From 07398428f3c51708d72ac1a6c8e65db3dc60012d Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 9 Dec 2024 05:29:48 +0000 Subject: [PATCH] Add changes --- _data/containers.yaml | 24 +- _data/repos.yml | 502 +- _recipes/CNCLgithub/pytower/Singularity | 56 + .../containers/reprostim-timesync/Singularity | 189 + _recipes/asgeissler/RNA-Schlange/Singularity | 141 + assets/js/repos.js | 41076 ++++++++-------- 6 files changed, 21376 insertions(+), 20612 deletions(-) create mode 100644 _recipes/CNCLgithub/pytower/Singularity create mode 100644 _recipes/ReproNim/reprostim/containers/reprostim-timesync/Singularity create mode 100644 _recipes/asgeissler/RNA-Schlange/Singularity diff --git a/_data/containers.yaml b/_data/containers.yaml index 54bd0db2..1c4c1b1e 100644 --- a/_data/containers.yaml +++ b/_data/containers.yaml @@ -1,5 +1,5 @@ bases: -- count: 2132 +- count: 2133 name: ubuntu - count: 451 name: nvidia/cuda @@ -25,7 +25,7 @@ bases: name: Characterisation-Virtual-Laboratory/CharacterisationVL-Software - count: 88 name: nfcore/base -- count: 71 +- count: 72 name: neurodebian - count: 53 name: fedora @@ -215,6 +215,8 @@ bases: name: bids/example - count: 8 name: nickjer/singularity-r +- count: 8 + name: condaforge/mambaforge - count: 8 name: broadinstitute/gatk - count: 7 @@ -231,8 +233,6 @@ bases: name: nickjer/singularity-rstudio - count: 7 name: archlinux/base -- count: 7 - name: condaforge/mambaforge - count: 7 name: khanlab/neuroglia-core - count: 7 @@ -2144,7 +2144,7 @@ bases: - count: 1 name: ubuntu16.04 bootstraps: -- count: 7028 +- count: 7031 name: docker - count: 511 name: shub @@ -2917,8 +2917,6 @@ orgs: name: asciinema - count: 1 name: quay.io/ohpc -- count: 1 - name: condaforge - count: 1 name: mercury - count: 1 @@ -3113,6 +3111,8 @@ orgs: name: cloudpg - count: 1 name: wangyinz +- count: 1 + name: condaforge - count: 1 name: dezordi - count: 1 @@ -3154,8 +3154,8 @@ orgs: - count: 1 name: thomasrobinson tags: - latest: 747 - other: 6634 + latest: 748 + other: 6636 versions: /nrs/funke/singularity/linajea/pylp_base: v1.5.img: 1 @@ -3695,7 +3695,7 @@ versions: latest: 1 condaforge/mambaforge: 4.10.3-1: 1 - latest: 1 + latest: 2 conradbailey/defaults/hpcc: sha256.38d0deec523f0557882ed1bea1e2b9b747285ff5d7a875f1375f11f085945b04: 1 containers.las.iastate.edu/ml-gpu-base: @@ -4645,7 +4645,7 @@ versions: latest: 1 neurodebian: artful-non-free: 3 - bookworm: 2 + bookworm: 3 bullseye: 3 bullseye-non-free: 2 buster: 6 @@ -6013,7 +6013,7 @@ versions: '16.10': 4 '17.04': 8 '17.10': 5 - '18.04': 411 + '18.04': 412 '18.04 ': 9 18.04': 1 '18.10': 10 diff --git a/_data/repos.yml b/_data/repos.yml index 137acfa3..ba3ebe3f 100644 --- a/_data/repos.yml +++ b/_data/repos.yml @@ -2270,8 +2270,8 @@ ACEnglish/truvari: >\n

All documentation about Truvari is on the WIKI. Additional information about using Truvari can be found in Discussions

\n" - stargazers_count: 321 - subscribers_count: 14 + stargazers_count: 324 + subscribers_count: 13 topics: - structural-variation - vcf @@ -2283,7 +2283,7 @@ ACEnglish/truvari: - sv-merging - benchmarking - sequencing - updated_at: 1729911196.0 + updated_at: 1732813185.0 AI-Planning/autoscale: data_format: 2 description: null @@ -3165,7 +3165,7 @@ AJResearchGroup/plinkr: \ id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"\ #faq\">\n\

See doc/faq.md

\n" - stargazers_count: 7 + stargazers_count: 8 subscribers_count: 1 topics: - gwas @@ -3173,7 +3173,7 @@ AJResearchGroup/plinkr: - plink2 - r - r-package - updated_at: 1708238471.0 + updated_at: 1733295988.0 AMarinhoSN/tutorial-cCC: data_format: 2 description: null @@ -10249,8 +10249,8 @@ BioDynaMo/biodynamo: >License\n

BioDynaMo is Apache 2.0 licensed.

\n" - stargazers_count: 98 - subscribers_count: 13 + stargazers_count: 104 + subscribers_count: 14 topics: - simulation - high-performance @@ -10264,7 +10264,7 @@ BioDynaMo/biodynamo: - cancer - large-scale - parallel - updated_at: 1729181995.0 + updated_at: 1733548383.0 Bioconductor/bioconductor_docker: data_format: 2 description: Docker Containers for Bioconductor @@ -14327,6 +14327,108 @@ CNCLgithub/psiturk-sing: subscribers_count: 3 topics: [] updated_at: 1601405204.0 +CNCLgithub/pytower: + data_format: 2 + description: Random block tower generator with analysis of physical characteristics + filenames: + - Singularity + full_name: CNCLgithub/pytower + latest_release: null + readme: "

pytower

\n

Contributing

\n

All team members must

\n
    \n\ +
  1. Create a branch based off the current master
    2. \n
  2. Add commits to that\ + \ new branch
    3. \n
  3. push the new branch and submit a pull request to master
    4. \n\ +
\n

Setup

\n\ +

Config

\n\ +

Simple setups on local hosts should run fine with the default.conf.\n\ + However, if there are any issues with singularity the create a user.conf\n\ + with correct attributes.

\n

default.conf reads as:

\n
[ENV]\n\
+    exec:singularity # the path to\
+    \ singularity binary\npath:julia-cont  # the path to the singularity container\npython:pyenv   \
+    \  # the name of the conda environment\n\
+    julia_depot:.julia  # the relative\
+    \ path to set JULIA_DEPOT_PATH\nmounts:
\n

As of now there\ + \ are some extraneous sections in default.conf which\ncould be of\ + \ use later.

\n
[PATHS]\ndatabases:output/databases\ntraces:output/traces\nrenders:output/renders
\n\ +

Environment building

\n

Simply run setup.sh\ + \ in the root of this repo as follows

\n
chmod +x setup.sh\n./setup.sh
\n

You will be prompted for sudo\ + \ when building the container.

\n

setup.sh will then create\ + \ the container at the path specified in the config (julia-cont by\ + \ default).

\n
\n

NOTE: Like many commands in this setup, variables\ + \ will be bound to those specified in user.conf if present or default.conf

\n\ +
\n

Runtime

\n

Interacting with the container

\n

After running setup.sh, you can now use run.sh\ + \ to use the environment.

\n

The synatx of run.sh is simply:

\n\ + 
./run.sh <command>
\n

Where command\ + \ can be any arbitrary bash expression.

\n

For example, you can probe the\ + \ python version in the conda environment using:

\n
>: ./run.sh\
+    \ python3 --version\nNo user config found, using default\nINFO for ENV\n     \
+    \   path => julia-cont\n        mounts => \n        exec => singularity\n\
+    \        julia_depot => .julia\n        python => pyenv\nPython 3.6.8 ::\
+    \ Anaconda, Inc.\n\n
\n

As you can see ./run.sh first

\n\ +
    \n
  1. Loads the available config
    2. \n
  2. Reads out the config
    3. \n
  3. Executes\ + \ the command
    4. \n
\n

Interacting with Julia

\n\ +

Getting into the julia repl is simply

\n
>: ./run.sh\
+    \ julia\n
\n
No user config found, using default\nINFO for\
+    \ ENV\n        path => julia-cont\n        mounts => \n        exec =>\
+    \ singularity\n        julia_depot => .julia\n        python => pyenv\n\
+    \               _\n   _       _ _(_)_     |  Documentation: https://docs.julialang.org\n\
+    \  (_)     | (_) (_)    |\n   _ _   _| |_  __ _   |  Type \"?\" for help, \"]?\"\
+    \ for Pkg help.\n  | | | | | | |/ _` |  |\n  | | |_| | | | (_| |  |  Version 1.1.0\
+    \ (2019-01-21)\n _/ |\\__'_|_|_|\\__'_|  |  Official https://julialang.org/ release\n\
+    |__/                   |\n\njulia> \n
\n

To make sure that JULIA_DEPOT_PATH\ + \ is set to that in the config:

\n
julia> DEPOT_PATH\n\
+    1-element Array{String,1}:\n \".julia\"\n\
+    \njulia> \n
\n

Running package

\n\ +

To activate the package (ie rely on Project.toml and Manifest.toml)\ + \ run

\n
(v1.1) pkg> activate .\n\n(kinezoo)\
+    \ pkg> instantiate\n  Updating registry at `.julia/registries/General`\n  Updating git-repo\
+    \ `https://github.com/JuliaRegistries/General.git`\n
\n

You can then reference code\ + \ within the repo or import dependencies:

\n
julia> using\
+    \ LightGraphs
\n

You can additionally pre-compile dependencies using:

\n\ + 
(kinezoo) pkg> precompile\nPrecompiling project...
\n" + stargazers_count: 0 + subscribers_count: 2 + topics: [] + updated_at: 1618965313.0 CNCLgithub/rooms-psiturk: data_format: 2 description: null @@ -54734,7 +54836,7 @@ NYU-Molecular-Pathology/LG-PACT: - html - makefile - markdown - updated_at: 1732740930.0 + updated_at: 1733171020.0 NYU-Molecular-Pathology/NGS580-nf: data_format: 2 description: Target exome sequencing analysis for NYU NGS580 gene panel @@ -73007,7 +73109,7 @@ UniversalDataTool/universal-data-tool: \ title=\"Code\">\U0001F4BB\n\n \n\n\n\n\n

This\ \ project follows the all-contributors specification. Contributions of any kind welcome!

\n" - stargazers_count: 1964 + stargazers_count: 1965 subscribers_count: 37 topics: - computer-vision @@ -73030,7 +73132,7 @@ UniversalDataTool/universal-data-tool: - labeling - labeling-tool - hacktoberfest - updated_at: 1732900462.0 + updated_at: 1733335701.0 VALENCE-software/VALENCE: data_format: 2 description: quantum chemistry software @@ -80568,8 +80670,8 @@ alexlancaster/pypop: filenames: - src/obsolete/Singularity full_name: alexlancaster/pypop - latest_release: v1.1.0 - stargazers_count: 21 + latest_release: v1.1.2 + stargazers_count: 22 subscribers_count: 8 topics: - population-genomics @@ -80577,7 +80679,7 @@ alexlancaster/pypop: - bioinformatics - open-source - free-software - updated_at: 1721685890.0 + updated_at: 1732580917.0 alexpacheco/lurc-ood-apps: data_format: 2 description: Repository for Open OnDemand Applications on Lehigh's HPC clusters @@ -85112,6 +85214,256 @@ asgeissler/OCyRS-pipeline: subscribers_count: 2 topics: [] updated_at: 1727253743.0 +asgeissler/RNA-Schlange: + data_format: 2 + description: The straightforward RNA-seq Snakemake pipeline + filenames: + - Singularity + full_name: asgeissler/RNA-Schlange + latest_release: null + readme: "

RNA-Schlange

\n

The RNA-Schlange (German word for snake)\n\ + pipeline assesses the quality of RNA-seq data in a\nplug and play approach. Thus,\ + \ a user should not need to provide any additional\nconfigurations aside from\ + \ providing the read files, the genome, and\na rudimentary sample sheet file.\n\ + This pipeline supports both microbial and eukaryotic\ + \ experimental data,\nas well ass both single-end and paired-end\ + \ sequencing data.\nAlternatively, a user can specify a set of SRA runs that should\ + \ be downloaded.\nIf you use the RNA-Schlange, please consider citing:

\n\ +

\"Exploring the regulatory potential of RNA structures in 202 cyanobacterial\ + \ genomes\"
\nAS Geissler, EC Alvarez, C Anthon, NU Frigaard, J Gorodkin,\ + \ and SE Seemann
\nsubmitted

\n

\"\

\n

RNA-Schlange\ + \ uses the following tools:

\n\n

Setup

\n\ + \n\ +

When using the run.sh helper script,\nSnakemake will automatically\ + \ install\nthe remaining dependencies (e.g. fastp)\nby creating new conda\ + \ environments within the workflow directory.

\n

User guide

\n

All\ + \ computations and data will be stored within the directory in which\nyou downloaded\ + \ this pipeline. There are two scenarios on how to use\nRNA-Schlange:

\n

Scenario 1: User provided\ + \ reads

\n\ +

In case you have RNA-seq data of your own, please create a folder data\n\ + and place your files there.\nAlso provide the genomic sequence and annotation\ + \ for your organism of\ninterest; please name the files\ngenome.fna.gz\ + \ and genome.gff.gz.\nYou might want to download them from\neither\ + \ RefSeq,\n\ + ENA, or\n\ + a species specific

\n

Finally, write a file samples.csv that\ + \ describes each\nread file that you have provided in data.\nThe\ + \ file should be comma separated (that is a ',' between each value)\nand contain\ + \ the columns 'file', 'batch', 'sample', and 'condition'.\nIf your experiment\ + \ is a pair-end RNA-seq dataset, also add the optional\n'pair' column.

\n

In\ + \ case that your sequencing facility provides md5 checksums, consider\nwriting\ + \ a checksum.txt file of the form

\n
    abc032358XXX\
+    \  file1_1.fast.gz\n    XYZ987654321  file1_2.fast.gz\n    ....\n
\n\ +

RNA-Schlange will then initiate a comparison of hash to verify that there\n\ + were not issues during the download.

\n

For example, the input data could\ + \ look like:

\n
    \u251C\u2500\u2500 data\n    \u2502\_\_ \u251C\
+    \u2500\u2500 file1_1.fastq.gz\n    \u2502\_\_ \u251C\u2500\u2500 file1_2.fastq.gz\n\
+    \    \u2502\_\_ \u251C\u2500\u2500 file2_1.fastq.gz\n    \u2502\_\_ \u251C\u2500\
+    \u2500 file2_2.fastq.gz\n    \u2502\_\_ \u2514\u2500\u2500 ...\n    \u251C\u2500\
+    \u2500 genome.fna.gz\n    \u251C\u2500\u2500 (checksum.txt) # optional\n    \u251C\
+    \u2500\u2500 genome.gff.gz\n    \u251C\u2500\u2500 samples.csv\n
\n\ +

With the file samples.csv describing the reads

\n
\
+    \    batch,sample,condition,pair,file\n    batch1,A,control,R1,file1_1.fastq.gz\n\
+    \    batch1,A,control,R2,file1_2.fastq.gz\n    batch1,B,control,R1,file2_1.fastq.gz\n\
+    \    batch1,B,control,R2,file2_2.fastq.gz\n    ...\n
\n

The pipline\ + \ will compute an analysis folder (see below) in which\nall files corresponding\ + \ to the samples are names\nbatch_sample_condition. Therefore the\ + \ pipeline only accepts\nsample/condition/batch with\nalpha-numeric names (incl.\ + \ dash, -0-9a-zA-Z).\nFor paired-end reads, the values for the pairs\ + \ are either R1 or R2.

\n

Scenario 2: Public data

\n\ +

RNA-Schlange supports you to specify\nrun accession numbers to download from\ + \ the\nSRA database.\n\ + All you needed to specify is a comma separated file\nspecifying the 'run' and\ + \ 'condition'.\nIf the data that will be downloaded is single-end, please\nname\ + \ the file sra-SE.csv.\nFor paired-end data name the file `sra-PE.csv.

\n\ +

A hypothetical sra\\*.csv should look like, example from the airway dataset:

\n
    run,condition\n   \
+    \ SRR1039508,N61311-control\n    SRR1039509,N61311-case\n    SRR1039512,N052611-control\n\
+    \    SRR1039513,N052611-case\n
\n
Pipeline execution
\n\ +

All that is needed to start the pipeline is to execute the helper script with:

\n\ + 
    bash run.sh\n
\n

If you specified the data via SRA\ + \ entries, you would need to run the script\ntwice (once for the download and\ + \ once for the quality assessment).\nIn the helper script, Snakemake is set to\ + \ automatically install the software\ndependencies with conda.

\n

Cluster execution and singularity

\n\ +

Alternatively, if you prefer the computations to run in a cluster,\nRNA-Schlange\ + \ comes with support for slurm.\nSimply use bash run_slurm.sh\ + \ after adapting the configurations to you\nsystem in clusterprofile_slurm/config.yaml.

\n\ +

If you prefer to use singularity to handle the dependencies,\nthen please use\n\ + bash run_slurm_singularity.sh and the configuration\nclusterprofile_slurm_singularity/config.yaml.\n\ + For this use case, the pipeline will download a pre-build\ncontainerized\ncontainerized\nimage that includes all dependencies. Thanks\ + \ to the ORAS standard,\nyou can use this image also for a docker environment.

\n\ +

Recommendation: Set the conda-prefix and singularity-prefix\n\ + to paths on your server for centralized storage of the dependencies and\ncontainer\ + \ images.\nThen dependencies won't be re-installed for each new workflow instance\n\ + (saving time and storage).

\n

Note on SRA downloading: Due\ + \ this pipelines internal coding to conditionally\nhandle user provided or SRA\ + \ deposited RNA-seq data, it is not possible to\nsplit the downloading part into\ + \ multiple jobs. Uset the\nrun.sh or run_singularity.sh\ + \ helpers for the downloading part\n(can be submitted to a queue as a single job).

\n\ +

Pipeline analysis\ + \ output

\n\ +

All computatioal results of the pipeline are stored in the analysis\n\ + directory.

\n

If you provided a checksum.txt file that specified\ + \ the\nper fastq file the expected checksums (see above), then\nthe pipeline creates\ + \ states the observed checksums in\nchecksum.txt with potential difference\ + \ to the expected\nchecksums listed in differing-checksum.txt.

\n\ +

The intermediary results of the pipeline are stored\nin computaitonal-chronological\ + \ order as indicated by numeric\nprefixes per folder:

\n\n
    \n
  1. The combined\ + \ expression count matrix collected from\nthe individual Salmon runs in counts.tsv.
    2. \n\ +
  2. Relative biotype of expressed genes shown in the barplot\nbiotype-content.png.
    3. \n\ +
  3. Scatter plots between the samples in a condition\nscatter-plots.png.
    4. \n\ +
  4. A printicpal component analysis (PCA) plot with\ncondition and batch indication\ + \ of the top $100$ genes with\nmost variance in expression pca.png.
    5. \n\ +
  5. A tentative analysis of differentially expressed genes\nas detected by DESeq2\ + \ at a false-discovery rate (FDR) of\n$0.05$ (see optional parameters below) are\ + \ in\nputative-diff-expression-summary.tsv and\nputative-diff-expression.tsv.
    6. \n\ +
  6. Finally, a heatmap showing expression levels of the putative\ndifferentially\ + \ expresses genes is shown in 1heatmap.png`.
    7. \n
\n\n

Optional configurations

\n\ +

RNA-Schlange attempts to provide a near configuration-free\nexperience of assessing\ + \ the overall RNA-seq data quality.\nHowever, a user could still adapt pipeline\ + \ in the\nconfig.yaml file, which is format in\nthe\nYAML format.

\n

Provided transcript/gene sequences and Gencode

\n

Instead of having the pipeline extract\ + \ the sequences for the genes annotatated\nin the genome.gff.gz,\ + \ you can provide already given gene/transcript sequences\nFor example, you can\ + \ use the transcript sequences for mouse or human\nfrom the GENCODE project:

\n
    wget $URL -O analysis/30_genes.fna.gz\n\
+
\n

In case of GENCODE provided sequences, please adapt the config.yaml:

\n\ + 
    salmon_index_args: [\n      '--gencode'\n    ]\n
\n\ +

fastp quality filtering

\n

The default paramters\ + \ are set to

\n\n

Alternative parameters are shown in the\nfastp handbook, which allow for:

\n\n

Although fastp allows for an automatic adapter sequence\ + \ detection,\nthe pipeline states the Illumina universal adapter sequences\nfor\ + \ explicitly checking for potential contamination by these sequences.\nAlterantive\ + \ adapter sequences are listed adapter_list.fa.

\n

SortMeRNA ribosomal RNA removal

\n\ +

Per default, the 16S, 18S, and 23S ribosomal RNAs are\nchoosen for the removal\ + \ steps.\nThe removal is relative to represantative squences collected\nby SortMeRNA.\ + \ Additional\nfilter sets are listed in the\nhandbook.

\n
    sortmerna: [\n      'silva-bac-16s-id90',\n\
+    \      'silva-arc-16s-id95',\n      'silva-euk-18s-id95',\n      'silva-bac-23s-id98',\n\
+    \      'silva-arc-23s-id98',\n      'silva-euk-28s-id98'\n    ]\n
\n\ +

DESeq2 analysis

\n

The assessment of putative\ndifferential\ + \ gene expression is per default relative to the\nFDR $\\alpha = 0.05$. An alternative\ + \ significance level can be specified\nin the configuraiton.\nFurther, if any\ + \ of the generated plots might in their dimension not\nbe suitable for a dataset,\ + \ other sizes can be specified.\nThe dimensions of the plots is given as a string\ + \ of the format\n'x' in inches.

\n
    dge: {\n      alpha: 0.05,\n\
+    \      dim_pca: '12x8',\n      dim_scatter: '20x20',\n      dim_biotype: '8x6'\n\
+    \    }\n
\n

Developer note

\n

When using this pipeline\ + \ in it's containerized form (e.g.\nrun_singularity.sh),\ + \ then an image that was build with the following commands\nwill be downloaded.

\n\ + 
    # The rule for fasterq-dump download from SRA is only contitionally\n\
+    \    # loaded, therefore there is a separate Dockerfile just for this part.\n\
+    \    snakemake --containerize > Dockerfile.fasterq\n    # After a complete\
+    \ run of the pipeline (to make sure all works), snapshot the conda envs\n    snakemake\
+    \ --containerize > Dockerfile\n    # MANUALLY copy paste and adapt step1/2\
+    \ part from Dockerfile.fasterq over\n    # Convert to a singularity file\n   \
+    \ # mambaforge does not have curl installed -> use wget\n    # `curl URL -o\
+    \ PATH` becomes `wget URL -O PATH`\n    # spython incorrectly doubles the '/environment.yaml/environment.yaml'\n\
+    \    spython recipe Dockerfile                              | \\\n        sed\
+    \ 's,curl \\([^ ]*\\) -o \\([^ ]*\\),wget \\1 -O \\2,' | \\\n        sed 's,/environment.yaml/environment.yaml,/environment.yaml,'\
+    \ > Singularity\n    singularity build --fakeroot rnaschlange-0.1.sif Singularity\n\
+    \    # setup repositry credential\n    singularity remote login --username <USER>\
+    \ oras://ghcr.io\n    # + enter secret access token\n    # upload image\n    singularity\
+    \ push rnaschlange-0.1.sif oras://ghcr.io/asgeissler/rnaschlange:0.1\n
\n" + stargazers_count: 0 + subscribers_count: 2 + topics: [] + updated_at: 1727253808.0 ashokdahal/FrameFieldLearning_Anaconda_Windows: data_format: 2 description: null @@ -86152,7 +86504,7 @@ ay-lab/selfish: \ class=\"pl-k\">import numpy as np\nmatrix\ \ = np.load(\"/path/to/output/selfish.npy\")\n\ + pl-c1\">load(\"/path/to/output/selfish.npy\")\n\

Citation

' - stargazers_count: 3 + stargazers_count: 4 subscribers_count: 4 topics: - nextflow @@ -158347,7 +158699,7 @@ maxemil/ALE-pipeline: - bioinformatics - singularity-container - pipeline - updated_at: 1715001082.0 + updated_at: 1732734824.0 maxemil/PhyloMagnet: data_format: 2 description: screening metagenomes for arbitrary lineages, using gene-centric assembly @@ -162499,14 +162851,14 @@ miykael/fmriflows: who created the great fMRIflows logo.

' - stargazers_count: 60 + stargazers_count: 62 subscribers_count: 7 topics: - python - neuroimaging - fmri - bids - updated_at: 1721799562.0 + updated_at: 1731393825.0 mjakobs/fastqsplit_singularity: data_format: 2 description: 'A singularity container for `fastqsplit`: https://github.com/supernifty/fastqsplit' @@ -170499,7 +170851,7 @@ netreconlab/ca-server: }\n\n# Allow access to the rest\ \ of your ca-server api\nlocation /ca/ {\n proxy_pass http://ca-server:3000/;\n}
\n" - stargazers_count: 8 + stargazers_count: 9 subscribers_count: 2 topics: - certificate-authority @@ -170511,7 +170863,7 @@ netreconlab/ca-server: - certificate-signing-request - csr - hacktoberfest - updated_at: 1729619973.0 + updated_at: 1733198947.0 netreconlab/clamav: data_format: 2 description: ' This repo provides a Singularity image version for ClamAV, an anti-virus @@ -172259,14 +172611,14 @@ nextgenusfs/funannotate: ' - stargazers_count: 322 + stargazers_count: 323 subscribers_count: 16 topics: - genome-annotation - gene-models - comparative-genomics - ncbi-submission - updated_at: 1729779443.0 + updated_at: 1733345263.0 nf-core/ddamsproteomics: data_format: 2 description: Quantitative shotgun MS proteomics @@ -174751,10 +175103,10 @@ nservant/HiC-Pro: Logs: logs/dixon_2M/ice_1000000.log\nLogs: logs/dixon_2M_2/ice_500000.log\nLogs:\ \ logs/dixon_2M_2/ice_1000000.log\n\nreal\t2m15,736s\nuser\t4m3,277s\nsys\t0m24,423s\n\ \n\n" - stargazers_count: 387 + stargazers_count: 388 subscribers_count: 18 topics: [] - updated_at: 1733028684.0 + updated_at: 1733361034.0 nterhoeven/reper: data_format: 2 description: reper - Genome-wide identification, classification and quantification @@ -176749,33 +177101,33 @@ openPMD/openPMD-api: >
import openpmd_api\
     \ as io\n\n# ...\n\nseries = io.Series(= io.Series(\"samples/git-sample/data%T.h5\", io.Access.read_only)\n\
+    >io.Access.read_only)\n\
     \nfor k_i, i in series.iterations.items():\n  \
+    \ class=\"pl-c1\">iterations.items():\n  \
     \  print(\"Iteration: {0}\"\
-    .format(k_i))\n\
+    .format(k_i))\n\
     \n    for k_m, m in i.meshes.items():\n\
+    >i.meshes.items():\n\
     \        print(\"  Mesh '{0}'\
-    \ attributes:\".format(.format(k_m))\n        for a\
     \ in m.attributes:\n            print(\"    {0}\".format(attributes:\n            print(\"    {0}\".format(a))\n\n    for k_p, p in\
-    \ i.particles.items():\n        print(i.particles.items():\n        print(\"  Particle species '{0}' attributes:\".format(k_p))\n        format(k_p))\n        for a in\
-    \ p.attributes:\n  \
+    \ p.attributes:\n  \
     \          print(\"    {0}\"\
-    .format(a))
\n\ + .format(a))\n\

More!

\n\ @@ -177003,33 +177355,31 @@ openPMD/openPMD-api: \ CMAKE_PREFIX_PATH=$HOME/somepath:$CMAKE_PREFIX_PATH\n

Use the following lines in\ \ your project's CMakeLists.txt:

\n
# supports:           \
-    \            COMPONENTS MPI NOMPI HDF5 ADIOS2\nfind_package(openPMD\
-    \ 0.16.0 CONFIG)\n\nif(openPMD_FOUND)\n\
-    \    target_link_libraries(YourTarget PRIVATE openPMD::openPMD)\nendif()
\n\ -

Alternatively, add the openPMD-api repository source directly to your\ - \ project and use it via:

\n
add_subdirectory(\"path/to/source/of/openPMD-api\"\
-    )\n\ntarget_link_libraries(YourTarget PRIVATE openPMD::openPMD)
\n

For development\ - \ workflows, you can even automatically download and build openPMD-api from within\ - \ a depending CMake project.\nJust replace the add_subdirectory call\ - \ with:

\n
include(FetchContent)\nset(CMAKE_POLICY_DEFAULT_CMP0077\
-    \ NEW)\nset(openPMD_BUILD_CLI_TOOLS\
-    \ OFF)\nset(openPMD_BUILD_EXAMPLES\
-    \ OFF)\nset(openPMD_BUILD_TESTING\
-    \ OFF)\nset(openPMD_BUILD_SHARED_LIBS\
-    \ OFF)  # precedence over BUILD_SHARED_LIBS if needed\nset(openPMD_INSTALL OFF)            # or instead use:\n# set(openPMD_INSTALL ${BUILD_SHARED_LIBS})\
-    \  # only install if used as a shared library\nset(openPMD_USE_PYTHON\
+    >
# supports:                       COMPONENTS MPI NOMPI\
+    \ HDF5 ADIOS2\nfind_package(openPMD 0.17.0\
+    \ CONFIG)\n\nif(openPMD_FOUND)\n    target_link_libraries(YourTarget PRIVATE\
+    \ openPMD::openPMD)\nendif()
\n

Alternatively,\ + \ add the openPMD-api repository source directly to your project and use it via:

\n\ + 
add_subdirectory(\"path/to/source/of/openPMD-api\")\n\ntarget_link_libraries(YourTarget PRIVATE openPMD::openPMD)
\n\ +

For development workflows, you can even automatically download and build openPMD-api\ + \ from within a depending CMake project.\nJust replace the add_subdirectory\ + \ call with:

\n
include(FetchContent)\nset(CMAKE_POLICY_DEFAULT_CMP0077 NEW)\nset(openPMD_BUILD_CLI_TOOLS\
+    \ OFF)\nset(openPMD_BUILD_EXAMPLES\
+    \ OFF)\nset(openPMD_BUILD_TESTING\
+    \ OFF)\nset(openPMD_BUILD_SHARED_LIBS\
+    \ OFF)  # precedence over BUILD_SHARED_LIBS\
+    \ if needed\nset(openPMD_INSTALL OFF)            # or instead use:\n\
+    # set(openPMD_INSTALL ${BUILD_SHARED_LIBS})  # only install\
+    \ if used as a shared library\nset(openPMD_USE_PYTHON\
     \ OFF)\nFetchContent_Declare(openPMD\n  GIT_REPOSITORY\
     \ \"https://github.com/openPMD/openPMD-api.git\"\n\
-    \  GIT_TAG        \"0.16.0\")\nFetchContent_MakeAvailable(openPMD)
\n\ + \ GIT_TAG \"0.17.0\")\nFetchContent_MakeAvailable(openPMD)\n\

Manually

openPMD-standard by Axel Huebl (HZDR,\ \ now LBNL) and contributors\n\n\n" - stargazers_count: 138 + stargazers_count: 142 subscribers_count: 11 topics: - openpmd @@ -177184,7 +177534,7 @@ openPMD/openPMD-api: - opendata - metadata - cpp17 - updated_at: 1728680719.0 + updated_at: 1733337290.0 openhackathons-org/AI-Profiler: data_format: 2 description: 'This material contains content on how to profile and optimize simple @@ -177873,7 +178223,7 @@ openhackathons-org/gpubootcamp: rel="nofollow">OpenACC Slack Channel.

' - stargazers_count: 524 + stargazers_count: 525 subscribers_count: 23 topics: - machine-learning @@ -177888,7 +178238,7 @@ openhackathons-org/gpubootcamp: - rapidsai - openmp - ai4hpc - updated_at: 1732263806.0 + updated_at: 1733451794.0 openhackathons-org/nways_multi_gpu: data_format: 2 description: N-Ways to Multi-GPU Programming @@ -220995,7 +221345,7 @@ tbepler/topaz: - denoising - cryo-em - topaz-em - updated_at: 1732932591.0 + updated_at: 1733240774.0 tdalford/bilby_relative_binning: data_format: 2 description: null @@ -226704,7 +227054,7 @@ ucberkeley/brc-cyberinfrastructure: ' stargazers_count: 10 - subscribers_count: 15 + subscribers_count: 14 topics: [] updated_at: 1710307936.0 ucr-hpcc/hpcc_modules: @@ -234304,7 +234654,7 @@ wheaton5/souporcell:

So generally

\n
consensus.py -c clusters.tsv -a alt.mtx -r ref.mtx\
     \ --soup_out soup.txt -v <freebayes vcf> --vcf_out cluster_genotypes.vcf\
     \ --output_dir .\n
\n" - stargazers_count: 168 + stargazers_count: 169 subscribers_count: 11 topics: - scrna-seq @@ -234313,7 +234663,7 @@ wheaton5/souporcell: - bioinformatics - computational-biology - genomics - updated_at: 1730915536.0 + updated_at: 1733285106.0 willgpaik/centos7_aci: data_format: 2 description: Centos 7 base image for ACI @@ -235169,10 +235519,10 @@ wsjeon/maddpg-rllib: ' - stargazers_count: 51 + stargazers_count: 50 subscribers_count: 2 topics: [] - updated_at: 1731152852.0 + updated_at: 1731949950.0 wtsi-hgi/nf_cellbender: data_format: 2 description: Single cell Nextflow cellbender pipeline. diff --git a/_recipes/CNCLgithub/pytower/Singularity b/_recipes/CNCLgithub/pytower/Singularity new file mode 100644 index 00000000..f4ffb0f4 --- /dev/null +++ b/_recipes/CNCLgithub/pytower/Singularity @@ -0,0 +1,56 @@ +Bootstrap: docker +From: ubuntu:18.04 + + +%environment +# setup PATH to point to julia, conda, and blender +export PATH=$PATH:/julia/bin +export PATH=$PATH:/miniconda/bin +export PATH=$PATH:/blender +export LANG=en_US.UTF-8 + +%runscript + exec bash "$@" + +%post + +# System level packages +apt-get update +apt-get install -y build-essential \ + graphviz \ + git \ + wget \ + ffmpeg \ + libglu1 \ + libxi6 +apt-get clean + +# Build context +mkdir /build-ctx && cd /build-ctx + +# Setup blender +wget "https://yale.box.com/shared/static/nn6n5iyo5m4tzl5u9yoy2dvv1ohk22xj.xz" \ + -O blender.tar.gz +tar -xf blender.tar.gz +mv blender-2.* /blender +chmod +x /blender/blender + + +# Setup conda +wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O conda.sh +bash conda.sh -b -p /miniconda + +# Add an sbatch workaround +echo '#!/bin/bash\nssh -y "$HOSTNAME" sbatch "$@"' > /usr/bin/sbatch +chmod +x /usr/bin/sbatch + +# Add an scancel workaround +echo '#!/bin/bash\nssh -y "$HOSTNAME" scancel "$@"' > /usr/bin/scancel +chmod +x /usr/bin/scancel + +# Add an srun workaround +echo '#!/bin/bash\nssh -y "$HOSTNAME" srun "$@"' > /usr/bin/srun +chmod +x /usr/bin/srun + +# clean up +rm -rf /build-ctx diff --git a/_recipes/ReproNim/reprostim/containers/reprostim-timesync/Singularity b/_recipes/ReproNim/reprostim/containers/reprostim-timesync/Singularity new file mode 100644 index 00000000..05ac70df --- /dev/null +++ b/_recipes/ReproNim/reprostim/containers/reprostim-timesync/Singularity @@ -0,0 +1,189 @@ +# Generated by Neurodocker and Reproenv. + +Bootstrap: docker +From: neurodebian:bookworm + +%environment +export LANG="en_US.UTF-8" +export LC_ALL="en_US.UTF-8" +export ND_ENTRYPOINT="/neurodocker/startup.sh" + +%post +export ND_ENTRYPOINT="/neurodocker/startup.sh" +apt-get update -qq +apt-get install -y -q --no-install-recommends \ + apt-utils \ + bzip2 \ + ca-certificates \ + curl \ + locales \ + unzip +rm -rf /var/lib/apt/lists/* +sed -i -e 's/# en_US.UTF-8 UTF-8/en_US.UTF-8 UTF-8/' /etc/locale.gen +dpkg-reconfigure --frontend=noninteractive locales +update-locale LANG="en_US.UTF-8" +chmod 777 /opt && chmod a+s /opt +mkdir -p /neurodocker +if [ ! -f "$ND_ENTRYPOINT" ]; then + echo '#!/usr/bin/env bash' >> "$ND_ENTRYPOINT" + echo 'set -e' >> "$ND_ENTRYPOINT" + echo 'export USER="${USER:=`whoami`}"' >> "$ND_ENTRYPOINT" + echo 'if [ -n "$1" ]; then "$@"; else /usr/bin/env bash; fi' >> "$ND_ENTRYPOINT"; +fi +chmod -R 777 /neurodocker && chmod a+s /neurodocker + +apt-get update -qq +apt-get install -y -q --no-install-recommends \ + build-essential \ + curl \ + git \ + gnupg \ + less \ + libasound2-dev \ + libcanberra-gtk3-module \ + libgtk-3-dev \ + libusb-1.0-0-dev \ + libwxgtk-media3.2-dev \ + libwxgtk-webview3.2-dev \ + libwxgtk3.2-dev \ + ncdu \ + pavucontrol \ + pigz \ + pkg-config \ + portaudio19-dev \ + procps \ + pulseaudio \ + pulseaudio-utils \ + python3 \ + python3-pip \ + strace \ + sudo \ + time \ + tree \ + vim \ + wget +rm -rf /var/lib/apt/lists/* + +git clone https://github.com/wieluk/psychopy_linux_installer/ /opt/psychopy-installer; cd /opt/psychopy-installer; git checkout 21b1ac36ee648e00cc3b68fd402c1e826270dad6 + +/opt/psychopy-installer/psychopy_linux_installer.sh --install_dir=/opt/psychopy --psychopy_version=2024.1.4 --bids_version=2023.2.0 --python_version=3.10.14 --wxpython_version=4.2.1 -v -f + +/opt/psychopy/psychopy_*/bin/pip install qrcode pyzbar opencv-python numpy click pydantic sounddevice scipy pydub pyaudio reedsolo psychopy-sounddevice + +bash -c 'ln -s /opt/psychopy/psychopy_*/bin/psychopy /usr/local/bin/' + +bash -c 'b=$(ls /opt/psychopy/psychopy_*/bin/python3); echo -e "#!/bin/sh\n$b \"\$@\"" >| /usr/local/bin/python3; chmod a+x /usr/local/bin/python3' + +# Save specification to JSON. +printf '{ \ + "pkg_manager": "apt", \ + "existing_users": [ \ + "root" \ + ], \ + "instructions": [ \ + { \ + "name": "from_", \ + "kwds": { \ + "base_image": "neurodebian:bookworm" \ + } \ + }, \ + { \ + "name": "env", \ + "kwds": { \ + "LANG": "en_US.UTF-8", \ + "LC_ALL": "en_US.UTF-8", \ + "ND_ENTRYPOINT": "/neurodocker/startup.sh" \ + } \ + }, \ + { \ + "name": "run", \ + "kwds": { \ + "command": "export ND_ENTRYPOINT=\\"/neurodocker/startup.sh\\"\\napt-get update -qq\\napt-get install -y -q --no-install-recommends \\\\\\n apt-utils \\\\\\n bzip2 \\\\\\n ca-certificates \\\\\\n curl \\\\\\n locales \\\\\\n unzip\\nrm -rf /var/lib/apt/lists/*\\nsed -i -e '"'"'s/# en_US.UTF-8 UTF-8/en_US.UTF-8 UTF-8/'"'"' /etc/locale.gen\\ndpkg-reconfigure --frontend=noninteractive locales\\nupdate-locale LANG=\\"en_US.UTF-8\\"\\nchmod 777 /opt && chmod a+s /opt\\nmkdir -p /neurodocker\\nif [ ! -f \\"$ND_ENTRYPOINT\\" ]; then\\n echo '"'"'#!/usr/bin/env bash'"'"' >> \\"$ND_ENTRYPOINT\\"\\n echo '"'"'set -e'"'"' >> \\"$ND_ENTRYPOINT\\"\\n echo '"'"'export USER=\\"${USER:=`whoami`}\\"'"'"' >> \\"$ND_ENTRYPOINT\\"\\n echo '"'"'if [ -n \\"$1\\" ]; then \\"$@\\"; else /usr/bin/env bash; fi'"'"' >> \\"$ND_ENTRYPOINT\\";\\nfi\\nchmod -R 777 /neurodocker && chmod a+s /neurodocker" \ + } \ + }, \ + { \ + "name": "install", \ + "kwds": { \ + "pkgs": [ \ + "build-essential", \ + "pkg-config", \ + "git", \ + "sudo", \ + "libgtk-3-dev", \ + "libwxgtk3.2-dev", \ + "libwxgtk-media3.2-dev", \ + "libwxgtk-webview3.2-dev", \ + "libcanberra-gtk3-module", \ + "libusb-1.0-0-dev", \ + "portaudio19-dev", \ + "libasound2-dev", \ + "pulseaudio", \ + "pavucontrol", \ + "pulseaudio-utils", \ + "vim", \ + "wget", \ + "strace", \ + "time", \ + "ncdu", \ + "gnupg", \ + "curl", \ + "procps", \ + "pigz", \ + "less", \ + "tree", \ + "python3", \ + "python3-pip" \ + ], \ + "opts": null \ + } \ + }, \ + { \ + "name": "run", \ + "kwds": { \ + "command": "apt-get update -qq\\napt-get install -y -q --no-install-recommends \\\\\\n build-essential \\\\\\n curl \\\\\\n git \\\\\\n gnupg \\\\\\n less \\\\\\n libasound2-dev \\\\\\n libcanberra-gtk3-module \\\\\\n libgtk-3-dev \\\\\\n libusb-1.0-0-dev \\\\\\n libwxgtk-media3.2-dev \\\\\\n libwxgtk-webview3.2-dev \\\\\\n libwxgtk3.2-dev \\\\\\n ncdu \\\\\\n pavucontrol \\\\\\n pigz \\\\\\n pkg-config \\\\\\n portaudio19-dev \\\\\\n procps \\\\\\n pulseaudio \\\\\\n pulseaudio-utils \\\\\\n python3 \\\\\\n python3-pip \\\\\\n strace \\\\\\n sudo \\\\\\n time \\\\\\n tree \\\\\\n vim \\\\\\n wget\\nrm -rf /var/lib/apt/lists/*" \ + } \ + }, \ + { \ + "name": "run", \ + "kwds": { \ + "command": "git clone https://github.com/wieluk/psychopy_linux_installer/ /opt/psychopy-installer; cd /opt/psychopy-installer; git checkout 21b1ac36ee648e00cc3b68fd402c1e826270dad6" \ + } \ + }, \ + { \ + "name": "run", \ + "kwds": { \ + "command": "/opt/psychopy-installer/psychopy_linux_installer.sh --install_dir=/opt/psychopy --psychopy_version=2024.1.4 --bids_version=2023.2.0 --python_version=3.10.14 --wxpython_version=4.2.1 -v -f" \ + } \ + }, \ + { \ + "name": "run", \ + "kwds": { \ + "command": "/opt/psychopy/psychopy_*/bin/pip install qrcode pyzbar opencv-python numpy click pydantic sounddevice scipy pydub pyaudio reedsolo psychopy-sounddevice" \ + } \ + }, \ + { \ + "name": "run", \ + "kwds": { \ + "command": "bash -c '"'"'ln -s /opt/psychopy/psychopy_*/bin/psychopy /usr/local/bin/'"'"'" \ + } \ + }, \ + { \ + "name": "run", \ + "kwds": { \ + "command": "bash -c '"'"'b=$\(ls /opt/psychopy/psychopy_*/bin/python3\); echo -e \\"#!/bin/sh\\\\n$b \\\\\\"\\\\$@\\\\\\"\\" >| /usr/local/bin/python3; chmod a+x /usr/local/bin/python3'"'"'" \ + } \ + }, \ + { \ + "name": "entrypoint", \ + "kwds": { \ + "args": [ \ + "python3" \ + ] \ + } \ + } \ + ] \ +}' > /.reproenv.json +# End saving to specification to JSON. + +%runscript +python3 diff --git a/_recipes/asgeissler/RNA-Schlange/Singularity b/_recipes/asgeissler/RNA-Schlange/Singularity new file mode 100644 index 00000000..abe9b556 --- /dev/null +++ b/_recipes/asgeissler/RNA-Schlange/Singularity @@ -0,0 +1,141 @@ +Bootstrap: docker +From: condaforge/mambaforge:latest +Stage: spython-base + +%files +envs/r.yaml /conda-envs/81349e987b92efdd9c42d5622123e303/environment.yaml +envs/sortmerna.yaml /conda-envs/b3a58c9dd8d8c7ef4943a32053eca134/environment.yaml +%labels +io.github.snakemake.containerized="true" +io.github.snakemake.conda_env_hash="6361a69b2f86b2a5d26ea0bfeea7b71b4228009ab7e104c5587d090032e5dd4a" +%post + +# Step 1: Retrieve conda environments + +# Conda environment: +# source: https://github.com/snakemake/snakemake-wrappers/raw/v1.10.0/bio/sra-tools/fasterq-dump/environment.yaml +# prefix: /conda-envs/85633ff8bea713d372cb9152f291c3a8 +# channels: +# - conda-forge +# - bioconda +# - nodefaults +# dependencies: +# - sra-tools >2.9.1 +# - pigz >=2.6 +# - pbzip2 >=1.1 +# - snakemake-wrapper-utils =0.3 +mkdir -p /conda-envs/85633ff8bea713d372cb9152f291c3a8 +wget https://github.com/snakemake/snakemake-wrappers/raw/v1.10.0/bio/sra-tools/fasterq-dump/environment.yaml -O /conda-envs/85633ff8bea713d372cb9152f291c3a8/environment.yaml + +# Conda environment: +# source: envs/r.yaml +# prefix: /conda-envs/81349e987b92efdd9c42d5622123e303 +# channels: +# - conda-forge +# - bioconda +# - defaults +# dependencies: +# - r-base +# - r-cowplot +# - r-essentials >= 4.0 +# - r-ggrepel +# - r-ggsci +# - r-magick +# - r-pheatmap +# - r-rcolorbrewer +# - r-tidyverse +# - bioconductor-biostrings +# - bioconductor-bsgenome +# - bioconductor-deseq2 +# - bioconductor-plyranges +# - bioconductor-rtracklayer +mkdir -p /conda-envs/81349e987b92efdd9c42d5622123e303 + +# Conda environment: +# source: envs/sortmerna.yaml +# prefix: /conda-envs/b3a58c9dd8d8c7ef4943a32053eca134 +# channels: +# - bioconda +# - defaults +# dependencies: +# - sortmerna =4.3.4 +mkdir -p /conda-envs/b3a58c9dd8d8c7ef4943a32053eca134 + +# Conda environment: +# source: https://github.com/snakemake/snakemake-wrappers/raw/v1.10.0/bio/fastp/environment.yaml +# prefix: /conda-envs/ead20a3f8bbfa36fbb2e0c3f905c1787 +# channels: +# - conda-forge +# - bioconda +# - nodefaults +# dependencies: +# - fastp =0.20 +mkdir -p /conda-envs/ead20a3f8bbfa36fbb2e0c3f905c1787 +wget https://github.com/snakemake/snakemake-wrappers/raw/v1.10.0/bio/fastp/environment.yaml -O /conda-envs/ead20a3f8bbfa36fbb2e0c3f905c1787/environment.yaml + +# Conda environment: +# source: https://github.com/snakemake/snakemake-wrappers/raw/v1.12.0/bio/fastqc/environment.yaml +# prefix: /conda-envs/573927d1a2f1de4bfdd03a5385f50ed8 +# channels: +# - conda-forge +# - bioconda +# - nodefaults +# dependencies: +# - fastqc ==0.11.9 +mkdir -p /conda-envs/573927d1a2f1de4bfdd03a5385f50ed8 +wget https://github.com/snakemake/snakemake-wrappers/raw/v1.12.0/bio/fastqc/environment.yaml -O /conda-envs/573927d1a2f1de4bfdd03a5385f50ed8/environment.yaml + +# Conda environment: +# source: https://github.com/snakemake/snakemake-wrappers/raw/v3.2.0/bio/multiqc/environment.yaml +# prefix: /conda-envs/a74c0ac65c84ed438731c3397703af31 +# channels: +# - conda-forge +# - bioconda +# - nodefaults +# dependencies: +# - multiqc =1.18 +mkdir -p /conda-envs/a74c0ac65c84ed438731c3397703af31 +wget https://github.com/snakemake/snakemake-wrappers/raw/v3.2.0/bio/multiqc/environment.yaml -O /conda-envs/a74c0ac65c84ed438731c3397703af31/environment.yaml + +# Conda environment: +# source: https://github.com/snakemake/snakemake-wrappers/raw/v3.2.0/bio/salmon/index/environment.yaml +# prefix: /conda-envs/58a327d476856b9082ebbfd2ce43537c +# channels: +# - conda-forge +# - bioconda +# - nodefaults +# dependencies: +# - salmon =1.10.2 +mkdir -p /conda-envs/58a327d476856b9082ebbfd2ce43537c +wget https://github.com/snakemake/snakemake-wrappers/raw/v3.2.0/bio/salmon/index/environment.yaml -O /conda-envs/58a327d476856b9082ebbfd2ce43537c/environment.yaml + +# Conda environment: +# source: https://github.com/snakemake/snakemake-wrappers/raw/v3.2.0/bio/salmon/quant/environment.yaml +# prefix: /conda-envs/c9b8e6e6cf962163ad419bc658b71b79 +# channels: +# - bioconda +# - conda-forge +# - nodefaults +# dependencies: +# - salmon =1.10.2 +# - gzip =1.13 +# - bzip2 =1.0.8 +mkdir -p /conda-envs/c9b8e6e6cf962163ad419bc658b71b79 +wget https://github.com/snakemake/snakemake-wrappers/raw/v3.2.0/bio/salmon/quant/environment.yaml -O /conda-envs/c9b8e6e6cf962163ad419bc658b71b79/environment.yaml + +# Step 2: Generate conda environments + +mamba env create --prefix /conda-envs/81349e987b92efdd9c42d5622123e303 --file /conda-envs/81349e987b92efdd9c42d5622123e303/environment.yaml && \ +mamba env create --prefix /conda-envs/b3a58c9dd8d8c7ef4943a32053eca134 --file /conda-envs/b3a58c9dd8d8c7ef4943a32053eca134/environment.yaml && \ +mamba env create --prefix /conda-envs/ead20a3f8bbfa36fbb2e0c3f905c1787 --file /conda-envs/ead20a3f8bbfa36fbb2e0c3f905c1787/environment.yaml && \ +mamba env create --prefix /conda-envs/573927d1a2f1de4bfdd03a5385f50ed8 --file /conda-envs/573927d1a2f1de4bfdd03a5385f50ed8/environment.yaml && \ +mamba env create --prefix /conda-envs/a74c0ac65c84ed438731c3397703af31 --file /conda-envs/a74c0ac65c84ed438731c3397703af31/environment.yaml && \ +mamba env create --prefix /conda-envs/58a327d476856b9082ebbfd2ce43537c --file /conda-envs/58a327d476856b9082ebbfd2ce43537c/environment.yaml && \ +mamba env create --prefix /conda-envs/c9b8e6e6cf962163ad419bc658b71b79 --file /conda-envs/c9b8e6e6cf962163ad419bc658b71b79/environment.yaml && \ +mamba env create --prefix /conda-envs/85633ff8bea713d372cb9152f291c3a8 --file /conda-envs/85633ff8bea713d372cb9152f291c3a8/environment.yaml && \ +mamba clean --all -y + +%runscript +exec /bin/bash "$@" +%startscript +exec /bin/bash "$@" diff --git a/assets/js/repos.js b/assets/js/repos.js index 3ef97360..0c0d974c 100644 --- a/assets/js/repos.js +++ b/assets/js/repos.js @@ -2,2088 +2,1719 @@ var data = [ { "data_format": 2, - "description": "Snakemake pipeline for QC of low-coverage skim-sequencing of G\u0026T seq data", + "description": "get rstudio on PSU ACI", "filenames": [ - "Singularity.def" + "Singularity.ml", + "Singularity" ], - "full_name": "EI-CoreBioinformatics/SkimSeqQC", + "full_name": "d-bohn/rstudio_aci", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSkimSeqQC Pipeline\u003c/h2\u003e\u003ca id=\"user-content-skimseqqc-pipeline\" class=\"anchor\" aria-label=\"Permalink: SkimSeqQC Pipeline\" href=\"#skimseqqc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSnakemake pipeline for QC of low-coverage skim-sequencing of G\u0026amp;T seq data\u003c/p\u003e\n\u003cp\u003eExpects reads to be in a directory called \u003ccode\u003eREADS\u003c/code\u003e with filenames in the format: \u003ccode\u003e{sample}_cDNA_R[12].fastq.gz\u003c/code\u003e and \u003ccode\u003e{sample}_gDNA_R[12].fastq.gz\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eModify the config file \u003ccode\u003econfig.yaml\u003c/code\u003e to set run_name and choose min number of gDNA and cDNA reads (see example below).\u003c/p\u003e\n\u003cp\u003eFirst run the \u003ccode\u003eGenerateSamplesheet.smk\u003c/code\u003e pipeline which just runs fastp on the raw reads to count reads and generate a \"samplesheet\":\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j 100 -p --snakefile GenerateSamplesheet.smk --cluster-config cluter.json --latency-wait 60 --cluster \"sbatch -p {cluster.partition} -c {cluster.c} --mem={cluster.memory} --job-name={cluster.J} --time={cluster.time}\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the full pipeline:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j 100 -p --snakefile SkimSeqQC.smk --cluster-config cluster.json --latency-wait 60 --cluster \"sbatch -p {cluster.partition} -c {cluster.c} --mem={cluster.memory} --job-name={cluster.J} --time={cluster.time}\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample \u003ccode\u003econfig.yaml\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun_name: PLATE_NAME\nsamplesheet: samplesheet.tsv\nfastq_dir: \"READS\"\n\n# Minimum gDNA read count to be passed to SPAdes\nmin_gDNA_read_count: 70000\n\n# Minimum cDNA read count to be passed to Trinity\nmin_cDNA_read_count: 50000\n\n# Minimum rRNA length to report in top blast hits vs pr2 DB\n# This is the length of the blast alignment not the annotated gene\nmin_rRNA_blast_length: 500\n\n# Interval to use for calculating random kmer uniquess (not currently reported in summary)\nrku_gDNA_interval: 25000\nrku_cDNA_interval: 25000\n\n# Map gDNA reads against the corresponding genome assembly\nmap_gDNA: True\n\n# Map cDNA reads against the corresponding genome assembly\nmap_cDNA: True\n\n# Identity threshold for clustering transcriptome assembly\ncdhit_identity_threshold: 0.98\n\n# How many reads to classify using centrifuge [-u/--upto \u0026lt;int\u0026gt; stop after first \u0026lt;int\u0026gt; reads/pairs (no limit)]\ncentrifuge_upto: 1000000\n\n# Paths to databases\nbbduk_adapters: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/bbmap/resources/adapters.fa\npr2_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/pr2/5.0.0/pr2_version_5.0.0_SSU_taxo_long.fasta\ndiamond_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/diamond/reference_proteomes.dmnd\ntaxonomy_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/taxdump/fullnamelineage.dmp\nkraken2_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/kraken2/\ncentrifuge_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/centrifuge/centrifugedb\ncentrifuge_NT_database: /ei/public/databases/centrifuge/ncbi/nt_20200707/nt\n\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erstudio_aci\u003c/h1\u003e\u003ca id=\"user-content-rstudio_aci\" class=\"anchor\" aria-label=\"Permalink: rstudio_aci\" href=\"#rstudio_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and workflow for building \u003ca href=\"https://www.rocker-project.org/\" rel=\"nofollow\"\u003erocker/verse\u003c/a\u003e\nin Docker Hub and modifying it with Singularity Hub for use with PSU\nACI HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003essh\u003c/code\u003e into the PSU ACI HPC with X11 flags.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh USERID@aci-b.aci.ics.psu.edu -X -Y\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart an interactive session using \u003ccode\u003eqsub\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -A open -I -X -l walltime=24:00:00 -l nodes=2:ppn=20 -l pmem=10gb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom ACI, start \u003ccode\u003escreen\u003c/code\u003e and then execute the following code to\ncreate an \u003ccode\u003eRStudio\u003c/code\u003e image running at address \u003ccode\u003e127.0.0.1:8787\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escreen\n\nsingularity pull -n rstudio_aci.simg shub://d-bohn/rstudio_aci\n\nsingularity exec rstudio_aci.simg rserver --www-address=127.0.0.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, press \u003ccode\u003eCTRL+A+D\u003c/code\u003e to detach the screen while allowing the process to continue running in the background.\u003c/p\u003e\n\u003cp\u003eFinally, start your preferred browser and navigate to \u003ccode\u003e127.0.0.1\u003c/code\u003e. For\nexample, firefox:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://jpetucci-firefox_icsaci\n\nsingularity exec jpetucci-firefox_icsaci-master-latest.simg /opt/firefox/./firefox\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e1). A \u003ccode\u003eshiny\u003c/code\u003e server should also start when executing this image,\nthe server should be running on port \u003ccode\u003e3838\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1678468349.0 + "updated_at": 1562649674.0 }, { "data_format": 2, - "description": "A package that sets up everything you need to run the simulator.", + "description": "Build a Singularity container for Photoscan", "filenames": [ "Singularity" ], - "full_name": "abersailbot/simulator", + "full_name": "ucb-rit/singularity-photoscan", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSimulator Instructions\u003c/h1\u003e\u003ca id=\"user-content-simulator-instructions\" class=\"anchor\" aria-label=\"Permalink: Simulator Instructions\" href=\"#simulator-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package uses the sails simulator and boatd to simulate a robot sailing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePre-requisites\u003c/h3\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003elibjansson-dev\u003c/p\u003e\n\u003cp\u003ePython 2.7 or 3.x\u003c/p\u003e\n\u003cp\u003eFor sails-ui\u003c/p\u003e\n\u003cp\u003elibgirepository1.0-dev\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCheckout Code\u003c/h3\u003e\u003ca id=\"user-content-checkout-code\" class=\"anchor\" aria-label=\"Permalink: Checkout Code\" href=\"#checkout-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheckout this repository and its submodules\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone --recursive https://github.com/abersailbot/simulator\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompile sails\u003c/h3\u003e\u003ca id=\"user-content-compile-sails\" class=\"anchor\" aria-label=\"Permalink: Compile sails\" href=\"#compile-sails\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ecd sailsd\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall python dependencies\u003c/h3\u003e\u003ca id=\"user-content-install-python-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install python dependencies\" href=\"#install-python-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsing Anaconda (optional)\u003c/h4\u003e\u003ca id=\"user-content-using-anaconda-optional\" class=\"anchor\" aria-label=\"Permalink: Using Anaconda (optional)\" href=\"#using-anaconda-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall Anaconda from \u003ca href=\"http://www.anaconda.org\" rel=\"nofollow\"\u003ewww.anaconda.org\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAnaconda has its own copy of Python (and many other packages), its huge but probably has more up to date packages than your OS.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda create -n boatd python=3.7 anaconda\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda activate boatd\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-forge jansson\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-forge pygobject\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsing Virtualenv (optional)\u003c/h4\u003e\u003ca id=\"user-content-using-virtualenv-optional\" class=\"anchor\" aria-label=\"Permalink: Using Virtualenv (optional)\" href=\"#using-virtualenv-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e*** Don\u0027t do this if you are using Anaconda. ***\u003c/p\u003e\n\u003cp\u003eUsing a virtual env is a lighter weight method of isolating your Python configuration.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m venv simulator-env\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esource simulator-env/bin/activate\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eor for python2\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython -m virtualenv simulator-env\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esource simulator-env/bin/activate\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstalling Packages\u003c/h4\u003e\u003ca id=\"user-content-installing-packages\" class=\"anchor\" aria-label=\"Permalink: Installing Packages\" href=\"#installing-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003epip install python-boatdclient python-sailsd gobject PyGObject\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall boatd as a python package\u003c/h3\u003e\u003ca id=\"user-content-install-boatd-as-a-python-package\" class=\"anchor\" aria-label=\"Permalink: Install boatd as a python package\" href=\"#install-boatd-as-a-python-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ecd boatd\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython setup.py install\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfigure boatd port\u003c/h3\u003e\u003ca id=\"user-content-configure-boatd-port\" class=\"anchor\" aria-label=\"Permalink: Configure boatd port\" href=\"#configure-boatd-port\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBoatd\u0027s default port is 2222, but this config uses 2223 (because i\u0027ve got an SSH tunnel using 2222).\nChange this by editing boatd.yml and boatd_client.py in the boatdclient Python package.\nThe script set_port.sh will read the config file and automatically set\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning\u003c/h2\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThree components must be launched:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe sails simulator\u003c/li\u003e\n\u003cli\u003eBoatd\u003c/li\u003e\n\u003cli\u003eThe behaviour to control the simulated boat via boatd\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOptionally you can launch the sails-ui graphical interface.\u003c/p\u003e\n\u003cp\u003eThe script run.sh will launch all four of these.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Singularity\u003c/h3\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall Singularity, see \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e for instructions.\u003c/p\u003e\n\u003cp\u003eDownload the container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull shub://abersailbot/simulator:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRunning the container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run abersailbot-simulator-master-latest.simg\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can either run the behaviour inside another container:\u003c/p\u003e\n\u003cp\u003esingularity exec abersailbot-simulator-master-latest.simg /opt/simulator/simulator-behaviour/waypoint-behaviour\u003c/p\u003e\n\u003cp\u003eOr execute your own behaviour outside the container. Note you\u0027ll have to change boatd-client to use port 2223 by editing\u003c/p\u003e\n\u003cp\u003eEdit boatd_client.py in your Python library directory and change:\u003c/p\u003e\n\u003cp\u003eclass Boatd(object):\u003cbr\u003e\ndef \u003cstrong\u003einit\u003c/strong\u003e(self, host=\u0027localhost\u0027, port= 2222):\u003c/p\u003e\n\u003cp\u003eto\u003c/p\u003e\n\u003cp\u003eclass Boatd(object):\u003cbr\u003e\ndef \u003cstrong\u003einit\u003c/strong\u003e(self, host=\u0027localhost\u0027, port= 2223):\u003c/p\u003e\n\u003cp\u003eOr run the fix_port.sh script in the root directory of this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Docker\u003c/h3\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running with Docker\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStopping everything\u003c/h2\u003e\u003ca id=\"user-content-stopping-everything\" class=\"anchor\" aria-label=\"Permalink: Stopping everything\" href=\"#stopping-everything\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script stop.sh\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuild a Singularity container that includes Photoscan, using a Singularity recipe file\u003c/h1\u003e\u003ca id=\"user-content-build-a-singularity-container-that-includes-photoscan-using-a-singularity-recipe-file\" class=\"anchor\" aria-label=\"Permalink: Build a Singularity container that includes Photoscan, using a Singularity recipe file\" href=\"#build-a-singularity-container-that-includes-photoscan-using-a-singularity-recipe-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild a Photoscan container using Singularity to run on Jetstream, Savio, or anyehwere else the Singularity container runtime is installed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThese instructions are current as of 08 August 2018, for Photoscan v1.4.3; as of this date, the \u003ccode\u003eSingularity\u003c/code\u003e file in this repo folder builds this version of Photoscan. It\u0027s a good idea to include the Photoscan version used in the name the Singularity container, as in \u003ccode\u003ephotoscan_1_4_3.simg\u003c/code\u003e, below.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/ucberkeley/brc-cyberinfrastructure\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e brc-cyberinfrastructure/photoscancontainer\n$ sudo singularity build photoscan_1_4_3.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor a simple test that Photoscan runs in the container, it is not necessary to have a license:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /home/masover/:/opt photoscan_1_4_3.simg /usr/local/photoscan-pro/photoscan.sh --version -platform offscreen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe smoke-test passes if you get something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eAgisoft PhotoScan Professional Version: 1.4.3 build 6185 (64 bit)\nCopyright (C) 2017 Agisoft LLC.\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA simple functional test (which requires access to a license server that serves a valid Photoscan license) is included in the subdirectory \"container-test\" (a Python script and a set of images that the script references). Invocation of the script would look something like this (replacing, as appropriate: the /global/scratch... paths; as well as the reference to a license server on UC Berkeley\u0027s shared HPC cluster, Savio, with another license server):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSINGULARITYENV_RLM_LICENSE=5053@lmgr0@brc.berkeley.edu singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /global/scratch/username/photoscan/:/opt /global/scratch/username/containers/photoscan_1_4_3.simg /usr/local/photoscan-pro/photoscan.sh -r /opt/photoscan-test.py -platform offscreen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA successful response will look something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eAddPhotos: filenames = /opt/images/coffeecup-1.jpg\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e/opt/images/coffeecup-2.jpg\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e/opt/images/coffeecup-3.jpg\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e/opt/images/coffeecup-4.jpg\nSaveProject: chunks = 0, path = /opt/photoscan-test.psz\nsaved project \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e 0.014898 sec\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDeeper exercise of Photoscan functionality should probably not use the sample images of a coffee cup. Instead, use images provided by the Photoscan vendor (Agisoft) at \u003ca href=\"http://www.agisoft.com/downloads/sample-data/\" rel=\"nofollow\"\u003ehttp://www.agisoft.com/downloads/sample-data/\u003c/a\u003e with appropriate changes to the/a test script.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003cem\u003eThese instructions supercede a previous set that involved building a Docker container and pulling it into Singularity. As of Singularity v2.6.0 and Photoscan 1.4.3, the method given here is the one that works (and is faster besides). This repository was replicated from \u003ca href=\"https://github.com/ucberkeley/brc-cyberinfrastructure/tree/master/photoscancontainer\"\u003ehttps://github.com/ucberkeley/brc-cyberinfrastructure/tree/master/photoscancontainer\u003c/a\u003e on 2019-02-15.\u003c/em\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 11, + "subscribers_count": 17, "topics": [], - "updated_at": 1588465186.0 + "updated_at": 1550266906.0 + }, + { + "data_format": 2, + "description": "Pipeline for Microbial Analysis (Quality control, Assembly, Annotation, Resistome, Virulome, Plasmid, Serotype, Prophages, Capsule, O-Locus, Closest genome and Genome Browser", + "filenames": [ + "modules/phigaro/Singularity" + ], + "full_name": "lcerdeira/Pipa", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/assets/PipaLogo.jpeg\"\u003e\u003cimg src=\"/assets/PipaLogo.jpeg\" alt=\"PIPA_Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePIPA\u003c/h1\u003e\u003ca id=\"user-content-pipa\" class=\"anchor\" aria-label=\"Permalink: PIPA\" href=\"#pipa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a7fe5fb2b697cc730b88bb0a32f4f63a028ab93b2bd841f2ff490ed7c3911c1d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f636f756e742f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a7fe5fb2b697cc730b88bb0a32f4f63a028ab93b2bd841f2ff490ed7c3911c1d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f636f756e742f6c63657264656972612f70697061\" alt=\"Code Count\" data-canonical-src=\"https://img.shields.io/github/languages/count/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/08b864adbd2221c4390737cee649add68be8487d237da9e0bd1f122911ccc351/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/08b864adbd2221c4390737cee649add68be8487d237da9e0bd1f122911ccc351/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f6c63657264656972612f70697061\" alt=\"Main Code Base\" data-canonical-src=\"https://img.shields.io/github/languages/top/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e)\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c5776335a454c60a3b541aea37c439a6c79384844ae65c20ef44294a0bd8eba4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d312e302d726564\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c5776335a454c60a3b541aea37c439a6c79384844ae65c20ef44294a0bd8eba4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d312e302d726564\" alt=\"Version\" data-canonical-src=\"https://img.shields.io/badge/version-1.0-red\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/91d49e5df623c07567903b3f59b0a194b4c5b128b17a3b7208eae32ecb336903/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d47504c76332d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/91d49e5df623c07567903b3f59b0a194b4c5b128b17a3b7208eae32ecb336903/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d47504c76332d626c7565\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/license-GPLv3-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a7330a27eb4f3b4a817c818e46a1b38712bb6d8f0e6fa0e378cc5b486dc6f2fe/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a7330a27eb4f3b4a817c818e46a1b38712bb6d8f0e6fa0e378cc5b486dc6f2fe/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f6c63657264656972612f70697061\" alt=\"Last Commit\" data-canonical-src=\"https://img.shields.io/github/last-commit/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e)\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e8a24a7914809ae13c3bffdc4637f367d0f74caf4e47d1c4f4739215a604b95f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d7261772f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8a24a7914809ae13c3bffdc4637f367d0f74caf4e47d1c4f4739215a604b95f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d7261772f6c63657264656972612f70697061\" alt=\"Open Issues\" data-canonical-src=\"https://img.shields.io/github/issues-raw/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e)\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/56c3438d581ac116cb5340ac5ca4a36699988658dc616e2483847715b88e90bc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f7265706f2d73697a652f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/56c3438d581ac116cb5340ac5ca4a36699988658dc616e2483847715b88e90bc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f7265706f2d73697a652f6c63657264656972612f70697061\" alt=\"Repo Size\" data-canonical-src=\"https://img.shields.io/github/repo-size/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#Description\"\u003eDescription\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline for Microbial Genomic Analysis\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNeed to be root of system to be installed.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./setup.sh\u003c/code\u003e to install all necessary libraries.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDr Louise Cerdeira - \u003ca href=\"mailto:Louise.Cerdeira@gmail.com\"\u003eLouise.Cerdeira@gmail.com\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 2, + "topics": [], + "updated_at": 1722552774.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.mathematica" + "Singularity.def" ], - "full_name": "uit-no/apptainer-mathematica", - "latest_release": "0.0.3", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLocal Mathematica via Apptainer/Singularity\u003c/h1\u003e\u003ca id=\"user-content-local-mathematica-via-apptainersingularity\" class=\"anchor\" aria-label=\"Permalink: Local Mathematica via Apptainer/Singularity\" href=\"#local-mathematica-via-apptainersingularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eApptainer or Singularity\u003c/li\u003e\n\u003cli\u003eA valid Mathematica license file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Download the container image\u003c/h3\u003e\u003ca id=\"user-content-step-1-download-the-container-image\" class=\"anchor\" aria-label=\"Permalink: Step 1: Download the container image\" href=\"#step-1-download-the-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, pull the image from the release page:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/uit-no/apptainer-mathematica/releases/download/0.0.1/mathematica.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ apptainer pull https://github.com/uit-no/apptainer-mathematica/releases/download/0.0.1/mathematica.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Locate your Mathematica license file\u003c/h3\u003e\u003ca id=\"user-content-step-2-locate-your-mathematica-license-file\" class=\"anchor\" aria-label=\"Permalink: Step 2: Locate your Mathematica license file\" href=\"#step-2-locate-your-mathematica-license-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEnsure you have a valid Mathematica license file accessible on your local machine. This is required to run the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning a Mathematica Script (.wl)\u003c/h3\u003e\u003ca id=\"user-content-running-a-mathematica-script-wl\" class=\"anchor\" aria-label=\"Permalink: Running a Mathematica Script (.wl)\" href=\"#running-a-mathematica-script-wl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run your Mathematica script, use the following command with singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run --bind path_to_license_file:/root/.WolframEngine/Licensing/mathpass mathematica.sif your_script.wl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor with Apptainer\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ apptainer run --bind path_to_license_file:/root/.WolframEngine/Licensing/mathpass mathematica.sif your_script.wl\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "FrancescoEgidioFaggion/SwEng", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject\" href=\"#se4hpcproject\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2 -- From build to release and manual job execution\u003c/h2\u003e\u003ca id=\"user-content-step-2----from-build-to-release-and-manual-job-execution\" class=\"anchor\" aria-label=\"Permalink: Step 2 -- From build to release and manual job execution\" href=\"#step-2----from-build-to-release-and-manual-job-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFocus now on the correct implementation of the matrix multiplication you\nfind in \u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e. This is a\nparallel implementation that uses MPI and reads the matrices to be\nmultiplied from two files, matrixA.txt and matrixB.txt. In these files\nthe first row contains the matrix dimensions (number of rows and\ncolumns), while the other rows contain the matrix itself.\u003c/p\u003e\n\u003cp\u003eYour task is to perform the following steps:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation\u003c/strong\u003e: Use the template available here\n\u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e to create your own\ngithub repository. Add to this repository the tests you have created in\nStep1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutomating the build, test and release processes\u003c/strong\u003e: Create a CI/CD\npipeline that, when someone pushes files in the repo, executes the\nbuilding and testing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContainerizing the application\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDefine a Singularity container descriptor for the matrix\nmultiplication program and push it in your repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtend the created action to create a container image from your\ndescription.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExecuting on the cluster\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a job.sh file to run your containerized application. Make\nsure that the standard output and error are mapped to txt files.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransfer on Galileo100 your job script and the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSubmit your job to the cluster and check whether it works correctly.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePush on your github repository your job.sh file and the files\nobtained from the execution of the matrix multiplication.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3 -- Automating a job submission with containerization\u003c/h2\u003e\u003ca id=\"user-content-step-3----automating-a-job-submission-with-containerization\" class=\"anchor\" aria-label=\"Permalink: Step 3 -- Automating a job submission with containerization\" href=\"#step-3----automating-a-job-submission-with-containerization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtend the action you have created at step 3 to automate completely the\nprocess from a push on the repository to the execution of the\ncontainerized software on SLURM. To do so, you will have to move your\ncontainer from the runner to the cluster. You can either use the scp\ncommand or you can publish your image on the Singularity registry and\nthen pull it from the cluster. Don\u0027t forget to handle your secrets\nproperly! You do not want to leave passwords and authentication tokens\nvisible to everybody, so you will use the \u003ca href=\"https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions?tool=cli\"\u003esecrets\nmechanism\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1692957745.0 + "updated_at": 1717597291.0 }, { "data_format": 2, - "description": "Singularity image for VirtualBox", + "description": "A singularity image for the MGEfinder software", "filenames": [ "Singularity" ], - "full_name": "bihealth/singularity-virtualbox", + "full_name": "bhattlab/MGEfinder-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMaxQuant in Singularity\u003c/h1\u003e\u003ca id=\"user-content-maxquant-in-singularity\" class=\"anchor\" aria-label=\"Permalink: MaxQuant in Singularity\" href=\"#maxquant-in-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 6, "topics": [], - "updated_at": 1593810130.0 + "updated_at": 1586559532.0 }, { "data_format": 2, - "description": "Scripts to run Numerical Weather Prediction procedures, integrating with nwpconf and ecFlow", + "description": null, "filenames": [ - "Singularity.nwprun_f36", - "Singularity.nwprun_r8", - "Singularity.bufr2netcdf_r8", - "Singularity.simc_tools_r8", - "Singularity.simc_tools_debug_r8" + "Singularity.v2", + "Singularity.v3", + "Singularity.v1", + "Singularity.v5", + "Singularity.v6", + "Singularity.v4" ], - "full_name": "ARPA-SIMC/nwprun", + "full_name": "BensonYang1999/hpl-cuda-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNwprun\u003c/h1\u003e\u003ca id=\"user-content-nwprun\" class=\"anchor\" aria-label=\"Permalink: Nwprun\" href=\"#nwprun\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNwprun combines the configuration and scripting framework\n\u003ca href=\"https://github.com/ARPA-SIMC/nwpconf\"\u003enwpconf\u003c/a\u003e with the ECMWF\n\u003ca href=\"https://software.ecmwf.int/wiki/display/ECFLOW/\" rel=\"nofollow\"\u003eecFlow\u003c/a\u003e workflow\nmanager to create complete suites running Numerical Weather Prediction\nmodels on HPC systems.\u003c/p\u003e\n\u003cp\u003eIt is targeted at the generation and management of operational model\nsuites contaning the typical tasks involved in continuous and\nintermittent atmospheric data assimilation (using various techniques\nincluding ensemble data assimilation), and forecasting (both in\ndeterministic and in ensemble modes). The main target is real time\nsuites, but there are options for applying the system to long-period\nresearch and reanalysis suites.\u003c/p\u003e\n\u003cp\u003eNwprun includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea set of job templates for performing the different parts of the\necFlow workflow using the nwpconf framework\u003c/li\u003e\n\u003cli\u003ea set of ecFlow include files to be used by the jobs, targeted at\nslurm and pbs schedulers\u003c/li\u003e\n\u003cli\u003ea generic python module for generating ecFlow suites\u003c/li\u003e\n\u003cli\u003esome python suite generators, using the indicated module for\ngenerating specifical suite definitions\u003c/li\u003e\n\u003cli\u003ea set of configuration trees for a number of NWP suites using the\nnwpconf framework\u003c/li\u003e\n\u003cli\u003ea set of shell script to be run as cron jobs for performing\nancillary operations related to operational NWP, mainly access to\ninput data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe practical configuration files and python suite generators included\nin the package are used in the Italian LAMI modelling suites both on\n\u003ca href=\"https://www.cineca.it/\" rel=\"nofollow\"\u003eCineca\u003c/a\u003e and on\n\u003ca href=\"https://www.arpae.it/sim\" rel=\"nofollow\"\u003eArpae-SIMC\u003c/a\u003e HPC systems.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 12, - "topics": [ - "ecflow", - "nwp", - "workflow" + "subscribers_count": 1, + "topics": [], + "updated_at": 1557155075.0 + }, + { + "data_format": 2, + "description": "Nemo Utility for Testing SETTE", + "filenames": [ + "Singularity.nemo", + "base_def/Singularity.nemo_baseOS" ], - "updated_at": 1732203799.0 + "full_name": "jdha/NUTS", + "latest_release": "0.0.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNUTS\u003c/h1\u003e\u003ca id=\"user-content-nuts\" class=\"anchor\" aria-label=\"Permalink: NUTS\" href=\"#nuts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNemo Utility for Testing SETTE\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 2, + "topics": [], + "updated_at": 1724894895.0 }, { "data_format": 2, - "description": "a collection of lmod modules", + "description": "Arkiweb docker image", "filenames": [ - "singularity/macs2/2.1.2.1r0/Singularity", - "singularity/pindel/cgpPindel_2.0.1/Singularity", - "singularity/picard/2.18.17r0/Singularity", - "singularity/trimgalore/0.5.0r0/Singularity", - "singularity/repenrich2/20190521r0/Singularity", - "singularity/bedops/2.4.35r0/Singularity", - "singularity/mfold/3.6r0/Singularity", - "singularity/gatsby.js/Singularity", - "singularity/iclipro/0.1.1r0/Singularity", - "singularity/rmats/4.0.2r0/Singularity", - "singularity/rseqc/3.0.0r0/Singularity", - "singularity/cutadapt/1.18r0/Singularity", - "singularity/bedtools/2.28.0r0/Singularity", - "singularity/bedtools/2.27.1r0/Singularity", - "singularity/meme/5.0.2r0/Singularity", - "singularity/fastqc/0.11.8r0/Singularity", - "singularity/bowtie2/2.3.5.1r0/Singularity", - "singularity/bowtie2/2.3.4.3r0/Singularity", - "singularity/subread/1.6.3r0/Singularity", - "singularity/samtools/1.9r0/Singularity", - "singularity/samtools/1.9r1/Singularity", - "singularity/star/2.6.1dr0/Singularity", - "singularity/star/2.7.0fr0/Singularity", - "singularity/fastp/0.19.5r0/Singularity", - "singularity/fastp/0.20.0r0/Singularity", - "singularity/R/Bioconductor_3.11/Singularity", - "singularity/R/3.6.0r0/Singularity", - "singularity/openjdk/7u211r0/Singularity", - "singularity/openjdk/8u181r0/Singularity", - "singularity/openjdk/7u181r0/Singularity", - "singularity/openjdk/8u212r0/Singularity", - "singularity/hisat2/2.1.0r0/Singularity", - "singularity/flexbar/3.5.0r0/Singularity", - "singularity/flexbar/3.4.0r0/Singularity", - "singularity/crossmap/0.3.1r0/Singularity", - "singularity/crossmap/0.3.2r0/Singularity", - "singularity/deeptools/3.1.2r0/Singularity" + "Singularity" ], - "full_name": "imbforge/sysops", + "full_name": "ARPA-SIMC/arkiweb-docker-image", "latest_release": null, - "readme": "\u003cp\u003eA collection of stuff to keep the systems up and running\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003earkiweb-docker-image\u003c/h1\u003e\u003ca id=\"user-content-arkiweb-docker-image\" class=\"anchor\" aria-label=\"Permalink: arkiweb-docker-image\" href=\"#arkiweb-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ARPA-SIMC/arkiweb/\"\u003eArkiweb\u003c/a\u003e recently became\nincompatible with the \u003ca href=\"https://github.com/ARPA-SIMC/arkimet/\"\u003earkimet\u003c/a\u003e\nC++ API\u0027s. This package allows to create a docker container including\na web server, arkiweb and an arkiweb-compatible version of arkimet, to\nbe run within a host having a newer arkimet version, replacing arkiweb\non the host. This allows to keep arkiweb running while keeping arkimet\nupdated to the latest version.\u003c/p\u003e\n\u003cp\u003eThe web server in the host talks with the web server in the container\nthrough apache \u003ccode\u003emod_proxy\u003c/code\u003e module, while the arkiweb in the container\ninteracts with the arkimet datasets in the host through the host\narkimet server http interface.\u003c/p\u003e\n\u003cp\u003eFor more detailed instruction on how to build and start the docker\nimage and configure the system, see the \u003ca href=\"HOWTO_it.md\"\u003eHOWTO\u003c/a\u003e in\nItalian.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 7, "topics": [], - "updated_at": 1599664216.0 + "updated_at": 1636458123.0 }, { "data_format": 2, - "description": "Mycobacterial pre-processing pipeline", + "description": "Counter RNA seq Window (CRAW) compute and visualize the coverage of RNA seq experiment.", "filenames": [ - "singularity/Singularity.ppBedtools", - "singularity/Singularity.ppKraken2", - "singularity/Singularity.ppBowtie2", - "singularity/Singularity.ppFastp", - "singularity/Singularity.ppMykrobe", - "singularity/Singularity.ppFastqc", - "singularity/Singularity.ppPerljson", - "singularity/Singularity.ppBwa", - "singularity/Singularity.ppFqtools" + "Singularity.1.0", + "Singularity" ], - "full_name": "oxfordmmm/preprocessing", + "full_name": "C3BI-pasteur-fr/craw", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMycobacterial Pre-processing Pipeline\u003c/h1\u003e\u003ca id=\"user-content-mycobacterial-pre-processing-pipeline\" class=\"anchor\" aria-label=\"Permalink: Mycobacterial Pre-processing Pipeline\" href=\"#mycobacterial-pre-processing-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCleans and QCs reads with fastp and FastQC, classifies with Kraken2 \u0026amp; Mykrobe, removes non-bacterial content, and - by alignment to any minority genomes - disambiguates mixtures of bacterial reads.\u003c/p\u003e\n\u003cp\u003eTakes as input one directory containing pairs of fastq(.gz) or bam files.\nProduces as output one directory per sample, containing the relevant reports \u0026amp; a pair of cleaned fastqs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow is designed to run with either docker \u003ccode\u003e-profile docker\u003c/code\u003e or singularity \u003ccode\u003e-profile singularity\u003c/code\u003e. Before running the workflow using singularity, the singularity images for the workflow will need to be built by running \u003ccode\u003esingularity/singularity_pull.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eE.g. to run the workflow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity --filetype fastq --input_dir fq_dir --pattern \"*_R{1,2}.fastq.gz\" --unmix_myco yes \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\nnextflow run main.nf -profile docker --filetype bam --input_dir bam_dir --unmix_myco no \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParams\u003c/h2\u003e\u003ca id=\"user-content-params\" class=\"anchor\" aria-label=\"Permalink: Params\" href=\"#params\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following parameters should be set in \u003ccode\u003enextflow.config\u003c/code\u003e or specified on the command line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003einput_dir\u003c/strong\u003e\u003cbr\u003e\nDirectory containing fastq OR bam files\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efiletype\u003c/strong\u003e\u003cbr\u003e\nFile type in input_dir. Either \"fastq\" or \"bam\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003epattern\u003c/strong\u003e\u003cbr\u003e\nRegex to match fastq files in input_dir, e.g. \"*_R{1,2}.fq.gz\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eoutput_dir\u003c/strong\u003e\u003cbr\u003e\nOutput directory\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eunmix_myco\u003c/strong\u003e\u003cbr\u003e\nDo you want to disambiguate mixed-mycobacterial samples by read alignment? Either \"yes\" or \"no\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003especies\u003c/strong\u003e\u003cbr\u003e\nPrincipal species in each sample, assuming genus Mycobacterium. Default \u0027null\u0027. If parameter used, takes 1 of 10 values: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ekraken_db\u003c/strong\u003e\u003cbr\u003e\nDirectory containing \u003ccode\u003e*.k2d\u003c/code\u003e Kraken2 database files (obtain from \u003ca href=\"https://benlangmead.github.io/aws-indexes/k2\" rel=\"nofollow\"\u003ehttps://benlangmead.github.io/aws-indexes/k2\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie2_index\u003c/strong\u003e\u003cbr\u003e\nDirectory containing Bowtie2 index (obtain from ftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/hg19_1kgmaj_bt2.zip). The specified path should NOT include the index name\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie_index_name\u003c/strong\u003e\u003cbr\u003e\nName of the bowtie index, e.g. hg19_1kgmaj\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp\u003eFor more information on the parameters run \u003ccode\u003enextflow run main.nf --help\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCheckpoints\u003c/h2\u003e\u003ca id=\"user-content-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Checkpoints\" href=\"#checkpoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheckpoints used throughout this workflow to fail a sample/issue warnings:\u003c/p\u003e\n\u003cp\u003eprocesses preprocessing_checkFqValidity or preprocessing_checkBamValidity\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e(Fail) If sample does not pass fqtools \u0027validate\u0027 or samtools \u0027quickcheck\u0027, as appropriate.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eprocess preprocessing_countReads\u003cbr\u003e\n2. (Fail) If sample contains \u0026lt; 100k pairs of raw reads.\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_fastp\u003cbr\u003e\n3. (Fail) If sample contains \u0026lt; 100k pairs of cleaned reads, required to all be \u0026gt; 50bp (cleaning using fastp with --length_required 50 --average_qual 10 --low_complexity_filter --correction --cut_right --cut_tail --cut_tail_window_size 1 --cut_tail_mean_quality 20).\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_kraken2\u003cbr\u003e\n4. (Fail) If the top family hit is not Mycobacteriaceae\u003cbr\u003e\n5. (Fail) If there are fewer than 100k reads classified as Mycobacteriaceae \u003cbr\u003e\n6. (Warn) If the top family classification is mycobacterial, but this is not consistent with top genus and species classifications\u003cbr\u003e\n7. (Warn) If the top family is Mycobacteriaceae but no G1 (species complex) classifications meet minimum thresholds of \u0026gt; 5000 reads or \u0026gt; 0.5% of the total reads (this is not necessarily a concern as not all mycobacteria have a taxonomic classification at this rank) \u003cbr\u003e\n8. (Warn) If sample is mixed or contaminated - defined as containing reads \u0026gt; the 5000/0.5% thresholds from multiple non-human species\u003cbr\u003e\n9. (Warn) If sample contains multiple classifications to mycobacterial species complexes, each meeting the \u0026gt; 5000/0.5% thresholds\u003cbr\u003e\n10. (Warn) If no species classification meets the 5000/0.5% thresholds\u003cbr\u003e\n11. (Warn) If no genus classification meets the 5000/0.5% thresholds\u003cbr\u003e\n12. (Fail) If no family classification meets the 5000/0.5% thresholds (redundant given point 5)\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_identifyBacterialContaminants\u003cbr\u003e\n13. (Fail) If the sample is not contaminated and the top species hit is not one of the 10 supported Mycobacteria:\\ abscessus|africanum|avium|bovis|chelonae|chimaera|fortuitum|intracellulare|kansasii|tuberculosis\u003cbr\u003e\n14. (Fail) If the sample is not contaminated and the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003cbr\u003e\n15. (Warn) If the top species hit is supported by \u0026lt; 75% coverage\u003cbr\u003e\n16. (Warn) If the top species hit has a median coverage depth \u0026lt; 10-fold\u003cbr\u003e\n17. (Warn) If we are unable to associate an NCBI taxon ID to any given contaminant species, which means we will not be able to locate its genome, and thereby remove it as a contaminant\u003cbr\u003e\n18. (Warn) If we are unable to determine a URL for the latest RefSeq genome associated with a contaminant species\u0027 taxon ID\u003cbr\u003e\n19. (Warn) If no complete genome could be found for a contaminant species. The workflow will proceed with alignment-based contaminant removal, but you\u0027re warned that there\u0027s reduced confidence in detecting reads from this species\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_downloadContamGenomes\u003cbr\u003e\n20. (Fail) If a contaminant is detected but we are unable to download a representative genome, and thereby remove it\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_summarise\u003cbr\u003e\n21. (Fail) If after having taken an alignment-based approach to decontamination, Kraken still detects a contaminant species\u003cbr\u003e\n22. (Fail) If after having taken an alignment-based approach to decontamination, the top species hit is not one of the 10 supported Mycobacteria\u003cbr\u003e\n23. (Fail) If, after successfully removing contaminants, the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCRAW_singularity\u003c/h1\u003e\u003ca id=\"user-content-craw_singularity\" class=\"anchor\" aria-label=\"Permalink: CRAW_singularity\" href=\"#craw_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity definition files for Counter RnAseq Window\u003c/p\u003e\n\u003cp\u003eCRAW compute and visualize the coverage of RNA seq experiment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHomepage project: \u003ca href=\"https://gitlab.pasteur.fr/bneron/craw\" rel=\"nofollow\"\u003ehttps://gitlab.pasteur.fr/bneron/craw\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFull documentation: \u003ca href=\"http://bneron.pages.pasteur.fr/craw\" rel=\"nofollow\"\u003ehttp://bneron.pages.pasteur.fr/craw\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 12, "topics": [], - "updated_at": 1665133955.0 + "updated_at": 1554456657.0 }, { "data_format": 2, - "description": "Singularity image for AFLGo (https://github.com/aflgo/aflgo)", + "description": "BSMAP is a short reads mapping software for bisulfite sequencing reads.", "filenames": [ - "Singularity.1604" + "2.90/Singularity" ], - "full_name": "shub-fuzz/aflgo", - "latest_release": "0.0.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAFLGo\u003c/h1\u003e\u003ca id=\"user-content-aflgo\" class=\"anchor\" aria-label=\"Permalink: AFLGo\" href=\"#aflgo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/aflgo/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/aflgo/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5085\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for AFLGo (\u003ca href=\"https://github.com/aflgo/aflgo\"\u003ehttps://github.com/aflgo/aflgo\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name aflgo.sif https://github.com/shub-fuzz/aflgo/releases/download/0.0.2/shub-fuzz-aflgo.1604.sif\n\nsingularity shell aflgo.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "pscedu/singularity-bsmap", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bsmap/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bsmap/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bsmap/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bsmap/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bd2eaf083681486a10c0520e03a0a148590eea13bd6758c649b236ab6a147168/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62736d6170\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bd2eaf083681486a10c0520e03a0a148590eea13bd6758c649b236ab6a147168/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62736d6170\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bsmap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/90dbaf60a7819cb50da6c9d684ae66e45fe1126b4a405a06963a4a1b5af27525/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62736d6170\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90dbaf60a7819cb50da6c9d684ae66e45fe1126b4a405a06963a4a1b5af27525/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62736d6170\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bsmap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2545c029ce8c2db8d5c4130530ee4ef326e5f362b25c0ad91e3a1e3e83593bd4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62736d6170\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2545c029ce8c2db8d5c4130530ee4ef326e5f362b25c0ad91e3a1e3e83593bd4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62736d6170\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bsmap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/05de20bfc1f89c9a27c99af6439257ab0e37e91536e272e93bc4d0995c9f88c3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62736d6170\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/05de20bfc1f89c9a27c99af6439257ab0e37e91536e272e93bc4d0995c9f88c3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62736d6170\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bsmap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-bsmap\u003c/h1\u003e\u003ca id=\"user-content-singularity-bsmap\" class=\"anchor\" aria-label=\"Permalink: singularity-bsmap\" href=\"#singularity-bsmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for bsmap.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebsmap\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bsmap/2.90\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bsmap\u003c/code\u003e as \u003ccode\u003e2.90.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1623682654.0 + "subscribers_count": 4, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1636519626.0 }, { "data_format": 2, "description": null, "filenames": [ - "tests/Singularity.py3.6.8_biopython1.73_mod", - "tests/Singularity.py3.6.3_biopython1.73", - "tests/Singularity.py3.7.1_biopython1.73_mod", - "tests/Singularity.py3.7.3_biopython1.73_mod", - "tests/Singularity.py3.7.1_biopython1.73", - "tools/Singularity.deepvariant_0.8.0", - "tools/Singularity.salmon_0.14.1", - "tools/Singularity.vcftools_0.1.16", - "tools/Singularity.krakenuniq_0.5.8", - "tools/Singularity.vcflib_1.0.0-rc2", - "tools/Singularity.plink_1.90beta5", - "tools/Singularity.meraculous_2.2.6", - "tools/Singularity.sambamba_0.6.9", - "tools/Singularity.stacks_2.3e", - "tools/Singularity.biopython_1.73", - "tools/Singularity.deepbinner_0.2.0", - "tools/Singularity.star_2.7.0c", - "tools/Singularity.mummer_4.0.0beta2", - "tools/Singularity.shinotate_1.5.8.918", - "tools/Singularity.scrmshaw_20180523", - "tools/Singularity.cutadapt_2.6", - "tools/Singularity.bbmap_38.50b", - "tools/Singularity.blobtools_1.0.1", - "tools/Singularity.gatk_4.1.0.0", - "tools/Singularity.trinity_2.8.4", - "tools/Singularity.flye_2.5", - "tools/Singularity.stacks_2.0Beta9", - "tools/Singularity.ensemble-vep_96.1", - "tools/Singularity.hmmer_3.2.1", - "tools/Singularity.spades_3.13.0", - "tools/Singularity.bracken_2.2", - "tools/Singularity.R_3.6.0", - "tools/Singularity.swarm_2.2.2", - "tools/Singularity.bioconductor_3.9", - "tools/Singularity.freebayes_1.2.0", - "tools/Singularity.vt_0.57721", - "tools/Singularity.pychopper_0.6.1", - "tools/Singularity.apollo_2.2.0", - "tools/Singularity.mothur_1.40.5", - "tools/Singularity.racon_1.4.7", - "tools/Singularity.minimap2_2.17r941", - "tools/Singularity.sra_2.9.2", - "tools/Singularity.BUSCO_3.0.2", - "tools/Singularity.kraken_2.0.8beta", - "tools/Singularity.transdecoder_5.3.0", - "tools/Singularity.bwa_0.7.17", - "tools/Singularity.last_973", - "tools/Singularity.kollector_1.0.1", - "tools/Singularity.quast_5.0.2", - "tools/Singularity.clustalo_1.2.4", - "tools/Singularity.R-Mfuzz_2.38.0", - "tools/Singularity.borgbackup_1.1.6", - "tools/Singularity.purge_haplotigs_20181203", - "pipelines/Singularity.five-accessions", - "pipelines/Singularity.racon-chunks_0.0.4", - "pipelines/Singularity.basecall_wrapper_0.0.32_albacore_2.3.3", - "pipelines/Singularity.racon-chunks_py36", - "pipelines/Singularity.pinfish", - "utils/Singularity.pigz_2.4.0", - "utils/Singularity.optaweb-employee-rostering", - "utils/Singularity.samtools_1.9", - "utils/Singularity.optaplanner_7.23.0", - "utils/Singularity.openshift" + "Singularity" ], - "full_name": "TomHarrop/singularity-containers", + "full_name": "challenge-engine/test-starting-kit", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity containers\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: Singularity containers\" href=\"#singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/996\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipelines\u003c/h2\u003e\u003ca id=\"user-content-pipelines\" class=\"anchor\" aria-label=\"Permalink: Pipelines\" href=\"#pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tomharrop/5acc/\"\u003efive-accessions\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://jgi.doe.gov/data-and-tools/bbtools/\" rel=\"nofollow\"\u003eBBMap 38.00\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bioconductor.org/help/docker/\" rel=\"nofollow\"\u003eBioconductor 3.7\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://biopython.org/\" rel=\"nofollow\"\u003eBiopython 1.72\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/borgbackup/borg\"\u003eborgbackup 1.1.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://busco.ezlab.org/\" rel=\"nofollow\"\u003eBUSCO 3.0.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.clustal.org/omega/\" rel=\"nofollow\"\u003eClustal Omega 1.2.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ekg/freebayes\"\u003efreebayes 1.2.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DerrickWood/kraken2\"\u003ekraken2 2.0.7-beta\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.hmmer.org/\" rel=\"nofollow\"\u003eHMMER 3.2.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://jgi.doe.gov/data-and-tools/meraculous/\" rel=\"nofollow\"\u003emeraculous 2.2.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2 2.11 r797\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.mothur.org/wiki/Main_Page\" rel=\"nofollow\"\u003eMothur 1.40.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://mummer4.github.io/\" rel=\"nofollow\"\u003emummer 4.0.0beta2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cog-genomics.org/plink/1.9/\" rel=\"nofollow\"\u003eplink 1.09 beta 5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rrwick/Porechop\"\u003ePorechop 0.2.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://r-project.org/\" rel=\"nofollow\"\u003eR 3.5.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://halfonlab.ccr.buffalo.edu/scrmshaw.html\" rel=\"nofollow\"\u003eSCRMshaw 05142018\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/COMBINE-lab/salmon/releases\"\u003eSalmon 0.11.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://catchenlab.life.illinois.edu/stacks/\" rel=\"nofollow\"\u003eStacks 2.0b\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003eSpades 3.12.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/torognes/swarm\"\u003eSwarm 2.2.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TransDecoder/TransDecoder/wiki\"\u003eTransDecoder 5.3.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/trinityrnaseq/trinityrnaseq\"\u003eTrinity 2.6.6\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etest-starting-kit\u003c/h1\u003e\u003ca id=\"user-content-test-starting-kit\" class=\"anchor\" aria-label=\"Permalink: test-starting-kit\" href=\"#test-starting-kit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd13\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1574294473.0 + "updated_at": 1620137534.0 }, { "data_format": 2, - "description": "Singularity definitions for agalma", + "description": "Simple example container with Nix and Python", "filenames": [ - "Singularity.latest", - "versions/Singularity.1.0.1", - "versions/Singularity.1.0.0" + "Singularity" ], - "full_name": "brevans/agalma", + "full_name": "XSEDE/nix-container-python-mandle", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity \u0026amp; agalma\u003c/h1\u003e\u003ca id=\"user-content-singularity--agalma\" class=\"anchor\" aria-label=\"Permalink: Singularity \u0026amp; agalma\" href=\"#singularity--agalma\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e definition file is meant to closely mirror the dockerfile for agalma. Singularity containers are well suited for running docker-like workflows in multi-user contexts, such as HPC clusters. Please see the \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003elinux\u003c/a\u003e or \u003ca href=\"http://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003eMacOS\u003c/a\u003e install instructions to get singularity.\u003c/p\u003e\n\u003cp\u003eTo build this Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo $(which singularity) build agalma.simg Singularity.latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo pull this Singularity image from singularity-hub and run the agalma tests in current directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run shub://brevans/agalma:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-python-mandle\u003c/h1\u003e\u003ca id=\"user-content-nix-container-python-mandle\" class=\"anchor\" aria-label=\"Permalink: nix-container-python-mandle\" href=\"#nix-container-python-mandle\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis directory is a full example of using the docker-centos-nix-python template to\ncontainerize a very simple Python3 app.\u003c/p\u003e\n\u003cp\u003eThis app allows you to create a GIF file with a straight-line zoom-in of the Mandlebrot set.\nRunning the bare container will show the various commandline options available, which\nmay be confusing, as this was written immediately following in-depth perusal of\n\u003ca href=\"https://en.wikipedia.org/wiki/Mandelbrot_set\" rel=\"nofollow\"\u003eThe Wikipedia article on the Mandlebrot Set\u003c/a\u003e.\nIf you have some time available and are interested in this sort of thing, please go down\nthe rabbithole, but otherwise view this as a somewhat helpful example.\u003c/p\u003e\n\u003cp\u003eThe following steps should allow you to test this out on a system with docker and singularity installed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker build -t $USER/python-mandle .\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker run -v $PWD:$PWD -it $USER/python-mandle $PWD/mandle_ex.gif\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build mandle.sif mandle.def\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esingularity run mandle.sif -n 2 sing_mandle_ex.gif\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor submission on an HPC system using SLURM, you could use the following:\n(Assuming you\u0027ve uploaded this .sif file locally to APPDIR)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#/bin/bash\n#SBATCH -N 1\n#SBATCH -n 24\n#SBATCH -o mandle_%A.out\n\nmodule load singularity/3.5 #Versions above 3.6 are incompatible with lower versions!\n\nWORKDIR=/scratch/myuser\nAPPDIR=/home/myuser/images/\n\nsingularity run $APPDIR/mandle.sif -n 24 $WORKDIR/my_mandle.gif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 16, "topics": [], - "updated_at": 1512665600.0 + "updated_at": 1628539859.0 }, { "data_format": 2, - "description": null, + "description": "The straightforward RNA-seq Snakemake pipeline", "filenames": [ "Singularity" ], - "full_name": "baxpr/mniconn", - "latest_release": "v3.3.0-beta2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emniconn\u003c/h1\u003e\u003ca id=\"user-content-mniconn\" class=\"anchor\" aria-label=\"Permalink: mniconn\" href=\"#mniconn\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputes functional connectivity maps and matrices for a specified set of ROIs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ewremovegm_niigz\u003c/code\u003e, \u003ccode\u003ewkeepgm_niigz\u003c/code\u003e, \u003ccode\u003ewmeanfmri_niigz\u003c/code\u003e. Preprocessed fMRI data from \u003ca href=\"https://github.com/baxpr/connprep\"\u003econnprep\u003c/a\u003e. This may be supplied in atlas space or subject native space, as long as the ROI image is in the same space.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ewroi_niigz\u003c/code\u003e. ROI image. This may be an image existing within the container (e.g. the MNI space \u0027AABHHIP_LR.nii.gz\u0027). Or, it may be any supplied image. In the latter case, \u003ccode\u003ewroilabel_csv\u003c/code\u003e must also be supplied; this file must contain Label and Region columns, or may be the STATS output of a slant assessor.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ewt1_niigz\u003c/code\u003e. T1 image for the PDF report.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline\u003c/h2\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-label=\"Permalink: Pipeline\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eResample the ROI image to match the fMRI. It\u0027s assumed both are already aligned and in the same space as the ROI image.\u003c/li\u003e\n\u003cli\u003eExtract mean time series from the supplied fMRI for each ROI in the ROI image.\u003c/li\u003e\n\u003cli\u003eCompute functional connectivity: \u003ccode\u003eR\u003c/code\u003e, the correlation coefficient; and \u003ccode\u003eZ\u003c/code\u003e, the Fisher transformed correlation, \u003ccode\u003eatanh(R) * sqrt(N-3)\u003c/code\u003e where \u003ccode\u003eN\u003c/code\u003e is number of time points. The ROI-to_ROI matrix is computed, and also voxelwise connectivity maps.\u003c/li\u003e\n\u003cli\u003eGenerate a PDF report and organize outputs for XNAT.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "asgeissler/RNA-Schlange", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRNA-Schlange\u003c/h1\u003e\u003ca id=\"user-content-rna-schlange\" class=\"anchor\" aria-label=\"Permalink: RNA-Schlange\" href=\"#rna-schlange\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003cem\u003eRNA-Schlange\u003c/em\u003e (German word for \u003cem\u003esnake\u003c/em\u003e)\npipeline assesses the quality of RNA-seq data in a\nplug and play approach. Thus, a user should not need to provide any additional\nconfigurations aside from providing the read files, the genome, and\na rudimentary sample sheet file.\nThis pipeline supports both \u003cstrong\u003emicrobial\u003c/strong\u003e and \u003cstrong\u003eeukaryotic\u003c/strong\u003e experimental data,\nas well ass both \u003cstrong\u003esingle-end\u003c/strong\u003e and \u003cstrong\u003epaired-end\u003c/strong\u003e sequencing data.\nAlternatively, a user can specify a set of SRA runs that should be downloaded.\nIf you use the \u003cem\u003eRNA-Schlange\u003c/em\u003e, please consider citing:\u003c/p\u003e\n\u003cp\u003e\"Exploring the regulatory potential of RNA structures in 202 cyanobacterial genomes\"\u003cbr\u003e\nAS Geissler, EC Alvarez, C Anthon, NU Frigaard, J Gorodkin, and SE Seemann\u003cbr\u003e\n\u003cem\u003esubmitted\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"RNA-Schlange.jpg\"\u003e\u003cimg src=\"RNA-Schlange.jpg\" alt=\"Figure of the pipeline workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRNA-Schlange uses the following tools:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003esra-toolkit\u003c/strong\u003e for downloading data from SRA\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efastp\u003c/strong\u003e for trimming, filtering, and adapter removal\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSortMeRNA\u003c/strong\u003e for removal of ribosomal RNA\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSalmon\u003c/strong\u003e + \u003cstrong\u003eDESeq2\u003c/strong\u003e to quickly assess expression levels and\nover library content\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFastQC\u003c/strong\u003e + \u003cstrong\u003eMultiQC\u003c/strong\u003e for reporting and summarizing quality reports\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html#\" rel=\"nofollow\"\u003econda\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003einstall Snakemake and mamba\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ conda install -n base -c conda-forge mamba\n $ mamba install -c bioconda snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload this pipeline\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ git clone asgeissler/RNA-Schlange\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhen using the \u003ccode\u003erun.sh\u003c/code\u003e helper script,\nSnakemake will automatically install\nthe remaining dependencies (\u003cem\u003ee.g.\u003c/em\u003e fastp)\nby creating new conda environments within the workflow directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUser guide\u003c/h2\u003e\u003ca id=\"user-content-user-guide\" class=\"anchor\" aria-label=\"Permalink: User guide\" href=\"#user-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll computations and data will be stored within the directory in which\nyou downloaded this pipeline. There are two scenarios on how to use\nRNA-Schlange:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eScenario 1: User provided reads\u003c/h3\u003e\u003ca id=\"user-content-scenario-1-user-provided-reads\" class=\"anchor\" aria-label=\"Permalink: Scenario 1: User provided reads\" href=\"#scenario-1-user-provided-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn case you have RNA-seq data of your own, please create a folder \u003ccode\u003edata\u003c/code\u003e\nand place your files there.\nAlso provide the genomic sequence and annotation for your organism of\ninterest; please name the files\n\u003ccode\u003egenome.fna.gz\u003c/code\u003e and \u003ccode\u003egenome.gff.gz\u003c/code\u003e.\nYou might want to download them from\neither \u003ca href=\"https://www.ncbi.nlm.nih.gov/refseq/\" rel=\"nofollow\"\u003eRefSeq\u003c/a\u003e,\n\u003ca href=\"https://www.ebi.ac.uk/ena/browser/home\" rel=\"nofollow\"\u003eENA\u003c/a\u003e, or\na species specific\u003c/p\u003e\n\u003cp\u003eFinally, write a file \u003ccode\u003esamples.csv\u003c/code\u003e that describes each\nread file that you have provided in \u003ccode\u003edata\u003c/code\u003e.\nThe file should be comma separated (that is a \u0027,\u0027 between each value)\nand contain the columns \u0027file\u0027, \u0027batch\u0027, \u0027sample\u0027, and \u0027condition\u0027.\nIf your experiment is a pair-end RNA-seq dataset, also add the optional\n\u0027pair\u0027 column.\u003c/p\u003e\n\u003cp\u003eIn case that your sequencing facility provides md5 checksums, consider\nwriting a \u003ccode\u003echecksum.txt\u003c/code\u003e file of the form\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e abc032358XXX file1_1.fast.gz\n XYZ987654321 file1_2.fast.gz\n ....\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRNA-Schlange will then initiate a comparison of hash to verify that there\nwere not issues during the download.\u003c/p\u003e\n\u003cp\u003eFor example, the input data could look like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e \u251c\u2500\u2500 data\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 file1_1.fastq.gz\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 file1_2.fastq.gz\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 file2_1.fastq.gz\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 file2_2.fastq.gz\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 genome.fna.gz\n \u251c\u2500\u2500 (checksum.txt) # optional\n \u251c\u2500\u2500 genome.gff.gz\n \u251c\u2500\u2500 samples.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the file \u003ccode\u003esamples.csv\u003c/code\u003e describing the reads\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e batch,sample,condition,pair,file\n batch1,A,control,R1,file1_1.fastq.gz\n batch1,A,control,R2,file1_2.fastq.gz\n batch1,B,control,R1,file2_1.fastq.gz\n batch1,B,control,R2,file2_2.fastq.gz\n ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe pipline will compute an analysis folder (see below) in which\nall files corresponding to the samples are names\n\u003ccode\u003ebatch_sample_condition\u003c/code\u003e. Therefore the pipeline only accepts\nsample/condition/batch with\nalpha-numeric names (incl. dash, \u003ccode\u003e-0-9a-zA-Z\u003c/code\u003e).\nFor paired-end reads, the values for the pairs are either R1 or R2.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eScenario 2: Public data\u003c/h3\u003e\u003ca id=\"user-content-scenario-2-public-data\" class=\"anchor\" aria-label=\"Permalink: Scenario 2: Public data\" href=\"#scenario-2-public-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRNA-Schlange supports you to specify\nrun accession numbers to download from the\n\u003ca href=\"https://www.ncbi.nlm.nih.gov/sra\" rel=\"nofollow\"\u003eSRA\u003c/a\u003e database.\nAll you needed to specify is a comma separated file\nspecifying the \u0027run\u0027 and \u0027condition\u0027.\nIf the data that will be downloaded is single-end, please\nname the file \u003ccode\u003esra-SE.csv\u003c/code\u003e.\nFor paired-end data name the file `sra-PE.csv.\u003c/p\u003e\n\u003cp\u003eA hypothetical \u003ccode\u003esra\\*.csv\u003c/code\u003e should look like, example from the \u003ca href=\"https://bioconductor.org/packages/release/data/experiment/vignettes/airway/inst/doc/airway.html\" rel=\"nofollow\"\u003eairway\u003c/a\u003e dataset:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e run,condition\n SRR1039508,N61311-control\n SRR1039509,N61311-case\n SRR1039512,N052611-control\n SRR1039513,N052611-case\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline execution\u003c/h2\u003e\u003ca id=\"user-content-pipeline-execution\" class=\"anchor\" aria-label=\"Permalink: Pipeline execution\" href=\"#pipeline-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll that is needed to start the pipeline is to execute the helper script with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e bash run.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you specified the data via SRA entries, you would need to run the script\ntwice (once for the download and once for the quality assessment).\nIn the helper script, Snakemake is set to automatically install the software\ndependencies with conda.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCluster execution and singularity\u003c/h3\u003e\u003ca id=\"user-content-cluster-execution-and-singularity\" class=\"anchor\" aria-label=\"Permalink: Cluster execution and singularity\" href=\"#cluster-execution-and-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, if you prefer the computations to run in a cluster,\nRNA-Schlange comes with support for \u003cem\u003eslurm\u003c/em\u003e.\nSimply use \u003ccode\u003ebash run_slurm.sh\u003c/code\u003e after adapting the configurations to you\nsystem in \u003ccode\u003eclusterprofile_slurm/config.yaml\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf you prefer to use singularity to handle the dependencies,\nthen please use\n\u003ccode\u003ebash run_slurm_singularity.sh\u003c/code\u003e and the configuration\n\u003ccode\u003eclusterprofile_slurm_singularity/config.yaml\u003c/code\u003e.\nFor this use case, the pipeline will download a pre-build\ncontainerized\n\u003ca href=\"https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#containerization-of-conda-based-workflows\" rel=\"nofollow\"\u003econtainerized\u003c/a\u003e\nimage that includes all dependencies. Thanks to the ORAS standard,\nyou can use this image also for a docker environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendation:\u003c/strong\u003e Set the \u003ccode\u003econda-prefix\u003c/code\u003e and \u003ccode\u003esingularity-prefix\u003c/code\u003e\nto paths on your server for centralized storage of the dependencies and\ncontainer images.\nThen dependencies won\u0027t be re-installed for each new workflow instance\n(saving time and storage).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote on SRA downloading:\u003c/strong\u003e Due this pipelines internal coding to conditionally\nhandle user provided or SRA deposited RNA-seq data, it is not possible to\nsplit the downloading part into multiple jobs. Uset the\n\u003ccode\u003erun.sh\u003c/code\u003e or \u003ccode\u003erun_singularity.sh\u003c/code\u003e helpers for the downloading part\n(can be submitted to a queue as a single job).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline analysis output\u003c/h2\u003e\u003ca id=\"user-content-pipeline-analysis-output\" class=\"anchor\" aria-label=\"Permalink: Pipeline analysis output\" href=\"#pipeline-analysis-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll computatioal results of the pipeline are stored in the \u003ccode\u003eanalysis\u003c/code\u003e\ndirectory.\u003c/p\u003e\n\u003cp\u003eIf you provided a \u003ccode\u003echecksum.txt\u003c/code\u003e file that specified the\nper fastq file the expected checksums (see above), then\nthe pipeline creates states the observed checksums in\n\u003ccode\u003echecksum.txt\u003c/code\u003e with potential difference to the expected\nchecksums listed in \u003ccode\u003ediffering-checksum.txt\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe intermediary results of the pipeline are stored\nin computaitonal-chronological order as indicated by numeric\nprefixes per folder:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e10_raw\u003c/code\u003e:\u003cbr\u003e\nContains symbolic links to the files in \u003ccode\u003edata\u003c/code\u003e but\nwith renaming to \u003ccode\u003ebatch\\_sample\\_condition(\\_pair)\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e11_discarded\u003c/code\u003e, \u003ccode\u003e11_unpaired\u003c/code\u003e, \u003ccode\u003e12_clean\u003c/code\u003e:\u003cbr\u003e\nThese folders contain the discarded, unpaired, and quality filtered\nclean reads, as processed by \u003cstrong\u003efastp\u003c/strong\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e13_report/\\*.html\u003c/code\u003e:\u003cbr\u003e\nPer file, \u003cstrong\u003efastp\u003c/strong\u003e procudes html accessible reports that showcase\nthe before/after filtering statistics. Additionally, the report\nshows detailed statistics onj\npotential adapter contamination or distribution of insert sizes of\npaired-end reads.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e15_fastqc/10_raw\u003c/code\u003e, \u003ccode\u003e15_fastqc/12_clean\u003c/code\u003e, \u003ccode\u003e50_fastqc_before\u003c/code\u003e,\nand \u003ccode\u003e51_fastqc_after\u003c/code\u003e:\u003cbr\u003e\n\u003cstrong\u003eFastQC\u003c/strong\u003e is a popular tools for reporting sequenceing reads quality.\nThe \u003ccode\u003e15_fastqc\u003c/code\u003e folder contains the indibidual reports, while\nthe comprehensive \u003cstrong\u003eMultiQC\u003c/strong\u003e report that aggregates the information\nfrom all files are in the \u003ccode\u003e50_fastqc_before\u003c/code\u003e and \u003ccode\u003e51_fastqc_after\u003c/code\u003e\nfolder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e20_db\u003c/code\u003e, \u003ccode\u003e21_ribosomal_RNA\u003c/code\u003e, \u003ccode\u003e22_non-ribosomal_RNA\u003c/code\u003e:\u003cbr\u003e\nThese directories correspond to the ribosomal RNA filtering\nof \u003cstrong\u003eSortMeRNA\u003c/strong\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e30_genes.fna.gz\u003c/code\u003e, \u003ccode\u003e31_salmon_index\u003c/code\u003e, \u003ccode\u003e32_salmon\u003c/code\u003e:\u003cbr\u003e\nFor a quality control assessment of expression levels,\nRNA-Schlange quantifies expression for all genes (coding and non-coding)\nannotated in the user-provided \u003ccode\u003egenome.gff.gz\u003c/code\u003e with \u003cstrong\u003eSalmon\u003c/strong\u003e.\nThere files and folders contain the index for the gene sequences\nand the output files of \u003cstrong\u003eSalmon\u003c/strong\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e40_survey\u003c/code\u003e:\u003cbr\u003e\nBased on the expresison levels, this folder contains the\ninformation on\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eThe combined expression count matrix collected from\nthe individual Salmon runs in \u003ccode\u003ecounts.tsv\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eRelative biotype of expressed genes shown in the barplot\n\u003ccode\u003ebiotype-content.png\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eScatter plots between the samples in a condition\n\u003ccode\u003escatter-plots.png\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eA printicpal component analysis (PCA) plot with\ncondition and batch indication of the top $100$ genes with\nmost variance in expression \u003ccode\u003epca.png\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eA tentative analysis of differentially expressed genes\nas detected by \u003cstrong\u003eDESeq2\u003c/strong\u003e at a false-discovery rate (FDR) of\n$0.05$ (see optional parameters below) are in\n\u003ccode\u003eputative-diff-expression-summary.tsv\u003c/code\u003e and\n\u003ccode\u003eputative-diff-expression.tsv\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eFinally, a heatmap showing expression levels of the putative\ndifferentially expresses genes is shown in 1heatmap.png`.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e53_main_multiqc\u003c/code\u003e:\u003cbr\u003e\nA summarizing \u003cstrong\u003eMultiQC\u003c/strong\u003e report of the filtering, ribosomal removal,\nand expression levels quantification.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptional configurations\u003c/h2\u003e\u003ca id=\"user-content-optional-configurations\" class=\"anchor\" aria-label=\"Permalink: Optional configurations\" href=\"#optional-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRNA-Schlange attempts to provide a near configuration-free\nexperience of assessing the overall RNA-seq data quality.\nHowever, a user could still adapt pipeline in the\n\u003ccode\u003econfig.yaml\u003c/code\u003e file, which is format in\nthe\n\u003ca href=\"https://en.wikipedia.org/wiki/YAML#Basic_components\" rel=\"nofollow\"\u003eYAML format\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProvided transcript/gene sequences and Gencode\u003c/h3\u003e\u003ca id=\"user-content-provided-transcriptgene-sequences-and-gencode\" class=\"anchor\" aria-label=\"Permalink: Provided transcript/gene sequences and Gencode\" href=\"#provided-transcriptgene-sequences-and-gencode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of having the pipeline extract the sequences for the genes annotatated\nin the \u003ccode\u003egenome.gff.gz\u003c/code\u003e, you can provide already given gene/transcript sequences\nFor example, you can use the transcript sequences for mouse or human\nfrom the \u003ca href=\"https://www.gencodegenes.org/\" rel=\"nofollow\"\u003eGENCODE project\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e wget $URL -O analysis/30_genes.fna.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn case of GENCODE provided sequences, please adapt the \u003ccode\u003econfig.yaml\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e salmon_index_args: [\n \u0027--gencode\u0027\n ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003efastp quality filtering\u003c/h3\u003e\u003ca id=\"user-content-fastp-quality-filtering\" class=\"anchor\" aria-label=\"Permalink: fastp quality filtering\" href=\"#fastp-quality-filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe default paramters are set to\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEnsure a an overall average\n\u003ca href=\"https://en.wikipedia.org/wiki/Phred_quality_score\" rel=\"nofollow\"\u003ePhred score\u003c/a\u003e\nquality above $20$ (probabity of incorrect base call $\u0026lt; 0.01$).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLess then $10%$ of positions are read are under a score of $20$.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReads have a minimal length of $40$ nucleotides.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe adapter contamination and clipping is done for the\nIllumina universal adapter sequences\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e fastp_args: [\n \u0027--average_qual=20\u0027,\n \u0027--qualified_quality_phred=20\u0027,\n \u0027--unqualified_percent_limit=10\u0027,\n \u0027--length_required=40\u0027,\n \u0027--adapter_sequence=AGATCGGAAGAGCACACGTCTGAACTCCAGTCA\u0027,\n \u0027--adapter_sequence_r2=AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT\u0027\n ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlternative parameters are shown in the\n\u003ca href=\"https://github.com/OpenGene/fastp\"\u003efastp handbook\u003c/a\u003e, which allow for:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDe-duplication\u003c/li\u003e\n\u003cli\u003eUnque molecular identifier (UMI) processing\u003c/li\u003e\n\u003cli\u003epolyX tail trimmign\u003c/li\u003e\n\u003cli\u003eglobal trimming\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlthough fastp allows for an automatic adapter sequence detection,\nthe pipeline states the Illumina universal adapter sequences\nfor explicitly checking for potential contamination by these sequences.\nAlterantive adapter sequences are listed \u003ccode\u003eadapter_list.fa\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSortMeRNA ribosomal RNA removal\u003c/h3\u003e\u003ca id=\"user-content-sortmerna-ribosomal-rna-removal\" class=\"anchor\" aria-label=\"Permalink: SortMeRNA ribosomal RNA removal\" href=\"#sortmerna-ribosomal-rna-removal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePer default, the 16S, 18S, and 23S ribosomal RNAs are\nchoosen for the removal steps.\nThe removal is relative to represantative squences collected\nby \u003cstrong\u003eSortMeRNA\u003c/strong\u003e. Additional\nfilter sets are listed in the\n\u003ca href=\"https://github.com/biocore/sortmerna/wiki/User-manual-v4.0\"\u003ehandbook\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sortmerna: [\n \u0027silva-bac-16s-id90\u0027,\n \u0027silva-arc-16s-id95\u0027,\n \u0027silva-euk-18s-id95\u0027,\n \u0027silva-bac-23s-id98\u0027,\n \u0027silva-arc-23s-id98\u0027,\n \u0027silva-euk-28s-id98\u0027\n ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDESeq2 analysis\u003c/h3\u003e\u003ca id=\"user-content-deseq2-analysis\" class=\"anchor\" aria-label=\"Permalink: DESeq2 analysis\" href=\"#deseq2-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe assessment of putative\ndifferential gene expression is per default relative to the\nFDR $\\alpha = 0.05$. An alternative significance level can be specified\nin the configuraiton.\nFurther, if any of the generated plots might in their dimension not\nbe suitable for a dataset, other sizes can be specified.\nThe dimensions of the plots is given as a string of the format\n\u0027x\u0027 in inches.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e dge: {\n alpha: 0.05,\n dim_pca: \u002712x8\u0027,\n dim_scatter: \u002720x20\u0027,\n dim_biotype: \u00278x6\u0027\n }\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeveloper note\u003c/h2\u003e\u003ca id=\"user-content-developer-note\" class=\"anchor\" aria-label=\"Permalink: Developer note\" href=\"#developer-note\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen using this pipeline in it\u0027s containerized form (\u003cem\u003ee.g.\u003c/em\u003e\n\u003ccode\u003erun_singularity.sh\u003c/code\u003e), then an image that was build with the following commands\nwill be downloaded.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # The rule for fasterq-dump download from SRA is only contitionally\n # loaded, therefore there is a separate Dockerfile just for this part.\n snakemake --containerize \u0026gt; Dockerfile.fasterq\n # After a complete run of the pipeline (to make sure all works), snapshot the conda envs\n snakemake --containerize \u0026gt; Dockerfile\n # MANUALLY copy paste and adapt step1/2 part from Dockerfile.fasterq over\n # Convert to a singularity file\n # mambaforge does not have curl installed -\u0026gt; use wget\n # `curl URL -o PATH` becomes `wget URL -O PATH`\n # spython incorrectly doubles the \u0027/environment.yaml/environment.yaml\u0027\n spython recipe Dockerfile | \\\n sed \u0027s,curl \\([^ ]*\\) -o \\([^ ]*\\),wget \\1 -O \\2,\u0027 | \\\n sed \u0027s,/environment.yaml/environment.yaml,/environment.yaml,\u0027 \u0026gt; Singularity\n singularity build --fakeroot rnaschlange-0.1.sif Singularity\n # setup repositry credential\n singularity remote login --username \u0026lt;USER\u0026gt; oras://ghcr.io\n # + enter secret access token\n # upload image\n singularity push rnaschlange-0.1.sif oras://ghcr.io/asgeissler/rnaschlange:0.1\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1625437966.0 + "updated_at": 1727253808.0 }, { "data_format": 2, - "description": "dot and other graphviz executable in a simple singularity container", + "description": "Methylation and mqtl analysis of the developing knee", "filenames": [ - "singularity/Singularity.v1" + "Singularity" ], - "full_name": "cokelaer/graphviz4all", + "full_name": "CBFLivUni/Epigenetics-OA-Risk-Human-Skeletal-Dev", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egraphviz4all\u003c/h1\u003e\u003ca id=\"user-content-graphviz4all\" class=\"anchor\" aria-label=\"Permalink: graphviz4all\" href=\"#graphviz4all\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDEPRECATED, Aug 2020\u003c/strong\u003e: This is now part of \u003ca href=\"https://damona.readthedocs.io\" rel=\"nofollow\"\u003ehttps://damona.readthedocs.io\u003c/a\u003e project.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edamona install graphviz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA container with graphviz (\u003ca href=\"http://www.graphviz.org/\" rel=\"nofollow\"\u003ehttp://www.graphviz.org/\u003c/a\u003e) executables (dot, circo, etc).\u003c/p\u003e\n\u003cp\u003eThis is for Singularity 2.4 at least and is available on singularity-hub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name graphviz.img shub://cokelaer/graphviz4all:v1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eConversion of the dot file into SVG conterpart:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./graphviz.img dot -Tsvg test.dot -o test.svg\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDifferential methylation and mQTL analysis of cartilage tissue in the developing knee joint\u003c/h1\u003e\u003ca id=\"user-content-differential-methylation-and-mqtl-analysis-of-cartilage-tissue-in-the-developing-knee-joint\" class=\"anchor\" aria-label=\"Permalink: Differential methylation and mQTL analysis of cartilage tissue in the developing knee joint\" href=\"#differential-methylation-and-mqtl-analysis-of-cartilage-tissue-in-the-developing-knee-joint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/CBFLivUni/SarahRice_mQTL/assets/8311721/7200daa9-99a6-4db5-8f9a-6c48971f405b\"\u003e\u003cimg src=\"https://github.com/CBFLivUni/SarahRice_mQTL/assets/8311721/7200daa9-99a6-4db5-8f9a-6c48971f405b\" width=\"70%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow\u003c/h2\u003e\u003ca id=\"user-content-workflow\" class=\"anchor\" aria-label=\"Permalink: Workflow\" href=\"#workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code required to reproduce the bioinformatics analysis of the methylation and genotyping arrays.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e01_Methyl_Preprocess.Rmd\u003c/strong\u003e\u003cbr\u003e\nQuality control and normalisation of the EPIC methylation arrays\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e02_Methyl_EDA.Rmd\u003c/strong\u003e\u003cbr\u003e\nPrincipal component analysis of the methylation data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e03_Methyl_Differential.Rmd\u003c/strong\u003e\u003cbr\u003e\nDifferential methylated site and region analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e04_DMR_mFuzzClustering.Rmd\u003c/strong\u003e\u003cbr\u003e\nMfuzz clustering of CpGs and enrichment analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e05_processROADMAP.Rmd\u003c/strong\u003e\u003cbr\u003e\nOverlap of DMRs with ROADMAP chondrocyte chromatin states and ATAC-seq data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e06_DMR_TFMotifs.qmd\u003c/strong\u003e\u003cbr\u003e\nEnrichment anaylsis of transcription factor motifs in DMRs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e07_genotype_arrays.Rmd\u003c/strong\u003e\u003cbr\u003e\nGenotyping array processing (using bash scripts), quality control and identification of GWAS OA SNP LD proxies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e08_matrixQTL_prep.Rmd\u003c/strong\u003e\u003cbr\u003e\nPreparation of methylation and genotyping data for mQTL analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e09_matrixQTL_analysis.Rmd\u003c/strong\u003e\u003cbr\u003e\nIdentification and plotting of mQTLs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10_mQTLComparisons.qmd\u003c/strong\u003e\u003cbr\u003e\nComparison of the mQTLs against existing mQTLs from foetal brain and comparison of CpGs in the developmental DMRs vs the mQTLs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e11_Colocalisation.qmd\u003c/strong\u003e\u003cbr\u003e\nColocalisation of the mQTL SNPs with osteoarthritis GWAS data\u003c/p\u003e\n\u003cp\u003eAll \u003ccode\u003eRmd\u003c/code\u003e and \u003ccode\u003eqmd\u003c/code\u003e files can be rendered within RStudio.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the needed R packages\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRScript install/installRLibs.R\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [ - "dot", - "circo", - "graphviz", - "singularity" + "colocalization", + "genotyping", + "methylation", + "mqtl", + "osteoarthritis" ], - "updated_at": 1597173467.0 + "updated_at": 1723046147.0 }, { "data_format": 2, - "description": null, + "description": "Random block tower generator with analysis of physical characteristics", "filenames": [ - "singularity/Singularity", - "singularity/docker-to-singularity/Singularity" + "Singularity" ], - "full_name": "snic-nsc/nscjekyllsetup", + "full_name": "CNCLgithub/pytower", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWhat is included\u003c/h1\u003e\u003ca id=\"user-content-what-is-included\" class=\"anchor\" aria-label=\"Permalink: What is included\" href=\"#what-is-included\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe Dockerfile in this repo is used to build a docker container with Jekyll 2.1.1, under rbenv (v2.4.1), with all the required gem files, to run the NSC webpages.\u003c/li\u003e\n\u003cli\u003eA second rbenv (v2.4.0) is also installed and setup with Jekyll 3.4.2, and can be used to test code requiring a more current Jekyll.\u003c/li\u003e\n\u003cli\u003eThere is a script (compile.sh) which can be used if you want to generate html code for the webpage, without actually logging onto the container.\u003c/li\u003e\n\u003cli\u003eThere\u0027s also a Singularity recipe, to build a singularity container.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Installation\u003c/h2\u003e\u003ca id=\"user-content-docker-installation\" class=\"anchor\" aria-label=\"Permalink: Docker Installation\" href=\"#docker-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe prebuilt container is also available on the Docker hub, and can be pulled down.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull pchengi/nscjekyll\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild locally from Dockerfile\u003c/h2\u003e\u003ca id=\"user-content-build-locally-from-dockerfile\" class=\"anchor\" aria-label=\"Permalink: Build locally from Dockerfile\" href=\"#build-locally-from-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou can also build the docker container yourself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e git clone https://github.com/snic-nsc/nscjekyllsetup.git\n cd nscjekyllsetup\n sudo docker build -t nscjekyll .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStarting the docker container\u003c/h2\u003e\u003ca id=\"user-content-starting-the-docker-container\" class=\"anchor\" aria-label=\"Permalink: Starting the docker container\" href=\"#starting-the-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e sudo docker run --rm -i -d -v \u0026lt;path to checked out nscweb repo\u0026gt;:/mnt -p 4000:4000 --name nscjekyll nscjekyll bash\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThe above command starts the container, and mounts your checked out nscweb directory onto /mnt directory on the container; it also proxies port 4000 on the container onto your host machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScripted html code generation\u003c/h2\u003e\u003ca id=\"user-content-scripted-html-code-generation\" class=\"anchor\" aria-label=\"Permalink: Scripted html code generation\" href=\"#scripted-html-code-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou can generate the html code for the files in the nscweb repo, without having to login into the container, using the compile.sh script on the container. It\u0027ll write the generated files to the _site directory, within your repo. It will output the compilation message(s) onto the terminal, and also return the exit code returned by jekyll, which can be used to test if the compilation was successful. Note that the \u003ccode\u003ecompile.sh\u003c/code\u003e script takes an argument; if \u003ccode\u003ensc\u003c/code\u003e is specified, it uses \u003ccode\u003ejekyll 2.1.1\u003c/code\u003e, else it will use a more current version of Jekyll, \u003ccode\u003ejekyll 3.5.2\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker exec -it nscjekyll bash /home/nscuser/compile.sh nsc\nConfiguration file: /home/nscuser/mnt/_config.yml\n Source: /home/nscuser/mnt\n Destination: /home/nscuser/mnt/_site\n Generating... \n done.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eServing the contents using Jekyll\u003c/h2\u003e\u003ca id=\"user-content-serving-the-contents-using-jekyll\" class=\"anchor\" aria-label=\"Permalink: Serving the contents using Jekyll\" href=\"#serving-the-contents-using-jekyll\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIn order to serve the file contents using Jekyll, simply do the following:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker exec -it nscjekyll bash\nsource rubyenv nsc\ncd mnt\njekyll serve --watch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAt this point, if you don\u0027t see errors on the console, you should be able to point the browser on your host machine to localhost:4000 and view the pages.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity installation\u003c/h2\u003e\u003ca id=\"user-content-singularity-installation\" class=\"anchor\" aria-label=\"Permalink: Singularity installation\" href=\"#singularity-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe singularity build recipe is found in the singularity directory, in this repo.\u003c/li\u003e\n\u003cli\u003eTo build:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd singularity\nsudo singularity build nscjekyll.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eTo simply compile pages (such as via a script)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind \u0026lt;checked-out nscweb directory\u0026gt;:/mnt nscjekyll.simg bash /usr/local/src/nscjekyllsetup/compile.sh nsc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRun the jekyll web server, to serve pages, you could do one of the following:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind \u0026lt;checked-out nscweb directory\u0026gt;:/mnt nscjekyll.simg bash\nsource /usr/local/src/nscjekyllsetup/rubyenv nsc\ncd /mnt\njekyll serve --watch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell nscjekyll.simg\nsource /usr/local/src/nscjekyllsetup/rubyenv nsc\ncd \u0026lt;checked-out nscweb directory\u0026gt;\njekyll serve --watch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConverting Docker to Singularity\u003c/h2\u003e\u003ca id=\"user-content-converting-docker-to-singularity\" class=\"anchor\" aria-label=\"Permalink: Converting Docker to Singularity\" href=\"#converting-docker-to-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you don\u0027t wish to build a singularity container from scratch, using the recipe, you can convert it from a prebuilt docker image.\u003c/li\u003e\n\u003cli\u003eTo do this, execute the build.sh script in docker-to-singularity folder, under `singularity\u0027.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epytower\u003c/h1\u003e\u003ca id=\"user-content-pytower\" class=\"anchor\" aria-label=\"Permalink: pytower\" href=\"#pytower\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll team members must\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a branch based off the current master\u003c/li\u003e\n\u003cli\u003eAdd commits to that new branch\u003c/li\u003e\n\u003cli\u003epush the new branch and submit a pull request to master\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfig\u003c/h3\u003e\u003ca id=\"user-content-config\" class=\"anchor\" aria-label=\"Permalink: Config\" href=\"#config\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimple setups on local hosts should run fine with the \u003ccode\u003edefault.conf\u003c/code\u003e.\nHowever, if there are any issues with \u003ccode\u003esingularity\u003c/code\u003e the create a \u003ccode\u003euser.conf\u003c/code\u003e\nwith correct attributes.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edefault.conf\u003c/code\u003e reads as:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[ENV]\u003c/span\u003e\nexec:singularity \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the path to singularity binary\u003c/span\u003e\npath:julia-cont \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the path to the singularity container\u003c/span\u003e\npython:pyenv \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the name of the conda environment\u003c/span\u003e\njulia_depot:.julia \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the relative path to set JULIA_DEPOT_PATH\u003c/span\u003e\nmounts:\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs of now there are some extraneous sections in \u003ccode\u003edefault.conf\u003c/code\u003e which\ncould be of use later.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[PATHS]\u003c/span\u003e\ndatabases:output/databases\ntraces:output/traces\nrenders:output/renders\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment building\u003c/h3\u003e\u003ca id=\"user-content-environment-building\" class=\"anchor\" aria-label=\"Permalink: Environment building\" href=\"#environment-building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimply run \u003ccode\u003esetup.sh\u003c/code\u003e in the root of this repo as follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will be prompted for sudo when building the container.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esetup.sh\u003c/code\u003e will then create the container at the path specified in the config (\u003ccode\u003ejulia-cont\u003c/code\u003e by default).\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNOTE: Like many commands in this setup, variables will be bound to those specified in \u003ccode\u003euser.conf\u003c/code\u003e if present or \u003ccode\u003edefault.conf\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRuntime\u003c/h2\u003e\u003ca id=\"user-content-runtime\" class=\"anchor\" aria-label=\"Permalink: Runtime\" href=\"#runtime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInteracting with the container\u003c/h3\u003e\u003ca id=\"user-content-interacting-with-the-container\" class=\"anchor\" aria-label=\"Permalink: Interacting with the container\" href=\"#interacting-with-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter running \u003ccode\u003esetup.sh\u003c/code\u003e, you can now use \u003ccode\u003erun.sh\u003c/code\u003e to use the environment.\u003c/p\u003e\n\u003cp\u003eThe synatx of \u003ccode\u003erun.sh\u003c/code\u003e is simply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./run.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecommand\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003ecommand\u003c/code\u003e can be any arbitrary bash expression.\u003c/p\u003e\n\u003cp\u003eFor example, you can probe the python version in the conda environment using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;: ./run.sh python3 --version\nNo user config found, using default\nINFO for ENV\n path =\u0026gt; julia-cont\n mounts =\u0026gt; \n exec =\u0026gt; singularity\n julia_depot =\u0026gt; .julia\n python =\u0026gt; pyenv\nPython 3.6.8 :: Anaconda, Inc.\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs you can see \u003ccode\u003e./run.sh\u003c/code\u003e first\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLoads the available config\u003c/li\u003e\n\u003cli\u003eReads out the config\u003c/li\u003e\n\u003cli\u003eExecutes the command\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteracting with Julia\u003c/h2\u003e\u003ca id=\"user-content-interacting-with-julia\" class=\"anchor\" aria-label=\"Permalink: Interacting with Julia\" href=\"#interacting-with-julia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGetting into the \u003ccode\u003ejulia\u003c/code\u003e repl is simply\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;: ./run.sh julia\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eNo user config found, using default\nINFO for ENV\n path =\u0026gt; julia-cont\n mounts =\u0026gt; \n exec =\u0026gt; singularity\n julia_depot =\u0026gt; .julia\n python =\u0026gt; pyenv\n _\n _ _ _(_)_ | Documentation: https://docs.julialang.org\n (_) | (_) (_) |\n _ _ _| |_ __ _ | Type \"?\" for help, \"]?\" for Pkg help.\n | | | | | | |/ _` | |\n | | |_| | | | (_| | | Version 1.1.0 (2019-01-21)\n _/ |\\__\u0027_|_|_|\\__\u0027_| | Official https://julialang.org/ release\n|__/ |\n\njulia\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo make sure that \u003ccode\u003eJULIA_DEPOT_PATH\u003c/code\u003e is set to that in the config:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-julia\"\u003e\u003cpre\u003ejulia\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eDEPOT_PATH\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003eelement Array{String,\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e}\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.julia\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\njulia\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning package\u003c/h3\u003e\u003ca id=\"user-content-running-package\" class=\"anchor\" aria-label=\"Permalink: Running package\" href=\"#running-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo activate the package (ie rely on \u003ccode\u003eProject.toml\u003c/code\u003e and \u003ccode\u003eManifest.toml\u003c/code\u003e) run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-julia\"\u003e\u003cpre\u003e(v1.\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e) pkg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e activate .\n\n(kinezoo) pkg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e instantiate\n Updating registry at \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e.julia/registries/General\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\n Updating git\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003erepo \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ehttps://github.com/JuliaRegistries/General.git\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then reference code within the repo or import dependencies:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-julia\"\u003e\u003cpre\u003ejulia\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eusing\u003c/span\u003e LightGraphs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can additionally pre-compile dependencies using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-julia\"\u003e\u003cpre\u003e(kinezoo) pkg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e precompile\nPrecompiling project\u003cspan class=\"pl-k\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "stargazers_count": 0, + "subscribers_count": 2, + "topics": [], + "updated_at": 1618965313.0 + }, + { + "data_format": 2, + "description": "Molecular electrostatics singularity image", + "filenames": [ + "Singularity" + ], + "full_name": "nbcrrolls/electrostatics-singularity", + "latest_release": "v2.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container for molecular electrostatic calculations using PDB2PQR/APBS and Brownian dynamics with BrownDye.\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-molecular-electrostatic-calculations-using-pdb2pqrapbs-and-brownian-dynamics-with-browndye\" class=\"anchor\" aria-label=\"Permalink: Singularity container for molecular electrostatic calculations using PDB2PQR/APBS and Brownian dynamics with BrownDye.\" href=\"#singularity-container-for-molecular-electrostatic-calculations-using-pdb2pqrapbs-and-brownian-dynamics-with-browndye\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis singularity image contains a complete software environment for running \u003ca href=\"http://browndye.ucsd.edu/\" rel=\"nofollow\"\u003eBrownDye (version 1 and 2)\u003c/a\u003e simulations. It also includes \u003ca href=\"http://www.poissonboltzmann.org/\" rel=\"nofollow\"\u003ePDB2PQR\u003c/a\u003e and \u003ca href=\"http://www.poissonboltzmann.org/\" rel=\"nofollow\"\u003eAPBS\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease \u003ca href=\"http://eepurl.com/by4eQr\" rel=\"nofollow\"\u003eregister\u003c/a\u003e your use of APBS and PDB2PQR.\u003c/p\u003e\n\u003cp\u003eThe image has been verified to work on XSEDE \u003ca href=\"https://portal.xsede.org/sdsc-comet\" rel=\"nofollow\"\u003ecomet\u003c/a\u003e and \u003ca href=\"https://www.sdsc.edu/support/user_guides/tscc-quick-start.html\" rel=\"nofollow\"\u003eTSCC\u003c/a\u003e shared cluster at SDSC. It will automatically bind \u003ccode\u003e/cvmfs\u003c/code\u003e \u003ccode\u003e/oasis\u003c/code\u003e \u003ccode\u003e/projects\u003c/code\u003e \u003ccode\u003e/scratch\u003c/code\u003e directories, if available on the host.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing the container\u003c/h2\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull the singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://nbcrrolls/electrostatics-singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart bash shell in the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell nbcrrolls-electrostatics-singularity-master-latest.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow the container is running and we can start a BrownDye2 job (using the Thrombin example):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load browndye2\ncp -ai $BD2_PATH/examples/thrombin .\ncd thrombin\nsed -i \u0027s/-PE0//g\u0027 *\nsed -i \u0027s/\u0026lt;n_trajectories\u0026gt; 10000 /\u0026lt;n_trajectories\u0026gt; 1000 /\u0027 t_m_simulation.xml.bak\nmake all # takes about min to run\nmodule unload browndye2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if you want to use BrownDye version 1:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load browndye1\ncp -ai $BD1_PATH/thrombin-example .\ncd thrombin-example\nsed -i \u0027s/-PE0//g\u0027 *\nsed -i \u0027s/\u0026lt;n-trajectories\u0026gt; 10000 /\u0026lt;n-trajectories\u0026gt; 1000 /\u0027 input.xml.bak # limit the number of calculated trajectories\nmake all\nbd_top input.xml\nnam_simulation t-m-simulation.xml # this takes about 3 min to run\ncat results.xml\nmodule unload browndye1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter we are finished we can quit the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also access individual applications from the electrostatics container.\u003c/p\u003e\n\u003cp\u003eTo list available applications:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity apps nbcrrolls-electrostatics-singularity-master-latest.simg \napbs\npdb2pqr\nnam_simulation\nwe_simulation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run, for example, apbs calculation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec nbcrrolls-electrostatics-singularity-master-latest.simg apbs input.in\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app apbs nbcrrolls-electrostatics-singularity-master-latest.simg input.in\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis Singularity image is hosted on Singularity Hub: \u003ca href=\"https://singularity-hub.org/collections/2497\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch6 class=\"heading-element\"\u003eThis project is supported by \u003ca href=\"http://nbcr.ucsd.edu\" rel=\"nofollow\"\u003eNBCR\u003c/a\u003e.\u003c/h6\u003e\u003ca id=\"user-content-this-project-is-supported-by-nbcr\" class=\"anchor\" aria-label=\"Permalink: This project is supported by NBCR.\" href=\"#this-project-is-supported-by-nbcr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 4, "topics": [], - "updated_at": 1534270572.0 + "updated_at": 1556048171.0 }, { "data_format": 2, - "description": null, + "description": "A public Docker container for WRF 3.8.1 with Fitch patch", "filenames": [ - "containers/2024/methylKitDMRs/Singularity", - "containers/2024/dmrseq/Singularity", - "containers/2024/methylKitAvgMethylOverRegions/Singularity" + "Singularity" ], - "full_name": "BenGSt/repo_for_reizel_lab", + "full_name": "federatedcloud/Docker-WRF-3.8.1-Fitch", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBen Steinberg\u0027s Repository for Dr. Reizel\u0027s lab (The Epigenetic Editing Research Lab, Technion, Israel)\u003c/h1\u003e\u003ca id=\"user-content-ben-steinbergs-repository-for-dr-reizels-lab-the-epigenetic-editing-research-lab-technion-israel\" class=\"anchor\" aria-label=\"Permalink: Ben Steinberg\u0027s Repository for Dr. Reizel\u0027s lab (The Epigenetic Editing Research Lab, Technion, Israel)\" href=\"#ben-steinbergs-repository-for-dr-reizels-lab-the-epigenetic-editing-research-lab-technion-israel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains varius scripts for running the following genomic data analysis pipelines:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWGBS\u003c/li\u003e\n\u003cli\u003eRRBS (nugen ovation kit)\u003c/li\u003e\n\u003cli\u003eDMR finding + heatmaps\u003c/li\u003e\n\u003cli\u003eRNA-SEQ DEG\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker-WRF-3.8.1-Fitch\u003c/h1\u003e\u003ca id=\"user-content-docker-wrf-381-fitch\" class=\"anchor\" aria-label=\"Permalink: Docker-WRF-3.8.1-Fitch\" href=\"#docker-wrf-381-fitch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA public Docker container for WRF 3.8.1 with Fitch patches.\u003c/p\u003e\n\u003cp\u003eDocker image: \u003ca href=\"https://hub.docker.com/repository/docker/cornellcac/wrf\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image: \u003ca href=\"https://singularity-hub.org/collections/5227\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuild\u003c/h1\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Docker container can be built using the script \u003ca href=\"https://github.com/federatedcloud/Docker-WRF-3.8.1-Fitch/blob/main/docker-build.sh\"\u003e\u003ccode\u003edocker-build.sh\u003c/code\u003e\u003c/a\u003e,\nwhich will produce an output file named \u003ccode\u003ebuild_output.txt\u003c/code\u003e (included in the\n\u003ca href=\"https://github.com/federatedcloud/Docker-WRF-3.8.1-Fitch/blob/main/.gitignore\"\u003e\u003ccode\u003e.gitignore\u003c/code\u003e\u003c/a\u003e).\nThe build will take some time, so it is recommended to use a terminal multiplexer, such as tmux.\nOne can view the full output at any time using a text editor to open \u003ccode\u003ebuild_output.txt\u003c/code\u003e.\nTo determine what step the build it is at, one can do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat build_output.txt | grep Step | tail -n 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will print the current command Docker is executing to build the container.\nTo view Docker build errors, try:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat build_output.txt | grep ERROR\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is actually the last command in the \u003ccode\u003edocker-build.sh\u003c/code\u003e script, so Docker build\nerrors will be listed upon completion. If there are no errors listed the container\nwas built successfully. Code and dependencies should be checked independently of\na Docker build error list.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePatches\u003c/h2\u003e\u003ca id=\"user-content-patches\" class=\"anchor\" aria-label=\"Permalink: Patches\" href=\"#patches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince there are some \u003ca href=\"https://www2.mmm.ucar.edu/wrf/users/wrfv3.8/known-prob-3.8.1.html\" rel=\"nofollow\"\u003eknown problems with WRF 3.8.1\u003c/a\u003e,\nwe have implemented the following patches provided by the WRF Users page:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www2.mmm.ucar.edu/wrf/src/fix/module_radiation_driver.F.fix-for-v3.8.1.tar.gz\" rel=\"nofollow\"\u003e\u003ccode\u003emodule_radiation_driver.F\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www2.mmm.ucar.edu/wrf/src/fix/module_cu_g3_random_seed_fix.F.gz\" rel=\"nofollow\"\u003e\u003ccode\u003emodule_cu_g3.F\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www2.mmm.ucar.edu/wrf/src/fix/Registry.EM_COMMON.v381.tar.gz\" rel=\"nofollow\"\u003e\u003ccode\u003eRegistry.EM_COMMON\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll of these patches, as well as our custom patches, are included in the repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling\u003c/h2\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWRF and WPS compilation is performed in bash. Please see the \u003ca href=\"https://github.com/federatedcloud/Docker-WRF-3.8.1-Fitch/blob/main/Dockerfile\"\u003eDockerfile\u003c/a\u003e\nfor full commands.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 8, "topics": [], - "updated_at": 1733087762.0 + "updated_at": 1620413771.0 }, { "data_format": 2, - "description": "Singularity recipes for base-images containing mrtrix3.", + "description": null, "filenames": [ - "Singularity.3.0_RC3", - "Singularity.3.0_RC2" + "Singularity" ], - "full_name": "MPIB/singularity-mrtrix3", + "full_name": "CINECA-HPC/container_openmpi420_gnu930__spack160_ubuntu2004_x86_64", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-mrtrix3\u003c/h1\u003e\u003ca id=\"user-content-singularity-mrtrix3\" class=\"anchor\" aria-label=\"Permalink: singularity-mrtrix3\" href=\"#singularity-mrtrix3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/729\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipes for base-images containing mrtrix3.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emrtrix3 is pulled from its \u003ca href=\"https://github.com/MRtrix3/mrtrix3\"\u003egithub repository\u003c/a\u003e and build using cmake.\u003c/li\u003e\n\u003cli\u003ecmake and the build dependencies are installed through the debian repositories via \u003ccode\u003eapt-get install\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAt the end of the build process the image is cleaned up by:\n\u003cul\u003e\n\u003cli\u003eremoving cmake and build dependencies through \u003ccode\u003eapt-get purge\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003edeleting the package cache.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_openmpi420_gnu930__spack160_ubuntu2004_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_openmpi420_gnu930__spack160_ubuntu2004_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_openmpi420_gnu930__spack160_ubuntu2004_x86_64\" href=\"#container_openmpi420_gnu930__spack160_ubuntu2004_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1528782437.0 + "updated_at": 1614275862.0 }, { "data_format": 2, - "description": "Simple high quality GIF encoding", + "description": "VisiData is an interactive multitool for tabular data. ", "filenames": [ - "1.2.0/Singularity" + "2.7.1/Singularity", + "2.11.1/Singularity", + "2.8/Singularity", + "3.0.2/Singularity", + "2.10.2/Singularity", + "2.11/Singularity", + "2.6.1/Singularity", + "2.4/Singularity", + "3.1/Singularity" ], - "full_name": "icaoberg/singularity-gifgen", - "latest_release": "v1.2.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-gifgen\u003c/h1\u003e\u003ca id=\"user-content-singularity-gifgen\" class=\"anchor\" aria-label=\"Permalink: singularity-gifgen\" href=\"#singularity-gifgen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-gifgen/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-gifgen/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-gifgen/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-gifgen/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ddcb279561c5f18a20a4ba52ac610c17ff1211eb392eb5e0018b36cec8351abb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d67696667656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ddcb279561c5f18a20a4ba52ac610c17ff1211eb392eb5e0018b36cec8351abb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d67696667656e\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-gifgen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/da84737ceaa96074c0f93f7e597a610cf976277838f95500a1b152e68829e579/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d67696667656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/da84737ceaa96074c0f93f7e597a610cf976277838f95500a1b152e68829e579/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d67696667656e\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-gifgen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/11fa14457c5a8dcd4494066d37ab61b76bc998132ef9e8ce5bd91cb952c692ed/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d67696667656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11fa14457c5a8dcd4494066d37ab61b76bc998132ef9e8ce5bd91cb952c692ed/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d67696667656e\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-gifgen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a1cb8f35430cad386966f91019470b5e6b32c19e02067af5a14f31274ac16c69/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d67696667656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1cb8f35430cad386966f91019470b5e6b32c19e02067af5a14f31274ac16c69/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d67696667656e\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-gifgen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./images/joaquin_sabina-19dias_y_500noches.gif\"\u003e\u003cimg src=\"./images/joaquin_sabina-19dias_y_500noches.gif\" width=\"50%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003e\u003ca href=\"https://www.youtube.com/watch?v=NY_EOhHRTdo\" rel=\"nofollow\"\u003eJoaqu\u00edn Sabina - 19 d\u00edas y 500 noches\u003c/a\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-visidata", + "latest_release": "v3.0.2", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-visidata/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-visidata/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-visidata/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-visidata/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8ccda96e18c4e06a66abeac2ca4c6742ebe7cb601c19b51b52b4f3681991576b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7669736964617461\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ccda96e18c4e06a66abeac2ca4c6742ebe7cb601c19b51b52b4f3681991576b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7669736964617461\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-visidata\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9d1212476e5d75a4cb9a460f9b7b86da67f385469f99ad3ffaa66f99d3ccdaeb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7669736964617461\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9d1212476e5d75a4cb9a460f9b7b86da67f385469f99ad3ffaa66f99d3ccdaeb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7669736964617461\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-visidata\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/521097008ddb94c7331ce0e2b89298737f5dde3737ac8917329163c9e3e0cbab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7669736964617461\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/521097008ddb94c7331ce0e2b89298737f5dde3737ac8917329163c9e3e0cbab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7669736964617461\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-visidata\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b3b5cfe9b1897a6ba2ea5a4092977dc9e63453ef0404dd0b196e8f7a71fe946f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7669736964617461\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b3b5cfe9b1897a6ba2ea5a4092977dc9e63453ef0404dd0b196e8f7a71fe946f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7669736964617461\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-visidata\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-visidata\u003c/h1\u003e\u003ca id=\"user-content-singularity-visidata\" class=\"anchor\" aria-label=\"Permalink: singularity-visidata\" href=\"#singularity-visidata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.visidata.org/\" rel=\"nofollow\"\u003evisidata\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003evd\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/visidata/2.7.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/visidata\u003c/code\u003e as \u003ccode\u003e2.7.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [ "singularity", "utilities" ], - "updated_at": 1697611081.0 + "updated_at": 1729457707.0 }, { "data_format": 2, - "description": "Container (SIMG,Docker) recipes for a number of projects", + "description": "Multimodal Barlow Twins architecture trained via self-supervision using feature based augmentation.", "filenames": [ - "adni_simg/Singularity.adni_lashis_simg", - "grad_unwarp/Singularity.gradient_unwarp_singularity", - "NeuroImaging/Singularity.ants_fsl_robex", - "NeuroImaging/Singularity.ashs", - "NeuroImaging/Singularity.spm_fsl_mrtrix", - "NeuroImaging/Singularity.cpac", - "NeuroImaging/Singularity.freesurfer-6.0", - "NeuroImaging/Singularity.mrtrix", - "NeuroImaging/Singularity.adni_lashis_simg", - "NeuroImaging/Singularity.fsl_robex", - "DEEPSEACAT/Singularity.deepseacat_singularity" + "slp/Singularity" ], - "full_name": "thomshaw92/container_recipes", + "full_name": "Poulinakis-Konstantinos/Multimodal-Barlow-Twins", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContainer Recipes\u003c/h1\u003e\u003ca id=\"user-content-container-recipes\" class=\"anchor\" aria-label=\"Permalink: Container Recipes\" href=\"#container-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer (SIMG,Docker) recipes for a number of projects\u003c/p\u003e\n", + "readme": "\u003cp\u003eMaster Thesis for M.Sc. in Data Science and Machine Learning @ NTUA\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://dspace.lib.ntua.gr/xmlui/bitstream/handle/123456789/58314/Poulinakis_Konstantinos-Master_Thesis-2023-ML.pdf?sequence=1\u0026amp;isAllowed=y\" rel=\"nofollow\"\u003e\"Self Supervised Multimodal Learning for Emotion recognition\"\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Poulinakis-Konstantinos/Multimodal-Barlow-Twins/assets/75034778/7b0da713-da02-4dee-a05e-f65583632448\"\u003e\u003cimg src=\"https://github.com/Poulinakis-Konstantinos/Multimodal-Barlow-Twins/assets/75034778/7b0da713-da02-4dee-a05e-f65583632448\" alt=\"proposed architecture\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1585190745.0 + "updated_at": 1702207569.0 }, { "data_format": 2, - "description": null, + "description": "conda test", "filenames": [ - "singularity/Singularity.PyTorch", - "singularity/Singularity.PyTensorflow" + "Singularity" ], - "full_name": "huynhngoc/orion-slurm-gpu", + "full_name": "FelixKrueger/Singularity_Test2", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity_Test2\u003c/h1\u003e\u003ca id=\"user-content-singularity_test2\" class=\"anchor\" aria-label=\"Permalink: Singularity_Test2\" href=\"#singularity_test2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003econda test\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1667480068.0 + "updated_at": 1537794737.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.latest" + "Singularity" ], - "full_name": "EPI-APE/simu_IV", + "full_name": "ddbj/singularity_apache_jekyll", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity image\u306e\u30d3\u30eb\u30c9\u003c/h1\u003e\u003ca id=\"user-content-singularity-image\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" aria-label=\"Permalink: singularity image\u306e\u30d3\u30eb\u30c9\" href=\"#singularity-image\u306e\u30d3\u30eb\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build ubuntu-18.04-apache2-jekyll.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejekyll\u3067\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u3059\u308b\u30bd\u30fc\u30b9\u306e\u6e96\u5099\u003c/h1\u003e\u003ca id=\"user-content-jekyll\u3067\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u3059\u308b\u30bd\u30fc\u30b9\u306e\u6e96\u5099\" class=\"anchor\" aria-label=\"Permalink: jekyll\u3067\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u3059\u308b\u30bd\u30fc\u30b9\u306e\u6e96\u5099\" href=\"#jekyll\u3067\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u3059\u308b\u30bd\u30fc\u30b9\u306e\u6e96\u5099\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u9069\u5f53\u306a\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u4f5c\u6210\u3057\u3001jekyll\u306e\u30c7\u30fc\u30bf\u3092\u305d\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u5185\u306b\u7f6e\u304f\u3002\u003c/p\u003e\n\u003cp\u003estart_container-build.sh \u307e\u305f\u306f start_container-serve.sh \u306e SOURCE_DIR\u5909\u6570\u306e\u5024\u3092\u30c7\u30fc\u30bf\u3092\u5165\u308c\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d1\u30b9\u306b\u4fee\u6b63\u3059\u308b\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity instance \u306e\u8d77\u52d5\u003c/h1\u003e\u003ca id=\"user-content-singularity-instance-\u306e\u8d77\u52d5\" class=\"anchor\" aria-label=\"Permalink: singularity instance \u306e\u8d77\u52d5\" href=\"#singularity-instance-\u306e\u8d77\u52d5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ejekyll\u3092build\u3067\u5b9f\u884c\u3057\u3066apache2\u306eDocumentRoot\u306b\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u51fa\u529b\u3055\u305b\u3001\u751f\u6210\u3057\u305f\u30b5\u30a4\u30c8\u3092apache2\u3067\u516c\u958b\u3059\u308b\u5834\u5408\u306fstart_container-build.sh\u3092\u5b9f\u884c\u3059\u308b\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container-build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ejekyll\u3092serve\u3067\u5b9f\u884c\u3057\u3001jekyll\u306ehttp\u30b5\u30fc\u30d0\u3092apache2\u306e\u30ea\u30d0\u30fc\u30b9\u30d7\u30ed\u30ad\u30b7\u3067\u53d7\u3051\u308b\u5834\u5408\u306fstart_container-serve.sh\u3092\u5b9f\u884c\u3059\u308b\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container-serve.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3044\u305a\u308c\u306e\u5834\u5408\u3082httpd.conf.build\u307e\u305f\u306fhttpd.conf.serve\u306eListen\u30c7\u30a3\u30ec\u30af\u30c6\u30a3\u30d6\u306bsingularity instance\u3067\u4f7f\u7528\u3059\u308b\u30dd\u30fc\u30c8\u756a\u53f7\u3092\u8a2d\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1566030551.0 + "updated_at": 1593769075.0 }, { "data_format": 2, - "description": "selective regression testing on method level for node.js apps", + "description": "Build scripts to create singularity containers for kaldi + pop-up-archive", "filenames": [ - "docker/Singularity.snowflake" + "Singularity.in" ], - "full_name": "charlie-cyf/nodeSRT", + "full_name": "AudiovisualMetadataPlatform/kaldi-pua-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSelective Regression Testing for Node.js\u003c/h1\u003e\u003ca id=\"user-content-selective-regression-testing-for-nodejs\" class=\"anchor\" aria-label=\"Permalink: Selective Regression Testing for Node.js\" href=\"#selective-regression-testing-for-nodejs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003einstall:\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: install:\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# install dependencies\nnpm i\n\n# install NodeSRT package\ncd nodeSRT\nsudo npm i .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eusage:\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003euse \u003ccode\u003enodeSRT --help\u003c/code\u003e to get help \u003cbr\u003e\nuse this command to run nodeSRT\n\u003ccode\u003enodeSRT -b \u0026lt;codebase dir\u0026gt; -d \u0026lt;diff patch dir\u0026gt; \u0026lt;options\u0026gt;\u003c/code\u003e \u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eoptions\u003c/h3\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e-b, --basefolder \u0026lt;path\u0026gt;\u003c/code\u003e the path to codebase to be analyzed \u003cbr\u003e\n\u003ccode\u003e-d --diffFile \u0026lt;path\u0026gt;\u003c/code\u003e path to diff file \u003cbr\u003e\n\u003ccode\u003e--config \u0026lt;path\u0026gt;\u003c/code\u003e path to JSON config file \u003cbr\u003e\n\u003ccode\u003e--e2e \u0026lt;boolean\u0026gt;\u003c/code\u003e run analyze on e2e tests \u003cbr\u003e\n\u003ccode\u003e--skipGetDependency \u0026lt;boolean\u0026gt;\u003c/code\u003e skip get denpendency graph step, has to set \u003ccode\u003e--callGraph\u003c/code\u003e \u003ccode\u003e--fileDependencyGraph\u003c/code\u003e \u003ccode\u003e--E2EdenpendencyGraph\u003c/code\u003e as well \u003cbr\u003e\n\u003ccode\u003e--callGraph \u0026lt;path\u0026gt;\u003c/code\u003e path to callgraph \u003cbr\u003e\n\u003ccode\u003e--fileDependencyGraph \u0026lt;path\u0026gt;\u003c/code\u003e path to file denpendency graph \u003cbr\u003e\n\u003ccode\u003e--E2EdenpendencyGraph \u0026lt;path\u0026gt;\u003c/code\u003e dir to E2e dependencyGraph \u003cbr\u003e\n\u003ccode\u003e--excepts \u0026lt;array\u0026gt;\u003c/code\u003e array of files to be excluded when injection \u003cbr\u003e\n\u003ccode\u003e--docker \u0026lt;boolean\u0026gt;\u003c/code\u003e whether running in docker or ci environment, if it is then skip user inputs\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eoptions only in configuration files only: \u003cbr\u003e\n\u003ccode\u003eexcepts: \u0026lt;array of file paths\u0026gt;\u003c/code\u003e files to be excepted in injection step \u003cbr\u003e\n\u003ccode\u003ee2eTestSuite: \u0026lt;object\u0026gt;\u003c/code\u003e e2e configuration object, includes \u003ccode\u003especs\u003c/code\u003e and \u003ccode\u003eexclude\u003c/code\u003e to specify e2e test suite \u003cbr\u003e\n\u003ccode\u003eincludesE2E: \u0026lt;boolean\u0026gt;\u003c/code\u003e run analyze on e2e tests \u003cbr\u003e\n\u003ccode\u003eonlyE2E: \u0026lt;boolean\u0026gt;\u003c/code\u003e only run e2e analysis, skip unit tests \u003cbr\u003e\n\u003ccode\u003erunUnitTestsInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to run unit test. e.g. \u003ccode\u003enpm run test:unit\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003eE2EprerunInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to setup e2e tests \u003cbr\u003e\n\u003ccode\u003eE2EpostrunInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to run after e2e tests finished \u003cbr\u003e\n\u003ccode\u003ebuildE2EInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to build code for e2e tests \u003cbr\u003e\n\u003ccode\u003erunE2EInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to run e2e tests \u003cbr\u003e\n\u003ccode\u003eE2Edir: \u0026lt;string\u0026gt;\u003c/code\u003e path E2E test folder \u003cbr\u003e\n\u003ccode\u003eE2Etemp: \u0026lt;string\u0026gt;\u003c/code\u003e test folder to be copied to E2E test folder if you want to use your own E2E test folder \u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esince each End-to-end test suite set up differently, you might want to customize \u003ca href=\"https://github.com/charlie-cyf/nodeSRT/tree/master/e2eTestsHandler\"\u003ee2e test handler\u003c/a\u003e for your e2e test suite\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eexamples\u003c/h3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esample nodeSRT command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enodeSRT -b ./simorgh -d ./diff.patch --config ./nodeSRT/example/dockerConfig.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou can find some sample configuration files in \u003ca href=\"https://github.com/charlie-cyf/nodeSRT/tree/master/example\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow it works\u003c/h2\u003e\u003ca id=\"user-content-how-it-works\" class=\"anchor\" aria-label=\"Permalink: How it works\" href=\"#how-it-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSteps: \u003cbr\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-steps-\" class=\"anchor\" aria-label=\"Permalink: Steps: \" href=\"#steps-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003estatic analysis on each js file, generate dependency graph on functions and fields\u003c/li\u003e\n\u003cli\u003einject dynamic analysis code to each function\u003c/li\u003e\n\u003cli\u003erun tests one by one, get entities influenced by each tests. Store as json file.\u003c/li\u003e\n\u003cli\u003eparse diff file, identify changes.\u003c/li\u003e\n\u003cli\u003egenerate a list of tests on test-entities relationship.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edesign decisions\u003c/h3\u003e\u003ca id=\"user-content-design-decisions\" class=\"anchor\" aria-label=\"Permalink: design decisions\" href=\"#design-decisions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003esince the selection is at method level, if a modification is outsid of the method, we will run all tests have dependencies on this file.\u003c/li\u003e\n\u003cli\u003eAlthough astring can handle comments, some special comments are misplaced after injection. e.g. @ts-ignore Therefore, they need to be ignore when doing code injection. Otherwise we will have a build error.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003edocker comamnd\u003c/h2\u003e\u003ca id=\"user-content-docker-comamnd\" class=\"anchor\" aria-label=\"Permalink: docker comamnd\" href=\"#docker-comamnd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edocker build -t srtdock:latest .\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003edocker run --rm -ti srtdock\u003c/code\u003e \u003cbr\u003e\u003c/p\u003e\n\u003cp\u003epush to docker hub: \u003ca href=\"https://ropenscilabs.github.io/r-docker-tutorial/04-Dockerhub.html\" rel=\"nofollow\"\u003ehttps://ropenscilabs.github.io/r-docker-tutorial/04-Dockerhub.html\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker login\ndocker tag f645182d6e68 charlie9731/srtdock:v2\ndocker push charlie9731/srtdock\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build -s srtSingular.sif docker://charlie9731/srtdock:v2\nsingularity run -w -e srtSingular.sif \u0026lt;commit id\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRHEL install docker script\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo dnf config-manager --add-repo=https://download.docker.com/linux/centos/docker-ce.repo\nsudo dnf install -y vim git curl\nsudo dnf install --nobest docker-ce\nsudo groupadd docker\nsudo usermod -aG docker ec2-user\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eexperiment data\u003c/h2\u003e\u003ca id=\"user-content-experiment-data\" class=\"anchor\" aria-label=\"Permalink: experiment data\" href=\"#experiment-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eyou can find experiment data in \u003ca href=\"https://github.com/charlie-cyf/nodeSRT/tree/master/experiment\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Kaldi + PUA\u003c/h1\u003e\u003ca id=\"user-content-singularity-kaldi--pua\" class=\"anchor\" aria-label=\"Permalink: Singularity Kaldi + PUA\" href=\"#singularity-kaldi--pua\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild (working) Singularity containers with Kaldi and the Pop-Up-Archive\ntraining with both CPU and GPU support.\u003c/p\u003e\n\u003cp\u003eDisclaimer: With the exception of the scripts in the top directory, all\nof the content was either pulled directly or inspired by other sources,\nincluding (but not limited to):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/hipstas/kaldi-pop-up-archive/tags\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/hipstas/kaldi-pop-up-archive/tags\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kaldi-asr/kaldi/blob/master/misc/docker/ubuntu-cuda/Dockerfile\"\u003ehttps://github.com/kaldi-asr/kaldi/blob/master/misc/docker/ubuntu-cuda/Dockerfile\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/brandeis-llc/aapb-pua-kaldi-docker\"\u003ehttps://github.com/brandeis-llc/aapb-pua-kaldi-docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://xtra.arloproject.com/datasets/kaldi-pop-up-archive/repo_backups/\" rel=\"nofollow\"\u003ehttp://xtra.arloproject.com/datasets/kaldi-pop-up-archive/repo_backups/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlso, there are some really...unpleasant...scripts in this mix. They\u0027re not mine and I have no idea how they work, but they seem to, so hooray!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the containers\u003c/h2\u003e\u003ca id=\"user-content-building-the-containers\" class=\"anchor\" aria-label=\"Permalink: Building the containers\" href=\"#building-the-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe build_singularity.sh script will build the container. It takes one\nargument: either \u0027gpu\u0027 or \u0027cpu\u0027. The build process is nearly identical,\nbut if you select the \u0027gpu\u0027 option, it will require SUDO access to build\nthe container. It will ask you when it\u0027s time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the container\u003c/h2\u003e\u003ca id=\"user-content-running-the-container\" class=\"anchor\" aria-label=\"Permalink: Running the container\" href=\"#running-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe containers are designed to be standalone, but due to the scripts inside,\nthe do require a writable overlay filesystem. The script run_kaldi.sh\ntakes care of it -- it will create a sparce overlay filesystem which will\nbe discarded when the processing has finished.\u003c/p\u003e\n\u003cp\u003eWhen deploying, only the .sif files and run_kaldi.sh need to be copied to\nthe run-time server.\u003c/p\u003e\n\u003cp\u003eThe syntax to run it is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e run_kaldi.sh \u0026lt;mode\u0026gt; \u0026lt;media_directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe mode is either \u0027cpu\u0027 or \u0027gpu\u0027, which is used to select which image to\nuse.\u003c/p\u003e\n\u003cp\u003eThe media_directory should hold files and the transcripts will be placed\nin this directory in a transcripts directory\u003c/p\u003e\n\u003cp\u003eTo test it, try:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./run_kaldi.sh cpu test_files\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1621883262.0 + "updated_at": 1659367162.0 }, { "data_format": 2, - "description": null, + "description": "Singularity example 14: installing R packages", "filenames": [ - "Singularity.whatshap_491ec8e", - "Singularity.shapeit_v2.r904", - "Singularity.vcftools_0.1.16", - "Singularity.stacks_2.53", - "Singularity.easysfs_c2b26c5", - "Singularity.vcflib_1.0.1", - "Singularity.transindel_7098bd6", - "Singularity.sniffles_f958698", - "Singularity.bayescan_2.1", - "Singularity.deepvariant_0.9.0", - "Singularity.freebayes_1.3.1", - "Singularity.deepvariant_0.9.0-gpu" + "Singularity_1", + "Singularity_3", + "Singularity_5", + "Singularity_2" ], - "full_name": "TomHarrop/variant-utils", - "latest_release": null, + "full_name": "richelbilderbeek/singularity_example_14", + "latest_release": "v1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_example_14\u003c/h1\u003e\u003ca id=\"user-content-singularity_example_14\" class=\"anchor\" aria-label=\"Permalink: singularity_example_14\" href=\"#singularity_example_14\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity example 14: installing R packages.\u003c/p\u003e\n\u003cp\u003eThe goal of this example is to create a Singularity image with\nan R package installed and using it on an R script.\u003c/p\u003e\n\u003cp\u003eThe R package we\u0027ll use is \u003ca href=\"https://CRAN.R-project.org/package=glue\" rel=\"nofollow\"\u003eglue\u003c/a\u003e,\nas it is a simple R package without dependencies.\u003c/p\u003e\n\u003cp\u003eThis is the R script, called \u003ca href=\"script.R\"\u003escript.R\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eglue::glue(\"Hello {target}\", target = \"world\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eAttempt 3: clean up\u003c/code\u003e is the best way:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild with sudo (i.e. no \u003ccode\u003e--fakeroot\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003esend the script text to the container, not the script filename\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 1: Singularity does not run scripts\u003c/h1\u003e\u003ca id=\"user-content-attempt-1-singularity-does-not-run-scripts\" class=\"anchor\" aria-label=\"Permalink: Attempt 1: Singularity does not run scripts\" href=\"#attempt-1-singularity-does-not-run-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"Singularity_1\"\u003eSingularity_1\u003c/a\u003e is a minimal Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: r-base\n\n%post\n Rscript -e \u0027install.packages(\"glue\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_1.sh\"\u003ebuild_singularity_1.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_1\"\u003eSingularity_1\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_1.sif Singularity_1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_1.sh\"\u003erun_singularity_1.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_1\"\u003eSingularity_1\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_1.sif Singularity_1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine.\u003c/p\u003e\n\u003cp\u003eThe error GHA gives, however, is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRun ./run_singularity_1.sh\nFatal error: cannot open file \u0027script.R\u0027: No such file or directory\nError: Process completed with exit code 2.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is a common theme: Singularity cannot run scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 2: Singularity can run script text\u003c/h1\u003e\u003ca id=\"user-content-attempt-2-singularity-can-run-script-text\" class=\"anchor\" aria-label=\"Permalink: Attempt 2: Singularity can run script text\" href=\"#attempt-2-singularity-can-run-script-text\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"Singularity_2\"\u003eSingularity_2\u003c/a\u003e is a minimal Singularity container,\nwith a \u003ccode\u003erunscript\u003c/code\u003e section added:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: r-base\n\n%apprun R\nexec R \"$@\"\n\n%apprun Rscript\nexec Rscript \"$@\"\n\n%runscript\nexec Rscript \"$@\"\n# exec R \"$@\"\n\n%post\n Rscript -e \u0027install.packages(\"glue\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_2.sh\"\u003ebuild_singularity_2.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_2\"\u003eSingularity_2\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_2.sif Singularity_2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_2.sh\"\u003erun_singularity_2.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_2\"\u003eSingularity_2\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\ncat script.R | singularity exec singularity_2.sif R --vanilla --silent --no-echo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, also on GitHub Actions!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 3: clean up\u003c/h1\u003e\u003ca id=\"user-content-attempt-3-clean-up\" class=\"anchor\" aria-label=\"Permalink: Attempt 3: clean up\" href=\"#attempt-3-clean-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eTHIS IS THE BEST WAY\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e is a minimal Singularity container,\nwith a \u003ccode\u003erunscript\u003c/code\u003e section added:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: r-base\n\n%runscript\nexec R --vanilla --silent --no-echo \"$@\"\n\n%post\n Rscript -e \u0027install.packages(\"glue\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_3.sh\"\u003ebuild_singularity_3.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_3.sif Singularity_3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_3.sh\"\u003erun_singularity_3.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\ncat script.R | ./singularity_3.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, also on GitHub Actions!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 4: fakeroot experiment\u003c/h1\u003e\u003ca id=\"user-content-attempt-4-fakeroot-experiment\" class=\"anchor\" aria-label=\"Permalink: Attempt 4: fakeroot experiment\" href=\"#attempt-4-fakeroot-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this case, we\u0027ll re-use \u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e,\nyet build it differently, using the\n\u003ca href=\"https://sylabs.io/guides/3.8/user-guide/fakeroot.html?highlight=fakeroot\" rel=\"nofollow\"\u003efakeroot\u003c/a\u003e\nfeature.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_4.sh\"\u003ebuild_singularity_4.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsingularity build --fakeroot singularity_4.sif Singularity_3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_4.sh\"\u003erun_singularity_4.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_4\"\u003eSingularity_4\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\ncat script.R | ./singularity_4.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, but fails on GitHub Actions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRun ./build_singularity_4.sh\nFATAL: could not use fakeroot: no mapping entry found in /etc/subuid for root\nError: Process completed with exit code 255.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eApparently, GHA does not support that mapping.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 5: run script directly revised\u003c/h1\u003e\u003ca id=\"user-content-attempt-5-run-script-directly-revised\" class=\"anchor\" aria-label=\"Permalink: Attempt 5: run script directly revised\" href=\"#attempt-5-run-script-directly-revised\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e is a minimal Singularity container,\nwith a \u003ccode\u003erunscript\u003c/code\u003e section added:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: r-base\n\n%runscript\nexec Rscript \"$@\"\n\n%post\n Rscript -e \u0027install.packages(\"glue\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_5.sh\"\u003ebuild_singularity_5.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsingularity build --fakeroot singularity_5.sif Singularity_5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_5.sh\"\u003erun_singularity_5.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n./singularity_5.sif script.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, but fails on GitHub Actions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRun ./build_singularity_5.sh\nFATAL: could not use fakeroot: no mapping entry found in /etc/subuid for root\nError: Process completed with exit code 255.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 6: run script, container built with sudo\u003c/h1\u003e\u003ca id=\"user-content-attempt-6-run-script-container-built-with-sudo\" class=\"anchor\" aria-label=\"Permalink: Attempt 6: run script, container built with sudo\" href=\"#attempt-6-run-script-container-built-with-sudo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere we will re-use \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_6.sh\"\u003ebuild_singularity_6.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_6.sif Singularity_5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_6.sh\"\u003erun_singularity_6.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n./singularity_6.sif script.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, however, on GHA this goes the classic sideways again:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRun ./run_singularity_6.sh\nFatal error: cannot open file \u0027script.R\u0027: No such file or directory\nError: Process completed with exit code 2.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1606169142.0 + "updated_at": 1627629507.0 }, { "data_format": 2, - "description": "Singularity containers to run Pointwise 18.0", + "description": "testing building containers with singularity hub webhook ", "filenames": [ - "Singularity.template", - "Singularity.local", "Singularity" ], - "full_name": "stephansmit/pointwise_containers", + "full_name": "eharkins/singularityhub-test", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity containers for Pointwise\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers-for-pointwise\" class=\"anchor\" aria-label=\"Permalink: Singularity containers for Pointwise\" href=\"#singularity-containers-for-pointwise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainers to run \u003ca href=\"https://www.pointwise.com/\" rel=\"nofollow\"\u003ePointwise\u003c/a\u003e version 18.0.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container\u003c/h2\u003e\u003ca id=\"user-content-build-the-container\" class=\"anchor\" aria-label=\"Permalink: Build the container\" href=\"#build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLocal build\u003c/h3\u003e\u003ca id=\"user-content-local-build\" class=\"anchor\" aria-label=\"Permalink: Local build\" href=\"#local-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build pointwise_containers.sif Singularity.local\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Hub build\u003c/h3\u003e\u003ca id=\"user-content-singularity-hub-build\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub build\" href=\"#singularity-hub-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUpload the installer to a temporary location via \u003ca href=\"https://www.file.io/\" rel=\"nofollow\"\u003efile.io\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./upload_files.sh \u0026lt;Installer_Dir\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFill in the links in the recipe\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./make_recipe.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePush the image to github\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit add Singularity; git commit -m \"latest image\"; git push;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTrigger the build on \u003ca href=\"https://singularity-hub.org/collections/3396\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull a container\u003c/h2\u003e\u003ca id=\"user-content-pull-a-container\" class=\"anchor\" aria-label=\"Permalink: Pull a container\" href=\"#pull-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/pointwise_containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecute Pointwise script\u003c/h2\u003e\u003ca id=\"user-content-execute-pointwise-script\" class=\"anchor\" aria-label=\"Permalink: Execute Pointwise script\" href=\"#execute-pointwise-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo execute a pointwise script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSINGULARITYENV_pwid_LICENSE=\u0026lt;port\u0026gt;@\u0026lt;host\u0026gt; singularity exec pointwise_containers.sif /opt/pointwise/pointwise -b \u0026lt;script-name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;port\u0026gt;\u003c/code\u003e and \u003ccode\u003e\u0026lt;host\u0026gt;\u003c/code\u003e point to the license server\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3396\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1596800030.0 + "updated_at": 1548377344.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image for VirtualBox", "filenames": [ - "Singularity.PhaGCN", - "Singularity.smurf.v0.1", - "Singularity.mtags.v1.1_cs", - "Singularity.mongodb.v0.1", - "Singularity.qiime2-smurf.v0.1", - "Singularity.samestr", - "Singularity.fish_probes.v0.1", - "Singularity.mapseq.v.2.0.1alpha", - "Singularity.vknight.v0.12", - "Singularity.carveme", - "Singularity.reCOGnise.v0.1", - "Singularity.gffread", - "Singularity.prokka", - "Singularity.humann3", - "Singularity.vknight.v0.14_with_idtaxa", - "Singularity.reCOGnise.0.4.5", - "Singularity.bbmap", - "Singularity.vknight.v0.6.5", - "Singularity.vknight.v0.13_collate" + "Singularity" ], - "full_name": "cschu/container-forge", + "full_name": "bihealth/singularity-virtualbox", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMaxQuant in Singularity\u003c/h1\u003e\u003ca id=\"user-content-maxquant-in-singularity\" class=\"anchor\" aria-label=\"Permalink: MaxQuant in Singularity\" href=\"#maxquant-in-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1658328247.0 + "updated_at": 1593810130.0 }, { "data_format": 2, - "description": "Container for the BraTS 2023 challenge using the TCuPGAN model", + "description": null, "filenames": [ - "project/Singularity.recipe" + "src/llama/Singularity.def", + "src/translation/Singularity.def" ], - "full_name": "ramanakumars/brats2023-tcupgan", + "full_name": "timopetric/LLaMA-T5-Slovene-Paraphrasing", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebrats2023-tcupgan\u003c/h1\u003e\u003ca id=\"user-content-brats2023-tcupgan\" class=\"anchor\" aria-label=\"Permalink: brats2023-tcupgan\" href=\"#brats2023-tcupgan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer for the BraTS 2023 challenge using the TCuPGAN model\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLLaMA-T5-Slovene-Paraphrasing\u003c/h1\u003e\u003ca id=\"user-content-llama-t5-slovene-paraphrasing\" class=\"anchor\" aria-label=\"Permalink: LLaMA-T5-Slovene-Paraphrasing\" href=\"#llama-t5-slovene-paraphrasing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNatural Language Processing course 2022-23 at Faculty of Computer and Information Science at University of Ljubljana\u003c/h3\u003e\u003ca id=\"user-content-natural-language-processing-course-2022-23-at-faculty-of-computer-and-information-science-at-university-of-ljubljana\" class=\"anchor\" aria-label=\"Permalink: Natural Language Processing course 2022-23 at Faculty of Computer and Information Science at University of Ljubljana\" href=\"#natural-language-processing-course-2022-23-at-faculty-of-computer-and-information-science-at-university-of-ljubljana\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a NLP course project in which we explored ways of Slovene language sentence paraphrisation.\nThe best approach turned out to be traslating original Slovene sentence into English, use Vicuna/LLaMA (1st gen) to paraphrase the sentence and then translate the results back into Slovene.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe \u003ca href=\"report/NLP_project_Paraphrasing_Sentences.pdf\"\u003ePDF report describing the methodology is located here\u003c/a\u003e.\u003c/h3\u003e\u003ca id=\"user-content-the-pdf-report-describing-the-methodology-is-located-here\" class=\"anchor\" aria-label=\"Permalink: The PDF report describing the methodology is located here.\" href=\"#the-pdf-report-describing-the-methodology-is-located-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTeam members:\u003c/h3\u003e\u003ca id=\"user-content-team-members\" class=\"anchor\" aria-label=\"Permalink: Team members:\" href=\"#team-members\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eMatej Kranjec\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eTimotej Petri\u010d\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eDomen Vilar\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003c/h4\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEvaluation\u003c/h2\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eplot_distribution_scores.py\u003c/h4\u003e\u003ca id=\"user-content-plot_distribution_scorespy\" class=\"anchor\" aria-label=\"Permalink: plot_distribution_scores.py\" href=\"#plot_distribution_scorespy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script plots the distribution of scores and writes the best paraphrases to a file based on the provided scores and paraphrases directories.\u003c/p\u003e\n\u003cp\u003eArguments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--scores_path\u003c/code\u003e: Path to the directory containing the scores files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--paraphrases_path\u003c/code\u003e: Path to the directory containing the paraphrases files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_directory\u003c/code\u003e: Output directory for the best paraphrases.\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe best paraphrases will be written to a file named \"best_paraphrases_euparl_t5.txt\" in the specified output directory.\u003c/li\u003e\n\u003cli\u003eTwo distribution plots will be generated: \"maximum_values.png\" and \"first_values.png\" in the current directory where script will run.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003emanual_evaluation.py\u003c/h4\u003e\u003ca id=\"user-content-manual_evaluationpy\" class=\"anchor\" aria-label=\"Permalink: manual_evaluation.py\" href=\"#manual_evaluationpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script is used for manual evaluation of the best paraphrases.\u003c/p\u003e\n\u003cp\u003eArguments:\u003c/p\u003e\n\u003cp\u003eThe script accepts the following arguments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--file1_path\u003c/code\u003e: Path to the first file containing paraphrases.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--file2_path\u003c/code\u003e: Path to the second file containing paraphrases.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--file_original_path\u003c/code\u003e: Path to the file containing original sentences.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--scores_folder_path\u003c/code\u003e: Path to the folder where the evaluation scores will be stored.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote: If the specified \u003ccode\u003escores_folder_path\u003c/code\u003e does not exist, the script will create it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eevaluation/run.sh\u003c/h4\u003e\u003ca id=\"user-content-evaluationrunsh\" class=\"anchor\" aria-label=\"Permalink: evaluation/run.sh\" href=\"#evaluationrunsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach dataset block in the run.sh file contains the necessary variables for the evaluation. Make sure to modify these variables according to your dataset paths and filenames.\u003c/p\u003e\n\u003cp\u003eFor example, if you want to evaluate the \u003ccode\u003eeuroparl-llama\u003c/code\u003e dataset, uncomment the block of code for that dataset and update the following variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eDATASET_PATH\u003c/code\u003e variable represents the path to the dataset you want to evaluate.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDATASET_ORIG_SENTS_FILE\u003c/code\u003e variable represents the file containing the original sentences of the dataset.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDATASET_TRAN_SENTS_FILE\u003c/code\u003e variable represents the file containing the paraphrased sentences of the dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce you have made the necessary modifications, you can run the run.sh script. It will execute the evaluate.py script with the provided dataset variables.\u003c/p\u003e\n\u003cp\u003eIf you prefer to run the evaluation separately without using the run.sh script, you can directly execute the evaluate.py script and pass the dataset variables as command-line arguments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the T5 model\u003c/h2\u003e\u003ca id=\"user-content-run-the-t5-model\" class=\"anchor\" aria-label=\"Permalink: Run the T5 model\" href=\"#run-the-t5-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e*You have to have \u003ccode\u003etransformers\u003c/code\u003e python library installed. Preferrably the one with GPU/CUDA support.\n*The Singularity (Docker) image with the prepared env is already set up in the shared location \u003ccode\u003e/d/hpc/projects/FRI/tp1859/nlp_project8/lma/containers/hf.sif\u003c/code\u003e on \u003cem\u003eArnes HPC\u003c/em\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMove to \u003ccode\u003esrc/paraphrasing\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eDownload the \u003ccode\u003efinetune_t5-sl-small_v0.0.4-Euparl600k_ensl_b4_lr3E-05_g16_j38753698\u003c/code\u003e model from:\n\u003ca href=\"https://unilj-my.sharepoint.com/:f:/g/personal/tp1859_student_uni-lj_si/Eie-WJrrsIVAiJCFNQ8r28UBhKVq6vxhvNcud7RgXTr0tw?e=Xot15v\" rel=\"nofollow\"\u003ehttps://unilj-my.sharepoint.com/:f:/g/personal/tp1859_student_uni-lj_si/Eie-WJrrsIVAiJCFNQ8r28UBhKVq6vxhvNcud7RgXTr0tw?e=Xot15v\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRename the downloaded folder to \u003ccode\u003emodels/t5model\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003echange \u003ccode\u003eOUT_MODEL_CHECKPOINTS_DIR\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to \u003ccode\u003emodels\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003echange \u003ccode\u003eMODEL_CHECKPOINT_FIN_GLOB\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to \u003ccode\u003et5*\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003epython3 inference.py\u003c/code\u003e or \u003ccode\u003esbatch run.sbatch\u003c/code\u003e if running on \u003cem\u003eArnes HPC\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the LLaMA/Vicuna based model\u003c/h2\u003e\u003ca id=\"user-content-run-the-llamavicuna-based-model\" class=\"anchor\" aria-label=\"Permalink: Run the LLaMA/Vicuna based model\" href=\"#run-the-llamavicuna-based-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e*You have to have \u003ccode\u003etransformers\u003c/code\u003e python library installed. Preferrably the one with GPU/CUDA support.\n*The Singularity (Docker) image with the prepared env is already set up in the shared location \u003ccode\u003e/d/hpc/projects/FRI/tp1859/nlp_project8/lma/containers/hf.sif\u003c/code\u003e on \u003cem\u003eArnes HPC\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eSince the model weights setup is quite difficult to do and the weights transformations consume large amounts of disk (at least 60 GB with intermediate cleanups) and RAM (60GB) we have prepared the already converted weights and uploaded them to the shared \u003cem\u003eArnes HPC\u003c/em\u003e space at path: \u003ccode\u003e/d/hpc/projects/FRI/tp1859/nlp_project8/lma/model_hf_vicuna\u003c/code\u003e. The Vicuna/LLaMA 13B model is in half precision and takes about 25 GB of VRAM on the GPU.\u003c/p\u003e\n\u003cp\u003eYou can then run the paraphrase generation by modifying the \u003ccode\u003erun_llama.sbatch\u003c/code\u003e script in \u003ccode\u003esrc/llama\u003c/code\u003e directory. You should set \u003ccode\u003e--model-path\u003c/code\u003e to the above LLaMA model wights directory.\nThen change the \u003ccode\u003e--corpus-name\u003c/code\u003e to some string and set \u003ccode\u003e--file-in\u003c/code\u003e to a file containing 1 english sentence per line. Then you can run the inference by running \u003ccode\u003esbatch run_llama.sbatch\u003c/code\u003e. Output files will be in \u003ccode\u003eprocessed\u003c/code\u003e directory, logs in \u003ccode\u003elogs\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTranslation\u003c/h3\u003e\u003ca id=\"user-content-translation\" class=\"anchor\" aria-label=\"Permalink: Translation\" href=\"#translation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can run translation with NEmO model simillarly as described in T5 / Vicuna section. Needed files like \u003ccode\u003erun_translation.sbatch\u003c/code\u003e are in \u003ccode\u003esrc/translation\u003c/code\u003e dir.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReport\u003c/h3\u003e\u003ca id=\"user-content-report\" class=\"anchor\" aria-label=\"Permalink: Report\" href=\"#report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReport is located in folder \u003ccode\u003ereport\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1689367513.0 + "updated_at": 1709121666.0 }, { "data_format": 2, - "description": "Singularity support for Wire-Cell toolkit", + "description": null, "filenames": [ - "Singularity.sl7big", - "Singularity.sl7kc", - "Singularity.wclsdev", - "Singularity.sl7wclsdev", - "Singularity.sl7", - "Singularity.sl7mvp", - "Singularity.wct0.8.0-ub1804", - "Singularity.artdaq", - "Singularity.externals", - "Singularity.wctdev" + "Singularity.latest" ], - "full_name": "WireCell/wire-cell-singularity", + "full_name": "bioexcel/pmx_container", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b26407ffc70e9b6a282c6c8fb352ee6ffc8d0c07ce393e6a4ae150283c0951c4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d6875622d626c7565\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docker-hub-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePMX container\u003c/h1\u003e\u003ca id=\"user-content-pmx-container\" class=\"anchor\" aria-label=\"Permalink: PMX container\" href=\"#pmx-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePMX (python 3 version) docker and singularity containers used for \u003ca href=\"https://github.com/bioexcel/biobb_pmx\"\u003ebiobb_pmx\u003c/a\u003e BioExcel Building Blocks modules.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker Use\u003c/h3\u003e\u003ca id=\"user-content-docker-use\" class=\"anchor\" aria-label=\"Permalink: Docker Use\" href=\"#docker-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull mmbirb/pmx_biobb:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run mmbirb/pmx_biobb:latest \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Use\u003c/h3\u003e\u003ca id=\"user-content-singularity-use\" class=\"anchor\" aria-label=\"Permalink: Singularity Use\" href=\"#singularity-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name pmx_biobb.sif shub://bioexcel/pmx_biobb_container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec pmx_biobb.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" aria-label=\"Permalink: Copyright \u0026amp; Licensing\" href=\"#copyright--licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknolegements\u003c/h3\u003e\u003ca id=\"user-content-acknolegements\" class=\"anchor\" aria-label=\"Permalink: Acknolegements\" href=\"#acknolegements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe singularity container has been built through \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5aaf032aade8af7cf9f6e16cb3c7ba70c927ecd783c1cfff82dc0ed8062560df/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5aaf032aade8af7cf9f6e16cb3c7ba70c927ecd783c1cfff82dc0ed8062560df/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 8, "topics": [], - "updated_at": 1585692767.0 + "updated_at": 1601384286.0 }, { "data_format": 2, - "description": "PaCBAM is a C command line tool for the complete characterization of genomic regions and single nucleotide positions from next-generation sequencing data.", + "description": "A complete, cross-platform solution to record, convert and stream audio and video.", "filenames": [ - "containers/Singularity" + "4.4.1-r4/Singularity", + "6.0-r26/Singularity", + "4.4.1-r3/Singularity", + "4.3.1/Singularity", + "6.1.1/Singularity", + "5.0.1/Singularity", + "5.0-r1/Singularity" ], - "full_name": "gerbenvoshol/pacbam", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNON OFFICIAL REPOSITORY!! See \u003ca href=\"https://bitbucket.org/CibioBCG/pacbam/src/master/\" rel=\"nofollow\"\u003ehttps://bitbucket.org/CibioBCG/pacbam/src/master/\u003c/a\u003e for the official repository\u003c/h1\u003e\u003ca id=\"user-content-non-official-repository-see-httpsbitbucketorgcibiobcgpacbamsrcmaster-for-the-official-repository\" class=\"anchor\" aria-label=\"Permalink: NON OFFICIAL REPOSITORY!! See https://bitbucket.org/CibioBCG/pacbam/src/master/ for the official repository\" href=\"#non-official-repository-see-httpsbitbucketorgcibiobcgpacbamsrcmaster-for-the-official-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePaCBAM\u003c/h1\u003e\u003ca id=\"user-content-pacbam\" class=\"anchor\" aria-label=\"Permalink: PaCBAM\" href=\"#pacbam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaCBAM is a C command line tool for the complete characterization of genomic regions and single nucleotide positions from next-generation sequencing data.\u003cbr\u003e\nPaCBAM implements a fast and scalable multi-core computational engine, generates exhaustive output files for downstream analysis, introduces an innovative on-the-fly read duplicates filtering strategy and provides comprehensive visual reports.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompilation from source code\u003c/h2\u003e\u003ca id=\"user-content-compilation-from-source-code\" class=\"anchor\" aria-label=\"Permalink: Compilation from source code\" href=\"#compilation-from-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install PaCBAM clone the repository and compile the C source code.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/gerbenvoshol/pacbam.git \n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pacbam\nmake -f Makefile.linux\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse instead Makefile.macos and Makefile.mingw to compile PaCBAM on, respectively, macOS and Windows systems.\u003cbr\u003e\nSamtools library \u003ccode\u003elibbam.a\u003c/code\u003e has been generated for GNU/Linux, Windows and macOS systems.\u003cbr\u003e\nFor compilation on Windows we have added also \u003ccode\u003elibz.a\u003c/code\u003e library, while compilation on Linux/macOS requires the installation of the development \u003ccode\u003ezlib\u003c/code\u003e package.\u003cbr\u003e\nLibraries can be found in \u003ccode\u003e./lib\u003c/code\u003e directory.\u003cbr\u003e\nWindows libraries have been generated using MinGW.\u003cbr\u003e\nIf libraries are not working we suggest to download/recompile them again.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaCBAM expects as input a sorted and indexed BAM file, a BED file with the coordinates of the genomic regions of interest (namely the target, e.g. captured regions of a WES experiment), a VCF file specifying a list of SNPs within the target and a reference genome FASTA file.\u003cbr\u003e\nDifferent running modes and filtering/computation options are available.\u003cbr\u003e\nRunning PaCBAM executable will list all usage options.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: \n ./pacbam bam=string bed=string vcf=string fasta=string [mode=int] [threads=int] [mbq=int] [mrq=int] [mdc=int] [out=string]\n [dedup] [dedupwin=int] [regionperc=float] [strandbias]\n\nbam=string \n NGS data file in BAM format \nbed=string \n List of target captured regions in BED format \nvcf=string \n List of SNP positions in VCF format (no compressed files are admitted)\nfasta=string \n Reference genome FASTA format file \nmode=string \n Execution mode [0=RC+SNPs+SNVs|1=RC+SNPs+SNVs+PILEUP(not including SNPs)|2=SNPs|3=RC|4=PILEUP|6=BAMCOUNT]\n (default 6)\ndedup \n On-the-fly duplicates filtering\ndedupwin=int \n Flanking region around captured regions to consider in duplicates filtering [default 1000]\nthreads=int \n Number of threads used (if available) for the pileup computation\n (default 1)\nregionperc=float \n Fraction of the captured region to consider for maximum peak signal characterization\n (default 0.5)\nmbq=int \n Min base quality\n (default 20)\nmrq=int \n Min read quality\n (default 1)\nmdc=int \n Min depth of coverage that a position should have to be considered in the output\n (default 0)\nstrandbias \n Print strand bias count information\ngenotype \n Print genotype calls for input SNPs using a strategy based on an allelic fraction cutoff threshold at 20%\ngenotypeBT \n Print genotype calls for input SNPs using a strategy based on a binomial test with significance at 1%)\nout=string \n Path of output directory (default is the current directory)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFolder \u003ccode\u003eexamples\u003c/code\u003e contains a small example of a BAM file and correspoding target regions in BED format and a SNPs in target regions in VCF format.\u003cbr\u003e\nThe following command executes PaCBAM with mode 1, generating 4 output files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e../pacbam bam=NGSData.bam bed=TargetRegions.bed vcf=SNPsInTargetRegions.vcf fasta=/path-to-reference-genome/human_g1k_v37.fasta mode=1 out=./\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe reference genome to use in this example can be downloaded at\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eftp://ftp.ncbi.nlm.nih.gov/1000genomes/ftp/technical/reference/human_g1k_v37.fasta.gz\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDuplicates filtering\u003c/h4\u003e\u003ca id=\"user-content-duplicates-filtering\" class=\"anchor\" aria-label=\"Permalink: Duplicates filtering\" href=\"#duplicates-filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo activate the \u003cem\u003eon-the-fly read duplicates filtering\u003c/em\u003e add to the command \u003ccode\u003ededup\u003c/code\u003e. To enlarge the genomic window (default 1000) used at captured regions to find duplicated reads use \u003ccode\u003ededupwin=N\u003c/code\u003e with \u003ccode\u003eN\u003c/code\u003e integer number.\nWhen single end reads are used you can set \u003ccode\u003eW=0\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput files\u003c/h2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-label=\"Permalink: Output files\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach execution mode computes and generates a combination of the following files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDepth of coverage characterization of all genomic regions\u003c/h4\u003e\u003ca id=\"user-content-depth-of-coverage-characterization-of-all-genomic-regions\" class=\"anchor\" aria-label=\"Permalink: Depth of coverage characterization of all genomic regions\" href=\"#depth-of-coverage-characterization-of-all-genomic-regions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each region provides the mean depth of coverage, the GC content and the mean depth of coverage of the subregion (user specified, default 0.5 fraction) that maximizes the coverage peak signal (\u003ccode\u003ercS\u003c/code\u003e and corresponding genomic coordinates \u003ccode\u003efromS\u003c/code\u003e and \u003ccode\u003etoS\u003c/code\u003e), to account for the reduced coverage depth due to incomplete match of reads to the captured regions.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tfrom\tto\tfromS\ttoS\trc\trcS\tgc\n20\t68348\t68410\t68348\t68378\t130.40\t129.68\t0.48\n20\t76643\t77060\t76845\t77052\t81.18\t111.99\t0.41\n20\t123267\t123329\t123293\t123323\t93.00\t99.81\t0.50\n20\t126053\t126335\t126100\t126240\t32.55\t54.73\t0.44\n20\t138183\t138236\t138210\t138235\t78.08\t99.92\t0.51\n20\t139412\t139667\t139510\t139636\t117.86\t125.38\t0.39\n20\t168524\t168761\t168524\t168641\t69.79\t91.03\t0.39\n20\t170213\t170266\t170213\t170238\t13.91\t18.69\t0.40\n20\t207927\t207989\t207958\t207988\t96.40\t106.65\t0.48\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingle-base resolution pileup\u003c/h4\u003e\u003ca id=\"user-content-single-base-resolution-pileup\" class=\"anchor\" aria-label=\"Permalink: Single-base resolution pileup\" href=\"#single-base-resolution-pileup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each genomic position in the target provides the read depth of the 4 possible bases A, C, G and T, the total depth of coverage, the variants allelic fraction (VAF), the strand bias information for each base, the unique identifier (e.g. dbsnp id) if available.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\tref\tA\tC\tG\tT\taf\tcov\n20\t68348\tG\t0\t0\t129\t0\t0.000000\t129\n20\t68349\tC\t0\t130\t0\t0\t0.000000\t130\n20\t68350\tC\t0\t130\t0\t0\t0.000000\t130\n20\t68352\tT\t0\t0\t0\t130\t0.000000\t130\n20\t68353\tG\t0\t0\t130\t0\t0.000000\t130\n20\t68354\tA\t130\t0\t0\t0\t0.000000\t130\n20\t68355\tA\t130\t0\t0\t0\t0.000000\t130\n20\t68356\tT\t0\t0\t0\t130\t0.000000\t130\n20\t68357\tA\t130\t0\t0\t0\t0.000000\t130\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingle-base resolution pileup (mode 6: BAMCOUNT)\u003c/h4\u003e\u003ca id=\"user-content-single-base-resolution-pileup-mode-6-bamcount\" class=\"anchor\" aria-label=\"Permalink: Single-base resolution pileup (mode 6: BAMCOUNT)\" href=\"#single-base-resolution-pileup-mode-6-bamcount\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each genomic position in the target provides the read depth of the 4 possible bases A, C, G and T, the total depth of coverage, the allelic fraction (e.g. FracA), and the strand bias information for each base (e.g. StrandA).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr pos ref cov CountA FracA StrandA CountC FracC StrandC CountG FracG StrandG CountT FracT StrandT\n20 68348 G 129 0 0.0000 0.00 0 0.0000 0.00 129 0.0000 1.00 0 0.0000 0.00\n20 76643 C 19 0 0.0000 0.00 19 1.0000 0.79 0 0.0000 0.00 0 0.0000 0.00\n20 76644 A 19 19 1.0000 0.79 0 0.0000 0.00 0 1.0000 0.00 0 0.0000 0.00\n20 76645 G 19 0 0.0000 0.00 0 0.0000 0.00 19 0.0000 0.79 0 0.0000 0.00\n20 76646 G 19 0 0.0000 0.00 0 0.0000 0.00 19 0.0000 0.79 0 0.0000 0.00\n20 76647 T 15 0 0.0000 0.00 0 0.0000 0.00 0 0.0000 0.00 15 1.0000 1.00\n20 76648 A 15 15 1.0000 1.00 0 0.0000 0.00 0 1.0000 0.00 0 0.0000 0.00\n20 76649 G 15 0 0.0000 0.00 0 0.0000 0.00 15 0.0000 1.00 0 0.0000 0.00\n20 76650 C 15 0 0.0000 0.00 15 1.0000 1.00 0 0.0000 0.00 0 0.0000 0.00\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePositions with reads support for alternative base\u003c/h4\u003e\u003ca id=\"user-content-positions-with-reads-support-for-alternative-base\" class=\"anchor\" aria-label=\"Permalink: Positions with reads support for alternative base\" href=\"#positions-with-reads-support-for-alternative-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvides pileup information only for position with positive VAF, computed using the alternative base with highest read depth (if any).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\tref\talt\tA\tC\tG\tT\taf\tcov\n20\t76953\tG\tA\t1\t0\t99\t0\t0.010000\t100\n20\t126263\tC\tT\t0\t26\t0\t1\t0.037037\t27\n20\t139484\tA\tG\t156\t0\t1\t0\t0.006369\t157\n20\t139557\tA\tG\t99\t0\t1\t0\t0.010000\t100\n20\t139570\tC\tA\t1\t171\t0\t0\t0.005814\t172\n20\t139622\tC\tA\t1\t135\t0\t0\t0.007353\t136\n20\t168728\tT\tA\t56\t0\t0\t0\t1.000000\t56\n20\t209986\tA\tT\t227\t0\t0\t2\t0.008734\t229\n20\t210097\tC\tT\t0\t82\t0\t1\t0.012048\t83\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhen \u003ccode\u003estrandbias\u003c/code\u003e option is used, the output format is the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\tref\talt\tA\tC\tG\tT\taf\tcov\tArs\tCrs\tGrs\tTrs\n20\t76953\tG\tA\t1\t0\t99\t0\t0.010000\t100\t1\t0\t80\t0\n20\t126263\tC\tT\t0\t26\t0\t1\t0.037037\t27\t0\t0\t0\t0\n20\t139484\tA\tG\t156\t0\t1\t0\t0.006369\t157\t111\t0\t1\t0\n20\t139557\tA\tG\t99\t0\t1\t0\t0.010000\t100\t39\t0\t0\t0\n20\t139570\tC\tA\t1\t171\t0\t0\t0.005814\t172\t0\t91\t0\t0\n20\t139622\tC\tA\t1\t135\t0\t0\t0.007353\t136\t0\t67\t0\t0\n20\t168728\tT\tA\t56\t0\t0\t0\t1.000000\t56\t19\t0\t0\t0\n20\t209986\tA\tT\t227\t0\t0\t2\t0.008734\t229\t106\t0\t0\t1\n20\t210097\tC\tT\t0\t82\t0\t1\t0.012048\t83\t0\t37\t0\t0\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLast four columns represent the number of reads, for each base, that are on the reverse strand. This information can be used to compute strand bias at base-specific resolution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSNPs pileup\u003c/h4\u003e\u003ca id=\"user-content-snps-pileup\" class=\"anchor\" aria-label=\"Permalink: SNPs pileup\" href=\"#snps-pileup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvides pileup information for all positions specified in the input VCF and uses the alternative alleles specified in the VCF file for the VAFs calculations.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\trsid\tref\talt\tA\tC\tG\tT\taf\tcov\n20\t68351\trs757428359\tA\tG\t130\t0\t0\t0\t0.000000\t130\n20\t68363\trs200192457\tA\tT\t129\t0\t0\t0\t0.000000\t129\n20\t68373\trs745889706\tT\tC\t0\t0\t0\t130\t0.000000\t130\n20\t68375\trs754912258\tA\tG\t54\t0\t50\t0\t0.480769\t104\n20\t68396\trs138777928\tC\tT\t0\t141\t0\t0\t0.000000\t141\n20\t68397\trs748102612\tG\tA\t0\t0\t141\t0\t0.000000\t141\n20\t68406\trs771803424\tA\tG\t140\t0\t0\t0\t0.000000\t140\n20\t76654\trs564320474\tG\tT\t0\t0\t31\t0\t0.000000\t31\n20\t76658\trs745496891\tC\tA\t0\t49\t0\t0\t0.000000\t49\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhen \u003ccode\u003egenotype\u003c/code\u003e or \u003ccode\u003egenotypeBT\u003c/code\u003e option is used, the output format is the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\trsid\tref\talt\tA\tC\tG\tT\taf\tcov\tgenotype\n20\t68351\trs757428359\tA\tG\t130\t0\t0\t0\t0.000000\t130\t0/0\n20\t68363\trs200192457\tA\tT\t129\t0\t0\t0\t0.000000\t129\t0/0\n20\t68373\trs745889706\tT\tC\t0\t0\t0\t130\t0.000000\t130\t0/0\n20\t68375\trs754912258\tA\tG\t54\t0\t50\t0\t0.480769\t104\t0/1\n20\t68396\trs138777928\tC\tT\t0\t141\t0\t0\t0.000000\t141\t0/0\n20\t68397\trs748102612\tG\tA\t0\t0\t141\t0\t0.000000\t141\t0/0\n20\t68406\trs771803424\tA\tG\t140\t0\t0\t0\t0.000000\t140\t0/0\n20\t76654\trs564320474\tG\tT\t0\t0\t31\t0\t0.000000\t31\t0/0\n20\t76658\trs745496891\tC\tA\t0\t49\t0\t0\t0.000000\t49\t0/0\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e0/0\u003c/code\u003e, \u003ccode\u003e0/1\u003c/code\u003e and \u003ccode\u003e1/1\u003c/code\u003e represent, respectively, the reference base homozygous genotype, the heterozygous genotype and the alternative base homozygous genotype.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003egenotype\u003c/code\u003e option implements an allelic fraction cutoff method where heterozygous genotype is assigned when the position allelic fraction is in the range (0.2,0.8). The \u003ccode\u003egenotypeBT\u003c/code\u003e option, instead, implements a Binomial Test statistics at significance of 1% and with probabilities p=0.55 (reference) and q=45 (alternative) to account for the reference mapping bias.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisual reports\u003c/h2\u003e\u003ca id=\"user-content-visual-reports\" class=\"anchor\" aria-label=\"Permalink: Visual reports\" href=\"#visual-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaCBAM includes a script to generate visual data reports written in python.\u003cbr\u003e\nIt provides different graphs for every output file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erc: gc content and region coverage distributions \nsnps: total SNPs count, total distribution and quantile distributions of alternative heterozygous and alternative homozygous SNPs \npabs: base modification count and strand bias distribution \npileup: cumulative coverage and allelic fraction distributions \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRequirements\u003c/h4\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePython 3.6.8\u003cbr\u003e\nNumpy 1.17.3\u003cbr\u003e\nmatplotlib 3.1.1\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsage\u003c/h4\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe report scripts expect as input the prefix of the output files from PaCBAM and the mode in which it was runned.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage:\n ./pacbam_report.py -i/--input string -m/--mode int [-o/--output string] [-s/--strandBias]\n\n-i INPUT, --input INPUT\n\tSpecify the input file prefix\n-m MODE, --mode MODE\n\tSpecify the mode used\n-o OUTPUT, --output OUTPUT\n\tSpecify the output file name (Default input.pdf)\n-s, --strandBias\n\tPlots the strand bias distribution \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMode option:\u003cbr\u003e\n0 Files: .rc, .snps and .pabs\n1 Files: .rc, .snps, .pabs and .pileup\u003cbr\u003e\n2 Files: .snps\u003cbr\u003e\n3 Files: .rc\u003cbr\u003e\n4 Files: .pileup\u003c/p\u003e\n\u003cp\u003eStrandBias reporting is available only in modes 0 and 1.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample\u003c/h4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following command computes the visual reports for the example data.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./pacbam_report.py -i example/NGSData -m 1 -o reports/reports.pdf\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you are using a container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run cibiobcg/pacbam:latest pacbam_report.py -i example/NGSData -m 1 -o reports/reports.pdf\n\nsingularity run pacbam.simg pacbam_report.py -i example/NGSData -m 1 -o reports/reports.pdf\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput file\u003c/h4\u003e\u003ca id=\"user-content-output-file\" class=\"anchor\" aria-label=\"Permalink: Output file\" href=\"#output-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe report script produces a single pdf file with all the graphs of the choosen mode.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/987756c702d790521a0a71c2de2cd65a8b3885de017ec1cd6c377299f52d3bf2/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f63756d756c6174697665436f7665726167652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/987756c702d790521a0a71c2de2cd65a8b3885de017ec1cd6c377299f52d3bf2/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f63756d756c6174697665436f7665726167652e706e67\" alt=\"cumulativeCoverage\" data-canonical-src=\"https://bitbucket.org/CibioBCG/pacbam/raw/master/reports/cumulativeCoverage.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eExample of PaCBAM reporting the cumulative coverage distribution for all positions reported in the PaCBAM pileup output file.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/09d7d883d14f5aa9d81994decdb7ac3f2e800f9d9042008ff983726fa5f27847/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f534e507354797065732e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/09d7d883d14f5aa9d81994decdb7ac3f2e800f9d9042008ff983726fa5f27847/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f534e507354797065732e706e67\" alt=\"SNPsTypes\" data-canonical-src=\"https://bitbucket.org/CibioBCG/pacbam/raw/master/reports/SNPsTypes.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eExample of PaCBAM reporting on allelic fraction (AF) distribution of all positions contained in the PaCBAM SNPs output file. SNPs are classified as heterozygous or alternative homozygous based on standard AF thresholds. Classification is also reported stratified by coverage quartiles.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8a805b120be3aa27b58d3caa76b7aa80be5445c36cca7f4eaea709f473ceeccd/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f626173654d6f64696669636174696f6e2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8a805b120be3aa27b58d3caa76b7aa80be5445c36cca7f4eaea709f473ceeccd/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f626173654d6f64696669636174696f6e2e706e67\" alt=\"baseModification\" data-canonical-src=\"https://bitbucket.org/CibioBCG/pacbam/raw/master/reports/baseModification.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eExample of PaCBAM reporting on distribution of alternative bases found for each reference base across all positions reported in the PABS PaCBAM output file (i.e. all positions with non-zero variant allelic fraction).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2ec17742ed0fe43ceb795540d19cdd3f5c0238263a77b1b6f4f6278ce5c40ba9/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f726567696f6e436f7665726167652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2ec17742ed0fe43ceb795540d19cdd3f5c0238263a77b1b6f4f6278ce5c40ba9/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f726567696f6e436f7665726167652e706e67\" alt=\"regionCoverage\" data-canonical-src=\"https://bitbucket.org/CibioBCG/pacbam/raw/master/reports/regionCoverage.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eExample of PaCBAM reporting on mean depth of coverage distribution computed across all regions reported in the genomic regions of the PaCBAM output file. Distribution is reported both for regions overall mean coverage and for regions fractions maximizing mean coverage.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicence\u003c/h2\u003e\u003ca id=\"user-content-licence\" class=\"anchor\" aria-label=\"Permalink: Licence\" href=\"#licence\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaCBAM is released under \u003ca href=\"https://bitbucket.org/CibioBCG/pacbam/src/master/COPYING\" rel=\"nofollow\"\u003eMIT\u003c/a\u003e licence.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCite\u003c/h2\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-label=\"Permalink: Cite\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSamuel Valentini, Tarcisio Fedrizzi, Francesca Demichelis, Alessandro Romanel. \u003cstrong\u003ePaCBAM: fast and scalable processing of whole exome and targeted sequencing data\u003c/strong\u003e. \u003cem\u003eBMC Genomics\u003c/em\u003e, 20:1018, 2019.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-ffmpeg", + "latest_release": "v6.1.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ffmpeg/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ffmpeg/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ffmpeg/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ffmpeg/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/dab232334f74c7ae2794e7c211bd1ea6b661fc8702ecd7f97bfcfe0c4604c2af/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66666d706567\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dab232334f74c7ae2794e7c211bd1ea6b661fc8702ecd7f97bfcfe0c4604c2af/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66666d706567\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-ffmpeg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/007f76ba939ab1d9a9b8ef43f7fde47e28c7eca5f8e998621a845ba1ab7da75e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66666d706567\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/007f76ba939ab1d9a9b8ef43f7fde47e28c7eca5f8e998621a845ba1ab7da75e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66666d706567\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-ffmpeg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ad31dc0aac0e49620585afad5cf6d6ddbcd3bdff0f47163132dfc776b36c48bb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66666d706567\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad31dc0aac0e49620585afad5cf6d6ddbcd3bdff0f47163132dfc776b36c48bb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66666d706567\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-ffmpeg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7cf0d28924b6b4cd8a1ec7b8d3b53549bee40a1fe61baa24cf5d1d7a436ef1cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66666d706567\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7cf0d28924b6b4cd8a1ec7b8d3b53549bee40a1fe61baa24cf5d1d7a436ef1cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66666d706567\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-ffmpeg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1643639323.0 + "subscribers_count": 4, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1649191357.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "r01/Singularity.def" ], - "full_name": "NatoNathan/setapDocker", + "full_name": "7yl4r/thiago_spoccupancy", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esetapDocker\u003c/h1\u003e\u003ca id=\"user-content-setapdocker\" class=\"anchor\" aria-label=\"Permalink: setapDocker\" href=\"#setapdocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003estarting from ubuntu 24 with r-base installed\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esudo apt install -y cmake libssl-dev libudunits2-dev libgdal-dev\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003emight also need \u003ccode\u003egdal-bin\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRscript -e \"install.packages(\"spOccupancy\")\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRscript -e \"install.packages(\"stars\")\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRscript -e \"install.packages(\"ggplot2\")\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003erun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eRscript 5...\u003c/code\u003e5.Analysis_spOccupancy_MultiSp_SpatInteg_Summer.R\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is used to package the application for a SLURM supercomputer.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build `run.sif` from `Singularity.def` on local machine with sudo:\u003c/span\u003e\nsudo singularity build run.sif Singularity.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e transfer .sif to supercomputer\u003c/span\u003e\nrsync -hazv run.sif tylarmurray@circe.rc.usf.edu:.\n\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSLURM\u003c/h3\u003e\u003ca id=\"user-content-slurm\" class=\"anchor\" aria-label=\"Permalink: SLURM\" href=\"#slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eset up new run\u003c/h4\u003e\u003ca id=\"user-content-set-up-new-run\" class=\"anchor\" aria-label=\"Permalink: set up new run\" href=\"#set-up-new-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003emkdir\u003c/code\u003e run##, \u003ccode\u003ecd\u003c/code\u003e into it, then:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eTEMPLATE=run03\nmkdir old-slurm\ncat \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enew run from template \u003cspan class=\"pl-smi\"\u003e$TEMPLATE\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e README.md\n\nln -s ../data/Shapes Shapes\nln -s ../data/spOccupancy_MultiSpp_FullArea spOccupancy_MultiSpp_FullArea\nln -s ../data/Grid_OccEnv_Seasonal.txt Grid_OccEnv_Seasonal.txt\n\ncp ../\u003cspan class=\"pl-smi\"\u003e$TEMPLATE\u003c/span\u003e/5.Analysis_spOccupancy_MultiSp_SpatInteg_Summer.R \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ncp ../\u003cspan class=\"pl-smi\"\u003e$TEMPLATE\u003c/span\u003e/submit.sh \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\ngit add README.md 5.Analysis_spOccupancy_MultiSp_SpatInteg_Summer.R submit.sh\ngit add -f Grid_OccEnv_Seasonal.txt Shapes spOccupancy_MultiSpp_FullArea\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNOTE: for *winter runs, the file is still named \u003ccode\u003e*_Summer.R\u003c/code\u003e, just use a different \u003ccode\u003eTEMPLATE\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1696674218.0 + "updated_at": 1732456909.0 }, { "data_format": 2, - "description": null, + "description": "Singularity containers for running COVISE, OpenCOVER, and Vistle", "filenames": [ - "cme-lab/Singularity.hoomd", - "cme-lab/Singularity.cuda91", - "cme-lab/Singularity.cuda80", - "cme-lab/Singularity.base", - "cme-lab/Singularity.mbuild", - "cme-lab/Singularity.cuda92" + "Singularity.centos7", + "Singularity.vistle-client", + "Singularity.vistle-server", + "Singularity.covise-deps", + "Singularity.covise" ], - "full_name": "mikemhenry/cme-lab-images", + "full_name": "vistle/singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecme-lab-images\u003c/h1\u003e\u003ca id=\"user-content-cme-lab-images\" class=\"anchor\" aria-label=\"Permalink: cme-lab-images\" href=\"#cme-lab-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1188\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWork in progress\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity containers for COVISE, OpenCOVER and Vistle\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers-for-covise-opencover-and-vistle\" class=\"anchor\" aria-label=\"Permalink: Singularity containers for COVISE, OpenCOVER and Vistle\" href=\"#singularity-containers-for-covise-opencover-and-vistle\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains definition files for building \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e containers\nfor \u003ca href=\"https://www.hlrs.de/covise\" rel=\"nofollow\"\u003eCOVISE\u003c/a\u003e, \u003ca href=\"https://www.hlrs.de/opencover\" rel=\"nofollow\"\u003eOpenCOVER\u003c/a\u003e, and \u003ca href=\"https://vistle.io\" rel=\"nofollow\"\u003eVistle\u003c/a\u003e.\nThey are based on \u003ca href=\"https://www.centos.org\" rel=\"nofollow\"\u003eCentos 7\u003c/a\u003e.\nCOVISE and OpenCOVER are built within the same container, and Vistle builds on\ntop of this.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding\u003c/h2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003einstall singularity\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo make\u003c/code\u003e inside this directory (super user access is required for building Singularity containers)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing\u003c/h2\u003e\u003ca id=\"user-content-using\" class=\"anchor\" aria-label=\"Permalink: Using\" href=\"#using\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003erunning COVISE\n\u003ccode\u003esingularity run --nv covise.sif\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003erunning OpenCOVER\n\u003ccode\u003esingularity exec --nv covise.sif /usr/bin/opencover\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003erunning Vistle\n\u003ccode\u003esingularity run --nv vistle-client.sif\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you do not use the proprietary NVidia driver, you should omit \u003ccode\u003e--nv\u003c/code\u003e from the command lines.\nIn all three cases, you can append files to be opened, to the command line.\nAlternatively, you can just execute the containers directly, e.g. \u003ccode\u003e./vistle-client.sif\u003c/code\u003e.\nEditing your \u003ccode\u003erun-singularity\u003c/code\u003e script will allow to change default parameters.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [], - "updated_at": 1530915345.0 + "subscribers_count": 6, + "topics": [ + "singularity-containers", + "visualization", + "hpc", + "hlrs", + "vistle", + "covise" + ], + "updated_at": 1600420965.0 }, { "data_format": 2, - "description": null, + "description": "Singularity definition files for projects.", "filenames": [ - ".OLD/Singularity.Seurat_monocle2", - ".OLD/Singularity.Seurat_monocle", - ".OLD/Singularity.ChIPseq" + "Singularity.gvfn", + "Singularity.explorer" ], - "full_name": "dfernandezperez/Docker", + "full_name": "qlan3/singularity-deffile", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker\u003c/h1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity (old) and docker recipies for bioinformatic pipelines\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-deffile\u003c/h1\u003e\u003ca id=\"user-content-singularity-deffile\" class=\"anchor\" aria-label=\"Permalink: singularity-deffile\" href=\"#singularity-deffile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3126\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity definition files for projects.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1661190588.0 + "topics": [ + "singularity", + "singularity-hub", + "singularity-container" + ], + "updated_at": 1564610770.0 }, { "data_format": 2, - "description": "Scibian packaging for: singularity-container", + "description": "Nextflow workflow for automated IGV snapshots", "filenames": [ - "e2e/testdata/Singularity", - "examples/busybox/Singularity", - "examples/ubuntu/Singularity", - "examples/asciinema/Singularity", - "examples/almalinux/Singularity", - "examples/centos-arm64/Singularity", - "examples/fedora-arm64/Singularity", - "examples/apps/Singularity.cowsay", - "examples/apps/Singularity", - "examples/fedora/Singularity", - "examples/centos/Singularity", - "examples/shub/Singularity", - "examples/almalinux-arm64/Singularity", - "examples/raspbian/Singularity", - "examples/arch/Singularity", - "examples/opensuse-arm64/Singularity", - "examples/opensuse/Singularity", - "examples/docker/Singularity", - "examples/sle/Singularity", - "examples/instances/Singularity", - "examples/multistage/Singularity", - "examples/scratch/Singularity.busybox", - "examples/scratch/Singularity.alpine", - "examples/library/Singularity", - "examples/scientific/Singularity", - "examples/self/Singularity" + "containers/IGV/Singularity.IGV" ], - "full_name": "scibian/singularity-container", + "full_name": "stevekm/IGV-snapshot-nf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularityCE\u003c/h1\u003e\u003ca id=\"user-content-singularityce\" class=\"anchor\" aria-label=\"Permalink: SingularityCE\" href=\"#singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/sylabs/singularity/tree/main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cb144ed26ca93a9146b0669951f71e8538e59a23370f81a5f44a3a39f59cbecc/68747470733a2f2f636972636c6563692e636f6d2f67682f73796c6162732f73696e67756c61726974792f747265652f6d61696e2e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/sylabs/singularity/tree/main.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Links\u003c/h2\u003e\u003ca id=\"user-content-quick-links\" class=\"anchor\" aria-label=\"Permalink: Quick Links\" href=\"#quick-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#support\"\u003eGetting Support\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sylabs/singularity/discussions/categories/community-call\"\u003eMonthly Community Call\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sylabs/singularity/discussions/categories/roadmap\"\u003eRoadmap\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"LICENSE.md\"\u003eProject License\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eGuidelines for Contributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"CODE_OF_CONDUCT.md\"\u003eCode of Conduct\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is SingularityCE?\u003c/h2\u003e\u003ca id=\"user-content-what-is-singularityce\" class=\"anchor\" aria-label=\"Permalink: What is SingularityCE?\" href=\"#what-is-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularityCE is the Community Edition of Singularity, an open source container\nplatform designed to be simple, fast, and secure. Many container platforms are\navailable, but SingularityCE is designed for ease-of-use on shared systems and in\nhigh performance computing (HPC) environments. It features:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn immutable single-file container image format, supporting cryptographic\nsignatures and encryption.\u003c/li\u003e\n\u003cli\u003eIntegration over isolation by default. Easily make use of GPUs, high speed\nnetworks, parallel filesystems on a cluster or server.\u003c/li\u003e\n\u003cli\u003eMobility of compute. The single file SIF container format is easy to transport\nand share.\u003c/li\u003e\n\u003cli\u003eA simple, effective security model. You are the same user inside a container\nas outside, and cannot gain additional privilege on the host system by\ndefault.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularityCE is open source software, distributed under the \u003ca href=\"LICENSE.md\"\u003eBSD License\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started with SingularityCE\u003c/h2\u003e\u003ca id=\"user-content-getting-started-with-singularityce\" class=\"anchor\" aria-label=\"Permalink: Getting Started with SingularityCE\" href=\"#getting-started-with-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install SingularityCE from source, see the\n\u003ca href=\"INSTALL.md\"\u003einstallation instructions\u003c/a\u003e. For other installation options, see\n\u003ca href=\"https://www.sylabs.io/guides/latest/admin-guide/\" rel=\"nofollow\"\u003eour guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSystem administrators can learn how to configure SingularityCE, and get an\noverview of its architecture and security features in the\n\u003ca href=\"https://www.sylabs.io/guides/latest/admin-guide/\" rel=\"nofollow\"\u003eadministrator guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor users, see the \u003ca href=\"https://www.sylabs.io/guides/latest/user-guide/\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e\nfor details on how to run and build containers with SingularityCE.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing to SingularityCE\u003c/h2\u003e\u003ca id=\"user-content-contributing-to-singularityce\" class=\"anchor\" aria-label=\"Permalink: Contributing to SingularityCE\" href=\"#contributing-to-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCommunity contributions are always greatly appreciated. To start developing\nSingularityCE, check out the \u003ca href=\"CONTRIBUTING.md\"\u003eguidelines for contributing\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease note we have a \u003ca href=\"CODE_OF_CONDUCT.md\"\u003ecode of conduct\u003c/a\u003e. Please follow it in\nall your interactions with the project members and users.\u003c/p\u003e\n\u003cp\u003eOur roadmap, other documents, and user/developer meeting information can be\nfound in \u003ca href=\"https://github.com/sylabs/singularity/discussions/\"\u003eGitHub Discussions\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe also welcome contributions to our\n\u003ca href=\"https://github.com/sylabs/singularity-userdocs\"\u003euser guide\u003c/a\u003e and\n\u003ca href=\"https://github.com/sylabs/singularity-admindocs\"\u003eadmin guide\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get help with SingularityCE, check out the community spaces detailed at our\n\u003ca href=\"https://sylabs.io/singularity#community\" rel=\"nofollow\"\u003eCommunity Portal\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSee also our \u003ca href=\"SUPPORT.md\"\u003eSupport Guidelines\u003c/a\u003e for further information about the\nbest place, and how, to raise different kinds of issues and questions.\u003c/p\u003e\n\u003cp\u003eFor additional support, \u003ca href=\"https://sylabs.io/contact-us\" rel=\"nofollow\"\u003econtact Sylabs\u003c/a\u003e to receive\nmore information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommunity Calls \u0026amp; Roadmap\u003c/h2\u003e\u003ca id=\"user-content-community-calls--roadmap\" class=\"anchor\" aria-label=\"Permalink: Community Calls \u0026amp; Roadmap\" href=\"#community-calls--roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe maintain our roadmap on \u003ca href=\"https://github.com/sylabs/singularity/discussions/categories/roadmap\"\u003eGitHub\nDiscussions\u003c/a\u003e,\nso that it\u0027s easy to collect ideas for new features, and discuss which should be\nprioritized for the next release.\u003c/p\u003e\n\u003cp\u003eRegular community calls are held for the project, on the first Thursday of each\nmonth, via Zoom. The agenda for each call includes a demonstration of new\nfeatures, or a project / workflow related to SingularityCE. This is followed by\ndevelopment updates \u0026amp; discussion, before open questions. Meeting details are\nposted in \u003ca href=\"https://github.com/sylabs/singularity/discussions/categories/community-call\"\u003eGithub\nDiscussions\u003c/a\u003e,\nand recordings made available at the \u003ca href=\"https://www.youtube.com/c/SylabsInc/videos\" rel=\"nofollow\"\u003eSylabs YouTube\nChannel\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you work on a project related to Singularity, or use Singularity in an\ninteresting workflow, \u003ca href=\"mailto:community@sylabs.io\"\u003elet us know\u003c/a\u003e if you\u0027d like to\npresent to the community!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGo Version Compatibility\u003c/h2\u003e\u003ca id=\"user-content-go-version-compatibility\" class=\"anchor\" aria-label=\"Permalink: Go Version Compatibility\" href=\"#go-version-compatibility\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularityCE aims to maintain support for the two most recent stable versions\nof Go. This corresponds to the Go\n\u003ca href=\"https://github.com/golang/go/wiki/Go-Release-Cycle#release-maintenance\"\u003eRelease Maintenance Policy\u003c/a\u003e\nand \u003ca href=\"https://golang.org/security\" rel=\"nofollow\"\u003eSecurity Policy\u003c/a\u003e, ensuring critical bug\nfixes and security patches are available for all supported language versions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting Singularity\u003c/h2\u003e\u003ca id=\"user-content-citing-singularity\" class=\"anchor\" aria-label=\"Permalink: Citing Singularity\" href=\"#citing-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe SingularityCE software may be cited using our Zenodo DOI \u003ccode\u003e10.5281/zenodo.5564905\u003c/code\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSingularityCE Developers (2021) SingularityCE. 10.5281/zenodo.5564905\n\u003ca href=\"https://doi.org/10.5281/zenodo.5564905\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.5564905\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis is an \u0027all versions\u0027 DOI for referencing SingularityCE in a manner that is\nnot version-specific. You may wish to reference the particular version of\nSingularityCE used in your work. Zenodo creates a unique DOI for each release,\nand these can be found in the \u0027Versions\u0027 sidebar on the \u003ca href=\"https://doi.org/10.5281/zenodo.5564905\" rel=\"nofollow\"\u003eZenodo record page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease also consider citing the original publication describing Singularity:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eKurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for\nmobility of compute. PLoS ONE 12(5): e0177459.\n\u003ca href=\"https://doi.org/10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003ehttps://doi.org/10.1371/journal.pone.0177459\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eUnless otherwise noted, this project is licensed under a 3-clause BSD license\nfound in the \u003ca href=\"LICENSE.md\"\u003elicense file\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIGV-snapshot-nf\u003c/h1\u003e\u003ca id=\"user-content-igv-snapshot-nf\" class=\"anchor\" aria-label=\"Permalink: IGV-snapshot-nf\" href=\"#igv-snapshot-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn example Nextflow workflow for creating automated IGV snapshots of .bam files based on a list of target regions.\u003c/p\u003e\n\u003cp\u003eThis workflow is designed to show how to integrate \u003ca href=\"https://github.com/stevekm/IGV-snapshot-automator\"\u003eautomated IGV snapshotting\u003c/a\u003e into a Nextflow workflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, clone this repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/stevekm/IGV-snapshot-automator.git\ncd IGV-snapshot-automator\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainers\u003c/h3\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker and/or Singularity containers are used to package IGV, X11, and \u003ccode\u003exvfb\u003c/code\u003e required for functionality. Docker is required to build Singularity containers\u003c/p\u003e\n\u003cp\u003eTo build the Docker container for IGV:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd containers\nmake docker-build VAR=IGV\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo test out the IGV Docker container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-test VAR=IGV\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(optional) To build a Singuarity container for IGV, first build the Singularity Docker container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-build VAR=Singularity-2.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThis container is used to build Singularity containers\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo build the Singularity container for IGV:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-build VAR=IGV\n\n# test the container:\nmake singularity-test VAR=IGV\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThe Singularity container will be saved to \u003ccode\u003econtainers/IGV/IGV.simg\u003c/code\u003e, which you can upload to your remote server for usage\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the included demo workflow (from the parent repo directory):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eShould look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eIGV-snapshot-nf$ make run\n./nextflow run main.nf -profile \"docker\"\nN E X T F L O W ~ version 19.04.1\nLaunching `main.nf` [kickass_cray] - revision: 1823b32e4f\n~~~~~~~ IGV Pipeline ~~~~~~~\n* Project dir: /Users/steve/projects/IGV-snapshot-nf\n* Launch dir: /Users/steve/projects/IGV-snapshot-nf\n* Work dir: /Users/steve/projects/IGV-snapshot-nf/work\n* Profile: docker\n* Script name: main.nf\n* Script ID: 1823b32e4f4fbc1caa63b0c12b2d4340\n* Container engine: docker\n* Workflow session: 843f9541-9cc2-46c8-9005-89659c67ed80\n* Nextflow run name: kickass_cray\n* Nextflow version: 19.04.1, build 5072 (03-05-2019 12:29 UTC)\n* Launch command:\nnextflow run main.nf -profile docker\n[warm up] executor \u0026gt; local\nexecutor \u0026gt; local (1)\n[91/852794] process \u0026gt; run_IGV [100%] 1 of 1 \u2714\nCompleted at: 22-May-2019 15:27:46\nDuration : 1m 20s\nCPU hours : (a few seconds)\nSucceeded : 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample snapshot output:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/stevekm/IGV-snapshot-nf/output/output/snapshots/chr13_113976596_113976736.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/stevekm/IGV-snapshot-nf/output/output/snapshots/chr13_113976596_113976736.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoftware\u003c/h1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-label=\"Permalink: Software\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTested with macOS 10.12.6 and RHEL 7\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNextflow (requires Java 8+ and \u003ccode\u003ebash\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIGV 2.4.10\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePython\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 3, + "topics": [ + "igv", + "nextflow" + ], + "updated_at": 1558554996.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "Singularity.v1.0", + "Singularity.latest" + ], + "full_name": "wkpalan/singularity-snpeff-snpsift", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-snpeff-snpsft\u003c/h1\u003e\u003ca id=\"user-content-singularity-snpeff-snpsft\" class=\"anchor\" aria-label=\"Permalink: singularity-snpeff-snpsft\" href=\"#singularity-snpeff-snpsft\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a duplicate project with access to snpeff and snpsift as apps within a singularity container.\u003c/p\u003e\n\u003cp\u003eThis project is an updated format code available from qbicsoftware \u003ca href=\"https://github.com/qbicsoftware/qbic-singularity-snpeff\"\u003esnpEff\u003c/a\u003e container\u003c/p\u003e\n\u003cp\u003eThis is a containerized version of the genetic variant annotation tool \u003ca href=\"http://snpeff.sourceforge.net/\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e. We use \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e as container technology.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootstrap files with tags\u003c/h2\u003e\u003ca id=\"user-content-bootstrap-files-with-tags\" class=\"anchor\" aria-label=\"Permalink: Bootstrap files with tags\" href=\"#bootstrap-files-with-tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide always a bootstrap file (\u003ccode\u003eSingularity\u003c/code\u003e) tagged \u003ccode\u003e.latest\u003c/code\u003e which represents the most resent development status of the container. If you see version tags like \u003ccode\u003e.v1.0\u003c/code\u003e, this means that this is the recipe of a container with a stable version tag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to build the container\u003c/h2\u003e\u003ca id=\"user-content-how-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: How to build the container\" href=\"#how-to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/qbicsoftware/qbic-singularity-snpeff.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e qbic-singularity-snpeff\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSince Singularity 2.4, the build command from file looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build myContainer.simg \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSingularity file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also download the build and ready-to-use container from Singularity Hub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://qbicsoftware/qbic-singularity-snpeff:latest\nsingularity pull shub://qbicsoftware/qbic-singularity-snpeff:v1.0\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run the container and calling SnpEff\u003c/h2\u003e\u003ca id=\"user-content-how-to-run-the-container-and-calling-snpeff\" class=\"anchor\" aria-label=\"Permalink: How to run the container and calling SnpEff\" href=\"#how-to-run-the-container-and-calling-snpeff\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the container and calling SnpEff you just need to\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e myContainer.simg snpEff [arguments]\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e myContainer.simg snpEff -h\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDefining the reference genome\u003c/h2\u003e\u003ca id=\"user-content-defining-the-reference-genome\" class=\"anchor\" aria-label=\"Permalink: Defining the reference genome\" href=\"#defining-the-reference-genome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProviding them inside of the container would make the container big, so we think it is a better idea to mount the reference genome into the right folder inside the container, where snpEff automatically searches for reference genome databases.\u003c/p\u003e\n\u003cp\u003eYou can simple download the databases, unzip them on your filesystem, and bind its \u003ccode\u003edata\u003c/code\u003e directory into the container. If you use snpEff\u0027s \u003ccode\u003e-v\u003c/code\u003e verbose output option, you will see that it will find the pre-installed databases and will not try to download it.\u003c/p\u003e\n\u003cp\u003eHere is an example, where we downloaded the \u003cstrong\u003ehg19\u003c/strong\u003e reference genome with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_hg19.zip\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eon the host filesystem, unzipped it and bound it during the container execution.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B ./data/:/usr/local/lib/snpEff/data snpEff.simg snpEff -v hg19 myVCF.vcf\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthor\u003c/h2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/wkpalan\"\u003eKokulapala (Gokul) Wimalanathan\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1696519487.0 + "updated_at": 1523929389.0 }, { "data_format": 2, "description": null, "filenames": [ - "vowpal_wabbit-master/singularity/Singularity.debian-unstable-i386", - "vowpal_wabbit-master/singularity/Singularity.debian-unstable-amd64" + "Dockerfile/Singularity" ], - "full_name": "maaz-kang-92/vowpal_wabbit-master", + "full_name": "namzoo99/ecNAPP", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLet\u0027s look for neoantigens!\u003c/h1\u003e\u003ca id=\"user-content-lets-look-for-neoantigens\" class=\"anchor\" aria-label=\"Permalink: Let\u0027s look for neoantigens!\" href=\"#lets-look-for-neoantigens\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecreated by Harold and Mary of CBM LAB\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow of ecNAPP\u003c/h2\u003e\u003ca id=\"user-content-workflow-of-ecnapp\" class=\"anchor\" aria-label=\"Permalink: Workflow of ecNAPP\" href=\"#workflow-of-ecnapp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/86759935/198952280-ea38ed73-16d7-484f-af9a-475aa0b6af09.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/86759935/198952280-ea38ed73-16d7-484f-af9a-475aa0b6af09.png\" alt=\"ing\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFor the use of this script, please prepare\u003c/h2\u003e\u003ca id=\"user-content-for-the-use-of-this-script-please-prepare\" class=\"anchor\" aria-label=\"Permalink: For the use of this script, please prepare\" href=\"#for-the-use-of-this-script-please-prepare\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003cli\u003emosek license and reference data for AA-suite, svaba\u003c/li\u003e\n\u003cli\u003ecsv file consisted of:\u003c/li\u003e\n\u003c/ol\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ecolnames\u003c/th\u003e\n\u003cth\u003edefinition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eproject\u003c/td\u003e\n\u003ctd\u003eproject name\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebarcode\u003c/td\u003e\n\u003ctd\u003esample barcode\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edocker_bind_path\u003c/td\u003e\n\u003ctd\u003epath where docker will bind to (docker -v)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003einput\u003c/td\u003e\n\u003ctd\u003einput file directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eref\u003c/td\u003e\n\u003ctd\u003ereference genome build\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eworkdir\u003c/td\u003e\n\u003ctd\u003eworking directory where the pipeline will be at\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutdir\u003c/td\u003e\n\u003ctd\u003eoutput directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esvaba_ref\u003c/td\u003e\n\u003ctd\u003edirectory of reference for svaba (genome)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDBSNP\u003c/td\u003e\n\u003ctd\u003edirectory of reference for svaba (dbsnp)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emoseklic\u003c/td\u003e\n\u003ctd\u003edirectory of MOSEK license\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAArepo\u003c/td\u003e\n\u003ctd\u003edirectory of AA repo\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ethe \u003ccode\u003edocker_bind_path\u003c/code\u003e must be the parent folder of \u003ccode\u003einput\u003c/code\u003e, \u003ccode\u003eworkdir\u003c/code\u003e, \u003ccode\u003eoutdir\u003c/code\u003e, \u003ccode\u003esvaba_ref\u003c/code\u003e, and \u003ccode\u003eDBSNP\u003c/code\u003e. Check our \u003ca href=\"https://github.com/skadbswn/ecNAPP/blob/main/example.csv\"\u003eexample.csv\u003c/a\u003e for more info.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. \u003ca href=\"https://github.com/jluebeck/AmpliconSuite-pipeline\"\u003eAmpliconSuite-pipeline\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-1-ampliconsuite-pipeline\" class=\"anchor\" aria-label=\"Permalink: 1. AmpliconSuite-pipeline\" href=\"#1-ampliconsuite-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe arguments are absed on the \u003ccode\u003eHL-NF:AmpliconArchitect\u003c/code\u003e, which are \u003ccode\u003e--AA_extendmode EXPLORE --AA_runmode FULL\u003c/code\u003e.\nTo download MOSEK liscence(mosek.lic), visit \u003ca href=\"https://www.mosek.com/products/academic-licenses/\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor AA_DATA_REPO, visit \u003ca href=\"https://datasets.genepattern.org/?prefix=data/module_support_files/AmpliconArchitect/\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eGenome build should be downloaded with \u003ccode\u003e_indexed\u003c/code\u003e files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. \u003ca href=\"https://github.com/walaj/svaba\"\u003eSVABA\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-2-svaba\" class=\"anchor\" aria-label=\"Permalink: 2. SVABA\" href=\"#2-svaba\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe used docker image for our pipeline. Since \u003ccode\u003eSVABA\u003c/code\u003e does not have output argument, the \u003ccode\u003eBAM\u003c/code\u003e files need to be placed where the output should be placed using symlink.\u003c/p\u003e\n\u003cp\u003eAfter the run, script automatically removes the symlink.\u003c/p\u003e\n\u003cp\u003eFor the additional info of reference(\u003ccode\u003eDBSNP\u003c/code\u003e), please visit the official svaba github(\u003ca href=\"https://github.com/walaj/svaba\"\u003eHERE\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. \u003ca href=\"https://hub.docker.com/r/sachet/polysolver\" rel=\"nofollow\"\u003ePOLYSOLVER\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-3-polysolver\" class=\"anchor\" aria-label=\"Permalink: 3. POLYSOLVER\" href=\"#3-polysolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe used docker image for polysolver. Since it has its own reference inside the image, we can choose genome build by argument, \u003ccode\u003ehg19\u003c/code\u003e or \u003ccode\u003ehg38\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003ePolysolver gives output with fixed name \u003ccode\u003ewinners.hla.txt\u003c/code\u003e, so the output is created under barcode folder.\u003c/p\u003e\n\u003cp\u003eDon\u0027t worry, the input hla will have it\u0027s own name while going through the next process.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. \u003ca href=\"https://services.healthtech.dtu.dk/service.php?NetMHCpan-4.1\" rel=\"nofollow\"\u003enetMHCpan\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-4-netmhcpan\" class=\"anchor\" aria-label=\"Permalink: 4. netMHCpan\" href=\"#4-netmhcpan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003efor the final output of neoantigens, we are using \u003ccode\u003enetMHCpan4.1b\u003c/code\u003e to find peptides binding with MHC class I.\u003c/p\u003e\n\u003cp\u003ethe final output will be under this header:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003ePos\u003c/th\u003e\n\u003cth align=\"center\"\u003eMHC\u003c/th\u003e\n\u003cth align=\"center\"\u003ePeptide\u003c/th\u003e\n\u003cth align=\"center\"\u003eCore\u003c/th\u003e\n\u003cth align=\"center\"\u003eOf\u003c/th\u003e\n\u003cth align=\"center\"\u003eGp\u003c/th\u003e\n\u003cth align=\"center\"\u003eGl\u003c/th\u003e\n\u003cth align=\"center\"\u003eIp\u003c/th\u003e\n\u003cth align=\"center\"\u003eIl\u003c/th\u003e\n\u003cth align=\"center\"\u003eIcore\u003c/th\u003e\n\u003cth align=\"center\"\u003eidentity\u003c/th\u003e\n\u003cth align=\"center\"\u003eScore_EL\u003c/th\u003e\n\u003cth align=\"center\"\u003e%Rank_EL\u003c/th\u003e\n\u003cth align=\"center\"\u003eBindLevel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e1\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHLA-B*40:01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNETQRLLLL\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNETQRLLLL\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNETQRLLLL\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePEPLIST\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7000450\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.237\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u0026lt;= SB\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003ewhere:\n\u003cul\u003e\n\u003cli\u003ePos: Residue number (starting from 0) of the peptide in the protein sequence.\u003c/li\u003e\n\u003cli\u003eHLA: Specified MHC molecule / Allele name.\u003c/li\u003e\n\u003cli\u003ePeptide: Amino acid sequence of the potential ligand.\u003c/li\u003e\n\u003cli\u003eCore: The minimal 9 amino acid binding core directly in contact with the MHC.\u003c/li\u003e\n\u003cli\u003eOf: The starting position of the Core within the Peptide (if \u0026gt; 0, the method predicts a N-terminal protrusion).\u003c/li\u003e\n\u003cli\u003eGp: Position of the deletion, if any.\u003c/li\u003e\n\u003cli\u003eGl: Length of the deletion, if any.\u003c/li\u003e\n\u003cli\u003eIp: Position of the insertion, if any.\u003c/li\u003e\n\u003cli\u003eIl: Length of the insertion, if any.\u003c/li\u003e\n\u003cli\u003eIcore: Interaction core. This is the sequence of the binding core including eventual insertions of deletions.\u003c/li\u003e\n\u003cli\u003eIdentity: Protein identifier, i.e. the name of the FASTA entry.\u003c/li\u003e\n\u003cli\u003eScore: The raw prediction score.\u003c/li\u003e\n\u003cli\u003e%Rank: Rank of the predicted binding score compared to a set of random natural peptides. This measure is not affected by inherent bias of certain molecules towards higher or lower mean predicted affinities. Strong binders are defined as having %rank\u0026lt;0.5, and weak binders with %rank\u0026lt;2. We advise to select candidate binders based on %Rank rather than Score\u003c/li\u003e\n\u003cli\u003eBindLevel: (SB: Strong Binder, WB: Weak Binder). The peptide will be identified as a strong binder if the %Rank is below the specified threshold for the strong binders (by default, 0.5%). The peptide will be identified as a weak binder if the %Rank is above the threshold of the strong binders but below the specified threshold for the weak binders (by default, 2%).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1722259857.0 + "updated_at": 1666414969.0 }, { "data_format": 2, - "description": "Samtools is a suite of programs for interacting with high-throughput sequencing data.", + "description": ":whale: Script to build a Singularity image for CellOrganizer", "filenames": [ - "1.11.0/Singularity", - "1.13.0/Singularity", - "1.15.1/Singularity", - "1.10.0/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-samtools", - "latest_release": "v1.15.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-samtools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-samtools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-samtools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-samtools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a0eadf22f405a09437137ff0c6ec856f6babc4249d9dddd5da94a5eb3629156e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a0eadf22f405a09437137ff0c6ec856f6babc4249d9dddd5da94a5eb3629156e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-samtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e475e80b6537836f51d6ad5734082aa59be755d4c096eda82ed4d7c0e41a8c87/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e475e80b6537836f51d6ad5734082aa59be755d4c096eda82ed4d7c0e41a8c87/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\" alt=\"forks\" 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href=\"https://camo.githubusercontent.com/bb47bf9c1b1dbe0f0e158a4721b1cdb1dba3dd2b2e9596626f2610b4d0058515/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bb47bf9c1b1dbe0f0e158a4721b1cdb1dba3dd2b2e9596626f2610b4d0058515/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73616d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-samtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-samtools\u003c/h1\u003e\u003ca id=\"user-content-singularity-samtools\" class=\"anchor\" aria-label=\"Permalink: singularity-samtools\" href=\"#singularity-samtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.htslib.org/\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eace2sam\u003c/code\u003e, \u003ccode\u003eblast2sam.pl\u003c/code\u003e \u003ccode\u003ebowtie2sam.pl\u003c/code\u003e, \u003ccode\u003eexport2sam.pl\u003c/code\u003e, \u003ccode\u003efasta-sanitize.pl\u003c/code\u003e, \u003ccode\u003egenerate_binaries.sh\u003c/code\u003e, \u003ccode\u003einterpolate_sam.pl\u003c/code\u003e, \u003ccode\u003emaq2sam-long\u003c/code\u003e, \u003ccode\u003emaq2sam-short\u003c/code\u003e, \u003ccode\u003emd5fa\u003c/code\u003e, \u003ccode\u003emd5sum-lite\u003c/code\u003e, \u003ccode\u003enovo2sam.pl\u003c/code\u003e, \u003ccode\u003eplot-ampliconstats\u003c/code\u003e, \u003ccode\u003eplot-bamstats\u003c/code\u003e, \u003ccode\u003epsl2sam.pl\u003c/code\u003e, \u003ccode\u003esam2vcf.pl\u003c/code\u003e, \u003ccode\u003esamtools\u003c/code\u003e, \u003ccode\u003esamtools.pl\u003c/code\u003e, \u003ccode\u003eseq_cache_populate.pl\u003c/code\u003e, \u003ccode\u003esoap2sam.pl\u003c/code\u003e, \u003ccode\u003ewgsim\u003c/code\u003e, \u003ccode\u003ewgsim_eval.pl\u003c/code\u003e and \u003ccode\u003ezoom2sam.pl\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/samtools/1.13.0\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/samtools\u003c/code\u003e as \u003ccode\u003e1.13.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "murphygroup/singularity-cellorganizer", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-cellorganizer\u003c/h1\u003e\u003ca id=\"user-content-singularity-cellorganizer\" class=\"anchor\" aria-label=\"Permalink: singularity-cellorganizer\" href=\"#singularity-cellorganizer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://cloud.sylabs.io/library/icaoberg/default/cellorganizer\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/804b7bdd4c5bf1cd2bf36b4c08742c95c2876b82d7734f4643ea80b9b415d1c1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d73796c6162732e696f2d677265656e2e737667\" alt=\"Hosted\" data-canonical-src=\"https://img.shields.io/badge/hosted-sylabs.io-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://www.cellorganizer.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5a654b31b83404ad847088d2645b299c92b54f8ca8be41fc57f262a6e3854ac7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72656c656173652d76322e382e312d7265642e737667\" alt=\"Release\" data-canonical-src=\"https://img.shields.io/badge/release-v2.8.1-red.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-cellorganizer/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8d054afb0990477a5bfd0e7e3deb8d3decc96b775aec8459a41885f03d351d52/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d63656c6c6f7267616e697a65722e737667\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-cellorganizer.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-cellorganizer/network\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f5f8af960220ab57f10b2ea754be5ca79f5b9552135d71eb05aec0f8818a01e4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d63656c6c6f7267616e697a65722e737667\" alt=\"GitHub forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-cellorganizer.svg\" 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100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout CellOrganizer\u003c/h2\u003e\u003ca id=\"user-content-about-cellorganizer\" class=\"anchor\" aria-label=\"Permalink: About CellOrganizer\" href=\"#about-cellorganizer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/19100b052bbc72a5acb1f431bae9b7f7b1bf42f54f89c0c78d767e674b98bd8f/687474703a2f2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f43656c6c4f7267616e697a65724c6f676f322d3235302e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/19100b052bbc72a5acb1f431bae9b7f7b1bf42f54f89c0c78d767e674b98bd8f/687474703a2f2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f43656c6c4f7267616e697a65724c6f676f322d3235302e6a7067\" alt=\"CellOrganizer Logo\" data-canonical-src=\"http://www.cellorganizer.org/wp-content/uploads/2017/08/CellOrganizerLogo2-250.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e project provides tools for\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elearning generative models of cell organization directly from images\u003c/li\u003e\n\u003cli\u003estoring and retrieving those models\u003c/li\u003e\n\u003cli\u003esynthesizing cell images (or other representations) from one or more models\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eModel learning captures variation among cells in a collection of images. Images used for model learning and instances synthesized from models can be two- or three-dimensional static images or movies.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e can learn models of\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ecell shape\u003c/li\u003e\n\u003cli\u003enuclear shape\u003c/li\u003e\n\u003cli\u003echromatin texture\u003c/li\u003e\n\u003cli\u003evesicular organelle size, shape and position\u003c/li\u003e\n\u003cli\u003emicrotubule distribution.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese models can be conditional upon each other. For example, for a given synthesized cell instance, organelle position is dependent upon the cell and nuclear shape of that instance.\u003c/p\u003e\n\u003cp\u003eCell types for which generative models for at least some organelles have been built include human HeLa cells, mouse NIH 3T3 cells, and Arabidopsis protoplasts. Planned projects include mouse T lymphocytes and rat PC12 cells.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisites\u003c/h2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity v3.5.+\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCellOrganizer v2.8.1\u003c/h3\u003e\u003ca id=\"user-content-cellorganizer-v281\" class=\"anchor\" aria-label=\"Permalink: CellOrganizer v2.8.1\" href=\"#cellorganizer-v281\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFixes\u003c/h4\u003e\u003ca id=\"user-content-fixes\" class=\"anchor\" aria-label=\"Permalink: Fixes\" href=\"#fixes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDisplay shape space when dataset field is not present or empty.\u003c/li\u003e\n\u003cli\u003eGeneration of watertight SBML Spatial output has been corrected for translation errors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOther\u003c/h4\u003e\u003ca id=\"user-content-other\" class=\"anchor\" aria-label=\"Permalink: Other\" href=\"#other\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe following models have been rebuilt using this version of CellOrganizer. Updated models can be found in the model repository.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2D HeLa diffeomorphic framework\u003c/li\u003e\n\u003cli\u003e2D HeLa PCA framework\u003c/li\u003e\n\u003cli\u003e2D HeLa classic framework\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCellOrganizer for Galaxy now supports Galaxy server v19.05.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCellOrganizer v2.8.0\u003c/h3\u003e\u003ca id=\"user-content-cellorganizer-v280\" class=\"anchor\" aria-label=\"Permalink: CellOrganizer v2.8.0\" href=\"#cellorganizer-v280\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFeatures\u003c/h4\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAdded improved model for generating protein distributions during T cell synapse formation that only requires annotation of cell couples at a single time point model and improves synapse alignment. Includes training, synthesis and info demos.\u003c/li\u003e\n\u003cli\u003eAdded outline PCA model for 2D cell and nuclear shapes. Includes training, synthesis and info demos.\u003c/li\u003e\n\u003cli\u003eAdded SPHARM-RPDM model for 3D cell and nuclear shapes (see \u003ca href=\"https://doi.org/10.1093/bioinformatics/bty983\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/bty983\u003c/a\u003e). Includes training, synthesis and info demos.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFixes\u003c/h4\u003e\u003ca id=\"user-content-fixes-1\" class=\"anchor\" aria-label=\"Permalink: Fixes\" href=\"#fixes-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFixed issues with options.train.flag. Valid options should be nuclear, cell, framework, and protein.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eEnhancements\u003c/h4\u003e\u003ca id=\"user-content-enhancements\" class=\"anchor\" aria-label=\"Permalink: Enhancements\" href=\"#enhancements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eModularized and cleaned up img2slml.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning on Singularity\u003c/h2\u003e\u003ca id=\"user-content-running-on-singularity\" class=\"anchor\" aria-label=\"Permalink: Running on Singularity\" href=\"#running-on-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCellOrganizer v2.8.*\u003c/h2\u003e\u003ca id=\"user-content-cellorganizer-v28\" class=\"anchor\" aria-label=\"Permalink: CellOrganizer v2.8.*\" href=\"#cellorganizer-v28\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreating the container\u003c/h3\u003e\u003ca id=\"user-content-creating-the-container\" class=\"anchor\" aria-label=\"Permalink: Creating the container\" href=\"#creating-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo create the container, run this command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; bash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAccessing the container\u003c/h3\u003e\u003ca id=\"user-content-accessing-the-container\" class=\"anchor\" aria-label=\"Permalink: Accessing the container\" href=\"#accessing-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo access the container, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; singularity shell cellorganizer.sif\n\nSingularity: Invoking an interactive shell within container...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo list the possible apps, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity cellorganizer.img:~/singularity-cellorganizer\u0026gt; ls -lt /opt/cellorganizer-binaries/\n\ntotal 111821\n-rwxr-xr-x 1 14246 users 12699470 Mar 29 14:25 slml2report\n-rwxr-xr-x 1 14246 users 12471747 Mar 29 14:25 slml2info\n-rwxr-xr-x 1 14246 users 40728639 Mar 29 14:25 slml2img\n-rwxr-xr-x 1 14246 users 48604048 Mar 29 14:25 img2slml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Demos\u003c/h3\u003e\u003ca id=\"user-content-running-demos\" class=\"anchor\" aria-label=\"Permalink: Running Demos\" href=\"#running-demos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run a specific demo\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; cd demos/2D/demo2D**/\n\u0026gt; singularity run ~/path/to/cellorganizer.simg demo2D**.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen contributing to this repository, please first discuss the change you wish to make via issue, \u003ca href=\"mailto:cellorganizer-dev@compbio.cmu.edu\"\u003eemail\u003c/a\u003e, or any other method with the owners of this repository before making a change.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eSupport for \u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e has been provided by grants GM075205, GM090033 and GM103712 from the \u003ca href=\"http://www.nigms.nih.gov/\" rel=\"nofollow\"\u003eNational Institute of General Medical Sciences\u003c/a\u003e, grants MCB1121919 and MCB1121793 from the \u003ca href=\"http://nsf.gov/\" rel=\"nofollow\"\u003eU.S. National Science Foundation\u003c/a\u003e, by a Forschungspreis from the \u003ca href=\"http://www.humboldt-foundation.de/\" rel=\"nofollow\"\u003eAlexander von Humboldt Foundation\u003c/a\u003e, and by the \u003ca href=\"http://www.frias.uni-freiburg.de/lifenet?set_language=en\" rel=\"nofollow\"\u003eFreiburg Institute for Advanced Studies\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.mmbios.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/da21af862d75eb41d1c50d7556125597f1e9c95b7551e77d66d998b7eaf87ce5/68747470733a2f2f69312e77702e636f6d2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f4d4d42696f536c6f676f2d65313530333531373835373331332e6769663f683d3630\" alt=\"MMBioS\" data-canonical-src=\"https://i1.wp.com/www.cellorganizer.org/wp-content/uploads/2017/08/MMBioSlogo-e1503517857313.gif?h=60\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCopyright (c) 2007-2020 by the \u003ca href=\"http://murphylab.web.cmu.edu\" rel=\"nofollow\"\u003eMurphy Lab\u003c/a\u003e at the \u003ca href=\"http://www.cbd.cmu.edu\" rel=\"nofollow\"\u003eComputational Biology Department\u003c/a\u003e in \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 5, "topics": [ - "singularity", - "bioinformatics" + "cellorganizer", + "container", + "virtualization", + "bioimage-informatics", + "modeling-tools" ], - "updated_at": 1649388765.0 + "updated_at": 1587470789.0 }, { "data_format": 2, - "description": "Zork", + "description": "Singularity container recipe for Quantum Espresso and Yambo for cpu, architecture x86_64 based on CentOS 7. ", "filenames": [ "Singularity" ], - "full_name": "richelbilderbeek/singularity_example_8", + "full_name": "CINECA-HPC/container_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_example_8\u003c/h1\u003e\u003ca id=\"user-content-singularity_example_8\" class=\"anchor\" aria-label=\"Permalink: singularity_example_8\" href=\"#singularity_example_8\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://travis-ci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"pics/TravisCI.png\" alt=\"Travis CI logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://travis-ci.org/richelbilderbeek/singularity_example_8\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75bd9e451bb113ffdd02e04479079eb460b56ab2170c3c1742e4c9ec399b83f1/68747470733a2f2f7472617669732d63692e6f72672f72696368656c62696c6465726265656b2f73696e67756c61726974795f6578616d706c655f382e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/richelbilderbeek/singularity_example_8.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSingularity example 8: \u003ca href=\"https://github.com/richelbilderbeek/Zork\"\u003eZork\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64\" href=\"#container_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer recipes for Quantum Espresso and Yambo for cpu, architecture x86_64 based on CentOS 7. In the specific:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ecentOS 7\u003c/li\u003e\n\u003cli\u003eGNU compiler 7.3.1\u003c/li\u003e\n\u003cli\u003ePython 2.7\u003c/li\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003eOpenmpi 2.1.1\u003c/li\u003e\n\u003cli\u003eQuantum Espresso 6.5 ( with --enable-openmp=yes --enable-parallel=yes)\u003c/li\u003e\n\u003cli\u003eYambo 4.5.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis recipe works in the Cineca cluster (arch x86_64):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarconi Skylake\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1565352003.0 + "updated_at": 1604598436.0 }, { "data_format": 2, - "description": "ncview is a visual browser for netCDF format files.", + "description": null, "filenames": [ - "2.1.8/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-ncview", - "latest_release": "v2.1.8", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ncview/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ncview/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ncview/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ncview/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f299218f6bd16993a0a59184094020eaab425fa4df557e96c4204187ad7fb9f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6e6376696577\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f299218f6bd16993a0a59184094020eaab425fa4df557e96c4204187ad7fb9f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6e6376696577\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-ncview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8cab69331f2a79e6d4684003fe63745d10180043acfac10977a8b1718cd54409/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6e6376696577\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8cab69331f2a79e6d4684003fe63745d10180043acfac10977a8b1718cd54409/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6e6376696577\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-ncview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/82b02b9759f899d2ef35be705c342dafe5ef14ba5aff88e176f1993ec2a87b8b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6e6376696577\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/82b02b9759f899d2ef35be705c342dafe5ef14ba5aff88e176f1993ec2a87b8b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6e6376696577\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-ncview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e3ad3ddea45d0e7f1bacbb407e530eeaa1e847488bf9e5f1d5acdcfb7bfa147c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6e6376696577\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e3ad3ddea45d0e7f1bacbb407e530eeaa1e847488bf9e5f1d5acdcfb7bfa147c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6e6376696577\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-ncview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-ncview\u003c/h1\u003e\u003ca id=\"user-content-singularity-ncview\" class=\"anchor\" aria-label=\"Permalink: singularity-ncview\" href=\"#singularity-ncview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/63f21827549552a28b8a2897d85328e059db374c4f4d631be32eea2f935d8c67/687474703a2f2f6369727275732e756373642e6564752f7e7069657263652f736f6674776172652f6e63766965772f636f6e74726f6c5f322e676966\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/63f21827549552a28b8a2897d85328e059db374c4f4d631be32eea2f935d8c67/687474703a2f2f6369727275732e756373642e6564752f7e7069657263652f736f6674776172652f6e63766965772f636f6e74726f6c5f322e676966\" data-canonical-src=\"http://cirrus.ucsd.edu/~pierce/software/ncview/control_2.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.hpc.cineca.it/software/ncview#:~:text=Ncview%20is%20a%20visual%20browser,%2C%20invert%20the%20data%2C%20etc\" rel=\"nofollow\"\u003encview\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003encview\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/ncview/0.17.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/ncview\u003c/code\u003e as \u003ccode\u003e0.17.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "bstriner/tensorflow-xla-cuda-10.1-cudnn7-devel-ubuntu16.04", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etensorflow-xla-cuda-10.1-cudnn7-devel-ubuntu16.04\u003c/h1\u003e\u003ca id=\"user-content-tensorflow-xla-cuda-101-cudnn7-devel-ubuntu1604\" class=\"anchor\" aria-label=\"Permalink: tensorflow-xla-cuda-10.1-cudnn7-devel-ubuntu16.04\" href=\"#tensorflow-xla-cuda-101-cudnn7-devel-ubuntu1604\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "singularity", - "oceanography" - ], - "updated_at": 1649398866.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1560510385.0 }, { "data_format": 2, - "description": "BUSCO is a tool to assess completeness of genome assembly, gene set, and transcriptome.", + "description": null, "filenames": [ - "5.0.0_cv1/Singularity", - "5.2.2/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-busco", - "latest_release": "v5.2.2", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-busco/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-busco/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-busco/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-busco/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/425d93185f3da1c8104cf1995eea245773a65051714696e18a8c4fa9f8f6f510/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627573636f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/425d93185f3da1c8104cf1995eea245773a65051714696e18a8c4fa9f8f6f510/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627573636f\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-busco\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f66f1a3e9208a90e17fbcef3dda2d1d93c5a50781dc9b2ff48fb557c3a87e75e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627573636f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f66f1a3e9208a90e17fbcef3dda2d1d93c5a50781dc9b2ff48fb557c3a87e75e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627573636f\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-busco\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/69480c6012aa57c6cc42f91e786ec416d33c47877919882b87cf603674fbb454/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627573636f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/69480c6012aa57c6cc42f91e786ec416d33c47877919882b87cf603674fbb454/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627573636f\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-busco\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/22741fcd23b2c0a3194bce34abf262f6d082a68307f196a7c37c94cf155c56c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627573636f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22741fcd23b2c0a3194bce34abf262f6d082a68307f196a7c37c94cf155c56c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627573636f\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-busco\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-busco\u003c/h1\u003e\u003ca id=\"user-content-singularity-busco\" class=\"anchor\" aria-label=\"Permalink: singularity-busco\" href=\"#singularity-busco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/aa7bee9ba2fc3541e26ef39aa126247cb4bd0e759d745d742d78f1c538a92813/68747470733a2f2f627573636f2e657a6c61622e6f72672f686f6d652f627573636f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa7bee9ba2fc3541e26ef39aa126247cb4bd0e759d745d742d78f1c538a92813/68747470733a2f2f627573636f2e657a6c61622e6f72672f686f6d652f627573636f2e706e67\" width=\"40%\" data-canonical-src=\"https://busco.ezlab.org/home/busco.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://busco.ezlab.org\" rel=\"nofollow\"\u003ebusco\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebusco\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/busco/5.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/busco\u003c/code\u003e as \u003ccode\u003e5.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "ddbj/singularity_centos7_nginx", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enginx\u3092\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067\u5b9f\u884c\u3059\u308bsingularity image\u003c/h1\u003e\u003ca id=\"user-content-nginx\u3092\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067\u5b9f\u884c\u3059\u308bsingularity-image\" class=\"anchor\" aria-label=\"Permalink: nginx\u3092\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067\u5b9f\u884c\u3059\u308bsingularity image\" href=\"#nginx\u3092\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067\u5b9f\u884c\u3059\u308bsingularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u30d3\u30eb\u30c9\" href=\"#image\u306e\u30d3\u30eb\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build centos7_nginx.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003enginx\u306e\u8a2d\u5b9a\u003c/h2\u003e\u003ca id=\"user-content-nginx\u306e\u8a2d\u5b9a\" class=\"anchor\" aria-label=\"Permalink: nginx\u306e\u8a2d\u5b9a\" href=\"#nginx\u306e\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u7f6e\u3044\u3066\u3042\u308bnginx.conf\u306fDDBJ nginx\u3092\u30ea\u30d0\u30fc\u30b9\u30d7\u30ed\u30ad\u30b7\u3068\u3057\u3066\u4f7f\u3046\u8a2d\u5b9a\u306b\u306a\u3063\u3066\u3044\u308b\u3002\u9069\u5b9c\u4fee\u6b63\u3059\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003einstance\u306e\u8d77\u52d5\u003c/h2\u003e\u003ca id=\"user-content-instance\u306e\u8d77\u52d5\" class=\"anchor\" aria-label=\"Permalink: instance\u306e\u8d77\u52d5\" href=\"#instance\u306e\u8d77\u52d5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1639864357.0 + "subscribers_count": 7, + "topics": [], + "updated_at": 1581482569.0 }, { "data_format": 2, - "description": null, + "description": " Molecular graphics systems in a Singularity container", "filenames": [ + "Singularity.1.0", "Singularity" ], - "full_name": "alejandrox1/singularity-test", + "full_name": "OSC/sa_singularity_molgfx", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1090\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTesting Singularity\u003c/h1\u003e\u003ca id=\"user-content-testing-singularity\" class=\"anchor\" aria-label=\"Permalink: Testing Singularity\" href=\"#testing-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo is designed to have a small test case for the usage of an OpenMPI\nexecutable on the Stampede2 supercomputer.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"src/\"\u003esrc\u003c/a\u003e contains the code necessary to build an executable.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"module/\"\u003emodule\u003c/a\u003e Stampede2 currently has version 2.3.1 installed as the\nmodule \u003ccode\u003etacc-singularity\u003c/code\u003e. This is an atempt to install Singularity v2.5.1\non the stampede2 supercomputer, along with its dependencies (there were\nsignificant changes to the API and Singularity itself between versions 2.3.X\nand 2.4.X). This still needs work...\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTODO\u003c/strong\u003e:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFinish installation of \u003ccode\u003esquash-tools\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eMake modules for \u003ccode\u003elibarchive-dev\u003c/code\u003e and \u003ccode\u003esquash-tools\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes:\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes:\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e./src/mpi_hello_world\u003c/code\u003e hangs when executed on a container based off \u003ccode\u003eubuntu 16.04\u003c/code\u003e. When checking the system resources, \u003ccode\u003empirun singularity exec ubuntu mpi_hello_world\u003c/code\u003e is indeed creating MPI tasks, however processes hang\nindefinetely alternating between \u003ccode\u003eS\u003c/code\u003e and \u003ccode\u003eR\u003c/code\u003e states.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMPICH\u003c/code\u003e doesn\u0027t seem to work - hence the use of \u003ccode\u003eOpenMPI\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWork around for mounting PWD on stampede: \u003ccode\u003emkdir /work /scratch\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Molgfx\u003c/h1\u003e\u003ca id=\"user-content-singularity-molgfx\" class=\"anchor\" aria-label=\"Permalink: Singularity Molgfx\" href=\"#singularity-molgfx\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4301\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://github.com/OpenChemistry\"\u003eOpen Chemistry\u003c/a\u003e, Gabedit and Jmol. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003emolgfx.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build molgfx.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull molgfx.sif shub://OSC/sa_singularity_molgfx\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFind versions of molecular graphics systems\u003c/h3\u003e\u003ca id=\"user-content-find-versions-of-molecular-graphics-systems\" class=\"anchor\" aria-label=\"Permalink: Find versions of molecular graphics systems\" href=\"#find-versions-of-molecular-graphics-systems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity inspect -H molgfx.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart Avogadro2\u003c/h3\u003e\u003ca id=\"user-content-start-avogadro2\" class=\"anchor\" aria-label=\"Permalink: Start Avogadro2\" href=\"#start-avogadro2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAvogadro2 is started using the default exec command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e molgfx.sif avogadro2\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1528214682.0 + "updated_at": 1588619360.0 }, { "data_format": 2, - "description": "BSMAP is a short reads mapping software for bisulfite sequencing reads.", + "description": "Containers for arch x86_64 based on Centos 7 and 8 with GNU 7 and 8 compiler and different versions of Spack 0.15.4 and 0.16.0", "filenames": [ - "2.90/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-bsmap", + "full_name": "CINECA-HPC/container_spack_centos_x86_64", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bsmap/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bsmap/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bsmap/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bsmap/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bd2eaf083681486a10c0520e03a0a148590eea13bd6758c649b236ab6a147168/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62736d6170\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bd2eaf083681486a10c0520e03a0a148590eea13bd6758c649b236ab6a147168/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62736d6170\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bsmap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/90dbaf60a7819cb50da6c9d684ae66e45fe1126b4a405a06963a4a1b5af27525/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62736d6170\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90dbaf60a7819cb50da6c9d684ae66e45fe1126b4a405a06963a4a1b5af27525/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62736d6170\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bsmap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2545c029ce8c2db8d5c4130530ee4ef326e5f362b25c0ad91e3a1e3e83593bd4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62736d6170\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2545c029ce8c2db8d5c4130530ee4ef326e5f362b25c0ad91e3a1e3e83593bd4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62736d6170\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bsmap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/05de20bfc1f89c9a27c99af6439257ab0e37e91536e272e93bc4d0995c9f88c3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62736d6170\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/05de20bfc1f89c9a27c99af6439257ab0e37e91536e272e93bc4d0995c9f88c3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62736d6170\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bsmap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-bsmap\u003c/h1\u003e\u003ca id=\"user-content-singularity-bsmap\" class=\"anchor\" aria-label=\"Permalink: singularity-bsmap\" href=\"#singularity-bsmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for bsmap.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebsmap\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bsmap/2.90\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bsmap\u003c/code\u003e as \u003ccode\u003e2.90.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_spack_centos_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_spack_centos_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_spack_centos_x86_64\" href=\"#container_spack_centos_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainers for arch x86_64 based on Centos 7 with GNU 7 compiler and different versions of Spack\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e15.4\u003c/li\u003e\n\u003cli\u003e16.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIMPORTANT (NOT NECESSARY IF YOU START FROM A DOCKER IMAGE): When you are going to work inside the container remember to source these 2 file in order to set the proper module environment with spack and Lmod\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esource /opt/spack/share/spack/setup-env.sh\u003c/li\u003e\n\u003cli\u003esource /usr/share/lmod/8.2.7/init/sh\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [ - "singularity", - "bioinformatics" + "spack" ], - "updated_at": 1636519626.0 + "updated_at": 1614178565.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container for restrained-ensemble simulations using gmxapi", "filenames": [ - "Palma-II/Singularity.1.0.0" + "Singularity" ], - "full_name": "PointCloudSegementationOnALSandMLS/DataProcessingAndEvaluation", + "full_name": "jmhays/singularity-restrained-ensemble", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDataProcessingAndEvaluation\u003c/h1\u003e\u003ca id=\"user-content-dataprocessingandevaluation\" class=\"anchor\" aria-label=\"Permalink: DataProcessingAndEvaluation\" href=\"#dataprocessingandevaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains a collection of scripts that wer used for the thesis: Point Cloud Segmentation of Urban Architectural Structures from Aerial and Mobile LiDAR Scans using Neural Networks. For further information, please refer to the organisation \u003ca href=\"https://github.com/PointCloudSegementationOnALSandMLS\"\u003ePointCloudSegementationOnALSandMLS\u003c/a\u003e. In this repository we provide differnt data preperation, visualiztion and scripts. Using, this data still needs adjusting data paths and configurations for the differnt models. Further, it currently misses some documentation, which will be added soon.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThis repository consits of the following folders and scripts:\u003c/h3\u003e\u003ca id=\"user-content-this-repository-consits-of-the-following-folders-and-scripts\" class=\"anchor\" aria-label=\"Permalink: This repository consits of the following folders and scripts:\" href=\"#this-repository-consits-of-the-following-folders-and-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePALMA-II: Since the most computation were conducted on th PALMA-II HPC cluster of the University of M\u00fcnster. These scipts mainly bash scripts, which allow to start the calculations on the cluster. Further, it consists off the Apptainer/Singularity image used on the Cluster.\u003c/li\u003e\n\u003cli\u003echange_class_divisions: As we tested differen class divisions inside our training data, we here provide the scipts, which restructured the class labeling in the point clouds\u003c/li\u003e\n\u003cli\u003eclusters: In this folder we provide a method to cluster vertical objects in the point clouds. Further we provide the results in this folder. For further information, please read the Readme inside this folder.\u003c/li\u003e\n\u003cli\u003eevaluation: The scripts in this folder are used to evaluate the model performances. Please read the Readme inside the folder for further information.\u003c/li\u003e\n\u003cli\u003ehelperFunctions: In this folder we just provide one script with different helper and test functions we used.\u003c/li\u003e\n\u003cli\u003epreprocessing: In order to be able to start the training data, we had to perform several pre processing steps. Please read the Readme in this folder for further information.\u003c/li\u003e\n\u003cli\u003evisualization: This folder contains different visualization scripts. For more information please stick to te Readme in this folder\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-restrained-ensemble\u003c/h1\u003e\u003ca id=\"user-content-singularity-restrained-ensemble\" class=\"anchor\" aria-label=\"Permalink: singularity-restrained-ensemble\" href=\"#singularity-restrained-ensemble\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container for restrained-ensemble simulations using gmxapi\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 3, "topics": [], - "updated_at": 1708817415.0 + "updated_at": 1540327715.0 }, { "data_format": 2, - "description": null, + "description": "Metabobank tools", "filenames": [ - "Singularity.bioconductor_3.20.def", - "Singularity.bioconductor_3.14.def", - "Singularity.bioconductor_3.19.def old", - "Singularity.Muscle_5.1.0.def", - "Singularity.gemma_0.98.5.def", - "Singularity.BBMap_39.01.def", - "Singularity.plink_2.0.def", - "Singularity.redDog.def", - "Singularity.bioconductor_3.19.def", - "Singularity.bioconductor_3.12.def", - "Singularity.viralFlye_0.2.def", - "Singularity.bioconductor_3.18.def", - "Singularity.diamond_2.1.6.def", - "Singularity.circos-0.69-9.def", - "Singularity.masurca_4.0.9.def" + "Singularity" ], - "full_name": "sarahinwood/singularity-containers", + "full_name": "ddbj/metabobank_tools", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMetabobank tools\u003c/h1\u003e\u003ca id=\"user-content-metabobank-tools\" class=\"anchor\" aria-label=\"Permalink: Metabobank tools\" href=\"#metabobank-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003embexcel2idfsdrf.rb\u003c/h2\u003e\u003ca id=\"user-content-mbexcel2idfsdrfrb\" class=\"anchor\" aria-label=\"Permalink: mbexcel2idfsdrf.rb\" href=\"#mbexcel2idfsdrfrb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMetaboBank metadata excel \u304b\u3089 IDF/SDRF tsv \u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u003c/p\u003e\n\u003cp\u003eOptions\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e-i: MetaboBank metadata excel\u003c/li\u003e\n\u003cli\u003e-f: base filename (\u6307\u5b9a\u3057\u306a\u3044\u5834\u5408 .xlsx \u3092\u9664\u3044\u305f\u30a8\u30af\u30bb\u30eb\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u4f7f\u7528)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eruby mbexcel2idfsdrf.rb -i MBS-22_1_LC-MS_metadata.xlsx -f test\n\ntest.idf.txt\ntest.sdrf.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eruby mbexcel2idfsdrf.rb -i MBS-22_1_LC-MS_metadata.xlsx\n\nMBS-22_1_LC-MS_metadata.idf.txt\nMBS-22_1_LC-MS_metadata.sdrf.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ebase filename \u3092\u30d5\u30a1\u30a4\u30eb\u540d\u3068\u3057\u3066 IDF/SDRF \u30d5\u30a1\u30a4\u30eb\u304c\u751f\u6210\u3055\u308c\u308b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[base filename].idf.txt\u003c/li\u003e\n\u003cli\u003e[base filename].sdrf.txt\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIDF \u306f MAGE-TAB 1.1 \u4ee5\u964d\u3092\u3001SDRF \u306f Source Name \u4ee5\u964d\u3092\u51fa\u529b\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 9, "topics": [], - "updated_at": 1732498193.0 + "updated_at": 1720571144.0 }, { "data_format": 2, - "description": "robot learning repository for IRIS robots. ", + "description": "IQmol in a Singularity container", "filenames": [ - "docker/Singularity", - "docker/railrl_v9_cuda10-1_mj1-50-1-59_torch0-4-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_hand_tf_v1/Singularity", - "docker/railrl_hand_tf_v1/Singularity_cpu", - "docker/railrl_hand_v2/Singularity", - "docker/railrl_hand_v2/Singularity_cpu", - "docker/railrl_hand_v3/Singularity", - "docker/railrl_hand_v3/Singularity_cpu", - "docker/railrl_gpu_mujoco1-5-v4/singularity/Singularity", - "docker/railrl_v8_cuda10-1/Singularity", - "docker/railrl_v6_cuda9/Singularity", - "docker/railrl_v11_cuda10-1_mj2-0-2-2_torch0-3-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v7/Singularity", - "docker/railrl_v5/singularity/Singularity", - "docker/railrl_hand_v1/Singularity", - "docker/railrl_hand_v1/Singularity_cpu", - "docker/railrl_v10_cuda10-1_mj2-0-2-2_torch0-4-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v6_cuda8/Singularity", - "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", - "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", - "docker/railrl_ray/Singularity", - "docker/railrl_v9-5_cuda10-1_mj1-50-1-59_torch1-1-0_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v7_cuda8/Singularity", - "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch_cuda8", - "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch", - "docker/vitchyr/railrl_v15_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", - "docker/vitchyr/railrl_v15_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", - "experiments/ashvin/icml2020/singularity/Singularity" + "Singularity.2.13b", + "Singularity", + "Singularity.2.14", + "Singularity.2.11.2" ], - "full_name": "JonathanYang0127/iris_robot_learning", + "full_name": "OSC/sa_singularity_iqmol", "latest_release": null, - "readme": "\u003cp\u003eREADME last updated on: 01/24/2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erailrl\u003c/h1\u003e\u003ca id=\"user-content-railrl\" class=\"anchor\" aria-label=\"Permalink: railrl\" href=\"#railrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReinforcement learning framework.\nSome implemented algorithms:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/ddpg.py\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/sac.py\"\u003eSoft Actor Critic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/dqn_and_double_dqn.py\"\u003e(Double) Deep Q-Network (DQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/her.py\"\u003eHindsight Experience Replay (HER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/model_based_dagger.py\"\u003eMPC with Neural Network Model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/naf.py\"\u003eNormalized Advantage Function (NAF)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eWARNING: I haven\u0027t tested this NAF implementation much, so it may not match the paper\u0027s performance. I\u0027m pretty confident about the other two implementations though.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, checkout the example scripts, linked above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSome dependancies\u003c/h3\u003e\u003ca id=\"user-content-some-dependancies\" class=\"anchor\" aria-label=\"Permalink: Some dependancies\" href=\"#some-dependancies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo apt-get install swig\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate Conda Env\u003c/h3\u003e\u003ca id=\"user-content-create-conda-env\" class=\"anchor\" aria-label=\"Permalink: Create Conda Env\" href=\"#create-conda-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall and use the included ananconda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda env create -f docker/railrl/railrl-env.yml\n$ source activate railrl-env\n(railrl-env) $ # Ready to run examples/ddpg_cheetah_no_doodad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if you want you can use the docker image included.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Simulation Env Code\u003c/h3\u003e\u003ca id=\"user-content-download-simulation-env-code\" class=\"anchor\" aria-label=\"Permalink: Download Simulation Env Code\" href=\"#download-simulation-env-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vitchyr/multiworld\"\u003emultiworld\u003c/a\u003e (contains environments):\u003ccode\u003egit clone https://github.com/vitchyr/multiworld\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting\u003c/h3\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWriting more tests in progress. Run with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enose2 -v -B -s tests/regression\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e(Optional) Install doodad\u003c/h3\u003e\u003ca id=\"user-content-optional-install-doodad\" class=\"anchor\" aria-label=\"Permalink: (Optional) Install doodad\" href=\"#optional-install-doodad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI recommend installing \u003ca href=\"https://github.com/justinjfu/doodad\"\u003edoodad\u003c/a\u003e to\nlaunch jobs. Some of its nice features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEasily switch between running code locally, on a remote compute with\nDocker, on EC2 with Docker\u003c/li\u003e\n\u003cli\u003eEasily add your dependencies that can\u0027t be installed via pip (e.g. you\nborrowed someone\u0027s code)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you install doodad, also modify \u003ccode\u003eCODE_DIRS_TO_MOUNT\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePath to rllab directory\u003c/li\u003e\n\u003cli\u003ePath to railrl directory\u003c/li\u003e\n\u003cli\u003ePath to other code you want to juse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou\u0027ll probably also need to update the other variables besides the docker\nimages/instance stuff.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup Config File\u003c/h3\u003e\u003ca id=\"user-content-setup-config-file\" class=\"anchor\" aria-label=\"Permalink: Setup Config File\" href=\"#setup-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must setup the config file for launching experiments, providing paths to your code and data directories. Inside \u003ccode\u003erailrl/config/launcher_config.py\u003c/code\u003e, fill in the appropriate paths. You can use \u003ccode\u003erailrl/config/launcher_config_template.py\u003c/code\u003e as an example reference.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecp railrl/launchers/config-template.py railrl/launchers/config.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a policy and seeing results\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-policy-and-seeing-results\" class=\"anchor\" aria-label=\"Permalink: Visualizing a policy and seeing results\" href=\"#visualizing-a-policy-and-seeing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDuring training, the results will be saved to a file called under\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e is the directory set by \u003ccode\u003erailrl.launchers.config.LOCAL_LOG_DIR\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;exp_prefix\u0026gt;\u003c/code\u003e is given either to \u003ccode\u003esetup_logger\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;foldername\u0026gt;\u003c/code\u003e is auto-generated and based off of \u003ccode\u003eexp_prefix\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003einside this folder, you should see a file called \u003ccode\u003eparams.pkl\u003c/code\u003e. To visualize a policy, run\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e(railrl) $ python scripts/sim_policy LOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;/params.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have rllab installed, you can also visualize the results\nusing \u003ccode\u003erllab\u003c/code\u003e\u0027s viskit, described at\nthe bottom of \u003ca href=\"http://rllab.readthedocs.io/en/latest/user/cluster.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython rllab/viskit/frontend.py LOCAL_LOG_DIR/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eexp_prefix\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd paths\u003c/h3\u003e\u003ca id=\"user-content-add-paths\" class=\"anchor\" aria-label=\"Permalink: Add paths\" href=\"#add-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=$PYTHONPATH:/path/to/multiworld/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/doodad/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/viskit/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/railrl-private/repo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredit\u003c/h2\u003e\u003ca id=\"user-content-credit\" class=\"anchor\" aria-label=\"Permalink: Credit\" href=\"#credit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository was initially developed primarily by \u003ca href=\"https://github.com/vitchyr\"\u003eVitchyr\nPong\u003c/a\u003e, until July 2021, at which point it was\ntransferred to the RAIL Berkeley organization and is primarily maintained\nby \u003ca href=\"https://github.com/anair13\"\u003eAshvin Nair\u003c/a\u003e.\nOther major collaborators and contributions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mdalal2020\"\u003eMurtaza Dalal\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/stevenlin1111\"\u003eSteven Lin\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA lot of the coding infrastructure is based on\n\u003ca href=\"https://github.com/rll/rllab\"\u003erllab\u003c/a\u003e.\nThe serialization and logger code are basically a carbon copy of the rllab\nversions.\u003c/p\u003e\n\u003cp\u003eThe Dockerfile is based on the \u003ca href=\"https://github.com/openai/mujoco-py/blob/master/Dockerfile\"\u003eOpenAI mujoco-py\nDockerfile\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe SMAC code builds off of the \u003ca href=\"https://github.com/katerakelly/oyster\"\u003ePEARL\ncode\u003c/a\u003e, which built off of an older\nRLKit version.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity IQmol\u003c/h1\u003e\u003ca id=\"user-content-singularity-iqmol\" class=\"anchor\" aria-label=\"Permalink: Singularity IQmol\" href=\"#singularity-iqmol\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3599\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"http://iqmol.org/index.html\" rel=\"nofollow\"\u003eIQmol\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e or CentOS image \u003ca href=\"https://hub.docker.com/_/centos\" rel=\"nofollow\"\u003ecentos\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003eiqmol.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build iqmol.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull iqmol.sif shub://OSC/sa_singularity_iqmol\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart IQmol\u003c/h3\u003e\u003ca id=\"user-content-start-iqmol\" class=\"anchor\" aria-label=\"Permalink: Start IQmol\" href=\"#start-iqmol\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIQmol is started using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run iqmol.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as a native command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./iqmol.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 8, "topics": [], - "updated_at": 1697423014.0 + "updated_at": 1599018735.0 }, { "data_format": 2, - "description": null, + "description": "Testing the use of Github Actions to deploy singularity images", "filenames": [ - "docker/Singularity", - "docker/railrl_v9_cuda10-1_mj1-50-1-59_torch0-4-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_hand_v2/Singularity", - "docker/railrl_hand_v2/Singularity_cpu", - "docker/railrl_gpu_mujoco1-5-v4/singularity/Singularity", - "docker/railrl_v8_cuda10-1/Singularity", - "docker/railrl_v6_cuda9/Singularity", - "docker/railrl_v11_cuda10-1_mj2-0-2-2_torch0-3-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v7/Singularity", - "docker/railrl_v5/singularity/Singularity", - "docker/railrl_hand_v1/Singularity", - "docker/railrl_hand_v1/Singularity_cpu", - "docker/railrl_v10_cuda10-1_mj2-0-2-2_torch0-4-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v6_cuda8/Singularity", - "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", - "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", - "docker/railrl_ray/Singularity", - "docker/railrl_v9-5_cuda10-1_mj1-50-1-59_torch1-1-0_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v7_cuda8/Singularity", - "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch_cuda8", - "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch", - "experiments/ashvin/icml2020/singularity/Singularity" + "Singularity" ], - "full_name": "Asap7772/rail-rl-franka-eval", - "latest_release": null, - "readme": "\u003cp\u003eREADME last updated on: 01/24/2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erailrl\u003c/h1\u003e\u003ca id=\"user-content-railrl\" class=\"anchor\" aria-label=\"Permalink: railrl\" href=\"#railrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReinforcement learning framework.\nSome implemented algorithms:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/ddpg.py\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/sac.py\"\u003eSoft Actor Critic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/dqn_and_double_dqn.py\"\u003e(Double) Deep Q-Network (DQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/her.py\"\u003eHindsight Experience Replay (HER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/model_based_dagger.py\"\u003eMPC with Neural Network Model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/naf.py\"\u003eNormalized Advantage Function (NAF)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eWARNING: I haven\u0027t tested this NAF implementation much, so it may not match the paper\u0027s performance. I\u0027m pretty confident about the other two implementations though.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, checkout the example scripts, linked above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSome dependancies\u003c/h3\u003e\u003ca id=\"user-content-some-dependancies\" class=\"anchor\" aria-label=\"Permalink: Some dependancies\" href=\"#some-dependancies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo apt-get install swig\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate Conda Env\u003c/h3\u003e\u003ca id=\"user-content-create-conda-env\" class=\"anchor\" aria-label=\"Permalink: Create Conda Env\" href=\"#create-conda-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall and use the included ananconda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda env create -f docker/railrl/railrl-env.yml\n$ source activate railrl-env\n(railrl-env) $ # Ready to run examples/ddpg_cheetah_no_doodad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if you want you can use the docker image included.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Simulation Env Code\u003c/h3\u003e\u003ca id=\"user-content-download-simulation-env-code\" class=\"anchor\" aria-label=\"Permalink: Download Simulation Env Code\" href=\"#download-simulation-env-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vitchyr/multiworld\"\u003emultiworld\u003c/a\u003e (contains environments):\u003ccode\u003egit clone https://github.com/vitchyr/multiworld\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e(Optional) Install doodad\u003c/h3\u003e\u003ca id=\"user-content-optional-install-doodad\" class=\"anchor\" aria-label=\"Permalink: (Optional) Install doodad\" href=\"#optional-install-doodad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI recommend installing \u003ca href=\"https://github.com/justinjfu/doodad\"\u003edoodad\u003c/a\u003e to\nlaunch jobs. Some of its nice features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEasily switch between running code locally, on a remote compute with\nDocker, on EC2 with Docker\u003c/li\u003e\n\u003cli\u003eEasily add your dependencies that can\u0027t be installed via pip (e.g. you\nborrowed someone\u0027s code)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you install doodad, also modify \u003ccode\u003eCODE_DIRS_TO_MOUNT\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePath to rllab directory\u003c/li\u003e\n\u003cli\u003ePath to railrl directory\u003c/li\u003e\n\u003cli\u003ePath to other code you want to juse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou\u0027ll probably also need to update the other variables besides the docker\nimages/instance stuff.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup Config File\u003c/h3\u003e\u003ca id=\"user-content-setup-config-file\" class=\"anchor\" aria-label=\"Permalink: Setup Config File\" href=\"#setup-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must setup the config file for launching experiments, providing paths to your code and data directories. Inside \u003ccode\u003erailrl/config/launcher_config.py\u003c/code\u003e, fill in the appropriate paths. You can use \u003ccode\u003erailrl/config/launcher_config_template.py\u003c/code\u003e as an example reference.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecp railrl/launchers/config-template.py railrl/launchers/config.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a policy and seeing results\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-policy-and-seeing-results\" class=\"anchor\" aria-label=\"Permalink: Visualizing a policy and seeing results\" href=\"#visualizing-a-policy-and-seeing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDuring training, the results will be saved to a file called under\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e is the directory set by \u003ccode\u003erailrl.launchers.config.LOCAL_LOG_DIR\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;exp_prefix\u0026gt;\u003c/code\u003e is given either to \u003ccode\u003esetup_logger\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;foldername\u0026gt;\u003c/code\u003e is auto-generated and based off of \u003ccode\u003eexp_prefix\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003einside this folder, you should see a file called \u003ccode\u003eparams.pkl\u003c/code\u003e. To visualize a policy, run\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e(railrl) $ python scripts/sim_policy LOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;/params.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have rllab installed, you can also visualize the results\nusing \u003ccode\u003erllab\u003c/code\u003e\u0027s viskit, described at\nthe bottom of \u003ca href=\"http://rllab.readthedocs.io/en/latest/user/cluster.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython rllab/viskit/frontend.py LOCAL_LOG_DIR/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eexp_prefix\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd paths\u003c/h3\u003e\u003ca id=\"user-content-add-paths\" class=\"anchor\" aria-label=\"Permalink: Add paths\" href=\"#add-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=$PYTHONPATH:/path/to/multiworld/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/doodad/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/viskit/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/railrl-private/repo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredit\u003c/h2\u003e\u003ca id=\"user-content-credit\" class=\"anchor\" aria-label=\"Permalink: Credit\" href=\"#credit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA lot of the coding infrastructure is based on \u003ca href=\"https://github.com/rll/rllab\"\u003erllab\u003c/a\u003e.\nAlso, the serialization and logger code are basically a carbon copy.\u003c/p\u003e\n", + "full_name": "bailey-lab/deploy-singularity-testing", + "latest_release": "v0.1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edeploy-singularity-testing\u003c/h1\u003e\u003ca id=\"user-content-deploy-singularity-testing\" class=\"anchor\" aria-label=\"Permalink: deploy-singularity-testing\" href=\"#deploy-singularity-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting the use of Github Actions to deploy singularity images\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 6, "topics": [], - "updated_at": 1605834321.0 + "updated_at": 1635190346.0 }, { "data_format": 2, - "description": "My software stack of singularity images.", + "description": "Container for AnnotSV software. ", "filenames": [ - "Singularity.cuda8-comet", - "Singularity.cuda8-openmpi3.0", - "Singularity.cuda8-ml", - "Singularity.comet", - "Singularity.cuda8", - "Singularity.cuda8-bridges", - "Singularity.bridges", - "Singularity.cuda8-flux", - "Singularity.flux" + "Singularity_2.2", + "Singularity" ], - "full_name": "csadorf/singularity-recipes", + "full_name": "Clinical-Genomics-Lund/annotsv_container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMy software stack of singularity images.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnnotSV container for WGS pipeline\u003c/h2\u003e\u003ca id=\"user-content-annotsv-container-for-wgs-pipeline\" class=\"anchor\" aria-label=\"Permalink: AnnotSV container for WGS pipeline\" href=\"#annotsv-container-for-wgs-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity recipe for AnnotSV 2.3 and 2.2.\u003c/p\u003e\n\u003cp\u003esudo singularity build annotsv2.3.sif Singularity\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1546983060.0 + "updated_at": 1618833570.0 }, { "data_format": 2, - "description": "Review how to write a singularity image", + "description": null, "filenames": [ + "Singularity_custom", "Singularity" ], - "full_name": "j23414/singularity_event", + "full_name": "h1-the-swan/shortest_path_wos_disciplines", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_event\u003c/h1\u003e\u003ca id=\"user-content-singularity_event\" class=\"anchor\" aria-label=\"Permalink: singularity_event\" href=\"#singularity_event\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4858\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eReview how to write a singularity image\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCalculate shortest path citation distance between pairs of disciplines in Web of Science (WoS)\u003c/h1\u003e\u003ca id=\"user-content-calculate-shortest-path-citation-distance-between-pairs-of-disciplines-in-web-of-science-wos\" class=\"anchor\" aria-label=\"Permalink: Calculate shortest path citation distance between pairs of disciplines in Web of Science (WoS)\" href=\"#calculate-shortest-path-citation-distance-between-pairs-of-disciplines-in-web-of-science-wos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJason Portenoy 2019\u003c/p\u003e\n\u003cp\u003eFor a set of disciplines in WoS, calculate the pairwise shortest path citation distance between them by calculating the average distance between a sample of papers from each discipline.\u003c/p\u003e\n\u003cp\u003eUse \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e for containerization of python graph-tool and to run on HPC.\u003c/p\u003e\n\u003cp\u003eFirst, clean the input data using \u003ca href=\"src/data/clean_samples.py\"\u003esrc/data/clean_samples.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eNext, convert to JSON using \u003ca href=\"src/data/make_json.py\"\u003esrc/data/make_json.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe analysis can be run on HPC with Singularity using the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# example command\n\nsingularity exec graph_tool.sif python3 -m src.process.calc_shortest_path_distances \u0026lt;PATH_TO_CITATIONS_TSV\u0026gt; \u0026lt;PATH_TO_SAMPLES_JSON\u0026gt; ./data/processed/discipline000 --discipline-index 0 --undirected --debug\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will calculate shortest path distances for one discipline. It will, for each separate paper ID, calculate shortest path distances between that paper and all of the others in the JSON file. It will output this to a separate file in the output directory (\u003ccode\u003e./data/processed/discipline000\u003c/code\u003e in the example above). In the example command above, it will run this analysis for the first discipline (discipline 0) in the JSON file.\u003c/p\u003e\n\u003cp\u003eOn HPC, these jobs can run in the backfill queue on nodes with 200GB RAM. These jobs have a maximum of 4 hours. The command above will ignore everything that has already been calculated (in the output directory), and process as many IDs as it can before the time runs out. So, it may be necessary to run each discipline multiple times, until all the IDs have been processed.\u003c/p\u003e\n\u003cp\u003eThe script \u003ca href=\"src/modify_slurm_script.py\"\u003esrc/modify_slurm_script.py\u003c/a\u003e can generate multiple scripts to run on the Slurm scheduler on HPC. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m src.modify_slurm_script scripts/201910040930/calc_shortest_path_slurm.sh --start=0 --end=35 --log-dir logs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(In this example, there are 36 disciplines. The file \u003ccode\u003ecalc_shortest_path_slurm.sh\u003c/code\u003e is a template Slurm script.)\u003c/p\u003e\n\u003cp\u003eThen, the jobs can all be submitted at once with (for example):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor file in scripts/201910040930/calc_shortest_paths_discipline*.sh; do sbatch -p ckpt -A stf-ckpt --mail-user $EMAIL $file; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(\u003ccode\u003e$EMAIL\u003c/code\u003e is an environment variable storing your email address. You will be notified when the job starts and ends.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is a containerization tool. It is good for shared supercomputers because, unlike Docker, it does not require admin/root privileges and does not give access to others\u0027 files.\u003c/p\u003e\n\u003cp\u003eIt does, however, require root privileges to build the images. Therefore, you should build on a system for which you have root privileges. This will output a (large) Singularity image file (\u003ccode\u003e.sif\u003c/code\u003e), which can be uploaded to a different system and used without root privileges.\u003c/p\u003e\n\u003cp\u003eImages are built from a Singularity definition file (e.g., \u003ca href=\"./Singularity\"\u003e./Singularity\u003c/a\u003e). These can be built off of Docker containers. This is the case for the \u003ccode\u003e./Singularity\u003c/code\u003e definition file in this repo. There is an existing Docker image for \u003ccode\u003epython-graph-tool\u003c/code\u003e, which is used as the base for the image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build graph_tool.sif ./Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere is another alternative definition file: \u003ca href=\"./Singularity_custom\"\u003e./Singularity_custom\u003c/a\u003e. This is basically copying the Dockerfile from the \u003ccode\u003epython-graph-tool\u003c/code\u003e Docker image, into Singularity format. (This takes a while to build.)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build graph_tool_custom.sif ./Singularity_custom\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eTODO\u003c/h5\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eParallelize the distance calculations, after the graph data is loaded (using multiprocessing)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1602528896.0 + "updated_at": 1571469085.0 }, { "data_format": 2, - "description": null, + "description": "Scripts to build a docker and singularity with uboonecode", "filenames": [ - "Singularity", - "SingularityFedora" + "Singularity" ], - "full_name": "jolars/HessianScreening", + "full_name": "NuTufts/uboonecode-container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCode for the Hessian Screening Rule\u003c/h1\u003e\u003ca id=\"user-content-code-for-the-hessian-screening-rule\" class=\"anchor\" aria-label=\"Permalink: Code for the Hessian Screening Rule\" href=\"#code-for-the-hessian-screening-rule\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://arxiv.org/abs/2104.13026\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6388ff4fabd00a51d4bfac35fb9ed96aa426dd928c7525234fe887624a148bab/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d313233342e35363738392d6233316231622e737667\" alt=\"arXiv\" data-canonical-src=\"https://img.shields.io/badge/arXiv-1234.56789-b31b1b.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe results from the simulations, which were run on a dedicated HPC\ncluster, are stored in the \u003ca href=\"results/\"\u003eresults folder\u003c/a\u003e. The figures and\ntables in the paper, generated from these results, are stored in\n\u003ca href=\"figures/\"\u003e\u003ccode\u003efigures/\u003c/code\u003e\u003c/a\u003e and \u003ca href=\"tables/\"\u003e\u003ccode\u003etables/\u003c/code\u003e\u003c/a\u003e respectively.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReproducing the Results\u003c/h2\u003e\u003ca id=\"user-content-reproducing-the-results\" class=\"anchor\" aria-label=\"Permalink: Reproducing the Results\" href=\"#reproducing-the-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe results from our paper were run through a singularity container.\nCheck the releases for pre-built singularity containers that you can\ndownload and use.\u003c/p\u003e\n\u003cp\u003eTo reproduce the results, \u003cstrong\u003ealways\u003c/strong\u003e use the singularity container. To\nrun an experiment from the singularity container, call\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind results:/project/results container.sif \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003escript\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;script\u0026gt;\u003c/code\u003e should be a name of a script in the \u003ca href=\"experiments/\"\u003eexperiments\nfolder\u003c/a\u003e, such as \u003ccode\u003esimulateddata.R\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRe-building the Singularity Container\u003c/h3\u003e\u003ca id=\"user-content-re-building-the-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Re-building the Singularity Container\" href=\"#re-building-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to re-build the singularity container from scratch (or\nsimply want to clone the repo to your local drive), you can do so via\nthe following steps.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone the repository to your local hard drive. On linux, using SSH\nauthentication, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:jolars/HessianScreening.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the root of the repo and download\nthe data sets by running the following commands.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e HessianScreening\nRscript data-raw/arcene.R\nRscript data-raw/breheny-data.R\nRscript data-raw/libsvm-data.R\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the singularity container by calling\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build container.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen proceed as in \u003ca href=\"#reproducing-the-results\"\u003eReproducing the Results\u003c/a\u003e\nto run the experiments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Experiments without Singularity (Not Recommended!)\u003c/h3\u003e\u003ca id=\"user-content-running-experiments-without-singularity-not-recommended\" class=\"anchor\" aria-label=\"Permalink: Running Experiments without Singularity (Not Recommended!)\" href=\"#running-experiments-without-singularity-not-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, you may also reproduce the results by cloning this\nrepository, then either opening the \u003ccode\u003eHessianScreening.Rproj\u003c/code\u003e file in R\nStudio or starting R in the root directory of this folder (which will\nactivate the renv repository) and then run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003erenv\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003erestore()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto restore the project library. Then build the R package (see below) and\nrun the simulations directly by running the scripts in the experiments\nfolder. This is \u003cstrong\u003enot recommended\u003c/strong\u003e, however, since it, unlike the\nSingularity container approach, does not exactly reproduce the software\nenvironment used when these simulations where originally run and may\nresult in discrepancies due to differences in for instance operating\nsystems, compilers, BLAS/LAPACK implementations, and most\ncritically, the version of R.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eR Package\u003c/h2\u003e\u003ca id=\"user-content-r-package\" class=\"anchor\" aria-label=\"Permalink: R Package\" href=\"#r-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to build and experiment with the package, you can do so by\ncalling\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e R CMD INSTALL \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eprovided you have \u003ccode\u003ecd\u003c/code\u003eed to the root folder of this repository. First\nensure, however, that you have enabled the renv project library by\ncalling \u003ccode\u003erenv::restore()\u003c/code\u003e (see the section above).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData\u003c/h2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-label=\"Permalink: Data\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe datasets used in these simulations are stored in the \u003ca href=\"data/\"\u003edata\nfolder\u003c/a\u003e. Scripts to retrieve these datasets from their original\nsources can be found in \u003ca href=\"data-raw/\"\u003e\u003ccode\u003edata-raw/\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003euboonecode-container\u003c/h1\u003e\u003ca id=\"user-content-uboonecode-container\" class=\"anchor\" aria-label=\"Permalink: uboonecode-container\" href=\"#uboonecode-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker and singularity file to build uboonecode containers.\u003c/p\u003e\n\u003cp\u003eThis takes advantage of scisoft and an utility script, \u003ccode\u003epullProducts\u003c/code\u003e. The code is built on top of Scientific Linux 6.\u003c/p\u003e\n\u003cp\u003eGiven a tagged-release version of uboonecode, pullProducts, untars all dependencies into the \u003ccode\u003e/products\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eOnce built, one can go into the container and setup UPS, which is the utilty to setup different software products, by sourcing\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /products/setup\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, one can then list the version of uboonecode stored in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eups list -aK+ uboonecode\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Docker image\u003c/h2\u003e\u003ca id=\"user-content-build-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build Docker image\" href=\"#build-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOf course, you need to install docker.\u003c/p\u003e\n\u003cp\u003eTo build the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker build -t [container_name:tag] .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the last step of the Dockerfile installs LArCV2.\nThis is used by our group to make truth-labeled training data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the Docker image\u003c/h2\u003e\u003ca id=\"user-content-run-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Run the Docker image\" href=\"#run-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it [container_name:tag] bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSetup uboonecode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetup uboonecode [version] [qualifier]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun larsoft\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elar -c [fcl file] -s [input file]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild a singularity image, based on the docker image\u003c/h2\u003e\u003ca id=\"user-content-build-a-singularity-image-based-on-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build a singularity image, based on the docker image\" href=\"#build-a-singularity-image-based-on-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe use singularity\u0027s feature of importing a docker image.\nTo do that, we have to push the docker image to dockerhub.\nYou\u0027ll need a dockerhub account.\u003c/p\u003e\n\u003cp\u003eOnce you get an account, push to dockerhub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker login\ndocker push [container_name:tag]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen build the singularity image. Note that the build file has extra folders created to allow\nnetwork folders to be mounted on the Tufts Cluster.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build [output_image_name] Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the singularity image\u003c/h2\u003e\u003ca id=\"user-content-run-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Run the singularity image\" href=\"#run-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart the singularity container by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell [container image]\nprompt\u0026gt; bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen follow the instructions for the docker image to run \u003ccode\u003elar\u003c/code\u003e or build a development copy.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup and build a development copy\u003c/h2\u003e\u003ca id=\"user-content-setup-and-build-a-development-copy\" class=\"anchor\" aria-label=\"Permalink: Setup and build a development copy\" href=\"#setup-and-build-a-development-copy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHave confirmed that one can also setup and build a development version as well.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /products/setup\nsetup mrb\nmkdir workdir\ncd workdir\nsetup uboonecode [version] [qualifier]\nexport MRB_PROJECT=larsoft\nmrb newDev\nsource localProducts-XXXX/setup\ncd srcs\nmrb g uboonecode\nmrbsetenv\nmrb i\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHelper scripts\u003c/h3\u003e\u003ca id=\"user-content-helper-scripts\" class=\"anchor\" aria-label=\"Permalink: Helper scripts\" href=\"#helper-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repo also contains scripts to launch a job to build a development setup on the Tufts cluster.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1718911019.0 + "updated_at": 1532097120.0 }, { "data_format": 2, "description": null, "filenames": [ - "container/Singularity", - "container/single_container/Singularity" + "Singularity" ], - "full_name": "Clinical-Genomics-Lund/SomaticPanelPipeline", + "full_name": "aces/simulation_toolkit_singularity", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSimulation Toolkit for Coticometry Pipeline\u003c/h1\u003e\u003ca id=\"user-content-simulation-toolkit-for-coticometry-pipeline\" class=\"anchor\" aria-label=\"Permalink: Simulation Toolkit for Coticometry Pipeline\" href=\"#simulation-toolkit-for-coticometry-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTools in this repository can be used to simulate artificial lesions in the brain in order to estimate the sensitivity and specificity of lesion\ndetection, using different automated corticometry pipelines.\u003c/p\u003e\n\u003cp\u003eTo set up software you need the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the software packages needed to run the deformation-2.pl script. Please follow steps in: \u003ca href=\"https://github.com/aces/simulation_toolkit_singularity/blob/main/Singularity\"\u003ehttps://github.com/aces/simulation_toolkit_singularity/blob/main/Singularity\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain data from\n\u003ca href=\"https://ida.loni.usc.edu/collaboration/access/appLicense.jsp;jsessionid=B0278AF5FD413E9AC14512DF841FFCA4/\" rel=\"nofollow\"\u003ehttps://ida.loni.usc.edu/collaboration/access/appLicense.jsp;jsessionid=B0278AF5FD413E9AC14512DF841FFCA4/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun deformation pipeline\"\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUsage\nUsage: deformation.pl -input \u0026lt;.mnc\u0026gt; -output [options]\u003c/p\u003e\n\u003cp\u003eMandatory options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-deformation_ratio provide the ratio of deformation, values must be between 0.1 [shrinkage] to 1.50 [expansion] [e.g. 0.1,1.2,0.6,\u2026]\n\n-mask Specify a tolerance map file (.mnc) indicating voxels that have a different amount of error allowed e.g., CSF, background [e.g. your-mask.mnc]\n\n-coordinate Specify a hyperslab starting at \u0026lt;x\u0026gt; \u0026lt;y\u0026gt; \u0026lt;z\u0026gt; and extending in respective directions by \u0026lt;sizex\u0026gt; \u0026lt;sizey\u0026gt; \u0026lt;sizez\u0026gt; [e.g. 70 100 80 5 5 5]\n\n-tolerance_space Define the buffer area around the deformation region [default = 4]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOther options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-blur_determinant Blurring kernel size for blurring deformation determinant blurring kernel 0-1\n\n-error Specify the amount of error that is allowed between the specified determinant and the final determinant (per voxel) [default =0.00001]\n\n-iteration Specify the maximum number of iterations to update the deformations field (-1 means until convergence) [default 1000]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cp\u003e./deformation.pl -input ICBM_00100_t1_final.mnc -output Debugging_Folder -deformation_ratio 0.6 -coordinate 70 100 70 10 10 10 -tolerance_space 4 -blur_determinant 0.25 -error 0.00001 -iteration 100\u003c/p\u003e\n\u003cp\u003eThe locally-deformed output file name includes input parameters to simplify creating GLM matrices for statistical analysis.\u003c/p\u003e\n\u003cp\u003eICBM_00100_t1_final_deformed_by_0.4atROIx70-y100-z70dimx10.dimy10.dimz10.mnc.\u003c/p\u003e\n\u003cp\u003eThere following intermediate files are generated to help you do quality control and can be deleted:\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/block.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/blurred0.25determinant_r_0.4x70-y100-z70dimx10.dimy10.dimz10.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/DDDDdilated.mnc \u0026lt;\u0026lt;number of D\u0027s corresponds to the number of times the tolerance space (defined to be 4 in the commandline) is dilated\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/DDDDring.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/determinant_r_0.4_grid.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/determinant_r_0.4x70-y100-z70dimx10.dimy10.dimz10.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/determinant_r_0.4.xfm\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMPmask.mnc\u003c/p\u003e\n\u003cp\u003eALTERNATIVELY: If you don\u0027t want to use this Perl wrapper, then follow the instructions for creating your own deformations:\n\u003ca href=\"https://wiki.mouseimaging.ca/display/MICePub/Generating+deformation+fields\" rel=\"nofollow\"\u003ehttps://wiki.mouseimaging.ca/display/MICePub/Generating+deformation+fields\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSource code for deformation pipeline and dependencies (MINC):\n\u003ca href=\"https://github.com/Mouse-Imaging-Centre/generate_deformation_fields\"\u003ehttps://github.com/Mouse-Imaging-Centre/generate_deformation_fields\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eExample Data, Scripts and Statistical analysis used in our Frontier\u0027s Paper can be found here: \u003ca href=\"https://github.com/aces/simulation_toolkit_statistics\"\u003ehttps://github.com/aces/simulation_toolkit_statistics\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAll these tools and data needed will be made available via CBRAIN. To learn more, please contact us at \u003ca href=\"mailto:cbrain-support.mni@mcgill.ca\"\u003ecbrain-support.mni@mcgill.ca\u003c/a\u003e. In the subject line, pleasee be sure to write SIMULATION TOOLKIT.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 5, "topics": [], - "updated_at": 1732607811.0 + "updated_at": 1626116668.0 }, { "data_format": 2, - "description": "Comp-550 Fall 2023 term group project", + "description": "A BIDSapp for automated mixed model analyses of mindboggle outputs.", "filenames": [ "Singularity" ], - "full_name": "mathematiguy/enron-nlp-analysis", + "full_name": "C0C0AN/Mindboggle-MixedModels", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eenron-nlp-analysis\u003c/h1\u003e\u003ca id=\"user-content-enron-nlp-analysis\" class=\"anchor\" aria-label=\"Permalink: enron-nlp-analysis\" href=\"#enron-nlp-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComp-550 Fall 2023 term group project\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProject Setup and Execution Guide\u003c/h1\u003e\u003ca id=\"user-content-project-setup-and-execution-guide\" class=\"anchor\" aria-label=\"Permalink: Project Setup and Execution Guide\" href=\"#project-setup-and-execution-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis README provides instructions for setting up and running a project that uses Data Version Control (DVC). DVC is an open-source tool for data science and machine learning projects. It allows for tracking and versioning of datasets and machine learning models, making it easier to share and reproduce experiments and analyses.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore you start, ensure you have the following installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython (version as per project requirements)\u003c/li\u003e\n\u003cli\u003epip (Python package manager)\u003c/li\u003e\n\u003cli\u003evirtualenv (Python environment management tool)\u003c/li\u003e\n\u003cli\u003eDVC (Data Version Control)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup Instructions\u003c/h2\u003e\u003ca id=\"user-content-setup-instructions\" class=\"anchor\" aria-label=\"Permalink: Setup Instructions\" href=\"#setup-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Clone the Project Repository\u003c/h3\u003e\u003ca id=\"user-content-1-clone-the-project-repository\" class=\"anchor\" aria-label=\"Permalink: 1. Clone the Project Repository\" href=\"#1-clone-the-project-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the project from the provided source and navigate to the project directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Create and Activate a Python Virtual Environment\u003c/h3\u003e\u003ca id=\"user-content-2-create-and-activate-a-python-virtual-environment\" class=\"anchor\" aria-label=\"Permalink: 2. Create and Activate a Python Virtual Environment\" href=\"#2-create-and-activate-a-python-virtual-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython -m venv venv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn Windows: \u003ccode\u003evenv\\Scripts\\activate\u003c/code\u003e\nmacOS and Linux: \u003ccode\u003esource venv/bin/activate\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Install Project Dependencies\u003c/h3\u003e\u003ca id=\"user-content-3-install-project-dependencies\" class=\"anchor\" aria-label=\"Permalink: 3. Install Project Dependencies\" href=\"#3-install-project-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Download NLTK Dependencies\u003c/h3\u003e\u003ca id=\"user-content-4-download-nltk-dependencies\" class=\"anchor\" aria-label=\"Permalink: 4. Download NLTK Dependencies\" href=\"#4-download-nltk-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the following in the terminal:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m nltk.downloader -d data/nltk_data all\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Install spaCy Language Model\u003c/h3\u003e\u003ca id=\"user-content-5-install-spacy-language-model\" class=\"anchor\" aria-label=\"Permalink: 5. Install spaCy Language Model\" href=\"#5-install-spacy-language-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m spacy download en_core_web_sm\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the Project\u003c/h2\u003e\u003ca id=\"user-content-running-the-project\" class=\"anchor\" aria-label=\"Permalink: Running the Project\" href=\"#running-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eData Version Control (DVC)\u003c/h3\u003e\u003ca id=\"user-content-data-version-control-dvc\" class=\"anchor\" aria-label=\"Permalink: Data Version Control (DVC)\" href=\"#data-version-control-dvc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun dvc repro to execute the DVC pipeline. DVC manages the data processing stages as per the dvc.yaml file.\u003c/p\u003e\n\u003cp\u003eTo run the project, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edvc repro\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the entire pipeline from start to finish. If you wanna see the dag for the project, run \u003ccode\u003edvc dag\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1702773276.0 + "updated_at": 1580230589.0 }, { "data_format": 2, - "description": null, + "description": "Nextflow pipeline for microarray and RNA-seq", "filenames": [ "Singularity" ], - "full_name": "soulj/OAModelmicroRNA", + "full_name": "CBFLivUni/SkeletalVis-Transcriptomics", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eACL model of osteoarthritis mRNA and miRNA analysis\u003c/h1\u003e\u003ca id=\"user-content-acl-model-of-osteoarthritis-mrna-and-mirna-analysis\" class=\"anchor\" aria-label=\"Permalink: ACL model of osteoarthritis mRNA and miRNA analysis\" href=\"#acl-model-of-osteoarthritis-mrna-and-mirna-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/soulj/OAModelmicroRNA/blob/main/figures/Fig2C_MAPlot.png\"\u003e\u003cimg src=\"https://github.com/soulj/OAModelmicroRNA/raw/main/figures/Fig2C_MAPlot.png\" width=\"40%\" height=\"40%\" align=\"center\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following R notebooks can be used to generate the bioinformatics figures and tables shown in the paper:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e01_ACLmRNA.qmd - DESeq2 analysis of the ACL rupture model mRNA-seq\u003c/li\u003e\n\u003cli\u003e02_ACLmiRNA.qmd - DESeq2 analysis of the ACL rupture model smallRNA-seq\u003c/li\u003e\n\u003cli\u003e03_mir199DiffExp.qmd - RNA-seq Differential expression, gene ontology and target analysis of mir199 inhibited HACs\u003c/li\u003e\n\u003cli\u003e04_DMMDiffExp.qmd - DESeq2 analysis of the DMM OA model mRNA-seq\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the analysis\u003c/h2\u003e\u003ca id=\"user-content-running-the-analysis\" class=\"anchor\" aria-label=\"Permalink: Running the analysis\" href=\"#running-the-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReproducibly with singularity\u003c/h3\u003e\u003ca id=\"user-content-reproducibly-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Reproducibly with singularity\" href=\"#reproducibly-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter cloning/downloading this repository.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://docs.sylabs.io/guides/3.8/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload and run the singularity container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e singularity run https://pgb.liv.ac.uk/~jsoul/OAModelmicroRNA/analysis.img\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild the singularity container:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e sudo singularity build runAnalysis.img Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the analysis and render tables and figures with a single command:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e ./runAnalysis.img\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAlternatively using RScript\u003c/h3\u003e\u003ca id=\"user-content-alternatively-using-rscript\" class=\"anchor\" aria-label=\"Permalink: Alternatively using RScript\" href=\"#alternatively-using-rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall the needed R packages\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e RScript install/install.R\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the analysis and render the html notebooks\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e RScript runAnalysis.R\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRaw data processing\u003c/h2\u003e\u003ca id=\"user-content-raw-data-processing\" class=\"anchor\" aria-label=\"Permalink: Raw data processing\" href=\"#raw-data-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the smallRNA-seq data the nextflow core smrnaseq v1.1.0\nwas run using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run nf-core/smrnaseq -r 1.1.0 --input \"fastqFiles/*.fastq.gz\" --genome GRCm38 --protocol \u0027custom\u0027 --three_prime_adapter AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC --mirtrace_protocol illumina --max_cpus 6 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSkeletalvis pipeline was used to process the RNA-seq data (github.com/soulj/SkeletalVis-Pipeline)\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSkeletalVis-Transcriptomics\u003c/h1\u003e\u003ca id=\"user-content-skeletalvis-transcriptomics\" class=\"anchor\" aria-label=\"Permalink: SkeletalVis-Transcriptomics\" href=\"#skeletalvis-transcriptomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSkeletalVis-Transcriptomics\u003c/strong\u003e is a bioinformatics pipeline for reproducible analyses of microarray and RNA-seq data.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a portable workflow tool to run tasks across multiple compute infrastructures. This pipeline uses a singularity container containing all the software needed to run the analysis, making installation simple and the results reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline summary\u003c/h2\u003e\u003ca id=\"user-content-pipeline-summary\" class=\"anchor\" aria-label=\"Permalink: Pipeline summary\" href=\"#pipeline-summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003cstrong\u003eSkeletalVis-Transcriptomics\u003c/strong\u003e pipeline takes a sample table and a parameter file defining the experiment as input. If not provided microarray data and fastq files are automatically downloaded using the provided accession numbers/sample identifiers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFeatures:\u003c/h3\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features:\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Download of raw microarray data from GEO, fastq files either directly from ENA, via conversion of sra files from SRA\u003cbr\u003e\n(\u003cstrong\u003eb\u003c/strong\u003e) Microarray with affyQCReport and RNA-seq read quality trimming with trimmomatic, QC reports with fastqc and multiQC\u003cbr\u003e\n(\u003cstrong\u003ec\u003c/strong\u003e)\tRNA-seq Quantification using \u003ca href=\"https://pachterlab.github.io/kallisto/\" rel=\"nofollow\"\u003e\u003ccode\u003ekallisto\u003c/code\u003e\u003c/a\u003e and processing with tximport to produce a sample x gene expression table\u003cbr\u003e\n(\u003cstrong\u003ed\u003c/strong\u003e) Differential expression analysis with limma, DESeq2 and Characteristic Direction\u003cbr\u003e\n(\u003cstrong\u003ee\u003c/strong\u003e) Pathway and gene ontology enrichment analysis with goseq\u003ca href=\"https://bioconductor.org/packages/release/bioc/html/goseq.html\" rel=\"nofollow\"\u003e\u003ccode\u003egoseq\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003ef\u003c/strong\u003e) Active subnetwork identification with \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/15272936/\" rel=\"nofollow\"\u003e\u003ccode\u003eGIGA\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003ei\u003c/strong\u003e) Identify transcription factors potenitally driving differential expression with \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/31114921/\" rel=\"nofollow\"\u003e\u003ccode\u003eCHEA3\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses are run in parallel and in result of error you can resume with the \u003ccode\u003e-resume\u003c/code\u003e parameter to re-run the pipeline starting from the previous fault.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAnalyse an example dataset\u003c/h3\u003e\u003ca id=\"user-content-analyse-an-example-dataset\" class=\"anchor\" aria-label=\"Permalink: Analyse an example dataset\" href=\"#analyse-an-example-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTry the pipeline on an example dataset (all inputs will be automatically downloaded): -\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html#installation\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the pipeline\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow clone CBFLivUni/SkeletalVis-Transcriptomics\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/docs/latest/config.html\" rel=\"nofollow\"\u003e\u003ccode\u003eConfigure\u003c/code\u003e\u003c/a\u003e the resource profile for your HPC or local computer. A template for slurm schedulers is provided as an example in \u003ccode\u003enextflow.config\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThere is a utility function provided to help replace paths within the config text files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e```console\n bash scripts/install/replacePath.sh nextflow.config /mnt/hc-storage/groups/cbf/Nextflow/SkeletalVis-Transcriptomics `pwd -P`\n```\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003e\n\u003cp\u003eTest on the example dataset:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow run main.nf -profile slurm -params-file params/GSE152805.yaml -with-singularity library://jsoul/default/skeletalvis-transcriptomics:latest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAnalyse your own data\u003c/h3\u003e\u003ca id=\"user-content-analyse-your-own-data\" class=\"anchor\" aria-label=\"Permalink: Analyse your own data\" href=\"#analyse-your-own-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDefine the sampleTable for RNA-seq data\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCreate a tab seperated table with unique Sample names, SRR accession numbers (if download is needed) and any additional metadata e.g\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSample\u003c/th\u003e\n\u003cth\u003eFile\u003c/th\u003e\n\u003cth\u003eCondition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eControl_1\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eControl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eControl_2\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eControl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTreated_1\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eTreated\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTreated_2\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eTreated\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote for microarray data the metadata is retrived directly from GEO and we instead just need to specify the columns of interest that define variables to compare.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDefine the configuration\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMost parameters are set to sensible defaults within the main nextflow script, with only a few parameters required to be altered with typical use. Note the use of Groovy, python and R booleans.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eOptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eaccession\u003c/td\u003e\n\u003ctd\u003eA unique identifier for the experiment to be analysed e.g the GEO accession of the data - used to name output data and download fastq files\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especies\u003c/td\u003e\n\u003ctd\u003eThe species the reads originate from - used to create the kallisto index\u003c/td\u003e\n\u003ctd\u003eHuman, Mouse, Rat, Cow, Pig\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esingle\u003c/td\u003e\n\u003ctd\u003eIs the data single ended RNA-seq?\u003c/td\u003e\n\u003ctd\u003etrue, false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebatchCorrect\u003c/td\u003e\n\u003ctd\u003eShould batch effect correction (sva) be used?\u003c/td\u003e\n\u003ctd\u003eTRUE, FALSE\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eskipTrimming\u003c/td\u003e\n\u003ctd\u003eShould read trimming be skipped?\u003c/td\u003e\n\u003ctd\u003efalse (default), true\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eParameters should be defined within a yaml file. See \u003ccode\u003eparams/GSE152805.yaml\u003c/code\u003e for an example.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eaccession\u003c/code\u003e parameter defines the default search path for fastq.gz files (data/\u003ccode\u003eaccession\u003c/code\u003e/fastqFiles/). Trimmed unpaired reads e.g \"*_R0.fastq.gz\" are skipped by default. If fastq files are not found locally the data will be downloaded using the provided \u003ccode\u003eaccession\u003c/code\u003e number.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline with your own parameters\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow run soulj/SkeletalVis-Transcriptomics -profile slurm -params-file ownData.yaml -with-singularity library://jsoul/default/skeletalvis-transcriptomics\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting modules\u003c/h3\u003e\u003ca id=\"user-content-testing-modules\" class=\"anchor\" aria-label=\"Permalink: Testing modules\" href=\"#testing-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModules can be tested using the \u003ca href=\"https://pypi.org/project/pytest-workflow/\" rel=\"nofollow\"\u003e\u003ccode\u003epytest-workflow\u003c/code\u003e\u003c/a\u003e framework. Module test directories within the \u003ccode\u003etests\u003c/code\u003e folder contain a nextflow script and a configuration yaml file defining the test for each module.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall pytest-workflow\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003econda install pytest-workflow\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the tests - e.g to test the pathway enrichment module\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epytest --symlink --kwdof --tag pathwayEnrichment\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1683907187.0 + "subscribers_count": 2, + "topics": [ + "microarray", + "nextflow", + "pathway-analysis", + "rna-seq", + "transcriptomics" + ], + "updated_at": 1700837110.0 }, { "data_format": 2, "description": null, "filenames": [ - "vrep/Singularity+", - "vrep/Singularity-without-conda+", - "vrep/Singularity-without-conda", - "vrep/Singularity", - "vrep/Singularity-cupy" + "Singularity" ], - "full_name": "takuma-yoneda/singularity-envs", + "full_name": "raffenet/singularity-images", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1597803674.0 + "updated_at": 1598301904.0 }, { "data_format": 2, - "description": "du + rust = dust. Like du but more intuitive.", + "description": "Small utilities for working with fastq sequence files.", "filenames": [ - "0.8.3/Singularity", - "0.7.0/Singularity", - "0.6.0/Singularity", - "0.8.6/Singularity", - "0.6.1/Singularity", - "0.8.0/Singularity", - "0.5.4/Singularity", - "0.8.4/Singularity" + "0.8/Singularity" ], - "full_name": "pscedu/singularity-dust", - "latest_release": "v0.8.4", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-dust/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-dust/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-dust/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-dust/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/61adf552b3234b2843e4c95d2a40467bcf6ed674755f53c0298c3fef098cefcf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d64757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/61adf552b3234b2843e4c95d2a40467bcf6ed674755f53c0298c3fef098cefcf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d64757374\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-dust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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src=\"https://camo.githubusercontent.com/7b19576388c609249f90940002caee3d06bfc3a99c7d4613e3f12d95d8d755cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d64757374\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-dust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d9c4b5e46fea27c4a179d1ae397e785209ca6251d122ffd936a03692f6c1c471/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d64757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d9c4b5e46fea27c4a179d1ae397e785209ca6251d122ffd936a03692f6c1c471/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d64757374\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-dust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-dust\u003c/h1\u003e\u003ca id=\"user-content-singularity-dust\" class=\"anchor\" aria-label=\"Permalink: singularity-dust\" href=\"#singularity-dust\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bootandy/dust/raw/master/media/snap.png\"\u003e\u003cimg src=\"https://github.com/bootandy/dust/raw/master/media/snap.png\" alt=\"Example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/bootandy/dust\"\u003edust\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003edust\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/dust/0.8.6\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/dust\u003c/code\u003e as \u003ccode\u003e0.8.6.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-fastq-tools", + "latest_release": "v0.8", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-fastq-tools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fastq-tools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-fastq-tools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fastq-tools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c7b7a1fdf0e2c8b7598b24e1f68857d5983b78a67a4972bc8a40bfa100030283/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c7b7a1fdf0e2c8b7598b24e1f68857d5983b78a67a4972bc8a40bfa100030283/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-fastq-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a37739f85a6b18346dfaecdc54fee862893d98cb9580108c32e41972a5d908b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a37739f85a6b18346dfaecdc54fee862893d98cb9580108c32e41972a5d908b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-fastq-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bcbd2e3a37265324da540a52ea3c2beb2303fe9fd469e9866de39cc7eb2506da/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bcbd2e3a37265324da540a52ea3c2beb2303fe9fd469e9866de39cc7eb2506da/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fastq-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/34aaa5fff834d641182221cb6e6f46fdbe6285629013261f7298219aca1ce234/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34aaa5fff834d641182221cb6e6f46fdbe6285629013261f7298219aca1ce234/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fastq-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-fastq-tools\u003c/h1\u003e\u003ca id=\"user-content-singularity-fastq-tools\" class=\"anchor\" aria-label=\"Permalink: singularity-fastq-tools\" href=\"#singularity-fastq-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/dcjones/fastq-tools\"\u003efastq-tools\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efastq\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/fastq-tools/0.8\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/fastq-tools\u003c/code\u003e as \u003ccode\u003e0.8.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [ - "singularity", - "utilities" + "bioinformatics", + "singularity" ], - "updated_at": 1729743820.0 + "updated_at": 1650574199.0 }, { "data_format": 2, - "description": null, + "description": "Docker file for building MiCall execution environment to run under Kive", "filenames": [ - "Singularity.PGDSpider_2.1.1.5", - "Singularity.longshot", - "Singularity.FastQC_0.11.5", - "Singularity.plink_1.9", - "Singularity.minimap2_2.17", - "Singularity.BCFtools_1.9", - "Singularity.R_3.5.0", - "Singularity.AdapterRemoval", - "Singularity.plink_2.0", - "Singularity.STAR_2.7.1a", - "Singularity.BayeScan_2.1", - "Singularity.R_3.6.0", - "Singularity.BBMap_37.92", - "Singularity.vcflib", - "Singularity.samtools_1.9", - "Singularity.fastsimcoal_2.6", - "Singularity.VCFtools_0.1.17", - "Singularity.gatk_3.8.0" + "Singularity" ], - "full_name": "MarissaLL/singularity-containers", - "latest_release": null, - "readme": "\u003cp\u003eRecipes for Singularity containers, which are hosted on SingularityHub at\n\u003ca href=\"https://www.singularity-hub.org/collections/1290\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/1290\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "cfe-lab/kive-default-docker", + "latest_release": "v1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ekive-default-docker\u003c/h1\u003e\u003ca id=\"user-content-kive-default-docker\" class=\"anchor\" aria-label=\"Permalink: kive-default-docker\" href=\"#kive-default-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker file for building default execution environment to run Kive pipelines\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 11, "topics": [], - "updated_at": 1629949124.0 + "updated_at": 1538429377.0 }, { "data_format": 2, - "description": "get openpose on PSU ACI", + "description": null, "filenames": [ - "Singularity.gpu" + "Singularity" ], - "full_name": "d-bohn/openpose_aci", + "full_name": "Cloud-PG/CachingOnDemand_singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eopenpose_aci\u003c/h1\u003e\u003ca id=\"user-content-openpose_aci\" class=\"anchor\" aria-label=\"Permalink: openpose_aci\" href=\"#openpose_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and workflow for building \u003ca href=\"https://github.com/CMU-Perceptual-Computing-Lab/openpose\"\u003eOpenPose\u003c/a\u003e\nin Docker Hub and modifying it with Singularity Hub for use with PSU\nACI HPC clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: This is the GPU version of OpenPose, for the CPU-only version please\nrefer to the appropriately labelled branch.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003essh\u003c/code\u003e into the PSU ACI HPC with X11 flags.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh USERID@aci-b.aci.ics.psu.edu -X -Y\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart an interactive session using \u003ccode\u003eqsub\u003c/code\u003e. We need a lot of memory for\nthe CPU version of OpenPose\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -A open -I -X -l walltime=24:00:00 -l nodes=5:ppn=10 -l pmem=20gb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom ACI pull the OpenPose image and shell into it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n openpose_aci.simg shub://d-bohn/openpose_aci\n\nsingularity exec -n openpose_aci.simg /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, once inside the image you can run the example utilizing the following\ncode:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /opt/openpose\nmkdir data \u0026amp;\u0026amp; mkdir data/poses\n\n./build/examples/openpose/openpose.bin --video examples/media/video.avi --write_video ./data/result.avi --write_json ./data/poses --display 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImage Builds\u003c/h2\u003e\u003ca id=\"user-content-image-builds\" class=\"anchor\" aria-label=\"Permalink: Image Builds\" href=\"#image-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe OpenPose docker image was built on docker hub.\u003c/p\u003e\n\u003cp\u003eThe OpenPose singularity image was built using the docker image base and\nconverting it to a singularity image via singularity hub.\u003c/p\u003e\n\u003cp\u003eSetup for linking Github with Docker Hub and Singularity Hub can be found here:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.docker.com/docker-hub/\" rel=\"nofollow\"\u003edocker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki\"\u003eSingularity Hub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003eSingularity\u003c/code\u003e file specifies creating a Singularity-compatible image\nfrom the docker image, as well as adding access to folders within ACI, specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# ACI mappings so you can access your files.\nmkdir -p /storage/home\nmkdir -p /storage/work\nmkdir -p /gpfs/group\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe OpenFace docker image is large (\u0026gt; 3.7GB). It is built on Ubuntu 16.04.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe current image is built with only CPU support, but can easily be adapted to\ninclude GPU support when that is available (see first two \u003ccode\u003emake\u003c/code\u003e flags in \u003ccode\u003eDockerfile\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe CPU version is SLOW. The example above takes several minutes to\nexecute. Runs at between 0.3 and 0.1 frames/second.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container for XRootD proxy server\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-xrootd-proxy-server\" class=\"anchor\" aria-label=\"Permalink: Singularity container for XRootD proxy server\" href=\"#singularity-container-for-xrootd-proxy-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSet up log directory\u003c/h2\u003e\u003ca id=\"user-content-set-up-log-directory\" class=\"anchor\" aria-label=\"Permalink: Set up log directory\" href=\"#set-up-log-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe log directory should have the correct permisions:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo chown -R 998:996 /var/log/xrootd\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStart the service from local image\u003c/h2\u003e\u003ca id=\"user-content-start-the-service-from-local-image\" class=\"anchor\" aria-label=\"Permalink: Start the service from local image\" href=\"#start-the-service-from-local-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the image from github repo:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Cloud-PG/CachingOnDemand_singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e CachingOnDemand_singularity\n\nsudo singularity build xrd_proxy.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen start the service:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo REMOTE_HOST=XXX.XXX.XXX.XX PROXY_PORT=1124 REMOTE_PORT=31094 singularity instance start -B /var/log/xrootd/:/var/log/xrootd/ xrd_proxy.sif myproxy\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStart the service with SingularityHub image\u003c/h2\u003e\u003ca id=\"user-content-start-the-service-with-singularityhub-image\" class=\"anchor\" aria-label=\"Permalink: Start the service with SingularityHub image\" href=\"#start-the-service-with-singularityhub-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart the service with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo REMOTE_HOST=XXX.XXX.XXX.XX PROXY_PORT=1124 REMOTE_PORT=31094 singularity instance start -B /var/log/xrootd/:/var/log/xrootd/ shub://Cloud-PG/CachingOnDemand_singularity:latest myproxy\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1578090055.0 + "updated_at": 1574258154.0 }, { "data_format": 2, - "description": "Singularity recipe for installing NEMO prerequisites, and scripts for configuring and running AMM7 model", + "description": "Work with reticulate on Singularity", "filenames": [ "Singularity" ], - "full_name": "swarder/NEMO-AMM7-recipe", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNEMO-AMM7-recipe\u003c/h1\u003e\u003ca id=\"user-content-nemo-amm7-recipe\" class=\"anchor\" aria-label=\"Permalink: NEMO-AMM7-recipe\" href=\"#nemo-amm7-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for installing NEMO prerequisites, and scripts for configuring and running AMM7 model\u003c/p\u003e\n\u003cp\u003eScripts installing prerequisites and downloading NEMO source code are modified from \u003ca href=\"https://github.com/rcaneill/NEMO-installs\"\u003ehttps://github.com/rcaneill/NEMO-installs\u003c/a\u003e (Copyright (c) 2019 Romain Caneill)\nModified here under MIT licence \u003ca href=\"https://github.com/rcaneill/NEMO-installs/blob/master/LICENSE\"\u003ehttps://github.com/rcaneill/NEMO-installs/blob/master/LICENSE\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAMM7 configuration based on \u003ca href=\"https://zenodo.org/record/4022310\" rel=\"nofollow\"\u003ehttps://zenodo.org/record/4022310\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe pre-built image can be pulled from Singularity Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://swarder/NEMO-AMM7-recipe:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, the recipe can be built locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build NEMO_AMM7.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce pulled or built, launch the shell (replace file name as appropriate):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell NEMO-AMM7-recipe_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDefine working directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport WORKDIR=/home/$USER/nemo_workdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen configure AMM7 within container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd $WORKDIR\ncp /nemo/installations/configure_amm7.sh .\n./configure_amm7.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, run NEMO\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd $WORKDIR/NEMOGCM/CONFIG/AMM7_SURGE/EXP_tideonly\nmpirun -np 6 ./opa : -np 1 ./xios_server.exe\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "richelbilderbeek/reticulate_on_singularity", + "latest_release": "v0.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ereticulate_on_singularity\u003c/h1\u003e\u003ca id=\"user-content-reticulate_on_singularity\" class=\"anchor\" aria-label=\"Permalink: reticulate_on_singularity\" href=\"#reticulate_on_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo shows how to work with the R package \u003ccode\u003ereticulate\u003c/code\u003e\nto run a Python script on Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSteps\u003c/h2\u003e\u003ca id=\"user-content-steps\" class=\"anchor\" aria-label=\"Permalink: Steps\" href=\"#steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e \u003ccode\u003e%post\u003c/code\u003e section contains the build\u003c/li\u003e\n\u003cli\u003eThe \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e \u003ccode\u003e%test\u003c/code\u003e section contains the test\u003c/li\u003e\n\u003cli\u003eThe \u003ca href=\".github/workflows/build_sandbox.yaml\"\u003e.github/workflows/build_sandbox.yaml\u003c/a\u003e\nand \u003ca href=\".github/workflows/build_singularity.yaml\"\u003e.github/workflows/build_singularity.yaml\u003c/a\u003e\nshow the final usage\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1610120713.0 + "updated_at": 1638270295.0 }, { "data_format": 2, - "description": "Singularity images for Jupyter (based on minconda3 Docker image)", + "description": "work with RAR archives with tools in a Singularity container", "filenames": [ "Singularity" ], - "full_name": "bihealth/singularity-jupyter", + "full_name": "singularityhub/rar", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Image with Jupyter\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-with-jupyter\" class=\"anchor\" aria-label=\"Permalink: Singularity Image with Jupyter\" href=\"#singularity-image-with-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRar\u003c/h1\u003e\u003ca id=\"user-content-rar\" class=\"anchor\" aria-label=\"Permalink: Rar\" href=\"#rar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1080\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a tutorial brought to you by the \u003ca href=\"https://www.github.com/vsoch\"\u003edebugger dinosaur\u003c/a\u003e of \u003ca href=\"https://srcc.stanford.edu\" rel=\"nofollow\"\u003eStanford Research Computing\u003c/a\u003e and is part of the \u003ca href=\"https://vsoch.github.io/lessons/\" rel=\"nofollow\"\u003eResearch Computing Lessons\u003c/a\u003e series.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/98dfd6c0463046b3bbd40debb29f3dfb8850ed286459aaff417080c5787a4b56/68747470733a2f2f76736f63682e6769746875622e696f2f6c6573736f6e732f6173736574732f696d672f6c6f676f2d626f6f6b2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98dfd6c0463046b3bbd40debb29f3dfb8850ed286459aaff417080c5787a4b56/68747470733a2f2f76736f63682e6769746875622e696f2f6c6573736f6e732f6173736574732f696d672f6c6f676f2d626f6f6b2e706e67\" alt=\"\" width=\"200\" data-canonical-src=\"https://vsoch.github.io/lessons/assets/img/logo-book.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor complete instructions and documentation for using the container, please \u003ca href=\"https://vsoch.github.io/lessons/unrar-python/#rar-ing-with-a-container\" rel=\"nofollow\"\u003eread the lesson\u003c/a\u003e. If you need help, post an issue on this repository, or to the \u003ca href=\"https://github.com/vsoch/lessons\"\u003elessons repository\u003c/a\u003e directly! You can also request a tutorial or lesson to be added. The debugger dinosaur and Research Computing are here for you!\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, - "topics": [], - "updated_at": 1591308299.0 + "subscribers_count": 3, + "topics": [ + "rar", + "archive", + "singularity", + "singularity-container", + "cluster", + "hpc", + "srcc", + "srcc-lessons" + ], + "updated_at": 1527981066.0 }, { "data_format": 2, - "description": "Singularity image recipe", + "description": "Singularity container for the Dartmouth 2017 MIND Summer School", "filenames": [ - "tensorflow/Singularity.tf-1.14.0-gpu-py3", - "tensorflow/Singularity.tf-2.3.0-gpu", - "tensorflow/Singularity.tf-2.5.1-gpu", - "tensorflow/Singularity.tf-1.15.5-gpu", - "tensorflow/Singularity.tf-2.1.0-gpu-py3", - "tensorflow/Singularity.tf-2.0.0-gpu-py3", - "tensorflow/Singularity.tf-1.12.0-gpu-mlflow-py3", - "tensorflow/Singularity.tf-2.3.1-gpu", - "tensorflow/Singularity.tf-1.15.2-gpu-py3", - "tensorflow/Singularity.tf-1.12.0-gpu-py3", - "tensorflow/Singularity.tf-1.13.1-gpu-py3", - "tensorflow/Singularity.tf-2.4.3-gpu", - "tensorflow/Singularity.tf-2.2.0-gpu" + "Singularity" ], - "full_name": "myzkyuki/singularity_recipe", + "full_name": "mvdoc/mind-tools-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_recipe\u003c/h1\u003e\u003ca id=\"user-content-singularity_recipe\" class=\"anchor\" aria-label=\"Permalink: singularity_recipe\" href=\"#singularity_recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity definition file for mind-tools\u003c/h1\u003e\u003ca id=\"user-content-singularity-definition-file-for-mind-tools\" class=\"anchor\" aria-label=\"Permalink: Singularity definition file for mind-tools\" href=\"#singularity-definition-file-for-mind-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can pull directly this image from singularity hub with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e singularity pull shub://mvdoc/mind-tools-singularity\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1644473010.0 + "updated_at": 1502763490.0 }, { "data_format": 2, - "description": "Practicas de la asignatura", + "description": "Knime build in Singularity Hub", "filenames": [ - "Practica1/compiladores/singularity-ce-3.9.5/e2e/testdata/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/busybox/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/ubuntu/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/asciinema/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/centos-arm64/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/apps/Singularity.cowsay", - "Practica1/compiladores/singularity-ce-3.9.5/examples/apps/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/centos/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/shub/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/raspbian/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/arch/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/opensuse-arm64/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/opensuse/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/debian/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/docker/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/sle/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/instances/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/multistage/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/scratch/Singularity.busybox", - "Practica1/compiladores/singularity-ce-3.9.5/examples/scratch/Singularity.alpine", - "Practica1/compiladores/singularity-ce-3.9.5/examples/library/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/scientific/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/examples/self/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/e2e/testdata/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/busybox/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/ubuntu/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/asciinema/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/centos-arm64/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/apps/Singularity.cowsay", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/apps/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/centos/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/shub/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/raspbian/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/arch/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/opensuse-arm64/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/opensuse/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/debian/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/docker/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/sle/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/instances/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/multistage/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/scratch/Singularity.busybox", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/scratch/Singularity.alpine", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/library/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/scientific/Singularity", - "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/self/Singularity" + "Singularity" ], - "full_name": "EstebanGomez1/Planificacion-Automatica", + "full_name": "tin6150/knime", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1716230355.0 + "updated_at": 1504507455.0 }, { "data_format": 2, - "description": null, + "description": "Pipeline used to detect ORFs from Mouse Ribo-Seq during time at EBI/Gencode", "filenames": [ - "singularity/e2e/testdata/Singularity", - "singularity/examples/busybox/Singularity", - "singularity/examples/ubuntu/Singularity", - "singularity/examples/asciinema/Singularity", - "singularity/examples/apps/Singularity.cowsay", - "singularity/examples/apps/Singularity", - "singularity/examples/centos/Singularity", - "singularity/examples/shub/Singularity", - "singularity/examples/raspbian/Singularity", - "singularity/examples/arch/Singularity", - "singularity/examples/opensuse/Singularity", - "singularity/examples/debian/Singularity", - "singularity/examples/docker/Singularity", - "singularity/examples/sle/Singularity", - "singularity/examples/instances/Singularity", - "singularity/examples/multistage/Singularity", - "singularity/examples/scratch/Singularity.busybox", - "singularity/examples/scratch/Singularity.alpine", - "singularity/examples/library/Singularity", - "singularity/examples/scientific/Singularity", - "singularity/examples/self/Singularity" + "Singularity" ], - "full_name": "DeepLearningItalia/NLP-HandsOn-2", + "full_name": "JackCurragh/gencode-mouse-orfs", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGencode Mouse ORF prediciton Pipeline\u003c/h1\u003e\u003ca id=\"user-content-gencode-mouse-orf-prediciton-pipeline\" class=\"anchor\" aria-label=\"Permalink: Gencode Mouse ORF prediciton Pipeline\" href=\"#gencode-mouse-orf-prediciton-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline predicts Mouse ORFs from Ribo-Seq data using RiboSeq.Org Data Portal\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1628067602.0 + "updated_at": 1700502150.0 }, { "data_format": 2, - "description": "Core repository for neuroglia singularity image", + "description": "testing container for pushing to singularity-hub", "filenames": [ - "Singularity.v1.4", - "Singularity.v1.5", "Singularity" ], - "full_name": "khanlab/neuroglia-core", - "latest_release": "v1.5", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eneuroglia-core\u003c/h1\u003e\u003ca id=\"user-content-neuroglia-core\" class=\"anchor\" aria-label=\"Permalink: neuroglia-core\" href=\"#neuroglia-core\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image for neuroimaging dependencies. Base image for khanlab apps and containers. Includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNeurodebian\u003c/li\u003e\n\u003cli\u003eOctave\u003c/li\u003e\n\u003cli\u003eNipype\u003c/li\u003e\n\u003cli\u003eFSL\u003c/li\u003e\n\u003cli\u003eAFNI\u003c/li\u003e\n\u003cli\u003eC3D\u003c/li\u003e\n\u003cli\u003eFreesurfer\u0027s mri_convert and mris_convert\u003c/li\u003e\n\u003cli\u003eANTS\u003c/li\u003e\n\u003cli\u003edcm2niix\u003c/li\u003e\n\u003cli\u003eheudiconv\u003c/li\u003e\n\u003cli\u003ebids-validator\u003c/li\u003e\n\u003cli\u003eNiftyReg\u003c/li\u003e\n\u003cli\u003egradunwarp\u003c/li\u003e\n\u003cli\u003edcmstack\u003c/li\u003e\n\u003cli\u003eConnectome Workbench\u003c/li\u003e\n\u003cli\u003eDatalad-osf\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCommits and pull-requests to this repository rebuild the \u003ccode\u003elatest\u003c/code\u003e version on Docker Hub, which is updated nightly to Singularity Hub. Releases on Docker Hub and Singularity Hub are built whenever a tag named \u003ccode\u003ev.*\u003c/code\u003e is committed. To avoid re-building on minor commits (e.g. changes to documentation), use \u003ccode\u003e[skip ci]\u003c/code\u003e in the commit message.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/khanlab/neuroglia-core\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/835cd5c934ef218fbd0ef5bdc983afeca64e7f868c316e16de506c0216eada12/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f6e6575726f676c69612d636f72652e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/neuroglia-core.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/393\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDocker:\n\u003ccode\u003edocker pull khanlab/neuroglia-core\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSingularity:\n\u003ccode\u003esingularity pull khanlab/neuroglia-core\u003c/code\u003e\u003c/p\u003e\n", + "full_name": "vsoch/hello-world", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 4, "topics": [], - "updated_at": 1591844429.0 + "updated_at": 1545323609.0 }, { "data_format": 2, - "description": "Singularity container for Gate ", + "description": "recipe for containers", "filenames": [ - "geant4/Singularity" + "NucleoATAC/Singularity", + "RGT/Singularity", + "FitHiChIP/Singularity.FitHiChIP", + "Homer/Singularity" ], - "full_name": "tfunck/gate", + "full_name": "Tuteja-Lab/containers", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainers\u003c/h1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003erecipe for containers\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1557681223.0 + "updated_at": 1706042876.0 }, { "data_format": 2, - "description": "Singularity images and recipes", + "description": "Guppy is a data processing toolkit that contains the Oxford Nanopore Technologies\u2019 basecalling algorithms.", "filenames": [ - "lammps/Singularity.lammps_ase_kim", - "lammps/Singularity.lammps_prophet", - "lammps/Singularity.lammps_ase", - "lammps/Singularity.lammps", - "deal.II/Singularity.deal", - "acroread/Singularity.acroread", - "ubuntu/Singularity.1804", - "ubuntu/Singularity.2004", - "tesseract/Singularity.tesseract", - "nut/Singularity.nut", - "SLURM/Singularity.slurm", - "ase-twistd/Singularity.ase-twistd", - "mariadb/Singularity.mariadb", - "jmol/Singularity.jmol", - "kmos/Singularity.kmos3_9", - "kmos/Singularity.kmos", - "Atom/Singularity.atom", - "fuse-overlayfs/Singularity.fuse-overlayfs", - "mongodb/Singularity.mongodb", - "gdis-git/Singularity-slim.gdis", - "gdis-git/Singularity.gdis", - "AMPE/Singularity.ampe", - "pp/Singularity.pp2", - "tools/Singularity.mc", - "tools/Singularity.gnuplot", - "tools/Singularity.vim", - "tools/Singularity.ncdu", - "tools/Singularity.gawk", - "tools/Singularity.meld", - "jupyter/Singularity.jupyter", - "graphics/Singularity.graphics", - "graphics/Singularity.gnuplot_5.4a", - "graphics/Singularity.gnuplot_5.4", - "graphics/Singularity.gnuplot_4.6", - "graphics/Singularity.gnuplot_alpine", - "graphics/Singularity.gnuplot_4.6a", - "texlive/Singularity.texlive", - "VESTA/Singularity.vesta", - "obabel/Singularity.obabel", - "cuda/Singularity.u18.04_cuda9.2", - "qemu/Singularity.qemu-utils", - "MD2-lab/Singularity.md2-lab", - "emacs/Singularity.emacs", - "gdis/Singularity.gdis", - "aria2/Singularity.aria2c", - "mkdocs-serve/Singularity.mkdocs-serve", - "clease/Singularity.clease", - "rstudio-server/Singularity.rstudio-server", - "rstudio-server/Singularity.rstudio-desktop", - "rstudio-server/Singularity-22.04.rstudio-server", - "gromacs/Singularity.gromacs", - "xcrysden/Singularity.xcrysden", - "xcrysden/Singularity.xcrysden_1.5.60" + "6.0.0/Singularity" ], - "full_name": "pmitev/Teoroo-singularity", + "full_name": "pscedu/singularity-guppy", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2338\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTeoroo-singularity\u003c/h1\u003e\u003ca id=\"user-content-teoroo-singularity\" class=\"anchor\" aria-label=\"Permalink: Teoroo-singularity\" href=\"#teoroo-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-guppy/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-guppy/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-guppy/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-guppy/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/acd271ee64be7e03cce0aa20980a61a37f2a803c6c21475e4f5e2e750f91b847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/acd271ee64be7e03cce0aa20980a61a37f2a803c6c21475e4f5e2e750f91b847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ce3bd0aacdb7b3b72cb6bf39ce61e5bb1cc29a5784ded8bb4f63d60ba37d4e96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce3bd0aacdb7b3b72cb6bf39ce61e5bb1cc29a5784ded8bb4f63d60ba37d4e96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b6e3ded696588df6d0e3326ff98a3915f0822c93b919f7af672f1bbb878a4fac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6e3ded696588df6d0e3326ff98a3915f0822c93b919f7af672f1bbb878a4fac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3fe9defc0e02f64e7c2aa7b7bb20b84f9a2e6f0239ee4e213edd1a550157e8c0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3fe9defc0e02f64e7c2aa7b7bb20b84f9a2e6f0239ee4e213edd1a550157e8c0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6775707079\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-guppy\u003c/h1\u003e\u003ca id=\"user-content-singularity-guppy\" class=\"anchor\" aria-label=\"Permalink: singularity-guppy\" href=\"#singularity-guppy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://community.nanoporetech.com/protocols/Guppy-protocol/v/gpb_2003_v1_revac_14dec2018/linux-guppy\" rel=\"nofollow\"\u003eguppy\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/guppy/6.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/guppy\u003c/code\u003e as \u003ccode\u003e6.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [ "singularity", - "singularity-containers" + "bioinformatics" ], - "updated_at": 1730474659.0 + "updated_at": 1644268950.0 }, { "data_format": 2, - "description": "dockerize bidskit for TACC usage", + "description": null, "filenames": [ - "Singularity.TACC", - "Singularity" + "Singularity.itermae", + "Singularity.test", + "Singularity.test_base", + "Singularity.itermae-plus", + "Singularity.latest" ], - "full_name": "jungheejung/docker-bidskit", + "full_name": "darachm/itermae", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edocker-bidskit\u003c/h1\u003e\u003ca id=\"user-content-docker-bidskit\" class=\"anchor\" aria-label=\"Permalink: docker-bidskit\" href=\"#docker-bidskit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003edockerize bidskit for TACC usage\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eitermae\u003c/h1\u003e\u003ca id=\"user-content-itermae\" class=\"anchor\" aria-label=\"Permalink: itermae\" href=\"#itermae\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"https://darachm.gitlab.io/itermae/concept.html\" rel=\"nofollow\"\u003econcept here\u003c/a\u003e and\n\u003ca href=\"https://darachm.gitlab.io/itermae/usage/tutorial.html\" rel=\"nofollow\"\u003etutorial here\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eitermae\u003c/code\u003e is a command-line utility to recognize patterns in input sequences\nand generate outputs from groups recognized. Basically, it uses fuzzy regular\nexpression operations to (primarily) DNA sequence for purposes of DNA\nbarcode/tag/UMI parsing, sequence and quality -based filtering,\nand general output re-arrangment.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a09342327230d778e1e997dbd43b10e60ec7ce50b0b15deebe5a3a219d1d44b1/68747470733a2f2f6461726163686d2e6769746c61622e696f2f697465726d61652f5f696d616765732f70617273655f6469616772616d5f312e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a09342327230d778e1e997dbd43b10e60ec7ce50b0b15deebe5a3a219d1d44b1/68747470733a2f2f6461726163686d2e6769746c61622e696f2f697465726d61652f5f696d616765732f70617273655f6469616772616d5f312e737667\" alt=\"itermae diagram\" data-canonical-src=\"https://darachm.gitlab.io/itermae/_images/parse_diagram_1.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eitermae\u003c/code\u003e reads and makes FASTQ, FASTA, text-file, and SAM (tab-delimited)\nfiles using \u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003e\u003ccode\u003eBiopython\u003c/code\u003e\u003c/a\u003e sequence records\nto represent slice, and read/output formats.\nPattern matching uses the \u003ca href=\"https://pypi.org/project/regex/\" rel=\"nofollow\"\u003e\u003ccode\u003eregex\u003c/code\u003e\u003c/a\u003e library,\nand the tool is designed to function in command-line pipes from tools like\n\u003ca href=\"https://www.gnu.org/software/parallel/\" rel=\"nofollow\"\u003eGNU \u003ccode\u003eparallel\u003c/code\u003e\u003c/a\u003e\nto permit light-weight parallelization.\u003c/p\u003e\n\u003cp\u003eIt\u0027s usage might look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ezcat seq_data.fastqz | itermae --config my_config.yml -v \u0026gt; output.sam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ezcat seq_data.fastqz \\\n | parallel --quote --pipe -l 4 --keep-order -N 10000 \\\n itermae --config my_config.yml -v \u0026gt; output.sam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewith a \u003ccode\u003emy_config.yml\u003c/code\u003e file that may look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematches:\n - use: input\n pattern: NNNNNGTCCTCGAGGTCTCTNNNNNNNNNNNNNNNNNNNNCGTACGCTGCAGGTC\n marking: aaaaaBBBBBBBBBBBBBBBccccccccccccccccccccDDDDDDDDDDDDDDD\n marked_groups:\n a:\n name: sampleIndex\n repeat: 5\n B:\n allowed_errors: 2\n c:\n name: barcode\n repeat_min: 18\n repeat_max: 22\n D:\n allowed_insertions: 1\n allowed_deletions: 2\n allowed_substititions: 2\noutput_list:\n - name: \u0027barcode\u0027\n description: \u0027description+\" sample=\"+sampleIndex\u0027\n seq: \u0027barcode\u0027\n filter: \u0027statistics.median(barcode.quality) \u0026gt;= 35\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAvailability, installation, \u0027installation\u0027\u003c/h1\u003e\u003ca id=\"user-content-availability-installation-installation\" class=\"anchor\" aria-label=\"Permalink: Availability, installation, \u0027installation\u0027\" href=\"#availability-installation-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOptions:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eUse pip to install \u003ccode\u003eitermae\u003c/code\u003e, so\u003c/p\u003e\n\u003cp\u003epython3 -m pip install itermae\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou can clone this repo, and install it locally. Dependencies are in\n\u003ccode\u003erequirements.txt\u003c/code\u003e, so\n\u003ccode\u003epython3 -m pip install -r requirements.txt\u003c/code\u003e will install those.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou can use \u003ca href=\"https://syslab.org\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e to pull and run a\n\u003ca href=\"https://singularity-hub.org/collections/4537\" rel=\"nofollow\"\u003eSingularity image of itermae.py\u003c/a\u003e,\nwhere everything is already installed.\nThis is the recommended usage.\u003c/p\u003e\n\u003cp\u003eThis image is built with a few other tools,\nlike g/mawk, perl, and parallel, to make command line munging easier.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eitermae\u003c/code\u003e is envisioned to be used in a pipe-line where you just got your\nDNA sequencing FASTQ reads back, and you want to parse them.\nThe recommended interface is the YAML config file, as demonstrated\nin \u003ca href=\"https://darachm.gitlab.io/itermae/usage/tutorial.html\" rel=\"nofollow\"\u003ethe tutorial\u003c/a\u003e\nand detailed again in the\n\u003ca href=\"https://darachm.gitlab.io/itermae/usage/config.html\" rel=\"nofollow\"\u003econfiguration details\u003c/a\u003e.\nYou can also use a command-line argument interface as detailed more\n\u003ca href=\"https://darachm.gitlab.io/itermae/usage/examples.html\" rel=\"nofollow\"\u003ein the examples\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eI recommend you test this on small batches of data,\nthen stick it behind GNU \u003ccode\u003eparallel\u003c/code\u003e and feed the whole FASTQ file via\n\u003ccode\u003ezcat\u003c/code\u003e in on standard input.\nThis parallelizes with a small memory footprint, then\nyou write it out to disk (or stream into another tool).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThanks\u003c/h1\u003e\u003ca id=\"user-content-thanks\" class=\"anchor\" aria-label=\"Permalink: Thanks\" href=\"#thanks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAgain, the tool is built upon on the excellent work of\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/regex/\" rel=\"nofollow\"\u003e\u003ccode\u003eregex\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003e\u003ccode\u003eBiopython\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.gnu.org/software/parallel/\" rel=\"nofollow\"\u003e\u003ccode\u003eparallel\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDevelopment, helping\u003c/h1\u003e\u003ca id=\"user-content-development-helping\" class=\"anchor\" aria-label=\"Permalink: Development, helping\" href=\"#development-helping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny issues or advice are welcome as an\n\u003ca href=\"https://gitlab.com/darachm/itermae/-/issues\" rel=\"nofollow\"\u003eissue on the gitlab repo\u003c/a\u003e.\nComplaints are especially welcome.\u003c/p\u003e\n\u003cp\u003eFor development, see the\n\u003ca href=\"https://darachm.gitlab.io/itermae/package.html\" rel=\"nofollow\"\u003edocumentation as rendered from docstrings\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eA set of tests is written up with \u003ccode\u003epytest\u003c/code\u003e module, and can be run from inside\nthe cloned repo with \u003ccode\u003emake test\u003c/code\u003e.\nSee \u003ccode\u003emake help\u003c/code\u003e for more options, such as building, installing, and uploading.\u003c/p\u003e\n\u003cp\u003eThere\u0027s also a bash script with some longer runs in\n\u003ccode\u003eprofiling_tests\u003c/code\u003e, these generate longer runs for profiling purposes\nwith \u003ccode\u003ecProfile\u003c/code\u003e and \u003ccode\u003esnakeviz\u003c/code\u003e.\nBut is out of date. Todo is to re-configure and retest that for speed.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1594676027.0 + "updated_at": 1619137884.0 }, { "data_format": 2, - "description": null, + "description": "Test Singularity-Hub.org", "filenames": [ "Singularity" ], - "full_name": "ISU-HPC/orthomcl", + "full_name": "mandelkow/SgTest", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eorthomcl\u003c/h1\u003e\u003ca id=\"user-content-orthomcl\" class=\"anchor\" aria-label=\"Permalink: orthomcl\" href=\"#orthomcl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSgTest\u003c/h1\u003e\u003ca id=\"user-content-sgtest\" class=\"anchor\" aria-label=\"Permalink: SgTest\" href=\"#sgtest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTest Singularity-Hub.org\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1524255245.0 + "updated_at": 1530825852.0 }, { "data_format": 2, - "description": "try adding press to DipNet", + "description": "HMMER is used for searching sequence databases for sequence homologs, and for making sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs).", "filenames": [ - "diplomacy_research/containers/tensorflow-serving/Singularity", - "diplomacy_research/containers/research/Singularity", - "diplomacy_research/containers/ubuntu-cuda10/Singularity", - "diplomacy_research/containers/redis/Singularity", - "diplomacy_research/containers/albert-ai/Singularity" + "3.3.1/Singularity", + "3.3.2/Singularity" ], - "full_name": "wwongkamjan/dipnet_press", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSupervised and RL Models for No Press Diplomacy + press\u003c/h1\u003e\u003ca id=\"user-content-supervised-and-rl-models-for-no-press-diplomacy--press\" class=\"anchor\" aria-label=\"Permalink: Supervised and RL Models for No Press Diplomacy + press\" href=\"#supervised-and-rl-models-for-no-press-diplomacy--press\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source code used to develop a supervised and RL agent that can play the No Press version of Diplomacy.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/map_overview.png\"\u003e\u003cimg width=\"500\" src=\"docs/images/map_overview.png\" alt=\"Diplomacy Map Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the MIT License - see the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file for details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRestrictions: The trained weights provided with this repository are for research purposes only and cannot be used to power any bots on any website without my prior written consent, which may be withheld without reasons.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe data provider also prevents using its data to train any bots accessible on any website.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYou can play against the trained model by playing against \"KestasBot\" on webdiplomacy.net\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataset\u003c/h2\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe model was trained by using a dataset of 156,468 games (diplomacy-v1-27k-msgs.zip), which consists of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e16,633 games on non-standard maps (e.g. modern and ancmed) (other_maps.jsonl)\u003c/li\u003e\n\u003cli\u003e33,279 no-press games on the standard map (standard_no_press.jsonl)\u003c/li\u003e\n\u003cli\u003e50 press games on the standard map with messages (standard_press_with_msgs.jsonl)\u003c/li\u003e\n\u003cli\u003e106,456 press games on the standard map without messages (standard_press_without_msgs.jsonl)\u003c/li\u003e\n\u003cli\u003e50 public press games on the standard map with messages (standard_public_press.jsonl)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA dataset of 156,458 games with 13,469,536 messages is also being prepared, but it is not yet available.\u003c/p\u003e\n\u003cp\u003eAccess to the dataset used to train the model can be requested by sending an email to \u003ca href=\"mailto:webdipmod@gmail.com\"\u003ewebdipmod@gmail.com\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository can be installed in a conda environment with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecreate\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003en\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eactivate\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements_dev\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis package depends on Redis and singularity 3+. Singularity can be installed with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing Singularity v3.2.0\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e VERSION=v3.2.0\nsudo apt-get update -y\nsudo apt-get install -y build-essential libssl-dev uuid-dev libgpgme11-dev libseccomp-dev pkg-config squashfs-tools\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing GO 1.12.5\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GO_VERSION=1.12.5 OS=linux ARCH=amd64\nwget -nv https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\nsudo tar -C /usr/local -xzf go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\nrm -f go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GOPATH=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.go\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=/usr/local/go/bin:\u003cspan class=\"pl-smi\"\u003e${PATH}\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e${GOPATH}\u003c/span\u003e/bin\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e\ngo get github.com/golang/dep/cmd/dep\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Building from source\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e/src/github.com/sylabs\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e/src/github.com/sylabs\ngit clone https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\ngit checkout \u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\n./mconfig -p /usr/local\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ./builddir\nmake\nsudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe package is compatible with Python 3.5, 3.6, and 3.7.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTraining models\u003c/h3\u003e\u003ca id=\"user-content-training-models\" class=\"anchor\" aria-label=\"Permalink: Training models\" href=\"#training-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train a model:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e WORKING_DIR=/path/to/some/directory\n$ cp diplomacy-v1-27k-msgs.zip \u003cspan class=\"pl-smi\"\u003e$WORKING_DIR\u003c/span\u003e\n$ conda activate diplomacy\n$ python diplomacy_research/scripts/build_dataset.py\n$ python diplomacy_research/models/policy/order_based/train.py --model_id 12\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlaying against the SL and RL agents\u003c/h3\u003e\u003ca id=\"user-content-playing-against-the-sl-and-rl-agents\" class=\"anchor\" aria-label=\"Permalink: Playing against the SL and RL agents\" href=\"#playing-against-the-sl-and-rl-agents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to play against the published results by using the \u003ccode\u003eDipNetSLPlayer\u003c/code\u003e and \u003ccode\u003eDipNetRLPlayer\u003c/code\u003e players in \u003ccode\u003ediplomacy_research.players.benchmark_player\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThese players will automatically download a singularity container with the trained weights, and then launch a TF serving server to handle the requests.\u003c/p\u003e\n\u003cp\u003eA simple example on how to play a 7 bots game is:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etornado\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egen\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eujson\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGame\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexport\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eto_saved_game_format\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy_research\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eplayers\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ebenchmark_player\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDipNetSLPlayer\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy_research\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecluster\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003estart_io_loop\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estop_io_loop\u003c/span\u003e\n\n\u003cspan class=\"pl-en\"\u003e@\u003cspan class=\"pl-s1\"\u003egen\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecoroutine\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emain\u003c/span\u003e():\n \u003cspan class=\"pl-s\"\u003e\"\"\" Plays a local game with 7 bots \"\"\"\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eplayer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDipNetSLPlayer\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGame\u003c/span\u003e()\n\n \u003cspan class=\"pl-c\"\u003e# Playing game\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ewhile\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enot\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eis_game_done\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eorders\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eyield\u003c/span\u003e {\u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eplayer\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eget_orders\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epowers\u003c/span\u003e}\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_orders\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eorders\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eset_orders\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_orders\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eprocess\u003c/span\u003e()\n\n \u003cspan class=\"pl-c\"\u003e# Saving to disk\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027game.json\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027w\u0027\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edumps\u003c/span\u003e(\u003cspan class=\"pl-en\"\u003eto_saved_game_format\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e)))\n \u003cspan class=\"pl-en\"\u003estop_io_loop\u003c/span\u003e()\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e__name__\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027__main__\u0027\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003estart_io_loop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emain\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlaying against a model\u003c/h3\u003e\u003ca id=\"user-content-playing-against-a-model\" class=\"anchor\" aria-label=\"Permalink: Playing against a model\" href=\"#playing-against-a-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is also possible for humans to play against bots using the web interface. The player can be changed in \u003ccode\u003ediplomacy_research.scripts.launch_bot\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In a terminal window or tab - Launch React server (from diplomacy/diplomacy)\u003c/span\u003e\nnpm start\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In another terminal window or tab - Launch diplomacy server\u003c/span\u003e\npython -m diplomacy.server.run\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In a third terminal window or tab - Launch the bot script\u003c/span\u003e\npython diplomacy_research/scripts/launch_bot.py\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTrained weights and experiment logs\u003c/h3\u003e\u003ca id=\"user-content-trained-weights-and-experiment-logs\" class=\"anchor\" aria-label=\"Permalink: Trained weights and experiment logs\" href=\"#trained-weights-and-experiment-logs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo facilitate reproducibility, the experiments can be downloaded using the following links. These include hyperparameters, tensorboard graphs, output logs, and weights for each epoch.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOrder based LSTM model (order-based v12 - Accuracy of 61.3% - \u003cstrong\u003eDipNet SL\u003c/strong\u003e) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/order-based-lstm.zip\" rel=\"nofollow\"\u003eDownload - 5.4GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOrder based Transformer model (order-based v15 - Accuracy of 60.7%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/order-based-trsf.zip\" rel=\"nofollow\"\u003eDownload - 8.2GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eToken based LSTM model (token-based v10 - Accuracy of 60.3%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/token-based-lstm.zip\" rel=\"nofollow\"\u003eDownload - 6.0GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eToken based Transformer model (token-based v11 - Accuracy of 58.9%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/token-based-trsf.zip\" rel=\"nofollow\"\u003eDownload - 3.5GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRL Model (Bootstrapped from order-based v12 and value v1 - \u003cstrong\u003eDipNet RL\u003c/strong\u003e) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/rl-model.zip\" rel=\"nofollow\"\u003eDownload - 11.1GB\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGames against Albert (DAIDE)\u003c/h3\u003e\u003ca id=\"user-content-games-against-albert-daide\" class=\"anchor\" aria-label=\"Permalink: Games against Albert (DAIDE)\" href=\"#games-against-albert-daide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe 1v6 and 6v1 games played between DipNet SL and Albert (DAIDE) can be downloaded below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eList of games with power assignments \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/daide_albert_results.xlsx\" rel=\"nofollow\"\u003eDownload - 53KB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVisualisation of each game (svg and json) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/daide_albert_games.zip\" rel=\"nofollow\"\u003eDownload - 2.3GB\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "pscedu/singularity-hmmer", + "latest_release": "v3.3.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-hmmer/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-hmmer/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-hmmer/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-hmmer/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/799816fd70c8377213a151f0eaae10e558bb65c45e9abb0d00faf2b3cfb6250a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d686d6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/799816fd70c8377213a151f0eaae10e558bb65c45e9abb0d00faf2b3cfb6250a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d686d6d6572\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-hmmer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/99a5788907342c6b3f1efdb2bd7b133bc109d02d875ead69c068fca8e20f1044/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d686d6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/99a5788907342c6b3f1efdb2bd7b133bc109d02d875ead69c068fca8e20f1044/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d686d6d6572\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-hmmer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/fa9dcf57e459cf613f3f62f13cda857ba73eaf63a5c8e324654c90b8aad6e344/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d686d6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fa9dcf57e459cf613f3f62f13cda857ba73eaf63a5c8e324654c90b8aad6e344/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d686d6d6572\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-hmmer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6edb3fb4ea40a9e47df0faa9e813a800869971e1d990e660a609031ad4d81a18/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d686d6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6edb3fb4ea40a9e47df0faa9e813a800869971e1d990e660a609031ad4d81a18/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d686d6d6572\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-hmmer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-hmmer\u003c/h1\u003e\u003ca id=\"user-content-singularity-hmmer\" class=\"anchor\" aria-label=\"Permalink: singularity-hmmer\" href=\"#singularity-hmmer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/EddyRivasLab/hmmer\"\u003ehmmer\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ealimask\u003c/code\u003e, \u003ccode\u003ehmmbuild\u003c/code\u003e, \u003ccode\u003ehmmemit\u003c/code\u003e, \u003ccode\u003ehmmpgmd\u003c/code\u003e, \u003ccode\u003ehmmscan\u003c/code\u003e, \u003ccode\u003ehmmstat\u003c/code\u003e, \u003ccode\u003ephmmer\u003c/code\u003e, \u003ccode\u003ehmmc2\u003c/code\u003e, \u003ccode\u003ehmmfetch\u003c/code\u003e, \u003ccode\u003ehmmpgmd_shard\u003c/code\u003e, \u003ccode\u003ehmmsearch\u003c/code\u003e, \u003ccode\u003ejackhmmer\u003c/code\u003e, \u003ccode\u003enhmmer\u003c/code\u003e, \u003ccode\u003ehmmalign\u003c/code\u003e, \u003ccode\u003ehmmconvert\u003c/code\u003e, \u003ccode\u003ehmmlogo\u003c/code\u003e, \u003ccode\u003ehmmpress\u003c/code\u003e, \u003ccode\u003ehmmsim\u003c/code\u003e, \u003ccode\u003emakehmmerdb\u003c/code\u003e, \u003ccode\u003enhmmscan\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hmmer/3.3.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hmmer\u003c/code\u003e as \u003ccode\u003e3.3.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1645489677.0 + "subscribers_count": 3, + "topics": [ + "bioinformatics", + "singularity" + ], + "updated_at": 1653937435.0 }, { "data_format": 2, "description": null, "filenames": [ - "diplomacy_research/containers/tensorflow-serving/Singularity", - "diplomacy_research/containers/research/Singularity", - "diplomacy_research/containers/ubuntu-cuda10/Singularity", - "diplomacy_research/containers/redis/Singularity", - "diplomacy_research/containers/albert-ai/Singularity" + "Singularity" ], - "full_name": "tanushreebanerjee/paquette_2019", + "full_name": "rkalyanapurdue/geoedf-connector", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSupervised and RL Models for No Press Diplomacy\u003c/h1\u003e\u003ca id=\"user-content-supervised-and-rl-models-for-no-press-diplomacy\" class=\"anchor\" aria-label=\"Permalink: Supervised and RL Models for No Press Diplomacy\" href=\"#supervised-and-rl-models-for-no-press-diplomacy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source code used to develop a supervised and RL agent that can play the No Press version of Diplomacy.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/map_overview.png\"\u003e\u003cimg width=\"500\" src=\"docs/images/map_overview.png\" alt=\"Diplomacy Map Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the MIT License - see the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file for details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRestrictions: The trained weights provided with this repository are for research purposes only and cannot be used to power any bots on any website without my prior written consent, which may be withheld without reasons.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe data provider also prevents using its data to train any bots accessible on any website.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYou can play against the trained model by playing against \"KestasBot\" on webdiplomacy.net\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataset\u003c/h2\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe model was trained by using a dataset of 156,468 games (diplomacy-v1-27k-msgs.zip), which consists of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e16,633 games on non-standard maps (e.g. modern and ancmed) (other_maps.jsonl)\u003c/li\u003e\n\u003cli\u003e33,279 no-press games on the standard map (standard_no_press.jsonl)\u003c/li\u003e\n\u003cli\u003e50 press games on the standard map with messages (standard_press_with_msgs.jsonl)\u003c/li\u003e\n\u003cli\u003e106,456 press games on the standard map without messages (standard_press_without_msgs.jsonl)\u003c/li\u003e\n\u003cli\u003e50 public press games on the standard map with messages (standard_public_press.jsonl)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA dataset of 156,458 games with 13,469,536 messages is also being prepared, but it is not yet available.\u003c/p\u003e\n\u003cp\u003eAccess to the dataset used to train the model can be requested by sending an email to \u003ca href=\"mailto:webdipmod@gmail.com\"\u003ewebdipmod@gmail.com\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository can be installed in a conda environment with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecreate\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003en\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eactivate\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements_dev\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis package depends on Redis and singularity 3+. Singularity can be installed with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing Singularity v3.2.0\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e VERSION=v3.2.0\nsudo apt-get update -y\nsudo apt-get install -y build-essential libssl-dev uuid-dev libgpgme11-dev libseccomp-dev pkg-config squashfs-tools\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing GO 1.12.5\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GO_VERSION=1.12.5 OS=linux ARCH=amd64\nwget -nv https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\nsudo tar -C /usr/local -xzf go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\nrm -f go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GOPATH=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.go\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=/usr/local/go/bin:\u003cspan class=\"pl-smi\"\u003e${PATH}\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e${GOPATH}\u003c/span\u003e/bin\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e\ngo get github.com/golang/dep/cmd/dep\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Building from source\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e/src/github.com/sylabs\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e/src/github.com/sylabs\ngit clone https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\ngit checkout \u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\n./mconfig -p /usr/local\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ./builddir\nmake\nsudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe package is compatible with Python 3.5, 3.6, and 3.7.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTraining models\u003c/h3\u003e\u003ca id=\"user-content-training-models\" class=\"anchor\" aria-label=\"Permalink: Training models\" href=\"#training-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train a model:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e WORKING_DIR=/path/to/some/directory\n$ cp diplomacy-v1-27k-msgs.zip \u003cspan class=\"pl-smi\"\u003e$WORKING_DIR\u003c/span\u003e\n$ conda activate diplomacy\n$ python diplomacy_research/scripts/build_dataset.py\n$ python diplomacy_research/models/policy/order_based/train.py --model_id 12\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlaying against the SL and RL agents\u003c/h3\u003e\u003ca id=\"user-content-playing-against-the-sl-and-rl-agents\" class=\"anchor\" aria-label=\"Permalink: Playing against the SL and RL agents\" href=\"#playing-against-the-sl-and-rl-agents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to play against the published results by using the \u003ccode\u003eDipNetSLPlayer\u003c/code\u003e and \u003ccode\u003eDipNetRLPlayer\u003c/code\u003e players in \u003ccode\u003ediplomacy_research.players.benchmark_player\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThese players will automatically download a singularity container with the trained weights, and then launch a TF serving server to handle the requests.\u003c/p\u003e\n\u003cp\u003eA simple example on how to play a 7 bots game is:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etornado\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egen\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eujson\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGame\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexport\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eto_saved_game_format\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy_research\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eplayers\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ebenchmark_player\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDipNetSLPlayer\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy_research\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecluster\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003estart_io_loop\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estop_io_loop\u003c/span\u003e\n\n\u003cspan class=\"pl-en\"\u003e@\u003cspan class=\"pl-s1\"\u003egen\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecoroutine\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emain\u003c/span\u003e():\n \u003cspan class=\"pl-s\"\u003e\"\"\" Plays a local game with 7 bots \"\"\"\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eplayer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDipNetSLPlayer\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGame\u003c/span\u003e()\n\n \u003cspan class=\"pl-c\"\u003e# Playing game\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ewhile\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enot\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eis_game_done\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eorders\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eyield\u003c/span\u003e {\u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eplayer\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eget_orders\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epowers\u003c/span\u003e}\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_orders\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eorders\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eset_orders\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_orders\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eprocess\u003c/span\u003e()\n\n \u003cspan class=\"pl-c\"\u003e# Saving to disk\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027game.json\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027w\u0027\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edumps\u003c/span\u003e(\u003cspan class=\"pl-en\"\u003eto_saved_game_format\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e)))\n \u003cspan class=\"pl-en\"\u003estop_io_loop\u003c/span\u003e()\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e__name__\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027__main__\u0027\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003estart_io_loop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emain\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlaying against a model\u003c/h3\u003e\u003ca id=\"user-content-playing-against-a-model\" class=\"anchor\" aria-label=\"Permalink: Playing against a model\" href=\"#playing-against-a-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is also possible for humans to play against bots using the web interface. The player can be changed in \u003ccode\u003ediplomacy_research.scripts.launch_bot\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In a terminal window or tab - Launch React server (from diplomacy/diplomacy)\u003c/span\u003e\nnpm start\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In another terminal window or tab - Launch diplomacy server\u003c/span\u003e\npython -m diplomacy.server.run\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In a third terminal window or tab - Launch the bot script\u003c/span\u003e\npython diplomacy_research/scripts/launch_bot.py\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTrained weights and experiment logs\u003c/h3\u003e\u003ca id=\"user-content-trained-weights-and-experiment-logs\" class=\"anchor\" aria-label=\"Permalink: Trained weights and experiment logs\" href=\"#trained-weights-and-experiment-logs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo facilitate reproducibility, the experiments can be downloaded using the following links. These include hyperparameters, tensorboard graphs, output logs, and weights for each epoch.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOrder based LSTM model (order-based v12 - Accuracy of 61.3% - \u003cstrong\u003eDipNet SL\u003c/strong\u003e) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/order-based-lstm.zip\" rel=\"nofollow\"\u003eDownload - 5.4GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOrder based Transformer model (order-based v15 - Accuracy of 60.7%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/order-based-trsf.zip\" rel=\"nofollow\"\u003eDownload - 8.2GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eToken based LSTM model (token-based v10 - Accuracy of 60.3%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/token-based-lstm.zip\" rel=\"nofollow\"\u003eDownload - 6.0GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eToken based Transformer model (token-based v11 - Accuracy of 58.9%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/token-based-trsf.zip\" rel=\"nofollow\"\u003eDownload - 3.5GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRL Model (Bootstrapped from order-based v12 and value v1 - \u003cstrong\u003eDipNet RL\u003c/strong\u003e) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/rl-model.zip\" rel=\"nofollow\"\u003eDownload - 11.1GB\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGames against Albert (DAIDE)\u003c/h3\u003e\u003ca id=\"user-content-games-against-albert-daide\" class=\"anchor\" aria-label=\"Permalink: Games against Albert (DAIDE)\" href=\"#games-against-albert-daide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe 1v6 and 6v1 games played between DipNet SL and Albert (DAIDE) can be downloaded below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eList of games with power assignments \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/daide_albert_results.xlsx\" rel=\"nofollow\"\u003eDownload - 53KB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVisualisation of each game (svg and json) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/daide_albert_games.zip\" rel=\"nofollow\"\u003eDownload - 2.3GB\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egeoedf-connector\u003c/h1\u003e\u003ca id=\"user-content-geoedf-connector\" class=\"anchor\" aria-label=\"Permalink: geoedf-connector\" href=\"#geoedf-connector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1671746273.0 + "updated_at": 1592580440.0 }, { "data_format": 2, "description": null, "filenames": [ - "1.58.1/Singularity", - "1.75.0/Singularity" + "Singularity.R_3.6.0", + "Singularity.bioconductor_3.9", + "Singularity.R-Mfuzz_2.38.0" ], - "full_name": "pscedu/singularity-rust", + "full_name": "TomHarrop/r-singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rust/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rust/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rust/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rust/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cf560daa2034e21d03639b214b57dc77409c3c04cd700171ffeea3d55a611e92/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d72757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cf560daa2034e21d03639b214b57dc77409c3c04cd700171ffeea3d55a611e92/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d72757374\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-rust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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src=\"https://camo.githubusercontent.com/eee6f13f6796e4d3e45b0f400ba03ea0c7775f9021705157877a4007fa1fbeb3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d72757374\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-rust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c719c29ce3719447d26c7ef3c2f8a4fbda921774edb12d766d211af0720ba9e0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d72757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c719c29ce3719447d26c7ef3c2f8a4fbda921774edb12d766d211af0720ba9e0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d72757374\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-rust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-rust\u003c/h1\u003e\u003ca id=\"user-content-singularity-rust\" class=\"anchor\" aria-label=\"Permalink: singularity-rust\" href=\"#singularity-rust\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7174f54056f65968a2515417952a46695bb1eb3d588756dd31d57cdb9fe2b0b0/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f642f64352f527573745f70726f6772616d6d696e675f6c616e67756167655f626c61636b5f6c6f676f2e7376672f3132303070782d527573745f70726f6772616d6d696e675f6c616e67756167655f626c61636b5f6c6f676f2e7376672e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7174f54056f65968a2515417952a46695bb1eb3d588756dd31d57cdb9fe2b0b0/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f642f64352f527573745f70726f6772616d6d696e675f6c616e67756167655f626c61636b5f6c6f676f2e7376672f3132303070782d527573745f70726f6772616d6d696e675f6c616e67756167655f626c61636b5f6c6f676f2e7376672e706e67\" width=\"25%\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/d/d5/Rust_programming_language_black_logo.svg/1200px-Rust_programming_language_black_logo.svg.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.rust-lang.org/\" rel=\"nofollow\"\u003erust\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003erust\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/rust/1.58.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/rust\u003c/code\u003e as \u003ccode\u003e1.58.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1645584492.0 + "updated_at": 1568680401.0 }, { "data_format": 2, - "description": "My software stack.", + "description": "Singularity ML Box with PyTorch, Keras, Tensorflow", "filenames": [ - "Singularity.cuda8-comet", - "Singularity.comet", - "Singularity.cuda8", - "Singularity.cuda8-bridges", - "Singularity", - "Singularity.bridges", - "Singularity.latest", - "Singularity.cuda8-flux", - "Singularity.flux" + "Singularity.ubuntu-bionic-cuda10", + "Singularity.ubuntu-xenial-cuda10", + "Singularity.ubuntu-bionic-cuda92", + "Singularity.ubuntu-xenial-cuda92" ], - "full_name": "csadorf/software", + "full_name": "jeffacce/singularity-ml-box", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-ml-box\u003c/h1\u003e\u003ca id=\"user-content-singularity-ml-box\" class=\"anchor\" aria-label=\"Permalink: singularity-ml-box\" href=\"#singularity-ml-box\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity ML Box with PyTorch 1.0, Keras, Tensorflow, CUDA 10, Ubuntu 18.04 LTS\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1546998866.0 + "updated_at": 1551987453.0 }, { "data_format": 2, - "description": null, + "description": "CachingOnDemand provides recipes and PaaS description templates for an end to end deployment of an XCache cluster", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "ISU-HPC/big-scape", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebig-scape\u003c/h1\u003e\u003ca id=\"user-content-big-scape\" class=\"anchor\" aria-label=\"Permalink: big-scape\" href=\"#big-scape\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for big-scape\u003c/p\u003e\n\u003cp\u003eAdapted from \u003ca href=\"https://git.wageningenur.nl/medema-group/BiG-SCAPE/blob/master/Dockerfile\" rel=\"nofollow\"\u003ehttps://git.wageningenur.nl/medema-group/BiG-SCAPE/blob/master/Dockerfile\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "Cloud-PG/CachingOnDemand", + "latest_release": "v2.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOn-Demand XCache cluster\u003c/h1\u003e\u003ca id=\"user-content-on-demand-xcache-cluster\" class=\"anchor\" aria-label=\"Permalink: On-Demand XCache cluster\" href=\"#on-demand-xcache-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1e34aacbd16043130fdc18f969404611342511683f9225d581c4d58856802f51/68747470733a2f2f7472617669732d63692e6f72672f436c6f75642d50472f43616368696e674f6e44656d616e642e7376673f6272616e63683d6d6173746572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e34aacbd16043130fdc18f969404611342511683f9225d581c4d58856802f51/68747470733a2f2f7472617669732d63692e6f72672f436c6f75642d50472f43616368696e674f6e44656d616e642e7376673f6272616e63683d6d6173746572\" alt=\"travis build of ansible\" data-canonical-src=\"https://travis-ci.org/Cloud-PG/CachingOnDemand.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://cloud-pg.github.io/CachingOnDemand/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1547836893.0 + "updated_at": 1574268938.0 }, { "data_format": 2, - "description": null, + "description": "containers-ftw version of https://www.kaggle.com/c/flavours-of-physics", "filenames": [ - "Singularity.v2", - "Singularity.v3", - "Singularity.v1", - "Singularity.v5", - "Singularity.v6", - "Singularity.v4" + "Singularity" ], - "full_name": "BensonYang1999/hpl-cuda-singularity", + "full_name": "sci-f/flavours-of-physics.scif", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMu Tau Tau Tau ContainersFTW\u003c/h1\u003e\u003ca id=\"user-content-mu-tau-tau-tau-containersftw\" class=\"anchor\" aria-label=\"Permalink: Mu Tau Tau Tau ContainersFTW\" href=\"#mu-tau-tau-tau-containersftw\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/containers-ftw.png\"\u003e\u003cimg src=\"img/containers-ftw.png\" alt=\"img/containers-ftw.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.org/containers-ftw/flavours-of-physics-ftw\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ed5f9af035d26113e1a9dff5169a0af2fb904250e307d127a4a8497c23d0b714/68747470733a2f2f7472617669732d63692e6f72672f636f6e7461696e6572732d6674772f666c61766f7572732d6f662d706879736963732d6674772e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/containers-ftw/flavours-of-physics-ftw.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a competition orginally \u003ca href=\"https://www.kaggle.com/c/flavours-of-physics/data\" rel=\"nofollow\"\u003ehosted on Kaggle\u003c/a\u003e, reproduced here to encourage containerization of submissions by way of \u003ca href=\"https://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. If you aren\u0027t familiar with Singularity, it\u0027s a container (like Docker) that can be run securely on HPC architectures.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClone\u003c/h2\u003e\u003ca id=\"user-content-clone\" class=\"anchor\" aria-label=\"Permalink: Clone\" href=\"#clone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst fork the repo to your own username. For example, if my user name is \u003ccode\u003evsoch\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/vsoch/flavours-of-physics-ftw\ncd flavours-of-physics-ftw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity create --size 8000 container.ftw \nsudo singularity bootstrap container.ftw Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B data/input:/data/input -B analysis:/code --pwd /code container.ftw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow edit \u003ccode\u003emain.py\u003c/code\u003e, do better, and submit a PR to the contest repo for your entry. Want more details? keep reading!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompetition Overview\u003c/h2\u003e\u003ca id=\"user-content-competition-overview\" class=\"anchor\" aria-label=\"Permalink: Competition Overview\" href=\"#competition-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eGoals\u003c/strong\u003e: Read about the \u003ca href=\"https://www.kaggle.com/c/flavours-of-physics/data\" rel=\"nofollow\"\u003edata\u003c/a\u003e to get a breakdown of the data provided, and the \u003ca href=\"https://www.kaggle.com/c/flavours-of-physics\" rel=\"nofollow\"\u003ebackground and goals\u003c/a\u003e of the competition is beautifully described and shown on Kaggle.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBuild\u003c/strong\u003e: Build your container (see build section below), which will install dependencies and prepare data for you. If you find that you need any more, or any additional software or libraries, you can add them to the \u003ccode\u003e%post\u003c/code\u003e section of the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCode\u003c/strong\u003e: Once you have your container built, you can use it to develop and test your submission.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSubmit\u003c/strong\u003e: A submission to the competition means submitting a PR (pull request)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGoals\u003c/h3\u003e\u003ca id=\"user-content-goals\" class=\"anchor\" aria-label=\"Permalink: Goals\" href=\"#goals\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEvaluation for this competition is based on AUC (area under the curve), defined as area under the curve, which broadly gets at the ratio of false positives to false negatives for your model. In addition to this criteria, the \u003ca href=\"metrics.py\"\u003emetrics\u003c/a\u003e file includes multiple checks that physicists do to make sure that results are unbiased.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild\u003c/h3\u003e\u003ca id=\"user-content-build-1\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen you are ready to start your submission, you should fork the repo to your branch, and then clone the fork. For example, if my username on Github was \u003ccode\u003evsoch\u003c/code\u003e, I would fork and then do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/vsoch/flavours-of-physics-ftw\ncd flavours-of-physics-ftw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can build your image. You will need one dependency, that \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003eSingularity is installed\u003c/a\u003e. Building comes down to creating an image and then using \u003ccode\u003ebootstrap\u003c/code\u003e to build from the container recipe, \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity create --size 8000 container.ftw \nsudo singularity bootstrap container.ftw Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWork in your Container\u003c/h2\u003e\u003ca id=\"user-content-work-in-your-container\" class=\"anchor\" aria-label=\"Permalink: Work in your Container\" href=\"#work-in-your-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo shell into your container, you will want to mount the analysis folder, and the external data. You can do that like this. Note that we are making the present working directory (\u003ccode\u003epwd\u003c/code\u003e) our folder with analysis scripts:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B data/input:/data/input -B analysis:/code --pwd /code container.ftw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen you shell into your container, it probably will look the same, but if you do \u003ccode\u003els /\u003c/code\u003e you will see a file called \u003ccode\u003esingularity\u003c/code\u003e and root folders \u003ccode\u003e/data\u003c/code\u003e and \u003ccode\u003e/code\u003c/code\u003e that aren\u0027t on your host. If you look inside, you will see the data and\nanalysis scripts mounted!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003els /code\nREADME.md helpers main.py metrics.py results tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTry creating a file on the host, and you will see it change in the container, or vice versa. Thus, your general workflow will be the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erun things from within the container, using the python or ipython located at \u003ccode\u003e/opt/conda/bin\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eedit code in your editor of choice on your host machine\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment\u003c/h3\u003e\u003ca id=\"user-content-environment\" class=\"anchor\" aria-label=\"Permalink: Environment\" href=\"#environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to ever find data or results locations, these have been provided for you via environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINERSFTW_DATA\u003c/code\u003e: The base folder with data\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINERSFTW_RESULT\u003c/code\u003e: The folder where results are written to\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINERSFTW_WORK\u003c/code\u003e: The folder where your scripts live.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt\u0027s definitely a good idea if you are interested to shell around the container to understand where things are located, and test the variables to confirm they are the same:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eecho $CONT\n$CONTAINERSFTW_RESULT $CONTAINERSFTW_DATA $CONTAINERSFTW_WORK \necho $CONTAINERSFTW_DATA\n/data/input\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCode\u003c/h3\u003e\u003ca id=\"user-content-code\" class=\"anchor\" aria-label=\"Permalink: Code\" href=\"#code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can work from inside the container, or comfortable from the host in the \u003ccode\u003eanalysis\u003c/code\u003e folder (mapped to \u003ccode\u003e/code\u003c/code\u003e in the container). Your main work is going to be located at \u003ccode\u003e/code/main.py\u003c/code\u003e in the container, which is \u003ccode\u003eanalysis/main.py\u003c/code\u003e on the host. If you open up this file, you can start interactively working in an ipython terminal in the container to test commands. For example, from \u003ccode\u003e/code\u003c/code\u003e let\u0027s try loading the data in \u003ccode\u003eipython\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efrom sklearn.ensemble import GradientBoostingClassifier\nfrom helpers.data import load_data\nfrom helpers.logger import bot\n\ntrain = load_data(name=\"training\")\nDEBUG Loading training : /data/input/training.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand if you proceed through the rest of the script, you will produce an example result. You can also run the entire example without shelling into the container at all:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B data/input:/data/input -B analysis:/code --pwd /code container.ftw\nDEBUG Loading training : /data/input/training.csv\n\nChecking Agreement:\nDEBUG Loading check_agreement : /data/input/check_agreement.csv\nKS metric 0.0681705596239 True\n\nChecking Correlation:\nDEBUG Loading check_correlation : /data/input/check_correlation.csv\nCvM metric 0.000981509354914 True\n\nChecking AUC:\nAUC 0.834346382383\nDEBUG Loading test : /data/input/test.csv\nDEBUG submission : /code/results/submission.csv\nLOG Result saved to /code/results/submission.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe result file is what gets tested in the continuous integration.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdding Dependencies\u003c/h3\u003e\u003ca id=\"user-content-adding-dependencies\" class=\"anchor\" aria-label=\"Permalink: Adding Dependencies\" href=\"#adding-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you add dependencies (another python module, additional data that conforms to competition rules, etc) you should update the Singularity recipe, for example, we have marked in \u003ccode\u003e%post\u003c/code\u003e where you can add installation steps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e #########################################################\n # Install additional software / libraries here\n #########################################################\n\n pip install -y pokemon\n\n #########################################################\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eDo I have to use Python?\u003c/strong\u003e: Of course not! The base template image given to use is based on a choice by the creator (for example, lots of people use \u003ccode\u003escikit-learn\u003c/code\u003e in python for machine learning). At the end of the day, the evaluation is done over the text file in \u003ccode\u003e/analysis/results/submission\u003c/code\u003e and is ambivalent to how it is generated. Your submission (the container image) must simply run to generate it, and you are good.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor now, for additional FAQ please see our \u003ca href=\"https://containers-ftw.github.io\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1557155075.0 + "subscribers_count": 3, + "topics": [ + "containers-ftw", + "mu", + "tau", + "physics", + "competition", + "containers", + "singularity-containers" + ], + "updated_at": 1516487103.0 }, { "data_format": 2, - "description": null, + "description": "Container recipe for NECAT available here: https://github.com/xiaochuanle/NECAT", "filenames": [ - "deps/DPMC/lg/Singularity", - "deps/DPMC/DMC/Singularity", - "deps/DPMC/tensor/Singularity", - "deps/DPMC/HTB/Singularity" + "Singularity" ], - "full_name": "dilkas/wmc-without-parameters", + "full_name": "HuffordLab-Containers/necat", "latest_release": null, - "readme": "\u003cp\u003eThis repository holds all source code (that I am legally allowed to distribute) related to the following publications:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDilkas P., Belle V. \u003cstrong\u003eWeighted Model Counting with Conditional Weights for Bayesian Networks\u003c/strong\u003e. UAI 2021.\u003c/li\u003e\n\u003cli\u003eDilkas P., Belle V. \u003cstrong\u003eWeighted Model Counting Without Parameter Variables\u003c/strong\u003e. SAT 2021.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enecat\u003c/h1\u003e\u003ca id=\"user-content-necat\" class=\"anchor\" aria-label=\"Permalink: necat\" href=\"#necat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1656402559.0 + "updated_at": 1614611989.0 }, { "data_format": 2, - "description": null, + "description": "Container image with signalp and targetp programs for functional analysis pipelines", "filenames": [ - "chroma/Singularity.chroma.tog4", - "chroma/Singularity.chroma.base", - "chroma/Singularity.chroma.all-but-chroma", - "chroma/Singularity.chroma.chroma-docker", - "chroma/Singularity.chroma.chroma-only", - "chroma/Singularity.chroma.chroma" + "Singularity" ], - "full_name": "wkcwells/singularity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "full_name": "biocorecrg/sigtarp_docker", + "latest_release": "5.0b", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esigtarp_docker\u003c/h1\u003e\u003ca id=\"user-content-sigtarp_docker\" class=\"anchor\" aria-label=\"Permalink: sigtarp_docker\" href=\"#sigtarp_docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/152766566\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9fb76abe902d36be7979eb4e057c173eb23157d522a904a478f52585e65ca0da/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3135323736363536362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/152766566.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContainer image with \u003ca href=\"http://www.cbs.dtu.dk/services/SignalP/\" rel=\"nofollow\"\u003esignalP\u003c/a\u003e, \u003ca href=\"http://www.cbs.dtu.dk/services/TargetP/\" rel=\"nofollow\"\u003etargetP\u003c/a\u003e programs for functional analysis pipelines.\u003c/p\u003e\n\u003cp\u003eCreate a directory named \u003ccode\u003eexternal\u003c/code\u003e and place 2 directories with its associated files and binaries as downloaded from the links above. You need to be granted an academic license permission first.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esignalp (5.0b)\u003c/li\u003e\n\u003cli\u003etargetp (2.0)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eContainer recipes will grab the necessary files from these directories.\u003c/p\u003e\n\u003cp\u003eBuilding with \u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build sigtarp.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can avoid using sudo with \u003ccode\u003e--fakeroot\u003c/code\u003e Singularity build option.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1556152675.0 + "updated_at": 1638960392.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity.anaconda3-dask-numba" + "Singularity" ], - "full_name": "zonca/Python_HPC_2022", + "full_name": "thiago-rissi/time_encoding_experiment", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etime_encoder\u003c/h1\u003e\u003ca id=\"user-content-time_encoder\" class=\"anchor\" aria-label=\"Permalink: time_encoder\" href=\"#time_encoder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1657276145.0 + "updated_at": 1724562717.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.dropbox" + "Singularity" ], - "full_name": "ternaustralia/coesra-singularity-dropbox", + "full_name": "aswinnarayanan/ci-dev", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecoesra-singularity-dropbox\u003c/h1\u003e\u003ca id=\"user-content-coesra-singularity-dropbox\" class=\"anchor\" aria-label=\"Permalink: coesra-singularity-dropbox\" href=\"#coesra-singularity-dropbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eci-dev\u003c/h1\u003e\u003ca id=\"user-content-ci-dev\" class=\"anchor\" aria-label=\"Permalink: ci-dev\" href=\"#ci-dev\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1670904307.0 + "updated_at": 1598406354.0 }, { "data_format": 2, - "description": "Seeing scenes with multi-granularity", + "description": null, "filenames": [ - "env.d/Singularity.blender", - "env.d/Singularity.base" + "Singularity" ], - "full_name": "CNCLgithub/GranularScenes", + "full_name": "J-Andy/eQTL-analysis", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGranularScenes\u003c/h1\u003e\u003ca id=\"user-content-granularscenes\" class=\"anchor\" aria-label=\"Permalink: GranularScenes\" href=\"#granularscenes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSeeing scenes with multi-granularity!\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup and running\u003c/h2\u003e\u003ca id=\"user-content-setup-and-running\" class=\"anchor\" aria-label=\"Permalink: Setup and running\" href=\"#setup-and-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./setup.sh cont_build python julia\u003c/code\u003e to build the container and setup enviroment\u003c/li\u003e\n\u003cli\u003eEnter \u003ccode\u003e./run.sh julia\u003c/code\u003e to get into Julia REPL\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNOTE: when setting up Julia, if \u003ccode\u003ePkg\u003c/code\u003e complains about some packages not being registered, you can install them manually via \u003ccode\u003ePkg.add(git url)\u003c/code\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis project has automatic configuration!! This configuration is defined in \u003ccode\u003edefault.conf\u003c/code\u003e.\nYou should always prepend \u003ccode\u003e./run.sh\u003c/code\u003e before any command (including running programs like \u003ccode\u003ejulia\u003c/code\u003e) to ensure consistency.\nIf you wish to have different values than \u003ccode\u003edefault.conf\u003c/code\u003e, simply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp default.conf user.conf\nvi user.conf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e edit to your liking without adding new elements\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMac and Window users\u003c/h2\u003e\u003ca id=\"user-content-mac-and-window-users\" class=\"anchor\" aria-label=\"Permalink: Mac and Window users\" href=\"#mac-and-window-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use singularity you must have a virtual machine running.\nAssuming you have vagrant (and something like virtualbox) setup on your host, you can follow these steps\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing \u003ccode\u003esetup.sh\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-setupsh\" class=\"anchor\" aria-label=\"Permalink: Using setup.sh\" href=\"#using-setupsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing \u003ccode\u003erun.sh\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-runsh\" class=\"anchor\" aria-label=\"Permalink: Using run.sh\" href=\"#using-runsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvision the virtual machine defined in \u003ccode\u003eVagrantfile\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCreate a \u003ccode\u003euser.conf\u003c/code\u003e as described above\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: git will not track \u003ccode\u003euser.conf\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eModify \u003ccode\u003euser.conf\u003c/code\u003e such that \u003ccode\u003epath\u003c/code\u003e is set to route through vagrant\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e[ENV]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003epath:vagrant ssh -c singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributing rules\u003c/h3\u003e\u003ca id=\"user-content-contributing-rules\" class=\"anchor\" aria-label=\"Permalink: Contributing rules\" href=\"#contributing-rules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eplace all re-used code in packages (\u003ccode\u003esrc\u003c/code\u003e or \u003ccode\u003epydeps\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eplace all interactive code in \u003ccode\u003escripts\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003enot use \"hard\" paths. Instead update \u003ccode\u003ePATHS\u003c/code\u003e in the config.\u003c/li\u003e\n\u003cli\u003eadd contributions to branches derived from \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edev\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003enot use \u003ccode\u003egit add *\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003enot commit large files (checkpoints, datasets, etc). Update \u003ccode\u003esetup.sh\u003c/code\u003e accordingly.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject layout\u003c/h3\u003e\u003ca id=\"user-content-project-layout\" class=\"anchor\" aria-label=\"Permalink: Project layout\" href=\"#project-layout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package environment is located under \u003ccode\u003epydeps\u003c/code\u003e and can be imported using \u003ccode\u003eimport pydepts\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eLikewise, the Julia package is described under \u003ccode\u003esrc\u003c/code\u003e and \u003ccode\u003etest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAll scripts are located under \u003ccode\u003escripts\u003c/code\u003e and data/output is under \u003ccode\u003eoutput\u003c/code\u003e as specific in the project config (\u003ccode\u003edefault.conf\u003c/code\u003e or \u003ccode\u003euser.conf\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChanging the enviroment\u003c/h3\u003e\u003ca id=\"user-content-changing-the-enviroment\" class=\"anchor\" aria-label=\"Permalink: Changing the enviroment\" href=\"#changing-the-enviroment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add new python or julia packages use the provided package managers. The Python dependencies are listed under \u003ccode\u003eenv.d/requirements.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor julia you can also use \u003ccode\u003e] add \u003c/code\u003e in the REPL\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRelated works\u003c/h2\u003e\u003ca id=\"user-content-related-works\" class=\"anchor\" aria-label=\"Permalink: Related works\" href=\"#related-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eManuscript pending!\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConference submissions\u003c/h3\u003e\u003ca id=\"user-content-conference-submissions\" class=\"anchor\" aria-label=\"Permalink: Conference submissions\" href=\"#conference-submissions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBelledonne, M., Bao, Y., \u0026amp; Yildirim, I. (2022). Navigational affordances are automatically computed during scene perception: Evidence from behavioral change blindness and a computational model of active attention. \u003cem\u003eJournal of Vision, 22\u003c/em\u003e(14), 4128-4128.\u003c/p\u003e\n\u003cp\u003eBelledonne, M., \u0026amp; Yildirim, I. (2021). Automatic computation of navigational affordances explains selective processing of geometry in scene perception: Behavioral and computational evidence. In \u003cem\u003eProceedings of the Annual Meeting of the Cognitive Science Society\u003c/em\u003e (Vol. 43, No. 43).\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGene Knockout and Mutation Analysis\u003c/h1\u003e\u003ca id=\"user-content-gene-knockout-and-mutation-analysis\" class=\"anchor\" aria-label=\"Permalink: Gene Knockout and Mutation Analysis\" href=\"#gene-knockout-and-mutation-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project analyzes the association between specific cancer mutations and gene knockout experiments using Welch\u0027s t-test in R. The analysis is encapsulated in a Singularity container, which includes all necessary dependencies and R scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eComment: Extending the Analysis with Advanced Models\u003c/h2\u003e\u003ca id=\"user-content-comment-extending-the-analysis-with-advanced-models\" class=\"anchor\" aria-label=\"Permalink: Comment: Extending the Analysis with Advanced Models\" href=\"#comment-extending-the-analysis-with-advanced-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhile Welch\u2019s t-test is suitable for this toy example, more advanced methods would be necessary for larger datasets or when controlling for additional covariates. If we had more data or multiple covariates, I would be using linear regression or ANOVA. Linear regression would help model the relationship between mutation status and knockout outcomes while accounting for factors such as tissue type or growth conditions.\nFor efficiently handling large-scale data with many gene-mutation pairs, I would use the MatrixEQTL package in R (\u003ca href=\"https://github.com/andreyshabalin/MatrixEQTL\"\u003ehttps://github.com/andreyshabalin/MatrixEQTL\u003c/a\u003e). MatrixEQTL allows the use of linear models and is optimized for large-scale genomic data, automatically performs False Discovery Rate (FDR) correction and has some model customization options.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Container\u003c/h2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity Container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container runs the R script \u003ccode\u003emain.R\u003c/code\u003e which:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLoads mutation data (\u003ccode\u003eMutations.tsv\u003c/code\u003e) and gene knockout data (\u003ccode\u003eGene_KOs.tsv\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003ePerforms Welch\u2019s t-tests for each mutation-gene pair.\u003c/li\u003e\n\u003cli\u003eOutputs results with False Discovery Rate (FDR) correction.\u003c/li\u003e\n\u003cli\u003eGenerates Q-Q plots, histograms, and volcano plots.\u003c/li\u003e\n\u003cli\u003eSaves significant results and all plots in the output directory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation Instructions\u003c/h2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-label=\"Permalink: Installation Instructions\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Clone the repository\u003c/h3\u003e\u003ca id=\"user-content-1-clone-the-repository\" class=\"anchor\" aria-label=\"Permalink: 1. Clone the repository\" href=\"#1-clone-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the repository containing the \u003ccode\u003eSingularity\u003c/code\u003e definition file, R scripts, and data files:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/J-Andy/eQTL-analysis.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e eQTL-analysis\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Build the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-2-build-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: 2. Build the Singularity image\" href=\"#2-build-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build the Singularity image locally using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build r-analysis.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t have root access on the system, like me, you can use the remote build service:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build --remote r-analysis.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Running the Analysis\u003c/h3\u003e\u003ca id=\"user-content-3-running-the-analysis\" class=\"anchor\" aria-label=\"Permalink: 3. Running the Analysis\" href=\"#3-running-the-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter building the container, run the analysis using this script:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./run_analysis.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMake sure the \u003ccode\u003erun_analysis.sh\u003c/code\u003e script is executable\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e chmod +x run_analysis.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe shell script will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate an output directory in your current directory.\u003c/li\u003e\n\u003cli\u003eRun the Singularity container and bind the local output directory to the container\u2019s /usr/src/app/output.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Running Unit Tests\u003c/h3\u003e\u003ca id=\"user-content-4-running-unit-tests\" class=\"anchor\" aria-label=\"Permalink: 4. Running Unit Tests\" href=\"#4-running-unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the unit tests, use \u003ccode\u003erun_tests.sh \u003c/code\u003e script.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUnit Test Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eData Alignment Test:\nEnsures that the mutation data (Mutations.tsv) and gene knockout data (Gene_KOs.tsv) are properly aligned by common models (cell lines).\nThe test verifies that the aligned datasets share the same number of common cell lines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eT-test Validation:\nChecks that Welch\u2019s t-tests return valid p-values when comparing mutation presence with gene knockout outcomes.\nThe test simulates mutation and fold change data and ensures the p-value is numeric and less than 1.\nTest Output:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf all tests pass, the script will print a success message: \"All tests passed successfully!\"\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Outputs\u003c/h3\u003e\u003ca id=\"user-content-5-outputs\" class=\"anchor\" aria-label=\"Permalink: 5. Outputs\" href=\"#5-outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe results and plots generated by the analysis are saved in the output directory. These include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esignificant_mutation_gene_associations.tsv: A table of significant mutation-gene associations after FDR correction.\u003c/li\u003e\n\u003cli\u003eqqplot_pvalues.png: A Q-Q plot for visualizing the distribution of p-values.\u003c/li\u003e\n\u003cli\u003ehistogram_pvalues.png: A histogram displaying the p-value distribution.\u003c/li\u003e\n\u003cli\u003evolcano_plot.png: A volcano plot of mutation-gene associations.\u003c/li\u003e\n\u003cli\u003eBoxplots: Individual PNG files for each significant mutation-gene association.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload the Prebuilt Singularity Image\u003c/h3\u003e\u003ca id=\"user-content-download-the-prebuilt-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Download the Prebuilt Singularity Image\" href=\"#download-the-prebuilt-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you prefer not to build the container yourself, you can download the prebuilt Singularity image (\u003ccode\u003er-analysis.sif\u003c/code\u003e) from the following link:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://drive.google.com/file/d/17zTK5rTlSquPqw5A8xGvv1kwUP1szQDB/view?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/file/d/17zTK5rTlSquPqw5A8xGvv1kwUP1szQDB/view?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMachine Learning Models for Predicting CRISPR Knockout Effects\u003c/h1\u003e\u003ca id=\"user-content-machine-learning-models-for-predicting-crispr-knockout-effects\" class=\"anchor\" aria-label=\"Permalink: Machine Learning Models for Predicting CRISPR Knockout Effects\" href=\"#machine-learning-models-for-predicting-crispr-knockout-effects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClassical ML Approach\u003c/h3\u003e\u003ca id=\"user-content-classical-ml-approach\" class=\"anchor\" aria-label=\"Permalink: Classical ML Approach\" href=\"#classical-ml-approach\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each gene:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDefine the features as the binary mutations in the cell lines.\u003c/li\u003e\n\u003cli\u003eDefine the target: the target is a continuous variable \"knock-out effect\".\u003c/li\u003e\n\u003cli\u003eTrain a separate Random Forest regression model for each gene: each model is trained on the same mutation data (features), but the target variable changes based on the gene whose knock-out effect we are trying to predict.\u003c/li\u003e\n\u003cli\u003eI would then test several other models that people previously used in the literature, like Elastic Net Regression and Gradient Boosting Machines.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMulti-Output Models\u003c/h3\u003e\u003ca id=\"user-content-multi-output-models\" class=\"anchor\" aria-label=\"Permalink: Multi-Output Models\" href=\"#multi-output-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the current approach, I train separate models for each gene. However, there is an opportunity to take advantage of the correlation between gene knockouts by predicting multiple gene knockout effects simultaneously using a Multi-Output Random Forest or indeed multi-task learning with Convolutional Neural Networks (becasue neural network can be set up to have multiple output neurons, one for each target variable).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMulti-Task Linear Regression\u003c/h3\u003e\u003ca id=\"user-content-multi-task-linear-regression\" class=\"anchor\" aria-label=\"Permalink: Multi-Task Linear Regression\" href=\"#multi-task-linear-regression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn even more advanced approach would be to apply a Bayesian multi-task linear regression model or multi-task learning with CNNs to solve this problem. For example, see here: \u003ca href=\"https://www.sciencedirect.com/science/article/abs/pii/S1046202323002128?via%3Dihub\" rel=\"nofollow\"\u003ehttps://www.sciencedirect.com/science/article/abs/pii/S1046202323002128?via%3Dihub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImplementation\u003c/h3\u003e\u003ca id=\"user-content-implementation\" class=\"anchor\" aria-label=\"Permalink: Implementation\" href=\"#implementation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI implemented a simple RF model, for a single gene, in the script \u003ccode\u003erandom_forest_model_for_gene_knockout_prediction_using_mutation_data.py\u003c/code\u003e\nYou can also run it on Colab: \u003ca href=\"https://colab.research.google.com/drive/1aIjK0wWs3UwiVI_bn-OcuUrHaQCpnZZo?usp=sharing\" rel=\"nofollow\"\u003ehttps://colab.research.google.com/drive/1aIjK0wWs3UwiVI_bn-OcuUrHaQCpnZZo?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGeneralization to Unseen Data\u003c/h1\u003e\u003ca id=\"user-content-generalization-to-unseen-data\" class=\"anchor\" aria-label=\"Permalink: Generalization to Unseen Data\" href=\"#generalization-to-unseen-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInitial Model Build\u003c/h2\u003e\u003ca id=\"user-content-initial-model-build\" class=\"anchor\" aria-label=\"Permalink: Initial Model Build\" href=\"#initial-model-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe generalization of the model will primarily depend the diversity and size of training data and regularization techniques (i.e. feature selection) we used. We can assess the initial generalizability with k-fold cross-validation and bootstrapping.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePan-Cancer vs. Tissue-Specific Models\u003c/strong\u003e\u003c/em\u003e\nIn a broader context, models could be trained in two ways:\u003c/p\u003e\n\u003cp\u003e\u2022\tPan-cancer models pool data from multiple cancer types, treating the knockouts as a universal phenomenon across all cell lines. This assumes mutation-response relationships are similar across different tissue types.\u003c/p\u003e\n\u003cp\u003e\u2022\tTissue-specific models account for lineage-specific effects by training separate models for individual cancer types.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel in Production\u003c/h2\u003e\u003ca id=\"user-content-model-in-production\" class=\"anchor\" aria-label=\"Permalink: Model in Production\" href=\"#model-in-production\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the models are deployed to production two issues are likely to occur \u003cstrong\u003edata drift\u003c/strong\u003e (changing mutation patterns) and \u003cstrong\u003econcept drift\u003c/strong\u003e (shifting biological relationships/interpretation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Data Drift:\u003c/strong\u003e\nThis will occur when the distribution of mutations in the new cell lines is significantly different from the training data. For instance, if the model was trained on cell lines that mostly contained mutations in specific genes, but new cell lines have mutations in entirely different genes or exhibit new mutation patterns, the model\u0027s performance will degrade.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSuggested Solution:\u003c/strong\u003e\u003c/em\u003e To detect data drift, we could monitor the distribution of mutations (input features) in new cell lines and compare them with the original training data. We can use statistical tests like Kolmogorov-Smirnov test or domain classifiers (which distinguish between old and new data) to detect these changes. A significant drift will require model retraining.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Concept Drift:\u003c/strong\u003e\nThis I believe will be less of a concern. Concept drift refers to a change in the underlying relationship between the input features and the target variable over time. In our context this could happen when we introduce new experimental conditions, such as different cell environments or evolve resistant cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSuggested Solution:\u003c/strong\u003e\u003c/em\u003e I think detecting this type of drift in our context would be difficult and would have to be informed by wet-lab scientists (are they doing anything different?). We could monitor the model\u2019s predictions and their accuracy on new cell lines. If the KO effect predictions are consistently inaccurate for new data even after retraining that to me would indicate concept drift.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1727729194.0 + "updated_at": 1726235293.0 }, { "data_format": 2, - "description": "Achab In Singularity, a Singularity Container for Captain Achab (annotation workflow)", + "description": "Tidyverse singularity container", "filenames": [ "Singularity" ], - "full_name": "mobidic/Achabilarity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eACHABILARITY\u003c/h1\u003e\u003ca id=\"user-content-achabilarity\" class=\"anchor\" aria-label=\"Permalink: ACHABILARITY\" href=\"#achabilarity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAchabInsinguLARITY, a container to use captainAchab workflow easier !\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/achab_logo.png\"\u003e\u003cimg src=\"img/achab_logo.png\" width=\"350\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGoals\u003c/h2\u003e\u003ca id=\"user-content-goals\" class=\"anchor\" aria-label=\"Permalink: Goals\" href=\"#goals\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse a Singularity container which already has all tools to run captainAchab workflow.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/img/captainAchab.svg\"\u003e\u003cimg src=\"/img/captainAchab.svg\" alt=\"captain achab workflow description\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eFirst, build\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename.simg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e Singularity \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eThen run\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e/PATH/TO/ANNOVAR\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename.simg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -i workflow_inputs.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity help\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename.simg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptions\u003c/h2\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: Options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e-c | --conf \u0026lt;file.conf\u0026gt;\u003c/strong\u003e : To add a conf file\u003cbr\u003e\n\u003cstrong\u003e-o | --option \u0026lt;option.json\u0026gt;\u003c/strong\u003e : To add an option file\u003cbr\u003e\n\u003cstrong\u003e-v | --verbosity \u0026lt;1, 2, 3 or 4\u0026gt;\u003c/strong\u003e : To set verbosity level (ERROR : 1 | WARNING : 2 | INFO [default] : 3 | DEBUG : 4)\u003cbr\u003e\n\u003cstrong\u003e-h | --help\u003c/strong\u003e : Print help message in terminal and close the script (Help provided by -h concerns wrapper using)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMore informations\u003c/h2\u003e\u003ca id=\"user-content-more-informations\" class=\"anchor\" aria-label=\"Permalink: More informations\" href=\"#more-informations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAchabilarity is currently using a part of \u003ca href=\"https://github.com/mobidic/MobiDL\"\u003eMobiDL\u003c/a\u003e which is \u003ca href=\"https://github.com/mobidic/Captain-ACHAB\"\u003eCaptainAchab\u003c/a\u003e workflow.\u003cbr\u003e\nThis Singularity contains CentOS environnement and all requirements to run Captain Achab workflow (MPA, Phenolyzer, Achab) and few others (BCFTools, GATK4 ...).\u003cbr\u003e\n\u003cstrong\u003eMake sure you already have Annovar (and its database) to bind it. It is not include in this container.\u003c/strong\u003e\nBinding of ANNOVAR and data folder (inputs) will look like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /path/to/annovar/:/media -B /path/to/data/:/mnt achabilarity.simg -c /path/to/conf -i /path/to/json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe container will execute specific wrapper of cromwell (\u003ca href=\"https://github.com/mobidic/Crom-wellWrapped\"\u003eCrom-wellWrapped\u003c/a\u003e) which will generate the right cromwell command depending on options and arguments.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eMontpellier Bioinformatique pour le Diagnostique Clinique (MoBiDiC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCHU de Montpellier\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/logo-mobidic.png\"\u003e\u003cimg src=\"img/logo-mobidic.png\" alt=\"MoBiDiC\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://neuro-2.iurc.montp.inserm.fr/mobidic/\" rel=\"nofollow\"\u003eVisit our website\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n", + "full_name": "richelbilderbeek/tidyverse_singularity", + "latest_release": "v1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etidyverse_singularity\u003c/h1\u003e\u003ca id=\"user-content-tidyverse_singularity\" class=\"anchor\" aria-label=\"Permalink: tidyverse_singularity\" href=\"#tidyverse_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/richelbilderbeek/tidyverse_singularity/actions/workflows/build_singularity.yaml\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/tidyverse_singularity/actions/workflows/build_singularity.yaml/badge.svg\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container with a working version of the \u003ccode\u003esingularity\u003c/code\u003e R package.\u003c/p\u003e\n\u003cp\u003eIt does run remotely (i.e. on GitHub Actions),\nbut not on my Ubuntu 20.04 LTS laptop).\u003c/p\u003e\n\u003cp\u003eThis is a follow-up of a question I \u003ca href=\"https://stackoverflow.com/questions/71252123/singularity-container-with-singularity-fails-only-locally-with-libicui18n-so-66-ca\" rel=\"nofollow\"\u003eposted a question on StackOverflow\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1617369638.0 + "updated_at": 1645771472.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.latest" + "Singularity" ], - "full_name": "brentritzema/senior-project", + "full_name": "oogasawa/singularity_ubuntu18", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRoy and Brent\u0027s Senior Project\u003c/h1\u003e\u003ca id=\"user-content-roy-and-brents-senior-project\" class=\"anchor\" aria-label=\"Permalink: Roy and Brent\u0027s Senior Project\" href=\"#roy-and-brents-senior-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUbuntu18 Singularity\u003c/h1\u003e\u003ca id=\"user-content-ubuntu18-singularity\" class=\"anchor\" aria-label=\"Permalink: Ubuntu18 Singularity\" href=\"#ubuntu18-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux 18.04 (Bionic) + \u89e3\u6790\u7528\u30c4\u30fc\u30eb\u5168\u90e8\u5165\u308a\u306e Singularity\u30b3\u30f3\u30c6\u30ca\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u524d\u63d0\u003c/h2\u003e\u003ca id=\"user-content-\u524d\u63d0\" class=\"anchor\" aria-label=\"Permalink: \u524d\u63d0\" href=\"#\u524d\u63d0\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux\u306bdebootstrap\u4ed6\u306e\u5fc5\u8981\u306a\u30bd\u30d5\u30c8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt upgrade\nsudo apt install build-essential libtool automake libarchive-dev debootstrap git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity\u306fversion 2.x\u7cfb\u3067\u30823.x\u7cfb\u3067\u3082\u3088\u3044\u3002\uff08\u4eca\u306f\u3082\u30463.x\u7cfb\u63a8\u5968\uff09\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.5/admin-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\uff08Official Document)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u4f7f\u3044\u65b9\u003c/h2\u003e\u003ca id=\"user-content-\u4f7f\u3044\u65b9\" class=\"anchor\" aria-label=\"Permalink: \u4f7f\u3044\u65b9\" href=\"#\u4f7f\u3044\u65b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# \u30b3\u30f3\u30c6\u30ca\u306e\u30d3\u30eb\u30c9\ngit clone http://gitlab.ddbj.nig.ac.jp/oogasawa/singularity-ubuntu18\ncd singularity-ubuntu18\nmkdir $HOME/singularity-images\nsudo singularity build --sandbox $HOME/singularity-images/ubuntu18 Singularity\n\n# \u30b3\u30f3\u30c6\u30ca\u5185\u3067\u306e\u4f5c\u696d\nsudo singularity shell --write $HOME/singularity-images/ubuntu18\n\n# *.sif\u30d5\u30a1\u30a4\u30eb\u306b\u56fa\u3081\u3066\u30b9\u30d1\u30b3\u30f3\u306a\u3069\u5171\u7528\u8a08\u7b97\u6a5f\u306b\u30b3\u30d4\u30fc\nsudo singularity build ubuntu18.sif $HOME/singularity-images/ubuntu18\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1549999922.0 + "updated_at": 1622297627.0 }, { "data_format": 2, - "description": null, + "description": "A Singularity recipe for sistr --- a tool for Salmonella typing", "filenames": [ - "Singularity" + "Singularity", + "v1.0.2/Singularity.v1.0.2" ], - "full_name": "mathematiguy/star-stuff", + "full_name": "phgenomics-singularity/sistr", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eminimal-project\u003c/h1\u003e\u003ca id=\"user-content-minimal-project\" class=\"anchor\" aria-label=\"Permalink: minimal-project\" href=\"#minimal-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple template for future projects\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esistr --- A Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-sistr-----a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: sistr --- A Singularity Container\" href=\"#sistr-----a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1206\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for \u003ca href=\"https://github.com/peterk87/sistr_cmd\"\u003esistr\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name TMP_DIRECTORY shub://phgenomics-singularity/sistr@latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003esistr.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eUse the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./sistr.simg --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name mlst shub://phgenomics-singularitysistr@VERSION.NUMBER\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir HOME/singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name sistr shub://phgenomics-singularity/sistr@latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eLink the image to a folder in your \u003ccode\u003ePATH\u003c/code\u003e (e.g., \u003ccode\u003eHOME/bin\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eln -s HOME/singularity/sistr.simg HOME/bin/sistr\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esistr --help\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1723292845.0 + "updated_at": 1530577180.0 }, { "data_format": 2, - "description": "A simple template for future projects", + "description": "Ticket 297240", "filenames": [ - "Singularity" + "Singularity_approach_5", + "Singularity_approach_4", + "Singularity", + "Singularity_approach_3" ], - "full_name": "mathematiguy/minimal-project", + "full_name": "UPPMAX/ticket_297240", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eminimal-project\u003c/h1\u003e\u003ca id=\"user-content-minimal-project\" class=\"anchor\" aria-label=\"Permalink: minimal-project\" href=\"#minimal-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple template for future projects\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eticket_297240\u003c/h1\u003e\u003ca id=\"user-content-ticket_297240\" class=\"anchor\" aria-label=\"Permalink: ticket_297240\" href=\"#ticket_297240\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTicket 297240\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApproach 5: Use Singularity from BZL file\u003c/h2\u003e\u003ca id=\"user-content-approach-5-use-singularity-from-bzl-file\" class=\"anchor\" aria-label=\"Permalink: Approach 5: Use Singularity from BZL file\" href=\"#approach-5-use-singularity-from-bzl-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDue to file that 10x uses \u003ca href=\"https://github.com/10XGenomics/cellranger/blob/main/conda_spec.bzl\"\u003ehttps://github.com/10XGenomics/cellranger/blob/main/conda_spec.bzl\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHere is a Docker file: \u003ca href=\"https://hub.docker.com/r/chainguard/bazel\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/chainguard/bazel\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDocker pull command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull chainguard/bazel:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003ebazel build conda_spec.bzl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuild Singularity file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: chainguard/bazel:latest\n\n%post\n # From https://github.com/brucemoran/Singularity/blob/8eb44591284ffb29056d234c47bf8b1473637805/shub/bases/recipe.CentOs7-R_3.5.2#L21\n echo \u0027export LANG=en_US.UTF-8 LANGUAGE=C LC_ALL=C LC_CTYPE=C LC_COLLATE=C LC_TIME=C LC_MONETARY=C LC_PAPER=C LC_MEASUREMENT=C\u0027 \u0026gt;\u0026gt; $SINGULARITY_ENVIRONMENT\n\n cd /opt\n git clone https://github.com/10XGenomics/cellranger\n cd cellranger\n\n bazel build conda_spec.bzl\n\n%runscript\npython \"$@\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuild:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eError:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eERROR: The \u0027build\u0027 command is only supported from within a workspace (below a directory having a WORKSPACE file).\nSee documentation at https://bazel.build/concepts/build-ref#workspace\nFATAL: While performing build: while running engine: exit status 2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse tip from \u003ca href=\"https://stackoverflow.com/q/61869719\" rel=\"nofollow\"\u003ehttps://stackoverflow.com/q/61869719\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGives error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+ touch WORKSPACE\n+ bazel build conda_spec.bzl\nExtracting Bazel installation...\nStarting local Bazel server and connecting to it...\nWARNING: --enable_bzlmod is set, but no MODULE.bazel file was found at the workspace root. Bazel will create an empty MODULE.bazel file. Please consider migrating your external dependencies from WORKSPACE to MODULE.bazel. For more details, please refer to https://github.com/bazelbuild/bazel/issues/18958.\nWARNING: Target pattern parsing failed.\nERROR: Skipping \u0027conda_spec.bzl\u0027: couldn\u0027t determine target from filename \u0027conda_spec.bzl\u0027\nERROR: couldn\u0027t determine target from filename \u0027conda_spec.bzl\u0027\nINFO: Elapsed time: 7.913s\nINFO: 0 processes.\nERROR: Build did NOT complete successfully\nFATAL: While performing build: while running engine: exit status 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdded tip from \u003ca href=\"https://raw.githubusercontent.com/10XGenomics/cellranger/main/conda_spec.bzl\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/10XGenomics/cellranger/main/conda_spec.bzl\u003c/a\u003e to add content to WORKSPACE.\u003c/p\u003e\n\u003cp\u003eBuild again:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+ echo \u0027load(\":conda_spec.bzl\", \"anaconda_workspace\")\u0027\n+ echo \u0027anaconda_workspace()\u0027\n+ bazel build conda_spec.bzl\nExtracting Bazel installation...\nStarting local Bazel server and connecting to it...\nWARNING: --enable_bzlmod is set, but no MODULE.bazel file was found at the workspace root. Bazel will create an empty MODULE.bazel file. Please consider migrating your external dependencies from WORKSPACE to MODULE.bazel. For more details, please refer to https://github.com/bazelbuild/bazel/issues/18958.\nERROR: Error computing the main repository mapping: Every .bzl file must have a corresponding package, but \u0027//:conda_spec.bzl\u0027 does not have one. Please create a BUILD file in the same or any parent directory. Note that this BUILD file does not need to do anything except exist.\nComputing main repo mapping: \nFATAL: While performing build: while running engine: exit status 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOK, create an empty BUILD file ...\u003c/p\u003e\n\u003cp\u003eWith content in the BUILD file:\nRun again, new error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+ touch BUILD\n+ bazel build conda_spec.bzl\nExtracting Bazel installation...\nStarting local Bazel server and connecting to it...\nWARNING: --enable_bzlmod is set, but no MODULE.bazel file was found at the workspace root. Bazel will create an empty MODULE.bazel file. Please consider migrating your external dependencies from WORKSPACE to MODULE.bazel. For more details, please refer to https://github.com/bazelbuild/bazel/issues/18958.\nERROR: Failed to load Starlark extension \u0027@@tenx_bazel_rules//rules:conda_package_repository.bzl\u0027.\nCycle in the workspace file detected. This indicates that a repository is used prior to being defined.\nThe following chain of repository dependencies lead to the missing definition.\n - @@tenx_bazel_rules\nThis could either mean you have to add the \u0027@@tenx_bazel_rules\u0027 repository with a statement like `http_archive` in your WORKSPACE file (note that transitive dependencies are not added automatically), or move an existing definition earlier in your WORKSPACE file.\nERROR: Error computing the main repository mapping: cycles detected during computation of main repo mapping\nComputing main repo mapping: \nFATAL: While performing build: while running engine: exit status 37\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApproach 4: Use Singularity from conda\u003c/h2\u003e\u003ca id=\"user-content-approach-4-use-singularity-from-conda\" class=\"anchor\" aria-label=\"Permalink: Approach 4: Use Singularity from conda\" href=\"#approach-4-use-singularity-from-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple Singularity file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: mcr.microsoft.com/devcontainers/anaconda\n# From: pnnlmiscscripts/anaconda:latest\n\n\n%post\n # From https://github.com/brucemoran/Singularity/blob/8eb44591284ffb29056d234c47bf8b1473637805/shub/bases/recipe.CentOs7-R_3.5.2#L21\n echo \u0027export LANG=en_US.UTF-8 LANGUAGE=C LC_ALL=C LC_CTYPE=C LC_COLLATE=C LC_TIME=C LC_MONETARY=C LC_PAPER=C LC_MEASUREMENT=C\u0027 \u0026gt;\u0026gt; $SINGULARITY_ENVIRONMENT\n\n conda install hcc::cellranger\n\n%runscript\npython \"$@\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuild:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+ conda install hcc::cellranger\nChannels:\n - defaults\n - hcc\nPlatform: linux-64\nCollecting package metadata (repodata.json): done\nSolving environment: failed\n\nLibMambaUnsatisfiableError: Encountered problems while solving:\n - nothing provides bcftools 1.9.* needed by cellranger-3.0.2-py27_1\n\nCould not solve for environment specs\nThe following package could not be installed\n\u2514\u2500 cellranger is not installable because it requires\n \u2514\u2500 bcftools 1.9.* , which does not exist (perhaps a missing channel).\n\nFATAL: While performing build: while running engine: exit status 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eApproach 5 seems easier.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApproach 3: Use conda from module\u003c/h2\u003e\u003ca id=\"user-content-approach-3-use-conda-from-module\" class=\"anchor\" aria-label=\"Permalink: Approach 3: Use conda from module\" href=\"#approach-3-use-conda-from-module\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLoading the conda module:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWorks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham1 ~]$ module load conda\nThe variable CONDA_ENVS_PATH contains the location of your environments. Set it to your project\u0027s environments folder if you have one.\nOtherwise, the default is ~/.conda/envs. Remember to export the variable with export CONDA_ENVS_PATH=/proj/...\n\nYou may run \"source conda_init.sh\" to initialise your shell to be able\nto run \"conda activate\" and \"conda deactivate\" etc.\nJust remember that this command adds stuff to your shell outside the scope of the module system.\n\nREMEMBER TO USE \u0027conda clean -a\u0027 once in a while\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNaive install fails:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install cellranger\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham1 ~]$ conda install cellranger\nRetrieving notices: ...working... done\nChannels:\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - conda-forge\nPlatform: linux-64\nCollecting package metadata (repodata.json): - subdir mismatch\n\\ subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\n- subdir mismatch\n\\ subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\ndone\nSolving environment: failed\n\nPackagesNotFoundError: The following packages are not available from current channels:\n\n - cellranger\n\nCurrent channels:\n\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - https://conda.anaconda.org/conda-forge/linux-64\n - https://conda.anaconda.org/conda-forge/noarch\n\nTo search for alternate channels that may provide the conda package you\u0027re\nlooking for, navigate to\n\n https://anaconda.org\n\nand use the search bar at the top of the page.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDo conda install from the doc at \u003ca href=\"https://anaconda.org/hcc/cellranger\" rel=\"nofollow\"\u003ehttps://anaconda.org/hcc/cellranger\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install hcc::cellranger\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFails too:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham1 ~]$ conda install hcc::cellranger\nChannels:\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - hcc\n - conda-forge\nPlatform: linux-64\nCollecting package metadata (repodata.json): / subdir mismatch\n- subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\n/ subdir mismatch\n- subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\ndone\nSolving environment: / warning libmamba Added empty dependency for problem type SOLVER_RULE_UPDATE\nfailed\n\nPackagesNotFoundError: The following packages are not available from current channels:\n\n - hcc::cellranger\n\nCurrent channels:\n\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - https://conda.anaconda.org/hcc\n - https://conda.anaconda.org/conda-forge/linux-64\n - https://conda.anaconda.org/conda-forge/noarch\n\nTo search for alternate channels that may provide the conda package you\u0027re\nlooking for, navigate to\n\n https://anaconda.org\n\nand use the search bar at the top of the page.\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing -c:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham1 ~]$ conda install -c hcc cellranger\nChannels:\n - hcc\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - conda-forge\nPlatform: linux-64\nCollecting package metadata (repodata.json): | subdir mismatch\n/ subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\n- subdir mismatch\n\\ subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\ndone\nSolving environment: / warning libmamba Added empty dependency for problem type SOLVER_RULE_UPDATE\nfailed\n\nLibMambaUnsatisfiableError: Encountered problems while solving:\n - nothing provides openssl \u0026gt;=1.1.1,\u0026lt;1.1.2.0a0 needed by python-2.7.15-h9bab390_2\n\nCould not solve for environment specs\nThe following packages are incompatible\n\u251c\u2500 cellranger is installable with the potential options\n\u2502 \u251c\u2500 cellranger 3.0.2 would require\n\u2502 \u2502 \u2514\u2500 python \u0026gt;=2.7,\u0026lt;2.8.0a0 with the potential options\n\u2502 \u2502 \u251c\u2500 python [2.7.10|2.7.11|...|2.7.9], which can be installed;\n\u2502 \u2502 \u2514\u2500 python 2.7.15 would require\n\u2502 \u2502 \u2514\u2500 openssl \u0026gt;=1.1.1,\u0026lt;1.1.2.0a0 , which does not exist (perhaps a missing channel);\n\u2502 \u2514\u2500 cellranger 3.0.2 would require\n\u2502 \u2514\u2500 python \u0026lt;3 with the potential options\n\u2502 \u251c\u2500 python [2.7.10|2.7.11|...|2.7.9], which can be installed;\n\u2502 \u251c\u2500 python 2.7.15, which cannot be installed (as previously explained);\n\u2502 \u2514\u2500 python [1.0.1|1.2|...|2.6.9], which can be installed;\n\u2514\u2500 pin-1 is not installable because it requires\n \u2514\u2500 python 3.12.* , which conflicts with any installable versions previously reported.\n\nPins seem to be involved in the conflict. Currently pinned specs:\n - python 3.12.* (labeled as \u0027pin-1\u0027)\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSeems unsolvable on Rackham\u003c/p\u003e\n\u003cp\u003eFrom \u003ca href=\"https://anaconda.org/hcc/repo/installers\" rel=\"nofollow\"\u003ehttps://anaconda.org/hcc/repo/installers\u003c/a\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTo install a conda package from this channel, run:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003econda install --channel \"HCC\" package\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTakes us back to the earlier tried:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./conda install --channel \"HCC\" cellranger\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewith error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erichel@richel-N141CU:~/anaconda3/bin$ ./conda install --channel \"HCC\" cellranger\nChannels:\n - HCC\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): done\nSolving environment: failed\n\nLibMambaUnsatisfiableError: Encountered problems while solving:\n - nothing provides bcftools 1.9.* needed by cellranger-3.0.2-py27_1\n\nCould not solve for environment specs\nThe following package could not be installed\n\u2514\u2500 cellranger is not installable because it requires\n \u2514\u2500 bcftools 1.9.* , which does not exist (perhaps a missing channel).\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e[ABANDON] Approach 2: Use conda from local install\u003c/h2\u003e\u003ca id=\"user-content-abandon-approach-2-use-conda-from-local-install\" class=\"anchor\" aria-label=\"Permalink: [ABANDON] Approach 2: Use conda from local install\" href=\"#abandon-approach-2-use-conda-from-local-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/hcc/cellranger\" rel=\"nofollow\"\u003ehttps://anaconda.org/hcc/cellranger\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e[FAILS] Approach 1: use Python files on clusters\u003c/h2\u003e\u003ca id=\"user-content-fails-approach-1-use-python-files-on-clusters\" class=\"anchor\" aria-label=\"Permalink: [FAILS] Approach 1: use Python files on clusters\" href=\"#fails-approach-1-use-python-files-on-clusters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e module load bioinfo-tools lz4/1.9.2 cellranger/8.0.1\npython\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eimport sys\nsys.path.append(\u0027/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/\u0027)\nfrom cellranger import *\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWith \u003ccode\u003emodule load lz4/1.9.2\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-with-module-load-lz4192\" class=\"anchor\" aria-label=\"Permalink: With module load lz4/1.9.2\" href=\"#with-module-load-lz4192\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e module load bioinfo-tools lz4/1.9.2 cellranger/8.0.1\npython\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eimport sys\nsys.path.append(\u0027/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/\u0027)\nfrom cellranger import *\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;\u0026gt;\u0026gt; import cellranger.matrix as cr_matrix\nTraceback (most recent call last):\n File \"\u0026lt;stdin\u0026gt;\", line 1, in \u0026lt;module\u0026gt;\n File \"/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/cellranger/matrix.py\", line 19, in \u0026lt;module\u0026gt;\n import cellranger.cr_io as cr_io\n File \"/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/cellranger/cr_io.py\", line 17, in \u0026lt;module\u0026gt;\n import lz4.frame as lz4\nModuleNotFoundError: No module named \u0027lz4\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHowever, there is no Python code for \u003ccode\u003elz4\u003c/code\u003e on the cluster:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham2 1.9.2]$ pwd\n/sw/libs/lz4/1.9.2\n\n[richel@rackham2 1.9.2]$ find . | grep py\n./rackham/src/contrib/debian/copyright\n./rackham/src/contrib/meson/GetLz4LibraryVersion.py\n./rackham/src/contrib/meson/InstallSymlink.py\n./rackham/src/tests/test-lz4-list.py\n./rackham/src/tests/test-lz4-speed.py\n./rackham/src/tests/test-lz4-versions.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNo module load lz4\u003c/h3\u003e\u003ca id=\"user-content-no-module-load-lz4\" class=\"anchor\" aria-label=\"Permalink: No module load lz4\" href=\"#no-module-load-lz4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e module load bioinfo-tools cellranger/8.0.1\npython\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eimport sys\nsys.path.append(\u0027/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/\u0027)\nfrom cellranger import *\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;\u0026gt;\u0026gt; import cellranger.matrix as cr_matrix\nTraceback (most recent call last):\n File \"\u0026lt;stdin\u0026gt;\", line 1, in \u0026lt;module\u0026gt;\n File \"/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/cellranger/matrix.py\", line 19, in \u0026lt;module\u0026gt;\n import cellranger.cr_io as cr_io\n File \"/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/cellranger/cr_io.py\", line 17, in \u0026lt;module\u0026gt;\n import lz4.frame as lz4\nModuleNotFoundError: No module named \u0027lz4\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThanks for the help 10x!\u003c/h2\u003e\u003ca id=\"user-content-thanks-for-the-help-10x\" class=\"anchor\" aria-label=\"Permalink: Thanks for the help 10x!\" href=\"#thanks-for-the-help-10x\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom \u003ca href=\"https://github.com/10XGenomics/cellranger/tree/main\"\u003ehttps://github.com/10XGenomics/cellranger/tree/main\u003c/a\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePlease note that this source code is made available only for informational\npurposes. 10x does not provide support for interpreting,\nmodifying, building, or running this code.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThanks 10x!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommunication\u003c/h2\u003e\u003ca id=\"user-content-communication\" class=\"anchor\" aria-label=\"Permalink: Communication\" href=\"#communication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2024-08-26\u003c/h3\u003e\u003ca id=\"user-content-2024-08-26\" class=\"anchor\" aria-label=\"Permalink: 2024-08-26\" href=\"#2024-08-26\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo me to 10X:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eI do support like you, and a user wants to use the Python code of Cell Ranger.\u003c/p\u003e\n\u003cp\u003eI\u0027ve had multiple approaches to get these Python modules to work (notes at \u003ca href=\"https://github.com/UPPMAX/ticket_297240\"\u003ehttps://github.com/UPPMAX/ticket_297240\u003c/a\u003e ), such as using the Bazel file nicely provided at \u003ca href=\"https://github.com/10XGenomics/cellranger/blob/main/conda_spec.bzl\"\u003ehttps://github.com/10XGenomics/cellranger/blob/main/conda_spec.bzl\u003c/a\u003e , yet always I got stranded at a dead end/\u003c/p\u003e\n\u003cp\u003eHow do I use the Cell Ranger Python modules? Could there be a guide published to? Or a GitHub Actions build script on the GitHub repo?\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 6, "topics": [], - "updated_at": 1723292824.0 + "updated_at": 1724654624.0 }, { "data_format": 2, - "description": null, + "description": "OSGVO image for blaylockbk", "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "nilsec/mtrack", + "full_name": "opensciencegrid/osgvo-blaylockbk", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMTrack\u003c/h1\u003e\u003ca id=\"user-content-mtrack\" class=\"anchor\" aria-label=\"Permalink: MTrack\" href=\"#mtrack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAutomatic extraction of microtubules in electron microscopy volumes of neural tissue.\u003c/p\u003e\n", + "readme": "", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 16, "topics": [], - "updated_at": 1565039518.0 + "updated_at": 1498768856.0 }, { "data_format": 2, - "description": "Demuxlet workflows for snRNA and snATAC", + "description": "Singularity container for MaxQuant in CentOS 7.", "filenames": [ - "containers/general/Singularity", - "containers/demuxlet/Singularity" + "Singularity" ], - "full_name": "arushiv/sn_demuxlet", + "full_name": "bihealth/singularity-maxquant", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNextFlow pipeline for RNA Demuxlet in demuxlet_rna; ATAC demuxlet in demuxlet_atac\u003c/h2\u003e\u003ca id=\"user-content-nextflow-pipeline-for-rna-demuxlet-in-demuxlet_rna-atac-demuxlet-in-demuxlet_atac\" class=\"anchor\" aria-label=\"Permalink: NextFlow pipeline for RNA Demuxlet in demuxlet_rna; ATAC demuxlet in demuxlet_atac\" href=\"#nextflow-pipeline-for-rna-demuxlet-in-demuxlet_rna-atac-demuxlet-in-demuxlet_atac\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Demuxlet workflows take as input pruned bam files along with the QC metrics generated from the snRNA or snATAC workflows. Bam files are split into chunks of 1000 nuclei to expedite the Demuxlet run (can change this in the main.nf). Vcf files are prepped by selecting SNPs to be tested and samples to be kept. For RNA I\u0027ve used gencode v19 gene introns+exons - ENCODE blacklist regions (this bed file is in the data folder). For ATAC I\u0027ve used gencode introns+exons - ENCODE blacklist regions + ATAC-seq peaks in the bulk/previously available snATAC cell types from the tissue of interest. This might need to be updated according to your needs.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eContainers general and demuxlet carry the software to run different processes.\u003c/li\u003e\n\u003cli\u003eRNA Demuxlet requires pruned bam files and qc files from the \u003ca href=\"https://github.com/porchard/snRNAseq-NextFlow\"\u003eRNA workflow\u003c/a\u003e as input. One way to do this is to provide the directory paths of the snRNA pruned bam directory and the list of library names so the workflow fetches bam files of the form \u003ccode\u003e${pruned_bam_dir_path}/${library}-hg19.before-dedup.bam\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eATAC Demuxlet requires pruned bam files and qc files from the \u003ca href=\"https://github.com/porchard/snATACseq-NextFlow\"\u003eATAC workflow\u003c/a\u003e as input. One way to do this is to provide the directory paths of the snATAC pruned bam directory and the list of library names so the workflow fetches bam files of the form \u003ccode\u003e${params.pruned_bam_dir}/${library}-hg19.pruned.bam\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eEdit the \u003ccode\u003enextflow.config\u003c/code\u003e that has the config parameters such as executor, container paths etc. to suit your system.\u003c/li\u003e\n\u003cli\u003eUpdate the \u003ccode\u003elibrary-config.json\u003c/code\u003e file with information about the individual libraries.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun.sh\u003c/code\u003e includes an example run command.\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMaxQuant in Singularity\u003c/h1\u003e\u003ca id=\"user-content-maxquant-in-singularity\" class=\"anchor\" aria-label=\"Permalink: MaxQuant in Singularity\" href=\"#maxquant-in-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload MaxQuant ZIP into this directory.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esudo singularity build maxquant-2.0.3.0.sif Singularity\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1586194289.0 + "updated_at": 1640476469.0 }, { "data_format": 2, - "description": null, + "description": "MXE", "filenames": [ - "containers/Singularity.0.3.5", - "containers/Singularity.0.3.3", - "containers/Singularity.0.4.1", - "containers/Singularity.0.3.6", - "containers/Singularity.0.4.0" + "Singularity" ], - "full_name": "tdalford/bilby_relative_binning", + "full_name": "richelbilderbeek/singularity_example_9", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_example_5\u003c/h1\u003e\u003ca id=\"user-content-singularity_example_5\" class=\"anchor\" aria-label=\"Permalink: singularity_example_5\" href=\"#singularity_example_5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://travis-ci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"pics/TravisCI.png\" alt=\"Travis CI logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://travis-ci.org/richelbilderbeek/singularity_example_5\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/81763108d8ea59e0425cb30af7c68be2e5b88c0872592f0cab297d542fb2d83a/68747470733a2f2f7472617669732d63692e6f72672f72696368656c62696c6465726265656b2f73696e67756c61726974795f6578616d706c655f352e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/richelbilderbeek/singularity_example_5.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSingularity example 5: Ubuntu 19.04 (disco)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3310\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1601504336.0 + "updated_at": 1564746746.0 }, { "data_format": 2, - "description": "Singularity recipe to build https://github.com/brentp/smoove", + "description": "Singularity Container for PyTorch", "filenames": [ - "Singularity" + "Singularity", + "Singularity.0.5.0" ], - "full_name": "lorenzgerber/smoove-singularity", + "full_name": "vansky/pytorch", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esmoove-singularity\u003c/h1\u003e\u003ca id=\"user-content-smoove-singularity\" class=\"anchor\" aria-label=\"Permalink: smoove-singularity\" href=\"#smoove-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePyTorch\u003c/h1\u003e\u003ca id=\"user-content-pytorch\" class=\"anchor\" aria-label=\"Permalink: PyTorch\" href=\"#pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/312\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePyTorch Version: 0.5.0\u003c/p\u003e\n\u003cp\u003eMARCC NVidia GPU and installed drivers at testing time were: K80 \u0026amp; 396.26.\u003c/p\u003e\n\u003cp\u003eHere is a display of the job submission script: \u003ccode\u003epytorch_job.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#SBATCH -N 1\n#SBATCH -n 6\n#SBATCH -p gpu\n#SBATCH --gres=gpu:1\n#SBATCH -t 1:0:0\n\nmodule load cuda/9.0\nmodule load singularity\n\ncd mnist\n\n# redefine SINGULARITY_HOME to mount current working directory to base $HOME\nexport SINGULARITY_HOME=$PWD:/home/$USER\n\nsingularity pull --name pytorch.simg shub://marcc-hpc/pytorch\nsingularity exec --nv ./pytorch.simg python main.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eClone this repository and submit a job, for example on MARCC Blue Crab:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /scratch/users/$USER\ngit clone https://github.com/marcc-hpc/pytorch.git\ncd pytorch\nsbatch pytorch_job.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease open Github issues if you interested in correcting typos, adding\nexamples, or just providing feedback!\u003c/p\u003e\n\u003cp\u003eExample taken from \u003ca href=\"https://github.com/pytorch/examples\"\u003ehttps://github.com/pytorch/examples\u003c/a\u003e (examples/mnist) LICENSE\nis reflected to show attribution; the only modifications are to point to an\nempty data folder inside the mnist folder.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1544424250.0 + "updated_at": 1590676888.0 }, { "data_format": 2, - "description": "Build Singularity containers to run SpaDES simulations on HPC clusters.", + "description": "Getting YOLO up and running on ACI", "filenames": [ - "Singularity.spades_base", - "Singularity.spades_github-development", - "Singularity.spades_github-master" + "Singularity" ], - "full_name": "UBC-FRESH/spades-singularity", + "full_name": "d-bohn/yolo_aci", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003espades-singularity\u003c/h1\u003e\u003ca id=\"user-content-spades-singularity\" class=\"anchor\" aria-label=\"Permalink: spades-singularity\" href=\"#spades-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout this project\u003c/h2\u003e\u003ca id=\"user-content-about-this-project\" class=\"anchor\" aria-label=\"Permalink: About this project\" href=\"#about-this-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project implements a scripted framework for automating the process of building Singularity containers for running SpaDES simulations on HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI am super impatient, and refuse to take the time to understand what I am doing before running any commands. Just tell me how to do the thing right now!\u003c/h2\u003e\u003ca id=\"user-content-i-am-super-impatient-and-refuse-to-take-the-time-to-understand-what-i-am-doing-before-running-any-commands-just-tell-me-how-to-do-the-thing-right-now\" class=\"anchor\" aria-label=\"Permalink: I am super impatient, and refuse to take the time to understand what I am doing before running any commands. Just tell me how to do the thing right now!\" href=\"#i-am-super-impatient-and-refuse-to-take-the-time-to-understand-what-i-am-doing-before-running-any-commands-just-tell-me-how-to-do-the-thing-right-now\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build, sign, and push the base container flavour to the cloud image repository, simply run \u003ccode\u003emake all flavour=FLAVOUR\u003c/code\u003e, where \u003ccode\u003eFLAVOUR\u003c/code\u003e is one of \u003ccode\u003ebase\u003c/code\u003e, \u003ccode\u003egithub-master\u003c/code\u003e, or \u003ccode\u003egithub-development\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNot sure which flavour to use? Read on!\u003c/p\u003e\n\u003cp\u003eNote that, if you do not have Singularity installed yet, you will need to run \u003ccode\u003emake install-singularity\u003c/code\u003e first.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity container definition files\u003c/h2\u003e\u003ca id=\"user-content-singularity-container-definition-files\" class=\"anchor\" aria-label=\"Permalink: Singularity container definition files\" href=\"#singularity-container-definition-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity container definition files follow standard Singularity definition file naming conventions (i.e., they are prefixed with \u003ccode\u003eSingularity.\u003c/code\u003e followed by a \u003cem\u003etag\u003c/em\u003e string). There are three flavours (tags) defined in this project: \u003ccode\u003ebase\u003c/code\u003e, \u003ccode\u003egithub-master\u003c/code\u003e, and \u003ccode\u003egithub-development\u003c/code\u003e. Note that the R code that installs SpaDES packages for each flavour is contained in a script named \u003ccode\u003espades-setup_flavour.R\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can also create new custom flavours by copying and modifying some files from an existing flavour. New flavours should be compatible with automated make targets (as long as you did not break the filename patterns).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBase flavour\u003c/h3\u003e\u003ca id=\"user-content-base-flavour\" class=\"anchor\" aria-label=\"Permalink: Base flavour\" href=\"#base-flavour\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe base container flavour includes the latest stable CRAN versions of core SpaDES R packages. This base can be used to run SpaDES models directly (for simpler projects, where the CRAN packages are all you need). The base image also serves as a \u003cem\u003ebootstrap\u003c/em\u003e image for other flavours. The base container flavour is implemented in \u003ccode\u003eSingularity.spades_base\u003c/code\u003e and \u003ccode\u003espades-setup_base.R\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGitHub flavours\u003c/h3\u003e\u003ca id=\"user-content-github-flavours\" class=\"anchor\" aria-label=\"Permalink: GitHub flavours\" href=\"#github-flavours\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two GitHub container flavours (\u003ccode\u003egithub-master\u003c/code\u003e, \u003ccode\u003egithub-development\u003c/code\u003e). These install core SpaDES R packages from the latest code pushed to GitHub repositories for \u003ccode\u003emaster\u003c/code\u003e and \u003ccode\u003edevelopment\u003c/code\u003e branches, respectively. The GitHub container flavours are implemented in the \u003ccode\u003eSingularity.spades-github_BRANCH\u003c/code\u003e and \u003ccode\u003espades-setup_github-BRANCH\u003c/code\u003e (where \u003ccode\u003eBRANCH\u003c/code\u003e is one of \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edevelopment\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThe GitHub container flavours are \u003cem\u003ebootstrapped\u003c/em\u003e from the base container flavour. Defintion file implementation assumes that a local base container image is available in path \u003ccode\u003ebuild/spades.sif\u003c/code\u003e, so the base container must be built first (the base container will automatically get built if not present if you run \u003ccode\u003emake build flavour=FLAVOUR\u003c/code\u003e, where \u003ccode\u003eFLAVOUR\u003c/code\u003e is any value except for \u003ccode\u003ebase\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCustom flavours\u003c/h3\u003e\u003ca id=\"user-content-custom-flavours\" class=\"anchor\" aria-label=\"Permalink: Custom flavours\" href=\"#custom-flavours\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can create a custom container flavour but copying \u003ccode\u003eSingularity.spades_github-master\u003c/code\u003e and \u003ccode\u003espades-setup_github-master.R\u003c/code\u003e---rename these to \u003ccode\u003eSingularity.spades_foo\u003c/code\u003e and \u003ccode\u003espades-setup_foo.R\u003c/code\u003e (where \u003ccode\u003efoo\u003c/code\u003e is whatever unique flavour name you want) and modify as required. Minimally, you just need to edit one line of code in the Singularity definition file to point to \u003ccode\u003espades-setup_foo.R\u003c/code\u003e, and edit the code in \u003ccode\u003espades-setup_foo.R\u003c/code\u003e to install whatever versions of SpaDES R packages you need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMakefile details\u003c/h2\u003e\u003ca id=\"user-content-makefile-details\" class=\"anchor\" aria-label=\"Permalink: Makefile details\" href=\"#makefile-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eMakefile\u003c/code\u003e implements a number of make targets.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake build flavour=FLAVOUR sandbox=true\u003c/code\u003e to build a sandbox container (in \u003ccode\u003ebuild/spades_FLAVOUR_sandbox\u003c/code\u003e). See Singularity documentation for details on sandbox containers.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake build flavour=FLAVOUR\u003c/code\u003e to build a container as a single \u003cem\u003esingularity image file\u003c/em\u003e (in \u003ccode\u003ebuild/spades_FLAVOUR.sif\u003c/code\u003e). See Singularity documentation for details on SIF containers.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake push flavour=FLAVOUR\u003c/code\u003e to sign your SIF image and push it to your Sylabs cloud image library account. See the \u003ca href=\"https:%5Ccloud.sylabs.io\"\u003eSylabs Container Library\u003c/a\u003e to create and configure your account.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake all flavour=FLAVOUR\u003c/code\u003e to build and push your image in one step.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eYOLO_aci\u003c/h1\u003e\u003ca id=\"user-content-yolo_aci\" class=\"anchor\" aria-label=\"Permalink: YOLO_aci\" href=\"#yolo_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and workflow for building \u003ca href=\"https://pjreddie.com/darknet/yolo/\" rel=\"nofollow\"\u003eYOLO\u003c/a\u003e\nin Docker Hub and modifying it with Singularity Hub for use with PSU\nACI HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand line\u003c/h3\u003e\u003ca id=\"user-content-command-line\" class=\"anchor\" aria-label=\"Permalink: Command line\" href=\"#command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eImages\u003c/h4\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eVideos\u003c/h4\u003e\u003ca id=\"user-content-videos\" class=\"anchor\" aria-label=\"Permalink: Videos\" href=\"#videos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights \u0026lt;video file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython3\u003c/h3\u003e\u003ca id=\"user-content-python3\" class=\"anchor\" aria-label=\"Permalink: Python3\" href=\"#python3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eImages\u003c/h4\u003e\u003ca id=\"user-content-images-1\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython yolo.py --image /path/to/image.jpg --yolo yolo-coco\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eVideo\u003c/h4\u003e\u003ca id=\"user-content-video\" class=\"anchor\" aria-label=\"Permalink: Video\" href=\"#video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython3 yolo_video.py --input /path/to/vid.mp4 \\\n\t--output /path/to/output/save.avi \\\n\t--write_res /path/to/write/results.txt \\\n\t--yolo yolo-coco\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1716933506.0 + "updated_at": 1561693683.0 }, { "data_format": 2, - "description": "metarepo for tidying up container recipes, currently Singularity", + "description": "Testing conversion", "filenames": [ - "ubuntu/Singularity.ubuntu2004", - "pacbio/Singularity.pacbio", - "base/Singularity.base", - "r/Singularity.r", - "r/Singularity.r-plus", - "starcode/Singularity.starcode-v0.1.1", - "jupyter/Singularity.jupyter-plus-alignparse", - "jupyter/Singularity.jupyter-plus-bioconda", - "jupyter/Singularity.jupyter-plus", - "jupyter/Singularity.jupyter-plus-tensorflow-v2.5.0-compiled", - "jupyter/Singularity.jupyter", - "jupyter/Singularity.jupyter-plus-tensorflow-v2.2.0-compiled", - "jupyter/Singularity.jupyter-plus-tensorflow-v2.4.0-rc4-compiled", - "shell/Singularity.shell-plus", - "tensorflow/Singularity.tensorflow-v2.5.0-compiled", - "tensorflow/Singularity.tensorflow-v2.0.3-compiled", - "tensorflow/Singularity.tensorflow-v1.15.4-compiled-partial", - "tensorflow/Singularity.tensorflow-v2.4.0-rc4-compiled", - "tensorflow/Singularity.tensorflow-v2.2.0-compiled", - "bioinfmunger/Singularity.bioinfmunger", - "lh3-aligners/Singularity.lh3-aligners", - "bioconda/Singularity.bioconda" + "Singularity" ], - "full_name": "darachm/containers2", + "full_name": "lalet/Singularity-hcp-prefreesurfer", "latest_release": null, - "readme": "\u003cp\u003eThis is for tracking, hosting recipes for Singularity containers, such that\nit can get mirrored on Github and singularity-hub can get it.\u003c/p\u003e\n\u003cp\u003eOrganzation copied from \u003ca href=\"https://github.com/jlboat/BioinfoContainers\"\u003ejlboat\u003c/a\u003e.\n(Of course, makes total sense to just use tags to organize things!)\u003c/p\u003e\n\u003cp\u003eSome recipes are for individual tools, some are for workflows and so are\ncombos.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity-hcp-prefreesurfer\u003c/h1\u003e\u003ca id=\"user-content-singularity-hcp-prefreesurfer\" class=\"anchor\" aria-label=\"Permalink: Singularity-hcp-prefreesurfer\" href=\"#singularity-hcp-prefreesurfer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting conversion\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1641869104.0 + "subscribers_count": 2, + "topics": [ + "containers", + "singularity", + "docker" + ], + "updated_at": 1521423231.0 }, { "data_format": 2, - "description": null, + "description": "T1 mapping BIDS app", "filenames": [ "Singularity" ], - "full_name": "cfe-lab/proviral", - "latest_release": "v2.3.4", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReadme\u003c/h2\u003e\u003ca id=\"user-content-readme\" class=\"anchor\" aria-label=\"Permalink: Readme\" href=\"#readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eminimap2 (\u003ca href=\"https://github.com/lh3/minimap2\"\u003ehttps://github.com/lh3/minimap2\u003c/a\u003e) (must be available via commandline)\u003c/li\u003e\n\u003cli\u003eblast tools (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)\u003c/li\u003e\n\u003cli\u003eR and RSCRIPT (\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003ehttps://www.r-project.org/\u003c/a\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity builds\u003c/h3\u003e\u003ca id=\"user-content-singularity-builds\" class=\"anchor\" aria-label=\"Permalink: Singularity builds\" href=\"#singularity-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBuild all singularity images inside of the \u003ccode\u003esimages\u003c/code\u003e folder\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFiltering\u003c/h3\u003e\u003ca id=\"user-content-filtering\" class=\"anchor\" aria-label=\"Permalink: Filtering\" href=\"#filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAt the core of the proviral pipeline, data is read from \u003ccode\u003econtigs.csv\u003c/code\u003e and \u003ccode\u003econseqs.csv\u003c/code\u003e files produced by MiCall\u003c/li\u003e\n\u003cli\u003eFirst the pipeline reads through all of the contigs, then the contigs\u003c/li\u003e\n\u003cli\u003eWhen it does this (see the \u003ccode\u003efind_primers()\u003c/code\u003e function) it applies the following logic in this order for filtering/tagging:\n\u003col\u003e\n\u003cli\u003eIf a sample is not proviral, skip it. Do not attempt to find primers or anything, just log a message saying \u003ccode\u003esample X was skipped because it was non-proviral\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf a sample has 0 in the remap column of the \u003ccode\u003ecascade.csv\u003c/code\u003e file, tag that sequence with an error: \u003ccode\u003eNo contig/conseq constructed\u003c/code\u003e, do not analyze it or try to find primers, and write it to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file (which records all failures)\u003c/li\u003e\n\u003cli\u003eIf the \u003ccode\u003econsensus-percent-cutoff\u003c/code\u003e is NOT \u003ccode\u003eMAX\u003c/code\u003e, tag it with an error: \u003ccode\u003econtig not MAX\u003c/code\u003e and skip the sequence (do not try to find primers)\u003c/li\u003e\n\u003cli\u003eIf the reference of the sample is \u003ccode\u003eHIV1-CON-XX-Consensus-seed\u003c/code\u003e tag that sequence with an error: \u003ccode\u003eis V3 sequence\u003c/code\u003e, skip the sequence (do not try to find primers), and write it to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eIf there is an \u003ccode\u003eX\u003c/code\u003e in the middle of the sequence, tag that sequence with an error: \u003ccode\u003elow internal read coverage\u003c/code\u003e, skip the sequence (do not try to find primers), and write it to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eIf there are ANY non-TCGA characters in the sequence, tag that sequence with an error: \u003ccode\u003econtig sequence contained non-TCGA/gap\u003c/code\u003e, skip the sequence (do not try to find primers), and write it to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eFor each end (5\u0027 (fwd), 3\u0027 (rev)) of the sequence:\n\u003col\u003e\n\u003cli\u003eIf there are \u003ccode\u003eX\u003c/code\u003e characters found, try to remove them (if they are clustered) and if not possible to remove tag the fwd/rev end with a fwd/rev primer error: \u003ccode\u003elow read coverage in primer region\u003c/code\u003e, skip the fwd/rev end (do not try to find primers)\u003c/li\u003e\n\u003cli\u003eIf fwd/rev end has zero nucleotides found for primer, tag the fwd/rev end with a fwd/rev primer error: \u003ccode\u003eprimer was not found\u003c/code\u003e, skip to the next end if any\u003c/li\u003e\n\u003cli\u003eIf the fwd/rev primer is deemed not valid, tag the fwd/rev end with a fwd/rev primer error: \u003ccode\u003eprimer failed secondary validation\u003c/code\u003e, skip to the next end if any\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eWrite the sequence to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file regardless of tagged errors in any error column\u003c/li\u003e\n\u003cli\u003eLoad the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e files for both contigs and conseqs and for both of them apply the following filters in order:\n\u003col\u003e\n\u003cli\u003eRemove all rows where either the \u003ccode\u003eerror\u003c/code\u003e, \u003ccode\u003efwd_error\u003c/code\u003e, or \u003ccode\u003erev_error\u003c/code\u003e is tagged\u003c/li\u003e\n\u003cli\u003eRemove the primers from the sequences (for hivseqinr)\u003c/li\u003e\n\u003cli\u003eRemove rows where sample name appears twice (duplicates)\u003c/li\u003e\n\u003cli\u003eRemove rows where the reference contains \u003ccode\u003eunknown\u003c/code\u003e or \u003ccode\u003ereverse\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eFinally merge the filtered contigs and conseqs and write the final \u003ccode\u003e*filtered.csv\u003c/code\u003e file with conseqs taking precedence over contigs\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "khanlab/despot1", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edespot1: DESPOT1-HIFI T1 mapping BIDS app\u003c/h1\u003e\u003ca id=\"user-content-despot1--despot1-hifi-t1-mapping-bids-app\" class=\"anchor\" aria-label=\"Permalink: despot1: DESPOT1-HIFI T1 mapping BIDS app\" href=\"#despot1--despot1-hifi-t1-mapping-bids-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs:\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs:\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExample BIDS-like naming (acq- flag and DESPOT suffix are \u003cem\u003erequired\u003c/em\u003e):\nsub-01/anat/sub-01_acq-SPGR_flip-1_DESPOT.nii.gz\nsub-01/anat/sub-01_acq-SPGR_flip-2_DESPOT.nii.gz\nsub-01/anat/sub-01_acq-IRSPGR_DESPOT.nii.gz\u003c/p\u003e\n\u003cp\u003eThe SPGR json files must include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRepetitionTime\u003c/li\u003e\n\u003cli\u003eFlipAngle\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe IRSPGR json files must include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRepetitionTime\u003c/li\u003e\n\u003cli\u003eInversionTime\u003c/li\u003e\n\u003cli\u003eFlipAngle\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following variables are hardcoded in run.sh (need to be modified if they are different for your sequence):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enpulse=78 #readout pulses following inversion\nfield=3 #field strength\ninvmode=2 #number of inversions per slice\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverall pipeline:\u003c/h2\u003e\u003ca id=\"user-content-overall-pipeline\" class=\"anchor\" aria-label=\"Permalink: Overall pipeline:\" href=\"#overall-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eParse and extract parameters from JSON files\u003c/li\u003e\n\u003cli\u003eRigidly register images to the first SPGR (flirt)\u003c/li\u003e\n\u003cli\u003ePre-process images prior to C code (nifti -\u0026gt; analyze)\u003c/li\u003e\n\u003cli\u003eRun DESPOT1-HIFI fitting using pre-compiled C code (src from Sean Deoni)\u003c/li\u003e\n\u003cli\u003ePost-process T1map, B1map, M0map images after C code (analyze -\u0026gt; nifti)\u003c/li\u003e\n\u003cli\u003eGenerate synthetic T1w map (simple octave script)\u003c/li\u003e\n\u003cli\u003eGenerate hard and soft brain masks from M0map\n\u003cul\u003e\n\u003cli\u003eHard mask from bet\u003c/li\u003e\n\u003cli\u003eSoft mask generated by N4 on bet M0map, then 10th percentile cutoff (hard above, 0-1 below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eApply soft mask to T1map, T1w, and B1map (to downweight high-intensity boundary voxels), and hard mask to M0map\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput:\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\u2500\u2500 OUT_FOLDER\n \u2514\u2500\u2500 sub-P004\n \u2514\u2500\u2500 anat\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_B1map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_M0map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_proc-masked_B1map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_proc-masked_M0map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_proc-masked_T1map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_proc-masked_T1w.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_T1map.nii.gz\n \u2514\u2500\u2500 sub-P004_acq-DESPOT_T1w.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn \u003ccode\u003ework\u003c/code\u003e folder with intermediate files will also be generated\u003c/p\u003e\n\u003cp\u003eTo do:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esimilar app for DESPOT2-FM mapping..\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 2, "topics": [], - "updated_at": 1655928986.0 + "updated_at": 1591844418.0 }, { "data_format": 2, - "description": "Prokka: rapid prokaryotic genome annotation.", + "description": "Singularity container for MitoGraph.", "filenames": [ - "1.14.5/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-prokka", + "full_name": "mcw-rcc/mitograph", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emitograph\u003c/h1\u003e\u003ca id=\"user-content-mitograph\" class=\"anchor\" aria-label=\"Permalink: mitograph\" href=\"#mitograph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe and modulefile for running Mitograph on a Linux system. This requires Singularity to build the container file. You can adapt the modulefile for your needs or run the analysis from the command line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Mitograph container\u003c/h2\u003e\u003ca id=\"user-content-run-mitograph-container\" class=\"anchor\" aria-label=\"Permalink: Run Mitograph container\" href=\"#run-mitograph-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec mitograph.sif input_file\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [ - "singularity", - "bioinformatics" + "singularity-container" ], - "updated_at": 1624982164.0 + "updated_at": 1699631258.0 }, { "data_format": 2, - "description": "An example repository to deploy multiple containers to a Singularity Registry Server from CircleCI", + "description": "FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. ", "filenames": [ - "vanessa/greeting/Singularity.tag", - "vanessa/greeting/Singularity" + "0.11.9/Singularity", + "0.12.1/Singularity" ], - "full_name": "singularityhub/circle-ci-sregistry", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Builder Circle-CI\u003c/h1\u003e\u003ca id=\"user-content-singularity-builder-circle-ci\" class=\"anchor\" aria-label=\"Permalink: Singularity Builder Circle-CI\" href=\"#singularity-builder-circle-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".circleci/sregistry-circle.png\"\u003e\u003cimg src=\".circleci/sregistry-circle.png\" alt=\".circleci/sregistry-circle.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a simple example of how you can achieve:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eversion control of your recipes\u003c/li\u003e\n\u003cli\u003eversioning to include image hash \u003cem\u003eand\u003c/em\u003e commit id\u003c/li\u003e\n\u003cli\u003ebuild of associated container and\u003c/li\u003e\n\u003cli\u003epush to a storage endpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003efor a reproducible build workflow. Specifically, this example will use a \u003cem\u003esingle repository\u003c/em\u003e\nas a base to build \u003cem\u003emultiple containers\u003c/em\u003e and push to a shared \u003ca href=\"https://www.github.com/singularityhub/sregistry\"\u003eSingularity Registry Server\u003c/a\u003e based on the namespace organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should this be managed via Github?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGithub, by way of easy integration with continuous integration, is an easy way\nto have a workflow set up where multiple people can collaborate on a container recipe,\nthe recipe can be tested (with whatever testing you need), discussed in pull requests,\nand then finally pushed to your storage of choice or Singularity Registry.\nImportantly, you don\u0027t need to give your entire team manager permissions\nto the registry. An encrypted credential that only is accessible to\nadministrators can do the push upon merge of a discussed change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should I use this instead of a service?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou could use a remote builder, but if you do the build in a continuous integration\nservice you get complete control over it. This means everything from the version of\nSingularity to use, to the tests that you run for your container. You have a lot more\nfreedom in the rate of building, and organization of your repository, because it\u0027s you\nthat writes the configuration. Although the default would work for most, you can\nedit the build, setup, and circle configuration file in the\n\u003ca href=\".circleci\"\u003e.circleci\u003c/a\u003e folder to fit your needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/singularityhub/circle-ci-sregistry\"\u003ecircle-ci-sregistry\u003c/a\u003e repository is\nan example repository that will allow you to store multiple recipes within, and then deploy\nto a \u003ca href=\"https://www.github.com/singularityhub/sregistry\"\u003eSingularity Registey Server\u003c/a\u003e.\nWe use CircleCI to build and push to your Singularity Registry. You have the freedom\nto store as many recipes in one repository as you please, with the understanding that one\nrepository maps to one builder on CircleCI (in terms of time allowed). However, you should\nalso realize that since the build and deploy happens with pull requests, you can have the bulids\ngoing in parallel (up to the time limit, of course). You are also free to have multiple repositories\nto deploy separate containers, but you would then need to ensure that the namespaces (the folders\nnamed inside that map to collection names) do not overlap.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Setup\u003c/h3\u003e\u003ca id=\"user-content-1-setup\" class=\"anchor\" aria-label=\"Permalink: 1. Setup\" href=\"#1-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo deploy this template for your registry you can:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFork or download \u003ca href=\"https://www.github.com/singularityhub/circle-ci-sregistry\"\u003esingularityhub/circle-ci-sregistry\u003c/a\u003e to your own GitHub account. Since the container namespace comes from the folders within, the name of the repository itself is not incredibly important.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://circleci.com/docs/2.0/getting-started/#setting-up-your-build-on-circleci\" rel=\"nofollow\"\u003eConnect your repository\u003c/a\u003e to CircleCI\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Adding Containers\u003c/h3\u003e\u003ca id=\"user-content-2-adding-containers\" class=\"anchor\" aria-label=\"Permalink: 2. Adding Containers\" href=\"#2-adding-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow does building work? Each folder represents a namespace. For example, the folder \u003ccode\u003evanessa/greeting\u003c/code\u003e maps to a container collection \u003ccode\u003evanessa/greeting\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAdd a New Container\u003c/h4\u003e\u003ca id=\"user-content-add-a-new-container\" class=\"anchor\" aria-label=\"Permalink: Add a New Container\" href=\"#add-a-new-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis means that to add a new container collection namespace, just create a folder for it.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir -p vanessa/greeting\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHow do tags work? The tags within the folder correspond to the tags for the container namespace. For example, here\nis how to create the tag \"pancakes\" for the container collection \"vanessa/greeting.\"\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ touch vanessa/greeting/Singularity.pancakes\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity file without any tags maps to the tag \"latest\"\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ touch vanessa/greeting/Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThat\u0027s it! Write your recipe there, and then open a pull request to build the container. Once the container is built, you need to approve the Hold in the continuous integration, and then the container will be pushed.\nMerging (or generally pushing to master) doesn\u0027t do any deployment. All deployments must happen\nthrough this pull request and approve process.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFreezing a Container\u003c/h4\u003e\u003ca id=\"user-content-freezing-a-container\" class=\"anchor\" aria-label=\"Permalink: Freezing a Container\" href=\"#freezing-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want a container collection to build, just put a .frozen file in the collection folder.\nIf you want to freeze the entire collection namespace, just put the .frozen file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003etouch vanessa/greeting/.frozen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to freeze a particular container, add an equivalently named empty file with frozen as\nan extension.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003etouch vanessa/greeting/Singularity.pancakes.frozen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s a very manual way of doing it, but importantly, the status of your building is\nreflected in the repository (version controlled!).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCustom Build for a Container\u003c/h4\u003e\u003ca id=\"user-content-custom-build-for-a-container\" class=\"anchor\" aria-label=\"Permalink: Custom Build for a Container\" href=\"#custom-build-for-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to custom build a container, just add a build.sh file to the directory with the recipe.\nIt will be used instead of the default build.sh provided with the repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Connect to CircleCI\u003c/h3\u003e\u003ca id=\"user-content-3-connect-to-circleci\" class=\"anchor\" aria-label=\"Permalink: 3. Connect to CircleCI\" href=\"#3-connect-to-circleci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you go to your \u003ca href=\"https://circleci.com/dashboard\" rel=\"nofollow\"\u003eCircle Dashboard\u003c/a\u003e you can usually select a Github organization (or user) and then the repository, and then click the toggle button to activate it to build on commit --\u0026gt; push.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. CircleCI Environment\u003c/h3\u003e\u003ca id=\"user-content-4-circleci-environment\" class=\"anchor\" aria-label=\"Permalink: 4. CircleCI Environment\" href=\"#4-circleci-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to communicate with your Singularity Registry Server, you should generate a\ntoken (a credential to push) in your $HOME/.sregistry file. Then you should add the entire\ncontents of this file to an encrypted CircleCI environment variable (just copy paste in the entire thing)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat $HOME/.sregistry\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewrite this to the environment variable \u003ccode\u003eSREGISTRY_CREDENTIALS\u003c/code\u003e in CircleCI.\nIf you don\u0027t define this variable, the builds will happen, but the deploy will\nbe skipped.\u003c/p\u003e\n\u003cp\u003eThat should be it! You should then open pull requests to build containers,\nand then approve the Holds in the CircleCI interface to push to your registry. For example,\nhere is the workflow view right after a hold was approved (notice that the deploy step is\nrunning):\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".circleci/hold.png\"\u003e\u003cimg src=\".circleci/hold.png\" alt=\".circleci/hold.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAnd here is when the deploy is done!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".circleci/deploy.png\"\u003e\u003cimg src=\".circleci/deploy.png\" alt=\".circleci/deploy.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can check what will be deployed (and the command used) in the Build step, it will\nlook something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSREGISTRY_CLIENT=registry sregistry push --name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evanessa/greeting:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/home/circleci/repo/vanessa/greeting/Singularity.sif\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nSREGISTRY_CLIENT=registry sregistry push --name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evanessa/greeting:tag\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/home/circleci/repo/vanessa/greeting/Singularity.tag.sif\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNotice how the container namespace reflects the folder organization provided in\nthe repository here!\u003c/p\u003e\n\u003cp\u003eIf you are interested in learning more about CircleCI (extra features!) continue\nreading below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExtra: Get to Know CircleCi\u003c/h3\u003e\u003ca id=\"user-content-extra-get-to-know-circleci\" class=\"anchor\" aria-label=\"Permalink: Extra: Get to Know CircleCi\" href=\"#extra-get-to-know-circleci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs we are working with \u003ca href=\"https://www.circleci.com\" rel=\"nofollow\"\u003eCircle CI\u003c/a\u003e, here are some other features\nthat might be of interest.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCircle offers \u003ca href=\"https://support.circleci.com/hc/en-us/articles/115015481128-Scheduling-jobs-cron-for-builds-\" rel=\"nofollow\"\u003escheduled builds\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eCircleCI also offers \u003ca href=\"https://circleci.com/docs/enterprise/gpu-configuration/\" rel=\"nofollow\"\u003eGPU Builders\u003c/a\u003e if you want/need that sort of thing.\u003c/li\u003e\n\u003cli\u003eIf you don\u0027t want to use the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry\u003c/a\u003e to push to Google Storage, Drive, Globus, Dropbox, or your personal Singularity Registry, CircleCI will upload your artifacts directly to your \u003ca href=\"https://circleci.com/docs/2.0/deployment-integrations/#section=deployment\" rel=\"nofollow\"\u003edeployment\u003c/a\u003e location of choice.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "pscedu/singularity-fastqc", + "latest_release": "v0.12.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-FastQC/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-FastQC/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-FastQC/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-FastQC/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c8d644d0b2e64f21fb4cc1192ab1921c4633b7d5c7390c6bbeabe858d9b378fb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666173747163\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8d644d0b2e64f21fb4cc1192ab1921c4633b7d5c7390c6bbeabe858d9b378fb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666173747163\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-fastqc\" 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href=\"https://camo.githubusercontent.com/c64e867a78719d38b0be4bd1d4fa1fa248f47748a1f94a50a34663859db5cbc4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666173747163\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c64e867a78719d38b0be4bd1d4fa1fa248f47748a1f94a50a34663859db5cbc4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666173747163\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fastqc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bb023ccb6e3a745b5603ada1d21ff3936a69a2654e09c85b2cf69a041f4e0d1b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666173747163\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bb023ccb6e3a745b5603ada1d21ff3936a69a2654e09c85b2cf69a041f4e0d1b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666173747163\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fastqc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-fastqc\u003c/h1\u003e\u003ca id=\"user-content-singularity-fastqc\" class=\"anchor\" aria-label=\"Permalink: singularity-fastqc\" href=\"#singularity-fastqc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cad1f53832db2e5156a4bf58563606936b992127e490293214ae663ce59933f0/68747470733a2f2f65787465726e616c2d636f6e74656e742e6475636b6475636b676f2e636f6d2f69752f3f753d68747470732533412532462532467777772e62696f696e666f726d61746963732e626162726168616d2e61632e756b25324670726f6a656374732532466661737471632532466661737471632e706e6726663d31266e6f66623d31\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cad1f53832db2e5156a4bf58563606936b992127e490293214ae663ce59933f0/68747470733a2f2f65787465726e616c2d636f6e74656e742e6475636b6475636b676f2e636f6d2f69752f3f753d68747470732533412532462532467777772e62696f696e666f726d61746963732e626162726168616d2e61632e756b25324670726f6a656374732532466661737471632532466661737471632e706e6726663d31266e6f66623d31\" alt=\"Screenshot\" data-canonical-src=\"https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fwww.bioinformatics.babraham.ac.uk%2Fprojects%2Ffastqc%2Ffastqc.png\u0026amp;f=1\u0026amp;nofb=1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efastqc\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/FastQC/0.11.9\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/FastQC\u003c/code\u003e as \u003ccode\u003e0.11.9.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [ "singularity", - "sregistry", - "builder-repository" + "bioinformatics" ], - "updated_at": 1550648627.0 + "updated_at": 1649274180.0 }, { "data_format": 2, - "description": "pacbio tools", + "description": "Singularity container", "filenames": [ - "singularity/Singularity.v2", - "singularity/Singularity.v3", - "singularity/Singularity.v1" + "Singularity.def" ], - "full_name": "cokelaer/pacbio4all", + "full_name": "ParthDoshi97/bioinformatic_tools_container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epacbio4all\u003c/h1\u003e\u003ca id=\"user-content-pacbio4all\" class=\"anchor\" aria-label=\"Permalink: pacbio4all\" href=\"#pacbio4all\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA container with some of the pacbio tools. This is for Singularity 2.4 at least !\u003c/p\u003e\n\u003cp\u003e::\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name pacbio.img shub://cokelaer/pacbio4all:v2\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Container for Bioinformatics Tools\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-bioinformatics-tools\" class=\"anchor\" aria-label=\"Permalink: Singularity Container for Bioinformatics Tools\" href=\"#singularity-container-for-bioinformatics-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity container is designed to provide a comprehensive suite of bioinformatics tools for quality control, read trimming, alignment, and post-alignment analysis. The container is based on Ubuntu 20.04 and includes commonly used tools such as FastQC, MultiQC, Trimmomatic, STAR, Bowtie2, HISAT2, SAMtools, PICARD, HOMER, MACS2, and MEME Suite.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Control Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eFastQC\u003c/strong\u003e: A quality control tool for high throughput sequence data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMultiQC\u003c/strong\u003e: Aggregates results from bioinformatics analyses into a single report.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRead Trimming and Filtering Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTrimmomatic\u003c/strong\u003e: A flexible read trimming tool for Illumina NGS data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFastx Toolkit\u003c/strong\u003e: A collection of command line tools for Short-Reads FASTA/FASTQ files preprocessing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAlignment Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSTAR\u003c/strong\u003e: A fast RNA-seq read mapper.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBowtie2\u003c/strong\u003e: A fast and sensitive gapped read aligner.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHISAT2\u003c/strong\u003e: A fast and sensitive alignment program for mapping next-generation sequencing reads.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePost-Alignment Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSAMtools\u003c/strong\u003e: A suite of programs for interacting with high-throughput sequencing data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePICARD\u003c/strong\u003e: A set of command line tools for manipulating high-throughput sequencing data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHOMER\u003c/strong\u003e: A suite of tools for Motif Discovery and next-generation sequencing analysis.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMACS2\u003c/strong\u003e: Model-based Analysis of ChIP-Seq data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMEME Suite\u003c/strong\u003e: A collection of tools for the discovery and analysis of sequence motifs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the Singularity container, use the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build bioinformatics.sif Singularity.def\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the container, use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e bioinformatics.sif \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecommand\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor example, to run FastQC on a file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e bioinformatics.sif fastqc sample.fastq\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIncluded Tools and Versions\u003c/h2\u003e\u003ca id=\"user-content-included-tools-and-versions\" class=\"anchor\" aria-label=\"Permalink: Included Tools and Versions\" href=\"#included-tools-and-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eUbuntu\u003c/strong\u003e: 20.04\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePerl\u003c/strong\u003e: 5.40.0\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFastQC\u003c/strong\u003e: 0.12.1\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMultiQC\u003c/strong\u003e: Latest via pip\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTrimmomatic\u003c/strong\u003e: 0.39\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFastx Toolkit\u003c/strong\u003e: 0.0.13\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSTAR\u003c/strong\u003e: 2.7.11b\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBowtie2\u003c/strong\u003e: 2.4.2\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHISAT2\u003c/strong\u003e: Latest from GitHub\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSAMtools\u003c/strong\u003e: 1.10\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePICARD\u003c/strong\u003e: 2.23.9\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHOMER\u003c/strong\u003e: Latest via Perl script\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMACS2\u003c/strong\u003e: Latest via pip\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMEME Suite\u003c/strong\u003e: 5.5.5\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnvironment Variables\u003c/h2\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container sets the following environment variables:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e DEBIAN_FRONTEND=noninteractive\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/opt/STAR-2.7.11b/bin/Linux_x86_64_static:/opt/hisat2:/opt/bowtie2:/opt/samtools-1.10:/usr/local/bin:/opt/FastQC:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/root/homer/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/opt/local/bin:/opt/local/libexec/meme-5.5.5:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthor\u003c/h2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eParth Doshi\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersion\u003c/h2\u003e\u003ca id=\"user-content-version\" class=\"anchor\" aria-label=\"Permalink: Version\" href=\"#version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container includes tools for quality control, read trimming, alignment, and post-alignment analysis in bioinformatics.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLabels\u003c/h2\u003e\u003ca id=\"user-content-labels\" class=\"anchor\" aria-label=\"Permalink: Labels\" href=\"#labels\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAuthor\u003c/strong\u003e: Parth Doshi\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVersion\u003c/strong\u003e: 1.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e\nThis README provides a clear and concise overview of the container, its features, installation instructions, usage examples, included tools and versions, environment variables, and author information. Adjust the placeholders such as `YourName` and any other details as necessary.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1508516491.0 + "updated_at": 1721847941.0 }, { "data_format": 2, "description": null, "filenames": [ - "Recipes/Singularity_example", - "Recipes/Singularity_ompi", - "Recipes/Singularity_pytorch", - "Recipes/Singularity_mpich", - "Recipes/Singularity_spark_full", - "Recipes/Singularity_spark", - "Recipes/Singularity_tensorflow", - "Recipes/Singularity_pytorch_full" + "Singularity.samtools_1.10", + "Singularity.syri_2aff3ba", + "Singularity.blast_2.2.31", + "Singularity.salmontools_23eac84", + "Singularity.star_2.7.6a", + "Singularity.ngmlr_8d76779", + "Singularity.muscle_3.8.1551", + "Singularity.minimap2_2.17r941", + "Singularity.samblaster_0.1.24" ], - "full_name": "souzaitor/HPC-projects", + "full_name": "TomHarrop/align-utils", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePr\u00e9-requisitos\u003c/h1\u003e\u003ca id=\"user-content-pr\u00e9-requisitos\" class=\"anchor\" aria-label=\"Permalink: Pr\u00e9-requisitos\" href=\"#pr\u00e9-requisitos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSele\u00e7\u00e3o do Recipe\u003c/h2\u003e\u003ca id=\"user-content-sele\u00e7\u00e3o-do-recipe\" class=\"anchor\" aria-label=\"Permalink: Sele\u00e7\u00e3o do Recipe\" href=\"#sele\u00e7\u00e3o-do-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdicione o caminho do \u003cem\u003esingularity recipe\u003c/em\u003e desejado na vari\u00e1vel RECIPE no github (projeto \u0026gt;\u0026gt; Settings \u0026gt;\u0026gt; Secrets \u0026gt;\u0026gt; New Secret).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTemplate Integra\u00e7\u00e3o Cont\u00ednua Github\u003c/h1\u003e\u003ca id=\"user-content-template-integra\u00e7\u00e3o-cont\u00ednua-github\" class=\"anchor\" aria-label=\"Permalink: Template Integra\u00e7\u00e3o Cont\u00ednua Github\" href=\"#template-integra\u00e7\u00e3o-cont\u00ednua-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEsse projeto o template para uso do cluster da UFSCar, contendo integra\u00e7\u00e3o cont\u00ednua com o Google Drive e Amazon S3.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequisitos para Google Drive\u003c/h2\u003e\u003ca id=\"user-content-requisitos-para-google-drive\" class=\"anchor\" aria-label=\"Permalink: Requisitos para Google Drive\" href=\"#requisitos-para-google-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eEntre no \u003ca href=\"https://console.developers.google.com/apis/credentials\" rel=\"nofollow\"\u003econsole de credenciais de API do Google\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSe ainda n\u00e3o houver um projeto, crie um.\u003c/li\u003e\n\u003cli\u003eEm \"Ativar API e Servi\u00e7os\", busque por \"Google Drive\" e ative a permiss\u00e3o.\u003c/li\u003e\n\u003cli\u003eClique em \"Criar credenciais\".\u003c/li\u003e\n\u003cli\u003eSelecione \"ID do cliente do OAuth\".\u003c/li\u003e\n\u003cli\u003eEm \"Tipo de aplicativo\", selecione \"App para computador\".\u003c/li\u003e\n\u003cli\u003eD\u00ea um nome de identifica\u00e7\u00e3o para as credenciais e clique em \"criar\". V\u00e3o aparecer dois dados (\"Seu ID de cliente\" e \"Sua chave secreta de cliente\"), precisaremos dos dois no passo seguinte.\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 17 # selecione aqui o n\u00famero correspondente a op\u00e7\u00e3o \"Google Drive\"\nclient_id\u0026gt; conte\u00fado de \"Seu ID de cliente\"\nclient_secret\u0026gt; conte\u00fado de \"Sua chave secreta de cliente\"\nscope\u0026gt; 1 # Full access all files, excluding Application Data Folder\nroot_folder_id\u0026gt; deixe em branco\nservice_account_file\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/n\u0026gt; n\n\nSe j\u00e1 tiver o rclone instalado em seu computador, execute o comando exibido, caso contr\u00e1rio, o instale conforme indicado na p\u00e1gina oficial do rclone dependendo do seu sistema operacional (https://rclone.org/install/). A Seguir insira o resultado do comando exibido:\n\nconfig_token\u0026gt; c\u00f3digo fornecido pelo terminal ap\u00f3s autoriza\u00e7\u00e3o\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequisitos para Amazon S3\u003c/h2\u003e\u003ca id=\"user-content-requisitos-para-amazon-s3\" class=\"anchor\" aria-label=\"Permalink: Requisitos para Amazon S3\" href=\"#requisitos-para-amazon-s3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eEntre na \u003ca href=\"console.aws.amazon.com\"\u003eAWS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClique na seta ao lado de seu nome de usu\u00e1rio e em \"My Security Credentials\".\u003c/li\u003e\n\u003cli\u003eNa se\u00e7\u00e3o \"Access Keys\", clique em \"Create New Access Key\".\u003c/li\u003e\n\u003cli\u003eNa janela que aparece, clique em \"Show Access Key\".\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 4\nprovider\u0026gt; 1\nenv_auth\u0026gt; 1\naccess_key_id\u0026gt; conte\u00fado de \"Access Key ID\"\nsecret_access_key\u0026gt; conte\u00fado de \"Secret Access Key\"\nregion\u0026gt; 16\nendpoint\u0026gt; deixe em branco\nlocation_constraint\u0026gt; 16\nacl\u0026gt; deixe em branco\nserver_side_encryption\u0026gt; deixe em branco\nsse_kms_key_id\u0026gt; deixe em branco\nstorage_class\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1672685576.0 + "updated_at": 1603929017.0 }, { "data_format": 2, - "description": "Singularity Image for GENIE dependencies built with ROOT5 on Ubuntu 14.04", + "description": null, "filenames": [ - "Singularity" + "Containers/First experiments/not-used/Singularity def files/ubuntu-openmpi-master/Singularity", + "Containers/First experiments/not-used/Singularity def files/ubuntu-mvapich-master/Singularity", + "Containers/First experiments/not-used/Singularity def files/centos-mvapich-master/Singularity", + "Containers/First experiments/not-used/Singularity def files/centos-master/Singularity", + "Containers/First experiments/not-used/Singularity def files/ubuntu-master/Singularity", + "Containers/First experiments/not-used/Singularity def files/centos-openmpi-master/Singularity" ], - "full_name": "twongjirad/singularity-genie-deps-root5-ubuntu14.04", + "full_name": "radical-group/koubbe", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-cern-root5-ubuntu14.04\u003c/h1\u003e\u003ca id=\"user-content-singularity-cern-root5-ubuntu1404\" class=\"anchor\" aria-label=\"Permalink: singularity-cern-root5-ubuntu14.04\" href=\"#singularity-cern-root5-ubuntu1404\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Image for GENIE dependencies built with ROOT5 on Ubuntu 14.04\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ekoubbe\u003c/h1\u003e\u003ca id=\"user-content-koubbe\" class=\"anchor\" aria-label=\"Permalink: koubbe\" href=\"#koubbe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBelow you have a brief summary of the main work that I have been doing during my time in \u003ca href=\"http://radical.rutgers.edu\" title=\"Radical-Lab\" rel=\"nofollow\"\u003eRadical-Lab\u003c/a\u003e at Rutgers Universiry. For detailed information (descriptions, instructions, source code, results, etc.), please visit each section\u0027s topic.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#radical-cybertools-rct\"\u003eRadical-Cybertools (RCT)\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#hyperparameter-optimization-hpo\"\u003eHyperparameter Optimization (HPO)\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#containers\"\u003eContainers\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#facts\"\u003eFACTS\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#misc\"\u003eMisc\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#installation-of-stress-ng-executable\"\u003eInstallation of stress-ng executable\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#installation-of-mpi4py-on-xsede-bridges-using-gcc-compiler\"\u003eInstallation of mpi4py on XSEDE Bridges using GCC compiler\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#reference\"\u003eReference\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRadical-Cybertools (RCT)\u003c/h2\u003e\u003ca id=\"user-content-radical-cybertools-rct\" class=\"anchor\" aria-label=\"Permalink: Radical-Cybertools (RCT)\" href=\"#radical-cybertools-rct\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload RCT stack as per instructed on \u003ca href=\"https://radicalentk.readthedocs.io/en/latest/install.html\" rel=\"nofollow\"\u003eEnTK installation website\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ virtualenv -p python3.7 \\\u0026lt;VE name\\\u0026gt; \n$ source \\\u0026lt;path-to-VE\\\u0026gt;/bin/activate \n$ pip install radical.entk \n$ pip install radical.analytics\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSimple RP exercise\u003c/h3\u003e\u003ca id=\"user-content-simple-rp-exercise\" class=\"anchor\" aria-label=\"Permalink: Simple RP exercise\" href=\"#simple-rp-exercise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere I ran the getting started example provided with RP and verified correct functionality:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd \\\u0026lt;path-to-VE\\\u0026gt;/radical.pilot/examples \n$ python 00_getting_started.py xsede.bridges\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSimple EnTK exercise\u003c/h3\u003e\u003ca id=\"user-content-simple-entk-exercise\" class=\"anchor\" aria-label=\"Permalink: Simple EnTK exercise\" href=\"#simple-entk-exercise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere I wrote three suggested applications to get familiar with EnTK (the duration of the tasks can be arbitrary short):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e128 tasks concurrently, where each task is 1 core\u003c/li\u003e\n\u003cli\u003e8 tasks where each task is 16 cores\u003c/li\u003e\n\u003cli\u003e16 concurrent batches of 8 tasks (each of 1 core, but where in each batch each task runs sequentially i.e., one after the other.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe results of these applications are posted \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/RCT/First%20Example%20on%20EnTK/results/results.pdf\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHyperparameter Optimization (HPO)\u003c/h2\u003e\u003ca id=\"user-content-hyperparameter-optimization-hpo\" class=\"anchor\" aria-label=\"Permalink: Hyperparameter Optimization (HPO)\" href=\"#hyperparameter-optimization-hpo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to see my Initial Presentation on HPO, please visit \u003ca href=\"https://docs.google.com/presentation/d/12yYCymB0-m4qGEPdgg0XKipuziSUmEoVhI32XXhDOtc/edit?usp=sharing\" rel=\"nofollow\"\u003eHPO Initial Presentation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo install HyperSpace (on Bridges login node, make sure MPICH or OpenMPI is available):\u003c/p\u003e\n\u003cp\u003eIf Anaconda (or Miniconda) not installed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \n$ bash Miniconda3-latest-Linux-x86_64.sh \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eElse:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create --name \\\u0026lt;VE name\\\u0026gt; python=3.7 \n$ conda activate \\\u0026lt;VE name\\\u0026gt; \n$ pip install mpi4py \n$ git clone https://github.com/yngtodd/hyperspace.git \n$ cd hyperspace \n$ pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFirst thing I did was to reproduce results for the HyperSpace Styblinski-Tang benchmark (on Bridges compute node):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd benchmarks/styblinskitang/hyperdrive \n$ mpirun -n 4 python3 benchmark.py --ndims 2 --results \\\u0026lt;/path/to/save/results\\\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to visualize the results, install HyperSpace on your local machine this time and follow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda install mpi4py (through conda this time so MPI packages get installed as well) \n$ conda install scikit-learn seaborn \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollow the Jupyter Notebook located in the repo \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/HyperSpace/First%20benchmark/results/vis_results.ipynb\"\u003ehere\u003c/a\u003e in order to visualize results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePerforming HPO for the CHEERS project\u003c/h3\u003e\u003ca id=\"user-content-performing-hpo-for-the-cheers-project\" class=\"anchor\" aria-label=\"Permalink: Performing HPO for the CHEERS project\" href=\"#performing-hpo-for-the-cheers-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a brief overview of what the CHEERS project is, as well as experiments design and results, please visit the \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/docs/First%20approach.pdf\"\u003eCHEERS First Approach document\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eParallel Bayesian SMBO vs Grid Search\u003c/h4\u003e\u003ca id=\"user-content-parallel-bayesian-smbo-vs-grid-search\" class=\"anchor\" aria-label=\"Permalink: Parallel Bayesian SMBO vs Grid Search\" href=\"#parallel-bayesian-smbo-vs-grid-search\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter playing around with HyperSpace and managing to get a working hyperparameter optimization code, the first thing that I did was a comparison of this approach (parallel Bayesian SMBO) against the already existing Grid Search one. You can find it here: \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/hyperparams-opt/code/NIRONE2-5/Andy_comparison_3params.ipynb\"\u003eAndy_comparison_3params.ipynb\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOf course, you need to have HyperSpace installed beforehand:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eEasy HyperSpace install on XSEDE Comet with mvapich2:\n\n$ pip3 install virtualenv --user\n$ add virtualenv to .bashrc:\n\texport PATH=\"/home/karahbit/.local/bin:$PATH\"\n$ source .bashrc\n$ virtualenv -p python3 ve-cheers\n$ module load mpi4py\n$ source ve-cheers/bin/activate\n$ pip install seaborn scikit-optimize==0.5.2\n$ git clone https://github.com/yngtodd/hyperspace.git\n$ cd ~/hyperspace\n$ pip install .\n$ export MV2_ENABLE_AFFINITY=0\n$ srun --partition=debug --pty --nodes=2 --ntasks-per-node=24 -t 00:30:00 --wait=0 --export=ALL /bin/bash\n$ mpirun -n 4 python benchmarks/styblinskitang/hyperdrive/benchmark.py --ndims 2 --results /home/karahbit/hyperspace_results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWeak Scaling experiment\u003c/h4\u003e\u003ca id=\"user-content-weak-scaling-experiment\" class=\"anchor\" aria-label=\"Permalink: Weak Scaling experiment\" href=\"#weak-scaling-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs a natural next step, I went ahead and performed weak scaling experiments by running the following on local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./cheers_hyperspace_entk.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003enote: cheers_hyperspace_entk.sh is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/hyperparams-opt/code/NIRONE2-5/cheers_hyperspace_entk.sh\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003enote2: modify \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/hyperparams-opt/code/NIRONE2-5/cheers_hyperspace_entk.py\"\u003echeers_hyperspace_entk.py\u003c/a\u003e according to your needs (e.g. which dataset, # of hyperparams, which remote cluster, etc.).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eStrong Scaling experiment\u003c/h4\u003e\u003ca id=\"user-content-strong-scaling-experiment\" class=\"anchor\" aria-label=\"Permalink: Strong Scaling experiment\" href=\"#strong-scaling-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHyperSpace as it is has a method called \u201chyperdrive\u201d which runs each subspace/optimization on its own single rank/core. There is also \u201cdualdrive\u201d which runs 2 subspaces/optimizations per rank/core.\u003c/p\u003e\n\u003cp\u003eIn order to perform strong scaling, we would need to create more of these functions, e.g. quadrive, octadrive, etc (I made those names up), so we can run 4, 8, 16, etc. optimizations per MPI rank respectively.. Eventually, we would like to name this function something like \u201cmultidrive\u201d, and specify the number of optimizations we would like per rank/core.\u003c/p\u003e\n\u003cp\u003eThis requires new development, thus more time. I already started experimenting with \u201cdualdrive\u201d, but we can\u2019t perform strong scaling until this is done.\u003c/p\u003e\n\u003cp\u003eYou can find an issue created specifically for this purpose in the HyperSpace GitHub repo:\n\u003ca href=\"https://github.com/yngtodd/hyperspace/issues/31\"\u003ehttps://github.com/yngtodd/hyperspace/issues/31\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAs said before, you can see the results for both experiments in \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/docs/First%20approach.pdf\"\u003eCHEERS First Approach document\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainers\u003c/h2\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to see my Initial Presentation on Containers, please visit \u003ca href=\"https://docs.google.com/presentation/d/1ZA0dlyVj5jCw4b_unFurkM9Q9E7sMrNNn_DfLtdanfA/edit?usp=sharing\" rel=\"nofollow\"\u003eContainers Initial Presentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo see my final paper regarding containerization, please visit \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Misc/Technical%20Report/GeorgeKoubbe_Report.pdf\"\u003eCase Studies of executing containerized scientific applications on High-Performance Computing Platforms using RADICAL-Cybertools\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eMoreover, to see a step-by-step walkthrough of how to create and use Singularity containers on remote clusters (e.g. Bridges) using RCT, go to the following \u003ca href=\"https://github.com/radical-cybertools/radical.pilot/wiki/Singularity-Containers\"\u003ewiki\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe experiments design is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/docs/First%20Container%20Experiments%20Design%20Dec%2012%2C%202019.pdf\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExperiment 1\u003c/h3\u003e\u003ca id=\"user-content-experiment-1\" class=\"anchor\" aria-label=\"Permalink: Experiment 1\" href=\"#experiment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run experiment 1, make sure stress-ng executable is installed on Bridges and radical stack is installed on local machine. Then, execute on local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./stress_rp.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003enote: stress_rp.sh is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp1/stress_rp.sh\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003enote2: modify \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp1/stress_rp.py\"\u003estress_rp.py\u003c/a\u003e accordingly to run via RP the executable or the containerized executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExperiment 2\u003c/h3\u003e\u003ca id=\"user-content-experiment-2\" class=\"anchor\" aria-label=\"Permalink: Experiment 2\" href=\"#experiment-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are going to run a Singularity containerized MPI executable on Bind mode \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/mpi.html\" rel=\"nofollow\"\u003e(what is Bind mode?)\u003c/a\u003e. Same as with experiment 1, we are going to execute on local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./mpi_rp.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOn Bridges:\u003c/h5\u003e\u003ca id=\"user-content-on-bridges\" class=\"anchor\" aria-label=\"Permalink: On Bridges:\" href=\"#on-bridges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003enote: For further instructions on how to build the container and install/compile the executable, go \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Bridges/Bind-Intel19.5/instructions.txt\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003enote2: mpi_rp.sh is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Bridges/Bind-Intel19.5/mpi_rp.sh\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003enote3: modify \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Bridges/Bind-Intel19.5/mpi_rp.py\"\u003empi_rp.py\u003c/a\u003e accordingly to run via RP the executable or the containerized executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOn Comet:\u003c/h5\u003e\u003ca id=\"user-content-on-comet\" class=\"anchor\" aria-label=\"Permalink: On Comet:\" href=\"#on-comet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003enote: For further instructions on how to build the container and install/compile the executable, go \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Comet/Bind-IntelMPI/instructions.txt\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003enote2: mpi_rp.sh is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Comet/Bind-IntelMPI/mpi_rp.sh\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003enote3: modify \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Comet/Bind-IntelMPI/mpi_rp.py\"\u003empi_rp.py\u003c/a\u003e accordingly to run via RP the executable or the containerized executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFACTS\u003c/h2\u003e\u003ca id=\"user-content-facts\" class=\"anchor\" aria-label=\"Permalink: FACTS\" href=\"#facts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGetting started\u003c/h3\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMy initial work consisted on helping out in running simple FACTS \"modules\" on XSEDE Bridges and verifying correct functionality.\u003c/p\u003e\n\u003cp\u003eAfter this testing was done, I proceeded to package the \u003ca href=\"https://github.com/radical-collaboration/facts\"\u003eFACTS repo\u003c/a\u003e into a python pip package and uploaded it to the pip server for easy download of general users.\u003c/p\u003e\n\u003cp\u003eLastly, I was tasked with the containerization of the FACTS framework. As it is right now, automation is achieved by creating a virtual environment and installing FACTS along with its dependencies through PIP. This framework will launch the executables for the required modules on a remote machine, being an HPC cluster, etc.\u003c/p\u003e\n\u003cp\u003eSo, why do we need containers? What is the benefit that containers are going to bring to FACTS?\u003c/p\u003e\n\u003cp\u003eWe envision this at two levels:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eWe containerize at the framework level. This will allow us to take FACTS apart into individual modules, completely independent from one another, with their own container each. The end user won\u2019t have to know about anything else, no virtual environment, no dependencies, no other steps. We would take full advantage of the portability and reproducibility benefits of containers. Therefore, the end user can simply execute the containerized module on the local machine. We can use Docker for this purpose.\u003c/li\u003e\n\u003cli\u003eWe containerize at the executable level. There is a growing number of modules inside FACTS. Each module has 4 stages: pre-processing, fit, project, post-processing. Each stage has one executable (python3 script). We can use Singularity for this purpose.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFew notes to keep in mind:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInput data is not going to be included in the container. We can integrate (bind mount) it to the Docker container at the time of execution. Singularity already offers integration features that make this easier.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhere are we going to obtain the containers from?\tAs said before, each container would be representing a FACTS module. The containers can be downloaded from Docker Hub or the Singularity equivalent, for example, with every container being specific to the application and remote resource. Lastly, the end user would just need to execute the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainerization at the executable level\u003c/h3\u003e\u003ca id=\"user-content-containerization-at-the-executable-level\" class=\"anchor\" aria-label=\"Permalink: Containerization at the executable level\" href=\"#containerization-at-the-executable-level\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs an initial approach, I started containerizing at the executable level (Singularity) on Comet with the kopp14 module and data that Greg sent me. Once done, I characterized performance and looked for any overheads. You can read how to run the container from the following file: \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/FACTS/Containerizing%20FACTS/Executable%20level/src/Comet/facts/facts_re.sh\"\u003efacts_re.sh\u003c/a\u003e. You can find the results in the last slide of the presentation \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/docs/Containers%20Initial%20Presentation.pdf\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003enote: keep in mind that you would have to build the Singularity container from the definition file I provided by running the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build ./modules/kopp14/landwaterstorage/kopp14_landwaterstorage.sif kopp14_landwaterstorage.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainerization at the framework level\u003c/h3\u003e\u003ca id=\"user-content-containerization-at-the-framework-level\" class=\"anchor\" aria-label=\"Permalink: Containerization at the framework level\" href=\"#containerization-at-the-framework-level\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis was not a requirement at the moment, but for fun I proceeded to create a Dockerfile containerizing FACTS at the framework level. You can find the file \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/FACTS/Containerizing%20FACTS/Framework%20level/Dockerfile\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMisc\u003c/h2\u003e\u003ca id=\"user-content-misc\" class=\"anchor\" aria-label=\"Permalink: Misc\" href=\"#misc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere you have general information about my work, readings, meetings, weekly summaries, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation of stress-ng executable\u003c/h2\u003e\u003ca id=\"user-content-installation-of-stress-ng-executable\" class=\"anchor\" aria-label=\"Permalink: Installation of stress-ng executable\" href=\"#installation-of-stress-ng-executable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install stress-ng on Bridges login node:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ wget http://kernel.ubuntu.com/~cking/tarballs/stress-ng/stress-ng-0.09.34.tar.xz \n$ tar xvf stress-ng-0.09.34.tar.xz \n$ cd stress-ng-0.09.34 \n$ make \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRequest 1 node, 4 cores on RM partition for 8 hours:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ interact -p RM -N 1 -n 4 -t 8:00:00 \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMeasure Total Time of Execution of stress-ng python script through MPI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/usr/bin/time -v mpirun -n 2 python3 helloworld.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo see core usage on each node:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh r001 \n$ htop\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003enote: helloworld.py is located in the repo \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/HyperSpace/First%20benchmark/docs/Guides/stress-ng/helloworld.py\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation of mpi4py on XSEDE Bridges using GCC compiler\u003c/h2\u003e\u003ca id=\"user-content-installation-of-mpi4py-on-xsede-bridges-using-gcc-compiler\" class=\"anchor\" aria-label=\"Permalink: Installation of mpi4py on XSEDE Bridges using GCC compiler\" href=\"#installation-of-mpi4py-on-xsede-bridges-using-gcc-compiler\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf Anaconda (or Miniconda) not installed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \n$ bash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eElse:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create --name \\\u0026lt;VE name\\\u0026gt; python=3.7 \n$ conda activate \\\u0026lt;VE name\\\u0026gt; \n$ wget https://bitbucket.org/mpi4py/mpi4py/downloads/mpi4py-3.0.3.tar.gz \n$ tar -zxf mpi4py-3.0.3.tar.gz \u0026amp;\u0026amp; rm mpi4py-3.0.3.tar.gz\n$ cd mpi4py-3.0.3 \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003emodify mpi.cfg as instructed in \u003ca href=\"https://mpi4py.readthedocs.io/en/stable/install.html#using-pip-or-easy-install\" rel=\"nofollow\"\u003empi4py installation\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Open MPI example \n# ---------------- \n[openmpi] \nmpi_dir = /usr/mpi/gcc/openmpi-2.1.2-hfi \nmpicc = %(mpi_dir)s/bin/mpicc \nmpicxx = %(mpi_dir)s/bin/mpicxx \n#include_dirs = %(mpi_dir)s/include \n#libraries = mpi \nlibrary_dirs = %(mpi_dir)s/lib64:/opt/packages/gcc/9.2.0/bin/gcc \nruntime_library_dirs = %(library_dirs)s \n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ python setup.py build --mpi=openmpi \n$ python setup.py install \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReference\u003c/h2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-label=\"Permalink: Reference\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe local machine used throughout the proyects is a virtual machine with Ubuntu 16.04.6 LTS.\u003c/p\u003e\n\u003cp\u003eThe radical-stack used is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e python : 3.7.6\n pythonpath : \n virtualenv : /home/karahbit/ve-rct3\n\n radical.analytics : 0.90.7\n radical.entk : 1.0.2\n radical.pilot : 1.3.0\n radical.saga : 1.3.0\n radical.utils : 1.3.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor specific references, please visit each section\u0027s topic.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"http://radical.rutgers.edu\" rel=\"nofollow\"\u003ehttp://radical.rutgers.edu\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://radical-cybertools.github.io\" rel=\"nofollow\"\u003ehttp://radical-cybertools.github.io\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.psc.edu/bridges/user-guide\" rel=\"nofollow\"\u003ehttps://www.psc.edu/bridges/user-guide\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sdsc.edu/support/user_guides/comet.html\" rel=\"nofollow\"\u003ehttps://www.sdsc.edu/support/user_guides/comet.html\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://radicalpilot.readthedocs.io/en/stable\" rel=\"nofollow\"\u003ehttps://radicalpilot.readthedocs.io/en/stable\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://radicalentk.readthedocs.io/en/latest\" rel=\"nofollow\"\u003ehttps://radicalentk.readthedocs.io/en/latest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hyperspace.readthedocs.io/en/latest\" rel=\"nofollow\"\u003ehttps://hyperspace.readthedocs.io/en/latest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://containers-at-tacc.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://containers-at-tacc.readthedocs.io/en/latest/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.open-mpi.org\" rel=\"nofollow\"\u003ehttps://www.open-mpi.org\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://wiki.ubuntu.com/Kernel/Reference/stress-ng\" rel=\"nofollow\"\u003ehttps://wiki.ubuntu.com/Kernel/Reference/stress-ng\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAuthor: \u003ca href=\"https://github.com/karahbit\"\u003eGeorge Koubbe\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1496352524.0 + "updated_at": 1595518122.0 }, { "data_format": 2, - "description": "Testing container for playing with singularity", + "description": "The Common Workflow Language (CWL) is an open standard for describing analysis workflows and tools in a way that makes them portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. ", "filenames": [ - "Hello-World/Singularity", - "01-Building/Singularity.build" + "3.1.20211020155521/Singularity", + "3.1.20220210171524/Singularity" ], - "full_name": "Deadlyelder/Singularity-hello-world", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity-hello-world\u003c/h1\u003e\u003ca id=\"user-content-singularity-hello-world\" class=\"anchor\" aria-label=\"Permalink: Singularity-hello-world\" href=\"#singularity-hello-world\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting container for playing with singularity.\u003c/p\u003e\n\u003cp\u003eUsed for testing on HPC and pushing on the \u003ca href=\"https://www.singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity hub\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "pscedu/singularity-cwltool", + "latest_release": "v3.1.20211020155521", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-cwltool/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-cwltool/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-cwltool/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-cwltool/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0713bada75749349dcdd345d4398a0394c60a68411e755ae4cc4de586633d366/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d63776c746f6f6c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0713bada75749349dcdd345d4398a0394c60a68411e755ae4cc4de586633d366/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d63776c746f6f6c\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-cwltool\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/06c1ad52de3c2984a480093626061fe1e2abf5399e3cf525379c8a0731f7b66b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d63776c746f6f6c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/06c1ad52de3c2984a480093626061fe1e2abf5399e3cf525379c8a0731f7b66b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d63776c746f6f6c\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-cwltool\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c19f9097b3a5bd623b4adaea7d7bf114d414cbe08983fb427ec9b51c1199cf8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d63776c746f6f6c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c19f9097b3a5bd623b4adaea7d7bf114d414cbe08983fb427ec9b51c1199cf8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d63776c746f6f6c\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-cwltool\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6f169d24efbee5cd194f8ecf9395464851313f0c36e4c8041d21e801a50b53b0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d63776c746f6f6c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6f169d24efbee5cd194f8ecf9395464851313f0c36e4c8041d21e801a50b53b0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d63776c746f6f6c\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-cwltool\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-cwltool\u003c/h1\u003e\u003ca id=\"user-content-singularity-cwltool\" class=\"anchor\" aria-label=\"Permalink: singularity-cwltool\" href=\"#singularity-cwltool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/72df1b7e3dc95fb0111902a35b64a124d254b58a0058c67416ae0a95d09f10bc/68747470733a2f2f7777772e636f6d6d6f6e776c2e6f72672f43574c2d4c6f676f2d4865616465722e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72df1b7e3dc95fb0111902a35b64a124d254b58a0058c67416ae0a95d09f10bc/68747470733a2f2f7777772e636f6d6d6f6e776c2e6f72672f43574c2d4c6f676f2d4865616465722e706e67\" data-canonical-src=\"https://www.commonwl.org/CWL-Logo-Header.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.commonwl.org/\" rel=\"nofollow\"\u003ecwltool\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ecwltool\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/cwltool/3.1.20211020155521\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/cwltool\u003c/code\u003e as \u003ccode\u003e3.1.20211020155521.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, - "topics": [], - "updated_at": 1533107262.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1635309195.0 }, { "data_format": 2, - "description": "Singularity recipe that can be used to build a container for BRER simulations.", + "description": null, "filenames": [ - "Singularity.brer-cuda-10.0-ubuntu-18.04", - "Singularity.trainA", - "Singularity.nogpu", - "Singularity.comet", - "Singularity.0_0_7", - "Singularity.0_0_6" + "Singularity" ], - "full_name": "kassonlab/singularity-brer", + "full_name": "darachm/singularity_csvkit", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBRER: Singularity\u003c/h1\u003e\u003ca id=\"user-content-brer-singularity\" class=\"anchor\" aria-label=\"Permalink: BRER: Singularity\" href=\"#brer-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the Singularity recipe used to build containers for BRER simulations.\nA pre-built image is hosted on Sylabs Singularity \u003ca href=\"https://cloud.sylabs.io/library/kassonlab/default/brer\" rel=\"nofollow\"\u003elibrary\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe main project for running these simulations is hosted at \u003ca href=\"https://github.com/kassonlab/run_brer\"\u003ehttps://github.com/kassonlab/run_brer\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting the container\u003c/h2\u003e\u003ca id=\"user-content-getting-the-container\" class=\"anchor\" aria-label=\"Permalink: Getting the container\" href=\"#getting-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull directly from singularity library (recommended):\u003c/p\u003e\n\u003cpre lang=\"angular2html\"\u003e\u003ccode\u003esingularity pull --name singularity-brer.sif library://kassonlab/default/brer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor build it yourself:\u003c/p\u003e\n\u003cpre lang=\"angular2html\"\u003e\u003ccode\u003esudo singularity build singularity-brer.sif deffile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003edeffile\u003c/code\u003e is one of the recipe files in this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOnce you\u0027ve got the container\u003c/h2\u003e\u003ca id=\"user-content-once-youve-got-the-container\" class=\"anchor\" aria-label=\"Permalink: Once you\u0027ve got the container\" href=\"#once-youve-got-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe GROMACS build in this container is GPU-compatible (built with CUDA). In order to take advantage of this, use\nthe Singularity \u003ccode\u003eexec\u003c/code\u003e command with the \u003ccode\u003e--nv\u003c/code\u003e option:\u003c/p\u003e\n\u003cpre lang=\"angular2html\"\u003e\u003ccode\u003esingularity exec --nv singularity-brer.sif python3 my_run_script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003e--nv\u003c/code\u003e will bind the host nvidia drivers to the container, so be sure that your drivers are compatible with the CUDA version in the container (default CUDA 10.1).\u003c/p\u003e\n\u003cp\u003eAn example run script is provided on the \u003ca href=\"https://github.com/kassonlab/run_brer\"\u003emain project website\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eNote: There is no Python 2 installed in the container (\u003ccode\u003e/usr/bin/python\u003c/code\u003e is actually Python3). Any Python scripts you write that you wish to run in the container\nmust be compatible with Python 3.X\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMiscellaneous\u003c/h4\u003e\u003ca id=\"user-content-miscellaneous\" class=\"anchor\" aria-label=\"Permalink: Miscellaneous\" href=\"#miscellaneous\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e: Use \u003ccode\u003esingularity exec ...\u003c/code\u003e, not \u003ccode\u003esingularity run ...\u003c/code\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003eThis container is for providing \u003ccode\u003ecsvkit\u003c/code\u003e for a nextflow pipeline.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1594306038.0 + "updated_at": 1543722869.0 }, { "data_format": 2, - "description": "An Apptainer/Singularity container for using MuJoCo-mjx on GPU. ", + "description": null, "filenames": [ "Singularity" ], - "full_name": "DMackRus/Apptainer_mujoco-mjx", + "full_name": "oogasawa/singularity_jupyter_typescript", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eApptainer_RL\u003c/h1\u003e\u003ca id=\"user-content-apptainer_rl\" class=\"anchor\" aria-label=\"Permalink: Apptainer_RL\" href=\"#apptainer_rl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple container for using MuJoCo-mjx, and Jax on the GPU of your PC. Installs Cuda and CudNN as well as other python dependancies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe only requirment for using this repository is \u003ca href=\"https://apptainer.org/admin-docs/master/installation.html\" rel=\"nofollow\"\u003eSingularity/Apptainer\u003c/a\u003e Version \u0026gt;= 3.5.\u003c/p\u003e\n\u003cp\u003eOnce Singularity/Apptainer has been installed, you can simply build the container using /.build.sh (This will take a while depending on your machine and wifi connection, approximately 30 minutes) and then you can launch the container using /.run.sh.\u003c/p\u003e\n\u003cp\u003eIf you need to admin priveledges inside the container to isntall something, you can use /.write.sh.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCurrent Issues\u003c/h2\u003e\u003ca id=\"user-content-current-issues\" class=\"anchor\" aria-label=\"Permalink: Current Issues\" href=\"#current-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eRepository is hard coded to download Cuda 12.4 and Cudnn 9.5. I will make this flexible once I figure that out.\u003c/li\u003e\n\u003cli\u003eSome possible issue with jax installation. It seems to be using the GPU but there is a warning about a version mismatch between cuda and XLA.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eToDo\u003c/h1\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: ToDo\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAdd paths in post script\u003c/li\u003e\n\u003cli\u003eMake CUDA and CuDDn installations dynamic.\u003c/li\u003e\n\u003cli\u003eLook into fixing XLA and Jax mismatch?\u003c/li\u003e\n\u003cli\u003eMake some nice mjx-examples and have this repo clone them automatically.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-jupyter-typescript\u003c/h1\u003e\u003ca id=\"user-content-singularity-jupyter-typescript\" class=\"anchor\" aria-label=\"Permalink: singularity-jupyter-typescript\" href=\"#singularity-jupyter-typescript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Singularity container of Jupyter notebook for datascience + typescript,\ncreated by converting an official Docker image\n\u003ca href=\"https://hub.docker.com/r/jupyter/datascience-notebook/\" rel=\"nofollow\"\u003ejupyter/datascience-notebook\u003c/a\u003e,\nand \u003ca href=\"https://github.com/yunabe/tslab\"\u003eyunabe/tslab Jupyter kernel\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Singularity image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/oogasawa/singularity-jupyter-typescript\ncd singularity-jupyter-typescript\nsudo singularity build . singularity-jupyter-typescript.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the server as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance start singularity-jupyter-typescript.sif sing_typescript\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEnter (attach) the Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# List the running containers.\nsingularity instance list\n\n# Attach the container\nsingularity shell instance://sing_typescript\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the Jupyter notebook (or Jupyter Lab) from within the Singularity prompt.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://sing_typescript\nSingularity\u0026gt; jupyter lab --port=50000\n[I 01:28:50.619 LabApp] JupyterLab extension loaded from /opt/conda/lib/python3.8/site-packages/jupyterlab\n[I 01:28:50.619 LabApp] JupyterLab application directory is /opt/conda/share/jupyter/lab\n[I 01:28:50.621 LabApp] Serving notebooks from local directory: /home/oogasawa/tmp3/singularity-jupyter-typescript\n[I 01:28:50.621 LabApp] The Jupyter Notebook is running at:\n[I 01:28:50.621 LabApp] http://mayfly:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n[I 01:28:50.621 LabApp] or http://127.0.0.1:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n[I 01:28:50.621 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n[C 01:28:50.624 LabApp]\n\n To access the notebook, open this file in a browser:\n\t file:///home/oogasawa/.local/share/jupyter/runtime/nbserver-25-open.html\n\tOr copy and paste one of these URLs:\n\t\thttp://mayfly:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n\t or http://127.0.0.1:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\t\t\t\t\t \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can access the Jupyter software \u003ca href=\"http://localhost:50000/\" rel=\"nofollow\"\u003ehttp://localhost:50000/\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eStop the server (and return to the bash prompt) by Ctrl-C, and stop the container as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance stop sing_typescript\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "apptainer", - "mujoco", - "mujoco-environments", - "mujoco-mjx", - "reinforcement-learning", - "singularity" - ], - "updated_at": 1729862068.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1622297530.0 }, { "data_format": 2, - "description": null, + "description": "Label Me Application for PTdatax", "filenames": [ - "Singularity.latest" + "Singularity" ], - "full_name": "bioexcel/zip_container", + "full_name": "In-For-Disaster-Analytics/labelme", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/mmbirb/zip\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/339e4f315da6f622b2972753d519e70d7d9bd259f370f9e8ddc90738339303d7/68747470733a2f2f717561792e696f2f7265706f7369746f72792f62696f636f6e7461696e6572732f62696f62625f696f2f737461747573\" alt=\"\" data-canonical-src=\"https://quay.io/repository/biocontainers/biobb_io/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4075\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eZIP container\u003c/h1\u003e\u003ca id=\"user-content-zip-container\" class=\"anchor\" aria-label=\"Permalink: ZIP container\" href=\"#zip-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eZIP docker and singularity containers used for \u003ca href=\"https://github.com/bioexcel/biobb_template\"\u003ebiobb_template\u003c/a\u003e BioExcel Building Blocks modules.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker Use\u003c/h3\u003e\u003ca id=\"user-content-docker-use\" class=\"anchor\" aria-label=\"Permalink: Docker Use\" href=\"#docker-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull mmbirb/zip:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run mmbirb/zip:latest \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Use\u003c/h3\u003e\u003ca id=\"user-content-singularity-use\" class=\"anchor\" aria-label=\"Permalink: Singularity Use\" href=\"#singularity-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name zip.sif shub://bioexcel/zip_container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec zip.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" aria-label=\"Permalink: Copyright \u0026amp; Licensing\" href=\"#copyright--licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknolegements\u003c/h3\u003e\u003ca id=\"user-content-acknolegements\" class=\"anchor\" aria-label=\"Permalink: Acknolegements\" href=\"#acknolegements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe singularity container has been built through \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5aaf032aade8af7cf9f6e16cb3c7ba70c927ecd783c1cfff82dc0ed8062560df/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5aaf032aade8af7cf9f6e16cb3c7ba70c927ecd783c1cfff82dc0ed8062560df/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eThis template is the first in a \u003ca href=\"#next-templates\"\u003eseries of templates\u003c/a\u003e that will guide you through the process of creating a cookbook and running it on TACC systems. From simple ones that run a command to more complex ones that run a Python script using conda or a Jupyter Notebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA GitHub account\u003c/li\u003e\n\u003cli\u003eTACC account. If you don\u0027t have one, you can request one \u003ca href=\"https://accounts.tacc.utexas.edu/register\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eTo access TACC systems, you should have an \u003ca href=\"https://tacc.utexas.edu/use-tacc/allocations/\" rel=\"nofollow\"\u003eallocation\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003eYou can see your allocations \u003ca href=\"https://ptdatax.tacc.utexas.edu/workbench/allocations/approved\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eIf you don\u0027t have an allocation, you can request one \u003ca href=\"https://portal.tacc.utexas.edu/allocation-request\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTemplate Overview\u003c/h2\u003e\u003ca id=\"user-content-template-overview\" class=\"anchor\" aria-label=\"Permalink: Template Overview\" href=\"#template-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis template creates a simple Python script that will be used to demonstrate how to run a cookbook on a TACC cluster and obtain the output using a UI. The cookbook will use a CSV file stored on TACC storage and run a Python script that reads it, calculates the average of the values in the first column, and writes the result to a file.\u003c/p\u003e\n\u003cp\u003eIn this case, the file is small for demonstration purposes. However, you can use the same process to analyze large files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow does it work?\u003c/h3\u003e\u003ca id=\"user-content-how-does-it-work\" class=\"anchor\" aria-label=\"Permalink: How does it work?\" href=\"#how-does-it-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"app.json\"\u003e\u003ccode\u003eapp.json\u003c/code\u003e\u003c/a\u003e file: contains the definition of the Tapis application, including the application\u0027s name, description, Docker image, input files, and advanced options.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Dockerfile\"\u003e\u003ccode\u003eDockerfile\u003c/code\u003e\u003c/a\u003e: a Docker image is built from the \u003ca href=\"./Dockerfile\"\u003e\u003ccode\u003eDockerfile\u003c/code\u003e\u003c/a\u003e. The Docker image defines the runtime environment for the application and the files that will be used by the application.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"run.sh\"\u003e\u003ccode\u003erun.sh\u003c/code\u003e\u003c/a\u003e: contains all the commands that will be executed on the TACC cluster.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUpload files to TACC storage\u003c/h3\u003e\u003ca id=\"user-content-upload-files-to-tacc-storage\" class=\"anchor\" aria-label=\"Permalink: Upload files to TACC storage\" href=\"#upload-files-to-tacc-storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne of the goals of the template is to demonstrate how to use the TACC storage system to store the input and output files. So, you should upload the CSV file to the TACC storage system.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGo to the \u003ca href=\"https://portal.tacc.utexas.edu\" rel=\"nofollow\"\u003eTACC Portal\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eClick on the \"Data Files\" tab.\u003c/li\u003e\n\u003cli\u003eClick on the \"Add +\" button.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image.png\"\u003e\u003cimg src=\"images/image.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick on the \"Upload\" button.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image-1.png\"\u003e\u003cimg src=\"images/image-1.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSelect the file you want to upload and click \u003ccode\u003eUpload Selected\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eModify the Dockerfile\u003c/h3\u003e\u003ca id=\"user-content-modify-the-dockerfile\" class=\"anchor\" aria-label=\"Permalink: Modify the Dockerfile\" href=\"#modify-the-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eDockerfile\u003c/code\u003e is used to create a Docker image that will be used to run the Python script. In this case, the Docker image is created using the \u003ccode\u003emicroconda\u003c/code\u003e base image, which is a minimal image that contains conda.\u003c/p\u003e\n\u003cp\u003eFor example, the Dockerfile below installs \u003ccode\u003ecurl\u003c/code\u003e using \u003ccode\u003eapt-get\u003c/code\u003e. This is useful if you need to install packages that are not available in conda.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-dockerfile\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eRUN\u003c/span\u003e apt-get update \u0026amp;\u0026amp; apt-get install -y \\\n curl \\\n \u0026amp;\u0026amp; rm -rf /var/lib/apt/lists/*\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDefine conda dependencies using \u003ccode\u003eenvironment.yaml\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-define-conda-dependencies-using-environmentyaml\" class=\"anchor\" aria-label=\"Permalink: Define conda dependencies using environment.yaml\" href=\"#define-conda-dependencies-using-environmentyaml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eenvironment.yaml\u003c/code\u003e file is used to define the conda environment that will be used to run the Python script. In this case, the \u003ccode\u003eenvironment.yaml\u003c/code\u003e file contains the dependencies needed to run the Python script.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ebase\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003echannels\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003econda-forge\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003edependencies\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003epython=3.9.1\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003epandas=1.2.1\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJob run script\u003c/h3\u003e\u003ca id=\"user-content-job-run-script\" class=\"anchor\" aria-label=\"Permalink: Job run script\" href=\"#job-run-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003erun.sh\u003c/code\u003e file is used to run the Python script. It activates the conda environment and runs the Python script.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e -xe\n\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${_tapisExecSystemInputDir}\u003c/span\u003e\npython /code/main.py billing.csv \u003cspan class=\"pl-smi\"\u003e${_tapisExecSystemOutputDir}\u003c/span\u003e/output.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003erun.sh\u003c/code\u003e has two variables that are used to define the input and output directories. These variables are \u003ccode\u003e_tapisExecSystemInputDir\u003c/code\u003e and \u003ccode\u003e_tapisExecSystemOutputDir\u003c/code\u003e which are automatically set by the Tapis system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e_tapisExecSystemInputDir: The directory where the input files are staged\u003c/li\u003e\n\u003cli\u003e_tapisExecSystemOutputDir: The directory where the application writes the output files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate your cookbook\u003c/h2\u003e\u003ca id=\"user-content-create-your-cookbook\" class=\"anchor\" aria-label=\"Permalink: Create your cookbook\" href=\"#create-your-cookbook\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use this repository as a template to create your cookbook. Follow the steps below to create your cookbook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate a new repository\u003c/h3\u003e\u003ca id=\"user-content-create-a-new-repository\" class=\"anchor\" aria-label=\"Permalink: Create a new repository\" href=\"#create-a-new-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClick on the \"Use this template\" button to create a new repository\u003c/li\u003e\n\u003cli\u003eFill in the form with the information for your new repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild the Docker image\u003c/h3\u003e\u003ca id=\"user-content-build-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build the Docker image\" href=\"#build-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the repository\u003c/li\u003e\n\u003cli\u003eBuild the Docker image using the command below\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t cookbook-python \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003ePush the Docker image to a container registry\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker tag cookbook-python \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour-registry\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/cookbook-python\ndocker push \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour-registry\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/cookbook-python\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eModify the \u003ccode\u003eapp.json\u003c/code\u003e file\u003c/h3\u003e\u003ca id=\"user-content-modify-the-appjson-file\" class=\"anchor\" aria-label=\"Permalink: Modify the app.json file\" href=\"#modify-the-appjson-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach app has a unique \u003ccode\u003eid\u003c/code\u003e and \u003ccode\u003edescription\u003c/code\u003e. So, you should change these fields to match your app\u0027s name and description.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the \u003ccode\u003eapp.json\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eChange the values \u003ccode\u003eid\u003c/code\u003e and \u003ccode\u003edescription\u003c/code\u003e fields with the name and description as you wish.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate a New Application on the Cookbook UI\u003c/h3\u003e\u003ca id=\"user-content-create-a-new-application-on-the-cookbook-ui\" class=\"anchor\" aria-label=\"Permalink: Create a New Application on the Cookbook UI\" href=\"#create-a-new-application-on-the-cookbook-ui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eGo to \u003ca href=\"https://in-for-disaster-analytics.github.io/cookbooks-ui/#/apps\" rel=\"nofollow\"\u003eCookbook UI\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick on the \"Create Application\" button\u003c/li\u003e\n\u003cli\u003eFill in the form with the information from your \u003ccode\u003eapp.json\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eClick \"Create Application\"\u003c/li\u003e\n\u003cli\u003eA new application will be created, and you will be redirected to the application\u0027s page\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun your Cookbook\u003c/h3\u003e\u003ca id=\"user-content-run-your-cookbook\" class=\"anchor\" aria-label=\"Permalink: Run your Cookbook\" href=\"#run-your-cookbook\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eGo to the application\u0027s page on the Cookbook UI, if you are not already there\u003c/li\u003e\n\u003cli\u003eClick on the \"Run\" button on the right side of the page. This will open the Portal UI\u003c/li\u003e\n\u003cli\u003eClick on the \"Select\" button to choose the input file\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image-2.png\"\u003e\u003cimg src=\"images/image-2.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick \"Run\"\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCheck the output\u003c/h3\u003e\u003ca id=\"user-content-check-the-output\" class=\"anchor\" aria-label=\"Permalink: Check the output\" href=\"#check-the-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eAfter the job finishes, you can check the output by clicking on the \"Output location\" link on the job\u0027s page\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/job-finished.png\"\u003e\u003cimg src=\"images/job-finished.png\" alt=\"Show a job finished \" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eYou will be redirected to the output location, where you can see the output files generated by the job\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image-4.png\"\u003e\u003cimg src=\"images/image-4.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick on a file to see its content. In this case, the file is named \u003ccode\u003eoutput.txt\u003c/code\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image-3.png\"\u003e\u003cimg src=\"images/image-3.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNext templates\u003c/h2\u003e\u003ca id=\"user-content-next-templates\" class=\"anchor\" aria-label=\"Permalink: Next templates\" href=\"#next-templates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/In-For-Disaster-Analytics/Cookbook-Docker-Template\"\u003eRunning a command\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/In-For-Disaster-Analytics/Cookbook-Conda-Template\"\u003eRunning a Python script using conda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/In-For-Disaster-Analytics/Cookbook-Jupyter-Template\"\u003eRunning a Jupyter Notebook\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWilliam Mobley - \u003ca href=\"mailto:wmobley@tacc.utexas.edu\"\u003ewmobley@tacc.utexas.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMaximiliano Osorio - \u003ca href=\"mailto:maxiosorio@gmail.com\"\u003emaxiosorio@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 9, + "subscribers_count": 3, "topics": [], - "updated_at": 1584437236.0 + "updated_at": 1718223396.0 }, { "data_format": 2, - "description": "Use ImageMagick\u00ae to create, edit, compose, or convert digital images.", + "description": "Singularity recipe for keras", "filenames": [ - "7.1.0-61/Singularity", - "7.0.10-48/Singularity", - "7.1.0-2/Singularity", - "7.1.1-15/Singularity", - "6.9.11-60/Singularity", - "7.1.1-39/Singularity" + "Singularity1.2", + "Singularity1.0", + "Singularity1.1" ], - "full_name": "pscedu/singularity-imagemagick", - "latest_release": "v7.1.1-39", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-imagemagick/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-imagemagick/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-imagemagick/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-imagemagick/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5528404bef0c0f51ff4a1ccdf773e7a55f5ef21a4f7d406e5cabb7372308c736/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5528404bef0c0f51ff4a1ccdf773e7a55f5ef21a4f7d406e5cabb7372308c736/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-imagemagick\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca 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href=\"https://camo.githubusercontent.com/1f17716ff11baefd5fc71f673ef822f24f490b32808190a8f18d27991459a89a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1f17716ff11baefd5fc71f673ef822f24f490b32808190a8f18d27991459a89a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-imagemagick\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3ffdcfa0d7b9b3f63e1a8d0e85f1deef8abe74f43040c727749fadc5ea1b9558/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d696d6167656d616769636b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3ffdcfa0d7b9b3f63e1a8d0e85f1deef8abe74f43040c727749fadc5ea1b9558/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d696d6167656d616769636b\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-imagemagick\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eImageMagick\u003c/h1\u003e\u003ca id=\"user-content-imagemagick\" class=\"anchor\" aria-label=\"Permalink: ImageMagick\" href=\"#imagemagick\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e2097f450d2fe8919131656de2541415e0cb72d469c16b0211e5c5fbb0e129b6/68747470733a2f2f65787465726e616c2d636f6e74656e742e6475636b6475636b676f2e636f6d2f69752f3f753d6874747025334125324625324679656e7061692e696469732e636f6d2e747725324677702d636f6e74656e7425324675706c6f616473253246323031322532463131253246696d6167656d616769636b5f77697a6172645f7468756d622e6a706726663d31266e6f66623d31\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e2097f450d2fe8919131656de2541415e0cb72d469c16b0211e5c5fbb0e129b6/68747470733a2f2f65787465726e616c2d636f6e74656e742e6475636b6475636b676f2e636f6d2f69752f3f753d6874747025334125324625324679656e7061692e696469732e636f6d2e747725324677702d636f6e74656e7425324675706c6f616473253246323031322532463131253246696d6167656d616769636b5f77697a6172645f7468756d622e6a706726663d31266e6f66623d31\" alt=\"Logo\" data-canonical-src=\"https://external-content.duckduckgo.com/iu/?u=http%3A%2F%2Fyenpai.idis.com.tw%2Fwp-content%2Fuploads%2F2012%2F11%2Fimagemagick_wizard_thumb.jpg\u0026amp;f=1\u0026amp;nofb=1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://imagemagick.org/index.php\" rel=\"nofollow\"\u003eImageMagick\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eUse ImageMagick\u00ae to create, edit, compose, or convert bitmap images. It can read and write images in a variety of formats (over 200) including PNG, JPEG, GIF, HEIC, TIFF, DPX, EXR, WebP, Postscript, PDF, and SVG. Use ImageMagick to resize, flip, mirror, rotate, distort, shear and transform images, adjust image colors, apply various special effects, or draw text, lines, polygons, ellipses and B\u00e9zier curves.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "ResearchIT/singularity-keras", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingulairty Recipe for keras\u003c/h1\u003e\u003ca id=\"user-content-singulairty-recipe-for-keras\" class=\"anchor\" aria-label=\"Permalink: Singulairty Recipe for keras\" href=\"#singulairty-recipe-for-keras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains the recipe for a basic keras install with gpu support.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersions:\u003c/h2\u003e\u003ca id=\"user-content-versions\" class=\"anchor\" aria-label=\"Permalink: Versions:\" href=\"#versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e1.0 - Initial effort\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, - "topics": [ - "singularity", - "utilities", - "image-processing" - ], - "updated_at": 1729796703.0 + "subscribers_count": 7, + "topics": [], + "updated_at": 1531950701.0 }, { "data_format": 2, - "description": null, + "description": "A GPU enabled Singularity image for the BarraCUDA short read aligner", "filenames": [ - "anaconda3/Singularity.5.3.0", - "anaconda3/Singularity", - "rstudio/Singularity.3.5.1", - "rstudio/Singularity.3.4.4", - "rstudio/Singularity", - "gephi/Singularity.0.9.1", - "gephi/Singularity.0.9.2", - "jupyter/Singularity.4.4.0", - "jupyter/Singularity", - "anaconda2/Singularity.5.3.0", - "anaconda2/Singularity" + "Singularity" ], - "full_name": "uncch-rdmc/singularity-dev-images", + "full_name": "KevinSayers/BarraCUDA_Singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-dev-images\u003c/h1\u003e\u003ca id=\"user-content-singularity-dev-images\" class=\"anchor\" aria-label=\"Permalink: singularity-dev-images\" href=\"#singularity-dev-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBarraCUDA_Singularity\u003c/h1\u003e\u003ca id=\"user-content-barracuda_singularity\" class=\"anchor\" aria-label=\"Permalink: BarraCUDA_Singularity\" href=\"#barracuda_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA GPU enabled Singularity image for the BarraCUDA short read aligner\u003c/p\u003e\n\u003cp\u003eNote: The nvidia/cuda Docker container that is used for bootstrapping must be a devel version. The others lack the nvcc compiler.\u003c/p\u003e\n\u003cp\u003eBarraCUDA paper: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278344/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278344/\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1556725305.0 + "updated_at": 1501794254.0 }, { "data_format": 2, - "description": "xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed limit.", + "description": null, "filenames": [ - "0.8.0/Singularity", - "0.8.1/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-xxhash", - "latest_release": "v0.8.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-xxhash/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-xxhash/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/38e6751944f979b39f22301ecf8a0769e81dc664fd9356a0b3b91ba35f7601a4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d787868617368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/38e6751944f979b39f22301ecf8a0769e81dc664fd9356a0b3b91ba35f7601a4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d787868617368\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-xxhash\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7b513d4b9426f73a45bd6cef405c18ba52dcbce34d97cf4359f5ed52b501ad7b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d787868617368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b513d4b9426f73a45bd6cef405c18ba52dcbce34d97cf4359f5ed52b501ad7b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d787868617368\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-xxhash\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e10a48f4a1caa69da93117716e45f60bb3ed92a40e52c3e9d0398bbed6539585/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d787868617368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e10a48f4a1caa69da93117716e45f60bb3ed92a40e52c3e9d0398bbed6539585/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d787868617368\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-xxhash\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/eb2962cd520a014471cc5af4a22bde6bc6af147099c98ef8651a90eac25190d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d787868617368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb2962cd520a014471cc5af4a22bde6bc6af147099c98ef8651a90eac25190d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d787868617368\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-xxhash\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-xxhash\u003c/h1\u003e\u003ca id=\"user-content-singularity-xxhash\" class=\"anchor\" aria-label=\"Permalink: singularity-xxhash\" href=\"#singularity-xxhash\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://xxhash.com/\" rel=\"nofollow\"\u003exxhash\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003exxh128sum\u003c/code\u003e, \u003ccode\u003exxh32sum\u003c/code\u003e, \u003ccode\u003exxh64sum\u003c/code\u003e and \u003ccode\u003exxhsum\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/xxhash/0.8.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/xxhash\u003c/code\u003e as \u003ccode\u003e0.8.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "famishedrover/pyhipop-planner", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHiPOP -- Hierarchical Partial-Order Planning\u003c/h1\u003e\u003ca id=\"user-content-hipop----hierarchical-partial-order-planning\" class=\"anchor\" aria-label=\"Permalink: HiPOP -- Hierarchical Partial-Order Planning\" href=\"#hipop----hierarchical-partial-order-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAuthors:\u003c/p\u003e\n\u003cp\u003eCharles Lesire, Alexandre Albore,\u003c/p\u003e\n\u003cp\u003eONERA/DTIS, Univ. of Toulouse, France\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1633063373.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1700254560.0 }, { "data_format": 2, - "description": "Singularity Container Handle", + "description": "Sngularity recipe for jupyter lab on R studio", "filenames": [ - "Singularity.1_4", - "Singularity.old", - "Singularity.1_11" + "Singularity" ], - "full_name": "jrenslo/singularity", + "full_name": "ISU-HPC/jupyter", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Container Handle\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejupyter\u003c/h1\u003e\u003ca id=\"user-content-jupyter\" class=\"anchor\" aria-label=\"Permalink: jupyter\" href=\"#jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSngularity recipe for jupyter lab on R studio\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1542304210.0 + "updated_at": 1527790762.0 }, { "data_format": 2, - "description": "Pipeline for Microbial Analysis (Quality control, Assembly, Annotation, Resistome, Virulome, Plasmid, Serotype, Prophages, Capsule, O-Locus, Closest genome and Genome Browser", + "description": null, "filenames": [ - "modules/phigaro/Singularity" + "Singularity" ], - "full_name": "lcerdeira/Pipa", + "full_name": "callaghanmt-containers/r_rgdal_tmap_sf", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/assets/PipaLogo.jpeg\"\u003e\u003cimg src=\"/assets/PipaLogo.jpeg\" alt=\"PIPA_Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePIPA\u003c/h1\u003e\u003ca id=\"user-content-pipa\" class=\"anchor\" aria-label=\"Permalink: PIPA\" href=\"#pipa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a7fe5fb2b697cc730b88bb0a32f4f63a028ab93b2bd841f2ff490ed7c3911c1d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f636f756e742f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a7fe5fb2b697cc730b88bb0a32f4f63a028ab93b2bd841f2ff490ed7c3911c1d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f636f756e742f6c63657264656972612f70697061\" alt=\"Code Count\" data-canonical-src=\"https://img.shields.io/github/languages/count/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/08b864adbd2221c4390737cee649add68be8487d237da9e0bd1f122911ccc351/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/08b864adbd2221c4390737cee649add68be8487d237da9e0bd1f122911ccc351/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f6c63657264656972612f70697061\" alt=\"Main Code Base\" data-canonical-src=\"https://img.shields.io/github/languages/top/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e)\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c5776335a454c60a3b541aea37c439a6c79384844ae65c20ef44294a0bd8eba4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d312e302d726564\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c5776335a454c60a3b541aea37c439a6c79384844ae65c20ef44294a0bd8eba4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d312e302d726564\" alt=\"Version\" data-canonical-src=\"https://img.shields.io/badge/version-1.0-red\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/91d49e5df623c07567903b3f59b0a194b4c5b128b17a3b7208eae32ecb336903/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d47504c76332d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/91d49e5df623c07567903b3f59b0a194b4c5b128b17a3b7208eae32ecb336903/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d47504c76332d626c7565\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/license-GPLv3-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a7330a27eb4f3b4a817c818e46a1b38712bb6d8f0e6fa0e378cc5b486dc6f2fe/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a7330a27eb4f3b4a817c818e46a1b38712bb6d8f0e6fa0e378cc5b486dc6f2fe/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f6c63657264656972612f70697061\" alt=\"Last Commit\" data-canonical-src=\"https://img.shields.io/github/last-commit/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e)\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e8a24a7914809ae13c3bffdc4637f367d0f74caf4e47d1c4f4739215a604b95f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d7261772f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8a24a7914809ae13c3bffdc4637f367d0f74caf4e47d1c4f4739215a604b95f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d7261772f6c63657264656972612f70697061\" alt=\"Open Issues\" data-canonical-src=\"https://img.shields.io/github/issues-raw/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e)\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/56c3438d581ac116cb5340ac5ca4a36699988658dc616e2483847715b88e90bc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f7265706f2d73697a652f6c63657264656972612f70697061\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/56c3438d581ac116cb5340ac5ca4a36699988658dc616e2483847715b88e90bc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f7265706f2d73697a652f6c63657264656972612f70697061\" alt=\"Repo Size\" data-canonical-src=\"https://img.shields.io/github/repo-size/lcerdeira/pipa\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#Description\"\u003eDescription\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline for Microbial Genomic Analysis\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNeed to be root of system to be installed.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./setup.sh\u003c/code\u003e to install all necessary libraries.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDr Louise Cerdeira - \u003ca href=\"mailto:Louise.Cerdeira@gmail.com\"\u003eLouise.Cerdeira@gmail.com\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003er_rgdal_tmap_sf\u003c/h1\u003e\u003ca id=\"user-content-r_rgdal_tmap_sf\" class=\"anchor\" aria-label=\"Permalink: r_rgdal_tmap_sf\" href=\"#r_rgdal_tmap_sf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1722552774.0 + "updated_at": 1556721577.0 }, { "data_format": 2, - "description": "Singularity container for dropSeqPipe", + "description": "centos7 singularity container for HPC", "filenames": [ - "Singularity.v04", "Singularity" ], - "full_name": "seb-mueller/singularity_dropSeqPipe", + "full_name": "DoaneAS/dijon", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1569595505.0 + "updated_at": 1493118640.0 }, { "data_format": 2, - "description": "get rstudio on PSU ACI", + "description": "They keep messing up their docker repository", "filenames": [ - "Singularity.ml", "Singularity" ], - "full_name": "d-bohn/rstudio_aci", + "full_name": "ISU-HPC/Roary", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erstudio_aci\u003c/h1\u003e\u003ca id=\"user-content-rstudio_aci\" class=\"anchor\" aria-label=\"Permalink: rstudio_aci\" href=\"#rstudio_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and workflow for building \u003ca href=\"https://www.rocker-project.org/\" rel=\"nofollow\"\u003erocker/verse\u003c/a\u003e\nin Docker Hub and modifying it with Singularity Hub for use with PSU\nACI HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003essh\u003c/code\u003e into the PSU ACI HPC with X11 flags.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh USERID@aci-b.aci.ics.psu.edu -X -Y\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart an interactive session using \u003ccode\u003eqsub\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -A open -I -X -l walltime=24:00:00 -l nodes=2:ppn=20 -l pmem=10gb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom ACI, start \u003ccode\u003escreen\u003c/code\u003e and then execute the following code to\ncreate an \u003ccode\u003eRStudio\u003c/code\u003e image running at address \u003ccode\u003e127.0.0.1:8787\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escreen\n\nsingularity pull -n rstudio_aci.simg shub://d-bohn/rstudio_aci\n\nsingularity exec rstudio_aci.simg rserver --www-address=127.0.0.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, press \u003ccode\u003eCTRL+A+D\u003c/code\u003e to detach the screen while allowing the process to continue running in the background.\u003c/p\u003e\n\u003cp\u003eFinally, start your preferred browser and navigate to \u003ccode\u003e127.0.0.1\u003c/code\u003e. For\nexample, firefox:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://jpetucci-firefox_icsaci\n\nsingularity exec jpetucci-firefox_icsaci-master-latest.simg /opt/firefox/./firefox\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e1). A \u003ccode\u003eshiny\u003c/code\u003e server should also start when executing this image,\nthe server should be running on port \u003ccode\u003e3838\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRoary\u003c/h1\u003e\u003ca id=\"user-content-roary\" class=\"anchor\" aria-label=\"Permalink: Roary\" href=\"#roary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThey keep messing up their docker repository\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1562649674.0 + "updated_at": 1540592377.0 }, { "data_format": 2, "description": null, "filenames": [ - "containers/Singularity.omnia.rocm" + "Singularity" ], - "full_name": "PhilipVinc/uvtest2", + "full_name": "truatpasteurdotfr/singularity-d12-scipion-runtime-cuda123", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOmnia\u003c/h1\u003e\u003ca id=\"user-content-omnia\" class=\"anchor\" aria-label=\"Permalink: Omnia\" href=\"#omnia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a single repository containing all working codes of the group.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStructure\u003c/h2\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e/packages # contains packages used by multiple users\n # This contains both internal versions of public packages\n # (netket) and private packages.\n```\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebuilding a debian12 based singularity/apptainer container for scipion3 (using cuda 12.3 which is the oldest cuda version for debian12 as of 2024/07/08)\u003c/h1\u003e\u003ca id=\"user-content-building-a-debian12-based-singularityapptainer-container-for-scipion3-using-cuda-123-which-is-the-oldest-cuda-version-for-debian12-as-of-20240708\" class=\"anchor\" aria-label=\"Permalink: building a debian12 based singularity/apptainer container for scipion3 (using cuda 12.3 which is the oldest cuda version for debian12 as of 2024/07/08)\" href=\"#building-a-debian12-based-singularityapptainer-container-for-scipion3-using-cuda-123-which-is-the-oldest-cuda-version-for-debian12-as-of-20240708\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhy ?\u003c/h2\u003e\u003ca id=\"user-content-why-\" class=\"anchor\" aria-label=\"Permalink: Why ?\" href=\"#why-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eready to use runtime environment for scipion3\u003c/li\u003e\n\u003cli\u003ejust add miniconda and scipion3 (use a writable bind mount for /opt)\u003c/li\u003e\n\u003cli\u003eallow easy snapshoting/replacement for /opt and upgrade/downgrade\u003c/li\u003e\n\u003cli\u003eshare scipion with your co-worker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to:\u003c/h2\u003e\u003ca id=\"user-content-how-to\" class=\"anchor\" aria-label=\"Permalink: How to:\" href=\"#how-to\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ebuild the container\u003c/li\u003e\n\u003cli\u003euse the container with a mounted /opt to\n\u003cul\u003e\n\u003cli\u003einstall miniconda and scipion-installer\u003c/li\u003e\n\u003cli\u003einstall any plugin/binary\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003euse mksquashfs \"freeze\" your scipion and share it with the debian runtime\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ehelper scripts (read and modify to your needs especially \u003ccode\u003e_S\u003c/code\u003e and \u003ccode\u003e_B\u003c/code\u003e)\u003c/h2\u003e\u003ca id=\"user-content-helper-scripts-read-and-modify-to-your-needs-especially-_s-and-_b\" class=\"anchor\" aria-label=\"Permalink: helper scripts (read and modify to your needs especially _S and _B)\" href=\"#helper-scripts-read-and-modify-to-your-needs-especially-_s-and-_b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esetup.sh\u003c/code\u003e build the container and install scipion3 conda environment\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escipion3.sh\u003c/code\u003e run scipion3 from the container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003euse the artefact produced on github instead of building your own:\u003c/h2\u003e\u003ca id=\"user-content-use-the-artefact-produced-on-github-instead-of-building-your-own\" class=\"anchor\" aria-label=\"Permalink: use the artefact produced on github instead of building your own:\" href=\"#use-the-artefact-produced-on-github-instead-of-building-your-own\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eapptainer build ghrc-io-singularity-d12-scipion-runtime-cuda123.sif oras://ghcr.io/truatpasteurdotfr/singularity-d12-scipion-runtime-cuda123:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://scipion.i2pc.es/\" rel=\"nofollow\"\u003ehttps://scipion.i2pc.es/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCaveat\u003c/h2\u003e\u003ca id=\"user-content-caveat\" class=\"anchor\" aria-label=\"Permalink: Caveat\" href=\"#caveat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1732149899.0 + "updated_at": 1720732063.0 }, { "data_format": 2, "description": null, "filenames": [ - "containers/Singularity.omnia.rocm" + "Singularity" ], - "full_name": "PhilipVinc/uvtest3", + "full_name": "SciNetHPC/singularity-CRISPRCasFinder", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOmnia\u003c/h1\u003e\u003ca id=\"user-content-omnia\" class=\"anchor\" aria-label=\"Permalink: Omnia\" href=\"#omnia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a single repository containing all working codes of the group.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStructure\u003c/h2\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e/packages # contains packages used by multiple users\n # This contains both internal versions of public packages\n # (netket) and private packages.\n```\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1732150434.0 + "updated_at": 1542826836.0 }, { "data_format": 2, - "description": "A singularity image for the MGEfinder software", + "description": "Pandoc is a free and open-source document converter, widely used as a writing tool and as a basis for publishing workflows.", "filenames": [ - "Singularity" + "3.1.1/Singularity", + "2.18/Singularity", + "2.2.1/Singularity" ], - "full_name": "bhattlab/MGEfinder-singularity", - "latest_release": null, + "full_name": "pscedu/singularity-pandoc", + "latest_release": "v2.18", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-pandoc/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-pandoc/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-pandoc/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-pandoc/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4346713e9da7ba810ef27b2a44f8b33e47b02c264afc5c5e106fb984a26ad669/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d70616e646f63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4346713e9da7ba810ef27b2a44f8b33e47b02c264afc5c5e106fb984a26ad669/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d70616e646f63\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-pandoc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/03f7d32b0cccc34d047656666fe6bfaff4953ac365e41e0c4349d20c2c1bcb1a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d70616e646f63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/03f7d32b0cccc34d047656666fe6bfaff4953ac365e41e0c4349d20c2c1bcb1a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d70616e646f63\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-pandoc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ab81f0905c8940279814f23c661f2f0dcbed9e252ec51aef7e19e226a841b9aa/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d70616e646f63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ab81f0905c8940279814f23c661f2f0dcbed9e252ec51aef7e19e226a841b9aa/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d70616e646f63\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-pandoc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4dca258df814adeb29b104b0a0b9d77357a405889c2b22690fbd8b7ecea496f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d70616e646f63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4dca258df814adeb29b104b0a0b9d77357a405889c2b22690fbd8b7ecea496f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d70616e646f63\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-pandoc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-pandoc\u003c/h1\u003e\u003ca id=\"user-content-singularity-pandoc\" class=\"anchor\" aria-label=\"Permalink: singularity-pandoc\" href=\"#singularity-pandoc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://pandoc.org/\" rel=\"nofollow\"\u003epandoc\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003epandoc\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/pandoc/2.2.1\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/pandoc\u003c/code\u003e as \u003ccode\u003e2.2.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, - "topics": [], - "updated_at": 1586559532.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1715636760.0 }, { "data_format": 2, - "description": "Nemo Utility for Testing SETTE", + "description": "Singularity container with Alpine OS, and miniconda3 installed in /opt/miniconda. ", "filenames": [ - "Singularity.nemo", - "base_def/Singularity.nemo_baseOS" + "Singularity" ], - "full_name": "jdha/NUTS", - "latest_release": "0.0.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNUTS\u003c/h1\u003e\u003ca id=\"user-content-nuts\" class=\"anchor\" aria-label=\"Permalink: NUTS\" href=\"#nuts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNemo Utility for Testing SETTE\u003c/p\u003e\n", + "full_name": "carissableker/alpine-miniconda-singularity", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAlpine Miniconda Singularity\u003c/h1\u003e\u003ca id=\"user-content-alpine-miniconda-singularity\" class=\"anchor\" aria-label=\"Permalink: Alpine Miniconda Singularity\" href=\"#alpine-miniconda-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container with Alpine OS, and miniconda3 installed in /opt/miniconda.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot miniconda.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSylabs Container Library\u003c/h2\u003e\u003ca id=\"user-content-sylabs-container-library\" class=\"anchor\" aria-label=\"Permalink: Sylabs Container Library\" href=\"#sylabs-container-library\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://cbleker/dev/alpine-miniconda-singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInspired by:\u003c/h2\u003e\u003ca id=\"user-content-inspired-by\" class=\"anchor\" aria-label=\"Permalink: Inspired by:\" href=\"#inspired-by\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/layers/continuumio/miniconda3/4.8.2-alpine/images/sha256-b9c9f2c7748abdb3291ef2e9b04a8ef3e355f0d7e8030e7a07b8f26c11ed88be?context=explore\" rel=\"nofollow\"\u003econtinuumio/miniconda3:4.8.2-alpine\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Docker-Hub-frolvlad/docker-alpine-miniconda3\"\u003ehttps://github.com/Docker-Hub-frolvlad/docker-alpine-miniconda3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/show0k/alpine-jupyter-docker\"\u003ehttps://github.com/show0k/alpine-jupyter-docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcng/singularity/issues/5075\"\u003ehttps://github.com/hpcng/singularity/issues/5075\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1724894895.0 + "updated_at": 1601374050.0 }, { "data_format": 2, - "description": "Tools for monitoring HTCondor and other things", + "description": "Detects copy number variants in exomes and gene panels using high throughput DNA sequencing data", "filenames": [ "Singularity" ], - "full_name": "WIPACrepo/monitoring-scripts", - "latest_release": "0.4.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emonitoring-scripts\u003c/h1\u003e\u003ca id=\"user-content-monitoring-scripts\" class=\"anchor\" aria-label=\"Permalink: monitoring-scripts\" href=\"#monitoring-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome scripts for sending data to ES, or plotting it, or other misc activities.\u003c/p\u003e\n", + "full_name": "PhilPalmer/exomedepth", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/exomedepth\u003c/h1\u003e\u003ca id=\"user-content-nf-coreexomedepth\" class=\"anchor\" aria-label=\"Permalink: nf-core/exomedepth\" href=\"#nf-coreexomedepth\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDetects copy number variants in exomes and gene panels using high throughput DNA sequencing data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/exomedepth\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/015ae7651ea4b7da42335298fa163b8df51591214941b98882663f4bc8d97b3a/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f65786f6d6564657074682e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/exomedepth.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/780f0e426d3a9fd5f3f54407686be63867cb8093d09e36c9bcbad58b728a111d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/exomedepth\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/36aed7cd499155ccd53712f856d14059fbb3a99b7e042f8624b3ed424e18c3d3/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f65786f6d6564657074682e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/exomedepth.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/exomedepth pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1730332969.0 + "updated_at": 1576776775.0 }, { "data_format": 2, - "description": "VisiData is an interactive multitool for tabular data. ", + "description": "Container for SLEAP", "filenames": [ - "2.7.1/Singularity", - "2.11.1/Singularity", - "2.8/Singularity", - "3.0.2/Singularity", - "2.10.2/Singularity", - "2.11/Singularity", - "2.6.1/Singularity", - "2.4/Singularity", - "3.1/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-visidata", - "latest_release": "v3.0.2", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-visidata/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-visidata/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-visidata/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-visidata/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8ccda96e18c4e06a66abeac2ca4c6742ebe7cb601c19b51b52b4f3681991576b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7669736964617461\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ccda96e18c4e06a66abeac2ca4c6742ebe7cb601c19b51b52b4f3681991576b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7669736964617461\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-visidata\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9d1212476e5d75a4cb9a460f9b7b86da67f385469f99ad3ffaa66f99d3ccdaeb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7669736964617461\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9d1212476e5d75a4cb9a460f9b7b86da67f385469f99ad3ffaa66f99d3ccdaeb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7669736964617461\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-visidata\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/521097008ddb94c7331ce0e2b89298737f5dde3737ac8917329163c9e3e0cbab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7669736964617461\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/521097008ddb94c7331ce0e2b89298737f5dde3737ac8917329163c9e3e0cbab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7669736964617461\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-visidata\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b3b5cfe9b1897a6ba2ea5a4092977dc9e63453ef0404dd0b196e8f7a71fe946f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7669736964617461\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b3b5cfe9b1897a6ba2ea5a4092977dc9e63453ef0404dd0b196e8f7a71fe946f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7669736964617461\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-visidata\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-visidata\u003c/h1\u003e\u003ca id=\"user-content-singularity-visidata\" class=\"anchor\" aria-label=\"Permalink: singularity-visidata\" href=\"#singularity-visidata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.visidata.org/\" rel=\"nofollow\"\u003evisidata\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003evd\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/visidata/2.7.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/visidata\u003c/code\u003e as \u003ccode\u003e2.7.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "maouw/sleap-container", + "latest_release": "0.9.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esleap-container\u003c/h1\u003e\u003ca id=\"user-content-sleap-container\" class=\"anchor\" aria-label=\"Permalink: sleap-container\" href=\"#sleap-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis a \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003econtainer\u003c/a\u003e to run \u003ca href=\"https://sleap.ai\" rel=\"nofollow\"\u003eSLEAP\u003c/a\u003e jobs on the University of Washington \u003ca href=\"https://hyak.uw.edu\" rel=\"nofollow\"\u003eHyak\u003c/a\u003e cluster. You can use this container to run SLEAP training and prediction jobs on Hyak in a GPU-accelerated environment.\ns, which provide reproducible environments that can run anywhere and be shared with other researchers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore running this container, you\u0027ll need the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA Linux, macOS, or Windows machine\u003c/li\u003e\n\u003cli\u003eAn SSH client (usually included with Linux and macOS, and available for Windows through the built-in SSH client on Windows 10+, \u003ca href=\"https://learn.microsoft.com/en-us/windows/wsl/install\" rel=\"nofollow\"\u003eWSL2\u003c/a\u003e or \u003ca href=\"https://www.cs.odu.edu/~zeil/cs252/latest/Public/loggingin/cygwin.mmd.html\" rel=\"nofollow\"\u003eCygwin\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://hyak.uw.edu\" rel=\"nofollow\"\u003eHyak\u003c/a\u003e Klone access with compute resources\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFollow the instructions below to set up your machine correctly:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstalling SSH\u003c/h3\u003e\u003ca id=\"user-content-installing-ssh\" class=\"anchor\" aria-label=\"Permalink: Installing SSH\" href=\"#installing-ssh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLinux\u003c/h4\u003e\u003ca id=\"user-content-linux\" class=\"anchor\" aria-label=\"Permalink: Linux\" href=\"#linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using Linux, OpenSSH is probably installed already -- if not, you can install it via \u003ccode\u003eapt-get install openssh-client\u003c/code\u003e on Debian/Ubuntu or \u003ccode\u003eyum install openssh-clients\u003c/code\u003e on RHEL/CentOS/Rocky/Fedora. To open a terminal window, search for \"Terminal\" in your desktop environment\u0027s application launcher.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003emacOS\u003c/h4\u003e\u003ca id=\"user-content-macos\" class=\"anchor\" aria-label=\"Permalink: macOS\" href=\"#macos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re on macOS, OpenSSH will already be installed. To open a terminal window, open \u003ccode\u003e/Applications/Utilities/Terminal.app\u003c/code\u003e or search for \"Terminal\" in Launchpad or Spotlight.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWindows\u003c/h4\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Windows 10+, you can \u003ca href=\"https://learn.microsoft.com/en-us/windows/terminal/tutorials/ssh\" rel=\"nofollow\"\u003euse the built-in SSH client\u003c/a\u003e. You may also install a SSH client through \u003ca href=\"https://learn.microsoft.com/en-us/windows/wsl/install\" rel=\"nofollow\"\u003eWSL2\u003c/a\u003e or \u003ca href=\"https://www.cs.odu.edu/~zeil/cs252/latest/Public/loggingin/cygwin.mmd.html\" rel=\"nofollow\"\u003eCygwin\u003c/a\u003e (not recommended, needs additional setup). See the links for instructions on how to install these. You can start a terminal window by searching for \"Terminal\" in the Start menu.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetting up SSH keys to connect to Hyak compute nodes\u003c/h3\u003e\u003ca id=\"user-content-setting-up-ssh-keys-to-connect-to-hyak-compute-nodes\" class=\"anchor\" aria-label=\"Permalink: Setting up SSH keys to connect to Hyak compute nodes\" href=\"#setting-up-ssh-keys-to-connect-to-hyak-compute-nodes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore you are allowed to connect to a compute node where your SLEAP job will be running, you must add your SSH public key to the authorized keys on the login node of the Hyak Klone cluster.\u003c/p\u003e\n\u003cp\u003eIf you don\u0027t, you will receive an error like this when you try to connect to the compute node:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003ePermission denied (publickey,gssapi-keyex,gssapi-with-mic)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo set this up quickly on Linux, macOS, or Windows (WSL2/Cygwin), open a new terminal window \u003cstrong\u003eon your machine\u003c/strong\u003e and enter the following 2 commands before you try again. Replace \u003ccode\u003eyour-uw-netid\u003c/code\u003e with your UW NetID:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e[ \u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e-r\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.ssh/id_rsa ] \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e ssh-keygen -t rsa -b 4096 -N \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -C \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eyour-uw-netid@uw.edu\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -f \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.ssh/id_rsa\nssh-copy-id -o StrictHostKeyChecking=no -i \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.ssh/id_rsa your-uw-netid@klone.hyak.uw.edu\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"https://hyak.uw.edu/docs/setup/intracluster-keys\" rel=\"nofollow\"\u003eHyak documentation\u003c/a\u003e for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSet up the Apptainer cache directory on Hyak \u003ccode\u003eklone\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-set-up-the-apptainer-cache-directory-on-hyak-klone\" class=\"anchor\" aria-label=\"Permalink: Set up the Apptainer cache directory on Hyak klone\" href=\"#set-up-the-apptainer-cache-directory-on-hyak-klone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eApptainer containers can take up several gigabytes of space each. By default, Apptainer will store cached containers in your home directory (\u003ccode\u003e~\u003c/code\u003e), under \u003ccode\u003e~/.cache/apptainer\u003c/code\u003e. However, because home directory space on Hyak is limited to 10 GiB per user, you may want to set up a different cache directory.\u003c/p\u003e\n\u003cp\u003eWe advise setting up a cache directory under the \u003ccode\u003e/tmp\u003c/code\u003e directory or in the \u003ca href=\"https://hyak.uw.edu/docs/storage/gscratch/\" rel=\"nofollow\"\u003escrubbed\u003c/a\u003e directory, under \u003ccode\u003e/gscratch/scrubbed/your-uw-netid\u003c/code\u003e. To set this up, first connect to \u003ccode\u003eklone.hyak.uw.edu\u003c/code\u003e via SSH:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh your-uw-netid@klone.hyak.uw.edu \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Replace your-uw-netid with your UW NetID\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce you\u0027re logged in, create a directory for the cache and set the \u003ccode\u003eAPPTAINER_CACHEDIR\u003c/code\u003e environment variable to point to it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e$USER\u003c/span\u003e/apptainer-cache\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e APPTAINER_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e$USER\u003c/span\u003e/apptainer-cache\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, add the following line to your \u003ccode\u003e~/.bashrc\u003c/code\u003e file (or \u003ccode\u003e~/.zshrc\u003c/code\u003e if you use ZSH) to retain this setting across multiple logins:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport APPTAINER_CACHEDIR=\u003cspan class=\"pl-cce\"\u003e\\\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e$USER\u003c/span\u003e/apptainer-cache\u003cspan class=\"pl-cce\"\u003e\\\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis guide assumes that you are running SLEAP on your own machine, with an open SLEAP project that you are ready to start training on. If you need help creating a SLEAP project, consult the \u003ca href=\"https://sleap.ai/tutorials/tutorial.html\" rel=\"nofollow\"\u003eSLEAP documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo start training your model on the cluster, you must first create a \u003cem\u003etraining package\u003c/em\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA self-contained \u003cstrong\u003etraining job package\u003c/strong\u003e contains a .slp file with labeled data and images which will be used for training, as well as .json training configuration file(s). \u003ca href=\"https://sleap.ai/notebooks/Training_and_inference_using_Google_Drive.html\" rel=\"nofollow\"\u003e*\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExporting a training package\u003c/h3\u003e\u003ca id=\"user-content-exporting-a-training-package\" class=\"anchor\" aria-label=\"Permalink: Exporting a training package\" href=\"#exporting-a-training-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can create a training job package in the \u003ccode\u003esleap-label\u003c/code\u003e GUI by following the \u003ccode\u003eRun Training...\u003c/code\u003e option under the \u003ccode\u003ePredict\u003c/code\u003e menu:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/screenshots/01-main_dropdown_predict_run_training.png\"\u003e\u003cimg src=\"./docs/screenshots/01-main_dropdown_predict_run_training.png\" alt=\"SLEAP GUI: Main Window Run Training\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSet the parameters for your training job (refer to \u003ca href=\"https://sleap.ai/tutorials/initial-training.html\" rel=\"nofollow\"\u003eSLEAP documentation\u003c/a\u003e if you\u0027re not sure), and click \u003ccode\u003eExport training job package\u003c/code\u003e once you\u0027re done:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/screenshots/02-run_training_dialog.png\"\u003e\u003cimg src=\"./docs/screenshots/02-run_training_dialog.png\" alt=\"SLEAP GUI: Run Training Dialog\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNext, you should see a dialog that says, \u003ccode\u003eCreated training job package.\u003c/code\u003e Click \u003ccode\u003eShow Details...\u003c/code\u003e:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/screenshots/03-created_training_job_package.png\"\u003e\u003cimg src=\"./docs/screenshots/03-created_training_job_package.png\" alt=\"SLEAP GUI: Created training job package\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe full file path to the training package will be displayed (e.g., \u003ccode\u003e/home/me/sleap/my_training_job.zip\u003c/code\u003e). Select and copy this path:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/screenshots/04-created_training_job_package_details.png\"\u003e\u003cimg src=\"./docs/screenshots/04-created_training_job_package_details.png\" alt=\"SLEAP GUI: Run Training Dialog\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUploading a training package to the cluster\u003c/h3\u003e\u003ca id=\"user-content-uploading-a-training-package-to-the-cluster\" class=\"anchor\" aria-label=\"Permalink: Uploading a training package to the cluster\" href=\"#uploading-a-training-package-to-the-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow you must use the terminal on your computer to upload the training package to the Hyak cluster. You can find instructions on how to set up your terminal to access Hyak \u003ca href=\"https://uw-psych.github.io/compute_docs/hyak/start/connect-ssh.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOpen a terminal window \u003cstrong\u003eon your computer\u003c/strong\u003e and enter the following command to copy the training package to your home directory (\u003ccode\u003e~\u003c/code\u003e) on the cluster:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escp /home/me/sleap/my_training_job.zip your-uw-netid@klone.hyak.uw.edu: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Replace your-uw-netid with your UW NetID\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNOTE: You may need to log in with your UW NetID and two-factor authentication.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunnning the training package on the cluster\u003c/h3\u003e\u003ca id=\"user-content-runnning-the-training-package-on-the-cluster\" class=\"anchor\" aria-label=\"Permalink: Runnning the training package on the cluster\" href=\"#runnning-the-training-package-on-the-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the file has been copied, log in to the cluster via SSH:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh your-uw-netid@klone.hyak.uw.edu \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Replace your-uw-netid with your UW NetID\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExtracting the training package\u003c/h4\u003e\u003ca id=\"user-content-extracting-the-training-package\" class=\"anchor\" aria-label=\"Permalink: Extracting the training package\" href=\"#extracting-the-training-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe training package should be located in your home directory on \u003ccode\u003eklone\u003c/code\u003e. You can check by running \u003ccode\u003els\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003els \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.zip \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Should display all ZIP files in directory, including `my_training_job.zip`\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUnzip the package file to a new directory. Let\u0027s call it \u003ccode\u003etraining_job\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eunzip my_training_job.zip -d training_job\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAllocating a node on the cluster\u003c/h4\u003e\u003ca id=\"user-content-allocating-a-node-on-the-cluster\" class=\"anchor\" aria-label=\"Permalink: Allocating a node on the cluster\" href=\"#allocating-a-node-on-the-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are almost ready to launch the container. First, though, we need to allocate a job on the cluster. We will use the \u003ccode\u003esalloc\u003c/code\u003e command to do this.\u003c/p\u003e\n\u003cp\u003eThe following command will allocate a job on one node with 4 GPUs, 64 GB of memory, and 8 CPUs for 24 hours on the \u003ccode\u003egpu-a40\u003c/code\u003e partition available to the \u003ccode\u003eescience\u003c/code\u003e account. You can adjust these parameters as needed. For more information on the \u003ccode\u003esalloc\u003c/code\u003e command, see this page and the \u003ca href=\"https://slurm.schedmd.com/salloc.html\" rel=\"nofollow\"\u003esalloc documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esalloc --job-name sleap-train-test \\\n --account escience \\\n --partition gpu-a40 \\\n --gpus 4 \\\n --ntasks 1 \\\n --gpus-per-task=4 \\\n --mem 64G \\\n --cpus-per-task 4 \\\n --time 24:00:00\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen the allocation is ready, you will automatically connect to the compute node. When you exit this session, the allocation will automatically be released.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning SLEAP\u003c/h4\u003e\u003ca id=\"user-content-running-sleap\" class=\"anchor\" aria-label=\"Permalink: Running SLEAP\" href=\"#running-sleap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eVerifying GPU access\u003c/h5\u003e\u003ca id=\"user-content-verifying-gpu-access\" class=\"anchor\" aria-label=\"Permalink: Verifying GPU access\" href=\"#verifying-gpu-access\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you are connected to the node, you can verify that the SLEAP container has access to the GPUs by running the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run --nv --bind /gscratch oras://ghcr.io/maouw/sleap-container:latest python -c \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eimport sleap; sleap.system_summary()\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should get output that looks something like this:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eGPUs: 4/4 available\n Device: /physical_device:GPU:0\n Available: True\n Initalized: False\n Memory growth: None\n Device: /physical_device:GPU:1\n Available: True\n Initalized: False\n Memory growth: None\n Device: /physical_device:GPU:2\n Available: True\n Initalized: False\n Memory growth: None\n Device: /physical_device:GPU:3\n Available: True\n Initalized: False\n Memory growth: None\n6.40s user 6.24s system 88% cpu 14.277s total\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eTraining the model\u003c/h5\u003e\u003ca id=\"user-content-training-the-model\" class=\"anchor\" aria-label=\"Permalink: Training the model\" href=\"#training-the-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow, navigate to the directory where you unzipped the training package:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/training_job\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe next step is to launch the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run --nv --bind /gscratch oras://ghcr.io/maouw/sleap-container:latest bash train-script.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eApptainer will download the container image from GitHub and launch it on the node. The option \u003ccode\u003e--nv\u003c/code\u003e enables Nvidia GPU support. Once the container has launched, it will instruct \u003ccode\u003ebash\u003c/code\u003e to run the script \u003ccode\u003etrain-script.sh\u003c/code\u003e. This script will start the training job.\u003c/p\u003e\n\u003cp\u003eDuring training, you will see a lot of output in the terminal. After some time, if training is successful, the last of the output should look something similar to this:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eINFO:sleap.nn.evals:Saved predictions: models/231009_165437.centered_instance/labels_pr.train.slp\nINFO:sleap.nn.evals:Saved metrics: models/231009_165437.centered_instance/metrics.train.npz\nINFO:sleap.nn.evals:OKS mAP: 0.205979\nPredicting... \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% ETA: 0:00:00 3.3 FPS\nINFO:sleap.nn.evals:Saved predictions: models/231009_165437.centered_instance/labels_pr.val.slp\nINFO:sleap.nn.evals:Saved metrics: models/231009_165437.centered_instance/metrics.val.npz\nINFO:sleap.nn.evals:OKS mAP: 0.064026\n229.63s user 44.64s system 77% cpu 5:53.45s total\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce training finishes, you\u0027ll see a new directory (or two new directories for top-down training pipeline) containing all the model files SLEAP needs to use for inference:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003els models/\u003c/pre\u003e\u003c/div\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e231009_165437.centered_instance 231009_165437.centroid\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can use these model files to run inference on your own computer, or you can run inference on the cluster (consult the \u003ca href=\"https://sleap.ai/guides/remote.html\" rel=\"nofollow\"\u003eSLEAP documentation\u003c/a\u003e for more information).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownloading the model\u003c/h3\u003e\u003ca id=\"user-content-downloading-the-model\" class=\"anchor\" aria-label=\"Permalink: Downloading the model\" href=\"#downloading-the-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo copy the model files back to your computer, in a terminal where you are logged into \u003ccode\u003eklone.hyak.uw.edu\u003c/code\u003e, compress the model directory with \u003ccode\u003ezip\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/training_job\nzip -r trained_models.zip models\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, in a new terminal window \u003cem\u003eon your own computer\u003c/em\u003e, use the \u003ccode\u003escp\u003c/code\u003e command to copy the model files from \u003ccode\u003eklone\u003c/code\u003e to your computer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escp your-uw-netid@klone.hyak.uw.edu:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/training_job/trained_models.zip \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Replace your-uw-netid with your UW NetID\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will copy the file \u003ccode\u003etrained_models.zip\u003c/code\u003e to your current directory. You can then unzip the file and use the model files for inference on your own computer. Consult the \u003ca href=\"https://sleap.ai/guides/remote.html\" rel=\"nofollow\"\u003eSLEAP documentation\u003c/a\u003e for more information on running inference with a trained model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnding the cluster job\u003c/h3\u003e\u003ca id=\"user-content-ending-the-cluster-job\" class=\"anchor\" aria-label=\"Permalink: Ending the cluster job\" href=\"#ending-the-cluster-job\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eBe sure to end your cluster job when you are done!\u003c/strong\u003e This will free up resources for other users and potentially prevent you from being charged for time you are not using.\u003c/p\u003e\n\u003cp\u003eTo do this, go back to the terminal where you were running SLEAP on the cluster. (If you closed the terminal, you can log back in to the cluster with \u003ccode\u003essh klone.hyak.uw.edu\u003c/code\u003e.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIf you\u0027re still logged in to the compute node\u003c/strong\u003e, exit:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCancel the job allocation with the \u003ccode\u003escancel\u003c/code\u003e command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escancel --me --jobname sleap-train-test\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, exit the cluster:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSLEAP well!\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1729457707.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1719879342.0 }, { "data_format": 2, - "description": "This is an openxdmod container for the bruno super computer @ biohub sf", + "description": "RNA-seq analysis for Kannan et al. 2019", "filenames": [ - "Singularity" + "Singularity", + "Singularity.Bioc3.8" ], - "full_name": "romxero/czbiohub_openxdmod", + "full_name": "winni2k/kannan_et_al_2019", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1720476769.0 + "updated_at": 1551895749.0 }, { "data_format": 2, - "description": "Jupyterlab plugin to interface with the EVN Archive", + "description": "The codebase for the DI-LSTM neural network framework. ", "filenames": [ "Singularity" ], - "full_name": "aardk/EVN-Archive", + "full_name": "Randers114/DI-LSTM", "latest_release": null, + "readme": "\u003cp\u003eTo run the model execute \"python -m models.model_tester\".\u003c/p\u003e\n\u003cp\u003eFor more information, the project can be found at \"\u003ca href=\"https://projekter.aau.dk/projekter/da/studentthesis/estimating-travel-cost-distributions-of-paths-in-road-networks-using-dualinput-lstms(a9e50268-29d3-4123-9ef1-766bf20307b0).html\" rel=\"nofollow\"\u003ehttps://projekter.aau.dk/projekter/da/studentthesis/estimating-travel-cost-distributions-of-paths-in-road-networks-using-dualinput-lstms(a9e50268-29d3-4123-9ef1-766bf20307b0).html\u003c/a\u003e\" if you have AAU access.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1727784318.0 + "updated_at": 1594109503.0 }, { "data_format": 2, - "description": "Molecular electrostatics singularity image", + "description": "Singularity image for biocontainers bamtools", "filenames": [ "Singularity" ], - "full_name": "nbcrrolls/electrostatics-singularity", - "latest_release": "v2.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container for molecular electrostatic calculations using PDB2PQR/APBS and Brownian dynamics with BrownDye.\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-molecular-electrostatic-calculations-using-pdb2pqrapbs-and-brownian-dynamics-with-browndye\" class=\"anchor\" aria-label=\"Permalink: Singularity container for molecular electrostatic calculations using PDB2PQR/APBS and Brownian dynamics with BrownDye.\" href=\"#singularity-container-for-molecular-electrostatic-calculations-using-pdb2pqrapbs-and-brownian-dynamics-with-browndye\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis singularity image contains a complete software environment for running \u003ca href=\"http://browndye.ucsd.edu/\" rel=\"nofollow\"\u003eBrownDye (version 1 and 2)\u003c/a\u003e simulations. It also includes \u003ca href=\"http://www.poissonboltzmann.org/\" rel=\"nofollow\"\u003ePDB2PQR\u003c/a\u003e and \u003ca href=\"http://www.poissonboltzmann.org/\" rel=\"nofollow\"\u003eAPBS\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease \u003ca href=\"http://eepurl.com/by4eQr\" rel=\"nofollow\"\u003eregister\u003c/a\u003e your use of APBS and PDB2PQR.\u003c/p\u003e\n\u003cp\u003eThe image has been verified to work on XSEDE \u003ca href=\"https://portal.xsede.org/sdsc-comet\" rel=\"nofollow\"\u003ecomet\u003c/a\u003e and \u003ca href=\"https://www.sdsc.edu/support/user_guides/tscc-quick-start.html\" rel=\"nofollow\"\u003eTSCC\u003c/a\u003e shared cluster at SDSC. It will automatically bind \u003ccode\u003e/cvmfs\u003c/code\u003e \u003ccode\u003e/oasis\u003c/code\u003e \u003ccode\u003e/projects\u003c/code\u003e \u003ccode\u003e/scratch\u003c/code\u003e directories, if available on the host.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing the container\u003c/h2\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull the singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://nbcrrolls/electrostatics-singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart bash shell in the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell nbcrrolls-electrostatics-singularity-master-latest.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow the container is running and we can start a BrownDye2 job (using the Thrombin example):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load browndye2\ncp -ai $BD2_PATH/examples/thrombin .\ncd thrombin\nsed -i \u0027s/-PE0//g\u0027 *\nsed -i \u0027s/\u0026lt;n_trajectories\u0026gt; 10000 /\u0026lt;n_trajectories\u0026gt; 1000 /\u0027 t_m_simulation.xml.bak\nmake all # takes about min to run\nmodule unload browndye2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if you want to use BrownDye version 1:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load browndye1\ncp -ai $BD1_PATH/thrombin-example .\ncd thrombin-example\nsed -i \u0027s/-PE0//g\u0027 *\nsed -i \u0027s/\u0026lt;n-trajectories\u0026gt; 10000 /\u0026lt;n-trajectories\u0026gt; 1000 /\u0027 input.xml.bak # limit the number of calculated trajectories\nmake all\nbd_top input.xml\nnam_simulation t-m-simulation.xml # this takes about 3 min to run\ncat results.xml\nmodule unload browndye1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter we are finished we can quit the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also access individual applications from the electrostatics container.\u003c/p\u003e\n\u003cp\u003eTo list available applications:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity apps nbcrrolls-electrostatics-singularity-master-latest.simg \napbs\npdb2pqr\nnam_simulation\nwe_simulation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run, for example, apbs calculation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec nbcrrolls-electrostatics-singularity-master-latest.simg apbs input.in\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app apbs nbcrrolls-electrostatics-singularity-master-latest.simg input.in\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis Singularity image is hosted on Singularity Hub: \u003ca href=\"https://singularity-hub.org/collections/2497\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch6 class=\"heading-element\"\u003eThis project is supported by \u003ca href=\"http://nbcr.ucsd.edu\" rel=\"nofollow\"\u003eNBCR\u003c/a\u003e.\u003c/h6\u003e\u003ca id=\"user-content-this-project-is-supported-by-nbcr\" class=\"anchor\" aria-label=\"Permalink: This project is supported by NBCR.\" href=\"#this-project-is-supported-by-nbcr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "full_name": "researchapps/bamtools", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePicard\u003c/h1\u003e\u003ca id=\"user-content-picard\" class=\"anchor\" aria-label=\"Permalink: Picard\" href=\"#picard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity image to deploy the bamtools software.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 bamtools.img\nsudo singularity bootstrap bamtools.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the bamtools runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bamtools.img [args] ...\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1556048171.0 + "updated_at": 1484507858.0 }, { "data_format": 2, @@ -2091,97 +1722,105 @@ var data = "filenames": [ "Singularity" ], - "full_name": "rynge/hub-test", + "full_name": "deanpettinga/bbcRNA-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehub-test\u003c/h1\u003e\u003ca id=\"user-content-hub-test\" class=\"anchor\" aria-label=\"Permalink: hub-test\" href=\"#hub-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebbcRNA-singularity\u003c/h1\u003e\u003ca id=\"user-content-bbcrna-singularity\" class=\"anchor\" aria-label=\"Permalink: bbcRNA-singularity\" href=\"#bbcrna-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3616\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1501705378.0 + "updated_at": 1571763045.0 }, { "data_format": 2, - "description": "Simple Apptainer for TrajOptKP repository. Can be used to run TrajOptKP easily without needing to download packages manually. Also used for setting up GitHub actions.", + "description": null, "filenames": [ - "Singularity_static", "Singularity" ], - "full_name": "DMackRus/Apptainer_TrajOptKP", + "full_name": "mike-dixon/build_container_on_shub", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eApptainer_TrajOptKP\u003c/h1\u003e\u003ca id=\"user-content-apptainer_trajoptkp\" class=\"anchor\" aria-label=\"Permalink: Apptainer_TrajOptKP\" href=\"#apptainer_trajoptkp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimple Apptainer for TrajOptKP repository. Can be used to run TrajOptKP easily without needing to download packages manually. Also used for setting up GitHub actions.\u003c/p\u003e\n", + "readme": "\u003cp\u003eExamples for building containers on Singularity Hub\u003c/p\u003e\n\u003cp\u003e./tutorial_steps.txt : example steps, command-by-command\u003c/p\u003e\n\u003cp\u003e./Singularity : is a recipe file for building your container\u003c/p\u003e\n\u003cp\u003e./text_translate.py is a sample python script we can run with the container\u003c/p\u003e\n\u003cp\u003e./make_git_repo.sh is a script that uploads your Singularity repository to github\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1730815636.0 + "updated_at": 1537307784.0 }, { "data_format": 2, - "description": "BIRN Quality Assurance Tools", + "description": "Singularity container containing an environment for Ringer users", "filenames": [ "Singularity" ], - "full_name": "NenckaLab/BXH-XCEDE-QA-TOOLS", + "full_name": "gabriel-milan/atlas-singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003cem\u003eSingularity instance based on \u003ca href=\"https://github.com/flywheel-apps/bxh-xcede-tools-qa\"\u003eflywheel-apps/bxh-xcede-tools-qa\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDESCRIPTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUses the \u003ca href=\"https://www.nitrc.org/projects/bxh_xcede_tools/\" rel=\"nofollow\"\u003eNITRC BXH/XCEDE Tools\u003c/a\u003e to gather QA data, both fmriqa_phantomqa.pl and fmriqa_generate.pl show an HTML page with images, graphs, and other data.\u003c/p\u003e\n\u003cp\u003efmriqa_phantomqa.pl is used for the BIRN stability phantom and is the default use for this container\u003c/p\u003e\n\u003cp\u003efmriqa_generate.pl can be used for human fMRI data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINPUTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA directory of DICOM images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOUTPUT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn HTML file, an XML Summary, and graphs/images. The HTML file contains the relevant information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOPTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOptions are found in manifest.json. Includes whether to use phantom or human fMRI and the various choices for outputs.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eATLAS Singularity Environment\u003c/h1\u003e\u003ca id=\"user-content-atlas-singularity-environment\" class=\"anchor\" aria-label=\"Permalink: ATLAS Singularity Environment\" href=\"#atlas-singularity-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4521\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository provides \u003ccode\u003egabriel-milan/atlas-singularity\u003c/code\u003e, a singularity container containing an environment for Ringer users.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1688053174.0 + "updated_at": 1593796964.0 }, { "data_format": 2, - "description": "conda test", + "description": "Behavioral experiments for \"Adaptive computation as a new mechanism of human attention\"", "filenames": [ "Singularity" ], - "full_name": "FelixKrueger/Singularity_Test2", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity_Test2\u003c/h1\u003e\u003ca id=\"user-content-singularity_test2\" class=\"anchor\" aria-label=\"Permalink: Singularity_Test2\" href=\"#singularity_test2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003econda test\u003c/p\u003e\n", + "full_name": "CNCLgithub/mot-psiturk", + "latest_release": "0.1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMOT Psiturk\u003c/h1\u003e\u003ca id=\"user-content-mot-psiturk\" class=\"anchor\" aria-label=\"Permalink: MOT Psiturk\" href=\"#mot-psiturk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrent MOT psiturk for probe experiments\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003esingularity\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003etar\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esetup\u003c/h3\u003e\u003ca id=\"user-content-setup-1\" class=\"anchor\" aria-label=\"Permalink: setup\" href=\"#setup-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esee help\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis setup file will, by default, pull a container and data files from dropbox.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning psiturk\u003c/h2\u003e\u003ca id=\"user-content-running-psiturk\" class=\"anchor\" aria-label=\"Permalink: Running psiturk\" href=\"#running-psiturk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x start_psiturk.sh\n./start_psiturk.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAPI\u003c/h2\u003e\u003ca id=\"user-content-api\" class=\"anchor\" aria-label=\"Permalink: API\" href=\"#api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etask.js\u003c/h3\u003e\u003ca id=\"user-content-taskjs\" class=\"anchor\" aria-label=\"Permalink: task.js\" href=\"#taskjs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe majority of the experiment\u0027s functionality is described in \u003ccode\u003epsiturk/static/js/task.js\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe main class used to setup pages for both the experiment and instructions is defined as \u003ccode\u003ePage\u003c/code\u003e.\n\u003ccode\u003ePage\u003c/code\u003e handles both media presentation and scale setup. See the docstrings for more info.\u003c/p\u003e\n\u003cp\u003eThere are three other main elements, \u003ccode\u003eInstructionRunner\u003c/code\u003e, \u003ccode\u003eQuiz\u003c/code\u003e, and \u003ccode\u003eExperiment\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecss and html\u003c/h3\u003e\u003ca id=\"user-content-css-and-html\" class=\"anchor\" aria-label=\"Permalink: css and html\" href=\"#css-and-html\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main html files are located under \u003ccode\u003epsiturk/templates/\u003c/code\u003e and css is under \u003ccode\u003epsiturk/static/css\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNotabley, \u003ccode\u003estage.html\u003c/code\u003e describes the pages for experimental trials and \u003ccode\u003eslider.css\u003c/code\u003e describes some of the elements found in the scale.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, - "topics": [], - "updated_at": 1537794737.0 + "topics": [ + "attention", + "behavior", + "psiturk" + ], + "updated_at": 1669660072.0 }, { "data_format": 2, - "description": "Docker / Singularity image for using the iRODS client commands on HPC systems", + "description": "Small tools/scripts written in python", "filenames": [ "Singularity" ], - "full_name": "SystemsGenetics/irods-docker", + "full_name": "MDU-PHL/mdu-pytools", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eirods-docker\u003c/h1\u003e\u003ca id=\"user-content-irods-docker\" class=\"anchor\" aria-label=\"Permalink: irods-docker\" href=\"#irods-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the files for building a Docker or Singularity image of the iRODS client commands, as well the files to create an Environment Module (or Lmod module) for the client commands, for use on an HPC system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity documentation: \u003ca href=\"https://www.sylabs.io/guides/2.5/user-guide/index.html\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/guides/2.5/user-guide/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eiRODS documentation: \u003ca href=\"https://docs.irods.org/4.1.12/\" rel=\"nofollow\"\u003ehttps://docs.irods.org/4.1.12/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEnvironment Modules: \u003ca href=\"http://modules.sourceforge.net/\" rel=\"nofollow\"\u003ehttp://modules.sourceforge.net/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eLmod: \u003ca href=\"https://lmod.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://lmod.readthedocs.io/en/latest/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must have Singularity installed on a local machine and your HPC system. It is recommended that you use Singularity 2.4 or newer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build irods.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run an icommand:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec irods.simg \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that you need admin privileges to build the Singularity image, so you will most likely have to build the image on a local machine and then transfer the image to your HPC system.\u003c/p\u003e\n\u003cp\u003eOnce you\u0027ve built the image, you can use the icommands \"out-of-the-box\" by creating aliases for each icommand, for example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias iinit=\"singularity exec irods.simg iinit\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe scripts \u003ccode\u003einstall-irods-lmod.sh\u003c/code\u003e and \u003ccode\u003einstall-irods-tmod.sh\u003c/code\u003e respectively create an Lmod module or Environment Module which provides these aliases automatically. You may need to edit these scripts to work for your particular environment.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMDU Python Tools\u003c/h1\u003e\u003ca id=\"user-content-mdu-python-tools\" class=\"anchor\" aria-label=\"Permalink: MDU Python Tools\" href=\"#mdu-python-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/MDU-PHL/mdu-pytools\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/beff6ea1fbb945167b46572357722ddea3d563f13997f1a5c83a2f7870d3e87e/68747470733a2f2f636972636c6563692e636f6d2f67682f4d44552d50484c2f6d64752d7079746f6f6c732e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/MDU-PHL/mdu-pytools.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/17430b1f952dff83ad16be3bc6d7bc1b28e5256448bd1b13cf569a42088dc70d/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f6d64752d7079746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/17430b1f952dff83ad16be3bc6d7bc1b28e5256448bd1b13cf569a42088dc70d/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f6d64752d7079746f6f6c73\" alt=\"PyPI - Python Version\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/mdu-pytools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/770d75608dabaa929e1fcf0af23b0afb832d96ab70b51c9e6e34cfed3c69b6dc/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d64752d7079746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/770d75608dabaa929e1fcf0af23b0afb832d96ab70b51c9e6e34cfed3c69b6dc/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d64752d7079746f6f6c73\" alt=\"PyPI\" data-canonical-src=\"https://img.shields.io/pypi/v/mdu-pytools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/47b997d6848a3598820db5da51efb22983fc9e454a83c40caca5457c3f96ab45/68747470733a2f2f696d672e736869656c64732e696f2f707970692f6c2f6d64752d7079746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/47b997d6848a3598820db5da51efb22983fc9e454a83c40caca5457c3f96ab45/68747470733a2f2f696d672e736869656c64732e696f2f707970692f6c2f6d64752d7079746f6f6c73\" alt=\"PyPI - License\" data-canonical-src=\"https://img.shields.io/pypi/l/mdu-pytools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#mdu-python-tools\"\u003eMDU Python Tools\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#background\"\u003eBackground\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#tools\"\u003eTools\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#mdu-merge-ngs-lanes\"\u003emdu-merge-ngs-lanes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#mdu-sra-uploads\"\u003emdu-sra-uploads\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#environmental-variables-that-can-be-used-to-set-options\"\u003eEnvironmental variables that can be used to set options\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#development\"\u003eDevelopment\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#development-environment\"\u003eDevelopment environment\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBackground\u003c/h2\u003e\u003ca id=\"user-content-background\" class=\"anchor\" aria-label=\"Permalink: Background\" href=\"#background\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome simple tools in python for MDU\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emdu-merge-ngs-lanes\u003c/h3\u003e\u003ca id=\"user-content-mdu-merge-ngs-lanes\" class=\"anchor\" aria-label=\"Permalink: mdu-merge-ngs-lanes\" href=\"#mdu-merge-ngs-lanes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse it to correctly merge lanes from an Illumina run into the a single FASTQ.\u003c/p\u003e\n\u003cp\u003eGet help:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emdu-merge-ngs-lanes --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBasic usage:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emdu-merge-ngs-lanes -i /path/to/fastq_folder -o /path/to/output \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e cmd.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdvanced usage:\u003c/p\u003e\n\u003cp\u003eYou can split the output to muliple subfolders of the output folder by adding \u003ccode\u003e--subfolder\u003c/code\u003e\nto the command line. The option can be used multiple times, and takes two space separated values as input:\n\u003ccode\u003epath\u003c/code\u003e \u003ccode\u003eregex\u003c/code\u003e. The \u003ccode\u003epath\u003c/code\u003e gives a name of the subfolder in the output folder, and the \u003ccode\u003eregex\u003c/code\u003e expression\ndetermines which samples go in that subfolder.\u003c/p\u003e\n\u003cp\u003eFor instance, the command below will split samples starting the NTC in to a subfolder called \u003ccode\u003entc\u003c/code\u003e,\nwhile all other samples will be added to a subfolder called \u003ccode\u003edata\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emdu-merge-ngs-lanes -i /path/to/fastq -o /path/to/output --subfolder \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e(?!NTC).*\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --subfolder \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003entc\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e(?\u0026lt;=NTC).*\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e cmd.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emdu-sra-uploads\u003c/h3\u003e\u003ca id=\"user-content-mdu-sra-uploads\" class=\"anchor\" aria-label=\"Permalink: mdu-sra-uploads\" href=\"#mdu-sra-uploads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse to it to upload FASTQ data to NCBI SRA.\u003c/p\u003e\n\u003cp\u003eRequires a file with tab-separated values of \u003ccode\u003eMDU ID\u003c/code\u003e and \u003ccode\u003eAUSMDUID\u003c/code\u003e. For example:\u003c/p\u003e\n\u003cp\u003emdu1\\tausmdu1\u003c/p\u003e\n\u003cp\u003emdu2\\tausmdu2\u003c/p\u003e\n\u003cp\u003eGetting help:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emdu-sra-uploads --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eUsage: mdu-sra-upload [OPTIONS] ISOLATES\n\nOptions:\n -f, --folder TEXT Folder on NCBI to upload. Used to find the reads\n when submitting via the SRA portal. [default:\n mdu]\n -r, --reads-folder TEXT Where reads are located (uses MDU_READS env\n variable \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e available).\n -k, --ascp-key TEXT Path to ascp ssh upload key (uses ASCP_UPLOAD_KEY\n env variable \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e available). This can be obtained\n from the SRA Submission Portal.\n -s, --sra-subfolder TEXT SRA subfolder owned by you where data will copied\n to (uses SRA_SUBFOLDER env variable is available).\n --help Show this message and exit.\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBasic usage:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /path/for/upload\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e copy paste isolates.txt\u003c/span\u003e\nmdu-sra-uploads isolates.txt\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e when completing the submission, search for pre-uploaded files in the folder called mdu\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eEnvironmental variables that can be used to set options\u003c/h4\u003e\u003ca id=\"user-content-environmental-variables-that-can-be-used-to-set-options\" class=\"anchor\" aria-label=\"Permalink: Environmental variables that can be used to set options\" href=\"#environmental-variables-that-can-be-used-to-set-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eMDU_READS\u003c/code\u003e: full path to where FASTQ data is stored\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eASCP_UPLOAD_KEY\u003c/code\u003e: full path to where your Aspera NCBI upload key is located (obtain one from the SRA submission portal under the Aspera command line instructions)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSRA_FOLDER\u003c/code\u003e: path to your folder at SRA. Usually composed by your \u003ccode\u003eemail\u003c/code\u003e plus an \"_\" and some random alphanumeric characters. This can be obtained from SRA submission portal under the Aspera command line instructions (e.g., \u003ccode\u003ejohn.doe@doe.industries.com_qEWo9\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDevelopment environment\u003c/h3\u003e\u003ca id=\"user-content-development-environment\" class=\"anchor\" aria-label=\"Permalink: Development environment\" href=\"#development-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo develop with the same environment use \u003ccode\u003evagrant\u003c/code\u003e and \u003ccode\u003evirtualbox\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\nvagrant ssh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce logged in to the VM, the shared folder is in \u003ccode\u003e/vagrant\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, - "topics": [], - "updated_at": 1550696838.0 + "subscribers_count": 5, + "topics": [ + "bioinformatics", + "scripts", + "microbial-genomics" + ], + "updated_at": 1568675585.0 }, { "data_format": 2, - "description": "An agent for Azure Pipelines using a Singularity image", + "description": "Detect germline or somatic variants from normal or tumour/normal whole-genome or targeted sequencing", "filenames": [ "Singularity" ], - "full_name": "basnijholt/azure-singularity-agent", + "full_name": "UCL-BLIC/Sarek_v2.3.FIX1", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWIP: azure-singularity-agent\u003c/h1\u003e\u003ca id=\"user-content-wip-azure-singularity-agent\" class=\"anchor\" aria-label=\"Permalink: WIP: azure-singularity-agent\" href=\"#wip-azure-singularity-agent\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn agent for Azure Pipelines using a Singularity image\u003c/p\u003e\n\u003cp\u003eBuild with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build azure-singularity-agent.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eenv AZP_URL=https://dev.azure.com/\u0026lt;organization\u0026gt; AZP_TOKEN=\u0026lt;PAT token\u0026gt; AZP_AGENT_NAME=mydockeragent singularity run azure-singularity-agent.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSeems to not work because the resulting \u003ccode\u003esif\u003c/code\u003e is read-only.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\u003ca href=\"http://sarek.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/Sarek_logo.png\" alt=\"Sarek\" title=\"Sarek\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\u003ca id=\"\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAn open-source analysis pipeline to detect germline or somatic variants from whole genome or targeted sequencing\u003c/h4\u003e\u003ca id=\"user-content-an-open-source-analysis-pipeline-to-detect-germline-or-somatic-variants-from-whole-genome-or-targeted-sequencing\" class=\"anchor\" aria-label=\"Permalink: An open-source analysis pipeline to detect germline or somatic variants from whole genome or targeted sequencing\" href=\"#an-open-source-analysis-pipeline-to-detect-germline-or-somatic-variants-from-whole-genome-or-targeted-sequencing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.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\" alt=\"Nextflow version\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg?logo=data:image/svg+xml;base64,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\" 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src=\"https://camo.githubusercontent.com/6601edf424039dadb758444948fd35a899c0e55d1d38c24e2b00d7960710d180/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f5363694c6966654c61622f536172656b2e7376673f6c6f676f3d676974687562266c6f676f436f6c6f723d7768697465\" alt=\"Sarek version\" data-canonical-src=\"https://img.shields.io/github/release/SciLifeLab/Sarek.svg?logo=github\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/54024046\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8306a87ffc25c6750b3f82dcba1992ba2bab5802fd9cd64cf194612ec66f9a1/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f35343032343034362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/54024046.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.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\" 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data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?logo=data:image/png;base64,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\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/maxulysse/sarek\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/331acbc5ce8eb54738019206937dfb7d73368e38ab28d073f3bb1e9e13f01e74/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6d6178756c797373652f736172656b2e7376673f6c6f676f3d646f636b6572\" alt=\"Docker Container available\" data-canonical-src=\"https://img.shields.io/docker/automated/maxulysse/sarek.svg?logo=docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/CAW_logo.png\"\u003e\u003cimg align=\"right\" title=\"CAW\" src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/CAW_logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePreviously known as the Cancer Analysis Workflow (CAW),\nSarek is a workflow designed to run analyses on WGS data from regular samples or tumour / normal pairs, including relapse samples if required.\u003c/p\u003e\n\u003cp\u003eIt\u0027s built using \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a domain specific language for workflow building.\nSoftware dependencies are handled using \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e - container technologies that provide excellent reproducibility and ease of use.\nSingularity has been designed specifically for high-performance computing environments.\nThis means that although Sarek has been primarily designed for use with the Swedish \u003ca href=\"https://www.uppmax.uu.se\" rel=\"nofollow\"\u003eUPPMAX HPC systems\u003c/a\u003e, it should be able to run on any system that supports these two tools.\u003c/p\u003e\n\u003cp\u003eSarek was developed at the \u003ca href=\"https://ngisweden.scilifelab.se/\" rel=\"nofollow\"\u003eNational Genomics Infastructure\u003c/a\u003e and \u003ca href=\"https://www.nbis.se/\" rel=\"nofollow\"\u003eNational Bioinformatics Infastructure Sweden\u003c/a\u003e which are both platforms at \u003ca href=\"https://www.scilifelab.se/\" rel=\"nofollow\"\u003eSciLifeLab\u003c/a\u003e.\nIt is listed on the \u003ca href=\"https://bio.tools/Sarek\" rel=\"nofollow\"\u003eElixir - Tools and Data Services Registry\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow steps\u003c/h2\u003e\u003ca id=\"user-content-workflow-steps\" class=\"anchor\" aria-label=\"Permalink: Workflow steps\" href=\"#workflow-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSarek is built with several workflow scripts.\nA wrapper script contained within the repository makes it easy to run the different workflow scripts as a single job.\nTo test your installation, follow the \u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/TESTS.md\"\u003etests documentation.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRaw FastQ files or aligned BAM files (with or without realignment \u0026amp; recalibration) can be used as inputs.\nYou can choose which variant callers to use, plus the pipeline is capable of accommodating additional variant calling software or CNV callers if required.\u003c/p\u003e\n\u003cp\u003eThe worflow steps and tools used are as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003ePreprocessing\u003c/strong\u003e - \u003ccode\u003emain.nf\u003c/code\u003e \u003cem\u003e(based on \u003ca href=\"https://software.broadinstitute.org/gatk/best-practices/\" rel=\"nofollow\"\u003eGATK best practices\u003c/a\u003e)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eMap reads to Reference\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://bio-bwa.sourceforge.net/\" rel=\"nofollow\"\u003eBWA\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMark Duplicates\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK MarkDuplicates\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBase (Quality Score) Recalibration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK BaseRecalibrator\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK ApplyBQSR\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGermline variant calling\u003c/strong\u003e - \u003ccode\u003egermlineVC.nf\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eSNVs and small indels\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK HaplotyeCaller\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka2\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStructural variants\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/manta\"\u003eManta\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSomatic variant calling\u003c/strong\u003e - \u003ccode\u003esomaticVC.nf\u003c/code\u003e \u003cem\u003e(optional)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eSNVs and small indels\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eMuTect2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ekg/freebayes\"\u003eFreebayes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka2\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStructural variants\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/manta\"\u003eManta\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSample heterogeneity, ploidy and CNVs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Crick-CancerGenomics/ascat\"\u003eASCAT\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAnnotation\u003c/strong\u003e - \u003ccode\u003eannotate.nf\u003c/code\u003e \u003cem\u003e(optional)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eVariant annotation\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://snpeff.sourceforge.net/\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ensembl.org/info/docs/tools/vep/index.html\" rel=\"nofollow\"\u003eVEP (Variant Effect Predictor)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReporting\u003c/strong\u003e - \u003ccode\u003erunMultiQC.nf\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eReporting\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Sarek pipeline comes with documentation in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL.md\"\u003eInstallation documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL_RACKHAM.md\"\u003eInstallation documentation specific for UPPMAX \u003ccode\u003erackham\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL_BIANCA.md\"\u003eInstallation documentation specific for UPPMAX \u003ccode\u003ebianca\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/TESTS.md\"\u003eTests documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/REFERENCES.md\"\u003eReference files documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONFIG.md\"\u003eConfiguration and profiles documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INTERVALS.md\"\u003eIntervals documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/USAGE.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONDA.md\"\u003eRunning the pipeline using Conda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/PARAMETERS.md\"\u003eCommand line parameters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/USE_CASES.md\"\u003eExamples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INPUT.md\"\u003eInput files documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/PROCESS.md\"\u003eProcesses documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONTAINERS.md\"\u003eDocumentation about containers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/ASCAT.md\"\u003eComplementary information about ASCAT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/ANNOTATION.md\"\u003eComplementary information about annotations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/OUTPUT.md\"\u003eOutput documentation structure\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions \u0026amp; Support\u003c/h2\u003e\u003ca id=\"user-content-contributions--support\" class=\"anchor\" aria-label=\"Permalink: Contributions \u0026amp; Support\" href=\"#contributions--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you would like to contribute to this pipeline, please see the \u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/.github/CONTRIBUTING.md\"\u003econtributing guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor further information or help, don\u0027t hesitate to get in touch on \u003ca href=\"https://gitter.im/SciLifeLab/Sarek\" rel=\"nofollow\"\u003eGitter\u003c/a\u003e or contact us: \u003ca href=\"mailto:maxime.garcia@scilifelab.se\"\u003emaxime.garcia@scilifelab.se\u003c/a\u003e, \u003ca href=\"mailto:szilveszter.juhos@scilifelab.se\"\u003eszilveszter.juhos@scilifelab.se\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCHANGELOG\u003c/h2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-label=\"Permalink: CHANGELOG\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/CHANGELOG.md\"\u003eCHANGELOG\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMain authors:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MaxUlysse\"\u003eMaxime Garcia\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/szilvajuhos\"\u003eSzilveszter Juhos\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHelpful contributors:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/alneberg\"\u003eJohannes Alneberg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Sebastian-D\"\u003eSebastian DiLorenzo\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/J35P312\"\u003eJesper Eisfeldt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ewels\"\u003ePhil Ewels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gulfshores\"\u003eMax K\u00e4ller\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/malinlarsson\"\u003eMalin Larsson\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/marcelm\"\u003eMarcel Martin\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bjornnystedt\"\u003eBj\u00f6rn Nystedt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pallolason\"\u003ePall Olason\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arontommi\"\u003eAron Skaftason\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nilesh-tawari\"\u003eNilesh Tawari\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca href=\"https://www.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/SciLifeLab_logo.png\" alt=\"SciLifeLab\" title=\"SciLifeLab\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ngisweden.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/NGI_logo.png\" alt=\"NGI\" title=\"NGI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nbis.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/NBIS_logo.png\" alt=\"NBIS\" title=\"NBIS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1583410819.0 + "updated_at": 1633704576.0 }, { "data_format": 2, - "description": "testing building containers with singularity hub webhook ", + "description": "CentOS 7 Base Singularity Image", "filenames": [ "Singularity" ], - "full_name": "eharkins/singularityhub-test", + "full_name": "scleveland/centos7-base-singularity", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1254\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Recipe for base CentOS 7 image For the University of Hawaii HPC system\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-base-centos-7-image-for-the-university-of-hawaii-hpc-system\" class=\"anchor\" aria-label=\"Permalink: Singularity Recipe for base CentOS 7 image For the University of Hawaii HPC system\" href=\"#singularity-recipe-for-base-centos-7-image-for-the-university-of-hawaii-hpc-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains recipe to create a base CentOS-7 singularity containter\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to Use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity run shub://scleveland/centos7-base-singularity\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1548377344.0 + "updated_at": 1531340029.0 }, { "data_format": 2, @@ -2189,13 +1828,12 @@ var data = "filenames": [ "Singularity" ], - "full_name": "nglinhbao/EU-AI-Act-Assessment", + "full_name": "yiwan-rl/singularity_recipes", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEU AI Act Assessment\u003c/h1\u003e\u003ca id=\"user-content-eu-ai-act-assessment\" class=\"anchor\" aria-label=\"Permalink: EU AI Act Assessment\" href=\"#eu-ai-act-assessment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is designed to classify AI systems according to the \u003cstrong\u003eEU AI Act\u003c/strong\u003e using the \u003cstrong\u003eLLaMA 2 (7B)\u003c/strong\u003e model. The AI system is evaluated through several stages, including \u003cstrong\u003eInitial Risk Assessment\u003c/strong\u003e, \u003cstrong\u003eLevel-Based Risk Assessment\u003c/strong\u003e, and \u003cstrong\u003eRisk Categorization\u003c/strong\u003e. The purpose is to ensure that AI systems comply with regulations and are categorized according to their risk levels.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#project-description\"\u003eProject Description\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#features\"\u003eFeatures\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ai-risk-assessment-workflow\"\u003eAI Risk Assessment Workflow\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProject Description\u003c/h2\u003e\u003ca id=\"user-content-project-description\" class=\"anchor\" aria-label=\"Permalink: Project Description\" href=\"#project-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project evaluates AI systems to assess their compliance with the \u003cstrong\u003eEU AI Act\u003c/strong\u003e. The system follows a tree-based workflow, beginning with an \u003cstrong\u003eInitial Risk Assessment\u003c/strong\u003e. Based on the outcomes, it moves to \u003cstrong\u003eLevel-Based Risk Assessment\u003c/strong\u003e or determines whether the AI system poses an \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e. The core components of the system include structured prompts designed for different phases of risk assessment and a fine-tuned \u003cstrong\u003eLLaMA 2\u003c/strong\u003e model to generate responses.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAI Risk Categorization\u003c/strong\u003e based on \u003cstrong\u003eEU AI Act\u003c/strong\u003e principles.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLLaMA 2 (7B)\u003c/strong\u003e model integration for automated risk assessments.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCSV-driven prompt system\u003c/strong\u003e for systematic AI evaluation.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-step automated workflow\u003c/strong\u003e: Initial risk, level-based assessment, and ongoing monitoring.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisites\u003c/h3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEnsure you have the following installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.8+.\u003c/li\u003e\n\u003cli\u003eConda environment (optional but recommended).\u003c/li\u003e\n\u003cli\u003eHugging Face API Token (required to access the LLaMA 2 model).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClone the Repository\u003c/h3\u003e\u003ca id=\"user-content-clone-the-repository\" class=\"anchor\" aria-label=\"Permalink: Clone the Repository\" href=\"#clone-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/nglinhbao/AI-EU-Act-Assessment.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e AI-EU-Act-Assessment\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSet Up Virtual Environment (Optional)\u003c/h3\u003e\u003ca id=\"user-content-set-up-virtual-environment-optional\" class=\"anchor\" aria-label=\"Permalink: Set Up Virtual Environment (Optional)\" href=\"#set-up-virtual-environment-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create --name AI-Act python=3.10\nconda activate AI-Act\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall Required Packages\u003c/h3\u003e\u003ca id=\"user-content-install-required-packages\" class=\"anchor\" aria-label=\"Permalink: Install Required Packages\" href=\"#install-required-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install -r requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAuthenticate Hugging Face\u003c/h3\u003e\u003ca id=\"user-content-authenticate-hugging-face\" class=\"anchor\" aria-label=\"Permalink: Authenticate Hugging Face\" href=\"#authenticate-hugging-face\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEnsure you have a Hugging Face token to access the LLaMA 2 model.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehuggingface-cli login\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, you can export the token directly in your environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e HUGGINGFACEHUB_API_TOKEN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eyour_token_here\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSteps to Run the Assessment:\u003c/h3\u003e\u003ca id=\"user-content-steps-to-run-the-assessment\" class=\"anchor\" aria-label=\"Permalink: Steps to Run the Assessment:\" href=\"#steps-to-run-the-assessment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrepare the AI System Description\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a \u003ccode\u003e.csv\u003c/code\u003e file containing the description of the AI systems that you wish to assess.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./sample.csv\"\u003eExample\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRun the Assessment Script\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eTo start the AI system classification process, make sure you have the system description \u003ccode\u003e.csv\u003c/code\u003e file ready, and execute the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 main.py\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrompts and AI System Evaluation\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe system reads prompts from the \u003ccode\u003eCSV\u003c/code\u003e file (\u003ccode\u003eai_risk_prompts.csv\u003c/code\u003e) and evaluates the AI system based on its description.\u003c/li\u003e\n\u003cli\u003eIf the AI system poses an \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e, the process halts and returns the result. Otherwise, it proceeds through all stages to classify the system as \u003cstrong\u003eHigh Risk\u003c/strong\u003e, \u003cstrong\u003eLimited Risk\u003c/strong\u003e, or \u003cstrong\u003eMinimal Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eOutput\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe system provides a risk categorization based on the predefined prompts in the CSV file.\u003c/li\u003e\n\u003cli\u003eExample outcomes include: \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e, \u003cstrong\u003eHigh Risk\u003c/strong\u003e, \u003cstrong\u003eLimited Risk\u003c/strong\u003e, or \u003cstrong\u003eMinimal Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Output:\u003c/h3\u003e\u003ca id=\"user-content-example-output\" class=\"anchor\" aria-label=\"Permalink: Example Output:\" href=\"#example-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is an example of how the system would respond to various prompts:\u003c/p\u003e\n\u003cpre lang=\"plaintext\"\u003e\u003ccode\u003e(AI-Act) User:~/EU-AI-Act$ python3 main.py \nLoading checkpoint shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:02\u0026lt;00:00, 1.50s/it]\nAccuracy: 0.30\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAI Risk Assessment Workflow with Scoring System\u003c/h2\u003e\u003ca id=\"user-content-ai-risk-assessment-workflow-with-scoring-system\" class=\"anchor\" aria-label=\"Permalink: AI Risk Assessment Workflow with Scoring System\" href=\"#ai-risk-assessment-workflow-with-scoring-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./workflow.png\"\u003e\u003cimg src=\"./workflow.png\" alt=\"Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment follows these key stages, evaluated sequentially from top to bottom. Each prompt at every risk level is scored on a scale of \u003cstrong\u003e1 to 5\u003c/strong\u003e, where:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e1\u003c/strong\u003e means the system absolutely does not meet the criteria,\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e5\u003c/strong\u003e means the system absolutely meets the criteria.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each stage, if the \u003cstrong\u003emean score\u003c/strong\u003e is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system will be classified under that risk category.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e:\u003c/h3\u003e\u003ca id=\"user-content-1-unacceptable-risk\" class=\"anchor\" aria-label=\"Permalink: 1. Unacceptable Risk:\" href=\"#1-unacceptable-risk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe system is first evaluated against prompts to identify if it violates fundamental human rights, Union values, or carries unacceptable risks.\u003c/li\u003e\n\u003cli\u003eEach prompt at this stage receives a score from \u003cstrong\u003e1 to 5\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eIf the \u003cstrong\u003emean score\u003c/strong\u003e of all prompts in this stage is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system is classified as \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e, and the assessment halts. No further evaluation is carried out.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. \u003cstrong\u003eHigh Risk\u003c/strong\u003e:\u003c/h3\u003e\u003ca id=\"user-content-2-high-risk\" class=\"anchor\" aria-label=\"Permalink: 2. High Risk:\" href=\"#2-high-risk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf the system passes the \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e stage, it is assessed for \u003cstrong\u003eHigh Risk\u003c/strong\u003e factors such as:\n\u003cul\u003e\n\u003cli\u003eImpact on human life,\u003c/li\u003e\n\u003cli\u003eHandling of sensitive personal data,\u003c/li\u003e\n\u003cli\u003eAutonomous decision-making without human oversight.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach prompt in this stage is scored from \u003cstrong\u003e1 to 5\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eIf the \u003cstrong\u003emean score\u003c/strong\u003e for the prompts in this stage is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system is classified as \u003cstrong\u003eHigh Risk\u003c/strong\u003e, and further evaluation stops.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. \u003cstrong\u003eLimited Risk\u003c/strong\u003e:\u003c/h3\u003e\u003ca id=\"user-content-3-limited-risk\" class=\"anchor\" aria-label=\"Permalink: 3. Limited Risk:\" href=\"#3-limited-risk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf the system does not qualify as \u003cstrong\u003eHigh Risk\u003c/strong\u003e, it is evaluated for \u003cstrong\u003eLimited Risk\u003c/strong\u003e factors like:\n\u003cul\u003e\n\u003cli\u003eBias detection mechanisms,\u003c/li\u003e\n\u003cli\u003eSystem transparency,\u003c/li\u003e\n\u003cli\u003eFairness in decision-making processes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach prompt in this stage is scored from \u003cstrong\u003e1 to 5\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eIf the \u003cstrong\u003emean score\u003c/strong\u003e is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system is classified as \u003cstrong\u003eLimited Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. \u003cstrong\u003eMinimal Risk\u003c/strong\u003e:\u003c/h3\u003e\u003ca id=\"user-content-4-minimal-risk\" class=\"anchor\" aria-label=\"Permalink: 4. Minimal Risk:\" href=\"#4-minimal-risk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf none of the previous stages result in a classification, the system is evaluated for \u003cstrong\u003eMinimal Risk\u003c/strong\u003e, including:\n\u003cul\u003e\n\u003cli\u003eProper handling of non-sensitive data,\u003c/li\u003e\n\u003cli\u003eMinimal impact on users and society,\u003c/li\u003e\n\u003cli\u003eLow potential for bias or unfair treatment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach prompt in this stage is scored from \u003cstrong\u003e1 to 5\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eIf the \u003cstrong\u003emean score\u003c/strong\u003e is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system is classified as \u003cstrong\u003eMinimal Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Workflow (Scoring):\u003c/h3\u003e\u003ca id=\"user-content-example-workflow-scoring\" class=\"anchor\" aria-label=\"Permalink: Example Workflow (Scoring):\" href=\"#example-workflow-scoring\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eUnacceptable Risk Prompts\u003c/strong\u003e: Scores [5, 4, 2, 3].\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMean score = (5 + 4 + 2 + 3) / 4 = 3.5 \u2192 \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eClassification stops.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eHigh Risk Prompts\u003c/strong\u003e: Scores [2, 3, 4, 4].\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMean score = (2 + 3 + 4 + 4) / 4 = 3.25 \u2192 \u003cstrong\u003eHigh Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eLimited Risk Prompts\u003c/strong\u003e: Scores [2, 2, 3].\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMean score = (2 + 2 + 3) / 3 = 2.33 \u2192 Not classified as Limited Risk.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1732090527.0 + "updated_at": 1615835305.0 }, { "data_format": 2, @@ -2203,44 +1841,42 @@ var data = "filenames": [ "Singularity" ], - "full_name": "challenge-engine/test-starting-kit", + "full_name": "oogasawa/singularity_postgresql-12.0", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etest-starting-kit\u003c/h1\u003e\u003ca id=\"user-content-test-starting-kit\" class=\"anchor\" aria-label=\"Permalink: test-starting-kit\" href=\"#test-starting-kit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd13\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_postgresql\u003c/h1\u003e\u003ca id=\"user-content-singularity_postgresql\" class=\"anchor\" aria-label=\"Permalink: singularity_postgresql\" href=\"#singularity_postgresql\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3\u3067\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306esingularity image\u306e\u4f7f\u3044\u65b9\u003c/p\u003e\n\u003cp\u003e\u5bfe\u5fdc\u3059\u308bPostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e9.6.15\u003c/li\u003e\n\u003cli\u003e10.10\u003c/li\u003e\n\u003cli\u003e11.5\u003c/li\u003e\n\u003cli\u003e12.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u751f\u6210\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u751f\u6210\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u751f\u6210\" href=\"#image\u306e\u751f\u6210\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u81ea\u5206\u306e\u74b0\u5883\u3067image\u3092build\u3059\u308b\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u306f9.6.15, 10.10, 11.5, 12.0\u306e\u3044\u305a\u308c\u304b\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ddbj/singularity_postgresql.git\n$ cd singularity_postgresql\n$ sudo singularity build ubuntu-18.04-postgresql-\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;.simg Singularity.\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" href=\"#image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Hub\u306b\u767b\u9332\u3055\u308c\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u306f9.6.15, 10.10, 11.5, 12.0\u306e\u3044\u305a\u308c\u304b\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ddbj/singularity_postgresql.git\n$ cd singularity_postgresql\n$ singularity pull --name ubuntu-18.04-postgresql-\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;.simg shub://ddbj/singularity_postgresql:\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u751f\u6210\u307e\u305f\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3060\u3051\u3067\u306fPostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u5b9f\u884c\u3067\u304d\u307e\u305b\u3093\u3002 start_container.sh\u3092\u5b9f\u884c\u3057\u3066singularity instance\u3092\u8d77\u52d5\u3057\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\u3092\u884c\u3044\u307e\u3059\u3002\n\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u524d\u306b\u3001\u81ea\u5206\u306e\u74b0\u5883\u306b\u5408\u308f\u305b\u3066 start_container.sh \u306e CONTAINER_HOME, IMAGE, INSTANCE, PORT\u5909\u6570\u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCONTAINER_HOME\u306b\u306fgit clone\u3067\u3067\u304d\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u3092\u8a18\u8f09\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003cli\u003eIMAGE\u306b\u306f\u3001image\u751f\u6210\u307e\u305f\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u306e\u969b\u306b\u6307\u5b9a\u3057\u305f\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u8a18\u8f09\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003cli\u003ePORT\u5909\u6570\u306f5000\u4ee5\u4e0a\u3067\u4efb\u610f\u306e\u6574\u6570\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3059\u308b\u3068\u3001\u521d\u56de\u5b9f\u884c\u6642\u306b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u521d\u671f\u5316\u304c\u884c\u308f\u308c\u305f\u5f8c\u3067\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b5\u30fc\u30d0\u304c\u8d77\u52d5\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\nThe files belonging to this database system will be owned by user \"\u0026lt;\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u30e6\u30fc\u30b6\u30fc\u0026gt;\".\nThis user must also own the server process.\n\nThe database cluster will be initialized with locale \"C\".\nThe default text search configuration will be set to \"english\".\n\nData page checksums are disabled.\n\nfixing permissions on existing directory /usr/local/pgsql12/data ... ok\ncreating subdirectories ... ok\nselecting dynamic shared memory implementation ... posix\nselecting default max_connections ... 100\nselecting default shared_buffers ... 128MB\nselecting default time zone ... Japan\ncreating configuration files ... ok\nrunning bootstrap script ... ok\nperforming post-bootstrap initialization ... ok\nsyncing data to disk ... ok\n\ninitdb: warning: enabling \"trust\" authentication for local connections\nYou can change this by editing pg_hba.conf or using the option -A, or\n--auth-local and --auth-host, the next time you run initdb.\n\nSuccess. You can now start the database server using:\n\n pg_ctl -D /usr/local/pgsql12/data -l logfile start\n\nStopping pgsql instance of /gpfs1/lustre2/home/y-okuda/git/singularity_postgresql/ubuntu-18.04-postgresql-12.0.simg (PID=36513)\nwaiting for server to start.... done\nserver started\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity instance\u3092\u8d77\u52d5\u3057\u305f\u30e6\u30fc\u30b6\u30fc\uff08initdb\u3092\u5b9f\u884c\u3057\u305f\u30e6\u30fc\u30b6\u30fc\uff09\u304cPostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec instance://pgsql psql -d postgres -p 55432\npsql (12.0)\nType \"help\" for help.\n\npostgres=# alter role \"\u0026lt;\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u30e6\u30fc\u30b6\u30fc\u0026gt;\" with password \u0027\u0026lt;\u30d1\u30b9\u30ef\u30fc\u30c9\u0026gt;\u0027;\nALTER ROLE\npostgres=# \\q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30d1\u30b9\u30ef\u30fc\u30c9\u306e\u8a2d\u5b9a\u306b\u3088\u308asingularity instance\u306e\u5916\u304b\u3089\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u30a2\u30af\u30bb\u30b9\u306e\u969b\u306f-h\u30aa\u30d7\u30b7\u30e7\u30f3\u3067singularity instance\u3092\u5b9f\u884c\u3057\u3066\u3044\u308b\u30db\u30b9\u30c8\u540d\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ psql -d postgres -p 55432 -h at043\n\u30d1\u30b9\u30ef\u30fc\u30c9: \npsql (9.2.24, \u30b5\u30fc\u30d0\u30fc 12.0)\n\u6ce8\u610f\uff1a psql \u30d0\u30fc\u30b8\u30e7\u30f3 9.2, \u30b5\u30fc\u30d0\u30fc\u30d0\u30fc\u30b8\u30e7\u30f3 12.0.\n psql \u306e\u6a5f\u80fd\u306e\u4e2d\u3067\u3001\u52d5\u4f5c\u3057\u306a\u3044\u3082\u306e\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\n\"help\" \u3067\u30d8\u30eb\u30d7\u3092\u8868\u793a\u3057\u307e\u3059.\n\npostgres=# \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u5225\u30ce\u30fc\u30c9\u304b\u3089\u306e\u30a2\u30af\u30bb\u30b9\u3082\u53ef\u80fd\u3067\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh at044\nLast login: Fri Nov 1 20:25:26 2019 from at043\n$ psql -d postgres -p 55432 -h at043\n\u30d1\u30b9\u30ef\u30fc\u30c9: \npsql (9.2.24, \u30b5\u30fc\u30d0\u30fc 12.0)\n\u6ce8\u610f\uff1a psql \u30d0\u30fc\u30b8\u30e7\u30f3 9.2, \u30b5\u30fc\u30d0\u30fc\u30d0\u30fc\u30b8\u30e7\u30f3 12.0.\n psql \u306e\u6a5f\u80fd\u306e\u4e2d\u3067\u3001\u52d5\u4f5c\u3057\u306a\u3044\u3082\u306e\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\n\"help\" \u3067\u30d8\u30eb\u30d7\u3092\u8868\u793a\u3057\u307e\u3059.\n\npostgres=# \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u63d0\u4f9b\u3057\u3066\u3044\u308bpg_hba.conf\u306e\u8a18\u8ff0\u3067\u306f\u30a2\u30af\u30bb\u30b9\u53ef\u80fd\u306aIP\u30a2\u30c9\u30ec\u30b9\u306b\u5236\u9650\u304c\u304b\u304b\u3063\u3066\u3044\u307e\u305b\u3093\u3002\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1620137534.0 + "updated_at": 1618363302.0 }, { "data_format": 2, - "description": "Singularity example 14: installing R packages", + "description": "RepeatMasker singularity image recipe", "filenames": [ - "Singularity_1", - "Singularity_3", - "Singularity_5", - "Singularity_2" + "Singularity", + "Singularity.4.0.8" ], - "full_name": "richelbilderbeek/singularity_example_14", - "latest_release": "v1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_example_14\u003c/h1\u003e\u003ca id=\"user-content-singularity_example_14\" class=\"anchor\" aria-label=\"Permalink: singularity_example_14\" href=\"#singularity_example_14\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity example 14: installing R packages.\u003c/p\u003e\n\u003cp\u003eThe goal of this example is to create a Singularity image with\nan R package installed and using it on an R script.\u003c/p\u003e\n\u003cp\u003eThe R package we\u0027ll use is \u003ca href=\"https://CRAN.R-project.org/package=glue\" rel=\"nofollow\"\u003eglue\u003c/a\u003e,\nas it is a simple R package without dependencies.\u003c/p\u003e\n\u003cp\u003eThis is the R script, called \u003ca href=\"script.R\"\u003escript.R\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eglue::glue(\"Hello {target}\", target = \"world\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eAttempt 3: clean up\u003c/code\u003e is the best way:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild with sudo (i.e. no \u003ccode\u003e--fakeroot\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003esend the script text to the container, not the script filename\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 1: Singularity does not run scripts\u003c/h1\u003e\u003ca id=\"user-content-attempt-1-singularity-does-not-run-scripts\" class=\"anchor\" aria-label=\"Permalink: Attempt 1: Singularity does not run scripts\" href=\"#attempt-1-singularity-does-not-run-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"Singularity_1\"\u003eSingularity_1\u003c/a\u003e is a minimal Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: r-base\n\n%post\n Rscript -e \u0027install.packages(\"glue\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_1.sh\"\u003ebuild_singularity_1.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_1\"\u003eSingularity_1\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_1.sif Singularity_1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_1.sh\"\u003erun_singularity_1.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_1\"\u003eSingularity_1\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_1.sif Singularity_1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine.\u003c/p\u003e\n\u003cp\u003eThe error GHA gives, however, is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRun ./run_singularity_1.sh\nFatal error: cannot open file \u0027script.R\u0027: No such file or directory\nError: Process completed with exit code 2.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is a common theme: Singularity cannot run scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 2: Singularity can run script text\u003c/h1\u003e\u003ca id=\"user-content-attempt-2-singularity-can-run-script-text\" class=\"anchor\" aria-label=\"Permalink: Attempt 2: Singularity can run script text\" href=\"#attempt-2-singularity-can-run-script-text\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"Singularity_2\"\u003eSingularity_2\u003c/a\u003e is a minimal Singularity container,\nwith a \u003ccode\u003erunscript\u003c/code\u003e section added:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: r-base\n\n%apprun R\nexec R \"$@\"\n\n%apprun Rscript\nexec Rscript \"$@\"\n\n%runscript\nexec Rscript \"$@\"\n# exec R \"$@\"\n\n%post\n Rscript -e \u0027install.packages(\"glue\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_2.sh\"\u003ebuild_singularity_2.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_2\"\u003eSingularity_2\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_2.sif Singularity_2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_2.sh\"\u003erun_singularity_2.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_2\"\u003eSingularity_2\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\ncat script.R | singularity exec singularity_2.sif R --vanilla --silent --no-echo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, also on GitHub Actions!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 3: clean up\u003c/h1\u003e\u003ca id=\"user-content-attempt-3-clean-up\" class=\"anchor\" aria-label=\"Permalink: Attempt 3: clean up\" href=\"#attempt-3-clean-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eTHIS IS THE BEST WAY\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e is a minimal Singularity container,\nwith a \u003ccode\u003erunscript\u003c/code\u003e section added:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: r-base\n\n%runscript\nexec R --vanilla --silent --no-echo \"$@\"\n\n%post\n Rscript -e \u0027install.packages(\"glue\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_3.sh\"\u003ebuild_singularity_3.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_3.sif Singularity_3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_3.sh\"\u003erun_singularity_3.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\ncat script.R | ./singularity_3.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, also on GitHub Actions!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 4: fakeroot experiment\u003c/h1\u003e\u003ca id=\"user-content-attempt-4-fakeroot-experiment\" class=\"anchor\" aria-label=\"Permalink: Attempt 4: fakeroot experiment\" href=\"#attempt-4-fakeroot-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this case, we\u0027ll re-use \u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e,\nyet build it differently, using the\n\u003ca href=\"https://sylabs.io/guides/3.8/user-guide/fakeroot.html?highlight=fakeroot\" rel=\"nofollow\"\u003efakeroot\u003c/a\u003e\nfeature.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_4.sh\"\u003ebuild_singularity_4.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_3\"\u003eSingularity_3\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsingularity build --fakeroot singularity_4.sif Singularity_3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_4.sh\"\u003erun_singularity_4.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_4\"\u003eSingularity_4\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\ncat script.R | ./singularity_4.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, but fails on GitHub Actions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRun ./build_singularity_4.sh\nFATAL: could not use fakeroot: no mapping entry found in /etc/subuid for root\nError: Process completed with exit code 255.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eApparently, GHA does not support that mapping.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 5: run script directly revised\u003c/h1\u003e\u003ca id=\"user-content-attempt-5-run-script-directly-revised\" class=\"anchor\" aria-label=\"Permalink: Attempt 5: run script directly revised\" href=\"#attempt-5-run-script-directly-revised\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e is a minimal Singularity container,\nwith a \u003ccode\u003erunscript\u003c/code\u003e section added:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: r-base\n\n%runscript\nexec Rscript \"$@\"\n\n%post\n Rscript -e \u0027install.packages(\"glue\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_5.sh\"\u003ebuild_singularity_5.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsingularity build --fakeroot singularity_5.sif Singularity_5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_5.sh\"\u003erun_singularity_5.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n./singularity_5.sif script.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, but fails on GitHub Actions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRun ./build_singularity_5.sh\nFATAL: could not use fakeroot: no mapping entry found in /etc/subuid for root\nError: Process completed with exit code 255.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAttempt 6: run script, container built with sudo\u003c/h1\u003e\u003ca id=\"user-content-attempt-6-run-script-container-built-with-sudo\" class=\"anchor\" aria-label=\"Permalink: Attempt 6: run script, container built with sudo\" href=\"#attempt-6-run-script-container-built-with-sudo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere we will re-use \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"build_singularity_6.sh\"\u003ebuild_singularity_6.sh\u003c/a\u003e is a simple shell script\nto build a container from \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsudo -E singularity build singularity_6.sif Singularity_5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"run_singularity_6.sh\"\u003erun_singularity_6.sh\u003c/a\u003e is a simple shell script\nto run the container built from \u003ca href=\"Singularity_5\"\u003eSingularity_5\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n./singularity_6.sif script.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning this locally goes fine, however, on GHA this goes the classic sideways again:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRun ./run_singularity_6.sh\nFatal error: cannot open file \u0027script.R\u0027: No such file or directory\nError: Process completed with exit code 2.\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "tpall/repeatmasker-singularity", + "latest_release": "v0.9", + "readme": "\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.2671673\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/05b4f48cf033550d0ad2dce2ad5b66ea091ef2b8baff1ff6ca0ed9c41194328f/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e323637313637332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.2671673.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://travis-ci.org/tpall/repeatmasker-singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2ac9880bdf7d2fd57847145748defa6ac26f6aaf31cc4fe84e4668d2846e890a/68747470733a2f2f7472617669732d63692e6f72672f7470616c6c2f7265706561746d61736b65722d73696e67756c61726974792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/tpall/repeatmasker-singularity.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRepeatMasker singularity image\u003c/h1\u003e\u003ca id=\"user-content-repeatmasker-singularity-image\" class=\"anchor\" aria-label=\"Permalink: RepeatMasker singularity image\" href=\"#repeatmasker-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains singularity image recipes for RepeatMasker 4.0.8 and 4.0.9(p2) together with RepBase RepeatMasker Edition database v20181026. RepBase RM database was installed from the private copy downloaded from \u003ca href=\"https://www.girinst.org/repbase\" rel=\"nofollow\"\u003egirinst.org\u003c/a\u003e before them fully switching to a \u003ca href=\"https://www.girinst.org/repbase/sub_announcement.html\" rel=\"nofollow\"\u003esubscription-based funding model\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBuilt RM-4.0.9p2 image can be downloaded from Zenodo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCommand line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /path/to/repeatmasker409.simg /usr/local/bin/RepeatMasker test/seqs/small-1.fa\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1627629507.0 + "updated_at": 1557682456.0 }, { "data_format": 2, - "description": "Singularity example 1: Hello World", + "description": "Repository for a singularity container to run the prob_mbrl code", "filenames": [ "Singularity" ], - "full_name": "richelbilderbeek/singularity_example_1", - "latest_release": "v2.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_example_1\u003c/h1\u003e\u003ca id=\"user-content-singularity_example_1\" class=\"anchor\" aria-label=\"Permalink: singularity_example_1\" href=\"#singularity_example_1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/singularity_example_1/actions\"\u003e\u003cimg src=\"GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/singularity_example_1/workflows/build_singularity/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/singularity_example_1/workflows/build_singularity/badge.svg?branch=master\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/singularity_example_1/workflows/build_singularity/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/singularity_example_1/workflows/build_singularity/badge.svg?branch=develop\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSingularity example 1: Hello World.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e (a script) to see what the container does.\u003c/p\u003e\n\u003cp\u003eThis repo builds the container, runs it and uploads it.\u003c/p\u003e\n", + "full_name": "juancamilog/prob_mbrl_container", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eprob_mbrl_container\u003c/h1\u003e\u003ca id=\"user-content-prob_mbrl_container\" class=\"anchor\" aria-label=\"Permalink: prob_mbrl_container\" href=\"#prob_mbrl_container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1627290514.0 + "updated_at": 1568145713.0 }, { "data_format": 2, @@ -2248,1651 +1884,1645 @@ var data = "filenames": [ "Singularity" ], - "full_name": "researchapps/fasta-utilities", + "full_name": "CNCLgithub/psiturk-sing", + "latest_release": "v1.1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePsiturk singularity container\u003c/h1\u003e\u003ca id=\"user-content-psiturk-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Psiturk singularity container\" href=\"#psiturk-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe definition file describes a lightweight, apline-based container to run psiturk\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild or pull the container\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePulling\u003c/h3\u003e\u003ca id=\"user-content-pulling\" class=\"anchor\" aria-label=\"Permalink: Pulling\" href=\"#pulling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container is hosted at \u003ca href=\"https://cloud.sylabs.io/library/mebelledonne/default/psiturk-apline\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/mebelledonne/default/psiturk-apline\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCan be pulled using the following commands (see link above for up-to-date info)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Pull with Singularity\u003c/span\u003e\n$ singularity pull --arch amd64 library://mebelledonne/default/psiturk-apline:1.0.0\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Pull by unique ID (reproducible even if tags change)\u003c/span\u003e\n$ singularity pull library://mebelledonne/default/psiturk-apline:sha256.4fd46933f838844df2dc14e09651f07d3d322a13c7a0458942028e8347f2addb\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell psiturk.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e now inside the container\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /where/your/project/is/psiturk\npsiturk \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e + any addition args you might want to pass \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "stargazers_count": 0, + "subscribers_count": 3, + "topics": [], + "updated_at": 1601405204.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "Singularity", + "v0.8.13/DB20190619/Singularity.v0.8.13_DB20190619", + "v0.8/DB20180717/Singularity.v0.8_DB20180717", + "v0.8/DB20190618/Singularity.v0.8_DB20190618" + ], + "full_name": "phgenomics-singularity/abricate_k", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFasta Utilities\u003c/h1\u003e\u003ca id=\"user-content-fasta-utilities\" class=\"anchor\" aria-label=\"Permalink: Fasta Utilities\" href=\"#fasta-utilities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a Singularity build file for the \u003ca href=\"https://github.com/jimhester/fasta_utilities\"\u003efasta-utilities\u003c/a\u003e library.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Download this repo\u003c/h2\u003e\u003ca id=\"user-content-2-download-this-repo\" class=\"anchor\" aria-label=\"Permalink: 2. Download this repo\" href=\"#2-download-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/singularituhub/fasta-utilities\ncd fasta-utilities\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-3-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 3. Bootstrap the image\" href=\"#3-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create fasta-utils.img\nsudo singularity bootstrap fasta-utils.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. Run commands\u003c/h2\u003e\u003ca id=\"user-content-4-run-commands\" class=\"anchor\" aria-label=\"Permalink: 4. Run commands\" href=\"#4-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhat commands are in bin?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./fasta-utils.img ls\n2big.pl\t\t\t fetch_entrez.pl\t pairs_unsorted.pl\nCpG_count.pl\t\t fetch_gi.pl\t\t percent_GC.pl\nabsolute_coordinates.pl fetch_sra.pl\t\t regex_fasta.pl\nadd_type.pl\t\t filter_align.pl\t remap_file.pl\nalign_progress.pl\t filter_bam.pl\t\t remove_ambiguous.pl\nascii2csv.pl\t\t filter_reads.pl\t rename_script.pl\navg_coverage.pl\t\t fix_headers.pl\t\t reverse_complement.pl\nbed2fasta.pl\t\t generate_fasta.pl\t sam2fastq.pl\nbed2igv.pl\t\t generate_map.pl\t sam_lengths.pl\nbisulfite_convert.pl\t get_fasta.pl\t\t sequence_counts.pl\nblast_information.pl\t gff2bed.pl\t\t size.pl\ncalcN.pl\t\t gff2data_frame.pl\t size_select.pl\ncollapse_duplicates.pl\t grep.pl\t\t sort.pl\ncombine_bed.pl\t\t in_list.pl\t\t splice.pl\ncommify.pl\t\t lengths.pl\t\t split_fasta.pl\nconsensus.pl\t\t maf2bed.pl\t\t standardize_names.pl\ndistances.pl\t\t mate_pair2paired_end.pl subset_fasta.pl\nfasta2fastq.pl\t\t merge_records.pl\t trans_fasta.pl\nfasta_head.pl\t\t mpileup_consensus.pl\t trim_fasta.pl\nfasta_tail.pl\t\t mpileup_counts.pl\t unique_headers.pl\nfastq2fasta.pl\t\t pairs_sorted.pl\t wrap.pl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun a command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./fasta-utils.img perl add_type.pl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMount the data directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run -b /path/to/data:/data/ fasta-utils.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo specify inputs and outputs, and run with data (not tested)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run -b /path/to/data:/data/ fasta-utils.img perl in_list.pl [args]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eShell into a (writable) container to test changes (that you should then add to the build file \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell --writable fasta-utils.img\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbricate --- A Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-abricate-----a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Abricate --- A Singularity Container\" href=\"#abricate-----a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1288\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for \u003ca href=\"https://github.com/tseemann/abricate\"\u003eAbricate\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ePull the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name TMP_DIRECTORY shub://phgenomics-singularity/Abricate@latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003eAbricate.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./Abricate.simg --help\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name mlst shub://phgenomics-singularityAbricate@VERSION.NUMBER\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir HOME/singularity\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name Abricate shub://phgenomics-singularity/Abricate@latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLink the image to a folder in your \u003ccode\u003ePATH\u003c/code\u003e (e.g., \u003ccode\u003eHOME/bin\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eln -s HOME/singularity/Abricate.simg HOME/bin/Abricate\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e abricate --help\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdating the DB\u003c/h2\u003e\u003ca id=\"user-content-updating-the-db\" class=\"anchor\" aria-label=\"Permalink: Updating the DB\" href=\"#updating-the-db\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eRun the \u003ccode\u003eupdate_db.py\u003c/code\u003e script (default version is 0.8 at the moment)\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1484506830.0 + "updated_at": 1576546683.0 }, { "data_format": 2, - "description": "Arkiweb docker image", + "description": null, "filenames": [ "Singularity" ], - "full_name": "ARPA-SIMC/arkiweb-docker-image", + "full_name": "StefReck/OrcaNet", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003earkiweb-docker-image\u003c/h1\u003e\u003ca id=\"user-content-arkiweb-docker-image\" class=\"anchor\" aria-label=\"Permalink: arkiweb-docker-image\" href=\"#arkiweb-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ARPA-SIMC/arkiweb/\"\u003eArkiweb\u003c/a\u003e recently became\nincompatible with the \u003ca href=\"https://github.com/ARPA-SIMC/arkimet/\"\u003earkimet\u003c/a\u003e\nC++ API\u0027s. This package allows to create a docker container including\na web server, arkiweb and an arkiweb-compatible version of arkimet, to\nbe run within a host having a newer arkimet version, replacing arkiweb\non the host. This allows to keep arkiweb running while keeping arkimet\nupdated to the latest version.\u003c/p\u003e\n\u003cp\u003eThe web server in the host talks with the web server in the container\nthrough apache \u003ccode\u003emod_proxy\u003c/code\u003e module, while the arkiweb in the container\ninteracts with the arkimet datasets in the host through the host\narkimet server http interface.\u003c/p\u003e\n\u003cp\u003eFor more detailed instruction on how to build and start the docker\nimage and configure the system, see the \u003ca href=\"HOWTO_it.md\"\u003eHOWTO\u003c/a\u003e in\nItalian.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 0, "topics": [], - "updated_at": 1636458123.0 + "updated_at": 1628086621.0 }, { "data_format": 2, - "description": null, + "description": "Container configuration file for GPU+Singularity tests.", "filenames": [ - "containers/Singularity.omnia.rocm" + "Singularity", + "Singularity.1404", + "Singularity.1604" ], - "full_name": "PhilipVinc/uvtest", + "full_name": "ruycastilho/GPUtest", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOmnia\u003c/h1\u003e\u003ca id=\"user-content-omnia\" class=\"anchor\" aria-label=\"Permalink: Omnia\" href=\"#omnia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a single repository containing all working codes of the group.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStructure\u003c/h2\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e/packages # contains packages used by multiple users\n # This contains both internal versions of public packages\n # (netket) and private packages.\n```\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1732149584.0 + "updated_at": 1504295844.0 }, { "data_format": 2, - "description": null, + "description": "Docker and Singularity images for FSL 5.11 and Resting State FMRI pipeline (Nan-kuei Chen/Duke University)", "filenames": [ "Singularity" ], - "full_name": "rses-singularity/fsl-debian-stretch-singularity", + "full_name": "chidiugonna/nklab-neuro-fsl", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity image definition for FSL\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-definition-for-fsl\" class=\"anchor\" aria-label=\"Permalink: Singularity image definition for FSL\" href=\"#singularity-image-definition-for-fsl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMaking it easier to start using \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\" rel=\"nofollow\"\u003eFSL\u003c/a\u003e on e.g. HPC.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2514\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"LICENSE.txt\"\u003eLICENSE.txt\u003c/a\u003e, particularly the conditions regarding commercial use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning FSL within a Singularity container using this Singularity image definition\u003c/h2\u003e\u003ca id=\"user-content-running-fsl-within-a-singularity-container-using-this-singularity-image-definition\" class=\"anchor\" aria-label=\"Permalink: Running FSL within a Singularity container using this Singularity image definition\" href=\"#running-fsl-within-a-singularity-container-using-this-singularity-image-definition\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest way to start using FSL via this Singularity image is to\npull the image from the \u003ca href=\"http://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularityHub\u003c/a\u003e on-line repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/home/\u003cspan class=\"pl-smi\"\u003e$USER\u003c/span\u003e/singularity_cache\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SINGULARITY_CACHEDIR}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsingularity pull --name fsl-debian-stretch-singularity-latest.sif shub://rses-singularity/fsl-debian-stretch-singularity:latest \nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SINGULARITY_CACHEDIR}\u003c/span\u003e/fsl-debian-stretch-singularity-latest.sif\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e /bin/bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running \u003ccode\u003esingularity exec\u003c/code\u003e you are then able to run commands \u0027within\u0027 a FSL \u0027container\u0027 e.g.\n\u003ccode\u003efsl-selftest\u003c/code\u003e or \u003ccode\u003efsl5.0-gps\u003c/code\u003e. Note that most FSL commands start with \u003ccode\u003efsl5.0-\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA note re SingularityHub: the \u003ca href=\"https://www.singularity-hub.org/collections/2514\" rel=\"nofollow\"\u003eFSL image provided via SingularityHub\u003c/a\u003e is\nrebuilt whenever there is a push to this GitHub repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding a Singularity image and running a FSL container \u003cem\u003ewithout\u003c/em\u003e using SingularityHub\u003c/h2\u003e\u003ca id=\"user-content-building-a-singularity-image-and-running-a-fsl-container-without-using-singularityhub\" class=\"anchor\" aria-label=\"Permalink: Building a Singularity image and running a FSL container without using SingularityHub\" href=\"#building-a-singularity-image-and-running-a-fsl-container-without-using-singularityhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to use the SingularityHub-built image then you can build it yourself \u003cstrong\u003eon your own machine\u003c/strong\u003e (not HPC):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMake sure you have Singularity installed.\u003c/li\u003e\n\u003cli\u003eEnsure you\u0027re read the \u003ca href=\"LICENSE.txt\"\u003eFSL license\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eInspect the \u003ca href=\"Singularity\"\u003eSingularity image definition in this repo\u003c/a\u003e; this includes steps to:\n\u003cul\u003e\n\u003cli\u003eInstall FSL.\u003c/li\u003e\n\u003cli\u003eInstall the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEEDS\" rel=\"nofollow\"\u003eFSL Evaluation and Example Data Suite (FEEDS)\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStart building an image file:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo SINGULARITY_TMPDIR=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.cache/singularity singularity build ./fsl-debian-stretch-singularity.sif ./Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo start a FSL container using this image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e ./fsl-debian-stretch-singularity.sif /bin/bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen from the resulting shell start the FSL command you want to use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting FSL inside a container\u003c/h2\u003e\u003ca id=\"user-content-testing-fsl-inside-a-container\" class=\"anchor\" aria-label=\"Permalink: Testing FSL inside a container\" href=\"#testing-fsl-inside-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003efsl-selftest\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker and Singularity images for FSL 5.11 and Resting State FMRI pipeline (Nan-kuei Chen/Duke University)\u003c/h1\u003e\u003ca id=\"user-content-docker-and-singularity-images-for-fsl-511-and-resting-state-fmri-pipeline-nan-kuei-chenduke-university\" class=\"anchor\" aria-label=\"Permalink: Docker and Singularity images for FSL 5.11 and Resting State FMRI pipeline (Nan-kuei Chen/Duke University)\" href=\"#docker-and-singularity-images-for-fsl-511-and-resting-state-fmri-pipeline-nan-kuei-chenduke-university\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e for details of use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSummary\u003c/h1\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-label=\"Permalink: Summary\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the build scripts for Docker and Singularity images of the Duke resting pipeline that perform processing of resting state data using FSL (Jenkinson et al. 2012) tools and custom scripts.\u003c/p\u003e\n\u003cp\u003eversion information can be obtained as \u003ccode\u003edocker run --rm orbisys/nklab-neuro-fsl -V\u003c/code\u003e and \u003ccode\u003esingularity run orbisys/nklab-neuro-fsl -V\u003c/code\u003e\nhelp information can be obtained as \u003ccode\u003edocker run --rm orbisys/nklab-neuro-fsl -h\u003c/code\u003e and \u003ccode\u003esingularity run orbisys/nklab-neuro-fsl -h\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe Docker image will be quite large when built. It comes with version 5.11 of FSL and also includes gpu versions of eddy, probtrackx and bedpostx. It is recommended if possible to use the Singularity version of this container.\u003c/p\u003e\n\u003cp\u003eAlternatively if you do not want to build the docker image locally you can pull it from the Docker hub using the command \u003ccode\u003edocker run -it --rm -v $PWD:/opt/data orbisys/nklab-neuro-fsl\u003c/code\u003e or \u003ccode\u003edocker pull orbisys/nklab-neuro-fsl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe Singularity image will be about 6GB when built. It comes with version 5.11 of FSL and also includes gpu versions of eddy, probtrackx and bedpostx. Again if you prefer not to build this locally then a sister version of this singularity image can be downloaded as \u003ccode\u003eSingularity pull shub://chidiugonna/nklab-neuro-fsl\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe original python source \u003ccode\u003eresting_pipeline.py\u003c/code\u003e available at at [\u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e] has been slightly amended. These changes are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edata1\u003c/code\u003e has been selectively converted to dtype \u003ccode\u003enumpy.float64\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eslice indices have been cast as longs in certain instances.\u003c/li\u003e\n\u003cli\u003eBXH functionality is ignored. To explicitly use BXH info pass the flag --ignorebxh=N\u003c/li\u003e\n\u003cli\u003eChanges have been made in step 8 to force the diagonals of the correlation matrix to zero to prevent inconsistencies due to NaNs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSliding window functionality\u003c/h3\u003e\u003ca id=\"user-content-sliding-window-functionality\" class=\"anchor\" aria-label=\"Permalink: Sliding window functionality\" href=\"#sliding-window-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA new step has been added \u003ccode\u003e-7sw\u003c/code\u003e to enable sliding window functionality. In order to use this step you will need to use the \u003ccode\u003e--slidewin\u003c/code\u003e parameter which takes 2 numbers seperated by a comma. The 1st number is the window size in seconds and the second number is the shift in seconds between sequential windows. So for example \u003ccode\u003e--slidewin=60,3\u003c/code\u003e will use a window size of \u003ccode\u003e60\u003c/code\u003e seconds shifted by \u003ccode\u003e3\u003c/code\u003e seconds for each subsequent window. Keep in mind that the \u003ccode\u003e--tr\u003c/code\u003e (in milliseconds) parameter is required to calculate the number of volumes to use for each sliding window correlation. If you do not specify the --slidwin parameter and run step \u003ccode\u003e7sw\u003c/code\u003e then default values of \u003ccode\u003e30,3\u003c/code\u003e will be used. Sliding window files are exported to a new directory \u003ccode\u003eSlidingWindow_W_S\u003c/code\u003e and image files are consolidated into 4D volumes for viewing in FSL as a movie\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExtensions to Slice Correction functionality\u003c/h3\u003e\u003ca id=\"user-content-extensions-to-slice-correction-functionality\" class=\"anchor\" aria-label=\"Permalink: Extensions to Slice Correction functionality\" href=\"#extensions-to-slice-correction-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline has been extended to accept custom slice correction timing files. A python script \u003ccode\u003emake_fsl_stc.py\u003c/code\u003e has been bundled in this container which can take .json files created by dcm2niix. This python program will create a slice correction file with timing values and one with slices in order of acquisition. It can be called as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e where fmri.json is the json output from dcm2niix. custom names for the slice order and slice time files can be provided as parameters as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake_fsl_stc.py fmri.json --slicenum=/path/num.txt --slicetime=/path/time.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOtherwise these files default to \u003ccode\u003esliceorder.txt\u003c/code\u003e and \u003ccode\u003eslicetimes.txt\u003c/code\u003e in the current directory.\u003c/p\u003e\n\u003cp\u003eIf \u003ccode\u003e--slicetime\u003c/code\u003e is provided and --sliceorder is not then only the slicetimes textfile is created. The opposite is true if \u003ccode\u003e--slicenum\u003c/code\u003e is provided.\u003c/p\u003e\n\u003cp\u003eOnce these custom files have been created then they can be provided to the resting state pipeline using the full path as input to the \u003ccode\u003e--sliceorder\u003c/code\u003e parameter\n\u003ccode\u003e--sliceorder=/path/num.txt\u003c/code\u003e as follows \u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eplease note that the default custom slice file expected uses slice order. If you pass a text file with slice times then you will need to use another parameter \u003ccode\u003e--slicetimings=time\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Docker Image\u003c/h3\u003e\u003ca id=\"user-content-build-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build Docker Image\" href=\"#build-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have docker installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003erest-state-fmri\u003c/code\u003edirectory and check that have a Docker file \u003ccode\u003eDockerfile\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and the \u003ccode\u003esrc/resting_pipeline.py\u003c/code\u003e file have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow build the image as follows \u003ccode\u003esudo docker build -t orbisys/nklab-neuro-fsl .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Docker Image\u003c/h3\u003e\u003ca id=\"user-content-run-docker-image\" class=\"anchor\" aria-label=\"Permalink: Run Docker Image\" href=\"#run-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWithin Shell\u003c/h4\u003e\u003ca id=\"user-content-within-shell\" class=\"anchor\" aria-label=\"Permalink: Within Shell\" href=\"#within-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to a directory with a test NIFTII image and enter \u003ccode\u003edocker run -it --rm -v $PWD:/opt/data --entrypoint /bin/bash orbisys/nklab-neuro-fsl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe docker image should run and automatically start in \u003ccode\u003e/opt/data\u003c/code\u003e directory which is mapped to the original directory from which you ran the image. The prompt should look something like below:\n\u003ccode\u003eroot@62e040b47368:/opt/data#\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eYou can now run the pipeline with the shell as follows: \u003ccode\u003eresting_pipeline.py --func PBIA6_26386_20140402_045154_93696_magnitude.nii --throwaway=4 --steps=2,3,4,5,6,7 -o PBIA6_26386_20140402_045154_93696 --sliceorder=odd --tr=5000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAs a one line command\u003c/h4\u003e\u003ca id=\"user-content-as-a-one-line-command\" class=\"anchor\" aria-label=\"Permalink: As a one line command\" href=\"#as-a-one-line-command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to a directory with a test NIFTII image and enter:\n\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func moco14a0001.nii.gz --steps=1,2,3,4,5,6,7,8 -o 14a0001 --sliceorder=\"even\" --tr=3000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning Gui within docker\u003c/h4\u003e\u003ca id=\"user-content-running-gui-within-docker\" class=\"anchor\" aria-label=\"Permalink: Running Gui within docker\" href=\"#running-gui-within-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo access GUI interaces of programs in the docker image then use the construct shown next (Courtesy of work by Fabio Rehm [\u003ca href=\"https://fabiorehm.com/blog/2014/09/11/running-gui-apps-with-docker/\" rel=\"nofollow\"\u003ehttps://fabiorehm.com/blog/2014/09/11/running-gui-apps-with-docker/\u003c/a\u003e] ). For example to run FSL as GUI then perform the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v $HOME/.Xauthority:/home/developer/.Xauthority -it --net=host --pid=host --ipc=host orbisys/rest-state-fmri fsl\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Commands\u003c/h3\u003e\u003ca id=\"user-content-example-commands\" class=\"anchor\" aria-label=\"Permalink: Example Commands\" href=\"#example-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreate Slice Timing files from json\u003c/h4\u003e\u003ca id=\"user-content-create-slice-timing-files-from-json\" class=\"anchor\" aria-label=\"Permalink: Create Slice Timing files from json\" href=\"#create-slice-timing-files-from-json\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/nklab-neuro-fsl /opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\" class=\"anchor\" aria-label=\"Permalink: Run pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/nklab-neuro-fsl /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity Image\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Build Singularity Image\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have singularity 2.4 or greater installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-fsl\u003c/code\u003edirectory and check that you have a Singularity definiton file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow build the image as follows \u003ccode\u003esudo singularity build nklab-neuro-fsl.simg Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Singularity Image\u003c/h3\u003e\u003ca id=\"user-content-run-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Run Singularity Image\" href=\"#run-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou can now run the pipeline as follows: \u003ccode\u003esingularity run nklab-neuro-fsl.simg /opt/rsfmri_python/bin/resting_pipeline.py --func PBIA6_26386_20140402_045154_93696_magnitude.nii --throwaway=4 --steps=2,3,4,5,6,7 -o PBIA6_26386_20140402_045154_93696 --sliceorder=odd --tr=5000\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eYou can also run FSL commands (e.g. flirt) directly as follows: \u003ccode\u003esingularity run --nv rest-state-fmri.simg /opt/fsl/bin/flirt ....\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShell into Singularity Image\u003c/h3\u003e\u003ca id=\"user-content-shell-into-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Shell into Singularity Image\" href=\"#shell-into-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou can shell into the singularity image using: \u003ccode\u003esingularity shell nklab-neuro-fsl.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Commands\u003c/h3\u003e\u003ca id=\"user-content-example-commands-1\" class=\"anchor\" aria-label=\"Permalink: Example Commands\" href=\"#example-commands-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreate Slice Timing files from json\u003c/h4\u003e\u003ca id=\"user-content-create-slice-timing-files-from-json-1\" class=\"anchor\" aria-label=\"Permalink: Create Slice Timing files from json\" href=\"#create-slice-timing-files-from-json-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esingularity run -B $PWD:/opt/data nklab-neuro-fsl.simg /opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file-1\" class=\"anchor\" aria-label=\"Permalink: Run pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --rm -B $PWD:/opt/data nklab-neuro-fsl.simg /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eM. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1552339344.0 + "updated_at": 1533338767.0 }, { "data_format": 2, - "description": "Build amazing TUIs (Text User Interfaces) with this innovative Python framework.", + "description": "The BARI RStudio Singularity container, for the Discovery cluster", "filenames": [ - "1.8.0/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-rich-cli", - "latest_release": "v1.8.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rich-cli/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rich-cli/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rich-cli/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rich-cli/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bcc09e15c6913a24778356e114f64ed669e51406eccbf8671e3dc3ac18f5610f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726963682d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bcc09e15c6913a24778356e114f64ed669e51406eccbf8671e3dc3ac18f5610f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726963682d636c69\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-rich-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" 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src=\"https://camo.githubusercontent.com/6b3c80e5ab41877d9e48d6b59f00c710c486977b5ce9bdc53cfe8c03b8a3213a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d726963682d636c69\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-rich-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cbd43977ff4ce22f0fd3f1e8a332320dfe3f28bf48e8d503ed4329ce3f66b82e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726963682d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbd43977ff4ce22f0fd3f1e8a332320dfe3f28bf48e8d503ed4329ce3f66b82e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726963682d636c69\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-rich-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-rich-cli\u003c/h1\u003e\u003ca id=\"user-content-singularity-rich-cli\" class=\"anchor\" aria-label=\"Permalink: singularity-rich-cli\" href=\"#singularity-rich-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n \u003csource type=\"video/mp4\"\u003e\n\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.textualize.io/\" rel=\"nofollow\"\u003erich-cli\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003erich\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/rich-cli/1.8.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/rich-cli\u003c/code\u003e as \u003ccode\u003e1.8.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "BARIBoston/bari_singularity_rstudio", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebari_singularity_rstudio\u003c/h1\u003e\u003ca id=\"user-content-bari_singularity_rstudio\" class=\"anchor\" aria-label=\"Permalink: bari_singularity_rstudio\" href=\"#bari_singularity_rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source code for the BARI Singularity image, containing R packages for geospatial analysis. The full documentation for Singularity can be found on the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/index.html\" rel=\"nofollow\"\u003esyslabs.io documentation site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguring installed packages\u003c/h2\u003e\u003ca id=\"user-content-configuring-installed-packages\" class=\"anchor\" aria-label=\"Permalink: Configuring installed packages\" href=\"#configuring-installed-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe complete image build process is outlined in the \u003ca href=\"Singularity\"\u003eSingularity definition file\u003c/a\u003e. Most likely, you will want to edit the \u003ccode\u003ePACKAGES\u003c/code\u003e array in the \u003ccode\u003e%post\u003c/code\u003e section of the definition file.\u003c/p\u003e\n\u003cp\u003eFor full documentation, see the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/definition_files.html\" rel=\"nofollow\"\u003eThe Definition File\u003c/a\u003e section of the syslabs.io documentation site.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding\u003c/h2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo quickly build the BARI Singularity image, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build bari.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen prototyping, though, it will likely be more useful to use a temporary sandbox directory to iterate through commands before writing the final procedure to the Singularity definition file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build --sandbox bari-dev/ Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then access a root shell that will persist changes as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell --writable bari-dev/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce you are finished prototyping, it is best to edit the definition file so that future builds will reflect your updated process and you can see if it is working as intended from a clean base image, but if you are impatient, you can build an image directly from the temporary sandbox:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build bari.img bari-dev/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor full documentation, see the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/build_a_container.html\" rel=\"nofollow\"\u003eBuilding a container\u003c/a\u003e section of the syslabs.io documentation site.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1660873798.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1594835811.0 }, { "data_format": 2, - "description": "Build scripts to create singularity containers for kaldi + pop-up-archive", + "description": "Singularity containers to run Gmsh", "filenames": [ - "Singularity.in" + "Singularity.paraview", + "Singularity", + "Singularity.pvbatch" ], - "full_name": "AudiovisualMetadataPlatform/kaldi-pua-singularity", + "full_name": "stephansmit/gmsh_containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Kaldi + PUA\u003c/h1\u003e\u003ca id=\"user-content-singularity-kaldi--pua\" class=\"anchor\" aria-label=\"Permalink: Singularity Kaldi + PUA\" href=\"#singularity-kaldi--pua\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild (working) Singularity containers with Kaldi and the Pop-Up-Archive\ntraining with both CPU and GPU support.\u003c/p\u003e\n\u003cp\u003eDisclaimer: With the exception of the scripts in the top directory, all\nof the content was either pulled directly or inspired by other sources,\nincluding (but not limited to):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/hipstas/kaldi-pop-up-archive/tags\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/hipstas/kaldi-pop-up-archive/tags\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kaldi-asr/kaldi/blob/master/misc/docker/ubuntu-cuda/Dockerfile\"\u003ehttps://github.com/kaldi-asr/kaldi/blob/master/misc/docker/ubuntu-cuda/Dockerfile\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/brandeis-llc/aapb-pua-kaldi-docker\"\u003ehttps://github.com/brandeis-llc/aapb-pua-kaldi-docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://xtra.arloproject.com/datasets/kaldi-pop-up-archive/repo_backups/\" rel=\"nofollow\"\u003ehttp://xtra.arloproject.com/datasets/kaldi-pop-up-archive/repo_backups/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlso, there are some really...unpleasant...scripts in this mix. They\u0027re not mine and I have no idea how they work, but they seem to, so hooray!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the containers\u003c/h2\u003e\u003ca id=\"user-content-building-the-containers\" class=\"anchor\" aria-label=\"Permalink: Building the containers\" href=\"#building-the-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe build_singularity.sh script will build the container. It takes one\nargument: either \u0027gpu\u0027 or \u0027cpu\u0027. The build process is nearly identical,\nbut if you select the \u0027gpu\u0027 option, it will require SUDO access to build\nthe container. It will ask you when it\u0027s time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the container\u003c/h2\u003e\u003ca id=\"user-content-running-the-container\" class=\"anchor\" aria-label=\"Permalink: Running the container\" href=\"#running-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe containers are designed to be standalone, but due to the scripts inside,\nthe do require a writable overlay filesystem. The script run_kaldi.sh\ntakes care of it -- it will create a sparce overlay filesystem which will\nbe discarded when the processing has finished.\u003c/p\u003e\n\u003cp\u003eWhen deploying, only the .sif files and run_kaldi.sh need to be copied to\nthe run-time server.\u003c/p\u003e\n\u003cp\u003eThe syntax to run it is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e run_kaldi.sh \u0026lt;mode\u0026gt; \u0026lt;media_directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe mode is either \u0027cpu\u0027 or \u0027gpu\u0027, which is used to select which image to\nuse.\u003c/p\u003e\n\u003cp\u003eThe media_directory should hold files and the transcripts will be placed\nin this directory in a transcripts directory\u003c/p\u003e\n\u003cp\u003eTo test it, try:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./run_kaldi.sh cpu test_files\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container for gmsh\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-gmsh\" class=\"anchor\" aria-label=\"Permalink: Singularity container for gmsh\" href=\"#singularity-container-for-gmsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container with \u003ca href=\"http://gmsh.info/\" rel=\"nofollow\"\u003eGmsh\u003c/a\u003e and \u003ca href=\"http://www.coolprop.org/\" rel=\"nofollow\"\u003eCoolProp\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Container\u003c/h2\u003e\u003ca id=\"user-content-build-container\" class=\"anchor\" aria-label=\"Permalink: Build Container\" href=\"#build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build gmsh_containers.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull Image\u003c/h2\u003e\u003ca id=\"user-content-pull-image\" class=\"anchor\" aria-label=\"Permalink: Pull Image\" href=\"#pull-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/gmsh_containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Image\u003c/h3\u003e\u003ca id=\"user-content-run-image\" class=\"anchor\" aria-label=\"Permalink: Run Image\" href=\"#run-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run gmsh_containers test.geo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3419\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"h://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1659367162.0 + "updated_at": 1567069655.0 }, { "data_format": 2, - "description": "STAR-Fusion is a component of the Trinity Cancer Transcriptome Analysis Toolkit (CTAT).", + "description": null, "filenames": [ - "1.9.1/Singularity", - "1.11.1/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-star-fusion", - "latest_release": "v1.11.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-star-fusion/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-star-fusion/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-star-fusion/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-star-fusion/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/730795aae0a8346332ec211f8d0b3113e8a917e1b7106ffed407c7ebbb9102b2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/730795aae0a8346332ec211f8d0b3113e8a917e1b7106ffed407c7ebbb9102b2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-star-fusion\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/25696986acf6b6f3651c739b24804afb212695c2c561a2734e7e57862f1f7caf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/25696986acf6b6f3651c739b24804afb212695c2c561a2734e7e57862f1f7caf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-star-fusion\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5fe0c9ec591212ad970580635f0e0e0b0c9ce57201207253ba90659c15e8727f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5fe0c9ec591212ad970580635f0e0e0b0c9ce57201207253ba90659c15e8727f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-star-fusion\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f9d2339d9895863d2b09eabb921f4fedbb665f04823e79c1f16ad23296d0b609/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d737461722d667573696f6e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f9d2339d9895863d2b09eabb921f4fedbb665f04823e79c1f16ad23296d0b609/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d737461722d667573696f6e\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-star-fusion\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-star-fusion\u003c/h1\u003e\u003ca id=\"user-content-singularity-star-fusion\" class=\"anchor\" aria-label=\"Permalink: singularity-star-fusion\" href=\"#singularity-star-fusion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/STAR-Fusion/STAR-Fusion\"\u003eSTAR-Fusion\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/STAR-Fusion/1.11.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/STAR-Fusion\u003c/code\u003e as \u003ccode\u003e1.11.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "aseetharam/class2", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eclass2\u003c/h1\u003e\u003ca id=\"user-content-class2\" class=\"anchor\" aria-label=\"Permalink: class2\" href=\"#class2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "bioinformatics", - "singularity" - ], - "updated_at": 1668127864.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1599421677.0 }, { "data_format": 2, - "description": "asciinema [as-kee-nuh-muh] is a free and open source solution for recording terminal sessions and sharing them on the web.", + "description": "Singularity GENIE image built on Ubuntu", "filenames": [ - "2.2.0/Singularity", - "2.4.0/Singularity", - "2.1.0/Singularity", - "2.3.0/Singularity", - "2.0.2/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-asciinema", - "latest_release": "v2.3.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-asciinema/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-asciinema/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-asciinema/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-asciinema/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/35478770911b6ab1d5857192d6b81d9c33caee554eca26d82a100a76f4baa7ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d61736369696e656d61\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/35478770911b6ab1d5857192d6b81d9c33caee554eca26d82a100a76f4baa7ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d61736369696e656d61\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-asciinema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/95e58d1cf98658b615e9766aa1e980ae2f20a5766700b22cf6305a04a5dbc4d9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d61736369696e656d61\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/95e58d1cf98658b615e9766aa1e980ae2f20a5766700b22cf6305a04a5dbc4d9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d61736369696e656d61\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-asciinema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9c72cbedd0caeb1d743b89c88ffd0c58127bef0190ff870d55f77558955fb4d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d61736369696e656d61\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9c72cbedd0caeb1d743b89c88ffd0c58127bef0190ff870d55f77558955fb4d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d61736369696e656d61\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-asciinema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2057263ea33bb56562217253a6b2770574d3deddcbecd54e862b8c6aa31edc90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d61736369696e656d61\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2057263ea33bb56562217253a6b2770574d3deddcbecd54e862b8c6aa31edc90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d61736369696e656d61\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-asciinema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-asciinema\u003c/h1\u003e\u003ca id=\"user-content-singularity-asciinema\" class=\"anchor\" aria-label=\"Permalink: singularity-asciinema\" href=\"#singularity-asciinema\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/232377\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/42ed19c8f85a29cee03b6168a5afbc320999af106f8af5afa28b3ae8f072cf76/68747470733a2f2f61736369696e656d612e6f72672f612f3233323337372e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/232377.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://asciinema.org/\" rel=\"nofollow\"\u003easciinema\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003easciinema\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/asciinema/2.4.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/asciinema\u003c/code\u003e as \u003ccode\u003e2.4.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "twongjirad/singularity-genie-ubuntu", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-genie-ubuntu\u003c/h1\u003e\u003ca id=\"user-content-singularity-genie-ubuntu\" class=\"anchor\" aria-label=\"Permalink: singularity-genie-ubuntu\" href=\"#singularity-genie-ubuntu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComing soon.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1633086930.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1496374870.0 }, { "data_format": 2, - "description": "Singularity container description for BigStitcher", + "description": "singularity container for edgeR", "filenames": [ - "Singularity-BigStitcher" + "Singularity", + "Singularity.1" ], - "full_name": "PreibischLab/BigStitcher-Singularity", + "full_name": "deanpettinga/edger-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBigStitcher-Singularity\u003c/h1\u003e\u003ca id=\"user-content-bigstitcher-singularity\" class=\"anchor\" aria-label=\"Permalink: BigStitcher-Singularity\" href=\"#bigstitcher-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container description that automatically creates an Uber-JAR of the current BigStitcher version (including all dependencies) using local copy of the Oracle JDK.\u003c/p\u003e\n\u003cp\u003eCan easily be deployed for example on a cluster for parallel resaving.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Container: EdgeR\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-edger\" class=\"anchor\" aria-label=\"Permalink: Singularity Container: EdgeR\" href=\"#singularity-container-edger\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3573\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 11, + "subscribers_count": 2, "topics": [], - "updated_at": 1584624624.0 + "updated_at": 1569864443.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.trial" + "Singularity" ], - "full_name": "EdOates84/Sigularity_test", + "full_name": "phgenomics-singularity/iqtree", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSigularity_test\u003c/h1\u003e\u003ca id=\"user-content-sigularity_test\" class=\"anchor\" aria-label=\"Permalink: Sigularity_test\" href=\"#sigularity_test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1589221154.0 + "updated_at": 1576535464.0 }, { "data_format": 2, - "description": "Shared nextflow modules and assets", + "description": null, "filenames": [ - "pipelines/tumWgs/container/Singularity" + "Singularity" ], - "full_name": "Clinical-Genomics-Lund/nextflow-modules", + "full_name": "phgenomics-singularity/prokka", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enextflow-modules\u003c/h1\u003e\u003ca id=\"user-content-nextflow-modules\" class=\"anchor\" aria-label=\"Permalink: nextflow-modules\" href=\"#nextflow-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eShared nextflow modules and assets used at CMD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe basic structure of the pipeline is as follows\u003c/h1\u003e\u003ca id=\"user-content-the-basic-structure-of-the-pipeline-is-as-follows\" class=\"anchor\" aria-label=\"Permalink: The basic structure of the pipeline is as follows\" href=\"#the-basic-structure-of-the-pipeline-is-as-follows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 modules\n\u2502 \u251c\u2500\u2500 bwa\n\u2502 \u2502 \u2514\u2500\u2500 main.nf\n\u2502 \u251c\u2500\u2500 samtools\n\u2502 \u2502 \u2514\u2500\u2500 main.nf\n\u2502 \u2514\u2500\u2500 senteion\n\u2502 \u2514\u2500\u2500 bwa\n\u251c\u2500\u2500 pipeline\n\u2502 \u251c\u2500\u2500 micro\n\u2502 \u2502 \u251c\u2500\u2500 data\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 micro\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 SRR10490537_1.fastq.gz\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 SRR10490537_2.fastq.gz\n\u2502 \u2502 \u251c\u2500\u2500 main.nf\n\u2502 \u2502 \u2514\u2500\u2500 nextflow.config\n\u2502 \u2514\u2500\u2500 nextflow.config\n\u2514\u2500\u2500 README.md\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 3, "topics": [], - "updated_at": 1648196462.0 + "updated_at": 1564451005.0 }, { "data_format": 2, - "description": "Terminal string styling done right", + "description": "Pipeline to run the Paintor program and its associated visualization tools on GWAS summary statistics data", "filenames": [ - "4.1.0/Singularity", - "5.0.0/Singularity", - "5.0.1/Singularity" + "Singularity" ], - "full_name": "icaoberg/singularity-chalk-cli", - "latest_release": "v5.0.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-chalk-cli/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-chalk-cli/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-chalk-cli/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-chalk-cli/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1c66c240bc38df64015075ec6f68767bb7e4634a835398142bfc7c1999c34256/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1c66c240bc38df64015075ec6f68767bb7e4634a835398142bfc7c1999c34256/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-chalk-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/05b8d9fb69618bfe0ec53c85330e3b0f7b8c73c7a36486e22f25d2b16d12f27b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/05b8d9fb69618bfe0ec53c85330e3b0f7b8c73c7a36486e22f25d2b16d12f27b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-chalk-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a24818a83e2601459d1e387affd74934bd1718bbb3aa8380df073c768dc67828/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a24818a83e2601459d1e387affd74934bd1718bbb3aa8380df073c768dc67828/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-chalk-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/11483fce283481c4a2af404bb7eeb1692c1d85a3bb2eb4f9a22d485caad35407/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11483fce283481c4a2af404bb7eeb1692c1d85a3bb2eb4f9a22d485caad35407/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-chalk-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-chalk-cli\u003c/h1\u003e\u003ca id=\"user-content-singularity-chalk-cli\" class=\"anchor\" aria-label=\"Permalink: singularity-chalk-cli\" href=\"#singularity-chalk-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/chalk/chalk-cli/blob/main/screenshot.png?raw=true\"\u003e\u003cimg src=\"https://github.com/chalk/chalk-cli/raw/main/screenshot.png?raw=true\" width=\"50%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/chalk/chalk-cli\"\u003echalk-cli\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges (or similar)\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-or-similar\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges (or similar)\" href=\"#installing-the-container-on-bridges-or-similar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003echalk-cli\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/chalk-cli/4.1.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modules/chalk-cli\u003c/code\u003e as \u003ccode\u003e4.1.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample\u003c/h3\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec singularity-chalk-cli-4.1.0.sif chalk -t \u0027{red.bold Dungeons and Dragons {~bold.blue (with added fairies)}}\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/screenshot.png\"\u003e\u003cimg src=\"images/screenshot.png\" alt=\"Screenshot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlternative Installation\u003c/h2\u003e\u003ca id=\"user-content-alternative-installation\" class=\"anchor\" aria-label=\"Permalink: Alternative Installation\" href=\"#alternative-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003espack install npm\nspack load npm\nnpm install -g chalk-cli\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "sdjebali/PaintorPipe", + "latest_release": "v2.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"files/logo_PaintorPipe.png\"\u003e\u003cimg src=\"files/logo_PaintorPipe.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePaintorPipe\u003c/h1\u003e\u003ca id=\"user-content-paintorpipe\" class=\"anchor\" aria-label=\"Permalink: PaintorPipe\" href=\"#paintorpipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ePaintorPipe\u003c/code\u003e is a pipeline that perform fine-mapping analysis, using GWAS summary statistics data and diverse functionnal annotations, implemented in Nextflow.\nThis pipeline run the Paintor program and its associated visualization tools and can be run locally or on a slurm cluster and handles containerisation using Singularity.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"files/flowchart_PaintorPipe.png\"\u003e\u003cimg src=\"files/flowchart_PaintorPipe.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#dependencies\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pull-the-pre-built-container\"\u003ePull the pre-built container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#local-machine\"\u003eLocal Machine\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-on-a-compute-cluster-with-slurm\"\u003eSlurm compute cluster\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pipeline-parameters\"\u003ePipeline parameters\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#input-options\"\u003eInput options\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output-options\"\u003eOutput options\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#nextflow-options\"\u003eNextflow options\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#example-on-a-small-dataset\"\u003eExample on a small dataset\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#gwas-summary-statistics\"\u003eGWAS summary statistics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#functionnal-annotations\"\u003eFunctionnal Annotations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDependencies\u003c/h1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use this pipeline you will need:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eNextflow\u003c/code\u003e \u0026gt;= 21.10.6\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity\u003c/code\u003e \u0026gt;= 3.7.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFirst of all, you need to install GO and singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA small dataset \u003ccode\u003eCAD_META_extract\u003c/code\u003e is provided to test this pipeline (\u003ca href=\"#example-on-a-small-dataset\"\u003eExample on a small dataset\u003c/a\u003e section). To try it out, use one of the followings commands after pulling the singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull the pre-built container\u003c/h2\u003e\u003ca id=\"user-content-pull-the-pre-built-container\" class=\"anchor\" aria-label=\"Permalink: Pull the pre-built container\" href=\"#pull-the-pre-built-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can pull the image we built for the \u003ccode\u003ePaintorPipe\u003c/code\u003e from our repository on Sylabs cloud using the command bellow :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --arch amd64 library://zgerber/paintorpipe/mainimage:0.1\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLocal Machine\u003c/h2\u003e\u003ca id=\"user-content-local-machine\" class=\"anchor\" aria-label=\"Permalink: Local Machine\" href=\"#local-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are running the pipeline on a local machine with limited resources and want to use the default configuration (at least 2 CPUs/4G mem), use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -config nextflow.config --gwasFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata/input/CAD_META_extract\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --annotationsFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata/input/annotations.txt\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --ref_genome \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehg19\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --chromosome_header \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eChr\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --pvalue_nonlead \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e1\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --snp \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e100000\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --pp_threshold \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e0.001\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile singularity -resume\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning on a Compute Cluster with Slurm\u003c/h2\u003e\u003ca id=\"user-content-running-on-a-compute-cluster-with-slurm\" class=\"anchor\" aria-label=\"Permalink: Running on a Compute Cluster with Slurm\" href=\"#running-on-a-compute-cluster-with-slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have access to a compute cluster that uses the Slurm Workload Manager and you want to utilize the resources available there (at least 22 CPUs / 60 G mem), use this command with the slurm profile:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -config nextflow.config --gwasFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata/input/CAD_META_extract\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --annotationsFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata/input/annotations.txt\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --ref_genome \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehg19\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --chromosome_header \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eChr\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --pvalue_nonlead \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e1\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --snp \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e100000\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --pp_threshold \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e0.001\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile singularity, slurm -resume\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline parameters\u003c/h1\u003e\u003ca id=\"user-content-pipeline-parameters\" class=\"anchor\" aria-label=\"Permalink: Pipeline parameters\" href=\"#pipeline-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput options\u003c/h2\u003e\u003ca id=\"user-content-input-options\" class=\"anchor\" aria-label=\"Permalink: Input options\" href=\"#input-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth width=\"200px\"\u003eOption\u003c/th\u003e\n \u003cth width=\"200px\"\u003eBy default, example\u003c/th\u003e\n \u003cth width=\"350px\"\u003eDescription\u003c/th\u003e\n \u003cth width=\"90px\"\u003eRequired\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--gwasFile\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003epath/to/GWAS_FILE\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eThe GWAS file must contains 7 required columns : Allele1, Allele2, Effect (Beta), StdErr (SE), Pvalue, CHR, BP. The order is not important, but the name of the column is (see header parameters).\u003c/td\u003e\n \u003ctd align=\"center\"\u003eRequired\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--annotationsFile\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003epath/to/ANNOTATIONS_FILE\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eThe file should contains 2 columns separeted by tabulation. The first one is the name of the annotation (of the annotation file, or the annotation type for example) and the second is the associated path to the file.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eRequired\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--ref_genome\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003ehg19\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eOnly two values are allowed : \u0027hg19\u0027 or \u0027hg38\u0027. Make sure you are using the correct reference genome for your summary statistics GWAS file, because the results of the pipeline coulb be incorrect.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--population\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eEUR\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSpecifies the name of the mainland population : AFR, AMR, EAS, EUR, SAS.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--pvalue_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003ePvalue\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003ePvalue header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--stderr_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eStdErr\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eStandard Error (SE) header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--effect_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eEffect\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eEffect (BETA) header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--chromosome_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eCHR\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eChromosome header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--effectallele_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eAllele1\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eAllele with effect header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--altallele_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eAllele2\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eAllele without effect header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--position_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eBP\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eVariant position in base pair in the chromosome header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--rsID_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003ersID\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eUnique variant identifiant or markermane header\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003c/tr\u003e\n\u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--zheader_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eZscore\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eThe computed zscore is added in a new column, corresponding to the Effect/StdErr for each SNP, for each locus.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--kb\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e500\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSNPs selection distance in kilo bases upstream and downstream of the lead SNP during the split of the GWAS file.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--pp_treshold\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e0\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSignificant posterior probability threshold.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--snp\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e10000000\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eNumber of significant SNPs to keep.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--pvalue_lead\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e5e-08\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSignificant Pvalue threshold for lead SNP.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--pvalue_nonlead\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e1\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSignificant Pvalue threshold for other SNPs around the lead SNP.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput options\u003c/h2\u003e\u003ca id=\"user-content-output-options\" class=\"anchor\" aria-label=\"Permalink: Output options\" href=\"#output-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth width=\"200px\"\u003eOption\u003c/th\u003e\n \u003cth width=\"200px\"\u003eBy default, example\u003c/th\u003e\n \u003cth width=\"350px\"\u003eDescription\u003c/th\u003e\n \u003cth width=\"90px\"\u003eRequired\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_locus\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_locus\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_sorted_locus\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_sorted_locus\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_VCFandMAPfrom1000G\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_VCF_map_files\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_ld\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_ld\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_bed\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_bed\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_annotations\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_annotations\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_annotated_locus\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_annotated_locus\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_paintor\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_paintor\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_results\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_results\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_posteriorprob\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_posteriorprob\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_plot\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_plot\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_canvis\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_canvis\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNextflow options\u003c/h2\u003e\u003ca id=\"user-content-nextflow-options\" class=\"anchor\" aria-label=\"Permalink: Nextflow options\" href=\"#nextflow-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is written in Nextflow, which provides the following default options:\u003c/p\u003e\n\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth width=\"200px\"\u003eOption\u003c/th\u003e\n \u003cth width=\"200px\"\u003eBy default, example\u003c/th\u003e\n \u003cth width=\"350px\"\u003eDescription\u003c/th\u003e\n \u003cth width=\"90px\"\u003eRequired\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-profile\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003esingularity\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eProfile(s) to use when running the pipeline. Specify the profiles that fit your infrastructure among \u003ccode\u003esingularity\u003c/code\u003e, \u003ccode\u003eslurm\u003c/code\u003e.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eRequired\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-config\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003enextflow.config\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\n Configuration file tailored to your infrastructure and dataset.\n \u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-revision\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eversion\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eVersion of the pipeline to launch.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-work-dir\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edirectory\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eWork directory where all temporary files are written.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-resume\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003c/td\u003e\n \u003ctd\u003eResume the pipeline from the last completed process.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-with-report\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003c/td\u003e\n \u003ctd\u003eNextflow can create an HTML execution report. It is a single document that includes many useful metrics on pipeline execution\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-with-timeline\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003c/td\u003e\n \u003ctd\u003eNextflow can display a timeline in HTML format for all processes performed in the pipeline\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor more Nextflow options, see \u003ca href=\"https://www.nextflow.io/docs/latest/cli.html#run\" rel=\"nofollow\"\u003eNextflow\u0027s documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExample on a small dataset\u003c/h1\u003e\u003ca id=\"user-content-example-on-a-small-dataset\" class=\"anchor\" aria-label=\"Permalink: Example on a small dataset\" href=\"#example-on-a-small-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGWAS summary statistics\u003c/h2\u003e\u003ca id=\"user-content-gwas-summary-statistics\" class=\"anchor\" aria-label=\"Permalink: GWAS summary statistics\" href=\"#gwas-summary-statistics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eCAD_META_extract\u003c/code\u003e GWAS file is an extract of the GWAS results from the latest \u003ccode\u003eCoronary Artery Disease\u003c/code\u003e (CAD) meta-analysis involving 122,733 cases and 424,528 controls (van der Harst P et al, 2018).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eunzip CAD_META_extract.zip\nhead CAD_META_extract\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eMarkerName\tAllele1\tAllele2\tFreq1\tFreqSE\tMinFreq\tMaxFreq\tEffect\tStdErr\tPvalue\tDirection\tHetISq\tHetChiSq\tHetDf\tHetPVal\trsID\tChr\tBP\n9:34486713_A_G\ta\tg\t0.9363\t0.0023\t0.9346\t0.9394\t0.0058\t0.0115\t0.6106\t+-\t0\t0.663\t1\t0.4156\trs72735241\t9\t34486713\n4:187387354_G_T\tt\tg\t0.0359\t0.0045\t0.032\t0.041\t0.009\t0.0155\t0.5604\t-+\t0\t0.847\t1\t0.3574\trs73020749\t4\t187387354\n4:76326344_C_G\tc\tg\t0.1743\t0.0062\t0.1694\t0.1823\t-3e-04\t0.0075\t0.9641\t-+\t69.2\t3.252\t1\t0.07136\trs11727982\t4\t76326344\n1:88710048_C_T\tt\tc\t0.4234\t0.008\t0.4172\t0.4337\t-0.0064\t0.0057\t0.2601\t--\t0\t0.371\t1\t0.5427\trs6428642\t1\t88710048\n1:189237277_C_T\tt\tc\t0.5217\t0.0011\t0.5209\t0.5232\t0.005\t0.0056\t0.3742\t++\t0\t0.129\t1\t0.719\trs1578705\t1\t189237277\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eCAD_META_extract\u003c/code\u003e GWAS test file provided contains the 8 required columns :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAllele1\u003c/li\u003e\n\u003cli\u003eAllele2\u003c/li\u003e\n\u003cli\u003eEffect\u003c/li\u003e\n\u003cli\u003eStdErr\u003c/li\u003e\n\u003cli\u003eCHR\u003c/li\u003e\n\u003cli\u003eBP\u003c/li\u003e\n\u003cli\u003ersID\u003c/li\u003e\n\u003cli\u003ePvalue\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe chromosome column is the only column with an incorrect header entry. We need to provide the correct version of the header: \u003ccode\u003eChr\u003c/code\u003e instead of \u003ccode\u003eCHR\u003c/code\u003e with the \u003ccode\u003e--chromosome_header\u003c/code\u003e parameter (see \u003ca href=\"#usage\"\u003eusage\u003c/a\u003e part).\u003c/p\u003e\n\u003cp\u003eThis is important that the column names are correctly written. If you have supplementary columns like in the exampe above, you can keep them, the pipeline is going to ignore them. If you don\u0027t want to change the required column names in the file, like the \u003ccode\u003eChr\u003c/code\u003ecolumn, you have to indicate the alternative names with the header arguments when launching Nextflow command. Make sure the columns are separated by tabulations.\u003c/p\u003e\n\u003cp\u003eBe careful when running the pipe, about the reference genome version (\u003ccode\u003e--ref_genome\u003c/code\u003e parameter). By default, the pipeline uses hg19 version (more used). Depending on the GWAS dataset you want to fine map, you can change by hg38 version (more recent).\u003c/p\u003e\n\u003cp\u003eAdditionally, it is important to ensure that the pvalue for non-leader SNPs is correct. Indeed, if you run \u003ccode\u003ePaintorPipe\u003c/code\u003e on a small number of variants, as in the example here, the pipeline will generate errors. We recommend in the testing phases to keep the \u003ccode\u003e--pvalue_nonlead\u003c/code\u003e parameter at 1.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFunctionnal annotations\u003c/h2\u003e\u003ca id=\"user-content-functionnal-annotations\" class=\"anchor\" aria-label=\"Permalink: Functionnal annotations\" href=\"#functionnal-annotations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConcerning the annotation library, you can use the annotations given in the Paintor github wiki or directely following this link (Warning: This is a large 6.7 GB file).\u003c/p\u003e\n\u003cp\u003eOnce the annotation bed files are downloaded, you can write the \u003ccode\u003eannotations.txt\u003c/code\u003e file, to give to the pipeline, pointing to all annotation bed files (use tabulation) looking like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egenc.exon path/to/exons.proj.bed\ngenc.intron path/to/introns.proj.bed\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first column is the name of the functionnal annotation and the second is the path to the bed file. Above, an example for a run with 2 annotations (exons \u0026amp; introns). We recommand to use no more than 4 or 5 annotations per run.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should obtain 43 loci in the \u003ccode\u003eoutput_locus\u003c/code\u003e directory. Check the \u003ccode\u003eslurm-46703827.out\u003c/code\u003e output file in the \u003ccode\u003efiles\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use \u003ccode\u003ePaintorPipe\u003c/code\u003e for your analysis, please cite the publication as follows :\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGerber, Z., Fisun, M., Aschard, H., \u0026amp; Djebali, S. (2024). PaintorPipe: a pipeline for genetic variant fine-mapping using functional annotations. Bioinformatics Advances, 4(1), vbad188.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eYou can find the publication by following this link (\u003ca href=\"https://doi.org/10.1093/bioadv/vbad188\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioadv/vbad188\u003c/a\u003e).\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1653903915.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1715613103.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container for qiime1.9.1", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "kit-cml/DrugSimulationGPU-naver", + "full_name": "scleveland/qiime1.9.1-singularity", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1242\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Recipe for QIIME1.9.1 For the University of Hawaii HPC system\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-qiime191-for-the-university-of-hawaii-hpc-system\" class=\"anchor\" aria-label=\"Permalink: Singularity Recipe for QIIME1.9.1 For the University of Hawaii HPC system\" href=\"#singularity-recipe-for-qiime191-for-the-university-of-hawaii-hpc-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains recipe run \u003ca href=\"https://qiime.org\" rel=\"nofollow\"\u003eqiime1.9.1\u003c/a\u003e within a\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container running the Ubuntu Xenial OS,which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to Use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity run shub://scleveland/qiime1.9.1-singularity\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1723195578.0 + "updated_at": 1571075539.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "kit-cml/ORd_dynamic_BDF", + "full_name": "CNCLgithub/liquid_gen_model_depth_img", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eliquid_gen_model_depth_img\u003c/h1\u003e\u003ca id=\"user-content-liquid_gen_model_depth_img\" class=\"anchor\" aria-label=\"Permalink: liquid_gen_model_depth_img\" href=\"#liquid_gen_model_depth_img\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClone the repo\u003c/h2\u003e\u003ca id=\"user-content-clone-the-repo\" class=\"anchor\" aria-label=\"Permalink: Clone the repo\" href=\"#clone-the-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, clone this repo:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone git@github.com:CNCLgithub/liquid_gen_model_depth_img.git\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet started with setting up simulation engine\u003c/h2\u003e\u003ca id=\"user-content-get-started-with-setting-up-simulation-engine\" class=\"anchor\" aria-label=\"Permalink: Get started with setting up simulation engine\" href=\"#get-started-with-setting-up-simulation-engine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get started, you need to have SPlisHSPlasH installed inside this repo. You can either go to their website and follow the instructions, as in here: \u003ca href=\"https://splishsplash.readthedocs.io/en/latest/build_from_source.html\" rel=\"nofollow\"\u003ehttps://splishsplash.readthedocs.io/en/latest/build_from_source.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOr we provide a way to install, first download this singularity container (\u003ca href=\"https://yale.box.com/shared/static/j40o27bcfgjkzzgecltoh0vj0v411ph5.sif\" rel=\"nofollow\"\u003ehttps://yale.box.com/shared/static/j40o27bcfgjkzzgecltoh0vj0v411ph5.sif\u003c/a\u003e),\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ewget https://yale.box.com/shared/static/j40o27bcfgjkzzgecltoh0vj0v411ph5.sif -O ss.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen use the following command to install\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec ${path_to_downloaded_singularity_container} bash ./setup_ss.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003ewhere you can have \u003ccode\u003e./ss.sif\u003c/code\u003e instead of \u003ccode\u003e${path_to_downloaded_singularity_container}\u003c/code\u003e if you have been following along.\u003c/p\u003e\n\u003cp\u003eRemember to install SPlisHSPlasH inside this project directory. i.e., the structure should look like \u003ccode\u003eliquid_gen_model_depth_img/SPlisHSPlasH/\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet started with the model\u003c/h2\u003e\u003ca id=\"user-content-get-started-with-the-model\" class=\"anchor\" aria-label=\"Permalink: Get started with the model\" href=\"#get-started-with-the-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003euse \u003ccode\u003e./setup.sh all\u003c/code\u003e to install all the modeling environment. Please note that if packages for Julia cannot be installed in this way, run \u003ccode\u003e./run.sh julia\u003c/code\u003e to open Julia interface and install them manually.\nIf you were to install manually, type the following command in the interface:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusing Pkg; Pkg.instantiate()\nusing Pkg; Pkg.add(\"FileIO\")\nPkg.add(\"GeometryBasics\")\nPkg.add(\"MeshIO\")\nPkg.add(\"PyCall\")\nPkg.add(\"Reexport\")\nPkg.add(\"Formatting\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun model\u003c/h2\u003e\u003ca id=\"user-content-run-model\" class=\"anchor\" aria-label=\"Permalink: Run model\" href=\"#run-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the model, use \u003ccode\u003e./run.sh julia src/exp_basic.jl ${#}/{Scene_name}\u003c/code\u003e. Please note that {#} corresponding to the number in library. [1-box, 2-boxwithahole, 3-oneobject, 4-obstacle, 5-motor, 6-wall]\n{Scene_names} corresponds to the available scene names. The scene names are combinations of scene names, which are \"box\" \"boxwithahole\" \"oneobject\" \"obstacle\" \"motor\" \"wall\", and viscosities, which are \"1016\" \"104\" \"1\" \"4\" \"16\". Connect them using an underscore, such as \u003ccode\u003ebox_1016\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAs an example, if you were to run box scene at viscosity 4, then run \u003ccode\u003e./run.sh julia src/exp_basic.jl 1/box_4\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eA pre-built singularity container can be downloaded here: \u003ca href=\"https://yale.box.com/s/7xjvx27hijjaewezso8l0mbf6hsrq3ha\" rel=\"nofollow\"\u003ehttps://yale.box.com/s/7xjvx27hijjaewezso8l0mbf6hsrq3ha\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1727234462.0 + "updated_at": 1679446657.0 }, { "data_format": 2, - "description": "Simulate drug effect to get each component using GPU parallelisation", + "description": "GATK 4beta singularity", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "kit-cml/DrugSimulationGPU", + "full_name": "DoaneAS/gatk", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egatk\u003c/h1\u003e\u003ca id=\"user-content-gatk\" class=\"anchor\" aria-label=\"Permalink: gatk\" href=\"#gatk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGATK 4beta singularity\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1720503899.0 + "updated_at": 1499573007.0 }, { "data_format": 2, - "description": "github actions testing", + "description": "Singulary recipe for plasflow", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "martinghunt/gat", - "latest_release": "v0.0.6", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egat\u003c/h1\u003e\u003ca id=\"user-content-gat\" class=\"anchor\" aria-label=\"Permalink: gat\" href=\"#gat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003egithub actions testing\u003c/p\u003e\n", + "full_name": "ISU-HPC/plasflow", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eplasflow\u003c/h1\u003e\u003ca id=\"user-content-plasflow\" class=\"anchor\" aria-label=\"Permalink: plasflow\" href=\"#plasflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingulary recipe for plasflow\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1639739073.0 + "updated_at": 1525896053.0 }, { "data_format": 2, - "description": "singularity examples", + "description": "unofficial conversion of python from docker to singularity", "filenames": [ - "Singularity.python", - "Singularity.MG5_alone", - "Singularity.MG5_MA5_PY8_DEL", - "Singularity.cowsay", - "Singularity.MG5_MA5_PY8_ROOT", - "Singularity.MG5_MA5_PY8", - "Singularity.MG5" + "Singularity" ], - "full_name": "oliviermattelaer/singularity-recipe", + "full_name": "tin6150/python", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-recipe\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe\" class=\"anchor\" aria-label=\"Permalink: singularity-recipe\" href=\"#singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epython\u003c/h1\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eunofficial\ncontainerization of python,\u003c/p\u003e\n\u003cp\u003efirst intended as conversion of python from docker to singularity\u003c/p\u003e\n\u003cp\u003enow have github workflow build docker container, the convert to singularity during a pull process\u003c/p\u003e\n\u003cp\u003efor incorporation into software_module_farm\u003c/p\u003e\n\u003cp\u003eplan to be a fairly fat container with many python libraries, numby, pandas, and likely many less common one requested by researchers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eexample run command (in testing)\u003c/h1\u003e\u003ca id=\"user-content-example-run-command-in-testing\" class=\"anchor\" aria-label=\"Permalink: example run command (in testing)\" href=\"#example-run-command-in-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003edocker pull ghcr.io/tin6150/python:main\ndocker run -it --rm --entrypoint=/bin/bash ghcr.io/tin6150/python:main\ndocker run -it --rm --entrypoint=/bin/pip ghcr.io/tin6150/python:main list\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003einvoking docker from inside docker, passthru, not exactly dind (docker-in-docker)\u003c/h1\u003e\u003ca id=\"user-content-invoking-docker-from-inside-docker-passthru-not-exactly-dind-docker-in-docker\" class=\"anchor\" aria-label=\"Permalink: invoking docker from inside docker, passthru, not exactly dind (docker-in-docker)\" href=\"#invoking-docker-from-inside-docker-passthru-not-exactly-dind-docker-in-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003epushd ~/tin-gh/lbnl-science-it/atlas-run\u003c/p\u003e\n\u003cp\u003edocker run -it --rm --entrypoint=python3 -v \u003ccode\u003epwd\u003c/code\u003e:/mnt -v /var/run/docker.sock:/var/run/docker.sock \u003cbr\u003e\nghcr.io/tin6150/python:main \u003cbr\u003e\n-u ./atlas_run.py -v\n2\u0026gt;\u0026amp;1 | tee ./log_atlas_testv12.out\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ethe tee is to local fs, not inside the container\u003c/h1\u003e\u003ca id=\"user-content-the-tee-is-to-local-fs-not-inside-the-container\" class=\"anchor\" aria-label=\"Permalink: the tee is to local fs, not inside the container\" href=\"#the-tee-is-to-local-fs-not-inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\nsingularity pull --name python.sif docker://ghcr.io/tin6150/py-conda:main\n\n\n\n# historical info\n\nthis repo was started back in 2019... \n2024.0413 changed branch name from master to main\nrename used web gui, manually changing URL to \"/branches\" to get list to rename, set default.\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 3, "topics": [], - "updated_at": 1543410032.0 + "updated_at": 1714970469.0 }, { "data_format": 2, - "description": "Singularity recipe files for angsd (https://github.com/ANGSD/angsd)", + "description": null, "filenames": [ - "Singularity.0.925", - "Singularity.0.922", - "Singularity.0.919", - "Singularity.0.917", - "Singularity.0.921", - "Singularity.0.918", - "Singularity.0.937", - "Singularity.0.940", + "Singularity.v0.0.1", + "Singularity.v0.0.3", "Singularity", - "Singularity.0.923" + "Singularity.v0.0.2", + "Singularity.v0.0.4" ], - "full_name": "powerPlant/angsd-srf", + "full_name": "darachm/singularity_python3_for_darach", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2300\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the angsd program for analysing NGS data\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1972\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis container is for providing a python3 environment, as Darach likes it\nfor some bioinformatics pipelines. Expect this to grow as more stuff is used.\u003c/p\u003e\n\u003cp\u003eThe primary reason for this is so that it flows nicely through SingularityHub\nfor Nextflow pipelines.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1645138593.0 + "updated_at": 1584545946.0 }, { "data_format": 2, - "description": "Epilepsy prediction codes running on Docker", + "description": "singularity container for nanopolish", "filenames": [ - "Singularity.fea" + "Singularity" ], - "full_name": "hlya23dd/Code_evaluation_Container", + "full_name": "ISU-HPC/nanopolish", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enanopolish\u003c/h1\u003e\u003ca id=\"user-content-nanopolish\" class=\"anchor\" aria-label=\"Permalink: nanopolish\" href=\"#nanopolish\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity container for nanopolish\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1598271532.0 + "updated_at": 1561744596.0 }, { "data_format": 2, - "description": "Singularity containers to run Paraview", + "description": null, "filenames": [ - "Singularity.paraview", - "Singularity.pvbatch" + "Singularity" ], - "full_name": "stephansmit/paraview_containers", + "full_name": "krafczyk/j.jcp.2016.08.012", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParaview_containers\u003c/h1\u003e\u003ca id=\"user-content-paraview_containers\" class=\"anchor\" aria-label=\"Permalink: Paraview_containers\" href=\"#paraview_containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity containers to run paraview\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse\u003c/h2\u003e\u003ca id=\"user-content-use\" class=\"anchor\" aria-label=\"Permalink: Use\" href=\"#use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the GUI with paraview\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec paraview_containers.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\n\u003ca href=\"https://singularity-hub.org/collections/3435\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJulia article containerized with Docker and Singularity\u003c/h1\u003e\u003ca id=\"user-content-julia-article-containerized-with-docker-and-singularity\" class=\"anchor\" aria-label=\"Permalink: Julia article containerized with Docker and Singularity\" href=\"#julia-article-containerized-with-docker-and-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom docker hub without cloning this repo:\u003c/h3\u003e\u003ca id=\"user-content-from-docker-hub-without-cloning-this-repo\" class=\"anchor\" aria-label=\"Permalink: From docker hub without cloning this repo:\" href=\"#from-docker-hub-without-cloning-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker run -it adb16x/julia_test:fresh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen inside the container:\n\u003ccode\u003esh run.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe output is stored in \u003ccode\u003eresults.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBy cloning this repo:\u003c/h3\u003e\u003ca id=\"user-content-by-cloning-this-repo\" class=\"anchor\" aria-label=\"Permalink: By cloning this repo:\" href=\"#by-cloning-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker build --no-cache -t julia .\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker run -it julia\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen inside the container:\n\u003ccode\u003esh run.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe output is stored in \u003ccode\u003eresults.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eBuild image \u003cstrong\u003eand\u003c/strong\u003e run the code:\n\u003ccode\u003esudo singularity --debug build test.simg Singularity |\u0026amp; tee sing-build.output\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo into the image:\n\u003ccode\u003esingularity shell test.simg\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eView the results:\n\u003ccode\u003ecat /usr/local/data/results.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAny output/issues with the build can be view at: \u003ccode\u003ecat sing-build.output\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo reproduce all 6 rows, change the \u003ccode\u003enumOfRefinements\u003c/code\u003e in \u003ccode\u003e/data/examples/runExperiments.jl\u003c/code\u003e to 6. It consumes a lot of memory though.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1586875597.0 + "updated_at": 1544035761.0 }, { "data_format": 2, - "description": null, + "description": "A Docker Image to run LLaMA on TACC", "filenames": [ - "Singularity.v0.2.0", - "Singularity.v0.4.0", - "Singularity.v0.5.0", - "Singularity", - "Singularity.v0.1.0" + "Singularity" ], - "full_name": "darachm/singularity_runningJobs", - "latest_release": "v0.1.0", + "full_name": "mosoriob/tacc-jupyter-llama-docker-image", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1593741796.0 + "updated_at": 1702471258.0 }, { "data_format": 2, - "description": "This source code is now deprecated. For updated workflows visit", + "description": "test singularity definition", "filenames": [ - "build/Singularity.beta" + "Singularity" ], - "full_name": "glass-consortium/glasstools", + "full_name": "deanpettinga/alpine-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity image for GLASS workflows\u003c/h2\u003e\u003ca id=\"user-content-singularity-image-for-glass-workflows\" class=\"anchor\" aria-label=\"Permalink: Singularity image for GLASS workflows\" href=\"#singularity-image-for-glass-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003ca href=\"https://www.singularity-hub.org/collections/262\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6089d24b6232e35051c9f5d1f23d3b2440289a8bdab500934c1f194dbba13ccb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f53696e67756c61726974792d312e312e3273322d627269676874677265656e2e737667\" alt=\"Singularity 1.1.2s2\" data-canonical-src=\"https://img.shields.io/badge/Singularity-1.1.2s2-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/glasstools/keystone/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/183e5dd3d93e42f62573ab86aa911caf6c12dab38c41c6c08e2a495e8aee14f8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f446f636b65722d312e322e322d627269676874677265656e2e737667\" alt=\"Docker 1.2.2\" data-canonical-src=\"https://img.shields.io/badge/Docker-1.2.2-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/glass-consortium/glassdocs/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5874f7ca01d26ee4c18b1ba13da87158f986395e8ab9464d0a1bc0fd26477538/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f676c6173732d636f6e736f727469756d2f676c617373646f63732e737667\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/glass-consortium/glassdocs.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e18-Nov-2017\u003cbr\u003e\nv1.1.2s2\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eBuild details for \u003ccode\u003eglass-consortium/glasstools\u003c/code\u003e images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePS:\u003c/strong\u003e Documentation to run workflows is not yet ready. Visit \u003ca href=\"https://docker.glass-consortium.org\" rel=\"nofollow\"\u003ehttps://docker.glass-consortium.org\u003c/a\u003e for updates.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCurrent Build\u003c/h4\u003e\u003ca id=\"user-content-current-build\" class=\"anchor\" aria-label=\"Permalink: Current Build\" href=\"#current-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSee below on how to install Singularity version: \u003ccode\u003e2.4-install_718360bb.g718360bb\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://glass-consortium/glasstools:beta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAutomated build, when successfully built is available at Singularity Hub: \u003ca href=\"https://www.singularity-hub.org/collections/262\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/262\u003c/a\u003e with image tag: \u003ccode\u003eglass-consortium/glasstools:beta\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload using \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, v2.4 or higher.\u003c/li\u003e\n\u003cli\u003eAvoid running container as root. Singularity images does not require root privileges to run workflows.\u003c/li\u003e\n\u003cli\u003eDefault bind while running workflow is user ${HOME}.\u003c/li\u003e\n\u003cli\u003eFor better potability and disk mounts, ask your system admin to configure \u003ccode\u003e/etc/singularity/singularity.conf\u003c/code\u003e and set \u003ccode\u003eenable overlay = yes\u003c/code\u003e. Read \u003ca href=\"http://singularity.lbl.gov/docs-mount\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/docs-mount\u003c/a\u003e for details.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eManual build\u003c/h4\u003e\u003ca id=\"user-content-manual-build\" class=\"anchor\" aria-label=\"Permalink: Manual build\" href=\"#manual-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend pulling pre-built Singularity image from Singularity registry at \u003ca href=\"https://www.singularity-hub.org/collections/262\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/262\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eManual build is for improvement and debugging of current beta image, especially with reducing image size and adding shortcodes to additional GLASS workflows.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/glass-consortium/glasstools.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\n\nsingularity build glasstools_keystone_beta.simg Singularity.beta\nsingularity inspect glasstools_keystone_beta.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee file: \u003cem\u003eglasstools_keystone_beta.simg.inspect.log\u003c/em\u003e for image details.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to install Singularity\u003c/h3\u003e\u003ca id=\"user-content-how-to-install-singularity\" class=\"anchor\" aria-label=\"Permalink: How to install Singularity\" href=\"#how-to-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOne time installation, \u003cstrong\u003erequires admin privileges\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePlease ask your system administrator to install Singularity with following version. While installation should be done by IT administrator, running GLASS workflows does not require \u003ccode\u003esudo\u003c/code\u003e privilege. Also, unlike potential root escalation while running docker container, Singularity based workflows are more isolated from host environment and less vulnerable to root escalation. Visit \u003ca href=\"http://singularity.lbl.gov/user-guide#security-and-privilege-escalation\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/user-guide#security-and-privilege-escalation\u003c/a\u003e for more on security.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eGLASS workflows are using Singularity \u003ccode\u003ev2.4\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eFull version at the time of install: v2.4-install_718360bb.g718360bb\u003cbr\u003e\nCommit: \u003ca href=\"https://github.com/singularityware/singularity/commit/718360bb20b66cbafb85dd9d0a73bd6bb60c7a1f\"\u003ehttps://github.com/singularityware/singularity/commit/718360bb20b66cbafb85dd9d0a73bd6bb60c7a1f\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eFor better compatibility with pre-built GLASS image, please install Singularity from forked reposioty as follows:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eumask\u003c/span\u003e 0022\n\ngit clone https://github.com/glass-consortium/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# confirm last commit ID to be 718360bb20b66cbafb85dd9d0a73bd6bb60c7a1f for HEAD -\u0026gt; master branch\u003c/span\u003e\ngit log --name-status HEAD^..HEAD\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# fork master branch to a new branch, named install_718360bb\u003c/span\u003e\ngit checkout -b install_718360bb\ngit status\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThis will show...\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eOn branch install_718360bb\u003cbr\u003e\nnothing to commit, working tree clean\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./autogen.sh \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e ./configure --prefix=/usr/local \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e return exit code for compilation status\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$?\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# only one time, we need root privileges\u003c/span\u003e\nsudo make install\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# return to non-root user environment\u003c/span\u003e\nsudo -k\n\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${HOME}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e non-root user\u003c/span\u003e\n\nsingularity --version\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will show \u003ccode\u003e2.4-install_718360bb.g718360bb\u003c/code\u003e. If so, installation is identical to an environment used to build GLASS Singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBugs, issues\u003c/h3\u003e\u003ca id=\"user-content-bugs-issues\" class=\"anchor\" aria-label=\"Permalink: Bugs, issues\" href=\"#bugs-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReport issues related to setting up Docker/Singularity image and running workflows at \u003ca href=\"https://github.com/glass-consortium/glassdocs/issues\"\u003ehttps://github.com/glass-consortium/glassdocs/issues\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOriginal Singularity file was based on \u003ca href=\"https://github.com/jekriske/r-base\"\u003ehttps://github.com/jekriske/r-base\u003c/a\u003e by \u003ca href=\"https://github.com/jekriske\"\u003eJeff Kriske\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ealpine-singularity\u003c/h1\u003e\u003ca id=\"user-content-alpine-singularity\" class=\"anchor\" aria-label=\"Permalink: alpine-singularity\" href=\"#alpine-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etest singularity definition\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1585336352.0 + "updated_at": 1571758824.0 }, { "data_format": 2, - "description": null, + "description": "This pipeline analyses rna data, deeloped for OvCa and HCC samples", "filenames": [ - "Singularity.fmriprep_1.5.0_ciftify", - "Singularity.fmriprep_ciftify_short3", - "Singularity.fmriprep_1.5.0_basic", - "Singularity.repronim_fmriprep_oldconnectome", - "Singularity.repronim_fmriprep_1.5.0", - "Singularity.fmriprep_old_connectome", - "Singularity.fmriprep_ciftify_short2", - "Singularity.sing_fmriprep_ciftify", - "Singularity.fmriprep_ciftify_short6", - "Singularity.repronim_fmriprep_1.5.0_with_connectome", - "Singularity.ciftify_only", - "Singularity.fmriprep_1.5.0_no_connectome", - "Singularity.fmriprep_test", - "Singularity.fmriprep_ciftify_short5", - "Singularity.test", - "Singularity.fmriprep_ciftify", - "Singularity.fmriprep_ciftify_short", - "Singularity.fmriprep_ciftify4", - "Singularity.oldconnectome" + "Singularity" ], - "full_name": "kellyuw/SingularityRecipes", + "full_name": "Krebsro/nf-core-neointrons", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularityRecipes\u003c/h1\u003e\u003ca id=\"user-content-singularityrecipes\" class=\"anchor\" aria-label=\"Permalink: SingularityRecipes\" href=\"#singularityrecipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCollection of Singularity recipes for neuroimaging.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt; HEAD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/neointrons\u003c/h1\u003e\u003ca id=\"user-content-nf-coreneointrons\" class=\"anchor\" aria-label=\"Permalink: nf-core/neointrons\" href=\"#nf-coreneointrons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eThis pipeline analyses rna data, developed for OvCa and HCC data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/neointrons\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b518c18e7a0eb37dff64a817b9d7599868058ef6d149d40f2fa83d7482070f6b/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6e656f696e74726f6e732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/neointrons.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b9d943c6da4bb0a73fe6bc476304001a1e93cbc8ccadf264da8fe47716ab6c53/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/neointrons\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/454e72258d5a13e5ba99c19a69f42f9df6f888965ed224607e80a9a984bdd7dd/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6e656f696e74726f6e732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/neointrons.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/neointrons pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\n=======\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core-neointrons\u003c/h1\u003e\u003ca id=\"user-content-nf-core-neointrons\" class=\"anchor\" aria-label=\"Permalink: nf-core-neointrons\" href=\"#nf-core-neointrons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline analyses rna data, deeloped for OvCa and HCC samples\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003ef1b73feef4f24607374c98f4c1efc1f1bef016af\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1572493534.0 + "updated_at": 1541497875.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for Braker", "filenames": [ - "docker/Singularity.snowflake" + "Singularity", + "Singularity.2.1.2" ], - "full_name": "pnplab/bids-preproc", + "full_name": "ISU-HPC/braker", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epnplab\u0027s bids-preproc pipeline\u003c/h1\u003e\u003ca id=\"user-content-pnplabs-bids-preproc-pipeline\" class=\"anchor\" aria-label=\"Permalink: pnplab\u0027s bids-preproc pipeline\" href=\"#pnplabs-bids-preproc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn fmriprep superlayer to handle hcp/cloud or distributed scheduling for heavy longitudinal datasets.\u003c/p\u003e\n\u003cp\u003eIt does six things:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eabstract and orchestrate the preprocessing across any kind of distributed environments,\nit currently works with SLURM, but can easily be extended to any system, ie. ssh, amazon cloud, through any existing of the existing dask distributed/jobqueue implementation.\u003c/li\u003e\n\u003cli\u003eprovide an extra granularity to fmriprep at the level of session,\nas fmriprep currently only handles processing of either full dataset or of single participant at a time.\u003c/li\u003e\n\u003cli\u003ewrap tool execution calls around docker or singularity virtual containers (or none at all), with the same source code.\u003c/li\u003e\n\u003cli\u003earchive dataset with dar and only extract the relevant parts (ie. specific sessions or subjects) when needed on computing node for mutualised hypercomputing environments,\nas filesystem such as lustre, which we tested on the beluga cluster (compute canada):\n\u003cul\u003e\n\u003cli\u003ecause fmriprep to randomly hang indefinitely due to process getting stuck in D-state mode (pending kernel-level state, likely due to the network filesystem drivers)\u003c/li\u003e\n\u003cli\u003eare slow (\u003ccode\u003eseff\u003c/code\u003e averages to 2.17% of CPU utilization for 92.36% for memory usage).\u003c/li\u003e\n\u003cli\u003eare limited in the amount of file one can write (the 1M default per-user scratch file count limit is already broken out for a single dataset such as kaminati, when considering for fmriprep intermediary generated files)\nand inner compute-nodes storages are too limited (a few hundreds gigs only) to store a single dataset, or even a single subject, considering all fmriprep\u0027s intermediary generated files (for kaminati).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003emonkey patch currently broken fmriprep anatomic fast-track mechanism, which is buggy with some dataset, cf. \u003ca href=\"https://github.com/nipreps/smriprep/issues/224\"\u003ehttps://github.com/nipreps/smriprep/issues/224\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003emonitor unreachable dask workers (likely due to hypercomputer network congestion issues) and kill and reschedule their associated compute nodes, if dask+slurm is the used scheduler.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe architecture should enable easy changes of the pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eie. to partially download the dataset, such as a single session, at the relevant time instead of extracting it from dar.\u003c/li\u003e\n\u003cli\u003eie. to use a different orchestration system than slurm (for instance kubernetes, ..., basically anything, check both dask distributed and dask jobqueue documentations for most of the currently available options).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBefore use, you must:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esetup the license/freesurfer.txt file (get a license from freesurfer website and put it inside that file, cf. fmriprep doc)\u003c/li\u003e\n\u003cli\u003edownload the templateflow atlas using the script in \u003ccode\u003e./scripts/download-templateflow-data.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003edownload and build the relevant singularity images (this step is only required if singularity is used):\n\u003ccode\u003emkdir ../singularity-images/; cd ../singularity-images/; singularity build bids-validator-1.8.5.simg docker://bids/validator:v1.8.5 ; singularity build fmriprep-20.2.6.simg docker://nipreps/fmriprep:20.2.6 ; singularity build smriprep-0.8.1.simg docker://nipreps/smriprep:0.8.1\u003c/code\u003e.\nfile \u003ccode\u003econfig.py\u003c/code\u003e might have to be adapted to get the proper path.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSample usage:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003epython main.py --help\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython main.py --vm-engine docker --granularity subject --executor none --disable-mriqc \u0027/scratch/nuks/kaminati-bids\u0027 \u0027/scratch/nuks/kaminati-preproc\u0027\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e./preprocessing.sh --vm-engine singularity --granularity session --executor slurm --disable-mriqc --worker-memory-gb 64 --worker-cpu-count 16 --worker-count 7 --worker-walltime 2-12:00:00 --worker-local-dir \u0027$SLURM_TMPDIR/pnplab-kaminati\u0027 \u0027/scratch/nuks/kaminati-bids\u0027 \u0027/scratch/nuks/kaminati-preproc\u0027\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebraker\u003c/h1\u003e\u003ca id=\"user-content-braker\" class=\"anchor\" aria-label=\"Permalink: braker\" href=\"#braker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for Braker\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1638698896.0 + "updated_at": 1541795875.0 }, { "data_format": 2, - "description": null, + "description": "Anvi\u2019o is an open-source, community-driven analysis and visualization platform for microbial \u2018omics.", "filenames": [ - "Singularityfile.def" + "8/Singularity", + "7/Singularity" ], - "full_name": "shanzewang/ros_jackal_image", + "full_name": "pscedu/singularity-anvio", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eROS-Jackal\u003c/h1\u003e\u003ca id=\"user-content-ros-jackal\" class=\"anchor\" aria-label=\"Permalink: ROS-Jackal\" href=\"#ros-jackal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the repository for the paper \"\u003ca href=\"https://arxiv.org/abs/2210.04839\" rel=\"nofollow\"\u003eBenchmarking Reinforcement Learning Techniques for Autonomous Navigation\u003c/a\u003e\".\u003c/p\u003e\n\u003cp\u003eThe results shown in the paper use Condor Cluster to distribute 100 actors for collecting trajectories. This setting can greatly speed up the training and make it feasible to finish all the experiments presented in the paper, however Condor Cluster is relatively inaccessible to most users. Instead, to guarantee reproducibility, we provide this version of repository that distributes the actors over 10 Singularity containers that can run locally on a single machine.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/ros_jackal.git\ncd ros_jackal\n\u003c/code\u003e\u003c/pre\u003e\n\u003col\u003e\n\u003cli\u003eIn your virtual environment, install the python dependencies:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\" rel=\"nofollow\"\u003ehttps://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 is \u003cstrong\u003erequired\u003c/strong\u003e to build the image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Only do following step if you really need!) The code does not require ROS installation, since the rollout happens in the container, but if you have need to develop based on our repo, running ROS and Gazebo simulation out of the container enables GUI and is easier to debug. Follow steps below to install ROS dependencies (assume \u003ccode\u003emelodic\u003c/code\u003e ROS installed already):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo and required ros packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/ros_jackal.git\ngit clone https://github.com/jackal/jackal.git --branch melodic-devel\ngit clone https://github.com/jackal/jackal_simulator.git --branch melodic-devel\ngit clone https://github.com/jackal/jackal_desktop.git --branch melodic-devel\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eInstall ROS package dependencies\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/melodic/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=melodic\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBuild the workspace\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecatkin_make\nsource devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eVerify your installation: (this script will run open-ai gym environment for 5 episodes)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePull image file (modify the \u0026lt;FOLDER_PATH_TO_SAVE_IMAGE\u0026gt; in the command, image file size ~ 3G\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \u0026lt;PATH_TO_THIS_REPO\u0026gt;/local_buffer/image:latest.sif library://zifanxu/ros_jackal_image/image:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh \u0026lt;PATH_TO_THIS_REPO\u0026gt;/local_buffer/nav_benchmark.sif python3 test_env.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrain a deep RL navigation policy\u003c/h2\u003e\u003ca id=\"user-content-train-a-deep-rl-navigation-policy\" class=\"anchor\" aria-label=\"Permalink: Train a deep RL navigation policy\" href=\"#train-a-deep-rl-navigation-policy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train a navigation policy, you just need to specify a \u003ccode\u003e.yaml\u003c/code\u003e file that includes the parameters for specific experiment. For instance,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython train.py --config configs/e2e_default_TD3.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe provide the full list of \u003ccode\u003e.yaml\u003c/code\u003e files used in our experiment in the end.\u003c/p\u003e\n\u003cp\u003eThis repo saves the collected trajectories from each actor in a local buffer folder, also actors load the recent policy from this folder. By default, buffer folder is a folder named \u003ccode\u003elocal_buffer\u003c/code\u003e in current dictionary. You can specify a new folder as \u003ccode\u003eexport BUFFER_FOLDER=/PATH/TO/YOUR/BUFFER_FOLDER\u003c/code\u003e. The logging files can be found under folder \u003ccode\u003elogging\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrain in computing cluster\u003c/h2\u003e\u003ca id=\"user-content-train-in-computing-cluster\" class=\"anchor\" aria-label=\"Permalink: Train in computing cluster\" href=\"#train-in-computing-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCluster requires a shared file system, where multiple actors load the lastest policy, rollout, and save the trajectory in the \u003ccode\u003eBUFFER_FOLDER\u003c/code\u003e. Then, a critic collects trajectories from \u003ccode\u003eBUFFER_FOLDER\u003c/code\u003e and updates the policy.\u003c/p\u003e\n\u003cp\u003eThis is asyncronized training pipeline, namely the actors might fall behind and do not generate trajectories from the latest policy.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the Singularity image\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif library://zifanxu/ros_jackal_image/image:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOn critic computing node\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport BUFFER_PATH=\u0026lt;BUFFER_PATH\u0026gt;\n./singularity_run.sh \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif python train.py --config configs/e2e_default_TD3_cluster.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOn actor computing node 0 (you need to run \u003ccode\u003e0-50\u003c/code\u003e computing nodes as defined in line 60 in \u003ccode\u003econtainer_config.yaml\u003c/code\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport BUFFER_PATH=\u0026lt;BUFFER_PATH\u0026gt;\n./singularity_run.sh \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif python actor.py --id 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSuccess rate of policies trained with different neural network architectures and history lengths in static (top) and dynamic-wall (bottom) environments.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eStatic\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e57 \u00b1 7%\u003c/td\u003e\n\u003ctd\u003e42 \u00b1 2%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e51 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e55 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e68 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e46 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eDynamic box\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e50 \u00b1 5%\u003c/td\u003e\n\u003ctd\u003e35 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e46 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e48 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 1%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e42 \u00b1 5%\u003c/td\u003e\n\u003ctd\u003e40 \u00b1 1%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e52 \u00b1 1%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e44 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eDynamic wall\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e67 \u00b1 7%\u003c/td\u003e\n\u003ctd\u003e72 \u00b1 1%\u003c/td\u003e\n\u003ctd\u003e69 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e82 \u00b1 4%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e78 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e63 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e33 \u00b1 28%\u003c/td\u003e\n\u003ctd\u003e15 \u00b1 13%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate, survival time and traversal time of policies trained with different safe-RL methods, MPC with probabilistic transition model and DWA.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSafe-RL method\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMLP\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eLagrangian\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMPC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDWA\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuccess rate\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e74 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e70 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e43%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSurvival time\u003c/td\u003e\n\u003ctd\u003e8.0 \u00b1 1.5s\u003c/td\u003e\n\u003ctd\u003e16.2 \u00b1 2.5s\u003c/td\u003e\n\u003ctd\u003e55.7 \u00b1 4.9s\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e88.6s\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTraversal time\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e7.5 \u00b1 0.3s\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e8.6 \u00b1 0.2s\u003c/td\u003e\n\u003ctd\u003e24.7 \u00b1 2.0s\u003c/td\u003e\n\u003ctd\u003e38.5s\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate of policies trained with different model-based methods and different number of transition samples\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTransition samples\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e100k\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e500k\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e2000k\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e13 \u00b1 7%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e58 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDyna-style deterministic\u003c/td\u003e\n\u003ctd\u003e8 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e30 \u00b1 10%\u003c/td\u003e\n\u003ctd\u003e66 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMPC deterministic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e21 \u00b1 10%\u003c/td\u003e\n\u003ctd\u003e62 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDyna-style probabilistic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e48 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e70 \u00b1 1%\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMPC probabilistic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e70 \u00b1 3%\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate of policies trained with different number of training environments\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eEnvironments\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e250\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuccess rate\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e54 \u00b1 8%\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e72 \u00b1 6%\u003c/td\u003e\n\u003ctd\u003e74 \u00b1 2 %\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(See below for all the config files used to reproduce the experiments)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e \u2514\u2500configs\n \u2502 \u2514\u2500safe_rl\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2502 \u2514\u2500mlp.yaml\n \u2502 \u2502 \u2514\u2500lagrangian.yaml\n \u2502 \u2514\u2500architecture_static\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_wall\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_box\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500model_based\n \u2502 \u2502 \u2514\u2500dyna.yaml\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2514\u2500generalization\n \u2502 \u2502 \u2514\u2500num_world_50.yaml\n \u2502 \u2502 \u2514\u2500num_world_5.yaml\n \u2502 \u2502 \u2514\u2500num_world_10.yaml\n \u2502 \u2502 \u2514\u2500num_world_100.yaml\n \u2502 \u2502 \u2514\u2500num_world_250.yamlconfigs\n \u2502 \u2514\u2500safe_rl\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2502 \u2514\u2500mlp.yaml\n \u2502 \u2502 \u2514\u2500lagrangian.yaml\n \u2502 \u2514\u2500architecture_static\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_wall\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_box\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500model_based\n \u2502 \u2502 \u2514\u2500dyna.yaml\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2514\u2500generalization\n \u2502 \u2502 \u2514\u2500num_world_50.yaml\n \u2502 \u2502 \u2514\u2500num_world_5.yaml\n \u2502 \u2502 \u2514\u2500num_world_10.yaml\n \u2502 \u2502 \u2514\u2500num_world_100.yaml\n \u2502 \u2502 \u2514\u2500num_world_250.yaml\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-anvio/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-anvio/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-anvio/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-anvio/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/76e66b0528620e6de223824bc0811e43da5305bc225fde5e0da2581d636a4d79/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d616e76696f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/76e66b0528620e6de223824bc0811e43da5305bc225fde5e0da2581d636a4d79/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d616e76696f\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-anvio\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b266c3fbd482290cf4c5e346f3832162d4903334e17f80734b14d5a75a2d437e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d616e76696f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b266c3fbd482290cf4c5e346f3832162d4903334e17f80734b14d5a75a2d437e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d616e76696f\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-anvio\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e125772a3252bfd4304809a815f2de28bd93d1d43e97d512f356a8db123116ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d616e76696f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e125772a3252bfd4304809a815f2de28bd93d1d43e97d512f356a8db123116ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d616e76696f\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-anvio\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7becb11640fa75f5c844770229b3132b8ed32bee6c4001e30be8dd3c86e26513/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d616e76696f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7becb11640fa75f5c844770229b3132b8ed32bee6c4001e30be8dd3c86e26513/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d616e76696f\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-anvio\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-anvio\u003c/h1\u003e\u003ca id=\"user-content-singularity-anvio\" class=\"anchor\" aria-label=\"Permalink: singularity-anvio\" href=\"#singularity-anvio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4ec10e0b03386960c0b2a8183e88162122d72b83655bd09576000f057f5bb00f/68747470733a2f2f6d6572656e6c61622e6f72672f696d616765732f616e76696f2d6e6574776f726b2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ec10e0b03386960c0b2a8183e88162122d72b83655bd09576000f057f5bb00f/68747470733a2f2f6d6572656e6c61622e6f72672f696d616765732f616e76696f2d6e6574776f726b2e706e67\" data-canonical-src=\"https://merenlab.org/images/anvio-network.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://https://merenlab.org/software/anvio/\" rel=\"nofollow\"\u003eanvio\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eanvio-*\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/anvio/8\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/anvio\u003c/code\u003e as \u003ccode\u003e8.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1713930886.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1641398359.0 }, { "data_format": 2, - "description": "Superparameterization coupler", + "description": "Singularity image for (last working verison) of OpenSees", "filenames": [ "Singularity" ], - "full_name": "CloudResolvingClimateModeling/sp-coupler", - "latest_release": "v1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSuperparameterization of OpenIFS with DALES\u003c/h1\u003e\u003ca id=\"user-content-superparameterization-of-openifs-with-dales\" class=\"anchor\" aria-label=\"Permalink: Superparameterization of OpenIFS with DALES\" href=\"#superparameterization-of-openifs-with-dales\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.1968305\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c1d1645aa679213f22216cc946a1fab69f500e5442f02f1e947c083a061e921d/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313936383330352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1968305.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains a script for running the global atmospheric model \u003ca href=\"https://confluence.ecmwf.int/display/OIFS/OpenIFS+Home\" rel=\"nofollow\"\u003eOpenIFS\u003c/a\u003e\ncoupled to local cloud-resolving LES simulations. The LES used is \u003ca href=\"https://github.com/dalesteam/dales\"\u003eDALES\u003c/a\u003e,\nthe Dutch Atmospheric Large Eddy Simulation.\u003c/p\u003e\n\u003cp\u003eA description of the coupling procedure and simulation results are given in\u003c/p\u003e\n\u003cp\u003eJansson, F., van den Oord, G., Pelupessy, I., Gr\u00f6nqvist, J. H., Siebesma, A. P., \u0026amp; Crommelin, D. (2019). Regional superparameterization in a global circulation model using large eddy simulations. \u003ca href=\"https://doi.org/10.1029/2018MS001600\" rel=\"nofollow\"\u003eJournal of Advances in Modeling Earth Systems, 11\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInterfaces to the models are built with \u003ca href=\"https://bitbucket.org/omuse/omuse/src/default/\" rel=\"nofollow\"\u003eOMUSE\u003c/a\u003e.\nThe interfaces are documented in the \u003ca href=\"https://omuse.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eOMUSE documentation\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFredrik Jansson (TU Delft and CWI, Amsterdam),\nGijs van den Oord (Netherlands e-Science center, Amsterdam),\nInti Pelupessy (Netherlands e-Science center, Amsterdam),\nMaria Chertova (Netherlands e-Science center, Amsterdam),\nPier Siebesma (TU Delft and KNMI),\nDaan Crommelin (CWI, Amsterdam),\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code in this repository is available under the Apache 2.0 license.\u003c/p\u003e\n\u003cp\u003eDALES and OpenIFS have their own licenses.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity image\u003c/h2\u003e\u003ca id=\"user-content-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Singularity image\" href=\"#singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor easy setup of the superparameterized simulation, we provide a\nSingularity recipe. This recipe can be used to build a Singularity\ncontainer including everything required to run the simulation.\nSee the \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall singularity on a computer where you have root access.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the image. This step requires root access.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build sp.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe build procedure will ask for a user name and password for the OpenIFS git repository at ECMWF,\nto download the modified OpenIFS.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo run the simulation, launch a shell inside the container. This step does not require root access,\nand can be done on a different machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell sp.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default Singularity mounts the user\u0027s home directory inside the image. If you have the sp-coupler directory somewhere in your home directory,\nthe singularity shell will be opened there.\u003c/p\u003e\n\u003cp\u003eRun the example simulation with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./run_T21_sockets.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLimitations of the Singularity setup\u003c/h3\u003e\u003ca id=\"user-content-limitations-of-the-singularity-setup\" class=\"anchor\" aria-label=\"Permalink: Limitations of the Singularity setup\" href=\"#limitations-of-the-singularity-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s unclear whether the Singularity image supports running on multiple nodes. AMUSE launches the workers using MPI_COMM_SPAWN,\nand this may not work over multiple nodes in this setup. For large runs, we recommend a manual installation for now.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample case\u003c/h2\u003e\u003ca id=\"user-content-example-case\" class=\"anchor\" aria-label=\"Permalink: Example case\" href=\"#example-case\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains a small example case which can be run on a single workstation, with OpenIFS on a T21 grid coupled to two DALES models.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eoifs-input/\u003c/code\u003e contains the files required to run OpenIFS for the small T21 grid. This is the standard OpenIFS test case bundled with OpenIFS itself.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edales-input/\u003c/code\u003e contains files required for DALES. This is a case with 64 x 64 x 160 grid points. The horizontal resolution can easily be changed by editing the file \u003ccode\u003enamoptions.001\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun_T21_mpi.sh\u003c/code\u003e run example simulation using MPI. For simulations using one or more computer nodes.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun_T21_sockets.sh\u003c/code\u003e run example simulation using the AMUSE sockets channel. For simulations that fit within one node.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun_T21_nospawn.sh\u003c/code\u003e run example simulation with work-around for MPI that does not support spawn. Experimental, provided as-is.\u003c/p\u003e\n\u003cp\u003eIn the Singularity image, the sockets variant works immediately. The MPI variant requires the following command to load the openMPI module:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eeval `/usr/bin/modulecmd sh load mpi/openmpi-x86_64`\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe full run of 100 time steps took about 13h on a quad-core workstation (i7-4790).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel settings\u003c/h2\u003e\u003ca id=\"user-content-model-settings\" class=\"anchor\" aria-label=\"Permalink: Model settings\" href=\"#model-settings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModel settings and input data are provided in three places:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ean OpenIFS input directory, containing an initial state, and model settings in fort.4\u003c/li\u003e\n\u003cli\u003ea DALES input directory, containing model settings in namoptions.001\u003c/li\u003e\n\u003cli\u003eoptions for the model coupling, provided on the command line of the coupling script. For a list of them, run \u003ccode\u003e./spmaster.py --help\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor a small example, see \u003ccode\u003erun_T21_sockets.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll model output is organized in an output directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edales-work-nnn/ DALES work directory, one for each LES instance.\n surf_xy*.nc surface fields: liquid water path, rain water path, total water path, accumulated surface rain\n cross*.nc cross section fields of the LES volume\nles-input copy of the DALES input files.\noifs-work OpenIFS work directory, contains output from the global model, mainly in GRIB format.\nspifs.nc netCDF file containing vertical profiles and tendencies for the superparameterized columns.\ntiming.txt CPU time statistics per time step for all models.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpenIFS and DALES can be configured as usual with their respective input files, in particular the type and frequency of the output they provide.\nSee the model documentation for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFormat of spifs.nc\u003c/h3\u003e\u003ca id=\"user-content-format-of-spifsnc\" class=\"anchor\" aria-label=\"Permalink: Format of spifs.nc\" href=\"#format-of-spifsnc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe output file spifs.nc contains vertical profiles of model variables and superparameterization tendencies\nfor every superparameterized grid point and global model time step.\nThe data is organized in groups according to the grid point where the model is located,\nfor example all data for the DALES at grid point 888 is located in the group 888/ in the netCDF file.\nIn general, variables in upper case relate to the global model, and variables in lower case relate to the local model.\nForcings \u003cem\u003eon\u003c/em\u003e the global model are denoted e.g. f_T, and on the local model f_thl.\u003c/p\u003e\n\u003cp\u003eThe superparameterization coupler can also store profiles in spifs.nc for columns that are not superparameterized.\nThe data for these columns then contain only quantities for the global model, there are no forcings and no local model quantities.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eVertical coordinates\u003c/h4\u003e\u003ca id=\"user-content-vertical-coordinates\" class=\"anchor\" aria-label=\"Permalink: Vertical coordinates\" href=\"#vertical-coordinates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProfiles in the local model use \u003ccode\u003ezf\u003c/code\u003e, in the root group of the file, as vertical coordinate. These are constant in time and the same for all the local models.\nFor the global model, the vertical coordinate is \u003ccode\u003eZf\u003c/code\u003e, which depends on both the grid point and time (because the global model\u0027s\nlevels are not on a fixed height but defined by pressure, they vary in time and space).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eVariables\u003c/h4\u003e\u003ca id=\"user-content-variables\" class=\"anchor\" aria-label=\"Permalink: Variables\" href=\"#variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most important variables are summarized below.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOpenIFS Variable\u003c/th\u003e\n\u003cth\u003eUnit\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003elat, lon\u003c/td\u003e\n\u003ctd\u003edegrees\u003c/td\u003e\n\u003ctd\u003egrid point coordinates\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eU, V\u003c/td\u003e\n\u003ctd\u003em/s\u003c/td\u003e\n\u003ctd\u003evelocity components in x, y directions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eT\u003c/td\u003e\n\u003ctd\u003eK\u003c/td\u003e\n\u003ctd\u003etemperature\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSH\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003especific humidity (i.e. water vapor, not cloud condensate)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQL\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003especific cloud condensate, liquid\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQI\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003especific cloud condensate in the form of ice\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQT\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003etotal specific humidity, SH+QL+QI\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePf\u003c/td\u003e\n\u003ctd\u003ePa\u003c/td\u003e\n\u003ctd\u003epressure\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eA\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003ecloud fraction\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_U, f_V\u003c/td\u003e\n\u003ctd\u003em/s^2\u003c/td\u003e\n\u003ctd\u003eforcings on global model\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_T\u003c/td\u003e\n\u003ctd\u003eK/s\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_SH, f_QL, f_QI\u003c/td\u003e\n\u003ctd\u003ekg/kg/s\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDALES Variable\u003c/th\u003e\n\u003cth\u003eUnit\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eu, v\u003c/td\u003e\n\u003ctd\u003em/s\u003c/td\u003e\n\u003ctd\u003evelocity components in x, y directions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ethl\u003c/td\u003e\n\u003ctd\u003eK\u003c/td\u003e\n\u003ctd\u003eliquid water potential temperature\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eqt\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003etotal specific humidity\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eql\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003econdensed water specific humidity\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewthl\u003c/td\u003e\n\u003ctd\u003eK m/s\u003c/td\u003e\n\u003ctd\u003esurface heat flux\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewqt\u003c/td\u003e\n\u003ctd\u003em/s\u003c/td\u003e\n\u003ctd\u003esurface moisture flux\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_u, f_v\u003c/td\u003e\n\u003ctd\u003em/s^2\u003c/td\u003e\n\u003ctd\u003eforcings on local model\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_thl\u003c/td\u003e\n\u003ctd\u003eK/s\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_qt\u003c/td\u003e\n\u003ctd\u003ekg/kg/s\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSample Python script for reading spifs.nc\u003c/h4\u003e\u003ca id=\"user-content-sample-python-script-for-reading-spifsnc\" class=\"anchor\" aria-label=\"Permalink: Sample Python script for reading spifs.nc\" href=\"#sample-python-script-for-reading-spifsnc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA sample python script for extracting data from the spifs.nc file is provided in \u003ccode\u003eexamples/access-spifs-nc.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRequirements and manual installation procedure - Python 3 version\u003c/h1\u003e\u003ca id=\"user-content-requirements-and-manual-installation-procedure---python-3-version\" class=\"anchor\" aria-label=\"Permalink: Requirements and manual installation procedure - Python 3 version\" href=\"#requirements-and-manual-installation-procedure---python-3-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCartesius\u003c/h2\u003e\u003ca id=\"user-content-cartesius\" class=\"anchor\" aria-label=\"Permalink: Cartesius\" href=\"#cartesius\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emodule load 2020\nmodule load netCDF-Fortran/4.5.2-gompi-2020a\nmodule load CMake/3.16.4-GCCcore-9.3.0\nmodule load FFTW/3.3.8-gompi-2020a\nmodule load Hypre/2.18.2-foss-2020a\nmodule load Python/3.8.2-GCCcore-9.3.0\nmodule load ecCodes/2.18.0-foss-2020a-Python-3.8.2\n# OpenMPI 4.0.3\n\ngit clone https://github.com/omuse-geoscience/omuse/\ncd omuse\npython3 -m venv omuse_env_2000\nsource omuse_env_2000/bin/activate\n\npip install -e .\n\nexport DOWNLOAD_CODES=all\nexport SYST=gnu-fast\n\n# work-around for OMUSE not finding netCDF\nexport DALES_FCFLAGS=\"`nf-config --flibs` -fdefault-real-8 -cpp\"\n\n# install DALES\npython setup.py build_code --code-name dales --inplace\n\n\nexport OIFS_GRIB_API_DIR=/sw/arch/RedHatEnterpriseServer7/EB_production/2020/software/ecCodes/2.18.0-foss-2020a-Python-3.8.2\nexport OIFS_GRIB_API_LIB=\"-L$OIFS_GRIB_API_DIR/lib -leccodes_f90\"\nexport GRIB_SAMPLES_PATH=$OIFS_GRIB_API_DIR/share/eccodes/ifs_samples/grib1_mlgrib2/\nexport OIFS_LAPACK_LIB=\"-L/sw/arch/RedHatEnterpriseServer7/EB_production/2020/software/ScaLAPACK/2.1.0-gompi-2020a/lib -L/sw/arch/RedHatEnterpriseServer7/EB_production/2020/software/OpenBLAS/0.3.9-GCC-9.3.0/lib -lopenblas -lscalapack\"\n\n# install open-ifs - requires ECMWF username/password\npython setup.py build_code --code-name oifs --inplace\n\npip install scipy moviepy matplotlib h5py shapely psutil\n# ERROR: pandas 1.0.3 requires pytz\u0026gt;=2017.2, which is not installed. - ignoring this for now\n\n# install SP-coupler, this repository. \ncd \npip install scipy moviepy matplotlib h5py shapely psutil\ngit clone https://github.com/CloudResolvingClimateModeling/sp-coupler\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRequirements and manual installation procedure - Python 2 version\u003c/h1\u003e\u003ca id=\"user-content-requirements-and-manual-installation-procedure---python-2-version\" class=\"anchor\" aria-label=\"Permalink: Requirements and manual installation procedure - Python 2 version\" href=\"#requirements-and-manual-installation-procedure---python-2-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following applies to the Python2 version, year 2020 or before.\nThese instructions are becoming obsolete, since OMUSE has switched to Python 3.\u003c/p\u003e\n\u003cp\u003eFor initial tests, we recommend trying the Singularity image, since it simplifies the installation.\nThe singularity recipe in the file \u003ccode\u003eSingularity\u003c/code\u003e can also be used as instructions for a manual setup.\u003c/p\u003e\n\u003cp\u003eFor a manual setup, the following tools and libraries are required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eC and Fortran compilers, e.g. gcc and gfortran\u003c/li\u003e\n\u003cli\u003emake\u003c/li\u003e\n\u003cli\u003ecmake\u003c/li\u003e\n\u003cli\u003enetCDF4\u003c/li\u003e\n\u003cli\u003eeccodes or gribapi\u003c/li\u003e\n\u003cli\u003eMPI\u003c/li\u003e\n\u003cli\u003empi4py\u003c/li\u003e\n\u003cli\u003ethe following Python modules:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epip install --upgrade mercurial moviepy f90nml numpy scipy matplotlib nose h5py docutils netCDF4 shapely psutil\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, install the following programs, in this order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAMUSE \u003ca href=\"http://amusecode.org/\" rel=\"nofollow\"\u003ehttp://amusecode.org/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOMUSE \u003ca href=\"https://bitbucket.org/omuse/omuse\" rel=\"nofollow\"\u003ehttps://bitbucket.org/omuse/omuse\u003c/a\u003e\nThe OMUSE Makefiles downloads and builds the two models.\n\u003cul\u003e\n\u003cli\u003eOpenIFS (note: requires username/password from ECMWF)\u003c/li\u003e\n\u003cli\u003eDALES \u003ca href=\"https://github.com/CloudResolvingClimateModeling/dales\"\u003ehttps://github.com/CloudResolvingClimateModeling/dales\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that OpenIFS might require several environment variables to be set both at compilation and at runtime.\nSee \u003ca href=\"https://confluence.ecmwf.int/display/OIFS/OpenIFS+User+Guides\" rel=\"nofollow\"\u003ethe OpenIFS manual\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the above are installed, you will need to add the python modules to your PYTHONPATH:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=\u0026lt;AMUSE clone path\u0026gt;/src:\u0026lt;SP-coupler clone path\u0026gt;/splib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto run the main driver script in this repo, spmaster.py. To view all the superparametrization options and configurations (e.g. the choice of the superparametrized region), type\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./spmaster.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation notes for specific systems\u003c/h1\u003e\u003ca id=\"user-content-installation-notes-for-specific-systems\" class=\"anchor\" aria-label=\"Permalink: Installation notes for specific systems\" href=\"#installation-notes-for-specific-systems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation on Arch Linux\u003c/h2\u003e\u003ca id=\"user-content-installation-on-arch-linux\" class=\"anchor\" aria-label=\"Permalink: Installation on Arch Linux\" href=\"#installation-on-arch-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen configuring OMUSE, one must explicitly specify python2, since the default is python3.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd amuse\nPYTHON=python2 ./configure --with-netcdf=/usr/\nmake framework\n\nexport DOWNLOAD_CODES=all\n\ncd src/omuse/community/dales\nmake\n\ncd ../oifs\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation on Fedora\u003c/h2\u003e\u003ca id=\"user-content-installation-on-fedora\" class=\"anchor\" aria-label=\"Permalink: Installation on Fedora\" href=\"#installation-on-fedora\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFedora\u0027s netcdf require some extra settings, becuse the module files\nand .inc files are in different places. We specify the module path\nwith FCFLAGS: Another issue seen on Fedora is that make in the dales\ndirectory fails with \u003ccode\u003ebuild.py: error: No module named dalesreader\u003c/code\u003e. One solution is to add . to PYTHONPATH. This seems to confuse mercurial though.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFCFLAGS=-I/usr/lib64/gfortran/modules ./configure --with-netcdf=/usr\nmake framework\n\nexport DOWNLOAD_CODES=all\n\nexport PYTHONPATH=$PYTHONPATH:. # for dalesreader to be found when creating the interface code\ncd src/omuse/community/dales\nmake\n\ncd ../oifs\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation on ECMWF Cray system\u003c/h2\u003e\u003ca id=\"user-content-installation-on-ecmwf-cray-system\" class=\"anchor\" aria-label=\"Permalink: Installation on ECMWF Cray system\" href=\"#installation-on-ecmwf-cray-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInitial setup\u003c/h3\u003e\u003ca id=\"user-content-initial-setup\" class=\"anchor\" aria-label=\"Permalink: Initial setup\" href=\"#initial-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLoad modules\u003c/h4\u003e\u003ca id=\"user-content-load-modules\" class=\"anchor\" aria-label=\"Permalink: Load modules\" href=\"#load-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eprgenvswitchto intel\n\nmodule load python/2.7.12-01\nmodule load netcdf4/4.4.1\nmodule load cmake/3.12.0\nmodule load git\nmodule load eccodes\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOther settings\u003c/h4\u003e\u003ca id=\"user-content-other-settings\" class=\"anchor\" aria-label=\"Permalink: Other settings\" href=\"#other-settings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# https proxy\nexport https_proxy=proxy:2222\n\nexport AMUSE_DIR=$PERM/2019/amuse/\nexport PYTHONPATH=$PYTHONPATH:$AMUSE_DIR/src/\n\nsource $PERM/meteo/bin/activate\n\n# OpenIFS compilation options\nexport OIFS_COMP=intel\nexport OIFS_BUILD=craynomp\n\n# Cray setup: all compilers are invoked with these names:\nexport OIFS_FC=ftn\nexport OIFS_CC=cc\n\nexport OIFS_GRIB_API_DIR=$ECCODES_DIR\nexport OIFS_GRIB_API_LIB=\"-L $ECCODES_LIB_DIR -leccodes_f90 -leccodes\"\nexport OIFS_GRIB_API_INCLUDE=\"-I $ECCODES_INCLUDE_DIR\"\n\nexport FCFLAGS=\"-convert big_endian\"\n\n# On the Cray, we don\u0027t want any linking flags for Lapack\n# they are included when using the Cray compiler wrappers\nexport OIFS_LAPACK_LIB=\" \"\n\n# DALES compilation options\nexport SYST=ECMWF-intel\nexport DALES_FCFLAGS=\"-g -traceback -O3 -r8 -xHost -fpp\"\n#these flags apply to the interface only\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003evirtual Python environment\u003c/h4\u003e\u003ca id=\"user-content-virtual-python-environment\" class=\"anchor\" aria-label=\"Permalink: virtual Python environment\" href=\"#virtual-python-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epip install --user virtualenv\nPATH=$PATH:~/.local/bin/\n\ncd $PERM\nvirtualenv meteo\nsource $PERM/meteo/bin/activate\npip install --upgrade mercurial moviepy f90nml numpy scipy matplotlib nose h5py docutils netCDF4 shapely psutil\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003empi4py on ECMWF\u003c/h4\u003e\u003ca id=\"user-content-mpi4py-on-ecmwf\" class=\"anchor\" aria-label=\"Permalink: mpi4py on ECMWF\" href=\"#mpi4py-on-ecmwf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince mid-2018 the mpi4py installed with the python modules at ECMWF no longer works. It can be installed manually from source.\nThis should be done with the same set of compilers and modules loaded as used for everything else.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eactivate the virtual python environment, and with the intel compiler and our modules loaded.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd $PERM\nwget https://bitbucket.org/mpi4py/mpi4py/downloads/mpi4py-3.0.0.tar.gz -O mpi4py-3.0.0.tar.gz\ntar zxf mpi4py-3.0.0.tar.gz\ncd mpi4py-3.0.0\n\n# add an enry for the Cray system in mpi.cfg\ncat \u0026gt;\u0026gt; mpi.cfg \u0026lt;\u0026lt;EOF\n[cray]\nmpicc = cc\nmpicxx = CC\nextra_link_args = -shared\nEOF\n\npython setup.py build --mpi=cray\npython setup.py install \n\ncd $PERM\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eNotes\u003c/h5\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFJ tried to compile mpi4py with the gnu compiler (\u003ccode\u003eprgenvswitchto gnu\u003c/code\u003e). Compilation seemed OK, but python segfaulted when testing the coupled system. Compiling mpi4py with the intel compiler seems to work - no module changes needed.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://jaist-hpc.blogspot.com/2015/02/mpi4py.html\" rel=\"nofollow\"\u003eSource for mpi4py instructions\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe following instructions install omuse and amuse sibe by side in the directory $PERM/2019/.\nThen a symlink in amuse/src is created, to omuse/src/omuse, so that the path amuse/src/omuse/community still works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOMUSE\u003c/h3\u003e\u003ca id=\"user-content-omuse\" class=\"anchor\" aria-label=\"Permalink: OMUSE\" href=\"#omuse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd $PERM/2019\nhg clone --insecure https://bitbucket.org/omuse/omuse\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAmuse\u003c/h3\u003e\u003ca id=\"user-content-amuse\" class=\"anchor\" aria-label=\"Permalink: Amuse\" href=\"#amuse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/fjansson/amuse\ncd amuse\ngit checkout spawnless\n\ncd src\nln -s $PERM/2019/omuse/src/omuse omuse\n# so that the old path amuse/src/omuse/community still works\n\ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis version is our own no-spawn fork for use at ECMWF. Elsewhere, the official amuse can be used:\n\u003ca href=\"https://github.com/amusecode/amuse/\"\u003ehttps://github.com/amusecode/amuse/\u003c/a\u003e .\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#make AMUSE find the right python:\nexport PYTHON=python\n\n./configure FC=ftn CC=cc --with-netcdf=`nc-config --prefix`\n# some libraries will not be found, e.g. gsl. This is OK \n\nmake framework\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOpenIFS and DALES\u003c/h3\u003e\u003ca id=\"user-content-openifs-and-dales\" class=\"anchor\" aria-label=\"Permalink: OpenIFS and DALES\" href=\"#openifs-and-dales\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpenIFS and DALES can be cloned using the OMUSE make file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport DOWNLOAD_CODES=all\n# DOWNLOAD_CODES=all will checkout entire repo with ssh, intended for developers of the components.\n# DOWNLOAD_CODES=latest will (shallow) checkout latest revision only\n# DOWNLOAD_CODES=\u0026lt;anything else\u0026gt; will (shallow) checkout release tag spifs_v1.0.0\n\nexport AMUSE_DIR=$PERM/2019/amuse/\nexport PYTHONPATH=$PYTHONPATH:$AMUSE_DIR/src/\nexport PATH=$PATH:$AMUSE_DIR/bin/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003ecd community/dales\nmake\ncd ../..\n\ncd community/oifs\nmake\n# note: this downloads OpenIFS, which requires ECMWF credentials\n\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "researchapps/opensees", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 3, - "topics": [], - "updated_at": 1645814234.0 + "topics": [ + "singularity-container", + "singularity-containers", + "opensees" + ], + "updated_at": 1486514968.0 }, { "data_format": 2, - "description": null, + "description": "https://github.com/trinityrnaseq/trinityrnaseq for Hebbe via Singularity Hub", "filenames": [ - "pipelines/0037-cell_cell_interaction/env/Singularity.cell_cell_interaction", - "pipelines/0015-preprocessing/env/Singularity.preprocessing", - "pipelines/0025-qc_cluster/env/Singularity.sc_qc_cluster" + "Singularity" ], - "full_name": "ckrilow/dev-ckrilow", + "full_name": "SysBioChalmers/trinityrnaseq-hebbe", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDescription\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis README is pulled from a default template for workflows.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWorkflow template setup\u003c/h1\u003e\u003ca id=\"user-content-workflow-template-setup\" class=\"anchor\" aria-label=\"Permalink: Workflow template setup\" href=\"#workflow-template-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003elib\u003c/h2\u003e\u003ca id=\"user-content-lib\" class=\"anchor\" aria-label=\"Permalink: lib\" href=\"#lib\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003elib\u003c/code\u003e directory contains general libraries that may be referenced by multiple workflows, for instance cromwell configs and python configs. Currently nothing in this directory is used.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003epipelines\u003c/h2\u003e\u003ca id=\"user-content-pipelines\" class=\"anchor\" aria-label=\"Permalink: pipelines\" href=\"#pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEach pipeline is a full analysis. Think of it like the heading of a methods section in a paper. For instance if this were genetic summary statistics workflow, a pipeline might be \"fine-mapping\" that does both conditional and credible set analysis. Another pipeline may be \"colocalization\".\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePipelines may have numbers prior to their name (e.g., \u003ccode\u003eexample_pipeline_1\u003c/code\u003e to \u003ccode\u003e0025-example_pipeline_1\u003c/code\u003e). These numbers do not mean anything, but merely used to keep pipelines in their general order of execution. These are optional.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA pipeline consists of :\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eA workflow.\u003c/li\u003e\n\u003cli\u003eA \u003ccode\u003escripts\u003c/code\u003e directory with \u003cem\u003eall\u003c/em\u003e scripts referenced by that workflow (unless a general lib script is called). Scripts may have numbers prior to their name. These numbers do not mean anything, but merely used to keep scripts in their general order of execution. These are optional.\u003c/li\u003e\n\u003cli\u003eA \u003ccode\u003edocs\u003c/code\u003e directory that contains a documentation of the default parameters written in a style that is publishable as methods in a paper (including citations). Within the \u003ccode\u003edocs\u003c/code\u003e directory there may be a \u003ccode\u003ereference\u003c/code\u003e with any additional reference materials.\u003c/li\u003e\n\u003cli\u003eAn \u003ccode\u003eexample_runtime_setup\u003c/code\u003e directory contains files that give an example of actual config files and any other files used to run the pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003estudies\u003c/h2\u003e\u003ca id=\"user-content-studies\" class=\"anchor\" aria-label=\"Permalink: studies\" href=\"#studies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA studies directory should either exist within the workflow repo or be a separate repo that has the same name as the workflow repo, but with \u003ccode\u003estudies\u003c/code\u003e appended to it (e.g. \u003ccode\u003etemplate-workflow\u003c/code\u003e becomes \u003ccode\u003etemplate-workflow-studies\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eIf there is a standard set of plots that will always look the same way, a pipeline should generate such plots. Otherwise, all code to analyze the results of a pipeline run should be in the \u003ccode\u003estudies\u003c/code\u003e directory. For instance if this were genetic summary statistics workflow, \u003ccode\u003estudies\u003c/code\u003e may contain a \u003ccode\u003et2d\u003c/code\u003e directory and a \u003ccode\u003eweight\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eWithin a study is either an Jupyter notebook (either python or R kernel) or an R markdown file. Nearly all plots / analysis of the results of running the various pipelines should be done in the notebook / markdown file.\u003c/li\u003e\n\u003cli\u003eA study may also contain a scripts directory with scripts to aggregate data for a one off analysis (if the analysis is going to be repeated, consider making a new pipeline or adding it to an existing pipeline) or for special plots that cannot be done in the notebook / markdown file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNew workflow reminders\u003c/h1\u003e\u003ca id=\"user-content-new-workflow-reminders\" class=\"anchor\" aria-label=\"Permalink: New workflow reminders\" href=\"#new-workflow-reminders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Documentation\u003c/li\u003e\n\u003cli\u003e[ ] Environment version control\u003c/li\u003e\n\u003cli\u003e[ ] Pipeline version control\u003c/li\u003e\n\u003cli\u003e[ ] Git branches\u003c/li\u003e\n\u003cli\u003e[ ] Code review\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocumentation\u003c/h1\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBe sure to document your code!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnvironment version control\u003c/h1\u003e\u003ca id=\"user-content-environment-version-control\" class=\"anchor\" aria-label=\"Permalink: Environment version control\" href=\"#environment-version-control\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnalysis environment is controlled using conda. Each pipeline should have an \u003ccode\u003eenvironment.yml\u003c/code\u003e file with all of the packages used. If a required package or library is missing from conda (and therefore not in the \u003ccode\u003eenvironment.yml\u003c/code\u003e), it should be noted in the \u003ccode\u003eREADME.md\u003c/code\u003e of the pipeline.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda env \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e --no-builds \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep -v prefix \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep -v name \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e environment.yml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline version control\u003c/h1\u003e\u003ca id=\"user-content-pipeline-version-control\" class=\"anchor\" aria-label=\"Permalink: Pipeline version control\" href=\"#pipeline-version-control\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach pipeline within this workflow uses \u003ca href=\"https://pypi.org/project/bumpversion\" rel=\"nofollow\"\u003ebumpversion\u003c/a\u003e for automatic \u003ca href=\"https://semver.org\" rel=\"nofollow\"\u003esemantic versioning\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e bump the appropriate increment\u003c/span\u003e\nbumpversion patch --verbose --dry-run\nbumpversion minor --verbose --dry-run\nbumpversion major --verbose --dry-run\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e commit with tags\u003c/span\u003e\ngit push --tags\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGitHub forks\u003c/h1\u003e\u003ca id=\"user-content-github-forks\" class=\"anchor\" aria-label=\"Permalink: GitHub forks\" href=\"#github-forks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eForking the repository allows developers to work independently while retaining well-maintained code on the master fork. For instructions on how to fork, follow the \u003ca href=\"https://help.github.com/en/articles/fork-a-repo\"\u003eFork a repo\u003c/a\u003e instructions.\u003c/p\u003e\n\u003cp\u003eAfter forking the repo, clone the repo to your local desktop:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to use SSH\u003c/span\u003e\ngit clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/template-workflow.git\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to use Https\u003c/span\u003e\ngit clone https://github.com/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/template-workflow.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis creates a replica of the remote repository on your local desktop. \u003cem\u003eNote\u003c/em\u003e: When you create your local repository, it will also make a local clone of the remote repository (typically as \u003ccode\u003eorigin\u003c/code\u003e). So, your local master branch would simply be \u003ccode\u003emaster\u003c/code\u003e. But, your remote master branch will be \u003ccode\u003eorigin/master\u003c/code\u003e. You can also add multiple remote repositories. For instance, let us say our main repository is under the remote repository \u003ccode\u003emy_repo\u003c/code\u003e. We will want to add it as a remote repository, so we can fetch the most up-to-date code. You could add it by:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the my_repo remote repo to your local desktop -- this will allow you to pull and push to branches on the my_repo repository\u003c/span\u003e\ngit remote add my_repo git@github.com:my_repo/template-workflow.git\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGit branches\u003c/h1\u003e\u003ca id=\"user-content-git-branches\" class=\"anchor\" aria-label=\"Permalink: Git branches\" href=\"#git-branches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBranching is how git actually tracks code development. For more information, see the \u003ca href=\"https://www.atlassian.com/git/tutorials/using-branches\" rel=\"nofollow\"\u003eGit Branch Tutorial\u003c/a\u003e on Atlassian. If you want to add a new feature, pipeline, or fix a bug, a common work flow would look like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Update your local copy of the master branch to make sure you are getting the most up-to-date code\u003c/span\u003e\ngit pull\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create the branch on your local machine and switch in this branch \u003c/span\u003e\ngit checkout -b [name_of_your_new_branch]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Push the branch on github\u003c/span\u003e\ngit push origin [name_of_your_new_branch]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs you develop, you want to commit your work to your branch, so you don\u0027t lose it all if something happens!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Confirm we\u0027re on the right branch\u003c/span\u003e\ngit branch -a\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add all your work to be tracked (Note: there are many ways to add specific files, etc. See https://git-scm.com/docs/git-add for more information). The following command adds everything in your currently directory.\u003c/span\u003e\ngit add \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Commit your work to the branch with a message describing what\u0027s in the commit\u003c/span\u003e\ngit commit -m \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eCreated the scATAC-seq pipeline!\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can add a -u parameter to set-upstream for a push\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Alternatively, git will also automatically query you when you do your first push.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can also set this manually by adding a new remote for your branch:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003egit remote add [name_of_your_remote] [name_of_your_new_branch]\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Here is another push where we specify HEAD\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003egit push origin HEAD # HEAD pushes everything up to the most recent commit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCode review\u003c/h1\u003e\u003ca id=\"user-content-code-review\" class=\"anchor\" aria-label=\"Permalink: Code review\" href=\"#code-review\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a \u003ca href=\"https://help.github.com/en/articles/creating-a-pull-request\"\u003eGitHub Pull Request\u003c/a\u003e. A PR allows other developers a chance to go through and comment on lines of code they believe can be improved. In addition, it will tell you if the code you are trying to merge into the \u003ccode\u003emy_repo\u003c/code\u003e branch actually conflicts with code that already exists in the branch, so you don\u0027t overwrite someone else\u0027s work.\u003c/p\u003e\n\u003cp\u003eOnce another developer approves the PR, you have the go-ahead to merge your code! Congrats, you finished your feature!\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: There are some cases where you may just want to push directly to the my_repo fork, thereby avoiding code reviews. For instance, if you\u0027re working on a one-off project that you want people to be able to see, but no one else is necessarily working on, you can always push directly to the branches on my_repo fork. Or, you could also still go through the steps of a PR, but simply merge your own code without CR.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3239\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository hosts just the Singularity recipe that is used to automatically build the latest image up on Singularity Hub. A custom recipe was needed for the image to run on the Hebbe cluster.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/trinityrnaseq/trinityrnaseq\"\u003eThe original source code is here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain the \u003ccode\u003eSingularity\u003c/code\u003e file, the \u003ccode\u003eDockerfile\u003c/code\u003e from the \u003ccode\u003e2.8.5\u003c/code\u003e release was copied here and converted to a recipe with \u003ccode\u003espython\u003c/code\u003e after which it was manually tweaked. The original \u003ccode\u003erecipe.basic\u003c/code\u003e that uses the Docker image has been kept for reference.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1596817893.0 + "updated_at": 1571750826.0 }, { "data_format": 2, - "description": "Tool for gathering dicom files into organized tarballs, each containing unique study instance", + "description": "Singularity container for R language\u0027s current version.", "filenames": [ - "singularity/Singularity.v0.0.4", - "singularity/Singularity.v.0.0.6", - "singularity/Singularity.v0.0.3", - "singularity/Singularity.v0.0.2", - "singularity/Singularity" + "Singularity" ], - "full_name": "khanlab/dicom2tar", + "full_name": "romxero/R-singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://circleci.com/gh/khanlab/dicom2tar/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5df563272b031151262e6399cc5983d2599e0152273767935f1b2c66cf119507/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f6469636f6d327461722f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/dicom2tar/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edicom2tar\u003c/h1\u003e\u003ca id=\"user-content-dicom2tar\" class=\"anchor\" aria-label=\"Permalink: dicom2tar\" href=\"#dicom2tar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTool for extract compressed files(if any), sort dicom files according to given rule, or tar the sorted, to a destination directory.\u003c/p\u003e\n\u003cp\u003eCheck dicom2tar.py for example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo install on graham:\u003c/h3\u003e\u003ca id=\"user-content-to-install-on-graham\" class=\"anchor\" aria-label=\"Permalink: To install on graham:\" href=\"#to-install-on-graham\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emodule unload python\nmodule load python/2\nvirtualenv ~/python_dicom2tar\nsource ~/python_dicom2tar/bin/activate\npip install dicom2tar\ndeactivate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can then run it with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource ~/python_dicom2tar/bin/activate\ndicom2tar \u0026lt;input\u0026gt; \u0026lt;output\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e#R Singularity for Sherlock\u003c/p\u003e\n\u003cp\u003eThis is an R singularity image for Sherlock using Debian SID unstable distribution\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 0, "topics": [], - "updated_at": 1666690069.0 + "updated_at": 1562008683.0 }, { "data_format": 2, - "description": "A singularity container that ships MALT, the MEGAN alignment tool.", + "description": null, "filenames": [ - "Singularity.v0.4.0", - "Singularity.latest" + "Singularity" ], - "full_name": "qbicsoftware-archive/qbic-singularity-malt", + "full_name": "marcoserenelli/WESADevaluator", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eqbic-singularity-malt\u003c/h1\u003e\u003ca id=\"user-content-qbic-singularity-malt\" class=\"anchor\" aria-label=\"Permalink: qbic-singularity-malt\" href=\"#qbic-singularity-malt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/641\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Singularity container with MALT, the MEGAN alignment tool (\u003ca href=\"https://ab.inf.uni-tuebingen.de/software/malt\" rel=\"nofollow\"\u003ehttps://ab.inf.uni-tuebingen.de/software/malt\u003c/a\u003e), created by \u003ca href=\"https://ab.inf.uni-tuebingen.de/people/welcome.html/huson/welcome.html\" rel=\"nofollow\"\u003eDaniel H. Huson\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor further information or help reffering the \u003cem\u003econtainer\u003c/em\u003e, please contact: \u003ca href=\"mailto:sven.fillinger@qbic.uni-tuebingen.de\"\u003esven.fillinger@qbic.uni-tuebingen.de\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBootstrap files with tags\u003c/h3\u003e\u003ca id=\"user-content-bootstrap-files-with-tags\" class=\"anchor\" aria-label=\"Permalink: Bootstrap files with tags\" href=\"#bootstrap-files-with-tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide always a bootstrap file (\u003ccode\u003eSingularity\u003c/code\u003e) tagged \u003ccode\u003e.latest\u003c/code\u003e which represents the most resent development status of the container. If you see version tags like \u003ccode\u003e.v0.4.0\u003c/code\u003e, this means that this is the recipe of a container with a stable version tag of MALT.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to build the container\u003c/h3\u003e\u003ca id=\"user-content-how-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: How to build the container\" href=\"#how-to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/qbicsoftware/qbic-singularity-malt.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e qbic-singularity-malt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSince Singularity 2.4, the build command from file looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build myContainer.simg \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSingularity file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also download the build and ready-to-use container from Singularity Hub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://qbicsoftware/qbic-singularity-malt:latest\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to run the container and calling MALT\u003c/h3\u003e\u003ca id=\"user-content-how-to-run-the-container-and-calling-malt\" class=\"anchor\" aria-label=\"Permalink: How to run the container and calling MALT\" href=\"#how-to-run-the-container-and-calling-malt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the malt-run script, you just need to\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e myContainer.simg malt-run --help\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or even shorter\u003c/span\u003e\nsingularity run myContainer.simg --help \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or even more shorter\u003c/span\u003e\n./myContainer.simg --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthor\u003c/h2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sven1103\"\u003eSven Fillinger\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 11, - "topics": [ - "other" - ], - "updated_at": 1600938903.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1716120220.0 }, { "data_format": 2, - "description": "SpecBCFuzz - Fuzzing LTL Solvers", + "description": "temp to troubleshoot assignment of version", "filenames": [ - "Singularity_black_reduced_size_v92", - "Singularity_black_v052", - "Singularity_black_v092", - "Singularity_black_reduced_size_v54", - "Singularity_black_debian_v53" + "Singularity" ], - "full_name": "lzmths/SpecBCFuzz", + "full_name": "yarikoptic/neurodebian-singularity-temp", "latest_release": null, - "readme": "\u003cp\u003eChange pom.xml and update project.baseDir\nIt should address the SpecBCFuzz project. For example, lib is a subfolder of the SpecBCFuzz project.\u003c/p\u003e\n\u003cp\u003eAfter that, you must set the Main.java (src/main/).\u003c/p\u003e\n\u003cp\u003eYou should edit:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esolverBin_version (File instance).\u003c/li\u003e\n\u003cli\u003esolver_version (SATSolver instance).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCertify that solver_version is an element of the list solvers.\u003c/p\u003e\n\u003cp\u003eAfter that, you need to provide some parameters to run SpecBCFuzz.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cp\u003e-log=/home/user/outcomes -lib=/home/user/lib -id=1 -run=1 -id=10 -sattimeout=60 -experiment=searchbased:SemSyn-format:gore -popSize=50 -maxNumOfInd=600 -specs=/home/user/specs/atm -os=mac\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elog represents the folder for the outcomes;\u003c/li\u003e\n\u003cli\u003elib represents the folder for lib. You may reference a copy of /lib present in the SpecBCFuzz repository;\u003c/li\u003e\n\u003cli\u003eid indicates a single number or name for the execution;\u003c/li\u003e\n\u003cli\u003erun is the number of executions of the differential search fuzzing;\u003c/li\u003e\n\u003cli\u003esattimeout is the timeout for each LTL solver;\u003c/li\u003e\n\u003cli\u003eexperiment indicates the type of experiment. For instance, the format of inputs;\u003c/li\u003e\n\u003cli\u003epopSize indicates the population size for the genetic algorithm;\u003c/li\u003e\n\u003cli\u003emaxNumOfInd indicates the maximum number of individuals created in the genetic algorithm;\u003c/li\u003e\n\u003cli\u003especs is the path for the specification and boundary conditions;\u003c/li\u003e\n\u003cli\u003eos refers to the operating systems;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdditional parameters and options are available in src/main/Main.java. Feel free to test them or add new ones.\u003c/p\u003e\n\u003cp\u003eYou can find specifications and boundary conditions (divergences or conflicts in the specification) in the following repository: \u003ca href=\"https://github.com/SpecBCFuzz/repo\"\u003ehttps://github.com/SpecBCFuzz/repo\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1730392930.0 + "updated_at": 1503090511.0 }, { "data_format": 2, - "description": null, + "description": "Facial Emotion Recognition on PSU ACI", "filenames": [ - "pong_game/singularity/team_server/Singularity.def", - "pong_game/singularity/umpire/Singularity.def" + "Singularity" ], - "full_name": "WarwickRSE/basics-of-linux-scrtp-git", + "full_name": "d-bohn/emorec_aci", "latest_release": null, - "readme": "", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eopenpose_aci\u003c/h1\u003e\u003ca id=\"user-content-openpose_aci\" class=\"anchor\" aria-label=\"Permalink: openpose_aci\" href=\"#openpose_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and workflow for building some emotion recognition models on PSU\nACI HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003essh\u003c/code\u003e into the PSU ACI HPC with X11 flags.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh USERID@aci-b.aci.ics.psu.edu -X -Y\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart an interactive session using \u003ccode\u003eqsub\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -A open -I -X -l walltime=24:00:00 -l nodes=5:ppn=10 -l pmem=20gb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the container, and execute the \u003ccode\u003evideo_analysis.py\u003c/code\u003e script.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n emorec_aci.simg shub://d-bohn/emorec_aci\n\nsingularity exec emorec_aci.simg /bin/bash\n\npython3 /opt/emorec/video_analysis.py VIDEOFILE OUTDIR SAVENAME\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe arguments to be passed (in order) are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eVIDEOFILE\u003c/strong\u003e: Full path to video file to be analyzed\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eOUTDIR\u003c/strong\u003e: Full path to a writable director to store\nresults (e.g., \u003ccode\u003e/storage/home\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSAVENAME\u003c/strong\u003e: File name to save the results (e.g., \u003ccode\u003edata.csv\u003c/code\u003e).\nIt will save automatically to the specified output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImage Builds\u003c/h2\u003e\u003ca id=\"user-content-image-builds\" class=\"anchor\" aria-label=\"Permalink: Image Builds\" href=\"#image-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe emorec_aci docker image was built on docker hub.\u003c/p\u003e\n\u003cp\u003eThe emorec_aci singularity image was built using the docker image base and\nconverting it to a singularity image via singularity hub.\u003c/p\u003e\n\u003cp\u003eSetup for linking Github with Docker Hub and Singularity Hub can be found here:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.docker.com/docker-hub/\" rel=\"nofollow\"\u003edocker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki\"\u003eSingularity Hub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003eSingularity\u003c/code\u003e file specifies creating a Singularity-compatible image\nfrom the docker image, as well as adding access to folders within ACI, specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# ACI mappings so you can access your files.\nmkdir -p /storage/home\nmkdir -p /storage/work\nmkdir -p /gpfs/group\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCreate command function for automatic execution of \u003ccode\u003evideo_analysis.py\u003c/code\u003e\nscript\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAdd a \u003ccode\u003eArgumentParser\u003c/code\u003e and help files.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1731587629.0 + "updated_at": 1557428867.0 }, { "data_format": 2, - "description": "Singularity Recipe for scipion", + "description": null, "filenames": [ - "Singularity.1.1", - "Singularity.1.1.cuda", - "Singularity.2.0", - "Singularity.2.0.cuda" + "Singularity" ], - "full_name": "ResearchIT/scipion", + "full_name": "phgenomics-singularity/assemblers", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 7, - "topics": [ - "singularity", - "scipion" - ], - "updated_at": 1592515044.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1576539518.0 }, { "data_format": 2, - "description": "Singularity container with R, tidyverse, and other packages", + "description": null, "filenames": [ - "Singularity.v0.0.14", - "Singularity.v0.0.13", - "Singularity.v0.0.11", - "Singularity.v0.0.12", - "Singularity.v0.0.10", - "Singularity.v0.0.15" + "Singularity" ], - "full_name": "darachm/singularity_r_for_darach", + "full_name": "gijzelaerr/singularity-notebook", "latest_release": null, - "readme": "\u003cp\u003eThis container is for providing \u003ccode\u003eR\u003c/code\u003e and some packages, that Darach likes.\u003c/p\u003e\n\u003cp\u003eThe primary reason for this is so that it flows nicely through SingularityHub\nfor Nextflow pipelines.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1581093246.0 + "updated_at": 1489998021.0 }, { "data_format": 2, - "description": "This project contains build scripts, setup and how-to instructions.", + "description": "singularity container for chlist binary", "filenames": [ - "hpc/simplace/Singularityfile.def", - "hpc/simplace/Singularityfile_HM.def" + "Singularity" ], - "full_name": "zalf-rpm/build-pipeline", + "full_name": "ISU-HPC/chlist", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebuild-pipeline\u003c/h1\u003e\u003ca id=\"user-content-build-pipeline\" class=\"anchor\" aria-label=\"Permalink: build-pipeline\" href=\"#build-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project contains build scripts, setups, how-to instructions and examples.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1731428353.0 + "updated_at": 1580398633.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.rabbitMQ", - "Singularity.def", - "worker/Singularity.worker" + "Singularity" ], - "full_name": "tahahah/py-pacman", + "full_name": "jordiae/simpaux-release", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePyPacman\u003c/h1\u003e\u003ca id=\"user-content-pypacman\" class=\"anchor\" aria-label=\"Permalink: PyPacman\" href=\"#pypacman\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0c9e4d33bbb7708d007d67d3a5ef0de48b6f82ae6ba5a45524573ef512e0aeef/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c616e67756167652d707974686f6e2d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0c9e4d33bbb7708d007d67d3a5ef0de48b6f82ae6ba5a45524573ef512e0aeef/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c616e67756167652d707974686f6e2d626c7565\" alt=\"language\" data-canonical-src=\"https://img.shields.io/badge/language-python-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9d525da30058c10a3a4c3339225203b08f2e5ea08582d046ff1fd43b4dfda173/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6f72616e6765\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9d525da30058c10a3a4c3339225203b08f2e5ea08582d046ff1fd43b4dfda173/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6f72616e6765\" alt=\"license\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe classic game of Pacman built with Pygame, provided also with a Reinforcement Learning environment.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/pacman-example.gif\"\u003e\u003cimg src=\"res/pacman-example.gif\" alt=\"example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#quick-start\"\u003eQuick Start\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#game\"\u003eGame\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#rl-environment\"\u003eRL Enviroment\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuick Start\u003c/h1\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGame\u003c/h2\u003e\u003ca id=\"user-content-game\" class=\"anchor\" aria-label=\"Permalink: Game\" href=\"#game\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the requirements\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the Game with the classic maze\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython main.py -lay classic -snd\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the Game without music or sounds\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython main.py -lay classic\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the game with others option\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: main.py [-h] [-lay LAYOUT] [-snd] [-stt]\n\nArgument for the Pacman Game\n\noptional arguments:\n -h, --help show this help message and exit\n -lay LAYOUT, --layout LAYOUT\n Name of layout to load in the game\n -snd, --sound Activate sounds in the game\n -stt, --state Display the state matrix of the game\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRL Environment\u003c/h2\u003e\u003ca id=\"user-content-rl-environment\" class=\"anchor\" aria-label=\"Permalink: RL Environment\" href=\"#rl-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ePacmanEnv\u003c/code\u003e class extends the \u003ccode\u003egym.Env\u003c/code\u003e class, so if you already know how to\nuse the \u003cstrong\u003eopen ai gym\u003c/strong\u003e, the api is the same.\u003c/p\u003e\n\u003cp\u003eHere\u0027s a little example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egym\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egym\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emake\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027pacman-v0\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eframe_to_skip\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e)\n\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eepisode\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erange\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eepisodes\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ereset\u003c/span\u003e()\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erange\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emax_steps\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eaction\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eaction_space\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esample\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003eobs\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erewards\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edone\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einfo\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003estep\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eaction\u003c/span\u003e)\n \n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edone\u003c/span\u003e: \n \u003cspan class=\"pl-k\"\u003ebreak\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAgent class\u003c/h3\u003e\u003ca id=\"user-content-agent-class\" class=\"anchor\" aria-label=\"Permalink: Agent class\" href=\"#agent-class\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003esrc.env\u003c/code\u003e folder provides also an abstract class that you can use to make your own AI agent.\nYou can use it to make your own agent, train it and directly plug into the game and see\nhow will perform.\u003c/p\u003e\n\u003cp\u003eHere\u0027s how you can use it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esrc\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eagent\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAgent\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyAgent\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eAgent\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027my_agent\u0027\u003c/span\u003e\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e__init__\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003epass\u003c/span\u003e\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eact\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estate\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ekwargs\u003c/span\u003e):\n \u003cspan class=\"pl-s\"\u003e\"\"\"\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e The code that return the action to take\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \"\"\"\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003epass\u003c/span\u003e\n \n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003etrain\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ekwargs\u003c/span\u003e):\n \u003cspan class=\"pl-s\"\u003e\"\"\"\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Your code to train the agent\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \"\"\"\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003epass\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd after you\u0027re done with the training you can simply plug it into the game:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erun_agent\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003estr\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eagent\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyAgent\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003econtroller\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eController\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elayout_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eact_sound\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eact_state\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eai_agent\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eagent\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003econtroller\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_menu\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more examples check out the \u003ca href=\"./examples\"\u003e\u003ccode\u003eexamples\u003c/code\u003e\u003c/a\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTodos\u003c/h1\u003e\u003ca id=\"user-content-todos\" class=\"anchor\" aria-label=\"Permalink: Todos\" href=\"#todos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] refactor everything using ECS\u003c/li\u003e\n\u003cli\u003e[ ] implement fruit\u003c/li\u003e\n\u003cli\u003e[ ] flashing power pellet\u003c/li\u003e\n\u003cli\u003e[x] state matrix in another screen\u003c/li\u003e\n\u003cli\u003e[x] Provide an RL Environment so an AI agent can be trained\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMIT\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaolo D\u0027Elia\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esimpaux\u003c/h1\u003e\u003ca id=\"user-content-simpaux\" class=\"anchor\" aria-label=\"Permalink: simpaux\" href=\"#simpaux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimple auxiliary network for metalearning\u003c/p\u003e\n\u003cp\u003eThe ProtoNet++ code is built on top of the \u003ca href=\"https://github.com/tristandeleu/pytorch-meta/tree/master/examples/protonet\"\u003eProtoNet example\u003c/a\u003e by @tristandeleu.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esimpaux_dev.sh\u003c/code\u003e script depends on the helper scripts for establishing persistent ssh connections \u003ca href=\"https://github.com/vmichals/ssh_persistent_connection_helpers\"\u003ehere\u003c/a\u003e. You just have to clone the repo and put the local folder in your $PATH.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1730625041.0 + "updated_at": 1714350232.0 }, { "data_format": 2, - "description": "Hold me closer, tiny container...", + "description": "A shovill singularity recipe", "filenames": [ - "Singularity.tiny", - "Singularity" + "Singularity", + "v0.9.0/Singularity.v0.9.0" ], - "full_name": "singularityhub/tiny-container", + "full_name": "phgenomics-singularity/shovill", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eshovill\u003c/h1\u003e\u003ca id=\"user-content-shovill\" class=\"anchor\" aria-label=\"Permalink: shovill\" href=\"#shovill\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA shovill singularity recipe\u003c/p\u003e\n\u003cp\u003eSingularity container for Torsten Seemann\u0027s \u003ca href=\"https://github.com/tseemann/shovill\"\u003eSHOVILL\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name shovill shub://phgenomics-singularity/shovill@latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003eshovill.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eUse the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./shovill.simg --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name shovill shub://phgenomics-singularity/shovill@v0.9.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir $HOME/singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name shovill_v0.9.0 shub://phgenomics-singularity/shovill@v0.9.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eLink the image to a folder in your \u003ccode\u003e$PATH\u003c/code\u003e (e.g., \u003ccode\u003e$HOME/bin\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eln -s $HOME/singularity/shovill_v0.9.0.simg $HOME/bin/shovill\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshovill --help\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1566311836.0 + "updated_at": 1521520426.0 }, { "data_format": 2, - "description": "My Singularity recipe files", + "description": null, "filenames": [ - "gerda-tgsend/Singularity.def", - "root-cern/Singularity.def", - "asciinema/Singularity.def", - "bat/Singularity.def", - "julia/Singularity.def", - "itunes/Singularity.def", - "lilypond/Singularity.def", - "texlive/Singularity.def", - "centos-base/Singularity.def", - "arch-base/Singularity.def" + "Singularity" ], - "full_name": "gipert/Singularity.def", + "full_name": "stephansmit/vuenode_containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity recipe files\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-files\" class=\"anchor\" aria-label=\"Permalink: Singularity recipe files\" href=\"#singularity-recipe-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sylabs/singularity\"\u003eSingularity\u003c/a\u003e containers I use the most on HPC clusters.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNodeJS container\u003c/h1\u003e\u003ca id=\"user-content-nodejs-container\" class=\"anchor\" aria-label=\"Permalink: NodeJS container\" href=\"#nodejs-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer with \u003ca href=\"https://vuejs.org\" rel=\"nofollow\"\u003eVue.js\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container locally\u003c/h2\u003e\u003ca id=\"user-content-build-the-container-locally\" class=\"anchor\" aria-label=\"Permalink: Build the container locally\" href=\"#build-the-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build vuenode_containers.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull the container\u003c/h2\u003e\u003ca id=\"user-content-pull-the-container\" class=\"anchor\" aria-label=\"Permalink: Pull the container\" href=\"#pull-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/vuenode_containers:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3589\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "containers" - ], - "updated_at": 1587858477.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1569691552.0 }, { "data_format": 2, - "description": "Content for MAPNET Workshop in analysis of low-coverage population genomic data", + "description": "PlanDEM is a domain-independent planner that works with dynamically estimated action models.", "filenames": [ - "MAPGD/Singularity" + "misc/releases/latest/Singularity", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12" ], - "full_name": "MapNetNZ/Pop-Genomics-Workshop2019", + "full_name": "eyal-weiss/plandem-public", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePop-Genomics-Workshop2019\u003c/h1\u003e\u003ca id=\"user-content-pop-genomics-workshop2019\" class=\"anchor\" aria-label=\"Permalink: Pop-Genomics-Workshop2019\" href=\"#pop-genomics-workshop2019\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContent for MAPNET Workshop in analysis of low-coverage population genomic data\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LynchLab/MAPGD\"\u003ehttps://github.com/LynchLab/MAPGD\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSingularity\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/docs/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://singularityhub.github.io/\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eVagrant Box \u003ca href=\"https://app.vagrantup.com/sylabs/boxes/singularity-3.0-ubuntu-bionic64\" rel=\"nofollow\"\u003ehttps://app.vagrantup.com/sylabs/boxes/singularity-3.0-ubuntu-bionic64\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eJupyter \u003ca href=\"https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook\" rel=\"nofollow\"\u003ehttps://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMBIE TPP REPO\u003c/h2\u003e\u003ca id=\"user-content-mbie-tpp-repo\" class=\"anchor\" aria-label=\"Permalink: MBIE TPP REPO\" href=\"#mbie-tpp-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/PlantandFoodResearch/MBIE_TPP_Populations\"\u003ehttps://github.com/PlantandFoodResearch/MBIE_TPP_Populations\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReproducing the Conda Environment\u003c/h3\u003e\u003ca id=\"user-content-reproducing-the--conda-environment\" class=\"anchor\" aria-label=\"Permalink: Reproducing the Conda Environment\" href=\"#reproducing-the--conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003erun this under Linux.\u003c/li\u003e\n\u003cli\u003eassuming you have installed miniconda\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml \n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePlanDEM\u003c/h1\u003e\u003ca id=\"user-content-plandem\" class=\"anchor\" aria-label=\"Permalink: PlanDEM\" href=\"#plandem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlanDEM is a research project, developed at Bar-Ilan University,\nthat aims to build a domain-independent classical planning system\nwhich uses dynamically estimated action models.\nIt is based on the Fast Downward planning system,\nwith modifications that support dynamic action model estimation.\u003c/p\u003e\n\u003cp\u003eCopyright 2021--2023 PlanDEM contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlanDEM main repository: \u003ca href=\"https://github.com/eyal-weiss/plandem-public\"\u003ehttps://github.com/eyal-weiss/plandem-public\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested Software Versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested Software Versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of PlanDEM is tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to PlanDEM.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2021--2023 Eyal Weiss\u003c/li\u003e\n\u003cli\u003e2021--2023 Gal A. Kaminka\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eEyal: \u003ca href=\"mailto:eyal.weiss@biu.ac.il\"\u003eeyal.weiss@biu.ac.il\u003c/a\u003e, \u003ca href=\"https://sites.google.com/view/eyal-weiss\" rel=\"nofollow\"\u003ehttps://sites.google.com/view/eyal-weiss\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eGal: \u003ca href=\"mailto:galk@cs.biu.ac.il\"\u003egalk@cs.biu.ac.il\u003c/a\u003e, \u003ca href=\"https://u.cs.biu.ac.il/~kaminkg/\" rel=\"nofollow\"\u003ehttps://u.cs.biu.ac.il/~kaminkg/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePapers\u003c/h2\u003e\u003ca id=\"user-content-papers\" class=\"anchor\" aria-label=\"Permalink: Papers\" href=\"#papers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePlanning with Multiple Action-Cost Estimates, Eyal Weiss and Gal A. Kaminka, ICAPS 2023\u003c/li\u003e\n\u003cli\u003eA Generalization of the Shortest Path Problem to Graphs with Multiple Edge-Cost Estimates, Eyal Weiss, Ariel Felner and Gal A. Kaminka, ECAI 2023\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRelevant information appears in directories with the same name.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as Fast Downward.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as Fast Downward, but with the following choices in the run command:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo run ACE choose the search engine \"synchronic\". See documentation in plugin_synchronic_estimation.cc. To switch between estimator types, open the file synchronic_estimation_search.cc and modify the class of *estimator_ptr (currently two options: Estimator or OntarioEstimator) and the input parameters of get_estimator accordingly.\u003c/li\u003e\n\u003cli\u003eTo run BEAUTY choose the search engine \"beauty\". See documentation in plugin_beauty.cc. To run Anytime-BEAUTY choose the search engine \"anytime_beauty\". See documentation in anytime_beauty.cc.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of PlanDEM as covered by this license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePlanDEM is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nPlanDEM is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1553205187.0 + "updated_at": 1689680580.0 }, { "data_format": 2, "description": null, "filenames": [ - "HPOBench/hpobench/container/recipes/Singularity.template", - "HPOBench/hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine", - "HPOBench/hpobench/container/recipes/surrogates/Singularity.YAHPOGymBenchmark", - "HPOBench/hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", - "HPOBench/hpobench/container/recipes/mo/Singularity.AdultBenchmark", - "HPOBench/hpobench/container/recipes/mo/Singularity.CNNBenchmark", - "HPOBench/hpobench/container/recipes/od/Singularity.ODBenchmarks", - "HPOBench/hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", - "HPOBench/hpobench/container/recipes/ml/Singularity.ml_mmfb", - "HPOBench/hpobench/container/recipes/ml/Singularity.PyBNN", - "HPOBench/hpobench/container/recipes/ml/Singularity.SupportVectorMachine", - "HPOBench/hpobench/container/recipes/ml/Singularity.XGBoostBenchmark", - "HPOBench/hpobench/container/recipes/ml/Singularity.YahpoRawBenchmark", - "HPOBench/hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark", - "HPOBench/hpobench/container/recipes/nas/Singularity.TabularBenchmarks", - "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_101", - "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_201", - "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_1shot1", - "HPOBench/hpobench/container/recipes/rl/Singularity.learnaBenchmark", - "HPOBench/hpobench/container/recipes/rl/Singularity.Cartpole" + "misc/releases/latest/Singularity", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12" ], - "full_name": "jr2021/ManyFairHPO-AIES", + "full_name": "hejia-zhang/downward", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eManyFairHPO: Many-Objective Fairness-Aware Hyperparameter Optimization\u003c/h1\u003e\u003ca id=\"user-content-manyfairhpo-many-objective-fairness-aware-hyperparameter-optimization\" class=\"anchor\" aria-label=\"Permalink: ManyFairHPO: Many-Objective Fairness-Aware Hyperparameter Optimization\" href=\"#manyfairhpo-many-objective-fairness-aware-hyperparameter-optimization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this repository you will find all source code for experiments and analyses accompanying the submission \"A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes\" to the ACM/AAAI AIES \u002724 Conference.\u003c/p\u003e\n\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"flowchart_aies_ternary.png\"\u003e\u003cimg src=\"flowchart_aies_ternary.png\" alt=\"Image Description\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall dependencies\u003c/h3\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install dependencies\" href=\"#install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHPOBench\u003c/h4\u003e\u003ca id=\"user-content-hpobench\" class=\"anchor\" aria-label=\"Permalink: HPOBench\" href=\"#hpobench\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd HPOBench\npip install -e .\ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMO-HPOBenchExperimentUtils\u003c/h4\u003e\u003ca id=\"user-content-mo-hpobenchexperimentutils\" class=\"anchor\" aria-label=\"Permalink: MO-HPOBenchExperimentUtils\" href=\"#mo-hpobenchexperimentutils\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd MO-HPOBenchExperimentUtils\npip install -e .\ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun a ManyFairHPO Experiment\u003c/h2\u003e\u003ca id=\"user-content-run-a-manyfairhpo-experiment\" class=\"anchor\" aria-label=\"Permalink: Run a ManyFairHPO Experiment\" href=\"#run-a-manyfairhpo-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython -u runner.py --objective_name f1_multi --model_name rf --dataset_name adult --seed 0 --optimizer_name nsga3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalyze ManyFairHPO Results\u003c/h2\u003e\u003ca id=\"user-content-analyze-manyfairhpo-results\" class=\"anchor\" aria-label=\"Permalink: Analyze ManyFairHPO Results\" href=\"#analyze-manyfairhpo-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe logs of our experimental results are provided on \u003ca href=\"https://drive.google.com/file/d/1TcYjjctuSEAHxL_SWopcTJ_IHRNb7tIA/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e. Please download the \u003ccode\u003eexpiriments.pkl\u003c/code\u003e file and place it in your the main project directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epickle\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epkl\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027experiments.pkl\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027rb\u0027\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eexperiments\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epkl\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e] \u003cspan class=\"pl-s1\"\u003eexperiments\u003c/span\u003e[(\u003cspan class=\"pl-s\"\u003e\u0027f1_multi\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027rf\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027adult\u0027\u003c/span\u003e)][\u003cspan class=\"pl-s\"\u003e\u0027function_values\u0027\u003c/span\u003e]\n\n \u003cspan class=\"pl-s1\"\u003eval_f1\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eval_ddsp\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eval_deod\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eval_deop\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eval_invd\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.316078\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.081204\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.026665\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.031306\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.019893\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.331510\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.075258\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.023383\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.026932\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.018170\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.323927\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.076689\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.023580\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.027098\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.019064\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.318430\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.081030\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.026330\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.031248\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.020370\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.397887\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.055827\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.007331\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.006138\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.017072\u003c/span\u003e \n... ... ... ... ... ... \n\u003cspan class=\"pl-c1\"\u003e996\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1.000000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000000\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e997\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.973703\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.001843\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.001118\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.002210\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000258\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e998\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.905509\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.006025\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.003782\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.007564\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000940\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e999\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.405365\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.055905\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.014091\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.017897\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.013220\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.340335\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.067798\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.017519\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.021524\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.017845\u003c/span\u003e\n\n[\u003cspan class=\"pl-c1\"\u003e10010\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecolumns\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e[\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e] \u003cspan class=\"pl-s1\"\u003eexperiments\u003c/span\u003e[(\u003cspan class=\"pl-s\"\u003e\u0027f1_multi\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027rf\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027adult\u0027\u003c/span\u003e)][\u003cspan class=\"pl-s\"\u003e\u0027archive\u0027\u003c/span\u003e]\n\n \u003cspan class=\"pl-s1\"\u003emax_depth\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emax_features\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emin_samples_leaf\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emin_samples_split\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003en_estimators\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e28\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.896547\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e15\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e78\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e109\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e22\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.367562\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e13\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e57\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e179\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e49\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.435865\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e102\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e106\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e29\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.891923\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e19\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e11\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e18\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.806194\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e17\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e175\u003c/span\u003e\n... ... ... ... ... ...\n\u003cspan class=\"pl-c1\"\u003e996\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.004743\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e127\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e167\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e997\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.580992\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e14\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e83\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e173\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e998\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.643578\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e88\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e163\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e999\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.544248\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e89\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e167\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e9\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.608166\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e69\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e173\u003c/span\u003e\n\n[\u003cspan class=\"pl-c1\"\u003e10010\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecolumns\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eManyFairHPO-AIES\u003c/h1\u003e\u003ca id=\"user-content-manyfairhpo-aies\" class=\"anchor\" aria-label=\"Permalink: ManyFairHPO-AIES\" href=\"#manyfairhpo-aies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFast Downward\u003c/h1\u003e\u003ca id=\"user-content-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Fast Downward\" href=\"#fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1723300161.0 + "updated_at": 1663278115.0 }, { "data_format": 2, - "description": null, + "description": "\ud83c\udf5d Code for the paper \"Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning.\"", "filenames": [ - "Singularity.def" + "misc/releases/latest/Singularity", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12" ], - "full_name": "BeatriceDRM/BoinDiRaimondoMetallo_softeng2", + "full_name": "yaaig-ufrgs/NeuralFastDownward-FSM", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject\" href=\"#se4hpcproject\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOur project satisfies all the requests correctly.\u003c/p\u003e\n\u003cp\u003eFurthermore, we have been able to upload the two files .txt (respectively output.txt and error.txt) using an artifact.\nAs a result, in txtFilesAction, you can see the two .txt created by the action \"Upload the files\".\nInside txtFiles, you can see the .txt obtained by copy.\u003c/p\u003e\n\u003cp\u003eThese two folders are both present because, in this way, we were able to make a comparison and to be sure that the result was correct.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNeuralFastDownward-FSM\u003c/h1\u003e\u003ca id=\"user-content-neuralfastdownward-fsm\" class=\"anchor\" aria-label=\"Permalink: NeuralFastDownward-FSM\" href=\"#neuralfastdownward-fsm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCode for the paper \"Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning\".\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNeural Fast Downward is intended to help with generating training data for\nclassical planning domains, as well as, using machine learning techniques with\nFast Downward (especially, Tensorflow and PyTorch).\u003c/p\u003e\n\u003cp\u003eNeuralFastDownward-FSM is a fork from \u003ca href=\"https://github.com/PatrickFerber/NeuralFastDownward\"\u003eFerber\u0027s Neural Fast Downward\u003c/a\u003e, which in turn derives from \u003ca href=\"https://github.com/aibasel/downward\"\u003eFast Downward\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eImportant: you can find our experiments from the paper in the \u003ccode\u003epaper-experiments\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFast Instructions\u003c/h2\u003e\u003ca id=\"user-content-fast-instructions\" class=\"anchor\" aria-label=\"Permalink: Fast Instructions\" href=\"#fast-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePre-run\u003c/h3\u003e\u003ca id=\"user-content-pre-run\" class=\"anchor\" aria-label=\"Permalink: Pre-run\" href=\"#pre-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload and extract\n\u003ca href=\"https://pytorch.org/cppdocs/installing.html\" rel=\"nofollow\"\u003e\u003ccode\u003elibtorch\u003c/code\u003e\u003c/a\u003e to a directory \u003ccode\u003ep\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecd\u003c/code\u003e to the directory where the root of the cloned repository is located, then:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport Torch_DIR=p OR export PATH_TORCH=p\npip install -r requirements.txt\n./build.py release\n# And if interested in running FastDownward in debug mode:\n./build.py debug\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e3.1. If torch 1.9.0 is not found, install Python \u0026lt;= 3.9.10.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMessing with the neural network code\u003c/h3\u003e\u003ca id=\"user-content-messing-with-the-neural-network-code\" class=\"anchor\" aria-label=\"Permalink: Messing with the neural network code\" href=\"#messing-with-the-neural-network-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee\n\u003ca href=\"https://github.com/yaaig-ufrgs/NeuralFastDownward-FSM/tree/main/src/pytorch\"\u003e\u003ccode\u003esrc/pytorch/\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDefault arguments\u003c/h3\u003e\u003ca id=\"user-content-default-arguments\" class=\"anchor\" aria-label=\"Permalink: Default arguments\" href=\"#default-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/yaaig-ufrgs/NeuralFastDownward-FSM/tree/main/src/pytorch/utils/default_args.py\"\u003e\u003ccode\u003esrc/pytorch/utils/default_args.py\u003c/code\u003e\u003c/a\u003e and \u003ca href=\"https://github.com/yaaig-ufrgs/NeuralFastDownward-FSM/tree/main/src/pytorch/utils/parse_args.py\"\u003e\u003ccode\u003esrc/pytorch/utils/parse_args.py\u003c/code\u003e\u003c/a\u003e for lists of default argument values when invoking programs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenerating samples\u003c/h3\u003e\u003ca id=\"user-content-generating-samples\" class=\"anchor\" aria-label=\"Permalink: Generating samples\" href=\"#generating-samples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eusage: fast-sample.py [-h] [-tst-dir TEST_TASKS_DIR] [-stp STATESPACE] [-tech {rw,dfs,bfs,bfs_rw}] [-search {greedy,astar}]\n [-heur {ff,lmcut}] [-st {complete,complete_nomutex,forward_statespace}] [-max MAX_SAMPLES] [-scs SEARCHES]\n [-sscs SAMPLES_PER_SEARCH] [-rd REGRESSION_DEPTH] [-rdm REGRESSION_DEPTH_MULTIPLIER] [-s SEED]\n [-dups {all,interrollout,none}] [-ms MULT_SEED] [-c RANDOM_PERCENTAGE] [-rhg RESTART_H_WHEN_GOAL_STATE]\n [-sf {none,mutex,statespace}] [-bfsp BFS_PERCENTAGE] [-o OUTPUT_DIR] [-sai {none,partial,complete,both}]\n [-sui SUCCESSOR_IMPROVEMENT] [-suirule {supersets,subsets,samesets}] [-kd K_DEPTH] [-unit UNIT_COST]\n [-cores CORES] [-t MAX_TIME] [-m MEM_LIMIT] [-eval EVALUATOR] [-dbg DEBUG]\n instance {yaaig}\nfast-sample.py: error: the following arguments are required: instance, method\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe example below takes all the instances in the \u003ccode\u003eblocks\u003c/code\u003e directory and saves the\nsamples, facts and defaults files in the \u003ccode\u003esamples\u003c/code\u003e directory with an\nappropriate filename. In the example below, we\u0027re generating 1000 samples. Of the final sample set, 500 are generated using BFS+RW and the remaining will be randomly generated. Duplicates are only allowed between rollout, states are completed with mutexes, all h-value improvements are used and the regression depth is limited by facts/avg(eff).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-sample.py tasks/experiments/blocks yaaig --technique bfs_rw --state-representation complete --max-samples 1000 --seed 0 --allow-dups interrollout --restart-h-when-goal-state yes --sample-improvement both --statespace tasks/experiments/statespaces/statespace_blocks_probBLOCKS-7-0_hstar --successor-improvement yes --regression-depth facts_per_avg_effects --state-filtering mutex --bfs-percentage 0.1 --random-percentage 0.5 --cores 1 --output-dir samples\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTraining a neural network\u003c/h3\u003e\u003ca id=\"user-content-training-a-neural-network\" class=\"anchor\" aria-label=\"Permalink: Training a neural network\" href=\"#training-a-neural-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecuting \u003ccode\u003e./train.py -h\u003c/code\u003e will show how to use it with all\nthe possible arguments. Almost everything is modifiable, and the default neural\nnetwork is a ResNet.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: train.py [-h] [-mdl {hnn,resnet}] [-sb SAVE_BEST_EPOCH_MODEL] [-diff SAVE_GIT_DIFF] [-pte POST_TRAIN_EVAL] [-pat PATIENCE]\n [-o {regression,prefix,one-hot}] [-lo LINEAR_OUTPUT] [-f NUM_FOLDS] [-hl HIDDEN_LAYERS]\n [-hu HIDDEN_UNITS [HIDDEN_UNITS ...]] [-b BATCH_SIZE] [-lr LEARNING_RATE] [-e MAX_EPOCHS] [-t MAX_TRAINING_TIME]\n [-a {sigmoid,relu,leakyrelu}] [-w WEIGHT_DECAY] [-d DROPOUT_RATE] [-shs SHUFFLE_SEED] [-sh SHUFFLE] [-gpu USE_GPU]\n [-bi BIAS] [-tsize TRAINING_SIZE] [-spt SAMPLE_PERCENTAGE] [-us UNIQUE_SAMPLES] [-ust UNIQUE_STATES]\n [-biout BIAS_OUTPUT] [-of OUTPUT_FOLDER] [-s SEED] [-sp SCATTER_PLOT] [-spn PLOT_N_EPOCHS]\n [-wm {default,sqrt_k,1,01,xavier_uniform,xavier_normal,kaiming_uniform,kaiming_normal,rai}] [-lf {mse,rmse}]\n [-no NORMALIZE_OUTPUT] [-rst RESTART_NO_CONV] [-cdead CHECK_DEAD_ONCE] [-sibd SEED_INCREMENT_WHEN_BORN_DEAD]\n [-trd NUM_CORES] [-dnw DATA_NUM_WORKERS] [-hpred SAVE_HEURISTIC_PRED]\n [-addfn [{patience,output-layer,num-folds,hidden-layers,hidden-units,batch-size,learning-rate,max-epochs,max-training-time,activation,weight-decay,dropout-rate,shuffle-seed,shuffle,use-gpu,bias,bias-output,normalize-output,restart-no-conv,sample-percentage,training-size} [{patience,output-layer,num-folds,hidden-layers,hidden-units,batch-size,learning-rate,max-epochs,max-training-time,activation,weight-decay,dropout-rate,shuffle-seed,shuffle,use-gpu,bias,bias-output,normalize-output,restart-no-conv,sample-percentage,training-size} ...]]]\n samples\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe example below will train a neural network with a sampling file as input, utilizing seed 0 (for reproducibility), a max of 20 training epochs, ReLU activation, regression output, MSE loss function and Kaiming Uniform network initialization. The trained model will be saved in the \u003ccode\u003eresults\u003c/code\u003e folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./train.py samples/yaaig_blocks_probBLOCKS-7-0_tech-bfsrw_sui_dups-ir_sai-both_repr-complete_bnd-factseff_maxs-1000_rs-500_ss0 -s 0 -e 20 -a relu -o regression -of results -lf mse -wm kaiming_uniform\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEvaluating instances\u003c/h3\u003e\u003ca id=\"user-content-evaluating-instances\" class=\"anchor\" aria-label=\"Permalink: Evaluating instances\" href=\"#evaluating-instances\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecuting \u003ccode\u003e./test.py -h\u003c/code\u003e will show how to use it with all\nthe possible arguments.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: test.py [-h] [-tfc TRAIN_FOLDER_COMPARE] [-diff SAVE_GIT_DIFF] [-d DOMAIN_PDDL] [-a {astar,eager_greedy}]\n [-heu {nn,add,blind,ff,goalcount,hmax,lmcut,hstar}] [-hm HEURISTIC_MULTIPLIER] [-u UNARY_THRESHOLD] [-t MAX_SEARCH_TIME]\n [-m MAX_SEARCH_MEMORY] [-e MAX_EXPANSIONS] [-pt {all,best,epochs}] [-sdir SAMPLES_DIR] [-ffile FACTS_FILE]\n [-dfile DEFAULTS_FILE] [-atn AUTO_TASKS_N] [-atf AUTO_TASKS_FOLDER] [-ats AUTO_TASKS_SEED] [-dlog DOWNWARD_LOGS]\n [-unit-cost UNIT_COST]\n train_folder [problem_pddls [problem_pddls ...]]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe example below takes a network folder (the trained model is located within\nit) as the first argument and will automatically find 50 random (fixed seed as default)\ninstances of the same domain to use for testing. \u003ccode\u003e-t\u003c/code\u003e is the time limit to solve the task, \u003ccode\u003e-a\u003c/code\u003e is the search algorithm used.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./test.py results/nfd_train.yaaig_blocks_probBLOCKS-7-0_tech-bfsrw_sui_dups-ir_sai-both_repr-complete_bnd-factseff_maxs-1000_rs-500_ss0.ns0 -t 360 -a eager_greedy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning full experiments\u003c/h3\u003e\u003ca id=\"user-content-running-full-experiments\" class=\"anchor\" aria-label=\"Permalink: Running full experiments\" href=\"#running-full-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can create multiple files like \u003ccode\u003eexp_example.json\u003c/code\u003e and call \u003ccode\u003e./run.py exp_example.json\u003c/code\u003e. Batch experiments will be performed according to the content in the JSON files. All the empty/unspecified settings will be run as\ndefault, and missing sections will be ignored.\u003c/p\u003e\n\u003cp\u003eYou can find a multitude of examples in the \u003ccode\u003epaper-experiments\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eorigin/release\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1717602739.0 + "updated_at": 1690974593.0 }, { "data_format": 2, - "description": null, + "description": "Build Singularity containers to run SpaDES simulations on HPC clusters.", "filenames": [ - "Singularity_tf1", - "Singularity", - "tf1.12/Singularity.tf1.12", - "tf1.13/Singularity.tf1.13" + "Singularity.spades_github-development", + "Singularity.spades_base", + "Singularity.spades_github-master" ], - "full_name": "mani3/tensorflow-gpu-py3-singularity", + "full_name": "gparadis/spades-singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2179\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003espades-singularity\u003c/h1\u003e\u003ca id=\"user-content-spades-singularity\" class=\"anchor\" aria-label=\"Permalink: spades-singularity\" href=\"#spades-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout this project\u003c/h2\u003e\u003ca id=\"user-content-about-this-project\" class=\"anchor\" aria-label=\"Permalink: About this project\" href=\"#about-this-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project implements a scripted framework for automating the process of building Singularity containers for running SpaDES simulations on HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI am super impatient, and refuse to take the time to understand what I am doing before running any commands. Just tell me how to do the thing right now!\u003c/h2\u003e\u003ca id=\"user-content-i-am-super-impatient-and-refuse-to-take-the-time-to-understand-what-i-am-doing-before-running-any-commands-just-tell-me-how-to-do-the-thing-right-now\" class=\"anchor\" aria-label=\"Permalink: I am super impatient, and refuse to take the time to understand what I am doing before running any commands. Just tell me how to do the thing right now!\" href=\"#i-am-super-impatient-and-refuse-to-take-the-time-to-understand-what-i-am-doing-before-running-any-commands-just-tell-me-how-to-do-the-thing-right-now\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build, sign, and push the base container flavour to the cloud image repository, simply run \u003ccode\u003emake all flavour=FLAVOUR\u003c/code\u003e, where \u003ccode\u003eFLAVOUR\u003c/code\u003e is one of \u003ccode\u003ebase\u003c/code\u003e, \u003ccode\u003egithub-master\u003c/code\u003e, or \u003ccode\u003egithub-development\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNot sure which flavour to use? Read on!\u003c/p\u003e\n\u003cp\u003eNote that, if you do not have Singularity installed yet, you will need to run \u003ccode\u003emake install-singularity\u003c/code\u003e first.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity container definition files\u003c/h2\u003e\u003ca id=\"user-content-singularity-container-definition-files\" class=\"anchor\" aria-label=\"Permalink: Singularity container definition files\" href=\"#singularity-container-definition-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity container definition files follow standard Singularity definition file naming conventions (i.e., they are prefixed with \u003ccode\u003eSingularity.\u003c/code\u003e followed by a \u003cem\u003etag\u003c/em\u003e string). There are three flavours (tags) defined in this project: \u003ccode\u003ebase\u003c/code\u003e, \u003ccode\u003egithub-master\u003c/code\u003e, and \u003ccode\u003egithub-development\u003c/code\u003e. Note that the R code that installs SpaDES packages for each flavour is contained in a script named \u003ccode\u003espades-setup_flavour.R\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can also create new custom flavours by copying and modifying some files from an existing flavour. New flavours should be compatible with automated make targets (as long as you did not break the filename patterns).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBase flavour\u003c/h3\u003e\u003ca id=\"user-content-base-flavour\" class=\"anchor\" aria-label=\"Permalink: Base flavour\" href=\"#base-flavour\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe base container flavour includes the latest stable CRAN versions of core SpaDES R packages. This base can be used to run SpaDES models directly (for simpler projects, where the CRAN packages are all you need). The base image also serves as a \u003cem\u003ebootstrap\u003c/em\u003e image for other flavours. The base container flavour is implemented in \u003ccode\u003eSingularity.spades_base\u003c/code\u003e and \u003ccode\u003espades-setup_base.R\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGitHub flavours\u003c/h3\u003e\u003ca id=\"user-content-github-flavours\" class=\"anchor\" aria-label=\"Permalink: GitHub flavours\" href=\"#github-flavours\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two GitHub container flavours (\u003ccode\u003egithub-master\u003c/code\u003e, \u003ccode\u003egithub-development\u003c/code\u003e). These install core SpaDES R packages from the latest code pushed to GitHub repositories for \u003ccode\u003emaster\u003c/code\u003e and \u003ccode\u003edevelopment\u003c/code\u003e branches, respectively. The GitHub container flavours are implemented in the \u003ccode\u003eSingularity.spades-github_BRANCH\u003c/code\u003e and \u003ccode\u003espades-setup_github-BRANCH\u003c/code\u003e (where \u003ccode\u003eBRANCH\u003c/code\u003e is one of \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edevelopment\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThe GitHub container flavours are \u003cem\u003ebootstrapped\u003c/em\u003e from the base container flavour. Defintion file implementation assumes that a local base container image is available in path \u003ccode\u003ebuild/spades.sif\u003c/code\u003e, so the base container must be built first (the base container will automatically get built if not present if you run \u003ccode\u003emake build flavour=FLAVOUR\u003c/code\u003e, where \u003ccode\u003eFLAVOUR\u003c/code\u003e is any value except for \u003ccode\u003ebase\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCustom flavours\u003c/h3\u003e\u003ca id=\"user-content-custom-flavours\" class=\"anchor\" aria-label=\"Permalink: Custom flavours\" href=\"#custom-flavours\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can create a custom container flavour but copying \u003ccode\u003eSingularity.spades_github-master\u003c/code\u003e and \u003ccode\u003espades-setup_github-master.R\u003c/code\u003e---rename these to \u003ccode\u003eSingularity.spades_foo\u003c/code\u003e and \u003ccode\u003espades-setup_foo.R\u003c/code\u003e (where \u003ccode\u003efoo\u003c/code\u003e is whatever unique flavour name you want) and modify as required. Minimally, you just need to edit one line of code in the Singularity definition file to point to \u003ccode\u003espades-setup_foo.R\u003c/code\u003e, and edit the code in \u003ccode\u003espades-setup_foo.R\u003c/code\u003e to install whatever versions of SpaDES R packages you need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMakefile details\u003c/h2\u003e\u003ca id=\"user-content-makefile-details\" class=\"anchor\" aria-label=\"Permalink: Makefile details\" href=\"#makefile-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eMakefile\u003c/code\u003e implements a number of make targets.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake build flavour=FLAVOUR sandbox=true\u003c/code\u003e to build a sandbox container (in \u003ccode\u003ebuild/spades_FLAVOUR_sandbox\u003c/code\u003e). See Singularity documentation for details on sandbox containers.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake build flavour=FLAVOUR\u003c/code\u003e to build a container as a single \u003cem\u003esingularity image file\u003c/em\u003e (in \u003ccode\u003ebuild/spades_FLAVOUR.sif\u003c/code\u003e). See Singularity documentation for details on SIF containers.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake push flavour=FLAVOUR\u003c/code\u003e to sign your SIF image and push it to your Sylabs cloud image library account. See the \u003ca href=\"https:%5Ccloud.sylabs.io\"\u003eSylabs Container Library\u003c/a\u003e to create and configure your account.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake all flavour=FLAVOUR\u003c/code\u003e to build and push your image in one step.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1582782591.0 + "updated_at": 1602038529.0 }, { "data_format": 2, - "description": "Research code from 2018 that doesn\u0027t fit in a more specific library.", + "description": null, "filenames": [ - "Singularity.cpu", - "Singularity.gpu" + "misc/releases/21.12/Singularity.21.12", + "misc/releases/latest/Singularity", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/19.12/Singularity.19.12" ], - "full_name": "dmorrill10/research2018", + "full_name": "ipc2023-classical/planner3", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eresearch2018\u003c/h1\u003e\u003ca id=\"user-content-research2018\" class=\"anchor\" aria-label=\"Permalink: research2018\" href=\"#research2018\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eResearch code from 2018 that doesn\u0027t fit in a more specific library.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSymk \u003ca href=\"https://github.com/speckdavid/symk/actions?query=workflow%3A%22Linux+build%22\"\u003e\u003cimg src=\"https://github.com/speckdavid/symk/workflows/Linux%20build/badge.svg\" alt=\"Linux build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/speckdavid/symk/actions?query=workflow%3A%22MacOS+build%22\"\u003e\u003cimg src=\"https://github.com/speckdavid/symk/workflows/MacOS%20build/badge.svg\" alt=\"MacOS build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-symk--\" class=\"anchor\" aria-label=\"Permalink: Symk \" href=\"#symk--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSymk is a state-of-the-art classical \u003cem\u003eoptimal\u003c/em\u003e and \u003cem\u003etop-k planner\u003c/em\u003e based on symbolic search.\u003c/p\u003e\n\u003cp\u003eWith Symk, it is possible to find a \u003cem\u003esingle optimal plan\u003c/em\u003e or a \u003cem\u003eset of k different best plans\u003c/em\u003e with the lowest cost for a given planning task.\nIn addition, Symk natively supports a variety of PDDL features that are rarely supported by other planners, such as conditional effects, derived predicates with axioms, and state-dependent action costs.\nSee this readme file for more information on running Symk and the various configurations.\nWe appreciate citations when SymK is used in a scientific context (see \u003ca href=\"#references\"\u003eReferences\u003c/a\u003e for more details).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#dependencies\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#compiling-the-symk-planner\"\u003eCompiling the Symk Planner\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#apptainer-image\"\u003eApptainer Image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generating-a-single-optimal-solution\"\u003eGenerating A Single Optimal Solution\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#generating-multiple-solutions\"\u003eGenerating Multiple Solutions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#top-k-configurations\"\u003eTop-k Configurations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#top-q-configurations\"\u003eTop-q Configurations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#loopless-planning\"\u003eLoopless Planning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#other-configurations\"\u003eOther Configurations\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#plan-selection-framework\"\u003ePlan Selection Framework\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#unordered-plan-selector\"\u003eUnordered Plan Selector\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#new-plan-selector\"\u003eNew Plan Selector\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pitfalls-and-troubleshooting\"\u003ePitfalls and Troubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently we only support Linux systems. The following should install all necessary dependencies.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esudo apt-get -y install cmake g++ make python3 autoconf automake\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSymk should compile on MacOS with the GNU C++ compiler and clang with the same instructions described above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompiling the Symk Planner\u003c/h3\u003e\u003ca id=\"user-content-compiling-the-symk-planner\" class=\"anchor\" aria-label=\"Permalink: Compiling the Symk Planner\" href=\"#compiling-the-symk-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./build.py \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eApptainer Image\u003c/h3\u003e\u003ca id=\"user-content-apptainer-image\" class=\"anchor\" aria-label=\"Permalink: Apptainer Image\" href=\"#apptainer-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo simplify the installation process, we alternatively provide an executable \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e container (formerly known as Singularity). It accepts the same arguments as Symk (\u003ccode\u003efast-downward.py\u003c/code\u003e script; see below).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e# \u003cspan class=\"pl-s1\"\u003eDownload the image,\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eapptainer pull symk.sif oras://ghcr.io/speckdavid/symk:latest\u003c/span\u003e\n\n# \u003cspan class=\"pl-s1\"\u003eor build it yourself.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eapptainer build symk.sif Apptainer\u003c/span\u003e\n\n# \u003cspan class=\"pl-s1\"\u003eThen run the desired configuration (for other configurations see below).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e./symk.sif domain.pddl problem.pddl --search \"sym-bd()\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenerating A Single Optimal Solution\u003c/h2\u003e\u003ca id=\"user-content-generating-a-single-optimal-solution\" class=\"anchor\" aria-label=\"Permalink: Generating A Single Optimal Solution\" href=\"#generating-a-single-optimal-solution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend to use the following configuration which uses bidirectional search.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"sym-bd()\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOther configurations are forward or backward search: \u003ccode\u003e--search \"sym-fw()\"\u003c/code\u003e or \u003ccode\u003e--search \"sym-bw()\"\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenerating Multiple Solutions\u003c/h2\u003e\u003ca id=\"user-content-generating-multiple-solutions\" class=\"anchor\" aria-label=\"Permalink: Generating Multiple Solutions\" href=\"#generating-multiple-solutions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTop-k Configurations\u003c/h3\u003e\u003ca id=\"user-content-top-k-configurations\" class=\"anchor\" aria-label=\"Permalink: Top-k Configurations\" href=\"#top-k-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend to use the following configuration which uses bidirectional search and\nreports the best \u003cstrong\u003ek\u003c/strong\u003e plans. Note that you can also specify \u003ccode\u003enum_plans=infinity\u003c/code\u003e if you want to find all possible plans.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symk-bd(plan_selection=top_k(num_plans=**k**))\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTop-q Configurations\u003c/h3\u003e\u003ca id=\"user-content-top-q-configurations\" class=\"anchor\" aria-label=\"Permalink: Top-q Configurations\" href=\"#top-q-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend to use the following configuration which uses bidirectional search and\nreports the \u003cstrong\u003ek\u003c/strong\u003e plans with quality bound \u003cstrong\u003eq\u003c/strong\u003e. Quality \u003ccode\u003e1\u0026lt;=q\u0026lt;=infinity\u003c/code\u003e is a multiplier that is multiplied to the cost of the cheapest solution.\nFor example, \u003ccode\u003eq=1\u003c/code\u003e reports only the cheapest plans, where \u003ccode\u003equality=infinity\u003c/code\u003e corresponds to the top-k planning.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symq-bd(plan_selection=top_k(num_plans=**k**),quality=**q**)\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLoopless Planning\u003c/h3\u003e\u003ca id=\"user-content-loopless-planning\" class=\"anchor\" aria-label=\"Permalink: Loopless Planning\" href=\"#loopless-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to generate loopless/simple plans, i.e., plans that do not visit any state more than once. In general, the option to consider and generate only simple plans can be combined with any Symk search engine and with different plan selectors by setting the \u003ccode\u003esimple\u003c/code\u003e parameter to true. See the following two examples and our \u003ca href=\"https://gki.informatik.uni-freiburg.de/papers/vontschammer-etal-icaps2022.pdf\" rel=\"nofollow\"\u003eICAPS 2022 Paper\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symk-bd(simple=true,plan_selection=top_k(num_plans=**k**))\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symq-bd(simple=true,plan_selection=top_k(num_plans=**k**),quality=**q**)\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOther Configurations\u003c/h3\u003e\u003ca id=\"user-content-other-configurations\" class=\"anchor\" aria-label=\"Permalink: Other Configurations\" href=\"#other-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to run Symk also with forward or backward search instead of bidirectional search, e.g., with \u003ccode\u003e--search \"symk-fw(...)\"\u003c/code\u003e or \u003ccode\u003e--search \"symk-bw(...)\"\u003c/code\u003e. Depending on the domain, one of these configurations may be faster than bidirectional search (\u003ccode\u003e\"--search symk-bd(...)\"\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePlan Selection Framework\u003c/h2\u003e\u003ca id=\"user-content-plan-selection-framework\" class=\"anchor\" aria-label=\"Permalink: Plan Selection Framework\" href=\"#plan-selection-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to create plans until a number of plans or simply a single plan is found that meets certain requirements.\nFor this purpose it is possible to write your own plan selector. During the search, plans are created and handed over to a plan selector with an anytime behavior.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnordered Plan Selector\u003c/h3\u003e\u003ca id=\"user-content-unordered-plan-selector\" class=\"anchor\" aria-label=\"Permalink: Unordered Plan Selector\" href=\"#unordered-plan-selector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn example of a plan selector is the \u003ca href=\"src/search/symbolic/plan_selection/unordered_selector.cc\"\u003eunordered_selector\u003c/a\u003e, which considers two plans as equivalent if their action multi-sets are equivalent. In other words, plans with the same multi-set of actions form an equivalence class and only one representative plan is reported for each equivalence class.\nNote that plan selectors can be combined with the different planning configurations.\u003c/p\u003e\n\u003cp\u003eWe recommend to use the following configurations which use bidirectional search.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUnordered Top-k:\u003c/h4\u003e\u003ca id=\"user-content-unordered-top-k\" class=\"anchor\" aria-label=\"Permalink: Unordered Top-k:\" href=\"#unordered-top-k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symk-bd(plan_selection=unordered(num_plans=**k**))\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUnordered Top-q:\u003c/h4\u003e\u003ca id=\"user-content-unordered-top-q\" class=\"anchor\" aria-label=\"Permalink: Unordered Top-q:\" href=\"#unordered-top-q\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symq-bd(plan_selection=unordered(num_plans=**k**),quality=**q**)\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew Plan Selector\u003c/h3\u003e\u003ca id=\"user-content-new-plan-selector\" class=\"anchor\" aria-label=\"Permalink: New Plan Selector\" href=\"#new-plan-selector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTwo simple examples of plan selectors are the \u003ca href=\"src/search/symbolic/plan_selection/top_k_selector.cc\"\u003etop_k_selector\u003c/a\u003e and\nthe \u003ca href=\"src/search/symbolic/plan_selection/top_k_even_selector.cc\"\u003etop_k_even_selector\u003c/a\u003e.\nFor this purpose it is possible to write your own plan selector.\nThe most important function is \u003cem\u003eadd_plan\u003c/em\u003e, in which you can specify whether a newly generated plan shall be accepted or rejected.\nTo create your own plan selector, you can copy the \u003cem\u003e.cc\u003c/em\u003e and \u003cem\u003e.h\u003c/em\u003e files of one of these two selectors and adjust them accordingly. Also add the new file name to \u003ca href=\"src/search/DownwardFiles.cmake\"\u003eDownwardFiles.cmake\u003c/a\u003e, similar to the other selection files.\nFinally, if you want to find a plan with your \u003cem\u003eawesome_selector\u003c/em\u003e selector (the name of the selector you specified for the plugin in the \u003cem\u003e.cc\u003c/em\u003e file), you can use the following command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symk-bd(plan_selection=awesome_selector(num_plans=1))\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote, that you can also search for the best \u003cstrong\u003ek\u003c/strong\u003e plans using your selector.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePitfalls and Troubleshooting\u003c/h2\u003e\u003ca id=\"user-content-pitfalls-and-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Pitfalls and Troubleshooting\" href=\"#pitfalls-and-troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, the planner performs a relevance analysis and removes components such as variables and actions that are irrelevant to achieving the goal. Although such variables and actions can in principle lead to further (simple) plans, they are classified as irrelevant and removed when translating PDDL to SAS+. If you wish to \u003cstrong\u003eobtain all plans\u003c/strong\u003e (even the non-relevant ones), please use the following options:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py --translate --search domain.pddl problem.pddl --translate-options --keep-unimportant-variables --search-options --search \"symk-bd(plan_selection=top_k(num_plans=**k**))\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReferences\u003c/h1\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that several components of SymK have been developed and published separately.\nWe appreciate citations of these sources when used.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMain source\u003c/h3\u003e\u003ca id=\"user-content-main-source\" class=\"anchor\" aria-label=\"Permalink: Main source\" href=\"#main-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDavid Speck, Robert Mattm\u00fcller, Bernhard Nebel: Symbolic Top-k Planning. AAAI 2020: 9967-9974 \u003ca href=\"https://rlplab.com/papers/speck-et-al-aaai2020.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"https://rlplab.com/papers/speck-et-al-aaai2020.html\" rel=\"nofollow\"\u003e[bib]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLoopless Top-k planning\u003c/h3\u003e\u003ca id=\"user-content-loopless-top-k-planning\" class=\"anchor\" aria-label=\"Permalink: Loopless Top-k planning\" href=\"#loopless-top-k-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eJulian von Tschammer, Robert Mattm\u00fcller, David Speck: Loopless Top-K Planning. ICAPS 2022: 380-384 \u003ca href=\"https://rlplab.com/papers/vontschammer-et-al-icaps2022.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"https://rlplab.com/papers/vontschammer-et-al-icaps2022.html\" rel=\"nofollow\"\u003e[bib]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAxiom and derived predicate support\u003c/h3\u003e\u003ca id=\"user-content-axiom-and-derived-predicate-support\" class=\"anchor\" aria-label=\"Permalink: Axiom and derived predicate support\" href=\"#axiom-and-derived-predicate-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eDavid Speck, Florian Gei\u00dfer, Robert Mattm\u00fcller, \u00c1lvaro Torralba: Symbolic Planning with Axioms. ICAPS 2019: 464-472 \u003ca href=\"https://rlplab.com/papers/speck-et-al-icaps2019.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"https://rlplab.com/papers/speck-et-al-icaps2019.html\" rel=\"nofollow\"\u003e[bib]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eState-dependent action cost support\u003c/h3\u003e\u003ca id=\"user-content-state-dependent-action-cost-support\" class=\"anchor\" aria-label=\"Permalink: State-dependent action cost support\" href=\"#state-dependent-action-cost-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eDavid Speck: Symbolic Search for Optimal Planning with Expressive Extensions. Ph.D. thesis: University of Freiburg (2022) \u003ca href=\"https://rlplab.com/papers/speck-phd2022.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"https://rlplab.com/papers/speck-phd2022.html\" rel=\"nofollow\"\u003e[bib]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou can find examples of domains with state-dependent action cost \u003ca href=\"https://github.com/speckdavid/SDAC-Benchmarks\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge that SymK is based on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward (22.06): \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSymbolic Fast Downward: \u003ca href=\"https://people.cs.aau.dk/~alto/software.html\" rel=\"nofollow\"\u003ehttps://people.cs.aau.dk/~alto/software.html\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, SymK uses some external software, which can be found in the following folders\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"src/dd_libs/cudd-3.0.0\"\u003esrc/dd_libs/cudd-3.0.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"src/search/ext\"\u003esrc/search/ext\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"src/search/sdac_parser/boost_dependencies\"\u003esrc/search/sdac_parser/boost_dependencies\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eSymK is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nSymK is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1604944821.0 + "updated_at": 1688990794.0 }, { "data_format": 2, - "description": null, + "description": "Grouping of my docker recipes", "filenames": [ - "Singularity-old", - "Singularity.cuda9.0-tf1.13", - "Singularity.v8", - "Singularity.cuda10.0-tf2.0", - "Singularity.cuda9.0-tf1.14", - "Singularity.cuda-10.0", - "Singularity.cuda9.0-tf1.13-without_ofed", - "Singularity.cuda10.0-tf1.13-v2", - "Singularity.v7", - "Singularity.cuda9.0-tf1.13-v2", - "Singularity.cuda-9.0", - "Singularity.test-cuda9.0", - "Singularity.cuda9.0-tf1.13-fixed_ofed", - "Singularity.cuda9.0-tf1.13-with_ucx", - "Singularity", - "Singularity.cuda9.0-tf1.13-v3" + "pschive_test/Singularity", + "psrchive_py2/Singularity_old", + "psrchive_py2/Singularity", + "gitlab_runner/Singularity", + "psrchive_Allegro/Singularity", + "psrchive_py3/Singularity_psrchive_last", + "psrchive_py3/Singularity_old", + "psrchive_py3/Singularity", + "Stable_Diffusion/Singularity" ], - "full_name": "BensonYang1999/tensorflow-gpu", + "full_name": "louisbondonneau/Docker_receipts", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Containers\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: Singularity Containers\" href=\"#singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity + Docker receipts can be found on github \u003ca href=\"https://github.com/louisbondonneau/Docker_receipts\"\u003ehttps://github.com/louisbondonneau/Docker_receipts\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDepending on the installation singularity executable can be named \"singularity\" or \"apptainer\".\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun a contaner\u003c/h2\u003e\u003ca id=\"user-content-run-a-contaner\" class=\"anchor\" aria-label=\"Permalink: Run a contaner\" href=\"#run-a-contaner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eINSTALL\u003c/th\u003e\n\u003cth align=\"left\"\u003epschive_py2\u003c/th\u003e\n\u003cth align=\"left\"\u003epschive_py3\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epsrchive\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py2)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003etempo2\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003etempo1\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epresto\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (v2.2 py2)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (v4 py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edspsr\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py2)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epsrsalsa\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eSIGPROC\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eRFICLEAN\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eGPTOOL\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003ePlotX/TransientX\u003c/td\u003e\n\u003ctd align=\"left\"\u003eNOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - nenupy\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - AntPat\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - dreamBeam\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - psrqpy\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - clean.py\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py2)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eNOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - pyqt5\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - pyglow\u003c/td\u003e\n\u003ctd align=\"left\"\u003eNOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRUN pschive_py2 container\u003c/h3\u003e\u003ca id=\"user-content-run-pschive_py2-container\" class=\"anchor\" aria-label=\"Permalink: RUN pschive_py2 container\" href=\"#run-pschive_py2-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /databf:/databf -B /data:/data -B /cep:/cep /cep/lofar/pulsar/Singularity/pschive_py2.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRUN pschive_py3 container\u003c/h3\u003e\u003ca id=\"user-content-run-pschive_py3-container\" class=\"anchor\" aria-label=\"Permalink: RUN pschive_py3 container\" href=\"#run-pschive_py3-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /databf:/databf -B /data:/data -B /cep:/cep /cep/lofar/pulsar/Singularity/pschive_py3.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eknown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003epsrdata, hdf5... and other things in Vlad installed used by LOFAR are not installed at this time\u003c/li\u003e\n\u003cli\u003epython installation on your home or environment variables in your bashrc can affect the operation inside the container. To avoid this, add the following lines to the beginning of your ~/.bashrc ~/.bash_profile\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Check if we are inside a Singularity container\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e [ \u003cspan class=\"pl-k\"\u003e-n\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$SINGULARITY_CONTAINER\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e ]\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If we are inside a Singularity container, exit the script here\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild a container from nothing\u003c/h2\u003e\u003ca id=\"user-content-build-a-container-from-nothing\" class=\"anchor\" aria-label=\"Permalink: Build a container from nothing\" href=\"#build-a-container-from-nothing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003einstall Go and Singularity\u003c/h3\u003e\u003ca id=\"user-content-install-go-and-singularity\" class=\"anchor\" aria-label=\"Permalink: install Go and Singularity\" href=\"#install-go-and-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapt-get update\napt-get install -y build-essential libssl-dev uuid-dev libgpgme11-dev squashfs-tools libseccomp-dev wget pkg-config git cryptsetup libglib2.0-dev\nGO_VERSION=1.20.2 OS=linux ARCH=amd64\nwget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${GO_VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${GO_VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\ntar -C /usr/local -xzf /tmp/go\u003cspan class=\"pl-smi\"\u003e${GO_VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:/usr/local/go/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\ngit clone --recurse-submodules https://github.com/sylabs/singularity.git singularity\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\n./mconfig\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e builddir\nmake\nmake install\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ebuild python2 psrchive container\u003c/h3\u003e\u003ca id=\"user-content-build-python2-psrchive-container\" class=\"anchor\" aria-label=\"Permalink: build python2 psrchive container\" href=\"#build-python2-psrchive-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/louisbondonneau/Docker_receipts\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Docker_receipts/psrchive_py2\nsingularity build /cep/lofar/pulsar/Singularity/pschive_py2.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ebuild python3 psrchive container\u003c/h3\u003e\u003ca id=\"user-content-build-python3-psrchive-container\" class=\"anchor\" aria-label=\"Permalink: build python3 psrchive container\" href=\"#build-python3-psrchive-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/louisbondonneau/Docker_receipts\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Docker_receipts/psrchive_py3\nsingularity build /cep/lofar/pulsar/Singularity/pschive_py3.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etry a writable container\u003c/h3\u003e\u003ca id=\"user-content-try-a-writable-container\" class=\"anchor\" aria-label=\"Permalink: try a writable container\" href=\"#try-a-writable-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity run --writable-tmpfs\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etry without any interference of your personal environment\u003c/h3\u003e\u003ca id=\"user-content-try-without-any-interference-of-your-personal-environment\" class=\"anchor\" aria-label=\"Permalink: try without any interference of your personal environment\" href=\"#try-without-any-interference-of-your-personal-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity run --cleanenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003euse CUDA\u003c/h3\u003e\u003ca id=\"user-content-use-cuda\" class=\"anchor\" aria-label=\"Permalink: use CUDA\" href=\"#use-cuda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eon nancep5 there is a TESLA T4 that you can use with dspsr for example\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity run --nv ***\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e nvidia-smi\nFri May 12 12:20:25 2023\n+-----------------------------------------------------------------------------+\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-------------------------------+----------------------+----------------------+\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e GPU Name Persistence-M\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e Bus-Id Disp.A \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e Volatile Uncorr. ECC \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e Fan Temp Perf Pwr:Usage/Cap\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e Memory-Usage \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e GPU-Util Compute M. \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e MIG M. \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e===============================+======================+======================\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 0 Tesla T4 On \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 00000000:3B:00.0 Off \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 0 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e N/A 36C P8 9W / 70W \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 4MiB / 15360MiB \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 0% Default \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e N/A \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n+-------------------------------+----------------------+----------------------+\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTODO\u003c/h2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eajouter:\nspyder\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCONTAINER TEST\u003c/h2\u003e\u003ca id=\"user-content-container-test\" class=\"anchor\" aria-label=\"Permalink: CONTAINER TEST\" href=\"#container-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etempo1\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash /cep/lofar/pulsar/ephem_scripts/par_conv_to_tempo1.sh /databf/nenufar-pulsar/ES03/ephem/B1919+21.par\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003etempo2\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /usr/local/pulsar/tempo2/example_data\ntempo2 -f example1.par example1.tim -nofit\npsrchive_info \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Tempo2::Predictor support enabled~\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003epsrchive\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -c \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eimport psrchive\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npython /cep/lofar/pulsar/NenPlot...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003epsrcat\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epsrcat -E B1919+21\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003epsredit\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epsredit -c dm ....\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003epresto\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -c \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eimport presto\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npython /usr/local/pulsar/presto/tests/test_presto_python.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003epsrsalsa\u003c/p\u003e\n\u003cblockquote\u003e\n\u003c/blockquote\u003e\n\u003cp\u003edreamBeam\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ecalibration of a NenuFAR archive\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003edspsr\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epython -c \u0027import dspsr\u0027\ndspsr -A -L 10 -E /databf/nenufar-pulsar/ES03/ephem/B2217+47.par -b 512 -O B2217+47_D20220304T1154_59642_002110_0057_BEAM0_dspsr /databf/nenufar-pulsar/DATA/B2217+47/RAW/B2217+47_D20220304T1154_59642_002110_0057_BEAM0.0000.raw\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1570345087.0 + "updated_at": 1696427759.0 }, { "data_format": 2, - "description": "A place to keep my Singularity recipes", + "description": "singularity image for assembly course", "filenames": [ - "Singularity.paralogfinder", - "Singularity.tetrad", - "Singularity.gatk", - "Singularity.plink", - "Singularity.ipyrad", - "Singularity.secapr", - "Singularity.yamp", - "Singularity.paup", - "Singularity.kat", - "Singularity.sra", - "Singularity.yangsmith", - "Singularity.snapper", - "Singularity.chloe", - "Singularity.clumpak", - "Singularity.hyphy", - "Singularity.hybpiper", - "Singularity.structure", - "Singularity.paragone_conda", - "Singularity.phylo", - "Singularity.gapfiller", - "Singularity.unicycler", - "Singularity.faststructure", - "Singularity.igv", - "Singularity.getorganelle", - "Singularity.hybphaser", - "Singularity.trinity", - "Singularity.samtools", - "Singularity.paragone", - "Singularity.qiime", - "Singularity.stacks", - "Singularity.quast" + "Singularity" ], - "full_name": "bmichanderson/singularity-containers", + "full_name": "MontseTor/assembly_course", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-containers\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: singularity-containers\" href=\"#singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA place to keep my Singularity recipes.\nThis repository contains recipes in the format \"Singularity.[program]\" and is linked to Singularity Hub so that all commits trigger builds there. Since Singularity Hub is no longer automatically building, new commits are no longer built.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1727856342.0 + "updated_at": 1573119853.0 }, { "data_format": 2, - "description": null, + "description": "CondaEnv for DM analysis pipeline", "filenames": [ - "Singularity.1.0.0" + "Singularity" ], - "full_name": "pndni/freesurfer-5.3.0-container", + "full_name": "golamshaifullah/DManalysis_condaenv", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1558118428.0 + "updated_at": 1568984053.0 }, { "data_format": 2, - "description": null, + "description": "Fork of CoqGym to implement a simple ablation study for EECS595.", "filenames": [ - "Singularity.fortran", - "Singularity.mpi" + "Singularity" ], - "full_name": "thomas-robinson/hello_world", + "full_name": "M-XI/CoqGym-EECS595", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehello_world\u003c/h1\u003e\u003ca id=\"user-content-hello_world\" class=\"anchor\" aria-label=\"Permalink: hello_world\" href=\"#hello_world\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity.fortran\u003c/h2\u003e\u003ca id=\"user-content-singularityfortran\" class=\"anchor\" aria-label=\"Permalink: Singularity.fortran\" href=\"#singularityfortran\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the singularity Fortran container, you can use a \u003ccode\u003esingularity build\u003c/code\u003e command. This example uses \u003cstrong\u003efakeroot\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build -f fortran.sif Singularity.fortran\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then test the functionality of the container with different commands\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run fortran.sif\n./fortran.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e fortran.sif hello.x\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITYENV_HELLO=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eyo wassup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsingularity run fortran.sif\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITYENV_HELLO=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHola mundo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n./fortran.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eif using csh\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run fortran.sif\n./fortran.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e fortran.sif hello.x\nsetenv SINGULARITYENV_HELLO \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eyo wassup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsingularity run fortran.sif\nsetenv SINGULARITYENV_HELLO \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHola mundo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n./fortran.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity.mpi\u003c/h2\u003e\u003ca id=\"user-content-singularitympi\" class=\"anchor\" aria-label=\"Permalink: Singularity.mpi\" href=\"#singularitympi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the singularity MPI continer, you follow pretty much the same procedure\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build -f mpi.sif Singularity.mpi\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis container uses mpich, so you should use a compatible version of MPI (mvapich, impi, etc).\nDo not use openmpi.\u003c/p\u003e\n\u003cp\u003eYou can run the mpi.sif by using the appropriate MPI running command for your system on singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -n 10 singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e mpi.sif mpi_hello.x\nmpirun -n 10 singularity run mpi.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing slurm requires an extra argument to srun\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esrun --mpi=pmi2 -n 10 singularity run mpi.sif\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCoqGym\u003c/h1\u003e\u003ca id=\"user-content-coqgym\" class=\"anchor\" aria-label=\"Permalink: CoqGym\" href=\"#coqgym\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNOTE\u003c/h2\u003e\u003ca id=\"user-content-note\" class=\"anchor\" aria-label=\"Permalink: NOTE\" href=\"#note\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a fork of the original CoqGym repository, made in order to implement a simple ablation study of the ASTactic model below.\nThe following sections consist of the original README.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSTART OF ORIGINAL README\u003c/h2\u003e\u003ca id=\"user-content-start-of-original-readme\" class=\"anchor\" aria-label=\"Permalink: START OF ORIGINAL README\" href=\"#start-of-original-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/example_proof.jpg\"\u003e\u003cimg src=\"images/example_proof.jpg\" alt=\"Example proof\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCode for the paper:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://arxiv.org/abs/1905.09381\" rel=\"nofollow\"\u003eLearning to Prove Theorems via Interacting with Proof Assistants\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://www.cs.princeton.edu/~kaiyuy/\" rel=\"nofollow\"\u003eKaiyu Yang\u003c/a\u003e and \u003ca href=\"https://www.cs.princeton.edu/~jiadeng/\" rel=\"nofollow\"\u003eJia Deng\u003c/a\u003e\u003cbr\u003e\nInternational Conference on Machine Learning (ICML) 2019\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@inproceedings\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003eyang2019coqgym\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eLearning to Prove Theorems via Interacting with Proof Assistants\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eYang, Kaiyu and Deng, Jia\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebooktitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eInternational Conference on Machine Learning (ICML)\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2019\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor potential bugs, please open an issue. For any other questions, please ask in \u003ca href=\"https://github.com/princeton-vl/CoqGym/discussions\"\u003eDiscussions\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"#1-installing-coqgym\"\u003eInstalling CoqGym\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 1.1 \u003ca href=\"#11-dependencies\"\u003eDependencies\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 1.2 \u003ca href=\"#12-building-coq-serapi-coqhammer-and-the-coq-projects\"\u003eBuilding Coq, SerAPI, CoqHammer, and the Coq Projects\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 1.3 \u003ca href=\"#13-extracting-the-proofs-from-coq-code-optional\"\u003eExtracting the Proofs from Coq Code\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 1.4 \u003ca href=\"#14-downloading-the-pre-extracted-proofs-recommended\"\u003eDownloading the Pre-extracted Proofs\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#2-using-coqgym-in-a-container\"\u003eUsing CoqGym in a Container\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 2.1 \u003ca href=\"#21-dependencies\"\u003eDependencies\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 2.2 \u003ca href=\"#22-downloading-the-pre-built-container-image\"\u003eDownloading the Pre-built Container Image\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 2.3 \u003ca href=\"#23-using-the-container\"\u003eUsing the Container\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 2.4 \u003ca href=\"#24-building-the-container-by-yourself\"\u003eBuilding the Container by Yourself\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#3-data-format\"\u003eData Format\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 3.1 \u003ca href=\"#31-json-files\"\u003eJSON Files\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 3.2 \u003ca href=\"#32-lmdb-file\"\u003eLMDB File\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 3.3 \u003ca href=\"#33-glossary\"\u003eGloassary\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#4-data-utilities\"\u003eData Utilities\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 4.1 \u003ca href=\"#41-interacting-with-coqgym\"\u003eInteracting with CoqGym\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 4.2 \u003ca href=\"#42-parsing-coq-terms\"\u003eParsing Coq Terms\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 4.3 \u003ca href=\"#43-computing-dataset-statistics\"\u003eComputing Dataset Statistics\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#5-the-astactic-model\"\u003eThe ASTactic Model\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 5.1 \u003ca href=\"#51-prerequisites\"\u003ePrerequisites\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 5.2 \u003ca href=\"#52-extracting-proof-steps\"\u003eExtracting Proof Steps\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 5.3 \u003ca href=\"#53-training\"\u003eTraining\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 5.4 \u003ca href=\"#54-testing\"\u003eTesting\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#6-credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#7-contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Installing CoqGym\u003c/h2\u003e\u003ca id=\"user-content-1-installing-coqgym\" class=\"anchor\" aria-label=\"Permalink: 1. Installing CoqGym\" href=\"#1-installing-coqgym\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow these steps to obtain the CoqGym dataset and build the environment for interacting with it.\nAlternatively, you may also use CoqGym in a \u003ca href=\"#2-using-coqgym-in-a-container\"\u003econtainer\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.1 Dependencies\u003c/h3\u003e\u003ca id=\"user-content-11-dependencies\" class=\"anchor\" aria-label=\"Permalink: 1.1 Dependencies\" href=\"#11-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://opam.ocaml.org/\" rel=\"nofollow\"\u003eOPAM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.anaconda.com/distribution/\" rel=\"nofollow\"\u003eAnaconda Python 3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://symas.com/lmdb/\" rel=\"nofollow\"\u003eLMDB\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ruby-lang.org/en/\" rel=\"nofollow\"\u003eRuby\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.2 Building Coq, SerAPI, CoqHammer, and the Coq Projects\u003c/h3\u003e\u003ca id=\"user-content-12-building-coq-serapi-coqhammer-and-the-coq-projects\" class=\"anchor\" aria-label=\"Permalink: 1.2 Building Coq, SerAPI, CoqHammer, and the Coq Projects\" href=\"#12-building-coq-serapi-coqhammer-and-the-coq-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate an OPAM switch for OCaml 4.07.1+flambda: \u003ccode\u003eopam switch create 4.07.1+flambda \u0026amp;\u0026amp; eval $(opam env)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUpgrade the installed OPAM packages (optional): \u003ccode\u003eopam upgrade \u0026amp;\u0026amp; eval $(opam env)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eClone the repository: \u003ccode\u003egit clone https://github.com/princeton-vl/CoqGym\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall Coq, SerAPI and CoqHammer: \u003ccode\u003ecd CoqGym \u0026amp;\u0026amp; source install.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuild the Coq projects (can take a while): \u003ccode\u003ecd coq_projects \u0026amp;\u0026amp; make \u0026amp;\u0026amp; cd ..\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate and activate the conda environment: \u003ccode\u003econda env create -f coq_gym.yml \u0026amp;\u0026amp; conda activate coq_gym\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: \u003ca href=\"https://github.com/coq/coq\"\u003eCoq\u003c/a\u003e, \u003ca href=\"https://github.com/ejgallego/coq-serapi\"\u003eSerAPI\u003c/a\u003e, \u003ca href=\"https://github.com/lukaszcz/coqhammer\"\u003eCoqHammer\u003c/a\u003e, and the Coq projects in \u003ca href=\"./coq_projects\"\u003ecoq_projects\u003c/a\u003e directory are indendent software projects with their own code repositories, but please follow the instructions above to build the specific versions we need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.3 Extracting the Proofs from Coq Code (Optional)\u003c/h3\u003e\u003ca id=\"user-content-13-extracting-the-proofs-from-coq-code-optional\" class=\"anchor\" aria-label=\"Permalink: 1.3 Extracting the Proofs from Coq Code (Optional)\" href=\"#13-extracting-the-proofs-from-coq-code-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe include the code for extracting CoqGym from Coq source code. However, it is not guaranteed to reproduce exactly the same data. The timeout and other miscellaneous errors during the data extraction may be machine-dependent. For example, a faster machine is likely to have fewer timeout errors and thus can extract more proofs.\nFor benchmark purpose, please download and use our pre-extracted version of CoqGym.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCheck all Coq files and locate the proofs:\u003cbr\u003e\nFor each \u003ccode\u003e*.meta\u003c/code\u003e file in \u003ccode\u003e./coq_projects/\u003c/code\u003e, run \u003ccode\u003epython check_proofs.py --file /path/to/*.meta\u003c/code\u003e\u003cbr\u003e\nNow you have generated a \u003ccode\u003e*.json\u003c/code\u003e file in \u003ccode\u003e./data/\u003c/code\u003e corresponding to each \u003ccode\u003e*.meta\u003c/code\u003e file. The \u003ccode\u003eproofs\u003c/code\u003e field of the JSON object is a list containing the proof names.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtract the proofs:\u003cbr\u003e\nFor each \u003ccode\u003e*.meta\u003c/code\u003e file and each proof, run:\u003cbr\u003e\n\u003ccode\u003epython extract_proof.py --file /path/to/*.meta --proof $PROOF_NAME\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003epython extract_synthetic_proofs.py --file /path/to/*.meta --proof $PROOF_NAME\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePost-processing: \u003ccode\u003epython postprocess.py\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eCaveat\u003c/em\u003e: The steps above are computationally expensive. When we say \"For each XXX, run \u003ccode\u003eYYY\u003c/code\u003e\", the tasks are embarrassingly parallel, which means you can run them in parallel in any order. We do not provide the code for that because it depends on a particular HPC infrastructure.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.4 Downloading the Pre-extracted Proofs (Recommended)\u003c/h3\u003e\u003ca id=\"user-content-14-downloading-the-pre-extracted-proofs-recommended\" class=\"anchor\" aria-label=\"Permalink: 1.4 Downloading the Pre-extracted Proofs (Recommended)\" href=\"#14-downloading-the-pre-extracted-proofs-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the CoqGym dataset \u003ca href=\"https://drive.google.com/drive/folders/149m_17VkYYkl0kdSB4AI8zodCuTmPaA6?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUnzip the data and set the paths: \u003ccode\u003epython unzip_data.py\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eCaveat\u003c/em\u003e: The second step sets the absolute paths in the data. You have to re-do it whenever the absolote path of the \u003ccode\u003edata/\u003c/code\u003e directory changes (e.g. after moving the entire repo to another directory).\u003c/p\u003e\n\u003cp\u003eNow you are ready to interact with CoqGym! Run \u003ccode\u003epython eval_env.py\u003c/code\u003e to check if it terminates normally without raising an error.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Using CoqGym in a Container\u003c/h2\u003e\u003ca id=\"user-content-2-using-coqgym-in-a-container\" class=\"anchor\" aria-label=\"Permalink: 2. Using CoqGym in a Container\" href=\"#2-using-coqgym-in-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs a less painful alternative to \u003ca href=\"#1-installing-coqgym\"\u003einstalling CoqGym\u003c/a\u003e from scratch, we provide a pre-built Singularity container (There is also a 3rd-party \u003ca href=\"https://hub.docker.com/r/innochainver/coqgym\" rel=\"nofollow\"\u003edocker image\u003c/a\u003e that may be useful).\nFeel free to skip these steps if you have finished installing CoqGym.\nCurrently we do not support GPUs for the container, therefore you have to complete the installation steps manually if you want to train models on CoqGym using GPUs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.1 Dependencies\u003c/h3\u003e\u003ca id=\"user-content-21-dependencies\" class=\"anchor\" aria-label=\"Permalink: 2.1 Dependencies\" href=\"#21-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eSingularity (a.k.a. Apptainer)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.2 Downloading the Pre-built Container Image\u003c/h3\u003e\u003ca id=\"user-content-22-downloading-the-pre-built-container-image\" class=\"anchor\" aria-label=\"Permalink: 2.2 Downloading the Pre-built Container Image\" href=\"#22-downloading-the-pre-built-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container image can be downloaded \u003ca href=\"https://drive.google.com/drive/folders/13Rwa5no6W4MwSvdjRrENAdDCQqTWhcqy?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.3 Using the Container\u003c/h3\u003e\u003ca id=\"user-content-23-using-the-container\" class=\"anchor\" aria-label=\"Permalink: 2.3 Using the Container\" href=\"#23-using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eStart a shell session inside the container: \u003ccode\u003esingularity shell coq_gym.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003esource /.bashrc \u0026amp;\u0026amp; cd /CoqGym \u0026amp;\u0026amp; eval $(opam env) \u0026amp;\u0026amp; conda activate coq_gym\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou are now ready to use CoqGym! Try \u003ccode\u003epython eval_env.py\u003c/code\u003e to see if it terminates normally without raising an error.\u003cbr\u003e\nFor further instructions about how to use a Singularity container, please consult the documentation of Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.4 Building the Container by Yourself\u003c/h3\u003e\u003ca id=\"user-content-24-building-the-container-by-yourself\" class=\"anchor\" aria-label=\"Permalink: 2.4 Building the Container by Yourself\" href=\"#24-building-the-container-by-yourself\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide a \u003ca href=\"./Singularity\"\u003eSingularity recipe\u003c/a\u003e from which you can build the container by yourself.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eYou need to be on a Linux machine of which you have sudo privileges.\u003c/li\u003e\n\u003cli\u003eDownload the dataset \u003ca href=\"https://drive.google.com/drive/folders/149m_17VkYYkl0kdSB4AI8zodCuTmPaA6?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and put the files in your \u003ccode\u003eCoqGym/\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003esudo singularity build coq_gym.simg Singularity\u003c/code\u003e to build the container image \u003ccode\u003ecoq_gym.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eCaveat\u003c/em\u003e: If you run out of disk space when building the container, it may because your \u003ccode\u003e/tmp\u003c/code\u003e directory is not large enough. See \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/build_env.html#temporary-folders\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/build_env.html#temporary-folders\u003c/a\u003e for a workaround.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Data Format\u003c/h2\u003e\u003ca id=\"user-content-3-data-format\" class=\"anchor\" aria-label=\"Permalink: 3. Data Format\" href=\"#3-data-format\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe dataset contains three parts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003ccode\u003edata\u003c/code\u003e directory: \u003ccode\u003e*.json\u003c/code\u003e files corresponding to the \u003ccode\u003e*.v\u003c/code\u003e files in Coq source code, whose format is explained below. The \u003ccode\u003e*.json\u003c/code\u003e files contain all important information about the proofs: environment, local context, goals, tactics, proof trees, etc.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003ccode\u003esexp_cache\u003c/code\u003e directory: A LMDB file that serves as an index for the S-expressions in \u003ccode\u003e*.json\u003c/code\u003e files. The \u003ccode\u003e*.json\u003c/code\u003e files contain keys for querying \u003ccode\u003esexp_cache\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eprojs_split.json\u003c/code\u003e: A JSON object containing the training/validation/testing split\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.1 JSON Files\u003c/h3\u003e\u003ca id=\"user-content-31-json-files\" class=\"anchor\" aria-label=\"Permalink: 3.1 JSON Files\" href=\"#31-json-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach \u003ccode\u003e*.json\u003c/code\u003e file in \u003ccode\u003edata/\u003c/code\u003e corresponds to a Coq source file \u003ccode\u003e*.v\u003c/code\u003e in \u003ccode\u003ecoq_projects/\u003c/code\u003e. For example, \u003ccode\u003edata/StructTact/Assoc.json\u003c/code\u003e corresponds to \u003ca href=\"https://github.com/princeton-vl/CoqGym/blob/master/coq_projects/StructTact/Assoc.v\"\u003ecoq_projects/StructTact/Assoc.v\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe format of the JSON files is described below.\nThe hash codes are used as keys to query the LMDB \u003ccode\u003esexp_cache\u003c/code\u003e.\nConsult the \u003ca href=\"#33-glossary\"\u003eglossary\u003c/a\u003e for the terminology.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \u0027filename\u0027: \u0027Assoc.v\u0027, # the path of the Coq source file relative to the root directory of the Coq project\n \u0027coq_project\u0027: \u0027StructTact\u0027, # the name of the Coq project\n \u0027vernac_cmds\u0027: [ # a list of Coq commands [6] in the source file\n [\u0027Cd \"$COQGYM_ROOT/coq_projects/StructTact\".\u0027, \u0027VernacChdir\u0027, \u00273701e61f37b72b3e61788fce6317466b7bb92b55\u0027], # [raw command, command type, command AST (in hash code)]\n [\u0027Arguments a_equiv {_} {_} _ _ _.\u0027, \u0027VernacArguments\u0027, \u00276777d3c472595dae20427d0892ad03d38f70fde9\u0027],\n ...\n [\u0027Arguments a_equiv {_} {_} _ _ _.\u0027, \u0027VernacArguments\u0027, \u00276777d3c472595dae20427d0892ad03d38f70fde9\u0027],\n ],\n \u0027num_extra_cmds\u0027: 107, # the code in the original Coq file starts at vernac_cmds[num_extra_cmds]\n \u0027proofs\u0027: [ # a list of human-written proofs\n ...\n ],\n \u0027synthetic_proofs\u0027: [ # a list of synthetic proofs\n ...\n ],\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe format for a proof is as follows, taking the \u003ca href=\"https://github.com/princeton-vl/CoqGym/tree/master/coq_projects/StructTact/Assoc.v#L47\"\u003e\u003ccode\u003eget_set_same\u003c/code\u003e\u003c/a\u003e in \u003ccode\u003ecoq_projects/StructTact/Assoc.v\u003c/code\u003e as an example.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \u0027name\u0027: get_set_same, # the name of the theorem\n \u0027line_nb\u0027: 118, # the theorem is defined in $file_data[\u0027vernac_cmds\u0027][$line_nb]\n \n \u0027env_delta\u0027: { # the global environment relative to the previous proof in the same file\n \u0027add\u0027 : { # entries that should be added to the environment\n \u0027constants\u0027 : [\n {\n \u0027physical_path\u0027: \u0027coq/theories/Arith/PeanoNat.vo:Nat.mul_wd\u0027, # the unique identifier\n \u0027short_ident\u0027: \u0027PeanoNat.Nat.mul_wd\u0027, # the short identifier\n \u0027qualid\u0027: \u0027Coq.Arith.PeanoNat.Nat.mul_wd\u0027, # the qualified identifier [1]\n \u0027type\u0027: \u0027@Morphisms.Proper (forall (_ : nat) (_ : nat), nat) (@Morphisms.respectful nat (forall _ : nat, nat) (@eq nat) (@Morphisms.respectful nat nat (@eq nat) (@eq nat))) Nat.mul\u0027, # the type [7] of the constant\n \u0027sort\u0027: \u0027Prop\u0027, # the sort [2] of the constant (the type of its type) [2]\n \u0027opaque\u0027: False, # whether the constant is opaque or transparent [3]\n \u0027sexp\u0027: \u0027333b2895c8e62d21856476bf89fa9681c9058bb9\u0027 # the S-expression [4] of the constant produced by SerAPI\n }, \n ...\n ],\n \u0027inductives\u0027 : [\n {\n \u0027physical_path\u0027 : \u0027coq/theories/Init/Wf.vo:Acc\u0027,\n \u0027blocks\u0027: [ # a list of blocks in a mutual inductive definition [5]. For regular inductive definitions (most cases), the list has length 1\n {\n \u0027short_ident\u0027: \u0027Acc\u0027,\n \u0027qualid\u0027: \u0027Coq.Init.Wf.Acc\u0027,\n \u0027constructors\u0027: [\n [\u0027Acc_intro\u0027, \u0027forall (A : Type) (R : forall (_ : A) (_ : A), Prop) (x : A) (_ : forall (y : A) (_ : R y x), _UNBOUND_REL_6 A R y), _UNBOUND_REL_5 A R x\u0027], # [constructor name, constructor type]\n ...\n ]\n }\n ],\n \u0027is_record\u0027: False,\n \u0027sexp\u0027: \u002731537cb98179ad7d2de0dd2cc783b4672b34b25b\u0027\n }\n ...\n \n ],\n },\n \u0027subtract\u0027 : { # entries that should be removed from the environment\n \u0027constants\u0027 : [],\n \u0027inductives\u0027 : [],\n },\n },\n \n \u0027steps\u0027: [ # a list of proof steps\n {\n \u0027command\u0027: [\u0027induction l; intros; simpl; repeat (break_match; simpl); subst; congruence.\u0027, \u0027VernacExtend\u0027, \u0027f6d2cb314d72d23562e5f2ef2657bd2589d44794\u0027], # (raw command, command type, command AST (in hash code)) the Coq command (usually a tactic but also includes other commands such as +, -, *, etc.) \n \u0027goal_ids\u0027: { # the IDs of the goals in the current proof step\n \u0027fg\u0027: [27], # focused goals \n \u0027bg\u0027: [] . # unfocused goals\n }\n }\n ...\n ], \n \n \u0027goals\u0027 { # the goals\n 27\u0027: { # $goal_id -\u0026gt; goal\n \u0027id\u0027: 27, # the goal ID\n \u0027type\u0027: \u0027forall (k : K) (v : V) (l : list (prod K V)), @eq (option V) (assoc (assoc_set l k v) k) (@Some V v)\u0027, # the type (logical statement) of the goal\n \u0027hypotheses\u0027: [ # the local context, a list of local premises\n {\u0027idents\u0027: [\u0027K\u0027, \u0027V\u0027], \u0027term\u0027: [], \u0027type\u0027: \u0027Type\u0027, \u0027sexp\u0027: \u0027cd1531c49fce6657997962b5375a3ef0a59db34a\u0027} # {\u0027idents\u0027: [a list of identifiers of the premises (usually of length one)], \u0027term\u0027: [a list of Coq terms (usually empty)], \u0027type\u0027: the type (logical statement) of the premise}\n {\u0027idents\u0027: [\u0027K_eq_dec\u0027], \u0027term\u0027: [], \u0027type\u0027: \"forall k k\u0027 : K, sumbool (@eq K k k\u0027) (not (@eq K k k\u0027))\", \u0027sexp\u0027: \u00275f5f5bcf9e10621f8c0c4642c0eba3ff36cbfff8\u0027},\n ],\n }\n }, \n \n \u0027proof_tree\u0027 : { # the proof tree\n \u0027goal_id\u0027: 27, \u0027children\u0027: []\n }, \n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.2 LMDB File\u003c/h3\u003e\u003ca id=\"user-content-32-lmdb-file\" class=\"anchor\" aria-label=\"Permalink: 3.2 LMDB File\" href=\"#32-lmdb-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esexp_cache\u003c/code\u003e is a LMDB mapping hash codes in \u003ccode\u003e*.json\u003c/code\u003e files to their corresponding S-expressions. Below is a code snippet in Python for accessing them.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSexpCache\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esexp_cache\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSexpCache\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027sexp_cache\u0027\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esexp_cache\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e\u0027333b2895c8e62d21856476bf89fa9681c9058bb9\u0027\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.3 Glossary\u003c/h3\u003e\u003ca id=\"user-content-33-glossary\" class=\"anchor\" aria-label=\"Permalink: 3.3 Glossary\" href=\"#33-glossary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#qualified-identifiers-and-simple-identifiers\" rel=\"nofollow\"\u003equalified identifier\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#sorts\" rel=\"nofollow\"\u003esort\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#sorts\" rel=\"nofollow\"\u003eopaque, transparent\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://en.wikipedia.org/wiki/S-expression\" rel=\"nofollow\"\u003eS-expression\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#mutually-defined-inductive-types\" rel=\"nofollow\"\u003einductive definition, mutual inductive definition\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#mutually-defined-inductive-types\" rel=\"nofollow\"\u003eCoq (vernac) command\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#types\" rel=\"nofollow\"\u003etype\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. Data Utilities\u003c/h2\u003e\u003ca id=\"user-content-4-data-utilities\" class=\"anchor\" aria-label=\"Permalink: 4. Data Utilities\" href=\"#4-data-utilities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe include some tools for interacting with CoqGym, but they are NOT a part of the dataset.\nYou may implement your own tools for similar purposes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.1 Interacting with CoqGym\u003c/h3\u003e\u003ca id=\"user-content-41-interacting-with-coqgym\" class=\"anchor\" aria-label=\"Permalink: 4.1 Interacting with CoqGym\" href=\"#41-interacting-with-coqgym\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eeval_env.py\u003c/code\u003e enables the interaction with the proofs in CoqGym.\nSee \u003ca href=\"ASTactic/agent.py\"\u003eASTactic/agent.py\u003c/a\u003e for examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.2 Parsing Coq Terms\u003c/h3\u003e\u003ca id=\"user-content-42-parsing-coq-terms\" class=\"anchor\" aria-label=\"Permalink: 4.2 Parsing Coq Terms\" href=\"#42-parsing-coq-terms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003egallina.py\u003c/code\u003e: a parser the S-expressions of Coq terms.\nIt may be useful for learning the embeddings of the term ASTs.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eutils.py\u003c/code\u003e: functions for iterating through all proofs or Coq files in the dataset.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.3 Computing Dataset Statistics\u003c/h3\u003e\u003ca id=\"user-content-43-computing-dataset-statistics\" class=\"anchor\" aria-label=\"Permalink: 4.3 Computing Dataset Statistics\" href=\"#43-computing-dataset-statistics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003estats/count_human_proofs.py\u003c/code\u003e: count the number of human-written proofs in CoqGym.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003estats/count_synthetic_proofs.py\u003c/code\u003e: count the number of synthetic-written proofs in CoqGym.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003estats/proofs.py\u003c/code\u003e: compute some statistics of the proofs.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e5. The ASTactic Model\u003c/h2\u003e\u003ca id=\"user-content-5-the-astactic-model\" class=\"anchor\" aria-label=\"Permalink: 5. The ASTactic Model\" href=\"#5-the-astactic-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/astactic.jpg\"\u003e\u003cimg src=\"/images/astactic.jpg\" alt=\"ASTactic\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHere we describe how to train and test the ASTactic model on CoqGym.\nThe following content is NOT a part of the CoqGym dataset, and therefore you do not need it if you only want to access the data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.1 Prerequisites\u003c/h3\u003e\u003ca id=\"user-content-51-prerequisites\" class=\"anchor\" aria-label=\"Permalink: 5.1 Prerequisites\" href=\"#51-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMake sure CoqGym has been properly installed and configured. The \u003ccode\u003ecoq_gym\u003c/code\u003e conda environment is activated, the OPAM switch is on \u003ccode\u003e4.07.1+flambda\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAutomated theorem provers: \u003ca href=\"https://vprover.github.io\" rel=\"nofollow\"\u003eVampire\u003c/a\u003e, \u003ca href=\"http://cvc4.cs.stanford.edu/\" rel=\"nofollow\"\u003eCVC4\u003c/a\u003e, \u003ca href=\"http://www.eprover.org\" rel=\"nofollow\"\u003eEprover\u003c/a\u003e, and \u003ca href=\"https://github.com/Z3Prover/z3\"\u003eZ3\u003c/a\u003e. Install all of them and make sure they are accessible in PATH, otherwise you may see a performance degradation of the hammer baseline.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e: Install the correct version for your hardware in the conda environment \u003ccode\u003ecoq_gym\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eThe instructions below assume that you are in the \u003ca href=\"./ASTactic/\"\u003eASTactic\u003c/a\u003e directory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.2 Extracting Proof Steps\u003c/h3\u003e\u003ca id=\"user-content-52-extracting-proof-steps\" class=\"anchor\" aria-label=\"Permalink: 5.2 Extracting Proof Steps\" href=\"#52-extracting-proof-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe ASTactic model is trained on individual proof steps, rather than entire proofs.\nAfter obtaining the CoqGym dataset, run \u003ccode\u003epython extract_proof_steps.py\u003c/code\u003e. This can take a while, and you have the option to run it in parallel, please see the \u003ccode\u003e--filter\u003c/code\u003e option in the source code for details.\u003c/p\u003e\n\u003cp\u003eThe extracted proof steps are in \u003ccode\u003eproof_steps/\u003c/code\u003e. You can double-check the number of proof steps to make sure everything works as expected:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDirectory\u003c/th\u003e\n\u003cth\u003e# files\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eproof_steps/train\u003c/td\u003e\n\u003ctd\u003e121,644\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eproof_steps/valid\u003c/td\u003e\n\u003ctd\u003e68,180\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.3 Training\u003c/h3\u003e\u003ca id=\"user-content-53-training\" class=\"anchor\" aria-label=\"Permalink: 5.3 Training\" href=\"#53-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train on the proof steps in training + validation set: \u003ccode\u003epython main.py --no_validation --exp_id astactic\u003c/code\u003e\u003cbr\u003e\nThe \"astactic\" above is an experiment ID, and you may change it to other IDs. Model checkpoints will be saved to \u003ccode\u003eruns/astactic/checkpoints/\u003c/code\u003e. See \u003ccode\u003eoptions.py\u003c/code\u003e for command line options.\u003c/p\u003e\n\u003cp\u003eA pre-trained model can be downloaded \u003ca href=\"https://drive.google.com/drive/folders/1AzLaEpoGS3BPMUz9Bl63MHAFRqlF4CtH?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.4 Testing\u003c/h3\u003e\u003ca id=\"user-content-54-testing\" class=\"anchor\" aria-label=\"Permalink: 5.4 Testing\" href=\"#54-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAssuming you want to test the model checkpoint \u003ccode\u003eruns/astactic/checkpoints/model_003.pth\u003c/code\u003e on the proof \u003ccode\u003eget_set_same\u003c/code\u003e in \u003ccode\u003e../data/StructTact/Assoc.json\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTesting ASTactic:\u003cbr\u003e\n\u003ccode\u003epython evaluate.py ours ours-TEST --path runs/astactic/checkpoints/model_003.pth --file ../data/StructTact/Assoc.json --proof \"get_set_same\"\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTesting an automated tactic X (may be \"auto\", \"trivial\", \"easy\", \"intuition\", or \"hammer\"):\u003cbr\u003e\n\u003ccode\u003epython -u evaluate.py X X-TEST --file ../data/StructTact/Assoc.json --proof \"get_set_same\"\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTesting ASTactic+X:\u003cbr\u003e\n\u003ccode\u003epython -u evaluate.py ours+X ours+X-TEST --path runs/astactic/checkpoints/model_003.pth --file ../data/StructTact/Assoc.json --proof \"get_set_same\"\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eCaveat\u003c/em\u003e: Testing is computationally expensive, but the workloads are embarrassingly parallel, which means you can run them in parallel in any order. We do not provide the code for that because it depends on a particular HPC infrastructure.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e6. Credits\u003c/h2\u003e\u003ca id=\"user-content-6-credits\" class=\"anchor\" aria-label=\"Permalink: 6. Credits\" href=\"#6-credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe code is formatted using \u003ca href=\"https://github.com/psf/black\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7d770c433d6198d89f8c1e2f187b904a9721d176259d0e97157337741cc8e837/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e737667\" alt=\"Code style: black\" data-canonical-src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThis repo includes the codebase of \u003ca href=\"https://github.com/coq/coq\"\u003eCoq\u003c/a\u003e, \u003ca href=\"https://github.com/ejgallego/coq-serapi\"\u003eSerAPI\u003c/a\u003e, \u003ca href=\"https://github.com/lukaszcz/coqhammer\"\u003eCoqHammer\u003c/a\u003e, and the Coq projects in \u003ca href=\"./coq_projects\"\u003ecoq_projects\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e7. Contributing\u003c/h2\u003e\u003ca id=\"user-content-7-contributing\" class=\"anchor\" aria-label=\"Permalink: 7. Contributing\" href=\"#7-contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe welcome and appreciate contributions from the community. For bug fixes and relatively minor changes (such as comments, typos, etc.), feel free to submit a pull request directly. For anything beyond, please first post in \u003ca href=\"https://github.com/princeton-vl/CoqGym/discussions\"\u003eDiscussions\u003c/a\u003e before implementing.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1617038655.0 + "updated_at": 1702431652.0 }, { "data_format": 2, - "description": "Singularity containers to run SU2", + "description": "This is a pipeline to run basic RNA-seq analysis for single-end data.", "filenames": [ - "Singularity.blackbird_v7.0.2", - "Singularity.fork_blackbird_v7.0.2", - "Singularity.forkv2_blackbird_v7.0.2", - "Singularity.fork_dev", - "Singularity.dev", - "Singularity", - "Singularity.master" + "envs/Singularity.deseq2_QC", + "envs/Singularity.fastqscreen", + "envs/Singularity.glimma_env", + "envs/Singularity.omic_qc_wf", + "envs/Singularity.fastqc", + "envs/Singularity.runGO", + "envs/Singularity.rseqc", + "envs/Singularity.deseq2", + "envs/Singularity.trim", + "envs/Singularity.permutation" ], - "full_name": "stephansmit/su2_containers", + "full_name": "ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity containers for SU2\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers-for-su2\" class=\"anchor\" aria-label=\"Permalink: Singularity containers for SU2\" href=\"#singularity-containers-for-su2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainers to run \u003ca href=\"https://su2code.github.io/\" rel=\"nofollow\"\u003eSU2\u003c/a\u003e with \u003ca href=\"https://www.open-mpi.org/\" rel=\"nofollow\"\u003eOpen MPI\u003c/a\u003e version 1.10.2.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull a container\u003c/h2\u003e\u003ca id=\"user-content-pull-a-container\" class=\"anchor\" aria-label=\"Permalink: Pull a container\" href=\"#pull-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/su2_containers:master\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Local\u003c/h2\u003e\u003ca id=\"user-content-run-local\" class=\"anchor\" aria-label=\"Permalink: Run Local\" href=\"#run-local\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003empirun -np 6 singularity exec su2_containers_master.sif /SU2/bin/SU2_CFD SU2.cfg \u0026gt; log.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun SurfSara\u003c/h2\u003e\u003ca id=\"user-content-run-surfsara\" class=\"anchor\" aria-label=\"Permalink: Run SurfSara\" href=\"#run-surfsara\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#SBATCH -N 2\n#SBATCH -p normal\n#SBATCH -n 40\n\nmodule load mpi/openmpi/1.10.2\nmpirun --hostfile hostfile.txt -np 40 singularity exec su2_containers_master.sif /SU2/bin/SU2_CFD SU2.cfg \u0026gt; log.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3334\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/892af8861c721181ec79bb73e9fdeacca71296f1df88960ad9c1fc2ab1ab3067/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135352e322e312d627269676874677265656e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/892af8861c721181ec79bb73e9fdeacca71296f1df88960ad9c1fc2ab1ab3067/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135352e322e312d627269676874677265656e2e737667\" alt=\"Snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%E2%89%A55.2.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://travis-ci.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a49d71aead60190e71bf72c6971f4e53d9cb58b28bae978ab977e30e8912e1c4/68747470733a2f2f7472617669732d63692e636f6d2f6f6873752d63656461722d636f6d702d6875622f42756c6b2d524e412d7365712d706970656c696e652d53452e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bulk-rna-seq-pipeline-se\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#bulk-rna-seq-pipeline-se\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBulk-RNA-seq-pipeline-SE\u003c/h1\u003e\n\u003cp\u003ePipeline to run basic RNA-seq analysis on single-end data.\u003c/p\u003e\n\u003cp\u003eThis is a package of Python and R scripts that enable reading, processing and analysis of Omics\u0027 datasets.\nThis package implements the Snakemake management workflow system and is currently implemented to work with\nthe cluster management and job scheduling system SLURM. This snakemake workflow utilizes conda installations to download and use packages for further analysis, so please ensure that you have installed miniconda prior to use.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-questionsissues\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#questionsissues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions/issues\u003c/h1\u003e\n\u003cp\u003ePlease add an issue to the Omics-QC-pipeline repository. We would appreciate if your issue included sample code/files\n(as appropriate) so that we can reproduce your bug/issue.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eWe welcome contributors! For your pull requests, please include the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSample code/file that reproducibly causes the bug/issue\u003c/li\u003e\n\u003cli\u003eDocumented code providing fix\u003c/li\u003e\n\u003cli\u003eUnit tests evaluating added/modified methods.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-use\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse\u003c/h1\u003e\n\u003cp\u003eLocate raw files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAfter sequencing, your raw fastq files are placed in \u003ccode\u003e/path/to/sequencing/files\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /path/to/raw/data\n$ ls -alh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck md5sum.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ md5sum \u2013c md5sum.txt \u0026gt; md5sum_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMove your files into the archive to be stored.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mv /path/to/raw/data /path/to/archive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck md5sum again to ensure your sequencing files are not corrupted.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ md5sum \u2013c md5sum.txt \u0026gt; md5sum_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eClone this Pipeline into your working directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCreate a \u003ccode\u003esamples/raw\u003c/code\u003e directory, and a \u003ccode\u003elogs\u003c/code\u003e directory in your \u003ccode\u003ewdir()\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir logs\n$ mkdir samples\n$ cd samples\n$ mkdir raw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSymbollically link the fastq files of your samples to the \u003ccode\u003ewdir/samples/raw\u003c/code\u003e directory using a bash script loop in your terminal.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003els -1 /path/to/data/LIB*gz | while read gz; do\n R=$( basename $gz | cut -d \u0027_\u0027 -f 3 | awk \u0027{print $1\".fastq.gz\"}\u0027 )\n echo $R\n ln -s ${gz} ./${R}\ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUpload your metadata file to the \u003ccode\u003edata\u003c/code\u003e directory, with the correct formatting:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eColumns should read:\n\u003ccode\u003eStudyID Column2 Column3 ...\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEach row should be a sample, with subsequent desired information provided (RNA extraction date, etc.)\u003c/li\u003e\n\u003cli\u003eEdit omic_config.yaml to include only columns included in this metadata file:\n\u003cul\u003e\n\u003cli\u003eThis includes \u003ccode\u003emeta_columns_to_plot\u003c/code\u003e and \u003ccode\u003epca labels\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAll values in this file should be tab-separated\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEdit the \u003ccode\u003eomic_config.yaml\u003c/code\u003e in your \u003ccode\u003ewdir()\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eChange the \u003ccode\u003eproject_id\u003c/code\u003e to a unique project identifier\u003c/li\u003e\n\u003cli\u003eAdd appropriate contrasts based on your samples under the \u003ccode\u003e[diffexp][contrasts]\u003c/code\u003e section\u003c/li\u003e\n\u003cli\u003eAdd the path to your metadata file for the \u003ccode\u003eomic_meta_data\u003c/code\u003e and \u003ccode\u003esamples\u003c/code\u003e parameters\u003c/li\u003e\n\u003cli\u003eChange \u003ccode\u003ebase_dir\u003c/code\u003e to your current working directory\u003c/li\u003e\n\u003cli\u003eEnsure you have the correct \u003ccode\u003eassembly\u003c/code\u003e specified\n\u003cul\u003e\n\u003cli\u003eCurrent options for this are: hg19, hg38.89 (ensembl v89) and hg38.90 (ensembl v90)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDo a dry-run of snakemake to ensure proper execution before submitting it to the cluster (in your wdir).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ snakemake -np --verbose\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce your files are symbolically linked, you can submit the job to exacloud via your terminal window.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sbatch submit_snakemake.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo see how the job is running, look at your queue.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ squeue -u your_username\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-directed-acyclic-graph-dag-of-an-example-workflow-including-two-samples\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#directed-acyclic-graph-dag-of-an-example-workflow-including-two-samples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph (DAG) of an example workflow including two samples\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE/blob/master/data/dag.png\"\u003e\u003cimg src=\"https://github.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE/raw/master/data/dag.png\" alt=\"Example Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1593778647.0 + "updated_at": 1582765139.0 }, { "data_format": 2, - "description": null, + "description": "Analysis code supporting the publication \"Trim66\u2019s paternal deficiency causes intrauterine overgrowth\"", "filenames": [ - "Singularity.fun" + "snakemake-rna-seq/singularity/Singularity.qc", + "snakemake-rna-seq/singularity/Singularity.deeptools", + "snakemake-rna-seq/singularity/Singularity.samtools", + "snakemake-rna-seq/singularity/Singularity.R", + "snakemake-rna-seq/singularity/Singularity.picard", + "snakemake-rna-seq/singularity/Singularity.bedtools", + "snakemake-rna-seq/singularity/Singularity.SalmonTE", + "snakemake-rna-seq/singularity/Singularity.ncbi", + "snakemake-rna-seq/singularity/Singularity.Trimmomatic", + "snakemake-rna-seq/singularity/Singularity.alignment" ], - "full_name": "m-novikov/singularity_training", + "full_name": "boulardlab/trim66-testis", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-trim66s-paternal-deficiency-causes-intrauterine-overgrowth\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#trim66s-paternal-deficiency-causes-intrauterine-overgrowth\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrim66\u2019s paternal deficiency causes intrauterine overgrowth\u003c/h1\u003e\n\u003cp\u003eThis repository holds the analysis code for the paper \"Trim66\u2019s paternal deficiency causes intrauterine overgrowth\".\u003c/p\u003e\n\u003cp\u003eThe content is organized as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/\u003c/code\u003e holds scripts to reproduce most of the figures in the paper.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003enotebooks/\u003c/code\u003e directory holds both the rmarkdown and compiled version of a notebook comparing the RNA-seq datasets.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esnakemake-chip-seq/\u003c/code\u003e holds a Snakemake pipeline used to process the ChIP-seq data presented in the paper.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esnakemake-rna-seq/\u003c/code\u003e holds a Snakemake pipeline used to process the RNA-seq data presetend in the paper.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-the-snakemake-pipelines\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#running-the-snakemake-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Snakemake pipelines\u003c/h1\u003e\n\u003cp\u003eTo run the pipelines users need to \u003ca href=\"https://snakemake.readthedocs.io/en/v7.28.3/getting_started/installation.html#installation-via-conda-mamba\" rel=\"nofollow\"\u003einstall Snakemake as per the official docs\u003c/a\u003e. The pipelines were developed and run using Snakemake 7.28.3. Later version should also work.\u003c/p\u003e\n\u003cp\u003eUsers will need to download our raw data from the ENA archive following the link given in the pubblication.\u003c/p\u003e\n\u003cp\u003eWe strongly encourage users to familiarize themselves with Snakemake execution profiles, the Portable Encapsulted Projects standard and Singularity before running the pipelines.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-reproducing-figures\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#reproducing-figures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReproducing figures\u003c/h1\u003e\n\u003cp\u003eOnce the pipelines executed correctly, users can build the notebook in \u003ccode\u003enotebooks/compare_TRIM66_spermatid_libs.Rmd\u003c/code\u003e to generate Figure 4b and 4c, among the others.\u003c/p\u003e\n\u003cp\u003eIn addition, users can run the scripts in the \u003ccode\u003efigures\u003c/code\u003e directory to reproduce other figures:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/heatmap_markers.R\u003c/code\u003e produces figure S5;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/plot_sashimi.R\u003c/code\u003e produces figure 2;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/single_copy_genes_v2.R\u003c/code\u003e: produces figure S6, among other figures;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/te_v1.R\u003c/code\u003e produces figure 4a;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/scrna-seq-testis\u003c/code\u003e contains scripts to download and prepare the data for a basic Seurat analysis to generate figure 1B and S1b;\u003c/li\u003e\n\u003cli\u003eFigures 4d and 4e are generated from the Snakemake pipeline in \u003ccode\u003esnakemake-chip-seq\u003c/code\u003e;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/dotplot-npeaks.R\u003c/code\u003e generates figure 4f.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-reporting-issues\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#reporting-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReporting issues\u003c/h1\u003e\n\u003cp\u003eIf users encouter problems, please use the Issue tracker to open issues, we will get back to you.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1551277834.0 + "updated_at": 1707815581.0 }, { "data_format": 2, - "description": null, + "description": "Collection of singularity recipes", "filenames": [ - "Singularity.r_sf" + "clumpak/Singularity.clumpak_v1.1", + "raisd/Singularity.raisd_v2.9", + "ngsRelate/Singularity.ngsRelate_v2.0", + "selscan/Singularity.selscan_v1.3.0", + "genespace/Singularity.genespace_v1.2.3", + "gushr/Singularity.gushr_v1.0.0", + "ohana/Singularity.ohana_vlatest", + "circos/Singularity.circos_v0.69-9", + "xpclr/Singularity.xpclr_v1.2.1", + "PCAngsd/Singularity.PCAngsd_v0.99", + "PCAngsd/Singularity.PCAngsd_vlatest", + "ngsLD/Singularity.ngsLD_v1.1.1", + "angsd/Singularity.angsd_v0.938", + "angsd/Singularity.angsd_v0.933", + "braker/Singularity.braker_v2.6.1", + "lassip/Singularity.lassip_v1.1.1", + "interproscan/Singularity.interproscan_v5.61-93.0" ], - "full_name": "callaghanmt-containers/recipes", + "full_name": "James-S-Santangelo/singularity-recipes", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eARC at University of Leeds\u003c/h1\u003e\u003ca id=\"user-content-arc-at-university-of-leeds\" class=\"anchor\" aria-label=\"Permalink: ARC at University of Leeds\" href=\"#arc-at-university-of-leeds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container recipes\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/942\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSingularity.r_sf\u003c/em\u003e: Latest version of R built on top of Ubuntu 16.04. R \u0027sf\u0027 package plus associated helper libraries and packages.\u003c/p\u003e\n\u003cp\u003eCheck singularity.r_sf for details.\u003c/p\u003e\n\u003cp\u003eTo build: \u003ccode\u003esudo singularity build r_sf.simg Singularity.r_sf\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eThis repository contains Singularity recipes for genomics tools that I have not found available through other means (e.g., Conda, Docker).\u003c/p\u003e\n\u003cp\u003eSingularity images are available on \u003ca href=\"https://cloud.sylabs.io/library/james-s-santangelo\" rel=\"nofollow\"\u003eSylab\u0027s Cloud Library\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1524235012.0 + "updated_at": 1651280675.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.dev" + "Singularity.rocm", + "Singularity.power9", + "Singularity.nvidia" ], - "full_name": "pndni/minc-ants-fsl-and-fs-container", + "full_name": "Delaunay/training-container", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1555097278.0 + "updated_at": 1559741876.0 }, { "data_format": 2, - "description": null, + "description": "Base container images using CentOS 7", "filenames": [ - "Singularity.def" + "Singularity", + "Singularity.centos7-perl" ], - "full_name": "dome01polimi/santarsiero_ferrario_sweng_part2", + "full_name": "ISU-HPC/centos7-base", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject - Step 2 CI/CD Pipeline and automation\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject---step-2-cicd-pipeline-and-automation\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject - Step 2 CI/CD Pipeline and automation\" href=\"#se4hpcproject---step-2-cicd-pipeline-and-automation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe automated the build, test and release process with a CI/CD pipeline, triggering Github actions on \u0027push\u0027 events.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow file\u003c/h2\u003e\u003ca id=\"user-content-workflow-file\" class=\"anchor\" aria-label=\"Permalink: Workflow file\" href=\"#workflow-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe definition of the action workflow is defined in \u003ccode\u003e./github/workflows/c-cpp.yml\u003c/code\u003e. Here is a brief explanation of the code.\nThe job runs on the latest Ubuntu environment provided by GitHub.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall MPI: The first step updates the package list and installs MPI to run the matrix multiplication library.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheckout Repository: This step checks out the project\u0027s repository from GitHub, including any submodules.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild Application: It creates a build directory, runs CMake to generate build files, and compiles the project.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun Tests: The built application is tested by running an MPI job with two processes using mpirun.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Dependencies for Singularity: Several dependencies required to install Singularity (a container platform) are installed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Go: The Go programming language is installed, which is necessary for building Singularity.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet Up Go Environment: The Go binary path is added to the environment variables.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Singularity: Singularity is downloaded, compiled, and installed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVerify Singularity Installation: This step checks the installed version of Singularity to ensure it was installed correctly.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild Singularity Container: A Singularity container is built using a definition file (Singularity.def).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePrepare Files for Transfer to Galileo: The container file and a SLURM job script are moved into a directory (to_send) for the upcoming transfer to Galileo cluster.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSSH to Remote Server: Using sshpass for password-based SSH, the files are transferred to a remote server. The job script is executed on the remote server, and the output is printed. The server job queue status is also checked.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis workflow streamlines the process of building, testing, and deploying the C/C++ application, while also leveraging Singularity containers for consistent and portable execution environments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOn Galileo\u003c/h1\u003e\u003ca id=\"user-content-on-galileo\" class=\"anchor\" aria-label=\"Permalink: On Galileo\" href=\"#on-galileo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Galileo, cluster management and job scheduling is handled by SLURM. We run the container image \u003ccode\u003econtainer.sif\u003c/code\u003e through a script named \u003ccode\u003ejob.sh\u003c/code\u003e, both transfered previously during the workflow. \u003ccode\u003ejob.sh\u003c/code\u003e contains the command for \u003cem\u003esingularity\u003c/em\u003e to run the image and where to pipe the output, in addition to other setup variables.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7-base\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#centos7-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7-base\u003c/h1\u003e\n\u003cp\u003eBase container images using CentOS 7\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1717671915.0 + "updated_at": 1525366145.0 }, { "data_format": 2, - "description": null, + "description": "Convert a VCF into a MAF, where each variant is annotated to only one of all possible gene isoforms", "filenames": [ - "Singularity.def" + "1.6.21/Singularity" ], - "full_name": "evlabwebapps/langatlas", + "full_name": "pscedu/singularity-vcf2maf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLanA\u003c/h1\u003e\u003ca id=\"user-content-lana\" class=\"anchor\" aria-label=\"Permalink: LanA\" href=\"#lana\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to add a new page\u003c/h2\u003e\u003ca id=\"user-content-how-to-add-a-new-page\" class=\"anchor\" aria-label=\"Permalink: How to add a new page\" href=\"#how-to-add-a-new-page\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNavigation bar is defined at \u003ccode\u003e./components/Navigation.jsx\u003c/code\u003e file. In order to add\na new page to navbar you must define a new page inside \u003ccode\u003e./pages\u003c/code\u003e and add route\nat \u003ccode\u003eroutes.js\u003c/code\u003e file. Also do not forget to export page by updating \u003ccode\u003e./pages/index.jsx\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to build and push image to DockerHub\u003c/h2\u003e\u003ca id=\"user-content-how-to-build-and-push-image-to-dockerhub\" class=\"anchor\" aria-label=\"Permalink: How to build and push image to DockerHub\" href=\"#how-to-build-and-push-image-to-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eyarn build\ndocker build --tag \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour-username\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/evlabwebapps-langatlas:latest \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker push \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour-username\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/evlabwebapps-langatlas:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOR edit and run \u003ccode\u003ebuild_push.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to deploy on the server (same as for backend)\u003c/h2\u003e\u003ca id=\"user-content-how-to-deploy-on-the-server-same-as-for-backend\" class=\"anchor\" aria-label=\"Permalink: How to deploy on the server (same as for backend)\" href=\"#how-to-deploy-on-the-server-same-as-for-backend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to enter Vagrant VM, pull Docker images and recreate containers with updated images.\u003c/p\u003e\n\u003cp\u003eOn HPC:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /om2/user/amirov/vagrant_images/evlabwebapps/\nvagrant ssh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInside VM:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker-compose pull\ndocker-compose up -d\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDocker-compose on VM that is common for frontend and backend\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eversion\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e3.5\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003eservices\u003c/span\u003e:\n\n \u003cspan class=\"pl-ent\"\u003eadmin\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eaamirov/evlab-web-apps-admin:latest\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ebuild\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e.\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eenv_file\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.env\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003evolumes\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e./assets:/app/assets\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e./backend-data:/app/data\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eports\u003c/span\u003e:\n - \u003cspan class=\"pl-c1\"\u003e8000:8000\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003ebackend\u003c/span\u003e\n\n \u003cspan class=\"pl-ent\"\u003eredis\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eredis:5-alpine\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003ebackend\u003c/span\u003e\n\n \u003cspan class=\"pl-ent\"\u003ecelery\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eaamirov/evlab-web-apps-admin:latest\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ebuild\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e.\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eenv_file\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.env\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003evolumes\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e./assets:/app/assets\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e./backend-data:/app/data\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003ebackend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ecommand\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ecelery -A src.evlabwebapps worker -l INFO\u003c/span\u003e\n\n \u003cspan class=\"pl-ent\"\u003ecelery-beat\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eaamirov/evlab-web-apps-admin:latest\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ebuild\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e.\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eenv_file\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.env\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003ebackend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ecommand\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ecelery -A src.evlabwebapps beat -l INFO\u003c/span\u003e\n\n \u003cspan class=\"pl-ent\"\u003efrontend\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eaamirov/evlabwebapps-langatlas:latest\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eports\u003c/span\u003e:\n - \u003cspan class=\"pl-c1\"\u003e8760:8760\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ebackend\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eevlabwebapps\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLearn More\u003c/h2\u003e\u003ca id=\"user-content-learn-more\" class=\"anchor\" aria-label=\"Permalink: Learn More\" href=\"#learn-more\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can learn more in the \u003ca href=\"https://facebook.github.io/create-react-app/docs/getting-started\" rel=\"nofollow\"\u003eCreate React App documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo learn React, check out the \u003ca href=\"https://reactjs.org/\" rel=\"nofollow\"\u003eReact documentation\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-vcf2maf/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-vcf2maf/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6c32b67e6bcfd7b78cca6dcfe21c4556b4fba959e13dd4612bb3e0b2846abfe7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d766366326d6166\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6c32b67e6bcfd7b78cca6dcfe21c4556b4fba959e13dd4612bb3e0b2846abfe7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d766366326d6166\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-vcf2maf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3ccc0089fe10f56f8768d722e148118222cba7cc032e7f2e3b6222aa6ec402ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d766366326d6166\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3ccc0089fe10f56f8768d722e148118222cba7cc032e7f2e3b6222aa6ec402ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d766366326d6166\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-vcf2maf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0068d09a09f0dde007625472c34b7e25f43da89ecd63871960f9fe0072f18edc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d766366326d6166\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0068d09a09f0dde007625472c34b7e25f43da89ecd63871960f9fe0072f18edc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d766366326d6166\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-vcf2maf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ff4771367a9654e511e5615d881b40f20b2ecf30679402c3517d2695b88380cf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d766366326d6166\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff4771367a9654e511e5615d881b40f20b2ecf30679402c3517d2695b88380cf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d766366326d6166\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-vcf2maf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-vcf2maf\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-vcf2maf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-vcf2maf\u003c/h2\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/mskcc/vcf2maf\"\u003evcf2maf\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003evcf2maf\u003c/code\u003e, \u003ccode\u003evcf2vcf\u003c/code\u003e, \u003ccode\u003emaf2maf\u003c/code\u003e and \u003ccode\u003emaf2vcf\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/vcf2maf/1.6.21\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/vcf2maf\u003c/code\u003e as \u003ccode\u003e1.6.21.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1640121099.0 + "subscribers_count": 4, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1653962577.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.0.8.0" + "Singularityfile.def" ], - "full_name": "arcsUVA/caffe2", + "full_name": "pouya-codes/singularity_create_groups", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecaffe2\u003c/h1\u003e\u003ca id=\"user-content-caffe2\" class=\"anchor\" aria-label=\"Permalink: caffe2\" href=\"#caffe2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-create-groups\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#create-groups\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Groups\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-development-information\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#development-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment Information\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eBefore running any experiment to be sure you are using the latest commits of all modules run the following script:\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e(cd /projects/ovcare/classification/singularity_modules ; ./update_modules.sh --bcgsc-pass your/bcgsc/path)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eusage: app.py [-h] {from-experiment-manifest,from-arguments} ...\n\nSplits patches to groups by patient case and saves the path to these patches in a group file (i.e. /path/to/patient_groups.json).\nThe patient_groups.json file uses Mitch\u0027s format for groups i.e. it is a json file with the format\n\n{\n \"chunks\": [\n {\n \"id\": int,\n \"imgs\": list of paths to patches\n },\n ...DEAFULT_SEEDE || Total ||\n| Patient in Group 1 | 9 | 13 | 20 | 3 | 45 |\n| Patient in Group 2 | 9 | 13 | 20 | 3 | 45 |\n| Patient in Group 3 | 8 | 13 | 20 | 3 | 44 |\n| Whole Slide Image | 38 | 63 | 79 | 13 | 193 |\n\n|| Patch Counts || MMRD || P53ABN || P53WT || POLE || Total ||\n| Patch in Group 1 | 38659 | 43918 | 52645 | 1791 | 137013 |\n| Patch in Group 2 | 15261 | 71059 | 34979 | 17248 | 138547 |\n| Patch in Group 3 | 19431 | 51330 | 53700 | 7881 | 132342 |\n| Total | 73351 | 166307 | 141324 | 26920 | 407902 |\n\nWhat are **categories**?\n\n1) if the --is_binary flag is used, then categories=(\u0027Tumor\u0027, \u0027Normal\u0027) where \u0027Tumor\u0027 is any patch annotated as \u0027Tumor\u0027 and \u0027Normal\u0027 is any patch with annotated as \u0027Other\u0027, \u0027MucinousBorderlineTumor\u0027, \u0027Necrosis\u0027 or \u0027Stroma\u0027\n2) if the --is_binary flag is not used, then categories=subtype (i.e. CC, EC, MC, LGSC, HGSC)\n\n**balance_patches**:\n\nThe --balance_patches flag gives the following options (illustrated):\n\n1) `--balance_patches overall` sets every cell to the min cell.\nDEAFULT_SEEDll to the min cell in each group.\n\n|| Patch Counts || MMRD || P53ABN || P53WT || POLE || Total ||\n| Patch in Group 1 | 1791 | 1791 | 1791 | 1791 | 7164 |\n| Patch in Group 2 | 15261 | 15261 | 15261 | 15261 | 61044 |\n| Patch in Group 3 | 7881 | 7881 | 7881 | 7881 | 31524 |\n| Total | 24933 | 24933 | 24933 | 24933 | 99732 |\nset_random_seedup 1 | 30000 | 30000 | 30000 | 1791 | 91791 |\n| Patch in Group 2 | 15261 | 30000 | 30000 | 17248 | 92509 |\n| Patch in Group 3 | 19431 | 30000 | 30000 | 7881 | 87312 |\n| Total | 64692 | 90000 | 90000 | 26920 | 271612 |\n\n3) `--balance_patches group=cap_amt` caps the number of patches in every group by cap_amt.\n`--balance_patches group=135000`\n\n|| Patch Counts || MMRD || P53ABN || P53WT || POLE || Total ||\n| Patch in Group 1 | 38659 | 43918 | 50632 | 1791 | 135000 |\n| Patch in Group 2 | 15261 | 67512 | 34979 | 17248 | 135000 |\n| Patch in Group 3 | 19431 | 51330 | 53700 | 7881 | 132342 |\n| Total | 73351 | 162760 | 139311 | 26920 | 402342 |\n\n3) `--balance_patches category=cap_amt` caps the number of patches in every category by cap_amt.\n`--balance_patches category=100000`\n\n|| Patch Counts || MMRD || P53ABN || P53WT || POLE || Total ||\n| Patch in Group 1 | 38659 | 33333 | 33333 | 1791 | 107116 |\n| Patch in Group 2 | 15261 | 33333 | 33333 | 17248 | 99175 |\n| Patch in Group 3 | 19431 | 33333 | 33333 | 7881 | 93978 |\n| Total | 73351 | 99999 | 99999 | 26920 | 300269 |\n\n**max_patient_patches**:\n\nIf --max_patient_patches is set, then we will select max_patient_patches from each patient case, so if max_patient_patches=60 then we will select at most 60 patches across all slides belonging to the patient.\n\n (1) this will select patches uniformly across all slides belonging to the patient. For example if there are 3 slides, then we will sample 20 patches from each slide unless a few slides have \u0026lt;20 patches in which case we select \u0026gt;20 patches from the slides with enough patches until we have 60 in total.\n (2) this will select patches uniformly across all categories belonging to the patient. For example if categories=(\u0027Tumor\u0027, \u0027Normal\u0027) then we will select 30 patches each category unless one category has \u0026lt;30 slides in which case we select we select \u0026gt;30 patches in the other category until we have 60 in total.\n (3) --max_patient_patches is applied before --balance_patches if both flags are set\n\npositional arguments:\n {from-experiment-manifest,from-arguments}\n Choose whether to use arguments from experiment manifest or from commandline\n from-experiment-manifest\n Use experiment manifest\n\n from-arguments Use arguments\n\noptional arguments:\n -h, --help show this help message and exit\n\nusage: app.py from-experiment-manifest [-h] [--component_id COMPONENT_ID]\n experiment_manifest_location\n\npositional arguments:\n experiment_manifest_location\n\noptional arguments:\n -h, --help show this help message and exit\n\n --component_id COMPONENT_ID\n\nusage: app.py from-arguments [-h] [--seed SEED] [--n_groups N_GROUPS]\n [--subtypes SUBTYPES [SUBTYPES ...]]\n [--is_binary] [--is_multiscale]\n [--balance_patches BALANCE_PATCHES]\n [--patch_pattern PATCH_PATTERN]\n [--filter_labels FILTER_LABELS [FILTER_LABELS ...]]\n --out_location OUT_LOCATION\n [--min_patches MIN_PATCHES]\n [--max_patches MAX_PATCHES]\n [--max_patient_patches MAX_PATIENT_PATCHES]\n {use-extracted-patches,use-hd5} ...\n\npositional arguments:\n {use-extracted-patches,use-hd5}\n Specify how to load patches.\n There are 2 ways of loading patches: by use_extracted_patches and by use_hd5.\n use-extracted-patches\n Use extracted and saved patches\n\n use-hd5 Use hd5 files\n\noptional arguments:\n -h, --help show this help message and exit\n\n --seed SEED Seed for random shuffle.\n (default: 256)\n\n --n_groups N_GROUPS The number of groups in groups file.\n (default: 3)\n\n --subtypes SUBTYPES [SUBTYPES ...]\n Space separated words describing subtype=groupping pairs for this study. Example: if doing one-vs-rest on the subtypes MMRD vs P53ABN, P53WT and POLE then the input should be \u0027MMRD=0 P53ABN=1 P53WT=1 POLE=1\u0027\n (default: {\u0027MMRD\u0027: 0, \u0027P53ABN\u0027: 1, \u0027P53WT\u0027: 2, \u0027POLE\u0027: 3})\n\n --is_binary Whether we want to categorize patches by the Tumor/Normal category (true) or by the subtype category (false).\n (default: False)\n\n --is_multiscale Whether patches have multiple scales i.e. different magnifications. Not currently used.\n (default: False)\n\n --balance_patches BALANCE_PATCHES\n Optional method to balance patches. Can choose (1) (\u0027group\u0027, \u0027overall\u0027, \u0027category\u0027) or (2) one of (\u0027group=cap_amt\u0027, \u0027overall=cap_amt\u0027, \u0027category=cap_amt\u0027).In the case (1), we will balance out the patches in every group, category, or overall (see description for more details). In case (2), we will cap the number of patches in every group, category, or overall to the number cap_amt.\n (default: None)\n\n --patch_pattern PATCH_PATTERN\n \u0027/\u0027 separated words describing the directory structure of the patch paths. The words are (\u0027annotation\u0027, \u0027subtype\u0027, \u0027slide\u0027, \u0027patch_size\u0027, \u0027magnification\u0027). A non-multiscale patch can be contained in a directory /path/to/patch/rootdir/Tumor/MMRD/VOA-1234/1_2.png so its patch_pattern is annotation/subtype/slide. A multiscale patch can be contained in a directory /path/to/patch/rootdir/Stroma/P53ABN/VOA-1234/10/3_400.png so its patch_pattern is annotation/subtype/slide/magnification\n (default: annotation/subtype/slide)\n\n --filter_labels FILTER_LABELS [FILTER_LABELS ...]\n Space separated words describing label=value pairs to filter patch by label value. For example, if a dataset contains patche paths like path/to/patch/rootdir/Tumor/MMRD/VOA-1234/256/20/1_2.png and we want to select Tumor patches of pixel size 256 * 256 and 20x magnification then the patch_pattern is annotation/subtype/slide/patch_size/magnification and the select_labels is \u0027annotation=Tumor patch_size=256 magnification=20\u0027\n (default: {})\n\n --out_location OUT_LOCATION\n full path of the groups file (i.e. /path/to/patient_groups.json). An example is \u0027/projects/ovcare/classification/cchen/ml/data/local_ec_100/patient_groups.json\u0027\n (default: None)\n\n --min_patches MIN_PATCHES\n Only include from slides that have at least min_patches number of patches\n (default: None)\n\n --max_patches MAX_PATCHES\n Only include from slides that have at most max_patches number of patches\n (default: None)\n\n --max_patient_patches MAX_PATIENT_PATCHES\n Select at most max_patient_patches number of patches from each patient.\n (default: None)\n\nusage: app.py from-arguments use-extracted-patches [-h] --patch_location\n PATCH_LOCATION\n {use-manifest,use-origin}\n ...\n\npositional arguments:\n {use-manifest,use-origin}\n Specify how to define patient ID and slide ID:\n 1. use-manifest 2. origin\n use-manifest Use manifest file to locate slides.\n a CSV file with minimum of 4 column and maximum of 6 columns. The name of columns\n should be among [\u0027origin\u0027, \u0027patient_id\u0027, \u0027slide_id\u0027, \u0027slide_path\u0027, \u0027annotation_path\u0027, \u0027subtype\u0027].\n origin, slide_id, patient_id must be one of the columns.\n\n use-origin Use origin for detecting patient ID and slide ID.\n NOTE: It only works for German, OVCARE, and TCGA.\n\noptional arguments:\n -h, --help show this help message and exit\n\n --patch_location PATCH_LOCATION\n root directory of all patches of a study. The patch directory structure is \u0027/patch_location/patch_pattern/x_y.png\u0027. See --patch_pattern below. An example is \u0027/projects/ovcare/classification/cchen/ml/data/local_ec_100/patches_256_sorted\u0027\n (default: None)\n\nusage: app.py from-arguments use-extracted-patches use-manifest\n [-h] --manifest_location MANIFEST_LOCATION\n\noptional arguments:\n -h, --help show this help message and exit\n\n --manifest_location MANIFEST_LOCATION\n Path to manifest CSV file.\n (default: None)\n\nusage: app.py from-arguments use-extracted-patches use-origin\n [-h] [--dataset_origin DATASET_ORIGIN [DATASET_ORIGIN ...]]\n\noptional arguments:\n -h, --help show this help message and exit\n\n --dataset_origin DATASET_ORIGIN [DATASET_ORIGIN ...]\n List of the origins of the slide dataset the patches are generated from. Should be from (\u0027ovcare\u0027, \u0027tcga\u0027, \u0027german\u0027, \u0027other\u0027). (For multiple origins, works for TCGA+ovcare. Mix of Other origins must be tested.)\n (default: [\u0027ovcare\u0027])\n\nusage: app.py from-arguments use-hd5 [-h] --hd5_location HD5_LOCATION\n {use-manifest,use-origin} ...\n\npositional arguments:\n {use-manifest,use-origin}\n Specify how to define patient ID and slide ID:\n 1. use-manifest 2. origin\n use-manifest Use manifest file to locate slides.\n a CSV file with minimum of 4 column and maximum of 6 columns. The name of columns\n should be among [\u0027origin\u0027, \u0027patient_id\u0027, \u0027slide_id\u0027, \u0027slide_path\u0027, \u0027annotation_path\u0027, \u0027subtype\u0027].\n origin, slide_id, patient_id must be one of the columns.\n\n use-origin Use origin for detecting patient ID and slide ID.\n NOTE: It only works for German, OVCARE, and TCGA.\n\noptional arguments:\n -h, --help show this help message and exit\n\n --hd5_location HD5_LOCATION\n root directory of all hd5 of a study.\n (default: None)\n\nusage: app.py from-arguments use-hd5 use-manifest [-h] --manifest_location\n MANIFEST_LOCATION\n\noptional arguments:\n -h, --help show this help message and exit\n\n --manifest_location MANIFEST_LOCATION\n Path to manifest CSV file.\n (default: None)\n\nusage: app.py from-arguments use-hd5 use-origin [-h]\n [--dataset_origin DATASET_ORIGIN [DATASET_ORIGIN ...]]\n\noptional arguments:\n -h, --help show this help message and exit\n\n --dataset_origin DATASET_ORIGIN [DATASET_ORIGIN ...]\n List of the origins of the slide dataset the patches are generated from. Should be from (\u0027ovcare\u0027, \u0027tcga\u0027, \u0027german\u0027, \u0027other\u0027). (For multiple origins, works for TCGA+ovcare. Mix of Other origins must be tested.)\n (default: [\u0027ovcare\u0027])\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTODO: there is a chance --balance_patches sets empty groups. This happens if any patches for some (group, category) is zero.\nTODO: in create_groups, variables are named \u0027subtype\u0027 instead of \u0027category\u0027. That leads to confusion.\nTODO: further explain how --max_patient_patches works in description.\nTODO: make GroupCreator.group_summary() return DataFrame. Test against DataFrame output.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1550983020.0 + "updated_at": 1698864845.0 }, { "data_format": 2, - "description": "Snakemake pipeline for genome coassembly", + "description": "Singularity recipes for singularity images containing ANTs (Advanced Normalization Tools).", "filenames": [ - "Singularity.def" + "Singularity.2.2.0" ], - "full_name": "EI-CoreBioinformatics/CoassemblyPipeline", + "full_name": "MPIB/singularity-ants", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCoassemblyPipeline\u003c/h2\u003e\u003ca id=\"user-content-coassemblypipeline\" class=\"anchor\" aria-label=\"Permalink: CoassemblyPipeline\" href=\"#coassemblypipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSnakemake pipeline to generate and QC genome (co)assemblies from single-cell (e.g., G\u0026amp;T-Seq) or metagenome data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTHIS PIPELINE DOES NOT TRIM READS. IT EXPECTS THE INPUT READS TO HAVE ALREADY BEEN ADAPTER TRIMMED\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe input to this pipeline is a comma separated file. The first line specifies the name of the coassembly, the following lines contain 4 comma separated values to specify the gDNA and cDNA libraries:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esample name\u003c/li\u003e\n\u003cli\u003elibrary type (gDNA or cDNA)\u003c/li\u003e\n\u003cli\u003epath to forward reads\u003c/li\u003e\n\u003cli\u003epath to reverse reads\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSample names should not contain periods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExample input file: \u003ccode\u003einput.csv\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCOASSEMBLY_NAME\nSample1,gDNA,Sample1_gDNA_R1.fq.gz,Sample1_gDNA_R2.fq.gz\nSample1,cDNA,Sample1_cDNA_R1.fg.gz,Sample1_cDNA_R2.fq.gz\nSample2,gDNA,Sample2_gDNA_R1.fq.gz,Sample2_gDNA_R2.fq.gz\nSample2,cDNA,Sample2_cDNA_R1.fg.gz,Sample2_cDNA_R2.fq.gz\nSample3,gDNA,Sample3_gDNA_R1.fq.gz,Sample3_gDNA_R2.fq.gz\nSample4,cDNA,Sample4_cDNA_R1.fq.gz,Sample4_cDNA_R2.fq.gz\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample config file: \u003ccode\u003econfig.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# File listing input reads, better to set this at command line with \"--config input=XXX.csv\"\n# Should use absolute paths\ninput:\n\n# Mode to run SPAdes in - \"sc\" for single-cell or \"meta\" for metagenome\nassembly_type: [sc / meta]\n\n# Optional tools to run\nrun_checkm: true\nrun_busco: true\n\n# SPAdes parameters\nkmers: \"21,33,55,77\"\nmin_scaffold_length: 1000 # for bbtools reformat\n\n# BUSCO parameters\nbusco_version: 3\nbusco_database: XXXXX\n\n# MetaBat2 parameters\nmetabat2_min_contig: 1500\n\n# Tiara parameters\ntiara_min_length: 1000\n\n# EukRep parameters\neukrep_min_length: 1000\n\n# Blobtools parameters\ndiamond_database: XXXXX\nmegablast_database: XXXXX\nnodesdb: XXXXX\nn_chunks: 4 # chunk the fasta file into N splits for the megablastn search; randomly to balance as input fasta will be sorted by sequence length\n\n# CAT parameters\ncat_database: XXXXX\ntaxonomy_dir: XXXXX\ndiamond_path: XXXXX\n\n# pr2 database\npr2_database: XXXXX\n\n# cleanup options\ncleanup_spades: true\ncleanup_cat: true\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample command to launch snakemake:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -p --config input=input.csv -j 20 --retries 1 --latency-wait 60 --snakefile CoassemblyPipeline.smk --cluster-config cluster.json --cluster \"sbatch -p {cluster.partition} -c {cluster.c} --mem={cluster.memory} --job-name={cluster.J} --time={cluster.time} --exclude={cluster.exclude} -o slurm_logs/slurm.{cluster.J}.%j.out\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eThis pipeline runs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egenome assembly using \u003ca href=\"https://github.com/ablab/spades\"\u003eSPAdes\u003c/a\u003e in single-cell or metagenome mode\u003c/li\u003e\n\u003cli\u003eannotation of rRNA genes using \u003ca href=\"https://github.com/tseemann/barrnap\"\u003ebarrnap\u003c/a\u003e, followed by classification based on comparison with the \u003ca href=\"https://github.com/pr2database/pr2database\"\u003epr2\u003c/a\u003e database\u003c/li\u003e\n\u003cli\u003etaxonomic classification using:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DRL/blobtools\"\u003eBlobtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dutilh/CAT\"\u003eCAT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/patrickwest/EukRep\"\u003eEukRep\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ibe-uw/tiara/\"\u003eTiara\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eassembly stats using \u003ca href=\"https://github.com/ablab/quast\"\u003eQUAST\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ecoverage stats based on mapping reads with \u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e and \u003ca href=\"http://qualimap.conesalab.org/\" rel=\"nofollow\"\u003eQualiMap\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003econtig binning using \u003ca href=\"https://bitbucket.org/berkeleylab/metabat\" rel=\"nofollow\"\u003eMetaBAT2\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gitlab.com/ezlab/busco\" rel=\"nofollow\"\u003eBUSCO\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Ecogenomics/CheckM\"\u003eCheckM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eOptionally, aligns RNA-Seq reads using \u003ca href=\"https://github.com/DaehwanKimLab/hisat2\"\u003ehisat2\u003c/a\u003e and assembles transcripts using \u003ca href=\"https://github.com/gpertea/stringtie\"\u003eStringTie2\u003c/a\u003e. RNA-Seq reads can come from any source (e.g., single-cell, metatranscriptome, or isolate RNA-Seq)\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003eThe Singularity definition file \u003ccode\u003eSingularity.def\u003c/code\u003e contains most of the required software. Additional software requirements not included in the definition file (due to compatbility issues):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DRL/blobtools\"\u003eblobtools (v1.1.1)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gitlab.com/ezlab/busco\" rel=\"nofollow\"\u003ebusco (v3/4/5)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/patrickwest/EukRep\"\u003eEukRep\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ibe-uw/tiara\"\u003eTiara\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eExample DAG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"example_DAG.png\"\u003e\u003cimg src=\"example_DAG.png\" alt=\"Example Snakemake DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-ants\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-ants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-ants\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/660\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipes for base-images containing ANTs (Advanced Normalization Tools). You can get the \u003ca href=\"https://github.com/ANTsX/ANTs\"\u003ecode and documentation for ANTs through GitHub\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eANTs is pulled from its \u003ca href=\"https://github.com/ANTsX/ANTs\"\u003egithub repository\u003c/a\u003e and build using cmake.\u003c/li\u003e\n\u003cli\u003ecmake and its dependencies are installed through the debian repositories via \u003ccode\u003eapt-get install\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAt the end of the build process the image is cleaned up by:\n\u003cul\u003e\n\u003cli\u003eremoving cmake and its dependencies through \u003ccode\u003eapt-get purge\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003edeleting the package cache,\u003c/li\u003e\n\u003cli\u003edeleting the folder containing the cloned repository.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e$ANTSPATH\u003c/code\u003e and \u003ccode\u003e$PATH\u003c/code\u003e are set according to the \u003ca href=\"https://github.com/ANTsX/ANTs/wiki/Compiling-ANTs-on-Linux-and-Mac-OS\"\u003ecompilation guide\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eANTs executable should therefore be directly available.\u003c/li\u003e\n\u003cli\u003eSuccessful build and \u003ccode\u003e$PATH\u003c/code\u003e setup is tested through calling antsRegistration with the -h flag.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1724235234.0 + "updated_at": 1519211227.0 }, { "data_format": 2, - "description": null, + "description": "Singularity Container for SDAPS", "filenames": [ - "Singularity" + "Singularity.sdaps" ], - "full_name": "ddbj/singularity_apache_jekyll", + "full_name": "williamssanders/sdaps", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity image\u306e\u30d3\u30eb\u30c9\u003c/h1\u003e\u003ca id=\"user-content-singularity-image\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" aria-label=\"Permalink: singularity image\u306e\u30d3\u30eb\u30c9\" href=\"#singularity-image\u306e\u30d3\u30eb\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build ubuntu-18.04-apache2-jekyll.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejekyll\u3067\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u3059\u308b\u30bd\u30fc\u30b9\u306e\u6e96\u5099\u003c/h1\u003e\u003ca id=\"user-content-jekyll\u3067\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u3059\u308b\u30bd\u30fc\u30b9\u306e\u6e96\u5099\" class=\"anchor\" aria-label=\"Permalink: jekyll\u3067\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u3059\u308b\u30bd\u30fc\u30b9\u306e\u6e96\u5099\" href=\"#jekyll\u3067\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u3059\u308b\u30bd\u30fc\u30b9\u306e\u6e96\u5099\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u9069\u5f53\u306a\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u4f5c\u6210\u3057\u3001jekyll\u306e\u30c7\u30fc\u30bf\u3092\u305d\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u5185\u306b\u7f6e\u304f\u3002\u003c/p\u003e\n\u003cp\u003estart_container-build.sh \u307e\u305f\u306f start_container-serve.sh \u306e SOURCE_DIR\u5909\u6570\u306e\u5024\u3092\u30c7\u30fc\u30bf\u3092\u5165\u308c\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d1\u30b9\u306b\u4fee\u6b63\u3059\u308b\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity instance \u306e\u8d77\u52d5\u003c/h1\u003e\u003ca id=\"user-content-singularity-instance-\u306e\u8d77\u52d5\" class=\"anchor\" aria-label=\"Permalink: singularity instance \u306e\u8d77\u52d5\" href=\"#singularity-instance-\u306e\u8d77\u52d5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ejekyll\u3092build\u3067\u5b9f\u884c\u3057\u3066apache2\u306eDocumentRoot\u306b\u9759\u7684\u30d5\u30a1\u30a4\u30eb\u3092\u51fa\u529b\u3055\u305b\u3001\u751f\u6210\u3057\u305f\u30b5\u30a4\u30c8\u3092apache2\u3067\u516c\u958b\u3059\u308b\u5834\u5408\u306fstart_container-build.sh\u3092\u5b9f\u884c\u3059\u308b\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container-build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ejekyll\u3092serve\u3067\u5b9f\u884c\u3057\u3001jekyll\u306ehttp\u30b5\u30fc\u30d0\u3092apache2\u306e\u30ea\u30d0\u30fc\u30b9\u30d7\u30ed\u30ad\u30b7\u3067\u53d7\u3051\u308b\u5834\u5408\u306fstart_container-serve.sh\u3092\u5b9f\u884c\u3059\u308b\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container-serve.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3044\u305a\u308c\u306e\u5834\u5408\u3082httpd.conf.build\u307e\u305f\u306fhttpd.conf.serve\u306eListen\u30c7\u30a3\u30ec\u30af\u30c6\u30a3\u30d6\u306bsingularity instance\u3067\u4f7f\u7528\u3059\u308b\u30dd\u30fc\u30c8\u756a\u53f7\u3092\u8a2d\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sdaps_container\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#sdaps_container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esdaps_container\u003c/h1\u003e\n\u003cp\u003eSingularity Container for SDAPS\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eSingularity-Hub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://williamssanders/sdaps:sdaps\n./williamssanders-sdaps-master-sdaps.simg setup /fastscratch/ssander/sdaps/example_2 example.tex\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eBuild the container:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build -c sdaps.simg Singularity.sdaps\n./sdaps.simg setup /fastscratch/ssander/sdaps/example_1 example.tex\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 0, "topics": [], - "updated_at": 1593769075.0 + "updated_at": 1553200061.0 }, { "data_format": 2, - "description": "OpenEXR in a Singularity container", + "description": null, "filenames": [ - "Singularity.2.2", - "Singularity" + "Singularity.tensorflow_venv", + "Singularity.tensorflow-1.14" ], - "full_name": "OSC/sa_singularity_openexr", + "full_name": "MuhsinFatih/singularityimages", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity OpenEXR\u003c/h1\u003e\u003ca id=\"user-content-singularity-openexr\" class=\"anchor\" aria-label=\"Permalink: Singularity OpenEXR\" href=\"#singularity-openexr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3586\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.openexr.com/\" rel=\"nofollow\"\u003eOpenEXR\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003eopenexr.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build openexr.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name openexr.sif shub://OSC/sa_singularity_openexr\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRender .EXR image\u003c/h3\u003e\u003ca id=\"user-content-render-exr-image\" class=\"anchor\" aria-label=\"Permalink: Render .EXR image\" href=\"#render-exr-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eexrdisplay\u003c/code\u003e command is launched using the command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e openexr.sif exrdisplay -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e openexr.sif exrdisplay rendertest_0001.exr\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 0, "topics": [], - "updated_at": 1569951214.0 + "updated_at": 1565383245.0 }, { "data_format": 2, - "description": "A public Docker container for WRF 3.8.1 with Fitch patch", + "description": "gpu image for folding at home", "filenames": [ "Singularity" ], - "full_name": "federatedcloud/Docker-WRF-3.8.1-Fitch", + "full_name": "slaclab/folding-at-home-gpu", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker-WRF-3.8.1-Fitch\u003c/h1\u003e\u003ca id=\"user-content-docker-wrf-381-fitch\" class=\"anchor\" aria-label=\"Permalink: Docker-WRF-3.8.1-Fitch\" href=\"#docker-wrf-381-fitch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA public Docker container for WRF 3.8.1 with Fitch patches.\u003c/p\u003e\n\u003cp\u003eDocker image: \u003ca href=\"https://hub.docker.com/repository/docker/cornellcac/wrf\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image: \u003ca href=\"https://singularity-hub.org/collections/5227\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuild\u003c/h1\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Docker container can be built using the script \u003ca href=\"https://github.com/federatedcloud/Docker-WRF-3.8.1-Fitch/blob/main/docker-build.sh\"\u003e\u003ccode\u003edocker-build.sh\u003c/code\u003e\u003c/a\u003e,\nwhich will produce an output file named \u003ccode\u003ebuild_output.txt\u003c/code\u003e (included in the\n\u003ca href=\"https://github.com/federatedcloud/Docker-WRF-3.8.1-Fitch/blob/main/.gitignore\"\u003e\u003ccode\u003e.gitignore\u003c/code\u003e\u003c/a\u003e).\nThe build will take some time, so it is recommended to use a terminal multiplexer, such as tmux.\nOne can view the full output at any time using a text editor to open \u003ccode\u003ebuild_output.txt\u003c/code\u003e.\nTo determine what step the build it is at, one can do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat build_output.txt | grep Step | tail -n 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will print the current command Docker is executing to build the container.\nTo view Docker build errors, try:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat build_output.txt | grep ERROR\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is actually the last command in the \u003ccode\u003edocker-build.sh\u003c/code\u003e script, so Docker build\nerrors will be listed upon completion. If there are no errors listed the container\nwas built successfully. Code and dependencies should be checked independently of\na Docker build error list.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePatches\u003c/h2\u003e\u003ca id=\"user-content-patches\" class=\"anchor\" aria-label=\"Permalink: Patches\" href=\"#patches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince there are some \u003ca href=\"https://www2.mmm.ucar.edu/wrf/users/wrfv3.8/known-prob-3.8.1.html\" rel=\"nofollow\"\u003eknown problems with WRF 3.8.1\u003c/a\u003e,\nwe have implemented the following patches provided by the WRF Users page:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www2.mmm.ucar.edu/wrf/src/fix/module_radiation_driver.F.fix-for-v3.8.1.tar.gz\" rel=\"nofollow\"\u003e\u003ccode\u003emodule_radiation_driver.F\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www2.mmm.ucar.edu/wrf/src/fix/module_cu_g3_random_seed_fix.F.gz\" rel=\"nofollow\"\u003e\u003ccode\u003emodule_cu_g3.F\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www2.mmm.ucar.edu/wrf/src/fix/Registry.EM_COMMON.v381.tar.gz\" rel=\"nofollow\"\u003e\u003ccode\u003eRegistry.EM_COMMON\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll of these patches, as well as our custom patches, are included in the repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling\u003c/h2\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWRF and WPS compilation is performed in bash. Please see the \u003ca href=\"https://github.com/federatedcloud/Docker-WRF-3.8.1-Fitch/blob/main/Dockerfile\"\u003eDockerfile\u003c/a\u003e\nfor full commands.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-folding-at-home-gpu\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#folding-at-home-gpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efolding-at-home-gpu\u003c/h1\u003e\n\u003cp\u003egpu image for folding at home\u003c/p\u003e\n\u003cp\u003esimple merge of nvidia cl image with folding at home v7.5.1 to enable gpu processing.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 6, "topics": [], - "updated_at": 1620413771.0 + "updated_at": 1584940583.0 }, { "data_format": 2, - "description": "singularity def file for flair(fluka)", + "description": null, "filenames": [ - "flair.def", - "flair-cern.def" + "Singularity.latest" ], - "full_name": "ifurther/flair-def", + "full_name": "bioexcel/biobb_haddock", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/59df560cf8b0622113f818a5a58a208df19e819ebe6795409cacdad4c9514fea/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f62696f62622d686164646f636b2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"\" data-canonical-src=\"https://readthedocs.org/projects/biobb-haddock/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/biobb_haddock\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a477fdb1fd9bc9eb7ffa6cae6a019c6d4c3902fd468b3126f1b78e56c7dcff83/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://quay.io/repository/biocontainers/biobb_haddock\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca418e4db0b3de91a09a5df4a59446da015b6164598a8bc255918e911484f84f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d517561792e696f2d626c7565\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docker-Quay.io-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-biobb_haddock\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#biobb_haddock\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebiobb_haddock\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003ebiobb_haddock is the Biobb module collection to compute information-driven flexible protein-protein docking.\nBiobb (BioExcel building blocks) packages are Python building blocks that\ncreate new layer of compatibility and interoperability over popular\nbioinformatics tools.\nThe latest documentation of this package can be found in our readthedocs site:\n\u003ca href=\"http://biobb-haddock.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003elatest API documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-version\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h3\u003e\n\u003cp\u003ev3.8.0 2022.1\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eUsing PIP:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eImportant:\u003c/strong\u003e PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install \"biobb_haddock\u0026gt;=3.8.0\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: \u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing ANACONDA:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda install -c bioconda \"biobb_haddock\u0026gt;=3.8.0\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: With conda installation BioBBs can be used with the \u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e and the \u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing DOCKER:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull quay.io/biocontainers/biobb_haddock:3.8.0--pyhdfd78af_0\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run quay.io/biocontainers/biobb_haddock:3.8.0--pyhdfd78af_0 \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing SINGULARITY:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMacOS users\u003c/strong\u003e: it\u0027s strongly recommended to avoid Singularity and use \u003cstrong\u003eDocker\u003c/strong\u003e as containerization system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name biobb_haddock.sif shub://bioexcel/biobb_haddock\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec biobb_haddock.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe command list and specification can be found at the \u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#copyright--licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2022 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2022 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 9, "topics": [], - "updated_at": 1619686613.0 + "updated_at": 1654724526.0 }, { "data_format": 2, - "description": "Singularity definition files for various projects", + "description": null, "filenames": [ - "miniconda/Singularity", - "hauntedhouse/Singularity", - "hauntedhouse_freesurfer/Singularity" + "src/Singularity.def" ], - "full_name": "mvdoc/singularity-def", + "full_name": "currocam/BiRC-Gaussian-graphical-models", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1495630055.0 + "updated_at": 1698331009.0 }, { "data_format": 2, - "description": "Def File of Singularity", + "description": null, "filenames": [ - "def/vae-mnist.def", - "def/lafin.def", - "def/stargan.def", - "def/wav2pix.def", - "def/singan.def", - "def/contextual-attention.def", - "def/edge-connect.def", - "def/sc-fegan.def" + "anaconda2/Singularity.5.3.0", + "anaconda2/Singularity", + "anaconda3/Singularity.5.3.0", + "anaconda3/Singularity", + "gephi/Singularity.0.9.1", + "gephi/Singularity.0.9.2", + "jupyter/Singularity", + "jupyter/Singularity.4.4.0", + "rstudio/Singularity", + "rstudio/Singularity.3.5.1", + "rstudio/Singularity.3.4.4" ], - "full_name": "Nahuel-Mk2/def-space", + "full_name": "OdumInstitute/singularity-dev-images", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edef-space\u003c/h1\u003e\u003ca id=\"user-content-def-space\" class=\"anchor\" aria-label=\"Permalink: def-space\" href=\"#def-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository is def-space for Singularity\u003c/p\u003e\n", + "readme": "\u003ch1 id=\"user-content-singularity-dev-images\"\u003e\u003ca class=\"heading-link\" href=\"#singularity-dev-images\"\u003esingularity-dev-images\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1606189900.0 + "updated_at": 1556725305.0 }, { "data_format": 2, "description": null, "filenames": [ - "scripts/singularity_container_scripts/Singularity.def" + "misc/releases/21.12/Singularity.21.12", + "misc/releases/22.12/Singularity.22.12", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/22.06/Singularity.22.06" ], - "full_name": "aperonalope/TFM_computacional", + "full_name": "salome-eriksson/downward-unsolvability", "latest_release": null, - "readme": "\u003cp\u003eThis repository contains all the scripts and files that were used for the masters thesis titled \"vscRNAFinder: A biochemical-bioinformatic pipeline to detect small circular RNAs\"\u003c/p\u003e\n\u003cp\u003eVscRNAFinder is a bioquemical-bioinformatic pipeline aimed at detecting very small circle RNAs by detecting the repetitive units of rolling circle tandem repeats and aligning them to the genome.\nIt is a nextflow pipeline that uses a singularity container\u003c/p\u003e\n\u003cp\u003eThe scipts folder contain all the scripts used either for creating the graphs present on the masters thesis manuscript or the ones employed by nextflow\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2023 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 10, Clang 12\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 22.1.1 and SoPlex 6.0.3+. On Ubuntu we\ntest both CPLEX and SoPlex. On Windows we currently only test CPLEX,\nand on macOS we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2023 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2023 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2023 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2023 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2023 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2023 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2023 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2023 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2018-2020, 2023 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2021-2023 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2022-2023 Remo Christen\u003c/li\u003e\n\u003cli\u003e2023 Simon Dold\u003c/li\u003e\n\u003cli\u003e2023 Claudia S. Grundke\u003c/li\u003e\n\u003cli\u003e2023 Emanuele Tirendi\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1724876381.0 + "updated_at": 1696843043.0 }, { "data_format": 2, - "description": "Counter RNA seq Window (CRAW) compute and visualize the coverage of RNA seq experiment.", + "description": null, "filenames": [ - "Singularity.1.0", - "Singularity" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/22.12/Singularity.22.12", + "misc/releases/latest/Singularity", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06" ], - "full_name": "C3BI-pasteur-fr/craw", + "full_name": "ipc2023-classical/planner23", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCRAW_singularity\u003c/h1\u003e\u003ca id=\"user-content-craw_singularity\" class=\"anchor\" aria-label=\"Permalink: CRAW_singularity\" href=\"#craw_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity definition files for Counter RnAseq Window\u003c/p\u003e\n\u003cp\u003eCRAW compute and visualize the coverage of RNA seq experiment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHomepage project: \u003ca href=\"https://gitlab.pasteur.fr/bneron/craw\" rel=\"nofollow\"\u003ehttps://gitlab.pasteur.fr/bneron/craw\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFull documentation: \u003ca href=\"http://bneron.pages.pasteur.fr/craw\" rel=\"nofollow\"\u003ehttp://bneron.pages.pasteur.fr/craw\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 12, + "subscribers_count": 1, "topics": [], - "updated_at": 1554456657.0 + "updated_at": 1688990723.0 + }, + { + "data_format": 2, + "description": "Implementation of the Property-Directed Reachability algorithm in the Fast Downward planning system. Implementation of my masters thesis.", + "filenames": [ + "misc/releases/19.06/Singularity.19.06", + "misc/releases/latest/Singularity", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06" + ], + "full_name": "Tiim/fast-downward-pdr", + "latest_release": "pdr-final", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [ + "fast-downward", + "pdr-algorithm", + "problem-solving" + ], + "updated_at": 1683295351.0 }, { "data_format": 2, "description": null, "filenames": [ - "nextflow/modules/SVG/Singularity.def", - "nextflow/modules/SCENVI/Singularity.def" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/latest/Singularity", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06" ], - "full_name": "joechanlab/Xenium_lung_NE_Plasticity_2024", + "full_name": "IBM/shortest-optimal-downward", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eXenium Lung NE Plasticity\u003c/h1\u003e\u003ca id=\"user-content-xenium-lung-ne-plasticity\" class=\"anchor\" aria-label=\"Permalink: Xenium Lung NE Plasticity\" href=\"#xenium-lung-ne-plasticity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the nextflow pipeline and scripts to analyze Xenium data for lung NE plasticity project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!NOTE]\nIf you are new to Nextflow and nf-core, please refer to \u003ca href=\"https://nf-co.re/docs/usage/installation\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e on how to set-up Nextflow. Make sure to \u003ca href=\"https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline\" rel=\"nofollow\"\u003etest your setup\u003c/a\u003e with \u003ccode\u003e-profile test\u003c/code\u003e before running the workflow on actual data.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eFirst, prepare a samplesheet with your input data that looks as follows, where each row contains the sample name, path to Xenium Ranger output.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esamplesheet.csv\u003c/code\u003e:\u003c/p\u003e\n\u003cpre lang=\"csv\"\u003e\u003ccode\u003esample, xenium\nCONTROL_REP1, xenium_output_folder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, prepare a parameter YAML file that looks as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eparams.yml\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003esamplesheet\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e../results/samplesheet.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e path to the sample sheet\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutdir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e../results/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output folder\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eexperiment\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eXenium\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eQC\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003egene_information_file\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e../data/annotation.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e gene annotation file\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eextra_markers\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePHOX2B\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e custom markers to plot\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_memory\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e36.GB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e memory\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_cpus\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cpu\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow, you can run the pipeline. For local run on HPC with singularity installed, execute the following command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run ./main.nf \\\n -profile singularity \\\n -params-file ./params.yml \\\n -w ./work/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using MSKCC lilac, you can use the pre-defined \u003ccode\u003elilac\u003c/code\u003e profile that uses the LSF executor.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run ./main.nf \\\n -profile lilac \\\n -params-file ./params.yml \\\n -w ./work/\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1 id=\"user-content-a-planner-for-shortest-cost-optimal-planning-problem\"\u003e\u003ca class=\"heading-link\" href=\"#a-planner-for-shortest-cost-optimal-planning-problem\"\u003eA planner for shortest cost optimal planning problem\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003ch2 id=\"user-content-the-code-implements-two-approaches-in-two-separate-branches\"\u003e\u003ca class=\"heading-link\" href=\"#the-code-implements-two-approaches-in-two-separate-branches\"\u003eThe code implements two approaches in two separate branches\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eCost-algebraic A* in branch \u003ccode\u003eshortest-optimal-cost-algebra\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCost transformation with regular A* in branch \u003ccode\u003eshortest-optimal-cost-transformation\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCiting:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{katz-et-al-socs2022,\n title = \"On Producing Shortest Cost-Optimal Plans\",\n author = \"Michael Katz and Gabriele R{\\\"o}ger and Malte Helmert\",\n booktitle = \"Proceedings of the 15th Annual Symposium on\n Combinatorial Search (SoCS 2022)\",\n publisher = \"{AAAI} Press\",\n year = \"2022\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1728663251.0 + "updated_at": 1652293206.0 }, { "data_format": 2, - "description": null, + "description": "geant4 in contianer.", "filenames": [ - "Singularity-v0.1", "Singularity" ], - "full_name": "fksato/caffe2_singularity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecaffe2_singularity\u003c/h1\u003e\u003ca id=\"user-content-caffe2_singularity\" class=\"anchor\" aria-label=\"Permalink: caffe2_singularity\" href=\"#caffe2_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCaffe2 singularity environment for facebook research video models\u003c/h2\u003e\u003ca id=\"user-content-caffe2-singularity-environment-for-facebook-research-video-models\" class=\"anchor\" aria-label=\"Permalink: Caffe2 singularity environment for facebook research video models\" href=\"#caffe2-singularity-environment-for-facebook-research-video-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecomplete installation guide:\n\u003ca href=\"https://github.com/facebookresearch/VMZ/blob/master/tutorials/Installation_guide.md\"\u003ehttps://github.com/facebookresearch/VMZ/blob/master/tutorials/Installation_guide.md\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUbuntu Version\u003c/h2\u003e\u003ca id=\"user-content-ubuntu-version\" class=\"anchor\" aria-label=\"Permalink: Ubuntu Version\" href=\"#ubuntu-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e16.04\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOfficial Nvidia docker image\u003c/h2\u003e\u003ca id=\"user-content-official-nvidia-docker-image\" class=\"anchor\" aria-label=\"Permalink: Official Nvidia docker image\" href=\"#official-nvidia-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003envidia/cuda:10.0-cudnn7-devel-ubuntu16.04\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested on\u003c/h2\u003e\u003ca id=\"user-content-tested-on\" class=\"anchor\" aria-label=\"Permalink: Tested on\" href=\"#tested-on\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGPU architecture: Pascal\u003c/p\u003e\n\u003cp\u003eCuda version: 10.0\u003c/p\u003e\n\u003cp\u003eCudnn version: 7.4.2\u003c/p\u003e\n\u003cp\u003eCompute Compatibility: 6.0 (TORCH_CUDA_ARCH_LIST)\u003c/p\u003e\n", + "full_name": "ifurther/geant4-docker", + "latest_release": "11.0.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-geant4-docker\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#geant4-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egeant4-docker\u003c/h1\u003e\n\u003cp\u003egeant4 in contianer.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1571015501.0 + "updated_at": 1638461581.0 }, { "data_format": 2, - "description": "Create a NeuroDebian Singularity image with all Python packages I need.", + "description": null, "filenames": [ - "docker/NeuroDebian/Singularity", - "docker/python2.7/Singularity", - "docker/python3.6/Singularity" + "diffuser/Singularity.def" ], - "full_name": "feilong/neurodebian-singularity", + "full_name": "ShravanRavi2002/Diffusion_RL_RectifiedFlow", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1502647456.0 + "updated_at": 1680977053.0 }, { "data_format": 2, - "description": "SParsity-based Analysis of Reliable K-hubness (SPARK) library optimized to run on HPCs and computing clusters", + "description": "Singularity container with stack for LArCV/pytorch", "filenames": [ - "for_build/containers/Singularity" + "Singularity" ], - "full_name": "multifunkim/spark-hpc", + "full_name": "LArbys/singularity-larbys-pytorch", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003espark-hpc\u003c/h1\u003e\u003ca id=\"user-content-spark-hpc\" class=\"anchor\" aria-label=\"Permalink: spark-hpc\" href=\"#spark-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSParsity-based Analysis of Reliable K-hubness (SPARK) library optimized to run on HPCs and computing clusters\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-larbys-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-larbys-pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-larbys-pytorch\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1565355677.0 + "updated_at": 1527174404.0 }, { "data_format": 2, - "description": "Imputation workflow with sanger impuation server, originally prepared for sceQTL-Gen consortium but copied here on 30 August, 2021 when updating to hg38 for sceQTL-Gen consortium", + "description": null, "filenames": [ - "Singularity.WGpipeline", - "Singularity.Imputation" + "Singularity.4", + "Singularity.2", + "Singularity.update", + "Singularity.0", + "Singularity.3", + "Singularity.1" ], - "full_name": "powellgenomicslab/Imputation_pipeline", + "full_name": "ddbj/singularity_R-3.6.3-CRAN-Bioconductor-packages", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWG1-pipeline-QC\u003c/h1\u003e\u003ca id=\"user-content-wg1-pipeline-qc\" class=\"anchor\" aria-label=\"Permalink: WG1-pipeline-QC\" href=\"#wg1-pipeline-qc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was built to assist with imputation of SNP genotype data. The data will be preprocessed with instructions for imputation on Sanger Imputation server and finally processing after impation.\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/powellgenomicslab/Imputation_pipeline/wiki\"\u003eWiki\u003c/a\u003e for information on running the QC pipeline.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ubuntu2004-lts--r-363--cran-packages--bioconductor-packages-\u306e-singularity\u30a4\u30e1\u30fc\u30b8\u30ec\u30b7\u30d4\u30d5\u30a1\u30a4\u30eb\" class=\"anchor\" aria-hidden=\"true\" href=\"#ubuntu2004-lts--r-363--cran-packages--bioconductor-packages-\u306e-singularity\u30a4\u30e1\u30fc\u30b8\u30ec\u30b7\u30d4\u30d5\u30a1\u30a4\u30eb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eubuntu20.04 LTS + R-3.6.3 + CRAN packages + Bioconductor packages \u306e singularity\u30a4\u30e1\u30fc\u30b8\u30ec\u30b7\u30d4\u30d5\u30a1\u30a4\u30eb\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.0 : ubuntu-20.04 LTS\u306bapt\u3067R\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u5f8c\u3001R\u3092\u524a\u9664\u003c/li\u003e\n\u003cli\u003eSingularity.1 : Singularity.0\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306bR-3.6.3\u3092\u30bd\u30fc\u30b9\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003cli\u003eSingularity.2 : Singularity.1\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306bCRAN, Bioconductor\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003cli\u003eSingularity.3 : Singularity.2\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306bCRAN\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003cli\u003eSingularity.4 : Singularity.3\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306bCRAN\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u6b8b\u308a\u3068Bioconductor\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003cli\u003eSingularity.update : Singularity.4\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u5185\u306eR\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u66f4\u65b0\u30fb\u65b0\u898f\u8ffd\u52a0\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u30a4\u30e1\u30fc\u30b8\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u30a4\u30e1\u30fc\u30b8\u306e\u30d3\u30eb\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u30a4\u30e1\u30fc\u30b8\u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build ubuntu-20.04-R-install-base.simg Singularity.0 2\u0026gt;\u0026amp;1 | tee log.0\n$ sudo singularity build ubuntu-20.04-R-3.6.3.simg Singularity.1 2\u0026gt;\u0026amp;1 | tee log.1\n$ sudo singularity build ubuntu-20.04-R-3.6.3-2.simg Singularity.2 2\u0026gt;\u0026amp;1 | tee log.2\n$ sudo singularity build ubuntu-20.04-R-3.6.3-CRAN-packages.simg Singularity.3 2\u0026gt;\u0026amp;1 | tee log.3\n$ sudo singularity build ubuntu-20.04-R-3.6.3-CRAN-Bioconductor-packages.simg Singularity.4 2\u0026gt;\u0026amp;1 | tee log.4\n$ sudo singularity build ubuntu-20.04-R-3.6.3-CRAN-Bioconductor-packages-update.simg Singularity.update 2\u0026gt;\u0026amp;1 | tee log.update\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity.update\u3092\u4f7f\u3063\u3066\u30a4\u30e1\u30fc\u30b8\u3092\u30d3\u30eb\u30c9\u3057\u305f\u969b\u306e\u30ed\u30b0\u3067\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u5931\u6557\u3057\u305f\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u628a\u63e1\u3059\u308b\u3002\n\u4e0d\u8db3\u3057\u3066\u3044\u308b\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3092Singularity.update\u306b\u8ffd\u52a0\u3057\u3001\u518d\u5ea6\u30a4\u30e1\u30fc\u30b8\u306e\u30d3\u30eb\u30c9\u3092\u884c\u3046\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u751f\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306e\u30b5\u30a4\u30ba\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u751f\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306e\u30b5\u30a4\u30ba\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u751f\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306e\u30b5\u30a4\u30ba\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls -lh\n-rwxr-xr-x 1 root root 2.1G 6\u6708 5 14:29 ubuntu-20.04-R-3.6.3-2.simg\n-rwxr-xr-x 1 root root 143G 6\u6708 12 15:17 ubuntu-20.04-R-3.6.3-CRAN-Bioconductor-packages.simg\n-rwxr-xr-x 1 root root 17G 6\u6708 8 10:59 ubuntu-20.04-R-3.6.3-CRAN-packages.simg\n-rwxr-xr-x 1 root root 1.4G 5\u6708 28 14:56 ubuntu-20.04-R-3.6.3.simg\n-rwxr-xr-x 1 root root 562M 5\u6708 28 12:34 ubuntu-20.04-R-install-base.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u306a\u304b\u3063\u305fr\u30d1\u30c3\u30b1\u30fc\u30b8\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u306a\u304b\u3063\u305fr\u30d1\u30c3\u30b1\u30fc\u30b8\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u306a\u304b\u3063\u305fR\u30d1\u30c3\u30b1\u30fc\u30b8\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u306a\u3044\n\u003cul\u003e\n\u003cli\u003eBioconductor (1)\n\u003cul\u003e\n\u003cli\u003echarm : Bioconductor 3.10\u306b\u542b\u307e\u308c\u3066\u3044\u306a\u3044\u305f\u3081\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u4f9d\u5b58\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u4e0d\u8db3\n\u003cul\u003e\n\u003cli\u003eCRAN (11)\n\u003cul\u003e\n\u003cli\u003eBALD\uff1a\u003ca href=\"http://mcmc-jags.sourceforge.net\" rel=\"nofollow\"\u003eJAGS\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eBRugs\uff1a\u003ca href=\"http://www.openbugs.net/w/FrontPage\" rel=\"nofollow\"\u003eOpenBUGS\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eOpenCL\uff1aNVIDIA CUDA\u7b49\u3067\u306eOpenCL\u30e9\u30f3\u30bf\u30a4\u30e0\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eROracle\uff1aOracle Instant Client or Oracle Database Client\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eRcplex\uff1aIBM ILOG CPLEX\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eRsymphony\uff1a\u003ca href=\"https://projects.coin-or.org/SYMPHONY\" rel=\"nofollow\"\u003eSYMPHONY\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003ecplexAPI\uff1aIBM ILOG CPLEX\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003ekmcudaR\uff1aNVIDIA CUDA\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eqtbase\uff1aQt 4.x\u304c\u5fc5\u8981\u3002ubuntu 20.04\u306eapt\u30ea\u30dd\u30b8\u30c8\u30ea\u306b\u5165\u3063\u3066\u3044\u306a\u3044\u3002\u003c/li\u003e\n\u003cli\u003erLindo\uff1a\u003ca href=\"https://www.lindo.com/\" rel=\"nofollow\"\u003eLindo API\u003c/a\u003e\u304c\u5fc5\u8981\u3002LINDOAPI_HOME\u3092\u8a2d\u5b9a\u305b\u3088\u3002\u003c/li\u003e\n\u003cli\u003erunjags\uff1a\u003ca href=\"http://mcmc-jags.sourceforge.net\" rel=\"nofollow\"\u003eJAGS\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBioconductor\uff0811\uff09\n\u003cul\u003e\n\u003cli\u003eChemineOB\uff1a\u003ca href=\"http://openbabel.org/wiki/Main_Page\" rel=\"nofollow\"\u003eOpen Babel\u003c/a\u003e \u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eSharedObject\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003cli\u003emlm4omics\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003cli\u003ersbml\uff1alibsbml\u304c\u5fc5\u8981\uff08libsbml5-dev\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u304c\u9055\u3046\u3088\u3046\u3060\uff09\u3002\u003c/li\u003e\n\u003cli\u003exps\uff1a\u003ca href=\"https://root.cern.ch/releases\" rel=\"nofollow\"\u003eroot_v5.34.36\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eRcwl\uff1acwltool\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u304c\u3001cwlversion\u306e\u5224\u5b9a\u306b\u5931\u6557\u3057\u3066\u3044\u308b\u3002\u003c/li\u003e\n\u003cli\u003epermGPU\uff1aNVIDIA CUDA\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eMSGFplus\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003cli\u003escAlign\uff1atensorflow\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u3063\u3066tensorflow\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3002\u003c/li\u003e\n\u003cli\u003eMoonlightR\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003cli\u003eRariant\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u4f9d\u5b58R\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u4e0d\u8db3 (20)\n\u003cul\u003e\n\u003cli\u003eBANOVA\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eBayesPostEst\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eIsotopeR\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003ePortfolioOptim\uff1aRsymphony\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eROI.plugin.cplex\uff1aRcplex\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eROI.plugin.symphony\uff1aRsymphony\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eRcmdrPlugin.RMTCJags\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eTreeBUGS\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003ebayescount\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003ebfw\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eora\uff1aROracle\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003epivmet\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eqtpaint\uff1aqtbase\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eBiGGR\uff1arsbml\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eMPTmultiverse\uff1aTreeBUGS, runjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eRcwlPipelines\uff1aRcwl\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eMSGFgui\uff1aMSGFplus\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eReplication\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003echarmData\uff1acharm\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eproteomics\uff1aMSGFplus\u304c\u5fc5\u8981\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1648700971.0 + "updated_at": 1592213830.0 }, { "data_format": 2, - "description": "Scripts used for manuscript for Demuxafy", + "description": "Tools and information for building/running the Epoch Singularity container.", "filenames": [ - "files/Singularity.chord" + "Singularity/Singularity" ], - "full_name": "powellgenomicslab/Demuxafy_manuscript", - "latest_release": "v1.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDemuxafy_manuscript\u003c/h1\u003e\u003ca id=\"user-content-demuxafy_manuscript\" class=\"anchor\" aria-label=\"Permalink: Demuxafy_manuscript\" href=\"#demuxafy_manuscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "full_name": "PlasmaFAIR/epoch_containers", + "latest_release": "v0.3.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-epoch-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#epoch-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEpoch Containers\u003c/h1\u003e\n\u003cp\u003eTools and information for building/running \u003ca href=\"https://epochpic.github.io/\" rel=\"nofollow\"\u003eEpoch\u003c/a\u003e using Docker/Singularity\ncontainers. This repository is targeted at users of the Viking HPC cluster at the\nUniversity of York, but the contents may be of use to other Epoch users.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eContainers package up software and dependencies so that code compiled on one machine\ncan be reliably run on others. When used in conjunction with scientific software, they\nallow researchers to run code without needing to build it themselves, and they make\nit much easier to share reproducible workflows.\u003c/p\u003e\n\u003cp\u003eWe provide support for two container platforms: \u003ca href=\"https://docs.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and\n\u003ca href=\"https://docs.sylabs.io/guides/3.11/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. Docker is the most widely used platform, and\nhas been used here to build a \u0027base image\u0027 of Epoch on which other tools may be built.\nSingularity is an alternative that was designed from the ground up to be useable on\nHPC systems, so unlike Docker it can be run on multi-node architectures using MPI\nwithout issue.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-epoch-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-epoch-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Epoch with Singularity\u003c/h2\u003e\n\u003cp\u003eTo run Epoch on Viking, first create a directory within \u003ccode\u003e~/scratch\u003c/code\u003e in which you\nwant to run your code:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh \u0026lt;userid\u0026gt;@viking.york.ac.uk\n$ mkdir -p ~/scratch/epoch\n$ cd ~/scratch/epoch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou\u0027ll need to ensure your \u003ccode\u003einput.deck\u003c/code\u003e file is within this directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e From your own machine\u003c/span\u003e\n$ scp input.deck \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e@viking.york.ac.uk:/users/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scratch/epoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the Singularity container, you\u0027ll need to load the following modules:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ module load tools/Singularity mpi/OpenMPI\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then run using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e library://liampattinson/epoch/epoch.sif:latest \\\n run_epoch --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis should download and cache the container, and then display some help text.\nYou can then run using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e library://liampattinson/epoch/epoch.sif:latest \\\n run_epoch -d 2 -o \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that you should only run short tests on the login nodes. Let\u0027s break this down:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity exec\u003c/code\u003e: Run a singularity container with a user provided command.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elibrary://\u003c/code\u003e: Download and run a container from \u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003esylabs.io\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eliampattinson/epoch/epoch.sif:latest\u003c/code\u003e: The specific container we want to run. This\none is a prebuilt Epoch container using the \u003ccode\u003eSingularity/Singularity\u003c/code\u003e recipe file in\nthis repo. Note that the Singularity container is built on top of the Docker\ncontainer.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun_epoch\u003c/code\u003e: The scripting entrypoint to launch an Epoch variant.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d 2\u003c/code\u003e: Run 2D epoch. Can also be 1D and 3D.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o .\u003c/code\u003e: Location of the output directory, which should container you \u003ccode\u003einput.deck\u003c/code\u003e\nfile. Ensure this is somewhere within your scratch space!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--photons\u003c/code\u003e: Optional flag that switches on QED features.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor a simplified interface, we can also use the \u003ccode\u003eepoch_singularity.py\u003c/code\u003e script within\nthis repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./epoch_singularity.py -d 2 -o \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis script mimics Epoch\u0027s behaviour of prompting the user to input their output\ndirectory after the program is running, so the following also works:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e ./epoch_singularity.py -d 2 --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run using MPI, we put the \u003ccode\u003empirun\u003c/code\u003e command \u003cem\u003ebefore\u003c/em\u003e the \u003ccode\u003esingularity\u003c/code\u003e command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mpirun -n 2 \\\n singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e library://liampattinson/epoch/epoch.sif:latest \\\n run_epoch -d 2 -o \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --photons\n$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Or...\u003c/span\u003e\n$ mpirun -n 2 ./epoch_singularity.py -d 2 -o \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen running the \u003ccode\u003eepoch_singularity.py\u003c/code\u003e script with MPI, note that we must supply the\noutput directory via the \u003ccode\u003e-o\u003c/code\u003e flag, and can\u0027t input it using \u003ccode\u003eecho output_dir |\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor real runs, we\u0027ll want to run Epoch via the Slurm scheduler. See the \u003ccode\u003e./examples\u003c/code\u003e\nfolder for an example job script \u003ccode\u003erun_sbatch.sh\u003c/code\u003e and an example \u003ccode\u003einput.deck\u003c/code\u003e. Once we\nhave a job script, we can submit a job using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sbatch run_sbatch.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can check the progress of our job using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ squeue -u \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt is also possible to pull the container from the remote repo:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull epoch.sif library://liampattinson/epoch/epoch.sif:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will download the container image to the file \u003ccode\u003eepoch.sif\u003c/code\u003e (\u003ccode\u003e.sif\u003c/code\u003e denoting a\n\u0027Singularity Image Format\u0027 file). You can then use \u003ccode\u003eepoch.sif\u003c/code\u003e in place of\n\u003ccode\u003elibrary://account/repo/container\u003c/code\u003e in any of the commands above.\u003c/p\u003e\n\u003cp\u003eTo see help text for the Singularity container, first pull it using the methods above,\nand then try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run-help epoch.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to inspect the container, it has been set up so that the following\ncommand opens a bash shell inside of it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run epoch.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTry to avoid getting \u003ccode\u003esingularity exec\u003c/code\u003e and \u003ccode\u003esingularity run\u003c/code\u003e mixed up; the\nformer lets you specify which command you want to run, while the later runs a\npre-defined script.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-analysing-code-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#analysing-code-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalysing code output\u003c/h2\u003e\n\u003cp\u003eIt is recommended to analyse Epoch output data on your own machine rather than on\nViking:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ scp \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e@viking.york.ac.uk:/users/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scratch/epoch/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.sdf \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need a particular Python library to read \u003ccode\u003e.sdf\u003c/code\u003e files, and this is packaged with\nEpoch itself. To install this library, try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/Warwick-Plasma/epoch\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e epoch/epoch1d\n$ make sdfutils\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that the SDF Python library is not packaged with modern best-practices in mind\n(i.e. using virtual environments, uploading packages to PyPI/conda-forge). It will\ninstall to \u003ccode\u003e~/.local/lib/python3.x/site-packages\u003c/code\u003e regardless of whether you\u0027re in a\n\u003ccode\u003evenv\u003c/code\u003e or \u003ccode\u003econda\u003c/code\u003e environment. If you feel you know what you\u0027re doing, you can manually\ncopy/move the installed files to the environment of your choice after installing, but\nit\u0027s recommended to just use the base user environment.\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://epochpic.github.io/\" rel=\"nofollow\"\u003eEpoch docs\u003c/a\u003e for info on using SDF analysis tools.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-epoch-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-epoch-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Epoch with Docker\u003c/h2\u003e\n\u003cp\u003eTo run Epoch on your own machine, you\u0027ll first need to install Docker if you don\u0027t have\nit already.\u003c/p\u003e\n\u003cp\u003eThe Epoch Docker container can be found at \u003ccode\u003eghcr.io/plasmafair/epoch:latest\u003c/code\u003e.\nTo run it, try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -v /path/to/output/dir:/output \\\n ghcr.io/plasmafair/epoch:latest -d 2 --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBreaking down each component here:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edocker run\u003c/code\u003e starts up the container and runs its \u0027entrypoint\u0027, which is the script\n\u003ccode\u003erun_epoch\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--rm\u003c/code\u003e automatically removes the container after running.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-v /path/to/output/dir:/output\u003c/code\u003e mounts the directory \u003ccode\u003e/path/to/output/dir\u003c/code\u003e on the\nhost machine to \u003ccode\u003e/output\u003c/code\u003e on the container. \u003ccode\u003e/path/to/output/dir\u003c/code\u003e should contain\nyour \u003ccode\u003einput.deck\u003c/code\u003e file before running.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eghcr.io/plasmafair/epoch:latest\u003c/code\u003e is the container to run. This will be downloaded\nthe first time you run the container, and cached for future use. It is created using\nthe file \u003ccode\u003eDocker/Dockerfile\u003c/code\u003e in this repo.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d 2\u003c/code\u003e: Run 2D epoch. Can also be 1D and 3D.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--photons\u003c/code\u003e: Optional flag that switches on QED features.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that you shouldn\u0027t mount your current working directory. Provided the second path\nprovided to \u003ccode\u003e-v\u003c/code\u003e is \u003ccode\u003e/output\u003c/code\u003e, there\u0027s no need to provide an argument to the \u003ccode\u003e-o\u003c/code\u003e flag.\nIf you want to open an interactive shell inside the container, try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -it -v /path/to/output/dir:/output \\\n --entrypoint /bin/bash ghcr.io/plasmafair/epoch:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor a simplified interface, try using the script \u003ccode\u003eepoch_docker.py\u003c/code\u003e. To achieve the\nsame results as the call above, try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./epoch_docker.py -d 2 -o /path/to/output/dir --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that the \u003ccode\u003e-o\u003c/code\u003e flag here refers to the run location on the host machine, not the\nlocation in the docker container. If \u003ccode\u003e-o\u003c/code\u003e is not provided, this script mimics the\nbehaviour of Epoch itself by prompting the user to input their output directory after\nthe program starts:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e /path/to/output/dir \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e ./epoch_docker.py -d 2 --photons\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-docker-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-docker-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Docker images\u003c/h2\u003e\n\u003cp\u003eTo build a Docker image, enter the \u003ccode\u003eDocker\u003c/code\u003e directory and try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e -t epoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then run the container via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -v /path/to/output/dir:/output \\\n epoch -d 2 --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry\"\u003eonline docs\u003c/a\u003e to set up your GitHub account to permit pushing to\nthe GitHub Container Registry (GHCR). Once set up, you should tag your repo with the\nname it should use online:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker tag epoch ghcr.io/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_profile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/epoch:0.1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then push using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker push ghcr.io/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_profile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/epoch:0.1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-singularity-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Singularity images\u003c/h2\u003e\n\u003cp\u003eThe file \u003ccode\u003eSingularity/Singularity\u003c/code\u003e contains the definitions for an Epoch Singularity\ncontainer. As this builds on the Docker image, it doesn\u0027t do much beyond updating\nsome file access permissions.\u003c/p\u003e\n\u003cp\u003eDue to permission issues, we can\u0027t build new containers directly on Viking. However,\nwe can make use of the Sylabs remote builder. To use this, first go to\n\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003esylabs.io\u003c/a\u003e and create an account. From there, you should be able to generate\nan \u0027access token\u0027. After doing so, copy the generated token to a file \u003ccode\u003e.token\u003c/code\u003e on\nyour system. Then:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity remote login\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCopy-paste your access token when prompted. You can then build your image using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --remote epoch.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis may take some time. Once it\u0027s done, you should find the image file \u003ccode\u003eepoch.sif\u003c/code\u003e\nin your current directory. You can run this container directly using \u003ccode\u003esingularity exec\u003c/code\u003e\nas shown above.\u003c/p\u003e\n\u003cp\u003eIf you wish to share your container with others, you\u0027ll first need to sign it. This can\nbe done using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity keys newpair\n$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Fill in the prompts as they appear.\u003c/span\u003e\n$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the same email as your sylabs account.\u003c/span\u003e\n$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can leave passwords blank\u003c/span\u003e\n$ singularity sign epoch.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can check it worked using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity verify epoch.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, we can upload it to Sylabs using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity push epoch.sif library://\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_sylabs_account\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/epoch/epoch.sif:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn addition to uploading an image with the \u003ccode\u003e:latest\u003c/code\u003e tag, we may also want to upload a\nversion with a version code like \u003ccode\u003e:1.0\u003c/code\u003e. If we add new features to the container, we\ncan then upload version \u003ccode\u003e:1.1\u003c/code\u003e etc. If we change how the container works in such a way\nthat our users must interact with it differently (e.g. we might have renamed an existing\nexecutable), we can then upload version \u003ccode\u003e:2.0\u003c/code\u003e etc.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-hidden=\"true\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003eThis repo is licensed under the GNU GPLv3 license, as it contains files from the\nsimilarly-licensed \u003ca href=\"https://github.com/Warwick-Plasma/epoch\"\u003eEpoch repository\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1700598267.0 + "updated_at": 1688409808.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for QIIME 2 (https://docs.qiime2.org/)", "filenames": [ - "Singularity" + "Singularity.2019.4", + "Singularity.2020.6", + "Singularity.2021.11", + "Singularity.2018.11", + "Singularity.2021.2", + "Singularity.2022.2", + "Singularity.2019.1-picrust2", + "Singularity.2020.11-aldex2", + "Singularity.2020.11", + "Singularity.2019.10", + "Singularity.2022.8", + "Singularity.2018.2", + "Singularity.2021.4", + "Singularity.2019.7", + "Singularity.2020.2", + "Singularity.2019.7-picrust2", + "Singularity.2021.8", + "Singularity.2020.8", + "Singularity.2019.1" ], - "full_name": "callaghanmt-containers/python_jupyter", + "full_name": "powerPlant/qiime2-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epython_jupyter\u003c/h1\u003e\u003ca id=\"user-content-python_jupyter\" class=\"anchor\" aria-label=\"Permalink: python_jupyter\" href=\"#python_jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2268\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the QIIME 2 microbiome analysis package\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1553554603.0 + "updated_at": 1638265189.0 }, { "data_format": 2, "description": null, "filenames": [ - "volsung-cudnn8-runtime-ubuntu18.04/Singularity", - "vdt_base/Singularity" + "environments/Singularity.preproc" ], - "full_name": "nesi/containers", + "full_name": "yarikoptic/demo-cifar-preproc", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4539\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsing conventions described here.\n\u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/getting-started/naming\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/singularityhub-docs/docs/getting-started/naming\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-materials-for-a-basic-demo-of-datalad-functionalities\" class=\"anchor\" aria-hidden=\"true\" href=\"#materials-for-a-basic-demo-of-datalad-functionalities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaterials for a basic demo of DataLad functionalities\u003c/h1\u003e\n\u003cp\u003eTo demonstrate\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eComposition of datasets\u003c/li\u003e\n\u003cli\u003eAutomated recording of commands results\u003c/li\u003e\n\u003cli\u003ePublishing to GitHub and FigShare\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1611608990.0 + "updated_at": 1553541127.0 }, { "data_format": 2, - "description": null, + "description": "simple login wrapper for token entry to web applications", "filenames": [ - "Singularity" + "Singularity", + "docs/singularity/examples/sh_notebook/Singularity.notebook", + "docs/singularity/examples/hello-world/Singularity.helloworld", + "docs/singularity/examples/notebook/Singularity.notebook" ], - "full_name": "marchoeppner/metagenomic-profiling", - "latest_release": "1.2", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIKMB Metagenomic profiling pipeline\u003c/h1\u003e\u003ca id=\"user-content-ikmb-metagenomic-profiling-pipeline\" class=\"anchor\" aria-label=\"Permalink: IKMB Metagenomic profiling pipeline\" href=\"#ikmb-metagenomic-profiling-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipelines analyses short reads and identifies the most likely species in the respective sample.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation about the pipeline can be found in the \u003ccode\u003edocs/\u003c/code\u003e directory or under the links below:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "vsoch/sh_login", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-shell-login-portal\" class=\"anchor\" aria-hidden=\"true\" href=\"#shell-login-portal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell Login Portal\u003c/h1\u003e\n\u003cp\u003eThis is an experiment to provide a general web server to wrap access to\na particular port served by nginx. We do this by having the main nginx\nroot (/) serve as a proxy for the flask application, and then the Flask\napplication expects a particular environment variable (defined at runtime)\nto check against a token provided by the user. If the token is correct,\nthe Flask response adds a header to authenticate it as so, and returns\nthe response to the user. If the response is incorrect, the user is\nreturned permission denied (403). The user cannot go to the port to\nbypass the application because of the proxy, and not exposing the port\ndirectly.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker works fairly well, as we can not expose particular ports to the host\u003c/li\u003e\n\u003cli\u003eSingularity does not, because all ports are shared\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\"docs\"\u003edocs\u003c/a\u003e for details.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1612364252.0 + "updated_at": 1545323610.0 }, { "data_format": 2, - "description": "singularity image for deepribo", + "description": null, "filenames": [ - "Singularity" + "code/Singularity.def", + "code/Singularity_COMMIT.def" ], - "full_name": "RickGelhausen/deepribo_image", + "full_name": "inm7/vbc_mri_pipeline", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-containerized-structural-connectivity-sc-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#containerized-structural-connectivity-sc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerized structural connectivity (SC) pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREQUIREMENTS\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTo use the containerized SC pipeline, please install \u0027singularity\u0027 on your computing system: \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.3/user-guide/installation.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline uses Freesurfer. If you do not have a license, please register for Freesurfer: \u003ca href=\"https://surfer.nmr.mgh.harvard.edu/registration.html\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/registration.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEssential files\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecode/Singularity\u003c/code\u003e: Recipe file to be used with \u003ccode\u003esingularity build\u003c/code\u003e to generate a container image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecode/input.txt\u003c/code\u003e: Example pipeline parameter specification\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecode/container_SC_pipeline_JURECA.sh\u003c/code\u003e: Example SLURM submission scripts for the JURECA HPC system\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instruction\" class=\"anchor\" aria-hidden=\"true\" href=\"#instruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eINSTRUCTION\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. ARGUMENTS\u003c/h3\u003e\n\u003cp\u003eThere are three main paths for this pipeline: working path, raw data path, and target (result) path. These paths have to be specified by the end-users based on their own computing system.\u003c/p\u003e\n\u003cp\u003eThe containerized SC pipeline consists of 4 modules: preprocessing, tractography, atlas transformation, and reconstruction. The containerized SC pipeline uses 2 arguments (module script and input file) as below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_pipeline.sh /mnt_sc/working/path/input.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also run a sigle module as below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_preprocess.sh /mnt_sc/working/path/input.txt\nsingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_tractography.sh /mnt_sc/working/path/input.txt\nsingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_atlas_transformation.sh /mnt_sc/working/path/input.txt\nsingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_reconstruct.sh /mnt_sc/working/path/input.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first argument specifies a module script and the second argument specifies an input file of it.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. INPUT\u003c/h3\u003e\n\u003cp\u003eAn example of an input text file is the following.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Freesurfer license\n# ------------------\nemail=end.user@your-institute.de\ndigit=xxxxx\nline1=xxxxxxxxxxxxx\nline2=xxxxxxxxxxxxx\n\n# Input variables\n# ---------------\ngrp=INM # Name of dataset\ntract=100000 # Total number of streamlines for whole-brain tractography\natlname=atlas_prefix # Name of atlas for prefixing results\nnumparc=100 # Total number of regions in a given atlas\nshells=0,1000,2000,3000 # shells=0,1000,2000,3000 for HCP dwMRI, i.e., b-values\nnon_zero_shells=1000,2000,3000 # shells=1000,2000,3000 for HCP dwMRI\n\n# Paths setting\n# -------------\ntp=/mnt_tp # Target (result) path\nsp=/mnt_sp # Source (raw) data path\nfp=/mnt_fp # Subject\u0027s path for freesurfer\nap=/mnt_ap # Atlas path\natlas=atlas.nii.gz # Atlas on the MNI 1mm space (6th generation in FSL)\nmni=/usr/share/fsl/5.0/data/standard/MNI152_T1_1mm.nii.gz # Standard template for registration\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe parameters can be modified by the end-users. For licensing Freesurfer, they should get a license code via a registration with a license agreement and put the license code in the input text file. Input files should be prepared for each subject and each condition. For example, a process of 8 subjects with 2 conditions needs 16 input text files. All input text files should be in the working path, \u0027wp=/mount/path/to/scripts\u0027.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-data-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-data-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. DATA STRUCTURE\u003c/h3\u003e\n\u003cp\u003eThe raw data path should have a data structure (BIDS) as below (in case of /mnt_sp=/path/to/DATA_DIR, grp=INM-BIDS, and sbj=sub-01).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/mnt_sp/INM-BIDS/sub-01/anat/sub-01_T1w.json\n/mnt_sp/INM-BIDS/sub-01/anat/sub-01_T1w.nii.gz\n/mnt_sp/INM-BIDS/sub-01/dwi/sub-01_dwi.bval\n/mnt_sp/INM-BIDS/sub-01/dwi/sub-01_dwi.bvec\n/mnt_sp/INM-BIDS/sub-01/dwi/sub-01_dwi.json\n/mnt_sp/INM-BIDS/sub-01/dwi/sub-01_dwi.nii.gz\n\nDATA_DIR (/mnt_sp)\n\u251c\u2500\u2500 INM-BIDS\n\u2502 \u251c\u2500\u2500 sub-01\n\u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 anat\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sub-01_T1w.json\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 sub-01_T1w.nii.gz\n\u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 dwi\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sub-01_dwi.bval\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sub-01_dwi.bvec\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sub-01_dwi.json\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 sub-01_dwi.nii.gz\n. . .\n. . .\n. . .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-example-script-for-the-condor\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-example-script-for-the-condor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. EXAMPLE SCRIPT FOR THE CONDOR\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n\nCPUS=\u00272\u0027\nRAM=\u00278G\u0027\nDISK=\u002790G\u0027\nLOGS_DIR=\u0027/path/to/condor/logs/directory\u0027\nVBC_DWMRI=\u0027/path/to/container/Container_SC_pipeline.simg\u0027\nDATA_DIR=\u0027/path/to/data/directory/prior/to/BIDS\u0027\nATLAS_DIR=\u0027/path/to/atlas/directory\u0027\nOUTPUT_DIR=\u0027/path/to/output/directory\u0027\nFREESURFER_OUTPUT=\u0027/path/to/freesurfer/subjects/directory\u0027\nFREESURFER_LICENSE=\u0027/opt/freesurfer/6.0/license.txt\u0027\nINPUT_PARAMETERS=\u0027/path/to/input/text/file\u0027\n\n# create the logs dir if it doesn\u0027t exist\n[ ! -d \"${LOGS_DIR}\" ] \u0026amp;\u0026amp; mkdir -p \"${LOGS_DIR}\"\n\n# print the .submit header\nprintf \"# The environment\nuniverse = vanilla\ngetenv = True\nrequest_cpus = ${CPUS}\nrequest_memory = ${RAM}\nrequest_disk = ${DISK}\n\n# Execution\ninitial_dir = \\$ENV(HOME)/htcondor-templates/vbc_dwmri\nexecutable = /usr/bin/singularity\n\\n\"\n\n# loop over all subjects\nfor sub in 110411; do\n printf \"arguments = exec --cleanenv \\\n -B ${DATA_DIR}:/mnt_sp,${OUTPUT_DIR}:/mnt_tp,${FREESURFER_OUTPUT}:/mnt_fp,${ATLAS_DIR}:/mnt_ap,${FREESURFER_LICENSE}:/opt/freesurfer/license.txt,${INPUT_PARAMETERS}:/opt/input.txt \\\n ${VBC_DWMRI} \\\n /usr/local/bin/container_SC_pipeline.sh \\\n /opt/input.txt \\\n ${CPUS} \\\n ${sub}\\n\"\n printf \"log = ${LOGS_DIR}/\\$(Cluster).\\$(Process).${sub}.log\\n\"\n printf \"output = ${LOGS_DIR}/\\$(Cluster).\\$(Process).${sub}.out\\n\"\n printf \"error = ${LOGS_DIR}/\\$(Cluster).\\$(Process).${sub}.err\\n\"\n printf \"Queue\\n\\n\"\ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-5-example-script-for-the-slurm\" class=\"anchor\" aria-hidden=\"true\" href=\"#5-example-script-for-the-slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. EXAMPLE SCRIPT FOR THE SLURM\u003c/h3\u003e\n\u003cp\u003eBased on the optimized configuration for the containerized SC pipeline on JURECA at Forschungszentrum J\u00fclich, we provide a script to run the SC pipeline, container_SC_pipeline_JURECA.sh. With a modification of three lines in it, you can use the script on JURECA. This script uses 9 arguments: a module name, 8 subject IDs.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esimg_path=/path/to/container/Container_dwMRI.simg\nwp=/mnt_sc/path/to/scripts\nmnt=/local/path/to/mount\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following example is a script for the slurm system on JURECA. You can copy the following lines and create a file for \u0027sbatch\u0027, for instance, \u0027run_sc_pipeline.sbatch\u0027, then execute like this, \u0027sbatch run_sc_pipeline.sbatch\u0027.\u003c/p\u003e\n\u003cp\u003ePrepare 8 input files for each subject in the working path (wp=/mnt_sc/path/to/scripts) as below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003einput_sub-01.txt\ninput_sub-02.txt\ninput_sub-03.txt\ninput_sub-04.txt\ninput_sub-05.txt\ninput_sub-06.txt\ninput_sub-07.txt\ninput_sub-08.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, make a script for \u0027sbatch\u0027 as below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#SBATCH -J SC_pipeline\n#SBATCH -o slurm_logs/SC_pipeline-out.%j\n#SBATCH -e slurm_logs/SC_pipeline-err.%j\n#SBATCH -A ${project_account}\n#SBATCH --nodes=1\n#SBATCH --time=16:00:00\n#SBATCH --mail-user=end.user@your-institute.de\n#SBATCH --mail-type=All\n#SBATCH --partition=batch\n\nbash container_SC_pipeline_JURECA.sh Preprocess sub-01 sub-02 sub-03 sub-04 sub-05 sub-06 sub-07 sub-08\nwait\n\nbash container_SC_pipeline_JURECA.sh Tractography sub-01 sub-02 sub-03 sub-04 sub-05 sub-06 sub-07 sub-08\nwait\n\nbash container_SC_pipeline_JURECA.sh Atlas_transformation sub-01 sub-02 sub-03 sub-04 sub-05 sub-06 sub-07 sub-08\nwait\n\nbash container_SC_pipeline_JURECA.sh Reconstruction sub-01 sub-02 sub-03 sub-04 sub-05 sub-06 sub-07 sub-08\nwait\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEach module can perform independently. For instance, if the preprocessing module was already performed for considered subjects, then you can continue to perform on the tractography module for the given subjects. An advanced version will have more parameters such as tracking algorithms, tracking steps, tracking angles, and so forth.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-troubleshoot\" class=\"anchor\" aria-hidden=\"true\" href=\"#troubleshoot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTROUBLESHOOT\u003c/h2\u003e\n\u003cp\u003eIf you have a problem to use the containerized SC pipeline. Please contact Kyesam Jung (\u003ca href=\"mailto:k.jung@fz-juelich.de\"\u003ek.jung@fz-juelich.de\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis development was supported by European Union\u2019s Horizon 2020 research and innovation programme under grant agreement \u003ca href=\"https://cordis.europa.eu/project/id/826421\" rel=\"nofollow\"\u003eVirtualBrainCloud (H2020-EU.3.1.5.3, grant no. 826421)\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1580979437.0 + "updated_at": 1635942025.0 }, { "data_format": 2, - "description": "Spliced Transcripts Alignment to a Reference.", + "description": "Custom Linux Container Build for Large Scale File Parsing in High Performance Computing Environments", "filenames": [ - "2.7.9a/Singularity", - "2.7.10b/Singularity", - "2.7.6a/Singularity" + "base-image-ubuntu-22.04/base-image/.singularity.d/Singularity" ], - "full_name": "pscedu/singularity-star", - "latest_release": "v2.7.10b", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-star/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-star/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-star/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-star/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/045d3775a082afd77b840cd32fde10dd75e09c910307d3b4f7647c037b7a4ef6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73746172\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/045d3775a082afd77b840cd32fde10dd75e09c910307d3b4f7647c037b7a4ef6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73746172\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-star\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e558688ff5d4af5bf2ce91ee40e43c83deaa6078c30edf6b895a94497dcf95f7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73746172\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e558688ff5d4af5bf2ce91ee40e43c83deaa6078c30edf6b895a94497dcf95f7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73746172\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-star\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/89ccbf93867c716c4fb9024fc6d759c8722465a2221d907a0e42b806cb0478e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d73746172\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/89ccbf93867c716c4fb9024fc6d759c8722465a2221d907a0e42b806cb0478e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d73746172\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-star\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/42907d2df93df3e4e292a73cf6b96da5f0158eee37ee91309a0b50ceb8dbb50a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73746172\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/42907d2df93df3e4e292a73cf6b96da5f0158eee37ee91309a0b50ceb8dbb50a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73746172\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-star\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-star\u003c/h1\u003e\u003ca id=\"user-content-singularity-star\" class=\"anchor\" aria-label=\"Permalink: singularity-star\" href=\"#singularity-star\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/alexdobin/STAR\"\u003estar\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eSTAR\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/star/2.7.6a\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/star\u003c/code\u003e as \u003ccode\u003e2.7.6a.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "alexander-labarge/hpc-tika-build", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-high-performance-computing-hpc-file-parsing-solution---direct-access-to-gpu--cpu-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#high-performance-computing-hpc-file-parsing-solution---direct-access-to-gpu--cpu-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHigh-Performance Computing (HPC) File Parsing Solution - Direct Access to GPU \u0026amp; CPU Resources\u003c/h1\u003e\n\u003cp\u003eThis solution provides direct access to GPU and CPU resources for high-performance computing (HPC) and high-throughput computing (HTC) environments. Unlike enterprise-based container frameworks, which are designed for microservices and require root privileges to install and run applications, this solution is optimized for complex applications that require all available resources without special privileges.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-targeted-toolsets-implemented\" class=\"anchor\" aria-hidden=\"true\" href=\"#targeted-toolsets-implemented\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTargeted Toolsets Implemented\u003c/h2\u003e\n\u003cp\u003eThis solution uses the following targeted toolsets:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eApache Tika\u2122 by Oracle\u003c/li\u003e\n\u003cli\u003eApptainer (formerly Singularity) by Berkeley National Laboratory\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-initial-cause-for-solution-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#initial-cause-for-solution-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInitial Cause for Solution Development\u003c/h2\u003e\n\u003cp\u003eThe development of this solution was motivated by the need to parse 7.5 TB of digital forensics data produced and stored in a variety of non-standard formats. The parsing of all data is necessary to drive subsequent efforts wherein conjectures are made from the subsequent data parsed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-apptainer-for-hpc-instead-of-virtual-machines-or-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-apptainer-for-hpc-instead-of-virtual-machines-or-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy Apptainer for HPC instead of Virtual Machines or Docker\u003c/h2\u003e\n\u003cp\u003eApptainer/Singularity is a container platform created for the HPC/HTC use case and presents key concepts for the scientific community:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt\u2019s designed to execute applications with bare-metal performance while retaining a high level of security, portability, and reproducibility.\u003c/li\u003e\n\u003cli\u003eContainers run rootless to prohibit privilege escalation.\u003c/li\u003e\n\u003cli\u003eAble to Leverage GPUs, FPGAs, high-speed networks, and filesystems.\u003c/li\u003e\n\u003cli\u003eA container platform for building and running Linux containers that packages software, libraries, and runtime compilers in a self-contained environment.\n\u003cul\u003e\n\u003cli\u003eApplication portability (single image file, contain all dependencies)\u003c/li\u003e\n\u003cli\u003eReproducibility, run cross platform, provide support for legacy OS and apps.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAbility to run, and in modern systems also to be installed, without any root daemon or setuid privileges. This makes it safer for large computer centers with shared resources.\u003c/li\u003e\n\u003cli\u003ePreserves the permissions in the environment. The user outside the container can be the same user inside.\u003c/li\u003e\n\u003cli\u003eApptainer propagates most environment variables set on the host into the container, by default. Docker does not propagate any host environment variables into the container. Environment variables may change the behavior of software.\u003c/li\u003e\n\u003cli\u003eSimple integration with resource managers (SLURM in our case) and distributed computing frameworks because it runs as a regular application.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/945a382c-3488-4c65-a743-44f0a704c7a5\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/945a382c-3488-4c65-a743-44f0a704c7a5\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#development-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment Steps:\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-test-host-machine-bare-metal\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-host-machine-bare-metal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest Host Machine (Bare Metal):\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0c14fa9e-2508-4c80-9883-f016eb70484f\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0c14fa9e-2508-4c80-9883-f016eb70484f\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-apptainer---build-from-source-install-debian-packages-for-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-apptainer---build-from-source-install-debian-packages-for-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1: Apptainer - Build from Source/ Install Debian Packages for Dependencies\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get install -y \\\n build-essential \\\n libseccomp-dev \\\n pkg-config \\\n uidmap \\\n squashfs-tools \\\n squashfuse \\\n fuse2fs \\\n fuse-overlayfs \\\n fakeroot \\\n cryptsetup \\\n curl wget git \\\n \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GOVERSION=1.20.6 OS=linux ARCH=amd64 \\\n wget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n sudo tar -C /usr/local -xzf /home/service-typhon/Downloads/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:/usr/local/go/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc \\\n curl -sSfL https://raw.githubusercontent.com/golangci/golangci-lint/master/install.sh \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sh -s -- -b \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ego env GOPATH\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/bin v1.51.1 \\\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:$(go env GOPATH)/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc \\\n git clone https://github.com/apptainer/apptainer.git \\\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e apptainer \\\n git checkout v1.2.0 \\\n ./mconfig \\\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ./builddir \\\n make \\\n sudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-create-sandbox-directory--pull-ubuntu-2204---jammy-docker-container-base-ubuntu-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-create-sandbox-directory--pull-ubuntu-2204---jammy-docker-container-base-ubuntu-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2: Create Sandbox Directory / Pull Ubuntu 22.04 - Jammy Docker Container (Base Ubuntu Build)\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7aaf3e7e-cb74-40d1-9971-24808b0885f8\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7aaf3e7e-cb74-40d1-9971-24808b0885f8\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-3-convert-to-immutable-sif-image-for-future-builds---demonstrate-shell-access\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-3-convert-to-immutable-sif-image-for-future-builds---demonstrate-shell-access\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 3: Convert to Immutable .sif Image for Future Builds - Demonstrate Shell Access\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/88b8bf10-0e96-4ad3-8d91-6135140e9a00\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/88b8bf10-0e96-4ad3-8d91-6135140e9a00\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-4-definition-file-configuration-for-building-dependencies---1st-build-scuccessful\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-4-definition-file-configuration-for-building-dependencies---1st-build-scuccessful\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 4: Definition File Configuration for Building Dependencies - 1st Build Scuccessful\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/ce94c114-8b0b-44b2-a7c0-33c26e4e36b7\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/ce94c114-8b0b-44b2-a7c0-33c26e4e36b7\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0d3b4389-7329-4a06-b551-392b07586abc\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0d3b4389-7329-4a06-b551-392b07586abc\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-5-now-that-there-is-a-base-instance-working-lets-create-a-live-sandbox-for-testing-from-the-image-we-just-created\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-5-now-that-there-is-a-base-instance-working-lets-create-a-live-sandbox-for-testing-from-the-image-we-just-created\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 5: Now that There is a Base Instance Working, lets create a live sandbox for testing from the image we just created:\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/a45a0aed-0b5b-4cc9-abdb-5178ad5ac648\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/a45a0aed-0b5b-4cc9-abdb-5178ad5ac648\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-note-initial-containers-are-limited-to-64mb-in-size-fix\" class=\"anchor\" aria-hidden=\"true\" href=\"#note-initial-containers-are-limited-to-64mb-in-size-fix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNote: Initial Containers are limited to 64MB in size. Fix:\u003c/h4\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0f2b5e02-d8a1-42bc-995a-507b802c4c3a\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0f2b5e02-d8a1-42bc-995a-507b802c4c3a\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-6-create-a-new-file-system-overlay-add-as-a-layer-in-sif-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-6-create-a-new-file-system-overlay-add-as-a-layer-in-sif-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 6: Create a New File System Overlay/ add as a layer in SIF build:\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/441d3a4a-18cd-4c74-8cf8-b7ffe18167eb\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/441d3a4a-18cd-4c74-8cf8-b7ffe18167eb\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-7-build-tika-configure-properly---completed-success\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-7-build-tika-configure-properly---completed-success\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 7: Build Tika/ Configure Properly - Completed/ Success:\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7c7320ab-999b-45b9-bd3c-beb8a19c1230\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7c7320ab-999b-45b9-bd3c-beb8a19c1230\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-tika-dependency-install-script-implemented-at-post\" class=\"anchor\" aria-hidden=\"true\" href=\"#tika-dependency-install-script-implemented-at-post\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTIKA DEPENDENCY INSTALL SCRIPT IMPLEMENTED AT %POST\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Java\u003c/span\u003e\napt-get update\napt-get install -y software-properties-common\napt-get install -y wget\napt-get install -y default-jre\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Tesseract OCR\u003c/span\u003e\napt-get install -y tesseract-ocr\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install ImageMagick\u003c/span\u003e\napt-get install -y imagemagick\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Poppler\u003c/span\u003e\napt-get install -y poppler-utils\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install FFmpeg\u003c/span\u003e\napt-get install -y ffmpeg\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Tika\u003c/span\u003e\nwget https://dlcdn.apache.org/tika/2.8.0/tika-app-2.8.0.jar\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Maven\u003c/span\u003e\nwget https://dlcdn.apache.org/maven/maven-3/3.9.3/binaries/apache-maven-3.9.3-bin.tar.gz\ntar -xvf apache-maven-3.9.3-bin.tar.gz \nmv apache-maven-3.9.3 /opt\nM2_HOME=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eopt/apache-maven-3.9.3/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nPATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$M2_HOME\u003c/span\u003e/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-tika-automated-test-end-of-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#tika-automated-test-end-of-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTIKA AUTOMATED TEST END OF INSTALL:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e \u003cspan class=\"pl-en\"\u003echeck_tika_test\u003c/span\u003e {\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eChecking Tika test...\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e grep -q \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTIKA PASSED TEST - ALEX\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e /output-files/tika-test-file.txt.json\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika test passed. FOUND STRING: TIKA PASSED TEST - ALEX in file.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e============TIKA HPC BUILD COMPLETING FINAL STEPS================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika test failed.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\n}\n\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eStarting Tika... at \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einput-files directory contents:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ncp /opt/tika-test-file.txt /input-files\nls -l /input-files/\njava -jar /tika-app-2.8.0.jar -i /input-files -o /output-files -J\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika started.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika output complete.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eoutput-files directory contents:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nls -l /output-files\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eCompleted at: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eExtracting text from files...\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eExtracted JSON OUTPUT:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract text from files \u0026amp; ignore JSON text\u003c/span\u003e\nextracted_text=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003efind /output-files -type f -exec strings {} \u003cspan class=\"pl-cce\"\u003e\\;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep -vE \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e^{.*}$\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Print extracted text\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$extracted_text\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Check Tika test\u003c/span\u003e\ncheck_tika_test\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-8-final-beta-build-script-other-bash-scripts-embedded\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-8-final-beta-build-script-other-bash-scripts-embedded\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSTEP 8: FINAL BETA BUILD SCRIPT (OTHER BASH SCRIPTS EMBEDDED)\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/71d64b44-a95e-484b-ad2d-082d3bc9ab60\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/71d64b44-a95e-484b-ad2d-082d3bc9ab60\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1668134054.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1690283498.0 }, { "data_format": 2, - "description": "message of the day examples for Singularity containers", + "description": null, "filenames": [ - "graphic/Singularity", - "fortune/Singularity.lolcow", - "fortune/Singularity", - "asciiart/Singularity", - "general/Singularity", - "greeting/Singularity", - "help/Singularity" + "containers/Singularity.1.3.3.el7" ], - "full_name": "singularityhub/motd", + "full_name": "pestoura/OpenHPC", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMessage of the Day\u003c/h1\u003e\u003ca id=\"user-content-message-of-the-day\" class=\"anchor\" aria-label=\"Permalink: Message of the Day\" href=\"#message-of-the-day\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003efor Singularity containers\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/223333?speed=2\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8faea5a9de38c71271a17454687573c439d37ecfaec6d803dfcd064657aa7a5f/68747470733a2f2f61736369696e656d612e6f72672f612f3232333333332e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/223333.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that these were modified for Singularity 3.x due to a \u003ca href=\"https://github.com/singularityhub/motd/issues/2\"\u003eloss of functionality\u003c/a\u003e\nto customize the actions shell file. If you are looking for the original recipes for 2.x containers,\nsee \u003ca href=\"https://github.com/singularityhub/motd/tree/release/2.x\"\u003erelease/2.x\u003c/a\u003e. The current\nmaster should work on both.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is a message of the day?\u003c/h2\u003e\u003ca id=\"user-content-what-is-a-message-of-the-day\" class=\"anchor\" aria-label=\"Permalink: What is a message of the day?\" href=\"#what-is-a-message-of-the-day\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027ve ever logged into a linux cluster, or played a computer\ngame like Half Life or World of Warcraft, you might be greeted with some\nasciiart, or something along the lines of a \"tip of the day.\" This is more\nofficial called a \"message of the day,\" (short is \u003ca href=\"https://en.wikipedia.org/wiki/Motd_(Unix)\" rel=\"nofollow\"\u003emotd\u003c/a\u003e\nand there is a bit of \u003ca href=\"https://ownyourbits.com/2017/04/05/customize-your-motd-login-message-in-debian-and-ubuntu/\" rel=\"nofollow\"\u003ehistory behind it\u003c/a\u003e. In short, we print a message to the terminal\nfor the user to see when he or she first logs into a shell.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow can we use motd with containers?\u003c/h2\u003e\u003ca id=\"user-content-how-can-we-use-motd-with-containers\" class=\"anchor\" aria-label=\"Permalink: How can we use motd with containers?\" href=\"#how-can-we-use-motd-with-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the context of a container, we might want to give the user a friendly message\nif they shell inside. The simplest use case is to greet the user. A more useful\nuse case is to provide some help for how to interact with the container, or\nwhere to find documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do we add motd to Singularity containers?\u003c/h2\u003e\u003ca id=\"user-content-how-do-we-add-motd-to-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: How do we add motd to Singularity containers?\" href=\"#how-do-we-add-motd-to-singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf we are creating a Singularity container,\nwe can\u0027t just echo a message in the runscript, because this gets executed on\na shell \u003cem\u003eor\u003c/em\u003e a run. We need to edit the \u003ccode\u003e/.singularity.d/actions/shell\u003c/code\u003e\nscript that is executed \u003cstrong\u003eonly\u003c/strong\u003e on a shell.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity MOTDs\u003c/h1\u003e\u003ca id=\"user-content-singularity-motds\" class=\"anchor\" aria-label=\"Permalink: Singularity MOTDs\" href=\"#singularity-motds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this repository, we will provide you with a few fun examples for generating\nmessages of the day in Singularity containers.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"general\"\u003egeneral\u003c/a\u003e: will show you how to customize a message for shell, exec, run, or test.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"greeting\"\u003egreeting\u003c/a\u003e: a simple message of the day to greet the user\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"fortune\"\u003efortune\u003c/a\u003e: give the user a fortune instead, add a cow, and some color!\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"help\"\u003ehelp\u003c/a\u003e: show the container\u0027s %help section to the user when they shell inside\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"asciiart\"\u003easciiart\u003c/a\u003e: generate a greeting with awesome asciiart!\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"graphic\"\u003egraphic\u003c/a\u003e: generate a colored graphic to surprise the user with.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eClearly, many of these examples are for fun, and others are better for communicating\ninformation. I\u0027m of the firm belief that we should aspire for both - interaction\nwith containers should be both informative and fun.\u003c/p\u003e\n", + "readme": "\n\u003ch1\u003e\u003ca id=\"\" class=\"anchor\" aria-hidden=\"true\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/openhpc/ohpc/blob/master/docs/recipes/install/common/figures/ohpc_logo.png\"\u003e\u003cimg src=\"https://github.com/openhpc/ohpc/raw/master/docs/recipes/install/common/figures/ohpc_logo.png\" width=\"170\" valign=\"middle\" hspace=\"5\" alt=\"OpenHPC\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-community-building-blocks-for-hpc-systems\" class=\"anchor\" aria-hidden=\"true\" href=\"#community-building-blocks-for-hpc-systems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommunity building blocks for HPC systems\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThis stack provides a variety of common, pre-built ingredients required to\ndeploy and manage an HPC Linux cluster including provisioning tools, resource\nmanagement, I/O clients, runtimes, development tools, containers, and a variety of\nscientific libraries.\u003c/p\u003e\n\u003cp\u003eThere are currently two release series: \u003ca href=\"https://github.com/openhpc/ohpc/wiki/1.3.X\"\u003e1.3.x\u003c/a\u003e and \u003ca href=\"https://github.com/openhpc/ohpc/wiki/2.x\"\u003e2.x\u003c/a\u003e,\nwhich target different major Linux OS distributions. The 1.3.x series targets\nCentOS7 and SLES12 while the 2.x series targets CentOS8 and Leap15.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h3\u003e\n\u003cp\u003eOpenHPC provides pre-built binaries via repositories for use with standard\nLinux package manager tools (e.g. \u003ccode\u003eyum\u003c/code\u003e or \u003ccode\u003ezypper\u003c/code\u003e). To get started,\nyou can enable an OpenHPC repository locally through installation of an\n\u003ccode\u003eohpc-release\u003c/code\u003e RPM which includes gpg keys for package signing and defines\nthe URL locations for [base] and [update] package repositories. Installation\nguides tailored for each supported provisioning system and resource manager\nwith detailed example instructions for installing a cluster are also available.\nCopies of the \u003ccode\u003eohpc-release\u003c/code\u003e package and installation guides along with\nmore information is available on the relevant release series pages\n(\u003ca href=\"https://github.com/openhpc/ohpc/wiki/1.3.X\"\u003e1.3.x\u003c/a\u003e or \u003ca href=\"https://github.com/openhpc/ohpc/wiki/2.x\"\u003e2.x\u003c/a\u003e).\u003c/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-questions-comments-or-bug-reports\" class=\"anchor\" aria-hidden=\"true\" href=\"#questions-comments-or-bug-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions, Comments, or Bug Reports?\u003c/h3\u003e\n\u003cp\u003eSubscribe to the \u003ca href=\"https://groups.io/g/openhpc-users\" rel=\"nofollow\"\u003eusers email list\u003c/a\u003e or see the\n\u003ca href=\"https://openhpc.community/\" rel=\"nofollow\"\u003ehttps://openhpc.community/\u003c/a\u003e page for more pointers.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-additional-software-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#additional-software-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional Software Requests?\u003c/h3\u003e\n\u003cp\u003ePlease see the component \u003ca href=\"https://github.com/openhpc/submission\"\u003esubmission page\u003c/a\u003e for more information\nregarding new software inclusion requests.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-contributing-to-openhpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing-to-openhpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing to OpenHPC\u003c/h3\u003e\n\u003cp\u003ePlease see the steps described in \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-register-your-system\" class=\"anchor\" aria-hidden=\"true\" href=\"#register-your-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRegister your system\u003c/h3\u003e\n\u003cp\u003eIf you are using elements of OpenHPC, please consider registering your system(s)\nusing the \u003ca href=\"https://drive.google.com/open?id=1KvFM5DONJigVhOlmDpafNTDDRNTYVdolaYYzfrHkOWI\" rel=\"nofollow\"\u003eSystem Registration Form\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "singularity-container", - "motd", - "message-of-the-day" - ], - "updated_at": 1639382975.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1679150954.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "envs/Singularity.1", + "envs/Singularity.1.2", + "envs/Singularity.1.1" ], - "full_name": "marchoeppner/exome-seq", + "full_name": "adswa/test_simg", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExome-seq Pipeline\u003c/h1\u003e\u003ca id=\"user-content-exome-seq-pipeline\" class=\"anchor\" aria-label=\"Permalink: Exome-seq Pipeline\" href=\"#exome-seq-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline offers a end-to-end workflow for exome analysis using the GATK4 toolchain\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etrimming with Fastp\u003c/li\u003e\n\u003cli\u003eread alignment with BWA\u003c/li\u003e\n\u003cli\u003eduplicate marking using Picard MarkDuplicates\u003c/li\u003e\n\u003cli\u003equality score recalibration\u003c/li\u003e\n\u003cli\u003egvcf calling\u003c/li\u003e\n\u003cli\u003ejoint variant calling\n-- variant hard-filtering [default]\n-- variant recalibration (SNPs and Indels) and filtering [optional, off by default and only recommended for \u0026gt;= 30 exomes]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe result will be a multi-sample VCF file as well as a list of VCF files for each sample.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1568723827.0 + "updated_at": 1601186003.0 }, { "data_format": 2, - "description": "This repo contains the singularity container to run snpPhylo which will build a phyogenetic tree from SNPS", + "description": null, "filenames": [ - "Singularity.1.0.0" + "Singularity" ], - "full_name": "ISUGIFsingularity/snpPhylo", + "full_name": "callaghanmt/cont_autobuild", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esnpPhylo\u003c/h1\u003e\u003ca id=\"user-content-snpphylo\" class=\"anchor\" aria-label=\"Permalink: snpPhylo\" href=\"#snpphylo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains the singularity container to run snpPhylo which will build a phyogenetic tree from SNPS\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cont_autobuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#cont_autobuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econt_autobuild\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1529509581.0 + "updated_at": 1582590721.0 }, { "data_format": 2, - "description": "Genome Annotation Tools", + "description": null, "filenames": [ - "Singularity.FastQC", - "Singularity.SAMtools", - "Singularity.BRAKER", - "Singularity.Diamond", - "Singularity.BLAST", - "Singularity.Augustus", - "Singularity.RepeatModeler", - "Singularity.RepeatMasker", - "Singularity.GeneMark", - "Singularity.Trimmomatic", - "Singularity.SRAToolkit", - "Singularity.STAR" + "hello-world/Singularity", + "singularity-definitions/Singularity.git-session", + "singularity-definitions/Singularity.hello-world" ], - "full_name": "williamssanders/annotate", + "full_name": "kaczmarj/container-workshop", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eannotate\u003c/h1\u003e\u003ca id=\"user-content-annotate\" class=\"anchor\" aria-label=\"Permalink: annotate\" href=\"#annotate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenome Annotation Tools\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-code-for-container-workshop\" class=\"anchor\" aria-hidden=\"true\" href=\"#code-for-container-workshop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode for container workshop\u003c/h1\u003e\n\u003cp\u003eSee \u003ca href=\"https://www.eventbrite.com/e/reproducible-research-in-computational-science-tickets-41433469623\" rel=\"nofollow\"\u003ethe Eventbrite page\u003c/a\u003e for more information.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1627567602.0 + "updated_at": 1532718524.0 }, { "data_format": 2, - "description": "Kraken 2 is the newest version of Kraken, a taxonomic classification system using exact k-mer matches to achieve high accuracy and fast classification speeds.", + "description": "singularity image for biocontainers blast (anaconda)", "filenames": [ - "2.1.2/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-kraken2", + "full_name": "researchapps/blast", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-kraken2/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-kraken2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-kraken2/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-kraken2/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/41387ab28ffce0d6eb470b0985070ed2f26a5482cb76c030e7808e61c6f7bff6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6b72616b656e32\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/41387ab28ffce0d6eb470b0985070ed2f26a5482cb76c030e7808e61c6f7bff6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6b72616b656e32\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-kraken2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e09d4753dfe7d898dae179b7a4fb26537ecae9ea5ef93c14d72565bbc57bdf65/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6b72616b656e32\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e09d4753dfe7d898dae179b7a4fb26537ecae9ea5ef93c14d72565bbc57bdf65/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6b72616b656e32\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-kraken2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5c939d3d6c35cabd4124398d1bd40917486d7009350ee47978428e4fbd940497/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6b72616b656e32\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c939d3d6c35cabd4124398d1bd40917486d7009350ee47978428e4fbd940497/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6b72616b656e32\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-kraken2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8aa53054e06c22dace4c851fb49c4740abe4d366f4e21895d122b09a60899b65/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6b72616b656e32\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8aa53054e06c22dace4c851fb49c4740abe4d366f4e21895d122b09a60899b65/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6b72616b656e32\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-kraken2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-kraken2\u003c/h1\u003e\u003ca id=\"user-content-singularity-kraken2\" class=\"anchor\" aria-label=\"Permalink: singularity-kraken2\" href=\"#singularity-kraken2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003ekraken2\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the other scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/kraken2/2.1.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/kraken2\u003c/code\u003e as \u003ccode\u003e2.1.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-blast\" class=\"anchor\" aria-hidden=\"true\" href=\"#blast\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBlast\u003c/h1\u003e\n\u003cp\u003eThis is a singularity image to deploy blast.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install Singularity\u003c/h2\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Bootstrap the image\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 blast.img\nsudo singularity bootstrap blast.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Run commands\u003c/h2\u003e\n\u003cp\u003eHow to access the blast runtime executables?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./blast.img\n\nThis container provides the following executables:\n2to3\t\t genbrk\t\t python-config\nactivate\t gencfu\t\t python2\nblast_formatter gencnval\t\t python2.7\nblastdb_aliastool gendict\t\t rpsblast\nblastdbcheck\t gene_info_reader\t rpstblastn\nblastdbcmd\t genrb\t\t seedtop\nblastdbcp\t icu-config\t\t segmasker\nblastn\t\t icuinfo\t\t seqdb_demo\nblastp\t\t idle\t\t\t seqdb_perf\nblastx\t\t legacy_blast.pl\t smtpd.py\nc_rehash\t makeblastdb\t\t sqlite3\nconda\t\t makeconv\t\t tblastn\nconda-env\t makembindex\t\t tblastx\nconvert2blastmask makeprofiledb\t tclsh8.5\ndatatool\t openssl\t\t test_pcre\ndeactivate\t pip\t\t\t uconv\ndeltablast\t pkgdata\t\t update_blastdb.pl\nderb\t\t project_tree_builder wheel\ndustmasker\t psiblast\t\t windowmasker\neasy_install\t pydoc\t\t windowmasker_2.2.22_adapter.py\neasy_install-2.7 python\t\t wish8.5\n\nExample usage: blast.img blastn [args] [options]\n\n\n\n ./blast.img blastn\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1629226178.0 + "updated_at": 1484518249.0 }, { "data_format": 2, - "description": "Singularity container recipe for Deep Learning with GPU for architecture x86_64 based on a CentOS 7. In the specific: centOS 7 with cuda library (10.0-devel-centos7) GNU compiler 7 Python 3.6 OpenMPI 2.1.1 (compiled with support for psm2, pmix, verbs) Tensorflow 1.14.0 GPU (pip) Py-Torch 1.4.0 GPU (pip) Torchvision 0.5.0 CPU (pip) MxNet 1.5.1 CPU (pip) Horovod 0.19.1 (compiled with Tensorflow, Pytorch, MxNet) This recipe works on in the Cineca cluster (arch x86_64): Galileo", + "description": "Singularity definition file for pycortex", "filenames": [ "Singularity" ], - "full_name": "CINECA-HPC/container_deep_learning_gpu_centos7_x86_64", + "full_name": "mvdoc/pycortex-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_deep_learning_gpu_centos7_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_deep_learning_gpu_centos7_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_deep_learning_gpu_centos7_x86_64\" href=\"#container_deep_learning_gpu_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer recipes for Deep Learning with GPU for architecture x86_64 based on a CentOS 7. In the specific:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCentOS 7 with cuda library (10.0-devel-centos7)\u003c/li\u003e\n\u003cli\u003eGNU compiler 7\u003c/li\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003eOpenMPI 2.1.1 (compiled with support for psm2, pmix, verbs)\u003c/li\u003e\n\u003cli\u003eTensorflow 1.14.0 GPU (pip)\u003c/li\u003e\n\u003cli\u003ePy-Torch 1.4.0 GPU (pip)\u003c/li\u003e\n\u003cli\u003eTorchvision 0.5.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003eMxNet 1.5.1 CPU (pip)\u003c/li\u003e\n\u003cli\u003eHorovod 0.19.1 (compiled with Tensorflow, Pytorch, MxNet)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis recipe works on in the Cineca cluster (arch x86_64):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGalileo\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/604\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-pycortex-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#pycortex-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epycortex Singularity container\u003c/h1\u003e\n\u003cp\u003eThis repository contains a singularity definition file to create a\ncontainer with \u003ca href=\"https://gallantlab.github.io\" rel=\"nofollow\"\u003epycortex\u003c/a\u003e, FreeSurfer, and\nFSL. It installs the \u003ccode\u003eglrework-merged\u003c/code\u003e branch of pycortex. Pycortex\u0027s\nfilestore database needs to be mounted externally so that it is\npersistent, and must be pointed to \u003ccode\u003e/cortex-filestore\u003c/code\u003e inside the\ncontainer.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-build-the-container-this-assumes-singularity--242-or-pull-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-build-the-container-this-assumes-singularity--242-or-pull-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Build the container (this assumes Singularity \u0026gt;= 2.4.2), or pull from singularity hub\u003c/h3\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003esingularity build pycortex.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ealternatively, the image can be pulled from singularity-hub\u003c/p\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003esingularity pull --name pycortex.img shub://mvdoc/pycortex-singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-run-it-mounting-the-relevant-directories-eg\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-run-it-mounting-the-relevant-directories-eg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Run it mounting the relevant directories, e.g.\u003c/h3\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003esingularity run -B /path/to/my/data:/data \\\n -B /path/to/my/filestore:/cortex-filestore \\\n -e -c pycortex.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will start a shell inside the container; then one can run a jupyter\nnotebook session with\u003c/p\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003ejupyter notebook --no-browser --port=9999\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you need to use FreeSurfer, you should set the environment variable \u003ccode\u003eFS_LICENSE\u003c/code\u003e to point to your \u003ccode\u003elicense.txt\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003eexport FS_LICENSE=/path/to/license.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe container can also be used as a wrapper for commands, for example\u003c/p\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003e$ singularity run \\\n -B /path/to/my/filestore:/cortex-filestore \\\n -e -c pycortex.img \\\n \"python -c \u0027import cortex; print(cortex.__file__)\u0027\"\n\n/src/pycortex/cortex/__init__.py\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1604486860.0 + "updated_at": 1518741953.0 }, { "data_format": 2, - "description": "ncdu is a disk utility for Unix systems", + "description": "Eukaryotic Genome Annotation Pipeline", "filenames": [ - "2.5/Singularity", - "1.17/Singularity", - "2.2.2/Singularity", - "2.3.1/Singularity", - "1.13/Singularity", - "1.16/Singularity" + "1.8.15/Singularity", + "1.8.13/Singularity", + "1.8.9/Singularity" ], - "full_name": "pscedu/singularity-ncdu", - "latest_release": "v2.2.2", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ncdu/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ncdu/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ncdu/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ncdu/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f9e74e6eaa558f8de96b5323befbe89f15952145bcd584b1b75b07dc8f1b61d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6e636475\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f9e74e6eaa558f8de96b5323befbe89f15952145bcd584b1b75b07dc8f1b61d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6e636475\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-ncdu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e2a7e960201dfb14c0ba1a3ca02f73241aaf0e2fdd48ee298e3fa977c90e2112/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6e636475\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e2a7e960201dfb14c0ba1a3ca02f73241aaf0e2fdd48ee298e3fa977c90e2112/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6e636475\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-ncdu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/df2f5f5dec729dd2c276b41738797fc83987906652195bb30d4b10274d156bff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6e636475\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df2f5f5dec729dd2c276b41738797fc83987906652195bb30d4b10274d156bff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6e636475\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-ncdu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d5c7c6650a24f7f3338ee4f85a9f3c1a5b43212b105e88c8a22a47cfb460bea2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6e636475\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d5c7c6650a24f7f3338ee4f85a9f3c1a5b43212b105e88c8a22a47cfb460bea2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6e636475\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-ncdu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-ncdu\u003c/h1\u003e\u003ca id=\"user-content-singularity-ncdu\" class=\"anchor\" aria-label=\"Permalink: singularity-ncdu\" href=\"#singularity-ncdu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/753d93f57df7c50b16d94eddfe1805ea221ff7d1a321786617d9f12e280fc1e3/68747470733a2f2f6465762e796f7268656c2e6e6c2f696d672f6e63647568656c70322d322e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/753d93f57df7c50b16d94eddfe1805ea221ff7d1a321786617d9f12e280fc1e3/68747470733a2f2f6465762e796f7268656c2e6e6c2f696d672f6e63647568656c70322d322e706e67\" alt=\"Screenshot\" data-canonical-src=\"https://dev.yorhel.nl/img/ncduhelp2-2.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://dev.yorhel.nl/ncdu\" rel=\"nofollow\"\u003encdu\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003encdu\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/ncdu/2.3.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/ncdu\u003c/code\u003e as \u003ccode\u003e2.3.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2019-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-funannotate", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-funannotate/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-funannotate/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-funannotate/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-funannotate/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5db6f9744c3c7d051d19348b21768f84f789cc84db6989490b1098cdee6d38e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5db6f9744c3c7d051d19348b21768f84f789cc84db6989490b1098cdee6d38e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7ce313f3a5197862191f8b70f543e4fbba7c0c6032325bfb1113fbe4d57f88b1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ce313f3a5197862191f8b70f543e4fbba7c0c6032325bfb1113fbe4d57f88b1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ec0ba12b6cec391402dbbe6193f93d5322dcf2af0aaf708398f82c707693536f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ec0ba12b6cec391402dbbe6193f93d5322dcf2af0aaf708398f82c707693536f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/260091bdd6a589c218c92d76f07d447cf463bde01be55549ac6c70baff972a1b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/260091bdd6a589c218c92d76f07d447cf463bde01be55549ac6c70baff972a1b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-funannotate\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-funannotate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-funannotate\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/nextgenusfs/funannotate\"\u003efunannotate\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efunannotate\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/funannotate/1.8.15\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/funannotate\u003c/code\u003e as \u003ccode\u003e1.8.15.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ - "singularity", - "utilities" + "bioinformatics", + "singularity" ], - "updated_at": 1729794217.0 + "updated_at": 1651105066.0 }, { "data_format": 2, - "description": "Browsh is a fully-modern text-based browser.", + "description": "A powerful toolset for genome arithmetic.", "filenames": [ - "1.6.4/Singularity" + "2.30.0/Singularity", + "2.29.2/Singularity" ], - "full_name": "pscedu/singularity-browsh", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-browsh/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-browsh/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-browsh/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-browsh/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/aae04b7121c139bf037b44c4863a21114a989b54ecef4cbd4833136653229c4b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62726f777368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aae04b7121c139bf037b44c4863a21114a989b54ecef4cbd4833136653229c4b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62726f777368\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-browsh\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3af86587ec17b775233a49c21959f73ef8bc6b15c0051db0cb1d241235635441/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62726f777368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3af86587ec17b775233a49c21959f73ef8bc6b15c0051db0cb1d241235635441/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62726f777368\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-browsh\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1df3e071a59c704e00cf554ccca039f39a79e1af0aab077774f3667861022e67/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62726f777368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1df3e071a59c704e00cf554ccca039f39a79e1af0aab077774f3667861022e67/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62726f777368\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-browsh\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c26a4fa48fbb99d7dec96104a642f3370e1430043143f9d36faef559894144eb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62726f777368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c26a4fa48fbb99d7dec96104a642f3370e1430043143f9d36faef559894144eb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62726f777368\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-browsh\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-browsh\u003c/h1\u003e\u003ca id=\"user-content-singularity-browsh\" class=\"anchor\" aria-label=\"Permalink: singularity-browsh\" href=\"#singularity-browsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.brow.sh\" rel=\"nofollow\"\u003ebrowsh\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebrowsh\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/browsh/1.6.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/browsh\u003c/code\u003e as \u003ccode\u003e1.6.4.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-bedtools", + "latest_release": "v2.30.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bedtools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bedtools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bedtools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bedtools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/22f661e5e94b3846e5f54afaf85c1023c03126e1750ee8026abcc863f0440822/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626564746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22f661e5e94b3846e5f54afaf85c1023c03126e1750ee8026abcc863f0440822/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626564746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bedtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" 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src=\"https://camo.githubusercontent.com/645b58875deba1ebd7ec091eade1c85984090c2733a2582ae0eb4d5dc25ebdb8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626564746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bedtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0f0e78a9c8323c2222fcb5d8d3acc383e7ebeef75933d388482e97b0b7ce6584/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626564746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0f0e78a9c8323c2222fcb5d8d3acc383e7ebeef75933d388482e97b0b7ce6584/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626564746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bedtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-bedtools\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-bedtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bedtools\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c477879255fc6a4ecf10f46c416998a1d4cb8bb316920074503c7cfc17b11364/687474703a2f2f7777772e616e647265772e636d752e6564752f757365722f6963616f626572672f706f73742f73696e67756c61726974792d626564746f6f6c732d7570646174652f6c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c477879255fc6a4ecf10f46c416998a1d4cb8bb316920074503c7cfc17b11364/687474703a2f2f7777772e616e647265772e636d752e6564752f757365722f6963616f626572672f706f73742f73696e67756c61726974792d626564746f6f6c732d7570646174652f6c6f676f2e706e67\" width=\"10%\" data-canonical-src=\"http://www.andrew.cmu.edu/user/icaoberg/post/singularity-bedtools-update/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebedtools\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bedtools/2.30.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bedtools\u003c/code\u003e as \u003ccode\u003e2.30.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/tigers/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ "singularity", - "utilities" + "bioinformatics" ], - "updated_at": 1631148643.0 + "updated_at": 1668470461.0 }, { "data_format": 2, - "description": "Example Singularity MPI container (mpich and openmpi)", + "description": null, "filenames": [ - "Singularity.openmpi", - "Singularity.mpich" + "centos/xclock_centos/Singularity", + "centos/turbo_xfce_centos/Singularity.turbo_xfce_centos", + "centos/gnome_centos/Singularity", + "centos/xfce_centos/Singularity", + "ubuntu/gnome_ubuntu/Singularity", + "ubuntu/xclock_ubuntu/Singularity", + "ubuntu/nautilus_ubuntu/Singularity" ], - "full_name": "rse-ops/singularity-mpi", + "full_name": "nesi/nesi-singularity-recipes", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Flux\u003c/h1\u003e\u003ca id=\"user-content-singularity-flux\" class=\"anchor\" aria-label=\"Permalink: Singularity Flux\" href=\"#singularity-flux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will reproduce the example \u003ca href=\"https://docs.sylabs.io/guides/3.10/user-guide/mpi.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull the container with Singularity and oras:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull oras://ghcr.io/rse-ops/singularity-mpi:mpich\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou might want an allocation (with or without userns):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ salloc --userns\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTry running the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mpirun -n 6 singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e singularity-mpi_mpich.sif /opt/mpitest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eHello, I am rank 1/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 2/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 3/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 4/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 0/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 5/6\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then try running with flux\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ flux start mpirun -n 6 singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e singularity-mpi_mpich.sif /opt/mpitest\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4539\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1663784213.0 + "updated_at": 1604055970.0 }, { "data_format": 2, - "description": null, + "description": "bids app wrapper for microstructure diffusion toolbox", "filenames": [ + "Singularity.v0.1", "Singularity" ], - "full_name": "Saford91/openfoam-singularity", - "latest_release": null, + "full_name": "khanlab/mdt-bids", + "latest_release": "v0.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mdt-bids\" class=\"anchor\" aria-hidden=\"true\" href=\"#mdt-bids\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emdt-bids\u003c/h1\u003e\n\u003cp\u003ebids app wrapper for microstructure diffusion toolbox\u003c/p\u003e\n\u003cp\u003ePlease see \u003ca href=\"http://github.com/cbclab/mdt\"\u003ehttp://github.com/cbclab/mdt\u003c/a\u003e for more details\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: ./run.sh \u0026lt;bids_dir\u0026gt; \u0026lt;output_dir\u0026gt; participant \u0026lt;optional arguments\u0026gt;\n\n Required arguments:\n [--in_prepdwi_dir PREPDWI_DIR]\n [--model MODEL (e.g. NODDI)]\n\n Optional arguments:\n [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL...]]\n [--model_fit_opts \"[options for mdt-model-fit]\"\n [--create_protocol_opts \"[options for mdt-create-protocol]\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor HCP WU-Minn data (e.g. HCP 1200 3T), use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--create_protocol_opts \\\"--Delta 21.8e-3 --delta 12.9e-3 --TR 8800e-3 --TE 57e-3\\\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTO DO:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eread in json files to get TR and TE\u003c/li\u003e\n\u003cli\u003eset default --maxG as 0.08 (80 mT/m for our 3T and 7T)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1525881846.0 + "updated_at": 1591844448.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for ms3 mountainlab processor package", "filenames": [ - "Singularity" + "Singularity.v0.0.2" ], - "full_name": "GodloveD/lolcow", + "full_name": "magland/ml_ms3", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ml_ms3\" class=\"anchor\" aria-hidden=\"true\" href=\"#ml_ms3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml_ms3\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for ms3 mountainlab processor package\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1493671740.0 + "updated_at": 1535668701.0 + }, + { + "data_format": 2, + "description": "VCF normalization", + "filenames": [ + "Singularity/Singularity.v1.0", + "Singularity/Singularity.v1.1" + ], + "full_name": "IARCbioinfo/vcf_normalization-nf", + "latest_release": "v1.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-vcf_normalization-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#vcf_normalization-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evcf_normalization-nf\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow-pipeline-for-vcf-normalization\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-for-vcf-normalization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline for vcf normalization\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/vcf_normalization-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/972f6bfd365386090c6b3e3cd6e549a88d55259c394799f2be26101fa1495f52/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f7663665f6e6f726d616c697a6174696f6e2d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/vcf_normalization-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/vcf_normalization-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4381\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"vcf_normalization-nf.png\"\u003e\u003cimg src=\"vcf_normalization-nf.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eApply \u003ca href=\"http://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003ebcftools norm\u003c/a\u003e to decompose and normalize variants from a set of VCF (compressed with gzip/bgzip).\u003c/p\u003e\n\u003cp\u003eThis scripts takes a set of a folder containing \u003ca href=\"https://samtools.github.io/hts-specs/VCFv4.2.pdf\" rel=\"nofollow\"\u003ecompressed VCF files\u003c/a\u003e (\u003ccode\u003e*.vcf.gz\u003c/code\u003e) as an input.\nIt consists at four piped steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(optional) filtering of variants (\u003ccode\u003ebcftoolvs view -f\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003esplit multiallelic sites into biallelic records (\u003ccode\u003ebcftools norm -m -\u003c/code\u003e) and left-alignment and normalization (\u003ccode\u003e-f ref\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003esorting (\u003ccode\u003ebcftools sort \u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eduplicate removal (\u003ccode\u003ebcftools norm -d exact\u003c/code\u003e) and compression (\u003ccode\u003e-Oz\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExternal software:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCaution\u003c/strong\u003e: \u003ccode\u003ebcftools\u003c/code\u003e has to be in your $PATH. Try each of the commands \u003ccode\u003ebcftools\u003c/code\u003e and \u003ccode\u003ebgzip\u003c/code\u003e, if it returns the options this is ok.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--vcf_folder\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eFolder containing tumor zipped VCF files\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--ref\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/path/to/ref.fasta\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eReference fasta file indexed\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--output_folder\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003enormalized_VCF/\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eFolder to output resulting compressed vcf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--filter_opt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-f PASS\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eOptions for bcftools view\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--cpu\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eNumber of cpus to use\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--mem\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003ctd\u003eSize of memory used for mapping (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that the default is to filter variants with the PASS flag. To deactivate, use \u003ccode\u003e--filter_opt \" \"\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--help\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eSimple use case example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/vcf_normalization-nf -r v1.1 -profile singularity --vcf_folder VCF/ --ref ref.fasta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the pipeline without singularity just remove \"-profile singularity\". Alternatively, one can run the pipeline using a docker container (-profile docker) the conda receipe containing all required dependencies (-profile conda).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eVCF.gz, VCF.gz.tbi\u003c/td\u003e\n\u003ctd\u003eCompressed normalized VCF files with indexes\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:alcalan@iarc.fr\"\u003ealcalan@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:delhommet@students.iarc.fr\"\u003edelhommet@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "stargazers_count": 0, + "subscribers_count": 3, + "topics": [], + "updated_at": 1590414197.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.279", - "Singularity.conda-bpy", - "Singularity.deb", - "Singularity.pg", - "Singularity.conda", - "Singularity", - "Singularity.bpy" + "Singularity_recipev5.Shannon", + "Singularity_recipev2.R3.5", + "Singularity_recipev3.Rpackages", + "Singularity_recipe_4.0.3_libraries", + "Singularity_recipe_R4.0.3", + "Singularity_recipe_scenic", + "Singularity_recipev4.PyPackages" ], - "full_name": "darikg/blan_singularity_def", + "full_name": "elisadonnard/singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eblan_singularity_def\u003c/h1\u003e\u003ca id=\"user-content-blan_singularity_def\" class=\"anchor\" aria-label=\"Permalink: blan_singularity_def\" href=\"#blan_singularity_def\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1589238602.0 + "updated_at": 1616106303.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.readfish_77c11e2", + "Singularity.readfish_14ddf60", + "Singularity.clairvoyante_1.01", + "Singularity.minionqc_1.4.2", + "Singularity.guppy_4.2.2", + "Singularity.medaka_v0.10.1", + "Singularity.chopchop_a301f2d", + "Singularity.minknow_20.10.3", + "Singularity.qcat_1.0.1", + "Singularity.guppy-cpu_4.2.2", + "Singularity.porechop_0.2.4" ], - "full_name": "murphygroup/singularity-matlabmcr2018b", + "full_name": "TomHarrop/ont-containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-matlabmcr2018b\u003c/h1\u003e\u003ca id=\"user-content-singularity-matlabmcr2018b\" class=\"anchor\" aria-label=\"Permalink: singularity-matlabmcr2018b\" href=\"#singularity-matlabmcr2018b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.mathworks.com/products/compiler/matlab-runtime.html\" rel=\"nofollow\"\u003eMATLAB Runtime\u003c/a\u003e is a standalone set of shared libraries that enables the execution of compiled MATLAB applications or components on computers that do not have MATLAB installed. When used together, MATLAB, MATLAB Compiler, and the MATLAB Runtime enable you to create and distribute numerical applications or software components quickly and securely.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen contributing to this repository, please first discuss the change you wish to make via issue, \u003ca href=\"mailto:cellorganizer-dev@compbio.cmu.edu\"\u003eemail\u003c/a\u003e, or any other method with the owners of this repository before making a change.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eSupport for \u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e has been provided by grants GM075205, GM090033 and GM103712 from the \u003ca href=\"http://www.nigms.nih.gov/\" rel=\"nofollow\"\u003eNational Institute of General Medical Sciences\u003c/a\u003e, grants MCB1121919 and MCB1121793 from the \u003ca href=\"http://nsf.gov/\" rel=\"nofollow\"\u003eU.S. National Science Foundation\u003c/a\u003e, by a Forschungspreis from the \u003ca href=\"http://www.humboldt-foundation.de/\" rel=\"nofollow\"\u003eAlexander von Humboldt Foundation\u003c/a\u003e, and by the \u003ca href=\"http://www.frias.uni-freiburg.de/lifenet?set_language=en\" rel=\"nofollow\"\u003eFreiburg Institute for Advanced Studies\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.mmbios.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/da21af862d75eb41d1c50d7556125597f1e9c95b7551e77d66d998b7eaf87ce5/68747470733a2f2f69312e77702e636f6d2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f4d4d42696f536c6f676f2d65313530333531373835373331332e6769663f683d3630\" alt=\"MMBioS\" data-canonical-src=\"https://i1.wp.com/www.cellorganizer.org/wp-content/uploads/2017/08/MMBioSlogo-e1503517857313.gif?h=60\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCopyright (c) 2007-2019 by the \u003ca href=\"http://murphylab.web.cmu.edu\" rel=\"nofollow\"\u003eMurphy Lab\u003c/a\u003e at the \u003ca href=\"http://www.cbd.cmu.edu\" rel=\"nofollow\"\u003eComputational Biology Department\u003c/a\u003e in \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-service-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#service-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eService installation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eChange the paths in \u003ccode\u003elaunch_server.sh\u003c/code\u003e and copy it to its location, e.g. \u003ccode\u003e/usr/local/bin\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eCopy the example systemd service and replace the paths in \u003ccode\u003eExecStart\u003c/code\u003e with path to \u003ccode\u003elaunch_server.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall the systemd service\n\u003ccode\u003esudo cp config/guppy.service /etc/systemd/user/\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eStart the service\u003cbr\u003e\n\u003ccode\u003esystemctl --user enable guppy.timer\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esystemctl --user start guppy.timer\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1555428752.0 + "updated_at": 1607563658.0 }, { "data_format": 2, - "description": "This is a snakemake/singularity pipelin for metabarcoding data processing using the OBItools and dada2.", + "description": "MountainLab package with various spike sorting utilities", "filenames": [ - "workflow/envs/Singularity" + "Singularity.v0.1.7" ], - "full_name": "LafontRapnouilTristan/metabarcoding_pipelino", + "full_name": "magland/ml_spikeforest", "latest_release": null, - "readme": "\u003cp\u003eThis pipeline starts from raw foward (R1) and reverse (R2) \u003ccode\u003e.fastq\u003c/code\u003e files and a \u003ccode\u003e.tab\u003c/code\u003e ngsfilter file.\u003c/p\u003e\n\u003cp\u003eThis pipeline aims to respects the \u003ca href=\"https://www.go-fair.org/fair-principles/\" rel=\"nofollow\"\u003eFAIR\u003c/a\u003e principles using \u003ca href=\"https://snakemake.readthedocs.io/en/stable/#\" rel=\"nofollow\"\u003esnakemake\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDescription\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline for raw NGS metabarcoding data processing using a combination of the OBItools, dada2 and sumaclust.\u003c/p\u003e\n\u003cp\u003eYou\u0027ll find parameters used by the pipeline in the \u003ca href=\"config/config.yaml\"\u003econfig file\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDAG of the pipeline:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dag/dag.png\"\u003e\u003cimg src=\"dag/dag.png\" alt=\"DAG of the pipeline\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInput\u003c/h1\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequired\u003c/h2\u003e\u003ca id=\"user-content-required\" class=\"anchor\" aria-label=\"Permalink: Required\" href=\"#required\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment\u003c/h3\u003e\u003ca id=\"user-content-environment\" class=\"anchor\" aria-label=\"Permalink: Environment\" href=\"#environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run this pipeline you need \u003cstrong\u003esnakemake\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFiles\u003c/h3\u003e\u003ca id=\"user-content-files\" class=\"anchor\" aria-label=\"Permalink: Files\" href=\"#files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRaw illumina sequencing output for forward and reverse reads in \u003ccode\u003e.fastq\u003c/code\u003e format\u003c/p\u003e\n\u003cp\u003eForward file named \u003cem\u003eXXX_R1.fastq\u003c/em\u003e and reverse \u003cem\u003eXXX_R2.fastq\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, you will need a text file named \u003cem\u003eXXX_ngsfilter.tab\u003c/em\u003e as required by the \u003ca href=\"https://pythonhosted.org/OBITools/scripts/ngsfilter.html\" rel=\"nofollow\"\u003engsfilter\u003c/a\u003e command of the obitools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTree\u003c/h2\u003e\u003ca id=\"user-content-tree\" class=\"anchor\" aria-label=\"Permalink: Tree\" href=\"#tree\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is what your directory tree should look like in order to run the pipeline.\u003c/p\u003e\n\u003cp\u003eName with \u003ccode\u003e*.extension\u003c/code\u003e are file and other are folders.\u003c/p\u003e\n\u003cp\u003eThe different \u003cstrong\u003erun\u003c/strong\u003e will be independantly processed.\u003c/p\u003e\n\u003cp\u003eMake sure that you have a different folders containing associated resources.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- Snakefile\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- benchmarks\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- config\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- config.yaml\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- dag\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- log\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- report\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- resources\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run1\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run1_ngsfilter.tab\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run1_R1.fastq\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run1_R2.fastq\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run2\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run2_ngsfilter.tab\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run2_R1.fastq\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run2_R2.fastq\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- results\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- workflow\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- envs\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- R_env.yaml\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- obi_env.yaml\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- suma_env.yaml\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- rules\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 01-pairing.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 02-sort_alignments.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 03-demultiplex.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 04-dada_prep.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 05-filterandtrim.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 06-derep.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 07-obi_clean.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 08-abbundance_filt.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 09-bimera_rm.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 10-otu_clust.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 11-merge_clust.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 12-format_out.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 12-seq_tracking.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 12-taxassign.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 13-benchmark.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- scripts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- benchmark.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- derep_dada2.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- filtandtrim_dada2.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- rm_bimera_dada2.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- seq_tracking.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- taxassign_dada2.R\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline steps and tools\u003c/h1\u003e\u003ca id=\"user-content-pipeline-steps-and-tools\" class=\"anchor\" aria-label=\"Permalink: Pipeline steps and tools\" href=\"#pipeline-steps-and-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI - Pre-processing\u003c/h2\u003e\u003ca id=\"user-content-i---pre-processing\" class=\"anchor\" aria-label=\"Permalink: I - Pre-processing\" href=\"#i---pre-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1 - merging paired-end sequenced reads\u003c/h3\u003e\u003ca id=\"user-content-1---merging-paired-end-sequenced-reads\" class=\"anchor\" aria-label=\"Permalink: 1 - merging paired-end sequenced reads\" href=\"#1---merging-paired-end-sequenced-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e - split fasq for faster processing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBITools/scripts/obidistribute.html\" rel=\"nofollow\"\u003e\u003cem\u003eobidistribute\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e : number of files to split in, \u003ccode\u003enfile\u003c/code\u003e in \u003ca href=\"config/config.yaml\"\u003e\u003ccode\u003econfig\u003c/code\u003e\u003c/a\u003e. (between 2 and 1000).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e - align paired-end sequence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/illuminapairedend.html\" rel=\"nofollow\"\u003e\u003cem\u003eilluminapairedend\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e - merge output and remove temp files\u003c/p\u003e\n\u003cp\u003ebasic cat and rm UNIX commands.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2 - filtering alignments\u003c/h3\u003e\u003ca id=\"user-content-2---filtering-alignments\" class=\"anchor\" aria-label=\"Permalink: 2 - filtering alignments\" href=\"#2---filtering-alignments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obiannotate.html\" rel=\"nofollow\"\u003e\u003cem\u003eobiannotate\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-S\u003c/code\u003e : expression used for annotation, ali:\u003ccode\u003egood\u003c/code\u003e if alignment score \u0026gt; \u003ccode\u003eminscore\u003c/code\u003e in \u003ca href=\"config/config.yaml\"\u003e\u003ccode\u003econfig\u003c/code\u003e\u003c/a\u003e.\nelse \u003ccode\u003ebad\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obisplit.html\" rel=\"nofollow\"\u003e\u003cem\u003eobisplit\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-t\u003c/code\u003e : split according to a condition, here \u003ccode\u003eali = good\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-p\u003c/code\u003e : prefix of the resulting files.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3 - demultiplexing and tag/primer trimming\u003c/h3\u003e\u003ca id=\"user-content-3---demultiplexing-and-tagprimer-trimming\" class=\"anchor\" aria-label=\"Permalink: 3 - demultiplexing and tag/primer trimming\" href=\"#3---demultiplexing-and-tagprimer-trimming\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e - annotate average phred quality\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obiannotate.html\" rel=\"nofollow\"\u003e\u003cem\u003eobiannotate\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-S\u003c/code\u003e : expression used for annotation, Avgqphred:-int(math.log10(sum(sequence.quality)/len(sequence))*10)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e - demultiplex according to the ngsfilter file\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/ngsfilter.html\" rel=\"nofollow\"\u003e\u003cem\u003engsfilter\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-ngs\u003c/code\u003e : ngs filter used for the demultiplexing in a \u003ccode\u003e.tab\u003c/code\u003e format.\nCheck \u003ca href=\"##Required\"\u003einput\u003c/a\u003e for details about input format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-u\u003c/code\u003e : name of the unassigned output file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4 - prepare files for dada2\u003c/h3\u003e\u003ca id=\"user-content-4---prepare-files-for-dada2\" class=\"anchor\" aria-label=\"Permalink: 4 - prepare files for dada2\" href=\"#4---prepare-files-for-dada2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obisplit.html\" rel=\"nofollow\"\u003e\u003cem\u003eobisplit\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e : attribute to use for splitting, here \u003ccode\u003esample\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e : path to split into.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5 - sequence quality filtering and trimming\u003c/h3\u003e\u003ca id=\"user-content-5---sequence-quality-filtering-and-trimming\" class=\"anchor\" aria-label=\"Permalink: 5 - sequence quality filtering and trimming\" href=\"#5---sequence-quality-filtering-and-trimming\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003edada2\u003c/strong\u003e - \u003ca href=\"https://rdrr.io/bioc/dada2/man/filterAndTrim.html\" rel=\"nofollow\"\u003e\u003cem\u003efilterAndTrim\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etruncLen\u003c/code\u003e: 200, length at which perform trimming.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emaxN\u003c/code\u003e: 0, maximum number of accepted \u003ccode\u003eN\u003c/code\u003e nucleotides.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emaxEE\u003c/code\u003e: 2, maximum number of accepted errors.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etruncQ\u003c/code\u003e: 2,\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ematchIDs\u003c/code\u003e: TRUE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003everbose\u003c/code\u003e: TRUE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emultithread\u003c/code\u003e: 15\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e6 - sequence dereplication\u003c/h3\u003e\u003ca id=\"user-content-6---sequence-dereplication\" class=\"anchor\" aria-label=\"Permalink: 6 - sequence dereplication\" href=\"#6---sequence-dereplication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003edada2\u003c/strong\u003e - \u003ca href=\"https://rdrr.io/bioc/dada2/man/derepFastq.html\" rel=\"nofollow\"\u003e\u003cem\u003ederepFastq\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003en\u003c/code\u003e : number of sequence simutaneously processed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eII - Key processing\u003c/h2\u003e\u003ca id=\"user-content-ii---key-processing\" class=\"anchor\" aria-label=\"Permalink: II - Key processing\" href=\"#ii---key-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1 - sequencing and error elimination\u003c/h3\u003e\u003ca id=\"user-content-1---sequencing-and-error-elimination\" class=\"anchor\" aria-label=\"Permalink: 1 - sequencing and error elimination\" href=\"#1---sequencing-and-error-elimination\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obiclean.html\" rel=\"nofollow\"\u003e\u003cem\u003eobiclean\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-r\u003c/code\u003e : Threshold ratio between counts (rare/abundant counts) of two sequence records so that the less abundant one is a variant of the more abundant (default: 1, i.e. all less abundant sequences are variants)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-H\u003c/code\u003e : Select only sequences with the head status in a least one sample.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2 - Abundance filtering\u003c/h3\u003e\u003ca id=\"user-content-2---abundance-filtering\" class=\"anchor\" aria-label=\"Permalink: 2 - Abundance filtering\" href=\"#2---abundance-filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBITools/scripts/obigrep.html\" rel=\"nofollow\"\u003e\u003cem\u003eobigrep\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-s\u003c/code\u003e : Regular expression pattern to be tested against the sequence itself. The pattern is case insensitive. Here, \u003ccode\u003e\u0027^[acgt]+$\u0027\u003c/code\u003e , corresponding only to sequence containing no ambiguous nucleotids (\u003cem\u003ee.g.\u003c/em\u003e n).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e : Predicat to filter, here \u003ccode\u003ecount\u0026gt;{params.mincount}\u003c/code\u003e to filter on reads count.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIII - Post-processing\u003c/h2\u003e\u003ca id=\"user-content-iii---post-processing\" class=\"anchor\" aria-label=\"Permalink: III - Post-processing\" href=\"#iii---post-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1 - Chimera removal\u003c/h3\u003e\u003ca id=\"user-content-1---chimera-removal\" class=\"anchor\" aria-label=\"Permalink: 1 - Chimera removal\" href=\"#1---chimera-removal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003edada2\u003c/strong\u003e - \u003ca href=\"https://rdrr.io/bioc/dada2/man/removeBimeraDenovo.html\" rel=\"nofollow\"\u003e\u003cem\u003eremoveBimeraDenovo\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emultithread\u003c/code\u003e : number of thread to use for bimera detection.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2 Sequence clustering\u003c/h3\u003e\u003ca id=\"user-content-2-sequence-clustering\" class=\"anchor\" aria-label=\"Permalink: 2 Sequence clustering\" href=\"#2-sequence-clustering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esumaclust\u003c/strong\u003e - \u003ca href=\"https://git.metabarcoding.org/OBItools/sumaclust/-/wikis/home\" rel=\"nofollow\"\u003e\u003cem\u003esumaclust\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e : Score threshold for clustering (\u003cem\u003ee.g.\u003c/em\u003e 0.97).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e : Threads to use for clustering.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3 Merging Clusters\u003c/h3\u003e\u003ca id=\"user-content-3-merging-clusters\" class=\"anchor\" aria-label=\"Permalink: 3 Merging Clusters\" href=\"#3-merging-clusters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obiselect.html\" rel=\"nofollow\"\u003e\u003cem\u003eobiselect\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-c\u003c/code\u003e : Attribute used to categorize the sequence records, \u003cem\u003ei.e.\u003c/em\u003e \u003ccode\u003ecluster\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e : Indicates how many sequence records per group have to be retrieved, \u003cem\u003ei.e.\u003c/em\u003e \u003ccode\u003e1\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--merge\u003c/code\u003e : Attribute to merge, \u003cem\u003ei.e.\u003c/em\u003e \u003ccode\u003esample\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-f\u003c/code\u003e : function used to score the sequence, \u003cem\u003ei.e.\u003c/em\u003e \u003ccode\u003ecount\u003c/code\u003e to have the reads per sample.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-M\u003c/code\u003e : maximize the \u003ccode\u003e-f\u003c/code\u003e function and order sample IDs in the headers of the sequences by their reads count.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4 Output Formating\u003c/h3\u003e\u003ca id=\"user-content-4-output-formating\" class=\"anchor\" aria-label=\"Permalink: 4 Output Formating\" href=\"#4-output-formating\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obitab.html\" rel=\"nofollow\"\u003e\u003cem\u003eobitab\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e : String written in the table for the not available values (\u003cem\u003ei.e.\u003c/em\u003e NA).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d\u003c/code\u003e : Removes column containing the sequence definition in the output tab file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d\u003c/code\u003e : add column at the end of the tab for the sequence itself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5 Assign taxonomy\u003c/h3\u003e\u003ca id=\"user-content-5-assign-taxonomy\" class=\"anchor\" aria-label=\"Permalink: 5 Assign taxonomy\" href=\"#5-assign-taxonomy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003edada2\u003c/strong\u003e - \u003ca href=\"https://rdrr.io/bioc/dada2/man/assignTaxonomy.html\" rel=\"nofollow\"\u003e\u003cem\u003eassignTaxonomy\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003erefFasta\u003c/code\u003e : Path to the \u003ccode\u003e.fasta\u003c/code\u003e database used to assign taxonomy to the sequence table.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emultithread\u003c/code\u003e : Number of threads used to perform taxonomic assignment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIV - Workflow evaluation\u003c/h2\u003e\u003ca id=\"user-content-iv---workflow-evaluation\" class=\"anchor\" aria-label=\"Permalink: IV - Workflow evaluation\" href=\"#iv---workflow-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1 Sequence tracking\u003c/h3\u003e\u003ca id=\"user-content-1-sequence-tracking\" class=\"anchor\" aria-label=\"Permalink: 1 Sequence tracking\" href=\"#1-sequence-tracking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each step of the workflow, computes the total number of sequences and reads.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2 Benchmark\u003c/h3\u003e\u003ca id=\"user-content-2-benchmark\" class=\"anchor\" aria-label=\"Permalink: 2 Benchmark\" href=\"#2-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each step of the workflow, computes the amount of time and computing resources used and plot them.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ml_spikeforest\" class=\"anchor\" aria-hidden=\"true\" href=\"#ml_spikeforest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml_spikeforest\u003c/h1\u003e\n\u003cp\u003eSpike sorting tools\nMountainLab processor package\u003c/p\u003e\n\u003cp\u003eInstallation from conda (with python 3.6):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -c flatiron -c conda-forge ml_spikeforest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1667832079.0 + "updated_at": 1537460132.0 }, { "data_format": 2, - "description": "Singularity container for deploying smudgeplot", + "description": "These are a collection of scripts we commonly use on the command line for exploring data and for documentation", "filenames": [ - "Singularity" + "Singularity.1.0.1", + "Singularity.1.0.0" ], - "full_name": "HuffordLab-Containers/smudgeplot", + "full_name": "ISUGIFsingularity/utilities", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esmudgeplot\u003c/h1\u003e\u003ca id=\"user-content-smudgeplot\" class=\"anchor\" aria-label=\"Permalink: smudgeplot\" href=\"#smudgeplot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container for deploying smudgeplot\u003c/p\u003e\n\u003cp\u003eOriginal source for this package is found here:\n\u003ca href=\"https://github.com/KamilSJaron/smudgeplot\"\u003ehttps://github.com/KamilSJaron/smudgeplot\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-bioinformatic-scripts-that-we-commonly-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#bioinformatic-scripts-that-we-commonly-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBioinformatic scripts that we commonly use\u003c/h1\u003e\n\u003cp\u003eThis Singularity container is primarily for testing out how containers function. All of the functions included in the container will run without a container. Having them in a container results in a huge performance hit as singularity has to be called and these scripts do not have dependencies that could benefit from a container.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003enb\u003c/strong\u003e = notebook to add text to a notebook but not the readme file\n**readme **= readme will create a README file in the folder and put the date and text into this file along with a copy in the notebook\n\u003cstrong\u003ecreatehist.awk\u003c/strong\u003e = function that will take a binsize argument and a list of numbers and return a count of numbers within increments of binsize\n\u003cstrong\u003eintervalBins.awk\u003c/strong\u003e = modified createhist script that gives the intervals and counts of elements in the interval\n\u003cstrong\u003enew_Assemblathon.pl\u003c/strong\u003e = script that will create summary statistics from a fasta file usually used for genome assemblies (see Assemblathon2 paper)\n**seqlen.awk **= script that will take a fasta file and report the ID and the length of the sequence.\n\u003cstrong\u003ecolsum\u003c/strong\u003e = used to sum the Nth colum of a file.\n\u003cstrong\u003esummary\u003c/strong\u003e = give summary statistics of a column of numbers\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-clone-this-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#clone-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClone this repository\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emkdir isugif\ncd isugif\ngit clone git@github.com:ISUGIFsingularity/utilities.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-place-singularity-container-into-simg-folder-inside-this-repo\" class=\"anchor\" aria-hidden=\"true\" href=\"#place-singularity-container-into-simg-folder-inside-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlace singularity container into SIMG folder inside this repo\u003c/h3\u003e\n\u003cp\u003eYou can pull the singularity image using these commands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd utilities\nmkdir SIMG\ncd SIMG\nsingularity pull shub://ISUGIFsingularity/utilities:1.0.1\nln -s utilities_1.0.1.sif ISUGIFsingularity-utilities-master.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-add-alias-and-path\" class=\"anchor\" aria-hidden=\"true\" href=\"#add-alias-and-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdd Alias and PATH\u003c/h3\u003e\n\u003cp\u003ePlace the following into your .bashrc folder for container use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias UTILITIESgit=Path2thisRepo\nexport PATH=$PATH:$UTILITIESgit/wrappers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlace the following into your .bashrc folder to use scripts without container (preferred method unless testing container functions)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias UTILITIESgit=Path2thisRepo\nexport PATH=$PATH:$UTILITIESgit/utilities\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h3\u003e\n\u003cp\u003eFor this to function properly had to add \u003ccode\u003e--bind $UTILITIESgit:/mnt\u003c/code\u003e to the wrappers\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec --bind $UTILITIESgit:/mnt --bind $PWD $UTILITIESgit/SIMG/ISUGIFsingularity-utilities-master.simg /mnt/utilities/summary.sh\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1612751126.0 + "updated_at": 1560444723.0 }, { "data_format": 2, - "description": "bcftools \u2014 utilities for variant calling and manipulating VCFs and BCFs.", + "description": null, "filenames": [ - "1.10.2/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-bcftools", + "full_name": "KM3NeT/OrcaSong", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bcftools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bcftools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bcftools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bcftools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7ca9386ab21efbdc68e7ef3c00a6dc53638e07bd6b621e85510512c1273746ed/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626366746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ca9386ab21efbdc68e7ef3c00a6dc53638e07bd6b621e85510512c1273746ed/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626366746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bcftools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b2aa819c47bb92269300f234936b1a0be2eebd89037240af5c1474b7848152f0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626366746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b2aa819c47bb92269300f234936b1a0be2eebd89037240af5c1474b7848152f0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626366746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bcftools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9b54f3037e3481e0a19ad82ebd352d2138f1b42ac7e9d0495b4b99cbae77d08e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626366746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9b54f3037e3481e0a19ad82ebd352d2138f1b42ac7e9d0495b4b99cbae77d08e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626366746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bcftools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5afb6c31b9ddfeb6d0bbc5d7e455eaf2a4d8d3f63596867e32743e5cfc036d04/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626366746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5afb6c31b9ddfeb6d0bbc5d7e455eaf2a4d8d3f63596867e32743e5cfc036d04/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626366746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bcftools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-bcftools\u003c/h1\u003e\u003ca id=\"user-content-singularity-bcftools\" class=\"anchor\" aria-label=\"Permalink: singularity-bcftools\" href=\"#singularity-bcftools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/icaoberg/bcftools\"\u003ebcftools\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629217454.0 + "subscribers_count": 4, + "topics": [], + "updated_at": 1625135038.0 }, { "data_format": 2, - "description": "Numerical representation of mountains in atmospheric models", + "description": "WineHQ in a Singularity container", "filenames": [ + "Singularity.4.0.3", + "Singularity.5.0.0", "Singularity" ], - "full_name": "hertzsprung/thesis", - "latest_release": "jshaw-thesis-v2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNumerical representation of mountains in atmospheric models\u003c/h1\u003e\u003ca id=\"user-content-numerical-representation-of-mountains-in-atmospheric-models\" class=\"anchor\" aria-label=\"Permalink: Numerical representation of mountains in atmospheric models\" href=\"#numerical-representation-of-mountains-in-atmospheric-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling the thesis with Singularity\u003c/h2\u003e\u003ca id=\"user-content-compiling-the-thesis-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Compiling the thesis with Singularity\" href=\"#compiling-the-thesis-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e to compile the thesis. On Ubuntu 16.10 and later, \u003ccode\u003esudo apt-get install singularity-container\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce installed,\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eExecute the \u003ca href=\"https://github.com/hertzsprung/AtmosTests\"\u003eAtmosTests\u003c/a\u003e simulations\u003c/li\u003e\n\u003cli\u003eEdit \u003ca href=\"https://github.com/hertzsprung/thesis/blob/master/build.properties\"\u003e\u003ccode\u003ebuild.properties\u003c/code\u003e\u003c/a\u003e so that \u003ccode\u003eatmostests_builddir\u003c/code\u003e points to the \u003ccode\u003eAtmosTests/build\u003c/code\u003e directory\u003c/li\u003e\n\u003cli\u003eExecute \u003ccode\u003e./singularity.bootstrap.sh\u003c/code\u003e to bootstrap the Singularity image, \u003ccode\u003ethesis.img\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExecute \u003ccode\u003e./singularity.ninja.sh\u003c/code\u003e to compile \u003ccode\u003ethesis.pdf\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "OSC/sa_singularity_winehq", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-winehq\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-winehq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity WineHQ\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3891\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.winehq.org/\" rel=\"nofollow\"\u003eWineHQ\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003ewinehq.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build winehq.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploy\u003c/h2\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull winehq.sif shub://OSC/sa_singularity_winehq\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-64-bit-windows-binary\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-64-bit-windows-binary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun 64-bit Windows binary\u003c/h3\u003e\n\u003cp\u003eWineHQ is started using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run winehq.sif /path/to/windows_64bit_exe\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as a native command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./winehq.sif /path/to/windows_64bit_exe\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-32-bit-windows-binary\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-32-bit-windows-binary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun 32-bit Windows binary\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e winehq.sif wine /path/to/windows_32bit_exe\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1537256120.0 + "updated_at": 1581361908.0 }, { "data_format": 2, - "description": null, + "description": "Picard is a set of command line tools for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF. ", "filenames": [ - "Singularity" + "2.23.2/Singularity" ], - "full_name": "ricardovialle/singularity-rstudio-r4", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity RStudio Server\u003c/h1\u003e\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: Singularity RStudio Server\" href=\"#singularity-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eR 4.0.3\nRStudio 1.3.1903\u003c/p\u003e\n\u003cp\u003eBased on repo \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e\nSingularity image for \u003ca href=\"https://www.rstudio.com/products/rstudio/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e. It was built on top of the base\nSingularity image \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-rstudio.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-rstudio.simg rstudio.def\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --arch amd64 library://vigo332/default/singularity-rstudio-r4:v0.01\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRStudio Server\u003c/h3\u003e\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: RStudio Server\" href=\"#rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003erserver\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app rserver singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app rserver singularity-rstudio.simg --help\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecommand-line options:\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003everify:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --verify-installation arg (=0) verify the current installation\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eserver:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-working-dir arg (=/) program working directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-user arg (=rstudio-server) program user\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-daemonize arg (=0) run program as daemon\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-app-armor-enabled arg (=1) is app armor enabled for this session\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-set-umask arg (=1) set the umask to 022 on startup\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSimple Password Authentication\u003c/h4\u003e\u003ca id=\"user-content-simple-password-authentication\" class=\"anchor\" aria-label=\"Permalink: Simple Password Authentication\" href=\"#simple-password-authentication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo secure the RStudio Server you will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLaunch the container with the environment variable \u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e set to\na password of your choosing.\u003c/li\u003e\n\u003cli\u003eLaunch the \u003ccode\u003erserver\u003c/code\u003e command with the PAM helper script \u003ccode\u003erstudio_auth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAn example is given as:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRSTUDIO_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e singularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path=pam-helper \\\n --server-data-dir=/tmp\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow when you attempt to access the RStudio Server you will be presented with a\nlog in form. You can log in with your current user name and password you set in\n\u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLDAP Authentication -- To be verified\u003c/h4\u003e\u003ca id=\"user-content-ldap-authentication----to-be-verified\" class=\"anchor\" aria-label=\"Permalink: LDAP Authentication -- To be verified\" href=\"#ldap-authentication----to-be-verified\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnother option is using an LDAP (or Active Directory) server for\nauthentication. Configuration of the LDAP authentication script \u003ccode\u003eldap_auth\u003c/code\u003e is\nhandled through the following environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_HOST\u003c/code\u003e - the host name of the LDAP server\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_USER_DN\u003c/code\u003e - the formatted string (where \u003ccode\u003e%s\u003c/code\u003e is replaced with the\nusername supplied during log in) of the bind DN used for LDAP authentication\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e - the file containing the CA certificates used by\nthe LDAP server (default: use system CA certificates)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn example for an LDAP server with signed SSL certificate from a trusted CA:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nsingularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn example for an LDAP server with a self-signed SSL certificate:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_CERT_FILE=/ca-certs.pem\nsingularity run \\\n --bind /path/to/ca-certs.pem:/ca-certs.pem \\\n singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that we had to bind mount the CA certificates file from the host machine\ninto the container and specify the container\u0027s path in \u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e (not\nthe host\u0027s path).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR and Rscript\u003c/h3\u003e\u003ca id=\"user-content-r-and-rscript\" class=\"anchor\" aria-label=\"Permalink: R and Rscript\" href=\"#r-and-rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e for more information on how to run \u003ccode\u003eR\u003c/code\u003e and\n\u003ccode\u003eRscript\u003c/code\u003e from within this Singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-picard", + "latest_release": "v2.23.2", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-picard/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-picard/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f2ee028725767bd1588c30ee90365dddfc357c08ce7b5a43ed492ec5987e19f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d706963617264\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f2ee028725767bd1588c30ee90365dddfc357c08ce7b5a43ed492ec5987e19f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d706963617264\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-picard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0011e731d3015546848fd1f5982cbad69352604dc45cd908e9ff27c2773e8107/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d706963617264\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0011e731d3015546848fd1f5982cbad69352604dc45cd908e9ff27c2773e8107/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d706963617264\" alt=\"forks\" 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href=\"https://camo.githubusercontent.com/b6afcdc9a6fce9707e6a449e49036b6136dd0335325684f4c8605d441b992e1f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d706963617264\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6afcdc9a6fce9707e6a449e49036b6136dd0335325684f4c8605d441b992e1f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d706963617264\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-picard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-picard\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-picard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-picard\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/PIGER\"\u003ePicard\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003epicard\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/picard/2.23.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/picard\u003c/code\u003e as \u003ccode\u003e2.23.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, - "topics": [], - "updated_at": 1629302355.0 + "subscribers_count": 3, + "topics": [ + "bioinformatics", + "singularity" + ], + "updated_at": 1628991999.0 }, { "data_format": 2, - "description": "Singularity images for MITgcm testing and examples", + "description": null, "filenames": [ - "Singularity" + "Singularity.template", + "bamcmp/Singularity.bamcmp", + "star-fusion/Singularity.star-fusion", + "bcl2fastq/Singularity.bcl2fastq" ], - "full_name": "christophernhill/mitgcm-shub-images", + "full_name": "BUBioinformaticsHub/bubhub-singularity-apps", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emitgcm-shub-images\u003c/h1\u003e\u003ca id=\"user-content-mitgcm-shub-images\" class=\"anchor\" aria-label=\"Permalink: mitgcm-shub-images\" href=\"#mitgcm-shub-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity images for MITgcm testing and examples\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esee \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003ehttps://singularity-hub.org\u003c/a\u003e and \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov\u003c/a\u003e, and also \u003ca href=\"https://hub.docker.com\" rel=\"nofollow\"\u003ehttps://hub.docker.com\u003c/a\u003e and \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003ehttps://www.docker.com\u003c/a\u003e for background on material here.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn MITgcm containers through Docker and Singluarity can provide a way to test code and maintain strong portability and interoperability and more complete workflow reproducibility. Many other projects use Docker and Singularity to share code that is neither portable or interoperable.\u003c/p\u003e\n\u003cp\u003eThere are also images here that allow MITgcm to work with example codes that are not particularly portable or interoperable. This is only possible because the core MITgcm code is widely tested on many platforms to ensure portability and interoperability. Importantly, for this to work, Singularity and/or Docker should not be a way to create MITgcm setups that unecessairily break portability, interoperability, reproducibility or ease of use by adopting narrow software stacks or user unfriendly code practices.\u003c/p\u003e\n\u003cp\u003eMainstream code in MITgcm should continue to be written with goals, as far as possible, that most model configurations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ecan run on many platforms, including (but not limited to!) a wide array of Linux distributions, handheld devices, Windows, OSX and iOS devices, single board computers.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecan checkpoint and restart in order to run in multiple sub-steps.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esupports validating exact and statistical reproducibility, even in the presence of floating point round off\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003einclude appropriate documentation of key features and modes of use\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etries to make minimum necessary assumptions about domain geometry and/or model equation configuration(s) to which the code will be applied\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etries to maintain robust compatibility with automatice differentiation where relevant and possible\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFollowing these guidelines helps create code that can be applied to the broadest range of problems; spanning, for example, laboratory experiments to Jovian deep atmospheres to Earth system ecosystem sensitvity calculations. The guidelines also serve to make meaningful coupling to external components (such as atmospheric model suites) relatively straightforward and flexible.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-apps\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity apps\u003c/h1\u003e\n\u003cp\u003eThis repo contains Singularity build images for various bubhub tools\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1517003494.0 + "updated_at": 1539704222.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.tf-nightly", + "Singularity.compute-0-27", + "Singularity.compute-0-36" ], - "full_name": "callaghanmt-containers/decryptics", + "full_name": "bstriner/tensorflow-cuda-10.0-cudnn7-devel-ubuntu16.04", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edecryptics\u003c/h1\u003e\u003ca id=\"user-content-decryptics\" class=\"anchor\" aria-label=\"Permalink: decryptics\" href=\"#decryptics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tensorflow-cuda-100-cudnn7-devel-ubuntu1604\" class=\"anchor\" aria-hidden=\"true\" href=\"#tensorflow-cuda-100-cudnn7-devel-ubuntu1604\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etensorflow-cuda-10.0-cudnn7-devel-ubuntu16.04\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1553554241.0 + "updated_at": 1560847380.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.v0.0.1" ], - "full_name": "phgenomics-singularity/snippy", + "full_name": "baxpr/cerconn", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cerconn-cerebellar-functional-connectivity-maps\" class=\"anchor\" aria-hidden=\"true\" href=\"#cerconn-cerebellar-functional-connectivity-maps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecerconn: cerebellar functional connectivity maps\u003c/h1\u003e\n\u003cp\u003eSeed regions are the Buckner 7 set as produced by cersuit pipeline. Four sets are computed: with\nand without removal of the mean gray matter signal by regression; with and without erosion of the\nseed ROIs with a 1-voxel radius spherical kernel. Both bivariate Pearson correlation and partial\ncorrelation with respect to the other seed regions are computed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eFrom cersuit_v2 cerebellar segmentation pipeline\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecerseg_niigz Native space Buckner7 segmentation, ATLASES_NATIVE iw_Buckner_7Networks_u_a_c_rt1_seg1.nii.gz\nwcerseg_niigz Atlas space Buckner7 segmentation, ATLASES_SUIT Buckner_7Networks.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eFrom CAT12, e.g. cat12_ss2p0_v2 pipeline\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewmt1_niigz MNI space bias corrected T1, BIAS_NORM\nfwddef_niigz Forward deformation from native to MNI, DEF_FWD\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eFrom connectivity preprocessing pipeline connprep_v2\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eremovegm_niigz Native space with mean gray signal removed, FILTERED_REMOVEGM_NATIVE\nkeepgm_niigz Native space with mean gray signal kept, FILTERED_KEEPGM_NATIVE\nmeanfmri_niigz Native space mean fmri, MEAN_FMRI_NATIVE\nwmeanfmri_niigz MNI space mean fmri, MEAN_FMRI_MNI\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eOther options\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erefimg_nii Filename of existing image for geometry reference (\u0027avg152T1.nii\u0027,\u0027mask_ICV.nii\u0027)\nfwhm Smoothing kernel for connectivity maps, in mm\nout_dir Output directory\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eInfo for PDF report title if run on XNAT\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eproject\nsubject\nsession\nscan\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eFor testing only\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efsl_dir Location of FSL installation\nmagick_dir Location of ImageMagick binaries\nsrc_dir Location of pipeline shell scripts\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eROIS Seed ROI images in native fMRI geometry (eroded and non-eroded) and list of ROI labels\n\nEROIS Eroded seed ROI images in native T1 geometry\n\nFMRIMASK Native fMRI space mask used to exclude voxels without fMRI signal from seeds\n\nCONNMAP Connectivity maps for the seed ROIs. There are a number of different types:\n\n R_* Pearson correlation\n Z_* Fisher Z transform of Pearson correlation\n pR_* Partial correlation conditioning on the the other seeds\n pZ_* Fisher transform of the partial correlation\n \n *E* Indicates eroded seed ROIs (no E indicates uneroded ROIs)\n \n REMOVEGM Mean gray matter removed during preprocessing\n KEEPGM Mean gray matter retained during preprocessing\n \n _MNI Indicates MNI space images (no _MNI indicates native space)\n\nSCONNMAP Smoothed connectivity maps. As above.\n\nCONNMAT Connectivity matrices for seed ROIs. As above.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1576562320.0 + "updated_at": 1616681036.0 }, { "data_format": 2, - "description": "It is for ptsim using cvmfs in singularity conitaner", + "description": "Container with xrootd for file xfer from FNAL", "filenames": [ "Singularity" ], - "full_name": "ifurther/ptsim-singularity", + "full_name": "LArbys/singularity-xrootd", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-for-root-with-xrootd\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-for-root-with-xrootd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity for ROOT with XROOTD\u003c/h1\u003e\n\u003cp\u003eContainer to be used for file xfer from FNAL.\nIn order to xfer files, the container must have its certificates to FNAL updated periodically.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-steps-to-using-this-container-and-image-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#steps-to-using-this-container-and-image-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to using this container and image (from scratch)\u003c/h1\u003e\n\u003cp\u003ePart 1: build the container\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild the container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePart 2: move the container to Tufts\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ego to the Tufts cluster (and probably to some place your user directory)\u003c/li\u003e\n\u003cli\u003eclone this repository\u003c/li\u003e\n\u003cli\u003ecopy the container to the repository folder as there are scripts we will use\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePart 3: renew your user grid certificate on a MicroBooNE machine\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elog into the microboone machine\u003c/li\u003e\n\u003cli\u003eon the UBmachine: load/renew your certificates, find your UID\u003c/li\u003e\n\u003cli\u003eback on tufts cluster: make a copy of example_setup_container_X.sh and edit it as instructed\u003c/li\u003e\n\u003cli\u003estart the container, go to the repo dir, run the script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePart 4: make a list of files to transfer. either:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emake a filelist\u003c/li\u003e\n\u003cli\u003eretrieve or setup a SAM definition\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePart 5: setup xfer_script and run\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container\u003c/h2\u003e\n\u003cp\u003eFirst find a computer that has docker and singularity (e.g. meitner). You will also need \u003ccode\u003esudo\u003c/code\u003e access to build the container.\u003c/p\u003e\n\u003cp\u003eClone this repo onto a computer:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/larbys/singularity-xrootd\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, follow the steps below to grab the required certificates from uboonebuild and copy them to your local machine\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emake sure your computer is setup to be able to get a FNAL kerberos ticket (i.e. \u003ccode\u003ekinit\u003c/code\u003e workse)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eclone this repo to your computer\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eget a kerberos ticket: \u003ccode\u003ekinit [username]@FNAL.GOV\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003emake the directory \u003ccode\u003e/tmp/$USER\u003c/code\u003e to hold certificates (must be somewhere in \u003ccode\u003e/tmp\u003c/code\u003e in order for singularity to read from outside the new container)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eif running this not for the first time, make sure \u003ccode\u003e/tmp/$USER/grid-security\u003c/code\u003e and \u003ccode\u003e/tmp/$USER/vomses\u003c/code\u003e are removed from your \u003ccode\u003e/tmp/$USER/\u003c/code\u003e folder\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003escp -r \u003ccode\u003e/etc/grid-security\u003c/code\u003e and \u003ccode\u003e/etc/vomses\u003c/code\u003e to your \u003ccode\u003e/tmp/$USER\u003c/code\u003e folder from one of the uboone gpvms.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escp -r fnal-username@ubcomputer:/etc/grid-security /tmp/$USER/\nscp fnal-username@ubcomputer:/etc/vomses /tmp/$USER/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecp \u003ccode\u003e/etc/krb5.conf\u003c/code\u003e to \u003ccode\u003e/tmp/$USER/\u003c/code\u003e or get this from the web using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://cdcvs.fnal.gov/redmine/attachments/download/9616/krb5.conf /tmp/$USER/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ego into \u003ccode\u003eSingularity\u003c/code\u003e file (this is the build instructions), and set your username at the line\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport USER=your-name-here\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, build the container using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-xrootd.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBasically what is happening is that, while being built, the container can see your system\u0027s \u003ccode\u003e/tmp\u003c/code\u003e folder.\nSo we put the required security files into \u003ccode\u003e/tmp\u003c/code\u003e and these get copied into the container\u0027s \u003ccode\u003e/etc/\u003c/code\u003e folder when it is built.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing container\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ecopy the new container to the Tuft\u0027s grid at: \u003ccode\u003e/cluster/tufts/wongjiradlab/larbys/images/singularity-xrootd\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003estart the container using \u003ccode\u003esource start_container.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eyou\u0027ll see a prompt once you are in the container. type \u003ccode\u003ebash\u003c/code\u003e to start a bash shell\u003c/li\u003e\n\u003cli\u003enavigate back to the container folder: \u003ccode\u003ecd /cluster/kappa/wongjiradlab/larbys/images/singularity-xrootd\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ein another terminal, log into one of the uboone gpvms. refresh your vomses certificates.\u003c/li\u003e\n\u003cli\u003emake a copy of \u003ccode\u003eexample_setup_container_X.sh\u003c/code\u003e, where \u003ccode\u003eX\u003c/code\u003e is your FNAL username\u003c/li\u003e\n\u003cli\u003echange XXXXXX with your FNAL username and YYYYYY with your user id\u003c/li\u003e\n\u003cli\u003erun this script\u003c/li\u003e\n\u003cli\u003eyou can test that your container now has permissions to use xrootd to access PNFS by running: \u003ccode\u003epython test_setup.py\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1641846771.0 + "updated_at": 1614268311.0 }, { "data_format": 2, - "description": "Singularity port of Picard tools", + "description": null, "filenames": [ - "Singularity" + "Singularity.dev" ], - "full_name": "researchapps/singularity-picard", + "full_name": "pndni/minc-ants-and-fsl-container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Picard\u003c/h1\u003e\u003ca id=\"user-content-singularity-picard\" class=\"anchor\" aria-label=\"Permalink: Singularity Picard\" href=\"#singularity-picard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 picard.img\nsudo singularity bootstrap picard.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the picard runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./picard.img [args] ...\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1479258335.0 + "updated_at": 1555092034.0 }, { "data_format": 2, - "description": "container_openmpi_gnu/_centos7_x86_64", + "description": null, "filenames": [ - "Singularity" + "Singularity.latest" ], - "full_name": "CINECA-HPC/container_openmpi_gnu7_centos7_x86_64", + "full_name": "bioexcel/acpype_container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_openmpi_gnu7_centos7_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_openmpi_gnu7_centos7_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_openmpi_gnu7_centos7_x86_64\" href=\"#container_openmpi_gnu7_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003econtainer_openmpi_gnu/_centos7_x86_64\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/mmbirb/acpype\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b48b2d8417bd2482df9aa6a920101731c2f1a73416deb25291044c5278738d4a/68747470733a2f2f717561792e696f2f7265706f7369746f72792f62696f636f6e7461696e6572732f62696f62625f696f2f737461747573\" alt=\"\" data-canonical-src=\"https://quay.io/repository/biocontainers/biobb_io/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3787\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-acpype-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#acpype-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eACPYPE container\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eACPYPE docker and singularity containers used for \u003ca href=\"https://github.com/bioexcel/biobb_chemistry\"\u003ebiobb_chemistry\u003c/a\u003e BioExcel Building Blocks modules.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Use\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull mmbirb/acpype:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run mmbirb/acpype:latest \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Use\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name acpype.sif shub://bioexcel/acpype_container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec acpype.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" aria-hidden=\"true\" href=\"#copyright--licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-acknolegements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknolegements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknolegements\u003c/h3\u003e\n\u003cp\u003eThe singularity container has been built through \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1605607119.0 + "updated_at": 1584436958.0 }, { "data_format": 2, - "description": "official build specifications for scientific linux", + "description": "This is a Nextflow pipeline for generating sequencing reports for the SNP\u0026Seq Technology platform, NGI Uppsala, SciLifelab Genomics.", "filenames": [ - "Singularity", - "7.0/Singularity" + "images/Singularity.checkqc-3.6.0" ], - "full_name": "singularityhub/scientific-linux", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScientific Linux\u003c/h1\u003e\u003ca id=\"user-content-scientific-linux\" class=\"anchor\" aria-label=\"Permalink: Scientific Linux\" href=\"#scientific-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official library of scientific linux builds for Singularity images hosted on Singularity Hub. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003ethe builds are organized by folders, with one \u003ccode\u003eSingularity\u003c/code\u003e file per folder. This ensures that we can find the files programatically.\u003c/li\u003e\n\u003cli\u003ethe different image tags correspond to these folders, and the name of the tag is specified on Singularity Hub\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUnder Development\u003c/h2\u003e\u003ca id=\"user-content-under-development\" class=\"anchor\" aria-label=\"Permalink: Under Development\" href=\"#under-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese images are currently not connected to Singularity Hub, but this will be done in early 2017. The first effort will be to develop a core set of images, and then any necessary builders / templating systems that would be necessary to help with this process. If you are interested in contributing, please \u003ca href=\"http://singularity.lbl.gov/contributing-code\" rel=\"nofollow\"\u003ereach out\u003c/a\u003e!\u003c/p\u003e\n", + "full_name": "Molmed/seqreports", + "latest_release": "v1.1.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-seqreports-snpseq-run-folder-qc-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#seqreports-snpseq-run-folder-qc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eseqreports: SNP\u0026amp;Seq Run folder QC pipeline\u003c/h1\u003e\n\u003cp\u003eThis is a Nextflow pipeline for generating sequencing reports for the SNP\u0026amp;Seq Technology platform, NGI Uppsala, SciLifelab Genomics.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-requisites\u003c/h2\u003e\n\u003cp\u003eYou need to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstall Nextflow (e.g. using conda \u003ccode\u003econda create -n nextflow-env nextflow\u003c/code\u003e or downloading from \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow.io\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://singularity.lbl.gov/install-linux#adding-the-mirror-and-installing\" rel=\"nofollow\"\u003eSingularity (version \u0026gt; 2.6)\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(currently mandatory: see known issues) Download the fastq-screen database by downloading fastq-screen from \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/fastq_screen_v0.13.0.tar.gz\" rel=\"nofollow\"\u003ehere\u003c/a\u003e, extract the archive and then run \u003ccode\u003efastq_screen --get_genomes\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-the-nextflow-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-the-nextflow-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the nextflow pipeline\u003c/h2\u003e\n\u003cp\u003eAwesome, you\u0027re all set! Let\u0027s try generating reports for your favourite runfolder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Using parameters supplied in a config (see below)\u003c/span\u003e\n nextflow run -c custom.config -profile snpseq,singularity main.nf\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Using parameters supplied on the command line\u003c/span\u003e\n nextflow run -profile snpseq,singularity main.nf \\\n --run_folder \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/path/to/runfolder\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n --fastqscreen_databases \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/path/to/databases\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n --checkqc_config \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/path/to/checkqc.config\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-profiles\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-profiles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable profiles\u003c/h3\u003e\n\u003cp\u003eThese are the primary config profiles:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edev\u003c/code\u003e: Run locally with low memory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eirma\u003c/code\u003e: Uppmax slurm profile for use on the cluster \u003ccode\u003eirma\u003c/code\u003e (note: The parameter \u003ccode\u003eparams.project\u003c/code\u003e must be supplied).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esnpseq\u003c/code\u003e: Run locally with greater memory available than \u003ccode\u003edev\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity\u003c/code\u003e: Enables singularity and provides container URLs.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest\u003c/code\u003e: Run the pipeline using test data\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdditional profiles:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edebug\u003c/code\u003e: prints out the \u003ccode\u003eenv\u003c/code\u003e properties before executing processes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-supplying-a-custom-config-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#supplying-a-custom-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupplying a custom config file\u003c/h3\u003e\n\u003cp\u003eCustom config files can contain all command line parameters, nextflow parameters, and overriding options.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eresume = true\nparams.run_folder = \u0027/path/to/runfolder\u0027\nparams.fastqscreen_databases = \u0027/path/to/databases\u0027\nparams.checkqc_config = \u0027/path/to/checkqc.config\u0027\nworkDir = \u0027/path/to/temporary/storage/space\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h2\u003e\n\u003cp\u003eThere are two primary branches of this project:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emaster\u003c/code\u003e: The stable release branch\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edev\u003c/code\u003e: The development and test branch, to which pull requests should be made.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTests are run through GitHub Actions when pushing code to the repo. See instructions below on how to reproduce it locally.\u003c/p\u003e\n\u003cp\u003eTo keep the python parts of the project nice and tidy, we enforce that code should be formatted according to \u003ca href=\"https://github.com/psf/black\"\u003eblack\u003c/a\u003e.\nTo re-format your code with black, simply run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eblack .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-tests-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-tests-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning tests locally\u003c/h3\u003e\n\u003cp\u003eAssuming you have installed all pre-requisites (except the fastq screen database: test data comes with a minimal version of it), you can run tests locally by following these steps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# create virtual environment \nvirtualenv -p python3.9 venv/ \n\n# activate venv\nsource venv/bin/activate\n\n# install dependencies\npip install -r requirements-dev.txt\n\n# run tests\npytest tests/\n\n# perform black formatter check\nblack --check .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown issues:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eUnable to download genome indicies using \u003ccode\u003efastq_screen --get_genomes\u003c/code\u003e as wget within the container does not resolve the address correctly. Fastq Screen must be installed separately (e.g. with conda) and the genomes downloaded prior to running the workflow. The path to the databases must then be given using the \u003ccode\u003eparams.fastqscreen_databases\u003c/code\u003e parameter.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 10, "topics": [], - "updated_at": 1484506870.0 + "updated_at": 1644241679.0 }, { "data_format": 2, - "description": "Haploid bacterial assembly and automatic annotation implemented using Nextflow", + "description": "Build recipe for a singularity container running RStudio Server.", "filenames": [ + "Singularity.3.6.2", "Singularity" ], - "full_name": "BU-ISCIII/bacterial_assembly-nf", + "full_name": "gparadis/singularity-rstudio", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity RStudio Server\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nickjer/singularity-rstudio\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/291de9d065fa77b739def518b0430f977c5793f78b1b4ce88d235e61c42332ee/68747470733a2f2f7472617669732d63692e6f72672f6e69636b6a65722f73696e67756c61726974792d7273747564696f2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nickjer/singularity-rstudio.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/463\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.rstudio.com/products/rstudio/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e. It was built on top of the base\nSingularity image \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-rstudio.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-rstudio.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploy\u003c/h2\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name singularity-rstudio.simg shub://nickjer/singularity-rstudio\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRStudio Server\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003erserver\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app rserver singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app rserver singularity-rstudio.simg --help\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecommand-line options:\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003everify:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --verify-installation arg (=0) verify the current installation\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eserver:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-working-dir arg (=/) program working directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-user arg (=rstudio-server) program user\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-daemonize arg (=0) run program as daemon\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-app-armor-enabled arg (=1) is app armor enabled for this session\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-set-umask arg (=1) set the umask to 022 on startup\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-simple-password-authentication\" class=\"anchor\" aria-hidden=\"true\" href=\"#simple-password-authentication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimple Password Authentication\u003c/h4\u003e\n\u003cp\u003eTo secure the RStudio Server you will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLaunch the container with the environment variable \u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e set to\na password of your choosing.\u003c/li\u003e\n\u003cli\u003eLaunch the \u003ccode\u003erserver\u003c/code\u003e command with the PAM helper script \u003ccode\u003erstudio_auth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAn example is given as:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRSTUDIO_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e singularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper rstudio_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow when you attempt to access the RStudio Server you will be presented with a\nlog in form. You can log in with your current user name and password you set in\n\u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-ldap-authentication\" class=\"anchor\" aria-hidden=\"true\" href=\"#ldap-authentication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLDAP Authentication\u003c/h4\u003e\n\u003cp\u003eAnother option is using an LDAP (or Active Directory) server for\nauthentication. Configuration of the LDAP authentication script \u003ccode\u003eldap_auth\u003c/code\u003e is\nhandled through the following environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_HOST\u003c/code\u003e - the host name of the LDAP server\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_USER_DN\u003c/code\u003e - the formatted string (where \u003ccode\u003e%s\u003c/code\u003e is replaced with the\nusername supplied during log in) of the bind DN used for LDAP authentication\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e - the file containing the CA certificates used by\nthe LDAP server (default: use system CA certificates)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn example for an LDAP server with signed SSL certificate from a trusted CA:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nsingularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn example for an LDAP server with a self-signed SSL certificate:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_CERT_FILE=/ca-certs.pem\nsingularity run \\\n --bind /path/to/ca-certs.pem:/ca-certs.pem \\\n singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that we had to bind mount the CA certificates file from the host machine\ninto the container and specify the container\u0027s path in \u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e (not\nthe host\u0027s path).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-r-and-rscript\" class=\"anchor\" aria-hidden=\"true\" href=\"#r-and-rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR and Rscript\u003c/h3\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e for more information on how to run \u003ccode\u003eR\u003c/code\u003e and\n\u003ccode\u003eRscript\u003c/code\u003e from within this Singularity image.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1650379680.0 + "updated_at": 1597274627.0 }, { "data_format": 2, - "description": "Singularity container for canu", + "description": null, "filenames": [ - "Singularity.1.8.0" + "Singularity.biopython_1.78", + "Singularity.pandas_0.25.3" ], - "full_name": "ISUGIFsingularity/canu", + "full_name": "TomHarrop/py-containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container recipe and run script for the MaSuRCA assembler\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-recipe-and-run-script-for-the-masurca-assembler\" class=\"anchor\" aria-label=\"Permalink: Singularity container recipe and run script for the MaSuRCA assembler\" href=\"#singularity-container-recipe-and-run-script-for-the-masurca-assembler\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClone this repository\u003c/h3\u003e\u003ca id=\"user-content-clone-this-repository\" class=\"anchor\" aria-label=\"Permalink: Clone this repository\" href=\"#clone-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emkdir isugif\ncd isugif\ngit clone git@github.com:ISUGIFsingularity/canu.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlace singularity container into SIMG folder inside this repo\u003c/h3\u003e\u003ca id=\"user-content-place-singularity-container-into-simg-folder-inside-this-repo\" class=\"anchor\" aria-label=\"Permalink: Place singularity container into SIMG folder inside this repo\" href=\"#place-singularity-container-into-simg-folder-inside-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can pull the singularity image using these commands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd canu\nmkdir SIMG\ncd SIMG\nsingularity pull shub://ISUGIFsingularity/canu:1.8.0\nln -s ISUGIFsingularity-canu-master-1.8.0.simg ISUGIFsingularity-canu-master.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd Alias and PATH\u003c/h3\u003e\u003ca id=\"user-content-add-alias-and-path\" class=\"anchor\" aria-label=\"Permalink: Add Alias and PATH\" href=\"#add-alias-and-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlace the following into your .bashrc folder for container use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#make sure you are in the canu folder that corresponds to the Path2thisRepo\nexport canugit=`pwd`\nexport PATH=$PATH:$canugit/wrappers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlace the following into your .bashrc folder to use scripts without container (preferred method unless testing container functions)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport canugit=path2thisrepo\nexport PATH=$PATH:$canugit/canu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNotes\u003c/h3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor this to function properly had to add \u003ccode\u003e--bind $canugit:/mnt\u003c/code\u003e to the wrappers\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec --bind $canugit:/mnt --bind $PWD $canugit/SIMG/ISUGIFsingularity-canu-master.simg /mnt/canu/summary.sh\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1550868367.0 + "updated_at": 1602640832.0 }, { "data_format": 2, - "description": "Singularity container with a working version of the stringr R package", + "description": "Angsd_Singularity_Install", "filenames": [ "Singularity" ], - "full_name": "richelbilderbeek/stringr_singularity", + "full_name": "carte731/Angsd_Singularity_Install", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003estringr_singularity\u003c/h1\u003e\u003ca id=\"user-content-stringr_singularity\" class=\"anchor\" aria-label=\"Permalink: stringr_singularity\" href=\"#stringr_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/richelbilderbeek/stringr_singularity/actions/workflows/build_singularity.yaml\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/stringr_singularity/actions/workflows/build_singularity.yaml/badge.svg\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container with a working version of the \u003ccode\u003estringr\u003c/code\u003e R package.\u003c/p\u003e\n\u003cp\u003eIt does run remotely (i.e. on GitHub Actions),\nbut not on my Ubuntu 20.04 LTS laptop).\u003c/p\u003e\n\u003cp\u003eBecause I do not understand why, I\n\u003ca href=\"https://stackoverflow.com/questions/71252123/singularity-container-with-stringr-fails-only-locally-with-libicui18n-so-66-ca\" rel=\"nofollow\"\u003eposted a question on StackOverflow\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity install recipe for Angsd-Wrapper program. University of Minnesota - Twin Cities, Morrell Lab.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1645702896.0 + "updated_at": 1566918717.0 }, { "data_format": 2, - "description": "A repository with simple singularity recipes for tutorial purpose", + "description": null, "filenames": [ - "Singularity.ub16.04-step1", - "Singularity.ub16.04-step0", - "Singularity.ub16.04-step2", - "Singularity.ub16.04-step3", - "Singularity.ub16.04-step4" + "containers/Singularity", + "containers/Singularity_freesurfer_and_fastsurfer.def" ], - "full_name": "DeepLearnPhysics/playground-singularity", + "full_name": "neurodatascience/watts_up_compute", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://raw.githubusercontent.com/DeepLearnPhysics/playground-singularity/master/LICENSE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e581ac49b7e1e99fb951242be63f6fdc6ebbc20c89a97fca0de99e1f2e6ae87e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6d6173686170652f6170697374617475732e737667\" alt=\"license\" data-canonical-src=\"https://img.shields.io/github/license/mashape/apistatus.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/459\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eplayground-singularity\u003c/h1\u003e\u003ca id=\"user-content-playground-singularity\" class=\"anchor\" aria-label=\"Permalink: playground-singularity\" href=\"#playground-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA repository with simple singularity recipes for tutorial purpose. Checkout the \u003ca href=\"https://github.com/DeepLearnPhysics/playground-singularity/wiki\"\u003ewiki\u003c/a\u003e for documentation!\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-watts_up_compute\" class=\"anchor\" aria-hidden=\"true\" href=\"#watts_up_compute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewatts_up_compute\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-code-repo-to-assess-compute-costs-of-neuroimaging-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#code-repo-to-assess-compute-costs-of-neuroimaging-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode repo to assess compute costs of neuroimaging pipelines\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-motivation\" class=\"anchor\" aria-hidden=\"true\" href=\"#motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIncreasing supply of large datasets and machine-learning models\u003c/li\u003e\n\u003cli\u003eGrowing demand for computational resources exceeding Moore\u2019s law [\u003ca href=\"https://openai.com/blog/ai-and-compute/\" rel=\"nofollow\"\u003e1\u003c/a\u003e, \u003ca href=\"https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/\" rel=\"nofollow\"\u003e2\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/1907.10597\" rel=\"nofollow\"\u003e3\u003c/a\u003e, \u003ca href=\"https://dl.acm.org/doi/10.1145/3442188.3445922\" rel=\"nofollow\"\u003e4\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2104.10350\" rel=\"nofollow\"\u003e5\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eEstimated carbon footprint of AI model: 284,000 Kgs of CO2 (5x lifetime emissions of a car or 300x RT-flights for single passenger between NYC and SF [\u003ca href=\"https://openai.com/blog/ai-and-compute/\" rel=\"nofollow\"\u003e1\u003c/a\u003e, \u003ca href=\"https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/\" rel=\"nofollow\"\u003e2\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/1907.10597\" rel=\"nofollow\"\u003e3\u003c/a\u003e])\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eDataset sizes\u003c/th\u003e\n\u003cth align=\"center\"\u003eModel sizes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/Fig1b.png\"\u003e\u003cimg src=\"figures/Fig1b.png\" alt=\"Drawing\" align=\"middle\" width=\"500px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/Fig1c.png\"\u003e\u003cimg src=\"figures/Fig1c.png\" alt=\"Drawing\" align=\"middle\" width=\"570px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-experiment-objectives\" class=\"anchor\" aria-hidden=\"true\" href=\"#experiment-objectives\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExperiment objectives:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBenchmark following compute cost metrics for neuroimaging pipelines:\n\u003cul\u003e\n\u003cli\u003emodel complexity (parameters, FLOPs/MACs)\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://www.bnikolic.co.uk/blog/python/flops/2019/09/27/python-counting-events.html\" rel=\"nofollow\"\u003egeneral purpose\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sovrasov/flops-counter.pytorch\"\u003epytorch:ptflops\u003c/a\u003e (Primarily used)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003emodel energy/power consumption using several carbon trackers\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Breakend/experiment-impact-tracker\"\u003eexperiment-impact-tracker\u003c/a\u003e (Primarily used)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/lfwa/carbontracker\"\u003eCarbonTracker\u003c/a\u003e (in-progress)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mlco2/codecarbon\"\u003eCodeCarbon\u003c/a\u003e (in-progress)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003emodel runtime\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eComparisons:\n\u003cul\u003e\n\u003cli\u003ehardware: cpu vs gpu\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-repo-organization-ongoing\" class=\"anchor\" aria-hidden=\"true\" href=\"#repo-organization-ongoing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepo organization (ongoing)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/Watts_up_compute_org.jpg\"\u003e\u003cimg src=\"figures/Watts_up_compute_org.jpg\" alt=\"Drawing\" align=\"middle\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-preliminary-results-on-a-pilot-sample\" class=\"anchor\" aria-hidden=\"true\" href=\"#preliminary-results-on-a-pilot-sample\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreliminary results on a pilot sample\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDatasets: \u003ca href=\"https://www.ukbiobank.ac.uk/enable-your-research/register\" rel=\"nofollow\"\u003eUK Biobank sample\u003c/a\u003e (N=72)\u003c/li\u003e\n\u003cli\u003ePipelines: \u003ca href=\"https://surfer.nmr.mgh.harvard.edu/\" rel=\"nofollow\"\u003eFreeSurfer 6.0\u003c/a\u003e implementation with \u003ca href=\"https://nipype.readthedocs.io/en/latest/users/examples/smri_fsreconall.html\" rel=\"nofollow\"\u003eNipype\u003c/a\u003e vs. FastSurfer (deep-learning approach)\u003c/li\u003e\n\u003cli\u003eOutput: Volumetric brain segmentation and cortical thickness estimation with DKT parcellations (see figure below)\u003c/li\u003e\n\u003cli\u003eProc: CPU (Intel Xeon(R) Gold 6148 @ 2.40GHz) vs. GPU (Tesla V100-SXM2-16GB CUDA:11.0)\u003c/li\u003e\n\u003cli\u003eHPC location: Compute Canada @ Quebec, Canada (\u003ca href=\"https://en.wikipedia.org/wiki/Power_usage_effectiveness\" rel=\"nofollow\"\u003ePUE\u003c/a\u003e ~ 1.2)\u003c/li\u003e\n\u003cli\u003eCompute cost metrics\n\u003col\u003e\n\u003cli\u003eRuntime 2) Power draw 3) Carbon emissions\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eCompute cost tracker: experiment-impact-tracker\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/FreeSurfer_FastSurfer.png\"\u003e\u003cimg src=\"figures/FreeSurfer_FastSurfer.png\" alt=\"Drawing\" align=\"middle\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-compute-cost-benchmarks\" class=\"anchor\" aria-hidden=\"true\" href=\"#compute-cost-benchmarks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompute cost benchmarks:\u003c/h3\u003e\n\u003cp\u003eNote: The values in table are for processing of a single scan. A typical inference/deployment pipeline may do ~10k of these runs for a large dataset. And a model training/development pipeline may incur over 1M runs.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePipeline (single run)\u003c/th\u003e\n\u003cth\u003eRuntime (hrs): CPU\u003c/th\u003e\n\u003cth\u003eRuntime (hrs): GPU\u003c/th\u003e\n\u003cth\u003ePower (W-hrs): CPU\u003c/th\u003e\n\u003cth\u003ePower (W-hrs): GPU\u003c/th\u003e\n\u003cth\u003eCarbon Emissions (grams): CPU\u003c/th\u003e\n\u003cth\u003eCarbon Emissions (grams): GPU\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eFreeSurfer\u003c/td\u003e\n\u003ctd\u003e8.3 (1.03)\u003c/td\u003e\n\u003ctd\u003eN/A\u003c/td\u003e\n\u003ctd\u003e108.5 (19.8)\u003c/td\u003e\n\u003ctd\u003eN/A\u003c/td\u003e\n\u003ctd\u003e3.26 (0.5)\u003c/td\u003e\n\u003ctd\u003eN/A\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFastSurfer\u003c/td\u003e\n\u003ctd\u003e9.8 (0.74)\u003c/td\u003e\n\u003ctd\u003e1.6 (0.47)\u003c/td\u003e\n\u003ctd\u003e126.4 (16.1)\u003c/td\u003e\n\u003ctd\u003e26.7 (7.7)\u003c/td\u003e\n\u003ctd\u003e3.79 (0.5)\u003c/td\u003e\n\u003ctd\u003e0.80 (0.2)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1536417204.0 + "updated_at": 1639003401.0 }, { "data_format": 2, @@ -3900,722 +3530,694 @@ var data = "filenames": [ "Singularity" ], - "full_name": "kavonrtep/assembly_repeat_annotation_pipeline", + "full_name": "rkalyanapurdue/mpitest", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAssembly Repeat Annotation Pipeline\u003c/h1\u003e\u003ca id=\"user-content-assembly-repeat-annotation-pipeline\" class=\"anchor\" aria-label=\"Permalink: Assembly Repeat Annotation Pipeline\" href=\"#assembly-repeat-annotation-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is required to use the container. Singularity can be installed using conda environment.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n singularity3 -c conda-forge \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esingularity\u0026gt;=3.6\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nconda activate singularity3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image (.sif file) can be downloaded from \u003ca href=\"https://github.com/kavonrtep/assembly_repeat_annotation_pipeline/releases\"\u003ehttps://github.com/kavonrtep/assembly_repeat_annotation_pipeline/releases\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFormat of \u003ccode\u003econfig.yaml\u003c/code\u003e file is as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003egenome_fasta\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edata/CEN6_ver_220406_part.fasta\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutput_dir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eoutput\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ecustom_library\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edata/pisum_custom_library.fasta\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003etandem_repeat_library\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edata/FabTR_all_sequences_210901.db.RM_format.fasta\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e posible values are : sensitive, default, quick,\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e if missing, default is used\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003erepeatmasker_sensitivity\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edefault\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e perform library size reduction, possible values are True, False, if missinf True is used\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ereduce_library\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLines specifying \u003ccode\u003ecustom_library\u003c/code\u003e and \u003ccode\u003etandem_repeat_library\u003c/code\u003e are optional. File \u003ccode\u003ecustom_library\u003c/code\u003e is\nused by RepeatMasker fo similarity based annotation. Sequences must be as FASTA with\nsequence IDs in format \u003ccode\u003e\u0026gt;repeatname#class/subclass\u003c/code\u003e\nClassification categories are:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eClass_I/LINE\nClass_I/LTR/Ty1_copia\nClass_I/LTR/Ty1_copia/Ale\nClass_I/LTR/Ty1_copia/Angela\nClass_I/LTR/Ty1_copia/Bianca\nClass_I/LTR/Ty1_copia/Ikeros\nClass_I/LTR/Ty1_copia/Ivana\nClass_I/LTR/Ty1_copia/SIRE\nClass_I/LTR/Ty1_copia/TAR\nClass_I/LTR/Ty1_copia/Tork\nClass_I/LTR/Ty1_copia/Alexandra\nClass_I/LTR/Ty1_copia/Bryana\nClass_I/LTR/Ty1_copia/Ferco\nClass_I/LTR/Ty3_gypsy\nClass_I/LTR/Ty3_gypsy/chromovirus\nClass_I/LTR/Ty3_gypsy/chromovirus/Ferney\nClass_I/LTR/Ty3_gypsy/chromovirus/CRM\nClass_I/LTR/Ty3_gypsy/chromovirus/Reina\nClass_I/LTR/Ty3_gypsy/chromovirus/Tekay\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Tatius\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Athila\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Tat\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Tat/Ogre\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Tat/Retand\nClass_II/Subclass_1/TIR/EnSpm_CACTA\nClass_II/Subclass_1/TIR/hAT\nClass_II/Subclass_1/TIR/MITE\nClass_II/Subclass_1/TIR/MITE/Stowaway\nClass_II/Subclass_1/TIR/MuDR_Mutator\nClass_II/Subclass_1/TIR/PIF_Harbinger\nClass_II/Subclass_1/TIR/Tc1_Mariner\nClass_II/Subclass_2/Helitron\nrDNA_45S/18S\nrDNA_45S/25S\nrDNA_45S/5.8S\nrDNA_5S/5S\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is possible to include additional categories to repeat library. Note that \u003ccode\u003eSubclass_1\u003c/code\u003e repeats are used to clean \u003ccode\u003eClass_I/LTR\u003c/code\u003e library which is built by DANTE_LTR thus it is critical to use correct codes for \u003ccode\u003eClass_II/Subclass_1\u003c/code\u003e mobile elements in repeat library.\u003c/p\u003e\n\u003cp\u003eFile \u003ccode\u003ecustom_library\u003c/code\u003e is used by TideCluster to annotate discovered tandem repeats based\non the similarity. Format is the same as above repeat database. E.g.\n\u003ccode\u003e\u0026gt;sequence_id/Satellite/PisTR-B\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run annotation pipeline, execute following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /path/to/ -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e assembly_repeat_annotation_pipeline.sif -c config.yaml -t 20\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eParameter \u003ccode\u003e-t\u003c/code\u003e specifies the number of threads to use. Singularity parameter \u003ccode\u003e-B\u003c/code\u003e is used to bind the input and output directories to the container. Without this parameter, the container will not be able to access the input and output files. File \u003ccode\u003econfig.yaml\u003c/code\u003e must be also in directory which is accessible to the container. In the example above this is the current directory \u003ccode\u003e$PWD\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning pipeline on metacentrum\u003c/h2\u003e\u003ca id=\"user-content-running-pipeline-on-metacentrum\" class=\"anchor\" aria-label=\"Permalink: Running pipeline on metacentrum\" href=\"#running-pipeline-on-metacentrum\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse \u003ca href=\"./scripts/annotate_repeats_metacentrum.sh\"\u003e./scripts/annotate_repeats_metacentrum.sh\u003c/a\u003e script to run the pipeline on metacentrum. Adjust paths to the input files , output directory and singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput structure\u003c/h2\u003e\u003ca id=\"user-content-output-structure\" class=\"anchor\" aria-label=\"Permalink: Output structure\" href=\"#output-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container\u003c/h2\u003e\u003ca id=\"user-content-build-the-container\" class=\"anchor\" aria-label=\"Permalink: Build the container\" href=\"#build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the container, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSINGULARITY=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ewhich singularity\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\nsudo ionice -c3 \u003cspan class=\"pl-smi\"\u003e$SINGULARITY\u003c/span\u003e build images/assembly_repeat_annotation_pipeline_0.6.3.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChangelog:\u003c/h2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-label=\"Permalink: Changelog:\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ev 0.5.1 - graphical output to PDF added\u003c/li\u003e\n\u003cli\u003ev 0.5.2 - RepeatMasker sensitivity can be set\u003c/li\u003e\n\u003cli\u003ev 0.6.0 - REXdb Viridiplante v4.0, library size reduction added, RepeatMasker parallelization added, missing full LTR-RT handling added\u003c/li\u003e\n\u003cli\u003ev 0.6.1 DANTE_LTR update to 0.4.0.3 (bugfix)\u003c/li\u003e\n\u003cli\u003ev 0.6.2 bugfix in bigwig calculation\u003c/li\u003e\n\u003cli\u003ev 0.6.3 dante update to 0.2.5 - bugfix\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mpitest\" class=\"anchor\" aria-hidden=\"true\" href=\"#mpitest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003empitest\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1729236602.0 + "updated_at": 1567614468.0 }, { "data_format": 2, - "description": "Common tools for w3const project", + "description": "Singularity recipe for ROS Indigo and Kinetic", "filenames": [ - "Singularity" + "Singularity", + "indigo/Singularity.indigo", + "kinetic/Singularity.kinetic" ], - "full_name": "ddbj/w3const_base", + "full_name": "ISU-HPC/ros", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ew3const_base\u003c/h1\u003e\u003ca id=\"user-content-w3const_base\" class=\"anchor\" aria-label=\"Permalink: w3const_base\" href=\"#w3const_base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCommon tools for w3const project\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHow to build the container\u003c/h1\u003e\u003ca id=\"user-content-how-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: How to build the container\" href=\"#how-to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~\ngit clone https://github.com/ddbj/w3const_base.git\nsudo singularity build constbase.sif ~/w3const_base/Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ecuratortool\u003c/em\u003e directory contains the following scripts and binaries.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003egetblastdb_ncbi.sh\u003c/h2\u003e\u003ca id=\"user-content-getblastdb_ncbish\" class=\"anchor\" aria-label=\"Permalink: getblastdb_ncbi.sh\" href=\"#getblastdb_ncbish\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload blast/db data from NCBI by using aspera connect and decompress to the blastdb directory.\u003c/p\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /home/w3const/work-kosuge/constbase.sif getblastdb_ncbi.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eVariables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDBNAME ... blast db to be downloaded.\u003c/li\u003e\n\u003cli\u003eMAXTRY ... Retry download until the times, when a downloaded file is broken.\u003c/li\u003e\n\u003cli\u003eBASE ... Base directory for running the script.\u003c/li\u003e\n\u003cli\u003eDBSRC ... URL of NCBI data resource.\u003c/li\u003e\n\u003cli\u003eDATLOC ... Usually, the latest tar.gz archives from NCBI are placed. When the downloading was failed, the tar.gz files are copied from DATLOCF directory.\u003c/li\u003e\n\u003cli\u003eDATLOCF ... Former tar.gz archives from NCBI are placed.\u003c/li\u003e\n\u003cli\u003eJSONLOC ... Manifest json files from NCBI. Each file are downloaded based on the information in the json file.\u003c/li\u003e\n\u003cli\u003eBDB ... A directory where decompressed data are placed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esendgmail_w3const.py\u003c/h2\u003e\u003ca id=\"user-content-sendgmail_w3constpy\" class=\"anchor\" aria-label=\"Permalink: sendgmail_w3const.py\" href=\"#sendgmail_w3constpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSends email by using the google account. You can specify a sender address if you have set the other email address(es) (e.g. sender alias) on the account.\u003c/p\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /home/w3const/work-kosuge/constbase.sif sendgmail_w3const.py [-h] --sj subject --to email --body file [--cc email] [--bcc email] [--att file] [--sender address]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou must prepare credential and white list files in advance.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a credential file to run the script.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -m 700 ~/.sendgmail_w3const\necho \u0027GmailAccount:ApplicationPassword\u0027 \u0026gt; ~/.sendgmail_w3const/account\nchmod 600 ~/.sendgmail_w3const/account\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate a whitelist\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003etouch ~/.sendgmail_w3const/whitelist; chmod 600 ~/.sendgmail_w3const/whitelist\nWrite an email address to the whitelist in each line.\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eExample\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd /home/w3const\nsingularity exec /home/w3const/work-kosuge/constbase.sif sendgmail_w3const.py --sj \"\u3066\u3059\u3068\u3067\u3059\" --to addr1,addr2 --body /home/w3const/work-kosuge/emailbody.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esendmail_gw1.py\u003c/h2\u003e\u003ca id=\"user-content-sendmail_gw1py\" class=\"anchor\" aria-label=\"Permalink: sendmail_gw1.py\" href=\"#sendmail_gw1py\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can send email only when sender address is \u003cem\u003enig\u003c/em\u003e and \u003cem\u003eddbj\u003c/em\u003e domain. Works only in supercomputer nodes.\u003c/p\u003e\n\u003cp\u003eYou must prepare whitelist file as mentioned above.\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /home/w3const/work-kosuge/constbase.sif sendgmail_gw1.py --sender foo@ddbj.nig.ac.jp --sj \"\u3066\u3059\u3068\u3067\u3059\" --to addr1,addr2 --body /home/w3const/work-kosuge/emailbody.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003emakeUniVec_blastdb.sh\u003c/h2\u003e\u003ca id=\"user-content-makeunivec_blastdbsh\" class=\"anchor\" aria-label=\"Permalink: makeUniVec_blastdb.sh\" href=\"#makeunivec_blastdbsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload the UniVec fasta from NCBI and replace the local file with newwer ones. The script also prepares blast databases whose names are UniVec and UniVec_Core.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBefore use\u003c/h3\u003e\u003ca id=\"user-content-before-use\" class=\"anchor\" aria-label=\"Permalink: Before use\" href=\"#before-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEdit the directory name of BASE (line 8). It is used for base directory. You need to create \"UniVec\" directory under the base directory. The blast databases for UniVec and UniVec_Core are created to the directory designated by BLASTDIR (line 12).\u003c/p\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /home/w3const/work-kosuge/constbase.sif makeUniVec_blastdb.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esplitff.sh\u003c/h2\u003e\u003ca id=\"user-content-splitffsh\" class=\"anchor\" aria-label=\"Permalink: splitff.sh\" href=\"#splitffsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeparate a huge flatfile into small-sized flat files.\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esplitff.sh -f \u0026lt;flatfile\u0026gt; -s \u0026lt;number of lines\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003egetorganismdivFF.py\u003c/h2\u003e\u003ca id=\"user-content-getorganismdivffpy\" class=\"anchor\" aria-label=\"Permalink: getorganismdivFF.py\" href=\"#getorganismdivffpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eObtain or search taxnomyc division for a target entry from the flatfile, Entrez, or local taxonomy dump file. In the case of ENV or taxid=0, taxonomic division is obtained from Entrez search or tax dump file by using the beginning of the /organism as a query.\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ee.g.1\ngetorganismdivFF.py -i \u0026lt;flatfile\u0026gt; -a \u0026lt;accession num\u0026gt;\ne.g.2\ngetorganismdivFF.py -i \u0026lt;flatfile\u0026gt; -a \u0026lt;accession num\u0026gt; -p \u0026lt;tax dump directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ejParser \u0026amp; transChecker\u003c/h2\u003e\u003ca id=\"user-content-jparser--transchecker\" class=\"anchor\" aria-label=\"Permalink: jParser \u0026amp; transChecker\" href=\"#jparser--transchecker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://ddbj.nig.ac.jp/public/ddbj-cib/MSS/\" rel=\"nofollow\"\u003ehttps://ddbj.nig.ac.jp/public/ddbj-cib/MSS/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eblast \u0026amp; matrix\u003c/h2\u003e\u003ca id=\"user-content-blast--matrix\" class=\"anchor\" aria-label=\"Permalink: blast \u0026amp; matrix\" href=\"#blast--matrix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eftp://ftp.ncbi.nih.gov/blast/executables/blast+/\nftp://ftp.ncbi.nih.gov/blast/matrices\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003evecscrnfilter.py\u003c/h2\u003e\u003ca id=\"user-content-vecscrnfilterpy\" class=\"anchor\" aria-label=\"Permalink: vecscrnfilter.py\" href=\"#vecscrnfilterpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReads vecscreen result that carried out with options -outfmt 0 -text_output, and filter the results with the degree of blast matches.\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evecscrnfilter.py [-s|-m|-w] [alignment file]\n# \u0027-s\u0027 outputs only \u0027Strong match\u0027\n# \u0027-m\u0027 outputs \u0027Moderate match\u0027 \u0026amp; \u0027Strong Match\u0027\n# \u0027-w\u0027 outputs \u0027Weak match\u0027 besides \u0027Moderate match\u0027 \u0026amp; \u0027Strong match\u0027\n# They may contain \u0027Suspect origin\u0027 if included in the result\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSRA Toolkit\u003c/h2\u003e\u003ca id=\"user-content-sra-toolkit\" class=\"anchor\" aria-label=\"Permalink: SRA Toolkit\" href=\"#sra-toolkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLatest version of \u003ca href=\"https://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/\" rel=\"nofollow\"\u003ehttps://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAspera Connect\u003c/h2\u003e\u003ca id=\"user-content-aspera-connect\" class=\"anchor\" aria-label=\"Permalink: Aspera Connect\" href=\"#aspera-connect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.ibm.com/aspera/connect/\" rel=\"nofollow\"\u003ehttps://www.ibm.com/aspera/connect/\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ros\" class=\"anchor\" aria-hidden=\"true\" href=\"#ros\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eros\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for ROS Indigo and Kinetic\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 11, + "subscribers_count": 2, "topics": [], - "updated_at": 1727936224.0 + "updated_at": 1524505755.0 }, { "data_format": 2, - "description": "Base centos7 with miniconda installed", + "description": "build index for several aligners and writes a module file", "filenames": [ - "Singularity" + "Singularity.1.0.2", + "Singularity.1.0.1" ], - "full_name": "scleveland/centos7-miniconda", + "full_name": "ISUGIFsingularity/genomeModules", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Recipe for base CentOS 7 image with Miniconda Installed\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-base-centos-7-image-with-miniconda-installed\" class=\"anchor\" aria-label=\"Permalink: Singularity Recipe for base CentOS 7 image with Miniconda Installed\" href=\"#singularity-recipe-for-base-centos-7-image-with-miniconda-installed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains recipe to install MiniConda on a base CentOS-7 singularity containteri (shub://scleveland/centos7-base-singularity)\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to Use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity run shub://scleveland\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-genomemodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#genomemodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egenomeModules\u003c/h1\u003e\n\u003cp\u003ebuild index for several aligners and writes a module file\u003c/p\u003e\n\u003cp\u003eAfter cloning this repository\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-download-the-following-singularity-images-and-place-them-in-simg-folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-the-following-singularity-images-and-place-them-in-simg-folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload the following singularity images and place them in SIMG folder\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003esingularity pull shub://ISUGIFsingularity/genomeModules:1.0.2\u003c/li\u003e\n\u003cli\u003esingularity pull shub://ISUGIFsingularity/utilities:1.0.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-modify-the-following-environmental-variables-in-prepare_genome_modulessh\" class=\"anchor\" aria-hidden=\"true\" href=\"#modify-the-following-environmental-variables-in-prepare_genome_modulessh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModify the following environmental variables in prepare_genome_modules.sh\u003c/h4\u003e\n\u003cp\u003eUse full paths so that the module file will work correctly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport GENMODgit=\"/pylon5/mc48o5p/severin/isugif/genomeModules\"\nGENMOD=\"/pylon5/mc48o5p/severin/isugif/\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGENMODgit is the location of this github repository.\nGENMOD is the location where you would like to store genome modules and sequence files that this script generates.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-running-the-prepare-genome-modules-command-to-generate-a-genome-module-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-prepare-genome-modules-command-to-generate-a-genome-module-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the prepare genome modules command to generate a genome module file.\u003c/h4\u003e\n\u003cp\u003eI ran this on the Seriola dorsalis genome and its corresponding GFF3 file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eprepare_genome_modules.sh serdor v2 Serdor_V2.fasta Serdor_V2.gff3\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-adding-the-modules-to-your-module-path\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-the-modules-to-your-module-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding the modules to your module path.\u003c/h4\u003e\n\u003cp\u003emodule use $GENMOD/genomes/modules/\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example-of-a-genome-module\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-of-a-genome-module\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample of a genome module\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003emodule load serdor\nmodule show serdor\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e-------------------------------------------------------------------\n/pylon5/mc48o5p/severin/genmodTest/genomes/modules//serdor/v2:\n\nmodule-whatis serdor \nunsetenv GENOME \nunsetenv GMAPDB \nunsetenv GNAME \nsetenv GENOMEDIR .//genomes/sequences/serdor/v2/ \nsetenv GENOMEFASTA .//genomes/sequences/serdor/v2/serdor_v2.fasta \nsetenv GENOMEINTERVALS .//genomes/sequences/serdor/v2/serdor_v2_100kb_coords.bed \nsetenv GNAME serdor_v2 \nsetenv GMAPDB .//genomes/sequences/serdor/v2/ \nsetenv modulefile .//genomes/modules/serdor/v2 \nsetenv VERSION v2 \nsetenv serdor_v2_genome .//genomes/sequences/serdor/v2/ \nsetenv serdor_v2_GMAPDB .//genomes/sequences/serdor/v2/serdor_v2 \nsetenv serdor_v2_GNAME serdor_v2 \nsetenv serdor_v2_intervals100k .//genomes/sequences/serdor/v2/serdor_v2_100kb_coords.bed \nsetenv serdor_v2_cdna .//genomes/sequences/serdor/v2/serdor_v2.fasta \nsetenv serdor_v2_cdna .//genomes/sequences/serdor/v2/serdor_v2.gff3 \nsetenv serdor_v2_cdna .//genomes/sequences/serdor/v2/serdor_v2.cdna.fasta \nsetenv serdor_v2_cds .//genomes/sequences/serdor/v2/serdor_v2.cds.fasta \nsetenv serdor_v2_gene .//genomes/sequences/serdor/v2/serdor_v2.gene.fasta \nsetenv serdor_v2_pep .//genomes/sequences/serdor/v2/serdor_v2.pep.fasta \nsetenv serdor_v2_upstream3000 serdor_v2.upstream3000.fasta \n-------------------------------------------------------------------\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1543871839.0 + "updated_at": 1523035282.0 }, { "data_format": 2, - "description": "Container Template for the Soil and Water Assessment Toolkit", + "description": "Bayesian poissonian histogram decomposition engine for the GERDA experiment", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "XSEDE/singularity-swat681", + "full_name": "gipert/gerda-fitter", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoil \u0026amp; Water Assessment Tool\u003c/h1\u003e\u003ca id=\"user-content-soil--water-assessment-tool\" class=\"anchor\" aria-label=\"Permalink: Soil \u0026amp; Water Assessment Tool\" href=\"#soil--water-assessment-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container includes the Soil and Water Assessment Tool (\u003ca href=\"https://swat.tamu.edu/software/\" rel=\"nofollow\"\u003ehttps://swat.tamu.edu/software/\u003c/a\u003e)\nrevision 681,\nbuilt for use on amd64 Linux systems. The binary is installed at /usr/local/swat681/swat.\nAt run-time, any input files MUST be bind-mounted to /usr/local/swat681 - for example:\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".github/gerda-logo.png\"\u003e\u003cimg src=\".github/gerda-logo.png\" align=\"left\" height=\"80\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-gerda-fitter-\" class=\"anchor\" aria-hidden=\"true\" href=\"#gerda-fitter-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egerda-fitter \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/gipert/gerda-fitter/workflows/CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/gipert/gerda-fitter/workflows/CI/badge.svg\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eA fully JSON-configurable bayesian fitting engine (based on\n\u003ca href=\"https://github.com/bat/bat\"\u003eBAT\u003c/a\u003e and\n\u003ca href=\"https://github.com/root-project/root\"\u003eROOT\u003c/a\u003e) for data in the form of ROOT\nhistograms. Taylored on GERDA data and Probability Density Functions.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-compile-and-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#compile-and-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile and install\u003c/h3\u003e\n\u003cp\u003eRequirements\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/root-project/root\"\u003eROOT\u003c/a\u003e \u2265 v6.12/04\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/bat/bat\"\u003eBAT\u003c/a\u003e \u2265 v1.0.0 (with Cuba enabled)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen just \u003ccode\u003ePREFIX=/path/to/prefix make install\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, a Singularity container can be used:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esudo singularity build gerda-fitter.sif Singularity.def\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e gerda-fitter.sif gerda-fitter -h\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eUSAGE: gerda-fitter [-h|--help] json-config\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003egerda-fitter\u003c/code\u003e executable acceps a JSON config file as the only argument.\nExamples can be found in this repository under \u003ccode\u003econfig/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe JSON config file begins with some general settings:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"id\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"phIIAfterLAr\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// model name\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"logging\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"summary\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// BAT verbosity level, see manual\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"precision\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"kMedium\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// precision (number and length of Markov chains), see BAT manual\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"output-dir\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"../results\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// folder with fit results\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003esettings about the global mode search algorithm:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s\"\u003e\"global-mode-search\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"method\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"kOptMinuit\"\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// see the BAT manual to learn about the other algorithms\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003esettings about the numerical integration needed to compute the evidence:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s\"\u003e\"integration\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"enabled\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// enable/disable the integration step\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"method\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"kIntCuba\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// see the BAT manual to learn about the other algorithms\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"cuba-method\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"kCubaDivonne\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// see the Cuba manual\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"integrator-settings\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"kIntCuba\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// here you can tweak the Cuba integration settings\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"kCubaDivonne\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// here for the Divonne algorithm\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"niter-max\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e1E07\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"niter-min\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"flags\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n \"\u003cspan class=\"pl-s1\"\u003ekCubaVegas\u003c/span\u003e\" : { // here for Vegas...\n // ...\n }\n // ...\n }\n }\n },\n // ...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003esettings about the p-value determination\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s\"\u003e\"p-value\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"enabled\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// enable/disable the computation\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"iterations\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e1E07\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// play with this number until the p-value is stable\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand finally the fit configuration section \u003ccode\u003e\"fit\"\u003c/code\u003e, where everything about the data and\nthe fit components is specified in a modular fashion:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fit\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"parameters\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// define fit parameters globally\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"theoretical-expectations\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// import PDFs and associated parameters\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLet\u0027s start with the \u003ccode\u003e\"parameters\"\u003c/code\u003e section, here the fit parameters must be defined:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e\"parameters\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"alpha-slope-bege\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// unique internal name\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2E-5\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1E-4\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"long-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"#alpha-model BEGe - slope\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"units\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"cts\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"prior\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"histogram\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"priorfile.root:objname\"\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// specify prior via external TH1\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"alpha-offset-bege\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1E-1\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"long-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"#alpha-model BEGe - offset\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"units\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"cts\"\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"prior\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"TFormula\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"gaus:1,10,5\"\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// specify prior via TFormula\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"background\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fixed\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e1234\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// parameters can be fixed to a value (not fit parameters anymore)\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"long-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"Background model\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"units\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"cts\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then associated to PDFs in the \u003ccode\u003e\"theoretical-expectations\"\u003c/code\u003e section:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e\"theoretical-expectations\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// takes a list of files with data histograms\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"../data/gerda-data-bkgmodel-phaseII-v04.00-lar.root\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// takes a list of object names in the file\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"M1_enrBEGe\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// this is a 1D histogram\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"gerda-pdfs\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"../data/gerda-pdfs/v2.1\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// set here the path to the gerda-pdfs, if you want\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fit-range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e560\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2014\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2064\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5300\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// note the possibility to skip regions\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"rebin-factor\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// fixed-size rebin\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"rebin-factor\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"560:10:700,700:20:900,1000:100:5300\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// support for variable binning!\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// here you must specify a list of PDFs you want to use\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"M1_enrCoax\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"M2_enrGe\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// this is a 2D histogram\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fit-range-x\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e560\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2014\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2064\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5300\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fit-range-y\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e700\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5300\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"rebin-factor-x\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// or just \"rebin-factor\" to rebin both axes\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"rebin-factor-y\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// here you must specify a list of PDFs you want to use\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"../data/gerda-data-bkgmodel-phaseII-v04.00-raw.root\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethe keys in the \u003ccode\u003e\"theoretical-expectations\"\u003c/code\u003e dictionary must be paths to the\nfiles that contain histograms to be fitted (the data). Then for each of these\nfiles the user must specify what histograms (ROOT objects) the program should\ntry to fit. For every data histogram a list of fit components must be provided\nin the \u003ccode\u003e\"components\"\u003c/code\u003e array. The array is filled with JSON objects that can be\nof multiple types.\u003c/p\u003e\n\u003cp\u003eAs instance, one might want to use the GERDA PDFs distributed within\n\u003ca href=\"https://github.com/mppmu/gerda-mage-sim\"\u003egerda-mage-sim\u003c/a\u003e using the following\nstructure:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"gerda-pdfs\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"../data/gerda-pdfs/v2.1\"\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// the gerda-pdfs path might be set here to override the global one\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"part\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"cables/cables_all\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"Th228-cables\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// this parameter name must be defined in the \"parameters\" section!\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"isotope\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"Tl208-larveto\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e0.3539\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"Bi212-larveto\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// specify a mixture of isotopes\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"Co60-cables\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"isotope\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Co60-run68pca\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// no mixture here\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"part\"\u003c/span\u003e: \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// you can also specify a mixture of parts!\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s2_8220\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e52183\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s2_8408\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e25337\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s2_8570\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e79868\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s3_8220\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e55438\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s3_8405\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e43433\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s3_8570\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e24130\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor even provide manually a ROOT histogram:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"root-file\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"../data/gerda-pdfs/v2.0-RC/alphas/analytic/pdf-functions.root\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"alpha-offset\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"hist-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"flat\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor even a ROOT \u003ccode\u003eTFormula\u003c/code\u003e in the form \u003ccode\u003e\"formula:par1,par2,...\"\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"alpha-slope\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"TFormula\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"gaus:1,34,2\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLast but not least, observables that depend on the model parameters only can be\ndefined via JSON file with the following syntax:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e\"parameters\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"2nbb-half-life-bege\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// unique internal name\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"TFormula\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"1.13380E26/[2nbb-bege]\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// ROOT\u0027s TFormula\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"multiply-fit-parameter-by-pdf-integral\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// there\u0027s the possibility to multiply each parameter\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// above by the pdf integral in a range:\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// [2nbb-bege] -\u0026gt; ([2nbb-bege]*Int)\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e19\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e80\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e89\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// range for the integral\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"dataset\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"h_data\"\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// dataset pdf refers to\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2E-5\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1E-4\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"long-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"T_{1/2}^{2#nu} - BEGe\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"units\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"cts\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eModel parameters must be specified as they were a \u003ccode\u003eTFormula\u003c/code\u003e parameter,\nenclosing their name in square brackets.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-related-projects\" class=\"anchor\" aria-hidden=\"true\" href=\"#related-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRelated projects\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gipert/gerda-factory\"\u003egerda-factory\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 9, - "topics": [], - "updated_at": 1635279632.0 - }, - { - "data_format": 2, - "description": "Testing Apptainer container builds via GitHub Actions", - "filenames": [ - "dir/Singularity" + "subscribers_count": 2, + "topics": [ + "bayesian-statistics", + "histogram-decomposition", + "spectral-decomposition" ], - "full_name": "maouw/test-apptainer-gh", - "latest_release": "v0.0.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etest-apptainer-gh\u003c/h1\u003e\u003ca id=\"user-content-test-apptainer-gh\" class=\"anchor\" aria-label=\"Permalink: test-apptainer-gh\" href=\"#test-apptainer-gh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting Apptainer container builds via GitHub Actions\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1697496824.0 + "updated_at": 1637679682.0 }, { "data_format": 2, - "description": "Container with psychopy", + "description": null, "filenames": [ - "Singularity" + "Singularity.purge_haplotigs_0b9afdf", + "Singularity.circos_0.69-9", + "Singularity.busco_4.0.4", + "Singularity.racon_1.4.10", + "Singularity.ragtag_1.0.1", + "Singularity.quast_5.0.2", + "Singularity.gfatools_0.4r165", + "Singularity.agb_a41ac9e", + "Singularity.merqury_45fd3cc", + "Singularity.gffread_0.12.3" ], - "full_name": "mvdoc/singularity-psychopy", + "full_name": "TomHarrop/assembly-utils", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1508289994.0 + "updated_at": 1601346862.0 }, { "data_format": 2, - "description": "repository for collaborating with scg4 users on Singularity containers", + "description": null, "filenames": [ - "cbanders/Singularity" + "Singularity.racon-chunks_0.0.6" ], - "full_name": "researchapps/scg4", + "full_name": "TomHarrop/racon-chunks", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSCG4 Singularity\u003c/h1\u003e\u003ca id=\"user-content-scg4-singularity\" class=\"anchor\" aria-label=\"Permalink: SCG4 Singularity\" href=\"#scg4-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a repository for Singularity image build files to help users of SCG4 build \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. If you are a user and need help, please submit an issue and we will help you build a container! When you are happy with your container, we recommend that you add the \u003ccode\u003eSingularity\u003c/code\u003e file to a new repo, and build automatically with \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e. Generally, your workflow will look like the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAsk for help via an \u003ca href=\"https://www.github.com/researchapps/scg4/issues\"\u003eissue\u003c/a\u003e if you don\u0027t know how to start\u003c/li\u003e\n\u003cli\u003eCreate a build specification file, a text file called Singularity, for your software needs. You can start with another user\u0027s as an example.\u003c/li\u003e\n\u003cli\u003eAsk for help with your file! This is what this repo is here for. You can submit issues with questions, and we will discuss and work together on the issues.\u003c/li\u003e\n\u003cli\u003eTest your build locally.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis usually looks something like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity create --size 4000 mynewimage.img\n singularity bootstrap mynewimage.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf it has a runscript, you can run as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run mynewimage.img # or\n ./mynewimage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you are having trouble with the runscript, shell inside like this to look around. The runscript is a file at the base of the image (\u003ccode\u003e/\u003c/code\u003e) called singularity.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell mynewimage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also (on your local machine) use the \u003ccode\u003e--writable\u003c/code\u003e option to test installation of software. You should have your build file open in another window and copy down commands that work, and ensure that the entire build goes successfully from start to finish without an error. Remember, any command that you issue and don\u0027t write done is NOT reproducible!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Hub\u003c/h2\u003e\u003ca id=\"user-content-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub\" href=\"#singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThen once you are finished, and create a new repo linked to Singularity Hub, using the image on scg4 comes down to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e module load singularity/january2017\n singularity run shub://reponame/mynewimage\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1488052770.0 + "updated_at": 1578525260.0 }, { "data_format": 2, - "description": "A singularity recipe for GPU based machine learning", + "description": null, "filenames": [ - "Singularity.cu80dnn6", - "Singularity", - "Singularity.cu80dnn7", - "Singularity.cu90" + "1.1.3/Singularity" ], - "full_name": "ISU-HPC/machine-learning", + "full_name": "pscedu/singularity-infernal", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emachine-learning\u003c/h1\u003e\u003ca id=\"user-content-machine-learning\" class=\"anchor\" aria-label=\"Permalink: machine-learning\" href=\"#machine-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA singularity recipe for GPU based machine learning\u003c/p\u003e\n\u003cp\u003eCurrently includes the following applications/packages\u003c/p\u003e\n\u003cp\u003eKeras\u003c/p\u003e\n\u003cp\u003eMXNET\u003c/p\u003e\n\u003cp\u003escikit-learn\u003c/p\u003e\n\u003cp\u003etensorflow\u003c/p\u003e\n\u003cp\u003epytorch\u003c/p\u003e\n\u003cp\u003elasagne\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1550521785.0 + "updated_at": 1629078386.0 }, { "data_format": 2, - "description": "Model implementation for \"Adaptive computation as a new mechanism of human attention\"", + "description": null, "filenames": [ - "env.d/Singularity" + "Singularity" ], - "full_name": "CNCLgithub/mot", + "full_name": "murphygroup/singularity-matlabmcr2017a", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emot\u003c/h1\u003e\u003ca id=\"user-content-mot\" class=\"anchor\" aria-label=\"Permalink: mot\" href=\"#mot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImplementation of adaptive computation and a case study on multiple object tracking (Pylyshyn \u0026amp; Storm 1988).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup and running\u003c/h2\u003e\u003ca id=\"user-content-setup-and-running\" class=\"anchor\" aria-label=\"Permalink: Setup and running\" href=\"#setup-and-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone repository \u003ccode\u003egit clone https://github.com/CNCLgithub/mot\u003c/code\u003e and \u003ccode\u003ecd mot\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./env.d/setup.sh cont_pull python julia\u003c/code\u003e to build the container and setup python and Julia.\u003c/li\u003e\n\u003cli\u003eEnter \u003ccode\u003e./env.d/run.sh julia\u003c/code\u003e to get into Julia REPL\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./env.d/setup.sh datasets\u003c/code\u003e to download the datasets used for the experiments.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis project has automatic configuration!! This configuration is defined in \u003ccode\u003edefault.conf\u003c/code\u003e.\nYou should always prepend \u003ccode\u003e./run.sh\u003c/code\u003e before any command (including running programs like \u003ccode\u003ejulia\u003c/code\u003e) to ensure consistency.\nIf you wish to have different values than \u003ccode\u003edefault.conf\u003c/code\u003e, simply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp default.conf user.conf\nvi user.conf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e edit to your liking without adding new elements\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReplication details\u003c/h2\u003e\u003ca id=\"user-content-replication-details\" class=\"anchor\" aria-label=\"Permalink: Replication details\" href=\"#replication-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe project is organized into core routines (under \u003ccode\u003esrc\u003c/code\u003e) and user scripts (under \u003ccode\u003escripts\u003c/code\u003e).\nIn order to run the adaptive computation model from scratch:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun the relevant scripts under \u003ccode\u003escripts/experiments\u003c/code\u003e (batch scripts are provided for SLURM)\u003c/li\u003e\n\u003cli\u003eAggregrate model traces using \u003ccode\u003escripts/analysis/aggregate_chains.jl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExport the produces \"csv\" files to the \u003ca href=\"https://github.com/CNCLgithub/mot-analysis\"\u003eanalysis repo\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMore details can be found in the README for each section.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMac and Window users\u003c/h3\u003e\u003ca id=\"user-content-mac-and-window-users\" class=\"anchor\" aria-label=\"Permalink: Mac and Window users\" href=\"#mac-and-window-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use singularity you must have a virtual machine running.\nAssuming you have vagrant (and something like virtualbox) setup on your host, you can follow these steps\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributing Rules\u003c/h3\u003e\u003ca id=\"user-content-contributing-rules\" class=\"anchor\" aria-label=\"Permalink: Contributing Rules\" href=\"#contributing-rules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePlace all re-used code in packages (\u003ccode\u003esrc\u003c/code\u003e or \u003ccode\u003efunctional_scenes\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePlace all interactive code in \u003ccode\u003escripts\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDo not use \"hard\" paths. Instead refer to the paths in \u003ccode\u003eSPATHS\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAdd contributions to branches derived from \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edev\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAvoid \u003ccode\u003egit add *\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDo not commit large files (checkpoints, datasets, etc). Update \u003ccode\u003esetup.sh\u003c/code\u003e accordingly.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject layout\u003c/h3\u003e\u003ca id=\"user-content-project-layout\" class=\"anchor\" aria-label=\"Permalink: Project layout\" href=\"#project-layout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package environment is managed by as defined in \u003ccode\u003esetup.sh\u003c/code\u003e (specifically \u003ccode\u003eSENV[pyenv]\u003c/code\u003e)\nLikewise, the Julia package is described under \u003ccode\u003esrc\u003c/code\u003e and \u003ccode\u003etest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAll scripts are located under \u003ccode\u003escripts\u003c/code\u003e and data/output is under \u003ccode\u003eenv.d/spaths\u003c/code\u003e as specific in the project config (\u003ccode\u003edefault.conf\u003c/code\u003e or \u003ccode\u003euser.conf\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChanging the enviroment\u003c/h3\u003e\u003ca id=\"user-content-changing-the-enviroment\" class=\"anchor\" aria-label=\"Permalink: Changing the enviroment\" href=\"#changing-the-enviroment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add new python or julia packages use the provided package managers (\u003ccode\u003epoetry add\u003c/code\u003e or \u003ccode\u003ePkg.add \u003c/code\u003e for python and julia respectively.)\u003c/p\u003e\n\u003cp\u003eFor julia you can also use \u003ccode\u003e] add \u003c/code\u003e in the REPL\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003efor more info checkout \u003ca href=\"https://python-poetry.org/docs/cli/\" rel=\"nofollow\"\u003epoetry\u003c/a\u003e and \u003ca href=\"https://julialang.github.io/Pkg.jl/v1/managing-packages/\" rel=\"nofollow\"\u003ePkg\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-matlabmcr2017a\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-matlabmcr2017a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-matlabmcr2017a\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eWhen contributing to this repository, please first discuss the change you wish to make via issue, \u003ca href=\"mailto:cellorganizer-dev@compbio.cmu.edu\"\u003eemail\u003c/a\u003e, or any other method with the owners of this repository before making a change.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eSupport for \u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e has been provided by grants GM075205, GM090033 and GM103712 from the \u003ca href=\"http://www.nigms.nih.gov/\" rel=\"nofollow\"\u003eNational Institute of General Medical Sciences\u003c/a\u003e, grants MCB1121919 and MCB1121793 from the \u003ca href=\"http://nsf.gov/\" rel=\"nofollow\"\u003eU.S. National Science Foundation\u003c/a\u003e, by a Forschungspreis from the \u003ca href=\"http://www.humboldt-foundation.de/\" rel=\"nofollow\"\u003eAlexander von Humboldt Foundation\u003c/a\u003e, and by the \u003ca href=\"http://www.frias.uni-freiburg.de/lifenet?set_language=en\" rel=\"nofollow\"\u003eFreiburg Institute for Advanced Studies\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.mmbios.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/868693f5973d8c9980a960c4ff8b9608ae5b009bec64db9cc1b92ab5cb831892/68747470733a2f2f69312e77702e636f6d2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f4d4d42696f536c6f676f2d65313530333531373835373331332e6769663f683d3630\" alt=\"MMBioS\" data-canonical-src=\"https://i1.wp.com/www.cellorganizer.org/wp-content/uploads/2017/08/MMBioSlogo-e1503517857313.gif?h=60\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCopyright (c) 2007-2019 by the \u003ca href=\"http://murphylab.web.cmu.edu\" rel=\"nofollow\"\u003eMurphy Lab\u003c/a\u003e at the \u003ca href=\"http://www.cbd.cmu.edu\" rel=\"nofollow\"\u003eComputational Biology Department\u003c/a\u003e in \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, - "topics": [ - "adaptive-computation", - "attention", - "julia", - "object-tracking" - ], - "updated_at": 1669660200.0 + "topics": [], + "updated_at": 1554872376.0 }, { "data_format": 2, - "description": "Singularity Image with EigenH5 and some other R packages", + "description": "Singularity containers with ImageMagick", "filenames": [ "Singularity" ], - "full_name": "CreRecombinase/docker-eigenh5", + "full_name": "stephansmit/imagemagick_containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edocker-eigenh5\u003c/h1\u003e\u003ca id=\"user-content-docker-eigenh5\" class=\"anchor\" aria-label=\"Permalink: docker-eigenh5\" href=\"#docker-eigenh5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Image with EigenH5 and some other R packages\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2630\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-imagemagick-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#imagemagick-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImageMagick Containers\u003c/h1\u003e\n\u003cp\u003eSingularity containers with ImageMagick\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/imagemagick_containers\nsingularity shell imagemagick_containers_latest.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\n\u003ca href=\"https://singularity-hub.org/collections/3475\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1571526273.0 + "updated_at": 1567168206.0 }, { "data_format": 2, - "description": "Singularity for samtools", + "description": "testing registry for singularity hub and singularity registry", "filenames": [ - "Singularity" + "Singularity", + "Singularity.test", + "os/centos/Singularity", + "os/ubuntu/Singularity.14.04", + "os/ubuntu/Singularity" ], - "full_name": "hisplan/singularity-samtools", + "full_name": "singularityhub/hello-registry", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-samtools\u003c/h1\u003e\u003ca id=\"user-content-singularity-samtools\" class=\"anchor\" aria-label=\"Permalink: singularity-samtools\" href=\"#singularity-samtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity for samtools\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity 2.2 must be installed on your system. \u003ca href=\"http://singularity.lbl.gov/docs-quick-start-installation\" rel=\"nofollow\"\u003eHere\u003c/a\u003e is the instruction.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownload \u003ccode\u003eSingularity\u003c/code\u003e file from this git repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate an empty container image of 200MB:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity create -s 200 samtools.img\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBootstrap the image using the \u003ccode\u003eSingularity\u003c/code\u003e image definition file you downloaded from the previous step:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity bootstrap samtools.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run samtools.img --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther Notes\u003c/h2\u003e\u003ca id=\"user-content-other-notes\" class=\"anchor\" aria-label=\"Permalink: Other Notes\" href=\"#other-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThis uses Alpine Linux as base image.\u003c/li\u003e\n\u003cli\u003eNote that the image definition file being used here contains a bunch of commands that downloads and compiles the source code of samtools, which is the main reason why the container image requires about 200MB. It would be nice if Singularity provides a way to shrink the image down to the only necessary size. Another workaround would be \u003ccode\u003eDockerfile\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [ "singularity", - "samtools", - "container" + "container", + "testing" ], - "updated_at": 1486144479.0 + "updated_at": 1561904313.0 }, { "data_format": 2, - "description": "singularity images for openmind", + "description": "Singularity recipe for salmon", "filenames": [ + "Singularity.0.10.1", "Singularity" ], - "full_name": "atacchet/om-images", + "full_name": "ISU-HPC/salmon", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eom-images\u003c/h1\u003e\u003ca id=\"user-content-om-images\" class=\"anchor\" aria-label=\"Permalink: om-images\" href=\"#om-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity images for openmind\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-salmon\" class=\"anchor\" aria-hidden=\"true\" href=\"#salmon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esalmon\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for salmon \u003ca href=\"https://github.com/COMBINE-lab/salmon\"\u003ehttps://github.com/COMBINE-lab/salmon\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe docker file needs to be modified as the newer versions are installed in /home in the container which is not\nrecommended in our case.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1493648266.0 - }, - { - "data_format": 2, - "description": "Minimal implementations of distributed, recurrent, deep reinforcement learning algorithms", - "filenames": [ - "SingularityFile.def" - ], - "full_name": "Jjschwartz/miniDRL", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMiniDRL\u003c/h1\u003e\u003ca id=\"user-content-minidrl\" class=\"anchor\" aria-label=\"Permalink: MiniDRL\" href=\"#minidrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMinimal implementations of distributed deep reinforcement learning algorithms, with a focus on recurrent neural networks. Heavily inspired by \u003ca href=\"https://github.com/vwxyzjn/cleanrl\"\u003eCleanRL\u003c/a\u003e and \u003ca href=\"https://github.com/corl-team/CORL\"\u003eCORL\u003c/a\u003e this library provides high-quality and easy-to-follow stand-alone implementations of some distributed RL algorithms.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePrerequisites:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython \u0026gt;= 3.10 (tested with 3.10)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo install:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:Jjschwartz/miniDRL.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e miniDRL\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or to install all dependencies\u003c/span\u003e\npip install -e .[all]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun PPO on \u003ca href=\"https://gymnasium.farama.org/\" rel=\"nofollow\"\u003egymnasium\u003c/a\u003e \u003ccode\u003eCartPole-v1\u003c/code\u003e environment using four parallel workers (reduce number of workers if you have less than four cores, or feel free to increase it if you have more):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython minidrl/ppo/run_gym.py \\\n --env_id CartPole-v1 \\\n --total_timesteps 1000000 \\\n --num_workers 4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e open another terminal and run tensorboard from repo root directory\u003c/span\u003e\ntensorboard --logdir runs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo use experiment tracking with wandb, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewandb login \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e only required for the first time\u003c/span\u003e\npython minidrl/ppo/run_gym.py \\\n --env_id CartPole-v1 \\\n --total_timesteps 1000000 \\\n --num_workers 4 \\\n --track_wandb \\\n --wandb_project minidrltest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlgorithms\u003c/h2\u003e\u003ca id=\"user-content-algorithms\" class=\"anchor\" aria-label=\"Permalink: Algorithms\" href=\"#algorithms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains standalone implementations of some of the main distributed RL algorithms that support recurrent neural networks, including:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePPO - Single Machine\u003c/h3\u003e\u003ca id=\"user-content-ppo---single-machine\" class=\"anchor\" aria-label=\"Permalink: PPO - Single Machine\" href=\"#ppo---single-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://arxiv.org/abs/1707.06347\" rel=\"nofollow\"\u003epaper\u003c/a\u003e | \u003ca href=\"https://github.com/Jjschwartz/miniDRL/blob/main/minidrl/ppo/ppo.py\"\u003ecode\u003c/a\u003e | \u003ca href=\"https://github.com/Jjschwartz/miniDRL/blob/main/docs/ppo/ppo.md\"\u003edocs\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/ppo/figures/pong_vs_num_workers_wall_time.svg\"\u003e\u003cimg src=\"docs/ppo/figures/pong_vs_num_workers_wall_time.svg\" alt=\"Learning Curve by wall time vs num workers\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cem\u003eLearning curve of PPO - Single Machine on Atari Pong with different number of parallel workers\u003c/em\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR2D2\u003c/h3\u003e\u003ca id=\"user-content-r2d2\" class=\"anchor\" aria-label=\"Permalink: R2D2\" href=\"#r2d2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://openreview.net/forum?id=r1lyTjAqYX\" rel=\"nofollow\"\u003epaper\u003c/a\u003e | \u003ca href=\"https://github.com/Jjschwartz/miniDRL/tree/main/minidrl/r2d2\"\u003ecode\u003c/a\u003e | \u003ca href=\"https://github.com/Jjschwartz/miniDRL/blob/main/docs/r2d2/r2d2.md\"\u003edocs\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMaybe in the future\u003c/h3\u003e\u003ca id=\"user-content-maybe-in-the-future\" class=\"anchor\" aria-label=\"Permalink: Maybe in the future\" href=\"#maybe-in-the-future\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePPO - Multi Machine\u003c/li\u003e\n\u003cli\u003eIMPALA\u003c/li\u003e\n\u003cli\u003eR2D2 - Multi Machine\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "distributed-reinforcement-learning", - "ppo", - "pytorch", - "r2d2", - "reinforcement-learning", - "rnn" - ], - "updated_at": 1700237055.0 + "updated_at": 1528143444.0 }, { "data_format": 2, - "description": "Test what effect subssampling of a run has on QC", + "description": null, "filenames": [ - "Singularity" + "Singularity.v3.3.1" ], - "full_name": "JackCurragh/Subsample-Effect-On-QC", + "full_name": "baxpr/sct-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSubsample Effect On QC\u003c/h1\u003e\u003ca id=\"user-content-subsample-effect-on-qc\" class=\"anchor\" aria-label=\"Permalink: Subsample Effect On QC\" href=\"#subsample-effect-on-qc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSubsample a FASTQ to different degrees and run FASTQC on it. Output a MultiQC Report\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-spinal-cord-toolbox-in-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#spinal-cord-toolbox-in-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpinal Cord Toolbox in Singularity container\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003e(/opt/)fmri_pipeline/fmri_pipeline_launch.sh\u003c/code\u003e, \u003ccode\u003e(/opt/)mffe_pipeline/mffe_pipeline_launch.sh\u003c/code\u003e for a list of the inputs for each app, and \u003ccode\u003e(/opt/)test_mffe.sh\u003c/code\u003e, \u003ccode\u003e(/opt/)test_fmri.sh\u003c/code\u003e for example run scripts. Many of the inputs for the fmri app are outputs of the mffe app.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h2\u003e\n\u003cp\u003eOutput images are named as \u003ccode\u003e\u0026lt;geometry\u0026gt;_\u0026lt;contents\u0026gt;.nii.gz\u003c/code\u003e. The tag \u003ccode\u003e_template_\u003c/code\u003e indicates the image was derived from the PAM50 template; all others are derived from the subject images.\u003c/p\u003e\n\u003cp\u003eGeometries are\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efmri_ Native geometry of the fMRI\nmffe_ Native geometry of the mFFE\nt2sag_ Native geometry of the T2 sagittal.\nipmffe_ Iso-voxel padded geometry based on the native mFFE. This is used to accurately \n resample vertebral locations and ROIs between geometries.\nwarp_ Warp field between two geometries\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput contents from the mffe app are\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e_mffe Unprocessed mFFE\n\n_maskNN Registration mask, NN mm in size\n\n_cord Segmented spinal cord (\"seg\")\n_cord_labeled Vertebral label ROIs found on the t2sag\n_cord_labeled_discs Disc point labels found on the t2sag\n_cord_labeled_body Body center points from _cord_labeled\n\n_gm Segmented gray matter found on the mFFE\n_wm Segmented white matter found on the mFFE\n_csf Segmented CSF found on the mFFE\n_template_csf Atlas CSF compartment from the PAM50 template\n\n_synt2 Synthetic T2 built from the gray and white segmentations\n\nmffe_report.pdf QC report and view of results\nmffe_csa.csv Cross-sectional areas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput contents from the fmri app are\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e_fmri Unprocessed fMRI\n_fmri0 First volume of unprocessed fMRI\n_moco Motion corrected fMRI\n_moco_mean Mean of motion corrected fMRI volumes\n_regbp Filtered fMRI (confound regression and bandpass)\n\n_mffe Resampled mFFE\n\n_maskNN Registration mask, NN mm in size\n\n_cord Segmented spinal cord (\"seg\")\n_cord_labeled Vertebral label ROIs found on the t2sag\n_centerline Cord centerline\n\n_gm Segmented gray matter found on the mFFE\n_wm Segmented white matter found on the mFFE\n_csf Atlas CSF compartment from the PAM50 template\n\n_notspine \"Not spine\" region used to obtain confound signals\n\n_gmcut Gray matter cut into four horns\n_gmcutlabel Gray matter cut into four horns and marked by level\n \n_R_*_inslice Connectivity maps for within-slice seeds (R)\n_Z_*_inslice Connectivity maps for within-slice seeds (Z)\n\nfmri_report.pdf QC report and view of results\nR_inslice.csv ROI-to-ROI connectivity within slice (R)\nZ_inslice.csv ROI-to-ROI connectivity within slice (Z)\n\nfmri_gmcut.csv Label info for ROI images of same base filename\nfmri_gmcutlabel.csv\n\nphyslog_cardiac.csv Cardiac signal from physlog\nphyslog_respiratory.csv Respiratory signal from physlog\nricor.slibase.1D Physlog signals as output from RetroTS\nricor.csv Computed respiratory regressors\n\nfmri_moco_params.tsv Estimated fMRI motion parameters\nfmri_moco_params_X.nii.gz\nfmri_moco_params_Y.nii.gz\n\nvolume_acquisition_time.txt Volume acq time used for filtering (sec)\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1700500572.0 + "updated_at": 1586976537.0 }, { "data_format": 2, - "description": "Nextflow pipeline for running RiboMetric on Genomic BAMs (as per rdp.ucc.ie)", + "description": null, "filenames": [ - "Singularity" + "Singularity.basecall_wrapper_0.0.32_albacore_2.3.3" ], - "full_name": "JackCurragh/RiboMetric-nf", + "full_name": "TomHarrop/basecall_wrapper", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRiboMetric-nf\u003c/h1\u003e\u003ca id=\"user-content-ribometric-nf\" class=\"anchor\" aria-label=\"Permalink: RiboMetric-nf\" href=\"#ribometric-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRuns RiboMetric on Genomic BAMs\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDocker\u003c/h5\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -t pipeline-image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003edocker\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027pipeline-image:latest\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eConda\u003c/h5\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a conda definition yaml file \u003ca href=\"conda/example.yml\"\u003eeg. here\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCall the pipeline directly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with all the frills\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills \u0026lt;params-file\u0026gt; \u0026lt;profile name from nextflow.config\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills example_parameters.yml standard\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e11 samtools index $file\n12 mudskipper bulk --alignment ${file} --out ${file}.trans.bam --index $MUD\n13 samtools sort ${file}.trans.bam -o ${file}.trans.bam\n14 samtools index ${file}.trans.bam\n15 RiboMetric run -b ${file}.trans.bam -a $RiboMetric -o $dir --all\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1709747987.0 + "updated_at": 1567650634.0 }, { "data_format": 2, - "description": "Template repository for setting up new Nextflow pipelines", + "description": null, "filenames": [ - "Singularity" + "devops_pipeline/Singularity", + "devops_base/Singularity" ], - "full_name": "JackCurragh/Nextflow-Template", + "full_name": "ninamiolane/gnetree", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNextflow-Template\u003c/h1\u003e\u003ca id=\"user-content-nextflow-template\" class=\"anchor\" aria-label=\"Permalink: Nextflow-Template\" href=\"#nextflow-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTemplate repository for setting up new Nextflow pipelines\u003c/p\u003e\n\u003cp\u003eThis template has been informed by many other Nextflow pipelines and templates. Some are much more advanced than this template. Please see:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nextflow-io/awesome-nextflow\"\u003eAwesome-Nextflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AustralianBioCommons/Nextflow_DSL2_template\"\u003eAustralian BioCommons Template\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/epi2me-labs/wf-template\"\u003eepi2me labs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/FredHutch/workflow-template-nextflow\"\u003eFredHutch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/search?q=Nextflow%20Template\u0026amp;type=repositories\"\u003eAnd Many More\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGeneral Setup Notes\u003c/h2\u003e\u003ca id=\"user-content-general-setup-notes\" class=\"anchor\" aria-label=\"Permalink: General Setup Notes\" href=\"#general-setup-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTower\u003c/h4\u003e\u003ca id=\"user-content-tower\" class=\"anchor\" aria-label=\"Permalink: Tower\" href=\"#tower\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etower.nf allows GUI tracking of workflow progress\nSet it up using the instructions \u003ca href=\"https://help.tower.nf/22.4/getting-started/usage/#nextflow-with-tower\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e||=====================================||\u003c/h1\u003e\u003ca id=\"\" class=\"anchor\" aria-label=\"Permalink: ||=====================================||\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e|| Template README\u003c/h1\u003e\u003ca id=\"user-content----template-readme\" class=\"anchor\" aria-label=\"Permalink: || Template README\" href=\"#---template-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e|| \u003cb\u003eDelete Everything Above\u003c/b\u003e\n\u003c/h1\u003e\u003ca id=\"user-content----delete-everything-above\" class=\"anchor\" aria-label=\"Permalink: || Delete Everything Above\" href=\"#---delete-everything-above\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e||=====================================||\u003c/h1\u003e\u003ca id=\"user-content--1\" class=\"anchor\" aria-label=\"Permalink: ||=====================================||\" href=\"#-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e[Describe here what this pipeline does]\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDocker\u003c/h5\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -t pipeline-image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003edocker\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027pipeline-image:latest\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eConda\u003c/h5\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a conda definition yaml file \u003ca href=\"conda/example.yml\"\u003eeg. here\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCall the pipeline directly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with all the frills\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills \u0026lt;params-file\u0026gt; \u0026lt;profile name from nextflow.config\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills example_parameters.yml standard\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1681044968.0 + "updated_at": 1548186366.0 }, { "data_format": 2, - "description": null, + "description": "Singularity Recipes for larcv3", "filenames": [ - "docker/Singularity" + "recipes/cuda/Singularity.centos7-cuda-core", + "recipes/cuda/Singularity.centos7-cuda-core-mpich", + "recipes/cuda/torch/Singularity.centos7-cuda-torch", + "recipes/cuda/torch/Singularity.centos7-cuda-torch-mpich-larcv", + "recipes/cuda/torch/Singularity.centos7-cuda-torch-larcv", + "recipes/cuda/torch/Singularity.centos7-cuda-torch-mpich", + "recipes/cuda/tf/Singularity.centos7-cuda-tf-mpich", + "recipes/cuda/tf/Singularity.centos7-cuda-tf-mpich-larcv", + "recipes/cuda/tf/Singularity.centos7-cuda-tf", + "recipes/cuda/tf/Singularity.centos7-cuda-tf-larcv" ], - "full_name": "arustamm/fwix", + "full_name": "DeepLearnPhysics/larcv3-singularity", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1722714141.0 + "updated_at": 1584373427.0 }, { "data_format": 2, - "description": "repo for automated processing of Ribo-Seq (and associated RNA-seq) data ", + "description": "Singularity recipe for cDNA_cupcake", "filenames": [ - "Singularity" + "Singularity.5.8.0" ], - "full_name": "JackCurragh/riboseq_data_processing", + "full_name": "ISU-HPC/cDNA_cupcake", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRibo-Seq Data Processing\u003c/h1\u003e\u003ca id=\"user-content-ribo-seq-data-processing\" class=\"anchor\" aria-label=\"Permalink: Ribo-Seq Data Processing\" href=\"#ribo-seq-data-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e[Describe here what this pipeline does]\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDocker\u003c/h5\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -t pipeline-image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003edocker\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027pipeline-image:latest\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eConda\u003c/h5\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a conda definition yaml file \u003ca href=\"conda/example.yml\"\u003eeg. here\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCall the pipeline directly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with all the frills\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills \u0026lt;params-file\u0026gt; \u0026lt;profile name from nextflow.config\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills example_parameters.yml standard\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData Processing For \u003ca href=\"riboseq.org\"\u003eRiboSeq.org\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-data-processing-for-riboseqorg\" class=\"anchor\" aria-label=\"Permalink: Data Processing For RiboSeq.org\" href=\"#data-processing-for-riboseqorg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAutomated processing of Ribo-Seq (and associated RNA-Seq) data for \u003ca href=\"https://gwips.ucc.ie/\" rel=\"nofollow\"\u003eGWIPS-Viz\u003c/a\u003e and \u003ca href=\"https://trips.ucc.ie/\" rel=\"nofollow\"\u003eTRIPS-Viz\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-automated-processing-of-ribo-seq-and-associated-rna-seq-data-for-gwips-viz-and-trips-viz\" class=\"anchor\" aria-label=\"Permalink: Automated processing of Ribo-Seq (and associated RNA-Seq) data for GWIPS-Viz and TRIPS-Viz\" href=\"#automated-processing-of-ribo-seq-and-associated-rna-seq-data-for-gwips-viz-and-trips-viz\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout Riboseq.org\u003c/h2\u003e\u003ca id=\"user-content-about-riboseqorg\" class=\"anchor\" aria-label=\"Permalink: About Riboseq.org\" href=\"#about-riboseqorg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a set of resources for the analysis and visualisation of publically available ribosome profiling data produced and maintained by various members of LAPTI lab in the School of Biochemistry and Cell Biology at Univeristy College Cork. These resources are well documented in their respective publications\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGWIPS-Viz\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://doi.org/10.1093/nar/gkx790\" rel=\"nofollow\"\u003eGWIPS-viz: 2018 update (2018).\u003c/a\u003e Nucleic Acids Res\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://doi.org/10.1002/cpbi.50\" rel=\"nofollow\"\u003eThe GWIPS-viz Browser (2018).\u003c/a\u003e Current Protocols in Bioinformatics\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://dx.doi.org/10.1002/pmic.201400603%20\" rel=\"nofollow\"\u003eGWIPS-viz as a tool for exploring ribosome profiling evidence supporting the synthesis of alternative proteoforms (2015).\u003c/a\u003e Proteomics\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://dx.doi.org/10.1093/nar/gkt1035\" rel=\"nofollow\"\u003e GWIPS-viz: development of a ribo-seq genome browser (2014).\u003c/a\u003e Nucleic Acids Res\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1093/nar/gky842\" rel=\"nofollow\"\u003eTrips-Viz: a transcriptome browser for exploring Ribo-Seq data (2019).\u003c/a\u003e Nucleic Acids Res\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://dx.doi.org/10.1080/15476286.2016.1141862\" rel=\"nofollow\"\u003eRiboGalaxy: a browser based platform for the alignment, analysis and visualization of ribosome profiling data.\u003c/a\u003e RNA Biology-Viz\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e** Note: Ribogalaxy is being updated currently and functionality will be restored shortly (14-2-2022)**\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements-1\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003e Biopython \u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/pandas/\" rel=\"nofollow\"\u003e Pandas \u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/validators/\" rel=\"nofollow\"\u003e Validators \u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutline\u003c/h2\u003e\u003ca id=\"user-content-outline\" class=\"anchor\" aria-label=\"Permalink: Outline\" href=\"#outline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eProduce Database Of All Available Ribosome Profiling Studies\u003c/li\u003e\n\u003cli\u003eGather Metadata\u003c/li\u003e\n\u003cli\u003eFetch Files and Infer Gaps in Metadata\u003c/li\u003e\n\u003cli\u003eRun Pipeline\u003c/li\u003e\n\u003cli\u003eUpload to GWIPS \u0026amp; TRIPS\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Produce Database Of All Available Ribosome Profiling Studies\u003c/h2\u003e\u003ca id=\"user-content-1-produce-database-of-all-available-ribosome-profiling-studies\" class=\"anchor\" aria-label=\"Permalink: 1. Produce Database Of All Available Ribosome Profiling Studies\" href=\"#1-produce-database-of-all-available-ribosome-profiling-studies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn recent years the rate at which ribosome profiling studies have been published has steadily increased. When the riboseq.org resources were initiatlly developed the number of available ribo-seq datasets was managable via manual inclusion. Here we put in place a method that records the details of relevant ribosome profiling data deposited in GEO\u003c/p\u003e\n\u003cp\u003eInitially manual searching of GEO and SRA were used along with \u003ca href=\"10.3390/biology10101026\"\u003eARGEOS\u003c/a\u003e. The outputs of each of these methods were colated to find the set of unique datasets.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Gather Metadata\u003c/h2\u003e\u003ca id=\"user-content-2-gather-metadata\" class=\"anchor\" aria-label=\"Permalink: 2. Gather Metadata\" href=\"#2-gather-metadata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGEO and SRA run tables contain valuable metadata that may be important for the processing and cateloging of the datasets. In this step we use python scripts to glean what we can from the information available\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Fetch Files and Infer Gaps in Metadata\u003c/h2\u003e\u003ca id=\"user-content-3-fetch-files-and-infer-gaps-in-metadata\" class=\"anchor\" aria-label=\"Permalink: 3. Fetch Files and Infer Gaps in Metadata\" href=\"#3-fetch-files-and-infer-gaps-in-metadata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA common problem with reprocessing data for these resources is that the data is deposited in GEO and SRA with inconsistent metadata. In the stage of the process we carry out a number of steps to check for the relevant data in the provided metadata and where it is absent we infer it from the data itself. This relates to information such as cell type and treatment but also UMI position and adapter position/sequence.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. Run pipeline\u003c/h2\u003e\u003ca id=\"user-content-4-run-pipeline\" class=\"anchor\" aria-label=\"Permalink: 4. Run pipeline\" href=\"#4-run-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this stage we use nextflow to process the fetched reads following the schema below\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/JackCurragh/riboseq_data_processing/blob/main/images/pipeline.drawio.png\"\u003e\u003cimg src=\"https://github.com/JackCurragh/riboseq_data_processing/raw/main/images/pipeline.drawio.png\" alt=\"Deptiction of the data processing pipeline\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e5. Upload to GWIPS and TRIPS\u003c/h2\u003e\u003ca id=\"user-content-5-upload-to-gwips-and-trips\" class=\"anchor\" aria-label=\"Permalink: 5. Upload to GWIPS and TRIPS\" href=\"#5-upload-to-gwips-and-trips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis stage uses the metadata to upload the processed files to the web resources in an automated fashion\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cdna_cupcake\" class=\"anchor\" aria-hidden=\"true\" href=\"#cdna_cupcake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecDNA_cupcake\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for cDNA_cupcake\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1685092664.0 + "updated_at": 1545169004.0 }, { "data_format": 2, - "description": "official build specifications for tensorflow", + "description": "Small example TI method within a docker", "filenames": [ - "Singularity" + "R_dynwrap/Singularity.R_dynwrap", + "python_hdf5/Singularity.python_hdf5", + "R_text/Singularity.R_text", + "python_text/Singularity.python_text", + "R_hdf5/Singularity.R_hdf5", + "R_feather/Singularity.R_feather", + "R_rds/Singularity.R_rds", + "python_feather/Singularity.python_feather" ], - "full_name": "researchapps/tensorflow", + "full_name": "dynverse/dynwrap_tester", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTensorflow\u003c/h1\u003e\u003ca id=\"user-content-tensorflow\" class=\"anchor\" aria-label=\"Permalink: Tensorflow\" href=\"#tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a tensorflow image developed to work on the Sherlock cluster. We start with Docker bases to make life easy.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-creating-ti-methods-within-a-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-ti-methods-within-a-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating TI methods within a docker\u003c/h1\u003e\n\u003cp\u003eThis repository contains several examples of wrapping a TI method within a docker.\u003c/p\u003e\n\u003cp\u003eIt contains three main files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edefinition.yml\u003c/code\u003e Defining the input, output and parameters of the method\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDockerfile\u003c/code\u003e Used for building the docker, its entrypoint is used to run the method\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun.R\u003c/code\u003e Loads the data, infers a trajectory, and generates some output files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe docker image is automatically build at \u003ca href=\"https://hub.docker.com/r/dynverse/dynwrap_tester/builds/\" rel=\"nofollow\"\u003edockerhub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis method can be run directly from dockerhub using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003edynwrap\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eti_comp1\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e pull_docker_ti_method(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edynverse/dynwrap_tester\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)()\n\u003cspan class=\"pl-smi\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e infer_trajectory(\u003cspan class=\"pl-smi\"\u003etask\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eti_comp1\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1484507796.0 + "updated_at": 1539684245.0 }, { "data_format": 2, - "description": "C++ API \u0026 command-line toolkit for working with BAM data", + "description": null, "filenames": [ - "2.5.1/Singularity", - "2.5.2/Singularity" + "Singularity.1.0.0", + "Singularity.1.1.0" ], - "full_name": "pscedu/singularity-bamtools", - "latest_release": "v2.5.2", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/054141521af458b1312c4cb51f20a90cba03b91c428ea0010ed08382f5118785/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/054141521af458b1312c4cb51f20a90cba03b91c428ea0010ed08382f5118785/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bamtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5193a9daee0df89ed29bb24726e38247567acccb6fb574dadf1f299cf508f7c6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5193a9daee0df89ed29bb24726e38247567acccb6fb574dadf1f299cf508f7c6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bamtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/22739bec1bac08d0d267605f10cb285794413bb170278dec0274e32df1f3a5da/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22739bec1bac08d0d267605f10cb285794413bb170278dec0274e32df1f3a5da/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bamtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e9c1e69bc7ade96a1b70b1b296fc22b7df4bbb8415576d179012ecd76c87e28d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e9c1e69bc7ade96a1b70b1b296fc22b7df4bbb8415576d179012ecd76c87e28d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bamtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-bamtools\u003c/h1\u003e\u003ca id=\"user-content-singularity-bamtools\" class=\"anchor\" aria-label=\"Permalink: singularity-bamtools\" href=\"#singularity-bamtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/pezmaster31/bamtools\"\u003ebamtools\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebamtools\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bamtools/2.5.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bamtools\u003c/code\u003e as \u003ccode\u003e2.5.1\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pndni/freesurfer-6.0.1-container", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629217479.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1556655715.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.build" + "Singularity.centos" ], - "full_name": "bjorgve/hpc-build-box", - "latest_release": "0.0.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehpc-build-box\u003c/h1\u003e\u003ca id=\"user-content-hpc-build-box\" class=\"anchor\" aria-label=\"Permalink: hpc-build-box\" href=\"#hpc-build-box\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container provides an environment with key libraries and tools for high-performance computing (HPC) development. It includes MPI (Message Passing Interface), OpenMP (Open Multi-Processing), Eigen (C++ template library for linear algebra), and CMake build system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eMPI\u003c/strong\u003e: Pre-installed Open MPI for parallel computing.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOpenMP\u003c/strong\u003e: Support for multi-platform shared-memory parallel programming.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEigen\u003c/strong\u003e: Eigen 3.4 for high-level linear algebra operations.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCMake\u003c/strong\u003e: Version 3.25.0 for configuring and building your projects.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisites\u003c/h2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/guides/3.7/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed on your machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload\u003c/h2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-label=\"Permalink: Download\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo pull the latest version of the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull https://github.com/bjorgve/hpc-build-box/releases/download/0.0.2/bjorgve-hpc-build-box.build.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning CMake\u003c/h3\u003e\u003ca id=\"user-content-running-cmake\" class=\"anchor\" aria-label=\"Permalink: Running CMake\" href=\"#running-cmake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./bjorgve-hpc-build-box.build.sif cmake [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompiling Code with Make\u003c/h3\u003e\u003ca id=\"user-content-compiling-code-with-make\" class=\"anchor\" aria-label=\"Permalink: Compiling Code with Make\" href=\"#compiling-code-with-make\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./bjorgve-hpc-build-box.build.sif make [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Executables\u003c/h3\u003e\u003ca id=\"user-content-running-executables\" class=\"anchor\" aria-label=\"Permalink: Running Executables\" href=\"#running-executables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./bjorgve-hpc-build-box.build.sif ./executable [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInside the Container\u003c/h2\u003e\u003ca id=\"user-content-inside-the-container\" class=\"anchor\" aria-label=\"Permalink: Inside the Container\" href=\"#inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere\u0027s what gets installed in the container based on the \u003ccode\u003e.def\u003c/code\u003e file:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBasic build utilities (\u003ccode\u003ebuild-essential\u003c/code\u003e, \u003ccode\u003ewget\u003c/code\u003e, \u003ccode\u003egit\u003c/code\u003e, \u003ccode\u003ecurl\u003c/code\u003e, etc.)\u003c/li\u003e\n\u003cli\u003eOpenMP (\u003ccode\u003elibgomp1\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eOpen MPI (\u003ccode\u003elibopenmpi-dev\u003c/code\u003e, \u003ccode\u003eopenmpi-bin\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eBoost libraries (\u003ccode\u003elibboost-all-dev\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCMake 3.25.0\u003c/li\u003e\n\u003cli\u003eEigen 3.4\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions and Issues\u003c/h2\u003e\u003ca id=\"user-content-contributions-and-issues\" class=\"anchor\" aria-label=\"Permalink: Contributions and Issues\" href=\"#contributions-and-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFeel free to open issues or submit pull requests if you have suggestions or encounter issues.\u003c/p\u003e\n", + "full_name": "ertheisen/test", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1694033038.0 + "updated_at": 1570565785.0 }, { "data_format": 2, - "description": "Work I did for Google Summer of Code 2020", + "description": "Surface morphometry BIDS app", "filenames": [ - "Singularity.test2", - "Singularity", - "Singularity_Test/Singularity.test" + "Singularity.v0.1", + "Singularity" ], - "full_name": "timothydgreer/GSoC_2020", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGSoC_2020\u003c/h1\u003e\u003ca id=\"user-content-gsoc_2020\" class=\"anchor\" aria-label=\"Permalink: GSoC_2020\" href=\"#gsoc_2020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWork I did for Google Summer of Code 2020\u003c/p\u003e\n", + "full_name": "khanlab/surfmorph", + "latest_release": "v0.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-surfmorph\" class=\"anchor\" aria-hidden=\"true\" href=\"#surfmorph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esurfmorph\u003c/h1\u003e\n\u003cp\u003eSurface morphometry BIDS app\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1589700293.0 + "updated_at": 1591844426.0 }, { "data_format": 2, - "description": "Python Gene Expression Spatial Toolkit", + "description": "Singularity definition files and Dockerfiles for building RStudio and R packages on LURC\u0027s OOD portal", "filenames": [ - "singularity/Singularity.stretch" + "Singularity.r402-lugeo", + "Singularity.r402-lubio", + "Singularity.r353", + "Singularity.r402-base", + "Singularity.r363" ], - "full_name": "mfschmidt/PyGEST", + "full_name": "alexpacheco/lurc-ood-rstudio", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-lurc-ood-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#lurc-ood-rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elurc-ood-rstudio\u003c/h1\u003e\n\u003cp\u003eSingularity definition files and Dockerfiles for building RStudio and R packages on LURC\u0027s OOD portal\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1611368377.0 + "updated_at": 1615375080.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.borgbackup_1.1.13", - "Singularity.clang-upc_3.9.1" + "Singularity.biodiverse" ], - "full_name": "TomHarrop/misc-utils", + "full_name": "ternaustralia/coesra-singularity-biodiverse", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-biodiverse\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-biodiverse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-biodiverse\u003c/h1\u003e\n\u003cp\u003eAuthor: Hoang Nguyen\nCreated: 22 July 2019\nThis will create a image with Singularity 2.5.1\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1619659263.0 + "topics": [ + "coesra" + ], + "updated_at": 1563776892.0 }, { "data_format": 2, - "description": "Model Evaluation Toolkit Singularity Containers", + "description": "A quality control pipeline for illumina data set. This pipeline removes contaminants (e.g. Phix), performs fastqc, adapter cleaning and trimming and checks for contaminants", "filenames": [ - "Singularity", - "previous/Singularity.8.0", - "previous/Singularity.8.1" + "singularity/Singularity" ], - "full_name": "trwhitcomb/metcontainers", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emetcontainers\u003c/h1\u003e\u003ca id=\"user-content-metcontainers\" class=\"anchor\" aria-label=\"Permalink: metcontainers\" href=\"#metcontainers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModel Evaluation Toolkit Singularity Containers\u003c/p\u003e\n\u003cp\u003eUnofficial Singularity version of official MET Docker containers\u003c/p\u003e\n", + "full_name": "sequana/quality_control", + "latest_release": "v0.10.0", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1570823060.0 + "updated_at": 1634220788.0 }, { "data_format": 2, - "description": null, + "description": "Singularity definition files and Dockerfiles for CentOS desktop on LURC\u0027s OOD portal", "filenames": [ - "Singularity" + "Singularity.xfce", + "Singularity.mate", + "Singularity.molgfx" ], - "full_name": "feilong/artful-neurodebian", + "full_name": "alexpacheco/lurc-ood-desktop", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-lurc-ood-desktop\" class=\"anchor\" aria-hidden=\"true\" href=\"#lurc-ood-desktop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elurc-ood-desktop\u003c/h1\u003e\n\u003cp\u003eSingularity definition files and Dockerfiles for CentOS desktop on LURC\u0027s OOD portal\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1505485847.0 - }, - { - "data_format": 2, - "description": "Get Parflow built in a singularity container for distribution", - "filenames": [ - "Singularity.parflow_ompi_206", - "Singularity", - "Singularity.no_netcdf", - "base/Singularity.base", - "base/Singularity.nv_base", - "mpi/Singularity.mpich", - "mpi/Singularity.ompi", - "pf/Singularity.parflow_cuda", - "pf/Singularity.parflow", - "pf/Singularity.parflow_mpich", - "pf/Singularity.parflow_ompi", - "libs/Singularity.nv_libs", - "libs/Singularity.libs_mpich", - "libs/Singularity.libs", - "libs/Singularity.libs_ompi" - ], - "full_name": "arezaii/pf_singularity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParFlow Singularity Definition Files\u003c/h1\u003e\u003ca id=\"user-content-parflow-singularity-definition-files\" class=\"anchor\" aria-label=\"Permalink: ParFlow Singularity Definition Files\" href=\"#parflow-singularity-definition-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA set of singularity definition files that allow for building Singularity containers for ParFlow with\neither OMPI or MPICH mpi layers.\u003c/p\u003e\n\u003cp\u003eEach ParFlow container is built as a sci-app container, providing access to both sequential and parallel\nbuilds of ParFlow\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout Apps\u003c/h2\u003e\u003ca id=\"user-content-about-apps\" class=\"anchor\" aria-label=\"Permalink: About Apps\" href=\"#about-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epar = distributed build of ParFlow, -DPARFLOW_AMPS_SEQUENTIAL_IO=True\u003c/li\u003e\n\u003cli\u003eseq = sequential build of ParFlow, -DPARFLOW_AMPS_SEQUENTIAL_IO=False\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto run either:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eapp_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e/path/to/singularity_container.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e.tcl input file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee additional information about \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/definition_files.html?highlight=apps#apps\" rel=\"nofollow\"\u003eApps in Singularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eHost OS must have Singularity installed\u003c/li\u003e\n\u003cli\u003eTo build container from recipe file, user must have root access\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo Build Container\u003c/h2\u003e\u003ca id=\"user-content-to-build-container\" class=\"anchor\" aria-label=\"Permalink: To Build Container\" href=\"#to-build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGeneral build command is of the form:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edestination/path/to/singularity_container.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSingularity definition file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eas a specific example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/pf_singularity_ompi Singularity.parflow_ompi\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo Use ParFlow in Container\u003c/h2\u003e\u003ca id=\"user-content-to-use-parflow-in-container\" class=\"anchor\" aria-label=\"Permalink: To Use ParFlow in Container\" href=\"#to-use-parflow-in-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eexample of running the LW test case in \u003ccode\u003eparflow/test/washita/tcl_scripts\u003c/code\u003e directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app par \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/pf_singularity_ompi LW_Test.tcl\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull from Singularity Hub\u003c/h2\u003e\u003ca id=\"user-content-pull-from-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Pull from Singularity Hub\" href=\"#pull-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull shub://arezaii/pf_singularity:parflow_ompi\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen to use it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app par pf_singularity_parflow_ompi.sif LW_Test.tcl\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBecause singularity containers are immutable and ParFlow tests write to disk, you must expand the image to a writable sandbox.\nUnfortunately this requires super user access to do...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMake Container Writable\u003c/h3\u003e\u003ca id=\"user-content-make-container-writable\" class=\"anchor\" aria-label=\"Permalink: Make Container Writable\" href=\"#make-container-writable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, create a writable sandbox from the immutable container using Singularity\u0027s build command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory_to_create_for_sandbox/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esingularity_container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eas an example, if you had pulled the parflow_ompi image from shub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox pf_singularity_parflow_ompi_test/ pf_singularity_parflow_ompi.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere will now be a new directory pf_singularity_parflow_ompi_test/ that is the root of the container.\nEditing any of the folder contents will require super user permissions.\u003c/p\u003e\n\u003cp\u003eYou can enter a console into the container now by using the Singularity shell command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell --writable \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory_to_create_for_sandbox/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Tests\u003c/h3\u003e\u003ca id=\"user-content-run-tests\" class=\"anchor\" aria-label=\"Permalink: Run Tests\" href=\"#run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter making the container writable and accessing it through a shell, both documented above, running the ParFlow\ntests can be done by changing directories and exporting the PARFLOW_DIR environment variable for either distributed\nor sequential builds of ParFlow.\u003c/p\u003e\n\u003cp\u003eTake note of the ParFlow build and install directories in the container:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequential Build\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild directory: /home/parflow/build_seq\u003c/li\u003e\n\u003cli\u003einstall directory: /home/parflow/pfdir_seq\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDistributed Build\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild directory: /home/parflow/build_par\u003c/li\u003e\n\u003cli\u003einstall directory: /home/parflow/pfdir_par\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/parflow/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ebuild_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PARFLOW_DIR=/home/parflow/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003einstall_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e make \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1650137133.0 + "updated_at": 1614296417.0 }, { "data_format": 2, - "description": "The simulation frame work for Craig Rafter\u0027s PhD research", + "description": "Repository of singularity containers", "filenames": [ - "SingularityDef" + "nanopolish/Singularity.nanopolish" ], - "full_name": "cbrafter/SUMO_FRAMEWORK", + "full_name": "alexiswl/singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSUMO Framework\u003c/h1\u003e\u003ca id=\"user-content-sumo-framework\" class=\"anchor\" aria-label=\"Permalink: SUMO Framework\" href=\"#sumo-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe simulation framework for the PhD research of Craig B. Rafter at the\nUniversity of Southampton 2015-2019.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. SUMO API\u003c/h2\u003e\u003ca id=\"user-content-1-sumo-api\" class=\"anchor\" aria-label=\"Permalink: 1. SUMO API\" href=\"#1-sumo-api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBase classes for signals and connecting to the simulation\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Models\u003c/h2\u003e\u003ca id=\"user-content-2-models\" class=\"anchor\" aria-label=\"Permalink: 2. Models\" href=\"#2-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFiles describing the road networks for SUMO\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Signal Controllers\u003c/h2\u003e\u003ca id=\"user-content-3-signal-controllers\" class=\"anchor\" aria-label=\"Permalink: 3. Signal Controllers\" href=\"#3-signal-controllers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCodes for the signal controllers used in this research\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. Simulation\u003c/h2\u003e\u003ca id=\"user-content-4-simulation\" class=\"anchor\" aria-label=\"Permalink: 4. Simulation\" href=\"#4-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCodes that run simulations using the models and signal controllers\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e5. Results Analysis\u003c/h2\u003e\u003ca id=\"user-content-5-results-analysis\" class=\"anchor\" aria-label=\"Permalink: 5. Results Analysis\" href=\"#5-results-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eScripts for analysing the SUMO outputs\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eScripts for doing useful things\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1653390685.0 + "updated_at": 1522028262.0 }, { "data_format": 2, - "description": "Implementing the pytorch mnist example in a docker container.", + "description": null, "filenames": [ "Singularity" ], - "full_name": "LBaeriswyl/pytorch-mnist-docker", + "full_name": "NotTheKmers/main", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epytorch-mnist-docker\u003c/h1\u003e\u003ca id=\"user-content-pytorch-mnist-docker\" class=\"anchor\" aria-label=\"Permalink: pytorch-mnist-docker\" href=\"#pytorch-mnist-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImplementing the pytorch MNIST example in a docker or singularity container.\u003c/p\u003e\n\u003cp\u003eTo train for a single epoch on docker:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnsure docker is installed\u003c/li\u003e\n\u003cli\u003eEnsure you are in the root directory\u003c/li\u003e\n\u003cli\u003eExecute ./run_mnist_docker_container.sh\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo train for a single epoch on singularity:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnsure singularity is installed\u003c/li\u003e\n\u003cli\u003eEnsure you are in the root directory\u003c/li\u003e\n\u003cli\u003eExecute ./run_mnist_singularity_container.sh\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-replicate--reproduce-kmer-publication\" class=\"anchor\" aria-hidden=\"true\" href=\"#replicate--reproduce-kmer-publication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReplicate \u0026amp; Reproduce Kmer Publication\u003c/h1\u003e\n\u003cp\u003eWe have been tasked with replicating, reproducing, and extending the previous work of the \"These Are Not the K-mers You Are Looking For: Efficient Online K-mer Counting Using a Probabilistic Data Structure\" publication\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-directory\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest directory\u003c/h2\u003e\n\u003cp\u003ePut scratch and testing code in this directory\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1700971295.0 + "updated_at": 1532379702.0 }, { "data_format": 2, - "description": "Fetch FASTQ files, clean and collapse duplicated reads", + "description": "A test to see if we can make images through singularity hub", "filenames": [ "Singularity" ], - "full_name": "JackCurragh/Collapse-FASTQ", + "full_name": "s-andrews/singularitytest", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCollapse FASTQ Files\u003c/h1\u003e\u003ca id=\"user-content-collapse-fastq-files\" class=\"anchor\" aria-label=\"Permalink: Collapse FASTQ Files\" href=\"#collapse-fastq-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInputs\u003c/h3\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is general purpose for preparing FASTQ read files but was specifically developed to prepare data for \u003ca href=\"https://riboseq.org/\" rel=\"nofollow\"\u003eRiboSeq.Org\u003c/a\u003e.\nInputs for this pipeline may vary. Options include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStudy accession\u003c/li\u003e\n\u003cli\u003eSample accession list\u003c/li\u003e\n\u003cli\u003ePath to FASTQ directory\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutputs\u003c/h3\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCollapsed Read File\u003c/h4\u003e\u003ca id=\"user-content-collapsed-read-file\" class=\"anchor\" aria-label=\"Permalink: Collapsed Read File\" href=\"#collapsed-read-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe primary output is a gzipped collapsed read file of the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;read\u0026lt;read_number\u0026gt;_x\u0026lt;read_count\u0026gt;\nNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;read_number\u0026gt;\u003c/code\u003e signifies that this is the nth read processed.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;read_count\u0026gt;\u003c/code\u003e signifies how many times reads with this exact sequence was seen\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFastQC Report\u003c/h4\u003e\u003ca id=\"user-content-fastqc-report\" class=\"anchor\" aria-label=\"Permalink: FastQC Report\" href=\"#fastqc-report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ehtml\u003c/code\u003e report for each sample is outputted\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFastP\u003c/h4\u003e\u003ca id=\"user-content-fastp\" class=\"anchor\" aria-label=\"Permalink: FastP\" href=\"#fastp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ehtml\u003c/code\u003e report from FastP\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMultiQC Report\u003c/h4\u003e\u003ca id=\"user-content-multiqc-report\" class=\"anchor\" aria-label=\"Permalink: MultiQC Report\" href=\"#multiqc-report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCombined report for each pipeline run. Merged FastP and FastQC\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDocker\u003c/h5\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -t pipeline-image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003edocker\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027pipeline-image:latest\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eConda\u003c/h5\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a conda definition yaml file \u003ca href=\"conda/example.yml\"\u003eeg. here\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCall the pipeline directly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with all the frills\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills \u0026lt;params-file\u0026gt; \u0026lt;profile name from nextflow.config\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills example_parameters.yml standard\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularitytest\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularitytest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularitytest\u003c/h1\u003e\n\u003cp\u003eA test to see if we can make images through singularity hub\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1723558153.0 + "updated_at": 1537371665.0 }, { "data_format": 2, - "description": "test_singularity", + "description": null, "filenames": [ - "Singularity" + "Singularity.v3.0.0" ], - "full_name": "leepc12/test_singularity", + "full_name": "baxpr/ndw_wm_edat", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etest_singularity\u003c/h1\u003e\u003ca id=\"user-content-test_singularity\" class=\"anchor\" aria-label=\"Permalink: test_singularity\" href=\"#test_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etest_singularity\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1542209843.0 + "updated_at": 1543615703.0 }, { "data_format": 2, - "description": "Singularity container with Nix and OpenMPI to be used in XSEDE compute environment (currently in development)", + "description": "Performance Evaluation Process Algebra", "filenames": [ - "Singularity" + "gpanalyser/Singularity.gpanalyser", + "pepa/Singularity.pepa", + "ipc/Singularity.ipc", + "bio-pepa/Singularity.biopepa" ], - "full_name": "XSEDE/singularity-nix-openmpi", + "full_name": "williamssanders/pepa", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-nix-openmpi\u003c/h1\u003e\u003ca id=\"user-content-singularity-nix-openmpi\" class=\"anchor\" aria-label=\"Permalink: singularity-nix-openmpi\" href=\"#singularity-nix-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4462\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container with Nix and OpenMPI to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", + "readme": "{\"message\":\"API rate limit exceeded for installation ID 633759.\",\"documentation_url\":\"https://docs.github.com/rest/overview/resources-in-the-rest-api#rate-limiting\"}", "stargazers_count": 0, - "subscribers_count": 16, + "subscribers_count": 0, "topics": [], - "updated_at": 1637690636.0 + "updated_at": 1592227688.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity", + "Singularity.4.2.0", + "Singularity.4.3.0" ], - "full_name": "Raijeku/TensorOrder-tests", + "full_name": "MPIB/singularity-jags", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTensorOrder\u003c/h1\u003e\u003ca id=\"user-content-tensororder\" class=\"anchor\" aria-label=\"Permalink: TensorOrder\" href=\"#tensororder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Python 3 tool for automatically contracting tensor networks for weighted model counting on multiple CPUs and on a GPU.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with docker\u003c/h2\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running with docker\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBecause of the variety of dependencies used in the various graph decomposition tools, it is recommended to use the docker container to run TensorOrder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker container (for singlecore and multi-core) can be built with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensororder .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to leverage a GPU, you must compile the (larger) docker container with TensorFlow and gpu drivers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensororder-gpu -f Dockerfile-gpu .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container\u003c/h3\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce built, docker containers can be used as follows to run TensorOrder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i tensororder:latest python /src/tensororder.py --planner=\"line-Flow\" --weights=\"unweighted\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, this runs the tensor network contraction on all available CPU cores with numpy. One can also choose to use the GPU to perform the contraction. This requires \u003ca href=\"https://nvidia.github.io/nvidia-container-runtime/\" rel=\"nofollow\"\u003envidia-container-runtime\u003c/a\u003e to be installed.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --gpus all tensororder-gpu:latest python /src/tensororder.py --planner=\"line-Flow\" --weights=\"unweighted\" --tensor_library=\"tensorflow-gpu\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is also possible to connect to a TPU on Google Cloud to perform the tensor contractions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i tensororder:latest python /src/tensororder.py --timeout=\"1000\" --planner=\"line-Flow\" --weights=\"unweighted\" --tensor_library=\"jax-tpu\" --entry_type=\"float32\" --tpu=\"10.6.165.2\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBoth docker containers are compatible with \u003ca href=\"https://github.com/Kasekopf/Turbine\"\u003eTurbine\u003c/a\u003e to run experiments on Google Cloud.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with Singularity\u003c/h2\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere is also a \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available for TensorOrder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container-1\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity container can be built with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build tensororder Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container\u003c/h3\u003e\u003ca id=\"user-content-using-the-container-1\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce built, Singularity containers can be used as follows to run TensorOrder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./tensororder --method=\"line-Flow\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning without containers\u003c/h2\u003e\u003ca id=\"user-content-running-without-containers\" class=\"anchor\" aria-label=\"Permalink: Running without containers\" href=\"#running-without-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTensorOrder can also be used directly as a Python 3 tool. Since TensorOrder uses \u003ca href=\"https://cython.org/\" rel=\"nofollow\"\u003eCython\u003c/a\u003e, it must be compiled:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake -C src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMoreover, the various tensor methods each require additional setup. Consult the \u003ca href=\"Dockerfile\"\u003eDocker file\u003c/a\u003e for an example set of installation commands.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor KCMR-metis and KCMR-gn, METIS must be installed using the instructions \u003ca href=\"src/tensorcsp\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-Tamaki and factor-Tamaki, the tree-decomposition solver Tamaki must be compiled using the \u003ccode\u003eheuristic\u003c/code\u003e instructions \u003ca href=\"solvers/TCS-Meiji\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-Flow and factor-Flow, the tree-decomposition solver FlowCutter must be compiled using the instructions \u003ca href=\"solvers/flow-cutter-pace17\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-htd and factor-htd, the tree-decomposition solver htd must be compiled using the instructions \u003ca href=\"solvers/htd-master\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor factor-hicks, the branch-decomposition solver Hicks must be compiled using the Makefile \u003ca href=\"solvers/hicks\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-portfolio3 and line-portfolio3, all tree-decompositions solvers must be compiled, and the portfolio must be compiled using the instructions \u003ca href=\"solvers/portfolio\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce everything has been built, the primary script is located in \u003ccode\u003esrc/tensororder.py\u003c/code\u003e. Example usage is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython src/tensororder.py --method=\"line-Flow\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTensorOrder requires the following python packages (see \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e for a working set of exact version information if needed):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eclick\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enumpy\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython-igraph\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enetworkx\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecython\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ethreadpoolctl\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etensorflow\u003c/code\u003e (optional)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePublications\u003c/h3\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease cite the following article if you use our code in a publication:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://arxiv.org/abs/2006.15512\" rel=\"nofollow\"\u003eParallel Weighted Model Counting with Tensor Networks\u003c/a\u003e. Jeffrey M. Dudek and Moshe Y. Vardi. Proceedings of MCW\u002720.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1801\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://MPIB/singularity-jags\nsingularity exec singularity-jags_latest.sif jags\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-jags-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#jags-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJAGS singularity\u003c/h1\u003e\n\u003cp\u003eSingularity images containing \u003ca href=\"https://sourceforge.net/projects/mcmc-jags/\" rel=\"nofollow\"\u003eJAGS\u003c/a\u003e [1]:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebased on debian-slim\u003c/li\u003e\n\u003cli\u003edownloads and builds JAGS from: \u003ca href=\"https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Source/\" rel=\"nofollow\"\u003ehttps://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Source/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003elinks against libopenblas\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e[1]\n\u003ca href=\"https://sourceforge.net/projects/mcmc-jags/\" rel=\"nofollow\"\u003ehttps://sourceforge.net/projects/mcmc-jags/\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1706232720.0 + "updated_at": 1580121725.0 }, { "data_format": 2, - "description": "preterm birth project singularity container", + "description": "A singularity container for TB-profiler", "filenames": [ "Singularity" ], - "full_name": "CreRecombinase/ptb_container", + "full_name": "phgenomics-singularity/tbprofiler", "latest_release": null, "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1566848645.0 + "updated_at": 1578274663.0 }, { "data_format": 2, - "description": "Niftynet GPU singularity container", + "description": "Containerized DMC application for HPCs", "filenames": [ - "Singularity.0.6.0" + "setup/build/Singularity/Singularity.def", + "setup/build/Singularity/Singularity.ubuntu", + "setup/build/Singularity/SingularityUpdate.def", + "setup/build/Singularity/SingularityCore.def" ], - "full_name": "yinglilu/niftynet_gpu_singularity", + "full_name": "McCoyGroup/RynLib", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rynlib\" class=\"anchor\" aria-hidden=\"true\" href=\"#rynlib\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRynLib\u003c/h1\u003e\n\u003cp\u003eThis started out as a quick layer between python and entos for running DMC\u003c/p\u003e\n\u003cp\u003eIt\u0027s grown a bit...\u003c/p\u003e\n\u003cp\u003eYou can find some documentation \u003ca href=\"https//:mccoygroup.github.io/Documentation/RynLib\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1714773139.0 + "updated_at": 1613573199.0 }, { "data_format": 2, - "description": null, + "description": "A client-worker proxy using ZMQ to server SSNet predictions on the Tufts Cluster", "filenames": [ - "Singularity" + "container/Singularity", + "container/SingularityX", + "container/SingularityNoDrivers", + "container/Singularity390.30" ], - "full_name": "khanlab/MRtrix3Tissue_singularity", - "latest_release": "v1.0.0", - "readme": "\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -v /data:/data MRtrix3Tissue.simg\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "LArbys/SSNetServer", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ssnet-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#ssnet-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSSNet Server\u003c/h1\u003e\n\u003cp\u003eA client-worker proxy using ZMQ to server SSNet predictions on the Tufts Cluster\u003c/p\u003e\n\u003cp\u003eThe code is a copy of the paranoid pirate proxy from the ZeroMQ Guide\u003c/p\u003e\n\u003cp\u003eThere are two goals:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a production network where many clients read data event-by-event, send to a collection of workers, receive net output, and write to disk\u003c/li\u003e\n\u003cli\u003eCreate a training network where single client app asks workers for batch data\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-classes\" class=\"anchor\" aria-hidden=\"true\" href=\"#classes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClasses\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ssnetworkerssnetclient\" class=\"anchor\" aria-hidden=\"true\" href=\"#ssnetworkerssnetclient\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSSNetWorker/SSNetClient\u003c/h3\u003e\n\u003cp\u003eThese are base classes that are meant to handle the network portion of the code.\nThey are to be inherited by child classes that handle either the reading/writing of data or the processing through a network.\u003c/p\u003e\n\u003cp\u003eNote that child client and workers are meant to be implemented together so that they understand their messages.\nWe do not enforce a standard messaging protocol.\nThis is meant to reflect the fact that different tasks usually differ in the details of the input/output data required.\nThough similar, I am not smart enough to define generic behavior.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ssnetbroker\" class=\"anchor\" aria-hidden=\"true\" href=\"#ssnetbroker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSSNetBroker\u003c/h3\u003e\n\u003cp\u003eThis class is the proxy between clients and workers.\nIt need not know anything about the data it is passing.\nIt\u0027s only job is to balance the load and keep track of connected workers (through heartbeats).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-simplelarcv1client\" class=\"anchor\" aria-hidden=\"true\" href=\"#simplelarcv1client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimpleLArCV1Client\u003c/h3\u003e\n\u003cp\u003eThis is a very basic client that reads larcv1 event images and sends it out to the SSNetBroker.\nIt only handles Image2D objects for now.\nYou can provide it a list of producer names via the \u003ccode\u003eproduct_dict\u003c/code\u003e argument of the constructor.\nIt will prepare a numpy array for each image product given. The array shapes are\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e(batchsize, number of images in event container, height, width)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe message sent to the worker is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[frame 0] \"producer name\" (string)\n[frame 1] \u0027Plane 65535 (rows,cols) = (0,0) ... Left Top (0,0) ... Right Bottom (0,0)\u0027 (the output of ImageMeta::dump())\n[frame 2] numpy array for batch\n[frame 3] \"producer name\" (string)\n[frame 4] \u0027Plane 65535 (rows,cols) = (0,0) ... Left Top (0,0) ... Right Bottom (0,0)\u0027 (the output of ImageMeta::dump())\n[frame 5] numpy array for batch\n(and so on...)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe received message is expected in the same format\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[frame 0] \"returned array name\" (string)\n[frame 1] \u0027Plane 65535 (rows,cols) = (0,0) ... Left Top (0,0) ... Right Bottom (0,0)\u0027 (the output of ImageMeta::dump())\n[frame 2] numpy array \n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe arrays in the received messages will be saved to an output larcv file.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dummylarcv1worker\" class=\"anchor\" aria-hidden=\"true\" href=\"#dummylarcv1worker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDummyLArCV1Worker\u003c/h3\u003e\n\u003cp\u003eUsed for debugging. Expects message from SimpleLArCV1Client and dumps numpy array shapes to standard out.\nReturns:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[frame 0] \"dummy\"\n[frame 1] \u0027Plane 65535 (rows,cols) = (0,0) ... Left Top (0,0) ... Right Bottom (0,0)\u0027 (the output of ImageMeta::dump())\n[frame 2] numpy array, filled with zeros, whose size is from the first received image\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-caffelarcv1clientworker\" class=\"anchor\" aria-hidden=\"true\" href=\"#caffelarcv1clientworker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaffeLArCV1Client/Worker\u003c/h3\u003e\n\u003cp\u003eWorker processes all three planes using Caffe1.\u003c/p\u003e\n\u003cp\u003eUses the same message protocol as Simple/Dummy pair above.\u003c/p\u003e\n\u003cp\u003eClient sends the images for one plane for one event as one batch. To send all three planes, 3 sets of frames are shipped together.\u003c/p\u003e\n\u003cp\u003eThe worker processes one frame at a time. It knows which plane\u0027s network to use from the meta. Because processing is frameset at a time,\nonly one network is running, while the others are idle. This could be improved by modeling the broker as a majordomo server, which\nknows how to ship different flavor of requests to different flavor of workers.\u003c/p\u003e\n\u003cp\u003eGood enough for now, though.\u003c/p\u003e\n\u003cp\u003eOn Meitner, 0.44 secs per event (read file+fill batch+roundtrip+write output)x3 planes.\nSeveral threads, but in total mem usage between 2.5 to 3 GB.\n(Will want to see mem usage in tests on separate nodes for worker and proxy.)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1583525733.0 + "updated_at": 1569100609.0 }, { "data_format": 2, - "description": "A singularity recipe for AMRfinderplus", + "description": "Singularity Bootstrap file for DL LEE SSNet using Caffe-LArbys", "filenames": [ "Singularity", - "v3.2.1/20191209/Singularity.v3.2.1_20191209", - "v3.2.1/20191106/Singularity.v3.2.1_20191106", - "v3.1.1b/20190924/Singularity.v3.1.1b_20190924" + "SingularityTufts" ], - "full_name": "phgenomics-singularity/amrfinderplus", + "full_name": "LArbys/singularity-dllee-ssnet", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-dllee-ssnet\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-dllee-ssnet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-dllee-ssnet\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1575844748.0 + "updated_at": 1497814537.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.cuda10", + "Singularity", + "Singularity.tf" ], - "full_name": "photocyte/shournal_singularity", + "full_name": "callaghanmt-containers/ubuntu_cuda_cudnn_base", "latest_release": null, - "readme": "\u003cp\u003eImage building handled by singularity-hub.org\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://photocyte/shournal_singularity\nsingularity shell shournal_singularity_latest.sif \nsource /usr/share/shournal/SOURCE_ME.bash\nSHOURNAL_ENABLE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCurrently not working with error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshournal-run: shournal-run seems to lack the suid-bit (SETUID) for root. You can correct that by\nchown root shournal-run \u0026amp;\u0026amp; chmod u+s shournal-run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Singularity build does do that, but still doesn\u0027t work.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ubuntu_cuda_cudnn_base\" class=\"anchor\" aria-hidden=\"true\" href=\"#ubuntu_cuda_cudnn_base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eubuntu_cuda_cudnn_base\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1597950515.0 + "updated_at": 1556291490.0 }, { "data_format": 2, - "description": "Singularity Images for neurodebian", + "description": "example scientific filesystem to assess metrics across different solutions to a single problem, printing \"Hello World\"", "filenames": [ "Singularity" ], - "full_name": "singularityhub/neurodebian", + "full_name": "sci-f/container.scif", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hello-world-scientific-filesystem\" class=\"anchor\" aria-hidden=\"true\" href=\"#hello-world-scientific-filesystem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHello World Scientific Filesystem\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/containers-ftw.png\"\u003e\u003cimg src=\"img/containers-ftw.png\" alt=\"img/containers-ftw.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a Scientific Filesystem installed in a Singularity container, used to evaluate ~20 languages across different metrics for printing a simple \"Hello World,\" in dinosaur-speak of course! You can use the Makefile to build, clean, and run the container, and we will walk through the commands here. In all of these commands, we name the container based on the environment variable \u003ccode\u003e$CONTAINER\u003c/code\u003e (set in the \u003ca href=\"Makefile\"\u003eMakefile\u003c/a\u003e as \u003ccode\u003ehello-world\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eBuild the container!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build hello-world Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhat applications are available?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world apps\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the primary timing analysis.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/bin/bash test.sh hello-world\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [ - "neurodebian", + "singularity", "singularity-container", - "singularity-containers" + "scif", + "scientific-filesystem" ], - "updated_at": 1487334737.0 + "updated_at": 1516572904.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity", + "Singularity.master", + "Singularity.singularity3" ], - "full_name": "ISU-HPC/ml-intel", + "full_name": "stephansmit/shipyard_containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eml-intel\u003c/h1\u003e\u003ca id=\"user-content-ml-intel\" class=\"anchor\" aria-label=\"Permalink: ml-intel\" href=\"#ml-intel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMachine learning container with Intel python bindings\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-shipyard-container-to-run-containers-on-azure-shipyard\" class=\"anchor\" aria-hidden=\"true\" href=\"#shipyard-container-to-run-containers-on-azure-shipyard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShipyard container to run containers on Azure Shipyard\u003c/h1\u003e\n\u003cp\u003eContainers to run containers on \u003ca href=\"https://batch-shipyard.readthedocs.io/en/latest/00-introduction/%22\" rel=\"nofollow\"\u003eAzure Shipyard\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-the-container-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-the-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the container locally\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build su2_containers_master.sif Singularity.master\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull the container\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/shipyard_containers:master \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3377\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1524599924.0 + "updated_at": 1565710384.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container with nbconvert for conversion of jupyter notebooks to other formats", "filenames": [ - "Singularity", - "__Deprecated__/Singularity_0_19" + "Singularity.latex" ], - "full_name": "daverblair/singularity_vlpi", + "full_name": "vsoch/singularity-nbconvert", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_vlpi\u003c/h1\u003e\u003ca id=\"user-content-singularity_vlpi\" class=\"anchor\" aria-label=\"Permalink: singularity_vlpi\" href=\"#singularity_vlpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity file for VLPI project.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-latex-converter\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-latex-converter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Latex Converter\u003c/h1\u003e\n\u003cp\u003eThis container will help you to convert Jupyter notebooks to html pages.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eBefore using, make sure you have the latest version of \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pull\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull\u003c/h3\u003e\n\u003cp\u003eThe easiest thing is to pull the container from Singularity Hub where it\u0027s already built.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name latex.simg shub://vsoch/singularity-nbconvert:latex\nProgress |===================================| 100.0% \nDone. Container is at: /tmp/singularity/latex.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h3\u003e\n\u003cp\u003eThe container is a file sitting in your present working directory! To convert from Jupyter notebook (extension \u003ccode\u003e.ipynb\u003c/code\u003e) to pdf. It\u0027s primary function (called a runscript) is to perform a conversion, and that looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run latex.simg --to pdf test_notebook.ipynb\n[NbConvertApp] Converting notebook test_notebook.ipynb to pdf\n[NbConvertApp] Support files will be in test_notebook_files/\n[NbConvertApp] Making directory test_notebook_files\n[NbConvertApp] Writing 17358 bytes to notebook.tex\n[NbConvertApp] Building PDF\n[NbConvertApp] Running xelatex 3 times: [u\u0027xelatex\u0027, u\u0027notebook.tex\u0027]\n[NbConvertApp] Running bibtex 1 time: [u\u0027bibtex\u0027, u\u0027notebook\u0027]\n[NbConvertApp] WARNING | bibtex had problems, most likely because there were no citations\n[NbConvertApp] PDF successfully created\n[NbConvertApp] Writing 52431 bytes to test_notebook.pdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe call above can have any custom arguments that you would give to \u003ccode\u003ejupyter nbconvert\u003c/code\u003e. It doesn\u0027t necessarily have to convert to \u003ccode\u003e--pdf\u003c/code\u003e, and you can add other options. E.g., to see help:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run latex --help\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-exec\" class=\"anchor\" aria-hidden=\"true\" href=\"#exec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExec\u003c/h3\u003e\n\u003cp\u003eThe above command targets the nbconvert executable directly (via Jupyter), but you can also execute a custom command, the container has all of the dependencies like jupyter, nbconvert, etc. installed. For example, here I am listing the contents of the conda installation bin:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec latex ls /opt/conda/bin\n2to3\t\t infotocap\t\tpip\t\t tabs\nactivate\t jsonschema\t\tpydoc\t\t tclsh\nc_rehash\t jupyter\t\tpygmentize\t tclsh8.6\ncaptoinfo\t jupyter-kernelspec\tpython\t\t tic\nchardetect\t jupyter-migrate\tpython-config\t toe\nclear\t\t jupyter-nbconvert\tpython2\t\t tput\nconda\t\t jupyter-run\t\tpython2-config\t tset\nconda-env\t jupyter-troubleshoot\tpython2.7\t wheel\ndeactivate\t jupyter-trust\t\tpython2.7-config wish\neasy_install\t ncursesw6-config\treset\t\t wish8.6\neasy_install-2.7 openssl\t\tsmtpd.py\nidle\t\t pandoc\t\tsqlite3\ninfocmp\t\t pandoc-citeproc\tsqlite3_analyzer\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h2\u003e\n\u003cp\u003eDevelopment with Singularity is easiest when you build a \"sandbox,\" which is like building into a folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --sandbox latex/ Singularity.latex\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h3\u003e\n\u003cp\u003eYou can build the image with \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003eSingularity 2.4\u003c/a\u003e with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --writable latex.simg Singularity.latex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen it\u0027s time for a \"production\" build (squash fs image):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build latex.simg Singularity.latex\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1646980294.0 + "subscribers_count": 2, + "topics": [ + "nbconvert", + "singularity-container", + "singularity", + "jupyter", + "pdf" + ], + "updated_at": 1589940582.0 }, { "data_format": 2, - "description": "TensorFlow Singularity recipes.", + "description": "Singularity recipes for building mriqc container", "filenames": [ - "Singularity.1.12.0-py27", - "Singularity.1.6.0-py27", - "Singularity.1.13.1-py36", - "Singularity.1.12.0-py36", - "Singularity.1.13.0-py36", - "Singularity.1.14.0-py36", - "Singularity.1.6.0-py36" + "Singularity.0.10.4", + "Singularity.0.14.2", + "Singularity.0.11.0" ], - "full_name": "arcsUVA/tensorflow", + "full_name": "MPIB/singularity-mriqc", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etensorflow\u003c/h1\u003e\u003ca id=\"user-content-tensorflow\" class=\"anchor\" aria-label=\"Permalink: tensorflow\" href=\"#tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2235\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nTensorFlow Singularity recipes.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-mriqc\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-mriqc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-mriqc\u003c/h1\u003e\n\u003cp\u003eSingularity recipes for building mriqc container\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUses the official docker image of mriqc as base: \u003ca href=\"https://hub.docker.com/r/poldracklab/mriqc/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/poldracklab/mriqc/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRecipes purge and reinstall libgsl2, since there were issues with it when just using the base container.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 4, "topics": [], - "updated_at": 1567631554.0 + "updated_at": 1535361688.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for ML analysis in Python", "filenames": [ - "Singularity.ubuntubase:10.0-u", - "Singularity.tensorflowbase:ngc" + "envs/Singularity.1" ], - "full_name": "nckucch/singularity", + "full_name": "adswa/python-ml", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1557068381.0 + "updated_at": 1600779264.0 }, { "data_format": 2, - "description": "repository for singularity image of iMapSplice", + "description": "Singularity Image for SAIGE", "filenames": [ "Singularity" ], - "full_name": "cory-weller/iMapSplice.simg", + "full_name": "statgen/singularity-saige", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eiMapSplice.simg\u003c/h1\u003e\u003ca id=\"user-content-imapsplicesimg\" class=\"anchor\" aria-label=\"Permalink: iMapSplice.simg\" href=\"#imapsplicesimg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003erepository for singularity image of iMapSplice\u003c/p\u003e\n\u003cp\u003eput the \u003ccode\u003eSingularity\u003c/code\u003e recipe file into your directory and build an image (if you have root access):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build iMapSplice.simg ./Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, retrieve the pre-built image from singularity hub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull -n iMapSplice.simg shub://cory-weller/iMapSplice.simg\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 8, "topics": [], - "updated_at": 1551883466.0 + "updated_at": 1543852783.0 }, { "data_format": 2, - "description": "Singularity recipe that includes git-annex, RStan, Python 3, and Snakemake", + "description": "Singularity Image for EPACTS", "filenames": [ "Singularity" ], - "full_name": "kyleam/garps", + "full_name": "statgen/singularity-epacts", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "snakemake", - "singularity", - "git-annex", - "rstan" - ], - "updated_at": 1586815899.0 + "subscribers_count": 8, + "topics": [], + "updated_at": 1543509361.0 }, { "data_format": 2, "description": null, "filenames": [ - "testing-with-conveyors/bale_actor/singularity/Singularity.def" + "Singularity", + "Singularity-cpu" ], - "full_name": "singhalshubh/Conveyors-Design-Reinvented", + "full_name": "p-h/ZHAW_deep_voice", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-zhaw-deep-voice\" class=\"anchor\" aria-hidden=\"true\" href=\"#zhaw-deep-voice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eZHAW deep voice\u003c/h1\u003e\n\u003cp\u003eThe ZHAW deep voice is a package of multiple neural networks that try resolving the speaker clustering task. The goal is to provide a uniform way of data-access, -preprocession and analysis fo the results.\u003c/p\u003e\n\u003cp\u003eNote that the suite needs the TIMIT Dataset to function at this point. This is a paid product from the LDC and can be \u003ca href=\"https://www.ldc.upenn.edu/\" rel=\"nofollow\"\u003eobtained here.\u003c/a\u003e\nThis data also needs to be processed using the \u003ca href=\"https://www.ldc.upenn.edu/language-resources/tools/sphere-conversion-tools\" rel=\"nofollow\"\u003esph2pipe tool\u003c/a\u003e and be put in the folder common/data/training/TIMIT\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-deep-voice\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-deep-voice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing deep voice\u003c/h2\u003e\n\u003cp\u003eIf you simply want to use it, you can let docker do the work for you and let it import all needed packages.\u003c/p\u003e\n\u003cp\u003eIn any way, whether you fork and pull the source code or let docker handle it for you, the whole suite is controllable over a one file interface, controller.py.\nIt can be run from console with the following calling structure:\ncontroller.py [-h] [-setup] [-n network] [-train] [-test] [-plot] [-clear] [-debug] [-best] [-val# ne]\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e-help Display the help screen you are seeing here\u003c/li\u003e\n\u003cli\u003e-setup Create all\u003c/li\u003e\n\u003cli\u003e-n specifiy which network should be used. Available:\n\u0027pairwise_lstm\u0027, \u0027pairwise_kldiv\u0027, \u0027flow_me\u0027, \u0027luvo\u0027 and \u0027all\u0027 (without the single quotes)\u003c/li\u003e\n\u003cli\u003e-train Specify to train the chosen network\u003c/li\u003e\n\u003cli\u003e-test Specify to test the chosen network\u003c/li\u003e\n\u003cli\u003e-plot Specify to plot the results of the chosen network. If network is \u0027all\u0027, all results will be displayed in one single plot\u003c/li\u003e\n\u003cli\u003e-clear Clear the folder in experiments\u003c/li\u003e\n\u003cli\u003e-debug Set the logging level of Tensorflow to Debug\u003c/li\u003e\n\u003cli\u003e-best Just the best results of the networks will be used in -plot\u003c/li\u003e\n\u003cli\u003e-val# specify which speaker number you want to use (40, 60, 80) to test the networks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAs an example, you want to train, and test but not plot the network pairwise_lstm. you would call:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003econtroller.py -n pairwise_lstm -train -test\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content-general-remarks\" class=\"anchor\" aria-hidden=\"true\" href=\"#general-remarks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral remarks\u003c/h3\u003e\n\u003cp\u003eBefore you start with your training you should run the controller once with the setup flag. This can take a while, approximately around 10 minutes.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1677621757.0 + "updated_at": 1541411393.0 }, { "data_format": 2, - "description": null, + "description": "A singularity container for building and running the Crispred pipeline (https://dubshen.astro.su.se/wiki/index.php/CRISPRED)", "filenames": [ - "Singularity", - "nova/Singularity.S17-12-08-maxopt" + "Singularity" ], - "full_name": "dingp/singularity", + "full_name": "colinsauze/crispred_singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1519058894.0 + "updated_at": 1538673673.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.latest", - "Singularity.ubuntu_test" + "SingularityRubuntu_RnBeads_FINAL", + "SingularityRRBSNF_FINAL" ], - "full_name": "EPI-APE/sing_af", + "full_name": "AdrianS85/RRBS", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1551109916.0 + "updated_at": 1540935055.0 }, { "data_format": 2, @@ -4623,143 +4225,141 @@ var data = "filenames": [ "Singularity" ], - "full_name": "oogasawa/singularity_jupyter_datascience", + "full_name": "mpachkov/singularity_test", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-jupyter-datascience\u003c/h1\u003e\u003ca id=\"user-content-singularity-jupyter-datascience\" class=\"anchor\" aria-label=\"Permalink: singularity-jupyter-datascience\" href=\"#singularity-jupyter-datascience\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Singularity container of Jupyter notebook for datascience,\ncreated by converting an official Docker image\n\u003ca href=\"https://hub.docker.com/r/jupyter/datascience-notebook/\" rel=\"nofollow\"\u003ejupyter/datascience-notebook\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Singularity image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/oogasawa/singularity-jupyter-datascience\ncd singularity-jupyter-datascience\nsudo singularity build . singularity-jupyter-datascience.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the server as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance start singularity-jupyter-datascience.sif sing_jupyter_ds\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEnter (attach) the Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# List the running containers.\nsingularity instance list\n\n# Attach the container\nsingularity shell instance://sing_jupyter_ds\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the Jupyter notebook (or Jupyter Lab) from within the Singularity prompt.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://sing_jupyter_ds\nSingularity\u0026gt; jupyter lab --port=50000\n[I 01:28:50.619 LabApp] JupyterLab extension loaded from /opt/conda/lib/python3.8/site-packages/jupyterlab\n[I 01:28:50.619 LabApp] JupyterLab application directory is /opt/conda/share/jupyter/lab\n[I 01:28:50.621 LabApp] Serving notebooks from local directory: /home/oogasawa/tmp3/singularity-jupyter-datascience\n[I 01:28:50.621 LabApp] The Jupyter Notebook is running at:\n[I 01:28:50.621 LabApp] http://mayfly:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n[I 01:28:50.621 LabApp] or http://127.0.0.1:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n[I 01:28:50.621 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n[C 01:28:50.624 LabApp]\n\n To access the notebook, open this file in a browser:\n\t file:///home/oogasawa/.local/share/jupyter/runtime/nbserver-25-open.html\n\tOr copy and paste one of these URLs:\n\t\thttp://mayfly:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n\t or http://127.0.0.1:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\t\t\t\t\t \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can access the Jupyter software \u003ca href=\"http://localhost:50000/\" rel=\"nofollow\"\u003ehttp://localhost:50000/\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eStop the server (and return to the bash prompt) by Ctrl-C, and stop the container as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance stop sing_jupyter_ds\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_test\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_test\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1622297568.0 + "updated_at": 1536594044.0 }, { "data_format": 2, - "description": "Custom Linux Container Build for Large Scale File Parsing in High Performance Computing Environments", + "description": "Template repo for CircleCI DockerHub+SingularityHub continuous integration", "filenames": [ - "base-image-ubuntu-22.04/base-image/.singularity.d/Singularity" + "Singularity", + "Singularity.v0.3" ], - "full_name": "alexander-labarge/hpc-data-parser", + "full_name": "khanlab/template-circleci", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHigh-Performance Computing (HPC) File Parsing Solution - Direct Access to GPU \u0026amp; CPU Resources\u003c/h1\u003e\u003ca id=\"user-content-high-performance-computing-hpc-file-parsing-solution---direct-access-to-gpu--cpu-resources\" class=\"anchor\" aria-label=\"Permalink: High-Performance Computing (HPC) File Parsing Solution - Direct Access to GPU \u0026amp; CPU Resources\" href=\"#high-performance-computing-hpc-file-parsing-solution---direct-access-to-gpu--cpu-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis solution provides direct access to GPU and CPU resources for high-performance computing (HPC) and high-throughput computing (HTC) environments. Unlike enterprise-based container frameworks, which are designed for microservices and require root privileges to install and run applications, this solution is optimized for complex applications that require all available resources without special privileges.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTargeted Toolsets Implemented\u003c/h2\u003e\u003ca id=\"user-content-targeted-toolsets-implemented\" class=\"anchor\" aria-label=\"Permalink: Targeted Toolsets Implemented\" href=\"#targeted-toolsets-implemented\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis solution uses the following targeted toolsets:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eApache Tika\u2122 by Oracle\u003c/li\u003e\n\u003cli\u003eApptainer (formerly Singularity) by Berkeley National Laboratory\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInitial Cause for Solution Development\u003c/h2\u003e\u003ca id=\"user-content-initial-cause-for-solution-development\" class=\"anchor\" aria-label=\"Permalink: Initial Cause for Solution Development\" href=\"#initial-cause-for-solution-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe development of this solution was motivated by the need to parse 7.5 TB of digital forensics data produced and stored in a variety of non-standard formats. The parsing of all data is necessary to drive subsequent efforts wherein conjectures are made from the subsequent data parsed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhy Apptainer for HPC instead of Virtual Machines or Docker\u003c/h2\u003e\u003ca id=\"user-content-why-apptainer-for-hpc-instead-of-virtual-machines-or-docker\" class=\"anchor\" aria-label=\"Permalink: Why Apptainer for HPC instead of Virtual Machines or Docker\" href=\"#why-apptainer-for-hpc-instead-of-virtual-machines-or-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eApptainer/Singularity is a container platform created for the HPC/HTC use case and presents key concepts for the scientific community:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt\u2019s designed to execute applications with bare-metal performance while retaining a high level of security, portability, and reproducibility.\u003c/li\u003e\n\u003cli\u003eContainers run rootless to prohibit privilege escalation.\u003c/li\u003e\n\u003cli\u003eAble to Leverage GPUs, FPGAs, high-speed networks, and filesystems.\u003c/li\u003e\n\u003cli\u003eA container platform for building and running Linux containers that packages software, libraries, and runtime compilers in a self-contained environment.\n\u003cul\u003e\n\u003cli\u003eApplication portability (single image file, contain all dependencies)\u003c/li\u003e\n\u003cli\u003eReproducibility, run cross platform, provide support for legacy OS and apps.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAbility to run, and in modern systems also to be installed, without any root daemon or setuid privileges. This makes it safer for large computer centers with shared resources.\u003c/li\u003e\n\u003cli\u003ePreserves the permissions in the environment. The user outside the container can be the same user inside.\u003c/li\u003e\n\u003cli\u003eApptainer propagates most environment variables set on the host into the container, by default. Docker does not propagate any host environment variables into the container. Environment variables may change the behavior of software.\u003c/li\u003e\n\u003cli\u003eSimple integration with resource managers (SLURM in our case) and distributed computing frameworks because it runs as a regular application.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/945a382c-3488-4c65-a743-44f0a704c7a5\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/945a382c-3488-4c65-a743-44f0a704c7a5\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment Steps:\u003c/h2\u003e\u003ca id=\"user-content-development-steps\" class=\"anchor\" aria-label=\"Permalink: Development Steps:\" href=\"#development-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest Host Machine (Bare Metal):\u003c/h3\u003e\u003ca id=\"user-content-test-host-machine-bare-metal\" class=\"anchor\" aria-label=\"Permalink: Test Host Machine (Bare Metal):\" href=\"#test-host-machine-bare-metal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0c14fa9e-2508-4c80-9883-f016eb70484f\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0c14fa9e-2508-4c80-9883-f016eb70484f\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Apptainer - Build from Source/ Install Debian Packages for Dependencies\u003c/h3\u003e\u003ca id=\"user-content-step-1-apptainer---build-from-source-install-debian-packages-for-dependencies\" class=\"anchor\" aria-label=\"Permalink: Step 1: Apptainer - Build from Source/ Install Debian Packages for Dependencies\" href=\"#step-1-apptainer---build-from-source-install-debian-packages-for-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get install -y \\\n build-essential \\\n libseccomp-dev \\\n pkg-config \\\n uidmap \\\n squashfs-tools \\\n squashfuse \\\n fuse2fs \\\n fuse-overlayfs \\\n fakeroot \\\n cryptsetup \\\n curl wget git \\\n \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GOVERSION=1.20.6 OS=linux ARCH=amd64 \\\n wget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n sudo tar -C /usr/local -xzf /home/service-typhon/Downloads/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:/usr/local/go/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc \\\n curl -sSfL https://raw.githubusercontent.com/golangci/golangci-lint/master/install.sh \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sh -s -- -b \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ego env GOPATH\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/bin v1.51.1 \\\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:$(go env GOPATH)/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc \\\n git clone https://github.com/apptainer/apptainer.git \\\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e apptainer \\\n git checkout v1.2.0 \\\n ./mconfig \\\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ./builddir \\\n make \\\n sudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Create Sandbox Directory / Pull Ubuntu 22.04 - Jammy Docker Container (Base Ubuntu Build)\u003c/h3\u003e\u003ca id=\"user-content-step-2-create-sandbox-directory--pull-ubuntu-2204---jammy-docker-container-base-ubuntu-build\" class=\"anchor\" aria-label=\"Permalink: Step 2: Create Sandbox Directory / Pull Ubuntu 22.04 - Jammy Docker Container (Base Ubuntu Build)\" href=\"#step-2-create-sandbox-directory--pull-ubuntu-2204---jammy-docker-container-base-ubuntu-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7aaf3e7e-cb74-40d1-9971-24808b0885f8\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7aaf3e7e-cb74-40d1-9971-24808b0885f8\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Convert to Immutable .sif Image for Future Builds - Demonstrate Shell Access\u003c/h3\u003e\u003ca id=\"user-content-step-3-convert-to-immutable-sif-image-for-future-builds---demonstrate-shell-access\" class=\"anchor\" aria-label=\"Permalink: Step 3: Convert to Immutable .sif Image for Future Builds - Demonstrate Shell Access\" href=\"#step-3-convert-to-immutable-sif-image-for-future-builds---demonstrate-shell-access\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/88b8bf10-0e96-4ad3-8d91-6135140e9a00\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/88b8bf10-0e96-4ad3-8d91-6135140e9a00\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 4: Definition File Configuration for Building Dependencies - 1st Build Scuccessful\u003c/h3\u003e\u003ca id=\"user-content-step-4-definition-file-configuration-for-building-dependencies---1st-build-scuccessful\" class=\"anchor\" aria-label=\"Permalink: Step 4: Definition File Configuration for Building Dependencies - 1st Build Scuccessful\" href=\"#step-4-definition-file-configuration-for-building-dependencies---1st-build-scuccessful\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/ce94c114-8b0b-44b2-a7c0-33c26e4e36b7\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/ce94c114-8b0b-44b2-a7c0-33c26e4e36b7\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0d3b4389-7329-4a06-b551-392b07586abc\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0d3b4389-7329-4a06-b551-392b07586abc\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 5: Now that There is a Base Instance Working, lets create a live sandbox for testing from the image we just created:\u003c/h3\u003e\u003ca id=\"user-content-step-5-now-that-there-is-a-base-instance-working-lets-create-a-live-sandbox-for-testing-from-the-image-we-just-created\" class=\"anchor\" aria-label=\"Permalink: Step 5: Now that There is a Base Instance Working, lets create a live sandbox for testing from the image we just created:\" href=\"#step-5-now-that-there-is-a-base-instance-working-lets-create-a-live-sandbox-for-testing-from-the-image-we-just-created\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/a45a0aed-0b5b-4cc9-abdb-5178ad5ac648\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/a45a0aed-0b5b-4cc9-abdb-5178ad5ac648\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eNote: Initial Containers are limited to 64MB in size. Fix:\u003c/h4\u003e\u003ca id=\"user-content-note-initial-containers-are-limited-to-64mb-in-size-fix\" class=\"anchor\" aria-label=\"Permalink: Note: Initial Containers are limited to 64MB in size. Fix:\" href=\"#note-initial-containers-are-limited-to-64mb-in-size-fix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0f2b5e02-d8a1-42bc-995a-507b802c4c3a\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0f2b5e02-d8a1-42bc-995a-507b802c4c3a\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 6: Create a New File System Overlay/ add as a layer in SIF build:\u003c/h3\u003e\u003ca id=\"user-content-step-6-create-a-new-file-system-overlay-add-as-a-layer-in-sif-build\" class=\"anchor\" aria-label=\"Permalink: Step 6: Create a New File System Overlay/ add as a layer in SIF build:\" href=\"#step-6-create-a-new-file-system-overlay-add-as-a-layer-in-sif-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/441d3a4a-18cd-4c74-8cf8-b7ffe18167eb\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/441d3a4a-18cd-4c74-8cf8-b7ffe18167eb\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 7: Build Tika/ Configure Properly - Completed/ Success:\u003c/h3\u003e\u003ca id=\"user-content-step-7-build-tika-configure-properly---completed-success\" class=\"anchor\" aria-label=\"Permalink: Step 7: Build Tika/ Configure Properly - Completed/ Success:\" href=\"#step-7-build-tika-configure-properly---completed-success\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7c7320ab-999b-45b9-bd3c-beb8a19c1230\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7c7320ab-999b-45b9-bd3c-beb8a19c1230\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTIKA DEPENDENCY INSTALL SCRIPT IMPLEMENTED AT %POST\u003c/h4\u003e\u003ca id=\"user-content-tika-dependency-install-script-implemented-at-post\" class=\"anchor\" aria-label=\"Permalink: TIKA DEPENDENCY INSTALL SCRIPT IMPLEMENTED AT %POST\" href=\"#tika-dependency-install-script-implemented-at-post\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Java\u003c/span\u003e\napt-get update\napt-get install -y software-properties-common\napt-get install -y wget\napt-get install -y default-jre\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Tesseract OCR\u003c/span\u003e\napt-get install -y tesseract-ocr\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install ImageMagick\u003c/span\u003e\napt-get install -y imagemagick\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Poppler\u003c/span\u003e\napt-get install -y poppler-utils\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install FFmpeg\u003c/span\u003e\napt-get install -y ffmpeg\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Tika\u003c/span\u003e\nwget https://dlcdn.apache.org/tika/2.8.0/tika-app-2.8.0.jar\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Maven\u003c/span\u003e\nwget https://dlcdn.apache.org/maven/maven-3/3.9.3/binaries/apache-maven-3.9.3-bin.tar.gz\ntar -xvf apache-maven-3.9.3-bin.tar.gz \nmv apache-maven-3.9.3 /opt\nM2_HOME=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eopt/apache-maven-3.9.3/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nPATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$M2_HOME\u003c/span\u003e/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTIKA AUTOMATED TEST END OF INSTALL:\u003c/h4\u003e\u003ca id=\"user-content-tika-automated-test-end-of-install\" class=\"anchor\" aria-label=\"Permalink: TIKA AUTOMATED TEST END OF INSTALL:\" href=\"#tika-automated-test-end-of-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e \u003cspan class=\"pl-en\"\u003echeck_tika_test\u003c/span\u003e {\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eChecking Tika test...\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e grep -q \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTIKA PASSED TEST - ALEX\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e /output-files/tika-test-file.txt.json\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika test passed. FOUND STRING: TIKA PASSED TEST - ALEX in file.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e============TIKA HPC BUILD COMPLETING FINAL STEPS================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika test failed.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\n}\n\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eStarting Tika... at \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einput-files directory contents:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ncp /opt/tika-test-file.txt /input-files\nls -l /input-files/\njava -jar /tika-app-2.8.0.jar -i /input-files -o /output-files -J\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika started.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika output complete.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eoutput-files directory contents:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nls -l /output-files\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eCompleted at: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eExtracting text from files...\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eExtracted JSON OUTPUT:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract text from files \u0026amp; ignore JSON text\u003c/span\u003e\nextracted_text=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003efind /output-files -type f -exec strings {} \u003cspan class=\"pl-cce\"\u003e\\;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep -vE \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e^{.*}$\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Print extracted text\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$extracted_text\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Check Tika test\u003c/span\u003e\ncheck_tika_test\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSTEP 8: FINAL BETA BUILD SCRIPT (OTHER BASH SCRIPTS EMBEDDED)\u003c/h3\u003e\u003ca id=\"user-content-step-8-final-beta-build-script-other-bash-scripts-embedded\" class=\"anchor\" aria-label=\"Permalink: STEP 8: FINAL BETA BUILD SCRIPT (OTHER BASH SCRIPTS EMBEDDED)\" href=\"#step-8-final-beta-build-script-other-bash-scripts-embedded\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/71d64b44-a95e-484b-ad2d-082d3bc9ab60\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/71d64b44-a95e-484b-ad2d-082d3bc9ab60\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1503\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/khanlab/template-circleci\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c2b426ee0f066d201a83b9ac4156ab58b817dd4135c9dc90ebeac909f35a9925/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f74656d706c6174652d636972636c6563692e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/template-circleci.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-template-circleci\" class=\"anchor\" aria-hidden=\"true\" href=\"#template-circleci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etemplate-circleci\u003c/h1\u003e\n\u003cp\u003eKhanlab template repo for CircleCI DockerHub+SingularityHub continuous integration.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-functionality\" class=\"anchor\" aria-hidden=\"true\" href=\"#functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctionality:\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-every-commit\" class=\"anchor\" aria-hidden=\"true\" href=\"#every-commit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvery commit:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eBuilds Docker container\u003c/li\u003e\n\u003cli\u003eRuns tests (using built Docker container)\u003c/li\u003e\n\u003cli\u003eIf successful, pushes to Docker Hub with \u003ccode\u003elatest\u003c/code\u003e tag\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-every-night\" class=\"anchor\" aria-hidden=\"true\" href=\"#every-night\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvery night:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eDeploys on Singularity Hub (via recipe commit, from Docker container)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-make-a-release\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-make-a-release\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo make a release:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUse Github\u0027s Draft a New Release\u003c/li\u003e\n\u003cli\u003eInclude v* in the Tag name (e.g. v0.1)\u003c/li\u003e\n\u003cli\u003eWill then automatically build, test and deploy on DockerHub and SingularityHub with v* release tag\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGithub repo in Khanlab organization\u003c/li\u003e\n\u003cli\u003eDockerfile in that repo\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-set-up-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#set-up-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet-up Instructions:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eCopy the .circleci/config.yml to your repository\u003c/li\u003e\n\u003cli\u003eLogin to circleci.com, and add the project\u003c/li\u003e\n\u003cli\u003eLogin to singularity hub, and add the project\u003c/li\u003e\n\u003cli\u003eEdit \u003ccode\u003etest\u003c/code\u003e section of .circleci/config.yml to set-up tests\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1697541728.0 + "updated_at": 1569207854.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.larcv_fc", + "Singularity.larcv_cpu.txt", + "Singularity.larcv_fcAWS" ], - "full_name": "compmetagen/taxies", + "full_name": "jonmiller3/singularity_imgs", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_imgs\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_imgs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_imgs\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1544461967.0 + "updated_at": 1602882140.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.efficient" + "Singularity" ], - "full_name": "huynhngoc/CubiAI-VRU", + "full_name": "vsoch/BIDShcppipelines-debug", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCubiAI\u003c/h1\u003e\u003ca id=\"user-content-cubiai\" class=\"anchor\" aria-label=\"Permalink: CubiAI\" href=\"#cubiai\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDeep learning can detect elbow disease in dogs screened for elbow dysplasia\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenerate dataset\u003c/h2\u003e\u003ca id=\"user-content-generate-dataset\" class=\"anchor\" aria-label=\"Permalink: Generate dataset\" href=\"#generate-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edataset_gen/gen_normal_abnormal.py\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCheck for duplication\n\u003ccode\u003edataset_gen/QA_data_splitting.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun experiments locally\u003c/h2\u003e\u003ca id=\"user-content-run-experiments-locally\" class=\"anchor\" aria-label=\"Permalink: Run experiments locally\" href=\"#run-experiments-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython experiment_binary.py config/local/pretrain.json path_to_folder/pretrain --temp_folder path_to_temp_folder/pretrain --epochs 20\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun experiemts on Orion\u003c/h2\u003e\u003ca id=\"user-content-run-experiemts-on-orion\" class=\"anchor\" aria-label=\"Permalink: Run experiemts on Orion\" href=\"#run-experiemts-on-orion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_pretrain_binary.sh config/pretrain/b0_normal_level2.json b0_normal_level2 2\nsbatch slurm_pretrain_multiclass.sh config/pretrain/b0_normal_level1_level2.json b0_normal_level1_level2 2\n\nsbatch slurm_scratch_binary.sh config/scratch/b0_normal_level2.json b0_normal_level2 2\nsbatch slurm_scratch_multiclass.sh config/scratch/b0_normal_level1_level2.json b0_normal_level1_level2 2\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1724057222.0 + "updated_at": 1545323608.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.adapterremoval_2.3.1", - "Singularity.bbmap_38.86", - "Singularity.seqtk_1.3r106", - "Singularity.matlock_9fe3fdd", - "Singularity.cutadapt_2.10", - "Singularity.pysam_0.15.3" + "Singularity" ], - "full_name": "TomHarrop/seq-utils", + "full_name": "upendrak/pacbio_singularity", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-running-iso-seq3-analysis-on-a-test-data-using-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-iso-seq3-analysis-on-a-test-data-using-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Iso-Seq3 analysis on a Test data using Singularity container\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003eIsoSeq3\u003c/em\u003e contains the newest tools to identify transcripts in PacBio single-molecule sequencing data. Starting in SMRT Link v6.0.0, those tools power the IsoSeq3 GUI-based analysis application. A composable workflow of existing tools and algorithms, combined with a new clustering technique, allows to process the ever-increasing yield of PacBio machines with similar performance to IsoSeq1 and IsoSeq2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e This is an example of an end-to-end cmd-line-only workflow from this \u003ca href=\"https://github.com/PacificBiosciences/IsoSeq3\"\u003etutorial\u003c/a\u003e to get from subreads to polished isoforms; timings are system dependent.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-availability\" class=\"anchor\" aria-hidden=\"true\" href=\"#availability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailability\u003c/h2\u003e\n\u003cp\u003eThe Iso-Seq3 can be run using Singualrity container hosted on Singularity hub\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Singularity\u003c/h2\u003e\n\u003cp\u003eThere are many ways to \u003ca href=\"https://www.sylabs.io/guides/2.5.1/user-guide/quick_start.html#installation\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e but this quick start guide will only cover one.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/singularityware/singularity.git\ncd singularity\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Singularity must be installed as root to function properly.\u003c/p\u003e\n\u003cp\u003eAfter installing Singularity make sure to run the --help option gives an overview of Singularity options and subcommands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity --help\n\nUSAGE: singularity [global options...] \u0026lt;command\u0026gt; [command options...] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-the-test-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-the-test-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload the test data\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eFor each cell, the \u0026lt;movie\u0026gt;.subreads.bam and \u0026lt;movie\u0026gt;.subreads.bam.pbi are needed for processing.\n\n$ mkdir tutorial \u0026amp;\u0026amp; cd tutorial\n$ wget https://downloads.pacbcloud.com/public/dataset/RC0_1cell_2017/m54086_170204_081430.subreads.bam\n$ wget https://downloads.pacbcloud.com/public/dataset/RC0_1cell_2017/m54086_170204_081430.subreads.bam.pbi\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-pull-the-singularity-container-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-pull-the-singularity-container-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation: Pull the Singularity container from Singularity hub\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name isoseq.img shub://upendrak/pacbio_singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-consensus-calling\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-consensus-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1: Consensus calling\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec isoseq.img ccs --version\nccs 3.0.0 (commit a54f14a)\n\n$ nohup singularity exec isoseq.img ccs m54086_170204_081430.subreads.bam m54086_170204_081430.ccs.bam --noPolish --minPasses 1 \u0026amp;\n\nNote: This step takes a long time. On a 6 CPU VM, it took around 5 hrs to complete\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-primer-removal-and-demultiplexing\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-primer-removal-and-demultiplexing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2: Primer removal and demultiplexing\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec isoseq.img lima --version\nlima 1.7.0 (commit v1.7.0-2-g9479065)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat primers.fasta\n\n\u0026gt;primer_5p\nAAGCAGTGGTATCAACGCAGAGTACATGGG\n\u0026gt;primer_3p\nGTACTCTGCGTTGATACCACTGCTT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec isoseq.img lima m54086_170204_081430.ccs.bam primers.fasta demux.bam --isoseq --no-pbi --dump-clips \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls demux*\ndemux.json demux.lima.counts demux.lima.report demux.lima.summary demux.primer_5p--primer_3p.bam demux.primer_5p--primer_3p.subreadset.xml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-3-clustering-and-transcript-clean-up\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-3-clustering-and-transcript-clean-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 3: Clustering and transcript clean up\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ nohup singularity exec isoseq.img isoseq3 cluster demux.primer_5p--primer_3p.bam unpolished.bam --verbose \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls unpolished*\nunpolished.bam unpolished.bam.pbi unpolished.cluster unpolished.fasta unpolished.flnc.bam unpolished.flnc.bam.pbi unpolished.flnc.consensusreadset.xml unpolished.transcriptset.xml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-4-polishing\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-4-polishing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 4: Polishing\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ nohup singularity exec isoseq.img isoseq3 polish unpolished.bam m54086_170204_081430.subreads.bam polished.bam --verbose\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls polished*\npolished.bam polished.bam.pbi polished.hq.fasta.gz polished.hq.fastq.gz polished.lq.fasta.gz polished.lq.fastq.gz polished.transcriptset.xml\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1597614799.0 + "updated_at": 1533222076.0 }, { "data_format": 2, - "description": "Trying to get Slamdunk to work on CentOS 6", + "description": "A simple demo of singularity containers", "filenames": [ "Singularity" ], - "full_name": "FelixKrueger/SlamDunk_Shub", + "full_name": "colinsauze/singularity_example", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSlamDunk_Shub\u003c/h1\u003e\u003ca id=\"user-content-slamdunk_shub\" class=\"anchor\" aria-label=\"Permalink: SlamDunk_Shub\" href=\"#slamdunk_shub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTrying to get SlamDunk to work on our dev server and eventually on our cluster\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singualrity_example\" class=\"anchor\" aria-hidden=\"true\" href=\"#singualrity_example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingualrity_example\u003c/h1\u003e\n\u003cp\u003eA simple demo of singularity containers\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1538406117.0 + "updated_at": 1531999980.0 }, { "data_format": 2, - "description": "My repository for singularity hub containers.", + "description": null, "filenames": [ - "miniconda3_mamba/Singularity.miniconda3mamba", - "rinla/Singularity.rinla" + "Singularity" ], - "full_name": "votti/singularity-builds", + "full_name": "colinsauze/Bovine_DNA_RNA", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1591370752.0 + "updated_at": 1531844224.0 }, { "data_format": 2, - "description": null, + "description": "Singularity bootstrap which includes uboonecode, larcv3 and gallery-framework. ", "filenames": [ - "Singularity_MCR.def", - "Singularity.def" + "Singularity" ], - "full_name": "Remi-Gau/cat12-container", + "full_name": "lmlepin9/Singularity-uboonecode-gallery", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCAT-12 container\u003c/h1\u003e\u003ca id=\"user-content-cat-12-container\" class=\"anchor\" aria-label=\"Permalink: CAT-12 container\" href=\"#cat-12-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker and Apptainer image for \u003ca href=\"https://neuro-jena.github.io/cat/\" rel=\"nofollow\"\u003eCAT12\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eCAT12 8.1 r2042\nSPM12, version 7771 (standalone)\nMATLAB, version 9.3.0.713579 (R2017b)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage notes\u003c/h2\u003e\u003ca id=\"user-content-usage-notes\" class=\"anchor\" aria-label=\"Permalink: Usage notes\" href=\"#usage-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample\u003c/h3\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --tag cat12\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v \u003cspan class=\"pl-smi\"\u003e${PWD}\u003c/span\u003e/tests/data/MoAEpilot:/data \\\n --rm -it cat12 \\\n /data/sub-01/anat/sub-01_T1w.nii \\\n -b cat_standalone_segment.m\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-uboonecode-gallery\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-uboonecode-gallery\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity-uboonecode-gallery\u003c/h1\u003e\n\u003cp\u003eSingularity bootstrap which includes uboonecode, larcv3 and gallery-framework.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1724126570.0 + "updated_at": 1617376998.0 }, { "data_format": 2, - "description": "Software Engineering for HPC DevOps Project Part2", + "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "supertramp277/DevOps-Project_Part2", + "full_name": "kristinebilgrav/Containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject\" href=\"#se4hpcproject\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2 -- From build to release and manual job execution\u003c/h2\u003e\u003ca id=\"user-content-step-2----from-build-to-release-and-manual-job-execution\" class=\"anchor\" aria-label=\"Permalink: Step 2 -- From build to release and manual job execution\" href=\"#step-2----from-build-to-release-and-manual-job-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFocus now on the correct implementation of the matrix multiplication you\nfind in \u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e. This is a\nparallel implementation that uses MPI and reads the matrices to be\nmultiplied from two files, matrixA.txt and matrixB.txt. In these files\nthe first row contains the matrix dimensions (number of rows and\ncolumns), while the other rows contain the matrix itself.\u003c/p\u003e\n\u003cp\u003eYour task is to perform the following steps:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation\u003c/strong\u003e: Use the template available here\n\u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e to create your own\ngithub repository. Add to this repository the tests you have created in\nStep1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutomating the build, test and release processes\u003c/strong\u003e: Create a CI/CD\npipeline that, when someone pushes files in the repo, executes the\nbuilding and testing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContainerizing the application\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDefine a Singularity container descriptor for the matrix\nmultiplication program and push it in your repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtend the created action to create a container image from your\ndescription.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExecuting on the cluster\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a job.sh file to run your containerized application. Make\nsure that the standard output and error are mapped to txt files.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransfer on Galileo100 your job script and the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSubmit your job to the cluster and check whether it works correctly.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePush on your github repository your job.sh file and the files\nobtained from the execution of the matrix multiplication.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3 -- Automating a job submission with containerization\u003c/h2\u003e\u003ca id=\"user-content-step-3----automating-a-job-submission-with-containerization\" class=\"anchor\" aria-label=\"Permalink: Step 3 -- Automating a job submission with containerization\" href=\"#step-3----automating-a-job-submission-with-containerization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtend the action you have created at step 3 to automate completely the\nprocess from a push on the repository to the execution of the\ncontainerized software on SLURM. To do so, you will have to move your\ncontainer from the runner to the cluster. You can either use the scp\ncommand or you can publish your image on the Singularity registry and\nthen pull it from the cluster. Don\u0027t forget to handle your secrets\nproperly! You do not want to leave passwords and authentication tokens\nvisible to everybody, so you will use the \u003ca href=\"https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions?tool=cli\"\u003esecrets\nmechanism\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-jitter_container\" class=\"anchor\" aria-hidden=\"true\" href=\"#jitter_container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJitter_container\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1717622843.0 + "updated_at": 1623335528.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for badread (https://github.com/rrwick/Badread)", "filenames": [ - "Singularity.def" + "Singularity", + "Singularity.0.2.0" ], - "full_name": "FrancescoEgidioFaggion/SwEng", + "full_name": "powerPlant/badread-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject\" href=\"#se4hpcproject\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2 -- From build to release and manual job execution\u003c/h2\u003e\u003ca id=\"user-content-step-2----from-build-to-release-and-manual-job-execution\" class=\"anchor\" aria-label=\"Permalink: Step 2 -- From build to release and manual job execution\" href=\"#step-2----from-build-to-release-and-manual-job-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFocus now on the correct implementation of the matrix multiplication you\nfind in \u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e. This is a\nparallel implementation that uses MPI and reads the matrices to be\nmultiplied from two files, matrixA.txt and matrixB.txt. In these files\nthe first row contains the matrix dimensions (number of rows and\ncolumns), while the other rows contain the matrix itself.\u003c/p\u003e\n\u003cp\u003eYour task is to perform the following steps:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation\u003c/strong\u003e: Use the template available here\n\u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e to create your own\ngithub repository. Add to this repository the tests you have created in\nStep1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutomating the build, test and release processes\u003c/strong\u003e: Create a CI/CD\npipeline that, when someone pushes files in the repo, executes the\nbuilding and testing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContainerizing the application\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDefine a Singularity container descriptor for the matrix\nmultiplication program and push it in your repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtend the created action to create a container image from your\ndescription.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExecuting on the cluster\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a job.sh file to run your containerized application. Make\nsure that the standard output and error are mapped to txt files.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransfer on Galileo100 your job script and the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSubmit your job to the cluster and check whether it works correctly.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePush on your github repository your job.sh file and the files\nobtained from the execution of the matrix multiplication.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3 -- Automating a job submission with containerization\u003c/h2\u003e\u003ca id=\"user-content-step-3----automating-a-job-submission-with-containerization\" class=\"anchor\" aria-label=\"Permalink: Step 3 -- Automating a job submission with containerization\" href=\"#step-3----automating-a-job-submission-with-containerization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtend the action you have created at step 3 to automate completely the\nprocess from a push on the repository to the execution of the\ncontainerized software on SLURM. To do so, you will have to move your\ncontainer from the runner to the cluster. You can either use the scp\ncommand or you can publish your image on the Singularity registry and\nthen pull it from the cluster. Don\u0027t forget to handle your secrets\nproperly! You do not want to leave passwords and authentication tokens\nvisible to everybody, so you will use the \u003ca href=\"https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions?tool=cli\"\u003esecrets\nmechanism\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for badread, a long read simulator that can imitate many types of read problems.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1717597291.0 + "updated_at": 1613427062.0 }, { "data_format": 2, @@ -4767,653 +4367,721 @@ var data = "filenames": [ "Singularity" ], - "full_name": "ddbj/singularity-apache2-igvwebapp", + "full_name": "fcola000/test", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-apache2-igvwebapp\u003c/h1\u003e\u003ca id=\"user-content-singularity-apache2-igvwebapp\" class=\"anchor\" aria-label=\"Permalink: singularity-apache2-igvwebapp\" href=\"#singularity-apache2-igvwebapp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eigv-webapp\u3068apache2\u3092\u5b9f\u884c\u3059\u308bsingularity instance\u3092\u8d77\u52d5\u3059\u308b\u305f\u3081\u306e\u30d5\u30a1\u30a4\u30eb\u4e00\u5f0f\u3067\u3059\u3002\nsingularity image\u306f\u521d\u56de\u8d77\u52d5\u6642\u306bSylabs Cloud\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u521d\u671f\u8a2d\u5b9a\u003c/h2\u003e\u003ca id=\"user-content-\u521d\u671f\u8a2d\u5b9a\" class=\"anchor\" aria-label=\"Permalink: \u521d\u671f\u8a2d\u5b9a\" href=\"#\u521d\u671f\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ehttpd.conf\u003c/h3\u003e\u003ca id=\"user-content-httpdconf\" class=\"anchor\" aria-label=\"Permalink: httpd.conf\" href=\"#httpdconf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eServerRoot \"/usr/local/apache2\"\n\nListen 38080\nUser user1\nGroup group1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003euser1\u3092\u81ea\u5206\u306e\u30a2\u30ab\u30a6\u30f3\u30c8\u540d\u3001group1\u3092\u81ea\u5206\u306e\u30b0\u30eb\u30fc\u30d7\u540d\u300138080\u3092apache2\u304c\u4f7f\u7528\u3059\u308b\u30dd\u30fc\u30c8\u756a\u53f7\u306b\u4fee\u6b63\u3057\u307e\u3059\u3002\nnetstat\u30b3\u30de\u30f3\u30c9\u306738080\u304c\u672a\u4f7f\u7528\u306a\u3089\u5909\u66f4\u4e0d\u8981\u3067\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003epackage.json\u003c/h3\u003e\u003ca id=\"user-content-packagejson\" class=\"anchor\" aria-label=\"Permalink: package.json\" href=\"#packagejson\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"name\": \"igv-webapp\",\n \"version\": \"1.5.5\",\n \"description\": \"igv web app\",\n \"keywords\": [],\n \"author\": \"Douglass Turner and Jim Robinson\",\n \"license\": \"MIT\",\n \"scripts\": {\n \"start\": \"npx http-server -p 38081 dist\",\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e38081\u3092igv-webapp\u304c\u4f7f\u7528\u3059\u308b\u30dd\u30fc\u30c8\u756a\u53f7\u306b\u4fee\u6b63\u3057\u307e\u3059\u3002\nnetstat\u30b3\u30de\u30f3\u30c9\u306738081\u304c\u672a\u4f7f\u7528\u306a\u3089\u5909\u66f4\u4e0d\u8981\u3067\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esingularity instance\u306e\u8d77\u52d5\u003c/h2\u003e\u003ca id=\"user-content-singularity-instance\u306e\u8d77\u52d5\" class=\"anchor\" aria-label=\"Permalink: singularity instance\u306e\u8d77\u52d5\" href=\"#singularity-instance\u306e\u8d77\u52d5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u521d\u56de\u5b9f\u884c\u6642\u306b\u3001ubuntu-18.04-apache-2.4.48-igv-webapp-1.5.5_latest.sif \u304c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u307e\u3059\u3002\n\u307e\u305f\u3001cgi-bin, htdocs, logs\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002\nhtdocs\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3001igv-webapp\u3067\u8868\u793a\u3057\u305f\u3044bam\u30d5\u30a1\u30a4\u30eb\u3068\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u30d5\u30a1\u30a4\u30eb\u3092\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eigv-webapp\u3078\u306e\u30a2\u30af\u30bb\u30b9\u003c/h2\u003e\u003ca id=\"user-content-igv-webapp\u3078\u306e\u30a2\u30af\u30bb\u30b9\" class=\"anchor\" aria-label=\"Permalink: igv-webapp\u3078\u306e\u30a2\u30af\u30bb\u30b9\" href=\"#igv-webapp\u3078\u306e\u30a2\u30af\u30bb\u30b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a6\u30a7\u30d6\u30d6\u30e9\u30a6\u30b6\u3067 http://\u0026lt;\u30db\u30b9\u30c8\u306eIP\u30a2\u30c9\u30ec\u30b9\u0026gt;:\u0026lt;package.json\u306b\u8a2d\u5b9a\u3057\u305f\u30dd\u30fc\u30c8\u756a\u53f7\u0026gt; \u306b\u30a2\u30af\u30bb\u30b9\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n\u30c8\u30e9\u30c3\u30af\u306e\u8ffd\u52a0\u306f\u3001Tracks\u30e1\u30cb\u30e5\u30fc\u304b\u3089URL\u3092\u9078\u3073\u3001\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ehttp://\u0026lt;\u30db\u30b9\u30c8\u306eID\u30a2\u30c9\u30ec\u30b9\u0026gt;:\u0026lt;httpd.conf\u306b\u8a2d\u5b9a\u3057\u305f\u30dd\u30fc\u30c8\u756a\u53f7\u0026gt;/\u0026lt;htdocs\u306b\u914d\u7f6e\u3057\u305fbam\u30d5\u30a1\u30a4\u30eb\u0026gt;\u003c/li\u003e\n\u003cli\u003ehttp://\u0026lt;\u30db\u30b9\u30c8\u306eID\u30a2\u30c9\u30ec\u30b9\u0026gt;:\u0026lt;httpd.conf\u306b\u8a2d\u5b9a\u3057\u305f\u30dd\u30fc\u30c8\u756a\u53f7\u0026gt;/\u0026lt;htdocs\u306b\u914d\u7f6e\u3057\u305f\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u30d5\u30a1\u30a4\u30eb\u0026gt;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u3092\u958b\u3044\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etest\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1625812484.0 + "updated_at": 1616500401.0 }, { "data_format": 2, - "description": "Eddy -- a tool for correcting eddy currents and movements in diffusion data", + "description": null, "filenames": [ "Singularity" ], - "full_name": "lalet/Eddy", + "full_name": "fcola000/shub_test", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEddy\u003c/h1\u003e\u003ca id=\"user-content-eddy\" class=\"anchor\" aria-label=\"Permalink: Eddy\" href=\"#eddy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEddy -- a tool for correcting eddy currents and movements in diffusion data\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-shub_test\" class=\"anchor\" aria-hidden=\"true\" href=\"#shub_test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eshub_test\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1533670576.0 + "updated_at": 1616356054.0 }, { "data_format": 2, - "description": "Singularity container for meningotype", + "description": "Singularity Containers Developed for the University of Oslo", "filenames": [ - "Singularity" + "trial/Singularity", + "conda/Singularity", + "conda/Singularity.conda", + "conda/Singularity.def" ], - "full_name": "phgenomics-singularity/meningotype", + "full_name": "Economax/SingularityCo", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1553641042.0 + "updated_at": 1616284321.0 }, { "data_format": 2, - "description": "This repo contains a simgularity recipe for setting-up and running Knet easily.", + "description": "Singularity recipe files for sniffles (https://github.com/fritzsedlazeck/Sniffles)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.1.0.12a" ], - "full_name": "KnetML/singularity-images", + "full_name": "powerPlant/sniffles-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Image for Knet\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-for-knet\" class=\"anchor\" aria-label=\"Permalink: Singularity Image for Knet\" href=\"#singularity-image-for-knet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/686\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUSAGE\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: USAGE\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePull the base image\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name knet.simg shub://KnetML/singularity-images:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNot to loose your changes, you should create an overlay image.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity image.create --size 2048 overlay.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf you need more space, you can expand this image later\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity image.expand overlay.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eConnect to the container with the overlay image.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --overlay overlay.simg Knet.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to enable the gpu usage, you can bind your cuda and cudnn paths.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind \u0026lt;your cuda path\u0026gt;:/usr/local/cuda,\u0026lt;your cudnn path\u0026gt;:/opt/cudnn --overlay overlay.simg --nv Knet.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBuild Knet\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ejulia -e \u0027Pkg.build(\"Knet\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNow you can work under the \u003cem\u003e/workdir\u003c/em\u003e folder\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the image\u003c/h3\u003e\u003ca id=\"user-content-running-the-image\" class=\"anchor\" aria-label=\"Permalink: Running the image\" href=\"#running-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can also run the image directly. It starts an ipython notebook server. You can connect to this server on localhost:8888.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind \u0026lt;your cuda path\u0026gt;:/usr/local/cuda,\u0026lt;your cudnn path\u0026gt;:/opt/cudnn --overlay overlay.simg --nv Knet.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage on the kuacc cluster\u003c/h3\u003e\u003ca id=\"user-content-usage-on-the-kuacc-cluster\" class=\"anchor\" aria-label=\"Permalink: Usage on the kuacc cluster\" href=\"#usage-on-the-kuacc-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLoad necessary modules\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emodule load cuda/9.0\nmodule load cudnn/7.0.4/cuda-9.0\nmodule load singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSet SINGULARITY_CACHEDIR as your scratch\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITY_CACHEDIR=/scratch/users/username\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eOn a compute node with gpu\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind /usr/local/cuda-9.0/:/usr/local/cuda,/kuacc/apps/cudnn/v7.0.4_CUDA_9.0:/opt/cudnn --overlay overlay.img --nv Knet.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDEPENDENCIES\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: DEPENDENCIES\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"singularity.lbl.gov\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eCuda drivers and libraries (optional)\u003c/li\u003e\n\u003cli\u003eCudnn library (optinal)\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for Sniffles, a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore).\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "singularity", - "knet", - "julia" - ], - "updated_at": 1549274984.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1610677399.0 }, { "data_format": 2, - "description": "the definition of the singularity avogadro container", + "description": null, "filenames": [ - "Singularity" + "Singularity.v2.0.0" ], - "full_name": "mherkazandjian/singularity_avogadro", + "full_name": "baxpr/fsthalconnMNI-public", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fsthalconnmni-public\" class=\"anchor\" aria-hidden=\"true\" href=\"#fsthalconnmni-public\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efsthalconnMNI-public\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE: For this public version of the repository, the ROI images are not included due to the restrictions on the Morel set, meaning the code will not actually run.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInputs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessed fMRI data from \u003ca href=\"https://github.com/baxpr/connprep\"\u003ehttps://github.com/baxpr/connprep\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eThalamus regions of interest from \u003ca href=\"https://github.com/baxpr/freesurfer-singularity\"\u003ehttps://github.com/baxpr/freesurfer-singularity\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIncluded ROIs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\"Morel\" thalamic sub-regions from Krauth A, Blanc R, Poveda A, Jeanmonod D, Morel A, Sz\u00e9kely G. A mean three-dimensional atlas of the human thalamus: generation from multiple histological data. Neuroimage. 2010;49(3):2053\u20132062. doi:10.1016/j.neuroimage.2009.10.042. These images are copyright University of Zurich and ETH Zurich, Axel Krauth, Re\u0301mi Blanc, Alejandra Poveda, Daniel Jeanmonod, Anne Morel, Ga\u0301bor Sze\u0301kely. They may not be redistributed, or used for other than research purposes in academic institutions (see src/rois/ACDMY/Agreement.pdf).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\"ABIDE\" regions from Woodward ND, Giraldo-Chica M, Rogers B, Cascio CJ. Thalamocortical dysconnectivity in autism spectrum disorder: An analysis of the Autism Brain Imaging Data Exchange. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2(1):76\u201384. doi:10.1016/j.bpsc.2016.09.002\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNetwork maps from Yeo et al 2011 (\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174820/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174820/\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOutputs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSeed connectivity maps and matrices for all ROIs/networks specified in the roiinfo_csv file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eProcess:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eROI resampling. Freesurfer ROIs (all .mgz in the roiinfo_csv file) are already in native space aligned with the subject T1 and fMRI, so are only converted to nifti format. MNI space ROIs (all .nii.gz in roiinfo_csv are assumed to be MNI space) are warped back to native space in the T1 geometry using the supplied warp invdef_niigz.\u003c/li\u003e\n\u003cli\u003eFor each native space ROI image, the native space fMRIs (removegm_niigz and keepgm_niigz) are resampled to the ROI image geometry, and mean ROI signals are extracted.\u003c/li\u003e\n\u003cli\u003eConnectivity matrices are computed for the mean ROI signals for both the removegm and keepgm data.\u003c/li\u003e\n\u003cli\u003eThe mean ROI signals are used with the four filtered fMRI image sets (removegm_niigz, keepgm_niigz, wremovegm_niigz, wkeepgm_niigz) to compute connectivity maps for each of the four.\u003c/li\u003e\n\u003cli\u003eThe connectivity maps are smoothed by the provided fwhm.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1541811097.0 + "updated_at": 1616091420.0 }, { "data_format": 2, - "description": "Mumax3 application", + "description": "Singularity recipe files for ora (https://github.com/pseudogene/ora)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.2.0.0" ], - "full_name": "SupercomputingWales/scw_ood_mumax3", + "full_name": "powerPlant/ora-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBatch Connect - Mumax3\u003c/h1\u003e\u003ca id=\"user-content-batch-connect---mumax3\" class=\"anchor\" aria-label=\"Permalink: Batch Connect - Mumax3\" href=\"#batch-connect---mumax3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLaunch Mumax3 server.\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for the Bio::ORA, a featherweight object for identifying mammalian olfactory receptor genes.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/pseudogene/ora\"\u003ehttps://github.com/pseudogene/ora\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1705933791.0 + "updated_at": 1615862307.0 }, { "data_format": 2, - "description": "Python, R, Tensorflow, etc. Put a container on it!", + "description": null, "filenames": [ "Singularity" ], - "full_name": "greyspectrum/conda-tf", + "full_name": "pchengi/cmorfixer_env", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econda-tf\u003c/h1\u003e\u003ca id=\"user-content-conda-tf\" class=\"anchor\" aria-label=\"Permalink: conda-tf\" href=\"#conda-tf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Dockerfile will build a containerized conda environment with Python, R, and Tensorflow, among other packages.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding\u003c/h2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run this container on a high performance computing cluster, you will need to build it locally, as a Singularity image.\u003c/p\u003e\n\u003cp\u003eFirst, \u003ca href=\"https://sylabs.io/guides/3.2/user-guide/installation.html\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e on a system where you have root privileges.\u003c/p\u003e\n\u003cp\u003eThen, install \u003ca href=\"https://docs.docker.com/install/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, build a Singularity container from the Dockerfile in this repo using \u003ca href=\"https://github.com/singularityware/docker2singularity\"\u003edocker2singularity\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-environment-for-cmor-fixer\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-for-cmor-fixer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment for cmor-fixer\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA tool to create an environment to allow easy use of the \u003ca href=\"https://github.com/EC-Earth/cmor-fixer\"\u003ecmor-fixer tool\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecmorfixer_env is a singularity container which comes with preinstalled miniconda3\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h1\u003e\n\u003cp\u003eYou need the singularity program installed. Follow the instructions here, to install singularity on your machine.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/install-linux\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-to-download-a-prebuilt-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-download-a-prebuilt-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo download a prebuilt singularity image:\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eIf you\u0027d like to use a prebuilt image, you could download from the link below; if you\u0027d rather build the container yourself, follow the build instructing in the To build section.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://esg-dn2.nsc.liu.se/virtualtestbed/cmorfixerenv.simg\" rel=\"nofollow\"\u003eLink to prebuilt image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-to-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build cmorfixerenv.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-to-initialize-container-and-optionally-mount-external-filesystems\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-initialize-container-and-optionally-mount-external-filesystems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo initialize container (and optionally mount external filesystems)\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eIf you don\u0027t have to mount any non-root filesystems, you could start the container like this:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell cmorfixerenv.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf you don\u0027t see on the container the filesystem which is accessible on the host machine, you could try this, and once inside the container, you\u0027ll be able to see the filesystem mounted on /mnt.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell --bind \u0026lt;path to filesystem you want mounted on the container\u0026gt;:/mnt cmorfixerenv.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eInside the container, do the following\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esource /etc/bashrc\nactivateminiconda3\nconda activate cmorfixer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eExecute cmorfixer (present in /opt/cmor_fixer/cmor-fixer/cmor-fixer.py, in the container)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd /root\nscript -c \u0027/opt/cmor_fixer/cmor-fixer/cmor-fixer.py --verbose --forceid --olist --npp 1 --dry /mnt/CMIP6/ScenarioMIP/EC-Earth-Consortium/EC-Earth3/ssp126/\u0027 scriptout_cmorfix_dryrun\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1562875154.0 + "updated_at": 1611252471.0 }, { "data_format": 2, - "description": "Singularity", + "description": "GitHub repo for storing scripts related to simulation using JModelica. The initial focus is on simulation in HPC environments.", "filenames": [ - "Singularity" + "Singularity_Recipes/Singularity_Recipe_Py2_Compilation_Simulation", + "Singularity_Recipes/Singularity_Recipe_Py3_Simulation" ], - "full_name": "lalet/boutiques2singularity", + "full_name": "urbanopt/JModelica_simulation", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eboutiques2singularity\u003c/h1\u003e\u003ca id=\"user-content-boutiques2singularity\" class=\"anchor\" aria-label=\"Permalink: boutiques2singularity\" href=\"#boutiques2singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-jmodelica-simulation\" class=\"anchor\" aria-hidden=\"true\" href=\"#jmodelica-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJModelica Simulation\u003c/h1\u003e\n\u003cp\u003eGitHub repo for storing scripts related to simulation of Modelica models using JModelica. The initial focus is on simulation in HPC environments.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipes\u003c/h1\u003e\n\u003cp\u003eRecipes for building Singularity containers for compilation and simulation of Modelica models using PyModelica and PyFMI. Note that the recipe that would support compilation and simulation is for use with Python2 only, while a separate recipe supports simulation in Python3.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1507997466.0 + "updated_at": 1608681086.0 }, { "data_format": 2, - "description": "EL6/7 singularity images", + "description": null, "filenames": [ "Singularity" ], - "full_name": "khurtado/singularity-images", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-images\u003c/h1\u003e\u003ca id=\"user-content-singularity-images\" class=\"anchor\" aria-label=\"Permalink: singularity-images\" href=\"#singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "full_name": "baxpr/makerois-PMAT", + "latest_release": "v1.0.13", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-create-study-specific-roi-image-in-mni-space\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-study-specific-roi-image-in-mni-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate study-specific ROI image in MNI space\u003c/h1\u003e\n\u003cp\u003ePMAT resting state connectivity study.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h2\u003e\n\u003cp\u003eAll should be matched to the same T1 image.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eT1 image in atlas space (typically BIAS_NORM resource of cat12 assessor)\u003c/li\u003e\n\u003cli\u003eDeformation from T1 subject space to atlas space (typically DEF_FWD resource of cat12 assessor)\u003c/li\u003e\n\u003cli\u003eSUBJECT directory of Freesurfer output (typically SUBJECT resource of freesurfer_dev assessor)\u003c/li\u003e\n\u003cli\u003eTemporal lobe segmentation (typically SEG resource of Temporal_Lobe assessor)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003erois_PMAT.nii.gz Region of interest image\nrois_PMAT-labels.csv Region labels and volumes\nmakerois-PMAT.pdf Visual report of final ROI image\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-regions-of-interest\" class=\"anchor\" aria-hidden=\"true\" href=\"#regions-of-interest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRegions of interest\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-spheres-atlas-space\" class=\"anchor\" aria-hidden=\"true\" href=\"#spheres-atlas-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpheres (atlas space)\u003c/h3\u003e\n\u003cp\u003eSource: \u003cem\u003eLibby LA, Ekstrom AD, Ragland JD, Ranganath C. Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by high-resolution functional imaging. J Neurosci. 2012;32(19):6550-6560. doi:10.1523/JNEUROSCI.3711-11.2012\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMethod: \u003cem\u003eSchr\u00f6der TN, Haak K V., Jimenez NIZ, et al. Functional topography of the human entorhinal cortex. Elife. 2015;4(October 2016):1-17. doi:10.7554/eLife.06738\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-entorhinal-cortex-atlas-space\" class=\"anchor\" aria-hidden=\"true\" href=\"#entorhinal-cortex-atlas-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEntorhinal cortex (atlas space)\u003c/h3\u003e\n\u003cp\u003eAnterior lateral and posterior medial sections. Source and method: \u003cem\u003eSchr\u00f6der TN, Haak K V., Jimenez NIZ, et al. Functional topography of the human entorhinal cortex. Elife. 2015;4(October 2016):1-17. doi:10.7554/eLife.06738\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-temporal-lobe-subject-space-warped\" class=\"anchor\" aria-hidden=\"true\" href=\"#temporal-lobe-subject-space-warped\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemporal lobe (Subject space, warped)\u003c/h3\u003e\n\u003cp\u003eHead for anterior hippocampus; body and tail combined for posterior hippocampus. Method: \u003cem\u003ePlassard AJ, McHugo M, Heckers S, Landman BA. Multi-Scale Hippocampal Parcellation Improves Atlas-Based Segmentation Accuracy. Proc SPIE Int Soc Opt Eng. 2017 Feb 11;10133:101332D. doi: 10.1117/12.2254425. Epub 2017 Feb 24. PMID: 28781411; PMCID: PMC5544133.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-parahippocampal-perirhinal-subject-space-warped\" class=\"anchor\" aria-hidden=\"true\" href=\"#parahippocampal-perirhinal-subject-space-warped\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParahippocampal, perirhinal (Subject space, warped)\u003c/h3\u003e\n\u003cp\u003eGenerated by Freesurfer 6. Parahippocampal (1016,2016) and perirhinal (surface patch resampled to volume, overlap with parahippocampus was assigned to perirhinal). Method: \u003cem\u003eBruce Fischl, Andre van der Kouwe, Christophe Destrieux, Eric Halgren, Florent Segonne, David H. Salat, Evelina Busa, Larry J. Seidman, Jill Goldstein, David Kennedy, Verne Caviness, Nikos Makris, Bruce Rosen, and Anders M. Dale. Automatically Parcellating the Human Cerebral Cortex. Cerebral Cortex January 2004; 14:11-22.\u003c/em\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1528125811.0 + "updated_at": 1607988459.0 }, { "data_format": 2, - "description": "Singularity container providing OpenBLAS with OpenMP support on Alpine Linux", + "description": null, "filenames": [ "Singularity" ], - "full_name": "twesterhout/openblas-singularity", + "full_name": "tpall/htseq-paper-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eopenblas-singularity\u003c/h1\u003e\u003ca id=\"user-content-openblas-singularity\" class=\"anchor\" aria-label=\"Permalink: openblas-singularity\" href=\"#openblas-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container providing OpenBLAS with OpenMP and 64-bit ints on Alpine\nLinux. Pre-built container is available for download from\n\u003ca href=\"https://cloud.sylabs.io/library/_container/6026ee4e1e573cd09be5c186\" rel=\"nofollow\"\u003eSingularity Container\nLibrary\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-htseq-paper-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#htseq-paper-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehtseq-paper-singularity\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1616752652.0 + "updated_at": 1604657436.0 }, { "data_format": 2, - "description": "Singularity image containing Freesurfer and HCP Connectome Workbench", + "description": null, "filenames": [ - "Singularity" + "Singularity", + "other_images/Singularity.custom_openspiel" ], - "full_name": "chidiugonna/nklab-neuro-HCP", + "full_name": "buregab/openspiel_singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity image containing Freesurfer and HCP Conncectome Workbench\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-containing-freesurfer-and-hcp-conncectome-workbench\" class=\"anchor\" aria-label=\"Permalink: Singularity image containing Freesurfer and HCP Conncectome Workbench\" href=\"#singularity-image-containing-freesurfer-and-hcp-conncectome-workbench\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity image will be about 6GB when built using Singularity 2.4.2. It installs the latest development version of freesurfer and HCP Connectome Workbench\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Singularity Image\u003c/h2\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Build Singularity Image\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have at least singularity 2.4 installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-HCP\u003c/code\u003edirectory and check that you have a Singularity definiton file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow simply build the image as \u003ccode\u003esudo singularity build nklab-neuro-HCP.simg Singularity\u003c/code\u003e - note that the image name is assumed to be \u003ccode\u003enklab-neuro-HCP.simg\u003c/code\u003e but this can be changed to a more convenient label.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Singularity Image\u003c/h2\u003e\u003ca id=\"user-content-run-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Run Singularity Image\" href=\"#run-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can now run commands by simply appending them to the end of \u003ccode\u003esingularity run nklab-neuro-HCP.simg\u003c/code\u003e So for example to run \u003ccode\u003ewb_command\u003c/code\u003e simply enter \u003ccode\u003esingularity run nklab-neuro-tools.HCP wb_command ....\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eFor building openspiel singularity containers.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1533339170.0 + "updated_at": 1604380357.0 }, { "data_format": 2, - "description": "Singularity Hub test", + "description": "launch the C++ IDE Anjuta from a Singularity container", "filenames": [ "Singularity" ], - "full_name": "kav2k/fortune", + "full_name": "d-w-moore/anjuta_via_singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003efortune\u003c/h1\u003e\u003ca id=\"user-content-fortune\" class=\"anchor\" aria-label=\"Permalink: fortune\" href=\"#fortune\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Hub test\u003c/p\u003e\n\u003cp\u003eFortune Teller\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-anjuta-ide-via-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#anjuta-ide-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnjuta IDE via Singularity\u003c/h1\u003e\n\u003cp\u003eThe container includes libraries for building and debugging C++\nprograms with GCC 9, with C++17 support and Boost libraries. C/Xlib\napplications are also supported.\u003c/p\u003e\n\u003cp\u003eTo build the container under Singularity ~2.5.1 :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eget \u003ca href=\"http://sylabs.io\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e . If you\u0027re on Ubuntu/Debian,\nthe \u003ca href=\"https://neuro.debian.net\" rel=\"nofollow\"\u003eNeuroDebian\u003c/a\u003e repo can offer the\nmost up-to-date Singularity packages\u003c/li\u003e\n\u003cli\u003ein a local copy of this repo, use the build command:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build anjuta.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThe IDE can be lauched by running anjuta.simg as an executable\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./anjuta.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor via the singularity application\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run anjuta.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo alter an existing image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build --sandbox anjuta anjuta.simg\n$ sudo singularity shell --writable anjuta\nSingularity\u0026gt; apt update; apt install {custom-packages...}\nSingularity\u0026gt; exit\n$ sudo singularity build anjuta_updated.simg anjuta\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1505266397.0 + "updated_at": 1600000384.0 }, { "data_format": 2, - "description": "XCrySDen in a Singularity container", + "description": "An implementation for solving 3SAT (Exact Cover) using the Quantum Approximate Optimization Algorithm", "filenames": [ - "Singularity.1.6.2", - "Singularity" + "SingularityFile.def" ], - "full_name": "OSC/sa_singularity_xcrysden", + "full_name": "vivekkatial/qaoa-three-sat", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity XCrySDen\u003c/h1\u003e\u003ca id=\"user-content-singularity-xcrysden\" class=\"anchor\" aria-label=\"Permalink: Singularity XCrySDen\" href=\"#singularity-xcrysden\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4445\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7edda6b40df66cdf6d87ee014ce8a73af8830d12f325162978d3b72836ea332d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"http://www.xcrysden.org/Download.html\" rel=\"nofollow\"\u003eXCrysDen\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003excrysden.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build xcrysden.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull xcrysden.sif shub://OSC/sa_singularity_xcrysden\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart XCrysDen\u003c/h3\u003e\u003ca id=\"user-content-start-xcrysden\" class=\"anchor\" aria-label=\"Permalink: Start XCrysDen\" href=\"#start-xcrysden\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eXCrysDen is started using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run xcrysden.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as a native command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./xcrysden.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-qaoa-3sat--\" class=\"anchor\" aria-hidden=\"true\" href=\"#qaoa-3sat--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQAOA 3SAT \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4beb7225857c50a9391b71fbe998bc23c33b4d87ee15e3da9b7c1b7dfdc67a11/68747470733a2f2f7472617669732d63692e636f6d2f766976656b6b617469616c2f71616f612d74687265652d7361742e7376673f6272616e63683d6d6173746572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4beb7225857c50a9391b71fbe998bc23c33b4d87ee15e3da9b7c1b7dfdc67a11/68747470733a2f2f7472617669732d63692e636f6d2f766976656b6b617469616c2f71616f612d74687265652d7361742e7376673f6272616e63683d6d6173746572\" alt=\"\" data-canonical-src=\"https://travis-ci.com/vivekkatial/qaoa-three-sat.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://qaoa-three-sat.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/253d508d956ec9315fd5509c8d9cb82640904ab96c15672f2c65c9ec5c2de390/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f71616f612d74687265652d7361742f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/qaoa-three-sat/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eAn implementation for solving 3SAT (Exact Cover) using the Quantum Approximate Optimization Algorithm\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 1, "topics": [], - "updated_at": 1592244254.0 + "updated_at": 1607596883.0 }, { "data_format": 2, - "description": "Neuroglia-core (FSL,AFNI,ANTS) + minctools", + "description": "Singularity recipe files for aws-cli (https://github.com/aws/aws-cli)", "filenames": [ - "Singularity.v1.0.1a", "Singularity", - "Singularity.v1.0.0", - "Singularity.v1.0.1b" + "Singularity.2.0.43" ], - "full_name": "khanlab/neuroglia-core-minc", + "full_name": "powerPlant/aws-cli-srf", "latest_release": null, + "readme": "\u003cp\u003eSingularity recipe files for the AWS CLI v2 tool\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1541127620.0 + "updated_at": 1598486009.0 }, { "data_format": 2, - "description": "Singularity image for the EEMT project", + "description": "Singularity recipe files for entrez-direct (https://ftp.ncbi.nlm.nih.gov/entrez/entrezdirect/)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.13.8.20200819" ], - "full_name": "rynge/eemt-singularity", + "full_name": "powerPlant/entrez-direct-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eeemt-singularity\u003c/h1\u003e\u003ca id=\"user-content-eemt-singularity\" class=\"anchor\" aria-label=\"Permalink: eemt-singularity\" href=\"#eemt-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image for the EEMT project\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for Entrez Direct: E-utilities on the Unix Command Line to provide access to the NCBI\u0027s suite of interconnected databases\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1491087384.0 + "updated_at": 1598244762.0 }, { "data_format": 2, - "description": null, + "description": "The purpose of this project is to map Oxford Nanopore Sequencing data down to the species level", "filenames": [ - "Singularity" + "setup/Singularity" ], - "full_name": "mmirko/singularitytest", + "full_name": "JoshLoecker/MAPT", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularitytest\u003c/h1\u003e\u003ca id=\"user-content-singularitytest\" class=\"anchor\" aria-label=\"Permalink: singularitytest\" href=\"#singularitytest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mapt\" class=\"anchor\" aria-hidden=\"true\" href=\"#mapt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMAPT\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/JoshLoecker/MAPT/wiki\"\u003ePlease view the Wiki\u003c/a\u003e for more information.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport\u003c/h3\u003e\n\u003cp\u003eIf you need help, have questions, or have feature ideas please \u003ca href=\"https://github.com/JoshLoecker/MAPT/issues\"\u003eopen a new issue\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1605257877.0 + "updated_at": 1649438683.0 }, { "data_format": 2, - "description": "Pipeline for miRNA alignment and quantification", + "description": "This contains the latest docker and singularity images", "filenames": [ - "Singularity" + "Singularity_Ubuntu_18_04_Cuda_11_0", + "Singularity_Ubuntu_18_04_Cuda_11_1", + "Singularity_Ubuntu_18_04_Cuda_10_2", + "Singularity_Ubuntu_20_04_Cuda_11_1", + "Singularity_Ubuntu_16_04" ], - "full_name": "marchoeppner/miRNApipe", + "full_name": "shreyaskamathkm/Cluster_Images", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emiRNApipe\u003c/h1\u003e\u003ca id=\"user-content-mirnapipe\" class=\"anchor\" aria-label=\"Permalink: miRNApipe\" href=\"#mirnapipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline for miRNA alignment and quantification\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation about the pipeline can be found in the \u003ccode\u003edocs/\u003c/code\u003e directory or under the links below:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1554805649.0 + "updated_at": 1635787927.0 }, { "data_format": 2, - "description": "Singularity image for biocontainers bcftools", + "description": "Docker images", "filenames": [ - "Singularity" + "images/sc_qc_cluster/Singularity.sc_qc_cluster" ], - "full_name": "researchapps/bcftools", + "full_name": "letaylor/docker-letaylor-travis", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSamtools\u003c/h1\u003e\u003ca id=\"user-content-samtools\" class=\"anchor\" aria-label=\"Permalink: Samtools\" href=\"#samtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity image to deploy samtools.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 samtools.img\nsudo singularity bootstrap samtools.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the samtools runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./samtools.img\n\nProgram: samtools (Tools for alignments in the SAM format)\nVersion: 1.3.1 (using htslib 1.3.1)\n\nUsage: samtools \u0026lt;command\u0026gt; [options]\n\nCommands:\n -- Indexing\n dict create a sequence dictionary file\n faidx index/extract FASTA\n index index alignment\n\n -- Editing\n calmd recalculate MD/NM tags and \u0027=\u0027 bases\n fixmate fix mate information\n reheader replace BAM header\n rmdup remove PCR duplicates\n targetcut cut fosmid regions (for fosmid pool only)\n addreplacerg adds or replaces RG tags\n\n -- File operations\n collate shuffle and group alignments by name\n cat concatenate BAMs\n merge merge sorted alignments\n mpileup multi-way pileup\n sort sort alignment file\n split splits a file by read group\n quickcheck quickly check if SAM/BAM/CRAM file appears intact\n fastq converts a BAM to a FASTQ\n fasta converts a BAM to a FASTA\n\n -- Statistics\n bedcov read depth per BED region\n depth compute the depth\n flagstat simple stats\n idxstats BAM index stats\n phase phase heterozygotes\n stats generate stats (former bamcheck)\n\n -- Viewing\n flags explain BAM flags\n tview text alignment viewer\n view SAM\u0026lt;-\u0026gt;BAM\u0026lt;-\u0026gt;CRAM conversion\n depad convert padded BAM to unpadded BAM\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker-letaylor\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-letaylor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker-letaylor\u003c/h1\u003e\n\u003cp\u003eThis repo contains Docker images that are automatically built using Travis CI. It is not designed to scale to many images as each image is updated if any one image changes.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-automatically-push-images-to-docker-hub-using-travis-ci\" class=\"anchor\" aria-hidden=\"true\" href=\"#automatically-push-images-to-docker-hub-using-travis-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically push images to Docker Hub using Travis CI\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-edit-config-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-edit-config-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Edit config files\u003c/h2\u003e\n\u003cp\u003eEdit the following files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e.travis.yml\u003c/code\u003e : alter \u003ccode\u003e$IMAGE_NAME\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-give-travis-ci-access-to-upload-to-docker-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-give-travis-ci-access-to-upload-to-docker-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Give Travis CI access to upload to Docker Hub\u003c/h2\u003e\n\u003cp\u003eStore both \u003ccode\u003e$DOCKER_PASSWORD\u003c/code\u003e and \u003ccode\u003e$DOCKER_USERNAME\u003c/code\u003e securely in on Travis CI. These are used for authentication.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLogin to the account you want Travis to use to upload on \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eClick on your username on the top left and go to \u0027Account Settings\u0027.\u003c/li\u003e\n\u003cli\u003eOn the left hand panel, go to \u0027Security\u0027 and enter your password as requested.\u003c/li\u003e\n\u003cli\u003eNow we\u0027ll create an API token. Name it Travis CI.\u003c/li\u003e\n\u003cli\u003eCreate the token and copy it.\u003c/li\u003e\n\u003cli\u003eLogin to your account on \u003ca href=\"https://travis-ci.org\" rel=\"nofollow\"\u003etravis-ci.org\u003c/a\u003e and go to the repository that you want to add this automatic functionality to.\u003c/li\u003e\n\u003cli\u003eOn the right next to \u0027More options\u0027 go to \u0027Settings\u0027 in the hamburger menu.\u003c/li\u003e\n\u003cli\u003eAdd an environment variable with the name \u003ccode\u003eDOCKER_PASSWORD\u003c/code\u003e and give it the value of the API token that you copied from \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eAdd an environment variable with the name \u003ccode\u003eDOCKER_USERNAME\u003c/code\u003e and give it your \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e user name.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1484519232.0 + "updated_at": 1653062770.0 }, { "data_format": 2, - "description": "official build specifications for nginx", + "description": "Singularity recipe files for Mandalorion-Episode-II (https://github.com/rvolden/Mandalorion-Episode-II)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.6219d58" ], - "full_name": "singularityhub/nginx", + "full_name": "powerPlant/mandalorion-episode-ii-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNginx\u003c/h1\u003e\u003ca id=\"user-content-nginx\" class=\"anchor\" aria-label=\"Permalink: Nginx\" href=\"#nginx\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container is built using Circle CI, Google Storage, and Google Cloud Build, and \u003ca href=\"https://singularityhub.github.io/registry-org/singularityhub/nginx/\" rel=\"nofollow\"\u003ehosted on Singularity Static Registry\u003c/a\u003e. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003etags are supported based on the extension of the Singularity file, with an extensionless file corresponding to \"latest\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you are interested in local usage, see \u003ca href=\"#local-usage\"\u003eLocal Usage\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can I find here?\u003c/h2\u003e\u003ca id=\"user-content-what-can-i-find-here\" class=\"anchor\" aria-label=\"Permalink: What can I find here?\" href=\"#what-can-i-find-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository here serves the container under the namespace \u003ccode\u003esingularityhub/nginx\u003c/code\u003e. Specifically,\nit provides an example of using CircleCI to build with Google Cloud Build and push a container to Google Storage,\nand then update manifests at \u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e.\nIf you are interested in other container build templates, see \u003ca href=\"https://github.com/singularityhub/registry/wiki/build-templates\"\u003ethis page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does this work?\u003c/h2\u003e\u003ca id=\"user-content-how-does-this-work\" class=\"anchor\" aria-label=\"Permalink: How does this work?\" href=\"#how-does-this-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will submit this container to the (organizational) registry at\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e\nfor a final container uri corresponding to \u003ccode\u003ehttps://singularityhub.github.io/registry-org/singularityhub/busybox\u003c/code\u003e. Specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub/registry-org --) the organization registry\nsingularityhub/nginx --) a container collection\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethen on GitHub pages:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub.github.io/registry-org --) the registry interface\nsingularityhub.github.io/registry-org/singularityhub/nginx --) the added container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions\u003c/h1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e0. Fork the Repository\u003c/h2\u003e\u003ca id=\"user-content-0-fork-the-repository\" class=\"anchor\" aria-label=\"Permalink: 0. Fork the Repository\" href=\"#0-fork-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the repository here to your account, and make sure to add write permissions\nfor a machine user for the repository, and the machine user\u0027s key to CircleCI.\nThis means:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eadding the machine user as a collaborator to the repository (and accepting the invitation)\u003c/li\u003e\n\u003cli\u003econnecting the repository to CircleCI\u003c/li\u003e\n\u003cli\u003enavigating to the CircleCI project page logged in as the machine user to follow the project (button in upper right)\u003c/li\u003e\n\u003cli\u003egoing to the settings -\u0026gt; Checkout SSH keys to add the machine user key.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFull instructions are provided \u003ca href=\"https://github.com/singularityhub/registry/wiki/deploy-container-storage#2-creating-a-connected-repository\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Setup your Organizational Registry\u003c/h2\u003e\u003ca id=\"user-content-1-setup-your-organizational-registry\" class=\"anchor\" aria-label=\"Permalink: 1. Setup your Organizational Registry\" href=\"#1-setup-your-organizational-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you haven\u0027t done so, follow the instructions \u003ca href=\"https://github.com/singularityhub/registry/wiki/deploy-container-storage#organizational\"\u003ehere\u003c/a\u003e to create the organizational registry. You will need to\nupdate the environment variables in the top of the \u003ca href=\".circleci/config.yml\"\u003e.circleci/config.yml\u003c/a\u003e\nto reflect your repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e environment:\n\n # The GitHub username / reponame that the container will be submit to\n - REGISTRY_BASE: singularityhub/registry-org\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should only need to do this once. The example provided here uses\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Google Storage\u003c/h2\u003e\u003ca id=\"user-content-2-google-storage\" class=\"anchor\" aria-label=\"Permalink: 2. Google Storage\" href=\"#2-google-storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will be interacting with Google Storage via the \u003ca href=\"https://www.github.com/singularityhub/sregistry\"\u003esregistry\u003c/a\u003e\ncommand line client.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequired environment variables\u003c/h2\u003e\u003ca id=\"user-content-required-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Required environment variables\" href=\"#required-environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a Google Project and \u003ca href=\"https://cloud.google.com/sdk/docs/authorizing#authorizing_with_a_service_account\" rel=\"nofollow\"\u003ea service account\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Download the Service Account Key\u003c/h3\u003e\u003ca id=\"user-content-1-download-the-service-account-key\" class=\"anchor\" aria-label=\"Permalink: 1. Download the Service Account Key\" href=\"#1-download-the-service-account-key\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first download a service account key from the \u003ca href=\"https://console.cloud.google.com/iam-admin/serviceaccounts?_ga=2.213389911.-231410963.1512057989\" rel=\"nofollow\"\u003eservice accounts page\u003c/a\u003e. For the roles, add an admin for Google\nStorage (to store your container), along with Storage Object Admin and Google Build Admin.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/service-account.png\"\u003e\u003cimg src=\"img/service-account.png\" alt=\"img/service-account.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOnce you add the roles, you \u003cem\u003edo not need to add users\u003c/em\u003e to the account. You can next download\nthe service account key to your local machine, and move it to the repository folder.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/create-key.png\"\u003e\u003cimg src=\"img/create-key.png\" alt=\"img/create-key.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that the .gitignore includes *.json so it won\u0027t be added to your project!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Circle CI Secrets\u003c/h3\u003e\u003ca id=\"user-content-2-circle-ci-secrets\" class=\"anchor\" aria-label=\"Permalink: 2. Circle CI Secrets\" href=\"#2-circle-ci-secrets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have the \u003ccode\u003e\u0026lt;project-id\u0026gt;-\u0026lt;number\u0026gt;.json\u003c/code\u003e in the present working directory,\nyou can add the entire thing to your project as an encrypted environment variable.\nHere is how to copy paste the string from your terminal:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eproject-id\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdd the text output from the above to an environment variable\ncalled \u003ccode\u003eGOOGLE_APPLICATION_CREDENTIALS\u003c/code\u003e along with the following (all project secrets):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGOOGLE_COMPUTE_ZONE: the zone you want your compute builder to run in.\u003c/li\u003e\n\u003cli\u003eSREGISTRY_GOOGLE_PROJECT: the id of your project, easiest to find in the Google Project console url.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptionally, export a name for your bucket, \u003ccode\u003eSREGISTRY_GOOGLE_STORAGE_BUCKET\u003c/code\u003e\n(it will be created if it doesn\u0027t exist). It will default to your project id with sregistry- as a prefix.\nDon\u0027t forget to add the machine user to the repository, and then add its credential.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLocal Usage\u003c/h2\u003e\u003ca id=\"user-content-local-usage\" class=\"anchor\" aria-label=\"Permalink: Local Usage\" href=\"#local-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to build the container locally:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e git clone https://www.github.com/singularityhub/nginx\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e nginx\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFirst, let\u0027s talk about how we would run this image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo singularity build nginx.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will see the image buildin, including downloading of Docker layers, installation of nginx. Now let\u0027s run it, and we start a webserver:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./nginx.sif\nServing HTTP on 0.0.0.0 port 9999 ...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"nginx-basic.png\"\u003e\u003cimg src=\"nginx-basic.png\" alt=\"nginx-basic.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWelp, that was easy!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does it work?\u003c/h2\u003e\u003ca id=\"user-content-how-does-it-work\" class=\"anchor\" aria-label=\"Permalink: How does it work?\" href=\"#how-does-it-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow is this working? Let\u0027s look at the spec file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Bootstrap: docker\n From: ubuntu:16.04\n\n %runscript\n\n cd /data\n exec python3 -m http.server 9999\n\n %post\n\n mkdir /data\n echo \"\u0026lt;h2\u0026gt;Hello World!\u0026lt;/h2\u0026gt;\" \u0026gt;\u0026gt; /data/index.html\n apt-get update\n apt-get -y install python3 \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Header\u003c/h3\u003e\u003ca id=\"user-content-the-header\" class=\"anchor\" aria-label=\"Permalink: The Header\" href=\"#the-header\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe First line \u003ccode\u003ebootstrap\u003c/code\u003e says that we are going to bootstrap a \u003ccode\u003edocker\u003c/code\u003e image, specifically using the (\u003ccode\u003eFrom\u003c/code\u003e field) \u003ccode\u003eubuntu:16.04\u003c/code\u003e. You couldn\u0027t choose another distribution that you like, I just happen to like Debian.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e%post\u003c/h3\u003e\u003ca id=\"user-content-post\" class=\"anchor\" aria-label=\"Permalink: %post\" href=\"#post\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePost is the section where you put commands you want to run once to create your image. This includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstallation of software\u003c/li\u003e\n\u003cli\u003ecreation of files or folders\u003c/li\u003e\n\u003cli\u003emoving data, files into the container image\u003c/li\u003e\n\u003cli\u003eanalysis things\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe list is pretty obvious, but what about the last one, analysis things? Yes, let\u0027s say that we had a script thing that we wanted to run just once to produce a result that would live in the container. In this case, we would have that thing run in %post, and then give some interactive access to the result via the \u003ccode\u003e%runscript\u003c/code\u003e. In the case that you want your image to be more like a function and run the analysis (for example, if you want your container to take input arguments, run something, and deliver a result), then this command should go in the \u003ccode\u003e%runscript\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIn our case, since we are going to serve a simple web-based thing, we create a directory to work with (\u003ccode\u003e/data\u003c/code\u003e is easy to remember), write a terribly formatted \u003ccode\u003eindex.html\u003c/code\u003e there (for those that aren\u0027t web focused, a web server by default will render a file called \u003ccode\u003eindex.html\u003c/code\u003e from a root folder). We then install python, because it has a nice command for bringing up a quick web server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e%runscript\u003c/h3\u003e\u003ca id=\"user-content-runscript\" class=\"anchor\" aria-label=\"Permalink: %runscript\" href=\"#runscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e%runscript\u003c/code\u003e is the thing executed when we run our container. For this example, we basically change directories to data, and then use python to start up a little server on port 9999 to serve that folder. Anything in that folder will then be available to our local machine on port 9999, meaning the address \u003ccode\u003elocalhost:9999\u003c/code\u003e or \u003ccode\u003e127.0.0.1:9999\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample Use Cases\u003c/h2\u003e\u003ca id=\"user-content-example-use-cases\" class=\"anchor\" aria-label=\"Permalink: Example Use Cases\" href=\"#example-use-cases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have a folder locally with some static html files or other that you want to serve, you can map a directory to data when running the container. For example, let\u0027s map the $PWD to the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B .:/data nginx-basic.img \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e.\u003c/code\u003e is a stand in for the present working directory, I could have also done:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B $PWD:/data nginx-basic.img \nsingularity run -B /path/to/singularity-web/nginx-basic:/data nginx-basic.img \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that binding the directory at runtime WILL map your specified place to the directory (and not the file we saved there before) but it does NOT overwrite the file saved to the image. In other words, if we run the image again without binding, we see the original \"Hello World!\"\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for Mandalorion Episode II, Attack of the Isoforms\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularityhub", - "registry-template", - "static-registry", - "singularity" - ], - "updated_at": 1698809323.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1583274107.0 }, { "data_format": 2, - "description": "Fork from https://github.com/dbolya/yolact", + "description": null, "filenames": [ - "Singularity" + "Singularity.mash", + "Singularity.CAT_update", + "Singularity.art", + "Singularity.metawap_docker", + "Singularity.pasta", + "Singularity.cmseq_conda", + "Singularity.snakemake", + "Singularity.euk_decide", + "Singularity.dRep", + "Singularity.dbcan", + "Singularity.nanofilt", + "Singularity.metaeuk", + "Singularity.BUSCO4", + "Singularity.cmseq", + "Singularity.ploidyNGS", + "Singularity.metawrap", + "Singularity.sepp", + "Singularity.R", + "Singularity.VAMP", + "Singularity.puntseq", + "Singularity.VAMB_10.1", + "Singularity.VAMB", + "Singularity.mashmap", + "Singularity.comparem", + "Singularity.ncbi-downloader", + "Singularity.biopython", + "Singularity.spades", + "Singularity.minimap2", + "Singularity.BUSCO5", + "Singularity.bbmap", + "Singularity.sourmash", + "Singularity.raxml-ng", + "Singularity.nQuire", + "Singularity.fastani", + "Singularity.metabat2", + "Singularity.seqtk", + "Singularity.pysam", + "Singularity.krona", + "Singularity.kraken2", + "Singularity.bamm", + "Singularity.megahit", + "Singularity.ete3", + "Singularity.bioinfo", + "Singularity.trimal", + "Singularity.spades_3.13", + "Singularity.dRep3", + "Singularity.deeptools", + "Singularity.tree", + "Singularity.BUSCO414", + "Singularity.METAMVGL", + "Singularity.repeatmasker", + "Singularity.mummer", + "Singularity.iqtree", + "Singularity.eukcc_vanilla", + "Singularity.mafft", + "Singularity.bioconvert", + "Singularity.qiime2", + "Singularity.CAT", + "Singularity.bwa", + "Singularity.mmseq2", + "Singularity.famsa", + "Singularity.EukRep", + "Singularity.antismash_standalone", + "Singularity.spades_3.15" ], - "full_name": "sokovninn/yolact-artwin", + "full_name": "hexmek/container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\n\u003cstrong\u003eY\u003c/strong\u003eou \u003cstrong\u003eO\u003c/strong\u003enly \u003cstrong\u003eL\u003c/strong\u003eook \u003cstrong\u003eA\u003c/strong\u003et \u003cstrong\u003eC\u003c/strong\u003eoefficien\u003cstrong\u003eT\u003c/strong\u003es\u003c/h1\u003e\u003ca id=\"user-content-you-only-look-at-coefficients\" class=\"anchor\" aria-label=\"Permalink: You Only Look At CoefficienTs\" href=\"#you-only-look-at-coefficients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e \u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\n \u255a\u2588\u2588\u2557 \u2588\u2588\u2554\u255d\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u2550\u2588\u2588\u2554\u2550\u2550\u255d\n \u255a\u2588\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u255a\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2551 \n \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA simple, fully convolutional model for real-time instance segmentation. This is the code for our papers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1904.02689\" rel=\"nofollow\"\u003eYOLACT: Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003eYOLACT++: Better Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eYOLACT++ (v1.2) released! (\u003ca href=\"CHANGELOG.md\"\u003eChangelog\u003c/a\u003e)\u003c/h4\u003e\u003ca id=\"user-content-yolact-v12-released-changelog\" class=\"anchor\" aria-label=\"Permalink: YOLACT++ (v1.2) released! (Changelog)\" href=\"#yolact-v12-released-changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYOLACT++\u0027s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e (check out our journal paper \u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIn order to use YOLACT++, make sure you compile the DCNv2 code. (See \u003ca href=\"https://github.com/dbolya/yolact#installation\"\u003eInstallation\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFor a real-time demo, check out our ICCV video:\u003c/h4\u003e\u003ca id=\"user-content-for-a-real-time-demo-check-out-our-iccv-video\" class=\"anchor\" aria-label=\"Permalink: For a real-time demo, check out our ICCV video:\" href=\"#for-a-real-time-demo-check-out-our-iccv-video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=0pMfmo8qfpQ\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/70ee4ab932214f5d02c39f251a53743c6e0e8c964af4af267119e92691fb2f9c/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f30704d666d6f38716670512f302e6a7067\" alt=\"IMAGE ALT TEXT HERE\" data-canonical-src=\"https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSome examples from our YOLACT base model (33.5 fps on a Titan Xp and 29.8 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e):\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_0.png\"\u003e\u003cimg src=\"data/yolact_example_0.png\" alt=\"Example 0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_1.png\"\u003e\u003cimg src=\"data/yolact_example_1.png\" alt=\"Example 1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_2.png\"\u003e\u003cimg src=\"data/yolact_example_2.png\" alt=\"Example 2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repository and enter it:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/dbolya/yolact.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e yolact\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eSet up the environment using one of the following methods:\n\u003cul\u003e\n\u003cli\u003eUsing \u003ca href=\"https://www.anaconda.com/distribution/\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eRun \u003ccode\u003econda env create -f environment.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eManually with pip\n\u003cul\u003e\n\u003cli\u003eSet up a Python3 environment (e.g., using virtenv).\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"http://pytorch.org/\" rel=\"nofollow\"\u003ePytorch\u003c/a\u003e 1.0.1 (or higher) and TorchVision.\u003c/li\u003e\n\u003cli\u003eInstall some other packages:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Cython needs to be installed before pycocotools\u003c/span\u003e\npip install cython\npip install opencv-python pillow pycocotools matplotlib \u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to train YOLACT, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into \u003ccode\u003e./data/coco\u003c/code\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to evaluate YOLACT on \u003ccode\u003etest-dev\u003c/code\u003e, download \u003ccode\u003etest-dev\u003c/code\u003e with this script.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO_test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you want to use YOLACT++, compile deformable convolutional layers (from \u003ca href=\"https://github.com/CharlesShang/DCNv2/tree/pytorch_1.0\"\u003eDCNv2\u003c/a\u003e).\nMake sure you have the latest CUDA toolkit installed from \u003ca href=\"https://developer.nvidia.com/cuda-toolkit\" rel=\"nofollow\"\u003eNVidia\u0027s Website\u003c/a\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e external/DCNv2\npython setup.py build develop\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEvaluation\u003c/h1\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere are our YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on \u003ccode\u003etest-dev\u003c/code\u003e:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1yp7ZbbDwvMiFJEq4ptVKTYTI2VeRDXl0/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EUVpxoSXaqNIlssoLKOEoCcB1m0RpzGq_Khp5n1VX3zcUw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDarknet53-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.7\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1dukLrTzZQEuhzitGkHaGjphlmRJOjVnP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_darknet53_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/ERrao26c8llJn25dIyZPhwMBxUp2GdZTKIMUQA3t0djHLw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e29.8\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf_/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EYRWxBEoKU9DiblrWx2M89MBGFkVVB_drlRd_v5sdT3Hgg\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e700\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e23.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e31.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1lE4Lz5p25teiXV-6HdTiOJSnS7u7GBzg/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_im700_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/Eagg5RSc5hFEhp7sPtvLNyoBjhlf2feog7t8OQzHKKphjw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYOLACT++ models (released on December 16th, 2019):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1ZPu1YR2UzGHQD0o1rEqy-j5bmEm3lbyP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EcJAtMiEFlhAnVsDf00yWRIBUC4m8iE9NEEiV05XwtEoGw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e27.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.6\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/15id0Qq5eqRbkD-N3ZjDZXdCvRyIaHpFB/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EVQ62sF0SrJPrl_68onyHF8BpG7c05A8PavV4a849sZgEA\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo evalute the model, put the corresponding weights file in the \u003ccode\u003e./weights\u003c/code\u003e directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., \u003ccode\u003eyolact_base\u003c/code\u003e for \u003ccode\u003eyolact_base_54_800000.pth\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuantitative Results on COCO\u003c/h2\u003e\u003ca id=\"user-content-quantitative-results-on-coco\" class=\"anchor\" aria-label=\"Permalink: Quantitative Results on COCO\" href=\"#quantitative-results-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This should get 29.92 validation mask mAP last time I checked.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Output a COCOEval json to submit to the website or to use the run_coco_eval.py script.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This command will create \u0027./results/bbox_detections.json\u0027 and \u0027./results/mask_detections.json\u0027 for detection and instance segmentation respectively.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py.\u003c/span\u003e\npython run_coco_eval.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQualitative Results on COCO\u003c/h2\u003e\u003ca id=\"user-content-qualitative-results-on-coco\" class=\"anchor\" aria-label=\"Permalink: Qualitative Results on COCO\" href=\"#qualitative-results-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on COCO. From here on I\u0027ll use a confidence threshold of 0.15.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --display\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBenchmarking on COCO\u003c/h2\u003e\u003ca id=\"user-content-benchmarking-on-coco\" class=\"anchor\" aria-label=\"Permalink: Benchmarking on COCO\" href=\"#benchmarking-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run just the raw model on the first 1k images of the validation set\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark --max_images=1000\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImages\u003c/h2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on the specified image.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process an image and save it to another file.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a whole folder of images.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVideo\u003c/h2\u003e\u003ca id=\"user-content-video\" class=\"anchor\" aria-label=\"Permalink: Video\" href=\"#video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a video in real-time. \"--video_multiframe\" will process that many frames at once for improved performance.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you want, use \"--display_fps\" to draw the FPS directly on the frame.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a video and save it to another file. This uses the same pipeline as the ones above now, so it\u0027s fast!\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs you can tell, \u003ccode\u003eeval.py\u003c/code\u003e can do a ton of stuff. Run the \u003ccode\u003e--help\u003c/code\u003e command to see everything it can do.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython eval.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTraining\u003c/h1\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, we train on COCO. Make sure to download the entire dataset using the commands above.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo train, grab an imagenet-pretrained model and put it in \u003ccode\u003e./weights\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eFor Resnet101, download \u003ccode\u003eresnet101_reducedfc.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1tvqFPd4bJtakOlmn-uIA492g2qurRChj/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Resnet50, download \u003ccode\u003eresnet50-19c8e357.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1Jy3yCdbatgXa5YYIdTCRrSV0S9V5g1rn/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Darknet53, download \u003ccode\u003edarknet53.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRun one of the training commands below.\n\u003cul\u003e\n\u003cli\u003eNote that you can press ctrl+c while training and it will save an \u003ccode\u003e*_interrupt.pth\u003c/code\u003e file at the current iteration.\u003c/li\u003e\n\u003cli\u003eAll weights are saved in the \u003ccode\u003e./weights\u003c/code\u003e directory by default with the file name \u003ccode\u003e\u0026lt;config\u0026gt;_\u0026lt;epoch\u0026gt;_\u0026lt;iter\u0026gt;.pth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains using the base config with a batch size of 8 (the default).\u003c/span\u003e\npython train.py --config=yolact_base_config\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.\u003c/span\u003e\npython train.py --config=yolact_base_config --batch_size=5\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file\u0027s name.\u003c/span\u003e\npython train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the help option to see a description of all available command line arguments\u003c/span\u003e\npython train.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMulti-GPU Support\u003c/h2\u003e\u003ca id=\"user-content-multi-gpu-support\" class=\"anchor\" aria-label=\"Permalink: Multi-GPU Support\" href=\"#multi-gpu-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYOLACT now supports multiple GPUs seamlessly during training:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBefore running any of the scripts, run: \u003ccode\u003eexport CUDA_VISIBLE_DEVICES=[gpus]\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3).\u003c/li\u003e\n\u003cli\u003eYou should still do this if only using 1 GPU.\u003c/li\u003e\n\u003cli\u003eYou can check the indices of your GPUs with \u003ccode\u003envidia-smi\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThen, simply set the batch size to \u003ccode\u003e8*num_gpus\u003c/code\u003e with the training commands above. The training script will automatically scale the hyperparameters to the right values.\n\u003cul\u003e\n\u003cli\u003eIf you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you\u0027re using.\u003c/li\u003e\n\u003cli\u003eIf you want to allocate the images per GPU specific for different GPUs, you can use \u003ccode\u003e--batch_alloc=[alloc]\u003c/code\u003e where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to \u003ccode\u003ebatch_size\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLogging\u003c/h2\u003e\u003ca id=\"user-content-logging\" class=\"anchor\" aria-label=\"Permalink: Logging\" href=\"#logging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYOLACT now logs training and validation information by default. You can disable this with \u003ccode\u003e--no_log\u003c/code\u003e. A guide on how to visualize these logs is coming soon, but now you can look at \u003ccode\u003eLogVizualizer\u003c/code\u003e in \u003ccode\u003eutils/logger.py\u003c/code\u003e for help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePascal SBD\u003c/h2\u003e\u003ca id=\"user-content-pascal-sbd\" class=\"anchor\" aria-label=\"Permalink: Pascal SBD\" href=\"#pascal-sbd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe also include a config for training on Pascal SBD annotations (for rapid experimentation or comparing with other methods). To train on Pascal SBD, proceed with the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the dataset from \u003ca href=\"http://home.bharathh.info/pubs/codes/SBD/download.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. It\u0027s the first link in the top \"Overview\" section (and the file is called \u003ccode\u003ebenchmark.tgz\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eExtract the dataset somewhere. In the dataset there should be a folder called \u003ccode\u003edataset/img\u003c/code\u003e. Create the directory \u003ccode\u003e./data/sbd\u003c/code\u003e (where \u003ccode\u003e.\u003c/code\u003e is YOLACT\u0027s root) and copy \u003ccode\u003edataset/img\u003c/code\u003e to \u003ccode\u003e./data/sbd/img\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDownload the COCO-style annotations from \u003ca href=\"https://drive.google.com/open?id=1ExrRSPVctHW8Nxrn0SofU1lVhK5Wn0_S\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eExtract the annotations into \u003ccode\u003e./data/sbd/\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eNow you can train using \u003ccode\u003e--config=yolact_resnet50_pascal_config\u003c/code\u003e. Check that config to see how to extend it to other models.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI will automate this all with a script soon, don\u0027t worry. Also, if you want the script I used to convert the annotations, I put it in \u003ccode\u003e./scripts/convert_sbd.py\u003c/code\u003e, but you\u0027ll have to check how it works to be able to use it because I don\u0027t actually remember at this point.\u003c/p\u003e\n\u003cp\u003eIf you want to verify our results, you can download our \u003ccode\u003eyolact_resnet50_pascal_config\u003c/code\u003e weights from \u003ca href=\"https://drive.google.com/open?id=1yLVwtkRtNxyl0kxeMCtPXJsXFFyc_FHe\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. This model should get 72.3 mask AP_50 and 56.2 mask AP_70. Note that the \"all\" AP isn\u0027t the same as the \"vol\" AP reported in others papers for pascal (they use an averages of the thresholds from \u003ccode\u003e0.1 - 0.9\u003c/code\u003e in increments of \u003ccode\u003e0.1\u003c/code\u003e instead of what COCO uses).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCustom Datasets\u003c/h2\u003e\u003ca id=\"user-content-custom-datasets\" class=\"anchor\" aria-label=\"Permalink: Custom Datasets\" href=\"#custom-datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can also train on your own dataset by following these steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a COCO-style Object Detection JSON annotation file for your dataset. The specification for this can be found \u003ca href=\"http://cocodataset.org/#format-data\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Note that we don\u0027t use some fields, so the following may be omitted:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003einfo\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eliscense\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eUnder \u003ccode\u003eimage\u003c/code\u003e: \u003ccode\u003elicense, flickr_url, coco_url, date_captured\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecategories\u003c/code\u003e (we use our own format for categories, see below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a definition for your dataset under \u003ccode\u003edataset_base\u003c/code\u003e in \u003ccode\u003edata/config.py\u003c/code\u003e (see the comments in \u003ccode\u003edataset_base\u003c/code\u003e for an explanation of each field):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003emy_custom_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset_base\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecopy\u003c/span\u003e({\n \u003cspan class=\"pl-s\"\u003e\u0027name\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027My Dataset\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027train_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027train_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027valid_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027valid_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027has_gt\u0027\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027class_names\u0027\u003c/span\u003e: (\u003cspan class=\"pl-s\"\u003e\u0027my_class_id_1\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_2\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_3\u0027\u003c/span\u003e, ...)\n})\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA couple things to note:\n\u003cul\u003e\n\u003cli\u003eClass IDs in the annotation file should start at 1 and increase sequentially on the order of \u003ccode\u003eclass_names\u003c/code\u003e. If this isn\u0027t the case for your annotation file (like in COCO), see the field \u003ccode\u003elabel_map\u003c/code\u003e in \u003ccode\u003edataset_base\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf you do not want to create a validation split, use the same image path and annotations file for validation. By default (see \u003ccode\u003epython train.py --help\u003c/code\u003e), \u003ccode\u003etrain.py\u003c/code\u003e will output validation mAP for the first 5000 images in the dataset every 2 epochs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFinally, in \u003ccode\u003eyolact_base_config\u003c/code\u003e in the same file, change the value for \u003ccode\u003e\u0027dataset\u0027\u003c/code\u003e to \u003ccode\u003e\u0027my_custom_dataset\u0027\u003c/code\u003e or whatever you named the config object above. Then you can use any of the training commands in the previous section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreating a Custom Dataset from Scratch\u003c/h4\u003e\u003ca id=\"user-content-creating-a-custom-dataset-from-scratch\" class=\"anchor\" aria-label=\"Permalink: Creating a Custom Dataset from Scratch\" href=\"#creating-a-custom-dataset-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/dbolya/yolact/issues/70#issuecomment-504283008\"\u003ethis nice post by @Amit12690\u003c/a\u003e for tips on how to annotate a custom dataset and prepare it for use with YOLACT.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use YOLACT or this code base in your work, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{yolact-iccv2019,\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n title = {YOLACT: {Real-time} Instance Segmentation},\n booktitle = {ICCV},\n year = {2019},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor YOLACT++, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{yolact-plus-arxiv2019,\n title = {YOLACT++: Better Real-time Instance Segmentation},\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n year = {2019},\n eprint = {1912.06218},\n archivePrefix = {arXiv},\n primaryClass = {cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContact\u003c/h1\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor questions about our paper or code, please contact \u003ca href=\"mailto:dbolya@ucdavis.edu\"\u003eDaniel Bolya\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1635159103.0 + "updated_at": 1619700602.0 }, { "data_format": 2, - "description": "Evaluation Framework and Amazon DSSTNE", + "description": "start with raw plink, end with standardized QCed plink", "filenames": [ - "amazon-dsstne/Singularity" + "workflow/Singularity_defs.def" ], - "full_name": "fnplus/project-recSys", + "full_name": "pmonnahan/DataPrep", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRecSys\u003c/h1\u003e\u003ca id=\"user-content-recsys\" class=\"anchor\" aria-label=\"Permalink: RecSys\" href=\"#recsys\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEvaluation Framwork and Amazon DSSTNE\u003c/h2\u003e\u003ca id=\"user-content-evaluation-framwork-and-amazon-dsstne\" class=\"anchor\" aria-label=\"Permalink: Evaluation Framwork and Amazon DSSTNE\" href=\"#evaluation-framwork-and-amazon-dsstne\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-pre-imputation-qc-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-imputation-qc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-imputation QC pipeline\u003c/h1\u003e\n\u003cp\u003eThe purpose of this pipeline is to perform the following for a set of input PLINK datasets:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebasic QC (genotype/variant missingness, HWE, and minor allele frequency)\u003c/li\u003e\n\u003cli\u003eharmonize allele specifications with the GRCh37 reference genome\u003c/li\u003e\n\u003cli\u003eproduce a set of VCF files (separated by chromosome) for imputation\u003c/li\u003e\n\u003cli\u003emerge filtered datasets into a single dataset consisting only of overlapping sites.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA companion pipeline, which performs post-imputation QC, will download alongside the pre-imputation pipeline. To use the post-imputation pipeline, see the README in the postImpute directory.\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#snakemake\"\u003eSnakemake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-the-workflow\"\u003eRunning the workflow\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#other-notes\"\u003eOther Notes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#debugging-and-error-reports\"\u003eDebugging and error reports\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pipeline-overview\"\u003ePipeline Overview\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#input-data\"\u003eInput Data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#dataset-harmonization\"\u003eData Harmonization\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#reference-allele-fixing\"\u003eReference allele fixing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#basic-qc\"\u003eBasic QC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#merging-inputs-optional\"\u003eMerging Inputs (Optional)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#imputaton-preparation\"\u003eImputation Preparation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pmonnahan/DataPrep/blob/master/Pipeline_DAG.png\"\u003e\u003cimg src=\"https://github.com/pmonnahan/DataPrep/raw/master/Pipeline_DAG.png\" alt=\"Pipeline DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-snakemake\" class=\"anchor\" aria-hidden=\"true\" href=\"#snakemake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakemake\u003c/h3\u003e\n\u003cp\u003eThe pipeline is coordinated and run on an HPC (or locally) using \u003cem\u003eSnakemake\u003c/em\u003e. To install snakemake, first create a virtual environment via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load python3/3.6.3_anaconda5.0.1\nconda install -c conda-forge mamba\nmamba create -c conda-forge -c bioconda -n \u0026lt;your_environment_name\u0026gt; snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a new virtual environment and install \u003ccode\u003esnakemake\u003c/code\u003e. Then, activate this environment and perform following installations:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;your_environment_name\u0026gt;\nconda install numpy yaml pandas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnytime you need to run the pipeline, activate this environment beforehand via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;environment_name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you choose not to create an environment, you must ensure that these packages are installed and available for your python installation.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eThe installation of the individual programs used throughout this pipeline can be completely avoid by utilizing a Singularity image. This image is too large to be hosted on Github, although you can find the definitions file used to create the image \u003ca href=\"https://github.com/pmonnahan/AncInf/blob/master/singularity/Singularity_defs.def\"\u003ehere\u003c/a\u003e. Building of images is still not currently supported at MSI, so I used a Vagrant virtual machine, which comes with Singularity pre-configured/installed (\u003ca href=\"https://app.vagrantup.com/singularityware/boxes/singularity-2.4/versions/2.4\" rel=\"nofollow\"\u003ehttps://app.vagrantup.com/singularityware/boxes/singularity-2.4/versions/2.4\u003c/a\u003e). I can also share the img file directly upon request.\u003c/p\u003e\n\u003cp\u003eHowever, in order to utilize the singularity image, \u003cem\u003eSingularity\u003c/em\u003e must be installed on the HPC. Currently, the pipeline assumes that \u003cem\u003eSingularity\u003c/em\u003e will be available as a module and can be loaded into the environment via the command specified in the config.yml file, where it says \u0027singularity_module\u0027. The default setting will work for MSI at UMN.\u003c/p\u003e\n\u003cp\u003eSingularity settings in config.yml\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity:\n use_singularity: \u0027true\u0027\n image: \u0027/home/pmonnaha/pmonnaha/singularity/AncestryInference.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the workflow\u003c/h2\u003e\n\u003cp\u003eFirst, activate the virtual environment into which snakemake was installed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;environment_name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eClone the parent repository to the location where you want to store the output of the pipeline.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/pmonnahan/DataPrep.git preImputeQC\ncd preImputeQC\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe critical files responsible for executing the pipeline are contained in the \u003cem\u003e./workflow\u003c/em\u003e subdirectory contained within the cloned repo. They are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSnakefile\u003c/li\u003e\n\u003cli\u003econfig.yml\u003c/li\u003e\n\u003cli\u003ecluster.yaml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003cem\u003eSnakefile\u003c/em\u003e is the primary workhouse of snakemake, which specifies the dependencies of various parts of the pipeline and coordinates execution. No modifications to the \u003cem\u003eSnakefile\u003c/em\u003e are necessary.\u003c/p\u003e\n\u003cp\u003eIn order for the \u003cem\u003eSnakefile\u003c/em\u003e to locate all of the necessary input and correctly submit jobs to the cluster, \u003cstrong\u003eboth\u003c/strong\u003e the \u003cem\u003econfig.yaml\u003c/em\u003e and \u003cem\u003ecluster.yaml\u003c/em\u003e need to be modified. Open these files and change the required entries that are indicated with \u0027MODIFY\u0027. Other fields do not require modification, although this may be desired given the particulars of the run you wish to implement. Details on each entry in the config file (e.g. what the program expects in each entry as well as the purpose of the entry) are provided in the \u003cem\u003ePipeline Overview\u003c/em\u003e at the bottom. Note: Only use letters and numbers when naming output files or datasets as this may cause issues with the report creation.\u003c/p\u003e\n\u003cp\u003eThe entire pipeline can be executed on a local machine (not recommended) or on an HPC, and the \u003cem\u003ecluster.yaml\u003c/em\u003e file is required only for the latter. For a local run, change the \u003ccode\u003elocal_run\u003c/code\u003e entry to \u003ccode\u003etrue\u003c/code\u003e under the \u003ccode\u003erun_settings\u003c/code\u003e section of the config file, and launch snakemake from within the parent directory by the simple command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHowever, multiple steps in the pipeline have high resource demands, and so are unlikely to be able to be run locally. This option exists primarily for testing and troubleshooting, so the remainder of the documentation assumes that the pipeline will be executed on an HPC. In order to coordinate the use of the HPC, the following modifications to the snakemake command are required:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --cluster \"sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\" --cluster-config workflow/cluster_yale.yaml -j 32\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere -j specifies the number of jobs that can be submitted at once.\u003c/p\u003e\n\u003cp\u003eOne additional setting in the \u003cem\u003econfig.yml\u003c/em\u003e is needed in order to correctly submit jobs to the HPC. The relevant entries are under the \u003ccode\u003erun_settings\u003c/code\u003e section of the config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun_settings:\n local_run: \u0027false\u0027\n cluster_config: \u0027workflow/cluster_slurm.yaml\u0027\n scheduler: \u0027slurm\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere, it is necessary that the \u003ccode\u003ecluster_config\u003c/code\u003e entry is set to the path of the cluster_slurm.yaml file that will be used in the snakemake command. Also, the scheduler must correspond to the syntax used in the snakemake command and cluster.yaml file. I should point out that these additional changes are needed for responsibly using PLINK within a snakemake framework, and are not directly needed for snakemake. PLINK will attempt to auto-detect available resources upon running regardless of the resources that were requested when the job was submitted. Therefore, we have to read and parse the requested resources in the cluster config file in order for them to be communicated to PLINK from within the Snakefile.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-other-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther notes\u003c/h3\u003e\n\u003cp\u003eIt is recommended that \u003cem\u003esnakemake\u003c/em\u003e is run as an interactive session on an HPC. \u003cem\u003eSnakemake\u003c/em\u003e will launch the specified number (via the -j flag) of jobs, and then will hang and wait for them to finish. As jobs finish (and assuming no errors), \u003cem\u003esnakemake\u003c/em\u003e will launch additional jobs keeping the total running jobs at whatever -j is set for. Although \u003cem\u003esnakemake\u003c/em\u003e should not use a lot of memory, it could have long run times, which is generally not advisable on login nodes.\u003c/p\u003e\n\u003cp\u003eOne attractive feature of \u003cem\u003esnakemake\u003c/em\u003e is its ability to keep track of the progress and dependencies of the different stages of the pipeline. Specifically, if an error is encountered or the pipeline otherwise stops before the final step, \u003cem\u003esnakemake\u003c/em\u003e can resume the pipeline where it left off, avoiding redundant computation for previously completed tasks. To do so, simply resubmit the original \u003cem\u003esnakemake\u003c/em\u003e command.\u003c/p\u003e\n\u003cp\u003eTo run a specific part of the pipeline, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -R \u0026lt;rule_name\u0026gt; --cluster \"sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\" --cluster-config workflow/cluster_yale.yaml -j 20 --rerun-incomplete\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003cem\u003erule_name\u003c/em\u003e indicates the \u0027rule\u0027 (i.e. job) in the Snakefile that you wish to run. Or, you can request a specific file by providing the filename at the end of the command. You may need to include the -F (i.e. force) if the output file already exists and you want to overwrite it.\u003c/p\u003e\n\u003cp\u003eAlso, it is often very helpful to do a \u0027dry-run\u0027 of the pipeline in which the different steps and dependencies are printed to screen, but no actual jobs are executed. This can be helpful to ensure that config entries are correct, etc. To perform a dry-run, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -nrp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: It is convenient to make an alias in your ~/.bashrc file to run snakemake on the cluster without having to type the --cluster... part of the command every time. For me, it looked like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias snakeslurm=\"snakemake -k --cluster \u0027sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --job-name={cluster.job-name} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\u0027 --cluster-config workflow/cluster_slurm.yaml\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis way, I can just do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakeslurm -j 25\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo launch snakemake on the cluster.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-debugging-and-error-reports\" class=\"anchor\" aria-hidden=\"true\" href=\"#debugging-and-error-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebugging and error reports\u003c/h4\u003e\n\u003cp\u003eShould an error be encountered in a job, snakemake will halt the pipeline and indicate in the terminal that an error has occurred. The offending job will also be printed in red in the terminal window. More information on why the job failed can be found in the \u0027stdout\u0027 and \u0027stderr\u0027 files that are output to the \u003cem\u003e\u0027OandE\u0027\u003c/em\u003e directory and will be labelled with the jobname.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline Overview\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput Data\u003c/h3\u003e\n\u003cp\u003eUnder the \u0027query\u0027 section, you can specify the inputs for one or more datasets. Each dataset should be uniquely named (Note: avoid using periods or underscores when naming output files or datasets as this may cause issues with the report creation.) with values specified for the following \"keys\":\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003edata\u003c/strong\u003e: path to the PLINK files (just the PLINK prefix).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003echrom_key\u003c/strong\u003e: tab-delimited text file with 2 columns (no header). The first column contains the old chromosome names, and the second column contains the new names.\n\u003cul\u003e\n\u003cli\u003eUsed for converting to numeric names. e.g chr10 to 10.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eallele_key\u003c/strong\u003e: tab-delimited text file with 5 columns (no header). First column is snpID and following columns are: old_allele1 old_allele2 new_allele1 new_allele2.\n\u003cul\u003e\n\u003cli\u003eUsed for converting alleles with A/B specification to ACGT. Oftentimes provided in the dbGaP download. If alleles are already specified in ACGT format, this field can be set to \u0027none\u0027\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eID_key\u003c/strong\u003e: tab-delimited text file with 2 columns (no header). First column is old SNP ID and second column is new SNP ID.\n\u003cul\u003e\n\u003cli\u003eUsed for converting to rsID format. If SNP IDs are already in rs-format, this field can be set to \u0027none\u0027\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eflip_key\u003c/strong\u003e: text file with single column containing SNP rsIDs that need to be flipped in order to align strand to the hg19 reference genome.\n\u003cul\u003e\n\u003cli\u003eUsed to harmonize strand across datasets to the hg19 reference genome. Set this field to \u0027none\u0027 if all alleles are already on the same strand as the target reference genome.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEach of these fields are optional and providing \u0027none\u0027 as the entry will disable the steps associated with each key. However, these fields should only be set to \u0027none\u0027 if you are sure that they are not necessary (e.g. you have already fixed any existing strand issues across datasets).\u003c/p\u003e\n\u003cp\u003eExample of input specifications in the config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003equery:\n \"dataset1\":\n data: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n chrom_key: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n allele_key: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n ID_key: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n flip_key: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n \"dataset2\":\n data: \"PATH/TO/PLINK/PREFIX/FOR/DATASET2\"\n chrom_key: \"none\"\n allele_key: \"none\"\n ID_key: \"none\"\n flip_key: \"none\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePhenotypes of the samples must be specified by a tab-delimited text file where the first column contains the sample IDs (as they appear in the imputed VCF file) and the second column contains the phenotype. The path to this file can be provided in the field labelled \u0027phenotype_file\u0027 under the \u0027phenotype_data\u0027 field in the config.yml file.\u003c/p\u003e\n\u003cp\u003eSex of the samples must also be specified in a tab-delimited text file where the first column is sample ID and the second column is the sex specification according to PLINK. The path to this file can be provided in the field labelled \u0027sex_file\u0027 under the \u0027phenotype_data\u0027 field in the config.yml file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ephenotype_data: \n pheno_file: \"none\"\n sex_file: \"/path/to/sex/file\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h3\u003e\n\u003cp\u003eThe output is a set of PLINK files in the parent directory labelled with the value provided in the \u0027outname\u0027 entry of the config file. However, if \u0027merge\u0027 is set to \u0027false\u0027 in the config file, this final merge step is skipped, and the final output would be the set of QC\u0027ed plink files within each subdirectory labelled with the dataset names. Within each of these subdirectories, there will also be a set of VCF files, which are suitable for use in either the Michigan or TOPMed imputation servers.\u003c/p\u003e\n\u003cp\u003eThe other primary output is a PDF report containing a summary of various steps in the pipeline. It is \u003cstrong\u003ehighly recommended\u003c/strong\u003e that the user carefully review this report to confirm that everything seems in order. Particular attention should be paid to whether specific steps have resulted in major loss of markers as well as whether there is a positive correlation between allele frequencies in the 1000Genomes dataset and allele frequencies in each of the query datasets. These scatter plots are provided towards the end of the report, and if a substantial subset of the points exhibit an anti-correlation, this is indicative of a preponderance of strand errors that ought to be corrected (via the \u0027flip_key\u0027) prior to proceeding.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dataset-harmonization\" class=\"anchor\" aria-hidden=\"true\" href=\"#dataset-harmonization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDataset harmonization\u003c/h3\u003e\n\u003cp\u003eThe first step(s) in the pipeline aims to harmonize the naming of chromosomes, alleles, and variant IDs. This is accomplished via the 4 keys described above. While this pipeline generally attempts to simplify the QC process, it is extremely important that the user is acquainted well enough with each individual dataset to ensure that the appropriate keys are specified (or not specified).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reference-allele-fixing\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference-allele-fixing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference allele fixing\u003c/h3\u003e\n\u003cp\u003eIn contrast to a VCF, where alleles are specified with respect to a specified reference genome (reference versus alternative alleles), PLINK-formatted files often specify alleles as major/minor alleles based on the frequency in the dataset. Furthermore, many commonly used arrays will contain a mixture of SNPs genotyped on either the + or - strand. Lastly, the default behavior of PLINK is to automatically set the minor to A1 and the major allele to A2, which can unintentionally generate inconsistencies in allele specifications across datasets.\u003c/p\u003e\n\u003cp\u003eWith respect to a reference genome, two possible types of errors can occur:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFlipped strand: The genotype is specified with respect to the opposite strand relative to the reference genome.\u003c/li\u003e\n\u003cli\u003eSwapped allele: The genotype is specified on the same strand as the reference genome, but the A1 (minor) allele has been set to equal the \u0027reference\u0027 allele when it ought to be set to equal the non-reference/\u0027alternative\u0027 allele\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo identify these errors, we use the bcftools plugin \u0027+fixref\u0027, which requires not only the reference sequence (fasta) file, but also a VCF file containing variant sites that are used to identify mismatching alleles in the query dataset. Importantly, if the program determines that no strand issues exist and that the reference/alternative alleles have simply been swapped, then program will swap the major/minor alleles to match the reference. It will not perform any strand flipping, where it converts genotypes to be specified with respect to the nucleotide on the opposite strand. Although the program will attempt to identify these strand flips, it doesn\u0027t make the correction as the authors consider this a risky move that should not be handled in an automated fashion. Thus, flip-strand mismatches are ultimately removed. If there are a large number of these, the user should attempt to understand and resolve the source of the issue and rerun this pipeline.\u003c/p\u003e\n\u003cp\u003eBy default, the pipeline will download the following files for the hg19 reference genome:\u003c/p\u003e\n\u003cp\u003eReference fasta:\nftp://ftp.ncbi.nlm.nih.gov/1000genomes/ftp/technical/reference/human_g1k_v37.fasta.gz\u003c/p\u003e\n\u003cp\u003eReference VCF (1000Genomes):\nftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/VCF/All_20170710.vcf.gz\u003c/p\u003e\n\u003cp\u003eAn indication of whether alleles are now specified correctly is to plot frequency of an allele in the query population against the frequency in the reference population and look for an obviously positive correlation. Such plots are automatically produced in the PDF report as the final step in the pipeline.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basic-qc\" class=\"anchor\" aria-hidden=\"true\" href=\"#basic-qc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasic QC\u003c/h3\u003e\n\u003cp\u003eAfter alleles have been fixed as described above, a series of basic QC steps are performed on each dataset by the script \u003cem\u003e\u0027scripts/QC.py\u0027\u003c/em\u003e, with the filtering thresholds specified in the config file (see below).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eperform_QC: \u0027true\u0027\nQC:\n vm1: \"0.2\" # Initial variant missingness filter\n gm: \"0.1\" # Individual missingness filter\n vm2: \"0.05\" # Ultimate call rate for variants after removing low-callrate samples\n maf: \"0.01\" # mimimum Minor allele frequency\n hwe: \"0.0000001\" # p-value threshold for whether site follows hardy-weinberg\n mbs: \"0.0000001\" # p-value treshold for test of whether missingness varies by sex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe first wish to identify and remove individual samples that show high missingess across markers (specified by \u0027gm\u0027). However, to identify these individuals, we first need to remove variants that imputed poorly across all individuals (specified by \u0027vm1\u0027). After removing these individuals, we then remove variants with high missingness (specified by \u0027vm2\u0027). Since poor imputation will result in missing genotypes, this missingness filter indirectly filters for low quality imputation sites. Variants are also filtered based whether or not they show significant departures from Hardy-Weinberg Equilibrium (\u0027hwe\u0027 entry) and whether there is a significant association between missingness and sex (\u0027mbs\u0027 entry). We also remove rare variants based on the \u0027maf\u0027 value. Lastly, we remove indels, duplicate SNPs, and multi-allelic variants.\u003c/p\u003e\n\u003cp\u003eNote that testing for missigness by case/control status is generally recommended as well if the user wishes to proceed straight to SNP-based analyses such as GWAS. However, if the data is to be used for ancestry inference, it may make more sense to retain these SNPs.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-merging-inputs-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#merging-inputs-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMerging inputs (Optional)\u003c/h3\u003e\n\u003cp\u003eIf multiple input datasets were provided, an optional final step is to create a single merged dataset consisting of only the sites that overlap (i.e. passed filters) across all component datasets. This behavior is controlled by the \u0027merge\u0027 entry in the config file. To enable the merging behavior, set this to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emerge: \u0027true\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-imputaton-preparation\" class=\"anchor\" aria-hidden=\"true\" href=\"#imputaton-preparation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImputaton preparation\u003c/h3\u003e\n\u003cp\u003eAnother optional, final feature is to create a set of of VCF files (parsed by chromosome) for each of the input datasets. These VCFs can be used directly as input into either the Michigan Imputation Server or the TOPMed Imputation Server. The output of the imputation servers can then be used as input into the post-imputation QC pipeline (see README.md in the \u0027postImpute\u0027 directory).\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "machine-learning", - "recommendation-engine" - ], - "updated_at": 1560613253.0 + "topics": [], + "updated_at": 1617574369.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for getorganelle (https://github.com/Kinggerm/GetOrganelle)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.v1.6.2e" ], - "full_name": "2lambda123/amazon-dsstne", + "full_name": "powerPlant/getorganelle-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAmazon DSSTNE: Deep Scalable Sparse Tensor Network Engine\u003c/h1\u003e\u003ca id=\"user-content-amazon-dsstne-deep-scalable-sparse-tensor-network-engine\" class=\"anchor\" aria-label=\"Permalink: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine\" href=\"#amazon-dsstne-deep-scalable-sparse-tensor-network-engine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDSSTNE (pronounced \"Destiny\") is an open source software library for training and deploying recommendation\nmodels with sparse inputs, fully connected hidden layers, and sparse outputs. Models with weight matrices\nthat are too large for a single GPU can still be trained on a single host. DSSTNE has been used at Amazon\nto generate personalized product recommendations for our customers at Amazon\u0027s scale. It is designed for\nproduction deployment of real-world applications which need to emphasize speed and scale over experimental\nflexibility.\u003c/p\u003e\n\u003cp\u003eDSSTNE was built with a number of features for production recommendation workloads:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-GPU Scale\u003c/strong\u003e: Training and prediction\nboth scale out to use multiple GPUs, spreading out computation\nand storage in a model-parallel fashion for each layer.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLarge Layers\u003c/strong\u003e: Model-parallel scaling enables larger networks than\nare possible with a single GPU.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSparse Data\u003c/strong\u003e: DSSTNE is optimized for fast performance on sparse datasets, common in recommendation\nproblems. Custom GPU kernels perform sparse computation on the GPU, without filling in lots of zeroes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBenchmarks\u003c/h2\u003e\u003ca id=\"user-content-benchmarks\" class=\"anchor\" aria-label=\"Permalink: Benchmarks\" href=\"#benchmarks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003escottlegrand@ reported [near-linear scaling with multiple GPUs] on the MovieLens recommendation problem\n(\u003ca href=\"https://medium.com/@scottlegrand/first-dsstne-benchmarks-tldr-almost-15x-faster-than-tensorflow-393dbeb80c0f#.ghe74fu1q\" rel=\"nofollow\"\u003ehttps://medium.com/@scottlegrand/first-dsstne-benchmarks-tldr-almost-15x-faster-than-tensorflow-393dbeb80c0f#.ghe74fu1q\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eDirections on how to run a benchmark can be found in \u003ca href=\"benchmarks/Benchmark.md\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScaling up\u003c/h2\u003e\u003ca id=\"user-content-scaling-up\" class=\"anchor\" aria-label=\"Permalink: Scaling up\" href=\"#scaling-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://blogs.aws.amazon.com/bigdata/post/TxGEL8IJ0CAXTK/Generating-Recommendations-at-Amazon-Scale-with-Apache-Spark-and-Amazon-DSSTNE\" rel=\"nofollow\"\u003eUsing Spark in AWS EMR and Dockers in AWS ECS \u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"LICENSE\"\u003eLicense\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFollow \u003ca href=\"docs/getting_started/setup.md\"\u003eSetup\u003c/a\u003e for step by step instructions on installing and setting up DSSTNE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUser Guide\u003c/h2\u003e\u003ca id=\"user-content-user-guide\" class=\"anchor\" aria-label=\"Permalink: User Guide\" href=\"#user-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCheck \u003ca href=\"docs/getting_started/userguide.md\"\u003eUser Guide\u003c/a\u003e for detailed information about the features in DSSTNE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCheck \u003ca href=\"docs/getting_started/examples.md\"\u003eExamples\u003c/a\u003e to start trying your first Neural Network Modeling using DSSTNE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQ\u0026amp;A\u003c/h2\u003e\u003ca id=\"user-content-qa\" class=\"anchor\" aria-label=\"Permalink: Q\u0026amp;A\" href=\"#qa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"FAQ.md\"\u003eFAQ\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for the GetOrganelle toolkit to assembly organelle genomes from genome skimming data\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1681407189.0 + "updated_at": 1579837325.0 }, { "data_format": 2, - "description": "braker container generated by Nathan Weeks (USDA)", + "description": "Singularity recipe files for hapcol (https://github.com/AlgoLab/HapCol)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.97d4a5e" ], - "full_name": "HuffordLab-Containers/braker", + "full_name": "powerPlant/hapcol-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebraker\u003c/h1\u003e\u003ca id=\"user-content-braker\" class=\"anchor\" aria-label=\"Permalink: braker\" href=\"#braker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBraker container generated by \u003ca href=\"https://www.ars.usda.gov/people-locations/person?person-id=41062\" rel=\"nofollow\"\u003eNathan Weeks\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for the HapCol tool, a fast and memory-efficient method for haplotype assembly from long gapless reads\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1618511479.0 + "updated_at": 1579837367.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for sga (https://github.com/jts/sga)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.0.10.15" ], - "full_name": "baxpr/dticam", + "full_name": "powerPlant/sga-srf", "latest_release": null, - "readme": "\u003cp\u003edticam\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCamino\u003c/h2\u003e\u003ca id=\"user-content-camino\" class=\"anchor\" aria-label=\"Permalink: Camino\" href=\"#camino\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://camino.cs.ucl.ac.uk/\" rel=\"nofollow\"\u003ehttp://camino.cs.ucl.ac.uk/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eP. A. Cook, Y. Bai, S. Nedjati-Gilani, K. K. Seunarine, M. G. Hall, G. J. Parker, D. C. Alexander, \"Camino: Open-Source Diffusion-MRI Reconstruction and Processing\", International Society for Magnetic Resonance in Medicine, Seattle, WA, USA, p. 2759, May 2006\u003c/p\u003e\n\u003cp\u003eCiting Camino: \u003ca href=\"http://camino.cs.ucl.ac.uk/index.php?n=Main.Citations\" rel=\"nofollow\"\u003ehttp://camino.cs.ucl.ac.uk/index.php?n=Main.Citations\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3984\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the SGA tool, a de novo genome assembler based on the concept of string graphs\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1599669635.0 + "updated_at": 1579231330.0 }, { "data_format": 2, - "description": "A pagit singularity container", + "description": "Singularity recipe files for MrBayes (http://nbisweden.github.io/MrBayes)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.3.2.7a", + "Singularity.3.2.7a-gpu" ], - "full_name": "phgenomics-singularity/pagit", + "full_name": "powerPlant/mrbayes-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epagit\u003c/h1\u003e\u003ca id=\"user-content-pagit\" class=\"anchor\" aria-label=\"Permalink: pagit\" href=\"#pagit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA pagit singularity container\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3808\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the MrBayes program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary models\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1521839990.0 + "updated_at": 1574325488.0 }, { "data_format": 2, - "description": "sRNA phasing software singularity container", + "description": null, "filenames": [ - "Singularity" + "Singularity.0.1.0" ], - "full_name": "seb-mueller/singularity_srna_phasing", + "full_name": "arcsUVA/cryoCARE", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_srna_phasing\u003c/h1\u003e\u003ca id=\"user-content-singularity_srna_phasing\" class=\"anchor\" aria-label=\"Permalink: singularity_srna_phasing\" href=\"#singularity_srna_phasing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esRNA phasing software singularity container\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1577127401.0 + "updated_at": 1574198978.0 }, { "data_format": 2, - "description": "Using deep plant phenomics on PlantIT", + "description": "Setups for various images used on the dgx.", "filenames": [ - "Singularity" + "Singularity-PyTorch" ], - "full_name": "ChenHsieh/deepplantphenomics-PlantIT", + "full_name": "uri-ai-lab/singularity-images", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edeepplantphenomics-PlantIT\u003c/h1\u003e\u003ca id=\"user-content-deepplantphenomics-plantit\" class=\"anchor\" aria-label=\"Permalink: deepplantphenomics-PlantIT\" href=\"#deepplantphenomics-plantit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo is used to serve as a flow on \u003ca href=\"http://portnoy.cyverse.org/\" rel=\"nofollow\"\u003ePlantIT\u003c/a\u003e that can conduct the semantic section pre-trained model on \u003ca href=\"https://www.plant-phenotyping.org/datasets-home\" rel=\"nofollow\"\u003eIPPN dataset\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDeep plant phenomics is a python package developed and \u003ca href=\"https://www.frontiersin.org/articles/10.3389/fpls.2017.01190/full\" rel=\"nofollow\"\u003epublished by Ubbens and Stavness\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eA truncated version of deepplantphenomics python package is in the other_module dir for the usage of docker image building recipe.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eto do list\u003c/h2\u003e\u003ca id=\"user-content-to-do-list\" class=\"anchor\" aria-label=\"Permalink: to do list\" href=\"#to-do-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e[] check how to correctly save the output\n[] build singularity image\n[] prepare other scripts\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esemantic section example USAGE\u003c/h2\u003e\u003ca id=\"user-content-semantic-section-example-usage\" class=\"anchor\" aria-label=\"Permalink: semantic section example USAGE\" href=\"#semantic-section-example-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAny image in the specified dir with suffix \u0027_rgb.png\u0027 can be read in the model.\u003c/li\u003e\n\u003cli\u003eUse -p to specify the input path dir. The -ft will be removed.\u003c/li\u003e\n\u003cli\u003eLocal testing command (script correctly run but cannot obtain output files):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/to/image/:/home/ docker://bendjamin101001/plantit_dpp:v1 python3 semantic_section_example.py -p /home/ -ft jpg\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-images\u003c/h1\u003e\n\u003cp\u003eSetups for various images used on the dgx.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1604511244.0 + "updated_at": 1573772605.0 }, { "data_format": 2, - "description": "Singularity image for pandoc", + "description": "Singularity container for https://github.com/revbayes/revbayes", "filenames": [ "Singularity" ], - "full_name": "golamshaifullah/SingularityPandoc", + "full_name": "ResearchIT/revbayes-singularity", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1569936107.0 + "updated_at": 1589324901.0 }, { "data_format": 2, - "description": "Utility to prepare dicoms for conversion using BIDSKIT (https://github.com/jmtyszka/bidskit) ", + "description": "Singularity recipe files for plink2 (https://www.cog-genomics.org/plink/2.0/)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.v2.00a2LM" ], - "full_name": "chidiugonna/nklab-neuro-utils", + "full_name": "powerPlant/plink2-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enklab-neuro-utils\u003c/h1\u003e\u003ca id=\"user-content-nklab-neuro-utils\" class=\"anchor\" aria-label=\"Permalink: nklab-neuro-utils\" href=\"#nklab-neuro-utils\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA number of utilities for data management. Will be updated as time goes by.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e./src/nklab-bids-convert.py\u003c/code\u003e utility to stage dicoms for conversion by \u003ccode\u003ebidskit\u003c/code\u003e (\u003ca href=\"https://github.com/jmtyszka/bidskit\"\u003ehttps://github.com/jmtyszka/bidskit\u003c/a\u003e) into BIDS format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003enklab-bids-convert.py\u003c/h2\u003e\u003ca id=\"user-content-nklab-bids-convertpy\" class=\"anchor\" aria-label=\"Permalink: nklab-bids-convert.py\" href=\"#nklab-bids-convertpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis utility will walk down the hierarchy of dicom data (\u003ccode\u003edicomdir\u003c/code\u003e) and will copy it to a new directory (\u003ccode\u003estagedir\u003c/code\u003e) in the required format for Bidskit to convert into BIDS format. It is important that the dicom data is contained within one folder for each subject. If the data has been collected in multiple sessions then the parameter \u003ccode\u003e--sessions\u003c/code\u003e can be used to prompt the tool to cluster (K-means) the dicoms based on the acquired datetime. For example \u003ccode\u003e--sessions pre post\u003c/code\u003e would copy the dicom data into 2 sessions pre and post for bidskit. In some situations the acquired datetime may be incorrect and thus lead to incorrect clustering. An exceptions file \u003ccode\u003e--exceptionlist\u003c/code\u003e may then be provided to associate a misclassified dicom with one that has the correct datetime. See \u003ccode\u003e./example/exception.json\u003c/code\u003e for an example that associates the misclassified \u003ccode\u003e3SHELL_TENSOR\u003c/code\u003e with \u003ccode\u003e3SHELL_RPE\u003c/code\u003e . Note that the string values in the exception file are substrings of the actual dicom folder names that allow for unique identification. A frozen version of bidskit is also included with this repository which has been slightly adapted for our lab\u0027s needs. Please run the tool with the flag \u003ccode\u003e--stageonly\u003c/code\u003e to avoid running this version of bidskit and to just run the dicom preparation steps described above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Singularity Image\u003c/h2\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Build Singularity Image\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have singularity 2.4 or greater installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-utils\u003c/code\u003edirectory and check that you have a Singularity definition file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow simply build the image as \u003ccode\u003esudo singularity build nklab-neuro-utils.simg Singularity\u003c/code\u003e - note that the image name is assumed to be \u003ccode\u003enklab-neuro-utils.simg\u003c/code\u003e but this can be changed to a more convenient label.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild local Docker Image\u003c/h2\u003e\u003ca id=\"user-content-build-local-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build local Docker Image\" href=\"#build-local-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAt the moment the docker image is retrievable from docker hub using \u003ccode\u003edocker pull orbisys/nklab-neuro-utils\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild local Docker Image\u003c/h2\u003e\u003ca id=\"user-content-build-local-docker-image-1\" class=\"anchor\" aria-label=\"Permalink: Build local Docker Image\" href=\"#build-local-docker-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSimply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-utils\u003c/code\u003edirectory and check that you have the Docker definition file \u003ccode\u003eDocker\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow simply build the image as \u003ccode\u003esudo docker build -t mylocaldockerimage Docker\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3722\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the PLINK association analysis toolset\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1545416478.0 + "updated_at": 1572401216.0 }, { "data_format": 2, - "description": "This program computes the cross entropy for groups of sequences that have been assigned to groups on the basis of biochemical, physiological, or other biological property. ", + "description": "hackathon_intel_genci", "filenames": [ - "1.0.0/Singularity" + "Sarek/Singularity", + "Sarek/ScLifeLab/Singularity" ], - "full_name": "pscedu/singularity-gent", - "latest_release": "v1.0.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-gent/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-gent/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/dcfd71e46b35d08572edfb976a2e1b7650a33efc6ac4d731076e5546b74f2717/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d67656e74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dcfd71e46b35d08572edfb976a2e1b7650a33efc6ac4d731076e5546b74f2717/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d67656e74\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-gent\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2d09491d6d44288f5675b1f9f2d4b45e4bdb400503a96a60ff8ea2b2bc1301ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d67656e74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2d09491d6d44288f5675b1f9f2d4b45e4bdb400503a96a60ff8ea2b2bc1301ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d67656e74\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-gent\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/29113b835989f2bb185f6bdc85ce7f7acc824ba4d828617552974c7afbe674ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d67656e74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/29113b835989f2bb185f6bdc85ce7f7acc824ba4d828617552974c7afbe674ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d67656e74\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-gent\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f3dfac73d7955a4d00781ddc596da4ba22dd8ab32a636f6d31451b89188b4327/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d67656e74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f3dfac73d7955a4d00781ddc596da4ba22dd8ab32a636f6d31451b89188b4327/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d67656e74\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-gent\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-gent\u003c/h1\u003e\u003ca id=\"user-content-singularity-gent\" class=\"anchor\" aria-label=\"Permalink: singularity-gent\" href=\"#singularity-gent\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/icaoberg/gent\"\u003eGeNT\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "larosap/hackathon_intel_genci", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1703380680.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1573750055.0 }, { "data_format": 2, - "description": "Computes and tracks the accuracy of a mechanical watch", + "description": null, "filenames": [ - "Singularity" + "Singularity.1.026" ], - "full_name": "MatthewBonanni/Watch-Accuracy", + "full_name": "arcsUVA/patric", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWatch-Accuracy\u003c/h1\u003e\u003ca id=\"user-content-watch-accuracy\" class=\"anchor\" aria-label=\"Permalink: Watch-Accuracy\" href=\"#watch-accuracy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputes and tracks the accuracy of a mechanical watch\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1720245772.0 + "updated_at": 1570548606.0 }, { "data_format": 2, - "description": "Singularity Hub build recipe for a singularity container running R (based on https://github.com/nickjer/singularity-r).", + "description": "containers", "filenames": [ - "Singularity", - "Singularity.3.6.2" + "Singularity.py3_tfstable", + "Singularity.pyhon3" ], - "full_name": "gparadis/singularity-r", + "full_name": "LuisBonillaR/singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity R\u003c/h1\u003e\u003ca id=\"user-content-singularity-r\" class=\"anchor\" aria-label=\"Permalink: Singularity R\" href=\"#singularity-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nickjer/singularity-r\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3ac61eb7693a90c7386ae808085fe4f76602fa13f140b564dfc2ff5215deef9f/68747470733a2f2f7472617669732d63692e6f72672f6e69636b6a65722f73696e67756c61726974792d722e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nickjer/singularity-r.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/462\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7edda6b40df66cdf6d87ee014ce8a73af8830d12f325162978d3b72836ea332d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-r.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-r.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name singularity-r.simg shub://nickjer/singularity-r\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR\u003c/h3\u003e\u003ca id=\"user-content-r\" class=\"anchor\" aria-label=\"Permalink: R\" href=\"#r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eR\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-r.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app R singularity-r.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app R singularity-r.simg --version\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eR version 3.4.3 (2017-11-30) -- \"Kite-Eating Tree\"\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eCopyright (C) 2017 The R Foundation for Statistical Computing\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ePlatform: x86_64-pc-linux-gnu (64-bit)\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eR is free software and comes with ABSOLUTELY NO WARRANTY.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eYou are welcome to redistribute it under the terms of the\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGNU General Public License versions 2 or 3.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eFor more information about these matters see\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ehttp://www.gnu.org/licenses/.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRscript\u003c/h3\u003e\u003ca id=\"user-content-rscript\" class=\"anchor\" aria-label=\"Permalink: Rscript\" href=\"#rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eRscript\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app Rscript singularity-r.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app Rscript singularity-r.simg --version\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eR scripting front-end version 3.4.3 (2017-11-30)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-r\"\u003ehttps://github.com/nickjer/singularity-r\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1597276020.0 + "updated_at": 1610738330.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for paml (http://abacus.gene.ucl.ac.uk/software/paml.html)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.4.9i" ], - "full_name": "ikmb/assembly-bacteria", + "full_name": "powerPlant/paml-srf", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIKMB Genomes - Bacterial assembly and annotation from PE reads\u003c/h1\u003e\u003ca id=\"user-content-ikmb-genomes---bacterial-assembly-and-annotation-from-pe-reads\" class=\"anchor\" aria-label=\"Permalink: IKMB Genomes - Bacterial assembly and annotation from PE reads\" href=\"#ikmb-genomes---bacterial-assembly-and-annotation-from-pe-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline takes PE genomic reads, performs trimming using trim_galore, assembles draft genomes using Spades and annotates the assembly using Dfast_core.\u003c/p\u003e\n\u003cp\u003eIn addition, reads will (optionally) be mapped back to the genome sequence to obtain stats about mapping rate, insert size, duplication rates, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the pipeline\u003c/h2\u003e\u003ca id=\"user-content-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline\" href=\"#running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is currently configured to run on RZCluster. Other execute environment may be added in the future.\u003c/p\u003e\n\u003cp\u003eTo run the pipeline, you first have to clone the repository from git:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone git@git.ikmb.uni-kiel.de:bfx-core/NF-genomes-bacteria.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you already have a local copy, make sure it is up-to-date:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit pull\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWith a local installation of the pipeline, you can next proceed to run it in a folder where you would like to generate the various result files:\u003c/p\u003e\n\u003cp\u003eLoad the environment modules for Nextflow:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emodule load IKMB Java Nextflow\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNext, move to the folder where the pipeline will be run from:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd /path/to/run_folder\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFinally, run the pipeline by providing a path to the sample info:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow -c /path/to/nextflow.config run /path/to/main.nf --samples Samples.csv\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSample file\u003c/h2\u003e\u003ca id=\"user-content-sample-file\" class=\"anchor\" aria-label=\"Permalink: Sample file\" href=\"#sample-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe file Samples is a CSV formatted list of data sets to process. An example format is included in the template subfolder. Required information include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSample name\u003c/li\u003e\n\u003cli\u003eLibrary name\u003c/li\u003e\n\u003cli\u003ePath to forward read\u003c/li\u003e\n\u003cli\u003epath to reverse read\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResfinder\u003c/h2\u003e\u003ca id=\"user-content-resfinder\" class=\"anchor\" aria-label=\"Permalink: Resfinder\" href=\"#resfinder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Pipeline can optionally run the Resfinder program to detect the presence of typical resistance-confering genes. To enable the resfinder search, use the \u003ccode\u003e--resfinder\u003c/code\u003eflag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline generates all outputs in the folder \"outputs\". For each PE data set (a library), a folder will be created containing the assembly, the annotation and the re-mapped reads for computing statistics on coverage etc.\u003c/p\u003e\n\u003cp\u003eFurthermore, trimmed reads will be jointly placed in the folder \"trimgalore\". A compilation of statistics for the various analysis is included in the older MultiQC - for library-level analytics (library_multiqc.html) and the assembly, annotation etc (sample_multiqc.html).\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3399\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the PAML tool for phylogenetic analyses of DNA or protein sequences using maximum likelihood.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1695285476.0 + "updated_at": 1565742033.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "braker/Singularity.braker" + "Singularity.panoply" ], - "full_name": "aseetharam/containers", + "full_name": "ternaustralia/coesra-singularity-panoply", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainers\u003c/h1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-panoply\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-panoply\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-panoply\u003c/h1\u003e\n\u003cp\u003eHoang Nguyen\n25 July 2019\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1596396241.0 + "topics": [ + "coesra" + ], + "updated_at": 1610426866.0 }, { "data_format": 2, - "description": "Singularity recipe for hisat2", + "description": null, "filenames": [ - "Singularity" + "Singularity.jupyter" ], - "full_name": "ISU-HPC/hisat2", + "full_name": "ternaustralia/coesra-singularity-jupyter", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehisat2\u003c/h1\u003e\u003ca id=\"user-content-hisat2\" class=\"anchor\" aria-label=\"Permalink: hisat2\" href=\"#hisat2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for hisat2\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-jupyter\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1553692888.0 + "updated_at": 1610425229.0 }, { "data_format": 2, - "description": "Repo for singularity development and test", + "description": "Example of deployment of a Galaxy Production Instance using CVMFS with Ansible", "filenames": [ "Singularity" ], - "full_name": "mobidic/Dev-Singularity", + "full_name": "MiguelJulia/GCC2019_GalaxyAnsibleDeplyoment_CVMFS", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDev-Singularity\u003c/h1\u003e\u003ca id=\"user-content-dev-singularity\" class=\"anchor\" aria-label=\"Permalink: Dev-Singularity\" href=\"#dev-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRepo for singularity development and test\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-gcc2019_galaxyansibledeplyoment_cvmfs\" class=\"anchor\" aria-hidden=\"true\" href=\"#gcc2019_galaxyansibledeplyoment_cvmfs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGCC2019_GalaxyAnsibleDeplyoment_CVMFS\u003c/h1\u003e\n\u003cp\u003eExample of deployment of a Galaxy Production Instance using CVMFS with Ansible.\nFor more info, look into \u003ca href=\"https://galaxyproject.github.io/training-material/topics/admin/\" rel=\"nofollow\"\u003egalaxy admin training materials\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-deploying-a-galaxy-stance\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploying-a-galaxy-stance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploying a galaxy stance\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003eansible-playbook -i host cvmfs_playbook.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-restart-galaxy\" class=\"anchor\" aria-hidden=\"true\" href=\"#restart-galaxy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRestart galaxy\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003esudo su - galaxy\nsupervisorctl restart galaxy\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-variables-to-modify-for-quick-deployment\" class=\"anchor\" aria-hidden=\"true\" href=\"#variables-to-modify-for-quick-deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVariables to modify for quick deployment\u003c/h4\u003e\n\u003cp\u003eAdmin user name. This user is not created, still has to be registered the first time and it will automatically get admin permissions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egalaxy_config:\n galaxy:\n admin_users: admin@example.com\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBrand: Whatever appears on the banner\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egalaxy_config:\n galaxy:\n brand: \"Freiburg GCC\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-welcomehtml\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcomehtml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewelcome.html\u003c/h4\u003e\n\u003cp\u003eFrontpage is not created by default. You can find the template inside \u003ccode\u003egalaxy_root: /srv/galaxy\u003c/code\u003e, in \u003ccode\u003eserver/static/welcome.html.sample\u003c/code\u003e. Just create a \u003ccode\u003ewelcome.html\u003c/code\u003e page from this template in that same location and restart galaxy.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-deploying-your-ansible-managed-galaxy-into-a-container-not-working-yet\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploying-your-ansible-managed-galaxy-into-a-container-not-working-yet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploying your ansible-managed galaxy into a container (not working yet!)\u003c/h4\u003e\n\u003cp\u003eWe will use \u003ca href=\"https://github.com/ansible-community/ansible-bender\"\u003eansible-bender\u003c/a\u003e for this task. Your playbook will have to be adapted to this plugging standars as described in their documentation, or compare the differences between my cvmfs_playbook.yml and ansible-bender-test.yml to have a quick idea of how it has to be done.\u003c/p\u003e\n\u003cp\u003eMake sure you are running the right version of ansible, as ansible-bender only works with python3. Still, playbooks designed for python2 can still be used. You will also need to install \u003ca href=\"https://github.com/containers/buildah/blob/master/install.md\"\u003ebuildah\u003c/a\u003e and \u003ca href=\"https://github.com/containers/libpod/blob/master/install.md\"\u003epodman\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, you will need to configurate podman repo config file \u003ccode\u003e/etc/containers/registries.conf\u003c/code\u003e to tell it where to look for your containers. For example, to search in dokerhub add \u003ccode\u003e\u0027docker.io\u0027\u003c/code\u003e inside\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[registries.search]\nregistries = [\u0027docker.io\u0027]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe image is required to have python interpreter build in.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-galaxy-container-with-docker-idea---not-testet-yet\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-galaxy-container-with-docker-idea---not-testet-yet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding galaxy container with Docker (idea - not testet yet)\u003c/h4\u003e\n\u003cp\u003eUse galaxy-container \u003ca href=\"https://github.com/bgruening/docker-galaxy-stable/blob/master/galaxy/Dockerfile\"\u003eDockerfile\u003c/a\u003e as template.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 0, "topics": [], - "updated_at": 1528377792.0 + "updated_at": 1562583598.0 }, { "data_format": 2, - "description": null, + "description": "demo pipeline for testing different data chunking methods for MuTect2", "filenames": [ - "Singularity" + "containers/annovar-150617/Singularity.annovar-150617", + "containers/variant-calling-0.0.2/Singularity.variant-calling-0.0.2" ], - "full_name": "oogasawa/singularity_ubuntu1804", + "full_name": "stevekm/MuTect2_target_chunking", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUbuntu18 Singularity\u003c/h1\u003e\u003ca id=\"user-content-ubuntu18-singularity\" class=\"anchor\" aria-label=\"Permalink: Ubuntu18 Singularity\" href=\"#ubuntu18-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux 18.04 (Bionic) \u306eSingularity\u30b3\u30f3\u30c6\u30ca\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u524d\u63d0\u003c/h2\u003e\u003ca id=\"user-content-\u524d\u63d0\" class=\"anchor\" aria-label=\"Permalink: \u524d\u63d0\" href=\"#\u524d\u63d0\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux\u306bdebootstrap\u4ed6\u306e\u5fc5\u8981\u306a\u30bd\u30d5\u30c8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt upgrade\nsudo apt install build-essential libtool automake libarchive-dev debootstrap git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity\u306fversion 2.x\u7cfb\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\uff08Official Document)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u4f7f\u3044\u65b9\u003c/h2\u003e\u003ca id=\"user-content-\u4f7f\u3044\u65b9\" class=\"anchor\" aria-label=\"Permalink: \u4f7f\u3044\u65b9\" href=\"#\u4f7f\u3044\u65b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# \u30b3\u30f3\u30c6\u30ca\u306e\u30d3\u30eb\u30c9\ngit clone http://gitlab.ddbj.nig.ac.jp/oogasawa/singularity-ubuntu18\ncd singularity-ubuntu18\nmkdir $HOME/singularity-images\nsudo singularity build --sandbox $HOME/singularity-images/ubuntu18 Singularity\n\n# \u30b3\u30f3\u30c6\u30ca\u5185\u3067\u306e\u4f5c\u696d\nsudo singularity shell --write $HOME/singularity-images/ubuntu18\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mutect2-target-chunking\" class=\"anchor\" aria-hidden=\"true\" href=\"#mutect2-target-chunking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMuTect2 Target Chunking\u003c/h1\u003e\n\u003cp\u003eDemo pipeline for testing different data chunking methods for MuTect2.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://software.broadinstitute.org/gatk/documentation/tooldocs/3.8-0/org_broadinstitute_gatk_tools_walkers_cancer_m2_MuTect2.php\" rel=\"nofollow\"\u003eMuTect2\u003c/a\u003e is a common tool used for variant calling of tumor-normal pairs. However, it is limited to running only in single-threaded mode, which can lead to extremely long execution times.\u003c/p\u003e\n\u003cp\u003eThis demo pipeline uses different techniques to chunk the included list of target regions (\u003ccode\u003etargets.bed\u003c/code\u003e) into smaller segments to run in parallel, then aggregate all results for comparison to ensure that variant calls are the same across all chunking methods.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eThis pipeline comes pre-configured for usage on NYULMC\u0027s Big Purple HPC cluster using pre-built Singularity containers and pre-downloaded reference files.\u003c/p\u003e\n\u003cp\u003eIn order to use this pipeline on your system you will need to update the file paths saved in \u003ccode\u003enextflow.config\u003c/code\u003e for your system.\u003c/p\u003e\n\u003cp\u003eSingularity and Docker container recipes are included in the \u003ccode\u003econtainers\u003c/code\u003e directory.\u003c/p\u003e\n\u003cp\u003ePaths to input .bam files for tumor and normal samples are read from the file \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce correctly configured, the pipeline can be run with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1622297682.0 + "subscribers_count": 1, + "topics": [ + "nextflow", + "mutect2", + "variant-calling" + ], + "updated_at": 1562090008.0 }, { "data_format": 2, - "description": null, + "description": "singularity lc builds", "filenames": [ "Singularity" ], - "full_name": "oogasawa/singularity_ubuntu18_apache2", + "full_name": "iapalm/lc-builds", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUbuntu18 - Apache2 Singularity\u003c/h1\u003e\u003ca id=\"user-content-ubuntu18---apache2-singularity\" class=\"anchor\" aria-label=\"Permalink: Ubuntu18 - Apache2 Singularity\" href=\"#ubuntu18---apache2-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux 18.04 (Bionic) \u306bApache2\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fSingularity\u30b3\u30f3\u30c6\u30ca\u003c/p\u003e\n\u003cp\u003eApache2\u306f\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002\u003c/p\u003e\n\u003cp\u003e\u30dd\u30a4\u30f3\u30c8\u306fhttpd.conf\u3067\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3059\u308b\u3053\u3068\u3067\u3001\u6700\u7d42\u7684\u306broot\u6a29\u9650\u306a\u3057\u3067Apache2\u3092singularity\u30b3\u30f3\u30c6\u30ca\u3067\u8d77\u52d5\u3067\u304d\u308b\u3068\u3044\u3046\u3053\u3068\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u30dd\u30fc\u30c8\u756a\u53f7\u30921024\u3088\u308a\u5927\u304d\u304f\u3059\u308b\u3002\u003c/li\u003e\n\u003cli\u003eerror_log, access_log\u3092\u30e6\u30fc\u30b6\u30fc\u306e\u30db\u30fc\u30e0\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u66f8\u304d\u51fa\u3059\u3002\u003c/li\u003e\n\u003cli\u003ePidFile\u3092\u30e6\u30fc\u30b6\u30fc\u306e\u30db\u30fc\u30e0\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u66f8\u304d\u51fa\u3059\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u524d\u63d0\u003c/h2\u003e\u003ca id=\"user-content-\u524d\u63d0\" class=\"anchor\" aria-label=\"Permalink: \u524d\u63d0\" href=\"#\u524d\u63d0\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(1) Singularity version 2.x (2.6.0\u63a8\u5968)\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\uff08Official Document)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e(2) debootstrap\u3068git\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cp\u003eUbuntu Linux\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5834\u5408\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3059\u308b\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt upgrade\nsudo apt install debootstrap git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCentOS\u3067\u306f\u73fe\u6642\u70b9\u3067\u306f\u8a66\u3057\u3066\u3044\u306a\u3044\u304c\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8a18\u4e8b\u304c\u3042\u308b\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://linuxconfig.org/how-to-debootstrap-on-centos-linux\" rel=\"nofollow\"\u003ehttps://linuxconfig.org/how-to-debootstrap-on-centos-linux\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u4f7f\u3044\u65b9\u003c/h2\u003e\u003ca id=\"user-content-\u4f7f\u3044\u65b9\" class=\"anchor\" aria-label=\"Permalink: \u4f7f\u3044\u65b9\" href=\"#\u4f7f\u3044\u65b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# sandbox\u30b3\u30f3\u30c6\u30ca\u306e\u30d3\u30eb\u30c9\ngit clone http://gitlab.ddbj.nig.ac.jp/oogasawa/singularity-ubuntu18-apache2\ncd singularity-ubuntu18-apache2\nmkdir -f ~/singularity-images\nsudo singularity build --sandbox ~/singularity-images/ubuntu18-apache2 Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u540d \u003ccode\u003e~/singularity-images/\u003c/code\u003e, \u30a4\u30e1\u30fc\u30b8\u540dubuntu18-apache2\u306f\u304a\u597d\u307f\u3067\u5909\u3048\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# sandbox\u30b3\u30f3\u30c6\u30ca\u5185\u3067\u306e\u4f5c\u696d\nsudo singularity shell --writable ~/singularity-images/ubuntu18-apache2\n\n# simg\u30d5\u30a1\u30a4\u30eb\u306b\u56fa\u3081\u308b\u3002\nsudo singularity build ~/singularity-images/ubuntu18-apache2.simg ~/singularity-images/ubuntu18-apache2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eapache\u7528singularity\u30b3\u30f3\u30c6\u30ca\u306e\u8d77\u52d5\u3068\u505c\u6b62 (exec\u30b3\u30de\u30f3\u30c9\u306b\u3088\u308b)\u003c/h3\u003e\u003ca id=\"user-content-apache\u7528singularity\u30b3\u30f3\u30c6\u30ca\u306e\u8d77\u52d5\u3068\u505c\u6b62-exec\u30b3\u30de\u30f3\u30c9\u306b\u3088\u308b\" class=\"anchor\" aria-label=\"Permalink: apache\u7528singularity\u30b3\u30f3\u30c6\u30ca\u306e\u8d77\u52d5\u3068\u505c\u6b62 (exec\u30b3\u30de\u30f3\u30c9\u306b\u3088\u308b)\" href=\"#apache\u7528singularity\u30b3\u30f3\u30c6\u30ca\u306e\u8d77\u52d5\u3068\u505c\u6b62-exec\u30b3\u30de\u30f3\u30c9\u306b\u3088\u308b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# run your container as a service.\nsingularity instance.start ~/singulariy-images/ubuntu18-apache2.simg web1\n\n# start apache2 server\nsingularity exec instance://web1 apache_start.sh\n\n# stop apache2 server\nsingularity exec instance://web1 apache_stop.sh\n\n# instance.stop \nsingularity instance.start web1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/running_services.html\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/guides/2.6/user-guide/running_services.html\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1622297768.0 + "updated_at": 1560984106.0 }, { "data_format": 2, - "description": null, + "description": "Singularity images to run on the cluster", "filenames": [ - "Singularity" + "Singularity.py3_tf112_plus", + "Singularity.py3_tf114_lls", + "Singularity.py3_astro", + "Singularity.py3_tf112", + "Singularity.py3_tf115", + "Singularity.py3_tf114", + "Singularity.py3_tf113" ], - "full_name": "oogasawa/singularity_nodejs", + "full_name": "joaocaldeira/singularity_imgs", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_imgs\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_imgs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_imgs\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2968\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity images to run on the cluster\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1622297603.0 + "updated_at": 1590440777.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.v4.2.0" ], - "full_name": "CrossR/meng_project", + "full_name": "baxpr/fmriqa", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReinforcement Learning For Games\u003c/h1\u003e\u003ca id=\"user-content-reinforcement-learning-for-games\" class=\"anchor\" aria-label=\"Permalink: Reinforcement Learning For Games\" href=\"#reinforcement-learning-for-games\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall Instructions\u003c/h2\u003e\u003ca id=\"user-content-install-instructions\" class=\"anchor\" aria-label=\"Permalink: Install Instructions\" href=\"#install-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe way used throughout the project was via Singularity.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall Singularity - \u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/install-linux\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eThis must be done on a personal machine! Singularity needs \u003ccode\u003eroot\u003c/code\u003e\naccess for the initial build.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSwap to the repo folder.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003esudo singularity build starcraft.simg Singularity\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eThis is going to install everything, so takes a while.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCan be used either with:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esingularity shell -C starcraft.simg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esingularity exec starcraft.simg python run.py ${SCRIPT_ARGS}\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eIf running on a machine that has a GPU + CUDA, ensure to pass \u003ccode\u003e--nv\u003c/code\u003e\nafter \u003ccode\u003eshell\u003c/code\u003e or \u003ccode\u003eexec\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePoetry\u003c/h3\u003e\u003ca id=\"user-content-poetry\" class=\"anchor\" aria-label=\"Permalink: Poetry\" href=\"#poetry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnly used initially, so may not be fully working.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall SC2. If installed to a non-standard location set the \u003ccode\u003e$SC2PATH\u003c/code\u003e\nenvironment variable to point to the install location.\n\u003cul\u003e\n\u003cli\u003eAn example of the install and setting this variable is in the\n\u003ccode\u003eSingularity\u003c/code\u003e install file.\u003c/li\u003e\n\u003cli\u003eAlso install the maps, which are also listed in the \u003ccode\u003eSingularity\u003c/code\u003e file,\nalongside their password. They should be installed into \u003ccode\u003e$SC2PATH/Maps\u003c/code\u003e,\nwhere the maps folder may need making.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://github.com/sdispater/poetry\"\u003epoetry\u003c/a\u003e with \u003ccode\u003epip install poetry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOnce installed, use \u003ccode\u003epoetry install\u003c/code\u003e inside this repo. This will create a\nvirtualenv and install all needed packages into it.\u003c/li\u003e\n\u003cli\u003eThen you can call the scripts like so:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Basic example:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Map names can be gotten from the map file names that you just downloaded.\u003c/span\u003e\npoetry run python CNN/run.py --map_name MAP_NAME --model_name MODEL_NAME --training=False\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For example:\u003c/span\u003e\npoetry run python CNN/run.py --map_name MoveToBeacon --model_name TestModel --training=True\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To load that model back at a later date and continue training:\u003c/span\u003e\npoetry run python CNN/run.py --map_name MoveToBeacon --model_name TestModel --training=True --if_output_exists=continue\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To load that model back at a later date with no more training:\u003c/span\u003e\npoetry run python CNN/run.py --map_name MoveToBeacon --model_name TestModel --training=False\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To use that model in a secondary training phase:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This has not been tested, so its possible you\u0027ll need to instead run the initial model\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e with curriculum_num=0, since I don\u0027t remember how that code works.\u003c/span\u003e\npoetry run python CNN/run.py --map_name MoveToBeacon --model_name TestModel2 --training=True --curriculum_num=1 --previous_model=_files/models/TestModel\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis way is meant for PC dev work, so the TensorFlow version does not need a GPU.\nIf this is needed, then call \u003ccode\u003epoetry remove tensorflow\u003c/code\u003e and \u003ccode\u003epoetry add tensorflow-gpu\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eYou can also enter the poetry shell like so\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epoetry shell\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAt that point, calling \u003ccode\u003epython\u003c/code\u003e will use the project venv.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions to Run\u003c/h2\u003e\u003ca id=\"user-content-instructions-to-run\" class=\"anchor\" aria-label=\"Permalink: Instructions to Run\" href=\"#instructions-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn example script of running the CNN can be found in \u003ccode\u003eCNN\\runPySC2.sh\u003c/code\u003e, which\nuses Singularity. The paths in this script will need updating for a different\nuser.\u003c/p\u003e\n\u003cp\u003eTo run a specific pretrained CNN model, the script should be called as follows,\nwhere \u003ccode\u003eMAP_NAME\u003c/code\u003e is the map or mini-game in question and \u003ccode\u003eMODEL_NAME\u003c/code\u003e is the\nexact name of the model, as stored in \u003ccode\u003eCNN\\_files\\\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython run.py --map_name MAP_NAME --model_name MODEL_NAME --training=False\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe folders for the models should look as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCNN/_files/\n\u251c\u2500\u2500 models\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 test_model_20\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 checkpoint\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13000.data-00000-of-00001\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13000.index\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13000.meta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13500.data-00000-of-00001\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13500.index\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 model.ckpt-13500.meta\n\u2514\u2500\u2500 summaries\n \u2514\u2500\u2500 test_model_20\n \u2514\u2500\u2500 events.out.tfevents.1521839337.db12gpu1.arc3.leeds.ac.uk\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo train a new model, instead drop the \u003ccode\u003e--training=False\u003c/code\u003e. It is necessary to\nadd \u003ccode\u003e--if_output_exists=continue\u003c/code\u003e to continue training an already existing model.\nThe full set of runtime flags can be found in \u003ca href=\"CNN/run.py\"\u003erun.py\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFlags of interest are as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003etraining\u003c/code\u003e - If the model should be trained or not.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003evisualize\u003c/code\u003e - If the PyGame GUI should be shown. Useful for local running during testing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003en_envs\u003c/code\u003e - Number of games to run in parallel. \u003cstrong\u003eImportant\u003c/strong\u003e on a high powered machine to get the best performance.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esave_replays_every\u003c/code\u003e - How often a game replay should be saved, such that the training progress can be seen.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esave_permanently_every\u003c/code\u003e - Models are saved every few episodes, but are done in a rolling fashion. This flag is used to create models that will not be overwritten at any point\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecurriculum_num\u003c/code\u003e - What is the current curriculum number? Should be set for curriculum learning such that multiple models are loaded.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eprevious_model\u003c/code\u003e - Path to the previous model file, for curriculum learning.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enumber_episodes\u003c/code\u003e / \u003ccode\u003enumber_steps\u003c/code\u003e - The maximum episodes or steps to take before stopping. If either of these are met, will stop.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-functional-mri-qa-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#functional-mri-qa-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctional MRI QA pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eTest the matlab code before compiling: \u003ccode\u003esrc/testmatlab.m\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCompile: \u003ccode\u003ecompile_matlab.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTest the compiled runtime: \u003ccode\u003ebin/test_compiled_matlab.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuild the Singularity container: \u003ccode\u003eSingularity.v4.2.0\u003c/code\u003e, \u003ca href=\"https://www.singularity-hub.org/collections/2945\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/2945\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eSee \u003ccode\u003etest_sing_container.sh\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cp\u003eThe inputs must all be provided, in the correct order. Paths are with respect to the container root.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eName of the output directory\u003c/li\u003e\n\u003cli\u003eFilename of the T1 structural image (.nii.gz)\u003c/li\u003e\n\u003cli\u003eFilename of the segmented T1 image (.nii.gz), typically the SEG output of a MultiAtlas or SLANT pipeline\u003c/li\u003e\n\u003cli\u003eFilename of the 4D fMRI (.nii.gz)\u003c/li\u003e\n\u003cli\u003eXNAT project label\u003c/li\u003e\n\u003cli\u003eXNAT subject label\u003c/li\u003e\n\u003cli\u003eXNAT session label\u003c/li\u003e\n\u003cli\u003eXNAT scan label (of the fMRI)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-processing\" class=\"anchor\" aria-hidden=\"true\" href=\"#processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcessing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eMotion realignment and creation of mean fMRI\u003c/li\u003e\n\u003cli\u003eCoregister T1 to mean fMRI\u003c/li\u003e\n\u003cli\u003eCompute SNR and quality metrics\u003c/li\u003e\n\u003cli\u003eCarpet plots, graphical report\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003efmriqa.pdf PDF report\nrp_fmri.txt Realignment parameters (SPM12 style)\nfmriqa_stats.csv Summary stats\nfmriqa_stats_wide.csv Summary stats in wide format (XNAT/REDCap compatible)\nFD.txt Framewise displacement time series\nDVARS.txt DVARS time series\nglobal.txt Global mean time series\nmeanfmri.nii.gz Mean fMRI image after realignment\nmedian_voxel_displacement_mm.txt Framewise displacement, median over voxels\ntemporal_snr.nii.gz Temporal signal-to-noise ratio image\nvoxel_displacement_mm_95prctile.nii.gz Framewise displacement image (95th percentile over time)\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1561411617.0 + "updated_at": 1558037991.0 }, { "data_format": 2, - "description": "Singularity containers to run several python scripts", + "description": "Singularity recipe files for SqueezeMeta (https://github.com/jtamames/SqueezeMeta)", "filenames": [ "Singularity", - "Singularity.BladegeneratorQmethod", - "Singularity.InteractiveCoolProp" + "Singularity.1.0.0-beta", + "Singularity.0.4.4" ], - "full_name": "stephansmit/python_containers", + "full_name": "powerPlant/squeezemeta-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container for Python scripts\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-python-scripts\" class=\"anchor\" aria-label=\"Permalink: Singularity container for Python scripts\" href=\"#singularity-container-for-python-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Container\u003c/h2\u003e\u003ca id=\"user-content-build-container\" class=\"anchor\" aria-label=\"Permalink: Build Container\" href=\"#build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build python_containers.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull Image\u003c/h2\u003e\u003ca id=\"user-content-pull-image\" class=\"anchor\" aria-label=\"Permalink: Pull Image\" href=\"#pull-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/python_containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Image\u003c/h3\u003e\u003ca id=\"user-content-run-image\" class=\"anchor\" aria-label=\"Permalink: Run Image\" href=\"#run-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run python_containers test.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3414\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2930\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the SqueezeMeta fully automated metagenomics pipeline\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1569418419.0 + "updated_at": 1557458055.0 }, { "data_format": 2, - "description": "ul-fri-nlp-course-project-nhk created by GitHub Classroom", + "description": "Docker and Singularity images for Scanpy", "filenames": [ - "report/code/Singularity_py.def" + "Singularity" ], - "full_name": "UL-FRI-NLP-2023-2024/ul-fri-nlp-course-project-nhk", + "full_name": "VIB-CBD/scanpy-images", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNatural language processing course 2023/24: \u003ccode\u003eLLM Prompt Strategies for Commonsense-Reasoning Task\u003c/code\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-natural-language-processing-course-202324-llm-prompt-strategies-for-commonsense-reasoning-task\" class=\"anchor\" aria-label=\"Permalink: Natural language processing course 2023/24: LLM Prompt Strategies for Commonsense-Reasoning Task\" href=\"#natural-language-processing-course-202324-llm-prompt-strategies-for-commonsense-reasoning-task\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAuthors:\u003c/h3\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors:\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMatic Pristavnik Vre\u0161njak\u003c/li\u003e\n\u003cli\u003eMitja Kocjan\u010di\u010d\u003c/li\u003e\n\u003cli\u003eSongeun Hong\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdvisors:\u003c/h3\u003e\u003ca id=\"user-content-advisors\" class=\"anchor\" aria-label=\"Permalink: Advisors:\" href=\"#advisors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSlavko \u017ditnik\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject File Structure\u003c/h3\u003e\u003ca id=\"user-content-project-file-structure\" class=\"anchor\" aria-label=\"Permalink: Project File Structure\" href=\"#project-file-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eroot/\n\u2502\n\u2514\u2500\u2500 report/\n \u251c\u2500\u2500 code/ # Directory containing project code\n \u2502 \u251c\u2500\u2500 promt_COT.py.py\n \u2502 \u251c\u2500\u2500 few_shot.py\n \u2502 \u2514\u2500\u2500 ... # You can see the more files in the folder\n \u2502\n \u2514\u2500\u2500 Reports_compiled_submission/ # Directory containing PDF reports\n \u251c\u2500\u2500 submisson1_report.pdf\n \u251c\u2500\u2500 submisson2_report.pdf\n \u2514\u2500\u2500 submisson3_report.pdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOverview\u003c/h3\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project explores various prompting strategies to enhance the performance of Large Language Models (LLMs) on commonsense reasoning tasks. With the increasing use of LLMs in personal and commercial domains, developing effective prompts is crucial for generating relevant and informative responses. This study provides a comprehensive comparison of different prompting strategies to determine their effectiveness and applicability.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe surge in popularity of LLMs like ChatGPT, PaLM, and Gemini has led to their widespread use. These models, often based on the transformer architecture, are capable of handling tasks that require commonsense reasoning\u2014using everyday knowledge to solve problems. Several prompting strategies have been developed to improve LLM performance on these tasks.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrompting Strategies\u003c/h3\u003e\u003ca id=\"user-content-prompting-strategies\" class=\"anchor\" aria-label=\"Permalink: Prompting Strategies\" href=\"#prompting-strategies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eZero-shot\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelies on the model being sufficiently trained with minimal prompt modifications.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eExample:\u003c/em\u003e \"Classify the text into neutral, negative, or positive. Text: I think the vacation is okay. Sentiment:\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eFew-shot\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnhances the prompt by including examples of previously solved problems.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eExample:\u003c/em\u003e \"This is awesome! //Negative This is bad! //Positive Wow that movie was rad! //Positive What a horrible show! //\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eChain-of-thought\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCombines with few-shot, adding step-by-step reasoning to the examples.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eExample:\u003c/em\u003e \"The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1. A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False.\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMethodology\u003c/h3\u003e\u003ca id=\"user-content-methodology\" class=\"anchor\" aria-label=\"Permalink: Methodology\" href=\"#methodology\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eVarious models were evaluated, including Command (Cohere AI), Mistral (Mistral AI), LLAMA3 (Meta), and T5 (Google Research).\u003c/li\u003e\n\u003cli\u003ePerformance metrics included ROUGE, BLEU, and BERT Score.\u003c/li\u003e\n\u003cli\u003eDatasets used: Winograd Schema Challenge, MultiArith, SQuAD, among others.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConclusion\u003c/h3\u003e\u003ca id=\"user-content-conclusion\" class=\"anchor\" aria-label=\"Permalink: Conclusion\" href=\"#conclusion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project offers valuable insights into the effectiveness of different prompting strategies for improving LLM performance on commonsense reasoning tasks. The comprehensive comparison highlights the potential benefits and limitations of each strategy, guiding future research and practical implementations.\u003c/p\u003e\n", + "readme": "\u003cp\u003eDependency full Scanpy Docker and Scanpy images based on Alpine.\u003c/p\u003e\n\u003cp\u003eIncludes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLoompy\u003c/li\u003e\n\u003cli\u003eLouvain\u003c/li\u003e\n\u003cli\u003eigraph\u003c/li\u003e\n\u003cli\u003eipython\u003c/li\u003e\n\u003cli\u003eJupyter\u003c/li\u003e\n\u003cli\u003eCython\u003c/li\u003e\n\u003cli\u003eMulticoreTSNE\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1716587895.0 + "updated_at": 1556798801.0 }, { "data_format": 2, - "description": "Circos is a software package for visualizing data and information.", + "description": "Theano Singularity container scripts", "filenames": [ - "0.69-9/Singularity" + "Singularity.1.0.4-py36" ], - "full_name": "pscedu/singularity-circos", - "latest_release": "v0.69-9", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-circos/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-circos/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-circos/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-circos/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cdb759354edc534f7f5069578ef5dad67d8ec7d1242bf799e84570fd867204ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d636972636f73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cdb759354edc534f7f5069578ef5dad67d8ec7d1242bf799e84570fd867204ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d636972636f73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-circos\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c2ef2a0bed6acc8fe318bf27c8b6090c20d689daec5bb4dd2724224e5836357e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d636972636f73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c2ef2a0bed6acc8fe318bf27c8b6090c20d689daec5bb4dd2724224e5836357e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d636972636f73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-circos\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/025587ed22e26a065fbd6f9dc8981ddbb40ee487cfac9e2104c2a47796a93957/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d636972636f73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/025587ed22e26a065fbd6f9dc8981ddbb40ee487cfac9e2104c2a47796a93957/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d636972636f73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-circos\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5764b609b640af5998b1a3138590ba41649db0393f4be5b77cc884e7f45a962e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d636972636f73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5764b609b640af5998b1a3138590ba41649db0393f4be5b77cc884e7f45a962e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d636972636f73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-circos\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-circos\u003c/h1\u003e\u003ca id=\"user-content-singularity-circos\" class=\"anchor\" aria-label=\"Permalink: singularity-circos\" href=\"#singularity-circos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://circos.ca/\" rel=\"nofollow\"\u003ecircos\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ecircos\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/circos/0.69-9\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/circos\u003c/code\u003e as \u003ccode\u003e0.69-9.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCopy the data to \u003ccode\u003e/ocean\u003c/code\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-copy-the-data-to-ocean\" class=\"anchor\" aria-label=\"Permalink: Copy the data to /ocean\" href=\"#copy-the-data-to-ocean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ewget http://circos.ca/distribution/circos-current.tgz\nmkdir -p /ocean/datasets/community/genomics/circos\ntar -xvf circos-current.tgz \u0026amp;\u0026amp; rm -f circos-current.tgzmv -v circos-0.69-9/data /ocean/datasets/community/genomics/circos/\nrm -rfv circos-0.69-9\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "arcsUVA/theano", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-theano\" class=\"anchor\" aria-hidden=\"true\" href=\"#theano\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etheano\u003c/h1\u003e\n\u003cp\u003eTheano Singularity container scripts\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 5, - "topics": [ - "singularity", - "utlities" - ], - "updated_at": 1707362994.0 + "topics": [], + "updated_at": 1554499739.0 }, { "data_format": 2, - "description": "Singularity container for STACKS", + "description": null, "filenames": [ - "Singularity", - "v2Beta9/Singularity.v2.0Beta9", - "v2.0/Singularity.v2.0" + "Singularity" ], - "full_name": "phgenomics-singularity/stacks", + "full_name": "andquintero/singularity_builds", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003estacks\u003c/h1\u003e\u003ca id=\"user-content-stacks\" class=\"anchor\" aria-label=\"Permalink: stacks\" href=\"#stacks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container for STACKS\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_builds\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_builds\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1527118463.0 + "updated_at": 1554218133.0 }, { "data_format": 2, - "description": "testing container for singularity hub", + "description": "Batch Connect - OSC RStudio Server - Pitzer", "filenames": [ - "Singularity", - "subfolder/Singularity.apps", - "subfolder/Singularity.tacos" + "Singularity" ], - "full_name": "singularityhub/hello-world", - "latest_release": null, + "full_name": "OSC/bc_osc_rstudio_server_pitzer", + "latest_release": "v0.1.5", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-batch-connect---osc-rstudio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#batch-connect---osc-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch Connect - OSC RStudio Server\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bbf138f428dd98a7b779e572caebe1d8f6c369fb4f9ba270c27f4b29282e5530/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665725f7069747a65722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bbf138f428dd98a7b779e572caebe1d8f6c369fb4f9ba270c27f4b29282e5530/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665725f7069747a65722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_rstudio_server_pitzer.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an RStudio Server\nwithin an Pitzer batch job.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deprecated-application-warning\" class=\"anchor\" aria-hidden=\"true\" href=\"#deprecated-application-warning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeprecated application warning\u003c/h2\u003e\n\u003cp\u003eThis application no longer works. It raises an exception when users attempt to submit jobs.\nThis is because we now have functionality to submit to multiple clusters and\n\u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server\"\u003ethe generic application\u003c/a\u003e now submits\nto pitzer rendering this application useless.\u003c/p\u003e\n\u003cp\u003eFor historic versions, see the last released you can still view\n\u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server_pitzer/tree/v0.3.0\"\u003ev0.3.0\u003c/a\u003e as it was the last\nworking version of this application.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 6, "topics": [], - "updated_at": 1560109788.0 + "updated_at": 1673988953.0 }, { "data_format": 2, @@ -5421,333 +5089,362 @@ var data = "filenames": [ "Singularity" ], - "full_name": "oogasawa/singularity_latex", + "full_name": "chenhongluo/horovord", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-latex\u003c/h1\u003e\u003ca id=\"user-content-singularity-latex\" class=\"anchor\" aria-label=\"Permalink: singularity-latex\" href=\"#singularity-latex\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA singularity container of LaTeX typesetting system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Singularity image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/oogasawa/singularity-latex\ncd singularity-latex\nsudo singularity build singularity-latex.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCompile a LaTeX file. (a DVI file will be generated.)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec singularity-latex.sif platex doc.tex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGenerate a PDF file from a DVI file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec singularity-latex.sif dvipdfmx doc.dvi\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1622296093.0 + "updated_at": 1553181936.0 }, { "data_format": 2, - "description": "A Docker recipe for building a SNO+ environment for RAT. ", + "description": "Singularity recipe files for checkm (http://ecogenomics.github.io/CheckM)", "filenames": [ "Singularity", - "Singularity.old" - ], - "full_name": "snoplus/rat-container", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erat-container\u003c/h1\u003e\u003ca id=\"user-content-rat-container\" class=\"anchor\" aria-label=\"Permalink: rat-container\" href=\"#rat-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/snoplus/rat-container\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/99f4d06cdd8e2f776dcff3a325ee95b8a490476dcc0b026dfcb7390b143580c6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65726875622d626c7565\" alt=\"https://img.shields.io/badge/hosted-dockerhub-blue\" data-canonical-src=\"https://img.shields.io/badge/hosted-dockerhub-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity and Docker recipes to build a SNO+ environment for RAT.\u003c/p\u003e\n\u003cp\u003eFor regular usage, simply download the pre-built container with the following instructions for your container platform of choice. For advanced users, see the build instructions below.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIF THE DOCKERHUB LINK STOPS WORKING, SOMEONE MAY HAVE TO BUILD AND REUPLOAD THE CONTAINER TO DOCKERHUB DUE TO A CHANGE IN THEIR POLICY\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs of \u003cem\u003e\u003cstrong\u003eNovember 1, 2020\u003c/strong\u003e\u003c/em\u003e Docker is implementing an inactive image removal policy, meaning in a free account (which is where this container is hosted) if the container is not \u003cem\u003e\u003cstrong\u003eupdated or pulled for 6 consecutive months\u003c/strong\u003e\u003c/em\u003e it will be \u003cem\u003e\u003cstrong\u003edeleted\u003c/strong\u003e\u003c/em\u003e. This isn\u0027t a huge issue, someone will just have to do the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBuild the container manually from the image file in this repository according to the instructions below\u003c/li\u003e\n\u003cli\u003eUpload it to another Dockerhub repository\u003c/li\u003e\n\u003cli\u003eUpdate the download links that reference the Dockerhub location with the new location\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFEATURES\u003c/h1\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: FEATURES\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFull RAT-compatible environment, including ROOT5 (ROOT6 version now available), GEANT4 and scons\u003c/li\u003e\n\u003cli\u003eCan build any version of RAT\u003c/li\u003e\n\u003cli\u003eGUI output support on all operating systems\u003c/li\u003e\n\u003cli\u003eTensorFlow and CppFlow (CPU-only for the time being)\u003c/li\u003e\n\u003cli\u003eSingularity and Docker compatibility\u003c/li\u003e\n\u003cli\u003e*Cluster-compatible\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e*The image can be uploaded manually, pulled directly (if the cluster firewall permits) or run from /cvmfs; however, the cvmfs\nimage is not always up-to-date with the repo version. This has been \u003ca href=\"https://github.com/snoplus/rat-container/issues/8\"\u003eidentified as an issue\u003c/a\u003e with a possible solution posed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e[PLEASE READ]\u003c/h1\u003e\u003ca id=\"user-content-please-read\" class=\"anchor\" aria-label=\"Permalink: [PLEASE READ]\" href=\"#please-read\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity and Docker are similar tools but operate slightly differently. Singularity acts more like an overlay, where\nyou have access to your filesystem as you would \u003cstrong\u003eoutside\u003c/strong\u003e the container (with the same rights as you\u0027d have outside),\nwhereas Docker provides you with an isolated virtual filesystem (meaning you \u003cstrong\u003ecan\u0027t\u003c/strong\u003e access your files from outside\nthe container). In summary, it is best to \u003cstrong\u003emount\u003c/strong\u003e whatever directories you may need when running the container, whether\nin Docker or Singularity (see the section \"\u003cstrong\u003eTo write/execute files from directories outside of RAT/launch\ndirectory\u003c/strong\u003e\" below).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRegardless of whether you download or build the container, you can use and develop RAT as you see fit as it is external\nto the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstructions to install Singularity can be found \u003ca href=\"https://github.com/sylabs/singularity/blob/master/INSTALL.md\"\u003ehere.\u003c/a\u003e For\nDocker, instructions for each platform can be found \u003ca href=\"https://docs.docker.com/install/#supported-platforms\" rel=\"nofollow\"\u003ehere.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eFor Singularity, version 3.2+ is required\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eFor Docker, version 19.0+ is required\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eAs the DIRAC system no longer supports SL6, there is no longer a need to maintain an SL6 version when pushing new RAT releases to cvmfs. Therefore, the only image offered here is based on SL7.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTo be clear, if you wish to use the prebuilt image, then you do NOT need to clone this repo; simply follow the\ninstructions below.\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNew Video Tutorial (slightly outdated - no longer necessary to source the setup-env.sh on startup)\u003c/h1\u003e\u003ca id=\"user-content-new-video-tutorial-slightly-outdated---no-longer-necessary-to-source-the-setup-envsh-on-startup\" class=\"anchor\" aria-label=\"Permalink: New Video Tutorial (slightly outdated - no longer necessary to source the setup-env.sh on startup)\" href=\"#new-video-tutorial-slightly-outdated---no-longer-necessary-to-source-the-setup-envsh-on-startup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.snolab.ca/snoplus/private/DocDB/0062/006281/001/RAT%20container%20tutorial.mp4\" rel=\"nofollow\"\u003eAvailable here (Requires SNO+ DocDB access)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo download the pre-built container\u003c/h1\u003e\u003ca id=\"user-content-to-download-the-pre-built-container\" class=\"anchor\" aria-label=\"Permalink: To download the pre-built container\" href=\"#to-download-the-pre-built-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eIf on a shared system/cluster\u003c/strong\u003e, Singularity should be available so use the following command to obtain the latest\nversion of the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name rat-container.sif docker://snoplus/rat-container:root5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe tag (in the above command, \u003ccode\u003eroot5\u003c/code\u003e) can be replaced with the desired tag.\u003c/p\u003e\n\u003cp\u003eEnsure that the Singularity version you are using is \u003cstrong\u003e\u22653.2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the moment, certain clusters (like Cedar) have firewall rules preventing access to SingularityHub. There is a version of\nthe image located at \u003ccode\u003e/cvmfs/snoplus.egi.eu/sl7/sw/containers/rat-container.sif\u003c/code\u003e but keep in mind that it may not always be\nthe latest version (this shouldn\u0027t matter if you are simply building/running RAT).\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eIf on your own local machine\u003c/strong\u003e, Docker should be used as it is easier to install.\nThe command to obtain the latest version of the container for Docker is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull snoplus/rat-container:root5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe tag (in the above command, \u003ccode\u003eroot5\u003c/code\u003e) can be replaced with the desired tag.\u003c/p\u003e\n\u003cp\u003eDocker doesn\u0027t actually create a file in your working directory in the same way that Singularity does; rather, it\ndownloads the image layers and adds an entry to your local \u003cstrong\u003eDocker registry\u003c/strong\u003e which can be viewed by going:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker images\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis difference doesn\u0027t have an effect on how the container is actually used.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions on how to use the container with RAT\u003c/h1\u003e\u003ca id=\"user-content-instructions-on-how-to-use-the-container-with-rat\" class=\"anchor\" aria-label=\"Permalink: Instructions on how to use the container with RAT\" href=\"#instructions-on-how-to-use-the-container-with-rat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTo build RAT for the first time\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eClone RAT from GitHub (\u003cstrong\u003eNOTE\u003c/strong\u003e - If on Windows, make sure you run \u003ccode\u003egit config --global core.autocrlf input\u003c/code\u003e prior to\ncloning or else Git will automatically change the Unix line-endings to Windows (which \u003cstrong\u003ewill break the next steps\u003c/strong\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnter the following command, filling in the path to RAT with your own.\nThis will mount your RAT repo to the directory \u003ccode\u003e/rat\u003c/code\u003e inside the container:\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eSingularity\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B path/to/rat:/rat rat-container.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cem\u003eDocker\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti --init --rm -v /absolute/path/to/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e - the \u003ccode\u003e-v\u003c/code\u003e flag operates the same as \u003ccode\u003e-B\u003c/code\u003e in Singularity BUT you \u003cstrong\u003emust\u003c/strong\u003e provide it with an absolute path (one starting at /);\nrelative paths (the path from where you are now) will \u003cstrong\u003enot\u003c/strong\u003e work.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOnce in the container, Singularity users need to run the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /home/scripts/setup-env.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn \u003cstrong\u003eDocker\u003c/strong\u003e this is \u003cstrong\u003eunnecessary\u003c/strong\u003e as Docker sources it automatically on launch.\nYou may see a message about how it could not find \u003ccode\u003e/rat/env.sh\u003c/code\u003e; this is expected as you have not built RAT yet.\nIf the build is successful, you shouldn\u0027t see this message next time.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFinally, run this command to build RAT:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esource /home/scripts/build-rat.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, \u003ccode\u003escons\u003c/code\u003e can manually be called while in the \u003ccode\u003e/rat\u003c/code\u003e folder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRAT is now ready to use! Look at the instructions below for how to run it\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo exit the container (Singularity and Docker)\u003c/strong\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexit\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo run RAT\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFirst, get a shell into the container with your RAT bound into it:\n(It is \u003cstrong\u003eimportant\u003c/strong\u003e to \u003cstrong\u003emount your rat directory to /rat\u003c/strong\u003e as the build scripts look there for it!)\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eSingularity\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B path/to/rat:/rat rat-container.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cem\u003eDocker\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti --init --rm -v /absolute/path/to/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRAT is now ready for use, and you should be able to access the RAT repo itself at \u003ccode\u003e/rat\u003c/code\u003e. To use other\ndirectories, additional bind mounts are necessary (see below).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo use GUI apps like ROOT\u0027s TBrowser\u003c/strong\u003e:\n(This is based on CERN\u0027s documentation for \u003ca href=\"https://hub.docker.com/r/rootproject/root-ubuntu16/\" rel=\"nofollow\"\u003erunning ROOT with graphics\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe process is different on each OS but I will outline steps here to make it work on each. Note that these instructions\nassume that since you are on your own machine, you are using \u003cstrong\u003eDocker\u003c/strong\u003e. Singularity may work with graphics as it is, but\nthese Docker solutions are the only ones that are tested and confirmed to be working.\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003eLinux\u003c/strong\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti --init --rm --user $(id -u) -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v /absolute/path/to/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs you can see, the difference is a few extra options. As the command has gotten so large, you can \u003ca href=\"https://askubuntu.com/a/17538\" rel=\"nofollow\"\u003eset an alias in your .bashrc\u003c/a\u003e to something much shorter and more convenient.\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003eWindows 10\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAs of the new May 2020 Windows update, the Windows Subsystem for Linux (WSL) version 2 is out. Docker desktop can be\nconfigured to use this which is the recommended way to run Docker on Windows. Ensure WSL2 is enabled in the Docker Desktop\nsettings, then follow these instructions:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload and install \u003ca href=\"https://sourceforge.net/projects/xming/\" rel=\"nofollow\"\u003eXming\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eWhen Windows prompts you to allow it in the firewall, do so.\u003c/li\u003e\n\u003cli\u003eFinally, restart Xming and now run the following command in Powershell or WSL2:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --init --rm -ti -e DISPLAY=host.docker.internal:0 -v /absolute/path/to/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cstrong\u003emacOS\u003c/strong\u003e:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ca href=\"https://www.xquartz.org/\" rel=\"nofollow\"\u003eXQuartz\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOpen XQuartz, and then go XQuartz -\u0026gt; Preferences -\u0026gt; Security, and tick the box \"Allow connections from network clients\"\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003exhost + 127.0.0.1\u003c/code\u003e which will whitelist your local IP\u003c/li\u003e\n\u003cli\u003eFinally, you can run the container with the following:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm --init -ti -v /tmp/.X11-unix:/tmp/.X11-unix -v /absolute/path/to/rat:/rat -e DISPLAY=host.docker.internal:0 snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(The order \u003ccode\u003e-ti\u003c/code\u003e instead of \u003ccode\u003e-it\u003c/code\u003e seems to only matter for MacOS)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo update RAT\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eOutside of the container, \u003ccode\u003ecd\u003c/code\u003e into your RAT repo, and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit pull origin master\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThen, run the container:\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eSingularity\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B path/to/rat:/rat rat-container.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cem\u003eDocker\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti --init -v \"$(pwd)\"/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the RAT directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /rat\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFinally, run the build script (\u003ccode\u003e/home/scripts/build-rat.sh\u003c/code\u003e) or \u003ccode\u003escons\u003c/code\u003e directly to rebuild RAT:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escons\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo write/execute files from directories outside of RAT/launch directory\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAdd additional bind mounts to your Singularity or Docker command\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eSingularity\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B path/to/rat:/rat,/other/path:/stuff rat-container.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cem\u003eDocker\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --init --rm -ti -v /absolute/path/to/rat:/rat -v /other/path:/stuff snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNow in the container, you have access to /other/path at /stuff\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo use a specific branch of RAT\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnsure you git checkout to the branch OUTSIDE the container to avoid issues, then run RAT like above\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo use TensorFlow/cppflow\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe libraries are already installed (tensorflow at /usr/local/lib, cppflow repo is at /home/software) and\nthe environment variables are set in the setup-env.sh script, so you should be able to just use it after sourcing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e[ADVANCED]\u003c/h1\u003e\u003ca id=\"user-content-advanced\" class=\"anchor\" aria-label=\"Permalink: [ADVANCED]\" href=\"#advanced\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo build the container\u003c/h1\u003e\u003ca id=\"user-content-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: To build the container\" href=\"#to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build, you must have \u003cstrong\u003eroot permissions\u003c/strong\u003e and \u003cstrong\u003eDocker installed on your machine\u003c/strong\u003e. Docker installation instructions can be found \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for each OS.\u003c/p\u003e\n\u003cp\u003eTo rebuild the container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate into either \u003ccode\u003e/ROOT5\u003c/code\u003e or \u003ccode\u003e/ROOT6\u003c/code\u003e depending on which you would like to build off of\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEdit \u003ccode\u003eDockerfile\u003c/code\u003e, which is the recipe on what you would like to put into your container\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOnce you are happy with your changes, navigate back to the root of the repository and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t YOUR_CONTAINER_TAG -f ROOT5/Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003eYOUR_CONTAINER_TAG\u003c/code\u003e is the name you would like to give to your container. Also, ensure you change \u003ccode\u003eROOT5\u003c/code\u003e to \u003ccode\u003eROOT6\u003c/code\u003e if using that version\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis will build your container with your tag name, which you can then use in the same way as in the above guide, but instead of\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run ... snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou will now run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run ... YOUR_TAG_NAME\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[OPTIONAL] If you would like to share or back up your container image, you can push it to Dockerhub. You can follow \u003ca href=\"https://docs.docker.com/docker-hub/repos/#pushing-a-docker-container-image-to-docker-hub\" rel=\"nofollow\"\u003ethe official documentation\u003c/a\u003e to learn how\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo run multiple RAT instances\u003c/h1\u003e\u003ca id=\"user-content-to-run-multiple-rat-instances\" class=\"anchor\" aria-label=\"Permalink: To run multiple RAT instances\" href=\"#to-run-multiple-rat-instances\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to use multiple RAT instances simultaneously, then all you have to do is run an instance of this container\nwith each version of RAT that you want; do NOT try mounting multiple RATs to the SAME instance as the image was\nnot configured for this.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo modify Geant4\u003c/h1\u003e\u003ca id=\"user-content-to-modify-geant4\" class=\"anchor\" aria-label=\"Permalink: To modify Geant4\" href=\"#to-modify-geant4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to edit Geant4 for any reason, you will have to modify the recipe file and make your changes accordingly, then\nrebuild the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eF.A.Q.\u003c/h1\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: F.A.Q.\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOn macOS I see \"docker: Error response from daemon: Mounts denied: The path ... is not shared from OS X and is not known to Docker.\"\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis happens because Docker only allows mounting from 4 locations by default to follow Apple\u0027s sandbox guidelines; these locations are:\n\u003cpre\u003e\u003ccode\u003e/Users\n/tmp\n/private\n/Volumes\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003eEnsure your RAT repository is stored in one of these locations (the easiest would be simply under \u003ccode\u003e/Users/[your username]/rat\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eI\u0027m seeing \"/usr/bin/bash: /usr/bin/bash: cannot execute binary file\" when I try to run the container\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis happens because you have \u003ccode\u003ebash\u003c/code\u003e at the end of your run command; in the new version, this is no longer necessary as it\nwill launch bash by itself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eI\u0027m seeing \"Error getting image manifest using url...\" when I try to pull the container\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis seems to happen on the clusters, most likely due to the firewall. Try pulling the container on your local machine,\nand transfer the image to your cluster with scp.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eI\u0027m seeing errors when running scons to rebuild RAT after updating to a new RAT release\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis happens when you use the GUI-enabled docker command (not the standard command) when launching the container to rebuild\nRAT. Please review the instructions for how to update RAT above for the correct way to update.\u003c/li\u003e\n\u003cli\u003eThis can also happen if you don\u0027t run \u003ccode\u003escons\u003c/code\u003e within the \u003ccode\u003e/rat\u003c/code\u003e directory as it won\u0027t be able to find the correct files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhen I try to open the TBrowser/another GUI app, it doesn\u0027t show\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis is a known issue, and happens for two reasons. If you are trying to use the Docker version on your own machine, Docker\ndoes not have access to the display by default so there is some configuration required.\u003c/li\u003e\n\u003cli\u003eThe other issue is if you are trying to do this on a cluster with the Singularity version, you will notice the same thing.\nBecause you are remotely connected, the display is not configured by default to also connect.\u003c/li\u003e\n\u003cli\u003eKnown methods for getting a GUI working are listed in a section above for each OS under Docker.\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 0, - "subscribers_count": 4, - "topics": [], - "updated_at": 1639774730.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "Singularity" + "Singularity.1.0.12", + "Singularity.1.0.13", + "Singularity.1.0.10", + "Singularity.1.0.8", + "Singularity.1.1.3", + "Singularity.1.0.11", + "Singularity.1.0.7" ], - "full_name": "photocyte/genometools_singularity", + "full_name": "powerPlant/checkm-srf", "latest_release": null, - "readme": "\u003cp\u003eSingularity file for \u003ca href=\"https://github.com/genometools/genometools\"\u003egenometools\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eImage building handled by singularity-hub.org\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://photocyte/genometools_singularity\nsingularity exec --cleanenv genometools_singularity_latest.sif gt -h\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2464\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the CheckM set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1591204765.0 + "updated_at": 1598504920.0 }, { "data_format": 2, - "description": "Singularity and Docker containers for day to day life", + "description": "Singularity recipe files for Pblat (http://icebert.github.io/pblat/)", "filenames": [ "Singularity", - "centos-from-sh/Singularity", - "centos-from-dh/Singularity", - "_xp/Singularity", - "centos-from-me/Singularity" + "Singularity.2.0", + "Singularity.2.1" ], - "full_name": "abhi18av/cli-tools", + "full_name": "powerPlant/pblat-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecli-tools\u003c/h1\u003e\u003ca id=\"user-content-cli-tools\" class=\"anchor\" aria-label=\"Permalink: cli-tools\" href=\"#cli-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/806\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity and Docker containers for day to day life\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/abhi18av/cli-tools/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/abhi18av/cli-tools/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInstall Datalad\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.singularity-hub.org/collections/667\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/667\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2380\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for Pblat, the parallelized blat with multi-threads support\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1522664549.0 + "updated_at": 1550562816.0 }, { "data_format": 2, - "description": "BBTools is a suite of fast, multithreaded bioinformatics tools designed for analysis of DNA and RNA sequence data.", + "description": null, "filenames": [ - "Singularity" + "Singularity", + "Singularity.3.6.3" ], - "full_name": "sghignone/BBTools", + "full_name": "tpall/singularity-stan", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBBTools\u003c/h1\u003e\u003ca id=\"user-content-bbtools\" class=\"anchor\" aria-label=\"Permalink: BBTools\" href=\"#bbtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4220\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBBTools is a suite of fast, multithreaded bioinformatics tools designed for analysis of DNA and RNA sequence data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRQCFilter2\u003c/h3\u003e\u003ca id=\"user-content-rqcfilter2\" class=\"anchor\" aria-label=\"Permalink: RQCFilter2\" href=\"#rqcfilter2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRQCFilter2 is a revised version of RQCFilter that uses a common path for all dependencies.\nThe dependencies are available at \u003ca href=\"http://portal.nersc.gov/dna/microbial/assembly/bushnell/RQCFilterData.tar\" rel=\"nofollow\"\u003ehttp://portal.nersc.gov/dna/microbial/assembly/bushnell/RQCFilterData.tar\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePerforms quality-trimming, artifact removal, linker-trimming, adapter trimming, and spike-in removal using BBDuk.\nPerforms human/cat/dog/mouse/microbe removal using BBMap.\nIt requires 40 GB RAM for mousecatdoghuman, but only 1GB or so without them.\u003c/p\u003e\n\u003cp\u003eUsage: rqcfilter2.sh in=\u0027\u0027 path=\u0027\u0027 rqcfilterdata=\u0027\u0027\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1616702144.0 + "updated_at": 1603529584.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for Deformetrica on Centos 7", "filenames": [ "Singularity" ], - "full_name": "darachm/singularity_dada2", + "full_name": "willgpaik/deformetrica_aci", "latest_release": null, - "readme": "\u003cp\u003eThis container is for providing \u003ccode\u003edada2\u003c/code\u003e for some bioinformatics pipelines.\u003c/p\u003e\n\u003cp\u003eThe primary reason for this is so that it flows nicely through SingularityHub\nfor Nextflow pipelines.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-deformetrica_aci\" class=\"anchor\" aria-hidden=\"true\" href=\"#deformetrica_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edeformetrica_aci\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for Deformetrica on Centos 7 for ACI-ICS clusters\u003c/p\u003e\n\u003cp\u003e2019/2/14\u003cbr\u003e\nAnaconda3 ver. 2018.12\u003cbr\u003e\nDeformetrica 4.1\u003cbr\u003e\nGUI can be used through EoD\u003c/p\u003e\n\u003cp\u003eCommands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; source activate deformetrica \n\u0026gt; deformetrica \nOr, \n\u0026gt; deformetrica gui\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e2020/9/21\u003cbr\u003e\nGPU support is added\u003cbr\u003e\nAnaconda, Python, and Deformetrica are updated\u003c/p\u003e\n\u003cp\u003e2020/10/9\u003cbr\u003e\nPyTorch and PyKeOps are added\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1546750956.0 + "updated_at": 1602342657.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image running R tidyverse + some other libraries", "filenames": [ - "Singularity" + "Singularity", + "Singularity.3.6.3" ], - "full_name": "cmaumet/nipype_tutorial", + "full_name": "tpall/singularity-tidyverse", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNipype Tutorial Notebooks\u003c/h1\u003e\u003ca id=\"user-content-nipype-tutorial-notebooks\" class=\"anchor\" aria-label=\"Permalink: Nipype Tutorial Notebooks\" href=\"#nipype-tutorial-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/miykael/nipype_tutorial/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/56c6ab80fc1ecc6792d64bd85b8e69e8b5992d1defa85e34eded3ab11381af28/68747470733a2f2f636972636c6563692e636f6d2f67682f6d69796b61656c2f6e69707970655f7475746f7269616c2e7376673f7374796c653d736869656c64\" alt=\"CircleCi\" data-canonical-src=\"https://circleci.com/gh/miykael/nipype_tutorial.svg?style=shield\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/issues/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/51ab5247214bfa948735d8c78ac468a02aa35e06a4cc7382d3f1c5e57406a802/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/pulls/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ccc0801d4de090929a683cd8f409c9e9f73d71b8d6b26c744b2192308ab5d092/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d70722f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub pull-requests\" data-canonical-src=\"https://img.shields.io/github/issues-pr/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://GitHub.com/miykael/nipype_tutorial/graphs/contributors/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f02cdc559a57384d13bb14ef068095850e66cfade8c0fa1acb2c454722106bb1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub contributors\" data-canonical-src=\"https://img.shields.io/github/contributors/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/commits/master\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df38a8439e58dc92bec26953fe15dbafbcae2edf29c51c8e054fc98eed3966ab/68747470733a2f2f6769746875622d62617369632d6261646765732e6865726f6b756170702e636f6d2f636f6d6d6974732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub Commits\" data-canonical-src=\"https://github-basic-badges.herokuapp.com/commits/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/archive/master.zip\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2829b238d51781fa9e31df4b384ba49370c5229d509e5e26cd7a96ce7d9178d9/68747470733a2f2f6769746875622d73697a652d62616467652e6865726f6b756170702e636f6d2f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub size\" data-canonical-src=\"https://github-size-badge.herokuapp.com/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/miykael/nipype_tutorial/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad2f8b06984b0f1d27d678f47c1f11518aa015202885ef2b9e812daa5c84743b/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d69796b61656c2f6e69707970655f7475746f7269616c2e7376673f6d61784167653d32353932303030\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/docker/pulls/miykael/nipype_tutorial.svg?maxAge=2592000\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://hits.dwyl.io/miykael/nipype_tutorial\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ab300fb01bfcec0b74484c2d85efe06524b363f0460c63fe55fe2e3c84bd9ae/687474703a2f2f686974732e6477796c2e696f2f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub HitCount\" data-canonical-src=\"http://hits.dwyl.io/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the Nipype Tutorial in Jupyter Notebook format. You can access the tutorial in two ways:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://miykael.github.io/nipype_tutorial/\" rel=\"nofollow\"\u003eNipype Tutorial Homepage\u003c/a\u003e: This website contains a static, read-only version of all the notebooks.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://miykael.github.io/nipype_tutorial/notebooks/introduction_docker.html\" rel=\"nofollow\"\u003eNipype Tutorial Docker Image\u003c/a\u003e: This guide explains how to use Docker to run the notebooks interactively on your own computer. The nipype tutorial docker image is the best interactive way to learn Nipype.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFeedback, Help \u0026amp; Support\u003c/h1\u003e\u003ca id=\"user-content-feedback-help--support\" class=\"anchor\" aria-label=\"Permalink: Feedback, Help \u0026amp; Support\" href=\"#feedback-help--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to help with this tutorial or have any questions, feel free to fork the repo of the \u003ca href=\"https://github.com/miykael/nipype_tutorial\"\u003eNotebooks\u003c/a\u003e or interact with other contributors on the slack channel \u003ca href=\"https://brainhack.slack.com/messages/nipype/\" rel=\"nofollow\"\u003ebrainhack.slack.com/messages/nipype/\u003c/a\u003e. If you have any questions or found a problem, open a new \u003ca href=\"https://github.com/miykael/nipype_tutorial/issues\"\u003eissue on github\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThanks and Acknowledgment\u003c/h1\u003e\u003ca id=\"user-content-thanks-and-acknowledgment\" class=\"anchor\" aria-label=\"Permalink: Thanks and Acknowledgment\" href=\"#thanks-and-acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA huge thanks to \u003ca href=\"https://github.com/mwaskom\"\u003eMichael Waskom\u003c/a\u003e, \u003ca href=\"https://github.com/oesteban\"\u003eOscar Esteban\u003c/a\u003e, \u003ca href=\"https://github.com/chrisfilo\"\u003eChris Gorgolewski\u003c/a\u003e and \u003ca href=\"https://github.com/satra\"\u003eSatrajit Ghosh\u003c/a\u003e for their input to this tutorial! And a huge thanks to \u003ca href=\"https://github.com/djarecka/\"\u003eDorota Jarecka\u003c/a\u003e who updated this tutorial to Python 3 and is helping me with keeping this tutorial updated and running!\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2366\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-tidyverse\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-tidyverse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity tidyverse\u003c/h2\u003e\n\u003cp\u003eThis will run R tidyverse + some other packages, like \u003cem\u003ehere\u003c/em\u003e, \u003cem\u003ereadxl\u003c/em\u003e, \u003cem\u003elubridate\u003c/em\u003e, \u003cem\u003ebookdown\u003c/em\u003e, etc.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1538064645.0 + "updated_at": 1608284812.0 }, { "data_format": 2, - "description": "transcritp assembly programs in one container", + "description": "Singularity recipe files for RaGOO (https://github.com/malonge/RaGOO)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.1.02", + "Singularity.1.01" ], - "full_name": "aseetharam/transcript-assemblers", + "full_name": "powerPlant/ragoo-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDirect Evidence Pipeline Container \u003ca href=\"https://singularity-hub.org/collections/4893\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-direct-evidence-pipeline-container-\" class=\"anchor\" aria-label=\"Permalink: Direct Evidence Pipeline Container \" href=\"#direct-evidence-pipeline-container-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis image contains all the tools required to run the direct evidence pipeline. For the complete pipeline workflow, see this \u003ca href=\"\"\u003epaste some link here\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull evidence.sif shub://aseetharam/transcript-assemblers\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a \u003ccode\u003eevidence.sif\u003c/code\u003e image, with the required programs pre-installed. All the programs required for evidence-based gene prediction are installed in this image and you can either run the pipeline by hand or use the Snakemake/Nextflow workflows to run them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisities\u003c/h3\u003e\u003ca id=\"user-content-prerequisities\" class=\"anchor\" aria-label=\"Permalink: Prerequisities\" href=\"#prerequisities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run this container you\u0027ll need \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach tool has their own usage, so please check individual tools and their requirements if you are running them by hand.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Transcript assemblers:\nsingularity run --cleanenv evidence.sif Trinity\nsingularity run --cleanenv evidence.sif class\nsingularity run --cleanenv evidence.sif strawberry\nsingularity run --cleanenv evidence.sif stringtie\nsingularity run --cleanenv evidence.sif cufflinks\n\n# ORF predictor\nsingularity run --cleanenv evidence.sif TransDecoder.LongOrfs\nsingularity run --cleanenv evidence.sif orfipy\n\n# Mikado-related\nsingularity run --cleanenv evidence.sif mikado\nsingularity run --cleanenv evidence.sif diamond\nsingularity run --cleanenv evidence.sif portcullis\nsingularity run --cleanenv evidence.sif samtools\nsingularity run --cleanenv evidence.sif prodigal\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment Variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ePATH\u003c/code\u003e Location for all the installed tools\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePYTHONDONTWRITEBYTECODE\u003c/code\u003e set to true\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTRINITY_HOME\u003c/code\u003e to utlilites to work\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample run\u003c/h2\u003e\u003ca id=\"user-content-example-run\" class=\"anchor\" aria-label=\"Permalink: Example run\" href=\"#example-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor running on your data:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewill update this section later\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eToDo\u003c/h2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: ToDo\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eAdd \u003ca href=\"https://github.com/NBISweden/AGAT\"\u003eAGAT\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd \u003ca href=\"\"\u003e\u003ccode\u003ephylostratR\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd \u003ca href=\"\"\u003eTESorter\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd \u003ca href=\"\"\u003eOrthoFinder\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd \u003ca href=\"\"\u003eBioAwk\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eArun Seetharam\u003c/strong\u003e - \u003cem\u003eauthor \u0026amp; maintainer\u003c/em\u003e - \u003ca href=\"\"\u003eWebPage\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee also the list of \u003ca href=\"https://github.com/your/repository/contributors\"\u003econtributors\u003c/a\u003e who\nparticipated in this project.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2341\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the RaGOO tool to order and orient genome assembly contigs via Minimap2 alignments to a reference genome\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1618590617.0 + "updated_at": 1550774761.0 }, { "data_format": 2, - "description": "This is a read-only mirror of the git repos at https://bioconductor.org", + "description": "Singularity recipe files for NovoGraph (https://github.com/NCBI-Hackathons/NovoGraph)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.1.0.0" ], - "full_name": "bioc/TSRchitect", + "full_name": "powerPlant/novograph-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTSRchitect: Promoter identification from diverse types of large-scale TSS profiling data\u003c/h1\u003e\u003ca id=\"user-content-tsrchitect-promoter-identification-from-diverse-types-of-large-scale-tss-profiling-data\" class=\"anchor\" aria-label=\"Permalink: TSRchitect: Promoter identification from diverse types of large-scale TSS profiling data\" href=\"#tsrchitect-promoter-identification-from-diverse-types-of-large-scale-tss-profiling-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe TSRchitect repository encompasses an \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e\npackage developed in the \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eBrendel Group\u003c/a\u003e for analyses\nof transcription start site data.\nThe code conforms to our \u003ca href=\"https://brendelgroup.github.io/\" rel=\"nofollow\"\u003eRAMOSE\u003c/a\u003e\nphilosophy: it generates \u003cstrong\u003ereproducible\u003c/strong\u003e, \u003cstrong\u003eaccurate\u003c/strong\u003e, and \u003cstrong\u003emeaningful\u003c/strong\u003e\nresults; it is \u003cstrong\u003eopen\u003c/strong\u003e (source) and designed to be \u003cstrong\u003escalable\u003c/strong\u003e and\n\u003cstrong\u003eeasy\u003c/strong\u003e to use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start \u003ca href=\"https://singularity-hub.org/collections/1204\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-quick-start-\" class=\"anchor\" aria-label=\"Permalink: Quick Start \" href=\"#quick-start-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInput to TSRchitect will be transcription profiling read alignment data in \u003ccode\u003ebam\u003c/code\u003e\nor \u003ccode\u003ebed\u003c/code\u003e format as well as the appropriate genome annotation (if\navailable).\nOutput consists of predicted Transcription Start Sites (TSS) and Transcription\nStart Regions (TSR) as well as statistics summarizing the distribution and\ncharacteristics of identified TSSs and TSRs.\u003c/p\u003e\n\u003cp\u003eAll the TSRchitect dependencies are encapsulated in a\n\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available from\n\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\nThus, once you know what you are doing, execution could be as simple as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name tsr.simg shub://BrendelGroup/TSRchitect\nsingularity exec tsr.simg R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich will bring up an \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e console with the\nTSRchitect library and all its prerequisites available.\nFor example, in that console, you should see\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eR version 3.5.3 (2019-03-11) -- \"Great Truth\"\n...\n\u0026gt; packageVersion(\"TSRchitect\")\n[1] \u00271.13.5\u0027\n\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRealistic Start\u003c/h2\u003e\u003ca id=\"user-content-realistic-start\" class=\"anchor\" aria-label=\"Permalink: Realistic Start\" href=\"#realistic-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the\n\u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\nOnce all preparatory steps are taken care of, see the \u003ca href=\"./demo/HOWTO.md\"\u003eHOWTO\u003c/a\u003e\ndocument for examples of how to load data into TSRchitect and predict and\ncharacterize promoters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ and References\u003c/h2\u003e\u003ca id=\"user-content-faq-and-references\" class=\"anchor\" aria-label=\"Permalink: FAQ and References\" href=\"#faq-and-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see\n\u003ca href=\"https://github.com/vpbrendel/TSRchitect/wiki/FAQ\"\u003eV. Brendel\u0027s TSRchitect FAQ\u003c/a\u003e\nfor usage examples and suggestions.\u003c/p\u003e\n\u003cp\u003eIf you find \u003cem\u003eTSRchitect\u003c/em\u003e useful, you may cite:\u003c/p\u003e\n\u003cp\u003eRaborn RT, Sridharan K, Brendel VP (2017)\n\u003cem\u003eTSRchitect: Promoter identification from large-scale TSS profiling data.\u003c/em\u003e\ndoi: 10.18129/B9.bioc.TSRchitect, \u003ca href=\"https://doi.org/doi:10.18129/B9.bioc.TSRchitect\" rel=\"nofollow\"\u003ehttps://doi.org/doi:10.18129/B9.bioc.TSRchitect\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOur own publications will be linked here in due course.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease direct all comments and suggestions to\n\u003ca href=\"mailto:vbrendel@indiana.edu\"\u003eVolker Brendel\u003c/a\u003e\nat \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eIndiana University\u003c/a\u003e and\n\u003ca href=\"mailto:rtraborn@asu.edu\"\u003eTaylor Raborn\u003c/a\u003e at his current address.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2342\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the NovoGraph tool to construct a genome graph representation of long-read-based de novo sequence assemblies\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1689261643.0 + "updated_at": 1550014659.0 }, { "data_format": 2, - "description": "Creates graphs from problem instance pddl inputs", + "description": "Singularity recipe files for SWAN (http://bitbucket.org/charade/swan)", "filenames": [ - "Singularity" + "Singularity.3516c2f" ], - "full_name": "JesseBrouw/GraphCreate", + "full_name": "powerPlant/swan-srf", "latest_release": null, - "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2354\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the SWAN tool for SV detection\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1671531108.0 + "updated_at": 1550114140.0 }, { "data_format": 2, - "description": "SE4HPC @POLIMI", + "description": "A thin Singularity image used as an alternative to Proot to wrap applications in an arbitrary file system.", "filenames": [ "Singularity" ], - "full_name": "fdallac/HPC-DevOps-exercise-part2", + "full_name": "OSC/centos7-launcher", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBrief description of CI/CD Github Actions Workflows\u003c/h1\u003e\u003ca id=\"user-content-brief-description-of-cicd-github-actions-workflows\" class=\"anchor\" aria-label=\"Permalink: Brief description of CI/CD Github Actions Workflows\" href=\"#brief-description-of-cicd-github-actions-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eauto-test.yaml\u003c/strong\u003e: Simple CI/CD workflow for building the executable (using CMake) and running the Unit Tests defined in \"test\\test_matrix_multiplication.cpp\"\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eauto-test-in-container.yaml\u003c/strong\u003e: CI/CD workflow for building a Docker image (from the Dockerfile in the repo) and push the image in Github Container Registry, generating a package and the artifact attestation. Then the Docker image is runned in order to launch Unit Tests, which are written in a test.log artifact (see an example in \"outputs\" folder)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eauto-test-container-on-cineca-cluster.yaml\u003c/strong\u003e: CI/CD workflow for building a Singularity container (from the Singularity file in the repo), deploy it into the CINECA cluster (Galileo100) and run the container. Output and error logs are then upload as artifacts (see examples in \"outputs\" folder)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject\" href=\"#se4hpcproject\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2 -- From build to release and manual job execution\u003c/h2\u003e\u003ca id=\"user-content-step-2----from-build-to-release-and-manual-job-execution\" class=\"anchor\" aria-label=\"Permalink: Step 2 -- From build to release and manual job execution\" href=\"#step-2----from-build-to-release-and-manual-job-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFocus now on the correct implementation of the matrix multiplication you\nfind in \u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e. This is a\nparallel implementation that uses MPI and reads the matrices to be\nmultiplied from two files, matrixA.txt and matrixB.txt. In these files\nthe first row contains the matrix dimensions (number of rows and\ncolumns), while the other rows contain the matrix itself.\u003c/p\u003e\n\u003cp\u003eYour task is to perform the following steps:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation\u003c/strong\u003e: Use the template available here\n\u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e to create your own\ngithub repository. Add to this repository the tests you have created in\nStep1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutomating the build, test and release processes\u003c/strong\u003e: Create a CI/CD\npipeline that, when someone pushes files in the repo, executes the\nbuilding and testing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContainerizing the application\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDefine a Singularity container descriptor for the matrix\nmultiplication program and push it in your repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtend the created action to create a container image from your\ndescription.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExecuting on the cluster\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a job.sh file to run your containerized application. Make\nsure that the standard output and error are mapped to txt files.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransfer on Galileo100 your job script and the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSubmit your job to the cluster and check whether it works correctly.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePush on your github repository your job.sh file and the files\nobtained from the execution of the matrix multiplication.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3 -- Automating a job submission with containerization\u003c/h2\u003e\u003ca id=\"user-content-step-3----automating-a-job-submission-with-containerization\" class=\"anchor\" aria-label=\"Permalink: Step 3 -- Automating a job submission with containerization\" href=\"#step-3----automating-a-job-submission-with-containerization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtend the action you have created at step 3 to automate completely the\nprocess from a push on the repository to the execution of the\ncontainerized software on SLURM. To do so, you will have to move your\ncontainer from the runner to the cluster. You can either use the scp\ncommand or you can publish your image on the Singularity registry and\nthen pull it from the cluster. Don\u0027t forget to handle your secrets\nproperly! You do not want to leave passwords and authentication tokens\nvisible to everybody, so you will use the \u003ca href=\"https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions?tool=cli\"\u003esecrets\nmechanism\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7-launcher\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos7-launcher\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7-launcher\u003c/h1\u003e\n\u003cp\u003eA Singularity image used wrap applications RStudio \u003ccode\u003erserver\u003c/code\u003e instances in an arbitrary file system for use with \u003ca href=\"http://openondemand.org/\" rel=\"nofollow\"\u003eOnDemand\u003c/a\u003e. Tested as compatible with Singularity 2.x and 3.x.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage:\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-2x\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-2x\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity 2.x\u003c/h3\u003e\n\u003cp\u003eTODO...\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-3x\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-3x\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity 3.x\u003c/h3\u003e\n\u003cp\u003eTODO...\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [], - "updated_at": 1717595406.0 + "updated_at": 1550176998.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for MAPGD (https://github.com/LynchLab/MAPGD)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.0.4.38-d3edee2" ], - "full_name": "kavonrtep/cenh3_chip_seq_pipeline", - "latest_release": "0.1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCHIP seq analysis pipeline\u003c/h1\u003e\u003ca id=\"user-content-chip-seq-analysis-pipeline\" class=\"anchor\" aria-label=\"Permalink: CHIP seq analysis pipeline\" href=\"#chip-seq-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is designed to analyze ChIP-seq data using a series of tools like epic2, Macs3 and deeptools. The pipeline is intended to be used for detection of broad peaks. The pipeline is encapsulated in a Singularity container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is required to use the container. Singularity can be installed using conda environment.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n singularity3 -c conda-forge \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esingularity\u0026gt;=3.6\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nconda activate singularity3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image (.sif file) can be downloaded from \u003ca href=\"https://github.com/kavonrtep/cenh3_chip_seq_pipeline/releases\"\u003ehttps://github.com/kavonrtep/cenh3_chip_seq_pipeline/releases\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInput files for the pipeline are provided in \u003ccode\u003econfig.yaml\u003c/code\u003e file. The file contains the following fields:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003egenome_fasta\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/path/to/genome.fasta\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003esamples\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003einput\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/path/to/input.fastq.gz\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003echip\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/path/to/chip.fastq.gz\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutput_dir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/path/to/output\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInput and ChIP could be \u003ccode\u003efastq\u003c/code\u003e or \u003ccode\u003efastq.gz\u003c/code\u003e files. To run the pipeline, execute the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /path/to/ -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e chipseq_pipeline.sif -c config.yaml -t 20\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eParameter \u003ccode\u003e-t\u003c/code\u003e specifies the number of threads to use. Singularity parameter \u003ccode\u003e-B\u003c/code\u003e is used to bind the input and output directories to the container. Without this parameter, the container will not be able to access the input and output files. File \u003ccode\u003econfig.yaml\u003c/code\u003e must be also in directory which is accessible to the container. In the example above this is the current directory \u003ccode\u003e$PWD\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf you have files in different directories, you can specify multiple \u003ccode\u003e-B\u003c/code\u003e parameters. For example if your \u003ccode\u003econfig.yaml\u003c/code\u003e is:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003egenome_fasta\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/mnt/data/genomes/genome.fasta\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003esamples\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003einput\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/mnt/data/fastq_reads/input.fastq.gz\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003echip\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/mnt/data/fastq_reads/chip.fastq.gz\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutput_dir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/mnt/data/outputs/output\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen you can use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /mnt/data/genomes \\\n-B /mnt/data/fastq_reads -B /mnt/data/outputs \\\n-B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e chipseq_pipeline.sif -c config.yaml -t 20\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr you can use the following command to bind the whole directory \u003ccode\u003e/mnt/data\u003c/code\u003e to the container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /mnt/data -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e chipseq_pipeline.sif -c config.yaml -t 20\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput structure\u003c/h2\u003e\u003ca id=\"user-content-output-structure\" class=\"anchor\" aria-label=\"Permalink: Output structure\" href=\"#output-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline will create the following files and folders in the output directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eoutput/\n\u251c\u2500\u2500 epic2/\n\u2502 \u251c\u2500\u2500 epic2_all.bs2000.csv\n\u2502 \u251c\u2500\u2500 epic2_all.default.csv\n\u2502 \u251c\u2500\u2500 epic2_unique.bs2000.csv\n\u2502 \u2514\u2500\u2500 epic2_unique.default.csv\n\u251c\u2500\u2500 macs3/\n\u2502 \u251c\u2500\u2500 macs3_all_peaks.broadPeak\n\u2502 \u251c\u2500\u2500 macs3_all_peaks.gappedPeak\n\u2502 \u251c\u2500\u2500 macs3_all_peaks.xls\n\u2502 \u251c\u2500\u2500 macs3_unique_peaks.broadPeak\n\u2502 \u251c\u2500\u2500 macs3_unique_peaks.gappedPeak\n\u2502 \u2514\u2500\u2500 macs3_unique_peaks.xls\n\u251c\u2500\u2500 mapped_reads/\n\u2502 \u251c\u2500\u2500 chip.all.bam\n\u2502 \u251c\u2500\u2500 chip.all.sorted.bam\n\u2502 \u251c\u2500\u2500 chip.all.sorted.bam.csi\n\u2502 \u251c\u2500\u2500 chip.unique.sorted.bam\n\u2502 \u251c\u2500\u2500 chip.unique.sorted.bam.csi\n\u2502 \u251c\u2500\u2500 input.all.bam\n\u2502 \u251c\u2500\u2500 input.all.sorted.bam\n\u2502 \u251c\u2500\u2500 input.all.sorted.bam.csi\n\u2502 \u251c\u2500\u2500 input.unique.sorted.bam\n\u2502 \u2514\u2500\u2500 input.unique.sorted.bam.csi\n\u251c\u2500\u2500 chip_vs_input_all.bs2000.bw \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from bamCompare, bin witdh 2000bp\u003c/span\u003e\n\u251c\u2500\u2500 chip_vs_input_all.bs200.bw \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from bamCompare, bin witdh 200bp\u003c/span\u003e\n\u251c\u2500\u2500 chip_vs_input_unique.bs2000.bw \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from bamCompare on uniquely mapped reads, bin with 2000bp\u003c/span\u003e\n\u251c\u2500\u2500 chip_vs_input_unique.bs200.bw \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from bamCompare on uniquely mapped reads, bin with 200bp\u003c/span\u003e\n\u251c\u2500\u2500 epic2_all.bs2000.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from epic2, bin width 2000bp\u003c/span\u003e\n\u251c\u2500\u2500 epic2_all.default.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from epic2, default bin width (200bp)\u003c/span\u003e\n\u251c\u2500\u2500 epic2_unique.bs2000.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from epic2 on uniquely mapped reads, bin width 2000bp\u003c/span\u003e\n\u251c\u2500\u2500 epic2_unique.default.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from epic2 on uniquely mapped reads, default bin width (200bp)\u003c/span\u003e\n\u251c\u2500\u2500 macs3_all_peaks.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from macs3, broadPeaks as bedgraph\u003c/span\u003e\n\u2514\u2500\u2500 macs3_unique_peaks.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from macs3 on uniquely mapped reads, broadPeaks as bedgraph\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container\u003c/h2\u003e\u003ca id=\"user-content-build-the-container\" class=\"anchor\" aria-label=\"Permalink: Build the container\" href=\"#build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the container, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSINGULARITY=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ewhich singularity\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\nsudo \u003cspan class=\"pl-smi\"\u003e$SINGULARITY\u003c/span\u003e build chipseq_pipeline.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "powerPlant/mapgd-srf", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2319\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the MAPGD series of related programs for the analysis of low coverage population genomic data or for the analysis of pooled data\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1719908278.0 + "updated_at": 1549853299.0 }, { "data_format": 2, - "description": "Nextflow pipeline to analyse gene expression in plants", + "description": "Singularity recipe files for DIAMOND (https://github.com/bbuchfink/diamond)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.v0.9.15", + "Singularity.v0.9.18", + "Singularity.v0.9.22", + "Singularity.v0.9.16", + "Singularity.v0.9.19", + "Singularity.v0.9.21", + "Singularity.v0.9.20", + "Singularity.v0.9.23", + "Singularity.v0.9.24", + "Singularity.v0.9.17" ], - "full_name": "lifebit-ai/Plant-RNASeq", + "full_name": "powerPlant/diamond-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/plant-rnaseq\u003c/h1\u003e\u003ca id=\"user-content-nf-coreplant-rnaseq\" class=\"anchor\" aria-label=\"Permalink: nf-core/plant-rnaseq\" href=\"#nf-coreplant-rnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNextflow pipeline to analyse gene expression in plants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/plant-rnaseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/08cebaf367d11339b060cd29e866226fea535bdb77cd3c6ea6d27626115e8723/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f706c616e742d726e617365712e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/plant-rnaseq.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b9d943c6da4bb0a73fe6bc476304001a1e93cbc8ccadf264da8fe47716ab6c53/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/plant-rnaseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/076f241f2cae896efc7d9ebd3bd36558690eeb6574cb81c81410fc61a47d4d9c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f706c616e742d726e617365712e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/plant-rnaseq.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/plant-rnaseq pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2322\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the DIAMOND Accelerated BLAST compatible local sequence aligner\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1550739422.0 + "updated_at": 1549857110.0 }, { "data_format": 2, - "description": "Utilities and models for Arabic Natural Language Processing.", + "description": "Singularity recipe files for pcl (https://github.com/PointCloudLibrary/pcl)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.1.9.1", + "Singularity.1.9.0", + "Singularity.1.8.1" ], - "full_name": "clu-ling/wanlp-2021", + "full_name": "powerPlant/pcl-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003earabic-nlp\u003c/h1\u003e\u003ca id=\"user-content-arabic-nlp\" class=\"anchor\" aria-label=\"Permalink: arabic-nlp\" href=\"#arabic-nlp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModels and utilities for Arabic NLP.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDevelopment\u003c/h1\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is organized as a Python module. We recommend using \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e for development.\u003c/p\u003e\n\u003cp\u003ePlease note if you need to install additional dependencies, you\u0027ll need to alter the \u003ccode\u003erequirements.txt\u003c/code\u003e and rebuild the docker image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -f Dockerfile -t \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re only using the existing dependencies, you can simply download the published Docker image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run code interactively in the iPython interpreter:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app parsertongue/arabic-nlp:latest ipython\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run code interactively in a Jupyter notebook, run the following command and open your browser to \u003ca href=\"http://localhost:8889\" rel=\"nofollow\"\u003elocalhost:8889\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app -p 8889:9999 parsertongue/arabic-nlp:latest launch-notebook\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest\u003c/h2\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTests are written by \u003ca href=\"https://docs.python.org/3.8/library/unittest.html#unittest.TestCase\" rel=\"nofollow\"\u003eextending the \u003ccode\u003eTestCase\u003c/code\u003e class\u003c/a\u003e from the \u003ccode\u003eunittest\u003c/code\u003e module in the Python standard library. All tests can be found in the \u003ca href=\"./tests\"\u003e\u003ccode\u003etests\u003c/code\u003e\u003c/a\u003e directory.\u003c/p\u003e\n\u003cp\u003eAll tests can be run using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e test-all\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest\u003c/h2\u003e\u003ca id=\"user-content-test-1\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTests are written by \u003ca href=\"https://docs.python.org/3.8/library/unittest.html#unittest.TestCase\" rel=\"nofollow\"\u003eextending the \u003ccode\u003eTestCase\u003c/code\u003e class\u003c/a\u003e from the \u003ccode\u003eunittest\u003c/code\u003e module in the Python standard library. All tests can be foun\nd in the \u003ca href=\"./tests\"\u003e\u003ccode\u003etests\u003c/code\u003e\u003c/a\u003e directory.\u003c/p\u003e\n\u003cp\u003eAll tests can be run using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e test-all\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnit tests\u003c/h3\u003e\u003ca id=\"user-content-unit-tests\" class=\"anchor\" aria-label=\"Permalink: Unit tests\" href=\"#unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run just the unit tests, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e green -vvv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eType hints\u003c/h3\u003e\u003ca id=\"user-content-type-hints\" class=\"anchor\" aria-label=\"Permalink: Type hints\" href=\"#type-hints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code makes use of Python type hints. To perform type checking, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e mypy --ignore-missing-imports --follow-imports=skip --strict-optional /app\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning an experiment\u003c/h3\u003e\u003ca id=\"user-content-running-an-experiment\" class=\"anchor\" aria-label=\"Permalink: Running an experiment\" href=\"#running-an-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe snippet below will run an experiment using \u003ca href=\"./tests/toy-data\"\u003etoy data\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app parsertongue/arabic-nlp:latest cdd-base-model --config /app/tests/toy-data/test-experiment-config.yml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a Singularity image from a published Docker image, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull cdd.sif docker://parsertongue/arabic-nlp:latest\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# HPC\u003c/span\u003e\n\nTo \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e the image on the UA HPC with GPU acceleration, first request an interactive session:\n\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ebash\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003eqsub -I \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-N interactive-gpu \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-W group_list=mygroupnamehere \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-q standard \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-l select=1:ncpus=2:mem=16gb:ngpus=1 \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-l cput=3:0:0 \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-l walltime=1:0:0\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe following modules are necessary:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load singularity\nmodule load cuda11\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNOTE: not all clusters have the \u003ccode\u003ecuda11\u003c/code\u003e module installed.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --nv --no-home /path/to/your.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo check that the GPU is found, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003envidia-smi\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, confirm that PyTorch can locate the GPU:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epython\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"import torch;print(f\u0027CUDA?:\u003cspan class=\"pl-cce\"\u003e\\t\u003c/span\u003e{torch.cuda.is_available()}\u0027);print(f\u0027GPU:\u003cspan class=\"pl-cce\"\u003e\\t\u003c/span\u003e{torch.cuda.get_device_name(0)}\u0027)\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLocal Singularity build\u003c/h1\u003e\u003ca id=\"user-content-local-singularity-build\" class=\"anchor\" aria-label=\"Permalink: Local Singularity build\" href=\"#local-singularity-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNOTE: These instructions assume you\u0027ve already installed Singularity (\u0026gt;= v3.7.0) locally on a Linux system.\u003c/p\u003e\n\u003cp\u003eFirst, we\u0027ll build a docker image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -f Dockerfile -t \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, we\u0027ll use the \u003ca href=\"https://sylabs.io/guides/3.7/user-guide/appendix.html#docker-daemon-archive\" rel=\"nofollow\"\u003e\u003ccode\u003edocker-daemon\u003c/code\u003e bootstrap agent\u003c/a\u003e to build a Singularity image from this docker image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build arabic-nlp.sif docker-daemon://parsertongue/arabic-nlp:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, we\u0027ll test that image works:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --nv arabic-nlp.sif train-arabert --help\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2329\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the pcl Point Cloud Library\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "arabic-nlp" - ], - "updated_at": 1697769625.0 + "topics": [], + "updated_at": 1550093426.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for crema (https://github.com/gbgolding/crema)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.fe4cf7a" ], - "full_name": "marchoeppner/wgs-calling", + "full_name": "powerPlant/crema-srf", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIKMB WGS variant calling pipeline\u003c/h1\u003e\u003ca id=\"user-content-ikmb-wgs-variant-calling--pipeline\" class=\"anchor\" aria-label=\"Permalink: IKMB WGS variant calling pipeline\" href=\"#ikmb-wgs-variant-calling--pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline generates human whole-genome variant calls from short read files.\u003c/p\u003e\n\u003cp\u003ePlease not that this code is not yet final!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation about the pipeline can be found in the \u003ccode\u003edocs/\u003c/code\u003e directory or under the links below:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/recommendations.md\"\u003eRecommendations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2320\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the crema tool to classify RNAs by Ensemble Machine learning Algorithms\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1564730498.0 + "updated_at": 1550200930.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for OpenDroneMap (https://www.opendronemap.org/)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.0.4.1", + "Singularity.0.4.0" ], - "full_name": "vrothenbergUCSD/Salk_LSD", + "full_name": "powerPlant/opendronemap-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCOGS 185 Advanced Machine Learning - Final Project\u003c/h1\u003e\u003ca id=\"user-content-cogs-185-advanced-machine-learning---final-project\" class=\"anchor\" aria-label=\"Permalink: COGS 185 Advanced Machine Learning - Final Project\" href=\"#cogs-185-advanced-machine-learning---final-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLocal Shape Descriptors\nNeuron segmentation task\u003c/p\u003e\n\u003cp\u003eThis is a forked repository of \u003ca href=\"https://github.com/funkelab/lsd_nm_experiments\"\u003ehttps://github.com/funkelab/lsd_nm_experiments\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLSD NM experiments tutorial\u003c/h1\u003e\u003ca id=\"user-content-lsd-nm-experiments-tutorial\" class=\"anchor\" aria-label=\"Permalink: LSD NM experiments tutorial\" href=\"#lsd-nm-experiments-tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGeneral notes\u003c/h2\u003e\u003ca id=\"user-content-general-notes\" class=\"anchor\" aria-label=\"Permalink: General notes\" href=\"#general-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA tutorial for running training/inference of networks used for paper (in\nsingularity containers).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTested on Ubuntu 18.04 with Quadro P6000 (24gb gpu ram)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssuming \u003ccode\u003esingularity\u003c/code\u003e is installed and setup\n(some tutorials \u003ca href=\"https://docs.sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and\n\u003ca href=\"https://singularity-tutorial.github.io/01-installation/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssuming conda is installed and setup (helpful \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e if needed)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/funkelab/lsd_nm_experiments.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCreate simple environment for fetching containers:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n test_env python=3.8\nconda activate test_env\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you just need \u003ca href=\"https://boto3.amazonaws.com/v1/documentation/api/latest/index.html\" rel=\"nofollow\"\u003eboto3\u003c/a\u003e for fetching containers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install boto3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you also want to view data with \u003ca href=\"https://github.com/google/neuroglancer\"\u003eneuroglancer\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install boto3 neuroglancer h5py zarr\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownloading container(s)\u003c/h2\u003e\u003ca id=\"user-content-downloading-containers\" class=\"anchor\" aria-label=\"Permalink: Downloading container(s)\" href=\"#downloading-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edownload_imgs.py\u003c/code\u003e will download the singularity container used in the paper (\u003ccode\u003elsd:v0.8.img\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eWe found that sometimes this singularity container throws errors because of deprecated cuda versions that don\u0027t play well with updated drivers\u003c/li\u003e\n\u003cli\u003eWe made another image (\u003ccode\u003elsd_legacy.img\u003c/code\u003e) that should handle this. If running into \u003ccode\u003elibcublas\u003c/code\u003e or \u003ccode\u003egcc\u003c/code\u003e errors with the original container, consider using the newer one\u003c/li\u003e\n\u003cli\u003eTo download (uncomment legacy img if desired):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython download_imgs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOr building legacy container from source:\u003c/h2\u003e\u003ca id=\"user-content-or-building-legacy-container-from-source\" class=\"anchor\" aria-label=\"Permalink: Or building legacy container from source:\" href=\"#or-building-legacy-container-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNvidia has deprecated the conda packages of the cudnn / cudatoolkit versions used in the original singularity container (6.0/8.0)\u003c/li\u003e\n\u003cli\u003eThis sometimes causes problems with updated drivers (even though the point of containerization is to solve this...)\u003c/li\u003e\n\u003cli\u003eCan get around by installing these from tars, specifically these:\n\u003cpre\u003e\u003ccode\u003ehttps://anaconda.org/numba/cudatoolkit/8.0/download/osx-64/cudatoolkit-8.0-3.tar.bz2\nhttps://repo.anaconda.com/pkgs/free/linux-64/cudnn-6.0.21-cuda8.0_0.tar.bz2\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esetup.sh\u003c/code\u003e will fetch these packages and use them to install directly into the conda environment when creating the Singularity container\u003c/li\u003e\n\u003cli\u003eThe other packages are just specified in the \u003ccode\u003elsd_legacy.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo build legacy image locally:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e./setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote - the original container runs fine with \u003ccode\u003eexec\u003c/code\u003e, the legacy one uses \u003ccode\u003erun\u003c/code\u003e. To be honest, not sure what the problem is here\u003c/li\u003e\n\u003cli\u003eSo \u003ccode\u003esingularity exec --nv lsd:v0.8.img python -c \"import tensorflow\"\u003c/code\u003e will work, and\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity run --nv lsd_legacy.img python -c \"import tensorflow\"\u003c/code\u003e will work, but\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity exec --nv lsd_legacy.img python -c \"import tensorflow\"\u003c/code\u003e will cause import errors\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFetching data\u003c/h2\u003e\u003ca id=\"user-content-fetching-data\" class=\"anchor\" aria-label=\"Permalink: Fetching data\" href=\"#fetching-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to 01_data directory (\u003ccode\u003ecd 01_data\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efetch_data.py\u003c/code\u003e will download training data from the aws bucket using a json file (\u003ccode\u003edatasets.json\u003c/code\u003e) specifying the training volumes for each dataset\u003c/li\u003e\n\u003cli\u003eThe script defaults to just downloading the first volume for each dataset (one for each of zebrafinch, fib25, hemi, or three total)\u003c/li\u003e\n\u003cli\u003edownload data:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython fetch_data.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eyou could also use the singularity container to download the data, but we already have boto3 in the basic env we created anyway\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating masks\u003c/h2\u003e\u003ca id=\"user-content-creating-masks\" class=\"anchor\" aria-label=\"Permalink: Creating masks\" href=\"#creating-masks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecreate_masks.py\u003c/code\u003e will create a \u003ccode\u003elabels_mask\u003c/code\u003e that we use for training to constrain random locations\u003c/li\u003e\n\u003cli\u003eIf you installed zarr into the conda environment, you can just run with:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython create_masks.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eotherwise, using the singularity container:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../lsd:v0.8.img python create_masks.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eor \u003ccode\u003esingularity run...\u003c/code\u003e if using legacy container\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eViewing the data\u003c/h2\u003e\u003ca id=\"user-content-viewing-the-data\" class=\"anchor\" aria-label=\"Permalink: Viewing the data\" href=\"#viewing-the-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you installed neuroglancer into your environment, you can view the data with \u003ccode\u003eview_data.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ee.g \u003ccode\u003epython -i view_data.py -d funke/fib25/training/tstvol-520-1.zarr\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you are viewing remotely, you could also set the bind address with -b (defaults to localhost)\u003c/li\u003e\n\u003cli\u003eThere are some good little packages \u0026amp; tutorials for using neuroglancer differently. Examples\n\u003ca href=\"https://github.com/funkelab/funlib.show.neuroglancer\"\u003ehere\u003c/a\u003e, \u003ca href=\"https://connectomics.readthedocs.io/en/latest/external/neuroglancer.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\nand \u003ca href=\"https://github.com/google/neuroglancer/tree/master/python/examples\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample fib25 training data:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/fib25_training_data.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/fib25_training_data.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownloading network checkpoints\u003c/h2\u003e\u003ca id=\"user-content-downloading-network-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Downloading network checkpoints\" href=\"#downloading-network-checkpoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efetch_checkpoint.py\u003c/code\u003e will download a specified network checkpoint for a given dataset to the target folder in \u003ccode\u003e02_train\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eWe can start with the baseline affinities for the zebrafinch dataset just using conda boto3 (or use singularity if desired):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython fetch_checkpoint.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining a network\u003c/h2\u003e\u003ca id=\"user-content-training-a-network\" class=\"anchor\" aria-label=\"Permalink: Training a network\" href=\"#training-a-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to the zebrafinch baseline directory (\u003ccode\u003ecd ../02_train/zebrafinch/baseline\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eWe start by creating our network in \u003ccode\u003emknet.py\u003c/code\u003e (e.g placeholders to match to the trained graphs):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python mknet.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIt should print a bunch of layer names and tensor shapes (that looks like a sideways U-Net) to the command line\u003c/li\u003e\n\u003cli\u003eCheck the files in the directory (e.g \u003ccode\u003etree .\u003c/code\u003e), it should now look like:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e.\n\u251c\u2500\u2500 checkpoint\n\u251c\u2500\u2500 config.json\n\u251c\u2500\u2500 config.meta\n\u251c\u2500\u2500 mknet.py\n\u251c\u2500\u2500 predict.py\n\u251c\u2500\u2500 predict_scan.py\n\u251c\u2500\u2500 train_net_checkpoint_400000.data-00000-of-00001\n\u251c\u2500\u2500 train_net_checkpoint_400000.index\n\u251c\u2500\u2500 train_net_checkpoint_400000.meta\n\u251c\u2500\u2500 train_net.json\n\u251c\u2500\u2500 train_net.meta\n\u251c\u2500\u2500 train.py\n\u2514\u2500\u2500 view_batch.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eTrain for 1 iteration:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python train.py 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eYou\u0027ll see that gunpowder will print \u003ccode\u003eERROR:tensorflow:Couldn\u0027t match files for checkpoint ./train_net_checkpoint_500000\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis is because it checks the \u003ccode\u003echeckpoint\u003c/code\u003e file which specifies iteration 500000 (since this network was trained for longer than the optimal checkpoint)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf we view the batch, we\u0027ll see that the predictions are all grey, since it really only trained for a single iteration and didn\u0027t use the checkpoint (\u003ccode\u003epython -i view_batch.py -f snapshots/batch_1.hdf\u003c/code\u003e):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_batch_1.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_batch_1.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo fix, simply edit this \u003ccode\u003echeckpoint\u003c/code\u003e file to point to the downloaded checkpoint iteration instead (e.g 500000 -\u0026gt; 400000)\u003c/li\u003e\n\u003cli\u003eNow running the above won\u0027t do anything, because of line 27 in \u003ccode\u003etrain.py\u003c/code\u003e:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etrained_until\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emax_iteration\u003c/span\u003e:\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSo just make sure to train to the checkpoint + n, eg for 1 extra iteration:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python train.py 400001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe can then view the saved batch, e.g:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython -i view_batch.py -f snapshots/batch_400001.hdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_batch_400k.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_batch_400k.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Inference\u003c/h2\u003e\u003ca id=\"user-content-running-inference\" class=\"anchor\" aria-label=\"Permalink: Running Inference\" href=\"#running-inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFor the lsds experiments, we ran everything from inference through evaluation in a blockwise fashion using \u003ca href=\"https://github.com/funkelab/daisy\"\u003edaisy\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor inference, this meant having a blockwise prediction script that called a gunpowder predict pipeline inside each process\u003c/li\u003e\n\u003cli\u003eFor example, this \u003ca href=\"https://github.com/funkelab/lsd/blob/master/lsd/tutorial/scripts/01_predict_blockwise.py\"\u003escript\u003c/a\u003e would distribute this \u003ca href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/02_train/zebrafinch/baseline/predict.py\"\u003escript\u003c/a\u003e by using a \u003ca href=\"http://funkey.science/gunpowder/api.html?highlight=daisy#gunpowder.DaisyRequestBlocks\" rel=\"nofollow\"\u003eDaisyRequestBlocks\u003c/a\u003e gunpowder node\u003c/li\u003e\n\u003cli\u003eIf you just want to run inference on a small volume (in memory), you can instead use a \u003ca href=\"http://funkey.science/gunpowder/api.html?highlight=scan#gunpowder.Scan\" rel=\"nofollow\"\u003eScan\u003c/a\u003e node\u003c/li\u003e\n\u003cli\u003eWe added adapted all blockwise inference scripts to also use scan nodes (e.g \u003ccode\u003epredict_scan.py\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eExample run on zfinch training data:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python predict_scan.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eResulting affinities:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_preds.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_preds.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMultitask (MTLSD)\u003c/h2\u003e\u003ca id=\"user-content-multitask-mtlsd\" class=\"anchor\" aria-label=\"Permalink: Multitask (MTLSD)\" href=\"#multitask-mtlsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThis can be run exactly the same as the baseline above.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInside \u003ccode\u003e01_data/fetch_checkpoint.py\u003c/code\u003e, uncomment \u003ccode\u003emtlsd\u003c/code\u003e and run.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to \u003ccode\u003e02_train/zebrafinch/mtlsd\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate network (\u003ccode\u003e... python mknet.py\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eChange \u003ccode\u003echeckpoint\u003c/code\u003e file iteration to match downloaded checkpoint (500000 -\u0026gt; 400000)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTrain (\u003ccode\u003e... python train.py 400001\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eView batch (\u003ccode\u003e... python -i view_batch.py ...\u003c/code\u003e) -\u0026gt; will also show LSDs\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePredict (\u003ccode\u003e... python predict_scan.py\u003c/code\u003e) -\u0026gt; will write out LSDs and Affs\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis will also give us LSDs:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/mtlsd_zfinch_preds_lsds.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/mtlsd_zfinch_preds_lsds.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTip - you can view the different components of the LSDs by adjusting the shader in neuroglancer, e.g changing \u003ccode\u003e0,1,2\u003c/code\u003e to \u003ccode\u003e3,4,5\u003c/code\u003e will show the diagonal entries of the covariance component (or the direction the processes move):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003evoid main() {\n emitRGB(\n vec3(\n toNormalized(getDataValue(3)),\n toNormalized(getDataValue(4)),\n toNormalized(getDataValue(5)))\n );\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eOr to view a single channel of the 10d lsds, (e.g channel 6):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003evoid main() {\n float v = toNormalized(getDataValue(6));\n vec4 rgba = vec4(0,0,0,0);\n if (v != 0.0) {\n rgba = vec4(colormapJet(v), 1.0);\n }\n emitRGBA(rgba);\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAutocontext (ACLSD and ACRLSD)\u003c/h2\u003e\u003ca id=\"user-content-autocontext-aclsd-and-acrlsd\" class=\"anchor\" aria-label=\"Permalink: Autocontext (ACLSD and ACRLSD)\" href=\"#autocontext-aclsd-and-acrlsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThese networks rely on a pretrained LSD (raw -\u0026gt; LSDs) network, eg:\u003c/li\u003e\n\u003cli\u003eACLSD: \u003ccode\u003eRaw -\u0026gt; LSDs -\u0026gt; Affs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eACRLSD: \u003ccode\u003eRaw -\u0026gt; LSDs + cropped Raw -\u0026gt; Affs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBecause of this, they are more computationally expensive (since training requires extra context to first predict the lsds)\u003c/li\u003e\n\u003cli\u003eThey ran using 23.5 GB of available 24 GB GPU RAM when testing on quadro p6000. If you have less than that you will likely run into cuda OOM errors\u003c/li\u003e\n\u003cli\u003eIf you have access to sufficient gpu memory, to start navigate to \u003ccode\u003e01_data\u003c/code\u003e and uncomment \u003ccode\u003elsd\u003c/code\u003e + run script to get the pretrained lsd checkpoint\u003c/li\u003e\n\u003cli\u003eGo to lsd directory (\u003ccode\u003ecd ../02_train/zebrafinch/lsd\u003c/code\u003e) and follow same instructions as baseline and mtlsd nets above\u003c/li\u003e\n\u003cli\u003eOnce you have the lsd checkpoints (and \u003ccode\u003etest_prediction.zarr\u003c/code\u003e with \u003ccode\u003epred_lsds\u003c/code\u003e following prediction), start with a basic autocontext network (aclsd).\u003c/li\u003e\n\u003cli\u003eGet the aclsd checkpoint, navigate to appropriate directory, change checkpoint file as before and train network.\u003c/li\u003e\n\u003cli\u003eVisualizing the resulting batch shows us the larger raw context needed for predicting the lsds to ensure that the output affinities remain the same size:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/aclsd_training_batch.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/aclsd_training_batch.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun prediction as before - note, will not run if lsds have not been predicted first. This script could be adapted to predict the lsds on-the-fly using just the lsds and affs checkpoints\u003c/li\u003e\n\u003cli\u003eThe same can be done for the ACRLSD network (note, requires a merge provider during prediction as this network takes both lsds and raw data as input)\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTodos: add consolidated fib25/hemi nets to tutorial\u003c/h2\u003e\u003ca id=\"user-content-todos-add-consolidated-fib25hemi-nets-to-tutorial\" class=\"anchor\" aria-label=\"Permalink: Todos: add consolidated fib25/hemi nets to tutorial\" href=\"#todos-add-consolidated-fib25hemi-nets-to-tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2266\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the OpenDroneMap Drone Mapping Software\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1716585661.0 + "updated_at": 1549336324.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for Racon (https://github.com/isovic/racon/)", "filenames": [ "Singularity", - "Singularity1" + "Singularity.1.3.2", + "Singularity.1.3.1", + "Singularity.1.4.3", + "Singularity.1.4.2", + "Singularity.1.3.3", + "Singularity.1.4.7", + "Singularity.1.3.0", + "Singularity.1.4.0" ], - "full_name": "DoaneAS/RforSingularity", + "full_name": "powerPlant/racon-srf", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2269\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the Racon consensus module for raw de novo DNA assembly of long uncorrected reads\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1496472410.0 + "updated_at": 1590711591.0 }, { "data_format": 2, - "description": "Building a singularity container for PHCpack", + "description": "Singularity recipe files for EddyPro Engine (https://github.com/LI-COR/eddypro-engine)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.6.2.1", + "Singularity.5.2.1", + "Singularity.6.0.0", + "Singularity.5.2.0", + "Singularity.6.1.0", + "Singularity.5.1.1", + "Singularity.6.2.0" ], - "full_name": "ARCLeeds/PHCpack_sing", + "full_name": "powerPlant/eddypro-engine-srf", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4512\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eA singularity container for PHCpack\u003c/h1\u003e\u003ca id=\"user-content-a-singularity-container-for-phcpack\" class=\"anchor\" aria-label=\"Permalink: A singularity container for PHCpack\" href=\"#a-singularity-container-for-phcpack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA repo containing the Singularity recipe file for build \u003ca href=\"https://github.com/janverschelde/PHCpack/\"\u003ePHCpack\u003c/a\u003e. Also builds\nthe \u003ca href=\"https://github.com/Macaulay2/M2\"\u003eMacaulay2 language\u003c/a\u003e from source.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetails:\u003c/h2\u003e\u003ca id=\"user-content-details\" class=\"anchor\" aria-label=\"Permalink: Details:\" href=\"#details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAuthor: Alex Coleman \u003ca href=\"mailto:a.coleman1@leeds.ac.uk\"\u003ea.coleman1@leeds.ac.uk\u003c/a\u003e\u003cbr\u003e\nDate: 2020-06-29\u003cbr\u003e\nOriginating request: REQTSK0356325\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTODO:\u003c/h2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO:\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] get Macauley2 built\u003c/li\u003e\n\u003cli\u003e[x] get PHCpack built\u003c/li\u003e\n\u003cli\u003e[x] create acceptable interface for user\u003c/li\u003e\n\u003cli\u003e[x] write documentation for use\u003c/li\u003e\n\u003cli\u003e[x] upload to Singularity Hub (optional)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing the container\u003c/h2\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdetails\u003e\n \u003csummary\u003eFor University of Leeds users!\u003c/summary\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIf you want to use this container on ARC3 or ARC4\u003c/h2\u003e\u003ca id=\"user-content-if-you-want-to-use-this-container-on-arc3-or-arc4\" class=\"anchor\" aria-label=\"Permalink: If you want to use this container on ARC3 or ARC4\" href=\"#if-you-want-to-use-this-container-on-arc3-or-arc4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need to load the singularity module first before running the following commands:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ module load singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cp\u003eTo download this container from Singularity Hub:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ singularity build PHCpack_sing shub://ARCLeeds/PHCpack_sing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should download a file called PHCpack_sing to your current directory which you can use in the following way:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e\n# open a Macaulay2 interactive development environment\n$ singularity run PHCpack_sing\n\n# You can also run an existing .m2 script file by doing the following:\n$ singularity exec PHCpack_sing --script name_of_script.m2\n\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2272\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the EddyPro eddy covariance data processing software\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "macaulay2", - "phcpack", - "singularity-container", - "singularity-hub" - ], - "updated_at": 1593596546.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1549923689.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for cdo (https://www.mpimet.mpg.de/cdo/)", "filenames": [ - "Singularity.spid" + "Singularity", + "Singularity.1.9.3", + "Singularity.1.9.5", + "Singularity.1.7.0" ], - "full_name": "romxero/spid_singularity", + "full_name": "powerPlant/cdo-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eApptainer Definition file for SPID.\u003c/h1\u003e\u003ca id=\"user-content-apptainer-definition-file-for-spid\" class=\"anchor\" aria-label=\"Permalink: Apptainer Definition file for SPID.\" href=\"#apptainer-definition-file-for-spid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2262\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the Climate Data Operators toolset\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1710191251.0 + "updated_at": 1549335527.0 }, { "data_format": 2, - "description": "Singularity Bootstrap File for CERN ROOT6 built on Ubuntu 16.10", + "description": "sparkle planning challenge", "filenames": [ "Singularity" ], - "full_name": "twongjirad/singularity-cern-root6-yakkety", + "full_name": "hejm37/sysu-planner", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-cern-root6-yakkety\u003c/h1\u003e\u003ca id=\"user-content-singularity-cern-root6-yakkety\" class=\"anchor\" aria-label=\"Permalink: singularity-cern-root6-yakkety\" href=\"#singularity-cern-root6-yakkety\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains Bootstrap file to build singularity image\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sysu-planner\" class=\"anchor\" aria-hidden=\"true\" href=\"#sysu-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esysu-planner\u003c/h1\u003e\n\u003cp\u003eThe SYSU-Planner is a two-stage planner designed to solve classical planning problems. It first performs the 1-BFWS (\u003ca href=\"https://people.eng.unimelb.edu.au/nlipovetzky/papers/aaai17-BFWS-novelty-exploration.pdf\" rel=\"nofollow\"\u003eNir and Hector 2017\u003c/a\u003e) with very fast speed. If it fails to find a solution, it will then perform a modified online refinement algorithm named \u003ca href=\"http://ada.liacs.nl/events/sparkle-planning-19/documents/solver_description/SYSU-planner-description.pdf\" rel=\"nofollow\"\u003eForward-RHC\u003c/a\u003e (see also \u003ca href=\"https://ipc2018-classical.bitbucket.io/planner-abstracts/team8.pdf\" rel=\"nofollow\"\u003eMaximilian and Jorg 2018\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-and-run-with-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-and-run-with-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild and run with container\u003c/h2\u003e\n\u003cp\u003eUsing the planner with \u003ca href=\"https://sylabs.io/docs/#singularity\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is rather simple. First install Singularity following \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003ethis guide\u003c/a\u003e. Then run the following script in CLI and you will have the plan file \u003cem\u003esas_plan\u003c/em\u003e under \u003cem\u003e$RUNDIR\u003c/em\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build planner.img sysu-planner/Singularity\nmkdir rundir\ncp path/to/domain.pddl rundir\ncp path/to/problem.pddl rundir\nRUNDIR=\"$(pwd)/rundir\"\nDOMAIN=\"$RUNDIR/domain.pddl\"\nPROBLEM=\"$RUNDIR/problem.pddl\"\nPLANFILE=\"$RUNDIR/sas_plan\"\nsingularity run -C -H $RUNDIR planner.img $DOMAIN $PROBLEM $PLANFILE $COSTBOUND\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-supported-problems\" class=\"anchor\" aria-hidden=\"true\" href=\"#supported-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupported problems\u003c/h3\u003e\n\u003cp\u003eThe formulation of supported problems is the same as \u003ca href=\"https://ipc2018-classical.bitbucket.io/#pddl\" rel=\"nofollow\"\u003eIPC 2018\u003c/a\u003e. We also provide a set of supported domains and problems in \u003ca href=\"https://github.com/hejm37/benchmark-domains\"\u003ebenchmark-domains\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-notes-on-playing-with-the-source-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes-on-playing-with-the-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes on playing with the source code\u003c/h2\u003e\n\u003cp\u003eThe source code of the planner contains two part:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBFWS-public and its dependency, LAPKT-public\u003c/li\u003e\n\u003cli\u003efast-downward-conjunctions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen planner should be invoked in the fast-downward-conjunctions part (using --dual option and it will call BFWS-public/fd-version/bfws.py to perform 1-BFWS, see \u003ca href=\"https://github.com/hejm37/sysu-planner/blob/master/Singularity\"\u003ethe Singularity script\u003c/a\u003e for more details).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-potential-failures\" class=\"anchor\" aria-hidden=\"true\" href=\"#potential-failures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePotential Failures\u003c/h3\u003e\n\u003cp\u003eIf the above build has failed, it may appears to be a cmake cache fail. In this case, remove the \u003cem\u003ebuilds\u003c/em\u003e (if it exists) directory under fast-downward-conjunctions and rerun the singularity command shall solve the problem.\u003c/p\u003e\n\u003cp\u003eOr it may appears to be a scons build fail. In this case, remove all the \u003cem\u003e.sconsign.dblite\u003c/em\u003e files under the directory shall solve the problem.\u003c/p\u003e\n\u003cp\u003eBoth cases would occur if the planner was built outside a container.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1496270730.0 + "updated_at": 1563536767.0 }, { "data_format": 2, - "description": "Singualrity recipe for BamM, GroopM and CheckM on Conda", + "description": "Omero client Singularity recipes.", "filenames": [ - "Singularity" + "Singularity.5.4.0", + "Singularity.5.4.10" ], - "full_name": "ISU-HPC/bamm-groopm-checkm", + "full_name": "arcsUVA/omero-client", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebamm-groopm-checkm\u003c/h1\u003e\u003ca id=\"user-content-bamm-groopm-checkm\" class=\"anchor\" aria-label=\"Permalink: bamm-groopm-checkm\" href=\"#bamm-groopm-checkm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingualrity recipe for BamM, GroopM, CheckM and RefineM on Conda\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-omero-client\" class=\"anchor\" aria-hidden=\"true\" href=\"#omero-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eomero-client\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2227\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nOmero client Singularity recipes\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1535561436.0 + "updated_at": 1557760203.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for Qt5 on Centos 7 and Ubuntu 16.04", "filenames": [ - "Singularity.def" + "Singularity", + "Singularity.ubuntu", + "Singularity.qt5", + "dsistudio_mrtrix3/Singularity.dsi_mrtrix3", + "dsistudio_mrtrix3/Singularity.dsi_mrtrix3_ants_fsl_fmriprep", + "dsistudio_mrtrix3/Singularity.dsi_mrtrix3_fsl", + "dsistudio_mrtrix3/Singularity.dsi_mrtrix3_centos8", + "dsistudio_mrtrix3/Singularity.dsi_mrtrix3_ants" ], - "full_name": "FrancescoPesce/SE4HPC_project_part2", + "full_name": "willgpaik/qt5_aci", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject - part 2\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject---part-2\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject - part 2\" href=\"#se4hpcproject---part-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRepository for the second part of the second project of SE4HPC by Miotti Michele and Francesco Pesce.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe file \u003ccode\u003etest/test_matrix_multiplication.cpp\u003c/code\u003e contains the tests from part 1 that are passed by the\nnew implementation of the matrix multiplication function.\u003c/li\u003e\n\u003cli\u003eThe file \u003ccode\u003eSingularity.def\u003c/code\u003e contains a container descriptor for the program.\u003c/li\u003e\n\u003cli\u003eThe file \u003ccode\u003ejob.sh\u003c/code\u003e is used to run the containerized application using SLURM and MPI. It is supposed to be executed using sbatch, as using srun may result in issues.\u003c/li\u003e\n\u003cli\u003eThe file \u003ccode\u003e.github/workflows/ci.yml\u003c/code\u003e contains the Github actions used to execute the pipeline. At the moment of the last commit, the workflow runs with no errors.\u003c/li\u003e\n\u003cli\u003eThe files \u003ccode\u003eoutput.txt\u003c/code\u003e and \u003ccode\u003eerror.txt\u003c/code\u003e contain the expected output and error from the execution of the application. We verified that they match with the ones obtained on the cluster.\u003c/li\u003e\n\u003cli\u003eWe made minor changes to the rest of the repository to be able to use the file \u003ccode\u003esrc/matrix_mult.cpp\u003c/code\u003e in the tests.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-qt5_aci\" class=\"anchor\" aria-hidden=\"true\" href=\"#qt5_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eqt5_aci\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for Qt5 on Centos 7 and Ubuntu 16.04 For ICS\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE: DO NOT rebuild \"Singularity.dsi_mrtrix3\" image.\u003c/strong\u003e\u003cbr\u003e\n(Last successful build was Mar 12 2019)\u003c/p\u003e\n\u003cp\u003eSingularity recipe for DSI Studio and MRtrix3 is updated on \u003cstrong\u003edsistudio_mrtrix3\u003c/strong\u003e folder\u003c/p\u003e\n\u003cp\u003eIf you want to install DSI Studio and MRtrix3 on Basic Qt5 Container,\u003cbr\u003e\ndownlaod \"dsistudio_mrtrix3_install.sh\" to preferred location\nand follow commands inside Singularity environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; chmod +x dsistudio_mrtrix3_install.sh \n\u0026gt; ./dsistudio_mrtrix3_install.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e2019/2/21\u003cbr\u003e\nUnable to use \u003cstrong\u003eGCC 8.2.1\u003c/strong\u003e due to build failure =\u0026gt; Going back to \u003cstrong\u003eGCC 7.3.1\u003c/strong\u003e\u003cbr\u003e\n(Failed to resolve the issue at this moment)\u003c/p\u003e\n\u003cp\u003e\u003cdel\u003e2019/5/13\u003cbr\u003e\nUpdated dsistudio_mrtrix3_install.sh due to Qt version issue\u003cbr\u003e\n(Requires Qt 5.12.2 or above: \u003ca href=\"https://github.com/frankyeh/DSI-Studio/issues/34\"\u003ehttps://github.com/frankyeh/DSI-Studio/issues/34\u003c/a\u003e)\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e2019/5/24\u003cbr\u003e\nReverted changes made on 2019/5/13\u003c/p\u003e\n\u003cp\u003e2019/6/24\u003cbr\u003e\n\u003cdel\u003eNewer version qt5 installation recipe added (in progress)\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e2019/7/22\u003cbr\u003e\nQt is updated to 5.12 with Qt Charts (for DSI Studio)\u003c/p\u003e\n\u003cp\u003e2019/7/24\u003cbr\u003e\nQt SVG is added (for MRtrix 3)\u003cbr\u003e\n32-bit EoD graphics libraries are disable (to aviod warnings)\u003c/p\u003e\n\u003cp\u003e2019/7/29\u003cbr\u003e\nNVIDIA driver is added to DSI Studio MRtrix3 container\u003c/p\u003e\n\u003cp\u003e2019/11/10\u003cbr\u003e\nQt version 5.12.5 is used\u003c/p\u003e\n\u003cp\u003e2020/4/24\u003cbr\u003e\nUbuntu 16.04 version added with Qt 5.14.2\u003c/p\u003e\n\u003cp\u003e2020/6/20\u003cbr\u003e\nQt5 container is updated to have nvidia driver\u003c/p\u003e\n\u003cp\u003e2020/7/27\u003cbr\u003e\nUbuntu container is updated to have NVIDIA driver (Ubuntu 16.04 based)\u003c/p\u003e\n\u003cp\u003e2020/9/28\u003cbr\u003e\nQt5 container is updated to use CUDA 9.1 version (for FSL with CUDA)\u003cbr\u003e\n(Reference: \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GPU\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GPU\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e2020/10/20\u003cbr\u003e\nQt5X11Extras is added to the Qt5 recipe\u003cbr\u003e\n(Ubuntu container will not be updated unless necessary)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1717606364.0 + "updated_at": 1618004326.0 }, { "data_format": 2, - "description": null, + "description": "Container to run various Game AI workloads", "filenames": [ "Singularity" ], - "full_name": "rynge/osgvo-ubuntu-18.04-testing", + "full_name": "sbutcher/minigym-container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eosgvo-ubuntu-18.04-testing\u003c/h1\u003e\u003ca id=\"user-content-osgvo-ubuntu-1804-testing\" class=\"anchor\" aria-label=\"Permalink: osgvo-ubuntu-18.04-testing\" href=\"#osgvo-ubuntu-1804-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOSG VO\u0027s Ubuntu 18.04 base image - testing\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-minigym-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#minigym-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eminigym-container\u003c/h1\u003e\n\u003cp\u003eContainer to run various Game AI workloads\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1543883829.0 + "updated_at": 1548062559.0 }, { "data_format": 2, @@ -5755,53 +5452,56 @@ var data = "filenames": [ "Singularity" ], - "full_name": "suouyang/linved", + "full_name": "djarecka/tmp_nipype_tut", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLineVD\u003c/h1\u003e\u003ca id=\"user-content-linevd\" class=\"anchor\" aria-label=\"Permalink: LineVD\" href=\"#linevd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository provides the code for \u003ca href=\"https://arxiv.org/pdf/2203.05181.pdf\" rel=\"nofollow\"\u003eLineVD: Statement-level Vulnerability Detection using Graph Neural Networks\u003c/a\u003e. The environment can be built using \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or by following / following the commands in the Singularity file. To start, clone the repository and navigate to the root directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory Structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory Structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre lang=\"dir\"\u003e\u003ccode\u003e(main module) \u251c\u2500\u2500 sastvd\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 codebert\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 helpers\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 ivdetect\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 linevd\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 scripts\n \u251c\u2500\u2500 storage\n(memoization) \u2502\u00a0\u00a0 \u251c\u2500\u2500 cache\n(raw data) \u2502\u00a0\u00a0 \u251c\u2500\u2500 external\n(csvs) \u2502\u00a0\u00a0 \u251c\u2500\u2500 outputs\n(models) \u2502\u00a0\u00a0 \u2514\u2500\u2500 processed\n(tests) \u2514\u2500\u2500 tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining LineVD from scratch\u003c/h2\u003e\u003ca id=\"user-content-training-linevd-from-scratch\" class=\"anchor\" aria-label=\"Permalink: Training LineVD from scratch\" href=\"#training-linevd-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild and initialise environment and download dataset\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build main.sif Singularity\nsingularity run main.sif -p initialise\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFeature extraction (Increase NUM_JOBS if running on HPC for speed up)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e main.sif python sastvd/scripts/prepare.py\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e main.sif python sastvd/scripts/getgraphs.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTrain model (Training takes around 1-2 hours using GTX 3060)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --nv main.sif python sastvd/scripts/train_best.py\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nipype-tutorial-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#nipype-tutorial-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNipype Tutorial Notebooks\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/miykael/nipype_tutorial/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/669c934f828c73340c0d591ed4b423ef3fa0193e787bfe385915e82dae5ed8fc/68747470733a2f2f636972636c6563692e636f6d2f67682f6d69796b61656c2f6e69707970655f7475746f7269616c2e7376673f7374796c653d736869656c64\" alt=\"CircleCi\" data-canonical-src=\"https://circleci.com/gh/miykael/nipype_tutorial.svg?style=shield\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/issues/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ea29b9a6350d6278064569a97945097dcdeedf9e93740b62ef46df808891fd37/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/pulls/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb7044b2c212e415ec4669de3bb9767f22bfed317ade3070bac8d41ea2a71529/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d70722f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub pull-requests\" data-canonical-src=\"https://img.shields.io/github/issues-pr/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://GitHub.com/miykael/nipype_tutorial/graphs/contributors/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7702816785d6120ca455fda7995bccb5bbdde3e3a92f859f27f866ad34bc55f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub contributors\" data-canonical-src=\"https://img.shields.io/github/contributors/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/commits/master\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fdcae12a957784eff34edadd6ded9a9a8cdf6354ce4d5c5b9d16727d838ecc23/68747470733a2f2f6769746875622d62617369632d6261646765732e6865726f6b756170702e636f6d2f636f6d6d6974732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub Commits\" data-canonical-src=\"https://github-basic-badges.herokuapp.com/commits/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/archive/master.zip\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fb9081bb8ee87986aea94736dd73ee86c56308df8e0b21ee9803cbe6976e3fab/68747470733a2f2f6769746875622d73697a652d62616467652e6865726f6b756170702e636f6d2f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub size\" data-canonical-src=\"https://github-size-badge.herokuapp.com/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/miykael/nipype_tutorial/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3658dcdcaf69e757f1454f83966a15fcdf8b7bcb1d3b4427ffb4226668659eb6/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d69796b61656c2f6e69707970655f7475746f7269616c2e7376673f6d61784167653d32353932303030\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/docker/pulls/miykael/nipype_tutorial.svg?maxAge=2592000\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://hits.dwyl.io/miykael/nipype_tutorial\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c19a46ac2503dae747aeea217a7a854e711a4c95b5814a8c85c59aa5c9920a61/687474703a2f2f686974732e6477796c2e696f2f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub HitCount\" data-canonical-src=\"http://hits.dwyl.io/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the Nipype Tutorial in Jupyter Notebook format. You can access the tutorial in two ways:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://miykael.github.io/nipype_tutorial/\" rel=\"nofollow\"\u003eNipype Tutorial Homepage\u003c/a\u003e: This website contains a static, read-only version of all the notebooks.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://miykael.github.io/nipype_tutorial/notebooks/introduction_docker.html\" rel=\"nofollow\"\u003eNipype Tutorial Docker Image\u003c/a\u003e: This guide explains how to use Docker to run the notebooks interactively on your own computer. The nipype tutorial docker image is the best interactive way to learn Nipype.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-feedback-help--support\" class=\"anchor\" aria-hidden=\"true\" href=\"#feedback-help--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeedback, Help \u0026amp; Support\u003c/h1\u003e\n\u003cp\u003eIf you want to help with this tutorial or have any questions, feel free to fork the repo of the \u003ca href=\"https://github.com/miykael/nipype_tutorial\"\u003eNotebooks\u003c/a\u003e or interact with other contributors on the slack channel \u003ca href=\"https://brainhack.slack.com/messages/nipype/\" rel=\"nofollow\"\u003ebrainhack.slack.com/messages/nipype/\u003c/a\u003e. If you have any questions or found a problem, open a new \u003ca href=\"https://github.com/miykael/nipype_tutorial/issues\"\u003eissue on github\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-thanks-and-acknowledgment\" class=\"anchor\" aria-hidden=\"true\" href=\"#thanks-and-acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThanks and Acknowledgment\u003c/h1\u003e\n\u003cp\u003eA huge thanks to \u003ca href=\"https://github.com/mwaskom\"\u003eMichael Waskom\u003c/a\u003e, \u003ca href=\"https://github.com/oesteban\"\u003eOscar Esteban\u003c/a\u003e, \u003ca href=\"https://github.com/chrisfilo\"\u003eChris Gorgolewski\u003c/a\u003e and \u003ca href=\"https://github.com/satra\"\u003eSatrajit Ghosh\u003c/a\u003e for their input to this tutorial! And a huge thanks to \u003ca href=\"https://github.com/djarecka/\"\u003eDorota Jarecka\u003c/a\u003e who updated this tutorial to Python 3 and is helping me with keeping this tutorial updated and running!\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1687515622.0 + "updated_at": 1547566090.0 }, { "data_format": 2, - "description": "Singularity recipes for Docker image gedet-base", + "description": "Bioinformatic tools in a singularity container", "filenames": [ - "Singularity.julia06-avx2" + "containers/Singularity", + "containers/Singularity.etoki", + "containers/Singularity.lyveset" ], - "full_name": "mppmu/gedet-base-singularity", + "full_name": "EnriqueDoster/sing_biotools", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sing_biotools\" class=\"anchor\" aria-hidden=\"true\" href=\"#sing_biotools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esing_biotools\u003c/h1\u003e\n\u003cp\u003eBioinformatic tools in a singularity container\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1516277260.0 + "updated_at": 1606287922.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container for Scanfold", "filenames": [ "Singularity" ], - "full_name": "callaghanmt-containers/evolinc", + "full_name": "ResearchIT/Scanfold", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eevolinc\u003c/h1\u003e\u003ca id=\"user-content-evolinc\" class=\"anchor\" aria-label=\"Permalink: evolinc\" href=\"#evolinc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [], - "updated_at": 1556291432.0 + "updated_at": 1570729149.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "containers/Singularity" ], - "full_name": "tschoonj/singularity-centos6", + "full_name": "stevekm/bwa-bench", "latest_release": null, + "readme": "", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1563792023.0 + "updated_at": 1549319905.0 }, { "data_format": 2, @@ -5809,259 +5509,269 @@ var data = "filenames": [ "Singularity" ], - "full_name": "eugtsa/tf_pytorch_singularity", + "full_name": "dfornika/nf-core-cpo", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etf_pytorch_singularity\u003c/h1\u003e\u003ca id=\"user-content-tf_pytorch_singularity\" class=\"anchor\" aria-label=\"Permalink: tf_pytorch_singularity\" href=\"#tf_pytorch_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-corecpo\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-corecpo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/cpo\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic Analysis of Carbapenem Resistant Organisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/cpo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b4a4d26450e93f9c13ce85f059bb61ebe27051414d40e4f4ba81966ca0029a4/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f63706f2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/cpo.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/cpo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4bc4e99ea4ca2a2f9b15fda9e4d3855153c0fd74431b920ed885080d46e0cc73/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f63706f2e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/cpo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/cpo pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003ch3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003enf-core/cpo was originally written by Dan Fornika.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1621430186.0 + "updated_at": 1544054866.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "singularity/Singularity.petibm0.5-xenial", + "singularity/Singularity.petibm0.5.1-xenial", + "singularity/Singularity.petibm0.4.2-xenial" ], - "full_name": "ldynia/singularity-analyzer", + "full_name": "mesnardo/petibm-decoupledibpm", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-decoupled-immersed-boundary-projection-method-with-petibm\" class=\"anchor\" aria-hidden=\"true\" href=\"#decoupled-immersed-boundary-projection-method-with-petibm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDecoupled Immersed Boundary Projection Method with PetIBM\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/mesnardo/petibm-decoupledibpm/raw/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ccf186e7288af6d88a1f6a930c0fcc4e7a8a9936b34e07629d815d1eab4d977/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d425344253230332d2d436c617573652d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.docker.com/u/mesnardo/repository/docker/mesnardo/petibm-decoupledibpm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b8d9674ae17bb539afa71ecc4169a1ee5a6a9242d8f9e12a10f4583093ba57c3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65722d2d6875622d696e666f726d6174696f6e616c2e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/hosted-docker--hub-informational.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3171\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-flow-over-a-stationary-circular-cylinder-re40-and-100\" class=\"anchor\" aria-hidden=\"true\" href=\"#flow-over-a-stationary-circular-cylinder-re40-and-100\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlow over a stationary circular cylinder ($Re=40$ and $100$)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/cylinder2dRe40/189_markers/figures/wz_0005000.png\"\u003e\u003cimg src=\"runs/cylinder2dRe40/189_markers/figures/wz_0005000.png\" alt=\"cylinderRe40_vorticity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Vorticity contours around the cylinder at Reynolds number $40$. (Contour levels between $-3D/U_\\infty$ and $3D/U_\\infty$ with increments of $0.4$.)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/cylinder2dRe100/189_markers/figures/wz_0020000.png\"\u003e\u003cimg src=\"runs/cylinder2dRe100/189_markers/figures/wz_0020000.png\" alt=\"cylinderRe100_vorticity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Vorticity contours around the cylinder at Reynolds number $100$ after $200$ time units of flow simulation. (Contour levels between $-3D/U_\\infty$ and $3D/U_\\infty$ with increments of $0.4$.)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/cylinder2dRe40/189_markers/figures/cp_0005000.png\"\u003e\u003cimg src=\"runs/cylinder2dRe40/189_markers/figures/cp_0005000.png\" alt=\"cylinderRe40_pressure_coefficient\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Pressure coefficient along the upper and lower surfaces of the cylinder at Reynolds number $40$. We compare with the results from Li et al. (2016).\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/cylinder2dRe100/189_markers/figures/pressure_coefficient.png\"\u003e\u003cimg src=\"runs/cylinder2dRe100/189_markers/figures/pressure_coefficient.png\" alt=\"cylinderRe100_pressure_coefficient\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Pressure coefficient along the upper and lower surfaces of the cylinder at Reynolds number $100$. We compare with the results from Li et al. (2016).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-flow-around-an-inline-oscillating-circular-cylinder-re100\" class=\"anchor\" aria-hidden=\"true\" href=\"#flow-around-an-inline-oscillating-circular-cylinder-re100\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlow around an inline oscillating circular cylinder ($Re=100$)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/algo1/figures/vorticity.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/algo1/figures/vorticity.png\" alt=\"oscillatingcylinderRe100_vorticity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Contours of the vorticity field around an inline oscillating cylinder at different phase angles ($\\phi = 2 \\pi f t$): $\\phi = 0^o$ (left) and $\\phi = 288^o$ (right). (Contour levels between $-20 U_m / D$ and $20 U_m / D$ using $30$ increments.)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/algo1/figures/pressure.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/algo1/figures/pressure.png\" alt=\"oscillatingcylinderRe100_pressure\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Contours of the pressure field around an inline oscillating cylinder at different phase angles ($\\phi = 2 \\pi f t$): $\\phi = 0^o$ (left) and $\\phi = 288^o$ (right). (Contour levels between $-1 \\rho U_m^2$ and $1 \\rho U_m^2$ using $50$ increments.)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/algo1/figures/velocity_profiles.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/algo1/figures/velocity_profiles.png\" alt=\"oscillatingcylinderRe100_velocity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Profile of the velocity components ($u$: left, $v$: right) at four locations along the centerline for various phase angles $\\phi$.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/drag_coefficient.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/drag_coefficient.png\" alt=\"oscillatingcylinderRe100_drag_coefficient\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the drag coefficient of the inline oscillating cylinder obtained using different algorithms. We also show zooms at early and developed stages.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_dt.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_dt.png\" alt=\"oscillatingcylinderRe100_drag_coefficient_dt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_dx.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_dx.png\" alt=\"oscillatingcylinderRe100_drag_coefficient_dx\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the drag coefficient obtained with Algorithm 1 for different time-step sizes and different grid sizes.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/temporal_error.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/temporal_error.png\" alt=\"oscillatingcylinderRe100_temporal_error\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Variations of the $L_\\infty$ and $L_2$ norm errors of the streamwise velocity as a function of the computational time-step size.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/spatial_error.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/spatial_error.png\" alt=\"oscillatingcylinderRe100_temporal_error\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Variations of the $L_\\infty$ and $L_2$ norm errors of the streamwise velocity as a function of the computational grid spacing.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_lag.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_lag.png\" alt=\"oscillatingcylinderRe100_cd_lag\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the drag coefficient using Algorithm 3 with force-prediction scheme 3. We compared the history obtained with different Lagrangian mesh resolutions: $N_b = 500$ Lagrangian markers on the boundary and $N_b = 202$ markers (the latter one corresponding to the same resolution as the Eulerian background grid).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-flow-around-an-impulsively-started-circular-cylinder-re40\" class=\"anchor\" aria-hidden=\"true\" href=\"#flow-around-an-impulsively-started-circular-cylinder-re40\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlow around an impulsively started circular cylinder (Re=40)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/translatingcylinder2dRe40/figures/drag_coefficients.png\"\u003e\u003cimg src=\"runs/translatingcylinder2dRe40/figures/drag_coefficients.png\" alt=\"translatingcylinder2dRe40_cd\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the drag coefficient of the impulsively started cylinder. Comparison with the analytical solution of Bar-Lev \u0026amp; Yang (1997) and the numerical results from Taira \u0026amp; Colonius (2007).\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/translatingcylinder2dRe40/dt=0.0005/figures/vorticity.png\"\u003e\u003cimg src=\"runs/translatingcylinder2dRe40/dt=0.0005/figures/vorticity.png\" alt=\"translatingcylinder2dRe40_wz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Vorticity contours around the impulsively started circular cylinder at $t=1.0$ (left) and $t=3.5$ (right). Contour levels between $-3 \\omega_z D / U_o$ and $3 \\omega_z D / U_o$ with increments of $0.4$.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/translatingcylinder2dRe40/figures/recirculation_lengths.png\"\u003e\u003cimg src=\"runs/translatingcylinder2dRe40/figures/recirculation_lengths.png\" alt=\"translatingcylinder2dRe40_lw\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the recirculation length measured in the reference frame of the impulsively start cylinder at Reynolds number 40 and for different time-step sizes.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-three-dimensional-flow-around-an-inline-oscillating-sphere-re7854\" class=\"anchor\" aria-hidden=\"true\" href=\"#three-dimensional-flow-around-an-inline-oscillating-sphere-re7854\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThree-dimensional flow around an inline oscillating sphere ($Re=78.54$)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingsphere/figures/pressure.png\"\u003e\u003cimg src=\"runs/oscillatingsphere/figures/pressure.png\" alt=\"sphere_pressure\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Contours of the pressure field in the $x$/$y$ at $z=0$ at three phase angles. Contour levels between $-2 p / \\rho U_m^2$ and $2 p / \\rho U_m^2$ with $30$ increments.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1509974976.0 + "updated_at": 1581529613.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.v2.0.0" ], - "full_name": "adswa/nilearn-container", + "full_name": "baxpr/fmri_modularity", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1616512270.0 + "updated_at": 1550158474.0 }, { "data_format": 2, - "description": "A sample for Singularity container providing pynwb with h5py build with mpi support", + "description": null, "filenames": [ - "Singularity" + "Singularity.v1.0.0" ], - "full_name": "dandi-containers/pynwb-mpi", - "latest_release": null, + "full_name": "baxpr/fmri_conncalc", + "latest_release": "v1.0.0-rc0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fmri_conncalc\" class=\"anchor\" aria-hidden=\"true\" href=\"#fmri_conncalc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efmri_conncalc\u003c/h1\u003e\n\u003cp\u003ePreprocessing and functional connectivity computation for fMRI\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eHere is an example for the \"jsins\" version of the processor, as described in\n\u003ca href=\"conncalc_jsins_v1.0.0.yaml\"\u003econncalc_jsins_v1.0.0.yaml\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity\n run\n --bind \u0026lt;INDIR\u0026gt;:/INPUTS\n --bind \u0026lt;OUTDIR\u0026gt;:/OUTPUTS\n baxpr-fmri_conncalc-master-v1.0.0.simg\n magick_path /usr/bin\n param_file params_JSins.csv\n wroi_file rois_JSins.nii.gz\n roi_file \u0027\u0027\n roiinfo_file rois_JSins.csv\n coregmat_file /INPUTS/coreg_mat.txt \\\n deffwd_file /INPUTS/y_deffwd.nii.gz \\\n ct1_file /INPUTS/ct1.nii.gz \\\n wgm_file /INPUTS/wgm.nii.gz \\\n wcseg_file /INPUTS/wcseg.nii.gz \\\n func_file /INPUTS/fmri.nii.gz \\\n project PROJECT_LABEL \\\n subject SUBJECT_LABEL \\\n session SESSION_LABEL \\\n scan SCAN_LABEL \\\n out_dir /OUTPUTS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe inputs \u003ccode\u003ecoregmat_file\u003c/code\u003e, \u003ccode\u003edeffwd_file\u003c/code\u003e, \u003ccode\u003ect1_file\u003c/code\u003e, \u003ccode\u003ewgm_file\u003c/code\u003e, \u003ccode\u003ewcseg_file\u003c/code\u003e would typically be obtained from the outputs of the \u003ccode\u003eMAGM_Coreg_Normalize_v2\u003c/code\u003e spider.\u003c/p\u003e\n\u003cp\u003eThe outputs are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efmri_conncalc.pdf Report\nparams.csv Parameters used in the analysis\nFD.txt Framewise displacement time series\nDVARS.txt Framewise variance time series\nbadvols.txt Scrubbed volumes indicator time series\nrp_adfunc.txt Realignment (motion) values\nwmeanadfunc.nii.gz Mean functional image in standard space\nwadfunc.nii.gz Slice time corrected and realigned functional images in standard space\nrroi_labels.nii.gz Region of interest label image\nroi_snr.nii.gz ROI SNR image\nroi_info.csv ROI info\nroi_labels.csv ROI names (if available)\n\nSeries of results repeated for each of the four processing streams\n(keep or remove mean gray matter; scrub or no scrub):\n\n confounds_removegm_noscrub.txt Confound (filter) matrix\n connectivity_matrix_R_removegm_noscrub.csv Connectivity matrix\n filtered_removegm_noscrub.nii.gz Filtered functional images\n roi_timeseries_removegm_noscrub.csv Filtered ROI time series\n stats_removegm_noscrub.txt Various statistics\n Zmap_removegm_noscrub.nii.gz Unsmoothed ROI connectivity maps\n sZmap_removegm_noscrub.nii.gz Smoothed ROI connectivity maps\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eThe built singularity container \u003ccode\u003ebaxpr-fmri_conncalc-master-v1.0.0.simg\u003c/code\u003e (URL is shub://baxpr/fmri_conncalc:v1.0.0) is stand-alone with no external dependencies. The compiled matlab \u003ca href=\"bin/run_fmri_conncalc.sh\"\u003erun_fmri_conncalc.sh\u003c/a\u003e requires only the appropriate MATLAB Runtime to execute. To build these there are two stages:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCompile the MATLAB code into a stand-alone executable, using \u003ca href=\"compile_matlab.sh\"\u003ecompile_matlab.sh\u003c/a\u003e. This requires a full MATLAB installation (R2017a, v92) and SPM12 (\u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/\" rel=\"nofollow\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/\u003c/a\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the singularity container. In addition to a few specific OS packages, this requires the MATLAB Compiled Runtime. All are specified to be downloaded during the build in the singularity recipe \u003ca href=\"Singularity.v1.0.0\"\u003eSingularity.v1.0.0\u003c/a\u003e. The container help text gives build instructions. Alternatively the built container can be obtained from singularity-hub:\n\u003ccode\u003esingularity pull shub://baxpr/fmri_conncalc:v1.0.0\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-peculiarities-of-specific-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#peculiarities-of-specific-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePeculiarities of specific pipelines\u003c/h2\u003e\n\u003cp\u003eSome critical analysis parameters are specified in the \u003ccode\u003eparam_file\u003c/code\u003e, e.g. \u003ccode\u003eparams_JSins.csv\u003c/code\u003e. This is a reference to a file that\u0027s in the built container, but these can also be viewed in the code repository e.g. \u003ca href=\"src/params/params_JSins.csv\"\u003esrc/params/params_JSins.csv\u003c/a\u003e. The parameters get as detailed as the repetition time of the fMRI scans. If the needed parameter file is not in the container already:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAdd the new parameter file in \u003ccode\u003esrc/params\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUpdate the matlab compilation code to include it with \u003ccode\u003e-a\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRecompile the matlab\u003c/li\u003e\n\u003cli\u003eCommit to github. Note that the compiled matlab executable is stored using LFS\u003c/li\u003e\n\u003cli\u003eRebuild the container (increment the patch number, e.g. 1.0.0 to 1.0.1)\u003c/li\u003e\n\u003cli\u003eCreate an updated YAML file appropriate for the parameter set\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jsins-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#jsins-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ejsins version\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"conncalc_jsins_v1.0.0.yaml\"\u003econncalc_jsins_v1.0.0.yaml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eStandard space regions of interest are used, \u003ca href=\"src/params/JS_insula/rois_JSins.nii.gz\"\u003erois_JSins.nii.gz\u003c/a\u003e, identical for every subject.\u003c/p\u003e\n\u003cp\u003eConnectivity matrix is computed (Pearson bivariate correlation R). A connectivity map is computed for each ROI (Fisher Z transform applied to Pearson bivariate correlation). Spatial smoothing is applied to the connectivity maps only.\u003c/p\u003e\n\u003cp\u003eParameter settings in \u003ca href=\"src/params/params_JSins.csv\"\u003eparams_JSins.csv\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFMRI repetition time (TR) is assumed to be 2.000 sec\u003c/li\u003e\n\u003cli\u003eUse all fMRI volumes (none dropped)\u003c/li\u003e\n\u003cli\u003eNo slice timing correction\u003c/li\u003e\n\u003cli\u003e6mm FWHM Gaussian spatial smoothing applied to connectivity maps\u003c/li\u003e\n\u003cli\u003eFilter settings (confound regressor matrix):\n\u003cul\u003e\n\u003cli\u003e0.01 Hz - 0.10 Hz bandpass filter (Fourier basis)\u003c/li\u003e\n\u003cli\u003e6 motion parameters (translation and rotation)\u003c/li\u003e\n\u003cli\u003e6 first differences of motion parameters\u003c/li\u003e\n\u003cli\u003eFirst 6 principal components of voxel time series from the eroded white matter/CSF compartment\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor scrubbed results, volumes before and after an excursion of FD \u0026gt; 0.5 are removed. DVARS is not used for scrubbing.\u003c/li\u003e\n\u003cli\u003eConnectivity maps are saved for each ROI.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-szhab-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#szhab-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eszhab version\u003c/h3\u003e\n\u003cp\u003eNo YAML available yet.\u003c/p\u003e\n\u003cp\u003eSubject-specific regions of interest are used, as described in the native space ROI image supplied as input. This image must be in the same space as the subject\u0027s native space structural.\u003c/p\u003e\n\u003cp\u003eConnectivity matrix is computed (Pearson bivariate correlation R of filtered time series). Spatial smoothing is not used.\u003c/p\u003e\n\u003cp\u003eParameter settings in \u003ccode\u003eparams_SZhab.csv\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFMRI repetition time (TR) is assumed to be 2.000 sec\u003c/li\u003e\n\u003cli\u003e5 initial volumes are dropped, and the following 60 volumes are used for the analysis\u003c/li\u003e\n\u003cli\u003eNo slice timing correction\u003c/li\u003e\n\u003cli\u003eFilter settings (confound regressor matrix):\n\u003cul\u003e\n\u003cli\u003e0.01 Hz - 0.15 Hz bandpass filter (Fourier basis)\u003c/li\u003e\n\u003cli\u003e6 motion parameters (translation and rotation)\u003c/li\u003e\n\u003cli\u003eFirst 3 principal components of voxel time series from the eroded white matter/CSF compartment\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor scrubbed results, volumes before and after an excursion of FD \u0026gt; 0.5 are removed. DVARS is not used for scrubbing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-general-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#general-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral pipeline\u003c/h2\u003e\n\u003cp\u003eOther than the above, processing proceeds as follows.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDrop functional volumes as specified.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePerform slice timing correction as specified. (SPM12 slice timing correction)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePerform motion realignment: two-stage alignment to mean image. (SPM12 realignment)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCoregister the mean functional image to the T1 weighted structural using a rigid body transform. The structural is first skull-stripped by zeroing all voxels that were not labeled by the multiatlas segmentation. The transformation is then applied to all functional volumes. (SPM12 coregistration)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eQuality parameters are computed: framewise displacement FD and framewise signal variance DVARS. Volumes exceeding scrubbing criteria are marked (\"badvols\").\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe functional and structural images are warped to standard space using the supplied nonlinear transform (forward deformation image). (SPM12 deformation tools)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe supplied standard space ROI image file is resampled to match the standard space fMRI geometry. (SPM12 reslice)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConnectivity computation. All filtering is done in a single step: a design matrix of confounds is created (see lists above), it is fit to each voxel time series, and the residuals are extracted. Then bivariate Pearson correlation is computed between ROI residual time series to produce the connectivity matrix. Fisher transformed correlation between ROIs/voxel residual time series is used to produce connectivity maps if that option is selected.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1571608712.0 + "updated_at": 1543615331.0 }, { "data_format": 2, - "description": "PySecDec base-OS Linux Containers", + "description": "This is singularity 2.6.0 image for PHEnix -1.4a", "filenames": [ - "Singularity" + "Singularity-2.6.0" ], - "full_name": "mppmu/mppmu_pysecdec-base_img", + "full_name": "Amjadhpc/PHEnix", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1538140519.0 + "updated_at": 1539686949.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for freesurfer", "filenames": [ "Singularity" ], - "full_name": "currymj/singularity-vm-kpdmetric", + "full_name": "ResearchIT/singularity-freesurfer", "latest_release": null, - "readme": "\u003cp\u003eThis repository is what I used to build a Singularity image in order to run KPDMetric with all its dependencies on HPC Clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVagrantfile\u003c/h1\u003e\u003ca id=\"user-content-vagrantfile\" class=\"anchor\" aria-label=\"Permalink: Vagrantfile\" href=\"#vagrantfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are on a Linux system, you don\u0027t need to worry about the Vagrantfile. Singularity can only be run on Linux, so on macOS or Windows we need to set up a Linux VM to build the container in. This Vagrantfile does that. It expects the VMware provider, which is not free; you\u0027ll have to change that if you want to use VirtualBox.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis file gives the recipe to build the Singularity container. It requires a couple things:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esingscript.sh\u003c/code\u003e which does some python-related stuff (needs to be called from bash)\u003c/li\u003e\n\u003cli\u003eyou must download CPLEX for Linux and use the installer to install to the directory \u003ccode\u003e./opt\u003c/code\u003e in the same directory as this repository.\u003c/li\u003e\n\u003cli\u003eyou must build the driver code from KPDMetric as a jar and save it as Simulation1.jar -- easiest to do this once by hand in Eclipse by exporting an executable jar.\u003c/li\u003e\n\u003cli\u003eyou must have an internet connection as the build script will clone a github repository.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 6, "topics": [], - "updated_at": 1509658881.0 + "updated_at": 1603915556.0 }, { "data_format": 2, - "description": "Singularity repo to package docker artifacts:base:latest container for running GobyWeb plugins with Singularity.", + "description": null, "filenames": [ "Singularity" ], - "full_name": "CampagneLaboratory/GobyWeb-Singularity", + "full_name": "mosoriob/pegasus_montage-workflow-v2", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGobyWeb-Singularity\u003c/h1\u003e\u003ca id=\"user-content-gobyweb-singularity\" class=\"anchor\" aria-label=\"Permalink: GobyWeb-Singularity\" href=\"#gobyweb-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity repo to package docker artifacts:base:latest container for running GobyWeb plugins with Singularity\u003c/p\u003e\n\u003cp\u003eTo build the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall singularity (Mac: \u003ca href=\"http://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/install-mac\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eActivate the vagrant VM\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003evagrant init singularityware/singularity-2.4\nvagrant up\nvagrant ssh\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInside the VM\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd /vagrant\ngit clone git@github.com:CampagneLaboratory/GobyWeb-Singularity.git\ncd GobyWeb-Singularity/\nsudo singularity build --writable CampagneLaboratory-GobyWeb-Singularity-master-latest.img shub://CampagneLaboratory/GobyWeb-Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eAt this point, we can exit the VM and copy the image file: CampagneLaboratory-GobyWeb-Singularity-master-latest.img\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-montage-workflow-v2\" class=\"anchor\" aria-hidden=\"true\" href=\"#montage-workflow-v2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emontage-workflow-v2\u003c/h1\u003e\n\u003cp\u003eA new Python DAX generator version of the classic Montage workflow. This workflow uses the \u003ca href=\"http://montage.ipac.caltech.edu\" rel=\"nofollow\"\u003eMontage\ntoolkit\u003c/a\u003e to re-project, background correct and add astronomical\nimages into custom mosaics.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://montage.ipac.caltech.edu\" rel=\"nofollow\"\u003eMontage\u003c/a\u003e - version 4.0 or later\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.astropy.org/\" rel=\"nofollow\"\u003eAstroPy\u003c/a\u003e - version 1.0 or later\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-plan-a-montage-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#plan-a-montage-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlan a Montage Workflow\u003c/h2\u003e\n\u003cp\u003eThe \u003cem\u003e./montage-workflow.py\u003c/em\u003e Python script sets up a \u003cem\u003edata/\u003c/em\u003e directory with a Pegasus DAX,\nimage tables and region headers. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./montage-workflow.py --center \"56.7 24.0\" --degrees 2.0 \\\n --band dss:DSS2B:blue --band dss:DSS2R:green --band dss:DSS2IR:red\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a 2x2 degree mosaic centered on 56.7 24.0, with 3 bands making up the\nred, green, and blue channels for the final JPEG output. A 2 degree workflow has a lot\nof input images and thus the workflow becomes wide. I simplified version of the workflow\nlooks like:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/dax1.png?raw=true\"\u003e\u003cimg src=\"docs/images/dax1.png?raw=true\" alt=\"DAX 1\" title=\"DAX 1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h2\u003e\n\u003cp\u003eThe quickest way to get started is to use the \u003cem\u003e./example-dss.sh\u003c/em\u003e\nscript. It shows how to use the \u003cem\u003emontage-workflow.py\u003c/em\u003e DAX generator to set up and plan\n2 degree workflows as described above. Example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./example-dss.sh \n\nAdding band 1 (dss DSS2B -\u0026gt; blue)\nRunning sub command: mArchiveList dss DSS2B \"56.7 24.00\" 2.2 2.2 data/1-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 1-images.tbl region-oversized.hdr 1-raw.tbl 1-projected.tbl 1-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 1-raw.tbl 1-diffs.tbl\n[struct stat=\"OK\", count=120]\n\nAdding band 2 (dss DSS2R -\u0026gt; green)\nRunning sub command: mArchiveList dss DSS2R \"56.7 24.00\" 2.2 2.2 data/2-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 2-images.tbl region-oversized.hdr 2-raw.tbl 2-projected.tbl 2-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 2-raw.tbl 2-diffs.tbl\n[struct stat=\"OK\", count=120]\n\nAdding band 3 (dss DSS2IR -\u0026gt; red)\nRunning sub command: mArchiveList dss DSS2IR \"56.7 24.00\" 2.2 2.2 data/3-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 3-images.tbl region-oversized.hdr 3-raw.tbl 3-projected.tbl 3-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 3-raw.tbl 3-diffs.tbl\n[struct stat=\"OK\", count=120]\n2016.06.02 21:46:32.455 PDT: \n2016.06.02 21:46:32.461 PDT: ----------------------------------------------------------------------- \n2016.06.02 21:46:32.466 PDT: File for submitting this DAG to HTCondor : montage-0.dag.condor.sub \n2016.06.02 21:46:32.471 PDT: Log of DAGMan debugging messages : montage-0.dag.dagman.out \n2016.06.02 21:46:32.476 PDT: Log of HTCondor library output : montage-0.dag.lib.out \n2016.06.02 21:46:32.481 PDT: Log of HTCondor library error messages : montage-0.dag.lib.err \n2016.06.02 21:46:32.487 PDT: Log of the life of condor_dagman itself : montage-0.dag.dagman.log \n2016.06.02 21:46:32.492 PDT: \n2016.06.02 21:46:32.497 PDT: -no_submit given, not submitting DAG to HTCondor. You can do this with: \n2016.06.02 21:46:32.507 PDT: ----------------------------------------------------------------------- \n2016.06.02 21:46:33.387 PDT: Your database is compatible with Pegasus version: 4.6.1 \n2016.06.02 21:46:33.392 PDT: \n\nI have concretized your abstract workflow. The workflow has been entered \ninto the workflow database with a state of \"planned\". The next step is \nto start or execute your workflow. The invocation required is\n\npegasus-run /data/scratch/rynge/montage2/montage-workflow-v2/work/1464929190\n\n2016.06.02 21:46:33.419 PDT: Time taken to execute is 2.961 seconds \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning the workflow produces fits and jpeg mosaics for each band, as well as a combined color one:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/pleiades.jpg?raw=true\"\u003e\u003cimg src=\"docs/images/pleiades.jpg?raw=true\" alt=\"Pleiades\" title=\"Pleiades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1516396334.0 + "updated_at": 1535257330.0 }, { "data_format": 2, - "description": "testing running clair with a singularity image", + "description": null, "filenames": [ "Singularity" ], - "full_name": "vsoch/singularity-clair", + "full_name": "weatherlab/metview", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Clair\u003c/h1\u003e\u003ca id=\"user-content-singularity-clair\" class=\"anchor\" aria-label=\"Permalink: Singularity Clair\" href=\"#singularity-clair\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a test for running clair with Singularity. I haven\u0027t figured out exactly how this works yet :)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild with Makefile\u003c/h2\u003e\u003ca id=\"user-content-build-with-makefile\" class=\"anchor\" aria-label=\"Permalink: Build with Makefile\" href=\"#build-with-makefile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild with Singularity\u003c/h2\u003e\u003ca id=\"user-content-build-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Build with Singularity\" href=\"#build-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity create clair.img\nsudo singularity bootstrap clair.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the server?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run clair.img\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-metview\" class=\"anchor\" aria-hidden=\"true\" href=\"#metview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emetview\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 3, - "topics": [ - "singularity", - "singularity-container", - "clair", - "coreos", - "container", - "security", - "vulnerability" - ], - "updated_at": 1545323610.0 + "topics": [], + "updated_at": 1523286570.0 }, { "data_format": 2, - "description": null, + "description": "PreFreeSurfer-Converting Docker to Singularity (centos7-reprozip.fslbuild-centos5)", "filenames": [ "Singularity" ], - "full_name": "kfox1111/p8compute", + "full_name": "soudabeh19/centos7-reprozip.fslbuild-centos5", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7-reprozipfslbuild-centos5\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos7-reprozipfslbuild-centos5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7-reprozip.fslbuild-centos5\u003c/h1\u003e\n\u003cp\u003ePreFreeSurfer-Converting Docker to Singularity (centos7-reprozip.fslbuild-centos5)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1539124833.0 + "updated_at": 1521572666.0 }, { "data_format": 2, - "description": "Singularity recipe for LaTeX.", + "description": "Nextflow + Singularity/Docker demo for CentOS 6.8 without OverlayFS", "filenames": [ - "Singularity" + "containers/demo1/Singularity.demo1", + "containers/base/Singularity.base" ], - "full_name": "robertodr/latex", + "full_name": "stevekm/NYU-phoenix-docker-singularity-nextflow-demo", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity recipe for LaTeX\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-latex\" class=\"anchor\" aria-label=\"Permalink: Singularity recipe for LaTeX\" href=\"#singularity-recipe-for-latex\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4518\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/4518\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull --name latex.sif shub://bast/latex\n$ singularity run latex.sif example.tex\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nyu-phoenix-hpc-dockersingularity-nextflow-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#nyu-phoenix-hpc-dockersingularity-nextflow-demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNYU phoenix HPC Docker/Singularity Nextflow Demo\u003c/h1\u003e\n\u003cp\u003eDemo on how to run a Nextflow pipeline on the HPC using Singularity containers built from Docker.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003eClone this repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/stevekm/NYU-phoenix-docker-singularity-nextflow-demo.git\ncd NYU-phoenix-docker-singularity-nextflow-demo\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-remote-hpc-phoenix\" class=\"anchor\" aria-hidden=\"true\" href=\"#remote-hpc-phoenix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRemote HPC (phoenix)\u003c/h2\u003e\n\u003cp\u003eTo run this workflow on the NYU phoenix HPC system, use the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run-p\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003einstall Nextflow to the current directory\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eextract a pre-built demo Singularity image from this repository\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun the Nextflow pipeline using the Singularity image\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-local\" class=\"anchor\" aria-hidden=\"true\" href=\"#local\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal\u003c/h2\u003e\n\u003cp\u003eTo run this workflow on your local computer (Docker required), use the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run-l\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003einstall Nextflow to the current directory\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ebuild the Docker containers included in this repository\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun the Nextflow pipeline using the Docker containers\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMakefile\u003c/code\u003e: shortcuts to common actions used in the demo\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emain.nf\u003c/code\u003e: main Nextflow pipeline file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enextflow.config\u003c/code\u003e: Nextflow configuration file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebin\u003c/code\u003e: directory for scripts to use inside the Nextflow pipeline; its contents will be prepended to your \u003ccode\u003ePATH\u003c/code\u003e when pipeline tasks are executed\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003econtainers\u003c/code\u003e: directory containing Docker and Singularity container files, along with documentation on their setup \u0026amp; usage\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-software-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware Requirements\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-local--remote-hpc-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#local--remote-hpc-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elocal \u0026amp; remote HPC server\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eJava 8 (for Nextflow)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGraphViz Dot (to compile flowchart)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-local-only\" class=\"anchor\" aria-hidden=\"true\" href=\"#local-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elocal only\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDocker version 17.12.0-ce, build c97c6d6\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVagrant version 2.0.1 (for tesing Singularity containers)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-remote-hpc-server-only\" class=\"anchor\" aria-hidden=\"true\" href=\"#remote-hpc-server-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eremote HPC server only\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity version 2.4.2\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1620251806.0 + "updated_at": 1521145930.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity" + "Singularity_recipev1.0", + "Singularity_recipe_R.3.4.1", + "Singularity_add.R_packages", + "Singularity_hicpro_v1", + "Singularity.add_python_packages", + "Singularity_recipe0_part1", + "Singularity.add_g2gtools", + "Singularity_recipev1.0_addR.3.4.3", + "Singularity_recipev1.R-3-4-3", + "Singularity_recipe_MMARGE" ], - "full_name": "pnnlhep/p8compute", + "full_name": "pranithavangala/singularity", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1539125493.0 + "updated_at": 1609299433.0 }, { "data_format": 2, - "description": "Singularity container for PRIMME (PReconditioned Iterative MultiMethod Eigensolver) library", + "description": "Singularity Recipe for GEOS-Chem", "filenames": [ "Singularity" ], - "full_name": "twesterhout/primme-singularity", + "full_name": "geoschem/Singularity_GC", "latest_release": null, + "readme": "\u003ch2\u003e\u003ca id=\"user-content-note-this-repository-is-obsolete-and-has-been-archived\" class=\"anchor\" aria-hidden=\"true\" href=\"#note-this-repository-is-obsolete-and-has-been-archived\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTE: THIS REPOSITORY IS OBSOLETE AND HAS BEEN ARCHIVED\u003c/h2\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [ - "singularity-containers", - "exact-diagonalization", - "svd", - "c" + "geos-chem", + "singularity-container", + "docker-image" ], - "updated_at": 1597061752.0 + "updated_at": 1674873388.0 }, { "data_format": 2, - "description": "Singularity recipe for CRISPR-DAV", + "description": " Build for docker and singularity containers for Multi Atlas", "filenames": [ - "Singularity" + "Singularity", + "Singularity.2.1.0" ], - "full_name": "ISU-HPC/crispr-dav", + "full_name": "VUIIS/Multi_Atlas_app", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecrispr-dav\u003c/h1\u003e\u003ca id=\"user-content-crispr-dav\" class=\"anchor\" aria-label=\"Permalink: crispr-dav\" href=\"#crispr-dav\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for CRISPR-DAV\u003c/p\u003e\n\u003cp\u003eAdapted form the original Docker recipe \u003ca href=\"https://github.com/pinetree1/crispr-dav\"\u003ehttps://github.com/pinetree1/crispr-dav\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-multi_atlas_app\" class=\"anchor\" aria-hidden=\"true\" href=\"#multi_atlas_app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti_Atlas_app\u003c/h1\u003e\n\u003cp\u003eThis includes everything required (except for the \"full-multi-atlas\" directory) to build a docker and corresponding singularity container for the Multi Atlas pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/vuiiscci/multi_atlas/tags/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/734\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Instructions:\u003c/h1\u003e\n\u003cp\u003eJust clone and run \u003ccode\u003ebuild.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vuiiscci/Multi_Atlas_app.git\ncd Multi_Atlas_app/\n./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE that you must have full-multi-atlas directory which contains atlases.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Instructions:\u003c/h1\u003e\n\u003cp\u003eFor docker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run --rm \\\n-v $(pwd)/INPUTS/:/INPUTS/ \\\n-v $(pwd)/OUTPUTS:/OUTPUTS/ \\\n--user $(id -u):$(id -g) \\\nvuiiscci/multi_atlas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e \\\n-B INPUTS/:/INPUTS \\\n-B OUTPUTS/:/OUTPUTS \\\nshub://vuiiscci/Multi_Atlas_app\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1524505816.0 + "updated_at": 1674914637.0 }, { "data_format": 2, - "description": null, + "description": "Virtual Research Environment for Sara Server - container build scripts", "filenames": [ "Singularity" ], - "full_name": "phgenomics-singularity/mash_kmc", + "full_name": "54r4/sara-server-vre", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sara-server-vre\" class=\"anchor\" aria-hidden=\"true\" href=\"#sara-server-vre\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esara-server-vre\u003c/h1\u003e\n\u003cp\u003eVirtual Research Environment for Sara Server - container build scripts\u003c/p\u003e\n\u003cp\u003eThis is the VRE main spec containing a Java Runtime Environment plus Eclipse\nused for the development of the SARA service.\nA local postgres database is integrated, too. The source is a docker repo\nwhich is being pulled on build time and used to locally run a postgresql\nserver using udocker.\nThis VRE has no external requirements whatsoever once the image has been built.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-prebuild-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-prebuild-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse prebuild image\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e cd /tmp\n singularity pull --name \"sara-server-vre.img\" shub://c1t4r/sara-server-vre\n ./sara-server-vre.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-local-image-singularity-23\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-local-image-singularity-23\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild local image (Singularity 2.3)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ecd /tmp\nsingularity create -s 2048 sara-server-vre.img\nsingularity bootstrap sara-server-vre.img ./Singularity\n./sara-server-vre.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-local-image-singularity-24\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-local-image-singularity-24\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild local image (Singularity 2.4)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build sara-server-vre.simg ./Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1576562938.0 + "updated_at": 1546985098.0 }, { "data_format": 2, - "description": "The Singularity file to create an image with nvBowtie and nvBWT. Available on Singularity Hub", + "description": null, "filenames": [ "Singularity" ], - "full_name": "KevinSayers/nvBowtie_Singularity", + "full_name": "markxiao/freesurfer", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003envBowtie_Singularity\u003c/h1\u003e\u003ca id=\"user-content-nvbowtie_singularity\" class=\"anchor\" aria-label=\"Permalink: nvBowtie_Singularity\" href=\"#nvbowtie_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity file to create an image with nvBowtie and nvBWT. Available on Singularity Hub\u003c/p\u003e\n\u003cp\u003eThe Singularity file can be used to bootstrap a container with nvBowtie. This is a GPU enabled Bowtie2 implementation developed by NVIDIA.\u003c/p\u003e\n\u003cp\u003eMore information: \u003ca href=\"https://developer.nvidia.com/nvbio\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/nvbio\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-freesurfer\" class=\"anchor\" aria-hidden=\"true\" href=\"#freesurfer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efreesurfer\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1503243479.0 + "updated_at": 1618603672.0 }, { "data_format": 2, - "description": "Singularity recipe for Pandoc.", + "description": null, "filenames": [ "Singularity" ], - "full_name": "robertodr/pandoc", + "full_name": "markxiao/fsl", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity recipe for Pandoc\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-pandoc\" class=\"anchor\" aria-label=\"Permalink: Singularity recipe for Pandoc\" href=\"#singularity-recipe-for-pandoc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4516\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/4516\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull --name pandoc.sif shub://bast/pandoc\n$ singularity run pandoc.sif --from=markdown --to=rst --output=README.rst README.md\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fsl\" class=\"anchor\" aria-hidden=\"true\" href=\"#fsl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efsl\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1620331522.0 + "updated_at": 1618603672.0 }, { "data_format": 2, - "description": "Docker image for Lattice Element Method", + "description": "R wrapper for bamdb", "filenames": [ - "Singularity" + "src/bamdb/Singularity.bamdb" ], - "full_name": "cb-geo/lem-container", + "full_name": "D-Lo/bambi", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker image for Lattice Element Method\u003c/h1\u003e\u003ca id=\"user-content-docker-image-for-lattice-element-method\" class=\"anchor\" aria-label=\"Permalink: Docker image for Lattice Element Method\" href=\"#docker-image-for-lattice-element-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eKrishna Kumar\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/cbgeo/lem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a191f0a1b6c4e1b41f032c2dd99bc4a5cabbc62a875f08e24da458d24bc4fc6a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f717561792d2d696d6167652d636267656f2d2d6c656d2d6666363962342e737667\" alt=\"Quay image\" data-canonical-src=\"https://img.shields.io/badge/quay--image-cbgeo--lem-ff69b4.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/cbgeo/lem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9982af777dc0e058b77b89824b8290f275c85fe153068c0cc68132431d64dabe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d2d6875622d636267656f2d2d6c656d2d6666363962342e737667\" alt=\"Docker hub\" data-canonical-src=\"https://img.shields.io/badge/docker--hub-cbgeo--lem-ff69b4.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/cb-geo/lem-container\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72966832e29d839ef2277b9d204355a9ff8c427bc681c73ce96b4fd1a443ec92/68747470733a2f2f636972636c6563692e636f6d2f67682f63622d67656f2f6c656d2d636f6e7461696e65722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/cb-geo/lem-container.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.org/cb-geo/docker-lem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9ba1a6181039c2f5cabd4a0f4b027382a31a54459e71aeacf82a64a07bea37a4/68747470733a2f2f6170692e7472617669732d63692e6f72672f63622d67656f2f646f636b65722d6c656d2e737667\" alt=\"Build status\" data-canonical-src=\"https://api.travis-ci.org/cb-geo/docker-lem.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://microbadger.com/images/cbgeo/lem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98e02b5982223671602e697fff2687b7c71a4ba023a12a4fb37767334a36e39e/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f636267656f2f6c656d2e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/cbgeo/lem.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClang 4.0\u003c/li\u003e\n\u003cli\u003eCMake\u003c/li\u003e\n\u003cli\u003eCUDA\u003c/li\u003e\n\u003cli\u003eEigen3\u003c/li\u003e\n\u003cli\u003eGCC 7\u003c/li\u003e\n\u003cli\u003eIntel TBB\u003c/li\u003e\n\u003cli\u003eIntel MKL\u003c/li\u003e\n\u003cli\u003eVim\u003c/li\u003e\n\u003cli\u003eVoro++\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing the docker image\u003c/h1\u003e\u003ca id=\"user-content-using-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Using the docker image\" href=\"#using-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe docker image can be used directly from Quay.io or Docker Hub\u003c/li\u003e\n\u003cli\u003ePull the docker image \u003ccode\u003edocker pull quay.io/cbgeo/lem\u003c/code\u003e or \u003ccode\u003edocker pull cbgeo/lem\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo launch the \u003ccode\u003ecbgeo/ca-abm\u003c/code\u003e docker container, run \u003ccode\u003edocker run -ti cbgeo/lem:latest /bin/bash\u003c/code\u003e or \u003ccode\u003edocker run -ti quay.io/cbgeo/lem:latest /bin/bash\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo login as root\u003c/h1\u003e\u003ca id=\"user-content-to-login-as-root\" class=\"anchor\" aria-label=\"Permalink: To login as root\" href=\"#to-login-as-root\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLaunching docker as root user: \u003ccode\u003edocker exec -u 0 -ti \u0026lt;containerid\u0026gt; /bin/bash\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCreating an image from the docker file\u003c/h1\u003e\u003ca id=\"user-content-creating-an-image-from-the-docker-file\" class=\"anchor\" aria-label=\"Permalink: Creating an image from the docker file\" href=\"#creating-an-image-from-the-docker-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eTo build an image from docker file run as root \u003ccode\u003edocker build -t \"cbgeo/lem\" /path/to/Dockerfile\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edocker history\u003c/code\u003e will show you the effect of each command has on the overall size of the file.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/mskilab/bambi\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/47c82ab2d405aa684f3a5004ed8fc79887c025105127effda9ce1d35b5568974/68747470733a2f2f7472617669732d63692e6f72672f6d736b696c61622f62616d62692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/mskilab/bambi.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/mskilab/bambi?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ccb3814df2f3f1c65e518dd49a10732518ba754f251e50546a0d42ec9fd9cdab/68747470733a2f2f696d672e736869656c64732e696f2f636f6465636f762f632f6769746875622f6d736b696c61622f62616d62692e737667\" alt=\"codecov.io\" data-canonical-src=\"https://img.shields.io/codecov/c/github/mskilab/bambi.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bambi\" class=\"anchor\" aria-hidden=\"true\" href=\"#bambi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi\u003c/h1\u003e\n\u003cp\u003eR package for querying 10x WGS and single-cell BAMs\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/gUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/bamUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-bambi-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#bambi-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi commands\u003c/h2\u003e\n\u003cp\u003eInstantiate a bambi object:\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003egrab_bx()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_bx(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_cb()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_cb(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_ub()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_ub(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003efetch_by_tag()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003efetch_by_tag(tag, tag_queries, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstantiate a \u003ccode\u003ebambi\u003c/code\u003e object\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003elibrary(bambi)\n\n\u0026gt; hcc1143_subset = bambi$new(bam_file = \"subsetHCC1143_phased_possorted0001.bam\", bamdb_path=\"subsetHCC1143_phased_possorted0001_lmdb\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCall methods\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003e\u0026gt; hcc1143_subset$grab_bx(\u0027CGACGTGTCCTCTAGC-1\u0027)\nGRanges object with 2 ranges and 11 metadata columns:\n seqnames ranges strand |\n \u0026lt;Rle\u0026gt; \u0026lt;IRanges\u0026gt; \u0026lt;Rle\u0026gt; |\n [1] chr1 [147975454, 147975580] + |\n [2] chr1 [147975675, 147975824] - |\n qname flag mapq cigar\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;character\u0026gt;\n [1] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 99 16 127M\n [2] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 147 16 150M\n rnext pnext tlen\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt;\n [1] = 147975676 371\n [2] = 147975455 -371\n seq\n \u0026lt;character\u0026gt;\n [1] ATGTCTTCTTCCTCATTATCTGGCACTGGTTAGGAAGCACTCATCTCCATGAAGTCATCTTTTGTTAATTCCTCTGGTGTGGTGTGTATTAGCTCTTAAATTCCTCCAAGATCCATATCTTGCAACC\n [2] ATCTGGACACAAATTGTACTTTTGTCCAGCACGAATTTATTGTTTTGAGTTTCATGGTTTTCTATATCAACTGATGACATCTTGAAAGGTGTAAGCCTTCCAGACTTCCATGATGTTCTCTCTATTGGGTTTCTCTTTTGCAATGTTGAC\n qual\n \u0026lt;character\u0026gt;\n [1] JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJAJFJJJJJJJJJFJJJJJJJJJJFJJJJFFFJJJFJJJJJJAAJFJJJFAFAFFFJAA\u0026lt;7F\u0026lt;\n [2] A\u0026lt;7FFFJFFFAJJAAAJJF\u0026lt;F\u0026lt;7A-\u0026lt;AA-\u0026lt;\u0026lt;\u0026lt;AFFJJJJJJJJFFJAFFAAFJFJJJAFFJJJJJJJJJJFJFAJJJJJJFJJJJJJ\u0026lt;FFJJJFJJJFJJJJJJJJJJJJJFJJJJFFJ7JJJJF\u0026lt;JJJJJJJJJJJJJJJJJJJFFAA\u0026lt;\n BX qwidth\n \u0026lt;character\u0026gt; \u0026lt;integer\u0026gt;\n [1] CGACGTGTCCTCTAGC-1 127\n [2] CGACGTGTCCTCTAGC-1 150\n -------\n seqinfo: 1 sequence from an unspecified genome; no seqlengths\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "lem", - "container" - ], - "updated_at": 1518214261.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1531085438.0 }, { "data_format": 2, - "description": "Singularity Image for NLPipe", + "description": "Singularity Recipe for Tofu2", "filenames": [ + "Singularity.v17", "Singularity" ], - "full_name": "vanatteveldt/nlpipe-singularity", + "full_name": "ResearchIT/tofu2", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enlpipe-singularity\u003c/h1\u003e\u003ca id=\"user-content-nlpipe-singularity\" class=\"anchor\" aria-label=\"Permalink: nlpipe-singularity\" href=\"#nlpipe-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Image for NLPipe\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre lang=\"{sh}\"\u003e\u003ccode\u003esudo singularity build nlpipe.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun the alpino service:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre lang=\"{sh}\"\u003e\u003ccode\u003esingularity instance.start nlpipe.img nlpipe\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the worker:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre lang=\"{sh}\"\u003e\u003ccode\u003esingularity run --app worker instance://nlpipe /tmp/nlpipedata alpinocoref\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eTo test, run (in a separate shell) the client:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre lang=\"{sh}\"\u003e\u003ccode\u003esingularity run --app client instance://nlpipe /tmp/nlpipedata alpinocoref process_inline \"dit is een test\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich should print an XML file after about 10 seconds.\u003c/p\u003e\n\u003cp\u003e(Note: these are my very first steps into singularity-land, so all feedback is appreciated!)\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-for-tofu2\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-for-tofu2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipe for Tofu2\u003c/h1\u003e\n\u003cp\u003eThis repo contains recipes to run \u003ca href=\"https://github.com/PacificBiosciences/IsoSeq_SA3nUP/wiki/%5BBeta%5D-ToFU2:-running-and-installing-ToFU2#install\"\u003eTofu2\u003c/a\u003e\nwithin a \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eVersions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ev17 - Tofu2 installed on Ubuntu\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Use:\u003c/h2\u003e\n\u003cp\u003eRun example:\u003c/p\u003e\n\u003cp\u003esingularity run shub://ResearchIT/tofu2 run_preCluster.py --cpus=4\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-alternative-method\" class=\"anchor\" aria-hidden=\"true\" href=\"#alternative-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternative method:\u003c/h2\u003e\n\u003cp\u003euse the provided bash wrapper and module file to use the tofu2 singularity container like a standard module\n(this assumes you have a singularity/2.4 module)\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003emodule load tofu2/v17\ntofu2 run_preCluster.py --cpus=4\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1515629557.0 + "subscribers_count": 6, + "topics": [ + "tofu", + "pacbio", + "singularity" + ], + "updated_at": 1522255502.0 }, { "data_format": 2, @@ -6069,822 +5779,803 @@ var data = "filenames": [ "Singularity" ], - "full_name": "singularityhub/redis", + "full_name": "tanhnhn/singularityhub-sregistry", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRedis\u003c/h1\u003e\u003ca id=\"user-content-redis\" class=\"anchor\" aria-label=\"Permalink: Redis\" href=\"#redis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official library of redis builds for Singularity images hosted on Singularity Hub. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003eeach branch corresponds with a different verison, or tag.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create redis.img\nsudo singularity bootstrap redis.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-registry\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Registry\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"http://joss.theoj.org/papers/050362b7e7691d2a5d0ebed8251bc01e\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4cb65855144c475cbe5584c579404a17e3e6984f958da24427dbe46b6202eb3c/687474703a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f30353033363262376537363931643261356430656265643832353162633031652f7374617475732e737667\" alt=\"status\" data-canonical-src=\"http://joss.theoj.org/papers/050362b7e7691d2a5d0ebed8251bc01e/status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.1012531\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/411f713db9ba01edfcb60386aaa1dff3e4ed4464707b95d889900a88d8f54936/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313031323533312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1012531.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://singularityhub.github.io/sregistry\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-singularity-registry\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-singularity-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is Singularity Registry\u003c/h2\u003e\n\u003cp\u003eSingularity Registry is a management and storage of Singularity images for an institution or user to deploy locally. It does not manage building, but serves endpoints to obtain and save containers. The Registry is expected to be available for use in the Fall.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-images-included\" class=\"anchor\" aria-hidden=\"true\" href=\"#images-included\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages Included\u003c/h2\u003e\n\u003cp\u003eSingularity Registry consists of several Docker images, and they are integrated to work together using \u003ca href=\"docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e. The images are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003evanessa/sregistry\u003c/strong\u003e: is the main uwsgi application, which serves a Django (python-based) application.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003enginx\u003c/strong\u003e: pronounced (engine-X) is the webserver. The starter application is configured for http, however you should follow the instructions to set up https properly.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eworker\u003c/strong\u003e: is the same uwsgi image, but with a running command that is specialized to perform tasks. The tasks are run via \u003ca href=\"http://www.celeryproject.org/\" rel=\"nofollow\"\u003ecelery\u003c/a\u003e, a distributed job queue that fits nicely into Django. The celery worker uses a\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eredis\u003c/strong\u003e: database to organize the jobs themselves.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more information about Singularity Registry, please reference the \u003ca href=\"https://singularityhub.github.io/sregistry\" rel=\"nofollow\"\u003edocs\u003c/a\u003e. If you have any issues, please \u003ca href=\"https://github.com/singularityhub/sregistry/issues\"\u003elet me know\u003c/a\u003e!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis code is licensed under the Affero GPL, version 3.0 or later \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1484275274.0 + "updated_at": 1513562903.0 }, { "data_format": 2, - "description": "official build specifications for picard tools", + "description": "Singularity container for samtools ", "filenames": [ - "Singularity" + "Singularity", + "old/Singularity.v1.6" ], - "full_name": "researchapps/picard", + "full_name": "stevekm/singularity-samtools-demo", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePicard\u003c/h1\u003e\u003ca id=\"user-content-picard\" class=\"anchor\" aria-label=\"Permalink: Picard\" href=\"#picard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official build file repository for the Picard software for Singularity Hub.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 picard.img\nsudo singularity bootstrap picard.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the picard runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./picard.img [args] ...\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h1\u003e\n\u003cp\u003eThis assumes you are building a Singularity container locally on a Mac\u003c/p\u003e\n\u003cp\u003eMake sure you\u0027ve already installed Vagrant, since its needed to run Singularity on a Mac\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebrew cask install virtualbox\nbrew cask install vagrant\nbrew cask install vagrant-manager\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have trouble install Vagrant with homebrew, try using \u003ca href=\"https://releases.hashicorp.com/vagrant/2.0.1/vagrant_2.0.1_x86_64.dmg\" rel=\"nofollow\"\u003ethis\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-creating-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating the Container\u003c/h1\u003e\n\u003cp\u003eThe workflow for creating a Singularity container on a Mac through Vagrant is saved in the included \u003ccode\u003eMakefile\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eMake the container by running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake container\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd run a test on the created container with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003eIf everything worked, the following files should be created:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esingularity-vm/image/singularity-container-samtools\u003c/code\u003e: the Singularity container file for samtools\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esingularity-vm/image/samtools-version.txt\u003c/code\u003e: the output from running samtools inside the container, should look like this:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esamtools 1.6\nUsing htslib 1.6\nCopyright (C) 2017 Genome Research Ltd.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/install-mac\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://app.vagrantup.com/singularityware/boxes/singularity-2.4\" rel=\"nofollow\"\u003ehttps://app.vagrantup.com/singularityware/boxes/singularity-2.4\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://releases.hashicorp.com/vagrant/2.0.1/vagrant_2.0.1_x86_64.dmg\" rel=\"nofollow\"\u003ehttps://releases.hashicorp.com/vagrant/2.0.1/vagrant_2.0.1_x86_64.dmg\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/docs-build-container\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/docs-build-container\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/docs-recipes\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/docs-recipes\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/qbicsoftware/qbic-singularity-samtools\"\u003ehttps://github.com/qbicsoftware/qbic-singularity-samtools\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [], - "updated_at": 1484507753.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "singularity-container" + ], + "updated_at": 1521728818.0 }, { "data_format": 2, - "description": "Gym Environment to simulate the Layout Problem as a Markov Decision Process to be solved by Reinforcement Learning", + "description": "FEniCS containers for CARC systems", "filenames": [ - "Singularity.def" + "Singularity.docker", + "Singularity.ubuntu" ], - "full_name": "hendrikunger/factorySim", + "full_name": "UNM-CARC/FEniCS", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003efactorySim\u003c/h1\u003e\u003ca id=\"user-content-factorysim\" class=\"anchor\" aria-label=\"Permalink: factorySim\" href=\"#factorysim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGym Environment to simulate the Layout Problem as a Markov Decision Process to be solved by Reinforcement Learning\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning instructions\u003c/h2\u003e\u003ca id=\"user-content-running-instructions\" class=\"anchor\" aria-label=\"Permalink: Running instructions\" href=\"#running-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse Docker host with Nvidia drivers installed.\nClone repository to Docker host.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/hendrikunger/factorySim.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e factorySim\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Docker image using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t factorysim \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun image with appropriate command e.g.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm -it --gpus all --shm-size=12gb factorysim:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eshm-size needs to be greater than 30% of RAM of Docker host\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll files from github repository are located in the default location /home/ray/factorySim. Training scripts can be run from this location as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeveloping instructions\u003c/h2\u003e\u003ca id=\"user-content-developing-instructions\" class=\"anchor\" aria-label=\"Permalink: Developing instructions\" href=\"#developing-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone Repository to your local machine or use Docker container from above\nNavigate to the factorySim/env directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/hendrikunger/factorySim.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e factorySim\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are not using docker you need to install dependecies using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapt-get update\napt-get install build-essential ibcairo2-dev pkg-config python3-dev\npip install -r requirements_factorySim.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIFC Open Shell is not in the index and needs to be installed manually.\nDownload appropriate version from \u003ca href=\"http://ifcopenshell.org/python\" rel=\"nofollow\"\u003ehttp://ifcopenshell.org/python\u003c/a\u003e and unpack into site packages directory of your Python installation.\ne.g.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget https://s3.amazonaws.com/ifcopenshell-builds/ifcopenshell-python-37-v0.6.0-517b819-linux64.zip\nunzip -q ifcopenshell-python-37-v0.6.0-517b819-linux64.zip -d \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/anaconda3/lib/python3.7/site-packages\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNavigate to the factorySim/env directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e env\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBuild a local package of factorySim using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m pip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fenics\" class=\"anchor\" aria-hidden=\"true\" href=\"#fenics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFEniCS\u003c/h1\u003e\n\u003cp\u003eThis repository contains a FEniCS container for UNM CARC high performance systems\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.docker - Singularity container built from the standard FEniCS docker container\u003c/li\u003e\n\u003cli\u003eSingularity.ubuntu - Singularity container built from the FEniCS ubuntu packages\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1716906355.0 + "updated_at": 1511832970.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipes for CARC systems", "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/latest/Singularity", - "misc/releases/22.12/Singularity.22.12", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06" + "Singularity.centos", + "Singularity.ubuntu-ompi", + "Singularity.ubuntu-mpich" ], - "full_name": "ipc2023-classical/planner1", + "full_name": "UNM-CARC/singularity-test", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScorpion\u003c/h1\u003e\u003ca id=\"user-content-scorpion\" class=\"anchor\" aria-label=\"Permalink: Scorpion\" href=\"#scorpion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eScorpion is a classical planning system that extends \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003eFast\nDownward\u003c/a\u003e. The main extensions are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enovel state-of-the-art algorithms for optimal classical planning\u003c/li\u003e\n\u003cli\u003eadditional search algorithms\u003c/li\u003e\n\u003cli\u003eseveral new plugin options and utilities\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"#differences-between-scorpion-and-fast-downward\"\u003ebelow\u003c/a\u003e for a detailed list\nof extensions. We regularly port the latest changes from Fast Downward to\nScorpion and also integrate some features from Scorpion back into Fast Downward.\u003c/p\u003e\n\u003cp\u003ePlease use the following reference when citing Scorpion:\nJendrik Seipp, Thomas Keller and Malte Helmert.\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003eSaturated Cost Partitioning for Optimal Classical Planning\u003c/a\u003e.\nJournal of Artificial Intelligence Research 67, pp. 129-167. 2020.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions\u003c/h2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the dependencies (the table below lists which versions are tested):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install cmake g++ git make python3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor plugins based on linear programming (e.g., \u003ccode\u003eocp()\u003c/code\u003e, \u003ccode\u003epho()\u003c/code\u003e) you need\nto \u003ca href=\"https://www.fast-downward.org/LPBuildInstructions\" rel=\"nofollow\"\u003eadd an LP solver\u003c/a\u003e. Then\ncompile the planner with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand see the available options with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py --help # driver\n./fast-downward.py --search -- --help # search component\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more details (including build instructions for macOS and Windows), see the\ndocumentation about\n\u003ca href=\"https://www.fast-downward.org/ObtainingAndRunningFastDownward\" rel=\"nofollow\"\u003ecompiling\u003c/a\u003e\nand \u003ca href=\"https://www.fast-downward.org/PlannerUsage\" rel=\"nofollow\"\u003erunning\u003c/a\u003e the planner. The\n\u003ca href=\"https://jendrikseipp.github.io/scorpion\" rel=\"nofollow\"\u003eplugin documentation\u003c/a\u003e shows\nwhich plugins are available (heuristics, search algorithms, etc.) and how\nto use them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRecommended configuration\u003c/h3\u003e\u003ca id=\"user-content-recommended-configuration\" class=\"anchor\" aria-label=\"Permalink: Recommended configuration\" href=\"#recommended-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend using the following configuration:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py \\\n --transform-task preprocess-h2 \\\n ../benchmarks/gripper/prob01.pddl \\\n --search \"astar(scp_online([\n projections(sys_scp(max_time=100, max_time_per_restart=10)),\n cartesian()],\n saturator=perimstar, max_time=1000, interval=10K, orders=greedy_orders()),\n pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003epreprocess-h2\u003c/code\u003e call prunes irrelevant operators in a preprocessing\nstep. The search configuration uses \u003ca href=\"https://ojs.aaai.org/index.php/SOCS/article/view/18535\" rel=\"nofollow\"\u003epartial order\nreduction\u003c/a\u003e and\nmaximizes over\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003ediverse\u003c/a\u003e,\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/3503\" rel=\"nofollow\"\u003esubset-saturated\u003c/a\u003e\ncost partitioning heuristics computed\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/15976/\" rel=\"nofollow\"\u003eonline\u003c/a\u003e during\nthe search. The underlying abstractions are \u003ca href=\"https://www.ijcai.org/proceedings/2019/780\" rel=\"nofollow\"\u003eSys-SCP pattern\ndatabases\u003c/a\u003e and \u003ca href=\"https://jair.org/index.php/jair/article/view/11217\" rel=\"nofollow\"\u003eCartesian\nabstractions\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e(In \u003ca href=\"https://lab.readthedocs.io/\" rel=\"nofollow\"\u003eDownward Lab\u003c/a\u003e you can use\n\u003ccode\u003eadd_algorithm(name=\"scorpion\", repo=\"path/to/repo\", rev=\"scorpion\", component_options=[], driver_options=[\"--transform-task\", \"preprocess-h2\", \"--alias\", \"scorpion\"]\u003c/code\u003e to run the recommended Scorpion configuration.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eApptainer image\u003c/h4\u003e\u003ca id=\"user-content-apptainer-image\" class=\"anchor\" aria-label=\"Permalink: Apptainer image\" href=\"#apptainer-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo simplify the installation process, we provide an executable\n\u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e container (formerly known as Singularity).\nIt accepts the same arguments as the \u003ccode\u003efast-downward.py\u003c/code\u003e script (see above).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Download the image,\napptainer pull scorpion.sif oras://ghcr.io/jendrikseipp/scorpion:latest\n\n# or build it yourself.\napptainer build scorpion.sif Apptainer\n\n# Then run recommended configuration (available via \"scorpion\" alias).\n./scorpion.sif --transform-task preprocess-h2 --alias scorpion PROBLEM_FILE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIPC 2018 version\u003c/h3\u003e\u003ca id=\"user-content-ipc-2018-version\" class=\"anchor\" aria-label=\"Permalink: IPC 2018 version\" href=\"#ipc-2018-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you prefer to run the Scorpion version from IPC 2018 (which uses an\nolder Fast Downward version and different abstractions), we recommend\nusing the \u003ca href=\"https://bitbucket.org/ipc2018-classical/team44/src/ipc-2018-seq-opt/\" rel=\"nofollow\"\u003eScorpion IPC\nrepo\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDifferences between Scorpion and Fast Downward\u003c/h2\u003e\u003ca id=\"user-content-differences-between-scorpion-and-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Differences between Scorpion and Fast Downward\" href=\"#differences-between-scorpion-and-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDiff between the latest merged version of Fast Downward and Scorpion:\n\u003ca href=\"https://github.com/jendrikseipp/scorpion/compare/main...scorpion\"\u003ehttps://github.com/jendrikseipp/scorpion/compare/main...scorpion\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScorpion comes with the\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/13708\" rel=\"nofollow\"\u003eh\u00b2-preprocessor\u003c/a\u003e\nby Vidal Alc\u00e1zar and \u00c1lvaro Torralba that prunes irrelevant operators.\nPass \u003ccode\u003e--transform-task preprocess-h2\u003c/code\u003e to use it.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003e--transform-task\u003c/code\u003e command allows you to run arbitrary preprocessing\ncommands that transform the SAS+ output from the translator before\npassing it to the search.\u003c/li\u003e\n\u003cli\u003eScorpion uses a\n\u003ca href=\"https://github.com/greg7mdp/parallel-hashmap\"\u003ephmap::flat_hash_set\u003c/a\u003e to check\nfor duplicate states, which often drastically reduces the peak memory usage,\ncompared to Fast Downward\u0027s \u003ccode\u003eIntHashSet\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf \u003ca href=\"https://ccache.dev/\" rel=\"nofollow\"\u003eccache\u003c/a\u003e is installed (recommended), Scorpion\nuses it to cache compilation files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew translator options\u003c/h3\u003e\u003ca id=\"user-content-new-translator-options\" class=\"anchor\" aria-label=\"Permalink: New translator options\" href=\"#new-translator-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse \u003ccode\u003e--dump-predicates\u003c/code\u003e and \u003ccode\u003e--dump-static-atoms\u003c/code\u003e to write files with\ninformation that\u0027s useful for learning domain control knowledge.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew plugin options\u003c/h3\u003e\u003ca id=\"user-content-new-plugin-options\" class=\"anchor\" aria-label=\"Permalink: New plugin options\" href=\"#new-plugin-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., search_strategy=incremental)\u003c/code\u003e: use \u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/6667\" rel=\"nofollow\"\u003eincremental search for\nCartesian abstraction\nrefinement\u003c/a\u003e\n(default).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., pick_flawed_abstract_state={batch_min_h, ...})\u003c/code\u003e:\nfind all current flaws, then iteratively repair the flaw that\u0027s closest to the goal\n(\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/19819\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, default=\u003ccode\u003ebatch_min_h\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., pick_split={max_cover, ...}, tiebreak_split={max_refined, ...})\u003c/code\u003e:\nsmarter strategies for splitting a flawed abstract state\n(\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/19819\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, default=\u003ccode\u003emax_cover\u003c/code\u003e\nand \u003ccode\u003emax_refined\u003c/code\u003e for tiebreaking).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar,cartesian}(..., dot_graph_verbosity={silent, write_to_console, write_to_file})\u003c/code\u003e:\nwrite intermediate abstractions as Graphviz dot files to stdout or to files (default=\u003ccode\u003esilent\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ehillclimbing(..., max_generated_patterns=200)\u003c/code\u003e: limit the number of\npatterns generated by hill climbing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esystematic(..., pattern_type=interesting_general)\u003c/code\u003e: compute interesting\npatterns for general cost partitioning.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew cost partitioning algorithms for abstraction heuristics\u003c/h3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-abstraction-heuristics\" class=\"anchor\" aria-label=\"Permalink: New cost partitioning algorithms for abstraction heuristics\" href=\"#new-cost-partitioning-algorithms-for-abstraction-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe use Cartesian abstractions in the example configurations below\n(\u003ccode\u003e[cartesian()]\u003c/code\u003e). You can also use pattern database heuristics, e.g.,\n\u003ccode\u003e[projections(systematic(2))]\u003c/code\u003e, or mix abstractions, e.g.,\n\u003ccode\u003e[projections(systematic(3)), cartesian()]\u003c/code\u003e. Some of the algorithms below\nare also part of vanilla Fast Downward, but are only implemented for PDB\nheuristics.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning:\n\u003ccode\u003eocp([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003ecanonical_heuristic([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], opportunistic=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], ..., opportunistic=true)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003egzocp([cartesian()], ...)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003escp([cartesian()], ...)\u003c/code\u003e (offline), \u003ccode\u003escp_online([cartesian()], ...)\u003c/code\u003e (online)\u003c/li\u003e\n\u003cli\u003e(Saturated) post-hoc optimization:\n\u003ccode\u003epho([cartesian()], ..., saturated={false,true})\u003c/code\u003e (offline),\n\u003ccode\u003eoperatorcounting([pho_abstraction_constraints([cartesian()], saturated={false,true})])\u003c/code\u003e (online)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can also compute the maximum over abstraction heuristics:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emaximize([cartesian()])\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe plugin documentation shows all options for \u003ca href=\"https://jendrikseipp.github.io/scorpion/Evaluator/#cost_partitioning_heuristics\" rel=\"nofollow\"\u003ecost partitioning\nheuristics\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNew pattern collection generators\u003c/h2\u003e\u003ca id=\"user-content-new-pattern-collection-generators\" class=\"anchor\" aria-label=\"Permalink: New pattern collection generators\" href=\"#new-pattern-collection-generators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSystematic patterns with size limits:\n\u003ccode\u003esys_scp(max_pattern_size=X, max_pdb_size=Y, max_collection_size=Z, ..., saturate=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSys-SCP patterns:\n\u003ccode\u003esys_scp(...)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew cost partitioning algorithms for landmark heuristics\u003c/h3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-landmark-heuristics\" class=\"anchor\" aria-label=\"Permalink: New cost partitioning algorithms for landmark heuristics\" href=\"#new-cost-partitioning-algorithms-for-landmark-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExample using A* search and saturated cost partitioning over BJOLP\nlandmarks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--evaluator\n \"lmc=lmcount(lm_merged([lm_rhw(), lm_hm(m=1)]),\n admissible=true, cost_partitioning=suboptimal, greedy=true,\n reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\"\n--search\n \"astar(lmc, lazy_evaluator=lmc)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDifferent cost partitioning algorithms (all need \u003ccode\u003eadmissible=true\u003c/code\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=optimal)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003elmcount(..., cost_partitioning=canonical)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePost-hoc optimization:\n\u003ccode\u003elmcount(..., cost_partitioning=pho)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=true, scoring_function=min_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=false, scoring_function=max_heuristic)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNew search engines\u003c/h2\u003e\u003ca id=\"user-content-new-search-engines\" class=\"anchor\" aria-label=\"Permalink: New search engines\" href=\"#new-search-engines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBreadth-first search (without overhead of the more general \u003ccode\u003eeager()\u003c/code\u003e search):\n\u003ccode\u003ebrfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDepth-first search:\n\u003ccode\u003edfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExhaustive search (useful for dumping the reachable state space of small input tasks):\n\u003ccode\u003edump_reachable_search_space()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIDA* search:\n\u003ccode\u003eidastar(cegar(cache_estimates=false))\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIterative width search:\n\u003ccode\u003eiw(width=2)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Tests\u003c/h1\u003e\n\u003cp\u003eThis repository contains test singularity recipes for Ubuntu and CentOS repository builds for\nHPC systems at the UNM Center for Advanced Research Computing. These recipes are generally built\nusing Singularity Hub, which links to this repository, and are meant for debugging basic\ncontainer setups that are then used to develop other more complex recipes.\u003c/p\u003e\n\u003cp\u003eNote that these containers pull the CARC modules //into// the containers when they run so that\ncode compiled outside the container can run inside the container. That\u0027s rarely something you want to\ndo, as one of the main point of containers is that they\u0027re stable and reproducible.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1688990534.0 + "updated_at": 1536783389.0 }, { "data_format": 2, - "description": null, + "description": "Repository used to build Singularity containers of HD software", "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/22.12/Singularity.22.12", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06" + "Singularity" ], - "full_name": "fai-saarland/fd-action-policy-testing", + "full_name": "faustus123/hdsingularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAutomatic Metamorphic Test Oracles for Action-Policy Testing\u003c/h1\u003e\u003ca id=\"user-content-automatic-metamorphic-test-oracles-for-action-policy-testing\" class=\"anchor\" aria-label=\"Permalink: Automatic Metamorphic Test Oracles for Action-Policy Testing\" href=\"#automatic-metamorphic-test-oracles-for-action-policy-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source code for action-policy testing based on the paper \"Automatic Metamorphic Test Oracles for Action-Policy Testing\".\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{eisenhut-et-al-icaps23, \n title = {Automatic Metamorphic Test Oracles for Action-Policy Testing},\n author = {Eisenhut, Jan and Torralba, \u00c1lvaro and Christakis, Maria and Hoffmann, J\u00f6rg},\n booktitle = {Proceedings of the 33rd International Conference on Automated Planning and Scheduling ({ICAPS}\u002723), 2023},\n year = {2023}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe build on the Fast Downward fuzzing extension by Steinmetz and others (whose source code can be accessed \u003ca href=\"https://doi.org/10.5281/zenodo.6323289\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{steinmetz-et-al-icaps22, \n title = {Debugging a Policy: Automatic Action-Policy Testing in AI Planning},\n author = {Steinmetz, Marcel and Fi\u0161er, Daniel and Eniser, Hasan Ferit and Ferber, Patrick and Gros, Timo P. and Heim, Philippe and H\u00f6ller, Daniel and Schuler, Xandra and W\u00fcstholz, Valentin and Christakis, Maria and Hoffmann, J\u00f6rg},\n booktitle = {Proceedings of the 32nd International Conference on Automated Planning and Scheduling ({ICAPS}\u002722), 2022},\n doi = {10.1609/icaps.v32i1.19820}, \n url = {https://ojs.aaai.org/index.php/ICAPS/article/view/19820}, \n year = {2022}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe computation of dominance functions is based on:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{torralba-ijcai2017,\n author = {Torralba, \u00c1lvaro},\n title = {From Qualitative to Quantitative Dominance Pruning for Optimal Planning},\n booktitle = {Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, {IJCAI-17}},\n pages = {4426--4432},\n doi = {10.24963/ijcai.2017/618},\n url = {https://doi.org/10.24963/ijcai.2017/618},\n year = {2017},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the Project\u003c/h2\u003e\u003ca id=\"user-content-building-the-project\" class=\"anchor\" aria-label=\"Permalink: Building the Project\" href=\"#building-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease build the project as a part of the \u003ca href=\"https://github.com/fai-saarland/bughive/\"\u003ethe bughive framework\u003c/a\u003e using the Makefile provided there. The Makefile target is \u003ccode\u003efd-action-policy-testing\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eYou need to have \u003ca href=\"https://www.boost.org/\" rel=\"nofollow\"\u003eboost\u003c/a\u003e installed. The minimum required version is \u003ccode\u003e1.74\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCode\u003c/h2\u003e\u003ca id=\"user-content-code\" class=\"anchor\" aria-label=\"Permalink: Code\" href=\"#code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe related to policy testing is primarily located in \u003ccode\u003esrc/search/policy_testing/\u003c/code\u003e.\nThe source code for the computation of dominance functions can be found in \u003ccode\u003esrc/search/policy_testing/simulations/\u003c/code\u003e.\nWe made slight modifications to the code base since we ran the experiments for the paper, e.g., to make sure one can compile the source code using more recent compilers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheck out the test drivers in \u003ca href=\"https://github.com/fai-saarland/bughive/tree/master/test_drivers\"\u003ethe bughive repository\u003c/a\u003e to learn how to easily invoke the tool.\u003c/p\u003e\n\u003cp\u003eA possible search configuration could be \u003ccode\u003epool_fuzzer(testing_method=\u0026lt;oracle\u0026gt;)\u003c/code\u003e, where \u003ccode\u003e\u0026lt;oracle\u0026gt;\u003c/code\u003e is one of the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003earas(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eatomic_unrelaxation_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ebounded_lookahead_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecomposite_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edummy_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eestimator_based_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eiterative_improvement_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eunrelaxation_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003eiterative_improvement_oracle\u003c/code\u003e implements the bound maintenance oracles (BMOs), while \u003ccode\u003eatomic_unrelaxation_oracle\u003c/code\u003e and \u003ccode\u003eunrelaxation_oracle\u003c/code\u003e implement state morphing oracles (SMOs).\u003c/p\u003e\n\u003cp\u003eYou can learn about the options supported by an oracle (say \u003ccode\u003eunrelaxation_oracle\u003c/code\u003e) by calling \u003ccode\u003e./fast-downward.py --search -- --help unrelaxation_oracle\u003c/code\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hdsingularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#hdsingularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehdsingularity\u003c/h1\u003e\n\u003cp\u003eRepository used to build Singularity containers of HD software\u003c/p\u003e\n\u003cp\u003eCheckout singularity-hub.org for details\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1708951067.0 + "updated_at": 1501591637.0 }, { "data_format": 2, - "description": null, + "description": "Python from source for use with singularity", "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/latest/Singularity", - "misc/releases/22.12/Singularity.22.12", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06" + "Singularity" ], - "full_name": "ipc2023-classical/planner4", + "full_name": "sbutcher/container-python", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIPC 2023 Apptainer Recipes\u003c/h2\u003e\u003ca id=\"user-content-ipc-2023-apptainer-recipes\" class=\"anchor\" aria-label=\"Permalink: IPC 2023 Apptainer Recipes\" href=\"#ipc-2023-apptainer-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe optimal configuration of our planner requires LP support. To build\nthe Apptainer recipe, you need an installer for CPLEX at a location\navailable under $IPC_THIRD_PARTY. We use version 22.1.1 for the\ncompetition, but in theory any version of CPLEX should be fine. For the\nApptainer recipe to work out of the box, the installer needs to be\nnamed as follows: cplex_studio2211.linux_x86_64.bin\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 10, Clang 12\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\nThe third party software OSI shipped within this repository is\nlicensed under the Eclipse Public License version 2.0 (EPL 2.0).\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-python\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer-python\u003c/h1\u003e\n\u003cp\u003ePython from source for use with singularity\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1688990844.0 + "updated_at": 1525427896.0 }, { "data_format": 2, - "description": null, + "description": "singularity container for use with singularity hub", "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/latest/Singularity", - "misc/releases/22.12/Singularity.22.12", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06" + "Singularity" ], - "full_name": "ipc2023-classical/planner28", + "full_name": "sbutcher/container-R", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIPC 2023 Apptainer Recipes\u003c/h2\u003e\u003ca id=\"user-content-ipc-2023-apptainer-recipes\" class=\"anchor\" aria-label=\"Permalink: IPC 2023 Apptainer Recipes\" href=\"#ipc-2023-apptainer-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe optimal configurations of our planner require LP support. To build\nthe Apptainer recipes, you need an installer for CPLEX at a location\navailable under $IPC_THIRD_PARTY. We use version 22.1.1 for the\ncompetition, but in theory any version of CPLEX is fine. For the\nApptainer recipe to work out of the box, the installer needst to be\nnamed as follows: cplex_studio2211.linux_x86_64.bin\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 10, Clang 12\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\nThe third party software OSI shipped within this repository is\nlicensed under the Eclipse Public License version 2.0 (EPL 2.0).\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-r\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer-R\u003c/h1\u003e\n\u003cp\u003esingularity container for use with singularity hub\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 3, "topics": [], - "updated_at": 1688990783.0 + "updated_at": 1525440620.0 }, { "data_format": 2, "description": null, "filenames": [ - "samples/Singularity/filenames/Singularity" + "Singularity" ], - "full_name": "0xahu/mywork", + "full_name": "touala/rce_tools", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLinguist\u003c/h1\u003e\u003ca id=\"user-content-linguist\" class=\"anchor\" aria-label=\"Permalink: Linguist\" href=\"#linguist\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/github/linguist/actions\"\u003e\u003cimg src=\"https://github.com/github/linguist/workflows/Run%20Tests/badge.svg\" alt=\"Actions Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://codespaces.new/github-linguist/linguist\" rel=\"nofollow\"\u003e\u003cimg src=\"https://github.com/codespaces/badge.svg\" alt=\"Open in GitHub Codespaces\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis library is used on GitHub.com to detect blob languages, ignore binary or vendored files, suppress generated files in diffs, and generate language breakdown graphs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"/docs/how-linguist-works.md\"\u003eHow Linguist works\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/docs/overrides.md\"\u003eChange Linguist\u0027s behaviour with overrides\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eContributing guidelines\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the gem:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egem install github-linguist\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLinguist is a Ruby library so you will need a recent version of Ruby installed.\nThere are known problems with the macOS/Xcode supplied version of Ruby that causes problems installing some of the dependencies.\nAccordingly, we highly recommend you install a version of Ruby using Homebrew, \u003ccode\u003erbenv\u003c/code\u003e, \u003ccode\u003ervm\u003c/code\u003e, \u003ccode\u003eruby-build\u003c/code\u003e, \u003ccode\u003easdf\u003c/code\u003e or other packaging system, before attempting to install Linguist and the dependencies.\u003c/p\u003e\n\u003cp\u003eLinguist uses \u003ca href=\"https://github.com/brianmario/charlock_holmes\"\u003e\u003ccode\u003echarlock_holmes\u003c/code\u003e\u003c/a\u003e for character encoding and \u003ca href=\"https://github.com/libgit2/rugged\"\u003e\u003ccode\u003erugged\u003c/code\u003e\u003c/a\u003e for libgit2 bindings for Ruby.\nThese components have their own dependencies.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003echarlock_holmes\n\u003cul\u003e\n\u003cli\u003ecmake\u003c/li\u003e\n\u003cli\u003epkg-config\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://site.icu-project.org/\" rel=\"nofollow\"\u003eICU\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zlib.net/\" rel=\"nofollow\"\u003ezlib\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003erugged\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://curl.haxx.se/libcurl/\" rel=\"nofollow\"\u003elibcurl\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.openssl.org\" rel=\"nofollow\"\u003eOpenSSL\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou may need to install missing dependencies before you can install Linguist.\nFor example, on macOS with \u003ca href=\"http://brew.sh/\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew install cmake pkg-config icu4c\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn Ubuntu:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get install build-essential cmake pkg-config libicu-dev zlib1g-dev libcurl4-openssl-dev libssl-dev ruby-dev\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eApplication usage\u003c/h3\u003e\u003ca id=\"user-content-application-usage\" class=\"anchor\" aria-label=\"Permalink: Application usage\" href=\"#application-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLinguist can be used in your application as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ruby\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003erequire\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027rugged\u0027\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003erequire\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027linguist\u0027\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003erepo\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eRugged\u003c/span\u003e::\u003cspan class=\"pl-v\"\u003eRepository\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003enew\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027.\u0027\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eproject\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eLinguist\u003c/span\u003e::\u003cspan class=\"pl-v\"\u003eRepository\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003enew\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003erepo\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erepo\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ehead\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003etarget_id\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eproject\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elanguage\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e#=\u0026gt; \"Ruby\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eproject\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elanguages\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e#=\u0026gt; { \"Ruby\" =\u0026gt; 119387 }\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand line usage\u003c/h3\u003e\u003ca id=\"user-content-command-line-usage\" class=\"anchor\" aria-label=\"Permalink: Command line usage\" href=\"#command-line-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGit Repository\u003c/h4\u003e\u003ca id=\"user-content-git-repository\" class=\"anchor\" aria-label=\"Permalink: Git Repository\" href=\"#git-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA repository\u0027s languages stats can also be assessed from the command line using the \u003ccode\u003egithub-linguist\u003c/code\u003e executable.\nWithout any options, \u003ccode\u003egithub-linguist\u003c/code\u003e will output the language breakdown by percentage and file size.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /path-to-repository\ngithub-linguist\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can try running \u003ccode\u003egithub-linguist\u003c/code\u003e on the root directory in this repository itself:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e66.84% 264519 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e24.68% 97685 C\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e6.57% 25999 Go\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.29% 5098 Lex\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.32% 1257 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.31% 1212 Dockerfile\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAdditional options\u003c/h4\u003e\u003ca id=\"user-content-additional-options\" class=\"anchor\" aria-label=\"Permalink: Additional options\" href=\"#additional-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003e\u003ccode\u003e--rev REV\u003c/code\u003e\u003c/h5\u003e\u003ca id=\"user-content---rev-rev\" class=\"anchor\" aria-label=\"Permalink: --rev REV\" href=\"#--rev-rev\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e--rev REV\u003c/code\u003e flag will change the git revision being analyzed to any \u003ca href=\"https://git-scm.com/docs/gitrevisions#_specifying_revisions\" rel=\"nofollow\"\u003egitrevisions(1)\u003c/a\u003e compatible revision you specify.\u003c/p\u003e\n\u003cp\u003eThis is useful to analyze the makeup of a repo as of a certain tag, or in a certain branch.\u003c/p\u003e\n\u003cp\u003eFor example, here is the popular \u003ca href=\"https://github.com/jekyll/jekyll\"\u003eJekyll open source project\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist jekyll\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e70.64% 709959 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e23.04% 231555 Gherkin\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e3.80% 38178 JavaScript\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.19% 11943 HTML\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.79% 7900 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.23% 2279 Dockerfile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.13% 1344 Earthly\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.10% 1019 CSS\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.06% 606 SCSS\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.02% 234 CoffeeScript\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.01% 90 Hack\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd here is Jekyll\u0027s published website, from the gh-pages branch inside their repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist jekyll --rev origin/gh-pages\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e100.00% 2568354 HTML\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003e\u003ccode\u003e--breakdown\u003c/code\u003e\u003c/h5\u003e\u003ca id=\"user-content---breakdown\" class=\"anchor\" aria-label=\"Permalink: --breakdown\" href=\"#--breakdown\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e--breakdown\u003c/code\u003e or \u003ccode\u003e-b\u003c/code\u003e flag will additionally show the breakdown of files by language.\u003c/p\u003e\n\u003cp\u003eYou can try running \u003ccode\u003egithub-linguist\u003c/code\u003e on the root directory in this repository itself:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist --breakdown\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e66.84% 264519 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e24.68% 97685 C\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e6.57% 25999 Go\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.29% 5098 Lex\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.32% 1257 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.31% 1212 Dockerfile\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eRuby:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGemfile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eRakefile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ebin/git-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ebin/github-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eext/linguist/extconf.rb\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003egithub-linguist.gemspec\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003elib/linguist.rb\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u2026\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003e\u003ccode\u003e--json\u003c/code\u003e\u003c/h5\u003e\u003ca id=\"user-content---json\" class=\"anchor\" aria-label=\"Permalink: --json\" href=\"#--json\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e--json\u003c/code\u003e or \u003ccode\u003e-j\u003c/code\u003e flag output the data into JSON format.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist --json\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e{\"Dockerfile\":{\"size\":1212,\"percentage\":\"0.31\"},\"Ruby\":{\"size\":264519,\"percentage\":\"66.84\"},\"C\":{\"size\":97685,\"percentage\":\"24.68\"},\"Lex\":{\"size\":5098,\"percentage\":\"1.29\"},\"Shell\":{\"size\":1257,\"percentage\":\"0.32\"},\"Go\":{\"size\":25999,\"percentage\":\"6.57\"}}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis option can be used in conjunction with \u003ccode\u003e--breakdown\u003c/code\u003e to get a full list of files along with the size and percentage data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist --breakdown --json\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e{\"Dockerfile\":{\"size\":1212,\"percentage\":\"0.31\",\"files\":[\"Dockerfile\",\"tools/grammars/Dockerfile\"]},\"Ruby\":{\"size\":264519,\"percentage\":\"66.84\",\"files\":[\"Gemfile\",\"Rakefile\",\"bin/git-linguist\",\"bin/github-linguist\",\"ext/linguist/extconf.rb\",\"github-linguist.gemspec\",\"lib/linguist.rb\",...]}}\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingle file\u003c/h4\u003e\u003ca id=\"user-content-single-file\" class=\"anchor\" aria-label=\"Permalink: Single file\" href=\"#single-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively you can find stats for a single file using the \u003ccode\u003egithub-linguist\u003c/code\u003e executable.\u003c/p\u003e\n\u003cp\u003eYou can try running \u003ccode\u003egithub-linguist\u003c/code\u003e on files in this repository itself:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist grammars.yml\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003egrammars.yml: 884 lines (884 sloc)\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e type: Text\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e mime type: text/x-yaml\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e language: YAML\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDocker\u003c/h4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have Docker installed you can build an image and run Linguist within a container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003edocker build -t linguist \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003edocker run --rm -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e -w \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e -t linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e66.84% 264519 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e24.68% 97685 C\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e6.57% 25999 Go\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.29% 5098 Lex\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.32% 1257 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.31% 1212 Dockerfile\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003edocker run --rm -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e -w \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e -t linguist github-linguist --breakdown\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e66.84% 264519 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e24.68% 97685 C\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e6.57% 25999 Go\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.29% 5098 Lex\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.32% 1257 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.31% 1212 Dockerfile\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eRuby:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGemfile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eRakefile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ebin/git-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ebin/github-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eext/linguist/extconf.rb\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003egithub-linguist.gemspec\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003elib/linguist.rb\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u2026\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease check out our \u003ca href=\"CONTRIBUTING.md\"\u003econtributing guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe language grammars included in this gem are covered by their repositories\u0027 respective licenses.\n\u003ca href=\"/vendor/README.md\"\u003e\u003ccode\u003evendor/README.md\u003c/code\u003e\u003c/a\u003e lists the repository for each grammar.\u003c/p\u003e\n\u003cp\u003eAll other files are covered by the MIT license, see \u003ca href=\"./LICENSE\"\u003e\u003ccode\u003eLICENSE\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rce_tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#rce_tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erce_tools\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1710228788.0 + "updated_at": 1665495879.0 }, { "data_format": 2, - "description": "Singularity image with CharGer and R libraries for germline small variants workflow.", + "description": "singularity image for gmx 2019", "filenames": [ "Singularity" ], - "full_name": "NagaComBio/singularity_gSmVs", + "full_name": "jmhays/singularity-gromacs", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity image with CharGer and R libraries for germline small variants workflow.\u003c/h3\u003e\u003ca id=\"user-content-singularity-image-with-charger-and-r-libraries-for-germline-small-variants-workflow\" class=\"anchor\" aria-label=\"Permalink: Singularity image with CharGer and R libraries for germline small variants workflow.\" href=\"#singularity-image-with-charger-and-r-libraries-for-germline-small-variants-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the singularity image in a cloud instance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# In a CentOS\n# If the CentOS 8.\nsudo dnf --disablerepo \u0027*\u0027 --enablerepo=extras swap centos-linux-repos centos-stream-repos\nsudo yum update\nsudo yum install git singularity\n\n# Clone the repo \ngit clone https://github.com/NagaComBio/singularity_gSmVs.git\ncd singularity_gSmVs/ \n\n#Build the image\nsudo singularity build gSmVs_${version}.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-gromacs\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-gromacs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-gromacs\u003c/h1\u003e\n\u003cp\u003esingularity image for gmx 2019\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1638799534.0 + "updated_at": 1562360506.0 }, { "data_format": 2, - "description": "Study interactions hosts and microorganisms at metabolic level.", + "description": null, "filenames": [ - "recipes/Singularity" + "Singularity.def", + "Singularity-test.def" ], - "full_name": "AuReMe/HoloInteract", + "full_name": "lalilalalalu/fuchs-container", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9aad17786c19226289cd84f82f2faf5eb0f1adc91c3e70780e7991c2a3298b60/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e392d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/python-3.9-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ba71cd0e231da422d29505443b30ee08fd26a9a935c2211fb4488ded3a7a0a2f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e31302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/python-3.10-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8279d3fba19d512de9201aa06a134661bb4283ef557ab58803fc41de8f0f479/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e31312d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/python-3.11-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a98887fbf555547fa3ddd96ce41fc24b99e450f80fcb4a45416698ef0f8499f6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d756e66696e69736865642d6f72616e67652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/documentation-unfinished-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ae131aa8ea6fc913efeff23640a7aebbe070e256be101f082c5cd0f4a68d2dd5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f77696b692d6e6f6e6578697374656e742d7265642e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/wiki-nonexistent-red.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\n\u003cstrong\u003eHoloInteract\u003c/strong\u003e - Metabolic Interaction in Holobionts\u003c/h1\u003e\u003ca id=\"user-content-holointeract---metabolic-interaction-in-holobionts\" class=\"anchor\" aria-label=\"Permalink: HoloInteract - Metabolic Interaction in Holobionts\" href=\"#holointeract---metabolic-interaction-in-holobionts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUses the \u003ca href=\"\"\u003eMetage2Metabo library\u003c/a\u003e to generate and manipulate metabolic networks.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePiPy \u003ccode\u003erequirements.txt\u003c/code\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-pipy-requirementstxt\" class=\"anchor\" aria-label=\"Permalink: PiPy requirements.txt\" href=\"#pipy-requirementstxt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epython \u0026gt;=3.9\u003c/li\u003e\n\u003cli\u003esetuptools\u0026gt;=65.5.1\u003c/li\u003e\n\u003cli\u003erich\u0026gt;=13.5.3\u003c/li\u003e\n\u003cli\u003ematplotlib\u0026gt;=3.8.0\u003c/li\u003e\n\u003cli\u003epandas\u0026gt;=1.5.3\u003c/li\u003e\n\u003cli\u003eplotly\u0026gt;=5.17.0\u003c/li\u003e\n\u003cli\u003eseaborn\u0026gt;=0.12.2\u003c/li\u003e\n\u003cli\u003eMetage2Metabo\u0026gt;=1.5.4\u003c/li\u003e\n\u003cli\u003escipy\u0026gt;=1.11.2\u003c/li\u003e\n\u003cli\u003epadmet\u0026gt;=5.0.1\u003c/li\u003e\n\u003cli\u003eete3\u0026gt;=3.1.3\u003c/li\u003e\n\u003cli\u003enumpy\u0026gt;=1.26.0\u003c/li\u003e\n\u003cli\u003estatsmodels\u0026gt;=0.14.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLocal\u003c/h4\u003e\u003ca id=\"user-content-local\" class=\"anchor\" aria-label=\"Permalink: Local\" href=\"#local\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eontosunburst : \u003ca href=\"https://github.com/PaulineGHG/Ontology_sunburst.git\"\u003ehttps://github.com/PaulineGHG/Ontology_sunburst.git\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuick install\u003c/h3\u003e\u003ca id=\"user-content-quick-install\" class=\"anchor\" aria-label=\"Permalink: Quick install\" href=\"#quick-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn HoloInteract directory :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash install_dependencies.sh\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommands\u003c/h2\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSubcommands available through \u003ccode\u003eholointeract\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMetabolic Analysis : \u003ccode\u003eholointeract metabolic_analysis\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-metabolic-analysis--holointeract-metabolic_analysis\" class=\"anchor\" aria-label=\"Permalink: Metabolic Analysis : holointeract metabolic_analysis\" href=\"#metabolic-analysis--holointeract-metabolic_analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eArguments :\u003c/h4\u003e\u003ca id=\"user-content-arguments-\" class=\"anchor\" aria-label=\"Permalink: Arguments :\" href=\"#arguments-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--comm\u003c/code\u003e, \u003ccode\u003e--community_networks\u003c/code\u003e : path to community networks in SBML\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--host\u003c/code\u003e, \u003ccode\u003e--host_networks\u003c/code\u003e : path to hosts networks in SBML\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o\u003c/code\u003e, \u003ccode\u003e--output\u003c/code\u003e : path to output directory\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-s\u003c/code\u003e, \u003ccode\u003e--seeds\u003c/code\u003e : path to seeds SBML file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e, \u003ccode\u003e--name\u003c/code\u003e : output files name (default=run)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-m\u003c/code\u003e, \u003ccode\u003e--scopes_method\u003c/code\u003e : method of scopes generation\n\u003ccode\u003e[\u0027solo\u0027, \u0027coop\u0027, \u0027full\u0027]\u003c/code\u003e (default=\u003ccode\u003e\u0027coop\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cm\u003c/code\u003e, \u003ccode\u003e--clustering_method\u003c/code\u003e : method for linkage in clustering (default=\u003ccode\u003e\u0027ward\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--max_clust\u003c/code\u003e : maximal number of cluster for the dendrogram division (default=\u003ccode\u003e10\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cpu\u003c/code\u003e : number of cpu to use (default=\u003ccode\u003e1\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTest example :\u003c/h4\u003e\u003ca id=\"user-content-test-example-\" class=\"anchor\" aria-label=\"Permalink: Test example :\" href=\"#test-example-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003eholointeract metabolic_analysis --comm small_example/inputs/community/ --host small_example/inputs/hosts/ -o small_example/outputs/ -s small_example/inputs/seeds/seeds_seawater_artefact.sbml -n test -m coop \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCoevolution Analysis : \u003ccode\u003eholointeract coevolution\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-coevolution-analysis--holointeract-coevolution\" class=\"anchor\" aria-label=\"Permalink: Coevolution Analysis : holointeract coevolution\" href=\"#coevolution-analysis--holointeract-coevolution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eArguments :\u003c/h4\u003e\u003ca id=\"user-content-arguments--1\" class=\"anchor\" aria-label=\"Permalink: Arguments :\" href=\"#arguments--1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--comm\u003c/code\u003e, \u003ccode\u003e--community_networks\u003c/code\u003e : path to community networks in SBML\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--host\u003c/code\u003e, \u003ccode\u003e--host_networks\u003c/code\u003e : path to hosts networks in SBML\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o\u003c/code\u003e, \u003ccode\u003e--output\u003c/code\u003e : path to output directory\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-s\u003c/code\u003e, \u003ccode\u003e--seeds\u003c/code\u003e : path to seeds SBML file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e, \u003ccode\u003e--name\u003c/code\u003e : output files name (default=run)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cm\u003c/code\u003e, \u003ccode\u003e--clustering_method\u003c/code\u003e : method for linkage in clustering (default=\u003ccode\u003e\u0027ward\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--max_clust\u003c/code\u003e : maximal number of cluster for the dendrogram division (default=\u003ccode\u003e10\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e, \u003ccode\u003e--phylo_tree\u003c/code\u003e : path to phylogenetic tree (Newick format) (default=\u003ccode\u003eNone\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cor\u003c/code\u003e, \u003ccode\u003e--correction\u003c/code\u003e : correction to apply to p-values\n\u003ccode\u003e[\u0027bonferroni\u0027, \u0027benjamini\u0027, None]\u003c/code\u003e (default=\u003ccode\u003eNone\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cpu\u003c/code\u003e : number of cpu to use (default=\u003ccode\u003e1\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTest example :\u003c/h4\u003e\u003ca id=\"user-content-test-example--1\" class=\"anchor\" aria-label=\"Permalink: Test example :\" href=\"#test-example--1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003eholointeract coevolution --comm small_example/inputs/community/ --host small_example/inputs/hosts/ -o small_example/outputs/ -s small_example/inputs/seeds/seeds_seawater_artefact.sbml -n test -p small_example/inputs/SpeciesTree_rooted.txt \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHelp available\u003c/h3\u003e\u003ca id=\"user-content-help-available\" class=\"anchor\" aria-label=\"Permalink: Help available\" href=\"#help-available\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eholointeract -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eholointeract \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003esubcommand\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -h\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequired Inputs\u003c/h2\u003e\u003ca id=\"user-content-required-inputs\" class=\"anchor\" aria-label=\"Permalink: Required Inputs\" href=\"#required-inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommunity Networks (\u003ccode\u003e--comm\u003c/code\u003e, \u003ccode\u003e--community_networks\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-community-networks---comm---community_networks\" class=\"anchor\" aria-label=\"Permalink: Community Networks (--comm, --community_networks)\" href=\"#community-networks---comm---community_networks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDirectory containing SBML networks files for each community organism.\u003c/p\u003e\n\u003cp\u003eEach organism must be placed in the directory named after its natural host.\u003c/p\u003e\n\u003cp\u003eHost names in community networks directory and host networks directory must coincide.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample :\u003c/h4\u003e\u003ca id=\"user-content-example-\" class=\"anchor\" aria-label=\"Permalink: Example :\" href=\"#example-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\u251c\u2500\u2500 Community_networks\n\u2502 \u251c\u2500\u2500 Host_1\n\u2502 \u2502 \u251c\u2500\u2500 Microorganism_1.sbml \n\u2502 \u2502 \u251c\u2500\u2500 Microorganism_2.sbml \n\u2502 \u251c\u2500\u2500 Host_2\n\u2502 \u2502 \u251c\u2500\u2500 Microorganism_3.sbml \n\u2502 \u2502 \u251c\u2500\u2500 Microorganism_4.sbml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHost Networks (\u003ccode\u003e--host\u003c/code\u003e, \u003ccode\u003e--host_networks\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-host-networks---host---host_networks\" class=\"anchor\" aria-label=\"Permalink: Host Networks (--host, --host_networks)\" href=\"#host-networks---host---host_networks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDirectory containing SBML networks files for each host.\u003c/p\u003e\n\u003cp\u003eHost names in community networks directory and host networks directory must coincide.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample :\u003c/h4\u003e\u003ca id=\"user-content-example--1\" class=\"anchor\" aria-label=\"Permalink: Example :\" href=\"#example--1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\u251c\u2500\u2500 Host_networks\n\u2502 \u251c\u2500\u2500 Host_1.sbml \n\u2502 \u251c\u2500\u2500 Host_2.sbml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSeeds (\u003ccode\u003e-s\u003c/code\u003e, \u003ccode\u003e--seeds\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-seeds--s---seeds\" class=\"anchor\" aria-label=\"Permalink: Seeds (-s, --seeds)\" href=\"#seeds--s---seeds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeeds are list of compounds corresponding to a common growth medium shared between all hosts.\u003c/p\u003e\n\u003cp\u003eArtefacts compounds should be added to seeds to avoid cycles + cofactors.\u003c/p\u003e\n\u003cp\u003eThe seeds file must be given at SBML format.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePhylogenetic Tree (\u003ccode\u003e-p\u003c/code\u003e, \u003ccode\u003e--phylo_tree\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-phylogenetic-tree--p---phylo_tree\" class=\"anchor\" aria-label=\"Permalink: Phylogenetic Tree (-p, --phylo_tree)\" href=\"#phylogenetic-tree--p---phylo_tree\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe phylogenetic tree is used to calculate phylogenetic distance between each pair of hosts.\u003c/p\u003e\n\u003cp\u003eThe file must be given at Newick format.\u003c/p\u003e\n\u003cp\u003eAll the hosts must be present in the tree.\u003c/p\u003e\n\u003cp\u003eAll the host names must coincide to the names in community networks directory\nand host networks directory.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1696949006.0 + "updated_at": 1667472176.0 }, { "data_format": 2, - "description": "Modified version of AMR++ ", + "description": null, "filenames": [ - "envs/containers/Singularity" + "Singularity.def" ], - "full_name": "passdan/AMR-local-mod", + "full_name": "annaLtyler/CAPE_transcripts", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository is a modified version of the \u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus\"\u003eAMR++ core repository\u003c/a\u003e with some notable modifications, changes, and parameterised for running on Cardiff University Biosciences compute cluster (Trinity). For usage and tutorials refer to the core AMR++ documentation.\u003c/p\u003e\n\u003cp\u003eNotable changes to original pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntegrated fastp \u0026amp; bowtie2 as QC and alignment packages\n\u003cul\u003e\n\u003cli\u003eNote: Default alignment for megares is kept as BWA consistent with original AMR++ and can be changed by parameter. Bowtie2 alignment against megaresDB has significant change on alignment rate.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBased upon modified docker image (passdan/amrplusplus-update)\u003c/li\u003e\n\u003cli\u003eSome code tweaks and fixes to work with singularity-slurm submission and repair nextflow channel bugs\u003c/li\u003e\n\u003cli\u003eBespoke job submission and result caputre to fit our specific requirements\u003c/li\u003e\n\u003cli\u003eAdded Bracken processing with new results\n\u003cul\u003e\n\u003cli\u003eNote: Two new workflow parameters: \u0027standard_AMR_wKraken_and_bracken\u0027 and \u0027kraken_and_braken\u0027 (for already filtered/host removed input)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCodebase is provided as-is and is hyper-locally modified for our infrastructure. If you are not concerned about bracken output or fastp \u0026amp; bowtie2 you probably want to work from the original AMR++ repository.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePreparing the install\u003c/h1\u003e\u003ca id=\"user-content-preparing-the-install\" class=\"anchor\" aria-label=\"Permalink: Preparing the install\" href=\"#preparing-the-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDownload the github repository with git clone and the url above\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with singularity\u003c/h2\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eThe pipeline is designed and configured to run with singularity \u0026amp; slurm. If you are using these then no further installation preparation is required.\nYou may choose to pre-build the singulariy images in advance if wanted. Note: config/singularity_slurm.conf is where singularity images can be defined.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload bowtie2 index files to remove contamination/host DNA\u003c/h2\u003e\u003ca id=\"user-content-download-bowtie2-index-files-to-remove-contaminationhost-dna\" class=\"anchor\" aria-label=\"Permalink: Download bowtie2 index files to remove contamination/host DNA\" href=\"#download-bowtie2-index-files-to-remove-contaminationhost-dna\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eEither download directly, or build your indexes to be filtered against from user supplied fasta files.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e[Recommended] download human genome indexes directly from: \u003ca href=\"https://bowtie-bio.sourceforge.net/bowtie2/index.shtml\" rel=\"nofollow\"\u003ehttps://bowtie-bio.sourceforge.net/bowtie2/index.shtml\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEdit Slurm submission scripts\u003c/h2\u003e\u003ca id=\"user-content-edit-slurm-submission-scripts\" class=\"anchor\" aria-label=\"Permalink: Edit Slurm submission scripts\" href=\"#edit-slurm-submission-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eAn example slurm script defines these parameters:\n\u003cul\u003e\n\u003cli\u003eworkdir: Location where processing will be performed (advice: use high speed location on your processing node i.e. /tmp)\u003c/li\u003e\n\u003cli\u003einstalldir: Location of this github repo on your system\u003c/li\u003e\n\u003cli\u003eresultsdir: Where do you want the main outputs to be transfered to (not the full working folders \u0026amp; outputs)\u003c/li\u003e\n\u003cli\u003erun: Name of the run for annotation and folder where the fastq files are\nDefault input read location is: \u003ccode\u003e$workdir/$run/fastq\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the pipeline\u003c/h1\u003e\u003ca id=\"user-content-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline\" href=\"#running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDefault pipeline is \u003ccode\u003estandard_AMR_wKraken_and_bracken\u003c/code\u003e which will run from raw fastqs to the endpoint. Alternatives are to use mid-process data, kraken only etc. by modifying the --pipeline parameter\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eAvailable pipelines:\n - demo: Run a demonstration of AMR++\n - standard_AMR: Run the standard AMR++ pipeline\n - fast_AMR: Run the fast AMR++ pipeline without host removal.\n - standard_AMR_wKraken: Run the standard AMR++ pipeline with Kraken\n - **NEW** standard_AMR_wKraken_and_bracken: Run the standard AMR++ pipeline with Kraken AND Bracken\n\nAvailable subworkflows:\n - eval_qc: Run FastQC analysis\n - trim_qc: Run trimming and quality control\n - rm_host: Remove host reads\n - resistome: Perform resistome analysis\n - align: Perform alignment to MEGARes database\n - kraken: Perform Kraken analysis\n - **NEW** kraken_and_bracken: Perform Kraken and Bracken analysis\n - qiime2: Perform QIIME 2 analysis\n - bam_resistome: Perform resistome analysis on BAM files\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSubmit as a slurm \u0026amp; singularity job with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch AMRplusplus_full.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou\u0027re finished!\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1693998493.0 + "updated_at": 1641489227.0 }, { "data_format": 2, - "description": "Code for snATAC + snRNA analyses", + "description": "Updated dockers for FEniCS 2019 (legacy FEniCS)", "filenames": [ - "eqtl/scan/scripts/Singularity", - "eqtl/susie/scripts/Singularity", - "sn_processing/rna/scripts/Singularity", - "sn_processing/atac/scripts/Singularity", - "directionality/smr/scripts/Singularity" + "dockerfiles/stable/Singularity", + "dockerfiles/dev-env/Singularity" ], - "full_name": "ParkerLab/sn_muscle_2023", + "full_name": "terjekv/fenics-docker", "latest_release": null, - "readme": "\u003cp\u003eCode for key analyses in the manuscript Single-nucleus chromatin and gene expression profiling across hundreds of skeletal muscle samples reveals context-specific regulation. Annotations in hg38.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo run workflow:\u003c/h1\u003e\u003ca id=\"user-content-to-run-workflow\" class=\"anchor\" aria-label=\"Permalink: To run workflow:\" href=\"#to-run-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSpecify all paths in config.yaml\u003c/h2\u003e\u003ca id=\"user-content-specify-all-paths-in-configyaml\" class=\"anchor\" aria-label=\"Permalink: Specify all paths in config.yaml\" href=\"#specify-all-paths-in-configyaml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModify scripts/nextflow.config accordingly. It currently provides configuration to run on the slurm scheduler.\u003c/h2\u003e\u003ca id=\"user-content-modify-scriptsnextflowconfig-accordingly-it-currently-provides-configuration-to-run-on-the-slurm-scheduler\" class=\"anchor\" aria-label=\"Permalink: Modify scripts/nextflow.config accordingly. It currently provides configuration to run on the slurm scheduler.\" href=\"#modify-scriptsnextflowconfig-accordingly-it-currently-provides-configuration-to-run-on-the-slurm-scheduler\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo generate the sigularity container, use the \u003ccode\u003eSingularity\u003c/code\u003e file in the scripts directory to generate one as:\u003c/h2\u003e\u003ca id=\"user-content-to-generate-the-sigularity-container-use-the-singularity-file-in-the-scripts-directory-to-generate-one-as\" class=\"anchor\" aria-label=\"Permalink: To generate the sigularity container, use the Singularity file in the scripts directory to generate one as:\" href=\"#to-generate-the-sigularity-container-use-the-singularity-file-in-the-scripts-directory-to-generate-one-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --remote container_name.sig scripts/Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the Nextflow workflow as:\u003c/h2\u003e\u003ca id=\"user-content-run-the-nextflow-workflow-as\" class=\"anchor\" aria-label=\"Permalink: Run the Nextflow workflow as:\" href=\"#run-the-nextflow-workflow-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003enextflow -C scripts/nextflow.config run scripts/main.nf -params-file config.yaml -resume -with-trace trace.txt\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker-for-fenics\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-for-fenics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker for FEniCS\u003c/h1\u003e\n\u003cp\u003eThis repository contains the scripts for building various Docker\nimages for \u003ca href=\"http://fenicsproject.org\" rel=\"nofollow\"\u003eFEniCS\u003c/a\u003e. The built images\nare available on \u003ca href=\"https://quay.io/organization/fenicsproject/\" rel=\"nofollow\"\u003equay.io\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://fenics.readthedocs.org/projects/containers/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/216da3db9027c7d6a1857be2a6ef086a77ed5dca0de68a1be21b21a464f1c7ca/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f66656e6963732d636f6e7461696e6572732f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/fenics-containers/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eTo install Docker for your platform (Windows, macOS, Linux, cloud\nplatforms, etc.), follow the instructions at\n\u003ca href=\"https://docs.docker.com/engine/getstarted/step_one/\" rel=\"nofollow\"\u003edocker.com\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOnce you have Docker installed, you can run any of the images below\nusing the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti quay.io/fenicsproject/\u0026lt;image-name\u0026gt;:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo start with you probably want to try the \u003ccode\u003estable:current\u003c/code\u003e image\nwhich includes a full stable version of FEniCS with PETSc, SLEPc,\npetsc4py and slepc4py already compiled. This image has been checked by\nthe FEniCS Project team:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti quay.io/fenicsproject/stable:current\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to share your current working directory into the container\nuse the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti -v $(pwd):/home/fenics/shared quay.io/fenicsproject/\u0026lt;image-name\u0026gt;:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to be able to view the plots in your web browser, use the following\ncommand:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti -p 127.0.0.1:8000:8000 -v $(pwd):/home/fenics/shared quay.io/fenicsproject/\u0026lt;image-name\u0026gt;:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsers with SELinux-enabled Linux distributions (Redhat, Fedora, CentOS, and others)\nwill need to add the \u003ccode\u003e:z\u003c/code\u003e flag to the volume mount, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti -v $(pwd):/home/fenics/shared:z quay.io/fenicsproject/\u0026lt;image-name\u0026gt;:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-experimental-singularity-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#experimental-singularity-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExperimental: Singularity support\u003c/h2\u003e\n\u003cp\u003eThis repository contains a script to build \u003ccode\u003edev-env\u003c/code\u003e and \u003ccode\u003estable\u003c/code\u003e\nimages that are compatible with the Singularity container runtime\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd dockerfiles\n./build-singularity-images.sh\ncd stable\nsingularity run -e stable.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease report any problems in the issue tracker.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eMore extensive documentation, including suggested workflows, is\navailable at \u003ca href=\"https://fenics-containers.readthedocs.org/\" rel=\"nofollow\"\u003ehttps://fenics-containers.readthedocs.org/\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003cp\u003eWe currently offer following end-user images. A full description of\nthe images can be found at \u003ca href=\"https://fenics-containers.readthedocs.org/\" rel=\"nofollow\"\u003ehttps://fenics-containers.readthedocs.org/\u003c/a\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eImage name\u003c/th\u003e\n\u003cth\u003eBuild status\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003estable\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/stable\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/244f27cc8397f31b11cbc2e780751c02b5e6be4fbc35b65fb734720b77f799b8/68747470733a2f2f717561792e696f2f7265706f7369746f72792f66656e69637370726f6a6563742f737461626c652f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/fenicsproject/stable/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eStable release, with PETSc and SLEPc.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edev\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003emaster\u003c/code\u003e version\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edev-env\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/dev-env\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9f60a447cec97d984a5e6d237ecb10b88e9a81054a289c509e46bd0e794561c3/68747470733a2f2f717561792e696f2f7265706f7369746f72792f66656e69637370726f6a6563742f6465762d656e762f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/fenicsproject/dev-env/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDevelopment environment with PETSc and SLEPc.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebase\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/base\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a89219955af8c2e29e3b80191c6b09fbcd0a4aec08fd3c6a796b2194eb459231/68747470733a2f2f717561792e696f2f7265706f7369746f72792f66656e69637370726f6a6563742f626173652f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/fenicsproject/base/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eBase image, not for end users.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: The \u003cem\u003eBuild status\u003c/em\u003e column refers to the latest \u003cem\u003eattempted\u003c/em\u003e\nbuild. Even if a build is marked as failed, there will still be a\nworking image available on the \u003ccode\u003elatest\u003c/code\u003e tag that you can use.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tagging-policies\" class=\"anchor\" aria-hidden=\"true\" href=\"#tagging-policies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTagging policies\u003c/h2\u003e\n\u003cp\u003eWe currently maintain tags on the \u003ccode\u003estable\u003c/code\u003e and \u003ccode\u003edev-env\u003c/code\u003e images.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-stable\" class=\"anchor\" aria-hidden=\"true\" href=\"#stable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003estable\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eYou can view the tags on the \u003ccode\u003estable\u003c/code\u003e image here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/stable?tab=tags\" rel=\"nofollow\"\u003ehttps://quay.io/repository/fenicsproject/stable?tab=tags\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe tagging policy for \u003ccode\u003estable\u003c/code\u003e image is as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003e:latest\u003c/code\u003e (default) tag refers to the latest image built by\nquay.io. The prior \u003ccode\u003e:latest\u003c/code\u003e image is automatically deleted by\nquay.io, unless it has been assigned another tag.\u003c/li\u003e\n\u003cli\u003eWe maintain a set of rolling release tags, e.g. \u003ccode\u003e:2016.1.0.r1\u003c/code\u003e,\n\u003ccode\u003e2016.1.0.r2\u003c/code\u003e that contain the \u003ccode\u003exxxx.x.x\u003c/code\u003e version of FEniCS, but\ncontain minor updates \u003ccode\u003e.rx\u003c/code\u003e to underlying dependencies (e.g. PETSc)\nand the container environment. These images have been checked\nthoroughly by the FEniCS project team.\u003c/li\u003e\n\u003cli\u003eThe latest rolling release is tagged with a \u003cem\u003emoving\u003c/em\u003e tag \u003ccode\u003e:current\u003c/code\u003e.\nThis tag is the default tag used by the \u003ccode\u003ebin/fenicsproject\u003c/code\u003e script\nwhen the user specifies \u003ccode\u003estable\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eWhen we release a new stable version of FEniCS the last rolling release\n\u003ccode\u003exxxx.x.x.rx\u003c/code\u003e of the image for the previous version will be tagged \u003ccode\u003exxxx.x.x\u003c/code\u003e for\npermanent archival. We will endeavour to keep all \u003ccode\u003exxxx.x.x.rx\u003c/code\u003e tags\nas well, but this is not guaranteed. We will always keep the last rolling\nrelease \u003ccode\u003exxxx.x.x\u003c/code\u003e tag.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dev-env\" class=\"anchor\" aria-hidden=\"true\" href=\"#dev-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003edev-env\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eYou can view the tags on the \u003ccode\u003edev-env\u003c/code\u003e image here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/dev-env?tab=tags\" rel=\"nofollow\"\u003ehttps://quay.io/repository/fenicsproject/dev-env?tab=tags\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe tagging policy for the \u003ccode\u003edev-env\u003c/code\u003e image is as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003e:latest\u003c/code\u003e (default) tag refers to the latest image build by\nquay.io. The prior \u003ccode\u003e:latest\u003c/code\u003e image is automatically deleted by\nquay.io, unless it has been assigned another tag.\u003c/li\u003e\n\u003cli\u003eWhen we release a new stable version of FEniCS the last \u003ccode\u003e:latest\u003c/code\u003e image is\ntagged \u003ccode\u003exxxx.x.x\u003c/code\u003e for permanent archival. This could be useful if you\nwant to compile an old version of FEniCS.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#development-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment images\u003c/h2\u003e\n\u003cp\u003eDue to the shutdown of our Bamboo build service, \u003ccode\u003edev\u003c/code\u003e images\nare no longer produced automatically.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-process\" class=\"anchor\" aria-hidden=\"true\" href=\"#process\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcess\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003eDockerfile\u003c/code\u003es in this repository are built and distributed as\nDocker images by quay.io. For this to happen automatically on a change\nin a \u003ccode\u003eDockerfile\u003c/code\u003e we have setup a \u003ca href=\"https://docs.quay.io/guides/building.html\" rel=\"nofollow\"\u003ebuild\ntrigger\u003c/a\u003e on quay.io for\neach image (e.g. \u003ccode\u003estable\u003c/code\u003e). Setting up a trigger requires\nadministrator access on this bitbucket repository and the\n\u003ccode\u003efenicsproject\u003c/code\u003e quay.io team.\u003c/p\u003e\n\u003cp\u003eThe tagging policy is described in the section \u0027Tagging policies\u0027. To\ncreate tags you need to be an administrator on the \u003ccode\u003efenicsproject\u003c/code\u003e\nquay.io team. The procedure is described\n\u003ca href=\"https://docs.quay.io/guides/tag-operations.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Currently all\ntags are created manually via the web interface.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eJack S. Hale (\u003ca href=\"mailto:jack.hale@uni.lu\"\u003ejack.hale@uni.lu\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eLizao Li (\u003ca href=\"mailto:lzlarryli@gmail.com\"\u003elzlarryli@gmail.com\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eGarth N. Wells (\u003ca href=\"mailto:gnw20@cam.ac.uk\"\u003egnw20@cam.ac.uk\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1699458196.0 + "updated_at": 1668124636.0 }, { "data_format": 2, - "description": null, + "description": "Learning how to use the workflow called nextflow", "filenames": [ - "singularity/Singularity.minimac4" + "nf-training/Singularity" ], - "full_name": "h3abionet/chipimputation_evaluate_chips", + "full_name": "ayoraind/nf-training", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eChip imputation evaluation Workflow h3abionet/chipimputation_evaluate_chips\u003c/h1\u003e\u003ca id=\"user-content-chip-imputation-evaluation-workflow-h3abionetchipimputation_evaluate_chips\" class=\"anchor\" aria-label=\"Permalink: Chip imputation evaluation Workflow h3abionet/chipimputation_evaluate_chips\" href=\"#chip-imputation-evaluation-workflow-h3abionetchipimputation_evaluate_chips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/h3abionet/chipimputation\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/613f3026f3cde9349d4ad1ff0e6842e170600d7473949e030192e71b08edaabc/68747470733a2f2f7472617669732d63692e6f72672f68336162696f6e65742f63686970696d7075746174696f6e2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/h3abionet/chipimputation.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/780f0e426d3a9fd5f3f54407686be63867cb8093d09e36c9bcbad58b728a111d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/h3abionet/chipimputation\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a9dd183a714011418b2104dcad694fcdbbfbf66fdca3c46b96018a56edf79026/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f63686970696d7075746174696f6e2e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/chipimputation.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is to evaluate the imputation performance and accuracy of different arrays starting from sequence data.\nIt masks non tag variants for each array, and then impute to a reference panel using Minimac.\u003cbr\u003e\nIt is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner.\u003cbr\u003e\nIt comes with singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe evaluate_chips pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and Configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eConfiguration for other clusters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup (native cluster)\u003c/h3\u003e\u003ca id=\"user-content-setup-native-cluster\" class=\"anchor\" aria-label=\"Permalink: Setup (native cluster)\" href=\"#setup-native-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHeadnode\u003c/h4\u003e\u003ca id=\"user-content-headnode\" class=\"anchor\" aria-label=\"Permalink: Headnode\" href=\"#headnode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e (can be installed as local user)\u003c/li\u003e\n\u003cli\u003eNXF_HOME needs to be set, and must be in the PATH\u003c/li\u003e\n\u003cli\u003eNote that we\u0027ve experienced problems running Nextflow when NXF_HOME is on an NFS mount.\u003c/li\u003e\n\u003cli\u003eThe Nextflow script also needs to be invoked in a non-NFS folder\u003c/li\u003e\n\u003cli\u003eJava 1.8+\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompute nodes\u003c/h4\u003e\u003ca id=\"user-content-compute-nodes\" class=\"anchor\" aria-label=\"Permalink: Compute nodes\" href=\"#compute-nodes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe compute nodes need to have singularity installed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe compute nodes need access to shared storage for input, references, output\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe following commands need to be available in PATH on the compute nodes, in case of unavailabitity of singularity.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eminimac4\u003c/code\u003e from \u003ca href=\"http://mathgen.stats.ox.ac.uk/impute/impute_v2.html\" rel=\"nofollow\"\u003eMINIMAC4\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evcftools\u003c/code\u003e from \u003ca href=\"https://vcftools.github.io/index.html\" rel=\"nofollow\"\u003eVCFtools\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebcftools\u003c/code\u003efrom \u003ca href=\"https://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebgzip\u003c/code\u003e from \u003ca href=\"http://www.htslib.org\" rel=\"nofollow\"\u003ehtslib\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eeagle\u003c/code\u003e from \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/\" rel=\"nofollow\"\u003eEagle\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython2.7\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e with the following packages ...\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nextflow-training-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-training-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow Training Guide.\u003c/h1\u003e\n\u003cp\u003eWelcome to the Nextflow training repo. We are excited to have you on the path to writing reproducible and scalable scientific workflows using Nextflow. This guide complements the full Nextflow documentation - if you ever have any doubts, head over to the docs located \u003ca href=\"https://www.nextflow.io/docs/latest/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThere are two main ways to get started with Seqera\u0027s Nextflow training course.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Locally - best if you are already confident with Git and Docker, or working offline. Follow the instructions \u003ca href=\"https://training.seqera.io/#_local_installation\" rel=\"nofollow\"\u003ehere\u003c/a\u003e, section 1.1.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGitpod - (recommended), is a containerized environment with all the programs and data pre-installed. Simply click the link and login via a GitHub account to start the tutorial. The full instructions are below.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gitpod-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#gitpod-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGitpod requirements:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eA GitHub account\u003c/li\u003e\n\u003cli\u003eWeb browser (Google Chrome, Firefox)\u003c/li\u003e\n\u003cli\u003eInternet connection\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gitpod-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#gitpod-quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGitpod quick start\u003c/h2\u003e\n\u003cp\u003eTo run Gitpod:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClick the following URL:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://gitpod.io/#https://github.com/seqeralabs/nf-training-public\" rel=\"nofollow\"\u003ehttps://gitpod.io/#https://github.com/seqeralabs/nf-training-public\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e(which is our Github repository URL, prefixed with \u003ca href=\"https://gitpod.io/#\" rel=\"nofollow\"\u003ehttps://gitpod.io/#\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLog in to your Github account (and allow authorization).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce you have signed in, Gitpod should load (skip prebuild if asked).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-explore-your-gitpod-ide\" class=\"anchor\" aria-hidden=\"true\" href=\"#explore-your-gitpod-ide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExplore your Gitpod IDE\u003c/h2\u003e\n\u003cp\u003eYou should now see something similar to the following:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/asciidocs/img/gitpod.welcome.png\"\u003e\u003cimg src=\"/asciidocs/img/gitpod.welcome.png\" alt=\"PNG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe sidebar\u003c/strong\u003e allows you to customize your Gitpod environment and perform basic tasks (copy, paste, open files, search, git, etc.). Click the Explorer button to see which files are in this repository.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe terminal\u003c/strong\u003e allows you to run all the programs in the repository. For example, both \u003ccode\u003enextflow\u003c/code\u003e and \u003ccode\u003edocker\u003c/code\u003e are installed and can be executed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe main window\u003c/strong\u003e allows you to view and edit files. Clicking on a file in the explorer will open it within the main window. You should also see the nf-training material browser (\u003ca href=\"https://training.seqera.io/\" rel=\"nofollow\"\u003ehttps://training.seqera.io/\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eTo test that the environment is working correctly, type the following into the terminal:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow info\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should come up with the Nextflow version and runtime information:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eVersion: 22.04.2 build 5701\nCreated: 16-05-2022 17:52 UTC\nSystem: Linux 5.16.20-051620-generic\nRuntime: Groovy 3.0.10 on OpenJDK 64-Bit Server VM 11.0.13+8-LTS\nEncoding: UTF-8 (UTF-8)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-gitpod-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#gitpod-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGitpod resources\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eGitpod gives you up to 50 hours per month to run the environment for free.\u003c/li\u003e\n\u003cli\u003eIt includes up to 16 cpus and 30GB of workspace.\u003c/li\u003e\n\u003cli\u003eGitpod will timeout after 30 minutes. However any changes are saved for up to two week (see next section for reopening a timed out session).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"http://www.gitpod.io\" rel=\"nofollow\"\u003ewww.gitpod.io\u003c/a\u003e for more details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reopening-a-gitpod-session\" class=\"anchor\" aria-hidden=\"true\" href=\"#reopening-a-gitpod-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReopening a Gitpod session\u003c/h3\u003e\n\u003cp\u003eYou can reopen an environment by going to \u003ca href=\"https://gitpod.io/workspaces\" rel=\"nofollow\"\u003ehttps://gitpod.io/workspaces\u003c/a\u003e and finding your previous environment, then clicking the button with three dots and selecting Open.\u003c/p\u003e\n\u003cp\u003eIf you save the URL from your previous Gitpod environment, you can just paste this into your browser to open the previous environment.\u003c/p\u003e\n\u003cp\u003eAlternatively, you can start a new workspace by following the Gitpod URL:\n\u003ca href=\"https://gitpod.io/#https://github.com/seqeralabs/nf-training-public\" rel=\"nofollow\"\u003ehttps://gitpod.io/#https://github.com/seqeralabs/nf-training-public\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tutorial provides all the scripts, so don\u0027t worry if you have lost your environment. In the \u003ccode\u003enf-training\u003c/code\u003e directory, you can find the main scripts used in the tutorial.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-saving-files-from-gitpod-to-your-local-machine\" class=\"anchor\" aria-hidden=\"true\" href=\"#saving-files-from-gitpod-to-your-local-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSaving files from Gitpod to your local machine.\u003c/h3\u003e\n\u003cp\u003eTo save your files, select your file of interest from the explorer panel, then right click the file to click \u003ccode\u003eDownload\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-copyright\" class=\"anchor\" aria-hidden=\"true\" href=\"#copyright\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\" rel=\"nofollow\"\u003e\u003cimg alt=\"Creative Commons License\" src=\"https://camo.githubusercontent.com/3f6af33ec372f6eb8a74152e311d8f3ba281cbfb44b003d825de68bcbcffbe9d/68747470733a2f2f692e6372656174697665636f6d6d6f6e732e6f72672f6c2f62792d6e632d6e642f342e302f38387833312e706e67\" data-canonical-src=\"https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCopyright 2020-2022, Seqera Labs. All examples and descriptions are licensed under the \u003ca href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\" rel=\"nofollow\"\u003eCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1630671596.0 + "updated_at": 1667983606.0 }, { "data_format": 2, - "description": null, + "description": "\u522b\u4eba\u7684", "filenames": [ - "singularity/Singularity.example_recipe", - "singularity/Singularity.plotting", - "singularity/Singularity.align" + "W-Unet/Wave-U-Net-Pytorch-master/Wave-U-Net-Pytorch-master/Singularity" ], - "full_name": "liluacrobat/Shotgun_inStrain", + "full_name": "fxd98/W-Unet", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBhattlab workflows\u003c/h1\u003e\u003ca id=\"user-content-bhattlab-workflows\" class=\"anchor\" aria-label=\"Permalink: Bhattlab workflows\" href=\"#bhattlab-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputational workflows for metagenomics tasks, packaged with Snakemake, Singularity and Conda.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTable of contents\u003c/h3\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"manual/setup.md\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/running.md\"\u003eRunning a workflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eAvailable workflows\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"manual/preprocessing.md\"\u003e\u003cstrong\u003ePreprocessing\u003c/strong\u003e metagenomic data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/assembly.md\"\u003eMetagenomic \u003cstrong\u003eAssembly\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/binning.md\"\u003eMetagenomic \u003cstrong\u003eBinning\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/dRep.md\"\u003e\u003cstrong\u003eDeReplication\u003c/strong\u003e of binned genomes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/inStrain.md\"\u003e\u003cstrong\u003einStrain\u003c/strong\u003e strain-diversity aware comparison of samples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bhattlab/kraken2_classification\"\u003eMetagenomic classification with \u003cstrong\u003eKraken2\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/sourmash.md\"\u003e\u003cstrong\u003eSourmash\u003c/strong\u003e read comparison\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/download_sra.md\"\u003e\u003cstrong\u003eDownload SRA\u003c/strong\u003e data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/arg.md\"\u003e\u003cstrong\u003eARG detection\u003c/strong\u003e with RGI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/viral.md\"\u003e\u003cstrong\u003eViral\u003c/strong\u003e contig prediction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/comparative_genomics.md\"\u003eComparative microbial genomics pipelines\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuickstart\u003c/h3\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re in the Bhatt lab and working on SCG, this command is an example of how to run the workflows. Other users will need to change these options (see \u003ca href=\"manual/running.md\"\u003eRunning a workflow\u003c/a\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --configfile config_preprocessing.yaml \\\n--snakefile ~/projects/bhattlab_workflows/preprocessing/preprocessing.snakefile \\\n--profile scg --jobs 100 --use-singularity \\\n--singularity-args \u0027--bind /labs/,/oak/,/home/\u0027\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-w-unet\" class=\"anchor\" aria-hidden=\"true\" href=\"#w-unet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eW-Unet\u003c/h1\u003e\n\u003cp\u003e\u522b\u4eba\u7684\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1629397031.0 + "updated_at": 1668261473.0 }, { "data_format": 2, - "description": "collection of performance tools in centos (iperf3, perf, htop, etc)", + "description": "Glances is a cross-platform system monitoring tool written in Python.", "filenames": [ - "Singularity" + "3.2.3.1/Singularity", + "3.3.1/Singularity", + "3.3.0.4/Singularity" ], - "full_name": "tin6150/perf_tools", - "latest_release": null, + "full_name": "pscedu/singularity-glances", + "latest_release": "v3.3.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-glances/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-glances/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-glances/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-glances/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/43ec72356b9caba3c3acfed806b0652e417e6059a5b6f51dea1f5dda0835d137/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d676c616e636573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/43ec72356b9caba3c3acfed806b0652e417e6059a5b6f51dea1f5dda0835d137/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d676c616e636573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-glances\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/95213954ba87ff8602673ef562afff42953930ec138ce96d3683c4a4536d2d84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d676c616e636573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/95213954ba87ff8602673ef562afff42953930ec138ce96d3683c4a4536d2d84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d676c616e636573\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-glances\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2d14c85860042ad12ca8177c832aba51fcbe93fc1b6fb018b78e3f4cde7022ec/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d676c616e636573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2d14c85860042ad12ca8177c832aba51fcbe93fc1b6fb018b78e3f4cde7022ec/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d676c616e636573\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-glances\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0d7e4849ce048818bdf2750b8463028635aa22f5a5797b1d02e5c3a24d979db0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d676c616e636573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d7e4849ce048818bdf2750b8463028635aa22f5a5797b1d02e5c3a24d979db0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d676c616e636573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-glances\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-glances\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-glances\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-glances\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/829c005628a650de0afbef7aa42f2aae5916381323380abb38aa97edf74873ef/68747470733a2f2f6e69636f6c6172676f2e6769746875622e696f2f676c616e6365732f7075626c69632f696d616765732f73637265656e73686f742d776964652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/829c005628a650de0afbef7aa42f2aae5916381323380abb38aa97edf74873ef/68747470733a2f2f6e69636f6c6172676f2e6769746875622e696f2f676c616e6365732f7075626c69632f696d616765732f73637265656e73686f742d776964652e706e67\" width=\"50%\" data-canonical-src=\"https://nicolargo.github.io/glances/public/images/screenshot-wide.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://nicolargo.github.io/glances/\" rel=\"nofollow\"\u003eglances\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eglances\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/glances/3.3.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/glances\u003c/code\u003e as \u003ccode\u003e3.3.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1670404321.0 + }, + { + "data_format": 2, + "description": "Denovo Assembly from FASTQ files", + "filenames": [ + "singularity/Singularity" + ], + "full_name": "sequana/denovo", + "latest_release": null, + "stargazers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1705619466.0 + "updated_at": 1668763760.0 }, { "data_format": 2, - "description": null, + "description": "Recenta de container singularity para rodar o VASP", "filenames": [ "Singularity" ], - "full_name": "darachm/singularity_grinder", + "full_name": "natanmr/vasp-container", "latest_release": null, - "readme": "\u003cp\u003eThis container is for providing \u003ccode\u003egrinder\u003c/code\u003e for some bioinformatics pipelines.\u003c/p\u003e\n\u003cp\u003eThe primary reason for this is so that it flows nicely through SingularityHub\nfor Nextflow pipelines.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-vasp-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#vasp-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evasp-container\u003c/h1\u003e\n\u003cp\u003eReceita de container singularity para rodar o VASP\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1546565343.0 + "updated_at": 1668887511.0 }, { "data_format": 2, "description": null, "filenames": [ - "docker/Singularity.nvidia.def" + "Singularity" ], - "full_name": "GeoSymCodes/devito", + "full_name": "JesseBrouw/Thesis", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDevito: Fast Stencil Computation from Symbolic Specification\u003c/h1\u003e\u003ca id=\"user-content-devito-fast-stencil-computation-from-symbolic-specification\" class=\"anchor\" aria-label=\"Permalink: Devito: Fast Stencil Computation from Symbolic Specification\" href=\"#devito-fast-stencil-computation-from-symbolic-specification\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-core\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-core/badge.svg\" alt=\"Build Status for the Core backend\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-mpi\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-mpi/badge.svg\" alt=\"Build Status with MPI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-gpu\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-gpu/badge.svg\" alt=\"Build Status on GPU\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/devitocodes/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3341c4237b01c40446dbf572166724d26ed6e3ce3b371353f8a932b9ae54f396/68747470733a2f2f636f6465636f762e696f2f67682f64657669746f636f6465732f64657669746f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Code Coverage\" data-canonical-src=\"https://codecov.io/gh/devitocodes/devito/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://join.slack.com/t/devitocodes/shared_invite/zt-gtd2yxj9-Y31YKk_7lr9AwfXeL2iMFg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/71e4f5a15e2e4dd4c87f9f57a0c6661196ed4542f236eb982803dbd090bd99e4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636861742d6f6e253230736c61636b2d253233333643354630\" alt=\"Slack Status\" data-canonical-src=\"https://img.shields.io/badge/chat-on%20slack-%2336C5F0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://devitocodes.github.io/devito-performance\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75162422b65fe2f61b15722be747fa13ebc1d80ecfeeccbee2462ab769c89da3/687474703a2f2f696d672e736869656c64732e696f2f62616467652f62656e63686d61726b656425323062792d6173762d626c75652e7376673f7374796c653d666c6174\" alt=\"asv\" data-canonical-src=\"http://img.shields.io/badge/benchmarked%20by-asv-blue.svg?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d6895be18a87329c268ffb103d3a4541dea612dd39066dc7f6f0ec0ff0400c2/68747470733a2f2f62616467652e667572792e696f2f70792f64657669746f2e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/devito.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/devitocodes/devito/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e91e1d353a8b6acf0b42547ac3901f2c30138a3abaaa3d3c242da30b5b4f8426/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/devitocodes/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fd36e0228b4c25e857e9ac2cf81d9b88dc56b5c50e75e39586fcbaa1c1a1007c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65726875622d696d616765732d696d706f7274616e742e7376673f6c6f676f3d446f636b65723f636f6c6f723d626c756576696f6c6574266c6162656c3d646f636b657226736f72743d73656d766572\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/badge/dockerhub-images-important.svg?logo=Docker?color=blueviolet\u0026amp;label=docker\u0026amp;sort=semver\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.devitoproject.org\" rel=\"nofollow\"\u003eDevito\u003c/a\u003e is a Python package to implement\noptimized stencil computation (e.g., finite differences, image processing,\nmachine learning) from high-level symbolic problem definitions. Devito builds\non \u003ca href=\"http://www.sympy.org/en/index.html\" rel=\"nofollow\"\u003eSymPy\u003c/a\u003e and employs automated code\ngeneration and just-in-time compilation to execute optimized computational\nkernels on several computer platforms, including CPUs, GPUs, and clusters\nthereof.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#about-devito\"\u003eAbout Devito\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#resources\"\u003eResources\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/devitocodes/devito/blob/master/FAQ.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#performance\"\u003ePerformance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#get-in-touch\"\u003eGet in touch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#interactive-jupyter-notebooks\"\u003eInteractive jupyter notebooks\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout Devito\u003c/h2\u003e\u003ca id=\"user-content-about-devito\" class=\"anchor\" aria-label=\"Permalink: About Devito\" href=\"#about-devito\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDevito provides a functional language to implement sophisticated operators that\ncan be made up of multiple stencil computations, boundary conditions, sparse\noperations (e.g., interpolation), and much more. A typical use case is\nexplicit finite difference methods for approximating partial differential\nequations. For example, a 2D diffusion operator may be implemented with Devito\nas follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGrid\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eshape\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e))\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTimeFunction\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027f\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003espace_order\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eeqn\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eEq\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edt\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elaplace\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eop\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eOperator\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eEq\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eforward\u003c/span\u003e, \u003cspan class=\"pl-en\"\u003esolve\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eeqn\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eforward\u003c/span\u003e)))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn \u003ccode\u003eOperator\u003c/code\u003e generates low-level code from an ordered collection of \u003ccode\u003eEq\u003c/code\u003e (the\nexample above being for a single equation). This code may also be compiled and\nexecuted\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003et\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etimesteps\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere is virtually no limit to the complexity of an \u003ccode\u003eOperator\u003c/code\u003e -- the Devito\ncompiler will automatically analyze the input, detect and apply optimizations\n(including single- and multi-node parallelism), and eventually generate code\nwith suitable loops and expressions.\u003c/p\u003e\n\u003cp\u003eKey features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA functional language to express finite difference operators.\u003c/li\u003e\n\u003cli\u003eStraightforward mechanisms to adjust the discretization.\u003c/li\u003e\n\u003cli\u003eConstructs to express sparse operators (e.g., interpolation), classic linear\noperators (e.g., convolutions), and tensor contractions.\u003c/li\u003e\n\u003cli\u003eSeamless support for boundary conditions and adjoint operators.\u003c/li\u003e\n\u003cli\u003eA flexible API to define custom stencils, sub-domains, sub-sampling,\nand staggered grids.\u003c/li\u003e\n\u003cli\u003eGeneration of highly optimized parallel code (SIMD vectorization, CPU and\nGPU parallelism via OpenMP and OpenACC, multi-node parallelism via MPI,\nblocking, aggressive symbolic transformations for FLOP reduction, etc.).\u003c/li\u003e\n\u003cli\u003eDistributed NumPy arrays over multi-node (MPI) domain decompositions.\u003c/li\u003e\n\u003cli\u003eInspection and customization of the generated code.\u003c/li\u003e\n\u003cli\u003eAutotuning framework to ease performance tuning.\u003c/li\u003e\n\u003cli\u003eSmooth integration with popular Python packages such as NumPy, SymPy, Dask,\nand SciPy, as well as machine learning frameworks such as TensorFlow and\nPyTorch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to try Devito is through Docker using the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# get the code\ngit clone https://github.com/devitocodes/devito.git\ncd devito\n\n# start a jupyter notebook server on port 8888\ndocker-compose up devito\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter running the last command above, the terminal will display a URL such as\n\u003ccode\u003ehttps://127.0.0.1:8888/?token=XXX\u003c/code\u003e. Copy-paste this URL into a browser window\nto start a \u003ca href=\"https://jupyter.org/\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e notebook session where you can go\nthrough the \u003ca href=\"https://github.com/devitocodes/devito/tree/master/examples\"\u003etutorials\u003c/a\u003e\nprovided with Devito or create your own notebooks.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://devitocodes.github.io/devito/download.html\" rel=\"nofollow\"\u003eSee here\u003c/a\u003e for detailed installation\ninstructions and other options. If you encounter a problem during installation, please\nsee the\n\u003ca href=\"https://github.com/devitocodes/devito/wiki/Installation-Issues\"\u003einstallation issues\u003c/a\u003e we\nhave seen in the past.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResources\u003c/h2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-label=\"Permalink: Resources\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo learn how to use Devito,\n\u003ca href=\"https://github.com/devitocodes/devito/blob/master/examples\"\u003ehere\u003c/a\u003e is a good\nplace to start, with lots of examples and tutorials.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.devitoproject.org/\" rel=\"nofollow\"\u003ewebsite\u003c/a\u003e also provides access to other\ninformation, including documentation and instructions for citing us.\u003c/p\u003e\n\u003cp\u003eSome FAQs are discussed \u003ca href=\"FAQ.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePerformance\u003c/h2\u003e\u003ca id=\"user-content-performance\" class=\"anchor\" aria-label=\"Permalink: Performance\" href=\"#performance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are interested in any of the following\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGeneration of parallel code (CPU, GPU, multi-node via MPI);\u003c/li\u003e\n\u003cli\u003ePerformance tuning;\u003c/li\u003e\n\u003cli\u003eBenchmarking operators;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ethen you should take a look at this\n\u003ca href=\"https://github.com/devitocodes/devito/blob/master/benchmarks/user\"\u003eREADME\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eYou may also be interested in\n\u003ca href=\"https://www.devitocodes.com/blog/thematrix\" rel=\"nofollow\"\u003eTheMatrix\u003c/a\u003e -- a cross-architecture\nbenchmarking framework showing the performance of several production-grade\nseismic operators implemented with Devito.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet in touch\u003c/h2\u003e\u003ca id=\"user-content-get-in-touch\" class=\"anchor\" aria-label=\"Permalink: Get in touch\" href=\"#get-in-touch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re using Devito, we would like to hear from you. Whether you\nare facing issues or just trying it out, join the\n\u003ca href=\"https://join.slack.com/t/devitocodes/shared_invite/zt-gtd2yxj9-Y31YKk_7lr9AwfXeL2iMFg\" rel=\"nofollow\"\u003econversation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive jupyter notebooks\u003c/h2\u003e\u003ca id=\"user-content-interactive-jupyter-notebooks\" class=\"anchor\" aria-label=\"Permalink: Interactive jupyter notebooks\" href=\"#interactive-jupyter-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tutorial jupyter notebook are available interactively at the public \u003ca href=\"https://mybinder.org/v2/gh/devitocodes/devito/master\" rel=\"nofollow\"\u003ebinder\u003c/a\u003e jupyterhub.\u003c/p\u003e\n", + "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1693333728.0 + "updated_at": 1668694985.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for 3D DNA (https://github.com/theaidenlab/3d-dna)", "filenames": [ - "core_libraries/subtrees/PreQual/Singularity", - "core_libraries/subtrees/MRtrix3/Singularity" + "Singularity", + "Singularity.180922" ], - "full_name": "Saurabh1826/CNT_Research_Template", + "full_name": "powerPlant/3d-dna-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCNT Research Repository Template\u003c/h1\u003e\u003ca id=\"user-content-cnt-research-repository-template\" class=\"anchor\" aria-label=\"Permalink: CNT Research Repository Template\" href=\"#cnt-research-repository-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ff9fd36258ece5b69dde5c086c71002d1ced07b38936b8d845c96c1531c1a0d2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e322e312d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff9fd36258ece5b69dde5c086c71002d1ced07b38936b8d845c96c1531c1a0d2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e322e312d626c7565\" alt=\"version\" data-canonical-src=\"https://img.shields.io/badge/version-0.2.1-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e3e94e150cc0334e54f7f11dadcb26c15b6661f36e71dccfeeebe76dbe7a8488/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069702e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e3e94e150cc0334e54f7f11dadcb26c15b6661f36e71dccfeeebe76dbe7a8488/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069702e737667\" alt=\"pip\" data-canonical-src=\"https://img.shields.io/pypi/v/pip.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/51cd8eed7edeb654616449db2a9bcf24c72762a19e4e9771980375413e5f4224/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f34\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/51cd8eed7edeb654616449db2a9bcf24c72762a19e4e9771980375413e5f4224/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f34\" alt=\"https://img.shields.io/pypi/pyversions/\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of this template is to consolidate shared libraries and enable consistent workflows and tests for most projects in the CNT lab. Users will be able to quickly load code from tested common libraries, or load their own personal code, in an object oriented manner.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePrerequisites\u003c/h1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use this repository, you must have access to either Python or Matlab.\u003c/p\u003e\n\u003cp\u003eWe also highly recommend the use of a virtual environment, conda environment, or similar software to manage distributions. Examples for their use can be found in the documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to install any of the common library code, we provide instructions for both Python and Matlab below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython\u003c/h2\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor python packages, python wheels and tarballs can be found in: CNT_Development/core_libraries/python/.\u003c/p\u003e\n\u003cp\u003eTo install, run:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epip install foo.whl\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eor\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epip install foo.tar.gz\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere foo is the name of the library of interest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMatlab\u003c/h2\u003e\u003ca id=\"user-content-matlab\" class=\"anchor\" aria-label=\"Permalink: Matlab\" href=\"#matlab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd37\u200d\u2640\ufe0f In development.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocumentation\u003c/h1\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis template is intended to be used as both an environment and a simple wrapper for research code. Before beginning, we highly recommend that a virtual environment (or equivalent) is created for each\nproject to ensure your dependencies and code are properly referenced. Examples for creating virtual environments is provided below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRepository Structure\u003c/h2\u003e\u003ca id=\"user-content-repository-structure\" class=\"anchor\" aria-label=\"Permalink: Repository Structure\" href=\"#repository-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA hyperlink enabled repository tree is available within the \u003ca href=\"./repository_structure.md\"\u003erepository_structure\u003c/a\u003e markdown file. We demonstrate the use of git-ginored files and folders by displaying those\nentries with a \u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e symbol.\u003c/p\u003e\n\u003cp\u003eA short description of some of the top-level directories and files are as follows:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecore_libraries\u003c/h3\u003e\u003ca id=\"user-content-core_libraries\" class=\"anchor\" aria-label=\"Permalink: core_libraries\" href=\"#core_libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains the submodules and build files that make up the core libraries used for lab-wide projects.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edata_pointers\u003c/h3\u003e\u003ca id=\"user-content-data_pointers\" class=\"anchor\" aria-label=\"Permalink: data_pointers\" href=\"#data_pointers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains pointers to data contained on Borel and Lief. Data requests should reference these data pointers to prevent duplication before downloading new data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edocuments\u003c/h3\u003e\u003ca id=\"user-content-documents\" class=\"anchor\" aria-label=\"Permalink: documents\" href=\"#documents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains various research documents associated with a project (i.e. SoPs, Pipeline diagrams, etc.) as well as code documentation (e.g.document strings) for the various libraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eexamples\u003c/h3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains example python and matlab scripts for various research tasks as well as how to use common libraries and environments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ereference_data\u003c/h3\u003e\u003ca id=\"user-content-reference_data\" class=\"anchor\" aria-label=\"Permalink: reference_data\" href=\"#reference_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains data that can be used for building targets or conducting unit tests.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esample_data\u003c/h3\u003e\u003ca id=\"user-content-sample_data\" class=\"anchor\" aria-label=\"Permalink: sample_data\" href=\"#sample_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains sample data that might be used in any of the lab-wide projects.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003escripts\u003c/h3\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-label=\"Permalink: scripts\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains user-defined scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eunit_tests\u003c/h3\u003e\u003ca id=\"user-content-unit_tests\" class=\"anchor\" aria-label=\"Permalink: unit_tests\" href=\"#unit_tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains unit tests for validating new/altered code at both the machine level and model level.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003euser_data\u003c/h3\u003e\u003ca id=\"user-content-user_data\" class=\"anchor\" aria-label=\"Permalink: user_data\" href=\"#user_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder is meant to store user data. Data in this repository is private by default and will not be uploaded to public repositories.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e.gitignore\u003c/h3\u003e\u003ca id=\"user-content-gitignore\" class=\"anchor\" aria-label=\"Permalink: .gitignore\" href=\"#gitignore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis file helps prevent certain files from being uploaded to the public repository. This can be to avoid excess data volumes, or to protect sensitive information. By default, the ignored files and\nfolders are designed for the development of a lab-wide template, and users should adjust the settings to match their own needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVirtual Environments\u003c/h1\u003e\u003ca id=\"user-content-virtual-environments\" class=\"anchor\" aria-label=\"Permalink: Virtual Environments\" href=\"#virtual-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython\u003c/h2\u003e\u003ca id=\"user-content-python-1\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConda\u003c/h3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend using the pre-built environment files provided to start your project. These files can be found in the following subfolders: core_libraries/python/*/*yml and can be installed using the following command:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda env create -f foo.yml\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere foo is the name of the environment.\u003c/p\u003e\n\u003cp\u003eFor those who wish to create their own environment, we introduced some of the basics below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreation\u003c/h4\u003e\u003ca id=\"user-content-creation\" class=\"anchor\" aria-label=\"Permalink: Creation\" href=\"#creation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda create --name myenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere myenv is the name of the environment you wish to create.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eListing environments\u003c/h4\u003e\u003ca id=\"user-content-listing-environments\" class=\"anchor\" aria-label=\"Permalink: Listing environments\" href=\"#listing-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda env list\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eActivating Environment\u003c/h4\u003e\u003ca id=\"user-content-activating-environment\" class=\"anchor\" aria-label=\"Permalink: Activating Environment\" href=\"#activating-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda activate myenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere myenv is the name of the environment you wish to activate.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDeactivating an environment\u003c/h4\u003e\u003ca id=\"user-content-deactivating-an-environment\" class=\"anchor\" aria-label=\"Permalink: Deactivating an environment\" href=\"#deactivating-an-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda deactivate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMore information\u003c/h4\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-label=\"Permalink: More information\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information, please read: \u003ca href=\"https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment\" rel=\"nofollow\"\u003ehttps://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVirtual Environment\u003c/h3\u003e\u003ca id=\"user-content-virtual-environment\" class=\"anchor\" aria-label=\"Permalink: Virtual Environment\" href=\"#virtual-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst make sure you have venv installed. If not, you can pip install it as follows: pip install venv\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreation\u003c/h4\u003e\u003ca id=\"user-content-creation-1\" class=\"anchor\" aria-label=\"Permalink: Creation\" href=\"#creation-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003epython3 -m venv /path/to/new/virtual/environment\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eListing environments\u003c/h4\u003e\u003ca id=\"user-content-listing-environments-1\" class=\"anchor\" aria-label=\"Permalink: Listing environments\" href=\"#listing-environments-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003elsvirtualenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYou may need to install virutalenvwrapper to use this command. ( pip install virtualenvwrapper. ) If it doesn\u0027t populate to your path, check the package directory for the executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eActivating Environment\u003c/h4\u003e\u003ca id=\"user-content-activating-environment-1\" class=\"anchor\" aria-label=\"Permalink: Activating Environment\" href=\"#activating-environment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esource /path/to/venv/bin/activate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDeactivating an environment\u003c/h4\u003e\u003ca id=\"user-content-deactivating-an-environment-1\" class=\"anchor\" aria-label=\"Permalink: Deactivating an environment\" href=\"#deactivating-an-environment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003edeactivate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(Type this command in your shell.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMatlab\u003c/h2\u003e\u003ca id=\"user-content-matlab-1\" class=\"anchor\" aria-label=\"Permalink: Matlab\" href=\"#matlab-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd37\u200d\u2642\ufe0f\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContact Us\u003c/h1\u003e\u003ca id=\"user-content-contact-us\" class=\"anchor\" aria-label=\"Permalink: Contact Us\" href=\"#contact-us\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny questions should be directed to the data science team. Contact information is provided below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:bjprager@seas.upenn.edu\"\u003eBrian Prager\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:asuncion@seas.upenn.edu\"\u003eJoshua Asuncion\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2286\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the 3D de novo assembly (3D DNA) pipeline\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1686633899.0 + "updated_at": 1669167969.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for ImageMagick (imagemagick.org)", "filenames": [ - "llava-container-train/Singularity", - "llava-container/Singularity" + "Singularity.7.1.0.52", + "Singularity" ], - "full_name": "uw-psych/llava-container", + "full_name": "powerPlant/imagemagick-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ellava-container\u003c/h1\u003e\u003ca id=\"user-content-llava-container\" class=\"anchor\" aria-label=\"Permalink: llava-container\" href=\"#llava-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container provides a convenient way to run \u003ca href=\"https://github.com/haotian-liu/LLaVA\"\u003eLLaVA\u003c/a\u003e on Hyak.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning LLaVA on Hyak \ud83c\udf47\u003c/h2\u003e\u003ca id=\"user-content-running-llava-on-hyak-\" class=\"anchor\" aria-label=\"Permalink: Running LLaVA on Hyak \ud83c\udf47\" href=\"#running-llava-on-hyak-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you\u0027ll need to log in to Hyak. If you\u0027ve never set this up, go \u003ca href=\"https://uw-psych.github.io/compute_docs\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh your-uw-netid@klone.hyak.uw.edu\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, you\u0027ll need to request a compute node. You can do this with the \u003ccode\u003esalloc\u003c/code\u003e command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Request a GPU node with 8 CPUs, 2 GPUs, 64GB of RAM, and 1 hour of runtime:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (Note: you may need to change the account and partition)\u003c/span\u003e\nsalloc --account escience --partition gpu-a40 --mem 64G -c 8 --time 1:00:00 --gpus 2\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOne you\u0027re logged in to the compute node, you should set up your cache directories and Apptainer settings.\u003c/p\u003e\n\u003cp\u003e\ud83d\udc49 \u003cem\u003eIf you\u0027re following this tutorial, \u003cstrong\u003eyou should do this every time you\u0027re running LLaVA on Hyak!\u003c/strong\u003e This is because the default settings for Apptainer will use your home directory for caching, which will quickly fill up your home directory and cause your jobs to fail.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Do this in every session where you\u0027re running LLaVA on Hyak!\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set up cache directories:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e APPTAINER_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/.cache/apptainer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e HUGGINGFACE_HUB_CACHE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/.cache/huggingface\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${APPTAINER_CACHEDIR}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HUGGINGFACE_HUB_CACHE}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set up Apptainer:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e APPTAINER_BIND=/gscratch APPTAINER_WRITABLE_TMPFS=1 APPTAINER_NV=1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, you can run LLaVA. Let\u0027s try with the sample image on LLaVA\u0027s repository:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/046162f35e7e2d7d7ca3f2a1cde03e4be84890fb656cbeb3babc584ba669c310/68747470733a2f2f6c6c6176612d766c2e6769746875622e696f2f7374617469632f696d616765732f766965772e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/046162f35e7e2d7d7ca3f2a1cde03e4be84890fb656cbeb3babc584ba669c310/68747470733a2f2f6c6c6176612d766c2e6769746875622e696f2f7374617469632f696d616765732f766965772e6a7067\" alt=\"Sample image\" data-canonical-src=\"https://llava-vl.github.io/static/images/view.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run LLaVA:\u003c/span\u003e\napptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n llava-run \\\n --model-path liuhaotian/llava-v1.5-7b \\\n --image-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://llava-vl.github.io/static/images/view.jpg\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --query \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eWhat\u0027s going on here?\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Description of the arguments:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e llava-run: the command to run in the container\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --model-path: the name of the model to use\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --image-file: the URL of the image to use\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --query: what to ask the model\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf it\u0027s working, you should see output that looks something like this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe image features a pier extending out into a large body of water, possibly a lake or a river. The pier is made of wood and has a few benches placed on it, providing a place for people to sit and enjoy the view. The water appears calm and serene, making it an ideal spot for relaxation and contemplation.\u003c/p\u003e\n\u003cp\u003eIn the background, there are mountains visible, adding to the picturesque scenery. The pier is situated in front of a forest, creating a peaceful and natural atmosphere.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eWhen you\u0027re done, you can exit the compute node with the command \u003ccode\u003eexit\u003c/code\u003e or \u003ccode\u003eCtrl-D\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChat mode \ud83d\udde3\ufe0f\u003c/h3\u003e\u003ca id=\"user-content-chat-mode-\ufe0f\" class=\"anchor\" aria-label=\"Permalink: Chat mode \ud83d\udde3\ufe0f\" href=\"#chat-mode-\ufe0f\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor chat, just pass \u003ccode\u003e--chat\u003c/code\u003e instead of \u003ccode\u003e--query\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n llava-run \\\n --model-path liuhaotian/llava-v1.5-7b \\\n --image-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://llava-vl.github.io/static/images/view.jpg\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --chat\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning other commands \ud83c\udfc3\u003c/h3\u003e\u003ca id=\"user-content-running-other-commands-\" class=\"anchor\" aria-label=\"Permalink: Running other commands \ud83c\udfc3\" href=\"#running-other-commands-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to a different command, such as one of the commands that comes with LLaVA, you can pass it after the image name:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n python -m llava.serve.cli\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImproving startup time \ud83d\ude80\u003c/h3\u003e\u003ca id=\"user-content-improving-startup-time-\" class=\"anchor\" aria-label=\"Permalink: Improving startup time \ud83d\ude80\" href=\"#improving-startup-time-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you notice slowness when launching the container, you can try extracting the container image to a sandbox directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set up a sandbox directory:\u003c/span\u003e\nSANDBOX=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/tmp/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/sandbox/llava\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e mkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edirname \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SANDBOX}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract the container image to the sandbox:\u003c/span\u003e\napptainer build --sandbox \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SANDBOX}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e oras://ghcr.io/uw-psych/llava-container/llava-container:latest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run LLaVA by passing the sandbox directory instead of the image URL:\u003c/span\u003e\napptainer run \\\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SANDBOX}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n llava-run \\\n --model-path liuhaotian/llava-v1.5-7b \\\n --image-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://llava-vl.github.io/static/images/view.jpg\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --query \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eWhat\u0027s going on here?\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the web interface \ud83d\udd78\ufe0f\u003c/h3\u003e\u003ca id=\"user-content-running-the-web-interface-\ufe0f\" class=\"anchor\" aria-label=\"Permalink: Running the web interface \ud83d\udd78\ufe0f\" href=\"#running-the-web-interface-\ufe0f\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIncluded in the container is a wrapper script for the LLaVA web interface. To run it, you can use the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n hyak-llava-web\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis script will print out a command to set up an SSH tunnel to the web interface. You can then open the web interface by visiting \u003ccode\u003ehttp://localhost:8000\u003c/code\u003e in your browser. The output should look something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To access the gradio web server, run the following command on your local machine: \u003c/span\u003e\nssh -o StrictHostKeyChecking=no -N -L 8000:localhost:53641 -J altan@klone.hyak.uw.edu altan@g3021\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should be able to copy and paste this command into your terminal to set up the SSH tunnel. Then, you can open \u003ccode\u003ehttp://localhost:8000\u003c/code\u003e in your browser to access the web interface.\u003c/p\u003e\n\u003cp\u003eTo configure the web interface, you can set the following environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMODEL_PATHS\u003c/code\u003e: a list of model paths, quoted and separated by space (default: \"liuhaotian/llava-v1.5-7b\")\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAvailable models include, but are not limited to:\n\u003cul\u003e\n\u003cli\u003eliuhaotian/llava-v1.5-7b\u003c/li\u003e\n\u003cli\u003eliuhaotian/llava-v1.5-13b\u003c/li\u003e\n\u003cli\u003eliuhaotian/llava-v1.5-7b-lora\u003c/li\u003e\n\u003cli\u003eliuhaotian/llava-v1.5-13b-lora\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md\"\u003ehttps://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md\u003c/a\u003e for more details.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eGRADIO_CONTROLLER_PORT\u003c/code\u003e: the port number for the gradio controller (or leave it empty to use a random port)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eLOCAL_HTTP_PORT\u003c/code\u003e: the port number to print for the local HTTP server SSH tunnel command (default: 8000)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MODEL_PATHS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eliuhaotian/llava-v1.5-13b\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the 13b model instead of the 7b model\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LOCAL_HTTP_PORT=9000 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use port 9000 instead of 8000\u003c/span\u003e\napptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n hyak-llava-web\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\ud83d\udc49 \u003cem\u003eYou need to select the model from the dropdown to start. If the model doesn\u0027t appear in the dropdown, wait a few seconds and refresh the page.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ccode\u003ellava-run\u003c/code\u003e\u003c/h2\u003e\u003ca id=\"user-content-llava-run\" class=\"anchor\" aria-label=\"Permalink: llava-run\" href=\"#llava-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ellava-run.py\u003c/code\u003e script is a modification of \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/run_llava.py\"\u003e\u003ccode\u003eLLaVA/lava/eval/run_llava.py\u003c/code\u003e\u003c/a\u003e that adds support for loading 4- and 8-bit models as found in \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/cli.py\"\u003e\u003ccode\u003eLaVA/llava/serve/cli.py\u003c/code\u003e\u003c/a\u003e, as well as a chat mode that allows you to have a conversation with the model.\u003c/p\u003e\n\u003cp\u003eThe following describes the usage of \u003ccode\u003ellava-run\u003c/code\u003e:\u003c/p\u003e\n\u003cpre lang=\"plain\"\u003e\u003ccode\u003eThis container provides a convenient way to run LLaVA. In addition to the LLaVA\nmodule, it includes the commands:\n - `llava-run`, a command-line wrapper for LLaVA inference\n - `hyak-llava-web`, a wrapper to launch the gradio web interface and issue an\n SSH connection string you can copy to open a tunnel to your own computer.\n \nTo run LLaVA with the `llava-run` script, use the following command:\n apptainer run --nv --writable-tmpfs \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n llava-run [llava-run arguments]\n\nYou must pass the \"--nv\" flag to enable GPU support.\n\nDepending on your intended use, you may also want to pass the \"--bind\" flag\nto mount a directory from the host system into the container.\n\nTo specify a directory to use for the HuggingFace model cache and enable access\nto /gscratch, use the following command:\n apptainer run --nv --writable-tmpfs \\\n --env HUGGINGFACE_HUB_CACHE=/path/to/cache \\\n --bind /gscratch \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n llava-run [llava-run arguments]\n\n\nThe following describes the usage of this script:\n\nllava-run [-h] [--model-path PATH] [--model-base PATH] --image-file\n\tIMAGE [IMAGE ...] (--query QUERY [QUERY ...] | --chat)\n\t[--json]\n\t[--conv-mode {v0,v1,vicuna_v1,llama_2,plain,v0_plain,llava_v0,v0_mmtag,llava_v1,v1_mmtag,llava_llama_2,mpt}]\n\t[--stack-sep SEP] [--temperature FLOAT] [--top_p FLOAT]\n\t[--num_beams N] [--max_new_tokens N]\n\t[--load-8bit | --load-4bit] [--device {cuda,cpu}]\n\t[--hf-cache-dir DIR]\n\noptions:\n -h, --help show this help message and exit\n --model-path PATH Model path\n --model-base PATH Model base (required for \u0027lora\u0027 models)\n --image-file IMAGE [IMAGE ...]\n Path or URL to image (provide multiple to process in\n batch; use --sep delimiter within paths to stack image\n inputs )\n --query QUERY [QUERY ...]\n Query (can be specified multiple times, e.g. --query a\n --query b)\n --chat Use chat instead of query\n --json Produce JSON output\n --conv-mode {v0,v1,vicuna_v1,llama_2,plain,v0_plain,llava_v0,v0_mmtag,llava_v1,v1_mmtag,llava_llama_2,mpt}\n Conversation mode\n --stack-sep SEP Internal separator for stacked image files (default:\n \",\")\n --temperature FLOAT Temperature (default: 0.2)\n --top_p FLOAT Top p (default: 1.0)\n --num_beams N Number of beams (default: 1)\n --max_new_tokens N Max new tokens (default: 512)\n --load-8bit Load 8bit model\n --load-4bit Load 4bit model\n --device {cuda,cpu} Device to use\n --hf-cache-dir DIR HuggingFace cache directory\n \n For details on the arguments, see the LLaVA documentation and the usage infor-\n mation for llava.eval.run_llava and llava.serve.cli.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee the \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/README.md\"\u003edocumentation\u003c/a\u003e for LLaVA or the source code for \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/run_llava.py\"\u003e\u003ccode\u003ellava/eval/run_llava.py\u003c/code\u003e\u003c/a\u003e and \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/cli.py\"\u003e\u003ccode\u003ellava/serve/cli.py\u003c/code\u003e\u003c/a\u003e for more information on the arguments.\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for ImageMagick \u003ca href=\"https://imagemagick.org/\" rel=\"nofollow\"\u003ehttps://imagemagick.org/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGenerate symlinks like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec imagemagick-7.1.0.52.sif ls -1 /opt/imagemagick/bin | xargs -L1 ln -s imagemagick-7.1.0.52.sif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1706071611.0 + "updated_at": 1667792525.0 }, { "data_format": 2, "description": null, "filenames": [ - "misc/releases/22.12/Singularity.22.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/latest/Singularity" + "Singularity.v0.4", + "Singularity" ], - "full_name": "silvansievers/pddl-symmetries", + "full_name": "cschu/nevermore", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 10, Clang 12\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1659431272.0 + "updated_at": 1639441314.0 }, { "data_format": 2, - "description": "R containers", + "description": "This repository provides a series of Singularity recipe files used to easily deploy numerous bioinformatics softwares through containers.All these Singularity recipes are ready to be used by the bioinformatics community and have been developed to be integrated into the workflow manager TOGGLe http://toggle.southgreen.fr.", "filenames": [ - "Singularity.3.6.0" + "Singularity.sRNA_pipeline.def" ], - "full_name": "arcsUVA/R", + "full_name": "SouthGreenPlatform/singularityRecipeFiles", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eR\u003c/h1\u003e\u003ca id=\"user-content-r\" class=\"anchor\" aria-label=\"Permalink: R\" href=\"#r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eR containers\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/514ee51de8b3543f550d8ab786b179568b58039afa34f1f55c753c4e8045b1db/687474703a2f2f7777772e736f757468677265656e2e66722f73697465732f736f757468677265656e2e66722f7468656d65732f736f757468677265656e2f6c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/514ee51de8b3543f550d8ab786b179568b58039afa34f1f55c753c4e8045b1db/687474703a2f2f7777772e736f757468677265656e2e66722f73697465732f736f757468677265656e2e66722f7468656d65732f736f757468677265656e2f6c6f676f2e706e67\" alt=\"\" data-canonical-src=\"http://www.southgreen.fr/sites/southgreen.fr/themes/southgreen/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipe Files\u003c/h1\u003e\n\u003cp\u003eThis repository provides a series of \u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e recipe files used to easily deploy numerous bioinformatics containers.\u003cbr\u003e\nAll the singularity containers are ready to be used by the bioinformatics community and to be integrated into the \u003ca href=\"http://toggle.southgreen.fr\" rel=\"nofollow\"\u003eTOGGLe\u003c/a\u003e workflow manager.\u003c/p\u003e\n\u003cp\u003eThe images are based on either 16.04 or 18.04 Ubuntu versions. All compiled images can be found at \u003ca href=\"http://bioinfo-storage.ird.fr/SingularityImages\" rel=\"nofollow\"\u003ehttp://bioinfo-storage.ird.fr/SingularityImages\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContact : Ndomassi Tando (\u003ca href=\"mailto:ndomassi.tando@ird.fr\"\u003endomassi.tando@ird.fr\u003c/a\u003e)\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSoftware\u003c/th\u003e\n\u003cth\u003eVersion\u003c/th\u003e\n\u003cth\u003eMaintainer\u003c/th\u003e\n\u003cth\u003etested and deployed on\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"http://www.bcgsc.ca/platform/bioinfo/software/abyss\" rel=\"nofollow\"\u003eAbyss\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e1.9\u003c/td\u003e\n\u003ctd\u003eVal\u00e9rie NOEL (UMR MIVEGEC)\u003c/td\u003e\n\u003ctd\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/SouthGreenPlatform/trainings/blob/gh-pages/images/logo_ird.png\"\u003e\u003cimg src=\"https://github.com/SouthGreenPlatform/trainings/raw/gh-pages/images/logo_ird.png\" height=\"20\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (i-trop cluster)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/jdidion/atropos\"\u003eatropos\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e1.1.14\u003c/td\u003e\n\u003ctd\u003eNdomassi TANDO (UMR DIADE)\u003c/td\u003e\n\u003ctd\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/SouthGreenPlatform/trainings/blob/gh-pages/images/logo_ird.png\"\u003e\u003cimg src=\"https://github.com/SouthGreenPlatform/trainings/raw/gh-pages/images/logo_ird.png\" height=\"20\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (i-trop cluster)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://bedtools.readthedocs.io/en/latest/index.html\" rel=\"nofollow\"\u003ebedtools\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.27.1\u003c/td\u003e\n\u003ctd\u003eValentin KLEIN (UMR DIADE)\u003c/td\u003e\n\u003ctd\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/SouthGreenPlatform/trainings/blob/gh-pages/images/logo_ird.png\"\u003e\u003cimg src=\"https://github.com/SouthGreenPlatform/trainings/raw/gh-pages/images/logo_ird.png\" height=\"20\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (i-trop cluster)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"http://hannonlab.cshl.edu/fastx_toolkit/\" rel=\"nofollow\"\u003eFASTX-Toolkit\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e0.0.13\u003c/td\u003e\n\u003ctd\u003eVal\u00e9rie NOEL (UMR MIVEGEC)\u003c/td\u003e\n\u003ctd\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/SouthGreenPlatform/trainings/blob/gh-pages/images/logo_ird.png\"\u003e\u003cimg src=\"https://github.com/SouthGreenPlatform/trainings/raw/gh-pages/images/logo_ird.png\" height=\"20\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (i-trop cluster)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, - "topics": [], - "updated_at": 1573410996.0 + "subscribers_count": 13, + "topics": [ + "recipe-files", + "singularity-containers" + ], + "updated_at": 1580131447.0 }, { "data_format": 2, "description": null, "filenames": [ - "docker/Singularity.def" + "docker/Singularity.nvidia.def" ], - "full_name": "mandarc64/RA", + "full_name": "guaacoelho/elastic_UMA", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mlcommons-algoperf-training-algorithms-benchmark\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#mlcommons-algoperf-training-algorithms-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMLCommons\u2122 AlgoPerf: Training Algorithms Benchmark\u003c/h1\u003e\n\u003cbr\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"#\"\u003e\u003cimg width=\"600\" src=\".assets/mlc_logo.png\" alt=\"MLCommons Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://arxiv.org/abs/2306.07179\" rel=\"nofollow\"\u003ePaper (arXiv)\u003c/a\u003e \u2022\n \u003ca href=\"/CALL_FOR_SUBMISSIONS.md\"\u003eCall for Submissions\u003c/a\u003e \u2022\n \u003ca href=\"/GETTING_STARTED.md\"\u003eGetting Started\u003c/a\u003e \u2022\n \u003ca href=\"/COMPETITION_RULES.md\"\u003eCompetition Rules\u003c/a\u003e \u2022\n \u003ca href=\"/DOCUMENTATION.md\"\u003eDocumentation\u003c/a\u003e \u2022\n \u003ca href=\"/CONTRIBUTING.md\"\u003eContributing\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/CI.yml\"\u003e\u003cimg src=\"https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/CI.yml/badge.svg\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/linting.yml\"\u003e\u003cimg src=\"https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/linting.yml/badge.svg\" alt=\"Lint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/mlcommons/algorithmic-efficiency/blob/main/LICENSE.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44bc63cdf9bc4b15dcf019006fc6e19bc712fc031f39fab78c0c4595b2967e93/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4170616368655f322e302d626c75652e737667\" alt=\"License: Apache 2.0\" data-canonical-src=\"https://img.shields.io/badge/License-Apache_2.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/google/yapf\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8bb70b59f8586325e23af2a66f72b16f21e0d8acf8cee54d403bf376e54ede6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d796170662d6f72616e6765\" alt=\"Code style: yapf\" data-canonical-src=\"https://img.shields.io/badge/code%20style-yapf-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cem\u003eAlgoPerf\u003c/em\u003e is a suite of benchmarks and competitions to measure neural network training speedups due to algorithmic improvements in both training algorithms and models. This is the repository for the \u003cem\u003eAlgoPerf: Training Algorithms benchmark\u003c/em\u003e and its associated competition. It is developed by the \u003ca href=\"https://mlcommons.org/en/groups/research-algorithms/\" rel=\"nofollow\"\u003eMLCommons Algorithms Working Group\u003c/a\u003e. This repository holds the \u003ca href=\"/COMPETITION_RULES.md\"\u003e\u003cstrong\u003ecompetition rules\u003c/strong\u003e\u003c/a\u003e, the \u003ca href=\"/DOCUMENTATION.md\"\u003e\u003cstrong\u003etechnical documentation\u003c/strong\u003e\u003c/a\u003e of the benchmark, \u003ca href=\"/GETTING_STARTED.md\"\u003e\u003cstrong\u003egetting started guides\u003c/strong\u003e\u003c/a\u003e, and the benchmark code. For a detailed description of the benchmark design, see our \u003ca href=\"https://arxiv.org/abs/2306.07179\" rel=\"nofollow\"\u003e\u003cstrong\u003epaper\u003c/strong\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!IMPORTANT]\nUpcoming Deadline:\nRegistration deadline to express non-binding intent to submit: \u003cstrong\u003eJanuary 28th, 2024\u003c/strong\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-table-of-contents-\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#table-of-contents-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents \n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#call-for-submissions\"\u003eCall for Submissions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#competition-rules\"\u003eCompetition Rules\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#technical-documentation-of-the-benchmark--faqs\"\u003eTechnical Documentation of the Benchmark \u0026amp; FAQs\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#paper-and-citing-the-algoperf-benchmark\"\u003ePaper and Citing the AlgoPerf Benchmark\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eYou can install this package and dependencies in a \u003ca href=\"/GETTING_STARTED.md#python-virtual-environment\"\u003ePython virtual environment\u003c/a\u003e or use a \u003ca href=\"/GETTING_STARTED.md#docker\"\u003eDocker/Singularity/Apptainer container\u003c/a\u003e (recommended).\nWe recommend using a Docker container (or alternatively, a Singularity/Apptainer container) to ensure a similar environment to our scoring and testing environments.\nBoth options are described in detail in the \u003ca href=\"/GETTING_STARTED.md\"\u003e\u003cstrong\u003eGetting Started\u003c/strong\u003e\u003c/a\u003e document.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTL;DR to install the Jax version for GPU run:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[pytorch_cpu]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[jax_gpu]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -f \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehttps://storage.googleapis.com/jax-releases/jax_cuda_releases.html\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[full]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTL;DR to install the PyTorch version for GPU run:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[jax_cpu]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[pytorch_gpu]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -f \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehttps://download.pytorch.org/whl/torch_stable.html\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[full]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eFor detailed instructions on developing and scoring your own algorithm in the benchmark see the \u003ca href=\"/GETTING_STARTED.md\"\u003eGetting Started\u003c/a\u003e document.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTL;DR running a JAX workload:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 submission_runner.py \\\n --framework=jax \\\n --workload=mnist \\\n --experiment_dir=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/experiments \\\n --experiment_name=my_first_experiment \\\n --submission_path=baselines/adamw/jax/submission.py \\\n --tuning_search_space=baselines/adamw/tuning_search_space.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTL;DR running a PyTorch workload:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 submission_runner.py \\\n --framework=pytorch \\\n --workload=mnist \\\n --experiment_dir=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/experiments \\\n --experiment_name=my_first_experiment \\\n --submission_path=baselines/adamw/jax/submission.py \\\n --tuning_search_space=baselines/adamw/tuning_search_space.json\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-call-for-submissions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#call-for-submissions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCall for Submissions\u003c/h2\u003e\n\u003cp\u003eThe \u003ca href=\"/CALL_FOR_SUBMISSIONS.md\"\u003eCall for Submissions\u003c/a\u003e announces the first iteration of the AlgoPerf: Training Algorithms competition based on the benchmark by the same name.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-competition-rules\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#competition-rules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompetition Rules\u003c/h3\u003e\n\u003cp\u003eThe competition rules for the \u003cem\u003eAlgoPerf: Training Algorithms\u003c/em\u003e competition can be found in the separate \u003ca href=\"/COMPETITION_RULES.md\"\u003e\u003cstrong\u003eCompetition Rules\u003c/strong\u003e\u003c/a\u003e document.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-technical-documentation-of-the-benchmark--faqs\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#technical-documentation-of-the-benchmark--faqs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTechnical Documentation of the Benchmark \u0026amp; FAQs\u003c/h3\u003e\n\u003cp\u003eWe provide additional technical documentation of the benchmark and answer frequently asked questions in a separate \u003ca href=\"/DOCUMENTATION.md\"\u003e\u003cstrong\u003eDocumentation\u003c/strong\u003e\u003c/a\u003e page. Suggestions, clarifications and questions can be raised via pull requests, creating an issue, or by sending an email to the \u003ca href=\"mailto:algorithms@mlcommons.org\"\u003eworking group\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eWe invite everyone to look through our rules, documentation, and codebase and submit issues and pull requests, e.g. for rules changes, clarifications, or any bugs you might encounter. If you are interested in contributing to the work of the working group and influence the benchmark\u0027s design decisions, please \u003ca href=\"https://mlcommons.org/en/groups/research-algorithms/\" rel=\"nofollow\"\u003ejoin the weekly meetings\u003c/a\u003e and consider becoming a member of the working group.\u003c/p\u003e\n\u003cp\u003eOur \u003ca href=\"/CONTRIBUTING.md\"\u003e\u003cstrong\u003eContributing\u003c/strong\u003e\u003c/a\u003e document provides further MLCommons contributing guidelines and additional setup and workflow instructions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe \u003cem\u003eAlgoPerf\u003c/em\u003e codebase is licensed under the \u003ca href=\"/LICENSE.md\"\u003eApache License 2.0\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-paper-and-citing-the-algoperf-benchmark\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#paper-and-citing-the-algoperf-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePaper and Citing the AlgoPerf Benchmark\u003c/h2\u003e\n\u003cp\u003eIn our paper \u003ca href=\"http://arxiv.org/abs/2306.07179\" rel=\"nofollow\"\u003e\"Benchmarking Neural Network Training Algorithms\"\u003c/a\u003e we motivate, describe, and justify the \u003cem\u003eAlgoPerf: Training Algorithms\u003c/em\u003e benchmark.\u003c/p\u003e\n\u003cp\u003eIf you are using the \u003cem\u003eAlgoPerf benchmark\u003c/em\u003e, its codebase, baselines, or workloads, please consider citing our paper:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"http://arxiv.org/abs/2306.07179\" rel=\"nofollow\"\u003eDahl, Schneider, Nado, et al.\u003cbr\u003e\n\u003cstrong\u003eBenchmarking Neural Network Training Algorithms\u003c/strong\u003e\u003cbr\u003e\n\u003cem\u003earXiv 2306.07179\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@Misc\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003eDahl2023AlgoPerf\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e{Benchmarking Neural Network Training Algorithms}\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eDahl, George E. and Schneider, Frank and Nado, Zachary and Agarwal, Naman and Sastry, Chandramouli Shama and Hennig, Philipp and Medapati, Sourabh and Eschenhagen, Runa and Kasimbeg, Priya and Suo, Daniel and Bae, Juhan and Gilmer, Justin and Peirson, Abel L. and Khan, Bilal and Anil, Rohan and Rabbat, Mike and Krishnan, Shankar and Snider, Daniel and Amid, Ehsan and Chen, Kongtao and Maddison, Chris J. and Vasudev, Rakshith and Badura, Michal and Garg, Ankush and Mattson, Peter\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2023\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003earchiveprefix\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003earXiv\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eeprint\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2306.07179\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n}\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-devito-fast-stencil-computation-from-symbolic-specification\" class=\"anchor\" aria-hidden=\"true\" href=\"#devito-fast-stencil-computation-from-symbolic-specification\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevito: Fast Stencil Computation from Symbolic Specification\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-core\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-core/badge.svg\" alt=\"Build Status for the Core backend\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-mpi\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-mpi/badge.svg\" alt=\"Build Status with MPI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-gpu\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-gpu/badge.svg\" alt=\"Build Status on GPU\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/devitocodes/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3371fe5bdd570d040c748fb93a3e18ce00797c85315f2d05364781a1e5b9aa53/68747470733a2f2f636f6465636f762e696f2f67682f64657669746f636f6465732f64657669746f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Code Coverage\" data-canonical-src=\"https://codecov.io/gh/devitocodes/devito/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://join.slack.com/t/devitocodes/shared_invite/zt-gtd2yxj9-Y31YKk_7lr9AwfXeL2iMFg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f0d0a8f3b06c0808c75575af15a74159d9d34f2bc02997c0f262dd916e0bf948/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636861742d6f6e253230736c61636b2d253233333643354630\" alt=\"Slack Status\" data-canonical-src=\"https://img.shields.io/badge/chat-on%20slack-%2336C5F0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://devitocodes.github.io/devito-performance\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3015b96f702ce7bd0e41f18aa7d7dfb69af77789127d64634a2223f829dbcee1/687474703a2f2f696d672e736869656c64732e696f2f62616467652f62656e63686d61726b656425323062792d6173762d626c75652e7376673f7374796c653d666c6174\" alt=\"asv\" data-canonical-src=\"http://img.shields.io/badge/benchmarked%20by-asv-blue.svg?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/249b65986b967e7268f743fa8e3face99c98762feaa8d1417d07769b1d3385bf/68747470733a2f2f62616467652e667572792e696f2f70792f64657669746f2e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/devito.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/devitocodes/devito/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/581c077bdbc6ca6899c86d0acc6145ae85e9d80e6f805a1071793dbe48917982/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.devitoproject.org\" rel=\"nofollow\"\u003eDevito\u003c/a\u003e is a Python package to implement\noptimized stencil computation (e.g., finite differences, image processing,\nmachine learning) from high-level symbolic problem definitions. Devito builds\non \u003ca href=\"http://www.sympy.org/en/index.html\" rel=\"nofollow\"\u003eSymPy\u003c/a\u003e and employs automated code\ngeneration and just-in-time compilation to execute optimized computational\nkernels on several computer platforms, including CPUs, GPUs, and clusters\nthereof.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#about-devito\"\u003eAbout Devito\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#resources\"\u003eResources\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#performance\"\u003ePerformance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#get-in-touch\"\u003eGet in touch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#interactive-jupyter-notebooks\"\u003eInteractive jupyter notebooks\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-devito\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-devito\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout Devito\u003c/h2\u003e\n\u003cp\u003eDevito provides a functional language to implement sophisticated operators that\ncan be made up of multiple stencil computations, boundary conditions, sparse\noperations (e.g., interpolation), and much more. A typical use case is\nexplicit finite difference methods for approximating partial differential\nequations. For example, a 2D diffusion operator may be implemented with Devito\nas follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGrid\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eshape\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e))\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTimeFunction\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027f\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003espace_order\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eeqn\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eEq\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edt\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elaplace\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eop\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eOperator\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eEq\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eforward\u003c/span\u003e, \u003cspan class=\"pl-en\"\u003esolve\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eeqn\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eforward\u003c/span\u003e)))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn \u003ccode\u003eOperator\u003c/code\u003e generates low-level code from an ordered collection of \u003ccode\u003eEq\u003c/code\u003e (the\nexample above being for a single equation). This code may also be compiled and\nexecuted\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003et\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etimesteps\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere is virtually no limit to the complexity of an \u003ccode\u003eOperator\u003c/code\u003e -- the Devito\ncompiler will automatically analyze the input, detect and apply optimizations\n(including single- and multi-node parallelism), and eventually generate code\nwith suitable loops and expressions.\u003c/p\u003e\n\u003cp\u003eKey features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA functional language to express finite difference operators.\u003c/li\u003e\n\u003cli\u003eStraightforward mechanisms to adjust the discretization.\u003c/li\u003e\n\u003cli\u003eConstructs to express sparse operators (e.g., interpolation), classic linear\noperators (e.g., convolutions), and tensor contractions.\u003c/li\u003e\n\u003cli\u003eSeamless support for boundary conditions and adjoint operators.\u003c/li\u003e\n\u003cli\u003eA flexible API to define custom stencils, sub-domains, sub-sampling,\nand staggered grids.\u003c/li\u003e\n\u003cli\u003eGeneration of highly optimized parallel code (SIMD vectorization, CPU and\nGPU parallelism via OpenMP and OpenACC, multi-node parallelism via MPI,\nblocking, aggressive symbolic transformations for FLOP reduction, etc.).\u003c/li\u003e\n\u003cli\u003eDistributed NumPy arrays over multi-node (MPI) domain decompositions.\u003c/li\u003e\n\u003cli\u003eInspection and customization of the generated code.\u003c/li\u003e\n\u003cli\u003eAutotuning framework to ease performance tuning.\u003c/li\u003e\n\u003cli\u003eSmooth integration with popular Python packages such as NumPy, SymPy, Dask,\nand SciPy, as well as machine learning frameworks such as TensorFlow and\nPyTorch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe easiest way to try Devito is through Docker using the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# get the code\ngit clone https://github.com/devitocodes/devito.git\ncd devito\n\n# start a jupyter notebook server on port 8888\ndocker-compose up devito\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter running the last command above, the terminal will display a URL such as\n\u003ccode\u003ehttps://127.0.0.1:8888/?token=XXX\u003c/code\u003e. Copy-paste this URL into a browser window\nto start a \u003ca href=\"https://jupyter.org/\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e notebook session where you can go\nthrough the \u003ca href=\"https://github.com/devitocodes/devito/tree/master/examples\"\u003etutorials\u003c/a\u003e\nprovided with Devito or create your own notebooks.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://devitocodes.github.io/devito/download.html\" rel=\"nofollow\"\u003eSee here\u003c/a\u003e for detailed installation\ninstructions and other options. If you encounter a problem during installation, please\nsee the\n\u003ca href=\"https://github.com/devitocodes/devito/wiki/Installation-Issues\"\u003einstallation issues\u003c/a\u003e we\nhave seen in the past.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h2\u003e\n\u003cp\u003eTo learn how to use Devito,\n\u003ca href=\"https://github.com/devitocodes/devito/blob/master/examples\"\u003ehere\u003c/a\u003e is a good\nplace to start, with lots of examples and tutorials.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.devitoproject.org/\" rel=\"nofollow\"\u003ewebsite\u003c/a\u003e also provides access to other\ninformation, including documentation and instructions for citing us.\u003c/p\u003e\n\u003cp\u003eSome FAQs are discussed \u003ca href=\"https://github.com/devitocodes/devito/wiki/FAQ\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-performance\" class=\"anchor\" aria-hidden=\"true\" href=\"#performance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePerformance\u003c/h2\u003e\n\u003cp\u003eIf you are interested in any of the following\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGeneration of parallel code (CPU, GPU, multi-node via MPI);\u003c/li\u003e\n\u003cli\u003ePerformance tuning;\u003c/li\u003e\n\u003cli\u003eBenchmarking operators;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ethen you should take a look at this\n\u003ca href=\"https://github.com/devitocodes/devito/blob/master/benchmarks/user\"\u003eREADME\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eYou may also be interested in\n\u003ca href=\"https://www.devitocodes.com/blog/thematrix\" rel=\"nofollow\"\u003eTheMatrix\u003c/a\u003e -- a cross-architecture\nbenchmarking framework showing the performance of several production-grade\nseismic operators implemented with Devito.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-get-in-touch\" class=\"anchor\" aria-hidden=\"true\" href=\"#get-in-touch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGet in touch\u003c/h2\u003e\n\u003cp\u003eIf you\u0027re using Devito, we would like to hear from you. Whether you\nare facing issues or just trying it out, join the\n\u003ca href=\"https://join.slack.com/t/devitocodes/shared_invite/zt-gtd2yxj9-Y31YKk_7lr9AwfXeL2iMFg\" rel=\"nofollow\"\u003econversation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-interactive-jupyter-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#interactive-jupyter-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive jupyter notebooks\u003c/h2\u003e\n\u003cp\u003eThe tutorial jupyter notebook are available interactively at the public \u003ca href=\"https://mybinder.org/v2/gh/devitocodes/devito/master\" rel=\"nofollow\"\u003ebinder\u003c/a\u003e jupyterhub.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1704437124.0 + "updated_at": 1669732936.0 }, { "data_format": 2, - "description": "Demo recipe ", + "description": "https://github.com/pygments/pygments.git", "filenames": [ - "Singularity", - "Singularity.3.8.6" + "tests/examplefiles/singularity/Singularity" ], - "full_name": "ISU-HPC/singularity_demo", + "full_name": "sailfishos-mirror/pygments", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_demo\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity_demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_demo\u003c/h1\u003e\n\u003cp\u003eDemo recipe\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1546982676.0 + "updated_at": 1641488312.0 }, { "data_format": 2, - "description": "Graphviz is a package of open-source tools initiated by AT\u0026T Labs Research for drawing graphs specified in DOT language scripts.", + "description": "christmas-devcontainers-talk", "filenames": [ - "3.0.0/Singularity", - "2.38.0/Singularity", - "2.48.0/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-graphviz", - "latest_release": "v2.44.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-graphviz/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-graphviz/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-graphviz/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-graphviz/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4f27d7f7ac7b9a86a1f0f6c45d19b90496b7c8ce89d5004d3fe96d163fe99e73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d677261706876697a\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4f27d7f7ac7b9a86a1f0f6c45d19b90496b7c8ce89d5004d3fe96d163fe99e73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d677261706876697a\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-graphviz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e6789d316f02fdfe74574853fb2870a7e4b7b6cccca76d201ff81cb1ac2adfbe/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d677261706876697a\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e6789d316f02fdfe74574853fb2870a7e4b7b6cccca76d201ff81cb1ac2adfbe/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d677261706876697a\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-graphviz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b71cbcc295d522b3323d66e9141fdec85461c9d128011383fac4956c54d95d73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d677261706876697a\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b71cbcc295d522b3323d66e9141fdec85461c9d128011383fac4956c54d95d73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d677261706876697a\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-graphviz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5227dbb6ba22ff0c43933a4284a416f0f8d311e9972bf97e5383fe334f545102/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d677261706876697a\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5227dbb6ba22ff0c43933a4284a416f0f8d311e9972bf97e5383fe334f545102/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d677261706876697a\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-graphviz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-graphviz\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-graphviz\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-graphviz\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/960789693fa68a8f442f9c6cc7d6a117639f1a792ec84c96648ad4764c385fcf/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f656e2f342f34382f477261706876697a4c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/960789693fa68a8f442f9c6cc7d6a117639f1a792ec84c96648ad4764c385fcf/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f656e2f342f34382f477261706876697a4c6f676f2e706e67\" width=\"25%\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/en/4/48/GraphvizLogo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://graphviz.org/\" rel=\"nofollow\"\u003egraphviz\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003egraphviz\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/graphviz/2.44.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/graphviz\u003c/code\u003e as \u003ccode\u003e 2.44.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-docker-image\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build Docker image\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003edbuild.sh\u003c/code\u003e to build the Docker image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./dbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-the-cwl-workflow\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-run-the-cwl-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run the CWL workflow\u003c/h2\u003e\n\u003cp\u003eTo run the workflow, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load anaconda3\npip install --user cwl-runner cwltool udocker\ncwl-runner --singularity dot.cwl example.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "ARCLeeds/christmas-devcontainers-talk", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-devcontainers-talk-for-christmas-conference-2022\" class=\"anchor\" aria-hidden=\"true\" href=\"#devcontainers-talk-for-christmas-conference-2022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevcontainers talk for Christmas Conference 2022\u003c/h1\u003e\n\u003cp\u003eThis is a toy repository that includes some MPI-enabled Markov chain random walks to search a 2D space for Santa \u003cg-emoji class=\"g-emoji\" alias=\"santa\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f385.png\"\u003e\ud83c\udf85\u003c/g-emoji\u003e!\u003c/p\u003e\n\u003cp\u003eIt\u0027s intention is to showcase using containers to enable portable and scalable code reuse.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eThis repository contains a Dockerfile for creating a container image and running locally.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e -t find-santa:latest\n\n$ mkdir santa-search-outputs\n\n$ docker run -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/santa-search-outputs:/app/figures find-santa:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then check inside \u003ccode\u003esanta-search-outputs\u003c/code\u003e directory to find the data visualisation plot.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-apptainer\" class=\"anchor\" aria-hidden=\"true\" href=\"#apptainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApptainer\u003c/h3\u003e\n\u003cp\u003eThis repository includes an \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e definition file that can be built using Apptainer.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ apptainer build find-santa.sif Singularity.def\n\n$ mpiexec -np 4 apptainer \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e find-santa.sif conda run -n devcontainers python /app/src/random_walk.py\n\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, + "topics": [], + "updated_at": 1670011770.0 + }, + { + "data_format": 2, + "description": "A small collection of programs for converting non-TIFF format images to TIFF and for manipulating and interogating the contents of TIFF images.", + "filenames": [ + "4.2.0/Singularity" + ], + "full_name": "pscedu/singularity-libtiff-tools", + "latest_release": "v4.2.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-libtiff-tools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-libtiff-tools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-libtiff-tools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-libtiff-tools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/274bd4774f9c09a10655a9b440ba3c1171dc46ed6817776efaf7c5579311ba9b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/274bd4774f9c09a10655a9b440ba3c1171dc46ed6817776efaf7c5579311ba9b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-libtiff-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2cdb477414e2bc11157f8ac70f0f08f6aca89f9a77440f50e1dba8e8105dca92/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2cdb477414e2bc11157f8ac70f0f08f6aca89f9a77440f50e1dba8e8105dca92/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-libtiff-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ac7590eb9a5062fa43ca709328ec101fb9dfe119dbe72eb8825d7d9b56ce2440/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ac7590eb9a5062fa43ca709328ec101fb9dfe119dbe72eb8825d7d9b56ce2440/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-libtiff-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8ccb90532f566ba1f408e14595e4b820b5f0bfce3dbdb8ba092c5a1a937dbe4b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ccb90532f566ba1f408e14595e4b820b5f0bfce3dbdb8ba092c5a1a937dbe4b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-libtiff-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-libtiff-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-libtiff-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-libtiff-tools\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f630a7ca0123289ce19e2c391cf329e94cb29966ba21c84444284358d998749d/687474703a2f2f7777772e6c6962746966662e6f72672f696d616765732f717561642e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f630a7ca0123289ce19e2c391cf329e94cb29966ba21c84444284358d998749d/687474703a2f2f7777772e6c6962746966662e6f72672f696d616765732f717561642e6a7067\" data-canonical-src=\"http://www.libtiff.org/images/quad.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://www.libtiff.org/tools.html\" rel=\"nofollow\"\u003elibtiff-tools\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/libtiff-tools/4.2.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/libtiff-tools\u003c/code\u003e as \u003ccode\u003e4.2.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/tigers/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 3, "topics": [ "singularity", "utilities" ], - "updated_at": 1649134093.0 + "updated_at": 1670379411.0 }, { "data_format": 2, - "description": "A Singularity Recipe to create a base CentOS container image", + "description": "fastq quality assessment and filtering tool", "filenames": [ - "Singularity", - "cuda7.5-devel/Singularity.cu75", - "cuda-8.0-cudnn7/Singularity.cu80dnn7", - "cuda-9.1-devel/Singularity.cu91", - "cuda-8.0-devel/Singularity.cu80dnn6", - "cuda-9.0-devel/Singularity.cu90" + "Singularity-Test", + "Singularity" ], - "full_name": "ISU-HPC/ml-base", + "full_name": "PaulaAlessio/FastqArazketa", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ml-base\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#ml-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml-base\u003c/h1\u003e\n\u003cp\u003eA Singularity Recipe to create a base CentOS container image\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fastqpuri-an-fq-quality-control-and-filter-tool\" class=\"anchor\" aria-hidden=\"true\" href=\"#fastqpuri-an-fq-quality-control-and-filter-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFastqPuri, an fq quality control and filter tool\u003c/h1\u003e\n\u003cp\u003eSoftware and source code of \u003ccode\u003eFastqPuri\u003c/code\u003e. It creates quality reports of\n\u003ccode\u003efastq\u003c/code\u003e files and filters them removing low quality reads, reads\ncontaining too many N\u0027s or contamination reads (unwanted rRNA reads,\nimpurities coming from another organism, ...).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eClone the repository, or download the source. Make sure that\nyour system supplies the following dependencies for FastqPuri.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOS: Linux (clang, gcc), Mac OS (clang, gcc), OpenBSD (clang)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecmake\u003c/code\u003e (at least version 2.8),\u003c/li\u003e\n\u003cli\u003ea \u003ccode\u003eC\u003c/code\u003e compiler supporting the \u003ccode\u003ec11\u003c/code\u003e standard\n(change the compiler flags otherwise),\u003c/li\u003e\n\u003cli\u003epandoc (optional, see documentation in \u003ccode\u003ePANDOC.md\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eRscript\u003c/code\u003e (optional),\u003c/li\u003e\n\u003cli\u003eFollowing \u003ccode\u003eR\u003c/code\u003e packages installed (optional):\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003epheatmap\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eknitr\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ermarkdown\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e FastqPuri will work without the optional dependencies\nbut will skip creating html reports if they are not available.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cmake -H. -Bbuild/ [-DRSCRIPT=/path/to/my/R/bin/Rscript] [-DCMAKE_INSTALL_PREFIX=/path/to/my/root] ... \n$ cd build \n$ make \n$ sudo make install \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen running \u003ccode\u003ecmake\u003c/code\u003e, there are some variables you can set\nusing the option -D followed by the variable name. These variables are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCMAKE_C_COMPILER\u003c/code\u003e: \u003ccode\u003eC\u003c/code\u003e compiler (default \u003ccode\u003egcc\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCMAKE_C_FLAGS\u003c/code\u003e: compiler flags (default \u003ccode\u003e-Wall -O3 -march=native -std=c11\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCMAKE_INSTALL_PREFIX\u003c/code\u003e: root path for \u003ccode\u003emake install\u003c/code\u003e, e.g. to\nredirect to a directory with user access (default /usr/local),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePANDOC\u003c/code\u003e: \u003ccode\u003epandoc\u003c/code\u003e executable (default \u003ccode\u003epandoc\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eRSCRIPT\u003c/code\u003e: \u003ccode\u003eRscript\u003c/code\u003e executable (default \u003ccode\u003eRscript\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eREAD_MAXLEN\u003c/code\u003e: Maximum Illumina read length\u003c/li\u003e\n\u003cli\u003e(default 400),\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe executables will be created in the folder \u003ccode\u003ebin\u003c/code\u003e and installed in \u003ccode\u003e/usr/local/bin\u003c/code\u003e.\n\u003ccode\u003eR\u003c/code\u003e scripts will be installed in \u003ccode\u003e/usr/local/share/FastqPuri/R\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWARNING:\u003c/strong\u003e do not move the executables that depend on \u003ccode\u003eR\u003c/code\u003e scripts,\nanywhere else, unless you also move the corresponding \u003ccode\u003eR\u003c/code\u003e scripts respecting\nthe local folder structure.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-executables\" class=\"anchor\" aria-hidden=\"true\" href=\"#executables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecutables\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eQreport\u003c/code\u003e: creates a quality report in html format (see \u003ccode\u003eREADME_Qreport.md\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSreport\u003c/code\u003e: creates a summary report in html format on a set of samples,\nregarding either the original files or the filtering process\n(see \u003ccode\u003eREADME_Sreport.md\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emakeBloom\u003c/code\u003e: creates a bloom filter from a fasta file of a certain size,\nand stores it in a file (see \u003ccode\u003eREADME_makeBloom.md\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emakeTree\u003c/code\u003e: creates a tree of a certain depth from a fasta file and stores\nit in a file (see \u003ccode\u003eREADME_makeTree.md\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etrimFilter\u003c/code\u003e: performs the filtering process for single-end data\n(see \u003ccode\u003eREADME_trimFilter.md\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etrimFilterPE\u003c/code\u003e: performs the filtering process for double stranded data\n(see \u003ccode\u003eREADME_trimFilterPE.md\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn exemplar work flow could be:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eQreport\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSreport\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emakeBloom\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etrimFilter\u003c/code\u003e or \u003ccode\u003etrimFilterPE\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eQreport\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSreport\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation-of-the-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation-of-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation of the code\u003c/h2\u003e\n\u003cp\u003eA Doxygen documentation of the code is available:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ehtml\u003c/code\u003e version under the folder \u003ccode\u003ehtml\u003c/code\u003e (open \u003ccode\u003eindex.html\u003c/code\u003e with a browser).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epdf\u003c/code\u003e version: \u003ccode\u003elatex/refman.pdf\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-a-docker-container-to-run-fastqpuri\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-a-docker-container-to-run-fastqpuri\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse a docker container to run FastqPuri\u003c/h2\u003e\n\u003cp\u003eThe file \u0027Dockerfile\u0027 documents the exact linux installation we used\nfor testing. If you have a docker installation ready on your machine,\nyou may want to use a docker container for easy installation and\ncapsulated usage of FastqPuri. After cloning this project from github\nand change to its main directory, you may install a docker container\nas follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker build -t fastqpuri .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a container based on the debian linux distribution\ncovering all dependencies including R and pandoc. As soon as such a\ncontainer is installed, you can use it either interactively:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -v $PWD:/tmp -it fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor by running a pipeline implemented in an executable bash script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -v $PWD:/tmp fastqpuri ./pipeline.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that this call generates results in the docker container\ndirectory \u003ccode\u003e/tmp\u003c/code\u003e but also keeps them after closing the docker container\nlocally where the container was started.\u003c/p\u003e\n\u003cp\u003eInstead of generating the docker container yourself with \u0027docker\nbuild\u0027, you can also pull a pre-built image from the docker hub as\nfollows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker pull clottaz/fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can run such a pre-built image with \u0027docker run\u0027 by indicating the\nimages as \u0027clottaz/fastqpuri\u0027.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-a-singularity-container-to-run-fastqpuri\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-a-singularity-container-to-run-fastqpuri\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse a singularity container to run FastqPuri\u003c/h2\u003e\n\u003cp\u003eAlternativly, if you have singularity installed on your machine, you\ncan call our docker container for FastqPuri as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell --bind .:/tmp docker://clottaz/fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis call opens a shell within the container.\nWith \u003ccode\u003e--bind\u003c/code\u003e we mount the current directory also in the container.\nThe syntax is as follows: --bind src:dest; src is the source path on\nthe host and dest is the destination path in the container, i.e. where\nyou would like to make the source path available in your container.\nNote that this destination path in your container should be an existing\ndirectory, the operation will fail if you do not create the directory first.\nHence, when we call \u003ccode\u003esingularity shell\u003c/code\u003e like this, the working directory\nin the container is \u003ccode\u003e/tmp\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, in order to execute a script from the current\ndirectory, call singularity as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run --bind .:/tmp docker://clottaz/fastqpuri /tmp/pipeline.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that \u003ccode\u003e/tmp/pipeline.sh\u003c/code\u003e relates to the call within the\ncontainer. Thus, \u003ccode\u003epipeline.sh\u003c/code\u003e is located in the directory where singularity\nrun is executed, but will be made available to the container via the \u003ccode\u003e--bind\u003c/code\u003e\nparameter.\u003c/p\u003e\n\u003cp\u003eIf you want to invoke a function of FastqPuri, you can use the \u0027exec\u0027\ncommand like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec docker://clottaz/fastqpuri Qreport -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor invoke a script located in your home directory (assuming that\nrun_ex_TREE.sh is located in your home directory):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec docker://clottaz/fastqpuri $HOME/run_ex_TREE.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity documentation can be found here: \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/docs/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-via-bioconda--under-construction\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-via-bioconda--under-construction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation via bioconda \u003cstrong\u003e-under construction\u003c/strong\u003e.\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eWe are currently working on a bioconda environment for FastqPuri.\nIf you follow the instructions below, it is quite likely that\nFastqPuri will not yet properly run from the bioconda environment.\nSorry about that and please stay tuned!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBioconda is a channel for the conda package manager specializing in\nbioinformatics software. Have a look at the reference:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBjoern Gruening, Ryan Dale, Andreas Sjoedin, Brad A. Chapman, Jillian\nRowe, Christopher H. Tomkins-Tinch, Renan Valieris, the Bioconda\nTeam, and Johannes Koester. 2018. Bioconda: Sustainable and\nComprehensive Software Distribution for the Life Sciences. Nature\nMethods, 2018.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo find out how to use bioconda, see \u003ca href=\"https://bioconda.github.io\" rel=\"nofollow\"\u003ehttps://bioconda.github.io\u003c/a\u003e.\nFor installing FastqPuri in a bioconda environment, you have to install\neither \u003ccode\u003eminiconda\u003c/code\u003e or \u003ccode\u003eanaconda\u003c/code\u003e and register channels as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda config --add channels defaults\n$ conda config --add channels bioconda\n$ conda config --add channels conda-forge\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can install \u003ccode\u003efastqpuri\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda install fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eActually, you may also want to use a specific environment for the\nsequencing quality control:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create -n qc fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis call installs \u003ccode\u003eFastqPuri\u003c/code\u003e directly in a separate environment.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003ePaula P\u00e9rez Rubio,\nClaudio Lottaz,\nJulia Engelmann\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eGPL v3 (see LICENSE.txt)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1533063693.0 + "updated_at": 1670411214.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.harp", + "Singularity.utils" ], - "full_name": "Shadowphax/bc_icts_rstudio_server", + "full_name": "BerglandLab/HS-reconstruction-gwas", "latest_release": null, - "readme": "\u003ch1 id=\"user-content-batch-connect---osc-rstudio-server\"\u003e\u003ca class=\"heading-link\" href=\"#batch-connect---osc-rstudio-server\"\u003eBatch Connect - OSC RStudio Server\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_rstudio_server.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an RStudio Server\nwithin an Owens batch job.\u003c/p\u003e\n\u003ch2 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule restore\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job before\nlaunching the RStudio Server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ewithout Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e 3.3.2+ (earlier versions are untested but may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rstudio.com/products/rstudio-server/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e 1.0.136+ (earlier versions are untested by may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://proot-me.github.io/\" rel=\"nofollow\"\u003ePRoot\u003c/a\u003e 5.1.0+ (used to setup fake bind mount)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eor with Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e 2.4.2+\u003c/li\u003e\n\u003cli\u003eA Singularity image similar to \u003ca href=\"https://www.singularity-hub.org/collections/463\" rel=\"nofollow\"\u003enickjer/singularity-rstudio\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorresponding module to launch the above Singularity image (see\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio/blob/master/example_module/\"\u003eexample_module\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-install\"\u003e\u003ca class=\"heading-link\" href=\"#install\"\u003eInstall\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2 id=\"user-content-contributing\"\u003e\u003ca class=\"heading-link\" href=\"#contributing\"\u003eContributing\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server/fork\"\u003ehttps://github.com/OSC/bc_osc_rstudio_server/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocumentation, website content, and logo is licensed under\n\u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC-BY-4.0\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCode is licensed under MIT (see LICENSE.txt)o\u003c/li\u003e\n\u003cli\u003eRStudio, Shiny and the RStudio logo are all registered trademarks of RStudio.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-rserver-command-line-arguements\"\u003e\u003ca class=\"heading-link\" href=\"#rserver-command-line-arguements\"\u003eRServer command line arguements\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis was the output of \u003ccode\u003e--help\u003c/code\u003e from version \u003ccode\u003e2021.09.1\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecommand-line options:\n\nverify:\n --verify-installation arg (=0) Runs verification mode to verify the \n current installation.\n\nserver:\n --server-working-dir arg (=/) The default working directory of the \n rserver process.\n --server-user arg (=rstudio-server) The user account of the rserver \n process.\n --server-daemonize arg (=0) Indicates whether or not the rserver \n process should run as a daemon.\n --server-pid-file arg (=/var/run/rstudio-server.pid)\n The path to a file where the rserver \n daemon\u0027s pid is written.\n --server-app-armor-enabled arg (=0) Indicates whether or not to enable \n AppArmor profiles for the rserver \n process.\n --server-set-umask arg (=1) If enabled, sets the rserver process \n umask to 022 on startup, which causes \n new files to have rw-r-r permissions.\n --secure-cookie-key-file arg If set, overrides the default path of \n the secure-cookie-key file used for \n encrypting cookies.\n --server-data-dir arg (=/var/run/rstudio-server)\n Path to the data directory where \n RStudio Server will write run-time \n state.\n --server-add-header arg Adds a header to all responses from \n RStudio Server. This option can be \n specified multiple times to add \n multiple headers.\n\nwww:\n --www-address arg (=0.0.0.0) The network address that RStudio Server\n will listen on for incoming \n connections.\n --www-port arg The port that RStudio Server will bind \n to while listening for incoming \n connections. If left empty, the port \n will be automatically determined based \n on your SSL settings (443 for SSL, 80 \n for no SSL).\n --www-root-path arg (=/) The path prefix added by a proxy to the\n incoming RStudio URL. This setting is \n used so RStudio Server knows what path \n it is being served from. If running \n RStudio Server behind a path-modifying \n proxy, this should be changed to match \n the base RStudio Server URL.\n --www-local-path arg (=www) The relative path from the RStudio \n installation directory, or absolute \n path where web assets are stored.\n --www-symbol-maps-path arg (=www-symbolmaps)\n The relative path from the RStudio \n installation directory, or absolute \n path, where symbol maps are stored.\n --www-use-emulated-stack arg (=0) Indicates whether or not to use GWT\u0027s \n emulated stack.\n --www-thread-pool-size arg (=2) The size of the threadpool from which \n requests will be serviced. This may be \n increased to enable more concurrency, \n but should only be done if the \n underlying hardware has more than 2 \n cores. It is recommended to use a value\n that is \u0026lt;= to the number of hardware \n cores, or \u0026lt;= to two times the number of\n hardware cores if the hardware utilizes\n hyperthreading.\n --www-proxy-localhost arg (=1) Indicates whether or not to proxy \n requests to localhost ports over the \n main server port. This should generally\n be enabled, and is used to proxy HTTP \n traffic within a session that belongs \n to code running within the session \n (e.g. Shiny or Plumber APIs)\n --www-verify-user-agent arg (=1) Indicates whether or not to verify \n connecting browser user agents to \n ensure they are compatible with RStudio\n Server.\n --www-same-site arg The value of the \u0027SameSite\u0027 attribute \n on the cookies issued by RStudio \n Server. Accepted values are \u0027none\u0027 or \n \u0027lax\u0027. The value \u0027none\u0027 should be used \n only when RStudio is hosted into an \n iFrame. For compatibility with some \n browsers (i.e. Safari 12), duplicate \n cookies will be issued by RStudio \n Server when \u0027none\u0027 is used.\n --www-frame-origin arg (=none) Specifies the allowed origin for the \n iFrame hosting RStudio if iFrame \n embedding is enabled.\n --www-enable-origin-check arg (=0) If enabled, cause RStudio to enforce \n that incoming request origins are from \n the host domain. This can be added for \n additional security. See \n https://cheatsheetseries.owasp.org/chea\n tsheets/Cross-Site_Request_Forgery_Prev\n ention_Cheat_Sheet.html#verifying-origi\n n-with-standard-headers\n --www-allow-origin arg Specifies an additional origin that \n requests are allowed from, even if it \n does not match the host domain. Used if\n origin checking is enabled. May be \n specified multiple times for multiple \n origins.\n\nrsession:\n --rsession-which-r arg The path to the main R program (e.g. \n /usr/bin/R). This should be set if no \n versions are specified in \n /etc/rstudio/r-versions and the default\n R installation is not available on the \n system path.\n --rsession-path arg (=rsession) The relative path from the RStudio \n installation directory, or absolute \n path to the rsession executable.\n --rldpath-path arg (=r-ldpath) The path to the r-ldpath script which \n specifies extra library paths for R \n versions.\n --rsession-ld-library-path arg Specifies additional LD_LIBRARY_PATHs \n to use for R sessions.\n --rsession-config-file arg If set, overrides the path to the \n /etc/rstudio/rsession.conf \n configuration file. The specified path \n may be a relative path from the RStudio\n installation directory, or an absolute \n path.\n --rsession-proxy-max-wait-secs arg (=10)\n The maximum time to wait in seconds for\n a successful response when proxying \n requests to rsession.\n --rsession-memory-limit-mb arg (=0) The limit in MB that an rsession \n process may consume.\n --rsession-stack-limit-mb arg (=0) The limit in MB that an rsession \n process may consume for its stack.\n --rsession-process-limit arg (=0) The maximum number of allowable \n rsession processes.\n\ndatabase:\n --database-config-file arg If set, overrides the path to the \n /etc/rstudio/database.conf \n configuration file.\n --db-command arg Executes the shell command specified \n injecting the current database \n configuration in the command.\n\nauth:\n --auth-none arg (=1) If set, disables multi-user \n authentication. Workbench/Pro features \n may not work in this mode.\n --auth-validate-users arg (=0) Indicates whether or not to validate \n that authenticated users exist on the \n target system. Disabling this option \n may cause issues to start or to run a \n session.\n --auth-stay-signed-in-days arg (=30) The number of days to keep a user \n signed in when using the \"Stay Signed \n In\" option. Will only take affect when \n auth-timeout-minutes is 0 (disabled).\n --auth-timeout-minutes arg (=60) The number of minutes a user will stay \n logged in while idle before required to\n sign in again. Set this to 0 (disabled)\n to enable legacy timeout \n auth-stay-signed-in-days.\n --auth-encrypt-password arg (=1) Indicates whether or not to encrypt the\n password sent from the login form. For \n security purposes, we strongly \n recommend you leave this enabled.\n --auth-login-page-html arg (=/etc/rstudio/login.html)\n The path to a file containing \n additional HTML customization for the \n login page.\n --auth-rdp-login-page-html arg (=/etc/rstudio/rdplogin.html)\n The path to a file containing \n additional HTML customization for the \n login page, as seen by RDP users.\n --auth-required-user-group arg Specifies a group that users must be in\n to be able to use RStudio.\n --auth-minimum-user-id arg (=auto) Specifies a minimum user id value. \n Users with a uid lower than this value \n may not use RStudio.\n --auth-pam-helper-path arg (=rserver-pam)\n The relative path from the RStudio \n installation directory, or absolute \n path where the PAM helper binary \n resides.\n --auth-pam-require-password-prompt arg (=1)\n Indicates whether or not to require the\n \"Password: \" prompt before sending the \n password via PAM. In most cases, this \n should be enabled. If using a custom \n PAM password prompt, you may need to \n disable this setting if PAM logins do \n not work correctly.\n --auth-pam-requires-priv arg (=1) Deprecated - will always be true.\n --auth-sign-in-throttle-seconds arg (=5)\n The minimum amount of time a user must \n wait before attempting to sign in again\n after signing out.\n --auth-revocation-list-dir arg If set, overrides the path to the \n directory which contains the revocation\n list to be used for storing expired \n tokens. As of RStudio Server 1.4, this \n has been moved to database storage, and\n so this setting is deprecated, but will\n be used to port over any existing \n file-based expired tokens.\n --auth-cookies-force-secure arg (=0) Indicates whether or not auth cookies \n should be forcefully marked as secure. \n This should be enabled if running an \n SSL terminator infront of RStudio \n Server. Otherwise, cookies will be \n marked secure if SSL is configured.\n\nmonitor:\n --monitor-interval-seconds arg (=60) The interval in seconds at which the \n monitor is probed for new data.\n\ngeneral:\n --help print help message\n --test-config test to ensure the config file is valid\n --config-file arg configuration file\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hs-reconstruction-gwas\" class=\"anchor\" aria-hidden=\"true\" href=\"#hs-reconstruction-gwas\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHS-reconstruction-gwas\u003c/h1\u003e\n\u003cp\u003eThis repository contains the scripts used to generate and process data, as well as generate figures, for the manuscript:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAccurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCory A. Weller (\u003ca href=\"mailto:caw5cv@virginia.edu\"\u003ecaw5cv@virginia.edu\u003c/a\u003e) \u0026amp; Alan O. Bergland (\u003ca href=\"mailto:aob2x@virginia.edu\"\u003eaob2x@virginia.edu\u003c/a\u003e)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eThis workflow allows for Singularity containers to process data in a reproducible manner without installing required programs and libraries. You will first need to install singularity on your system, if it is not already available. Many HPC systems already have pre-loaded \u003ccode\u003esingularity\u003c/code\u003e that can be loaded as a module.\u003c/p\u003e\n\u003cp\u003eOtherwise, install singularity 3.x following the instructions from \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003esylabs.io\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThen, you can retrieve the pre-built singularity image files from Singularity Hub.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name harp.sif shub://cory-weller/HS-reconstruction-gwas:harp\nsingularity pull --name utils.sif shub://cory-weller/HS-reconstruction-gwas:utils\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1646833582.0 + "updated_at": 1670518878.0 }, { "data_format": 2, - "description": "[read-only mirror]", + "description": "Nextflow workflow to run DPclust on a series of samples", "filenames": [ "Singularity" ], - "full_name": "unlhcc/bc-hcc-rstudio-server", + "full_name": "IARCbioinfo/DPclust-nf", "latest_release": null, - "readme": "\u003ch1 id=\"user-content-batch-connect---hcc-rstudio-server\"\u003e\u003ca class=\"heading-link\" href=\"#batch-connect---hcc-rstudio-server\"\u003eBatch Connect - HCC RStudio Server\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://git.unl.edu/hcc/bc-hcc-rstudio-server/-/commits/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eed68de7ee579cda26a799a79e6376b967f69b7e355d2eca9b2a46e88e54c904/68747470733a2f2f6769742e756e6c2e6564752f6863632f62632d6863632d7273747564696f2d7365727665722f6261646765732f6d61737465722f706970656c696e652e737667\" alt=\"pipeline status\" data-canonical-src=\"https://git.unl.edu/hcc/bc-hcc-rstudio-server/badges/master/pipeline.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an \u003ca href=\"https://www.rstudio.com/products/rstudio-server/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e\nwithin a SLURM batch job.\u003c/p\u003e\n\u003cp\u003eBased off of \u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server\"\u003ehttps://github.com/OSC/bc_osc_rstudio_server\u003c/a\u003e.\u003c/p\u003e\n\u003ch2 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule restore\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job before\nlaunching the RStudio Server.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://apptainer.org\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e 2.4.2+\u003c/li\u003e\n\u003cli\u003eAn Apptainer image similar to \u003ca href=\"https://hub.docker.com/r/rocker/rstudio\" rel=\"nofollow\"\u003erocker/rstudio\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-buildinstall\"\u003e\u003ca class=\"heading-link\" href=\"#buildinstall\"\u003eBuild/Install\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eGitlab CI will automatically build both CentOS 7 and 8 RPMs.\nThey can be installed directly via \u003ccode\u003eyum\u003c/code\u003e for testing.\u003c/p\u003e\n\u003cp\u003eFor production, add to the per-cluster common repos and require via puppet.\u003c/p\u003e\n\u003ch2 id=\"user-content-contributing\"\u003e\u003ca class=\"heading-link\" href=\"#contributing\"\u003eContributing\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://git.unl.edu/hcc/bc-hcc-rstudio-server/-/forks/new\" rel=\"nofollow\"\u003ehttps://git.unl.edu/hcc/bc-hcc-rstudio-server/-/forks/new\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocumentation, website content, and logo is licensed under\n\u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC-BY-4.0\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCode is licensed under MIT (see LICENSE.txt)\u003c/li\u003e\n\u003cli\u003eRStudio, Shiny and the RStudio logo are all registered trademarks of RStudio.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-name\" class=\"anchor\" aria-hidden=\"true\" href=\"#name\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eName\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-empty-template-for-nextflow-pipelines-short-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#empty-template-for-nextflow-pipelines-short-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEmpty template for nextflow pipelines (short description)\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/template-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e3a0e94b24410397271294a485f985c85941b292ba2c7cbf3fefb26bf1e0c76b/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f74656d706c6174652d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/template-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/template-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1404\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/94193130\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5decad826d7116b1c950b6b48c06052496f141e803525b088ce3cdcaaa4d7b88/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f39343139333133302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/94193130.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"template-nf.png\"\u003e\u003cimg src=\"template-nf.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eExternal software:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003einput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003einput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpecify the test files location\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param1\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param2\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param3\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param4\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--flag2\u003c/td\u003e\n\u003ctd\u003e....\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e...\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-detailed-description-optional-section\" class=\"anchor\" aria-hidden=\"true\" href=\"#detailed-description-optional-section\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetailed description (optional section)\u003c/h2\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-hidden=\"true\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/template-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib1*\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support (link to specific gitter chatroom)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib2\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib3\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eTester\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#references-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences (optional)\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#faq-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ (optional)\u003c/h2\u003e\n", "stargazers_count": 0, - "subscribers_count": 9, + "subscribers_count": 4, "topics": [], - "updated_at": 1646361327.0 + "updated_at": 1670593101.0 }, { "data_format": 2, - "description": null, + "description": "The singularity definition file of curp container and the workflow to build and upload sif file to GHCR.", "filenames": [ "Singularity" ], - "full_name": "OSC/bc_osc_rstudio_server_quick", + "full_name": "passive-radio/curp-singularity", "latest_release": "v0.0.1", - "readme": "\u003ch1 id=\"user-content-batch-connect---osc-rstudio-server\"\u003e\u003ca class=\"heading-link\" href=\"#batch-connect---osc-rstudio-server\"\u003eBatch Connect - OSC RStudio Server\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_rstudio_server.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an RStudio Server\nwithin an Owens batch job.\u003c/p\u003e\n\u003ch2 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule restore\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job before\nlaunching the RStudio Server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ewithout Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e 3.3.2+ (earlier versions are untested but may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rstudio.com/products/rstudio-server/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e 1.0.136+ (earlier versions are untested by may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://proot-me.github.io/\" rel=\"nofollow\"\u003ePRoot\u003c/a\u003e 5.1.0+ (used to setup fake bind mount)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eor with Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e 2.4.2+\u003c/li\u003e\n\u003cli\u003eA Singularity image similar to \u003ca href=\"https://www.singularity-hub.org/collections/463\" rel=\"nofollow\"\u003enickjer/singularity-rstudio\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorresponding module to launch the above Singularity image (see\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio/blob/master/example_module/\"\u003eexample_module\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-install\"\u003e\u003ca class=\"heading-link\" href=\"#install\"\u003eInstall\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2 id=\"user-content-contributing\"\u003e\u003ca class=\"heading-link\" href=\"#contributing\"\u003eContributing\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server/fork\"\u003ehttps://github.com/OSC/bc_osc_rstudio_server/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-curp-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#curp-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecurp-singularity\u003c/h1\u003e\n\u003cp\u003eThe singularity definition file of curp container and the workflow to build and upload sif file to GHCR.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-pull-and-use-pre-built-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-pull-and-use-pre-built-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to pull and use pre-built image\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull curp_singularity.sif oras://ghcr.io/passive-radio/curp-singularity:latest\nsingularity \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e curp_singularity.sif\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 11, + "subscribers_count": 1, "topics": [], - "updated_at": 1570733859.0 + "updated_at": 1670898005.0 }, { "data_format": 2, - "description": "A singularity recipe for SALSA: A tool to scaffold long read assemblies with Hi-C data ", + "description": "Slurm Docker and Apptainer commands", "filenames": [ - "Singularity" + "singularity/Singularity.recipe" ], - "full_name": "ISU-HPC/SALSA", + "full_name": "Yessense/slurm_ml_pipeline", "latest_release": null, - "readme": "\u003ch1 id=\"user-content-salsa\"\u003e\u003ca class=\"heading-link\" href=\"#salsa\"\u003eSALSA\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003eA singularity recipe for SALSA: A tool to scaffold long read assemblies with Hi-C data\u003c/p\u003e\n\u003cp\u003eThe executables are located in /SALSA/*.py\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1526316679.0 + "updated_at": 1671531473.0 }, { "data_format": 2, - "description": "Heavy quark evolution framework in heavy-ion collisions", + "description": null, "filenames": [ "Singularity" ], - "full_name": "Yingru/hic_HQ", + "full_name": "CNCLgithub/eeg-psiturk", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hic_hq\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#hic_hq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehic_HQ\u003c/h1\u003e\n\u003cp\u003eA framework of heavy quark evolution in heavy-ion collisions\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#0-work-locally--make-sure-you-have-root-right-\"\u003e0. Work locally (make sure you have root right)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#1-work-with-cloud-computing-system\"\u003e1. Work with cloud computing system\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#11-install--docker--in-chameleon-instance\"\u003e1.1 Install \u003ccode\u003eDocker\u003c/code\u003e in Chameleon instance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#12a-build-a--docker--container-from--dockerfile-\"\u003e1.2a Build a \u003ccode\u003eDocker\u003c/code\u003e container from \u003cem\u003eDockerfile\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#12b-instead-of-12a--pull-a--docker--image-from--dockerhub-\"\u003e1.2b Instead of 1.2a, pull a \u003ccode\u003eDocker\u003c/code\u003e image from \u003cem\u003edockerhub\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#13-install--singularity--in-chameleon-instance\"\u003e1.3 Install \u003ccode\u003esingularity\u003c/code\u003e in Chameleon instance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#13a-pull--singularity--container-from--dockerhub-\"\u003e1.3a Pull \u003ccode\u003esingularity\u003c/code\u003e container from \u003ccode\u003edockerhub\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#13b-or-instead-13a--build--singularity--image-from-recipe\"\u003e1.3b Or instead 1.3a, build \u003ccode\u003esingularity\u003c/code\u003e image from recipe\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#2-remaining-issue--to-be-done-\"\u003e2 Remaining issue (to be done)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-0-work-locally-make-sure-you-have-root-right-or-have-the-all-the-dependencies\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#0-work-locally-make-sure-you-have-root-right-or-have-the-all-the-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e0. Work locally (make sure you have root right, or have the all the dependencies)\u003c/h2\u003e\n\u003cp\u003eprerequisites:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython3, numpy, scipy, hdf5, pip\u003c/li\u003e\n\u003cli\u003eC/C++/Fortran compilers ==\u0026gt; tested: GNU gcc/g++/gfortran 4.8.4 version\u003c/li\u003e\n\u003cli\u003ecmake (2.8+), boost (1.54+), HDF5 (1.8.11)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eor you can install all the dependencies using \u003ccode\u003einstall_software.sh\u003c/code\u003e (on a ubunut14.04 OS)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Yingru/hic_HQ.git\n\ncd hic_HQ/\nbash install_software.sh # returns a tar.gz file where contains all the modules\n\n\nmkdir test\ncp hic_HQ-osg/hic_HQ-osg.tar.gz test/\ncd test/\ntar -xzf hic_HQ-osg.tar.gz\ncp -r ../workdir/ hic_HQ-osg\ncd hic_HQ-osg/workdir\n\n\npython3 python3 run-events_cD.py args.conf 0\n# args.conf set up parameters ($\\alpha_s, \\hat{q}_{min}, \\hat{q}_{slope}, \\gamma$)\n# parameter_df.dat are diffusion parameters (particle_ID, hydro_ID, HQ list ...)\n# parameter_hd.dat are hadronization parameters (particle_ID ...)\n# HQ_sample.conf are initially sample HQ list parameters\n# vishnew.conf are hydro parameters (shear, bulk, edensity ...)\n# 0 is jobID, useful when you run parallel jobs\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-work-with-cloud-computing-system\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#1-work-with-cloud-computing-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Work with cloud computing system\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.chameleoncloud.org/\" rel=\"nofollow\"\u003e\u003cstrong\u003eChameleon\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[tutorial, get started]((\u003ca href=\"https://chameleoncloud.readthedocs.io/en/latest/getting-started/index.html\" rel=\"nofollow\"\u003ehttps://chameleoncloud.readthedocs.io/en/latest/getting-started/index.html\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCreate an account, join/create a project\u003c/li\u003e\n\u003cli\u003eLoggin in through \u003ca href=\"https://chi.uc.chameleoncloud.org/\" rel=\"nofollow\"\u003eUChicago\u003c/a\u003e or \u003ca href=\"https://chi.tacc.chameleoncloud.org/\" rel=\"nofollow\"\u003eTACC\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReserve a node, launch instance (I choosed \u003cstrong\u003eUbuntu14.04\u003c/strong\u003e), create a key pair, associate IP address\u003c/li\u003e\n\u003cli\u003eaccess your instance\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e# download the .pem key pair\nchmod 600 yx59chameleonkey.pem\nssh-add yx59chameleonkey.pem\nssh cc@ip_address\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-11-install-docker-in-chameleon-instance\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#11-install-docker-in-chameleon-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1 Install \u003ccode\u003eDocker\u003c/code\u003e in Chameleon instance\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003essh cc@192.5.87.178\n\n# check OS version\nlsb_release -a\n\n# install docker and its dependencies\n# 1. you can use the default installation, such as apt-get to install from OS repository\n# 2. install from source (17.03.2 version)\n\nmkdir Install \u0026amp;\u0026amp; cd Install\nsudo apt-get install libsystemd-journal0\nwget https://download.docker.com/linux/ubuntu/dists/trusty/pool/stable/amd64/docker-ce_17.03.2~ce-0~ubuntu-trusty_amd64.deb\nsudo dpkg -i docker-ce_17.03.2~ce-0~ubuntu-trusty_amd64.deb\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-12a-build-a-docker-container-from-dockerfile\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#12a-build-a-docker-container-from-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.2a Build a \u003ccode\u003eDocker\u003c/code\u003e container from \u003cem\u003eDockerfile\u003c/em\u003e\n\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# build the docker image\ngit clone https://github.com/Yingru/hic_HQ.git\ncd hic_HQ/\nsudo docker build -t hic_hq:v1 .\n\n# check docker images\nsudo docker images\ncd workdir/\n\n# to run the executable\n# run-events_cD.py is the pipeline script\n# args.conf changes the parameters ($alpha_s, \\hat{q}_{min}, \\hat{q}_{slope}, \\gamma$\n# 0 is the jobID (useful to run parallel events)\nsudo docker run -v `pwd`:/var/hic_HQ-osg/results hic_hq:v1 python3 run-events_cD.py args.conf 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-12b-instead-of-12a-pull-a-docker-image-from-dockerhub\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#12b-instead-of-12a-pull-a-docker-image-from-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.2b Instead of 1.2a, pull a \u003ccode\u003eDocker\u003c/code\u003e image from \u003cem\u003edockerhub\u003c/em\u003e\n\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# distinguish from previous case, dockerhub autimatically assign tag as latest\nsudo docker pull yingruxu/hic_hq:latest\nsudo docker images\ncd workdir/\nsudo docker run -v `pwd`:/var/hic_HQ-osg/results yingruxu/hic_hq:latest python3 run-events_cD.py args.conf 1\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-13-install-singularity-in-chameleon-instance\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#13-install-singularity-in-chameleon-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.3 Install \u003ccode\u003esingularity\u003c/code\u003e in Chameleon instance\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e#singularity dependencies\nsudo apt-get update\nsudo apt-get install libarchive-dev python dh-autoreconf build-essential\n\n# install the maste branch\ngit clone https://github.com/singularityware/singularity.git\ncd singularity\n\n# ERRRR, their master branch is not consistent with tutorial!\ngit checkout vault/release-2.5\n\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-13a-pull-singularity-container-from-dockerhub\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#13a-pull-singularity-container-from-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.3a Pull \u003ccode\u003esingularity\u003c/code\u003e container from \u003ccode\u003edockerhub\u003c/code\u003e\n\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# check version, better use 2.5.2 (for some reason, the older version 2.3 doesn\u0027t pull)\nsingularity --version\n\ncd workdir/\nsudo apt-get update \u0026amp;\u0026amp; sudo apt-get install squashfs-tools \nsingularity pull docker://yingruxu/hic_hq\n\n# convert this to a writable container\nsingularity build --writable hic_hq_write.img hic_hq.simg\n\n# or build from dockerhub (not sure what is the difference)\nsingularity build --writable hic_hq_write.img docker://yingruxu/hic_hq\n\n\n# doesn\u0027t work? read-only filesystem? I am not able to write? -- fixed\n# now the second question, not enough space\nsudo singularity shell --writable -B $PWD:/var/hic_HQ-osg/results hic_hq_write.img\ncd /var/hic_HQ-osg/results/\n# for some reason need to set locale?\necho \"LC_ALL=en_US.UTF-8\" \u0026gt;\u0026gt; /etc/environment\necho \"en_US.UTF-8 UTF-8\" \u0026gt;\u0026gt; /etc/locale.gen\necho \"LANG=en_US.UTF-8\" \u0026gt; /etc/locale.conf\nlocale-gen en_US.UTF-8\n\npython3 run-events_cD.py args.conf 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-13b-or-instead-13a-build-singularity-image-from-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#13b-or-instead-13a-build-singularity-image-from-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.3b Or instead 1.3a, build \u003ccode\u003esingularity\u003c/code\u003e image from recipe\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# remember to build writable image\nsudo singularity build --writable hic_hq.img Singularity\n\n# to test singularity container interactively\nsudo singularity shell --writable -B $PWD:/var/hic_HQ-osg/results hic_hq.img\n\n\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt; HEAD\n# to run trento events\nsudo singularity exec --writable -B $PWD:/var/hic_HQ-osg/results hic_hq.img /var/hic_HQ-osg/bin/trento Pb Pb 10 --output initial.hdf5\n\n# to run full events\nsudo singularity exec --writable -B $PWD:/var/hic_HQ-osg/results hic_hq.img python3 /var/hic_HQ-osg/results/run-events_cD.py /var/hic_HQ-osg/results/args.conf 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-remaining-issue-to-be-done\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#2-remaining-issue-to-be-done\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2 Remaining issue (to be done)\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eChange the \u003cem\u003eDockerfile\u003c/em\u003e to add the \u003ccode\u003elocale\u003c/code\u003e information (it is fine with \u003ccode\u003eDocker\u003c/code\u003e container, but cause trouble when using \u003ccode\u003esingularity pull/build\u003c/code\u003e from \u003cem\u003eDockerhub\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003eRight now I still need \u003ccode\u003eroot\u003c/code\u003e privilege to be able to write in a singularity container filesystem (even though I already choose the \u003ccode\u003e--writable\u003c/code\u003e option, need to fix that\u003c/li\u003e\n\u003cli\u003eWhile running in a \u003ccode\u003esingularity\u003c/code\u003e container, the space limit is reached? (use \u003ccode\u003e--sandbox\u003c/code\u003e instead of \u003ccode\u003e--writable\u003c/code\u003e?)\n=======\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; 6c170142da31ead53fd2857f8755f37b4a68a8be\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-psiturk-experiment\" class=\"anchor\" aria-hidden=\"true\" href=\"#psiturk-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePsiturk Experiment\u003c/h1\u003e\n\u003cp\u003ePsiturk experiment used in Galileo (response slider) style experiments.\u003c/p\u003e\n\u003cp\u003eBased off of \u003ca href=\"https://github.com/CNCLgithub/rooms-psiturk\"\u003eCNCLgithub/rooms-psiturk\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-linux\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Linux\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edependencies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003esingularity\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003etar\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetup\u003c/h3\u003e\n\u003cp\u003esee help\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh --help\n./setup.sh cont data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis setup file will, by default, pull a container and data files from box.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-mac\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-mac\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Mac\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dependencies-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edependencies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003econda\u003c/li\u003e\n\u003cli\u003etar\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setup-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetup\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh --help\n./setup.sh data env\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-psiturk\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-psiturk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning psiturk\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda activate eeg-psiturk-env\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e psiturk/\npsiturk server on\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-api\" class=\"anchor\" aria-hidden=\"true\" href=\"#api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAPI\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-taskjs\" class=\"anchor\" aria-hidden=\"true\" href=\"#taskjs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etask.js\u003c/h3\u003e\n\u003cp\u003eThe majority of the experiment\u0027s functionality is described in \u003ccode\u003epsiturk/static/js/task.js\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe main class used to setup pages for both the experiment and instructions is defined as \u003ccode\u003ePage\u003c/code\u003e.\n\u003ccode\u003ePage\u003c/code\u003e handles both media presentation and scale setup. See the docstrings for more info.\u003c/p\u003e\n\u003cp\u003eThere are three other main elements, \u003ccode\u003eInstructionRunner\u003c/code\u003e, \u003ccode\u003eQuiz\u003c/code\u003e, and \u003ccode\u003eExperiment\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-css-and-html\" class=\"anchor\" aria-hidden=\"true\" href=\"#css-and-html\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecss and html\u003c/h3\u003e\n\u003cp\u003eThe main html files are located under \u003ccode\u003epsiturk/templates/\u003c/code\u003e and css is under \u003ccode\u003epsiturk/static/css\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNotabley, \u003ccode\u003estage.html\u003c/code\u003e describes the pages for experimental trials and \u003ccode\u003eslider.css\u003c/code\u003e describes some of the elements found in the scale.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1534526792.0 + "updated_at": 1670977729.0 }, { "data_format": 2, - "description": "Provides Visidata using Debian Stretch as Singularity Image", + "description": null, "filenames": [ - "Singularity" + "test/core/044-singularity-nonsharedfs-minimal/image/Singularity" ], - "full_name": "paulklemm/visidata-singularity", + "full_name": "Jtg003/https-github.com-pegasus-isi-pegasus", "latest_release": null, + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"doc/sphinx/images/pegasusfront-black-reduced.png\"\u003e\u003cimg src=\"doc/sphinx/images/pegasusfront-black-reduced.png\" width=\"200\" alt=\"Pegasus WMS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pegasus-workflow-management-system\" class=\"anchor\" aria-hidden=\"true\" href=\"#pegasus-workflow-management-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePegasus Workflow Management System\u003c/h2\u003e\n\u003cp align=\"left\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d86f06b102fe2b21a15c2fe7b335a1fa19d1a8e67a2086236348bcf6e2bc83b8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f706567617375732d6973692f706567617375733f636f6c6f723d626c7565266c6162656c3d4c6963656e6365\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d86f06b102fe2b21a15c2fe7b335a1fa19d1a8e67a2086236348bcf6e2bc83b8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f706567617375732d6973692f706567617375733f636f6c6f723d626c7565266c6162656c3d4c6963656e6365\" data-canonical-src=\"https://img.shields.io/github/license/pegasus-isi/pegasus?color=blue\u0026amp;label=Licence\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/eb3de5004fcc489334124e42bd6c5141eac62cd9bd5a0ac8abdc70b3abf70041/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f706567617375732d6973692f706567617375733f6c6162656c3d4c6174657374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb3de5004fcc489334124e42bd6c5141eac62cd9bd5a0ac8abdc70b3abf70041/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f706567617375732d6973692f706567617375733f6c6162656c3d4c6174657374\" data-canonical-src=\"https://img.shields.io/github/v/tag/pegasus-isi/pegasus?label=Latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/defd5997fcf06cfa5e84a7b31da92ac209152dd742f4a0f4d1ca47d7e649fc3f/68747470733a2f2f696d672e736869656c64732e696f2f707970692f646d2f706567617375732d776d733f636f6c6f723d677265656e266c6162656c3d50795049253230446f776e6c6f616473\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/defd5997fcf06cfa5e84a7b31da92ac209152dd742f4a0f4d1ca47d7e649fc3f/68747470733a2f2f696d672e736869656c64732e696f2f707970692f646d2f706567617375732d776d733f636f6c6f723d677265656e266c6162656c3d50795049253230446f776e6c6f616473\" data-canonical-src=\"https://img.shields.io/pypi/dm/pegasus-wms?color=green\u0026amp;label=PyPI%20Downloads\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6dd2629a781aaaf2b5f44f4adb568746dfc3d9601a4f93a55a752a436140e3ba/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732d616e6f6e2f706567617375732d6973692f706567617375733f636f6c6f723d677265656e266c6162656c3d436f6e7472696275746f7273\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6dd2629a781aaaf2b5f44f4adb568746dfc3d9601a4f93a55a752a436140e3ba/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732d616e6f6e2f706567617375732d6973692f706567617375733f636f6c6f723d677265656e266c6162656c3d436f6e7472696275746f7273\" data-canonical-src=\"https://img.shields.io/github/contributors-anon/pegasus-isi/pegasus?color=green\u0026amp;label=Contributors\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003ePegasus WMS is a configurable system for mapping and executing scientific\nworkflows over a wide range of computational infrastructures including laptops,\ncampus clusters, supercomputers, grids, and commercial and academic clouds.\nPegasus has been used to run workflows with up to 1 million tasks that process\ntens of terabytes of data at a time.\u003c/p\u003e\n\u003cp\u003ePegasus WMS bridges the scientific domain and the execution environment by\nautomatically mapping high-level workflow descriptions onto distributed\nresources. It automatically locates the necessary input data and computational\nresources required by a workflow, and plans out all of the required data\ntransfer and job submission operations required to execute the workflow.\nPegasus enables scientists to construct workflows in abstract terms without\nworrying about the details of the underlying execution environment or the\nparticulars of the low-level specifications required by the middleware (Condor,\nGlobus, Amazon EC2, etc.). In the process, Pegasus can $ ant dist and optimize the\nworkflow to enable efficient, high-performance execution of large\nworkflows on complex, distributed infrastructures.\u003c/p\u003e\n\u003cp\u003ePegasus has a number of features that contribute to its usability and\neffectiveness:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePortability / Reuse \u2013 User created workflows can easily be run in different\nenvironments without alteration. Pegasus currently runs workflows on top of\nCondor pools, Grid infrastructures such as Open Science Grid and XSEDE,\nAmazon EC2, Google Cloud, and HPC clusters. The same workflow can run on a\nsingle system or across a heterogeneous set of resources.\u003c/li\u003e\n\u003cli\u003ePerformance \u2013 The Pegasus mapper can reorder, group, and prioritize tasks in\norder to increase overall workflow performance.\u003c/li\u003e\n\u003cli\u003eScalability \u2013 Pegasus can easily scale both the size of the workflow, and\nthe resources that the workflow is distributed over. Pegasus runs workflows\nranging from just a few computational tasks up to 1 million. The number of\nresources involved in executing a workflow can scale as needed without any\nimpediments to performance.\u003c/li\u003e\n\u003cli\u003eProvenance \u2013 By default, all jobs in Pegasus are launched using the\nKickstart wrapper that captures runtime provenance of the job and helps in\ndebugging. Provenance data is collected in a database, and the data can be\nqueried with tools such as pegasus-statistics, pegasus-plots, or directly\nusing SQL.\u003c/li\u003e\n\u003cli\u003eData Management \u2013 Pegasus handles replica selection, data transfers and\noutput registration in data catalogs. These tasks are added to a workflow as\nauxilliary jobs by the Pegasus planner.\u003c/li\u003e\n\u003cli\u003eReliability \u2013 Jobs and data transfers are automatically retried in case of\nfailures. Debugging tools such as pegasus-analyzer help the user to debug the\nworkflow in case of non-recoverable failures.\u003c/li\u003e\n\u003cli\u003eError Recovery \u2013 When errors occur, Pegasus tries to recover when possible\nby retrying tasks, by retrying the entire workflow, by providing workflow-level\ncheckpointing, by re-mapping portions of the workflow, by trying alternative\ndata sources for staging data, and, when all else fails, by providing a rescue\nworkflow containing a description of only the work that remains to be done.\nIt cleans up storage as the workflow is executed so that data-intensive\nworkflows have enough space to execute on storage-constrained resources.\nPegasus keeps track of what has been done (provenance) including the locations\nof data used and produced, and which software was used with which parameters.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eYou can find more information about Pegasus on the \u003ca href=\"http://pegasus.isi.edu\" rel=\"nofollow\"\u003ePegasus Website\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePegasus has an extensive \u003ca href=\"http://pegasus.isi.edu/documentation/\" rel=\"nofollow\"\u003eUser Guide\u003c/a\u003e\nthat documents how to create, plan, and monitor workflows.\u003c/p\u003e\n\u003cp\u003eWe recommend you start by completing the Pegasus Tutorial from \u003ca href=\"https://pegasus.isi.edu/documentation/user-guide/tutorial.html\" rel=\"nofollow\"\u003eChapter 3 of the\nPegasus User Guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe easiest way to install Pegasus is to use one of the binary packages\navailable on the \u003ca href=\"http://pegasus.isi.edu/downloads\" rel=\"nofollow\"\u003ePegasus downloads page\u003c/a\u003e.\nConsult \u003ca href=\"https://pegasus.isi.edu/documentation/user-guide/installation.html\" rel=\"nofollow\"\u003eChapter 2 of the Pegasus User Guide\u003c/a\u003e\nfor more information about installing Pegasus from binary packages.\u003c/p\u003e\n\u003cp\u003eThere is documentation on the Pegasus website for the Python, Java and R\n\u003ca href=\"https://pegasus.isi.edu/documentation/reference-guide/api-reference.html\" rel=\"nofollow\"\u003eAbstract Workflow Generator APIs\u003c/a\u003e.\nWe strongly recommend using the Python API which is feature complete, and also\nallows you to invoke all the pegasus command line tools.\u003c/p\u003e\n\u003cp\u003eYou can use \u003cem\u003epegasus-init\u003c/em\u003e command line tool to run several examples\non your local machine. Consult \u003ca href=\"https://pegasus.isi.edu/documentation/user-guide/example-workflows.html\" rel=\"nofollow\"\u003eChapter 4 of the Pegasus\nUser Guide\u003c/a\u003e\nfor more information.\u003c/p\u003e\n\u003cp\u003eThere are also examples of how to \u003ca href=\"https://pegasus.isi.edu/documentation/user-guide/execution-environments.html\" rel=\"nofollow\"\u003eConfigure Pegasus for Different Execution\nEnvironments\u003c/a\u003e\nin the Pegasus User Guide.\u003c/p\u003e\n\u003cp\u003eIf you need help using Pegasus, please contact us. See the [contact page]\n(\u003ca href=\"http://pegasus.isi.edu/contact\" rel=\"nofollow\"\u003ehttp://pegasus.isi.edu/contact\u003c/a\u003e) on the Pegasus website for more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding from Source\u003c/h2\u003e\n\u003cp\u003ePegasus can be compiled on any recent Linux or Mac OS X system.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-source-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#source-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource Dependencies\u003c/h3\u003e\n\u003cp\u003eIn order to build Pegasus from source, make sure you have the following installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003cli\u003eJava 8 or higher\u003c/li\u003e\n\u003cli\u003ePython 3.5 or higher\u003c/li\u003e\n\u003cli\u003eR\u003c/li\u003e\n\u003cli\u003eAnt\u003c/li\u003e\n\u003cli\u003egcc\u003c/li\u003e\n\u003cli\u003eg++\u003c/li\u003e\n\u003cli\u003emake\u003c/li\u003e\n\u003cli\u003etox 3.14.5 or higher\u003c/li\u003e\n\u003cli\u003emysql (optional, required to access MySQL databases)\u003c/li\u003e\n\u003cli\u003epostgresql (optional, required to access PostgreSQL databases)\u003c/li\u003e\n\u003cli\u003ePython pyyaml\u003c/li\u003e\n\u003cli\u003ePython GitPython\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOther packages may be required to run unit tests, and build MPI tools.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-hidden=\"true\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling\u003c/h3\u003e\n\u003cp\u003eAnt is used to compile Pegasus.\u003c/p\u003e\n\u003cp\u003eTo get a list of build targets run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ant -p\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe targets that begin with \"dist\" are what you want to use.\u003c/p\u003e\n\u003cp\u003eTo build a basic binary tarball (excluding documentation), run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ant dist\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the release tarball (including documentation), run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ant dist-release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting packages will be created in the \u003ccode\u003edist\u003c/code\u003e subdirectory.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1545049200.0 + "updated_at": 1671659562.0 }, { "data_format": 2, - "description": "Run IGV in a XFCE-based Singularity container", + "description": "pydocbrowser website", "filenames": [ - "Singularity" + "build/sources/pygments-2.12.0/tests/examplefiles/singularity/Singularity", + "build/sources/pygments-2.14.0/tests/examplefiles/singularity/Singularity", + "build/sources/pygments-2.11.2/tests/examplefiles/singularity/Singularity", + "build/sources/pygments-2.13.0/tests/examplefiles/singularity/Singularity" ], - "full_name": "bihealth/singularity-igv", + "full_name": "pydocbrowser/pydocbrowser.github.io", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pydocbrowser/pydocbrowser\"\u003epydocbrowser\u003c/a\u003e website\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pydocbrowser/pydocbrowser.github.io/actions/workflows/build.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pydocbrowser/pydocbrowser.github.io/actions/workflows/build.yml/badge.svg\" alt=\"build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1612971331.0 + "updated_at": 1650700618.0 }, { "data_format": 2, - "description": null, + "description": "repository for running scripts on Orion", "filenames": [ - "Singularity.centos7-cuda-tf1.11.0-torch0.4.1", - "Singularity.centos7-tensorflow-cpu" + "Singularity" ], - "full_name": "apphys/hpsim_rl_singularity", + "full_name": "LingoNMBU/DAT300-CA2-Orion", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity recipe\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1542320831.0 + "updated_at": 1667572209.0 }, { "data_format": 2, - "description": null, + "description": "Run assembler (Canu, flye, hifiasm) on a set of long read files", "filenames": [ - "Singularity.v2.1.0" + "singularity/Singularity" ], - "full_name": "baxpr/mp2rage", + "full_name": "sequana/lora", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mp2rage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#mp2rage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emp2rage\u003c/h1\u003e\n\u003cp\u003eReconstructs a T1-weighted image from images at multiple inversion times following Marques et al. 2009. The robust adjustment (beta factor) of O\u0027Brien 2014 is also implemented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PF, Gruetter R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage. 2010 Jan 15;49(2):1271-81. doi:10.1016/j.neuroimage.2009.10.002. PMID: 19819338.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/19819338\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/pubmed/19819338\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe large spatial inhomogeneity in transmit B(1) field (B(1)(+)) observable in human MR images at high static magnetic fields (B(0)) severely impairs image quality. To overcome this effect in brain T(1)-weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T(1)-weighted images where the result image was free of proton density contrast, T(2) contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B(1)(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T(1)-weighted images, acquired within 12 min, high-resolution 3D T(1) maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T(1) maps were validated in phantom experiments. In humans, the T(1) values obtained at 7 T were 1.15+/-0.06 s for white matter (WM) and 1.92+/-0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T(1) values obtained (0.81+/-0.03 s for WM and 1.35+/-0.05 for GM) were once again found to be in very good agreement with values in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eO\u0027Brien KR, Kober T, Hagmann P, et al. Robust T1-weighted Structural Brain Imaging and Morphometry at 7T Using MP2RAGE PLoS One. 2014;9(6):e99676. Published 2014 Jun 16. doi:10.1371/journal.pone.0099676\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/24932514/\" rel=\"nofollow\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/24932514/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePurpose: To suppress the noise, by sacrificing some of the signal homogeneity for numerical stability, in uniform T1 weighted (T1w) images obtained with the magnetization prepared 2 rapid gradient echoes sequence (MP2RAGE) and to compare the clinical utility of these robust T1w images against the uniform T1w images.\u003c/p\u003e\n\u003cp\u003eMaterials and methods: 8 healthy subjects (29.0 \u00b1 4.1 years; 6 Male), who provided written consent, underwent two scan sessions within a 24 hour period on a 7T head-only scanner. The uniform and robust T1w image volumes were calculated inline on the scanner. Two experienced radiologists qualitatively rated the images for: general image quality; 7T specific artefacts; and, local structure definition. Voxel-based and volume-based morphometry packages were used to compare the segmentation quality between the uniform and robust images. Statistical differences were evaluated by using a positive sided Wilcoxon rank test.\u003c/p\u003e\n\u003cp\u003eResults: The robust image suppresses background noise inside and outside the skull. The inhomogeneity introduced was ranked as mild. The robust image was significantly ranked higher than the uniform image for both observers (observer 1/2, p-value = 0.0006/0.0004). In particular, an improved delineation of the pituitary gland, cerebellar lobes was observed in the robust versus uniform T1w image. The reproducibility of the segmentation results between repeat scans improved (p-value = 0.0004) from an average volumetric difference across structures of \u2248 6.6% to \u2248 2.4% for the uniform image and robust T1w image respectively.\u003c/p\u003e\n\u003cp\u003eConclusions: The robust T1w image enables MP2RAGE to produce, clinically familiar T1w images, in addition to T1 maps, which can be readily used in uniform morphometry packages.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1595274818.0 + "updated_at": 1673268784.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for dvc (https://github.com/iterative/dvc)", "filenames": [ - "Singularity.v1.0.0" + "Singularity.2.40.0", + "Singularity.1.11.16", + "Singularity.1.6.1", + "Singularity.2.8.2", + "Singularity", + "Singularity.2.1.0", + "Singularity.2.8.1" ], - "full_name": "baxpr/dwi-reorder", + "full_name": "powerPlant/dvc-srf", "latest_release": null, + "readme": "\u003cp\u003eSingularity recipe files for the DVC tool for Data Version Control\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1574202643.0 + "updated_at": 1665710810.0 }, { "data_format": 2, - "description": null, + "description": "Code for blog Reproducibility in Tensorflow/PyTorch/JAX", "filenames": [ - "Singularity", - "spades.v3.7/Singularity", - "spades.v3.11/Singularity", - "shovill.v.1.9/Singularity" + "Singularity.recipe" ], - "full_name": "kristyhoran/multi_assembler_singularity", + "full_name": "WolodjaZ/reproduc-ml-tutorial", "latest_release": null, - "readme": "\u003cp\u003eA singulairty recipe which incorporates shovill and skesa\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-reproducibility-in-tensorflowpytorchjax\" class=\"anchor\" aria-hidden=\"true\" href=\"#reproducibility-in-tensorflowpytorchjax\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReproducibility in Tensorflow/PyTorch/JAX\u003c/h1\u003e\n\u003cp\u003eThis is an example repository from my blog on \u003ca href=\"https://wolodjaz.github.io/blogs/\" rel=\"nofollow\"\u003eReproducibility in Tensorflow/PyTorch/JAX\u003c/a\u003e, so please read it first.\u003c/p\u003e\n\u003cp\u003eThe structure of this repository differs from the one in the blog due to the addition of \u003ca href=\"https://mybinder.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eBinder\u003c/a\u003e settings. The repository structure is as follows:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003ereproduc-ml-tutorial/\n workspace/ # Default location for data sets, logs, models, parameter files.\n train.yaml # Train hyper-parameters.\n .dockerignore # Docker ignore file that prevents workspace directory to be sent to docker server.\n DockerBuildfile # Docker recipe.\n environment.yml # Conda environment config file for mybinder\n index.ipynb # Example notebook from Reproducibility in Tensorflow/PyTorch/JAX part 2\n mlcube.yaml # MLCube definition file.\n train_jax.py # Python source code training simple neural network using MNIST data set with JAX.\n train_pytorch.py # Python source code training simple neural network using MNIST data set with PyTorch.\n train_tensorflow.py # Python source code training simple neural network using MNIST data set with Tensorflow.\n requirements.txt # Python project dependencies.\n run.sh # Main bash script that lunches python script based on passed argument\n Singularity.recipe # Singularity recipe.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-reproducibility-in-tensorflowpytorchjax-part-22--notebook\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-reproducibility-in-tensorflowpytorchjax-part-22--notebook\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the \"Reproducibility in Tensorflow/PyTorch/JAX Part 2/2\" Notebook\u003c/h2\u003e\n\u003cp\u003eTo run the notebook, you can pull this repository and launch \u003ccode\u003eindex.ipynb\u003c/code\u003e locally, but you can also click on the badge below to test running it on BinderHub without pulling the repository \u003cg-emoji class=\"g-emoji\" alias=\"sunglasses\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f60e.png\"\u003e\ud83d\ude0e\u003c/g-emoji\u003e:\n\u003ca href=\"https://mybinder.org/v2/gh/WolodjaZ/reproduc-ml-tutorial/HEAD?labpath=index.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/581c077bdbc6ca6899c86d0acc6145ae85e9d80e6f805a1071793dbe48917982/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-main-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#main-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMain Project\u003c/h2\u003e\n\u003cp\u003eIn addition to running the notebook, you can also run the main application where you can train MNIST datasets on a basic neural network made in Pytorch/Jax/Tensorflow. You will build a docker image or a singularity image and launch it to run the training. Everything, including logs and data, will be saved under the \u003ccode\u003eworkspace\u003c/code\u003e directory. There is also a \u003ccode\u003etrain.yaml\u003c/code\u003e file where I have defined all the parameters used for the scripts. You can check and change them if you want to.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Project\u003c/h2\u003e\n\u003cp\u003eTo run the project, we are using \u003ca href=\"https://github.com/mlcommons/mlcube\"\u003eMLCube\u003c/a\u003e, which provides the contract for our pipeline, as defined in the file \u003ccode\u003emlcube.yaml\u003c/code\u003e. Based on this file and the framework, you will need to first configure our environment by building our images. Before doing so, please install mlcube:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install mlcube\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, create our images:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prepare docker image\u003c/span\u003e\nmlcube configure --mlcube=. --platform=docker\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prepare singularity image\u003c/span\u003e\nmlcube configure --mlcube=. --platform=singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can now run our pipelines by choosing which platform and framework to use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\nmlcube run --mlcube=. --platform=docker --task=pytorch\nmlcube run --mlcube=. --platform=docker --task=tensorflow\nmlcube run --mlcube=. --platform=docker --task=jax\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\nmlcube run --mlcube=. --platform=singularity --task=pytorch\nmlcube run --mlcube=. --platform=singularity --task=tensorflow\nmlcube run --mlcube=. --platform=singularity --task=jax\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running the commands, pipeline will start the training process and the log and models will be saved under the \u003ccode\u003eworkspace\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-additional-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#additional-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional Resources:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCheck out my blog \u003ca href=\"https://wolodjaz.github.io/blogs/\" rel=\"nofollow\"\u003eReproducibility in Tensorflow/PyTorch/JAX\u003c/a\u003e for more information on the topic.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mybinder.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eBinder\u003c/a\u003e is a great tool for creating and sharing custom computing environments with others.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mlcommons/mlcube\"\u003eMLCube\u003c/a\u003e is a useful tool that provides a consistent interface for machine learning models in containers like Docker.\u003c/li\u003e\n\u003cli\u003eFor more guidance on reproducible research, check out \u003ca href=\"https://the-turing-way.netlify.app/reproducible-research/reproducible-research.html\" rel=\"nofollow\"\u003eThe Turing Way\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-and-grand-finally\" class=\"anchor\" aria-hidden=\"true\" href=\"#and-grand-finally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnd Grand Finally\u003c/h2\u003e\n\u003cp\u003eClosing comment offered by chatGPT \u003cg-emoji class=\"g-emoji\" alias=\"robot\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f916.png\"\u003e\ud83e\udd16\u003c/g-emoji\u003e\u003c/p\u003e\n\u003cp\u003eWe\u0027re so glad you\u0027ve given our project a try! Your feedback is incredibly valuable to us as we continue to improve and update the project. Whether you have questions, comments, or suggestions, please don\u0027t hesitate to reach out to us by emailing us at \u003ca href=\"mailto:vladimirzaigrajew@gmail.com\"\u003evladimirzaigrajew@gmail.com\u003c/a\u003e or by opening an issue on the GitHub repository. Thank you for your support!\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1543190575.0 + "updated_at": 1673392331.0 }, { "data_format": 2, - "description": "lowcharts is meant to be used in those scenarios where we have numerical data in text files that we want to display in the terminal to do a basic analysis.", + "description": null, "filenames": [ - "0.4.2/Singularity", - "0.5.8/Singularity", - "0.5.7/Singularity" + "TFD/Singularity.0.4", + "TFD/Singularity" ], - "full_name": "pscedu/singularity-lowcharts", - "latest_release": "v0.5.8", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-lowcharts/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-lowcharts/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-lowcharts/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-lowcharts/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f7fc9ce8a0f943ba64af6c034355a9c31eecd12def757563ede07b3784c8f519/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c6f77636861727473\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f7fc9ce8a0f943ba64af6c034355a9c31eecd12def757563ede07b3784c8f519/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c6f77636861727473\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-lowcharts\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca 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src=\"https://camo.githubusercontent.com/ebc83f1df7b32d114034b8d95b0d270b8512149b150348054f1be74c202b63d2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c6f77636861727473\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-lowcharts\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-lowcharts\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-lowcharts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-lowcharts\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/juan-leon/lowcharts/main/resources/histogram-example.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/juan-leon/lowcharts/main/resources/histogram-example.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/juan-leon/lowcharts\"\u003elowcharts\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003elowcharts\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/lowcharts/0.4.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/lowcharts\u003c/code\u003e as \u003ccode\u003e0.4.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "fromstar/Project_ASA_2022", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-project_asa_2022\" class=\"anchor\" aria-hidden=\"true\" href=\"#project_asa_2022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject_ASA_2022\u003c/h1\u003e\n\u003cp\u003eIn this project a smart house environment is simulated. In the scenario presented, the presence of people in the various rooms, the production of electricity by photovoltaic panels, the cleanliness and temperature of the rooms\nare monitored. Thanks to this information a main smart agent (HouseAgent)\nknows everything that happens in the house and is able to manage the agents\nin charge of cleaning the various rooms. Two other agents (LightAgent and\nShutterAgent) are in charge of lighting a room if a person is present. Depending on the natural brightness, it is decided whether the shutters must be opened\nor the lights must be switched on, so as to guarantee energy savings. Two last\nrobots agents are tasked with cleaning the floors of the house and are the sole\nplanning agents. The various agents can exchange information each other in\norder to perform tasks in different places.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eJavascript, Node.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003cp\u003eTo install the module use this command in the main folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enpm install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the code use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enpm run test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enode ./src/houseworld/HouseWorld.js\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-asa_assignment_3\" class=\"anchor\" aria-hidden=\"true\" href=\"#asa_assignment_3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eASA_assignment_3\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-domain\" class=\"anchor\" aria-hidden=\"true\" href=\"#domain\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDomain\u003c/h3\u003e\n\u003cp\u003eThis sample domain file uses the key-in extension which cannot be used in simulation. In the simulation, therefore, the problem is circumvented through the use of predicates with characteristics that still allow to distinguish different types.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e;; domain file: domain-robot1.pddl\n(define (domain robot1)\n (:requirements :strips :typing)\n (:types\n robot\n room \n base_station \n )\n \n (:predicates\n (is_in_room ?robot - robot ?room1 - room)\n (is_adjacent ?room1 - room ?room2 - room)\n (is_in_bs ?base_station - base_station ?robot - room)\n (is_dirty ?room - room)\n (bs_in_room ?base_station - base_station ?room - room) \n )\n \n (:action Move\n :parameters (?robot ?room1 ?room2 ?base_station)\n :precondition (and\n (is_in_room ?robot ?room1)\n (is_adjacent ?room1 ?room2)\n )\n :effect (and\n (not (is_in_room ?robot ?room1))\n (is_in_room ?robot ?room2)\n (not (is_in_bs ?base_station ?robot))\n )\n )\n \n (:action Clean\n :parameters (?room ?robot)\n :precondition (and\n (is_in_room ?robot ?room)\n (is_dirty ?room)\n )\n :effect (and\n (not (is_dirty ?room))\n )\n )\n \n (:action Charge\n :parameters (?robot ?base_station ?room)\n :precondition (and\n (is_in_room ?robot ?room)\n (bs_in_room ?base_station ?room)\n (not (is_in_bs ?base_station ?robot))\n )\n :effect (and\n (is_in_bs ?base_station ?robot)\n )\n )\n)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-problem\" class=\"anchor\" aria-hidden=\"true\" href=\"#problem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProblem\u003c/h3\u003e\n\u003cp\u003eThis sample problem file contains all the information about the environment that the agent knows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e;; problem file: problem-robot1.pddl\n(define (problem robot1)\n (:domain robot1)\n (:objects\n office - room\n tavern - room\n basement_bathroom - room\n base_station1 - base_station\n robot1 - robot\n )\n (:init\n (is_adjacent office tavern)\n (is_adjacent tavern office)\n (is_adjacent tavern basement_bathroom)\n (is_adjacent basement_bathroom tavern)\n (bs_in_room base_station1 tavern)\n (is_in_room robot1 tavern)\n (is_in_bs base_station1 robot1)\n (is_dirty tavern)\n (is_dirty office)\n )\n (:goal\n (and (not (is_dirty tavern)) (not (is_dirty basement_bathroom)) (not (is_dirty office)) (is_in_bs base_station1 robot1))\n )\n)\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1635967869.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1674056102.0 }, { "data_format": 2, "description": null, "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/latest/Singularity", - "misc/releases/22.12/Singularity.22.12", - "misc/releases/21.12/Singularity.21.12" + "Singularity" ], - "full_name": "ipc2023-classical/planner25", + "full_name": "DanKaptijn/souporcellCopy", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-scorpion\" class=\"anchor\" aria-hidden=\"true\" href=\"#scorpion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScorpion\u003c/h1\u003e\n\u003cp\u003eScorpion is a classical planning system that extends \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003eFast\nDownward\u003c/a\u003e. The main extensions are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enovel state-of-the-art algorithms for optimal classical planning\u003c/li\u003e\n\u003cli\u003eadditional search algorithms\u003c/li\u003e\n\u003cli\u003eseveral new plugin options and utilities\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"#differences-between-scorpion-and-fast-downward\"\u003ebelow\u003c/a\u003e for a detailed list\nof extensions. We regularly port the latest changes from Fast Downward to\nScorpion and also integrate some features from Scorpion back into Fast Downward.\u003c/p\u003e\n\u003cp\u003ePlease use the following reference when citing Scorpion:\nJendrik Seipp, Thomas Keller and Malte Helmert.\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003eSaturated Cost Partitioning for Optimal Classical Planning\u003c/a\u003e.\nJournal of Artificial Intelligence Research 67, pp. 129-167. 2020.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h2\u003e\n\u003cp\u003eInstall the dependencies (the table below lists which versions are tested):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install cmake g++ git make python3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor plugins based on linear programming (e.g., \u003ccode\u003eocp()\u003c/code\u003e, \u003ccode\u003epho()\u003c/code\u003e) you need\nto \u003ca href=\"https://www.fast-downward.org/LPBuildInstructions\" rel=\"nofollow\"\u003eadd an LP solver\u003c/a\u003e. Then\ncompile the planner with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand see the available options with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py --help # driver\n./fast-downward.py --search -- --help # search component\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more details (including build instructions for macOS and Windows), see the\ndocumentation about\n\u003ca href=\"https://www.fast-downward.org/ObtainingAndRunningFastDownward\" rel=\"nofollow\"\u003ecompiling\u003c/a\u003e\nand \u003ca href=\"https://www.fast-downward.org/PlannerUsage\" rel=\"nofollow\"\u003erunning\u003c/a\u003e the planner. The\n\u003ca href=\"https://jendrikseipp.github.io/scorpion\" rel=\"nofollow\"\u003eplugin documentation\u003c/a\u003e shows\nwhich plugins are available (heuristics, search algorithms, etc.) and how\nto use them.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-recommended-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#recommended-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecommended configuration\u003c/h3\u003e\n\u003cp\u003eWe recommend using the following configuration:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py \\\n --transform-task preprocess-h2 \\\n ../benchmarks/gripper/prob01.pddl \\\n --search \"astar(scp_online([\n projections(sys_scp(max_time=100, max_time_per_restart=10)),\n cartesian()],\n saturator=perimstar, max_time=1000, interval=10K, orders=greedy_orders()),\n pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003epreprocess-h2\u003c/code\u003e call prunes irrelevant operators in a preprocessing\nstep. The search configuration uses \u003ca href=\"https://ojs.aaai.org/index.php/SOCS/article/view/18535\" rel=\"nofollow\"\u003epartial order\nreduction\u003c/a\u003e and\nmaximizes over\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003ediverse\u003c/a\u003e,\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/3503\" rel=\"nofollow\"\u003esubset-saturated\u003c/a\u003e\ncost partitioning heuristics computed\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/15976/\" rel=\"nofollow\"\u003eonline\u003c/a\u003e during\nthe search. The underlying abstractions are \u003ca href=\"https://www.ijcai.org/proceedings/2019/780\" rel=\"nofollow\"\u003eSys-SCP pattern\ndatabases\u003c/a\u003e and \u003ca href=\"https://jair.org/index.php/jair/article/view/11217\" rel=\"nofollow\"\u003eCartesian\nabstractions\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e(In \u003ca href=\"https://lab.readthedocs.io/\" rel=\"nofollow\"\u003eDownward Lab\u003c/a\u003e you can use\n\u003ccode\u003eadd_algorithm(name=\"scorpion\", repo=\"path/to/repo\", rev=\"scorpion\", component_options=[], driver_options=[\"--transform-task\", \"preprocess-h2\", \"--alias\", \"scorpion\"]\u003c/code\u003e to run the recommended Scorpion configuration.)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-apptainer-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#apptainer-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApptainer image\u003c/h4\u003e\n\u003cp\u003eTo simplify the installation process, we provide an executable\n\u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e container (formerly known as Singularity).\nIt accepts the same arguments as the \u003ccode\u003efast-downward.py\u003c/code\u003e script (see above).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Download the image,\napptainer pull scorpion.sif oras://ghcr.io/jendrikseipp/scorpion:latest\n\n# or build it yourself.\napptainer build scorpion.sif Apptainer\n\n# Then run recommended configuration (available via \"scorpion\" alias).\n./scorpion.sif --transform-task preprocess-h2 --alias scorpion PROBLEM_FILE\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ipc-2018-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#ipc-2018-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIPC 2018 version\u003c/h3\u003e\n\u003cp\u003eIf you prefer to run the Scorpion version from IPC 2018 (which uses an\nolder Fast Downward version and different abstractions), we recommend\nusing the \u003ca href=\"https://bitbucket.org/ipc2018-classical/team44/src/ipc-2018-seq-opt/\" rel=\"nofollow\"\u003eScorpion IPC\nrepo\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-differences-between-scorpion-and-fast-downward\" class=\"anchor\" aria-hidden=\"true\" href=\"#differences-between-scorpion-and-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDifferences between Scorpion and Fast Downward\u003c/h2\u003e\n\u003cp\u003eDiff between the latest merged version of Fast Downward and Scorpion:\n\u003ca href=\"https://github.com/jendrikseipp/scorpion/compare/main...scorpion\"\u003ehttps://github.com/jendrikseipp/scorpion/compare/main...scorpion\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScorpion comes with the\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/13708\" rel=\"nofollow\"\u003eh\u00b2-preprocessor\u003c/a\u003e\nby Vidal Alc\u00e1zar and \u00c1lvaro Torralba that prunes irrelevant operators.\nPass \u003ccode\u003e--transform-task preprocess-h2\u003c/code\u003e to use it.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003e--transform-task\u003c/code\u003e command allows you to run arbitrary preprocessing\ncommands that transform the SAS+ output from the translator before\npassing it to the search.\u003c/li\u003e\n\u003cli\u003eScorpion uses a\n\u003ca href=\"https://github.com/greg7mdp/parallel-hashmap\"\u003ephmap::flat_hash_set\u003c/a\u003e to check\nfor duplicate states, which often drastically reduces the peak memory usage,\ncompared to Fast Downward\u0027s \u003ccode\u003eIntHashSet\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf \u003ca href=\"https://ccache.dev/\" rel=\"nofollow\"\u003eccache\u003c/a\u003e is installed (recommended), Scorpion\nuses it to cache compilation files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-translator-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-translator-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew translator options\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUse \u003ccode\u003e--dump-predicates\u003c/code\u003e and \u003ccode\u003e--dump-static-atoms\u003c/code\u003e to write files with\ninformation that\u0027s useful for learning domain control knowledge.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-plugin-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-plugin-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew plugin options\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., search_strategy=incremental)\u003c/code\u003e: use \u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/6667\" rel=\"nofollow\"\u003eincremental search for\nCartesian abstraction\nrefinement\u003c/a\u003e\n(default).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., pick_flawed_abstract_state={batch_min_h, ...})\u003c/code\u003e:\nfind all current flaws, then iteratively repair the flaw that\u0027s closest to the goal\n(\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/19819\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, default=\u003ccode\u003ebatch_min_h\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., pick_split={max_cover, ...}, tiebreak_split={max_refined, ...})\u003c/code\u003e:\nsmarter strategies for splitting a flawed abstract state\n(\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/19819\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, default=\u003ccode\u003emax_cover\u003c/code\u003e\nand \u003ccode\u003emax_refined\u003c/code\u003e for tiebreaking).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar,cartesian}(..., dot_graph_verbosity={silent, write_to_console, write_to_file})\u003c/code\u003e:\nwrite intermediate abstractions as Graphviz dot files to stdout or to files (default=\u003ccode\u003esilent\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ehillclimbing(..., max_generated_patterns=200)\u003c/code\u003e: limit the number of\npatterns generated by hill climbing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esystematic(..., pattern_type=interesting_general)\u003c/code\u003e: compute interesting\npatterns for general cost partitioning.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-abstraction-heuristics\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-cost-partitioning-algorithms-for-abstraction-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew cost partitioning algorithms for abstraction heuristics\u003c/h3\u003e\n\u003cp\u003eWe use Cartesian abstractions in the example configurations below\n(\u003ccode\u003e[cartesian()]\u003c/code\u003e). You can also use pattern database heuristics, e.g.,\n\u003ccode\u003e[projections(systematic(2))]\u003c/code\u003e, or mix abstractions, e.g.,\n\u003ccode\u003e[projections(systematic(3)), cartesian()]\u003c/code\u003e. Some of the algorithms below\nare also part of vanilla Fast Downward, but are only implemented for PDB\nheuristics.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning:\n\u003ccode\u003eocp([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003ecanonical_heuristic([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], opportunistic=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], ..., opportunistic=true)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003egzocp([cartesian()], ...)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003escp([cartesian()], ...)\u003c/code\u003e (offline), \u003ccode\u003escp_online([cartesian()], ...)\u003c/code\u003e (online)\u003c/li\u003e\n\u003cli\u003e(Saturated) post-hoc optimization:\n\u003ccode\u003epho([cartesian()], ..., saturated={false,true})\u003c/code\u003e (offline),\n\u003ccode\u003eoperatorcounting([pho_abstraction_constraints([cartesian()], saturated={false,true})])\u003c/code\u003e (online)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can also compute the maximum over abstraction heuristics:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emaximize([cartesian()])\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe plugin documentation shows all options for \u003ca href=\"https://jendrikseipp.github.io/scorpion/Evaluator/#cost_partitioning_heuristics\" rel=\"nofollow\"\u003ecost partitioning\nheuristics\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-pattern-collection-generators\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-pattern-collection-generators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew pattern collection generators\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSystematic patterns with size limits:\n\u003ccode\u003esys_scp(max_pattern_size=X, max_pdb_size=Y, max_collection_size=Z, ..., saturate=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSys-SCP patterns:\n\u003ccode\u003esys_scp(...)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-landmark-heuristics\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-cost-partitioning-algorithms-for-landmark-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew cost partitioning algorithms for landmark heuristics\u003c/h3\u003e\n\u003cp\u003eExample using A* search and saturated cost partitioning over BJOLP\nlandmarks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--evaluator\n \"lmc=lmcount(lm_merged([lm_rhw(), lm_hm(m=1)]),\n admissible=true, cost_partitioning=suboptimal, greedy=true,\n reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\"\n--search\n \"astar(lmc, lazy_evaluator=lmc)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDifferent cost partitioning algorithms (all need \u003ccode\u003eadmissible=true\u003c/code\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=optimal)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003elmcount(..., cost_partitioning=canonical)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePost-hoc optimization:\n\u003ccode\u003elmcount(..., cost_partitioning=pho)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=true, scoring_function=min_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=false, scoring_function=max_heuristic)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-search-engines\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-search-engines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew search engines\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBreadth-first search (without overhead of the more general \u003ccode\u003eeager()\u003c/code\u003e search):\n\u003ccode\u003ebrfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDepth-first search:\n\u003ccode\u003edfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExhaustive search (useful for dumping the reachable state space of small input tasks):\n\u003ccode\u003edump_reachable_search_space()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIDA* search:\n\u003ccode\u003eidastar(cegar(cache_estimates=false))\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIterative width search:\n\u003ccode\u003eiw(width=2)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-souporcell\" class=\"anchor\" aria-hidden=\"true\" href=\"#souporcell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esouporcell\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/wheaton5/souporcell/blob/master/souporcell_star.png\"\u003e\u003cimg src=\"https://github.com/wheaton5/souporcell/raw/master/souporcell_star.png\" width=\"100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePreprint manuscript of this method available at \u003ca href=\"https://www.biorxiv.org/content/10.1101/699637v1\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/699637v1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003esouporcell is a method for clustering mixed-genotype scRNAseq experiments by individual.\u003c/p\u003e\n\u003cp\u003eThe inputs are just the possorted_genome_bam.bam, and barcodes.tsv as output from \u003ca href=\"https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger\" rel=\"nofollow\"\u003ecellranger\u003c/a\u003e.\nsouporcell is comprised of 6 steps with the first 3 using external tools and the final using the code provided here.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRemapping (\u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCalling candidate variants (\u003ca href=\"https://github.com/ekg/freebayes\"\u003efreebayes\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCell allele counting (\u003ca href=\"https://github.com/10XGenomics/vartrix\"\u003evartrix\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eClustering cells by genotype (souporcell.py)\u003c/li\u003e\n\u003cli\u003eCalling doublets (troublet)\u003c/li\u003e\n\u003cli\u003eCalling cluster genotypes and inferring amount of ambient RNA (consensus.py)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-easy-installation-linux-recommended\" class=\"anchor\" aria-hidden=\"true\" href=\"#easy-installation-linux-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEasy Installation (Linux) (recommended)\u003c/h2\u003e\n\u003cp\u003eDownload singularity image (1.3gb) (singularity is similar to docker but safe for clusters)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://wheaton5/souporcell\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you are running on a scientific cluster, they will likely have singularity, contact your sysadmin for more details.\nIf you are running on your own linux box you may need to install \u003ca href=\"https://www.sylabs.io/guides/3.2/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003erequires singularity \u0026gt;= 3.0\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewhich singularity\nsingularity --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should now be able to run souporcell_pipeline.py through the singularity container. Singularity automatically mounts the current working directory and directories downstream from where you run it, otherwise you would need to manually mount those directories. Therefor you can run it from a directory that is upstream of all of the inputs. Input files are the cellranger bam, cellranger barcodes file, and a reference fasta. The cellranger bam is located in the cellranger outs directory and is called possorted_genome_bam.bam. The barcodes file is located in the cellranger outs/filtered_gene_bc_matrices/\u0026lt;ref_name\u0026gt;/barcodes.tsv. The reference fasta should be of the same species but does not necessarily need to be the exact cellranger reference.\u003c/p\u003e\n\u003cp\u003eThe options for using souporcell are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec souporcell_latest.sif souporcell_pipeline.py -h\nusage: souporcell_pipeline.py [-h] -i BAM -b BARCODES -f FASTA -t THREADS -o\n OUT_DIR -k CLUSTERS [-p PLOIDY]\n [--min_alt MIN_ALT] [--min_ref MIN_REF]\n [--max_loci MAX_LOCI] [--restarts RESTARTS]\n [--common_variants COMMON_VARIANTS]\n [--known_genotypes KNOWN_GENOTYPES]\n [--known_genotypes_sample_names KNOWN_GENOTYPES_SAMPLE_NAMES [KNOWN_GENOTYPES_SAMPLE_NAMES ...]]\n [--skip_remap SKIP_REMAP] [--ignore IGNORE]\n\nsingle cell RNAseq mixed genotype clustering using sparse mixture model\nclustering with tensorflow.\n\noptional arguments:\n -h, --help show this help message and exit\n -i BAM, --bam BAM cellranger bam\n -b BARCODES, --barcodes BARCODES\n barcodes.tsv from cellranger\n -f FASTA, --fasta FASTA\n reference fasta file\n -t THREADS, --threads THREADS\n max threads to use\n -o OUT_DIR, --out_dir OUT_DIR\n name of directory to place souporcell files\n -k CLUSTERS, --clusters CLUSTERS\n number cluster, tbd add easy way to run on a range of\n k\n -p PLOIDY, --ploidy PLOIDY\n ploidy, must be 1 or 2, default = 2\n --min_alt MIN_ALT min alt to use locus, default = 10.\n --min_ref MIN_REF min ref to use locus, default = 10.\n --max_loci MAX_LOCI max loci per cell, affects speed, default = 2048.\n --restarts RESTARTS number of restarts in clustering, when there are \u0026gt; 12\n clusters we recommend increasing this to avoid local\n minima\n --common_variants COMMON_VARIANTS\n common variant loci or known variant loci vcf, must be\n vs same reference fasta\n --known_genotypes KNOWN_GENOTYPES\n known variants per clone in population vcf mode, must\n be .vcf right now we dont accept gzip or bcf sorry\n --known_genotypes_sample_names KNOWN_GENOTYPES_SAMPLE_NAMES [KNOWN_GENOTYPES_SAMPLE_NAMES ...]\n which samples in population vcf from known genotypes\n option represent the donors in your sample\n --skip_remap SKIP_REMAP\n don\u0027t remap with minimap2 (not recommended unless in\n conjunction with --common_variants\n --ignore IGNORE set to True to ignore data error assertions\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA typical command looks like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /path/to/souporcell_latest.sif souporcell_pipeline.py -i /path/to/possorted_genome_bam.bam -b /path/to/barcodes.tsv -f /path/to/reference.fasta -t num_threads_to_use -o output_dir_name -k num_clusters\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above command will run all six steps of the pipeline and it will require up to 24gb of ram for human (minimap2 bam index is high water mark for memory). For smaller genomes, fewer clusters, lower --max-loci will require less memory. Note that souporcell will require roughly 2x the amount of diskspace that the input bam file takes up. This dataset should take several hours to run on 8 threads mostly due to read processing, remapping, and variant calling.\u003c/p\u003e\n\u003cp\u003eIf you have a common snps file you may want to use the --common_variants option with or without the --skip_remap option. This option will skip conversion to fastq, remapping with minimap2, and reattaching barcodes, and the --common_variants will remove the freebayes step. Each which will save a significant amount of time, but --skip-remap isn\u0027t recommended without --common_variants.\u003c/p\u003e\n\u003cp\u003eCommon variant files from 1k genomes filtered to variants \u0026gt;= 2% allele frequency in the population and limited to SNPs can be found here for GRCh38\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download\u0026amp;confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate \u0027https://docs.google.com/uc?export=download\u0026amp;id=13aebUpEKrtjliyT9rYzRijtkNJVUk5F_\u0027 -O- | sed -rn \u0027s/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p\u0027)\u0026amp;id=13aebUpEKrtjliyT9rYzRijtkNJVUk5F_\" -O common_variants_grch38.vcf \u0026amp;\u0026amp; rm -rf /tmp/cookies.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor for hg19 here\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download\u0026amp;confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate \u0027https://docs.google.com/uc?export=download\u0026amp;id=1lw4T6d7uXsm9dt39ZtEwpuB2VTY3wK1y\u0027 -O- | sed -rn \u0027s/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p\u0027)\u0026amp;id=1lw4T6d7uXsm9dt39ZtEwpuB2VTY3wK1y\" -O common_variants_hg19.vcf \u0026amp;\u0026amp; rm -rf /tmp/cookies.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-practicetesting-data-set-demuxlet-paper-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#practicetesting-data-set-demuxlet-paper-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePractice/Testing data set: Demuxlet paper data\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://sra-pub-src-1.s3.amazonaws.com/SRR5398235/A.merged.bam.1 -O A.merged.bam\nwget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM2560nnn/GSM2560245/suppl/GSM2560245_barcodes.tsv.gz\ngunzip GSM2560245_barcodes.tsv.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if you don\u0027t have a human reference sitting around, grab one here\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget http://cf.10xgenomics.com/supp/cell-exp/refdata-cellranger-GRCh38-3.0.0.tar.gz\ntar -xzvf refdata-cellranger-GRCh38-3.0.0.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow you should be ready to test it out\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /path/to/souporcell_latest.sif souporcell_pipeline.py -i A.merged.bam -b GSM2560245_barcodes.tsv -f refdata-cellranger-GRCh38-3.0.0/fasta/genome.fa -t 8 -o demux_data_test -k 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should require about 20gb of ram mostly because of the minimap2 indexing step. I might soon host an index and reference for human to make this less painful.\u003c/p\u003e\n\u003cp\u003eThe important files are\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eclusters.tsv\u003c/li\u003e\n\u003cli\u003ecluster_genotypes.vcf\u003c/li\u003e\n\u003cli\u003eambient_rna.txt\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eclusters.tsv will look like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebarcode status assignment log_loss_singleton log_loss_doublet cluster0 cluster1\nAAACCTGAGATCCGAG-1 singlet 0 -152.7778890920112 -190.5463095948822 -43.95302689281067 -101.63377524087669\nAAACCTGAGCACCGTC-1 singlet 0 -78.56014177554212 -96.66255440088581 -20.949294849836267 -52.57478083591962\nAAACCTGAGTACGATA-1 singlet 0 -216.0188863327174 -281.3888392065457 -63.059016939362536 -159.5450834682198\nAAACCTGGTACATGTC-1 singlet 1 -47.189434469216565 -96.30865717225866 -62.652900832546955 -15.284168900754413\nAAACCTGTCTACTCAT-1 singlet 0 -129.30104434183454 -167.87811467946756 -41.09158213888751 -106.3201962010145\nAAACCTGTCTTGTCAT-1 singlet 0 -85.99781433701455 -110.81701038967158 -24.518165091815554 -60.05279033826837\nAAACGGGCACTGTTAG-1 singlet 0 -154.26595878718032 -191.05465308213363 -31.356408693487197 -81.61186496254497\nAAACGGGCATCATCCC-1 singlet 1 -46.33205678267174 -80.24152434540565 -50.78221280006256 -14.615983876840312\nAAACGGGGTAGGGTAC-1 singlet 0 -240.5237900569412 -302.91575436035924 -71.79370547349878 -154.08594135029728\nAAACGGGTCGGCATCG-1 singlet 0 -166.66827966974532 -226.56795157885028 -51.08790637893961 -148.04625123166286\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the cell barcode, singlet/doublet status, cluster, log_loss_singleton, log_loss_doublet, followed by log loss for each cluster.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ecluster_genotypes.vcf is a vcf with genotypes for each cluster for each variant in the input vcf from freebayes\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eambient_rna.txt just contains the ambient RNA percentage detected\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-hard-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#hard-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHard install\u003c/h2\u003e\n\u003cp\u003eInstead of using singularity you can install everything independently (not recommended, but shouldn\u0027t be too bad)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/wheaton5/souporcell.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eput souporcell directory on your PATH\nrequires samtools, bcftools, htslib, python3, freebayes, vartrix, minimap2 all on your PATH\nI suggest you use the conda env I have set up by using the following command if you have conda or miniconda\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f /path/to/souporcell/souporcell_env.yaml\nconda activate souporcell\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will also need Rust and to compile the two rust binaries\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl --proto \u0027=https\u0027 --tlsv1.2 -sSf https://sh.rustup.rs | sh\ncd /path/to/souporcell/souporcell \u0026amp;\u0026amp; cargo build --release\ncd /path/to/souporcell/troublet \u0026amp;\u0026amp; cargo build --release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eotherwise python packages tensorflow, pyvcf, pystan, pyfaidx, numpy, scipy are required, but as the versions change, I do recommend using the presetup env.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-through-the-pipeline-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-through-the-pipeline-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run through the pipeline script\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esouporcell_pipeline.py -i /path/to/possorted_genome_bam.bam -b /path/to/barcodes.tsv -f /path/to/reference.fasta -t num_threads_to_use -o output_dir_name -k num_clusters\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-things-step-by-step-not-through-the-pipeline-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-things-step-by-step-not-through-the-pipeline-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run things step by step not through the pipeline script\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-remapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-remapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Remapping\u003c/h3\u003e\n\u003cp\u003eWe discuss the need for remapping in our manuscript. We need to keep track of cell barcodes and and UMIs, so we first create a fastq with those items encoded in the readname.\nRequires python 3.0, modules pysam, argparse (pip install/conda install depending on environment)\nEasiest to first add the souporcell directory to your PATH variable with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PATH=/path/to/souporcell:$PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the renamer.py script to put some of the quality information in the read name. For human data this step will typically take several hours and the output fq file will be somewhat larger than the input bam\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython renamer.py --bam possorted_genome_bam.bam --barcodes barcodes.tsv --out fq.fq\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we must remap these reads using minimap2 (similar results have been seen with hisat2)\nRequires \u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e\nand add /path/to/minimap2 to your PATH. For human data the remapping will typically require more than 12 Gb memory and may take a few hours to run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eminimap2 -ax splice -t 8 -G50k -k 21 -w 11 --sr -A2 -B8 -O12,32 -E2,1 -r200 -p.5 -N20 -f1000,5000 -n2 -m20 -s40 -g2000 -2K50m --secondary=no \u0026lt;reference_fasta_file\u0026gt; \u0026lt;fastq_file\u0026gt; \u0026gt; minimap.sam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(note the -t 8 as the number of threads, change this as needed)\nNow we must retag the reads with their cell barcodes and UMIs\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython retag.py --sam minimap.sam --out minitagged.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we must sort and index our bam.\nRequires \u003ca href=\"http://www.htslib.org/\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esamtools sort minitagged.bam minitagged_sorted.bam\nsamtools index minitagged_sorted.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-calling-candidate-variants\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-calling-candidate-variants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Calling candidate variants\u003c/h3\u003e\n\u003cp\u003eYou may wish to break this into multiple jobs such as 1 job per chromosome and merge after but the basic command is the following.\nRequires \u003ca href=\"https://github.com/ekg/freebayes\"\u003efreebayes\u003c/a\u003e and add /path/to/freebayes/bin to your PATH\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efreebayes -f \u0026lt;reference_fasta\u0026gt; -iXu -C 2 -q 20 -n 3 -E 1 -m 30 --min-coverage 6 --max-coverage 100000 minitagged_sorted.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-cell-allele-counting\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-cell-allele-counting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Cell allele counting\u003c/h3\u003e\n\u003cp\u003eRequires \u003ca href=\"https://github.com/10XGenomics/vartrix\"\u003evartrix\u003c/a\u003e\nand add /path/to/vartrix to your PATH\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evartrix --umi --mapq 30 -b \u0026lt;bam file\u0026gt; -c \u0026lt;barcode tsv\u0026gt; --scoring-method coverage --threads 8 --ref-matrix ref.mtx --out-matrix alt.mtx -v \u0026lt;freebayes vcf\u0026gt; --fasta \u0026lt;fasta file used for remapping\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003enote the --threads argument and use an appropriate number of threads for your system.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-clustering-cells-by-genotype\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-clustering-cells-by-genotype\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Clustering cells by genotype\u003c/h3\u003e\n\u003cp\u003eRust required. To install rust:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://sh.rustup.rs -sSf | sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand to build souporcell clustering\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/souporcell/souporcell\ncargo build --release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd add /path/to/souporcell/souporcell/target/release to your path\nusage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esouporcell -h\nsouporcell 2.4\nHaynes Heaton \u0026lt;whheaton@gmail.com\u0026gt;\nclustering scRNAseq cells by genotype\n\nUSAGE:\n souporcell [OPTIONS] --alt_matrix \u0026lt;alt_matrix\u0026gt; --barcodes \u0026lt;barcodes\u0026gt; --num_clusters \u0026lt;num_clusters\u0026gt; --ref_matrix \u0026lt;ref_matrix\u0026gt;\n\nFLAGS:\n -h, --help Prints help information\n -V, --version Prints version information\n\nOPTIONS:\n -a, --alt_matrix \u0026lt;alt_matrix\u0026gt; alt matrix from vartrix\n -b, --barcodes \u0026lt;barcodes\u0026gt; cell barcodes\n --initialization_strategy \u0026lt;initialization_strategy\u0026gt;\n cluster initialization strategy, defaults to kmeans++, valid values are kmeans++, random_uniform,\n middle_variance, random_cell_assignment\n --known_cell_assignments \u0026lt;known_cell_assignments\u0026gt;\n tsv with barcodes and their known assignments\n\n -g, --known_genotypes \u0026lt;known_genotypes\u0026gt;\n NOT YET IMPLEMENTED population vcf/bcf of known genotypes if available.\n \n --known_genotypes_sample_names \u0026lt;known_genotypes_sample_names\u0026gt;...\n NOT YET IMPLEMENTED sample names, must be samples from the known_genotypes vcf\n\n --min_alt \u0026lt;min_alt\u0026gt;\n minimum number of cells containing the alt allele for the variant to be used for clustering\n\n --min_alt_umis \u0026lt;min_alt_umis\u0026gt; min alt umis to use locus for clustering\n --min_ref \u0026lt;min_ref\u0026gt;\n minimum number of cells containing the ref allele for the variant to be used for clustering\n\n --min_ref_umis \u0026lt;min_ref_umis\u0026gt; min ref umis to use locus for clustering\n -k, --num_clusters \u0026lt;num_clusters\u0026gt; number of clusters\n -r, --ref_matrix \u0026lt;ref_matrix\u0026gt; ref matrix from vartrix\n -r, --restarts \u0026lt;restarts\u0026gt; number of random seedings\n --seed \u0026lt;seed\u0026gt; optional random seed\n -t, --threads \u0026lt;threads\u0026gt; number of threads to use\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo generally something along the lines of\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esouporcell -a alt.mtx -r ref.mtx -b barcodes.tsv -k \u0026lt;num_clusters\u0026gt; -t 8 \u0026gt; clusters_tmp.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(note clusters_tmp.tsv output as the doublet caller outputs the final clusters file)\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-5-calling-doublets\" class=\"anchor\" aria-hidden=\"true\" href=\"#5-calling-doublets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. Calling doublets\u003c/h3\u003e\n\u003cp\u003eRust required.\nBuild troublet:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/souporcell/troublet\ncargo build --release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd add /path/to/souporcell/troublet/target/release to your path\nThe usage is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etroublet -h\ntroublet 2.4\nHaynes Heaton \u0026lt;whheaton@gmail.com\u0026gt;\nIntergenotypic doublet detection given cluster assignments and cell allele counts\n\nUSAGE:\n troublet [OPTIONS] --alts \u0026lt;alts\u0026gt; --clusters \u0026lt;clusters\u0026gt;\n\nFLAGS:\n -h, --help Prints help information\n -V, --version Prints version information\n\nOPTIONS:\n -a, --alts \u0026lt;alts\u0026gt; alt allele counts per cell in sparse matrix format out of vartrix\n -c, --clusters \u0026lt;clusters\u0026gt; cluster file output from schism\n -b, --debug \u0026lt;debug\u0026gt;... print debug info for index of cells listed\n -d, --doublet_prior \u0026lt;doublet_prior\u0026gt; prior on doublets. Defaults to 0.5\n --doublet_threshold \u0026lt;doublet_threshold\u0026gt; doublet posterior threshold, defaults to 0.90\n -r, --refs \u0026lt;refs\u0026gt; ref allele counts per cell in sparse matrix format out of vartrix\n --singlet_threshold \u0026lt;singlet_threshold\u0026gt; singlet posterior threshold, defaults to 0.90\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo generally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etroublet -a alt.mtx -r ref.mtx --clusters clusters_tmp.tsv \u0026gt; clusters.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-6-genotype-and-ambient-rna-coinference\" class=\"anchor\" aria-hidden=\"true\" href=\"#6-genotype-and-ambient-rna-coinference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6. Genotype and ambient RNA coinference\u003c/h3\u003e\n\u003cp\u003ePython3 required with modules pystan, pyvcf, pickle, math, scipy, gzip (pip install should work for each)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econsensus.py -h\nusage: consensus.py [-h] -c CLUSTERS -a ALT_MATRIX -r REF_MATRIX [-p PLOIDY]\n --soup_out SOUP_OUT --vcf_out VCF_OUT --output_dir\n OUTPUT_DIR -v VCF\n\nconsensus genotype calling and ambient RNA estimation\n\noptional arguments:\n -h, --help show this help message and exit\n -c CLUSTERS, --clusters CLUSTERS\n tsv cluster file from the troublet output\n -a ALT_MATRIX, --alt_matrix ALT_MATRIX\n alt matrix file\n -r REF_MATRIX, --ref_matrix REF_MATRIX\n ref matrix file\n -p PLOIDY, --ploidy PLOIDY\n ploidy, must be 1 or 2, defaults to 2\n --soup_out SOUP_OUT soup output\n --vcf_out VCF_OUT vcf output\n --output_dir OUTPUT_DIR\n output directory\n -v VCF, --vcf VCF vcf file from which alt and ref matrix were created\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo generally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econsensus.py -c clusters.tsv -a alt.mtx -r ref.mtx --soup_out soup.txt -v \u0026lt;freebayes vcf\u0026gt; --vcf_out cluster_genotypes.vcf --output_dir .\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1688990761.0 + "updated_at": 1674048314.0 }, { "data_format": 2, - "description": null, + "description": "HTCondor execution point setup and configuration for using HTCondor file transfer to synchronize a directory between two hosts", "filenames": [ - "Singularity", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/latest/Singularity", - "misc/releases/22.12/Singularity.22.12", - "misc/releases/21.12/Singularity.21.12" + "Singularity.def" ], - "full_name": "ipc2023-classical/planner8", + "full_name": "htcondor/htcondor-file-transfer", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-scorpion\" class=\"anchor\" aria-hidden=\"true\" href=\"#scorpion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScorpion\u003c/h1\u003e\n\u003cp\u003eScorpion is an optimal classical planner that uses saturated cost\npartitioning to combine multiple abstraction heuristics. It also contains\nimplementations of many other cost partitioning algorithms over\nabstraction and landmark heuristics. Scorpion is based on the \u003ca href=\"https://github.com/aibasel/downward\"\u003eFast\nDownward planning system\u003c/a\u003e (version 22.06),\nwhich is described below. We regularly port the latest changes from Fast Downward\nto Scorpion and also try to port Scorpion features back to Fast Downward.\u003c/p\u003e\n\u003cp\u003ePlease use the following reference when citing Scorpion:\nJendrik Seipp, Thomas Keller and Malte Helmert.\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003eSaturated Cost Partitioning for Optimal Classical Planning\u003c/a\u003e.\nJournal of Artificial Intelligence Research 67, pp. 129-167. 2020.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h2\u003e\n\u003cp\u003eAfter installing the requirements (see below), compile the planner with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand see the available options with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py --help # driver\n./fast-downward.py --search -- --help # search component\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more details (including build instructions for Windows), see the\ndocumentation about\n\u003ca href=\"https://www.fast-downward.org/ObtainingAndRunningFastDownward\" rel=\"nofollow\"\u003ecompiling\u003c/a\u003e\nand \u003ca href=\"https://www.fast-downward.org/PlannerUsage\" rel=\"nofollow\"\u003erunning\u003c/a\u003e the planner. The\n\u003ca href=\"https://jendrikseipp.github.io/scorpion\" rel=\"nofollow\"\u003eplugin documentation\u003c/a\u003e shows\nwhich plugins are available (heuristics, search algorithms, etc.) and how\nto use them.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-recommended-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#recommended-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecommended configuration\u003c/h3\u003e\n\u003cp\u003eWe recommend using the following configuration:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py \\\n --transform-task preprocess-h2 \\\n ../benchmarks/gripper/prob01.pddl \\\n --search \"astar(scp_online([\n projections(sys_scp(max_time=100, max_time_per_restart=10)),\n cartesian()],\n saturator=perimstar, max_time=1000, interval=10K, orders=greedy_orders()),\n pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003epreprocess-h2\u003c/code\u003e call prunes irrelevant operators in a preprocessing\nstep. The search configuration uses \u003ca href=\"https://ojs.aaai.org/index.php/SOCS/article/view/18535\" rel=\"nofollow\"\u003epartial order\nreduction\u003c/a\u003e and\nmaximizes over\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003ediverse\u003c/a\u003e,\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/3503\" rel=\"nofollow\"\u003esubset-saturated\u003c/a\u003e\ncost partitioning heuristics computed\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/15976/\" rel=\"nofollow\"\u003eonline\u003c/a\u003e during\nthe search. The underlying abstractions are \u003ca href=\"https://www.ijcai.org/proceedings/2019/780\" rel=\"nofollow\"\u003eSys-SCP pattern\ndatabases\u003c/a\u003e and \u003ca href=\"https://jair.org/index.php/jair/article/view/11217\" rel=\"nofollow\"\u003eCartesian\nabstractions\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e(In \u003ca href=\"https://lab.readthedocs.io/\" rel=\"nofollow\"\u003eDownward Lab\u003c/a\u003e you can use\n\u003ccode\u003eadd_algorithm(name=\"scorpion\", repo=\"path/to/repo\", rev=\"scorpion\", component_options=[], driver_options=[\"--transform-task\", \"preprocess-h2\", \"--alias\", \"scorpion\"]\u003c/code\u003e to run the recommended Scorpion configuration.)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h4\u003e\n\u003cp\u003eTo simplify the installation process, we provide an executable\n\u003ca href=\"https://github.com/hpcng/singularity\"\u003eSingularity\u003c/a\u003e container for\nScorpion. It accepts the same arguments as the \u003ccode\u003efast-downward.py\u003c/code\u003e script\n(see above).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Download the container (tested with Singularity 3.5),\nsingularity pull scorpion.sif library://jendrikseipp/default/scorpion:latest\n\n# or build the container yourself.\nsudo singularity build scorpion.sif Singularity\n\n# Then run recommended configuration (available via \"scorpion\" alias).\n./scorpion.sif --transform-task preprocess-h2 --alias scorpion PROBLEM_FILE\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ipc-2018-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#ipc-2018-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIPC 2018 version\u003c/h3\u003e\n\u003cp\u003eIf you prefer to run the Scorpion version from IPC 2018 (which uses an\nolder Fast Downward version and different abstractions), we recommend\nusing the \u003ca href=\"https://bitbucket.org/ipc2018-classical/team44/src/ipc-2018-seq-opt/\" rel=\"nofollow\"\u003eScorpion IPC\nrepo\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-differences-between-scorpion-and-fast-downward\" class=\"anchor\" aria-hidden=\"true\" href=\"#differences-between-scorpion-and-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDifferences between Scorpion and Fast Downward\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eScorpion comes with the\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/13708\" rel=\"nofollow\"\u003eh\u00b2-preprocessor\u003c/a\u003e\nby Vidal Alc\u00e1zar and \u00c1lvaro Torralba that prunes irrelevant operators.\nPass \u003ccode\u003e--transform-task preprocess-h2\u003c/code\u003e to use it.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003e--transform-task\u003c/code\u003e command allows you to run arbitrary preprocessing\ncommands that transform the SAS+ output from the translator before\npassing it to the search.\u003c/li\u003e\n\u003cli\u003eScorpion uses a\n\u003ca href=\"https://github.com/greg7mdp/parallel-hashmap\"\u003ephmap::flat_hash_set\u003c/a\u003e to check\nfor duplicate states, which often drastically reduces the peak memory usage,\ncompared to Fast Downward\u0027s \u003ccode\u003eIntHashSet\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf \u003ca href=\"https://ccache.dev/\" rel=\"nofollow\"\u003eccache\u003c/a\u003e is installed (recommended), Scorpion\nuses it to cache compilation files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-translator-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-translator-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew translator options\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUse \u003ccode\u003e--dump-predicates\u003c/code\u003e and \u003ccode\u003e--dump-static-atoms\u003c/code\u003e to write files with\ninformation that\u0027s useful for learning domain control knowledge.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-plugin-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-plugin-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew plugin options\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecegar(..., search_strategy=incremental)\u003c/code\u003e: use \u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/6667\" rel=\"nofollow\"\u003eincremental search for\nCartesian abstraction\nrefinement\u003c/a\u003e\n(default).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ehillclimbing(..., max_generated_patterns=200)\u003c/code\u003e: limit the number of\npatterns generated by hill climbing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esystematic(..., pattern_type=interesting_general)\u003c/code\u003e: compute interesting\npatterns for general cost partitioning.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-abstraction-heuristics\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-cost-partitioning-algorithms-for-abstraction-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew cost partitioning algorithms for abstraction heuristics\u003c/h3\u003e\n\u003cp\u003eWe use Cartesian abstractions in the example configurations below\n(\u003ccode\u003e[cartesian()]\u003c/code\u003e). You can also use pattern database heuristics, e.g.,\n\u003ccode\u003e[projections(systematic(2))]\u003c/code\u003e, or mix abstractions, e.g.,\n\u003ccode\u003e[projections(systematic(3)), cartesian()]\u003c/code\u003e. Some of the algorithms below\nare also part of vanilla Fast Downward, but are only implemented for PDB\nheuristics.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning:\n\u003ccode\u003eocp([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003ecanonical_heuristic([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], opportunistic=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], ..., opportunistic=true)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003egzocp([cartesian()], ...)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003escp([cartesian()], ...)\u003c/code\u003e (offline), \u003ccode\u003escp_online([cartesian()], ...)\u003c/code\u003e (online)\u003c/li\u003e\n\u003cli\u003e(Saturated) post-hoc optimization:\n\u003ccode\u003epho([cartesian()], ..., saturated={false,true})\u003c/code\u003e (offline),\n\u003ccode\u003eoperatorcounting([pho_abstraction_constraints([cartesian()], saturated={false,true})])\u003c/code\u003e (online)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can also compute the maximum over abstraction heuristics:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emaximize([cartesian()])\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe plugin documentation shows all options for \u003ca href=\"https://jendrikseipp.github.io/scorpion/Evaluator/#cost_partitioning_heuristics\" rel=\"nofollow\"\u003ecost partitioning\nheuristics\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-pattern-collection-generators\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-pattern-collection-generators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew pattern collection generators\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSystematic patterns with size limits:\n\u003ccode\u003esys_scp(max_pattern_size=X, max_pdb_size=Y, max_collection_size=Z, ..., saturate=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSys-SCP patterns:\n\u003ccode\u003esys_scp(...)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-landmark-heuristics\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-cost-partitioning-algorithms-for-landmark-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew cost partitioning algorithms for landmark heuristics\u003c/h3\u003e\n\u003cp\u003eExample using A* search and saturated cost partitioning over BJOLP\nlandmarks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--evaluator\n \"lmc=lmcount(lm_merged([lm_rhw(), lm_hm(m=1)]),\n admissible=true, cost_partitioning=suboptimal, greedy=true,\n reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\"\n--search\n \"astar(lmc, lazy_evaluator=lmc)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDifferent cost partitioning algorithms (all need \u003ccode\u003eadmissible=true\u003c/code\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=optimal)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003elmcount(..., cost_partitioning=canonical)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePost-hoc optimization:\n\u003ccode\u003elmcount(..., cost_partitioning=pho)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=true, scoring_function=min_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=false, scoring_function=max_heuristic)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-search-engines\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-search-engines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew search engines\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBreadth-first search (without overhead of the more general \u003ccode\u003eeager()\u003c/code\u003e search):\n\u003ccode\u003ebrfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDepth-first search:\n\u003ccode\u003edfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExhaustive search (useful for dumping the reachable state space of small input tasks):\n\u003ccode\u003edump_reachable_search_space()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIDA* search:\n\u003ccode\u003eidastar(cegar(cache_estimates=false))\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIterative width search:\n\u003ccode\u003eiw(width=2)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1688990870.0 + "updated_at": 1674233118.0 }, { "data_format": 2, - "description": "Use Docker as a shell to store a Singularity image", + "description": "Container recipes for OpenVINO", "filenames": [ - "Singularity" + "ubuntu18/2019/singularity/Singularity.2019_R3_c_omp-py36-gcc75-ubuntu18", + "ubuntu18/2019/singularity/Singularity.2019_R3.1_cg_omp-py36-gcc75-ubuntu18", + "ubuntu18/2019/singularity/Singularity.2019_pre-release-1_c_omp-py36-gcc75-ubuntu18", + "ubuntu18/2019/singularity/Singularity.2019_R3.1_c_omp-py36-gcc75-ubuntu18", + "ubuntu18/2019/singularity/Singularity.2019_R3.1_c_tbb-py36-gcc75-ubuntu18", + "ubuntu18/2019/singularity/Singularity.2019_R3.1_cg_tbb-py36-gcc75-ubuntu18" ], - "full_name": "singularityhub/singularity-in-docker", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-in-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-in-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity in Docker\u003c/h1\u003e\n\u003cp\u003eThis is a proof of concept for packaging a Singularity container in a Docker\nimage, only with purpose to store it in a Docker Registry for pulling later.\nOf course you\u0027d need Docker or a tool like \u003ca href=\"https://github.com/deislabs/oras\"\u003eoras\u003c/a\u003e to handle the pull.\nUse at your own risk! I don\u0027t know if there are rules against this sort of thing.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/poopies.png\"\u003e\u003cimg src=\"img/poopies.png\" alt=\"img/poopies.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eBuild the Singularity container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build busybox.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen test it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./busybox.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen build the docker container, giving the Singularity container as a build arg.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build -t vanessa/singularity-in-docker --build-arg container=busybox.sif \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMake sure it\u0027s there:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run -it vanessa/singularity-in-docker \n/ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls /\u003c/span\u003e\nbin dev home root tmp var\nbusybox.sif etc proc sys usr\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen push to wherever you like! When it\u0027s time to pull and use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker pull vanessa/singularity-in-docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can run with a different entrypoint, detached, to keep it running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run -d --rm --name squiggles vanessa/singularity-in-docker tail -f /dev/null\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen copy the Singularity container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker cp squiggles:/busybox.sif exported-busybox.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTada!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./exported-busybox.sif \nRun run run run runnnnn\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd stop your squiggles.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker stop squiggles\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "fenz-org/OpenVino", + "latest_release": "0.0.4", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-openvino\" class=\"anchor\" aria-hidden=\"true\" href=\"#openvino\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenVino\u003c/h1\u003e\n\u003cp\u003eContainer recipes for OpenVINO\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1567362681.0 + "updated_at": 1675191249.0 }, { "data_format": 2, - "description": "This repository is an AI Bootcamp material that consist of a workflow for NLP", + "description": "Bin for holding recipe files", "filenames": [ - "Singularity_riva_speech", - "Singularity_tao" + "bullseye_minio/Singularity", + "apache_gunicorn_flask/Singularity", + "nginx_gunicorn_flask/Singularity" ], - "full_name": "openhackathons-org/End-to-End-NLP", + "full_name": "hamrhein/containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-end-to-end-nlp-bootcamp\" class=\"anchor\" aria-hidden=\"true\" href=\"#end-to-end-nlp-bootcamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnd-to-End NLP Bootcamp\u003c/h1\u003e\n\u003cp\u003eThis repository contains the material for the \u003cstrong\u003eEnd-To-End NLP\u003c/strong\u003e bootcamp, the goal of which is to build a complete end-to-end NLP pipeline for Question Answering application. This bootcamp will introduce participants to multiple NVIDIA\u00ae SDKs, most notably NVIDIA TAO Toolkit, NVIDIA TensorRT\u2122, and NVIDIA RIVA. Participants will also have hands-on experience in data preprocessing, model training, optimization, and deployment at scale.\u003c/p\u003e\n\u003cp\u003eThe content is structured in 3 modules, plus an introductory notebook and two challenge notebooks:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOverview of \u003cstrong\u003eEnd-To-End NLP\u003c/strong\u003e bootcamp\u003c/li\u003e\n\u003cli\u003eLab 1: Data preprocessing\u003c/li\u003e\n\u003cli\u003eLab 2: Transfer learning with NVIDIA TAO (QA training)\u003c/li\u003e\n\u003cli\u003eLab 3: Custom model deployment on RIVA\u003c/li\u003e\n\u003cli\u003eChallenge 1: building SQuAD dataset\u003c/li\u003e\n\u003cli\u003eChallenge 2: deploying custom dataset on RIVA\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tutorial-duration\" class=\"anchor\" aria-hidden=\"true\" href=\"#tutorial-duration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTutorial duration\u003c/h2\u003e\n\u003cp\u003eThe total bootcamp material would take approximately 8 hours. It is recommended to divide the teaching of the material into two days, covering Lab 1 in one session and Lab 2 \u0026amp; 3 in the next session.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning using Singularity\u003c/h2\u003e\n\u003cp\u003eUpdate coming soon\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-using-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-using-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning using Docker\u003c/h2\u003e\n\u003cp\u003eRun the material via a python virtual environment and Docker containers. Root privileges are required using \u003ccode\u003esudo\u003c/code\u003e. If you don\u0027t have root privileges on your local system, please follow the above instructions on how to run the lab using Singularity.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installing-the-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the prerequisites\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003edocker-ce\u003c/code\u003e by following the \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003eofficial instructions\u003c/a\u003e. Once you have installed docker-ce, follow the \u003ca href=\"https://docs.docker.com/engine/install/linux-postinstall/\" rel=\"nofollow\"\u003epost-installation steps\u003c/a\u003e to ensure that docker can be run without \u003ccode\u003esudo\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003envidia-container-toolkit\u003c/code\u003e by following the \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\" rel=\"nofollow\"\u003einstall-guide\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet an NGC account and API key:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGo to the \u003ca href=\"https://ngc.nvidia.com/\" rel=\"nofollow\"\u003eNGC\u003c/a\u003e website and click on \u003ccode\u003eRegister for NGC\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eClick on the \u003ccode\u003eContinue\u003c/code\u003e button where \u003ccode\u003eNVIDIA Account (Use existing or create a new NVIDIA account)\u003c/code\u003e is written.\u003c/li\u003e\n\u003cli\u003eFill in the required information and register, then proceed to log in with your new account credentials.\u003c/li\u003e\n\u003cli\u003eIn the top right corner, click on your username and select \u003ccode\u003eSetup\u003c/code\u003e in the dropdown menu.\u003c/li\u003e\n\u003cli\u003eProceed and click on the \u003ccode\u003eGet API Key\u003c/code\u003e button.\u003c/li\u003e\n\u003cli\u003eNext, you will find a \u003ccode\u003eGenerate API Key\u003c/code\u003e button in the upper right corner. After clicking on this button, a dialog box should appear and you have to click on the \u003ccode\u003eConfirm\u003c/code\u003e button.\u003c/li\u003e\n\u003cli\u003eFinally, copy the generated API key and username and save them somewhere on your local system.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall NGC CLI\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLog in with your account credentials at \u003ca href=\"https://ngc.nvidia.com/\" rel=\"nofollow\"\u003eNGC\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eIn the top right corner, click on your username and select \u003ccode\u003eSetup\u003c/code\u003e in the dropdown menu.\u003c/li\u003e\n\u003cli\u003eProceed and click on the \u003ccode\u003eDownloads\u003c/code\u003e button in the CLI panel.\u003c/li\u003e\n\u003cli\u003eSelect \u003ccode\u003eAMD64 Linux\u003c/code\u003e and follow the instructions.\u003c/li\u003e\n\u003cli\u003eOpen the terminal on your local system and log in to the NGC docker registry (\u003ccode\u003envcr.io\u003c/code\u003e) using the command \u003ccode\u003edocker login nvcr.io\u003c/code\u003e and enter \u003ccode\u003e$oauthtoken\u003c/code\u003e as Username and your \u003ccode\u003eAPI Key\u003c/code\u003e as Password.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-install-tao-toolkit-and-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-tao-toolkit-and-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall TAO Toolkit and dependencies\u003c/h3\u003e\n\u003cp\u003eTAO Toolkit is a Python pip package that is hosted on the NVIDIA PyIndex. The package uses the docker restAPI under the hood to interact with the NGC Docker registry to pull and instantiate the underlying docker containers. You must have an NGC account and an API key associated with your account.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-virtualvenwrapper-approach\" class=\"anchor\" aria-hidden=\"true\" href=\"#virtualvenwrapper-approach\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVirtualvenwrapper approach\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003envidia-container-toolkit \u0026gt; 1.3.0-1\u003c/code\u003e from \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun docker without root\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esudo groupadd docker\u003c/li\u003e\n\u003cli\u003esudo usermod -aG docker $USER\u003c/li\u003e\n\u003cli\u003enewgrp docker\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epip3 install python=3.6.9\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate virtualvenwrapper launcher\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt install python-pip python3-pip unzip\npip3 install --upgrade pip\n\npip3 install virtualenvwrapper\n\nexport VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3\nexport WORKON_HOME=/home/user/.virtualenvs\nexport PATH=/home/user/.local/bin:$PATH\nsource /home/user/.local/bin/virtualenvwrapper.sh\n\nmkvirtualenv -p /usr/bin/python3 launcher\n\nworkon launcher\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that \u003ccode\u003euser\u003c/code\u003e should be replaced with the local machine user\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003e\n\u003cp\u003eTAO and Jupyter notebook installation\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip3 install jupyterlab\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip3 install nvidia-tao\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInvoke the entrypoints using the this command \u003ccode\u003etao -h\u003c/code\u003e. You should see the following output:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eusage: tao \n {list,stop,info,augment,bpnet,classification,detectnet_v2,dssd,emotionnet,faster_rcnn,fpenet,gazenet,gesturenet,\n heartratenet,intent_slot_classification,lprnet,mask_rcnn,punctuation_and_capitalization,question_answering,\n retinanet,speech_to_text,ssd,text_classification,converter,token_classification,unet,yolo_v3,yolo_v4,yolo_v4_tiny}\n ...\n\nLauncher for TAO\n\noptional arguments:\n-h, --help show this help message and exit\n\ntasks:\n {list,stop,info,augment,bpnet,classification,detectnet_v2,dssd,emotionnet,faster_rcnn,fpenet,gazenet,gesturenet,heartratenet\n ,intent_slot_classification,lprnet,mask_rcnn,punctuation_and_capitalization,question_answering,retinanet,speech_to_text,\n ssd,text_classification,converter,token_classification,unet,yolo_v3,yolo_v4,yolo_v4_tiny}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more info, visit the \u003ca href=\"https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html\" rel=\"nofollow\"\u003eTAO Toolkit documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-other-dependencies-to-run-the-lab\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-other-dependencies-to-run-the-lab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall other dependencies to run the lab:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e pip3 install spacy-langdetect\n pip3 install -U spacy[cuda114]\n python3 -m spacy download en_core_web_sm \n pip3 install pyspellchecker\n pip3 install openpyxl\n pip3 install -U transformers==3.0.0\n pip3 install nltk\n #python3 -m nltk.downloader punkt\n #pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116\n #pip3 install Cython \n pip3 install jupyterlab\n pip3 install ipywidgets\n pip3 install gdown\n pip3 install soundfile\n \n #nemo installation\n pip install Cython\n pip install nemo_toolkit[all]\n pip3 install pynini\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-all-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-all-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun All Notebooks\u003c/h3\u003e\n\u003cp\u003eActivate virtualvenwrapper launcher \u003ccode\u003eworkon launcher\u003c/code\u003e (you may be required to export path as executed in 4. above)\u003c/p\u003e\n\u003cp\u003eYou are to run the ALL notebooks in the \u003ccode\u003elauncher\u003c/code\u003e environment.\u003c/p\u003e\n\u003cp\u003eLaunch the jupyter lab with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ejupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=~/End-to-End-NLP/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRemember to set the \u003ccode\u003e--notebook-dir\u003c/code\u003e to the location where the \u003ccode\u003eproject folder\u003c/code\u003e where this material is located.\u003c/p\u003e\n\u003cp\u003eThen, open jupyter lab in the browser at \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e and start working on the lab by clicking on the \u003ccode\u003eStart_here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cp\u003eCongratulations, you\u0027ve successfully built and deployed an end-to-end computer vision pipeline!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown issues\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tao\" class=\"anchor\" aria-hidden=\"true\" href=\"#tao\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTAO\u003c/h3\u003e\n\u003cp\u003ea. When installing the TAO Toolkit Launcher to your host machine\u2019s native python3 as opposed to the recommended route of using a virtual environment, you may get an error saying that \u003ccode\u003etao binary wasn\u2019t found\u003c/code\u003e. This is because the path to your \u003ccode\u003etao\u003c/code\u003e binary installed by pip wasn\u2019t added to the \u003ccode\u003ePATH\u003c/code\u003e environment variable in your local machine. In this case, please run the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eexport PATH=$PATH:~/.local/bin\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eb. When training, you can see an error message stating:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eResource exhausted: OOM when allocating tensor...\nERROR: Ran out of GPU memory, please lower the batch size, use a smaller input resolution, use a smaller backbone, or enable model parallelism for supported TLT architectures (see TLT documentation).\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs the error says, you ran out of GPU memory. Try playing with batch size to reduce the memory footprint.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ngc\" class=\"anchor\" aria-hidden=\"true\" href=\"#ngc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNGC\u003c/h3\u003e\n\u003cp\u003eYou can see an error message stating:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003engc: command not found ...\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can resolve this by setting the path to ngc within the conda launcher environment as:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eecho \"export PATH=\\\"\\$PATH:$(pwd)/ngc-cli\\\"\" \u0026gt;\u0026gt; ~/.bash_profile \u0026amp;\u0026amp; source ~/.bash_profile\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-riva-speech-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#riva-speech-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRiva Speech Server\u003c/h3\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n\u003cp\u003eBin for holding recipe files\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1683562054.0 + "updated_at": 1674604619.0 }, { "data_format": 2, - "description": null, + "description": "stable-diffusion-webui(AUTOMATIC1111\u7248) \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fsingularity image\u3092\u4f5c\u6210\u30fb\u5b9f\u884c\u3059\u308b\u305f\u3081\u306esingularity\u5b9a\u7fa9\u30d5\u30a1\u30a4\u30eb\u3068\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8", "filenames": [ - "Singularity" + "Singularity.sdwebui", + "Singularity.repositories", + "Singularity.base" ], - "full_name": "UMMS-Biocore/trinitiySing", + "full_name": "oct1971/singularity_stable_diffusion_webui", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cp\u003eExecutables\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_stable_diffusion_webui\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_stable_diffusion_webui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_stable_diffusion_webui\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui\"\u003estable-diffusion-webui(AUTOMATIC1111\u7248)\u003c/a\u003e \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fsingularity image\u3092\u4f5c\u6210\u30fb\u5b9f\u884c\u3059\u308b\u305f\u3081\u306esingularity\u5b9a\u7fa9\u30d5\u30a1\u30a4\u30eb\u3068\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u203binstall.py\u3092\u542b\u3080extension\u306fWebUI\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u307e\u305b\u3093\u3002\u305d\u306e\u3088\u3046\u306aextension\u306b\u3064\u3044\u3066\u306f\u3001Singularity.sdwebui\u30d5\u30a1\u30a4\u30eb\u306binstall.py\u4e2d\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u3044\u308b\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u30b3\u30de\u30f3\u30c9\u3092\u8ffd\u52a0\u3057\u3066\u30a4\u30e1\u30fc\u30b8\u3092\u518d\u751f\u6210\u3057\u3001extensions\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306bextension\u306e\u30ea\u30dd\u30b8\u30c8\u30ea\u3092git clone\u3059\u308b\u3053\u3068\u3067\u4f7f\u7528\u306f\u53ef\u80fd\u3067\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-wsl2-ubuntu2004-singularity-39\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\" class=\"anchor\" aria-hidden=\"true\" href=\"#wsl2-ubuntu2004-singularity-39\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWSL2, ubuntu20.04, singularity 3.9\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30da\u30fc\u30b8\u306e\u624b\u9806\u306b\u5f93\u3063\u3066Windows10/11\u306bWSL2, ubuntu20.04, NVIDIA driver, libnvidia-container-tools, singularity3.9\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003eLinux\u3067\u4f7f\u7528\u3059\u308b\u5834\u5408\u306fNVIDIA driver, singularity3.9\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3092\u884c\u3063\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/2022/03/wsl2-gpu/\" rel=\"nofollow\"\u003ehttps://sylabs.io/2022/03/wsl2-gpu/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u307e\u305f\u3001\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u306e\u5b9f\u884c\u7528\u306bMicrosoft Store\u304b\u3089Windows Termnal\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304a\u304f\u3053\u3068\u3092\u304a\u52e7\u3081\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u306fWSL2\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6642\u306b\u540c\u6642\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u305fUbuntu on Windows\u3084Windows Terminal\u3067\u958b\u3044\u305fubuntu\u306e\u30b7\u30a7\u30eb\u3067\u5b9f\u884c\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306eclone\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306eclone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306eclone\u003c/h2\u003e\n\u003cp\u003eclone\u3059\u308b\u5834\u6240\u306f\u3069\u3053\u3067\u3082\u69cb\u3044\u307e\u305b\u3093\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/oct1971/singularity_stable_diffusion_webui\n$ cd singularity_stable_diffusion_webui\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-image\u306ebuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-image\u306ebuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity image\u306ebuild\u003c/h2\u003e\n\u003cp\u003esingularity image\u306ebuild\u306f\u7ba1\u7406\u8005\u6a29\u9650\u304c\u5fc5\u8981\u306a\u305f\u3081\u3001sudo\u3092\u4ed8\u3051\u3066\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u203bcudnn\u5c0e\u5165\u306e\u305f\u3081\u3001\u30d9\u30fc\u30b9\u30a4\u30e1\u30fc\u30b8\u3092 nvidia/cuda:11.3.0-cudnn8-devel-ubuntu20.04 \u306b\u5909\u66f4\u3057\u307e\u3057\u305f\u3002\u6539\u3081\u3066 base image\u306ebuild \u304b\u3089\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\uff082022-10-12\uff09\u3002\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-base-image\u306ebuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#base-image\u306ebuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebase image\u306ebuild\u003c/h3\u003e\n\u003cp\u003eubuntu 20.04\u306bpython3.10, cuda11.3, cudnn8 \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo bash build_base_image.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-repositories-image\u306ebuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#repositories-image\u306ebuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erepositories image\u306ebuild\u003c/h3\u003e\n\u003cp\u003ebase image\u306bstable-diffusion-webui\u3067\u4f7f\u7528\u3059\u308b\u30ea\u30dd\u30b8\u30c8\u30ea\u7b49\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo bash build_repositories_image.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sdwebui-image\u306ebuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#sdwebui-image\u306ebuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esdwebui image\u306ebuild\u003c/h3\u003e\n\u003cp\u003estable-diffusion-webui(AUTOMATIC1111\u7248) \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo bash build_sdwebui_image.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u66f4\u65b0\u983b\u5ea6\u306e\u9ad8\u3044stable-diffusion-webui\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3092\u5206\u96e2\u3057\u3066\u3044\u307e\u3059\u306e\u3067\u3001\u66f4\u65b0\u304c\u3042\u3063\u305f\u5834\u5408\u306f\u901a\u5e38sdwebui image\u306ebuild\u306e\u307f\u518d\u5b9f\u884c\u3057\u307e\u3059\u3002\nstable-diffusion-webui\u304c\u5185\u90e8\u3067\u4f7f\u7528\u3057\u3066\u3044\u308b\u30ea\u30dd\u30b8\u30c8\u30ea\u306e\u8ffd\u52a0\u7b49\u304c\u3042\u3063\u305f\u5834\u5408\u306f\u003ca href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs#manual-installation\"\u003eManual Installation\u003c/a\u003e\u306e\u5185\u5bb9\u3092\u53c2\u8003\u306bSingularity.repositories\u3092\u4fee\u6b63\u3057\u3001repositories.sif\u3092\u518dbuild\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u521d\u671f\u8a2d\u5b9a\u306e\u5b9f\u884c\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u521d\u671f\u8a2d\u5b9a\u306e\u5b9f\u884c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u521d\u671f\u8a2d\u5b9a\u306e\u5b9f\u884c\u003c/h2\u003e\n\u003cp\u003esingularity\u3067\u5b9f\u884c\u3055\u308c\u308b\u30b3\u30f3\u30c6\u30ca\u5185\u306f\u4e00\u90e8\u3092\u9664\u3044\u3066\u66f8\u304d\u8fbc\u307f\u7981\u6b62\u3067\u3042\u308b\u305f\u3081\u3001stable-diffusion-webui\u306e\u5b9f\u884c\u5f8c\u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u308b\u30d5\u30a1\u30a4\u30eb\u306e\u4fdd\u5b58\u5834\u6240\u306f\u30b3\u30f3\u30c6\u30ca\u5b9f\u884c\u6642\u306b\u30b3\u30f3\u30c6\u30ca\u5185\u306b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u30d0\u30a4\u30f3\u30c9\u3057\u307e\u3059\u3002\u307e\u305f\u3001\u30d5\u30a1\u30a4\u30eb\u30b5\u30a4\u30ba\u306e\u5927\u304d\u3044model\u30d5\u30a1\u30a4\u30eb\u3082\u30a4\u30e1\u30fc\u30b8\u5185\u306b\u5165\u308c\u306a\u3044\u3088\u3046\u306b\u3057\u3066\u3044\u307e\u3059\u3002\u305d\u308c\u3089\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u30fb\u30d5\u30a1\u30a4\u30eb\u306e\u6e96\u5099\u3092\u884c\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u203bdata_dir\u4ee5\u5916\u306b ~/.cache \u4ee5\u4e0b\u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u308b\u30d5\u30a1\u30a4\u30eb\u3082\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u203blattent-diffusion\u304c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u30d5\u30a1\u30a4\u30eb\u306f\u3001\u30b3\u30f3\u30c6\u30ca\u3092\u8d77\u52d5\u3057\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306brepositories\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304c\u4f5c\u6210\u3055\u308c\u3001\u305d\u306e\u4e2d\u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003estable-diffusion-webui(AUTOMATIC1111\u7248) \u306b\u3066\u753b\u50cf\u51fa\u529b\u5148\u306bmodel\u306ehash\u5024\u306e\u30b5\u30d6\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u4f7f\u3048\u308b\u3088\u3046\u306b\u306a\u3063\u305f\u305f\u3081\u3001model\u3054\u3068\u306e\u51fa\u529b\u5148\u306e\u4f5c\u6210\u304c\u4e0d\u8981\u306b\u306a\u308a\u307e\u3057\u305f\u3002init_model_integration.sh \u306fmodel\u5225\u306e\u51fa\u529b\u5148\u3092\u751f\u6210\u3057\u307e\u305b\u3093\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash init_model_integration.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-model\u306e\u914d\u7f6e\" class=\"anchor\" aria-hidden=\"true\" href=\"#model\u306e\u914d\u7f6e\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emodel\u306e\u914d\u7f6e\u003c/h2\u003e\n\u003cp\u003emodel\u30d5\u30a1\u30a4\u30eb\u306f\u5225\u9014\u7528\u610f\u3057\u3001data_dir/models/Stable-diffusion/ \u306b\u30ea\u30cd\u30fc\u30e0\u305b\u305a\u306b\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://huggingface.co/CompVis/stable-diffusion-v-1-4-original\" rel=\"nofollow\"\u003e\u672c\u5bb6model\u003c/a\u003e: sd-v1-4.ckpt\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://huggingface.co/hakurei/waifu-diffusion\" rel=\"nofollow\"\u003ewaifu-diffuion model\u003c/a\u003e: wd-v1-2-full-ema.ckpt\n\u003cul\u003e\n\u003cli\u003eOriginal PyTorch Model Download Link \u3088\u308a\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://huggingface.co/naclbit/trinart_stable_diffusion_v2\" rel=\"nofollow\"\u003etrinart2 model\u003c/a\u003e: trinart2_step60000.ckpt, trinart2_step95000.ckpt, trinart2_step115000.ckpt\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-esrgan\u306emodel\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\" class=\"anchor\" aria-hidden=\"true\" href=\"#esrgan\u306emodel\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eESRGAN\u306emodel\u306e\u914d\u7f6e\uff08\u30aa\u30d7\u30b7\u30e7\u30f3\uff09\u003c/h2\u003e\n\u003cp\u003eESRGAN\u306emodel\u306f data_dir/models/ESRGAN/ \u306b\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-swinir\u306emodel\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\" class=\"anchor\" aria-hidden=\"true\" href=\"#swinir\u306emodel\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSwinIR\u306emodel\u306e\u914d\u7f6e\uff08\u30aa\u30d7\u30b7\u30e7\u30f3\uff09\u003c/h2\u003e\n\u003cp\u003eSwinIR\u306emodel\u306f data_dir/models/SwinIR/ \u306b\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307fembedding\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307fembedding\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307fembedding\u306e\u914d\u7f6e\uff08\u30aa\u30d7\u30b7\u30e7\u30f3\uff09\u003c/h2\u003e\n\u003cp\u003etextual inversion\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307fembedding\u306f data_dir/embeddings/ \u306b\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-stable-diffusion-webui\u306e\u8d77\u52d5\" class=\"anchor\" aria-hidden=\"true\" href=\"#stable-diffusion-webui\u306e\u8d77\u52d5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estable-diffusion-webui\u306e\u8d77\u52d5\u003c/h2\u003e\n\u003cp\u003e\u751f\u6210\u3055\u308c\u305f\u753b\u50cf\u306foutputs\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3001\u30bb\u30fc\u30d6\u3057\u305f\u753b\u50cf\u306flog\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u4fdd\u5b58\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u203b\u3053\u306e\u5f8c\u306estable-diffusion-webui\u306e\u521d\u671f\u8a2d\u5b9a\u3067 \u0027Save images to a subdirectory\u0027, \u0027Save grids to subdirectory\u0027 \u306b\u30c1\u30a7\u30c3\u30af\u3092\u5165\u308c\u3001 \u0027Directory name pattern\u0027 \u3092 \u0027[model_hash]\u0027 \u3068\u3059\u308b\u3068\u4f7f\u7528\u3057\u3066\u3044\u308bmodel\u3054\u3068\u306b\u30b5\u30d6\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_instance.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-stable-diffusion-webui\u306e\u521d\u671f\u8a2d\u5b9a\" class=\"anchor\" aria-hidden=\"true\" href=\"#stable-diffusion-webui\u306e\u521d\u671f\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estable-diffusion-webui\u306e\u521d\u671f\u8a2d\u5b9a\u003c/h2\u003e\n\u003cp\u003eSettings\u30bf\u30d6\u3067\u4ee5\u4e0b\u306e\u8a2d\u5b9a\u3092\u884c\u3044\u3001Apply settings\u3092\u30af\u30ea\u30c3\u30af\u3057\u3066\u8a2d\u5b9a\u3092\u4fdd\u5b58\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOutput directory for txt2img images: /outputs/txt2img-images\u003c/li\u003e\n\u003cli\u003eOutput directory for img2img images: /outputs/img2img-images\u003c/li\u003e\n\u003cli\u003eOutput directory for images from extras tab: /outputs/extras-images\u003c/li\u003e\n\u003cli\u003eOutput directory for txt2img grids: /outputs/txt2img-grids\u003c/li\u003e\n\u003cli\u003eOutput directory for img2img grids: /outputs/img2img-grids\u003c/li\u003e\n\u003cli\u003eDirectory for saving images using the Save button: /log/images\u003c/li\u003e\n\u003cli\u003eFont for image grids that have text: /usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\u003c/li\u003e\n\u003cli\u003eSave images to a subdirectory: \u30c1\u30a7\u30c3\u30af\u003c/li\u003e\n\u003cli\u003eSave grids to subdirectory: \u30c1\u30a7\u30c3\u30af\u003c/li\u003e\n\u003cli\u003eDirectory name pattern: [model_hash]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u8a2d\u5b9a\u5185\u5bb9\u306f data_dir/ui-config.json, data_dir/config.json \u306b\u66f8\u304d\u8fbc\u307e\u308c\u307e\u3059\u306e\u3067\u3001Batch count\u306e\u4e0a\u9650\u5909\u66f4\u7b49\u306f\u3053\u3061\u3089\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u203b\u5f53\u74b0\u5883\u3067\u306f\u3001\"Apply color correction to img2img results to match original colors.\" \u306b\u30c1\u30a7\u30c3\u30af\u304c\u5165\u3063\u3066\u3044\u308b\u3068SD upscale\u3067\u306e\u51fa\u529b\u6642\u306b\u9ed2\u305a\u3093\u3060\u8272\u306b\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u3002\u305d\u306e\u5834\u5408\u306f\u3053\u3061\u3089\u306e\u30c1\u30a7\u30c3\u30af\u3092\u5916\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-textual-inversion\u3067\u4f7f\u7528\u3059\u308b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3064\u3044\u3066\" class=\"anchor\" aria-hidden=\"true\" href=\"#textual-inversion\u3067\u4f7f\u7528\u3059\u308b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3064\u3044\u3066\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etextual inversion\u3067\u4f7f\u7528\u3059\u308b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3064\u3044\u3066\u003c/h2\u003e\n\u003cp\u003einit_model_integration.sh \u306e\u5b9f\u884c\u3067\u3001inputs \u3068 preprocessed_inputs \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u4f5c\u6210\u3057\u3066\u3042\u308a\u307e\u3059\u3002textual inversion \u306e\u753b\u9762\u3067\u3001Source directory \u306b inputs/, Destination directory \u306b preprocessed_inputs/, Dataset directory \u306b preprocessed_inputs/ \u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-stable-diffusion-webui\u306e\u505c\u6b62\" class=\"anchor\" aria-hidden=\"true\" href=\"#stable-diffusion-webui\u306e\u505c\u6b62\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estable-diffusion-webui\u306e\u505c\u6b62\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067\u505c\u6b62\u3055\u305b\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity instance stop sdwebui\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-windows\u306e\u30a8\u30af\u30b9\u30d7\u30ed\u30fc\u30e9\u304b\u3089stable-diffusion-webui\u3067\u751f\u6210\u3057\u305f\u753b\u50cf\u3078\u306e\u30a2\u30af\u30bb\u30b9\" class=\"anchor\" aria-hidden=\"true\" href=\"#windows\u306e\u30a8\u30af\u30b9\u30d7\u30ed\u30fc\u30e9\u304b\u3089stable-diffusion-webui\u3067\u751f\u6210\u3057\u305f\u753b\u50cf\u3078\u306e\u30a2\u30af\u30bb\u30b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows\u306e\u30a8\u30af\u30b9\u30d7\u30ed\u30fc\u30e9\u304b\u3089stable-diffusion-webui\u3067\u751f\u6210\u3057\u305f\u753b\u50cf\u3078\u306e\u30a2\u30af\u30bb\u30b9\u003c/h2\u003e\n\u003cp\u003e\u30a8\u30af\u30b9\u30d7\u30ed\u30fc\u30e9\u306e\u30a2\u30c9\u30ec\u30b9\u30d0\u30fc\u306b \u003ccode\u003e\\\\wsl\\Ubuntu\\home\\\u0026lt;\u3042\u306a\u305f\u306e\u30a2\u30ab\u30a6\u30f3\u30c8\u540d\u0026gt;\\\u0026lt;\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u3092clone\u3057\u305f\u5834\u6240\u0026gt;\u003c/code\u003e\u3092\u5165\u529b\u3057\u3066\u958b\u3044\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1519685222.0 + "updated_at": 1674777883.0 }, { "data_format": 2, - "description": "A base Singularity container for PRESTO, including dependencies and environment, converted from docker-PRESTO", + "description": null, "filenames": [ "Singularity" ], - "full_name": "federatedcloud/singularity-PRESTO", + "full_name": "ionut94/IPC-23-CPC", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-presto\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-presto\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-PRESTO\u003c/h1\u003e\n\u003cp\u003eA base Singularity container for PRESTO, including dependencies and environment, converted from docker-PRESTO\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4510\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCopyright (C) 2003-2016 Malte Helmert\nCopyright (C) 2008-2016 Gabriele Roeger\nCopyright (C) 2010-2016 Jendrik Seipp\nCopyright (C) 2010, 2011, 2013-2016 Silvan Sievers\nCopyright (C) 2012-2016 Florian Pommerening\nCopyright (C) 2016 Martin Wehrle\nCopyright (C) 2013, 2015 Salome Simon\nCopyright (C) 2014, 2015 Patrick von Reth\nCopyright (C) 2015 Manuel Heusner, Thomas Keller\nCopyright (C) 2009-2014 Erez Karpas\nCopyright (C) 2014 Robert P. Goldman\nCopyright (C) 2010-2012 Andrew Coles\nCopyright (C) 2010, 2012 Patrik Haslum\nCopyright (C) 2003-2011 Silvia Richter\nCopyright (C) 2009-2011 Emil Keyder\nCopyright (C) 2010, 2011 Moritz Gronbach, Manuela Ortlieb\nCopyright (C) 2011 Vidal Alc\u00e1zar Saiz, Michael Katz, Raz Nissim\nCopyright (C) 2010 Moritz Goebelbecker\nCopyright (C) 2007-2009 Matthias Westphal\nCopyright (C) 2009 Christian Muise\n\nFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1622819998.0 + "updated_at": 1674825486.0 }, { "data_format": 2, - "description": "Novel genomes can be analyzed by GeneMark-ES, an algorithm utilizing models parameterized by unsupervised training. Notably, GeneMark-ES has a special option for fungal genomes to account for fungal-specific intron organization. ", + "description": null, "filenames": [ - "4.65/Singularity" + "Singularity.def" ], - "full_name": "pscedu/singularity-genemark-es", + "full_name": "mysteryresearcher/sampling-in-optimal-sgd", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-genemark-es/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-genemark-es/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-genemark-es/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-genemark-es/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/268bf28da5aff3f043cd0714535301fca5138222750e4a5728a5c25e2e832847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/268bf28da5aff3f043cd0714535301fca5138222750e4a5728a5c25e2e832847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-genemark-es\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/23ee7cd28d76bd5cef900d9662da76f2c280c918c273ee7fc6998621242d6e5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/23ee7cd28d76bd5cef900d9662da76f2c280c918c273ee7fc6998621242d6e5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-genemark-es\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a38b44e2f6b70d1fd02cecabb5e1e98970228f7141ff2c262872ebd57619e047/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a38b44e2f6b70d1fd02cecabb5e1e98970228f7141ff2c262872ebd57619e047/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-genemark-es\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/74af0a874b012a1b673b1febc99b28067b350741dc8da571c81f4e6231b52442/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/74af0a874b012a1b673b1febc99b28067b350741dc8da571c81f4e6231b52442/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-genemark-es\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-genemark-es\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-genemark-es\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-genemark-es\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for GeneMark-ES.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ecountFullySupportedTranscripts.py\u003c/code\u003e, \u003ccode\u003eflag_anchored_elements.py\u003c/code\u003e, \u003ccode\u003egenerateReport.py\u003c/code\u003e, \u003ccode\u003epredictionAnalysis.py\u003c/code\u003e, \u003ccode\u003eselectSupportedSubsets.py\u003c/code\u003e, \u003ccode\u003ebed_to_gff.pl\u003c/code\u003e, \u003ccode\u003ebp_seq_select.pl\u003c/code\u003e, \u003ccode\u003ebuild_mod.pl\u003c/code\u003e, \u003ccode\u003ecalc_introns_from_gtf.pl\u003c/code\u003e, \u003ccode\u003echange_path_in_perl_scripts.pl\u003c/code\u003e, \u003ccode\u003ecompare_intervals_exact.pl\u003c/code\u003e, \u003ccode\u003egc_distr.pl\u003c/code\u003e, \u003ccode\u003eget_below_gc.pl\u003c/code\u003e, \u003ccode\u003eget_sequence_from_GTF.pl\u003c/code\u003e, \u003ccode\u003egmes_petap.pl\u003c/code\u003e, \u003ccode\u003ehc_exons2hints.pl\u003c/code\u003e, \u003ccode\u003ehistogram.pl\u003c/code\u003e, \u003ccode\u003emake_nt_freq_mat.pl\u003c/code\u003e, \u003ccode\u003eparse_ET.pl\u003c/code\u003e, \u003ccode\u003eparse_by_introns.pl\u003c/code\u003e, \u003ccode\u003eparse_gibbs.pl\u003c/code\u003e, \u003ccode\u003eparse_set.pl\u003c/code\u003e, \u003ccode\u003epredict_genes.pl\u003c/code\u003e, \u003ccode\u003ereformat_gff.pl\u003c/code\u003e, \u003ccode\u003erescale_gff.pl\u003c/code\u003e, \u003ccode\u003ernaseq_introns_to_gff.pl\u003c/code\u003e, \u003ccode\u003erun_es.pl\u003c/code\u003e, \u003ccode\u003erun_hmm_pbs.pl\u003c/code\u003e, \u003ccode\u003escan_for_bp.pl\u003c/code\u003e, \u003ccode\u003estar_to_gff.pl\u003c/code\u003e and \u003ccode\u003everify_evidence_gmhmm.pl\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/GeneMark-ES/4.8.25\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/Genemark-ES\u003c/code\u003e as \u003ccode\u003e4.8.25.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/genemark-ess/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sharper-rates-and-flexible-framework-for-nonconvex-sgd-with-client-and-data-sampling\" class=\"anchor\" aria-hidden=\"true\" href=\"#sharper-rates-and-flexible-framework-for-nonconvex-sgd-with-client-and-data-sampling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-install-singularity-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/introduction.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (optional)\u003c/h3\u003e\n\u003cp\u003eIf you don\u0027t want to install Singularity, make sure that you have all dependecies from Singularity.def (python3, numpy, pytorch, etc.)\u003c/p\u003e\n\u003cp\u003ea. Pull an image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://k3nfalt/default/python_ml:sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb. Open a shell console of the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv ~/python_ml_sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-prepare-scripts-for-experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-prepare-scripts-for-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Prepare scripts for experiments\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/page_ab/config_quadratic.py \n--experiments_name EXPERIMENT_NAME --num_nodes_list 1000 \n--theretical_step_size --step_size_range -8 10 --number_of_iterations 10000 --cpus_per_task 1 \n--noise_lambdas 0.0 0.1 0.5 1.0 10.0 --dim 10 --samplings \u0027original_page\u0027 \u0027uniform_with_replacement\u0027 \u0027importance\u0027 \n--strongly_convex_constant 0.001 --generate_type worst_case --batch_size 1 10 25 50 100 500 1000 \n--dumps_path SOME_PATH --dataset_path PATH_TO_FOLDER_WITH_DATASET\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-execute-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-execute-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Execute scripts\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esh SOME_PATH/EXPERIMENT_NAME/singularity_*.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-plot-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-plot-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Plot results\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003epython3 code/distributed_optimization_library/experiments/plots/page_ab/quad_prog_plot.py \n--dumps_paths SOME_PATH/EXPERIMENT_NAME\n--output_path SOME_OUTPUT_PATH --filter_sampling importance original_page --filter_noise_lambda 0.1 --batch_experiment\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOne can find all other scripts \u003ca href=\"code/distributed_optimization_library/experiments/plots/page_ab/scripts.txt\"\u003ehere\u003c/a\u003e that generate experiments from the paper.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1631406552.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1652883626.0 }, { "data_format": 2, - "description": "NextFlow pipeline: fastq -\u003e SNV CNV -\u003e loqusdb", + "description": null, "filenames": [ - "resources/Singularity" + "Singularity.def" ], - "full_name": "Clinical-Genomics-Lund/ffpe-nextflow", + "full_name": "mysteryresearcher/dasha-partial-participation", "latest_release": null, - "readme": "\u003ch3\u003e\u003ca id=\"user-content-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow\u003c/h3\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-install-singularity-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/introduction.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (optional)\u003c/h3\u003e\n\u003cp\u003eIf you don\u0027t want to install Singularity, make sure that you have all dependecies from Singularity.def (python3, numpy, pytorch, etc.)\u003c/p\u003e\n\u003cp\u003ea. Pull an image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://k3nfalt/default/python_ml:sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb. Open a shell console of the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv ~/python_ml_sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-prepare-scripts-for-experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-prepare-scripts-for-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Prepare scripts for experiments\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/dasha_partial_participation/config_libsvm_dasha_partial_particiaption.py \n--dumps_path SOME_PATH --dataset_path PATH_TO_FOLDER_WITH_DATASET --dataset real-sim \n--experiments_name EXPERIMENT_NAME \n--num_nodes_list 100 --step_size_range -10 0 --number_of_seeds 1 --number_of_iterations 5000000 \n--algorithm_names zero_marina_sync_stochastic zero_marina_partial_participation_stochastic --cpus_per_task 11 \n--number_of_processes 10 --time 10 --parallel --compressors rand_k --number_of_coordinates 200 --quality_check_rate 1000 \n--oracle stochastic --mega_batch 10000 --batch_size 1 --function stochastic_logistic_regression --logistic_regression_nonconvex 0.001 \n--partial_participation_probabilities 1.0 0.5 0.1 0.01\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-execute-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-execute-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Execute scripts\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esh SOME_PATH/EXPERIMENT_NAME/singularity_*.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-plot-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-plot-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Plot results\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/plots/dasha_partial_participation/plot_vr-marina_real-sim_stochastic.py \n--dumps_paths SOME_PATH/EXPERIMENT_NAME \n--output_path SOME_PATH_FOR_PLOTS \n--ignore_methods \"VR-MARINA (online)\" \"DASHA-MVR\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOne can find all other scripts \u003ca href=\"https://github.com/mysteryresearcher/dasha-partial-participation/blob/submission_neurips2022/code/distributed_optimization_library/experiments/plots/dasha_partial_participation/script.txt\"\u003ehere\u003c/a\u003e that generate experiments from the paper.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1559647892.0 + "updated_at": 1650602862.0 }, { "data_format": 2, - "description": "Singularity recipe for vg and toil-vg", + "description": null, "filenames": [ "Singularity" ], - "full_name": "ISU-HPC/vg-toil-vg", + "full_name": "CshlSiepelLab/SimPol", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-vg-toil-vg\" class=\"anchor\" aria-hidden=\"true\" href=\"#vg-toil-vg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evg-toil-vg\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for vg and toil-vg\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-simpol--simulating-the-dynamics-of-rna-polymerase-rnap-on-dna-template\" class=\"anchor\" aria-hidden=\"true\" href=\"#simpol--simulating-the-dynamics-of-rna-polymerase-rnap-on-dna-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimPol \u2014 Simulating the dynamics of RNA Polymerase (RNAP) on DNA template\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eSimPol tracks the independent movement of RNAPs along the DNA templates of a large\nnumber of cells. It accepts several key\nuser-specified parameters, including the initiation rate, pause-escape rate,\na constant or variable elongation rate, the mean and variance of pause sites across cells,\nas well as the center-to-center spacing constraint between RNAPs,\nthe number of cells being simulated, the gene length, and the total time of transcription.\nThe simulator simply allows each RNAP to move forward or not,\nin time slices of $10^{-4}$ minutes, according to the specified position-specific\nrate parameters. It assumes that at most one movement of each RNAP can occur per\ntime slice. The simulator monitors for collisions between adjacent RNAPs, prohibiting\none RNAP to advance if it is at the boundary of the allowable distance from the next.\nAfter running for the specified time, SimPol outputs either a file in HDF5 format or files in CSV format that records all RNAP position for the last specified number of steps. See more options and details about parameters and outputs below.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/simulator.png\"\u003e\u003cimg src=\"figures/simulator.png\" alt=\"simulator\" width=\"600\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\tFig.1 Design of SimPol (\u201cSimulator of Polymerases\u201d)\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Environment\u003c/h2\u003e\n\u003cp\u003eWe provide two different approaches to set up the environment for SimPol, one with\n\u003ca href=\"https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\nand the other with \u003ca href=\"https://docs.sylabs.io/guides/latest/user-guide/quick_start.html#\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\nPre-built debug and release executables can be found in the bin folder\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\nconda activate simpol\n\nbin/simPol_Release --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot simPol.sif Singularity\n\nsingularity shell simPol.sif\n\nbin/simPol_Release --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild from source\u003c/h2\u003e\n\u003cp\u003eThere is the option to build in either debug mode with debug symbols or release mode with compiler optimizations (e.g. -O2).\u003c/p\u003e\n\u003cp\u003eTo create a build directory in release mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake -S . -B Release/ -D CMAKE_BUILD_TYPE=Release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo clean the release mode build directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake --build Release/ --target clean\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build in release mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake --build Release/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: Replace all instances of \u0027Release\u0027 with \u0027Debug\u0027 to build in Debug mode\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Locally\u003c/h2\u003e\n\u003cp\u003eSet Number of Threads [By default uses all available threads]\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport OMP_NUM_THREADS=\u0026lt;number of threads to use\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun in Release Mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Release/simPol [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun in Debug Mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Debug/simPol [options]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun program with file containing command line arguments\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./Release/simPol $(\u0026lt;simPol_arguments.dat)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-with-uge-workload-manager-within-cshl\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-with-uge-workload-manager-within-cshl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun with UGE Workload Manager (within CSHL)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -pe OpenMP 5 -cwd -b y ./Release/simPol -n 100 --csv 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote: This allocates 5 threads for the program in the OpenMP parallel environment\u003c/li\u003e\n\u003cli\u003eCheck available parallel environments: qconf -spl\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eOptions:\n\t-h, --help\n\t\tShow this help message and exit\n\n\t-k INTEGER, --tssLen=INTEGER\n\t\tdefine the mean of pause sites across cells [default 50bp]\n\n\t--kSd=DOUBLE\n\t\tdefine the standard deviation of pause sites across cells [default 0]\n\n\t--kMin=INTEGER\n\t\tupper bound of pause site allowed [default 17bp]\n\n\t--kMax=INTEGER\n\t\tlower bound of pause site allowed [default 200bp]\n\n\t--geneLen=INTEGER\n\t\tdefine the length of the whole gene [default 2000bp]\n\n\t-a DOUBLE, --alpha=DOUBLE\n\t\tinitiation rate [default 1 event per min]\n\n\t-b DOUBLE, --beta=DOUBLE\n\t\tpause release rate [default 1 event per min]\n\n\t-z DOUBLE, --zeta=DOUBLE\n\t\tthe mean of elongation rates across sites [default 2000bp per min]\n\n\t--zetaSd=DOUBLE\n\t\tthe standard deviation of elongation rates across sites [default 1000]\n\n\t--zetaMax=DOUBLE\n\t\tthe maximum of elongation rates allowed [default 2500bp per min]\n\n\t--zetaMin=DOUBLE\n\t\tthe minimum of elongation rates allowed [default 1500bp per min]\n\n\t--zetaVec=CHARACTER\n\t\ta file contains vector to scale elongation rates. All cells share the same set of parameters [default \"\"]\n\n\t-n INTEGER, --cellNum=INTEGER\n\t\tNumber of cells being simulated [default 10]\n\n\t-s INTEGER, --polSize=INTEGER\n\t\tPolymerase II size [default 33bp]\n\n\t--addSpace=INTEGER\n\t\tAdditional space in addition to RNAP size [default 17bp]\n\n\t-t DOUBLE, --time=DOUBLE\n\t\tTotal time of simulating data in a cell [default 0.1 min]\n\n\t--hdf5=INTEGER\n\t\tRecord position matrix to HDF5 file for remaining number of steps specified [default: 0 steps]\n\n\t--csv=INTEGER\n\t\tRecord position matrix to csv file for remaining number of steps specified [default: 1 step]\n\n\t-d CHARACTER, --outputDir=CHARACTER\n\t\tDirectory for saving results [default: \u0027results\u0027]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cp\u003eAfter simulation, SimPol produces multiple output files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eprobability_vector.csv\u003c/li\u003e\n\u003cli\u003epause_sites.csv\u003c/li\u003e\n\u003cli\u003ecombined_cell_data.csv: Stores the total # of polymerase at each site across all cells\u003c/li\u003e\n\u003cli\u003eposition_matrices.h5: An HDF5 file containing a group of position matrices for the last number of specified steps. The file can be viewed by importing it into the HDFView app. Download Here: \u003ca href=\"https://www.hdfgroup.org/downloads/hdfview/#download\" rel=\"nofollow\"\u003ehttps://www.hdfgroup.org/downloads/hdfview/#download\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eUsing HighFive interface to generate HDF5 file \u003ca href=\"https://github.com/BlueBrain/HighFive\"\u003ehttps://github.com/BlueBrain/HighFive\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUses chunking and ZLIB (deflate) compression at level 9\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003epositions/position_matrix_#.csv: CSV files containing the position matrix at a specified step\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sampling-nascent-rna-sequencing-read-counts\" class=\"anchor\" aria-hidden=\"true\" href=\"#sampling-nascent-rna-sequencing-read-counts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSampling nascent RNA sequencing read counts\u003c/h2\u003e\n\u003cp\u003eIf you prefer to simulate nascent RNA sequencing read counts in addition to the RNAP positions,\nyou can also follow the tutorial in \u003ccode\u003escripts/sample_read_counts.Rmd\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eZhao, Y., Liu, L. \u0026amp; Siepel, A. Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells. 2022.10.19.512929 Preprint at \u003ca href=\"https://doi.org/10.1101/2022.10.19.512929\" rel=\"nofollow\"\u003ebioRxiv\u003c/a\u003e (2022).\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1567801955.0 + "updated_at": 1675094116.0 }, { "data_format": 2, - "description": null, + "description": "Symbolic Bidirectional A* with Error", "filenames": [ "Singularity" ], - "full_name": "stephansmit/inkscape_containers", + "full_name": "valcazar/SymBAE", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-inkscape-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#inkscape-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInkscape containers\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-the-container-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-the-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the container locally\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build inkscape_containers_latest.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull the container\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/inkscape_containers:latest \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3588\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCopyright (C) 2003-2016 Malte Helmert\nCopyright (C) 2008-2016 Gabriele Roeger\nCopyright (C) 2010-2016 Jendrik Seipp\nCopyright (C) 2010, 2011, 2013-2016 Silvan Sievers\nCopyright (C) 2012-2016 Florian Pommerening\nCopyright (C) 2016 Martin Wehrle\nCopyright (C) 2013, 2015 Salome Simon\nCopyright (C) 2014, 2015 Patrick von Reth\nCopyright (C) 2015 Manuel Heusner, Thomas Keller\nCopyright (C) 2009-2014 Erez Karpas\nCopyright (C) 2014 Robert P. Goldman\nCopyright (C) 2010-2012 Andrew Coles\nCopyright (C) 2010, 2012 Patrik Haslum\nCopyright (C) 2003-2011 Silvia Richter\nCopyright (C) 2009-2011 Emil Keyder\nCopyright (C) 2010, 2011 Moritz Gronbach, Manuela Ortlieb\nCopyright (C) 2011 Vidal Alc\u00e1zar Saiz, Michael Katz, Raz Nissim\nCopyright (C) 2010 Moritz Goebelbecker\nCopyright (C) 2007-2009 Matthias Westphal\nCopyright (C) 2009 Christian Muise\n\nFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1569690911.0 + "updated_at": 1675605071.0 }, { "data_format": 2, - "description": "official build specifications for busybox", + "description": "Singularity container for Python and Keras", "filenames": [ "Singularity" ], - "full_name": "singularityhub/busybox", + "full_name": "JasonKChow/singPyKeras", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-busybox\" class=\"anchor\" aria-hidden=\"true\" href=\"#busybox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBusyBox\u003c/h1\u003e\n\u003cp\u003eThis is a library of busybox builds for Singularity images \u003ca href=\"https://singularityhub.github.io/registry-org/singularityhub/busybox/\" rel=\"nofollow\"\u003ehosted on Singularity Static Registry\u003c/a\u003e. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003etags are supported based on the extension of the Singularity file, with an extensionless file corresponding to \"latest\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-can-i-find-here\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-can-i-find-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat can I find here?\u003c/h2\u003e\n\u003cp\u003eThe repository here serves the container under the namespace \u003ccode\u003esingularityhub/busybox\u003c/code\u003e. Specifically,\nit provides an example of using CircleCI to build and push a container to Google Storage,\nand then update manifests at \u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e.\nIf you are interested in other container build templates, see \u003ca href=\"https://github.com/singularityhub/registry/wiki/build-templates\"\u003ethis page\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-does-this-work\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-does-this-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow does this work?\u003c/h2\u003e\n\u003cp\u003eWe will submit this container to the (organizational) registry at\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e\nfor a final container uri corresponding to \u003ccode\u003ehttps://singularityhub.github.io/registry-org/singularityhub/busybox\u003c/code\u003e. Specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub/registry-org --) the organization registry\nsingularityhub/busybox --) a container collection\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethen on GitHub pages:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub.github.io/registry-org --) the registry interface\nsingularityhub.github.io/registry-org/singularityhub/busybox --) the added container\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-0-fork-the-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#0-fork-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e0. Fork the Repository\u003c/h2\u003e\n\u003cp\u003eFor the repository here to your account, and make sure to add write permissions\nfor a machine user for the repository, and the machine user\u0027s key to CircleCI.\nThis means:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eadding the machine user as a collaborator to the repository (and accepting the invitation)\u003c/li\u003e\n\u003cli\u003econnecting the repository to CircleCI\u003c/li\u003e\n\u003cli\u003enavigating to the CircleCI project page logged in as the machine user to follow the project (button in upper right)\u003c/li\u003e\n\u003cli\u003egoing to the settings -\u0026gt; Checkout SSH keys to add the machine user key.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFull instructions are provided \u003ca href=\"https://github.com/singularityhub/registry/wiki/deploy-container-storage#2-creating-a-connected-repository\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-setup-your-organizational-registry\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-setup-your-organizational-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Setup your Organizational Registry\u003c/h2\u003e\n\u003cp\u003eIf you haven\u0027t done so, follow the instructions \u003ca href=\"https://github.com/singularityhub/registry/wiki/deploy-container-storage#organizational\"\u003ehere\u003c/a\u003e to create the organizational registry. You will need to\nupdate the environment variables in the top of the \u003ca href=\".circleci/config.yml\"\u003e.circleci/config.yml\u003c/a\u003e\nto reflect your repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e environment:\n\n # The GitHub username / reponame that the container will be submit to\n - REGISTRY_BASE: singularityhub/registry-org\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should only need to do this once. The example provided here uses\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-google-storage\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-google-storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Google Storage\u003c/h2\u003e\n\u003cp\u003eWe will be interacting with Google Storage via the \u003ca href=\"https://www.github.com/singularityhub/sregistry\"\u003esregistry\u003c/a\u003e\ncommand line client.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-required-environment-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#required-environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired environment variables\u003c/h2\u003e\n\u003cp\u003eCreate a Google Project and \u003ca href=\"https://cloud.google.com/sdk/docs/authorizing#authorizing_with_a_service_account\" rel=\"nofollow\"\u003ea service account\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-download-the-service-account-key\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-download-the-service-account-key\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Download the Service Account Key\u003c/h3\u003e\n\u003cp\u003eYou should first download a service account key from the \u003ca href=\"https://console.cloud.google.com/iam-admin/serviceaccounts?_ga=2.213389911.-231410963.1512057989\" rel=\"nofollow\"\u003eservice accounts page\u003c/a\u003e. For the roles, add an admin for Google\nStorage (to store your container). If you want to use the Google Cloud Builder (a similar\nconfiguration, example at \u003ca href=\"https://www.github.com/singularityhub/nginx\"\u003enginx\u003c/a\u003e) then you can also add Google Build.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/service-account.png\"\u003e\u003cimg src=\"img/service-account.png\" alt=\"img/service-account.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOnce you add the roles, you \u003cem\u003edo not need to add users\u003c/em\u003e to the account. You can next download\nthe service account key to your local machine, and move it to the repository folder.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/create-key.png\"\u003e\u003cimg src=\"img/create-key.png\" alt=\"img/create-key.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that the .gitignore includes *.json so it won\u0027t be added to your project!\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-circle-ci-secrets\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-circle-ci-secrets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Circle CI Secrets\u003c/h3\u003e\n\u003cp\u003eOnce you have the \u003ccode\u003e\u0026lt;project-id\u0026gt;-\u0026lt;number\u0026gt;.json\u003c/code\u003e in the present working directory,\nyou can add the entire thing to your project as an encrypted environment variable.\nHere is how to copy paste the string from your terminal:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eproject-id\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdd the text output from the above to an environment variable\ncalled \u003ccode\u003eGOOGLE_APPLICATION_CREDENTIALS\u003c/code\u003e along with the following (all project secrets):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGOOGLE_COMPUTE_ZONE: the zone you want your compute builder to run in.\u003c/li\u003e\n\u003cli\u003eSREGISTRY_GOOGLE_PROJECT: the id of your project, easiest to find in the Google Project console url.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptionally, export a name for your bucket, \u003ccode\u003eSREGISTRY_GOOGLE_STORAGE_BUCKET\u003c/code\u003e\n(it will be created if it doesn\u0027t exist). It will default to your project id with sregistry- as a prefix.\nDon\u0027t forget to add the machine user to the repository, and then add its credential.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singpykeras\" class=\"anchor\" aria-hidden=\"true\" href=\"#singpykeras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingPyKeras\u003c/h1\u003e\n\u003cp\u003eSingularity container for Python and Keras. Check releases for built images.\u003c/p\u003e\n\u003cp\u003eTo build:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build pyTF.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo use/test:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv pyTF.sif python kerasTest.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo get into environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv pyTF.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo get just an interactive python\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv pyTF.sif python\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "singularityhub", - "singularity", - "sregistry-org", - "static-registry", - "registry", - "registry-template" - ], - "updated_at": 1549553036.0 + "topics": [], + "updated_at": 1676527668.0 }, { "data_format": 2, - "description": "R container with baySeq and riboseq libraries", + "description": "pipeline for imputing snps on 1000g hg38 reference. repurposed from sceQTL-Gen for specific lab use", "filenames": [ - "Singularity" + "Singularity.Imputation" ], - "full_name": "callaghanmt-containers/riboseqbayseq", - "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-singularity-container-build-script-for-riboseqr-and-bayseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container-build-script-for-riboseqr-and-bayseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container build script for riboSeqR and baySeq\u003c/h2\u003e\n\u003cp\u003eBoth packages are obtained from Bioconductor and require RCurl as a prerequisite.\u003c/p\u003e\n\u003cp\u003eRCurl needs the Ubuntu \u003ccode\u003elibcurl-dev\u003c/code\u003e package which is also installed\u003c/p\u003e\n\u003cp\u003eTo build:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build riboseqbayseq.simg Singularity\u003c/code\u003e\u003c/p\u003e\n", + "full_name": "powellgenomicslab/SNP_imputation_1000g_hg38", + "latest_release": "v0.0.2", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-powell-lab-imputation-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#powell-lab-imputation-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePowell Lab Imputation Pipeline\u003c/h1\u003e\n\u003cp\u003eRepurposed pipeline from Urmo for the sceQTL-Gen Consortium. Update requirements so more suitable for more general use\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/powellgenomicslab/SNP_imputation_1000g_hg38/wiki/SNP-Genotype-Imputation-Using-1000G-hg38-Reference\"\u003eWiki\u003c/a\u003e for information on running the SNP imputation pipeline.\u003c/p\u003e\n\u003cp\u003eThese documents were put together by Drew Neavin on 16 November, 2021.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1536747304.0 + "updated_at": 1648702994.0 }, { "data_format": 2, - "description": "seqtk singulairty container", + "description": null, "filenames": [ - "Singularity" + "devops_pipeline/Singularity", + "devops_base/Singularity" ], - "full_name": "phgenomics-singularity/seqtk", + "full_name": "ninamiolane/connect", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1576530783.0 + "updated_at": 1582874207.0 }, { "data_format": 2, - "description": "A Docker/Singularity container for packaging pulsar searching software", + "description": "This material contains content on how to profile and optimize simple Pytorch mnist code using NVIDIA Nsight Systems and Pytorch Profiler ", "filenames": [ "Singularity" ], - "full_name": "federatedcloud/pulsar-pipeline-container", + "full_name": "openhackathons-org/AI-Profiler", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker-pulsar-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-pulsar-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker-pulsar-pipeline\u003c/h1\u003e\n\u003cp\u003eA Docker/Singularity container for packaging pulsar searching software\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4541\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-optimizing-a-deep-neural-network-dnn-training-program\" class=\"anchor\" aria-hidden=\"true\" href=\"#optimizing-a-deep-neural-network-dnn-training-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimizing a Deep Neural Network (DNN) training program\u003c/h1\u003e\n\u003cp\u003eThis folder contains contents for AI training program profiling.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNVIDIA Nsight Systems\u003c/li\u003e\n\u003cli\u003ePyTorch Profiler with TensorBoard Plugin\u003c/li\u003e\n\u003cli\u003eTensorBoard Visualization\u003c/li\u003e\n\u003cli\u003eOptimization Techniques\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eTo run this tutorial you will need a machine with NVIDIA GPU.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall the latest \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eTo be able to see the profiler output, please download NVIDIA Nsight Systems\u0027 latest version from \u003ca href=\"https://developer.nvidia.com/nsight-systems\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eLinux ubuntu OS\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on containers\u003c/h2\u003e\n\u003cp\u003eTo start with, you will have to build a Docker or Singularity container.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Container\u003c/h3\u003e\n\u003cp\u003eTo build a docker container, run:\n\u003ccode\u003esudo docker build --network=host -t \u0026lt;imagename\u0026gt;:\u0026lt;tagnumber\u0026gt; .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor instance:\n\u003ccode\u003esudo docker build -t pytorch:1.0 .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe code labs have been written using Jupyter notebooks and a Dockerfile has been built to simplify deployment. In order to serve the docker instance for a student, it is necessary to expose port 8888 from the container, for instance, the following command would expose port 8888 inside the container as port 8888 on the lab machine:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -it --rm --network=host -v ~/ai_profiler/workspace:/workspace pytorch:1.0 jupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--gpus\u003c/code\u003e flag is used to enable \u003ccode\u003eall\u003c/code\u003e NVIDIA GPUs during container runtime. The \u003ccode\u003e--rm\u003c/code\u003e flag is used to clean an temporary images created during the running of the container. The \u003ccode\u003e-it\u003c/code\u003e flag enables killing the jupyter server with ctrl-c.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--ipc=host --ulimit memlock=-1 --ulimit stack=67108864\u003c/code\u003e enable sufficient memory allocation to run pytorch within the docker environment.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ejupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e command launch the jupyter notebook inside the container. The flag \u003ccode\u003e-v\u003c/code\u003e allows the mapping of working directory on your local machine \u003ccode\u003e~/ai_profiler/profiler/workspace:/workspace\u003c/code\u003e to \u003ccode\u003eworspace\u003c/code\u003e directory inside the container.\u003c/p\u003e\n\u003cp\u003eThen, open the jupyter notebook in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e\nStart working on the lab by clicking on the \u003ccode\u003estart_here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container\u003c/h3\u003e\n\u003cp\u003eTo build the singularity container, run:\n\u003ccode\u003esudo singularity build --fakeroot \u0026lt;image_name\u0026gt;.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFore example:\n\u003ccode\u003esingularity build --fakeroot pytorch.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, run the container:\n\u003ccode\u003esingularity run --nv --bind ~/ai_profiler/workspace:/workspace pytorch.simg jupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--nv\u003c/code\u003e flag is used to enable \u003ccode\u003eall\u003c/code\u003e NVIDIA GPUs during container runtime. The \u003ccode\u003e--bind\u003c/code\u003e allows the mapping of working directory on your local machine \u003ccode\u003e~/ai_profiler/profiler/workspace:/workspace\u003c/code\u003e to \u003ccode\u003eworspace\u003c/code\u003e directory inside the container.\u003c/p\u003e\n\u003cp\u003eThen, open the jupyter notebook in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e\nStart working on the lab by clicking on the \u003ccode\u003eStart_Here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-local-machine\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-local-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Local Machine\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall PyTorch \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall essentials:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e pip3 install jupyterlab\n pip3 install ipywidgets\n pip3 install torch_tb_profiler\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eInstall NVIDIA Nsight Systems version 2022.1.1 from \u003ca href=\"https://developer.nvidia.com/nsight-systems\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and set path. Please run \u003ccode\u003ensys --version\u003c/code\u003e from the terminal to ensure you are using the version 2022.1.1 or above\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e#Tutorial Duration\nThe total bootcamp material would take 2 hours.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1622819898.0 + "updated_at": 1675267909.0 }, { "data_format": 2, - "description": "Singularity image with a selection of neuro processing packages and tools", + "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "chidiugonna/nklab-neuro-tools", + "full_name": "manasi-sharma/language-OG-diffuser", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-image-containing-neuroimaging-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-image-containing-neuroimaging-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity image containing Neuroimaging software\u003c/h1\u003e\n\u003cp\u003eThis Singularity image will be about 20GB when built using Singularity 2.4.2. It comes with FSL 5.10 including eddy_cuda8.0, Mrtrix 3RC2, Freesurfer 6.0.0, Afni 18.0.21, ANTS 2.2.0, MRIQC v0.1, Julia v0.6.1 and The Duke Resting State fMRI pipeline. It also has CUDA 8.0 toolkit libraries installed.\u003c/p\u003e\n\u003cp\u003eThe image can be built using Singularity build in singularity2.4.2\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Singularity Image\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have singularity 2.4 installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-tools\u003c/code\u003edirectory and check that you have a Singularity definiton file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow simply build the image as \u003ccode\u003esudo singularity build nklab-neuro-tools.simg Singularity\u003c/code\u003e - note that the image name is assumed to be \u003ccode\u003enklab-neuro-tools.simg\u003c/code\u003e but this can be changed to a more convenient label.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Singularity Image\u003c/h2\u003e\n\u003cp\u003eYou can now run commands by simply appending them to the end of \u003ccode\u003esingularity run nklab-neuro-tools.simg\u003c/code\u003e So for example to run an FSL command like flirt directly the following would be entered: \u003ccode\u003esingularity run nklab-neuro-tools.simg flirt ....\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-cuda-compatibility\" class=\"anchor\" aria-hidden=\"true\" href=\"#cuda-compatibility\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCuda Compatibility\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou can run Cuda-8.0 compatible executables by using the \u003ccode\u003e--nv\u003c/code\u003e parameter. The example provided next shows how to accomplish this with \u003ccode\u003eeddy-cuda8.0\u003c/code\u003e:\n\u003ccode\u003esingularity run --nv rsfmri.img /opt/fsl/bin/eddy_cuda8.0 --imain=G1_1_OFF_28271_cgm --mask=G1_1_OFF_28271_cgm0_brain_mask --acqp=acqparams.txt --index=index.txt --bvecs=bvecs --bvals=bvals --out=G1_1_OFF_28271_cgm_eddy\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-shell-into-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#shell-into-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell into Singularity Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou can also shell into the singularity image using: \u003ccode\u003esingularity shell nklab-neuro-tools.simg\u003c/code\u003e and then run commands at the command line within the container.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eProvided below are notes on specific aspects of the container that may be useful.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-resting-state-fmri-pipeline-nan-kuei-chenduke-university\" class=\"anchor\" aria-hidden=\"true\" href=\"#resting-state-fmri-pipeline-nan-kuei-chenduke-university\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResting State FMRI pipeline (Nan-kuei Chen/Duke University)\u003c/h2\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e for details of use.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe original python source \u003ccode\u003eresting_pipeline.py\u003c/code\u003e available at at [\u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e] has been slightly amended and is included in this repository in the folder \u003ccode\u003esrc\u003c/code\u003e. These changes are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edata1\u003c/code\u003e has been selectively converted to dtype \u003ccode\u003enumpy.float64\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eslice indices have been cast as longs in certain instances.\u003c/li\u003e\n\u003cli\u003eBXH functionality is ignored. To explicitly use BXH info pass the flag --ignorebxh=N\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sliding-window-functionality\" class=\"anchor\" aria-hidden=\"true\" href=\"#sliding-window-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSliding window functionality\u003c/h3\u003e\n\u003cp\u003eA new step has been added \u003ccode\u003e-7sw\u003c/code\u003e to enable sliding window functionality. In order to use this step you will need to use the \u003ccode\u003e--slidewin\u003c/code\u003e parameter which takes 2 numbers seperated by a comma. The 1st number is the window size in seconds and the second number is the shift in seconds between sequential windows. So for example \u003ccode\u003e--slidewin=60,3\u003c/code\u003e will use a window size of \u003ccode\u003e60\u003c/code\u003e seconds shifted by \u003ccode\u003e3\u003c/code\u003e seconds for each subsequent window. Keep in mind that the \u003ccode\u003e--tr\u003c/code\u003e (in milliseconds) parameter is required to calculate the number of volumes to use for each sliding window correlation. If you do not specify the --slidwin parameter and run step \u003ccode\u003e7sw\u003c/code\u003e then default values of \u003ccode\u003e30,3\u003c/code\u003e will be used. Sliding window files are exported to a new directory \u003ccode\u003eSlidingWindow_W_S\u003c/code\u003e and image files are consolidated into 4D volumes for viewing in FSL as a movie\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-extensions-to-slice-correction-functionality\" class=\"anchor\" aria-hidden=\"true\" href=\"#extensions-to-slice-correction-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtensions to Slice Correction functionality\u003c/h3\u003e\n\u003cp\u003eThe pipeline has been extended to accept custom slice correction timing files. A python script make_fsl_stc.py has been bundled in this container which can take .json files created by dcm2niix. This python program will create a slice correction file with timing values and one with slices in order of acquisition. It can be called as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e where fmri.json is the json output from dcm2niix. custom names for the slice order and slice time files can be provided as parameters as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake_fsl_stc.py fmri.json --slicenum=/path/num.txt --slicetime=/path/time.txt\u003c/code\u003e otherwise these files default to \u003ccode\u003esliceorder.txt\u003c/code\u003e and \u003ccode\u003eslicetimes.txt\u003c/code\u003e in the current directory.\u003c/p\u003e\n\u003cp\u003eOnce these custom files have been created then they can be provided to the resting state pipeline using the full path as input to the \u003ccode\u003e--sliceorder\u003c/code\u003e parameter\n\u003ccode\u003e--sliceorder=/path/num.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eplease note that the default custom slice file expected uses slice order. If you pass a text file with slice times then you will need to use another parameter \u003ccode\u003e--slicetimings=time\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-example-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample Commands\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-create-slice-timing-files-from-json\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-slice-timing-files-from-json\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Slice Timing files from json\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esingularity run -B $PWD:/opt/data nklab-neuro-tools.simg /opt/rsfmri_python/bin/make_fsl_stc.py /opt/data/fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --rm -B $PWD:/opt/data nklab-neuro-tools.simg /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-planning-with-diffusion--\" class=\"anchor\" aria-hidden=\"true\" href=\"#planning-with-diffusion--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanning with Diffusion \u00a0\u00a0 \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eTraining and visualizing of diffusion models from \u003ca href=\"https://diffusion-planning.github.io/\" rel=\"nofollow\"\u003ePlanning with Diffusion for Flexible Behavior Synthesis\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/main\"\u003emain branch\u003c/a\u003e contains code for training diffusion models and planning via value-function guided sampling on the D4RL locomotion environments.\nThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/kuka\"\u003ekuka branch\u003c/a\u003e contains block-stacking experiments.\nThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/maze2d\"\u003emaze2d branch\u003c/a\u003e contains goal-reaching via inpainting in the Maze2D environments.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/184a6bb0418c60d778dcfa9f81dfddfdea0c0a3238b1d0cc4aa8666dd32ad3ca/68747470733a2f2f646966667573696f6e2d706c616e6e696e672e6769746875622e696f2f696d616765732f64696666757365722d636172642e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/184a6bb0418c60d778dcfa9f81dfddfdea0c0a3238b1d0cc4aa8666dd32ad3ca/68747470733a2f2f646966667573696f6e2d706c616e6e696e672e6769746875622e696f2f696d616765732f64696666757365722d636172642e706e67\" width=\"60%\" title=\"Diffuser model\" data-canonical-src=\"https://diffusion-planning.github.io/images/diffuser-card.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUpdates\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e12/09/2022: Diffuser (the RL model) has been integrated into \u003cg-emoji class=\"g-emoji\" alias=\"hugs\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f917.png\"\u003e\ud83e\udd17\u003c/g-emoji\u003e Diffusers (the Hugging Face diffusion model library)! See \u003ca href=\"https://huggingface.co/docs/diffusers/using-diffusers/rl\" rel=\"nofollow\"\u003ethese docs\u003c/a\u003e for how to run Diffuser using their pipeline.\u003c/li\u003e\n\u003cli\u003e10/17/2022: A bug in the value function scaling has been fixed in \u003ca href=\"https://github.com/jannerm/diffuser/commit/3d7361c2d028473b601cc04f5eecd019e14eb4eb\"\u003ethis commit\u003c/a\u003e. Thanks to \u003ca href=\"https://scholar.google.com/citations?user=Q6UMpRYAAAAJ\u0026amp;hl=en\" rel=\"nofollow\"\u003ePhilemon Brakel\u003c/a\u003e for catching it!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eLoad a pretrained diffusion model and sample from it in your browser with \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003escripts/diffuser-sample.ipynb\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate diffuser\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-pretrained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-pretrained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing pretrained models\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-downloading-weights\" class=\"anchor\" aria-hidden=\"true\" href=\"#downloading-weights\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownloading weights\u003c/h3\u003e\n\u003cp\u003eDownload pretrained diffusion models and value functions with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./scripts/download_pretrained.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis command downloads and extracts a \u003ca href=\"https://drive.google.com/file/d/1wc1m4HLj7btaYDN8ogDIAV9QzgWEckGy/view?usp=share_link\" rel=\"nofollow\"\u003etarfile\u003c/a\u003e containing \u003ca href=\"https://drive.google.com/drive/folders/1ie6z3toz9OjcarJuwjQwXXzDwh1XnS02?usp=sharing\" rel=\"nofollow\"\u003ethis directory\u003c/a\u003e to \u003ccode\u003elogs/pretrained\u003c/code\u003e. The models are organized according to the following structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u2514\u2500\u2500 logs/pretrained\n \u251c\u2500\u2500 ${environment_1}\n \u2502 \u251c\u2500\u2500 diffusion\n \u2502 \u2502 \u2514\u2500\u2500 ${experiment_name}\n \u2502 \u2502 \u251c\u2500\u2500 state_${epoch}.pt\n \u2502 \u2502 \u251c\u2500\u2500 sample-${epoch}-*.png\n \u2502 \u2502 \u2514\u2500\u2500 {dataset, diffusion, model, render, trainer}_config.pkl\n \u2502 \u251c\u2500\u2500 values\n \u2502 \u2502 \u2514\u2500\u2500 ${experiment_name}\n \u2502 \u2502 \u251c\u2500\u2500 state_${epoch}.pt\n \u2502 \u2502 \u2514\u2500\u2500 {dataset, diffusion, model, render, trainer}_config.pkl\n \u2502 \u2514\u2500\u2500 plans\n \u2502 \u2514\u2500\u2500 defaults\n \u2502 \u251c\u2500\u2500 0\n \u2502 \u251c\u2500\u2500 1\n \u2502 \u251c\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 149\n \u2502\n \u251c\u2500\u2500 ${environment_2}\n \u2502 \u2514\u2500\u2500 ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003estate_${epoch}.pt\u003c/code\u003e files contain the network weights and the \u003ccode\u003econfig.pkl\u003c/code\u003e files contain the instantation arguments for the relevant classes.\nThe png files contain samples from different points during training of the diffusion model.\nWithin the \u003ccode\u003eplans\u003c/code\u003e subfolders, there are the results of 150 evaluation trials for each environment using the default hyperparameters.\u003c/p\u003e\n\u003cdetails\u003e\n\u003csummary\u003eTo aggregate the results of the evaluations in the \u003ccode\u003elogs\u003c/code\u003e folder, run \u003ccode\u003epython scripts/read_results.py\u003c/code\u003e. (Expand to view the output of this command on the plans downloaded from Google Drive.)\n\u003c/summary\u003e\n\u003cpre\u003e\u003ccode\u003ehopper-medium-replay-v2 | defaults | logs/pretrained/hopper-medium-replay-v2/plans | 150 scores\n 93.6 +/- 0.37\nhopper-medium-v2 | defaults | logs/pretrained/hopper-medium-v2/plans | 150 scores\n 74.3 +/- 1.36\nhopper-medium-expert-v2 | defaults | logs/pretrained/hopper-medium-expert-v2/plans | 150 scores\n 103.3 +/- 1.30\nwalker2d-medium-replay-v2 | defaults | logs/pretrained/walker2d-medium-replay-v2/plans | 150 scores\n 70.6 +/- 1.60\nwalker2d-medium-v2 | defaults | logs/pretrained/walker2d-medium-v2/plans | 150 scores\n 79.6 +/- 0.55\nwalker2d-medium-expert-v2 | defaults | logs/pretrained/walker2d-medium-expert-v2/plans | 150 scores\n 106.9 +/- 0.24\nhalfcheetah-medium-replay-v2 | defaults | logs/pretrained/halfcheetah-medium-replay-v2/plans | 150 scores\n 37.7 +/- 0.45\nhalfcheetah-medium-v2 | defaults | logs/pretrained/halfcheetah-medium-v2/plans | 150 scores\n 42.8 +/- 0.32\nhalfcheetah-medium-expert-v2 | defaults | logs/pretrained/halfcheetah-medium-expert-v2/plans | 150 scores\n 88.9 +/- 0.25\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eTo create the table of offline RL results from the paper, run \u003ccode\u003epython plotting/table.py\u003c/code\u003e. This will print a table that can be copied into a Latex document. (Expand to view table source.)\u003c/summary\u003e\n\u003cpre\u003e\u003ccode\u003e\\definecolor{tblue}{HTML}{1F77B4}\n\\definecolor{tred}{HTML}{FF6961}\n\\definecolor{tgreen}{HTML}{429E9D}\n\\definecolor{thighlight}{HTML}{000000}\n\\newcolumntype{P}{\u0026gt;{\\raggedleft\\arraybackslash}X}\n\\begin{table*}[hb!]\n\\centering\n\\small\n\\begin{tabularx}{\\textwidth}{llPPPPPPPPr}\n\\toprule\n\\multicolumn{1}{r}{\\bf \\color{black} Dataset} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} Environment} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} BC} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} CQL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} IQL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} DT} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} TT} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MOPO} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MOReL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MBOP} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} Diffuser} \\\\ \n\\midrule\nMedium-Expert \u0026amp; HalfCheetah \u0026amp; $55.2$ \u0026amp; $91.6$ \u0026amp; $86.7$ \u0026amp; $86.8$ \u0026amp; $95.0$ \u0026amp; $63.3$ \u0026amp; $53.3$ \u0026amp; $\\textbf{\\color{thighlight}105.9}$ \u0026amp; $88.9$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.3$}} \\\\ \nMedium-Expert \u0026amp; Hopper \u0026amp; $52.5$ \u0026amp; $\\textbf{\\color{thighlight}105.4}$ \u0026amp; $91.5$ \u0026amp; $\\textbf{\\color{thighlight}107.6}$ \u0026amp; $\\textbf{\\color{thighlight}110.0}$ \u0026amp; $23.7$ \u0026amp; $\\textbf{\\color{thighlight}108.7}$ \u0026amp; $55.1$ \u0026amp; $103.3$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.3$}} \\\\ \nMedium-Expert \u0026amp; Walker2d \u0026amp; $\\textbf{\\color{thighlight}107.5}$ \u0026amp; $\\textbf{\\color{thighlight}108.8}$ \u0026amp; $\\textbf{\\color{thighlight}109.6}$ \u0026amp; $\\textbf{\\color{thighlight}108.1}$ \u0026amp; $101.9$ \u0026amp; $44.6$ \u0026amp; $95.6$ \u0026amp; $70.2$ \u0026amp; $\\textbf{\\color{thighlight}106.9}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.2$}} \\\\ \n\\midrule\nMedium \u0026amp; HalfCheetah \u0026amp; $42.6$ \u0026amp; $44.0$ \u0026amp; $\\textbf{\\color{thighlight}47.4}$ \u0026amp; $42.6$ \u0026amp; $\\textbf{\\color{thighlight}46.9}$ \u0026amp; $42.3$ \u0026amp; $42.1$ \u0026amp; $44.6$ \u0026amp; $42.8$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.3$}} \\\\ \nMedium \u0026amp; Hopper \u0026amp; $52.9$ \u0026amp; $58.5$ \u0026amp; $66.3$ \u0026amp; $67.6$ \u0026amp; $61.1$ \u0026amp; $28.0$ \u0026amp; $\\textbf{\\color{thighlight}95.4}$ \u0026amp; $48.8$ \u0026amp; $74.3$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.4$}} \\\\ \nMedium \u0026amp; Walker2d \u0026amp; $75.3$ \u0026amp; $72.5$ \u0026amp; $\\textbf{\\color{thighlight}78.3}$ \u0026amp; $74.0$ \u0026amp; $\\textbf{\\color{thighlight}79.0}$ \u0026amp; $17.8$ \u0026amp; $\\textbf{\\color{thighlight}77.8}$ \u0026amp; $41.0$ \u0026amp; $\\textbf{\\color{thighlight}79.6}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.55$}} \\\\ \n\\midrule\nMedium-Replay \u0026amp; HalfCheetah \u0026amp; $36.6$ \u0026amp; $45.5$ \u0026amp; $44.2$ \u0026amp; $36.6$ \u0026amp; $41.9$ \u0026amp; $\\textbf{\\color{thighlight}53.1}$ \u0026amp; $40.2$ \u0026amp; $42.3$ \u0026amp; $37.7$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.5$}} \\\\ \nMedium-Replay \u0026amp; Hopper \u0026amp; $18.1$ \u0026amp; $\\textbf{\\color{thighlight}95.0}$ \u0026amp; $\\textbf{\\color{thighlight}94.7}$ \u0026amp; $82.7$ \u0026amp; $\\textbf{\\color{thighlight}91.5}$ \u0026amp; $67.5$ \u0026amp; $\\textbf{\\color{thighlight}93.6}$ \u0026amp; $12.4$ \u0026amp; $\\textbf{\\color{thighlight}93.6}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.4$}} \\\\ \nMedium-Replay \u0026amp; Walker2d \u0026amp; $26.0$ \u0026amp; $77.2$ \u0026amp; $73.9$ \u0026amp; $66.6$ \u0026amp; $\\textbf{\\color{thighlight}82.6}$ \u0026amp; $39.0$ \u0026amp; $49.8$ \u0026amp; $9.7$ \u0026amp; $70.6$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.6$}} \\\\ \n\\midrule\n\\multicolumn{2}{c}{\\bf Average} \u0026amp; 51.9 \u0026amp; \\textbf{\\color{thighlight}77.6} \u0026amp; \\textbf{\\color{thighlight}77.0} \u0026amp; 74.7 \u0026amp; \\textbf{\\color{thighlight}78.9} \u0026amp; 42.1 \u0026amp; 72.9 \u0026amp; 47.8 \u0026amp; \\textbf{\\color{thighlight}77.5} \\hspace{.6cm} \\\\ \n\\bottomrule\n\\end{tabularx}\n\\vspace{-.0cm}\n\\caption{\n}\n\\label{table:locomotion}\n\\end{table*}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/diffusion-planning/diffusion-planning.github.io/blob/master/images/table.png\"\u003e\u003cimg src=\"https://github.com/diffusion-planning/diffusion-planning.github.io/raw/master/images/table.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003ch3\u003e\u003ca id=\"user-content-planning\" class=\"anchor\" aria-hidden=\"true\" href=\"#planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanning\u003c/h3\u003e\n\u003cp\u003eTo plan with guided sampling, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs/pretrained\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--logbase\u003c/code\u003e flag points the \u003ca href=\"scripts/plan_guided.py#L22-L30\"\u003eexperiment loaders\u003c/a\u003e to the folder containing the pretrained models.\nYou can override planning hyperparameters with flags, such as \u003ccode\u003e--batch_size 8\u003c/code\u003e, but the default\nhyperparameters are a good starting point.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining from scratch\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eTrain a diffusion model with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/train.py --dataset halfcheetah-medium-expert-v2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe default hyperparameters are listed in \u003ca href=\"config/locomotion.py#L22-L65\"\u003elocomotion:diffusion\u003c/a\u003e.\nYou can override any of them with flags, eg, \u003ccode\u003e--n_diffusion_steps 100\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTrain a value function with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/train_values.py --dataset halfcheetah-medium-expert-v2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"config/locomotion.py#L67-L108\"\u003elocomotion:values\u003c/a\u003e for the corresponding default hyperparameters.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003ePlan using your newly-trained models with the same command as in the pretrained planning section, simply replacing the logbase to point to your new models:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"config/locomotion.py#L110-L149\"\u003elocomotion:plans\u003c/a\u003e for the corresponding default hyperparameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeferred f-strings.\u003c/strong\u003e Note that some planning script arguments, such as \u003ccode\u003e--n_diffusion_steps\u003c/code\u003e or \u003ccode\u003e--discount\u003c/code\u003e,\ndo not actually change any logic during planning, but simply load a different model using a deferred f-string.\nFor example, the following flags:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e---horizon 32 --n_diffusion_steps 20 --discount 0.997\n--value_loadpath \u0027f:values/defaults_H{horizon}_T{n_diffusion_steps}_d{discount}\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill resolve to a value checkpoint path of \u003ccode\u003evalues/defaults_H32_T20_d0.997\u003c/code\u003e. It is possible to\nchange the horizon of the diffusion model after training (see \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for an example),\nbut not for the value function.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile . -t diffuser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTest the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm --gpus all \\\n --mount type=bind,source=$PWD,target=/home/code \\\n --mount type=bind,source=$HOME/.d4rl,target=/root/.d4rl \\\n diffuser \\\n bash -c \\\n \"export PYTHONPATH=$PYTHONPATH:/home/code \u0026amp;\u0026amp; \\\n python /home/code/scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot diffuser.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTest the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv --writable-tmpfs diffuser.sif \\\n bash -c \\\n \"pip install -e . \u0026amp;\u0026amp; \\\n python scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-azure\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-azure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Azure\u003c/h2\u003e\n\u003ch4\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eTag the Docker image (built in the \u003ca href=\"#Docker\"\u003eDocker section\u003c/a\u003e) and push it to Docker Hub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport DOCKER_USERNAME=$(docker info | sed \u0027/Username:/!d;s/.* //\u0027)\ndocker tag diffuser ${DOCKER_USERNAME}/diffuser:latest\ndocker image push ${DOCKER_USERNAME}/diffuser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eUpdate \u003ca href=\"azure/config.py\"\u003e\u003ccode\u003eazure/config.py\u003c/code\u003e\u003c/a\u003e, either by modifying the file directly or setting the relevant \u003ca href=\"azure/config.py#L47-L52\"\u003eenvironment variables\u003c/a\u003e. To set the \u003ccode\u003eAZURE_STORAGE_CONNECTION\u003c/code\u003e variable, navigate to the \u003ccode\u003eAccess keys\u003c/code\u003e section of your storage account. Click \u003ccode\u003eShow keys\u003c/code\u003e and copy the \u003ccode\u003eConnection string\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload \u003ca href=\"https://docs.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10\" rel=\"nofollow\"\u003e\u003ccode\u003eazcopy\u003c/code\u003e\u003c/a\u003e: \u003ccode\u003e./azure/download.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h4\u003e\n\u003cp\u003eLaunch training jobs with \u003ccode\u003epython azure/launch.py\u003c/code\u003e. The launch script takes no command-line arguments; instead, it launches a job for every combination of hyperparameters in \u003ca href=\"azure/launch_train.py#L36-L38\"\u003e\u003ccode\u003eparams_to_sweep\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-viewing-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#viewing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eViewing results\u003c/h4\u003e\n\u003cp\u003eTo rsync the results from the Azure storage container, run \u003ccode\u003e./azure/sync.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo mount the storage container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a blobfuse config with \u003ccode\u003e./azure/make_fuse_config.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./azure/mount.sh\u003c/code\u003e to mount the storage container to \u003ccode\u003e~/azure_mount\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo unmount the container, run \u003ccode\u003esudo umount -f ~/azure_mount; rm -r ~/azure_mount\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{janner2022diffuser,\n title = {Planning with Diffusion for Flexible Behavior Synthesis},\n author = {Michael Janner and Yilun Du and Joshua B. Tenenbaum and Sergey Levine},\n booktitle = {International Conference on Machine Learning},\n year = {2022},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe diffusion model implementation is based on Phil Wang\u0027s \u003ca href=\"https://github.com/lucidrains/denoising-diffusion-pytorch\"\u003edenoising-diffusion-pytorch\u003c/a\u003e repo.\nThe organization of this repo and remote launcher is based on the \u003ca href=\"https://github.com/jannerm/trajectory-transformer\"\u003etrajectory-transformer\u003c/a\u003e repo.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1533338985.0 + "updated_at": 1675500146.0 }, { "data_format": 2, "description": null, "filenames": [ - "material/scientific/Singularity", - "material/tensorflow/Singularity", - "material/hello/Singularity", - "material/centos/Singularity", - "material/mpi/Singularity", - "material/ubuntu/Singularity" + "singularity/Singularity" ], - "full_name": "DataSystemsGroupUT/singularity-tutorial", + "full_name": "oxfordmmm/Bugflow_DSL2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-a-practical-guide-to-singularity---ut-data-engineering-fall-2021\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-practical-guide-to-singularity---ut-data-engineering-fall-2021\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA Practical Guide to Singularity - UT Data Engineering (Fall 2021)\u003c/h1\u003e\n\u003cp\u003eThis guide will introduce you to Singularity, a containerization system for scientific computing environments that is available on many scientific computing clusters. Containers allow you to package the environment that your code depends on inside of a portable unit. This is extremely useful for ensuring that your code can be run portably on other machines. It is also useful for installing software, packages, libraries, etc. in environments where you do not have root privileges, like an HPC account.\nThe repository contains the guide and files for the practical session of Singularity containers for the course Data Engineering at the University of Tartu.\nIt is divided in four parts and it goes from the installation process, knowing basic commands and finally a more advanced exercise.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-i-installing-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-i-installing-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart I. Installing Singularity\u003c/h2\u003e\n\u003cp\u003eYou have two options to get Singularity installed on your machine.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-option-1-the-docker-way-recommended-for-the-practice-session\" class=\"anchor\" aria-hidden=\"true\" href=\"#option-1-the-docker-way-recommended-for-the-practice-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 1: The Docker way (recommended for the practice session)\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003edocker\u003c/code\u003e and \u003ccode\u003egit\u003c/code\u003e should be installed on your machine. Then we need to create a container that has the dependencies and binary of singularity in it. The container to run uses the \u003ccode\u003ejcrm/singularity\u003c/code\u003e image that was built with a custom \u003ca href=\"./Dockerfile\"\u003eDockerfile\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDownload the contents of the repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/DataSystemsGroupUT/singularity-tutorial.git\n$ cd singularity-tutorial\n$ docker run --name singularity -v $(pwd)/material:/material -it --privileged jcrm/singularity:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTest that the installation works.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity --version\nsingularity version 3.8.0\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-option-2-the-traditional-way\" class=\"anchor\" aria-hidden=\"true\" href=\"#option-2-the-traditional-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 2: The traditional way\u003c/h3\u003e\n\u003cp\u003eDepending on your machine, install the dependencies and the singularity program.\nThe \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eofficial website\u003c/a\u003e provides a comprehensive manual to get it done.\u003c/p\u003e\n\u003cp\u003eTest that installation works.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity --version\nsingularity version 3.8.0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow clone the repository locally. If you have \u003ccode\u003egit\u003c/code\u003e, then just execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/DataSystemsGroupUT/singularity-tutorial.git\n$ cd singularity-tutorial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNB!\u003c/strong\u003e In the following sections we will assume that commands and examples will run under the \"Docker way\" configuration.\u003c/p\u003e\n\u003cp\u003eNow you\u0027re ready to go :)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-ii-first-steps-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-ii-first-steps-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart II. First steps with Singularity\u003c/h2\u003e\n\u003cp\u003eSingularity instantiates containers from images that define their environment. Singularity images are stored in \u003ccode\u003e.sif\u003c/code\u003e files.\nYou build a .sif file by defining your environment in a text file and providing that definition to the command singularity build.\nBuilding an image file does require root privileges, so it is most convenient to build the image on your local machine or workstation and then copy it to your HPC cluster.\nOnce you\u0027ve uploaded your image to your HPC cluster, you can submit a batch job that runs singularity exec with the image file you created and the command you want to run.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning containers\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eExample 1\u003c/strong\u003e: Latest Ubuntu image from the Docker Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run docker://ubuntu:latest\n$ docker run ubuntu:latest # Docker equivalent\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eExample 2\u003c/strong\u003e: Any image from the Docker Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run docker://godlovedc/lolcow\n$ docker run godlovedc/lolcow # Docker equivalent\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eExample 3\u003c/strong\u003e: Pre-built \u003ccode\u003e.sif\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run hello/hello.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can run containers from different sources.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e*.sif Singularity Image Format (SIF)\n*.sqsh SquashFS format. Native to Singularity 2.4+\n*.img ext3 format. Native to Singularity versions \u0026lt; 2.4\ndirectory/ sandbox format. Directory containing a valid root file\ninstance://* A local running instance of a container\nlibrary://* A SIF container hosted on a Library\ndocker://* A Docker/OCI container hosted on Docker Hub\nshub://* A container hosted on Singularity Hub\noras://* A SIF container hosted on an OCI registry\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-our-own-container-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-our-own-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding our own container image\u003c/h3\u003e\n\u003cp\u003eTo build a singularity container, we use the \u003ccode\u003ebuild\u003c/code\u003e command. The \u003ccode\u003ebuild\u003c/code\u003e command installs an OS, sets up a container\u0027s environment and installs the apps we will need.\nThe \u003ccode\u003ebuild\u003c/code\u003e command accepts a target as input and produces a container as output.\nTo use the \u003ccode\u003ebuild\u003c/code\u003e command, we need a \u003cstrong\u003erecipe file\u003c/strong\u003e (a.k.a definition file).\u003c/p\u003e\n\u003cp\u003eA Singularity recipe file is a set of instructions telling Singularity what software to install in the container.\nA Singularity Definition file is divided in two parts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eHeader :\u003c/strong\u003e Describes configuration of the base operating system within the container. The most important keyword here is \u003ccode\u003eBootstrap\u003c/code\u003e and you can find the supported options in the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/appendix.html?highlight=bootstrap\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: debootstrap\nOSVersion: trusty\nMirrorURL: http://us.archive.ubuntu.com/ubuntu/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSections :\u003c/strong\u003e Group definitions of the container. Each section is defined by the \u003ccode\u003e%\u003c/code\u003e character and a reserved keyword:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e%runscript\n echo \"This is what happens when you run the container...\"\n\n%post\n echo \"Hello from inside the container\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere we can see an overview of the valid sections. The complete reference can be found \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/definition_files.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%setup groups commands to be executed first on the host system\n%files copies files into the container\n%app* redundant to build different containers for each app\n%post installs new software and libraries, write configuration files, create new directories\n%test runs at the very end of the build process to validate the container using a method of your choice\n%environment defines environment variables used at runtime\n%startscript groups files executed when the instance start command is issued\n%runscript groups commands to be executed when the container image is run\n%labels used to add metadata to the file\n%help adds information to the metadata file in the container during the build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Singularity source code contains several example definition files in the \u003ccode\u003e/examples\u003c/code\u003e subdirectory.\nLet\u0027s take its \u003ccode\u003eubuntu\u003c/code\u003e example definition that has been copied to the \u003ccode\u003ematerial/ubuntu\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat /material/ubuntu/Singularity\nBootStrap: debootstrap\nOSVersion: trusty\nMirrorURL: http://us.archive.ubuntu.com/ubuntu/\n\n\n%runscript\n echo \"This is what happens when you run the container...\"\n\n\n%post\n echo \"Hello from inside the container\"\n sed -i \u0027s/$/ universe/\u0027 /etc/apt/sources.list\n apt-get update\n apt-get -y install vim\n apt-get clean\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow let\u0027s use this definition file as a starting point to build our \u003ccode\u003eubuntu.sif\u003c/code\u003e container. Note that the build command requires \u003ccode\u003esudo\u003c/code\u003e privileges when executing in non-docker mode.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/ubuntu\n$ singularity build ubuntu.sif Singularity\n$ singularity run ubuntu.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can also spawn a shell within the container and interact with it. For this we execute the \u003ccode\u003eshell\u003c/code\u003e command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell ubuntu.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDepending on the environment on your host system you may see your prompt change. Let\u0027s see the information of the OS running in the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity\u0026gt; cat /etc/os-release\nNAME=\"Ubuntu\"\nVERSION=\"14.04, Trusty Tahr\"\nID=ubuntu\nID_LIKE=debian\nPRETTY_NAME=\"Ubuntu 14.04 LTS\"\nVERSION_ID=\"14.04\"\nHOME_URL=\"http://www.ubuntu.com/\"\nSUPPORT_URL=\"http://help.ubuntu.com/\"\nBUG_REPORT_URL=\"http://bugs.launchpad.net/ubuntu/\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs an additional experiment, let\u0027s build the lolcow program in two different ways. These two ways will only differ in the bootstrap agent and they will contain the same definitions for the sections. This is described below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%runscript\n fortune | cowsay | lolcat\n\n%files\n install-dependencies.sh install-dependencies.sh\n\n%post\n echo \"Hello from inside the container\"\n sh -x install-dependencies.sh\n\n%environment\n export PATH=/usr/games:$PATH\n export LC_ALL=C\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first way uses the \u003ccode\u003eubuntu.sif\u003c/code\u003e image that we previously built.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: localimage\nFrom: /material/ubuntu/ubuntu.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLet\u0027s build the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/lolcow\n$ singularity build lolcow-localimage.sif lolcow-localimage.def\n$ singularity run lolcow-localimage.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe second way uses the base library, which is commonly used for Singularity containerized environments.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: library\nFrom: ubuntu:18.04\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLet\u0027s build and run the second image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/lolcow\n$ singularity build lolcow-library.sif lolcow-library.def\n$ singularity run lolcow-library.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRemember that Singularity can build containers in several different file formats. The default is to build a \u003ca href=\"https://en.wikipedia.org/wiki/SquashFS\" rel=\"nofollow\"\u003esquashfs\u003c/a\u003e image. The \u003ccode\u003esquashfs\u003c/code\u003e format is compressed and immutable making it a good choice for reproducible, production-grade containers. However, if you want to shell into a container and have more freedom with it, you should build a sandbox (which is just a directory). This is great when you are still developing your container and don\u0027t yet know what should be included in the recipe file.\nThe command would look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity --sandbox build lolcow-library.sif lolcow-library.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-iii-data-intensive-application\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-iii-data-intensive-application\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart III. Data intensive application\u003c/h2\u003e\n\u003cp\u003eFor this part we will execute a Tensorflow program (borrowed from \u003ca href=\"https://github.com/easy-tensorflow/easy-tensorflow/tree/master/3_Neural_Network\"\u003ehere\u003c/a\u003e) that trains a neural network to classify MNIST data of handwriting images. It also logs the progress of the training and saves the result into a file.\nSince we want to avoid installing all the dependencies of tensorflow in a blank Singularity image, we better use the \u003ccode\u003etensorflow/tensorflow:1.15.5\u003c/code\u003e image from the Docker Hub. Additionally we install the \u003ccode\u003ematplotlib\u003c/code\u003e dependency in the \u003ccode\u003e%post\u003c/code\u003e stage.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: tensorflow/tensorflow:1.15.5\n\n%post\n pip install matplotlib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe definition of the image can be found in \u003ca href=\"material/tensorflow/Singularity\"\u003ematerial/tensorflow/Singularity\u003c/a\u003e.\nNow we can build this definition into a \u003ccode\u003e.sif\u003c/code\u003e image file using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/tensorflow\n$ singularity build tensorflow.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis ran the commands we defined in the \u003ccode\u003e%post\u003c/code\u003e section inside a container and\nafterwards saved the state of the container in the image \u003ccode\u003etensorflow.sif\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eLet\u0027s run our Tensorflow program in a container based on the image we just built.\nBefore executing the command we have to copy the python source code files into the new container.\nWe achieve this by adding the \u003ccode\u003e--bind\u003c/code\u003e flag and specifying the source and destintation paths to mount.\nFinally we run the program using the\u003ccode\u003esh\u003c/code\u003e command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --bind /material/tensorflow/:/material tensor.sif sh -c \"cd /material \u0026amp;\u0026amp; python main.py\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis program does not take long to run. Once it finishes, it creates the file \u003ccode\u003eout.png\u003c/code\u003e with the correct and misclassified examples.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/plot.png\"\u003e\u003cimg src=\"images/plot.png\" alt=\"Plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWorth to mention that, for convenience, Singularity\n\u003ca href=\"https://www.sylabs.io/guides/3.2/user-guide/bind_paths_and_mounts.html\" rel=\"nofollow\"\u003ebinds a few important directories by default\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYour home directory\u003c/li\u003e\n\u003cli\u003eThe current working directory\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e/sys\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e/proc\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eothers (depending on the version of Singularity)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-iv-advanced-usage-of-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-iv-advanced-usage-of-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart IV. Advanced Usage of Singularity\u003c/h2\u003e\n\u003cp\u003eFor this part it is necessary to get access to an HPC cluster or set it up locally.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-mpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#mpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMPI\u003c/h3\u003e\n\u003cp\u003eYou can run Singularity containers via MPI. You\u0027ll need to have MPI installed within the container.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf you are working on a single node, you can run MPI within a container.\u003c/li\u003e\n\u003cli\u003eHowever, more commonly you would use the MPI executable on your HPC cluster to execute Singularity containers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe key thing in order to use the system MPI to run Singularity containers is to make sure the MPI installed inside the container is compatible with the MPI installed on the HPC.\nThe easiest way to ensure this is to have the version inside the container be the same version as the MPI module you plan to use on any HPC cluster. You can see these modules with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ module load gcc # load the gcc version of interest\n$ module avail openmpi # see the MPI versions available for that gcc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere is an example of running a Singularity container via MPI. Fist we build the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/mpi\n$ singularity build openmpi.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will prepare the \u003ccode\u003empitest.c\u003c/code\u003e to execute MPI natively on the HPC cluster.\nThe program is simple. It ranks the completion order of MPI executors.\nFor that we launch 2 processes per node on all allocated nodes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ module load gcc openmpi\n$ mpirun -n 2 singularity run openmpi.sif /opt/mpitest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-slurm\" class=\"anchor\" aria-hidden=\"true\" href=\"#slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSLURM\u003c/h3\u003e\n\u003cp\u003eIf your target system is setup with a batch system such as SLURM, a standard way to execute MPI applications is through a batch script. The following example illustrates the context of a batch script for Slurm that aims at starting a Singularity container on each node allocated to the execution of the job. It can easily be adapted for all major batch systems available.\nHere\u0027s an example of running a Singularity container with SLURM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#SBATCH --job-name singularity-mpi\n#SBATCH -N $NNODES # total number of nodes\n#SBATCH --time=00:05:00 # Max execution time\n\nmpirun -n $NP singularity exec openmpi.sif /opt/mpitest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-gpucuda\" class=\"anchor\" aria-hidden=\"true\" href=\"#gpucuda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGPU/CUDA\u003c/h3\u003e\n\u003cp\u003eYou can easily use a Singularity container that does computation on a GPU. Singularity supports NVIDIA\u2019s CUDA GPU compute framework.\nBy using the \u003ccode\u003e--nv\u003c/code\u003e flag when running Singularity, the NVIDIA drivers in the HPC cluster are dynamically mounted into the container at run time. The container should provide the CUDA toolkit, using a version of the toolkit that is compatible with the NVIDIA driver version in the HPC.\u003c/p\u003e\n\u003cp\u003eHere\u0027s an example of running a Singularity container based on a Docker container that provides GPU-using software.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run --nv docker://pytorch/pytorch:1.6.0-cuda10.1-cudnn7-runtime\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conclusion\" class=\"anchor\" aria-hidden=\"true\" href=\"#conclusion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConclusion\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eWe have learned the necessary commands of Singularity to start producing containers that can run in HPC environments.\u003c/li\u003e\n\u003cli\u003eSingularity enables isolation, reproducibility and security in HPC environments.\u003c/li\u003e\n\u003cli\u003eIts use is mostly targeted to scientific applications with intensive performance requirements.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sylabs/singularity-userdocs/blob/master/mpi.rst\"\u003ehttps://github.com/sylabs/singularity-userdocs/blob/master/mpi.rst\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/maheshbabuadapa/Singularity-Tutorial\"\u003ehttps://github.com/maheshbabuadapa/Singularity-Tutorial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs-research-it.berkeley.edu/services/high-performance-computing/user-guide/software/using-software/using-singularity-savio\" rel=\"nofollow\"\u003ehttps://docs-research-it.berkeley.edu/services/high-performance-computing/user-guide/software/using-software/using-singularity-savio\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bdusell/singularity-tutorial\"\u003ehttps://github.com/bdusell/singularity-tutorial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1637825788.0 + "updated_at": 1676460377.0 }, { "data_format": 2, @@ -6892,279 +6583,231 @@ var data = "filenames": [ "Singularity" ], - "full_name": "ipelupessy/test-singularity", + "full_name": "asfistonlavie/TEFLoN2", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-teflon2\" class=\"anchor\" aria-hidden=\"true\" href=\"#teflon2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTEFLoN2\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1522955804.0 + "updated_at": 1658824620.0 }, { "data_format": 2, - "description": "informations and configurations for OpenFLUID containerization", + "description": null, "filenames": [ - "v2.1.3/Singularity", - "v2.1.9/Singularity", - "v1.7.2/Singularity", - "v2.1.5/Singularity", - "v2.1.4/Singularity", - "v2.1.2/Singularity", - "v2.1.8/Singularity", - "v2.1.6/Singularity", - "v2.1.7/Singularity", - "v2.0.2/Singularity", - "v2.1.10/Singularity", - "v2.1.11/Singularity" + "v4.7.1/Singularity", + "v4.9.1/Singularity" ], - "full_name": "OpenFLUID/openfluid-containers", + "full_name": "yh549848/singularity-code-server-stacks", "latest_release": null, - "readme": "\u003cp\u003eThis repository contains configuration files for Docker and Singularity containerization of OpenFLUID.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1616516042.0 + "updated_at": 1676597534.0 }, { "data_format": 2, "description": null, "filenames": [ - "Recipes/Singularity_pytorch", - "Recipes/Singularity_pytorch_full", - "Recipes/Singularity_spark_full", - "Recipes/Singularity_mpich", - "Recipes/Singularity_example", - "Recipes/Singularity_ompi", - "Recipes/Singularity_tensorflow", - "Recipes/Singularity_spark" + "envs/containers/Singularity" ], - "full_name": "Yasmim-Fernandes/Ufscar-hpc-template-ci", + "full_name": "EnriqueDoster/AMRplusplus", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-pr\u00e9-requisitos\" class=\"anchor\" aria-hidden=\"true\" href=\"#pr\u00e9-requisitos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePr\u00e9-requisitos\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sele\u00e7\u00e3o-do-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#sele\u00e7\u00e3o-do-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSele\u00e7\u00e3o do Recipe\u003c/h2\u003e\n\u003cp\u003eAdicione o caminho do \u003cem\u003esingularity recipe\u003c/em\u003e desejado na vari\u00e1vel RECIPE no github (projeto \u0026gt;\u0026gt; Settings \u0026gt;\u0026gt; Secrets \u0026gt;\u0026gt; New Secret).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-template-integra\u00e7\u00e3o-cont\u00ednua-github\" class=\"anchor\" aria-hidden=\"true\" href=\"#template-integra\u00e7\u00e3o-cont\u00ednua-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemplate Integra\u00e7\u00e3o Cont\u00ednua Github\u003c/h1\u003e\n\u003cp\u003eEsse projeto o template para uso do cluster da UFSCar, contendo integra\u00e7\u00e3o cont\u00ednua com o Google Drive e Amazon S3.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requisitos-para-google-drive\" class=\"anchor\" aria-hidden=\"true\" href=\"#requisitos-para-google-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequisitos para Google Drive\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eEntre no \u003ca href=\"https://console.developers.google.com/apis/credentials\" rel=\"nofollow\"\u003econsole de credenciais de API do Google\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSe ainda n\u00e3o houver um projeto, crie um com permiss\u00e3o para a \"Google Drive API\".\u003c/li\u003e\n\u003cli\u003eClique em \"Criar credenciais\".\u003c/li\u003e\n\u003cli\u003eSelecione \"ID do cliente do OAuth\".\u003c/li\u003e\n\u003cli\u003eEm \"Tipo de aplicativo\", selecione \"App para computador\".\u003c/li\u003e\n\u003cli\u003eD\u00ea um nome de identifica\u00e7\u00e3o para as credenciais e clique em \"criar\". V\u00e3o aparecer dois dados (\"Seu ID de cliente\" e \"Sua chave secreta de cliente\"), precisaremos dos dois no passo seguinte.\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 13\nclient_id\u0026gt; conte\u00fado de \"Seu ID de cliente\"\nclient_secret\u0026gt; conte\u00fado de \"Sua chave secreta de cliente\"\nscope\u0026gt; 1\nroot_folder_id\u0026gt; deixe em branco\nservice_account_file\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/n\u0026gt; n\n\nCopie o url e cole no navegador no computador local. Autorize e:\n\nEnter verification code\u0026gt; c\u00f3digo fornecido pelo navegador ap\u00f3s autoriza\u00e7\u00e3o\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requisitos-para-amazon-s3\" class=\"anchor\" aria-hidden=\"true\" href=\"#requisitos-para-amazon-s3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequisitos para Amazon S3\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eEntre na \u003ca href=\"console.aws.amazon.com\"\u003eAWS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClique na seta ao lado de seu nome de usu\u00e1rio e em \"My Security Credentials\".\u003c/li\u003e\n\u003cli\u003eNa se\u00e7\u00e3o \"Access Keys\", clique em \"Create New Access Key\".\u003c/li\u003e\n\u003cli\u003eNa janela que aparece, clique em \"Show Access Key\".\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 4\nprovider\u0026gt; 1\nenv_auth\u0026gt; 1\naccess_key_id\u0026gt; conte\u00fado de \"Access Key ID\"\nsecret_access_key\u0026gt; conte\u00fado de \"Secret Access Key\"\nregion\u0026gt; 16\nendpoint\u0026gt; deixe em branco\nlocation_constraint\u0026gt; 16\nacl\u0026gt; deixe em branco\nserver_side_encryption\u0026gt; deixe em branco\nsse_kms_key_id\u0026gt; deixe em branco\nstorage_class\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-amr-bioinformatic-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-bioinformatic-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ bioinformatic pipeline\u003c/h1\u003e\n\u003cp\u003e(\u003ca href=\"https://megares.meglab.org/\" rel=\"nofollow\"\u003ehttps://megares.meglab.org/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eAMR++ is a bioinformatic pipeline meant to aid in the analysis of raw sequencing reads to characterize the profile of antimicrobial resistance genes, or resistome. AMR++ was developed to work in conjuction with the the MEGARes database which contains sequence data for approximately 9,000 hand-curated antimicrobial resistance genes accompanied by an annotation structure that is optimized for use with high throughput sequencing and metagenomic analysis. The acyclical annotation graph of MEGARes allows for accurate, count-based, hierarchical statistical analysis of resistance at the population level, much like microbiome analysis, and is also designed to be used as a training database for the creation of statistical classifiers.\u003c/p\u003e\n\u003cp\u003eThe goal of many metagenomics studies is to characterize the content and relative abundance of sequences of interest from the DNA of a given sample or set of samples. You may want to know what is contained within your sample or how abundant a given sequence is relative to another.\u003c/p\u003e\n\u003cp\u003eOften, metagenomics is performed when the answer to these questions must be obtained for a large number of targets where techniques like multiplex PCR and other targeted methods would be too cumbersome to perform. AMR++ can process the raw data from the sequencer, identify the fragments of DNA, and count them. It also provides a count of the polymorphisms that occur in each DNA fragment with respect to the reference database.\u003c/p\u003e\n\u003cp\u003eAdditionally, you may want to know if the depth of your sequencing (how many reads you obtain that are on target) is high enough to identify rare organisms (organisms with low abundance relative to others) in your population. This is referred to as rarefaction and is calculated by randomly subsampling your sequence data at intervals between 0% and 100% in order to determine how many targets are found at each depth.\u003c/p\u003e\n\u003cp\u003eWith AMR++, you will obtain alignment count files for each sample that are combined into a count matrix that can be analyzed using any statistical and mathematical techniques that can operate on a matrix of observations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/FAQs.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/update_details.md\"\u003eDetails on AMR++ updates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-amr-demonstration\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ demonstration\u003c/h2\u003e\n\u003cp\u003eCreate the anaconda environment for AMR++. This will work for both the nextflow version and snakemake version.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -c conda-forge -n mamba_base mamba\nconda activate mamba_base\nmamba create -c conda-forge -c bioconda -n AMR++ snakemake nextflow git make cxx-compiler singularity\nmamba activate AMR++\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eClone the AMR++ repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/EnriqueDoster/AMRplusplus.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBrief tutorial for nextflow pipeline test run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e AMRplusplus\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run command to perform the demonstration pipeline using the singularity profile\u003c/span\u003e\nnextflow run main_AMR++.nf -profile singularity --pipeline demo\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-using-amr-to-analyze-your-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-amr-to-analyze-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing AMR++ to analyze your data\u003c/h1\u003e\n\u003cp\u003eAMR++ is customizable to suit your computing needs and analyze your data. Primarily, the \u003ccode\u003e-profile\u003c/code\u003e paramater allows you to choose between running AMR++ using a singularity container, docker container, anaconda packages, or a local installation of your software.\nAll parameters used to control how AMR++ analyzes your data can also be changed as needed in a variety of ways. For full information, review this \u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003econfiguration document.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a brief example, the default parameters were run using this command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile singularity --pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo change the reads that were analyzed, you should specify the ```--reads`` parameters. Here, we can use regular expressions to point to your samples in a different directory.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile singularity --pipeline demo --reads \"path/to/your/reads/*_R{1,2}.fastq.gz\" \u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-choosing-a-modified-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#choosing-a-modified-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoosing a modified pipeline\u003c/h2\u003e\n\u003cp\u003eAMR++ analyzes data by combining workflows that takes a set of sequencing reads through various bioinformatic software. We recommend our standard AMR++ pipeline as a comprehensive way to start from raw sequencing reads, QC assessment, host DNA removal, and resistome analysis with MEGARes. However, users might only want to replicate portions of the pipeline and have more control over their computing needs. Using the \u003ccode\u003e--pipeline\u003c/code\u003e parameter, users can change how AMR++ runs.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimple demonstration\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline fast_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR_wKraken\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\nNon-host reads \u0026gt; Microbiome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epipeline fragments\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline multiqc\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate sample QC\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline trim\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming using trimmomatic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline rmhost\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to host DNA using bwa and remove contaminants\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline resistome\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to MEGARes using bwa, perform rarefaction and resistome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline kraken\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eClassify reads taxanomically using kraken\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1607370441.0 + "updated_at": 1665339720.0 }, { "data_format": 2, - "description": "Singularity recipe files for APSIM Classic (https://github.com/APSIMInitiative/APSIMClassic)", + "description": "Testing SingularityHub integration", "filenames": [ - "Singularity", - "Singularity.7.10-r49ace54f9c8a670190aef9d8d0fb9d5477bb1534", - "Singularity.7.9-r4047" + "Singularity.fun" ], - "full_name": "powerPlant/apsim-srf", + "full_name": "mmarinriera/Singularity_training", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the APSIM Classic version of the Agricultural Production Systems sIMulator\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainer-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#maintainer-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainer Notes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRecipes for APSIM 7.9 use the upstream SVN repository (no longer available)\u003c/li\u003e\n\u003cli\u003ePlease see comments inside the recipes for the reasons why some upstream files are overwritten during the build process\u003c/li\u003e\n\u003cli\u003eThe Cotton Model requires a password, which needs to be obtained by the model owner and placed under \u003ccode\u003efiles/CottonPassword.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1586904956.0 + "updated_at": 1551276494.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "envs/containers/Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-pytorch-a40", + "full_name": "Microbial-Ecology-Group/MHplusplus", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-testing-image-for-a40-gpupytorch-\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-image-for-a40-gpupytorch-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etesting image for a40 gpu/pytorch \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-pytorch-a40/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-pytorch-a40/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003edocker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-pytorch-a40:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell oras://ghcr.io/truatpasteurdotfr/singularity-docker-pytorch-a40:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-----in-development----\" class=\"anchor\" aria-hidden=\"true\" href=\"#----in-development----\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e--- In development ---\u003c/h1\u003e\n\u003ch1\u003e\u003ca id=\"user-content-mh-bioinformatic-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#mh-bioinformatic-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMH++ bioinformatic pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/FAQs.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/update_details.md\"\u003eDetails on AMR++ updates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-amr-demonstration\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ demonstration\u003c/h2\u003e\n\u003cp\u003eCreate the anaconda environment for AMR++. This will work for both the nextflow version and snakemake version.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -c conda-forge -n mamba_base mamba\nconda activate mamba_base\nmamba create -c conda-forge -c bioconda -n AMR++ snakemake nextflow git make cxx-compiler singularity\nmamba activate AMR++\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eClone the AMR++ repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/EnriqueDoster/AMRplusplus.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBrief tutorial for nextflow pipeline test run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e AMRplusplus\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run command to perform the demonstration pipeline using the singularity profile\u003c/span\u003e\nnextflow run main_AMR++.nf -profile singularity --pipeline demo\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-using-amr-to-analyze-your-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-amr-to-analyze-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing AMR++ to analyze your data\u003c/h1\u003e\n\u003cp\u003eAMR++ is customizable to suit your computing needs and analyze your data. Primarily, the \u003ccode\u003e-profile\u003c/code\u003e paramater allows you to choose between running AMR++ using a singularity container, docker container, anaconda packages, or a local installation of your software.\nAll parameters used to control how AMR++ analyzes your data can also be changed as needed in a variety of ways. For full information, review this \u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003econfiguration document.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a brief example, the default parameters were run using this command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile singularity --pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo change the reads that were analyzed, you should specify the ```--reads`` parameters. Here, we can use regular expressions to point to your samples in a different directory.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile singularity --pipeline demo --reads \"path/to/your/reads/*_R{1,2}.fastq.gz\" \u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-choosing-a-modified-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#choosing-a-modified-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoosing a modified pipeline\u003c/h2\u003e\n\u003cp\u003eAMR++ analyzes data by combining workflows that takes a set of sequencing reads through various bioinformatic software. We recommend our standard AMR++ pipeline as a comprehensive way to start from raw sequencing reads, QC assessment, host DNA removal, and resistome analysis with MEGARes. However, users might only want to replicate portions of the pipeline and have more control over their computing needs. Using the \u003ccode\u003e--pipeline\u003c/code\u003e parameter, users can change how AMR++ runs.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimple demonstration\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline fast_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR_wKraken\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\nNon-host reads \u0026gt; Microbiome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epipeline fragments\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline multiqc\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate sample QC\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline trim\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming using trimmomatic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline rmhost\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to host DNA using bwa and remove contaminants\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline resistome\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to MEGARes using bwa, perform rarefaction and resistome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline kraken\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eClassify reads taxanomically using kraken\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1636479915.0 + "updated_at": 1667397676.0 }, { "data_format": 2, - "description": null, + "description": "Material for the GPU course ML-variant", "filenames": [ - "Singularity" + "singularity/Singularity.tensorflow_gpu-py3", + "singularity/Singularity.pytorch_gpu-py3", + "singularity/Singularity.tensorflow_cpu-py3" ], - "full_name": "Samip1211/MongoImage", + "full_name": "mmoelle1/GPU_Cource_ML", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1565456485.0 + "updated_at": 1667300805.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.PhaGCN" ], - "full_name": "GeertvanGeest/test_shub", + "full_name": "cschu/phagcn_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-test_shub\" class=\"anchor\" aria-hidden=\"true\" href=\"#test_shub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etest_shub\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1618210665.0 + "updated_at": 1669215943.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "Singularity.hpc" + "SingularityRecipe" ], - "full_name": "hqhv/oneapi", + "full_name": "CRC-901-On-the-Fly-Computing/executor-bootup", "latest_release": null, + "readme": "\u003cp\u003eThis repository contains shell scripts that are supposed to be executed within a Docker container when basic services are deployed in the Testbed.\nThe shell script downloads the source code, runs the verification, runs the compilation and finally launches the SEDE executor.\nThe Docker container that is created for basic services has the following file system structure:\u003c/p\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003cp\u003e\u251c\u2500 cpachecker\n\u251c\u2500 hooks\u003cbr\u003e\n\u251c\u2500 sede\u003cbr\u003e\n\u251c\u2500 src\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Folder src contains the C, Java or Python code of basic services. This container must contain a compile.sh for the compilation. The compile script may call another build tool like gradle or make.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Source code is downloaded from a ServiceCodeProvider repository.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Cerificates (*.proof) for C implementations must be in the same directory as the .*c file and must have a specific file name pattern: _.proof. For example, the name of the proof for the analysis sign for the C implementation service_grey_cpu.c must be service_grey_cpu_sign.proof.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; The configuration files that are necessary for the SEDE executor must be in the folder src/main/resources/config.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Folder hooks contains shell scripts for downloading the source code, running the verification, and running the compilation.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Folder sede contains the SEDE executor logic.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; The script run.sh executes all scripts in the hooks folder in alphanumerical order and starts the SEDE server in the end.\u003c/p\u003e\n\u003cp\u003eInstallation\nThe following software needs to be installed inside the Docker container:\u003c/p\u003e\n\u003cp\u003ecurl |\ngit |\njavac / gcc |\ngradle / make\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1611066291.0 - }, - { - "data_format": 2, - "description": "OpenFOAM atmospheric test cases", - "filenames": [ - "Singularity" - ], - "full_name": "hertzsprung/AtmosTests", - "latest_release": "jshaw-thesis", - "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1517845038.0 + "updated_at": 1669321582.0 }, { "data_format": 2, - "description": "cutadapt removes adapter sequences from sequencing reads.", + "description": "Project for I519", "filenames": [ - "2.10/Singularity" + "SingularityPRJ.def" ], - "full_name": "pscedu/singularity-cutadapt", + "full_name": "ginnymortensen/gamortenPRJ", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-cutadapt/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-cutadapt/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1a971ebb81368ce57d4702d1ac0fa0534e1de4e11c2f3a1fc98cb5c5203ae017/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6375746164617074\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1a971ebb81368ce57d4702d1ac0fa0534e1de4e11c2f3a1fc98cb5c5203ae017/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6375746164617074\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-cutadapt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/460062e41af5fcb53936a892aaf05699f5552d0c369d8c9d05308a15ecb18032/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6375746164617074\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/460062e41af5fcb53936a892aaf05699f5552d0c369d8c9d05308a15ecb18032/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6375746164617074\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-cutadapt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5403066544c7ca2002d9c3183ace39dbb1aabbed23f96489c23f815b84b3a31f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6375746164617074\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5403066544c7ca2002d9c3183ace39dbb1aabbed23f96489c23f815b84b3a31f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6375746164617074\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-cutadapt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d60627004d3b1d480327260e840077df4c84113f1dcd462a39d25dfc08daae2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6375746164617074\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d60627004d3b1d480327260e840077df4c84113f1dcd462a39d25dfc08daae2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6375746164617074\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-cutadapt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-cutadapt\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-cutadapt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-cutadapt\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://cutadapt.readthedocs.io/en/stable\" rel=\"nofollow\"\u003ecutadapt\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ecutadapt\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/cutadapt/2.10\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/cutadapt\u003c/code\u003e as \u003ccode\u003e2.10.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629217124.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1670041069.0 }, { "data_format": 2, - "description": "Contains the material presented at CCD lab meeting on 11/13/2019", + "description": null, "filenames": [ - "examples/Singularity.pytorch-docker", - "examples/Singularity.julia", - "examples/Singularity.conda", - "examples/Singularity.fasttext" + "Singularity.bwa", + "Singularity.gatk" ], - "full_name": "CNCLgithub/singularity_workshop_2019", + "full_name": "mkgoita/containers", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1573678647.0 + "updated_at": 1671889682.0 }, { "data_format": 2, - "description": "An adaptive planner for IPC ", + "description": null, "filenames": [ "Singularity" ], - "full_name": "zyf505/CPC0", + "full_name": "saviodot/singularity_MACS2", "latest_release": null, - "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCopyright (C) 2003-2016 Malte Helmert\nCopyright (C) 2008-2016 Gabriele Roeger\nCopyright (C) 2010-2016 Jendrik Seipp\nCopyright (C) 2010, 2011, 2013-2016 Silvan Sievers\nCopyright (C) 2012-2016 Florian Pommerening\nCopyright (C) 2016 Martin Wehrle\nCopyright (C) 2013, 2015 Salome Simon\nCopyright (C) 2014, 2015 Patrick von Reth\nCopyright (C) 2015 Manuel Heusner, Thomas Keller\nCopyright (C) 2009-2014 Erez Karpas\nCopyright (C) 2014 Robert P. Goldman\nCopyright (C) 2010-2012 Andrew Coles\nCopyright (C) 2010, 2012 Patrik Haslum\nCopyright (C) 2003-2011 Silvia Richter\nCopyright (C) 2009-2011 Emil Keyder\nCopyright (C) 2010, 2011 Moritz Gronbach, Manuela Ortlieb\nCopyright (C) 2011 Vidal Alc\u00e1zar Saiz, Michael Katz, Raz Nissim\nCopyright (C) 2010 Moritz Goebelbecker\nCopyright (C) 2007-2009 Matthias Westphal\nCopyright (C) 2009 Christian Muise\n\nFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_macs2\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_macs2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_MACS2\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1613781111.0 + "updated_at": 1616690622.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.mg5_ma5_madspin" + "Singularity.4.0.14", + "Singularity.4.4.2" ], - "full_name": "HenryDayHall/madspin_singularity", + "full_name": "sschmeier/container-fishtank-gpu", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1602173663.0 + "updated_at": 1624344477.0 }, { "data_format": 2, - "description": "Singularity Ubuntu container with the Paraview stack", + "description": "TransDecoder identifies candidate coding regions within transcript sequences.", "filenames": [ "Singularity" ], - "full_name": "CHPC-UofU/Singularity-ubuntu-paraview", + "full_name": "sghignone/TransDecoder", "latest_release": null, + "readme": "\u003ch2\u003e\u003ca id=\"user-content-transdecoder-v550\" class=\"anchor\" aria-hidden=\"true\" href=\"#transdecoder-v550\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTransDecoder v.5.5.0\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5159\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe current build is based on the bioconda distribution of Brian Haas\u0027 transdecoder 5.5.0.\u003c/p\u003e\n\u003cp\u003eTransDecoder identifies candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to the genome using Tophat and Cufflinks.\u003c/p\u003e\n\u003cp\u003eVisit the project \u003ca href=\"https://github.com/TransDecoder/TransDecoder/wiki\"\u003ewiki\u003c/a\u003e for all TransDecoder documentation.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1492111584.0 - }, - { - "data_format": 2, - "description": "Singularity dependency container, neuroglia-core + DWI software (camino, mrtrix, unring)", - "filenames": [ - "Singularity", - "Singularity.v1.4.1" + "topics": [ + "miniconda3", + "singularity", + "singularity-hub", + "singularity-recipe" ], - "full_name": "khanlab/neuroglia-dwi", - "latest_release": "v1.5", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-neuroglia-dwi\" class=\"anchor\" aria-hidden=\"true\" href=\"#neuroglia-dwi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eneuroglia-dwi\u003c/h1\u003e\n\u003cp\u003eSingularity image for neuroimaging dependencies. Supplements \u003ca href=\"http://www.github.com/khanlab/neuroglia-core\"\u003ehttp://www.github.com/khanlab/neuroglia-core\u003c/a\u003e with additional DWI software. Includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emrtrix3\u003c/li\u003e\n\u003cli\u003ecamino\u003c/li\u003e\n\u003cli\u003eunring\u003c/li\u003e\n\u003cli\u003eDKE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCommits and pull-requests to this repository rebuild the \u003ccode\u003elatest\u003c/code\u003e version on Docker Hub, which is updated nightly to Singularity Hub. Releases on Docker Hub and Singularity Hub are built whenever a tag named \u003ccode\u003ev.*\u003c/code\u003e is committed. To avoid re-building on minor commits (e.g. changes to documentation), use \u003ccode\u003e[skip ci]\u003c/code\u003e in the commit message.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/khanlab/neuroglia-core\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6231f7b29a2b680358e7d9c865672c500cdd9b75198b457634e3cc4c3a78cb70/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f6e6575726f676c69612d6477692e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/neuroglia-dwi.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/451\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDocker:\n\u003ccode\u003edocker pull khanlab/neuroglia-dwi\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSingularity:\n\u003ccode\u003esingularity pull khanlab/neuroglia-dwi\u003c/code\u003e\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1591844442.0 + "updated_at": 1612624905.0 }, { "data_format": 2, - "description": "Singularity images for everyday research work.", + "description": null, "filenames": [ - "Singularity.deepo-cpu", - "Singularity.pymc3", - "Singularity.datasci", - "Singularity.deepo-cpu-nlp" + "Singularity" ], - "full_name": "hans/research-labs", + "full_name": "thomas-robinson/single-point-land", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1648667607.0 + "updated_at": 1613156529.0 }, { "data_format": 2, - "description": "The GATK is the industry standard for identifying SNPs and indels in germline DNA and RNAseq data. ", + "description": "Computational Analysis of gene Family Evolution (CAFE)", "filenames": [ - "4.2.0.0/Singularity", - "4.1.9.0/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-gatk", - "latest_release": "v4.2.0.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-gatk/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-gatk/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/93c4f349e621f15ffd8933b4c7d4ea8eea7b6f9156528a6b483b1b47d8064a91/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6761746b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93c4f349e621f15ffd8933b4c7d4ea8eea7b6f9156528a6b483b1b47d8064a91/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6761746b\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-gatk\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/744613aa898037bbfe237a7943e118e4fe72355964b8726f785c33e44de131e0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6761746b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/744613aa898037bbfe237a7943e118e4fe72355964b8726f785c33e44de131e0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6761746b\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-gatk\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5d9f7edad1535dfc343a82ee05a1cee751f4185de5e88e9959f1e306baf3af56/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6761746b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d9f7edad1535dfc343a82ee05a1cee751f4185de5e88e9959f1e306baf3af56/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6761746b\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-gatk\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/936434bf1d55721ba57e95e4bb99cfb8b78d330106b7ffebafb8911c75556071/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6761746b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/936434bf1d55721ba57e95e4bb99cfb8b78d330106b7ffebafb8911c75556071/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6761746b\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-gatk\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-gatk\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-gatk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-gatk\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f0a6e98fde6f4e7dd338612de8a154b1169c4572acc8ca3ffed117a81e28d4be/68747470733a2f2f7468656d652e7a646173736574732e636f6d2f7468656d655f6173736574732f323337383336302f646630383566313534333231666161633931353964646135376635303130336238376134663734332e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f0a6e98fde6f4e7dd338612de8a154b1169c4572acc8ca3ffed117a81e28d4be/68747470733a2f2f7468656d652e7a646173736574732e636f6d2f7468656d655f6173736574732f323337383336302f646630383566313534333231666161633931353964646135376635303130336238376134663734332e706e67\" alt=\"Logo\" data-canonical-src=\"https://theme.zdassets.com/theme_assets/2378360/df085f154321faac9159dda57f50103b87a4f743.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://gatk.broadinstitute.org/hc/en-us\" rel=\"nofollow\"\u003eGATK\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003egatk\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/gatk/4.1.9.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/gatk\u003c/code\u003e as \u003ccode\u003e4.1.9.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "sghignone/CAFE", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cafe\" class=\"anchor\" aria-hidden=\"true\" href=\"#cafe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCAFE\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-computational-analysis-of-gene-family-evolution-cafe\" class=\"anchor\" aria-hidden=\"true\" href=\"#computational-analysis-of-gene-family-evolution-cafe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComputational Analysis of gene Family Evolution (CAFE)\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5151\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe current build is based on the bioconda distribution of the Hahn Lab CAFE v.4.2.1.\u003c/p\u003e\n\u003cp\u003eThe purpose of CAFE is to analyze changes in gene family size in a way that accounts for phylogenetic history and provides a statistical foundation for evolutionary inferences. The program uses a birth and death process to model gene gain and loss across a user-specified phylogenetic tree. The distribution of family sizes generated under this model can provide a basis for assessing the significance of the observed family size differences among taxa.\u003c/p\u003e\n\u003cp\u003eCAFE v4.2.1 is the latest in a regular series of releases to the CAFE application. The manual and various tutorials may be viewed on the website (\u003ca href=\"https://hahnlab.github.io/CAFE/\" rel=\"nofollow\"\u003ehttps://hahnlab.github.io/CAFE/\u003c/a\u003e) . This document describes how to download and use CAFE v4.2.1. (credits: \u003ca href=\"https://github.com/hahnlab/CAFE\"\u003ehttps://github.com/hahnlab/CAFE\u003c/a\u003e)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [ "singularity", - "bioinformatics" + "singularity-hub", + "singularity-recipe", + "miniconda3" ], - "updated_at": 1628991719.0 + "updated_at": 1612624956.0 }, { "data_format": 2, - "description": "Pipeline for preprocessing fMRI data ", + "description": null, "filenames": [ - "TheBrainPipeline/preprocessing/Singularity_Containers/Singularity", - "TheBrainPipeline/preprocessing/Singularity_Containers/.ipynb_checkpoints/Singularity-checkpoint" + "Singularity" ], - "full_name": "niblunc/NIBL", + "full_name": "juanca09/default", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-neuropsychology-of-ingestive-behavior-lab\" class=\"anchor\" aria-hidden=\"true\" href=\"#neuropsychology-of-ingestive-behavior-lab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNeuropsychology of Ingestive Behavior Lab\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/niblunc/TheBrainPipeline/tree/master/TheBrainPipeline\"\u003eTheBrainPipeline\u003c/a\u003e : analysis scripts and files, such as decoding\u003cbr\u003e\n\u003ca href=\"https://github.com/niblunc/TheBrainPipeline/tree/master/OsirixFiles\"\u003eOsirix_Files\u003c/a\u003e : scripts used to prep data from OsiriX \u003cbr\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-default\" class=\"anchor\" aria-hidden=\"true\" href=\"#default\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edefault\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1583185636.0 + "updated_at": 1612274393.0 }, { "data_format": 2, @@ -7172,67 +6815,69 @@ var data = "filenames": [ "Singularity" ], - "full_name": "ctpelok77/ipc2018-delfi", + "full_name": "kristinebilgrav/Retro_files", "latest_release": null, - "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-retro_files\" class=\"anchor\" aria-hidden=\"true\" href=\"#retro_files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRetro_files\u003c/h1\u003e\n\u003cp\u003eContains files used to run retroseq and analyse outcome\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1660771542.0 + "updated_at": 1621431178.0 }, { "data_format": 2, "description": null, "filenames": [ - "0.39/Singularity.0.39" + "Singularity" ], - "full_name": "yh549848/singularity-trimmomatic", + "full_name": "kristinebilgrav/Vep_retro_containers", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-jitterbug\" class=\"anchor\" aria-hidden=\"true\" href=\"#jitterbug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJitterbug\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1602826148.0 + "updated_at": 1617190998.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.snowflake" + "Singularity" ], - "full_name": "longgangfan/ubuntu2004uwgeo-sig", + "full_name": "nicspalla/openmpi_centos_x86_64", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ubuntu2004uwgeo-sig\" class=\"anchor\" aria-hidden=\"true\" href=\"#ubuntu2004uwgeo-sig\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eubuntu2004uwgeo-sig\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-openmpi_centos_x86_64\" class=\"anchor\" aria-hidden=\"true\" href=\"#openmpi_centos_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopenmpi_centos_x86_64\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1621586669.0 + "updated_at": 1605260984.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity" + "Singularity.v1.0.0" ], - "full_name": "ddesvillechabrol/lora", + "full_name": "baxpr/segwarp", "latest_release": null, + "readme": "\u003cp\u003eWarp SEG output of a multi-atlas assessor to MNI space using the supplied SPM warp field.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1678461554.0 + "updated_at": 1605062943.0 }, { "data_format": 2, - "description": null, + "description": "Code used to generate summaries, models and figures for article \"A field-wide assessment of differential high throughput sequencing reveals widespread bias\".", "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "mohammadreza-sheykhmousa/FFS", + "full_name": "tpall/geo-htseq-paper", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-polygonal-building-segmentation-by-frame-field-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#polygonal-building-segmentation-by-frame-field-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/h1\u003e\n\u003cp\u003eWe add a frame field output to an image segmentation neural network to improve segmentation quality\nand provide structural information for the subsequent polygonization step.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/frame_field_sample.png\"\u003e\u003cimg src=\"images/frame_field_sample.png\" width=\"512\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 1: Close-up of our additional frame field output on a test image.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/model_training.png\"\u003e\u003cimg src=\"images/model_training.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 2: Given an overhead image, the model outputs an edge mask, an interior mask,\n and a frame field for buildings. The total loss includes terms that align the masks and\n frame field to ground truth data as well as regularizers to enforce smoothness of the\n frame field and consistency between the outputs.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/schematic_polygonization.png\"\u003e\u003cimg src=\"images/schematic_polygonization.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 3: Given classification maps and a frame field as input, we optimize skeleton polylines to\n align to the frame field using an Active Skeleton Model (ASM) and detect corners using\n the frame field, simplifying non-corner vertices.\n\u003c/p\u003e\n\u003cp\u003eThis repository contains the official code for the paper:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://www-sop.inria.fr/members/Nicolas.Girard/\" rel=\"nofollow\"\u003eNicolas Girard\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/smirnov/\" rel=\"nofollow\"\u003eDmitriy Smirnov\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/jsolomon/\" rel=\"nofollow\"\u003eJustin Solomon\u003c/a\u003e,\n\u003ca href=\"https://www-sop.inria.fr/members/Yuliya.Tarabalka/\" rel=\"nofollow\"\u003eYuliya Tarabalka\u003c/a\u003e\u003cbr\u003e\nCVPR 2021\u003cbr\u003e\n\u003cstrong\u003e[\u003ca href=\"https://arxiv.org/abs/2004.14875\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, \u003ca href=\"https://www.youtube.com/watch?v=226pPTBsNJ8\u0026amp;t=8s\" rel=\"nofollow\"\u003evideo\u003c/a\u003e]\u003c/strong\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-git-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#git-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGit submodules\u003c/h2\u003e\n\u003cp\u003eThis project uses various git submodules that should be cloned too.\u003c/p\u003e\n\u003cp\u003eTo clone a repository including its submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive --jobs 8 \u0026lt;URL to Git repo\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you already have cloned the repository and now want to load it\u2019s submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --init --recursive --jobs 8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more about explanations about using submodules and git, see \u003ca href=\"SUBMODULES.md\"\u003eSUBMODULES.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eThe easiest way to setup environment is to use the Docker image provided in the \u003ca href=\"docker\"\u003edocker\u003c/a\u003e (see README inside the folder).\u003c/p\u003e\n\u003cp\u003eOnce the docker container is built and launched, execute the \u003ca href=\"setup.sh\"\u003esetup.sh\u003c/a\u003e script inside to install required packages.\u003c/p\u003e\n\u003cp\u003eThe environment in the container is now ready for use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda environment\u003c/h2\u003e\n\u003cp\u003eAlternatively you can install all dependencies in a conda environment.\nI provide my environment specifications in the \u003ca href=\"environment.yml\"\u003eenvironment.yml\u003c/a\u003e which you can use to create your environment own with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h1\u003e\n\u003cp\u003eSeveral datasets are used in this work.\nWe typically put all datasets in a \"data\" folder which we link to the \"/data\" folder in the container (with the \u003ccode\u003e-v\u003c/code\u003e argument when running the container).\nEach dataset has it\u0027s own sub-folder, usually named with a short version of that dataset\u0027s name.\nEach dataset sub-folder should have a \"raw\" folder inside containing all the original folders and files fo the datset.\nWhen pre-processing data, \"processed\" folders will be created alongside the \"raw\" folder.\u003c/p\u003e\n\u003cp\u003eFor example, here is an example working file structure inside the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/data \n|-- AerialImageDataset\n |-- raw\n |-- train\n | |-- aligned_gt_polygons_2\n | |-- gt\n | |-- gt_polygonized\n | |-- images\n `-- test\n |-- aligned_gt_polygons_2\n |-- images\n`-- mapping_challenge_dataset\n |-- raw\n |-- train\n | |-- images\n | |-- annotation.json\n | `-- annotation-small.json\n `-- val\n `-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf however you would like to use a different folder for the datasets (for example while not using Docker),\nyou can change the path to datasets in config files.\nYou can modify the \"data_dir_candidates\" list in the config to only include your path.\nThe training script checks this list of paths one at a time and picks the first one that exists.\nIt then appends the \"data_root_partial_dirpath\" directory to get to the dataset.\u003c/p\u003e\n\u003cp\u003eYou can find some of the data we used in this shared \"data\" folder: \u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inria-aerial-image-labeling-dataset\" class=\"anchor\" aria-hidden=\"true\" href=\"#inria-aerial-image-labeling-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInria Aerial Image Labeling Dataset\u003c/h2\u003e\n\u003cp\u003eLink to the dataset: \u003ca href=\"https://project.inria.fr/aerialimagelabeling/\" rel=\"nofollow\"\u003ehttps://project.inria.fr/aerialimagelabeling/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor the Inria dataset, the original ground truth is just a collection of raster masks.\nAs our method requires annotations to be polygons in order to compute the ground truth angle for the frame field, we made 2 versions of the dataset:\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria OSM dataset\u003c/em\u003e has aligned annotations pulled from OpenStreetMap.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria Polygonized dataset\u003c/em\u003e has polygon annotations obtained from using our frame field polygonization algorithm on the original raster masks.\nThis was done by running the \u003ccode\u003epolygonize_mask.py\u003c/code\u003e script like so:\n\u003ccode\u003epython polygonize_mask.py --run_name inria_dataset_osm_mask_only.unet16 --filepath ~/data/AerialImageDataset/raw/train/gt/*.tif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can find this new ground truth for both cases in the shared \"data\" folder (\u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-the-mainpy-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-mainpy-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the main.py script\u003c/h1\u003e\n\u003cp\u003eExecute \u003ca href=\"main.py\"\u003emain.py\u003c/a\u003e script to train a model, test a model or use a model on your own image.\nSee the help of the main script with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython main.py --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe script can be launched on multiple GPUs for multi-GPU training and evaluation.\nSimply set the \u003ccode\u003e--gpus\u003c/code\u003e argument to the number of gpus you want to use.\nHowever, for the first launch of the script on a particular dataset (when it will pre-process the data),\nit is best to leave it at 1 as I did not implement multi-GPU synchronization when pre-processing datasets.\u003c/p\u003e\n\u003cp\u003eAn example use is for training a model with a certain config file, like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained\u003c/code\u003e\nwhich will train the Unet-Resnet101 on the CrowdAI Mapping Challenge dataset.\nThe batch size can be adjusted like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained -b \u0026lt;new batch size\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen training is done, the script can be launched in eval mode, to evaluate the trained model:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval\u003c/code\u003e.\nDepending on the eval parameters of the config file, running this will output results on the test dataset.\u003c/p\u003e\n\u003cp\u003eFinally, if you wish to compute AP and AR metrics with the COCO API, you can run:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval_coco\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-launch-inference-on-one-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#launch-inference-on-one-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch inference on one image\u003c/h2\u003e\n\u003cp\u003eMake sure the run folder has the correct structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- \u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the [main.py] script like so (filling values for arguments run_name and in_filepath):\n\u003ccode\u003epython main.py --run_name \u0026lt;run_name\u0026gt; --in_filepath \u0026lt;your_image_filepath\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe outputs will be saved next to the input image\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-trained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload trained models\u003c/h2\u003e\n\u003cp\u003eWe provide already-trained models so you can run inference right away.\nDownload here: \u003ca href=\"https://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\u003c/a\u003e.\nEach model was trained in a \"run\", whose folder (named with the format \u003ccode\u003e\u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\u003c/code\u003e) you can download at the provided link.\nYou should then place those runs in a folder named \"runs\" inside the \"frame_field_learning\" folder like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard | 2020-06-02 07:57:31\n| | |-- mapping_dataset.unet_resnet101_pretrained.field_off.train_val | 2020-09-07 11:54:48\n| | |-- mapping_dataset.unet_resnet101_pretrained.train_val | 2020-09-07 11:28:51\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBecause Google Drive reformats folder names, you have to rename the run folders as above.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-hidden=\"true\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCite:\u003c/h1\u003e\n\u003cp\u003eIf you use this code for your own research, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{Girard_2021_CVPR,\n author = {Girard, Nicolas and Smirnov, Dmitriy and Solomon, Justin and Tarabalka, Yuliya},\n title = {Polygonal Building Extraction by Frame Field Learning},\n booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n month = {June},\n year = {2021},\n pages = {5891-5900}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tpall/geo-htseq-paper/workflows/CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/tpall/geo-htseq-paper/workflows/CI/badge.svg\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-geo-htseq-paper\" class=\"anchor\" aria-hidden=\"true\" href=\"#geo-htseq-paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeo-htseq-paper\u003c/h1\u003e\n\u003cp\u003eWe analyzed the field of expression profiling by high throughput sequencing, or RNA-seq, in terms of replicability and reproducibility, using data from the GEO (Gene Expression Omnibus) repository. Our work puts an upper bound of 56% to field-wide reproducibility, based on the types of files submitted to GEO.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting data\u003c/h2\u003e\n\u003cp\u003eGot to \u003ca href=\"https://zenodo.org/record/6795313\" rel=\"nofollow\"\u003ehttps://zenodo.org/record/6795313\u003c/a\u003e and download data archive, let\u0027s say, to your Downloads folder.\u003c/p\u003e\n\u003cp\u003eThen create new folder, e.g. \"geo-htseq\" and enter this folder\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir geo-htseq\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e geo-htseq\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCopy downloaded dataset to your working directory and uncompress:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Downloads/geo-htseq.tar.gz \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ntar -xzvf geo-htseq.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRemove tar.gz archive from working directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erm geo-htseq.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow you should have dataset in \"output\" subdirectory ready for analysis.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-workflow-graph\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflow-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow graph\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"resources/images/rulegraph.pdf\"\u003e\u003cimg src=\"resources/images/rulegraph.pdf\" alt=\"rulegraph\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1636198482.0 + "updated_at": 1656954496.0 }, { "data_format": 2, @@ -7240,371 +6885,358 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-centos7-ci", + "full_name": "marcjwilliams1/rstudiosrvrV4", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-building-a-centos7-singularity-and-docker-image-for-ci\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-a-centos7-singularity-and-docker-image-for-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a centos7 singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003einitial docker image project \u003ca href=\"https://github.com/truatpasteurdotfr/docker-c7-ci\"\u003ehttps://github.com/truatpasteurdotfr/docker-c7-ci\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eadding support for singularity format to be used directly\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-caveat\" class=\"anchor\" aria-hidden=\"true\" href=\"#caveat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-ci/actions/workflows/docker-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-ci/actions/workflows/docker-publish.yml/badge.svg\" alt=\"Docker build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-centos7-ci:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-ci/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-ci/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos7-ci:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4911\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for R studio server with Rv4.0.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [], + "updated_at": 1605122458.0 + }, + { + "data_format": 2, + "description": "The definition files for creating singularity containers that can run in the WashU HPC", + "filenames": [ + "Singularity.def" + ], + "full_name": "humanconnectome/hcp-pipelines-singularity", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-definitions-for-hcp-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-definitions-for-hcp-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Definitions for HCP Pipelines\u003c/h1\u003e\n\u003cp\u003eThe definition files for creating singularity containers for the XNAT pipelines\nwrapper code so that it can run in the WashU HPC.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cloning-with-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#cloning-with-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning with Submodules\u003c/h2\u003e\n\u003cp\u003eDon\u0027t forget to pull down the submodules as well, with the \u003ccode\u003e--recursive\u003c/code\u003e flag.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/humanconnectome/hcp-pipelines-singularity --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCommand\u003c/th\u003e\n\u003cth\u003eTask\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emake clean\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRemove previous container image.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emake update\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eUpdate all the git submodule repos.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emake build\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eGenerate a container image from .def file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emake upload\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eUpload the container to correct location in the HPC.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1635152901.0 + "updated_at": 1610395015.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for GroIMP (http://www.grogra.de/software/groimp)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.1.6-jre8-cuda+sundials-2.7.0", + "Singularity.1.6-cuda", + "Singularity.1.6-jre8-cuda" ], - "full_name": "mwanakijiji/rrlyrae_metallicity", + "full_name": "powerPlant/groimp-srf", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rrlyrae_metallicity\" class=\"anchor\" aria-hidden=\"true\" href=\"#rrlyrae_metallicity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003errlyrae_metallicity\u003c/h1\u003e\n\u003cp\u003eThis is a package for determining metallicities from med-res RRab spectroscopy. See --- for documentation.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://coveralls.io/github/mwanakijiji/rrlyrae_metallicity?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2bb0fd2bc008af8b9f3e3838890e25c208723b50f910daa5e509bba2111d27c8/68747470733a2f2f636f766572616c6c732e696f2f7265706f732f6769746875622f6d77616e616b696a696a692f72726c797261655f6d6574616c6c69636974792f62616467652e7376673f6272616e63683d6d6173746572\" alt=\"Coverage Status\" data-canonical-src=\"https://coveralls.io/repos/github/mwanakijiji/rrlyrae_metallicity/badge.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for GroIMP, a 3D-modelling platform\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1641769814.0 + "updated_at": 1651533257.0 + }, + { + "data_format": 2, + "description": "Singularity recipe files for pinfish (https://github.com/nanoporetech/pinfish)", + "filenames": [ + "Singularity", + "Singularity.0.1.0" + ], + "full_name": "powerPlant/pinfish-srf", + "latest_release": null, + "readme": "\u003cp\u003eSingularity recipe files for the pinfish collection of tools helping to make sense of long transcriptomics data (long cDNA reads, direct RNA reads)\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [], + "updated_at": 1583274123.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity_jlabsolidbase_devel", - "Singularity.1.0.2" + "Singularity" ], - "full_name": "jlabsolid/container", + "full_name": "arezaii/pf_singularity_demo", "latest_release": null, - "readme": "\u003cp\u003eContainer\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-parflow-singularity-container-demonstration\" class=\"anchor\" aria-hidden=\"true\" href=\"#parflow-singularity-container-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParFlow Singularity Container Demonstration\u003c/h1\u003e\n\u003cp\u003eThe Singularity container is built with ParFlow installed as a SCIF-app, providing access to both sequential and parallel\nbuilds of ParFlow. See additional information about \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/definition_files.html?highlight=apps#apps\" rel=\"nofollow\"\u003eApps in Singularity\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eHost OS must have Singularity installed (See \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html\" rel=\"nofollow\"\u003eInstalling Singularity\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-linux-hosts\" class=\"anchor\" aria-hidden=\"true\" href=\"#linux-hosts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinux Hosts\u003c/h2\u003e\n\u003cp\u003eVerify Singularity is installed with the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, see the Quickstart directions below\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-windowsmac-hosts\" class=\"anchor\" aria-hidden=\"true\" href=\"#windowsmac-hosts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows/Mac Hosts\u003c/h2\u003e\n\u003cp\u003eFollow the instructions to \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html#install-on-windows-or-mac\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMake sure you are ssh\u0027d into the Vagrant box before beginning the Quickstart steps below\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant ssh\nvagrant@vagrant:~$ singularity --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eSteps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/arezaii/pf_singularity_demo\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ecd to the repository directory\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd pf_singularity_demo\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003erun the shell script to execute tests for Little Washita domain on 1 processor, for 1 timestep\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./run_test.sh LW 1 1 1 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-performance-test-cases\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-performance-test-cases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Performance Test Cases\u003c/h2\u003e\n\u003cp\u003eThe shell script run_test.sh facilitates running tests on different domains.\u003c/p\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./run_test.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edomain\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eP\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eQ\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eR\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eTimeSteps\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edomain is a test domain defined below\u003c/li\u003e\n\u003cli\u003eP, Q, R are integers defining processor topology in X, Y, Z directions\u003c/li\u003e\n\u003cli\u003eTimesteps is number of timesteps to execute\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-domains\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-domains\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest Domains\u003c/h2\u003e\n\u003cp\u003eThere are several test domains for performance analysis contained in the perf_tests folder.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLW - Little Washita\u003c/li\u003e\n\u003cli\u003eclayl - ClayL\u003c/li\u003e\n\u003cli\u003econus_ru - CONUS Clip - Run off\u003c/li\u003e\n\u003cli\u003econus_tfg - CONUS Clip - Terrain Following Grid\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-little-washita\" class=\"anchor\" aria-hidden=\"true\" href=\"#little-washita\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLittle Washita\u003c/h3\u003e\n\u003cp\u003eNatural model of the Little Washita watershed in Oklahoma.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDomain Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumber of Cells: 84,050, 41x41x50 (X,Y,Z)\u003c/li\u003e\n\u003cli\u003eHorizontal Resolution: 1km\u003c/li\u003e\n\u003cli\u003eVertical Resolution: 2m\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTechnical Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCLM enabled with NLDAS Forcings\u003c/li\u003e\n\u003cli\u003eTimestep: 1hr\u003c/li\u003e\n\u003cli\u003eSuburface: Heterogeneous\u003c/li\u003e\n\u003cli\u003eInitial Condition: Pressure file from spin-up\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-clayl\" class=\"anchor\" aria-hidden=\"true\" href=\"#clayl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClayL\u003c/h3\u003e\n\u003cp\u003eSynthetic model with completely flat surface and many thin, vertical layers\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDomain Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumber of Cells: 2.4M for 1 core. Scales with processor count, 100Px100Qx240 (X,Y,Z)\u003c/li\u003e\n\u003cli\u003eHorizontal Resolution: 1m\u003c/li\u003e\n\u003cli\u003eVertical Resolution: 0.025m\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTechnical Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNo CLM, constant simulated rain on top surface @ .0008 mm/hr\u003c/li\u003e\n\u003cli\u003eTimestep 1hr\u003c/li\u003e\n\u003cli\u003eSubsurface: Homogeneous\u003c/li\u003e\n\u003cli\u003eInitial Condition: Dry\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conus-run-off\" class=\"anchor\" aria-hidden=\"true\" href=\"#conus-run-off\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCONUS Run-off\u003c/h3\u003e\n\u003cp\u003eNatural topography with an impervious surface (parking lot simulation)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDomain Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumber of Cells: 1,562,500 1250x1250x1 (X,Y,Z)\u003c/li\u003e\n\u003cli\u003eHorizontal Resolution: 1km\u003c/li\u003e\n\u003cli\u003eVertical Resolution: 0.10m\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTechnical Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNo CLM, period of 1 hour simulated rain on top surface @ .005 mm/hr, then recession for 1000 hours\u003c/li\u003e\n\u003cli\u003eTimestep: 6 minutes\u003c/li\u003e\n\u003cli\u003eSubsurface: Homogeneous\u003c/li\u003e\n\u003cli\u003eInitial Condition: Dry\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conus-terrain-following-grid\" class=\"anchor\" aria-hidden=\"true\" href=\"#conus-terrain-following-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCONUS Terrain Following Grid\u003c/h3\u003e\n\u003cp\u003eNatural topography with the terrain following grid (TFG) feature enabled\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDomain Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumber of Cells: 1,125,000 750x750x2 (X,Y,Z)\u003c/li\u003e\n\u003cli\u003eHorizontal Resolution: 1km\u003c/li\u003e\n\u003cli\u003eVertical Resolution: toplayer=1m, bottomlayer=100m\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTechnical Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNo CLM, seepage face boundary condition type on top layer, @ 0.00001\u003c/li\u003e\n\u003cli\u003eTimestep: 100000\u003c/li\u003e\n\u003cli\u003eSubsurface: Homogeneous\u003c/li\u003e\n\u003cli\u003eInitial Condition: Water Table at 45m above lower boundary\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-apps\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout Apps\u003c/h2\u003e\n\u003cp\u003eThe demo container has two apps installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epar = distributed build of ParFlow, -DPARFLOW_AMPS_SEQUENTIAL_IO=False\u003c/li\u003e\n\u003cli\u003eseq = sequential build of ParFlow, -DPARFLOW_AMPS_SEQUENTIAL_IO=True\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eapp_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e/path/to/singularity_container.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e.tcl input file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee additional information about \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/definition_files.html?highlight=apps#apps\" rel=\"nofollow\"\u003eApps in Singularity\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-build-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Build Container\u003c/h2\u003e\n\u003cp\u003eThe quickest way to build is to use a remote build service such as \u003ca href=\"https://cloud.sylabs.io/builder\" rel=\"nofollow\"\u003ecloud.sylabs.io\u003c/a\u003e\nIf a user has root access, they can build from the definition file, conventionally named Singularity.\u003c/p\u003e\n\u003cp\u003eGeneral build command is of the form:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edestination/path/to/singularity_container.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSingularity definition file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eas a specific example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/pf_singularity_demo.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-use-parflow-in-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-use-parflow-in-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Use ParFlow in Container\u003c/h2\u003e\n\u003cp\u003eExample of running the LW test case in \u003ccode\u003eparflow/test/washita/tcl_scripts\u003c/code\u003e directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app par \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/pf_singularity_demo.sif LW_Test.tcl\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-from-sylabs-cloud\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-from-sylabs-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull from Sylabs Cloud\u003c/h2\u003e\n\u003cp\u003eTo pull the pre-built image from Sylabs Cloud:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull [destination image name] library://arezaii/default/parflow_demo:master\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting\u003c/h2\u003e\n\u003cp\u003eBecause singularity containers are write protected and ParFlow tests write to disk, you must expand the image to a writable sandbox.\nThis requires super user access, similar to building a container from the definition file.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-make-container-writable\" class=\"anchor\" aria-hidden=\"true\" href=\"#make-container-writable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMake Container Writable\u003c/h3\u003e\n\u003cp\u003eFirst, create a writable sandbox from the immutable container using Singularity\u0027s build command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory_to_create_for_sandbox/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esingularity_container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eas an example, if you had pulled the parflow_ompi image from shub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox parflow_demo_master_sandbox/ parflow_demo_master.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere will now be a new directory parflow_demo_master_sandbox/ that is the root of the container.\nEditing any of the folder contents will require super user permissions.\u003c/p\u003e\n\u003cp\u003eYou can enter a console into the container now by using the Singularity shell command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell --writable \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory_to_create_for_sandbox/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Tests\u003c/h3\u003e\n\u003cp\u003eAfter making the container writable and accessing it through a shell, both documented above, running the ParFlow\ntests can be done by changing directories and exporting the PARFLOW_DIR environment variable for either distributed\nor sequential builds of ParFlow.\u003c/p\u003e\n\u003cp\u003eTake note of the ParFlow build and install directories in the container:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequential Build\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild directory: /home/parflow/build_seq\u003c/li\u003e\n\u003cli\u003einstall directory: /home/parflow/pfdir_seq\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDistributed Build\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild directory: /home/parflow/build_par\u003c/li\u003e\n\u003cli\u003einstall directory: /home/parflow/pfdir_par\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/parflow/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ebuild_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PARFLOW_DIR=/home/parflow/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003einstall_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e make \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1521236311.0 + "updated_at": 1583512107.0 }, { "data_format": 2, - "description": "Applied nuclear physics relevant software, containerized. Including Geant4 and Root.", + "description": "Owncloud", "filenames": [ - "Singularity" + "Singularity.owncloud" ], - "full_name": "peter-jansson/appnuc", - "latest_release": "0.6.3", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-appnuc-applied-nuclear-physics-relevant-software-containerized\" class=\"anchor\" aria-hidden=\"true\" href=\"#appnuc-applied-nuclear-physics-relevant-software-containerized\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappnuc: Applied nuclear physics relevant software, containerized.\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.6841830\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe0491dd1b21254f68c00e841d95cb67f03343dd15eaf13e20280daa72ec13a7/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e363834313833302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.6841830.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/peter-jansson/appnuc/actions/workflows/docker-image.yml/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/peter-jansson/appnuc/actions/workflows/docker-image.yml/badge.svg?branch=master\" alt=\"Docker build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/peter-jansson/appnuc/actions/workflows/apptainer-image.yml/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/peter-jansson/appnuc/actions/workflows/apptainer-image.yml/badge.svg?branch=master\" alt=\"Apptainer build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn Ubuntu Linux 22.04 based image/container with a bunch of standard programs that are useful for scientific work in the field of applied nuclear physics. In addition to relevant software listed \u003ca href=\"scripts/install-apt-packages.sh\"\u003ehere\u003c/a\u003e and \u003ca href=\"scripts/install-pip-packages.sh\"\u003ehere\u003c/a\u003e, the following list of software packages are installed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://geant4.web.cern.ch/\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e monte carlo framework, version 11.1.1.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://root.cern.ch/\" rel=\"nofollow\"\u003eRoot\u003c/a\u003e data analysis framework, version 6.26/10.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://dx.doi.org/10.18434/T48G6X\" rel=\"nofollow\"\u003eXCOM\u003c/a\u003e program from NIST, version 3.1.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis containerized solution can be referenced as:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePeter Jansson; \"appnuc: Applied nuclear physics relevant software, containerized\"; GitHub software repository: \u003ca href=\"https://github.com/peter-jansson/appnuc\"\u003epeter-jansson/appnuc\u003c/a\u003e; Version: 0.6.3; DOI: \u003ca href=\"https://doi.org/10.5281/zenodo.6841830\" rel=\"nofollow\"\u003e10.5281/zenodo.6841830\u003c/a\u003e; 2023-03-31\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis work is licensed under the \u003ca href=\"LICENSE\"\u003eGNU Lesser General Public License v3.0 (LGPL-3)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f400597fcdcb66eeb5702e037732d66d7eecdf94f4f363a2dde0da21c4ba9ec4/68747470733a2f2f7777772e676e752e6f72672f67726170686963732f6c67706c76332d776974682d746578742d3135347836382e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f400597fcdcb66eeb5702e037732d66d7eecdf94f4f363a2dde0da21c4ba9ec4/68747470733a2f2f7777772e676e752e6f72672f67726170686963732f6c67706c76332d776974682d746578742d3135347836382e706e67\" alt=\"LGPL-3\" data-canonical-src=\"https://www.gnu.org/graphics/lgplv3-with-text-154x68.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eA \u003ca href=\"https://docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image named \u003ccode\u003eappnuc\u003c/code\u003e can built using the Dockerfile, by the command\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker build -t appnuc:latest -t appnuc:0.6.3 .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe image can be started in a container by, e.g., the command\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -i -t appnuc bash -l\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSignificantly more information on how to mount a local file system to the container as well as other command line options is available in the \u003ca href=\"https://docs.docker.com/engine/reference/commandline/cli/\" rel=\"nofollow\"\u003eDocker documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-apptainer-former-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#apptainer-former-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApptainer (former Singularity)\u003c/h2\u003e\n\u003cp\u003eAn \u003ca href=\"http://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e file containing the same containerized software can be built using the definition file, named \u003ccode\u003eSingularity\u003c/code\u003e. E.g. using the command\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eapptainer build appnuc-0.6.3.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eto build \u003ccode\u003eappnuc-0.6.3.sif\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"http://apptainer.org/docs\" rel=\"nofollow\"\u003eApptainer documentation\u003c/a\u003e for more information.\u003c/p\u003e\n", + "full_name": "ternaustralia/coesra-singularity-owncloud", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-owncloud\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-owncloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-owncloud\u003c/h1\u003e\n\u003cp\u003eAuthor: Hoang Nguyen\nCreated: 22 July 2019\nThis will create a image with Singularity 2.5.1\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [ - "applied-nuclear-physics", - "singularity", - "apptainer", - "docker", - "geant4", - "geant4-simulation", - "root", - "root-cern", - "xcom" + "coesra" ], - "updated_at": 1671696032.0 + "updated_at": 1610426521.0 }, { "data_format": 2, - "description": null, + "description": "Knime", "filenames": [ - "Singularity" + "Singularity.knime" ], - "full_name": "hkong1/fhirql", + "full_name": "ternaustralia/coesra-singularity-knime", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-a-fhir-has-been-lit-on-this-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-fhir-has-been-lit-on-this-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA FHIR has been lit on this server\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-fhirql\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-fhirql\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is fhirql\u003c/h2\u003e\n\u003cp\u003eFhirql is a spring boot adaptation of hapi fhir server. This can be used as a template for extending generic FHIR server for specific use cases. See the example projects below. I have updated it to FHIR-R4 and spring-boot 2.2.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-fhir-r4-hl7-fast-healthcare-interoperability-resources-release-4\" class=\"anchor\" aria-hidden=\"true\" href=\"#fhir-r4-hl7-fast-healthcare-interoperability-resources-release-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFHIR\u00ae R4 (HL7 Fast Healthcare Interoperability Resources, Release 4)\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-other-projects-that-using-this-as-backend\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-projects-that-using-this-as-backend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther projects that using this as backend\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/E-Health/fhirform\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"fire\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f525.png\"\u003e\ud83d\udd25\u003c/g-emoji\u003e The FHIRForm framework for managing healthcare eForms\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/E-Health/drishti\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"eyes\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f440.png\"\u003e\ud83d\udc40\u003c/g-emoji\u003e Drishti | An mHealth sense-plan-act framework!\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ejava 8\u003c/li\u003e\n\u003cli\u003emaven 3\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Use:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/dermatologist/fhirql.git\nmvn spring-boot:run\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAccess UI at \u003ca href=\"http://localhost:8080/fhir\" rel=\"nofollow\"\u003ehttp://localhost:8080/fhir\u003c/a\u003e and FHIR BASE at \u003ca href=\"http://localhost:8080/fhir/fhir\" rel=\"nofollow\"\u003ehttp://localhost:8080/fhir/fhir\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-extend\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-extend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to extend\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThis uses spring boot Web.\u003c/li\u003e\n\u003cli\u003eOverride the default UI by adding files with the same name to WEB-INF/templates (Thymeleaf).\u003c/li\u003e\n\u003cli\u003eFor example this application overrides tmpl-head.html and tmpl-home-welcome.html\u003c/li\u003e\n\u003cli\u003eThe list of original templates are \u003ca href=\"https://github.com/jamesagnew/hapi-fhir/tree/master/hapi-fhir-testpage-overlay/src/main/webapp/WEB-INF/templates\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003ePre-build docker container of overlay branch is available for testing and can be deployed using the following command. Access it at \u003ca href=\"http://localhost:8080/fhirql\" rel=\"nofollow\"\u003ehttp://localhost:8080/fhirql\u003c/a\u003e\n(Docker container is for testing only.)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -d --name fhirserver -p 8080:8080 beapen/fhir\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-hidden=\"true\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthor\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://nuchange.ca\" rel=\"nofollow\"\u003eBell Eapen\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-knime\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-knime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-knime\u003c/h1\u003e\n\u003cp\u003eAuthor: Hoang Nguyen\nCreated: 22 July 2019\nThis will create a image with Singularity 2.5.1\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1603378426.0 + "subscribers_count": 2, + "topics": [ + "coesra" + ], + "updated_at": 1670882548.0 }, { "data_format": 2, "description": null, "filenames": [ - "ploi/planning/FD/misc/releases/latest/Singularity", - "ploi/planning/FD/misc/releases/19.12/Singularity.19.12", - "ploi/planning/FD/misc/releases/20.06/Singularity.20.06", - "ploi/planning/FD/misc/releases/19.06/Singularity.19.06" + "Singularity.macroecodesktop" ], - "full_name": "alestarbucks/ofappdl", + "full_name": "ternaustralia/coesra-singularity-macroecodesktop", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-object-filtering-in-automatic-planning-problems-using-deep-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#object-filtering-in-automatic-planning-problems-using-deep-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eObject Filtering in Automatic Planning Problems using Deep Learning\u003c/h1\u003e\n\u003cp\u003eThis README file is explicitly dedicated to serve as the guide of use of the source code associated to Alejandro \u00c1lvarez Conejo\u0027s Final Bachelor Thesis in order to run the project in any local computer. Note that these instructions are described to be applicable to Linux-based systems.\u003c/p\u003e\n\u003cp\u003eThis repository contains three main folders, which are referred to in this annex as \u003ccode\u003emodules\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003eploi\u003c/code\u003e folder contains all the code related to the execution of the main algorithm for PLOI. It includes the code related to the guiders, the planners (including Fast-Downward) and the GNN implementation, as well as the main scripts that allow the whole project to work as discussed in the main body of the thesis. Note that inside the \u003ccode\u003emodel\u003c/code\u003e folder the model and data set files for the conducted tests can be found.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003egenerators\u003c/code\u003e folder contains the scripts that were used to generate the training and test problems. Inside, there is a folder dedicated to each of the domains of study and all of their versions, including the scripts that were used for the first approach described in chapter 5.3 in the \u003ccode\u003eunconnectednoise\u003c/code\u003e subfolder.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003epddlgym\u003c/code\u003e folder, which contains all the code related to the PDDLGym module. It has to be modified in order to include the domains of study inside its existing library of domains and example problems. Note that the original code for this module was also modified in order to make it more flexible to several valid syntaxes in PDDL. These modifications are not related to the core algorithm and thus have not been thoroughly detailed but the code inside the \u003ccode\u003eparser\u003c/code\u003e file of this module can be compared to the original parser in PDDLGym\u2019s original repository in order to examine the specifics of these changes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-projects-source-code-and-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-projects-source-code-and-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the project\u2019s source code and dependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall basic dependencies: cmake, g++, make, git, Python 3.6 or higher and pip, if these are not already installed.\u003c/li\u003e\n\u003cli\u003eClone the thesis\u2019 repository using the following command:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/alestarbucks/ofappdl\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eNavigate to the \u003ccode\u003eploi\u003c/code\u003e folder and install the requirements for that module:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install -r requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRepeat the same operation for the PDDLGym module.\n4.\tAdditionally, install wandb to avoid missing dependencies:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install wandb\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eCreate a symbolic link called \u003ccode\u003evalidate\u003c/code\u003e on the machine\u2019s path, pointing to the VAL validator\u2019s binary:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo ln -s \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_ofappdl\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/ofappdl/val/bin/Validate /usr/local/bin/validate\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to check that the symbolic link is working as intended, try to enter the command \u003ccode\u003evalidate\u003c/code\u003e in the command line and expect an output showing the usage of the command.\n6.\tBuild the Fast-Downward planner by navigating to ploi/planning/fd and running the following command (it may take a few minutes):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./build.py\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eBefore the first run and every time that a new domain is added to the PDDLGym module, re-install it using the version that exists in the repository. From the root folder of the repository, run:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install -e ./pddlgym\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis command is automatically included in the provided shell script that runs the project, so it is not explicitly needed to execute this step if such script is used.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-including-a-new-domain\" class=\"anchor\" aria-hidden=\"true\" href=\"#including-a-new-domain\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIncluding a new domain\u003c/h2\u003e\n\u003cp\u003eIn order to use PLOI for the purpose of applying it to other domains, a few changes must be made inside both the \u003ccode\u003epddlgym\u003c/code\u003e module and the \u003ccode\u003eploi\u003c/code\u003e module:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFirst, add the domain. Navigate to \u003ccode\u003epddlgym/pddlgym/pddl\u003c/code\u003e and copy the domain file inside that folder.\u003c/li\u003e\n\u003cli\u003eLikewise, add the training and test problems in two separate folders called \u003ccode\u003e\u0026lt;domain name\u0026gt;\u003c/code\u003e and \u003ccode\u003e\u0026lt;domain name\u0026gt;_test\u003c/code\u003e, respectively, inside the aforementioned folder.\u003c/li\u003e\n\u003cli\u003eOpen the \u003ccode\u003e__init__.py\u003c/code\u003e file inside pddlgym/pddlgym. Locate the list of environments after line 34 (\u003ccode\u003efor env_name, kwargs in [\u003c/code\u003e) and add the following lines, completing with the same name as the domain that was added in 1:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e(\u003cspan class=\"pl-s\"\u003e\"\u0026lt;domain name\u0026gt;\"\u003c/span\u003e,\n {\u003cspan class=\"pl-s\"\u003e\"operators_as_actions\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"dynamic_action_space\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e}\n)\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eThe domain has now been added to the PDDLGym module and now it must be included in the PLOI module. For this, open the \u003ccode\u003emain.py\u003c/code\u003e file inside the ploi module and locate the \u003ccode\u003epddlgym_env_names\u003c/code\u003e dictionary. Add an entry in which the key is the name to which the domain will be referred in the invoking command inside the PLOI module, and the value is the name of the domain inside the PDDLGym module that was used for steps 1 to 3. For clarity, using the same name for both is recommended.\u003c/li\u003e\n\u003cli\u003eIn case of using the provided shell script to run the project, set the \u003ccode\u003eDOMAIN_NAME\u003c/code\u003e variable to match the key of the previously added entry in the dictionary mentioned in step 4.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the project\u003c/h2\u003e\n\u003cp\u003eThe main command that triggers the start of the project\u2019s execution takes the following parameters:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--domain_name\u003c/code\u003e (required): The name of the domain of study to which the selected method is intended to be applied. It must be consistent and match the name chosen in the process detailed in the previous section.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--train_planner_name\u003c/code\u003e: The name of the planner used for training. In the experiments detailed in this report, this planner was fd-opt-lmcut (the optimal variant of FD).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--test_planner_name\u003c/code\u003e (required): The name of the planner used for testing. In the experiments detailed in this report, this planner was fd-lama-first (the satisficing variant of FD).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--guider_name\u003c/code\u003e (required): The name of the guider to be used, between gnn-bce-10 (GNN guider) or no-guidance (for standard planning or random score).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_seeds\u003c/code\u003e (required): The number of seeds which will be used to randomly initialize the model\u2019s weights before training. The learning phase will be repeated as many times as seeds are specified, and only the best model will be selected. Only one seed was used for the experiments in this thesis.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_train_problems\u003c/code\u003e (default to 0): The number of training problems to be considered.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_test_problems\u003c/code\u003e (required): The number of testing problems to be considered.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--do_incremental_planning\u003c/code\u003e (required): 1 or 0. Whether or not to use incremental planning, i.e., for PLOI or random scoring, whether it implements random score guidance or GNN-based guidance. For standard planning this flag must be set to 0.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--greedy_search\u003c/code\u003e (default to 0): 1 or 0. Indicates whether the greedy search algorithm is implemented in the phase of training data collection.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--timeout\u003c/code\u003e (required): Time in seconds that each test problem is dedicated before time running out and the problem being skipped. For this thesis, this time span was of 120 seconds.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_epochs\u003c/code\u003e (default 1001): Number of epochs that will constitute the learning phase.\nThe command is then executed as:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 main.py [flags]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe provided shell script called \u003ccode\u003emyrun.sh\u003c/code\u003e inside the PLOI module serves as an easy way to control the experimental process. The selected domain and method must be uncommented from the file and the script will run the appropriate command to execute the required experimental run.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1624570598.0 + "topics": [ + "coesra" + ], + "updated_at": 1610426323.0 }, { "data_format": 2, - "description": "Repository for \u0027Biased Exploration for Satisificing Heuristic Search\u0027 at ICAPS22", + "description": "Python wrapper for submitting jobs via bsub with the option to do so in a container environment.", "filenames": [ - "downward/misc/releases/latest/Singularity", - "downward/misc/releases/19.12/Singularity.19.12", - "downward/misc/releases/20.06/Singularity.20.06", - "downward/misc/releases/19.06/Singularity.19.06" + "singularity/Singularity" ], - "full_name": "Kurorororo/biased-exploration", + "full_name": "funkelab/funlib.run", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-biased-exploration-for-satisficing-heuristic-search\" class=\"anchor\" aria-hidden=\"true\" href=\"#biased-exploration-for-satisficing-heuristic-search\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBiased Exploration for Satisficing Heuristic Search\u003c/h1\u003e\n\u003cp\u003eThis repository is for our ICAPS 2022 paper, \u003ca href=\"https://tidel.mie.utoronto.ca/pubs/biased-exploration-icaps22.pdf\" rel=\"nofollow\"\u003eBiased Exploration for Satisficing Heuristic Search\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-classical-planning\" class=\"anchor\" aria-hidden=\"true\" href=\"#classical-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClassical Planning\u003c/h2\u003e\n\u003cp\u003eOur implementation is on top of \u003ca href=\"https://www.fast-downward.org/\" rel=\"nofollow\"\u003eFast Downward\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e downward\npython3 build.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eType-LAMA with Softmin-Type(h) using two type-based buckets\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehlm=lmcount(lm_factory=lm_rhw(reasonable_orders=true),transform=adapt_costs(one),pref=false)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elazy(alt([single(hff),single(hff,pref_only=true),softmin_type_based([hff,g]),single(hlm),single(hlm,pref_only=true),softmin_type_based([hlm,g])],boost=1000),preferred=[hff,hlm],cost_type=one,reopen_closed=false)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eType-LAMA with Softmin-Type(h)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehlm=lmcount(lm_factory=lm_rhw(reasonable_orders=true),transform=adapt_costs(one),pref=false)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elazy(alt([single(hff),single(hff,pref_only=true),single(hlm),single(hlm,pref_only=true),softmin_type_based([hff,g])],boost=1000),preferred=[hff,hlm],cost_type=one,reopen_closed=false)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSoftmin-Type(h) with FF\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eeager(alt([single(hff), softmin_type_based([hff, g()], ignore_size=true)]), cost_type=one)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLin-Type(h) with FF\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eeager(alt([single(hff), linear_weighted_type_based([hff, g()])]), cost_type=one)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e3-Type(h) with FF\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eeager(alt([single(hff), nth_type_based([hff, g()], n=3)]), cost_type=one)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eType(h) with FF\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eeager(alt([single(hff), softmin_type_based([hff, g()], ignore_size=true, ignore_weights=true)]))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-synthetic-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#synthetic-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSynthetic Data\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 random_digraph.py -o result.json\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-funlibrun\" class=\"anchor\" aria-hidden=\"true\" href=\"#funlibrun\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efunlib.run\u003c/h1\u003e\n\u003cp\u003ePython wrapper for submitting jobs via bsub with the option to do so in a container environment.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003emake install-full\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis creates a funlib.run config file ~/.funlib.run\nthat contains default parameters that\ncan be overwritten for each specific run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enum_gpus = 1\nmemory = 25600\nworking_directory = .\nsingularity = \"\"\nhost = \"\"\nqueue = \"normal\"\nenvironment = \"\"\nbatch = False\nmount_dirs = \"\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThere are three useful ways to use funlib.run:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDirect usage via command line arguments (overwrites config file defaults):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython run.py -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epython train.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -c 5 -g 1 -q normal -s path-to-singularity-image\n\npython run_singularity.py -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epython mknet.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -s path-to-singularity-image\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eIndirect call via another script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efunlib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003erun\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erun\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erun_singularity\u003c/span\u003e\n\n\u003cspan class=\"pl-en\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"python train.py\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_cpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_gpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003equeue\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"normal\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003erun_singularity\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"python mknet.py\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esingularity_image\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"path_to_image\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCommand creation and subsequent call:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efunlib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003erun\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erun\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erun_singularity\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esubprocess\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003echeck_call\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003erun_command\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"python train.py\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_cpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_gpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003equeue\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"normal\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003echeck_call\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003erun_command\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eshell\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003erun_singularity_command\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erun_singularity\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"python mknet.py\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esingularity_image\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"path_to_image\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003echeck_call\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003erun_singularity_command\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eshell\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-daisy\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-daisy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Daisy\u003c/h2\u003e\n\u003cp\u003eWhen used with daisy.call do not expand the cmd to a string via setting expand=False:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ecmd\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ebase_command\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003equeue\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003equeue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_gpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_cpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003enum_cpus\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esingularity_image\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003esingularity_container\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emount_dirs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emount_dirs\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexpand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003edaisy\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecall\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecmd\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elog_out\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elog_out\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elog_err\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elog_err\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1655238687.0 + "updated_at": 1635345979.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "singularity/Singularity.BlendIt.def" ], - "full_name": "shots47s/cbrain-plugins-mriqc", + "full_name": "housw/BlendIt", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1560259505.0 + "updated_at": 1623019661.0 }, { "data_format": 2, - "description": "TRACULA Pipeline", + "description": "Nextflow workflow for finding conserved motifs intersecting with splice junctions", "filenames": [ - "Singularity", - "Singularity.v2.0.0", - "Singularity.v2.1.1" + "Singularity" ], - "full_name": "ccmvumc/TRACULA", - "latest_release": "v2.1.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tracula\" class=\"anchor\" aria-hidden=\"true\" href=\"#tracula\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTRACULA\u003c/h1\u003e\n\u003cp\u003eTRACULA Pipeline\u003c/p\u003e\n", + "full_name": "czbiohub/splicemotifs", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-corebedtools-intersect\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-corebedtools-intersect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/bedtools-intersect\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eIntersect lots of bed files with lots of other bed files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/bedtools-intersect\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/811368779316af4f70b4dd35fc2c24cebcc4dc194cd63234e130384ec38ac89f/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f626564746f6f6c732d696e746572736563742e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/bedtools-intersect.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/bedtools-intersect\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca7e06b0d2929a9cba14da1892e90c6d4673a695806cb07ea82e89a1cbecef92/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f626564746f6f6c732d696e746572736563742e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/bedtools-intersect.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/bedtools-intersect pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1621015992.0 + "updated_at": 1564673719.0 }, { "data_format": 2, - "description": null, + "description": "RNA-seq analysis pipeline based on Snakemake", "filenames": [ - "Selector/hclib/modules/bale_actor/singularity/Singularity.def" + "Singularity" ], - "full_name": "youssefelmougy/tempSC", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-an-asynchronous-distributed-actor-based-approach-to-jaccard-similarity-for-genome-comparisons\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-asynchronous-distributed-actor-based-approach-to-jaccard-similarity-for-genome-comparisons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAn Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-abstract\" class=\"anchor\" aria-hidden=\"true\" href=\"#abstract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbstract\u003c/h2\u003e\n\u003cp\u003eThe computation of genome similarity is important in computational biology applications, and is assessed by calculating Jaccard similarity of DNA sequencing sets. However, it\u2019s challenging to find solutions that can compute Jaccard similarity with the efficiency and scalability needed to fully utilize capabilities of modern HPC hardware. We introduce a novel algorithm for computing Jaccard similarity for genome comparisons, founded on an actor-based programming model. Our algorithm takes advantage of fine-grained asynchronous computations, distributed/shared memory model, and the Fine-grained Asynchronous Bulk-Synchronous Parallelism execution model. Our performance results on the NERSC Perlmutter supercomputer demonstrate that this approach scales to 16,384 cores, showing an average of 3.6\u00d7 and 5.5\u00d7 improvement in execution time and hardware counters compared to a state-of-the-art baseline. Moreover, we propose a novel compiler approach enabling programmers to optionally develop distributed code using the familiar BSP-based Partitioned Global Address Space model while automatically generating Actor-based code for improved performance.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation Instructions\u003c/h2\u003e\n\u003cp\u003eThe following installation instructions are for the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-load-the-appropriate-modules-to-prepare-for-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#load-the-appropriate-modules-to-prepare-for-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLoad the appropriate modules to prepare for setup\u003c/h3\u003e\n\u003cp\u003eThis loads the modules for both Selector and GenomeAtScale to prepare for setup.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource scripts/modules.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-first-time-setup-and-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#first-time-setup-and-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFirst time setup and installation\u003c/h3\u003e\n\u003cp\u003eThis sets up and installs both the Selector and GenomeAtScale applications and their backend runtimes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource scripts/setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Instructions\u003c/h2\u003e\n\u003cp\u003eThe following running instructions are for the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC).\u003c/p\u003e\n\u003cp\u003eThe run script (\u003ccode\u003e/scripts/run.sh\u003c/code\u003e) has 4 options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e source /scripts/run.sh [selector | ctf | both] [1...inf] [1...inf] [0...5]\n \n [selector | ctf | both] Selects which application (or both) to run\n [1...inf] Selects the number of cores for the run\n [1...inf] Selects the number of nodes for the run\n [0...5] Selects the set of HWPC to collect (0:none, 1:L1DA/L1DM/L1IA/L1IM, 2:L2DR/L2DM/L2IR/L2IM, 3:TLBDM/TLBIM, 4:BRINS/BRMSP, 5:INS/CYC)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: when selecting the number of nodes for the run, please remember that GenomeAtScale uses 32 cores/node and Selector uses either 32 or 64 cores/node.\u003c/p\u003e\n\u003cp\u003eFor example, \u003ccode\u003esource /scripts/run.sh selector 1024 16 2\u003c/code\u003e will run an experiment for the Selector application using 1024 cores on 16 nodes, collecting L2 cache statistics.\u003c/p\u003e\n\u003cp\u003eThis will submit an sbatch file to the run queue at Perlmutter. At job completion, a \u003ccode\u003ejaccard_selector.out\u003c/code\u003e or \u003ccode\u003ejaccard_ctf.out\u003c/code\u003e or both will be created, showing the CMD output of the run. Moreover, if HWPC were collected, a directory with the structure \u003ccode\u003ejaccard*+pat+*\u003c/code\u003e will be created in \u003ccode\u003e/Selector/hclib/modules/bale_actor/jaccard-selector/\u003c/code\u003e or \u003ccode\u003e/GenomeAtScale/jaccard-ctf/\u003c/code\u003e or both. Please see the Output Interpretation section for instructions on how to understand these results.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-interpretation\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-interpretation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput Interpretation\u003c/h2\u003e\n\u003cp\u003eThe following instructions are for understanding the results and relating them to the results found in the paper.\u003c/p\u003e\n\u003cp\u003eAt the completion of each run, there are two outputs that are created:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejaccard_selector.out OR jaccard_ctf.out OR both Output file from submitted job\njaccard_selector+pat+* OR jaccard+pat+* OR both Output folder (in respective directory) from a CrayPat run if HWPC were collected\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e*.out\u003c/code\u003e files contain the execution times of the run for the specific version. This result directly relates to Figure 2 (q) in the paper. An example output is shown below, where \u003ccode\u003e0.06150 seconds\u003c/code\u003e would be reported as the resulting value for the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e...\nRunning jaccard on 128 threads\nK-mer Matrix is 15000x5000 and has 15248 nonzeros.\n\nJaccard Similarity Matrix is 5000x5000 and has 12497374 values.\n\nRunning Jaccard Similarity K-mers (selector): \n 0.06150 seconds\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003ejaccard*+pat+*\u003c/code\u003e folders contain information dumped by the CrayPat profiler (for more information see \u003ca href=\"https://docs.nersc.gov/tools/performance/craypat/\" rel=\"nofollow\"\u003ehttps://docs.nersc.gov/tools/performance/craypat/\u003c/a\u003e). To generate human-readable content, we run \u003ccode\u003epat_report\u003c/code\u003e on the respective directory. This will display information of interest for the specified HWPC in the run, and will directly relate to Figures 2 (a-p). An example output is shown below, where we can see the L1 cache statistics which would be reported as the resulting values for the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@perlmutter: ~\u0026gt; pat_report $PWD/Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector+pat+190420-8718377t\n CrayPat/X: Version 23.02.0 Revision a53634a72 01/11/23 17:17:09\n\n Number of PEs (MPI ranks): 128\n\n Numbers of PEs per Node: 64 PEs on each of 2 Nodes\n\n Numbers of Threads per PE: 2\n\n Number of Cores per Socket: 64\n\n Execution start time: Sun Mar 19 10:25:36 2023\n\n System name and speed: nid004836 2.552 GHz (nominal)\n\n AMD Milan CPU Family: 25 Model: 1 Stepping: 1\n\n Core Performance Boost: 256 PEs have CPB capability\n\n\n Current path to data file:\n /Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector+pat+190420-8718377t (RTS, 2 data files)\n\n ...\n ...\n\n Processing step 7 of 10\n Notes for table 5:\n ...\n ...\n ==============================================================================\n USER / #1.selector_jaccard\n ------------------------------------------------------------------------------\n Time% 2.8% \n Time 0.060836 secs\n Imb. Time 0.000013 secs\n Imb. Time% 0.0% \n Calls 16.438 /sec 1.0 calls\n PAPI_L1_DCM 0.057G/sec 2,369,390.898 misses\n PAPI_L1_DCA 2.252G/sec 110,478,052.633 refs\n Average Time per Call 0.060836 secs\n CrayPat Overhead : Time 0.0% \n perf::PERF_COUNT_HW_CACHE_L1I:ACCESS 1,214,778 \n perf::PERF_COUNT_HW_CACHE_L1I:MISS 5,868\n ==============================================================================\n\n ...\n ...\n\n Hardware performance counter events:\n PAPI_L1_DCM Level 1 data cache misses\n PAPI_L1_DCA Level 1 data cache accesses\n perf::PERF_COUNT_HW_CACHE_L1I:ACCESS Undocumented counter\n perf::PERF_COUNT_HW_CACHE_L1I:MISS Undocumented counter\n\n Estimated minimum instrumentation overhead per call of a traced function,\n which was subtracted from the data shown in this report\n (for raw data, use the option: -s overhead=include):\n Time 0.114 microsecs\n\n Number of traced functions that were called: 7\n\n (To see the list, specify: -s traced_functions=show)\nuser@perlmutter: ~\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-top-level-directory-organization\" class=\"anchor\" aria-hidden=\"true\" href=\"#top-level-directory-organization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-Level Directory Organization\u003c/h2\u003e\n\u003cp\u003eThe folder structure of this repository is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e.\n\u251c\u2500\u2500 Selector # Contains files for the Actor-based runtime and the Jaccard k-mer Selector application\n\u2502 \u251c\u2500\u2500 hclib # Contains the HClib library and the Actor-based runtime\n\u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 modules \n\u2502 \u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 bale_actor \n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 jaccard-selector # Contains the Jaccard k-mer Selector application files\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 jaccard_kmer_selector.cpp # Application code for Selector version of Jaccard similarity for genome comparisons\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 jaccard_kmer_locality_selector.cpp # Application code for locality-aware Selector version of Jaccard similarity for genome comparisons\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 kmer_matrix.mtx # K-mer matrix file for evaluation\n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 ... \n\u251c\u2500\u2500 GenomeAtScale # Contains files for the CTF library and the GenomeAtScale application\n\u2502 \u251c\u2500\u2500 ctf # Contains the CTF library\n\u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u251c\u2500\u2500 jaccard-ctf # Contains the GenomeAtScale (jaccard-ctf) files\n\u2502 \u2502 \u251c\u2500\u2500 jaccard.cxx # Application code for GenomeAtScale\n\u2502 \u2502 \u251c\u2500\u2500 kmer_matrix.zip # K-mer matrix files for evaluation\n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 ... \n\u251c\u2500\u2500 ActorCode_from_PGASOpenMP # Contains PGAS-OpenMP code and translated Actor-based code (Section 6)\n\u251c\u2500\u2500 scripts # Contains installation, running, and modules scripts and sample Perlmutter sbatch files\n\u2502 \u251c\u2500\u2500 setup.sh # Installation and build script for the system backends and application code for both the Selector application and the GenomeAtScale application\n\u2502 \u251c\u2500\u2500 run.sh # Run script for both the selector application and GenomeAtScale application\n\u2502 \u251c\u2500\u2500 modules.sh # Modules script to prepare for running experiments (only used following first time setup using setup.sh, has to be re-run everytime you login to a cluster/supercomputer)\n\u2502 \u2514\u2500\u2500 ... \n\u2514\u2500\u2500 README.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eIf you use our application in your work, please cite \u003ca href=\"\"\u003eour paper\u003c/a\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eYoussef Elmougy, Akhiro Hayashi, Jun Shirako, and Vivek Sarkar. 2023. An Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eCorresponding author: Youssef Elmougy (\u003ca href=\"mailto:yelmougy3@gatech.edu\"\u003eyelmougy3@gatech.edu\u003c/a\u003e)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), through the Advanced Graphical Intelligence Logical Computing Environment (AGILE) research program, under Army Research Office (ARO) contract number W911NF22C0083. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government.\u003c/p\u003e\n", + "full_name": "tgac-vumc/RNA-seq", + "latest_release": "v1.0.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rna-seq-analysis-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#rna-seq-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRNA-seq analysis pipeline\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://snakemake.bitbucket.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e0a726dc69516d51067fd9fc2074a9f2dc9d44eb069ae05434a36f580af32f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b653d3d352e32352e302d627269676874677265656e2e7376673f7374796c653d666c61742d737175617265\" alt=\"Snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake==5.25.0-brightgreen.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/3066\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c9d2afb620129b7ba0f4d918b77bfdb2b91c595cd6c6d013e950ee6e3c2bbc55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d73696e67756c61726974792d2d6875622d7265642e737667\" alt=\"singularity-hub\" data-canonical-src=\"https://img.shields.io/badge/install%20with-singularity--hub-red.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e225eb3891735f81d51e8e6aa377429328cfd43656973ff807bffe9234bc28c7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d636f6e64612d677265656e2e737667\" alt=\"miniconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-conda-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a \u003ca href=\"https://snakemake.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e based pipeline for RNA-seq used in the \u003ca href=\"http://www.tgac.nl/\" rel=\"nofollow\"\u003eTumor Genome Core Analysis\u003c/a\u003e housed in the \u003ca href=\"https://www.vumc.com/departments/cancer-center-amsterdam.htm\" rel=\"nofollow\"\u003eCancer Center Amsterdam\u003c/a\u003e, at \u003ca href=\"https://www.vumc.nl/\" rel=\"nofollow\"\u003eAmsterdam UMC location VUmc\u003c/a\u003e and part of the Department of Pathology.\u003c/p\u003e\n\u003cp\u003eThe pipeline processes raw data from FastQ inputs (\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e, \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003eTrimmomatic\u003c/a\u003e), aligns the reads (\u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTAR\u003c/a\u003e), generates gene counts (\u003ca href=\"http://bioinf.wehi.edu.au/featureCounts/\" rel=\"nofollow\"\u003efeatureCounts\u003c/a\u003e) and performs quality-control on the results (\u003ca href=\"https://multiqc.info/\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e). Paired-end (PE) and single read (SR) are supported.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/RNA-seq/blob/master/DAG_RNAseq.png\"\u003e\u003cimg width=\"850\" height=\"483\" src=\"https://github.com/tgac-vumc/RNA-seq/raw/master/DAG_RNAseq.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe pipeline is preliminary used in linux environment with conda/singularity available.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Conda\u003c/h3\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-installing-miniconda-3\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-installing-miniconda-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1: Installing Miniconda 3\u003c/h3\u003e\n\u003cp\u003eFirst, please open a terminal or make sure you are logged into your Linux VM. Assuming that you have a 64-bit system, on Linux, download and install Miniconda 3 with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn MacOS X, download and install with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh\nbash Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-downloading-repository--creating-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-downloading-repository--creating-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2: Downloading repository \u0026amp; creating environment\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emkdir snakemake_RNAseq\ncd snakemake_RNAseq\ngit clone https://github.com/tgac-vumc/RNA-seq\nconda env create --name RNAseq --file env.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003c/h3\u003e\n\u003cp\u003eThe singularity container holds a virtual environment of CentOS 7 and it\u0027s available with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tgac-vumc/RNA-seq\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-path-configuration--running-the-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#path-configuration--running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePath Configuration \u0026amp; Running the pipeline\u003c/h2\u003e\n\u003cp\u003eBefore attempting to run the pipeline, please open \u003cem\u003econfig.yaml\u003c/em\u003e. Inside, you will encounter \u003cstrong\u003ePath Configuration\u003c/strong\u003e and \u003cstrong\u003eSoftware Options\u003c/strong\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOn \u003cstrong\u003ePath configuration\u003c/strong\u003e, first, you have to choose whether your data is PE or SR and after change the fastq path to the path where your fastq files are actually stored.\u003c/li\u003e\n\u003cli\u003eOn \u003cstrong\u003eSoftware Options\u003c/strong\u003e, you will find several options that can be modified by the user. Please, have a look at it before running the pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll the software used in the pipeline is installed by conda or executed in a wrapper. We recommend to run the pipeline from a different location than the pipeline path, like the example below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -s PATH_TO_PIPELINE/Snakefile --use-conda --cores=24\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith --use-conda option, the pipeline will create environments to run rules based on \u003cem\u003eenv.yaml\u003c/em\u003e.\n\u003cstrong\u003eNote\u003c/strong\u003e the pipeline assumes that \u003cem\u003econfig.yaml\u003c/em\u003e is available at the location where the pipeline is executed.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1681587929.0 + "updated_at": 1625231941.0 }, { "data_format": 2, - "description": null, + "description": "Affinity Representing Instance Descriptors", "filenames": [ - "Singularity.UbuntuMOE-xenial", - "Singularity.YelpMOE" + "singularity/Singularity" ], - "full_name": "aminnayebi/ContainerMOE", + "full_name": "funkelab/arid", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1554405415.0 + "updated_at": 1562764827.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v1.0.1", - "Singularity.v1.0.0" + "Singularity.v2.2.0" ], - "full_name": "bud42/RWML", - "latest_release": "v1.0.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rwml\" class=\"anchor\" aria-hidden=\"true\" href=\"#rwml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRWML\u003c/h1\u003e\n", + "full_name": "baxpr/connprep", + "latest_release": "v2.2.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-connprep\" class=\"anchor\" aria-hidden=\"true\" href=\"#connprep\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econnprep\u003c/h1\u003e\n\u003cp\u003eProduce preprocessed fMRI images ready for connectivity analysis.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eDrop initial or final volumes as specified. Default: Analyze all volumes.\u003c/li\u003e\n\u003cli\u003eGet the TR (volume acquisition time) from pixdim[4] field of the Nifti header.\u003c/li\u003e\n\u003cli\u003eSlice timing correction. Default: none.\u003c/li\u003e\n\u003cli\u003eHead motion realignment (SPM12 two-stage) and production of mean fMRI.\u003c/li\u003e\n\u003cli\u003eRigid body coregistration of mean fMRI to T1 structural.\u003c/li\u003e\n\u003cli\u003eCompute volume quality metrics FD, DVARS.\u003c/li\u003e\n\u003cli\u003eReslice realigned fMRI to native space, and also warp to MNI space using CAT12 transform.\u003c/li\u003e\n\u003cli\u003eRemove confounds from the native and MNI space fMRIs by simultaneous regression. Defaults:\n\u003cul\u003e\n\u003cli\u003e0.01 - 0.10 Hz bandpass filter\u003c/li\u003e\n\u003cli\u003e6 estimated motion parameters and their first differences\u003c/li\u003e\n\u003cli\u003e6 principal components from the white matter + CSF compartment\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRepeat the confound removal, additionally removing the mean signal of the gray matter compartment.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003enum_initial_vols_to_drop 0 Number of initial volumes to drop\nnum_vols_to_analyze all Total number of volumes to analyze\nbandpasslo_hz 0.01 Low edge of bandpass filter in Hz\nbandpasshi_hz 0.10 High edge of bandpass filter\nmot_PCs 6 Number of PCs of motion params to remove\nmotderiv_PCs 6 Same for motion derivatives\nwmcsf_PCs 6 Same for white matter/CSF compartment\nslorder none Slice timing correction, SPM12 nomenclature \nfmri_niigz fMRI images, 4D Nifti\nmt1_niigz T1 structural\ndeffwd_niigz Forward deformation of T1 to MNI\ngray_niigz Gray matter volume fraction\nwhite_niigz White matter volume fraction\ncsf_niigz CSF volume fraction\nproject XNAT project label\nsubject XNAT subject label\nsession XNAT session label\nscan XNAT scan label\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003econnprep.pdf Processing report\nrp_adfmri.txt Realignment parameters\nFD.txt Framewise displacement\nDVARS.txt Framewise noise\nfiltered_keepgm_noscrub_nadfmri.nii.gz Filtered data, native space, gray matter signal retained\nfiltered_keepgm_noscrub_wadfmri.nii.gz Filtered data, MNI space, gray matter signal retained\nfiltered_removegm_noscrub_nadfmri.nii.gz Filtered data, native space, gray matter signal removed\nfiltered_removegm_noscrub_wadfmri.nii.gz Filtered data, MNI space, gray matter signal removed\nmeanadfmri.nii.gz Mean fMRI, native space\nwmeanadfmri.nii.gz Mean fMRI, MNI space\nstats_keepgm_noscrub.txt Processing info when gray matter signal retained\nstats_removegm_noscrub.txt Processing info when gray matter signal removed\ngm_mask.nii.gz Native space gray matter mask\nwmcsf_mask.nii.gz Native space white matter/CSF mask\nconfounds_keepgm_noscrub.txt Confounds matrix when gray matter signal retained\nconfounds_removegm_noscrub.txt Confounds matrix when gray matter signal removed\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1612386680.0 + "updated_at": 1595372367.0 }, { "data_format": 2, - "description": null, + "description": "Nextflow workflow for assembling large, diploid, eukaryotic genomes (2 gigabases haploid size or bigger)", "filenames": [ - "Singularity/Singularity.v1.0" + "Singularity" ], - "full_name": "Monia234/IARC-RNA-seq", + "full_name": "czbiohub/nf-large-assembly", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-rna-fusions\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-rna-fusions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-rna-fusions\u003c/h1\u003e\n\u003cp\u003eA nextflow pipeline to call somatic rna fusions\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-czbiohubnf-large-assembly\" class=\"anchor\" aria-hidden=\"true\" href=\"#czbiohubnf-large-assembly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eczbiohub/nf-large-assembly\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eAssemble large diploid eukaryotic genomes (2 gigabases haploid size or bigger)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/czbiohub/nf-large-assembly\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8d428dc306e8c519b4952b8239ab3eace188860f1c5dfabe1a4059c42c067a1e/68747470733a2f2f7472617669732d63692e6f72672f637a62696f6875622f6e662d6c617267652d617373656d626c792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/czbiohub/nf-large-assembly.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/nf-large-assembly\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/767f13dee3d8a1039b493b285b876f4ef216154825cb6401031b09e8d959b916/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6e662d6c617267652d617373656d626c792e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/nf-large-assembly.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe czbiohub/nf-large-assembly pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1644245608.0 + "updated_at": 1556036860.0 }, { "data_format": 2, - "description": "GNU Midnight Commander is a visual file manager, licensed under GNU General Public License and therefore qualifies as Free Software.", + "description": "test of nf-core create", "filenames": [ - "4.8.28/Singularity", - "4.8.25/Singularity", - "4.8.26/Singularity", - "4.8.29/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-mc", - "latest_release": "v4.8.29", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-mc/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-mc/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-mc/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-mc/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/20f9b62353e523d3ab71a141bef01e7c6c9b266b5d21ca495fec6bcf5149a691/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/20f9b62353e523d3ab71a141bef01e7c6c9b266b5d21ca495fec6bcf5149a691/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d63\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-mc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/197b32bc5699c413928a8dc98e7fc7ba78e4232c6d05026a53625842fe00f633/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/197b32bc5699c413928a8dc98e7fc7ba78e4232c6d05026a53625842fe00f633/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d63\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-mc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d972290aeff699a37c55f27a4af658b413ceba5d7bc7697189e546d9cc80fa22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6d63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d972290aeff699a37c55f27a4af658b413ceba5d7bc7697189e546d9cc80fa22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6d63\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-mc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3210b070126fcd60c3222ffb78e07d55b8223b3eb1c9f4e7e8cb2b33fb54931c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3210b070126fcd60c3222ffb78e07d55b8223b3eb1c9f4e7e8cb2b33fb54931c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d63\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-mc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-mc\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-mc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-mc\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/07885183f92acdf4c924ec41b91e32bf00dd0b1f3f820c477551a32ec8dfad98/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f392f39622f4d69646e696768745f436f6d6d616e6465725f342e372e302e395f6f6e5f5562756e74755f31312e30342e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/07885183f92acdf4c924ec41b91e32bf00dd0b1f3f820c477551a32ec8dfad98/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f392f39622f4d69646e696768745f436f6d6d616e6465725f342e372e302e395f6f6e5f5562756e74755f31312e30342e706e67\" alt=\"Image\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/commons/9/9b/Midnight_Commander_4.7.0.9_on_Ubuntu_11.04.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/mc\"\u003emc\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003emc\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/mc/4.8.29\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/mc\u003c/code\u003e as \u003ccode\u003e4.8.29.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "czbiohub/nf-core-test", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-coretest\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-coretest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/test\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003etest of nf-core\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/test\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5656ec3ca80ae8775904761dfc7b47e3357d325de15a8d013edd4a0093630611/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f746573742e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/test.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/test\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8a74c7ad053a343b2d1b30e0ef0f86afe191999cfc823635773862aefd840fd2/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f746573742e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/test.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/test pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1676698058.0 + "topics": [], + "updated_at": 1554245021.0 }, { "data_format": 2, - "description": "Set of Singularity HPC containers", + "description": null, "filenames": [ - "fenics/Singularity" + "Singularity" ], - "full_name": "kma/HPC-Container", + "full_name": "ResearchIT/SimNIBS", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC-Container\u003c/h1\u003e\n\u003cp\u003eSet of Singularity containers\u003c/p\u003e\n", + "readme": "\u003ch3\u003e\u003ca id=\"user-content-simnibs-singularity-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#simnibs-singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimNIBS singularity recipe\u003c/h3\u003e\n\u003cp\u003eBefore building, place the SimNIBS source tarball in the /tmp directory. (recipe version 2.1.1)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1530297750.0 + "updated_at": 1546981375.0 }, { "data_format": 2, - "description": "w2l", + "description": "Batch Connect - Example Shiny App that runs on OSC OnDemand", "filenames": [ - "Singularity", - "Singularity.gpu" + "ext/Singularity" ], - "full_name": "klm122/w2l", + "full_name": "OSC/bc_osc_example_shiny", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-w2l\" class=\"anchor\" aria-hidden=\"true\" href=\"#w2l\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ew2l\u003c/h1\u003e\n\u003cp\u003ew2l\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-wip-batch-connect---osc-example-shiny-app\" class=\"anchor\" aria-hidden=\"true\" href=\"#wip-batch-connect---osc-example-shiny-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[WIP] Batch Connect - OSC Example Shiny App\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a8152a2780451d58acdca1e79b03f771d6e84ae12087e6e7a824b6759b715dc1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f6578616d706c655f7368696e792e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a8152a2780451d58acdca1e79b03f771d6e84ae12087e6e7a824b6759b715dc1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f6578616d706c655f7368696e792e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_example_shiny.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app designed for OSC OnDemand that launches a Shiny App within\nan Owens batch job.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e x.y.z+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_example_shiny/fork\"\u003ehttps://github.com/OSC/bc_osc_example_shiny/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 11, "topics": [], - "updated_at": 1645905985.0 + "updated_at": 1527005209.0 }, { "data_format": 2, - "description": "IMPICA is notoriously difficult to build, so I made this so it would build if you have docker and mount for my research use.", + "description": null, "filenames": [ - "singularity/Singularity" + "IMAGES/methylator/Singularity", + "WGBS/DMT_analysis/Singularity_Methylator.def" ], - "full_name": "utcs-scea/Impica-Builder", + "full_name": "kirsho/DASH", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-dash-dazl-scarlet-hygromycin\" class=\"anchor\" aria-hidden=\"true\" href=\"#dash-dazl-scarlet-hygromycin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDASH (DAzl-Scarlet-Hygromycin)\u003c/h1\u003e\n\u003cp\u003eDescription of WGBS analysis for the \u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.05.03.442415v1\" rel=\"nofollow\"\u003epreprint\u003c/a\u003e \u003cstrong\u003e\"A genome-wide knock-out screen for actors of epigenetic silencing reveals new regulators of germline genes and 2-cell like cell state\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDefossez \u003ca href=\"http://parisepigenetics.com/dmdeg/\" rel=\"nofollow\"\u003elab\u003c/a\u003e, Epigenetics \u0026amp; cell fate \u003ca href=\"http://parisepigenetics.com/fr/\" rel=\"nofollow\"\u003eUnit\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1636746170.0 + "updated_at": 1658847725.0 }, { "data_format": 2, - "description": "code_aster containers", + "description": null, "filenames": [ - "Singularity.common.default", - "Singularity.salome_meca.cwa", - "Singularity.seq.default", - "Singularity.mpi.asterxx", - "Singularity.mpi.default" + "haz/docker/fd/Singularity" ], - "full_name": "codeaster/container", + "full_name": "FlorianPommerening/core-challenge-2022-solvers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers-for-code_aster\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers-for-code_aster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers for code_aster\u003c/h1\u003e\n\u003cp\u003eThis repository provides some recipes to build containers for\n\u003ca href=\"https://www.code-aster.org/\" rel=\"nofollow\"\u003ecode_aster\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003efor \u003ca href=\"https://docs.docker.com/\" rel=\"nofollow\"\u003edocker\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003efor \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eIt should be considered as a work in progress.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor example, additional work is needed to execute a containerized version of\ncode_aster from an existing\n\u003ca href=\"https://www.code-aster.org/spip.php?article302\" rel=\"nofollow\"\u003esalome_meca\u003c/a\u003e\ninstallation.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThe repository contains recipes to build a sequential and a parallel\nversion for the development branch (\u003ccode\u003edefault\u003c/code\u003e) which refers to the \u003ccode\u003elatest\u003c/code\u003e\ntag on docker images.\nThe code_aster version is named \u003ccode\u003eunstable\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-list-of-code_aster-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#list-of-code_aster-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eList of code_aster images\u003c/h2\u003e\n\u003cp\u003eExecutable images:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecodeastersolver/codeaster-seq\u003c/code\u003e: Sequential version of code_aster.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecodeastersolver/codeaster-mpi\u003c/code\u003e: Parallel version of code_aster.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIntermediate layer with prerequisites:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecodeastersolver/codeaster-common\u003c/code\u003e: Prerequisites for the sequential and\nparallel versions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis image can also be used to build your own development version.\u003c/p\u003e\n\u003cp\u003eSingularity recipes are simple \u003cem\u003econversions\u003c/em\u003e that use the Docker images as\nbootstrap.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tags\" class=\"anchor\" aria-hidden=\"true\" href=\"#tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTags\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003elatest\u003c/code\u003e: It refers to the last head of the \u003ccode\u003edefault\u003c/code\u003e branch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eNo more for the moment...\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild images\u003c/h2\u003e\n\u003cp\u003eSee available targets:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen choose your target between \u003ccode\u003eseq\u003c/code\u003e and \u003ccode\u003empi\u003c/code\u003e, or \u003ccode\u003ebuild\u003c/code\u003e to build all:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEnvironment files added in the \u003ccode\u003eenv.d\u003c/code\u003e directory are sourced before calling\n\u003ccode\u003edocker\u003c/code\u003e/\u003ccode\u003esingularity\u003c/code\u003e builder. It may be useful for example to configure the\nenvironment to pass a proxy.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-a-shell-using-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-a-shell-using-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a shell using the image:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm -it codeastersolver/codeaster-seq:latest\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-a-testcase-using-testcase-files-embedded-in-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-a-testcase-using-testcase-files-embedded-in-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a testcase using testcase files embedded in the image:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm codeastersolver/codeaster-seq:latest as_run --nodebug_stderr --test zzzz100f\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-a-testcase-using-files-out-of-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-a-testcase-using-files-out-of-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a testcase using files out of the image:\u003c/h3\u003e\n\u003cp\u003eIn this example the data files are extracted from the \u003cem\u003eimage\u003c/em\u003e.\nIn the real life, these files are for example created from salome_meca.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e create a temporary container to access the testcase files\u003c/span\u003e\ndocker run --name astercp codeastersolver/codeaster-seq:latest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e copy files\u003c/span\u003e\nmkdir workdir\ndocker cp astercp:/scif/apps/aster/share/aster/tests/sslv155a.comm workdir/\ndocker cp astercp:/scif/apps/aster/share/aster/tests/sslv155a.mmed workdir/\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e clean the temporary container\u003c/span\u003e\ndocker rm astercp\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e create the export file\u003c/span\u003e\ndocker run --rm codeastersolver/codeaster-seq:latest as_run --get_export sslv155a --nodebug_stderr \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \\\n sed -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003es#/scif/apps/aster/share/aster/tests#.#g\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e workdir/export\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf the \u003ccode\u003eexport\u003c/code\u003e file is manually created, the version can be addressed just\nby name (\u003ccode\u003eP version unstable\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eNow, run a code_aster container using local files:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm --volume \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/workdir:/aster codeastersolver/codeaster-seq:latest \\\n as_run --nodebug_stderr /aster/export\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-validation\" class=\"anchor\" aria-hidden=\"true\" href=\"#validation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eValidation\u003c/h3\u003e\n\u003cp\u003eTo limit the size of the binary images only few testcases are available in the\ninstallation directory.\nThe 3800+ testcases can be extracted from the source tree from the\n\u003ca href=\"https://bitbucket.org/code_aster/codeaster-src\" rel=\"nofollow\"\u003eBitbucket repository\u003c/a\u003e\n(see below).\nChecking all the 3800 testcases takes about 15-20h cpu.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSome prerequisites are not yet available within the container\n(miss3d, ecrevisse, etc.). So, all the tests that are using these tools\nare currently in failure.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo execute the existing testcases, use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -t codeastersolver/codeaster-seq:latest run_testcases unstable\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to copy the result files\u003c/span\u003e\ndocker cp -a \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONTAINER\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:/home/aster/resutest \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eDESTINATION\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse the following commands to download all the 3800+ testcases from the\n\u003ca href=\"https://bitbucket.org/code_aster/codeaster-src\" rel=\"nofollow\"\u003eBitbucket repository\u003c/a\u003e and\nexecute them.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e download the testcases out of the container\u003c/span\u003e\nwget https://bitbucket.org/code_aster/codeaster-src/get/default.tar.gz\ntar xzf default.tar.gz\nmv code_aster-codeaster-src-\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/astest \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e rm -rf code_aster-codeaster-src-\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e mount \u0027astest\u0027 and run testcases in the container\u003c/span\u003e\ndocker run -t --volume \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/astest:/home/aster/tests codeastersolver/codeaster-seq:latest \\\n run_testcases --tests=/home/aster/tests unstable\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 3, "topics": [], - "updated_at": 1575303352.0 + "updated_at": 1663181341.0 }, { "data_format": 2, - "description": "Diamond aligner Docker image", + "description": null, "filenames": [ "Singularity" ], - "full_name": "biocorecrg/diamond_docker", + "full_name": "smfsamir/transformer-gnn", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-diamond-docker-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#diamond-docker-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiamond Docker images\u003c/h1\u003e\n\u003cp\u003eDiamond aligner Docker image\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/biocorecrg/diamond/builds/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/50336b251df61eac194e273e6751254dd983989ce3ad82bd5782d5367ad795c7/68747470733a2f2f646f636b65726275696c646261646765732e7175656c6c746578742e65752f7374617475732e7376673f6f7267616e697a6174696f6e3d62696f636f7265637267267265706f7369746f72793d6469616d6f6e64\" alt=\"Docker Build Status\" data-canonical-src=\"https://dockerbuildbadges.quelltext.eu/status.svg?organization=biocorecrg\u0026amp;repository=diamond\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-gnn-fast\" class=\"anchor\" aria-hidden=\"true\" href=\"#gnn-fast\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGNN-Fast\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003eTo make it easy for you to get started with GitLab, here\u0027s a list of recommended next steps.\u003c/p\u003e\n\u003cp\u003eAlready a pro? Just edit this README.md and make it your own. Want to make it easy? \u003ca href=\"#editing-this-readme\"\u003eUse the template at the bottom\u003c/a\u003e!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-add-your-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#add-your-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdd your files\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file\" rel=\"nofollow\"\u003eCreate\u003c/a\u003e or \u003ca href=\"https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file\" rel=\"nofollow\"\u003eupload\u003c/a\u003e files\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line\" rel=\"nofollow\"\u003eAdd files using the command line\u003c/a\u003e or push an existing Git repository with the following command:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd existing_repo\ngit remote add origin https://gitlab.com/smfsamir/gnn-fast.git\ngit branch -M main\ngit push -uf origin main\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-integrate-with-your-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#integrate-with-your-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntegrate with your tools\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://gitlab.com/smfsamir/gnn-fast/-/settings/integrations\" rel=\"nofollow\"\u003eSet up project integrations\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-collaborate-with-your-team\" class=\"anchor\" aria-hidden=\"true\" href=\"#collaborate-with-your-team\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCollaborate with your team\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/members/\" rel=\"nofollow\"\u003eInvite team members and collaborators\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html\" rel=\"nofollow\"\u003eCreate a new merge request\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically\" rel=\"nofollow\"\u003eAutomatically close issues from merge requests\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/merge_requests/approvals/\" rel=\"nofollow\"\u003eEnable merge request approvals\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html\" rel=\"nofollow\"\u003eAutomatically merge when pipeline succeeds\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-and-deploy\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-and-deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest and Deploy\u003c/h2\u003e\n\u003cp\u003eUse the built-in continuous integration in GitLab.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/ci/quick_start/index.html\" rel=\"nofollow\"\u003eGet started with GitLab CI/CD\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/application_security/sast/\" rel=\"nofollow\"\u003eAnalyze your code for known vulnerabilities with Static Application Security Testing(SAST)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/topics/autodevops/requirements.html\" rel=\"nofollow\"\u003eDeploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/clusters/agent/\" rel=\"nofollow\"\u003eUse pull-based deployments for improved Kubernetes management\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/ci/environments/protected_environments.html\" rel=\"nofollow\"\u003eSet up protected environments\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-editing-this-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#editing-this-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEditing this README\u003c/h1\u003e\n\u003cp\u003eWhen you\u0027re ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to \u003ca href=\"https://www.makeareadme.com/\" rel=\"nofollow\"\u003emakeareadme.com\u003c/a\u003e for this template.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-suggestions-for-a-good-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#suggestions-for-a-good-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSuggestions for a good README\u003c/h2\u003e\n\u003cp\u003eEvery project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-name\" class=\"anchor\" aria-hidden=\"true\" href=\"#name\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eName\u003c/h2\u003e\n\u003cp\u003eChoose a self-explaining name for your project.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eLet people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-badges\" class=\"anchor\" aria-hidden=\"true\" href=\"#badges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBadges\u003c/h2\u003e\n\u003cp\u003eOn some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-visuals\" class=\"anchor\" aria-hidden=\"true\" href=\"#visuals\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisuals\u003c/h2\u003e\n\u003cp\u003eDepending on what you are making, it can be a good idea to include screenshots or even a video (you\u0027ll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eWithin a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eUse examples liberally, and show the expected output if you can. It\u0027s helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport\u003c/h2\u003e\n\u003cp\u003eTell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-roadmap\" class=\"anchor\" aria-hidden=\"true\" href=\"#roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRoadmap\u003c/h2\u003e\n\u003cp\u003eIf you have ideas for releases in the future, it is a good idea to list them in the README.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eState if you are open to contributions and what your requirements are for accepting them.\u003c/p\u003e\n\u003cp\u003eFor people who want to make changes to your project, it\u0027s helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.\u003c/p\u003e\n\u003cp\u003eYou can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors-and-acknowledgment\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors-and-acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors and acknowledgment\u003c/h2\u003e\n\u003cp\u003eShow your appreciation to those who have contributed to the project.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eFor open source projects, say how it is licensed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-project-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#project-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject status\u003c/h2\u003e\n\u003cp\u003eIf you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1567699875.0 + "updated_at": 1663265237.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.ubuntu", - "Singularity.cell2location", - "Singularity.irods.4.2.8" + "Singularity" ], - "full_name": "prete/singularity-recipes", + "full_name": "rses-singularity/caffe-cpu", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-caffe-cpu\" class=\"anchor\" aria-hidden=\"true\" href=\"#caffe-cpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaffe (CPU)\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1606249308.0 + "updated_at": 1542376576.0 }, { "data_format": 2, - "description": "Singularity container with Spack", + "description": "Singularity file for Cornell-MOE based off git clone https://github.com/wujian16/Cornell-MOE.git", "filenames": [ - "Singularity.spack-root", - "Singularity.spack-lmod", - "Singularity.spack-bowtie", - "Singularity.spack-rhel", - "Singularity.spackbase", - "Singularity.spack-fastqvalidator", - "Singularity.spack" + "Singularity" ], - "full_name": "baberlevi/spack-singularity", + "full_name": "belledon/moe-sing", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-work-in-progress\" class=\"anchor\" aria-hidden=\"true\" href=\"#work-in-progress\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ework in progress\u003c/h1\u003e\n\u003cp\u003eattempt to build a base singularity image with spack that can be used as the bootstrap for\nother singularity images that would perform the spack install of a particular package\u003c/p\u003e\n\u003cp\u003ecurrently having an issue with stage directory for spack attempting to write to\nthe immutable squashfs\u003c/p\u003e\n\u003cp\u003eas expected, the child container will happily install during %post since it can write\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-moe-sing\" class=\"anchor\" aria-hidden=\"true\" href=\"#moe-sing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emoe-sing\u003c/h1\u003e\n\u003cp\u003eSingularity file for Cornell-MOE based off git clone \u003ca href=\"https://github.com/wujian16/Cornell-MOE.git\"\u003ehttps://github.com/wujian16/Cornell-MOE.git\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTested on Singularity 2.4\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1521583740.0 + "updated_at": 1516305918.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.ExplainAI2", - "Singularity.ubuntu_tf", - "Singularity.physio", - "Singularity.centos_torch3", - "Singularity.centos_tf2", - "Singularity.ubuntu_pre", - "Singularity.centos_tf", - "Singularity.centos_torch2", - "Singularity.ExplainAI", - "Singularity.Spektral", - "Singularity.ubuntu_torch", - "Singularity.torch_mmf", - "Singularity.centos_torch", - "Singularity.jax", - "Singularity.mac_local", - "Singularity.pytorch", - "Singularity.torch" + "src/pddlstream/downward/misc/releases/20.06/Singularity.20.06", + "src/pddlstream/downward/misc/releases/19.12/Singularity.19.12", + "src/pddlstream/downward/misc/releases/19.06/Singularity.19.06", + "src/pddlstream/downward/misc/releases/latest/Singularity" ], - "full_name": "cyang31/containers", + "full_name": "Gaoyuan-Liu/Non-prehensile-Augmented-TAMP", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-non-prehensile-augmented-tamp\" class=\"anchor\" aria-hidden=\"true\" href=\"#non-prehensile-augmented-tamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNon-Prehensile Augmented TAMP\u003c/h1\u003e\n\u003cp\u003eRobotic manipulation in cluttered environments requires synergistic planning among prehensile and non-prehensile actions. Previous work on sampling-based Task and Motion Planning (TAMP) algorithms, e.g. PDDLStream, provide a fast and generalizable solution for multi-modal manipulation. However, they are likely to fail in cluttered scenarios where no collision-free grasping approaches can be sampled without preliminary manipulations.\nTo extend the ability of sampling-based algorithms, we integrate a vision-based Reinforcement Learning (RL) non-prehensile procedure, namely pusher, the pushing actions generated by pusher can eliminate interlocked situations and make the problem solvable. Also, the sampling-based algorithm evaluates the pushing actions by providing rewards in the training process, thus the pusher can learn to avoid situations containing irreversible failures.\nThe proposed hybrid planning method is validated on a cluttered bin picking problem and implemented in both simulation and real world. Results show that the pusher can effectively improve the success ratio of the previous sampling-based algorithm, while the sampling-based algorithm can help the pusher to learn pushing skills.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Gaoyuan-Liu/Non-prehensile-Augmented-TAMP/blob/main/pics/intro.png\"\u003e\u003cimg src=\"https://github.com/Gaoyuan-Liu/Non-prehensile-Augmented-TAMP/raw/main/pics/intro.png\" width=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-video\" class=\"anchor\" aria-hidden=\"true\" href=\"#video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVideo\u003c/h2\u003e\n\u003cp\u003eThe method introduction and experiments:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://youtu.be/brXAh9BH_Qw\" rel=\"nofollow\"\u003e\u003cimg src=\"https://github.com/Gaoyuan-Liu/Non-prehensile-Augmented-TAMP/raw/main/pics/youtube.png\" alt=\"Watch the video\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo:\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:Gaoyuan-Liu/panda_tamp.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003eComplie DownwardFast:\n\u003cpre\u003e\u003ccode\u003ecd panda_tamp/src/pddlstream\n\n./downward/build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003eCompile IKFast:\n\u003cpre\u003e\u003ccode\u003ecd panda_tamp/src/pddlstream/examples/pybullet/utils/\n\npybullet-planning$ (cd pybullet_tools/ikfast/franka_panda; python setup.py)\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNvigate terminal to \u003ccode\u003esrc/panda_pddlstream\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate pddlstream\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun panda_pddl in pybullet:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m examples.pybullet.panda.run_pybullet -n 3 -v\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun panda_pddl with Franka:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eroslaunch panda_control franka_following.launch \n\npython -m examples.pybullet.panda.run -n 3 -v\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-trainning\" class=\"anchor\" aria-hidden=\"true\" href=\"#trainning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrainning\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRun moveit motion planner, go to to \u003ccode\u003ews_moveit\u003c/code\u003e workspace\n\u003cpre\u003e\u003ccode\u003esource devel/setup.bash\n\nroslaunch panda_moveit_config demo.launch\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003eRun trainning scripts, go to \u003ccode\u003esrc/pddlstream/examples/pybullet/panda\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-ros-interpretation\" class=\"anchor\" aria-hidden=\"true\" href=\"#ros-interpretation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eROS Interpretation\u003c/h2\u003e\n\u003cp\u003eAfter PDDLStream solve the problem, the \u003ccode\u003esolution\u003c/code\u003e after post process returns a list \u003ccode\u003ecommands\u003c/code\u003e, the elements in the list are classes defined in \u003ccode\u003epanda_primitives\u003c/code\u003e. Therefore, the main pourpose of ROS interpretation is to interpret the \u003ccode\u003epanda_primitives\u003c/code\u003e to ROS commands.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-debug\" class=\"anchor\" aria-hidden=\"true\" href=\"#debug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebug\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-general\" class=\"anchor\" aria-hidden=\"true\" href=\"#general\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eThe defaut top grasping pose is in \u003ccode\u003epanda_utils.py\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-moveit-cartesian-path\" class=\"anchor\" aria-hidden=\"true\" href=\"#moveit-cartesian-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMoveit cartesian path\u003c/h3\u003e\n\u003cp\u003eThe \u003ca href=\"https://thomasweng.com/moveit_cartesian_jump_threshold/\" rel=\"nofollow\"\u003epost\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pybullet-camera\" class=\"anchor\" aria-hidden=\"true\" href=\"#pybullet-camera\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePybullet camera\u003c/h3\u003e\n\u003cp\u003eThe \u003ca href=\"https://towardsdatascience.com/simulate-images-for-ml-in-pybullet-the-quick-easy-way-859035b2c9dd\" rel=\"nofollow\"\u003epost\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest\u003c/h3\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1632080282.0 + "updated_at": 1662124624.0 }, { "data_format": 2, "description": null, "filenames": [ - "2.0.3/Singularity" + "install/Singularity" ], - "full_name": "yh549848/singularity-blastxmlparser", + "full_name": "BrennanGambling/mindboogle", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1645547232.0 + "updated_at": 1662174191.0 }, { "data_format": 2, @@ -7612,172 +7244,157 @@ var data = "filenames": [ "Singularity" ], - "full_name": "snystrom/bioconductor_docker_meme", + "full_name": "thanhtlx/linevd2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-bioconductor-docker-with-meme-suite\" class=\"anchor\" aria-hidden=\"true\" href=\"#bioconductor-docker-with-meme-suite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBioconductor Docker with MEME Suite\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4716\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBuilds the bioconductor docker container with the \u003ca href=\"meme-suite.org\"\u003ememe-suite\u003c/a\u003e v5.1.1, using python3.7.1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e Currently only builds from the \u003ccode\u003ebioconductor_docker:devel\u003c/code\u003e container. In the future, I will support stable releases.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eBuild the Docker image from Dockerfile:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t snystrom/bioconductor_docker_meme\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePull from Dockerhub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull snystrom/bioconductor_docker_meme:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -e PASSWORD=\u0026lt;password\u0026gt; -p 8787:8787 -v \u0026lt;drive/to/mount\u0026gt;:/mnt/\u0026lt;location\u0026gt; snystrom/bioconductor_docker_meme\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhile running, go to \u003ca href=\"https://localhost:8787/\" rel=\"nofollow\"\u003ehttps://localhost:8787/\u003c/a\u003e and login with \u003ccode\u003erstudio:\u0026lt;password\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo enter the container at the commandline while running:\n\u003cstrong\u003eNOTE:\u003c/strong\u003e this will enter as \u003ccode\u003eroot\u003c/code\u003e not the \u003ccode\u003erstudio\u003c/code\u003e user\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it snystrom/bioconductor_docker_meme /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-linevd\" class=\"anchor\" aria-hidden=\"true\" href=\"#linevd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLineVD\u003c/h1\u003e\n\u003cp\u003eThis repository provides the code for \u003ca href=\"https://arxiv.org/pdf/2203.05181.pdf\" rel=\"nofollow\"\u003eLineVD: Statement-level Vulnerability Detection using Graph Neural Networks\u003c/a\u003e. The environment can be built using \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or by following / following the commands in the Singularity file. To start, clone the repository and navigate to the root directory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory Structure\u003c/h2\u003e\n\u003cpre lang=\"dir\"\u003e\u003ccode\u003e(main module) \u251c\u2500\u2500 sastvd\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 codebert\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 helpers\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 ivdetect\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 linevd\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 scripts\n \u251c\u2500\u2500 storage\n(memoization) \u2502\u00a0\u00a0 \u251c\u2500\u2500 cache\n(raw data) \u2502\u00a0\u00a0 \u251c\u2500\u2500 external\n(csvs) \u2502\u00a0\u00a0 \u251c\u2500\u2500 outputs\n(models) \u2502\u00a0\u00a0 \u2514\u2500\u2500 processed\n(tests) \u2514\u2500\u2500 tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training-linevd-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-linevd-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining LineVD from scratch\u003c/h2\u003e\n\u003cp\u003eBuild and initialise environment and download dataset\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build main.sif Singularity\nsingularity run main.sif -p initialise\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFeature extraction (Increase NUM_JOBS if running on HPC for speed up)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e main.sif python sastvd/scripts/prepare.py\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e main.sif python sastvd/scripts/getgraphs.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTrain model (Training takes around 1-2 hours using GTX 3060)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --nv main.sif python sastvd/scripts/train_best.py\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -H /mnt/hdd/thuonglc/linevd/ --nv --env TUNE_DISABLE_STRICT_METRIC_CHECKING=1 main.sif python sastvd/scripts/train_best.py 16\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1618423035.0 + "updated_at": 1663593135.0 }, { "data_format": 2, - "description": "Singularity recipe files for busco (https://gitlab.com/ezlab/busco)", + "description": "Collection of numerical and analytical tools for the solution of nonlinear matter-wave equation in Bose Einstein Condensates (BEC)", "filenames": [ - "Singularity.4.1.4", - "Singularity", - "Singularity.4.0.2", - "Singularity.4.1.0", - "Singularity.4.0.0", - "Singularity.4.0.6", - "Singularity.4.0.4", - "Singularity.5.1.2", - "Singularity.4.0.1", - "Singularity.4.0.5", - "Singularity.4.1.1", - "Singularity.5.2.2", - "Singularity.4.1.2" + "Singularity" ], - "full_name": "powerPlant/busco-srf", + "full_name": "lorenzifrancesco/soliton-BEC", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the BUSCO tool for Benchmarking Universal Single-Copy Ortholog assessment\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-soliton-bec\" class=\"anchor\" aria-hidden=\"true\" href=\"#soliton-bec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esoliton-BEC\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/AshtonSBradley/FourierGPE.jl/actions\"\u003e\u003cimg src=\"https://github.com/AshtonSBradley/FourierGPE.jl/workflows/CI/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/AshtonSBradley/FourierGPE.jl\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7fe44b2d2126e2133bbad1e9b91a108b030bc57ca00f6e0e1c3b636a0811ab8e/68747470733a2f2f636f6465636f762e696f2f67682f417368746f6e53427261646c65792f466f75726965724750452e6a6c2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Coverage\" data-canonical-src=\"https://codecov.io/gh/AshtonSBradley/FourierGPE.jl/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCollection of numerical and analytical tools for the solution of nonlinear matter-wave equation in Bose Einstein Condensates (BEC)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1629171754.0 + "updated_at": 1648714594.0 }, { "data_format": 2, - "description": "Containers for game AI", + "description": "Mostly command-line utilities for automating cumbersome processes", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "sbutcher/game-container", + "full_name": "mfromano/utils", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-game-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#game-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egame-container\u003c/h1\u003e\n\u003cp\u003eContainers for game AI\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-utils\" class=\"anchor\" aria-hidden=\"true\" href=\"#utils\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eutils\u003c/h1\u003e\n\u003cp\u003eMostly command-line utilities for automating cumbersome processes\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1547647598.0 + "updated_at": 1671898445.0 }, { "data_format": 2, - "description": "Diffusion NLP project", + "description": null, "filenames": [ - "Singularity", - "Diffusion-LM/Singularity" + "Testes_ate_21_10_2022/facies_classification_benchmark/my_benchmark-box/.singularity.d/Singularity", + "Testes_ate_21_10_2022/thurbridi/my_thurbridi/.singularity.d/Singularity" ], - "full_name": "mathematiguy/diffusion-nlp", + "full_name": "elis-essantos/sdumontHome", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-diffusion-nlp\" class=\"anchor\" aria-hidden=\"true\" href=\"#diffusion-nlp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ediffusion-nlp\u003c/h1\u003e\n\u003cp\u003eThis project attempts to reproduce the paper \"Diffusion-LM Improves Controllable Text Generation\" by Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., \u0026amp; Hashimoto, T. B. (2022), available here: \u003ca href=\"https://arxiv.org/pdf/2205.14217.pdf\" rel=\"nofollow\"\u003ehttps://arxiv.org/pdf/2205.14217.pdf\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory structure\u003c/h2\u003e\n\u003cp\u003eThere are 3 significant subfolders of this repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ediffusion_lm\u003c/code\u003e - contains code towards a from scratch reproduction of the authors\u0027 work. It includes a \u003ccode\u003emodel.py\u003c/code\u003e model definition file in PyTorch, which implements the forward pass of the model as closely as I could figure out from the paper and also by looking through their source code. It is supported by \u003ccode\u003enotebooks\u003c/code\u003e, which contains my investigations of the model design, and also \u003ccode\u003etests\u003c/code\u003e where I implemented some tests for testing the model code.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDiffusion-LM\u003c/code\u003e - contains a fork of the original source code for the paper at \u003ca href=\"https://github.com/XiangLi1999/Diffusion-LM\"\u003ehttps://github.com/XiangLi1999/Diffusion-LM\u003c/a\u003e. There I have containerized the project so it can be run reliably on other computers. The full details of the fork are documented there.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMLRC-2022-Report\u003c/code\u003e - is a latex project containing a report written by myself for the completion of a Class Project for Comp-599 Natural Language Understanding at McGill University, fall 2022 semester.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-how-to-get-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-get-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to get started\u003c/h1\u003e\n\u003cp\u003eThe only software dependencies for this repository is GNU Make and Singularity. On Ubuntu systems, make can be installed simply via \u003ccode\u003esudo apt install make\u003c/code\u003e. Instructions for how to install Singularity are available here: \u003ca href=\"https://docs.sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehttps://docs.sylabs.io/guides/3.5/user-guide/quick_start.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you are interested in running \u003ccode\u003ediffusion_lm\u003c/code\u003e, then you will need to build the singularity container in this directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Build the singularity container for this project\nmake container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen once you have done that, you can start a local Jupyterlab server via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Start local jupyterlab server\nmake jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe server will be listening at \u003ccode\u003elocalhost:8888\u003c/code\u003e and has a default password of \"jupyter\".\u003c/p\u003e\n\u003cp\u003eYou can also run other \u003ccode\u003emake\u003c/code\u003e commands, such as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Build the latex report at MLRC-2022-Report/article.pdf\nmake report\n\n# Run pytest unit tests\nmake test\n\n# Attempt to train the diffusion_lm model (not working)\nmake train\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is everything you would need to know to get around this repository. Building the singularity container does take time, so if you insist on not using it you can still install the python requirements for the project with \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e, although it is recommended to do this inside of a python environment of some sort.\u003c/p\u003e\n\u003cp\u003eYou can still run the make commands outside of the singularity container with \u003ccode\u003emake \u0026lt;command\u0026gt; RUN=\u003c/code\u003e - this suppresses the \u003ccode\u003esingularity exec\u003c/code\u003e command, but this will only work if you have the dependencies installed on your machine.\u003c/p\u003e\n", + "readme": "", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1671466565.0 + "updated_at": 1671998603.0 }, { "data_format": 2, - "description": "Master Thesis for Robotics Master", + "description": "HTCondor execution point setup and configuration for using HTCondor file transfer to synchronize a directory between two hosts", "filenames": [ - "vision/src/vision/pythonClasses/deeplab/SingularityResNest", - "vision/src/vision/pythonClasses/darknet/Singularity" + "Singularity.def" ], - "full_name": "GuiMateus/thesis", + "full_name": "brianaydemir/htcondor_file_transfer_ep", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-3d-volumetric-and-semantic-environment-reconstruction\" class=\"anchor\" aria-hidden=\"true\" href=\"#3d-volumetric-and-semantic-environment-reconstruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3D Volumetric and Semantic Environment Reconstruction\u003c/h1\u003e\n\u003cp\u003eThis repo contains the materials used in the Master\u0027s Thesis from Guilherme Mateus at Aalborg University. The pipeline contained in it creates 3D semantical and volumetric reconstructions of environments using Deep Learning. This implementation is done using ROS melodic as a framework of communication.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".images/gitImage.png\"\u003e\u003cimg src=\".images/gitImage.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA small description of each package is given below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eontologies\u003c/strong\u003e: Handles object ontonlogies.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eservice\u003c/strong\u003e: Consists of services files for system communication.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003erealsense-ros\u003c/strong\u003e: Gathers data using realsense camera.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003euserInterface\u003c/strong\u003e: Provides a GUI for users to control the system.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003evision\u003c/strong\u003e: Handles screw detection using YOLOv4 and DeepLabV3+.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eThe system contains YOLOv4 and DeepLabV3+. However, YOLOv4 still has to be manually built under \u003ccode\u003ecatkin_ws/src/release/vision/src/vision/pythonClasses/darknet.py\u003c/code\u003e, for that follow the instructions on the \u003ca href=\"https://github.com/AlexeyAB/darknet\"\u003erepo\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOBS: To build darknet you need to get a CMake version bigger than 3.12, which is not compatible with ROS. Do not uninstall the current version installed in the system, instead use a local CMake version.\u003c/p\u003e\n\u003cp\u003eIn case of problems with DeepLabV3+, follow the \u003ca href=\"https://github.com/jfzhang95/pytorch-deeplab-xception\"\u003erepo\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePre-trained models and configs can be found by using \u003ccode\u003e./setup.sh\u003c/code\u003e. The weights are stored under \u003ccode\u003e/opt/vision/\u003c/code\u003e, therefore to use the weights models the script needs root permissions. Alternatively the weights paths must be manually changed in \u003ccode\u003ecatkin_ws/src/release/vision/src/vision/pythonClasses/detectObjects.py\u003c/code\u003e and \u003ccode\u003ecatkin_ws/src/release/vision/src/vision/pythonClasses/segmentationInit.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf it still doesn\u0027t work, I don\u0027t know mate, ask my parrot, he might know it better than me or something like that.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cp\u003eThis requires a system setup with ROS. It is recommended to use \u003ccode\u003eUbuntu 18.04\u003c/code\u003e with \u003ccode\u003eROS Melodic\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-creating-workspace-and-cloning-the-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-workspace-and-cloning-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating workspace and cloning the repository\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e create a catkin workspace\u003c/span\u003e\nmkdir -p catkin_ws/src \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e catkin_ws/src\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Clone the repository from bitbucket.\u003c/span\u003e\ngit clone --recursive https://guimateus@bitbucket.org/guimateus/thesis.git\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install dependencies\u003c/span\u003e\nsudo apt update -qq \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\nrosdep update\nrosdep install --from-paths \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --ignore-src --rosdistro melodic -y\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003einstall python catkin tools. Needed for catkin build command\u003c/span\u003e\nsudo apt-get install python-catkin-tools\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e build the workspace\u003c/span\u003e\ncatkin build\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installing-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling dependencies\u003c/h3\u003e\n\u003cp\u003eGo to Intel Realsense website and \u003ca href=\"https://www.intelrealsense.com/developers/\" rel=\"nofollow\"\u003einstall the SDK for Linux\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-launching-the-system\" class=\"anchor\" aria-hidden=\"true\" href=\"#launching-the-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunching The System\u003c/h3\u003e\n\u003cp\u003eTo launch system type the following to a terminal window.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch launch_nodes main.launch\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-reconstructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-reconstructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning reconstructions\u003c/h2\u003e\n\u003cp\u003eThis is the user interface of the system\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".images/GUI3D.png\"\u003e\u003cimg src=\".images/GUI3D.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFirst use offline reconstruction to detect static objects in the environment. Then, to perform an online reconstruction create ontological relations using the tab of the interface shown below, and select an object of interest under the \"Object Selection\" tab.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".images/ontologiesTabNew.png\"\u003e\u003cimg src=\".images/ontologiesTabNew.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe results can be visualized in \"Object Detection\", \"Object Segmentation\", and \"3D Reconstruction\".\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-future-works\" class=\"anchor\" aria-hidden=\"true\" href=\"#future-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFuture works\u003c/h2\u003e\n\u003cp\u003eSome possible future works to increase quality of the repo:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSegmentation change\u003c/strong\u003e: The qualitative results of the segmentation network are not satisfying, therefore it must be changed.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSimplifying setup\u003c/strong\u003e: Setup can be a bit convoluted, so maybe I can make it a bit easier.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eImprove ontologies framework\u003c/strong\u003e: Could be cool to have some extra functionalities in ontologies and maybe use a stochastic method.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eImprove addition of new objects\u003c/strong\u003e: Kind of hard to add custom objects to system right now, have to make the training framework easier.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eParrots\u003c/strong\u003e: This git repo critically lacks parrots.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".images/sam.jpg\"\u003e\u003cimg src=\".images/sam.jpg\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e[Guilherme Mateus Martins]\u003c/strong\u003e - \u003ca href=\"mailto:gmateu16@student.aau.dk\"\u003eemail\u003c/a\u003e - \u003ca href=\"https://bitbucket.org/%7Bba72de4e-9cb6-4e73-89db-24d4d8f12fe7%7D/\" rel=\"nofollow\"\u003eGit Profile\u003c/a\u003e - \u003ca href=\"https://www.linkedin.com/in/guilherme-mateus-346b58b5/\" rel=\"nofollow\"\u003eLinkedIn\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eAalborg university\u003c/li\u003e\n\u003cli\u003eDimitris Chrysostomou\u003c/li\u003e\n\u003cli\u003eSome other cool people\u003c/li\u003e\n\u003cli\u003eMy computer for being a real trooper and not dying after this repo made it crash several times\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1641547757.0 + "updated_at": 1658411822.0 }, { "data_format": 2, - "description": "Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.", + "description": "openjdk:8 based release of CANU, a PacBio assembler", "filenames": [ - "2.10.8/Singularity", - "2.10.9/Singularity", - "2.11.0/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-sra-toolkit", - "latest_release": "v2.11.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-sra-toolkit/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-sra-toolkit/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-sra-toolkit/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-sra-toolkit/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/31e3cb09d81e6fb61f9191e5ce617c6808d24498265a7a0c0f5b82303b18306d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31e3cb09d81e6fb61f9191e5ce617c6808d24498265a7a0c0f5b82303b18306d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-sra-toolkit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/829e9dae945b99db01be6a45bd00195069d38a971309a3673faa34fcc622e860/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/829e9dae945b99db01be6a45bd00195069d38a971309a3673faa34fcc622e860/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-sra-toolkit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ab0f5d5286d9642bb3b041fca0d27ceef55d8806e1f752c89fb699f6f7794e03/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ab0f5d5286d9642bb3b041fca0d27ceef55d8806e1f752c89fb699f6f7794e03/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-sra-toolkit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/753805fc4e3f4b095bd00c1a208047377e71c91f500b66d8730044e6298c3a8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/753805fc4e3f4b095bd00c1a208047377e71c91f500b66d8730044e6298c3a8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-sra-toolkit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-sra-toolkit\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-sra-toolkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-sra-toolkit\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/ncbi/sra-tools\"\u003esra-toolkit\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003esra-toolkit\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/sra-toolkit/2.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/sra-toolkit\u003c/code\u003e as \u003ccode\u003e 2.11.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "sghignone/canu", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-canu\" class=\"anchor\" aria-hidden=\"true\" href=\"#canu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecanu\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ - "singularity", - "bioinformatics" + "container", + "docker-container", + "dockerfile", + "dna-assembly", + "pacbio" ], - "updated_at": 1629226848.0 + "updated_at": 1662449005.0 }, { "data_format": 2, - "description": "Singularity recipes for ALCF-Theta", + "description": "WaveUnet for Saraga Dataset (Indian Carnatic Music)", "filenames": [ - "singularity_recipes/Singularity.py36", - "singularity_recipes/Singularity.hello_world", - "singularity_recipes/Singularity.mpich33" + "Singularity" ], - "full_name": "Romit-Maulik/Theta_Containers", + "full_name": "its-rajesh/WaveUnet", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers-on-theta\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers-on-theta\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers on Theta\u003c/h1\u003e\n\u003cp\u003eSingularity recipes for ALCF-Theta\u003c/p\u003e\n\u003cp\u003eSingularity hub is discontinued. One must build on dockerhub and pull on ALCF.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-waveunet-implementation-for-saraga-dataset-indian-carnatic-music\" class=\"anchor\" aria-hidden=\"true\" href=\"#waveunet-implementation-for-saraga-dataset-indian-carnatic-music\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWaveUnet Implementation for Saraga Dataset (Indian Carnatic Music)\u003c/h1\u003e\n\u003cp\u003eActual Network: \u003ca href=\"https://github.com/f90/Wave-U-Net-Pytorch\"\u003ehttps://github.com/f90/Wave-U-Net-Pytorch\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSaraga Carnatic Dataset:\u003c/p\u003e\n\u003cp\u003eIt has five stems: Mixed, Vocal, Violin, Mrindangam Right and Mrindangam Left.\nConverting Mrindangam left and right into single audio file (mrindangam)\nExpecting Four stem output namely: Vocal, violin, mrindangam and others\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWith Bleeding (Actual Dataset)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSDR:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWithout Bleeding (Bleeding Removed Dataset)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSDR:\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1619207429.0 + "updated_at": 1665079190.0 }, { "data_format": 2, - "description": null, + "description": "a Singularity recipe with openmpi 2.1.1 on base centos 7 to run on the Cineca clusters x86_64 based", "filenames": [ "Singularity" ], - "full_name": "Freakey17/CP4TP", + "full_name": "simarocchi/openmpi_centos7_x86_64", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-openmpi_centos7_x86_64\" class=\"anchor\" aria-hidden=\"true\" href=\"#openmpi_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopenmpi_centos7_x86_64\u003c/h1\u003e\n\u003cp\u003ea Singularity recipe with openmpi 2.1.1 on base centos 7 to run on the Cineca clusters x86_64 based\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1557407944.0 + "updated_at": 1605098444.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "Singularity.test2" + "diamond-with-ncbidb/Singularity" ], - "full_name": "rsm5139/singularity", + "full_name": "AsagaKosho/containers", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1551716847.0 + "updated_at": 1652842419.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "mshow34jt/analysis_container", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-analysis_container\" class=\"anchor\" aria-hidden=\"true\" href=\"#analysis_container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eanalysis_container\u003c/h1\u003e\n\u003cp\u003egit clone \u003ca href=\"http://github.com/mshow34jt/analysis_container\"\u003ehttp://github.com/mshow34jt/analysis_container\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ecd analysis_container\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eto build with Docker\u003c/h3\u003e\n\u003cp\u003edocker build -t analysis:v1 .\u003c/p\u003e\n\u003cp\u003eexecute with:\u003cbr\u003e\ndocker run --rm -d --network host --name analysis -v $PWD/log:/data/log -v $PWD/ldms:/data/ldms -v $PWD/slurm:/data/slurm -v /etc/localtime:/etc/localtime analysis:v1\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-proceed-with-singularity-as-an-alternative\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-proceed-with-singularity-as-an-alternative\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo proceed with Singularity as an alternative:\u003c/h3\u003e\n\u003cp\u003edocker save analysis:v1 \u0026gt;analysisv1.tar\u003c/p\u003e\n\u003cp\u003esingularity build analysis.sif docker-archive://analysisv1.tar\u003c/p\u003e\n\u003cp\u003ealternatively build without docker requires root or fakeroot setup\nsteps to build image (sif file) and start instance (example):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIn the wscont/ folder, as the container owner user, run ./dock2sing.sh (generates Singularity.def)\u003c/li\u003e\n\u003cli\u003eBe sure to setup \"fakeroot\" requirements first if not there already.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/cli/singularity_config_fakeroot.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/cli/singularity_config_fakeroot.html\u003c/a\u003e\nsingularity build --fakeroot analysis.sif Singularity.def\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-move-the-file-to-the-desired-host-and-there-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#move-the-file-to-the-desired-host-and-there-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMove the file to the desired host, and there run\u2026\u003c/h3\u003e\n\u003cp\u003esingularity instance start --bind /storage/nvme0n1/ncsa/eclipse/store_function_csv/spool/:/data/ldms --bind /storage/slurm/eclipse/spool-bitzer/job_detail:/data/slurm --bind /etc/localtime:/etc/localtime --bind /storage/nvme0n1/ncsa/log:/data/log analysis.sif analysis\u003c/p\u003e\n\u003cp\u003eThe first time the container is started, you will need to prime the database with test data and metadata for the metrics\u003cbr\u003e\nI do it interactively with singularity shell instance://analysis\u003cbr\u003e\ncat tests.csv |./inserttests.pl\u003cbr\u003e\ncat eclipse_md.csv |./insertmd.pl\nexit\u003c/p\u003e\n\u003cp\u003esingularity run instance://analysis /jobmon/bin/init.sh \u0026amp;\u003c/p\u003e\n", + "full_name": "jt2gtwci/HessianScreeningRule", + "latest_release": "v0.2.0", + "readme": "\n\u003ch1\u003e\u003ca id=\"user-content-code-for-the-hessian-screening-rule\" class=\"anchor\" aria-hidden=\"true\" href=\"#code-for-the-hessian-screening-rule\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode for the Hessian Screening Rule\u003c/h1\u003e\n\n\n\u003ch2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h2\u003e\n\u003cp\u003eThe results from the simulations are stored in the \u003ca href=\"results/\"\u003eresults\nfolder\u003c/a\u003e. The figures and tables in the paper, generated from\nthese results, are stored in \u003ca href=\"figures/\"\u003e\u003ccode\u003efigures/\u003c/code\u003e\u003c/a\u003e and\n\u003ca href=\"tables/\"\u003e\u003ccode\u003etables/\u003c/code\u003e\u003c/a\u003e respectively.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reproducing-the-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#reproducing-the-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReproducing the Results\u003c/h2\u003e\n\u003cp\u003eTo reproduce the results, we recommend you use the singularity\ncontainer. See the release section on GitHub and download the container\nfrom there. To run an experiment from the singularity container, call\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --no-home --bind results:/project/results container.sif \u0026lt;script\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;script\u0026gt;\u003c/code\u003e should be a name of a script to run from the\n\u003ca href=\"experiments/\"\u003eexperiments folder\u003c/a\u003e folder, such as\n\u003ccode\u003eexperiments/simulateddata.R\u003c/code\u003e. The results will then be output to the\n\u003ccode\u003eresults\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-re-building-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#re-building-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRe-building the Singularity Container\u003c/h3\u003e\n\u003cp\u003eIf you want to re-build the singularity container (or simply want to\nclone the repo to your local drive), you can do so via the following\nsteps.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone the repository to your local hard drive. On linux, using SSH\nauthentication, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:jt2gtwci/HessianScreeningRule.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the root of the repo and build the singularity container\nby calling\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd HessianScreeningRule\nsudo singularity build container.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen proceed as in \u003ca href=\"#reproducing-the-results\"\u003eReproducing the Results\u003c/a\u003e\nto run the experiments.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-experiments-without-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-experiments-without-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Experiments without Singularity\u003c/h3\u003e\n\u003cp\u003eAlternatively, you may also reproduce the results by cloning this\nrepository, then either opening the \u003ccode\u003eHessianScreening.Rproj\u003c/code\u003e file in R\nStudio or starting R in the root directory of this folder (which will\nactivate the renv repository) and then run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erenv::restore()\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto restore the project library. Then build the R package (see below) and\nrun the simulations directly by running the scripts in the experiments\nfolder. This is not recommended, however, since it, unlike the\nSingularity container approach, does not exactly reproduce the software\nenvironment used when these simulations where originally run and may\nresult in discrepancies due to differences in for instance operating\nsystems, compilers, and BLAS/LAPACK implementations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-r-package\" class=\"anchor\" aria-hidden=\"true\" href=\"#r-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR Package\u003c/h2\u003e\n\u003cp\u003eIf you want to build and experiment with the package, you can do so by\ncalling\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e R CMD INSTALL .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003cp\u003eThe data sets used for the project are not stored on this repository and\nhave to be downloaded by running the script found in\n\u003ca href=\"data-raw/\"\u003e\u003ccode\u003edata-raw/\u003c/code\u003e\u003c/a\u003e. This does not apply when you use the\nsingularity container, however, since the data sets are stored inside it\n(and could technically be retrieved from it too).\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1636506455.0 + "updated_at": 1652962267.0 }, { "data_format": 2, - "description": "EPACTS container", + "description": "Modified copy of GEMMA version 0.93 (Zhou and Stephens) for use with bugs", "filenames": [ "Singularity" ], - "full_name": "CHPC-UofU/Singularity-ubuntu-epacts", - "latest_release": null, + "full_name": "danny-wilson/gemma0.93b", + "latest_release": "v0.1", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1504217055.0 + "updated_at": 1653040520.0 }, { "data_format": 2, @@ -7785,44 +7402,46 @@ var data = "filenames": [ "Singularity" ], - "full_name": "lixuekai2001/brain-inversion", + "full_name": "michalpolic/yolact", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-you-only-look-at-coefficients\" class=\"anchor\" aria-hidden=\"true\" href=\"#you-only-look-at-coefficients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eY\u003c/strong\u003eou \u003cstrong\u003eO\u003c/strong\u003enly \u003cstrong\u003eL\u003c/strong\u003eook \u003cstrong\u003eA\u003c/strong\u003et \u003cstrong\u003eC\u003c/strong\u003eoefficien\u003cstrong\u003eT\u003c/strong\u003es\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e \u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\n \u255a\u2588\u2588\u2557 \u2588\u2588\u2554\u255d\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u2550\u2588\u2588\u2554\u2550\u2550\u255d\n \u255a\u2588\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u255a\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2551 \n \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA simple, fully convolutional model for real-time instance segmentation. This is the code for our papers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1904.02689\" rel=\"nofollow\"\u003eYOLACT: Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003eYOLACT++: Better Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-yolact-v12-released-changelog\" class=\"anchor\" aria-hidden=\"true\" href=\"#yolact-v12-released-changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYOLACT++ (v1.2) released! (\u003ca href=\"CHANGELOG.md\"\u003eChangelog\u003c/a\u003e)\u003c/h4\u003e\n\u003cp\u003eYOLACT++\u0027s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e (check out our journal paper \u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIn order to use YOLACT++, make sure you compile the DCNv2 code. (See \u003ca href=\"https://github.com/dbolya/yolact#installation\"\u003eInstallation\u003c/a\u003e)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-for-a-real-time-demo-check-out-our-iccv-video\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-a-real-time-demo-check-out-our-iccv-video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor a real-time demo, check out our ICCV video:\u003c/h4\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=0pMfmo8qfpQ\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c9e061dab1a6754a417d6eb14803f1104cce54aa9231aa0d779e2523694ed23e/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f30704d666d6f38716670512f302e6a7067\" alt=\"IMAGE ALT TEXT HERE\" data-canonical-src=\"https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSome examples from our YOLACT base model (33.5 fps on a Titan Xp and 29.8 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e):\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_0.png\"\u003e\u003cimg src=\"data/yolact_example_0.png\" alt=\"Example 0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_1.png\"\u003e\u003cimg src=\"data/yolact_example_1.png\" alt=\"Example 1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_2.png\"\u003e\u003cimg src=\"data/yolact_example_2.png\" alt=\"Example 2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repository and enter it:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/dbolya/yolact.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e yolact\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eSet up the environment using one of the following methods:\n\u003cul\u003e\n\u003cli\u003eUsing \u003ca href=\"https://www.anaconda.com/distribution/\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eRun \u003ccode\u003econda env create -f environment.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eManually with pip\n\u003cul\u003e\n\u003cli\u003eSet up a Python3 environment (e.g., using virtenv).\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"http://pytorch.org/\" rel=\"nofollow\"\u003ePytorch\u003c/a\u003e 1.0.1 (or higher) and TorchVision.\u003c/li\u003e\n\u003cli\u003eInstall some other packages:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Cython needs to be installed before pycocotools\u003c/span\u003e\npip install cython\npip install opencv-python pillow pycocotools matplotlib \u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to train YOLACT, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into \u003ccode\u003e./data/coco\u003c/code\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to evaluate YOLACT on \u003ccode\u003etest-dev\u003c/code\u003e, download \u003ccode\u003etest-dev\u003c/code\u003e with this script.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO_test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you want to use YOLACT++, compile deformable convolutional layers (from \u003ca href=\"https://github.com/CharlesShang/DCNv2/tree/pytorch_1.0\"\u003eDCNv2\u003c/a\u003e).\nMake sure you have the latest CUDA toolkit installed from \u003ca href=\"https://developer.nvidia.com/cuda-toolkit\" rel=\"nofollow\"\u003eNVidia\u0027s Website\u003c/a\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e external/DCNv2\npython setup.py build develop\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-hidden=\"true\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvaluation\u003c/h1\u003e\n\u003cp\u003eHere are our YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on \u003ccode\u003etest-dev\u003c/code\u003e:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1yp7ZbbDwvMiFJEq4ptVKTYTI2VeRDXl0/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EUVpxoSXaqNIlssoLKOEoCcB1m0RpzGq_Khp5n1VX3zcUw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDarknet53-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.7\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1dukLrTzZQEuhzitGkHaGjphlmRJOjVnP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_darknet53_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/ERrao26c8llJn25dIyZPhwMBxUp2GdZTKIMUQA3t0djHLw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e29.8\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf_/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EYRWxBEoKU9DiblrWx2M89MBGFkVVB_drlRd_v5sdT3Hgg\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e700\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e23.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e31.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1lE4Lz5p25teiXV-6HdTiOJSnS7u7GBzg/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_im700_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/Eagg5RSc5hFEhp7sPtvLNyoBjhlf2feog7t8OQzHKKphjw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYOLACT++ models (released on December 16th, 2019):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1ZPu1YR2UzGHQD0o1rEqy-j5bmEm3lbyP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EcJAtMiEFlhAnVsDf00yWRIBUC4m8iE9NEEiV05XwtEoGw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e27.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.6\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/15id0Qq5eqRbkD-N3ZjDZXdCvRyIaHpFB/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EVQ62sF0SrJPrl_68onyHF8BpG7c05A8PavV4a849sZgEA\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo evalute the model, put the corresponding weights file in the \u003ccode\u003e./weights\u003c/code\u003e directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., \u003ccode\u003eyolact_base\u003c/code\u003e for \u003ccode\u003eyolact_base_54_800000.pth\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quantitative-results-on-coco\" class=\"anchor\" aria-hidden=\"true\" href=\"#quantitative-results-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuantitative Results on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This should get 29.92 validation mask mAP last time I checked.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Output a COCOEval json to submit to the website or to use the run_coco_eval.py script.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This command will create \u0027./results/bbox_detections.json\u0027 and \u0027./results/mask_detections.json\u0027 for detection and instance segmentation respectively.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py.\u003c/span\u003e\npython run_coco_eval.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-qualitative-results-on-coco\" class=\"anchor\" aria-hidden=\"true\" href=\"#qualitative-results-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQualitative Results on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on COCO. From here on I\u0027ll use a confidence threshold of 0.15.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --display\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-benchmarking-on-coco\" class=\"anchor\" aria-hidden=\"true\" href=\"#benchmarking-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarking on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run just the raw model on the first 1k images of the validation set\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark --max_images=1000\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on the specified image.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process an image and save it to another file.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a whole folder of images.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-video\" class=\"anchor\" aria-hidden=\"true\" href=\"#video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVideo\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a video in real-time. \"--video_multiframe\" will process that many frames at once for improved performance.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you want, use \"--display_fps\" to draw the FPS directly on the frame.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a video and save it to another file. This uses the same pipeline as the ones above now, so it\u0027s fast!\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs you can tell, \u003ccode\u003eeval.py\u003c/code\u003e can do a ton of stuff. Run the \u003ccode\u003e--help\u003c/code\u003e command to see everything it can do.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython eval.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining\u003c/h1\u003e\n\u003cp\u003eBy default, we train on COCO. Make sure to download the entire dataset using the commands above.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo train, grab an imagenet-pretrained model and put it in \u003ccode\u003e./weights\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eFor Resnet101, download \u003ccode\u003eresnet101_reducedfc.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1tvqFPd4bJtakOlmn-uIA492g2qurRChj/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Resnet50, download \u003ccode\u003eresnet50-19c8e357.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1Jy3yCdbatgXa5YYIdTCRrSV0S9V5g1rn/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Darknet53, download \u003ccode\u003edarknet53.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRun one of the training commands below.\n\u003cul\u003e\n\u003cli\u003eNote that you can press ctrl+c while training and it will save an \u003ccode\u003e*_interrupt.pth\u003c/code\u003e file at the current iteration.\u003c/li\u003e\n\u003cli\u003eAll weights are saved in the \u003ccode\u003e./weights\u003c/code\u003e directory by default with the file name \u003ccode\u003e\u0026lt;config\u0026gt;_\u0026lt;epoch\u0026gt;_\u0026lt;iter\u0026gt;.pth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains using the base config with a batch size of 8 (the default).\u003c/span\u003e\npython train.py --config=yolact_base_config\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.\u003c/span\u003e\npython train.py --config=yolact_base_config --batch_size=5\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file\u0027s name.\u003c/span\u003e\npython train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the help option to see a description of all available command line arguments\u003c/span\u003e\npython train.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-multi-gpu-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#multi-gpu-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-GPU Support\u003c/h2\u003e\n\u003cp\u003eYOLACT now supports multiple GPUs seamlessly during training:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBefore running any of the scripts, run: \u003ccode\u003eexport CUDA_VISIBLE_DEVICES=[gpus]\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3).\u003c/li\u003e\n\u003cli\u003eYou should still do this if only using 1 GPU.\u003c/li\u003e\n\u003cli\u003eYou can check the indices of your GPUs with \u003ccode\u003envidia-smi\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThen, simply set the batch size to \u003ccode\u003e8*num_gpus\u003c/code\u003e with the training commands above. The training script will automatically scale the hyperparameters to the right values.\n\u003cul\u003e\n\u003cli\u003eIf you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you\u0027re using.\u003c/li\u003e\n\u003cli\u003eIf you want to allocate the images per GPU specific for different GPUs, you can use \u003ccode\u003e--batch_alloc=[alloc]\u003c/code\u003e where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to \u003ccode\u003ebatch_size\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-logging\" class=\"anchor\" aria-hidden=\"true\" href=\"#logging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogging\u003c/h2\u003e\n\u003cp\u003eYOLACT now logs training and validation information by default. You can disable this with \u003ccode\u003e--no_log\u003c/code\u003e. A guide on how to visualize these logs is coming soon, but now you can look at \u003ccode\u003eLogVizualizer\u003c/code\u003e in \u003ccode\u003eutils/logger.py\u003c/code\u003e for help.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pascal-sbd\" class=\"anchor\" aria-hidden=\"true\" href=\"#pascal-sbd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePascal SBD\u003c/h2\u003e\n\u003cp\u003eWe also include a config for training on Pascal SBD annotations (for rapid experimentation or comparing with other methods). To train on Pascal SBD, proceed with the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the dataset from \u003ca href=\"http://home.bharathh.info/pubs/codes/SBD/download.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. It\u0027s the first link in the top \"Overview\" section (and the file is called \u003ccode\u003ebenchmark.tgz\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eExtract the dataset somewhere. In the dataset there should be a folder called \u003ccode\u003edataset/img\u003c/code\u003e. Create the directory \u003ccode\u003e./data/sbd\u003c/code\u003e (where \u003ccode\u003e.\u003c/code\u003e is YOLACT\u0027s root) and copy \u003ccode\u003edataset/img\u003c/code\u003e to \u003ccode\u003e./data/sbd/img\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDownload the COCO-style annotations from \u003ca href=\"https://drive.google.com/open?id=1ExrRSPVctHW8Nxrn0SofU1lVhK5Wn0_S\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eExtract the annotations into \u003ccode\u003e./data/sbd/\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eNow you can train using \u003ccode\u003e--config=yolact_resnet50_pascal_config\u003c/code\u003e. Check that config to see how to extend it to other models.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI will automate this all with a script soon, don\u0027t worry. Also, if you want the script I used to convert the annotations, I put it in \u003ccode\u003e./scripts/convert_sbd.py\u003c/code\u003e, but you\u0027ll have to check how it works to be able to use it because I don\u0027t actually remember at this point.\u003c/p\u003e\n\u003cp\u003eIf you want to verify our results, you can download our \u003ccode\u003eyolact_resnet50_pascal_config\u003c/code\u003e weights from \u003ca href=\"https://drive.google.com/open?id=1yLVwtkRtNxyl0kxeMCtPXJsXFFyc_FHe\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. This model should get 72.3 mask AP_50 and 56.2 mask AP_70. Note that the \"all\" AP isn\u0027t the same as the \"vol\" AP reported in others papers for pascal (they use an averages of the thresholds from \u003ccode\u003e0.1 - 0.9\u003c/code\u003e in increments of \u003ccode\u003e0.1\u003c/code\u003e instead of what COCO uses).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-custom-datasets\" class=\"anchor\" aria-hidden=\"true\" href=\"#custom-datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustom Datasets\u003c/h2\u003e\n\u003cp\u003eYou can also train on your own dataset by following these steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a COCO-style Object Detection JSON annotation file for your dataset. The specification for this can be found \u003ca href=\"http://cocodataset.org/#format-data\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Note that we don\u0027t use some fields, so the following may be omitted:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003einfo\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eliscense\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eUnder \u003ccode\u003eimage\u003c/code\u003e: \u003ccode\u003elicense, flickr_url, coco_url, date_captured\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecategories\u003c/code\u003e (we use our own format for categories, see below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a definition for your dataset under \u003ccode\u003edataset_base\u003c/code\u003e in \u003ccode\u003edata/config.py\u003c/code\u003e (see the comments in \u003ccode\u003edataset_base\u003c/code\u003e for an explanation of each field):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003emy_custom_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset_base\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecopy\u003c/span\u003e({\n \u003cspan class=\"pl-s\"\u003e\u0027name\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027My Dataset\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027train_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027train_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027valid_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027valid_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027has_gt\u0027\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027class_names\u0027\u003c/span\u003e: (\u003cspan class=\"pl-s\"\u003e\u0027my_class_id_1\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_2\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_3\u0027\u003c/span\u003e, ...)\n})\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA couple things to note:\n\u003cul\u003e\n\u003cli\u003eClass IDs in the annotation file should start at 1 and increase sequentially on the order of \u003ccode\u003eclass_names\u003c/code\u003e. If this isn\u0027t the case for your annotation file (like in COCO), see the field \u003ccode\u003elabel_map\u003c/code\u003e in \u003ccode\u003edataset_base\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf you do not want to create a validation split, use the same image path and annotations file for validation. By default (see \u003ccode\u003epython train.py --help\u003c/code\u003e), \u003ccode\u003etrain.py\u003c/code\u003e will output validation mAP for the first 5000 images in the dataset every 2 epochs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFinally, in \u003ccode\u003eyolact_base_config\u003c/code\u003e in the same file, change the value for \u003ccode\u003e\u0027dataset\u0027\u003c/code\u003e to \u003ccode\u003e\u0027my_custom_dataset\u0027\u003c/code\u003e or whatever you named the config object above. Then you can use any of the training commands in the previous section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-creating-a-custom-dataset-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-a-custom-dataset-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a Custom Dataset from Scratch\u003c/h4\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/dbolya/yolact/issues/70#issuecomment-504283008\"\u003ethis nice post by @Amit12690\u003c/a\u003e for tips on how to annotate a custom dataset and prepare it for use with YOLACT.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h1\u003e\n\u003cp\u003eIf you use YOLACT or this code base in your work, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{yolact-iccv2019,\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n title = {YOLACT: {Real-time} Instance Segmentation},\n booktitle = {ICCV},\n year = {2019},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor YOLACT++, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{yolact-plus-arxiv2019,\n title = {YOLACT++: Better Real-time Instance Segmentation},\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n year = {2019},\n eprint = {1912.06218},\n archivePrefix = {arXiv},\n primaryClass = {cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eFor questions about our paper or code, please contact \u003ca href=\"mailto:dbolya@ucdavis.edu\"\u003eDaniel Bolya\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1652667265.0 + "updated_at": 1653076832.0 }, { "data_format": 2, - "description": "Singularity recipe files for sex-detector-plusplus (https://gitlab.in2p3.fr/sex-det-family/sex-detector-plusplus)", + "description": null, "filenames": [ - "Singularity", - "Singularity.00f7d723" + "Singularity.binutils-2.36.1-GCCcore-11.1.0-centos7.def", + "Singularity.zlib-1.2-centos7.def" ], - "full_name": "powerPlant/sex-detector-plusplus-srf", + "full_name": "jkwmoore/centos7-eb-singularity-image", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for SEX-DETector, a tool for the statistical inferrence of sex-linked genes from RNA / DNA reads from a cross (parents and set of childrens)\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7-eb-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos7-eb-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7-eb-singularity-image\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1600917082.0 + "updated_at": 1653324040.0 }, { "data_format": 2, - "description": "Centos 7 base image for ACI", + "description": "BRAKER is a pipeline for fully automated prediction of protein coding gene structures with GeneMark-ES/ET and AUGUSTUS in novel eukaryotic genomes.", "filenames": [ - "Singularity", - "Singularity.cuda9.1", - "Singularity.gpu", - "Singularity.test" + "2.1.5/Singularity", + "2.1.6/Singularity" ], - "full_name": "willgpaik/centos7_aci", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7_aci\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos7_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7_aci\u003c/h1\u003e\n\u003cp\u003eCentos 7 base image for ACI Singualarity recipe\u003cbr\u003e\nThis recipe may include unnecessary packages for certain software installation.\u003cbr\u003e\nSize of CPU-only container: ~1 GB\u003cbr\u003e\nSize of GPU container: ~2.6 GB\u003c/p\u003e\n\u003cp\u003eMore packages will be added in the future\u003c/p\u003e\n\u003cp\u003e2019/2/17\n\u003cstrong\u003eCentos 7\u003c/strong\u003e with \u003cstrong\u003eGCC 8\u003c/strong\u003e\u003cbr\u003e\nTo enable GCC 8,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; source /opt/rh/devtoolset-8/enable\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e2019/3/1\u003cbr\u003e\nOpenMPI is added to \u003ccode\u003e$PATH\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e2019/3/11\u003cbr\u003e\nOpenMPI is updated to version 2.1.6\u003c/p\u003e\n\u003cp\u003e2019/4/12\u003cbr\u003e\nBoost 1.70.0 in added\u003c/p\u003e\n\u003cp\u003e2019/7/19\u003cbr\u003e\n\u003cdel\u003ePython 2 and 3 are updated to version 2.7.16 and version 3.7.4\u003c/del\u003e\u003cbr\u003e\nOpenMPI is updated to version 4.0.1\u003c/p\u003e\n\u003cp\u003e2019/7/21\u003cbr\u003e\n\u003cdel\u003eFew Python packages are added\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e2019/7/22\u003cbr\u003e\n\u003cdel\u003eFew corrections are made including Python\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e2019/7/23\u003cbr\u003e\nPythons are replaced with packages\u003cbr\u003e\nTo enable Python 2.7.16,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; source /opt/rh/python27/enable\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSystem version of python is 3.6.8\u003c/p\u003e\n\u003cp\u003e2019/7/30\u003cbr\u003e\ndevtoolset-7 GCC is added (some software can\u0027t be built with GCC 8)\u003c/p\u003e\n\u003cp\u003e2019/11/9\u003cbr\u003e\nCMake 3.15.5 is added\u003c/p\u003e\n\u003cp\u003e2019/11/22\u003cbr\u003e\nOpenMPI is downgraded to 1.10.1 to match version on ACI\u003c/p\u003e\n\u003cp\u003e2020/2/12\u003cbr\u003e\nBoost is upgraded to 1.72.0 and CMake is upgraded to 3.16.4\u003c/p\u003e\n\u003cp\u003e2020/3/2\u003cbr\u003e\nGPU version is added\u003c/p\u003e\n\u003cp\u003e2020/9/21\u003cbr\u003e\nMinor updates are made (regarding libxkb)\u003c/p\u003e\n\u003cp\u003e2020/9/28\u003cbr\u003e\nRecipe for CUDA 9.1 is added (for FSL with CUDA)\u003c/p\u003e\n\u003cp\u003e2020/10/11\u003cbr\u003e\nBoost is upgraded to 1.74.0 and CMake is upgraded to 3.18.4\u003cbr\u003e\nR 4.0.3 is added (Curl 7.72.0 and XZ 5.2.5 are added for R)\u003cbr\u003e\nVirtualGL is downgraded to 2.5.2 to match system version\u003c/p\u003e\n\u003cp\u003e2020/10/18\u003cbr\u003e\nUDUNITS 2.2.26 is added\u003c/p\u003e\n\u003cp\u003e2020/10/20\u003cbr\u003e\nTix-devel, Tx-devel, TkInter-devel, LAPACK-devel, and BLAS-devel are added\u003c/p\u003e\n", + "full_name": "pscedu/singularity-braker2", + "latest_release": "v2.1.6", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-braker2/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-braker2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-braker2/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-braker2/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/19dcb2c13dec2c34ca260c8ca2b2a8209ecde7a198e864340ede4eba62cb3d33/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6272616b657232\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/19dcb2c13dec2c34ca260c8ca2b2a8209ecde7a198e864340ede4eba62cb3d33/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6272616b657232\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-braker2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0bd8da7fc9970e7e157de2eec966b6db39f4c9445336118b4feae68787406ca0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6272616b657232\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0bd8da7fc9970e7e157de2eec966b6db39f4c9445336118b4feae68787406ca0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6272616b657232\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-braker2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/1538a0ecc9226a4026e275016281ee2daead4806b53f7afec5b265acf1ff03ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6272616b657232\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1538a0ecc9226a4026e275016281ee2daead4806b53f7afec5b265acf1ff03ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6272616b657232\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-braker2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c222f5a51b626a831c4b4d20b0afdb3b1157ec9ca835a8441f90b570f5fa7f0c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6272616b657232\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c222f5a51b626a831c4b4d20b0afdb3b1157ec9ca835a8441f90b570f5fa7f0c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6272616b657232\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-braker2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-braker2\" class=\"anchor\" href=\"#singularity-braker2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-braker2\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/a84569b5f282590e3c4947c993c063920444aca07bdf99e5914d7abcc82d939a/68747470733a2f2f7777772e62696f727869762e6f72672f636f6e74656e742f62696f727869762f6561726c792f323032302f30382f31312f323032302e30382e31302e3234353133342f46312e6c617267652e6a7067\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a84569b5f282590e3c4947c993c063920444aca07bdf99e5914d7abcc82d939a/68747470733a2f2f7777772e62696f727869762e6f72672f636f6e74656e742f62696f727869762f6561726c792f323032302f30382f31312f323032302e30382e31302e3234353133342f46312e6c617267652e6a7067\" width=\"50%\" data-canonical-src=\"https://www.biorxiv.org/content/biorxiv/early/2020/08/11/2020.08.10.245134/F1.large.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/Gaius-Augustus/BRAKER\"\u003eBRAKER2\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/braker2/2.1.5\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/BRAKER2\u003c/code\u003e as \u003ccode\u003e2.1.5.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1603227322.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1649280757.0 }, { "data_format": 2, @@ -7830,128 +7449,97 @@ var data = "filenames": [ "Singularity" ], - "full_name": "kiwiroy/singularity-perlbrew", + "full_name": "truatpasteurdotfr/miniforge3-bioconda-perl-bioperl", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2845\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-perlbrew\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-perlbrew\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-perlbrew\u003c/h1\u003e\n\u003cp\u003eA simple ubuntu base with perlbrew installed. Useful as a base image for brewing\nspecific versions of perl.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-a-miniforge3-based-container-with-bioconda-bioperl\" class=\"anchor\" href=\"#a-miniforge3-based-container-with-bioconda-bioperl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea miniforge3 based container with bioconda-bioperl\u003c/h1\u003e\n\u003cp\u003eUsing \u003ca href=\"https://github.com/conda-forge/miniforge/\"\u003ehttps://github.com/conda-forge/miniforge/\u003c/a\u003e instead of miniconda3 from Anaconda.com\u003c/p\u003e\n\u003cp\u003edocker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/miniforge3-bioconda-perl-bioperl:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv oras://ghcr.io/truatpasteurdotfr/miniforge3-bioconda-perl-bioperl:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1556532781.0 + "updated_at": 1651682376.0 }, { "data_format": 2, "description": null, "filenames": [ - "Kaysera/Singularity.def" + "Singularity-base-ubuntu20.04-intel2021.1.1" ], - "full_name": "Kaysera/test-reproducibility", + "full_name": "NOAA-GFDL/HPC-ME", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-test-for-future-simd-reproducibility\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-for-future-simd-reproducibility\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest for future SIMD reproducibility\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hpc-me-hpc-portable-containers-for-model-environments\" class=\"anchor\" href=\"#hpc-me-hpc-portable-containers-for-model-environments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC-ME: HPC Portable Containers for Model Environments\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contents\" class=\"anchor\" href=\"#contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#what-is-hpc-me\"\u003eWhat is HPC-ME\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#list-of-current-compilers\"\u003eList of current compilers/MPI/OS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#list-of-current-libraries\"\u003eList of current libraries\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-build\"\u003eHow to build\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-use\"\u003eHow to use\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#gfdl-example\"\u003eGFDL example\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#planned-improvements\"\u003ePlanned improvements\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-hpc-me\" class=\"anchor\" href=\"#what-is-hpc-me\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is HPC-ME\u003c/h2\u003e\n\u003cp\u003eHPC Portable Container - Model Environments is a set of Dockerfiles, Singularity Definition files, and containers to provide portable model environments for scientific applications that require the same set of libraries. The ultimate goal is to have a community-based list of libraries that are needed for compiling, executing, and post-processing earth science models. We all use many of the same underlying libraries, and by working together we can agree upon a community-based approach to making container usage as standardized as possible.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-list-of-current-compilersmpios\" class=\"anchor\" href=\"#list-of-current-compilersmpios\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eList of current compilers/MPI/OS\u003c/h2\u003e\n\u003cp\u003eFor each container, there is a full version that contains the programming environment and a smaller runtime environment that can be used to run compiled executables. (The runtime container definition files will be added soon.)\n#- \u003ca href=\"Dockerfile_gnu_ubuntu20.04\"\u003egcc 8/mpich/ubuntu 20.04\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"Dockerfile_gnu_rhel8\"\u003egcc 8/mpich/RHEL8\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Dockerfile_intel_ubuntu18.04\"\u003eintel oneAPI 2022.1/mpich(impi)/ubuntu 18.04\u003c/a\u003e\n#- \u003ca href=\"Dockerfile_intel_centos8\"\u003eintel oneAPI 2021.4/mpich(impi)/centos 8\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-list-of-current-libraries\" class=\"anchor\" href=\"#list-of-current-libraries\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eList of current libraries\u003c/h2\u003e\n\u003cp\u003eThis is the current list of most of the libraries used in the HPC-ME containers (We are trying to keep this up-to-date).\nThe complete lit should be found in the respective YAML file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#automake\" rel=\"nofollow\"\u003eautomake@1.16.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#bacio\" rel=\"nofollow\"\u003ebacio@2.4.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#berkeley-db\" rel=\"nofollow\"\u003eberkeley-db@18.1.40\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#bison\" rel=\"nofollow\"\u003ebison@3.7.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#bzip2\" rel=\"nofollow\"\u003ebzip2@1.0.8\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#cmake\" rel=\"nofollow\"\u003ecmake@3.21.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#crtm\" rel=\"nofollow\"\u003ecrtm@2.3.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#curl\" rel=\"nofollow\"\u003ecurl@7.78.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#diffutils\" rel=\"nofollow\"\u003ediffutils@3.7\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#esmf\" rel=\"nofollow\"\u003eesmf@8.1.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#expat\" rel=\"nofollow\"\u003eexpat@2.4.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#g2\" rel=\"nofollow\"\u003eg2@3.4.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#g2tmpl\" rel=\"nofollow\"\u003eg2tmpl@1.10.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#gdbm\" rel=\"nofollow\"\u003egdbm@1.19\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#gsl\" rel=\"nofollow\"\u003egsl@2.7\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#hdf5\" rel=\"nofollow\"\u003ehdf5@1.10.7\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#intel-mpi\" rel=\"nofollow\"\u003eintel-mpi@2019.10.317\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#ip\" rel=\"nofollow\"\u003eip@3.3.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#ip2\" rel=\"nofollow\"\u003eip2@1.1.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#jasper\" rel=\"nofollow\"\u003ejasper@2.0.32\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libbsd\" rel=\"nofollow\"\u003elibbsd@0.11.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libiconv\" rel=\"nofollow\"\u003elibiconv@1.16\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libjpeg-turbo\" rel=\"nofollow\"\u003elibjpeg-turbo@2.1.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libmd\" rel=\"nofollow\"\u003elibmd@1.0.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libpng\" rel=\"nofollow\"\u003elibpng@1.6.37\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libsigsegv\" rel=\"nofollow\"\u003elibsigsegv@2.13\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libxml2\" rel=\"nofollow\"\u003elibxml2@2.9.12\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libyaml\" rel=\"nofollow\"\u003elibyaml@0.2.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#m4\" rel=\"nofollow\"\u003em4@1.4.19\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#nasm\" rel=\"nofollow\"\u003enasm@2.15.05\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#ncurses\" rel=\"nofollow\"\u003encurses@6.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#nemsio\" rel=\"nofollow\"\u003enemsio@2.5.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#netcdf-c\" rel=\"nofollow\"\u003enetcdf-c@4.8.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#netcdf-fortran\" rel=\"nofollow\"\u003enetcdf-fortran@4.5.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#numactl\" rel=\"nofollow\"\u003enumactl@2.0.14\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#openssl\" rel=\"nofollow\"\u003eopenssl@1.1.1l\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#parallel-netcdf\" rel=\"nofollow\"\u003eparallel-netcdf@1.12.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#perl\" rel=\"nofollow\"\u003eperl@5.34.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#pkgconf\" rel=\"nofollow\"\u003epkgconf@1.8.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#readline\" rel=\"nofollow\"\u003ereadline@8.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#sfcio\" rel=\"nofollow\"\u003esfcio@1.4.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#sigio\" rel=\"nofollow\"\u003esigio@2.3.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#sp\" rel=\"nofollow\"\u003esp@2.3.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#udunits\" rel=\"nofollow\"\u003eudunits@2.2.28\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#w3emc\" rel=\"nofollow\"\u003ew3emc@2.9.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#w3nco\" rel=\"nofollow\"\u003ew3nco@2.4.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#wrf-io\" rel=\"nofollow\"\u003ewrf-io@1.2.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#xerces-c\" rel=\"nofollow\"\u003exerces-c@3.2.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#xz\" rel=\"nofollow\"\u003exz@5.2.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#zlib\" rel=\"nofollow\"\u003ezlib@1.2.11\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#lmod\" rel=\"nofollow\"\u003elmod@8.5.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#nccmp\" rel=\"nofollow\"\u003enccmp@1.8.6.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#nco\" rel=\"nofollow\"\u003enco@4.7.9\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#cray-netcdf\" rel=\"nofollow\"\u003ecray-netcdf@4.6.3.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#cray-hdf5\" rel=\"nofollow\"\u003ecray-hdf5@1.10.5.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#uberftp\" rel=\"nofollow\"\u003euberftp\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-build\" class=\"anchor\" href=\"#how-to-build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to build\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eWe plan to make this step optional soon.\u003c/strong\u003e In order to build the Docker images, you will need access to a computer with root-like access, and either docker or singularity installed. If you do not have root-like access to a suitable machine, you can still run images that were already created (e.g. on Docker hub), and we plan on hosting runnable Docker images along with the Dockerfiles in this repository soon. If you have root-like access and docker, start by choosing one of the currently supported model environments from the list above. Then build the Docker container from the Dockerfile using docker build; for example, to build the gcc8/mpich/ubuntu18 container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --file Dockerfile_gnu_ubuntu20.04 . --tag hpc-me.ubuntu.gnu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe build process takes approximately 2-3 hours, as the packages are downloaded and compiled using Spack. After a successful build, you will see that the image was built and tagged successfully:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSuccessfully built 90a878af77b4\nSuccessfully tagged hpc-me.rhel8.gnu:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you may run the container using docker or singularity on the same host. To run the image on a different machine, pushing the image to Docker Hub is recommended. Note that you will need a DockerHub account to do this (replace USER with your Docker user ID in the examples below). For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker tag hpc-me.rhel8.gnu USER/hpc-me.rhel8.gnu\ndocker login\ndocker push USER/hpc-me.rhel8.gnu:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-use\" class=\"anchor\" href=\"#how-to-use\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use\u003c/h2\u003e\n\u003cp\u003eWe plan to make improvements on this process. Also, while we plan on making Docker images available on the GitHub container registry, currently you must build the images yourself. Please start with the \u003ca href=\"#how-to-build\"\u003eBuild instructions\u003c/a\u003e to generate a Docker image with your desired OS/compiler HPC-ME environment. Then you may run the container using docker or singularity; singularity is more likely than docker to be available on HPC environments.\u003c/p\u003e\n\u003cp\u003eThe usage documentation consists of some general notes on serial/parallel usage, files inside and outside the container, downloading the containers, and then specific usage scenarios:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#serial-applications-using-docker\"\u003eSerial applications using docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#serial-applications-using-singularity\"\u003eSerial applications using singularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#parallel-applications-using-singularity\"\u003eParallel applications using singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-serial-and-parallel-usage\" class=\"anchor\" href=\"#serial-and-parallel-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSerial and parallel usage\u003c/h3\u003e\n\u003cp\u003eHPC-ME containers are intended for both serial and parallel applications. Serial applications include compiling model executables, generating input grids, and post-processing model output. Earth system, climate, and weather models require parallelism to run efficiently, and use one of the Message Passage Interface (MPI) implementations OpenMPI, Intel MPI, or mpich. GCC-based HPC-ME containers use the mpich-based MPI library, which is widely available on most HPC sites, and the Intel-based containers contain both mpich and Intel MPI.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-notes-on-filesystems-and-writing-files\" class=\"anchor\" href=\"#notes-on-filesystems-and-writing-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes on filesystems and writing files\u003c/h3\u003e\n\u003cp\u003eWe recommend not saving or modifying files within the environment container, and instead create and modify files on your regular filesystem. To do this, you will need to connect your filesystem to your container using bind mounts.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-downloading-containers-and-managing-images-on-the-filesystem\" class=\"anchor\" href=\"#downloading-containers-and-managing-images-on-the-filesystem\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownloading containers and managing images on the filesystem\u003c/h3\u003e\n\u003cp\u003eOnce you have pushed your images to DockerHub, you will need to download them before using. In the examples below, replace USER with your Docker Hub ID. If using docker,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull USER/hpc-me.rhel8.gnu:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using singularity,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull docker://USER/hpc-me.rhel8.gnu:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using singularity, the image file (SIF format) is saved to the current working directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; ls *.sif\n-rwxr-xr-x 532M Dec 10 16:09 hpc-me.rhel8.gnu_latest.sif*\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using docker, the downloaded image is handled by the central docker service.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-serial-applications-using-docker\" class=\"anchor\" href=\"#serial-applications-using-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSerial applications using docker\u003c/h3\u003e\n\u003cp\u003eYou may activate an interactive shell within the desired HPC-ME container using docker. After running the container, the compilers and tools available within the container will be accessible in your PATH; e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; docker run -it hpc-me.rhel8.gnu:latest\n\n[root@0d2cf64e1175 /]# which nf-config\n/opt/view/bin/nf-config\n\n[root@0d2cf64e1175 /]# nf-config --version\nnetCDF-Fortran 4.5.3\n\n[root@0d2cf64e1175 /]# nf-config --cflags\n-I/opt/software/linux-rhel8-x86_64/gcc-8.4.1/netcdf-fortran-4.5.3-g5qfkdlp36unt2s4j4wyrc6heh2sa64n/include\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-serial-applications-using-singularity\" class=\"anchor\" href=\"#serial-applications-using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSerial applications using singularity\u003c/h3\u003e\n\u003cp\u003eSingularity can run Docker images and is more likely to be available on HPC environments. As with docker run, the HPC-ME tools and compilers are available in the shell, somewhat similar to loading a set of Environment Modules prepared by site administrators.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;singularity run hpc-me.rhel8.gnu_latest.sif\n\nSingularity\u0026gt; which nf-config\n/opt/view/bin/nf-config\n\nSingularity\u0026gt; nf-config --version\nnetCDF-Fortran 4.5.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-parallel-applications-using-singularity\" class=\"anchor\" href=\"#parallel-applications-using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParallel applications using singularity\u003c/h3\u003e\n\u003cp\u003eHPC-ME containers can provide the runtime environment for MPI applications. For instance, one could compile an MPI application using the instructions above using one of the HPC-ME development containers; and then run the application using the corresponding runtime HPC-ME container.\u003c/p\u003e\n\u003cp\u003ePlease note that we are continuing to improve the usability of HPC-ME containers as well as provide more usage examples.\u003c/p\u003e\n\u003cp\u003eUsually, GFDL climate models are run on gaea by submitting a runscript to the Slurm scheduler. The runscript loads needed runtime Environment Modules, prepares input directories and files, and executes the MPI executable using srun. The HPC-ME containers provide the necessary runtime environment, obviating the need for loading Environment Modules. Currently, our approach for using the HPC-ME containers is as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a new container, starting with the desired HPC-ME runtime container\u003c/li\u003e\n\u003cli\u003eAdd the MPI-compiled executable to the container filesystem\u003c/li\u003e\n\u003cli\u003eSet the MPI-compiled executable to as the container\u0027s command (so that when the container is run the MPI executable within the container runs)\u003c/li\u003e\n\u003cli\u003eRun the singularity container SIF file using srun within the runscript, replacing the traditional MPI executable.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eReplace \"srun executable.x\" with \"srun singularity run container.SIF\"\u003c/li\u003e\n\u003cli\u003eAdd --mpi=pmi2 to the srun call, which connects the system MPI to the container MPI to the singularity run call\u003c/li\u003e\n\u003cli\u003eBind the working directory so that the container has access to the input files and can write output files (singularity run -B=/path/to/workdir)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eSubmit the modified runscript to the scheduler\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe plan to provide more examples and usage scenarios, such as using the HPC-ME containers as-is (i.e. not creating a new container as described above)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-gfdl-example\" class=\"anchor\" href=\"#gfdl-example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGFDL example\u003c/h2\u003e\n\u003cp\u003eAn example of using an HPC-ME container with the GFDL FRE workflow can be found \u003ca href=\"GFDL_EXAMPLE.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-planned-improvements\" class=\"anchor\" href=\"#planned-improvements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanned improvements\u003c/h2\u003e\n\u003cp\u003eHPC-ME is a work in progress under active development, so please check back or follow the repository for more updates.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-cache\" class=\"anchor\" href=\"#build-cache\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild cache\u003c/h3\u003e\n\u003cp\u003eWe are working to create a build cache for the libraries listed so that building the containers is quick and easy.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-github-container-registry\" class=\"anchor\" href=\"#github-container-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGithub container registry\u003c/h3\u003e\n\u003cp\u003eWe are working to add CI capability to this repository, so that the containers will be automatically built and stored in the github container registry. This will make building unnecessary for most cases, though users may build the containers themselves if they wish (e.g. for custom modifications).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-more-usage-examples-and-documentation-especially-for-mpi-applications\" class=\"anchor\" href=\"#more-usage-examples-and-documentation-especially-for-mpi-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore usage examples and documentation, especially for MPI applications\u003c/h3\u003e\n\u003cp\u003eWe are still learning how to best use the HPC-ME containers with MPI appliations, so please check back.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h3\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is provided\non an \u0027as is\u0027 basis and the user assumes responsibility for its use. DOC has\nrelinquished control of the information and no longer has responsibility to\nprotect the integrity, confidentiality, or availability of the information. Any\nclaims against the Department of Commerce stemming from the use of its GitHub\nproject will be governed by all applicable Federal law. Any reference to\nspecific commercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply their\nendorsement, recommendation or favoring by the Department of Commerce. The\nDepartment of Commerce seal and logo, or the seal and logo of a DOC bureau,\nshall not be used in any manner to imply endorsement of any commercial product\nor activity by DOC or the United States Government.\u003c/p\u003e\n\u003cp\u003eThis project code is made available through GitHub but is managed by NOAA-GFDL\nat \u003ca href=\"https://gitlab.gfdl.noaa.gov\" rel=\"nofollow\"\u003ehttps://gitlab.gfdl.noaa.gov\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [], - "updated_at": 1655130007.0 + "updated_at": 1650907447.0 }, { "data_format": 2, - "description": null, + "description": "Flappie singularity image =\u003e https://github.com/nanoporetech/flappie", "filenames": [ - "os_recipes/Singularity.SuSE", - "os_recipes/Singularity.deboot.ubuntu", - "os_recipes/Singularity.centos7", - "os_recipes/Singularity.4.2.5", - "os_recipes/Singularity.archive.debian", - "os_recipes/Singularity.centos6", - "os_recipes/Singularity.base-4.2.5", - "os_recipes/Singularity.usmirror.debian", - "docs/Singularity.3_0.debian9", - "store_pw/Singularity.pw_embed", - "store_pw/Singularity.4.2.5", - "store_pw/Singularity.python-4.2.5", - "store_pw/Singularity.base-4.2.5", - "store_pw/Singularity.pw_encrypt" + "Singularity" ], - "full_name": "d-w-moore/new_d2c", + "full_name": "romxero/flappie_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-installing-and-running-slurm-on-ubuntu-16-or-18\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-and-running-slurm-on-ubuntu-16-or-18\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling and Running SLURM on ubuntu 16 or 18\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-slurm\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall SLURM\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install slurm-wlm\ngit clone http://github.com/d-w-moore/new_d2c\ncd new_d2c\nperl process_slurm_template.pl | sudo dd of=/etc/slurm-llnl/slurm.conf\nsudo systemctl restart slurmctld slurmd\nsudo systemctl enable slurmctld slurmd\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto test:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esudo apt install bc\u003c/li\u003e\n\u003cli\u003elocate command file slurm_install_test.sh containing:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e #!/bin/bash\n bc -l \u0026lt;\u0026lt;\u0026lt;\"scale=4000;a(1)*4\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003erun the above mentioned test script using : \u003ccode\u003esbatch \u0026lt;script\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003etype: \u003ccode\u003esqueue\u003c/code\u003e and note the job present (most likely running)\u003c/li\u003e\n\u003cli\u003ewhen it disappears from queue (\u003ccode\u003ewatch -n1 squeue\u003c/code\u003e), look for \u003ccode\u003eslurm-\u0026lt;JOBNUM\u0026gt;.out\u003c/code\u003e\ncontaining the job\u0027s output\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-datacompute-automated-setup---install-irods-hook-scripts-for-slurm-prolog--epilog\" class=\"anchor\" aria-hidden=\"true\" href=\"#datacompute-automated-setup---install-irods-hook-scripts-for-slurm-prolog--epilog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData/Compute automated setup - install iRODS hook scripts for slurm prolog / epilog\u003c/h2\u003e\n\u003cp\u003eThe following command will setup prolog and epilog scripts to be run (pre- and post-,\nrespectively) for each job executed by SLURM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo ./slurm_hook_setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1561308424.0 + "updated_at": 1555445240.0 }, { "data_format": 2, - "description": "Singularity container script for 10x Genomics SuperNova software", + "description": "Dynamic-programming optimizer to solve exact literal-weighted SAT (Boolean MPE)", "filenames": [ - "Singularity.2.0.0" + "lg/Singularity" ], - "full_name": "arcsUVA/supernova", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-supernova\" class=\"anchor\" aria-hidden=\"true\" href=\"#supernova\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esupernova\u003c/h1\u003e\n\u003cp\u003eSingularity container script for 10x Genomics SuperNova software\u003c/p\u003e\n", + "full_name": "vuphan314/DPO", + "latest_release": "v0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dpo-dynamic-programming-optimizer\" class=\"anchor\" href=\"#dpo-dynamic-programming-optimizer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPO (dynamic-programming optimizer)\u003c/h1\u003e\n\u003cp\u003eDPO runs in two phases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe planning phase constructs a project-join tree for an XOR-CNF formula.\u003c/li\u003e\n\u003cli\u003eThe execution phase computes the maximum and a maximizer from the constructed join tree.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cloning-this-repository\" class=\"anchor\" href=\"#cloning-this-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning this repository\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vuphan314/DPO\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDir \u003ca href=\"./lg/\"\u003e\u003ccode\u003elg/\u003c/code\u003e\u003c/a\u003e: DPO\u0027s planner\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"./dmc/\"\u003e\u003ccode\u003edmc/\u003c/code\u003e\u003c/a\u003e: DPO\u0027s executor\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"./eval/\"\u003e\u003ccode\u003eeval/\u003c/code\u003e\u003c/a\u003e: empirical evaluation\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" href=\"#acknowledgment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgment\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/ADDMC\"\u003eADDMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/approxmc\"\u003eBIRD\u003c/a\u003e: Soos, Meel\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cs.rochester.edu/u/kautz/Cachet\" rel=\"nofollow\"\u003eCachet\u003c/a\u003e: Sang, Beame, Kautz\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/msoos/cryptominisat\"\u003eCryptoMiniSat\u003c/a\u003e: Soos\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003eCUDD package\u003c/a\u003e: Somenzi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://davidkebo.com/cudd#cudd6\" rel=\"nofollow\"\u003eCUDD visualization\u003c/a\u003e: Kebo\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts\u003c/a\u003e: Beck\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/DPMC\"\u003eDPMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kit-algo/flow-cutter-pace17\"\u003eFlowCutter\u003c/a\u003e: Strasser\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mabseher/htd\"\u003ehtd\u003c/a\u003e: Abseher, Musliu, Woltran\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://reasoning.cs.ucla.edu/minic2d\" rel=\"nofollow\"\u003eminiC2D\u003c/a\u003e: Oztok, Darwiche\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mccompetition.org\" rel=\"nofollow\"\u003eModel Counting Competition\u003c/a\u003e: Hecher, Fichte\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Kasekopf/SlurmQueen\"\u003eSlurmQueen\u003c/a\u003e: Dudek\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://trolando.github.io/sylvan\" rel=\"nofollow\"\u003eSylvan\u003c/a\u003e: van Dijk\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/TCS-Meiji/PACE2017-TrackA\"\u003eTamaki\u003c/a\u003e: Tamaki\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/TensorOrder\"\u003eTensorOrder\u003c/a\u003e: Dudek, Duenas-Osorio, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/WAPS\"\u003eWAPS\u003c/a\u003e: Gupta, Sharma, Roy, Meel\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1551891095.0 + "updated_at": 1652124481.0 }, { "data_format": 2, - "description": "Singularity recipe files for slim (https://github.com/MesserLab/SLiM)", + "description": "Dynamic-programming existential-random stochastic SAT solver", "filenames": [ - "Singularity", - "Singularity.3.4+1c85d00", - "Singularity.3.5" + "lg/Singularity" ], - "full_name": "powerPlant/slim-srf", - "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for Selection on Linked Mutations: A forward population genetic simulation for studying linkage effects, such as hitchhiking, background selection, and Hill-Robertson interference\u003c/p\u003e\n", + "full_name": "vuphan314/DPER", + "latest_release": "v0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dper-dynamic-programming-existential-random-stochastic-sat-solver\" class=\"anchor\" href=\"#dper-dynamic-programming-existential-random-stochastic-sat-solver\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPER (dynamic-programming existential-random stochastic SAT solver)\u003c/h1\u003e\n\u003cp\u003eDPER runs in two phases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe planning phase constructs a graded project-join tree for a CNF formula.\u003c/li\u003e\n\u003cli\u003eThe execution phase computes the maximum and a maximizer from the constructed tree.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cloning-this-repository\" class=\"anchor\" href=\"#cloning-this-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning this repository\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vuphan314/DPER\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDir \u003ca href=\"lg\"\u003e\u003ccode\u003elg\u003c/code\u003e\u003c/a\u003e: DPER\u0027s planner\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"dmc\"\u003e\u003ccode\u003edmc\u003c/code\u003e\u003c/a\u003e: DPER\u0027s executor\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"eval\"\u003e\u003ccode\u003eeval\u003c/code\u003e\u003c/a\u003e: empirical evaluation\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" href=\"#acknowledgment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgment\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/ADDMC\"\u003eADDMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/approxmc\"\u003eBIRD\u003c/a\u003e: Soos, Meel\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cs.rochester.edu/u/kautz/Cachet\" rel=\"nofollow\"\u003eCachet\u003c/a\u003e: Sang, Beame, Kautz\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003eCUDD package\u003c/a\u003e: Somenzi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://davidkebo.com/cudd#cudd6\" rel=\"nofollow\"\u003eCUDD visualization\u003c/a\u003e: Kebo\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts\u003c/a\u003e: Beck\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/DPMC\"\u003eDPMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kit-algo/flow-cutter-pace17\"\u003eFlowCutter\u003c/a\u003e: Strasser\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mabseher/htd\"\u003ehtd\u003c/a\u003e: Abseher, Musliu, Woltran\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://reasoning.cs.ucla.edu/minic2d\" rel=\"nofollow\"\u003eminiC2D\u003c/a\u003e: Oztok, Darwiche\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mccompetition.org\" rel=\"nofollow\"\u003eModel-counting competition\u003c/a\u003e: Hecher, Fichte\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Kasekopf/SlurmQueen\"\u003eSlurmQueen\u003c/a\u003e: Dudek\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://trolando.github.io/sylvan\" rel=\"nofollow\"\u003eSylvan\u003c/a\u003e: van Dijk\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/TCS-Meiji/PACE2017-TrackA\"\u003eTamaki\u003c/a\u003e: Tamaki\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/TensorOrder\"\u003eTensorOrder\u003c/a\u003e: Dudek, Duenas-Osorio, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/WAPS\"\u003eWAPS\u003c/a\u003e: Gupta, Sharma, Roy, Meel\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1607459916.0 + "updated_at": 1652162476.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "scripts/Singularity" ], - "full_name": "jganong/singularity-test", + "full_name": "waglecn/mabs", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mabs\" class=\"anchor\" href=\"#mabs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emabs\u003c/h1\u003e\n\u003cp\u003eauthor:\u003ca href=\"mailto:nwaglechner@gmail.com\"\u003enwaglechner@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-basic-setup\" class=\"anchor\" href=\"#basic-setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasic Setup\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/waglecn/mabs.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eConda and snakemake\u003c/p\u003e\n\u003cp\u003eMiniconda available from:\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ehttps://docs.conda.io/en/latest/miniconda.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePython 3.8.3 Miniconda\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \nbash Miniconda3-latest-Linux-X86_64.sh\nconda env create --name mabs --file environment.yaml\nconda activate mabs\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e- note the version of python installed in the the mabs environment is not necessarily the same as the default miniconda python version\n- asking for ete3 in the default environment will required python 3.6 (200921)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-required-files\" class=\"anchor\" href=\"#required-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired files:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGATK3 jar file\n\u003cul\u003e\n\u003cli\u003eavailable from \u003ca href=\"https://console.cloud.google.com/storage/browser/gatk-software/package-archive/gatk\" rel=\"nofollow\"\u003ehttps://console.cloud.google.com/storage/browser/gatk-software/package-archive/gatk\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eused \u0027\u0027\u0027GenomeAnalysisTK-3.8-1-0-gf15c1c3ef.tar.bz2\u0027\u0027\u0027\u003c/li\u003e\n\u003cli\u003esee config.yaml\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eadapters for trimming - see config.yaml\n\u003cul\u003e\n\u003cli\u003elook for adapter files bundled with trimmomatic, ie.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003elocate TruSeq3-PE.fa\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eKraken database\n\u003ca href=\"ftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/\" rel=\"nofollow\"\u003eftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget ftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/minikraken_8GB_202003.tgz\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-how-to-run\" class=\"anchor\" href=\"#how-to-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --configfile config.yaml --cores 8 --use-conda --conda-prefix /path/to/.snakemake/conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse config.default.yaml as a template for other config files.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h1\u003e\n\u003cp\u003e200915\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003estrange bug causing infinite loop in snakemake downloading refseq genomes. I think this is because of the dynamic() output/input in rules. Checking this out, seeing if the bug happens if I run entire pipeline from scratch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e200917\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enoticed a bug in running shovill, increased expected memory usage. Shovill version 0.9.0 running from an older miniconda. Removed miniconda, started from scratch, and pinned Shovill 1.1.0 in shovill.yaml\u003c/li\u003e\n\u003cli\u003eafter fixing, rerunning seems to work with example data, then works after deleting the mashtree and refseq_download directories.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e210302\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eon vs masking before gubbins vs after see \u003ca href=\"https://github.com/sanger-pathogens/gubbins/issues/275\"\u003ehttps://github.com/sanger-pathogens/gubbins/issues/275\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200902\" class=\"anchor\" href=\"#todo-200902\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200902\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e[ ]download internal project data - deferred\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[X] configurable data-dir - 200914\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edownload external project data\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[X] refseq genomes - done 200904\u003c/li\u003e\n\u003cli\u003e[ ] genomes from Bryant et al, SRA\n\u003cul\u003e\n\u003cli\u003eneed to know what these are\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[X] download reference assemblies - 200908\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003efirst used all contig assemblies, changed to \u0027complete\u0027 keyword\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ereading in samples somehow, obviously this depends on how/where they are downloaded (see previous TODO item) and the data that is already downloaded\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eneed a dummy rule that requires these as input in order to define wildcards\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[X] basic Snakefile - 200905\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[X] build workflow part 1\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[X] index reference assemblies - deferred 200914\n\u003cul\u003e\n\u003cli\u003emoved to resources/alignment_references\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] pre-trim QC - done 200908\u003c/li\u003e\n\u003cli\u003e[X] trim - done 200909\n\u003cul\u003e\n\u003cli\u003especify adapter files, add variable to config\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] post-trim QC done 200909\u003c/li\u003e\n\u003cli\u003e[X] kraken check - done 200910\n\u003cul\u003e\n\u003cli\u003e[X] download kraken db automatically - deferred, added to Required files\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] genome assembly on raw reads - 200914\n\u003cul\u003e\n\u003cli\u003e[X] Erm(41) identification on assembly - 200912\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] kraken2 on assembly - 200912\u003c/li\u003e\n\u003cli\u003e[X] mashtree assembly - 200913\u003c/li\u003e\n\u003cli\u003e[X] map everything to ATCC 19977 for basic coverage - 200914\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[ ] build workflow part 2 on available assemblies\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[X] tree-guided MRCA - 200915\u003c/li\u003e\n\u003cli\u003e[X] MRCA-guided MLST - 200913\u003c/li\u003e\n\u003cli\u003e[X] MRCA-guided reference mapping - 200921\u003c/li\u003e\n\u003cli\u003e[ ] Optional: Mark duplicates with picard\u003c/li\u003e\n\u003cli\u003e[X] read filtering - see Martin et al 2018 and Lee et al 2020\n\u003cul\u003e\n\u003cli\u003e[X] filter soft clips - 200922\u003c/li\u003e\n\u003cli\u003e[X] optional GATK realignment, but see for why it was removed in 2015 for gatk4 \u003ca href=\"https://github.com/broadinstitute/gatk-docs/blob/master/blog-2012-to-2019/2016-06-21-Changing_workflows_around_calling_SNPs_and_indels.md?id=7847\"\u003ehttps://github.com/broadinstitute/gatk-docs/blob/master/blog-2012-to-2019/2016-06-21-Changing_workflows_around_calling_SNPs_and_indels.md?id=7847\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e[X] added 200923, optional 200924\u003c/li\u003e\n\u003cli\u003eintially added gatk4, got errors and followed the rabbit-hole\u003c/li\u003e\n\u003cli\u003eto follow Martin et al, added conda env with gatk3.8, since the resulting bam can be used with any downstream variant caller\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] annotate regions of interest\n\u003cul\u003e\n\u003cli\u003eremove PP/PPE regions (BED file)\n\u003cul\u003e\n\u003cli\u003e[X] identify PP/PPE - 200927\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] zero coverage of reference\u003c/li\u003e\n\u003cli\u003e[ ] remove phage, tnp, IS\u003c/li\u003e\n\u003cli\u003e[X] merge ROI BED files\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] MRCA-guided variant calling with bcftools - 200922\n\u003cul\u003e\n\u003cli\u003e[X] bcftools mpileup - 200923\u003c/li\u003e\n\u003cli\u003e[X] called variants - 200923\u003c/li\u003e\n\u003cli\u003e[X] variant filtering\n\u003cul\u003e\n\u003cli\u003e[X] basic Martin et al - 200925\u003c/li\u003e\n\u003cli\u003e[ ] density filter - see \u003ca href=\"https://github.com/c2-d2/within-host-diversity/blob/master/fastq_to_vcf_pipeline.py#L122\"\u003ehttps://github.com/c2-d2/within-host-diversity/blob/master/fastq_to_vcf_pipeline.py#L122\u003c/a\u003e line\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] variant annotation with SNPEff\u003c/li\u003e\n\u003cli\u003e[X] SNP-tree construction\n\u003cul\u003e\n\u003cli\u003e[X] SNP extraction - custom? merge vcf as per Robyn 201006\u003c/li\u003e\n\u003cli\u003e[X] - merge SNPs - 201013\u003c/li\u003e\n\u003cli\u003e[X] concatenate cSNPSs (exclude hSNPs) 201016\n\u003cul\u003e\n\u003cli\u003esnp-sites ? snippy?\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] - vcfmerge 201014\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200911\" class=\"anchor\" href=\"#todo-200911\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200911\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[X] add trimming parameters to config file - 200921\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200914\" class=\"anchor\" href=\"#todo-200914\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200914\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003esub-species type assemblies are hard-coded in scripts/tree_MRCA.py, it would be useful for these to be configurable but adds layers of complexity to snakefile\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200920\" class=\"anchor\" href=\"#todo-200920\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200920\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eAdded GATK info to REQUIREMENTS, and config.yaml\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200926\" class=\"anchor\" href=\"#todo-200926\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200926\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Tune variant filtering\u003c/li\u003e\n\u003cli\u003e[X] TODO big question here - use stats from part 1 to make \u003cem\u003enew\u003c/em\u003e sample_sheet with QC pass samples? No\n\u003cul\u003e\n\u003cli\u003e[X] make list to prune from SNP alignment - not needed 201012\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] need separate list of in-complete genomes, as MRCA-guided MLST didn\u0027t work as expected, tree has wrong structure (samples from pt 29 should be mmas) - Fixed 201006, need to convert gbff files before mashtree can read\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-201010\" class=\"anchor\" href=\"#todo-201010\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 201010\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[X] start density filter\u003c/li\u003e\n\u003cli\u003e[X] merge completed results without recalculating shovill assemblies for old samples - 201010\u003c/li\u003e\n\u003cli\u003e[X] merge 0-coverage bed files and PE_PPE bed files 201013\u003c/li\u003e\n\u003cli\u003e[X] filter merged bed from vcf\n\u003cul\u003e\n\u003cli\u003e[X] compress vcf with bcftools\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-201013\" class=\"anchor\" href=\"#todo-201013\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 201013\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[X] complete density filter - 20-11-23\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-201015\" class=\"anchor\" href=\"#todo-201015\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 201015\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[X] incorporate \u003ca href=\"https://github.com/phac-nml/mab_mabscessus\"\u003ehttps://github.com/phac-nml/mab_mabscessus\u003c/a\u003e 211021\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-210323\" class=\"anchor\" href=\"#210323\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e210323\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003emerging script\u003c/li\u003e\n\u003cli\u003ecopy results_folder1 and results_folder2 into results_merge folder\u003c/li\u003e\n\u003cli\u003eremove the gubbins folder\u003c/li\u003e\n\u003cli\u003eremove the SNP_phylo folder\u003c/li\u003e\n\u003cli\u003eremove the files in MRCA_ref_folder, but keep the individual reference sub-folders\u003c/li\u003e\n\u003cli\u003eremove the mashtree folder\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003erun snakemake with the following targets, in this order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emashtree/assembly_mashtree.complete.tree\u003c/li\u003e\n\u003cli\u003estage1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003etouch ./MRCA_ref_mapping/\u003cem\u003e/tempRGSC.merged.\u003c/em\u003e.sorted.bam.bai\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.intervals\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA.bam\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA.mpileup\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.vcf.gz\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.failed.vcf.gz\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.AD_failed.vcf.gz\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.hvar.vcf.gz\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA.0cov.bed\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.hvar_DF.bed\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003estage2\u003c/li\u003e\n\u003cli\u003estage3 to generate the merged output (gubbins, SNP phylo, merged beds, etc)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1606934860.0 + "updated_at": 1651613417.0 }, { "data_format": 2, - "description": null, + "description": "Uniform and Weighted Sampling using Dynamic Programming", "filenames": [ - "Singularity.conda_torch", - "Singularity.torch3", - "Singularity.tf2new", - "Singularity.ubuntu_tf", - "Singularity.tf_einops", - "Singularity.ubuntu_pre", - "Singularity.centos_tf", - "Singularity.centos_torch2", - "Singularity.conda", - "Singularity.ExplainAI", - "Singularity.geometric", - "Singularity.tf23", - "Singularity.Spektral", - "Singularity.tf2", - "Singularity.ubuntu_torch", - "Singularity.torch2", - "Singularity.centos_torch", - "Singularity.tf2b1", - "Singularity.torch" + "dmc/Singularity", + "lg/Singularity" ], - "full_name": "alex-chunhui-yang/container", + "full_name": "allrtaken/DPSampler", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dpmc-dynamic-programming-for-model-counting\" class=\"anchor\" href=\"#dpmc-dynamic-programming-for-model-counting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPMC (Dynamic Programming for Model Counting)\u003c/h1\u003e\n\u003cp\u003eDPMC computes weighted model counts of formulas in conjunctive normal form (CNF)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe DPMC framework runs in two phases:\n\u003cul\u003e\n\u003cli\u003ePlanning phase: \u003ca href=\"./lg\"\u003eLG\u003c/a\u003e or \u003ca href=\"./htb\"\u003eHTB\u003c/a\u003e constructs a join tree of a CNF formula\u003c/li\u003e\n\u003cli\u003eExecution phase: \u003ca href=\"./dmc\"\u003eDMC\u003c/a\u003e computes the model count of the formula using the join tree\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopers:\n\u003cul\u003e\n\u003cli\u003eJeffrey Dudek\u003c/li\u003e\n\u003cli\u003eVu Phan\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-releases\" class=\"anchor\" href=\"#releases\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://github.com/vardigroup/DPMC/releases\"\u003eReleases\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e2021/05/25: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/mc-2021\"\u003emc-2021\u003c/a\u003e \u003ca href=\"https://zenodo.org/badge/latestdoi/280443175\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a66989e99eb192ab9857e39b3f1e218d0f4b7bcd8b478436fdace72cf61b408c/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3238303434333137352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/280443175.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"./mcc\"\u003eModel Counting Competition MC-2021\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2021/05/23: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/v2.0.0\"\u003ev2.0.0\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eSAT-2021 paper: \u003cstrong\u003eProCount: Weighted Projected Model Counting with Graded Project-Join Trees\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors: Jeffrey M. Dudek, Vu H. N. Phan, Moshe Y. Vardi\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2020/07/20: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/v1.0.0\"\u003ev1.0.0\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eCP-2020 paper: \u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2008.08748\" rel=\"nofollow\"\u003eDPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors: Jeffrey M. Dudek, Vu H. N. Phan, Moshe Y. Vardi\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-example-files\" class=\"anchor\" href=\"#example-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"./examples\"\u003eExample files\u003c/a\u003e\n\u003c/h2\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" href=\"#acknowledgment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"./ACKNOWLEDGMENT.md\"\u003eAcknowledgment\u003c/a\u003e\n\u003c/h2\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1617573462.0 + "updated_at": 1652210102.0 }, { "data_format": 2, @@ -7959,535 +7547,423 @@ var data = "filenames": [ "Singularity" ], - "full_name": "shots47s/MAGetBrain_Sinularity", + "full_name": "Hydroinformatics/singularity-swat681wr-main", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-magetbrain_sinularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#magetbrain_sinularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMAGetBrain_Sinularity\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-soil--water-assessment-tool\" class=\"anchor\" href=\"#soil--water-assessment-tool\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoil \u0026amp; Water Assessment Tool\u003c/h1\u003e\n\u003cp\u003eThis container includes the Soil and Water Assessment Tool (\u003ca href=\"https://swat.tamu.edu/software/\" rel=\"nofollow\"\u003ehttps://swat.tamu.edu/software/\u003c/a\u003e)\nrevision 681,\nbuilt for use on amd64 Linux systems. The binary is installed at /usr/local/swat681/swat.\nAt run-time, any input files MUST be bind-mounted to /usr/local/swat681 - for example:\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1534432676.0 - }, - { - "data_format": 2, - "description": "CS 361 Evolutionary Computation and Artificial Life project. ", - "filenames": [ - "third-party/force-cover/Singularity" - ], - "full_name": "koellingh/empirical-p53-simulator", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-evolutionary-algorithm\" class=\"anchor\" aria-hidden=\"true\" href=\"#evolutionary-algorithm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvolutionary Algorithm\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/anyaevostinar/evo-algo/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff63e2cc80517f7b8e8246b33025f569d757ede0ae65c7ea57418d79e5a3709d/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f25324676657273696f6e2d62616467652e6a736f6e\" alt=\"version\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fversion-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2bde10efc09d993aad000b3101be56d5410d0ced348c4c7bbedbc7ffce79d630/68747470733a2f2f696d672e736869656c64732e696f2f7472617669732f616e796165766f7374696e61722f65766f2d616c676f2e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/travis/anyaevostinar/evo-algo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://evo-algo.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2af827df5df15ff6f5c6a9fff5021e631a0049aa2274f98e983135bb66f0ed81/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f65766f2d616c676f2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/evo-algo/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://evo-algo.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e035446b6d1c7a911bd4b203bd581f2116c7873352a90d4797dc08119abbd0e/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f253246646f63756d656e746174696f6e2d636f7665726167652d62616467652e6a736f6e\" alt=\"documentation coverage\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fdocumentation-coverage-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869814fe0b90d0baadc37140ac31d6d9c0e4e00c2854a51f1030c40678bc13a4/68747470733a2f2f636f6465636f762e696f2f67682f616e796165766f7374696e61722f65766f2d616c676f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"code coverage status\" data-canonical-src=\"https://codecov.io/gh/anyaevostinar/evo-algo/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo/search?q=todo+OR+fixme\u0026amp;type=\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4455f1d1625d643fe17506cb6ad50a9a6612ce62ab4667dc60b75616241c7534/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e636f6d25324665766f2d616c676f253246646f746f2d62616467652e6a736f6e\" alt=\"dotos\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.com%2Fevo-algo%2Fdoto-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db51ac0d7785eb561dcfc0cbccfc0cfed9872513120f8f732b1301504c4eb32e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f616e796165766f7374696e61722f65766f2d616c676f2e7376673f7374796c653d666c61742d737175617265266c6f676f3d676974687562266c6162656c3d5374617273266c6f676f436f6c6f723d7768697465\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/anyaevostinar/evo-algo.svg?style=flat-square\u0026amp;logo=github\u0026amp;label=Stars\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn evolutionary algorithm\u003c/p\u003e\n\u003cp\u003eCheck out the live in-browser web app at \u003ca href=\"https://anyaevostinar.github.io/evo-algo\" rel=\"nofollow\"\u003ehttps://anyaevostinar.github.io/evo-algo\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: MIT license\u003c/li\u003e\n\u003cli\u003eDocumentation: \u003ca href=\"https://evo-algo.readthedocs.io\" rel=\"nofollow\"\u003ehttps://evo-algo.readthedocs.io\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/assets/cookie.gif\"\u003e\u003cimg src=\"docs/assets/cookie.gif\" alt=\"cookie monster example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThis package was created with \u003ca href=\"https://github.com/audreyr/cookiecutter\"\u003eCookiecutter\u003c/a\u003e and the \u003ca href=\"https://github.com/devosoft/cookiecutter-empirical-project\"\u003edevosoft/cookiecutter-empirical-project\u003c/a\u003e project template.\u003c/p\u003e\n\u003cp\u003eThis package uses \u003ca href=\"https://github.com/devosoft/Empirical#readme\"\u003eEmpirical\u003c/a\u003e, a library of tools for scientific software development, with emphasis on also being able to build web interfaces using Emscripten.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eTo install \u003ca href=\"https://github.com/devosoft/Empirical\"\u003eEmpirical\u003c/a\u003e, pull down a clone of the Empirical repository. See \u003ca href=\"https://empirical.readthedocs.io/en/latest/QuickStartGuides\" rel=\"nofollow\"\u003eQuick Start Guides\u003c/a\u003e for directions on cloning and using the library.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1615848203.0 + "updated_at": 1652372813.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.specter", + "Singularity", + "Singularity.jupyter", + "conda-cudf/Singularity.conda-cudf", + "elastic_search/Singularity", + "semantic_scholar/Singularity", + "mental-ability-proj/Singularity.mental-ability", + "vocab_comp/Singularity.vocab_comp" ], - "full_name": "mmore500/tag-olympics", + "full_name": "ghoshmainak/singularity-recipe", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-recipe\" class=\"anchor\" href=\"#singularity-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-recipe\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5061\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-this-singularity-container-contains\" class=\"anchor\" href=\"#this-singularity-container-contains\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis singularity container contains:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eConda environment\u003c/li\u003e\n\u003cli\u003eNumpy\u003c/li\u003e\n\u003cli\u003eNotebook=6.0.3\u003c/li\u003e\n\u003cli\u003ePandas\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-conda-cudf-recipe\" class=\"anchor\" href=\"#conda-cudf-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econda-cudf recipe\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/containers/15169\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is an extention of singularity-recipe. This container contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eConda environment\u003c/li\u003e\n\u003cli\u003eNotebook=6.0.3\u003c/li\u003e\n\u003cli\u003eNumpy\u003c/li\u003e\n\u003cli\u003ecudf=0.13\u003c/li\u003e\n\u003cli\u003ecudatoolkit=10.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-mental-ability-project-recipe\" class=\"anchor\" href=\"#mental-ability-project-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emental-ability-project recipe\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/containers/15485\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis container is meant for my own project on mental ability. It contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython development environment\u003c/li\u003e\n\u003cli\u003epip3\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003escipy\u003c/li\u003e\n\u003cli\u003escikit-learn\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003ejupyter\u003c/li\u003e\n\u003cli\u003ejupyterlab\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003estatmodels\u003c/li\u003e\n\u003cli\u003enltk\u003c/li\u003e\n\u003cli\u003espacy\u003c/li\u003e\n\u003cli\u003efasttext\u003c/li\u003e\n\u003cli\u003econtractions\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003ecurl\u003c/li\u003e\n\u003cli\u003enano and vim\u003c/li\u003e\n\u003cli\u003etransformers\u003c/li\u003e\n\u003cli\u003ePyTorch\u003c/li\u003e\n\u003cli\u003egit\u003c/li\u003e\n\u003cli\u003edask\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-elastic-search-recipe\" class=\"anchor\" href=\"#elastic-search-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eelastic search recipe\u003c/h1\u003e\n\u003cp\u003eIt contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython development environment\u003c/li\u003e\n\u003cli\u003epip3\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003ecurl\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003egit\u003c/li\u003e\n\u003cli\u003ejsonmerge\u003c/li\u003e\n\u003cli\u003ejsonlines\u003c/li\u003e\n\u003cli\u003eparquet\u003c/li\u003e\n\u003cli\u003eelasticsearch\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1635955138.0 + "updated_at": 1639749845.0 }, { "data_format": 2, - "description": "Recipe for funannotate pipeline Singularity recipy for UA HPC", + "description": "example Singularity files", "filenames": [ - "Singularity" + "cowsay/Singularity" ], - "full_name": "dshyshlov/funannotate_singularity", + "full_name": "cyverse-education/intro2singularity", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-intro2singularity\" class=\"anchor\" href=\"#intro2singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eintro2singularity\u003c/h1\u003e\n\u003cp\u003eexample Singularity files\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1602202847.0 + "updated_at": 1652622862.0 }, { "data_format": 2, - "description": null, + "description": "compute", "filenames": [ - "Singularity.horovod_cpu", - "Singularity.openmpi_cuda", - "Singularity.cpu_tf2.2_torch1.5_hvd0.19", - "Singularity.cpu_tf1.14_torch1.1_hvd0.16", - "Singularity.horovod_cpu_centos", - "Singularity.julia_deps", - "Singularity.gpu", - "Singularity.test2", - "Singularity.test", - "Singularity.horovod_gpu" + "Singularity.def" ], - "full_name": "EliseJ/kay_singularity_images", + "full_name": "Aku02/cc", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-images-for-mldl-stack-on-kay\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-images-for-mldl-stack-on-kay\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity images for ML/DL stack on Kay\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cc\" class=\"anchor\" href=\"#cc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecc\u003c/h1\u003e\n\u003cp\u003ecompute\u003c/p\u003e\n\u003cp\u003esingularity run --nv conda.sif\u003c/p\u003e\n\u003cp\u003esingularity run --nv --bind /scratch:/home/akash02 scratch/conda.sif\u003c/p\u003e\n\u003cp\u003e$ sudo singularity build --nv --nvccli --sandbox test conda.sif\u003c/p\u003e\n\u003cp\u003esingularity shell --nv --nvccli conda.sif\u003c/p\u003e\n\u003cp\u003esrun --mem=16G --cpus-per-task=2 --time=3:0:0 --gres=gpu:t4:1 --pty bash\u003c/p\u003e\n\u003cp\u003esingularity run --nv --nvccli --bind cc:/user_mnt cc/test/\u003c/p\u003e\n\u003cp\u003esudo singularity run --nv --nvccli --writable --bind cc:/root cc/product/\u003c/p\u003e\n\u003cp\u003esudo singularity run --nv banmo.sif\nsudo singularity run --nv --nvccli --writable --bind cc:/root cc/test/\u003c/p\u003e\n\u003cp\u003esudo singularity build --nv --nvccli --rocm product/ Singularity.def\u003c/p\u003e\n\u003cp\u003esudo singularity build --nv --nvccli banmo.sif --tmpdir=$SINGULARITY_TMPDIR docker-daemon://banmo:latest\u003c/p\u003e\n\u003cp\u003esudo singularity build --nv --sandbox --nvccli --rocm test/ Singularity.def\u003c/p\u003e\n\u003cp\u003eERROR conda.core.link:_execute(699): An error occurred while installing package \u0027conda-forge::cudatoolkit-dev-11.3.1-py39h3811e60_0\u0027.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1612268805.0 + "updated_at": 1651513695.0 }, { "data_format": 2, - "description": "Singularity container recipes for bioinformatic workflows", + "description": null, "filenames": [ - "Singularity", - "cellranger-atac/Singularity", - "cellranger-rna/Singularity_cellranger-rna_4.0.0" + "Singularity.def" ], - "full_name": "perllb/singularity", + "full_name": "Garuda-1/Thesis-2022", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity container recipes for bioinformatics workflows\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e-- Build container with\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esudo -E singularity build \u0026lt;.sif image file\u0026gt; \u0026lt; container recipe \u0026gt;\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1604059761.0 + "updated_at": 1652887962.0 }, { "data_format": 2, "description": null, "filenames": [ + "Singularity.full", "Singularity" ], - "full_name": "Saford91/centos7-singularity", + "full_name": "leo-cazenille/multiAE-ME", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-multiae-me\" class=\"anchor\" href=\"#multiae-me\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emultiAE-ME\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1500478470.0 + "updated_at": 1650020409.0 }, { "data_format": 2, - "description": "Container Library of Apptainer definition files.", + "description": null, "filenames": [ - "Singularity.digits", - "Singularity.tensorflow", - "Singularity.theano", - "ciml/Singularity.tape-0.4", - "ciml/Singularity.sparkr-2.3.1", - "ciml/Singularity.r-3.6.1", - "ciml/Singularity.esm-0.3.1", - "ciml/Singularity.pyspark-3.1.2", - "tensorflow/Singularity.tensorflow-2.8.2-ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "tensorflow/Singularity.tensorflow-2.5.0-ubuntu-18.04-cuda-11.2-openmpi-4.0.5", - "tensorflow/Singularity.tensorflow-2.7.3-ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "tensorflow/Singularity.tensorflow-2.5.3-ubuntu-18.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "tensorflow/Singularity.tensorflow-2.5.1-ubuntu-18.04-cuda-11.2-mlnx-ofed-4.7-3.2.9.0-openmpi-4.0.5", - "tensorflow/Singularity.tensorflow-2.3.0-ubuntu-18.04-cuda-10.1.168-openmpi-3.1.4", - "hpl/Singularity.hpl-2.3-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3-openblas-0.3.18", - "hpl/Singularity.hpl-2.3-ubuntu-18.04-openmpi-4.0.5-openblas-0.3.14", - "hpl/Singularity.hpl-2.3-ubuntu-18.04-openmpi-3.1.6-openblas-0.3.10", - "hpl/Singularity.hpl-2.3-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6-openblas-0.3.18", - "hpl/Singularity.hpl-2.3-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6-openblas-0.3.18", - "hpl/Singularity.hpl-2.3-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3-openblas-0.3.18", - "hpl/Singularity.hpl-2.3-ubuntu-18.04-openmpi-3.1.4-openblas-0.3.10", - "visit/Singularity.visit-3.1.4-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "beast/Singularity.beast-1.10.4-ubuntu-18.04-cuda-10.2", - "beast/Singularity.beast-2.6.1-ubuntu-18.04-cuda-10.2", - "pytorch/Singularity.pytorch-1.8.2-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "pytorch/Singularity.pytorch-1.10.2-ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "ubuntu/Singularity.ubuntu-18.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "ubuntu/Singularity.ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "ubuntu/Singularity.ubuntu-20.04-cuda-11.2", - "ubuntu/Singularity.ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "ubuntu/Singularity.ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0", - "ubuntu/Singularity.ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "ubuntu/Singularity.ubuntu-18.04-cuda-10.2", - "ubuntu/Singularity.ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "ubuntu/Singularity.ubuntu-20.04", - "ubuntu/Singularity.ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "ubuntu/Singularity.ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "ubuntu/Singularity.ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0", - "ubuntu/Singularity.ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0", - "ubuntu/Singularity.ubuntu-18.04", - "ubuntu/Singularity.ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "ubuntu/Singularity.ubuntu-18.04-cuda-11.2", - "ubuntu/Singularity.ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "ubuntu/Singularity.ubuntu-18.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0", - "ubuntu/Singularity.ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0", - "ubuntu/Singularity.ubuntu-18.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "torch/Singularity.torch-extras", - "torch/Singularity.torch", - "ior/Singularity.ior-3.3.0rc1-ubuntu-18.04-openmpi-3.1.4", - "ior/Singularity.ior-3.3.0rc1-ubuntu-18.04-openmpi-3.1.6", - "ior/Singularity.ior-3.3.0-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "ior/Singularity.ior-3.3.0-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "ior/Singularity.ior-3.3.0-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "ior/Singularity.ior-3.3.0-ubuntu-18.04-openmpi-4.0.5", - "ior/Singularity.ior-3.3.0-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "centos/Singularity.centos-7.9.2009-mvapich-2.3.2", - "centos/Singularity.centos-7.9.2009-openmpi-3.1.4", - "centos/Singularity.centos-7.9.2009", - "centos/Singularity.centos-7.7.1908-openmpi-4.0.5", - "centos/Singularity.centos-7.7.1908-cuda-11.0", - "centos/Singularity.centos-7.9.2009-cuda-10.1.168", - "centos/Singularity.centos-7.7.1908-openmpi-3.1.6", - "centos/Singularity.centos-7.7.1908", - "centos/Singularity.centos-7.7.1908-cuda-11.0-openmpi-3.1.6", - "centos/Singularity.centos-7.7.1908-cuda-11.0-openmpi-4.0.5", - "rstudio/Singularity.rstudio", - "miniconda/Singularity.miniconda3-py38-4.11.0-ubuntu-20.04", - "miniconda/Singularity.miniconda2-py27-4.8.3-ubuntu-18.04", - "miniconda/Singularity.miniconda3-py39-4.9.2-ubuntu-18.04", - "miniconda/Singularity.miniconda3-py39-4.11.0-ubuntu-20.04", - "miniconda/Singularity.miniconda3-py38-4.9.2-ubuntu-18.04", - "miniconda/Singularity.miniconda3-py37-4.9.2-ubuntu-18.04", - "miniconda/Singularity.miniconda3-py37-4.11.0-ubuntu-20.04", - "anaconda/Singularity.anaconda3-py39-2021.11-ubuntu-20.04", - "anaconda/Singularity.anaconda2-py27-2019.10-ubuntu-18.04", - "fenics/Singularity.fenics-2019.1.0-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "mxnet/Singularity.mxnet-1.7.0-ubuntu-18.04-cuda-10.1.168-openmpi-3.1.4", - "gromacs/Singularity.gromacs-2020.7-ubuntu-18.04-cuda-10.2", - "singularity/Singularity.singularity-3.7.4-ubuntu-18.04", - "keras/Singularity.keras-py3", - "keras/Singularity.keras-py2", - "stream/Singularity.stream-5.10-ubuntu-18.04", - "paraview/Singularity.paraview-5.9.0-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6-osmesa-20.1.5", - "rnaseq/Singularity.rnaseq", - "deepbench/Singularity.deepbench-da81ba7-ubuntu-18.04-cuda-11.2-mlnx-ofed-4.7-3.2.9.0-openmpi-3.1.6", - "deepbench/Singularity.deepbench-da81ba7-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "xcrysden/Singularity.xcrysden-1.6.2-ubuntu-18.04", - "spark/Singularity.spark-3.2.1-hadoop-3.2-ubuntu-20.04", - "spark/Singularity.spark-2.3.1-hadoop-2.7-ubuntu-18.04", - "spark/Singularity.spark-3.1.2-hadoop-3.2-ubuntu-18.04", - "omb/Singularity.omb-5.6.3-centos-7.9.2009-mvapich-2.3.2", - "omb/Singularity.omb-5.8-ubuntu-18.04-cuda-11.2-mlnx-ofed-4.7-3.2.9.0-openmpi-4.0.5", - "omb/Singularity.omb-5.9-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "omb/Singularity.omb-5.9-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "omb/Singularity.omb-5.7-ubuntu-18.04-openmpi-4.0.5", - "omb/Singularity.omb-5.8-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.6-1.0.1.1-openmpi-3.1.4", - "omb/Singularity.omb-5.9-ubuntu-20.04-cuda-11.4-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "omb/Singularity.omb-5.7-ubuntu-18.04-mlnx-ofed-4.7-3.2.9.0-openmpi-3.1.6", - "omb/Singularity.omb-5.7-centos-7.7.1908-openmpi-3.1.6", - "omb/Singularity.omb-5.9-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "omb/Singularity.omb-5.6.3-ubuntu-18.04-openmpi-3.1.4", - "omb/Singularity.omb-5.6.3-ubuntu-18.04-mvapich-2.3.2", - "omb/Singularity.omb-5.9-ubuntu-20.04-cuda-11.4-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "omb/Singularity.omb-5.9-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "omb/Singularity.omb-5.7-centos-7.7.1908-cuda-11.0-openmpi-3.1.6", - "omb/Singularity.omb-5.9-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", - "omb/Singularity.omb-5.6.3-ubuntu-18.04-cuda-10.1.168-openmpi-3.1.4", - "omb/Singularity.omb-5.7-ubuntu-18.04-cuda-11.2-openmpi-4.0.5", - "omb/Singularity.omb-5.6.3-ubuntu-18.04-openmpi-3.1.6", - "omb/Singularity.omb-5.7-ubuntu-18.04-mlnx-ofed-4.7-3.2.9.0-openmpi-4.0.5", - "omb/Singularity.omb-5.7-centos-7.7.1908-openmpi-4.0.5", - "omb/Singularity.omb-5.7-ubuntu-18.04-mlnx-ofed-4.7-3.2.9.0-mvapich-2.3.6", - "omb/Singularity.omb-5.9-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", - "omb/Singularity.omb-5.8-ubuntu-18.04-mlnx-ofed-4.6-1.0.1.1-openmpi-3.1.4", - "omb/Singularity.omb-5.6.3-centos-7.9.2009-openmpi-3.1.4" + "Singularity" ], - "full_name": "acchapm1/containerlibrary", + "full_name": "carshadi/tiff2octree-singularity", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1680805708.0 + "updated_at": 1651625714.0 }, { "data_format": 2, - "description": null, + "description": "Research on the effects of mixing and matching dataset towards audio separation", "filenames": [ - "Singularity" + "museparation/waveunet/Singularity" ], - "full_name": "shailapar/build_container_on_shub", + "full_name": "B-lanc/Museparation", "latest_release": null, - "readme": "\u003cp\u003eExamples for building containers on Singularity Hub\u003c/p\u003e\n\u003cp\u003e./tutorial_steps.txt : example steps, command-by-command\u003c/p\u003e\n\u003cp\u003e./Singularity : is a recipe file for building your container\u003c/p\u003e\n\u003cp\u003e./text_translate.py is a sample python script we can run with the container\u003c/p\u003e\n\u003cp\u003e./make_git_repo.sh is a script that uploads your Singularity repository to github\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-museparation\" class=\"anchor\" href=\"#museparation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMuseparation\u003c/h1\u003e\n\u003cp\u003eResearch on the effects of mixing and matching dataset towards audio separation\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1558647668.0 + "updated_at": 1649044884.0 }, { "data_format": 2, - "description": "If you are going to build off of basic Empirical, this is the project for you", + "description": "The Bootcamp of the Ghent Quantum Chemistry Group, aimed at achieving the initial competences needed in order to be able to contribute to our electronic structure method development group.", "filenames": [ - "third-party/force-cover/Singularity" + "Singularity" ], - "full_name": "piperwelch/Basic-Empirical-Starter-carlcs361s01w21-6", + "full_name": "GQCG-edu/bootcamp", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-evolutionary-algorithm\" class=\"anchor\" aria-hidden=\"true\" href=\"#evolutionary-algorithm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvolutionary Algorithm\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/anyaevostinar/evo-algo/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff63e2cc80517f7b8e8246b33025f569d757ede0ae65c7ea57418d79e5a3709d/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f25324676657273696f6e2d62616467652e6a736f6e\" alt=\"version\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fversion-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2bde10efc09d993aad000b3101be56d5410d0ced348c4c7bbedbc7ffce79d630/68747470733a2f2f696d672e736869656c64732e696f2f7472617669732f616e796165766f7374696e61722f65766f2d616c676f2e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/travis/anyaevostinar/evo-algo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://evo-algo.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2af827df5df15ff6f5c6a9fff5021e631a0049aa2274f98e983135bb66f0ed81/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f65766f2d616c676f2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/evo-algo/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca 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src=\"https://camo.githubusercontent.com/869814fe0b90d0baadc37140ac31d6d9c0e4e00c2854a51f1030c40678bc13a4/68747470733a2f2f636f6465636f762e696f2f67682f616e796165766f7374696e61722f65766f2d616c676f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"code coverage status\" data-canonical-src=\"https://codecov.io/gh/anyaevostinar/evo-algo/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo/search?q=todo+OR+fixme\u0026amp;type=\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4455f1d1625d643fe17506cb6ad50a9a6612ce62ab4667dc60b75616241c7534/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e636f6d25324665766f2d616c676f253246646f746f2d62616467652e6a736f6e\" alt=\"dotos\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.com%2Fevo-algo%2Fdoto-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db51ac0d7785eb561dcfc0cbccfc0cfed9872513120f8f732b1301504c4eb32e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f616e796165766f7374696e61722f65766f2d616c676f2e7376673f7374796c653d666c61742d737175617265266c6f676f3d676974687562266c6162656c3d5374617273266c6f676f436f6c6f723d7768697465\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/anyaevostinar/evo-algo.svg?style=flat-square\u0026amp;logo=github\u0026amp;label=Stars\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn evolutionary algorithm\u003c/p\u003e\n\u003cp\u003eCheck out the live in-browser web app at \u003ca href=\"https://anyaevostinar.github.io/evo-algo\" rel=\"nofollow\"\u003ehttps://anyaevostinar.github.io/evo-algo\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: MIT license\u003c/li\u003e\n\u003cli\u003eDocumentation: \u003ca href=\"https://evo-algo.readthedocs.io\" rel=\"nofollow\"\u003ehttps://evo-algo.readthedocs.io\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/assets/cookie.gif\"\u003e\u003cimg src=\"docs/assets/cookie.gif\" alt=\"cookie monster example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThis package was created with \u003ca href=\"https://github.com/audreyr/cookiecutter\"\u003eCookiecutter\u003c/a\u003e and the \u003ca href=\"https://github.com/devosoft/cookiecutter-empirical-project\"\u003edevosoft/cookiecutter-empirical-project\u003c/a\u003e project template.\u003c/p\u003e\n\u003cp\u003eThis package uses \u003ca href=\"https://github.com/devosoft/Empirical#readme\"\u003eEmpirical\u003c/a\u003e, a library of tools for scientific software development, with emphasis on also being able to build web interfaces using Emscripten.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eTo install \u003ca href=\"https://github.com/devosoft/Empirical\"\u003eEmpirical\u003c/a\u003e, pull down a clone of the Empirical repository. See \u003ca href=\"https://empirical.readthedocs.io/en/latest/QuickStartGuides\" rel=\"nofollow\"\u003eQuick Start Guides\u003c/a\u003e for directions on cloning and using the library.\u003c/p\u003e\n", + "readme": "\u003cp align=\"center\"\u003e\n\u003ca href=\"media/bootcamp.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"media/bootcamp.png\" width=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eIn this boot camp you will learn the minimal set of computer skills that are required to survive \u003ca href=\"https://gqcg.github.io/\" rel=\"nofollow\"\u003ein our computational chemistry group\u003c/a\u003e. We will first focus on acquiring high-level skills using freely available resources that run in your browser. After you have obtained these skills, we will break free from the confines of those resources and transition to running software on your local system and in the cloud. Finally, you will apply the skills you have learned by implementing Restricted Hartree-Fock using PySCF.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-schedule\" class=\"anchor\" href=\"#schedule\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSchedule\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTraining\u003c/th\u003e\n\u003cth\u003eTechnologies\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"training/browser.md\"\u003eCoding in the browser\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eGithub, LaTeX/Overleaf, SciPy-Stack/Colab\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"training/local.md\"\u003eCoding locally\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eGit, VSCode, Docker, Jupyter, VSCode: LaTeX workshop\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"training/cloud.md\"\u003eCoding in the cloud\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eHPC/modules, Singularity/Apptainer\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"project/README.md\"\u003eCapstone project\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u00a0PySCF, RHF\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1615683851.0 + "subscribers_count": 3, + "topics": [ + "training", + "gqcg" + ], + "updated_at": 1656513940.0 }, { "data_format": 2, - "description": "A container for PyMultinest", + "description": "ShellCheck, a static analysis tool for shell scripts", "filenames": [ - "Singularity" + "0.5.0/Singularity", + "0.8.0/Singularity" ], - "full_name": "sysmso/singularity-multinest", + "full_name": "pscedu/singularity-shellcheck", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-multinest\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-multinest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-multinest\u003c/h1\u003e\n\u003cp\u003eA container for PyMultinest\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-shellcheck/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-shellcheck/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-shellcheck/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-shellcheck/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/405923c0ac019718e1ce7ee2305e5e1e59721f91e675a8b0905b5c8b1e2abfbc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/405923c0ac019718e1ce7ee2305e5e1e59721f91e675a8b0905b5c8b1e2abfbc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-shellcheck\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/bd63ff1e920842a88281143fb261678d5a3c13a0b9aa2b77daa4f01c294053b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bd63ff1e920842a88281143fb261678d5a3c13a0b9aa2b77daa4f01c294053b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-shellcheck\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3501f937ae3c9e631fa7626a21c08282d81835d5df168737e339adbc9b8e6d41/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3501f937ae3c9e631fa7626a21c08282d81835d5df168737e339adbc9b8e6d41/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-shellcheck\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f24e35c82c0a0127f9824f4cb911bf49ccdadbd7871a842b8ad8292cfbae64c6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f24e35c82c0a0127f9824f4cb911bf49ccdadbd7871a842b8ad8292cfbae64c6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-shellcheck\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-shellcheck\" class=\"anchor\" href=\"#singularity-shellcheck\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-shellcheck\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/koalaman/shellcheck.net\"\u003eshellcheck\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eshellcheck\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/shellcheck/0.8.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/shellcheck\u003c/code\u003e as \u003ccode\u003e0.8.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1602594100.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1649646255.0 }, { "data_format": 2, "description": null, "filenames": [ - "deepcell-tf/Singularity.0.1", - "DS5559/Singularity-0.1", - "tensorflow/Singularity.2.0.0-py36", - "tensorflow/Singularity.1.12.0-py36", - "tensorflow/Singularity.1.6.0-py36", - "tensorflow/Singularity.1.13.1-py36", - "tensorflow/Singularity.1.12.0-py27", - "tensorflow/Singularity.1.12.3-py36", - "tensorflow/Singularity.2.1.0-py37-rs8wa", - "tensorflow/Singularity.1.6.0-py27", - "tensorflow/Singularity.2.1.0-py37", - "tensorflow/Singularity.1.14.0-py36", - "patric/Singularity.1.026", - "rhessys/Singularity.1", - "rhessys/Singularity.3.3", - "rhessys/Singularity.3", - "rhessys/Singularity.2", - "kaggle/Singularity-0.0", - "kaggle/Singularity-0.1", - "pytorch/Singularity.1.3.1-py36", - "pytorch/Singularity.1.0.0-py36", - "pytorch/Singularity.1.4.0-py37", - "cryoCARE/Singularity.0.1.0", - "danpos/Singularity.2.2.2", - "cp-analyst/Singularity.2.2.1", - "maxquant/Singularity.1.6.7.0", - "caffe2/Singularity.0.8.0", - "supernova/Singularity.2.0.0", - "anaconda/Singularity.2019.10-cuda10.0-cudnn7.6-py3.6", - "anaconda/Singularity.2019.10-cuda10.0-cudnn7.6-py3.7", - "anaconda/Singularity.2019.10-cuda9.0-cudnn7.6-py2.7", - "anaconda/Singularity.2019.10-cuda9.0-cudnn7.6-py3.6", - "anaconda/Singularity.cuda10.0-cudnn7.4-py3.6", - "anaconda/Singularity.cuda9.0-cudnn7.4-py3.6", - "lolcow/Singularity.1.0.0", - "theano/Singularity.1.0.4-py36", - "hydrator/Singularity.0.0.2", - "hydrator/Singularity.0.0.10", - "cellprofiler/Singularity.2.2.0", - "cellprofiler/Singularity.3.0.0", - "cellprofiler/Singularity.3.1.8", - "cellprofiler/Singularity.3.1.9", - "p4vasp/Singularity.0.3.30", - "anvio/Singularity.6.2-alpine", - "anvio/Singularity.6.2", - "inkscape/Singularity.0.92.3", - "sumo/Singularity.1.3.1", - "omero-client/Singularity.5.6.1", - "omero-client/Singularity.5.4.10", - "rstudio_server/Singularity.1.1.463", - "rstudio_server/Singularity.1.0.143", - "vg/Singularity.1.22.0", - "vg/Singularity.1.23.0", - "R/Singularity.3.6.0", - "R/Singularity-3.6.0", - "electron/Singularity" + "2/images/Singularity.def", + "4/images/Singularity.def", + "3/images/Singularity.def", + "1/images/Singularity.def" ], - "full_name": "uvarc/singularity-scripts", + "full_name": "alcidesmig/hpc-ufscar-cluster", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-scripts\u003c/h1\u003e\n\u003cp\u003eCollection of Singularity container recipe files.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1614097316.0 + "updated_at": 1643401257.0 }, { "data_format": 2, - "description": "ngs pipelines _ nextflow/singularity workflows", + "description": null, "filenames": [ - "scATAC_cellranger/container_singularity/Singularity" + "Singularity" ], - "full_name": "perllb/ngs_pipelines", + "full_name": "truatpasteurdotfr/singularity-cryolo-cuda10", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-singularity-container-for-cryolo-using-cuda-version-10\" class=\"anchor\" href=\"#building-a-singularity-container-for-cryolo-using-cuda-version-10\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a singularity container for crYOLO using CUDA version 10\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\n\u003ca href=\"https://github.com/truatpasteurdotfr/singularity-cryolo-cuda10/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-cryolo-cuda10/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run --nv oras://ghcr.io/truatpasteurdotfr/singularity-cryolo-cuda10:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLICENSE:\nThe same as crYOLO (free for academic use, see \u003ca href=\"https://cryolo.readthedocs.io/en/stable/other/license.html\" rel=\"nofollow\"\u003ehttps://cryolo.readthedocs.io/en/stable/other/license.html\u003c/a\u003e)\ncopy retrieved from \u003ca href=\"https://raw.githubusercontent.com/MPI-Dortmund/cryolo/9ea47f694fc68bcdaedf550a128558b8e77855bc/source/other/license.rst\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/MPI-Dortmund/cryolo/9ea47f694fc68bcdaedf550a128558b8e77855bc/source/other/license.rst\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1668091545.0 + "updated_at": 1650023541.0 }, { "data_format": 2, - "description": "This repo is intended for the tutorial in the Planning Meeting held on the 24th of October, 2018", + "description": "Ancestry ", "filenames": [ - "demoPlanner/Singularity", - "runPlanningTool/planners/OPTIC-Base/Singularity", - "runPlanningTool/planners/team40/Singularity" + "Singularity" ], - "full_name": "ionut94/KCL-PlanningTutorial", + "full_name": "jahaltom/RIA", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-kcl-planningtutorial\" class=\"anchor\" aria-hidden=\"true\" href=\"#kcl-planningtutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKCL-PlanningTutorial\u003c/h1\u003e\n\u003cp\u003eThis repo is intended for the tutorial in the Planning Meeting held on the 24th of October, 2018\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dev-repo-for-runplanningtool-is-here\" class=\"anchor\" aria-hidden=\"true\" href=\"#dev-repo-for-runplanningtool-is-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDev repo for runPlanningTool is \u003ca href=\"https://github.com/momartinm/runPlanningTool.git\"\u003ehere\u003c/a\u003e\n\u003c/h2\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-rna-seq-inferred-ancestry-ria\" class=\"anchor\" href=\"#rna-seq-inferred-ancestry-ria\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRNA-Seq Inferred Ancestry (RIA)\u003c/h1\u003e\n\u003cp\u003eRIA is a method for infering super-population (Africa, Europe, South Asia, East Asia, and America) identity from Human RNA-seq data.\nRIA leverages data from 1000 genomes project and utilizes a machine learning approach that involves principal component analysis and support vector machine.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/jahaltom/RNA-Seq-Ancestry-Inference/blob/main/FlowChart.png?raw=true\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/jahaltom/RNA-Seq-Ancestry-Inference/raw/main/FlowChart.png?raw=true\" alt=\"alt text\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eUsing Conda 4.10.3, create the conda enviroment and activate:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate Ancestry\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor you can use the Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull ria.sif library://aseetharam/ancestry/ria:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou can access the tools inside the container by prefixing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\nsingularity exec --bind $PWD ria.sif snakemake \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-data-preparation\" class=\"anchor\" href=\"#data-preparation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Preparation\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e1000 Genomes Project:\u003c/strong\u003e\nThe snakemake script \"Prepare_1KGP\" downloads chr(1-22) level VCF files from 1000 Genomes Project phase 3 on GRCh38 (\u003ca href=\"https://www.internationalgenome.org/data-portal/data-collection/grch38\" rel=\"nofollow\"\u003ehttps://www.internationalgenome.org/data-portal/data-collection/grch38\u003c/a\u003e, \u003ca href=\"https://doi.org/10.12688/wellcomeopenres.15126.2\" rel=\"nofollow\"\u003ehttps://doi.org/10.12688/wellcomeopenres.15126.2\u003c/a\u003e) while filtering out indels. It also indexes and creates a BED for each filtered VCF file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j 22 -s Prepare_1KGP --cluster \"sbatch -t 01:00:00 -c 4 -N 1\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eGRCh38 Reference Genome\u003c/strong\u003e\nThe bash script \"Prepare_Reference_Genome\" will download the Human genome GRCh38 fasta(GCA_000001405.15_GRCh38_no_alt_plus_hs38d1_analysis_set.fna.gz) and the corresponding gtf, and will create a seqence dictionary and index file for the fasta. It also creates a STAR index.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch Prepare_Reference_Genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-raw-data-retrieval-from-sra-qc-and-star-2-pass\" class=\"anchor\" href=\"#raw-data-retrieval-from-sra-qc-and-star-2-pass\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRaw data retrieval from SRA, QC, and STAR 2-Pass\u003c/h2\u003e\n\u003cp\u003eThe snakemake script \"STAR_SRA\" takes in a list of run accession IDs \"RAids.txt\" and fetches the raw fastq files from SRA and then uses Trimgalore for QC. The reads are then ran through STAR 2-Pass mode for enhanced novel SJ detection. The SJ.out.tab file for the 2nd pass is made by combining all SJ.out.tab files from the first pass and removing SJ\u0027s that are supported by 2 or less unique mappers.\u003c/p\u003e\n\u003cp\u003eFor just 1 study, create a list of the corresponding run accession IDs \"RAids.txt\" and run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j 50 -k -s STAR_SRA --cluster \"sbatch -t 8:00:00 -c 30 -N 1\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor multiple studies, create 2 files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSRP: List of unique study accession IDs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eERP126405\nERP127339\nSRP293106\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003elist: 2 column file of study accession IDs and corresponding run accession IDs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eERP124749 ERR4777044\nERP124749 ERR4777043\nERP126405 ERR5104751\nERP126405 ERR5104750\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run STAR_SRA on all studies using this script. This will make it so each study gets its own combined SJ.out.tab file for the 2nd pass.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat SRP | while read i; do \n\tcat list | grep \"$i\" | awk \u0027{print $2}\u0027 \u0026gt; RAids.txt\n\tsnakemake -j 300 -k -s STAR_SRA --cluster \"sbatch -t 8:00:00 -c 30 -N 1 -p RM-shared\"\n\trm output/all.SJ.out.tab\ndone\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-infer-ancestry\" class=\"anchor\" href=\"#infer-ancestry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInfer Ancestry\u003c/h2\u003e\n\u003cp\u003ePerforms GATK best practices workflow for RNAseq short variant discovery (SNPs + Indels). Intersects varaint data from GATK with 1000 Genomes Project ancestry informative SNPs to gather common loci. Performs PCA on variant data via PLINK and SVM model is implemented for ancestry inference.\u003c/p\u003e\n\u003cp\u003eSplit RAids.txt so snakemake doesnt stall.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esplit -l 100 RAids.txt\n\nls *xa* | cat \u0026gt; splits\n\ncat splits | while read i; do\n\tcat $i \u0026gt; RAids.txt\n\tsnakemake -j 300 -k -s InferAncestry.py --cluster \"sbatch -t 02:00:00 -c 7 -p RM-shared\"\ndone\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1540504981.0 + "updated_at": 1645029635.0 }, { "data_format": 2, - "description": null, + "description": "R package for nsphs_ml_qt", "filenames": [ - "hpc_files/singularity_hpc_files/Singularity.bld" + "Singularity", + "scripts_bianca/Singularity" ], - "full_name": "ammunk/distributed-training-pytorch", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-demo-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#demo-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo scripts\u003c/h1\u003e\n\u003cp\u003eThis repository contains demo scripts for running distributed training of deep\nneural networks using PyTorch. These scripts are written according to the\ninformation found at (\u003ca href=\"https://github.com/ammunk/hpc\"\u003ehttps://github.com/ammunk/hpc\u003c/a\u003e)\u003c/p\u003e\n", + "full_name": "richelbilderbeek/nsphs_ml_qt", + "latest_release": "v0.3", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nsphs_ml_qt\" class=\"anchor\" href=\"#nsphs_ml_qt\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ensphs_ml_qt\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=master\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/be413dbe544678e8710f09ecb2ee97d7a26b0a853e40a539069148252157700f/68747470733a2f2f636f6465636f762e696f2f67682f72696368656c62696c6465726265656b2f6e737068735f6d6c5f71742f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=develop\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt?branch=develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d51e07e8b43e2d93b35336c3c0437fdb29a65ac9e5d54406d980daf81cd2567f/68747470733a2f2f636f6465636f762e696f2f67682f72696368656c62696c6465726265656b2f6e737068735f6d6c5f71742f6272616e63682f646576656c6f702f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt/branch/develop/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll code for \u003cg-emoji class=\"g-emoji\" alias=\"lock\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f512.png\"\u003e\ud83d\udd12\u003c/g-emoji\u003e \u003ca href=\"https://github.com/AJResearchGroup/article_nsphs_ml_qt\"\u003earticle_nsphs_ml_qt\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_bianca/README.md\"\u003eWorkflow on Bianca\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_rackham/README.md\"\u003eWorkflow on Rackham\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_local/README.md\"\u003eWorkflow on local computer\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eResults: \u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt_results\"\u003esee the \u0027nsphs_ml_qt_results\u0027 repository\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"man/figures/example_architecture.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"man/figures/example_architecture.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"man/figures/example_dimred.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"man/figures/example_dimred.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"man/figures/legend_HO_tiny.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"man/figures/legend_HO_tiny.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1646720319.0 + "updated_at": 1655910726.0 }, { "data_format": 2, - "description": null, + "description": "Work with Python installed at a custom location", "filenames": [ - "Singularity.torch_mmf", - "Singularity.torch" + "Singularity" ], - "full_name": "ChunCun/container", - "latest_release": null, + "full_name": "richelbilderbeek/ormr", + "latest_release": "v0.6.2.1", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ormr\" class=\"anchor\" href=\"#ormr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eormr\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=master\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/ormr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80f61013497de9c4ba38bd7d37d57f2baf9ad486b3e667b76823a2fa7acb1783/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6f726d722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/ormr/coverage.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=master\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=master\" alt=\"build_singularity\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=develop\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/ormr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e40a61ddb8d3cee1a4e177f20956ab6b1887a9d5a422c8e9f9024859f4c23af/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6f726d722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/ormr/coverage.svg?branch=develop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=develop\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=develop\" alt=\"build_singularity\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"man/figures/ormr_logo_50.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"man/figures/ormr_logo_50.png\" alt=\"ormr logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWork with Python installed at a custom location.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-goal\" class=\"anchor\" href=\"#goal\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows a user to install Python packages,\ncreate a Conda environment and run Python scripts\nwith only one point of contact.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e heavily depends on \u003ccode\u003ereticulate\u003c/code\u003e, the latter being\nmore powerful and flexible. \u003ccode\u003eormr\u003c/code\u003e, however, focuses\non making it trivially simple to install Python\npackages and run Python scripts.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-install-ormr\" class=\"anchor\" href=\"#install-ormr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall \u003ccode\u003eormr\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003eAs \u003ccode\u003eormr\u003c/code\u003e is developed on the \u003ccode\u003emaster\u003c/code\u003e branch, only a release\nis tested to work:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eremotes::install_github(\"richelbilderbeek/ormr\", ref = \"v0.6.1\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee FAQ why one needs to install a release.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-examples\" class=\"anchor\" href=\"#examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e uses one point of contact, \u003ccode\u003eormr_folder_name\u003c/code\u003e.\nFor convenience, there is also a default \u003ccode\u003eormr_folder_name\u003c/code\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall a Python package\u003c/li\u003e\n\u003cli\u003eRun a Python script\u003c/li\u003e\n\u003cli\u003eRun a Python script with command-line arguments\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAlso, \u003ccode\u003eormr\u003c/code\u003e uses \u003cstrong\u003eeager loading\u003c/strong\u003e, which means that\nit will setup everything it needs for you. For example,\nif you want to run a Python script from a new \u003ccode\u003eormr_folder_name\u003c/code\u003e,\nit will create a Conda environment there for you as well.\u003c/p\u003e\n\u003cp\u003eNote that \u003ccode\u003ecreate_default_conda_env\u003c/code\u003e conveniently returns the\n\u003ccode\u003eormr_folder_name\u003c/code\u003e used to work with this environment.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-1-install-a-python-package\" class=\"anchor\" href=\"#1-install-a-python-package\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install a Python package\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall_python_package(\n \u003cspan class=\"pl-v\"\u003epackage_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003escipy\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\ninstall_python_package(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epackage_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003escipy\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-2-run-a-python-script\" class=\"anchor\" href=\"#2-run-a-python-script\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Run a Python script\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehello_world.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script(\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehello_world.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-3-run-a-python-script-with-command-line-arguments\" class=\"anchor\" href=\"#3-run-a-python-script-with-command-line-arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Run a Python script with command-line arguments\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshow_args.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script_with_args(\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworld\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshow_args.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script_with_args(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworld\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-faq\" class=\"anchor\" href=\"#faq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-the-goal-of-ormr\" class=\"anchor\" href=\"#what-is-the-goal-of-ormr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is the goal of \u003ccode\u003eormr\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows a user to install Python packages,\ncreate a Conda environment and run Python scripts\nwith only one point of contact.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-in-what-context-is-ormr-useful\" class=\"anchor\" href=\"#in-what-context-is-ormr-useful\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIn what context is \u003ccode\u003eormr\u003c/code\u003e useful?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e was written to write simpler\n\u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (a type of containerization\nsoftware, similar to Docker) scripts.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ereticulate\u003c/code\u003e is great when using its default folders on a local computer.\nHowever, for a Singularity container, it is recommended to install\nlibraries in a systems folder. In that setting, \u003ccode\u003ereticulate\u003c/code\u003e is\nharder to work with.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows to install install Python packages,\ncreate a Conda environment and run Python scripts\nin any folder easily, for example,\nin a system folder (\u003ccode\u003e/opt/ormr\u003c/code\u003e) of a Singularity container.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-not-just-use-reticulate\" class=\"anchor\" href=\"#why-not-just-use-reticulate\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy not just use \u003ccode\u003ereticulate\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e heavily depends on \u003ccode\u003ereticulate\u003c/code\u003e, the latter being\nmore powerful and flexible.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e, however, focuses\non making it trivially simple to install Python\npackages and run Python scripts,\ndue to eager loading.\nAdditionally, \u003ccode\u003eormr\u003c/code\u003e has a more extensive documentation,\nand 100% code coverage.\u003c/p\u003e\n\u003cp\u003eBeyond the domain of \u003ccode\u003eormr\u003c/code\u003e, use \u003ccode\u003ereticulate\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-do-you-mean-with-eager-loading\" class=\"anchor\" href=\"#what-do-you-mean-with-eager-loading\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat do you mean with eager loading?\u003c/h2\u003e\n\u003cp\u003eEager loading is the opposite of lazy loading.\u003c/p\u003e\n\u003cp\u003eHere, it is defined as \u0027if you want \u003ccode\u003eormr\u003c/code\u003e to do B, which depends on\nthe setup of A\u0027, \u003ccode\u003eormr\u003c/code\u003e will setup A, then do B. For example, to install\na package to a certain \u003ccode\u003eormr_folder_name\u003c/code\u003e (\u0027to do B\u0027), \u003ccode\u003eormr\u003c/code\u003e\nwill create a Conda environment for that (\u0027the setup of A\u0027).\u003c/p\u003e\n\u003cp\u003eThis means that no setup code is necessary.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-does-one-need-to-install-a-release-instead-of-just-master\" class=\"anchor\" href=\"#why-does-one-need-to-install-a-release-instead-of-just-master\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy does one need to install a release, instead of just \u003ccode\u003emaster\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003eThe development of \u003ccode\u003eormr\u003c/code\u003e takes place on the \u003ccode\u003emaster\u003c/code\u003e branch.\nHence, \u003ccode\u003emaster\u003c/code\u003e will break regularily.\nA specific release is tested to build correctly.\u003c/p\u003e\n\u003cp\u003eThe reason for this non-traditional workflow, is that the\nSingularity script always installs the \u003ccode\u003emaster\u003c/code\u003e branch,\nas it cannot detect the \u003ccode\u003egit\u003c/code\u003e branch is being built by.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" href=\"#there-is-a-feature-i-miss\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThere is a feature I miss\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" href=\"#i-want-to-collaborate\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI want to collaborate\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting code\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" href=\"#i-think-i-have-found-a-bug\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI think I have found a bug\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting bugs\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" href=\"#theres-something-else-i-want-to-say\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThere\u0027s something else I want to say\u003c/h2\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-do-i-contribute\" class=\"anchor\" href=\"#how-do-i-contribute\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do I contribute?\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-is-the-package-called-ormr\" class=\"anchor\" href=\"#why-is-the-package-called-ormr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy is the package called \u003ccode\u003eormr\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003eThis name is a pun on \u003ccode\u003ereticulate\u003c/code\u003e. \u003ccode\u003ereticulate\u003c/code\u003e is named after a\ntype of snake. \u003ccode\u003eormr\u003c/code\u003e is written in Sweden. In Swedish, \u003ccode\u003eorm\u003c/code\u003e, is a snake.\nFollowing the common tradtion of adding an \u003ccode\u003er\u003c/code\u003e to the end of an R package\nname (e.g \u003ccode\u003edplyr\u003c/code\u003e, \u003ccode\u003etidyr\u003c/code\u003e, etc) resulted in \u003ccode\u003eormr\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-about-the-logo\" class=\"anchor\" href=\"#what-about-the-logo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat about the logo?\u003c/h2\u003e\n\u003cp\u003eThe original snake image was found when searching for a\npublic domain image of a snake, using the following DuckDuckGo image seach:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttps://duckduckgo.com/?q=orm+.png\u0026amp;t=ffab\u0026amp;iar=images\u0026amp;iaf=license%3APublic%2Ctype%3Aclipart\u0026amp;iax=images\u0026amp;ia=images\u0026amp;iai=https%3A%2F%2Fcdn.pixabay.com%2Fphoto%2F2016%2F03%2F31%2F15%2F10%2Fcartoon-1293047_1280.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter that, the image was modified using KolourPaint and the R logo was added.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/search;query=ormr\" rel=\"nofollow\"\u003eFind the latest \u0027ormr\u0027 Singularity container\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-links\" class=\"anchor\" href=\"#links\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinks\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/reticulate_on_singularity\"\u003ehttps://github.com/richelbilderbeek/reticulate_on_singularity\u003c/a\u003e:\ndemo how to run \u003ccode\u003ereticulate\u003c/code\u003e within a Singularity container, without \u003ccode\u003eormr\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1605677713.0 + "updated_at": 1639766183.0 }, { "data_format": 2, - "description": "Centos 8 base image for Roar", + "description": "This is the repository for the workshop taught at ISPW 2022 in Sydney", "filenames": [ - "Singularity", - "Singularity.gpu" + "files/daskdev/Singularity.dask" ], - "full_name": "willgpaik/centos8_roar", + "full_name": "ardimirzaei/ispw2022-abm-workshop", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos8_roar\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos8_roar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos8_roar\u003c/h1\u003e\n\u003cp\u003e\u003cdel\u003eCentos\u003c/del\u003e Rocky Linux 8 base image for Roar\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-note\" class=\"anchor\" aria-hidden=\"true\" href=\"#note\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTE\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eThis recipe may include unnecessary packages for certain software installation\u003c/li\u003e\n\u003cli\u003eMore packages will be added in the future\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-updates\" class=\"anchor\" aria-hidden=\"true\" href=\"#updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUpdates\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e2020/11/13\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInitial recipe added\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e2021/03/22\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDefault Python3 is updated to Python 3.8\u003c/li\u003e\n\u003cli\u003eLapack, BLAS, OpenBLAS, ATLAS, and NetCDF are added\u003c/li\u003e\n\u003cli\u003eCMake 3.19.7, Boost 1.75.0, and R 4.0.4 are added\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e2022/10/31\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eImage changed from Centos 8 to Rocky Linux 8\u003c/li\u003e\n\u003cli\u003eDefault Python3 is updated to Python 3.9\u003c/li\u003e\n\u003cli\u003eCMake and R are removed due to later version can be installed from package repo\u003c/li\u003e\n\u003cli\u003eBoost is updated to 1.80.0\u003c/li\u003e\n\u003cli\u003e(Changes are applied to non-GPU version only)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ispw-2022-abm-workshop\" class=\"anchor\" href=\"#ispw-2022-abm-workshop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eISPW 2022 ABm Workshop\u003c/h1\u003e\n\u003cp\u003eForked from SIH\n--Update this readme.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1667245535.0 + "subscribers_count": 1, + "topics": [ + "abm", + "complex-systems", + "pharmacy", + "workshop" + ], + "updated_at": 1654581868.0 }, { "data_format": 2, - "description": "Learning temporal planning models", + "description": "Deplete Fastq files from human or other content", "filenames": [ - "planners/team1/src/Singularity" + "singularity/Singularity" ], - "full_name": "sjimenezgithub/tmodeling", + "full_name": "sequana/depletion", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tmodeling\" class=\"anchor\" aria-hidden=\"true\" href=\"#tmodeling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etmodeling\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1574164945.0 + "updated_at": 1648819859.0 }, { "data_format": 2, - "description": null, + "description": "Raw format to OME-TIFF converter.", "filenames": [ - "Singularity.td_base_ml" + "3.0.0/Singularity" ], - "full_name": "TurbulentDynamics/tdEnvSetup", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-turbulent-dynamics\" class=\"anchor\" aria-hidden=\"true\" href=\"#turbulent-dynamics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTurbulent Dynamics\u003c/h1\u003e\n\u003cp\u003eTurbulent Dynamics developes Maching Learning, MPI and iOS applications for both High Performance Computing (Supercomputing), edge devices (Xavier, Raspberry PI, MyriadX) and MacOS. Minimising system admin workload is not trivial so this guide was created to try setup a common dominator for all projects.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#Environment-setup\"\u003eEnvironment setup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Simple-Cluster-Diagnostics\"\u003eSimple Cluster Diagnostics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Coding-Guidelines-and-Visualisations\"\u003eCoding Guidelines and Visualisations\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-environment-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment setup\u003c/h1\u003e\n\u003cp\u003eTurbulent Dynamics developes Maching Learning, MPI and iOS applications for both High Performance Computing (Supercomputing) edge devices (Xavier, Raspberry PI, MyriadX) and MacOS. Minimising system admin workload is not trivial, as different devices require a different stack, especially edge devices, and sometimes sudo is not available (on HPC systems). This drives out environment and app choices.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eAvoid sudo installs by using Brew for basic tools.\u003c/li\u003e\n\u003cli\u003eAvoid sudo and allow multiple versions of apps using Spack (also compiles all dependencies giving performance advantages).\u003c/li\u003e\n\u003cli\u003eUse containers where possible (Edge devices struggle or are unable).\u003c/li\u003e\n\u003cli\u003eUse Python Venv, for ML Tensorflow and tools.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDevice\u003c/th\u003e\n\u003cth\u003eUse Case\u003c/th\u003e\n\u003cth\u003eNotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHPC System\u003c/td\u003e\n\u003ctd\u003eTraining ML and Large Scale MPI apps 100s nodes\u003c/td\u003e\n\u003ctd\u003eSudo not available\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWorkstations (with AMD GPU)\u003c/td\u003e\n\u003ctd\u003eTraining ML\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWorkstations (with Nvidia GPU)\u003c/td\u003e\n\u003ctd\u003eTraining ML, rebuilding Xavier/Nano and MPI app testing\u003c/td\u003e\n\u003ctd\u003eNvidia SDK limits to Ubuntu 18.04\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMacOS (AMD GPU)\u003c/td\u003e\n\u003ctd\u003eVisualisations in Metal and iOS apps\u003c/td\u003e\n\u003ctd\u003eDevelop in Swift\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA Xavier/Nano\u003c/td\u003e\n\u003ctd\u003eML Inferencing\u003c/td\u003e\n\u003ctd\u003eLimited to Cuda 10.0, Tensorflow 1.14\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMyriadX (Intel Compute Stick)\u003c/td\u003e\n\u003ctd\u003eML Inferencing\u003c/td\u003e\n\u003ctd\u003eOpenVINO limits to Ubuntu 16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRaspberry Pi\u003c/td\u003e\n\u003ctd\u003eML Inferencing\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_0_basics_and_brew.md\"\u003eInstall basics and brew on both MacOS and Linux\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_1_with_spack.md\"\u003eInstall spack and some applications\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_2_python_modules.md\"\u003eInstall python modules\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_3_OpenVINO_on_Ubuntu_16_04.md\"\u003eInstall OpenVINO on Ubuntu 16.04\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_3_nvidia_for_Ubuntu_18_04.md\"\u003eInstall Nvidia CUDA and tools on Ubuntu 18.04\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_4_nvidia_docker2_base_ml_container.md\"\u003eInstall docker, nvidia-docker2 and run TD_Base_Nvidia_ML container\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_5_singularity.md.md\"\u003eInstall singularity and run TD_Base_Nvidia_ML container\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_6_optional_apps.md\"\u003eOptional Apps\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/spack_swift_package.py\"\u003e(WIP) Use Spack to install Swift\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/swift_for_ubuntu.md\"\u003e(WIP) Install Swift on Ubuntu\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-simple-cluster-diagnostics\" class=\"anchor\" aria-hidden=\"true\" href=\"#simple-cluster-diagnostics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimple Cluster Diagnostics\u003c/h1\u003e\n\u003cp\u003eSimple utility to check if OpenMP, MPI and cuda are working as expected.\n\u003ca href=\"diagnostics_hello_world_mpi_openmp_gpu/README.md\"\u003eDiagnostics OpenMP, MPI, GPU\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-coding-guidelines-and-visualisations\" class=\"anchor\" aria-hidden=\"true\" href=\"#coding-guidelines-and-visualisations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCoding Guidelines and Visualisations\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"dev_info/index.md\"\u003eCoding guidelines\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://turbulentdynamics.github.io/tdEnvSetup/graphics/arrows.html\" rel=\"nofollow\"\u003eVector Identifiers\u003c/a\u003e The vectors are numbered differently than usual LBM implementations\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://turbulentdynamics.github.io/tdEnvSetup/graphics/cube.html\" rel=\"nofollow\"\u003eItem Identifiers\u003c/a\u003e The cells in the outer shell of the lattice grid has been given an identification\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://turbulentdynamics.github.io/tdEnvSetup/graphics/1000.html\" rel=\"nofollow\"\u003eVisualisation 1000 cubes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "pscedu/singularity-raw2ometiff", + "latest_release": "v3.0.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-raw2ometiff/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-raw2ometiff/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-raw2ometiff/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-raw2ometiff/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8d0d22e8f29e65a71263e2fb6bce4826c7ce3f09aed55e6dbde2928785d212f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8d0d22e8f29e65a71263e2fb6bce4826c7ce3f09aed55e6dbde2928785d212f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-raw2ometiff\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7822cb72c205ba7e407c8e3c73f1753c3051e55c850a256628b591b7640cb064/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7822cb72c205ba7e407c8e3c73f1753c3051e55c850a256628b591b7640cb064/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-raw2ometiff\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ee493a8b01574ccdadc2be17adf48265c4d4d5b8d4dd7528eebab32317323e41/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ee493a8b01574ccdadc2be17adf48265c4d4d5b8d4dd7528eebab32317323e41/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-raw2ometiff\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/48898dd432d34088bdf217be2af09312648ecdf47ab776fb4de050c5338365bd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/48898dd432d34088bdf217be2af09312648ecdf47ab776fb4de050c5338365bd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-raw2ometiff\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-raw2ometiff\" class=\"anchor\" href=\"#singularity-raw2ometiff\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-raw2ometiff\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/d6ac5fb6156e1b197fd547d7632a7b90cb6aac15112aff196834a376c3065baf/68747470733a2f2f7777772e676c656e636f65736f6674776172652e636f6d2f696d672f6c6f676f2e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d6ac5fb6156e1b197fd547d7632a7b90cb6aac15112aff196834a376c3065baf/68747470733a2f2f7777772e676c656e636f65736f6674776172652e636f6d2f696d672f6c6f676f2e737667\" width=\"25%\" data-canonical-src=\"https://www.glencoesoftware.com/img/logo.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/glencoesoftware/raw2ometiff\"\u003eraw2ometiff\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eraw2ometiff\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/raw2ometiff/3.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/raw2ometiff\u003c/code\u003e as \u003ccode\u003e3.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, - "topics": [], - "updated_at": 1635665969.0 + "topics": [ + "singularity", + "utilities", + "image-processing" + ], + "updated_at": 1633063422.0 + }, + { + "data_format": 2, + "description": "A command-line benchmarking tool.", + "filenames": [ + "1.13.0/Singularity", + "1.11.0/Singularity" + ], + "full_name": "pscedu/singularity-hyperfine", + "latest_release": "v1.11.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hyperfine\" class=\"anchor\" href=\"#singularity-hyperfine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hyperfine\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/hyperfine\"\u003ehyperfine\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehyperfine\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hyperfine/1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hyperfine\u003c/code\u003e as \u003ccode\u003e1.11.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 2, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1649205323.0 + }, + { + "data_format": 2, + "description": "BLAST finds regions of similarity between biological sequences.", + "filenames": [ + "2.13.0/Singularity", + "2.11.0/Singularity", + "2.9.0/Singularity" + ], + "full_name": "pscedu/singularity-blast", + "latest_release": "v2.13.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-blast/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-blast/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/34775a544d028af1a76f53887556e0b47d8a40289aa78e79056c5e13dcbc48e4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626c617374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34775a544d028af1a76f53887556e0b47d8a40289aa78e79056c5e13dcbc48e4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626c617374\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-blast\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e33eb8d62dc7df0e7a911833138fefaed18e36044c13f7a3aa53c104c1ffc719/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626c617374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e33eb8d62dc7df0e7a911833138fefaed18e36044c13f7a3aa53c104c1ffc719/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626c617374\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-blast\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c141231c49671d4a45e13d6d79f61b30ec62be1462b950fac124cfb21cc2d206/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626c617374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c141231c49671d4a45e13d6d79f61b30ec62be1462b950fac124cfb21cc2d206/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626c617374\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-blast\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0ec9871992e72eb86a829ac86878026892749bddbb4623030eb745093184def6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626c617374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0ec9871992e72eb86a829ac86878026892749bddbb4623030eb745093184def6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626c617374\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-blast\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-blast\" class=\"anchor\" href=\"#singularity-blast\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-blast\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the other scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/blast/2.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/blast\u003c/code\u003e as \u003ccode\u003e2.11.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 4, + "topics": [ + "bioinformatics", + "singularity" + ], + "updated_at": 1636731786.0 }, { "data_format": 2, "description": null, "filenames": [ - "external/oskar/singularity/Singularity.base-dep", - "external/oskar/singularity/Singularity.python3" + "Singularity" ], - "full_name": "kernsuite-debian/everybeam", + "full_name": "khourhin/uber_container", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-everybeam-library\" class=\"anchor\" aria-hidden=\"true\" href=\"#everybeam-library\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEveryBeam library\u003c/h1\u003e\n\u003cp\u003eThis package can be used to compute the beam response for a variety of\nradio telescopes, i.e.:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLOFAR\u003c/li\u003e\n\u003cli\u003eOSKAR\u003c/li\u003e\n\u003cli\u003eMWA\u003c/li\u003e\n\u003cli\u003eVLA\u003c/li\u003e\n\u003cli\u003eATCA\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis package also provides an abstract interface to a selection of beam responses for apperture arrays (LOFAR/OSKAR), and beamformed versions thereof. Currently implemented are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHamaker LOFAR model\u003c/li\u003e\n\u003cli\u003eOSKAR spherical wave model\u003c/li\u003e\n\u003cli\u003eOSKAR-dipole: work in progress\u003c/li\u003e\n\u003cli\u003eLOBEs: work in progress. A coefficient file is currently only available for a limited number of LOFAR stations. Selecting the LOBEs model defaults back to Hamaker, in case no coefficient file is available.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEveryBeam replaces the stand alone version of the LOFAR station response library (LOFARBeam).\u003c/p\u003e\n\u003cp\u003eEveryBeam is licensed under the terms of the GNU GPL3 license.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation-and-installation-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation-and-installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation and Installation Instructions\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.astron.nl/citt/EveryBeam/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e along with \u003ca href=\"https://www.astron.nl/citt/EveryBeam/build-instructions.html\" rel=\"nofollow\"\u003einstallation instructions\u003c/a\u003e can be found at the provided links.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-dp3\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-dp3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with DP3\u003c/h2\u003e\n\u003cp\u003eTo use Everybeam within \u003ca href=\"https://git.astron.nl/RD/DP3\" rel=\"nofollow\"\u003eDP3\u003c/a\u003e - the streaming visibility framework - DP3 needs to be compiled against EveryBeam. To do so, make sure DP3 can find EveryBeam by adding the EveryBeam install dir to the \u003ccode\u003eCMAKE_PREFIX_PATH\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA test measurement set is included in DP3 (\u003ccode\u003etNDP3-generic.in_MS.tgz\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eTo simulate visibilities with a certain element model, use \u003ccode\u003eDP3 DP3.parset\u003c/code\u003e with \u003ccode\u003eDP3.parset\u003c/code\u003e a parset file with the following contents:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emsin=tNDP3-generic.MS\nmsout=.\nsteps=[predict]\npredict.usebeammodel=True\npredict.elementmodel=oskardipole\npredict.sourcedb=tNDP3-generic.MS/sky # sourcedb file\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-wsclean\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-wsclean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with WSClean\u003c/h2\u003e\n\u003cp\u003eTo use EveryBeam with \u003ca href=\"https://gitlab.com/aroffringa/wsclean\" rel=\"nofollow\"\u003eWSClean\u003c/a\u003e (for A-term or primary beam corrections), WSClean needs to be compiled against EveryBeam. In order to do so, make sure WSClean can find EveryBeam by adding the EveryBeam install dir to the \u003ccode\u003eCMAKE_PREFIX_PATH\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1663586637.0 + "updated_at": 1653684576.0 }, { "data_format": 2, - "description": null, + "description": "Bio-Formats image file format to raw format converter.", + "filenames": [ + "0.3.0/Singularity" + ], + "full_name": "pscedu/singularity-bioformats2raw", + "latest_release": "v3.0.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bioformats2raw/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bioformats2raw/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-bioformats2raw/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bioformats2raw/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/393a92fbeeb7f1545c3869f957dc859760052e0eff8eeb8f95b409800de2d2b2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/393a92fbeeb7f1545c3869f957dc859760052e0eff8eeb8f95b409800de2d2b2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bioformats2raw\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/444f4d60540e76ad5618bb8884c8f9d5a6a1f61fcf2a94363f379f6c6b074c5c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/444f4d60540e76ad5618bb8884c8f9d5a6a1f61fcf2a94363f379f6c6b074c5c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bioformats2raw\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/91fa772cacd77c7d5f540224712927de4888ac6dc960cd56dc1d23edeac9a1b9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/91fa772cacd77c7d5f540224712927de4888ac6dc960cd56dc1d23edeac9a1b9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bioformats2raw\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f6dea7ba6ee602447c05fc1a8d8498d7d44f22ad6f72fb6ad78b424301cd6d14/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f6dea7ba6ee602447c05fc1a8d8498d7d44f22ad6f72fb6ad78b424301cd6d14/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bioformats2raw\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bioformats2raw\" class=\"anchor\" href=\"#singularity-bioformats2raw\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bioformats2raw\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/d6ac5fb6156e1b197fd547d7632a7b90cb6aac15112aff196834a376c3065baf/68747470733a2f2f7777772e676c656e636f65736f6674776172652e636f6d2f696d672f6c6f676f2e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d6ac5fb6156e1b197fd547d7632a7b90cb6aac15112aff196834a376c3065baf/68747470733a2f2f7777772e676c656e636f65736f6674776172652e636f6d2f696d672f6c6f676f2e737667\" width=\"25%\" data-canonical-src=\"https://www.glencoesoftware.com/img/logo.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/glencoesoftware/bioformats2raw\"\u003ebioformats2raw\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebioformats2raw\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bioformats2raw/3.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bioformats2raw\u003c/code\u003e as \u003ccode\u003e3.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 3, + "topics": [ + "singularity", + "utilities", + "image-processing" + ], + "updated_at": 1649185211.0 + }, + { + "data_format": 2, + "description": "singularity container", "filenames": [ + "Singularity.salad", "Singularity", - "model_preprocess/Singularity" + "Singularity.pokemon" ], - "full_name": "lsx1980/3D_model_reconstruction", + "full_name": "dcasciotti/alexrequest", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-3d-root-model-reconstruction\" class=\"anchor\" aria-hidden=\"true\" href=\"#3d-root-model-reconstruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3D root model reconstruction\u003c/h1\u003e\n\u003cp\u003eThe software package was integrated as a module at PlantIT website at : \u003ca href=\"https://portnoy.cyverse.org/\" rel=\"nofollow\"\u003ehttps://portnoy.cyverse.org/\u003c/a\u003e.\n(Collaborate with Cyverse \u003ca href=\"https://www.cyverse.org/\" rel=\"nofollow\"\u003ehttps://www.cyverse.org/\u003c/a\u003e ) . Users are welcomed to registered as an user to try this package via PlantIT website.\u003c/p\u003e\n\u003cp\u003eThe software package was also available at Dockerhub (\u003ca href=\"https://hub.docker.com/r/computationalplantscience/3d-model-reconstruction\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/computationalplantscience/3d-model-reconstruction\u003c/a\u003e) for advanced users to run locally via singularity at Linux environment:\u003c/p\u003e\n\u003cp\u003eThis software can be run by docker container, users do not need to install many libraries and compile complex source files.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup-docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Docker container\u003c/h1\u003e\n\u003cp\u003eOS requirements\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTo install Docker container (https://docs.docker.com/engine/install/ubuntu/): \n\nTo install Docker Engine, you need the 64-bit version of one of these Ubuntu versions:\n\nUbuntu Groovy 20.10\nUbuntu Focal 20.04 (LTS)\nUbuntu Bionic 18.04 (LTS)\nUbuntu Xenial 16.04 (LTS)\n\nDocker Engine is supported on x86_64 (or amd64), armhf, and arm64 architectures.\n\nUninstall old versions\n$ sudo apt-get remove docker docker-engine docker.io containerd runc\n\nSet up the repository\n\nUpdate the apt package index and install packages to allow apt to use a repository over HTTPS:\n\n$ sudo apt-get update\n\n$ sudo apt-get install \\\n apt-transport-https \\\n ca-certificates \\\n curl \\\n gnupg-agent \\\n software-properties-common\n\nAdd Docker\u2019s official GPG key:\n\n$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -\n\nVerify that you now have the key with the fingerprint 9DC8 5822 9FC7 DD38 854A E2D8 8D81 803C 0EBF CD88, by searching for the last 8 characters of the fingerprint.\n\n$ sudo apt-key fingerprint 0EBFCD88\n\npub rsa4096 2017-02-22 [SCEA]\n 9DC8 5822 9FC7 DD38 854A E2D8 8D81 803C 0EBF CD88\nuid [ unknown] Docker Release (CE deb) \u0026lt;docker@docker.com\u0026gt;\nsub rsa4096 2017-02-22 [S]\n\n$ sudo add-apt-repository \\\n \"deb [arch=amd64] https://download.docker.com/linux/ubuntu \\\n $(lsb_release -cs) \\\n stable\"\n\nUpdate the apt package index, and install the latest version of Docker Engine and containerd, or go to the next step to install a specific version:\n\n$ sudo apt-get update\n$ sudo apt-get install docker-ce docker-ce-cli containerd.io\n\nVerify that Docker Engine is installed correctly by running the hello-world image.\n\n$ sudo docker run hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-this-container-by-building-it-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-this-container-by-building-it-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun this container by building it locally:\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e# Clone source code to your local path\n$ git clone https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo.git\n\n# Enter into the source code folder named as \"cd 3D_model_reconstruction_demo\"\n$ cd 3D_model_reconstruction_demo/\n\n# Build docker container locally named as \"3d_model_reconstruction\" using \"Dockerfile\" in the same folder, note: docker repository name must be lowercase.\n$ docker build -t 3d_model_reconstruction -f Dockerfile .\n\n# Run the docker container by linking docker container data path to user\u0027s image data folder local path\n# Note: please replace $path_to_image_folder as your local image data folder path, \n# Suggest to check your image folder path using \"pwd\" command\n# Example: $ docker run -v /home/suxing/example/root_images:/images -it 3d_model_reconstruction\n\n$ docker run -v /$path_to_image_folder:/images -it 3d_model_reconstruction\n\n# After launch the docker container, run \"pipeline.sh\" or \"pipeline.sh\" insider the container\n$ root@0529cde0b988:/opt/code# ./pipeline.sh\nor $ root@0529cde0b988:/opt/code# python3 pipeline.py\n\n# Get 3d model result named as \"dense.0.ply\"\n# After the container was executed successfully with image data files, user should be able to see output in your command window like this:\n\u0027\u0027\u0027\nLoading option-0000.ply, 48656 vertices ...\nSave to /images/dense.nvm ... done\nSave /images/dense.0.ply ...done\n----------------------------------------------------------------\n\u0027\u0027\u0027\nThe 3D model file was in ply format(https://en.wikipedia.org/wiki/PLY_(file_format)), it is located inside your image folder, its name is \"dense.0.ply\".\npath = \"/$path_to_image_folder/dense.0.ply\"\n\nTo visualize the 3d model file, suggest to install Meshlab(https://www.meshlab.net/) or cloudcompare(https://www.danielgm.net/cc/)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-hidden=\"true\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthor\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esuxing liu(suxingliu@gmail.com)\nWesley Paul Bonelli(wbonelli@uga.edu)\n\nReference:\nVisualSFM\n[Anders Damsgaard](mailto:adamsgaard@ucsd.edu) with contributions by Caleb Adams and Connor P Doherty.\nChangchang Wu ( wucc1130@gmail.com )\n+ Structure from Motion\n[1] Changchang Wu, \"Towards Linear-time Incremental Structure From Motion\", 3DV 2013\n[2] Changchang Wu, \"VisualSFM: A Visual Structure from Motion System\", http://ccwu.me/vsfm/, 2011\n+ Bundle Adjustment\n[3] Changchang Wu, Sameer Agarwal, Brian Curless, and Steven M. Seitz, \"Multicore Bundle Adjustment\", CVPR 2011 \n+ Feature Detection\n[4] Changchang Wu, \"SiftGPU: A GPU implementation of Scale Invaraint Feature Transform (SIFT)\", http://cs.unc.edu/~ccwu/siftgpu, 2007\n\nCOLMAP\nhttps://colmap.github.io\nAuthor: Johannes L. Schoenberger (jsch-at-demuc-dot-de)\n@inproceedings{schoenberger2016sfm,\n author={Sch\\\"{o}nberger, Johannes Lutz and Frahm, Jan-Michael},\n title={Structure-from-Motion Revisited},\n booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},\n year={2016},\n}\n\n@inproceedings{schoenberger2016mvs,\n author={Sch\\\"{o}nberger, Johannes Lutz and Zheng, Enliang and Pollefeys, Marc and Frahm, Jan-Michael},\n title={Pixelwise View Selection for Unstructured Multi-View Stereo},\n booktitle={European Conference on Computer Vision (ECCV)},\n year={2016},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDocker container was maintained by Wesley Paul Bonelli. it was deployed to Plant IT website by Wesley Paul Bonelli (\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eSingularity container overlay issues were solved by [Saravanaraj Ayyampalayam] (\u003ca href=\"https://github.com/raj76\"\u003ehttps://github.com/raj76\u003c/a\u003e) (\u003ca href=\"mailto:raj76@uga.edu\"\u003emailto:raj76@uga.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eSpecial thanks to Chris Cotter building the container recipe for testing and debugging.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-hidden=\"true\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTodo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGPU cuda version container\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eGNU Public License\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" href=\"#singularity-deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"img/shpc.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" href=\"#1-template-or-fork\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Template or Fork\u003c/h3\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" href=\"#1-write-your-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" href=\"#2-update-the-version-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Update the VERSION file\u003c/h3\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/releases.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" href=\"#3-how-to-develop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How to Develop\u003c/h3\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" href=\"#4-how-to-pull\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. How to pull\u003c/h3\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1614652430.0 + "updated_at": 1652978087.0 }, { "data_format": 2, - "description": null, + "description": "Recipe files used to compile SLURM (https://github.com/SchedMD/slurm) in powerPlant", "filenames": [ - "envs/illumina/Singularity" + "21.08.8-2/centos7/singularity/Singularity.21.08.8-2-make", + "21.08.8-2/centos7/singularity/Singularity.21.08.8-2-rpm" ], - "full_name": "here0009/SARS-Cov2_Snakemake_Pipeline", + "full_name": "powerPlant/slurm-build", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sarscov2_snakemake_pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#sarscov2_snakemake_pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSarsCov2_Snakemake_Pipeline\u003c/h1\u003e\n\u003cp\u003eThis is a snakemake pipeline used for analyse SarsCov2 sequence data generated by illumina machine.\nThis pipelien was based on \u003ca href=\"https://github.com/artic-network/fieldbioinformatics\"\u003eARTIC network\u0027s fieldbioinformatics tools\u003c/a\u003e, \u003ca href=\"https://github.com/dridk/Sars-CoV-2-NGS-pipeline\"\u003eSars-CoV-2-NGS-pipeline\u003c/a\u003e and \u003ca href=\"https://github.com/connor-lab/ncov2019-artic-nf\"\u003encov2019-artic-nf\u003c/a\u003e with some updates:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003efastqc\u003c/code\u003e and was used to generate the qc report of input data.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003equast\u003c/code\u003e was used to generate the sequence assembly report.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/cov-lineages/pangolin\"\u003epangolin\u003c/a\u003e was used for the typing of SarsCov-2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCorGat\u003c/code\u003e was used to annotate the sequence, and generate alle frequency reports\nYou need to clone \u003ca href=\"https://github.com/matteo14c/CorGAT\"\u003eCorGat\u003c/a\u003e and specify the directory in the config files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emultiqc\u003c/code\u003e was used to generate the final report.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe workflow shows like below:\u003c/p\u003e\n\u003cp\u003eA test_data file was provided to test the pipeline.\nYou may test the pipeline by dry-run\n\u003ccode\u003esnakemake -s sars2.smk -n\u003c/code\u003e\nthen run the pipeline:\n\u003ccode\u003esnakemake -s sars2.smk -j 4 --use-conda\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWARNING - THIS REPO IS UNDER ACTIVE DEVELOPMENT AND ITS BEHAVIOUR MAY CHANGE AT \u003cstrong\u003eANY\u003c/strong\u003e TIME.\u003c/p\u003e\n\u003cp\u003ePLEASE ENSURE THAT YOU READ BOTH THE README AND THE CONFIG FILE AND UNDERSTAND THE EFFECT OF THE OPTIONS ON YOUR DATA!\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1622116428.0 + "updated_at": 1652918976.0 }, { "data_format": 2, - "description": "A Singularity File for Running Trinity on the HPCC", + "description": "VNC Server in a Singularity container", "filenames": [ - "Singularity" + "Singularity", + "Singularity.2.1.2" ], - "full_name": "msuefishlab/trinity_singularity", + "full_name": "nickjer/singularity-vncserver", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-vnc-server\" class=\"anchor\" href=\"#singularity-vnc-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity VNC Server\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/603\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://turbovnc.org/\" rel=\"nofollow\"\u003eTurboVNC\u003c/a\u003e with the inclusion of \u003ca href=\"https://github.com/novnc/websockify/\"\u003ewebsockify\u003c/a\u003e for\nconnecting to the VNC server from within your browser using \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build\" class=\"anchor\" href=\"#build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-vncserver.simg\u003c/code\u003e\nwith:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-vncserver.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-deploy\" class=\"anchor\" href=\"#deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploy\u003c/h2\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name singularity-vncserver.simg shub://nickjer/singularity-vncserver\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run\" class=\"anchor\" href=\"#run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-vncserver\" class=\"anchor\" href=\"#vncserver\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evncserver\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003evncserver\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-vncserver.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app vncserver singularity-vncserver.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app vncserver singularity-vncserver.simg\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eYou will require a password to access your desktops.\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003ePassword:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eVerify:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eWould you like to enter a view-only password (y/n)? n\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eDesktop \u0027TurboVNC: dev:1 (nickjer)\u0027 started on display dev:1\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eCreating default startup script /home/nickjer/.vnc/xstartup.turbovnc\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eStarting applications specified in /home/nickjer/.vnc/xstartup.turbovnc\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eLog file is /home/nickjer/.vnc/dev:1.log\u003c/span\u003e\n\n$ \u003cspan class=\"pl-s1\"\u003esingularity run --app vncserver singularity-vncserver.simg -kill :1\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eKilling Xvnc process ID 9738\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-vncpasswd\" class=\"anchor\" href=\"#vncpasswd\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evncpasswd\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003evncpasswd\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app vncpasswd singularity-vncserver.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emypassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e singularity run --app vncpasswd singularity-vncserver.simg -f \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e vnc_passwd\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eWarning: password truncated to the length of 8.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-websockify\" class=\"anchor\" href=\"#websockify\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewebsockify\u003c/h3\u003e\n\u003cp\u003eIn some cases you may not want to download and install a VNC client on your\nlocal machine. In those cases you can actually use the \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e client which\nruns completely in your browser.\u003c/p\u003e\n\u003cp\u003eIn order to connect to the VNC server with \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e you will need to enable\n\u003ca href=\"https://github.com/novnc/websockify/\"\u003ewebsockify\u003c/a\u003e which will translate the incoming websocket traffic from \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e\nto normal TCP traffic proxied to the listening VNC server.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ewebsockify\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app websockify singularity-vncserver.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAssuming you started a \u003ccode\u003evncserver\u003c/code\u003e above listening on port \u003ccode\u003e5901\u003c/code\u003e (display port\n\u003ccode\u003e:1\u003c/code\u003e), you will launch \u003ccode\u003ewebsockify\u003c/code\u003e on the same machine with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app websockify singularity-vncserver.simg 8000 localhost:5901\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eWebSocket server settings:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e - Listen on :8000\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e - No SSL/TLS support (no cert file)\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e - proxying from :8000 to localhost:5901\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen from your browser using the \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e client, connect to the machine running\nthe VNC server and port \u003ccode\u003e8000\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIt is recommended you either setup SSL for a secure connection or host it\nfrom behind a reverse proxy with SSL already enabled.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-vncserver\"\u003ehttps://github.com/nickjer/singularity-vncserver\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1528136809.0 + "updated_at": 1581617600.0 }, { "data_format": 2, - "description": "Singularity recipe files for winnowmap (https://github.com/marbl/Winnowmap)", + "description": null, "filenames": [ - "Singularity.2.0.0" + "Singularity.v8", + "Singularity.v4", + "Singularity.v2", + "Singularity.v6", + "Singularity.v3", + "Singularity.va", + "Singularity.v5", + "Singularity.v1", + "Singularity.v9", + "Singularity.v7" ], - "full_name": "powerPlant/winnowmap-srf", + "full_name": "sternacht/tf_singu", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for Winnowmap, a long-read mapping algorithm optimized for mapping ONT and PacBio reads to repetitive reference sequences.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/marbl/Winnowmap\"\u003ehttps://github.com/marbl/Winnowmap\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1615953867.0 + "updated_at": 1560171965.0 }, { "data_format": 2, - "description": "Docker image to get DeepLabCutCore running on cloud GPUs.", + "description": null, "filenames": [ - "Singularity" + "RNAja/envs/Singularity.RNAja.def" ], - "full_name": "bchaselab/DeepLabCut-HPC", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker/badge.svg\"\u003e\u003cimg src=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker%20Image%20CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker%20Image%20CI/badge.svg\" alt=\"Docker Image CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/76a9b0a79aec38ba07bbf90a456c47bbdbca1fd005e919d62ec4fbc0cb2719d4/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6663617475732f646565706c6162637574\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/76a9b0a79aec38ba07bbf90a456c47bbdbca1fd005e919d62ec4fbc0cb2719d4/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6663617475732f646565706c6162637574\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/fcatus/deeplabcut\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/62efa830e023afaea0c6e665046187228790fcc4c07825b174254f2d1694a96d/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6663617475732f646565706c6162637574\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/62efa830e023afaea0c6e665046187228790fcc4c07825b174254f2d1694a96d/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6663617475732f646565706c6162637574\" alt=\"Docker Image Size (latest by date)\" data-canonical-src=\"https://img.shields.io/docker/image-size/fcatus/deeplabcut\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-from-dockerhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#from-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFrom \u003ca href=\"https://hub.docker.com/repository/docker/fcatus/deeplabcut\" rel=\"nofollow\"\u003eDockerhub\u003c/a\u003e\n\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker pull fcatus/deeplabcut:latest\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-use-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-use-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Use With Singularity\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull docker://fcatus/deeplabcut:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor build it from a singularity file\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ vim singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eBootstrap\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edocker\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eFrom\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efcatus/deeplabcut:latest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --remote deeplabcut.sif singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-from-a-singularity-definition-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-a-singularity-definition-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild From a Singularity \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/definition_files.html\" rel=\"nofollow\"\u003eDefinition File\u003c/a\u003e\n\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the definition file\u003c/span\u003e\n$ wget https://git.io/JJvBb\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Customize the definition file (optional)\u003c/span\u003e\n$ vim dlc.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build remotely from the definition file\u003c/span\u003e\n$ singularity build --remote deeplabcut.sif dlc.def\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more information about using \u003ccode\u003esingularity build\u003c/code\u003e, see \u003ca href=\"https://sylabs.io/guides/3.1/user-guide/cli/singularity_build.html\" rel=\"nofollow\"\u003eSingularity Build\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "Aucomte/RNAja", + "latest_release": "0.1.0", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "docker", - "deeplabcut", - "clone", - "slurm", - "hpc", - "singularity" - ], - "updated_at": 1617137580.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1646051177.0 }, { "data_format": 2, @@ -8495,94 +7971,148 @@ var data = "filenames": [ "Singularity" ], - "full_name": "monaghaa/mytranslator", + "full_name": "talha-naveed97/orion_test", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1638480957.0 + "updated_at": 1646090180.0 }, { "data_format": 2, - "description": "Singularity recipe for Pathway-Tools and mpwt.", + "description": "Some util functions for machine learning experiments", "filenames": [ "Singularity" ], - "full_name": "ArnaudBelcour/mpwt-singularity", + "full_name": "martinmamql/mini-tool-box", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mini-tool-box\" class=\"anchor\" href=\"#mini-tool-box\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emini-tool-box\u003c/h1\u003e\n\u003cp\u003eSome util functions for machine learning experiments\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, + "topics": [], + "updated_at": 1642610373.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "Singularity" + ], + "full_name": "raveancic/scRNAaltas_TNBC_mm", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-scrnaaltas_tnbc_mm\" class=\"anchor\" href=\"#scrnaaltas_tnbc_mm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escRNAaltas_TNBC_mm\u003c/h1\u003e\n\u003cp\u003eA pipeline for the scRNAseq data analysis of TNBC mouse model\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/raveancic/scRNAaltas_TNBC_mm/tree/master/cl_crt_FASTQ2countmat\"\u003eStep\u003c/a\u003e - Create the count matrix/bam file from FASTQ files. (sankemake pipeline - singularity container - PBS cluster). This step is the one published in \u003ca href=\"https://www.nature.com/articles/s41420-022-00893-x\" rel=\"nofollow\"\u003eCarpen et al., 2022, Cell Death Discovery\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "stargazers_count": 0, + "subscribers_count": 3, "topics": [ - "pathway-tools" + "scrna-seq-analysis", + "snakemake", + "snakemake-pipeline", + "cellranger", + "singularity", + "scrna", + "pbs" ], - "updated_at": 1643893785.0 + "updated_at": 1646907794.0 }, { "data_format": 2, - "description": "If you are going to build off of basic Empirical, this is the project for you", + "description": "Repository for automatic software installation with a Singularity container containing EasyBuild. ", "filenames": [ - "third-party/force-cover/Singularity" + "scripts/Singularity.eb-4.5.0-Lmod-ubuntu20-LTR", + "scripts/Singularity.eb-4.5.0-Lmod-rocky8", + "scripts/Singularity.eb-4.4.2-Lmod-ubuntu20-LTR", + "scripts-23-01-2022/Singularity.eb-4.5.0-Lmod-ubuntu20-LTR", + "scripts-23-01-2022/Singularity.eb-4.5.0-Lmod-rocky8", + "scripts-23-01-2022/Singularity.eb-4.4.2-Lmod-ubuntu20-LTR", + "scripts-combined/easybuild/Singularity.eb-4.5.0-Lmod-ubuntu20-LTR", + "scripts-combined/easybuild/Singularity.eb-4.5.0-Lmod-rocky8", + "scripts-combined/easybuild/Singularity.eb-4.4.2-Lmod-ubuntu20-LTR" ], - "full_name": "EGBWright/ArbitriumSimulation", + "full_name": "sassy-crick/software-installation", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-evolutionary-algorithm\" class=\"anchor\" aria-hidden=\"true\" href=\"#evolutionary-algorithm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvolutionary Algorithm\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/anyaevostinar/evo-algo/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff63e2cc80517f7b8e8246b33025f569d757ede0ae65c7ea57418d79e5a3709d/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f25324676657273696f6e2d62616467652e6a736f6e\" alt=\"version\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fversion-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2bde10efc09d993aad000b3101be56d5410d0ced348c4c7bbedbc7ffce79d630/68747470733a2f2f696d672e736869656c64732e696f2f7472617669732f616e796165766f7374696e61722f65766f2d616c676f2e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/travis/anyaevostinar/evo-algo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://evo-algo.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2af827df5df15ff6f5c6a9fff5021e631a0049aa2274f98e983135bb66f0ed81/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f65766f2d616c676f2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/evo-algo/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://evo-algo.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e035446b6d1c7a911bd4b203bd581f2116c7873352a90d4797dc08119abbd0e/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f253246646f63756d656e746174696f6e2d636f7665726167652d62616467652e6a736f6e\" alt=\"documentation coverage\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fdocumentation-coverage-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869814fe0b90d0baadc37140ac31d6d9c0e4e00c2854a51f1030c40678bc13a4/68747470733a2f2f636f6465636f762e696f2f67682f616e796165766f7374696e61722f65766f2d616c676f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"code coverage status\" data-canonical-src=\"https://codecov.io/gh/anyaevostinar/evo-algo/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo/search?q=todo+OR+fixme\u0026amp;type=\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4455f1d1625d643fe17506cb6ad50a9a6612ce62ab4667dc60b75616241c7534/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e636f6d25324665766f2d616c676f253246646f746f2d62616467652e6a736f6e\" alt=\"dotos\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.com%2Fevo-algo%2Fdoto-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db51ac0d7785eb561dcfc0cbccfc0cfed9872513120f8f732b1301504c4eb32e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f616e796165766f7374696e61722f65766f2d616c676f2e7376673f7374796c653d666c61742d737175617265266c6f676f3d676974687562266c6162656c3d5374617273266c6f676f436f6c6f723d7768697465\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/anyaevostinar/evo-algo.svg?style=flat-square\u0026amp;logo=github\u0026amp;label=Stars\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn evolutionary algorithm\u003c/p\u003e\n\u003cp\u003eCheck out the live in-browser web app at \u003ca href=\"https://anyaevostinar.github.io/evo-algo\" rel=\"nofollow\"\u003ehttps://anyaevostinar.github.io/evo-algo\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: MIT license\u003c/li\u003e\n\u003cli\u003eDocumentation: \u003ca href=\"https://evo-algo.readthedocs.io\" rel=\"nofollow\"\u003ehttps://evo-algo.readthedocs.io\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/assets/cookie.gif\"\u003e\u003cimg src=\"docs/assets/cookie.gif\" alt=\"cookie monster example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThis package was created with \u003ca href=\"https://github.com/audreyr/cookiecutter\"\u003eCookiecutter\u003c/a\u003e and the \u003ca href=\"https://github.com/devosoft/cookiecutter-empirical-project\"\u003edevosoft/cookiecutter-empirical-project\u003c/a\u003e project template.\u003c/p\u003e\n\u003cp\u003eThis package uses \u003ca href=\"https://github.com/devosoft/Empirical#readme\"\u003eEmpirical\u003c/a\u003e, a library of tools for scientific software development, with emphasis on also being able to build web interfaces using Emscripten.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eTo install \u003ca href=\"https://github.com/devosoft/Empirical\"\u003eEmpirical\u003c/a\u003e, pull down a clone of the Empirical repository. See \u003ca href=\"https://empirical.readthedocs.io/en/latest/QuickStartGuides\" rel=\"nofollow\"\u003eQuick Start Guides\u003c/a\u003e for directions on cloning and using the library.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-automatic-software-installation-script\" class=\"anchor\" href=\"#automatic-software-installation-script\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatic software installation script\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction:\u003c/h2\u003e\n\u003cp\u003eThe aim of the script is to install the software inside a container, and thus the so installed software is independent from the OS as much as possible, and also takes care of different architectures. The idea comes from the EESSI project and how the software is installed in there. So kudos to them!!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to\" class=\"anchor\" href=\"#how-to\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow-to:\u003c/h2\u003e\n\u003cp\u003eBefore the script can run, there are a few files which need to be adjusted.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003einstall.sh\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esoftwarelist.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esoftwarelist.yaml\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003einstall.sh\u003c/code\u003e does basically the whole magic. There are a few lines at the top which need to be changed to reflect where the software needs to go. The most important are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSOFTWARE_INSTDIR\u003c/code\u003e which is where the software tree and all the helper stuff lives\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBINDDIR\u003c/code\u003e is the directory which needs to be bound inside the container as per default Singularity does only mount \u003ccode\u003e/tmp\u003c/code\u003e and \u003ccode\u003e/home\u003c/code\u003e it seems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou also might want to look at:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINER_VERSION\u003c/code\u003e which is the name of the sif-file, i.e. the container\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eEB_VERSION\u003c/code\u003e which is the version of EasyBuild to be used for building software. If that does not exist, it should be automatically installed\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSW_LIST\u003c/code\u003e contains a simple list of the EasyConfig files to be installed. All in one line with a blank between them.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSW_YAML\u003c/code\u003econtains the software to be installed as an EasyStack file in \u003ccode\u003eyaml\u003c/code\u003e format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth the \u003ccode\u003eSW_LIST\u003c/code\u003e and the \u003ccode\u003eSW_YAML\u003c/code\u003e are independent from each other. So as long as the file got a content, it will be used.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esoftware.sh\u003c/code\u003e will be created on the fly in the right directory, using the various template files, and does contain the list of software which needs to be installed which will be pulled in by the \u003ccode\u003esoftwarelist.txt\u003c/code\u003e file. The EasyStack file, so it exists, will be places in the correct directory.\nIf you need to change any of the paths where the software will be installed, you will need to look into \u003ccode\u003esoftware.tmpl\u003c/code\u003e, the Singularity Definition file \u003ccode\u003eSingularity.eb-4.4.2-Lmod-ubuntu20-LTR\u003c/code\u003e and both the \u003ccode\u003einstall.sh\u003c/code\u003e and \u003ccode\u003einteractive-install.sh\u003c/code\u003e files.\nNote: You can mount any folder outside the container but you will need to make sure that the \u003ccode\u003eMODULEPATH\u003c/code\u003e variable are identical inside and outside the container. Thus, if you are using like in our example \u003ccode\u003e/apps/easybuild\u003c/code\u003e as the root install directory, the \u003ccode\u003eMODULEPATH\u003c/code\u003e then needs to be set to for example \u003ccode\u003e/apps/easybuild/modules/all\u003c/code\u003e inside and outside the container!\u003c/p\u003e\n\u003cp\u003eThere is currently one bad hack in the \u003ccode\u003einstall.sh\u003c/code\u003e script, which is the architecture where the container is running on is determined by a fixed-path script! That will be tidied up at one point, so please be aware of this!\nThe idea about using \u003ccode\u003earchspec.py\u003c/code\u003e is that outside the container you got different paths where to install the software, but one common path for all the source files. If you are only having one type of architecture, you can set that manually at the top of the file.\u003c/p\u003e\n\u003cp\u003eThe first time the script runs, it will create the directory structure but then stops as the Singularity container is not in place. For the full automated installation, we would download the container from somewhere. However, as this only needs to be done once, it is left for now like this.\u003c/p\u003e\n\u003cp\u003eOnce the container in the right folder we are upgrading EasyBuild to the latest version. This way, a module file is created automatically. Once that is done, the software will be installed if required.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements:\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e \u0026gt;= 2.7.x and \u003ccode\u003efusermount\u003c/code\u003e \u0026gt;= 2.9.7\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-do\" class=\"anchor\" href=\"#to-do\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Do:\u003c/h2\u003e\n\u003cp\u003eIt needs to be tested on Lustre but that does currently not work as \u003ccode\u003efusermount\u003c/code\u003e on at the current cluster is too old.\u003c/p\u003e\n\u003cp\u003eAlso, as mentioned above, the \u003ccode\u003earchpsec.py\u003c/code\u003e needs to be installed in a better way.\u003c/p\u003e\n\u003cp\u003eFinally, it somehow would be nice to include \u003ccode\u003e--cuda-compute-capabilities=8.0\u003c/code\u003e for the A100 GPU builds automatically to make it a bit more fool-proved.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1615402009.0 + "updated_at": 1639737533.0 }, { "data_format": 2, - "description": "singularity recipes", + "description": "MLPerf Inference containers recipes", "filenames": [ - "Singularity.tf2p4_addons", - "Singularity.tf2p1", - "Singularity.tf2p4_costum", - "Singularity.tf2_cuda", - "Singularity.skimage", - "Singularity.tf2_addons", - "Singularity.tf2", - "Singularity.tf2_cuda_pip", - "Singularity.comet", - "Singularity.pandas", - "Singularity.torch", - "Singularity.tf2p1_addons", - "Singularity..torch1p8" + "v0.5/Singularity.v0.5", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_cg_omp-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_rt_c_tbb-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_c_omp-py36-gcc75-ubuntu18-avx2", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2020.3.1_src_c_omp-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_c_omp-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_omp-py36-gcc75-ubuntu18-avx2", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_omp-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_omp-py38-gcc75-ubuntu20", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_omp-py36-gcc75-ubuntu18-sse42", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_rt_cg_tbb-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_cg_tbb-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_cg_omp-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_cg_tbb-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_c_omp-py36-gcc75-ubuntu18-sse42", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_c_tbb-py36-gcc75-ubuntu18", + "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_tbb-py36-gcc75-ubuntu18", + "v0.7/Singularity.v0.7", + "v0.7/python/MXNet/singularity/Singularity.v0.7-MXNet-1.7.0-py38-gcc93-ubuntu20", + "v0.7/python/MXNet/singularity/Singularity.v0.7-MXNet-1.8.0-py38-gcc93-ubuntu20", + "v0.7/python/MXNet/singularity/Singularity.v0.7-MXNet-1.7.0_6ae469a-py38-gcc93-ubuntu20", + "v0.7/cpp/TensorFlow/singularity/Singularity.v0.7-TensorFlow-v2.3.0-py38-gcc93-ubuntu20", + "v0.7/cpp/TensorFlow/singularity/Singularity.v0.7-TensorFlow-v2.3.0-py38-gcc93-ubuntu20_cl", + "v0.7/cpp/TensorFlow/singularity/Singularity.v0.7-TensorFlow-v2.3.0-py38-gcc10-ubuntu20", + "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_rt_cg_tbb-py36-gcc75-ubuntu18", + "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2021.1pre_src_c_tbb-py36-gcc75-ubuntu18", + "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_src_c_omp-py36-gcc75-ubuntu18", + "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2021.1pre_src_c_omp-py36-gcc75-ubuntu18", + "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_src_c_tbb-py36-gcc75-ubuntu18", + "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_rt_c_tbb-py36-gcc75-ubuntu18", + "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_src_cg_omp-py36-gcc75-ubuntu18", + "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_src_cg_tbb-py36-gcc75-ubuntu18", + "v1.1/Singularity.v1.1", + "v1.1/cpp/OpenVINO/singularity/Singularity.v1.1-OpenVINO-2021.4_src_c_tbb-py38-gcc93-ubuntu20", + "v1.1/cpp/OpenVINO/singularity/Singularity.v1.1-OpenVINO-2021.4_src_c_omp-py38-gcc93-ubuntu20", + "v1.1/cpp/OpenVINO/singularity/Singularity.v1.1-OpenVINO-2021.1pre_src_c_omp-py36-gcc75-ubuntu18", + "v1.0/Singularity.v1.0" ], - "full_name": "xiyaojin/singularity", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cp\u003esingularity recipes\u003c/p\u003e\n", + "full_name": "provarepro/mlperf_inference", + "latest_release": "0.1.9", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mlperf-inference\" class=\"anchor\" href=\"#mlperf-inference\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMLPerf Inference\u003c/h1\u003e\n\u003cp\u003eMLPerf Inference containers recipes\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1622692314.0 + "updated_at": 1641476524.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "container/Singularity.vep-96.0" ], - "full_name": "robomorelli/horovod_torch_nccl", + "full_name": "vsarsani/Genetic-Characterization-Nextflow", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-horovod_torch_nccl\" class=\"anchor\" aria-hidden=\"true\" href=\"#horovod_torch_nccl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehorovod_torch_nccl\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-genetic-characterization-of-a-phenotype-nextflow-pipeline\" class=\"anchor\" href=\"#genetic-characterization-of-a-phenotype-nextflow-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenetic Characterization of a Phenotype Nextflow-pipeline\u003c/h1\u003e\n\u003cp\u003eThis pipeline performs the following functions to do a comprehensive genetic characterization of a phenotype marker (ex: height )\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eQC of GWAS Summary Statistics files, allele matching.\u003c/li\u003e\n\u003cli\u003eTrans-ancestry meta-analysis using various approaches. ( Fixed and random effects).\u003c/li\u003e\n\u003cli\u003eIdentification of Lead Variants and gene annotation from the meta-analysis results.\u003c/li\u003e\n\u003cli\u003eConditional analysis using GCTA COJO.\u003c/li\u003e\n\u003cli\u003eDistributional and Manhanttan plots of meta-analysis and conditional analysis.\u003c/li\u003e\n\u003cli\u003eWindow-based fine-mapping around lead variants to identify causal variants.\u003c/li\u003e\n\u003cli\u003eWindow-based fine-mapping around lead variants to identify eQTL colocalization.\u003c/li\u003e\n\u003cli\u003eeQTL based summary mendelian randomization.\u003c/li\u003e\n\u003cli\u003ePRS score construction from causal variants.\u003c/li\u003e\n\u003cli\u003eEnrichment analysis.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe pipeline has a total of ten processes. The tools used for all the ten processes are containerized in the \u003ca href=\"https://github.com/vsarsani/Genetic-Characterization-Nextflow/blob/master/container/Dockerfile\"\u003edocker image \u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vsarsani/Genetic-Characterization-Nextflow.git\ncd Nextflow-pipeline\ngit checkout dev_nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-nextflow\" class=\"anchor\" href=\"#nextflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003emake install\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-docker-image-installion\" class=\"anchor\" href=\"#docker-image-installion\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker image installion\u003c/h1\u003e\n\u003cp\u003eTo install the docker image for all the process tools using Docker, run the Makefile command in the container directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd container\nmake docker-build\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-how-to-run-the-pipeline\" class=\"anchor\" href=\"#how-to-run-the-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the pipeline\u003c/h1\u003e\n\u003cp\u003eIn order to run the pipeline, you need GWAS Summary files obtained from a study or multiple studies. Please use the following command.\n\u003ccode\u003e./nextflow run main.nf -resume --gwas-files ukb_bmi.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOtherwise, you can also run the whole pipeline by using the following one liner,\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./nextflow run main.nf\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1616241702.0 + "updated_at": 1647271167.0 }, { "data_format": 2, - "description": "Standalone Singularity file for CAMISIM fork", + "description": null, "filenames": [ - "Singularity.cami_python2" + "Singularity.recipe" ], - "full_name": "KatSteinke/singularity-camisim-standalone", + "full_name": "robbieperrott/Hons", "latest_release": null, + "readme": "\u003cp\u003eThis repository contains\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA Jenkinsfile, which contains parameters to be fed to a Jenkins pipeline job.\u003c/li\u003e\n\u003cli\u003eA Singularity recipe file, which specifies how to build the Singularity container on the target server.\u003c/li\u003e\n\u003cli\u003eRobbie\u0027s final research paper.\u003c/li\u003e\n\u003cli\u003eA poster summarizing the contents of our paper.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOur final mark was 72 percent.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1618570284.0 + "updated_at": 1647336586.0 }, { "data_format": 2, @@ -8590,637 +8120,516 @@ var data = "filenames": [ "Singularity" ], - "full_name": "juanca09/dino", + "full_name": "truatpasteurdotfr/singularity-docker-miniconda-quicksom", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-dino--a-nice-dinosaurio-\" class=\"anchor\" aria-hidden=\"true\" href=\"#dino--a-nice-dinosaurio-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edino ( A nice dinosaurio )\u003c/h1\u003e\n\u003cp\u003eYou need a GitHub account\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eLog into Github\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAdd a git repository ( ex:hello )\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLog into Singularity Hub ( \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003ehttps://singularity-hub.org\u003c/a\u003e ) as the github user\u003c/p\u003e\n\u003cp\u003eIn the Hub add a new collection ( with the repository )\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone the git project\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone\n\ngit clone git@github.com:\u0026lt;USER\u0026gt;/hello.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ein the directory \"hello\" add a Singularity definition file as \"Singularity\"\u003c/p\u003e\n\u003cp\u003eEx:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap:docker\nFrom:ubuntu:16.04\n\n%labels\nMAINTAINER juanca09\nSPECIES Dinosaur\n\n %environment\nRAWR_BASE=/code\nexport RAWR_BASE\n\n %runscript\necho \"This gets run when you run the image!\" \nexec /bin/bash /code/dino.sh \"$@\"\n\n\n%post \necho \"This section happens once after bootstrap to build the image.\" \nmkdir -p /code \necho \"echo \\\"RoooAAAARRRRR !!!!\\\"\" \u0026gt;\u0026gt; /code/dino.sh\nchmod u+x /code/dino.sh \n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCommit and push the project\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-a-miniconda-based-quicksom-httpsgithubcombougui505quicksom-container-with-pymolpytorch\" class=\"anchor\" href=\"#a-miniconda-based-quicksom-httpsgithubcombougui505quicksom-container-with-pymolpytorch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea miniconda based quicksom (\u003ca href=\"https://github.com/bougui505/quicksom\"\u003ehttps://github.com/bougui505/quicksom\u003c/a\u003e) container with pymol/pytorch\u003c/h1\u003e\n\u003cp\u003edocker: \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-miniconda-quicksom/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-miniconda-quicksom/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-quicksom:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv oras://ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-quicksom:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-test-the-examples\" class=\"anchor\" href=\"#test-the-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etest the examples\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/bougui505/quicksom.git\n$ cd quicksom\n$ singularity --nv -B `pwd` oras://ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-quicksom:latest\nSingularity\u0026gt; dcd2npy --pdb data/2lj5.pdb --dcd data/2lj5.dcd --select \u0027name CA\u0027\nSingularity\u0026gt; time quicksom_fit -i data/2lj5.npy -o data/som_2lj5.p --n_iter 100 --batch_size 50 --periodic --alpha 0.5\nSingularity\u0026gt; quicksom_gui -i data/som_2lj5.p\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1613312484.0 + "updated_at": 1647383705.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "Singularity.5.28.2", - "Singularity.5.28.0", - "Singularity.5.28.1" + "singularity/Singularity" ], - "full_name": "kiwiroy/singularity-perl", + "full_name": "Egrt/https---huggingface.co-spaces-Egrt-Luuu", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2846\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-perl\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-perl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-perl\u003c/h1\u003e\n\u003cp\u003eUbuntu images with perl installed using perlbrew.\u003c/p\u003e\n", + "readme": "\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-title-luuuemoji-colorfrom-redcolorto-purplesdk-gradiosdk_version-2812app_file-apppypinned-falselicense-apache-20\" class=\"anchor\" href=\"#title-luuuemoji-colorfrom-redcolorto-purplesdk-gradiosdk_version-2812app_file-apppypinned-falselicense-apache-20\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etitle: Luuu\nemoji: \u003cg-emoji class=\"g-emoji\" alias=\"earth_africa\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f30d.png\"\u003e\ud83c\udf0d\u003c/g-emoji\u003e\ncolorFrom: red\ncolorTo: purple\nsdk: gradio\nsdk_version: 2.8.12\napp_file: app.py\npinned: false\nlicense: apache-2.0\u003c/h2\u003e\n\u003cp\u003eCheck out the configuration reference at \u003ca href=\"https://huggingface.co/docs/hub/spaces#reference\" rel=\"nofollow\"\u003ehttps://huggingface.co/docs/hub/spaces#reference\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1556534425.0 + "updated_at": 1647768660.0 }, { "data_format": 2, - "description": "Singularity recipe files for nanopolish (https://github.com/jts/nanopolish)", + "description": null, "filenames": [ - "Singularity", - "Singularity.3.8.3-1.el7" + "Singularity.def" ], - "full_name": "powerPlant/nanopolish-srf", + "full_name": "piyu2181/singularity", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for nanopolish \u003ca href=\"https://github.com/jts/nanopolish\"\u003ehttps://github.com/jts/nanopolish\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1639350932.0 + "updated_at": 1565737347.0 }, { "data_format": 2, - "description": "proof of concept for running singularity in a singularity container", + "description": "Planning problem generation using Graph Neural Networks and Reinforcement Learning.", "filenames": [ - "Singularity" + "src/fast-downward/misc/releases/19.06/Singularity.19.06", + "src/fast-downward/misc/releases/20.06/Singularity.20.06", + "src/fast-downward/misc/releases/21.12/Singularity.21.12", + "src/fast-downward/misc/releases/19.12/Singularity.19.12" ], - "full_name": "lkirk/singularity-in-singularity", + "full_name": "ari-dasci/S-PlanningProblemGeneration", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-in-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-in-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity in singularity\u003c/h1\u003e\n\u003cp\u003eThis is a proof-of-concept to show that it is indeed possible to run nested singularity processes.\nMy purpose for doing this is to create containers that can run applications that are in other other containers, allowing me to decompose the containers into small, purpose-built units.\u003c/p\u003e\n\u003cp\u003eTo test this for yourself, you can do the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esudo singularity build container.sif Singularity\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esingularity shell container.sif\u003c/span\u003e\n\n# \u003cspan class=\"pl-s1\"\u003ethen, go ahead and try running\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esingularity shell container.sif\u003c/span\u003e\n# \u003cspan class=\"pl-s1\"\u003eas many \u003cspan class=\"pl-c1\"\u003etimes\u003c/span\u003e as you want\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere is an example session where I nest 3 containers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@host$ singularity shell container.sif\nSingularity\u0026gt; singularity shell container.sif\nINFO: Converting SIF file to temporary sandbox...\nSingularity\u0026gt; singularity shell container.sif\nINFO: Converting SIF file to temporary sandbox...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd the resulting process tree (reported by htop):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity runtime parent\n\u251c\u2500 /bin/bash --norc\n\u2502 \u2514\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 /bin/bash --norc\n\u2502 \u2502 \u2514\u2500 Singularity runtime parent\n\u2502 \u2502 \u251c\u2500 /bin/bash --norc\n\u2502 \u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2502 \u2514\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2514\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u2514\u2500 Singularity runtime parent\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you do not want to coerce conversion to a temporary sandbox on every call (it can be time intensive for large images), you can simply create the sandbox upfront:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@host$ singularity build --sandbox test container.sif\nWARNING: \u0027nodev\u0027 mount option set on /tmp, it could be a source of failure during build process\nINFO: Starting build...\nINFO: Verifying bootstrap image container.sif\nINFO: Creating sandbox directory...\nINFO: Build complete: test\nuser@host$ singularity shell container.sif\nSingularity\u0026gt; singularity shell test\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1626825365.0 + "updated_at": 1648654350.0 }, { "data_format": 2, - "description": "Singularity recipe files for truvari (https://github.com/spiralgenetics/truvari)", + "description": null, "filenames": [ - "Singularity", - "Singularity.2.1.0" + "Singularity" ], - "full_name": "powerPlant/truvari-srf", + "full_name": "aminhaghparast/deep-variant", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for truvari, a Structural variant toolkit for benchmarking, annotating and more for VCFs.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://raw.githubusercontent.com/nf-core/deepvariant/master/docs/images/deepvariant_logo.png\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/nf-core/deepvariant/master/docs/images/deepvariant_logo.png\" alt=\"deepvariant\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-nf-coredeepvariant\" class=\"anchor\" href=\"#nf-coredeepvariant\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/deepvariant\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eDeep Variant as a Nextflow pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/deepvariant\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/554b3a00bbca0efb91acd93d9efc7929d4f25be25b8c7e5a58a31906f742ac65/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6465657076617269616e742e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/deepvariant.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6478e9f9fab44bd81e58f3ac9c53bd07b4447d3ce541c677c184903c7466e52/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413531382e31302e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A518.10.1-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/nf-core/Lobby\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75a58bd7ba3966d16ebf50e1d2d55a4d21c67fa281370f9b823c2f4e53532b03/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769747465722d2532306a6f696e253230636861742532302545322538362539322d3466623939612e737667\" alt=\"Gitter\" data-canonical-src=\"https://img.shields.io/badge/gitter-%20join%20chat%20%E2%86%92-4fb99a.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/deepvariant\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b79519758c23c61efc7c090d99e6c194456d4d72c071d9fb892501ca0be4f1c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6465657076617269616e742e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/deepvariant.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Nextflow pipeline for running the \u003ca href=\"https://github.com/google/deepvariant\"\u003eGoogle DeepVariant variant caller\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-deepvariant-and-why-in-nextflow\" class=\"anchor\" href=\"#what-is-deepvariant-and-why-in-nextflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is DeepVariant and why in Nextflow?\u003c/h2\u003e\n\u003cp\u003eThe Google Brain Team in December 2017 released a \u003ca href=\"https://www.ebi.ac.uk/training/online/course/human-genetic-variation-i-introduction/variant-identification-and-analysis/what-variant\" rel=\"nofollow\"\u003eVariant Caller\u003c/a\u003e based on DeepLearning: DeepVariant.\u003c/p\u003e\n\u003cp\u003eIn practice, DeepVariant first builds images based on the BAM file, then it uses a DeepLearning image recognition approach to obtain the variants and eventually it converts the output of the prediction in the standard VCF format.\u003c/p\u003e\n\u003cp\u003eDeepVariant as a Nextflow pipeline provides several advantages to the users. It handles automatically, through \u003cstrong\u003epreprocessing steps\u003c/strong\u003e, the creation of some extra needed indexed and compressed files which are a necessary input for DeepVariant, and which should normally manually be produced by the users.\nVariant Calling can be performed at the same time on \u003cstrong\u003emultiple BAM files\u003c/strong\u003e and thanks to the internal parallelization of Nextflow no resources are wasted.\nNextflow\u0027s support of Docker allows to produce the results in a computational reproducible and clean way by running every step inside of a \u003cstrong\u003eDocker container\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFor more detailed information about Google\u0027s DeepVariant please refer to \u003ca href=\"https://github.com/google/deepvariant\"\u003egoogle/deepvariant\u003c/a\u003e or this \u003ca href=\"https://research.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e. \u003cbr\u003e\nFor more information about DeepVariant in Nextflow please refer to this \u003ca href=\"https://blog.lifebit.ai/post/deepvariant/?utm_campaign=documentation\u0026amp;utm_source=github\u0026amp;utm_medium=web\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eWarning DeepVariant can be very computationally intensive to run.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo \u003cstrong\u003etest\u003c/strong\u003e the pipeline you can run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/deepvariant -profile test,docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA typical run on \u003cstrong\u003ewhole genome data\u003c/strong\u003e looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/deepvariant --genome hg19 --bam yourBamFile --bed yourBedFile -profile standard,docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn this case variants are called on the bam files contained in the testdata directory. The hg19 version of the reference genome is used.\nOne vcf files is produced and can be found in the folder \"results\"\u003c/p\u003e\n\u003cp\u003eA typical run on \u003cstrong\u003ewhole exome data\u003c/strong\u003e looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/deepvariant --exome --genome hg19 --bam_folder myBamFolder --bed myBedFile -profile standard,docker\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe nf-core/deepvariant documentation is split into the following files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/reference_genomes.md\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/about.md\"\u003eMore about DeepVariant\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-more-about-the-pipeline\" class=\"anchor\" href=\"#more-about-the-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore about the pipeline\u003c/h2\u003e\n\u003cp\u003eAs shown in the following picture, the worklow both contains \u003cstrong\u003epreprocessing steps\u003c/strong\u003e ( light blue ones ) and proper \u003cstrong\u003evariant calling steps\u003c/strong\u003e ( darker blue ones ).\u003c/p\u003e\n\u003cp\u003eSome input files ar optional and if not given, they will be automatically created for the user during the preprocessing steps. If these are given, the preprocessing steps are skipped. For more information about preprocessing, please refer to the \"INPUT PARAMETERS\" section.\u003c/p\u003e\n\u003cp\u003eThe worklow \u003cstrong\u003eaccepts one reference genome and multiple BAM files as input\u003c/strong\u003e. The variant calling for the several input BAM files will be processed completely indipendently and will produce indipendent VCF result files. The advantage of this approach is that the variant calling of the different BAM files can be parallelized internally by Nextflow and take advantage of all the cores of the machine in order to get the results at the fastest.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://github.com/nf-core/deepvariant/blob/master/pics/pic_workflow.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/nf-core/deepvariant/raw/master/pics/pic_workflow.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThis pipeline was originally developed at \u003ca href=\"https://lifebit.ai/?utm_campaign=documentation\u0026amp;utm_source=github\u0026amp;utm_medium=web\" rel=\"nofollow\"\u003eLifebit\u003c/a\u003e, by @luisas, to ease and reduce cost for variant calling analyses\u003c/p\u003e\n\u003cp\u003eMany thanks to nf-core and those who have helped out along the way too, including (but not limited to): @ewels, @MaxUlysse, @apeltzer, @sven1103 \u0026amp; @pditommaso\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1613598092.0 + "updated_at": 1651618731.0 }, { "data_format": 2, - "description": "GSOC 2020 @ Red Hen \u0026 Vitrivr", + "description": "A symbolic generalized MaxSAT solver", "filenames": [ - "openpose_singularity/Singularity.openpose_v1.60", - "openpose_singularity/Singularity.frankier_gsoc2020", - "attic/vitrivr_singularity/Singularity.adampro", - "attic/vitrivr_singularity/Singularity.cineast" + "dmc/Singularity", + "lg/Singularity" ], - "full_name": "frankier/gsoc2020", + "full_name": "zzwonder/DPMS", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/frankier/gsoc2020/wiki\"\u003eProgress is on the wiki.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository is for small odds/ends and to point to other places where the\nactual coding has taken place including forks of other projects.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eattic\u003c/code\u003e contains old and abandoned work:\n\u003cul\u003e\n\u003cli\u003eHand pose annotation\u003c/li\u003e\n\u003cli\u003eSingularity def files for Cineast (Docker is used now)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eopenpose_singularity\u003c/code\u003e contains Singularity container for OpenPose\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingslurm\u003c/code\u003e (Snakemake SLURM profile) Run SLURM outside container by\ncommunicating over the filesystem\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eskelshop\u003c/code\u003e contains a \u003cem\u003esubmodule\u003c/em\u003e for the skelshop utility, which contains\nall the Python code/Snakemake pipelines, for skeleton dumping, tracking,\nsegmentation, and embedding pipelines\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eforks\u003c/code\u003e contains \u003cem\u003esubmodules\u003c/em\u003e with forks of existing repos:\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003evitrivr-ng\u003c/code\u003e, \u003ccode\u003ecineast\u003c/code\u003e \u0026amp; \u003ccode\u003ecottontail\u003c/code\u003e are forks of Vitrivr projects\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ejavacpp-presets-add-openpose\u003c/code\u003e: OpenPose JavaCPP binding\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eopencv_wrapper\u003c/code\u003e: Add a couple of extra methods\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eopenpose\u003c/code\u003e: Improve Python API and enable broken tracking\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evitrivr_pilot\u003c/code\u003e contains scripts to deploy pilot Vitrivr instance\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erefreeze_hand_tracking\u003c/code\u003e contains code to refreeze a pretrained hand\ndetection model\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dpms-dynamic-programming-for-generalized-maxsat\" class=\"anchor\" href=\"#dpms-dynamic-programming-for-generalized-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPMS (Dynamic Programming for Generalized MaxSAT)\u003c/h1\u003e\n\u003cp\u003eDPMS handles generalized MaxSAT problems in an extended DIMACS format (described below)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe DPMC framework runs in two phases:\n\u003cul\u003e\n\u003cli\u003ePlanning phase: \u003ca href=\"./lg\"\u003eLG\u003c/a\u003e constructs a (graded) project-join tree of a generalized MaxSAT formula\u003c/li\u003e\n\u003cli\u003eExecution phase: \u003ca href=\"./dmc\"\u003eDMC\u003c/a\u003e computes the answer to a generalized MaxSAT formula using the (graded) project-join tree\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-linux\" class=\"anchor\" href=\"#installation-linux\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation (Linux)\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eautomake 1.16\u003c/li\u003e\n\u003cli\u003ecmake 3.16\u003c/li\u003e\n\u003cli\u003eg++ 9.3\u003c/li\u003e\n\u003cli\u003egmp 6.2\u003c/li\u003e\n\u003cli\u003emake 4.2\u003c/li\u003e\n\u003cli\u003ealready included as git submodules:\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003ecudd 3.0\u003c/a\u003e (a slightly modified version for DPMS is inlcuded)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts 2.2\u003c/a\u003e (included)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/trolando/sylvan\"\u003esylvan 1.5\u003c/a\u003e(included)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-install-submodules\" class=\"anchor\" href=\"#install-submodules\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall submodules:\u003c/h3\u003e\n\u003cp\u003eIn addmc/libraries/, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./install.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-lg-tree-builder\" class=\"anchor\" href=\"#compile-lg-tree-builder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile LG (Tree Builder)\u003c/h3\u003e\n\u003cp\u003eSee \u003ca href=\"./lg/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-dmc-executor\" class=\"anchor\" href=\"#compile-dmc-executor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile DMC (Executor)\u003c/h3\u003e\n\u003cp\u003eSee \u003ca href=\"./dmc/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-example-command-line\" class=\"anchor\" href=\"#usage-example-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage Example (Command Line)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ecnfFile=\"examples/pbtest.wbo\" \u0026amp;\u0026amp; bash -c \"lg/build/lg \u0027lg/solvers/flow-cutter-pace17/flow_cutter_pace17 -p 100\u0027\" \u0026lt; $cnfFile | dmc/dmc --cf=$cnfFile --mx=1 --mb=999\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure to use \"--mx=1\" to enable maxSAT.\u003c/p\u003e\n\u003cp\u003eChange \"999\" in \"--mb=999\" to a better upper bound of optimal cost (e.g., the result of o-line of a MaxSAT solver). For a WBO or partial MaxSAT instance, --mb is set to be the trivial bound which can be read from the instance, unless the user gives a better bound.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-problem-format-of-generalized-maxsat\" class=\"anchor\" href=\"#problem-format-of-generalized-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProblem format of Generalized MaxSAT\u003c/h2\u003e\n\u003cp\u003eSome examples of each type of problem can be found in examples/\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-generalized-maxsat-and-weighted-maxsat\" class=\"anchor\" href=\"#generalized-maxsat-and-weighted-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e(generalized) MaxSAT and weighted MaxSAT\u003c/h3\u003e\n\u003cp\u003eThe Max-CNF-SAT problems (.cnf) should use the DIMACS format: \u003ca href=\"https://www.ieee.org/conferences/publishing/templates.html\" rel=\"nofollow\"\u003ehttps://www.ieee.org/conferences/publishing/templates.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor XOR constraints, use \u0027x\u0027 at the beginning of a line\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ex1 xor x2 xor \\neg x3 =\u0026gt; x 1 2 -3 0.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor weighted MaxSAT (.cnf), use \"p wcnf nvars nclauses total-Soft-Weight\" instead of \"p cnf nvars nclauses\" in header. For each clause line, put the weight at the beginning of a line, then the first literal.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pseudo-boolean-optimization-wbo\" class=\"anchor\" href=\"#pseudo-boolean-optimization-wbo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePseudo-Boolean optimization (WBO)\u003c/h3\u003e\n\u003cp\u003eFor PB constraints (.wbo), here is an example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+1 x1 +1 x2 \u0026gt;= 1 ;\n[90] -1 x1 -1 x2 \u0026gt;= -1 ;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first constraint is a hard constraint. The second constraint is soft with weight 90.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-min-maxsat\" class=\"anchor\" href=\"#min-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMin-MaxSAT\u003c/h3\u003e\n\u003cp\u003eA Min-MaxSAT problem file is same with a MaxSAT file except that there is a \u0027vm\u0027 line indicating the min variables. Variables that do not appear in the vm line are all max variables.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1603876660.0 + "updated_at": 1641000935.0 }, { "data_format": 2, - "description": "Docker Environment for running 21cmFAST", + "description": "Tensorflow running in an Arch Linux Singularity container. Working towards JupyterHub SingularityHub Interop", "filenames": [ "Singularity" ], - "full_name": "nkern/21cmfast_env", + "full_name": "chiroptical/tensorflow-jupyterhub", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-21cmfast_env\" class=\"anchor\" aria-hidden=\"true\" href=\"#21cmfast_env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e21cmfast_env\u003c/h1\u003e\n\u003cp\u003eDocker environment for running 21cmFAST on ubuntu\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-container-with-tensorflow-and-jupyter-notebook\" class=\"anchor\" href=\"#singularity-container-with-tensorflow-and-jupyter-notebook\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container with Tensorflow and Jupyter Notebook\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eIntent is to run Tensorflow on GPU compute nodes through JupyterHub\n\u003cul\u003e\n\u003cli\u003eIf you would like this built for another driver, submit an issue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBorrowed \u003ccode\u003elinks.sh\u003c/code\u003e from \u003ca href=\"https://github.com/drorlab/tf-singularity\"\u003ehttps://github.com/drorlab/tf-singularity\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eI extracted CuDNN here because the download link expires\u003c/li\u003e\n\u003cli\u003eBuilding the Singularity container:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity create -s 3072 tensorflow-jupyterhub.img\n$ sudo singularity bootstrap tensorflow-jupyterhub.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRunning local jupyter server:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run tensorflow-jupyterhub.img\n[I 21:58:36.327 NotebookApp] Serving notebooks from local directory: \u0026lt;some directory\u0026gt;\n[I 21:58:36.327 NotebookApp] 0 active kernels \n[I 21:58:36.327 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/?token=\u0026lt;some token\u0026gt;\n[I 21:58:36.327 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n[C 21:58:36.329 NotebookApp] \n \n Copy/paste this URL into your browser when you connect for the first time,\n to login with a token:\n http://localhost:8888/?token=\u0026lt;some token\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to just run a script:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec tensorflow-jupyterhub.img python hello-world.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mcburton\"\u003emcburton\u003c/a\u003e and I are working on JupyterHub\nplugins to handle Singularity Hub images cleanly.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-do\" class=\"anchor\" href=\"#to-do\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Do\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003ePossibly indicate any bloat in the image and clear it out, if possible\n\u003cul\u003e\n\u003cli\u003eTensorflow DockerHub Compressed Image with GPU is 2 GB, mine is 3 GB\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWorking on JupyterHub plugin to deploy images from SingularityHub\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1503421722.0 + "updated_at": 1497564620.0 }, { "data_format": 2, "description": null, "filenames": [ - "context/ocserv-container/Singularity.def", - "context/openconnect-container/Singularity.def" + "Singularity" ], - "full_name": "cooperative-computing-lab/userlevel-vpn-tun-tap", + "full_name": "porchard/RNAseq-NextFlow", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-userlevel-vpn-tun-tap\" class=\"anchor\" aria-hidden=\"true\" href=\"#userlevel-vpn-tun-tap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003euserlevel-vpn-tun-tap\u003c/h1\u003e\n\u003cp\u003eSetup of a virtual network interface inside a singularity container using\nnetwork namespaces. All the network traffic of the container is routed to the\nvirtual interface and then a vpn server (ocserv). The interface gets its ip\nfrom the vpn server.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pre-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-setup\u003c/h2\u003e\n\u003cp\u003eThe following is needed to allow a user to manipulate namespaces at the compute nodes:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the following line to /etc/sysctl.d/99-sysctl.conf\u003c/span\u003e\nuser.max_user_namespaces=10000\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esysctl -p\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe machine running the VPN host needs the following changes:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the following line to /etc/sysctl.d/99-sysctl.conf\u003c/span\u003e\nuser.max_user_namespaces=10000\nnet.ipv4.ip_forward=1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand similarly run \u003ccode\u003esysctl -p\u003c/code\u003e afterwards. These are the only steps that require\nroot at the execution sites.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the containers\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-singularity-image-for-the-vpn-clients\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-singularity-image-for-the-vpn-clients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the singularity image for the VPN clients:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e context/openconnect-container\n$ sudo singularity build vpncms-client.sif Singularity.def\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../..\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe build process installs openconnect and its dependencies using the\ncmssw/cms:rhel7 image as a base. It will also compile from source \u003ccode\u003evpnns\u003c/code\u003e,\n\u003ccode\u003eocproxy\u0027 and \u003c/code\u003etsocks`, the alternative programs to use openconnect without\nroot privileges.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-singularity-image-for-the-vpn-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-singularity-image-for-the-vpn-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the singularity image for the VPN server:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e context/ocserv-container\n$ sudo singularity build vpncms-server.sif Singularity.def\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../..\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-vpn-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-vpn-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the VPN server\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-without-root-privileges\" class=\"anchor\" aria-hidden=\"true\" href=\"#without-root-privileges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWithout root privileges:\u003c/h3\u003e\n\u003cp\u003eTo ensure that all processes are termianted when the singularity container\nterminates, we execute the image inside an instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./launch-vpn-server --image context/ocserv-container/vpncms-server.img --instance vpn_server --add-user myvpnuser:myvpnpasswd --port 8443\nAdded user: myvpnuser\nSERVER PIN:\npin-sha256:XXXXXXX...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe make note of the server pin printed, as we will need it when connecting the clients.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-root-privileges\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-root-privileges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith root privileges:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo ./launch-vpn-server --image context/ocserv-container/vpncms-server.img --instance vpn_server --add-user myvpnuser:myvpnpasswd --port 8443 --privileged\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-launch-some-vpn-clients\" class=\"anchor\" aria-hidden=\"true\" href=\"#launch-some-vpn-clients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch some vpn clients;\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./launch-vpn-client --image context/openconnect-container/vpncms-client.sif \\\n --server MACHINE_WHERE_OCSERV_RUNS:8443 \\\n --servercert pin-sha256:XXXXXXX... \\\n --user myvpnuser \\\n --passwd myvpnpasswd \\\n --vpn-mode ns \\\n -- /bin/bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003elaunch-vpn-client\u003c/code\u003e script simply starts/stops an instance of the singularity\ncontainer so that no openconnect services are left behind The real virtual interface\nsetup magic happens in /etc/cms-vpn/vpn-start.sh.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-adding-cvmfs-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-cvmfs-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding cvmfs support\u003c/h2\u003e\n\u003cp\u003ecvmfs can be provided using cvmfsexec via fusermount and singularity. We do\nthis by creating a self-contained cvmfsexec distribution and using it as the\nsingularity executable:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/cvmfs/cvmfsexec.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e cvmfsexec\n$ ./makedist -s -m rhel7-x86_64 osg\n$ ./makedist -s -o /tmp/singularity-cmvfsexec\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\n$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGCVMFS_REPOSITORIES=cms.cern.ch,atlas.cern.ch,oasis.opensciencegrid.org\n$ ./launch-vpn-client --image context/openconnect-container/vpncms-client.sif \\\n --server MACHINE_WHERE_OCSERV_RUNS:8443 \\\n --servercert pin-sha256:XXXXXXX... \\\n --user myvpnuser \\\n --passwd myvpnpasswd \\\n --vpn-mode ns \\\n --singularity /tmp/singularity-cmvfsexec \\\n -- ls /cvmfs/cms.cern.ch\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-paired-end-rna-seq-data\" class=\"anchor\" href=\"#nextflow-pipeline-for-paired-end-rna-seq-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow pipeline for paired-end RNA-seq data\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eIf you have Singularity installed, you can use the config provided here (\u0027Singularity\u0027) to build a container with all the dependencies.\u003c/p\u003e\n\u003cp\u003eOtherwise, you\u0027ll need to have the following installed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSTAR\u003c/li\u003e\n\u003cli\u003efastqc\u003c/li\u003e\n\u003cli\u003esamtools\u003c/li\u003e\n\u003cli\u003eQoRTs\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI\u0027ve used this pipeline with NextFlow v. 19.04.1\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003ePaths to various generic files (STAR indices and chromosome size files) must be included in the nextflow.config file -- check that file and change paths accordingly.\u003c/p\u003e\n\u003cp\u003eYou\u0027ll also need to set the params.results variable -- either in the nextflow.config file itself, or on the command line when you run the pipeline (\u0027--results /path/to/results\u0027).\u003c/p\u003e\n\u003cp\u003eLastly, you\u0027ll need to include information about each RNA-seq library, including the genome to which it should be mapped, and the paths to the fastq files for each readgroup. Organize this information in a JSON file, as in library-config.json.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eOnce you have all of the above information, you can run the pipeline as follows (in this case, indicating the path to the results on the command line):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run -with-singularity /path/to/Singularity.simg -params-file library-config.json --results /path/to/results /path/to/main.nf\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1614364980.0 + "updated_at": 1642007112.0 }, { "data_format": 2, - "description": "Proteomics pipeline", + "description": null, "filenames": [ - "Singularity/singularity-master/singularity-master/examples/shub/Singularity", - "Singularity/singularity-master/singularity-master/examples/scientific/Singularity", - "Singularity/singularity-master/singularity-master/examples/arch/Singularity", - "Singularity/singularity-master/singularity-master/examples/ubuntu/Singularity", - "Singularity/singularity-master/singularity-master/examples/centos/Singularity", - "Singularity/singularity-master/singularity-master/examples/docker/Singularity", - "Singularity/singularity-master/singularity-master/examples/scratch/Singularity.busybox", - "Singularity/singularity-master/singularity-master/examples/scratch/Singularity.alpine", - "Singularity/singularity-master/singularity-master/examples/self/Singularity", - "Singularity/singularity-master/singularity-master/examples/busybox/Singularity", - "Singularity/singularity-master/singularity-master/examples/apps/Singularity", - "Singularity/singularity-master/singularity-master/examples/apps/Singularity.cowsay", - "Singularity/singularity-master/singularity-master/examples/instances/Singularity", - "Singularity/singularity-master/singularity-master/examples/asciinema/Singularity", - "Singularity/singularity-master/singularity-master/examples/sle/Singularity", - "Singularity/singularity-master/singularity-master/examples/raspbian/Singularity", - "Singularity/singularity-master/singularity-master/examples/library/Singularity", - "Singularity/singularity-master/singularity-master/examples/multistage/Singularity", - "Singularity/singularity-master/singularity-master/examples/opensuse/Singularity", - "Singularity/singularity-master/singularity-master/e2e/testdata/Singularity" + "Singularity" ], - "full_name": "HayleyPrice/Pipeline", + "full_name": "ddbj/singularity_guacamole_mysql", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_guacamole_mysql\" class=\"anchor\" href=\"#singularity_guacamole_mysql\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_guacamole_mysql\u003c/h1\u003e\n\u003cp\u003eRemote Desktop \u3084 VNC \u306e\u63a5\u7d9a\u3092 HTTP \u306b\u5909\u63db\u3057\u3066 HTML5 \u30a6\u30a7\u30d6\u30d6\u30e9\u30a6\u30b6\u3067\u8868\u793a\u3059\u308b Apache Guacamole \u3092 singularity instance \u3067\u5b9f\u884c\u3059\u308b\u305f\u3081\u306e\u30ec\u30b7\u30d4\u30d5\u30a1\u30a4\u30eb\u30fb\u521d\u671f\u5316\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u3059\u3002\u30e6\u30fc\u30b6\u30fc\u8a8d\u8a3c\u306bMySQL\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003eguacamole 1.3\u3067\u3059\u3067\u306b\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u5b9f\u884c\u3057\u3066\u3044\u308b\u5834\u5408\u306f\u3001\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4e00\u5ea6\u7d42\u4e86\u3057\u3066\u300csingularity image\u306e\u30d3\u30eb\u30c9\u300d\u3092\u5b9f\u884c\u5f8c\u3001\u300c\u30c7\u30fc\u30bf\u306e\u79fb\u884c\u300d\u307e\u3067\u9032\u3093\u3067\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-image-\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" href=\"#singularity-image-%E3%81%AE%E3%83%93%E3%83%AB%E3%83%89\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity image \u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067 singularity image \u3092\u30d3\u30eb\u30c9\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build guacamole.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMySQL, Tomcat\u306b\u3064\u3044\u3066\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u5148\u306b\u7f6e\u304b\u308c\u3066\u3044\u308b\u30d0\u30fc\u30b8\u30e7\u30f3\u304c\u9650\u5b9a\u3055\u308c\u3066\u3044\u308b\u305f\u3081\u3001\u30d5\u30a1\u30a4\u30eb\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3067\u304d\u305a\u306b\u30d3\u30eb\u30c9\u306b\u5931\u6557\u3059\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u306e\u5834\u5408\u306f\u30d5\u30a1\u30a4\u30eb\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u5148\u3092\u898b\u3066\u3001Singularity\u30d5\u30a1\u30a4\u30eb\u4e2d\u306e\u4ee5\u4e0b\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306e\u8a18\u8ff0\u3092\u9069\u5b9c\u5909\u66f4\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMYSQL_VERSION=\"5.6.51\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eTOMCAT_VERSION=\"9.0.56\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-\u521d\u671f\u8a2d\u5b9a\" class=\"anchor\" href=\"#%E5%88%9D%E6%9C%9F%E8%A8%AD%E5%AE%9A\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u521d\u671f\u8a2d\u5b9a\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067 singularity isntance \u8d77\u52d5\u306e\u305f\u3081\u306e\u521d\u671f\u8a2d\u5b9a\u3092\u884c\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u5b9f\u884c\u524d\u306b init.sh \u5185\u306e MYSQL_ROOT_PASSWD, MYSQL_GUACAMOLE_USER_PASSWD, MYSQL_PORT, GUACAMOLE_PORT, TOMCAT_SHUTDOWN_PORT, TOMCAT_PORT \u306e\u5024\u3092\u9069\u5b9c\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\"Enter current password for root (enter for none):\" \u3068\u8868\u793a\u3055\u308c\u305f\u3068\u3053\u308d\u3067\u51e6\u7406\u304c\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u306b\u306a\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u30ea\u30bf\u30fc\u30f3\u30ad\u30fc\u3092\u62bc\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u6b21\u306b\u3001\"Set root password? [Y/n]\" \u3068\u8868\u793a\u3055\u308c\u305f\u3068\u3053\u308d\u3067Y\u3092\u5165\u529b\u3057\u3001MySQL\u306eroot\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u3053\u3053\u3067\u3001init.sh\u306eMYSQL_ROOT_PASSWD\u306b\u8a2d\u5b9a\u3057\u305f\u5024\u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u4ee5\u964d\u306f\u3059\u3079\u3066Y\u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u51e6\u7406\u304c\u5b8c\u4e86\u3059\u308b\u3068\u3001data\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3068start_container.sh\u30d5\u30a1\u30a4\u30eb\u304c\u751f\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash init.sh\nexec init_mysql.sh\nperl: warning: Setting locale failed.\nperl: warning: Please check that your locale settings:\n\tLANGUAGE = \"ja_JP\",\n\tLC_ALL = (unset),\n\tLANG = \"ja_JP.UTF-8\"\n are supported and installed on your system.\nperl: warning: Falling back to the standard locale (\"C\").\nWARNING: Could not write to config file ./my.cnf: Read-only file system\n\nInstalling MySQL system tables...2021-03-17 18:46:46 0 [Warning] TIMESTAMP with implicit DEFAULT value is deprecated. Please use --explicit_defaults_for_timestamp server option (see documentation for more details).\n2021-03-17 18:46:46 0 [Note] Ignoring --secure-file-priv value as server is running with --bootstrap.\n2021-03-17 18:46:46 0 [Note] ./bin/mysqld (mysqld 5.6.51) starting as process 18851 ...\n2021-03-17 18:46:46 18851 [Note] InnoDB: Using atomics to ref count buffer pool pages\n2021-03-17 18:46:46 18851 [Note] InnoDB: The InnoDB memory heap is disabled\n2021-03-17 18:46:46 18851 [Note] InnoDB: Mutexes and rw_locks use GCC atomic builtins\n2021-03-17 18:46:46 18851 [Note] InnoDB: Memory barrier is not used\n2021-03-17 18:46:46 18851 [Note] InnoDB: Compressed tables use zlib 1.2.11\n2021-03-17 18:46:46 18851 [Note] InnoDB: Using CPU crc32 instructions\n2021-03-17 18:46:46 18851 [Note] InnoDB: Initializing buffer pool, size = 128.0M\n2021-03-17 18:46:46 18851 [Note] InnoDB: Completed initialization of buffer pool\n2021-03-17 18:46:46 18851 [Note] InnoDB: The first specified data file ./ibdata1 did not exist: a new database to be created!\n2021-03-17 18:46:46 18851 [Note] InnoDB: Setting file ./ibdata1 size to 12 MB\n2021-03-17 18:46:46 18851 [Note] InnoDB: Database physically writes the file full: wait...\n2021-03-17 18:46:46 18851 [Note] InnoDB: Setting log file ./ib_logfile101 size to 48 MB\n2021-03-17 18:46:46 18851 [Note] InnoDB: Setting log file ./ib_logfile1 size to 48 MB\n2021-03-17 18:46:47 18851 [Note] InnoDB: Renaming log file ./ib_logfile101 to ./ib_logfile0\n2021-03-17 18:46:47 18851 [Warning] InnoDB: New log files created, LSN=45781\n2021-03-17 18:46:47 18851 [Note] InnoDB: Doublewrite buffer not found: creating new\n2021-03-17 18:46:47 18851 [Note] InnoDB: Doublewrite buffer created\n2021-03-17 18:46:47 18851 [Note] InnoDB: 128 rollback segment(s) are active.\n2021-03-17 18:46:47 18851 [Warning] InnoDB: Creating foreign key constraint system tables.\n2021-03-17 18:46:47 18851 [Note] InnoDB: Foreign key constraint system tables created\n2021-03-17 18:46:47 18851 [Note] InnoDB: Creating tablespace and datafile system tables.\n2021-03-17 18:46:47 18851 [Note] InnoDB: Tablespace and datafile system tables created.\n2021-03-17 18:46:47 18851 [Note] InnoDB: Waiting for purge to start\n2021-03-17 18:46:47 18851 [Note] InnoDB: 5.6.51 started; log sequence number 0\n2021-03-17 18:46:47 18851 [Note] RSA private key file not found: /usr/local/mysql/data//private_key.pem. Some authentication plugins will not work.\n2021-03-17 18:46:47 18851 [Note] RSA public key file not found: /usr/local/mysql/data//public_key.pem. Some authentication plugins will not work.\n2021-03-17 18:46:53 18851 [Note] Binlog end\n2021-03-17 18:46:53 18851 [Note] InnoDB: FTS optimize thread exiting.\n2021-03-17 18:46:53 18851 [Note] InnoDB: Starting shutdown...\n2021-03-17 18:46:54 18851 [Note] InnoDB: Shutdown completed; log sequence number 1625977\nOK\n\nFilling help tables...2021-03-17 18:46:54 0 [Warning] TIMESTAMP with implicit DEFAULT value is deprecated. Please use --explicit_defaults_for_timestamp server option (see documentation for more details).\n2021-03-17 18:46:54 0 [Note] Ignoring --secure-file-priv value as server is running with --bootstrap.\n2021-03-17 18:46:54 0 [Note] ./bin/mysqld (mysqld 5.6.51) starting as process 18875 ...\n2021-03-17 18:46:54 18875 [Note] InnoDB: Using atomics to ref count buffer pool pages\n2021-03-17 18:46:54 18875 [Note] InnoDB: The InnoDB memory heap is disabled\n2021-03-17 18:46:54 18875 [Note] InnoDB: Mutexes and rw_locks use GCC atomic builtins\n2021-03-17 18:46:54 18875 [Note] InnoDB: Memory barrier is not used\n2021-03-17 18:46:54 18875 [Note] InnoDB: Compressed tables use zlib 1.2.11\n2021-03-17 18:46:54 18875 [Note] InnoDB: Using CPU crc32 instructions\n2021-03-17 18:46:54 18875 [Note] InnoDB: Initializing buffer pool, size = 128.0M\n2021-03-17 18:46:54 18875 [Note] InnoDB: Completed initialization of buffer pool\n2021-03-17 18:46:54 18875 [Note] InnoDB: Highest supported file format is Barracuda.\n2021-03-17 18:46:54 18875 [Note] InnoDB: 128 rollback segment(s) are active.\n2021-03-17 18:46:54 18875 [Note] InnoDB: Waiting for purge to start\n2021-03-17 18:46:55 18875 [Note] InnoDB: 5.6.51 started; log sequence number 1625977\n2021-03-17 18:46:55 18875 [Note] RSA private key file not found: /usr/local/mysql/data//private_key.pem. Some authentication plugins will not work.\n2021-03-17 18:46:55 18875 [Note] RSA public key file not found: /usr/local/mysql/data//public_key.pem. Some authentication plugins will not work.\n2021-03-17 18:46:55 18875 [Note] Binlog end\n2021-03-17 18:46:55 18875 [Note] InnoDB: FTS optimize thread exiting.\n2021-03-17 18:46:55 18875 [Note] InnoDB: Starting shutdown...\n2021-03-17 18:46:56 18875 [Note] InnoDB: Shutdown completed; log sequence number 1625987\nOK\n\nTo start mysqld at boot time you have to copy\nsupport-files/mysql.server to the right place for your system\n\nPLEASE REMEMBER TO SET A PASSWORD FOR THE MySQL root USER !\nTo do so, start the server, then issue the following commands:\n\n ./bin/mysqladmin -u root password \u0027new-password\u0027\n ./bin/mysqladmin -u root -h dbod04 password \u0027new-password\u0027\n\nAlternatively you can run:\n\n ./bin/mysql_secure_installation\n\nwhich will also give you the option of removing the test\ndatabases and anonymous user created by default. This is\nstrongly recommended for production servers.\n\nSee the manual for more instructions.\n\nYou can start the MySQL daemon with:\n\n cd . ; ./bin/mysqld_safe \u0026amp;\n\nYou can test the MySQL daemon with mysql-test-run.pl\n\n cd mysql-test ; perl mysql-test-run.pl\n\nPlease report any problems at http://bugs.mysql.com/\n\nThe latest information about MySQL is available on the web at\n\n http://www.mysql.com\n\nSupport MySQL by buying support/licenses at http://shop.mysql.com\n\nWARNING: Could not copy config file template ./support-files/my-default.cnf to\n./my.cnf, may not have access rights to do so.\nYou may want to copy the file manually, or create your own,\nit will then be used by default by the server when you start it.\n\nexec mysql_secure_installation\nINFO: instance started successfully\nperl: warning: Setting locale failed.\nperl: warning: Please check that your locale settings:\n\tLANGUAGE = \"ja_JP\",\n\tLC_ALL = (unset),\n\tLANG = \"ja_JP.UTF-8\"\n are supported and installed on your system.\nperl: warning: Falling back to the standard locale (\"C\").\n\n\n\nNOTE: RUNNING ALL PARTS OF THIS SCRIPT IS RECOMMENDED FOR ALL MySQL\n SERVERS IN PRODUCTION USE! PLEASE READ EACH STEP CAREFULLY!\n\nIn order to log into MySQL to secure it, we\u0027ll need the current\npassword for the root user. If you\u0027ve just installed MySQL, and\nyou haven\u0027t set the root password yet, the password will be blank,\nso you should just press enter here.\n\nEnter current password for root (enter for none): \nOK, successfully used password, moving on...\n\nSetting the root password ensures that nobody can log into the MySQL\nroot user without the proper authorisation.\n\nSet root password? [Y/n] Y\nNew password: \nRe-enter new password: \nPassword updated successfully!\nReloading privilege tables..\n ... Success!\n\n\nBy default, a MySQL installation has an anonymous user, allowing anyone\nto log into MySQL without having to have a user account created for\nthem. This is intended only for testing, and to make the installation\ngo a bit smoother. You should remove them before moving into a\nproduction environment.\n\nRemove anonymous users? [Y/n] Y\n ... Success!\n\nNormally, root should only be allowed to connect from \u0027localhost\u0027. This\nensures that someone cannot guess at the root password from the network.\n\nDisallow root login remotely? [Y/n] Y\n ... Success!\n\nBy default, MySQL comes with a database named \u0027test\u0027 that anyone can\naccess. This is also intended only for testing, and should be removed\nbefore moving into a production environment.\n\nRemove test database and access to it? [Y/n] Y\n - Dropping test database...\n ... Success!\n - Removing privileges on test database...\n ... Success!\n\nReloading the privilege tables will ensure that all changes made so far\nwill take effect immediately.\n\nReload privilege tables now? [Y/n] Y\n ... Success!\n\n\n\n\nAll done! If you\u0027ve completed all of the above steps, your MySQL\ninstallation should now be secure.\n\nThanks for using MySQL!\n\n\nCleaning up...\nsetup guacamole database\nWarning: Using a password on the command line interface can be insecure.\nWarning: Using a password on the command line interface can be insecure.\nWarning: Using a password on the command line interface can be insecure.\nWarning: Using a password on the command line interface can be insecure.\nWarning: Using a password on the command line interface can be insecure.\nINFO: Stopping guacamole instance of /home/okuda/singularity/ubuntu-18.04-guacamole-1.3.0-mysql/guacamole.sif (PID=18915)\ncreate server.xml\ncreate guacamole_home\nINFO: instance started successfully\nINFO: Stopping guacamole instance of /home/okuda/singularity/ubuntu-18.04-guacamole-1.3.0-mysql/guacamole.sif (PID=19214)\ncreate guacamole.properties\ncreate start_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-instance-\u306e\u8d77\u52d5\" class=\"anchor\" href=\"#singularity-instance-%E3%81%AE%E8%B5%B7%E5%8B%95\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity instance \u306e\u8d77\u52d5\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067 singularity instance \u3092\u8d77\u52d5\u3057\u307e\u3059\u3002instance \u306e\u8d77\u52d5\u5f8c\u3001instance \u5185\u3067mysqld, guacd, tomcat\u3000\u304c\u8d77\u52d5\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\nINFO: instance started successfully\nguacd[22]: INFO:\tGuacamole proxy daemon (guacd) version 1.3.0 started\nUsing CATALINA_BASE: /opt/tomcat\nUsing CATALINA_HOME: /opt/tomcat\nUsing CATALINA_TMPDIR: /opt/tomcat/temp\nUsing JRE_HOME: /usr\nUsing CLASSPATH: /opt/tomcat/bin/bootstrap.jar:/opt/tomcat/bin/tomcat-juli.jar\nUsing CATALINA_OPTS: \nTomcat started.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-\u30c7\u30fc\u30bf\u306e\u79fb\u884c\" class=\"anchor\" href=\"#%E3%83%87%E3%83%BC%E3%82%BF%E3%81%AE%E7%A7%BB%E8%A1%8C\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u30c7\u30fc\u30bf\u306e\u79fb\u884c\u003c/h2\u003e\n\u003cp\u003eguacamole 1.3\u3067\u4f5c\u6210\u6e08\u307f\u306estart_container.sh\u3092\u4f7f\u3063\u3066\u65b0\u3057\u3044\u30a4\u30e1\u30fc\u30b8\u3067\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u8d77\u52d5\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\nINFO: instance started successfully\nguacd[25]: INFO:\tGuacamole proxy daemon (guacd) version 1.4.0 started\nUsing CATALINA_BASE: /opt/tomcat\nUsing CATALINA_HOME: /opt/tomcat\nUsing CATALINA_TMPDIR: /opt/tomcat/temp\nUsing JRE_HOME: /usr/lib/jvm/java-11-openjdk-amd64\nUsing CLASSPATH: /opt/tomcat/bin/bootstrap.jar:/opt/tomcat/bin/tomcat-juli.jar\nUsing CATALINA_OPTS: \nTomcat started.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u5185\u306b\u5165\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://guacamole\nSingularity\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eguacamole-auth-jdbc-mysql-1.3.0.jar\u306e\u30b7\u30f3\u30dc\u30ea\u30c3\u30af\u30ea\u30f3\u30af\u3092guacamole-auth-jdbc-mysql-1.4.0.jar\u306e\u30b7\u30f3\u30dc\u30ea\u30c3\u30af\u30ea\u30f3\u30af\u306b\u5909\u66f4\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity\u0026gt; ls -l /etc/guacamole/extensions/\ntotal 4\nlrwxrwxrwx 1 okuda okuda 82 Mar 17 2021 guacamole-auth-jdbc-mysql-1.3.0.jar -\u0026gt; /usr/local/src/guacamole-auth-jdbc-1.3.0/mysql/guacamole-auth-jdbc-mysql-1.3.0.jar\nSingularity\u0026gt; ln -s /usr/local/src/guacamole-auth-jdbc-1.4.0/mysql/guacamole-auth-jdbc-mysql-1.4.0.jar /etc/guacamole/extensions/\nSingularity\u0026gt; ls -l /etc/guacamole/extensions/\ntotal 8\nlrwxrwxrwx 1 okuda okuda 82 Mar 17 2021 guacamole-auth-jdbc-mysql-1.3.0.jar -\u0026gt; /usr/local/src/guacamole-auth-jdbc-1.3.0/mysql/guacamole-auth-jdbc-mysql-1.3.0.jar\nlrwxrwxrwx 1 okuda okuda 82 Jan 17 12:06 guacamole-auth-jdbc-mysql-1.4.0.jar -\u0026gt; /usr/local/src/guacamole-auth-jdbc-1.4.0/mysql/guacamole-auth-jdbc-mysql-1.4.0.jar\nSingularity\u0026gt; rm /etc/guacamole/extensions/guacamole-auth-jdbc-mysql-1.3.0.jar \nSingularity\u0026gt; ls -l /etc/guacamole/extensions/\ntotal 4\nlrwxrwxrwx 1 okuda okuda 82 Jan 17 12:06 guacamole-auth-jdbc-mysql-1.4.0.jar -\u0026gt; /usr/local/src/guacamole-auth-jdbc-1.4.0/mysql/guacamole-auth-jdbc-mysql-1.4.0.jar\nSingularity\u0026gt; exit\nexit\n$\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u518d\u8d77\u52d5\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity instance stop guacamole\nINFO: Stopping guacamole instance of /home/okuda/singularity/ubuntu-18.04-guacamole-1.4.0-mysql/guacamole.sif (PID=29810)\n$ bash start_container.sh \nINFO: instance started successfully\nguacd[26]: INFO:\tGuacamole proxy daemon (guacd) version 1.4.0 started\nUsing CATALINA_BASE: /opt/tomcat\nUsing CATALINA_HOME: /opt/tomcat\nUsing CATALINA_TMPDIR: /opt/tomcat/temp\nUsing JRE_HOME: /usr/lib/jvm/java-11-openjdk-amd64\nUsing CLASSPATH: /opt/tomcat/bin/bootstrap.jar:/opt/tomcat/bin/tomcat-juli.jar\nUsing CATALINA_OPTS: \nTomcat started.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-guacamole-\u3078\u306e\u30a2\u30af\u30bb\u30b9\" class=\"anchor\" href=\"#guacamole-%E3%81%B8%E3%81%AE%E3%82%A2%E3%82%AF%E3%82%BB%E3%82%B9\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eguacamole \u3078\u306e\u30a2\u30af\u30bb\u30b9\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"http://localhost\" rel=\"nofollow\"\u003ehttp://localhost\u003c/a\u003e:\u0026lt;TOMCAT_PORT\u306e\u5024\u0026gt;/guacamole \u3092\u30a6\u30a7\u30d6\u30d6\u30e9\u30a6\u30b6\u3067\u958b\u3044\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u8d77\u52d5\u76f4\u5f8c\u306e\u30e6\u30fc\u30b6\u30fc\u540d\u3001\u30d1\u30b9\u30ef\u30fc\u30c9\u306f\u3044\u305a\u308c\u3082 guacadmin \u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1645798954.0 + "updated_at": 1642040783.0 }, { "data_format": 2, - "description": "Analysis scripts and code for Paramormyrops RNA-seq project", + "description": null, "filenames": [ - "trinity_singularity/Singularity" + "Singularity.def" ], - "full_name": "msuefishlab/paramormyrops_rnaseq", + "full_name": "mysteryresearcher/dasha", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-paramormyrops_rnaseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#paramormyrops_rnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eparamormyrops_rnaseq\u003c/h1\u003e\n\u003cp\u003eAnalysis scripts and code for our research article: Losilla, M., Luecke, D.M. \u0026amp; Gallant, J.R. The transcriptional correlates of divergent electric organ discharges in Paramormyrops electric fish. BMC Evol Biol 20, 6 (2020). \u003ca href=\"https://doi.org/10.1186/s12862-019-1572-3\" rel=\"nofollow\"\u003ehttps://doi.org/10.1186/s12862-019-1572-3\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains files with the code we used in our analysis.\u003c/p\u003e\n\u003cp\u003eThe table below serves as a guide to understand the flow of the code. It details the order in which the code was executed, along with a description and comments of each step. Notes are shown in \u003cstrong\u003ebold\u003c/strong\u003e text.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e that a Singularity file is provided in the folder trinity_singularity to run on high performance computing systems. This would allow any user capable of running Singularity images to recreate the exact computing environment used for these analyses, though it is not required.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003escript/command file\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003cth\u003ecomments\u003c/th\u003e\n\u003cth\u003eadditional_outputs (These are provided in the folder named additional_files)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_01_FastQCraw.sh\u003c/td\u003e\n\u003ctd\u003eassess quality of raw reads\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_02_trim_rename_unzip.sh\u003c/td\u003e\n\u003ctd\u003etrim, rename and unzip reads\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_03_FastQCtrimmed.sh\u003c/td\u003e\n\u003ctd\u003eassess quality of trimmed reads\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eThe NCBI transcripts file we used as reference for the align and count steps was from: NCBI Paramormyrops kingsleyae Annotation Release 100, based on genome assembly PKINGS_0.1. We downloaded the transcripts file from here: ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/002/872/115/GCF_002872115.1_PKINGS_0.1 We used the file called: rna.fna.gz, and removed the sole rRNA transcript present: XR_002837744.1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecmd_generate_gene_to_trans_file.txt\u003c/td\u003e\n\u003ctd\u003egenerate a gene-to-transcript list from the NCBI transcripts file\u003c/td\u003e\n\u003ctd\u003ethis list is required by the align and count steps\u003c/td\u003e\n\u003ctd\u003egene-trans-map.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04a_RSEMindex.sh\u003c/td\u003e\n\u003ctd\u003eIndex the NCBI transcripts file\u003c/td\u003e\n\u003ctd\u003ecalls the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04a_bash.sh\u003c/td\u003e\n\u003ctd\u003eIndex the NCBI transcripts file\u003c/td\u003e\n\u003ctd\u003eexecutes commands within the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04b_RSEMperIndiv.sh\u003c/td\u003e\n\u003ctd\u003eAligns reads to NCBI transcripts file and counts reads per gene\u003c/td\u003e\n\u003ctd\u003ecalls the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04b_bash.sh\u003c/td\u003e\n\u003ctd\u003eAligns reads to NCBI transcripts file and counts reads per gene\u003c/td\u003e\n\u003ctd\u003eexecutes commands within the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04c_matrices.sh\u003c/td\u003e\n\u003ctd\u003eBuild gene expression matrices\u003c/td\u003e\n\u003ctd\u003ecalls the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04c_bash.sh\u003c/td\u003e\n\u003ctd\u003eBuild gene expression matrices\u003c/td\u003e\n\u003ctd\u003eexecutes commands within the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eAt this point the gene expression matrices (RSEM.gene.counts.matrix and RSEM.gene.TMM.counts.matrix ) use gene names and symbols from the NCBI transcriptome. However, EntrezGeneIDs are preferred for downstream analyses. Therefore, I converted their gene names and symbols to Pkings EntrezGeneIDs with the next R code. The converted files were assigned to the original file names. The original files were first renamed to: \u0026lt;orginal name\u0026gt;_ORIG_gene_symbols\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etranslate_gene_IDs.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e\n\u003cli\u003e Replace gene names and symbols with EntrezGeneIDs in the gene expression matrices\u003c/li\u003e \u003cli\u003e generate a file with the columns Pking EntrezGeneID, gene name, gene symbol and type of gene for each of the predicted 27610 P. kingsleyae genes. This file is named Dic.PkingEntrezGeneID-to-name_symbol_type.txt \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003eThis code runs on the renamed files\u003c/td\u003e\n\u003ctd\u003eDic.PkingEntrezGeneID-to-name_symbol_type.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_05a_DE_analyses.sh\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e Data exploration - Correlation matrix, PCA \u003c/li\u003e \u003cli\u003e DGE and MA plots - all 10 possible pairwise OTU comparisons \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003ecalls the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_05a_bash_DE_genes.sh\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e Data exploration - Correlation matrix, PCA \u003c/li\u003e \u003cli\u003e DGE and MA plots - all 10 possible pairwise OTU comparisons \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cli\u003e executes commands within the singularity container \u003c/li\u003e\n\u003cli\u003e We modified 2) to use the function estimateDisp() instead of the functions estimateCommonDisp() and estimateTagwiseDisp() \u003c/li\u003e\n\u003c/td\u003e\n\u003ctd\u003euses the samples.txt file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClustering_of_DEG_mean.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e For each phenotype pair, extract the genes that meet the expression filters (Set B groups) \u003c/li\u003e \u003cli\u003e plot expression patterns of the genes in each group from 1) \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003egenerates black \u0026amp; white and colored plots for Set B genes (These plots served informational purposes)\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egenerate_suppl_files_DEG_comparisons_and_groups.Rmd\u003c/td\u003e\n\u003ctd\u003egenerate the supplemental files with the details of the \u003col\u003e \u003cli\u003e 10 DGE comparisons and \u003c/li\u003e \u003cli\u003e Set B groups \u003c/li\u003e\n\u003c/ol\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_06_blastp.sh\u003c/td\u003e\n\u003ctd\u003eblast P. kingsleyae proteins to D. rerio proteins\u003c/td\u003e\n\u003ctd\u003eoutput is split into 7 files, we merged all to one file afterwards\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAnnotation_wrangling.Rmd\u003c/td\u003e\n\u003ctd\u003eFor each ontology, generate two \u0027dictionaries\u0027: \u003col\u003e \u003cli\u003e Pking Entrez Gene IDs to D. rerio GO IDs \u003c/li\u003e \u003cli\u003e D. rerio GO IDs to GO terms \u003c/li\u003e \u003c/ol\u003e\n\u003c/td\u003e\n\u003ctd\u003eFiles from 2) were not used in later scripts, they served as references\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e Dic.PkingEntrezGeneID-to-GO.{ontology}.txt \u003c/li\u003e \u003cli\u003e Dic.{ontology}.GOid_to_term.txt \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eenrichment_on_Pkings _all_10_DGE_comparisons.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e GO enrichment on all 10 DGE comparisons \u003c/li\u003e \u003cli\u003e Horizontal bar plot significant GO terms\u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cli\u003e Xcel file from 1) is part of the supplementary files \u003c/li\u003e\n\u003cli\u003e This code also produces a file with information on each upregulated gene annotated to enriched GO terms, including how many GO terms the gene was annotated to for a given upregulated list and ontology (frequency). The file served informational purposes \u003c/li\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eenrichment_on_Pkings_clusters.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e GO enrichment on Set B groups \u003c/li\u003e \u003cli\u003e Horizontal bar plot significant GO terms \u003c/li\u003e \u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cli\u003e Xcel file from 1) is part of the supplementary files \u003c/li\u003e \u003cli\u003e the horizontal bar plot from 2) served informational purposes) \u003c/li\u003e \u003cli\u003e This code also produces a file with information on each upregulated gene annotated to enriched GO terms, including how many GO terms the gene was annotated to for a given upregulated list and ontology (frequency). The file served informational purposes \u003c/li\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eset_C.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e Intersect upregulated genes from Sets A\u0027 and B (these intersected genes are Set C) \u003c/li\u003e \u003cli\u003e GO enrichment on Set C genes \u003c/li\u003e \u003cli\u003e plot expression patterns of Set C genes \u003c/li\u003e \u003cli\u003e Horizontal bar plot significant GO terms \u003c/li\u003e \u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003eThe outputs are: \u003col\u003e \u003cli\u003e one file per list of upregulated genes \u003c/li\u003e \u003cli\u003e one file per list of enriched GO terms \u003c/li\u003e \u003cli\u003e Xcel file with upregulated genes (consolidation of output 1) \u003c/li\u003e \u003cli\u003e Xcel file with enriched GO terms (consolidation of output 2) \u003c/li\u003e \u003cli\u003e Xcel file with information on each upregulated gene annotated to enriched GO terms, including how many GO terms the gene was annotated to for a given upregulated list and ontology (frequency). The file served informational purposes \u003c/li\u003e \u003cli\u003e Color plots for Set C genes expression patterns \u003c/li\u003e \u003cli\u003e Horizontal bar plot with enriched GO terms \u003c/li\u003e \u003c/ol\u003e \u003cli\u003e Outputs 3) and 4) are part of the supplemental files \u003c/li\u003e \u003cli\u003e Outputs 6) and 7) make up Figs. 4-6 \u003c/li\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dasha-distributed-nonconvex-optimization-with-communication-compression-optimal-oracle-complexity-and-without-client-synchronization\" class=\"anchor\" href=\"#dasha-distributed-nonconvex-optimization-with-communication-compression-optimal-oracle-complexity-and-without-client-synchronization\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity and Without Client Synchronization\u003c/h1\u003e\n\u003cp\u003eThis repository provides the code to reproduce the experiments of the submission for The Thirty-ninth International Conference on Machine Learning (ICML 2022)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-install-singularity-optional\" class=\"anchor\" href=\"#1-install-singularity-optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/introduction.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (optional)\u003c/h3\u003e\n\u003cp\u003eIf you don\u0027t want to install Singularity, make sure that you have all dependecies from Singularity.def (python3, numpy, pytorch, etc.)\u003c/p\u003e\n\u003cp\u003ea. Pull an image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://k3nfalt/default/python_ml:sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb. Open a shell console of the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv ~/python_ml_sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-prepare-scripts-for-experiments\" class=\"anchor\" href=\"#2-prepare-scripts-for-experiments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Prepare scripts for experiments\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/zero_marina/config_libsvm_zero_marina.py \n--dumps_path SOME_PATH --dataset_path PATH_TO_FOLDER_WITH_DATASET --dataset mushrooms \n--experiments_name EXPERIMENT_NAME --num_nodes_list 5 \n--step_size_range -10 4 --number_of_seeds 1 --number_of_iterations 21000 \n--algorithm_names zero_marina marina --function nonconvex \n--compressors rand_k --number_of_coordinates 10 --quality_check_rate 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-execute-scripts\" class=\"anchor\" href=\"#3-execute-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Execute scripts\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esh SOME_PATH/EXPERIMENT_NAME/singularity_*.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-4-plot-results\" class=\"anchor\" href=\"#4-plot-results\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Plot results\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/plots/zero_marina/plot_marina_mushrooms_gradient.py \n--dumps_paths SOME_PATH/EXPERIMENT_NAME\n--output_path SOME_PATH_FOR_PLOTS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOne can find all other scripts \u003ca href=\"https://github.com/mysteryresearcher/dasha/blob/ac7d0dce798898fb6255e7c0ab181def8ac88f48/code/distributed_optimization_library/experiments/plots/zero_marina/script.txt#L1\"\u003ehere\u003c/a\u003e that generate experiments from the paper.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1602949531.0 + "updated_at": 1642513577.0 }, { "data_format": 2, - "description": "Singularity recipe files for spades (git@github:powerPlant/spades-srf.git)", + "description": "A curriculum framework", "filenames": [ - "Singularity.cami2-submission", - "Singularity.v3.10.0", - "Singularity.v3.8.1", - "Singularity.v0.5-recomb", - "Singularity.v3.12.0", - "Singularity.v3.9.0", - "Singularity.spaligner-paper", - "Singularity.v3.11.1", - "Singularity.v3.13.0", - "Singularity.v3.8.0", - "Singularity.v3.10.1", - "Singularity.v3.14.0", - "Singularity.template", - "Singularity.cloudspades-paper", - "Singularity.v3.13.1", - "Singularity.v3.8.2", - "Singularity.v3.11.0", - "Singularity.metaplasmid-paper", - "templates/Singularity.template" + "pddlgym_planners/FD/misc/releases/19.06/Singularity.19.06", + "pddlgym_planners/FD/misc/releases/latest/Singularity", + "pddlgym_planners/FD/misc/releases/19.12/Singularity.19.12", + "pddlgym_planners/FD/misc/releases/20.06/Singularity.20.06" ], - "full_name": "powerPlant/spades-srf", + "full_name": "nitsan57/CDM_torch", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for spades\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cdm_torch\" class=\"anchor\" href=\"#cdm_torch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCDM_torch\u003c/h1\u003e\n\u003cp\u003eA curriculum framework\ncheck out cdm.ipynb\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1580700253.0 + "updated_at": 1642618241.0 }, { "data_format": 2, - "description": null, + "description": "Pycharm in Singularity", "filenames": [ "Singularity" ], - "full_name": "tomuram/singularity_recipes", - "latest_release": null, + "full_name": "serheang/pycharm_singularity", + "latest_release": "pycharm", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-build-status-badge-\" class=\"anchor\" href=\"#build-status-badge-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild status badge: \u003ca href=\"https://github.com/serheang/pycharm_singularity/workflows/build-singularity-container/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/serheang/pycharm_singularity/workflows/build-singularity-container/badge.svg\" alt=\"badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-pycharm_singularity\" class=\"anchor\" href=\"#pycharm_singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epycharm_singularity\u003c/h1\u003e\n\u003cp\u003ePycharm in Singularity container.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1625019915.0 + "updated_at": 1642676609.0 }, { "data_format": 2, - "description": "Recipes for Singularity images used by Singularity Hub.", + "description": null, "filenames": [ - "Singularity.Root6.Ubuntu-18.04", - "Singularity.Root6.Geant4.OptSim.Ubuntu-18.04", - "Singularity.Root6.Geant4.Ubuntu-18.04" + "Singularity" ], - "full_name": "PPKoller/SHub", + "full_name": "zellerlab/vortex_light", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-shub\" class=\"anchor\" aria-hidden=\"true\" href=\"#shub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSHub\u003c/h1\u003e\n\u003cp\u003eRecipes for Singularity images to be built on Singularity Hub.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4666\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-argoncube-optical-simulation--\" class=\"anchor\" aria-hidden=\"true\" href=\"#argoncube-optical-simulation--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArgonCube Optical Simulation \u003ca href=\"https://github.com/PPKoller/ArCubeOptSim\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ac28190b3bdb446d46b2760854ecec42927bd2ae802d0729c6b0e72449b56082/68747470733a2f2f6769746875622e6769746875626173736574732e636f6d2f696d616765732f6d6f64756c65732f6c6f676f735f706167652f4769744875622d4d61726b2e706e67\" width=\"30\" data-canonical-src=\"https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://argoncube.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://github.com/PPKoller/SHub/raw/master/.ArCube_Logo.png\" width=\"100\" align=\"right\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-pull-the-container-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-pull-the-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Pull the container image:\u003c/h3\u003e\n\u003cp\u003eThe optical simulation software container can be pulled directly via the Singularity command:\u003cbr\u003e\n(size ~ 1.4G)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://PPKoller/SHub:root6.geant4.optsim.ubuntu-18.04\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-image-default-checks\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-image-default-checks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Image default checks:\u003c/h3\u003e\n\u003cp\u003ePerforming the Singularity default checks should return \u003ccode\u003ePASS: (retval=0)\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emv PPKoller-SHub-master-root6.geant4.optsim.ubuntu-18.04.simg OptSim.simg\nsingularity check --tag default OptSim.simg\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-export-io-binding-paths\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-export-io-binding-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Export I/O binding paths:\u003c/h3\u003e\n\u003cp\u003eUsing the environment variable \u003ccode\u003e$SINGULARITY_BINDPATH\u003c/code\u003e there won\u0027t be any need to bind I/O paths manually later.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir input output\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_BINDPATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einput/:/input,output/:/output\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Run instructions:\u003c/h3\u003e\n\u003cp\u003eRunning the container without any arguments will return a list of the available apps including a short description on what it does and what parameters you might need to provide.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run OptSim.simg\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-5-run-apps\" class=\"anchor\" aria-hidden=\"true\" href=\"#5-run-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. Run apps:\u003c/h3\u003e\n\u003cp\u003eThere are five apps available within the container: four simulaion related apps that run the optical simulation with different levels of user defined input and one app that allows you to build the photon look-up-table using the output created by running the simulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe selected voxels will be processed sequentially. Separate container calls are needed for parallel processing.\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esim\u003c/strong\u003e\u003cbr\u003e\nRun the simulation on voxels no. 0 to 9 using the default statistics, voxel geometry and optical properties.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app sim OptSim.simg 0 10\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistics\u003c/em\u003e: 1\u0027000 events per voxel / 10\u0027000 photons per event\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVoxel geometry\u003c/em\u003e: 32 x 128 x 32 voxels / 9.460 x 9.858 x 9.692 mm\u003csup\u003e3\u003c/sup\u003e (drift x vertical x beam)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOpt. properties\u003c/em\u003e: \u003ca href=\"https://github.com/PPKoller/ArCubeOptSim/tree/LUT/resources/datafiles\"\u003ePPKoller/ArCubeOptSim/tree/LUT/resources/datafiles\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esim_usr_geo\u003c/strong\u003e\u003cbr\u003e\nRun the simulation on voxels no. 0 to 9 with user defined statistics and voxel geometry. Herefore, the file \u003ccode\u003eOptSim_LUT_voxel_table.txt\u003c/code\u003e has to be placed in the folder \u003ccode\u003einput/\u003c/code\u003e before executing the simulation.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app sim_usr_geo OptSim.simg 0 10\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eOptSim_LUT_voxel_table.txt\u003c/code\u003e can be created by the Jupyter Notebook provided \u003ca href=\"create_OptSim_LUT_voxel_table.ipynb\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esim_usr_opt\u003c/strong\u003e\u003cbr\u003e\nRun the simulation on voxels no. 0 to 9 with user defined optical properties. Herefore, a folder \u003ccode\u003edatafiles/\u003c/code\u003e containing all optical properties files has to be placed in the folder \u003ccode\u003einput/\u003c/code\u003e before executing the simulation.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app sim_usr_opt OptSim.simg 0 10\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe folder \u003ccode\u003edatafiles/\u003c/code\u003e containing the default optical properties files can be found \u003ca href=\"https://github.com/PPKoller/ArCubeOptSim/tree/LUT/resources/datafiles\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esim_usr\u003c/strong\u003e\u003cbr\u003e\nRun the simulation on voxels no. 0 to 9 with user defined statistics, voxel geometry and optical properties. (see instructions above)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app sim_usr OptSim.simg 0 10\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003elut / lut_usr\u003c/strong\u003e\u003cbr\u003e\nBuild the photon look-up-table using the output created by running the simulation. Herefore, voxel number \u00270\u0027 needs to have been processed and the respective root file \u003ccode\u003eOptSim_00000000.root\u003c/code\u003e has to be present in \u003ccode\u003eoutput/root_files/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app lut OptSim.simg\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAnd in case the simulation was run with user defined statistics and voxel geometry:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app lut_usr OptSim.simg\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-6-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#6-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6. Output\u003c/h3\u003e\n\u003cp\u003eAfter running the optical simulation, log and error files will appear in \u003ccode\u003eoutput/log_files/\u003c/code\u003e and root files will appear in \u003ccode\u003eoutput/root_files/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAfter running the LUT builder, the photon look-up-table will apper in \u003ccode\u003eoutput/\u003c/code\u003e as \u003ccode\u003eOptSim_LUT_ArgonCube2x2.root\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[optional]\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-user-defined-tpb-thickness\" class=\"anchor\" aria-hidden=\"true\" href=\"#user-defined-tpb-thickness\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser defined TPB thickness\u003c/h4\u003e\n\u003cp\u003ePlace the file \u003ccode\u003epreinit.mac\u003c/code\u003e with custom TPB thickness in the folder \u003ccode\u003einput/\u003c/code\u003e before executing the simulation. The default \u003ccode\u003epreinit.mac\u003c/code\u003e can be found \u003ca href=\"https://github.com/PPKoller/ArCubeOptSim/tree/LUT/resources/macros/preinit.mac\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-build-and-shell-into-writable-sandbox-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-and-shell-into-writable-sandbox-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild and shell into writable sandbox image:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox OptSim OptSim.simg\nsudo singularity shell --writable OptSim\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-build-compressed-read-only-squashfs-image-from-sandbox-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-compressed-read-only-squashfs-image-from-sandbox-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild compressed read-only squashfs image from sandbox image:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build OptSim_edited.simg OptSim\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-vortex_light\" class=\"anchor\" href=\"#vortex_light\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evortex_light\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installing-locally-and-running-from-local-installation\" class=\"anchor\" href=\"#installing-locally-and-running-from-local-installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling locally and running from local installation\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo from GitHub.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/zellerlab/vortex_light.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate a conda environment with NextFlow, e.g. by using the provided \u003ccode\u003eenvironment.yml\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd vortex_light\nconda env create -f environment.yml\nconda activate vortex_light\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eMake a copy of the \u003ccode\u003econfig/run.config\u003c/code\u003e file and adjust it to your environment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run /path/to/vortex_light/main.nf --input_dir /path/to/input_files --output_dir /path/to/output_dir -c /path/to/run.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote: Nextflow itself requires at least \u003ccode\u003e5GB\u003c/code\u003e of memory.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-from-github\" class=\"anchor\" href=\"#running-from-github\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning from GitHub\u003c/h3\u003e\n\u003cp\u003eThis requires a local nextflow installation. If you don\u0027t have one, see Steps 1/2 above.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eMake a local copy of the \u003ca href=\"https://raw.githubusercontent.com/zellerlab/vortex_light/main/config/run.config\" rel=\"nofollow\"\u003erun configuration file\u003c/a\u003e and adjust to your environment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run zellerlab/vortex_light --input_dir /path/to/input_files --output_dir /path/to/output_dir -c /path/to/run.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote: Nextflow itself requires at least \u003ccode\u003e5GB\u003c/code\u003e of memory.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input-parameters\" class=\"anchor\" href=\"#input-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput parameters\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input_dir\u003c/code\u003e should be a folder with bam files or with gzipped fastq files. For fastq files, individual samples should be separated into individual folders.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_dir\u003c/code\u003e is \u003ccode\u003evlight_out\u003c/code\u003e in the local directory by default.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--skip_\u0026lt;analysis\u0026gt;\u003c/code\u003e, \u003ccode\u003e--run_\u0026lt;analysis\u0026gt;\u003c/code\u003e skips, resp. explicitly requires execution of the specified analysis (\u003ccode\u003epathseq\u003c/code\u003e, \u003ccode\u003ebase_counts\u003c/code\u003e (read counts post pre-processing), \u003ccode\u003ekraken2\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--publishMode\u003c/code\u003e allows to switch between various modes of how results files are placed in the \u003ccode\u003eoutput_dir\u003c/code\u003e (cf. NextFlow documentation)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ekraken2\u003c/code\u003e can only run when the parameter \u003ccode\u003ekraken_database\u003c/code\u003e is set.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epathseq\u003c/code\u003e can only run when the parameter \u003ccode\u003epathseq_database\u003c/code\u003e is set.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eOutputs\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe output folder contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea subdirectory per sample (named \u003ccode\u003e\u0026lt;sample\u0026gt;\u003c/code\u003e) with\n\u003cul\u003e\n\u003cli\u003ethe kraken2 report \u003ccode\u003e\u0026lt;sample\u0026gt;.kraken2_report.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ethe library size \u003ccode\u003e\u0026lt;sample\u0026gt;.libsize.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003epathseq output\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.bam\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.bam.sgi\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.score_metrics\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.scores\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNote that by default, all files in the output folder are symlinks into the work dir! Before you delete the work dir, ensure you have dereferenced copies. Alternatively, change the --publishMode parameter to \u003ccode\u003ecopy\u003c/code\u003e or \u003ccode\u003elink\u003c/code\u003e (if the target file system supports hard links).\u003c/strong\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1612530237.0 + "updated_at": 1642692226.0 }, { "data_format": 2, - "description": null, + "description": "Version 3 of OnDemand apps", "filenames": [ - "Singularity.Bowtie2", - "Singularity", - "Singularity.FastQC", - "Singularity.bedtools", - "Singularity.samtools", - "Singularity.methylkit" + "rstudio_server_app/Singularity", + "shiny_app/ext/Singularity" ], - "full_name": "thakk/biobase", + "full_name": "CHPC-UofU/OOD-apps-v3", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-containers-for-bioinformatics-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-containers-for-bioinformatics-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity containers for bioinformatics tools\u003c/h1\u003e\n\u003cp\u003eBioinformatics related singularity container recipies.\u003c/p\u003e\n\u003cp\u003eBase is CentOS 8.\u003c/p\u003e\n\u003cp\u003eCurrently two containers are implemented:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebasic tools:\n\u003cul\u003e\n\u003cli\u003eSamtools\u003c/li\u003e\n\u003cli\u003eBEDTools\u003c/li\u003e\n\u003cli\u003eFastQC\u003c/li\u003e\n\u003cli\u003eBowtie2\u003c/li\u003e\n\u003cli\u003eMultiQC\u003c/li\u003e\n\u003cli\u003eCutadapt\u003c/li\u003e\n\u003cli\u003eSTAR\u003c/li\u003e\n\u003cli\u003eHisat2\u003c/li\u003e\n\u003cli\u003ePicard\u003c/li\u003e\n\u003cli\u003eTrimmomatic\u003c/li\u003e\n\u003cli\u003eSamblaster\u003c/li\u003e\n\u003cli\u003eVarScan\u003c/li\u003e\n\u003cli\u003eVcfanno\u003c/li\u003e\n\u003cli\u003ePlink\u003c/li\u003e\n\u003cli\u003eMACS2\u003c/li\u003e\n\u003cli\u003eHomer\u003c/li\u003e\n\u003cli\u003eNextFlow\u003c/li\u003e\n\u003cli\u003enf-core\u003c/li\u003e\n\u003cli\u003eMAGeCK\u003c/li\u003e\n\u003cli\u003eTrimGalore\u003c/li\u003e\n\u003cli\u003eBismark\u003c/li\u003e\n\u003cli\u003eUCSC tools\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003emethylKit (built from basic):\n\u003cul\u003e\n\u003cli\u003eR + Bioconductor\u003c/li\u003e\n\u003cli\u003emethylkit\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003esamtools (built from Alpine Linux 3.10.3)\n\u003cul\u003e\n\u003cli\u003eNote, automated Singularity Hub build does not seem to work correctly as this recipe uses multistage build to minimize container size\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-availability\" class=\"anchor\" aria-hidden=\"true\" href=\"#availability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailability\u003c/h2\u003e\n\u003cp\u003eBasic tools container is available at Singularity hub: shub://thakk/biobase\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ood-apps\" class=\"anchor\" href=\"#ood-apps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOOD-apps\u003c/h1\u003e\n\u003cp\u003eRepository of CHPC\u0027s Open OnDemand apps\u003c/p\u003e\n\u003cp\u003eVersion 3.0\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etemplated filling of job parameters\u003c/li\u003e\n\u003cli\u003edynamic filling of application versions (module files)\u003c/li\u003e\n\u003cli\u003ethe templates are in directory app-templates\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis is a repository of interactive apps used at CHPC with Open OnDemand.\u003c/p\u003e\n\u003cp\u003eEach subdirectory has its own README.md which contains information about this particular app.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1589801761.0 + "updated_at": 1643047602.0 }, { "data_format": 2, - "description": "Singularity containers for tools using the MAGICIAN pipeline", + "description": "Singularity recipe files for sqlite-tools (http://www.sqlite.org/)", "filenames": [ - "drep/Singularity.drep", - "camisim_ks_fork/Singularity.cami_python2", - "bbmap_36.49_metabat2_latest/Singularity.bbmap_from_metabat" + "Singularity.3.36.0", + "Singularity" ], - "full_name": "KatSteinke/magician-singularity-containers", + "full_name": "powerPlant/sqlite-tools-srf", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5332\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for sqlite-tools to provide sqldiff\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1617363865.0 + "updated_at": 1643154969.0 }, { "data_format": 2, "description": null, "filenames": [ - "setup/Singularity" + "Singularity" ], - "full_name": "smithhenryd/GSoC_2020_UnderrepresentedMessagesAndDemocrats-", + "full_name": "canceromics/LncPipe", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-gsoc_2020_underrepresentedmessagesanddemocrats\" class=\"anchor\" aria-hidden=\"true\" href=\"#gsoc_2020_underrepresentedmessagesanddemocrats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGSoC_2020_UnderrepresentedMessagesAndDemocrats:\u003c/h1\u003e\n\u003cp\u003eThe 2020 Google Summer of Code project \"Understanding Messages to Underrepresented Racial, Ethnic, Gender, and Sexual Groups on Social Media by Democratic Politicians and their Electoral Implications\" is contributed by Henry Smith with \u003ca href=\"http://www.redhenlab.org/\" rel=\"nofollow\"\u003eRed Hen Lab\u003c/a\u003e. Work on the project is completed under the mentorship of \u003ca href=\"http://home.jsjoo.com/\" rel=\"nofollow\"\u003eDr. Jungeock Joo\u003c/a\u003e and \u003ca href=\"https://bywords.github.io/\" rel=\"nofollow\"\u003eDr. Kunwoo Park\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gsoc-2020-blog\" class=\"anchor\" aria-hidden=\"true\" href=\"#gsoc-2020-blog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGSOC 2020 Blog:\u003c/h2\u003e\n\u003cp\u003eDetailed weekly updates during summer 2020 can be found at the project\u0027s \u003ca href=\"https://smithhenryd.github.io/UnderrepresentedMessagesAndDemocrats.github.io/\" rel=\"nofollow\"\u003eblog page\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-project-directory\" class=\"anchor\" aria-hidden=\"true\" href=\"#project-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject Directory:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/smithhenryd/GSoC_2020_UnderrepresentedMessagesAndDemocrats-/tree/master/background\"\u003ebackground\u003c/a\u003e details preliminary information relevant to the research project and topic. This folder currently contains the original proposal as well as a brief summary of related political science research.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/smithhenryd/GSoC_2020_UnderrepresentedMessagesAndDemocrats-/tree/master/electoral_outcomes_data\"\u003eelectoral_outcomes_data\u003c/a\u003e includes data collected from \u003ca href=\"https://ballotpedia.org/Election_results,_2018\" rel=\"nofollow\"\u003eBallotpedia\u003c/a\u003e summarizing 2018 U.S. midterm election outcomes. The current data details primary and general election outcomes in racially and ethnically diverse congressional districts, measured by the proportion of individuals that identify as people of color (POC).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/smithhenryd/GSoC_2020_UnderrepresentedMessagesAndDemocrats-/tree/master/imgs_data\"\u003eimgs_data\u003c/a\u003e contains information pertaining to the 2018 Facebook images dataset collected by Dr. Jungseock Joo and his colleagues. The dataset consists of images shared on Facebook from January 1 - November 5, 2018 by U.S. politicians who competed for the U.S. House, Senate, and state governorships during the 2018 general election.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-background-and-motivation\" class=\"anchor\" aria-hidden=\"true\" href=\"#background-and-motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground and Motivation:\u003c/h2\u003e\n\u003cp\u003eThe importance of underrepresented voters is not new to the Democratic party: a 2017 poll of registered voters by the Pew Research Institute of U.S. Politics and Policy estimated that only fifty-nine percent of self-identified Democrats/lean Democrats label themselves as white, compared to the eighty-nine percent of Republicans/lean Republicans. This figure is down from an estimated sixty-seven percent in 2007 and seventy-five percent in 1997. The same report approximates that Black voters constitute nineteen percent of this Democratic base, Hispanic voters twelve percent, and Asian together with other underrepresented racial/ethnic groups constitute ten percent [6].\u003c/p\u003e\n\u003cp\u003eMoreover, recent elections suggest the emergence of the LGBT community, which we classify as underrepresented gender and sexual individuals, as one of the most solid Democratic voting blocs. Exit polling by NBC following the 2018 midterm elections indicated that while LGBT voters constituted only six percent of the electorate, upwards of eighty-two percent of these voters supported the Democratic candidate [1].\u003c/p\u003e\n\u003cp\u003eDespite the distinct importance of these groups to the Democratic party, it is not clear that the party knows how to effectively mobilize underrepresented voters. This harrowing reality came to the forefront of the news cycle following a decade-low Black voter turnout during the 2016 election [4]. In response to this fall in turnout, to which many have attributed Democratic presidential candidate Hillary Clinton\u2019s loss, the Democratic National Committee (DNC) pledged $2.5 million for the funding of programs to increase turnout among underrepresented groups during the 2018 midterm elections [3].\u003c/p\u003e\n\u003cp\u003eOf particular interest to our research is how politicians themselves aim to mobilize these communities through social media. Past research has underscored the importance of social media as spaces for underrepresented racial, gender, and sexual groups. In conflict with the narrative that a lack of access to technology divides disadvantaged racial groups, a recent study has shown that online platforms in fact embolden social networks between these groups [2]. Likewise, it is estimated that eighty percent of LGBT adults engage on at least one social media website, which is much greater than the fifty-eight percent of the general public [5].\u003c/p\u003e\n\u003cp\u003eKeeping this in mind, we seek to answer the following questions:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eHow do Democratic politicians present themselves to underrepresented racial, gender, and sexual groups on social media platforms through visual content?\u003c/li\u003e\n\u003cli\u003eWhich traits displayed in these images are perceived most positively/negatively by underrepresented voters?\u003c/li\u003e\n\u003cli\u003eHow do visual messages predict primary election outcomes in diverse electoral districts?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eSources:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[1] Fitzsimons, T. (2018, November 08). Record LGBT support for Democrats in midterms, NBC News Exit Poll shows. NBC News. Retrieved from \u003ca href=\"https://www.nbcnews.com/feature/nbc-out/record-lgbt-support-democrats-midterms-nbc-news-exit-poll-shows-n934211\" rel=\"nofollow\"\u003ehttps://www.nbcnews.com/feature/nbc-out/record-lgbt-support-democrats-midterms-nbc-news-exit-poll-shows-n934211\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2] Amy L. Gonzales. 2015. Disadvantaged Minorities\u2019 Use of the Internet to Expand Their Social Networks. Communication Research 44, 4 (2017), 467-486.\u003c/li\u003e\n\u003cli\u003e[3] Herndon, A. W. (2018, June 21). Democrats Plan New Effort to Target Minority Voters. The New York Times. Retrieved from \u003ca href=\"https://www.nytimes.com/2018/06/21/us/politics/democrats-minority-voters-midterms.html?smtyp=cur\u0026amp;smid=tw-nytimes\" rel=\"nofollow\"\u003ehttps://www.nytimes.com/2018/06/21/us/politics/democrats-minority-voters-midterms.html?smtyp=cur\u0026amp;smid=tw-nytimes\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[4] Krogstad, J. M. and Lopez, M. H. (2017, May 12). Black voter turnout fell in 2016, even as a record number of Americans cast ballots. Pew Research Center, Washington, D.C. Retrieved from \u003ca href=\"https://www.pewresearch.org/fact-tank/2017/05/12/black-voter-turnout-fell-in-2016-even-as-a-record-number-of-americans-cast-ballots/\" rel=\"nofollow\"\u003ehttps://www.pewresearch.org/fact-tank/2017/05/12/black-voter-turnout-fell-in-2016-even-as-a-record-number-of-americans-cast-ballots/\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[5] \u201cA Survey of LGBT Americans.\u201d Pew Research Center, Washington, D.C. (2013, June 13) \u003ca href=\"https://www.pewsocialtrends.org/2013/06/13/a-survey-of-lgbt-americans/\" rel=\"nofollow\"\u003ehttps://www.pewsocialtrends.org/2013/06/13/a-survey-of-lgbt-americans/\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[6] \u201cWide Gender Gap, Growing Educational Divide in Voters\u2019 Party Identification.\u201d Pew Research Center, Washington, D.C. (2018, March 20) \u003ca href=\"https://www.people-press.org/2018/03/20/wide-gender-gap-growing-educational-divide-in-voters-party-identification/\" rel=\"nofollow\"\u003ehttps://www.people-press.org/2018/03/20/wide-gender-gap-growing-educational-divide-in-voters-party-identification/\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-lncpipe\" class=\"anchor\" href=\"#lncpipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/likelet/LncPipe/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/baf841111dc28f78162a4622b162743688582f72fdd1489701abaed0dbedeb6c/68747470733a2f2f696d672e736869656c64732e696f2f6175722f6c6963656e73652f79616f7572742e737667\" alt=\"AUR\" data-canonical-src=\"https://img.shields.io/aur/license/yaourt.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://nextflow.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4592602bf49949ce2bf5d14fd5d8f82ff4d9da11fcc13f9afaadaa60e0f915e0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e32342e302d627269676874677265656e2e737667\" alt=\"nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.24.0-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-overall\" class=\"anchor\" href=\"#overall\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverall\u003c/h2\u003e\n\u003cp\u003eRecently, long noncoding RNA molecules (lncRNA) captured widespread attentions for their critical\nroles in diverse biological process and important implications in variety of human diseases and\ncancers. Identification and profiling of lncRNAs is a fundamental step to advance our knowledge\non their function and regulatory mechanisms. However, RNA sequencing based lncRNA discovery is\ncurrently limited due to complicated operations and implementation of the tools involved. Therefore, we present a one-stop multi-tool integrated pipeline called \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e focused on characterizing lncRNAs from raw transcriptome sequencing data.\nThe pipeline was developed based on a popular workflow framework \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report. \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\n\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#schematic-diagram\"\u003eSchematic diagram\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation-and-quick-start\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#run-docker\"\u003eRun Docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/likelet/LncPipeTestData\"\u003eRun with example data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#interactive-reports\"\u003eInteractive reports\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#parameters\"\u003eParameters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#faq\"\u003eFAQ\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#acknowledgements\"\u003eAcknowledgements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-schematic-diagram\" class=\"anchor\" href=\"#schematic-diagram\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSchematic diagram\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e\u003cbr\u003e\nLncPipe is implemented with Nextflow pipeline management system. To run LncPipe. \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e should be pre-installed at POSIX compatible system (Linux, Solaris, OS X, etc), It requires BASH and Java 7 or higher to be installed. We do not recommend running the pipes in the Windows since most of bioinformatic tools are not supported.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start\u003c/h2\u003e\n\u003cp\u003eHere, we show step by step installation of \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e in a linux system as an example (adopted from \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003eNextFlow\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the NextFlow executable package by pasting the following command into your terminal window:\u003c/p\u003e\n\u003cp\u003ewget -qO- get.nextflow.io | bash\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIt will create the \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e main executable file in the current directory.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOptionally, move the nextflow file to a directory accessible by your \u003ccode\u003e$PATH\u003c/code\u003e variable (only required to avoid typing the full path to this file each time you need to run it). Of course, you can download the lastest binary version of NextFlow by yourself from \u003ca href=\"https://github.com/nextflow-io/nextflow/releases\"\u003ehere\u003c/a\u003e and add the path to your system environment.All those pipelines were written in \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e commands. For more details, please see \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eDownload the LncPipe github repository by:\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/likelet/LncPipe.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eConfigure the design.file with experimental conditions and replicate info\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eConfigure your data and reference files in \u003cem\u003enextflow.config\u003c/em\u003e or \u003cem\u003edocker.config\u003c/em\u003e or \u003cem\u003esingularity.config\u003c/em\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003e\n\u003cp\u003eRun LncPipe nextflow pipeline:\u003c/p\u003e\n\u003cp\u003enextflow -c nextflow.config run LncRNAanalysisPipe.nf\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eor docker command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nextflow -c docker.config run LncRNAanalysisPipe.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor singularity command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # create image \n singularity build lncPipe.image docker://bioinformatist/lncpipe\n # run command \n nextflow -c singularity.config run LncRNAanalysisPipe.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e__7.Run with test data __ .\u003c/p\u003e\n\u003cp\u003ePlZ go to \u003ca href=\"https://github.com/likelet/LncPipeTestData\"\u003ehttps://github.com/likelet/LncPipeTestData\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-prepare-input-files\" class=\"anchor\" href=\"#prepare-input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare input files\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-references-index-and-annotation-filesmandatory\" class=\"anchor\" href=\"#references-index-and-annotation-filesmandatory\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences, index and annotation files(Mandatory).\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cg-emoji class=\"g-emoji\" alias=\"blush\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f60a.png\"\u003e\ud83d\ude0a\u003c/g-emoji\u003ePlease keep the consistency of your genome sequence,index library and annotation files (Important!): genome version, chromosome format, gtf coordinated e.g. The dependent third-party softwares may stop for any discrepencies in file-formatting.\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenome reference (genome fasta file with suffix \u003ccode\u003e.fa\u003c/code\u003e etc. )\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenome Index for alignment (hisat2 or tophat or STAR)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGENCODE gene annotation file in GTF format\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLNCipedia gene annotation file in GTF format.(set null if not available for your species)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRaw sequence file with *.fastq.gz / *.fq.gz suffixed\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-species\" class=\"anchor\" href=\"#species\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecies\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;Currently, LncPipe has been tested for detection of lncRNAs in \u0027humans\u0027 only.\nHowever, LncPipe can be manually configured to run the anlysis for other species as well and requires additional files \"known_protein_coding.gtf\" and \"known_lncRNA.gtf\" for coding probability calculations. More information on usage for non-human species can be found here. \n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eReference files for humans\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ehisat index built from Genome:\n\u003ca href=\"http://cancerbio.info/pub/hg38_hisat_index.tar.gz\" rel=\"nofollow\"\u003ehttp://cancerbio.info/pub/hg38_hisat_index.tar.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenome reference:\n\u003ca href=\"ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/GRCh38.p10.genome.fa.gz\" rel=\"nofollow\"\u003eftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/GRCh38.p10.genome.fa.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGENCODE gene annotation:\n\u003ca href=\"ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/gencode.v27.annotation.gtf.gz\" rel=\"nofollow\"\u003eftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/gencode.v27.annotation.gtf.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLNCipedia gene annotation:\n\u003ca href=\"https://lncipedia.org/downloads/lncipedia_5_0_hc_hg38.gtf\" rel=\"nofollow\"\u003ehttps://lncipedia.org/downloads/lncipedia_5_0_hc_hg38.gtf\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRaw sequence file with *.fastq.gz / *.fq.gz suffixed\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReference files for mouse\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ehisat index built from Genome\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenome reference:\u003cbr\u003e\n\u003ca href=\"ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M16/GRCm38.p5.genome.fa.gz\" rel=\"nofollow\"\u003eftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M16/GRCm38.p5.genome.fa.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGENCODE gene annotation:\n\u003ca href=\"ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M16/gencode.vM16.annotation.gtf.gz\" rel=\"nofollow\"\u003eftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M16/gencode.vM16.annotation.gtf.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLNCipedia gene annotation: null\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRaw sequence file with *.fastq.gz / *.fq.gz suffixed\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-docker\" class=\"anchor\" href=\"#run-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Docker\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ePrepare input files as mentioned earlier.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eModify the \u003ccode\u003edocker.config\u003c/code\u003e in \u003ccode\u003emandatory\u003c/code\u003e section.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall docker and download the latest LncPipe build using:\n\u003ccode\u003edocker pull bioinformatist/lncpipe\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun LncPipe using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nextflow -c docker.config run LncRNAanalysisPipe.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe docker image for LncPipe is available on the docker-hub (\u003ca href=\"https://hub.docker.com/r/bioinformatist/lncpipe/tags/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/bioinformatist/lncpipe/tags/\u003c/a\u003e).\nAlternatively, nextflow can automatically pull image from docker.io. \u003ccode\u003eDockerfile\u003c/code\u003e recorded that what we have done with the image. For user from local China looking to pull the docker image can use this \u003ca href=\"https://github.com/likelet/Blogs_tips/blob/master/README.md#setting-docker-download-mirror-site\"\u003emirror site instead\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eTO Install softwares locally on your machine, please see install instructions \u003ca href=\"https://github.com/likelet/LncPipe/blob/master/InstallSoftwareLocally.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-interactive-reports\" class=\"anchor\" href=\"#interactive-reports\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive reports\u003c/h2\u003e\n\u003cp\u003eThe results of LncPipe are summarized and visualized via interactive plots by our novel R package \u003ca href=\"https://github.com/bioinformatist/LncPipeReporter\"\u003eLncPipeReporter\u003c/a\u003e. Users can also try LncPipeReporter as stand-alone for visualizing known and novel lncRNAs.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003eAs a nextflow-based analysis pipeline, LncPipe allow users edit configure file \u003ccode\u003enextflow.config\u003c/code\u003e to set the index files and default file path parameters instead of typing them into the command line.\u003c/p\u003e\n\u003cp\u003eTo configure, please go to \u003ccode\u003eparams\u003c/code\u003e line, and set the following information of various file locations and system environment settings\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-groovy\"\u003e\u003cpre\u003e params {\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e/*\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e User setting options (mandatory)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e \u003cspan class=\"pl-c\"\u003e*/\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e input file and genome reference\u003c/span\u003e\n fastq_ext \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e*_{1,2}.fq.gz\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n fasta_ref \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/data/database/hg38/genome.fa\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n design \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edesign.file\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n hisat2_index \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/data/database/hg38/hisatIndex/grch38_snp_tran/genome_snp_tran\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n cpatpath\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/opt/CPAT-1.2.3\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003ehuman gtf only\u003c/span\u003e\n gencode_annotation_gtf \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/data/database/hg38/Annotation/gencode.v24.annotation.gtf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n lncipedia_gtf \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/data/database/hg38/Annotation/lncipedia_4_0_hg38.gtf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e set \"null\" if you are going to perform analysis on other species\u003c/span\u003e\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e additional options for non-human species, else leaving them unchanged\u003c/span\u003e\n species\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehuman\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e mouse , zebrafish, fly\u003c/span\u003e\n known_coding_gtf\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n known_lncRNA_gtf\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003efor test\u003c/span\u003e\n cpatpath \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/home/zhaoqi/software/CPAT/CPAT-1.2.2/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e/*\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e User setting options (optional)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e \u003cspan class=\"pl-c\"\u003e*/\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e tools setting\u003c/span\u003e\n star_idex \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eset if star used\u003c/span\u003e\n bowtie2_index \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eset if tophat used\u003c/span\u003e\n aligner \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehisat\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e or \"star\",\"tophat\"\u003c/span\u003e\n sam_processor\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esambamba\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eor \"samtools(deprecated)\"\u003c/span\u003e\n qctools \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003efastp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e or \"afterqc\",\"fastp\",\"fastqc\"\u003c/span\u003e\n detools \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eedger\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eor \"deseq2\",\"noiseq\" not supported yet\u003c/span\u003e\n quant \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ekallisto\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e or \u0027htseq\u0027\u003c/span\u003e\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eother setting\u003c/span\u003e\n singleEnd \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\n unstrand \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\n skip_combine \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\n lncRep_Output \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ereporter.html\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n lncRep_theme \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003enpg\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n lncRep_cdf_percent \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e\n lncRep_max_lnc_len \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10000\u003c/span\u003e\n lncRep_min_expressed_sample \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e\n mem\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e60\u003c/span\u003e\n cpu\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e\n }\n\n manifest {\n homePage \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehttps//github.com/likelet/LncPipe\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n description \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eLncPipe:a Nextflow-based Long non-coding RNA analysis PIPELINE\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n mainScript \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eLncRNAanalysisPipe.nf\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n }\n\n\n timeline {\n \u003cspan class=\"pl-c1\"\u003eenabled\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003efile\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etimeline.html\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n }\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-parameters\" class=\"anchor\" href=\"#parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThose parameters would cover the setting from \u003ccode\u003enextflow.config\u003c/code\u003e file\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eMandatory(plz configure those options in \u003cem\u003enextflow.config\u003c/em\u003e or \u003cem\u003edocker.config\u003c/em\u003e file)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth align=\"right\"\u003eExample/Default value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003e.\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003einput folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--species\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003ehuman\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eYour species, mouse, fly and zebra fish are also supported\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fastq_ext\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003e*_{1,2}.fastq.gz\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003einput raw paired reads\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--out_folder\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003e.\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eoutput folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--design\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003eFALSE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ea txt file that stored experimental design information, plz see details from \u003ccode\u003e--design\u003c/code\u003e section below\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003eReferences\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRequired\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--star_index/--bowtie2_index/--hisat2_index\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003ePath to STAR?bowtie2/hisat2(mutually exclusive) index(required if not set in config file)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fasta\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePath to Fasta reference(required if not set in config file)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--gencode_annotation_gtf\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAn annotation file from GENCODE database for annotating lncRNAs(required if not set in config file). e.g. gencode.v26.annotation.gtf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--lncipedia_gtf\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAn annotation file from LNCipedia database for annotating lncRNAs(required if not set in config file) e.g. \u003ca href=\"http://www.lncipedia.org/downloads/lncipedia_4_0_hc_hg38.gtf\" rel=\"nofollow\"\u003elncipedia_4_0_hc_hg38.gtf\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003esoftware path (should not setting when using docker )\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRequired\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpatpath\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eHome folder of cpat installed location\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cblockquote\u003e\n\u003cp\u003esince cpat may call model data from its home path, users should specified where the model file is located in. Especially users install cpat by themselves without our install code.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eOptional\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--singleEnd\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eFALSE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003especify that the reads are single ended\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--merged_gtf\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eFALSE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSkip mapping and assembly step by directly providing assembled merged gtf files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--unstrand\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eFALSE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003especify that library is unstrand specific\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--aligner\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003estar\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAligner for reads mapping (optional), STAR is default and supported only at present,\u003cem\u003estar\u003c/em\u003e/\u003cem\u003etophat\u003c/em\u003e/\u003cem\u003ehisat2\u003c/em\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--qctools\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003efastp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eTools for assess raw reads quality or filtered by \u003cem\u003efastp\u003c/em\u003e, \u003cem\u003efastqc\u003c/em\u003e, \u003cem\u003eafterqc\u003c/em\u003e or \u003cem\u003enone\u003c/em\u003e(skip qc step)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003eLncPipeReporter options\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--lncRep_Output\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ereporter.html\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSpecify report file name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--lncRep_theme\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003enpg\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePlot theme setting in interactive plot. Values from \u003ca href=\"https://github.com/road2stat/ggsci\"\u003eggsci\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--lncRep_min_expressed_sample\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e50\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum expressed gene allowed in each sample, 50 default. Samples not passed were filtered from analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ccode\u003e--fastq_ext\u003c/code\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eRaw fastq files are required for de-novo analysis.This parameters should be set according to your paired or singled reads file names.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Sample1_1.fq.gz\n Sample1_2.fq.gz\n Sample2_1.fq.gz\n Sample2_2.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can input pattern \u003ccode\u003e*_{1,2}.fq.gz\u003c/code\u003e to make the all paired-end file recognized by \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eFor singled reads file, file pattern should be fed with \u003ccode\u003e--singleEnd\u003c/code\u003e parameter specified\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--star_idex?--bowtie2_index/--hisat2_index\u003c/code\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThis parameter is \u003cem\u003erequired\u003c/em\u003e when not configured in nextflow.config file. It specify the star/tophat/hisat2(mutually exclusive) index folder built before running \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e .\nIf you don\u0027t know what it is?You can use \u003ccode\u003e--fasta\u003c/code\u003e to specify the reference sequence data. The index file would be built by \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e automatically.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ccode\u003e--design\u003c/code\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eExperimental design file matrix for differential expression analysis. Default: \u003ccode\u003enull\u003c/code\u003e\nFormat:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eWT:Sample1,Sample2,Sample3\nKO:Sample1,Sample2,Sample3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhile \u003ccode\u003eKO/WT\u003c/code\u003e represents the two experimental condition, and sample1, sample2, sample3 are replicates which should be comma-delimited in the same line .\u003c/p\u003e\n\u003cp\u003eFor sample names, it should be the sample as the prefix of fastq files which was trimmed by \u003ccode\u003e--fastq_ext\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cp\u003eif fastq file names are \u003ccode\u003eSample1_1.fq.gz, Sample1_2.fq.gz\u003c/code\u003e that comes from one sample and your \u003ccode\u003e--fastq_ext\u003c/code\u003e is set as \u003ccode\u003e*_{1,2}.fq.gz\u003c/code\u003e, the sample name\nshould be Sample1.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eResult\u003c/code\u003e folder under current path(default) or output_folder set by user. A typical structure of \u003ccode\u003eResult\u003c/code\u003e is follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Result/\n \u251c\u2500\u2500 QC\n \u2502 \u251c\u2500\u2500 N1141_1.clean_fastqc.html\n \u2502 \u251c\u2500\u2500 N1141_2.clean_fastqc.html\n \u2502 \u251c\u2500\u2500 N1177_1.clean_fastqc.html\n \u2502 \u2514\u2500\u2500 N1177_2.clean_fastqc.html\n \u251c\u2500\u2500 Identified_lncRNA\n \u2502 \u251c\u2500\u2500 all_lncRNA_for_classifier.gtf\n \u2502 \u251c\u2500\u2500 final_all.fa\n \u2502 \u251c\u2500\u2500 final_all.gtf\n \u2502 \u251c\u2500\u2500 lncRNA.fa\n \u2502 \u251c\u2500\u2500 protein_coding.fa\n \u2502 \u2514\u2500\u2500 protein_coding.final.gtf\n \u251c\u2500\u2500 LncReporter\n \u2502 \u251c\u2500\u2500 Differential_Expression_analysis.csv\n \u2502 \u2514\u2500\u2500 Report.html\n \u251c\u2500\u2500 Quantification\n \u2502 \u251c\u2500\u2500 kallisto.count.txt\n \u2502 \u2514\u2500\u2500 kallisto.tpm.txt\n \u2514\u2500\u2500 Star_alignment\n \u251c\u2500\u2500 STAR_N1141\n \u2502 \u251c\u2500\u2500 N1141Aligned.sortedByCoord.out.bam\n \u2502 \u251c\u2500\u2500 N1141Log.final.out\n \u2502 \u251c\u2500\u2500 N1141Log.out\n \u2502 \u251c\u2500\u2500 N1141Log.progress.out\n \u2502 \u2514\u2500\u2500 N1141SJ.out.tab\n \u2514\u2500\u2500 STAR_N1177\n \u251c\u2500\u2500 N1177Aligned.sortedByCoord.out.bam\n \u251c\u2500\u2500 N1177Log.final.out\n \u251c\u2500\u2500 N1177Log.out\n \u251c\u2500\u2500 N1177Log.progress.out\n \u2514\u2500\u2500 N1177SJ.out.tab\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eQC\u003c/code\u003e stored the Quality control output generated by FastQC or AfterQC software.\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eIdentified_lncRNA\u003c/code\u003e contains all assembled lncRNA and their sequences. \u003cem\u003eall_lncRNA_for_classifier.gtf\u003c/em\u003e includes both novel and known lncRNA features in \u003ca href=\"http://www.ensembl.org/info/website/upload/gff.html\" rel=\"nofollow\"\u003eGTF format\u003c/a\u003e;\n\u003cem\u003elncRNA.fa\u003c/em\u003e is all lncRNA sequences in fasta format. \u003cem\u003eprotein_coding.final.gtf\u003c/em\u003e and \u003cem\u003eprotein_coding.fa\u003c/em\u003e are protein coding information extracted from gencode annotation. \u003cem\u003efinal_all.gtf\u003c/em\u003e and \u003cem\u003efinal_all.fa\u003c/em\u003e are combined files for further analysis.\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAlignment\u003c/code\u003e are hisat/tophat/STAR aligner standard output\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eQuantification\u003c/code\u003e are estimated abundance using kallisto. \u003cem\u003ekallisto.count.txt\u003c/em\u003e stored reads count matrix and \u003cem\u003ekallisto.tpm.txt\u003c/em\u003e are tpm(Transcripts Per Kilobase Million) matrix.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLncReporter\u003c/code\u003e stored the interactive report file and differential expression matrix generated by LncPipeReporter which wrapped EdgeR.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tips\" class=\"anchor\" href=\"#tips\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTips\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cg-emoji class=\"g-emoji\" alias=\"blush\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f60a.png\"\u003e\ud83d\ude0a\u003c/g-emoji\u003ePlz keep the consistency of your genome sequence, index library and annotation files: genome version, chromosome format, gtf coordinated e.g. The third-party software may stop for any of the above reasons.\u003c/li\u003e\n\u003cli\u003e\n\u003cg-emoji class=\"g-emoji\" alias=\"confused\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f615.png\"\u003e\ud83d\ude15\u003c/g-emoji\u003eSetting your analysis parameters always in config file, differ project should corresponding to differ configurations for reproductive analysis. To rerun a project, you can just specify -c \u003ccode\u003eyour.config\u003c/code\u003e in your command, which can also help you to record analysis parameters.\u003c/li\u003e\n\u003cli\u003e\n\u003cg-emoji class=\"g-emoji\" alias=\"open_mouth\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f62e.png\"\u003e\ud83d\ude2e\u003c/g-emoji\u003eRun analysis on docker container, no much to say.\u003c/li\u003e\n\u003cli\u003e\n\u003cg-emoji class=\"g-emoji\" alias=\"grimacing\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f62c.png\"\u003e\ud83d\ude2c\u003c/g-emoji\u003eAlways use the latest version to be away from the known bugs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" href=\"#acknowledgement\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThanks to the author of \u003ca href=\"https://github.com/OpenGene/AfterQC\"\u003eAfterQC\u003c/a\u003e, Shifu Chen, for his help on providing a gzip output support to meet the require of LncPipe. Thanks to the internal test by Hongwan Zhang and Yan Wang from SYSUCC Cancer bioinformatics platform.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-faq\" class=\"anchor\" href=\"#faq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cem\u003e1. PLEK throws an error \"/data/software/PLEK.1.2/PLEK.py:line12: $\u0027\\r\u0027: can not find command\", how to fix?\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA: using the follow command as suggested in the installation section.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e perl -CD -pi -e\u0027tr/\\x{feff}//d \u0026amp;\u0026amp; s/[\\r\\n]+/\\n/\u0027 *.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cem\u003e2. IOError: [Errno 2] No such file or directory: \u0027/opt/CPAT-1.2.3/dat/Human_Hexamer.tsv\u0027?\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA: The cpat command required the \u003ccode\u003eHuman_Hexamer.tsv\u003c/code\u003e to predict lncRNA coding potential, plz check your \u003ccode\u003ecpatpath\u003c/code\u003e parameters.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cem\u003e3. When using htseq to quantify transicript, it throws \"Error occured when reading beginning of SAM/BAM file. \u0027csamtools.AlignedRead\u0027 object has no attribute \u0027reference_start\u0027 \"\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA: It\u0027s a version conflict caused by htseq and hisat generated bamfile, a possible solution for this is to install the old version of htseq\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003eFor implementation:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/likelet\"\u003eQi Zhao\u003c/a\u003e \u003ca href=\"mailto:zhaoqi@sysucc.org.cn\"\u003ezhaoqi@sysucc.org.cn\u003c/a\u003e, Sun Yat-sen University Cancer Center;\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://icannotendure.space\" rel=\"nofollow\"\u003eYu Sun\u003c/a\u003e \u003ca href=\"mailto:sun_yu@mail.nankai.edu.cn\"\u003esun_yu@mail.nankai.edu.cn\u003c/a\u003e, Nan kai University;\u003cbr\u003e\nFor project design and new feature request:\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/likelet\"\u003eQi Zhao\u003c/a\u003e \u003ca href=\"mailto:zhaoqi@sysucc.org.cn\"\u003ezhaoqi@sysucc.org.cn\u003c/a\u003e, Sun Yat-sen University Cancer Center;\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"\"\u003eZhixiang Zuo\u003c/a\u003e \u003ca href=\"mailto:zuozhx@sysucc.org.cn\"\u003ezuozhx@sysucc.org.cn\u003c/a\u003e, Sun Yat-sen University Cancer Center;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eWe strongly recommend users open new issues if they have questions or find bugs.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"LICENSE\"\u003eGPL v3 license\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eQi Zhao, Yu Sun, Dawei Wang, Hongwan Zhang, Kai Yu, Jian Zheng, Zhixiang Zuo. LncPipe: A Nextflow-based pipeline for identification and analysis of long non-coding RNAs from RNA-Seq data. Journal of Genetics and Genomics. 2018. (\u003cem\u003eIn press\u003c/em\u003e)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "data-cleaning", - "statistics", - "political-science", - "political-parties", - "python", - "election-analysis", - "election-data" - ], - "updated_at": 1640627843.0 + "topics": [], + "updated_at": 1643344466.0 }, { "data_format": 2, - "description": "Attempt at Docker/GATK Port to Singularity for MSU HPCC", + "description": null, "filenames": [ "Singularity" ], - "full_name": "msuefishlab/gatk_singularity", + "full_name": "stela2502/singularityImages", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularityimages\" class=\"anchor\" href=\"#singularityimages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularityImages\u003c/h1\u003e\n\u003cp\u003eThis git repo is a skelleton of my work I have done on singularity images.\nThese images are used on aurora-ls2 to run analyses on the blades instead of the frontend.\u003c/p\u003e\n\u003cp\u003eAll of that documention is in our Bioinformatics Slack Howto channel.\u003c/p\u003e\n\u003cp\u003eThe software I install I mainly install from within the singularity image. Hence the usage of shell.sh.\u003c/p\u003e\n\u003cp\u003eInstaling Python modules is tricky as pip3 always installs in a private path and not the global unless told otherwise.\nHence only I with my username on the computer I build the images could use the modules.\u003c/p\u003e\n\u003cp\u003eA solution could be to use some conda approach, but as this here will be a singularity image we could also try to install globaly:\u003c/p\u003e\n\u003cp\u003ePython solution:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install --prefix=/usr/local \u0026lt;package name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1521034490.0 + "updated_at": 1643377152.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Studenten/XiaoyuSun/Polygonization-by-Frame-Field-Learning/singularity/Singularity", + "Studenten/Polygonization-by-Frame-Field-Learning-master-3bandRGB/singularity/Singularity" ], - "full_name": "juanca09/tgv", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tgv\" class=\"anchor\" aria-hidden=\"true\" href=\"#tgv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etgv\u003c/h1\u003e\n", + "full_name": "vissed-kad/github_demo", + "latest_release": "v1.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-objectherkenning-met-deeplearning-technieken\" class=\"anchor\" href=\"#objectherkenning-met-deeplearning-technieken\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eObjectherkenning met Deeplearning technieken\u003c/h1\u003e\n\u003cp\u003eDeze repository bevat folders en bestanden van de projecten van het Objectherkenningsteam.\u003c/p\u003e\u003cp\u003eZie de info in de onderliggende folder(s) voor meer informatie.\u003c/p\u003e\n\u003cp\u003etest 1234\ntest 5678\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1612281173.0 + "updated_at": 1643653877.0 }, { "data_format": 2, - "description": "Test using singularityhub", + "description": "centos8 container to run brave ", "filenames": [ - "Singularity", - "Singularity.centostest", - "Singularity.basic" + "Singularity" ], - "full_name": "nbarlowATI/shub-test", + "full_name": "truatpasteurdotfr/singularity-docker-centos8-brave", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-shub-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#shub-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eshub-test\u003c/h1\u003e\n\u003cp\u003eTest using singularityhub\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-docker-centos8-brave-using-stream8-now-that-centos8-is-eoled\" class=\"anchor\" href=\"#singularity-docker-centos8-brave-using-stream8-now-that-centos8-is-eoled\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-centos8-brave (using stream8 now that centos8 is EOL\u0027ed)\u003c/h1\u003e\n\u003cp\u003ecentos8 container to run brave built from github actions\u003c/p\u003e\n\u003cp\u003eRunning without installation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos8-brave:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuilding:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-docker-centos8-brave.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload and rename:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name singularity-docker-centos8-brave.sif oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos8-brave:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning with a separate $HOME (here ~/singularity.d/home/singularity-docker-centos8-brave)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p ~/singularity.d/home/singularity-docker-centos8-brave\nsingularity run -B /run -H ~/singularity.d/home/singularity-docker-centos8-brave singularity-docker-centos8-brave.sif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1617891470.0 + "updated_at": 1635199842.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.0.9.18" ], - "full_name": "CN-Healthborn/el7tf1.12gpu", + "full_name": "Famingzhao/pySCENIC", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nova-el7-tensorflow-gpu\" class=\"anchor\" aria-hidden=\"true\" href=\"#nova-el7-tensorflow-gpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enova-el7-tensorflow-gpu\u003c/h1\u003e\n\u003cp\u003eConfigurations for docker and singularity for making OSG-compatible CENTOS7 container with GPU-accelerated tensorflow and keras installed.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1603475388.0 + "updated_at": 1643982275.0 }, { "data_format": 2, - "description": "Age Group Prediction in TV news (Open Source)", + "description": "Files of FWI Paper", "filenames": [ - "Singularity.trial", - "Singularity.newsage" + "devito/docker/Singularity.nvidia.def" ], - "full_name": "Xiaoyu-Lu/GSoC_2020", - "latest_release": null, - "readme": "\u003cp\u003eGSoC 2020: Age Group Prediction in TV news\u003c/p\u003e\n", + "full_name": "felipeaugustogudes/paper-fwi", + "latest_release": "v1.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-effectiveness-and-computational-efficiency-of-absorbing-boundary-conditions-for-full-waveform-inversion\" class=\"anchor\" href=\"#effectiveness-and-computational-efficiency-of-absorbing-boundary-conditions-for-full-waveform-inversion\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEffectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-authors-daiae-iglesia-dolci-felipe-a-g-silva-pedro-s-peixoto-and-ernani-v-volpe\" class=\"anchor\" href=\"#authors-daiae-iglesia-dolci-felipe-a-g-silva-pedro-s-peixoto-and-ernani-v-volpe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors: Daiae Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto and Ernani V. Volpe\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-department-of-mechanical-engineering-of-polytechnic-school-university-of-s\u00e3o-paulo\" class=\"anchor\" href=\"#department-of-mechanical-engineering-of-polytechnic-school-university-of-s%C3%A3o-paulo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDepartment of Mechanical Engineering of Polytechnic School, University of S\u00e3o Paulo\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-department-of-applied-mathematics-institute-of-mathematics-and-statistics-university-of-s\u00e3o-paulo\" class=\"anchor\" href=\"#department-of-applied-mathematics-institute-of-mathematics-and-statistics-university-of-s%C3%A3o-paulo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDepartment of Applied Mathematics, Institute of Mathematics and Statistics, University of S\u00e3o Paulo\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contacts-dolciuspbr-felipeaugustoguedesuspbr-pedrospimeuspbr-ernvolpeuspbr\" class=\"anchor\" href=\"#contacts-dolciuspbr-felipeaugustoguedesuspbr-pedrospimeuspbr-ernvolpeuspbr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContacts: \u003ca href=\"mailto:dolci@usp.br\"\u003edolci@usp.br\u003c/a\u003e, \u003ca href=\"mailto:felipe.augusto.guedes@usp.br\"\u003efelipe.augusto.guedes@usp.br\u003c/a\u003e, \u003ca href=\"mailto:pedrosp@ime.usp.br\"\u003epedrosp@ime.usp.br\u003c/a\u003e, \u003ca href=\"mailto:ernvolpe@usp.br\"\u003eernvolpe@usp.br\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eImportant Informations:\u003c/strong\u003e These codes are part of the Project Software Technologies for Modeling and Inversion (STMI) at RCGI in the University of Sao Paulo.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1606450294.0 + "updated_at": 1644293938.0 }, { "data_format": 2, - "description": "Singularity container for Samviewer", + "description": "Nextflow pipeline for single cell analysis", "filenames": [ "Singularity" ], - "full_name": "CHPC-UofU/Singularity-ubuntu-samviewer", + "full_name": "soulj/SkeletalVis-SingleCell", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-skeletalvis-singlecell\" class=\"anchor\" href=\"#skeletalvis-singlecell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSkeletalVis-SingleCell\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eSkeletalVis-SingleCell\u003c/strong\u003e is a bioinformatics pipeline for reproducible analyses of 10x Genomics single-cell RNA-sequencing data.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a portable workflow tool to run tasks across multiple compute infrastructures. This pipeline uses a singularity container containing all the software needed to run the analysis, making installation simple and the results reproducible.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline-summary\" class=\"anchor\" href=\"#pipeline-summary\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline summary\u003c/h2\u003e\n\u003cp\u003eThe \u003cstrong\u003eSkeletalVis-SingleCell\u003c/strong\u003e pipeline takes a sample table and a parameter file defining the experiment as input. If not provided fastq files are automatically downloaded using the provided sample identifiers.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-features\" class=\"anchor\" href=\"#features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures:\u003c/h3\u003e\n\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Download of fastq files either directly from ENA, via conversion of sra or bam files from SRA\u003cbr\u003e\n(\u003cstrong\u003eb\u003c/strong\u003e)\tQuantification using \u003ca href=\"https://www.kallistobus.tools/\" rel=\"nofollow\"\u003e\u003ccode\u003ekallisto-bustools\u003c/code\u003e\u003c/a\u003e to produce cell x gene matrices\u003cbr\u003e\n(\u003cstrong\u003ec\u003c/strong\u003e) Flexible filtering of \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/DropletUtils.html\" rel=\"nofollow\"\u003e\u003ccode\u003eempty droplets\u003c/code\u003e\u003c/a\u003e, quality control and thresholding\u003cbr\u003e\n(\u003cstrong\u003ed\u003c/strong\u003e) Normalisation and cell cycle effect removal\u003cbr\u003e\n(\u003cstrong\u003ee\u003c/strong\u003e) Automatic cell type annotation with \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/SingleR.html\" rel=\"nofollow\"\u003e\u003ccode\u003eSingleR\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003ef\u003c/strong\u003e) Clustering and visualisation with \u003ca href=\"https://satijalab.org/seurat/\" rel=\"nofollow\"\u003e\u003ccode\u003eSeurat\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003eg\u003c/strong\u003e) Marker gene identification and pathway analysis\u003cbr\u003e\n(\u003cstrong\u003eh\u003c/strong\u003e) Cell crosstalk analysis of ligand-receptor predictions using \u003ca href=\"https://github.com/saezlab/liana\"\u003e\u003ccode\u003eliana\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003ei\u003c/strong\u003e) Sample integration and differential expression analysis between conditions with \u003ca href=\"https://github.com/MarioniLab/miloR\"\u003e\u003ccode\u003emiloR\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses are run in parallel and in result of error you can resume with the \u003ccode\u003e-resume\u003c/code\u003e parameter to re-run the pipeline starting from the previous fault.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-analyse-an-example-dataset\" class=\"anchor\" href=\"#analyse-an-example-dataset\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalyse an example dataset\u003c/h3\u003e\n\u003cp\u003eTry the pipeline on an example dataset (all inputs will be automatically downloaded): -\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html#installation\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/docs/latest/config.html\" rel=\"nofollow\"\u003e\u003ccode\u003eConfigure\u003c/code\u003e\u003c/a\u003e the resource profile for your HPC or local computer. A template for slurm schedulers is provided as an example in \u003ccode\u003enextflow.config\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the pipeline and test on the example dataset with a single command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow run soulj/SkeletalVis-SingleCell -profile slurm -params-file GSE152805.yaml -with-singularity library://jsoul/default/singlecell:latest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-analyse-your-own-data\" class=\"anchor\" href=\"#analyse-your-own-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalyse your own data\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eDefine the sampleTable\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCreate a tab seperated table with unique Sample names, SRR accession numbers (if download is needed) and any additional metadata e.g\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSample\u003c/th\u003e\n\u003cth\u003eFile\u003c/th\u003e\n\u003cth\u003eCondition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eControl_1\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eControl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eControl_2\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eControl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTreated_1\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eTreated\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTreated_2\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eTreated\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDefine the configuration\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMost parameters are set to sensible defaults within the main nextflow script, with only 5 parameters required to be altered with typical use:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eOptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eaccession\u003c/td\u003e\n\u003ctd\u003eThe GEO accession of the data - used to name output data and download fastq files\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edownloadSite\u003c/td\u003e\n\u003ctd\u003eThe site to download the raw data from if needed\u003c/td\u003e\n\u003ctd\u003eSRA, ENA, SRA_BAM\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especies\u003c/td\u003e\n\u003ctd\u003eThe species the reads originate from - used to create the kallisto bus index\u003c/td\u003e\n\u003ctd\u003ehuman, mouse\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003echemistry\u003c/td\u003e\n\u003ctd\u003eThe chemistry used for the 10x Genomics experiment\u003c/td\u003e\n\u003ctd\u003e10xv1, 10xv2, 10xv3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ereplciates\u003c/td\u003e\n\u003ctd\u003eDoes the experiment contain replicated treatments to perform differential expression analysis?\u003c/td\u003e\n\u003ctd\u003etrue, false\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eParameters should be defined within a yaml file. See \u003ccode\u003eparams/GSE152805.yaml\u003c/code\u003e for an example.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline with your own parameters\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow run soulj/SkeletalVis-SingleCell -profile slurm -params-file ownData.yaml -with-singularity library://jsoul/default/skeletalvis-singlecell\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-testing-modules\" class=\"anchor\" href=\"#testing-modules\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting modules\u003c/h3\u003e\n\u003cp\u003eModules can be tested using the \u003ca href=\"https://pypi.org/project/pytest-workflow/\" rel=\"nofollow\"\u003e\u003ccode\u003epytest-workflow\u003c/code\u003e\u003c/a\u003e framework. Module test directories within the \u003ccode\u003etests\u003c/code\u003e folder contain a nextflow script and a configuration yaml file defining the test for each module.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall pytest-workflow\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003econda install pytest-workflow\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the tests - e.g to test the GSEA module\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epytest --symlink --kwdof --tag gsea\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1498859914.0 + "updated_at": 1644406348.0 }, { "data_format": 2, - "description": null, + "description": "Files to create singularity container for CHPC deeplearning module", "filenames": [ - "container/Singularity" + "Singularity.deeplearning" ], - "full_name": "Genomic-Medicine-Linkoping/nextflow_rnaseqfus", + "full_name": "CHPC-UofU/deeplearning-module", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-deeplearning-module\" class=\"anchor\" href=\"#deeplearning-module\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edeeplearning-module\u003c/h1\u003e\n\u003cp\u003eThis repo contains files to construct the container for the CHPC deeplearning\nmodule.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1622550367.0 + "updated_at": 1644445691.0 }, { "data_format": 2, "description": null, "filenames": [ - "0.0.0.9000/Singularity.0.0.0.9000" + "Singularity.salad", + "Singularity", + "Singularity.pokemon" ], - "full_name": "yh549848/singularity-raptranker", - "latest_release": null, + "full_name": "mwittep/EAGER", + "latest_release": "v1.92.56", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" href=\"#singularity-deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"img/shpc.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" href=\"#1-template-or-fork\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Template or Fork\u003c/h3\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" href=\"#1-write-your-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" href=\"#2-update-the-version-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Update the VERSION file\u003c/h3\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/releases.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" href=\"#3-how-to-develop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How to Develop\u003c/h3\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" href=\"#4-how-to-pull\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. How to pull\u003c/h3\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1602825895.0 + "updated_at": 1644482849.0 }, { "data_format": 2, "description": null, "filenames": [ - "dockerfiles/Singularity-dota.simg", - "dockerfiles/Singularity-dotaservice.simg" + "singularity/student/Singularity", + "singularity/base/Singularity" ], - "full_name": "bglick13/dotaservice", + "full_name": "UIUC-cs484/uiuccs484parallelprog", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-dotaservice\" class=\"anchor\" aria-hidden=\"true\" href=\"#dotaservice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDotaService\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dotaservice-icon.png\"\u003e\u003cimg src=\"dotaservice-icon.png\" alt=\"dotaservice icon\" width=\"128\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eNOTE: The project that uses the dotaservice in a k8s environment is the \u003ca href=\"https://github.com/TimZaman/dotaclient\"\u003eDotaClient\u003c/a\u003e repo.\u003c/p\u003e\n\u003cp\u003eDotaService is a service to play Dota 2 through gRPC. There are first class python bindings\nand examples, so you can play dota as you would use the OpenAI gym API.\u003c/p\u003e\n\u003cp\u003eIt\u0027s fully functional and super lightweight. Starting Dota \u003ccode\u003eobs = env.reset()\u003c/code\u003e takes 5 seconds,\nand each \u003ccode\u003eobs = env.step(action)\u003c/code\u003e in the environment takes between 10 and 30 ms.\u003c/p\u003e\n\u003cp\u003eYou can even set the config of \u003ccode\u003erender=True\u003c/code\u003e and you can watch the game play live. Each game will\nhave a uuid and folder associated where there\u0027s a Dota demo (replay) and console logs.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"demo.gif\"\u003e\u003cimg src=\"demo.gif\" alt=\"demo\" width=\"600\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-dotaservice-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-dotaservice-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun DotaService Locally\u003c/h2\u003e\n\u003cp\u003eRun the DotaService so you can connect your client to it later. Only one client per server\nis supported, and only one DotaService per VM (eg local or one per docker container).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m dotaservice\n\u0026gt;\u0026gt;\u0026gt; Serving on 127.0.0.1:13337\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-dotaservice-distributed\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-dotaservice-distributed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun DotaService Distributed\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"docker/README.md\"\u003edocker/README.md\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo run two dockerservice instances, one on port \u003ccode\u003e13337\u003c/code\u003e and one on \u003ccode\u003e13338\u003c/code\u003e, f.e. run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -dp 13337:13337 ds\ndocker run -dp 13338:13337 ds\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can run as many as you want, until you run out of ports or ip addresses. If you are wearing\nyour fancy pants, use Kubernetes to deploy gazillions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-client-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#client-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClient Code\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egrpclib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eclient\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eChannel\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprotobuf\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eDotaService_grpc\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDotaServiceStub\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprotobuf\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eDotaService_pb2\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAction\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprotobuf\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eDotaService_pb2\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Connect to the DotaService.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDotaServiceStub\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eChannel\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027127.0.0.1\u0027\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e13337\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e# Get the initial observation.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eobservation\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ereset\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e())\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erange\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e):\n \u003cspan class=\"pl-c\"\u003e# Sample an action from the action protobuf\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eaction\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAction\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eMoveToLocation\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e.., \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e.., \u003cspan class=\"pl-s1\"\u003ez\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e..)\n \u003cspan class=\"pl-c\"\u003e# Take an action, returning the resulting observation.\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eobservation\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003estep\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eaction\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis is very useful to provide an environment for reinforcement learning, and service aspect of it makes it\nespecially useful for distributed training. I am planning to provide a client python\nmodule for this (\u003ccode\u003ePyDota\u003c/code\u003e) that mimics typical OpenAI gym APIs. Maybe I won\u0027t even make PyDota\nand the gRPC client is enough.\u003c/p\u003e\n\u003cdiv\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dotaservice.png\"\u003e\u003cimg src=\"dotaservice.png\" alt=\"dotaservice connections\" width=\"680\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.7\u003c/li\u003e\n\u003cli\u003eUnix: MacOS, Ubuntu. A dockerfile is also provided see: \u003ca href=\"docker/README.md\"\u003edocker/README.md\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eInstalling from pypi:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install dotaservice\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor development; installing from source:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(Optional) Compile the protos for Python (run from repository root):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m grpc_tools.protoc -I. --python_out=. --python_grpc_out=. --grpc_python_out=. dotaservice/protos/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.proto\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h1\u003e\n\u003cp\u003eMy dev notes: \u003ca href=\"NOTES.md\"\u003eNOTES.md\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eOpenAI Dota crew\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://karpathy.github.io/2016/05/31/rl/\" rel=\"nofollow\"\u003eKarpathy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eJan Ivanecky\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Nostrademous\"\u003eNostrademous\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containers\" class=\"anchor\" href=\"#containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n\u003cp\u003eContainer declarations and other tools for building the containers for CS 484.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-vmfarm-setup-via-ansible\" class=\"anchor\" href=\"#vmfarm-setup-via-ansible\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVMFarm setup via ansible\u003c/h2\u003e\n\u003cp\u003eThese Ansible scripts assume CentOS_7.\u003c/p\u003e\n\u003cp\u003eInstall Ansible on your fresh VM.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo yum install ansible\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDepending on your setup, you may have a single VM, or you may have an administrative VM and several student VMs.\u003c/p\u003e\n\u003cp\u003eIn either case, you will need to create a file named \u003ccode\u003e/etc/ansible/hosts\u003c/code\u003e (or in older versions of Ansible, \u003ccode\u003e/etc/ansible/hosts/ansiblehosts\u003c/code\u003e) on the admin machine (or single machine).\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://docs.ansible.com/ansible/2.9/\" rel=\"nofollow\"\u003ehttps://docs.ansible.com/ansible/2.9/\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-single-vm\" class=\"anchor\" href=\"#single-vm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingle VM\u003c/h3\u003e\n\u003cp\u003eThe host file should look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[admin]\nlocalhost ansible_connection=local\n[all]\nlocalhost ansible_connection=local\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-multiple-vms-admin--individual-student-vms\" class=\"anchor\" href=\"#multiple-vms-admin--individual-student-vms\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMultiple VMs (admin + individual student VMs)\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e[admin]\nlocalhost ansible_connection=local\n[students]\nstudenthost1.anydomain.edu\nstudenthost2.anydomain.edu\nstudenthost3.anydomain.edu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have difficulty connecting to the student machines, please see \u003ca href=\"https://docs.ansible.com/ansible/latest/user_guide/connection_details.html\" rel=\"nofollow\"\u003ehttps://docs.ansible.com/ansible/latest/user_guide/connection_details.html\u003c/a\u003e . You may need to setup an SSH key.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-ansible-scripts\" class=\"anchor\" href=\"#running-ansible-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Ansible scripts\u003c/h3\u003e\n\u003cp\u003eSSH to the admin machine, clone this repo and run the following commands. (These take a long time, you should probably use a \u003ccode\u003escreen\u003c/code\u003e session for them.)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStart a bash terminal as root:\u003c/em\u003e \u003ccode\u003esudo bash\u003c/code\u003e .\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\nansible-playbook ./ansible/vmfarm/0_basepkgs.yml\nansible-playbook ./ansible/vmfarm/0a_disable_aslr.yml\nansible-playbook ./ansible/vmfarm/0b_mpi.yml\nansible-playbook ./ansible/vmfarm/cmake_installer.yml\nansible-playbook ./ansible/vmfarm/gtest.yml\nansible-playbook ./ansible/vmfarm/gbench.yml\nansible-playbook ./ansible/vmfarm/charm.yml\nansible-playbook ./ansible/vmfarm/hpctoolkitall.yml\n\nrm -rf /tmp/gtest /tmp/gbench /tmp/charm\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e /tmp/hpctoolkit\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n\nyum clean all \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e rm -rf /var/cache/yum\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker-container-building\" class=\"anchor\" href=\"#docker-container-building\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker container building\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eYou probably don\u0027t have to do this. Be absolutely certain beforehand.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo begin with, you shouldn\u0027t need to do this unless you have altered the Ansible scripts that build something in the container.\u003c/p\u003e\n\u003cp\u003eIf future generations of TAs decide to use a newer version of Charm or to radically change the environment for the MPs, it may be necessary to build new docker containers. Otherwise, please find working Docker containers at \u003ca href=\"https://hub.docker.com/u/uiuccs484parallelprog\" rel=\"nofollow\"\u003ehttps://hub.docker.com/u/uiuccs484parallelprog\u003c/a\u003e assignments should be done using the \u003ccode\u003euiuccs484parallelprog/cs484_student\u003c/code\u003e container.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-building-docker-containers\" class=\"anchor\" href=\"#building-docker-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding docker containers\u003c/h3\u003e\n\u003cp\u003eYou can build the docker containers by cloning this repo, then running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash ./docker/build.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eSTOP\u003c/em\u003e\nIf you have altered the Ansible or Docker scripts, you should increment the version number for the docker image. The version number is in the script \u003ccode\u003e./docker/build.sh\u003c/code\u003e .\u003c/p\u003e\n\u003cp\u003eIf you are logged in to docker hub and a member of the group \u003ccode\u003euiuccs484parallelprog\u003c/code\u003e, you can push these images to make them available to the world.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container-building\" class=\"anchor\" href=\"#singularity-container-building\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container building\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eHopefully you don\u0027t have to do this. If you update the docker container, then you may need to.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTODO: Write this.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1585923678.0 + "updated_at": 1539027275.0 }, { "data_format": 2, - "description": null, + "description": "Code repository for a project focused on diagnostic prediction from whole blood slides ", "filenames": [ - "Singularity" + "pipeline_tf2/Singularity.def" ], - "full_name": "dylanturpin/shub_test", + "full_name": "josegcpa/wbs-prediction", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-a-complete-computational-assessment-of-the-cytomorphological-determinants-of-myelodyplastic-syndromes\" class=\"anchor\" href=\"#a-complete-computational-assessment-of-the-cytomorphological-determinants-of-myelodyplastic-syndromes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA complete computational assessment of the cytomorphological determinants of myelodyplastic syndromes\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-motivation\" class=\"anchor\" href=\"#motivation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003cp\u003eThis is the repository for \u003ca href=\"\"\u003ePLACEHOLDER\u003c/a\u003e. In this work, we use the whole blood slides of \u0026gt;300 individuals with myelodyplastic syndromes and anaemias and use them to develop a method that is capable of predicting a disease and retrieving examples of cells which are relevant for each classification.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-code-map\" class=\"anchor\" href=\"#code-map\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode map\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-software\" class=\"anchor\" href=\"#software\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003epython\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esnakemake\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e (analysis and plotting)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-required-python-packages\" class=\"anchor\" href=\"#required-python-packages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired python packages\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003eopencv-python\u003c/code\u003e, \u003ccode\u003etensorflow==1.12\u003c/code\u003e, \u003ccode\u003escikit-image\u003c/code\u003e, \u003ccode\u003eh5py\u003c/code\u003e, \u003ccode\u003ealbumentations\u003c/code\u003e, \u003ccode\u003epsutil\u003c/code\u003e, \u003ccode\u003epytorch\u003c/code\u003e, \u003ccode\u003etifffile\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-project-enumeration\" class=\"anchor\" href=\"#project-enumeration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject enumeration\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003epipeline\u003c/code\u003e - contains the pipeline for WBC and RBC detection and characterisation from WBS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esimulations\u003c/code\u003e - contains simulations validating MILe-ViCe\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emile-vice\u003c/code\u003e - contains the code to train and run MILe-ViCe on the output from \u003ccode\u003epipeline\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erbc-segmentation\u003c/code\u003e - contains the code to train a predictor that filters poorly predictions for detected RBC\u003c/li\u003e\n\u003cli\u003e(STILL TESTING) \u003ccode\u003evae-characterisation\u003c/code\u003e - characterisation of blood cells using a beta-variational autoencoder\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1574885235.0 + "topics": [ + "morphometrics", + "image-analysis", + "bioimage-analysis", + "deep-learning", + "machine-learning" + ], + "updated_at": 1641212653.0 }, { "data_format": 2, - "description": null, + "description": "CBL-D (quinault) singularity and docker image for CI", "filenames": [ "Singularity" ], - "full_name": "robomorelli/singularity_test", + "full_name": "truatpasteurdotfr/singularity-docker-quinault-ci", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_test\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_test\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-cbl-d-quinault-singularity-and-docker-image-for-ci\" class=\"anchor\" href=\"#building-a-cbl-d-quinault-singularity-and-docker-image-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a CBL-D (quinault) singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCBL-D (Common Base Linux - Delridge)\u003c/li\u003e\n\u003cli\u003eDebian 10 based (quinault)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Azure/CloudShell\"\u003ehttps://github.com/Azure/CloudShell\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Azure/CloudShell/blob/master/linux/base.Dockerfile\"\u003ehttps://github.com/Azure/CloudShell/blob/master/linux/base.Dockerfile\u003c/a\u003e for \u003ccode\u003eFROM sbidprod.azurecr.io/quinault\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://boxofcables.dev/building-cbl-d-microsofts-other-linux-distro/\" rel=\"nofollow\"\u003ehttps://boxofcables.dev/building-cbl-d-microsofts-other-linux-distro/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eLICENSE copied verbatim from \u003ca href=\"https://raw.githubusercontent.com/Azure/CloudShell/master/LICENSE\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/Azure/CloudShell/master/LICENSE\u003c/a\u003e as of 2022/02/13\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-quinault-ci/actions/workflows/docker-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-quinault-ci/actions/workflows/docker-publish.yml/badge.svg\" alt=\"Docker build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-quinault-ci:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-quinault-ci/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-quinault-ci/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-quinault-ci:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1610876939.0 + "updated_at": 1644758743.0 }, { "data_format": 2, - "description": "Jupyter Miniconda Python 3 and Singularity Container", + "description": "RNA-seq raw reads processing pipeline through alignment", "filenames": [ - "Singularity.jupyter3", - "Singularity.rstudio", - "Singularity.rbase", - "Singularity.ecmwf.odb", - "Singularity.jupyter23", - "Singularity.jupyter2rttov", - "Singularity.centos8", - "Singularity.stuff", - "Singularity.jupyter3ec", - "Singularity.centos", - "Singularity.centos.apps", - "Singularity.jedi", - "Singularity.gitlab", - "Singularity.jupyter3rttov", - "Singularity.lehre", - "Singularity.intelpy", - "Singularity.jupyter2" + "Singularity.hg19v1.centos" ], - "full_name": "MBlaschek/singularity-jupyter", + "full_name": "ertheisen/cloudsrest_centos", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3843\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-jupyter-and-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter-and-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter and Singularity\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eUpdated: 25.11.2019, new singularity version 3.5\u003c/strong\u003e\n\u003cstrong\u003eContainers are on singularity-hub now: \u003ca href=\"https://singularity-hub.org/collections/3843\" rel=\"nofollow\"\u003eMyCollections\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJupyter Miniconda Python 3 and Singularity Container\u003c/p\u003e\n\u003cp\u003eThis is an update from \u003ca href=\"https://github.com/singularityhub/jupyter\"\u003e\u003c/a\u003e the offical jupyter singularity container that requires root permissions to run:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[NEW] Only need root permissions to create the container\u003c/li\u003e\n\u003cli\u003e[NEW] Miniconda (smaller in size)\u003c/li\u003e\n\u003cli\u003e[NEW] runscript gives informaiton\u003c/li\u003e\n\u003cli\u003e[NEW] Using CentOS 6.10 not Ubuntu anymore\u003c/li\u003e\n\u003cli\u003e[NEW] GLIBC 2.12 compatibility to CentOS 6.10 (Final)\u003c/li\u003e\n\u003cli\u003e[NEW] Build NCAR WRF containers with singularity \u003ca href=\"https://github.com/NCAR/container-wrf\"\u003eNCAR WRF containers\u003c/a\u003e\nIf you haven\u0027t installed singularity, do that with \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ethese instructions\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownlaod Receipie files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.centos (Base only Centos)\u003c/li\u003e\n\u003cli\u003eSingularity.jupyter23 (Miniconda, Jupyter Python2 \u0026amp; Python 3)\u003c/li\u003e\n\u003cli\u003eSingularity.jupyter3 (Miniconda, Jupyter Python 3)\u003c/li\u003e\n\u003cli\u003eSingularity.jupyter3x (Miniconda, Jupyter Python 3, \u003ca href=\"https://confluence.ecmwf.int/display/ECC\" rel=\"nofollow\"\u003eEccodes\u003c/a\u003e, cfgrib from ECMWF)\u003c/li\u003e\n\u003cli\u003eSingualrity.jupyter3ec (Miniconda, Jupyter Python 3, Eccodes library manual build, \u003cstrong\u003edeprecated\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eSingualrity.jupyter3rttov (Miniconda, Jupyter Python 3, \u003ca href=\"https://www.nwpsaf.eu/site/software/rttov/\" rel=\"nofollow\"\u003eRTTOV\u003c/a\u003e from EUMETSAT (not included due to license))\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eClone the Repository and manually build containers:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e git clone https://github.com/MBlaschek/singularity-jupyter jupyter\n cd jupyter \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRetrieve Containers from singularity hub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull shub://MBlaschek/singularity-jupyter:[TAG]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTags are the names above (centos, jupyter23, jupyter3, ...):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull shub://MBlaschek/singularity-jupyter:centos\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-create\" class=\"anchor\" aria-hidden=\"true\" href=\"#create\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCREATE\u003c/h2\u003e\n\u003cp\u003eFirst create the CentOS container that is used by all the others.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build centos610.sif Singularity.centos\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLet\u0027s now create the notebook container:\nIf you build locally, then just edit the Recipie to use the local image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Local centos 6.10 image\nBootstrap: localimage\nFrom: centos610.sif\n# Bootstrap: shub\n# From: MBlaschek/singularity-jupyter:centos\n# most recent and debian image\n# BootStrap: docker\n# From: continuumio/miniconda3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eJupyter Python 3 Notebook Container: \u003ccode\u003eSingularity.jupyter3\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eJupyter Python 2 \u0026amp; 3 Notebook Container: \u003ccode\u003eSingularity.jupyter23\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eJupyter Python 2 \u0026amp; 3 Notebook + Eccodes Library: \u003ccode\u003eSingularity.jupyter3x\u003c/code\u003e (depends on the image from \u003ccode\u003ejupyter3.sif \u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can choose now if you prefer a writeable container (for development, installation of additional packages, ...) or a deployment container (read_only, default) \u003ca href=\"http://singularity.lbl.gov/docs-build-container\" rel=\"nofollow\"\u003eread more\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build --writeable jupyter3.sif Singularity.jupyter3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor for deployment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build jupyter3.sif Singularity.jupyter3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Notebook server Recipies include a line at the end that is quite important for jupyter to run properly:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export JUPYTER_RUNTIME_DIR=$PWD/.runtime\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis line tells jupyter to use a specific directory for its runtime. Otherwise it would try to use the default \u003ccode\u003eXDG_RUNTIME_DIR\u003c/code\u003e, which is by default set to \u003ccode\u003e/run/user/...\u003c/code\u003e and not accessable via the container.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRUN\u003c/h2\u003e\n\u003cp\u003eThen to run the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run jupyter3.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003egives Information on the container and it\u0027s apps (notebook)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity Container\n Container Centos 6.10 (docker)\n Glibc: 2.12-1.212.el6.x86_64\n Installed: wget, git, curl, bzip2 ca-certificates\n\n SCIF (Apps): notebook\n Container.Glibc : 2.12-1.212.el6.x86_64\n Container.OS : CentOS 6.10\n Definition.Author : M. Blaschek\n Definition.Author.Email : michael.blaschek@univie.ac.at\n Definition.File.Date : 5.11.2019\n Definition.File.Version : 1.0\n org.label-schema.build-date : Thursday_28_November_2019_8:49:15_UTC\n org.label-schema.schema-version : 1.0\n org.label-schema.usage : /.singularity.d/runscript.help\n org.label-schema.usage.singularity.deffile.bootstrap : shub\n org.label-schema.usage.singularity.deffile.from : MBlaschek/singularity-jupyter:centos\n org.label-schema.usage.singularity.runscript.help : /.singularity.d/runscript.help\n org.label-schema.usage.singularity.version : 3.4.2\n Bye Bye\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003elaunch the notebook:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity run jupyter3.sif notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003elaunch the console:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity run jupyter3.sif ipython\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor as a singularity instances (background server):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance start jupyter3.sif Jupy3\nsingularity run instance://Jupy3 notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor as an instance with remote access (default is just localhost):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run instance://Jupy3 notebook --ip=$(hostname) \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnyway you should see output like this:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"jupyter.png\"\u003e\u003cimg src=\"jupyter.png\" alt=\"jupyter.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe current directory is where your server starts. In your browser you should be able to navigate to the link from the console:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"jupyterweb.png\"\u003e\u003cimg src=\"jupyterweb.png\" alt=\"jupyterweb.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThere is a \u003ccode\u003e.jupyter3.log\u003c/code\u003e file that shows this output.\u003c/p\u003e\n\u003cp\u003eThe password is \u003cstrong\u003esuper-secret\u003c/strong\u003e. You can change that easily within the Singularity file.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-ipykernel-and-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#ipykernel-and-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIPYKernel and Containers\u003c/h2\u003e\n\u003cp\u003eIn order to use your container with an existing notebook server you need to register your container kernel with that server.\nOther people have done this:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/clemsonciti/singularity-in-jupyter-notebook\"\u003eTensorflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/mattpitkin/35ac19214048e96c391e948d7ec34ca5\"\u003eKernel\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo register your container, in the \u003ccode\u003e${HOME}/.local/share/jupyter/kernels\u003c/code\u003e create a new directory, e.g. myimage, and add a \u003ccode\u003ekernel.json\u003c/code\u003e file containing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"language\": \"python\",\n \"argv\": [\"/usr/bin/singularity\",\n \"exec\",\n \"/dir/to/your/image/jupyter3.sif\",\n \"/opt/conda/bin/python\",\n \"-m\",\n \"ipykernel\",\n \"-f\",\n \"{connection_file}\"\n ],\n \"display_name\": \"Python 3 (Singularity)\"\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eChange the path to your image and singularity executable. Then start a jupyter notebook with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e jupyter notebook \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand there should be a usable Python 3 (Singularity) kernel option! Check your Jupyter paths, like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e jupyter --paths\n \n config:\n /home/user/.jupyter\n /opt/anaconda2/etc/jupyter\n /usr/local/etc/jupyter\n /etc/jupyter\n data:\n /home/user/.local/share/jupyter\n /opt/anaconda2/share/jupyter\n /usr/local/share/jupyter\n /usr/share/jupyter\n runtime:\n /run/user/1000/jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand make sure the runtime directory is accessable from inside the container. In this example it isn\u0027t. There I need to change this to something like this, before I run the server again:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export JUPYTER_RUNTIME_DIR=$HOME/.local/share/jupyter/runtime\n jupyter notebook \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat should solve the issue and make your contained jupyter environment accessable via your notebook server. :)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-runtime-dir\" class=\"anchor\" aria-hidden=\"true\" href=\"#runtime-dir\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRUNTIME DIR\u003c/h4\u003e\n\u003cp\u003eI came across a few problems, which related to the \u003ccode\u003eRUNTIME_DIR\u003c/code\u003e and is quite import to run your server without root permissions.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e XDG_RUNTIME_DIR=/run/user/1000 # Default in Ubuntu/Linux (inside the container)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat is not a good path. Therefore we change it to a defined path inside the container (already in the singularity file).\nThe following shows a way around, not necessary if you use the above recipe.\u003c/p\u003e\n\u003cp\u003eThis directory \u003ccode\u003e/run/user/..\u003c/code\u003e is not accessable by default from inside the container.\nTo register your container, in the \u003ccode\u003e${HOME}/.local/share/jupyter/kernels\u003c/code\u003e create a new directory, e.g. myimage, and add a \u003ccode\u003ekernel.json\u003c/code\u003e file containing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"language\": \"python\",\n \"argv\": [\"/usr/bin/singularity\",\n \"exec\",\n \"-B\",\n \"/run/user:/run/user\",\n \"/dir/to/your/image/jupyter.img\",\n \"/opt/conda/bin/python\",\n \"-m\",\n \"ipykernel\",\n \"-f\",\n \"{connection_file}\"\n ],\n \"display_name\": \"Python 3 (Singularity)\"\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere adding the \u003ccode\u003e-B /run/user:/run/user\u003c/code\u003e option is important, which allows the container to have access.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-r-studio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#r-studio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR-Studio Server\u003c/h1\u003e\n\u003cp\u003eThis is a lightly modified version of what \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003enickjer\u003c/a\u003e has done. The Modifications allow to run the R-Studio server as an instance.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance start rserver.sif RStudio\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsually the R-Studio server runs on port 9090.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-syntax-highlighting\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-syntax-highlighting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Syntax Highlighting\u003c/h1\u003e\n\u003cp\u003eThere is a nice repo \u003ca href=\"https://github.com/singularityhub/singularity.lang\"\u003esingularity.lang\u003c/a\u003e, where this can be added for Gedit, Nano and Vim. For Atom there is a highlighting as well. Works well.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-appalachianhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#appalachianhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappalachian.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1662971530.0 + "updated_at": 1560527292.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.nanocomp", - "Singularity.parallel", - "Singularity.pomoxis", - "Singularity.OligoMiner", - "Singularity.bedops", - "Singularity.AP_master", - "Singularity.salmon", - "Singularity.freebayes", - "Singularity.seqkit", - "Singularity.yacrd", - "Singularity.PEPPER", - "Singularity.HELEN", - "Singularity.medaka", - "Singularity.R", - "Singularity.busco", - "Singularity.slamdunk", - "Singularity.marvel", - "Singularity.medakaGPU", - "Singularity.mashmap", - "Singularity.TailfindR", - "Singularity.mosdepth", - "Singularity.cutadapt", - "Singularity.pycoQC", - "Singularity.bowtie", - "Singularity.hiC-pro", - "Singularity.ngmlr.txt", - "Singularity.deep-variant", - "Singularity.bedtools", - "Singularity.Repeatmasker", - "Singularity.filtlong", - "Singularity.samtools", - "Singularity.sratoolkit", - "Singularity.homer-tools", - "Singularity.purge_dups", - "Singularity.STAR", - "Singularity.mummer", - "Singularity.guppy", - "Singularity.nanopolish", - "Singularity.kentUtils", - "Singularity.quast", - "Singularity.albacore" + "Singularity.ubuntu_base" ], - "full_name": "dominik-handler/AP_singu", - "latest_release": null, + "full_name": "miquelmassot/singularity-deploy", + "latest_release": "0.0.2", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" href=\"#singularity-deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/miquelmassot/singularity-deploy/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/miquelmassot/singularity-deploy/actions/workflows/builder.yml/badge.svg\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBased on: \u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1612162065.0 + "updated_at": 1644837961.0 }, { "data_format": 2, - "description": "image_preprocess", + "description": "My collection of singularity containers recipes", "filenames": [ - "Singularity" + "busco/Singularity.busco", + "Biocontainer/Singularity.Biocontainers", + "DIRT/Singularity.DIRT", + "genome-annotation/Singularity.genome-annotation" ], - "full_name": "lsx1980/image_preprocess", + "full_name": "raj76/singularity", "latest_release": null, - "readme": "\u003cp\u003e\"\"\"\nVersion: 1.5\u003c/p\u003e\n\u003cp\u003eSummary: image pre-processingfor 3D model reconstruction\u003c/p\u003e\n\u003cp\u003eAuthor: suxing liu\u003c/p\u003e\n\u003cp\u003eAuthor-email: \u003ca href=\"mailto:suxingliu@gmail.com\"\u003esuxingliu@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUSAGE:\u003c/p\u003e\n\u003cp\u003epython pipeline.py -p /path_to_image_folder/ -ft jpg\u003c/p\u003e\n\u003cp\u003eparameter list:\u003c/p\u003e\n\u003cp\u003eargument:\n(\"-p\", \"--path\", required = True, help = \"path to image file\")\n(\"-ft\", \"--filetype\", required = True, help = \"Image filetype\")\u003c/p\u003e\n\u003cp\u003esingularity build --writable image_preprocess.img Singularity\nsingularity exec image_preprocess.img python /opt/code/pipeline.py -p /path_to_image_folder/ -ft jpg\n\"\"\"\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cp\u003eMy collection of singularity containers recipes\n\u003ca href=\"https://singularity-hub.org/collections/611\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1561479834.0 + "updated_at": 1518905174.0 }, { "data_format": 2, - "description": null, + "description": "RAxML - Randomized Axelerated Maximum Likelihood.", "filenames": [ - "Singularity" + "8.2.9/Singularity" ], - "full_name": "yuechenwangwyc/topaz", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-topaz\" class=\"anchor\" aria-hidden=\"true\" href=\"#topaz\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTopaz\u003c/h1\u003e\n\u003cp\u003eA pipeline for particle detection in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Topaz also includes methods for micrograph and tomogram denoising using deep denoising models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCheck out our \u003ca href=\"https://github.com/tbepler/topaz/discussions\"\u003eDiscussion\u003c/a\u003e section for general help, suggestions, and tips on using Topaz.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v025\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v025\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.5\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eAdded Relion integration scripts\u003c/li\u003e\n\u003cli\u003eTopaz extract can now write particle coordinates to one file per input micrograph\u003c/li\u003e\n\u003cli\u003eAdded Gaussian filter option for after 3D denoising\u003c/li\u003e\n\u003cli\u003eAdded info on Topaz Workshops\u003c/li\u003e\n\u003cli\u003eTopaz GUI update\u003c/li\u003e\n\u003cli\u003eVarious bug fixes\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v024\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v024\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.4\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eAdded 3D denoising with \u003cstrong\u003etopaz denoise3d\u003c/strong\u003e and two pretrained 3D denoising models\u003c/li\u003e\n\u003cli\u003eAdded argument for setting number of threads to multithreaded commands\u003c/li\u003e\n\u003cli\u003eTopaz GUI update\u003c/li\u003e\n\u003cli\u003eVarious bug fixes\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v023\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v023\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.3\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eImprovements to the pretrained denoising models\u003c/li\u003e\n\u003cli\u003eTopaz now includes pretrained particle picking models\u003c/li\u003e\n\u003cli\u003eUpdated tutorials\u003c/li\u003e\n\u003cli\u003eUpdated GUI to include denoising commands\u003c/li\u003e\n\u003cli\u003eDenoising paper preprint is available \u003ca href=\"https://doi.org/10.1101/838920\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v022\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.2\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThe Topaz publication is out \u003ca href=\"https://doi.org/10.1038/s41592-019-0575-8\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBug fixes and GUI update\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v020\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.0\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTopaz now supports the newest versions of pytorch (\u0026gt;= 1.0.0). If you have pytorch installed for an older version of topaz, it will need to be upgraded. See installation instructions for details.\u003c/li\u003e\n\u003cli\u003eAdded \u003cstrong\u003etopaz denoise\u003c/strong\u003e, a command for denoising micrographs using neural networks.\u003c/li\u003e\n\u003cli\u003eUsability improvements to the GUI.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAn Nvidia GPU with CUDA support for GPU acceleration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBasic Unix/Linux knowledge.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003e(Recommended) Click here to install \u003cem\u003eusing Anaconda\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eIf you do not have the Anaconda python distribution, \u003ca href=\"https://www.anaconda.com/download\" rel=\"nofollow\"\u003eplease install it following the instructions on their website\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe strongly recommend installing Topaz into a separate conda environment. To create a conda environment for Topaz:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n topaz python=3.6 # or 2.7 if you prefer python 2\nsource activate topaz # this changes to the topaz conda environment, \u0027conda activate topaz\u0027 can be used with anaconda \u0026gt;= 4.4 if properly configured\n# source deactivate # returns to the base conda environment\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMore information on conda environments can be found \u003ca href=\"https://conda.io/docs/user-guide/tasks/manage-environments.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-topaz\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-topaz\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Topaz\u003c/h2\u003e\n\u003cp\u003eTo install the precompiled Topaz package and its dependencies, including pytorch:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install topaz -c tbepler -c pytorch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis installs pytorch from the official channel. To install pytorch for specific cuda versions, you will need to add the \u0027cudatoolkit=X.X\u0027 package. E.g. to install pytorch for CUDA 9.0:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install cudatoolkit=9.0 -c pytorch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor combined into a single command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install topaz cudatoolkit=9.0 -c tbepler -c pytorch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for additional pytorch installation instructions.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it! Topaz is now installed in your anaconda environment.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here to install \u003cem\u003eusing Pip\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eWe strongly recommend installing Topaz into a \u003cem\u003evirtual environment\u003c/em\u003e. See \u003ca href=\"https://virtualenv.pypa.io/en/latest/installation/\" rel=\"nofollow\"\u003einstallation instructions\u003c/a\u003e and \u003ca href=\"https://virtualenv.pypa.io/en/latest/userguide/\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e for virtualenv.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-topaz-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-topaz-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Topaz\u003c/h2\u003e\n\u003cp\u003eTo install Topaz for Python 3.X\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install topaz-em\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efor Python 2.7\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install topaz-em\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for additional pytorch installation instructions, including how to install pytorch for specific CUDA versions.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it! Topaz is now installed through pip.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here to install \u003cem\u003eusing Docker\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eDo you have Docker installed? If not, \u003cem\u003eclick here\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-linuxmacos--command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#linuxmacos--command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinux/MacOS \u00a0\u00a0 \u003cem\u003e(command line)\u003c/em\u003e\n\u003c/h2\u003e\n\u003cp\u003eDownload and install Docker 1.21 or greater for \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eLinux\u003c/a\u003e or \u003ca href=\"https://store.docker.com/editions/community/docker-ce-desktop-mac\" rel=\"nofollow\"\u003eMacOS\u003c/a\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eConsider using a Docker \u0027convenience script\u0027 to install (search on your OS\u0027s Docker installation webpage).\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eLaunch docker according to your Docker engine\u0027s instructions, typically \u003ccode\u003edocker start\u003c/code\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e You must have sudo or root access to \u003cem\u003einstall\u003c/em\u003e Docker. If you do not wish to \u003cem\u003erun\u003c/em\u003e Docker as sudo/root, you need to configure user groups as described here: \u003ca href=\"https://docs.docker.com/install/linux/linux-postinstall/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/install/linux/linux-postinstall/\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-windows--gui--command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#windows--gui--command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows \u00a0\u00a0 \u003cem\u003e(GUI \u0026amp; command line)\u003c/em\u003e\n\u003c/h2\u003e\n\u003cp\u003eDownload and install \u003ca href=\"https://docs.docker.com/toolbox/toolbox_install_windows/\" rel=\"nofollow\"\u003eDocker Toolbox for Windows\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eLaunch Kitematic.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf on first startup Kitematic displays a red error suggesting that you run using VirtualBox, do so.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e \u003ca href=\"https://docs.docker.com/toolbox/toolbox_install_mac/\" rel=\"nofollow\"\u003eDocker Toolbox for MacOS\u003c/a\u003e has not yet been tested.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is Docker?\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=YFl2mCHdv24\" rel=\"nofollow\"\u003eThis tutorial explains why Docker is useful.\u003c/a\u003e\u003c/p\u003e\n\n\u003cbr\u003e\n\u003cp\u003eA Dockerfile is provided to build images with CUDA support. Build from the github repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t topaz https://github.com/tbepler/topaz.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor download the source code and build from the source directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/tbepler/topaz\ncd topaz\ndocker build -t topaz .\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here to install \u003cem\u003eusing Singularity\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eA prebuilt Singularity image for Topaz is available \u003ca href=\"https://singularity-hub.org/collections/2413\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and can be installed with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://nysbc/topaz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you can run topaz from within the singularity image with (paths must be changed appropriately):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv -B /mounted_path:/mounted_path /path/to/singularity/container/topaz_latest.sif /usr/local/conda/bin/topaz\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here to install \u003cem\u003efrom source\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRecommended: install Topaz into a virtual Python environment\u003c/em\u003e\u003cbr\u003e\nSee \u003ca href=\"https://conda.io/docs/user-guide/tasks/manage-environments.html\" rel=\"nofollow\"\u003ehttps://conda.io/docs/user-guide/tasks/manage-environments.html\u003c/a\u003e or \u003ca href=\"https://virtualenv.pypa.io/en/stable/\" rel=\"nofollow\"\u003ehttps://virtualenv.pypa.io/en/stable/\u003c/a\u003e for setting one up.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-the-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-the-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall the dependencies\u003c/h4\u003e\n\u003cp\u003eTested with python 3.6 and 2.7\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epytorch (\u0026gt;= 1.0.0)\u003c/li\u003e\n\u003cli\u003etorchvision\u003c/li\u003e\n\u003cli\u003epillow (\u0026gt;= 6.2.0)\u003c/li\u003e\n\u003cli\u003enumpy (\u0026gt;= 1.11)\u003c/li\u003e\n\u003cli\u003epandas (\u0026gt;= 0.20.3)\u003c/li\u003e\n\u003cli\u003escipy (\u0026gt;= 0.19.1)\u003c/li\u003e\n\u003cli\u003escikit-learn (\u0026gt;= 0.19.0)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEasy installation of dependencies with conda\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install numpy pandas scikit-learn\nconda install -c pytorch pytorch torchvision\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more info on installing pytorch for your CUDA version see \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003ehttps://pytorch.org/get-started/locally/\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-download-the-source-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-the-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload the source code\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/tbepler/topaz\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-topaz-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-topaz-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Topaz\u003c/h4\u003e\n\u003cp\u003eMove to the source code directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd topaz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, this will be the most recent version of the topaz source code. To install a specific older version, checkout that commit. For example, for v0.1.0 of Topaz:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit checkout v0.1.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that older Topaz versions may have different dependencies. Refer to the README for the specific Topaz version.\u003c/p\u003e\n\u003cp\u003eInstall Topaz into your Python path including the topaz command line interface\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install for development use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003eTopaz is also available through \u003ca href=\"https://sbgrid.org/software/titles/topaz\" rel=\"nofollow\"\u003eSBGrid\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-tutorial\" class=\"anchor\" aria-hidden=\"true\" href=\"#tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTutorial\u003c/h1\u003e\n\u003cp\u003eThe tutorials are presented in Jupyter notebooks. Please install Jupyter following the instructions \u003ca href=\"http://jupyter.org/install\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"tutorial/01_quick_start_guide.ipynb\"\u003eQuick start guide\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"tutorial/02_walkthrough.ipynb\"\u003eComplete walkthrough\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"tutorial/03_cross_validation.ipynb\"\u003eCross validation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"tutorial/04_denoising.ipynb\"\u003eMicrograph denoising\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe tutorial data can be downloaded \u003ca href=\"http://bergerlab-downloads.csail.mit.edu/topaz/topaz-tutorial-data.tar.gz\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo run the tutorial steps on your own system, you will need to install \u003ca href=\"http://jupyter.org/install\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e and \u003ca href=\"https://matplotlib.org/\" rel=\"nofollow\"\u003ematplotlib\u003c/a\u003e which is used for visualization.\u003c/p\u003e\n\u003cp\u003eWith Anaconda this can be done with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install jupyter matplotlib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you installed Topaz using anaconda, make sure these are installed into your Topaz evironment.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-user-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#user-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser guide\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here for a description of the Topaz pipeline and its commands\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe command line interface is structured as a single entry command (topaz) with different steps defined as subcommands. A general usage guide is provided below with brief instructions for the most important subcommands in the particle picking pipeline.\u003c/p\u003e\n\u003cp\u003eTo see a list of all subcommands with a brief description of each, run \u003ccode\u003etopaz --help\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-image-preprocessing\" class=\"anchor\" aria-hidden=\"true\" href=\"#image-preprocessing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImage preprocessing\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-downsampling-topaz-downsample\" class=\"anchor\" aria-hidden=\"true\" href=\"#downsampling-topaz-downsample\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownsampling (topaz downsample)\u003c/h4\u003e\n\u003cp\u003eIt is recommened to downsample and normalize images prior to model training and prediction.\u003c/p\u003e\n\u003cp\u003eThe downsample script uses the discrete Fourier transform to reduce the spacial resolution of images. It can be used as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etopaz downsample --scale={downsampling factor} --output={output image path} {input image path} \n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz downsample [-h] [-s SCALE] [-o OUTPUT] [-v] file\n\npositional arguments:\n file\n\noptional arguments:\n -h, --help show this help message and exit\n -s SCALE, --scale SCALE\n downsampling factor (default: 4)\n -o OUTPUT, --output OUTPUT\n output file\n -v, --verbose print info\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-normalization-topaz-normalize\" class=\"anchor\" aria-hidden=\"true\" href=\"#normalization-topaz-normalize\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNormalization (topaz normalize)\u003c/h4\u003e\n\u003cp\u003eThe normalize script can then be used to normalize the images. This script fits a two component Gaussian mixture model with an additional scaling multiplier per image to capture carbon pixels and account for differences in exposure. The pixel values are then adjusted by dividing each image by its scaling factor and then subtracting the mean and dividing by the standard deviation of the dominant Gaussian mixture component. It can be used as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etopaz normalize --destdir={directory to put normalized images} [list of image files]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz normalize [-h] [-s SAMPLE] [--niters NITERS] [--seed SEED]\n [-o DESTDIR] [-v]\n files [files ...]\n\npositional arguments:\n files\n\noptional arguments:\n -h, --help show this help message and exit\n -s SAMPLE, --sample SAMPLE\n pixel sampling factor for model fit (default: 100)\n --niters NITERS number of iterations to run for model fit (default:\n 200)\n --seed SEED random seed for model initialization (default: 1)\n -o DESTDIR, --destdir DESTDIR\n output directory\n -v, --verbose verbose output\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-single-step-preprocessing-topaz-preprocess\" class=\"anchor\" aria-hidden=\"true\" href=\"#single-step-preprocessing-topaz-preprocess\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingle-step preprocessing (topaz preprocess)\u003c/h4\u003e\n\u003cp\u003eBoth downsampling and normalization can be performed in one step with the preprocess script.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etopaz preprocess --scale={downsampling factor} --destdir={directory to put processed images} [list of image files]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz preprocess [-h] [-s SCALE] [-t NUM_WORKERS]\n [--pixel-sampling PIXEL_SAMPLING] [--niters NITERS]\n [--seed SEED] -o DESTDIR [-v]\n files [files ...]\n\npositional arguments:\n files\n\noptional arguments:\n -h, --help show this help message and exit\n -s SCALE, --scale SCALE\n rescaling factor for image downsampling (default: 4)\n -t NUM_WORKERS, --num-workers NUM_WORKERS\n number of processes to use for parallel image\n downsampling (default: 0)\n --pixel-sampling PIXEL_SAMPLING\n pixel sampling factor for model fit (default: 100)\n --niters NITERS number of iterations to run for model fit (default:\n 200)\n --seed SEED random seed for model initialization (default: 1)\n -o DESTDIR, --destdir DESTDIR\n output directory\n -v, --verbose verbose output\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-model-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#model-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModel training\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-file-formats\" class=\"anchor\" aria-hidden=\"true\" href=\"#file-formats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile formats\u003c/h4\u003e\n\u003cp\u003eThe training script requires a file listing the image file paths and another listing the particle coordinates. Coordinates index images from the top left. These files should be tab delimited with headers as follows:\u003c/p\u003e\n\u003cp\u003eimage file list\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eimage_name\tpath\n...\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eparticle coordinates\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eimage_name\tx_coord\ty_coord\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-train-region-classifiers-with-labeled-particles-topaz-train\" class=\"anchor\" aria-hidden=\"true\" href=\"#train-region-classifiers-with-labeled-particles-topaz-train\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrain region classifiers with labeled particles (topaz train)\u003c/h4\u003e\n\u003cp\u003eModels are trained using the \u003ccode\u003etopaz train\u003c/code\u003e command. For a complete list of training arguments, see\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etopaz train --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-segmentation-and-particle-extraction\" class=\"anchor\" aria-hidden=\"true\" href=\"#segmentation-and-particle-extraction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSegmentation and particle extraction\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-segmention-topaz-segment-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#segmention-topaz-segment-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSegmention (topaz segment, optional)\u003c/h4\u003e\n\u003cp\u003eImages can be segmented using the \u003ccode\u003etopaz segment\u003c/code\u003e command with a trained model.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz segment [-h] [-m MODEL] [-o DESTDIR] [-d DEVICE] [-v]\n paths [paths ...]\n\npositional arguments:\n paths paths to image files for processing\n\noptional arguments:\n -h, --help show this help message and exit\n -m MODEL, --model MODEL\n path to trained classifier\n -o DESTDIR, --destdir DESTDIR\n output directory\n -d DEVICE, --device DEVICE\n which device to use, \u0026lt;0 corresponds to CPU (default:\n GPU if available)\n -v, --verbose verbose mode\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-particle-extraction-topaz-extract\" class=\"anchor\" aria-hidden=\"true\" href=\"#particle-extraction-topaz-extract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParticle extraction (topaz extract)\u003c/h4\u003e\n\u003cp\u003ePredicted particle coordinates can be extracted directly from saved segmented images (see above) or images can be segmented and particles extracted in one step given a trained model using the \u003ccode\u003etopaz extract\u003c/code\u003e command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz extract [-h] [-m MODEL] [-r RADIUS] [-t THRESHOLD]\n [--assignment-radius ASSIGNMENT_RADIUS]\n [--min-radius MIN_RADIUS] [--max-radius MAX_RADIUS]\n [--step-radius STEP_RADIUS] [--num-workers NUM_WORKERS]\n [--targets TARGETS] [--only-validate] [-d DEVICE]\n [-o OUTPUT]\n paths [paths ...]\n\npositional arguments:\n paths paths to image files for processing\n\noptional arguments:\n -h, --help show this help message and exit\n -m MODEL, --model MODEL\n path to trained subimage classifier, if no model is\n supplied input images must already be segmented\n -r RADIUS, --radius RADIUS\n radius of the regions to extract\n -t THRESHOLD, --threshold THRESHOLD\n score quantile giving threshold at which to terminate\n region extraction (default: 0.5)\n --assignment-radius ASSIGNMENT_RADIUS\n maximum distance between prediction and labeled target\n allowed for considering them a match (default: same as\n extraction radius)\n --min-radius MIN_RADIUS\n minimum radius for region extraction when tuning\n radius parameter (default: 5)\n --max-radius MAX_RADIUS\n maximum radius for region extraction when tuning\n radius parameters (default: 100)\n --step-radius STEP_RADIUS\n grid size when searching for optimal radius parameter\n (default: 5)\n --num-workers NUM_WORKERS\n number of processes to use for extracting in parallel,\n 0 uses main process (default: 0)\n --targets TARGETS path to file specifying particle coordinates. used to\n find extraction radius that maximizes the AUPRC\n --only-validate flag indicating to only calculate validation metrics.\n does not report full prediction list\n -d DEVICE, --device DEVICE\n which device to use, \u0026lt;0 corresponds to CPU\n -o OUTPUT, --output OUTPUT\n file path to write\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis script uses the non maxima suppression algorithm to greedily select particle coordinates and remove nearby coordinates from the candidates list. Two additional parameters are involved in this process.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eradius: coordinates within this parameter of selected coordinates are removed from the candidates list\u003c/li\u003e\n\u003cli\u003ethreshold: specifies the score quantile below which extraction stops\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe radius parameter can be tuned automatically given a set of known particle coordinates by finding the radius which maximizes the average precision score. In this case, predicted coordinates must be assigned to target coordinates which requires an additional distance threshold (--assignment-radius).\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-choosing-a-final-particle-list-threshold-topaz-precision_recall_curve\" class=\"anchor\" aria-hidden=\"true\" href=\"#choosing-a-final-particle-list-threshold-topaz-precision_recall_curve\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoosing a final particle list threshold (topaz precision_recall_curve)\u003c/h4\u003e\n\u003cp\u003eParticles extracted using Topaz still have scores associated with them and a final particle list should be determined by choosing particles above some score threshold. The \u003ccode\u003etopaz precision_recall_curve\u003c/code\u003e command can facilitate this by reporting the precision-recall curve for a list of predicted particle coordinates and a list of known target coordinates. A threshold can then be chosen to optimize the F1 score or for specific recall/precision levels on a heldout set of micrographs.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz precision_recall_curve [-h] [--predicted PREDICTED]\n [--targets TARGETS] -r ASSIGNMENT_RADIUS\n\noptional arguments:\n -h, --help show this help message and exit\n --predicted PREDICTED\n path to file containing predicted particle coordinates\n with scores\n --targets TARGETS path to file specifying target particle coordinates\n -r ASSIGNMENT_RADIUS, --assignment-radius ASSIGNMENT_RADIUS\n maximum distance between prediction and labeled target\n allowed for considering them a match\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here for a description of the model architectures, training methods, and training radius\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-model-architectures\" class=\"anchor\" aria-hidden=\"true\" href=\"#model-architectures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModel architectures\u003c/h4\u003e\n\u003cp\u003eCurrently, there are several model architectures available for use as the region classifier\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eresnet8 [receptive field = 71]\u003c/li\u003e\n\u003cli\u003econv127 [receptive field = 127]\u003c/li\u003e\n\u003cli\u003econv63 [receptive field = 63]\u003c/li\u003e\n\u003cli\u003econv31 [receptive field = 31]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eResNet8 gives a good balance of performance and receptive field size. Conv63 and Conv31 can be better choices when less complex models are needed.\u003c/p\u003e\n\u003cp\u003eThe number of units in the base layer can be set with the --units flag. ResNet8 always doubles the number of units when the image is strided during processing. Conv31, Conv63, and Conv127 do not by default, but the --unit-scaling flag can be used to set a multiplicative factor on the number of units when striding occurs.\u003c/p\u003e\n\u003cp\u003eThe pooling scheme can be changed for the conv* models. The default is not to perform any pooling, but max pooling and average pooling can be used by specifying \"--pooling=max\" or \"--pooling=avg\".\u003c/p\u003e\n\u003cp\u003eFor a detailed layout of the architectures, use the --describe flag.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-training-methods\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining methods\u003c/h4\u003e\n\u003cp\u003eThe PN method option treats every coordinate not labeled as positive (y=1) as negative (y=0) and then optimizes the standard classification objective:\n$$ \\piE_{y=1}[L(g(x),1)] + (1-\\pi)E_{y=0}[L(g(x),0)] $$\nwhere $\\pi$ is a parameter weighting the positives and negatives, $L$ is the misclassifiaction cost function, and $g(x)$ is the model output.\u003c/p\u003e\n\u003cp\u003eThe GE-binomial method option instead treats coordinates not labeled as positive (y=1) as unlabeled (y=?) and then optimizes an objective including a generalized expectation criteria designed to work well with minibatch SGD.\u003c/p\u003e\n\u003cp\u003eThe GE-KL method option instead treats coordinates not labeled as positive (y=1) as unlabeled (y=?) and then optimizes the objective:\n$$ E_{y=1}[L(g(x),1)] + \\lambdaKL(\\pi, E_{y=?}[g(x)]) $$\nwhere $\\lambda$ is a slack parameter (--slack flag) that specifies how strongly to weight the KL divergence of the expecation of the classifier over the unlabeled data from $\\pi$.\u003c/p\u003e\n\u003cp\u003eThe PU method uses the objective function proposed by Kiryo et al. (2017)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-radius\" class=\"anchor\" aria-hidden=\"true\" href=\"#radius\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRadius\u003c/h4\u003e\n\u003cp\u003eThis sets how many pixels around each particle coordinate are treated as positive, acting as a form of data augmentation. These coordinates follow a distribution that results from which pixel was selected as the particle center when the data was labeled. The radius should be chosen to be large enough that it covers a reasonable region of pixels likely to have been selected but not so large that pixels outside of the particles are labeled as positives.\u003c/p\u003e\n\n\u003cp\u003eA user guide is also built into the \u003ca href=\"https://emgweb.nysbc.org/topaz.html\" rel=\"nofollow\"\u003eTopaz GUI\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-integration\" class=\"anchor\" aria-hidden=\"true\" href=\"#integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntegration\u003c/h1\u003e\n\u003cp\u003eTopaz also integrates with RELION, CryoSPARC, Scipion, and Appion. You can find information and tutorials here:\u003c/p\u003e\n\u003cp\u003eRELION: \u003ca href=\"https://github.com/tbepler/topaz/tree/master/relion_run_topaz\"\u003ehttps://github.com/tbepler/topaz/tree/master/relion_run_topaz\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCryoSPARC: \u003ca href=\"https://guide.cryosparc.com/processing-data/all-job-types-in-cryosparc/deep-picking/deep-picking\" rel=\"nofollow\"\u003ehttps://guide.cryosparc.com/processing-data/all-job-types-in-cryosparc/deep-picking/deep-picking\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eScipion: \u003ca href=\"https://github.com/scipion-em/scipion-em-topaz\"\u003ehttps://github.com/scipion-em/scipion-em-topaz\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-topaz-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#topaz-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTopaz\u003c/h3\u003e\n\u003cp\u003eBepler, T., Morin, A., Rapp, M., Brasch, J., Shapiro, L., Noble, A.J., Berger, B. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat Methods 16, 1153\u20131160 (2019). \u003ca href=\"https://doi.org/10.1038/s41592-019-0575-8\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41592-019-0575-8\u003c/a\u003e\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eBibtex\u003c/summary\u003e\u003cp\u003e\n\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@Article{Bepler2019,\nauthor={Bepler, Tristan\nand Morin, Andrew\nand Rapp, Micah\nand Brasch, Julia\nand Shapiro, Lawrence\nand Noble, Alex J.\nand Berger, Bonnie},\ntitle={Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs},\njournal={Nature Methods},\nyear={2019},\nissn={1548-7105},\ndoi={10.1038/s41592-019-0575-8},\nurl={https://doi.org/10.1038/s41592-019-0575-8}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003ch3\u003e\u003ca id=\"user-content-topaz-denoise\" class=\"anchor\" aria-hidden=\"true\" href=\"#topaz-denoise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTopaz-Denoise\u003c/h3\u003e\n\u003cp\u003eBepler, T., Kelley, K., Noble, A.J., Berger, B. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat Commun 11, 5208 (2020). \u003ca href=\"https://doi.org/10.1038/s41467-020-18952-1\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41467-020-18952-1\u003c/a\u003e\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eBibtex\u003c/summary\u003e\u003cp\u003e\n\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@Article{Bepler2020_topazdenoise,\nauthor={Bepler, Tristan\nand Kelley, Kotaro\nand Noble, Alex J.\nand Berger, Bonnie},\ntitle={Topaz-Denoise: general deep denoising models for cryoEM and cryoET},\njournal={Nature Communications},\nyear={2020},\nissn={2041-1723},\ndoi={10.1038/s41467-020-18952-1},\nurl={https://doi.org/10.1038/s41467-020-18952-1}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003ch1\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h1\u003e\n\u003cdetails\u003e\u003csummary\u003eTristan Bepler\u003c/summary\u003e\u003cp\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/tbepler.png\"\u003e\u003cimg src=\"images/tbepler.png\" width=\"120\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\u003c/details\u003e\n\u003cdetails\u003e\u003csummary\u003eAlex J. Noble\u003c/summary\u003e\u003cp\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/anoble.png\"\u003e\u003cimg src=\"images/anoble.png\" width=\"120\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\u003c/details\u003e\n\u003ch1\u003e\u003ca id=\"user-content-topaz-workshop\" class=\"anchor\" aria-hidden=\"true\" href=\"#topaz-workshop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTopaz Workshop\u003c/h1\u003e\n\u003cp\u003eTo request a Topaz Workshop for academic or non-academic purposes, send a request to:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;alexjnoble [at] gmail [dot] com\u0026gt;\u003c/em\u003e \u0026amp; \u003cem\u003e\u0026lt;tbepler [at] gmail [dot] com\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eTopaz is open source software released under the \u003ca href=\"https://github.com/tbepler/topaz/blob/master/LICENSE\"\u003eGNU General Public License, Version 3\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bugs--suggestions\" class=\"anchor\" aria-hidden=\"true\" href=\"#bugs--suggestions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBugs \u0026amp; Suggestions\u003c/h1\u003e\n\u003cp\u003ePlease report bugs and make specific feature requests and suggestions for improvements as a \u003ca href=\"https://github.com/tbepler/topaz/issues\"\u003eGithub issue\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor general help, questions, suggestions, tips, and installation/setup assistance, please take a look at our new \u003ca href=\"https://github.com/tbepler/topaz/discussions\"\u003eDiscussion\u003c/a\u003e section.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-raxml", + "latest_release": "v8.2.9", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-raxml/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-raxml/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-raxml/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-raxml/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/277233f105b68116448cd7db2faac6b9dedc5603317bc3be2789550b8554d1e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7261786d6c\" 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href=\"https://camo.githubusercontent.com/ee982fcac05c22d0d030c924d411c219e655450543d9c54220f0f105c072ede2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7261786d6c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ee982fcac05c22d0d030c924d411c219e655450543d9c54220f0f105c072ede2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7261786d6c\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-raxml\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-raxml\" class=\"anchor\" href=\"#singularity-raxml\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-raxml\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://cme.h-its.org/exelixis/web/software/raxml\" rel=\"nofollow\"\u003eraxml\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eraxml\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/raxml/8.2.9\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/raxml\u003c/code\u003e as \u003ccode\u003e8.2.9.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1611916919.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1644856111.0 }, { "data_format": 2, - "description": "local settings", + "description": null, "filenames": [ - "examples/shub/Singularity", - "examples/scientific/Singularity", - "examples/arch/Singularity", - "examples/ubuntu/Singularity", - "examples/centos/Singularity", - "examples/docker/Singularity", - "examples/scratch/Singularity.busybox", - "examples/scratch/Singularity.alpine", - "examples/debian/Singularity", - "examples/self/Singularity", - "examples/busybox/Singularity", - "examples/apps/Singularity", - "examples/apps/Singularity.cowsay", - "examples/instances/Singularity", - "examples/asciinema/Singularity", - "examples/raspbian/Singularity", - "examples/library/Singularity", - "examples/multistage/Singularity", - "examples/opensuse/Singularity" + "docker/Singularity.snowflake" ], - "full_name": "frankwillmore/alcf-singularity", + "full_name": "nuKs/bids-preproc", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/sylabs/singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1646c42a348a1331feb3842e34171e866c139adbae2608ba5fbd2c022c9c20f/68747470733a2f2f7472617669732d63692e6f72672f73796c6162732f73696e67756c61726974792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/sylabs/singularity.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/sylabs/singularity/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff56e7dd170e08e53c09fda12031315bb91f5b4220f2d3cfaf46044700f32fa1/68747470733a2f2f636972636c6563692e636f6d2f67682f73796c6162732f73696e67756c61726974792f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/sylabs/singularity/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://goreportcard.com/report/github.com/sylabs/singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/179d3d939b6a64c4f021860776fdc6243bc26409e966f1aa6bd7d35ca9593fea/68747470733a2f2f676f7265706f7274636172642e636f6d2f62616467652f6769746875622e636f6d2f73796c6162732f73696e67756c6172697479\" alt=\"Go Report Card\" data-canonical-src=\"https://goreportcard.com/badge/github.com/sylabs/singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eGuidelines for Contributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\".github/PULL_REQUEST_TEMPLATE.md\"\u003ePull Request Template\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"LICENSE.md\"\u003eProject License\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity is an open source container platform designed to be simple, fast, and secure. Singularity is optimized for \u003ca href=\"https://www.sylabs.io/2018/09/singularity-is-enterprise-performance-computing/\" rel=\"nofollow\"\u003eEPC\u003c/a\u003e and HPC workloads, allowing untrusted users to run untrusted containers in a trusted way.\u003c/p\u003e\n\u003cp\u003eCheck out \u003ca href=\"https://www.sylabs.io/singularity/whos-using-singularity/\" rel=\"nofollow\"\u003ewho is using Singularity\u003c/a\u003e and some \u003ca href=\"https://www.sylabs.io/category/how-tos/\" rel=\"nofollow\"\u003euse cases of Singularity\u003c/a\u003e on our website.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started with Singularity\u003c/h2\u003e\n\u003cp\u003eTo install Singularity from source, see the \u003ca href=\"INSTALL.md\"\u003einstallation instructions\u003c/a\u003e. For other installation options, see \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eour website\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor system administrators, see the \u003ca href=\"https://www.sylabs.io/guides/3.0/admin-guide/\" rel=\"nofollow\"\u003eadministrator documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor users, see the \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003euser documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing-to-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing-to-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing to Singularity\u003c/h2\u003e\n\u003cp\u003eCommunity contributions are always greatly appreciated. To start developing Singularity, check out the \u003ca href=\"CONTRIBUTING.md\"\u003eguidelines for contributing\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe also welcome contributions to our \u003ca href=\"https://github.com/sylabs/singularity-userdocs\"\u003euser docs\u003c/a\u003e and \u003ca href=\"https://github.com/sylabs/singularity-admindocs\"\u003eadmin docs\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport\u003c/h2\u003e\n\u003cp\u003eTo get help with Singularity, check out the \u003ca href=\"https://www.sylabs.io/singularity/community/\" rel=\"nofollow\"\u003eCommunity Portal\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor additional support, \u003ca href=\"https://www.sylabs.io/contact/\" rel=\"nofollow\"\u003econtact us\u003c/a\u003e to receive more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cite-as\" class=\"anchor\" aria-hidden=\"true\" href=\"#cite-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCite as:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459. https://doi.org/10.1371/journal.pone.0177459\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe also have a Zenodo citation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer, Gregory M.. (2016). Singularity 2.1.2 - Linux application and environment\ncontainers for science. 10.5281/zenodo.60736\n\nhttps://doi.org/10.5281/zenodo.60736\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eUnless otherwise noted, this project is licensed under a 3-clause BSD license found in the \u003ca href=\"LICENSE.md\"\u003elicense file\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1558040154.0 + "updated_at": 1644864844.0 }, { "data_format": 2, - "description": "Run a jupyter notebook server within singularity container.", + "description": "jq is a lightweight and flexible command-line JSON processor.", "filenames": [ - "Singularity" + "1.6/Singularity" ], - "full_name": "kma/singularity-jupyter", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-jupyter\u003c/h1\u003e\n\u003cp\u003eThis example shows how to run a jupyter notebook server within singularity container.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-create-and-bootstrap-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-and-bootstrap-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate and bootstrap the container\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity create -s 1200 jupyter.img\n$ sudo singularity bootstrap jupyter.img Singularity \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-singularity-hub-to-pull-this-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-singularity-hub-to-pull-this-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse singularity-hub to pull this container\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003e$ singularity pull shub://906\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ singularity pull shub://kma/singularity-jupyter:master\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the container\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run jupyter.img\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will starts jupyter server on port 8888. The current directory will be used as the notebook direcory.\nYou can connect to the server and select the notebook file \u003ca href=\"python_heat2d.ipynb\"\u003epython_heat2d.ipynb\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-jq", + "latest_release": "v1.6", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-jq/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-jq/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-jq/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-jq/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7ba2d90cdf83d5e2c1ff43f36457bd7662a4c884cfaba4962f33517ebbcc87c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6a71\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ba2d90cdf83d5e2c1ff43f36457bd7662a4c884cfaba4962f33517ebbcc87c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6a71\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-jq\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f57604a88aedfdf5b00774448eb1e1458a22026714624a4ed775fe8f7948d252/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6a71\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f57604a88aedfdf5b00774448eb1e1458a22026714624a4ed775fe8f7948d252/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6a71\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-jq\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8f97344032bc1f53bcc9a24cb971084b86a8b67ee2c9e9cae802a9dd84cf569c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6a71\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8f97344032bc1f53bcc9a24cb971084b86a8b67ee2c9e9cae802a9dd84cf569c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6a71\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-jq\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0e1fe273a8da2bb31a820d69ebb9fce673aaed18c07e2b45b4ad319d38a46954/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6a71\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e1fe273a8da2bb31a820d69ebb9fce673aaed18c07e2b45b4ad319d38a46954/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6a71\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-jq\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-jq\" class=\"anchor\" href=\"#singularity-jq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-jq\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/2a6480b8877ea9a4e8d36292d5b1a2a918d31be6029767a0dc047dbe8c48d977/68747470733a2f2f737465646f6c616e2e6769746875622e696f2f6a712f6a712e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a6480b8877ea9a4e8d36292d5b1a2a918d31be6029767a0dc047dbe8c48d977/68747470733a2f2f737465646f6c616e2e6769746875622e696f2f6a712f6a712e706e67\" width=\"50%\" data-canonical-src=\"https://stedolan.github.io/jq/jq.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://stedolan.github.io/jq/\" rel=\"nofollow\"\u003ejq\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ejq\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/jq/1.6\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/jq\u003c/code\u003e as \u003ccode\u003e1.6.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1493997701.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1644901477.0 }, { "data_format": 2, - "description": "Files to build Singularity images for running the Monte-Carlo event generator Sherpa", + "description": "The jp command is a command line interface to JMESPath, an expression language for manipulating JSON.", "filenames": [ - "Singularity.fitting_centos6", - "Singularity.sherpa-rel-2-2-7_68ab0c9c5_Caesar", - "Singularity.sherpa-2.2.6", - "Singularity.rivet_centos6", - "Singularity.sherpa-tmp-cherrypick-ewvirt-into-master_HEAD_centos6", - "Singularity.sherpa-rel-2-2-9_HEAD_centos6", - "Singularity.sherpa-master_2dc43a3d_Asterix", - "Singularity.plotting", - "Singularity.mceg", - "Singularity.sherpa-rel-2-2-7_12338b5d_Bossix", - "Singularity.sherpa-master_HEAD_centos6", - "Singularity.plotting_centos6", - "Singularity.sherpa-openmpi.devtoolset", - "Singularity.sherpa-2.2.6_centos6", - "Singularity.rivet", - "Singularity.sherpa-rel-2-2-7_HEAD_centos6", - "Singularity.mceg_centos6" + "0.2.1/Singularity" ], - "full_name": "ebothmann/sherpa-singularity", - "latest_release": null, + "full_name": "pscedu/singularity-jp", + "latest_release": "v0.2.1", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-jp/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-jp/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-jp/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-jp/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/05b134868e56bf8c8fa7dc13f4470cf13cad0b7161546e09c789aafe22deec6e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6a70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/05b134868e56bf8c8fa7dc13f4470cf13cad0b7161546e09c789aafe22deec6e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6a70\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-jp\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/92fedab816aca4c765ddc450267fdad82b3f6c32c306fde8ca397c0a1aedec59/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6a70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/92fedab816aca4c765ddc450267fdad82b3f6c32c306fde8ca397c0a1aedec59/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6a70\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-jp\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/434a811330df95d0d19571e1ec67b47ba8e8e026d81c1f24c5f0eb3fae746f35/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6a70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/434a811330df95d0d19571e1ec67b47ba8e8e026d81c1f24c5f0eb3fae746f35/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6a70\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-jp\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/9e077544905c5c3ee2d0d7b1f83444e58ecffcb7d201f604c5eb3e114c1e18b3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6a70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e077544905c5c3ee2d0d7b1f83444e58ecffcb7d201f604c5eb3e114c1e18b3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6a70\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-jp\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-jp\" class=\"anchor\" href=\"#singularity-jp\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-jp\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://jmespath.org/\" rel=\"nofollow\"\u003ejp\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ejp\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/jp/0.2.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/jp\u003c/code\u003e as \u003ccode\u003e0.2.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1603222289.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1644903522.0 }, { "data_format": 2, - "description": "Testing Singularity container and Singularity-hub", + "description": "Custom implementation of neurodocker (https://github.com/ReproNim/neurodocker)", "filenames": [ "Singularity" ], - "full_name": "kma/singularity-lab", + "full_name": "achennings/neurodocker", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-use-case\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-use-case\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity use case\u003c/h1\u003e\n\u003cp\u003eCreate a reproducible container image to run a simple python program (\u003ccode\u003edata_alaysys.py\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThis code takes a csv file and plots results in two separated pdf files.\u003c/p\u003e\n\u003cp\u003eThe csv can be found \u003ca href=\"http://info.iut-bm.univ-fcomte.fr/staff/mazouzi/docs/ganglia-metrics.csv\" rel=\"nofollow\"\u003e[here]\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-create-a-container-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-a-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate a container locally\u003c/h2\u003e\n\u003cp\u003eRun \u003ccode\u003ebuild-local\u003c/code\u003e to create and bootstrap a container (This action needs root access).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity create -s 1000 mycontainer.img\n$ sudo singularity bootstrap mycontainer.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-python-code-inside-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-python-code-inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun python code inside the container\u003c/h2\u003e\n\u003cp\u003eRun \u003ccode\u003erun-local.sh\u003c/code\u003e to execute python code inside the container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ wget http://info.iut-bm.univ-fcomte.fr/staff/mazouzi/docs/ganglia-metrics.csv\n\n$ ./mycontainer.img data_analysis\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-image-container-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-image-container-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull image container from singularity-hub\u003c/h2\u003e\n\u003cp\u003eIf you don\u0027t root access, singularity-hub can create images by providing a specification file. See the \u003ca href=\"https://singularity-hub.org/faq\" rel=\"nofollow\"\u003e[documentation]\u003c/a\u003e for more details .\u003c/p\u003e\n\u003cp\u003eThe image corresponding to the \u003ccode\u003eSingularity\u003c/code\u003e file can be pulled from \u003ca href=\"https://singularity-hub.org/containers/842/\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/containers/842/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePull image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull shub://842\nOr\n$ singularity pull shub://kma/singularity-lab:master\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun python code using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kma-singularity-lab-master.img python data_analysis.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./kma-singularity-lab-master.img data_analysis.py\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-neurodocker\" class=\"anchor\" href=\"#neurodocker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eneurodocker\u003c/h1\u003e\n\u003cp\u003eCustom implementation of neurodocker (\u003ca href=\"https://github.com/ReproNim/neurodocker\"\u003ehttps://github.com/ReproNim/neurodocker\u003c/a\u003e)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1493818555.0 + "updated_at": 1645031040.0 }, { "data_format": 2, @@ -9228,41 +8637,40 @@ var data = "filenames": [ "Singularity" ], - "full_name": "timo-singularity/rivet", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erecipes\u003c/h1\u003e\n", + "full_name": "NagaComBio/singularity_gcnvplotting", + "latest_release": "v0.2.0", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-for-gcnvplotting_v010sif\" class=\"anchor\" href=\"#for-gcnvplotting_v010sif\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor gcnvplotting_v0.1.0.sif\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/NagaComBio/singularity_gcnvplotting.git\ncd singularity_gcnvplotting\nsudo singularity build gcnvplotting_v0.1.0.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1622810530.0 + "updated_at": 1640253212.0 }, { "data_format": 2, - "description": "Singularity recipe(s) for LSDalton.", + "description": "Code related to the installation and use of the openface on PSU\u0027s ACI systems ", "filenames": [ - "Singularity.latest-gcc-9.3.0" + "Singularity" ], - "full_name": "bast/lsdalton", + "full_name": "behav/openface", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipes-for-lsdalton\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipes-for-lsdalton\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca href=\"https://sylabs.io/guides/latest/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e recipe(s) for \u003ca href=\"https://gitlab.com/dalton/lsdalton/\" rel=\"nofollow\"\u003eLSDalton\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5142\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/5142\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull --name lsdalton shub://bast/lsdalton:latest-gcc-9.3.0\n$ ./lsdalton myexample.dal mymolecule.mol\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-openface_ics\" class=\"anchor\" href=\"#openface_ics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopenface_ics\u003c/h1\u003e\n\u003cp\u003eCode and workflow for building \u003ca href=\"https://github.com/TadasBaltrusaitis/OpenFace\"\u003eOpenFace\u003c/a\u003e\nin Docker Hub and modifying it with Singularity Hub for use with PSU\nACI HPC clusters.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eFrom ACI, executing the following code should create an \u003ccode\u003eOpenFace\u003c/code\u003e image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://d-bohn/openface_ics:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-image-builds\" class=\"anchor\" href=\"#image-builds\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImage Builds\u003c/h2\u003e\n\u003cp\u003eThe OpenFace docker image was built from scratch on docker hub following the\n\u003ca href=\"https://github.com/TadasBaltrusaitis/OpenFace/wiki/Unix-Installation\"\u003edocumentation\u003c/a\u003e provided by it\u0027s maintainers.\u003c/p\u003e\n\u003cp\u003eThe OpenFace singularity image was built using the docker image base and\nconverting it to a singularity image via singularity hub.\u003c/p\u003e\n\u003cp\u003eSetup for linking Github with Docker Hub and Singularity Hub can be found here:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.docker.com/docker-hub/\" rel=\"nofollow\"\u003edocker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki\"\u003eSingularity Hub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003eSingularity\u003c/code\u003e file specifies creating a Singularity-compatible image\nfrom the docker image, as well as adding access to folders within ACI, specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# ACI mappings so you can access your files.\nmkdir -p /storage/home\nmkdir -p /storage/work\nmkdir -p /gpfs/group\nmkdir -p /gpfs/scratch\nmkdir -p /var/spool/torque\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe OpenFace docker image is large (\u0026gt; 6GB). It is built on Ubuntu 18.04.\nNot sure if it can be reduced in size as the executables rely on several\nlarge libraries.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSeveral important updates for \u003ccode\u003efaciallandmarkdetector\u003c/code\u003e are hosted on\nthe maintainer\u0027s cloud account. Might be prudent to download them\nseparately and/or include them in the repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSome functionality for real-time video viewing is not available\nwhen run in a container (at least not as of now).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1612249375.0 + "updated_at": 1556740713.0 }, { "data_format": 2, "description": null, "filenames": [ - "scripts/Singularity" + "Singularity" ], - "full_name": "SCXsunchenxi/Auto-Pytorch", + "full_name": "yhisaki/exp_pfrl", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-auto-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#auto-pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuto-PyTorch\u003c/h1\u003e\n\u003cp\u003eCopyright (C) 2019 \u003ca href=\"http://www.automl.org/\" rel=\"nofollow\"\u003eAutoML Group Freiburg\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis a very early pre-alpha version of our upcoming Auto-PyTorch.\nSo far, Auto-PyTorch supports featurized data (classification, regression) and image data (classification).\u003c/p\u003e\n\u003cp\u003eThe newest features in Auto-PyTorch for tabular data are described in the paper \u003ca href=\"https://arxiv.org/abs/2006.13799\" rel=\"nofollow\"\u003e\"Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL\"\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eClone repository\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e install/path\n$ git clone https://github.com/automl/Auto-PyTorch.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Auto-PyTorch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to contribute to this repository switch to our current develop branch\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git checkout develop\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall pytorch:\n\u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ehttps://pytorch.org/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInstall Auto-PyTorch:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat requirements.txt \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e xargs -n 1 -L 1 pip install\n$ python setup.py install\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h2\u003e\n\u003cp\u003eCode for the \u003ca href=\"https://arxiv.org/abs/2006.13799\" rel=\"nofollow\"\u003epaper\u003c/a\u003e is available under \u003ccode\u003eexamples/ensemble\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor a detailed tutorial, please refer to the jupyter notebook in \u003ca href=\"https://github.com/automl/Auto-PyTorch/tree/master/examples/basics\"\u003ehttps://github.com/automl/Auto-PyTorch/tree/master/examples/basics\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn a nutshell:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# data and metric imports\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodel_selection\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_digits\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ereturn_X_y\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\u003cspan class=\"pl-v\"\u003eX_train\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eX_test\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_test\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \\\n \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodel_selection\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etrain_test_split\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erandom_state\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# running Auto-PyTorch\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"tiny_cs\"\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# config preset\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003elog_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027info\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e300\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e90\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003evalidation_split\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.3\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ey_pred\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003epredict\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX_test\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"Accuracy score\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eaccuracy_score\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ey_test\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_pred\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMore examples with datasets:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e examples/\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003eHow to configure Auto-PyTorch for your needs:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Print all possible configuration options.\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e().\u003cspan class=\"pl-en\"\u003eprint_help\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# You can use the constructor to configure Auto-PyTorch.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elog_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027info\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e300\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e90\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# You can overwrite this configuration in each fit call.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elog_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027debug\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e900\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e150\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# You can use presets to configure the config space.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# Available presets: full_cs, medium_cs (default), tiny_cs.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# These are defined in autoPyTorch/core/presets.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# tiny_cs is recommended if you want fast results with few resources.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# full_cs is recommended if you have many resources and a very high search budget.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"full_cs\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Enable or disable components using the Auto-PyTorch config:\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enetworks\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e\"resnet\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"shapedresnet\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"mlpnet\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"shapedmlpnet\"\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# You can take a look at the search space.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# Each hyperparameter belongs to a node in Auto-PyTorch\u0027s ML Pipeline.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# The names of the hyperparameters are prefixed with the name of the node: NodeName:hyperparameter_name.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# If a hyperparameter belongs to a component: NodeName:component_name:hyperparameter_name.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# Call with the same arguments as fit.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eget_hyperparameter_search_space\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003evalidation_split\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.3\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# You can configure the search space of every hyperparameter of every component:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eHyperparameterSearchSpaceUpdates\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esearch_space_updates\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eHyperparameterSearchSpaceUpdates\u003c/span\u003e()\n\n\u003cspan class=\"pl-s1\"\u003esearch_space_updates\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enode_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"NetworkSelector\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ehyperparameter\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"shapedresnet:activation\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003evalue_range\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e\"relu\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"sigmoid\"\u003c/span\u003e])\n\u003cspan class=\"pl-s1\"\u003esearch_space_updates\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enode_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"NetworkSelector\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ehyperparameter\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"shapedresnet:blocks_per_group\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003evalue_range\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e],\n \u003cspan class=\"pl-s1\"\u003elog\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehyperparameter_search_space_updates\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003esearch_space_updates\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEnable ensemble building (for featurized data):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetEnsemble\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorchEnsemble\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetEnsemble\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"tiny_cs\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e300\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e90\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDisable pynisher if you experience issues when using cuda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"tiny_cs\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elog_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027info\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e300\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e90\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecuda\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003euse_pynisher\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis program is free software: you can redistribute it and/or modify\nit under the terms of the Apache license 2.0 (please see the LICENSE file).\u003c/p\u003e\n\u003cp\u003eThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\u003c/p\u003e\n\u003cp\u003eYou should have received a copy of the Apache license 2.0\nalong with this program (see LICENSE file).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@incollection\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003emendoza-automlbook18a\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eHector Mendoza and Aaron Klein and Matthias Feurer and Jost Tobias Springenberg and Matthias Urban and Michael Burkart and Max Dippel and Marius Lindauer and Frank Hutter\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eTowards Automatically-Tuned Deep Neural Networks\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2018\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003emonth\u003c/span\u003e = dec,\n \u003cspan class=\"pl-s\"\u003eeditor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eHutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebooktitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eAutoML: Methods, Sytems, Challenges\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epublisher\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eSpringer\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003echapter\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e7\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epages\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e141--156\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003enote\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eTo appear.\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Previously, the name of the project was AutoNet. Since this was too generic, we changed the name to AutoPyTorch. AutoNet 2.0 in the reference mention above is indeed AutoPyTorch.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003eAuto-PyTorch is developed by the \u003ca href=\"http://www.automl.org/\" rel=\"nofollow\"\u003eAutoML Group of the University of Freiburg\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1609655576.0 + "updated_at": 1645156767.0 }, { "data_format": 2, @@ -9270,420 +8678,434 @@ var data = "filenames": [ "Singularity" ], - "full_name": "marcjwilliams1/rstudio_julia", + "full_name": "talha-naveed97/orion", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5054\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for R studio server with Rv4.0 + julia v1.5.3\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1611507934.0 + "updated_at": 1646176107.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "containers/Singularity.0.4.1", + "containers/Singularity.0.4.0", + "containers/Singularity.0.3.5", + "containers/Singularity.0.3.3", + "containers/Singularity.0.3.6" ], - "full_name": "jganong/ubuntu-focal-foiegras", + "full_name": "Samanwaya1301/tidal-heating-bilby", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1607375887.0 + "updated_at": 1638182089.0 }, { "data_format": 2, - "description": "singularity container to run Ian Jonsen\u0027s foieGras package", + "description": "Demultiplexing and QC pipeline for Illumina and 10X Single Cell sequencing data", "filenames": [ "Singularity" ], - "full_name": "jganong/ubuntu-bionic-R-4.0.3-foieGras", + "full_name": "csawye01/nf-core-demultiplex-crick", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nf-coredemultiplex\" class=\"anchor\" href=\"#nf-coredemultiplex\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/demultiplex\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eDemultiplexing pipeline for Illumina data\u003c/strong\u003e\n\u003cstrong\u003eIN PROGRESS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/demultiplex\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ae3bf24b0d68bb5e81863eb358c7f3cd3a383647e932a785a123565bf2d13391/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f64656d756c7469706c65782e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/demultiplex.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/demultiplex\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c838bd17591342d038d2a3b9de19e08588f2ae0043530f3eb082113f2651bac7/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f64656d756c7469706c65782e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/demultiplex.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003enf-core/demultiplex\u003c/strong\u003e is a bioinformatics demultiplexing pipeline used for multiple types of data input from sequencing runs.\nThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-sample-sheet-format\" class=\"anchor\" href=\"#sample-sheet-format\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSample Sheet Format\u003c/h3\u003e\n\u003cp\u003eThe sample sheet must fall into the same format as seen below to adhere to the Illumina standards with the additional column of DataAnalysisType and ReferenceGenome to ensure 10X sample will be processed correctly. Order of columns does not matter but the case of column names does.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eLane\u003c/th\u003e\n\u003cth\u003eSample_ID\u003c/th\u003e\n\u003cth\u003eUser_Sample_Name\u003c/th\u003e\n\u003cth\u003eindex\u003c/th\u003e\n\u003cth\u003eindex2\u003c/th\u003e\n\u003cth\u003eSample_Project\u003c/th\u003e\n\u003cth\u003eReferenceGenome\u003c/th\u003e\n\u003cth\u003eDataAnalysisType\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eABC11A2\u003c/td\u003e\n\u003ctd\u003eU_ABC0_BS_GL_DNA\u003c/td\u003e\n\u003ctd\u003eCGATGT\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003ePM10000\u003c/td\u003e\n\u003ctd\u003eHomo sapiens\u003c/td\u003e\n\u003ctd\u003eWhole Exome\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eSAG100A10\u003c/td\u003e\n\u003ctd\u003eSAG100A10\u003c/td\u003e\n\u003ctd\u003eSI-GA-C1\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eSC18100\u003c/td\u003e\n\u003ctd\u003eMus musculus\u003c/td\u003e\n\u003ctd\u003e10X-3prime\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e3\u003c/td\u003e\n\u003ctd\u003eCAP200A11\u003c/td\u003e\n\u003ctd\u003eUN1800_AE_6\u003c/td\u003e\n\u003ctd\u003eiCLIP\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003ePM18200\u003c/td\u003e\n\u003ctd\u003eHomo sapiens\u003c/td\u003e\n\u003ctd\u003eOther\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pipeline-summary\" class=\"anchor\" href=\"#pipeline-summary\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline summary\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eReformatting the input sample sheet\n\u003cul\u003e\n\u003cli\u003eScript looks for \u003ccode\u003eiCLIP\u003c/code\u003e in the index column of the sample sheet and collapses the iCLIP samples into one per lane.\u003c/li\u003e\n\u003cli\u003eSplits 10X single cell samples into 10X, 10X-ATAC and 10X-DNA .csv files by searching in the sample sheet column DataAnalysisType for \u003ccode\u003e10X-3prime\u003c/code\u003e, \u003ccode\u003e10X-ATAC\u003c/code\u003e and \u003ccode\u003e10X-CNV\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOutputs the results of needing to run specific processes in the pipeline (can be only 10X single cell samples, mix of 10X single cell with non single cell samples or all non single cell samples)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eChecking the sample sheet for downstream error causing samples such as:\n\u003cul\u003e\n\u003cli\u003ea mix of short and long indexes on the same lane\u003c/li\u003e\n\u003cli\u003ea mix of single and dual indexes on the same lane\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eProcesses that only run if there are issues within the sample sheet found by the sample sheet check process (CONDITIONAL):\n\u003col\u003e\n\u003cli\u003eCreates a new sample sheet with any samples that would cause an error removed and create a a txt file of a list of the removed problem samples\u003c/li\u003e\n\u003cli\u003eRun \u003ca href=\"http://emea.support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html\" rel=\"nofollow\"\u003e\u003ccode\u003ebcl2fastq\u003c/code\u003e\u003c/a\u003e on the newly created sample sheet and output the Stats.json file\u003c/li\u003e\n\u003cli\u003eParsing the Stats.json file for the indexes that were in the problem samples list.\u003c/li\u003e\n\u003cli\u003eRecheck newly made sample sheet for any errors or problem samples that did not match any indexes in the Stats.json file. If there is still an issue the pipeline will exit at this stage.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eSingle cell 10X sample processes (CONDITIONAL):\nWill run either CellRanger, CellRangerATAC, CellRangerDNA depending on the samplesheet data type\nNOTE: Must create CONFIG to point to CellRanger genome References\n\u003col\u003e\n\u003cli\u003eCell Ranger mkfastq runs only when 10X samples exist. This will run the process with \u003ca href=\"https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger\" rel=\"nofollow\"\u003e\u003ccode\u003eCellRanger\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/what-is-cell-ranger-atac\" rel=\"nofollow\"\u003e\u003ccode\u003eCellRanger ATAC\u003c/code\u003e\u003c/a\u003e, and \u003ca href=\"https://support.10xgenomics.com/single-cell-dna/software/pipelines/latest/what-is-cell-ranger-dna\" rel=\"nofollow\"\u003e\u003ccode\u003eCell Ranger DNA\u003c/code\u003e\u003c/a\u003e depending on which sample sheet has been created.\u003c/li\u003e\n\u003cli\u003eCell Ranger Count runs only when 10X samples exist. This will run the process with \u003ca href=\"https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/count\" rel=\"nofollow\"\u003e\u003ccode\u003eCell Ranger Count\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\" rel=\"nofollow\"\u003e\u003ccode\u003eCell Ranger ATAC Count\u003c/code\u003e\u003c/a\u003e, and \u003ca href=\"https://support.10xgenomics.com/single-cell-dna/software/pipelines/latest/using/cnv\" rel=\"nofollow\"\u003e\u003ccode\u003eCell Ranger DNA CNV\u003c/code\u003e\u003c/a\u003edepending on the output from Cell Ranger mkfastq. 10X reference genomes can be downloaded from the 10X site, a new config would have to be created to point to the location of these. Must add config to point Cell Ranger to genome references if used outside the Crick profile.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://emea.support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html\" rel=\"nofollow\"\u003e\u003ccode\u003ebcl2fastq\u003c/code\u003e\u003c/a\u003e (CONDITIONAL):\n\u003col\u003e\n\u003cli\u003eRuns on either the original sample sheet that had no error prone samples or on the newly created sample sheet created from the extra steps.\u003c/li\u003e\n\u003cli\u003eThis is only run when there are samples left on the sample sheet after removing the single cell samples.\u003c/li\u003e\n\u003cli\u003eThe arguments passed in bcl2fastq are changeable parameters that can be set on the command line when initiating the pipeline. Takes into account if Index reads will be made into FastQ\u0027s as well\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003e\u003ccode\u003eFastQC\u003c/code\u003e\u003c/a\u003e runs on the pooled fastq files from all the conditional processes.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/\" rel=\"nofollow\"\u003e\u003ccode\u003eFastQ Screen\u003c/code\u003e\u003c/a\u003e runs on the pooled results from all the conditional processes.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://multiqc.info/docs/\" rel=\"nofollow\"\u003e\u003ccode\u003eMultiQC\u003c/code\u003e\u003c/a\u003e runs on each projects FastQC results produced.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://multiqc.info/docs/\" rel=\"nofollow\"\u003e\u003ccode\u003eMultiQC_all\u003c/code\u003e\u003c/a\u003e runs on all FastQC results produced.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/demultiplex pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/reference_genomes.md\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003eCredits\nThe nf-core/demultiplex pipeline was written by Chelsea Sawyer of the The Bioinformatics \u0026amp; Biostatistics Group for use at The Francis Crick Institute, London.\nMany thanks to others who have helped out along the way too, including (but not limited to): \u003ca href=\"https://github.com/ChristopherBarrington\"\u003e\u003ccode\u003e@ChristopherBarrington\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://github.com/drpatelh\"\u003e\u003ccode\u003e@drpatelh\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://github.com/danielecook\"\u003e\u003ccode\u003e@danielecook\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://github.com/escudem\"\u003e\u003ccode\u003e@escudem\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://github.com/crickbabs\"\u003e\u003ccode\u003e@crickbabs\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1607375064.0 + "updated_at": 1638199013.0 }, { "data_format": 2, - "description": "Singularity description files", + "description": null, "filenames": [ - "fusorsv/Singularity", - "mousegwas/Singularity" + "Singularity" ], - "full_name": "asafpr/singularity", + "full_name": "truatpasteurdotfr/singularity-debian9-visualstudio", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-vstudio-on-debian9-toy-system-for-singularity-for-ci\" class=\"anchor\" href=\"#building-a-vstudio-on-debian9-toy-system-for-singularity-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a vstudio on debian9 toy system for singularity for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-debian9-visualstudio/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-debian9-visualstudio/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-debian9-visualstudio:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1616613441.0 + "updated_at": 1638365824.0 }, { "data_format": 2, - "description": "This is the Artifact Description repository for the CGO21 paper: YaskSite \u2013 Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures", + "description": null, "filenames": [ "Singularity" ], - "full_name": "seasite-project/CGO21_YaskSite_AD", - "latest_release": "CGO21v0.3", - "readme": "\u003ch1\u003e\u003ca id=\"user-content--cgo21_yasksite_ad-\" class=\"anchor\" aria-hidden=\"true\" href=\"#-cgo21_yasksite_ad-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cins\u003e CGO21_YaskSite_AD \u003c/ins\u003e\u003c/h1\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup-phase\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-phase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup phase\u003c/h1\u003e\n\u003cp\u003eSteps 1 to 3 guide you through setting up.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-11\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-11\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1.1\u003c/h2\u003e\n\u003cp\u003eClone this repository and go to the cloned directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/seasite-project/CGO21_YaskSite_AD.git\ncd CGO21_YaskSite_AD\ngit checkout CGO21v0.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-12\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-12\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1.2\u003c/h2\u003e\n\u003cp\u003eFor the next steps we need singularity v 3.6.4 or higher.\nIf singularity is not installed, you can install singularity with the following script if you have root access.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./install_singularity.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2\u003c/h2\u003e\n\u003cp\u003eDownload the singularity container.\u003c/p\u003e\n\u003cp\u003eThe pre-build container is available under the following link \u003ca href=\"https://doi.org/10.5281/zenodo.4415558\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.4415558\u003c/a\u003e\nand can be installed using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://zenodo.org/record/4415558/files/YS_CGO.sif?download=1 -O YS_CGO.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-3\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 3\u003c/h2\u003e\n\u003cp\u003eOnce singularity image is downloaded on the benchmarking system the first step is to run the app called build.\nThis installs YaskSite. It should be done at runtime since the YaskSite does machine specific configuration\nat build time. Run the following to do this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app build YS_CGO.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-phase\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-phase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun phase\u003c/h1\u003e\n\u003cp\u003eStep 4 illustrates how to run the app to reproduce results.\nIt is recommended the settings in the paper are followed to get comparable results.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-4\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 4\u003c/h2\u003e\n\u003cp\u003eRun the apps corresponding to YaskSite and Offsite. There are also pre-configured apps that helps to\nreproduce data in figures of the paper. To see the list of available apps use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run-help YS_CGO.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe method to run each apps are described in corresponding app\u0027s help. For example help on how to run Fig4 app\n(reproduces results in Fig4 of the paper) can be obtained using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run-help --app Fig4 YS_CGO.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "truatpasteurdotfr/singularity-debian10-visualstudio", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-vstudio-on-debian10-toy-system-for-singularity-for-ci\" class=\"anchor\" href=\"#building-a-vstudio-on-debian10-toy-system-for-singularity-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a vstudio on debian10 toy system for singularity for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-debian10-visualstudio/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-debian10-visualstudio/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-debian10-visualstudio:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1609764345.0 + "updated_at": 1638370657.0 }, { "data_format": 2, - "description": "Bayesian Atmospheric Radiative Transfer (BART) packaged in a Singularity container https://github.com/davecwright3/bart-singularity", + "description": "singularity recipe for https://github.com/chienchi/amplicon_coverage_plot", "filenames": [ "Singularity" ], - "full_name": "davecwright3/bart-singularity", + "full_name": "dcgc-bfx/singularity-amplicon_coverage_plot", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4946\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bart-singularity-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#bart-singularity-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBART Singularity Guide\u003c/h1\u003e\n\u003cp\u003eThe Singularity image has BART installed at \u003ccode\u003e/bart_dir\u003c/code\u003e. The \u003ccode\u003e$topdir\u003c/code\u003e environment variable is set to this directory inside the image. This means that the instructions for the demo listed here \u003ca href=\"https://github.com/exosports/BART/tree/master/examples/demo\"\u003ehttps://github.com/exosports/BART/tree/master/examples/demo\u003c/a\u003e still work, but we need to mount a directory for outputs into the container for two reasons:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe demo expects your output directory to be parallel to the BART directory\u003c/li\u003e\n\u003cli\u003eThe container file system is read-only (this is only a problem because of (1); being read-only is actually preferred because it helps ensure reproducible results)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eIf the output directory wasn\u0027t required to be parallel to BART, you could run the container anywhere in \u003ccode\u003e$HOME\u003c/code\u003e because Singularity mounts \u003ccode\u003e$HOME\u003c/code\u003e of the current user into the container by default\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe image has a directory parallel to BART that is meant for output at \u003ccode\u003e/bart_dir/run\u003c/code\u003e. Make a directory on your host system where you want to store results. For the sake of this guide, let\u0027s say it\u0027s under your current directory at \u003ccode\u003edemo/run\u003c/code\u003e and you have pulled the singularity image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name bart.sif shub://davecwright3/bart-singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto your current directory as well. Then start a shell in the singularity container with the bind mount specified\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B demo/run:/bart_dir/run bart.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe BART conda environment will be automatically activated. Now just \u003ccode\u003ecd $topdir/run\u003c/code\u003e and follow the instructions here \u003ca href=\"https://github.com/exosports/BART/tree/master/examples/demo\"\u003ehttps://github.com/exosports/BART/tree/master/examples/demo\u003c/a\u003e if you would like to do a demo run. You can \u003ccode\u003eexit\u003c/code\u003e the container whenever you are done, and your results will remain in your \u003ccode\u003edemo/run\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eBayesian Atmospheric Radiative Transfer (BART), a code to infer\nproperties of planetary atmospheres based on observed spectroscopic\ninformation.\u003c/p\u003e\n\u003cp\u003eThis project was completed with the support of the NASA Planetary\nAtmospheres Program, grant NNX12AI69G, held by Principal Investigator\nJoseph Harrington. Principal developers included graduate students\nPatricio E. Cubillos and Jasmina Blecic, programmer Madison Stemm, and\nundergraduates M. Oliver Bowman and Andrew S. D. Foster. The included\n\u0027transit\u0027 radiative transfer code is based on an earlier program of\nthe same name written by Patricio Rojo (Univ. de Chile, Santiago) when\nhe was a graduate student at Cornell University under Joseph\nHarrington. Statistical advice came from Thomas J. Loredo and Nate\nB. Lust.\u003c/p\u003e\n\u003cp\u003eCopyright (C) 2015-2016 University of Central Florida.\nAll rights reserved.\u003c/p\u003e\n\u003cp\u003eThis is a test version only, and may not be redistributed to any third\nparty. Please refer such requests to us. This program is distributed\nin the hope that it will be useful, but WITHOUT ANY WARRANTY; without\neven the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\nPURPOSE.\u003c/p\u003e\n\u003cp\u003eOur intent is to release this software under an open-source,\nreproducible-research license, once the code is mature and the first\nresearch paper describing the code has been accepted for publication\nin a peer-reviewed journal. We are committed to development in the\nopen, and have posted this code on github.com so that others can test\nit and give us feedback. However, until its first publication and\nfirst stable release, we do not permit others to redistribute the code\nin either original or modified form, nor to publish work based in\nwhole or in part on the output of this code. By downloading, running,\nor modifying this code, you agree to these conditions. We do\nencourage sharing any modifications with us and discussing them\nopenly.\u003c/p\u003e\n\u003cp\u003eWe welcome your feedback, but do not guarantee support. Please send\nfeedback or inquiries to:\nPatricio Cubillos \u003ca href=\"mailto:patricio.cubillos@oeaw.ac.at\"\u003epatricio.cubillos@oeaw.ac.at\u003c/a\u003e\nJasmina Blecic \u003ca href=\"mailto:jasmina@physics.ucf.edu\"\u003ejasmina@physics.ucf.edu\u003c/a\u003e\nJoseph Harrington \u003ca href=\"mailto:jh@physics.ucf.edu\"\u003ejh@physics.ucf.edu\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eor alternatively,\nJoseph Harrington, Patricio Cubillos, and Jasmina Blecic\nUCF PSB 441\n4111 Libra Drive\nOrlando, FL 32816-2385\nUSA\u003c/p\u003e\n\u003cp\u003eThank you for testing BART!\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-amplicon_coverage_plot\" class=\"anchor\" href=\"#singularity-amplicon_coverage_plot\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-amplicon_coverage_plot\u003c/h1\u003e\n\u003cp\u003esingularity recipe for \u003ca href=\"https://github.com/chienchi/amplicon_coverage_plot\"\u003ehttps://github.com/chienchi/amplicon_coverage_plot\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eImages are stored here: \u003ca href=\"https://cloud.sylabs.io/library/fabianrost84/dcgc-bfx/amplicon_coverage_plot\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/fabianrost84/dcgc-bfx/amplicon_coverage_plot\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1604965509.0 + "updated_at": 1638796921.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.FMS-gcc10-openmpi-netcdf4.6.3-ubuntu", - "Singularity", - "Singularity.FMS-gcc10-openmpi-netcdf4.6.3-ubuntu-compile" + "Singularity" ], - "full_name": "thomas-robinson/fms_containers", + "full_name": "Mauricemonashuniversity/Epileptic-seizure-prediction", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fms_containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#fms_containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efms_containers\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1604411747.0 + "updated_at": 1640038462.0 }, { "data_format": 2, - "description": "Singularity recipe files for edta (https://github.com/oushujun/EDTA)", + "description": null, "filenames": [ - "Singularity", - "Singularity.1.8.3", - "Singularity.1.9.0" + "recipes/Singularity.def" ], - "full_name": "powerPlant/edta-srf", - "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the Extensive de novo TE Annotator tool\u003c/p\u003e\n", + "full_name": "stigrj/ghcr_sandbox", + "latest_release": "v2.0.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-testing-out-ghcr-workflows\" class=\"anchor\" href=\"#testing-out-ghcr-workflows\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting out GHCR workflows\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1603071842.0 + "updated_at": 1641893233.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "2.26.10/Singularity" ], - "full_name": "shreyaskamathkm/singularity_meshroom", + "full_name": "yh549848/singularity-picard", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_meshroom\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_meshroom\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_meshroom\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1602807348.0 + "updated_at": 1641914982.0 }, { "data_format": 2, - "description": null, + "description": "Playground for Julia environments to test on Milgram ", "filenames": [ "Singularity" ], - "full_name": "lehtiolab/nf-deqms", + "full_name": "CNCLgithub/JuliaHPCApp", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-lehtiolabnf-deqms\" class=\"anchor\" aria-hidden=\"true\" href=\"#lehtiolabnf-deqms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elehtiolab/nf-deqms\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eA small pipeline to re-run DEqMS on existing results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0fcfc6847f4944e0c46cb62bb190c0110bafa56ce455c12dd23051df8d710a4a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30312e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.01.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/lehtiolab/nf-deqms\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4068dc15ebffdfaa7d220510750dd7bcde75393d91d3fe2d05dc15190c515246/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c656874696f6c61622f6e662d6465716d732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/lehtiolab/nf-deqms.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThis workflow reruns DEqMS analysis on existing results, e.g. from the \u003ca href=\"https://github.com/lehtiolab/ddamsproteomics\"\u003elehtiolab/ddamsproteomics\u003c/a\u003e pipeline. It exists so one can use orthogonal sample groups (CTRL vs TREAT, old vs young) and rerun, or perhaps correct a mistake in the sample annotation, without having to re-search an entire set of spectra against a protein sequence database.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun pipeline:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/nf-deqms --proteins proteins.txt --peptides peptides.txt --genes genes.txt --ensg ensg.txt --sampletable samples.txt -profile standard,docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can leave out any accession that you do not have or are not interested in (e.g. \u003ccode\u003e--ensg\u003c/code\u003e in a Swissprot analysis).\u003c/p\u003e\n\u003cp\u003eThe lehtiolab/nf-deqms pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere is more extensive documentation on the options inside the main.nf file.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003elehtiolab/nf-deqms was originally written by Jorrit Boekel and tries to follow the \u003ca href=\"https://nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e best practices and templates.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1605692054.0 + "updated_at": 1641927437.0 }, { "data_format": 2, - "description": "Singularity recipe files for bedops (https://github.com/bedops/bedops)", + "description": "Apache Druid singularity container for holberton school student records and such", "filenames": [ - "Singularity", - "Singularity.2.4.39" + "Singularity" ], - "full_name": "powerPlant/bedops-srf", + "full_name": "romxero/Singularity_Apache_Druid", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the BEDOPS open-source command-line toolkit that performs highly efficient and scalable Boolean and other set operations, statistical calculations, archiving, conversion and other management of genomic data of arbitrary scale.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-apache-druid-in-a-singularity-container-this-is-used-for-testing-and-for-creating-a-database-for-interactive-use-by-holberton-tulsa\" class=\"anchor\" href=\"#apache-druid-in-a-singularity-container-this-is-used-for-testing-and-for-creating-a-database-for-interactive-use-by-holberton-tulsa\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApache Druid in a singularity container. This is used for testing and for creating a database for interactive use by Holberton Tulsa.\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1596773368.0 + "updated_at": 1642026259.0 }, { "data_format": 2, - "description": "Singularity recipe files for REPET (https://urgi.versailles.inra.fr/Tools/REPET)", + "description": null, "filenames": [ - "Singularity.3.0", "Singularity" ], - "full_name": "powerPlant/repet-srf", + "full_name": "biobox-info/fragpipe", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for REPET\n(\u003ca href=\"https://urgi.versailles.inra.fr/Tools/REPET\" rel=\"nofollow\"\u003ehttps://urgi.versailles.inra.fr/Tools/REPET\u003c/a\u003e), used to detect, annotate and\nanalyse repeats in genomic sequences, specifically designed for transposable\nelements (TEs).\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fragpipe\" class=\"anchor\" href=\"#fragpipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFragpipe\u003c/h1\u003e\n\u003cp\u003eFragpipe latest version: 1.0.0\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1602104190.0 + "updated_at": 1642084970.0 }, { "data_format": 2, - "description": "parallel gzipper in pure python", + "description": null, "filenames": [ - "Singularity.alpine" + "ext/Singularity" ], - "full_name": "d-w-moore/zipit", + "full_name": "clemsonciti/ood_rshiny", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-zipit\" class=\"anchor\" aria-hidden=\"true\" href=\"#zipit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ezipit\u003c/h1\u003e\n\u003cp\u003eThis repo contains two scripts useful for gzipping and checking large files\nas quickly as possible leveraging the parallelism of your machine.\u003c/p\u003e\n\u003cp\u003eThey require only that python be installed, and they depend only on modules\nincluded in the Python Standard Library -- particularly, of course, gzip.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-zipitpy\" class=\"anchor\" aria-hidden=\"true\" href=\"#zipitpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ezipit.py\u003c/h2\u003e\n\u003cp\u003eExample uses:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./zipit.py -v large.tar # =\u0026gt; Creates large.tar.gz at default level of parallelism.\n # (-v verbosely informs of the piece-wise gzip tasks)\n\n $ ./zipit.py -qm large.tar # =\u0026gt; creates large.tar.gz using all available CPU\u0027s\n\n $ some_command | ./zipit.py - \u0026gt; out.gz # =\u0026gt; gzips from the stdin stream, onto stdout\n\n $ docker export cimg | ./zipit.py \\ # =\u0026gt; export and compress the filesystem of\n -d cimg.dig - \u0026gt;cimg.tgz # a docker container\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testzippy\" class=\"anchor\" aria-hidden=\"true\" href=\"#testzippy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etestzip.py\u003c/h2\u003e\n\u003cp\u003eExample use (for context, see the final \u003ccode\u003ezipit.py\u003c/code\u003e example above):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./testzip.py cimg.tgz cimg.dig # =\u0026gt; tests the gzipped file\u0027s integrity using a digest file\n # (returns 0 if the integrity is good)\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-batch-connect---example-jupyter-notebook-server-palmetto\" class=\"anchor\" href=\"#batch-connect---example-jupyter-notebook-server-palmetto\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch Connect - Example Jupyter Notebook Server Palmetto\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/3a55d969e5c049c5a679159fb5e33007b3341d832eb29fbf0419a7ea1df72b22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6578616d706c655f6a7570797465722e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3a55d969e5c049c5a679159fb5e33007b3341d832eb29fbf0419a7ea1df72b22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6578616d706c655f6a7570797465722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_example_jupyter.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/d7ea6810dec03748ecd3a16bc0028d6d3ba3f7f01daa23e53e07ed1740b54420/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6f73632f62635f6578616d706c655f6a7570797465722e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d7ea6810dec03748ecd3a16bc0028d6d3ba3f7f01daa23e53e07ed1740b54420/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6f73632f62635f6578616d706c655f6a7570797465722e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/github/license/osc/bc_example_jupyter.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app that launches a Jupyter Notebook server within a\nbatch job.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://jupyter.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eJupyter Notebook\u003c/a\u003e 4.2.3+ (earlier\nversions are untested but may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.openssl.org/\" rel=\"nofollow\"\u003eOpenSSL\u003c/a\u003e 1.0.1+ (used to hash the Jupyter Notebook\nserver password)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOptional\u003c/strong\u003e software:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e\n6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based CLI\nused to load appropriate environments within the batch job before launching\nthe Jupyter Notebook server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eThese are command line only installation directions.\u003c/p\u003e\n\u003cp\u003eWe start by downloading a zipped package of this code. This allows us to start\nwith a fresh directory that has no git history as we will be building off of\nthis.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the zip from the GitHub page\u003c/span\u003e\nwget https://github.com/OSC/bc_example_jupyter/archive/master.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a catchy directory\u003c/span\u003e\nmkdir my_jupyter_app\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Unzip the downloaded file into this directory\u003c/span\u003e\ntar xzvf master.tar.gz -C my_jupyter_app --strip-components=1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Change the working directory to this new directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e my_jupyter_app\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFrom here you will make any modifications to the code that you would like and\nversion your changes in your own repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Version our app by making a new Git repository\u003c/span\u003e\ngit init\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make all your code changes while testing them in the OnDemand Dashboard\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ...\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the files to the Git repository\u003c/span\u003e\ngit add --all\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Commit the staged files to the Git repository\u003c/span\u003e\ngit commit -m \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emy first commit\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_example_jupyter/fork\"\u003ehttps://github.com/OSC/bc_example_jupyter/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1602285708.0 + "updated_at": 1642298553.0 }, { "data_format": 2, - "description": "Singularity for HPC", + "description": null, "filenames": [ - "Singularity.centos7-python3.7-transformers3.0.2-ImageCrawl", - "Singularity.centos7-python3.8-transformers4.11.0-ImageCrawl", - "Singularity.centos7-python3.7-transformers2.11.0-ImageCrawl" + "Singularity_CPU", + "Singularity_GPU" ], - "full_name": "sina-ehsani/hpc-singularity", + "full_name": "ddbj/singularity_alphafold", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehpc-singularity\u003c/h1\u003e\n\u003cp\u003eSingularity for HPC\u003c/p\u003e\n\u003cp\u003eMake sure the sigularity is built on \u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003ehttps://sylabs.io\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eif ready use:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull shub://sinaehsani6/hpc-singularity:centos7-python3.7-transformers3.0.2-imagecrawl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTransformer 2.11.0:\n\u003ccode\u003esingularity pull shub://sinaehsani6/hpc-singularity:centos7-python3.7-transformers2.11.0-imagecrawl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMake sure the imagecrawl is updated (latest commit)\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_alphafold\" class=\"anchor\" href=\"#singularity_alphafold\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_alphafold\u003c/h1\u003e\n\u003cp\u003eUbuntu 18.04\u306balphafold 2.1\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fsingularity image\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306eSingularity definition file\u3067\u3059\u3002GPU\u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u306fSingularity_GPU\u3001GPU\u3092\u4f7f\u7528\u3057\u306a\u3044\u5834\u5408\u306fSingularity_CPU\u3092\u4f7f\u7528\u3057\u3066image\u3092\u30d3\u30eb\u30c9\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-image\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" href=\"#image%E3%81%AE%E3%83%93%E3%83%AB%E3%83%89\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eimage\u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build alphafold-2.1-xPU.sif Singularity_xPU\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 6, "topics": [], - "updated_at": 1641850034.0 + "updated_at": 1642384956.0 }, { "data_format": 2, - "description": "HPC-AI 2020 | Training Project NEMO - Nucleus for European Modelling of the Ocean", + "description": null, "filenames": [ - "Slurm Script/Singularity.nemo.apps", - "Slurm Script/Singularity.CENTOS-7.7-NEMO-MOFED" + "Singularity" ], - "full_name": "soycoder/nemo", + "full_name": "lawlessrd/SCZ-WM-pipeline", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content--nemo---ocean\" class=\"anchor\" aria-hidden=\"true\" href=\"#-nemo---ocean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"ocean\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f30a.png\"\u003e\ud83c\udf0a\u003c/g-emoji\u003e NEMO - ocean\u003c/h1\u003e\n\u003cp\u003eHPC-AI 2020 | Training Project - NEMO: Nucleus for European Modelling of the Ocean\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content--docker-images---centos\" class=\"anchor\" aria-hidden=\"true\" href=\"#-docker-images---centos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"floppy_disk\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4be.png\"\u003e\ud83d\udcbe\u003c/g-emoji\u003e Docker Images - CentOS\u003c/h2\u003e\n\u003cp\u003eThank you for an image (\u003ca href=\"https://hub.docker.com/r/wangyoucao577/centos7-gcc7.4\" rel=\"nofollow\"\u003ewangyoucao577/centos7-gcc7.4\u003c/a\u003e)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content--tag\" class=\"anchor\" aria-hidden=\"true\" href=\"#-tag\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"bookmark\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f516.png\"\u003e\ud83d\udd16\u003c/g-emoji\u003e Tag\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/layers/soycoder/centos7/nemo-ocean/images/sha256-c7bdaa3614e1fc1bbef31bdb05ac997e64b11abff716d00315807b1b79ad13c3\" rel=\"nofollow\"\u003e:nemo-ocean\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content--environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"sunrise_over_mountains\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f304.png\"\u003e\ud83c\udf04\u003c/g-emoji\u003e Environment\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eHPC-X to build an out-of-box MPI environment\u003c/li\u003e\n\u003cli\u003eBoost library\u003c/li\u003e\n\u003cli\u003eHDF5 Parallellibrary\u003c/li\u003e\n\u003cli\u003eNETCDF Parallel library with HDF5\u003c/li\u003e\n\u003cli\u003eNETCDF-FortranParallel library with NETCDF Parallel\u003c/li\u003e\n\u003cli\u003eXIOS\u003c/li\u003e\n\u003cli\u003eGYREwith GNUgfortran + HPC-X OpenMPI\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-html-basic\"\u003e\u003cpre\u003e/usr/mpi/gcc/openmpi-3.1.1rc1/bin/mpirun \\\n-mca pml ucx -x UCX_NET_DEVICES=mlx5_0:1 \\\n-mca mpi_show_mca_params 1 -mca pml_ucx_verbose 9 \\\n/usr/mpi/gcc/openmpi-3.1.1rc1/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\n\n/usr/mpi/gcc/openmpi-3.1.1rc1/bin/mpirun -n 2 \\\n-mca pml ucx -x UCX_NET_DEVICES=mlx5_0:1 \\\n/usr/mpi/gcc/openmpi-3.1.1rc1/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\n/usr/bin/time -p mpirun -n 2 \\\n-mca pml ucx -x UCX_TLS=rc UCX_NET_DEVICES=mlx5_0:1 ./nemo\n\n/usr/bin/time -p mpirun -n 2 \\\n-mca -x UCX_TLS=rc -x UCX_NET_DEVICES=mlx5_0:1 ./nemo\n\n/usr/bin/time -p mpirun -n 2 \\\n-mca -x UCX_TLS=rc -x UCX_NET_DEVICES=ib0 \\\n/home/hpc/nemo/apps/hpcx-v2.6.0-gcc-MLNX_OFED_LINUX-4.7-1.0.0.1-redhat7.7-x86_64/ompi/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\nibstat\n\n\nNow step into the container and install MOFED:\n\n$ sudo singularity exec -w u16.04-sandbox/ bash\n(singularity)# cd MOFED/MLNX_OFED_LINUX-4.3-1.0.1.0-ubuntu16.04-x86_64\n(singularity)# ./mlnxofedinstall\n\n\n! -- (nemo) singularity exec -w nemo.sif bash\n\n\n## Run container\nTo use Singularity in Mellanox/HPCX need to load env module: `module load tools/singularity`\n.\n\nRun `osu_latency` test:\n```sh\n$ mpirun -np 2 --map-by node -mca btl self singularity exec hpcx-u16.04.simg /hpcx/ompi-a7df\nd94/tests/osu-micro-benchmarks-5.3.2/osu_latency\n# OSU MPI Latency Test v5.3.2\n# Size Latency (us)\n0 1.55\n1 1.55\n2 1.55\n4 1.55\n8 1.54\n16 1.55\n32 1.55\n64 1.65\n128 2.19\n256 2.23\n512 2.35\n1024 2.64\n2048 2.89\n4096 3.51\n8192 5.00\n16384 6.44\n32768 8.91\n65536 14.12\n131072 25.05\n262144 27.31\n524288 49.03\n1048576 92.53\n2097152 178.95\n4194304 351.24\n\n\n\n$hpcx_mpi_dir/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\ncd /home/hpc/nemo/apps/hpcx-v2.6.0-gcc-MLNX_OFED_LINUX-4.7-1.0.0.1-redhat7.7-x86_64\n\nmpirun \\\n-mca pml ucx -x UCX_NET_DEVICES=mlx5_0:1 \\\n-mca mpi_show_mca_params 1 -mca pml_ucx_verbose 9 \\\n./ompi/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\n\nmpirun \\\n-mca mpi_show_mca_params 1 -mca pml_ucx_verbose 9 \\\n./ompi/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\n\n\n/usr/bin/time -p mpirun -np 4 \\\n--map-by core -report-bindings \\\n-mca io ompio -x UCX_NET_DEVICES=mlx5_0:1 ./nemo\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-scz-white-matter-pipeline\" class=\"anchor\" href=\"#scz-white-matter-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSCZ White Matter Pipeline\u003c/h1\u003e\n\u003cp\u003eThis spider will preprocess fMRI data as well as corresponding T1 data, extract mean time-courses of each predefined ROI and compute the correlation matrices between white matter ROIs and gray matter ROIs. Please see Gao\u2019s publications [1, 2] for more details. The spider will also compute FALFF, ALFF and ReHo maps.\u003c/p\u003e\n\u003cp\u003eThis XNAT spider is currently designed for three databases (ADNI_23, BLSA and OASIS-3) which are proposed to be analyzed in white matter reanalysis project (PI: Dr. Gore and Dr. Landman).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h2\u003e\n\u003cp\u003efMRI (.nii.gz)\u003c/p\u003e\n\u003cp\u003eT1 (.nii.gz)\u003c/p\u003e\n\u003cp\u003eConfiguration file (.mat)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput:\u003c/h2\u003e\n\u003cp\u003ePreprocessed fMRI in MNI space:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../scz_OUTPUTS/result1_corrmatrix/FunImgARCFWD/1/Detrend_4DVolume.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTissue probability maps (gray matter and white matter) in MNI space:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../scz_OUTPUTS/result1_corrmatrix/T1ImgNewSegment/1/wc1T1.nii.gz\n\n../scz_OUTPUTS/result1_corrmatrix/T1ImgNewSegment/1/wc2T1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFunctional connectivity matrices between white matter ROIs and gray matter ROIs:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../scz_OUTPUTS/result1_corrmatrix/matr_1.mat\n\n../scz_OUTPUTS/result1_corrmatrix/matr_1.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMean time-courses of the white and gray matter ROIs:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result1_corrmatrix/tc_1.mat\t\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhole-brain ALFF maps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result2_wm_alff/ALFF_FunImgARCFWD/ALFFMap_1.nii.gz\n\n../ scz_OUTPUTS/result2_wm_alff/ALFF_FunImgARCFWD/mALFFMap_1.nii.gz\n\n../ scz_OUTPUTS/result2_wm_alff/ALFF_FunImgARCFWD/zALFFMap_1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhole-brain FALFF maps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result3_wm_falff/fALFF_FunImgARCFWD/fALFFMap_1.nii.gz\n\n../ scz_OUTPUTS/result3_wm_falff/fALFF_FunImgARCFWD/mfALFFMap_1.nii.gz\n\n../ scz_OUTPUTS/result3_wm_falff/fALFF_FunImgARCFWD/zfALFFMap_1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhole brain ReHo maps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result4_wm_reho/ReHo_FunImgARCFWD/ReHoMap_1.nii.gz\n\n../ scz_OUTPUTS/result4_wm_reho/ReHo_FunImgARCFWD/mReHoMap_1.nii.gz\n\n../ scz_OUTPUTS/result4_wm_reho/ReHo_FunImgARCFWD/zReHoMap_1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhole brain maps of degree of centrality:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/DegreeCentrality_PositiveBinarizedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/DegreeCentrality_PositiveWeightedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/mDegreeCentrality_PositiveBinarizedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/mDegreeCentrality_PositiveWeightedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/zDegreeCentrality_PositiveBinarizedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/zDegreeCentrality_PositiveWeightedSumBrainMap_1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h1\u003e\n\u003cp\u003e[1] Gao Y, Sengupta A, Li M, et al. (2020) Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer\u2019s disease. PLoS ONE 15(10): e0240513. \u003ca href=\"https://doi.org/10.1371/journal.pone.0240513\" rel=\"nofollow\"\u003ehttps://doi.org/10.1371/journal.pone.0240513\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e[2] Gao Y, Li M, Huang AS. Lower functional connectivity of white matter during rest and working memory tasks is associated with cognitive impairments in schizophrenia. Schizophr Res. 2021 Jul;233:101-110. doi: 10.1016/j.schres.2021.06.013. Epub 2021 Jun 29. PMID: 34215467; PMCID: PMC8442250.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1603363757.0 + "updated_at": 1642706993.0 }, { "data_format": 2, - "description": "Singularity recipe files for sortmerna (https://github.com/biocore/sortmerna)", + "description": "singularity recipes for bioinformatic analysis", "filenames": [ - "Singularity", - "Singularity.4.3.2", - "Singularity.4.3.6", - "Singularity.3.0.3", - "Singularity.4.2.0", - "Singularity.4.3.4" + "Singularity.vcf_processing.v1.0", + "Singularity.dysgu.v1.3.0", + "Singularity.sv_call.v1.0", + "Singularity.bcftools.v1.10.2", + "Singularity.qcbam.v1.0", + "Singularity.align_dedup.v1.0", + "Singularity.expansion_hunter.v5.0.0", + "Singularity.Rstudio", + "Singularity.pygenometracks", + "Singularity.GADO-v1.0.4", + "Singularity.HapCUT2", + "Singularity.sv_processing.v1.0", + "Singularity.expansion_hunter.v3.2.2", + "Singularity.hail", + "Singularity.V2_anno.var2reg", + "Singularity.Exomiser-v12.1.0", + "Singularity.variantstore", + "Singularity.GREEN-VARAN_v1", + "Singularity.shiny.server" ], - "full_name": "powerPlant/sortmerna-srf", + "full_name": "edg1983/Singularity_images", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" href=\"#singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity recipes\u003c/h1\u003e\n\u003cp\u003eThese are singularity recipes for images used in our bionformatic analysis.\nSome images are bundled with supplementary resources for analysis.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-supporting-files\" class=\"anchor\" href=\"#supporting-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esupporting files\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-resources-folder\" class=\"anchor\" href=\"#resources-folder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eresources folder\u003c/h4\u003e\n\u003cp\u003eSome supporting files are needed for the analysis.\nSee description file in the resources folder for the list of expected files and folders\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-custom-scripts\" class=\"anchor\" href=\"#custom-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecustom scripts\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-tools-folder\" class=\"anchor\" href=\"#tools-folder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etools folder\u003c/h4\u003e\n\u003cp\u003eSome supporting scripts are included in the tools folder and are copied into the corresponding images\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 2, + "topics": [], + "updated_at": 1636544746.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "3.1.6/Singularity", + "2.0.19/Singularity" + ], + "full_name": "yh549848/singularity-q", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the SortMeRNA local sequence alignment tool for filtering, mapping and clustering.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1659497761.0 + "updated_at": 1643962791.0 }, { "data_format": 2, - "description": "Singularity recipe files for trinityrnaseq (https://github.com/trinityrnaseq/trinityrnaseq)", + "description": null, "filenames": [ - "Singularity.2.14.0", - "Singularity", - "Singularity.2.13.2", - "Singularity.2.9.0", - "Singularity.2.8.6", - "Singularity.2.9.1", - "Singularity.2.10.0" + "Singularity/Singularity.v1.0" ], - "full_name": "powerPlant/trinityrnaseq-srf", + "full_name": "Monia234/IARC-imputation", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the Trinity RNA-Seq de novo transcriptome assembly\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-genotyping-imputation---pipeline-v10\" class=\"anchor\" href=\"#genotyping-imputation---pipeline-v10\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenotyping imputation : Pipeline V1.0\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-a-nextflow-pipeline-to-realise-a-datasets-genotyping-imputation\" class=\"anchor\" href=\"#a-nextflow-pipeline-to-realise-a-datasets-genotyping-imputation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA nextflow pipeline to realise a dataset\u0027s genotyping imputation\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/Imputation-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e3a0e94b24410397271294a485f985c85941b292ba2c7cbf3fefb26bf1e0c76b/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f74656d706c6174652d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/template-nf.svg?style=svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/imputation-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4533\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/94193130\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5decad826d7116b1c950b6b48c06052496f141e803525b088ce3cdcaaa4d7b88/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f39343139333133302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/94193130.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"template-nf.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"template-nf.png\" alt=\"Workflow representation\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eThe pipeline used to perform the imputation of several targets datasets processed with standard input.\u003c/p\u003e\n\u003cp\u003eHere is a summary of the method :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessing of data : by using the nextflow script Preparation.nf with create a directory \"file/\" with all the dependencies.\u003c/li\u003e\n\u003cli\u003eFirst step : Origin estimation of sample from the target dataset by using admixture tools and the hapmap dataset as reference.\u003c/li\u003e\n\u003cli\u003eSecond step : Series of SNPs filters and quality checking from the target dataset before the imputation step.\u003c/li\u003e\n\u003cli\u003eThird step : VCF production\u003c/li\u003e\n\u003cli\u003eLast step : Phasing and imputation\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the Usage section to test the full pipeline with your target dataset.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eThe pipeline works under Linux distributions.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExternal software:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eLiftOver : conda install ucsc-liftover\u003c/li\u003e\n\u003cli\u003ePlink (PLINK v1.90b6.12 64-bit (28 Oct 2019)) : conda install plink\u003c/li\u003e\n\u003cli\u003eAdmixture (ADMIXTURE Version 1.3.0) : conda install admixture\u003c/li\u003e\n\u003cli\u003ePerl : conda install perl\u003c/li\u003e\n\u003cli\u003eTerm::ReadKey module : conda install perl-termreadkey\u003c/li\u003e\n\u003cli\u003eBcfTools : conda install bcftools\u003c/li\u003e\n\u003cli\u003eeagle 2.4.1 : \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-50002.2\" rel=\"nofollow\"\u003eSee instructions\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eminimac4 : conda install cmake ; pip install cget ; git clone \u003ca href=\"https://github.com/statgen/Minimac4.git\"\u003ehttps://github.com/statgen/Minimac4.git\u003c/a\u003e ; cd Minimac4 ; bash install.sh\u003c/li\u003e\n\u003cli\u003eSamtools : conda install samtools\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eFile to download :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"zzz.bwh.harvard.edu/plink/dist/hapmap_r23a.zip\"\u003eHapmap Dataset\u003c/a\u003e : as reference\u0027s dataset for admixture\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.hagsc.org/hgdp/data/hgdp.zip\" rel=\"nofollow\"\u003eHGDP Dataset\u003c/a\u003e : for the dataset\u0027s test, you have to use the toMap.py \u0026amp; toPed.py in the \u0027converstion\u0027 directory to convert files in the .map/.ped plink format. Next you have to convert this last output in the .bed/.bam/.fam plink format by using plink line command and run the imputation\u0027s pipeline.\u003c/li\u003e\n\u003cli\u003ePerl tool : \u003ca href=\"https://www.well.ox.ac.uk/~wrayner/tools/\" rel=\"nofollow\"\u003eHRC-1000G-check-bim-NoReadKey.pl\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.well.ox.ac.uk/~wrayner/tools/1000GP_Phase3_combined.legend.gz\" rel=\"nofollow\"\u003e1000GP_Phase3_combined.legend\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eLiftOver tool : \u003ca href=\"http://hgdownload.cse.ucsc.edu/goldenpath/hg19/liftOver/hg19ToHg38.over.chain.gz\" rel=\"nofollow\"\u003ehg19ToHg38.over.chain\u003c/a\u003e \u0026amp; \u003ca href=\"http://hgdownload.cse.ucsc.edu/goldenpath/hg18/liftOver/hg18ToHg38.over.chain.gz\" rel=\"nofollow\"\u003ehg18ToHg38.over.chain\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePeparation dataset tool : \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2432498/bin/pone.0002551.s003.xls\" rel=\"nofollow\"\u003epone.0002551.s003.xls\u003c/a\u003e (Convert it in .csv format)\u003c/li\u003e\n\u003cli\u003eAdmixture tool : relationships_w_pops_121708.txt\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/zhanxw/checkVCF/raw/master/checkVCF.py\"\u003eCheckVCF\u003c/a\u003e, \u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz\" rel=\"nofollow\"\u003eFasta file in V37\u003c/a\u003e \u0026amp; \u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/GRCh38_reference_genome/\" rel=\"nofollow\"\u003eFasta file in V38\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/GRCh38_positions/\" rel=\"nofollow\"\u003e1000G Reference in Hg38\u003c/a\u003e with the \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-320005.3.2\" rel=\"nofollow\"\u003edoc\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate \u003ca href=\"https://imputationserver.readthedocs.io/en/latest/create-reference-panels/#create-legend-files\" rel=\"nofollow\"\u003elegend\u003c/a\u003e, \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-320005.3.2\" rel=\"nofollow\"\u003ebcf\u003c/a\u003e \u0026amp; \u003ca href=\"https://imputationserver.readthedocs.io/en/latest/create-reference-panels/#create-m3vcf-files\" rel=\"nofollow\"\u003em3vcf\u003c/a\u003e files for the reference\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eOther to know :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSee the Usage part to create the environment to run the pipeline. All the necessary dependencies are download with the using of the script Preparation.nf. To run it, you\u0027ll need to install the next software : in2csv(1.0.5), liftOver, plink, Minimac3(2.0.1) \u0026amp; bcftools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software of the main scritp by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input\" class=\"anchor\" href=\"#input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePlink datasets\u003c/td\u003e\n\u003ctd\u003eCorresponds to the target dataset to be analysed. Composed by the following files : bed, bim \u0026amp; fam\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInput environment\u003c/td\u003e\n\u003ctd\u003ePath to your input directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-parameters\" class=\"anchor\" href=\"#parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-mandatory\" class=\"anchor\" href=\"#mandatory\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--target\u003c/td\u003e\n\u003ctd\u003emy_target\u003c/td\u003e\n\u003ctd\u003ePattern of the target dataset which do the link with the file .bed/.bim./fam for plink\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input\u003c/td\u003e\n\u003ctd\u003euser/main_data/\u003c/td\u003e\n\u003ctd\u003eThe path of the main directory where we can find 2 directory : my_target/ + files/\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output\u003c/td\u003e\n\u003ctd\u003euser/my_result/\u003c/td\u003e\n\u003ctd\u003eThe path of the main directory where you want to place your results\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optional\" class=\"anchor\" href=\"#optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--script\u003c/td\u003e\n\u003ctd\u003emy/directory/script/bin\u003c/td\u003e\n\u003ctd\u003eThe path of the bin script directory, to be able to run the annexe programme grom the pipeline\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--geno1\u003c/td\u003e\n\u003ctd\u003e0.03\u003c/td\u003e\n\u003ctd\u003eFirst genotyping call rate plink threshold, apply in the full target dataset\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--geno2\u003c/td\u003e\n\u003ctd\u003e0.03\u003c/td\u003e\n\u003ctd\u003eSecond genotyping call rate plink threshold, apply in the target dataset divide by population\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--maf\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003eMinor allele frequencies plink threshold, apply in the full target dataset\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--pihat\u003c/td\u003e\n\u003ctd\u003e0.185\u003c/td\u003e\n\u003ctd\u003eMinimum pi_hat value use for the relatedness test, 0.185 is halfway between the expected IBD for third- and second-degree relatives\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hwe\u003c/td\u003e\n\u003ctd\u003e1e-8\u003c/td\u003e\n\u003ctd\u003eHardy-Weinberg Equilibrium plink p-value threshold\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--legend\u003c/td\u003e\n\u003ctd\u003eALL.chr_GRCh38.genotypes.20170504.legend\u003c/td\u003e\n\u003ctd\u003eFile to use as .legend\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fasta\u003c/td\u003e\n\u003ctd\u003eGRCh38_full_analysis_set_plus_decoy_hla.fa\u003c/td\u003e\n\u003ctd\u003eFile to use as fasta reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--chain\u003c/td\u003e\n\u003ctd\u003ehg18ToHg38.over.chain\u003c/td\u003e\n\u003ctd\u003eFile to use as liftover conversion\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--VCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/vcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as VCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--BCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/bcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as BCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--M3VCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/m3vcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as M3VCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--conversion\u003c/td\u003e\n\u003ctd\u003ehg38/hg18/hg19\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cloud\u003c/td\u003e\n\u003ctd\u003ehg38/hg18/hg19\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--token_Michighan\u003c/td\u003e\n\u003ctd\u003epath/to/my_token.txt\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--token_TOPMed\u003c/td\u003e\n\u003ctd\u003epath/to/my_token.txt\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--QC_cloud\u003c/td\u003e\n\u003ctd\u003emy/directory/donwload_imputation_server\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-flags\" class=\"anchor\" href=\"#flags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003ePrepare the environment to run the imputation pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir data\ncd data\nnextflow run IARCbioinfo/Imputation-nf/bin/Preparation.nf --out /data/\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePaste the bim/bed/fam plink target files in a directory, and the directory in your \"data/\" directory. You have to call the plink files and your directory with the same pattern, as the following exemple : data/target/target{.bed,.bim,.fam}. So now you have 2 directories in your \"data/\" repertory :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e_ data/my_target/ : with the plink target files (my_target.bed, my_target.bim, my_target.fam).\u003c/p\u003e\n\u003cp\u003e_ data/files/ : with all the dependencies.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the imputation pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eIf you want to run the imputation in one of the server (Michigan and/or TOPMed Imputation), you need you write your token acces in a file and to give it in argument. For example :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ --cloud on --token_Michighan /folder/my_token_Michighan.txt --token_TOPMed /folder/my_token_TOPMed.txt -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce your imputation data is downloaded, you can run the end of the QC analysis :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ --QC_cloud /downloaded_imputation_server_file/ -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-detailed-description-optional-section\" class=\"anchor\" href=\"#detailed-description-optional-section\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetailed description (optional section)\u003c/h2\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" href=\"#directed-acyclic-graph\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/Imputation-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributions\" class=\"anchor\" href=\"#contributions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGabriel Aur\u00e9lie\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:gabriela@students.iarc.fr\"\u003egabriela@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLipinski Boris\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"mailto:LipinskiB@students.iarc.fr\"\u003eLipinskiB@students.iarc.fr\u003c/a\u003e / \u003ca href=\"mailto:boris.lipinski@etu.univ-lyon1.fr\"\u003eboris.lipinski@etu.univ-lyon1.fr\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references-optional\" class=\"anchor\" href=\"#references-optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences (optional)\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-faq-optional\" class=\"anchor\" href=\"#faq-optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ (optional)\u003c/h2\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-test-pipeline\" class=\"anchor\" href=\"#test-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etest-pipeline\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1645140013.0 + "updated_at": 1644245707.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity_1.0.0" + "Singularity/Singularity.v1.0", + "Singularity/Singularity.v1.1" ], - "full_name": "daviesdrew/variantcalling", + "full_name": "Monia234/IARC-fastqc", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"\" class=\"anchor\" aria-hidden=\"true\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/nf-core-illuminavariantcalling_logo.png\"\u003e\u003cimg src=\"docs/images/nf-core-illuminavariantcalling_logo.png\" alt=\"nf-core/illuminavariantcalling\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eIllumina paired end reads variant calling pipeline\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/nf-core/illuminavariantcalling/actions\"\u003e\u003cimg src=\"https://github.com/nf-core/illuminavariantcalling/workflows/nf-core%20CI/badge.svg\" alt=\"GitHub Actions CI Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/nf-core/illuminavariantcalling/actions\"\u003e\u003cimg src=\"https://github.com/nf-core/illuminavariantcalling/workflows/nf-core%20linting/badge.svg\" alt=\"GitHub Actions Linting Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1a7b876aea11f8490a824ae9376e2b0108e8b19b424effa1b67d0a7afcfe096e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413531392e31302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/illuminavariantcalling\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/609e7a6579baf2276f34ef713d9cc0b55f7fd62e2c5c7618d40423779d41fd44/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f696c6c756d696e6176617269616e7463616c6c696e672e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/illuminavariantcalling.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003ei. Install \u003ca href=\"https://nf-co.re/usage/installation\" rel=\"nofollow\"\u003e\u003ccode\u003enextflow\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eii. Install either \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003e\u003ccode\u003eDocker\u003c/code\u003e\u003c/a\u003e or \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e for full pipeline reproducibility (please only use \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003e\u003ccode\u003eConda\u003c/code\u003e\u003c/a\u003e as a last resort; see \u003ca href=\"https://nf-co.re/usage/configuration#basic-configuration-profiles\" rel=\"nofollow\"\u003edocs\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eiii. Download the pipeline and test it on a minimal dataset with a single command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/illuminavariantcalling -profile test,\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edocker/singularity/conda/institute\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePlease check \u003ca href=\"https://github.com/nf-core/configs#documentation\"\u003enf-core/configs\u003c/a\u003e to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use \u003ccode\u003e-profile \u0026lt;institute\u0026gt;\u003c/code\u003e in your command. This will enable either \u003ccode\u003edocker\u003c/code\u003e or \u003ccode\u003esingularity\u003c/code\u003e and set the appropriate execution settings for your local compute environment.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eiv. Start running your own analysis!\u003c/p\u003e\n\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/illuminavariantcalling -profile \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edocker/singularity/conda/institute\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e*_R{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --genome GRCh37\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"docs/usage.md\"\u003eusage docs\u003c/a\u003e for all of the available options when running the pipeline.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe nf-core/illuminavariantcalling pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/installation\" rel=\"nofollow\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/local_installation\" rel=\"nofollow\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/adding_own_config\" rel=\"nofollow\"\u003eAdding your own system config\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/reference_genomes\" rel=\"nofollow\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003enf-core/illuminavariantcalling was originally written by Drew Davies.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions-and-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions-and-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions and Support\u003c/h2\u003e\n\u003cp\u003eIf you would like to contribute to this pipeline, please see the \u003ca href=\".github/CONTRIBUTING.md\"\u003econtributing guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor further information or help, don\u0027t hesitate to get in touch on \u003ca href=\"https://nfcore.slack.com/channels/illuminavariantcalling\" rel=\"nofollow\"\u003eSlack\u003c/a\u003e (you can join with \u003ca href=\"https://nf-co.re/join/slack\" rel=\"nofollow\"\u003ethis invite\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\n\n\u003cp\u003eYou can cite the \u003ccode\u003enf-core\u003c/code\u003e publication as follows:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eThe nf-core framework for community-curated bioinformatics pipelines.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhilip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso \u0026amp; Sven Nahnsen.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNat Biotechnol.\u003c/em\u003e 2020 Feb 13. doi: \u003ca href=\"https://dx.doi.org/10.1038/s41587-020-0439-x\" rel=\"nofollow\"\u003e10.1038/s41587-020-0439-x\u003c/a\u003e.\u003cbr\u003e\nReadCube: \u003ca href=\"https://rdcu.be/b1GjZ\" rel=\"nofollow\"\u003eFull Access Link\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fastqc-nf\" class=\"anchor\" href=\"#fastqc-nf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efastqc-nf\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quality-control-of-raw-sequencing-reads\" class=\"anchor\" href=\"#quality-control-of-raw-sequencing-reads\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality control of raw sequencing reads\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/fastqc-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d355ed64b381b5e3e497a32c3b032d9becd558aebd39a0da28073fbe613dfd81/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f6661737471632d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/fastqc-nf.svg?style=svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/fastqc-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4559\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/IARCbioinfo/fastqc-nf/blob/master/fastqc-nf.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/IARCbioinfo/fastqc-nf/raw/master/fastqc-nf.png\" alt=\"fastqc-nf\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003ePerform quality control of Fasta files.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eFastQC: see official installation \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003cli\u003eMultiqc: see official installation \u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bam-input-files\" class=\"anchor\" href=\"#bam-input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBAM input files\u003c/h3\u003e\n\u003cp\u003eIn order to process BAM files, we convert fastq files to bam files with:\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003ca href=\"http://samtools.sourceforge.net/\" rel=\"nofollow\"\u003e\u003cem\u003esamtools\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input\" class=\"anchor\" href=\"#input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing FASTQ files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003ePath to output folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-parameters\" class=\"anchor\" href=\"#parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-optional\" class=\"anchor\" href=\"#optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eExample value\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ext\u003c/td\u003e\n\u003ctd\u003efastq.gz\u003c/td\u003e\n\u003ctd\u003eExtension of files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--multiqc_config\u003c/td\u003e\n\u003ctd\u003enone\u003c/td\u003e\n\u003ctd\u003econfig yaml file for multiqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eNumber of cpu used by fastqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e10\u003c/td\u003e\n\u003ctd\u003eSize of memory used for mapping (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-flags\" class=\"anchor\" href=\"#flags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h3\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003enextflow run IARCbioinfo/fastqc-nf -r v1.1 -profile singularity --input_folder input --output_folder results\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run the pipeline with docker or conda instead of singularity, just replace \"-profile singularity\" with \"-profile docker\" or \"-profile conda\", respectively. To run with your own local installation of softwares, just remove \"-profile singularity\"\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003emultiqc_fastqc_report.html\u003c/td\u003e\n\u003ctd\u003emultiQC report for fastQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emultiqc_fastqc_report_data\u003c/td\u003e\n\u003ctd\u003edata used for the multiQC report HTMLs\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributions\" class=\"anchor\" href=\"#contributions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@fellows.iarc.fr\"\u003eAlcalaN@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1593036214.0 + "updated_at": 1644245739.0 }, { "data_format": 2, - "description": "Rnnotator is an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome.", + "description": null, "filenames": [ - "Singularity" + "SingularityFile" ], - "full_name": "sghignone/Rnnotator", + "full_name": "AMarinhoSN/tutorial-cCC", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rnnotator\" class=\"anchor\" aria-hidden=\"true\" href=\"#rnnotator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRnnotator\u003c/h1\u003e\n\u003cp\u003eRnnotator is an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome.\u003c/p\u003e\n\u003cp\u003eRnnotator must be run on a 64-bit Linux architecture. Before running Rnnotator the\nfollowing prerequisites must be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBlat v. 34 (\u003ca href=\"http://genome.ucsc.edu/FAQ/FAQblat.html#blat3\" rel=\"nofollow\"\u003ehttp://genome.ucsc.edu/FAQ/FAQblat.html#blat3\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eVelvet 1.0.15 (\u003ca href=\"http://www.ebi.ac.uk/~zerbino/velvet/\" rel=\"nofollow\"\u003ehttp://www.ebi.ac.uk/~zerbino/velvet/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eAMOS (\u003ca href=\"http://sourceforge.net/apps/mediawiki/amos/index.php\" rel=\"nofollow\"\u003ehttp://sourceforge.net/apps/mediawiki/amos/index.php\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eVmatch 2.0 (\u003ca href=\"http://www.vmatch.de/\" rel=\"nofollow\"\u003ehttp://www.vmatch.de/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003ebwa 0.5.8c (\u003ca href=\"http://bio-bwa.sourceforge.net/\" rel=\"nofollow\"\u003ehttp://bio-bwa.sourceforge.net/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eMUMmer (\u003ca href=\"http://sourceforge.net/projects/mummer/\" rel=\"nofollow\"\u003ehttp://sourceforge.net/projects/mummer/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eBioPerl (\u003ca href=\"http://www.bioperl.org\" rel=\"nofollow\"\u003ehttp://www.bioperl.org\u003c/a\u003e) -- base system\u003c/li\u003e\n\u003cli\u003ePerl modules: Parallel::ForkManager, Tree (\u003ca href=\"http://search.cpan.org/\" rel=\"nofollow\"\u003ehttp://search.cpan.org/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional prerequisites are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOases 0.1.18 (\u003ca href=\"http://www.ebi.ac.uk/~zerbino/oases/\" rel=\"nofollow\"\u003ehttp://www.ebi.ac.uk/~zerbino/oases/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eBambus 2.33 (\u003ca href=\"http://www.cbcb.umd.edu/software/bambus/\" rel=\"nofollow\"\u003ehttp://www.cbcb.umd.edu/software/bambus/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSopra 1.0 (\u003ca href=\"mailto:dayarian@physics.rutgers.edu\"\u003edayarian@physics.rutgers.edu\u003c/a\u003e) x1 \u2013 x4 scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003esg\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-tutorial-ccc\" class=\"anchor\" href=\"#tutorial-ccc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etutorial-cCC\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, + "topics": [], + "updated_at": 1643312784.0 + }, + { + "data_format": 2, + "description": "BEDOPS is an open-source command-line toolkit that performs highly efficient and scalable Boolean and other set operations, statistical calculations, archiving, conversion and other management of genomic data of arbitrary scale.", + "filenames": [ + "2.4.40/Singularity", + "2.4.39/Singularity" + ], + "full_name": "pscedu/singularity-bedops", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bedops/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bedops/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-bedops/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bedops/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/046e5d2f53345f56b267b5b3fb70cf631199ba19ea8a20c754afd1fd5cae6098/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6265646f7073\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/046e5d2f53345f56b267b5b3fb70cf631199ba19ea8a20c754afd1fd5cae6098/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6265646f7073\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bedops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/1fd7fd2c301c82aa5c557690a7df3c9c3565a7badad9f11502e8fe21f7bcfbf5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6265646f7073\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1fd7fd2c301c82aa5c557690a7df3c9c3565a7badad9f11502e8fe21f7bcfbf5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6265646f7073\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bedops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/2b60ce6402987813ce63df1d7f2cdedee3f8108e37c69d00089eb9e576b81249/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6265646f7073\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b60ce6402987813ce63df1d7f2cdedee3f8108e37c69d00089eb9e576b81249/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6265646f7073\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bedops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/9e0c74991471aaa28ae5b127672f0979c5a9e3e79e590771d4df28ad0b47fad3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6265646f7073\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e0c74991471aaa28ae5b127672f0979c5a9e3e79e590771d4df28ad0b47fad3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6265646f7073\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bedops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bedops\" class=\"anchor\" href=\"#singularity-bedops\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bedops\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/fcce2433d40cda32c9340a2c9e3b2d6c1ebd0bbb8e1361a72ae3ecd2514681ad/68747470733a2f2f6265646f70732e72656164746865646f63732e696f2f656e2f6c61746573742f5f7374617469632f6c6f676f5f776974685f6c6162656c5f76332e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fcce2433d40cda32c9340a2c9e3b2d6c1ebd0bbb8e1361a72ae3ecd2514681ad/68747470733a2f2f6265646f70732e72656164746865646f63732e696f2f656e2f6c61746573742f5f7374617469632f6c6f676f5f776974685f6c6162656c5f76332e706e67\" width=\"75%\" data-canonical-src=\"https://bedops.readthedocs.io/en/latest/_static/logo_with_label_v3.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for BEDOPS.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebedops\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/BEDOPS/2.4.40\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/BEDOPS\u003c/code\u003e as \u003ccode\u003e2.4.40.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 3, "topics": [ - "pipeline", "singularity", - "singularity-recipe", - "rnaseq", - "docker", - "dockerfile" + "bioinformatics" ], - "updated_at": 1612716290.0 + "updated_at": 1631926426.0 }, { "data_format": 2, - "description": null, + "description": "BLAST-Like Alignment Tool.", "filenames": [ - "Singularity.kepler" + "36/Singularity" ], - "full_name": "ternaustralia/coesra-singularity-kepler", + "full_name": "pscedu/singularity-blat", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-kepler\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-kepler\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-kepler\u003c/h1\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-blat/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-blat/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-blat/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-blat/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fde072f38ccaf86b9b38a5136b7663dd158f14a4e1cf1278108e06da76103d06/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626c6174\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fde072f38ccaf86b9b38a5136b7663dd158f14a4e1cf1278108e06da76103d06/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626c6174\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-blat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/af4ec22e9ffbf62e6d73e3a37e86694f53da06d4e9bb1f1340e3229c13ef3bb7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626c6174\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/af4ec22e9ffbf62e6d73e3a37e86694f53da06d4e9bb1f1340e3229c13ef3bb7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626c6174\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-blat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c1a11abb9c1ade82245064c947accad2be8fb585c711780038651614310a9e2f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626c6174\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c1a11abb9c1ade82245064c947accad2be8fb585c711780038651614310a9e2f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626c6174\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-blat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fcaf031a92e8a29f5eef49aae1244a4d6365b34bcc206de848f3405b25751c32/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626c6174\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fcaf031a92e8a29f5eef49aae1244a4d6365b34bcc206de848f3405b25751c32/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626c6174\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-blat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-blat\" class=\"anchor\" href=\"#singularity-blat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-blat\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/djhshih/blat\"\u003eBLAT\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eblat\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/BLAT/36\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/BLAT\u003c/code\u003e as \u003ccode\u003e36.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [ - "coesra" + "singularity", + "bioinformatics" ], - "updated_at": 1610425796.0 + "updated_at": 1631929745.0 }, { "data_format": 2, - "description": "Singularity recipe files for vg (https://github.com/vgteam/vg)", + "description": "FLASH (Fast Length Adjustment of SHort reads) is a very fast and accurate software tool to merge paired-end reads from next-generation sequencing experiments. ", "filenames": [ - "Singularity.1.8.0", - "Singularity", - "Singularity.1.12.0", - "Singularity.1.12.1", - "Singularity.1.9.0", - "Singularity.1.11.0", - "Singularity.1.13.0", - "Singularity.1.10.0" + "1.2.11/Singularity" ], - "full_name": "powerPlant/vg-srf", + "full_name": "pscedu/singularity-flash", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2311\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the vg tools for working with genome variation graphs\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-flash/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-flash/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-flash/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-flash/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/86a31ece843f2a6eac62c15a795deb81ca5d718c06548f2c2a47b1da60ac0398/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666c617368\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/86a31ece843f2a6eac62c15a795deb81ca5d718c06548f2c2a47b1da60ac0398/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666c617368\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-flash\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8438148a71d5669e7f72c44baa07c726adaf97954d4d37637024d1ad4ffe1838/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666c617368\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8438148a71d5669e7f72c44baa07c726adaf97954d4d37637024d1ad4ffe1838/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666c617368\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-flash\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c561b50b92370071ff56fae3c65507316dd34d582f558974abcac0f51a9fe052/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666c617368\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c561b50b92370071ff56fae3c65507316dd34d582f558974abcac0f51a9fe052/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666c617368\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-flash\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b15c615f56761a00ff252428c0c999278f063be89c1895fc26e7d55f2a9417fc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666c617368\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b15c615f56761a00ff252428c0c999278f063be89c1895fc26e7d55f2a9417fc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666c617368\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-flash\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-flash\" class=\"anchor\" href=\"#singularity-flash\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-flash\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://ccb.jhu.edu/software/FLASH/\" rel=\"nofollow\"\u003eFLASH\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eflash\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/flash/1.2.11\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/flash\u003c/code\u003e as \u003ccode\u003e1.2.11.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1549578706.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1631930117.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.1.3.1-py36", - "Singularity.1.0.0-py36" + "Singularity" ], - "full_name": "arcsUVA/pytorch", + "full_name": "oogasawa/singularity-img-gridengine-master", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epytorch\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-img-gridengine-master\" class=\"anchor\" href=\"#singularity-img-gridengine-master\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-img-gridengine-master\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1573410610.0 + "updated_at": 1631970804.0 }, { "data_format": 2, - "description": "singularity scripts for cellprofiler", + "description": null, "filenames": [ - "Singularity.3.1.8", - "Singularity.2.2.0", - "Singularity.3.0.0" + "Singularity" ], - "full_name": "arcsUVA/cellprofiler", + "full_name": "oogasawa/singularity-img-gridengine-client", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-img-ubuntu16-gridengine-client\" class=\"anchor\" href=\"#singularity-img-ubuntu16-gridengine-client\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-img-ubuntu16-gridengine-client\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1556734065.0 + "updated_at": 1631971328.0 }, { "data_format": 2, - "description": "Singularity recipe files for Bismark (https://github.com/FelixKrueger/Bismark)", + "description": "MAXCUT Simulation Code", "filenames": [ - "Singularity", - "Singularity.0.19.1", - "Singularity.0.23.0", - "Singularity.0.23.1", - "Singularity.0.20.0" + "SingularityFile.def" ], - "full_name": "powerPlant/bismark-srf", + "full_name": "fenellamcandrew/aqc-maxcut", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2263\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the Bismark bisulfite mapping and methylation calling program\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-adiabatic-quantum-computing-for-maxcut\" class=\"anchor\" href=\"#adiabatic-quantum-computing-for-maxcut\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdiabatic Quantum Computing for MAXCUT\u003c/h1\u003e\n\u003cp\u003eMAXCUT Simulation Code\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh -i \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.ssh/experimentr.pem ubuntu@\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eMY_IP_ADDRESS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1635284848.0 + "updated_at": 1632103728.0 }, { "data_format": 2, - "description": "Singularity recipe files for Portcullis (https://github.com/maplesond/portcullis)", + "description": null, "filenames": [ - "Singularity.1.1.0", - "Singularity", - "Singularity.1.1.1", - "Singularity.1.1.2" + "Singularity" ], - "full_name": "powerPlant/portcullis-srf", + "full_name": "truatpasteurdotfr/singularity-docker-stream8-chrome", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2267\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for Portcullis, a program for PORTable CULLing of Invalid Splice junctions from pre-aligned RNA-seq data\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-docker-stream8-chrome\" class=\"anchor\" href=\"#singularity-docker-stream8-chrome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-stream8-chrome\u003c/h1\u003e\n\u003cp\u003eGoogle Chrome container based on a CentOS Stream 8 x86_64 docker image built from github actions\u003c/p\u003e\n\u003cp\u003e(toy) singularity image produced by github actions available at \u003ccode\u003eghcr.io/truatpasteurdotfr/singularity-docker-stream8-chrome:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why\" class=\"anchor\" href=\"#why\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eworkaround solution when a Chrome release is not running on CentOS-7 because the required glibc is not satisfied\n(yes, I know... CentOS-7 is not on the list of approved OS).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-without-installation-\" class=\"anchor\" href=\"#running-without-installation-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning without installation: \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-chrome/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-chrome/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-docker-stream8-chrome:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building\" class=\"anchor\" href=\"#building\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-docker-stream8-chrome.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1549336366.0 + "updated_at": 1638391965.0 }, { "data_format": 2, - "description": "Snakemake workflow for analysis and assembly of viral genomes from IonTorrent AmpliSeq data.", + "description": null, "filenames": [ "Singularity" ], - "full_name": "peterk87/viral-ampliseq-assembly", - "latest_release": "v1.0.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-snakemake-workflow-viral-ampliseq-assembly\" class=\"anchor\" aria-hidden=\"true\" href=\"#snakemake-workflow-viral-ampliseq-assembly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakemake workflow: viral-ampliseq-assembly\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://snakemake.bitbucket.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/de7b3ae9d2ddd7970750ed14a267d738217987e5635a19380de6f3b2ec3216e6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135352e352e342d627269676874677265656e2e737667\" alt=\"Snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%E2%89%A55.5.4-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/peterk87/viral-ampliseq-assembly\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9ca62ba99cb6a38032432759aa450c99bf81b9671bab9e21e2492c47bf7cf065/68747470733a2f2f7472617669732d63692e6f72672f70657465726b38372f766972616c2d616d706c697365712d617373656d626c792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/peterk87/viral-ampliseq-assembly.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3359\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://snakemake.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e workflow for analysis and assembly of viral genomes such as Classical Swine Fever Virus (\u003ca href=\"https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=11096\" rel=\"nofollow\"\u003eCSFV\u003c/a\u003e) from IonTorrent AmpliSeq data.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePreprocessing\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDuplicate reads were removed using \u003ca href=\"https://broadinstitute.github.io/picard/\" rel=\"nofollow\"\u003ePicard\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReads were trimmed with \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003eTrimmomatic\u003c/a\u003e prior to \u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003eSPAdes\u003c/a\u003e assembly\u003c/li\u003e\n\u003cli\u003eBAM file stats computed using \u003ca href=\"https://samtools.github.io/\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e (coverage depth, extent, extent per genome, # of reads mapped)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReference Genome Selection\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownloading of all Classical swine fever virus (\u003ca href=\"https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=11096\" rel=\"nofollow\"\u003eCSFV\u003c/a\u003e) (or FMDV, Ebola, Zika) virus genomes from \u003ca href=\"https://www.ncbi.nlm.nih.gov/books/NBK25501/\" rel=\"nofollow\"\u003eNCBI Entrez API\u003c/a\u003e using \u003ca href=\"https://biopython.org/\" rel=\"nofollow\"\u003eBioPython\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mash.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eMash\u003c/a\u003e screen of deduplicated reads against all reference genomes with sketch size of 10000 and sketch k-mer size of 16, sorting by Mash screen identity to find top reference genome for read mapping and variant calling\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRead Mapping \u0026amp; Variant Calling\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRead mapping with \u003ca href=\"https://github.com/lh3/bwa\"\u003eBWA MEM\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRemoval of duplicate reads with \u003ca href=\"https://samtools.github.io/\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVariant calling with \u003ca href=\"https://github.com/ekg/freebayes\"\u003eFreeBayes\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://snpeff.sourceforge.net/SnpEff.html\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e was used to predict and report variant effects using reference genome annotation\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDe Novo Assembly\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003eSPAdes\u003c/a\u003e de novo assembly of trimmed deduplicated reads.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://quast.sourceforge.net/quast.html\" rel=\"nofollow\"\u003eQUAST\u003c/a\u003e quality assessment of assemblies\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eQuality Control\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://multiqc.info/\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e interactive report of \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e, \u003ca href=\"https://samtools.github.io/\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e, \u003ca href=\"http://quast.sourceforge.net/quast.html\" rel=\"nofollow\"\u003eQUAST\u003c/a\u003e, \u003ca href=\"http://snpeff.sourceforge.net/SnpEff.html\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePhylogenetic Tree\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePhylogenetic tree constructed with \u003ca href=\"http://www.iqtree.org/\" rel=\"nofollow\"\u003eIQ-TREE\u003c/a\u003e (or \u003ca href=\"http://bioinformatics.hungry.com/clearcut/\" rel=\"nofollow\"\u003eClearcut\u003c/a\u003e if a quick and dirty tree is okay)\u003c/li\u003e\n\u003cli\u003eInteractive HTML phylogenetic tree visualization with \u003ca href=\"http://phylocanvas.org/\" rel=\"nofollow\"\u003ePhyloCanvas\u003c/a\u003e using \u003ca href=\"https://github.com/peterk87/shiptv\"\u003eshiptv\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePeter Kruczkiewicz (@peterk87)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-0-install-pre-requisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-0-install-pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 0: Install pre-requisites\u003c/h3\u003e\n\u003cp\u003eRunning this workflow with \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is recommended, but you can use \u003ca href=\"https://conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e if you prefer. The Singularity image will come with all the dependencies bundled together in a single file.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-singularity-recommended\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-singularity-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (recommended)\u003c/h4\u003e\n\u003cp\u003eFollow the instructions for installing Singularity \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/quick_start.html#quick-start\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-setup-and-activate-the-conda-environment-if-not-using-singularity-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-and-activate-the-conda-environment-if-not-using-singularity-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup and activate the \u003ca href=\"https://conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e environment if not using \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (optional)\u003c/h4\u003e\n\u003cp\u003eInstall \u003ca href=\"https://conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e if you haven\u0027t already following \u003ca href=\"https://conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ethese instructions\u003c/a\u003e and setup the \u003ca href=\"https://bioconda.github.io/user/install.html#set-up-channels\" rel=\"nofollow\"\u003eBioConda channel\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDownload or \u003ccode\u003egit clone\u003c/code\u003e this repo\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/peterk87/viral-ampliseq-assembly.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e viral-ampliseq-assembly\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e create a conda environment named \"viral-ampliseq-assembly-1.0.0\"\u003c/span\u003e\nconda env create -f environment.yml\nconda activate viral-ampliseq-assembly-1.0.0\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install snakemake into this env\u003c/span\u003e\nconda install -y snakemake\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run Snakemake on the test directory\u003c/span\u003e\nsnakemake --directory test/\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-install-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-install-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1: Install workflow\u003c/h3\u003e\n\u003cp\u003eIf you simply want to use this workflow, download and extract the \u003ca href=\"https://github.com/peterk87/viral-ampliseq-assembly/releases\"\u003elatest release\u003c/a\u003e.\nIf you intend to modify and further develop this workflow, fork this repository. Please consider providing any generally applicable modifications via a pull request.\u003c/p\u003e\n\u003cp\u003eIn any case, if you use this workflow in a paper, don\u0027t forget to give credits to the authors by citing the URL of this repository and, if available, its DOI (see above).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-configure-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-configure-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2: Configure workflow\u003c/h3\u003e\n\u003cp\u003eCreate an analysis directory, copy and modify the example \u003ccode\u003econfig.yaml\u003c/code\u003e and \u003ccode\u003esamples.tsv\u003c/code\u003e files to suit your needs.\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir ~/my-ampliseq-analysis\ncp viral-ampliseq-assembly/config.yaml ~/my-ampliseq-analysis/\ncp viral-ampliseq-assembly/samples.tsv ~/my-ampliseq-analysis/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEdit your \u003ccode\u003econfig.yaml\u003c/code\u003e as needed.\u003c/p\u003e\n\u003cp\u003eAdd sample entries to your \u003ccode\u003esamples.tsv\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esample bam_file\nSample1 bams/Sample1.bam\nSample2 bams/Sample2.bam\nSample3 bams/Sample3.bam\n... \u0026lt;more sample entries\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003ebam_file\u003c/code\u003e can be the relative or absolute path to a sample\u0027s BAM file.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-iq-tree-maximum-likelihood-or-clearcut-rnj-tree\" class=\"anchor\" aria-hidden=\"true\" href=\"#iq-tree-maximum-likelihood-or-clearcut-rnj-tree\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca href=\"http://www.iqtree.org/\" rel=\"nofollow\"\u003eIQ-TREE\u003c/a\u003e maximum-likelihood or \u003ca href=\"http://bioinformatics.hungry.com/clearcut/\" rel=\"nofollow\"\u003eClearcut\u003c/a\u003e RNJ tree\u003c/h4\u003e\n\u003cp\u003eIn your \u003ccode\u003econfig.yaml\u003c/code\u003e the \u003ccode\u003efast_tree\u003c/code\u003e parameter controls which method (ML or RNJ) is used for phylogenetic tree construction.\u003c/p\u003e\n\u003cp\u003eIf you want a quick and dirty tree, set\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003efast_tree\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ein your \u003ccode\u003econfig.yaml\u003c/code\u003e to generate a Relaxed Neighbor Joining (RNJ) tree.\u003c/p\u003e\n\u003cp\u003eOtherwise, if you want a high accuracy phylogenetic tree and are willing to wait for it, then set\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003efast_tree\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto use \u003ca href=\"http://www.iqtree.org/\" rel=\"nofollow\"\u003eIQ-TREE\u003c/a\u003e to generate a maximum-likelihood phylogenetic tree with 1000 ultrafast bootstraps (UFBoot) (see \u003ca href=\"http://dx.doi.org/10.1093/molbev/mst024\" rel=\"nofollow\"\u003eMinh et al., 2016\u003c/a\u003e for more info on UFBoot).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-3-execute-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-3-execute-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 3: Execute workflow\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eIf you do not have \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed then remove the \u003ccode\u003e--use-singularity\u003c/code\u003e flag\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTest your configuration by performing a dry-run via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity -n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the workflow locally via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity --cores $N\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eusing \u003ccode\u003e$N\u003c/code\u003e cores.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-cluster-execution\" class=\"anchor\" aria-hidden=\"true\" href=\"#cluster-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCluster execution\u003c/h4\u003e\n\u003cp\u003e\u003cem\u003eNote: You may need to install the \u003ccode\u003edrmaa\u003c/code\u003e Python library (\u003ccode\u003epip install drmaa\u003c/code\u003e)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eYou can execute the workflow on a SLURM/DRMAA cluster environment with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --directory test --use-singularity --drmaa \" -c 4 -p YourClusterQueueName --mem=4096 \" -j 8 -w 60\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the workflow on the test data in the \u003ccode\u003etest/\u003c/code\u003e directory with 4 CPUs and 4G memory per job and 8 jobs at once (\u003ccode\u003e-j 8\u003c/code\u003e) while waiting 60 seconds for output files to appear on the shared filesystem (\u003ccode\u003e-w 60\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThe cluster partition or queue to schedule jobs to is specified with \u003ccode\u003e-p YourClusterQueueName\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe above will run each rule or job with 4 CPUs and 4GB memory each, which may be way more than needed or not enough so you could create a YAML (or JSON) file to specify default and specific resource requirements for some steps:\u003c/p\u003e\n\u003cp\u003eExample \u003ccode\u003ecluster-config.yaml\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003e__default__\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003epartition\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eYourClusterQueueName\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1024\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003esamtools_index_bam_initial\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e16384\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003espades_assembly\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e16384\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ebwa_mem\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4096\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emafft_msa\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4096\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eiqtree\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4096\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003esnpeff\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4096\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWith the \u003ccode\u003ecluster-config.yaml\u003c/code\u003e, run the workflow in a cluster environment via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --directory test --use-singularity --cluster-config cluster-config.yaml --drmaa \" -c {cluster.cpu} -p {cluster.partition} --mem={cluster.memory} \" -j 8 -w 60\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the above command and \u003ccode\u003ecluster-config.yaml\u003c/code\u003e, by default, a rule or step in the workflow will only use 1 CPU and request 1G of memory, while the rules like \u003ccode\u003eiqtree\u003c/code\u003e or \u003ccode\u003espades_assembly\u003c/code\u003e will request more CPUs and memory from the SLURM/DRMAA scheduler.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003eSnakemake documentation\u003c/a\u003e for further details.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting\u003c/h2\u003e\n\u003cp\u003eTests cases are in the subfolder \u003ccode\u003etest\u003c/code\u003e. They should be executed via continuous integration with Travis CI.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003eIf you were to copy the files in \u003ccode\u003etest\u003c/code\u003e (\u003ccode\u003esamples.tsv\u003c/code\u003e, \u003ccode\u003ebam/\u003c/code\u003e and \u003ccode\u003econfig.yaml\u003c/code\u003e) to a new directory \u003ccode\u003emy-analysis-directory\u003c/code\u003e and run the workflow on that directory, i.e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --directory my-analysis-directory/ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e other args\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe contents of \u003ccode\u003emy-analysis-directory\u003c/code\u003e should look like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emy-analysis-directory\n\u251c\u2500\u2500 phylogeny \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Phylogenetic Tree Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 genome-metadata.tsv\n\u2502 \u2514\u2500\u2500 tree.html\n\u251c\u2500\u2500 config.yaml \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e INPUT: Workflow Execution Config File \u003c/span\u003e\n\u251c\u2500\u2500 qc \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Quality Control Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 multiqc.html \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e MultiQC report file\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 fastqc \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e FastQC Output\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 Sample1.html\n\u2502 \u2502 \u2514\u2500\u2500 Sample1_fastqc.zip\n\u2502 \u251c\u2500\u2500 multiqc_data\n\u2502 \u2502 \u251c\u2500\u2500 [Text files]\n\u2502 \u2514\u2500\u2500 quast \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e QUAST Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 report.tex\n\u2502 \u251c\u2500\u2500 icarus_viewers\n\u2502 \u2502 \u2514\u2500\u2500 contig_size_viewer.html\n\u2502 \u251c\u2500\u2500 report.html\n\u2502 \u251c\u2500\u2500 basic_stats\n\u2502 \u2502 \u251c\u2500\u2500 [QUAST PDFs]\n\u2502 \u251c\u2500\u2500 icarus.html\n\u2502 \u251c\u2500\u2500 transposed_report.tex\n\u2502 \u251c\u2500\u2500 quast.log\n\u2502 \u251c\u2500\u2500 report.pdf\n\u2502 \u251c\u2500\u2500 report.txt\n\u2502 \u251c\u2500\u2500 .snakemake_timestamp\n\u2502 \u251c\u2500\u2500 report.tsv\n\u2502 \u251c\u2500\u2500 transposed_report.tsv\n\u2502 \u2514\u2500\u2500 transposed_report.txt\n\u251c\u2500\u2500 variant_calling \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Variant Calling Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 Sample1-filtered.vcf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Filtered variants for Sample1 in VCF format\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 Sample1.vcf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Unfiltered variants for Sample1 in VCF format\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 snpeff \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SnpEff Output\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 Sample1\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 [SnpEff specific files]\n\u2502 \u2502 \u251c\u2500\u2500 Sample1.vcf\n\u2502 \u2502 \u251c\u2500\u2500 Sample1.csv\n\u2502 \u2502 \u251c\u2500\u2500 Sample1.html \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SnpEff report for Sample1\u003c/span\u003e\n\u2502 \u2502 \u2514\u2500\u2500 Sample1.genes.txt\n\u2502 \u2514\u2500\u2500 Sample1-vcf.tsv \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SnpEff annotated variants in a tab-delimited table\u003c/span\u003e\n\u251c\u2500\u2500 mapping \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Read Mapping Output\u003c/span\u003e\n\u2502 \u2514\u2500\u2500 Sample1 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Read mapping output and summary files for Sample1\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 Sample1-extent.tsv\n\u2502 \u251c\u2500\u2500 Sample1-genome_extent.tsv\n\u2502 \u251c\u2500\u2500 Sample1-idxstats.tsv\n\u2502 \u251c\u2500\u2500 Sample1.bam\n\u2502 \u251c\u2500\u2500 Sample1-depth.tsv\n\u2502 \u251c\u2500\u2500 Sample1-idxstats-sorted.tsv\n\u2502 \u251c\u2500\u2500 Sample1-idxstats-top_mapped.txt\n\u2502 \u2514\u2500\u2500 Sample1.bam.bai\n\u251c\u2500\u2500 bam \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input directory with Sample1 BAM file specified in config.yaml\u003c/span\u003e\n\u2502 \u2514\u2500\u2500 a.bam\n\u251c\u2500\u2500 consensus \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Consensus Sequence Output\u003c/span\u003e\n\u2502 \u2514\u2500\u2500 Sample1.fasta \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Consensus sequence for Sample1 from reference mapping and variant calling\u003c/span\u003e\n\u251c\u2500\u2500 logs \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Log files for various tools\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etool name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u2502 \u2502 \u2514\u2500\u2500 Sample1.log\n\u251c\u2500\u2500 samples.tsv \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e INPUT: tab-delimited table with 2 fields: \"sample\" and \"bam_file\"\u003c/span\u003e\n\u251c\u2500\u2500 references \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Reference Genomes Downloaded From NCBI\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 Sample1 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Top Reference Genome\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 reference.gff\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.bwt\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.pac\n\u2502 \u2502 \u251c\u2500\u2500 reference.genbank\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.amb\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.ann\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.sa\n\u2502 \u2502 \u251c\u2500\u2500 reference.fasta\n\u2502 \u2502 \u2514\u2500\u2500 reference-no_ambig.fasta.fai\n\u2502 \u251c\u2500\u2500 csf.msh \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Mash sketch database from \"csf.fasta\"\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 csf.genbank \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e CSFV genomes downloaded from NCBI in GenBank format\u003c/span\u003e\n\u2502 \u2514\u2500\u2500 csf.fasta \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e CSFV genomes downloaded from NCBI in FASTA format\u003c/span\u003e\n\u251c\u2500\u2500 assembly \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Assembly Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 spades \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SPAdes assembly outputs for each input sample\u003c/span\u003e\n\u2502 \u2502 \u2514\u2500\u2500 Sample1 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SPAdes assembly output for Sample1\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 before_rr.fasta\n\u2502 \u2502 \u251c\u2500\u2500 params.txt\n\u2502 \u2502 \u251c\u2500\u2500 contigs.paths\n\u2502 \u2502 \u251c\u2500\u2500 input_dataset.yaml\n\u2502 \u2502 \u251c\u2500\u2500 \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSPAdes specific output directories\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 scaffolds.paths\n\u2502 \u2502 \u251c\u2500\u2500 contigs.fasta\n\u2502 \u2502 \u251c\u2500\u2500 spades.log\n\u2502 \u2502 \u251c\u2500\u2500 assembly_graph.fastg\n\u2502 \u2502 \u251c\u2500\u2500 dataset.info\n\u2502 \u2502 \u251c\u2500\u2500 scaffolds.fasta\n\u2502 \u2502 \u2514\u2500\u2500 assembly_graph_with_scaffolds.gfa\n\u2502 \u2514\u2500\u2500 spades-Sample1.fasta\n\u251c\u2500\u2500 benchmarks \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Benchmark runtime info for tools in workflow\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ebenchmark tab-delimited files \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003evarious tools\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e workflow\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u251c\u2500\u2500 msa \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Multiple sequence alignment (MSA) output and IQ-TREE/Clearcut phylogenetic tree\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 alignment.fasta\n\u2502 \u251c\u2500\u2500 samples-pre-aln.fasta\n\u2502 \u2514\u2500\u2500 alignment.fasta.treefile\n\u2514\u2500\u2500 preprocess \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Preprocessing Output of Input BAM Files \u003c/span\u003e\n \u251c\u2500\u2500 samtools \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Initial BAM file stats output\u003c/span\u003e\n \u2502 \u251c\u2500\u2500 depth\n \u2502 \u2502 \u251c\u2500\u2500 Sample1-extent.tsv\n \u2502 \u2502 \u251c\u2500\u2500 Sample1-genome_extent.tsv\n \u2502 \u2502 \u2514\u2500\u2500 Sample1.tsv\n \u2502 \u251c\u2500\u2500 flagstat\n \u2502 \u2502 \u2514\u2500\u2500 Sample1.flagstat\n \u2502 \u251c\u2500\u2500 index\n \u2502 \u2502 \u2514\u2500\u2500 Sample1.done\n \u2502 \u2514\u2500\u2500 idxstats\n \u2502 \u251c\u2500\u2500 Sample1-top_mapped.txt\n \u2502 \u251c\u2500\u2500 Sample1.tsv\n \u2502 \u2514\u2500\u2500 Sample1-sorted.tsv\n \u251c\u2500\u2500 fastqs \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Deduplicated reads in FASTQ format\u003c/span\u003e\n \u2502 \u2514\u2500\u2500 Sample1.fastq\n \u251c\u2500\u2500 mash \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Mash Screen results\u003c/span\u003e\n \u2502 \u251c\u2500\u2500 Sample1-screen_references-sorted.tsv\n \u2502 \u2514\u2500\u2500 Sample1-screen_references.tsv\n \u251c\u2500\u2500 trimmed_fastqs \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trimmomatic trimmed reads\u003c/span\u003e\n \u2502 \u2514\u2500\u2500 Sample1.fastq\n \u2514\u2500\u2500 dedup \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Deduplicated BAM files\u003c/span\u003e\n \u251c\u2500\u2500 Sample1.bam\n \u251c\u2500\u2500 Sample1.metrics.txt\n \u2514\u2500\u2500 Sample1.bam.bai\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "truatpasteurdotfr/singularity-c7-openapi-basekit", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-c7-openapi-basekit\" class=\"anchor\" href=\"#singularity-c7-openapi-basekit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-c7-openapi-basekit\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1566573045.0 + "updated_at": 1635331812.0 }, { "data_format": 2, - "description": null, + "description": "Repository containing code for the paper \"Shared neural codes for visual and semantic information about familiar others in a common representational space\"", "filenames": [ - "Singularity" + "singularity/Singularity-neurodocker" ], - "full_name": "melnel000/Sarek_CBIO", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"\" class=\"anchor\" aria-hidden=\"true\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"http://sarek.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/Sarek_logo.png\" alt=\"Sarek\" title=\"Sarek\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003ch4\u003e\u003ca id=\"user-content-an-open-source-analysis-pipeline-to-detect-germline-or-somatic-variants-from-whole-genome-or-targeted-sequencing\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-open-source-analysis-pipeline-to-detect-germline-or-somatic-variants-from-whole-genome-or-targeted-sequencing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAn open-source analysis pipeline to detect germline or somatic variants from whole genome or targeted sequencing\u003c/h4\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8165e759b147d5dfd77c2603211746a0ec20eae5aaea1c6a882604a6093c564c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e7376673f6c6f676f3d646174613a696d6167652f7376672b786d6c3b6261736536342c5044393462577767646d567963326c76626a30694d5334774969426c626d4e765a476c755a7a3069565652474c54676949484e305957356b59577876626d5539496d3576496a382b50484e325a794167494868746247357a4f6d526a50534a6f644852774f6938766348567962433576636d63765a474d765a57786c6257567564484d764d5334784c7949674943423462577875637a706a597a30696148523063446f764c324e795a57463061585a6c593239746257397563793576636d6376626e4d6a49694167494868746247357a4f6e4a6b5a6a30696148523063446f764c336433647935334d793576636d63764d546b354f5338774d6938794d6931795a47597463336c75644746344c57357a497949674943423462577875637a707a646d6339496d6830644841364c79393364336375647a4d7562334a6e4c7a49774d44417663335a6e49694167494868746247357a50534a6f644852774f693876643364334c6e637a4c6d39795a7938794d4441774c334e325a7949674943423462577875637a707a623252706347396b615430696148523063446f764c334e765a476c77623252704c6e4e7664584a6a5a575a76636d646c4c6d356c64433945564551766332396b615842765a476b744d43356b644751694943416765473173626e4d366157357263324e6863475539496d6830644841364c793933643363756157357263324e686347557562334a6e4c3235686257567a6347466a5a584d766157357263324e68634755694943416764326c6b64476739496a45794c6a63354f5449794f473174496941674947686c6157646f644430694d5449754f4441304f4441356257306949434167646d6c6c64304a76654430694d434177494451314c6a4d314d5455354e4341304e53347a4e7a457a4e6a6b694943416761575139496e4e325a7a63324e54496949434167646d567963326c76626a30694d5334784969416749476c7561334e6a5958426c4f6e5a6c636e4e7062323439496a41754f544567636a457a4e7a49314969416749484e765a476c77623252704f6d52765932356862575539496d356c6548526d624739334c575a68646d6c6a62323474643268706447557563335a6e496a34674944786b5a575a7a49434167494342705a4430695a47566d637a63324e5451694943382b494341386332396b615842765a476b36626d46745a5752326157563349434167494342705a443069596d467a5a53496749434167494842685a32566a62327876636a306949325a6d5a6d5a6d5a6949674943416749474a76636d526c636d4e76624739795053496a4e6a59324e6a59324969416749434167596d39795a4756796233426859326c30655430694d53347749694167494341676157357263324e68634755366347466e5a57397759574e7064486b39496a41754d4349674943416749476c7561334e6a5958426c4f6e42685a32567a6147466b62336339496a49694943416749434270626d747a593246775a54703662323974505349334c6a6b784f5455354e546b694943416749434270626d747a593246775a54706a654430694d6a41754d54457a4d6a4d3149694167494341676157357263324e686347553659336b39496a497a4c6a45324d7a6b774f4349674943416749476c7561334e6a5958426c4f6d5276593356745a5735304c5856756158527a50534a77654349674943416749476c7561334e6a5958426c4f6d4e31636e4a6c626e5174624746355a584939496d7868655756794d5349674943416749484e6f6233646e636d6c6b50534a6d5957787a5a5349674943416749475a706443317459584a6e61573474644739775053497749694167494341675a6d6c304c573168636d6470626931735a575a305053497749694167494341675a6d6c304c573168636d6470626931796157646f644430694d4349674943416749475a706443317459584a6e61573474596d3930644739745053497749694167494341676157357263324e686347553664326c755a4739334c5864705a48526f505349784f54497749694167494341676157357263324e686347553664326c755a4739334c57686c6157646f644430694d5441784e5349674943416749476c7561334e6a5958426c4f6e6470626d5276647931345053497749694167494341676157357263324e686347553664326c755a4739334c586b39496a41694943416749434270626d747a593246775a5470336157356b623363746257463461573170656d566b5053497849694176506941675047316c6447466b5958526849434167494342705a4430696257563059575268644745334e6a5533496a34674943416750484a6b5a6a70535245592b494341674943416750474e6a4f6c6476636d73674943416749434167494342795a47593659574a76645851394969492b4943416749434167494341385a474d365a6d397962574630506d6c745957646c4c334e325a797434625777384c32526a4f6d5a76636d31686444346749434167494341674944786b597a70306558426c494341674943416749434167494342795a475936636d567a6233567959325539496d6830644841364c79397764584a734c6d39795a79396b5979396b5932317064486c775a53395464476c7362456c745957646c496941765069416749434167494341675047526a4f6e52706447786c506a77765a474d3664476c306247552b49434167494341675043396a597a705862334a7250694167494341384c334a6b5a6a70535245592b494341384c32316c6447466b5958526850694167504763674943416749476c7561334e6a5958426c4f6d7868596d567350534a4d59586c6c6369417849694167494341676157357263324e68634755365a334a76645842746232526c50534a7359586c6c636949674943416749476c6b50534a7359586c6c636a45694943416749434230636d467563325a76636d3039496e52795957357a624746305a5367784d5451754d5441304d7a63734c5451314d6934314d7a4d324e696b6950694167494341386347463061434167494341674943427a64486c735a5430695a6d6c7362446f6a5a6d5a6d5a6d5a6d49694167494341674943426b50534a74494330784d5451754d5441304d7a63734e4455314c6a51324e545979494441734f4334344e6a457a4d7941774c6a49774d7a457a4c4441754d4459774e53426a49444d754f4463794f544d734d5334784d7a6b304d79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alt=\"Nextflow version\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg?logo=data:image/svg+xml;base64,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\" 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src=\"https://camo.githubusercontent.com/720a0b93892db5c772d24eb7dc2fd6fefb2b556eff92ee7ae6a2963a40a8dd5a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f5363694c6966654c61622f536172656b2e7376673f6c6f676f3d676974687562266c6f676f436f6c6f723d7768697465\" alt=\"Sarek version\" data-canonical-src=\"https://img.shields.io/github/release/SciLifeLab/Sarek.svg?logo=github\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/54024046\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2794ec0225017cde71e3ed51dd8393510fe23a950955ef03f7439d7c0f288f83/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f35343032343034362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/54024046.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.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\" 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data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?logo=data:image/png;base64,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\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/maxulysse/sarek\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bc3bec2ef3bf857d42e0bff8df09f0e81595bbd7dbc2681d0feadd729acb4bc0/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6d6178756c797373652f736172656b2e7376673f6c6f676f3d646f636b6572\" alt=\"Docker Container available\" data-canonical-src=\"https://img.shields.io/docker/automated/maxulysse/sarek.svg?logo=docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/CAW_logo.png\"\u003e\u003cimg align=\"right\" title=\"CAW\" src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/CAW_logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePreviously known as the Cancer Analysis Workflow (CAW),\nSarek is a workflow designed to run analyses on WGS data from regular samples or tumour / normal pairs, including relapse samples if required.\u003c/p\u003e\n\u003cp\u003eIt\u0027s built using \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a domain specific language for workflow building.\nSoftware dependencies are handled using \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e - container technologies that provide excellent reproducibility and ease of use.\nSingularity has been designed specifically for high-performance computing environments.\nThis means that although Sarek has been primarily designed for use with the Swedish \u003ca href=\"https://www.uppmax.uu.se\" rel=\"nofollow\"\u003eUPPMAX HPC systems\u003c/a\u003e, it should be able to run on any system that supports these two tools.\u003c/p\u003e\n\u003cp\u003eSarek was developed at the \u003ca href=\"https://ngisweden.scilifelab.se/\" rel=\"nofollow\"\u003eNational Genomics Infastructure\u003c/a\u003e and \u003ca href=\"https://www.nbis.se/\" rel=\"nofollow\"\u003eNational Bioinformatics Infastructure Sweden\u003c/a\u003e which are both platforms at \u003ca href=\"https://www.scilifelab.se/\" rel=\"nofollow\"\u003eSciLifeLab\u003c/a\u003e.\nIt is listed on the \u003ca href=\"https://bio.tools/Sarek\" rel=\"nofollow\"\u003eElixir - Tools and Data Services Registry\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-workflow-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflow-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow steps\u003c/h2\u003e\n\u003cp\u003eSarek is built with several workflow scripts.\nA wrapper script contained within the repository makes it easy to run the different workflow scripts as a single job.\nTo test your installation, follow the \u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/TESTS.md\"\u003etests documentation.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRaw FastQ files or aligned BAM files (with or without realignment \u0026amp; recalibration) can be used as inputs.\nYou can choose which variant callers to use, plus the pipeline is capable of accommodating additional variant calling software or CNV callers if required.\u003c/p\u003e\n\u003cp\u003eThe worflow steps and tools used are as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003ePreprocessing\u003c/strong\u003e - \u003ccode\u003emain.nf\u003c/code\u003e \u003cem\u003e(based on \u003ca href=\"https://software.broadinstitute.org/gatk/best-practices/\" rel=\"nofollow\"\u003eGATK best practices\u003c/a\u003e)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eMap reads to Reference\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://bio-bwa.sourceforge.net/\" rel=\"nofollow\"\u003eBWA\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMark Duplicates\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK MarkDuplicates\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBase (Quality Score) Recalibration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK BaseRecalibrator\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK ApplyBQSR\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGermline variant calling\u003c/strong\u003e - \u003ccode\u003egermlineVC.nf\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eSNVs and small indels\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK HaplotyeCaller\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka2\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStructural variants\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/manta\"\u003eManta\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSomatic variant calling\u003c/strong\u003e - \u003ccode\u003esomaticVC.nf\u003c/code\u003e \u003cem\u003e(optional)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eSNVs and small indels\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eMuTect2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ekg/freebayes\"\u003eFreebayes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka2\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStructural variants\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/manta\"\u003eManta\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSample heterogeneity, ploidy and CNVs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Crick-CancerGenomics/ascat\"\u003eASCAT\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAnnotation\u003c/strong\u003e - \u003ccode\u003eannotate.nf\u003c/code\u003e \u003cem\u003e(optional)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eVariant annotation\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://snpeff.sourceforge.net/\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ensembl.org/info/docs/tools/vep/index.html\" rel=\"nofollow\"\u003eVEP (Variant Effect Predictor)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReporting\u003c/strong\u003e - \u003ccode\u003erunMultiQC.nf\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eReporting\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe Sarek pipeline comes with documentation in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL.md\"\u003eInstallation documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL_RACKHAM.md\"\u003eInstallation documentation specific for UPPMAX \u003ccode\u003erackham\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL_BIANCA.md\"\u003eInstallation documentation specific for UPPMAX \u003ccode\u003ebianca\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/TESTS.md\"\u003eTests documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/REFERENCES.md\"\u003eReference files documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONFIG.md\"\u003eConfiguration and profiles documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INTERVALS.md\"\u003eIntervals documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/USAGE.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/PARAMETERS.md\"\u003eCommand line parameters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/USE_CASES.md\"\u003eExamples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INPUT.md\"\u003eInput files documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/PROCESS.md\"\u003eProcesses documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONTAINERS.md\"\u003eDocumentation about containers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/ASCAT.md\"\u003eMore information about ASCAT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/OUTPUT.md\"\u003eOutput documentation structure\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions--support\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions \u0026amp; Support\u003c/h2\u003e\n\u003cp\u003eIf you would like to contribute to this pipeline, please see the \u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/.github/CONTRIBUTING.md\"\u003econtributing guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor further information or help, don\u0027t hesitate to get in touch on \u003ca href=\"https://gitter.im/SciLifeLab/Sarek\" rel=\"nofollow\"\u003eGitter\u003c/a\u003e or contact us: \u003ca href=\"mailto:maxime.garcia@scilifelab.se\"\u003emaxime.garcia@scilifelab.se\u003c/a\u003e, \u003ca href=\"mailto:szilveszter.juhos@scilifelab.se\"\u003eszilveszter.juhos@scilifelab.se\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-hidden=\"true\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCHANGELOG\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/CHANGELOG.md\"\u003eCHANGELOG\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eMain authors:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MaxUlysse\"\u003eMaxime Garcia\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/szilvajuhos\"\u003eSzilveszter Juhos\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHelpful contributors:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/alneberg\"\u003eJohannes Alneberg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Sebastian-D\"\u003eSebastian DiLorenzo\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/J35P312\"\u003eJesper Eisfeldt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ewels\"\u003ePhil Ewels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gulfshores\"\u003eMax K\u00e4ller\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/malinlarsson\"\u003eMalin Larsson\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/marcelm\"\u003eMarcel Martin\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bjornnystedt\"\u003eBj\u00f6rn Nystedt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pallolason\"\u003ePall Olason\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arontommi\"\u003eAron Skaftason\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca href=\"https://www.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/SciLifeLab_logo.png\" alt=\"SciLifeLab\" title=\"SciLifeLab\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ngisweden.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/NGI_logo.png\" alt=\"NGI\" title=\"NGI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nbis.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/NBIS_logo.png\" alt=\"NBIS\" title=\"NBIS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "mvdoc/identity-decoding", + "latest_release": "1.0.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-shared-neural-codes-for-visual-and-semantic-information-about-familiar-faces-in-a-common-representational-space\" class=\"anchor\" href=\"#shared-neural-codes-for-visual-and-semantic-information-about-familiar-faces-in-a-common-representational-space\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShared neural codes for visual and semantic information about familiar faces in a common representational space\u003c/h1\u003e\n\u003cp\u003eThis repository contains the code for the analyses reported in \u003cem\u003eShared neural codes for visual and semantic information about familiar faces in a common representational space\u003c/em\u003e by Matteo Visconti di Oleggio Castello, James V. Haxby, \u0026amp; M. Ida Gobbini published in the \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe reference for the associated publication is\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVisconti di Oleggio Castello, M., Haxby, J. V., \u0026amp; Gobbini, M. I. Shared neural codes for visual and semantic information about familiar faces in a common representational space \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e (2021). \u003ca href=\"https://doi.org/10.1073/pnas.2110474118\" rel=\"nofollow\"\u003ehttps://doi.org/10.1073/pnas.2110474118\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis repository can be cited as\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVisconti di Oleggio Castello, M., Haxby, J. V., \u0026amp; Gobbini, M. I. (2021). mvdoc/identity-decoding. \u003cem\u003eZenodo\u003c/em\u003e. \u003ca href=\"https://doi.org/10.5281/zenodo.5645003\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.5645003\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/344613702\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31de84d904523cf98d5215b7c3dac0af54476f3416c24e0ee28469dc04ef9647/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3334343631333730322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/344613702.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-disclaimer--how-to-get-help\" class=\"anchor\" href=\"#disclaimer--how-to-get-help\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer \u0026amp; how to get help\u003c/h2\u003e\n\u003cp\u003eThese scripts are shared in a format that is suitable for archival and review. All analyses were run inside a singularity container (shared in the current repository) on a local cluster and on \u003ca href=\"https://rc.dartmouth.edu/index.php/discovery-overview/\" rel=\"nofollow\"\u003eDiscovery, Dartmouth\u0027s HPC cluster\u003c/a\u003e. The paths listed in these scripts need to be modified in order to run the scripts on a different system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIf you have any questions related to the code, please open an issue in this repository or contact us via email (see corresponding author in the publication).\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-data\" class=\"anchor\" href=\"#data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003cp\u003eThe raw data is available on OpenNeuro as the dataset \u003ccode\u003eds003834\u003c/code\u003e: \u003ca href=\"https://openneuro.org/datasets/ds003834\" rel=\"nofollow\"\u003ehttps://openneuro.org/datasets/ds003834\u003c/a\u003e.\nIf you use the data, please cite the corresponding publication:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVisconti di Oleggio Castello, M., Haxby, J. V., \u0026amp; Gobbini, M. I. Shared neural codes for visual and semantic information about familiar faces in a common representational space. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e (2021). \u003ca href=\"https://doi.org/10.5281/zenodo.5645003\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.5645003\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-repository-structure\" class=\"anchor\" href=\"#repository-structure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepository structure\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"singularity\"\u003e\u003ccode\u003esingularity\u003c/code\u003e\u003c/a\u003e contains code to generate the singularity image that was used to run all analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"src\"\u003e\u003ccode\u003esrc\u003c/code\u003e\u003c/a\u003e contains a python package (\u003ccode\u003efamfaceangles\u003c/code\u003e) containing various general functions used in the analysis scripts\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts\"\u003e\u003ccode\u003escripts\u003c/code\u003e\u003c/a\u003e contains the scripts used for the analyses reported in the manuscript\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn the following sections we describe each file in detail.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h3\u003e\n\u003cp\u003eThis folder contains the following files\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity-neurodocker\u003c/code\u003e: a singularity definition file for the image used in all analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecreate-image.sh\u003c/code\u003e: a bash script to generate the singularity image. Note that the syntax used in this script is for singularity versions 2.X. New versions of singularity will need a different syntax, and they have not been tested with this definition file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-src\" class=\"anchor\" href=\"#src\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esrc\u003c/h3\u003e\n\u003cp\u003eThis folder contains the python package \u003ccode\u003efamfaceangles\u003c/code\u003e with helper functions used in the analysis scripts. It can be installed as any other python package (e.g., \u003ccode\u003epip install -e src\u003c/code\u003e)\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-scripts\" class=\"anchor\" href=\"#scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escripts\u003c/h3\u003e\n\u003cp\u003eThis folder contains the following scripts\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-preprocessing\" class=\"anchor\" href=\"#preprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessing\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/00preproc/run-fmriprep103-singularity.sh\"\u003e\u003ccode\u003e00preproc/run-fmriprep103-singularity.sh\u003c/code\u003e\u003c/a\u003e calls fmriprep to preprocess the data.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/00preproc/prepare-fsaverage6-suma.sh\"\u003e\u003ccode\u003e00preproc/prepare-fsaverage6-suma.sh\u003c/code\u003e\u003c/a\u003e prepares the \u003cem\u003efsaverage6\u003c/em\u003e surfaces to be used with SUMA.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/00preproc/make-maskmedial-fsaverage6.sh\"\u003e\u003ccode\u003e00preproc/make-maskmedial-fsaverage6.sh\u003c/code\u003e\u003c/a\u003e creates a mask in NIML format to remove medial nodes in \u003cem\u003efsaverage6\u003c/em\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-hyperalignment\" class=\"anchor\" href=\"#hyperalignment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperalignment\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/run_preproc_hpal.py\"\u003e\u003ccode\u003e01hyperalignment/run_preproc_hpal.py\u003c/code\u003e\u003c/a\u003e preprocesses the data from \u003cem\u003eThe Grand Budapest Hotel\u003c/em\u003e to be used for hyperalignment.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/run_preproc_hpal_singularity.sh\"\u003e\u003ccode\u003e01hyperalignment/run_preproc_hpal_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/run_hpal.py\"\u003e\u003ccode\u003e01hyperalignment/run_hpal.py\u003c/code\u003e\u003c/a\u003e runs the hyperalignment algorithm.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/run_hpal_singularity.sh\"\u003e\u003ccode\u003e01hyperalignment/run_hpal_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/apply_hpal.py\"\u003e\u003ccode\u003e01hyperalignment/apply_hpal.py\u003c/code\u003e\u003c/a\u003e applies the hyperalignment transformations to the input data.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/apply_hpal_singularity.sh\"\u003e\u003ccode\u003e01hyperalignment/apply_hpal_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-glm\" class=\"anchor\" href=\"#glm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGLM\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_model.py\"\u003e\u003ccode\u003e02glm/run_glm_model.py\u003c/code\u003e\u003c/a\u003e runs a GLM model for the face perception experiment using the specified model.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_blockrun_singularity.sh\"\u003e\u003ccode\u003e02glm/run_glm_blockrun_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity on anatomically-aligned data and a BLOCK model estimated within each run.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_blockrun_hpal_singularity.sh\"\u003e\u003ccode\u003e02glm/run_glm_blockrun_hpal_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity on hyperaligned data and a BLOCK model estimated within each run.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_localizer_bwsj.py\"\u003e\u003ccode\u003e02glm/run_glm_localizer_bwsj.py\u003c/code\u003e\u003c/a\u003e runs the GLM model for the hyperaligned localizer data.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_localizer_bwsj_singularity.sh\"\u003e\u003ccode\u003e02glm/run_glm_localizer_bwsj_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/workflows.py\"\u003e\u003ccode\u003e02glm/workflows.py\u003c/code\u003e\u003c/a\u003e contains additional functions and Nipype workflows required to run the GLM models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-mvpa\" class=\"anchor\" href=\"#mvpa\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMVPA\u003c/h4\u003e\n\u003cp\u003eBetween-subject searchlight decoding\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_bwsbj.py\"\u003e\u003ccode\u003e03mvpa/run_sl_bwsbj.py\u003c/code\u003e\u003c/a\u003e runs between-subject whole-brain searchlight decoding.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_bwsbj_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_bwsbj_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity on hyperaligned data.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_bwsbj_fsaverage6_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_bwsbj_fsaverage6_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity on anatomically-aligned data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBetween-subject ROI decoding\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_bwsj_roi_v2.py\"\u003e\u003ccode\u003e03mvpa/run_bwsj_roi_v2.py\u003c/code\u003e\u003c/a\u003e runs between-subject decoding analyses within manually defined ROIs.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_bwsj_roi_v2_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_bwsj_roi_v2_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_roi.py\"\u003e\u003ccode\u003e03mvpa/run_sl_roi.py\u003c/code\u003e\u003c/a\u003e contains some additional functions needed for ROI decoding.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWithin-subject searchlight decoding\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl.py\"\u003e\u003ccode\u003e03mvpa/run_sl.py\u003c/code\u003e\u003c/a\u003e runs within-subject whole-brain searchlight decoding.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_blockrun_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_blockrun_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_blockrun_permutation_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_blockrun_permutation_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity to generate permuted maps.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCross-validated RSA\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_cvrsa.py\"\u003e\u003ccode\u003e03mvpa/run_sl_cvrsa.py\u003c/code\u003e\u003c/a\u003e runs within-subject searchlight cross-validated RSA.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_cvrsa_blockrun_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_cvrsa_blockrun_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_rsa_target.py\"\u003e\u003ccode\u003e03mvpa/run_rsa_target.py\u003c/code\u003e\u003c/a\u003e runs model-based RSA by comparing the cross-validated brain RDMs with model RDMs.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_rsa_target_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_rsa_target_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-statistics\" class=\"anchor\" href=\"#statistics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStatistics\u003c/h4\u003e\n\u003cp\u003ePermutation testing for between-subject MVPC\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_permtest_bootstrap.py\"\u003e\u003ccode\u003e04stat/run_permtest_bootstrap.py\u003c/code\u003e\u003c/a\u003e runs permutation testing with bootstrapping.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_permtest_bwsbj_bootstrap_singularity.sh\"\u003e\u003ccode\u003e04stat/run_permtest_bwsbj_bootstrap_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_fam-diff_bwsj_identity.sh\"\u003e\u003ccode\u003e04stat/make_fam-diff_bwsj_identity.sh\u003c/code\u003e\u003c/a\u003e creates difference maps (familiar - visual) from precomputed accuracy maps.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_permtest_famdiff_bwsbj_bootstrap_singularity.sh\"\u003e\u003ccode\u003e04stat/run_permtest_famdiff_bwsbj_bootstrap_singularity.sh\u003c/code\u003e\u003c/a\u003e runs permutation testing on the familiar - visual difference maps.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make-maskfdrval-diff-identity-bsmvpc.sh\"\u003e\u003ccode\u003e04stat/make-maskfdrval-diff-identity-bsmvpc.sh\u003c/code\u003e\u003c/a\u003e makes a mask that highlights significant nodes for the familiar - visual difference map.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThreshold-Free Cluster Enhancement for within-subject MVPC and RSA\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_tfce_fsaverage6_cosmo.m\"\u003e\u003ccode\u003e04stat/run_tfce_fsaverage6_cosmo.m\u003c/code\u003e\u003c/a\u003e runs the CoSMoMVPA TFCE code for within-subject MVPC.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_tfce_fsaverage6_cosmo_blockrun_hpalsubjs_singularity.sh\"\u003e\u003ccode\u003e04stat/run_tfce_fsaverage6_cosmo_blockrun_hpalsubjs_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_blockrun_fsaverage6-hpal.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_blockrun_fsaverage6-hpal.sh\u003c/code\u003e\u003c/a\u003e creates thresholded maps based on the TFCE values.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_blockrun_fsaverage6-hpal_all.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_blockrun_fsaverage6-hpal_all.sh\u003c/code\u003e\u003c/a\u003e calls the previous script for all comparisons of interest.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_tfce_cvrsa_fsaverage6_cosmo.m\"\u003e\u003ccode\u003e04stat/run_tfce_cvrsa_fsaverage6_cosmo.m\u003c/code\u003e\u003c/a\u003e runs the CoSMoMVPA TFCE code for within-subject cross-validated RSA.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_tfce_cvrsa_fsaverage6_cosmo_singularity.sh\"\u003e\u003ccode\u003e04stat/run_tfce_cvrsa_fsaverage6_cosmo_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_cvrsa_fsaverage6.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_cvrsa_fsaverage6.sh\u003c/code\u003e\u003c/a\u003e creates thresholded maps based on the TFCE values.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_cvrsa_fsaverage6_singularity.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_cvrsa_fsaverage6_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_cvrsa_fsaverage6_all.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_cvrsa_fsaverage6_all.sh\u003c/code\u003e\u003c/a\u003e calls the previous script for all comparisons of interest.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-visualization\" class=\"anchor\" href=\"#visualization\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisualization\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/05viz/drive-suma-blockrun-fsaverage6-group-hpal.sh\"\u003e\u003ccode\u003e05viz/drive-suma-blockrun-fsaverage6-group-hpal.sh\u003c/code\u003e\u003c/a\u003e shows an example call to \u003ccode\u003eDriveSuma\u003c/code\u003e to generate surface plots.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" href=\"#acknowledgements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the NSF grant #1835200 to M. Ida Gobbini. We would like to thank Swaroop Guntupalli, Yaroslav Halchenko, Carlo Cipolli, and the members of the Gobbini and Haxby lab for helpful discussions during the development of this project.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1541579046.0 + "updated_at": 1636025062.0 }, { "data_format": 2, @@ -9691,13 +9113,13 @@ var data = "filenames": [ "Singularity" ], - "full_name": "ResearchIT/scanindel", + "full_name": "truatpasteurdotfr/singularity-docker-centos7-openapi-basekit", "latest_release": null, - "readme": "\u003ch3\u003e\u003ca id=\"user-content-scanindel-singularity-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#scanindel-singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScanIndel Singularity recipe\u003c/h3\u003e\n\u003cp\u003eScanIndel is a python program to detect indels (insertions and deletions) from NGS data by re-align and de novo assemble soft clipped reads.\u003c/p\u003e\n\u003cp\u003eOriginal repository \u003ca href=\"https://github.com/cauyrd/ScanIndel\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker-c7-openapi-basekit-\" class=\"anchor\" href=\"#docker-c7-openapi-basekit-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker-c7-openapi-basekit \u003ca href=\"https://github.com/truatpasteurdotfr/docker-c7-openapi-basekit/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/docker-c7-openapi-basekit/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 2, "topics": [], - "updated_at": 1539032220.0 + "updated_at": 1635355882.0 }, { "data_format": 2, @@ -9705,281 +9127,277 @@ var data = "filenames": [ "Singularity" ], - "full_name": "sbutcher/container-setc", + "full_name": "anoyaro84/snakemake_ChIPseq", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-setc\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-setc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer-setc\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-chip-seq-analysis-pipeline-based-on-snakemake\" class=\"anchor\" href=\"#chip-seq-analysis-pipeline-based-on-snakemake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChIP-seq analysis pipeline based on snakemake\u003c/h1\u003e\n\u003cp\u003eThis is an snakemake-based Peak calling pipeline used in Zwart lab at the Netherlands Cancer Institute.\nThe pipeline obtains ChIP-seq data from diverse sources (remote/local path or GEO) and process them accordingly to produce peak lists in bed format and coverage profiles in tdf format.\u003c/p\u003e\n\u003cp\u003eRoughly, the pipeline takes the following steps to produce the outcome:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownloading raw data (either bam/fastq files) from the specified locations (local, remote, or GEO) in DataList.csv\u003c/li\u003e\n\u003cli\u003eAlignment with bwa-mem (in case of fastq files)\u003c/li\u003e\n\u003cli\u003eMarking duplicate reads with picard\u003c/li\u003e\n\u003cli\u003eRemoving low-quality reads (retain reads with mapping quality \u0026gt; 20)\u003c/li\u003e\n\u003cli\u003ePeak calling with MACS1.4/MACS2/DFilter (support more than one peak callers)\u003c/li\u003e\n\u003cli\u003eTaking intersection between the peaks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that PeakPairs.csv is used to specify ChIP-seq vs input pairs, and config.yaml is used for specifiying optional parameters in softwares.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe pipeline is preliminary used in linux environment with conda/singularity available. Singularity is used only for DFilter (one of two peak callers used) within the pipeline. Currently, the pipeline is tested with conda version 4.5.4 and singularity version 2.5.1.\u003c/p\u003e\n\u003cp\u003eFor downloading repository \u0026amp; creating evnironment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/anoyaro84/snakemake_ChIPseq\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e snakemake_ChIPseq\nconda env create --file env/snakemake.yaml\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install phantompeak tools\u003c/span\u003e\ngit submodule init\ngit submodule update\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe most of softwares used in the pipeline is installed by conda or excuted in wrapper.\nOnly exception is the phantompeak, the software used for estimating the fragment length that can be used by MACS2.\nPhantompeak tools is included as a submodule, for which you can install with the last two commands.\u003c/p\u003e\n\u003cp\u003eWe recommend to run the pipeline from a different location than pipeline path, like below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake -s PATH_TO_PIPELINE/Snakefile --use-conda --use-singularity --cores=24\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWith --use-conda option, the pipeline will create environments to run rules based on .yaml files in env/.\nThe --use-singulairty option applies only to DFilter peak caller. The singularity container holds a virtual environment of Ubuntu with DFilter installed.\u003c/p\u003e\n\u003cp\u003eNote that the pipeline assumes that there is the following three files available at the location where the pipeline is executed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003econfig.yaml\u003c/li\u003e\n\u003cli\u003eDataList.csv\u003c/li\u003e\n\u003cli\u003ePeakPairs.csv\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee below for more details on how to prepare these input files.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-preparing-input-files\" class=\"anchor\" href=\"#preparing-input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparing Input files\u003c/h2\u003e\n\u003cp\u003eFor DatList.csv, it is expected to have the following structure (in csv format):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eID\u003c/th\u003e\n\u003cth\u003eSource\u003c/th\u003e\n\u003cth\u003ePath\u003c/th\u003e\n\u003cth\u003eFormat\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eIdentifier of each sequencing data\u003c/td\u003e\n\u003ctd\u003eSource of the files, can either be remote (forge), local, or GEO\u003c/td\u003e\n\u003ctd\u003e(local/remote) path to the file. (ignored if Source is GEO)\u003c/td\u003e\n\u003ctd\u003eEither fastq or bam (ignored if Source is GEO)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe pipeline will take either fastq/bam files from GEO, remote/local locations based on the table above.\u003c/p\u003e\n\u003cp\u003eFor GEO, GSM ID is required for ID, which will be used as an quiry to GEO database. For remote/local files, ID should be a part of the file name. The pipeline greps bam/fastq files with ID on the specified path. The pipeline grabs bam/fastq files with ID on the specified path. If there is none or multiple files with the specified ID on the path, it will give an error.\u003c/p\u003e\n\u003cp\u003eFor PeakPairs.csv, signal and input pairs need to be specified in the following format (in csv):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSignal\u003c/th\u003e\n\u003cth\u003eInput\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eID of ChIP-seq data\u003c/td\u003e\n\u003ctd\u003eID of Input data\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that IDs used in the PeakPairs.csv should be available in ID column of DataList.csv.\u003c/p\u003e\n\u003cp\u003eFor config.yaml, you can copy it from this repository and modify the parameters based on your need.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1538491698.0 + "updated_at": 1534943715.0 }, { "data_format": 2, - "description": null, + "description": "This is a repo which holds the codebase for our class project on NLP.", "filenames": [ - "Singularity.ubuntu" + "singularity/Singularity.debian-unstable-amd64", + "singularity/Singularity.debian-unstable-i386" ], - "full_name": "UNM-CARC/heudiconv", + "full_name": "ravisha2396/NLPProject", "latest_release": null, - "readme": "\u003cp\u003eNot much\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"/logo_assets/vowpal-wabbits-github-logo@3x.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/logo_assets/vowpal-wabbits-github-logo@3x.png\" height=\"auto\" width=\"100%\" alt=\"Vowpal Wabbit\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://dev.azure.com/vowpalwabbit/Vowpal%20Wabbit/_build/latest?definitionId=23\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/58819a50d93dd6bfee30aecaa0f72d7e66623fd462c5ac37bdc427f3058ae723/68747470733a2f2f696d672e736869656c64732e696f2f617a7572652d6465766f70732f6275696c642f766f7770616c7761626269742f33393334313133632d396532622d346462632d383937322d3732616239623962343334322f32333f6c6162656c3d4c696e75782532306275696c64266c6f676f3d417a7572652532304465766f7073\" alt=\"Linux build status\" data-canonical-src=\"https://img.shields.io/azure-devops/build/vowpalwabbit/3934113c-9e2b-4dbc-8972-72ab9b9b4342/23?label=Linux%20build\u0026amp;logo=Azure%20Devops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://dev.azure.com/vowpalwabbit/Vowpal%20Wabbit/_build/latest?definitionId=14\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d539f0bca2e4c6aca53fbbbf2a4efb7be920f95b698171172d1af967aa5025d7/68747470733a2f2f696d672e736869656c64732e696f2f617a7572652d6465766f70732f6275696c642f766f7770616c7761626269742f33393334313133632d396532622d346462632d383937322d3732616239623962343334322f31343f6c6162656c3d57696e646f77732532306275696c64266c6f676f3d417a7572652532304465766f7073\" alt=\"Windows build status\" data-canonical-src=\"https://img.shields.io/azure-devops/build/vowpalwabbit/3934113c-9e2b-4dbc-8972-72ab9b9b4342/14?label=Windows%20build\u0026amp;logo=Azure%20Devops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://dev.azure.com/vowpalwabbit/Vowpal%20Wabbit/_build/latest?definitionId=22\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/214f203a660ee423d4694b193d4839c0bcd320402462f1030b0d25f33588b0a9/68747470733a2f2f696d672e736869656c64732e696f2f617a7572652d6465766f70732f6275696c642f766f7770616c7761626269742f33393334313133632d396532622d346462632d383937322d3732616239623962343334322f32323f6c6162656c3d4d61634f532532306275696c64266c6f676f3d417a7572652532304465766f7073\" alt=\"MacOS build status\" data-canonical-src=\"https://img.shields.io/azure-devops/build/vowpalwabbit/3934113c-9e2b-4dbc-8972-72ab9b9b4342/22?label=MacOS%20build\u0026amp;logo=Azure%20Devops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://codecov.io/gh/VowpalWabbit/vowpal_wabbit\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/32898f10a7c61069a273521aea6b4becacfc4d776e96dd0e747f03e286b1b824/68747470733a2f2f636f6465636f762e696f2f67682f566f7770616c5761626269742f766f7770616c5f7761626269742f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/VowpalWabbit/vowpal_wabbit/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://lgtm.com/projects/g/JohnLangford/vowpal_wabbit/alerts/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e389698afd7de10a602e5e1a705d05c192a37638521b67a3ca2fac8d937b69e/68747470733a2f2f696d672e736869656c64732e696f2f6c67746d2f616c657274732f672f4a6f686e4c616e67666f72642f766f7770616c5f7761626269742e7376673f6c6f676f3d6c67746d266c6f676f57696474683d3138\" alt=\"Total Alerts\" data-canonical-src=\"https://img.shields.io/lgtm/alerts/g/JohnLangford/vowpal_wabbit.svg?logo=lgtm\u0026amp;logoWidth=18\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/VowpalWabbit\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/64efc8a80a3424a0595bf90fcae3ee2ef1878436f3c22137aef60e11f4ca9126/68747470733a2f2f6261646765732e6769747465722e696d2f566f7770616c5761626269742e737667\" alt=\"Gitter chat\" data-canonical-src=\"https://badges.gitter.im/VowpalWabbit.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the \u003cem\u003eVowpal Wabbit\u003c/em\u003e fast online learning code.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-vowpal-wabbit\" class=\"anchor\" href=\"#why-vowpal-wabbit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy Vowpal Wabbit?\u003c/h2\u003e\n\u003cp\u003eVowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state of the art algorithms with performance in mind.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eInput Format.\u003c/strong\u003e The input format for the learning algorithm is substantially more flexible than might be expected. Examples can have features consisting of free form text, which is interpreted in a bag-of-words way. There can even be multiple sets of free form text in different namespaces.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSpeed.\u003c/strong\u003e The learning algorithm is fast -- similar to the few other online algorithm implementations out there. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eScalability.\u003c/strong\u003e This is not the same as fast. Instead, the important characteristic here is that the memory footprint of the program is bounded independent of data. This means the training set is not loaded into main memory before learning starts. In addition, the size of the set of features is bounded independent of the amount of training data using the hashing trick.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFeature Interaction.\u003c/strong\u003e Subsets of features can be internally paired so that the algorithm is linear in the cross-product of the subsets. This is useful for ranking problems. The alternative of explicitly expanding the features before feeding them into the learning algorithm can be both computation and space intensive, depending on how it\u0027s handled.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki\"\u003eVisit the wiki to learn more.\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eFor the most up to date instructions for getting started on Windows, MacOS or Linux \u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Getting-started\"\u003eplease see the wiki\u003c/a\u003e. This includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Getting-started\"\u003eInstalling with a package manager\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Dependencies\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Building\"\u003eBuilding\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Tutorial\"\u003eTutorial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1536784012.0 + "updated_at": 1636245564.0 }, { "data_format": 2, - "description": null, + "description": "repo hosting personal example scripts and notebooks for various pieces of software by OPIG", "filenames": [ - "ext/Singularity" + "webdevel/ubuntu/.singularity.d/Singularity" ], - "full_name": "OSC/bc_osc_rshiny", + "full_name": "broncio123/software_hands-on", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-wip-batch-connect---osc-shiny-app-launcher\" class=\"anchor\" aria-hidden=\"true\" href=\"#wip-batch-connect---osc-shiny-app-launcher\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[WIP] Batch Connect - OSC Shiny App Launcher\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5e71a5a07e8e6e15f922edb76b5fb68e7ee6087e336807c38095861b8c7f5fc2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f7368696e795f6c61756e636865722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e71a5a07e8e6e15f922edb76b5fb68e7ee6087e336807c38095861b8c7f5fc2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f7368696e795f6c61756e636865722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/shiny_launcher.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app designed for OSC OnDemand that launches a Shiny App within\nan Owens batch job.\u003c/p\u003e\n\u003cp\u003eThe Shiny app is included a submodule and each deployment can modified which\nShiny app to deploy using git config to specify the URL for the submodule. This\nway the launcher code can be reused for multiple apps but the launcher and the\napp itself can be managed separately.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e x.y.z+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eTODO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_example_shiny/fork\"\u003ehttps://github.com/OSC/bc_osc_example_shiny/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cp\u003esoftware_hands-on\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1628181440.0 + "updated_at": 1638391053.0 }, { "data_format": 2, - "description": "Adapt the BEaST skull stripping method for 7T MRI as a BIDS app", + "description": "Based on the original Sregistry: https://github.com/singularityhub/sregistry - Deploy the Singularity Sregistry as rootless containers with podman-compose. Also added data persistence for the PostgreSQL database and rootless setup for SSL and PAM authentication.", "filenames": [ - "Singularity.v0.0.1a" + "Singularity" ], - "full_name": "Martybird/7TBEaST", + "full_name": "hashkeks/sregistry-podman-compose", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-7tbeast\" class=\"anchor\" aria-hidden=\"true\" href=\"#7tbeast\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e7TBEaST\u003c/h1\u003e\n\u003cp\u003eAdapt the BEaST skull stripping method for 7T MRI as a BIDS app\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-registry-server---podman-compose-edition\" class=\"anchor\" href=\"#singularity-registry-server---podman-compose-edition\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Registry Server - podman-compose edition\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-podman-compose\" class=\"anchor\" href=\"#what-is-podman-compose\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is podman-compose\u003c/h2\u003e\n\u003cp\u003ePodman-compose is the podman equivalent to docker-compose, using the podman container engine. It allows for the creation of rootless containers running in user namespace. For more information see \u003ca href=\"https://podman.io/\" rel=\"nofollow\"\u003ehttps://podman.io/\u003c/a\u003e and \u003ca href=\"https://github.com/containers/podman-compose\"\u003ehttps://github.com/containers/podman-compose\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-are-the-differences-to-the-original-singularity-registry-server\" class=\"anchor\" href=\"#what-are-the-differences-to-the-original-singularity-registry-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat are the differences to the original Singularity Registry Server\u003c/h2\u003e\n\u003cp\u003eThis version of the Singularity Registry Server is set-up to work in a non-root environment.\nI \u003cstrong\u003edid not\u003c/strong\u003e change the code of the applications.\nI \u003cstrong\u003edid\u003c/strong\u003e change the folder structure and the docker-compose.yml file and provide documentation to make this setup run with podman-compose.\nThis setup in it\u0027s current configuration is meant to be run with valid SSL certificates. You can change that by deactivating the corresponding settings in the docker-compose.yml and shub/settings/config.py files.\nIn the end you still have to make your configurations (like setting your services addresses, renaming your instance, enabling authentication, etc.) according to the original documentation which you can find at \u003ca href=\"https://singularityhub.github.io/sregistry/\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/sregistry/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe differences in detail:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eChanged the docker-compose.yml\n\u003cul\u003e\n\u003cli\u003eVolume paths are not taken from uwsgi directly, but are defined per service. Consquence: You don\u0027t need a nginx user on your host system anymore and don\u0027t have permissions problems after deactivating PAM again.\u003c/li\u003e\n\u003cli\u003eVolume mapping for PAM files changed.\u003c/li\u003e\n\u003cli\u003eVolume mapping for SSL certs changed.\u003c/li\u003e\n\u003cli\u003eVolume mapping for PostgreSQL database added, so it can save data persistently without initiating a backup procedure.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eA PAM folder with a \u0027shadow\u0027 file was added. You need to copy the information of configured users from your /etc/shadow into this file since rootless containers do not have access to the original /etc/shadow.\u003c/li\u003e\n\u003cli\u003eAn SSL directory with subdirectories was added to save and access cert files in the rootless environment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-to-do-besides-doing-the-usual-configuration\" class=\"anchor\" href=\"#what-to-do-besides-doing-the-usual-configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat to do besides doing the usual configuration\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eYou \u003cstrong\u003eneed\u003c/strong\u003e to change the ownership of the sregistry/minio-images folder to the user that is used inside the minio container with the UID and GID 1.\nTo do so, execute the following command inside the sregistry folder:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epodman unshare chown -R 1:1 minio-images\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will change the ownership to the UID that will be used in user namespace and represents the user with UID 1 inside the minio container.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYou can put your SSL cert and key into the according folders in the sregistry/ssl folder\u003c/li\u003e\n\u003cli\u003eYou can put your user info from /etc/shadow into sregistry/PAM/shadow\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-who-worked-on-this\" class=\"anchor\" href=\"#who-worked-on-this\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWho worked on this\u003c/h3\u003e\n\u003ctable\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/hashkeks\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/34633191?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eCedric Casper\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/hashkeks\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/kkaftan\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/74317121?v=4\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eKevin Kaftan\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/kkaftan\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-the-following-section-is-taken-from-the-original-sregistry-repo-itself-and-does-not-have-to-do-with-our-changes\" class=\"anchor\" href=\"#the-following-section-is-taken-from-the-original-sregistry-repo-itself-and-does-not-have-to-do-with-our-changes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe following section is taken from the original Sregistry repo itself and does not have to do with our changes.\u003c/h2\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-registry-server\" class=\"anchor\" href=\"#singularity-registry-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Registry Server\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"http://joss.theoj.org/papers/050362b7e7691d2a5d0ebed8251bc01e\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/77382cd0ef59a3538ed515392195d8541e46ce977b42c3838e930e6ccf221bfb/68747470733a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f30353033363262376537363931643261356430656265643832353162633031652f7374617475732e737667\" alt=\"status\" data-canonical-src=\"https://joss.theoj.org/papers/050362b7e7691d2a5d0ebed8251bc01e/status.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/singularityhub/sregistry/actions?query=branch%3Amaster+workflow%3Asregistry-ci\"\u003e\u003cimg src=\"https://github.com/singularityhub/sregistry/workflows/sregistry-ci/badge.svg?branch=master\" alt=\"GitHub actions status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.1012531\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/411f713db9ba01edfcb60386aaa1dff3e4ed4464707b95d889900a88d8f54936/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313031323533312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1012531.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2f835bc9b4458adb32cf016ec029863ab35c3b89d29ecc3a14494909424d38b5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538422532302532302545322539372538462532302532302545322539372538422d6f72616e6765\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B%20%20%E2%97%8F%20%20%E2%97%8B-orange\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"#contributors-\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/194f21da62ea53d158311e06473f9ec192dea9c1f3f6423c9c3f12aff583b546/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f616c6c5f636f6e7472696275746f72732d32302d6f72616e67652e7376673f7374796c653d666c61742d737175617265\" alt=\"All Contributors\" data-canonical-src=\"https://img.shields.io/badge/all_contributors-20-orange.svg?style=flat-square\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://singularityhub.github.io/sregistry\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\n\n\n\u003ctable\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://vsoch.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/814322?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eVanessasaurus\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=vsoch\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=vsoch\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"tschoonj.github.io\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/65736?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eTom Schoonjans\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=tschoonj\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=tschoonj\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"antoinecully.com\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/6448924?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eAntoine Cully\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=Aneoshun\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=Aneoshun\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://dctrud.sdf.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/4522799?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eDavid Trudgian\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=dctrud\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/serlophug\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/20574493?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eSergio L\u00f3pez Huguet\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=serlophug\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=serlophug\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/jbd\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/169483?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003ejbd\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=jbd\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=jbd\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"http://alex.hirzel.us/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/324152?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eAlex Hirzel\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=alhirzel\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=alhirzel\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"http://tangiblecomputationalbiology.blogspot.com\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/207407?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eSteffen M\u00f6ller\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=smoe\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=smoe\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"www.onerussian.com\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/39889?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eYaroslav Halchenko\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=yarikoptic\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=yarikoptic\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"http://sourceforge.net/u/victorsndvg/profile/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/6474985?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003evictorsndvg\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=victorsndvg\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=victorsndvg\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"arfon.org\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/4483?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eArfon Smith\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=arfon\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=arfon\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://ransomwareroundup.com\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/9367754?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eBrie Carranza\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=bbbbbrie\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=bbbbbrie\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://orcid.org/0000-0002-6178-3585\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/145659?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eDan Fornika\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=dfornika\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=dfornika\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/RonaldEnsing\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/8299064?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eRonald Ensing\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=RonaldEnsing\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=RonaldEnsing\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/vladdoster\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/10052309?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003evladdoster\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=vladdoster\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/pini-gh\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/1241814?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003epini-gh\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=pini-gh\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/0nebody\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/26727168?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003e0nebody\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=0nebody\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/dtrudg\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/4522799?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003edtrudg\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=dtrudg\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/craigwindell\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/44250868?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003ecraigwindell\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=craigwindell\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/hashkeks\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/34633191?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eCedric\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=hashkeks\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n\u003c/table\u003e\n\n\n\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-singularity-registry\" class=\"anchor\" href=\"#what-is-singularity-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is Singularity Registry\u003c/h2\u003e\n\u003cp\u003eSingularity Registry Server is a server to provide management and storage of\nSingularity images for an institution or user to deploy locally.\nIt does not manage building but serves endpoints to obtain and save containers.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-images-included\" class=\"anchor\" href=\"#images-included\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages Included\u003c/h2\u003e\n\u003cp\u003eSingularity Registry consists of several Docker images, and they are integrated\nto work together using \u003ca href=\"docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe images are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003evanessa/sregistry\u003c/strong\u003e: is the main uWSGI application, which serves a Django (python-based) application.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003enginx\u003c/strong\u003e: pronounced (engine-X) is the webserver. The starter application is configured for HTTP. However, you should follow our \u003ca href=\"https://singularityhub.github.io/sregistry/docs/install/server#ssl\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e to set up HTTPS properly. Note that we build a custom NGINX image that takes advantage of the \u003ca href=\"https://www.nginx.com/resources/wiki/modules/upload/\" rel=\"nofollow\"\u003enginx-upload-module\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eworker\u003c/strong\u003e: is the same uWSGI image, but with a running command for queueing jobs and processing them in the background. These jobs run via \u003ca href=\"https://github.com/rq/django-rq\"\u003edjango-rq\u003c/a\u003e backed by a\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eredis\u003c/strong\u003e: database to organize the jobs themselves.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003escheduler\u003c/strong\u003e jobs can be scheduled using the scheduler.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more information about Singularity Registry Server, please reference the\n\u003ca href=\"https://singularityhub.github.io/sregistry\" rel=\"nofollow\"\u003edocs\u003c/a\u003e. If you have any issues,\nplease \u003ca href=\"https://github.com/singularityhub/sregistry/issues\"\u003elet me know\u003c/a\u003e!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis code is licensed under the MPL 2.0 \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1530840788.0 + "updated_at": 1637673514.0 }, { "data_format": 2, - "description": null, + "description": "A collection of Singularity images", "filenames": [ - "Singularity.prc-0_8_0" + "recipes/diffTF/Singularity.diffTF_conda", + "recipes/diffTF/Singularity.diffTF_R", + "recipes/RNA-Seq/Singularity.RNA_Seq_R", + "recipes/RNA-Seq/Singularity.RNA_seq_conda", + "recipes/RNA-Seq/Singularity.RNA_seq_fastqc", + "recipes/ATAC-Seq/Singularity.ATAC_seq_conda2", + "recipes/ATAC-Seq/Singularity.ATAC_seq_conda", + "recipes/ATAC-Seq/Singularity.ATAC_Seq_R", + "recipes/VariantCalling/Singularity.Variant-Calling_R", + "recipes/VariantCalling/Singularity.Variant-Calling_conda" ], - "full_name": "d-w-moore/singularity-python-irodsclient", + "full_name": "chrarnold/Singularity_images", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_images\" class=\"anchor\" href=\"#singularity_images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity_images\u003c/h1\u003e\n\u003cp\u003eA collection of Singularity images\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1530133683.0 + "updated_at": 1637101837.0 }, { "data_format": 2, - "description": "for singularity biuld", + "description": null, "filenames": [ "Singularity" ], - "full_name": "d-w-moore/singularity-icommands-4.2.1", - "latest_release": null, + "full_name": "hmgu-itg/single-point-analysis-pipeline", + "latest_release": "0.0.1", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-snakefile-order\" class=\"anchor\" href=\"#snakefile-order\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakefile order\u003c/h2\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eread-config.smk\u003c/li\u003e\n\u003cli\u003evariant-qc.smk\u003c/li\u003e\n\u003cli\u003esingle-cohort.smk\u003c/li\u003e\n\u003cli\u003emeta-analysis.smk\u003c/li\u003e\n\u003cli\u003edetect-peaks.smk\u003c/li\u003e\n\u003cli\u003epeakplot.smk\u003c/li\u003e\n\u003cli\u003ecojo.smk\u003c/li\u003e\n\u003cli\u003equery.smk\u003c/li\u003e\n\u003cli\u003egwas.smk\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-questions\" class=\"anchor\" href=\"#questions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions\u003c/h2\u003e\n\u003cp\u003eQ. Why do the \u003ccode\u003efreq\u003c/code\u003e and \u003ccode\u003efreq_geno\u003c/code\u003e column values in the \u003ccode\u003e.jma.cojo\u003c/code\u003e file differ?\nA. \u003ccode\u003efreq_geno\u003c/code\u003e column is the frequency of the \u003ccode\u003erefA\u003c/code\u003e column allele in the input bfile (you can use \u003ccode\u003eplink --freq\u003c/code\u003e to check).\nThe \u003ccode\u003efreq\u003c/code\u003e column value is the exact value extracted from the input cojofile, where the cojofile was created from the corresponding metal file.\nSo the \u003ccode\u003efreq\u003c/code\u003e column value comes from the \u003ccode\u003eAlt_Freq\u003c/code\u003e column value in the metal file, and the \u003ccode\u003eAlt_Freq\u003c/code\u003e column value is the \"weighted average of frequency for Alt allele across all studies\".\nThe \u003ccode\u003efreq_geno\u003c/code\u003e and \u003ccode\u003efreq\u003c/code\u003e column values differ because \u003ccode\u003efreq_geno\u003c/code\u003e is just the allele frequency of the variant from the genotype file (plink bfile) that was combined from all cohorts,\nwhereas \u003ccode\u003efreq\u003c/code\u003e column is the weighted average of frequency across cohorts (calculated by metal).\u003c/p\u003e\n\u003cp\u003eQ. When I try to run a rule, I get an error saying \u003ccode\u003eText file busy\u003c/code\u003e. What do I do?\nA. Delete the script and restore it using \u003ccode\u003egit restore workflow/script/problematic_script.sh\u003c/code\u003e. Your rules should run normally after doing this\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1527027070.0 + "updated_at": 1636694692.0 }, { "data_format": 2, - "description": "Singularity images for deep learning software", + "description": "MR preprocessing for the Healthy Brain Ageing clinic at the Thompson Institute, USC.", "filenames": [ - "Singularity.py3_fast2", - "Singularity.py3_tf1gnt", - "Singularity.py3_dmda", - "Singularity.py3_trch", - "Singularity.py2_tf17", - "Singularity.py2_tf110", - "Singularity.py3_tf2gnt", - "Singularity.py3_tf" + "lesion-segmentation_src/Singularity", + "qatools_src/Singularity", + "deep-brain-net_src/Singularity" ], - "full_name": "gnperdue/singularity_imgs", + "full_name": "jakepalmer/TI-HBA-MRprep", "latest_release": null, - "readme": "\u003cp\u003eSingularity containers (with inspiration from J. Simone, \u003ca href=\"https://github.com/TomaszGolan/mlmpr\"\u003eT. Golan\u003c/a\u003e, and \u003ca href=\"https://github.com/DeepLearnPhysics/larcv2-singularity\"\u003eK. Terao\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/998\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePull, e.g. \u003ccode\u003e$ singularity pull shub://gnperdue/singularity_imgs:py2_tf17\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity.py2_tf110\u003c/code\u003e - See \u003ca href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/Dockerfile.devel-gpu\"\u003eTF\u003c/a\u003e for base package definition.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ti-hba-mr-preprocessing\" class=\"anchor\" href=\"#ti-hba-mr-preprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTI HBA MR Preprocessing\u003c/h1\u003e\n\u003cp\u003eThis is a basic preprocessing pipeline for MRI data from the Healthy Brain Ageing Clinic at the Thompson Institute, USC.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline-overview\" class=\"anchor\" href=\"#pipeline-overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline overview\u003c/h2\u003e\n\u003cp\u003eThese are the steps of the pipeline. These steps are explained in more detail below, along with links to helpful resources/documentation and citations.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDicoms are converted to a BIDS compliant dataset with HeuDiConv.\u003c/li\u003e\n\u003cli\u003eAutomatic QC for the T1-weighted scan using MRIQC.\u003c/li\u003e\n\u003cli\u003eSubcortical segmentation and cortical parcellation with FastSurfer (includes QC).\u003c/li\u003e\n\u003cli\u003eBrain age prediction with DeepBrainNet.\u003c/li\u003e\n\u003cli\u003eWMH segmentation with FSL\u0027s BIANCA.\u003c/li\u003e\n\u003cli\u003eDWI preprocessing with QSIprep.\u003c/li\u003e\n\u003cli\u003ersfMRI preprocessing with fMRIprep.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eEach of these steps should be cited appropriately if used in publication (citations included below).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ideas-behind-implementation\" class=\"anchor\" href=\"#ideas-behind-implementation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIdeas behind implementation\u003c/h3\u003e\n\u003cp\u003eThe pipeline was developed with the following ideas in mind:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esubmit_jobs.sh\u003c/code\u003e orchestrates the pipeline by submitting a job on the HPC for each participant. For regular use, this is the only file that should need editing, e.g. editing paths and PBS parameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun_pipeline.py\u003c/code\u003e includes the main processing pipeline and simply wraps the Singularity commands for each step.\u003c/li\u003e\n\u003cli\u003eEach step is implemented in its own container on the HPC. Containers can be built from Dockerfile/Singularity files in the \u003ccode\u003e*_src\u003c/code\u003e folders or from published containters (noted in each section below).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo setup it requires building multiple containers, but the idea was for this pipeline to remain \u0027modular\u0027 so that each processing step is independent and can be modified/removed without affecting the rest of the pipeline (with the exception of dicom to BIDS conversion being required for all subsequent steps). Similarly, the pipeline can be extended by adding a container, processing script/command and a function in the \u003ccode\u003erun_pipeline.py\u003c/code\u003e script.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-assumed-input-file-structure\" class=\"anchor\" href=\"#assumed-input-file-structure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAssumed input file structure\u003c/h2\u003e\n\u003cp\u003eThe pipeline takes dicoms as its input with the assumed file structure before processing being:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u251c\u2500\u2500 bids\n\u251c\u2500\u2500 derivatives\n\u251c\u2500\u2500 dicom\n \u251c\u2500\u2500 HBA_0001_T1\n \u251c\u2500\u2500 RESEARCH_PROTOCOLS_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 AAHEAD_SCOUT_64CH_HEAD_COIL_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 MPRAGE_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 EP2D_DIFF_QBALL96DIR_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n ...\n \u251c\u2500\u2500 HBA_0002_T1\n \u251c\u2500\u2500 RESEARCH_PROTOCOLS_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 AAHEAD_SCOUT_64CH_HEAD_COIL_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 MPRAGE_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 EP2D_DIFF_QBALL96DIR_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n ...\n ...\n\u251c\u2500\u2500 TI-HBA-MRprep\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere...\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edicom\u003c/code\u003e = where the dicoms will be copied for each participant to be processed\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebids\u003c/code\u003e = the BIDS compliant data converted from \u003ccode\u003edicom\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ederivatives\u003c/code\u003e = the pipeline outputs\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTI-HBA-MRprep\u003c/code\u003e = the code in this repository\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-intended-usage\" class=\"anchor\" href=\"#intended-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntended usage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eMake sure directory structure exists as shown \u003ca href=\"##Assumed-input-file-structure\"\u003eabove\u003c/a\u003e in the analysis directory on the HPC.\u003c/li\u003e\n\u003cli\u003eClone this repo and move to the HPC.\u003c/li\u003e\n\u003cli\u003eCopy dicoms to process into the \u003ccode\u003edicom\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eUpdate/check the schedular parameters in \u003ccode\u003esubmit_jobs.sh\u003c/code\u003e. It might take some testing to get these right, afterwhich they most likely won\u0027t need to be changed often.\u003c/li\u003e\n\u003cli\u003eUpdate/check the file paths in \u003ccode\u003esubmit_jobs.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eWhen ready to run the pipeline, type the following in terminal:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /path/on/HPC\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e submit_jobs.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e...where \u003ccode\u003e/path/on/HPC\u003c/code\u003e is the appropriate path to the data and code on the HPC.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-detailed-processing-steps\" class=\"anchor\" href=\"#detailed-processing-steps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetailed processing steps\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e FastSurfer, QSIprep and fMRIprep require a FreeSurfer license, which can be obtained for free from \u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/License\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. The file needs to be passed to the \u003ccode\u003esubmit_jobs.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dicom-to-bids\" class=\"anchor\" href=\"#dicom-to-bids\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDicom to BIDS\u003c/h3\u003e\n\u003cp\u003eBIDS is a standard for structuring neuroimaging datasets that is being increasingly implemented that allows a consistent interface and documentation of datasets. A number of open source pipelines expect input to be in BIDS format.\u003c/p\u003e\n\u003cp\u003eHeuDiConv has been developed to automate the conversion from dicom to BIDS. It requires some setup (i.e. putting together a \u003ccode\u003eheuristic.py\u003c/code\u003e file to provide the rules for conversion), however this will generally only need to be setup once and has been done (see \u003ccode\u003eheudiconv_src/heuristic.py\u003c/code\u003e). This would need updating if the MRI sequences change. Example commands to help with the setup are included in the comments in the docstring for the \u003ccode\u003erunDcm2BIDS\u003c/code\u003e function in the \u003ccode\u003erun_pipeline.py\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eFor more info see \u003ca href=\"https://bids.neuroimaging.io/\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e and \u003ca href=\"https://heudiconv.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eHeuDiConv\u003c/a\u003e documentation, also this HeuDiConv \u003ca href=\"https://reproducibility.stanford.edu/bids-tutorial-series-part-2a/\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e and \u003ca href=\"https://github.com/bids-standard/bids-starter-kit/wiki/\"\u003ewiki\u003c/a\u003e. The HeuDiConv \u003ca href=\"https://heudiconv.readthedocs.io/en/latest/installation.html#docker\" rel=\"nofollow\"\u003econtainer\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-mriqc\" class=\"anchor\" href=\"#mriqc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMRIQC\u003c/h3\u003e\n\u003cp\u003eThis is an automated QC pipeline for T1-weighted, T2-weighted and fMRI sequences (if present in BIDS folder). It produces visual reports and a range of QC metrics that may be useful for further analysis.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://mriqc.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e, \u003ca href=\"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184661\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e and the \u003ca href=\"https://hub.docker.com/r/poldracklab/mriqc/\" rel=\"nofollow\"\u003econtainer\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-fastsurfer\" class=\"anchor\" href=\"#fastsurfer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFastSurfer\u003c/h3\u003e\n\u003cp\u003eFastSurfer is a deep learning implementation of FreeSurfer. It provides essentially the same output but is faster (as you may have guessed) and more accurate.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://deep-mi.org/research/fastsurfer/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e, \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811920304985\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e and the \u003ca href=\"https://github.com/Deep-MI/FastSurfer\"\u003egithub\u003c/a\u003e which also includes \u003ca href=\"https://github.com/Deep-MI/FastSurfer/tree/master/Docker\"\u003eDockerfiles\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-fastsurfer-qc\" class=\"anchor\" href=\"#fastsurfer-qc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFastSurfer QC\u003c/h4\u003e\n\u003cp\u003eThis is just a quick visual QC step for the output of FastSurfer and is run automatically. It produces a CSV file with some QC metrics (some of which overlap with MRIQC) and screenshots to check the segmentation and cortical parcellation.\u003c/p\u003e\n\u003cp\u003eThis is only designed for quick, preliminary visual QC and full visual QC should be completed before any statistical analysis for publication (see \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811921004511\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for discussion of QC approaches).\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://github.com/Deep-MI/qatools-python\"\u003edocumentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-deepbrainnet\" class=\"anchor\" href=\"#deepbrainnet\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeepBrainNet\u003c/h3\u003e\n\u003cp\u003eThis is a deep learning model developed for the prediction of brain age. It produces a single predicted age based on the T1-weighted input, which can then be used to calculate a difference score with chronological age.\u003c/p\u003e\n\u003cp\u003eThe model has been implemented in \u003ca href=\"https://antsx.github.io/ANTsPyNet/docs/build/html/utilities.html\" rel=\"nofollow\"\u003eANTsPyNet\u003c/a\u003e, including the preprocessing steps, which is used in \u003ccode\u003edeep-brain-net_src/run_prediction.py\u003c/code\u003e. The Dockerfile/Singularity file is also included in the \u003ccode\u003edeep-brain-net_src\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://academic.oup.com/brain/article/143/7/2312/5863667?login=true\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e for more info about the model development and interpretation and original \u003ca href=\"https://github.com/vishnubashyam/DeepBrainNet\"\u003ecode\u003c/a\u003e from authors.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-wmh-segmentation-with-bianca\" class=\"anchor\" href=\"#wmh-segmentation-with-bianca\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWMH segmentation with BIANCA\u003c/h3\u003e\n\u003cp\u003eBIANCA requires some pre/post processing. The steps used are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocess T1 and FLAIR with \u003ccode\u003efsl_anat\u003c/code\u003e (see \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/fsl_anat\" rel=\"nofollow\"\u003edocs\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a white matter mask with \u003ccode\u003emake_bianca_mask\u003c/code\u003e (see BIANCA \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BIANCA/Userguide#Data_preparation\" rel=\"nofollow\"\u003edocs\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCreate \u003ccode\u003emasterfile.txt\u003c/code\u003e as input for BIANCA\u003c/li\u003e\n\u003cli\u003eThe BIANCA output is a probability image, so apply thresholding (default to 0.9 here)\u003c/li\u003e\n\u003cli\u003eExtract the total WMH number and volume\u0027\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBIANCA also requires some manually labeled WMH masks as training data. A recent \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811921004663?via%3Dihub#bib0013\" rel=\"nofollow\"\u003epaper\u003c/a\u003e suggested the use of consistent training labels may be beneficial to avoid inter-rater variability between manual segmentations. Currently, this pipeline makes use of manual segmentations provided by those authors (included in container) for the training labels. This could be changed in future if a sample of HBA participants were manually segmented.\u003c/p\u003e\n\u003cp\u003eBIANCA \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BIANCA/Userguide#Data_preparation\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e, \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fslcourse/lectures/practicals/seg_struc/#bianca\" rel=\"nofollow\"\u003etutorial\u003c/a\u003e and \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811916303251?via%3Dihub\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e, as well as the \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811921004663?via%3Dihub#bib0013\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e and discussion for training labels that can be found \u003ca href=\"https://issues.dpuk.org/eugeneduff/wmh_harmonisation/-/tree/master/BIANCA_training_datasets\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-qsiprep\" class=\"anchor\" href=\"#qsiprep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQSIprep\u003c/h3\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eIMPORTANT:\u003c/strong\u003e This step has not been tested extensively. The defaults have been used for almost all options, however these should be checked before using this data in any further analysis.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eQSIprep is a BIDS app that runs preprocessing and reconstruction of DWI data. Only preprocessing is completed here but QSIprep is also an excellent tool to use for further analysis. Visual QC reports are also produced which provide and easy way to check the quality of the DWI data.\u003c/p\u003e\n\u003cp\u003eQSIprep utilises a number of software packages that should be references (as well as the QSIprep citation). Example citation information with references in produced as part of processing and can be found in the \u003ccode\u003elogs\u003c/code\u003e folder of the output.\u003c/p\u003e\n\u003cp\u003eSome steps in QSIprep (particularly eddy current correction and disortion correction with TOPUP) are resource intensive. Currently the pipeline is set to allow QSIprep\u0027s underlying workflow manager (\u003ca href=\"https://nipype.readthedocs.io/en/latest/#\" rel=\"nofollow\"\u003eNipype\u003c/a\u003e) to manage the CPU and RAM usage by detecting how many CPUs are available and using 90% of available RAM (see MultiProc section \u003ca href=\"https://miykael.github.io/nipype_tutorial/notebooks/basic_plugins.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://qsiprep.readthedocs.io/en/latest/index.html\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e, \u003ca href=\"https://www.nature.com/articles/s41592-021-01185-5\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e, Docker \u003ca href=\"https://hub.docker.com/r/pennbbl/qsiprep/\" rel=\"nofollow\"\u003eimage\u003c/a\u003e and info for using with \u003ca href=\"https://qsiprep.readthedocs.io/en/latest/installation.html#singularity-container\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-fmriprep\" class=\"anchor\" href=\"#fmriprep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efMRIprep\u003c/h3\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eIMPORTANT:\u003c/strong\u003e This step has not been tested extensively. The defaults have been used for almost all options, however these should be checked before using this data in any further analysis.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003efMRIprep is another BIDS app for preprocessing fMRI data. As for QSIprep, fMRIprep uses several software packages that should also be referenced. Visual QC reports are also produced.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://fmriprep.org/en/latest/index.html\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e, \u003ca href=\"https://www.nature.com/articles/s41592-018-0235-4\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e, Docker \u003ca href=\"https://hub.docker.com/r/nipreps/fmriprep\" rel=\"nofollow\"\u003eimage\u003c/a\u003e and info for using with \u003ca href=\"https://fmriprep.org/en/latest/installation.html#containerized-execution-docker-and-singularity\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "singularity-hub", - "singularity-container" - ], - "updated_at": 1593117348.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1630381276.0 }, { "data_format": 2, - "description": "Singularity Recipe for High-Performance GEOS-Chem (GCHP)", + "description": "Container for R with libraries for LBNL Energy Technology Area project", "filenames": [ "Singularity" ], - "full_name": "geoschem/Singularity_GCHP", + "full_name": "tin6150/r4eta", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-this-repository-is-obsolete-and-has-been-archived\" class=\"anchor\" aria-hidden=\"true\" href=\"#this-repository-is-obsolete-and-has-been-archived\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTHIS REPOSITORY IS OBSOLETE AND HAS BEEN ARCHIVED\u003c/h2\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1674873388.0 + "updated_at": 1635819130.0 }, { "data_format": 2, - "description": " Build for docker and singularity containers for FMRIQA", + "description": null, "filenames": [ - "Singularity", - "Singularity.4.0.0" + "Singularity" ], - "full_name": "VUIIS/FMRIQA_app", + "full_name": "genomic-medicine-sweden/RareDisease_RNA_workflow", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fmriqa_app\" class=\"anchor\" aria-hidden=\"true\" href=\"#fmriqa_app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFMRIQA_app\u003c/h1\u003e\n\u003cp\u003eThis includes everything required (except for the \"spm12r6225_with_vbm8r435_compiled\" directory and \"FMRIQA_v4_0_0\" compiled MATLAB executable, which are too large to commit) to build a docker and corresponding singularity container for the FMRIQA pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/vuiiscci/fmriqa/tags/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.singularity-hub.org/collections/920\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Instructions:\u003c/h1\u003e\n\u003cp\u003eJust clone and run \u003ccode\u003ebuild.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vuiiscci/FMRIQA_app.git\ncd FMRIQA_app/\n./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE that you must have \"spm12r6225_with_vbm8r435_compiled\" directory and \"FMRIQA_v4_0_0\" compiled MATLAB executable.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Instructions:\u003c/h1\u003e\n\u003cp\u003eFor docker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run --rm \\\n-v $(pwd)/INPUTS/:/INPUTS/ \\\n-v $(pwd)/OUTPUTS:/OUTPUTS/ \\\n--user $(id -u):$(id -g) \\\nvuiiscci/fmriqa\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e \\\n-B INPUTS/:/INPUTS \\\n-B OUTPUTS/:/OUTPUTS \\\nshub://vuiiscci/FMRIQA_app\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-raredisease_rna_workflow\" class=\"anchor\" href=\"#raredisease_rna_workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRareDisease_RNA_workflow\u003c/h1\u003e\n\u003cp\u003enextflow main.nf --help\u003c/p\u003e\n\u003cp\u003erun a single sample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow main.nf -profile singularity --r1 read1.fq.gz --r2 --read2.fq.gz --sample sampleID --output output_directory -c config.conf\n\noptionally, a vcf file may be provided:\n\nnextflow main.nf -profile singularity --samplesheet sample.csv --output output_directory --vcf input.vcf -c config.conf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003erun all samples in a samplesheet:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow main.nf -profile singularity --samplesheet sample.csv --output output_directory -c config.conf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethe samplesheet is a comma-separated file with the following header:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esample,r1,r2,vcf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe sample, r1 and r2 are mandatory, the vcf column may be left empty\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-setup\" class=\"anchor\" href=\"#setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetup\u003c/h1\u003e\n\u003cp\u003eModify the config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereference_dir : specify the folder with all your references \n\nSTAR_ref_dir : the star reference index folder\n\nref :the reference fasta file (dict and fai file required)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe pipeline will automatically download and cache the latest singularity image.\u003c/p\u003e\n\u003cp\u003eAlternatively you can download the singularity collection:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://J35P312/RareDisease_RNA_workflow\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr install all dependencies, as listed in dependencies\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edependencies\u003c/h1\u003e\n\u003cp\u003eWhen using singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow\nsingularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eotherwise:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow\nsamtools\nSTAR\ngatk\nstringtie\npicard\nstar-fusion\nfusioncatcher\nArriba\t\nmultiQC\nfastQC\nBootstrapAnn (https://github.com/J35P312/BootstrapAnn)\nucsc-wigtobigwig\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 9, "topics": [], - "updated_at": 1674914637.0 + "updated_at": 1630424912.0 }, { "data_format": 2, - "description": "Singularity recipe for NMRPipe", + "description": null, "filenames": [ - "Singularity", - "Singularity.212_64" + "Singularity" ], - "full_name": "ResearchIT/NMRPipe", + "full_name": "dcgc-bfx/singularity-sc-rhapsody", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-for-nmrpipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-for-nmrpipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipe for NMRPipe\u003c/h1\u003e\n\u003cp\u003eThis repo contains the recipe to run \u003ca href=\"https://www.ibbr.umd.edu/nmrpipe/\" rel=\"nofollow\"\u003eNMRPipe\u003c/a\u003e\nwithin a \u003ca href=\"https://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, which can be built using \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eVersions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e212_64 - NMRPipe linux212_64 built on centos7.4\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singlecell-sc-rhapsody\" class=\"anchor\" href=\"#singlecell-sc-rhapsody\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esinglecell-sc-rhapsody\u003c/h1\u003e\n\u003cp\u003eDCGC singularity recipe for containerized versions of the BD Rhapsody Targeted Analysis and Whole Transcriptome Analysis (WTA) pipelines (available at \u003ca href=\"https://bitbucket.org/CRSwDev/cwl/src/master/\" rel=\"nofollow\"\u003ehttps://bitbucket.org/CRSwDev/cwl/src/master/\u003c/a\u003e).\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 4, "topics": [], - "updated_at": 1523030864.0 + "updated_at": 1630594642.0 }, { "data_format": 2, - "description": " Build for docker and singularity containers for temporal lobe segmentation", + "description": null, "filenames": [ - "Singularity.3.1.0", "Singularity" ], - "full_name": "VUIIS/Temporal_Lobe_app", + "full_name": "tsgoten/multi-agent-tc", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-temporal_lobe_app\" class=\"anchor\" aria-hidden=\"true\" href=\"#temporal_lobe_app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemporal_Lobe_app\u003c/h1\u003e\n\u003cp\u003eThis includes everything required to build a docker and corresponding singularity container for the Temporal Lobe pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/vuiiscci/temporal_lobe/tags/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.singularity-hub.org/collections/828\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Instructions:\u003c/h1\u003e\n\u003cp\u003eJust clone and run \u003ccode\u003ebuild.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vuiiscci/Temporal_Lobe_app.git\ncd Temporal_Lobe_app/\n./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Instructions:\u003c/h1\u003e\n\u003cp\u003eFor docker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run --rm \\\n-v $(pwd)/INPUTS/:/INPUTS/ \\\n-v $(pwd)/OUTPUTS:/OUTPUTS/ \\\n--user $(id -u):$(id -g) \\\nvuiiscci/temporal_lobe\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e \\\n-B INPUTS/:/INPUTS \\\n-B OUTPUTS/:/OUTPUTS \\\nshub://vuiiscci/Temporal_Lobe_app\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-transactive_control\" class=\"anchor\" href=\"#transactive_control\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etransactive_control\u003c/h1\u003e\n\u003cp\u003eCode meant to support and simulate the Social Game that will be launched in 2020. Elements of transactive control and behavioral engineering will be tested and designed here\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://dvc.org/doc/install\" rel=\"nofollow\"\u003edvc\u003c/a\u003e (with google drive support)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOn linux this is \u003ccode\u003epip install \u0027dvc[gdrive]\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall Docker, if you have not already.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc remote add -d gdrive gdrive://1qaTn6IYd3cpiyJegDwwEhZ3LwrujK3_x\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc pull\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./run.sh\u003c/code\u003e from the root of the repo. This will put you in a shell in the docker container with the \u003ccode\u003erl_algos/logs\u003c/code\u003e directory mounted\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 StableBaselines.py sac test_experiment\u003c/code\u003e in the docker container to start an experiment with the name \u003ccode\u003etest_experiment\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003etensorboard --logdir rl_algos/logs\u003c/code\u003e from outside the docker container to view the logs\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-12202020\" class=\"anchor\" href=\"#12202020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e12/20/2020\u003c/h3\u003e\n\u003cp\u003eThis repository has been cleaned and updated for use. It contains: (1) The OpenAI gym environment \"OfficeLearn\", in the \"gym-socialgame\" folder, and (2) implementations of Reinforcement learning algorithms in \"rl_algos\" folder. In the \"simulations\" folder are various datasets for setting up and training models associated with the gym simulation.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-912020\" class=\"anchor\" href=\"#912020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e9/1/2020\u003c/h3\u003e\n\u003cp\u003eThe most recent running of the code involves navigating to the rl_algos/ directory, then running the python command for the vanilla version:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac\u003c/p\u003e\n\u003cp\u003eAdding in the planning model can be done with the following flags:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac --planning_steps=10 --planning_model=Oracle --num_steps=10000\u003c/p\u003e\n\u003cp\u003ePlease see transactive_control/gym-socialgame/gym_socialgame/envs for files pertaining to the setup of the environment. The socialgame_env.py contains a lot of the information necessary for understanding how the agent steps through the environment. The reward.py file contains information on the variety of reward types available for testing. agents.py contains information on the deterministic people that we created for our simulation.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gym-socialgame\" class=\"anchor\" href=\"#gym-socialgame\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egym-socialgame\u003c/h3\u003e\n\u003cp\u003eOpenAI Gym environment for a social game.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1592512741.0 + "updated_at": 1630639192.0 }, { "data_format": 2, - "description": null, + "description": "R docker container for scanem", "filenames": [ "Singularity" ], - "full_name": "thehyve/singularity-jupyter", + "full_name": "jacobhepkema/scanem-r", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-jupyter\u003c/h1\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://quay.io/repository/jacobhepkema/scanem-r\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/85a1c7b34a5e0ff0bab3c5a2d59f5bdb663afbcd0fecbe64eeaea4d3cb247771/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6a61636f626865706b656d612f7363616e656d2d722f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/jacobhepkema/scanem-r/status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-scanem-r\" class=\"anchor\" href=\"#scanem-r\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escanem-r\u003c/h1\u003e\n\u003cp\u003eR docker/singularity container for scanem. Docker container on quay.io (see above), singularity container at \u003ccode\u003eshub://jacobhepkema/scanem-r:latest\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1674903596.0 + "updated_at": 1630677641.0 }, { "data_format": 2, - "description": "This is a github MIRROR of the main ocellaris repo on bitbucket (https://bitbucket.org/ocellarisproject/ocellaris). NO pull request or issues should go to this repo, please! This repository is only here to support Singularity Hub which lacks bitbucket support. The code in this repository may be severely out of date! It is synced with bitbucket manually and may be months or years behind!", + "description": "D\u00e9mo conteneur PRECIS", "filenames": [ - "containers/Singularity" + "Singularity" ], - "full_name": "TormodLandet/Ocellaris", + "full_name": "cclerget/demo-precis", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1553974960.0 + "updated_at": 1494259937.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "IHEC/Singularity.ihec" + "Singularity" ], - "full_name": "pranit123-hub/gemBS", + "full_name": "cclerget/test-wh", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-hidden=\"true\" href=\"#news\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNews\u003c/h1\u003e\n\u003cp\u003eFirst release of gemBS-rs, a complete rewrite of the gemBS pipeline (apart from the mapper) in Rust bringing increased\nstability while maintaining the high performance of gemBS: \u003ca href=\"https://github.com/heathsc/gemBS-rs.git\"\u003ehttps://github.com/heathsc/gemBS-rs.git\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-gembs\" class=\"anchor\" aria-hidden=\"true\" href=\"#gembs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egemBS\u003c/h1\u003e\n\u003cp\u003egemBS is a high performance bioinformatic pipeline designed for highthroughput analysis\nof DNA methylation data from whole genome bisulfites sequencing data\n(WGBS). It combines GEM3, a high performance read aligner and\nbs_call, a high performance variant and methyation caller, into a streamlined and efficient pipeline for\nbisulfite sueqnce analysis.\u003c/p\u003e\n\u003cp\u003eThe manuscript describing the pipeline is available \u003ca href=\"https://www.biorxiv.org/content/early/2017/10/11/201988\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-hidden=\"true\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003egemBS is licensed under GPL.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h2\u003e\n\u003cp\u003eUse \u003ccode\u003egit clone --recursive\u003c/code\u003e to retrieve the complete source code including the code from external projects such as \u003ccode\u003ebs_call\u003c/code\u003e and \u003ccode\u003egem3-mapper\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBefore starting the installation of gemBS, you will need to install\nor check the installation of several packages.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ea) gcc with development libraries\nb) python3, pip3, matplotlib, multiprocess\nc) zlib, lzma, openssl, libcurl, libncurses, wget, pigz\u003c/p\u003e\n\u003cp\u003eIf you are working on a clean (fairly recent) Ubuntu installation, you\ncan install everything required with the followiwg commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get update\nsudo apt-get install -y python3 build-essential git python3-pip wget pigz\nsudo apt-get install -y zlib1g-dev libbz2-dev\nsudo apt-get install -y libncurses5-dev liblzma-dev libssl-dev libcurl4-openssl-dev\npip3 install matplotlib multiprocess\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the gemBS distribution if you haven\u0027t already done so:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS.git\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse python install command:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo install to the standard system location (i.e., so that all users\ncan use gemBS):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e``python3 setup.py install``\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install to the user\u0027s home directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e``python3 setup.py install --user``\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-check-your-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#check-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheck your installation\u003c/h2\u003e\n\u003cp\u003eFor checking your installation follow this\n\u003ca href=\"http://statgen.cnag.cat/gemBS/v3/UserGuide/_build/html/example.html\" rel=\"nofollow\"\u003eworked example\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eDocumentation can be found at\n\u003ca href=\"http://statgen.cnag.cat/gemBS/v3/UserGuide/_build/html/index.html\" rel=\"nofollow\"\u003egemBS\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-hidden=\"true\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChangelog:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e3.5.5 Fix logging bug caused by trimming change in 3.5.3\n3.5.4 Fix bug in the output of strand specific cpg txt files (not\n encode Bed files) where the \u0027C\u0027 entry was not being printed\n3.5.3 Allow for read end specific trimming in bs_call\n3.5.3 Enable range checks and asserts in bs_call all target; add bs_call debug target\n3.5.2 Correct problems with gcc10. Move to htslib/samtools/bcftools version 1.11\n3.5.1 Check if C compiler requires --std=c99 flag for standards conformant behaviour\n3.5.1 Make sure bgzip is copied correctly during installation\n3.5.0 Make bs_call process contig pools from largest to smallest (this change alters the sqlite db format so\n if you have a previously started gemBS run you should (a) remove the .gemBS directory, (b) redo the\n \u0027gemBS prepare\u0027 step to recreate the db file and (3) run \u0027gemBS db-sync\u0027. \n3.5.0 Switch bs_call and snpxtr to use the new dbSNP index format\n3.5.0 Add ability of dbSNP to read the new JSON and VCF dbSNP format files\n that are now used for human and non-human species respectively\n3.5.0 Add multithreading to dbSNP_idx\n3.5.0 Change format of dbSNP index to allow (a) efficient loading\n of SNP data for individual contigs and (b) parallel index creation \n3.5.0 Rewrite mextr and snpxtr as standalone tools rather than\n bcftools plugins. Now multithreaded and (relatively) memoryefficient\n3.5.0 Replace bedToBigBed and wigToBigWig to reduce memory usage\n and improve speed\n3.4.5 Fix crash when using the -k (keep-mismatch) flag, and fix rare hangs at end of processing\n3.4.4 Sort input bcf files to bcftools concat stage to ensure reproducibility.\n3.4.4 Add extra sort keys when generating pools to ensure stability of pool membership in the event of multiple contigs\n having the same size\n3.4.3 Remove calculation of the goodness of filter (GOF) as this is expensive, non-standard and unreliable. Removing this\n removes the dependency on GSL.\n3.4.3 Add autodetection of output format to bs_call (unless explicitly specified on the command line)\n3.4.2 Add CRAM support (via make_cram option in configuration file)\n3.4.1 Add benchmark-mode that does not write date or program version numbers into SAM/BAM or VCF/BCF files\n Switch to samtools, bcftools and htslib v1.10\n3.4.0 Move to new bs_call version (2.1.0) which is more efficient\n in memory use and can read BAMs and write BCFs natively.\n The new bs_call requires a faidx indexed reference, so gemBS\n no creates this during indexing.\n3.4.0 Add switches to give more control to threads and memory\n usage in mapping and calling stages\n3.3.3 Remove legacy pathway for config files with no header line (fix issue \u0027error in gemBS index #65)\n3.3.2 Fix error where header line for wig files could be omitted\n3.3.2 Fix generation of non_cpg files\n3.3.1 Fix Attribute error bug due to not checking if conversion is a list\n3.3.0 Make new release for IHEC\n3.3.0 Switch conversion default in IHEC_standard configuration to 0.01,0.05 rather than auto, which can give odd results if conversion controls not present or not working correctly\n3.3.0 Fix bug where conversion parameters could be ignored\n3.2.13 Fix formatting bug in mextr with multiple samples (not triggered in normal gemBS use)\n3.2.12 Ensure that conversion statistics are correctly calculated for non-stranded or reverse conversion protocols\n3.2.11 Introduce reverse_conversion option for mapping where read 1 is G2A converted and read 2 is C2T converted\n3.2.10 Correct regex patch for single end reads\n3.2.9 Update Singularity and Dockerfile recipes to allow kemp utils to be built correctly\n3.2.9 Make setup.py and gemBS/commands.py read the version information from gemBS/version.py, so ensuring consistency\n3.2.9 Fix bug added in last version where options in config file were not being taken into account\n3.2.8 Fix mis specification errors in long options for mextr. \n3.2.8 Fix bug where mextr (methyl extract plugin for bcftools) would crash if cpg output option was not set.\n3.2.7 Apply patches for bugs in handling single end reads (suggested by I. Moghul)\n3.2.7 Changed regex for filenames to make it more general (suggested by I. Moghul)\n3.2.7 Fixed bug (reported by chhylp123) where zero arguments to some options were being ignored\n3.2.6 Cleaned up compilation and cleaning of gemBS tools\n3.2.6 Fixed python error if either the over conversion reference sequence was not defined\n3.2.6 Added check in bs_call that conversion parameters are valid (between 0 and 1)\n3.2.6 Perform more stringent sanity checking on conversion vaalues when autocomputed by gemBS\n3.2.6 Use --diasble-lzma configuration flag for samtools and bcftools as we don\u0027t need it and it removes an unneccesary dependency\n3.2.6 Add install options --disable-cuda (on by default) and --enable-cuda that affect GEM3 comppilation\n3.2.6 Bug fix with incorrect handling of duplicate reads\n3.2.5 Minor bug fix - correct error with non-paired end non-bisulfite reads\n3.2.4 Modify the bisulfite processing in gem-mapper to be more efficient (in particular for the non-stranded option)\n3.2.4 Modify gemBS to use the new conversion options for gem-mapper\n3.2.4 Switch gem-mapper to use option --underconversion-sequence and --overconversion-sequence rather than --underconversion_sequence to be consistent with other options\n3.2.3 Fixed bug if conversion parameters were not set\n3.2.2 Rework non-stranded mode so that both possible conversions are tried and the results merged\n3.2.2 Fix bug where non-stranded flag was not being passed to mapper in paired end mode\n3.2.1 Move warning message from bscall from stdout to stderr\n3.2.1 Switch Singularity build to use Ubuntu 16.04 rather than 18.04 to allow the image to work in CentOS 6 (Docker build changed as well to keep the two in sync)\n3.2.1 Fix undeclared variable bugs and missing --ignore-deps option in merge-bcfs\n3.2.1 Add default for dbSNP_index if dbSNP_files is set\n3.2.1 Add gsl-path install option\n3.2.0 Make new release\n3.1.0 Make installation process more modular. Allow for sub-installs\n3.1.0 Add support for reading config from ${index_dir}/gemBS.json if it exists\n3.1.0 Add --reference-bias option to mextr and gemBS extract\n3.1.0 Add support for non-bisulfite mapping of individual datasets\n3.1.0 Allow white space in variable values\n3.1.0 Allow fallback to gzip if pigz not present\n3.1.0 Add --dry-run, --json, --ignore-db and --ignore-dep to extract command\n3.1.0 Add --ignore-dep option to call and merge-bcfs commands\n3.1.0 Add SNP extraction function to extract command\n3.0 Make v3.0 release\n3.0 Merge with master branch.\n3.0 Bump samtools sort memory limit to 2G\n3.0 Add extra_references option for reference generation\n3.0 Allow input files to mapping to be shell commands\n3.0 Add links to documentation\n3.0 Upload new yeast example and add documentation\n3.0 Add --dir option to gemBS\n3.0 Add --ignore-db options for --dry-run / --json\n3.0 Add --json output option for dry runs\n3.0 Update help text to match new functions\n3.0 Introduce standard analysis configurations stored within distribution\n3.0 Switch gem3-mapper distribution to gembs branch on official gem3-mapper repo\n3.0 Removal of incomplete files and roll back of db in the event of pipeline failure\n3.0 Automatic removal of individual BAMs and BCFs after successful merging\n3.0 Prevent pipelines hanging in event of failure\n3.0 Generate ENCODE bed and bigbed files\n3.0 Switch to python 3\n3.0 Switch to mextr for BCF filtering\n3.0 Include fetch and build of samtools / bcftools during build process\n3.0 Add dry-run capability to map and call commands\n3.0 Introduce contig pools to automatically group small contigs\n3.0 Automatic generation of contig.size files from index build\n3.0 Allow use of in memory sqlite3 db as an option\n3.0 Allow multiple instances of gemBS (possible on different hosts) to work \n simultaneously on the same analysis\n3.0 Reduce and simply commands\n3.0 Add Dockerfile\n3.0 Add multi-threading and multi-processing options for most commands\n3.0 Use sqlite3 to track progress of analyses, file paths etc.\n3.0 Added more flexible configuration options (new csv format + new configuration file)\n3.0 Remove test dataset from distribution (distribute from web site)\n2.1.0 Ensure commands run during pipeline come from installation\n2.1.0 Added Singularity build recipe\n2.1.0 Add new command gemBS direct-mapping\n2.1.0 Fixed Makefile clean in tools\n2.0.2 Fixed bug related with the percentage of High Quality Variant in Variants summary report.\n2.0.2 Check temporary directory existence.\n2.0.2 Fixed QualityNonRefCpg sample name in png image.\n2.0.2 Fixed mapper issues related with aligning performace.\n2.0.2 Fixed arguments for Under/Over Conversion sequence name in gem3-mapper\n2.0.1 On bscall repository, fixed argument -k about discarded reads that do not form proper pairs.\n2.0 Check tmp folder before starting mapping process.\n2.0 Added Left and Right Trimming optional arguments to gemBS bscall.\n2.0 Added GC Coverage correlation value to BS Call Stats Summary.\n2.0 Fixed error when reporting complete path to not found bam files.\n2.0 Fixed iteration over sampleBams dictionary in MergeAll method.\n2.0 Updated: Avoid redo indexing when merging just one file.\n2.0 Changed conversion formula.\n2.0 Added parameter for dbSNP.\n2.0 Added threads to bscall.\n2.0 Removed CpGs reports. Already done from bscall report.\n2.0 Fixed bs_call makefile for the gcc to be used.\n2.0 New bscall version. Generates JSON report.\n2.0 Removed gemBS options snp-stats,cpg-report,cpg-stats.\n2.0 Added summary report from the bs_call json stats\n2.0 New BSCall Report. From bscall son file generates three types of reports:\n Mapping and Coverage Report\n Bs-Genotypes Calls Report\n Methylation Statistics report\n1.7 Added non stranded read conversion parameter\n1.7 Fixed SE crash when estimating overlapped bases.\n1.7 Fixed gem-index (gem3) to follow fastq and SAM specifications. \n Modified gem3-mapper repository external module.\n New external module https://github.com/heathsc/gem3-mapper.git\n1.7 Fixed threads parameter to samtools merge\n1.7 Fixed threads parameter to gem-mapper\n1.7 Removed Indels Field on Variants Report.\n1.7 Added Overlapping Bases at Mapping Report\n1.7 Modified Base Counts Overall, removed Base Counts general and Base Counts Overall\n1.7 New Dinucleotide CpGs Report\n New table dinucleotide stats\n New plots for Informative Reads and CpGs\n Methylation levels plots for different types of CpGs\n Summary Table\n1.7 New Readme file to inform about report test\n1.7 New basic statis table for Variants Report\n1.7 Removed parameter -r (reference length) parameter for mapping reports command (gemBS bsMap).\n1.6 New CpGs Density plot, include box plos, bar plot and fitting curve\n1.6 Change name at CpG report:\n \"Heterozygous\" for \"Alternative CX\"\n \"De Novo CpGs Methylation Status\" for \"Non Reference CpGs\"\n \"CpGs with SNP\" for \"SNPs (CX) at Reference CpGs\"\n1.6 CpGs Report Simplified to Q\u0026gt;20\n1.6 BigWig Default parameters for filtering CpG per a given quality and a total number of supported informative reads \n1.5 Initial Release \n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-developers\" class=\"anchor\" aria-hidden=\"true\" href=\"#developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopers\u003c/h2\u003e\n\u003cp\u003egemBS:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarcos Fernandez-Callejo - \u003ca href=\"mailto:marcos.fernandez@cnag.crg.eu\"\u003emarcos.fernandez@cnag.crg.eu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimon Heath - \u003ca href=\"mailto:simon.heath@gmail.com\"\u003esimon.heath@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003egem mapper:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSantiago Marco-Sola - \u003ca href=\"mailto:santiagomsola@gmail.com\"\u003esantiagomsola@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ebisulfite caller and filtering:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimon Heath - \u003ca href=\"mailto:simon.heath@gmail.com\"\u003esimon.heath@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1664976329.0 + "updated_at": 1547670364.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "Singularity.0.4" + "Singularity" ], - "full_name": "Altava/tfd_time", - "latest_release": "0.4", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-temporalfastdownward\" class=\"anchor\" aria-hidden=\"true\" href=\"#temporalfastdownward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemporalFastDownward\u003c/h1\u003e\n\u003cp\u003eThe version of Temporal Fast Downward.\nThis version has been ported to Python3 (tested with Python3.8)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInformation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://gki.informatik.uni-freiburg.de/tools/tfd/\" rel=\"nofollow\"\u003ehttp://gki.informatik.uni-freiburg.de/tools/tfd/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e$ ./build\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003ePatrick Eyerich, Robert Mattm\u00fcller and Gabriele R\u00f6ger.\nUsing the Context-enhanced Additive Heuristic for Temporal and Numeric Planning.\nIn Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS 2009), 2009.\u003c/p\u003e\n", + "full_name": "vigo332/singularity-rstudio-r4", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" href=\"#singularity-rstudio-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity RStudio Server\u003c/h1\u003e\n\u003cp\u003eR 4.0.3\nRStudio 1.3.1903\u003c/p\u003e\n\u003cp\u003eBased on repo \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e\nSingularity image for \u003ca href=\"https://www.rstudio.com/products/rstudio/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e. It was built on top of the base\nSingularity image \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build\" class=\"anchor\" href=\"#build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-rstudio.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-rstudio.simg rstudio.def\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-deploy\" class=\"anchor\" href=\"#deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploy\u003c/h2\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --arch amd64 library://vigo332/default/singularity-rstudio-r4:v0.01\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run\" class=\"anchor\" href=\"#run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" href=\"#rstudio-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRStudio Server\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003erserver\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app rserver singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app rserver singularity-rstudio.simg --help\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecommand-line options:\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003everify:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --verify-installation arg (=0) verify the current installation\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eserver:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-working-dir arg (=/) program working directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-user arg (=rstudio-server) program user\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-daemonize arg (=0) run program as daemon\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-app-armor-enabled arg (=1) is app armor enabled for this session\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-set-umask arg (=1) set the umask to 022 on startup\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-simple-password-authentication\" class=\"anchor\" href=\"#simple-password-authentication\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimple Password Authentication\u003c/h4\u003e\n\u003cp\u003eTo secure the RStudio Server you will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLaunch the container with the environment variable \u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e set to\na password of your choosing.\u003c/li\u003e\n\u003cli\u003eLaunch the \u003ccode\u003erserver\u003c/code\u003e command with the PAM helper script \u003ccode\u003erstudio_auth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAn example is given as:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRSTUDIO_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e singularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path=pam-helper \\\n --server-data-dir=/tmp\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow when you attempt to access the RStudio Server you will be presented with a\nlog in form. You can log in with your current user name and password you set in\n\u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-ldap-authentication----to-be-verified\" class=\"anchor\" href=\"#ldap-authentication----to-be-verified\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLDAP Authentication -- To be verified\u003c/h4\u003e\n\u003cp\u003eAnother option is using an LDAP (or Active Directory) server for\nauthentication. Configuration of the LDAP authentication script \u003ccode\u003eldap_auth\u003c/code\u003e is\nhandled through the following environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_HOST\u003c/code\u003e - the host name of the LDAP server\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_USER_DN\u003c/code\u003e - the formatted string (where \u003ccode\u003e%s\u003c/code\u003e is replaced with the\nusername supplied during log in) of the bind DN used for LDAP authentication\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e - the file containing the CA certificates used by\nthe LDAP server (default: use system CA certificates)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn example for an LDAP server with signed SSL certificate from a trusted CA:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nsingularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn example for an LDAP server with a self-signed SSL certificate:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_CERT_FILE=/ca-certs.pem\nsingularity run \\\n --bind /path/to/ca-certs.pem:/ca-certs.pem \\\n singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that we had to bind mount the CA certificates file from the host machine\ninto the container and specify the container\u0027s path in \u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e (not\nthe host\u0027s path).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-r-and-rscript\" class=\"anchor\" href=\"#r-and-rscript\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR and Rscript\u003c/h3\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e for more information on how to run \u003ccode\u003eR\u003c/code\u003e and\n\u003ccode\u003eRscript\u003c/code\u003e from within this Singularity image.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1665578634.0 + "updated_at": 1631568351.0 }, { "data_format": 2, "description": null, "filenames": [ - "bc3.10-rs125042r362/Singularity", - "bc3.12-r405rs125/Singularity", - "bc3.15-r421tv132rs2022072.576/Singularity" + "rstudio_server_app/Singularity" ], - "full_name": "yh549848/singularity-rstudio-rnaseq", + "full_name": "CHPC-UofU/OOD-pe-apps", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ood-apps\" class=\"anchor\" href=\"#ood-apps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOOD-apps\u003c/h1\u003e\n\u003cp\u003eRepository of CHPC\u0027s PE Open OnDemand apps\u003c/p\u003e\n\u003cp\u003eThis is a repository of interactive apps used at CHPC Protected Environment with Open OnDemand.\u003c/p\u003e\n\u003cp\u003eEach subdirectory has its own README.md which contains information about this particular app.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1665633331.0 + "updated_at": 1631895259.0 }, { "data_format": 2, "description": null, "filenames": [ - "waveunet/Singularity" + "Singularity" ], - "full_name": "bbaysal/BSS", + "full_name": "truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-bss\" class=\"anchor\" aria-hidden=\"true\" href=\"#bss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBSS\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-docker-centos8-warewulf4-builder-\" class=\"anchor\" href=\"#singularity-docker-centos8-warewulf4-builder-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-centos8-warewulf4-builder \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003ewarewulf4 builder container based on a CentOS 8 x86_64 docker image built from github actions\u003c/p\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why\" class=\"anchor\" href=\"#why\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eprovide a minimal builder for warewulf4\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti ghcr.io/truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder:latest rpmbuild -ta warewulf-4.2.0.tar.gz \n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1666637248.0 + "updated_at": 1635198514.0 }, { "data_format": 2, - "description": "OpenHPC recipe for NVIDIA\u0027s container maker", + "description": "Markdown Files to Explain Running anvi\u0027o in Singularity", "filenames": [ - "Singularity.def", - "container-backups/Singularity.def" + "anvio-pangenomics/Singularity" ], - "full_name": "kaisucode/ohpc-container-recipe", + "full_name": "rbartelme/anvio-singularity", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-openhpc-container-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#openhpc-container-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenHPC Container Recipe\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec ohpc-recipe4.simg python /benchmark.py\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003enote: scale down the memory usage in \u003ccode\u003eVagrantfile\u003c/code\u003e if your system can\u0027t support the specified amount (4096)\u003c/p\u003e\n\u003cp\u003eThis is a container recipe for \u003ca href=\"https://github.com/NVIDIA/hpc-container-maker\"\u003eNVIDIA\u0027s HPC container maker\u003c/a\u003e. The base image is \u003ca href=\"https://quay.io/repository/ohpc/ohpc-gnu9\" rel=\"nofollow\"\u003eOpenHPC\u0027s development environment\u003c/a\u003e, with added Python, TensorFlow, and Keras support\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e[share]$ nvcc --version\nnvcc: NVIDIA (R) Cuda compiler driver\nCopyright (c) 2005-2015 NVIDIA Corporation\nBuilt on Tue_Aug_11_14:27:32_CDT_2015\nCuda compilation tools, release 7.5, V7.5.17\u003c/p\u003e\n\u003cp\u003emodule: loading \u0027cuda/7.5.18\u0027\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003etensorflow-2.6.0\ncuDNN 8.1\ncuda 11.2\u003c/p\u003e\n\u003cp\u003ein sbatch script,\nmodule load cuda/11.3.1\nmodule load cudnn/8.1.0\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.tensorflow.org/install/source\" rel=\"nofollow\"\u003ehttps://www.tensorflow.org/install/source\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage examples\u003c/h2\u003e\n\u003ch4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehpccm --recipe ohpc-recipe.py --format docker \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e Dockerfile\ndocker build -t ohpc-recipe -f Dockerfile \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/python_scripts/:/mnt/python_scripts/ -it --rm ohpc-recipe python3.7 /mnt/python_scripts/test.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h4\u003e\n\u003cp\u003eNote: For singularity builds, root access is required. If you are on MacOS or Windows, please check out the instructions \u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html#mac\" rel=\"nofollow\"\u003ehere\u003c/a\u003e on how to use Vagrant to build a Singularity virtual machine\u003c/p\u003e\n\u003cp\u003ehpccm --recipe ohpc-recipe.py --singularity-version=3.8 --format singularity \u0026gt; Singularity.def\nversion 3.8 for multistage\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehpccm --recipe ohpc-recipe.py --format singularity \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e Singularity.def\nsudo singularity build ohpc-recipe.simg Singularity.def\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --nv ohpc-recipe.simg python3 \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/python_scripts/benchmark.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn alternate solution is to build using Docker, then rebuild as singularity\n\u003ccode\u003esingularity build ohpc-recipe.simg docker://kevinhsuk/ohpc-recipe\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-running-anvio-in-singularity-containers\" class=\"anchor\" href=\"#running-anvio-in-singularity-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning anvi\u0027o in Singularity containers\u003c/h1\u003e\n\u003cp\u003eRyan Bartelme, PhD\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-preparation\" class=\"anchor\" href=\"#preparation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparation\u003c/h2\u003e\n\u003cp\u003eIf you want to test anvi\u0027o on an HPC system, here are a few strategies:\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pulling-anvio-docker-image-into-singularity\" class=\"anchor\" href=\"#pulling-anvio-docker-image-into-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePulling anvi\u0027o docker image into Singularity\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eStart by using singularity to pull the latest version of the anvi\u0027o image from dockerhub:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull docker://meren/anvio\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAfter seeing the standard output of the docker pull command, Singularity will print out something like:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eINFO: Creating SIF file...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAnd the \u003ccode\u003e*.sif\u003c/code\u003e file should appear in the directory:\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls\nanvio_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThe latest docker image of anvi\u0027o will \u003cstrong\u003eNOT\u003c/strong\u003e have the databases configured. This is also an opportune time to create your own customized docker image from the \u003ccode\u003emeren/anvio:latest\u003c/code\u003e docker image tag.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-making-your-own-dockerfile-to-customize-your-anvio-runtime\" class=\"anchor\" href=\"#making-your-own-dockerfile-to-customize-your-anvio-runtime\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaking your own Dockerfile to customize your anvi\u0027o runtime\u003c/h2\u003e\n\u003cp\u003eSee an example: \u003ca href=\"anvio-dbconfig/Dockerfile\"\u003eDockerfile\u003c/a\u003e this runs through the database configurations for anvi\u0027o. (As of 03-25-21 this does not properly compile the 3d structure db\u0027s)\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuring-anvio-singularity-containers\" class=\"anchor\" href=\"#configuring-anvio-singularity-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguring anvi\u0027o Singularity containers\u003c/h2\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-docker-container-image-customization\" class=\"anchor\" href=\"#docker-container-image-customization\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker container image customization\u003c/h3\u003e\n\u003cp\u003eIn this case I used a \u003ca href=\"anvio-pangenomics/Dockerfile\"\u003eDockerfile\u003c/a\u003e, where I am building off the \u003ccode\u003eanvio-dbconfig\u003c/code\u003e \u003ca href=\"anvio-dbconfig/Dockerfile\"\u003eimage\u003c/a\u003e. The modifications include an installation of \u003ca href=\"https://github.com/kblin/ncbi-genome-download\"\u003encbi-genome-download\u003c/a\u003e using the anvio conda environment \u003ca href=\"https://github.com/rbartelme/anvio-singularity/blob/bacaaec5130fdb188647c4cdac72aaa275e277b8/anvio-pangenomics/Dockerfile#L4\"\u003epip\u003c/a\u003e and setting the \u003ca href=\"anvio-pangenomics/entrypoint.sh\"\u003eentrypoint\u003c/a\u003e to the conda environment of anvio for the docker runtime. Note \u003ca href=\"anvio-pangenomics/profile\"\u003eprofile\u003c/a\u003e is included to make sure the container sources the \u003ccode\u003e.bashrc\u003c/code\u003e for the conda path.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-building-singularity-images\" class=\"anchor\" href=\"#building-singularity-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Singularity images\u003c/h3\u003e\n\u003cp\u003eOur local cluster singularity version:\u003c/p\u003e\n\u003cpre lang=\"[rbartelme@gpu06\"\u003e\u003ccode\u003esingularity-ce version 3.8.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eBuilding from the Docker image above:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e \u003cem\u003eThis required \u003ccode\u003esudo su\u003c/code\u003e on our local cluster, which I have access to, this has not been tested with \u003ccode\u003e--fakeroot\u003c/code\u003e yet.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo su\u003c/code\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity build statement, using Singularity \u003ca href=\"anvio-pangenomics/Singularity\"\u003erecipe\u003c/a\u003e:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity build anvio-pangenomics.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet ownership of Singularity \u003ccode\u003e*.sif\u003c/code\u003e file and set group permissions.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo chown rbartelme:iplant-everyone anvio-pangenomics.sif\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRead up on job scheduling with your HPC\u0027s IT team documentation\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-example-with-slurm-singularity-and-snakemake\" class=\"anchor\" href=\"#example-with-slurm-singularity-and-snakemake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample with SLURM, Singularity, and Snakemake\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-snakemake-workflows-with-singularity\" class=\"anchor\" href=\"#snakemake-workflows-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakemake Workflows with Singularity\u003c/h3\u003e\n\u003cp\u003eAnvi\u0027o has awesome snakemake \u003ca href=\"\"\u003eworkflows\u003c/a\u003e built in! This is the \"end-to-end\" approach for all your HPC or cloud compute needs.\u003c/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-comparative-genomics\" class=\"anchor\" href=\"#comparative-genomics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComparative Genomics\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eExample json input for Comparative Genomics Workflow:\u003c/strong\u003e\u003c/p\u003e\n\u003chr\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1667364910.0 + "updated_at": 1628704470.0 }, { "data_format": 2, - "description": null, + "description": "Multi-Label Multi/Single-Class Image Segmentation", "filenames": [ - "Singularity-mpi.def", - "Singularity-test.def", - "Singularity.def" + "Singularity" ], - "full_name": "lalilalalalu/fuchs-and-local-container", + "full_name": "kbronik2017/Multi_Label_Segmentation_UCL", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/UCLBrain/MSLS/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f9cab98cc8f1052f0c0096b8b462cf9b2280a706b1adc0895d8a4859f3743314/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f55434c427261696e2f4d534c53\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/network\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6c7a087c43d2a65a6ee22ec8ce2e004a29212c4b9619b117f4b2bbabf98a6c5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f55434c427261696e2f4d534c53\" alt=\"GitHub forks\" data-canonical-src=\"https://img.shields.io/github/forks/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/10fab859502fd8c9b24dde937e50fecba7449e94ea057774713238ab3ec754fc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f55434c427261696e2f4d534c53\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d525d837b03e3b7a24b6322fc2197a134fa12e6a96188e5f18d6cd92236aa47/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f55434c427261696e2f4d534c53\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-multi-label-multisingle-class-image-segmentation\" class=\"anchor\" href=\"#multi-label-multisingle-class-image-segmentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-Label Multi/Single-Class Image Segmentation\u003c/h1\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/diag.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/diag.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-publication\" class=\"anchor\" href=\"#publication\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublication\u003c/h1\u003e\n\u003cp\u003eLe Zhang, Ryutaro Tanno, Kevin Bronik, Chen Jin, Parashkev Nachev, Frederik Barkhof, Olga Ciccarelli, and Daniel C. Alexander, Learning to Segment When Experts Disagree, International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI). Springer, Cham, 2020.\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/Miccai_2020_abs.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg align=\"center\" height=\"1000\" src=\"images/Miccai_2020_abs.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\n \n \u003cp\u003eClick here to see full pdf file: \u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/MICCAI_2020.pdf\"\u003eLink to PDF\u003c/a\u003e\u003c/p\u003e\n \n\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-gui-program\" class=\"anchor\" href=\"#running-the-gui-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the GUI Program!\u003c/h1\u003e\n\u003cp\u003eFirst, user needs to install Anaconda \u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003ehttps://www.anaconda.com/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda env create -f conda_environment_Training_Inference.yml \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efinally\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python Training_Inference_GUI.py \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/GUI.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/GUI.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-program-from-the-command-line\" class=\"anchor\" href=\"#running-the-program-from-the-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Program from the command line!\u003c/h1\u003e\n\u003cp\u003eFirst\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen for training\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Training_without_GUI.py [or annotation_network_Training_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efor testing\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Inference_without_GUI.py [or annotation_network_Inference_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-testing-the-program\" class=\"anchor\" href=\"#testing-the-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting the Program!\u003c/h1\u003e\n\u003cp\u003eExamples of Training and Testing subjects can be found in: \u003ca href=\"https://github.com/UCLBrain/MSLS/tree/master/examples\"\u003ehttps://github.com/UCLBrain/MSLS/tree/master/examples\u003c/a\u003e (which will allow users to quickly and easily train and test the program)\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/bin_seg_ex.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/bin_seg_ex.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e..................................................................................................................................................................\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/multi_seg_ex.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/multi_seg_ex.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1667539488.0 + "topics": [ + "segmentation", + "multi-label" + ], + "updated_at": 1628544698.0 }, { "data_format": 2, @@ -9987,567 +9405,550 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28", + "full_name": "caoky8989/LVAD", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-a-miniconda-based--container-with-python-39-with-cudnn-81-cuda-112-with-tensorflow-gpu-28\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-miniconda-based--container-with-python-39-with-cudnn-81-cuda-112-with-tensorflow-gpu-28\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea miniconda based container with python 3.9 with cudnn 8.1 cuda 11.2 with tensorflow-gpu 2.8\u003c/h1\u003e\n\u003cp\u003edocker: \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv oras://ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1667825933.0 + "updated_at": 1628703776.0 }, { "data_format": 2, - "description": null, + "description": "Implements GA-DQN tuner which consists of a genetic algorithm that uses two deep Q-network agents.", "filenames": [ "Singularity" ], - "full_name": "pranavad/tipsytowers", + "full_name": "lhutton1/ga-dqn-tuner", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tipsytowers\" class=\"anchor\" aria-hidden=\"true\" href=\"#tipsytowers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etipsytowers\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-generating-high-performance-code-for-deep-learning-workloads-a-reinforcement-learning-based-approach\" class=\"anchor\" href=\"#generating-high-performance-code-for-deep-learning-workloads-a-reinforcement-learning-based-approach\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerating high-performance code for deep learning workloads: a reinforcement learning based approach.\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003eImplemented as part of a final year dissertation. Should not be considered for production use.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis project aims to apply reinforcement learning to auto-tuning in AutoTVM (part of the TVM machine learning compiler),\nin order to improve the experience of the end user. Currently, reinforcement learning is applied to the GATuner - a genetic algorithm\nthat repeatedly applies elitism, 2-point crossover and mutation to a population. Named \u003cstrong\u003eGA-DQN\u003c/strong\u003e, the new tuner uses two independent\ndeep Q-network (DQN)\u0027s that are applied to crossover and mutation. Crossover is completed by allowing DQN to suggest the point at\nwhich to crossover a gene, while, mutation is completed by allowing DQN to select which detail to randomly mutate. In addition, an evaluation\nframework is provided to assess the performance of GA-DQN.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/assets/ga-dqn-pipeline.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/assets/ga-dqn-pipeline.png\" alt=\"GA-DQN tuning pipeline\" title=\"GA-DQN tuning pipeline\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eTo use the tuner, TVM must be installed and visible within your python environment. Due to needing additional features not available in a released\nversion of TVM, a forked version of TVM is used which applies a small amount debugging code and a fix to the PyTorch front-end parser. A pinned\nversion is also used as TVM is mostly in a development stage and the API\u0027s used are unstable. Consequently, the GA-DQN tuner has only been tested\nwith this specific commit, along with small modifications ontop. The required version can be pulled from git like so:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive https://github.com/lhutton1/tvm.git tvm\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e tvm\ngit checkout autotvm-measure-remote-time\ngit checkout d2452502b9486a7993d9dec3d04e449efdd81cf7\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTVM also requires a number of dependencies such as: Cuda, Python3.6, LLVM, XGBoost (for the XGBTuner) and PyTorch (for the GA-DQN tuner). As such, we recommend using a containerised environment powered by Singularity. Similar to docker, an image must be built from which containers can be run based on the image. First install Singularity, then build the image using a simple script provided:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Singularity\u003c/span\u003e\nsudo wget -O- http://neuro.debian.net/lists/xenial.us-ca.full \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo tee /etc/apt/sources.list.d/neurodebian.sources.list \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\n sudo apt-key adv --recv-keys --keyserver hkp://pool.sks-keyservers.net:80 0xA5D32F012649A5A9 \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\n sudo apt-get update\n \nsudo apt-get install -y singularity-container\n \n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build image\u003c/span\u003e\n./create_image.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFrom this a container can be created and GA-DQN can be run from within this container using the presented shell:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./create_container.sh rl-tuner.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow in the shell, test your container works correctly by attempting to run the evaluation framework help prompt:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython driver.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote: This has been tested on a Ubuntu 18.04 setup and is not guaranteed to work with other operating systems. These scripts have also been tested on the University of Leeds HPC cluster, ARC.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: it is possible to build TVM and install its dependencies from scratch, although this is not recommended due to the number of packages required. The process required should be similar to that provided in \u003ccode\u003ecreate_image.sh\u003c/code\u003e script. However, it is recommended you create a new virtual environment for python in this process.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-rl-tuner\" class=\"anchor\" href=\"#rl-tuner\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRL Tuner\u003c/h2\u003e\n\u003cp\u003eGA-DQN is a tuner that combines advancements in reinforcement learning and the genetic algorithm tuner that currently exists in TVM. Two independent deep Q-network (DQN)\u0027s are used to suggest where to crossover genes and which detail of a gene to mutate.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-ga-tuner\" class=\"anchor\" href=\"#ga-tuner\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGA Tuner\u003c/h2\u003e\n\u003cp\u003eThe GA tuner is code obtained from the open source TVM compiler. It is here for convenience and to allow a small amount of debug code to be added so that it can be evaluated. This work is not my own.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-evaluation-framework-tools\" class=\"anchor\" href=\"#evaluation-framework-tools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvaluation framework (tools)\u003c/h2\u003e\n\u003cp\u003eProvides a series of tools and experiments to quickly test various tuning algorithms in AutoTVM. Use tune and benchmark commands on a series of pre-trained models to evaluate random, genetic algorithm, extreme gradient boost and GA-DQN algorithms. Use the experiment framework to evaluate various aspects of GA-DQN, with graphical monitoring.\u003c/p\u003e\n\u003cp\u003eA command line driver is provided for this framework:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython driver.py -m=tune -c=../config-example.json\npython driver.py -m=benchmark -c=../config-example.json\npython driver.py -m=experiment -c=../config-example.json\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-ga-dqn-pipeline-example\" class=\"anchor\" href=\"#ga-dqn-pipeline-example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGA-DQN pipeline example\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"/assets/ga-dqn-pipeline-example.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/assets/ga-dqn-pipeline-example.png\" alt=\"GA-DQN pipeline example\" title=\"GA-DQN pipeline example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1665153756.0 + "updated_at": 1628544168.0 }, { "data_format": 2, - "description": null, + "description": "nextflow pipeline for cellranger atac 10x analysis and qc", "filenames": [ - "Singularity" + "container/Singularity_sc-atac-10x-builder" ], - "full_name": "psadil/cat12_app", + "full_name": "perllb/ctg-sc-atac-10x", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cat12_app\" class=\"anchor\" aria-hidden=\"true\" href=\"#cat12_app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecat12_app\u003c/h1\u003e\n\u003cp\u003eBundle cat12 as prefect workflow\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install cat12_app\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecat12_app\u003c/code\u003e was created by Patrick Sadil. It is licensed under the terms of the MIT license.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecat12_app\u003c/code\u003e was created with \u003ca href=\"https://cookiecutter.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003e\u003ccode\u003ecookiecutter\u003c/code\u003e\u003c/a\u003e and the \u003ccode\u003epy-pkgs-cookiecutter\u003c/code\u003e \u003ca href=\"https://github.com/py-pkgs/py-pkgs-cookiecutter\"\u003etemplate\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ctg-sc-atac-10x\" class=\"anchor\" href=\"#ctg-sc-atac-10x\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ectg-sc-atac-10x\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-preprocessing-of-10x-chromium-sc-atac-data-with-cellranger\" class=\"anchor\" href=\"#nextflow-pipeline-for-preprocessing-of-10x-chromium-sc-atac-data-with-cellranger\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline for preprocessing of 10x chromium sc-ATAC data with cellranger.\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDesigned to handle multiple projects in one sequencing run (but also works with only one project)\u003c/li\u003e\n\u003cli\u003eSupports mm10 and hg38 references, but can also be run with custom reference genome and annotation (must be added via nextflow.config). See custom genome below.\u003c/li\u003e\n\u003cli\u003eSupports nuclei samples\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUSAGE\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone and build the Singularity container for this pipeline: \u003ca href=\"https://github.com/perllb/ctg-sc-atac-10x/tree/master/container/ctg-sc-atac-10x\"\u003ehttps://github.com/perllb/ctg-sc-atac-10x/tree/master/container/ctg-sc-atac-10x\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit your samplesheet to match the example samplesheet. See section \u003ccode\u003eSampleSheet\u003c/code\u003e below\u003c/li\u003e\n\u003cli\u003eEdit the nextflow.config file to fit your project and system.\u003c/li\u003e\n\u003cli\u003eRun pipeline\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enohup nextflow run pipe-sc-atac-10x.nf \u0026gt; log.pipe-sc-atac-10x.txt \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input-files\" class=\"anchor\" href=\"#input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files\u003c/h2\u003e\n\u003cp\u003eThe following files must be in the runfolder to start pipeline successfully.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSamplesheet (\u003ccode\u003eCTG_SampleSheet.sc-atac-10x.csv\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-samplesheet-requirements\" class=\"anchor\" href=\"#samplesheet-requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSamplesheet requirements:\u003c/h3\u003e\n\u003cp\u003eNote: no header! only the rows shown below, starting with the column names.\nNote: Must be in comma-separated values format (.csv)\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSample_ID\u003c/th\u003e\n\u003cth\u003eindex\u003c/th\u003e\n\u003cth\u003eSample_Project\u003c/th\u003e\n\u003cth\u003eSample_Species\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSi1\u003c/td\u003e\n\u003ctd\u003eSI-GA-D9\u003c/td\u003e\n\u003ctd\u003e2021_012\u003c/td\u003e\n\u003ctd\u003ehuman\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSi2\u003c/td\u003e\n\u003ctd\u003eSI-GA-H9\u003c/td\u003e\n\u003ctd\u003e2021_012\u003c/td\u003e\n\u003ctd\u003ehuman\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSample1\u003c/td\u003e\n\u003ctd\u003eSI-GA-C9\u003c/td\u003e\n\u003ctd\u003e2021_013\u003c/td\u003e\n\u003ctd\u003emouse\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSample2\u003c/td\u003e\n\u003ctd\u003eSI-GA-C9\u003c/td\u003e\n\u003ctd\u003e2021_013\u003c/td\u003e\n\u003ctd\u003emouse\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-the-nf-pipeline-takes-the-following-columns-from-samplesheet-to-use-in-channels\" class=\"anchor\" href=\"#the-nf-pipeline-takes-the-following-columns-from-samplesheet-to-use-in-channels\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe nf-pipeline takes the following Columns from samplesheet to use in channels:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSample_ID\u003c/code\u003e : ID of sample. Sample_ID can only contain a-z, A-Z and \"_\". E.g space and hyphen (\"-\") are not allowed! If \u0027Sample_Name\u0027 is present, it will be ignored.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eindex\u003c/code\u003e : Must use index ID (10x ID) if dual index. For single index, the index sequence works too.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSample_Project\u003c/code\u003e : Project ID. E.g. 2021_033, 2021_192.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSample_Species\u003c/code\u003e : Only \u0027human\u0027/\u0027mouse\u0027/\u0027custom\u0027 are accepted. If species is not human or mouse, set \u0027custom\u0027. This custom reference genome has to be specified in the nextflow config file. See below how to edit the config file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-samplesheet-template\" class=\"anchor\" href=\"#samplesheet-template\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSamplesheet template\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eSamplesheet name \u003ccode\u003eCTG_SampleSheet.sc-atac-10x.csv\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eSample_ID,index,Sample_Project,Sample_Species \nSi1,Sn1,SI-GA-D9,2021_012,human \nSi2,Sn2,SI-GA-H9,2021_012,human \nSample1,S1,SI-GA-C9,2021_013,mouse \nSample2,S23,SI-GA-C9,2021_013,mouse\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline-steps\" class=\"anchor\" href=\"#pipeline-steps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline steps:\u003c/h2\u003e\n\u003cp\u003eCellranger version: cellranger atac v2.0.0\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eDemultiplexing\u003c/code\u003e (cellranger mkfastq): Converts raw basecalls to fastq, and demultiplex samples based on index (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/mkfastq\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/mkfastq\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFastQC\u003c/code\u003e: FastQC calculates quality metrics on raw sequencing reads (\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/a\u003e). MultiQC summarizes FastQC reports into one document (\u003ca href=\"https://multiqc.info/\" rel=\"nofollow\"\u003ehttps://multiqc.info/\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAlign\u003c/code\u003e + \u003ccode\u003eCounts\u003c/code\u003e (cellranger count): Aligns fastq files to reference genome, counts genes for each cell/barcode, perform secondary analysis such as clustering and generates the cloupe files (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAggregation\u003c/code\u003e (cellranger aggr): Automatically creates the input csv pointing to molecule_info.h5 files for each sample to be aggregated and executes aggregation (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/aggr\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/aggr\u003c/a\u003e). This is only run if there is more than one sample pr project.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCellranger count metrics\u003c/code\u003e (bin/ctg-sc-count-metrics-concat.py): Collects main count metrics (#cells and #reads/cell etc.) from each sample and collect in table\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emultiQC\u003c/code\u003e: Compile fastQC and cellranger count metrics in multiqc report\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emd5sum\u003c/code\u003e: md5sum of all generated files\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ectg-PROJ_ID-output\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eqc\u003c/code\u003e: Quality control output.\n\u003cul\u003e\n\u003cli\u003ecellranger metrics: Main metrics summarising the count / cell output\u003c/li\u003e\n\u003cli\u003efastqc output (\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003emultiqc output: Summarizing FastQC output and demultiplexing (\u003ca href=\"https://multiqc.info/\" rel=\"nofollow\"\u003ehttps://multiqc.info/\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efastq\u003c/code\u003e: Contains raw fastq files from cellranger mkfastq.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecount-cr\u003c/code\u003e: Cellranger count output. Here you find gene/cell count matrices, secondary analysis output, and more. See (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\u003c/a\u003e) for more information on the output files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esummaries\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eweb-summary files which provide an overview of essential metrics from the 10x run.\u003c/li\u003e\n\u003cli\u003ecloupe files which can be used to explore the data interactively in the Loupe browser (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/visualization/latest/what-is-loupe-cell-browser\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/visualization/latest/what-is-loupe-cell-browser\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eaggregate\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eOutput from cellranger aggregation. This is only run if there is more than one sample pr project.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ectg-md5.PROJ_ID.txt\u003c/code\u003e: text file with md5sum recursively from output dir root\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-container\" class=\"anchor\" href=\"#container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-custom-genome\" class=\"anchor\" href=\"#custom-genome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustom genome\u003c/h2\u003e\n\u003cp\u003eIf custom genome (not hg38 or mm10) is used\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSet \"Sample_Species\" column to \u0027custom\u0027 in samplesheet:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSample_ID\u003c/th\u003e\n\u003cth\u003eSample_Name\u003c/th\u003e\n\u003cth\u003eindex\u003c/th\u003e\n\u003cth\u003eSample_Project\u003c/th\u003e\n\u003cth\u003eSample_Species\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSi1\u003c/td\u003e\n\u003ctd\u003eSn1\u003c/td\u003e\n\u003ctd\u003eSI-GA-D9\u003c/td\u003e\n\u003ctd\u003eproj_2021_012\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003ecustom\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSi2\u003c/td\u003e\n\u003ctd\u003eSn2\u003c/td\u003e\n\u003ctd\u003eSI-GA-H9\u003c/td\u003e\n\u003ctd\u003eproj_2021_012\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003ecustom\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eIn nextflow.config, set\n\u003ccode\u003ecustom_genome=/PATH/TO/CUSTOMGENOME\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-add-custom-genes-eg-reporters-to-cellranger-annotation\" class=\"anchor\" href=\"#add-custom-genes-eg-reporters-to-cellranger-annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdd custom genes (e.g. reporters) to cellranger annotation\u003c/h3\u003e\n\u003cp\u003eYou can use this script to add custom genes to the cellranger ref\n\u003ca href=\"https://github.com/perllb/ctg-cellranger-add2ref\"\u003ehttps://github.com/perllb/ctg-cellranger-add2ref\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003enextflow version 19.04.1.5072\u003c/li\u003e\n\u003cli\u003eSingularity (v 3.7.0-1.el7)\u003c/li\u003e\n\u003cli\u003ejava (openjdk version \"10.0.2\" 2018-07-17)\u003c/li\u003e\n\u003cli\u003eOpenJDK Runtime Environment Zulu10.3+5 (build 10.0.2+13)\u003c/li\u003e\n\u003cli\u003eOpenJDK 64-Bit Server VM Zulu10.3+5 (build 10.0.2+13, mixed mode)\u003c/li\u003e\n\u003cli\u003eSingularity container (\u003ca href=\"https://github.com/perllb/ctg-sc-atac-10x/blob/main/container/Singularity_sc-atac-10x-builder\"\u003ehttps://github.com/perllb/ctg-sc-atac-10x/blob/main/container/Singularity_sc-atac-10x-builder\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCellranger 10x ATAC or ARC references (e.g. refdata-cellranger-arc-GRCh38-2020-A-2.0.0 and refdata-cellranger-arc-mm10-2020-A-2.0.0)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1668356057.0 + "updated_at": 1629907530.0 }, { "data_format": 2, - "description": null, + "description": "Code and scripts for the bluebird bio technical exam", "filenames": [ - "controllers/PythonBlocks/downward/misc/releases/19.12/Singularity.19.12", - "controllers/PythonBlocks/downward/misc/releases/21.12/Singularity.21.12", - "controllers/PythonBlocks/downward/misc/releases/20.06/Singularity.20.06", - "controllers/PythonBlocks/downward/misc/releases/22.06/Singularity.22.06", - "controllers/PythonBlocks/downward/misc/releases/latest/Singularity", - "controllers/PythonBlocks/downward/misc/releases/19.06/Singularity.19.06" + "question_1/RNAseq_DE_analysis/environments/Singularity" ], - "full_name": "dylankrieg/block-stacking", + "full_name": "esha-joshi/bluebird_bio_exam", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bluebird_bio_exam\" class=\"anchor\" href=\"#bluebird_bio_exam\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebluebird_bio_exam\u003c/h1\u003e\n\u003cp\u003eCode and scripts for the bluebird bio technical exam taken on 2021-07-21\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-question_1\" class=\"anchor\" href=\"#question_1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003equestion_1\u003c/h2\u003e\n\u003cp\u003eThis directory contains the Nextflow file, Singularity config files, R script for DE analysis and additional bash scripts for pre-processing for the implementation to analyze the cancer cell-lines. There is README describing the software requirements, dependencies and running of the program as well.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-question_2\" class=\"anchor\" href=\"#question_2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003equestion_2\u003c/h2\u003e\n\u003cp\u003eThis directory contains the R script for making the SQL queries to the UCSC database to generate a BED file for BRCA1 and BRCA2.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1668580222.0 + "updated_at": 1628654340.0 }, { "data_format": 2, - "description": "Resource monitor that shows usage and stats for processor, memory, disks, network and processes.", + "description": "R and bioinformatic packages Singularity container", "filenames": [ - "1.0.68/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-bpytop", - "latest_release": "v1.0.68", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bpytop/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bpytop/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bpytop/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bpytop/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6086d29389cadd3479a8523980b1ec9dd37bee0ee7f391f6e84294e38555ec6b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627079746f70\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6086d29389cadd3479a8523980b1ec9dd37bee0ee7f391f6e84294e38555ec6b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627079746f70\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bpytop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1dcdee2b45c87cb810de1b868a733f10f65a3a4a6ccf69522c58b89d6da6cae2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627079746f70\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1dcdee2b45c87cb810de1b868a733f10f65a3a4a6ccf69522c58b89d6da6cae2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627079746f70\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bpytop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/33f6a11260743e3ce7aaa7df6f4f4605ae789e6bac5ed0488e1e3b17338a5e64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627079746f70\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33f6a11260743e3ce7aaa7df6f4f4605ae789e6bac5ed0488e1e3b17338a5e64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627079746f70\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bpytop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c6693f25e42eaf97446bdc6be30ff9d42dab77028422ec377407a7c3f33c4635/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627079746f70\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6693f25e42eaf97446bdc6be30ff9d42dab77028422ec377407a7c3f33c4635/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627079746f70\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bpytop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-bpytop\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-bpytop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bpytop\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for bpytop.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebpytop\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bpytop/1.2.13\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bpytop\u003c/code\u003e as \u003ccode\u003e1.2.13.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "sylvainschmitt/singularity-r-bioinfo", + "latest_release": "0.0.3", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-r-and-bioinformatic-packages-singularity-container\" class=\"anchor\" href=\"#r-and-bioinformatic-packages-singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR and bioinformatic packages Singularity container\u003c/h1\u003e\n\u003cp\u003eSylvain Schmitt\nAugust 6, 2021\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eR and bioinformatic packages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis container includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e 4.0.3\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etidyverse\u003c/code\u003e 1.3.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBiostrings\u003c/code\u003e 2.58.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evcfR\u003c/code\u003e 1.12.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evroom\u003c/code\u003e 1.3.2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecsv2sql\u003c/code\u003e 0.1.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ereshape2\u003c/code\u003e 1.4.4\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003etidyverse\u003c/code\u003e is an opinionated collection of R packages designed for\ndata science. All packages share an underlying design philosophy,\ngrammar, and data structures.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://www.tidyverse.org/\" rel=\"nofollow\"\u003ehttps://www.tidyverse.org/\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eBiostrings\u003c/code\u003e is a memory efficient string containers, string matching\nalgorithms, and other utilities, for fast manipulation of large\nbiological sequences or sets of sequences.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://bioconductor.org/packages/release/bioc/html/Biostrings.html\" rel=\"nofollow\"\u003ehttps://bioconductor.org/packages/release/bioc/html/Biostrings.html\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eThe R package \u003ccode\u003evcfR\u003c/code\u003e is a set of tools designed to read, write,\nmanipulate and analyze VCF data.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://knausb.github.io/vcfR_documentation/\" rel=\"nofollow\"\u003ehttps://knausb.github.io/vcfR_documentation/\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003evroom\u003c/code\u003e is the fastest delimited reader for R, 1.23 GB/sec.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://vroom.r-lib.org/\" rel=\"nofollow\"\u003ehttps://vroom.r-lib.org/\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ecsv2sql\u003c/code\u003e is a wrapper to convert csv files to sql database.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://github.com/kcf-jackson/csv2sql\"\u003ehttps://github.com/kcf-jackson/csv2sql\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ereshape2\u003c/code\u003e is an R package written by Hadley Wickham that makes it easy\nto transform data between wide and long formats.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://seananderson.ca/2013/10/19/reshape/\" rel=\"nofollow\"\u003ehttps://seananderson.ca/2013/10/19/reshape/\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eSingularity container based on the recipe:\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-r-bioinfo/blob/main/Singularity\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eImage singularity (V\u0026gt;=3.3) is automatically test and built and pushed\non the registry using\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/test.yml\"\u003etest.yml\u003c/a\u003e\n\u0026amp;\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/builder.yml\"\u003ebuilder.yml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ebuild\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build Biostrings.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003epull\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull https://github.com/sylvainschmitt/singularity-r-bioinfo/releases/download/0.0.3/sylvainschmitt-singularity-r-bioinfo.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esnakemake\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s1\"\u003esingularity\u003c/span\u003e: \n \u003cspan class=\"pl-s\"\u003e\"https://github.com/sylvainschmitt/singularity-r-bioinfo/releases/download/0.0.3/sylvainschmitt-singularity-r-bioinfo.latest.sif\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, + "topics": [], + "updated_at": 1628683690.0 + }, + { + "data_format": 2, + "description": "Examples of Dockerfiles and Singularity recipes", + "filenames": [ + "python-env/Singularity" + ], + "full_name": "kaczmarj/container-examples", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-container-examples\" class=\"anchor\" href=\"#container-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer examples\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-build-with-docker\" class=\"anchor\" href=\"#build-with-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild with docker\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-bedtoolsdockerfile\" class=\"anchor\" href=\"#bedtoolsdockerfile\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebedtools.Dockerfile\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag bedtools --file bedtools.Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-condadockerfile\" class=\"anchor\" href=\"#condadockerfile\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econda.Dockerfile\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag conda --file conda.Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-jupyter-notebook\" class=\"anchor\" href=\"#running-jupyter-notebook\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erunning jupyter notebook\u003c/h3\u003e\n\u003cp\u003eplease note that we set \u003ccode\u003e--ip 0.0.0.0\u003c/code\u003e. and we need to publish the port from the\ncontainer onto the host. otherwise, the port is only accessible inside the container\nand will not be seen by our web browser (which is outside of the container).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it --publish 8888:8888 conda --port 8888 --ip 0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tensorflow24dockerfile\" class=\"anchor\" href=\"#tensorflow24dockerfile\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etensorflow24.Dockerfile\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensorflow:2.4 --file tensorflow24.Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-python-env\" class=\"anchor\" href=\"#python-env\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epython-env\u003c/h2\u003e\n\u003cp\u003ethis is an example of building a docker image for a python environment. that directory\nincludes a \u003ccode\u003erequirements.txt\u003c/code\u003e file, which lists dependencies. we copy that file into\nthe docker image when it is being built, and we install the python packages listed\nthere.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag mypyenv python-env\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-build-with-singularity\" class=\"anchor\" href=\"#build-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild with singularity\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-python-env-1\" class=\"anchor\" href=\"#python-env-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epython-env\u003c/h2\u003e\n\u003cp\u003ethis example builds a singularity image of \u003ccode\u003epython-env\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd python-env\nsudo singularity build python-env.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample of running the image. arguments after the image name are passed to the\nentrypoint. because our entrypoint is \u003ccode\u003epython\u003c/code\u003e, the command-line arguments are passed\nto that.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run python-env.sif -c \u0027import numpy; print(numpy.__version__)\u0027\n1.21.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand here\u0027s an example to show that users stay themselves in containers...\u003c/p\u003e\n\u003cp\u003eremember, just be yourself.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec python-env.sif whoami\njakub\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethis is not the case in docker. you need \u003ccode\u003esudo\u003c/code\u003e to run the containers, so inside the\ncontainer, you can be root. this is not ideal, especially on shared clusters.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [], + "updated_at": 1628778703.0 + }, + { + "data_format": 2, + "description": "The FASTX-Toolkit is a collection of command line tools for Short-Reads FASTA/FASTQ files preprocessing.", + "filenames": [ + "0.0.14/Singularity" + ], + "full_name": "pscedu/singularity-fastx-toolkit", + "latest_release": null, + "stargazers_count": 0, + "subscribers_count": 2, "topics": [ "singularity", - "utilities" + "bioinformatics" ], - "updated_at": 1670890527.0 + "updated_at": 1628888079.0 }, { "data_format": 2, - "description": "Docker and Singularity images to run Biodiverse software", + "description": "Command line ASCII boxes unlimited!", "filenames": [ - "SingularityDef.def", - "SingularityDef_NoPerlbrew.def" + "1.3/Singularity" ], - "full_name": "vmikk/biodiverse-docker", - "latest_release": "v.1.0.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-biodiverse\" class=\"anchor\" aria-hidden=\"true\" href=\"#biodiverse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBiodiverse\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/vmikk/biodiverse-docker/blob/main/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1491d736cc21d494e4262c1cd8e116d4f865ff2f4bd64a2d79fa990778e324c8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f766d696b6b2f62696f646976657273652d646f636b6572\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/vmikk/biodiverse-docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vmikk/biodiverse\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ae1010d045b7a869f8b06b818b364a2ec0227e7f3d7fe3ab8cb4f280c386b732/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f486f737465642d446f636b65724875622d626c7565\" alt=\"Hosted_DockerHub\" data-canonical-src=\"https://img.shields.io/badge/Hosted-DockerHub-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/vmiks/gbif/biodiverse\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe9171fa5097d0f35af6c0988f42c6d6571880fc954aea1ee3a4259dc7603ae8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f486f737465642d53696e67756c61726974794c6962726172792d626c7565\" alt=\"Hosted_SingularityLibrary\" data-canonical-src=\"https://img.shields.io/badge/Hosted-SingularityLibrary-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains definition files the \u003ca href=\"https://shawnlaffan.github.io/biodiverse/\" rel=\"nofollow\"\u003eBiodiverse\u003c/a\u003e (Laffan et al., 2010) containers.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-docker-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker image\u003c/h1\u003e\n\u003cp\u003eTo build the \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image with Biodiverse run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag biodiverse --file Dockerfile_NoPerlbrew . \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eDockerfile_NoPerlbrew\u003c/code\u003e should be present in the current directory.\u003c/p\u003e\n\u003cp\u003eA ready-to-use image is available at \u003ca href=\"https://hub.docker.com/r/vmikk/biodiverse\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull vmikk/biodiverse:1.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity image\u003c/h1\u003e\n\u003cp\u003eTo build the \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image with Biodiverse run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build Biodiverse.sif SingularityDef_NoPerlbrew.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eSingularityDef_NoPerlbrew.def\u003c/code\u003e should be present in the current directory.\u003c/p\u003e\n\u003cp\u003eA ready-to-use image is available at the \u003ca href=\"https://cloud.sylabs.io/library/vmiks/gbif/biodiverse\" rel=\"nofollow\"\u003eSingularity Library\u003c/a\u003e and can be downloaded with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --arch amd64 library://vmiks/gbif/biodiverse:1-0-0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h1\u003e\n\u003cp\u003eLaffan SW, Lubarsky E, Rosauer DF (2010) Biodiverse, a tool for the spatial analysis of biological and related diversity. Ecography, 33: 643-647. \u003ca href=\"https://onlinelibrary.wiley.com/doi/10.1111/j.1600-0587.2010.06237.x\" rel=\"nofollow\"\u003eDOI: 10.1111/j.1600-0587.2010.06237.x\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "icaoberg/singularity-boxes", + "latest_release": "1.3", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-boxes/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-boxes/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-boxes/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-boxes/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a3f198a87deb5330effce45fa5f8a588e4f8f261b175507874e098ebd9458adf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d626f786573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3f198a87deb5330effce45fa5f8a588e4f8f261b175507874e098ebd9458adf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d626f786573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-boxes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/6d68d10803a8f101a59999b34c1be8b23084c82097bd1506f6b69eca3546ed7b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d626f786573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6d68d10803a8f101a59999b34c1be8b23084c82097bd1506f6b69eca3546ed7b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d626f786573\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-boxes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/699ba19f5376b37177283fe5acc1e075c4a846491fedbbedc3fc349b3693bc30/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d626f786573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/699ba19f5376b37177283fe5acc1e075c4a846491fedbbedc3fc349b3693bc30/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d626f786573\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-boxes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0e74e828eef6e3d5d7627cf27223de60469083957af4cdf436c4b767a7e870d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d626f786573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e74e828eef6e3d5d7627cf27223de60469083957af4cdf436c4b767a7e870d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d626f786573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-boxes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-boxes\" class=\"anchor\" href=\"#singularity-boxes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-boxes\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://boxes.thomasjensen.com/\" rel=\"nofollow\"\u003eboxes\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [ - "biodiversity", - "docker", - "endemism", - "phylogenetic-diversity", - "singularity" + "singularity", + "utilities" ], - "updated_at": 1650613175.0 + "updated_at": 1631084542.0 }, { "data_format": 2, - "description": "Pulsar Timing Environments", + "description": "The ViennaRNA Package consists of a C code library and several stand-alone programs for the prediction and comparison of RNA secondary structures.", "filenames": [ - "containers/Singularity" + "2.4.14/Singularity" ], - "full_name": "ipta/pulsar-env", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/IPTA/pulsar-env/actions/workflows/test_CondaEnv.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/IPTA/pulsar-env/actions/workflows/test_CondaEnv.yml/badge.svg\" alt=\"Conda Env Test\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/IPTA/pulsar-env/actions/workflows/test_Singularity.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/IPTA/pulsar-env/actions/workflows/test_Singularity.yml/badge.svg\" alt=\"Apptainer Build (Ubuntu)\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-pulsar-timing-environments\" class=\"anchor\" aria-hidden=\"true\" href=\"#pulsar-timing-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePulsar Timing Environments\u003c/h1\u003e\n\u003cp\u003eThis repository offers a centeralized location for the IPTA Pulsar Timing \u0026amp; Data Combination Teams\u0027 environment.\u003c/p\u003e\n\u003cp\u003eCurrently, this repository presents the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn Anaconda Environment for Pulsar Science (\u003ccode\u003eanaconda_env.yml\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eSingularity/Apptainer Container for HPC Resources (\u003ccode\u003econtainers/Singularity\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-of-the-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-of-the-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation of the Conda Environment\u003c/h2\u003e\n\u003cp\u003ePlease note, we highly encourage using a fresh install of \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003eMambaforge\u003c/a\u003e or \u003ca href=\"https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html\" rel=\"nofollow\"\u003eMicroMamba\u003c/a\u003eover a default install of Anaconda/Miniconda. If you must use an Anaconda/Miniconda installation, from a fresh environment install the \u003ca href=\"https://github.com/mamba-org/mamba\"\u003eMamba Environment \u0026amp; Package Handler\u003c/a\u003e via \u003ccode\u003econda install -c conda-forge mamba\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e As of \u003ccode\u003econda\u003c/code\u003e version 22.11, \u003ccode\u003elibmamba\u003c/code\u003e can be used as a solver to speed up basic Anaconda installs (though there are growing pains). You can find out more \u003ca href=\"https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community\" rel=\"nofollow\"\u003eat the official posting\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo install this environment in your flavor of Anaconda, proceed through the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone this directory: \u003ccode\u003egit clone https://github.com/ipta/pulsar-env.git\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEnter the cloned directory: \u003ccode\u003ecd pulsar-env\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUsing \u003ccode\u003emamba\u003c/code\u003e, install the environment: \u003ccode\u003emamba env create -f anaconda-env.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eActivate the environment: \u003ccode\u003emamba activate IPTA_Env\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-important-note-regarding-the-included-openmpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#important-note-regarding-the-included-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Note Regarding the Included OpenMPI\u003c/h3\u003e\n\u003cp\u003eFor Linux 64, Open MPI is built with CUDA awareness but this support is disabled by default. To enable it, please set the environment variable \u003ccode\u003eOMPI_MCA_opal_cuda_support=true\u003c/code\u003e before launching your MPI processes. Equivalently, you can set the MCA parameter in the command line: \u003ccode\u003empiexec --mca opal_cuda_support 1 ...\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIn addition, the UCX support is also built but disabled by default. To enable it, first install UCX (\u003ccode\u003econda install -c conda-forge ucx\u003c/code\u003e). Then, set the environment variables \u003ccode\u003eOMPI_MCA_pml=\"ucx\"\u003c/code\u003e and \u003ccode\u003eOMPI_MCA_osc=\"ucx\"\u003c/code\u003e before launching your MPI processes. Equivalently, you can set the MCA parameters in the command line: \u003ccode\u003empiexec --mca pml ucx --mca osc ucx ...\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNote that you might also need to set \u003ccode\u003eUCX_MEMTYPE_CACHE=n\u003c/code\u003e for CUDA awareness via UCX. Please consult UCX\u0027s documentation for detail.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-viennarna", + "latest_release": "v2.4.14", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-viennarna/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-viennarna/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/57ccfd894f989623760a4ef3f10260f771a1fc248f5bf2218fa3c1f6a92ed7b7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/57ccfd894f989623760a4ef3f10260f771a1fc248f5bf2218fa3c1f6a92ed7b7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-viennarna\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/edfc9b50cfd2fd753e7bf402f10073da81e6ac134bb5f9d5c154d8ca266689e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/edfc9b50cfd2fd753e7bf402f10073da81e6ac134bb5f9d5c154d8ca266689e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-viennarna\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/db9a3b8ac1a85b49dcb1110778b0b6f49678043557b898cfcf02c5c8ad330245/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db9a3b8ac1a85b49dcb1110778b0b6f49678043557b898cfcf02c5c8ad330245/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-viennarna\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/09f05ae67886e7f3f5559d192fecf3d45a6bb4495adbb67e1891e55a262309d9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/09f05ae67886e7f3f5559d192fecf3d45a6bb4495adbb67e1891e55a262309d9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-viennarna\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-viennarna\" class=\"anchor\" href=\"#singularity-viennarna\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-viennarna\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://rna.tbi.univie.ac.at\" rel=\"nofollow\"\u003eviennarna\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eAnalyseDists\u003c/code\u003e, \u003ccode\u003eAnalyseSeqs\u003c/code\u003e, \u003ccode\u003eKinfold\u003c/code\u003e, \u003ccode\u003eRNA2Dfold\u003c/code\u003e, \u003ccode\u003eRNAaliduplex\u003c/code\u003e, \u003ccode\u003eRNAalifold\u003c/code\u003e, \u003ccode\u003eRNAcofold\u003c/code\u003e, \u003ccode\u003eRNAdistance\u003c/code\u003e, \u003ccode\u003eRNAduplex\u003c/code\u003e, \u003ccode\u003eRNAeval\u003c/code\u003e, \u003ccode\u003eRNAfold\u003c/code\u003e, \u003ccode\u003eRNAforester\u003c/code\u003e, \u003ccode\u003eRNAheat\u003c/code\u003e, \u003ccode\u003eRNAinverse\u003c/code\u003e, \u003ccode\u003eRNALalifold\u003c/code\u003e, \u003ccode\u003eRNALfold\u003c/code\u003e, \u003ccode\u003eRNApaln\u003c/code\u003e, \u003ccode\u003eRNApdist\u003c/code\u003e, \u003ccode\u003eRNAparconv\u003c/code\u003e, \u003ccode\u003eRNAPKplex\u003c/code\u003e, \u003ccode\u003eRNAplex\u003c/code\u003e, \u003ccode\u003eRNAplfold\u003c/code\u003e, \u003ccode\u003eRNAplot\u003c/code\u003e, \u003ccode\u003eRNAsnoop\u003c/code\u003e, \u003ccode\u003eRNAsubopt\u003c/code\u003e, \u003ccode\u003eRNAup\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/viennarna/2.4.14\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/viennarna\u003c/code\u003e as \u003ccode\u003e2.4.14.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, - "topics": [], - "updated_at": 1669905442.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1631407623.0 }, { "data_format": 2, - "description": "libmicropython touch screen OS for nxp mxmrt 1062 and/or a souped up Teensy 4.1", + "description": "Aspera Connect helps you securely move file and folders of any size.", "filenames": [ - "ports/libmicropython/IRIDESCENT/__PYTHONMODULES/music21_deps/pygments-master/tests/examplefiles/singularity/Singularity" + "3.11.0.5/Singularity" ], - "full_name": "8888clockradio/iridescentmicropython", + "full_name": "pscedu/singularity-aspera-connect", "latest_release": null, - "readme": "\u003cp\u003eiridescentmicropython\nANY COMMERCIAL USE OF ANY IRIDESCENT FILES REQUIRES LICENSING contact \u003ca href=\"mailto:george@georgerosar.com\"\u003egeorge@georgerosar.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eplease email \u003ca href=\"mailto:george@georgerosar.com\"\u003egeorge@georgerosar.com\u003c/a\u003e if you want to be a contributer\u003c/p\u003e\n\u003cp\u003eCopyright 2023 George Charles Rosar II\u003c/p\u003e\n\u003cp\u003eTeensy 4.1 should have at least 16MB or more of external RAM soldered into Teensy 4.1 PSRAM pads. Should either be soldered or connected to the Teensy Audio Adapter Card, also Teensy Audio Adapter Card should have an additional 2Gbit of Flash RAM soldered in the Audio Adapter.\u003c/p\u003e\n\u003cp\u003eThe MOST IMPORTANT development issue is getting micropython to recieve and send text to Serial.print() or Serial.read(), mphalport.cpp is not functioning properly.\u003c/p\u003e\n\u003cp\u003einstalling\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir iridescentBUILD; cd iridescentBUILD\ngit clone https://github.com/8888clockradio/iridescentmicropython.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eedit iridescentBUILD/iridescentmicropython/toolchain.mk\u003c/p\u003e\n\u003cp\u003echange LIBPATHFILEDROP, COMPILERPATH, TOOLSPATH and maybe also IS_WINDOWS_TOOLCHAIN_QUESTION_MARK to the proper values. Use absolute paths, works better for the tiered makefile system\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLIBPATHFILEDROP=\"/Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/lib/clang-runtimes/armv7em_hard_fpv5_d16/lib\"\nCOMPILERPATH=\"/Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/bin\"\nTOOLSPATH=\"/Applications/Teensyduino.app/Contents/Java/hardware/tools\"\nCROSSCOMPILEPREFIX = clang\nADDITIONAL_TOOLS = llvm\nCLANG_TOOLCHAIN = --config \"$(COMPILERPATH)/armv7em_hard_fpv5_d16.cfg\"\n\nIS_WINDOWS_TOOLCHAIN_QUESTION_MARK=\n#uncomment below if windows\n#IS_WINDOWS_TOOLCHAIN_QUESTION_MARK=.exe\n\nAR := \"$(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\"\nAS := \"$(COMPILERPATH)/$(ADDITIONAL_TOOLS)-as$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(ASFLAGS)\nCC := \"$(COMPILERPATH)/$(CROSSCOMPILEPREFIX)$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(CLANG_TOOLCHAIN)\nCPP := $(CC) -E\nCXX := \"$(COMPILERPATH)/$(CROSSCOMPILEPREFIX)++$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(CLANG_TOOLCHAIN)\n#GDB = $(CROSS_COMPILE)-gdb\nLD := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ld$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nOBJCOPY := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-objcopy$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nSIZE := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-size$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nSTRIP := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-strip$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nAR := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nar := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto the path of your LLVM clang and clang++ toolchain, download LLVM-embedded-toolchain-for-Arm\n\u003ca href=\"https://github.com/ARM-software/LLVM-embedded-toolchain-for-Arm/releases/download/release-15.0.2/LLVMEmbeddedToolchainForArm-15.0.2-Windows-x86_64.zip\"\u003ehttps://github.com/ARM-software/LLVM-embedded-toolchain-for-Arm/releases/download/release-15.0.2/LLVMEmbeddedToolchainForArm-15.0.2-Windows-x86_64.zip\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewindows\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\n\u003ca href=\"https://github.com/ARM-software/LLVM-embedded-toolchain-for-Arm/releases/download/release-15.0.2/LLVMEmbeddedToolchainForArm-15.0.2-Linux-x86_64.tar.gz\"\u003ehttps://github.com/ARM-software/LLVM-embedded-toolchain-for-Arm/releases/download/release-15.0.2/LLVMEmbeddedToolchainForArm-15.0.2-Linux-x86_64.tar.gz\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elinux\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor (PREFERRED)\n\u003ca href=\"https://github.com/8888clockradio/iridescentmicropython/raw/main/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64.tar.gz\"\u003ehttps://github.com/8888clockradio/iridescentmicropython/raw/main/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64.tar.gz\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emacOS x64 Intel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ekeep lib/clang-runtimes/armv7em_hard_fpv5_d16/lib in the LIBPATHFILEDROP and make sure you add /bin to COMPILERPATH\u003c/p\u003e\n\u003cp\u003echange /Applications/Teensyduino.app in TOOLSPATH if Teensyduino is installed in a non-standard location\u003c/p\u003e\n\u003cp\u003ecopy the .tar.gz file to iridescentBUILD/\nextract the .tar.gz file in iridescentBUILD/\nshould look like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLIBPATHFILEDROP=\"/Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/lib/clang-runtimes/armv7em_hard_fpv5_d16/lib\"\nCOMPILERPATH=\"/Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/bin\"\nTOOLSPATH=\"/Applications/Teensyduino.app/Contents/Java/hardware/tools\"\nCROSSCOMPILEPREFIX = clang\nADDITIONAL_TOOLS = llvm\nCLANG_TOOLCHAIN = --config \"$(COMPILERPATH)/armv7em_hard_fpv5_d16.cfg\"\n\nIS_WINDOWS_TOOLCHAIN_QUESTION_MARK=\n#uncomment below if windows\n#IS_WINDOWS_TOOLCHAIN_QUESTION_MARK=.exe\n\nAR := \"$(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\"\nAS := \"$(COMPILERPATH)/$(ADDITIONAL_TOOLS)-as$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(ASFLAGS)\nCC := \"$(COMPILERPATH)/$(CROSSCOMPILEPREFIX)$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(CLANG_TOOLCHAIN)\nCPP := $(CC) -E\nCXX := \"$(COMPILERPATH)/$(CROSSCOMPILEPREFIX)++$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(CLANG_TOOLCHAIN)\n#GDB = $(CROSS_COMPILE)-gdb\nLD := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ld$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nOBJCOPY := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-objcopy$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nSIZE := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-size$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nSTRIP := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-strip$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nAR := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nar := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou then need to copy the FPU library for heavy mathematics (specifically needed for audio, which isn\u0027t required yet\u2013 but this step is still required for linking) (THE REGULAR TEENSY LIBRARY USES SOFT FLOAT ON A HARD FLOAT BULD?! \u2013 THIS IS CORRECTED HERE)\ndownload: \u003ca href=\"https://github.com/ARM-software/CMSIS/raw/master/CMSIS/Lib/GCC/libarm_cortexM7lfdp_math.a\"\u003ehttps://github.com/ARM-software/CMSIS/raw/master/CMSIS/Lib/GCC/libarm_cortexM7lfdp_math.a\u003c/a\u003e\nand place into the $(LIBPATHFILEDROP) defined in toolchain.mk so like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp ~/Downloads/libarm_cortexM7lfdp_math.a /Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/lib/clang-runtimes/armv7em_hard_fpv5_d16/lib/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto build:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd iridescentmicropython/ports/libmicropython\nmake submodules #only need to run make submodules once usually\nmake clean; make V=1 #you can repeat this specific command to rebuild from scratch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eif you want to get daring copy the python modules for kivy, virtual environment, numpy, intelbinhex, pygame, matplotlib, music21, et cetera :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp -R iridescentBUILD/iridescentmicropython/ports/libmicropython/modulesTakenOut/* iridescentBUILD/iridescentmicropython/ports/libmicropython/modules/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand build again\ndoubtful there\u0027s any hardware that will support it at the moment, however due to tiny flash ram size on hardware\u003c/p\u003e\n\u003cp\u003ea board is in development for this firmware/OS\u003c/p\u003e\n\u003cp\u003eif you have kdbg installed through brew\nyou can run to debug in a very basic way\nNOTE: probably doesn\u0027t work since addition of clang\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd iridescentmicropython/ports/libmicropython; ./kdbg.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTHIS PROBABLY DOESN\u0027T MATTER ANYMORE\nNOTE: need to add FLASHMEM to all micropython boot up steps and modify startup.c to run boot clock start\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/ports/libmicropython/IRIDESCENT/cores-master/teensy4/startup.c\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003egenerate extern blocks on FLASHMEM with #include \u0026lt;avr/pgmspace.h\u0026gt; from:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/ports/libmicropython/board_init.c\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAND THESE:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/system_MIMXRT1062.c\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/system_MIMXRT1062.h\niridescentBUILD/iridescentmicropython/ports/libmicropython/boards/MIMXRT1062_clock_config.c\niridescentBUILD/iridescentmicropython/ports/libmicropython/boards/MIMXRT1062_clock_config.h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy inserting in: iridescentBUILD/iridescentmicropython/ports/libmicropython/IRIDESCENT/cores-master/teensy4/startup.c in function void ResetHandler(void)\u003c/p\u003e\n\u003cp\u003eALSO THESE FILES PROBABLY NEED FLASHMEM TOO (just in .h files) on functions (plus #include \u0026lt;avr/pgmspace.h\u0026gt;):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/fsl_device_registers.h\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/drivers/fsl_gpio.h\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/drivers/fsl_iomuxc.h\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/drivers/fsl_clock.h\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/drivers/fsl_lpuart.h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLD Script is located:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/ports/libmicropython/IRIDESCENT/imxmrt_ld/picoimxrt1062_t41.ld\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eTHIS MATTERS THO\nMost of the desktop OS will be based off this concept, as matlibplot and kivy will work together with music21:\nSo either build GUI with matlibplot through kivy or just through kivy\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/ports/libmicropython/modulesTakenOut/kivy/garden/garden/matplotlib/examples\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-aspera-connect/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-aspera-connect/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-aspera-connect/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-aspera-connect/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/9163de09dca2f8ae61af41ff4d7baf31f1a6c6d4b86d2a361d93909e155b3ab5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9163de09dca2f8ae61af41ff4d7baf31f1a6c6d4b86d2a361d93909e155b3ab5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-aspera-connect\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5ec7de4d9f3ea32793203e28d5147d5dc6cc8b9bd7cf2ab08eb5692796de5c23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ec7de4d9f3ea32793203e28d5147d5dc6cc8b9bd7cf2ab08eb5692796de5c23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-aspera-connect\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/adf1ed132e277181fe81068629a01b5494834d8fb83cc84fc3b132e50f6e08ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/adf1ed132e277181fe81068629a01b5494834d8fb83cc84fc3b132e50f6e08ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-aspera-connect\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ca701dddc8865d68b18f35dbdd6e33054e2977f14c0409a15baeb30435b74962/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca701dddc8865d68b18f35dbdd6e33054e2977f14c0409a15baeb30435b74962/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-aspera-connect\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-aspera-connect\" class=\"anchor\" href=\"#singularity-aspera-connect\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-aspera-connect\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eascp\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/aspera-connect/3.11.0.5\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/aspera-connect\u003c/code\u003e as \u003ccode\u003e3.11.0.5.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally. As of today, Does not work on MacOSX.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1672487253.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1629217755.0 }, { "data_format": 2, - "description": null, + "description": "BWA is a program for aligning sequencing reads against a large reference genome (e.g. human genome). ", "filenames": [ - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/latest/Singularity", - "misc/releases/19.06/Singularity.19.06" + "0.7.17a/Singularity", + "0.7.3a/Singularity" ], - "full_name": "utop1an/rule-based-heuristic", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "pscedu/singularity-bwa", + "latest_release": "v0.7.3a", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bwa/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bwa/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-bwa/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bwa/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/dbf644fd78a6a349b822d2b6db36a3f1ab2e4155f5d67671d27202073ac6cb6a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627761\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dbf644fd78a6a349b822d2b6db36a3f1ab2e4155f5d67671d27202073ac6cb6a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627761\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bwa\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0f4fa8203031531a1e542e55475a3668dbdc3d10e36a3ce505f5f098b465b1ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627761\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0f4fa8203031531a1e542e55475a3668dbdc3d10e36a3ce505f5f098b465b1ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627761\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bwa\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c10a09139792e339dfd18dbbab5079f2bf2027194d2dcfc6796e85d1bab3b88/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627761\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c10a09139792e339dfd18dbbab5079f2bf2027194d2dcfc6796e85d1bab3b88/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627761\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bwa\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a1977c9c08b068aecc0940ff8e8665b3e38fd423b0e217273d28b8ada424e70a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627761\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1977c9c08b068aecc0940ff8e8665b3e38fd423b0e217273d28b8ada424e70a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627761\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bwa\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bwa\" class=\"anchor\" href=\"#singularity-bwa\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bwa\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/Bwa\"\u003ebwa\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebwa\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bwa/0.7.3a\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bwa\u003c/code\u003e as \u003ccode\u003e0.7.3a.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1672794609.0 + "subscribers_count": 3, + "topics": [ + "bioinformatics", + "singularity" + ], + "updated_at": 1629083200.0 }, { "data_format": 2, - "description": "Nextflow workflow for benchmarking biohansel and Snippy with NCBI SRA genomes", + "description": "Container used to run IMI spikeScreen", "filenames": [ "Singularity" ], - "full_name": "peterk87/nf-biohansel-sra-benchmark", + "full_name": "IMIMF-UNILJSI/spikeScreenContainer", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-biohansel-sra-benchmark\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-biohansel-sra-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-biohansel-sra-benchmark\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://dev.azure.com/peterkruczkiewicz0831/nf-biohansel-sra-benchmark/_build/latest?definitionId=2\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5bffa2b25d6120420c8ab60e72373cb3d1ee278f01b086b8f0abb4d334b9bb23/68747470733a2f2f6465762e617a7572652e636f6d2f70657465726b7275637a6b69657769637a303833312f6e662d62696f68616e73656c2d7372612d62656e63686d61726b2f5f617069732f6275696c642f7374617475732f70657465726b38372e6e662d62696f68616e73656c2d7372612d62656e63686d61726b3f6272616e63684e616d653d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://dev.azure.com/peterkruczkiewicz0831/nf-biohansel-sra-benchmark/_apis/build/status/peterk87.nf-biohansel-sra-benchmark?branchName=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3444\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNextflow workflow for benchmarking \u003ca href=\"https://github.com/phac-nml/biohansel\"\u003ebiohansel\u003c/a\u003e and \u003ca href=\"https://github.com/tseemann/snippy/\"\u003eSnippy\u003c/a\u003e with NCBI SRA genomes.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pre-reqs\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-reqs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-reqs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOne or more directories each with the following files (see \u003ccode\u003eschemes/enteritidis_v1.0.7\u003c/code\u003e for an example)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eaccessions\u003c/code\u003e - List of SRA run accessions (e.g. \u003ccode\u003eSRR8820085\u003c/code\u003e) in a file (one accession per line)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme.fasta\u003c/code\u003e - biohansel scheme definition file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eref.gb\u003c/code\u003e - Genbank format reference genome\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emetadata.tsv\u003c/code\u003e tab delimited metadata file or empty file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInput scheme directory included with this repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eschemes\n\u2514\u2500\u2500 enteritidis_v1.0.7\n \u251c\u2500\u2500 accessions\n \u251c\u2500\u2500 metatadata.tsv\n \u251c\u2500\u2500 ref.gb\n \u2514\u2500\u2500 scheme.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eShow help message:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run peterk87/nf-biohansel-sra-benchmark --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eShould see something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 19.07.0-edge\nLaunching `main.nf` [drunk_dalembert] - revision: 97a449f5b6\n==================================================================\npeterk87/nf-biohansel-sra-benchmark ~ version 1.0dev\n==================================================================\n\nGit info: null - null [null]\n\nUsage:\n The typical command for running the pipeline is as follows:\n\n nextflow run peterk87/nf-biohansel-sra-benchmark \\\n --outdir results \\\n --schemesdir schemes \\\n --n_genomes 96 \\\n --iterations 10 \\\n -work workdir \\\n -profile standard\n\nOptions:\n --outdir Output directory (default: results)\n --schemesdir Directory with subtyping schemes and accessions to benchmark with biohansel (default: schemes)\n --n_genomes Number of SRA genomes to download and analyze per scheme (default: 96)\n --iterations Number of iterations per biohansel benchmark (default: 10)\n --thread_combos List of integer number of threads to test biohansel and snippy with delimited by comma (default: 1,2,4,8,16,32)\nOther options:\n -w/--work-dir The temporary directory where intermediate data will be saved (default: work)\n -profile Configuration profile to use. [singularity, conda, slurm] (default: standard)\nCluster options:\n -profile Only \"-profile slurm\" is accepted\n --slurm_queue Name of SLURM queue to submit jobs to (e.g. \"HighPriority\").\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun test profile creating Conda environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run peterk87/nf-biohansel-sra-benchmark -profile test,conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun included benchmark dataset with Singularity and default parameters (i.e. 96 genomes, 10 iterations for biohansel, run Snippy and biohansel with 1,2,4,8,16,32 threads/CPUs):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# clone/download this repo so that the scheme included with this repo can be run with the workflow\ngit clone https://github.com/peterk87/nf-biohansel-sra-benchmark.git\nnextflow run peterk87/nf-biohansel-sra-benchmark -profile singularity --schemesdir nf-biohansel-sra-benchmark/schemes\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun above on a cluster with SLURM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/peterk87/nf-biohansel-sra-benchmark.git\nnextflow run peterk87/nf-biohansel-sra-benchmark -profile singularity,slurm --slurm_queue \u0026lt;QueueName\u0026gt; --schemesdir nf-biohansel-sra-benchmark/schemes\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-run-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-run-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline run information\u003c/h2\u003e\n\u003cp\u003eWithin your output directory (e.g. \u003ccode\u003eresults/\u003c/code\u003e), you should find a \u003ccode\u003epipeline_info\u003c/code\u003e directory with runtime information about your analysis including trace information (see \u003ca href=\"https://www.nextflow.io/docs/latest/tracing.html\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/docs/latest/tracing.html\u003c/a\u003e for more info about these output files)\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-spikescreencontainer\" class=\"anchor\" href=\"#spikescreencontainer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003espikeScreenContainer\u003c/h1\u003e\n\u003cp\u003eContainer used to run IMI spikeScreen\nThis repo is meant to increase portability through automatic automatic container builds on shub.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1566494312.0 + "updated_at": 1629128736.0 }, { "data_format": 2, - "description": "Getting up to speed with Singularity", + "description": "ASCIIGenome is a genome browser based on command line interface and designed for console terminals.", "filenames": [ - "Singularity" + "1.16.0/Singularity" ], - "full_name": "netscruff/SingularityTest", - "latest_release": null, + "full_name": "pscedu/singularity-asciigenome", + "latest_release": "v1.16.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-asciigenome/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-asciigenome/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-asciigenome/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-asciigenome/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fc6d0c140fd5a29bf338bd86c146a7c92b4c8062434faaf70ef0f6c0deb67aa6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fc6d0c140fd5a29bf338bd86c146a7c92b4c8062434faaf70ef0f6c0deb67aa6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-asciigenome\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4ebf7a2007abd25cbc70d78eea4b3e18e84b103736a8a5edb364f1677212c427/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ebf7a2007abd25cbc70d78eea4b3e18e84b103736a8a5edb364f1677212c427/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-asciigenome\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/6e5331506177fef5e181d308bb0030987e5b6b8ed2eac29c791694f6339a6115/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6e5331506177fef5e181d308bb0030987e5b6b8ed2eac29c791694f6339a6115/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-asciigenome\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b6afd6a92e3bec558c0c64e9a5bdc6d25c18335230778d4b78f8b2f869f15e4d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6afd6a92e3bec558c0c64e9a5bdc6d25c18335230778d4b78f8b2f869f15e4d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-asciigenome\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-asciigenome\" class=\"anchor\" href=\"#singularity-asciigenome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-asciigenome\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/fc29589526166dc2ee7ce341088c93b17e02dca1cf81bba1cf477425249fa4f3/68747470733a2f2f617363696967656e6f6d652e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6c656973686d616e69615f7472616e736372697074732e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fc29589526166dc2ee7ce341088c93b17e02dca1cf81bba1cf477425249fa4f3/68747470733a2f2f617363696967656e6f6d652e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6c656973686d616e69615f7472616e736372697074732e706e67\" width=\"50%\" data-canonical-src=\"https://asciigenome.readthedocs.io/en/latest/_images/leishmania_transcripts.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/dariober/ASCIIGenome\"\u003easciigenome\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003easciigenome\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/asciigenome/1.16.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/asciigenome\u003c/code\u003e as \u003ccode\u003e1.16.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1511296488.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629217403.0 }, { "data_format": 2, "description": null, "filenames": [ - "v4.7.1/Singularity" + "Singularity" ], - "full_name": "yh549848/singularity-code-server", + "full_name": "porchard/ATACseq-NextFlow", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-atac-seq-data\" class=\"anchor\" href=\"#nextflow-pipeline-for-atac-seq-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow pipeline for ATAC-seq data\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eIf you have Singularity installed, you can use the config provided here (\u0027Singularity\u0027) to build a container with all the dependencies.\u003c/p\u003e\n\u003cp\u003eOtherwise, you\u0027ll need to have the following installed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecta\u003c/li\u003e\n\u003cli\u003ebedtools\u003c/li\u003e\n\u003cli\u003ebwa\u003c/li\u003e\n\u003cli\u003epicardtools\u003c/li\u003e\n\u003cli\u003efastqc\u003c/li\u003e\n\u003cli\u003esamtools\u003c/li\u003e\n\u003cli\u003eataqv\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis pipeline works with NextFlow versions \u0026gt;= 20.07.1\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003ePaths to various generic files (e.g., bwa indices) must be included in the nextflow.config file -- check that file and change paths accordingly. These include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBlacklist bed files for each genome\u003c/li\u003e\n\u003cli\u003eChrom size files for each genome\u003c/li\u003e\n\u003cli\u003eBWA indices\u003c/li\u003e\n\u003cli\u003eTSS files (BED6 files denoting TSS positions)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou\u0027ll also need to set the params.results variable -- either in the nextflow.config file itself, or on the command line when you run the pipeline (\u0027--results /path/to/results\u0027).\u003c/p\u003e\n\u003cp\u003eLastly, you\u0027ll need to include information about each ATAC-seq library, including the genome that each library should be mapped to and the paths to the fastq files for each readgroup. Organize this information in a JSON file, as in library-config.json.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eOnce you have all of the above information, you can run the pipeline as follows (in this case, indicating the path to the results on the command line):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run -with-singularity /path/to/Singularity.simg -with-trace -with-report -with-dag -with-timeline -params-file library-config.json --results /path/to/results /path/to/main.nf\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1675855529.0 + "updated_at": 1629490022.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity Recipe for accessing GPU" + "Singularity" ], - "full_name": "salammemphis/GPU-and-singularity", + "full_name": "baxpr/sct-fmri", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe to access GPU from host machine. It will spin up a jupyter notebook from singularity.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-cuda-110-and-tensorflow-220-and-keras-240\" class=\"anchor\" aria-hidden=\"true\" href=\"#cuda-110-and-tensorflow-220-and-keras-240\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCUDA 11.0 and tensorflow 2.2.0 and keras 2.4.0\u003c/h1\u003e\n\u003cp\u003eBootstrap: docker\n#From: tensorflow/tensorflow:latest-gpu-py3-jupyter\nFrom: nvcr.io/nvidia/tensorflow:20.08-tf2-py3\n%help\nThis container is made by Shahinur Alam (\u003ca href=\"mailto:sajonbuet@gmail.com\"\u003esajonbuet@gmail.com\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e%setup\u003c/p\u003e\n\u003cp\u003e%files\u003c/p\u003e\n\u003cp\u003e%labels\nMaintainer Shahinur\nVersion 1.0\u003c/p\u003e\n\u003cp\u003e%environment\u003c/p\u003e\n\u003cp\u003e%post\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install numpy pandas sklearn webcolors plotly matplotlib statsmodels factorial pynrrd pillow\npip install torch\npip install scikit-image medpy Tables nilearn SimpleITK h5py glob3 nibabel tifffile scipy opencv-python\npip install --upgrade keras\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e%runscript\necho \"Arguments received: $*\"\nport=$1\nroot_dir=$2\nport=\u003ccode\u003eecho $port| sed \u0027s/ *$//g\u0027\u003c/code\u003e\nroot_dir=\u003ccode\u003eecho $root_dir| sed \u0027s/ *$//g\u0027\u003c/code\u003e\necho $port\njupyter notebook --no-browser --port $port --notebook-dir $root_dir --ip 0.0.0.0\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-works-with-cuda-101\" class=\"anchor\" aria-hidden=\"true\" href=\"#works-with-cuda-101\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorks with CUDA 10.1\u003c/h1\u003e\n\u003cp\u003eBootstrap: docker\nFrom: tensorflow/tensorflow:latest-gpu-py3-jupyter\n%help\nThis container is made by Shahinur Alam (\u003ca href=\"mailto:sajonbuet@gmail.com\"\u003esajonbuet@gmail.com\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e%setup\u003c/p\u003e\n\u003cp\u003e%files\u003c/p\u003e\n\u003cp\u003e%labels\nMaintainer Shahinur\nVersion 1.0\u003c/p\u003e\n\u003cp\u003e%environment\u003c/p\u003e\n\u003cp\u003e%post\n#pip install numpy pandas sklearn webcolors plotly matplotlib statsmodels factorial pynrrd pillow\n#pip install torch\n#pip install scikit-image medpy Tables tensorflow_addons nilearn SimpleITK h5py glob3 nibabel tifffile scipy opencv-python\npip uninstall -y tensorflow tensorflow-addons tensorflow-estimator tensorflow-gpu tensorboard tensorboard-plugin-wit\npip install --upgrade keras\npip install --upgrade tensorflow\npip install tensorflow-addons==0.11.2\npip install tensorflow-estimator==2.3.0\npip install tensorflow-gpu==2.3.0\npip install tensorboard==2.3.0\npip install tensorboard-plugin-wit==1.7.0\u003c/p\u003e\n\u003cp\u003e%runscript\necho \"Arguments received: $*\"\nport=$1\nroot_dir=$2\nport=\u003ccode\u003eecho $port| sed \u0027s/ *$//g\u0027\u003c/code\u003e\nroot_dir=\u003ccode\u003eecho $root_dir| sed \u0027s/ *$//g\u0027\u003c/code\u003e\necho $port\njupyter notebook --no-browser --port $port --notebook-dir $root_dir --ip 0.0.0.0\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-sct-fmri-processing\" class=\"anchor\" href=\"#sct-fmri-processing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSCT fMRI processing\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003etest_container.sh\u003c/code\u003e for an example run command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--fmri_niigz 4D spinal cord fMRI, fully qualified path and filename\n--masksize Size of mask to create in mm\n--label_info Text to label the PDF, e.g. from XNAT project/subject\n--out_dir Outputs directory (and working directory)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline\" class=\"anchor\" href=\"#pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003esrc/main.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003efmri0.nii.gz First volume of fMRI\n\nfmri_mask??.nii.gz Created analysis mask\n\nfmri_centerline.nii.gz Cord centerline\nfmri_centerline.csv\n\nfmri_moco.nii.gz Moco outputs\nfmri_moco_mean.nii.gz\nmoco_params.tsv\nmoco_params_x.nii.gz\nmoco_params_y.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1675440928.0 + "updated_at": 1629493665.0 }, { "data_format": 2, "description": null, "filenames": [ - "planner/downward/misc/releases/19.12/Singularity.19.12", - "planner/downward/misc/releases/20.06/Singularity.20.06", - "planner/downward/misc/releases/latest/Singularity", - "planner/downward/misc/releases/19.06/Singularity.19.06" + "1.0.3/Singularity" ], - "full_name": "drexlerd/downward-hffpi", + "full_name": "yh549848/singularity-sicer2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-downward-hffpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#downward-hffpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edownward-hffpi\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone recursively\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003egit clone --recursively \u0026lt;link_to_repo\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate python3 virtual environment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython3 -m venv --prompt hffpi .venv\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eActivate virtual environment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource .venv/bin/activate\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eInstall python packages (needed for experimental code)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epip install -r requirements.txt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInstall planner\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./planner/downward/build.py\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-the-planner\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-the-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the planner\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003eexperiments/experiment-hffpi.py\u003c/code\u003e on example callstrings.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-the-experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-the-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the experiments\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd experiments\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e./experiment-hffpi.py --all\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1675861881.0 + "updated_at": 1629665658.0 }, { "data_format": 2, "description": null, "filenames": [ - "recipes/single-cell-genomics/mosaic/Singularity.mosaic-v03", - "recipes/peakcallers/macs2/Singularity.macs2-2271", - "recipes/peakcallers/hiddendomains/Singularity.hiddendomains-31", - "recipes/quality-control/fastqc/Singularity.fastqc-0119cv6", - "recipes/quality-control/fastqc/Singularity.fastqc-0119cv8", - "recipes/quality-control/fastqc/Singularity.fastqc-0119cv7", - "recipes/mapping/bowtie2samtools/Singularity.bowtie2samtools-v245v115", - "recipes/mapping/bowtie2/Singularity.bowtie2-245", - "recipes/mapping/bowtie2/Singularity.bowtie2-241cv1", - "recipes/fastq-operations/parallelfastqdump/Singularity.parallelfastqdump-v063", - "recipes/fastq-operations/trimgalore/Singularity.trimgalore-v067", - "recipes/os-environments/alpine/Singularity.alpine-3160", - "recipes/rpackages/bioconductor/genomeinfodb/Singularity.genomeinfodb-1323", - "recipes/rpackages/bioconductor/genomicranges/Singularity.genomicranges-1480", - "recipes/rpackages/snakemake-pipelines/chipseq/Singularity.snakemakechipseq-v001", - "recipes/rpackages/bedtools/Singularity.bedr-107", - "recipes/image-analysis/deeplabcut/Singularity.deeplabcut-2202", - "recipes/image-analysis/cellpose/Singularity.cellpose-2.0.5", - "recipes/image-analysis/chimerax/Singularity.chimerax-1.3", - "recipes/chipseq/spikchip/Singularity.spikchip-v099", - "recipes/chipseq/spikchipcustom/Singularity.spikchipcustom-v099", - "recipes/analysissuites/picardtools/Singularity.picardtools-2221", - "recipes/analysissuites/picardtools/Singularity.picardtools-2271", - "recipes/analysissuites/deeptools/Singularity.deeptools-351", - "recipes/analysissuites/samtools/Singularity.samtools-114", - "recipes/analysissuites/samtools/Singularity.samtools-115", - "recipes/analysissuites/bedops/Singularity.bedops-2440" + "2.63/Singularity" ], - "full_name": "descostesn/singularityhub-emblrome", + "full_name": "yh549848/singularity-ngsplot", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularityhub-embl-rome-gitlab-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularityhub-embl-rome-gitlab-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularityhub EMBL Rome (Gitlab version)\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pulling\"\u003ePulling\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThis repository aims at sharing singularity images among the EMBL community. We try to follow a strict model to provide uniformly designed singularities. Please let us know if we should modify anything.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant: Since we do not have a premium account, I tried to find some workaround. We cannot secure completely that master will stay green. Please be careful to strictly follow the instructions.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pulling\" class=\"anchor\" aria-hidden=\"true\" href=\"#pulling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePulling\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePlease read the entire section before trying to pull any singularities\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTo pull an existing singularity, first have a look at the image of interest in the list \u003ca href=\"https://git.embl.de/descoste/singularityhub-emblrome/container_registry\" rel=\"nofollow\"\u003ehere\u003c/a\u003e or in this \u003ca href=\"https://git.embl.de/descoste/singularityhub-emblrome/-/tree/main/recipes\" rel=\"nofollow\"\u003efolder\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the script below in a \u003ccode\u003edownload.sh\u003c/code\u003e file and run the command: \u003ccode\u003ebash dowload.sh username containername imagename\u003c/code\u003e. For example, \u003ccode\u003ebash download.sh descoste fastqcv0019cv8.sif \u0027fastqc:0119cv8\u0027\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/usr/bin/bash\n\nUSERNAME=$1\nCONTAINERNAME=$2\nIMAGE=$3\n\nsingularity pull --docker-username $USERNAME --docker-password $SINGULARITY_DOCKER_PASSWORD $CONTAINERNAME oras://git.embl.de:4567/descoste/singularityhub-emblrome/$IMAGE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eImportant\u003c/strong\u003e: You need to define a git token to be able to use the \u003ccode\u003e$SINGULARITY_DOCKER_PASSWORD\u003c/code\u003e variable. Follow these steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClick on your avatar at the top right of your gitlab page.\u003c/li\u003e\n\u003cli\u003eClick on \u003ccode\u003epreferences\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eClick on \u003ccode\u003eAccess Tokens\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eEnter a Token name. ex: \"singularitypull\".\u003c/li\u003e\n\u003cli\u003eIn the \u003ccode\u003eSelect scopes\u003c/code\u003e section, select \u003ccode\u003eread_registry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eClick \u003ccode\u003eCreate personal access token\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAt the beginning of the new loaded page, click on the folder icon to copy your new personal access token.\u003c/li\u003e\n\u003cli\u003eEdit your \u003ccode\u003e.bashrc\u003c/code\u003e (\u003ccode\u003eemacs -nw ~/.bashrc\u003c/code\u003e or \u003ccode\u003evim ~/.bashrc\u003c/code\u003e) by adding \u003ccode\u003eexport SINGULARITY_DOCKER_PASSWORD=\"paste_your_copied_access_token_here\"\u003c/code\u003e wherever you like.\u003c/li\u003e\n\u003cli\u003eAfter closing your editor, run \u003ccode\u003eexec bash\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eNow try to pull a particular singularity following the instructions above.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Make sure that you do use bash and not something else like zsh.\u003c/p\u003e\n\u003cp\u003eIf it does not work please do:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eAdd the remote: \u003ccode\u003esingularity remote add --no-login embl https://git.embl.de:4567\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUse the remote: \u003ccode\u003esingularity remote use embl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eLog to the remote: \u003ccode\u003esingularity remote login oras://git.embl.de:4567\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eThis repository is maintained by Nicolas Descostes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant: Since we do not have a premium account, I tried to find some workaround. We cannot secure completely that master will stay green. Please be careful to strictly follow the instructions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant Bis: Each singularity should contain a single tool. Contact me ahead if you plan otherwise.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo add a new singularity recipe, you need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone the repository: \u003ccode\u003egit clone git@git.embl.de:descoste/singularityhub-emblrome.git\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEnter the folder: \u003ccode\u003ecd singularityhub-emblrome/\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePosition yourself on the \"submission\" branch: \u003ccode\u003egit checkout submission\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eMake sure that the content of the branch is up-to-date: \u003ccode\u003egit reset --hard main\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd a singularity recipe inside \u003ccode\u003erecipes\u003c/code\u003e in the adapted folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eRespect the naming format \u003ccode\u003eSingularity.toolName-tag\u003c/code\u003e (with a upper-case S). Please use common sense to choose the folder\u003c/strong\u003e. If you are not sure, please contact me by email or by chat.\u003c/p\u003e\n\u003cp\u003eFor instance, if you want to submit fastqc version 0119cv8, your file name will be \u003ccode\u003eSingularity.fastqc-0119cv8\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eCommit and push to the repository: `git add myrecipe \u0026amp;\u0026amp; git commit -m \"initial commit\" \u0026amp;\u0026amp; git push origin submission\"\u003c/li\u003e\n\u003cli\u003eModify \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e in the \"submission area\" using the following template (replace \u003ccode\u003etoolName\u003c/code\u003e, \u003ccode\u003etag\u003c/code\u003e, and \u003ccode\u003epath_to_recipe_folder\u003c/code\u003e):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003etoolName-tag-test:\n extends: .templateTest\n variables:\n BASENAME: toolName\n TAG: tag\n RECIPE_PATH: recipes/path_to_recipe_folder_without_file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor instance, if you want to submit fastqc version 0119cv8, your rule name will be \u003ccode\u003efastqc-0119cv8-test\u003c/code\u003e and the path to the recipe \u003ccode\u003erecipes/quality-control/fastqc\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote 1:\u003c/strong\u003e There is no slash at the end of the path and the file name is \u003cstrong\u003enot\u003c/strong\u003e precised.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote 2:\u003c/strong\u003e The BASENAME and the TAG are used to create the file name (Singularity.BASENAME-TAG). Please verify that it matches.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eIn the following instruction, \u003cstrong\u003eplease add toolName-tag-test` as a commit message\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003ePush the file \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e to the repository: \u003ccode\u003egit add .gitlab-ci.yml \u0026amp;\u0026amp; git commit -m \"toolName-tag-test\" \u0026amp;\u0026amp; git push origin submission\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eVisit this \u003ca href=\"https://git.embl.de/descoste/singularityhub-emblrome/-/merge_requests\" rel=\"nofollow\"\u003epage\u003c/a\u003e to submit a merge request.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eAs title: toolName-tag-test\u003c/li\u003e\n\u003cli\u003edescription: A one-line sentence to explain what the tool is. Please precise any important information as well.\u003c/li\u003e\n\u003cli\u003eReviewer: Choose Nicolas Descostes\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBe careful:\u003c/strong\u003e Uncheck the \u003ccode\u003eDelete source branch when merge request is accepted.\u003c/code\u003e before submitting.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003eNow it is time to test the build of your singularity. You will see a gear on the right of \u003ccode\u003eDetached merge request pipeline #32160 waiting for manual action for \u003c/code\u003e. Click on it and hit the play button next to your rule.\u003c/li\u003e\n\u003cli\u003eIn the \u003ccode\u003eCI/CD \u0026gt; jobs\u003c/code\u003e (menu on the left), you can see your job running.\u003c/li\u003e\n\u003cli\u003eOnce your job passes the test (green checkmark), I will merge and deploy your singularity. I will let you know when this is done.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1674643692.0 + "updated_at": 1629677826.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image for alienpy", "filenames": [ "Singularity" ], - "full_name": "rses-singularity/tfgpu-theano-pytorch-keras", + "full_name": "adriansev/alienpy.sing", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tensorflow-gpu-theano-keras-and-pytorch-gpu-with-opencv\" class=\"anchor\" aria-hidden=\"true\" href=\"#tensorflow-gpu-theano-keras-and-pytorch-gpu-with-opencv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTensorflow (GPU), Theano, Keras and PyTorch (GPU) with OpenCV\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-listing\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-listing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware listing\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ev1\n\u003cul\u003e\n\u003cli\u003eUbuntu 16.04\u003c/li\u003e\n\u003cli\u003eCUDA 8 + cuDNN 6\u003c/li\u003e\n\u003cli\u003ePython 3.5\u003c/li\u003e\n\u003cli\u003eTheano 1.0.0\u003c/li\u003e\n\u003cli\u003eTensorflow (GPU) 1.4.1\u003c/li\u003e\n\u003cli\u003ePyTorch (GPU) 0.3.0\u003c/li\u003e\n\u003cli\u003eOpenCV 3.3.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-alienpysing\" class=\"anchor\" href=\"#alienpysing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ealienpy.sing\u003c/h1\u003e\n\u003cp\u003eSingularity image for alienpy\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1674934803.0 + "updated_at": 1629840214.0 }, { "data_format": 2, - "description": null, + "description": "Docker recipe for building Interproscan", "filenames": [ + "Singularity.open", "Singularity" ], - "full_name": "rses-singularity/digits", - "latest_release": null, + "full_name": "biocorecrg/interproscan_docker", + "latest_release": "5.48-83.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-interproscan_docker\" class=\"anchor\" href=\"#interproscan_docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einterproscan_docker\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/150708687\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5867fa2b54b675356b6c4b17144ce558f6902bee46de35012c7bdafc38d90f88/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3135303730383638372e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/150708687.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContainer recipes for building \u003ca href=\"https://interproscan-docs.readthedocs.io\" rel=\"nofollow\"\u003eInterproscan\u003c/a\u003e. Both \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and \u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e versions are provided (the latter recomended).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to use Interproscan external privative software, these programs must be obtained first with granted academic permissions.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cbs.dtu.dk/services/SignalP/\" rel=\"nofollow\"\u003eSignalP\u003c/a\u003e \u003ccode\u003esignalp-4.1b.Linux.tar.Z\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cbs.dtu.dk/services/TMHMM/\" rel=\"nofollow\"\u003eTMHMM\u003c/a\u003e \u003ccode\u003etmhmm-2.0c.Linux.tar.gz\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://phobius.sbc.su.se/\" rel=\"nofollow\"\u003ePhobious\u003c/a\u003e \u003ccode\u003ephobius101_linux.tar.gz\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eRegarding phobius: \u003ca href=\"https://www.biostars.org/p/238642/\" rel=\"nofollow\"\u003ehttps://www.biostars.org/p/238642/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eKeep in mind that some other modifications are also needed in those programs above in advance, e. g., replacing \u003ccode\u003e/usr/bin/perl\u003c/code\u003e for \u003ccode\u003e/usr/bin/env perl\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLast software package versions of Interproscan include the whole data by default. For container performance and distribution, we don\u0027t keep Interproscan data directory.\u003c/p\u003e\n\u003cp\u003eIt is important to ensure that program and data versions match and that this is adequately reflected in \u003ccode\u003einterproscan.properties\u003c/code\u003e or \u003ccode\u003einterproscan.open.properties\u003c/code\u003e files. Otherwise Interproscan is not likely to work.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pregenerated-images\" class=\"anchor\" href=\"#pregenerated-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePregenerated images\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://biocore.crg.eu/iprscan/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/biocorecrg/interproscan\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-from-docker-recipes\" class=\"anchor\" href=\"#building-from-docker-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding from Docker recipes\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e# With privative software\ndocker build -t iprscan:5.48-83.0 -f Dockerfile .\nsudo singularity build iprscan-5.48-83.0.sif docker-daemon://iprscan:5.48-83.0\n# Without privative software\ndocker build -t iprscan-open:5.48-83.0 -f Dockerfile.open .\nsudo singularity build iprscan-5.48-83.0.open.sif docker-daemon://iprscan-open:5.48-83.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-from-singularity-recipes\" class=\"anchor\" href=\"#building-from-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding from Singularity recipes\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e# With privative software\nsudo singularity build iprscan-5.48-83.0.sif Singularity\n# Without privative software\nsudo singularity build iprscan-5.48-83.0.open.sif Singularity.open\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can avoid using \u003ccode\u003esudo\u003c/code\u003e with \u003ccode\u003e--fakeroot\u003c/code\u003e Singularity build option.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eFor running the container images, it is mandatory to mount a data directory that fits the same Interproscan version. Below some example commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Docker\ndocker run --volume /path/to/data:/usr/local/interproscan/data --volume /path/to/scratch:/scratch -t biocorecrg/interproscan:5.48-83.0 /usr/local/interproscan/interproscan.sh -i /scratch/test.fa --goterms --iprlookup --pathways -o /scratch/out_interpro -f TSV\n\n# Singularity\nsingularity exec -e iprscan-5.47-82.0.open.sif /usr/local/interproscan/interproscan.sh -i /path/to/test2.fa --goterms --iprlookup --pathways -o /path/to/out_interpro -f TSV\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTES\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eMoreover, keep into account that a user with suitable permissions may need first to index \u003ccode\u003e/usr/local/interproscan/data\u003c/code\u003e directory (e.g., with \u003ccode\u003epython3 /usr/local/interproscan/initial_setup.py\u003c/code\u003e). You can use the very container images. Details here: \u003ca href=\"https://interproscan-docs.readthedocs.io/en/5.48-83.0/HowToRun.html\" rel=\"nofollow\"\u003ehttps://interproscan-docs.readthedocs.io/en/5.48-83.0/HowToRun.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDepending on your setup, you may need to change \u003ccode\u003eSINGULARITY_TMPDIR\u003c/code\u003e (and \u003ccode\u003eSINGULARITY_CACHEDIR\u003c/code\u003e) environment variables for pointing to a location with enough space. More details at: \u003ca href=\"https://singularity.hpcng.org/admin-docs/master/installation.html\" rel=\"nofollow\"\u003ehttps://singularity.hpcng.org/admin-docs/master/installation.html\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [], - "updated_at": 1674934803.0 + "updated_at": 1631532581.0 }, { "data_format": 2, - "description": null, + "description": "Copy of the template_project_escape to test the GitHub CI", "filenames": [ - "Singularity" + "Singularity/Singularity" ], - "full_name": "rses-singularity/theano", - "latest_release": null, + "full_name": "escape2020/template_project_escape", + "latest_release": "v0.1.4", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-template_project_escape\" class=\"anchor\" href=\"#template_project_escape\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etemplate_project_escape\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.4923992\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869b462bf3a319fe4fc2ffa52fb6b0f7c8e42eb4e0ed4bc2482306b9fd5aafab/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343932333939322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4923992.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/-/commits/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a25ea71f12537b69d5aca8b409685333243d4e65ceb91c5a401dadb6eacea20e/68747470733a2f2f6769746c61622e696e3270332e66722f657363617065323032302f7770332f74656d706c6174655f70726f6a6563745f6573636170652f6261646765732f6d61737465722f706970656c696e652e737667\" alt=\"pipeline status\" data-canonical-src=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/badges/master/pipeline.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fd551ba4b042d89480347a0e74e31af63b356b2cac1116c7b80038f41b04a581/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://camo.githubusercontent.com/64f9054d866a78f16e1451647c19b22fc159a58191e004612257ce5277bd6db9/68747470733a2f2f63646e2e65736f2e6f72672f696d616765732f6c617267652f616e6e3138303834612e6a7067\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/64f9054d866a78f16e1451647c19b22fc159a58191e004612257ce5277bd6db9/68747470733a2f2f63646e2e65736f2e6f72672f696d616765732f6c617267652f616e6e3138303834612e6a7067\" width=\"640\" height=\"453\" data-canonical-src=\"https://cdn.eso.org/images/large/ann18084a.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eA simple template project to provide software to ESCAPE.\u003c/p\u003e\n\u003cp\u003eThis repository shows the \u003cstrong\u003ebasic content\u003c/strong\u003e that should be included in a project (following the\n\u003ca href=\"https://opensource.guide/starting-a-project/\" rel=\"nofollow\"\u003eopensource guide\u003c/a\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn \u003ca href=\"https://help.github.com/en/github/creating-cloning-and-archiving-repositories/licensing-a-repository#where-does-the-license-live-on-my-repository\"\u003eopen source\u003c/a\u003e\n\u003cstrong\u003elicense\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://help.github.com/en/github/getting-started-with-github/create-a-repo#commit-your-first-change\"\u003e\u003cstrong\u003eREADME\u003c/strong\u003e file\u003c/a\u003e,\nsimilar to this one.\u003c/li\u003e\n\u003cli\u003eContributing guidelines.\n\u003cul\u003e\n\u003cli\u003eSee below the general guidelines for the ESCAPE repository.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://opensource.guide/code-of-conduct/\" rel=\"nofollow\"\u003ecode of conduct\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003eCheck why is a good idea to add one.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe repository itself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt would be highly suitable to include too:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA setup file as well as the basic commands to install the library (see below).\u003c/li\u003e\n\u003cli\u003eA \u003ccode\u003e.gitignore\u003c/code\u003e file.\u003c/li\u003e\n\u003cli\u003eUnitary and integration tests, and ideally a CI pipeline.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ePlease feel free to clone / fork / template this project!\u003c/strong\u003e (For example, look to left of the\n\u003ccode\u003eClone or download\u003c/code\u003e button in the \u003ca href=\"https://github.com/garciagenrique/template_project_escape\"\u003eGitHub\u003c/a\u003e site).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor a detailed explanation of how to submit a contribution to a project / repository (Fork, create a branch, make\na pull request...), you can have a look to the \u003ca href=\"https://opensource.guide/how-to-contribute/#how-to-submit-a-contribution\" rel=\"nofollow\"\u003eopensource guide\u003c/a\u003e\nand/or the \u003ca href=\"https://git-scm.com/doc\" rel=\"nofollow\"\u003egit\u0027s documentation\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eNot that if you have login GitLab by using the \u003ccode\u003e[Shibbolenth]\u003c/code\u003e service (eduGAIN, F\u00e9d\u00e9ration d\u0027Identit\u00e9s\nRENATER), you will need to \u003ca href=\"https://gitlab.in2p3.fr/help/ssh/README#generating-a-new-ssh-key-pair\" rel=\"nofollow\"\u003eadd a SSH key\u003c/a\u003e to\nyour GitLab profile if you want to \u0027push\u0027 your changes to the server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contribute-to-the-escape-ossr\" class=\"anchor\" href=\"#contribute-to-the-escape-ossr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContribute to the ESCAPE OSSR\u003c/h1\u003e\n\u003cp\u003eIf you want to provide software to the ESCAPE repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the \u003ca href=\"https://escape2020.pages.in2p3.fr/wp3/ossr-pages/page/contribute/contribute_ossr/\" rel=\"nofollow\"\u003eESCAPE OSSR guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor ESCAPE members, follow the steps detailed in \u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/onboarding\" rel=\"nofollow\"\u003ethe onboarding project\u003c/a\u003e\nto finalise your contribution and the same onboarding process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAll the code provided should be uploaded to the \u003ca href=\"https://zenodo.org/communities/escape2020/\" rel=\"nofollow\"\u003eZenodo ESCAPE community\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the following \u003ca href=\"https://escape2020.pages.in2p3.fr/wp3/ossr-pages/page/contribute/publish_tutorial/\" rel=\"nofollow\"\u003etutorial on how to publish content in Zenodo\u003c/a\u003e,\nand how to automatise the upload of each new release of your project.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-this-project-also-includes\" class=\"anchor\" href=\"#this-project-also-includes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis project also includes\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-1-how-to-automatise-the-building-of-a-singularity-image-and-upload-it-to-zenodo-using-the-gitlab-ci\" class=\"anchor\" href=\"#1-how-to-automatise-the-building-of-a-singularity-image-and-upload-it-to-zenodo-using-the-gitlab-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. How to automatise the building of a Singularity image and upload it to Zenodo using the GitLab-CI\u003c/h2\u003e\n\u003cp\u003eA working example of how to automatise the GitLab-CI to;\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecreate a Singularity image / container of your code,\u003c/li\u003e\n\u003cli\u003emake it available as a downloadable artifact within your project and\u003c/li\u003e\n\u003cli\u003eupload it to the \u003ca href=\"https://zenodo.org/communities/escape2020\" rel=\"nofollow\"\u003eESCAPE OSSR\u003c/a\u003e,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ecan be found in the \u003ccode\u003e.singularityci\u003c/code\u003e, and \u003ccode\u003eSingularity\u003c/code\u003e directories and in the \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e file - the\n\u003ccode\u003ebuild_singularity_image\u003c/code\u003e stage. Please read carefully all the README files.\u003c/p\u003e\n\u003cp\u003eFor an easy example of how to create a Singularity receipt from scratch (and its corresponding container when executed),\nplease have a look to the \u003ccode\u003esingularity_utils\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-2-how-to-automatise-the-building-of-a-docker-container-and-upload-it-to-the-gitlab-container-registry\" class=\"anchor\" href=\"#2-how-to-automatise-the-building-of-a-docker-container-and-upload-it-to-the-gitlab-container-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. How to automatise the building of a Docker container and upload it to the GitLab Container Registry\u003c/h2\u003e\n\u003cp\u003eAn example can be found in the \u003ccode\u003eDocker\u003c/code\u003e directory and in the \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e file - the\n\u003ccode\u003ebuild_docker_image\u003c/code\u003e stage.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h1\u003e\n\u003cp\u003eExample of how to show installing instructions (and indeed the way to install this project).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e $ git clone https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape.git\n $ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e template_project_escape\n $ pip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-citing\" class=\"anchor\" href=\"#citing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCiting\u003c/h1\u003e\n\u003cp\u003eExample of citing (as well as the DOI to cite this project),\u003c/p\u003e\n\u003cp\u003eIn case of citing this repository, use the following DOI:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ev2.2 \u003ca href=\"https://doi.org/10.5281/zenodo.4923992\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869b462bf3a319fe4fc2ffa52fb6b0f7c8e42eb4e0ed4bc2482306b9fd5aafab/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343932333939322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4923992.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev2.1 \u003ca href=\"https://doi.org/10.5281/zenodo.4790629\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8f54e2f50cdf0c4d1bd5183d6472cae8b708efacd6a1319b443d8e456f41b7f/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343739303632392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4790629.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev2.0 \u003ca href=\"https://doi.org/10.5281/zenodo.3884963\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c27201272a77fc3ab3029c8c2c452e02a71736b7adaa0926bc45d3ac825598ed/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333838343936332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3884963.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev1.1 \u003ca href=\"https://doi.org/10.5281/zenodo.3743490\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbfe31d3a9bd48ef4554b414c3c2325276269a476a79ec0217aa9feccc87cecd/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333734333439302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3743490.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev1.0 \u003ca href=\"https://doi.org/10.5281/zenodo.3572655\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ec734ee4c1c793eaa8f9c2b189a6eae6d7d2ccb5f2a92adeb8af479409cc7bb/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333537323635352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3572655.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDo not forget to include your code / container into the \u003ca href=\"https://zenodo.org/communities/escape2020/\" rel=\"nofollow\"\u003eZenodo ESCAPE community\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003e\u003cstrong\u003eNote that\u003c/strong\u003e\u003c/em\u003e a DOI will be assigned in the moment create a new record/entry in Zenodo.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003ePlease check the licenses of the code within the \u003ccode\u003e.singularityci\u003c/code\u003e directory before adding this template\nto your project.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-report-an-issue--ask-a-question\" class=\"anchor\" href=\"#report-an-issue--ask-a-question\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReport an issue / Ask a question\u003c/h1\u003e\n\u003cp\u003eUse the \u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/-/issues\" rel=\"nofollow\"\u003eGitLab repository Issues\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eEmail to vuillaume [at] lapp.in2p3.fr / garcia [at] lapp.in2p3.fr.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1674934802.0 + "updated_at": 1631872285.0 }, { "data_format": 2, - "description": " MrBayes, a program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary models.", + "description": null, "filenames": [ - "Singularity.3.2.7a-mpi" + "Singularity.rstudio" ], - "full_name": "sghignone/MrBayes", + "full_name": "ternaustralia/coesra-singularity-rstudio", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mrbayes\" class=\"anchor\" aria-hidden=\"true\" href=\"#mrbayes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMrBayes\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4216\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMrBayes v.3.2.7, a program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary models.\u003c/p\u003e\n\u003cp\u003eThe current release is based on MrBayes version 3.2.7a, released March 6, 2019. This version is compiled with MPI support and without the Beagle library\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coesra-singularity-rstudio\" class=\"anchor\" href=\"#coesra-singularity-rstudio\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-rstudio\u003c/h1\u003e\n\u003cp\u003eHoang Nguyen\n25 July 2019\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [ - "singularity", - "container", - "bayesian-inference", - "phylogenomics", - "phylogenetics" + "coesra" ], - "updated_at": 1663758431.0 + "updated_at": 1610424737.0 }, { "data_format": 2, - "description": "Building an online mousetracking tool", + "description": null, "filenames": [ - "Singularity" + "Singularity.qgis" ], - "full_name": "paulstillman/Online-Mousetracking", + "full_name": "ternaustralia/coesra-singularity-qgis", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-online-mousetracking\" class=\"anchor\" aria-hidden=\"true\" href=\"#online-mousetracking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOnline-Mousetracking\u003c/h1\u003e\n\u003cp\u003eBuilding an online mousetracking tool\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coesra-singularity-qgis\" class=\"anchor\" href=\"#coesra-singularity-qgis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-qgis\u003c/h1\u003e\n\u003cp\u003eHoang Nguyen 24 July 2019\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1663958736.0 + "updated_at": 1610427940.0 }, { "data_format": 2, "description": null, "filenames": [ - "bc3.10-rs125042r362/Singularity", - "bc3.12-r405rs125042/Singularity", - "bc3.15-r421tv132rs2022072.576/Singularity" + "Singularity.canopy" ], - "full_name": "yh549848/singularity-rstudio-methylseq", + "full_name": "ternaustralia/coesra-singularity-canopy", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coesra-singularity-canopy\" class=\"anchor\" href=\"#coesra-singularity-canopy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-canopy\u003c/h1\u003e\n\u003cp\u003eAuthor: Hoang Nguyen\nCreated: 22 July 2019\nThis will create a image with Singularity 2.5.1\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1665633405.0 - }, - { - "data_format": 2, - "description": "Quantifying the life of pollen.", - "filenames": [ - "singularity/Singularity" + "topics": [ + "coesra" ], - "full_name": "cedarwarman/pollen_cv", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-pollen_cv\" class=\"anchor\" aria-hidden=\"true\" href=\"#pollen_cv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epollen_cv\u003c/h1\u003e\n\u003cp\u003eQuantifying the life of pollen.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1665173991.0 + "updated_at": 1610425023.0 }, { "data_format": 2, "description": null, "filenames": [ - "analysis/assembly/containers/Singularity.canu" + "util/PATRIC/Singularity" ], - "full_name": "justicengom/head_to_head_pipeline-", + "full_name": "adamlabadorf/bf500", "latest_release": null, - "readme": "\u003ch3\u003e\u003ca id=\"user-content-preprint\" class=\"anchor\" aria-hidden=\"true\" href=\"#preprint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprint\u003c/h3\u003e\n\u003cblockquote\u003e\n\u003cp\u003eHall, M. B. \u003cem\u003eet al\u003c/em\u003e. Nanopore sequencing for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e drug susceptibility testing and outbreak investigation. \u003cem\u003eMedrxiv\u003c/em\u003e 2022.03.04.22271870 (2022) \u003ca href=\"https://doi.org/10.1101/2022.03.04.22271870\" rel=\"nofollow\"\u003edoi:10.1101/2022.03.04.22271870\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003cp\u003eThis repository holds the pipelines/scripts used for our paper analysing Illumina and\nNanopore for \u003cem\u003eM.tuberculosis\u003c/em\u003e drug resistance calling and transmission clustering.\u003c/p\u003e\n\u003cp\u003eFor people wanting to analyse their Nanopore data in the same manner as we did in this paper, we would suggest using \u003ca href=\"https://github.com/mbhall88/tbpore\"\u003ehttps://github.com/mbhall88/tbpore\u003c/a\u003e, which is a python program that runs the drug resistance prediction and clustering (with a smaller decontamination database) components of this pipeline. It is actively maintained and much easier to use.\u003c/p\u003e\n\u003cp\u003eAll pipelines require the following dependencies to be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://snakemake.github.io/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e (and\n\u003ca href=\"https://github.com/mamba-org/mamba\"\u003eMamba\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/docs\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eThe Python library \u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003e\u003ccode\u003epandas\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee subdirectories for more specific information about different pipelines. They are\nnested according to their dependence on the outputs of each pipeline.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"data/QC\"\u003eQuality Control\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/assembly\"\u003eAssembly\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"analysis/baseline_variants\"\u003eBaseline variant analysis\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/transmission_clustering\"\u003eTransmission clustering\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/resistance_prediction\"\u003eDrug Resistance Prediction\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following pipelines are not relevant to the work in the final paper.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"data/H37Rv_PRG\"\u003eH37Rv PRG construction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/pandora_variants\"\u003ePandora variant analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-data-availability\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-availability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData availability\u003c/h1\u003e\n\u003cp\u003eAll data is submitted under the Project accession \u003cstrong\u003ePRJEB49093\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe accessions and all relevant sample metadata for this study can be found at \u003ca href=\"https://doi.org/10.6084/m9.figshare.19304648\" rel=\"nofollow\"\u003ehttps://doi.org/10.6084/m9.figshare.19304648\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe raw Nanopore data is available to download from: \u003ca href=\"https://ftp.ebi.ac.uk/pub/databases/ont_tb_eval2022/\" rel=\"nofollow\"\u003ehttps://ftp.ebi.ac.uk/pub/databases/ont_tb_eval2022/\u003c/a\u003e. See the sample metadata file for mappings between samples and the relevant Nanopore runs and barcode numbers.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bf500---bioinformatics-engineering\" class=\"anchor\" href=\"#bf500---bioinformatics-engineering\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBF500 - Bioinformatics Engineering)\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://adamlabadorf.github.io/bf500/\" rel=\"nofollow\"\u003eGo to the Github Pages site\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1671672013.0 + "updated_at": 1631274450.0 }, { "data_format": 2, - "description": null, + "description": "One place for all the different container recipes", "filenames": [ - "Singularity" + "uboonecode/Singularity.uboonecode", + "ubdl/Singularity.ubdldeps.u16.04_py3.6.11", + "ubdl/Singularity.ubdldev", + "ubdl/Singularity.ubdldev.python3", + "sparseconvnet/Singularity.sparseconvnet" ], - "full_name": "rhassett-cshl/SimPolv2", + "full_name": "LArbys/larbys-containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-simpol--simulating-the-dynamics-of-rna-polymerase-rnap-on-dna-template\" class=\"anchor\" aria-hidden=\"true\" href=\"#simpol--simulating-the-dynamics-of-rna-polymerase-rnap-on-dna-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimPol \u2014 Simulating the dynamics of RNA Polymerase (RNAP) on DNA template\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eSimPol tracks the independent movement of RNAPs along the DNA templates of a large\nnumber of cells. It accepts several key\nuser-specified parameters, including the initiation rate, pause-escape rate,\na constant or variable elongation rate, the mean and variance of pause sites across cells,\nas well as the center-to-center spacing constraint between RNAPs,\nthe number of cells being simulated, the gene length, and the total time of transcription.\nThe simulator simply allows each RNAP to move forward or not,\nin time slices of $10^{-4}$ minutes, according to the specified position-specific\nrate parameters. It assumes that at most one movement of each RNAP can occur per\ntime slice. The simulator monitors for collisions between adjacent RNAPs, prohibiting\none RNAP to advance if it is at the boundary of the allowable distance from the next.\nAfter running for the specified time, SimPol outputs either a file in HDF5 format or files in CSV format that records all RNAP position for the last specified number of steps. See more options and details about parameters and outputs below.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/simulator.png\"\u003e\u003cimg src=\"figures/simulator.png\" alt=\"simulator\" width=\"600\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\tFig.1 Design of SimPol (\u201cSimulator of Polymerases\u201d)\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Environment\u003c/h2\u003e\n\u003cp\u003eWe provide two different approaches to set up the environment for SimPol, one with\n\u003ca href=\"https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\nand the other with \u003ca href=\"https://docs.sylabs.io/guides/latest/user-guide/quick_start.html#\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\nPre-built debug and release executables can be found in the bin folder\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\nconda activate simpol\n\nbin/simPol_Release --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot simPol.sif Singularity\n\nsingularity shell simPol.sif\n\nbin/simPol_Release --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild from source\u003c/h2\u003e\n\u003cp\u003eThere is the option to build in either debug mode with debug symbols or release mode with compiler optimizations (e.g. -O2).\u003c/p\u003e\n\u003cp\u003eTo create a build directory in release mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake -S . -B Release/ -D CMAKE_BUILD_TYPE=Release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo clean the release mode build directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake --build Release/ --target clean\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build in release mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake --build Release/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: Replace all instances of \u0027Release\u0027 with \u0027Debug\u0027 to build in Debug mode\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Locally\u003c/h2\u003e\n\u003cp\u003eSet Number of Threads [By default uses all available threads]\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport OMP_NUM_THREADS=\u0026lt;number of threads to use\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun in Release Mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Release/simPol [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun in Debug Mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Debug/simPol [options]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun program with file containing command line arguments\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./Release/simPol $(\u0026lt;simPol_arguments.dat)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-with-uge-workload-manager-within-cshl\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-with-uge-workload-manager-within-cshl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun with UGE Workload Manager (within CSHL)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -pe OpenMP 5 -cwd -b y ./Release/simPol -n 100 --csv 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote: This allocates 5 threads for the program in the OpenMP parallel environment\u003c/li\u003e\n\u003cli\u003eCheck available parallel environments: qconf -spl\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eOptions:\n\t-h, --help\n\t\tShow this help message and exit\n\n\t-k INTEGER, --tssLen=INTEGER\n\t\tdefine the mean of pause sites across cells [default 50bp]\n\n\t--kSd=DOUBLE\n\t\tdefine the standard deviation of pause sites across cells [default 0]\n\n\t--kMin=INTEGER\n\t\tupper bound of pause site allowed [default 17bp]\n\n\t--kMax=INTEGER\n\t\tlower bound of pause site allowed [default 200bp]\n\n\t--geneLen=INTEGER\n\t\tdefine the length of the whole gene [default 2000bp]\n\n\t-a DOUBLE, --alpha=DOUBLE\n\t\tinitiation rate [default 1 event per min]\n\n\t-b DOUBLE, --beta=DOUBLE\n\t\tpause release rate [default 1 event per min]\n\n\t-z DOUBLE, --zeta=DOUBLE\n\t\tthe mean of elongation rates across sites [default 2000bp per min]\n\n\t--zetaSd=DOUBLE\n\t\tthe standard deviation of elongation rates across sites [default 1000]\n\n\t--zetaMax=DOUBLE\n\t\tthe maximum of elongation rates allowed [default 2500bp per min]\n\n\t--zetaMin=DOUBLE\n\t\tthe minimum of elongation rates allowed [default 1500bp per min]\n\n\t--zetaVec=CHARACTER\n\t\ta file contains vector to scale elongation rates. All cells share the same set of parameters [default \"\"]\n\n\t-n INTEGER, --cellNum=INTEGER\n\t\tNumber of cells being simulated [default 10]\n\n\t-s INTEGER, --polSize=INTEGER\n\t\tPolymerase II size [default 33bp]\n\n\t--addSpace=INTEGER\n\t\tAdditional space in addition to RNAP size [default 17bp]\n\n\t-t DOUBLE, --time=DOUBLE\n\t\tTotal time of simulating data in a cell [default 0.1 min]\n\n\t--hdf5=INTEGER\n\t\tRecord position matrix to HDF5 file for remaining number of steps specified [default: 0 steps]\n\n\t--csv=INTEGER\n\t\tRecord position matrix to csv file for remaining number of steps specified [default: 1 step]\n\n\t-d CHARACTER, --outputDir=CHARACTER\n\t\tDirectory for saving results [default: \u0027results\u0027]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cp\u003eAfter simulation, SimPol produces multiple output files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eprobability_vector.csv\u003c/li\u003e\n\u003cli\u003epause_sites.csv\u003c/li\u003e\n\u003cli\u003ecombined_cell_data.csv: Stores the total # of polymerase at each site across all cells\u003c/li\u003e\n\u003cli\u003eposition_matrices.h5: An HDF5 file containing a group of position matrices for the last number of specified steps. The file can be viewed by importing it into the HDFView app. Download Here: \u003ca href=\"https://www.hdfgroup.org/downloads/hdfview/#download\" rel=\"nofollow\"\u003ehttps://www.hdfgroup.org/downloads/hdfview/#download\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eUsing HighFive interface to generate HDF5 file \u003ca href=\"https://github.com/BlueBrain/HighFive\"\u003ehttps://github.com/BlueBrain/HighFive\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUses chunking and ZLIB (deflate) compression at level 9\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003epositions/position_matrix_#.csv: CSV files containing the position matrix at a specified step\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sampling-nascent-rna-sequencing-read-counts\" class=\"anchor\" aria-hidden=\"true\" href=\"#sampling-nascent-rna-sequencing-read-counts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSampling nascent RNA sequencing read counts\u003c/h2\u003e\n\u003cp\u003eIf you prefer to simulate nascent RNA sequencing read counts in addition to the RNAP positions,\nyou can also follow the tutorial in \u003ccode\u003escripts/sample_read_counts.Rmd\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eZhao, Y., Liu, L. \u0026amp; Siepel, A. Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells. 2022.10.19.512929 Preprint at \u003ca href=\"https://doi.org/10.1101/2022.10.19.512929\" rel=\"nofollow\"\u003ebioRxiv\u003c/a\u003e (2022).\u003c/p\u003e\n", + "readme": "\u003cp\u003eRepository to hold various Docker and singularity container building scripts\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-are-containers\" class=\"anchor\" href=\"#what-are-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat are containers?\u003c/h2\u003e\n\u003cp\u003eContainers according to Amazon:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eContainers provide a standard way to package your application\u0027s code, configurations, and dependencies into a single object.\nContainers share an operating system installed on the server and run as resource-isolated processes, ensuring quick,\nreliable, and consistent deployments, regardless of environment.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eAs far as our group is concerned, we use containers to be able to run the same piece of code on\nthe various compute platforms we have access to.\nThis is primary the Tufts cluster, which requires us to put our code into \u003ccode\u003eSingularity\u003c/code\u003e containers.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-whats-the-repo-for\" class=\"anchor\" href=\"#whats-the-repo-for\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat\u0027s the repo for?\u003c/h2\u003e\n\u003cp\u003eWe hold instructions on how to build particularly useful containers for our work.\nIn addition to packing up the code, containers can be built on top of another allow us to build, for example,\na container holding the common dependencies of our different software packages.\u003c/p\u003e\n\u003cp\u003eThis allows one to build a container for a specific analysis without having to repackage the whole stack of code again.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-are-you-going-to-make-me-build-all-of-these-myself\" class=\"anchor\" href=\"#are-you-going-to-make-me-build-all-of-these-myself\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAre you going to make me build all of these myself?\u003c/h2\u003e\n\u003cp\u003eNo! We keep copies of the containers on our \u003ca href=\"dockerhub\"\u003edockerhub\u003c/a\u003e and \u003ca href=\"https://www.singularity-hub.org/collections/2494\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e hub pages.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-containers-and-the-heirarchy\" class=\"anchor\" href=\"#containers-and-the-heirarchy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers (and the heirarchy)\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/232863cf1838592aed70d01a47bdf2e517095bcbf4637bbf2fccda3c9dc523ab/68747470733a2f2f672e67726176697a6f2e636f6d2f736f757263652f637573746f6d5f6d61726b31303f68747470732533412532462532467261772e67697468756275736572636f6e74656e742e636f6d2532464c41726279732532466c61726279732d636f6e7461696e6572732532466d6173746572253246636f6e7461696e65725f67726170682e646f74\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/232863cf1838592aed70d01a47bdf2e517095bcbf4637bbf2fccda3c9dc523ab/68747470733a2f2f672e67726176697a6f2e636f6d2f736f757263652f637573746f6d5f6d61726b31303f68747470732533412532462532467261772e67697468756275736572636f6e74656e742e636f6d2532464c41726279732532466c61726279732d636f6e7461696e6572732532466d6173746572253246636f6e7461696e65725f67726170682e646f74\" alt=\"Alt text\" data-canonical-src=\"https://g.gravizo.com/source/custom_mark10?https%3A%2F%2Fraw.githubusercontent.com%2FLArbys%2Flarbys-containers%2Fmaster%2Fcontainer_graph.dot\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eContainer\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescripton\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eubuntu\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003ca href=\"https://hub.docker.com/r/nvidia/cuda/\" rel=\"nofollow\"\u003envidia containers\u003c/a\u003e which include cuda and cuDNN libraries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eROOT\u003c/td\u003e\n\u003ctd align=\"left\"\u003ebuild of CERN\u0027s \u003ca href=\"https://github.com/root-project/root\"\u003eROOT\u003c/a\u003e data-analysis library\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eOpenCV\u003c/td\u003e\n\u003ctd align=\"left\"\u003eopen source \u003ca href=\"https://github.com/opencv/opencv\"\u003elibrary\u003c/a\u003e of computer vision algorithms\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ePyTorch\u003c/td\u003e\n\u003ctd align=\"left\"\u003edeep learning \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003elibrary\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eSparseConvNet\u003c/td\u003e\n\u003ctd align=\"left\"\u003eincludes submanifold convolution library for pytorch\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003edllee_unified\u003c/td\u003e\n\u003ctd align=\"left\"\u003ecurrent-gen analysis code for MicroBooNE DL low-energy excess analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eubdl\u003c/td\u003e\n\u003ctd align=\"left\"\u003erepository with next-gen LArbys tools for MicroBooNE DL-working group analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-specific-versions\" class=\"anchor\" href=\"#specific-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecific Versions\u003c/h2\u003e\n\u003cp\u003eHere we list official stack versions to be used for production and analysis studies\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eStack Name\u003c/th\u003e\n\u003cth\u003eubuntu\u003c/th\u003e\n\u003cth\u003epython\u003c/th\u003e\n\u003cth\u003eROOT\u003c/th\u003e\n\u003cth\u003eOpenCV\u003c/th\u003e\n\u003cth\u003ePyTorch\u003c/th\u003e\n\u003cth\u003eSubConvNet (nutufts-fork)\u003c/th\u003e\n\u003cth\u003edllee_unified\u003c/th\u003e\n\u003cth\u003eubdl\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003edllee_unified\u003c/td\u003e\n\u003ctd\u003e16.04 LTS+CUDA 10.0+cuDNN 7\u003c/td\u003e\n\u003ctd\u003e2.7\u003c/td\u003e\n\u003ctd\u003e6.16/00\u003c/td\u003e\n\u003ctd\u003e3.4\u003c/td\u003e\n\u003ctd\u003e1.0.1post2\u003c/td\u003e\n\u003ctd\u003etagXXXXXX\u003c/td\u003e\n\u003ctd\u003etagXXXXXXXX\u003c/td\u003e\n\u003ctd\u003en/a\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eubdl\u003c/td\u003e\n\u003ctd\u003e16.04 LTS+CUDA 10.0+cuDNN 7\u003c/td\u003e\n\u003ctd\u003e2.7\u003c/td\u003e\n\u003ctd\u003e6.16/00\u003c/td\u003e\n\u003ctd\u003e3.4\u003c/td\u003e\n\u003ctd\u003e1.0.1post2\u003c/td\u003e\n\u003ctd\u003etagXXXXXX\u003c/td\u003e\n\u003ctd\u003en/a\u003c/td\u003e\n\u003ctd\u003etagxxxx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eubdl dependences\u003c/td\u003e\n\u003ctd\u003e16.04 LTS+CUDA 11.0+cuDNN 8\u003c/td\u003e\n\u003ctd\u003e3.6.11\u003c/td\u003e\n\u003ctd\u003e6.22/06\u003c/td\u003e\n\u003ctd\u003e3.4.11\u003c/td\u003e\n\u003ctd\u003e1.7.1\u003c/td\u003e\n\u003ctd\u003e7dfbd0f\u003c/td\u003e\n\u003ctd\u003en/a\u003c/td\u003e\n\u003ctd\u003en/a\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe \u003ccode\u003eubdl dependencies\u003c/code\u003e container is used to build the \u003ccode\u003eubdl\u003c/code\u003e repository on Tufts.\nThis provides a development environment.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-built-containers-on-tufts\" class=\"anchor\" href=\"#built-containers-on-tufts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilt containers on Tufts\u003c/h2\u003e\n\u003cp\u003eOn the Tufts Cluster you can find the containers at:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/cluster/tufts/wongjiradlab/larbys/larbys-containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-instructions\" class=\"anchor\" href=\"#instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h1\u003e\n\u003cp\u003eWe use two packages: \u003ca href=\"https://www.docker.com/why-docker\" rel=\"nofollow\"\u003edocker\u003c/a\u003e and \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTypically, we will use \u003ccode\u003edocker\u003c/code\u003e to build the containers and then convert the docker image into a \u003ccode\u003esingularity\u003c/code\u003e container.\u003c/p\u003e\n\u003cp\u003eIn the end, it is not important what tool we use to build the containers (one could use just singularity), but ultimately we must end up with a singularity container to run on the Tufts cluster. (The reason is that docker is not supported on the cluster due to security concerns with docker.)\u003c/p\u003e\n\u003cp\u003eYou can run both docker and singularity from your personal machine. You can also use lab machines at:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTufts: meitner, rubin\u003c/li\u003e\n\u003cli\u003eMIT: nudot, trex\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto build your containers.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-do-i-need-to-do-to-build-a-container\" class=\"anchor\" href=\"#what-do-i-need-to-do-to-build-a-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat do I need to do to build a container?\u003c/h2\u003e\n\u003cp\u003e(still under construction)\u003c/p\u003e\n\u003cp\u003eIn general, you just need to know the instructions you\u0027d type to install the software in question.\nYou put those instructions into a recipe file and tell docker or singularity to build the container.\u003c/p\u003e\n\u003cp\u003eAs an example, we will use the anticipated most-likely case, which is to make a container with a new version of analysis code (\u003ccode\u003eubdl\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eIn the folder \u003ccode\u003eubdl\u003c/code\u003e, there is the docker recipe file to build this container.\nIt probably looks something like the following (assuming it hasn\u0027t changed too much since the time this README was written):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFROM larbys/sparseconvnet:ubuntu16.04_latest\n\nMAINTAINER taritree.wongjirad@tufts.edu\n\n# UBDL\nRUN apt-get update -y \u0026amp;\u0026amp; apt install -y rsync \u0026amp;\u0026amp; apt-get autoremove -y \u0026amp;\u0026amp; apt-get clean -y\nRUN pip install pyyaml typing figcan zmq\nRUN cd /usr/local \u0026amp;\u0026amp; git clone --recursive https://github.com/larbys/ubdl \u0026amp;\u0026amp; \\\n cd ubdl \u0026amp;\u0026amp; chmod +x setenv.sh \u0026amp;\u0026amp; chmod +x buildall.sh \u0026amp;\u0026amp; chmod +x configure.sh\nRUN cd /usr/local/ubdl/larcv \u0026amp;\u0026amp; cp misc/FindCUDA.cmake /usr/local/share/cmake-3.13/Modules/\nRUN cd /usr/local/ubdl \u0026amp;\u0026amp; bash -c \"source /usr/local/root/build/bin/thisroot.sh \u0026amp;\u0026amp; source setenv.sh \u0026amp;\u0026amp; source configure.sh \u0026amp;\u0026amp; source buildall.sh\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first line tells docker to build off of an existing image.\nThis happens to be the \u003ccode\u003elarbys/sparseconvnet\u003c/code\u003e image,\nwhich contains the software stack up to the Sparse Convolutional Network library.\nThe SparseConvNet library is the last dependency for the \u003ccode\u003eubdl\u003c/code\u003e code.\nSo all that\u0027s left to finish the container is to build \u003ccode\u003eubdl\u003c/code\u003e into the container.\u003c/p\u003e\n\u003cp\u003eThe docker file is just the list of instructions to install \u003ccode\u003eubdl\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo build it, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t larbys/ubdl:dev . -f Dockerfile_ubuntu16.04\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e-t\u003c/code\u003e flag is to the set the \"name\" or \"tag\" of the image.\n\u003ccode\u003e.\u003c/code\u003e tells Docker where to find the docker recipe file.\nAnd \u0027-f\u0027 is what recipe file to use (in \u0027.\u0027).\u003c/p\u003e\n\u003cp\u003eWith the image with ubdl built, the next step if one wants to create a container to run\nat Tufts, is to create a singularity container.\nLike the docker build file above,\nwe list the commands we would run to configure the computer for \u003ccode\u003eubdl\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAs an example, in the \u003ccode\u003eubdl\u003c/code\u003e folder,\nyou\u0027ll see a file called \u003ccode\u003eSingularity.ubdl\u003c/code\u003e,\nwhich contains the instructions to build the \u003ccode\u003eubdl\u003c/code\u003e repository.\nIt\u0027ll look something that the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebootstrap: docker\nFrom: larbys/ubdl:latest\n\n%post\n mkdir -p /cluster/home\n mkdir -p /cluster/kappa\n mkdir -p /cluster/shared\n mkdir -p /opt/shared\n\n%environment\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-alternative-build-ubdl-outside-the-container\" class=\"anchor\" href=\"#alternative-build-ubdl-outside-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternative, build \u003ccode\u003eubdl\u003c/code\u003e outside the container\u003c/h2\u003e\n\u003cp\u003eHere, we of course start with the container we built with docker above, \u003ccode\u003elarbys/ubdl:latest\u003c/code\u003e.\nYou can see all we do is create four folders.\nThese folders server to provide a mount point for our container to the network storage area.\nWhen making singularity containers for the Tufts cluster,\nplease include these commands.\u003c/p\u003e\n\u003cp\u003eNote that the instructinos here were about installing \u003ccode\u003eubdl\u003c/code\u003e into the container.\nHowever, an alternative is to clone the \u003ccode\u003eubdl\u003c/code\u003e code into some folder and then compile that source\nusing the libraries found in the container.\nWe provide the \u003ccode\u003eubdl-dependencies\u003c/code\u003e container for this.\u003c/p\u003e\n\u003cp\u003eInstructions on how to do that can be found \u003ca href=\"https://github.com/LArbys/ubdl/wiki/Build-development-copy-of-UBDL-with-container\"\u003ehere\u003c/a\u003e\nas part of the \u003ccode\u003eubdl\u003c/code\u003e wiki.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1673019793.0 + "updated_at": 1629120346.0 }, { "data_format": 2, - "description": "ABC-MK estimations", + "description": null, "filenames": [ - "scripts/singularity/Singularity" + "Singularity.mpich33" ], - "full_name": "jmurga/MKtest.jl", + "full_name": "cjknight/singularity_test", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-abc-mk\" class=\"anchor\" aria-hidden=\"true\" href=\"#abc-mk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eABC-MK\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://jmurga.github.io/MKtest.jl/dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/56f8252ba8e9d3f0b810769543f77823d2fe031ce560d4c2d69fb1fcad800383/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732d6c61746573742d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docs-latest-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMKtest.jl is a Julia package including a fast Approximate Bayesian Computation version of the McDonald-Kreitman test (ABC-MK) presented in \u003ca href=\"https://doi.org/10.1038/s41559-019-0890-6\" rel=\"nofollow\"\u003eUricchio et al. (2019)\u003c/a\u003e. The new ABC-MK implementation significantly improves the efficiency of the population genetics inferences. Following \u003ca href=\"https://doi.org/10.1038/s41559-019-0890-6\" rel=\"nofollow\"\u003eUricchio et al.(2019)\u003c/a\u003e, the analytical estimations were used to explore the effect of background selection and selective interference on weakly beneficial alleles. Nonetheless, we developed a more straightforward and computationally efficient ABC-based inference procedure that accounts for the DFE of deleterious and beneficial alleles and partial recombination between selected genomic elements. Our approach estimates $\\alpha$, $\\alpha_W$, $\\alpha_S$, and the Gamma distribution DFE parameters.\u003c/p\u003e\n\u003cp\u003eIn addition, the package automatizes other MK-like analyses parsing polymorphic and divergence data as well as including several extensions such as \u003ca href=\"https://doi.org/10.1371/journal.pgen.1005774\" rel=\"nofollow\"\u003eGrapes\u003c/a\u003e, \u003ca href=\"https://doi.org/10.1073/pnas.1220835110\" rel=\"nofollow\"\u003eaMK\u003c/a\u003e, \u003ca href=\"https://doi.org/10.1093/g3journal/jkac206\" rel=\"nofollow\"\u003eimputedMK\u003c/a\u003e or \u003ca href=\"https://doi.org/10.1038/4151024a\" rel=\"nofollow\"\u003efwwMK\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://jmurga.github.io/MKtest.jl/dev\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e for details.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_test\" class=\"anchor\" href=\"#singularity_test\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_test\u003c/h1\u003e\n\u003cp\u003eSimple singularity example originally from here: \u003ca href=\"https://github.com/jtchilders/singularity_image_recipes\"\u003ehttps://github.com/jtchilders/singularity_image_recipes\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eTrying to replicate steps here: \u003ca href=\"https://www.alcf.anl.gov/support-center/theta/singularity-theta\" rel=\"nofollow\"\u003ehttps://www.alcf.anl.gov/support-center/theta/singularity-theta\u003c/a\u003e .\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1646232582.0 + "updated_at": 1627936122.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for RATTLE.", "filenames": [ - "Singularity" + "Singularity", + "Singularity-0.0" ], - "full_name": "amanmdesai/singularity-python-packages-demo", + "full_name": "powerPlant/rattle-srf", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-python-packages\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-python-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-python-packages\u003c/h1\u003e\n", + "readme": "\u003cp\u003eSingularity recipe for RATTLE : Reference-free reconstruction and quantification of transcriptomes from long-read sequencing\n\u003ca href=\"https://github.com/comprna/RATTLE\"\u003ehttps://github.com/comprna/RATTLE\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 0, "topics": [], - "updated_at": 1675056243.0 + "updated_at": 1627958435.0 }, { "data_format": 2, - "description": "Open OnDemand Apps used by the ACCRE Visualization Portal", + "description": "TOMTOM docker/singularity container for scanem", "filenames": [ - "rstudio/Singularity", - "rstudio_gpu/Singularity" + "Singularity" ], - "full_name": "accre/ood_apps", + "full_name": "jacobhepkema/scanem-motif", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ood_apps\" class=\"anchor\" aria-hidden=\"true\" href=\"#ood_apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eood_apps\u003c/h1\u003e\n\u003cp\u003eOpen OnDemand Apps used by the ACCRE Visualization Portal\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://quay.io/repository/jacobhepkema/scanem-motif\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f763ec0804bf9dcf1c8c53c453a9add6992333ec5501b757f4c23948408962c5/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6a61636f626865706b656d612f7363616e656d2d6d6f7469662f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/jacobhepkema/scanem-motif/status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-scanem-motif\" class=\"anchor\" href=\"#scanem-motif\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escanem-motif\u003c/h1\u003e\n\u003cp\u003eTOMTOM docker/singularity container for \u003ca href=\"https://github.com/jacobhepkema/scanem\"\u003e\u003cstrong\u003escanem\u003c/strong\u003e\u003c/a\u003e. Quay.io docker repo at \u003ca href=\"https://quay.io/repository/jacobhepkema/scanem-motif\" rel=\"nofollow\"\u003ehttps://quay.io/repository/jacobhepkema/scanem-motif\u003c/a\u003e (see build status above).\u003c/p\u003e\n\u003cp\u003eUsually this container is used in the Nextflow pipeline for \u003ca href=\"https://github.com/jacobhepkema/scanem\"\u003e\u003cstrong\u003escanem\u003c/strong\u003e\u003c/a\u003e. This container contains the MEME suite, which includes the Tomtom motif comparison tool\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eShobhit Gupta, JA Stamatoyannopolous, Timothy Bailey and William Stafford Noble, \"Quantifying similarity between motifs\", Genome Biology, 8(2):R24, 2007.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRun tools by prepending \u003ccode\u003e/opt/bin\u003c/code\u003e to your command, e.g. \u003ccode\u003e/opt/bin/tomtom [args]\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 11, + "subscribers_count": 1, "topics": [], - "updated_at": 1663612575.0 + "updated_at": 1627983632.0 }, { "data_format": 2, - "description": "A suite for benchmarking sparse format conversions using IntelMKL, SPARSKIT and TACO. The tool uses 14 sparse data sources from https://sparse.tamu.edu/ for benchmarking. It also uses a singularity conrtainer making it easy to run test on various machines.", + "description": "Docker image, environent, and scripts to convert dockerfiles to singularity recipes.", "filenames": [ - "container/Singularity.experiments.def", - "container/Singularity.intel-mkl.def", - "container/Singularity.taco-experiments.def", - "container/Singularity.sparskit.def" + "examples/cusignal/Singularity.def", + "examples/seti_bl/Singularity.def" ], - "full_name": "BoiseState-AdaptLab/Sparse_Format_Conversion_Experiments", + "full_name": "jeffreyegan/docker2singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sparse_format_conversion_experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#sparse_format_conversion_experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSparse_Format_Conversion_Experiments\u003c/h1\u003e\n\u003cp\u003eA suite for benchmarking sparse format conversions using IntelMKL, SPARSKIT and TACO. The tool uses 14 sparse data sources from \u003ca href=\"https://sparse.tamu.edu/\" rel=\"nofollow\"\u003ehttps://sparse.tamu.edu/\u003c/a\u003e for benchmarking. It also uses a singularity conrtainer making it easy to run test on various machines.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker2singularity\" class=\"anchor\" href=\"#docker2singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker2singularity\u003c/h1\u003e\n\u003cp\u003eDocker image, environent, and scripts to convert dockerfiles to singularity recipes.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eStage the \u003ccode\u003eDockerfile\u003c/code\u003e you wish to convert in the \u003ccode\u003econvert\u003c/code\u003e directory and then run the following at terminal to execute conversion to a \u003ccode\u003eSingularity.def\u003c/code\u003e output file. The output is produced int he same \u003ccode\u003econvert\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v ~/repos/docker2singularity/convert:/convert -it docker2singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1594315503.0 + "updated_at": 1628008337.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.root5", + "Singularity.17.09", + "Singularity.18.02.1", + "Singularity.18.02" ], - "full_name": "rses-singularity/torch", + "full_name": "NuWro/builds", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-torch\" class=\"anchor\" aria-hidden=\"true\" href=\"#torch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTorch\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nuwro-singularity-recipes\" class=\"anchor\" href=\"#nuwro-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNuWro Singularity recipes\u003c/h1\u003e\n\u003cp\u003eThis repository contains Singularity recipes for containers with \u003ca href=\"https://github.com/NuWro/nuwro\"\u003eNuWro\u003c/a\u003e releases (starting from 17.09).\u003c/p\u003e\n\u003cp\u003eThe builds can be found in \u003ca href=\"https://singularity-hub.org/collections/265\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eInstructions on how to use NuWro containers can be found in \u003ca href=\"https://nuwro.github.io/user-guide/singularity/\" rel=\"nofollow\"\u003eUser Guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor more information about Singularity please visit \u003ca href=\"http://singularity.lbl.gov/user-guide\" rel=\"nofollow\"\u003eSingularity Used Guide\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1542376613.0 + "updated_at": 1522301666.0 }, { "data_format": 2, - "description": "mpi 4.1.4", + "description": "Singularity recipe files for bonito (https://github.com/nanoporetech/bonito)", "filenames": [ - "Singularity" + "Singularity.0.3.6", + "Singularity", + "Singularity.0.4.0" ], - "full_name": "riro3277/SimvascularSIngularity", + "full_name": "powerPlant/bonito-srf", "latest_release": null, + "readme": "\u003cp\u003eSingularity recipe files for bonito, a PyTorch Basecaller for Oxford Nanopore Reads\n\u003ca href=\"https://github.com/nanoporetech/bonito\"\u003ehttps://github.com/nanoporetech/bonito\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1664487305.0 + "updated_at": 1627353613.0 }, { "data_format": 2, - "description": "GNU Octave is software featuring a high-level programming language, primarily intended for numerical computations", + "description": null, "filenames": [ - "6.3.0/Singularity", - "7.3.0/Singularity", - "6.2.0/Singularity", - "7.1.0/Singularity", - "7.2.0/Singularity", - "6.4.0/Singularity" + "docker/Singularity.snowflake" ], - "full_name": "pscedu/singularity-octave", - "latest_release": "v7.2.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-octave/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-octave/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-octave/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-octave/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f0eef640ab704754409e5b3805cf764e1ae5c4a70f474b51e86e21ca7c7ce71a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6f6374617665\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f0eef640ab704754409e5b3805cf764e1ae5c4a70f474b51e86e21ca7c7ce71a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6f6374617665\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-octave\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/af95265639886aed45e4f673056ff5d595df2b5d5760eb36abe563365c430bb7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6f6374617665\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/af95265639886aed45e4f673056ff5d595df2b5d5760eb36abe563365c430bb7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6f6374617665\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-octave\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4f62994add717900b8861b5c16791ebf7c20c850bdd05ed86990f15629ef2696/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6f6374617665\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4f62994add717900b8861b5c16791ebf7c20c850bdd05ed86990f15629ef2696/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6f6374617665\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-octave\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a17a4ce476d76eb74b11af4b5598c58d76785b012ef2ed56b3eb98106a68fdab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6f6374617665\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a17a4ce476d76eb74b11af4b5598c58d76785b012ef2ed56b3eb98106a68fdab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6f6374617665\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-octave\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-octave\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-octave\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-octave\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/21d293a8bfd3418f027c62e2ef688e8549d00a26a56b2c3bb9810bc6d5adf754/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f362f36612f476e752d6f63746176652d6c6f676f2e7376672f3139323070782d476e752d6f63746176652d6c6f676f2e7376672e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/21d293a8bfd3418f027c62e2ef688e8549d00a26a56b2c3bb9810bc6d5adf754/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f362f36612f476e752d6f63746176652d6c6f676f2e7376672f3139323070782d476e752d6f63746176652d6c6f676f2e7376672e706e67\" width=\"15%\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/6/6a/Gnu-octave-logo.svg/1920px-Gnu-octave-logo.svg.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.gnu.org/software/octave/\" rel=\"nofollow\"\u003eOctave\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eoctave-cli\u003c/code\u003e, \u003ccode\u003epandoc\u003c/code\u003e and \u003ccode\u003egnuplot\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/octave/6.3.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/octave\u003c/code\u003e as \u003ccode\u003e6.3.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "nuKs/preprocessing", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-useful-utilities-for-single-cell-processing-with-alevin-fry\" class=\"anchor\" href=\"#useful-utilities-for-single-cell-processing-with-alevin-fry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful utilities for single-cell processing with alevin-fry\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/COMBINE-lab/alevin-fry\"\u003eAlevin-fry\u003c/a\u003e is a fast, accurate and memory-frugal tool for preprocessing single-cell and single-nucleus RNA-seq data. You can read more about alevin-fry in \u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.06.29.450377v1\" rel=\"nofollow\"\u003eits pre-print\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis respoistory contains scripts, functions and utilities that are useful for preparing data for processing with alevin-fry, as well as for reading alevin-fry data into other packages for downstream analysis.\u003c/p\u003e\n\u003cp\u003eThe different utilities are broken down in this repository by the language in which they are written (right now, Python, R and bash). A brief listing of\nthe available utilities currently in the repository is:\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-r-language\" class=\"anchor\" href=\"#r-language\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR language\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e \u2014 A script to build a spliced + intron (splici) ref for indexing and quantification with \u003ccode\u003ealevin-fry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esplici.R\u003c/code\u003e \u2014 Contains the \u003ccode\u003emake_splici_txome\u003c/code\u003e function, which is the function called by the \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e wrapper script. If you want to build a splici reference programatically in R code, you can use this function.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecellRangerLikeEmptyDrops.R\u003c/code\u003e \u2014 An implementation of the hybrid UMI count filtering and \u003ca href=\"https://github.com/MarioniLab/DropletUtils\"\u003e\u003ccode\u003eemptyDrops\u003c/code\u003e\u003c/a\u003e used by CellRanger (and subsequently by \u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTARsolo\u003c/a\u003e). This R implementation is a translation of the implemntation in STARsolo, which itself was reverse-engineered from CellRanger.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eload_fry.R\u003c/code\u003e \u2014 Contains a function to load \u003ccode\u003ealevin-fry\u003c/code\u003e output (including from USA mode quantification) into a \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html\" rel=\"nofollow\"\u003e\u003ccode\u003eSingleCellExperiment\u003c/code\u003e\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-python-language\" class=\"anchor\" href=\"#python-language\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython language\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eload_fry.py\u003c/code\u003e \u2014 Contains a Python function \u003ccode\u003eload_fry\u003c/code\u003e which is intended to load \u003ccode\u003ealevin-fry\u003c/code\u003e output (including from USA mode quantification) into a \u003ca href=\"https://github.com/theislab/scanpy\"\u003e\u003ccode\u003eScanpy\u003c/code\u003e\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bash\" class=\"anchor\" href=\"#bash\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBash\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eget_10x_permit_lists.sh\u003c/code\u003e \u2014 Provides a script to download the 10x chromium v2 or v3 permit lists.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esimpleaf\u003c/code\u003e \u2014 Provides a script to run the entire \u003ccode\u003esalmon -\u0026gt; alevin-fry (generate-permit-list \u0026gt; collate \u0026gt; quant)\u003c/code\u003e pipeline, though providing only a simplified set of options.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-simpleaf\" class=\"anchor\" href=\"#using-simpleaf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing simpleaf\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003esimpleaf\u003c/code\u003e script that resides in the \u003ccode\u003ebash\u003c/code\u003e subdirectory is intended to simply the running of \u003ccode\u003ealevin-fry\u003c/code\u003e in common usage scenarios. By limiting some of the different options that can be set, it provides a streamlined way to build the splici reference and index in a single command, as well as to process an experiment from raw FASTQ files to a count matrix in a single command.\u003c/p\u003e\n\u003cp\u003eTo work properly, \u003ccode\u003esimpleaf\u003c/code\u003e has a few requirements. First, it should be run from \u003cem\u003ewithin\u003c/em\u003e the \u003ccode\u003ebash\u003c/code\u003e subdirectory of this repository. This is because it currently makes assumptions about the relative paths of the scripts \u003ccode\u003eget_10x_permit_lists.sh\u003c/code\u003e and \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e. Additionally, the following environment variables are used within \u003ccode\u003esimpleaf\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eALEVIN_FRY_HOME\u003c/code\u003e \u003cstrong\u003eREQUIRED\u003c/strong\u003e \u2014 This directory will be used for persistent configuration and small file (\u0026lt;1G) storage between runs. If you provide a directory and it doesn\u0027t exist, it will be created. It is easiest to just set this in your enviornment globally so that the same home can be used over many runs without you having to provide the variable explicitly each time. A good choice for this variable might be something like \u003ccode\u003e~/.alevin_fry_home\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSALMON_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ccode\u003esalmon\u003c/code\u003e executable of version \u0026gt;= 1.5.1. If not provided, the script will assume it can simply invoke \u003ccode\u003esalmon\u003c/code\u003e in the current enviornment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eFRY_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ccode\u003ealevin-fry\u003c/code\u003e executable of version \u0026gt;= 0.4.0. If not provided, the script will assume it can simply invoke \u003ccode\u003ealevin-fry\u003c/code\u003e in the current enviornment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eTIME_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ca href=\"https://www.gnu.org/software/time/\" rel=\"nofollow\"\u003eGNU time\u003c/a\u003e executable; this is different from the shell \u003ccode\u003etime\u003c/code\u003e command, and on most linux systems exists at \u003ccode\u003e/usr/bin/time\u003c/code\u003e. If this variable is not provided, the script will assume it can use \u003ccode\u003e/usr/bin/time\u003c/code\u003e. On OSX systems, you should install GNU time explicitly. This can be done with \u003ca href=\"https://anaconda.org/conda-forge/time\" rel=\"nofollow\"\u003econda\u003c/a\u003e or homebrew.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003esimpleaf\u003c/code\u003e script has two sub-commands:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eindex\u003c/code\u003e \u2014 The \u003ccode\u003eindex\u003c/code\u003e command will take a reference genome FASTA and GTF as input, build a splici reference using the \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e script, and then build a sparse \u003ccode\u003esalmon\u003c/code\u003e index on the resulting reference. \u003cstrong\u003eNote\u003c/strong\u003e: The \u003ccode\u003eindex\u003c/code\u003e command requires the \u003ccode\u003eRscript\u003c/code\u003e executable to be in the path, as well as all of theR packages that are required by \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e. The relevant options (which you can obtain by running \u003ccode\u003e./simpleaf index -h\u003c/code\u003e) are:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eUsage: ./simpleaf index [options]\n options:\n -f, --fasta REQUIRED genome reference FASTA file\n -g, --gtf REQUIRED GTF file with gene annotations\n -l, --rlen REQUIRED the target read length the index will be built for\n -o, --output REQUIRED path to output directory (will be created if it doesn\u0027t exist)\n -s, --spliced OPTIONAL path to FASTA file with extra spliced sequence to add to the index\n -u, --unspliced OPTIONAL path to FASTA file with extra unspliced sequence to add to the index\n -d, --dedup FLAG OPTIONAL deduplicate identical sequences inside the R script when building the splici reference\n -t, --threads OPTIONAL number of threads to use when running [default: min(16, num cores)]\n -h, --help display this help message\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003equant\u003c/code\u003e \u2014 The \u003ccode\u003equant\u003c/code\u003e command takes as input the index, reads, and relevant information about the experiment (e.g. chemistry), and runs all of the steps of the \u003ccode\u003ealevin-fry\u003c/code\u003e pipeline, from mapping with \u003ccode\u003esalmon\u003c/code\u003e through \u003ccode\u003equant\u003c/code\u003e with \u003ccode\u003ealevin-fry\u003c/code\u003e. The relevant options (which you can obtain by running \u003ccode\u003e./simpleaf quant -h\u003c/code\u003e) are:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eUsage: ./simpleaf quant [options]\n options:\n -1, --r1 REQUIRED comma separated list of left reads\n -2, --r2 REQUIRED comma separated list of right reads\n -i, --index REQUIRED path to a (sparse or dense) salmon splici index\n -o, --output REQUIRED path to output directory (will be created if it doesn\u0027t exist)\n -f, --fmode REQUIRED permit list filter mode, one of {knee, k, unfilt, u}\n -c, --chem REQUIRED chemistry of experiment, one of {v2, v3}\n -r, --res REQUIRED resolution strategy for alevin-fry, one of {cr-like, cr-like-em}\n -m, --t2g REQUIRED three-column txp-to-gene file to pass to alevin-fry quant command\n -t, --threads OPTIONAL number of threads to use when running [default: min(16, num cores)]\n -h, --help display this help message\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "numerical-computation" - ], - "updated_at": 1633062005.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1626495005.0 }, { "data_format": 2, - "description": "Gnuplot is a portable command-line driven graphing utility for Linux, OS/2, MS Windows, OSX, VMS, and many other platforms. ", + "description": null, "filenames": [ - "5.4.5/Singularity", - "5.4/Singularity" + "docker/Singularity.snowflake" ], - "full_name": "pscedu/singularity-gnuplot", - "latest_release": "v5.4.5", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-gnuplot/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-gnuplot/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-gnuplot/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-gnuplot/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-gnuplot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/74c6e2027d7219588767058a259260a20db37f4dd71ce7104696072751036512/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d676e75706c6f74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/74c6e2027d7219588767058a259260a20db37f4dd71ce7104696072751036512/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d676e75706c6f74\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-gnuplot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-gnuplot\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-gnuplot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-gnuplot\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d8469d53dc10c159651aa0f44ad8eced294e0cdb843467ee1ec27671a69f551e/687474703a2f2f676e75706c6f742e736f75726365666f7267652e6e65742f64656d6f2f616e696d617465322e312e676966\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d8469d53dc10c159651aa0f44ad8eced294e0cdb843467ee1ec27671a69f551e/687474703a2f2f676e75706c6f742e736f75726365666f7267652e6e65742f64656d6f2f616e696d617465322e312e676966\" alt=\"Plot\" data-canonical-src=\"http://gnuplot.sourceforge.net/demo/animate2.1.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://gnuplot.info/\" rel=\"nofollow\"\u003egnuplot\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003egnuplot\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/gnuplot/5.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/gnuplot\u003c/code\u003e as \u003ccode\u003e5.4.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pnplab/preprocessing", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-useful-utilities-for-single-cell-processing-with-alevin-fry\" class=\"anchor\" href=\"#useful-utilities-for-single-cell-processing-with-alevin-fry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful utilities for single-cell processing with alevin-fry\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/COMBINE-lab/alevin-fry\"\u003eAlevin-fry\u003c/a\u003e is a fast, accurate and memory-frugal tool for preprocessing single-cell and single-nucleus RNA-seq data. You can read more about alevin-fry in \u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.06.29.450377v1\" rel=\"nofollow\"\u003eits pre-print\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis respoistory contains scripts, functions and utilities that are useful for preparing data for processing with alevin-fry, as well as for reading alevin-fry data into other packages for downstream analysis.\u003c/p\u003e\n\u003cp\u003eThe different utilities are broken down in this repository by the language in which they are written (right now, Python, R and bash). A brief listing of\nthe available utilities currently in the repository is:\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-r-language\" class=\"anchor\" href=\"#r-language\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR language\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e \u2014 A script to build a spliced + intron (splici) ref for indexing and quantification with \u003ccode\u003ealevin-fry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esplici.R\u003c/code\u003e \u2014 Contains the \u003ccode\u003emake_splici_txome\u003c/code\u003e function, which is the function called by the \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e wrapper script. If you want to build a splici reference programatically in R code, you can use this function.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecellRangerLikeEmptyDrops.R\u003c/code\u003e \u2014 An implementation of the hybrid UMI count filtering and \u003ca href=\"https://github.com/MarioniLab/DropletUtils\"\u003e\u003ccode\u003eemptyDrops\u003c/code\u003e\u003c/a\u003e used by CellRanger (and subsequently by \u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTARsolo\u003c/a\u003e). This R implementation is a translation of the implemntation in STARsolo, which itself was reverse-engineered from CellRanger.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eload_fry.R\u003c/code\u003e \u2014 Contains a function to load \u003ccode\u003ealevin-fry\u003c/code\u003e output (including from USA mode quantification) into a \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html\" rel=\"nofollow\"\u003e\u003ccode\u003eSingleCellExperiment\u003c/code\u003e\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-python-language\" class=\"anchor\" href=\"#python-language\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython language\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eload_fry.py\u003c/code\u003e \u2014 Contains a Python function \u003ccode\u003eload_fry\u003c/code\u003e which is intended to load \u003ccode\u003ealevin-fry\u003c/code\u003e output (including from USA mode quantification) into a \u003ca href=\"https://github.com/theislab/scanpy\"\u003e\u003ccode\u003eScanpy\u003c/code\u003e\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bash\" class=\"anchor\" href=\"#bash\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBash\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eget_10x_permit_lists.sh\u003c/code\u003e \u2014 Provides a script to download the 10x chromium v2 or v3 permit lists.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esimpleaf\u003c/code\u003e \u2014 Provides a script to run the entire \u003ccode\u003esalmon -\u0026gt; alevin-fry (generate-permit-list \u0026gt; collate \u0026gt; quant)\u003c/code\u003e pipeline, though providing only a simplified set of options.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-simpleaf\" class=\"anchor\" href=\"#using-simpleaf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing simpleaf\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003esimpleaf\u003c/code\u003e script that resides in the \u003ccode\u003ebash\u003c/code\u003e subdirectory is intended to simply the running of \u003ccode\u003ealevin-fry\u003c/code\u003e in common usage scenarios. By limiting some of the different options that can be set, it provides a streamlined way to build the splici reference and index in a single command, as well as to process an experiment from raw FASTQ files to a count matrix in a single command.\u003c/p\u003e\n\u003cp\u003eTo work properly, \u003ccode\u003esimpleaf\u003c/code\u003e has a few requirements. First, it should be run from \u003cem\u003ewithin\u003c/em\u003e the \u003ccode\u003ebash\u003c/code\u003e subdirectory of this repository. This is because it currently makes assumptions about the relative paths of the scripts \u003ccode\u003eget_10x_permit_lists.sh\u003c/code\u003e and \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e. Additionally, the following environment variables are used within \u003ccode\u003esimpleaf\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eALEVIN_FRY_HOME\u003c/code\u003e \u003cstrong\u003eREQUIRED\u003c/strong\u003e \u2014 This directory will be used for persistent configuration and small file (\u0026lt;1G) storage between runs. If you provide a directory and it doesn\u0027t exist, it will be created. It is easiest to just set this in your enviornment globally so that the same home can be used over many runs without you having to provide the variable explicitly each time. A good choice for this variable might be something like \u003ccode\u003e~/.alevin_fry_home\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSALMON_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ccode\u003esalmon\u003c/code\u003e executable of version \u0026gt;= 1.5.1. If not provided, the script will assume it can simply invoke \u003ccode\u003esalmon\u003c/code\u003e in the current enviornment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eFRY_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ccode\u003ealevin-fry\u003c/code\u003e executable of version \u0026gt;= 0.4.0. If not provided, the script will assume it can simply invoke \u003ccode\u003ealevin-fry\u003c/code\u003e in the current enviornment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eTIME_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ca href=\"https://www.gnu.org/software/time/\" rel=\"nofollow\"\u003eGNU time\u003c/a\u003e executable; this is different from the shell \u003ccode\u003etime\u003c/code\u003e command, and on most linux systems exists at \u003ccode\u003e/usr/bin/time\u003c/code\u003e. If this variable is not provided, the script will assume it can use \u003ccode\u003e/usr/bin/time\u003c/code\u003e. On OSX systems, you should install GNU time explicitly. This can be done with \u003ca href=\"https://anaconda.org/conda-forge/time\" rel=\"nofollow\"\u003econda\u003c/a\u003e or homebrew.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003esimpleaf\u003c/code\u003e script has two sub-commands:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eindex\u003c/code\u003e \u2014 The \u003ccode\u003eindex\u003c/code\u003e command will take a reference genome FASTA and GTF as input, build a splici reference using the \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e script, and then build a sparse \u003ccode\u003esalmon\u003c/code\u003e index on the resulting reference. \u003cstrong\u003eNote\u003c/strong\u003e: The \u003ccode\u003eindex\u003c/code\u003e command requires the \u003ccode\u003eRscript\u003c/code\u003e executable to be in the path, as well as all of theR packages that are required by \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e. The relevant options (which you can obtain by running \u003ccode\u003e./simpleaf index -h\u003c/code\u003e) are:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eUsage: ./simpleaf index [options]\n options:\n -f, --fasta REQUIRED genome reference FASTA file\n -g, --gtf REQUIRED GTF file with gene annotations\n -l, --rlen REQUIRED the target read length the index will be built for\n -o, --output REQUIRED path to output directory (will be created if it doesn\u0027t exist)\n -s, --spliced OPTIONAL path to FASTA file with extra spliced sequence to add to the index\n -u, --unspliced OPTIONAL path to FASTA file with extra unspliced sequence to add to the index\n -d, --dedup FLAG OPTIONAL deduplicate identical sequences inside the R script when building the splici reference\n -t, --threads OPTIONAL number of threads to use when running [default: min(16, num cores)]\n -h, --help display this help message\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003equant\u003c/code\u003e \u2014 The \u003ccode\u003equant\u003c/code\u003e command takes as input the index, reads, and relevant information about the experiment (e.g. chemistry), and runs all of the steps of the \u003ccode\u003ealevin-fry\u003c/code\u003e pipeline, from mapping with \u003ccode\u003esalmon\u003c/code\u003e through \u003ccode\u003equant\u003c/code\u003e with \u003ccode\u003ealevin-fry\u003c/code\u003e. The relevant options (which you can obtain by running \u003ccode\u003e./simpleaf quant -h\u003c/code\u003e) are:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eUsage: ./simpleaf quant [options]\n options:\n -1, --r1 REQUIRED comma separated list of left reads\n -2, --r2 REQUIRED comma separated list of right reads\n -i, --index REQUIRED path to a (sparse or dense) salmon splici index\n -o, --output REQUIRED path to output directory (will be created if it doesn\u0027t exist)\n -f, --fmode REQUIRED permit list filter mode, one of {knee, k, unfilt, u}\n -c, --chem REQUIRED chemistry of experiment, one of {v2, v3}\n -r, --res REQUIRED resolution strategy for alevin-fry, one of {cr-like, cr-like-em}\n -m, --t2g REQUIRED three-column txp-to-gene file to pass to alevin-fry quant command\n -t, --threads OPTIONAL number of threads to use when running [default: min(16, num cores)]\n -h, --help display this help message\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1668307366.0 + "subscribers_count": 0, + "topics": [], + "updated_at": 1626495060.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity.0.1.1", + "Singularity.0.1", + "Singularity" ], - "full_name": "paplessix/Recvis22", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-planning-with-diffusion--\" class=\"anchor\" aria-hidden=\"true\" href=\"#planning-with-diffusion--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanning with Diffusion \u00a0\u00a0 \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eTraining and visualizing of diffusion models from \u003ca href=\"https://diffusion-planning.github.io/\" rel=\"nofollow\"\u003ePlanning with Diffusion for Flexible Behavior Synthesis\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/main\"\u003emain branch\u003c/a\u003e contains code for training diffusion models and planning via value-function guided sampling on the D4RL locomotion environments.\nThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/kuka\"\u003ekuka branch\u003c/a\u003e contains block-stacking experiments.\nThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/maze2d\"\u003emaze2d branch\u003c/a\u003e contains goal-reaching via inpainting in the Maze2D environments.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/184a6bb0418c60d778dcfa9f81dfddfdea0c0a3238b1d0cc4aa8666dd32ad3ca/68747470733a2f2f646966667573696f6e2d706c616e6e696e672e6769746875622e696f2f696d616765732f64696666757365722d636172642e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/184a6bb0418c60d778dcfa9f81dfddfdea0c0a3238b1d0cc4aa8666dd32ad3ca/68747470733a2f2f646966667573696f6e2d706c616e6e696e672e6769746875622e696f2f696d616765732f64696666757365722d636172642e706e67\" width=\"60%\" title=\"Diffuser model\" data-canonical-src=\"https://diffusion-planning.github.io/images/diffuser-card.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUpdates\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e12/09/2022: Diffuser (the RL model) has been integrated into \u003cg-emoji class=\"g-emoji\" alias=\"hugs\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f917.png\"\u003e\ud83e\udd17\u003c/g-emoji\u003e Diffusers (the Hugging Face diffusion model library)! See \u003ca href=\"https://huggingface.co/docs/diffusers/using-diffusers/rl\" rel=\"nofollow\"\u003ethese docs\u003c/a\u003e for how to run Diffuser using their pipeline.\u003c/li\u003e\n\u003cli\u003e10/17/2022: A bug in the value function scaling has been fixed in \u003ca href=\"https://github.com/jannerm/diffuser/commit/3d7361c2d028473b601cc04f5eecd019e14eb4eb\"\u003ethis commit\u003c/a\u003e. Thanks to \u003ca href=\"https://scholar.google.com/citations?user=Q6UMpRYAAAAJ\u0026amp;hl=en\" rel=\"nofollow\"\u003ePhilemon Brakel\u003c/a\u003e for catching it!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eLoad a pretrained diffusion model and sample from it in your browser with \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003escripts/diffuser-sample.ipynb\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate diffuser\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-pretrained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-pretrained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing pretrained models\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-downloading-weights\" class=\"anchor\" aria-hidden=\"true\" href=\"#downloading-weights\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownloading weights\u003c/h3\u003e\n\u003cp\u003eDownload pretrained diffusion models and value functions with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./scripts/download_pretrained.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis command downloads and extracts a \u003ca href=\"https://drive.google.com/file/d/1wc1m4HLj7btaYDN8ogDIAV9QzgWEckGy/view?usp=share_link\" rel=\"nofollow\"\u003etarfile\u003c/a\u003e containing \u003ca href=\"https://drive.google.com/drive/folders/1ie6z3toz9OjcarJuwjQwXXzDwh1XnS02?usp=sharing\" rel=\"nofollow\"\u003ethis directory\u003c/a\u003e to \u003ccode\u003elogs/pretrained\u003c/code\u003e. The models are organized according to the following structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u2514\u2500\u2500 logs/pretrained\n \u251c\u2500\u2500 ${environment_1}\n \u2502 \u251c\u2500\u2500 diffusion\n \u2502 \u2502 \u2514\u2500\u2500 ${experiment_name}\n \u2502 \u2502 \u251c\u2500\u2500 state_${epoch}.pt\n \u2502 \u2502 \u251c\u2500\u2500 sample-${epoch}-*.png\n \u2502 \u2502 \u2514\u2500\u2500 {dataset, diffusion, model, render, trainer}_config.pkl\n \u2502 \u251c\u2500\u2500 values\n \u2502 \u2502 \u2514\u2500\u2500 ${experiment_name}\n \u2502 \u2502 \u251c\u2500\u2500 state_${epoch}.pt\n \u2502 \u2502 \u2514\u2500\u2500 {dataset, diffusion, model, render, trainer}_config.pkl\n \u2502 \u2514\u2500\u2500 plans\n \u2502 \u2514\u2500\u2500 defaults\n \u2502 \u251c\u2500\u2500 0\n \u2502 \u251c\u2500\u2500 1\n \u2502 \u251c\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 149\n \u2502\n \u251c\u2500\u2500 ${environment_2}\n \u2502 \u2514\u2500\u2500 ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003estate_${epoch}.pt\u003c/code\u003e files contain the network weights and the \u003ccode\u003econfig.pkl\u003c/code\u003e files contain the instantation arguments for the relevant classes.\nThe png files contain samples from different points during training of the diffusion model.\nWithin the \u003ccode\u003eplans\u003c/code\u003e subfolders, there are the results of 150 evaluation trials for each environment using the default hyperparameters.\u003c/p\u003e\n\u003cdetails\u003e\n\u003csummary\u003eTo aggregate the results of the evaluations in the \u003ccode\u003elogs\u003c/code\u003e folder, run \u003ccode\u003epython scripts/read_results.py\u003c/code\u003e. (Expand to view the output of this command on the plans downloaded from Google Drive.)\n\u003c/summary\u003e\n\u003cpre\u003e\u003ccode\u003ehopper-medium-replay-v2 | defaults | logs/pretrained/hopper-medium-replay-v2/plans | 150 scores\n 93.6 +/- 0.37\nhopper-medium-v2 | defaults | logs/pretrained/hopper-medium-v2/plans | 150 scores\n 74.3 +/- 1.36\nhopper-medium-expert-v2 | defaults | logs/pretrained/hopper-medium-expert-v2/plans | 150 scores\n 103.3 +/- 1.30\nwalker2d-medium-replay-v2 | defaults | logs/pretrained/walker2d-medium-replay-v2/plans | 150 scores\n 70.6 +/- 1.60\nwalker2d-medium-v2 | defaults | logs/pretrained/walker2d-medium-v2/plans | 150 scores\n 79.6 +/- 0.55\nwalker2d-medium-expert-v2 | defaults | logs/pretrained/walker2d-medium-expert-v2/plans | 150 scores\n 106.9 +/- 0.24\nhalfcheetah-medium-replay-v2 | defaults | logs/pretrained/halfcheetah-medium-replay-v2/plans | 150 scores\n 37.7 +/- 0.45\nhalfcheetah-medium-v2 | defaults | logs/pretrained/halfcheetah-medium-v2/plans | 150 scores\n 42.8 +/- 0.32\nhalfcheetah-medium-expert-v2 | defaults | logs/pretrained/halfcheetah-medium-expert-v2/plans | 150 scores\n 88.9 +/- 0.25\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eTo create the table of offline RL results from the paper, run \u003ccode\u003epython plotting/table.py\u003c/code\u003e. This will print a table that can be copied into a Latex document. (Expand to view table source.)\u003c/summary\u003e\n\u003cpre\u003e\u003ccode\u003e\\definecolor{tblue}{HTML}{1F77B4}\n\\definecolor{tred}{HTML}{FF6961}\n\\definecolor{tgreen}{HTML}{429E9D}\n\\definecolor{thighlight}{HTML}{000000}\n\\newcolumntype{P}{\u0026gt;{\\raggedleft\\arraybackslash}X}\n\\begin{table*}[hb!]\n\\centering\n\\small\n\\begin{tabularx}{\\textwidth}{llPPPPPPPPr}\n\\toprule\n\\multicolumn{1}{r}{\\bf \\color{black} Dataset} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} Environment} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} BC} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} CQL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} IQL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} DT} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} TT} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MOPO} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MOReL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MBOP} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} Diffuser} \\\\ \n\\midrule\nMedium-Expert \u0026amp; HalfCheetah \u0026amp; $55.2$ \u0026amp; $91.6$ \u0026amp; $86.7$ \u0026amp; $86.8$ \u0026amp; $95.0$ \u0026amp; $63.3$ \u0026amp; $53.3$ \u0026amp; $\\textbf{\\color{thighlight}105.9}$ \u0026amp; $88.9$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.3$}} \\\\ \nMedium-Expert \u0026amp; Hopper \u0026amp; $52.5$ \u0026amp; $\\textbf{\\color{thighlight}105.4}$ \u0026amp; $91.5$ \u0026amp; $\\textbf{\\color{thighlight}107.6}$ \u0026amp; $\\textbf{\\color{thighlight}110.0}$ \u0026amp; $23.7$ \u0026amp; $\\textbf{\\color{thighlight}108.7}$ \u0026amp; $55.1$ \u0026amp; $103.3$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.3$}} \\\\ \nMedium-Expert \u0026amp; Walker2d \u0026amp; $\\textbf{\\color{thighlight}107.5}$ \u0026amp; $\\textbf{\\color{thighlight}108.8}$ \u0026amp; $\\textbf{\\color{thighlight}109.6}$ \u0026amp; $\\textbf{\\color{thighlight}108.1}$ \u0026amp; $101.9$ \u0026amp; $44.6$ \u0026amp; $95.6$ \u0026amp; $70.2$ \u0026amp; $\\textbf{\\color{thighlight}106.9}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.2$}} \\\\ \n\\midrule\nMedium \u0026amp; HalfCheetah \u0026amp; $42.6$ \u0026amp; $44.0$ \u0026amp; $\\textbf{\\color{thighlight}47.4}$ \u0026amp; $42.6$ \u0026amp; $\\textbf{\\color{thighlight}46.9}$ \u0026amp; $42.3$ \u0026amp; $42.1$ \u0026amp; $44.6$ \u0026amp; $42.8$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.3$}} \\\\ \nMedium \u0026amp; Hopper \u0026amp; $52.9$ \u0026amp; $58.5$ \u0026amp; $66.3$ \u0026amp; $67.6$ \u0026amp; $61.1$ \u0026amp; $28.0$ \u0026amp; $\\textbf{\\color{thighlight}95.4}$ \u0026amp; $48.8$ \u0026amp; $74.3$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.4$}} \\\\ \nMedium \u0026amp; Walker2d \u0026amp; $75.3$ \u0026amp; $72.5$ \u0026amp; $\\textbf{\\color{thighlight}78.3}$ \u0026amp; $74.0$ \u0026amp; $\\textbf{\\color{thighlight}79.0}$ \u0026amp; $17.8$ \u0026amp; $\\textbf{\\color{thighlight}77.8}$ \u0026amp; $41.0$ \u0026amp; $\\textbf{\\color{thighlight}79.6}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.55$}} \\\\ \n\\midrule\nMedium-Replay \u0026amp; HalfCheetah \u0026amp; $36.6$ \u0026amp; $45.5$ \u0026amp; $44.2$ \u0026amp; $36.6$ \u0026amp; $41.9$ \u0026amp; $\\textbf{\\color{thighlight}53.1}$ \u0026amp; $40.2$ \u0026amp; $42.3$ \u0026amp; $37.7$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.5$}} \\\\ \nMedium-Replay \u0026amp; Hopper \u0026amp; $18.1$ \u0026amp; $\\textbf{\\color{thighlight}95.0}$ \u0026amp; $\\textbf{\\color{thighlight}94.7}$ \u0026amp; $82.7$ \u0026amp; $\\textbf{\\color{thighlight}91.5}$ \u0026amp; $67.5$ \u0026amp; $\\textbf{\\color{thighlight}93.6}$ \u0026amp; $12.4$ \u0026amp; $\\textbf{\\color{thighlight}93.6}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.4$}} \\\\ \nMedium-Replay \u0026amp; Walker2d \u0026amp; $26.0$ \u0026amp; $77.2$ \u0026amp; $73.9$ \u0026amp; $66.6$ \u0026amp; $\\textbf{\\color{thighlight}82.6}$ \u0026amp; $39.0$ \u0026amp; $49.8$ \u0026amp; $9.7$ \u0026amp; $70.6$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.6$}} \\\\ \n\\midrule\n\\multicolumn{2}{c}{\\bf Average} \u0026amp; 51.9 \u0026amp; \\textbf{\\color{thighlight}77.6} \u0026amp; \\textbf{\\color{thighlight}77.0} \u0026amp; 74.7 \u0026amp; \\textbf{\\color{thighlight}78.9} \u0026amp; 42.1 \u0026amp; 72.9 \u0026amp; 47.8 \u0026amp; \\textbf{\\color{thighlight}77.5} \\hspace{.6cm} \\\\ \n\\bottomrule\n\\end{tabularx}\n\\vspace{-.0cm}\n\\caption{\n}\n\\label{table:locomotion}\n\\end{table*}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/diffusion-planning/diffusion-planning.github.io/blob/master/images/table.png\"\u003e\u003cimg src=\"https://github.com/diffusion-planning/diffusion-planning.github.io/raw/master/images/table.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003ch3\u003e\u003ca id=\"user-content-planning\" class=\"anchor\" aria-hidden=\"true\" href=\"#planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanning\u003c/h3\u003e\n\u003cp\u003eTo plan with guided sampling, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs/pretrained\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--logbase\u003c/code\u003e flag points the \u003ca href=\"scripts/plan_guided.py#L22-L30\"\u003eexperiment loaders\u003c/a\u003e to the folder containing the pretrained models.\nYou can override planning hyperparameters with flags, such as \u003ccode\u003e--batch_size 8\u003c/code\u003e, but the default\nhyperparameters are a good starting point.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining from scratch\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eTrain a diffusion model with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/train.py --dataset halfcheetah-medium-expert-v2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe default hyperparameters are listed in \u003ca href=\"config/locomotion.py#L22-L65\"\u003elocomotion:diffusion\u003c/a\u003e.\nYou can override any of them with flags, eg, \u003ccode\u003e--n_diffusion_steps 100\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTrain a value function with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/train_values.py --dataset halfcheetah-medium-expert-v2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"config/locomotion.py#L67-L108\"\u003elocomotion:values\u003c/a\u003e for the corresponding default hyperparameters.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003ePlan using your newly-trained models with the same command as in the pretrained planning section, simply replacing the logbase to point to your new models:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"config/locomotion.py#L110-L149\"\u003elocomotion:plans\u003c/a\u003e for the corresponding default hyperparameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeferred f-strings.\u003c/strong\u003e Note that some planning script arguments, such as \u003ccode\u003e--n_diffusion_steps\u003c/code\u003e or \u003ccode\u003e--discount\u003c/code\u003e,\ndo not actually change any logic during planning, but simply load a different model using a deferred f-string.\nFor example, the following flags:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e---horizon 32 --n_diffusion_steps 20 --discount 0.997\n--value_loadpath \u0027f:values/defaults_H{horizon}_T{n_diffusion_steps}_d{discount}\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill resolve to a value checkpoint path of \u003ccode\u003evalues/defaults_H32_T20_d0.997\u003c/code\u003e. It is possible to\nchange the horizon of the diffusion model after training (see \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for an example),\nbut not for the value function.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile . -t diffuser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTest the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm --gpus all \\\n --mount type=bind,source=$PWD,target=/home/code \\\n --mount type=bind,source=$HOME/.d4rl,target=/root/.d4rl \\\n diffuser \\\n bash -c \\\n \"export PYTHONPATH=$PYTHONPATH:/home/code \u0026amp;\u0026amp; \\\n python /home/code/scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot diffuser.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTest the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv --writable-tmpfs diffuser.sif \\\n bash -c \\\n \"pip install -e . \u0026amp;\u0026amp; \\\n python scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-azure\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-azure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Azure\u003c/h2\u003e\n\u003ch4\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eTag the Docker image (built in the \u003ca href=\"#Docker\"\u003eDocker section\u003c/a\u003e) and push it to Docker Hub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport DOCKER_USERNAME=$(docker info | sed \u0027/Username:/!d;s/.* //\u0027)\ndocker tag diffuser ${DOCKER_USERNAME}/diffuser:latest\ndocker image push ${DOCKER_USERNAME}/diffuser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eUpdate \u003ca href=\"azure/config.py\"\u003e\u003ccode\u003eazure/config.py\u003c/code\u003e\u003c/a\u003e, either by modifying the file directly or setting the relevant \u003ca href=\"azure/config.py#L47-L52\"\u003eenvironment variables\u003c/a\u003e. To set the \u003ccode\u003eAZURE_STORAGE_CONNECTION\u003c/code\u003e variable, navigate to the \u003ccode\u003eAccess keys\u003c/code\u003e section of your storage account. Click \u003ccode\u003eShow keys\u003c/code\u003e and copy the \u003ccode\u003eConnection string\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload \u003ca href=\"https://docs.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10\" rel=\"nofollow\"\u003e\u003ccode\u003eazcopy\u003c/code\u003e\u003c/a\u003e: \u003ccode\u003e./azure/download.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h4\u003e\n\u003cp\u003eLaunch training jobs with \u003ccode\u003epython azure/launch.py\u003c/code\u003e. The launch script takes no command-line arguments; instead, it launches a job for every combination of hyperparameters in \u003ca href=\"azure/launch_train.py#L36-L38\"\u003e\u003ccode\u003eparams_to_sweep\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-viewing-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#viewing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eViewing results\u003c/h4\u003e\n\u003cp\u003eTo rsync the results from the Azure storage container, run \u003ccode\u003e./azure/sync.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo mount the storage container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a blobfuse config with \u003ccode\u003e./azure/make_fuse_config.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./azure/mount.sh\u003c/code\u003e to mount the storage container to \u003ccode\u003e~/azure_mount\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo unmount the container, run \u003ccode\u003esudo umount -f ~/azure_mount; rm -r ~/azure_mount\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{janner2022diffuser,\n title = {Planning with Diffusion for Flexible Behavior Synthesis},\n author = {Michael Janner and Yilun Du and Joshua B. Tenenbaum and Sergey Levine},\n booktitle = {International Conference on Machine Learning},\n year = {2022},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe diffusion model implementation is based on Phil Wang\u0027s \u003ca href=\"https://github.com/lucidrains/denoising-diffusion-pytorch\"\u003edenoising-diffusion-pytorch\u003c/a\u003e repo.\nThe organization of this repo and remote launcher is based on the \u003ca href=\"https://github.com/jannerm/trajectory-transformer\"\u003etrajectory-transformer\u003c/a\u003e repo.\u003c/p\u003e\n", + "full_name": "dcgc-bfx/singularity-base-conda", + "latest_release": "v0.1-alpha", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/dcgc-bfx/dcgc-base-conda/workflows/Build/badge.svg?branch=main\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/dcgc-bfx/dcgc-base-conda/workflows/Build/badge.svg?branch=main\" alt=\"Build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5252\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-dcgc-base-conda\" class=\"anchor\" href=\"#dcgc-base-conda\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edcgc-base-conda\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1672051619.0 + "updated_at": 1626686541.0 }, { "data_format": 2, - "description": "Singularity bootstrap files inheriting from tensorflow Docker images", + "description": "modified version of nicMSlesions", "filenames": [ "Singularity" ], - "full_name": "zhaojuanwendy/singularity-tensorflow", + "full_name": "jstutters/nicpython36", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-tensorflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-tensorflow\u003c/h1\u003e\n\u003cp\u003eStore singularity bootstrap files for tensorflow with accre mount points included.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ms_cnn\" class=\"anchor\" href=\"#ms_cnn\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMS_CNN\u003c/h1\u003e\n\u003cp\u003e[This is a modified version of nicMSlesions (\u003ca href=\"https://github.com/NIC-VICOROB/nicMSlesions\"\u003ehttps://github.com/NIC-VICOROB/nicMSlesions\u003c/a\u003e)]\n\u003cbr\u003e\n\u003ca href=\"CNN.jpeg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"300\" src=\"CNN.jpeg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-this--version-support-additionally-the-following-functionalities\" class=\"anchor\" href=\"#this--version-support-additionally-the-following-functionalities\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis version support additionally the following functionalities:\u003c/h1\u003e\n\u003cdl\u003e\n \u003cdt\u003e(1) Runnable on a Mac system/computer\u003c/dt\u003e\n \u003cdt\u003e(2) Cold start and warm start support:\u003c/dt\u003e\n \u003cdd\u003e- Allowing to re-create the architecture of the model\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use the saved weights of the model\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use the training configuration and avoiding to run preprocessing again\u003c/dd\u003e\n \u003cdd\u003e- Allowing to resume training exactly where it left off(interrupting the training is \n allowed throughout the training process)\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use pretrained model\u003c/dd\u003e\n \u003cdt\u003e(3) Supporting Python 3\u003c/dt\u003e\n \u003cdt\u003e(4) Integrated Tensorborad [to provide the measurements and visualisations of TensorFlow execution (to understand, debug, and optimisation of the TensorFlow programs)]\u003c/dt\u003e\n \u003cdt\u003e(5) Checking whether a file or directory is relevant for Training and Testing\u003c/dt\u003e \n \u003cdt\u003e(6) Easy HPC (High Performance Computing) support\u003c/dt\u003e \n \u003cdt\u003e(7) Bias correction of masks using FSL\u003c/dt\u003e\n \u003cdt\u003e(8) Registration, moving all images to the Flair, T1 or Standard space\u003c/dt\u003e\n\u003c/dl\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"BR.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"500\" src=\"BR.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"note.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"100\" src=\"note.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n# Running the Program!\n\u003cp\u003eThis modified version can be run with or without a GUI (similar to original version)\u003c/p\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"GUI_NM.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"500\" src=\"GUI_NM.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003ch1\u003e\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-program-on-the-hpc-cluster-using-nvidia-gpuswithout-any-additional-librarydependency-installation\" class=\"anchor\" href=\"#running-the-program-on-the-hpc-cluster-using-nvidia-gpuswithout-any-additional-librarydependency-installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Program on the HPC cluster using NVIDIA GPUs(without any additional library/dependency installation):\u003c/h1\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"hpc.jpeg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"200\" src=\"hpc.jpeg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003eFirst, user will need to be sure that \"singularity\"\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/\u003c/a\u003e\nis available on local or remote machine.\u003c/p\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity pull docker://kbronik/ms_cnn_ucl:latest \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running the above, a singularity image using docker hub (docker://kbronik/ms_cnn_ucl:latest) will be generated:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - path to singularity//..///ms_cnn_ucl_latest.sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity run --nv (path to singularity)//..///ms_cnn_ucl_latest.sif python (path to nicpython36)/nic_train_network_batch.py (or other nic-python code)\u003c/pre\u003e\u003c/div\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"note_HPC.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"120\" src=\"note_HPC.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003ch1\u003e\n\u003ca id=\"user-content-for-an-interactive-session\" class=\"anchor\" href=\"#for-an-interactive-session\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor an interactive session:\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity shell (path to singularity)//..///ms_cnn_ucl_latest.sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate idp\n - python (path to nicpython36)/app.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-for-an-interactive-session-tensorflow-on-cpu-only\" class=\"anchor\" href=\"#for-an-interactive-session-tensorflow-on-cpu-only\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor an interactive session (TensorFlow on CPU only):\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e docker://kbronik/ms-ucl-cnn-cpu:CPU_Latest python (path to nicpython36)/app.py \u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1646281475.0 + "updated_at": 1624933808.0 }, { "data_format": 2, - "description": "Hello World image for Singularity", + "description": "A Nextflow pipeline for automatically running QC on Nano runs", "filenames": [ - "Singularity" + "environments/illumina/Singularity" ], - "full_name": "amanmdesai/hello-world-singularity", - "latest_release": "v1.0.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hello-world-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#hello-world-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehello-world-singularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/amanmdesai/hello-world-singularity/actions/workflows/singularity-build-deploy.yml\"\u003e\u003cimg src=\"https://github.com/amanmdesai/hello-world-singularity/actions/workflows/singularity-build-deploy.yml/badge.svg?branch=master\" alt=\"Singularity Build Deploy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA simple singularity image to demonstrate how to use singularity.\u003c/p\u003e\n\u003cp\u003eTo pull this image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull oras://ghcr.io/amanmdesai/hello-world-singularity:latest\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "WalesGenePark/NanoSeqQC", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nanoseqqc\" class=\"anchor\" href=\"#nanoseqqc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNanoSeqQC\u003c/h1\u003e\n\u003cp\u003eA Nextflow pipeline for automatically running QC on Nano runs\u003c/p\u003e\n\u003cp\u003eWARNING - UNDER CURRENT DEVELOPMENT AND NOT FULLY FUNCTIONAL\u003c/p\u003e\n\u003cp\u003elarge sections of nextflow coding are based off the excellent ncov2019-artic-nf pipeline \u003ccode\u003econnor-lab/ncov2019-artic-nf\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h4\u003e\n\u003chr\u003e\n\u003cp\u003eThe running of this will automatically take fastq reads from a Nano sequencing read, run FastP read diagnostics and trimming before performing some comparative statistics based on library metadata such as RIN and concentration.\nAdditionally, reads will be run through Kraken2 to confirm species profile (and lack of contamination!)\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick-start\u003c/h4\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-illumina\" class=\"anchor\" href=\"#illumina\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIllumina\u003c/h5\u003e\n\u003cp\u003e\u003ccode\u003enextflow run WalesGenePark/NanoSeqQC --profile singularity,slurm --prefix \"job_output\" --directory /path/to/reads --outdir /path/to/outfile\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOptions\u003cbr\u003e\n--fastpInputVer (paired, single, merged)\u003c/p\u003e\n\u003chr\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h4\u003e\n\u003cp\u003eAn up-to-date version of Nextflow is required because the pipeline is written in DSL2. Following the instructions at \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/\u003c/a\u003e to download and install Nextflow should get you a recent-enough version.\u003c/p\u003e\n\u003cp\u003e1: git clone the repository\u003cbr\u003e\n2: chmod +x the two scripts in NanoSeqQC/scripts/\u003cbr\u003e\n3: run the singularity build\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-executor\" class=\"anchor\" href=\"#executor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecutor\u003c/h4\u003e\n\u003cp\u003eBy default, the pipeline runs locally unless specifying \u003ccode\u003e-profile slurm\u003c/code\u003e to send to a SLURM cluster.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-config\" class=\"anchor\" href=\"#config\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfig\u003c/h4\u003e\n\u003cp\u003eCommon config options are set in \u0027conf/base.config\u0027.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1672229206.0 + "updated_at": 1627380153.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.v1.0.0" ], - "full_name": "amanmdesai/singularity-python-packages", + "full_name": "mchugomk/cat12_long", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-python-packages\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-python-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-python-packages\u003c/h1\u003e\n", + "readme": "", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1673367679.0 + "updated_at": 1627066402.0 }, { "data_format": 2, @@ -10555,606 +9956,592 @@ var data = "filenames": [ "Singularity" ], - "full_name": "rses-singularity/tensorflow-cpu", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tensorflow-gpu-and-keras\" class=\"anchor\" aria-hidden=\"true\" href=\"#tensorflow-gpu-and-keras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTensorflow (GPU) and Keras\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "baxpr/bedpost-singularity", + "latest_release": "v3.0.0", + "readme": "\u003cp\u003eRuns FSL\u0027s bedpostx on the input DWI data set, and creates a PDF report of the results.\nQuite simple - see /opt/src/pipeline.sh for the main script.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1542376589.0 + "updated_at": 1626106357.0 }, { "data_format": 2, - "description": null, + "description": "FastTree infers approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences. ", "filenames": [ - "Singularity" + "2.1.11/Singularity" ], - "full_name": "aarandad/ampseq_workflow", - "latest_release": "v0.0.4", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ampseq-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#ampseq-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAmpSeq Workflow\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setting-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#setting-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetting Parameters\u003c/h3\u003e\n\u003cp\u003eModify the nextflow.config file:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ereadDIR\u003c/td\u003e\n\u003ctd\u003eThe folder that contains all the fastq files (\u003cem\u003erequired\u003c/em\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutDIR\u003c/td\u003e\n\u003ctd\u003eThe folder where you want the resulting data to be save (default \u0027results\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esequencer\u003c/td\u003e\n\u003ctd\u003eThe sequencer used to produce your data (default \u0027nextseq\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC_only\u003c/td\u003e\n\u003ctd\u003eWhether to only run QC related workflows or all workflows\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erefseq_fasta \u003cstrong\u003eor\u003c/strong\u003e genome\u003c/td\u003e\n\u003ctd\u003ePath to reference sequences \u003cstrong\u003eor\u003c/strong\u003e path to genome (\u003cem\u003eone\u003c/em\u003e is \u003cstrong\u003erequired\u003c/strong\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdditionally, the nextflow parameter \u003ccode\u003e-profile\u003c/code\u003e can be use to target the infrastructure you wish to run the pipeline on. The different profiles are listed below, including any setup that is required.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eIf using singularity, please run the command below to generate the singularity image.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build ampseq_workflow.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then include the \u003ccode\u003esingularity\u003c/code\u003e profile on the command line.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: you should also include executor you wish to run\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR single --refseq_fasta v4_refseq.fasta --target v4 -profile sge,singularity -c conf/custom.config\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBelow is an example using the genome parameter:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single -w \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/work --target v4 -profile sge,singularity --genome PlasmoDB-59_Pfalciparum3D7_Genome.fasta -c conf/custom.config\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eThe pipeline can be easily run with docker and is the recommended way to run it when not using an HPC.\u003c/p\u003e\n\u003cp\u003eFollow the steps below to setup your docker image:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003edocker\u003c/a\u003e is a prerequisite.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t aarandad/ampseq_worfklow \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd you\u0027re done! To run the pipeline, simply add \u003ccode\u003e-profile docker\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR single --target v4-profile -profile docker\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda\u003c/h3\u003e\n\u003cp\u003eTo use conda, you must first install either \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda\u003c/a\u003e. Once installed, include the \u003ccode\u003econda\u003c/code\u003e profile on the command line.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR single --target v3 -profile conda\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-customizing-for-your-institution\" class=\"anchor\" aria-hidden=\"true\" href=\"#customizing-for-your-institution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustomizing for your institution\u003c/h3\u003e\n\u003cp\u003eThere is a file named \u003ccode\u003ecustom.config\u003c/code\u003e in \u003ccode\u003econf/\u003c/code\u003e that can be used to tailor processes to your environment. By default,\nthis file is used to tailor the pipeline for Wynton HPC at UCSF. This file may be altered to fit your institution\u0027s profile.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h3\u003e\n\u003cp\u003ePotential ways to execute the pipeline:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e local executor\u003c/span\u003e\nnextflow run main.nf --target v3\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e with a profile (currently only supports sge)\u003c/span\u003e\nnextflow run main.nf -profile sge --target v3\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run with singularity on an HPC with specified reference sequences\u003c/span\u003e\nnextflow run main.nf --readDIR single -profile sge,singularity --refseq_fasta v4_refseq.fasta --target v4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or locally with docker\u003c/span\u003e\nnextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single/ --target v4 -profile docker --refseq_fasta v4_refseq.fasta\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e genomes can be provided in lieu of reference sequences, which will be generated with the amplicon table\u003c/span\u003e\nnextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single/ -w \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/work --target v4 -profile docker --genome PlasmoDB-59_Pfalciparum3D7_Genome.fasta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to resume after some processes were successfully executed, add -resume at the end of it\u003c/p\u003e\n", + "full_name": "pscedu/singularity-fasttree", + "latest_release": "v2.1.11", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-fasttree/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fasttree/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-fasttree/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fasttree/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/776c7e7d72b508b47ce8bd555bc38368be7629ae529461cdfcfaf5af2920c141/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6661737474726565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/776c7e7d72b508b47ce8bd555bc38368be7629ae529461cdfcfaf5af2920c141/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6661737474726565\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-fasttree\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/6ba1353dc0bb816d3bca4dafd9f90d560fb7d5234867b2ac4b80d0557d1bdc90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6661737474726565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6ba1353dc0bb816d3bca4dafd9f90d560fb7d5234867b2ac4b80d0557d1bdc90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6661737474726565\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-fasttree\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a6aa9ae28b1371a303884250204eea3a43e273bae8eb19f063f732416f9d6bec/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6661737474726565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a6aa9ae28b1371a303884250204eea3a43e273bae8eb19f063f732416f9d6bec/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6661737474726565\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fasttree\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0cc26b73063b0fb2d5f502b2eeb83e3568bac891ce91b016300d28524b83b564/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6661737474726565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0cc26b73063b0fb2d5f502b2eeb83e3568bac891ce91b016300d28524b83b564/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6661737474726565\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fasttree\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-fasttree\" class=\"anchor\" href=\"#singularity-fasttree\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-fasttree\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eFastTree\u003c/code\u003e, \u003ccode\u003eFastTreeMP\u003c/code\u003e and \u003ccode\u003eFastTreeDbl\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/FastTree/2.1.11\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/FastTree\u003c/code\u003e as \u003ccode\u003e2.1.11.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1659049973.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629226128.0 }, { "data_format": 2, - "description": "DSL 2 version of https://github.com/jhoneycuttr/nf-wgs ", + "description": "Nextflow pipelines for a variety of bioinformatics outputs", "filenames": [ - "Singularity" + "nextstrain/environments/Singularity" ], - "full_name": "Finterly/nf-wgs-dsl2", + "full_name": "matt-sd-watson/nextflow_for_bioinformatics", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-optimized-gatk4-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#optimized-gatk4-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimized GATK4 Pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-1-nextflow-dsl-2-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-1-nextflow-dsl-2-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart 1 Nextflow DSL 2 Workflow\u003c/h2\u003e\n\u003cp\u003eAdapted from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Karaniare/Optimized_GATK4_pipeline\"\u003ehttps://github.com/Karaniare/Optimized_GATK4_pipeline\u003c/a\u003e (shell script)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jhoneycuttr/nf-wgs\"\u003ehttps://github.com/jhoneycuttr/nf-wgs\u003c/a\u003e (Nextflow DSL 1)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setting-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#setting-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetting Parameters\u003c/h3\u003e\n\u003cp\u003eModify the nextflow.config file:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameters\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003einputdir\u003c/td\u003e\n\u003ctd\u003eThe folder that contains all the fastq files (default \u0027data\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutdir\u003c/td\u003e\n\u003ctd\u003eThe folder where you want the resulting data to be save (default \u0027results\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erefdir\u003c/td\u003e\n\u003ctd\u003eThe folder that contains reference genomes and bed files (default \u0027genomes\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etrimadapter\u003c/td\u003e\n\u003ctd\u003eThe adapter used for initial trimming of reads (default \u0027TruSeq3-PE.fa\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOther Parameters\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ereads\u003c/td\u003e\n\u003ctd\u003eThe fastq files in the inputdir folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eref\u003c/td\u003e\n\u003ctd\u003eThe reference genome (default \u0027Pf3D7_human.fa\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erscript\u003c/td\u003e\n\u003ctd\u003eThe r script for generating report\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdditionally, the nextflow parameter \u003ccode\u003e-profile\u003c/code\u003e can be use to target the infrastructure you wish to run the pipeline on. The different profiles are listed below, including any setup that is required.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eUnder construction \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e !\u003c/p\u003e\n\u003cp\u003eIf using singularity, please run the command below to generate the singularity image.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build nf-wgs-dsl2.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then include the \u003ccode\u003esingularity\u003c/code\u003e profile on the command line.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: you should also include executor you wish to run\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile sge,singularity -c conf/custom.config\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBelow is an example using the genome parameter:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single -w \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/work --target v4 -profile sge,singularity --genome PlasmoDB-59_Pfalciparum3D7_Genome.fasta -c conf/custom.config\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eThe pipeline can be easily run with docker and is the recommended way to run it when not using an HPC.\u003c/p\u003e\n\u003cp\u003eFollow the steps below to setup your docker image:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003edocker\u003c/a\u003e is a prerequisite.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t finterly/nf-wgs-dsl2 \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd you\u0027re done! To run the pipeline, simply add \u003ccode\u003e-profile docker\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile docker\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda\u003c/h3\u003e\n\u003cp\u003eUnder construction \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e !\u003c/p\u003e\n\u003cp\u003eTo use conda, you must first install either \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda\u003c/a\u003e. Once installed, include the \u003ccode\u003econda\u003c/code\u003e profile on the command line.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR single --target v3 -profile conda\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-customizing-for-your-institution\" class=\"anchor\" aria-hidden=\"true\" href=\"#customizing-for-your-institution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustomizing for your institution\u003c/h3\u003e\n\u003cp\u003eThere is a file named \u003ccode\u003ecustom.config\u003c/code\u003e in \u003ccode\u003econf/\u003c/code\u003e that can be used to tailor processes to your environment. By default,\nthis file is used to tailor the pipeline for Wynton HPC at UCSF. This file may be altered to fit your institution\u0027s profile.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h3\u003e\n\u003cp\u003eUnder construction \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e !\u003c/p\u003e\n\u003cp\u003ePotential ways to execute the pipeline:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e local executor\u003c/span\u003e\nnextflow run main.nf\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e with a profile (currently only supports sge)\u003c/span\u003e\nnextflow run main.nf -profile sge\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run with singularity on an HPC with specified reference sequences\u003c/span\u003e\nnextflow run main.nf --readDIR single -profile sge,singularity --refseq_fasta v4_refseq.fasta --target v4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or locally with docker\u003c/span\u003e\nnextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single/ --target v4 -profile docker --refseq_fasta v4_refseq.fasta\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e genomes can be provided in lieu of reference sequences, which will be generated with the amplicon table\u003c/span\u003e\nnextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single/ -w \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/work --target v4 -profile docker --genome PlasmoDB-59_Pfalciparum3D7_Genome.fasta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to resume after some processes were successfully executed, add -resume at the end of it\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow_for_bioinformatics\" class=\"anchor\" href=\"#nextflow_for_bioinformatics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextflow_for_bioinformatics\u003c/h1\u003e\n\u003cp\u003eNextflow pipelines for routine bioinformatics analyses\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-nextstrain\" class=\"anchor\" href=\"#nextstrain\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextstrain\u003c/h2\u003e\n\u003cp\u003eThe nextstrain workflow is the most up-to-date and maintained pipeline in this repo. It can be used to generate a serie sof parallel nextstrain builds or for parameter testing. A specific README for this pipeline is provided in the named directory.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-rna-seq-and-tree_annotation\" class=\"anchor\" href=\"#rna-seq-and-tree_annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erna-seq and tree_annotation\u003c/h2\u003e\n\u003cp\u003eIn development.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1671034942.0 + "updated_at": 1629726178.0 }, { "data_format": 2, - "description": "Container with Jupyter and rstudio server", + "description": "Recipes for docker and singularity containers for COHERENT projects", "filenames": [ - "Singularity.0.2.0", - "Singularity.0.2.1", - "Singularity", - "Singularity.0.1" + "geant4/Singularity_geant4", + "geant4/Singularity" ], - "full_name": "dcgc-bfx/singularity-jupyter-rstudio", + "full_name": "NuTufts/coherent-containers", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/dcgc-bfx/dcgc-jupyter-rstudio/workflows/Build/badge.svg?branch=main\"\u003e\u003cimg src=\"https://github.com/dcgc-bfx/dcgc-jupyter-rstudio/workflows/Build/badge.svg?branch=main\" alt=\"Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5253\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dcgc-jupyter-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#dcgc-jupyter-rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edcgc-jupyter-rstudio\u003c/h1\u003e\n\u003cp\u003eContainer with Jupyter and rstudio server\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coherent-containers\" class=\"anchor\" href=\"#coherent-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoherent-containers\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1622118476.0 + "updated_at": 1626189399.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.4.4.2", - "Singularity.4.0.14" + "Singularity" ], - "full_name": "sschmeier/fishtank-gpu2", + "full_name": "yuma-35/wave-U-guiter", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fishtank-gpu2\" class=\"anchor\" aria-hidden=\"true\" href=\"#fishtank-gpu2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efishtank-gpu2\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-wave-u-net-pytorch\" class=\"anchor\" href=\"#wave-u-net-pytorch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWave-U-Net (Pytorch)\u003c/h1\u003e\n\u003cp\u003eImproved version of the \u003ca href=\"https://arxiv.org/abs/1806.03185\" rel=\"nofollow\"\u003eWave-U-Net\u003c/a\u003e for audio source separation, implemented in Pytorch.\u003c/p\u003e\n\u003cp\u003eClick \u003ca href=\"www.github.com/f90/Wave-U-Net\"\u003ehere\u003c/a\u003e for the original Wave-U-Net implementation in Tensorflow.\nYou can find more information about the model and results there as well.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-improvements\" class=\"anchor\" href=\"#improvements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImprovements\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eMulti-instrument separation by default, using a separate standard Wave-U-Net for each source (can be set to one model as well)\u003c/li\u003e\n\u003cli\u003eMore scalable to larger data: A depth parameter D can be set that employs D convolutions for each single convolution in the original Wave-U-Net\u003c/li\u003e\n\u003cli\u003eMore configurable: Layer type, resampling factor at each level etc. can be easily changed (different normalization, residual connections...)\u003c/li\u003e\n\u003cli\u003eFast training: Preprocesses the given dataset by saving the audio into HDF files, which can be read very quickly during training, thereby avoiding slowdown due to resampling and decoding\u003c/li\u003e\n\u003cli\u003eModular thanks to Pytorch: Easily replace components of the model with your own variants/layers/losses\u003c/li\u003e\n\u003cli\u003eBetter output handling: Separate output convolution for each source estimate with linear activation so amplitudes near 1 and -1 can be easily predicted, at test time thresholding to valid amplitude range [-1,1]\u003c/li\u003e\n\u003cli\u003eFixed or dynamic resampling: Either use fixed lowpass filter to avoid aliasing during resampling, or use a learnable convolution\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003eGPU strongly recommended to avoid very long training times.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-option-1-direct-install-recommended\" class=\"anchor\" href=\"#option-1-direct-install-recommended\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 1: Direct install (recommended)\u003c/h3\u003e\n\u003cp\u003eSystem requirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinux-based OS\u003c/li\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://mega-nerd.com/libsndfile/\" rel=\"nofollow\"\u003elibsndfile\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ffmpeg.org/\" rel=\"nofollow\"\u003effmpeg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eCUDA 10.1 for GPU usage\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eClone the repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/f90/Wave-U-Net-Pytorch.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRecommended: Create a new virtual environment to install the required Python packages into, then activate the virtual environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evirtualenv --python /usr/bin/python3.6 waveunet-env\nsource waveunet-env/bin/activate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstall all the required packages listed in the \u003ccode\u003erequirements.txt\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-option-2-singularity\" class=\"anchor\" href=\"#option-2-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 2: Singularity\u003c/h3\u003e\n\u003cp\u003eWe also provide a Singularity container which allows you to avoid installing the correct Python, CUDA and other system libraries, however we don\u0027t provide specific advice on how to run the container and so only do this if you have to or know what you are doing (since you need to mount dataset paths to the container etc.)\u003c/p\u003e\n\u003cp\u003eTo pull the container, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://f90/Wave-U-Net-Pytorch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the container from the directory where you cloned this repository to, using the commands listed further below in this readme.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-download-datasets\" class=\"anchor\" href=\"#download-datasets\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload datasets\u003c/h1\u003e\n\u003cp\u003eTo directly use the pre-trained models we provide for download to separate your own songs, now skip directly to the \u003ca href=\"#test\"\u003elast section\u003c/a\u003e, since the datasets are not needed in that case.\u003c/p\u003e\n\u003cp\u003eTo start training your own models, download the \u003ca href=\"https://sigsep.github.io/datasets/musdb.html\" rel=\"nofollow\"\u003efull MUSDB18HQ dataset\u003c/a\u003e and extract it into a folder of your choice. It should have two subfolders: \"test\" and \"train\" as well as a README.md file.\u003c/p\u003e\n\u003cp\u003eYou can of course use your own datasets for training, but for this you would need to modify the code manually, which will not be discussed here. However, we provide a loading function for the normal MUSDB18 dataset as well.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-training-the-models\" class=\"anchor\" href=\"#training-the-models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining the models\u003c/h1\u003e\n\u003cp\u003eTo train a Wave-U-Net, the basic command to use is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3.6 train.py --dataset_dir /PATH/TO/MUSDB18HQ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere the path to MUSDB18HQ dataset needs to be specified, which contains the \u003ccode\u003etrain\u003c/code\u003e and \u003ccode\u003etest\u003c/code\u003e subfolders.\u003c/p\u003e\n\u003cp\u003eAdd more command line parameters as needed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--cuda\u003c/code\u003e to activate GPU usage\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--hdf_dir PATH\u003c/code\u003e to save the preprocessed data (HDF files) to custom location PATH, instead of the default \u003ccode\u003ehdf\u003c/code\u003e subfolder in this repository\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--checkpoint_dir\u003c/code\u003e and \u003ccode\u003e--log_dir\u003c/code\u003e to specify where checkpoint files and logs are saved/loaded\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--load_model checkpoints/model_name/checkpoint_X\u003c/code\u003e to start training with weights given by a certain checkpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more config options, see \u003ccode\u003etrain.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTraining progress can be monitored by using Tensorboard on the respective \u003ccode\u003elog_dir\u003c/code\u003e.\nAfter training, the model is evaluated on the MUSDB18HQ test set, and SDR/SIR/SAR metrics are reported for all instruments and written into both the Tensorboard, and in more detail also into a \u003ccode\u003eresults.pkl\u003c/code\u003e file in the \u003ccode\u003echeckpoint_dir\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content--test-trained-models-on-songs\" class=\"anchor\" href=\"#-test-trained-models-on-songs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-test\"\u003e\u003c/a\u003e Test trained models on songs!\u003c/h1\u003e\n\u003cp\u003eWe provide the default model in a pre-trained form as download so you can separate your own songs right away.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-downloading-our-pretrained-models\" class=\"anchor\" href=\"#downloading-our-pretrained-models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownloading our pretrained models\u003c/h2\u003e\n\u003cp\u003eDownload our pretrained model \u003ca href=\"https://www.dropbox.com/s/r374hce896g4xlj/models.7z?dl=1\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nExtract the archive into the \u003ccode\u003echeckpoints\u003c/code\u003e subfolder in this repository, so that you have one subfolder for each model (e.g. \u003ccode\u003eREPO/checkpoints/waveunet\u003c/code\u003e)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-pretrained-model\" class=\"anchor\" href=\"#run-pretrained-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun pretrained model\u003c/h2\u003e\n\u003cp\u003eTo apply our pretrained model to any of your own songs, simply point to its audio file path using the \u003ccode\u003einput_path\u003c/code\u003e parameter:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3.6 predict.py --load_model checkpoints/waveunet/model --input \"audio_examples/Cristina Vane - So Easy/mix.mp3\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAdd \u003ccode\u003e--cuda \u003c/code\u003e when using a GPU, it should be much quicker\u003c/li\u003e\n\u003cli\u003ePoint \u003ccode\u003e--input\u003c/code\u003e to the music file you want to separate\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default, output is written where the input music file is located, using the original file name plus the instrument name as output file name. Use \u003ccode\u003e--output\u003c/code\u003e to customise the output directory.\u003c/p\u003e\n\u003cp\u003eTo run your own model:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePoint \u003ccode\u003e--load_model\u003c/code\u003e to the checkpoint file of the model you are using. If you used non-default hyper-parameters to train your own model, you must specify them here again so the correct model is set up and can receive the weights!\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1614062687.0 + "updated_at": 1624842105.0 }, { "data_format": 2, - "description": "Singularity recipe files for sambamba (https://github.com/biod/sambamba)", + "description": "Docker image", "filenames": [ - "Singularity.0.8.0", - "Singularity" + "Singularity.latest" ], - "full_name": "powerPlant/sambamba-srf", + "full_name": "AdamWilsonLab/docker_geospatial_plus", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for Sambamba, a high performance highly parallel robust and fast tool (and library), written in the D programming language, for working with SAM and BAM files.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-geospatial-plus\" class=\"anchor\" href=\"#geospatial-plus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeospatial Plus\u003c/h1\u003e\n\u003cp\u003eBuilding on the versioned geospatial Rocker image.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-github-actions\" class=\"anchor\" href=\"#github-actions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGithub Actions\u003c/h1\u003e\n\u003cp\u003eThis repository uses GitHub Actions to test the docker image prior to making it available as a GitHub package.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1613348079.0 + "updated_at": 1624971946.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.cosmic_tagging_tf_2010" + "Singularity" ], - "full_name": "maxpkatz/singularity_image_files", + "full_name": "mherkazandjian/ismcpak", "latest_release": null, - "readme": "", + "readme": "\u003cp\u003e\u003ca href=\"https://cloud.sylabs.io/library/_container/5f9bd736bccfe9cf4578f166\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-quickstart\" class=\"anchor\" href=\"#quickstart\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h1\u003e\n\u003cp\u003eTo run a quick example, the following container can be used:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone -b alpha-master https://github.com/mherkazandjian/ismcpak.git ~/ismcpak\n$ cd ~/ismcpak/tests\n$ singularity exec library://mher/default/ismcpak:latest mpiexec python run_singleMesh.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is a package which implements some utilities useful for modelling and\nanalyzing simulation output of PDRs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ejupyter notebooks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo run a jupyter server inside the container with the full ismcpak environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --scratch /run/user library://mher/default/ismcpak:latest jupyter-lab\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-the-container-locally\" class=\"anchor\" href=\"#build-the-container-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the container locally\u003c/h2\u003e\n\u003cp\u003eThe following command build the singularity container on a local machine. The\nonly prerequisite is to have singularity installed and to have sudo access.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone -b alpha-master https://github.com/mherkazandjian/ismcpak.git ~/ismcpak\n$ cd ~/ismcpak\n$ sudo make singularity\n$ cd tests\n$ singularity exec ../container.sif mpiexec python run_singleMesh.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h1\u003e\n\u003cp\u003eamuse - mpich\nPyQt4\nipython\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installing-the-pdr-code\" class=\"anchor\" href=\"#installing-the-pdr-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the PDR code\u003c/h1\u003e\n\u003cp\u003eThe PDR code should be copied into:\namuse/src/amuse/community/pdr\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-compiling-the-pdr-code\" class=\"anchor\" href=\"#compiling-the-pdr-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling the PDR code\u003c/h1\u003e\n\u003cp\u003eThe PDR code can be compiled using:\n~\u0026gt; cd amuse/src/amuse/community/pdr\n~\u0026gt; make all\nThe generates the libpdr.a library\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-setting-up-the-working-environment\" class=\"anchor\" href=\"#setting-up-the-working-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetting up the working environment\u003c/h1\u003e\n\u003cp\u003eThe path to ismcpak should be added to the PYTHONPATH environment variable. For\nbash, the following line should be added:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=/PATH/TO/ismcpak:$PYTHONPATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto tcsh :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetenv PYTHONPATH /PATH/TO/ismcpak:$PYTHONPATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-code\" class=\"anchor\" href=\"#running-the-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the code\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-basic-test---single-model\" class=\"anchor\" href=\"#basic-test---single-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasic test - single model\u003c/h2\u003e\n\u003cp\u003eThe PDR code can only be run through the AMUSE ( \u003ca href=\"http://amusecode.org\" rel=\"nofollow\"\u003ehttp://amusecode.org\u003c/a\u003e ).\nDepending on the mpi environment installed with AMUSE, it might be\nnecessary to launch the mpd deamon before executing either:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e~\u0026gt; mpirun -np 1 python run_singleMesh.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e~\u0026gt; python run_singleMesh.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-a-grid-of-models\" class=\"anchor\" href=\"#running-a-grid-of-models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a Grid of models\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-setup-the-working-environment-variables\" class=\"anchor\" href=\"#setup-the-working-environment-variables\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetup the working environment variables\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003esource setdev\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-install-the-pdr-code-into-amuse-make-sure-the-correct\" class=\"anchor\" href=\"#install-the-pdr-code-into-amuse-make-sure-the-correct\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einstall the pdr code into amuse (make sure the correct\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-path-of-amuse-is-set-in-setenv\" class=\"anchor\" href=\"#path-of-amuse-is-set-in-setenv\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epath of amuse is set in setenv\u003c/h1\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003emake pdr_install\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-after-these-two-steps-the-tests\" class=\"anchor\" href=\"#after-these-two-steps-the-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eafter these two steps, the tests\u003c/h1\u003e\n\u003cp\u003erun_singleMesh.py\nchemical_network_pdr_code.py\nshould run without errors\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eto run a grid, use the following under ismcpak:\n~\u0026gt; ipython --pylab=qt tests/run_oneSidedGrid.py\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eafter the model data is written to\ntests/oneSidedGrid/meshes\nwe need to construct the database files .db using constructReadArchive.py\n~\u0026gt; ipython --pylab=qt constructReadArchive.py\u003c/p\u003e\n\u003cp\u003eafter the database is constructed we must have the file\nmeshes.db meshes.db.info\nin the output directory and a message\narchive integrity test passed\nmust be displayed\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eafter creating the database, a reference file must be generated which\nstores information about the parameters which have been used in\ngenerating the data. A template of this file is located under\nruns/tests/templateDir/used_params.py\nwhere the parameters used by run_oneSidedGrid.py should be filled in\nby hand. Once the values are changed :\n~\u0026gt; python used_parms.py\ngenerates the pickle file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eset the desired display parameters in analyzeArchive.py and invoke :\n~\u0026gt; ipython --pylab=qt analyzeArchive.py\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eto generate the radex databases, the bottom part of analyzeArchive.py should be enabled to\nallow radex databases to be computed and written do disk. Set the desired values of\nAv to compute and the species whose emission will be computed and re-run:\n~\u0026gt; ipython --pylab=qt analyzeArchive.py\nAs a check, the data in\ntests/oneSidedGrid/radexDbs\nshould have directories with the Avs we have set and each directory should\nhave files for each species we have specified.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eafter producing the radex database files, we can convert that data to ascii data using :\n~\u0026gt; ipython ismcpak2Ascii.py\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h1\u003e\n\u003cp\u003eTHIS SOFTWARE IS PROVIDED UNDER THE GPL LICENSE BY THE COPYRIGHT HOLDERS AND\nCONTRIBUTORS \u201cAS IS\u201d AND DOES NOT EXPRESS OR PROVIDE IMPLIED WARRANTIES,\nINCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND F\nITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\nOWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\nEXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT\nOF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\nINTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT\n, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY\nWAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH\nDAMAGE.\u003c/p\u003e\n\u003cp\u003eSee LICENSE.txt for more information about the GPL license.\u003c/p\u003e\n\u003cp\u003ePlease cite the following papers if any part of this package is used in your\nresearch.\u003c/p\u003e\n\u003cp\u003eKazandjian, M. V., Meijerink, R., Pelupessy, I., Israel, F. P., Spaans, M.,\narXiv:1403.7000\u003c/p\u003e\n\u003cp\u003eKazandjian, M. V., Meijerink, R., Pelupessy, I., Israel, F. P., Spaans, M.,\n2012, A\u0026amp;A, 542, A65, 26\u003c/p\u003e\n\u003cp\u003eMeijerink, R., Spaans, M., \u0026amp; Israel, F. P. 2007, A\u0026amp;A, 461, 793\u003c/p\u003e\n\u003cp\u003eMeijerink, R. \u0026amp; Spaans, M. 2005, A\u0026amp;A, 436, 397\u003c/p\u003e\n\u003cp\u003eIsmpak makes makes use of \"Radex\" internally to compute the line emissions. Please\nreference the RADEX paper as well:\u003c/p\u003e\n\u003cp\u003eVan der Tak, F.F.S., Black, J.H., Sch\u00f6ier, F.L., Jansen, D.J., van Dishoeck, E.F. 2007, A\u0026amp;A 468, 627\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1609779903.0 + "updated_at": 1625261030.0 }, { "data_format": 2, - "description": "definition files for containers used in Hanlab", + "description": null, "filenames": [ - "singularity.R.3.6.3.Bioc/R.3.6.3.Bioc.def", - "singularity.Rconda/R.3.6.3.def", - "singularity.mkl/mkl.def", - "singularity.mkl/mkl.ubuntu.def", - "singularity.R.4.0.2.Bioc/R.4.0.2.Bioc.def", - "singularity.py37.ml.openblas/py37.ml.openblas.def", - "singularity.R.3.6.3.phylo/R.3.6.3.phylo.def", - "singularity.SAD/SAD.def", - "singularity.phylo/phylo.def", - "singularity.py37.ml.mkl/py37.ml.mkl.def", - "singularity.rnaseq/rnaseq.def" + "misc/releases/20.06/Singularity.20.06", + "misc/releases/latest/Singularity", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/19.06/Singularity.19.06" ], - "full_name": "HanLabUNLV/hanlab_singularity_defs", + "full_name": "salome-eriksson/downward-issue751-prototype", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fast-downward\" class=\"anchor\" href=\"#fast-downward\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast Downward\u003c/h1\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" href=\"#tested-software-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1648241982.0 + "updated_at": 1625214736.0 }, { "data_format": 2, - "description": "Singularity definition files for building various software to run on HPC systems", + "description": "METHYLPY, is an analysis pipeline for DNA methylation data.", "filenames": [ - "coinfinder.def", - "octopus.def", - "demultiplex.def", - "sibeliusz.def", - "orthofinder.def", - "torstyverse.def", - "openmpibase.def", - "amiga.def", - "panx.def", - "instrain.def", - "eggnogmapper.def", - "motulizer.def", - "orthofinder_usemem.def", - "raxspectree.def", - "tychfinder.def", - "wgasuite.def", - "checkm.def", - "pheniqs.def" + "1.4.3/Singularity" ], - "full_name": "slhogle/singularity_def_files", - "latest_release": null, + "full_name": "pscedu/singularity-methylpy", + "latest_release": "v1.4.3", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-methylpy/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-methylpy/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-methylpy/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-methylpy/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/68dce811b911761f8ba92aae500e0e1400720248c31724fc8f3215bead82ab11/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d657468796c7079\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/68dce811b911761f8ba92aae500e0e1400720248c31724fc8f3215bead82ab11/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d657468796c7079\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-methylpy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fe10607a3df63b8791aff64a9cb4897318e187e28b12c3a563a6a0a76bbb8f73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d657468796c7079\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe10607a3df63b8791aff64a9cb4897318e187e28b12c3a563a6a0a76bbb8f73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d657468796c7079\" alt=\"forks\" 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href=\"https://camo.githubusercontent.com/c59e0399bb825884a9fa4f45b91bdba428f86d1decc49df207e011187efa29b1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d657468796c7079\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c59e0399bb825884a9fa4f45b91bdba428f86d1decc49df207e011187efa29b1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d657468796c7079\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-methylpy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-methylpy\" class=\"anchor\" href=\"#singularity-methylpy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-methylpy\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/METHYLPY\"\u003eMETHYLPY\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the Perl scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/methylpy/1.4.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/methylpy\u003c/code\u003e as \u003ccode\u003e1.4.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1639279998.0 + "subscribers_count": 1, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629218072.0 }, { "data_format": 2, - "description": "patroon with openms singularity image", + "description": null, "filenames": [ - "Singularity" + "volsung-cudnn8-runtime-ubuntu18.04/Singularity", + "vdt_base/Singularity" ], - "full_name": "romxero/patroonOpenmsSingularity", + "full_name": "AvciRecep/chaste_nesi", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4539\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsing conventions described here.\n\u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/getting-started/naming\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/singularityhub-docs/docs/getting-started/naming\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1659137230.0 + "updated_at": 1625528992.0 }, { "data_format": 2, - "description": "Recipes and definition files for building singularity", + "description": "Command Line Interface and Python API for Forskalle", "filenames": [ - "flameshot/Singularity", - "ansible/Singularity" + "Singularity" ], - "full_name": "serheang/singularity", + "full_name": "csf-ngs/forskalle-api", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://sylabs.io/guides/3.6/user-guide/introduction.html\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to build\u003c/h2\u003e\n\u003cp\u003eThe simplest way to build a singularity container is to build from docker:\n\u003ccode\u003esingularity pull docker://centos:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eHowever, if you have a definition file like this:\ndocker.def:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: centos:7\n\n%labels\n\tAUTHOR SerTan\n\tVERSION 1.0\n\n%environment\n\texport PATH=/usr/local/bin:$PATH\n\texport LANG=en_US.UTF-8\n\texport LC_ALL=C\n\n%files\n\n%post\n\tyum -y install emacs\n\n%runscript\n\techo \"This is a container\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you can build the SIF from it:\n\u003ccode\u003esudo singularity build test.sif docker.def\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can refer to this \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html\" rel=\"nofollow\"\u003equickstart guide\u003c/a\u003e to have more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the image?\u003c/h2\u003e\n\u003cp\u003eTo run a SIF:\n\u003ccode\u003esingularity run -B $XDG_RUNTIME_DIR \u0026lt;sif file\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt require to bind $XDG_RUNTIME_DIR into the container so that we can utilize the host\u0027s X session capacity.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fsk-api--cli\" class=\"anchor\" href=\"#fsk-api--cli\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFSK API + cli\u003c/h1\u003e\n\u003cp\u003ePython library for Fsk3 API. Will add functionality as needed.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstall from the VBCF.NGS repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install git+https://ngs.vbcf.ac.at/repo/software/forskalle-api.git\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or github\u003c/span\u003e\npip3 install git+https://github.com/csf-ngs/forskalle-api.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-cli\" class=\"anchor\" href=\"#cli\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCLI\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003efsk-cli [command] [options] etc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePoint it at your favorite Forskalle instance either by\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esetting environment variables: \u003ccode\u003eFSK_API_BASE\u003c/code\u003e and \u003ccode\u003eFSK_API_KEY\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eusing a config file in \u003ccode\u003e~/.fsk_api.yml\u003c/code\u003e, please see \u003ca href=\"doc/\"\u003edoc/\u003c/a\u003e for an example\u003c/li\u003e\n\u003cli\u003eproviding \u003ccode\u003e--base\u003c/code\u003e and \u003ccode\u003e--key\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTry \u003ccode\u003efsk-cli --help\u003c/code\u003e for some hints!\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-examples\" class=\"anchor\" href=\"#examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h4\u003e\n\u003cp\u003eSet all sequenced samples of a multiplex to Ok:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003efsk-cli multi get M4711 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e jq \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.multiplex_samples[].sample_id\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \\\n \u003cspan class=\"pl-k\"\u003ewhile\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e sample_id\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e \n fsk-cli set-sequencing-status \u003cspan class=\"pl-smi\"\u003e$sample_id\u003c/span\u003e --status Ok\n \u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn place editing with jq and updating:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e update all request lanes to status Ready\u003c/span\u003e\nfsk-cli request get R4711 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \\\n jq \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.request_lanes[].status=\"Ready\"\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \\\n fsk-cli request update R4711\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-library\" class=\"anchor\" href=\"#library\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLibrary\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003efrom forskalle_api import FskApi\n\nfsk_api = FskApi()\nsample_json = fsk_api.get_sample(54321)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003efrom forskalle_api import FskApi\nfrom forskalle_api.auto.queryparams import IlluminaRunFilters\nfrom forskalle_api.fsk_query import FskQuery\n\nfsk_api = FskApi()\nirf = IlluminaRunFilters(sequenced_after=\"2020-05-01\")\nq = FskQuery(filters=irf)\nruns = fsk_api.get_runs_illumina(q)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere is no API-doc or similar, but we all love reading python source code!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-models\" class=\"anchor\" href=\"#models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModels\u003c/h3\u003e\n\u003cp\u003eModels and Query Parameters are autogenerated from forskalle. Return values of most api calls are thin class layers with type hints, e.g. forskalle_api.auto.models.Sample with all properties and relationships to allow easy navigation in your source code editor.\u003c/p\u003e\n\u003cp\u003eYou can also find de/serialization helpers (serializeSample from Class to dict, plainToSample from dict to Class).\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1660644642.0 + "updated_at": 1625599328.0 }, { "data_format": 2, - "description": "Eugene is an integrative genome annotation software", + "description": null, "filenames": [ - "eugene/singularity/4.3/Singularity" + "ext/Singularity" ], - "full_name": "tschiex/eugene", - "latest_release": "v4.3a", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-welcome-to-eugene\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcome-to-eugene\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to eugene\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-an-integrative-gene-finder-for-eukaryotic-and-prokaryotic-genomes\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-integrative-gene-finder-for-eukaryotic-and-prokaryotic-genomes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAn integrative gene finder for eukaryotic and prokaryotic genomes\u003c/h2\u003e\n\u003cp\u003eThis software is OSI Certified Open Source Software. OSI Certified is\na certification mark of the Open Source Initiative. eugene is\ngoverned by the ARTISTIC LICENSE (see \u003ca href=\"http://www.opensource.org\" rel=\"nofollow\"\u003ewww.opensource.org\u003c/a\u003e). Please see\nthe file COPYING for details. For documentation, please see the files\nin the doc subdirectory. For building and installation instructions\nplease see the INSTALL file. For creating a new eugene release, please\nsee the RELEASE file.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor more information\u003c/h2\u003e\n\u003cp\u003eVisit eugene\u0027s web site at \u003ca href=\"http://eugene.toulouse.inrae.fr\" rel=\"nofollow\"\u003eINRAE\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "OSC/shiny_launcher", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-wip-batch-connect---osc-shiny-app-launcher\" class=\"anchor\" href=\"#wip-batch-connect---osc-shiny-app-launcher\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[WIP] Batch Connect - OSC Shiny App Launcher\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/5e71a5a07e8e6e15f922edb76b5fb68e7ee6087e336807c38095861b8c7f5fc2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f7368696e795f6c61756e636865722e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e71a5a07e8e6e15f922edb76b5fb68e7ee6087e336807c38095861b8c7f5fc2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f7368696e795f6c61756e636865722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/shiny_launcher.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app designed for OSC OnDemand that launches a Shiny App within\nan Owens batch job.\u003c/p\u003e\n\u003cp\u003eThe Shiny app is included a submodule and each deployment can modified which\nShiny app to deploy using git config to specify the URL for the submodule. This\nway the launcher code can be reused for multiple apps but the launcher and the\napp itself can be managed separately.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e x.y.z+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eTODO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_example_shiny/fork\"\u003ehttps://github.com/OSC/bc_osc_example_shiny/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1660811863.0 + "updated_at": 1569007230.0 }, { "data_format": 2, - "description": "Code repository for my Masters thesis with the title \"Temporal Planning for Droplet Routing on Microfluidic Biochips\".", + "description": "Singularity recipe for singularity-term-img-cli", "filenames": [ - "Singularity" + "4.1.0/Singularity" ], - "full_name": "Altava/droplet-routing", + "full_name": "icaoberg/singularity-term-img-cli", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-droplet-routing\" class=\"anchor\" aria-hidden=\"true\" href=\"#droplet-routing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edroplet-routing\u003c/h1\u003e\n\u003cp\u003eCode repository for my Masters thesis with the title \"Temporal Planning for Droplet Routing on Microfluidic Biochips\".\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-term-img-cli\" class=\"anchor\" href=\"#singularity-term-img-cli\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-term-img-cli\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sindresorhus/term-img-cli/raw/main/screenshot.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/sindresorhus/term-img-cli/raw/main/screenshot.jpg\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sindresorhus/term-img-cli\"\u003eterm-img\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eterm-img\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/term-img/3.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/term-img\u003c/code\u003e as \u003ccode\u003e3.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1654011059.0 + "updated_at": 1622923859.0 }, { "data_format": 2, - "description": "This is the Singularity file for build singularity image of biomarkers module", + "description": null, "filenames": [ - "Biomarkers/Singularity" + "containers/Singularity" ], - "full_name": "tperezdevelopment/Singularity-Tools", - "latest_release": "1.0.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity-Tools\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/270368691\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b74d900bff6714a691edb3ec8bc54abcbf1653a66cc2dfeb1eb05e5e3f452b05/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3237303336383639312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/270368691.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eList of Singularity file to build Tools\u003c/p\u003e\n", + "full_name": "bananaeat/Cinnamon_assembly", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cinnamon\" class=\"anchor\" href=\"#cinnamon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCinnamon\u003c/h1\u003e\n\u003cp\u003eThis directory contains the code for the Cinnamon language compiler. This compiler is described in the paper:\u003c/p\u003e\n\u003cp\u003eCinnamon: A Domain-Specific Language for Binary Profiling and Monitoring,\nMahwish Arif, Ruoyu Zhou, Hsi-Ming Ho and Timothy M. Jones,\nCGO 2021\u003c/p\u003e\n\u003cp\u003ePlease cite this paper if you produce any work that builds upon this code and / or data.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-licence\" class=\"anchor\" href=\"#licence\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicence\u003c/h2\u003e\n\u003cp\u003eCinnamon is released under an Apache licence.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-cinnamon\" class=\"anchor\" href=\"#building-cinnamon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Cinnamon\u003c/h2\u003e\n\u003cp\u003eCinnamon can currently target three different binary frameworks; Janus, Pin and Dyninst. To build the compiler:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003eexport CINNAMON_ROOT = /path/to/cinnamon-source\ncd $(CINNAMON_ROOT)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the Cinnamon backend for Janus:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003emake TARGET=janus\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the Cinnamon backend for Pin:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003emake TARGET=pin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the Cinnamon backend for Dyninst:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003emake TARGET=dyninst\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-compiling-a-sample-program\" class=\"anchor\" href=\"#compiling-a-sample-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling a sample program\u003c/h2\u003e\n\u003cp\u003eCinnamon sample programs are available in the \u003ccode\u003etests\u003c/code\u003e directory. The following commands will compile the Cinnamon program \u003ccode\u003eins.dsl\u003c/code\u003e and integrate the resulting code into one of the target frameworks. You will need to set the path to your target framework installation in the respective scripts:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003e$(CINNAMON_ROOT)/Scripts/compileToJanus.py $CINNAMON_ROOT/tests/ins.dsl\n$(CINNAMON_ROOT)/Scripts/compileToPin.py $CINNAMON_ROOT/tests/ins.dsl\n$(CINNAMON_ROOT)/Scripts/compileToDyn.py $CINNAMON_ROOT/tests/ins.dsl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter this, the final tool can be built and run using the target framework\u0027s build instructions.\u003c/p\u003e\n\u003cp\u003eIf you just want to compile the Cinnamon DSL code and not yet integrate it into a target framework, run the following command. This will generate a number of different files containing relevant code for the cinnamon program:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003ecd $CINNAMON_ROOT\n./bdc $CINNAMON_ROOT/tests/ins.dsl\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-target-frameworks\" class=\"anchor\" href=\"#target-frameworks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTarget frameworks\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-janus\" class=\"anchor\" href=\"#janus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJanus\u003c/h3\u003e\n\u003cp\u003eYou can get the Janus implementation with placeholders, templates and utility libraries for Cinnamon from the main Janus repository at \u003ca href=\"https://github.com/timothymjones/Janus.git\"\u003ehttps://github.com/timothymjones/Janus.git\u003c/a\u003e, then switch to the \u003ccode\u003ecinnamon\u003c/code\u003e branch.\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003egit clone https://github.com/timothymjones/Janus.git\ncd Janus\ngit checkout -b cinnamon origin/cinnamon\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext set \u003ccode\u003eJanusPATH\u003c/code\u003e in \u003ccode\u003ecompileToJanus.py\u003c/code\u003e to be the location that you have cloned Janus.\u003c/p\u003e\n\u003cp\u003eOnce the code for Janus has been generated and integrated (after running the \u003ccode\u003ecompileToJanus.py\u003c/code\u003e script from above), you can build the final tool using the following commands:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003e(cd build; cmake ..; make -j8)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the final tool on the target binary:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003e./janus/jdsl_run \u0026lt;target_binary\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pin\" class=\"anchor\" href=\"#pin\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePin\u003c/h3\u003e\n\u003cp\u003eEverything required for Pin is contained within the \u003ccode\u003etargets/Pin\u003c/code\u003e directory. Copy the \u003ccode\u003eMyDSLTool\u003c/code\u003e directory to \u003ccode\u003epath-to-your-pin-root-dir/source/tools\u003c/code\u003e, where \u003ccode\u003epath-to-your-pin-root\u003c/code\u003e should be self-explanatory.\u003c/p\u003e\n\u003cp\u003eNext set \u003ccode\u003ePinPATH=your-pin-root-dir/source/tools/MyDSLTool\u003c/code\u003e in \u003ccode\u003ecompileToPin.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the code for Pin has been generated and integrated (after running the \u003ccode\u003ecompileToPin.py\u003c/code\u003e script from above), you can build the final tool using the following commands:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003ecd your-pin-root-dir/source/tools/MyDSLTool\nmake obj-intel64/MyDSLTool.so\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the final tool on the target binary:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003eyour-pin-root-dir/pin -t obj-intel64/MyDSLTool.so -- \u0026lt;target_binary\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dyninst\" class=\"anchor\" href=\"#dyninst\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDyninst\u003c/h3\u003e\n\u003cp\u003eYou can obtain Dyninst version 10.1.0 as follows:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003ewget https://github.com/dyninst/dyninst/archive/v10.1.0.tar.gz``\ntar xzvf v10.1.0.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce extracted, add \u003ccode\u003ec_LoadInsn\u003c/code\u003e and \u003ccode\u003ec_StoreInsn\u003c/code\u003e into \u003ccode\u003eenum InsnCategory\u003c/code\u003e in \u003ccode\u003edyninst-10.1.0/instructionAPI/h/InstructionCategories.h\u003c/code\u003e and then build by following the Dyninst build instructions.\u003c/p\u003e\n\u003cp\u003eEverything else required for Dyninst is contained within the \u003ccode\u003etargets/Dyninst\u003c/code\u003e directory. Copy the \u003ccode\u003eMyDSLTool\u003c/code\u003e directory to \u003ccode\u003epath-to-your-dyn-root-dir/examples\u003c/code\u003e, where \u003ccode\u003epath-to-your-dyn-root-dir\u003c/code\u003e should be self-explanatory.\u003c/p\u003e\n\u003cp\u003eNext set \u003ccode\u003eDynPATH=path-to-your-dyn-root-dir/examples/MyDSLTool\u003c/code\u003e in \u003ccode\u003ecompileToDyn.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the code for Dyninst has been generated and integrated (after running the \u003ccode\u003ecompileToDyn.py\u003c/code\u003e script from above), you can build the final tool using the following commands:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003ecd path-to-your-dyn-root-dir/examples/MyDSLTool\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the final tool on the target binary:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003epath-to-your-dyn-root-dir/examples/MyDSLTool/DSLtool -m static -o \u0026lt;output_binary\u0026gt; \u0026lt;input_binary\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1661186876.0 + "updated_at": 1625671641.0 }, { "data_format": 2, - "description": null, + "description": "Simple terminal UI for git commands.", "filenames": [ - "Singularity.cellranger" + "0.28.2/Singularity", + "0.23.1/Singularity" ], - "full_name": "georgia-katsoula/cellranger", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/shpc.png\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-template-or-fork\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Template or Fork\u003c/h3\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-write-your-singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-update-the-version-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Update the VERSION file\u003c/h3\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/releases.png\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-how-to-develop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How to Develop\u003c/h3\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-how-to-pull\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. How to pull\u003c/h3\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", + "full_name": "icaoberg/singularity-lazygit", + "latest_release": "v0.28.2", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-lazygit/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-lazygit/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ce43f9f05edc280dcb72fb4ca8be46c0dab1ad9b88f48b7c2d9b8273288d266f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6c617a79676974\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce43f9f05edc280dcb72fb4ca8be46c0dab1ad9b88f48b7c2d9b8273288d266f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6c617a79676974\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-lazygit\" 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rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8240aa02d92e2a9a5ee84af2261f39ed2c8fc86a0c2f54f6e1f6bab629e0fe5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6c617a79676974\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-lazygit\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/cef5da0642ce33fb4fb6a644a1c76721ffe638963e959e6812c008aa8c6ce693/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6c617a79676974\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cef5da0642ce33fb4fb6a644a1c76721ffe638963e959e6812c008aa8c6ce693/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6c617a79676974\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-lazygit\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-lazygit\" class=\"anchor\" href=\"#singularity-lazygit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-lazygit\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"images/screenshot.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/screenshot.png\" alt=\"Screenshot\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://github.com/jesseduffield/lazygit\"\u003elazygit\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003elazygit\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/lazygit/4.8.25\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/lazygit\u003c/code\u003e as \u003ccode\u003e4.8.25.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/lazygits/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1661761753.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1625268998.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "sequana/variant_calling", - "latest_release": "v0.12.0", + "full_name": "baxpr/demo-singularity-matlab-fsl", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-demo-singularity-container-for-matlab-plus-fsl\" class=\"anchor\" href=\"#demo-singularity-container-for-matlab-plus-fsl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo singularity container for Matlab plus FSL\u003c/h1\u003e\n\u003cp\u003eThis example container takes a Nifti image as input, zeroes out a hole in it of\nthe specified diameter, and saves the result to a new Nifti file. Quick,\npointless, and easy to tell whether it worked right.\u003c/p\u003e\n\u003cp\u003eThis is one way to organize a Matlab-based Singularity container -\nperhaps most easily conceived of as a series of wrappers around the main\ncodebase. Done this way, it\u0027s fairly easy to work on each piece in isolation,\nproblem-solving from the inside out.\u003c/p\u003e\n\u003cp\u003eThis container also includes an installation of FSL, which has a lot of handy\ntools including fsleyes to make the QA PDF. The FSL parts could be removed from\nthe Singularity file if FSL isn\u0027t used, to end up with a smaller container.\nContrariwise, all the Matlab parts could be removed to end up with an FSL-only\ncontainer.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity container\n| Primary entrypoint (shell script)\n| | X11 wrapper\n| | | Shell script preprocessing\n| | | Matlab processing (compiled)\n| | | | Matlab entrypoint\n| | | | Matlab main function\n| | | \\ Matlab sub-functions / codebase\n\\ \\ \\ Shell script postprocessing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDependencies in terms of the actual files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity\n src/pipeline_entrypoint.sh\n src/pipeline_main.sh\n src/copy_inputs.sh\n src/preprocessing.sh\n matlab/bin/run_matlab_entrypoint.sh\n matlab/bin/matlab_entrypoint\n / matlab/src/matlab_entrypoint.m \\ Used for compilation,\n | matlab/src/matlab_main.m | but not at container\n \\ matlab/src/* / runtime\n src/postprocessing.sh\n src/make_pdf.sh\n src/finalize.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe process of putting it together is described below. The scripts and code in\nthis repository are extensively commented, so if something isn\u0027t clear here,\nit\u0027s probably explained in the Singularity file or the example code.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-matlab-part\" class=\"anchor\" href=\"#matlab-part\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMatlab part\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-write-the-basic-matlab-code\" class=\"anchor\" href=\"#write-the-basic-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWrite the basic Matlab code\u003c/h3\u003e\n\u003cp\u003eWrite Matlab code that does what\u0027s needed. Put it in \u003ccode\u003ematlab/src\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA popular toolbox for reading and writing Nifti files that\u0027s available on Matlab\nCentral has a lot of insidious bugs and is not being maintained. Matlab\u0027s own\nfunctions for Nifti files are quite limited. Here is an alternative, which is\nused in this example:\n\u003ca href=\"https://github.com/VUIIS/spm_readwrite_nii\"\u003ehttps://github.com/VUIIS/spm_readwrite_nii\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-write-the-matlab-entrypoint\" class=\"anchor\" href=\"#write-the-matlab-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWrite the Matlab entrypoint\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/src/matlab_entrypoint.m\u003c/code\u003e exists to take command line arguments, parse\nthem, and call the main code. A convenient way to set things up is to write a\nmain function that takes a structure as its sole input, with the structure\ncontaining whatever inputs are needed. See \u003ccode\u003ematlab/src/matlab_main.m\u003c/code\u003e for an\nexample of this.\u003c/p\u003e\n\u003cp\u003eCouple of things to note in the entrypoint code are the quit/exit sections at\nbeginning and end. The bit at the beginning allows the executable to run during\nthe container build, without actually doing anything - this is needed to extract\nthe CTF archive into the container at the only time the container is writeable\n(h/t \u003ca href=\"https://twitter.com/annash128\" rel=\"nofollow\"\u003ehttps://twitter.com/annash128\u003c/a\u003e for figuring that one out). The bit at the\nend exits matlab when the function is finished. Without it, the running Matlab\nprocess won\u0027t release execution back to the calling script when it\u0027s done.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-test-the-matlab-entrypoint\" class=\"anchor\" href=\"#test-the-matlab-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the Matlab entrypoint\u003c/h3\u003e\n\u003cp\u003eThe script \u003ccode\u003ematlab/src/test_matlab_entrypoint.m\u003c/code\u003e is an example of how to do\nthis. The appropriate Matlab must be installed on the testing computer.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-the-matlab-code\" class=\"anchor\" href=\"#compile-the-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile the Matlab code\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/compile_matlab.sh\u003c/code\u003e shows how. Many compiled executables are likely to be\ntoo large to store on github. Git LFS may be a solution.\n\u003ca href=\"https://docs.github.com/en/github/managing-large-files/working-with-large-files\"\u003ehttps://docs.github.com/en/github/managing-large-files/working-with-large-files\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-test-the-compiled-matlab-code\" class=\"anchor\" href=\"#test-the-compiled-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the compiled Matlab code\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/test_compiled_matlab.sh\u003c/code\u003e. The appropriate Matlab Runtime must be\ninstalled on the testing computer.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-shell-script-part\" class=\"anchor\" href=\"#shell-script-part\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell script part\u003c/h2\u003e\n\u003cp\u003eAll of the below procedures could be done in the matlab part, if desired,\ninstead of in shell script. If so, parsing inputs should be done following the\nexample in \u003ccode\u003ematlab/src/matlab_entrypoint.m\u003c/code\u003e. But it\u0027s often easier to move\nfiles, create the QA PDF, etc using shell script and FSL. So that\u0027s what we are\ndoing in this example. All this code is in the \u003ccode\u003esrc\u003c/code\u003e directory.\u003c/p\u003e\n\u003cp\u003eAll the shell scripts called from \u003ccode\u003esrc/pipeline_entrypoint.sh\u003c/code\u003e \"know\" the\nenvironment variables that are exported there. This is a very convenient way to\npass along the input arguments, although it isn\u0027t entirely transparent, because\nthere\u0027s no hint in the shell scripts where the variables\u0027 values are coming from\nunless we explain it in the comments.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-main-entrypoint\" class=\"anchor\" href=\"#main-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMain entrypoint\u003c/h3\u003e\n\u003cp\u003eThis is \u003ccode\u003esrc/pipeline_entrypoint.sh\u003c/code\u003e. It uses bash to parse the command line\ninputs and export them to environment variables so they\u0027re accessible. Then it\ncalls the primary shell script \u003ccode\u003esrc/pipeline_main.sh\u003c/code\u003e which in turn calls\neverything else. The main script is run in xvfb to provide a virtual display,\noften needed by matlab and required for fsleyes.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-copy-inputs\" class=\"anchor\" href=\"#copy-inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopy inputs\u003c/h3\u003e\n\u003cp\u003eWe copy input files to the output/working directory so we don\u0027t mess them up.\nThis also is an opportunity to rename them to something consistent. It\u0027s very\nconvenient to hard-code the filenames so we don\u0027t have to store and manipulate\nfilenames in environment variables or the like. Also, this makes it easy to\nproduce output files with consistent names - outputs of one pipeline may serve\nas inputs to another, and it\u0027s much easier to manage this if filenames are the\nsame for every run, or at least consistent.\u003c/p\u003e\n\u003cp\u003eWe generally assume the output directory starts out empty and will not be\ninterfered with by any other processes - this is true for XNAT/DAX, but\nimportant to be aware of in other contexts.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preprocessing\" class=\"anchor\" href=\"#preprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessing\u003c/h3\u003e\n\u003cp\u003eFor this example, there is no preprocessing before the matlab part. But initial\nFSL steps or similar could be put here: \u003ccode\u003esrc/preprocessing.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-postprocessing\" class=\"anchor\" href=\"#postprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePostprocessing\u003c/h3\u003e\n\u003cp\u003eThere isn\u0027t any postprocessing for this example either, but there could be:\n\u003ccode\u003esrc/postprocessing.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pdf-creation\" class=\"anchor\" href=\"#pdf-creation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePDF creation\u003c/h3\u003e\n\u003cp\u003eAll assessors on VUIIS XNAT require a PDF QA report of some sort. For this\nexample, a display of the segmented ROIs overlaid on the T1 is created using\nfsleyes and ImageMagick, \u003ccode\u003esrc/make_pdf.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003ePDF creation can be done in Matlab instead. It\u0027s hard to make these look good.\nAn example with some tricks, including a \u003ccode\u003e.fig\u003c/code\u003e file painstakingly made with\nMatlab\u0027s GUIDE, is\n\u003ca href=\"https://github.com/baxpr/connprep/blob/855dadc/src/connectivity_filter.m#L271\"\u003ehttps://github.com/baxpr/connprep/blob/855dadc/src/connectivity_filter.m#L271\u003c/a\u003e\nA way to show slices of functional images with a nice red/blue colormap is\n\u003ca href=\"https://github.com/baxpr/connprep/blob/855dadc/src/make_network_maps.m\"\u003ehttps://github.com/baxpr/connprep/blob/855dadc/src/make_network_maps.m\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-finalizing-the-output\" class=\"anchor\" href=\"#finalizing-the-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFinalizing the output\u003c/h3\u003e\n\u003cp\u003eAll Niftis must be compressed for storage on XNAT, and outputs can be organized\nin an easily understandable way: \u003ccode\u003esrc/finalize.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eWrite an informative README - so tedious, yet so helpful. Include the\nappropriate citations for all the methods and software you have used. Even\nessentially write the methods section for a paper that uses the pipeline. Here\u0027s\nan excellent example: \u003ca href=\"https://github.com/MASILab/PreQual\"\u003ehttps://github.com/MASILab/PreQual\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAlternatively, git-ify some documentation like this:\n\u003ca href=\"https://github.com/VUIIS/dax/tree/main/docs\"\u003ehttps://github.com/VUIIS/dax/tree/main/docs\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eto get something like this:\n\u003ca href=\"https://dax.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://dax.readthedocs.io/en/latest/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-container\" class=\"anchor\" href=\"#building-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container\u003c/h2\u003e\n\u003cp\u003eBe sure the Matlab code is newly compiled, see above. You can run\n\u003ccode\u003ematlab/check_for_compilation.sh\u003c/code\u003e first to make sure there\u0027s no source code\nnewer than the compiled executable.\u003c/p\u003e\n\u003cp\u003eThen from the root directory of the working copy of the repo, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build \u0026lt;container_name\u0026gt;.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGood practice: before you build, create a release on github (if using github) or\nat least tag the commit you are about to build. Give the container a versioned\nname like \u003ccode\u003edemo-singularity-matlab-fsl_v1.0.0.simg\u003c/code\u003e that matches the repository\nname and release version/tag.\u003c/p\u003e\n\u003cp\u003eExternal binaries such as Matlab Runtime and FSL can be included by copying\nlocal copies into the container in the Singularity file\u0027s \u003ccode\u003e%files\u003c/code\u003e section. This\ntends to be a little faster when multiple builds are needed during debugging,\nor necessary for files that are not available to download, and this is what\u0027s\nbeing done in the example Singularity file. Alternatively, binaries or install\nfiles can be downloaded from their source at build time - there are some\ncommented-out sections in the Singularity file showing how that is done. (Thanks\n\u003ca href=\"https://github.com/praitayini\"\u003ehttps://github.com/praitayini\u003c/a\u003e for exploring this in detail)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-container\" class=\"anchor\" href=\"#running-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the container\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003etest_singularity_container.sh\u003c/code\u003e for an example run command and some\nimportant info.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h3\u003e\n\u003cp\u003ePaths to files are relative to the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--t1_niigz A T1 image\n--seg_niigz Its corresponding segmentation from e.g. slant pipeline\n--diameter_mm Diameter of the hole to zero out, in mm (default 30)\n\n--project Labels from XNAT, used only to annotate the QA PDF\n--subject (default UNK_*)\n--session\n--scan\n\n--out_dir Where outputs will be stored (default /OUTPUTS)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePDF/holed_image.pdf QA report\nHOLED_T1/holed_t1.nii.gz T1 image with a hole in it\nHOLED_SEG/holed_seg.nii.gz Segmentation with a hole in it\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1646920385.0 + "updated_at": 1625430481.0 }, { "data_format": 2, - "description": "ENIGMA CHR DTI repository", + "description": "Singularity recipe for bat", "filenames": [ - "singularity/Singularity.def" + "0.17.1/Singularity" ], - "full_name": "kcho/ENIGMA_CHR_DTI", - "latest_release": "example_dwi_data_light", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-enigma-chr-dti-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#enigma-chr-dti-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eENIGMA CHR DTI pipeline\u003c/h1\u003e\n\u003cp\u003eKevin Cho and Yoobin Kwak\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:kevincho@bwh.harvard.edu\"\u003ekevincho@bwh.harvard.edu\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:yoobinkwak@gmail.com\"\u003eyoobinkwak@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction\u003c/li\u003e\n\u003cli\u003eCitation\u003c/li\u003e\n\u003cli\u003eInstallation\u003c/li\u003e\n\u003cli\u003eArranging data for the pipeline\u003c/li\u003e\n\u003cli\u003eRunning the ENIGMA CHR DTI Pipeline\u003c/li\u003e\n\u003cli\u003eSharing outputs to other teams\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eENIGMA CHR DTI pipeline is a toolbox for analyzing diffusion weighted imaging (DWI) data developed for ENIGMA-CHR DTI project. The pipeline expects dicom files of a single DWI scan arranged in a required structure (decribed in \"Arranging data for the pipeline\") and automatically processes available data.\u003c/p\u003e\n\u003cp\u003eThe dicom files will be converted to a Nifti file, bval, and bvec file along with the BIDS sidecar json file. Then the following steps will be applied to each subject data.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGibbs unring (FSL)\u003c/li\u003e\n\u003cli\u003eFSL Eddy (6.0.4)\u003c/li\u003e\n\u003cli\u003eTensor decomposition to create fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) maps.\u003c/li\u003e\n\u003cli\u003eSkeletonization of the FA, AD, MD and RD maps using PNL-TBSS.\u003c/li\u003e\n\u003cli\u003eExtraction of mean diffusion measures in the major JHU bundles.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo increase the homogeneity of the diffusion acquisition parameters within the site, the pipeline curates the following dicom tags from all data, and highlight in the report if there is any deviation in dicom tags within a site.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSeriesDescription\u003c/li\u003e\n\u003cli\u003eImageType\u003c/li\u003e\n\u003cli\u003eAcquisitionMatrix\u003c/li\u003e\n\u003cli\u003eDeviceSerialNumber\u003c/li\u003e\n\u003cli\u003eEchoTime\u003c/li\u003e\n\u003cli\u003eFlipAngle\u003c/li\u003e\n\u003cli\u003eInPlanePhaseEncodingDirection\u003c/li\u003e\n\u003cli\u003eMagneticFieldStrength\u003c/li\u003e\n\u003cli\u003eManufacturer\u003c/li\u003e\n\u003cli\u003eManufacturerModelName\u003c/li\u003e\n\u003cli\u003eProtocolName\u003c/li\u003e\n\u003cli\u003eRepetitionTime\u003c/li\u003e\n\u003cli\u003eSequenceName\u003c/li\u003e\n\u003cli\u003eSliceThickness\u003c/li\u003e\n\u003cli\u003eSoftwareVersions\u003c/li\u003e\n\u003cli\u003eSpacingBetweenSlices\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlthough it\u0027s recommended to provide dicom data as the input to the pipeline, you can also provide diffusion files in the nifti format if your DWI data requires a specific dicom to nifti conversion or if the dicom files not available by some reason. You would need to provide DWI nifti file, bvector file, bvalue file in a structure that the pipeline expects. Pleaes make sure you are providing the raw nifti file without any preprocessing. If any of the three files is missing, the pipeline will raise an error. (See \u003ccode\u003eArranging data for the pipeline\u003c/code\u003e section.) Please let the study coordinator know your situation, and the study coordinate will guide you.\u003c/p\u003e\n\u003cp\u003eThe toolbox is deployed in a container, so as long as either Docker or Singularity is installed on the server, the toolbox should be functional regardless of the operating system.\nPlease note the pipeline does not support Apple Mac with M1 Chips yet, due to an issue with tensorflow installation on M1 Chip machines. Also, since this pipeline is specifically developed for ENIGMA-CHR DTI project, it does not support EPI distortion correction using reverse-encoding maps or field maps. If your data for ENIGMA-CHR project has multiple DWI series, blip-up / blip-down, fieldmaps, or other reverse-encoding diffusion scans, please reach out to the coordinating team.\u003c/p\u003e\n\u003cp\u003ePlease let the study coordinator know if you don\u0027t have powerful enough servers to process your diffusion data. The study coordinator will arrange a cloud server for you to run the pipeline.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eThis toolbox uses the following softwares. Please cite them if you use this pipeline in your study.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rordenlab/dcm2niix\"\u003e\u003ccode\u003edcm2niix\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pnlbwh/CNN-Diffusion-MRIBrain-Segmentation\"\u003eCNN based diffusion MRI brain segmentation tool\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://fsl.fmrib.ox.ac.uk/\" rel=\"nofollow\"\u003eFSL (and FSL unring)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ANTsX/ANTs\"\u003eANTs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pnlbwh/TBSS\"\u003ePNL TBSS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kcho/objPipe\"\u003e\u003ccode\u003eobjPipe\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pnlbwh/eddy-squeeze\"\u003e\u003ccode\u003eeddy-squeeze\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pnlbwh/nifti-snapshot\"\u003e\u003ccode\u003enifti-snapshot\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewith Docker\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eInstall and configure Docker Desktop\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.docker.com/products/docker-desktop/\" rel=\"nofollow\"\u003eDownload Docker Desktop\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003ewith at least 4 cores (12 cores preferably) and 4 GB RAM (16 GB preferably)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDownload ENIGMA CHR DTI docker image.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn terminal or power-shell, type\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker pull kcho/enigma-chr-pipeline\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewith Singularity\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity build enigma-chr-pipeline.simg docker://kcho/enigma-chr-pipeline\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\u003ca href=\"how_to_test_pipeline.md\"\u003eTest the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-arranging-data-for-the-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#arranging-data-for-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArranging data for the pipeline\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-if-you-are-providing-dicom-files-to-the-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#if-you-are-providing-dicom-files-to-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf you are providing dicom files to the pipeline\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e/Users/kc244/enigma_chr_data \u0026lt;- it could be somewhere else\n\u2514\u2500\u2500 sourcedata\n \u251c\u2500\u2500 subject_01\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610335278017249630.dcm\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610335278017249631.dcm\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610431388254021154.dcm\n \u251c\u2500\u2500 subject_02\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610335278017239630.dcm\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610335278011723631.dcm\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.202204261043138825403154.dcm\n \u2514\u2500\u2500 subject_XX\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-if-you-are-providing-nifti-files-to-the-pipeline-as-the-raw-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#if-you-are-providing-nifti-files-to-the-pipeline-as-the-raw-input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf you are providing nifti files to the pipeline as the raw input\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e/Users/kc244/enigma_chr_data \u0026lt;- it could be somewhere else\n\u2514\u2500\u2500 rawdata\n \u00a0\u00a0 \u251c\u2500\u2500 subject_01\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01.nii.gz\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01.bvec\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_01.bval\n \u00a0\u00a0 \u251c\u2500\u2500 subject_02\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_02.nii.gz\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_02.bvec\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_02.bval\n \u00a0\u00a0 \u251c\u2500\u2500 ...\n \u00a0\u00a0 \u2514\u2500\u2500 subject_XX\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-enigma-chr-dti-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-enigma-chr-dti-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the ENIGMA CHR DTI Pipeline\u003c/h2\u003e\n\u003cp\u003eOnce you have your dicom files arranged for each subject, run following command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ enigma_chr_dir=/Users/kc244/enigma_chr_data # set this to your data location\n$ docker run -it -v ${enigma_chr_dir}:/data kcho/enigma-chr-pipeline\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ enigma_chr_dir=/Users/kc244/enigma_chr_data # set this to your data location\n$ singularity run -e -B ${enigma_chr_dir}:/data:rw enigma-chr-pipeline.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eThe pipeline is expected to take about 2~3 hours to process a single subject data.\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-if-you-are-providing-nifti-data-to-the-pipeline-follow-the-steps-below\" class=\"anchor\" aria-hidden=\"true\" href=\"#if-you-are-providing-nifti-data-to-the-pipeline-follow-the-steps-below\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf you are providing nifti data to the pipeline, follow the steps below.\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ enigma_chr_dir=/Users/kc244/enigma_chr_data # set this to your data location\n$ docker run -it \\\n -v ${enigma_chr_dir}:/data \\\n enigma_chr_pipeline xvfb-run -a python /opt/ENIGMA_CHR_DTI/scripts/enigma_chr_pipeline.py -b /data --nifti_input\n\n# for singularity\n$ singularity shell -e -B ${enigma_chr_dir}:/data \\\n enigma_chr_pipeline.simg xvfb-run -a python /opt/ENIGMA_CHR_DTI/scripts/enigma_chr_pipeline.py -b /data --nifti_input\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sharing-outputs-to-other-teams\" class=\"anchor\" aria-hidden=\"true\" href=\"#sharing-outputs-to-other-teams\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSharing outputs to other teams\u003c/h2\u003e\n\u003cp\u003eRun the code below to collect and compress the files to share.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -it -v ${enigma_chr_dir}:/data kcho/enigma-chr-pipeline collect_outputs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run -e -B ${enigma_chr_dir}:/data:rw enigma-chr-pipeline.simg collect_outputs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere is the list of files collected by \u003ccode\u003ecollect_outputs.py\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/Users/kc244/enigma_chr_data\n derivatives/\n \u251c\u2500\u2500 eddy_qc\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 eddy_summary.html\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_02\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 eddy_summary.html\n \u251c\u2500\u2500 screenshots\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_02\n \u251c\u2500\u2500 tbss\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 snapshots\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ENIGMA\\ Template\\ FA\\ skeleton.jpg\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ENIGMA\\ Template\\ FA.jpg\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 Mean\\ FA\\ skeleton.jpg\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 mean\\ FA.jpg\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 stats\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 AD_combined_roi.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 AD_combined_roi_avg.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 FA_combined_roi.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 FA_combined_roi_avg.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 MD_combined_roi.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 MD_combined_roi_avg.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 RD_combined_roi.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u2514\u2500\u2500 RD_combined_roi_avg.csv\n \u2514\u2500\u2500 web_summary\n \u251c\u2500\u2500 Study.html\n \u251c\u2500\u2500 Study.pdf\n \u251c\u2500\u2500 subject_01\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01.html\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_01.pdf\n \u2514\u2500\u2500 subject_02\n \u251c\u2500\u2500 subject_02.html\n \u2514\u2500\u2500 subject_02.pdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-enter-into-the-image-shell\" class=\"anchor\" aria-hidden=\"true\" href=\"#enter-into-the-image-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnter into the image shell\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ enigma_chr_dir=/Users/kc244/enigma_chr_data # set this to your data location\n$ docker run -it \\\n -v ${enigma_chr_dir}:/data \\\n enigma_chr_pipeline /bin/bash\n\n# for singularity\n$ singularity shell -e -B ${enigma_chr_dir}:/data \\\n enigma_chr_pipeline.simg /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "icaoberg/singularity-bat", + "latest_release": "v0.17.1", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bat\" class=\"anchor\" href=\"#singularity-bat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bat\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/7b7c397acc5b91b4c4cf7756015185fe3c5f700f70d256a212de51294a0cf673/68747470733a2f2f696d6775722e636f6d2f724773646e44652e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b7c397acc5b91b4c4cf7756015185fe3c5f700f70d256a212de51294a0cf673/68747470733a2f2f696d6775722e636f6d2f724773646e44652e706e67\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/bat\"\u003ebat\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebat\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bat/0.17.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modules/bat\u003c/code\u003e as \u003ccode\u003e0.17.1\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1651669505.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1622870361.0 }, { "data_format": 2, "description": null, "filenames": [ - "misc/releases/20.06/Singularity.20.06", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/latest/Singularity" + "Singularity.def" ], - "full_name": "silvansievers/pddl-symmetry-reduction", + "full_name": "granek/jupyter-MIC-2021", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hts-jupyter-notebook-container\" class=\"anchor\" href=\"#hts-jupyter-notebook-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHTS Jupyter notebook container\u003c/h1\u003e\n\u003cp\u003eWe are offering a series of 6 workshops on biological assays and data analysis for HIV researchers.\nThis series is funded by an R25 grand from the National Institute of Allergies and Infectious Disease (NIAID).\nOur goal is to provide educational enrichment for HIV researchers on current assay technologies and the statistical and bioinformatic analysis techniques necessary to process such data.\u003c/p\u003e\n\u003cp\u003eThis is the source for the Docker container used to run the course Jupyter\nnotebooks.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-using-the-image\" class=\"anchor\" href=\"#using-the-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing the image\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install-docker\" class=\"anchor\" href=\"#install-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall docker\u003c/h2\u003e\n\u003cp\u003eTo run a container on your local machine or laptop, download the docker program from \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003ehttps://www.docker.com\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-image-on-your-local-computer\" class=\"anchor\" href=\"#run-image-on-your-local-computer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun image on your local computer\u003c/h2\u003e\n\u003cp\u003eOnce you have the docker program installed, open the program (you should get a terminal screen with command line). Enter the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull dukehtscourse/jupyter-hts-2019\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will pull down the course docker image from dockerhub. It may take a few minutes. Next, run the command to start a container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --name hts-course -v YOUR_DIRECTORY_WITH_COURSE_MATERIAL:/home/jovyan/work \\\n-d -p 127.0.0.1\\:9999\\:8888 \\\n-e PASSWORD=\"YOUR_CHOSEN_NOTEBOOK_PASSWORD\" \\\n-e NB_UID=1000 \\\n-t dukehtscourse/jupyter-hts-2019\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe most important parts of this verbiage are the \u003ccode\u003eYOUR_DIRECTORY_WITH_COURSE_MATERIALS\u003c/code\u003e and \u003ccode\u003eYOUR_CHOSEN_NOTEBOOK_PASSWORD\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYOUR_DIRECTORY_WITH_COURSE_MATERIALS\u003c/code\u003e (Bind mounting): The directory name is the one you extracted your course materials into. So, if you put them in your home directory, it might look something like: \u003ccode\u003e-v /home/janice/HTS2019-notebooks:/home/jovyan/work\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eYOUR_CHOSEN_NOTEBOOK_PASSWORD\u003c/code\u003e: The password is whatever you want to use to password protect your notebook. Now, this command is running the notebook so that it is only \u0027seen\u0027 by your local computer - no one else on the internet can access it, and you cannot access it remotely, so the password is a bit of overkill. Use it anyway. An example might be: \u003ccode\u003e-e PASSWORD=\"Pssst_this_is_Secret\"\u003c/code\u003e except that this is a terrible password and you should follow standard rules of not using words, include a mix of capital and lowercase and special symbols. etc.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d -p 127.0.0.1\\:9999\\:8888\u003c/code\u003e part of the command is telling docker to run the notebook so that it is only visible to the local machine. It is absolutely possible to run it as a server to be accessed across the web - but there are some security risks associated, so if you want to do this proceed with great caution and get help.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOf course, it would be better either configure HTTPS (see the options section below) or run an Nginx proxy in front of the container instance so you get https (encryption) instead of http.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-open-the-jupyter-in-your-browser\" class=\"anchor\" href=\"#open-the-jupyter-in-your-browser\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpen the Jupyter in your browser\u003c/h3\u003e\n\u003cp\u003eOpen a browser and point it to \u003ca href=\"http://127.0.0.1:9999\" rel=\"nofollow\"\u003ehttp://127.0.0.1:9999\u003c/a\u003e\nYou should get to a Jupyter screen asking for a password. This is the password you created in the docker run command.\nNow, you should be able to run anything you like from the course. Depending on your laptop\u0027s resources (RAM, cores), this might be slow, so be aware and start by testing only one file (vs the entire course data set).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-stopping-docker\" class=\"anchor\" href=\"#stopping-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStopping Docker\u003c/h3\u003e\n\u003cp\u003eThe container will continue running, even if you do not have Jupyter open in a web browser. If you don\u0027t plan to use it for a while, you might want to shut it down so it isn\u0027t using resources on your computer. Here are two ways to do that:\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-kitematic\" class=\"anchor\" href=\"#kitematic\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKitematic\u003c/h4\u003e\n\u003cp\u003eIncluded in the \u003ca href=\"https://docs.docker.com/docker-for-mac/\" rel=\"nofollow\"\u003eDocker for Mac\u003c/a\u003e and the \u003ca href=\"https://docs.docker.com/docker-for-windows/\" rel=\"nofollow\"\u003eDocker for Windows\u003c/a\u003e installations.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-commandline\" class=\"anchor\" href=\"#commandline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommandline\u003c/h4\u003e\n\u003cp\u003eYou may want to familiarize yourself with the following Docker commands.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003edocker stop\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker rm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker ps -a\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker images\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker rmi\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-windows-note\" class=\"anchor\" href=\"#windows-note\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows Note\u003c/h3\u003e\n\u003cp\u003eThese instructions have not been tested in a Windows environment. If you have problems with them, please give us feedback\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-image-on-a-server\" class=\"anchor\" href=\"#run-image-on-a-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun image on a server\u003c/h2\u003e\n\u003cp\u003eTo run on a remote server you will want to use a slightly different command from above, because you \u003cem\u003ewill need to connect remotely\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --name hts-course \\\n-v YOUR_DIRECTORY_WITH_COURSE_MATERIAL:/home/jovyan/work \\\n-d -p 8888:8888 \\\n-e USE_HTTPS=\"yes\" \\\n-e PASSWORD=\"YOUR_CHOSEN_NOTEBOOK_PASSWORD\" \\\n-e NB_UID=1000 \\\n-t dukehtscourse/jupyter-hts-2019\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-options\" class=\"anchor\" href=\"#options\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions\u003c/h2\u003e\n\u003cp\u003eYou may customize the execution of the Docker container and the Notebook server it contains with the following optional arguments.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-e PASSWORD=\"YOURPASS\"\u003c/code\u003e - Configures Jupyter Notebook to require the given password. Should be conbined with \u003ccode\u003eUSE_HTTPS\u003c/code\u003e on untrusted networks.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-e USE_HTTPS=yes\u003c/code\u003e - Configures Jupyter Notebook to accept encrypted HTTPS connections. If a \u003ccode\u003epem\u003c/code\u003e file containing a SSL certificate and key is not provided (see below), the container will generate a self-signed certificate for you.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e(v4.0.x)\u003c/strong\u003e \u003ccode\u003e-e NB_UID=1000\u003c/code\u003e - Specify the uid of the \u003ccode\u003ejovyan\u003c/code\u003e user. Useful to mount host volumes with specific file ownership.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-e GRANT_SUDO=yes\u003c/code\u003e - Gives the \u003ccode\u003ejovyan\u003c/code\u003e user passwordless \u003ccode\u003esudo\u003c/code\u003e capability. Useful for installing OS packages. \u003cstrong\u003eYou should only enable \u003ccode\u003esudo\u003c/code\u003e if you trust the user or if the container is running on an isolated host.\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-v /some/host/folder/for/work:/home/jovyan/work\u003c/code\u003e - Host mounts the default working directory on the host to preserve work even when the container is destroyed and recreated (e.g., during an upgrade).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e(v3.2.x)\u003c/strong\u003e \u003ccode\u003e-v /some/host/folder/for/server.pem:/home/jovyan/.ipython/profile_default/security/notebook.pem\u003c/code\u003e - Mounts a SSL certificate plus key for \u003ccode\u003eUSE_HTTPS\u003c/code\u003e. Useful if you have a real certificate for the domain under which you are running the Notebook server.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e(v4.0.x)\u003c/strong\u003e \u003ccode\u003e-v /some/host/folder/for/server.pem:/home/jovyan/.local/share/jupyter/notebook.pem\u003c/code\u003e - Mounts a SSL certificate plus key for \u003ccode\u003eUSE_HTTPS\u003c/code\u003e. Useful if you have a real certificate for the domain under which you are running the Notebook server.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-e INTERFACE=10.10.10.10\u003c/code\u003e - Configures Jupyter Notebook to listen on the given interface. Defaults to \u0027*\u0027, all interfaces, which is appropriate when running using default bridged Docker networking. When using Docker\u0027s \u003ccode\u003e--net=host\u003c/code\u003e, you may wish to use this option to specify a particular network interface.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-e PORT=8888\u003c/code\u003e - Configures Jupyter Notebook to listen on the given port. Defaults to 8888, which is the port exposed within the Dockerfile for the image. When using Docker\u0027s \u003ccode\u003e--net=host\u003c/code\u003e, you may wish to use this option to specify a particular port.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-course-image-with-singularity\" class=\"anchor\" href=\"#running-the-course-image-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Course Image with Singularity\u003c/h2\u003e\n\u003cp\u003eDocker requires root permissions to run, so you are unlikely to be able to run Docker on a computer that you are not fully in control of. As an alternative you can run the course image with \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, another container system. Singularity is similar to Docker, and can run Docker images, but you do not need special permissions to run Singularity images \u003cem\u003eor\u003c/em\u003e Docker images with Singularity (as long as Singularity is actually installed on the computer).\u003c/p\u003e\n\u003cp\u003eThe following command uses Singularity to start up a container from the course Jupyter image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-the-course-image-on-a-slurm-cluster\" class=\"anchor\" href=\"#running-the-course-image-on-a-slurm-cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Course Image on a SLURM cluster\u003c/h3\u003e\n\u003cp\u003eWe will use the example of the Duke Computer Cluster, but these instructions should be easily adaptable to other clusters\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFrom your computer run this to connect to DCC:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh NetID@dcc-login-03.oit.duke.edu\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOnce you are connected run this to start a tmux session:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003etmux new -s jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOnce you have started a tmux session you can start up Jupyter with this command:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun singularity exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: the first time you run this, it might take a VERY long time to download the Docker image and build the Singularity image from it\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eRunning this command will print a bunch of stuff. You can ignore everything except the last two lines, which will say something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttp://dcc-chsi-01:8889/?token=08172007896ad29bb5fbd92f6f3f516a8b2f7303ed7f1df3\nor http://127.0.0.1:8889/?token=08172007896ad29bb5fbd92f6f3f516a8b2f7303ed7f1df3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou need this information for the next few steps. For the next step you need the \u201cdcc-chsi-01:8889\u201d part.\n\u201cdcc-chsi-01\u201d is the compute node that Jupyter is running on and \u201c8889\u201d is the port it is listening on. You may get a different value every time you start the container.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eYou want to run the following command in another terminal on your computer to set up port forwarding.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh -L PORT:NODE.rc.duke.edu:PORT NetID@dcc-login-03.oit.duke.edu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this command you want to replace \u201cPORT\u201d with the value you got for port from the srun command and replace \u201cNODE\u201d with the compute node that was printed by the srun command. So for the example above, the ssh port forwarding command would be:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -L 8889:dcc-chsi-01.rc.duke.edu:8889 NetID@dcc-login-03.oit.duke.edu\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eNow you can put the last line that the srun command printed in your web browser and it should open your Jupyter instance running on DCC.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe Jupyter session keeps running until you explicitly shut it down. If the port forwarding SSH connection drops you will need to restart SSH with the same command, but you don\u2019t need to restart Jupyter.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere are two ways to explicitly shut down Jupyter:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eWithin Jupyter, click on the \u003cem\u003eJupyter\u003c/em\u003e logo in the top left to go to the main Jupyter page, then click \"Quit\" in the top right\u003c/li\u003e\n\u003cli\u003eDo control-C twice in the terminal where you started Jupyter. If this connection dropped, you can reconnect to it with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh NetID@dcc-login-03.oit.duke.edu\ntmux a -t jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter shutting down the Jupyter session you can type \u003ccode\u003eexit\u003c/code\u003e at the terminal to close the tmux session.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf you need more memory or more cpus you can use the \u003ccode\u003e--mem\u003c/code\u003e and/or \u003ccode\u003e--cpus-per-task\u003c/code\u003e arguments to in the \u201csrun\u201d, for example to request 4 CPUs and 10GB of RAM:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun --cpus-per-task=4 --mem=10G singularity exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eIf you have high priority access to a partition you can request that partition be used with the \u003ccode\u003e-A\u003c/code\u003e and \u003ccode\u003e-p\u003c/code\u003e arguments to \u003ccode\u003esrun\u003c/code\u003e:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun -A chsi -p chsi singularity exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eYou might want to access files that are outside of your home directory. Within a singularity container your access to the host computer is\nlimited: by default, from inside the container you can only access your home directory. If you want to access directories that are outside your home\ndirectory, you have to tell \u003cem\u003eSingularity\u003c/em\u003e when you start the container with the \u003ccode\u003e--bind\u003c/code\u003e command line argument. For example:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun singularity --bind /work/josh:/work/josh exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eYou can combine several of these command line flags:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun -A chsi -p chsi --cpus-per-task=4 --mem=10G singularity exec --bind /work/josh:/work/josh docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eIt is strongly recommended to set the \u003ccode\u003eSINGULARITY_CACHEDIR\u003c/code\u003e environment variables in your .bashrc or when running \u003ccode\u003esrun\u003c/code\u003e. This environment variable specifies where the Docker image (and the Singularity image built from it) are saved. If this variable is not specified, singularity will cache images in \u003ccode\u003e$HOME/.singularity/cache\u003c/code\u003e, which can fill up quickly. This is discussed in the \u003ca href=\"https://sylabs.io/guides/3.7/user-guide/build_env.html#cache-folders\" rel=\"nofollow\"\u003eSingularity Documentation\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITY_CACHEDIR=\"/work/josh/singularity_cache\"; srun -A chsi -p chsi --cpus-per-task=4 --mem=10G singularity exec --bind /work/josh:/work/josh docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-install-singularity\" class=\"anchor\" href=\"#install-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Singularity\u003c/h3\u003e\n\u003cp\u003eHere are instructions for installing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/2.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity version 2.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.2/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity version 3.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/singularity-desktop-macos/\" rel=\"nofollow\"\u003eSingularity Desktop for macOS (Alpha Preview)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1657796847.0 + "updated_at": 1623704048.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "2.2.1/Singularity" ], - "full_name": "ddbj/singularity_omegafold", + "full_name": "icaoberg/singularity-hisat2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_omegafold\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_omegafold\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_omegafold\u003c/h1\u003e\n\u003cp\u003eUbuntu 20.04\u306bomegafold v1.1.0\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fsingularity image\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306eSingularity definition file\u3067\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-image\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" aria-hidden=\"true\" href=\"#image\u306e\u30d3\u30eb\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eimage\u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build omegafold-1.1.0.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3-login_gpuq\u3067\u306e\u5b9f\u884c\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3-login_gpuq\u3067\u306e\u5b9f\u884c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3 login_gpu.q\u3067\u306e\u5b9f\u884c\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv omegafold-1.1.0.sif python3 /opt/OmegaFold/main.py input.fasta output_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30ab\u30ec\u30f3\u30c8\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b output_dir \u304c\u4f5c\u6210\u3055\u308c\u3001\u305d\u306e\u4e2d\u306b\u7d50\u679c\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3-intelq\u3067\u306e\u5b9f\u884c\u7528\u30b8\u30e7\u30d6\u30b9\u30af\u30ea\u30d7\u30c8\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3-intelq\u3067\u306e\u5b9f\u884c\u7528\u30b8\u30e7\u30d6\u30b9\u30af\u30ea\u30d7\u30c8\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3 intel.q\u3067\u306e\u5b9f\u884c\u7528\u30b8\u30e7\u30d6\u30b9\u30af\u30ea\u30d7\u30c8\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#$ -S /bin/sh\n#$ -cwd\n#$ -l s_vmem=2G\n#$ -l mem_req=2G\n#$ -l intel\n#$ -pe def_slot 16\nN=16\nsingularity exec /home/y-okuda/singularity/omegafold/omegafold-1.1.0.sif \\\nsh -c \"\\\nexport OMP_NUM_THREADS=${N}; \\\npython3 /opt/OmegaFold/main.py \\\n--device cpu \\\n/home/y-okuda/singularity/omegafold/input.fasta \\\n/home/y-okuda/singularity/omegafold/output_dir \\\n\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eN\u306b\u8a2d\u5b9a\u3057\u305f\u6570\u306eCPU\u30b3\u30a2\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u540c\u3058\u5024\u3092 -pe def_slot \u306b\u3082\u8a2d\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ea61f9228ca14a66e58889a560447e0b7c8ba73ddbaa594055242ace96eb0a84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ea61f9228ca14a66e58889a560447e0b7c8ba73ddbaa594055242ace96eb0a84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4796300b08f76b423ee0574c15e8bd8ad15b0a389a36fd3f58549b8bb5df8690/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4796300b08f76b423ee0574c15e8bd8ad15b0a389a36fd3f58549b8bb5df8690/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/25eeccddaeb5bf9a3f7053b646f9b7bf540d33b801c371db1850d745da46fc95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/25eeccddaeb5bf9a3f7053b646f9b7bf540d33b801c371db1850d745da46fc95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f14a5cb988478f36746bb17a364f669ee4d02a32a6f6fddfbccaff9dc50a8379/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f14a5cb988478f36746bb17a364f669ee4d02a32a6f6fddfbccaff9dc50a8379/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hisat\" class=\"anchor\" href=\"#singularity-hisat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hisat\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://daehwankimlab.github.io/hisat2/\" rel=\"nofollow\"\u003ehisat2\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts \u003ccode\u003ehisat2*\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hisat2/2.2.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hisat2\u003c/code\u003e as \u003ccode\u003e2.2.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-singularity-definition-file\" class=\"anchor\" href=\"#building-the-image-using-the-singularity-definition-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the Singularity definition file\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1661924246.0 + "updated_at": 1624060173.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for graphviz", "filenames": [ - "Singularity.def" + "2.44.0/Singularity" ], - "full_name": "roitberg-group/lammps-ani", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-lammps-ani\" class=\"anchor\" aria-hidden=\"true\" href=\"#lammps-ani\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLAMMPS-ANI\u003c/h1\u003e\n\u003cp\u003eA plugin to run torchani on LAMMPS.\u003cbr\u003e\nOn hipergator, the compiled program and a working example script could be found at \u003ccode\u003e/blue/roitberg/apps/lammps-ani/examples/water/submit.sh\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirement\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirement\u003c/h2\u003e\n\u003cp\u003eRun an interactive session\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esrun --qos=roitberg --account=roitberg --nodes=1 --ntasks=2 --cpus-per-task=2 --mem=20gb --gres=gpu:2 --partition=hpg-ai -t 10:00:00 --pty /bin/bash -i\nmodule load cuda/11.4.3 gcc/9.3.0 openmpi/4.0.5 cmake/3.21.3 git/2.30.1 singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003epytorch and cudnn\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge\nconda install -c conda-forge cudnn=8.3.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity--docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity--docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity \u0026amp; Docker Container\u003c/h2\u003e\n\u003cp\u003eYou could use the pre-built \u003ca href=\"https://github.com/roitberg-group/lammps-ani/pkgs/container/lammps-ani\"\u003edocker container\u003c/a\u003e to avoid compiling the program by yourself.\u003c/p\u003e\n\u003cp\u003eSome HPCs provide Singularity instead of Docker. The following shows the instruction for Singularity usage:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive git@github.com:roitberg-group/lammps-ani.git\nsingularity pull -F docker://ghcr.io/roitberg-group/lammps-ani:master\nmkdir -p \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/singularity-home\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e exec into container\u003c/span\u003e\nSINGULARITYENV_CUDA_VISIBLE_DEVICES=\u003cspan class=\"pl-smi\"\u003e$CUDA_VISIBLE_DEVICES\u003c/span\u003e singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --cleanenv -H \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/singularity-home:/home --nv lammps-ani_master.sif /bin/bash\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e test\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e lammps-ani\nnvidia-smi \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e external/torchani_sandbox \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e python setup.py install --ext --user \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../../ \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e tests/ \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e python save_ani.py \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e ./test_all.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./build.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun example\u003c/h2\u003e\n\u003cp\u003emake sure \u003ccode\u003eLAMMPS_PLUGIN_PATH\u003c/code\u003e and \u003ccode\u003eLAMMPS_ROOT\u003c/code\u003e are set correctly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport LAMMPS_PLUGIN_PATH=/blue/roitberg/apps/lammps-ani/build/\ncd examples/water/\nmpirun -np 8 ${LAMMPS_ROOT}/build/lmp_mpi -in in.lammps\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "icaoberg/singularity-graphviz", + "latest_release": "v2.44.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-graphviz\" class=\"anchor\" href=\"#singularity-graphviz\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-graphviz\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/960789693fa68a8f442f9c6cc7d6a117639f1a792ec84c96648ad4764c385fcf/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f656e2f342f34382f477261706876697a4c6f676f2e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/960789693fa68a8f442f9c6cc7d6a117639f1a792ec84c96648ad4764c385fcf/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f656e2f342f34382f477261706876697a4c6f676f2e706e67\" alt=\"Logo\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/en/4/48/GraphvizLogo.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://graphviz.org/\" rel=\"nofollow\"\u003egraphviz \u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003egraphviz \u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/graphviz/2.44.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modules/graphviz \u003c/code\u003e as \u003ccode\u003e 2.44.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [], - "updated_at": 1649451939.0 + "subscribers_count": 1, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1622859227.0 }, { "data_format": 2, - "description": "Use SNP genotype information pulled from single cell RNA-seq data to predict ancestries", + "description": "Singularity recipe for dust", "filenames": [ - "Singularity.ancestry_prediction_scRNAseq" + "0.5.4/Singularity" ], - "full_name": "powellgenomicslab/ancestry_prediction_scRNAseq", + "full_name": "icaoberg/singularity-dust", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ancestry_prediction_scrnaseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#ancestry_prediction_scrnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eancestry_prediction_scRNAseq\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-dust\" class=\"anchor\" href=\"#singularity-dust\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-dust\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/bootandy/dust/raw/master/media/snap.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/bootandy/dust/raw/master/media/snap.png\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/bootandy/dust\"\u003edust\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003edust\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/dust/0.5.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/dust\u003c/code\u003e as \u003ccode\u003e0.5.4\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1661089200.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1622860644.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for hyperfine", "filenames": [ - "program/HiC-Pro_3.1.0/Singularity" + "1.11.0/Singularity" ], - "full_name": "hermanzhaozzzz/snakepipes_Hi-C", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-snakepipes_hi-c\" class=\"anchor\" aria-hidden=\"true\" href=\"#snakepipes_hi-c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esnakepipes_Hi-C\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003e\u987b\u77e5\u003c/strong\u003e\uff1a\u672c\u4ed3\u5e93\u8fd8\u5728\u6784\u5efa\u4e2d\uff0c\u6682\u65f6\u53ea\u4f5c\u53c2\u8003\uff01\uff01\u003c/p\u003e\n\u003cp\u003e\u53c2\u8003\u548c\u5f15\u7528\u4e86\u4e00\u4e9b\u003ca href=\"https://github.com/nservant/HiC-Pro\"\u003eHiC Pro\u003c/a\u003e\u7684\u4ee3\u7801\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u73af\u5883\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u73af\u5883\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u73af\u5883\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install python r-base bowtie2 samtools iced r-ggplot2 r-rcolorbrewer\nconda install -c bioconda java-jdk hicexplorer\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u6211\u7528\u7684\u7248\u672c\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e python=3.9.13\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e R=4.0.5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e bowtie2=2.4.5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e samtools=1.15.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e iced=0.5.10\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e java-jdk=1.8 # java openjdk version \"1.8.0_312\"\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e hicexplorer=3.7.2\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u7528\u6cd5\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u7528\u6cd5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u7528\u6cd5\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-0-\u6d4b\u5e8f\u8d28\u91cf\u63a7\u5236\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-0-\u6d4b\u5e8f\u8d28\u91cf\u63a7\u5236\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estep 0 \u6d4b\u5e8f\u8d28\u91cf\u63a7\u5236\u003c/h3\u003e\n\u003cp\u003e\u4f7f\u7528 \u003ca href=\"https://github.com/hermanzhaozzzz/snakepipes_fastqc-multiqc\"\u003esnakepipes_fastqc-multiqc\u003c/a\u003e\u8fdb\u884c\u8d28\u91cf\u63a7\u5236\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-\u8fd0\u884csnakemake-pipeline\u751f\u6210hi-c-contact-matrix\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-\u8fd0\u884csnakemake-pipeline\u751f\u6210hi-c-contact-matrix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estep 1 \u8fd0\u884cSnakemake Pipeline\uff0c\u751f\u6210Hi-C contact matrix\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u56de\u8d34Hi-C reads\u4ee5\u53ca\u751f\u6210RAW\u77e9\u9635ICE\u6821\u6b63\u77e9\u9635\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003evalidPairs convert to .hic file(Juicer)\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e HiC\ngit clone https://github.com/hermanzhaozzzz/snakepipes_fastqc-multiqc.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e snakepipes_fastqc-multiqc\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e use jupyterlab or runipy to run step01_generate_samples.ipynb\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e get samples.json and check it\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e dry run, rm -n to run pipeline\u003c/span\u003e\nsnakemake -pr -j 8 -s step02_run_mapping_and_generate_matrix.py -n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output as below\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e HiC|\u21d2 tree . -L 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e .\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 bam\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 fastq\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 hic_file\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 matrix\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 qc\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 quality_checks\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 snakepipes_fastqc-multiqc\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 snakepipes_Hi-C\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 temp_files\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u2514\u2500\u2500 valid_pairs\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-convert-validpairs-to-juicer-hic\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-convert-validpairs-to-juicer-hic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estep 2 Convert ValidPairs to Juicer .hic\u00b6\u003c/h3\u003e\n", + "full_name": "icaoberg/singularity-hyperfine", + "latest_release": "v1.11.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" 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data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hyperfine\" class=\"anchor\" href=\"#singularity-hyperfine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hyperfine\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/hyperfine\"\u003ehyperfine\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehyperfine\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hyperfine/1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hyperfine\u003c/code\u003e as \u003ccode\u003e1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1658684345.0 - }, - { - "data_format": 2, - "description": "Singularity recipe files for DeepVariant (https://github.com/google/deepvariant)", - "filenames": [ - "Singularity.1.0.0", - "Singularity", - "Singularity.1.4.0-gpu", - "Singularity.1.4.0" + "topics": [ + "singularity", + "utilities" ], - "full_name": "powerPlant/deepvariant-srf", - "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-singularity-recipe-files-for-deepvariant\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-files-for-deepvariant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity recipe files for Deepvariant\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/google/deepvariant\"\u003ehttps://github.com/google/deepvariant\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGenerate symlinks for executables\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec deepvariant.1.4.0.sif find /opt/deepvariant/bin -type f -executable -printf \"%f\\n\" | xargs -L1 ln -s deepvariant.1.4.0.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gpu-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#gpu-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGPU support\u003c/h2\u003e\n\u003cp\u003eSet \u003ccode\u003eSINGULARITY_NV=true\u003c/code\u003e to enable GPU support where required. Useful in environment modules, like,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Required to enable GPU\nsetenv SINGULARITY_NV true\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 0, - "subscribers_count": 0, - "topics": [], - "updated_at": 1657769856.0 + "updated_at": 1624059711.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for methylpy", "filenames": [ - "Singularity" + "1.4.3/Singularity" ], - "full_name": "baxpr/makerois-PMAT-fs", - "latest_release": "v1.0.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-create-study-specific-roi-image-in-mni-space\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-study-specific-roi-image-in-mni-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate study-specific ROI image in MNI space\u003c/h1\u003e\n\u003cp\u003ePMAT resting state connectivity study. Freesurfer-based ROIs for followup analysis.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h2\u003e\n\u003cp\u003eAll should be matched to the same T1 image.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eT1 image in atlas space (typically BIAS_NORM resource of cat12 assessor)\u003c/li\u003e\n\u003cli\u003eDeformation from T1 subject space to atlas space (typically DEF_FWD resource of cat12 assessor)\u003c/li\u003e\n\u003cli\u003eSUBJECT directory of Freesurfer output (typically SUBJECT resource of freesurfer_dev assessor)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003erois_PMAT_fs.nii.gz Region of interest image\nrois_PMAT_fs-labels.csv Region labels and volumes\nmakerois-PMAT-fs.pdf Visual report of final ROI image\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-regions-of-interest\" class=\"anchor\" aria-hidden=\"true\" href=\"#regions-of-interest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRegions of interest\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-visual-regions-subject-space-warped\" class=\"anchor\" aria-hidden=\"true\" href=\"#visual-regions-subject-space-warped\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisual regions (Subject space, warped)\u003c/h3\u003e\n\u003cp\u003eGenerated by Freesurfer 6. Region indices in \u003ccode\u003esrc/rois-visual-a2009s.csv\u003c/code\u003e. Method: \u003cem\u003eBruce Fischl, Andre van der Kouwe, Christophe Destrieux, Eric Halgren, Florent Segonne, David H. Salat, Evelina Busa, Larry J. Seidman, Jill Goldstein, David Kennedy, Verne Caviness, Nikos Makris, Bruce Rosen, and Anders M. Dale. Automatically Parcellating the Human Cerebral Cortex. Cerebral Cortex January 2004; 14:11-22.\u003c/em\u003e\u003c/p\u003e\n", + "full_name": "icaoberg/singularity-methylpy", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hyperfine\" class=\"anchor\" href=\"#singularity-hyperfine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hyperfine\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/hyperfine\"\u003ehyperfine\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehyperfine\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hyperfine/1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hyperfine\u003c/code\u003e as \u003ccode\u003e1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1658942371.0 + "updated_at": 1622730662.0 }, { "data_format": 2, - "description": "\u62ff\u6765\u505a\u6027\u80fd\u4f18\u5316...fork from https://github.com/ot4f/stgcn_gan", + "description": "Singularity recipe for ABySS", "filenames": [ - "Singularity" + "2.1.5/Singularity" ], - "full_name": "asifreal/stgcn_gan", + "full_name": "icaoberg/singularity-abyss", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-stgcn_gan\" class=\"anchor\" aria-hidden=\"true\" href=\"#stgcn_gan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estgcn_gan\u003c/h1\u003e\n\u003cp\u003eTraining STGCN with WGAN\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hyperfine\" class=\"anchor\" href=\"#singularity-hyperfine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hyperfine\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/hyperfine\"\u003ehyperfine\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehyperfine\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hyperfine/1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hyperfine\u003c/code\u003e as \u003ccode\u003e1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1658914223.0 + "updated_at": 1622730467.0 }, { "data_format": 2, - "description": "Script allowing to convert a NIfTI file with ROIs to the DICOM SEG format.", + "description": "Singularity recipe for shellcheck", "filenames": [ - "Singularity.nifti-to-seg" + "0.5.0/Singularity" ], - "full_name": "roger-schaer/nifti-to-seg", + "full_name": "icaoberg/singularity-shellcheck", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nifti-to-seg-converter\" class=\"anchor\" aria-hidden=\"true\" href=\"#nifti-to-seg-converter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNIfTI to SEG Converter\u003c/h1\u003e\n\u003cp\u003eThis project allows you to convert a NIfTI file containing\none or more non-overlapping regions-of-interest (ROIs)\ninto the DICOM Segmentation (SEG) format.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eThe following instructions will help you to perform your\nfirst NIfTI to SEG conversion.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cp\u003eYou can either run the project directly with Python, or\nuse Docker instead. If you want to run it directly with\nPython, you need to install the dependencies listed in\nrequirements.txt:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enumpy\ngit+https://github.com/roger-schaer/pydicom-seg.git#egg=pydicom-seg\nSimpleITK\npalettable\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-general-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#general-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral usage\u003c/h3\u003e\n\u003cp\u003eThe script expects the following arguments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-i, --dicom_input\u003c/code\u003e : The path of the folder with the\noriginal DICOM images (from which ROIs were extracted)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-n, --nifti_roi\u003c/code\u003e : The path of the NIfTI file containing\nthe ROI(s) to convert to DICOM SEG\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o, --output_seg\u003c/code\u003e : The path where the created DICOM SEG\nfile should be saved\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-l, --label_map\u003c/code\u003e : \u003cem\u003e(OPTIONAL)\u003c/em\u003e The path to a CSV file containing\npairs of \u003ccode\u003e\u0026lt;label_id\u0026gt;,\u0026lt;label_name\u0026gt;\u003c/code\u003e entries\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d, --match-orientation\u003c/code\u003e : \u003cem\u003e(OPTIONAL)\u003c/em\u003e No value;\npresence of argument indicates that orientation of NIfTI file will be matched to DICOM images\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-s, --match-size\u003c/code\u003e : \u003cem\u003e(OPTIONAL)\u003c/em\u003e No value;\npresence of argument indicates that size of NIfTI file will be matched to DICOM images.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo execute the script, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython nifti_to_seg.py -i /path/to/dicom/images -n /path/to/nifti.nii -o /path/to/seg.dcm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen the script is executed, it will analyze the provided\nNIfTI file to identify the various ROIs saved within. This\nis done by detecting the \u003cstrong\u003eunique\u003c/strong\u003e pixel values present in\nthe image.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-without-a-label-map-file-manual-label-name-entry\" class=\"anchor\" aria-hidden=\"true\" href=\"#without-a-label-map-file-manual-label-name-entry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWithout a label map file (manual label name entry)\u003c/h4\u003e\n\u003cp\u003eIf you have not provided a label map file path, you will then\nbe prompted to map each of these values to a string describing\nthe content of the associated ROI. To know which pixel value\ncorresponds to which ROI, you may need to refer to the software\nthat generated the NIfTI file (e.g. ITK-SNAP, which uses label\nnumbers starting from 1).\u003c/p\u003e\n\u003cp\u003eThe output looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFound X regions in the NIfTI file, please input a name for each of them.\n(1/X) - Please insert a name for the region with the assigned number N: ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the names have been input, the SEG file will be\ngenerated and saved at the path provided in the \u003ccode\u003e-o\u003c/code\u003e\nargument.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-with-a-label-map-file-bulk-processing\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-a-label-map-file-bulk-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith a label map file (bulk processing)\u003c/h4\u003e\n\u003cp\u003eInstead of inputting the label mappings manually, you can also provide\nthe \u003ccode\u003e-l\u003c/code\u003e / \u003ccode\u003e--label_map\u003c/code\u003e parameter pointing to a CSV file containing\npairs of \u003ccode\u003e\u0026lt;label_id\u0026gt;,\u0026lt;label_name\u0026gt;\u003c/code\u003e entries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE :\u003c/strong\u003e This methods requires you to know in advance the existing\npixel values in the NIfTI segmentation file. Only exhaustive files\ncontaining a label for each identified pixel value are accepted.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Docker\u003c/h2\u003e\n\u003cp\u003eTo run the script using docker, use the following syntax:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it \\\n-v /path/to/data/on/host:/data \\\nmedgift/nifti-to-seg:latest \\\n--dicom_input=/data/dicom_folder \\\n--nifti_roi=/data/seg.nii \\\n--output_seg=/data/seg.dcm \\\n--label_map=/data/labels.csv (OPTIONAL)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe parameters are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--rm\u003c/code\u003e removes the container once the script completes.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-it\u003c/code\u003e allows interacting with the container in the console.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emedgift/nifti-to-seg:latest\u003c/code\u003e is the Docker image.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-v\u003c/code\u003e maps a folder from your computer to the container (on \u003ccode\u003e/data\u003c/code\u003e).\nPut all necessary files in that folder (DICOM \u0026amp; NIfTI), and the\noutput will be written there as well.\u003c/li\u003e\n\u003cli\u003eThe other parameters are the same as for general Python usage.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Singularity\u003c/h2\u003e\n\u003cp\u003eSee the \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e pages for setup.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Image\u003c/h3\u003e\n\u003cp\u003eEnter the directory where this readme file is located.\nBuild the singularity image with name \u003cem\u003emeshtool.sif\u003c/em\u003e by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build nifti_to_seg.sif Singularity.nifti-to-seg\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-meshtool-from-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-meshtool-from-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning MeshTool from Singularity Image\u003c/h3\u003e\n\u003cp\u003eYou can enter a shell in the singularity container by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -e /path/to/nifti_to_seg.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLeave the singularity shell again with \u003ccode\u003eexit\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRoger Schaer\u003c/strong\u003e - \u003cem\u003eInitial work\u003c/em\u003e - \u003ca href=\"https://github.com/roger-schaer\"\u003eroger-schaer\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis project is licensed under the MIT License - see the \u003ca href=\"LICENSE.md\"\u003eLICENSE.md\u003c/a\u003e file for details\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/razorx89\"\u003erazorx89\u003c/a\u003e for the great work\non \u003ca href=\"https://github.com/razorx89/pydicom-seg\"\u003epydicom-seg\u003c/a\u003e,\nwhich is the core of this script\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp align=\"center\"\u003e\n \u003ca href=\"https://camo.githubusercontent.com/762c1129f266494bbbb3faff3d673040cf7b1f19d45c6e13f49b08de12f5116a/68747470733a2f2f692e70617374652e706963732f38373031383966616466363638613935386338616163383366333865373939632e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/762c1129f266494bbbb3faff3d673040cf7b1f19d45c6e13f49b08de12f5116a/68747470733a2f2f692e70617374652e706963732f38373031383966616466363638613935386338616163383366333865373939632e706e67\" width=\"300\" align=\"left\" data-canonical-src=\"https://i.paste.pics/870189fadf668a958c8aac83f38e799c.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-pema\" class=\"anchor\" href=\"#pema\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePEMA:\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-a-flexible-pipeline-for-environmental-dna-metabarcoding-analysis-of-the-16s18s-rrna-its-and-coi-marker-genes\" class=\"anchor\" href=\"#a-flexible-pipeline-for-environmental-dna-metabarcoding-analysis-of-the-16s18s-rrna-its-and-coi-marker-genes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S rRNA, ITS and COI marker genes\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003ePEMA is reposited in\u003c/em\u003e \u003ca href=\"https://hub.docker.com/r/hariszaf/pema\" rel=\"nofollow\"\u003e\u003cem\u003eDocker Hub\u003c/em\u003e\u003c/a\u003e \u003cem\u003eas well as in\u003c/em\u003e \u003ca href=\"https://singularity-hub.org/collections/2295\" rel=\"nofollow\"\u003e\u003cem\u003eSingularity Hub\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-a-pema-tutorial-can-be-found-here\" class=\"anchor\" href=\"#a-pema-tutorial-can-be-found-here\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA PEMA tutorial can be found \u003ca href=\"https://docs.google.com/presentation/d/1lVH23DPa2NDNBhVvOTRoip8mraw8zfw8VQwbK4vkB1U/edit?fbclid=IwAR14PpWfPtxB8lLBBnoxs7UbG3IJfkArrJBS5f2kRA__kvGDUb8wiJ2Cy_s#slide=id.g57f092f54d_1_21\" rel=\"nofollow\"\u003e\u003cstrong\u003ehere\u003c/strong\u003e\u003c/a\u003e.\u003c/h4\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-for-any-troubles-you-may-have-when-running-pema-or-for-any-potential-improvevments-you-would-like-to-suggest-please-share-on-the-pema-gitter-community\" class=\"anchor\" href=\"#for-any-troubles-you-may-have-when-running-pema-or-for-any-potential-improvevments-you-would-like-to-suggest-please-share-on-the-pema-gitter-community\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor any troubles you may have when running PEMA or for any potential improvevments you would like to suggest, please share on the \u003ca href=\"https://gitter.im/pema-helpdesk/community\" rel=\"nofollow\"\u003ePEMA Gitter community\u003c/a\u003e.\u003c/h4\u003e\n\n\u003cp\u003e\u003ca href=\"https://gitter.im/pema-helpdesk/community?utm_source=badge\u0026amp;utm_medium=badge\u0026amp;utm_campaign=pr-badge\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7385c04b449351f12fb57a4bd6f9791ebd68a483493399e50a8f096fadde4246/68747470733a2f2f6261646765732e6769747465722e696d2f70656d612d68656c706465736b2f636f6d6d756e6974792e737667\" alt=\"Gitter\" data-canonical-src=\"https://badges.gitter.im/pema-helpdesk/community.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/400c4e52df43f6a0ab8a89b74b1a78d1a64da56a7848b9110c9d2991bb7c3105/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pema-biodiversity-in-all-its-different-levels\"\u003ePEMA: biodiversity in all its different levels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#a-container-based-tool\"\u003e A container-based tool\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#how-to-run-pema\"\u003eHow to run PEMA\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#parameters-file\"\u003eParameters\u0027 file\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pema-on-hpc\"\u003ePEMA on HPC\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#prerequisites-1\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installing-1\"\u003eInstalling\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-pema-1\"\u003eRunning PEMA\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#example\"\u003eExample\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pema-on-a-simple-pc\"\u003ePEMA on a simple PC\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#prerequisites\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installing\"\u003eInstalling\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-pema\"\u003eRunning PEMA\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#step-1---build-a-docker-container\"\u003eStep 1 - Build a Docker container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#step-2---run-pema\"\u003eStep 2 - Run PEMA\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#the-phyloseq-r-package\"\u003ephyloseq - for a downstream ecological analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#acknowledgments\"\u003eAcknowledgments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-diff\"\u003e\u003cpre\u003e\u003cspan class=\"pl-mi1\"\u003e\u003cspan class=\"pl-mi1\"\u003e+\u003c/span\u003e convertion of the Illumina raw data is now implemented in the framework of PEMA\u003c/span\u003e\n\u003cspan class=\"pl-mi1\"\u003e\u003cspan class=\"pl-mi1\"\u003e+\u003c/span\u003e PEMA now supports 2 extra marker genes, 18S rRNA and ITS. \u003c/span\u003e\n\u003cspan class=\"pl-mi1\"\u003e\u003cspan class=\"pl-mi1\"\u003e+\u003c/span\u003e PEMA is now available for macOS!\u003c/span\u003e\n\u003cspan class=\"pl-mi1\"\u003e\u003cspan class=\"pl-mi1\"\u003e+\u003c/span\u003e for anything feel free to contact me at: haris-zaf@hcmr.gr\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-pema-biodiversity-in-all-its-different-levels\" class=\"anchor\" href=\"#pema-biodiversity-in-all-its-different-levels\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePEMA: biodiversity in all its different levels\u003c/h1\u003e\n\u003cp\u003ePEMA supports the metabarcoding analysis of four marker genes, \u003cstrong\u003e16S rRNA\u003c/strong\u003e (Bacteria), \u003cstrong\u003eITS\u003c/strong\u003e (Fungi) as well as \u003cstrong\u003eCOI\u003c/strong\u003e and \u003cstrong\u003e18S rRNA\u003c/strong\u003e (metazoa). As input, PEMA accepts .fastq.gz files as returned by Illumina sequencing platforms.\u003c/p\u003e\n\u003cp\u003ePEMA processes the reads from each sample and \u003cstrong\u003ereturns an OTU- or an ASV-table with the taxonomies\u003c/strong\u003e of the taxa found and their abundances in each sample. It also returns statistics and a FASTQC diagram about the quality of the reads for each sample. Finally, PEMA supports \u003cstrong\u003edownstream ecological analysis\u003c/strong\u003e of the profiles retrieved, facilitated by the \u003ca href=\"http://joey711.github.io/phyloseq/index.html\" rel=\"nofollow\"\u003ephyloseq\u003c/a\u003e R package.\u003c/p\u003e\n\u003cp\u003ePEMA supports both OTU clustering (thanks to VSEARCH and CROP algorithms) and ASV inference (via SWARM) for all four marker genes.\u003c/p\u003e\n\u003cp\u003eFor the case of the 16S rRNA marker gene, PEMA includes two separate approaches for taxonomy assignment: alignment-based and phylogenetic-based. For the latter, a reference tree of 1000 taxa was created using SILVA_132_SSURef, EPA-ng and RaxML-ng.\u003c/p\u003e\n\u003cp\u003ePEMA has been implemented in \u003ca href=\"https://pcingola.github.io/BigDataScript/\" rel=\"nofollow\"\u003eBigDataScript\u003c/a\u003e programming language. BDS\u2019s ad hoc task parallelism and task synchronization, supports heavyweight computation. Thus, PEMA inherits such features and it also supports roll-back checkpoints and on-demand partial pipeline execution. In addition, PEMA takes advantage of all the computational power available on a specific machine; for example, if PEMA is executed on a personal laptop with 4 cores, it is going to use all four of them.\u003c/p\u003e\n\u003cp\u003eFinally, container-based technologies such as Docker and Singularity, make PEMA easy accessible for all operating systems.\nAs you can see in the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/help_files/GitHub%20tutorial.pdf\"\u003ePEMA_tutorial.pdf\u003c/a\u003e, once you have either Docker or Singularity on your computational environment (see below which suits your case better), running PEMA is cakewalk. You can also find the \u003ca href=\"https://docs.google.com/presentation/d/1lVH23DPa2NDNBhVvOTRoip8mraw8zfw8VQwbK4vkB1U/edit?usp=sharing\" rel=\"nofollow\"\u003e\u003cstrong\u003ePEMA tutorial\u003c/strong\u003e\u003c/a\u003e as a Google Slides file.\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-a-container-based-tool\" class=\"anchor\" href=\"#a-container-based-tool\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA container-based tool\u003c/h1\u003e\n\u003cp\u003ePEMA can run either on a HPC environment (server, cluster etc) or on a simple PC. However, we definitely suggest to run it on an HPC environment to exploit the full potential of PEMA. Running on a powerful server or a cluster can be time-saving since it would require significantly less computational time than in a common PC. However, for analyses with a small number of samples, a common PC can suffice.\u003c/p\u003e\n\u003cp\u003eThere is one \u003cstrong\u003emajor difference\u003c/strong\u003e between running PEMA on a common PC than running it on a HPC environment. In the first case, PEMA runs through \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003e\u003cstrong\u003eDocker\u003c/strong\u003e\u003c/a\u003e, while in the latter one, it runs through \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOn the following chapters, you can find how to install PEMA both in Docker and Singlularity including examples.\u003c/p\u003e\n\u003cp\u003eRunning PEMA is exactly \u003cstrong\u003ethe same\u003c/strong\u003e procedure in both of those cases.\u003c/p\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-run-pema\" class=\"anchor\" href=\"#how-to-run-pema\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run PEMA\u003c/h2\u003e\n\u003cp\u003eAssuming you have either Docker or Singularity on your system (see below how to get them).\nYou need to create a directory where you will have everything PEMA needs - we will call it \u003cem\u003e\u003cstrong\u003eanalysis directory\u003c/strong\u003e\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn this directory, you need to add the following \u003cstrong\u003emandatory\u003c/strong\u003e files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003e\u003cstrong\u003eparameters.tsv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file (you can download it from this repository and then \u003cstrong\u003ecomplete it\u003c/strong\u003e according to the needs of your analysis)\u003c/li\u003e\n\u003cli\u003ea subdirectory called \u003cem\u003e\u003cstrong\u003emydata\u003c/strong\u003e\u003c/em\u003e where your .fastq.gz files will be located \u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf your need to perform phyloseq, in the analysis directory you also need to add the following \u003cstrong\u003eoptionally\u003c/strong\u003e files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/phyloseq_in_PEMA.R\"\u003e\u003cem\u003e\u003cstrong\u003ephyloseq_in_PEMA.R\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e which you can also download from this repository and set it the way you want (that is an R script which we have implemented and has some main features that need to stay always the same in order to be executed as part of PEMA and some parts where the user can set what exactly needs to get from the phyloseq package)\u003c/li\u003e\n\u003cli\u003ethe \u003ca href=\"https://raw.githubusercontent.com/hariszaf/pema/master/analysis_directory/metadata.csv\" rel=\"nofollow\"\u003e\u003cem\u003e\u003cstrong\u003emetadata.csv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file which has to be in a \u003cstrong\u003ecomma separated\u003c/strong\u003e format (you can find an example of this file on PEMA\u0027s GitHub repository).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-attention--\" class=\"anchor\" href=\"#attention--\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eAttention!\u003c/strong\u003e \u003cbr\u003e\n\u003c/h3\u003e\n\u003cp\u003ePEMA will \u003cstrong\u003efail\u003c/strong\u003e unless you name the aforementioned files and directories \u003cstrong\u003eexactly\u003c/strong\u003e as described above.\n\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eHere is an example of how your \u003cem\u003eanalysis directory\u003c/em\u003e should be in case you do want a phyloseq analysis:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@home-PC:~/Desktop/analysis_directory$ ls\nmydata parameters.tsv phyloseq_in_PEMA.R metadata.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand in case you do not:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@home-PC:~/Desktop/analysis_directory$ ls\nmydata parameters.tsv \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/hariszaf/pema/tree/master/analysis_directory\"\u003e\u003cstrong\u003eHere\u003c/strong\u003e\u003c/a\u003e you can find an example of an \u003cem\u003eanalysis directory\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eAfter you have prepared this \u003cem\u003eanalysis directory\u003c/em\u003e you are ready to run PEMA (see below).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAn extended list with PEMA\u0027s ouput can be found \u003ca href=\"https://github.com/hariszaf/pema/blob/master/help_files/PEMA\u0027s%20output%20files.md\"\u003e\u003cstrong\u003ehere\u003c/strong\u003e\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-parameters-file\" class=\"anchor\" href=\"#parameters-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u0027 file\u003c/h1\u003e\n\u003cp\u003eThe most crucial component in running PEMA is the parameters file. This file must be located \u003cstrong\u003ein\u003c/strong\u003e the \u003cem\u003eanalysis directory\u003c/em\u003e and the user needs to fill it \u003cstrong\u003eevery time\u003c/strong\u003e PEMA is about to be called. If you need more than one analyses to run, then you need to make copies of the parameters\u0027 file and have one of those in eah of the analysis directrories you create.\u003c/p\u003e\n\u003cp\u003eSo, here is the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003e\u003cstrong\u003eparameters.tsv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file as it looks like, in a study case of our own.\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-pema-on-hpc\" class=\"anchor\" href=\"#pema-on-hpc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePEMA on HPC\u003c/h1\u003e\n\u003cp\u003ePEMA is best to run on HPC (server, cluster, cloud). Usually environmental data are quite large and the whole process has huge computational demands. To get PEMA running on your HPC you (actually your system administrator) need to install Singularity as described below.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/strong\u003e is a free, cross-platform and open-source computer program that performs operating-system-level virtualization also known as containerization. One of the main uses of Singularity is to bring containers and reproducibility to scientific computing and the high-performance computing (HPC) world.\u003c/p\u003e\n\u003cp\u003eSingularity needs a Linux/Unix system to run.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing\" class=\"anchor\" href=\"#installing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling\u003c/h2\u003e\n\u003cp\u003eAfter you install Singularity in your environment and open it, you need to download PEMA\u0027s image from Singularity Hub, by running the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull shub://hariszaf/pema:v.1.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow you have PEMA on your environment. But there is still one really \u003cstrong\u003eimportant\u003c/strong\u003e thing that you need to do! Please \u003cstrong\u003edownload\u003c/strong\u003e the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003eparameters.tsv\u003c/em\u003e\u003c/a\u003e file and move it or copy it to the same directory with your raw data.\u003c/p\u003e\n\u003cp\u003eNow you are ready to go!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-pema\" class=\"anchor\" href=\"#running-pema\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning PEMA\u003c/h2\u003e\n\u003cp\u003eSingularity permits the use of a job scheduler that allocates computional resources on clusters and at the same time, works as a queuing system, as \u003cstrong\u003e\u003ca href=\"https://slurm.schedmd.com/overview.html\" rel=\"nofollow\"\u003eSlurm\u003c/a\u003e\u003c/strong\u003e. This way you are able to create a job as you usually do in your system and after editing the parameters file as needed, run PEMA as a job on your cluster.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example\" class=\"anchor\" href=\"#example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e#SBATCH --partition=batch\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=20\n#SBATCH --mem=\n# Memory per node specification is in MB. It is optional.\n# The default limit is 3000MB per core.\n#SBATCH --job-name=\"testPema\"\n#SBATCH --output=PEMA.output\n#SBATCH --mail-user=haris-zafr@hcmr.gr\n#SBATCH --mail-type=ALL\n#SBATCH --requeue\n\n\nsingularity run -B /\u0026lt;path\u0026gt;/\u0026lt;of\u0026gt;/\u0026lt;input\u0026gt;/\u0026lt;directory\u0026gt;/:/mnt/analysis /\u0026lt;path\u0026gt;/\u0026lt;of\u0026gt;/\u0026lt;PEMA_container\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the above example, we set the cluster \"Zorba\", to run PEMA in 1 node, with 20 cores.\u003c/p\u003e\n\u003cp\u003eFor further information, you can always check \u003ca href=\"https://docs.google.com/presentation/d/1lVH23DPa2NDNBhVvOTRoip8mraw8zfw8VQwbK4vkB1U/edit?fbclid=IwAR14PpWfPtxB8lLBBnoxs7UbG3IJfkArrJBS5f2kRA__kvGDUb8wiJ2Cy_s#slide=id.g57f092f54d_1_21\" rel=\"nofollow\"\u003ePEMA\u0027s tutorial\u003c/a\u003e.\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-pema-on-a-simple-pc\" class=\"anchor\" href=\"#pema-on-a-simple-pc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePEMA on a simple PC\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites-1\" class=\"anchor\" href=\"#prerequisites-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eTo run PEMA in a simple PC on your own environment, you first need to install \u003ca href=\"https://docs.docker.com/install/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, in case you do not already have it.\u003c/p\u003e\n\u003cp\u003eYou should check your software version. A version of Docker is avalable for all Windows, Mac and Linux. If you have Windows 10 Pro or your Mac\u0027s hardware in after 2010, then you can insall Docker straightforward. Otherwise, you need to install the \u003ca href=\"https://docs.docker.com/toolbox/\" rel=\"nofollow\"\u003eDocker toolbox\u003c/a\u003e instead. You can check if your System Requirements are according to the ones mentioned below in order to be sure what you need to do.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSystem Requirements\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e**__Windows 10 64bit__**:\nPro, Enterprise or Education (1607 Anniversary Update, Build 14393 or later).\nVirtualization is enabled in BIOS. Typically, virtualization is enabled by default.\nThis is different from having Hyper-V enabled. For more detail see Virtualization must be enabled in Troubleshooting.\nCPU SLAT-capable feature.\nAt least 4GB of RAM.\n\n**__Mac__**\nMac hardware must be a 2010 or newer model, with Intel\u2019s hardware support for memory management unit (MMU)\nvirtualization, including Extended Page Tables (EPT) and Unrestricted Mode. You can check to see if your machine\nhas this support by running the following command in a terminal:\nsysctl kern.hv_support macOS El Capitan 10.11 and newer macOS releases are supported.\nWe recommend upgrading to the latest version of macOS.\nAt least 4GB of RAM\nVirtualBox prior to version 4.3.30 must NOT be installed (it is incompatible with Docker for Mac).\nIf you have a newer version of VirtualBox installed, it\u2019s fine.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-1\" class=\"anchor\" href=\"#installing-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling\u003c/h2\u003e\n\u003cp\u003eAfter you install Docker in your environment and run it, the only thing you need to do, is to download PEMA\u0027s image, by running the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull hariszaf/pema\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe PEMA image file is a quite large (~3Gb), so it will take a while until it is downloaded in your computer system.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-pema-1\" class=\"anchor\" href=\"#running-pema-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning PEMA\u003c/h2\u003e\n\u003cp\u003eRunning PEMA has two discrete steps.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-step-1---build-a-docker-container\" class=\"anchor\" href=\"#step-1---build-a-docker-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1 - Build a Docker container\u003c/h3\u003e\n\u003cp\u003eAt first, you need to let Docker have access in your dataset. To provide access you need to run the following command and specifying the path to where your data is stored, i.e. changing the \u0026lt;path_to_analysis_directory\u0026gt; accordingly:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it -v /\u0026lt;path_to_analysis_directory\u0026gt;/:/mnt/analysis hariszaf/pema\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter you run the command above, you have now built a Docker container, in which you can run PEMA!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-step-2---run-pema\" class=\"anchor\" href=\"#step-2---run-pema\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2 - Run PEMA\u003c/h3\u003e\n\u003cp\u003eNow, being inside the PEMA container, the only thing remaining to do, is to run PEMA\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./PEMA_v1.bds\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePEMA is now running. The runtime of PEMA depends on the computational features of your environment, on the size of your data, as well as the parameters you chose.\u003c/p\u003e\n\u003cp\u003ePlease, keep in mind that when you need to copy a whole directory, then you always have to put \"/\" in the end of the path that describes where the directory is located.\u003c/p\u003e\n\u003cp\u003eFinally, you will find the PEMA output in the analysis directory on your computer. \u003cbr\u003e\nAs the output directory is mounted into the built Docker container, you can copy its contents wherever you want. However, in case you want to remove it permanently, you need to do this as a sudo user.\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-the-phyloseq-r-package\" class=\"anchor\" href=\"#the-phyloseq-r-package\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe \"phyloseq\" R package\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003efor a downstream ecological analysis of OTUs/ASVs retrieved\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePEMA performs all the basic functions of the \"phyloseq\" R package. In addition, it performs certain functions of the \u003ca href=\"https://cran.r-project.org/web/packages/vegan/index.html\" rel=\"nofollow\"\u003e\u003cem\u003e\u003cstrong\u003evegan\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e R package.\u003c/p\u003e\n\u003cp\u003eWhen the user asks for a downstream analysis using the \"phyloseq\" R package, then an extra input file called \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/phyloseq_in_PEMA.R\"\u003e\u003cem\u003e\u003cstrong\u003e\"phyloseq_script.R\"\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e needs to be imported in the \"analysis_directory\". In PEMA\u0027s main repository, you can find a template of this file; this file needs to be as it would run on your own computer, as you would run \u003cem\u003ephyloseq\u003c/em\u003e in any case. PEMA will create the \u003cem\u003e\"phyloseq object\"\u003c/em\u003e automatically and then it will perform the analysis as asked. The output will be placed in an extra subfolder in the main output directory of PEMA called \u003cem\u003ephyloseq_analysis\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition, the \u003cem\u003e\u003cstrong\u003emetadata.tsv\u003c/strong\u003e\u003c/em\u003e file is also required when the phyloseq option has been selected. An example of this file you can find \u003ca href=\"https://raw.githubusercontent.com/hariszaf/pema/master/analysis_directory/metadata.csv\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" href=\"#acknowledgments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h1\u003e\n\u003cp\u003ePEMA uses a series of tools, datasets as well as Big Data Script language. We thank all the groups that developed them.\nThe tools \u0026amp; databases that PEMA uses are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBigDataScript programming language - \u003ca href=\"https://pcingola.github.io/BigDataScript/\" rel=\"nofollow\"\u003ehttps://pcingola.github.io/BigDataScript/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFASTQC - \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u03a4rimmomatic - \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003ehttp://www.usadellab.org/cms/?page=trimmomatic\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCutadapt - \u003ca href=\"https://cutadapt.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ehttps://cutadapt.readthedocs.io/en/stable/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBayesHammer - included in SPAdes - \u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003ehttp://cab.spbu.ru/software/spades/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePANDAseq - \u003ca href=\"https://github.com/neufeld/pandaseq\"\u003ehttps://github.com/neufeld/pandaseq\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOBITools - \u003ca href=\"https://pythonhosted.org/OBITools/welcome.html\" rel=\"nofollow\"\u003ehttps://pythonhosted.org/OBITools/welcome.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBLAST Command Line Applications - \u003ca href=\"https://www.ncbi.nlm.nih.gov/books/NBK52640/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK52640/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVSEARCH-2.9.1 - \u003ca href=\"https://github.com/torognes/vsearch/releases/tag/v2.9.1\"\u003ehttps://github.com/torognes/vsearch/releases/tag/v2.9.1\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSWARM - \u003ca href=\"https://github.com/torognes/swarm\"\u003ehttps://github.com/torognes/swarm\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCROP - \u003ca href=\"https://github.com/tingchenlab/CROP\"\u003ehttps://github.com/tingchenlab/CROP\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCREST - \u003ca href=\"https://github.com/lanzen/CREST\"\u003ehttps://github.com/lanzen/CREST\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRDPClassifier - \u003ca href=\"https://github.com/rdpstaff/classifier\"\u003ehttps://github.com/rdpstaff/classifier\u003c/a\u003e\n(RPDtools are required in order to execute RDPClassifier)\u003c/li\u003e\n\u003cli\u003eSILVA db - \u003ca href=\"https://www.arb-silva.de/no_cache/download/archive/current/Exports/\" rel=\"nofollow\"\u003ehttps://www.arb-silva.de/no_cache/download/archive/current/Exports/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMIDORI db - \u003ca href=\"http://reference-midori.info/index.html\" rel=\"nofollow\"\u003ehttp://reference-midori.info/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\"phat\" algorithm, from the \"gappa\" package - \u003ca href=\"https://github.com/lczech/gappa/wiki/Subcommand:-phat\"\u003ehttps://github.com/lczech/gappa/wiki/Subcommand:-phat\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMAFFT - \u003ca href=\"https://mafft.cbrc.jp/alignment/software/\" rel=\"nofollow\"\u003ehttps://mafft.cbrc.jp/alignment/software/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRAxML -ng - \u003ca href=\"https://github.com/amkozlov/raxml-ng\"\u003ehttps://github.com/amkozlov/raxml-ng\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePaPaRa - \u003ca href=\"https://cme.h-its.org/exelixis/web/software/papara/index.html\" rel=\"nofollow\"\u003ehttps://cme.h-its.org/exelixis/web/software/papara/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEPA-ng - \u003ca href=\"https://github.com/Pbdas/epa-ng\"\u003ehttps://github.com/Pbdas/epa-ng\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ephyloseq R package - \u003ca href=\"http://joey711.github.io/phyloseq/index.html\" rel=\"nofollow\"\u003ehttp://joey711.github.io/phyloseq/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003evegan R package - \u003ca href=\"https://cran.r-project.org/web/packages/vegan/index.html\" rel=\"nofollow\"\u003ehttps://cran.r-project.org/web/packages/vegan/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnd of course the container-based technologies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker - \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehttps://www.docker.com/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity - \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003ehttps://sylabs.io/singularity/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003ePEMA is under the GNU GPLv3 license (for 3rd party components separate licenses apply).\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h1\u003e\n\u003cp\u003eHaris Zafeiropoulos, Ha Quoc Viet, Katerina Vasileiadou, Antonis Potirakis, Christos Arvanitidis, Pantelis Topalis, Christina Pavloudi, Evangelos Pafilis, PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes, GigaScience, Volume 9, Issue 3, March 2020, giaa022, \u003ca href=\"https://doi.org/10.1093/gigascience/giaa022\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/gigascience/giaa022\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1654849338.0 + "updated_at": 1622859451.0 }, { "data_format": 2, - "description": "Final year Major Project", + "description": null, "filenames": [ - "gdown.pl/Singularity" + "Singularity" ], - "full_name": "arshagarwal/FA-GAN", + "full_name": "baxpr/demo-singularity-spm-freeview", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-official-implementation-of-the-paper-titled-fa-gan-high-resolution-face-aging\" class=\"anchor\" aria-hidden=\"true\" href=\"#official-implementation-of-the-paper-titled-fa-gan-high-resolution-face-aging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOfficial Implementation of the paper titled \u003ca href=\"https://ieeexplore.ieee.org/document/9514090\" rel=\"nofollow\"\u003eFA-GAN: High Resolution Face-Aging\u003c/a\u003e\n\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-steps-to-run-using-docker-using-nvidia-gpus\" class=\"anchor\" aria-hidden=\"true\" href=\"#steps-to-run-using-docker-using-nvidia-gpus\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to run using docker using Nvidia gpus:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall docker using \u003ca href=\"https://docs.docker.com/engine/install/ubuntu/\" rel=\"nofollow\"\u003edocker installation guide\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eInstall nvidia-docker2 using \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker\" rel=\"nofollow\"\u003envidia-docker2 installation guide\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNote: Ensure to check the latest versions of nvidia cuda runtime and coroborrate with pytorch cuda requirements , this guide was uploaded on 4/9/2020.\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild the image using \u003ccode\u003edocker build -f docker -t slimgan:gpu\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eRun the Container using \u003ccode\u003edocker run --gpus all --shm-size 10G -it slimgan:gpu\u003c/code\u003e.\n5.Run the \u003ccode\u003envidia-smi\u003c/code\u003e to check if gpus work.\u003c/li\u003e\n\u003cli\u003eRun thr command \u003ccode\u003epython main.py --rafd_image_dir Big_slim --num_iters 30000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 --resume_iters 20000 --num_workers 5\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-steps-to-run-the-code-on-google-colab-just-3-lines-of-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#steps-to-run-the-code-on-google-colab-just-3-lines-of-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to run the code on Google colab (just 3 lines of code):\u003c/h2\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eClone the code by running the command \u003ccode\u003e!git clone https://username:password@github.com/arshagarwal/C_slim_gan.git -b arsh_spectral\u003c/code\u003e .\n\u003cstrong\u003eReplace the username and password with your github username and password respectively.\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003ecd C_slim_gan\u003c/code\u003e to navigate to the \u003cstrong\u003eC_slim_gan\u003c/strong\u003e directory.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003e!bash import_dataset.sh\u003c/code\u003e to import the \u003cstrong\u003eBig slim dataset\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003e! python main.py --rafd_image_dir Big_slim --num_iters 20000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 \u003c/code\u003e to train on the \u003cstrong\u003eBig_slim_dataset\u003c/strong\u003e.\n\u003cstrong\u003eAlternatively to train on custom dataset replace the \u003ccode\u003eslim_dataset/Train_dataset\u003c/code\u003e string with the path of your custom dataset.\u003c/strong\u003e\u003cbr\u003e\nFor further options such as \u003cstrong\u003enumber of epochs, batch_size etc\u003c/strong\u003e refer \u003cstrong\u003emain.py\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-note-after-running-step-3-after-every-sample-stepth-iteration-generated-samples-will-be-stored-in-stargansamples-folder-for-performance-evaluation\" class=\"anchor\" aria-hidden=\"true\" href=\"#note-after-running-step-3-after-every-sample-stepth-iteration-generated-samples-will-be-stored-in-stargansamples-folder-for-performance-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNote: After running step 3 after every sample stepth iteration generated samples will be stored in stargan/samples folder for performance evaluation.\u003c/h3\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-continuing-training-from-20000-iteration-to-30000-iteration-follow\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-continuing-training-from-20000-iteration-to-30000-iteration-follow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor Continuing training from 20000 iteration to 30000 iteration follow:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003epython main.py --rafd_image_dir Big_slim --num_iters 30000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 --resume_iters 20000\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-demo-singularity-container-with-spm12-and-freeview\" class=\"anchor\" href=\"#demo-singularity-container-with-spm12-and-freeview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo singularity container with SPM12 and Freeview\u003c/h1\u003e\n\u003cp\u003eSPM12-based pipelines require a little extra work to get them compiled and working in a\ncontainer. Freesurfer\u0027s Freeview is also included here, as it\u0027s very handy for creating\nthe PDF QA report.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/baxpr/demo-singularity-matlab-fsl\"\u003ehttps://github.com/baxpr/demo-singularity-matlab-fsl\u003c/a\u003e for a lot of detailed info about\nputting Matlab code into singularity containers. This example uses the same structure.\u003c/p\u003e\n\u003cp\u003eA licensed Matlab installation is required to compile the Matlab code, but is not needed\nto run the compiled executable in the container.\u003c/p\u003e\n\u003cp\u003eSPM12 (\u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" rel=\"nofollow\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/a\u003e) is not in this repository and must\nbe installed separately on the compilation host. Edit \u003ccode\u003ematlab/compile_matlab.sh\u003c/code\u003e to point\nto it.\u003c/p\u003e\n\u003cp\u003eSPM requires jumping an extra hurdle at the compilation step - we use a modified version\nof SPM\u0027s own compiler function \u003ccode\u003espm_make_standalone.m\u003c/code\u003e, found at\n\u003ccode\u003ematlab/spm_make_standalone_local.m\u003c/code\u003e. This process captures a lot of dependencies that\ncould otherwise easily be left out of the executable, with the resulting symptom that it\ncompiles just fine but fails at run time with various cryptic error messages. In addition\nto SPM12, everything in the \u003ccode\u003ematlab/src\u003c/code\u003e directory is included in the path at compile time.\nIf Matlab toolboxes are used, they will need to be added to the list of included toolboxes\nin \u003ccode\u003ematlab/spm_make_standalone_local.m\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe compiled Matlab executable is stored on github using git LFS. A regular git clone will\ndownload a pointer text file instead of the executable binary. The result of building a\ncontainer from that will be a cryptic error message - so, compile it yourself. Or, if\nstoring on github, download it manually and replace the pointer text file, or include this\nstep in the Singularity file if helpful - example here:\n\u003ca href=\"https://github.com/baxpr/gf-fmri/blob/master/Singularity.v1.3.4#L65\"\u003ehttps://github.com/baxpr/gf-fmri/blob/master/Singularity.v1.3.4#L65\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFreesurfer requires a license to run:\n\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall#License\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall#License\u003c/a\u003e\nBest practice is to store your license file on the host that will run the container, and\nbind it to the container at runtime - NOT to include your own license file in the\ncontainer itself.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1654958671.0 + "updated_at": 1625615789.0 }, { "data_format": 2, - "description": null, + "description": "Hosts DockerFiles to build MRtrix3 containers", "filenames": [ "Singularity" ], - "full_name": "dcgc-bfx/singularity-base", + "full_name": "MRtrix3/containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-dcgc-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#dcgc-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edcgc-base\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containers-for-mrtrix3\" class=\"anchor\" href=\"#containers-for-mrtrix3\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers for \u003cem\u003eMRtrix3\u003c/em\u003e\n\u003c/h1\u003e\n\u003cp\u003eHosts recipe files to build \u003cem\u003eMRtrix3\u003c/em\u003e containers\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-docker\" class=\"anchor\" href=\"#using-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Docker\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-terminal-command\" class=\"anchor\" href=\"#run-terminal-command\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun terminal command\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it mrtrix3 \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf not built locally, \u003ccode\u003edocker\u003c/code\u003e will download the latest image from DockerHub.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-gui\" class=\"anchor\" href=\"#run-gui\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun GUI\u003c/h4\u003e\n\u003cp\u003eThese instructions are for Linux.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003exhost +local:root\ndocker run --rm -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY mrtrix3 mrview\nxhost -local:root # Run this when finished.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-locally-build-docker-image\" class=\"anchor\" href=\"#locally-build-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocally build Docker image\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag mrtrix3 .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSet \u003ccode\u003eDOCKER_BUILDKIT=1\u003c/code\u003e to build parts of the Docker image in parallel, which can speed up build time.\nUse \u003ccode\u003e--build-arg MAKE_JOBS=4\u003c/code\u003e to build \u003cem\u003eMRtrix3\u003c/em\u003e with 4 processors (can substitute this with any number of processors \u0026gt; 0); if omitted, \u003cem\u003eMRtrix3\u003c/em\u003e will be built using a single thread only.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-singularity\" class=\"anchor\" href=\"#using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-build-container-natively\" class=\"anchor\" href=\"#build-container-natively\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild container natively\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build MRtrix3_\u0026lt;version\u0026gt;.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-convert-from-docker-container\" class=\"anchor\" href=\"#convert-from-docker-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConvert from Docker container\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build MRtrix3_\u0026lt;version\u0026gt;.sif docker://mrtrix/mrtrix3:\u0026lt;version\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-terminal-command-1\" class=\"anchor\" href=\"#run-terminal-command-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun terminal command\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003eMRtrix3_\u0026lt;version\u0026gt;.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-gui-1\" class=\"anchor\" href=\"#run-gui-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun GUI\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec -B /run MRtrix3_\u0026lt;version\u0026gt;.sif mrview\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-developers-update-minified-external-dependencies\" class=\"anchor\" href=\"#developers-update-minified-external-dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopers: Update minified external dependencies\u003c/h2\u003e\n\u003cp\u003eThis process can only be completed by those with write access to the \u003ca href=\"https://osf.io/5rwp3/\" rel=\"nofollow\"\u003e\"\u003cem\u003eMRtrix3\u003c/em\u003e container dependencies\" OSF project\u003c/a\u003e.\nThese files contain \"minified\" versions of external neuroimaging software package dependencies, containing only those components that are utilised by \u003cem\u003eMRtrix3\u003c/em\u003e scripts.\nThese files should only need to be updated if:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn \u003cem\u003eMRtrix3\u003c/em\u003e update introduces a new feature that invokes some new external software tool not previously utilised;\u003c/li\u003e\n\u003cli\u003eA requisite update occurs in one of these external softwares.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the \u003ccode\u003edocker\u003c/code\u003e and \u003ccode\u003eneurodocker\u003c/code\u003e Python packages.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install docker neurodocker\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the ART ACPCdetect tool from NITRC into the working directory.\u003c/p\u003e\n\u003cp\u003eThis cannot be downloaded directly via e.g. \u003ccode\u003ewget\u003c/code\u003e, as it requires logging in to NITRC; instead, visit the following link with a web browser:\n\u003ca href=\"https://www.nitrc.org/frs/download.php/10595/acpcdetect_v2.0_LinuxCentOS6.7.tar.gz\" rel=\"nofollow\"\u003e\u003ccode\u003ehttps://www.nitrc.org/frs/download.php/10595/acpcdetect_v2.0_LinuxCentOS6.7.tar.gz\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload test data necessary for minification process.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl -fL -# https://github.com/MRtrix3/script_test_data/archive/master.tar.gz | tar xz\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpdate file \u003ccode\u003eminify.Dockerfile\u003c/code\u003e to install the desired versions of external software packages.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild Docker image for \u003ccode\u003eneurodocker-minify\u003c/code\u003e, with complete installations of external packages.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eDOCKER_BUILDKIT=1 docker build --tag mrtrix3:minify --file minify.Dockerfile --build-arg MAKE_JOBS=4 .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eDOCKER_BUILDKIT=1\u003c/code\u003e enables BuildKit, which builds separate build stages in parallel.\nThis can speed up Docker build times in some circumstances.\nIn this case, ANTs and \u003cem\u003eMRtrix3\u003c/em\u003e will be compiled in parallel, and other downloads will be performed at the same time as well.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eMAKE_JOBS\u003c/code\u003e argument controls how many cores are used for compilation of ANTs and \u003cem\u003eMRtrix3\u003c/em\u003e.\nIf BuildKit is utilised, do not specify all of the available threads; specify half or fewer, so that threads are not unnecessarily split across jobs and RAM usage is not excessive.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a minified version of the Docker image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -itd --name mrtrix3 --security-opt=seccomp:unconfined --volume $(pwd)/script_test_data-master:/mnt mrtrix3:minify\nneurodocker-minify --dirs-to-prune /opt --container mrtrix3 --commands \"bash cmds-to-minify.sh\"\ndocker export mrtrix3 | docker import - mrtrix3:minified\ndocker stop mrtrix3\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenerate tarballs for each of the utilised dependencies.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p tarballs\ndocker run --rm -itd --workdir /opt --name mrtrix3 \\\n --volume $(pwd)/tarballs:/output mrtrix3:minified bash\ndocker exec mrtrix3 bash -c \"tar c art | pigz -9 \u0026gt; /output/acpcdetect_\u0026lt;version\u0026gt;.tar.gz\"\ndocker exec mrtrix3 bash -c \"tar c ants | pigz -9 \u0026gt; /output/ants_\u0026lt;version\u0026gt;.tar.gz\"\ndocker exec mrtrix3 bash -c \"tar c fsl | pigz -9 \u0026gt; /output/fsl_\u0026lt;version\u0026gt;.tar.gz\"\ndocker stop mrtrix3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor each tarball, manually replace text \"\u003ccode\u003e\u0026lt;version\u0026gt;\u003c/code\u003e\" with the version number of that particular software that was installed in the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpload these files to \u003ca href=\"https://osf.io/nfx85/\" rel=\"nofollow\"\u003eOSF\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFile \u003ccode\u003eDockerfile\u003c/code\u003e can then be modified to download the desired versions of external software packages.\nAs OSF file download links do not contain file names, which would otherwise indicate the version of each software to be downloaded, please ensure that comments within that file are updated to indicate the version of that software within the tarball.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 10, "topics": [], - "updated_at": 1633510107.0 + "updated_at": 1612696118.0 }, { "data_format": 2, - "description": "Docker image for MGKit", + "description": null, "filenames": [ - "Singularity.def" + "BlueprintPipeline/Resource/gemBS-2.1.1/Singularity" ], - "full_name": "frubino/mgkit-docker-repo", + "full_name": "Irfanwustl/Research_code", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker-image-for-mgkit\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-image-for-mgkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Image for MGKit\u003c/h1\u003e\n\u003cp\u003eThis is a new Dockerfile that allows the use of MGKit using a container. You can run the scripts directly, for example:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -i frubino/mgkit:latest sampling-utils rand_seq\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWill run the \u003ccode\u003esampling-utils rand_seq\u003c/code\u003e to create some randome FASTA sequences. Commands can be piped as well:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -i frubino/mgkit:latest sampling-utils rand_seq | docker run --rm -i frubino/mgkit:latest fasta-utils translate\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWill translate the random sequneces from the first command. Highly suggested to use an alias, such as:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ealias mgkit=\u0027docker run --rm -i frubino/mgkit:latest\u0027\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis way the above command becomes:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emgkit sampling-utils rand_seq | mgkit fasta-utils translate\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you want to run interactively a series of commands you can use \u003ccode\u003ebash\u003c/code\u003e instead of another command, but remember to add the \u003ccode\u003e-t\u003c/code\u003e option:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -it frubino/mgkit:latest bash\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-branch\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-branch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild branch\u003c/h1\u003e\n\u003cp\u003eA \u003ccode\u003efrubino/mgkit:build\u003c/code\u003e branch is present to allow the creation of Conda packages. Checkout the branch with \u003ccode\u003egit checkout build\u003c/code\u003e. A script is included to build the image and environment are used to specify output directory inside the container, the Python version to use to build and the MGKit version to use\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cblockquote\u003e\n\u003cp\u003eYou need to modify the version of MGKit manually with a tag or commit id (after the \u003ccode\u003e@\u003c/code\u003e in the \u003ccode\u003epip\u003c/code\u003e line)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThere are 2 options to use this image with \u003cem\u003eSingularity\u003c/em\u003e, 1) create a Docker image using the \u003ccode\u003eDockerfile.singularity\u003c/code\u003e and then pull it or 2) building it with \u003cem\u003eSingularity\u003c/em\u003e, for example with \u003ca href=\"https://cloud.sylabs.io/\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/\u003c/a\u003e (command \u003ccode\u003esingularity build --remote\u003c/code\u003e) if \u003ccode\u003eroot\u003c/code\u003e access is not available.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Image\u003c/h2\u003e\n\u003cp\u003eThe main difference between the 2 \u003ccode\u003eDockerfile\u003c/code\u003e is that the Singularity version removes any use of a specific user, because that is mostly done to mount a directory in the image. Also instead of using a version of MGKit in \u003ccode\u003econda\u003c/code\u003e PyPI is used instead.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest\u003c/h2\u003e\n\u003cp\u003eTry to run: \u003ccode\u003esingularity exec mgkit_0.6.0.sif sampling-utils rand_seq | singularity exec mgkit_0.6.0.sif fasta-utils info\u003c/code\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCorrect for the image name used in the build process\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-research_code\" class=\"anchor\" href=\"#research_code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResearch_code\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "mgkit", - "bioinformatics", - "metagenomics", - "metagenomic-analysis", - "evolution" - ], - "updated_at": 1635513477.0 + "topics": [], + "updated_at": 1627949747.0 }, { "data_format": 2, - "description": "Test species and lineage calls made by mykrobe", + "description": null, "filenames": [ - "Python/Singularity.def" + "Singularity_fastqc", + "Singularity_multiqc", + "Singularity_trimmomatic" ], - "full_name": "Mykrobe-tools/mykrobe-lineage-test", + "full_name": "uf-icbr-bioinformatics/biocontainers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mykrobe-lineage-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#mykrobe-lineage-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emykrobe-lineage-test\u003c/h1\u003e\n\u003cp\u003eThis repository contains code for testing mykrobe species and lineage calls,\nand results of the testing.\nIt is intended for mykrobe developers, for testing mykrobe species/lineage calls\nand tracking the results.\u003c/p\u003e\n\u003cp\u003eThere are two directories:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003ePython/\u003c/code\u003e: this contains the code, and a Singularity definition file that\nmakes a container with the code plus the dependencies.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAnalysis/\u003c/code\u003e: contains results of testing mykrobe species and lineage calls.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor results, please see the readme in the \u003ccode\u003eAnalysis/\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThis repository has a main script called \u003ccode\u003emlt\u003c/code\u003e (acronym for \"mykrobe lineage\ntest\", yes we are testing species calls as well but\n\"mykrobe lineage species test\" seemed like a bad name!).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eThe easiest way is to build a singularity container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd Python\nsudo singularity build mlt Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun like that, singularity will make a container file called \u003ccode\u003emlt\u003c/code\u003e.\nYou can just treat it as an normal executable, no need to run\n\u003ccode\u003esingularity exec mlt\u003c/code\u003e unless you want to.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource\u003c/h3\u003e\n\u003cp\u003eIf you want to run locally, then you will need these in your \u003ccode\u003e$PATH\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eenaDataGet\u003c/code\u003e, which is from enaBrowserTools (have a look in \u003ccode\u003eSingularity.def\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emykrobe\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e(also \u003ccode\u003efastaq\u003c/code\u003e and \u003ccode\u003encbi-genome-download\u003c/code\u003e are required, but are installed when\nyou install \u003ccode\u003emlt\u003c/code\u003e because they are in the requirements file.)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen run \u003ccode\u003epip install .\u003c/code\u003e from the \u003ccode\u003ePython/\u003c/code\u003e directory. The required python\npackages will be installed (they are in \u003ccode\u003erequirements.txt\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eAlternatively, you could not do pip install, and instead do this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=/path_to/mykrobe-lineage-test/Python /path_to/mykrobe-lineage-test/Python/mlt/__main__.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat command is equivalent to running the script \u003ccode\u003emlt\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testing-lineage-calls\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-lineage-calls\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting lineage calls\u003c/h2\u003e\n\u003cp\u003eIn short, the process is:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePut your sample info in a TSV file.\u003c/li\u003e\n\u003cli\u003eDownload reads using \u003ccode\u003emlt download_data\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun mykrobe on all samples using \u003ccode\u003emlt run_mykrobe\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMake a summary of the results using \u003ccode\u003emlt summary\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sample-tsv\" class=\"anchor\" aria-hidden=\"true\" href=\"#sample-tsv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esample TSV\u003c/h3\u003e\n\u003cp\u003eAll the commands need a TSV of sample information. The format is like\nthis (this is made up data!):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eaccession source species lineage\nSRR12345678 ena Mycobacterium_tuberculosis lineage1.2.3\nGCF_1234567 genbank Mycobacterium_tuberculosis lineage2.3.4\nXY123456 refseq Mycobacterium_tuberculosis lineage3.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou must have columns \u003ccode\u003eaccession\u003c/code\u003e, \u003ccode\u003esource\u003c/code\u003e, \u003ccode\u003especies\u003c/code\u003e and \u003ccode\u003elineage\u003c/code\u003e. They\ncan be in any order (and any extra columns are ignored). The lineage can\nbe \"NA\" if there is no lineage call and you just want to test the species\ncall.\u003c/p\u003e\n\u003cp\u003eThe source must be \u003ccode\u003eena\u003c/code\u003e, \u003ccode\u003egenbank\u003c/code\u003e, or \u003ccode\u003erefseq\u003c/code\u003e, and the \u003ccode\u003eaccession\u003c/code\u003e column\nshould have the corresponding ENA run ID, or genbank/refseq genome accession.\nSince reads are needed for mykrobe, reads are simulated from genomes using\n\u003ccode\u003efastaq to_perfect_reads\u003c/code\u003e, making perfect reads (ie no snp/indel errors)\nof length 75bp, fragment size 200, and depth 20X.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-download-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload data\u003c/h3\u003e\n\u003cp\u003eWith a TSV file of samples \u003ccode\u003esamples.tsv\u003c/code\u003e in the above format, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emlt download_data --cpus 3 samples.tsv Reads\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat example downloads 3 samples in parallel. It makes a directory called\n\u003ccode\u003eReads\u003c/code\u003e containing the downloaded data. It will (well, \u003cem\u003eshould\u003c/em\u003e) not crash\non failed downloads, but carry on and get all the samples it can. Check\nstderr to see what happened.\u003c/p\u003e\n\u003cp\u003eYou can rerun on an existing directory and it will only try to get data\nthat is missing and skip the samples that are already downloaded.\nThis also means you can do hacks like different sample TSV files run\nagainst the same directory of a superset of reads, if you\u0027re feeling\nfancy.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-mykrobe\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-mykrobe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun mykrobe\u003c/h3\u003e\n\u003cp\u003eAssuming you have a directory of downloaded reads from \u003ccode\u003emlt download_data\u003c/code\u003e\ncalled \u003ccode\u003eReads/\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emlt run_mykrobe --cpus 10 samples.tsv Reads Results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat will run 10 samples in parallel. It makes a new directory (if it\ndoesn\u0027t exit already) called \u003ccode\u003eResults\u003c/code\u003e. As for \u003ccode\u003edownload_data\u003c/code\u003e, you can\nrerun against the same directory and it will only run samples that do not\nalready have a mykrobe json file of results. It will ignore samples in the TSV\nwith no reads in \u003ccode\u003eReads/\u003c/code\u003e. It\u0027s up to you to use the right TSV file/Reads\ndirectory/results directory - there is no sanity checking. This does allow\nfor more hacking and testing of samples.\u003c/p\u003e\n\u003cp\u003eIMPORTANT: the first time a sample is run in \u003ccode\u003eResults/\u003c/code\u003e, there is no\nskeletons file. If you ask for more than one CPU, the first sample will be\nrun on its own, making the skeletons file. Then the remaining samples are\nrun using multiprocessing, since they can then all use the skeletons file,\ninstead of all trying to make one at the same time and crashing.\u003c/p\u003e\n\u003cp\u003eThere is an option \u003ccode\u003e--panels_dir\u003c/code\u003e, which will use that option with mykrobe,\nso that you can override the default panels directory and use your own.\nYou probably want this, since the point here is to test species/lineage calls.\nIt is not recommended to change the panel and then use an existing results\ndirectory because the skeletons file that is already might be used!\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-summarise-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#summarise-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummarise results\u003c/h3\u003e\n\u003cp\u003eAssuming you have a samples TSV file \u003ccode\u003esamples.tsv\u003c/code\u003e, a directory of reads\ncalled \u003ccode\u003eReads/\u003c/code\u003e, and a directory of mykrobe runs called \u003ccode\u003eResults/\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emlt summary samples.tsv Reads Results summary.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat makes a new TSV file called \u003ccode\u003esummary.tsv\u003c/code\u003e. It is the same as \u003ccode\u003esamples.tsv\u003c/code\u003e,\nbut with added columns:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecalled_species\u003c/code\u003e and \u003ccode\u003ecalled_lineage\u003c/code\u003e. These are the calls made by mykrobe.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecorrect\u003c/code\u003e: this is \u003ccode\u003etrue|false\u003c/code\u003e, showing if the both the called species and\nlineage were correct. If the expected lineage is \"NA\", then the true/false\ncall only depends on the species.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNow be good and record the results in the \u003ccode\u003eAnalysis/\u003c/code\u003e directory and push\nto github.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-biocontainers\" class=\"anchor\" href=\"#biocontainers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebiocontainers\u003c/h1\u003e\n\u003cp\u003eThis repository contains recipes for containers used to perform QC, summary statistics, and pre-processing on NGS datasets.\u003c/p\u003e\n\u003cp\u003eIn the future, we may provide the containers themselves. Stay tuned. Work in progress.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1655217767.0 + "updated_at": 1623019187.0 }, { "data_format": 2, - "description": null, + "description": "Biobb_structure_utils is the Biobb module collection to modify or extract information from a PDB structure file, such as pulling out a particular model or chain, removing water molecules or ligands, or renumbering or sorting atoms or residues.", "filenames": [ - "Singularity", - "Singularity_flipped", - "Singularity_test", - "Singularity_backup", - "Singularity2" + "Singularity.latest" ], - "full_name": "mwanakijiji/rrlfe", + "full_name": "bioexcel/biobb_structure_utils", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rrlfe\" class=\"anchor\" aria-hidden=\"true\" href=\"#rrlfe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003errlfe\u003c/h1\u003e\n\u003cp\u003eA code base for generating and applying calibrations for retrieving [Fe/H] from low-res spectra of RR Lyrae variable stars. See \u003ca href=\"https://rrlfe.readthedocs.io/\" rel=\"nofollow\"\u003ehttps://rrlfe.readthedocs.io/\u003c/a\u003e for documentation.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://coveralls.io/github/mwanakijiji/rrlfe?branch=main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c783f25af909dcd1dc513f24cbf780405955d2d29da614210ef15dc39a291c35/68747470733a2f2f636f766572616c6c732e696f2f7265706f732f6769746875622f6d77616e616b696a696a692f72726c66652f62616467652e7376673f6272616e63683d6d61696e\" alt=\"Coverage Status\" data-canonical-src=\"https://coveralls.io/repos/github/mwanakijiji/rrlfe/badge.svg?branch=main\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-attribution\" class=\"anchor\" aria-hidden=\"true\" href=\"#attribution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAttribution\u003c/h2\u003e\n\u003cp\u003eIf this code has been useful for your work, please cite the source in the following BibTeX entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@article{esposito2018,\n Adsurl = {},\n Author = {},\n Doi = {},\n Eid = {},\n Journal = {},\n Keywords = {},\n Month = ,\n Pages = {},\n Title = {{}},\n Volume = ,\n Year = ,\n Bdsk-Url-1 = {}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d7c1b5de86a2921c1f759b175820fb443eba3f18bbf45e56e42f2cee72844627/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f62696f62622d7374727563747572652d7574696c732f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"\" data-canonical-src=\"https://readthedocs.org/projects/biobb-structure-utils/badge/?version=latest\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/biobb_structure_utils\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a477fdb1fd9bc9eb7ffa6cae6a019c6d4c3902fd468b3126f1b78e56c7dcff83/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://quay.io/repository/biocontainers/biobb_structure_utils\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca418e4db0b3de91a09a5df4a59446da015b6164598a8bc255918e911484f84f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d517561792e696f2d626c7565\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docker-Quay.io-blue\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3836\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-biobb_structure_utils\" class=\"anchor\" href=\"#biobb_structure_utils\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebiobb_structure_utils\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eBiobb_structure_utils is the Biobb module collection to modify or extract information from a PDB structure file, such as pulling out a particular model or chain, removing water molecules or ligands, or renumbering or sorting atoms or residues. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site:\n\u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003elatest API documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-version\" class=\"anchor\" href=\"#version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h3\u003e\n\u003cp\u003ev3.6.1 2021.2\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eUsing PIP:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eImportant:\u003c/strong\u003e PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install \"biobb_structure_utils\u0026gt;=3.6.1\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: \u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing ANACONDA:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda install -c bioconda \"biobb_structure_utils\u0026gt;=3.6.1\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: With conda installation BioBBs can be used with the \u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e and the \u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing DOCKER:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull quay.io/biocontainers/biobb_structure_utils:3.6.1--pyhdfd78af_0\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run quay.io/biocontainers/biobb_structure_utils:3.6.1--pyhdfd78af_0 \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing SINGULARITY:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMacOS users\u003c/strong\u003e: it\u0027s strongly recommended to avoid Singularity and use \u003cstrong\u003eDocker\u003c/strong\u003e as containerization system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name biobb_structure_utils.sif shub://bioexcel/biobb_structure_utils\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec biobb_structure_utils.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe command list and specification can be found at the \u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" href=\"#copyright--licensing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2021 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2021 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-acknolegements\" class=\"anchor\" href=\"#acknolegements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknolegements\u003c/h3\u003e\n\u003cp\u003eThis software uses functions to read and modify GRO files based in the \u003ca href=\"https://github.com/caizkun/gropy\"\u003eGROPY\u003c/a\u003e library created by Zhikun Cai (\u003ca href=\"mailto:caizkun@gmail.com\"\u003ecaizkun@gmail.com\u003c/a\u003e) under the \u003ca href=\"https://github.com/caizkun/gropy/blob/master/LICENSE\"\u003eMIT\u003c/a\u003e. In this project \u003ca href=\"https://github.com/caizkun/gropy\"\u003eGROPY\u003c/a\u003e has been adapted to Python 3 and our own needs.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 8, "topics": [], - "updated_at": 1648070883.0 + "updated_at": 1625224033.0 }, { "data_format": 2, - "description": "Parametric face image generator for mooney faces", + "description": null, "filenames": [ - "Singularity" + "bc3.10--rstudio125042r362/Singularity", + "bc3.12--rstudio125042r405/Singularity" ], - "full_name": "ShreyaKapoor18/parametric-face-image-generator", + "full_name": "yh549848/singularity-rstudio-rnaseqde", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-parametric-face-image-generator\" class=\"anchor\" aria-hidden=\"true\" href=\"#parametric-face-image-generator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eparametric-face-image-generator\u003c/h1\u003e\n\u003cp\u003eThis software enables you to generate fully parametric face images from the Basel Face Model 2017 as proposed in:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e[1] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"http://openaccess.thecvf.com/content_CVPRW_2019/papers/BEFA/Kortylewski_Analyzing_and_Reducing_the_Damage_of_Dataset_Bias_to_Face_CVPRW_2019_paper.pdf\" rel=\"nofollow\"\u003e\"Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data\"\u003c/a\u003e,\nIN: CVPRW (2019)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[2] Adam Kortylewski, Andreas Schneider, Thomas Gerig, Bernhard Egger, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"https://arxiv.org/abs/1802.05891\" rel=\"nofollow\"\u003e\"Training Deep Face Recognition Systems with Synthetic Data\"\u003c/a\u003e,\nIN: arXiv preprint (2018)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[3] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Kortylewski_Empirically_Analyzing_the_CVPR_2018_paper.pdf\" rel=\"nofollow\"\u003e\"Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems\"\u003c/a\u003e,\nIN: CVPRW (2018)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can control the variation of parameters such as pose, shape, color, camera and illumination based on your demand and application.\nThis dataset can be used for training and comparing machine learning techniques such as CNNs on a common ground as proposed in [1,3] by generating fully controlled training and test data.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rendering-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#rendering-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRendering Setup\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_0.png\"\u003e\u003cimg src=\"data/example_images/0_0.png\" alt=\"0_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1.png\"\u003e\u003cimg src=\"data/example_images/0_1.png\" alt=\"0_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2.png\"\u003e\u003cimg src=\"data/example_images/0_2.png\" alt=\"0_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/1_0.png\"\u003e\u003cimg src=\"data/example_images/1_0.png\" alt=\"1_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/1_1.png\"\u003e\u003cimg src=\"data/example_images/1_1.png\" alt=\"1_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/1_2.png\"\u003e\u003cimg src=\"data/example_images/1_2.png\" alt=\"1_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAbove you can see example face images sampled from this data generator. Each row shows different images of the same facial identity.\u003c/p\u003e\n\u003cp\u003eIn the \"controlled\" setup (top row), the model parameters are sampled at equidistant positions along a certain parameter , e.g. the yaw pose.\u003c/p\u003e\n\u003cp\u003eIn the \"random\" setup (bottom row), the model parameters are sampled randomly from a custom distribution.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rendering-different-image-modalities\" class=\"anchor\" aria-hidden=\"true\" href=\"#rendering-different-image-modalities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRendering Different Image Modalities\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1_depth.png\"\u003e\u003cimg src=\"data/example_images/0_1_depth.png\" alt=\"0_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2_depth.png\"\u003e\u003cimg src=\"data/example_images/0_2_depth.png\" alt=\"0_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_3_depth.png\"\u003e\u003cimg src=\"data/example_images/0_3_depth.png\" alt=\"0_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1_correspondence.png\"\u003e\u003cimg src=\"data/example_images/0_1_correspondence.png\" alt=\"1_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2_correspondence.png\"\u003e\u003cimg src=\"data/example_images/0_2_correspondence.png\" alt=\"1_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_3_correspondence.png\"\u003e\u003cimg src=\"data/example_images/0_3_correspondence.png\" alt=\"1_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can render different image modalities such as e.g. depth images (top row), color coded correspondence images (bottom row), normals, albedo or illumination.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rendering-face-regions\" class=\"anchor\" aria-hidden=\"true\" href=\"#rendering-face-regions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRendering Face Regions\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1_region_mask.png\"\u003e\u003cimg src=\"data/example_images/0_1_region_mask.png\" alt=\"0_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2_region_mask.png\"\u003e\u003cimg src=\"data/example_images/0_2_region_mask.png\" alt=\"0_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_3_region_mask.png\"\u003e\u003cimg src=\"data/example_images/0_3_region_mask.png\" alt=\"0_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1_region_mask_bfm09.png\"\u003e\u003cimg src=\"data/example_images/0_1_region_mask_bfm09.png\" alt=\"0_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2_region_mask_bfm09.png\"\u003e\u003cimg src=\"data/example_images/0_2_region_mask_bfm09.png\" alt=\"0_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_3_region_mask_bfm09.png\"\u003e\u003cimg src=\"data/example_images/0_3_region_mask_bfm09.png\" alt=\"0_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can render different region maps, while we provide two default ones.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-facial-landmarks\" class=\"anchor\" aria-hidden=\"true\" href=\"#facial-landmarks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFacial Landmarks\u003c/h3\u003e\n\u003cp\u003eFor each face image the location and visibilty of 19 facial landmarks is written in a .tlms file in the following format:\u003c/p\u003e\n\u003cp\u003e\"facial landmark name\" \"visibility\" \"x-position\" \"y-position\"\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003einstalled \u003ca href=\"http://www.oracle.com/technetwork/java/javase/downloads/index.html\" rel=\"nofollow\"\u003eJava\u003c/a\u003e (Version 8.0 or higher recommended)\u003c/li\u003e\n\u003cli\u003edownload jar and config file under \u003ca href=\"https://github.com/unibas-gravis/parametric-face-image-generator/releases\"\u003e\u003ccode\u003erelease\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://gravis.dmi.unibas.ch/PMM/\" rel=\"nofollow\"\u003edownload\u003c/a\u003e Basel Face Model 2017\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://gravis.dmi.unibas.ch/PMM/\" rel=\"nofollow\"\u003edownload\u003c/a\u003e Basel Illumination Prior 2017\u003c/li\u003e\n\u003cli\u003eget a dataset with backgrounds, e.g. the \u003ca href=\"http://www.robots.ox.ac.uk/~vgg/data/dtd/\" rel=\"nofollow\"\u003eDescribable Textures Dataset\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eadapt paths and configuration in \u003ccode\u003edata/config_files/example_config_controlled.json\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eFor generating images in the controlled setup execute:\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ejava -Xmx2g -cp generator.jar faces.apps.ControlledFaces -c data/config_files/example_config_controlled.json\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eFor generating images in the random setup execute:\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ejava -Xmx2g -cp generator.jar faces.apps.RandomFaces -c data/config_files/example_config_random.json\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-for-developers\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor Developers:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003einstalled \u003ca href=\"http://www.oracle.com/technetwork/java/javase/downloads/index.html\" rel=\"nofollow\"\u003eJava\u003c/a\u003e (Version 8.0 or higher recommended)\u003c/li\u003e\n\u003cli\u003einstalled \u003ca href=\"http://www.scala-sbt.org/release/tutorial/Setup.html\" rel=\"nofollow\"\u003esbt\u003c/a\u003e (only for compiling from sources)\u003c/li\u003e\n\u003cli\u003eclone repository\u003c/li\u003e\n\u003cli\u003ecompile and run using \u003ccode\u003esbt run -mem 2000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ewe provide a singularity container recipe file to run the data generator directly on compute servers\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-help-needed\" class=\"anchor\" aria-hidden=\"true\" href=\"#help-needed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHelp needed\u003c/h2\u003e\n\u003cp\u003eThere is a \u003ca href=\"https://groups.google.com/forum/#!forum/scalismo-faces\" rel=\"nofollow\"\u003escalismo-faces google group\u003c/a\u003e for general questions and discussion.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-background\" class=\"anchor\" aria-hidden=\"true\" href=\"#background\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h2\u003e\n\u003cp\u003eBesides the publications listed next, we have also freely available \u003ca href=\"http://gravis.dmi.unibas.ch/PMM/lectures/overview/\" rel=\"nofollow\"\u003electures and tutorials\u003c/a\u003e. Some of the topics covered are statistical shape modeling and model-based image analysis as part of our research about \u003ca href=\"http://gravis.dmi.unibas.ch/PMM/\" rel=\"nofollow\"\u003eProbabilistic Morphable Models\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-hidden=\"true\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublications\u003c/h2\u003e\n\u003cp\u003eIf you use this software you will need the Basel Face Model 2017, the Basel Illumination Prior 2017 and a dataset of backgrounds. Please cite the following papers:\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-data-generator---random-mode\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-generator---random-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Generator - Random Mode\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e[1] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"http://openaccess.thecvf.com/content_CVPRW_2019/papers/BEFA/Kortylewski_Analyzing_and_Reducing_the_Damage_of_Dataset_Bias_to_Face_CVPRW_2019_paper.pdf\" rel=\"nofollow\"\u003e\"Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data\"\u003c/a\u003e,\nIN: CVPRW (2019)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[2] Adam Kortylewski, Andreas Schneider, Thomas Gerig, Bernhard Egger, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"https://arxiv.org/abs/1802.05891\" rel=\"nofollow\"\u003e\"Training Deep Face Recognition Systems with Synthetic Data\"\u003c/a\u003e,\nIN: arXiv preprint (2018)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-data-generator---controlled-mode\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-generator---controlled-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Generator - Controlled Mode\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e[3] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Kortylewski_Empirically_Analyzing_the_CVPR_2018_paper.pdf\" rel=\"nofollow\"\u003e\"Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems\"\u003c/a\u003e,\nIN: CVPRW (2018)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basel-face-model-2017\" class=\"anchor\" aria-hidden=\"true\" href=\"#basel-face-model-2017\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasel Face Model 2017\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e[4] Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Luethi, Sandro Schoenborn and Thomas Vetter\n\u003ca href=\"https://arxiv.org/abs/1709.08398\" rel=\"nofollow\"\u003e\" Morphable Face Models - An Open Framework\"\u003c/a\u003e,\nIN: 13th IEEE Conference on Automatic Face and Gesture Recognition (FG 2018)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basel-illumination-prior-2017\" class=\"anchor\" aria-hidden=\"true\" href=\"#basel-illumination-prior-2017\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasel Illumination Prior 2017\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e[5] Bernhard Egger, Sandro Schoenborn, Andreas Schneider, Adam Kortylewski, Andreas Morel-Forster, Clemens Blumer and Thomas Vetter\n\u003ca href=\"http://gravis.dmi.unibas.ch/publications/2018/2018_Egger_IJCV.pdf\" rel=\"nofollow\"\u003e\"Occlusion-aware 3D Morphable Models and an Illumination Prior for Face Image Analysis\"\u003c/a\u003e,\nIN: International Journal of Computer Vision, 2018\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-background-dataset\" class=\"anchor\" aria-hidden=\"true\" href=\"#background-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground Dataset\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eA background dataset of your choice\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBernhard Egger\u003c/li\u003e\n\u003cli\u003eAdam Kortylewski\u003c/li\u003e\n\u003cli\u003eAndreas Morel-Forster\u003c/li\u003e\n\u003cli\u003eAndreas Schneider\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainers\" class=\"anchor\" aria-hidden=\"true\" href=\"#maintainers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainers\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eUniversity of Basel, Graphics and Vision research: \u003ca href=\"https://github.com/unibas-gravis\"\u003e@unibas-gravis\u003c/a\u003e, \u003ca href=\"http://gravis.cs.unibas.ch\" rel=\"nofollow\"\u003ehomepage\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License, Version 2.0\u003c/a\u003e, details see LICENSE\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCopyright 2017, University of Basel, Graphics and Vision Research\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-running-rstudio-server-in-a-conda-environment\" class=\"anchor\" href=\"#running-rstudio-server-in-a-conda-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Rstudio Server in a Conda Environment\u003c/h1\u003e\n\u003cp\u003eI usually rely on the \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003econda package manager\u003c/a\u003e to manage my environments during development. Thanks to \u003ca href=\"https://conda-forge.org/\" rel=\"nofollow\"\u003econda-forge\u003c/a\u003e and \u003ca href=\"https://bioconda.github.io/\" rel=\"nofollow\"\u003ebioconda\u003c/a\u003e most R packages are now also available through conda. For production,\nI \u003ca href=\"https://github.com/grst/containerize-conda\"\u003econvert them to containers\u003c/a\u003e as these are easier to share.\u003c/p\u003e\n\u003cp\u003eUnfortunately, there seems to be \u003ca href=\"https://community.rstudio.com/t/start-rstudio-server-session-in-conda-environment/12516/15\" rel=\"nofollow\"\u003eno straightforward way\u003c/a\u003e to use conda envs in Rstudio server. This repository provides three approaches to make rstudio server work with conda envs.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#running-rstudio-server-with-singularity\"\u003eRunning Rstudio Server in a Singularity Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-rstudio-server-with-podmandocker\"\u003eRunning Rstudio Server in a Docker/Podman Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-locally\"\u003eRunning Rstudio Server locally\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-rstudio-server-with-singularity\" class=\"anchor\" href=\"#running-rstudio-server-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Rstudio Server with Singularity\u003c/h2\u003e\n\u003cp\u003eWith this approach Rstudio Server runs in a Singularity container (based on \u003ca href=\"https://hub.docker.com/r/rocker/rstudio\" rel=\"nofollow\"\u003erocker/rstudio\u003c/a\u003e).\u003cbr\u003e\nThe conda environment gets mounted into the container - like that there\u0027s no need to rebuild the container to add a package and\n\u003ccode\u003einstall.packages\u003c/code\u003e can be used without issues. The container-based approach has the following benefits:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAuthentication works (\u003ca href=\"https://github.com/grst/rstudio-server-conda/issues/3\"\u003e#3\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSeveral separate instances of Rstudio server can run in parallel, even without the \u003cem\u003ePro\u003c/em\u003e version.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003emamba\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:grst/rstudio-server-conda.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e rstudio-server-conda/singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eActivate the target conda env or set the environment variable \u003ccode\u003eCONDA_PREFIX\u003c/code\u003e\nto point to the location of the conda env.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the \u003ccode\u003erun_singularity.sh\u003c/code\u003e script. In particular, you may need to add additional bind mounts\n(e.g. a global data directory).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExecute the \u003ccode\u003erun_singularity.sh\u003c/code\u003e script. It will automatically build the container if it is not available.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ePORT=8787 PASSWORD=notsafe ./run_singularity.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLog into Rstudio\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eopen rstudio server at \u003ccode\u003ehttp://localhost:8787\u003c/code\u003e (or whatever port you specified)\u003c/li\u003e\n\u003cli\u003elogin with your default username and the password you specified via the \u003ccode\u003ePASSWORD\u003c/code\u003e environment variable.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-rstudio-server-with-podmandocker\" class=\"anchor\" href=\"#running-rstudio-server-with-podmandocker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Rstudio Server with Podman/Docker\u003c/h2\u003e\n\u003cp\u003eThis approach is similar to \u003ca href=\"#running-rstudio-server-with-singularity\"\u003eSingularity\u003c/a\u003e, but uses\nDocker or Podman and a \u003ccode\u003edocker-compose.yml\u003c/code\u003e file instead.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-known-limitations\" class=\"anchor\" href=\"#known-limitations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown limitations\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eNo access to shared group directories (\u003ca href=\"https://github.com/grst/rstudio-server-conda/issues/14\"\u003e#14\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-prerequisites-1\" class=\"anchor\" href=\"#prerequisites-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://podman.io/\" rel=\"nofollow\"\u003ePodman\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/docker/compose\"\u003edocker-compose\u003c/a\u003e or \u003ca href=\"https://github.com/containers/podman-compose\"\u003epodman-compose\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003emamba\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage-1\" class=\"anchor\" href=\"#usage-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:grst/rstudio-server-conda.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the rstudio container (fetches the latest version of \u003ca href=\"https://hub.docker.com/r/rocker/rstudio\" rel=\"nofollow\"\u003erocker/rstudio\u003c/a\u003e and adds some custom scripts)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e rstudio-server-conda/docker\ndocker-compose build \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or podman-compose\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the docker-compose.yml file into your project directory and adjust the paths.\u003c/p\u003e\n\u003cp\u003eYou may want to add additional volumes with your data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e[...]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eports\u003c/span\u003e:\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e port on the host : port in the container (the latter is always 8787)\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e8889:8787\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003evolumes\u003c/span\u003e:\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e mount conda env into exactely the same path as on the host system - some paths are hardcoded in the env.\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/home/sturm/anaconda3/envs/R400:/home/sturm/anaconda3/envs/R400\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Share settings between rstudio instances\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/home/sturm/.local/share/rstudio/monitored/user-settings:/root/.local/share/rstudio/monitored/user-settings\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e mount the working directory containing your R project.\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/home/sturm/projects:/projects\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eenvironment\u003c/span\u003e:\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e password used for authentication\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003ePASSWORD=notsafe\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e repeat the path of the conda environment (must be identical to the path in \"volumes\")\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003eCONDAENV=/home/sturm/anaconda3/envs/R400\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun your project-specific instance of Rstudio-server\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker-compose up \u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLog into Rstudio\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOpen your server at \u003ccode\u003ehttp://localhost:8889\u003c/code\u003e (or whatever port you specified)\u003c/li\u003e\n\u003cli\u003eLogin with the user \u003ccode\u003erstudio\u003c/code\u003e (when using Docker) or \u003ccode\u003eroot\u003c/code\u003e (when using Podman) and the password you specified\nin the \u003ccode\u003edocker-compose.yml\u003c/code\u003e. If you are using Podman and login with \u003ccode\u003erstudio\u003c/code\u003e you won\u0027t have permissions to\naccess the mounted volumes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-locally\" class=\"anchor\" href=\"#running-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Locally\u003c/h2\u003e\n\u003cp\u003eWith this approach a locally installed Rstudio server is ran such that it uses the conda env.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-known-limitations-1\" class=\"anchor\" href=\"#known-limitations-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown limitations\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eno authentication (\u003ca href=\"https://github.com/grst/rstudio-server-conda/issues/3\"\u003e#3\u003c/a\u003e). Use this approach only in a secure network!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-prerequisites-2\" class=\"anchor\" href=\"#prerequisites-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rstudio.com/products/rstudio/download-server/\" rel=\"nofollow\"\u003erstudio server\u003c/a\u003e installed locally\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003emamba\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage-2\" class=\"anchor\" href=\"#usage-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repo\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/grst/rstudio-server-conda.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun rstudio server in the conda env\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd rstudio-server-conda/local\nconda activate my_project\n./start_rstudio_server.sh 8787 # use any free port number here. \n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConnect to Rstudio\u003c/p\u003e\n\u003cp\u003eYou should now be able to connect to rstudio server on the port you specify.\n\u003cstrong\u003eIf an R Session has previously been running, you\u0027ll need to rstart the Rsession now\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eObviously, if your env does not have a version of \u003ccode\u003eR\u003c/code\u003e installed, this will either not\nwork at all, or fall back to the system-wide R installation.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-how-it-works\" class=\"anchor\" href=\"#how-it-works\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow it works\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eRstudio server, can be started in non-daemonized mode by each user individually on a custom port (similar to a jupyter notebook). This instance can then run in a conda environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; conda activate my_project\n\u0026gt; /usr/lib/rstudio-server/bin/rserver \\\n --server-daemonize=0 \\\n --www-port 8787 \\\n --rsession-which-r=$(which R) \\\n --rsession-ld-library-path=$CONDA_PREFIX/lib\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo avoid additional problems with library paths, also \u003ccode\u003ersession\u003c/code\u003e needs to run within the conda environment. This is achieved by wrapping \u003ccode\u003ersession\u003c/code\u003e into the \u003ca href=\"https://github.com/grst/rstudio-server-conda/blob/master/local/rsession.sh\"\u003ersession.sh\u003c/a\u003e script. The path to the wrapped \u003ccode\u003ersession\u003c/code\u003e executable can be passed to \u003ccode\u003erserver\u003c/code\u003e as command line argument.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erserver # ...\n --rsession-path=rsession.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen using multiple users a unique \u003ccode\u003esecret-cookie-key\u003c/code\u003e has to be generated for each user. The path to the secret cookie key can be passed to \u003ccode\u003erserver\u003c/code\u003e as a command line parameter.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euuid \u0026gt; /tmp/rstudio-server/${USER}_secure-cookie-key\nrserver # ...\n --secure-cookie-key-file /tmp/rstudio-server/${USER}_secure-cookie-key\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1667551934.0 + "updated_at": 1623388496.0 }, { "data_format": 2, - "description": null, + "description": "A template project to provide software to ESCAPE.", "filenames": [ - "Singularity.def" + "Singularity/Singularity" ], - "full_name": "bsande6/fa1p1_luo", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fa1p1_luo\" class=\"anchor\" aria-hidden=\"true\" href=\"#fa1p1_luo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFA1p1_Luo\u003c/h1\u003e\n\u003cp\u003eRepository for Dr. Luo\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eBefore adding to this repo it is recommended to set up a .gitignore file and add the pycache folder\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-baseline-driving-network\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-baseline-driving-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun baseline driving network\u003c/h1\u003e\n\u003cp\u003eCheck that the config file for the assocated folder and configuration has the IMAGE_TRANSLATION config set to False\u003c/p\u003e\n\u003cp\u003epython coiltraine.py --gpus 0 --folder nocrash --exp resnet34imnet10S1 --single-process drive -de NocrashTraining_Town01 --docker carlagear\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-translation\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-translation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun translation\u003c/h1\u003e\n\u003cp\u003eCheck that the config file for the assocated folder and configuration has the IMAGE_TRANSLATION config set to True and the STYLE and AERIAL configs are False.\u003c/p\u003e\n\u003cp\u003eChoose translation checkpoint via the -name and --which_epoch parameters.\u003c/p\u003e\n\u003cp\u003epython coiltraine.py --gpus 0 --folder nocrash --exp resnet34imnet10S1 --single-process drive -de NocrashTraining_Town01 --docker carlagear --name finetune_fromEpoch400_episodes_1000epoch_weight2000.0 --which_epoch 200 --no_instance --n_downsample_global 2\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-translation-with-stylegan\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-translation-with-stylegan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun translation with styleGan\u003c/h1\u003e\n\u003cp\u003eThis is the model which is used for the aerial translation.\u003c/p\u003e\n\u003cp\u003eEnsure that the configuration file correctly set STYLE_TRANSLATION and AERIAL_TRANSLATION. You may also have to change these files in coil_global.py if they are not correctly adjusted.\u003c/p\u003e\n\u003cp\u003eBe sure to replace checkpoint path with the desired checkpoint\u003c/p\u003e\n\u003cp\u003epython coiltraine.py --gpus 0 --folder nocrash --exp resnet34imnet10S1 --single-process drive -de NocrashTraining_Town01 --docker carlagear --checkpoint_path pixel2style2pixel/checkpoints/carla_AtoG/checkpoints/iteration_1000000.pt\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-data_collector\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-data_collector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun data_collector\u003c/h1\u003e\n\u003cp\u003eThe data collection must be run under the old translation environment pix2pix\u003c/p\u003e\n\u003cp\u003epython multi_gpu_collection.py -pt /path/to/data/folder -d dataset_configuration_file\u003c/p\u003e\n", + "full_name": "garciagenrique/template_project_escape", + "latest_release": "v0.0.3-dev", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-template_project_escape\" class=\"anchor\" href=\"#template_project_escape\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etemplate_project_escape\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.4923992\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869b462bf3a319fe4fc2ffa52fb6b0f7c8e42eb4e0ed4bc2482306b9fd5aafab/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343932333939322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4923992.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/-/commits/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a25ea71f12537b69d5aca8b409685333243d4e65ceb91c5a401dadb6eacea20e/68747470733a2f2f6769746c61622e696e3270332e66722f657363617065323032302f7770332f74656d706c6174655f70726f6a6563745f6573636170652f6261646765732f6d61737465722f706970656c696e652e737667\" alt=\"pipeline status\" data-canonical-src=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/badges/master/pipeline.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fd551ba4b042d89480347a0e74e31af63b356b2cac1116c7b80038f41b04a581/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://camo.githubusercontent.com/64f9054d866a78f16e1451647c19b22fc159a58191e004612257ce5277bd6db9/68747470733a2f2f63646e2e65736f2e6f72672f696d616765732f6c617267652f616e6e3138303834612e6a7067\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/64f9054d866a78f16e1451647c19b22fc159a58191e004612257ce5277bd6db9/68747470733a2f2f63646e2e65736f2e6f72672f696d616765732f6c617267652f616e6e3138303834612e6a7067\" width=\"640\" height=\"453\" data-canonical-src=\"https://cdn.eso.org/images/large/ann18084a.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eA simple template project to provide software to ESCAPE.\u003c/p\u003e\n\u003cp\u003eThis repository shows the \u003cstrong\u003ebasic content\u003c/strong\u003e that should be included in a project (following the\n\u003ca href=\"https://opensource.guide/starting-a-project/\" rel=\"nofollow\"\u003eopensource guide\u003c/a\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn \u003ca href=\"https://help.github.com/en/github/creating-cloning-and-archiving-repositories/licensing-a-repository#where-does-the-license-live-on-my-repository\"\u003eopen source\u003c/a\u003e\n\u003cstrong\u003elicense\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://help.github.com/en/github/getting-started-with-github/create-a-repo#commit-your-first-change\"\u003e\u003cstrong\u003eREADME\u003c/strong\u003e file\u003c/a\u003e,\nsimilar to this one.\u003c/li\u003e\n\u003cli\u003eContributing guidelines.\n\u003cul\u003e\n\u003cli\u003eSee below the general guidelines for the ESCAPE repository.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://opensource.guide/code-of-conduct/\" rel=\"nofollow\"\u003ecode of conduct\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003eCheck why is a good idea to add one.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe repository itself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt would be highly suitable to include too:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA setup file as well as the basic commands to install the library (see below).\u003c/li\u003e\n\u003cli\u003eA \u003ccode\u003e.gitignore\u003c/code\u003e file.\u003c/li\u003e\n\u003cli\u003eUnitary and integration tests, and ideally a CI pipeline.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ePlease feel free to clone / fork / template this project!\u003c/strong\u003e (For example, look to left of the\n\u003ccode\u003eClone or download\u003c/code\u003e button in the \u003ca href=\"https://github.com/garciagenrique/template_project_escape\"\u003eGitHub\u003c/a\u003e site).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor a detailed explanation of how to submit a contribution to a project / repository (Fork, create a branch, make\na pull request...), you can have a look to the \u003ca href=\"https://opensource.guide/how-to-contribute/#how-to-submit-a-contribution\" rel=\"nofollow\"\u003eopensource guide\u003c/a\u003e\nand/or the \u003ca href=\"https://git-scm.com/doc\" rel=\"nofollow\"\u003egit\u0027s documentation\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eNot that if you have login GitLab by using the \u003ccode\u003e[Shibbolenth]\u003c/code\u003e service (eduGAIN, F\u00e9d\u00e9ration d\u0027Identit\u00e9s\nRENATER), you will need to \u003ca href=\"https://gitlab.in2p3.fr/help/ssh/README#generating-a-new-ssh-key-pair\" rel=\"nofollow\"\u003eadd a SSH key\u003c/a\u003e to\nyour GitLab profile if you want to \u0027push\u0027 your changes to the server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contribute-to-the-escape-ossr\" class=\"anchor\" href=\"#contribute-to-the-escape-ossr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContribute to the ESCAPE OSSR\u003c/h1\u003e\n\u003cp\u003eIf you want to provide software to the ESCAPE repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the \u003ca href=\"https://escape2020.pages.in2p3.fr/wp3/ossr-pages/page/contribute/contribute_ossr/\" rel=\"nofollow\"\u003eESCAPE OSSR guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor ESCAPE members, follow the steps detailed in \u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/onboarding\" rel=\"nofollow\"\u003ethe onboarding project\u003c/a\u003e\nto finalise your contribution and the same onboarding process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAll the code provided should be uploaded to the \u003ca href=\"https://zenodo.org/communities/escape2020/\" rel=\"nofollow\"\u003eZenodo ESCAPE community\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the following \u003ca href=\"https://escape2020.pages.in2p3.fr/wp3/ossr-pages/page/contribute/publish_tutorial/\" rel=\"nofollow\"\u003etutorial on how to publish content in Zenodo\u003c/a\u003e,\nand how to automatise the upload of each new release of your project.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-this-project-also-includes\" class=\"anchor\" href=\"#this-project-also-includes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis project also includes\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-1-how-to-automatise-the-building-of-a-singularity-image-and-upload-it-to-zenodo-using-the-gitlab-ci\" class=\"anchor\" href=\"#1-how-to-automatise-the-building-of-a-singularity-image-and-upload-it-to-zenodo-using-the-gitlab-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. How to automatise the building of a Singularity image and upload it to Zenodo using the GitLab-CI\u003c/h2\u003e\n\u003cp\u003eA working example of how to automatise the GitLab-CI to;\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecreate a Singularity image / container of your code,\u003c/li\u003e\n\u003cli\u003emake it available as a downloadable artifact within your project and\u003c/li\u003e\n\u003cli\u003eupload it to the \u003ca href=\"https://zenodo.org/communities/escape2020\" rel=\"nofollow\"\u003eESCAPE OSSR\u003c/a\u003e,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ecan be found in the \u003ccode\u003e.singularityci\u003c/code\u003e, and \u003ccode\u003eSingularity\u003c/code\u003e directories and in the \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e file - the\n\u003ccode\u003ebuild_singularity_image\u003c/code\u003e stage. Please read carefully all the README files.\u003c/p\u003e\n\u003cp\u003eFor an easy example of how to create a Singularity receipt from scratch (and its corresponding container when executed),\nplease have a look to the \u003ccode\u003esingularity_utils\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-2-how-to-automatise-the-building-of-a-docker-container-and-upload-it-to-the-gitlab-container-registry\" class=\"anchor\" href=\"#2-how-to-automatise-the-building-of-a-docker-container-and-upload-it-to-the-gitlab-container-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. How to automatise the building of a Docker container and upload it to the GitLab Container Registry\u003c/h2\u003e\n\u003cp\u003eAn example can be found in the \u003ccode\u003eDocker\u003c/code\u003e directory and in the \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e file - the\n\u003ccode\u003ebuild_docker_image\u003c/code\u003e stage.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h1\u003e\n\u003cp\u003eExample of how to show installing instructions (and indeed the way to install this project).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e $ git clone https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape.git\n $ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e template_project_escape\n $ pip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-citing\" class=\"anchor\" href=\"#citing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCiting\u003c/h1\u003e\n\u003cp\u003eExample of citing (as well as the DOI to cite this project),\u003c/p\u003e\n\u003cp\u003eIn case of citing this repository, use the following DOI:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ev2.2 \u003ca href=\"https://doi.org/10.5281/zenodo.4923992\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869b462bf3a319fe4fc2ffa52fb6b0f7c8e42eb4e0ed4bc2482306b9fd5aafab/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343932333939322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4923992.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev2.1 \u003ca href=\"https://doi.org/10.5281/zenodo.4790629\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8f54e2f50cdf0c4d1bd5183d6472cae8b708efacd6a1319b443d8e456f41b7f/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343739303632392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4790629.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev2.0 \u003ca href=\"https://doi.org/10.5281/zenodo.3884963\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c27201272a77fc3ab3029c8c2c452e02a71736b7adaa0926bc45d3ac825598ed/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333838343936332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3884963.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev1.1 \u003ca href=\"https://doi.org/10.5281/zenodo.3743490\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbfe31d3a9bd48ef4554b414c3c2325276269a476a79ec0217aa9feccc87cecd/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333734333439302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3743490.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev1.0 \u003ca href=\"https://doi.org/10.5281/zenodo.3572655\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ec734ee4c1c793eaa8f9c2b189a6eae6d7d2ccb5f2a92adeb8af479409cc7bb/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333537323635352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3572655.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDo not forget to include your code / container into the \u003ca href=\"https://zenodo.org/communities/escape2020/\" rel=\"nofollow\"\u003eZenodo ESCAPE community\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003e\u003cstrong\u003eNote that\u003c/strong\u003e\u003c/em\u003e a DOI will be assigned in the moment create a new record/entry in Zenodo.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003ePlease check the licenses of the code within the \u003ccode\u003e.singularityci\u003c/code\u003e directory before adding this template\nto your project.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-report-an-issue--ask-a-question\" class=\"anchor\" href=\"#report-an-issue--ask-a-question\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReport an issue / Ask a question\u003c/h1\u003e\n\u003cp\u003eUse the \u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/-/issues\" rel=\"nofollow\"\u003eGitLab repository Issues\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eEmail to vuillaume [at] lapp.in2p3.fr / garcia [at] lapp.in2p3.fr.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1668106102.0 + "updated_at": 1623346169.0 }, { "data_format": 2, - "description": "Source code, installation, configuration and submission scripts for exascale in situ visualization with ISAAC and PIConGPU", + "description": "container for gatk tools", "filenames": [ - "sources/crusher/picongpu/share/picongpu/dockerfiles/ubuntu-2004/Singularity", - "sources/summit/picongpu/share/picongpu/dockerfiles/ubuntu-2004/Singularity" + "Singularity" ], - "full_name": "benjha/sc2022_ISAAC_artifact", + "full_name": "aseetharam/gatk", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sc2022-artifact-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#sc2022-artifact-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSC\u00272022 Artifact Description\u003c/h1\u003e\n\u003cp\u003eWe reported the results of six experiments to evaluate the performance characteristics and portability of our in situ visualization solution. Three were run on Summit (\u003ccode\u003e64_gpus\u003c/code\u003e, \u003ccode\u003estrong_scaling\u003c/code\u003e, \u003ccode\u003eweak_scaling\u003c/code\u003e) and the other three on Crusher (\u003ccode\u003efirst_experiment\u003c/code\u003e, \u003ccode\u003esecond_experiment\u003c/code\u003e, \u003ccode\u003eweak_scaling\u003c/code\u003e). General simulations parameters:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eKelvin-Helmholtz instability simulation.\u003c/li\u003e\n\u003cli\u003e256x256x256 cells per GPU, additionally on Crusher: 512x512x512 cells per GPU.\u003c/li\u003e\n\u003cli\u003eFour particles per cell resulting in 134,217,728 macroparticles per GPU.\u003c/li\u003e\n\u003cli\u003eVolume, isosurface, particles and vector field visualization of three data sources. The threshold for isosurface visualization is set to the maximum of 1 for all sources to prevent any kind of early ray termination due to a valid isosurface.\u003c/li\u003e\n\u003cli\u003eTrilinear Interpolation is enabled, and the step size is set to the default of 0.5.\u003c/li\u003e\n\u003cli\u003eHalo exchange enabled.\u003c/li\u003e\n\u003cli\u003eTimings are averaged from 1440 time steps. Starting simulation time step is 10 to allow stabilization.\u003c/li\u003e\n\u003cli\u003eCamera view\u0027s animation is divided into four stages, each with 360 steps and a rotation around a different axis to cover most of the viewing angles.\u003c/li\u003e\n\u003cli\u003eISAAC streaming capabilities are disabled including image compression.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe interested reader can check the PIConGPU\u2019s documentation under this \u003ca href=\"https://picongpu.readthedocs.io\" rel=\"nofollow\"\u003elink\u003c/a\u003e for details on how to set up a simulation and a experiment. The configuration files used for the experiments are available following the next links:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSummit\n\u003cul\u003e\n\u003cli\u003e256x256x256 \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/simulation/summit/KelvinHelmholtz\"\u003eSimulation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/summit/64_gpus\"\u003e64_gpus\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/summit/strong_scaling\"\u003estrong_scaling\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/summit/weak_scaling\"\u003eweak_scaling\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCrusher\n\u003cul\u003e\n\u003cli\u003e256x256x256 \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/simulation/crusher/KelvinHelmholtz\"\u003eSimulation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e512x512x512 \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/simulation/crusher/KelvinHelmholtz_large\"\u003eSimulation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/crusher/first_experiment\"\u003efirst_experiment\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/crusher/second_experiment\"\u003esecond_experiment\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/crusher/weak_scaling\"\u003eweak_scaling\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExperiments are reproduced following the instructions of the next section.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation--running-experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation--running-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation \u0026amp; Running Experiments\u003c/h1\u003e\n\u003cp\u003eWe include three scripts to deploy the experiments in Summit and Crusher systems:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/blob/main/config_vars.sh\"\u003e\u003ccode\u003econfig_vars.sh\u003c/code\u003e\u003c/a\u003e. This script includes the configuration variables that should be set by the user to install, configure and submit the experiments to the batch system. This script is modifiable by the user and is used by the \u003ccode\u003einstall.sh\u003c/code\u003e and \u003ccode\u003erun_experimen.sh\u003c/code\u003e scripts.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/blob/main/install.sh\"\u003e\u003ccode\u003einstall.sh\u003c/code\u003e\u003c/a\u003e. This script compiles and installs ISAAC, and the Kelvin-Helmholtz instability simulation. This script is only runnable by the user and should not be modified.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/blob/main/run_experiment.sh\"\u003e\u003ccode\u003erun_experiment.sh\u003c/code\u003e\u003c/a\u003e. This script submits to the batch system the experiments described previously. This script is only runnable by the user and should not be modified.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe configuration variables defined in \u003ccode\u003econfig_vars.sh\u003c/code\u003e are described next:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eMAIL\u003c/code\u003e. Specifies what e-mail will receive a notification when a submitted experiment is running. This variable is optional.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePROJ_ID\u003c/code\u003e. Specifies what project id to use to submit a job. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e. Indicates the installation path of all software stack. Make sure \u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e is under \u003ccode\u003e$PROJWORK/\u0026lt;proj_id\u0026gt;/\u0026lt;user_id\u0026gt;\u003c/code\u003e. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMY_ISAAC_LOG_PATH\u003c/code\u003e. Specifies the path of the performance files generated when running the code. Make sure \u003ccode\u003eMY_ISAAC_LOG_PATH\u003c/code\u003e is under \u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMY_SIMULATIONS_PATH\u003c/code\u003e. Sets the simulations\u0027 path. Make sure it is under \u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMY_SIMULATION_NAME\u003c/code\u003e. Indicates the name of the simulation. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSYSTEM\u003c/code\u003e. Specifies the target cluster to install and execute the experiments. Available options are: \u003ccode\u003esummit\u003c/code\u003e, \u003ccode\u003ecrusher\u003c/code\u003e. This variable is mandatory\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eEXPERIMENT_NAME\u003c/code\u003e. Sets the experiment name that will be submitted to the batch system.\n\u003cul\u003e\n\u003cli\u003eOptions for summit are: \u003ccode\u003e64_gpus\u003c/code\u003e, \u003ccode\u003estrong_scaling\u003c/code\u003e, \u003ccode\u003eweak_scaling\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOptions for crusher are: \u003ccode\u003efirst_experiment\u003c/code\u003e, \u003ccode\u003esecond_experiment\u003c/code\u003e, \u003ccode\u003eweak_scaling\u003c/code\u003e. This variable is mandatory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFRAMEBUFFER\u003c/code\u003e. Sets the framebuffer resolution. This option is only used on \u003ccode\u003eSYSTEM=summit\u003c/code\u003e and \u003ccode\u003eEXPERIMENT_NAME=64_gpus\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eAvailable options: 720 , 1080 , 1440 , 2160.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eN_GPUS\u003c/code\u003e. Sets the number of GPUs for strong scaling and weak scaling experiments.\n\u003cul\u003e\n\u003cli\u003eOptions for \u003ccode\u003eSYSTEM=summit\u003c/code\u003e and \u003ccode\u003eEXPERIMENT_NAME=strong_scaling\u003c/code\u003e: 1, 2, 4, 8, 16, 32, 64, 128, 256, 512.\u003c/li\u003e\n\u003cli\u003eOptions for \u003ccode\u003eSYSTEM=summit\u003c/code\u003e and \u003ccode\u003eEXPERIMENT_NAME=weak_scaling\u003c/code\u003e: 1, 8, 64, 512, 1000, 2755, 4096, 5832, 8000, 10648, 13824.\u003c/li\u003e\n\u003cli\u003eOptions for \u003ccode\u003eSYSTEM=crusher\u003c/code\u003e and \u003ccode\u003eEXPERIMENT_NAME=weak_scaling\u003c/code\u003e: 1 , 8 , 64 , 216 , 512 , 1000.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstallation steps are as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLogin to Summit or Crusher.\u003c/li\u003e\n\u003cli\u003eClone this repository:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/benjha/sc2022_ISAAC_artifact.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eGo to \u003ccode\u003esc2022_ISAAC_artifact\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eSet executable the permissions for \u003ccode\u003einstall.sh\u003c/code\u003e and \u003ccode\u003erun_experiment.sh\u003c/code\u003e scripts:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003echmod +x install.sh\nchmod +x run_experiment.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eSet the next variables according to your preferences in config_vars.sh script:\n\u003ccode\u003eMAIL\u003c/code\u003e, \u003ccode\u003ePROJ_ID\u003c/code\u003e, \u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e, \u003ccode\u003eMY_ISAAC_LOG_PATH\u003c/code\u003e, \u003ccode\u003eMY_SIMULATIONS_PATH\u003c/code\u003e, \u003ccode\u003eMY_SIMULATION_NAME\u003c/code\u003e,\u003ccode\u003eSYSTEM\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport MAIL=\"mymail@myserver.com\"\nexport PROJ_ID=ABC123\nexport MY_INSTALLATION_PATH=$PROJWORK/ABC123/myuserid/sc2022_AD/summit\nexport MY_ISAAC_LOG_PATH=$MY_INSTALLATION_PATH/isaac_logs\nexport MY_SIMULATIONS_PATH=$MY_INSTALLATION_PATH/simulations\nexport MY_SIMULATION_NAME=kh_isaac_test\nexport SYSTEM=summit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote this example installs the software stack on Summit.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute the installation script only once per system:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./install.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-an-experiment\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-an-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning an experiment\u003c/h2\u003e\n\u003cp\u003eFor example, to run the weak_scaling experiment on Summit with 512 GPUs based on the previous section, follow the next steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSet the next variables in config_vars.sh script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport EXPERIMENT_NAME=weak_scaling\nexport N_GPUS=512\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun the run_experiment.sh script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./run_experiment.s\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe complete definition of variables in \u003ccode\u003econfig_vars.sh\u003c/code\u003e script for the 512 GPU weak scaling experiment on Summit is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport MAIL=\"mymail@myserver.com\"\nexport PROJ_ID=ABC123\nexport MY_INSTALLATION_PATH=$PROJWORK/ABC123/myuserid/sc2022_AD/summit\nexport MY_ISAAC_LOG_PATH=$MY_INSTALLATION_PATH/isaac_logs\nexport MY_SIMULATIONS_PATH=$MY_INSTALLATION_PATH/simulations\nexport MY_SIMULATION_NAME=kh_isaac_test\nexport SYSTEM=summit\nexport EXPERIMENT_NAME=weak_scaling\nexport N_GPUS=512\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor completeness, a \u003ccode\u003econfig_vars.sh\u003c/code\u003e script example that is used to install the software stack and run the Crusher\u0027s \u003ccode\u003esecond_experiment\u003c/code\u003e is shown next:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport MAIL=\"mymail@myserver.com\"\nexport PROJ_ID=ABC123\nexport MY_INSTALLATION_PATH=$PROJWORK/ABC123/myuserid/sc2022_AD/crusher\nexport MY_ISAAC_LOG_PATH=$MY_INSTALLATION_PATH/isaac_logs\nexport MY_SIMULATIONS_PATH=$MY_INSTALLATION_PATH/simulations\nexport MY_SIMULATION_NAME=kh_isaac_test\nexport SYSTEM=crusher\nexport EXPERIMENT_NAME=second_experiment\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4700\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-container-for-the-gatk\" class=\"anchor\" href=\"#container-for-the-gatk\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer for the GATK\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tools-included\" class=\"anchor\" href=\"#tools-included\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTools included\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"http://www.htslib.org/\" rel=\"nofollow\"\u003eSamTools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://bio-bwa.sourceforge.net/\" rel=\"nofollow\"\u003eBWA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.gnu.org/software/datamash/\" rel=\"nofollow\"\u003eDatamash\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gatk.broadinstitute.org/hc/en-us\" rel=\"nofollow\"\u003eGATK\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://broadinstitute.github.io/picard/\" rel=\"nofollow\"\u003ePicard Tools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lh3/bioawk\"\u003eBioAWK\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bedtools.readthedocs.io\" rel=\"nofollow\"\u003eBedTools\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePlease be sure to cite all the programs if you use this container.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eto pull the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name gatk.sif shub://aseetharam/gatk:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto use the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec gatk.sif samtools\nsingularity exec gatk.sif bwa\nsingularity exec gatk.sif datamash\nsingularity exec gatk.sif java -jar /gatk/gatk-package-4.1.8.1-local.jar\nsingularity exec gatk.sif java -jar /picard/picard.jar\nsingularity exec gatk.sif bioawk\nsingularity exec gatk.sif bedtools\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1649690153.0 + "updated_at": 1623344768.0 }, { "data_format": 2, - "description": "code accompanying \"Nanopore sequencing of a monkeypox virus strain isolated from a pustular lesion in the Central African Republic\"", + "description": "octopus Singularity container ", "filenames": [ "Singularity" ], - "full_name": "mvdenbog/MPXV_NanoPoreSeq", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mpxv_nanoporeseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#mpxv_nanoporeseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMPXV_NanoPoreSeq\u003c/h1\u003e\n\u003cp\u003eThis is snakefile code accompanying \"Nanopore sequencing of a monkeypox virus strain isolated from a pustular lesion in the Central African Republic\".\u003c/p\u003e\n\u003cp\u003eVandenbogaert M, Kwasiborski A, Gonofio E, Descorps-Decl\u00e8re S, Selekon B, Nkili Meyong AA, Ouilibona RS, Gessain A, Manuguerra JC, Caro V, Nakoune E, Berthet N. Nanopore sequencing of a monkeypox virus strain isolated from a pustular lesion in the Central African Republic. Sci Rep. 2022 Jun 24;12(1):10768. doi: 10.1038/s41598-022-15073-1. PMID: 35750759; PMCID: PMC9232561.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232561/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232561/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation--usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation--usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation \u0026amp; Usage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eUsing Docker/Singularity.\u003c/p\u003e\n\u003cp\u003eAll conda/python dependencies are defined in accompanying dependency files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003econda_installed_packages_base.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003econda_installed_packages_homopolish.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip38_installed_packages.txt\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe provided Singularity file is illustrative of the dependency definitions, and on building a target docker/singularity instance.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-preparation-of-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#preparation-of-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparation of data\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basecalling\" class=\"anchor\" aria-hidden=\"true\" href=\"#basecalling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasecalling\u003c/h3\u003e\n\u003cp\u003eInput data is supposed to be basecalled, prior to using the provided snakemake file.\u003c/p\u003e\n\u003cp\u003eExample basecalling instructions (below instructions are uinsg Guppy v 3.2.4, and are indicative only):\u003c/p\u003e\n\u003cp\u003eExample using CPUs:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edir=/opt/Guppy/ont-guppy-cpu_3.4.4/ont-guppy-cpu/bin\n\n${dir}/guppy_basecaller --kit ${kit} --flowcell ${flowcell} --barcode_kits ${barcode_kit} -i ${indir}/ -s ${outdir} --num_callers 4 --cpu_threads_per_caller 20 -q 4000 --qscore_filtering --min_qscore ${min_qscore} --disable_pings --trim_barcodes\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample using GPUs:\u003c/p\u003e\n\u003cp\u003eWorks on Tesla P100 only.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e${dir}/guppy_basecaller -i /data/fast5_pass/ --save_path /scratch/out/ --flowcell ${flowcell} --kit ${barcode_kit} --gpu_runners_per_device 8 -r --qscore_filtering --min_qscore 7 -x auto --disable_pings --trim_barcodes\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-organization-of-fastq-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#organization-of-fastq-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOrganization of FASTQ files\u003c/h3\u003e\n\u003cp\u003eand reference genome (here reference NC_003310).\u003c/p\u003e\n\u003cp\u003eWorking directory will be \u003ccode\u003e/scratch/\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /scratch/\nln ~/RawData/*.fastq .\nln ~/NC_003310.fasta .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-execution\" class=\"anchor\" aria-hidden=\"true\" href=\"#execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j10 -s Snakefile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eJupyter python/R notebooks for downstream analysis will be added shortly.\u003c/p\u003e\n", + "full_name": "sylvainschmitt/singularity-octopus", + "latest_release": "0.0.1", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-octopussingularity-container\" class=\"anchor\" href=\"#octopussingularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://github.com/luntergroup/octopus\"\u003eOctopus\u003c/a\u003e\n\u003ca href=\"https://github.com/hpcng/singularity\"\u003eSingularity\u003c/a\u003e container\u003c/h1\u003e\n\u003cp\u003eSylvain Schmitt\nApril 28, 2021\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBionformatics software Octopus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOctopus is a mapping-based variant caller that implements several\ncalling models within a unified haplotype-aware framework. Octopus takes\ninspiration from particle filtering by constructing a tree of haplotypes\nand dynamically pruning and extending the tree based on haplotype\nposterior probabilities in a sequential manner. This allows octopus to\nimplicitly consider all possible haplotypes at a given loci in\nreasonable time.\u003c/p\u003e\n\u003cp\u003eOctopus Version: 0.7.4\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://github.com/luntergroup/octopus\"\u003ehttps://github.com/luntergroup/octopus\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eSingularity container based on the recipe: Singularity.template.def\u003c/p\u003e\n\u003cp\u003ePackage installation using Miniconda3 V4.7.12\u003c/p\u003e\n\u003cp\u003eImage singularity (V\u0026gt;=3.3) is automatically test and built and pushed\non the registry using\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/test.yml\"\u003etest.yml\u003c/a\u003e\n\u0026amp;\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/builder.yml\"\u003ebuilder.yml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ebuild\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build octopus.sif Singularity\nsingularity run octopus.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e octopus.sif octopus -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003epull\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull https://github.com/sylvainschmitt/singularity-octopus/releases/download/0.0.1/sylvainschmitt-singularity-octopus.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esnakemake\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s1\"\u003esingularity\u003c/span\u003e: \n \u003cspan class=\"pl-s\"\u003e\"https://github.com/sylvainschmitt/singularity-octopus/releases/download/0.0.1/sylvainschmitt-singularity-octopus.latest.sif\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1658744372.0 + "updated_at": 1623243296.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.7", + "Singularity.12", + "Singularity.121", + "Singularity.11", + "Singularity.8", + "Singularity.5", + "Singularity.10", + "Singularity.9", + "Singularity.111", + "Singularity.15", + "Singularity.14", + "Singularity.6", + "Singularity.4", + "Singularity.3", + "Singularity.13" ], - "full_name": "kh11kim/kstar_rev", + "full_name": "masoudrezai/Singularity", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-welcome-to-the-page-of-k-planner----a-state-of-the-art-top-k-planner-integrating-the-k-algorithm-into-fast-downward\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcome-to-the-page-of-k-planner----a-state-of-the-art-top-k-planner-integrating-the-k-algorithm-into-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to the page of K* planner -- a state of the art Top-k planner integrating the K* algorithm into Fast Downward.\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# ./fast-downward.py \u0026lt;domain_file\u0026gt; \u0026lt;problem_file\u0026gt; --search \"kstar(heuristic,k=\u0026lt;number-of-plans\u0026gt;)\"\n\n./fast-downward.py examples/gripper/domain.pddl examples/gripper/prob01.pddl --search \"kstar(blind(),k=100)\"\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eheurisitic\u003c/em\u003e: any heuristic provided by Fast Downward\u003cbr\u003e\n(\u003ca href=\"http://www.fast-downward.org/Doc/Heuristic\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/Doc/Heuristic\u003c/a\u003e).\u003cbr\u003e\n\u003cstrong\u003eDisclaimer\u003c/strong\u003e: Optimality of K* is only guaranteed with an admissible and consistent heuristic.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h3\u003e\n\u003cp\u003eMichael Katz, Shirin Sohrabi, Octavian Udrea and Dominik Winterer\u003cbr\u003e\n\u003cstrong\u003eA Novel Iterative Approach to Top-k Planning\u003c/strong\u003e \u003ca href=\"https://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/download/17749/16971\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"/top_k.bib\"\u003e[bib]\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eIn ICAPS 2018\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h3\u003e\n\u003cp\u003eFor questions and comments please get in touch with Michael Katz (\u003ca href=\"mailto:michael.katz1@ibm.com\"\u003emichael.katz1@ibm.com\u003c/a\u003e).\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-octopussingularity-container\" class=\"anchor\" href=\"#octopussingularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://github.com/luntergroup/octopus\"\u003eOctopus\u003c/a\u003e\n\u003ca href=\"https://github.com/hpcng/singularity\"\u003eSingularity\u003c/a\u003e container\u003c/h1\u003e\n\u003cp\u003eSylvain Schmitt\nApril 28, 2021\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBionformatics software Octopus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOctopus is a mapping-based variant caller that implements several\ncalling models within a unified haplotype-aware framework. Octopus takes\ninspiration from particle filtering by constructing a tree of haplotypes\nand dynamically pruning and extending the tree based on haplotype\nposterior probabilities in a sequential manner. This allows octopus to\nimplicitly consider all possible haplotypes at a given loci in\nreasonable time.\u003c/p\u003e\n\u003cp\u003eOctopus Version: 0.7.4\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://github.com/luntergroup/octopus\"\u003ehttps://github.com/luntergroup/octopus\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eSingularity container based on the recipe: Singularity.template.def\u003c/p\u003e\n\u003cp\u003ePackage installation using Miniconda3 V4.7.12\u003c/p\u003e\n\u003cp\u003eImage singularity (V\u0026gt;=3.3) is automatically test and built and pushed\non the registry using\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/test.yml\"\u003etest.yml\u003c/a\u003e\n\u0026amp;\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/builder.yml\"\u003ebuilder.yml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ebuild\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build octopus.sif Singularity\nsingularity run octopus.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e octopus.sif octopus -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003epull\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull https://github.com/sylvainschmitt/singularity-octopus/releases/download/0.0.1/sylvainschmitt-singularity-octopus.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esnakemake\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s1\"\u003esingularity\u003c/span\u003e: \n \u003cspan class=\"pl-s\"\u003e\"https://github.com/sylvainschmitt/singularity-octopus/releases/download/0.0.1/sylvainschmitt-singularity-octopus.latest.sif\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1659371898.0 + "updated_at": 1623238419.0 }, { "data_format": 2, "description": null, "filenames": [ - "docker/Singularity.def" + "singularity_environment/Singularity" ], - "full_name": "benjrise/flood-detetection", + "full_name": "cpezzato/discrete_active_inference", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-discrete_active_inference-for-robotics\" class=\"anchor\" href=\"#discrete_active_inference-for-robotics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ediscrete_active_inference for robotics\u003c/h1\u003e\n\u003cp\u003eRepository for active inference and behavior trees for discrete decision making. This repository relies on a TIAGo simulation in a simplified retail store. Please read the associated paper for more theorethical considerations about the algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\"Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics\"\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCorrado Pezzato, Carlos Hernandez, Stefan Bonhof, Martijn Wisse, \u003ca href=\"https://arxiv.org/abs/2011.09756\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/2011.09756\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-content\" class=\"anchor\" href=\"#content\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContent\u003c/h2\u003e\n\u003cp\u003eThis repositiry contains a Matlab examples and a ROS package for active inference for task planning and execution.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-main-files\" class=\"anchor\" href=\"#main-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMain files\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eMatlab:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eaip.m\u003c/em\u003e the active inference algorithm for decision making is illustrated in the case of heterogeneous states and actions.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eexample.m\u003c/em\u003e example of use of active inference for discrete decision making in a robotic case where conflicts and preconditions checks are required. A robot is assumed to be able to navigate to a point (MoveBase), reach a location with its end effector (Move MPC), and pick and place things. Actions have preconditions and are assumed not instantaneous\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eROS:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe other folders are related to the ROS package containing a Python implementation of active inference and behavior trees. You can run an example use case with TIAGo in a simplified retail store after installation of the package ad dependancies.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSimulation Environment\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA singularity image can be downloaded from \u003ca href=\"https://drive.google.com/drive/folders/1DYuRWgCiiHCG4ck_7Pf_Kw4Kn-ZpZ-Oy?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, you can build the singularity yourself:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ecreate a sub directory called \u0027pkgs\u0027 (in the \u003ccode\u003esingularity_environment\u003c/code\u003e directory)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e mkdir pkgs\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003euse \u003ccode\u003evcstool\u003c/code\u003e (or \u003ccode\u003ewstool\u003c/code\u003e) to clone/download the dependencies (as specified in \u003ccode\u003eretail_store_lightweight_sim.repos\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e vcs import \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e retail_store_lightweight_sim.repos pkgs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdding packages to \u003ccode\u003epkg\u003c/code\u003e will allow \u003ccode\u003erosdep\u003c/code\u003e to install all required build and run dependencies into the image, so students can then proceed to build those packages in their own workspaces (otherwise builds would fail due to missing dependencies).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Packages in \u003ccode\u003epkg\u003c/code\u003e will be installed on the image, their source will \u003cstrong\u003enot\u003c/strong\u003e be included in the image itself, so there may be some elements that are not installed. So far I\u0027ve only noticed one required change.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eModify the \u003ccode\u003eCMakeList.txt\u003c/code\u003e file from the \u003ccode\u003epal_navigation_sm\u003c/code\u003e inside the \u003ccode\u003epkgs\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eChange the \u003ccode\u003einstall\u003c/code\u003e instruction (starts at line 10) by adding some scripts as follows.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003einstall(\nPROGRAMS\n scripts/map_setup.py\n scripts/pal_navigation_main_sm.py\n scripts/navigation.sh\n scripts/base_maps_symlink.sh\n scripts/cp_maps_to_home.sh\n scripts/cp_pose_to_home.sh\n DESTINATION \u003cspan class=\"pl-smi\"\u003e${CATKIN_PACKAGE_BIN_DESTINATION}\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003echeck the \u003ccode\u003eVERSION\u003c/code\u003e variable inside the \u003ccode\u003edocker_build.sh\u003c/code\u003e, \u003ccode\u003ebuild.sh\u003c/code\u003e and \u003ccode\u003eSingularity\u003c/code\u003e files. This version should match the version of your singularity install (\u003ccode\u003esingularity -v\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun \u003ccode\u003edocker_build.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./docker_build.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter some time and a successful build, a new docker image will be created. This requires Docker to be installed and configured.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun \u003ccode\u003ebuild.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./build.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAfter some time and a successful build, a new \u003ccode\u003e.simg\u003c/code\u003e should be generated by \u003ccode\u003esingularity\u003c/code\u003e in the \u003ccode\u003ecwd\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBehavior trees library\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInstall the BT library to use this package (tested in Ubuntu 18.04 with ROS Melodic). Before proceeding, it is recommended to to install the following dependencies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install libzmq3-dev libboost-dev\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also easily install the \u003ca href=\"https://github.com/BehaviorTree/BehaviorTree.CPP\"\u003eBehavior Tree library\u003c/a\u003e with the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install ros-$ROS_DISTRO-behaviortree-cpp-v3\nsudo apt-get update \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-code\" class=\"anchor\" href=\"#running-the-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the code\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUsing the virtual environment\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccess the simngularity image by using the regular Singularity \u003ccode\u003eshell\u003c/code\u003e action:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell /path/to/discrete_ai_tiago.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse the flag for nvidia drivers if applicable to your machine:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --nv /path/to/discrete_ai_tiago.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen source \u003ccode\u003e/opt/ros/melodic/setup.bash\u003c/code\u003e to access all the TIAGo dependencies installed on the image.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e /opt/ros/melodic/setup.bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eHow to run a simple example with TIAGo\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCreate a new workspace and clone this repository in the \u003ccode\u003esrc\u003c/code\u003e folder. Build the package using \u003ccode\u003ecatkin build\u003c/code\u003e. Run the three commands below from within the singularity image after sourcing \u003ccode\u003esource/devel/setup.bash\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch retail_store_simulation tiago_simulation.launch\nrosrun discrete_ai tiago_perception.py\nrosrun discrete_ai active_inference_server.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFrom a terminal outside the singularity image run the behavior tree:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erosrun discrete_ai demo_executeBT\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe expected outcome is the following:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"tiago_sim.gif\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"tiago_sim.gif\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: The sills used in this simulation are based on standard moveBase and moveIt actions, thus robustness (especially of IK solutions) might make TIAGo fail the grasp. Aruco detection can also imprecise and will be improved over time.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1660656835.0 + "updated_at": 1623232591.0 }, { "data_format": 2, - "description": "Singularity recipe files for glnexus (https://github.com/dnanexus-rnd/GLnexus)", + "description": "Multi-Label Multi/Single-Class Image Segmentation", "filenames": [ - "Singularity", - "Singularity.1.4.3" + "Singularity" ], - "full_name": "powerPlant/glnexus-srf", + "full_name": "kbronik2017/Multi_Label_Segmentation", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for GLnexus (\u003ca href=\"https://github.com/dnanexus-rnd/GLnexus\"\u003ehttps://github.com/dnanexus-rnd/GLnexus\u003c/a\u003e)\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/UCLBrain/MSLS/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f9cab98cc8f1052f0c0096b8b462cf9b2280a706b1adc0895d8a4859f3743314/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f55434c427261696e2f4d534c53\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/network\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6c7a087c43d2a65a6ee22ec8ce2e004a29212c4b9619b117f4b2bbabf98a6c5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f55434c427261696e2f4d534c53\" alt=\"GitHub forks\" data-canonical-src=\"https://img.shields.io/github/forks/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/10fab859502fd8c9b24dde937e50fecba7449e94ea057774713238ab3ec754fc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f55434c427261696e2f4d534c53\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d525d837b03e3b7a24b6322fc2197a134fa12e6a96188e5f18d6cd92236aa47/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f55434c427261696e2f4d534c53\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-multi-label-multisingle-class-image-segmentation\" class=\"anchor\" href=\"#multi-label-multisingle-class-image-segmentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-Label Multi/Single-Class Image Segmentation\u003c/h1\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/diag.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/diag.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-publication\" class=\"anchor\" href=\"#publication\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublication\u003c/h1\u003e\n\u003cp\u003eLe Zhang, Ryutaro Tanno, Kevin Bronik, Chen Jin, Parashkev Nachev, Frederik Barkhof, Olga Ciccarelli, and Daniel C. Alexander, Learning to Segment When Experts Disagree, International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI). Springer, Cham, 2020.\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/Miccai_2020_abs.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg align=\"center\" height=\"1000\" src=\"images/Miccai_2020_abs.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\n \n \u003cp\u003eClick here to see full pdf file: \u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/MICCAI_2020.pdf\"\u003eLink to PDF\u003c/a\u003e\u003c/p\u003e\n \n\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-gui-program\" class=\"anchor\" href=\"#running-the-gui-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the GUI Program!\u003c/h1\u003e\n\u003cp\u003eFirst, user needs to install Anaconda \u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003ehttps://www.anaconda.com/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda env create -f conda_environment_Training_Inference.yml \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efinally\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python Training_Inference_GUI.py \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/GUI.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/GUI.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-program-from-the-command-line\" class=\"anchor\" href=\"#running-the-program-from-the-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Program from the command line!\u003c/h1\u003e\n\u003cp\u003eFirst\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen for training\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Training_without_GUI.py [or annotation_network_Training_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efor testing\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Inference_without_GUI.py [or annotation_network_Inference_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-testing-the-program\" class=\"anchor\" href=\"#testing-the-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting the Program!\u003c/h1\u003e\n\u003cp\u003eExamples of Training and Testing subjects can be found in: \u003ca href=\"https://github.com/UCLBrain/MSLS/tree/master/examples\"\u003ehttps://github.com/UCLBrain/MSLS/tree/master/examples\u003c/a\u003e (which will allow users to quickly and easily train and test the program)\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/bin_seg_ex.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/bin_seg_ex.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e..................................................................................................................................................................\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/multi_seg_ex.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/multi_seg_ex.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n", "stargazers_count": 0, - "subscribers_count": 0, - "topics": [], - "updated_at": 1659481676.0 + "subscribers_count": 1, + "topics": [ + "segmentation", + "multi-label" + ], + "updated_at": 1628469613.0 }, { "data_format": 2, - "description": "Singularity recipe files for kraken-biom (https://github.com/smdabdoub/kraken-biom)", + "description": null, "filenames": [ - "Singularity", - "Singularity.1.2.0" + "util/PATRIC/Singularity" ], - "full_name": "powerPlant/kraken-biom-srf", + "full_name": "adamlabadorf/bf550", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for kraken-biom (\u003ca href=\"https://github.com/smdabdoub/kraken-biom\"\u003ehttps://github.com/smdabdoub/kraken-biom\u003c/a\u003e)\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bf550---foundations-in-programming-data-analytics-and-machine-learning-in-python\" class=\"anchor\" href=\"#bf550---foundations-in-programming-data-analytics-and-machine-learning-in-python\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBF550 - Foundations in Programming, Data Analytics, and Machine Learning in Python\u003c/h1\u003e\n\u003cp\u003e(unofficial title: Bioinformatics Engineering)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://adamlabadorf.github.io/bf550/\" rel=\"nofollow\"\u003eGo to the Github Pages site\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1659482439.0 + "updated_at": 1628214043.0 }, { "data_format": 2, - "description": null, + "description": "Validate and submit reads using Webin-CLI in batch.", "filenames": [ "Singularity" ], - "full_name": "cschu/profile_me_ci", + "full_name": "enasequence/ena-bulk-webincli", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ena-webin-cli-bulk-submission-tool\" class=\"anchor\" href=\"#ena-webin-cli-bulk-submission-tool\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eENA Webin-CLI Bulk Submission Tool\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThis tool is a wrapper to bulk submit read, un-annotated genome, targeted sequence or taxonomic reference data to the ENA using Webin-CLI.\u003c/p\u003e\n\u003cp\u003eThe tool requires an appropriate metadata spreadsheet which it uses to generate manifest files for the user and validate or submit their submission. The tool does not handle study and sample registration, therefore visit \u003ca href=\"https://ena-docs.readthedocs.io/en/latest/submit/general-guide.html\" rel=\"nofollow\"\u003eENA Submissions Documentation\u003c/a\u003e for more information on this. The documentation also provides information on manifest file fields for your type of submission (which correlate to the headers in the spreadsheet file).\u003c/p\u003e\n\u003cp\u003eAn example template spreadsheet has been provided (example_template_input.txt). This file is a tab-delimited text file, however the script also consumes spreadsheets in native MS Excel formats (e.g. .xslx) or comma-separated (.csv).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h4\u003e\n\u003cp\u003eTo ease in usage, the tool has been containerised using \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e. The only requirement is to have Docker \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003einstalled\u003c/a\u003e. Once installed, run the following commands to setup:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone the repository:\n\u003ccode\u003egit clone https://github.com/nadimm-rahman/ena-bulk-webincli.git \u0026amp;\u0026amp; cd ena-bulk-webincli\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuild the docker image:\n\u003ccode\u003edocker build --tag ena-bulk-webincli .\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eReady to go! Run the tool using docker using the following command:\n\u003ccode\u003edocker run --rm -v \u0026lt;LOCAL_DATA_DIRECTORY\u0026gt;:/data ena-bulk-webincli -h\u003c/code\u003e (for help)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026lt;LOCAL_DATA_DIRECTORY\u0026gt; is recommended to be the directory or parent directory on your machine containing your data files to submit. Below is an example command which would submit read data to the test server:\n\u003ccode\u003edocker run --rm -v pathto/data:/data ena-bulk-webincli -u Webin-XXXX -p XXXX -g reads -s example_template_read.txt -d /data -m submit -t\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNote: For data files to be submitted, relative file paths in accordance to \u003ccode\u003e\u0026lt;LOCAL_DATA_DIRECTORY\u0026gt;\u003c/code\u003e must be provided within the input spreadsheet.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-other\" class=\"anchor\" href=\"#other\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther\u003c/h4\u003e\n\u003cp\u003eTo use the tool without Docker:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone the repository:\n\u003ccode\u003egit clone https://github.com/nadimm-rahman/ena-bulk-webincli.git \u0026amp;\u0026amp; cd ena-bulk-webincli\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload the latest version of \u003ca href=\"https://github.com/enasequence/webin-cli/releases\"\u003eWebin-CLI\u003c/a\u003e installed.\u003c/li\u003e\n\u003cli\u003eDownload tool dependencies listed below.\u003c/li\u003e\n\u003cli\u003eEdit the \u0027Configuration\u0027 section at the top of bulk_webincli.py to include the full path to the Webin-CLI jar file and whether parallel processing should be carried out.\u003c/li\u003e\n\u003cli\u003eRun the tool using \u003ccode\u003epython bulk_webincli.py --help\u003c/code\u003e(for help)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe script accepts full paths to files (to be submitted e.g. fastq/fasta) within the input spreadsheet. To control location of outputs, a specific directory can be provided using the \u003ccode\u003e--directory/-d\u003c/code\u003e parameter, where the folders listed below will be generated.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eMandatory arguments include Webin submission account username and password, genetic context and metadata spreadsheet. Note that the \u003ccode\u003e--test/-t\u003c/code\u003e flag can be specified to use Webin test submission services.\u003c/p\u003e\n\u003cp\u003eBy default, the script utilises two additional directories:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u0027manifests\u0027 - which houses all generated manifest files and report files.\u003c/li\u003e\n\u003cli\u003e\u0027submissions\u0027 - housing all validation and submission related reports and files, includes analysis and receipt XMLs of submissions.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cp\u003eThe tool runs using \u003ca href=\"https://www.python.org/downloads/\" rel=\"nofollow\"\u003ePython3.6+\u003c/a\u003e and requires installation of \u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003ePython Pandas\u003c/a\u003e and \u003ca href=\"https://joblib.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ejoblib\u003c/a\u003e. This can be installed in a \u003ca href=\"https://docs.python.org/3/tutorial/venv.html\" rel=\"nofollow\"\u003evirtual environment\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1636023839.0 + "updated_at": 1625847484.0 }, { "data_format": 2, @@ -11162,665 +10549,685 @@ var data = "filenames": [ "Singularity" ], - "full_name": "psadil/cat12", + "full_name": "MontrealSergiy/deformation", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cat12\" class=\"anchor\" aria-hidden=\"true\" href=\"#cat12\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecat12\u003c/h1\u003e\n\u003cp\u003eTo build, run \u003ccode\u003ebuild_singularity\u003c/code\u003e as root e.g.,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo ./build_singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that the build expects to find a few files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e./code/main\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e./CAT12.zip (zipped standalone copy of CAT12, \u003ca href=\"https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2102\u0026amp;L=SPM\u0026amp;P=R8713\" rel=\"nofollow\"\u003ehttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2102\u0026amp;L=SPM\u0026amp;P=R8713\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e./MCR_R2017b_glnxa64_installer.zip (e.g., \u003ccode\u003ewget https://ssd.mathworks.com/supportfiles/downloads/R2017b/deployment_files/R2017b/installers/glnxa64/MCR_R2017b_glnxa64_installer.zip\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe script \u003ccode\u003erun_a2cps_segment\u003c/code\u003e provides a minimal wrapper around the container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./run_a2cps_segment T1w.nii.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run cat_standalone with a different template, \u003ccode\u003e\u0026lt;template\u0026gt;\u003c/code\u003e, on T1w image, \u003ccode\u003e\u0026lt;data\u0026gt;\u003c/code\u003e, try\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --cleanenv cat12.sif -b \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etemplate\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edata\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse the \u003ccode\u003e--cleanenv\u003c/code\u003e flag may not be necessary, depending on your host. When running with host Ubuntu 20.04, there were environment variables associated with Java that interfered with MATLAB. See the Singularity documentation on \u003ca href=\"https://sylabs.io/guides/3.8/user-guide/environment_and_metadata.html?highlight=cleanenv#environment-overview\" rel=\"nofollow\"\u003eenvironment variables\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prebuilt-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#prebuilt-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrebuilt container\u003c/h2\u003e\n\u003cp\u003eA verison of the container has been prebuilt and shared on \u003ca href=\"https://cloud.sylabs.io\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io\u003c/a\u003e. To use it, replace the container definition with \u003ccode\u003elibrary://psadil/default/cat\u003c/code\u003e, e. g.,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --cleanenv library://psadil/default/cat:0.0.1 -b \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etemplate\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edata\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-deformation-field\" class=\"anchor\" href=\"#deformation-field\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeformation field\u003c/h1\u003e\n\u003cp\u003eThis PERL script is a wrapper that is calling sequence of commands for generating deformation fields scrips\n\u003ca href=\"https://wiki.mouseimaging.ca/display/MICePub/Generating+deformation+fields\" rel=\"nofollow\"\u003ehttps://wiki.mouseimaging.ca/display/MICePub/Generating+deformation+fields\u003c/a\u003e\nSource code for deformation pipeline and dependencies (MINC):\n\u003ca href=\"https://github.com/Mouse-Imaging-Centre/generate_deformation_fields\"\u003ehttps://github.com/Mouse-Imaging-Centre/generate_deformation_fields\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cp\u003edeformation_2.pl -input ICBM_00100_t1_final.mnc \u0026lt;\u0026lt;this could be any anatomical minc file, for a collection of minc files\u0026gt;\u0026gt; -output dummy_hoho -deformation_ratio 0.6 -coordinate 70 100 70 10 10 10 -tolerance_space 4 \u0026lt;\u0026gt; -blur_determinant 0.25 \u0026lt;\u0026gt; -error 0.00001 \u0026lt;\u0026gt; -iteration 100\u003c/p\u003e\n\u003cp\u003eThe output of running this command looks like this:\nICBM_00100_t1_final_deformed_by_0.4atROIx70-y100-z70dimx10.dimy10.dimz10.mnc. \u003c/p\u003e\n\u003cp\u003eWe will also have a directory dummy_hoho/TMP that will contain the in-between-files.\u003c/p\u003e\n\u003cp\u003e$:/dummy_hoho/TMP$ ls\u003c/p\u003e\n\u003cp\u003eblock.mnc\u003c/p\u003e\n\u003cp\u003eblurred0.25determinant_r_0.4x70-y100-z70dimx10.dimy10.dimz10.mnc\u003c/p\u003e\n\u003cp\u003eDDDDdilated.mnc\u003c/p\u003e\n\u003cp\u003eDDDDring.mnc\u003c/p\u003e\n\u003cp\u003edeterminant_r_0.4_grid.mnc\u003c/p\u003e\n\u003cp\u003edeterminant_r_0.4x70-y100-z70dimx10.dimy10.dimz10.mnc\u003c/p\u003e\n\u003cp\u003edeterminant_r_0.4.xfm\u003c/p\u003e\n\u003cp\u003emask.mnc\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1659538764.0 + "updated_at": 1623632255.0 }, { "data_format": 2, - "description": "Singularity recipe files for gatk (https://github.com/broadinstitute/gatk)", + "description": "Massively Parallel, Portable, and Reproducible Tractography", "filenames": [ - "Singularity", - "Singularity.4.2.6.1" + "container/Singularity" ], - "full_name": "powerPlant/gatk-srf", + "full_name": "LLNL/MaPPeRTrac", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for gatk (\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003ehttps://github.com/broadinstitute/gatk\u003c/a\u003e)\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mappertrac\" class=\"anchor\" href=\"#mappertrac\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaPPeRTrac\u003c/h1\u003e\n\u003cp\u003eMassively Parallel, Portable, and Reproducible Tractography (MaPPeRTrac) is a brain tractography workflow for high performance computing. It incorporates novel technologies to simplify and accelerate neuroimaging research.\n\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-setup\" class=\"anchor\" href=\"#setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h3\u003e\n\u003cp\u003eRequirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.5+\u003c/li\u003e\n\u003cli\u003eSLURM job scheduling on a multi-node system\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cb\u003e1. Install NumPy and Parsl\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip3 install parsl numpy scipy\u003c/code\u003e\u003cbr\u003e\n(\u003ccode\u003epip3 install parsl numpy scipy --user\u003c/code\u003e for non-root systems)\u003c/p\u003e\n\u003cp\u003e\u003cb\u003e2. Clone repository\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone git@github.com:LLNL/MaPPeRTrac.git\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecd MaPPeRTrac/\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cb\u003e3. Load a Singularity container\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eRequirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity 3.0+ (\u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/guides/3.0/user-guide/\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBuilding the container:\u003cbr\u003e\ni. Obtain root access (you can copy and run the image in a non-root system afterwards).\u003cbr\u003e\nii. Place a Freesurfer \u003ccode\u003elicense.txt\u003c/code\u003e in the repo directory (\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/License\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/License\u003c/a\u003e).\u003cbr\u003e\niii. \u003ccode\u003e./container/build.sh\u003c/code\u003e\n\u003cbr\u003e\nNotes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMake sure to set \u003ccode\u003econtainer_path\u003c/code\u003e to the Singularity container\u0027s location.\u003c/li\u003e\n\u003cli\u003eIf you are having trouble building the container, try branch \u003ccode\u003eno_viz\u003c/code\u003e. This will disable render functionality.\u003c/li\u003e\n\u003cli\u003eAlternatively, download the image \u003ca href=\"https://drive.google.com/file/d/1lh0_5GO6-7qIznjvIcSMY-Ua8iBpZ4DJ/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\n\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cb\u003e4. Specify your DICOM or NIfTI data\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003ePlace your data in the same filesystem as the repository.\u003c/p\u003e\n\u003cp\u003eYou can download the example data \u003ca href=\"https://drive.google.com/file/d/1YC0QzWNohq173_zJaqZfnI5d6EPb9On2/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-launch\" class=\"anchor\" href=\"#launch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003e./s_run_all.py \u0026lt;config_json\u0026gt;\u003c/code\u003e\n\u003cbr\u003e\nSee \u003ccode\u003eexamples/dummy_config.json\u003c/code\u003e for example parameters.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-file-overview\" class=\"anchor\" href=\"#file-overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile Overview\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eTracktographyScripts/\n+- container/\n| +- build.sh\n| +- Singularity # Singularity build recipe\n|\n+- examples\n| +- dataset_description.json # Example of the BIDS dataset description\n| +- dummy_config.json # Example of the config JSON\n| +- dummy_dicom/\n| +- dummy_nifti/\n| +- dummy_subjects.json # Example of the subjects JSON\n|\n+- license.txt # Freesurfer license. NOTE: not included, required to build Singularity container\n+- LICENSE # MaPPeRTrac license.\n|\n+- lists/\n| +- connectome_idxs.txt # Brain region indices for .mat connectome files\n| +- list_edges_reduced.txt # Default edges to compute with Probtrackx and EDI (930 edges)\n| +- list_edges_all.txt # All possible edges (6643 edges)\n| +- render_targets.txt # NiFTI files to visualize with s4_render\n|\n+- README.md\n|\n+- s_run_all.py # Main script\n|\n+- subscripts/\n +- __init__.py\n +- maskseeds.py # Helper functions for s2b_freesurfer.py\n +- run_vtk.py # Helper script for s4_render.py\n +- s_debug.py # For debugging\n +- s1_dti_preproc.py\n +- s2a_bedpostx.py\n +- s2b_freesurfer.py\n +- s3_probtrackx.py\n +- s4_render.py\n +- utilities.py # General utility functions\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-overview\" class=\"anchor\" href=\"#output-overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput Overview\u003c/h3\u003e\n\u003cp\u003eThe following are the most important output files. This list is not comprehensive.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;OUTPUT DIRECTORY\u0026gt;/\n+- sourcedata/ # DICOM preprocessing data\n+- rawdata/ # BIDS-compliant NiFTI imaging data\n+- derivatives/\n +- sub-\u0026lt;SUBJECT NAME\u0026gt;\n +- [ses-\u0026lt;SESSION NAME\u0026gt;] # If session name specified, outputs will be in a session directory\n +- connectome_idxs.txt # Brain region indices for .mat connectome files\n +- connectome_#samples_oneway.txt # Oneway connectome in list form. Each edge has four columns:\n Column 1 is the source region\n Column 2 is the destination region\n Column 3 is number of fibers (NOF): the total count of successful streamlines between the two regions\n Column 4 is normalized NOF: the average density of successful streamlines the target region.\n +- connectome_#samples_twoway.txt # Twoway connectome in list form\n +- connectome_#samples_oneway_nof.mat # Oneway NOF connectome in matrix form\n +- connectome_#samples_twoway_nof.mat # Twoway NOF connectome in matrix form (should be symmetric)\n +- connectome_#samples_oneway_nof_normalized.mat # Oneway normalized NOF connectome in matrix form\n +- connectome_#samples_twoway_nof_normalized.mat # Twoway normalized NOF connectome in matrix form (should be symmetric)\n |\n +- EDI/\n | +- EDImaps/\n | +- FAtractsumsRaw.nii.gz # NiFTI image of total streamline density\n | +- FAtractsumsTwoway.nii.gz # NiFTI image of edge density (EDI). See Payabvash et al. (2019) for details.\n |\n +- log/ # Directory containing stdout and performance logs\n |\n +- render/ # Directory containing NiFTI image renders from step s4_render\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-config-parameterscommand-line-arguments\" class=\"anchor\" href=\"#config-parameterscommand-line-arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfig Parameters/Command Line Arguments\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eRequired Parameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003esubjects_json\u003c/td\u003e\n\u003ctd\u003eJSON file with input directories for each subject\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput_dir\u003c/td\u003e\n\u003ctd\u003eThe super-directory that will contain output directories for each subject.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003escheduler_name\u003c/td\u003e\n\u003ctd\u003eScheduler to be used for running jobs. Value is \"slurm\" for LLNL, \"cobalt\" for ANL, and \"grid_engine\" for UCSF.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOptional Parameter\u003c/th\u003e\n\u003cth\u003eDefault\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003esteps\u003c/td\u003e\n\u003ctd\u003es1 s2a s2b s3 s4\u003c/td\u003e\n\u003ctd\u003eSteps to run\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egpu_steps\u003c/td\u003e\n\u003ctd\u003es2a\u003c/td\u003e\n\u003ctd\u003eSteps to enable CUDA-enabled binaries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003escheduler_bank\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eScheduler bank to charge for jobs. Required for slurm and cobalt.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003escheduler_partition\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eScheduler partition to assign jobs. Required for slurm and cobalt.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003escheduler_options\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eString to prepend to the submit script to the scheduler\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egpu_options\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eString to prepend to the submit blocks for GPU-enabled steps, such as \u0027module load cuda/8.0;\u0027\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eworker_init\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eString to run before starting a worker, such as \u2018module load Anaconda; source activate env;\u2019\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtainer_path\u003c/td\u003e\n\u003ctd\u003econtainer/image.simg\u003c/td\u003e\n\u003ctd\u003ePath to Singularity container image\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eunix_username\u003c/td\u003e\n\u003ctd\u003e[[current user]]\u003c/td\u003e\n\u003ctd\u003eUnix username for Parsl job requests\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eunix_group\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eUnix group to assign file permissions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eforce\u003c/td\u003e\n\u003ctd\u003eFalse\u003c/td\u003e\n\u003ctd\u003eForce re-compute if checkpoints already exist\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egssapi\u003c/td\u003e\n\u003ctd\u003eFalse\u003c/td\u003e\n\u003ctd\u003eUse Kerberos GSS-API authentication\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003elocal_host_only\u003c/td\u003e\n\u003ctd\u003eTrue\u003c/td\u003e\n\u003ctd\u003eRequest all jobs on local machine, ignoring other hostnames\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eparsl_path\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003ePath to Parsl binaries, if not installed in /usr/bin or /usr/sbin\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erender_list\u003c/td\u003e\n\u003ctd\u003elists/render_targets.txt\u003c/td\u003e\n\u003ctd\u003eText file list of NIfTI outputs for s4_render (relative to each subject output directory)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epbtx_sample_count\u003c/td\u003e\n\u003ctd\u003e1000\u003c/td\u003e\n\u003ctd\u003eNumber of streamlines per seed voxel in s3_probtrackx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epbtx_random_seed\u003c/td\u003e\n\u003ctd\u003e[[random number]]\u003c/td\u003e\n\u003ctd\u003eRandom seed in s3_probtrackx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epbtx_max_memory\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003eMaximum memory per node (in GB) for s3_probtrackx. Default value of 0 indicates unlimited memory bound\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econnectome_idx_list\u003c/td\u003e\n\u003ctd\u003elists/connectome_idxs.txt\u003c/td\u003e\n\u003ctd\u003eText file with pairs of volumes and connectome indices\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ehistogram_bin_count\u003c/td\u003e\n\u003ctd\u003e256\u003c/td\u003e\n\u003ctd\u003eNumber of bins in NiFTI image histograms\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epbtx_edge_list\u003c/td\u003e\n\u003ctd\u003elists/list_edges_reduced.txt\u003c/td\u003e\n\u003ctd\u003eText file list of edges for steps s3_probtrackx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecompress_pbtx_results\u003c/td\u003e\n\u003ctd\u003eTrue\u003c/td\u003e\n\u003ctd\u003eCompress probtrackx outputs to reduce inode and disk space usage\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edynamic_walltime\u003c/td\u003e\n\u003ctd\u003eFalse\u003c/td\u003e\n\u003ctd\u003eRequest dynamically shortened walltimes, to gain priority on job queue\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_job_time\u003c/td\u003e\n\u003ctd\u003e00:15:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s1 on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_job_time\u003c/td\u003e\n\u003ctd\u003e00:45:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s2a on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_job_time\u003c/td\u003e\n\u003ctd\u003e10:00:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s2b on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_job_time\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s3 on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_job_time\u003c/td\u003e\n\u003ctd\u003e00:15:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s4 on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_cores_per_task\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s1_dti_preproc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_cores_per_task\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s2a_bedpostx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_cores_per_task\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s2b_freesurfer\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_cores_per_task\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s3_probtrackx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_cores_per_task\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s4_render\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s1_dti_preproc, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s2a_bedpostx, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s2b_freesurfer, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s3_probtrackx, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s4_render, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s2a\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s2b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(0.2 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(1.0 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s2a\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(1.0 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s2b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(1.0 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(0.1 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s2a\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s2b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebids_json\u003c/td\u003e\n\u003ctd\u003eexamples/dummy_bids_desc.json\u003c/td\u003e\n\u003ctd\u003eDescription file dataset_description.json, as specified at \u003ca href=\"https://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html\" rel=\"nofollow\"\u003ehttps://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebids_readme\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eFree form text file describing the dataset in more detail\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebids_session_name\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eName for the session timepoint (e.g. 2weeks)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-download-mri-images-from-openneuro\" class=\"anchor\" href=\"#download-mri-images-from-openneuro\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload MRI Images from OpenNeuro\u003c/h3\u003e\n\u003cp\u003eDownload MRI images from OpenNeuro repository by providing path to install data and accession ID of the MRI image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: subscripts/download_openneuro.py [-h] [--install-directory INSTALL_DIR] [-a ACC_NUM]\n\narguments:\n -h, --help show this help message and exit\n --install-directory INSTALL_DIR\n Path where data will be installed\n -a ACC_NUM, --accession ACC_NUM\n MRI Accession ID from OpenNeuro\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRequirements:\npython package datalad, git-annex\nInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c conda-forge datalad\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eon mac:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebrew install git-annex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eon linux:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c conda-forge git-annex\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h3\u003e\n\u003cp\u003eMaPPeRTrac is distributed under the terms of the BSD-3 License.\u003c/p\u003e\n\u003cp\u003eLLNL-CODE-811655\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 3, "topics": [], - "updated_at": 1659582311.0 + "updated_at": 1627333260.0 }, { "data_format": 2, "description": null, "filenames": [ - "fsl/singularity/Singularity.fsl" + "Singularity" ], - "full_name": "nikhil153/brain-diff", + "full_name": "QsingularityAi/polar-pfc-master_active-crystel", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-brain-diff\" class=\"anchor\" aria-hidden=\"true\" href=\"#brain-diff\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebrain-diff\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-goal-brainage-prediction-with-two-timepoints\" class=\"anchor\" aria-hidden=\"true\" href=\"#goal-brainage-prediction-with-two-timepoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal: Brainage prediction with two timepoints\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-replication\" class=\"anchor\" aria-hidden=\"true\" href=\"#replication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReplication\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- [Paper](https://doi.org/10.1016/j.media.2020.101871): Accurate brain age prediction with lightweight deep neural networks Han Peng, Weikang Gong, Christian F. Beckmann, Andrea Vedaldi, Stephen M Smith Medical Image Analysis (2021)\n- Code [repo](https://github.com/ha-ha-ha-han/UKBiobank_deep_pretrain)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-datasets\" class=\"anchor\" aria-hidden=\"true\" href=\"#datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDatasets\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- UKBB: notebooks/1_ukb_follow_up.ipynb\n- ADNI: notebooks/2_adni_follow_up.ipynb\n- Simulations: notebooks/7_brain_diff_sim.ipynb\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- Brainage replication: notebooks/4_brain_age.ipynb\n- Simulation: notebooks/8_brain_diff_sim_results.ipynb\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ukb-data-wrangling\" class=\"anchor\" aria-hidden=\"true\" href=\"#ukb-data-wrangling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUKB data wrangling\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-step-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003ecopy files from squashfs on Beluga\nSes-2 (n=40681): \u0026lt;neurohub_ukbb_t1w_bids_derivatives.squashfs\u0026gt;:/neurohub/ukbb/imaging/T1\nSes-3 (n=3208): \u0026lt;neurohub_ukbb_t1w_ses3_0_derivatives.squashfs\u0026gt;:/neurohub/ukbb/imaging\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-step-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e## move them in psudo-bids\nfor i in `ls | grep sub- | grep -v json`; do \n mkdir -p ../`echo $i | cut -d \"_\" -f1`/ses-2/anat; \n mv `echo $i | cut -d \"_\" -f1`* ../`echo $i | cut -d \"_\" -f1`/ses-2/anat/; \ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-adni-data-wrangling\" class=\"anchor\" aria-hidden=\"true\" href=\"#adni-data-wrangling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eADNI data wrangling\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003euse src/generate_adni_bids.py\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun instructions\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-simulations\" class=\"anchor\" aria-hidden=\"true\" href=\"#simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimulations:\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- Simple interactive runs: notebooks/7_brain_diff_sim.ipynb\n- Batch runs: src/run_simul.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sfcn-replication\" class=\"anchor\" aria-hidden=\"true\" href=\"#sfcn-replication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSFCN replication:\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- src/run_SFCN.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-slurm-setup-for-training-lsn\" class=\"anchor\" aria-hidden=\"true\" href=\"#slurm-setup-for-training-lsn\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eslurm setup for training LSN\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003emodule load singularity/3.8\u003c/li\u003e\n\u003cli\u003esingularity shell --overlay /project/rpp-aevans-ab/neurohub/ukbb/imaging/neurohub_ukbb_t1w_bids_derivatives.squashfs:ro --overlay /project/rpp-aevans-ab/neurohub/ukbb/imaging/neurohub_ukbb_t1w_bids_derivatives_ses3_0_bids.squashfs /home/nikhil/scratch/FastSurfer.sif\u003c/li\u003e\n\u003cli\u003e./run_LSN.sh\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-polar-pfc-master_active-crystel\" class=\"anchor\" href=\"#polar-pfc-master_active-crystel\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epolar-pfc-master_active-crystel\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1654635361.0 + "updated_at": 1624399268.0 }, { "data_format": 2, - "description": "A simple utility to convert a bunch of input fastq files into their reverse complement", + "description": null, "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "sequana/revcomp", - "latest_release": "v0.9.0", + "full_name": "shrutir11/lolcow", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-lolcow\" class=\"anchor\" href=\"#lolcow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elolcow\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1661892371.0 + "updated_at": 1624383824.0 }, { "data_format": 2, - "description": null, + "description": "Talking to Hinkskalle", "filenames": [ "Singularity" ], - "full_name": "baxpr/cersuit", - "latest_release": "v2.1.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cersuit\" class=\"anchor\" aria-hidden=\"true\" href=\"#cersuit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecersuit\u003c/h1\u003e\n\u003cp\u003eCerebellar segmentation with the \u003ca href=\"http://diedrichsenlab.org/imaging/suit.htm\" rel=\"nofollow\"\u003eSUIT atlas and toolbox\u003c/a\u003e. In the container, the pipeline is installed in the \u003ccode\u003e/opt/cersuit\u003c/code\u003e directory. Matlab code is in the \u003ccode\u003esrc\u003c/code\u003e directory, and the entrypoint is \u003ccode\u003esrc/cersuit.m\u003c/code\u003e. Compiled Matlab code for use in the singularity container without a Matlab license is in \u003ccode\u003ebin\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSee the \u003ccode\u003eexternal\u003c/code\u003e directory for links, references, and license information for the underlying SPM12 and SUIT Matlab software. \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki\" rel=\"nofollow\"\u003eFSL version 6.0.2\u003c/a\u003e is also used for image file manipulation and creating the QA PDF.\u003c/p\u003e\n\u003cp\u003eThe container has a full installation of both SPM12 (compiled) and FSL.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references-for-suit\" class=\"anchor\" aria-hidden=\"true\" href=\"#references-for-suit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences for SUIT\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1016/j.neuroimage.2006.05.056\" rel=\"nofollow\"\u003eDiedrichsen, J. (2006). A spatially unbiased atlas template of the human cerebellum. Neuroimage, 33, 1, p. 127-138.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1016/j.neuroimage.2009.01.045\" rel=\"nofollow\"\u003eDiedrichsen, J., Balsters, J. H., Flavell, J., Cussans, E., \u0026amp; Ramnani, N. (2009). A probabilistic atlas of the human cerebellum. Neuroimage 46(1):39-46.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1016/j.neuroimage.2010.10.035\" rel=\"nofollow\"\u003eDiedrichsen, J., Maderwald, S., Kuper, M., Thurling, M., Rabe, K., Gizewski, E. R., et al. (2011). Imaging the deep cerebellar nuclei: A probabilistic atlas and normalization procedure. Neuroimage 54(3):1786-94\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1371/journal.pone.0133402\" rel=\"nofollow\"\u003eDiedrichsen, J. \u0026amp; Zotow, E. (2015). Surface-based display of volume-averaged cerebellar data. PLoS One, 7, e0133402.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAdjustment of the source T1 file to axial data ordering using fslreorient2std, to meet a requirement of the SUIT toolbox.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTranslation-only alignment of the supplied gray matter image to SPM12\u0027s gray matter probabilistic atlas (TPM.nii). This is accomplished by aligning the centers of mass. Rotations are not estimated, to avoid an issue with SUIT\u0027s bounding box computation. The supplied gray matter image must be in register with the supplied T1. The estimated registration is saved to file and also applied to the T1.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSUIT estimation of the affine transformation and warp of the cerebellar area of the T1 to the SUIT atlas.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eResampling of the T1 and related images to the SUIT atlas space. Gray matter and white matter images are resampled both with and without modulation by the Jacobian.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eResampling of the SUIT-supplied atlases to the original T1 native space.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eComputation of regional volumes for the Lobules_SUIT atlas in the native T1 space.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-of-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-of-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage of the singularity container\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003esingularity_examples.sh\u003c/code\u003e for examples of using the container for SUIT warp estimation, and transformation from native to SUIT space and back using an existing estimated warp. The transformations can also be done directly from matlab with the \u003ccode\u003etransform_???.m\u003c/code\u003e functions in \u003ccode\u003esrc\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters-and-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters-and-inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters and inputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;temporary-home-dir\u0026gt; Matlab will use this for temp files\n\u0026lt;tmp-dir\u0026gt; Other location for temp files \n\u0026lt;input-dir\u0026gt; Directory containing the input T1 image file\n\u0026lt;output-dir\u0026gt; Outputs will be stored here\n\u0026lt;t1-niigz-filename\u0026gt; Filename of the input T1 - expecting \u0026lt;something\u0026gt;.nii.gz\n\u0026lt;mask-threshold\u0026gt; SPM mask threshold for separating brain from background\n\u0026lt;project-name\u0026gt; Project/subject/session/scan names from XNAT, if XNAT is\n\u0026lt;subject-name\u0026gt; used. These are only used to decorate the PDF report.\n\u0026lt;session-name\u0026gt; \n\u0026lt;scan-name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cp\u003ePDF report for quality assurance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePDF cersuit.pdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTransformation from native to atlas space. Apply in this order\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRIGID coreg_t1_to_mni.mat\nAFFINE Affine_c_t1_seg1.mat\nFLOWFIELD u_a_c_t1_seg1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCropped T1 in both spaces\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eT1_CROP_NATIVE c_t1.nii.gz\nT1_CROP_SUIT wc_t1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCerebellum mask, segmented gray matter and white matter volume fraction images in native and atlas space\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMASK_NATIVE c_t1_pcereb.nii.gz\nGRAY_NATIVE c_t1_seg1.nii.gz\nWHITE_NATIVE c_t1_seg2.nii.gz\nMASK_SUIT wc_t1_pcereb.nii.gz\nGRAY_SUIT wc_t1_seg1.nii.gz\nWHITE_SUIT wc_t1_seg2.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eJacobian-modulated gray and white matter images in atlas space\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eGRAYMOD_SUIT wdc_t1_seg1.nii.gz\nWHITEMOD_SUIT wdc_t1_seg2.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSegmented regions in native and atlas space, with lookup table\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eATLASES_NATIVE SUIT-supplied atlases resampled to original T1 space\nATLASES_SUIT The SUIT-supplied atlases themselves\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eVolumetry of segmented regions, computed from native space images. The \"Total\" is the volume of the atlas region after transformation to native space. The \"Gray\" is the sum of voxel gray matter fraction within the atlas region, in native space; similar for \"White\".\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNATIVE_VOLS iw_Lobules-SUIT_u_a_c_t1_seg1-volumes.csv\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "csf-ngs/hinkskalle-api", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hinkskalle-api\" class=\"anchor\" href=\"#hinkskalle-api\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHinkskalle API\u003c/h1\u003e\n\u003cp\u003eTalking to \u003ca href=\"https://github.com/csf-ngs/hinkskalle\"\u003eHinkskalle\u003c/a\u003e made easy\u003c/p\u003e\n\u003cp\u003eUse me to\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elist available downloads\u003c/li\u003e\n\u003cli\u003edownload data\u003c/li\u003e\n\u003cli\u003eupload data\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003ehinkskalle-api provides\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea small library with a thin wrapper over the JSON API\u003c/li\u003e\n\u003cli\u003ea CLI (\u003ccode\u003ehinkli\u003c/code\u003e: short for hink-cli, get it?)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eYou will need python3 and pip. Then you can run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install git+https://github.com/csf-ngs/hinkskalle-api\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-command-line-interface\" class=\"anchor\" href=\"#command-line-interface\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand Line Interface\u003c/h3\u003e\n\u003cp\u003eGet a list of available commands and options:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehinkli --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYour first step should be logging in:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e non-VBCF.NGS users get your own instance!\u003c/span\u003e\nhinkli --base https://singularity.ngs.vbcf.ac.at/ login\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e answer prompt for username and password\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe registry and token should now be stored in \u003ccode\u003e~/.hink_api.yml\u003c/code\u003e and available for further use.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-discovering--downloading-data\" class=\"anchor\" href=\"#discovering--downloading-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscovering \u0026amp; Downloading Data\u003c/h4\u003e\n\u003cp\u003eYour most likely use case will be downloading data provided via Hinkskalle.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e shows available collections of containers\u003c/span\u003e\nhinkli list-collections\nhinkli list-containers [collection]\nhinkli list-downloads [collection]/[container]\nhinkli pull [collection]/[container]:[tag]\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e username is optional, but can be provided, too:\u003c/span\u003e\nhinkli list-collections test.hase\nhinkli list-containers test.hase/[collection]\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e etc\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBasic structure:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA Collection holds a bunch of containers (topic, type, ...)\u003c/li\u003e\n\u003cli\u003eContainers hold tagged data\u003c/li\u003e\n\u003cli\u003eEach tag points to some data (some tags point to the same data)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf Hinkskalle shows you these downloads in your container \u003ccode\u003etest.hase/example/FAQ4711\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e- \u003cspan class=\"pl-ent\"\u003efilename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ebunch_of_reads.fastq.gz\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esize\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e41.5 MB\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003etags\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ebasecalled,20210621\u003c/span\u003e\n- \u003cspan class=\"pl-ent\"\u003efilename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003erawdata.tar.gz\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esize\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e41.5 TB\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003etags\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eraw\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can use these commands to download:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e either one fetches bunch_of_reads.fastq\u003c/span\u003e\nhinkli pull example/FAQ4711:basecalled\nhinkli pull example/FAQ4711:20210621\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e fetches rawdata.tar.gz\u003c/span\u003e\nhinkli pull example/FAQ4711:raw\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHinkli will even check the sha256 checksum for you!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-api\" class=\"anchor\" href=\"#api\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAPI\u003c/h3\u003e\n\u003cp\u003eNot documented - use at your own risk!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ehinkskalle_api\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eHinkApi\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eapi\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eHinkApi\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ecollections\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eapi\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003elist_collections\u003c/span\u003e()\n\u003cspan class=\"pl-c\"\u003e# etc\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003eBy default, hinkli reads its config from \u003ccode\u003e~/.hink_api.yml\u003c/code\u003e. This file should look like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ehink_api_base\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ehttps://singularity.ngs.vbcf.ac.at\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ehink_api_key\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eyour_super_secret_token\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can use these env variables to override:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eHINK_API_BASE\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eHINK_API_KEY\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHINK_API_CFG\u003c/code\u003e - to look for the config file in a different location\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-development\" class=\"anchor\" href=\"#development\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h1\u003e\n\u003cp\u003eYou can regenerate the models from the Hinkskalle swagger/openapi definition:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install git+https://ngs.vbcf.ac.at/repo/software/swagspotta.git\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e from pkg.ngs.vbcf.ac.at production:\u003c/span\u003e\nshare/create_models.sh\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e from your local hinkskalle dev server:\u003c/span\u003e\nshare/create_models.sh http://localhost:7660/swagger\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1659018685.0 + "updated_at": 1626991307.0 }, { "data_format": 2, "description": null, "filenames": [ - "SMiRL_Code/Singularity" + "Singularity.isafe", + "Singularity.breakseq", + "Singularity.pophuman", + "Singularity.abcmk" ], - "full_name": "KBoumghar/IFT4055-RL", + "full_name": "jmurga/bgd-pic", "latest_release": null, - "readme": "\u003cp\u003e#IFT4055 - Journal\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-02-05-2022\" class=\"anchor\" aria-hidden=\"true\" href=\"#02-05-2022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e02-05-2022\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eLearned about MDP and Q-function (see MDP.pdf)\u003c/li\u003e\n\u003cli\u003eSMiRL paper up to page 6 (see Smirl.pdf).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eQuestions I need to answer :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAuxiliary objective, what is this exactly?\u003c/li\u003e\n\u003cli\u003eMinimizing the R.H.S to get maximum reward\u003c/li\u003e\n\u003cli\u003eEstimate of state marginal (cannot seem to find reference for that)\u003c/li\u003e\n\u003cli\u003eHow / how fast can we find the distribution that fits our p_{\\theta_t}(s)\u003c/li\u003e\n\u003cli\u003eMaximum likelihood estimation : OK. Maximum likelihood state density estimation process???\u003c/li\u003e\n\u003cli\u003eWe can\u0027t assume independence of states like what I\u0027ve seen. What is used for Maximum likelihood?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhat I (think) I need to do next :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMore reading/watching on maximum likelihood in machine learning context\u003c/li\u003e\n\u003cli\u003eRead paper about DQN algorithm : \u003ca href=\"https://arxiv.org/pdf/1312.5602.pdf\" rel=\"nofollow\"\u003ehttps://arxiv.org/pdf/1312.5602.pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRead paper about TRPO algorithm\u003c/li\u003e\n\u003cli\u003ePart with Density estimation with learned representations?\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fast-downward\" class=\"anchor\" href=\"#fast-downward\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast Downward\u003c/h1\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" href=\"#tested-software-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1652813085.0 + "updated_at": 1624197047.0 }, { "data_format": 2, "description": null, "filenames": [ - "_profiler/Singularity" + "Singularity.3.0" ], - "full_name": "mozhgan-kch/HPC_Bootcamp", + "full_name": "onuryukselen/singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-hpc_bootcamp\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc_bootcamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC_Bootcamp\u003c/h1\u003e\n\u003cp\u003eThis repository contains training content for the HPC_Bootcamp materials. This repository includes the following file structure in the initial two levels:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e _advanced\n\u2502 \u251c\u2500\u2500 cuda_advanced\n\u2502 \u251c\u2500\u2500 multigpu\n\u2502 \u2514\u2500\u2500 openacc_advanced\n\u251c\u2500\u2500 _basic\n\u2502 \u251c\u2500\u2500 cuda_basic\n\u2502 \u251c\u2500\u2500 iso\n\u2502 \u251c\u2500\u2500 openacc_basic\n\u2502 \u2514\u2500\u2500 openmp\n\u251c\u2500\u2500 _profiler\n\u2502 \u251c\u2500\u2500 jupyter_notebook\n\u2502 \u251c\u2500\u2500 Presentations\n\u2502 \u2514\u2500\u2500 source_code\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 bootstrap.sh\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 _scripts\n\u2514\u2500\u2500 start_notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThe _\u003cem\u003eadvanced\u003c/em\u003e directory contains all of the advanced training materials for CUDA, OpenACC, and multiGPU.\u003c/li\u003e\n\u003cli\u003eThe _\u003cem\u003ebasic\u003c/em\u003e directory contains all of the introductory training materials for CUDA, Standard Languages, OpenMP Offloading, and OpenACC.\u003c/li\u003e\n\u003cli\u003eThe _\u003cem\u003eprofiler\u003c/em\u003e directory contains content on NVIDIA Nsight Systems and Compute.\u003c/li\u003e\n\u003cli\u003e_scripts directory contains container defintion files for each bootcamp type.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePlease note there is a container definition file for each content in \u003ccode\u003e_advanced\u003c/code\u003e, \u003ccode\u003e_basic\u003c/code\u003e, and \u003ccode\u003e_profiler\u003c/code\u003e directory and those can be used on their own without mixing with other contents. Please check the \u003ccode\u003eREADME.md\u003c/code\u003e file inside of each for more information.\u003c/p\u003e\n\u003cp\u003eYou can either clone the whole repository and isolate contents or you can only clone without any of the directories. Please follow below steps for each method.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-cloning-the-repo-with-all-the-direcotires-and-isolate-later-using-git-sparse-checkout\" class=\"anchor\" aria-hidden=\"true\" href=\"#cloning-the-repo-with-all-the-direcotires-and-isolate-later-using-git-sparse-checkout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning the repo with all the direcotires and isolate later using \u003ccode\u003egit sparse-checkout\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eYou can use the \u003ccode\u003eboostrap.sh\u003c/code\u003e script at the root of the repository to isolate the content. For example, by running \u003ccode\u003ebash ./bootstrap.sh openacc\u003c/code\u003e, your working directory will include all the content related to the OpenACC Bootcamp from basic to advanced. Now, you can run the \u003ccode\u003ebootstrap.sh\u003c/code\u003e command using one of the following pre-defined bootcamp contents: \u003ccode\u003enways-basic\u003c/code\u003e, \u003ccode\u003eopenacc\u003c/code\u003e, \u003ccode\u003eprofiling\u003c/code\u003e,\u003ccode\u003ecuda\u003c/code\u003e, \u003ccode\u003emultigpu\u003c/code\u003e. See example below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStep 1: clone the whole repository via \u003ccode\u003egit@github.com:mozhgan-kch/HPC_Bootcamp.git\u003c/code\u003e or \u003ccode\u003ehttps://github.com/mozhgan-kch/HPC_Bootcamp.git\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eStep 2: Navigate to the bootcamp folder via \u003ccode\u003ecd HPC_Bootcamp\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eStep 3: Run \u003ccode\u003ebash ./bootstrap.sh profiling\u003c/code\u003e , this example will isolate files required for the profiling material.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-cloning-the-repo-without-directories\" class=\"anchor\" aria-hidden=\"true\" href=\"#cloning-the-repo-without-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning the repo without directories\u003c/h3\u003e\n\u003cp\u003eYou can clone the repository and avoid filling in the working directory with the huge list of files by using the \u003ccode\u003e--no-checkout\u003c/code\u003e option as you clone. Try the below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --no-checkout git@github.com:mozhgan-kch/HPC_Bootcamp.git\ncd HPC_Bootcamp\ngit sparse-checkout init --cone\ngit checkout main\nbash ./bootstrap.sh profiling\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce done, navigate to \u003ccode\u003e_scripts\u003c/code\u003e via \u003ccode\u003ecd _scripts\u003c/code\u003e and build the container by following below steps.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-container-using-the-def-files-inside-the-_script-folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-container-using-the-def-files-inside-the-_script-folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container using the def files inside the \u003ccode\u003e_script\u003c/code\u003e folder\u003c/h3\u003e\n\u003cp\u003eTo build the singularity container, run:\n\u003ccode\u003esudo singularity build miniapp.simg {Name of the content}_Singularity\u003c/code\u003e , alternatively you can use \u003ccode\u003esingularity build --fakeroot miniapp.simg {Name of the content}_Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand copy the files to your local machine to make sure changes are stored locally:\n\u003ccode\u003esingularity run miniapp.simg cp -rT /labs ~/labs\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, run the container:\n\u003ccode\u003esingularity run --nv miniapp.simg jupyter-lab --notebook-dir=~/labs\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOnce inside the container, open the jupyter lab in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e, and start the lab by clicking on the \u003ccode\u003e_{Name of the content}.ipynb\u003c/code\u003e notebook. \u003ccode\u003e{Name of the content}\u003c/code\u003e can be \u003ccode\u003eprofiling\u003c/code\u003e. More alternatives will be added.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-container-using-the-def-files-inside-the-content-folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-container-using-the-def-files-inside-the-content-folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container using the def files inside the content folder\u003c/h3\u003e\n\u003cp\u003eAlternatively, you can build containers for each content by using the recipe inside of each content.\nExample : Build container for the \u003cem\u003e_profiler\u003c/em\u003e content. Navigate to \u003ccode\u003e_profiler\u003c/code\u003e directory and read the \u003ccode\u003eREADME.md\u003c/code\u003e file for more information.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cp\u003eDevelopment Branch\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1662909782.0 + "updated_at": 1623903591.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "1.3.1/Singularity", + "1.3.3/Singularity" ], - "full_name": "anastasiadoulab/machaon", + "full_name": "yh549848/singularity-rsem", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-machaon\" class=\"anchor\" aria-hidden=\"true\" href=\"#machaon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMachaon\u003c/h1\u003e\n\u003cbr\u003e\nThis repository contains an implementation for the method presented in the paper \"Identifying and \nprofiling structural similarities between Spike of SARS-CoV-2 and other viral or host proteins with \nMachaon\".\n\u003cp\u003ePlease consult this time-saving manual before you use Machaon. It contains an in-depth explanation\u003cbr\u003e\nabout installing, setting up and using this method.\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements\u003c/h2\u003e\n\u003cbr\u003e\n\u003cp\u003eThe target system for Machaon\u0027s development is Ubuntu 20.4. Machaon has limited functionality\u003cbr\u003e\non Windows and MacOS. Some post-processing modules utilize TM-Align and DSSP which are not\u003cbr\u003e\ncross-platform implementations. DSSP data might also be used for setting the targets of constrained\u003cbr\u003e\ncomparisons, which is Machaon\u0027s default behaviour.\u003c/p\u003e\n\u003cp\u003eThe recommended ways to use Machaon is either by working inside a Docker container or a Singularity\u003cbr\u003e\ncontainer or by working in an Ubuntu 20.4 environment with Anaconda (see instructions in the \u0027Installation\u0027\u003cbr\u003e\nsection below). On Windows, you could try WSL in order to get access to a UNIX environment (not tested):\u003cbr\u003e\n\u003ca href=\"https://docs.microsoft.com/en-us/windows/wsl/install\" rel=\"nofollow\"\u003ehttps://docs.microsoft.com/en-us/windows/wsl/install\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMachaon is an I/O (input/output) intensive implementation and the performance is depended on the\u003cbr\u003e\nstorage hardware and the storage optimizations of the host operating and file systems. For every\u003cbr\u003e\nPDB file that is analyzed, there is a corresponding set of serialized data objects in the form of\u003cbr\u003e\nbinary files (pickle Python package) which hold the necessary data for the calculation of each\u003cbr\u003e\nmetric. NVMe storage is highly recommended.\u003c/p\u003e\n\u003cp\u003eMachaon is a multi-core CPU application with moderate demands on RAM memory only for\u003cbr\u003e\npost-processing and target setup for constrained comparisons due to the required alignments\u003cbr\u003e\n(especially alignments in parallel).\u003c/p\u003e\n\u003cbr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-repository-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#repository-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepository contents\u003c/h2\u003e\n\u003cbr\u003e\n\u003cul\u003e\n\u003cli\u003eassess: this folder contains scripts for Machaon\u0027s benchmarking, evaluation and assessment\u003c/li\u003e\n\u003cli\u003econfig: configuration files\u003c/li\u003e\n\u003cli\u003edocs: It contains programming-related documentation and diagrams.\n\u003cul\u003e\n\u003cli\u003edocs/classes: Extensive API documentation for all the classes of this implementation.\u003cbr\u003e\nEach class has a dedicated HTML file with thorough description.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003esetup: Scripts for downloading and preparing some (optional) related data sources.\u003c/li\u003e\n\u003cli\u003esrc: source code\u003c/li\u003e\n\u003cli\u003etest: It contains an integrity test with testing data and expected outputs.\u003c/li\u003e\n\u003cli\u003edocker-compose.yml : A file used by Docker Compose tool.\u003c/li\u003e\n\u003cli\u003eDockerfile: A file with the commands needed to set up Machaon in a Docker container.\u003c/li\u003e\n\u003cli\u003eenvironment.yml: A file used by Anaconda Python package manager.\u003c/li\u003e\n\u003cli\u003eLICENSE.md: The license of this implementation.\u003c/li\u003e\n\u003cli\u003eREADME.md: Machaon\u0027s manual (the one you are reading).\u003c/li\u003e\n\u003cli\u003eSingularity: A file used to set up a Singularity container.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e \n\u003ch2\u003e\u003ca id=\"user-content-setup-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup instructions\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-local-data-sources\" class=\"anchor\" aria-hidden=\"true\" href=\"#local-data-sources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal data sources\u003c/h3\u003e\n\u003cbr\u003e\nEnrichment and meta-analysis stages rely on external data sources. There are fallbacks in place for \nsome of them (webservice calls) but it is strongly recommended utilizing the available static resources. \nThis will minimize network activity, greatly speed up the process and protect the respective third party \nweb services from burden. Be sure to have enough available disk space (at least 30GB) for the initial \ndownloads (at least 12GB after the preparation).\n\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: You can use the \u003cb\u003e\u0027noThirdPartyData\u0027\u003c/b\u003e flag in the configuration, ending up only with the comparison\u003cbr\u003e\nresults. This mode does not require the set up of local data sources or other external data access. The metrics\u003cbr\u003e\n\u003cb\u003edo not rely on external information \u003c/b\u003e apart from the PDB file. Therefore, you only need to collect a set of\u003cbr\u003e\nPDB files to compare with your PDB of choice . However, you will miss enrichment and gene ID-based filtering\u003cbr\u003e\nof the results along with the functionality of the evaluation, meta-analysis, presentation modules.\u003cbr\u003e\nAlso, you will not able to perform the domain scanning since it requires the residue positions of the domains\u003cbr\u003e\n(information found in UniProt data).\u003c/p\u003e\n\u003cp\u003eChoose a folder that will be the root data \u0026amp; cache folder of Machaon and \u003cb\u003ecopy\u003c/b\u003e there the .sh files located\u003cbr\u003e\nin the setup folder. You can use symbolic links if you need to have some resources in separate locations\u003cbr\u003e\n(\u003ca href=\"https://en.wikipedia.org/wiki/Symbolic_link\" rel=\"nofollow\"\u003ehttps://en.wikipedia.org/wiki/Symbolic_link\u003c/a\u003e). Make sure the scripts have adequate execution permissions:\u003cbr\u003e\n\u003ccode\u003echmod 770 *.sh\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-pdb-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#pdb-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePDB files:\u003c/h4\u003e\n\u003cp\u003eThere are two ways that you can obtain multiple PDB files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.rcsb.org/downloads\" rel=\"nofollow\"\u003ehttps://www.rcsb.org/downloads\u003c/a\u003e\u003cbr\u003e\nThe downloaded files need to be decompressed and renamed (execute in the downloaded files\u0027 folder):\u003cbr\u003e\n\u003ccode\u003els *.gz | parallel gunzip\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003efor f in *.ent; do mv -- \"$f\" \"${f%.ent}.pdb\"; done\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Unix or MacOS only) \u003ca href=\"https://www.rcsb.org/docs/programmatic-access/batch-downloads-with-shell-script\" rel=\"nofollow\"\u003ehttps://www.rcsb.org/docs/programmatic-access/batch-downloads-with-shell-script\u003c/a\u003e\u003cbr\u003e\nThe downloaded files need to be decompressed (execute in the downloaded files\u0027 folder):\u003cbr\u003e\n\u003ccode\u003els *.gz | parallel gunzip\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOf course, you can use RCSB search and retrieve relevant PDB IDs by a query of choice.\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: PDB files from AlphaFold\u0027s predictions are \u003cb\u003e fully \u003c/b\u003e supported. You can download them from here:\u003cbr\u003e\n\u003ca href=\"https://alphafold.ebi.ac.uk/download\" rel=\"nofollow\"\u003ehttps://alphafold.ebi.ac.uk/download\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can manage the files as below:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emkdir AF\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003etar -xvf UP000005640_9606_HUMAN_v3.tar -C AF\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecd AF\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003erm -rf *.cif.gz\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003els *.gz | parallel gunzip\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eImportant:\u003c/b\u003e Avoid underscores in custom PDB filenames. For example, in Ubuntu you can run:\u003cbr\u003e\n\u003ccode\u003erename.ul \u0027_\u0027 \u0027\u0027 *.pdb\u003c/code\u003e and remove an underscores from every filename in the folder.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-refseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#refseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRefSeq:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExecute \u003ccode\u003e./download_refseq_resources.sh\u003c/code\u003e\u003cbr\u003e\nIf there are any errors during the downloads, you could try to run the script a while\nlater (\u003ca href=\"https://www.biostars.org/p/493656\" rel=\"nofollow\"\u003ehttps://www.biostars.org/p/493656\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003eExecute \u003ccode\u003e./download_refseq_resources.sh\u003c/code\u003e again for a final verification of the\ndownloaded files\u0027 integrity and then execute:\u003cbr\u003e\n\u003ccode\u003e./prepare_refseq_resources.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-uniprot-mapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#uniprot-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUniprot mapping:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eIt is recommended to use a dedicated FTP transferring program than a browser for the following large\u003cbr\u003e\ndownloads (e.g. FileZilla: \u003ca href=\"https://filezilla-project.org/download.php\" rel=\"nofollow\"\u003ehttps://filezilla-project.org/download.php\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eVisit the following directory : \u003ca href=\"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping\" rel=\"nofollow\"\u003ehttps://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload the following files: idmapping_selected.tab.gz, idmapping.dat.gz (Be sure to have enough space for the downloads)\u003c/li\u003e\n\u003cli\u003eExecute \u003ccode\u003e./prepare_uniprot_resources.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation (Containers)\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h4\u003e\n\u003cp\u003eIf you are going to use Docker, you only need to specify your data storage in docker-compose.yml file:\u003cbr\u003e\n\u003ccode\u003e- MY_BIG_STORAGE_PATH:/opt/storage\u003c/code\u003e\u003cbr\u003e\n(replace MY_BIG_STORAGE_PATH with your path of choice)\u003c/p\u003e\n\u003cp\u003eand run the following command to build and launch a Machaon-ready container:\u003cbr\u003e\n\u003ccode\u003esudo docker-compose up -d\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can enter into the container and start working:\u003cbr\u003e\n\u003ccode\u003esudo docker exec -it \u0026lt;container\u0027s name\u0026gt; bash\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003econda activate machaon\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecd /opt/src\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003epython run.py -h\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe folder with the configurations (config) is the shared between the host system\u003cbr\u003e\nand container for ease of use (you can read and edit configuration files outside of\u003cbr\u003e\nthe container).\u003c/p\u003e\n\u003cp\u003eAlternatively, if you plan to run it in a Cloud VM instance, you need to modify the\u003cbr\u003e\nDocker configurations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edocker-compose.yml: Set your mounts accordingly (or remove the volume directive)\u003c/li\u003e\n\u003cli\u003eDockerfile: Add the following line before WORKDIR command:\u003cbr\u003e\n\u003ccode\u003eADD ./config /opt/machaon/config\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h4\u003e\n\u003cp\u003eThese are the instructions for creating a container with Singularity (\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://sylabs.io/docs/\u003c/a\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload the latest version from here: \u003ca href=\"https://github.com/sylabs/singularity/releases\"\u003ehttps://github.com/sylabs/singularity/releases\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExecute:\u003cbr\u003e\n\u003ccode\u003esingularity build --fakeroot machaon.sif Singularity\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esingularity run machaon.sif\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003econda activate machaon\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecd /opt/src\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003epython run.py -h\u003c/code\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-manual-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#manual-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eManual Installation\u003c/h3\u003e\n\u003cbr\u003e \nThis section is a walkthrough for manual installation (please also check Dockerfile, it contains all \nneeded commands but it is recommended to execute them separately). \n\u003ch4\u003e\u003ca id=\"user-content-modified-tm-align-compilation\" class=\"anchor\" aria-hidden=\"true\" href=\"#modified-tm-align-compilation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModified TM-Align compilation\u003c/h4\u003e\n\u003cp\u003eThis well-established method is used for 3D similarity computation by the evaluation module.\u003cbr\u003e\nMachaon can run without the presence of this executable but you will miss the 3D similarity\u003cbr\u003e\nevaluation of the final candidates in the Machaon\u0027s results.\u003c/p\u003e\n\u003cp\u003eAccording to the original documentation, TM-Align is compiled as:\u003cbr\u003e\n\u003ccode\u003eg++ -static -O3 -ffast-math -lm -o TMalign TMalign.cpp\u003c/code\u003e\u003cbr\u003e\n(You might need to install g++ first: \u003ccode\u003esudo apt-get install build-essential\u003c/code\u003e )\u003cbr\u003e\nMacOS users should omit \u0027-static\u0027 option.\nFor more, you can check: \u003ca href=\"https://zhanggroup.org/TM-align\" rel=\"nofollow\"\u003ehttps://zhanggroup.org/TM-align\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-dssp\" class=\"anchor\" aria-hidden=\"true\" href=\"#dssp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDSSP\u003c/h4\u003e\n\u003cp\u003eThis well-known method is used for protein secondary structure assignment, employed in constrained\u003cbr\u003e\nsearch mode and the Gene Ontology meta-analysis process of Machaon. Alternatively, you could use\u003cbr\u003e\nprotein or hydrophobicity-focused sequences that do not require this program otherwise Machaon\u003cbr\u003e\nwill use STRIDE instead (see next section).\u003c/p\u003e\n\u003cp\u003eBelow are the steps for the compilation of DSSP 4.0 in \u003cb\u003eUbuntu 20.4\u003c/b\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCMake:\u003cbr\u003e\n\u003ccode\u003esudo apt-get install cmake\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBoost:\u003cbr\u003e\n\u003ccode\u003esudo apt-get install libboost-all-dev\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor Ubuntu versions lower than 20.04, you need to install Boost from source if your latest version is lower than 1.70:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRemove previous Boost version:\u003cbr\u003e\n\u003ccode\u003eapt remove \u0027libboost.*-dev\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload and extract the latest version from: \u003ca href=\"https://www.boost.org/\" rel=\"nofollow\"\u003ehttps://www.boost.org/\u003c/a\u003e (greater than 1.70)\u003c/li\u003e\n\u003cli\u003eInstall:\u003cbr\u003e\n\u003ccode\u003echmod +x bootstrap.sh\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003e./boostrap.sh\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esudo ./b2 link=static install\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBZIP2:\u003cbr\u003e\n\u003ccode\u003esudo apt-get install libbz2-dev\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecifpp:\nMake sure you have cmake (\u003ccode\u003esudo apt install cmake \u003c/code\u003e) and follow the instructions in \u003ca href=\"https://github.com/PDB-REDO/libcifpp\"\u003ehttps://github.com/PDB-REDO/libcifpp\u003c/a\u003e\u003cbr\u003e\nYou might need also to install this before: \u003ca href=\"https://github.com/mhekkel/mrc\"\u003ehttps://github.com/mhekkel/mrc\u003c/a\u003e (\u003ca href=\"https://github.com/PDB-REDO/dssp/issues/4\"\u003ehttps://github.com/PDB-REDO/dssp/issues/4\u003c/a\u003e)\u003cbr\u003e\nFor Ubuntu 18.04 you also need to install these first of all:\u003cbr\u003e\n\u003ccode\u003esudo add-apt-repository ppa:ubuntu-toolchain-r/test\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esudo apt update\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esudo apt install gcc-9 g++-9\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eexport CC=/usr/bin/gcc-9\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eexport CXX=/usr/bin/g++-9\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDSSP: Please follow the instructions in \u003ca href=\"https://github.com/PDB-REDO/dssp\"\u003ehttps://github.com/PDB-REDO/dssp\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cb\u003eNote:\u003c/b\u003eThere are also other options to obtain DSSP files without setting up the program: \u003ca href=\"https://swift.cmbi.umcn.nl/gv/dssp/\" rel=\"nofollow\"\u003ehttps://swift.cmbi.umcn.nl/gv/dssp/\u003c/a\u003e\u003cbr\u003e\nIn that case, you should add them in a folder named \u0027dssp_cache\u0027 located in your specified root data \u0026amp; cache folder\u003cbr\u003e\n(\u0027rootDisk\u0027 parameter, more in \u0027Execution\u0027 section) .\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-stride\" class=\"anchor\" aria-hidden=\"true\" href=\"#stride\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSTRIDE\u003c/h4\u003e\n\u003cp\u003eSTRIDE is an established method for determining the protein secondary structure from PDB files.\nIt is used as a fallback solution for custom PDB files that do not fully follow the standard PDB\nformat and lack annotations. Please follow the instructions in \u003ca href=\"http://webclu.bio.wzw.tum.de/stride/\" rel=\"nofollow\"\u003ehttp://webclu.bio.wzw.tum.de/stride/\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-using-the-executables\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-the-executables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing the executables\u003c/h4\u003e\n\u003cp\u003eAfter the compilations, you have to copy the mkdssp, stride, TM-Align executables\u003cbr\u003e\ninto the directory of Machaon and give them the required execute permissions:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd machaon/src\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecp /home/\u0026lt;user\u0026gt;/.local/bin/mkdssp .\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecp /home/\u0026lt;user\u0026gt;/.local/bin/stride .\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003echmod 770 mkdssp \u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003echmod 770 stride \u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003echmod 770 TMalign \u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-required-system-libraries\" class=\"anchor\" aria-hidden=\"true\" href=\"#required-system-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired system libraries:\u003c/h4\u003e\n\u003cp\u003eYou need the poppler library in order to export the figures in the EPS format\nwith Python plotly library:\u003cbr\u003e\n\u003ccode\u003esudo apt-get install libpoppler-cpp-dev\u003c/code\u003e\nThis a graphics related library for Open3D:\n\u003ccode\u003esudo apt-get install libgl1-mesa-dev\u003c/code\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-python-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#python-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython environment:\u003c/h4\u003e\n\u003cp\u003eAn environment setup of Anaconda Python distribution is needed : \u003ca href=\"https://www.anaconda.com\" rel=\"nofollow\"\u003ehttps://www.anaconda.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis distribution allows easy setup of all the requisites for Machaon.\u003c/p\u003e\n\u003cp\u003eOnce you have an operational Anaconda-enabled terminal, move into the setup folder and execute\u003cbr\u003e\nthe following command to install all the required packages:\u003cbr\u003e\n\u003ccode\u003econda env create -f environment.yml\u003c/code\u003e\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-testing-your-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting your installation:\u003c/h3\u003e\n\u003cp\u003eRun the test script in the /test folder:\n\u003ccode\u003epython integrity_test.py\u003c/code\u003e\u003cbr\u003e\nIf there are no differences reported at the end, than your installation should be successful.\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-execution\" class=\"anchor\" aria-hidden=\"true\" href=\"#execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h2\u003e\n\u003cbr\u003e\n\u003cp\u003eAt first, you need to activate the previously installed environment in an Anaconda-enabled terminal:\u003cbr\u003e\n\u003ccode\u003econda activate machaon\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start:\u003c/h3\u003e\n\u003cp\u003eExecute the following script which is located in the src folder: \u003ccode\u003e run.py -h\u003c/code\u003e\u003cbr\u003e\nThis will display all the available options and their descriptions.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-batch-jobs-recommended\" class=\"anchor\" aria-hidden=\"true\" href=\"#batch-jobs-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch jobs (recommended):\u003c/h3\u003e\n\u003cp\u003eEdit \u003cb\u003econfig.yaml\u003c/b\u003e file in the src folder and run \u003cb\u003e batch_run.py\u003c/b\u003e. Below is an example entry with the default\u003cbr\u003e\nvalues. You could copy it and modify it according to your needs. Configurations with \"ignore : True\" field\u003cbr\u003e\nare ignored. You could also consult with the example configurations used for the Spike protein of SARS-CoV-2.\u003c/p\u003e\n\u003cpre\u003e - rootDisk: \"\" \n referencePDBID: \"\"\n overridePDBID: \"\"\n referenceChainID: \"\"\n referenceGeneID: \"\"\n referenceSequenceLength: 0\n comparisonMode: \"\"\n pdbDatasetPath: \"\"\n outputPath: \"\"\n excludedOrganisms: []\n excludedGeneNames: []\n excludedPDBIDs: []\n isReferenceViral: False\n GOProperty: \"\"\n GOTargetProperties: []\n GOSearch: \"\"\n GOAlignmentLevel: \"secondary\"\n noThirdPartyData: False\n pdbValidation: False\n GOAnalysisOnly: False \n\u003c/pre\u003e\n\u003cp\u003eAll the options are presented below:\u003c/p\u003e\n\u003cpre\u003e\u0027rootDisk\u0027: This will also be the caching location for the extracted features.\n\u0027referencePDBID\u0027: Choose the reference PDB IDs (1 search per reference)\n\u0027overridePDBID\u0027: Override the reference PDBID for Uniprot ID retrieval (for renamed reference PDB files, e.g. 6VXX_processed.pdb)\n\u0027referenceChainID\u0027: Choose the chain of the reference PDB\n\u0027referenceGeneID\u0027: Provide the gene id (Entrez) of the reference PDB\n\u0027referenceSequenceLength\u0027: Provide the protein sequence length of the reference protein\n\u0027comparisonMode\u0027: Choose \u0027whole\u0027, \u0027domain\u0027 or \u0027segment\u0027\n\u0027alignmentLevel\u0027: Choose \u0027primary\u0027, \u0027secondary\u0027, \u0027mixed\u0027, \u0027hydrophobicity\u0027. Default is \u0027mixed\u0027. (Only from segment scans)\n\u0027pdbDatasetPath\u0027: Relative path for PDB data folder\n\u0027outputPath\u0027: The location of the outputs (can be relative or full path)\n\u0027excludedOrganisms\u0027: Filtering out structures originating from the same organism as the reference one\n\u0027excludedGeneNames\u0027: Filtering out structures originating from the same gene as the reference one\n\u0027excludedPDBIDs\u0027: Exclude PDB IDs\n\u0027isReferenceViral\u0027: Meta-analysis skips the search in viral genome data for the reference, if it is not a viral protein\n\u0027GOProperty\u0027: Choose a property type for analysis: \u0027biologicalProcess\u0027, \u0027molecularFunction\u0027, \u0027cellularComponent\u0027\n\u0027GOTargetProperties\u0027: Choose properties for analysis\n\u0027GOSearch\u0027: Choose a term to be searched in all available GO Terms belonging to the results e.g. \u0027ubiquit\u0027 (could be a stem of a word)\n\u0027GOAlignmentLevel\u0027: Choose target alignment level : [\u0027primary\u0027, \u0027secondary\u0027, \u0027mixed\u0027, \u0027hydrophobicity\u0027. Default is \u0027secondary\u0027]\n\u0027noThirdPartyData\u0027: Do not use external local or online resources. PDB data only.\n\u0027GOAnalysisOnly\u0027: Perform only GO Meta-analysis (for completed searches).\n\u0027pdbValidation\u0027: Validation for PDB files. Every file assessed as invalid is skipped from the search (very strict and slow). \n\u0027ignore\u0027: If set to True, the configuration will be ignored. Useful for storing previous job details.\n\u0027*whatever*\u0027: You can include fields of your own, like tags or notes (e.g. \u0027date\u0027 : 14-4-2003). These are not considered by the program. \n\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-constrained-mode-segments\" class=\"anchor\" aria-hidden=\"true\" href=\"#constrained-mode-segments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConstrained mode (segments):\u003c/h4\u003e\n\u003cp\u003eConstrained search on segments requires also preset about the reference segment. This is set in\u003cbr\u003e\n\u003cb\u003esegments.yaml\u003c/b\u003e file in the src folder. Below is an empty template entry. You could also consult with\u003cbr\u003e\nthe example segment definitions used for the Spike protein of SARS-CoV-2.\u003c/p\u003e\n\u003cpre\u003e- referencePDBChain: \"\"\n residues: []\n residueRanges: \"\"\n known: False\n\u003c/pre\u003e\n\u003cp\u003eAll the options are presented below:\u003c/p\u003e\n\u003cpre\u003e\u0027referencePDBChain\u0027: The reference PDB ID and chain ID separated by a dot, \u0026lt;PDB ID\u0026gt;.\u0026lt;CHAIN ID\u0026gt; e.g. \"\"6VXX.A\"\n\u0027residues\u0027: List of residue positions (one-based indexing), e.g. [1, 2, 3, 4, 5]\n\u0027residueRanges\u0027: Range definitions separated by comma, e.g. \u00271-50,70-78\u0027\n\u0027known\u0027: Select True if the segment belongs to a known site like a binding site (considered by GO Meta-analysis module).\n\u0027ignore\u0027: If set to True, the configuration will be ignored. Useful for storing past segment presets.\n\u0027*whatever*\u0027: You can include fields of your own, like tags or notes (e.g. \u0027doi\u0027 : \u0027123.3456/1234.123\u0027). These are not considered by the program. \n\nNote: \u0027residues\u0027 and \u0027residueRanges\u0027 definitions are combined, e.g. [12, 15, 59] \nand \u002713-40, 47-52\u0027 would result to the selection of residue positions from 12 to 40, \nfrom 47 to 52 and 59 (duplicate definitions are removed).\n\u003c/pre\u003e\u003cbr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data-directory-structures\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-directory-structures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData directory structures\u003c/h2\u003e\n\u003cbr\u003e \n\u003ch4\u003e\u003ca id=\"user-content-output-folder-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-folder-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput folder structure\u003c/h4\u003e\n\u003cpre\u003e (a user-specified output folder)\n |\n |__ (a user-specified top directory name)\n |\n |__metrics/ (directory for the computed metrics for all structures in the dataset)\n |\n |__candidates/ (directory for the selected final set of candidate entries, \n | the final report is saved here [HTML file])\n |\n |__plots/ (directory for plots regarding the final set)\n |\n |__go/ (directory for GO meta-analysis, mini reports and related visualizations)\n\u003c/pre\u003e\n\u003cp\u003e\u003cb\u003eNote for constrained mode search on segments\u003c/b\u003e:The corresponding output files contain a suffix\u003cbr\u003e\n\"site\u0026lt;segment index\u0026gt;\" that signify the results for a particular segment. The index comes from the\u003cbr\u003e\nconfiguration order. In the \"metrics\" folder, there is a \"*_site\u0026lt;segment index\u0026gt;-parts.csv\" file that contains\u003cbr\u003e\nthe contiguous parts of the segment as determined by the method.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-root-folder-source-data--cache-full-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#root-folder-source-data--cache-full-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRoot folder (source data \u0026amp; cache), full structure\u003c/h4\u003e\n\u003cpre\u003e (a user-specified \u003cb\u003eroot\u003c/b\u003e folder)\n |\n |--DATA_\u0026lt;PDB directory name\u0026gt;_\u0026lt;whole or domain\u0026gt;/ \n | (directory for storing the extracted features of a PDB directory)\n |\n |--domains/ (directory for caching domain information by UniProt online requests)\n |\n |--dssp_cache/ (directory for caching DSSP results)\n |\n |--enrichment/ (directory for caching data enrichment of PDB chain entries)\n |\n |__entrez/ (cache directory for NCBI Entrez online requests)\n |\n |--pdbinfo/ (directory for caching extracted PDB meta-data)\n |\n |--prot_sec/ (directory for caching PDB sequence/secondary structure data)\n |\n |__refseq/ (RefSeq resources directory)\n |\n |--rcsbenrich/ (cache directory for RCSB enrichment data) \n |\n |--(user created PDB folders, \u003cb\u003eeach folder corresponds to a target dataset for a search\u003c/b\u003e)\n |\n |__idmapping_selected.tab.gz (UniProt idmapping resources)\n\u003c/pre\u003e\n\u003cp\u003eThere is also a cache file that is generated besides the scripts in src folder (go_cache.csv) that holds\u003cbr\u003e\nGene Ontology data.\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-format\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-format\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput format\u003c/h2\u003e\n\u003cbr\u003e\nThe outputs are human interpretable CSV files with headers:\n\u003cul\u003e\n\u003cli\u003emetrics directory has comma separated CSV files\u003c/li\u003e\n\u003cli\u003ecandidates directory has tab separated CSV files\u003c/li\u003e\n\u003cli\u003eoutputs of constrained searches include columns with serialized list contents which can be parsed with eval()\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-special-cases\" class=\"anchor\" aria-hidden=\"true\" href=\"#special-cases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecial Cases\u003c/h2\u003e\n\u003cbr\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIf you want to compare a polymer as a whole structure you could use pdb-tools :\n\u003ca href=\"https://github.com/haddocking/pdb-tools\"\u003ehttps://github.com/haddocking/pdb-tools\u003c/a\u003e\u003cbr\u003e\nand combine multiple chains to one. You should remove any pre-computed features of the old PDB\u003cbr\u003e\n(*_angles.pkl, *_distances.pkl, *_triangles.pkl) and the original PDB from the dataset (you could\u003cbr\u003e\nkeep these files in a separate location as back up). You need to decide which original \u0026lt;PDB ID\u0026gt; and\u003cbr\u003e\n\u0026lt;PDB chain ID\u0026gt; you will use as a reference for the third-party resources.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn case you encounter warnings about empty chain identifiers or missing chains, use pdb_chain\u003cbr\u003e\ncommand from pdb-tools: \u003ccode\u003epdb_chain -A no_chains.pdb \u0026gt; corrected.pdb\u003c/code\u003e to put a dummy identifier\nto a problematic PDB file.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSecondary structure data cannot be extracted from PDBs that lack experimental information so you may have to\nchange the target alignment level to primary or hydrophobicity (recommended) for constrained mode search on\nsegments (default is \u0027mixed\u0027) or GO metanalysis (default is 2D).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-trivia\" class=\"anchor\" aria-hidden=\"true\" href=\"#trivia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrivia\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://en.wikipedia.org/wiki/Machaon_(mythology)\" rel=\"nofollow\"\u003ehttps://en.wikipedia.org/wiki/Machaon_(mythology)\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/43cc1554b9e51a28dfa82673f6a9629d4b3f4151419378b68631040a1a5f52a2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg 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href=\"https://camo.githubusercontent.com/72066080adaa298a3f65d9ad3d27681498685739defe4d4061e06d30f8bf5277/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72066080adaa298a3f65d9ad3d27681498685739defe4d4061e06d30f8bf5277/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bamtools\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bamtools\" class=\"anchor\" href=\"#singularity-bamtools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bamtools\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/pezmaster31/bamtools\"\u003ebamtools\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebamtools\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bamtools/2.5.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bamtools\u003c/code\u003e as \u003ccode\u003e2.5.1\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1655488848.0 + "updated_at": 1623772549.0 }, { "data_format": 2, - "description": "blastfoam-CI-docker", + "description": "FLAC (/fl\u00e6k/; Free Lossless Audio Codec) is an audio coding format for lossless compression of digital audio.", "filenames": [ - "Singularity-openfoam.def" + "1.3.3/Singularity" ], - "full_name": "jiaqiwang969/blastfoam-project", + "full_name": "pscedu/singularity-flac", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-blastfoam-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#blastfoam-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eblastfoam-project\u003c/h1\u003e\n\u003cp\u003eAim: High resolution fvm simulation using \u003ca href=\"https://github.com/synthetik-technologies/blastfoam\"\u003eblastfoam\u003c/a\u003e scheme\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emv Dockerfile.step01 Dockerfile\ndocker build jiaqiknight/openfoam-blastfoam:v1 .\nmv Dockerfile.step02 Dockerfile\ndocker build jiaqiknight/openfoam-blastfoam:v2 .\nsingularity build openfoam-blastfoam-v2012.sif Singularity-openfoam.def\nsingularity shell openfoam-blastfoam-v2012.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-action-to-dockerhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-action-to-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Action to dockerhub\u003c/h3\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-flac/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-flac/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/37fadb899e2280d332672dd4ff8c55c77b6d1da4314ad56fb36c15142413fbda/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666c6163\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37fadb899e2280d332672dd4ff8c55c77b6d1da4314ad56fb36c15142413fbda/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666c6163\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-flac\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5ccca52861f2fb4e7486645f35b923f2cbc790f1e7ed709d7422b0c9f2dc19d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666c6163\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ccca52861f2fb4e7486645f35b923f2cbc790f1e7ed709d7422b0c9f2dc19d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666c6163\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-flac\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e8f82abbc9bacec399c48512a0390d8b4d29091355a9ce463d889ecb16cd4775/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666c6163\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8f82abbc9bacec399c48512a0390d8b4d29091355a9ce463d889ecb16cd4775/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666c6163\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-flac\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5325f2a609a18c54057940e4962347ba4f1beeef88a23a92c7149e8016cf4e13/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666c6163\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5325f2a609a18c54057940e4962347ba4f1beeef88a23a92c7149e8016cf4e13/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666c6163\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-flac\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-flac\" class=\"anchor\" href=\"#singularity-flac\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-flac\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/flac\"\u003eflac\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-about-this-repository\" class=\"anchor\" href=\"#about-this-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout this repository\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/4d9e61b063851d778ce9938e3f7bb848d158ebf589dbcd68bf21c5d5eeef6d40/68747470733a2f2f6d65646961322e67697068792e636f6d2f6d656469612f31334867774773584630616947592f67697068792e6769663f6369643d6563663035653437396d61316e736b74386d786278726c323076377375656868343931687532306b6973786878636265267269643d67697068792e6769662663743d67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4d9e61b063851d778ce9938e3f7bb848d158ebf589dbcd68bf21c5d5eeef6d40/68747470733a2f2f6d65646961322e67697068792e636f6d2f6d656469612f31334867774773584630616947592f67697068792e6769663f6369643d6563663035653437396d61316e736b74386d786278726c323076377375656868343931687532306b6973786878636265267269643d67697068792e6769662663743d67\" alt=\"DANGER\" data-canonical-src=\"https://media2.giphy.com/media/13HgwGsXF0aiGY/giphy.gif?cid=ecf05e479ma1nskt8mxbxrl20v7suehh491hu20kisxhxcbe\u0026amp;rid=giphy.gif\u0026amp;ct=g\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe purpose of this repository is to highlight how to deploy a Singularity and Spack together.\u003c/li\u003e\n\u003cli\u003eAt this moment, the workflow is expected to fail as we have not found a good solution to deploying the images (yet).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCOMMENT: \u003cstrong\u003eDo not deploy on any system.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the Perl scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/flac/1.3.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/flac\u003c/code\u003e as \u003ccode\u003e1.3.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1655572084.0 + "subscribers_count": 2, + "topics": [ + "utilities", + "singularity" + ], + "updated_at": 1628186027.0 }, { "data_format": 2, - "description": null, + "description": "QSYM - Concolic Execution Engine (https://github.com/sslab-gatech/qsym)", "filenames": [ - "Singularity" + "Singularity.1604", + "Singularity.1804" ], - "full_name": "kirsho/conda2sing", - "latest_release": null, + "full_name": "shub-fuzz/qsym", + "latest_release": "0.0.2", + "readme": "\u003cp\u003eSingularity Image for QSYM (\u003ca href=\"https://github.com/sslab-gatech/qsym\"\u003ehttps://github.com/sslab-gatech/qsym\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/qsym/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/qsym/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3625\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eQSYM - Concolic Execution Engine (\u003ca href=\"https://github.com/sslab-gatech/qsym\"\u003ehttps://github.com/sslab-gatech/qsym\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eusage:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name qsym.sif https://github.com/shub-fuzz/qsym/releases/download/0.0.2/shub-fuzz-qsym.1604.sif\n\nsingularity shell qsym.sif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1656054479.0 - }, - { - "data_format": 2, - "description": "Spatially explicit individual based model that simulates Coffee Leaf Rust epidemics on a coffee farm", - "filenames": [ - "Singularity.def" - ], - "full_name": "comses-education/spatialrust-model", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-spatialrust-coffee-leaf-rust-epidemic-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#spatialrust-coffee-leaf-rust-epidemic-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpatialRust: Coffee Leaf Rust Epidemic Model\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/06a717d034624fa1ef05f60d027c62477e5fb10c3803b2e488c18839125fa828/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538422532302532302545322539372538422532302532302545322539372538422d6f72616e6765\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B%20%20%E2%97%8B%20%20%E2%97%8B-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/comses-education/spatialrust-model/actions/workflows/docker-image.yml\"\u003e\u003cimg src=\"https://github.com/comses-education/spatialrust-model/actions/workflows/docker-image.yml/badge.svg\" alt=\"Docker Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/comses-education/spatialrust-model/actions/workflows/singularity-image.yml\"\u003e\u003cimg src=\"https://github.com/comses-education/spatialrust-model/actions/workflows/singularity-image.yml/badge.svg\" alt=\"Singularity Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSpatialRust is an individual based model that simulates Coffee Leaf Rust epidemics within a coffee farm.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installing-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling dependencies\u003c/h3\u003e\n\u003cp\u003eMove to this project\u0027s directory and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia install.jl\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-the-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the model\u003c/h3\u003e\n\u003cp\u003eThere are two script files available. You can run a single simulation using a fixed parameter set using \u003ccode\u003escripts/OneRun.jl\u003c/code\u003e as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia scripts/OneRun.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eResults from this single run will be saved in a \u003ccode\u003eresults\u003c/code\u003e folder as \u003ccode\u003esinglerun.csv\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe second option, \u003ccode\u003escripts/ParameterRuns.jl\u003c/code\u003e, lets you run a parameter exploration experiment. The default setup of this experiment will run 2700 simulations. To modify the parameter values to be evaluated or the replicates for each combination, open \u003ccode\u003escripts/ParameterRuns.jl\u003c/code\u003e and edit lines 11 to 14. Like the first option, you can run the script from bash:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia scripts/ParameterRuns.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eResults from this experiment will be saved in a \u003ccode\u003eresults\u003c/code\u003e folder as \u003ccode\u003eparameterexp.csv\u003c/code\u003e. Both scripts take care of creating the \u003ccode\u003eresults\u003c/code\u003e folder if it has not been created yet.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-on-open-science-grid\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-open-science-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Open Science Grid\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eEstablish an account on Open Science Grid\n\u003ca href=\"https://osg-htc.org/research-facilitation/accounts-and-projects/general/index.html\" rel=\"nofollow\"\u003ehttps://osg-htc.org/research-facilitation/accounts-and-projects/general/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a host alias for your OSG account (\u003ca href=\"https://github.com/comses-education/fair-osg-template#set-up-your-user-account-on-the-open-science-grid\"\u003ehttps://github.com/comses-education/fair-osg-template#set-up-your-user-account-on-the-open-science-grid\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eBuild a singularity image and deploy it to your OSG \u003ccode\u003e/public/\u0026lt;username\u0026gt;\u003c/code\u003e directory via \u003ccode\u003e$ make OSG_USERNAME=\u0026lt;your-osg-username\u0026gt; deploy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003essh into the OSG login node, cd into the \u003ccode\u003espatialrust\u003c/code\u003e directory and submit the generated \u003ccode\u003espatialrust.submit\u003c/code\u003e via \u003ccode\u003e$ condor_submit spatialrust.submit\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ethis runs the ParameterRuns.jl on OSG and should drop off a \u003ccode\u003eresults.zip\u003c/code\u003e file with the data in the same directory you submitted the job script.\u003c/li\u003e\n\u003c/ol\u003e\n", - "stargazers_count": 0, - "subscribers_count": 5, - "topics": [ - "agent-based-model", - "computational-model", - "julia", - "simulation" - ], - "updated_at": 1655789345.0 + "updated_at": 1623682731.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image for Angora (https://github.com/AngoraFuzzer/Angora)", "filenames": [ - "Singularity" + "Singularity.1604", + "Singularity.1804" ], - "full_name": "touala/MUMmer", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mummer\" class=\"anchor\" aria-hidden=\"true\" href=\"#mummer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMUMmer\u003c/h1\u003e\n\u003cp\u003eAdapted from \u003ca href=\"https://forgemia.inra.fr/gafl/singularity/mummer/\" rel=\"nofollow\"\u003ehttps://forgemia.inra.fr/gafl/singularity/mummer/\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "shub-fuzz/angora", + "latest_release": "0.0.2", + "readme": "\u003cp\u003eSingularity image for Angora (\u003ca href=\"https://github.com/AngoraFuzzer/Angora\"\u003ehttps://github.com/AngoraFuzzer/Angora\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/angora/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/angora/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3645\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name angora.sif https://github.com/shub-fuzz/angora/releases/download/0.0.2/shub-fuzz-angora.1604.sif\n\nsingularity shell angora.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003einteractive session:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell angora.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003estart fuzzing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec angora.sif /start_fuzzing [[ -n \u0026lt;# instances\u0026gt; ] -t ] \u0026lt;target_path\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1656321189.0 + "updated_at": 1623682691.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image for Ankou (https://github.com/SoftSec-KAIST/Ankou)", "filenames": [ - "Singularity" + "Singularity.1604" ], - "full_name": "VUIIS/examcardtotxt", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-examcard-conversion-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#examcard-conversion-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamcard Conversion Pipeline\u003c/h1\u003e\n\u003cp\u003eThis spider converts Philips examcards from DICOM format to PDF, HTML, and TXT formats. Special thanks goes to Sha Zhao from Manchester University.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h2\u003e\n\u003cp\u003eExamcard (.dcm)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs:\u003c/h2\u003e\n\u003cp\u003eExamcard (.pdf)\nExamcard (.html)\nExamcard (.txt)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-version-2212021\" class=\"anchor\" aria-hidden=\"true\" href=\"#version-2212021\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion 22.1.2021\u003c/h2\u003e\n", + "full_name": "shub-fuzz/ankou", + "latest_release": "0.0.2", + "readme": "\u003cp\u003eSingularity image for Ankou (\u003ca href=\"https://github.com/SoftSec-KAIST/Ankou\"\u003ehttps://github.com/SoftSec-KAIST/Ankou\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/ankou/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/ankou/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4173\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name ankou.sif https://github.com/shub-fuzz/ankou/releases/download/0.0.2/shub-fuzz-ankou.1604.sif\n\nsingularity shell ankou.sif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1655221055.0 + "updated_at": 1623682696.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image for Eclipser (https://github.com/SoftSec-KAIST/Eclipser)", "filenames": [ - "Singularity" + "Singularity.1604" ], - "full_name": "kirsho/yml2sing", - "latest_release": null, + "full_name": "shub-fuzz/eclipser", + "latest_release": "0.0.2", + "readme": "\u003cp\u003eSingularity image for Eclipser (\u003ca href=\"https://github.com/SoftSec-KAIST/Eclipser\"\u003ehttps://github.com/SoftSec-KAIST/Eclipser\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/eclipser/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/eclipser/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name eclipser.sif https://github.com/shub-fuzz/eclipser/releases/download/0.0.2/shub-fuzz-eclipser.1604.sif\n\nsingularity shell eclipser.sif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1655971591.0 + "updated_at": 1623682705.0 }, { "data_format": 2, - "description": null, + "description": "Singularity Image for AFL (https://github.com/google/AFL)", "filenames": [ - "Singularity.v1" + "Singularity.i386", + "Singularity.1604", + "Singularity.1804" ], - "full_name": "cschu/duk_singularity", - "latest_release": null, + "full_name": "shub-fuzz/afl", + "latest_release": "0.0.2", + "readme": "\u003cp\u003eSingularity Image for AFL (\u003ca href=\"https://github.com/google/AFL\"\u003ehttps://github.com/google/AFL\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/afl/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/afl/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name afl.sif https://github.com/shub-fuzz/afl/releases/download/0.0.2/shub-fuzz-afl.1604.sif\n\nsingularity shell afl.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epull Ubuntu 18.04 container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name afl.1804.sif https://github.com/shub-fuzz/afl/releases/download/0.0.2/shub-fuzz-afl.1804.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epull Ubuntu 16.04 i386 container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name afl_i386.sif https://github.com/shub-fuzz/afl/releases/download/0.0.2/shub-fuzz-afl.i386.sif\n\nsingularity pull --name afl_i386.sif shub://shub-fuzz/afl:i386\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1656415847.0 + "updated_at": 1623682579.0 }, { "data_format": 2, - "description": "Dockerfile for an image containing Conda, open-ssh and java-ready for installing IBM products", + "description": "Singularity image for honggfuzz (https://github.com/google/honggfuzz)", "filenames": [ - "Singularity" + "Singularity.i386", + "Singularity.1604", + "Singularity.1804", + "v21/Singularity.v21" ], - "full_name": "cfrioux/docker_conda_ssh", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker_conda_ssh\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker_conda_ssh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker_conda_ssh\u003c/h1\u003e\n\u003cp\u003eDockerfile for an image containing Conda, open-ssh and java-ready for installing IBM products\u003c/p\u003e\n", + "full_name": "shub-fuzz/honggfuzz", + "latest_release": "0.0.2", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/honggfuzz/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/honggfuzz/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3641\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity image for honggfuzz (\u003ca href=\"https://github.com/google/honggfuzz\"\u003ehttps://github.com/google/honggfuzz\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eusage:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name honggfuzz.sif https://github.com/shub-fuzz/honggfuzz/releases/download/0.0.2/shub-fuzz-honggfuzz.1604.sif\n\nsingularity shell honggfuzz.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003epull Ubuntu 18.04 container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name honggfuzz.1804.sif https://github.com/shub-fuzz/honggfuzz/releases/download/0.0.2/shub-fuzz-honggfuzz.1804.sif\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1656509993.0 + "updated_at": 1623682711.0 }, { "data_format": 2, "description": null, "filenames": [ - "planner/symk/Singularity", - "planner/symk/misc/releases/19.06/Singularity.19.06", - "planner/symk/misc/releases/19.12/Singularity.19.12", - "planner/symk/misc/releases/latest/Singularity" + "Singularity.openrefine" ], - "full_name": "zihangs/GRACE", + "full_name": "ternaustralia/coesra-singularity-openrefine", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-grace\" class=\"anchor\" aria-hidden=\"true\" href=\"#grace\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGRACE\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Environment\u003c/h3\u003e\n\u003cp\u003eThe docker image can be found \u003ca href=\"https://hub.docker.com/r/suzihang/grace\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003edocker run -it -v PathToGRACE:/mnt suzihang/grace /bin/bash\u003c/p\u003e\n\u003cp\u003eThe container should contain all dependency libraries (you can install other tools into the container). Then, build the planner with all the required dependencies in the container.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coesra-singularity-openrefine\" class=\"anchor\" href=\"#coesra-singularity-openrefine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-openrefine\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1659585039.0 + "subscribers_count": 2, + "topics": [ + "coesra" + ], + "updated_at": 1610426463.0 }, { "data_format": 2, - "description": "A singularity container for `fastqsplit`: https://github.com/supernifty/fastqsplit", + "description": "HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes as well as to a single reference genome. ", "filenames": [ - "Singularity.fastqsplit" + "2.2.1/Singularity" ], - "full_name": "mjakobs/fastqsplit_singularity", - "latest_release": "v1.0.3", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fastqsplit-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#fastqsplit-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFastqSplit Singularity Container\u003c/h1\u003e\n\u003cp\u003eA Singularity container for \u003ccode\u003efastqsplit\u003c/code\u003e by \u003ca href=\"https://github.com/supernifty/fastqsplit\"\u003ehttps://github.com/supernifty/fastqsplit\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eBased on a template by \u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instructions-for-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#instructions-for-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions for use\u003c/h2\u003e\n\u003cp\u003eTo pull this singularity container please run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/mjakobs/fastqsplit_singularity/releases/download/v1.0.2/mjakobs-fastqsplit_singularity.fastqsplit.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "pscedu/singularity-hisat2", + "latest_release": "v2.2.1", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ea61f9228ca14a66e58889a560447e0b7c8ba73ddbaa594055242ace96eb0a84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ea61f9228ca14a66e58889a560447e0b7c8ba73ddbaa594055242ace96eb0a84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4796300b08f76b423ee0574c15e8bd8ad15b0a389a36fd3f58549b8bb5df8690/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4796300b08f76b423ee0574c15e8bd8ad15b0a389a36fd3f58549b8bb5df8690/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/25eeccddaeb5bf9a3f7053b646f9b7bf540d33b801c371db1850d745da46fc95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/25eeccddaeb5bf9a3f7053b646f9b7bf540d33b801c371db1850d745da46fc95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f14a5cb988478f36746bb17a364f669ee4d02a32a6f6fddfbccaff9dc50a8379/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f14a5cb988478f36746bb17a364f669ee4d02a32a6f6fddfbccaff9dc50a8379/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hisat\" class=\"anchor\" href=\"#singularity-hisat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hisat\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://daehwankimlab.github.io/hisat2/\" rel=\"nofollow\"\u003ehisat2\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts \u003ccode\u003ehisat2\u003c/code\u003e, \u003ccode\u003ehisat2-build\u003c/code\u003e and \u003ccode\u003ehisat2-inspect\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hisat2/2.2.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hisat2\u003c/code\u003e as \u003ccode\u003e2.2.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-singularity-definition-file\" class=\"anchor\" href=\"#building-the-image-using-the-singularity-definition-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the Singularity definition file\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1652199670.0 + "topics": [ + "bioinformatics", + "singularity" + ], + "updated_at": 1629078604.0 }, { "data_format": 2, - "description": "FAIR+ template repository with support and scaffolding for Docker, Singularity, and the Open Science Grid", + "description": "bowtie2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences.", "filenames": [ - "Singularity.def" + "2.4.4/Singularity", + "2.2.5/Singularity", + "2.4.1/Singularity", + "2.4.2/Singularity" ], - "full_name": "comses-education/fair-osg-template", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fair-osg-template\" class=\"anchor\" aria-hidden=\"true\" href=\"#fair-osg-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efair-osg-template\u003c/h1\u003e\n\u003cp\u003eThis template repository provides scaffolding and support for adopting the \u003ca href=\"https://doi.org/10.15497/RDA00068\" rel=\"nofollow\"\u003eFAIR4RS Principles\u003c/a\u003e and containerization support for \u003ca href=\"https://docs.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, and the \u003ca href=\"https://opensciencegrid.org/\" rel=\"nofollow\"\u003eOpen Science Grid (OSG)\u003c/a\u003e. A basic Makefile is included to be customized with basic \u003ccode\u003ebuild | deploy | clean\u003c/code\u003e targets to build container images in Docker and Singularity and copy the generated Singularity image and model files to an OSG login node.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-fair4rs-principles\" class=\"anchor\" aria-hidden=\"true\" href=\"#fair4rs-principles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAIR4RS Principles\u003c/h2\u003e\n\u003cp\u003eMore details at \u003ca href=\"https://github.com/comses-education/fair-osg-template/wiki/FAIR-Principles-for-Research-Software\"\u003ethis template repository\u0027s wiki\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003cstrong\u003eFindable\u003c/strong\u003e: create a persistent identifier for each released / published version of the software\u003c/li\u003e\n\u003cli\u003e[ ] \u003cstrong\u003eAccessible\u003c/strong\u003e: make your software open source (good start, using this!), ensure that it is well documented with descriptive metadata and narrative documentation, and make sure that this metadata remains accessible even if the software is not\u003c/li\u003e\n\u003cli\u003e[ ] \u003cstrong\u003eInteroperable\u003c/strong\u003e: your software should read, write, and exchange data using domain-relevant \u003cem\u003eopen\u003c/em\u003e community standards (e.g., netCDF, HDF, domain-specific controlled vocabularies or ontologies, etc.)*\u003c/li\u003e\n\u003cli\u003e[ ] \u003cstrong\u003eReusable\u003c/strong\u003e: Software can be executed and understood, modified, built upon, or incorporated into other software - a clear and accessible license, detailed provenance metadata, qualified persistent references to other software dependencies, domain-relevant community standards*\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] add narrative documentation in durable text formats (e.g., PDF with no special extensions, .odt OpenOffice Document file, Markdown / plaintext) about your computational model ideally with visual diagrams, flowcharts, etc., that describe expected inputs, outputs, assumptions, and consider adhering to a structured, domain-specific protocols like the \u003ca href=\"https://www.jasss.org/23/2/7.html\" rel=\"nofollow\"\u003eODD Protocol for Describing Agent-Based and other Simulation Models\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] include a README.md with a quick start for new users that addresses the following basic concerns:\u003c/li\u003e\n\u003cli\u003e[ ] What assumptions if any are embedded in the model?\u003c/li\u003e\n\u003cli\u003e[ ] Is it possible to change or extend the model?\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-containerization-and-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#containerization-and-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerization and Scripts\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] specify pinned software and system dependencies to be installed in Docker and Singularity\u003c/li\u003e\n\u003cli\u003e[ ] identify an appropriate base image. You can use base images prefixed with \u003ccode\u003eosg-\u003c/code\u003e for common platforms\nlike NetLogo, Julia, Python, and R at \u003ca href=\"https://hub.docker.com/u/comses\" rel=\"nofollow\"\u003ehttps://hub.docker.com/u/comses\u003c/a\u003e or create your own based on an OSG blessed\nimage (e.g., \u003ca href=\"https://github.com/opensciencegrid/osgvo-ubuntu-20.04\"\u003ehttps://github.com/opensciencegrid/osgvo-ubuntu-20.04\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] customize job-wrapper.sh\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-this-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-this-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run this model\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] What does this model do?\u003c/li\u003e\n\u003cli\u003e[ ] How do I run it?\u003c/li\u003e\n\u003cli\u003e[ ] What are some example inputs? What are the expected outputs for those example inputs? Where do they live?\u003c/li\u003e\n\u003cli\u003e[ ] How do I analyze or understand the outputs?\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-the-open-science-grid\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-the-open-science-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on the Open Science Grid\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-set-up-your-user-account-on-the-open-science-grid\" class=\"anchor\" aria-hidden=\"true\" href=\"#set-up-your-user-account-on-the-open-science-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet up your user account on the Open Science Grid\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://osg-htc.org/research-facilitation/accounts-and-projects/general/index.html\" rel=\"nofollow\"\u003ehttps://osg-htc.org/research-facilitation/accounts-and-projects/general/index.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou must have already gone through the OSG facilitation process with access to an Open Science Grid login node before\n\u003ccode\u003e% make deploy\u003c/code\u003e will work and you should create an alias in your \u003ccode\u003e.ssh/config\u003c/code\u003e that assigns the name \u003ccode\u003eosg\u003c/code\u003e to your OSG\nlogin node.\u003c/p\u003e\n\u003cp\u003eFor example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eHost osg\n HostName login02.osgconnect.net\n User \u0026lt;your-assigned-osg-username\u0026gt;\n IdentityFile ~/.ssh/a-private-ssh-key that you generated and added to your OSG profile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information on connecting to OSG and generating SSH keys, please see\n\u003ca href=\"https://support.opensciencegrid.org/support/solutions/articles/12000027675-generate-ssh-keys-and-activate-your-osg-login\" rel=\"nofollow\"\u003ehttps://support.opensciencegrid.org/support/solutions/articles/12000027675-generate-ssh-keys-and-activate-your-osg-login\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-customize-entry-point-scripts-and-model-metadata\" class=\"anchor\" aria-hidden=\"true\" href=\"#customize-entry-point-scripts-and-model-metadata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustomize entry point scripts and model metadata\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# user to connect to OSG as\nOSG_USERNAME := ${USER}\n# name of this computational model, used as the namespace (for singularity, Docker, and as a folder to keep things\n# organized on the OSG filesystem login node). recommend that you use all lowercase alphanumeric with - or _ to\n# separate words, e.g., chime-abm or spatial-rust-model\nMODEL_NAME := ${OSG_MODEL_NAME}\n# the directory (in the container) where the computational model source\n# code or executable can be called, e.g., main.py | netlogo-headless.sh\nMODEL_CODE_DIRECTORY := /code\n# entrypoint script to be called by job-wrapper.sh\nENTRYPOINT_SCRIPT := /srv/run.sh\n# entrypoint script language\nENTRYPOINT_SCRIPT_EXECUTABLE := bash\n# the OSG output file to be transferred\nOSG_OUTPUT_FILES := output,results\n# the submit file to be executed on OSG via `condor_submit ${OSG_SUBMIT_FILE}`\nOSG_SUBMIT_FILENAME := ${OSG_MODEL_NAME}.submit\n# the initial entrypoint for the OSG job, calls ENTRYPOINT_SCRIPT\nOSG_JOB_SCRIPT := job-wrapper.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(TODO: set data via cookiecutter and cookiecutter.json in cookiecutter project + document further)\u003c/p\u003e\n\u003cp\u003eThese can be customized in the make command.\u003c/p\u003e\n\u003cp\u003eThen run\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake OSG_USERNAME=\u0026lt;your-username\u0026gt; build\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eto build Docker + Singularity images with the model + dependencies embedded or\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake OSG_USERNAME=\u0026lt;your-username\u0026gt; clean deploy\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eto build and then copy the images to your OSG login node and public directory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input-and-output-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-and-output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput and Output Files\u003c/h2\u003e\n\u003cp\u003eOSG defaults transfer all generated output files. If your model generates all files in a given directory, say \u003ccode\u003eoutput\u003c/code\u003e and/or \u003ccode\u003eresults\u003c/code\u003e, something like\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003etransfer_output_files = output,results\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eshould work, e.g., a comma separated list of\u003c/p\u003e\n\u003cp\u003eFor more information, see \u003ca href=\"https://htcondor.readthedocs.io/en/latest/users-manual/file-transfer.html#specifying-what-files-to-transfer\" rel=\"nofollow\"\u003ehttps://htcondor.readthedocs.io/en/latest/users-manual/file-transfer.html#specifying-what-files-to-transfer\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "pscedu/singularity-bowtie2", + "latest_release": "v2.4.4", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bowtie2/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bowtie2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/78e679d7d4d533686856a3aabf05836e6e4c4332ede5b3be04e448bcb0af367d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626f7774696532\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78e679d7d4d533686856a3aabf05836e6e4c4332ede5b3be04e448bcb0af367d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626f7774696532\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bowtie2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/1de77fb074acfa00a23f3b57ec7ee2899825e96179ba08e9726d43d479a8d305/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626f7774696532\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1de77fb074acfa00a23f3b57ec7ee2899825e96179ba08e9726d43d479a8d305/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626f7774696532\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bowtie2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/022a36667f17e7d4a64d83341d6f9b3ef4e8e2ca7bbb26a5e59ea5d45b8bd4ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626f7774696532\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/022a36667f17e7d4a64d83341d6f9b3ef4e8e2ca7bbb26a5e59ea5d45b8bd4ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626f7774696532\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bowtie2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/6a118105cb6b5b857541ef5a71ff99144f58396c20ec65334f65aef06d79ae43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626f7774696532\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6a118105cb6b5b857541ef5a71ff99144f58396c20ec65334f65aef06d79ae43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626f7774696532\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bowtie2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bowtie2\" class=\"anchor\" href=\"#singularity-bowtie2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bowtie2\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/bowtie2\"\u003ebowtie2\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the Perl scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bowtie2/2.4.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bowtie2\u003c/code\u003e as \u003ccode\u003e2.4.4.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [], - "updated_at": 1657275644.0 + "subscribers_count": 3, + "topics": [ + "bioinformatics", + "singularity" + ], + "updated_at": 1628991557.0 }, { "data_format": 2, - "description": "R package for nsphs_ml_qt", + "description": "PHYLIP is a free package of programs for inferring phylogenies.", "filenames": [ - "Singularity", - "scripts_local/issue_61/Singularity", - "scripts_bianca/Singularity" + "3.697/Singularity" ], - "full_name": "AJResearchGroup/nsphs_ml_qt", - "latest_release": "v0.3", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nsphs_ml_qt\" class=\"anchor\" aria-hidden=\"true\" href=\"#nsphs_ml_qt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ensphs_ml_qt\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/be413dbe544678e8710f09ecb2ee97d7a26b0a853e40a539069148252157700f/68747470733a2f2f636f6465636f762e696f2f67682f72696368656c62696c6465726265656b2f6e737068735f6d6c5f71742f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt?branch=develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d51e07e8b43e2d93b35336c3c0437fdb29a65ac9e5d54406d980daf81cd2567f/68747470733a2f2f636f6465636f762e696f2f67682f72696368656c62696c6465726265656b2f6e737068735f6d6c5f71742f6272616e63682f646576656c6f702f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt/branch/develop/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll code for \u003cg-emoji class=\"g-emoji\" alias=\"lock\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f512.png\"\u003e\ud83d\udd12\u003c/g-emoji\u003e \u003ca href=\"https://github.com/AJResearchGroup/article_nsphs_ml_qt\"\u003earticle_nsphs_ml_qt\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eVideo on workflow: \u003ca href=\"https://youtu.be/FSh6i0Vsf54\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"https://richelbilderbeek.nl/nsphs_ml_qt_workflow.ogv\" rel=\"nofollow\"\u003edownload\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_bianca/README.md\"\u003eWorkflow on Bianca\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_rackham/README.md\"\u003eWorkflow on Rackham\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_local/README.md\"\u003eWorkflow on local computer\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eResults: \u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt_results\"\u003esee the \u0027nsphs_ml_qt_results\u0027 repository\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/example_architecture.png\"\u003e\u003cimg src=\"man/figures/example_architecture.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/example_dimred.png\"\u003e\u003cimg src=\"man/figures/example_dimred.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/legend_HO_tiny.png\"\u003e\u003cimg src=\"man/figures/legend_HO_tiny.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "pscedu/singularity-phylip-suite", + "latest_release": "v3.697", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-phylip-suite/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-phylip-suite/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-phylip-suite/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-phylip-suite/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c170f91fccaa5cf129589585cae304316c06a6c4926ef489d6ae6cec8639c69d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c170f91fccaa5cf129589585cae304316c06a6c4926ef489d6ae6cec8639c69d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-phylip-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5e02750234489a3b6681aab63cc8c7dee07d7b579324265bc6a45b0d56dad6d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e02750234489a3b6681aab63cc8c7dee07d7b579324265bc6a45b0d56dad6d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-phylip-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a77ca9fdcc58325c1ddfe94710d8ad36e21b307dbe40570153f1e80be6cac559/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a77ca9fdcc58325c1ddfe94710d8ad36e21b307dbe40570153f1e80be6cac559/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-phylip-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/50a0a1f8e6d29153411b82f9caaa4f006a72cf5b55af95c617d808d70a1588b9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/50a0a1f8e6d29153411b82f9caaa4f006a72cf5b55af95c617d808d70a1588b9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-phylip-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-phylip-suite\" class=\"anchor\" href=\"#singularity-phylip-suite\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-phylip-suite\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/b55efde725f14299bbb17335a6c6f17cde58bd9f451a128b97c0cfb8fe5b4edc/68747470733a2f2f65766f6c7574696f6e2e67656e65746963732e77617368696e67746f6e2e6564752f7068796c69702e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b55efde725f14299bbb17335a6c6f17cde58bd9f451a128b97c0cfb8fe5b4edc/68747470733a2f2f65766f6c7574696f6e2e67656e65746963732e77617368696e67746f6e2e6564752f7068796c69702e676966\" alt=\"Logo\" data-canonical-src=\"https://evolution.genetics.washington.edu/phylip.gif\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://evolution.genetics.washington.edu/phylip.html\" rel=\"nofollow\"\u003ePHYLIP\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/phylip-suite/3.697\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/phylip-suite\u003c/code\u003e as \u003ccode\u003e3.697.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1657465920.0 + "subscribers_count": 3, + "topics": [ + "bioinformatics", + "singularity" + ], + "updated_at": 1629217939.0 }, { "data_format": 2, - "description": null, + "description": "FastANI is developed for fast alignment-free computation of whole-genome Average Nucleotide Identity (ANI)", "filenames": [ - "r4.1.3-bc3.14-cgrtextbook20200930/Singularity", - "r4.1.0-bc3.13-cgrtextbook20200930/Singularity" + "1.33/Singularity" ], - "full_name": "yh549848/singularity-r-notebook", - "latest_release": null, + "full_name": "pscedu/singularity-fastani", + "latest_release": "v1.3.3", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-fastani/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fastani/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8dc34ff6e6f588edb478bbdd040070223d6309ed57ff692ec669a99a4ce3c044/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66617374616e69\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8dc34ff6e6f588edb478bbdd040070223d6309ed57ff692ec669a99a4ce3c044/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66617374616e69\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-fastani\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/eee7b443f78bf774c4336377ac5dee69d66644752fb8ba1f4c38931628d44fb4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66617374616e69\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eee7b443f78bf774c4336377ac5dee69d66644752fb8ba1f4c38931628d44fb4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66617374616e69\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-fastani\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/27c0f180ef8bc2d2094b529129fd31169bf0e10b3a965f25fb6ef0d8b8311868/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66617374616e69\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/27c0f180ef8bc2d2094b529129fd31169bf0e10b3a965f25fb6ef0d8b8311868/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66617374616e69\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fastani\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/86bc49a261c15fbea5000ea905a561248a21175c8d281a5949cf47e66c9eae71/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66617374616e69\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/86bc49a261c15fbea5000ea905a561248a21175c8d281a5949cf47e66c9eae71/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66617374616e69\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fastani\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-fastani\" class=\"anchor\" href=\"#singularity-fastani\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-fastani\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"github.com/parbliss/fastani\"\u003efastANI\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efastANI\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/fastANI/1.33\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/fastANI\u003c/code\u003e as \u003ccode\u003e1.33.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1637718305.0 + "subscribers_count": 1, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1628991664.0 }, { "data_format": 2, - "description": "Unofficial Sniffles repository for singularity container", + "description": null, "filenames": [ - "Singularity" + "misc/releases/20.06/Singularity.20.06", + "misc/releases/latest/Singularity", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/19.06/Singularity.19.06" ], - "full_name": "touala/Sniffles", + "full_name": "No-Diehl/FD-SAT", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sniffles\" class=\"anchor\" aria-hidden=\"true\" href=\"#sniffles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSniffles\u003c/h1\u003e\n\u003cp\u003eUnofficial Sniffles repository for singularity container\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fast-downward\" class=\"anchor\" href=\"#fast-downward\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast Downward\u003c/h1\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1657955196.0 + "updated_at": 1625821718.0 }, { "data_format": 2, "description": null, "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/latest/Singularity" + "Singularity" ], - "full_name": "silvansievers/symmetric-lookups", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "VUIIS/demo-singularity-spm-freeview", + "latest_release": "v1.0.1", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-demo-singularity-container-with-spm12-and-freeview\" class=\"anchor\" href=\"#demo-singularity-container-with-spm12-and-freeview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo singularity container with SPM12 and Freeview\u003c/h1\u003e\n\u003cp\u003eSPM12-based pipelines require a little extra work to get them compiled and working in a\ncontainer. Freesurfer\u0027s Freeview is also included here, as it\u0027s very handy for creating\nthe PDF QA report. This example shows three different ways of creating image displays for\nthe QA PDF.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/baxpr/demo-singularity-matlab-fsl\"\u003ehttps://github.com/baxpr/demo-singularity-matlab-fsl\u003c/a\u003e for a lot of detailed info about\nputting Matlab code into singularity containers. This example uses the same structure.\u003c/p\u003e\n\u003cp\u003eA licensed Matlab installation is required to compile the Matlab code, but is not needed\nto run the compiled executable in the container.\u003c/p\u003e\n\u003cp\u003eSPM12 (\u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" rel=\"nofollow\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/a\u003e) is not in this repository and must\nbe installed separately on the compilation host. Edit \u003ccode\u003ematlab/compile_matlab.sh\u003c/code\u003e to point\nto it.\u003c/p\u003e\n\u003cp\u003eSPM requires jumping an extra hurdle at the compilation step - we use a modified version\nof SPM\u0027s own compiler function \u003ccode\u003espm_make_standalone.m\u003c/code\u003e, found at\n\u003ccode\u003ematlab/spm_make_standalone_local.m\u003c/code\u003e. This process captures a lot of dependencies that\ncould otherwise easily be left out of the executable, with the resulting symptom that it\ncompiles just fine but fails at run time with various cryptic error messages. In addition\nto SPM12, everything in the \u003ccode\u003ematlab/src\u003c/code\u003e directory is included in the path at compile time.\nIf Matlab toolboxes are used, they will need to be added to the list of included toolboxes\nin \u003ccode\u003ematlab/spm_make_standalone_local.m\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe compiled Matlab executable is stored on github using git LFS. A regular git clone will\ndownload a pointer text file instead of the executable binary. The result of building a\ncontainer from that will be a cryptic error message - so, compile it yourself. Or, if\nstoring on github, download it manually and replace the pointer text file, or include this\nstep in the Singularity file if helpful - example here:\n\u003ca href=\"https://github.com/baxpr/gf-fmri/blob/47b0552/Singularity.v1.3.4#L65\"\u003ehttps://github.com/baxpr/gf-fmri/blob/47b0552/Singularity.v1.3.4#L65\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFreesurfer requires a license to run:\n\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall#License\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall#License\u003c/a\u003e\nBest practice is to store your license file on the host that will run the container, and\nbind it to the container at runtime - NOT to include your own license file in the\ncontainer itself.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1659363562.0 + "updated_at": 1625837739.0 }, { "data_format": 2, - "description": null, + "description": "Notebook template using Fink API for the LSST broker workshop", "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/latest/Singularity" + "Singularity" ], - "full_name": "silvansievers/merge-strategies", + "full_name": "astrolabsoftware/fink-notebook-template", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fink-broker-tutorials\" class=\"anchor\" href=\"#fink-broker-tutorials\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFink broker tutorials\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://colab.research.google.com/github/astrolabsoftware/fink-notebook-template/blob/main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains materials (notebooks \u0026amp; presentation) to explore the \u003ca href=\"https://fink-broker.org\" rel=\"nofollow\"\u003eFink broker\u003c/a\u003e alert data. As of April 2021, Fink has collected more than 80 million alerts from the ZTF public stream, and processed more than 30 millions (after quality cuts). Among these, you will find extragalatic sources (supernovae, AGN, ...), galactic sources (many classes of transients incl. variables stars from our galaxy or gravitational microlensing events, ...) and moving objects from our Solar System (asteroids, comets, and made-man objects like space-debris!). Some sources are already confirmed, many are candidates!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-materials\" class=\"anchor\" href=\"#materials\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaterials\u003c/h2\u003e\n\u003cp\u003eThe repository contains a number of notebooks focusing on the use of the Fink REST API. We shortly present different science cases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExtragalactic science: AGN \u0026amp; supernovae (\u003ca href=\"extragalactic/extragalactic.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eGalactic science: variable stars \u0026amp; microlensing (\u003ca href=\"galactic/galactic.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSolar system science: asteroids, comets \u0026amp; space debris (\u003ca href=\"sso/sso.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eMulti-messenger astronomy: searching for kilonovae (\u003ca href=\"MMA/MMA.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eBroker interfaces: presentation on the livestream service, the Science Portal and its API, and the Fink TOM module (\u003ca href=\"interfaces/README.md\"\u003esee the presentation\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese sciences are not exhaustive and we welcome new collaborations to expand them!\u003c/p\u003e\n\u003cp\u003eYou can try the notebooks using Google Colab (follow the link above). You can also clone the repo, and try it locally (very little external libraries are required).\u003c/p\u003e\n\u003cp\u003eWe also provide a Singularity script to work in a contained environment (thanks @bregeon):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBuild with \u003ccode\u003esingularity build --fakeroot fink.sif Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun with \u003ccode\u003esingularity run fink.sif\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpen the link in your browser (from the host)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-contribute\" class=\"anchor\" href=\"#how-to-contribute\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to contribute\u003c/h2\u003e\n\u003cp\u003eHow to contribute:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone (or fork) this repo, and open a new branch.\u003c/li\u003e\n\u003cli\u003eCreate a new folder with a meaningful name (e.g. \u003ccode\u003esupernovae\u003c/code\u003e, \u003ccode\u003egrb\u003c/code\u003e, ...)\u003c/li\u003e\n\u003cli\u003eRead and copy an existing notebook to get an idea of the structure of a tutorial.\u003c/li\u003e\n\u003cli\u003eOnce your notebook is finished, open a Pull Request such that we review the tutorial and merge it!\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 6, "topics": [], - "updated_at": 1651653962.0 + "updated_at": 1625729812.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for multiqc (https://github.com/ewels/MultiQC)", "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/latest/Singularity" + "Singularity.1.6", + "Singularity.1.9", + "Singularity.1.11", + "Singularity.1.5", + "Singularity", + "Singularity.1.8", + "Singularity.1.7" ], - "full_name": "silvansievers/weak-stubborn-sets", + "full_name": "powerPlant/multiqc-srf", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for the MultiQC tool to aggregate results from bioinformatics analyses across many samples into a single report.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1659517719.0 + "updated_at": 1625703839.0 }, { "data_format": 2, - "description": "Singularity image for the scikit-hep software ecosystem", + "description": null, "filenames": [ "Singularity" ], - "full_name": "amanmdesai/singularity-scikit-hep", - "latest_release": "v1.0.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-scikit-hep\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-scikit-hep\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-scikit-hep\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/amanmdesai/singularity-scikit-hep/actions/workflows/singularity-build-deploy.yml\"\u003e\u003cimg src=\"https://github.com/amanmdesai/singularity-scikit-hep/actions/workflows/singularity-build-deploy.yml/badge.svg?branch=master\" alt=\"Singularity Build Deploy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/533611076\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fce93d17667e5605dd27f08f48424292886536d8ac123c1441b6e3a51b801dc4/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3533333631313037362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/533611076.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA singularity container for scikit-hep with python packages\u003c/p\u003e\n\u003cp\u003eTo pull this image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull oras://ghcr.io/amanmdesai/singularity-scikit-hep:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis image contains the python packages.\u003c/p\u003e\n\u003cp\u003enumpy, awkward, uproot4, scikit-hep-testdata, hist, particle, hepunits, matplotlib, boost-histogram, iminuit, zfit, vector, fastjet\u003c/p\u003e\n", + "full_name": "baxpr/conncalc", + "latest_release": "v1.0.4", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-conncalc\" class=\"anchor\" href=\"#conncalc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econncalc\u003c/h1\u003e\n\u003cp\u003eComputes functional connectivity maps and matrices for a specified set of ROIs.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eremovegm_niigz\u003c/code\u003e, \u003ccode\u003ekeepgm_niigz\u003c/code\u003e, \u003ccode\u003emeanfmri_niigz\u003c/code\u003e. Preprocessed fMRI data from\n\u003ca href=\"https://github.com/baxpr/connprep\"\u003econnprep\u003c/a\u003e. This may be supplied in atlas space or\nsubject native space. The first two are 4D time series, the last a single 3D image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eroi_niigz\u003c/code\u003e. ROI image. This may be an image existing within the container (e.g. the\nMNI space \u0027AABHHIP_LR.nii.gz\u0027, see src/rois/README.md). Or, it may be any supplied\nimage. In the latter case, \u003ccode\u003eroilabel_csv\u003c/code\u003e must also be supplied; this file must contain\nLabel and Region columns, or may be the STATS output of a slant assessor. The ROI\nimage must be already be aligned with the T1 and the fMRI (though needn\u0027t be sampled to\nthe same voxel grid or field of view) - no coregistration or warp is performed on any\nof the images.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003et1_niigz\u003c/code\u003e. T1 image for the PDF report.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emask_niigz\u003c/code\u003e. Brain mask - will be binarized and dilated and used to exclude any clearly\nex-brain voxels in the stored connectivity maps. Supply \u0027none\u0027 to mask to the entire\nvolume (i.e. no masking).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003econnmaps_out\u003c/code\u003e. \u0027yes\u0027 or \u0027no\u0027 to choose whether to additionally store voxelwise\nconnectivity images for each ROI in the ROI image.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline\" class=\"anchor\" href=\"#pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eResample the ROI image to match the fMRI voxel sampling. It\u0027s assumed both are already\naligned.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtract mean time series from the supplied fMRI for each ROI in the ROI image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompute functional connectivity. The ROI-to-ROI connectivity matrix is computed, and also\nvoxelwise connectivity Z maps if requested.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e, the correlation coefficient\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eZ\u003c/code\u003e, the Fisher transformed correlation, \u003ccode\u003eatanh(R) * sqrt(N-3)\u003c/code\u003e where \u003ccode\u003eN\u003c/code\u003e is number of time points\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eVdf\u003c/code\u003e, \u003ccode\u003ePdf\u003c/code\u003e, \u003ccode\u003eZdf\u003c/code\u003e autocorrelation-adjusted connectivity metrics from \u003ca href=\"https://github.com/asoroosh/xDF\"\u003ehttps://github.com/asoroosh/xDF\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenerate a PDF report and organize outputs for XNAT.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1662637680.0 + "updated_at": 1625759471.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "openfoam/Singularity.of-7-from-docker" + "Singularity" ], - "full_name": "ggruszczynski/singularity_recipies", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipies\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity recipies\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4746\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOfficial Documentation:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/getting-started/recipes\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/singularityhub-docs/docs/getting-started/recipes\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-diy\" class=\"anchor\" aria-hidden=\"true\" href=\"#diy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDIY\u003c/h2\u003e\n\u003cp\u003eHow to run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to build\u003c/span\u003e\nsudo singularity build image.sif recipe.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to run \u003c/span\u003e\nsingularity shell --cleanenv lolcow_latest.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Without the --cleanenv flag, the environment on the host system will be present within the container at run time.\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e lolcow_latest.sif cowsay moo\nsingularity run lolcow_latest.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to download\u003c/span\u003e\nsingularity pull shub://ggruszczynski/singularity_recipies\nsingularity run singularity_recipies_latest.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to build from docker hub\u003c/span\u003e\nsingularity pull docker://openfoam/openfoam7-paraview56\nsingularity shell --cleanenv openfoam7-paraview56_latest.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --cleanenv cat /etc/os-release\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to build from docker file\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e https://www.nas.nasa.gov/hecc/support/kb/converting-docker-images-to-singularity-for-use-on-pleiades_643.html\u003c/span\u003e\n\n$ sudo docker build -t ood-rstudio-bio.4.1.2 - \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e Dockerfile.4.1.2\n\n$ docker images\nREPOSITORY TAG IMAGE ID CREATED SIZE\nood-rstudio-bio.4.1.2 latest 9ab18b041cba 27 minutes ago 7.05GB\n\n$ docker save 9ab18b041cba -o ood_rstudio_bio_docker_412.tar\n$ singularity build ood_rstudio_bio_singularity_412.sif docker-archive://ood_rstudio_bio_docker_412.tar\n\n$ singularity build --sandbox lolcow docker-archive://lolcow.tar\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-openfoam-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#openfoam-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenFoam notes\u003c/h3\u003e\n\u003cp\u003eOF fundation: vX versioning + third party\nOF org: vYYMM versioning\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-mpi-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#mpi-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMPI notes\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://singularity.lbl.gov/faq\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/faq\u003c/a\u003e\nWhy do we call \u2018mpirun\u2019 from outside the container (rather than inside)?\nWith Singularity, the MPI usage model is to call \u2018mpirun\u2019 from outside the container, and reference the container from your \u2018mpirun\u2019 command. Usage would look like this:\u003c/p\u003e\n\u003cp\u003e$ mpirun -np 20 singularity exec container.img /path/to/contained_mpi_prog\nBy calling \u2018mpirun\u2019 outside the container, we solve several very complicated work-flow aspects. For example, if \u2018mpirun\u2019 is called from within the container it must have a method for spawning processes on remote nodes. Historically ssh is used for this which means that there must be an sshd running within the container on the remote nodes, and this sshd process must not conflict with the sshd running on that host! It is also possible for the resource manager to launch the job and (in Open MPI\u2019s case) the Orted processes on the remote system, but that then requires resource manager modification and container awareness.\u003c/p\u003e\n\u003cp\u003eIn the end, we do not gain anything by calling \u2018mpirun\u2019 from within the container except for increasing the complexity levels and possibly losing out on some added performance benefits (e.g. if a container wasn\u2019t built with the proper OFED as the host).\u003c/p\u003e\n\u003cp\u003eSee the Singularity on HPC page for more details.\u003c/p\u003e\n", + "full_name": "VUIIS/demo-singularity-matlab-fsl", + "latest_release": "v1.0.1", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-demo-singularity-container-for-matlab-plus-fsl\" class=\"anchor\" href=\"#demo-singularity-container-for-matlab-plus-fsl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo singularity container for Matlab plus FSL\u003c/h1\u003e\n\u003cp\u003eThis example container takes a Nifti image as input, zeroes out a hole in it of\nthe specified diameter, and saves the result to a new Nifti file. Quick,\npointless, and easy to tell whether it worked right.\u003c/p\u003e\n\u003cp\u003eThis is one way to organize a Matlab-based Singularity container -\nperhaps most easily conceived of as a series of wrappers around the main\ncodebase. Done this way, it\u0027s fairly easy to work on each piece in isolation,\nproblem-solving from the inside out.\u003c/p\u003e\n\u003cp\u003eThis container also includes an installation of FSL, which has a lot of handy\ntools including fsleyes to make the QA PDF. The FSL parts could be removed from\nthe Singularity file if FSL isn\u0027t used, to end up with a smaller container.\nContrariwise, all the Matlab parts could be removed to end up with an FSL-only\ncontainer.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity container\n| Primary entrypoint (shell script)\n| | X11 wrapper\n| | | Shell script preprocessing\n| | | Matlab processing (compiled)\n| | | | Matlab entrypoint\n| | | | Matlab main function\n| | | \\ Matlab sub-functions / codebase\n\\ \\ \\ Shell script postprocessing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDependencies in terms of the actual files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity\n src/pipeline_entrypoint.sh\n src/pipeline_main.sh\n src/copy_inputs.sh\n src/preprocessing.sh\n matlab/bin/run_matlab_entrypoint.sh\n matlab/bin/matlab_entrypoint\n / matlab/src/matlab_entrypoint.m \\ Used for compilation,\n | matlab/src/matlab_main.m | but not at container\n \\ matlab/src/* / runtime\n src/postprocessing.sh\n src/make_pdf.sh\n src/finalize.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe process of putting it together is described below. The scripts and code in\nthis repository are extensively commented, so if something isn\u0027t clear here,\nit\u0027s probably explained in the Singularity file or the example code.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-this-container-before-editing-anything\" class=\"anchor\" href=\"#building-this-container-before-editing-anything\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding this container before editing anything\u003c/h2\u003e\n\u003cp\u003eTry building this from scratch, to find any immediate issues:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eGet the installers for Matlab Compiled Runtime and FSL and place them in the\n\u003ccode\u003eexternal\u003c/code\u003e directory. URLs for these are in the \u003ccode\u003eSingularity\u003c/code\u003e file. Alternatively,\ncomment out the installer files in the \u0027%files\u0027 section and uncomment the download\nlines (\u0027wget\u0027) later - this way they will be downloaded as part of the build.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the container, following the instructions below\n\u003ca href=\"https://github.com/baxpr/demo-singularity-matlab-fsl#building-the-container\"\u003ehttps://github.com/baxpr/demo-singularity-matlab-fsl#building-the-container\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-matlab-part\" class=\"anchor\" href=\"#matlab-part\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMatlab part\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-write-the-basic-matlab-code\" class=\"anchor\" href=\"#write-the-basic-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWrite the basic Matlab code\u003c/h3\u003e\n\u003cp\u003eWrite Matlab code that does what\u0027s needed. Put it in \u003ccode\u003ematlab/src\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA popular toolbox for reading and writing Nifti files that\u0027s available on Matlab\nCentral has a lot of insidious bugs and is not being maintained. Matlab\u0027s own\nfunctions for Nifti files are quite limited. Here is an alternative, which is\nused in this example:\n\u003ca href=\"https://github.com/VUIIS/spm_readwrite_nii\"\u003ehttps://github.com/VUIIS/spm_readwrite_nii\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-write-the-matlab-entrypoint\" class=\"anchor\" href=\"#write-the-matlab-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWrite the Matlab entrypoint\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/src/matlab_entrypoint.m\u003c/code\u003e exists to take command line arguments, parse\nthem, and call the main code. A convenient way to set things up is to write a\nmain function that takes a structure as its sole input, with the structure\ncontaining whatever inputs are needed. See \u003ccode\u003ematlab/src/matlab_main.m\u003c/code\u003e for an\nexample of this.\u003c/p\u003e\n\u003cp\u003eCouple of things to note in the entrypoint code are the quit/exit sections at\nbeginning and end. The bit at the beginning allows the executable to run during\nthe container build, without actually doing anything - this is needed to extract\nthe CTF archive into the container at the only time the container is writeable\n(h/t \u003ca href=\"https://twitter.com/annash128\" rel=\"nofollow\"\u003ehttps://twitter.com/annash128\u003c/a\u003e for figuring that one out). The bit at the\nend exits matlab when the function is finished. Without it, the running Matlab\nprocess won\u0027t release execution back to the calling script when it\u0027s done.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-test-the-matlab-entrypoint\" class=\"anchor\" href=\"#test-the-matlab-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the Matlab entrypoint\u003c/h3\u003e\n\u003cp\u003eThe script \u003ccode\u003ematlab/src/test_matlab_entrypoint.m\u003c/code\u003e is an example of how to do\nthis. The appropriate Matlab must be installed on the testing computer.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-the-matlab-code\" class=\"anchor\" href=\"#compile-the-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile the Matlab code\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/compile_matlab.sh\u003c/code\u003e shows how. Many compiled executables are likely to be\ntoo large to store on github. Git LFS may be a solution.\n\u003ca href=\"https://docs.github.com/en/github/managing-large-files/working-with-large-files\"\u003ehttps://docs.github.com/en/github/managing-large-files/working-with-large-files\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-test-the-compiled-matlab-code\" class=\"anchor\" href=\"#test-the-compiled-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the compiled Matlab code\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/test_compiled_matlab.sh\u003c/code\u003e. The appropriate Matlab Runtime must be\ninstalled on the testing computer.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-shell-script-part\" class=\"anchor\" href=\"#shell-script-part\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell script part\u003c/h2\u003e\n\u003cp\u003eAll of the below procedures could be done in the matlab part, if desired,\ninstead of in shell script. If so, parsing inputs should be done following the\nexample in \u003ccode\u003ematlab/src/matlab_entrypoint.m\u003c/code\u003e. But it\u0027s often easier to move\nfiles, create the QA PDF, etc using shell script and FSL. So that\u0027s what we are\ndoing in this example. All this code is in the \u003ccode\u003esrc\u003c/code\u003e directory.\u003c/p\u003e\n\u003cp\u003eAll the shell scripts called from \u003ccode\u003esrc/pipeline_entrypoint.sh\u003c/code\u003e \"know\" the\nenvironment variables that are exported there. This is a very convenient way to\npass along the input arguments, although it isn\u0027t entirely transparent, because\nthere\u0027s no hint in the shell scripts where the variables\u0027 values are coming from\nunless we explain it in the comments.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-main-entrypoint\" class=\"anchor\" href=\"#main-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMain entrypoint\u003c/h3\u003e\n\u003cp\u003eThis is \u003ccode\u003esrc/pipeline_entrypoint.sh\u003c/code\u003e. It uses bash to parse the command line\ninputs and export them to environment variables so they\u0027re accessible. Then it\ncalls the primary shell script \u003ccode\u003esrc/pipeline_main.sh\u003c/code\u003e which in turn calls\neverything else. The main script is run in xvfb to provide a virtual display,\noften needed by matlab and required for fsleyes.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-copy-inputs\" class=\"anchor\" href=\"#copy-inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopy inputs\u003c/h3\u003e\n\u003cp\u003eWe copy input files to the output/working directory so we don\u0027t mess them up.\nThis also is an opportunity to rename them to something consistent. It\u0027s very\nconvenient to hard-code the filenames so we don\u0027t have to store and manipulate\nfilenames in environment variables or the like. Also, this makes it easy to\nproduce output files with consistent names - outputs of one pipeline may serve\nas inputs to another, and it\u0027s much easier to manage this if filenames are the\nsame for every run, or at least consistent.\u003c/p\u003e\n\u003cp\u003eWe generally assume the output directory starts out empty and will not be\ninterfered with by any other processes - this is true for XNAT/DAX, but\nimportant to be aware of in other contexts.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preprocessing\" class=\"anchor\" href=\"#preprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessing\u003c/h3\u003e\n\u003cp\u003eFor this example, there is no preprocessing before the matlab part. But initial\nFSL steps or similar could be put here: \u003ccode\u003esrc/preprocessing.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-postprocessing\" class=\"anchor\" href=\"#postprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePostprocessing\u003c/h3\u003e\n\u003cp\u003eThere isn\u0027t any postprocessing for this example either, but there could be:\n\u003ccode\u003esrc/postprocessing.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pdf-creation\" class=\"anchor\" href=\"#pdf-creation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePDF creation\u003c/h3\u003e\n\u003cp\u003eAll assessors on VUIIS XNAT require a PDF QA report of some sort. For this\nexample, a display of the segmented ROIs overlaid on the T1 is created using\nfsleyes and ImageMagick, \u003ccode\u003esrc/make_pdf.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003ePDF creation can be done in Matlab instead. It\u0027s hard to make these look good.\nAn example with some tricks, including a \u003ccode\u003e.fig\u003c/code\u003e file painstakingly made with\nMatlab\u0027s GUIDE, is\n\u003ca href=\"https://github.com/baxpr/connprep/blob/855dadc/src/connectivity_filter.m#L271\"\u003ehttps://github.com/baxpr/connprep/blob/855dadc/src/connectivity_filter.m#L271\u003c/a\u003e\nA way to show slices of functional images with a nice red/blue colormap is\n\u003ca href=\"https://github.com/baxpr/connprep/blob/855dadc/src/make_network_maps.m\"\u003ehttps://github.com/baxpr/connprep/blob/855dadc/src/make_network_maps.m\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-finalizing-the-output\" class=\"anchor\" href=\"#finalizing-the-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFinalizing the output\u003c/h3\u003e\n\u003cp\u003eAll Niftis must be compressed for storage on XNAT, and outputs can be organized\nin an easily understandable way: \u003ccode\u003esrc/finalize.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eWrite an informative README - so tedious, yet so helpful. Include the\nappropriate citations for all the methods and software you have used. Even\nessentially write the methods section for a paper that uses the pipeline. Here\u0027s\nan excellent example: \u003ca href=\"https://github.com/MASILab/PreQual\"\u003ehttps://github.com/MASILab/PreQual\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAlternatively, git-ify some documentation like this:\n\u003ca href=\"https://github.com/VUIIS/dax/tree/main/docs\"\u003ehttps://github.com/VUIIS/dax/tree/main/docs\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eto get something like this:\n\u003ca href=\"https://dax.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://dax.readthedocs.io/en/latest/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-container\" class=\"anchor\" href=\"#building-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container\u003c/h2\u003e\n\u003cp\u003eBe sure the Matlab code is newly compiled, see above. You can run\n\u003ccode\u003ematlab/check_for_compilation.sh\u003c/code\u003e first to make sure there\u0027s no source code\nnewer than the compiled executable.\u003c/p\u003e\n\u003cp\u003eThen from the root directory of the working copy of the repo, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build \u0026lt;container_name\u0026gt;.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGood practice: before you build, create a release on github (if using github) or\nat least tag the commit you are about to build. Give the container a versioned\nname like \u003ccode\u003edemo-singularity-matlab-fsl_v1.0.0.simg\u003c/code\u003e that matches the repository\nname and release version/tag.\u003c/p\u003e\n\u003cp\u003eExternal binaries such as Matlab Runtime and FSL can be included by copying\nlocal copies into the container in the Singularity file\u0027s \u003ccode\u003e%files\u003c/code\u003e section. This\ntends to be a little faster when multiple builds are needed during debugging,\nor necessary for files that are not available to download, and this is what\u0027s\nbeing done in the example Singularity file. Alternatively, binaries or install\nfiles can be downloaded from their source at build time - there are some\ncommented-out sections in the Singularity file showing how that is done. (Thanks\n\u003ca href=\"https://github.com/praitayini\"\u003ehttps://github.com/praitayini\u003c/a\u003e for exploring this in detail)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-container\" class=\"anchor\" href=\"#running-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the container\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003etest_singularity_container.sh\u003c/code\u003e for an example run command and some\nimportant info.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h3\u003e\n\u003cp\u003ePaths to files are relative to the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--t1_niigz A T1 image\n--seg_niigz Its corresponding segmentation from e.g. slant pipeline\n--diameter_mm Diameter of the hole to zero out, in mm (default 30)\n\n--label_info A label to annotate the QA PDF, e.g. info from XNAT\n\n--out_dir Where outputs will be stored (default /OUTPUTS)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePDF/holed_image.pdf QA report\nHOLED_T1/holed_t1.nii.gz T1 image with a hole in it\nHOLED_SEG/holed_seg.nii.gz Segmentation with a hole in it\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-container-with-dax\" class=\"anchor\" href=\"#running-the-container-with-dax\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the container with DAX\u003c/h2\u003e\n\u003cp\u003eWith a suitable configuration file, DAX (\u003ca href=\"https://github.com/VUIIS/dax\"\u003ehttps://github.com/VUIIS/dax\u003c/a\u003e) can run this on a cluster.\u003c/p\u003e\n\u003cp\u003eInstructions are here: \u003ca href=\"https://dax.readthedocs.io/en/latest/processors.html\" rel=\"nofollow\"\u003ehttps://dax.readthedocs.io/en/latest/processors.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn example is here:\n\u003ca href=\"https://github.com/VUIIS/dax_yaml_processor_examples/blob/master/demo-matfsl_v1.0.0_processor.yaml\"\u003ehttps://github.com/VUIIS/dax_yaml_processor_examples/blob/master/demo-matfsl_v1.0.0_processor.yaml\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1655827541.0 + "updated_at": 1626126045.0 }, { "data_format": 2, - "description": "Standalone scripts to assist with intermediate tasks in GeoEDF workflows", + "description": "Target/Integrative Genetic Element Retriever", "filenames": [ - "Singularity" + "5.32.1/Singularity" ], - "full_name": "geoedf/workflow-utils", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-geoedf-workflow-utilities\" class=\"anchor\" aria-hidden=\"true\" href=\"#geoedf-workflow-utilities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeoEDF Workflow Utilities\u003c/h1\u003e\n\u003cp\u003eStandalone scripts to assist with intermediate tasks in GeoEDF workflows\u003c/p\u003e\n", + "full_name": "pscedu/singularity-tiger", + "latest_release": "v5.32.1", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-tiger/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-tiger/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-tiger/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-tiger/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/45b51fb9658ca6d66b992d887a9258c238463b14dc9fb58c4ed17ba4e75f2efc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7469676572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b51fb9658ca6d66b992d887a9258c238463b14dc9fb58c4ed17ba4e75f2efc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7469676572\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-tiger\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/266d33a01bfbc88af8ef713b4ab3f12e4207aea6a693420544c401bcc4687384/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7469676572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/266d33a01bfbc88af8ef713b4ab3f12e4207aea6a693420544c401bcc4687384/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7469676572\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-tiger\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/cd42640e858ab21849faf7ea1ae7b80b386fea232400bb8666ab226125727f09/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7469676572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cd42640e858ab21849faf7ea1ae7b80b386fea232400bb8666ab226125727f09/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7469676572\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-tiger\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/049f66af13d64dfd0fc9fb6af0dd2b62c6d9f524419d096508747447c1c661ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7469676572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/049f66af13d64dfd0fc9fb6af0dd2b62c6d9f524419d096508747447c1c661ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7469676572\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-tiger\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-tiger\" class=\"anchor\" href=\"#singularity-tiger\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-tiger\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/tiger\"\u003etiger\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ednaStats.pl\u003c/code\u003e, \u003ccode\u003eislander.pl\u003c/code\u003e, \u003ccode\u003eresolve.pl\u003c/code\u003e, \u003ccode\u003etater.pl\u003c/code\u003e and \u003ccode\u003etiger.pl\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/tiger/4.8.25\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/tiger\u003c/code\u003e as \u003ccode\u003e4.8.25.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/tigers/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1655911232.0 + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629218294.0 }, { "data_format": 2, - "description": "Old copy of the nf-core methylseq workflow including hacked in NuGen/Tecan support", + "description": "Trimmomatic performs a variety of useful trimming tasks for illumina paired-end and single ended data", "filenames": [ - "Singularity" + "0.39/Singularity" ], - "full_name": "HPCBio/methylseq-old", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"\" class=\"anchor\" aria-hidden=\"true\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/methylseq_logo.png\"\u003e\u003cimg src=\"docs/images/methylseq_logo.png\" alt=\"nf-core/methylseq\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/methylseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1e11b31de3d567f647c562b736ad6e010ef787d1a8aa35dce459aba5b4587ed/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6d657468796c7365712e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/methylseq.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/17df893666bfa5cad487466d4476ab773ea5def560c04c50f45795017865e81c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/124913037\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/89f01223dd3cce114d92a5764aa2e589ddd0915df7208e879ab1d88a5cee4b31/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3132343931333033372e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/124913037.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/nf-core/Lobby\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75a58bd7ba3966d16ebf50e1d2d55a4d21c67fa281370f9b823c2f4e53532b03/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769747465722d2532306a6f696e253230636861742532302545322538362539322d3466623939612e737667\" alt=\"Gitter\" data-canonical-src=\"https://img.shields.io/badge/gitter-%20join%20chat%20%E2%86%92-4fb99a.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/methylseq/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fbe9131f0a48ef34c529ac997f1ac04e3b5df4586ceb45fcda42c1568a761456/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6d657468796c7365712e737667\" alt=\"Docker Container available\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/methylseq.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1091\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Container\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003enf-core/methylseq\u003c/strong\u003e is a bioinformatics best-practice analysis pipeline used for Methylation (BS-Seq) data analysis.\u003c/p\u003e\n\u003cp\u003eThe pipeline uses \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a bioinformatics workflow tool. It pre-processes raw data from FastQ inputs, aligns the reads and performs extensive quality-control on the results.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pipeline-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline Steps\u003c/h3\u003e\n\u003cp\u003eThe pipeline allows you to choose between running either \u003ca href=\"https://github.com/FelixKrueger/Bismark\"\u003eBismark\u003c/a\u003e or \u003ca href=\"https://github.com/brentp/bwa-meth\"\u003ebwa-meth\u003c/a\u003e / \u003ca href=\"https://github.com/dpryan79/methyldackel\"\u003eMethylDackel\u003c/a\u003e.\nChoose between workflows by using \u003ccode\u003e--aligner bismark\u003c/code\u003e (default) or \u003ccode\u003e--aligner bwameth\u003c/code\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eStep\u003c/th\u003e\n\u003cth\u003eBismark workflow\u003c/th\u003e\n\u003cth\u003ebwa-meth workflow\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGenerate Reference Genome Index \u003cem\u003e(optional)\u003c/em\u003e\n\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003ebwa-meth\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRaw data QC\u003c/td\u003e\n\u003ctd\u003eFastQC\u003c/td\u003e\n\u003ctd\u003eFastQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAdapter sequence trimming\u003c/td\u003e\n\u003ctd\u003eTrim Galore!\u003c/td\u003e\n\u003ctd\u003eTrim Galore!\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAlign Reads\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003ebwa-meth\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDeduplicate Alignments\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003ePicard MarkDuplicates\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eExtract methylation calls\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003eMethylDackel\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSample report\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSummary Report\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAlignment QC\u003c/td\u003e\n\u003ctd\u003eQualimap\u003c/td\u003e\n\u003ctd\u003eQualimap\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProject Report\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/methylseq pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003eThese scripts were originally written for use at the \u003ca href=\"https://portal.scilifelab.se/genomics/\" rel=\"nofollow\"\u003eNational Genomics Infrastructure\u003c/a\u003e at \u003ca href=\"http://www.scilifelab.se/\" rel=\"nofollow\"\u003eSciLifeLab\u003c/a\u003e in Stockholm, Sweden.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMain author:\n\u003cul\u003e\n\u003cli\u003ePhil Ewels (\u003ca href=\"https://github.com/ewels/\"\u003e@ewels\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eContributors:\n\u003cul\u003e\n\u003cli\u003eRickard Hammar\u00e9n (\u003ca href=\"https://github.com/Hammarn/\"\u003e@Hammarn\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eAlexander Peltzer (\u003ca href=\"https://github.com/apeltzer/\"\u003e@apeltzer\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "pscedu/singularity-trimmomatic", + "latest_release": "0.39", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-trimmomatic/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-trimmomatic/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/886b78ddfce96f64ae6be83548b2b219652f30a8ab8e6f78413fcf5c763a5fe3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/886b78ddfce96f64ae6be83548b2b219652f30a8ab8e6f78413fcf5c763a5fe3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-trimmomatic\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f83a71da75047a61c081c63788b045654ea1befe0da1eccfc4e3d46b96c656e5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f83a71da75047a61c081c63788b045654ea1befe0da1eccfc4e3d46b96c656e5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-trimmomatic\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/42de55829ff65764902a5aa22015e62165ede512bd9503a795afe4c4815d9e61/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/42de55829ff65764902a5aa22015e62165ede512bd9503a795afe4c4815d9e61/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-trimmomatic\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/77a4e6c187943c941ff1ee8267ebe7f72b3e5d5723a03d3ac4e91e826dc35c98/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/77a4e6c187943c941ff1ee8267ebe7f72b3e5d5723a03d3ac4e91e826dc35c98/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-trimmomatic\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-trimmomatic\" class=\"anchor\" href=\"#singularity-trimmomatic\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-trimmomatic\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/usadellab/Trimmomatic\"\u003eTrimmomatic\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003etrimmomatic\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/trimmomatic/0.39\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/trimmomatic\u003c/code\u003e as \u003ccode\u003e0.39.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, - "topics": [], - "updated_at": 1655919157.0 + "subscribers_count": 1, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1628992066.0 }, { "data_format": 2, - "description": "SPAdes \u2013 St. Petersburg genome assembler \u2013 is intended for both standard isolates and single-cell MDA bacteria assemblies.", + "description": "ABySS is a de novo sequence assembler that is designed for very short reads", "filenames": [ - "3.15.5/Singularity", - "3.15.3/Singularity", - "3.15.4/Singularity", - "3.14.1/Singularity" + "2.1.5/Singularity" ], - "full_name": "pscedu/singularity-spades", - "latest_release": "v3.15.5", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-spades/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-spades/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/11b78f1cdc09fbfab0186e430a46591d3424481741a5bf3fd4ba85e5fdf6ab64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d737061646573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11b78f1cdc09fbfab0186e430a46591d3424481741a5bf3fd4ba85e5fdf6ab64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d737061646573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-spades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4e45c3ef27ec17192287b575ac775bc38996f5ef4371b5c3d60a832ce806669e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d737061646573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e45c3ef27ec17192287b575ac775bc38996f5ef4371b5c3d60a832ce806669e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d737061646573\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-spades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1df46e77d3f98a8380250f3dc06ab84e8496724d7526816114756b5e5115363b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d737061646573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1df46e77d3f98a8380250f3dc06ab84e8496724d7526816114756b5e5115363b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d737061646573\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-spades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4819979457ec016fbbbd524e8046081eef419b6669035f155038c378d436a9dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d737061646573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4819979457ec016fbbbd524e8046081eef419b6669035f155038c378d436a9dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d737061646573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-spades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-spades\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-spades\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-spades\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/spades/3.15.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/spades\u003c/code\u003e as \u003ccode\u003e3.15.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-abyss", + "latest_release": "v2.1.5", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-abyss/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-abyss/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/61f7c02135e022542644bca33ec90a8b6bdc9cbb8ec4b0e3ec8b5480f035beaa/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6162797373\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/61f7c02135e022542644bca33ec90a8b6bdc9cbb8ec4b0e3ec8b5480f035beaa/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6162797373\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-abyss\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e1d01eb58e3d5d47f45f00fffe4ab16909d1541fcb3cf25e0c01bf5f9d0c8028/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6162797373\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1d01eb58e3d5d47f45f00fffe4ab16909d1541fcb3cf25e0c01bf5f9d0c8028/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6162797373\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-abyss\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b5bcaa1865f5b4c8a6901097150a4b2915c2e0a967f27a5cd175b8cd7c653ce3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6162797373\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b5bcaa1865f5b4c8a6901097150a4b2915c2e0a967f27a5cd175b8cd7c653ce3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6162797373\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-abyss\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/1d51b6d070f927bea877c0ebb5437d326390ba7459fc3a1baa9ecefc8c1f63ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6162797373\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1d51b6d070f927bea877c0ebb5437d326390ba7459fc3a1baa9ecefc8c1f63ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6162797373\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-abyss\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-abyss\" class=\"anchor\" href=\"#singularity-abyss\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-abyss\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/ABYSS\"\u003eABySS\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/ABySS/2.1.5\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/ABySS\u003c/code\u003e as \u003ccode\u003e2.1.5.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [ "singularity", "bioinformatics" ], - "updated_at": 1658280597.0 + "updated_at": 1628991345.0 }, { "data_format": 2, - "description": "Demonstration workflow with Alphafold in a Jupyter notebook", + "description": "This repository will hold: the build script to install singularity and other dependencies for it, and a definition file for the singularity container for HPC.", "filenames": [ - "container/Singularity.def" + "SingularitySC", + "Singularity" ], - "full_name": "parallelworks/alphafold-notebook-demo", + "full_name": "perminaa/SingularityHPC", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-alphafold-notebook-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#alphafold-notebook-demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ealphafold-notebook-demo\u003c/h1\u003e\n\u003cp\u003eDemonstration workflow with Alphafold in a Jupyter notebook\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003cp\u003eThe following components are necessary for setting up this workflow:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eAn Alphafold Singularity container. Please see instructions in \u003ccode\u003e./container\u003c/code\u003e for how to build an Alphafold container. Currently, it is assumed that this container is available at a \u003cstrong\u003ehard coded path\u003c/strong\u003e in \u003ccode\u003e./container/run_singularity_container.py\u003c/code\u003e in this line of code:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity_image = Client.load(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/public/apps/alphafold/alphafold.sif\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eA Conda (or pip) environment that has the \u003ccode\u003eabsl-py\u003c/code\u003e and \u003ccode\u003espython\u003c/code\u003e packages to launch the container. This workflow also uses \u003ccode\u003eparsl\u003c/code\u003e (but it is not required for using the container itself). For a cluster with Conda in a module, here is an example for how to create a local environment:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load conda3\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e /gs/gsfs0/hpc01/rhel8/apps/conda3/etc/profile.d/conda.sh\nconda create -y -p /gs/gsfs0/users/gstefan/work/alphafold/env -c conda-forge absl-py==0.13.0 spython=0.1.16 parsl\nconda activate /gs/gsfs0/users/gstefan/work/alphafold/env\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e/gs/gsfs0/users/gstefan/\u003c/code\u003e is your home directory.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003ePull this workflow code into your PW environment.\n\u003cstrong\u003eTODO: Add instructions here.\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the workflow from PW.\n\u003cstrong\u003eTODO: Add instructions here.\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-interactive-runs\" class=\"anchor\" aria-hidden=\"true\" href=\"#interactive-runs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive runs\u003c/h2\u003e\n\u003cp\u003eFor the purposes of testing Alphafold, it is possible to\nstart interactive Alphafold runs (i.e. manually launch the\napplication for an instance). Instructions for launching\nan interactive run are in \u003ccode\u003e./container\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-batch-runs\" class=\"anchor\" aria-hidden=\"true\" href=\"#batch-runs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch runs\u003c/h2\u003e\n\u003cp\u003eWhen you want to run many proteins with Alphafold, there are\ntwo options:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ethe workflow form (under construction) can be used to launch a batch run or\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emain.ipynb\u003c/code\u003e, the Jupyter notebook that contains the workflow code.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWhen users opt for the first option (the workflow form), the form simply\ngrabs the code out of \u003ccode\u003emain.ipynb\u003c/code\u003e and executes it. Users can use\n\u003ccode\u003emain.ipynb\u003c/code\u003e as a template for more complicated Alphafold workflows\nand/or directly modify some of the Alphafold options that are not\navailable in the workflow form. Jupyter notebooks (\u003ccode\u003e*.ipynb\u003c/code\u003e files)\ncan be opened, edited, and run on the platform by double clicking on\nthe file in the file browser pane.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-colabfold\" class=\"anchor\" aria-hidden=\"true\" href=\"#colabfold\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eColabFold\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sokrypton/ColabFold\"\u003eColabFold\u003c/a\u003e is a community-driven\nupdate to Alphafold underpinned by \u003ca href=\"https://colabfold.mmseqs.com/\" rel=\"nofollow\"\u003enew/updated databases\u003c/a\u003e\nand the MSA search process is accelerated by \u003ca href=\"https://github.com/soedinglab/MMseqs2\"\u003eMMseqs2\u003c/a\u003e.\nPlease see the colabfold directory for more information.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularityhpc\" class=\"anchor\" href=\"#singularityhpc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularityHPC\u003c/h1\u003e\n\u003cp\u003eThis repository will hold: the build script to install singularity and other dependencies for it, and a definition file for the singularity container for HPC.\u003c/p\u003e\n\u003cp\u003eTo install, run \u003ccode\u003egit clone https://github.com/perminaa/SingularityHPC.git \u0026amp;\u0026amp; cd SingularityHPC \u0026amp;\u0026amp; bash buildscript.sh\u003c/code\u003e. This will install and configure singularity\nand build a container called \u003ccode\u003eContainer.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run the container, you can use \u003ccode\u003esingularity shell Container.sif\u003c/code\u003e to run in the singularity shell or \u003ccode\u003esingularity exec Container.sif \u0026lt;command\u0026gt;\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1659444272.0 + "updated_at": 1626501688.0 }, { "data_format": 2, - "description": null, + "description": "R server within singularity container on HPC", "filenames": [ - "Recipes/Singularity_spark_full", - "Recipes/Singularity_numpy", - "Recipes/Singularity_pytorch", - "Recipes/Singularity_ompi", - "Recipes/Singularity_GPU", - "Recipes/Singularity_tensorflow", - "Recipes/Singularity_Python", - "Recipes/Singularity_mpich", - "Recipes/Singularity_pytorch_full", - "Recipes/Singularity_spark", - "Recipes/Singularity_sklearn", - "Recipes/Singularity_example" + "Singularity_bioc_python" ], - "full_name": "Gab0410/Cluster-HPC", + "full_name": "retogerber/singularity_rserver", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-pr\u00e9-requisitos\" class=\"anchor\" aria-hidden=\"true\" href=\"#pr\u00e9-requisitos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePr\u00e9-requisitos\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sele\u00e7\u00e3o-do-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#sele\u00e7\u00e3o-do-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSele\u00e7\u00e3o do Recipe\u003c/h2\u003e\n\u003cp\u003eAdicione o caminho do \u003cem\u003esingularity recipe\u003c/em\u003e desejado na vari\u00e1vel RECIPE no github (projeto \u0026gt;\u0026gt; Settings \u0026gt;\u0026gt; Secrets \u0026gt;\u0026gt; New Secret).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-template-integra\u00e7\u00e3o-cont\u00ednua-github\" class=\"anchor\" aria-hidden=\"true\" href=\"#template-integra\u00e7\u00e3o-cont\u00ednua-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemplate Integra\u00e7\u00e3o Cont\u00ednua Github\u003c/h1\u003e\n\u003cp\u003eEsse projeto o template para uso do cluster da UFSCar, contendo integra\u00e7\u00e3o cont\u00ednua com o Google Drive e Amazon S3.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requisitos-para-google-drive\" class=\"anchor\" aria-hidden=\"true\" href=\"#requisitos-para-google-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequisitos para Google Drive\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eEntre no \u003ca href=\"https://console.developers.google.com/apis/credentials\" rel=\"nofollow\"\u003econsole de credenciais de API do Google\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSe ainda n\u00e3o houver um projeto, crie um com permiss\u00e3o para a \"Google Drive API\" (Dashboard).\u003c/li\u003e\n\u003cli\u003eEm \"Tela de consentimento OAuth\", marque \"Interno\" na primeira p\u00e1gina, preencha os campos obrigat\u00f3rios na segunda, n\u00e3o preencha nada na terceira,\u003c/li\u003e\n\u003cli\u003eClique em Credenciais \u0026gt; Criar credenciais.\u003c/li\u003e\n\u003cli\u003eSelecione \"ID do cliente do OAuth\".\u003c/li\u003e\n\u003cli\u003eEm \"Tipo de aplicativo\", selecione \"App para computador\".\u003c/li\u003e\n\u003cli\u003eD\u00ea um nome de identifica\u00e7\u00e3o para as credenciais e clique em \"criar\". V\u00e3o aparecer dois dados (\"Seu ID de cliente\" e \"Sua chave secreta de cliente\"), precisaremos dos dois no passo seguinte.\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 17 # selecione aqui o n\u00famero correspondente a op\u00e7\u00e3o \"Google Drive\"\nclient_id\u0026gt; conte\u00fado de \"Seu ID de cliente\"\nclient_secret\u0026gt; conte\u00fado de \"Sua chave secreta de cliente\"\nscope\u0026gt; 1 # Full access all files, excluding Application Data Folder\nroot_folder_id\u0026gt; deixe em branco\nservice_account_file\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/n\u0026gt; n\n\nSe j\u00e1 tiver o rclone instalado em seu computador, execute o comando exibido, caso contr\u00e1rio, o instale conforme indicado na p\u00e1gina oficial do rclone dependendo do seu sistema operacional (https://rclone.org/install/). A Seguir insira o resultado do comando exibido:\n\nconfig_token\u0026gt; c\u00f3digo fornecido pelo terminal ap\u00f3s autoriza\u00e7\u00e3o\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requisitos-para-amazon-s3\" class=\"anchor\" aria-hidden=\"true\" href=\"#requisitos-para-amazon-s3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequisitos para Amazon S3\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eEntre na \u003ca href=\"console.aws.amazon.com\"\u003eAWS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClique na seta ao lado de seu nome de usu\u00e1rio e em \"My Security Credentials\".\u003c/li\u003e\n\u003cli\u003eNa se\u00e7\u00e3o \"Access Keys\", clique em \"Create New Access Key\".\u003c/li\u003e\n\u003cli\u003eNa janela que aparece, clique em \"Show Access Key\".\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 4\nprovider\u0026gt; 1\nenv_auth\u0026gt; 1\naccess_key_id\u0026gt; conte\u00fado de \"Access Key ID\"\nsecret_access_key\u0026gt; conte\u00fado de \"Secret Access Key\"\nregion\u0026gt; 16\nendpoint\u0026gt; deixe em branco\nlocation_constraint\u0026gt; 16\nacl\u0026gt; deixe em branco\nserver_side_encryption\u0026gt; deixe em branco\nsse_kms_key_id\u0026gt; deixe em branco\nstorage_class\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-r-server-in-singularity\" class=\"anchor\" href=\"#r-server-in-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR server in singularity\u003c/h1\u003e\n\u003cp\u003eThis workflow together with the script \u003ccode\u003esingRstudio.sh\u003c/code\u003e facilitates setting up an R server running in a singularity container on a HPC and accessing it on a local PC.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-workflow\" class=\"anchor\" href=\"#workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prepare-only-first-time\" class=\"anchor\" href=\"#prepare-only-first-time\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare (only first time)\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-on-local-pc\" class=\"anchor\" href=\"#on-local-pc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOn local PC\u003c/h3\u003e\n\u003cp\u003eSince building a singularity image requires root privilege it is often not possible to directly build on your HPC. A simple workaround is to build in on your local PC and the copy to the server.\nBuild Singularity image file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity_container.sif Singularity_bioc_python\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe given Singularity build file is just an example, to customize for your needs have a look at the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/build_a_container.html\" rel=\"nofollow\"\u003esingularity documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAfter building the image copy to server, e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escp singularity_container.sif SERVERNAME:/some/location\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively there is the possibily to build without sudo using the \u003ccode\u003e--remote\u003c/code\u003e flage. \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/cloud_library.html\" rel=\"nofollow\"\u003eSingularity documentation\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-setup\" class=\"anchor\" href=\"#setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-on-server\" class=\"anchor\" href=\"#on-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOn server\u003c/h3\u003e\n\u003cp\u003eMake sure a suitable temporary directory is available, e.g. \u003ccode\u003e~/tmp\u003c/code\u003e (the default).\u003c/p\u003e\n\u003cp\u003eDecide on the port you want to use, the default is 8788.\u003c/p\u003e\n\u003cp\u003eRun rserver with singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash singRstudio.sh -c singularity_container.sif -t ~/tmp -p 8789\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-on-local-pc-1\" class=\"anchor\" href=\"#on-local-pc-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOn local PC\u003c/h3\u003e\n\u003cp\u003eRedirect traffic from port on server to local port via ssh:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -Nf -L LOCALPORT:localhost:SERVERPORT SERVERNAME\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ereplacing \u003ccode\u003eLOCALPORT\u003c/code\u003e with the port you want to use on your local pc, \u003ccode\u003eSERVERPORT\u003c/code\u003e with the above specified port (default 8788) and \u003ccode\u003eSERVERNAME\u003c/code\u003e with the address of the server.\ne.g:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -Nf -L 8787:localhost:8788 user@myserver.com\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen open a browser and go to \u003ccode\u003ehttp://localhost:LOCALPORT\u003c/code\u003e again replacing \u003ccode\u003eLOCALPORT\u003c/code\u003e. Login with your server username and passwort (as specified with the \u003ccode\u003e-a\u003c/code\u003e argument, default: \u003ccode\u003epassword\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-other-options\" class=\"anchor\" href=\"#other-options\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther options:\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bind-local-directories-to-container\" class=\"anchor\" href=\"#bind-local-directories-to-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBind local directories to container\u003c/h3\u003e\n\u003cp\u003eTo connect directories to the container in a specific manner set the \u003ccode\u003e-b\u003c/code\u003e argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash singRstudio.sh -c singularity_container.sif -b \"local/dir/1:/absolute/container/dir/1,local/dir/2:/absolute/container/dir/2\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-local-r-library\" class=\"anchor\" href=\"#local-r-library\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elocal R library\u003c/h3\u003e\n\u003cp\u003eSince singularity containers are read-only, installing R packages is not possible. For reproducibility this is great as it is always clear what packages were used,\nbut sometimes it can be a nuissance when testing stuff. A workaround is to specify a local directory in which the packages are installed. This can be done setting\nthe \u003ccode\u003e-r\u003c/code\u003e argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash singRstudio.sh -c singularity_container.sif -r ~/my/R/library\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dry-run\" class=\"anchor\" href=\"#dry-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDry run\u003c/h3\u003e\n\u003cp\u003eTo just show the \"built\" singularity command without executing it add \u003ccode\u003e-d\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash singRstudio.sh -c singularity_container.sif -d\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1663111689.0 + "updated_at": 1626332421.0 }, { "data_format": 2, - "description": "Work in progress: A cookiecutter for singularity images", + "description": "msee is a command-line tool to read markdown file.", "filenames": [ - "{{cookiecutter.project_name}}/Singularity" + "0.3.5/Singularity" ], - "full_name": "amanmdesai/cookiecutter-singularity", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cookiecutter-project-for-singularity-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#cookiecutter-project-for-singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCookiecutter Project for Singularity images\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/amanmdesai/cookiecutter-docker-singularity/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/486e44f0e9c09c6186d86e72c96fdfc6574e09d8885cf0fe2b912e9cdbff847e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f616d616e6d64657361692f636f6f6b69656375747465722d646f636b65722d73696e67756c6172697479\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/amanmdesai/cookiecutter-docker-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-hidden=\"true\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePurpose\u003c/h2\u003e\n\u003cp\u003eCreate Singularity image definition files\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eEasily write customized singularity images\u003c/li\u003e\n\u003cli\u003eDeploy easily to github packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstructions will be added\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-it\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning it!\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-extension\" class=\"anchor\" aria-hidden=\"true\" href=\"#extension\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtension:\u003c/h2\u003e\n\u003cp\u003eAn extension either to include docker images here, or elsewhere is foreseen.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eWORK in Progress\nContributions are welcome and can be made by opening a PR or bug report.\u003c/p\u003e\n", + "full_name": "icaoberg/singularity-msee", + "latest_release": "v0.3.5", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-msee/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-msee/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8f15c74b6b0c9b138f9da5b4933fe28294369dc8e7eb93b731dc2ce072de357e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6d736565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8f15c74b6b0c9b138f9da5b4933fe28294369dc8e7eb93b731dc2ce072de357e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6d736565\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-msee\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/dc49ece7c14937e5c2802e8ab4824bc9c4606e21bd4f04eede89efffab599a2d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6d736565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc49ece7c14937e5c2802e8ab4824bc9c4606e21bd4f04eede89efffab599a2d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6d736565\" alt=\"Forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-msee\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/bc6a9157f824c9765da6596c0beb4a82c4f71d7a27b46cec8e32781fb8c3faad/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6d736565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bc6a9157f824c9765da6596c0beb4a82c4f71d7a27b46cec8e32781fb8c3faad/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6d736565\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-msee\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5858470ac4cc23a618c46e8f874d611a5c2ba2762846b37cae4b6767e4e8784f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6d736565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5858470ac4cc23a618c46e8f874d611a5c2ba2762846b37cae4b6767e4e8784f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6d736565\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-msee\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-msee\" class=\"anchor\" href=\"#singularity-msee\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-msee\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://cloud.githubusercontent.com/assets/157338/10902801/531ba216-823d-11e5-87ac-986b8d5ea4cc.png\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://cloud.githubusercontent.com/assets/157338/10902801/531ba216-823d-11e5-87ac-986b8d5ea4cc.png\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://www.npmjs.com/package/msee\" rel=\"nofollow\"\u003emsee\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/msees/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1663515196.0 + "topics": [ + "singularity", + "utilties" + ], + "updated_at": 1627585753.0 }, { "data_format": 2, - "description": "ffmpeg and pysoundfile in a Singularity image", + "description": "Repo for recipes to put on singularity hub", "filenames": [ - "Singularity" + "Singularity.dbspype", + "Singularity.xenial" ], - "full_name": "rses-singularity/singularity-ubuntu-xenial-ffmpeg-pysoundfile", + "full_name": "hbraunDSP/containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ffmpeg-and-pysoundfile-in-a-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#ffmpeg-and-pysoundfile-in-a-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003effmpeg and pysoundfile in a Singularity image\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e for the Python packages installed in the image (using \u003ccode\u003epip\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containers\" class=\"anchor\" href=\"#containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n\u003cp\u003ePRIVATE repo for recipes to put on singularity hub.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1503997989.0 + "updated_at": 1567609710.0 }, { "data_format": 2, - "description": "BIDS app to perform PET motion correction of dynamic data", + "description": "HTSlib A C library for reading/writing high-throughput sequencing data. ", "filenames": [ - "Singularity" + "1.13/Singularity" ], - "full_name": "mnoergaard/hmcpet", - "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-an-example-bids-app-template-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-example-bids-app-template-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAn example BIDS App (template repository)\u003c/h2\u003e\n\u003cp\u003eEvery BIDS App needs to follow a minimal set of command arguments common across\nall of the Apps. This allows users and developers to easily use and integrate\nBIDS Apps with their environment.\u003c/p\u003e\n\u003cp\u003eThis is a minimalist example of a BIDS App consisting of a Dockerfile and a simple\nentry point script (written in this case in Python) accepting the standard BIDS\nApps command line arguments. This repository can be used as a template for new BIDS Apps.\u003c/p\u003e\n\u003cp\u003eFor more information about the specification of BIDS Apps see \u003ca href=\"https://docs.google.com/document/d/1E1Wi5ONvOVVnGhj21S1bmJJ4kyHFT7tkxnV3C23sjIE/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eThis is a placeholder for a short description explaining to the user what your App will doing.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eProvide a link to the documentation of your pipeline.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-how-to-report-errors\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-report-errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to report errors\u003c/h3\u003e\n\u003cp\u003eProvide instructions for users on how to get help and report errors.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eDescribe how would you would like users to acknowledge use of your App in their papers (citation, a paragraph that can be copy pasted, etc.)\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eThis App has the following command line arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\tusage: run.py [-h]\n\t [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]\n\t bids_dir output_dir {participant,group}\n\n\tExample BIDS App entry point script.\n\n\tpositional arguments:\n\t bids_dir The directory with the input dataset formatted\n\t according to the BIDS standard.\n\t output_dir The directory where the output files should be stored.\n\t If you are running a group level analysis, this folder\n\t should be prepopulated with the results of\n\t the participant level analysis.\n\t {participant,group} Level of the analysis that will be performed. Multiple\n\t participant level analyses can be run independently\n\t (in parallel).\n\n\toptional arguments:\n\t -h, --help show this help message and exit\n\t --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]\n\t The label(s) of the participant(s) that should be\n\t analyzed. The label corresponds to\n\t sub-\u0026lt;participant_label\u0026gt; from the BIDS spec (so it does\n\t not include \"sub-\"). If this parameter is not provided\n\t all subjects will be analyzed. Multiple participants\n\t can be specified with a space separated list.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run it in participant level mode (for one participant):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --rm \\\n\t-v /Users/filo/data/ds005:/bids_dataset:ro \\\n\t-v /Users/filo/outputs:/outputs \\\n\tbids/example \\\n\t/bids_dataset /outputs participant --participant_label 01\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter doing this for all subjects (potentially in parallel), the group level analysis\ncan be run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --rm \\\n\t-v /Users/filo/data/ds005:/bids_dataset:ro \\\n\t-v /Users/filo/outputs:/outputs \\\n\tbids/example \\\n\t/bids_dataset /outputs group\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-special-considerations\" class=\"anchor\" aria-hidden=\"true\" href=\"#special-considerations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecial considerations\u003c/h3\u003e\n\u003cp\u003eDescribe whether your app has any special requirements. For example:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMultiple map reduce steps (participant, group, participant2, group2 etc.)\u003c/li\u003e\n\u003cli\u003eUnusual memory requirements\u003c/li\u003e\n\u003cli\u003eetc.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "pscedu/singularity-htslib", + "latest_release": "v1.13", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-htslib/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-htslib/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-htslib/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-htslib/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c19127d592d5b1774f1b776b581a4ee3e088dc8836040a4dcfb0112e233e0272/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6874736c6962\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c19127d592d5b1774f1b776b581a4ee3e088dc8836040a4dcfb0112e233e0272/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6874736c6962\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-htslib\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7eb94edba6c28c8efe671ccb26366254e3fa67b9ba22e17183a8353c985c30f7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6874736c6962\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7eb94edba6c28c8efe671ccb26366254e3fa67b9ba22e17183a8353c985c30f7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6874736c6962\" alt=\"Forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-htslib\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0c4e24ae23aaa5d0244c3ba0370b21835d696a720659445acd0879d08953dbf5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6874736c6962\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0c4e24ae23aaa5d0244c3ba0370b21835d696a720659445acd0879d08953dbf5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6874736c6962\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-htslib\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e0b439a0b671ca5e751012f4801e4febe27cb2fa88ea95ed862cca4a347af459/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6874736c6962\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e0b439a0b671ca5e751012f4801e4febe27cb2fa88ea95ed862cca4a347af459/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6874736c6962\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-htslib\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-htslib\" class=\"anchor\" href=\"#singularity-htslib\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-htslib\u003c/h2\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/samtools/htslib\"\u003ehtslib\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehtsfile\u003c/code\u003e, \u003ccode\u003etabix\u003c/code\u003e and \u003ccode\u003ebgzip\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/htslib/1.13\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/htslib\u003c/code\u003e as \u003ccode\u003e1.13.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1653596606.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629226143.0 }, { "data_format": 2, - "description": null, + "description": "\u5b8f\u57fa\u56e0\u7ec4\u76f8\u5173\u4ee3\u7801", "filenames": [ - "Singularity.zlib-1.2-centos8.def", - "Singularity.binutils-2.36.1-GCCcore-11.1.0-centos8.def" + "scripts/lathe/singularity/Singularity.quickmerge", + "scripts/lathe/singularity/Singularity.longread", + "scripts/lathe/singularity/Singularity.htsbox" ], - "full_name": "jkwmoore/centos8-eb-singularity-image", + "full_name": "JiaLonghao1997/Microbiome", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-microbiome\" class=\"anchor\" href=\"#microbiome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMicrobiome\u003c/h1\u003e\n\u003cp\u003e\u5b8f\u57fa\u56e0\u7ec4\u76f8\u5173\u4ee3\u7801\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1653574058.0 + "updated_at": 1626932334.0 }, { "data_format": 2, "description": null, "filenames": [ - "ext/Singularity" + "Singularity.tut0804", + "Singularity.05211526", + "Singularity", + "Singularity.386", + "Singularity.05201328", + "Singularity.sf", + "Singularity.05131402", + "Singularity.05221357", + "Singularity.1908121107", + "Singularity.cuda10" ], - "full_name": "dtenenba/bc_example_dan_rstudio", + "full_name": "timkphd/Containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-wip-batch-connect---osc-example-shiny-app\" class=\"anchor\" aria-hidden=\"true\" href=\"#wip-batch-connect---osc-example-shiny-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[WIP] Batch Connect - OSC Example Shiny App\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a8152a2780451d58acdca1e79b03f771d6e84ae12087e6e7a824b6759b715dc1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f6578616d706c655f7368696e792e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a8152a2780451d58acdca1e79b03f771d6e84ae12087e6e7a824b6759b715dc1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f6578616d706c655f7368696e792e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_example_shiny.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app designed for OSC OnDemand that launches a Shiny App within\nan Owens batch job.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e x.y.z+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_example_shiny/fork\"\u003ehttps://github.com/OSC/bc_osc_example_shiny/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-chpcs-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#chpcs-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCHPC\u0027s notes\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-functional-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#functional-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctional overview\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUses CHPC\u0027s R (3.6.1) which has shiny installed\u003c/li\u003e\n\u003cli\u003eTo run a webserver, use an openresty container running nginx\u003c/li\u003e\n\u003cli\u003eThe script.sh that launches the OOD app creates a nginx config file and Shiny app launcher, then runs R with the launcher, followed by looking for the Unix socket created by the R\u0027s Shiny, thich then gets used by the nginx startup\u003c/li\u003e\n\u003cli\u003eThe user shiny app path is specified in the job specs\u0027 input box\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that Shiny app can be also launched from the OOD\u0027s RStudio app by typing\nlibrary(\u0027shiny\u0027)\nrunApp(\"newdir\") - where \"newdir\" is the directory where app.R resides\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-applications-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#applications-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApplication\u0027s dependencies\u003c/h3\u003e\n\u003cp\u003eR libraries that are needed by the application need to either be installed centrally to CHPC\u0027s R libraries location, or to other shared directory location. The former approach risks potential version conflicts with other library dependencies (this is more of an issue in Python but is possible in R as well).\u003c/p\u003e\n\u003cp\u003eBest practice may be for the creator of the app to install all the dependencies to his/her home directory, and in the app modify the R library path (using the .libPaths function) to add this directory to it.\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container-build-scripts\" class=\"anchor\" href=\"#singularity-container-build-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container build Scripts\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-see-httpssingularity-huborgcollections2962\" class=\"anchor\" href=\"#see-httpssingularity-huborgcollections2962\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSee \u003ca href=\"https://singularity-hub.org/collections/2962\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/2962\u003c/a\u003e\n\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, , R, MPI (intel and openMPI ), python\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.05131402\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, basic stuff, does not actually install Intel Python\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.05201328\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.05211526\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.05221357 (Built)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.1908121107 (Built)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:latest, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.386 (Built)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBasic 32 bit with Fortran, c++ make, nano,vim\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.sf (Built)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:18.04, STAR-Fusion\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.tut0804\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1653954801.0 + "updated_at": 1627001875.0 }, { "data_format": 2, - "description": null, + "description": "RAdiation SEmiconductoR ", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "ZizZu94/covid19-ultrasound-img-prediction", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-covid19-ultrasound-image-score-prediction\" class=\"anchor\" aria-hidden=\"true\" href=\"#covid19-ultrasound-image-score-prediction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCovid19 Ultrasound image score prediction\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-models-resnet50-and-efficientnet-b0\" class=\"anchor\" aria-hidden=\"true\" href=\"#models-resnet50-and-efficientnet-b0\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModels: ResNet50 and EfficientNet-b0\u003c/h2\u003e\n", + "full_name": "dt-np/raser", + "latest_release": "v1.1", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-raser\" class=\"anchor\" href=\"#raser\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRASER\u003c/h1\u003e\n\u003cp\u003eRAdiation SEmiconductoR\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-build-with-singularity\" class=\"anchor\" href=\"#build-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild with Singularity\u003c/h1\u003e\n\u003cp\u003eBefore running the code, install the Singularity on your OS.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e./sinularity_build.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e./singularity_run.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eraser\u0026gt; geant4_build.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-run-with-singularity\" class=\"anchor\" href=\"#run-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun with Singularity\u003c/h1\u003e\n\u003cblockquote\u003e\n\u003cp\u003e./singularity_run.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eraser\u0026gt; ./run\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-raser-unit-test-after-you-change-some-codes\" class=\"anchor\" href=\"#raser-unit-test-after-you-change-some-codes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRaser unit test after you change some codes\u003c/h1\u003e\n\u003cblockquote\u003e\n\u003cp\u003e./singularity_run.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eraser\u0026gt; ./run 0.1.5\nraser\u0026gt; ./run 0.2.5\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIf the output is \"Successful\", the code your changed is OK.\nOtherwise, you should check the code your changed.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "classification", - "covid-19", - "deep-learning", - "efficientnet", - "neural-network", - "python", - "pytorch", - "resnet-50", - "ultrasound" - ], - "updated_at": 1654162475.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1630661539.0 }, { "data_format": 2, - "description": null, + "description": "Fast, reliable protein-coding gene prediction for prokaryotic genomes.", "filenames": [ - "downward/misc/releases/19.12/Singularity.19.12", - "downward/misc/releases/20.06/Singularity.20.06", - "downward/misc/releases/latest/Singularity", - "downward/misc/releases/19.06/Singularity.19.06" + "2.6.3/Singularity" ], - "full_name": "aymeric75/latplan", + "full_name": "pscedu/singularity-prodigal", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-prodigal/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-prodigal/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-prodigal/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-prodigal/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/dbf23f859880f70436ee9de9ee89a1528a5171defb337385614da4eee0ffeb2d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d70726f646967616c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dbf23f859880f70436ee9de9ee89a1528a5171defb337385614da4eee0ffeb2d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d70726f646967616c\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-prodigal\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f5f6ed86bbd6c79a6449e24738f02f9844e1fe9972bd1d162eb62316c06bf9dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d70726f646967616c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f5f6ed86bbd6c79a6449e24738f02f9844e1fe9972bd1d162eb62316c06bf9dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d70726f646967616c\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-prodigal\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4ef2a97630fd3ac7037f653270a9129fe20826c8bf90956c9b995c0eea86da02/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d70726f646967616c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ef2a97630fd3ac7037f653270a9129fe20826c8bf90956c9b995c0eea86da02/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d70726f646967616c\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-prodigal\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e41f95bac858eb80bd1956886531b10a1a335ab73e0c0cbdc7ab720cf22824cd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d70726f646967616c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e41f95bac858eb80bd1956886531b10a1a335ab73e0c0cbdc7ab720cf22824cd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d70726f646967616c\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-prodigal\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-prodigal\" class=\"anchor\" href=\"#singularity-prodigal\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-prodigal\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/prodigal\"\u003eprodigal\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eprodigal\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/prodigal/2.6.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/prodigal\u003c/code\u003e as \u003ccode\u003e2.6.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1654148749.0 + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629226411.0 }, { "data_format": 2, - "description": "Ba\u011flant\u0131 test ara\u00e7lar\u0131 i\u00e7eren Docker imaj\u0131", + "description": "Bismark is a program to map bisulfite treated sequencing reads to a genome of interest and perform methylation calls in a single step. ", "filenames": [ - "Singularity" + "0.22.3/Singularity" ], - "full_name": "gulnihalugur/testutils", + "full_name": "pscedu/singularity-bismark", "latest_release": null, - "readme": "\u003cp\u003eDocker imaji: curl, wget, ping, netcat, nslookup,host, dig, psql, mysql\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-kullanim\" class=\"anchor\" aria-hidden=\"true\" href=\"#kullanim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKullanim\u003c/h2\u003e\n\u003cp\u003eKubernetes\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ekubectl run --rm utils -it --generator=run-pod/v1 --image gulnihalugur/testutils bash\n# You will be seeing a bash prompt\n$ psql -h hostname -U test -d test\n...\n$ exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDocker Engine\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker pull gulnihalugur/testutils\n$ docker run --rm -it gulnihalugur/testutils bash\n\n# konteynir icinde\n$ ping google.com\n$ ifconfig\n...\n$ exit\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bismark/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bismark/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-bismark/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bismark/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ac6bb8f2b406618278034b709c736e772b92723686bd930d0ec0e0caaf278599/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6269736d61726b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ac6bb8f2b406618278034b709c736e772b92723686bd930d0ec0e0caaf278599/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6269736d61726b\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bismark\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5f93c1931fec2c3f8a38122749025c57f09fb930eb75c591ad412dfa911b97ae/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6269736d61726b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5f93c1931fec2c3f8a38122749025c57f09fb930eb75c591ad412dfa911b97ae/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6269736d61726b\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bismark\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f20c48b5b2226a8f0a7cff096f8cedfe80b073f0b801009d9684d408ee31ca07/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6269736d61726b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f20c48b5b2226a8f0a7cff096f8cedfe80b073f0b801009d9684d408ee31ca07/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6269736d61726b\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bismark\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/07c4815a6c07dc179ddc97de5e5faa3c7fec941cc2c81a94adaed0547946fd27/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6269736d61726b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/07c4815a6c07dc179ddc97de5e5faa3c7fec941cc2c81a94adaed0547946fd27/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6269736d61726b\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bismark\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bismark\" class=\"anchor\" href=\"#singularity-bismark\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bismark\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/FelixKrueger/Bismark\"\u003ebismark\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebismark\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bismark/0.22.3\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bismark\u003c/code\u003e as \u003ccode\u003e0.22.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, - "topics": [], - "updated_at": 1561217122.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629214967.0 }, { "data_format": 2, - "description": "Singularity container for minc built on centos 7", + "description": "Nextflow pipeline for inference of CRISPR edits from NGS (Amplicon Sequencing) data", "filenames": [ "Singularity" ], - "full_name": "pndni/minc-container", + "full_name": "Yield10Bio/crispedit", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-crispedit\" class=\"anchor\" href=\"#crispedit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecrispedit\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eNextflow pipeline for inference of CRISPR edits from NGS (Amplicon Sequencing) data\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun crispedit/crisedit.nf --in_fastq /Users/ngebremedhin/Downloads/VB26_18_3428_P1087_308811w_BF8.fastq --ref canola_badc_allgenes.fa --out_dir ${PWD}/myProject --project_name LC25 --bucket bioinformatics-analysis-netsanet --sgrna \"CCTTCTGAGCCCATGAACAAATC\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 19.04.1\nLaunching `crispedit/crisedit.nf` [hopeful_swanson] - revision: 607eaca8d5\n\n[warm up] executor \u0026gt; local\nexecutor \u0026gt; local (27)\n[a7/a397c1] process \u0026gt; mergeReads [100%] 1 of 1 \u2714\n[58/d46b33] process \u0026gt; clustering [100%] 1 of 1 \u2714\n[1d/5e8374] process \u0026gt; dereplication [100%] 1 of 1 \u2714\n[bb/cc12b3] process \u0026gt; mapHighFrequencyReads [100%] 1 of 1 \u2714\n[44/eec241] process \u0026gt; samToBam [100%] 1 of 1 \u2714\n[92/c84f30] process \u0026gt; indexBam [100%] 1 of 1 \u2714\n[d2/119094] process \u0026gt; identiyEdits [100%] 1 of 1 \u2714\n[a2/38c0af] process \u0026gt; prepForPSA [100%] 1 of 1 \u2714\n[a6/51cabd] process \u0026gt; performPSA [100%] 9 of 9 \u2714\n[bd/fd96a3] process \u0026gt; combineClustalOut [100%] 9 of 9 \u2714\n[33/0f5567] process \u0026gt; createFinalReport [100%] 1 of 1 \u2714\n\nCompleted at: 25-Jul-2019 11:37:08\nDuration : 12.4s\nCPU hours : (a few seconds)\nSucceeded : 27\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ebwa 0.7.17\u003c/li\u003e\n\u003cli\u003evsearch 2.18.0\u003c/li\u003e\n\u003cli\u003ebbmap 38.92\u003c/li\u003e\n\u003cli\u003esamtools=1.9\u003c/li\u003e\n\u003cli\u003eBiopython\u003c/li\u003e\n\u003cli\u003eclustalo 1.2.4\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003ecrispedit is written by Netsanet Gebremedhin.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1554308962.0 + "updated_at": 1631063991.0 }, { "data_format": 2, - "description": "Singularity build files for FSL and freesurfer", + "description": "ElikoPy is Python library aiming at easing the processing of diffusion imaging for microstructural analysis.", "filenames": [ - "Singularity.FSL-6.0.1_freesurfer-6.0.1_dev" + "Singularity_elikopy" ], - "full_name": "pndni/FSL-and-freesurfer", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-license-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#license-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense info\u003c/h1\u003e\n\u003cp\u003eWhile the actual code in this repository is covered by the provided \u003ca href=\"LICENSE\"\u003elicense\u003c/a\u003e,\nusing freesurfer and FSL requires accepting their respective licenses. By using this\ncontainer, you must agree to these licenses.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense\" rel=\"nofollow\"\u003eFreesurfer license\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\" rel=\"nofollow\"\u003eFSL license\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou must acquire a freesurfer license from\n\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/registration.html\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/registration.html\u003c/a\u003e\nEnsure that the license file is visible from the container,\nand set the environment variable FS_LICENSE to point to it\n(or copy the file to /opt/freesurfer/license.txt from\ninside the container)\u003c/p\u003e\n", + "full_name": "Hyedryn/elikopy", + "latest_release": "v0.2.2", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-elikopy\" class=\"anchor\" href=\"#elikopy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eElikoPy\u003c/h1\u003e\n\u003cp\u003eElikoPy is Python library aiming at easing the processing of diffusion imaging for microstructural analysis.\nThis Python library is based on\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDIPY, a python library for the analysis of MR diffusion imaging.\u003c/li\u003e\n\u003cli\u003eMicrostructure fingerprinting, a python library doing estimation of white matter microstructural properties from a dictionary of Monte Carlo diffusion MRI fingerprints.\u003c/li\u003e\n\u003cli\u003eFSL, a comprehensive library of analysis tools for FMRI, MRI and DTI brain imaging data.\u003c/li\u003e\n\u003cli\u003eDIAMOND, a c software that is characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion\u2010compartment imaging.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eElikoPy requires \u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython\u003c/a\u003e v3.7+ to run.\u003c/p\u003e\n\u003cp\u003eAfter cloning the repo, you can either firstly install all the python dependencies including optionnal dependency used to speed up the code:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install -r requirements.txt --user\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr you can install directly the library with only the mandatory dependencies (if you performed the previous step, you still need to perform this step):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ python3 setup.py install --user\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMicrostructure Fingerprinting is currently not avaible in the standard python repo, you can clone and install this library manually.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:rensonnetg/microstructure_fingerprinting.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e microstructure_fingerprinting\n$ python setup.py install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFSL also needs to be installed and availabe in our path if you want to perform mouvement correction or tbss.\u003c/p\u003e\n\u003cp\u003eUnfortunatly, the DIAMOND code is not publically available. If you do not have it in your possesion, you will not be able to use this algorithm. If you have it, simply add the executable to your path.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eTodo\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-development\" class=\"anchor\" href=\"#development\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h3\u003e\n\u003cp\u003eWant to contribute? Great!\u003c/p\u003e\n\u003cp\u003eDo not hesitate to open issue or pull request!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-todos\" class=\"anchor\" href=\"#todos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTodos\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eRelease a complete and accurate documentation for the library\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eFree Software, Hell Yeah!\u003c/strong\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1554399581.0 + "topics": [ + "microstructure-fingerprinting", + "fsl", + "tbss", + "python-library", + "diffusion-imaging", + "preprocessing", + "dmri", + "diamond", + "noddi", + "dti" + ], + "updated_at": 1627554863.0 }, { "data_format": 2, - "description": "Singularity recipe for centos7", + "description": null, "filenames": [ - "Singularity.dev" + "pin/conduit-binder/third-party/force-cover/Singularity" ], - "full_name": "pndni/centos7-base", + "full_name": "mmore500/conduit-quality-of-service-writeup", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1555436901.0 + "updated_at": 1631675356.0 }, { "data_format": 2, - "description": null, + "description": "Nextflow pipeline for inference of CRISPR edits from NGS (Amplicon Sequencing) data", "filenames": [ - "Singularity.ubuntu-20.04" + "Singularity" ], - "full_name": "zonca/singularity_github_ci", + "full_name": "gnetsanet/crispedit", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-test-build-singularity-containers-on-github-actions\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-build-singularity-containers-on-github-actions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest build Singularity containers on Github Actions\u003c/h2\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-crispedit\" class=\"anchor\" href=\"#crispedit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecrispedit\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eNextflow pipeline for inference of CRISPR edits from NGS (Amplicon Sequencing) data\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun crispedit/crisedit.nf --in_fastq /Users/ngebremedhin/Downloads/VB26_18_3428_P1087_308811w_BF8.fastq --ref canola_badc_allgenes.fa --out_dir ${PWD}/myProject --project_name LC25 --bucket bioinformatics-analysis-netsanet --sgrna \"CCTTCTGAGCCCATGAACAAATC\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 19.04.1\nLaunching `crispedit/crisedit.nf` [hopeful_swanson] - revision: 607eaca8d5\n\n[warm up] executor \u0026gt; local\nexecutor \u0026gt; local (27)\n[a7/a397c1] process \u0026gt; mergeReads [100%] 1 of 1 \u2714\n[58/d46b33] process \u0026gt; clustering [100%] 1 of 1 \u2714\n[1d/5e8374] process \u0026gt; dereplication [100%] 1 of 1 \u2714\n[bb/cc12b3] process \u0026gt; mapHighFrequencyReads [100%] 1 of 1 \u2714\n[44/eec241] process \u0026gt; samToBam [100%] 1 of 1 \u2714\n[92/c84f30] process \u0026gt; indexBam [100%] 1 of 1 \u2714\n[d2/119094] process \u0026gt; identiyEdits [100%] 1 of 1 \u2714\n[a2/38c0af] process \u0026gt; prepForPSA [100%] 1 of 1 \u2714\n[a6/51cabd] process \u0026gt; performPSA [100%] 9 of 9 \u2714\n[bd/fd96a3] process \u0026gt; combineClustalOut [100%] 9 of 9 \u2714\n[33/0f5567] process \u0026gt; createFinalReport [100%] 1 of 1 \u2714\n\nCompleted at: 25-Jul-2019 11:37:08\nDuration : 12.4s\nCPU hours : (a few seconds)\nSucceeded : 27\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003ecrispedit is written by Netsanet Gebremedhin.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1654214816.0 + "updated_at": 1631673344.0 }, { "data_format": 2, @@ -11828,360 +11235,364 @@ var data = "filenames": [ "Singularity" ], - "full_name": "VacantiLab/LehtioDDMSQuantSearch", + "full_name": "arabnejad/FabSim4", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-lehtiolabddamsproteomics\" class=\"anchor\" aria-hidden=\"true\" href=\"#lehtiolabddamsproteomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elehtiolab/ddamsproteomics\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eA Quantitative MS proteomics analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2e73ffd6aaca951b76d06bb9e625b9958985004799f48697d15d272d8754b96a/68747470733a2f2f7472617669732d63692e6f72672f6c656874696f6c61622f6464616d7370726f74656f6d6963732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/lehtiolab/ddamsproteomics.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/03c97559839c37998c3c1db1465217ff323c688ad1dbb4a617a90eefde35af1d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30312e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.01.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/219955514\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3cdf183590e0fd8280e9cf4762883d9e76e2b577ee19066a8d3debc07af0553/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3231393935353531342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/219955514.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3e006af0b85a286d286be1e4a41902ac68ed95f8253c038485c54ba693b93049/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c656874696f6c61622f6464616d7370726f74656f6d6963732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/lehtiolab/ddamsproteomics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThis workflow identifies peptides in mzML input data using \u003ca href=\"https://github.com/MSGFPlus/msgfplus\"\u003eMSGF+\u003c/a\u003e, and \u003ca href=\"https://github.com/percolator/percolator/\"\u003ePercolator\u003c/a\u003e, quantifies isobarically labeled samples with \u003ca href=\"https://github.com/openms/openms\"\u003eOpenMS\u003c/a\u003e, and precursor peptides with \u003ca href=\"https://github.com/fickludd/dinosaur\"\u003eDinosaur\u003c/a\u003e, and processes that output to formatted peptide and protein/gene tables using \u003ca href=\"https://github.com/lehtiolab/msstitch\"\u003eMsstitch\u003c/a\u003e. Optional PTM data is analyzed by \u003ca href=\"https://github.com/dfermin/lucxor\"\u003eLuciphor2\u003c/a\u003e, and differential expression analyses can be performed using \u003ca href=\"https://github.com/yafeng/deqms\"\u003eDEqMS\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun pipeline:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa --mods \u0027oxidation;carbamidomethylation\u0027 -profile standard,docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr for two sample sets of isobaric data you can:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa --mods \u0027oxidation;carbamidomethylation --isobaric \u0027setA:tmt10plex:126 setB:tmt10plex:127N\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more elaborate examples covering fractionation, PTMs, and more, the lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere is more extensive documentation on the options inside the main.nf file.\u003c/p\u003e\n\u003cp\u003eThe pipeline takes multiple mzML files as input and performs identification and quantification to output results and a QC report (\u003ca href=\"docs/example_qc.html\"\u003ean example can be found here\u003c/a\u003e)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003elehtiolab/ddamsproteomics was originally written by Jorrit Boekel and tries to follow the \u003ca href=\"https://nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e best practices and templates.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fabsim4\" class=\"anchor\" href=\"#fabsim4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFabSim4\u003c/h1\u003e\n\u003cp\u003eThis the migrated version of FabSim3 to Fabric2\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1654280178.0 + "updated_at": 1630410840.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + ".ci/github/Singularity" ], - "full_name": "Lipinski-B/DE-nf", - "latest_release": "v1.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-de-nf---pipeline-v10\" class=\"anchor\" aria-hidden=\"true\" href=\"#de-nf---pipeline-v10\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDE-nf : Pipeline V1.0\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-un-pipeline-nextflow-pour-r\u00e9aliser-une-analyse-dexpression-diff\u00e9rentielle-rnaseq-sur-un-ensemble-dindividus\" class=\"anchor\" aria-hidden=\"true\" href=\"#un-pipeline-nextflow-pour-r\u00e9aliser-une-analyse-dexpression-diff\u00e9rentielle-rnaseq-sur-un-ensemble-dindividus\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUn pipeline nextflow pour r\u00e9aliser une analyse d\u0027expression diff\u00e9rentielle RNAseq sur un ensemble d\u0027individus.\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/lipinskiboris/de-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5269\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\" width=\"100%\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"analyses-nf.png\"\u003e\u003cimg align=\"center\" width=\"60%\" src=\"analyses-nf.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eCe pipeline a \u00e9t\u00e9 d\u00e9velopp\u00e9 en vue de r\u00e9aliser des analyses RNAseq compl\u00e8tes \u00e0 partir de fichiers FASTA issus de s\u00e9quen\u00e7age NGS.\u003c/p\u003e\n\u003cp\u003eVoici un r\u00e9sum\u00e9 de la m\u00e9thode :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR\u00e9alisation d\u0027un index (optionnel).\u003c/li\u003e\n\u003cli\u003eAlignement des reads sur le g\u00e9nome de r\u00e9f\u00e9rence.\u003c/li\u003e\n\u003cli\u003eIntersection des fichiers SAM sur l\u0027annotation de r\u00e9f\u00e9rence.\u003c/li\u003e\n\u003cli\u003e\u00c9laboration de la matrice finale de comptage brute.\u003c/li\u003e\n\u003cli\u003eAnalyse d\u0027expression diff\u00e9rentielle sur R via le package DESeq2.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVeuillez consulter la section \"Usage\" pour tester le pipeline avec un ensemble de donn\u00e9es.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-d\u00e9pendences\" class=\"anchor\" aria-hidden=\"true\" href=\"#d\u00e9pendences\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eD\u00e9pendences\u003c/h2\u003e\n\u003cp\u003eLe pipeline est fonctionnel sous les distributions de Linux.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCe pipeline est enti\u00e8rement bas\u00e9 sur l\u0027utilisation de \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e. Il est fortement recommand\u00e9 de prendre connaissance de son \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003einstallation\u003c/a\u003e et de son utilisation avant d\u0027ex\u00e9cuter le pipeline.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSoftware \u00e0 installer :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSTAR (version 2.7.7a)\u003c/li\u003e\n\u003cli\u003eBWA (version 0.7.17-r1188)\u003c/li\u003e\n\u003cli\u003esamtools (version 1.9)\u003c/li\u003e\n\u003cli\u003efastqc (version 0.11)\u003c/li\u003e\n\u003cli\u003emultiqc (version 1.8)\u003c/li\u003e\n\u003cli\u003ehtseq-count (version 0.13.5)\u003c/li\u003e\n\u003cli\u003eR (version 4.0.3)\u003c/li\u003e\n\u003cli\u003ePackage R : DESeq2, edgeR, pheatmap, RColorBrewer, ggbeeswarm, genefilter, biomaRt, stringr, ggplot2, NMF, tidyverse.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFichier compl\u00e9mentaire n\u00e9cessaire :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFichier d\u0027annotation GTF : \u003ca href=\"https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/latest/\" rel=\"nofollow\"\u003ehg38\u003c/a\u003e ou \u003ca href=\"https://ftp.ncbi.nlm.nih.gov/genomes/all/annotation_releases/7160/102/GCF_006496715.1_Aalbo_primary.1/GCF_006496715.1_Aalbo_primary.1_genomic.gtf.gz\" rel=\"nofollow\"\u003eAedes albopictus\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFichier FNA pour l\u0027index : \u003ca href=\"https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes/\" rel=\"nofollow\"\u003ehg38\u003c/a\u003e ou \u003ca href=\"https://ftp.ncbi.nlm.nih.gov/genomes/all/annotation_releases/7160/102/GCF_006496715.1_Aalbo_primary.1/GCF_006496715.1_Aalbo_primary.1_genomic.fna.gz\" rel=\"nofollow\"\u003eAedes albopictus\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFichier XLS : M\u00e9tadonn\u00e9e (voir dossier data/ pour Aedes albopictus)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAutre :\nDes containers Docker et Singularity ont \u00e9galement \u00e9t\u00e9 \u00e9labor\u00e9 en vue de permettre aux utilisateurs de lancer le pipeline sans avoir \u00e0 installer toutes les d\u00e9pendances n\u00e9cessaires de la partie 2. Les installations des outils \u003ca href=\"https://docs.docker.com/engine/install/ubuntu/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e et \u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e sont n\u00e9cessaire au pr\u00e9alable. Voir la derni\u00e8re section de \"Usage\" pour plus de d\u00e9tails.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eFichier FASTA/FASTQ\u003c/td\u003e\n\u003ctd\u003eCorresponds aux fichiers FASTA/FASTQ d\u0027int\u00e9r\u00eat compress\u00e9s au format .gz.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-param\u00e8tres\" class=\"anchor\" aria-hidden=\"true\" href=\"#param\u00e8tres\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParam\u00e8tres\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-param\u00e8tres-obligatoires-\" class=\"anchor\" aria-hidden=\"true\" href=\"#param\u00e8tres-obligatoires-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParam\u00e8tres obligatoires :\u003c/h4\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eNom\u003c/th\u003e\n\u003cth\u003eExemple\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input\u003c/td\u003e\n\u003ctd\u003e/input/\u003c/td\u003e\n\u003ctd\u003eChemin vers le dossier o\u00f9 se trouvent les fichiers FASTA \u00e0 utiliser pour l\u0027analyse. Assurez-vous de n\u0027avoir que les fichiers FASTA d\u0027int\u00e9r\u00eats dans ce dossier et rien d\u0027autre.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output\u003c/td\u003e\n\u003ctd\u003e/output/\u003c/td\u003e\n\u003ctd\u003eChemin vers le dossier o\u00f9 se trouveront les diff\u00e9rents r\u00e9sultats issus du pipeline.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--GTF\u003c/td\u003e\n\u003ctd\u003e/data/fichier.gtf\u003c/td\u003e\n\u003ctd\u003eChemin o\u00f9 se trouve le fichier d\u0027annotation \u00e0 utiliser pour l\u0027index via STAR et l\u0027intersection via htseq-count.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-param\u00e8tres-obligatoires-compl\u00e9mentaires-pour-lindex-\" class=\"anchor\" aria-hidden=\"true\" href=\"#param\u00e8tres-obligatoires-compl\u00e9mentaires-pour-lindex-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParam\u00e8tres obligatoires compl\u00e9mentaires pour l\u0027index :\u003c/h4\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eNom\u003c/th\u003e\n\u003cth\u003eExemple\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--index\u003c/td\u003e\n\u003ctd\u003e/data/index\u003c/td\u003e\n\u003ctd\u003eChemin vers le dossier o\u00f9 se trouve l\u0027index STAR \u00e0 utiliser pour le pipeline. Si cette option n\u0027est pas utilis\u00e9e, merci de vous assurer de fournir l\u0027option --FNA en plus de l\u0027option --GTF pour r\u00e9aliser l\u0027index. Par d\u00e9faut, null.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eOu bien :\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--FNA\u003c/td\u003e\n\u003ctd\u003e/data/fichier.fna\u003c/td\u003e\n\u003ctd\u003eChemin o\u00f9 se trouve le fichier .fna \u00e0 fournir obligatoirement pour r\u00e9aliser l\u0027index si l\u0027option --index n\u0027est pas fourni.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-param\u00e8tres-optionellescompl\u00e9mentaires-\" class=\"anchor\" aria-hidden=\"true\" href=\"#param\u00e8tres-optionellescompl\u00e9mentaires-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParam\u00e8tres optionelles/compl\u00e9mentaires :\u003c/h4\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eNom\u003c/th\u003e\n\u003cth\u003eExemple\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mapper\u003c/td\u003e\n\u003ctd\u003eSTAR/BWA\u003c/td\u003e\n\u003ctd\u003eMapper \u00e0 utiliser. Par d\u00e9faut BWA (MEM).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--thread\u003c/td\u003e\n\u003ctd\u003eN\u003c/td\u003e\n\u003ctd\u003eNombre de thread \u00e0 utiliser pour le pipeline. Par d\u00e9faut 1.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--R\u003c/td\u003e\n\u003ctd\u003eon/off\u003c/td\u003e\n\u003ctd\u003eOption pour r\u00e9aliser (\"on\") ou non (\"off\") l\u0027analyse d\u0027expression diff\u00e9rentielle sur R par d\u00e9faut sur pipeline. Par d\u00e9faut, off.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--metadata\u003c/td\u003e\n\u003ctd\u003e/data/metadata.xls\u003c/td\u003e\n\u003ctd\u003eChemin o\u00f9 se trouve le fichier de m\u00e9tadonn\u00e9es \u00e0 utiliser pour l\u0027analyse d\u0027expression diff\u00e9rentielle sur R. Obligatoire si l\u0027option --R est mis sur \"on\"\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eLancement basique du pipeline, dans le cas o\u00f9 toutes les d\u00e9pendances sont install\u00e9es localement.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf --input /input/ --GTF /data/fichier.gtf --FNA /data/fichier.fna --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLa matrice de comptage r\u00e9sultant correspond au fichier finale.txt dans le dossier \"/output/merge/finale.txt\".\u003c/p\u003e\n\u003cp\u003eUn script DE.R est mis \u00e0 votre disposition dans le dossier \"bin/\" de ce r\u00e9pertoire git, afin de vous permettre de r\u00e9aliser par vous-m\u00eame l\u0027analyse de l\u0027expression diff\u00e9rentielle. Vous aurez donc besoin de la matrice finale pour terminer l\u0027analyse mais aussi d\u0027un fichier XLS r\u00e9pertoriant les m\u00e9tadonn\u00e9es des \u00e9chantillons d\u0027int\u00e9r\u00eats.\u003c/p\u003e\n\u003cp\u003eLe script DE.R se lance comme ceci :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/DE.r finale.txt /data/Metadata.xls\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eVous pouvez utiliser votre propre fichier XLS, dans ce cas il est recommand\u00e9 de suivre comme template le fichier \"Metadata.xls\" que vous trouverez dans le dossier \"data/\" de ce r\u00e9pertoire. Le but ici \u00e9tant de pouvoir permettre \u00e0 l\u0027utilisateur de r\u00e9aliser ses propres analyses exploratoires d\u0027expression diff\u00e9rentielle \u00e0 partir du template fourni dans le script DE.R\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eVous pouvez \u00e9galement lancer le pipeline avec la r\u00e9alisation d\u0027une analyse d\u0027expression diff\u00e9rentielle par d\u00e9faut sur R de fa\u00e7on automatique, via l\u0027option --R.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf --input /input/ --GTF /data/fichier.gtf --FNA /data/fichier.fna --R on --metadata /data/metadata.xls --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUn rapport sera mis \u00e0 votre disposition dans le dossier \"/output/R/\".\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eDans le cas o\u00f9 toutes les d\u00e9pendances sont install\u00e9es localement et vous souhaitez utiliser votre propre index STAR pour l\u0027analyse, vous pouvez suivre cette proc\u00e9dure. Attention pour des raisons de compatibilit\u00e9, l\u0027index ajout\u00e9 avec l\u0027option --index doit \u00eatre r\u00e9alis\u00e9 avec la m\u00eame version du mapper que celle utilis\u00e9e pour l\u0027alignement.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf --input /input/ --GTF /data/fichier.gtf --index /data/mapper_index --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eEnfin vous pouvez lancer le pipeline via l\u0027utilisation de containers Docker/Singularity via l\u0027option -profile.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf -profile docker --input /input/ --GTF /data/fichier.gtf --FNA /data/fichier.fna --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eou\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf -profile singularity --input /input/ --GTF /data/fichier.gtf --FNA /data/fichier.fna --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLipinski Boris\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:boris.lipinski@etu.univ-lyon1.fr\"\u003eboris.lipinski@etu.univ-lyon1.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDevelopeur \u00e0 contacter pour support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "full_name": "qwert2333/CEPCSW_training", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cepcsw\" class=\"anchor\" href=\"#cepcsw\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://cepc.github.io/CEPCSW/\" rel=\"nofollow\"\u003eCEPCSW\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.travis-ci.com/cepc/CEPCSW\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/51cb592ac6435ae6b6bdc6cca7a941779434c9db16df9857df2a94e6f239971b/68747470733a2f2f7777772e7472617669732d63692e636f6d2f636570632f4345504353572e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://www.travis-ci.com/cepc/CEPCSW.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/cepc/CEPCSW/actions\"\u003e\u003cimg src=\"https://github.com/cepc/CEPCSW/workflows/CI/badge.svg?branch=master\" alt=\"CI\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCEPC offline software prototype based on \u003ca href=\"https://github.com/key4hep\"\u003eKey4hep\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start\u003c/h2\u003e\n\u003cp\u003eSSH to lxslc7 (CentOS 7).\u003c/p\u003e\n\u003cp\u003eBefore run following commands, please make sure you setup the CVMFS:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone git@github.com:cepc/CEPCSW.git\n$ cd CEPCSW\n$ git checkout master # branch name\n$ source setup.sh\n$ ./build.sh\n$ ./run.sh Examples/options/helloalg.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-packages\" class=\"anchor\" href=\"#packages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackages\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eExamples: For new comers and users\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDetector: Geometry\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenerator: Physics Generator\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSimulation: Detector Simulation\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDigitization: Digitization\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReconstruction: Reconstruction\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-conventions-for-collections\" class=\"anchor\" href=\"#conventions-for-collections\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConventions for collections\u003c/h2\u003e\n\u003cp\u003eKeep the collection names compatible between the prototype and the existing CEPC software.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMCParticle\u003c/li\u003e\n\u003cli\u003eVXDCollection\u003c/li\u003e\n\u003cli\u003eSITCollection\u003c/li\u003e\n\u003cli\u003eTPCCollection\u003c/li\u003e\n\u003cli\u003eSETCollection\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1654520921.0 + "updated_at": 1631168746.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.main", + "Singularity.def" ], - "full_name": "przepiorkaGrzegorz/singularity_container", + "full_name": "shubavarshini/microbiome", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-microbiome\" class=\"anchor\" href=\"#microbiome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emicrobiome\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1660178824.0 + "updated_at": 1630678051.0 }, { "data_format": 2, - "description": "High Resolution Non-Deterministic Face Aging", + "description": "Singularity image for the presence_absence pipeline ", "filenames": [ - "gdown.pl/Singularity" + "Singularity" ], - "full_name": "arshagarwal/Face-AHQ-GAN2", - "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-steps-to-run-using-docker-using-nvidia-gpus\" class=\"anchor\" href=\"#steps-to-run-using-docker-using-nvidia-gpus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to run using docker using Nvidia gpus:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall docker using \u003ca href=\"https://docs.docker.com/engine/install/ubuntu/\" rel=\"nofollow\"\u003edocker installation guide\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eInstall nvidia-docker2 using \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker\" rel=\"nofollow\"\u003envidia-docker2 installation guide\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNote: Ensure to check the latest versions of nvidia cuda runtime and coroborrate with pytorch cuda requirements , this guide was uploaded on 4/9/2020.\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild the image using \u003ccode\u003edocker build -f docker -t slimgan:gpu\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eRun the Container using \u003ccode\u003edocker run --gpus all --shm-size 10G -it slimgan:gpu\u003c/code\u003e.\n5.Run the \u003ccode\u003envidia-smi\u003c/code\u003e to check if gpus work.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003epython main.py --img_dir ../data/celeba_hq/train --iters 20000,60000,100000 --img_size 128,256,512 --batch_size 16,8,2 --gpus 0,1 --c_dim 2 --num_workers 5\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-steps-to-run-the-code-on-google-colab-just-3-lines-of-code\" class=\"anchor\" href=\"#steps-to-run-the-code-on-google-colab-just-3-lines-of-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to run the code on Google colab (just 3 lines of code):\u003c/h2\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eClone the code by running the command \u003ccode\u003e!git clone https://username:password@github.com/arshagarwal/C_slim_gan.git -b arsh_spectral\u003c/code\u003e .\n\u003cstrong\u003eReplace the username and password with your github username and password respectively.\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003ecd C_slim_gan\u003c/code\u003e to navigate to the \u003cstrong\u003eC_slim_gan\u003c/strong\u003e directory.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003e!bash import_dataset.sh\u003c/code\u003e to import the \u003cstrong\u003eBig slim dataset\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003e! python main.py --rafd_image_dir Big_slim --num_iters 20000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 \u003c/code\u003e to train on the \u003cstrong\u003eBig_slim_dataset\u003c/strong\u003e.\n\u003cstrong\u003eAlternatively to train on custom dataset replace the \u003ccode\u003eslim_dataset/Train_dataset\u003c/code\u003e string with the path of your custom dataset.\u003c/strong\u003e\u003cbr\u003e\nFor further options such as \u003cstrong\u003enumber of epochs, batch_size etc\u003c/strong\u003e refer \u003cstrong\u003emain.py\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-note-after-running-step-3-after-every-sample-stepth-iteration-generated-samples-will-be-stored-in-stargansamples-folder-for-performance-evaluation\" class=\"anchor\" href=\"#note-after-running-step-3-after-every-sample-stepth-iteration-generated-samples-will-be-stored-in-stargansamples-folder-for-performance-evaluation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNote: After running step 3 after every sample stepth iteration generated samples will be stored in stargan/samples folder for performance evaluation.\u003c/h3\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-for-continuing-training-from-20000-iteration-to-30000-iteration-follow\" class=\"anchor\" href=\"#for-continuing-training-from-20000-iteration-to-30000-iteration-follow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor Continuing training from 20000 iteration to 30000 iteration follow:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003epython main.py --rafd_image_dir Big_slim --num_iters 30000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 --resume_iters 20000\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "vdclab/simg-PA_tools", + "latest_release": "0.0.2", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-presence_absence-tools-image\" class=\"anchor\" href=\"#singularity-presence_absence-tools-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity presence_absence tools image\u003c/h1\u003e\n\u003cp\u003eSingularity image for the presence_absence pipeline.\u003c/p\u003e\n\u003cp\u003eThis repository is created to be able to not depend on instalation or module loading for the presence abscence pipeline.\u003c/p\u003e\n\u003cp\u003eIn this Singularity container the following software and python library are installed :\u003c/p\u003e\n\u003cp\u003eSoftwares:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI BLAST+ == 2.10.1\u003c/li\u003e\n\u003cli\u003esilix == 1.2.11\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePython libraries:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003encbi-genome-download == 0.3.0\u003c/li\u003e\n\u003cli\u003eete3 == 3.1.2\u003c/li\u003e\n\u003cli\u003ematplotlib == 3.3.3\u003c/li\u003e\n\u003cli\u003epandas == 1.1.5\u003c/li\u003e\n\u003cli\u003ebiopython == 1.78\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1651478504.0 + "updated_at": 1629657667.0 }, { "data_format": 2, - "description": "Repository for Open OnDemand Applications on Lehigh\u0027s HPC clusters", + "description": "clone of temp_tc", "filenames": [ - "spark_r/Singularity" + "Singularity" ], - "full_name": "alexpacheco/lurc-ood-apps", + "full_name": "JoshLorDeveloper/temp_tc_clone", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-open-ondemand-applications\" class=\"anchor\" href=\"#open-ondemand-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpen OnDemand Applications\u003c/h1\u003e\n\u003cp\u003eOpen OnDemand Applications on Lehigh\u0027s HPC cluster.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMaple\u003c/li\u003e\n\u003cli\u003eMathematica\u003c/li\u003e\n\u003cli\u003eMATLAB\u003c/li\u003e\n\u003cli\u003eAbaqus\u003c/li\u003e\n\u003cli\u003eAnsys\u003c/li\u003e\n\u003cli\u003eDesktop Environment - XCFE\u003c/li\u003e\n\u003cli\u003eGNU Octave\u003c/li\u003e\n\u003cli\u003eSAS\u003c/li\u003e\n\u003cli\u003eVisualization Tools\n\u003cul\u003e\n\u003cli\u003eASE\u003c/li\u003e\n\u003cli\u003eAvogadro 2\u003c/li\u003e\n\u003cli\u003eGabedit\u003c/li\u003e\n\u003cli\u003eGaussView\u003c/li\u003e\n\u003cli\u003eParaview\u003c/li\u003e\n\u003cli\u003ePWGui\u003c/li\u003e\n\u003cli\u003ePyMol\u003c/li\u003e\n\u003cli\u003eVESTA\u003c/li\u003e\n\u003cli\u003eXCrysDen\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSTATA\u003c/li\u003e\n\u003cli\u003eDeepLabCut Desktop Application\u003c/li\u003e\n\u003cli\u003eSpyder\u003c/li\u003e\n\u003cli\u003eSpark + Jupyter\u003c/li\u003e\n\u003cli\u003eSpark + RStudio\u003c/li\u003e\n\u003cli\u003eX-Ray Crytallagraphic analysis tools - XDS, Phenix, CCP4, Cytoscape\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-transactive_control\" class=\"anchor\" href=\"#transactive_control\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etransactive_control\u003c/h1\u003e\n\u003cp\u003eCode meant to support and simulate the Social Game that will be launched in 2020. Elements of transactive control and behavioral engineering will be tested and designed here\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://dvc.org/doc/install\" rel=\"nofollow\"\u003edvc\u003c/a\u003e (with google drive support)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOn linux this is \u003ccode\u003epip install \u0027dvc[gdrive]\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall Docker, if you have not already.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc remote add -d gdrive gdrive://1qaTn6IYd3cpiyJegDwwEhZ3LwrujK3_x\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc pull\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./run.sh\u003c/code\u003e from the root of the repo. This will put you in a shell in the docker container with the \u003ccode\u003erl_algos/logs\u003c/code\u003e directory mounted\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 StableBaselines.py sac test_experiment\u003c/code\u003e in the docker container to start an experiment with the name \u003ccode\u003etest_experiment\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003etensorboard --logdir rl_algos/logs\u003c/code\u003e from outside the docker container to view the logs\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-12202020\" class=\"anchor\" href=\"#12202020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e12/20/2020\u003c/h3\u003e\n\u003cp\u003eThis repository has been cleaned and updated for use. It contains: (1) The OpenAI gym environment \"OfficeLearn\", in the \"gym-socialgame\" folder, and (2) implementations of Reinforcement learning algorithms in \"rl_algos\" folder. In the \"simulations\" folder are various datasets for setting up and training models associated with the gym simulation.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-912020\" class=\"anchor\" href=\"#912020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e9/1/2020\u003c/h3\u003e\n\u003cp\u003eThe most recent running of the code involves navigating to the rl_algos/ directory, then running the python command for the vanilla version:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac\u003c/p\u003e\n\u003cp\u003eAdding in the planning model can be done with the following flags:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac --planning_steps=10 --planning_model=Oracle --num_steps=10000\u003c/p\u003e\n\u003cp\u003ePlease see transactive_control/gym-socialgame/gym_socialgame/envs for files pertaining to the setup of the environment. The socialgame_env.py contains a lot of the information necessary for understanding how the agent steps through the environment. The reward.py file contains information on the variety of reward types available for testing. agents.py contains information on the deterministic people that we created for our simulation.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gym-socialgame\" class=\"anchor\" href=\"#gym-socialgame\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egym-socialgame\u003c/h3\u003e\n\u003cp\u003eOpenAI Gym environment for a social game.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1651619112.0 + "updated_at": 1636716500.0 }, { "data_format": 2, "description": null, "filenames": [ - "planner/symk/Singularity", - "planner/symk/misc/releases/19.06/Singularity.19.06", - "planner/symk/misc/releases/latest/Singularity", - "planner/symk/misc/releases/19.12/Singularity.19.12" + "Singularity" ], - "full_name": "zihangs/Janus", + "full_name": "DCAN-Labs/heudiconv-helper", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-janus\" class=\"anchor\" href=\"#janus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJanus\u003c/h1\u003e\n", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-bids-conversion-scripts\" class=\"anchor\" href=\"#bids-conversion-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBIDS Conversion Scripts\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eThis repository contains scripts that will assist in the conversion of raw DICOM data sets to \u003ca href=\"http://bids.neuroimaging.io/format\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e. The \"heuristics\" folder contains example scripts that have been used to convert data from DICOM to BIDS. \u003cstrong\u003eThis folder may be helpful to build your own heuristics script.\u003c/strong\u003e For additional information on how to create a heuristic script see the \u003ca href=\"https://github.com/nipy/heudiconv\"\u003eheudiconv\u003c/a\u003e github page.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-hosting\" class=\"anchor\" href=\"#container-hosting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Hosting\u003c/h3\u003e\n\u003cp\u003ePull the most recent container below, (\u003cstrong\u003eNOTE: you only have to do this once!\u003c/strong\u003e):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tjhendrickson/BIDS_scripts:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" href=\"#singularity-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Usage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/singularity/images/directory/imagename.img --help\nusage: [-h] [--output_dir OUTPUT_DIR] [--dicom_dir DICOM_DIR]\n [--study_name STUDY_NAME] [--ses_id SES_ID] [--subj_id SUBJ_ID]\n [--heuristic HEURISTIC] [--dry_run]\n\nScript that controls BIDS conversion for individual studies\n\noptional arguments:\n -h, --help show this help message and exit\n --output_dir OUTPUT_DIR\n The directory that the BIDS data will be outputted to\n --dicom_dir DICOM_DIR\n The directory that houses unzipped dicom\n directories/files.\n --study_name STUDY_NAME\n What is the shorthand name for this study?\n --ses_id SES_ID scanning session id\n --subj_id SUBJ_ID subject id\n --heuristic HEURISTIC\n Path to heuristic file, if the file is already within\n the container (i.e. within heuristics folder) you do\n not have to specify a path.\n --dry_run Dry run. A dicominfo_*.tsv file will generate within\n .heudiconv/\u0027subj_id\u0027/info directory which can be used\n to create heuristic script\n\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eThis application must be run with either the \"--heuristic\" or \"--dry_run\" argument, it will fail otherwise.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse the \"--dry_run\" argument to take a closer look at the acquistion parameters for a scanning session.\u003c/p\u003e\n\u003cp\u003eTo run a single participant with dry_run argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --dry_run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output a hidden folder (named .heudiconv) along with sub-folders based on arguments provided to \"--subj_id\" and \"--ses_id\" respectively.\nWithin the sub-folders will be a tsv file that begins with \"dicominfo\". \u003cstrong\u003eBased on the example above the path to the file will be \".heudiconv/1000/ses-10000/info/dicominfo_ses-10000.tsv\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse this tsv file to design a heuristics script to organize your eventual nifti data. \u003cstrong\u003eSee this tutorial for a how to on heuristic script creation (\u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman3\" rel=\"nofollow\"\u003eheuristics tutorial\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo run a single participant with heuristic argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir -B /path/to/heuristics/script:/heuristic.py \\\n /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --heuristic /heuristic.py\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-important-notes\" class=\"anchor\" href=\"#important-notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Notes\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gotchas\" class=\"anchor\" href=\"#gotchas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGotchas\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Bind Mounting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to run this container you will have to use \"bind mounting\", meaning you will have to link local folders/files to existing folders/files within the container with the -B flag. In the example above the local folder \"/home/timothy/sandbos_DO_NOT_DELETE/BIDS/142_CIFASD_4\" becomes \"/output_dir\" within the container as they are separated by a colon (:). \u003cstrong\u003eNotice that in both cases above the output and dicom folder and heuristic file are bound to /output_dir, /dicom_dir and /heuristic.py respectively, this is very important.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) DICOM Data Formatting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the restrictive nature of BIDS the dicom data must be in a particular format in order for the conversion to work properly. This application will copy dicom data directories by searching for either the --subj_id or --ses_id argument present within a dicom directory name, place them in a separate directory, and rearrange them. So for example if the dicom directory is named \"XYXY4776XYXY\" --subj_id 4776 the application will find the \"4776\" pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Subject ID and Session ID names\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou must use alphanumerics (i.e. letters or numbers) only (\u003cstrong\u003eno special characters\u003c/strong\u003e) with your subject IDs (subj_id) and session IDs (ses_id). \u003cstrong\u003eNote the \"--ses_id\" argument is optional\u003c/strong\u003e!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-best-practices\" class=\"anchor\" href=\"#best-practices\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBest Practices\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Initial Conversion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile testing the initial BIDS conversion it is best to start with one or two datasets and specify the \u0027--dry_run\u0027 argument (see above for an example of usage).\nThis will create a dicom_info tsv file which can be used for heuristic script creation.\nSee Step 3 of \u0027Run HeuDiConv on ses-001 scans to get the dicominfo file\u0027 within \u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman2\" rel=\"nofollow\"\u003eStanford BIDS Tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) BIDS Validator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce satisfied with an initial BIDS conversion, prior to running the conversion on an entire study first ensure that the BIDS converted dataset meets the BIDS specification by using the \u003ca href=\"http://incf.github.io/bids-validator/\" rel=\"nofollow\"\u003eBIDS validator web version\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Longitudinal Format\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen converting data to BIDS you can certainly have a cross sectonal directory format such as below:\nBIDS_output\nsub-01\nsub-02\nsub-03\nsub-04\netc\nHowever, I suggest placing data within a longitudinal directory format even if you have cross-sectional data:\nBIDS_output\nsub-01\nses-01\nses-02\nsub-02\nses-01\netc\u003c/p\u003e\n\u003cp\u003eYou can control the BIDS directory format by providing both the arguments --subj_id --ses_id for a conversion, if you only specify one of the two arguments the data will be outputted in a cross-sectional format.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1652720467.0 + "updated_at": 1634787198.0 }, { "data_format": 2, - "description": "The source code for the TAAMP project", + "description": "A Singularity container Definition File for running the Tensorflow Object Detection API and a demo Python script.", "filenames": [ - "downward/misc/releases/19.06/Singularity.19.06", - "downward/misc/releases/20.06/Singularity.20.06", - "downward/misc/releases/19.12/Singularity.19.12" + "singularity/Singularity" ], - "full_name": "ScazLab/TAAMP", + "full_name": "cedarwarman/object_detection_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-task-affordance-and-motion-planning-taamppproach\" class=\"anchor\" href=\"#task-affordance-and-motion-planning-taamppproach\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTask, Affordance, And Motion Planning (TAAMP)pproach\u003c/h1\u003e\n\u003cp\u003eWe used TAAMP, which is an affordance-based TAMP approach to expedite the search on tasks with contrained environment, or tasks that are infeasible due to environmental constraints. In this approach, we checked whether the environment allow the effects of certain actions. Or in other words, whether the environment can afford these actions. This is because some constraints imposed by the context, such as a very crowded surface that does not allow more objects to be placed on top of it as shown in the image below, is independent of robot configurations (e.g., grasp poses of the object). We refer to the quality of being \"place-able\" as affordance, and each action may have different affordances.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_7_zoom_in.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_7_zoom_in.png\" height=\"150\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe build upon PDDLStream, the state-of-the-art TAMP planner. The source code of PDDLStream can be found \u003ca href=\"https://github.com/caelan/pddlstream\"\u003ehere\u003c/a\u003e, and the original readme file can be found \u003ca href=\"PDDLSTREAM_README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone --recursive git@github.com:ScazLab/Affordance-based-TAMP.git\n$ cd Affordance-based-TAMP\nAffordance-based-TAMP$ ./downward/build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstall the dependencies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install pybullet numpy scipy\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-demonstrations\" class=\"anchor\" href=\"#demonstrations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemonstrations\u003c/h2\u003e\n\u003cp\u003eThis repository contains the demonstrations in simulation that are included in the paper. There are four types of tasks: unconstrained tasks, constrained tasks 1, constrained tasks 2, and infeasible tasks. Each type of task has a demonstration without the tool and one with the tool. In these tasks, a robot should cook the \"celery\" (the green block) by first placing it on the \"sink\" (the blue surface) and then placing it on the \"stove\" (the red surface). The \"radish\" (the cyan block) is not directly related to the goal. Images of each task is shown below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_1.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_2.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_2.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_3.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_3.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_4.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_4.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_5.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_5.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_6.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_6.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_7.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_7.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_8.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_8.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe compared our results with PDDLStream which doesn\u0027t have these affordance checks, and used them as control conditions.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preliminaries\" class=\"anchor\" href=\"#preliminaries\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreliminaries\u003c/h3\u003e\n\u003cp\u003eBefore you ran the code, you should update the directories in the urdf files in \u003ccode\u003eexamples/pybullet/utils/models\u003c/code\u003e and in \u003ccode\u003eexamples/pybullet/utils/models/drake/objects\u003c/code\u003e with the prefix \u003ccode\u003emeiying_\u003c/code\u003e. I attempted to use relative paths but the urdf cannot find the correct point cloud file. I apologize for any inconvenience.\u003c/p\u003e\n\u003cp\u003eYou also need to correct the path in the \u003ccode\u003eexamples/pybullet/utils/model/bb.json\u003c/code\u003e, \u003ccode\u003elearned_samples\\ur/simulator\\pointcloud\\tool.json\u003c/code\u003e, the \u003ccode\u003eget_package_dir()\u003c/code\u003e function in \u003ccode\u003eexamples/pybullet/utils/pybullet_tools/learn_affordance_tamp/constants.py\u003c/code\u003e. This is awarkward coding style, but I run out of time to fix it.\u003c/p\u003e\n\u003cp\u003eNote: If you would like to learn the affordances and use the generic affordance tests, you should train the tasks with TRI-STAR (steps omitted here. Please refer to the TRI-STAR readme file to see how to use the package; You also need to update the file location of the learned affordances in the function \u003ccode\u003e\\_get_goal_range\u003c/code\u003e in \u003ccode\u003emeiying_primitives.py\u003c/code\u003e).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-unconstrained-tasks\" class=\"anchor\" href=\"#unconstrained-tasks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnconstrained Tasks\u003c/h3\u003e\n\u003cp\u003eThe non-tool-use version was orginally included in \u003ca href=\"https://github.com/caelan/pddlstream/tree/main/examples/pybullet/kuka\"\u003ePDDLStream\u003c/a\u003e. We included this task to ensure that the task is friendly to the current planners. In the tool-use version, the robot should first retrieve the the green block with the L-shaped tool.\u003c/p\u003e\n\u003cp\u003eDemonstrations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-Tool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_1_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_2_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNon-Tool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_1_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_2_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can add the \u003ccode\u003e-viewer\u003c/code\u003e option to visualize the task and the solution, for example:\n\u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_1_test.run -viewer\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-constrained-tasks-1\" class=\"anchor\" href=\"#constrained-tasks-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConstrained Tasks 1\u003c/h3\u003e\n\u003cp\u003eIn these tasks, the environments are more constrainted than constrained tasks. However, the robots does not need to relocate objects that are not directly related to the goal (e.g., the cyan block). In the non-tool-use task, the robot needs to place the green block on the relatively crowded vlue surface which has limited space for the green block. In the tool-use task, the robot needs to retrieve the green block hiding under the orange tunnel with a T-shaped tool. In these tasks, the red blocks are immovable.\u003c/p\u003e\n\u003cp\u003eDemonstrations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-Tool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_3_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_4_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNon-Tool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_3_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_4_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-constrained-tasks-2\" class=\"anchor\" href=\"#constrained-tasks-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConstrained Tasks 2\u003c/h3\u003e\n\u003cp\u003eIn these tasks, the robots need to relocate objects that are not directly related to the goal (e.g., the cyan block). In the non-tool-use task, the robot needs to relocate the cyan block to make room for the green block. In the tool-use task, the robot needs to retrieve the L-shaped tool hiding under the orange tunnel with a T-shaped tool, in order to pull the green block towards itself with the T-shaped tool. In these tasks, the red blocks are also immovable.\u003c/p\u003e\n\u003cp\u003eDemonstrations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-Tool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_5_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_6_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNon-Tool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_5_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_6_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-infeasible-tasks\" class=\"anchor\" href=\"#infeasible-tasks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInfeasible Tasks\u003c/h3\u003e\n\u003cp\u003eIn these tasks, the robots cannot complete the tasks. The green block is hidding under immovable yellow contrainer, which makes it impossible to pick, pull or push the green block to retrieve it.\u003c/p\u003e\n\u003cp\u003eDemonstrations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-Tool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_7_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_8_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNon-Tool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_7_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_8_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-adding-new-examples\" class=\"anchor\" href=\"#adding-new-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding New Examples\u003c/h2\u003e\n\u003cp\u003eTo add a new example, one should first create a folder under \u003ccode\u003eexamples/pybullet\u003c/code\u003e. In this folder, one should create a \u003ccode\u003e__init__.py\u003c/code\u003e to initialize this folder as a package, a \u003ccode\u003edomain.pddl\u003c/code\u003e which defines the problem (e.g., the actions), a \u003ccode\u003estream.pddl\u003c/code\u003e with the streams to certify predicates or generate samples, and a \u003ccode\u003erun.py\u003c/code\u003e that defines the environment.\u003c/p\u003e\n\u003cp\u003eIf a new object is needed, one should create an urdf under \u003ccode\u003eexamples/pybullet/utils/models\u003c/code\u003e. If a pointcloud/mesh is needed, one should create an \u003ccode\u003eobj\u003c/code\u003e file, as well as a ply file with the same name for collision detection purposes.\u003c/p\u003e\n\u003cp\u003eWhen a new action is needed, the names of the correspondence affordance checks in the \u003ccode\u003estream.pddl\u003c/code\u003e should starts with the \u003ccode\u003etest\u003c/code\u003e and also include the word \u003ccode\u003efeasible\u003c/code\u003e so that these checks will be applied earlier in the search process when necessary.\u003c/p\u003e\n\u003cp\u003eWhen sampling for certain affordances are needed, and when fluents are needed (currently only support the AtPose fluent), the name of the affordance samplers should be added to \u003ccode\u003e./pddlstream/algorithms/scheduling/apply_fluents.py\u003c/code\u003e line 98. Note: this is by no means be considered as good coding style, but I did not have time to completely refactor the code. The purpose of this source code is to show the benefit of considering affordances.\u003c/p\u003e\n\u003cp\u003eNote: I only performed a minor code refactor before I upload this source code due to time constraints. I apologize for the messiness of the code.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-tensorflow-object-detection-in-singularity\" class=\"anchor\" href=\"#tensorflow-object-detection-in-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTensorflow Object Detection in Singularity\u003c/h1\u003e\n\u003cp\u003eThis repo contains a Singularity Definition File for making a container that runs the \u003ca href=\"https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md\"\u003eTensorflow Object Detection API\u003c/a\u003e. It also contains a Python script that runs a modified version of the API\u0027s \u003ca href=\"https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/eager_few_shot_od_training_tf2_colab.ipynb\"\u003eEager Few Shot Detector demo\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-singularity-container\" class=\"anchor\" href=\"#building-the-singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the Singularity container\u003c/h2\u003e\n\u003cp\u003eTo build the Singularity container with \u003ca href=\"https://cloud.sylabs.io/builder\" rel=\"nofollow\"\u003eRemote Builder\u003c/a\u003e, first add your credentials:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity remote login\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity -v build --remote ./singularity/tf_od.sif ./singularity/Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-demo\" class=\"anchor\" href=\"#running-the-demo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the demo\u003c/h2\u003e\n\u003cp\u003eTo run the demo with X11 forwarding and error message suppression:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv -B ~/.Xauthority ./singularity/tf_od.sif python3 ./python/eager_few_shot_od_training_tf2_singularity.py \u0026amp;\u0026gt;/dev/null \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI use this in an HPC environment, so putting it in the background and suppressing messages allows me to monitor the progress with \u003ccode\u003envtop\u003c/code\u003e or \u003ccode\u003envidia-smi\u003c/code\u003e in the same window. Adjust to suit your needs.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 9, + "subscribers_count": 1, "topics": [], - "updated_at": 1658517655.0 + "updated_at": 1632281930.0 }, { "data_format": 2, - "description": "A toolkit for aligning multi-modal images to the Allen CCF.", + "description": "Class apps for CHPC OnDemand", "filenames": [ - "CWLScripts/Singularity.def" + "MIB2020/Singularity" ], - "full_name": "dontminchenit/CCFAlignmentToolkit", + "full_name": "CHPC-UofU/OOD-class-apps", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ccfalignmenttoolkit\" class=\"anchor\" href=\"#ccfalignmenttoolkit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCCFAlignmentToolkit\u003c/h1\u003e\n\u003cp\u003eOne-time Functions (these are functions that only need to be run once. We will run these and will provide the end results as resources)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eConstruction of fMOST atlas\nFunction: antsMultivariateTemplateConstruction2.sh\nInputs: Collection of fMOST images to be used in atlas.\nOutputs: fMOST average atlas\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSequential Registration of fMOST atlas to CCF\nFunction: AtlasToCCFSequentialRegistration.py\nInputs: Atlas \u0026amp; labels for fMOST atlas and CCF\nOutputs: Transform between fMOST atlas -\u0026gt; CCF\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUser Runtime Functions (These are functions the users will run given new images)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRegistration of new fMOST image to fMOST atlas\nFunction: fMOSTRegisterToCCF.py\nInputs: New fMOST image (downsampled) and fMOST average atlas\nOutput: Transform between new fMOST image and fMOST atlas\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e2)Applying transforms to image\nFunction: ApplyTransfromTofMOST.py\nInputs: fMOST image; transform between fMOST image-\u0026gt;fMOST atlas; transform between fMOST atlas -\u0026gt; CCF\nOutputs: new fMOST image in CCF space\u003c/p\u003e\n\u003cp\u003e3)Applying transforms to neurons\nFunction: ApplyTransfromToSWC.py\nInputs: SWC in new fMOST image space; transform between fMOST image-\u0026gt;fMOST atlas; transform between fMOST atlas-\u0026gt;CCF\nOutputs: neurons (swc) in CCF space\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ood-apps\" class=\"anchor\" href=\"#ood-apps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOOD-apps\u003c/h1\u003e\n\u003cp\u003eRepository of CHPC\u0027s Class Open OnDemand apps\u003c/p\u003e\n\u003cp\u003eThis is a repository of interactive apps used at CHPC supported classes with Open OnDemand.\u003c/p\u003e\n\u003cp\u003eEach subdirectory has its own README.md which contains information about this particular app.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1649347077.0 + "updated_at": 1631833519.0 }, { "data_format": 2, "description": null, "filenames": [ - "Wave-U-Net-Pytorch/Singularity" + "pySCENIC-master/Singularity.0.9.18" ], - "full_name": "likelian/source-separation", + "full_name": "rahuldvs1904/pySCENIC-master", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-source-separation\" class=\"anchor\" href=\"#source-separation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esource-separation\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1650573000.0 + "updated_at": 1631722647.0 }, { "data_format": 2, - "description": "Definition files for singularity container", + "description": "AUGUSTUS is a program that predicts genes in eukaryotic genomic sequences.", "filenames": [ - "Singularity.test", - "Singularity.one-point-stats", - "Singularity.reach" + "3.4.0/Singularity" ], - "full_name": "piyanatk/singularity-containers", + "full_name": "pscedu/singularity-augustus", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" href=\"#singularity-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-containers\u003c/h1\u003e\n\u003cp\u003eDefinition files for singularity container\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-augustus/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-augustus/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-augustus/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-augustus/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b009594bd55f20cf925162d0fe98bf76f1a1f741fbee0db595e0980a750bb1ae/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6175677573747573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b009594bd55f20cf925162d0fe98bf76f1a1f741fbee0db595e0980a750bb1ae/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6175677573747573\" alt=\"Issue\" 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src=\"https://camo.githubusercontent.com/955c466d3c55ba981c6418256a37775f3ba366d3272280c174ef9e6ed43f5d27/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6175677573747573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-augustus\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-augustus\" class=\"anchor\" href=\"#singularity-augustus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-augustus\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for AUGUSTUS.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eaugustus\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/AUGUSTUS/3.4.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/AUGUSTUS\u003c/code\u003e as \u003ccode\u003e3.4.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1650540063.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1631583633.0 }, { "data_format": 2, "description": null, "filenames": [ - "AttentionASR/util/Singularity.def", - "wav2vec2.0bert/util/Singularity.def" + "tools/Singularity" ], - "full_name": "1vket/ASR", + "full_name": "psadil/meta", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1649148128.0 + "updated_at": 1635794729.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "container/Singularity.intel_am4", + "container/Singularity.intel_netcdf", + "container/Singularity.gnu" ], - "full_name": "Bandit42/gdown.pl", + "full_name": "nova0002/troubleshooting", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gdownpl\" class=\"anchor\" href=\"#gdownpl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egdown.pl\u003c/h1\u003e\n\u003cp\u003eGoogle Drive direct download of big files\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003ewget\u003c/em\u003e and \u003cem\u003ePerl\u003c/em\u003e must be in the PATH.\u003cbr\u003e\n\u003cstrong\u003eWindows\u003c/strong\u003e and \u003cstrong\u003elinux\u003c/strong\u003e compatible.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eUse Google Drive shareable links, viewable by anyone:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./gdown.pl \u0027gdrive file url\u0027 [\u0027desired file name\u0027] \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-example\" class=\"anchor\" href=\"#example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h1\u003e\n\u003cp\u003eFor example, to download \u003ca href=\"https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit\" rel=\"nofollow\"\u003ethis video\u003c/a\u003e from my \u003ca href=\"https://circulosmeos.wordpress.com/2015/03/04/axolotl-a-simple-plain-text-documentation-system/\" rel=\"nofollow\"\u003eaxolotl project\u003c/a\u003e, just copy the url, and give a file name if desired:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./gdown.pl https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit axolotl.mp4 \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-resuming-a-download\" class=\"anchor\" href=\"#resuming-a-download\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResuming a download\u003c/h1\u003e\n\u003cp\u003eIf you need to resume a download, please, use \u003ca href=\"https://github.com/circulosmeos/gdown.pl/tree/with-resume\"\u003e\u003cstrong\u003egdown.pl v2.0\u003c/strong\u003e here\u003c/a\u003e.\u003cbr\u003e\nAs long as a file name is indicated as second parameter, \u003cem\u003egdown.pl v2.0\u003c/em\u003e \u003cstrong\u003ewill try to resume the partially downloaded file\u003c/strong\u003e if a local incomplete file with that name already exists.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-version\" class=\"anchor\" href=\"#version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h1\u003e\n\u003cp\u003eThis version is \u003cstrong\u003ev1.4\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-warning\" class=\"anchor\" href=\"#warning\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWarning\u003c/h3\u003e\n\u003cp\u003ePlease, note that v1.2 (available between days 12 to 31 of Jan/2019) \u003cstrong\u003eshould not be used\u003c/strong\u003e, as it contains a bug that could result in unusable downloaded files. Proceed to overwrite with v1.4 in case you have it.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003cp\u003eA simple Docker file is provided, to build a simple Docker image with gdown.pl.\u003cbr\u003e\nThis has been used for pre-pulling data from a Google Drive to Kubernetes persistent volumes.\u003cbr\u003e\nThanks @anton-khodak\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eAn example \u003ca href=\"https://sylabs.io/guides/3.2/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e file is provided.\u003cbr\u003e\nBuild the container:\n\u003ccode\u003esudo singularity build (imagename) Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRun the container:\n\u003ccode\u003esingularity run (imagename) (gdown.pl args)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThanks to @ttbrunner\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eDistributed \u003ca href=\"http://www.gnu.org/licenses/gpl-3.0.html\" rel=\"nofollow\"\u003eunder GPL 3\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h1\u003e\n\u003cp\u003eThis software is provided \"as is\", without warranty of any kind, express or implied.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-more-info\" class=\"anchor\" href=\"#more-info\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore info\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files\" rel=\"nofollow\"\u003ehttps://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eby \u003ca href=\"loopidle@gmail.com\"\u003ecirculosmeos\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gfdl-am4-model\" class=\"anchor\" href=\"#gfdl-am4-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGFDL AM4 Model\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/102487636\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/878db836b9000fd7d9ff531257cade7343f3a3fdf8f764b5a7f1e8ef6ccc6abe/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3130323438373633362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/102487636.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository includes the public release of the GFDL AM4 model\ncode. The AM4 model is described in the\n\u003ca href=\"https://doi.org/10.1002/2017MS001208\" rel=\"nofollow\"\u003etwo\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.1002/2017MS001209\" rel=\"nofollow\"\u003earticles\u003c/a\u003e published in the\n\u003ca href=\"https://agupubs.onlinelibrary.wiley.com/journal/19422466\" rel=\"nofollow\"\u003eJournal of Advances in Modeling Earth Systems\n(JAMES)\u003c/a\u003e.\nMore information on the model and access to the output is available on\nthe \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eAM4 data and code\nsite\u003c/a\u003e at the\n\u003ca href=\"https://www.gfdl.noaa.gov\" rel=\"nofollow\"\u003eGeophysical Fluid Dynamics Laboratory\n(GFDL)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe layout of this package includes the following directories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esrc - The source code for the AM4 model\u003c/li\u003e\n\u003cli\u003eexec - The build directory with Makefiles for building the model executable\u003c/li\u003e\n\u003cli\u003erun - Sample run script and updated files needed for running\u003c/li\u003e\n\u003cli\u003eanalysis - Sample analysis scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cloning-instructions\" class=\"anchor\" href=\"#cloning-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning Instructions\u003c/h2\u003e\n\u003cp\u003eThis repository uses \u003ca href=\"https://git-scm.com/book/en/v2/Git-Tools-Submodules\" rel=\"nofollow\"\u003egit\nsubmodules\u003c/a\u003e to\npoint to other repositories. Thus, care should be taken when cloning,\nand updating the source to ensure all source. To obtain all source,\nuse the following git command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NOAA-GFDL/AM4.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--recursive\u003c/code\u003e option to \u003ccode\u003egit clone\u003c/code\u003e instructs git to recursively\nclone all submodules. In the event the repository was not cloned\nusing the \u003ccode\u003e--recursive\u003c/code\u003e option, the following step must be taken to\nobtain all sources:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# From within the AM4 parent directory\ngit submodule update --init --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-source-code\" class=\"anchor\" href=\"#source-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource Code\u003c/h2\u003e\n\u003cp\u003eAll model source is contained in the \u003ca href=\"src\"\u003esrc\u003c/a\u003e directory. GFDL\ntracks code using the git version control system. This package\nincludes a single version of the following GFDL model components. The\ngit hash listed corresponds to the commit hash in the internal GFDL\ngit repository.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComponent\u003c/th\u003e\n\u003cth\u003eCommit Hash\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_drivers\u003c/td\u003e\n\u003ctd\u003e5ee95d6abf0879594551dd7e6635dff4004c4010\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_param\u003c/td\u003e\n\u003ctd\u003e2e94acfd8621e85216bf822c395a8c3f15a511a5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_shared\u003c/td\u003e\n\u003ctd\u003ea557d4d7bab033ef1ad1d400a62fe07a97ccb477\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eice_param\u003c/td\u003e\n\u003ctd\u003e1553c8bc4f9a66791c89367b6f327147523155ed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eice_sis\u003c/td\u003e\n\u003ctd\u003eccc7328dcd79706dd5c17c8bab660222886fc80b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eland_lad2\u003c/td\u003e\n\u003ctd\u003ea220288ecb289bf9d793d051fc5076072874ce07\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe following components are available in the\n\u003ca href=\"https://github.com/NOAA-GFDL\"\u003eNOAA-GFDL\u003c/a\u003e github organization:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/MOM6\"\u003eMOM6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/coupler\"\u003ecoupler\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/FMS\"\u003eFMS\u003c/a\u003e (as \u003ca href=\"src/shared\"\u003eshared\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/GFDL_atmos_cubed_sphere\"\u003eGFDL_atmos_cubed_sphere (tag AM4.0)\u003c/a\u003e (as \u003ca href=\"src/atmos_cubed_sphere\"\u003eatmos_cubed_sphere\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-am4\" class=\"anchor\" href=\"#building-am4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding AM4\u003c/h2\u003e\n\u003cp\u003e###Containers\nThe \u003ca href=\"container\"\u003econtainer folder\u003c/a\u003e provides example Dockerfiles and Signularity\ndefinition files to use to build AM4 containers using either GCC/GFORTAN or\nIntel oneAPI. There is a script that can be used to build the intel\nsingularity containers, and the first step of this script can be used with the\nother GFDL climate models.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-from-source\" class=\"anchor\" href=\"#from-source\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFrom source\u003c/h3\u003e\n\u003cp\u003eThe \u003ca href=\"exec\"\u003eexec\u003c/a\u003e directory contains Makefiles that can be used to\nbuild the AM4 executable. These Makefiles were generated using the\n\u003ca href=\"https://github.com/NOAA-GFDL/mkmf\"\u003eMake Makefile (mkmf)\u003c/a\u003e program.\nIncluded in the exec direcgtory is a sample make template file for the\nIntel compilers (\u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e). This make\ntemplate can be used on any system with a relatively recent version of\nthe Intel compilers, the netCDF 4 library and the MPICH2 MPI library.\nIncluded in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file are\nadditional settings that can be modified during the build.\u003c/p\u003e\n\u003cp\u003eTo run the default build (-O3 -msse2), go to the exec directory and\nenter the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you would like to change some of the compiler options, there are several different\noptions to add to the make command. For example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake ISA=-xhost BLD_TYPE=REPRO\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill replace -msse with -xhost and -O3 with -O2. The three options for\n\u003ccode\u003eBLD_TYPE\u003c/code\u003e are\u003cbr\u003e\n\u003ccode\u003ePROD\u003c/code\u003e (-O3)\u003cbr\u003e\n\u003ccode\u003eREPRO\u003c/code\u003e (-O2)\u003cbr\u003e\n\u003ccode\u003eDEBUG\u003c/code\u003e (-O0 and other traps)\u003cbr\u003e\nAll of the make line options can be\nfound in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-obtaining-the-input-data\" class=\"anchor\" href=\"#obtaining-the-input-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eObtaining the input data\u003c/h2\u003e\n\u003cp\u003eThe input data required for running the AM4 model can be found on\n\u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eGFDL\u0027s data\nportal\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eAM4.tar.gz\u003c/code\u003e contains a configured run directory to run a\nsample experiment of the AM4 model. Included in the tar file is a\nREADME.AM4_run with more instructions on how to configure the AM4 run\ndirectory.\u003c/p\u003e\n\u003cp\u003eOn Linux systems, the \u003ccode\u003ewget\u003c/code\u003e command is usually sufficient to download the data\nfile:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo ensure the file downloaded is complete and not corrupted, download one of the two files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz.sha256\nwget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz.sig\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand run the following command that corresponds to the signature file downloaded:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esha256sum -c AM4_run.tar.gz.sha256\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003egpg --verify AM4_run.tar.gz.sig\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-am4\" class=\"anchor\" href=\"#running-am4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning AM4\u003c/h2\u003e\n\u003cp\u003eIncluded in the run directory is a sample run script for reference.\nTo run the AM4 sample experiment, first download the data file\nmentioned in \u003ca href=\"#obtaining-the-input-data\"\u003eObtaining the Input data\u003c/a\u003e\nsection. Replace diag_table and input.nml in the top level of the\nuntar\u0027d directory with the corresponding files in the run directory\nof this repository. Modify the variables in the configuration section\nin the sample run script, and then run the script.\u003c/p\u003e\n\u003cp\u003eThe sample data and run script are configured to run on 216\nprocessors. To run on a different number of processors, or modify the\nexperiment, refer to the \u003ccode\u003eREADME.AM4_run\u003c/code\u003e file included in the AM4\ndata tarball.\u003c/p\u003e\n\u003cp\u003eNote: The \u003ccode\u003einput.nml\u003c/code\u003e file (found in the AM4 data tarball) contains\nFortran namelists and namelist variables that modify, at run time, the\nmodel. To learn more about the settings in the \u003ccode\u003einput.nml\u003c/code\u003e file,\nplease refer to source code where the namelist/variable are defined.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-analysis-scripts\" class=\"anchor\" href=\"#analysis-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalysis Scripts\u003c/h2\u003e\n\u003cp\u003eSome of the climate analysis scripts run at NOAA GFDL and used in the\nAM4 documentation papers are located in the analysis directory.\nWithin each analysis suite, is a \u003ca href=\"https://jupyter-notebook.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ejupyter\nnotebook\u003c/a\u003e, both\nreadable and runnable from your local jupyter environment, provided\nall dependencies are installed.\u003c/p\u003e\n\u003cp\u003eE.g.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/cjs1/radiation_atmos_av_mon/radiation_atmos_av_mon.ipynb\"\u003eRadiation processor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/bw/bw_atmos_cru_ts_a1r/bw_atmos_monthly_cru_ts.1980-2014.ipynb\"\u003eLong-term DJF seasonal mean\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/bw/bw_atmos_zm_atl_pac_a1r/bw_atmos_atl_pac.1980-2014.ipynb\"\u003eZonal_mean_zonal_wind_stress\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/pcmdimetrics/portraitPlot-AM4.AMIP.ipynb\"\u003ePCMDI Metrics Portrait Plot\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-model-output-and-other-references\" class=\"anchor\" href=\"#model-output-and-other-references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModel output and Other References\u003c/h2\u003e\n\u003cp\u003ePlease refer to the \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eAM4 data and code\nsite\u003c/a\u003e for details\nabout where to find model and OBS data used in the papers.\u003c/p\u003e\n\u003cp\u003eFor all analysis figures and pertaining data, please use the AM4\ndocumentation papers as the original reference.\u003c/p\u003e\n\u003cp\u003ePlease direct your questions and feedback to\n\u003ca href=\"mailto:gfdl.climate.model.info@noaa.gov\"\u003egfdl.climate.model.info@noaa.gov\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h2\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is\nprovided on an \u0027as is\u0027 basis and the user assumes responsibility for\nits use. DOC has relinquished control of the information and no\nlonger has responsibility to protect the integrity, confidentiality,\nor availability of the information. Any claims against the Department\nof Commerce stemming from the use of its GitHub project will be\ngoverned by all applicable Federal law. Any reference to specific\ncommercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply\ntheir endorsement, recommendation or favoring by the Department of\nCommerce. The Department of Commerce seal and logo, or the seal and\nlogo of a DOC bureau, shall not be used in any manner to imply\nendorsement of any commercial product or activity by DOC or the United\nStates Government.\u003c/p\u003e\n\u003cp\u003eThis project code is made available through GitHub but is managed by\nNOAA-GFDL at \u003ca href=\"https://gitlab.gfdl.noaa.gov\" rel=\"nofollow\"\u003ehttps://gitlab.gfdl.noaa.gov\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1651087778.0 + "updated_at": 1631295050.0 }, { "data_format": 2, - "description": "Control + Camera code for the autonomous delivery robot developed for Albert Heijn as part of the Robotics Minor at TU Delft 2020", + "description": "Singularity container for playing 2048", "filenames": [ - "Gazebo/Singularity" + "Singularity" ], - "full_name": "Sh-Anand/delivery-fellow", + "full_name": "bbbbbrie/2048-container", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-delivery-fellow\" class=\"anchor\" href=\"#delivery-fellow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDelivery Fellow\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-2048-container\" class=\"anchor\" href=\"#2048-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2048-container\u003c/h1\u003e\n\u003cp\u003eA recipe for a Singularity container useful for playing 2048.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-construction\" class=\"anchor\" href=\"#construction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConstruction\u003c/h2\u003e\n\u003cp\u003eBuild the container with something like \u003ccode\u003esudo singularity build 2048.img Singularity\u003c/code\u003e or \u003ccode\u003ebuild-image.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-play-2048\" class=\"anchor\" href=\"#play-2048\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlay 2048!\u003c/h2\u003e\n\u003cp\u003eRun \u003ccode\u003esingularity exec 2048.img /usr/games/2048-qt\u003c/code\u003e or \u003ccode\u003eplay-2048.sh\u003c/code\u003e after building the container.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/436e8e13c282802e7481996df4fa6dc543fd7e18b520205aa794df403d2226b7/68747470733a2f2f692e696d6775722e636f6d2f64496c50474c642e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/436e8e13c282802e7481996df4fa6dc543fd7e18b520205aa794df403d2226b7/68747470733a2f2f692e696d6775722e636f6d2f64496c50474c642e706e67\" alt=\"Score: 128\" data-canonical-src=\"https://i.imgur.com/dIlPGLd.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1651228584.0 + "topics": [ + "singularity-container", + "2048", + "2048-game", + "container" + ], + "updated_at": 1556246890.0 }, { "data_format": 2, - "description": "A repo of container definitions and CI build support", + "description": null, "filenames": [ - "singularity/analysis/r/Singularity", - "singularity/analysis/python/Singularity", - "singularity/analysis/notebook/Singularity" + "bartender/Singularity" ], - "full_name": "lkirk/containers", + "full_name": "cory-weller/YKO-barseq", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containers\" class=\"anchor\" href=\"#containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers\u003c/h1\u003e\n\u003cp\u003eThis is my personal repo of container definitions and CI build support\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-background\" class=\"anchor\" href=\"#background\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h1\u003e\n\u003cp\u003eSince I use singularity and docker heavily in my analysis/development workflows, I needed a CI system for versioning/releasing containers.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-builds\" class=\"anchor\" href=\"#singularity-builds\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Builds\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/lkirk/analysis/r\" rel=\"nofollow\"\u003eR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/lkirk/analysis/python\" rel=\"nofollow\"\u003ePython\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/lkirk/analysis/notebook\" rel=\"nofollow\"\u003eJupyter Notebook\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003cp\u003eTools\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://quay.io/repository/lkirk/bwa\" rel=\"nofollow\"\u003eBWA\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/d8a712bb098b054f0b4be4e9e111d976dc1a1faf2dce9016f81fd76bc4d06462/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f6277612f7374617475733f746f6b656e3d38313862616539352d313133612d343762642d396561642d636630343435613137323739\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d8a712bb098b054f0b4be4e9e111d976dc1a1faf2dce9016f81fd76bc4d06462/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f6277612f7374617475733f746f6b656e3d38313862616539352d313133612d343762642d396561642d636630343435613137323739\" alt=\"\" data-canonical-src=\"https://quay.io/repository/lkirk/bwa/status?token=818bae95-113a-47bd-9ead-cf0445a17279\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://quay.io/repository/lkirk/samtools\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/c4de3955204a04af95d325f708841180e77c42bbe944739ed74609eb629450ee/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f73616d746f6f6c732f7374617475733f746f6b656e3d61316462633336612d633938352d343565312d393563642d353132333030653531383932\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c4de3955204a04af95d325f708841180e77c42bbe944739ed74609eb629450ee/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f73616d746f6f6c732f7374617475733f746f6b656e3d61316462633336612d633938352d343565312d393563642d353132333030653531383932\" alt=\"\" data-canonical-src=\"https://quay.io/repository/lkirk/samtools/status?token=a1dbc36a-c985-45e1-95cd-512300e51892\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://quay.io/repository/lkirk/bcftools\" rel=\"nofollow\"\u003eBcftools\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/6090ee9c9b88031c51de2fbe49de54b6c91686e8e676a2d49827af13be55f2d2/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f626366746f6f6c732f7374617475733f746f6b656e3d63363032653330612d316637392d346432622d383338372d633762656131393537366632\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6090ee9c9b88031c51de2fbe49de54b6c91686e8e676a2d49827af13be55f2d2/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f626366746f6f6c732f7374617475733f746f6b656e3d63363032653330612d316637392d346432622d383338372d633762656131393537366632\" alt=\"\" data-canonical-src=\"https://quay.io/repository/lkirk/bcftools/status?token=c602e30a-1f79-4d2b-8387-c7bea19576f2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-yko-barseq\" class=\"anchor\" href=\"#yko-barseq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYKO-barseq\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-singularity-image\" class=\"anchor\" href=\"#building-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding singularity image\u003c/h2\u003e\n\u003cp\u003eOn a computer with sudo access, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e while in directory containing Singularity file\u003c/span\u003e\nsudo singularity build bartender.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-bartender-extractor\" class=\"anchor\" href=\"#running-bartender-extractor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning bartender extractor\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \\\n -B /data/SBGE/cory/YKO-barseq:/home/wellerca/ \\\n bartender/bartender.sif bartender_extractor_com \\\n -f seq/MS3059950-600V3_19109498_S7_L001_R1_001.fastq \\\n -o pre \\\n -p CGAGC[34]C -m 1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRunning bartender extractor\nbartender_extractor seq/MS3059950-600V3_19109498_S7_L001_R1_001.fastq pre 1 \"(CGAG.|CGA.C|CG.GC|C.AGC|.GAGC)([ATCGN]{34})(C)\" CGAGC C 3 1\nTotally there are 1187764 reads in seq/MS3059950-600V3_19109498_S7_L001_R1_001.fastq file!\nTotally there are 1118562 valid barcodes from seq/MS3059950-600V3_19109498_S7_L001_R1_001.fastq file\nTotally there are 1118562 valid barcodes whose quality pass the quality condition\nThe estimated sequence error from the prefix and suffix parts is 0.0311966\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-formatting-barcodes\" class=\"anchor\" href=\"#formatting-barcodes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFormatting barcodes\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003eextracted_barcode.txt\u003c/code\u003e file contains a 34-mer nucleotide sequence, but we only\nwant the 20 nucleotide barcode sequence contained within.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epython3\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eformat_barcodes\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epy\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epre_barcode\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebarcodes\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-bartender-cluster\" class=\"anchor\" href=\"#running-bartender-cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning bartender cluster\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \\\n -B /data/SBGE/cory/YKO-barseq:/home/wellerca/ \\\n bartender/bartender.sif bartender_single_com \\\n -f barcodes.txt \\\n -o barcode_clusters \\\n -d 2 \\\n -s 5\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eoutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRunning bartender\nLoading barcodes from the file\nIt takes 00:00:01 to load the barcodes from barcodes.txt\nShortest barcode length: 20\nLongest barcode length: 20\nStart to group barcode with length 20\nUsing two sample unpooled test\nTransforming the barcodes into seed clusters\nInitial number of unique reads: 64431\nThe distance threshold is 2\nClustering iteration 1\nClustering iteration 2\nClustering iteration 3\nClustering iteration 4\nIdentified 18272 barcodes with length 20\nThe clustering process takes 00:00:01\nStart to dump clusters to file with prefix barcode_clusters\nStart to remove pcr effects\n***(Overall error rate estimated from the clustering result)***\nTotal number of clusters after removing PCR effects: 18272\nThe estimated error rate is 0.00340786\nThe overall running time 00:00:05 seconds.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-take-most-abundant-seq-consensus-per-cluster-and-plot\" class=\"anchor\" href=\"#take-most-abundant-seq-consensus-per-cluster-and-plot\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTake most abundant seq (consensus) per cluster and plot\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003edata.table\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003eggplot2\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003eggrepel\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003edat\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fread(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ebarcode_clusters_barcode.csv\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003edat\u003c/span\u003e[, \u003cspan class=\"pl-smi\"\u003e.SD\u003c/span\u003e[which.max(\u003cspan class=\"pl-smi\"\u003eFrequency\u003c/span\u003e)], \u003cspan class=\"pl-v\"\u003eby\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCluster.ID\u003c/span\u003e]\n\nsetnames(\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eUnique.reads\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econsensus\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e[, \u003cspan class=\"pl-smi\"\u003eFrequency\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNULL\u003c/span\u003e]\nsetkey(\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eCluster.ID\u003c/span\u003e)\nsetkey(\u003cspan class=\"pl-smi\"\u003edat\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eCluster.ID\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003edat.merge\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e merge(\u003cspan class=\"pl-smi\"\u003edat\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003edat.merge\u003c/span\u003e[, \u003cspan class=\"pl-k\"\u003elist\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eN\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e sum(\u003cspan class=\"pl-smi\"\u003eFrequency\u003c/span\u003e)), \u003cspan class=\"pl-v\"\u003eby\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e][order(\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e)]\n\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e[, \u003cspan class=\"pl-smi\"\u003eabundance_rank\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e.N\u003c/span\u003e]\n\nfwrite(\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003efile\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econsensus_counts.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003equote\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eF\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003erow.names\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eF\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecol.names\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eT\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003esep\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e,\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\n\n\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3000\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003etext_label\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e]\n\n\nggplot(\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e], aes(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eabundance_rank\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e geom_point() \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\nscale_y_continuous(\u003cspan class=\"pl-v\"\u003etrans\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elog10\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003ebreaks\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003ec(\u003cspan class=\"pl-c1\"\u003e1e0\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e1\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e2\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e3\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e4\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e5\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e6\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003elabels\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003ec(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e10\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e100\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e10000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e100000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1000000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\nlabs(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBarcodes ranked by abundance\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eAbundance\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etitle\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBarcodes with cluster counts \u0026gt;= 2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\ntheme_few(\u003cspan class=\"pl-c1\"\u003e12\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\ngeom_text_repel(aes(\u003cspan class=\"pl-v\"\u003elabel\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003etext_label\u003c/span\u003e))\n\nggplot(\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10000\u003c/span\u003e], aes(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eabundance_rank\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e geom_point() \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\nlabs(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBarcodes ranked by abundance\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eAbundance\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etitle\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBarcodes with cluster counts \u0026gt;= 2 and \u0026lt;= 100000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\ntheme_few(\u003cspan class=\"pl-c1\"\u003e12\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\ngeom_text_repel(aes(\u003cspan class=\"pl-v\"\u003elabel\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003etext_label\u003c/span\u003e))\n\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1647133221.0 + "updated_at": 1631115532.0 }, { "data_format": 2, - "description": "CheckM provides a set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes.", + "description": "Singularity container for RNA-Seq power analysis", "filenames": [ - "1.2.0/Singularity", - "1.1.3/Singularity" + "Singularity.rnaseqpower" ], - "full_name": "pscedu/singularity-checkm", - "latest_release": "v1.1.3", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-checkm/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-checkm/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-checkm/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-checkm/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c36d6207c7a83b2505f3c3da9648b2bde15022fb54c87c7d694d8aef86ba345/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d636865636b6d\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c36d6207c7a83b2505f3c3da9648b2bde15022fb54c87c7d694d8aef86ba345/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d636865636b6d\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-checkm\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/39b062bc9d3f1163144f8faf52a104ed79d50fb16f21cf3ab1bf888d2f31ffff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d636865636b6d\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39b062bc9d3f1163144f8faf52a104ed79d50fb16f21cf3ab1bf888d2f31ffff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d636865636b6d\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-checkm\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b2c55512b9909564abb8abf9ff50100608717f5342c5fcce30438dcc63f8eeeb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d636865636b6d\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b2c55512b9909564abb8abf9ff50100608717f5342c5fcce30438dcc63f8eeeb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d636865636b6d\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-checkm\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4e65c1f34358c555baa1e01b53582475c3621a1e12ad015e81ad9fc5dbc0a221/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d636865636b6d\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e65c1f34358c555baa1e01b53582475c3621a1e12ad015e81ad9fc5dbc0a221/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d636865636b6d\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-checkm\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-checkm\" class=\"anchor\" href=\"#singularity-checkm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-checkm\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/93019bef04184f04502c095cd35e9340a581b7ead3193c4e7b387aa845b96f59/687474703a2f2f65636f67656e6f6d6963732e6769746875622e696f2f436865636b4d2f696d672f636865636b6d2e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93019bef04184f04502c095cd35e9340a581b7ead3193c4e7b387aa845b96f59/687474703a2f2f65636f67656e6f6d6963732e6769746875622e696f2f436865636b4d2f696d672f636865636b6d2e706e67\" width=\"50%\" data-canonical-src=\"http://ecogenomics.github.io/CheckM/img/checkm.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://ecogenomics.github.io/CheckM/\" rel=\"nofollow\"\u003eCheckM\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003echeckm\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/CheckM/1.1.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/checkm\u003c/code\u003e as \u003ccode\u003e1.1.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "qbicsoftware/rnaseq-power-container", + "latest_release": "0.3.14", + "readme": "\u003cp\u003eCreates power or sample size matrix given different experimental parameters. Uploads created heatmaps as attachment to openBIS using attachi-cli and Dync.\u003c/p\u003e\n\u003cp\u003eUses \u003ca href=\"https://doi.org/doi:10.18129/B9.bioc.RnaSeqSampleSize\" rel=\"nofollow\"\u003eRnaSeqSampleSize\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eContainers are built using the \u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003eSingularity Deploy template\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1651352851.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1635346688.0 }, { "data_format": 2, - "description": null, + "description": "Operating Systems", "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/bioconda-perl-bioperl", + "full_name": "cassimpatel/COMP2211", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-a-miniconda-based-container-with-bioconda-bioperl\" class=\"anchor\" href=\"#a-miniconda-based-container-with-bioconda-bioperl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea miniconda based container with bioconda-bioperl\u003c/h1\u003e\n\u003cp\u003edocker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/bioconda-perl-bioperl:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv oras://ghcr.io/truatpasteurdotfr/bioconda-perl-bioperl:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-operating-systems-comp2211\" class=\"anchor\" href=\"#operating-systems-comp2211\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOperating Systems (COMP2211)\u003c/h1\u003e\n\u003cp\u003eNOTE: this repository does not seem to work, no source code seems to be committed or staged. Instructions to run are kept here, but find a copy of the operating system including all changes made within your UoL Linux File System in Documents\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eNote these instructions are for running on a UoL Linux terminal\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to the directory containing this README file\u003c/li\u003e\n\u003cli\u003eRun the following command: \u003ccode\u003esingularity shell xv6_tools.simg\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eThe terminal should now prompt you with \u003ccode\u003eSingularity\u0026gt; \u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eNotice you are no longer in the same folder, navigate into the \u003ccode\u003exv6-riscv\u003c/code\u003e directory\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003emake clean\u003c/code\u003e followed by \u003ccode\u003emake\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eStart up the Xv6 Operating system: \u003ccode\u003emake qemu\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOnce you are finished using the OS:\n\u003cul\u003e\n\u003cli\u003eHold \u003ccode\u003ectrl + a\u003c/code\u003e and click \u003ccode\u003ex\u003c/code\u003e to exit back to Singularity\u003c/li\u003e\n\u003cli\u003eIf you want to view new changes to the OS code: run \u003ccode\u003emake clean; make; make qemu\u003c/code\u003e again to restart the OS\u003c/li\u003e\n\u003cli\u003eTo exit Singularity: use command \u003ccode\u003eexit\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-shortcut-to-run\" class=\"anchor\" href=\"#shortcut-to-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShortcut to run\u003c/h2\u003e\n\u003cp\u003eNavigate to the top repository directory and use the commands below. Note you will have to run the first line, then the second.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell xv6_tools.simg\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Desktop/Git/COMP2211/xv6-riscv\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e make clean\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e make\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e make qemu\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1651593131.0 + "updated_at": 1641130398.0 }, { "data_format": 2, - "description": "The MEME Suite allows you to discover novel motifs in collections of unaligned nucleotide or protein sequences, and to perform a wide variety of other motif-based analyses.", + "description": "Scripts for building VirSorter2 Cyverse App", "filenames": [ - "5.4.1/Singularity", - "5.4.0/Singularity", - "5.3.3/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-meme-suite", - "latest_release": "v5.4.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-meme-suite/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-meme-suite/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-meme-suite/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-meme-suite/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/19fb6791fbb0d3b375a57c2d240aaaf0bcf3c3fc41c90ea1779d4b744a1b503b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/19fb6791fbb0d3b375a57c2d240aaaf0bcf3c3fc41c90ea1779d4b744a1b503b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-meme-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c583cd02f62c30b39ca677f51fb0f0594e8d44174063b0a9eff5e1da24824696/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c583cd02f62c30b39ca677f51fb0f0594e8d44174063b0a9eff5e1da24824696/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-meme-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f6e0b1113a2126c7e25c07091194a3965453a639e67de2097dc827e2dea8066c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f6e0b1113a2126c7e25c07091194a3965453a639e67de2097dc827e2dea8066c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-meme-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/20a21f7be1dd954bce6c17c8486d5d385ff4cc87c3c6b3574e99c4b9287e759c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/20a21f7be1dd954bce6c17c8486d5d385ff4cc87c3c6b3574e99c4b9287e759c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-meme-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-meme-suite\" class=\"anchor\" href=\"#singularity-meme-suite\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-meme-suite\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://meme-suite.org/meme/\" rel=\"nofollow\"\u003ememe-suite\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ememe-suite\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/meme-suite/5.4.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/meme-suite\u003c/code\u003e as \u003ccode\u003e5.4.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "jiarong/vs2-cyverse-app", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1649276065.0 + "updated_at": 1640407925.0 }, { "data_format": 2, - "description": "A visual approach to monitoring and managing the on campus HPC system known as Bender. ", + "description": null, "filenames": [ "Singularity" ], - "full_name": "wrightedu/Bender-Monitor", + "full_name": "yimengkong/6mASCOPE", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bender-monitor\" class=\"anchor\" href=\"#bender-monitor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBender-Monitor\u003c/h1\u003e\n\u003cp\u003eA visual approach to monitoring and managing the on campus HPC system known as Bender.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-6mascope\" class=\"anchor\" href=\"#6mascope\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6mASCOPE\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003e6mASCOPE is a toolbox to assess 6mA events in eukaryotic species using a quantitative deconvolution approach. By using a novel short-insert library (200~400bp) design with the PacBio sequencing Sequel II System, 6mASCOPE makes an effective use of the large number of circular consensus (CCS) reads to reliably capture deviations in IPD values at single molecule resolution. Taking an innovative metagenomic approach, 6mASCOPE deconvolves the DNA molecules from a gDNA sample into species and genomic regions of interests, and sources of contamination. Using a rationally designed machine learning model, 6mASCOPE enables sensitive and reliable 6mA quantification for each of the deconvolved composition.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-access\" class=\"anchor\" href=\"#access\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAccess\u003c/h2\u003e\n\u003cp\u003eThis current version is for manuscript review. Upon publication, we plan to release 6mASOCPE publically on our GitHub page \u003ca href=\"https://github.com/fanglab/6mascope\"\u003ehttps://github.com/fanglab/6mascope\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003e6mASCOPE is distributed as a fully functional image bypassing the need to install any dependencies others than the virtualization software. We recommend using Singularity, which can be installed on Linux systems and is often the preferred solution by HPC administrators (\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eQuick Start\u003c/a\u003e). \u003ccode\u003e6mASCOPE\u003c/code\u003e was tested extensively with Singularity v3.6.4.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load singularity/3.6.4 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Required only singularity/3.6.4 is a dynamic environment module. \u003c/span\u003e\nsingularity pull 6mASCOPE.sif library://yimengkong/default/6mascope:latest \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the image from cloud.sylabs.io; Make sure you have the network connection\u003c/span\u003e\nsingularity build --sandbox 6mASCOPE 6mASCOPE.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a writable container named 6mASCOPE\u003c/span\u003e\nsingularity run --no-home -w 6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start an interactive shell to use 6mASCOPE, type `exit` to leave\u003c/span\u003e\ninit_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Only required once when start using 6mASCOPE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e run_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Required every time when running 6mASCOPE\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe image retrieved from \u003ca href=\"https://cloud.sylabs.io/home\" rel=\"nofollow\"\u003eSylab Cloud\u003c/a\u003e with \u003ccode\u003esingularity pull\u003c/code\u003e (e.g. 6mASCOPE.sif) is already built and can be reused at will. Containers built with those instructions are writable meaning that results from 6mASCOPE analysis can be retrieved when the container is not running. Outputs for the following commands can be found at \u003ccode\u003e./path/to/6mASCOPE/home/6mASCOPE/\u003c/code\u003e. To re-run the same container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --no-home -w 6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Re-run container 6mASCOPE, type `exit` to leave\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e run_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Required every time when running 6mASCOPE\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tool-showcase\" class=\"anchor\" href=\"#tool-showcase\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTool showcase\u003c/h2\u003e\n\u003cp\u003eTo showcase the toolbox applications, we provide examples for the analysis of the Drosophila ~45min embryo dataset presented in our manuscript (Fig 5). The dataset can be downloaded with the following commands from within a 6mASCOPE container: \u003ccode\u003e6mASCOPE get_test_data\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-contamination-estimation\" class=\"anchor\" href=\"#contamination-estimation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContamination estimation\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-goal\" class=\"anchor\" href=\"#goal\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal\u003c/h4\u003e\n\u003cp\u003eTo get an idea about the overall contamination of a gDNA sample. This step helps users define the composition of a gDNA sample using a metagenomic approach to assign reads to different species.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-description-1\" class=\"anchor\" href=\"#description-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription:\u003c/h4\u003e\n\u003cp\u003eFor a given CCS dataset generated from short-insert library, 6mASCOPE will examine if there are contaminating species and calculate the proportion of reads mapped to the reference and top 50 contaminated species from reads that do not map to the eukaryotic species of interest.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eCCS reads file capturing all the genetic material in a gDNA sample (.fasta, pre-computed in the following example)\u003c/li\u003e\n\u003cli\u003eEukaryotic reference of genome of interest (.fasta)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs:\u003c/h4\u003e\n\u003cp\u003eFor a given CCS dataset generated from short-insert library, \u003ccode\u003e6mASCOPE\u003c/code\u003e will examine if there are contaminating species and calculate the proportion of reads mapped to the reference and top 50 contaminated species from reads that do not map to the eukaryotic species of interest.\u003c/p\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-example-of-the-output\" class=\"anchor\" href=\"#example-of-the-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample of the Output:\u003c/h5\u003e\n\u003cpre\u003e\u003ccode\u003eRemove 8491 possible inter-species chimeric reads for further analysis\n#total_CCS\tmapped_to_goi\tcontaminants\n666159\t640345 (96.1249%)\t25814 (3.87505%)\n\nTop 50 mapped species outside goi reference\n#Count\tSpecies\n 10836 Saccharomyces cerevisiae\n 2413 Acetobacter tropicalis\n 1524 Acetobacter pasteurianus\n 1479 Lactobacillus plantarum\n 882 Acetobacter sp.\n ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(Full species list can be viewed in \u003ccode\u003etest.contam.estimate.txt\u003c/code\u003e)\u003c/p\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-example-commands\" class=\"anchor\" href=\"#example-commands\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample commands:\u003c/h5\u003e\n\u003cpre\u003e\u003ccode\u003e6mASCOPE contam -c test.ccs.fasta -r test.ref.fasta -o test.contam.estimate.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this example, \u003ccode\u003etest.ccs.fasta\u003c/code\u003e includes CCS reads (674,650) from the Drosophila ~45min embryo reads dataset described in our manuscript and pre-filtered with command \u003ccode\u003e6mASCOPE ccs\u003c/code\u003e. Using 5 cores, runtime is ~12m51s. The output shows ~3.9% CCS reads come from contaminated sources other than Drosophila melanogaster, the genome of interest (goi). Please be noted, blastn is embedded within this step, which will need at least 32-64G RAM.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-6ma-analysis-using-quantitative-deconvolution\" class=\"anchor\" href=\"#6ma-analysis-using-quantitative-deconvolution\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6mA analysis using quantitative deconvolution\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-goal-1\" class=\"anchor\" href=\"#goal-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal:\u003c/h4\u003e\n\u003cp\u003eFor each source determined in \u003ccode\u003e6mASCOPE contam\u003c/code\u003e, this step will quantify the 6mA/A level and calculate the 6mA contribution (%) of each source to the total 6mA abundance in the gDNA sample.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-inputs-1\" class=\"anchor\" href=\"#inputs-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eThe same CCS reads file as explained above for Contamination Estimation (.fasta).\u003c/li\u003e\n\u003cli\u003eIPD and QV information of the CCS reads (pre-computed in the following example, ; this can be generated for new data with \u003ccode\u003e6mASCOPE ipd\u003c/code\u003e command, as explained in detailed tutorial).\u003c/li\u003e\n\u003cli\u003eUser defined groups besides the genome of interest. Examples as shown below. (Left columns: subgroup name. Right columns: contamination sources, use vertical line if multiple sources included within one subgroup).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eSaccharomyces Saccharomyces\nAcetobacter Acetobacter|Komagataeibacter\nLactobacillus Lactobacillus\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-outputs-1\" class=\"anchor\" href=\"#outputs-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs:\u003c/h4\u003e\n\u003cp\u003eA table including the following information: the proportion (%) of reads from each source out of the total number of reads; source-specific 6mA/A level with 95% confidence intervals (log10-transformed), and contribution (%) of each source to the total 6mA abundance in the gDNA sample (as presented in the manuscript Figure 5A, B, C)\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-example-commands-1\" class=\"anchor\" href=\"#example-commands-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample commands:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e6mASCOPE quant -c test.ccs.fasta -i test.IPD.out.A -o test -r test.ref.fasta -s subgroup.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this example, the file \u003ccode\u003etest.IPD.out.A\u003c/code\u003e includes the pre-calculated IPD and QV information on the CCS molecules (can be generated with \u003ccode\u003e6mASCOPE ipd\u003c/code\u003e). Only Adenines were included here to to reduce computational time and ease evaluation. \u003ccode\u003esubgroup.txt\u003c/code\u003e includes the pre-defined main contamination groups, inferred from the top mapped species and blast output from \u003ccode\u003e6mASCOPE contam\u003c/code\u003e. Using 5 cores, runtime is ~13m17s.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-example-output\" class=\"anchor\" href=\"#example-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample output:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e #Subgroup count ReadsProportion 6mAlevel(ppm) 6mAlevel(log10) UpCI DownCI subtotal(ppm) contribution(%)\n goi 640345 0.9612 2.0417 -5.69 -5.0 -6.0 1.9625 1.4431\n Saccharomyces 11011 0.0165 45.7088 -4.34 -3.9 -6.0 0.7542 0.5546\n Acetobacter 5757 0.0086 5495.4087 -2.26 -2.0 -2.5 47.2605 34.7522\n Lactobacillus 1517 0.0023 977.2372 -3.01 -2.7 -3.3 2.2476 1.6528\n others 7529 0.0113 7413.1024 -2.13 -1.9 -2.4 83.7681 61.5974\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"/docs/figures/test.6mASCOPE.ReadsProportion.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.ReadsProportion.png\" alt=\"The proportion of CCS reads from each group 6mA\" width=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n1. The % of total CCS reads mapped to different subgroups. Left: The % of CCS reads mapped to D. melanogaster (genome of interest) and contamintant subgroups. Right: The % of CCS reads mapped to different contaminant sources.\n\u003cp align=\"center\"\u003e\n \u003ca href=\"/docs/figures/test.6mASCOPE.6mAlevel.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.6mAlevel.png\" alt=\"Group-specific 6mA/A level prediction with confidence intervals 6mA\" width=\"500\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n2. 6mA quantification and 95% confidence intervals (log10-transformed) on CCS reads mapped to different subgroups. Please be noted, it is important to combine the estimated 6mA/A level with its confidence interval for reliable data interpretation. In this example, the 6mA/A level of Saccharomyces (45.7ppm) does not mean abundant 6mA events in this subgroup because it has a wide range of confidence interval (1-125ppm; -6.0 to -3.9 with log10 transformed). In the paper, an additional Sequel II run for this single species (higher yield) actually shows extremely low 6mA level (2ppm, confidence interval: 1-10ppm).\n\u003cp align=\"center\"\u003e\n \u003ca href=\"/docs/figures/test.6mASCOPE.6mAcontribution.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.6mAcontribution.png\" alt=\"Group-specific 6mA/A level prediction with confidence intervals 6mA\" width=\"300\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n3. Contribution (%) of each source to total 6mA abundance in the gDNA sample. CCS reads mapped to the D. melanogaster genome only explains 1.4% of the total 6mA events in the gDNA sample (green).\n\u003cp\u003eThese figures can be drawn with \u003ccode\u003esh ~/code/draw_example.sh test.6mASCOPE.txt\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eFor a comprehensive description of\u00a06mASCOPE including installation guide, data preprocessing and a detailed tutorial, including how to apply 6mASCOPE to your own datasets, please refer to the\u00a0\u003ca href=\"https://6mascope.readthedocs.io/en/latest/overview.html\" rel=\"nofollow\"\u003ecomplete documentation\u003c/a\u003e .\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1649360377.0 + "updated_at": 1637592376.0 }, { "data_format": 2, - "description": "FDUPES is a program for identifying or deleting duplicate files residing within specified directories.", + "description": null, "filenames": [ - "2.1.2/Singularity" + "Singularity.centos-7__openmpi-4.0.5__h5py" ], - "full_name": "pscedu/singularity-fdupes", - "latest_release": "v2.1.2", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-fdupes/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fdupes/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-fdupes/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fdupes/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" 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data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fdupes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0ce07a73d5d378e12e65c32c5066e0d1f9c188afcfe5184dc190f28137faf80c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666475706573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0ce07a73d5d378e12e65c32c5066e0d1f9c188afcfe5184dc190f28137faf80c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666475706573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fdupes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-fdupes\" class=\"anchor\" href=\"#singularity-fdupes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-fdupes\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/adrianlopezroche/fdupes\"\u003efdupes\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efdupes\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/fdupes/2.1.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/fdupes\u003c/code\u003e as \u003ccode\u003e2.1.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "mcduta/h5py-demo", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-experimenting-with-hdf5-in-python\" class=\"anchor\" href=\"#experimenting-with-hdf5-in-python\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExperimenting with HDF5 in Python\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-material\" class=\"anchor\" href=\"#material\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaterial\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ethis README;\u003c/li\u003e\n\u003cli\u003ethe associated python files;\u003c/li\u003e\n\u003cli\u003ea Singularity container for \u003ccode\u003eminiconda\u003c/code\u003e, \u003ccode\u003empi4py\u003c/code\u003e and \u003ccode\u003eh5py\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003ccode\u003ejupyter\u003c/code\u003e notebooks to experiment with MPI are very limited in scope by the very logic of parallel execution.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-reading\" class=\"anchor\" href=\"#reading\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReading\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://twiki.cern.ch/twiki/pub/Sandbox/JaredDavidLittleSandbox/PythonandHDF5.pdf\" rel=\"nofollow\"\u003ePython and HDF5\u003c/a\u003e by Andrew Collette (O\u0027Reilly, 2014)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ntrs.nasa.gov/api/citations/20180008456/downloads/20180008456.pdf\" rel=\"nofollow\"\u003eSome notes about chunks and compression\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs.h5py.org/en/stable/mpi.html#\" rel=\"nofollow\"\u003eh5py online documentation on parallel HDF5\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-the-container\" class=\"anchor\" href=\"#the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe container\u003c/h3\u003e\n\u003cp\u003eThe Singularity container for \u003ccode\u003eminiconda\u003c/code\u003e, \u003ccode\u003empi4py\u003c/code\u003e and \u003ccode\u003eh5py\u003c/code\u003e can be directly downloaded from \u003ca href=\"https://cloud.sylabs.io/library/mcduta/default/h5py\" rel=\"nofollow\"\u003eSyLabs\u003c/a\u003e using the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://mcduta/default/h5py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, it can be generated from the recipe provided\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build --fakeroot h5py_latest.sif Singularity.centos-7__openmpi-4.0.5__h5py\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-interactive-shell\" class=\"anchor\" href=\"#interactive-shell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive Shell\u003c/h3\u003e\n\u003cp\u003eTo experiment with the parallel Python scripts, obtain an interactive shell in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell h5py_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInteract with the shell, available containerised software and the underlying files system in the normal way, just as on any linux workstation.\u003c/p\u003e\n\u003cp\u003eBasic configuration settings can be checked once in a container shell, \u003cem\u003ee.g.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eorte-info --config\nh5pcc -showconfig\nconda list h5py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBoth executables as well as the expected HDF5 tools, \u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003eh5dump\u003c/code\u003e and \u003ccode\u003eh5ls\u003c/code\u003e are already in path. The above commands shows some details of how \u003ccode\u003eh5py\u003c/code\u003e was built (\u003cem\u003ei.e.\u003c/em\u003e on top of a parallel enabled build of HDF5 itself). See also \u003ca href=\"https://docs.h5py.org/en/stable/mpi.html#building-against-parallel-hdf5\" rel=\"nofollow\"\u003eh5py notes on building HDF5\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input-and-output\" class=\"anchor\" href=\"#input-and-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput and Output\u003c/h3\u003e\n\u003cp\u003eNeither the Python scripts nor the HDF5 files generated are part of the container. The Python scripts can be anywhere in a path on DLS storage. For the purpose of experimentation for I/O performance, the HDF5 files generated can be on a path that is mounted as \u003ccode\u003egpfs\u003c/code\u003e, \u003ccode\u003enfs\u003c/code\u003e or local \u003ccode\u003eext4\u003c/code\u003e (\u003cem\u003ee.g.\u003c/em\u003e local scratch or \u003ccode\u003e/tmp\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTip\u003c/strong\u003e: an easy way to verify what a certain path is mounted as is \u003ccode\u003edf -PT /path\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eControlling input and output can be done by bind-mounting paths in the Singularity container. For example, supposing the Python files are in \u003ccode\u003e$HOME/h5pytest\u003c/code\u003e and the output is to go to \u003ccode\u003e/dls/p45/path/to/somewhere\u003c/code\u003e, the command to start the Singularity shell is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind $HOME/h5pytest:/apps/input,/dls/p45/path/to/somewhere:/apps/output h5py_latest.sif\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce in a container shell, go to the designated output path in the container and experiment, \u003cem\u003ee.g.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /apps/output/\nmpirun -np 4 python /apps/input/h5py_write_demo.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAny files written to \u003ccode\u003e/apps/output\u003c/code\u003e are \"seen\" outside the container in the path \u003ccode\u003e/dls/p45/path/to/somewhere\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAn easier alternative to the above is to have the Python scripts and output in the same path, case in which bind-mounting the current working directory is sufficient. For example, the following command lands the Singularity shell in the current directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --home $PWD h5py_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAny files generated in the container shell are visible in \u003ccode\u003e$PWD\u003c/code\u003e outside.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-cluster\" class=\"anchor\" href=\"#cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCluster\u003c/h3\u003e\n\u003cp\u003eAn interactive session on the Hamilton cluster is a good idea for a) the availability of a significant number of cores on which the \u003ccode\u003empirun\u003c/code\u003e-launched Python processes can execute and b) the availability of \u003ccode\u003egpfs\u003c/code\u003e mounted paths. An example of request for an interactive job is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqrsh -pe openmpi-savu 20 -l h_rt=01:00:00,m_mem_free=8G -P tomography\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity is available on the cluster nodes.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-h5py-experiments\" class=\"anchor\" href=\"#h5py-experiments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eh5py\u003c/code\u003e Experiments\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-1\" class=\"anchor\" href=\"#exercise-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 1\u003c/h3\u003e\n\u003cp\u003eFirst, experiment with parallel writes and reads from local disk (\u003ccode\u003eext4\u003c/code\u003e file system). Create a user writable directory in \u003ccode\u003e/tmp\u003c/code\u003e and then obtain an interactive session on Hamilton. Use the commands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /tmp/$USER\nsingularity shell --bind $PWD:/apps/input,/tmp/$USER:/apps/output h5py_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce in the container shell, run the writer \u003ccode\u003eh5py\u003c/code\u003e demo with a varying number of processes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /apps/output/\nfor np in 4 8 16; do mpirun -np ${np} python /apps/input/h5py_write_demo.py; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEdit the file \u003ccode\u003eh5py_write_demo.py\u003c/code\u003e and observe the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe HDF5 files is open using the \u003ccode\u003empio\u003c/code\u003e driver and the operation makes use of the default MPI communicator \u003ccode\u003eMPI.COMM_WORLD\u003c/code\u003e;\u003c/li\u003e\n\u003cli\u003eeach process initialises only a part of the data that is written to file;\u003c/li\u003e\n\u003cli\u003ethere is no \u003cem\u003eglobal\u003c/em\u003e (across-process) view of the data; the variable \u003ccode\u003edataset\u003c/code\u003e is a handle for the data;\u003c/li\u003e\n\u003cli\u003edata initialisation is an \u003cem\u003eindependent\u003c/em\u003e \u003ccode\u003eh5py\u003c/code\u003e operation, while file open and close are \u003cem\u003ecollective\u003c/em\u003e (see the \u003ca href=\"https://docs.h5py.org/en/stable/mpi.html#collective-versus-independent-operations\" rel=\"nofollow\"\u003eh5py notes on this\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe data size is fixed, so increasing the number of processes means each process initialises and writes less data.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-2\" class=\"anchor\" href=\"#exercise-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 2\u003c/h3\u003e\n\u003cp\u003eNow, run the reader demo, which reads the data from the file written by the writer demo. Use the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor np in 4 8 16; do mpirun -np ${np} python /apps/input/h5py_read_demo.py; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEdit the file \u003ccode\u003eh5py_read_demo.py\u003c/code\u003e and observe the similarities with the writer demo.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-3\" class=\"anchor\" href=\"#exercise-3\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 3\u003c/h3\u003e\n\u003cp\u003eIn the read demo \u003ccode\u003eh5py_read_demo.py\u003c/code\u003e, print additional information on data read by each process, \u003cem\u003ee.g.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eprint (\" iproc = {}, shape = {}, data[0,0] = {}\".format(iproc, dataproc.shape, dataproc[0,0]))\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlace this just after the last \u003ccode\u003eMPI.Wtime\u003c/code\u003e call. Rerun the demo with 4 processes and understand the output. Now replace the \"process view\" of the data \u003ccode\u003edataproc[0,0]\u003c/code\u003e with the \"global view\" \u003ccode\u003edataset[0,0]\u003c/code\u003e and rerun. What happens?\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-4\" class=\"anchor\" href=\"#exercise-4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 4\u003c/h3\u003e\n\u003cp\u003eNow repeat the write and read runs above on \u003ccode\u003egpfs\u003c/code\u003e rather than \u003ccode\u003eetx4\u003c/code\u003e. Use an interactive cluster session and an appropriate path (\u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003e/dls/p45\u003c/code\u003e) that is mounted as \u003ccode\u003egpfs\u003c/code\u003e on Hamilton nodes. How do write/read times compare with \u003ccode\u003eext4\u003c/code\u003e?\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-5\" class=\"anchor\" href=\"#exercise-5\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 5\u003c/h3\u003e\n\u003cp\u003eRepeat the same operations, on the same path as the previous exercise but this time running the containe on a linux workstation, which mounts the path as \u003ccode\u003enfs\u003c/code\u003e (check!). How do results change?\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-6\" class=\"anchor\" href=\"#exercise-6\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 6\u003c/h3\u003e\n\u003cp\u003eEdit the file \u003ccode\u003eh5py_serial_chunking_demo.py\u003c/code\u003e and understand what it is programmed to do. The demo is serial and can be run outside the container, using the DLS python installation, \u003cem\u003ee.g.\u003c/em\u003e using \u003ccode\u003emodule load python/3.9\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNotice how the demo writes and then reads the same amount of data (simulating a stack of images) to and from HDF5 files. The first write/read is contiguous (\u003cem\u003ei.e.\u003c/em\u003e no chunks), the second is chunked and the third is chunked and also uses compression.\u003c/p\u003e\n\u003cp\u003eRun the demo on \u003ccode\u003egpfs03\u003c/code\u003e as well as \u003ccode\u003eext4\u003c/code\u003e. The chunked reads should show increased performance over the contiguous, and compressed read even more so.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe success of chunking depends entirely on the particular read data access pattern.\u003c/li\u003e\n\u003cli\u003eThe chunks are set at dataset creation time but can be changed using command line tools, \u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003eh5repack\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1633086411.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1639579950.0 }, { "data_format": 2, - "description": "Count your code, quickly.", + "description": "A Strudel2 singularity container based on the code for OpenOnDemand shell application", "filenames": [ - "12.1.2/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-tokei", - "latest_release": "v12.1.2", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-tokei/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-tokei/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-tokei/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-tokei/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/390a2ed4d417fdf45f007b0d91b70db9dc6ff55cff5fc8f811907b8a76d74102/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d746f6b6569\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/390a2ed4d417fdf45f007b0d91b70db9dc6ff55cff5fc8f811907b8a76d74102/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d746f6b6569\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-tokei\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7b139de678930691b4ec584c1314dc0c37bc85e1767f8a2d22394b60895ed619/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d746f6b6569\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b139de678930691b4ec584c1314dc0c37bc85e1767f8a2d22394b60895ed619/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d746f6b6569\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-tokei\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4a8856bc06400cf7ded9aef8652c3b21a9258182350126042ebd6e7a605fc4e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d746f6b6569\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4a8856bc06400cf7ded9aef8652c3b21a9258182350126042ebd6e7a605fc4e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d746f6b6569\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-tokei\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/933616d1190d66e34b2b8e2c452321c7f5aaf0eba14a74d8672c4f67db1312a0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d746f6b6569\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/933616d1190d66e34b2b8e2c452321c7f5aaf0eba14a74d8672c4f67db1312a0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d746f6b6569\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-tokei\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-tokei\" class=\"anchor\" href=\"#singularity-tokei\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-tokei\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e===============================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Language Files Lines Code Comments Blanks\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e===============================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e BASH 4 49 30 10 9\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e JSON 1 1332 1332 0 0\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Shell 1 49 38 1 10\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e TOML 2 77 64 4 9\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e-------------------------------------------------------------------------------\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Markdown 5 1355 0 1074 281\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e |- JSON 1 41 41 0 0\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e |- Rust 2 53 42 6 5\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e |- Shell 1 22 18 0 4\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e (Total) 1471 101 1080 290\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e-------------------------------------------------------------------------------\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Rust 19 3416 2840 116 460\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e |- Markdown 12 351 5 295 51\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e (Total) 3767 2845 411 511\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e===============================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Total 32 6745 4410 1506 829\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e===============================================================================\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/XAMPPRocky/tokei\"\u003etokei\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003etokei\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/tokei/12.1.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/tokei\u003c/code\u003e as \u003ccode\u003e12.1.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "l1ll1/terminal", + "latest_release": null, + "readme": "\u003cp\u003eThis container runs code derived from\n\u003ca href=\"https://osc.github.io/ood-documentation/master/applications/shell.html\" rel=\"nofollow\"\u003ehttps://osc.github.io/ood-documentation/master/applications/shell.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWhen starting the program as a batch job, it simply submits a tmux new-session\nWhen connecting to the program,\nit:\u003c/p\u003e\n\u003cp\u003ea) picks an unused port\nb) generates a random token for authenticaion\nc) runs a command like ssh localhost tmux attach-session \nd) proxys that command onto the unused port\ne) watches (using lsof) for connections to the port. if its been disconnected for 5 minutes it shuts down the proxy\nf) prints out the port and token in json format\u003c/p\u003e\n\u003cp\u003eBecause the proxy is inside the container, but the tmux server is outside we have to do a bit ssh localhost\nWhen doing this we supress operations relating to SSHKnowHosts (beacuse localhost is rarely the same localhost)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-debugging\" class=\"anchor\" href=\"#debugging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebugging:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCheck that you can start a tmux session via echo \"module load singularity\\nsingularity exec term.sif /start\" | sbatch This is what strudel2 does\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFind out which node your tmux is running on, login, singularity shell term.sif\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInside the singularity shell, try executing /params. Check that it gives json output. Check that it starts node /opt/shell/tmux.js and watchdog.py\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate an SSH tunnel to the port specified. Open the URL localhost:/tmux?token=\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1649568351.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1636670270.0 }, { "data_format": 2, - "description": "Spatially explicit individual based model that simulates Coffee Leaf Rust epidemics on a coffee farm", + "description": "Container recipes, usually related to HPC and scientific computing", "filenames": [ - "Singularity.def" + "cadabra/cadabra2-2.1.9-stretch/Singularity" ], - "full_name": "comses-education/coffee-leaf-rust-model", + "full_name": "jose-d/container-recipes", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-spatialrust\" class=\"anchor\" href=\"#spatialrust\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpatialRust\u003c/h1\u003e\n\u003cp\u003eSpatialRust is an individual based model that simulates Coffee Leaf Rust epidemics within a coffee farm.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installing-dependencies\" class=\"anchor\" href=\"#installing-dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling dependencies\u003c/h3\u003e\n\u003cp\u003eMove to this project\u0027s directory and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-julia\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eusing\u003c/span\u003e Pkg\nPkg\u003cspan class=\"pl-k\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eactivate\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\nPkg\u003cspan class=\"pl-k\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003einstantiate\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-the-model\" class=\"anchor\" href=\"#running-the-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the model\u003c/h3\u003e\n\u003cp\u003eThere are two script files available. You can run a single simulation using a fixed parameter set using \u003ccode\u003escripts/OneRun.jl\u003c/code\u003e as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia scripts/OneRun.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eResults from this single run will be saved in a \u003ccode\u003eresults\u003c/code\u003e folder as \u003ccode\u003esinglerun.csv\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe second option, \u003ccode\u003escripts/ParameterRuns.jl\u003c/code\u003e, lets you run a parameter exploration experiment. The default setup of this experiment will run 2700 simulations. To modify the parameter values to be evaluated or the replicates for each combination, open \u003ccode\u003escripts/ParameterRuns.jl\u003c/code\u003e and edit lines 11 to 14. Like the first option, you can run the script from bash:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia scripts/ParameterRuns.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eResults from this experiment will be saved in a \u003ccode\u003eresults\u003c/code\u003e folder as \u003ccode\u003eparameterexp.csv\u003c/code\u003e. Both scripts take care of creating the \u003ccode\u003eresults\u003c/code\u003e folder if it has not been created yet.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-container-recipes\" class=\"anchor\" href=\"#container-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer-recipes\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "agent-based-model", - "computational-model", - "julia", - "simulation" - ], - "updated_at": 1654288638.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1636393528.0 }, { "data_format": 2, "description": null, "filenames": [ - "LaMachine-master/Singularity.dev", - "LaMachine-master/Singularity" + "RStudio/Singularity", + "bc_desktop/Singularity" ], - "full_name": "AymanYac/Neonec-Deep-Classsifier", + "full_name": "SupercomputingWales/open-ondemand-apps", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-lamachine-deepclassifier--neonec-dutch-rd\" class=\"anchor\" href=\"#lamachine-deepclassifier--neonec-dutch-rd\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaMachine DeepClassifier : Neonec Dutch R\u0026amp;D\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-open-ondemand-apps\" class=\"anchor\" href=\"#open-ondemand-apps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopen-ondemand-apps\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://osc.github.io/ood-documentation/latest/\" rel=\"nofollow\"\u003eOpen-Ondemand\u003c/a\u003e provides a convenient interface for users to access remote servers such as HPC systems.\u003c/p\u003e\n\u003cp\u003eThis repository will store the versions as running on \u003ca href=\"https://portal.supercomputing.wales\" rel=\"nofollow\"\u003eSupercomputing Wales\u003c/a\u003e Hawk system.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-rstudio\" class=\"anchor\" href=\"#rstudio\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRstudio\u003c/h2\u003e\n\u003cp\u003eUsing Rocker container this spins up a Rstudio session. See Singularity file.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-jupyter\" class=\"anchor\" href=\"#jupyter\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter\u003c/h2\u003e\n\u003cp\u003eUses Anaconda as installed on Hawk to provide Jupyter session. If users install jupyter in their environments installed in home directory then the kernels for their environments also appear as an option.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-bc_desktop\" class=\"anchor\" href=\"#bc_desktop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebc_desktop\u003c/h2\u003e\n\u003cp\u003eTo allow remote desktop a container was created to allow the desktop (Mate in this case from EPEL) dependencies to be isolated from host OS which doesnt allow EPEL repository. This also supports VirtualGL and TurboVNC to provide 3D interface. Requires Slurm configurationt to support spinning up Xorg and provide a desktop.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1647881800.0 + "updated_at": 1635457389.0 }, { "data_format": 2, - "description": null, + "description": "DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types", + "filenames": [ + ".development/Singularity" + ], + "full_name": "JD2112/AlveolarCellTypeDeconvolution", + "latest_release": "v1.4.1", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-the-r-scripts-to-analyze-the-alveolar-macrophages-hla-drcd3--and-lymphocytes-cd3-specific-cell-types-from-dna-methylation-analysis\" class=\"anchor\" href=\"#the-r-scripts-to-analyze-the-alveolar-macrophages-hla-drcd3--and-lymphocytes-cd3-specific-cell-types-from-dna-methylation-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe R scripts to analyze the Alveolar macrophages (HLA-DR+/CD3-) and lymphocytes (CD3+) specific cell types from DNA methylation analysis.\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/JD2112/AlveolarCellTypeDeconvolution/actions/workflows/docker-image.yml\"\u003e\u003cimg src=\"https://github.com/JD2112/AlveolarCellTypeDeconvolution/actions/workflows/docker-image.yml/badge.svg?event=workflow_run\" alt=\"alv-decon\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-related-publication-published-in-epigenetics-2021-08-11\" class=\"anchor\" href=\"#related-publication-published-in-epigenetics-2021-08-11\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRelated publication: (Published in Epigenetics, 2021-08-11)\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eDas, J., Idh, N., Paues, J., Sikkeland, L. I. B., \u0026amp; Lerm, M.\u003c/em\u003e (2021). **DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types. \\ ** bioRxiv.\u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.03.12.435086v1\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2021.03.12.435086\u003c/a\u003e \\ \u003ca href=\"https://www.tandfonline.com/doi/full/10.1080/15592294.2021.1969499\" rel=\"nofollow\"\u003eEpigenetics link\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-create-package-and-r-script-files-according-to-the-analysis-or-result-in-the-manuscript\" class=\"anchor\" href=\"#create-package-and-r-script-files-according-to-the-analysis-or-result-in-the-manuscript\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate package and R script files according to the analysis (or Result in the manuscript).\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eDNA methylome analysis - till the normalizaed beta value calculation.\u003c/li\u003e\n\u003cli\u003eNormality calculation with Anderson\u0027s test (\u003cstrong\u003eTable 1\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003ePearson\u0027s rank correaltion analysis - Figures, Table (\u003cstrong\u003eFigure 2 - a. HLA-DR, b. CD3\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eBeanplot from the beta values of the whole dataset to describe the beta distribution over all samples (\u003cstrong\u003eFigure S1a\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eMann-Whitney test for the hypothesis - Figures, Table (F\u003cstrong\u003eigure 3a - HLA-DR and 3b. CD3\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eValidation of SI and BAL from Lung compartments (\u003cstrong\u003eFigure 4\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eTesting of 3 reference-free algorithms - algorithms testings, Venn Diagrams (\u003cstrong\u003eFigure 5a. HLA-DR and Figrue 5b. CD3\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eCell proportion analysis using the EpiDISH package (\u003cstrong\u003eFigure 6\u003c/strong\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-use-of-docker-image\" class=\"anchor\" href=\"#use-of-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse of Docker image\u003c/h2\u003e\n\u003cp\u003eDockerfile can be used for all R packages and repositories. The image file can be found here\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull jd21/alv-decon:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-functions-present-in-the-package\" class=\"anchor\" href=\"#functions-present-in-the-package\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctions present in the package\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFunctions\u003c/th\u003e\n\u003cth\u003eR scripts\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003cth\u003enotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eChAMPanalysis450K()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eChAMPanalysis.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003escript for DNA methylation using ChAMP\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eStatiscalAnalysisHLADR()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eStatisticalAnalysis.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eStatiscalAnalysisCD3()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eStatisticalAnalysis.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eValidationWithCysticFibrosis()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eValidationWithCF.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eCompareAnalysisRingh()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eStatisticalAnalysis.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003ehistogramPlot()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure2c.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003ehistogram analysis for beta values\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eAlveolarCellTypeDeconvolutionRefFreeEWAS()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eHouseman algorithm reference free analysis\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eAlveolarCellTypeDeconvolutionSVA()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eSVA analysis\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eAlveolarCellTypeDeconvolutionRefFreeCellMix()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eAlveolarCellTypeDeconvolutionTOAST()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eggplotRegression()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure4.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003esFigure1()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003esupplementaryFigureS1.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003esFigure2()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003esupplementaryFigureS2.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eqqPlot()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003esupplementaryFigureS3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eQ-Q plot for compare DNA methylome data\u003c/td\u003e\n\u003ctd\u003ea sub-function can also be used; gg_qq()\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "stargazers_count": 0, + "subscribers_count": 2, + "topics": [ + "dna-methylation", + "alveolar-macrophages", + "alveolar-lymphocytes", + "hla-dr", + "cd3", + "cell-deconvolution" + ], + "updated_at": 1639727537.0 + }, + { + "data_format": 2, + "description": "Exploratory research using graph neural networks", "filenames": [ "Singularity" ], - "full_name": "remiolsen/pin_hic_singularity", + "full_name": "davidhin/gnn-exploration", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-pin_hic_singularity\" class=\"anchor\" href=\"#pin_hic_singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epin_hic_singularity\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1647940031.0 + "updated_at": 1635288566.0 }, { "data_format": 2, "description": null, "filenames": [ - "SingularityLfH.def" + "Singularity" ], - "full_name": "LearningUAV/hallucination", + "full_name": "truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-docker-stream8-warewulf4-builder-\" class=\"anchor\" href=\"#singularity-docker-stream8-warewulf4-builder-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-stream8-warewulf4-builder \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003ewarewulf4 builder container based on a CentOS Stream 8 x86_64 docker image built from github actions\u003c/p\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why\" class=\"anchor\" href=\"#why\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eprovide a minimal builder for warewulf5\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti ghcr.io/truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec oras://ghcr.io/truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder:latest rpmbuild -ta warewulf-4.2.0.tar.gz \n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1646853452.0 + "updated_at": 1638431157.0 }, { "data_format": 2, @@ -12189,13 +11600,13 @@ var data = "filenames": [ "Singularity" ], - "full_name": "garciaml/BrainQCNet_GPU", + "full_name": "truatpasteurdotfr/singularity-docker-centos8-ci", "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-brainqcnet-version-for-gpu-compatible-with-cuda-cudnn--detection-of-artifacts-on-brain-t1-weighted-scans\" class=\"anchor\" href=\"#brainqcnet-version-for-gpu-compatible-with-cuda-cudnn--detection-of-artifacts-on-brain-t1-weighted-scans\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBrainQCNet [version for GPU compatible with CUDA, CuDNN] : Detection of Artifacts on Brain T1-weighted Scans\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/garciaml/BrainQCNet/blob/master/T1_low_quality_2.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/garciaml/BrainQCNet/raw/master/T1_low_quality_2.jpg\" width=\"2000\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor more information about the specification of BIDS Apps see \u003ca href=\"https://docs.google.com/document/d/1E1Wi5ONvOVVnGhj21S1bmJJ4kyHFT7tkxnV3C23sjIE/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eBrainQCNet is a software that automatically detects the presence of defaults on brain structural MRI scans.\u003c/p\u003e\n\u003cp\u003eIt is based on a Deep Learning algorithm, you can find more details on how it was built on the paper \u003ca href=\"https://link-to-preprint.com\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eIn order to understand how to use BainQCNet, please go \u003ca href=\"https://github.com/garciaml/BrainQCNet\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-how-to-report-errors\" class=\"anchor\" href=\"#how-to-report-errors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to report errors\u003c/h3\u003e\n\u003cp\u003eUsers can get help using the \u003ca href=\"https://groups.google.com/g/brainqcnet-users\" rel=\"nofollow\"\u003ebrainqcnet-users mailing list\u003c/a\u003e.\nAll bugs, concerns and enhancement requests for this software can be submitted \u003ca href=\"https://github.com/garciaml/BrainQCNet_GPU/issues\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h3\u003e\n\u003cp\u003eWhen using BrainQCNet, please include the following citation:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBrainQCNet: a Deep Learning attention-based model for multi-scale detection of artifacts in brain structural MRI scans\u003c/em\u003e, Melanie Garcia, Nico Dosenbach, Clare Kelly. bioRxiv 2022.03.11.483983; doi: \u003ca href=\"https://doi.org/10.1101/2022.03.11.483983\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2022.03.11.483983\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-centos-8-toy-system-for-singularity-and-docker-image-for-ci\" class=\"anchor\" href=\"#building-a-centos-8-toy-system-for-singularity-and-docker-image-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a CentOS 8 toy system for singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCentOS-8 system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos8-ci/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-centos8-ci/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-centos8-ci:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos8-ci:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1646931926.0 + "updated_at": 1635192721.0 }, { "data_format": 2, @@ -12203,307 +11614,320 @@ var data = "filenames": [ "Singularity" ], - "full_name": "garciaml/BrainQCNet_CPU", + "full_name": "truatpasteurdotfr/singularity-docker-stream8-ci", "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-brainqcnet-version-for-cpu--detection-of-artifacts-on-brain-t1-weighted-scans\" class=\"anchor\" href=\"#brainqcnet-version-for-cpu--detection-of-artifacts-on-brain-t1-weighted-scans\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBrainQCNet [version for CPU] : Detection of Artifacts on Brain T1-weighted Scans\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/garciaml/BrainQCNet/blob/master/T1_low_quality_2.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/garciaml/BrainQCNet/raw/master/T1_low_quality_2.jpg\" width=\"2000\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor more information about the specification of BIDS Apps see \u003ca href=\"https://docs.google.com/document/d/1E1Wi5ONvOVVnGhj21S1bmJJ4kyHFT7tkxnV3C23sjIE/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eBrainQCNet is a software that automatically detects the presence of defaults on brain structural MRI scans.\u003c/p\u003e\n\u003cp\u003eIt is based on a Deep Learning algorithm, you can find more details on how it was built on the paper \u003ca href=\"https://link-to-preprint.com\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eIn order to understand how to use BainQCNet, please go \u003ca href=\"https://github.com/garciaml/BrainQCNet\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-how-to-report-errors\" class=\"anchor\" href=\"#how-to-report-errors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to report errors\u003c/h3\u003e\n\u003cp\u003eUsers can get help using the \u003ca href=\"https://groups.google.com/g/brainqcnet-users\" rel=\"nofollow\"\u003ebrainqcnet-users mailing list\u003c/a\u003e.\nAll bugs, concerns and enhancement requests for this software can be submitted \u003ca href=\"https://github.com/garciaml/BrainQCNet_CPU/issues\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h3\u003e\n\u003cp\u003eWhen using BrainQCNet, please include the following citation:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBrainQCNet: a Deep Learning attention-based model for multi-scale detection of artifacts in brain structural MRI scans\u003c/em\u003e, Melanie Garcia, Nico Dosenbach, Clare Kelly. bioRxiv 2022.03.11.483983; doi: \u003ca href=\"https://doi.org/10.1101/2022.03.11.483983\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2022.03.11.483983\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-centos-stream-8-toy-system-for-singularity-and-docker-image-for-ci\" class=\"anchor\" href=\"#building-a-centos-stream-8-toy-system-for-singularity-and-docker-image-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a CentOS Stream 8 toy system for singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003estream8 system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-ci/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-ci/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-stream8-ci:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-stream8-ci:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1646931972.0 + "updated_at": 1635194959.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "piyu2181/singulariyu", + "full_name": "truatpasteurdotfr/singularity-docker-busybox", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-busybox-toy-system-for-singularity-and-docker-image-for-ci\" class=\"anchor\" href=\"#building-a-busybox-toy-system-for-singularity-and-docker-image-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a busybox toy system for singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-busybox/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-busybox/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-busybox:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-busybox:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1565736075.0 + "updated_at": 1635194705.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.speaker_tagging" + "Singularity" ], - "full_name": "oboratav/speaker-tagging", + "full_name": "remiolsen/fast5mod-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-red-hen-teletext-color-annotator\" class=\"anchor\" href=\"#red-hen-teletext-color-annotator\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRed Hen Teletext Color Annotator\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.redhenlab.org/home/the-cognitive-core-research-topics-in-red-hen/the-barnyard/convert-teletext-colors-to-speaker-tags\" rel=\"nofollow\"\u003eA Red Hen Lab project.\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eSome providers in certain countries use styling features available in DVB Teletext to color-code their closed captioning. These color codes can potentially be used to detect turn-taking between interlocutors.\u003c/p\u003e\n\u003cp\u003eThis program takes a \u003ccode\u003e.seg\u003c/code\u003e file, reads color tags inside it (if any), and outputs an annotated version of the same file.\u003c/p\u003e\n\u003cp\u003eThe tags it adds are in the form of:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[start]|[end]|CTG_0|[hex]/[text]\n\u003c/code\u003e\u003c/pre\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eField\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e[start]\u003c/td\u003e\n\u003ctd\u003eStarting timestamp of the annotation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e[end]\u003c/td\u003e\n\u003ctd\u003eEnding timestamp of the annotation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e[hex]\u003c/td\u003e\n\u003ctd\u003eHex color of the tag\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e[text]\u003c/td\u003e\n\u003ctd\u003eContents of the tag\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor instance:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e20200202214233.960|20200202214234.760|CTG_0|#ffff00/y nuevas pistas.\n20200202214233.960|20200202214234.760|CC1|\u0026lt;font color=\"#ffff00\"\u0026gt;y nuevas pistas.\u0026lt;/font\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003ehex/text\u003c/code\u003e pairs may repeat if more than one color tag exists in a single CC line, with each pair being separated by \u003ccode\u003e|\u003c/code\u003e like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e20200202214242.840|20200202214245.360|CTG_0|#ffff00/en busca de respuestas|#ffff00/a las nuevas tendencias.\n20200202214242.840|20200202214245.360|CC1|\u0026lt;font color=\"#ffff00\"\u0026gt;en busca de respuestas\u0026lt;/font\u0026gt; \u0026lt;font color=\"#ffff00\"\u0026gt;a las nuevas tendencias.\u0026lt;/font\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-install-and-use\" class=\"anchor\" href=\"#how-to-install-and-use\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Install and Use\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-via-docker\" class=\"anchor\" href=\"#via-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evia Docker\u003c/h3\u003e\n\u003cp\u003eInstalling and using the tool as a Docker container is by far the easiest way. Simply run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull oboratav/speaker-tagging\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd Docker will take care of the rest. To annotate a file, simply pipe it into the container, and capture its output:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat input_file.txt | docker run -i -a stdin -a stdout oboratav/speaker-tagging \u0026gt; output_file.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also use the \u003ccode\u003e-v\u003c/code\u003e flag to mount files from the local filesystem:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v /some/input/file.seg:/usr/data/input_file.seg -a stdout oboratav/speaker-tagging /usr/data/input_file.seg \u0026gt; output_file.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-directly\" class=\"anchor\" href=\"#directly\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectly\u003c/h3\u003e\n\u003cp\u003eYou can also skip Docker altogether and just clone this git repo, create a virtual environment, and install the requirements listed in \u003ccode\u003erequirements.txt\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-example-use-cases\" class=\"anchor\" href=\"#example-use-cases\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample Use Cases\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eFind occurrences of two different colors in the same line:\n\u003ccode\u003eCTG_0\\|.*([a-f0-9]{6}).*\\|(?!\\1)(?:[a-f0-9]{6})\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fast5mod-singularity\" class=\"anchor\" href=\"#fast5mod-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efast5mod-singularity\u003c/h1\u003e\n\u003cp\u003eSingulartized version of \u003ca href=\"https://github.com/nanoporetech/fast5mod\"\u003ehttps://github.com/nanoporetech/fast5mod\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1639347259.0 + "updated_at": 1635176825.0 }, { "data_format": 2, - "description": "Generate a singularity container for XDS", + "description": "Singularity images for tensorflow", "filenames": [ - "Singularity.xds_2021-Feb05" + "Singularity.cuda9.0-tf1.13-with_dali", + "Singularity.cuda9.0-tf1.13-ofed4.4", + "Singularity.cuda9.0-tf1.13-ofed4.0", + "Singularity.cuda9.0-tf1.13-without-ofed" ], - "full_name": "hoangnguyen177/xds-singularity-container", + "full_name": "Pepitaw/singularity_tensorflow", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-xds-singularity-container\" class=\"anchor\" href=\"#xds-singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003exds-singularity-container\u003c/h1\u003e\n\u003cp\u003eGenerate a singularity container for XDS\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_tensorflow\" class=\"anchor\" href=\"#singularity_tensorflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity_tensorflow\u003c/h1\u003e\n\u003cp\u003eSingularity images for tensorflow\nUsed for 2019 APAC HPC-AI\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1639537965.0 + "updated_at": 1634629001.0 }, { "data_format": 2, - "description": null, + "description": "Trigger repo1 on repos2 release", + "filenames": [ + "environments/illumina/Singularity" + ], + "full_name": "sofstam/repo1", + "latest_release": "v2.1.3", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-repo1\" class=\"anchor\" href=\"#repo1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepo1\u003c/h2\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [], + "updated_at": 1638278004.0 + }, + { + "data_format": 2, + "description": "sherlock vnc is a singularity container and job script to run xfce4 in a vnc session on the sherlock compute cluster", "filenames": [ "Singularity" ], - "full_name": "AdamWilsonLab/emma_docker", - "latest_release": "0.0.605", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-emma-docker-container\" class=\"anchor\" href=\"#emma-docker-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEMMA Docker Container\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-instructions\" class=\"anchor\" href=\"#instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quickstart\" class=\"anchor\" href=\"#quickstart\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -p 8787:8787 -e PASSWORD=yourpasswordhere adamwilsonlab/emma:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eVisit \u003ccode\u003elocalhost:8787\u003c/code\u003e in your browser and log in with username rstudio and the password you set. NB: Setting a password is now REQUIRED. Container will error otherwise.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-local-machine-no-password\" class=\"anchor\" href=\"#local-machine-no-password\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal machine (no password)\u003c/h2\u003e\n\u003cp\u003eIf you are running on a local machine with other security mechanisms, you can use the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm \\\n -p 127.0.0.1:8787:8787 \\\n -e DISABLE_AUTH=true \\\n adamwilsonlab/emma:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eThere are two methods to pull the docker image into Singularity as explained below.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-set-some-useful-environment-variables\" class=\"anchor\" href=\"#set-some-useful-environment-variables\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet some useful environment variables\u003c/h3\u003e\n\u003cp\u003eIf you don\u0027t do this you\u0027re likely to run out of space because the home directory doesn\u0027t have much room.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# mount project folder inside container:\nexport PROJECT_FOLDER=\"/projects/academic/adamw/\"\n# path to singularity container file. If you want to use a different image, you\u0027ll need\n# to update this line.\nexport DOCKER_PATH=\"docker://adamwilsonlab/emma:latest\"\nexport CONTAINER_PATH=\"/panasas/scratch/grp-adamw/singularity/$USER/AdamWilsonLab-emma_docker:latest.sif\"\n# to use for ssh:\nexport SERVER_URL=\"horae.ccr.buffalo.edu\"\n# folder to hold temporary singularity files - unique for each user:\n# export SINGULARITY_LOCALCACHEDIR=\"/panasas/scratch/grp-adamw/singularity/\"$USER\nexport SINGULARITY_LOCALCACHEDIR=\"/ssd_data/singularity/\"$USER\n\n# name the resulting sif file\nexport SIF_PATH=$SINGULARITY_LOCALCACHEDIR/\"AdamWilsonLab-emma_docker-latest.sif\"\n\n# define a few more folders used by singularity\nexport SINGULARITY_CACHEDIR=$SINGULARITY_LOCALCACHEDIR\nexport SINGULARITY_TMPDIR=$SINGULARITY_LOCALCACHEDIR\n\n# Create the folders if they don\u0027t already exist\nmkdir -p $SINGULARITY_LOCALCACHEDIR/tmp\nmkdir -p $SINGULARITY_LOCALCACHEDIR/run\nmkdir -p $SINGULARITY_LOCALCACHEDIR/rstudio\n\n\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-directly-from-docker-image-locally\" class=\"anchor\" href=\"#build-directly-from-docker-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild directly from Docker image locally\u003c/h3\u003e\n\u003cp\u003eBuild the .sif directly from the docker image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# build the singularity image - note this takes about 3 hours on horae!\nnohup singularity build --force $SIF_PATH $DOCKER_PATH \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003enohup\u003c/code\u003e simply allows it to keep running if the SSH connection is broken.\u003c/p\u003e\n\u003cp\u003eThen follow the \u003ca href=\"singularity_start.sh\"\u003e\u003ccode\u003esingularity_start.sh\u003c/code\u003e\u003c/a\u003e script.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-use-the-precompiled-sif-from-github\" class=\"anchor\" href=\"#use-the-precompiled-sif-from-github\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse the precompiled .sif from Github\u003c/h3\u003e\n\u003cp\u003eA .sif file is compiled using github actions when the version number of the image is updated in this repository. These can be found \u003ca href=\"https://github.com/AdamWilsonLab/emma_docker/releases\"\u003ehere\u003c/a\u003e. However, they are only produced if turned on in the GitHub actions \u003ccode\u003ebuilder.yml\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eYou will only need to run the following once (unless the image changes).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /panasas/scratch/grp-adamw/singularity/adamw\nrm AdamWilsonLab-emma_docker-latest.sif\nwget -O $SIF_PATH https://github.com/AdamWilsonLab/emma_docker/releases/download/0.0.530/AdamWilsonLab-emma_docker-latest.sif.zip\nunzip $SIF_PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen follow the \u003ca href=\"singularity_start.sh\"\u003e\u003ccode\u003esingularity_start.sh\u003c/code\u003e\u003c/a\u003e script.\u003c/p\u003e\n", + "full_name": "romxero/sherlock_vnc", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1641492090.0 + "updated_at": 1634276991.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "iqbal-lab-org/triphecta", + "full_name": "DCAN-Labs/BIDS_scripts", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/triphecta/actions/workflows/build.yaml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/triphecta/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-triphecta\" class=\"anchor\" href=\"#triphecta\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etriphecta\u003c/h1\u003e\n\u003cp\u003eUnder construction\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-bids-conversion-scripts\" class=\"anchor\" href=\"#bids-conversion-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBIDS Conversion Scripts\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eThis repository contains scripts that will assist in the conversion of raw DICOM data sets to \u003ca href=\"http://bids.neuroimaging.io/format\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e. The \"heuristics\" folder contains example scripts that have been used to convert data from DICOM to BIDS. \u003cstrong\u003eThis folder may be helpful to build your own heuristics script.\u003c/strong\u003e For additional information on how to create a heuristic script see the \u003ca href=\"https://github.com/nipy/heudiconv\"\u003eheudiconv\u003c/a\u003e github page.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-hosting\" class=\"anchor\" href=\"#container-hosting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Hosting\u003c/h3\u003e\n\u003cp\u003ePull the most recent container below, (\u003cstrong\u003eNOTE: you only have to do this once!\u003c/strong\u003e):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tjhendrickson/BIDS_scripts:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" href=\"#singularity-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Usage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/singularity/images/directory/imagename.img --help\nusage: [-h] [--output_dir OUTPUT_DIR] [--dicom_dir DICOM_DIR]\n [--study_name STUDY_NAME] [--ses_id SES_ID] [--subj_id SUBJ_ID]\n [--heuristic HEURISTIC] [--dry_run]\n\nScript that controls BIDS conversion for individual studies\n\noptional arguments:\n -h, --help show this help message and exit\n --output_dir OUTPUT_DIR\n The directory that the BIDS data will be outputted to\n --dicom_dir DICOM_DIR\n The directory that houses unzipped dicom\n directories/files.\n --study_name STUDY_NAME\n What is the shorthand name for this study?\n --ses_id SES_ID scanning session id\n --subj_id SUBJ_ID subject id\n --heuristic HEURISTIC\n Path to heuristic file, if the file is already within\n the container (i.e. within heuristics folder) you do\n not have to specify a path.\n --dry_run Dry run. A dicominfo_*.tsv file will generate within\n .heudiconv/\u0027subj_id\u0027/info directory which can be used\n to create heuristic script\n\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eThis application must be run with either the \"--heuristic\" or \"--dry_run\" argument, it will fail otherwise.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse the \"--dry_run\" argument to take a closer look at the acquistion parameters for a scanning session.\u003c/p\u003e\n\u003cp\u003eTo run a single participant with dry_run argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --dry_run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output a hidden folder (named .heudiconv) along with sub-folders based on arguments provided to \"--subj_id\" and \"--ses_id\" respectively.\nWithin the sub-folders will be a tsv file that begins with \"dicominfo\". \u003cstrong\u003eBased on the example above the path to the file will be \".heudiconv/1000/ses-10000/info/dicominfo_ses-10000.tsv\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse this tsv file to design a heuristics script to organize your eventual nifti data. \u003cstrong\u003eSee this tutorial for a how to on heuristic script creation (\u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman3\" rel=\"nofollow\"\u003eheuristics tutorial\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo run a single participant with heuristic argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir -B /path/to/heuristics/script:/heuristic.py \\\n /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --heuristic /heuristic.py\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-important-notes\" class=\"anchor\" href=\"#important-notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Notes\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gotchas\" class=\"anchor\" href=\"#gotchas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGotchas\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Bind Mounting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to run this container you will have to use \"bind mounting\", meaning you will have to link local folders/files to existing folders/files within the container with the -B flag. In the example above the local folder \"/home/timothy/sandbos_DO_NOT_DELETE/BIDS/142_CIFASD_4\" becomes \"/output_dir\" within the container as they are separated by a colon (:). \u003cstrong\u003eNotice that in both cases above the output and dicom folder and heuristic file are bound to /output_dir, /dicom_dir and /heuristic.py respectively, this is very important.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) DICOM Data Formatting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the restrictive nature of BIDS the dicom data must be in a particular format in order for the conversion to work properly. This application will copy dicom data directories by searching for either the --subj_id or --ses_id argument present within a dicom directory name, place them in a separate directory, and rearrange them. So for example if the dicom directory is named \"XYXY4776XYXY\" --subj_id 4776 the application will find the \"4776\" pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Subject ID and Session ID names\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou must use alphanumerics (i.e. letters or numbers) only (\u003cstrong\u003eno special characters\u003c/strong\u003e) with your subject IDs (subj_id) and session IDs (ses_id). \u003cstrong\u003eNote the \"--ses_id\" argument is optional\u003c/strong\u003e!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-best-practices\" class=\"anchor\" href=\"#best-practices\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBest Practices\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Initial Conversion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile testing the initial BIDS conversion it is best to start with one or two datasets and specify the \u0027--dry_run\u0027 argument (see above for an example of usage).\nThis will create a dicom_info tsv file which can be used for heuristic script creation.\nSee Step 3 of \u0027Run HeuDiConv on ses-001 scans to get the dicominfo file\u0027 within \u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman2\" rel=\"nofollow\"\u003eStanford BIDS Tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) BIDS Validator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce satisfied with an initial BIDS conversion, prior to running the conversion on an entire study first ensure that the BIDS converted dataset meets the BIDS specification by using the \u003ca href=\"http://incf.github.io/bids-validator/\" rel=\"nofollow\"\u003eBIDS validator web version\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Longitudinal Format\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen converting data to BIDS you can certainly have a cross sectonal directory format such as below:\nBIDS_output\nsub-01\nsub-02\nsub-03\nsub-04\netc\nHowever, I suggest placing data within a longitudinal directory format even if you have cross-sectional data:\nBIDS_output\nsub-01\nses-01\nses-02\nsub-02\nses-01\netc\u003c/p\u003e\n\u003cp\u003eYou can control the BIDS directory format by providing both the arguments --subj_id --ses_id for a conversion, if you only specify one of the two arguments the data will be outputted in a cross-sectional format.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 5, "topics": [], - "updated_at": 1639756790.0 + "updated_at": 1633800709.0 }, { "data_format": 2, - "description": "\ud83d\udc1f \ud83c\udf63 \ud83c\udf71 Highly-accurate \u0026 wicked fast transcript-level quantification from RNA-seq reads using selective alignment", + "description": "Recipe for deepspeed singularity container", "filenames": [ - "1.6.0/Singularity", - "1.5.2/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-salmon", + "full_name": "luukkonenr/deepspeed-torch-singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-salmon/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-salmon/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-salmon/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-salmon/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0b8ff9f174b15f24d56c3f8e2cf513b84784150c0375c38dc395c4b3278d6a17/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73616c6d6f6e\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0b8ff9f174b15f24d56c3f8e2cf513b84784150c0375c38dc395c4b3278d6a17/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73616c6d6f6e\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-salmon\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/2c22132440bbb02239d96119f1cf10791c102131494c8947ac8fe61fb5e02783/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73616c6d6f6e\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg 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style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7dd8453685087761442a45f4a77d6ddab1597f1cc80874ef308e2170ed183e6a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73616c6d6f6e\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7dd8453685087761442a45f4a77d6ddab1597f1cc80874ef308e2170ed183e6a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73616c6d6f6e\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-salmon\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-salmon\" class=\"anchor\" href=\"#singularity-salmon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-salmon\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/COMBINE-lab/salmon/raw/master/doc/salmon_logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg alt=\"salmon logo\" src=\"https://github.com/COMBINE-lab/salmon/raw/master/doc/salmon_logo.png\" width=\"600\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/COMBINE-lab/salmon\"\u003esalmon\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003esalmon\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/salmon/1.5.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/salmon\u003c/code\u003e as \u003ccode\u003e1.5.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch3\u003e\n\u003ca id=\"user-content-note-docker-workflow-with-gh-actions-is-broken-due-to-a-broken-dependency-since-debian-git-depenceny-for-image-has-been-removed\" class=\"anchor\" href=\"#note-docker-workflow-with-gh-actions-is-broken-due-to-a-broken-dependency-since-debian-git-depenceny-for-image-has-been-removed\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTE: Docker-workflow with GH-Actions is broken due to a broken dependency, since debian-git-depenceny for image has been removed.\u003c/h3\u003e\n\u003cp\u003eTODO: update image path.\nPrevious working image is still available.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-recipe-template-for-building-deepspeed-enabled-pytorch-container\" class=\"anchor\" href=\"#singularity-recipe-template-for-building-deepspeed-enabled-pytorch-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity-recipe-template for building Deepspeed-enabled pytorch-container\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install-singularity\" class=\"anchor\" href=\"#install-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall singularity\u003c/h2\u003e\n\u003cp\u003eFollow these instructions to install singularity on a system\n\u003ca href=\"https://github.com/hpcng/singularity/blob/master/INSTALL.md\"\u003ehttps://github.com/hpcng/singularity/blob/master/INSTALL.md\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNOTE: I\u0027ve used \u003cstrong\u003eSingularity version 3.5.3\u003c/strong\u003e, newest 3.8.3 gave me some errors and I think it uses later gcc or something like that which results in build problems with some of the libraries.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-option-1-building-a-container-on-your-own-machine\" class=\"anchor\" href=\"#option-1-building-a-container-on-your-own-machine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 1: Building a container on your own machine\u003c/h2\u003e\n\u003cp\u003eYou need root-privileges (or --fakeroot) to build containers.\nYou may need to set cachedir for singularity to avoid \u0027no space left on device\u0027-errors\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir $HOME/.cache/singularity/\nexport SINGULARITY_TMPDIR=/path/ e.g $HOME/.cache/singularity/\nexport SINGULARITY_CACHEDIR=/path/ e.g $HOME/.cache/singularity/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eBUILD:\u003c/strong\u003e \u003ccode\u003esudo -E singularity build container-name Singularity\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-option-2-pulling-ready-built-image-from-ghcr\" class=\"anchor\" href=\"#option-2-pulling-ready-built-image-from-ghcr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 2: Pulling ready-built image from ghcr\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITY_TMPDIR=/path/ e.g $HOME/.cache/singularity/\nexport SINGULARITY_CACHEDIR=/path/ e.g $HOME/.cache/singularity/\nsingularity pull NAME_FOR_IMG docker://ghcr.io/luukkonenr/deepspeed-torch-singularity:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-csc-environment\" class=\"anchor\" href=\"#running-on-csc-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on CSC-environment\u003c/h2\u003e\n\u003cp\u003eIf running on Mahti make sure your $HOME/.ssh/config is looking like this\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e host c???? g???? mahti* *.mahti.csc.fi\n IdentityFile ~/.ssh/id_rsa_mahti\n StrictHostKeyChecking no\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePut the following inside your slurm-script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#Load pdsh\nmodule load pdsh/2.31\n\n#Bind directory with pdsh to /usr/local/sbin in singularity\nexport SING_FLAGS=\"$SING_FLAGS -B /appl/spack/v014/install-tree/gcc-4.8.5/pdsh-2.31-cdzt5w/bin:/usr/local/sbin\"`\nexport SING_IMAGE=/PATH/TO/CONTAINER/deepspeed.sif # This needs to match the path inside your init_node.sh\nexport SING_FLAGS=$SING_FLAGS \"--nv\" # Enable GPU\nexport SING_FLAGS=$SING_FLAGS \"--contain\" # Shadow /home/$USER/ \nexport TORCH_EXT_DIR=/path/to/some/dir/ # I f you have existing dir with some ops, may cause a hang with a msg about using this torch_ext_dir. Try removing that dir and run your job again.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing plain singularity and \u003ccode\u003e--contain\u003c/code\u003e-flag shadowing the /user/home/ to avoid possible conflicting user-packages:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEXAMPLE:\u003c/strong\u003e\n\u003ccode\u003esingularity exec --contain $SING_IMAGE python -c \u0027ds_report\u0027\u003c/code\u003e\n\u003ccode\u003esingularity exec $SING_FLAGS $SING_IMAGE python -c \u0027ds_report\u0027\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eUsing csc singularity_wrapper (\u003cstrong\u003enot preferred\u003c/strong\u003e, may lead to conflicts especially on multinode-setup) :\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRUNNING:\u003c/strong\u003e\n\u003ccode\u003esingularity_wrapper exec deepspeed DEEPSPEED_ARGUMENTS path/to/python_script.py PYTHON_ARGUMENTS\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEXAMPLE:\u003c/strong\u003e\n\u003ccode\u003esingularity_wrapper exec deepspeed --hostfile=hostfile.txt --master_addr=$MASTER_NODE /projappl/project_2004600/risto/model3multi/training/trainer.py --train_data $TRAIN_DATA \\ ... \u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-changes-to-packages\" class=\"anchor\" href=\"#changes-to-packages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChanges to packages:\u003c/h2\u003e\n\u003cp\u003eThis version has been configured to use pdsh for inter-node communications. No other runners have been tested and may need spesific configurations.\n\u003ccode\u003e/opt/conda/lib/python3.8/site-packages/deepspeed/launcher/multinode_runner.py\u003c/code\u003e has been modified to contain relevant information about running python inside the container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eadded line \"source node_init.sh\" \u003cem\u003esee node_init.sh\u003c/em\u003e to PDSH-runner-class\u003c/li\u003e\n\u003cli\u003eexec argument \u003ccode\u003epython\u003c/code\u003e changed to \u003ccode\u003esingularity exec $SING_FLAGS $SING_IMAGE python\u003c/code\u003e to PDSH-runner-class\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIMPORTANT\u003c/strong\u003e: CSC singularity_wrapper exposes user-libraries even if we use \u003ccode\u003e--contain\u003c/code\u003e-flag so using it with this container is not a good idea.\n\u003ccode\u003e--contain\u003c/code\u003e-flag prevents usage of locally installed packages. Otherwise, conflicts with different versions of packages, especially included modified Deepspeed will cause problems.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eI\u0027ve tried to test get build process working with Github Actions but during build I encounter \"no space left on device\"-error and build crashes. Will try to get this working so newest img would always be ready to get pulled. However, Docker-workflow works.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-resources\" class=\"anchor\" href=\"#resources\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://singularity-tutorial.github.io/\" rel=\"nofollow\"\u003ehttps://singularity-tutorial.github.io/\u003c/a\u003e -- Basics of singularity usage\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/\u003c/a\u003e -- Singularity docs (v.3.5)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1639902426.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1637060857.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "requirements/Singularity.def" ], - "full_name": "porchard/snRNAseq-NextFlow", + "full_name": "nasa-cisto-ai/slump-detection", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-10x-snatac-seq-data\" class=\"anchor\" href=\"#nextflow-pipeline-for-10x-snatac-seq-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow pipeline for 10X snATAC-seq data\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eSingularity (v. 3) and NextFlow (\u0026gt;= v. 20.10.0). Containers with the software for each step are pulled from the Sylabs cloud library (\u003ca href=\"https://cloud.sylabs.io/library\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003ePaths to reference files must be included in the nextflow.config file -- check that file and change paths accordingly. These include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSTAR indices (compatible with STAR v. 2.7.9a)\u003c/li\u003e\n\u003cli\u003eGTF files\u003c/li\u003e\n\u003cli\u003eBarcode whitelist (for Chromium v3, that is the 3M-february-2018.txt file; for v2, that is the 737K-august-2016.txt file; for multiome, that is 737K-arc-v1.txt)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou\u0027ll also need to set the params.results variable -- either in the nextflow.config file itself, or on the command line when you run the pipeline (\u0027--results /path/to/results\u0027) as well as the 10X Chromium chemistry version (\u0027V2\u0027, \u0027V3\u0027, or \u0027multiome\u0027)\u003c/p\u003e\n\u003cp\u003eLastly, you\u0027ll need to include information about each RNA-seq library, including the genome to which it should be mapped, and the paths to the fastq files for each readgroup. Organize this information in a JSON file, as in library-config.json. For each readgroup, the \u00271\u0027 fastq file corresponds to the sequencing read including the UMI and the nucleus index; the \u00272\u0027 fastq file refers to the sequencing read representing the actual transcript. Also, note that the \u0027genome\u0027 attribute is given as a list (because I will be adding the ability to map to multiple genomes, in the case that nuclei from multiple species are mixed together).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eOnce you have all of the above information, you can run the pipeline as follows (in this case, indicating the path to the results on the command line):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run -params-file library-config.json --chemistry multiome --results /path/to/results /path/to/main.nf\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-slump-detection\" class=\"anchor\" href=\"#slump-detection\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSlump Detection\u003c/h1\u003e\n\u003cp\u003eSlump Detection as an instance segmentation problem.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-business-case\" class=\"anchor\" href=\"#business-case\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBusiness Case\u003c/h2\u003e\n\u003cp\u003eThe following repository stores several experiments for the task of instance and semantic\nsegmentation of slumps in very high-resolution satellite imagery. Many of the instructions\nlisted below are guided towards utilizing GSFC NASA Center for Climate Simulation (NCCS)\ncomputing resources, particularly the PRISM GPU cluster.\u003c/p\u003e\n\u003cp\u003eA system with NVIDIA GPUs is required to run the scripts located in this repository.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eprojects/detectron2: utilizes the detectron2 framework for the task of instance segmentation\nleveraging MaskRCNN and Fast RCNN. The backend engine is PyTorch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-summarized-steps\" class=\"anchor\" href=\"#summarized-steps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummarized Steps\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin.nccs.nasa.gov\nssh gpulogin1\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#Logging_In\"\u003eLogging-In\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Container_Environment_Installation\"\u003eContainer Environment Installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Working_Inside_Container\"\u003eWorking Inside a Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Getting_Started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Authors\"\u003eAuthors\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#References\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-logging-in-\" class=\"anchor\" href=\"#logging-in-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogging-In \u003ca name=\"user-content-Logging_In\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eYou will need an activate NCCS account together with a PIV Card or an RSA Token. Please refer\nto the following link for instructions on setting up login or any login related questions:\n\u003ca href=\"https://www.nccs.nasa.gov/nccs-users/instructional/logging-in/bastion-host\" rel=\"nofollow\"\u003eNCCS Logging-In\u003c/a\u003e.\nOnce you are all setup, you may login to the PRISM GPU cluster.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin.nccs.nasa.gov\nssh gpulogin1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-container-environment-installation-\" class=\"anchor\" href=\"#container-environment-installation-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Environment Installation \u003ca name=\"user-content-Container_Environment_Installation\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eAll the software and scripts from this repository can be ran within a container. Containers are\nsmall versions of operating systems that are meant to speed up the process of software development.\nThese containers are simply a binary file which has all the executables needed to run the software included.\u003c/p\u003e\n\u003cp\u003eThe NCCS provides Singularity as the default container runtime tool. In order to configure your\nenvironment to run Singularity containers, you will need to setup the environment variables listed below.\nFor this, you can simply add the following lines to your ~/.bashrc file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport SINGULARITY_CACHEDIR=\u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/.singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport SINGULARITY_TMPDIR=\u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/.singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTest the environment variables with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e[username@gpulogin1 \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e]$ \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_CACHEDIR\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_TMPDIR\u003c/span\u003e\n/att/nobackup/username/.singularity /att/nobackup/username/.singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to utilize the container for this project, we first need to download the image from a container\nregistry. The image for this project is located in \u003ca href=\"https://hub.docker.com/repository/docker/nasanccs/slump-detectron2\" rel=\"nofollow\"\u003eNASA NCCS DockerHub Repository\u003c/a\u003e. Docker containers can be pulled as Singularity containers to be executed on HPC\nenvironments. The following commands allow the download of the container from DockerHub and generates a\nfile with a .sif extension. Depending on the file system, this step can take several minutes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e\nmodule load singularity\nsingularity pull docker://docker.io/nasanccs/slump-detectron2:latest\nsingularity build --sandbox slump-detectron2_latest slump-detectron2_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-working-inside-a-container-\" class=\"anchor\" href=\"#working-inside-a-container-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorking Inside a Container \u003ca name=\"user-content-Working_Inside_Container\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eEach project provides a set of Slurm scripts that will execute code inside the container without having\nto login inside the image. You may skip this step and go straight to the project README if you are only\ninterested in running scripts from outside the container. This section is meant to help users developing\nand testing code inside the container to facilitate the development process.\u003c/p\u003e\n\u003cp\u003eTo get a session in one of the PRISM GPU nodes, you can run the following command. Additional instructions\nregarding Slurm can be found in the \u003ca href=\"https://www.nccs.nasa.gov/nccs-users/instructional/adapt-instructional/using-prism\" rel=\"nofollow\"\u003eNCCS website\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esalloc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will notice that the hostname will change to something similar to gpu***. This means that you are now\nlogged into one of the GPU nodes. To access the container image, you can run the command listed below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --nv -B /att/nobackup/username:/att/nobackup/username slump-detectron2_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere username is your NASA auid. From here, you can run any command inside the container image. Note that\nfor Singularity containers to have access to other paths within the HPC environment, we need to bind\ndirectories to particular locations in the container. The command above is binding your $NOBACKUP directory\nto be visible from inside the container.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started-\" class=\"anchor\" href=\"#getting-started-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started \u003ca name=\"user-content-Getting_Started\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eThe following is a summarized set of steps to get started and running in less than 5 minutes once the container image has been downloaded.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository into your ADAPT space\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e\ngit clone https://github.com/jordancaraballo/slump-detection.git\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCopy the data into the data/ directory\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp /data/location/.tif \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/data\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eGenerate train, test, and validation datasets\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch gen_dataset.sh\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eTrain a new model\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch train_detectron2.sh\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eClassify given imagery\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch predict_detectron2.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-project-specific-information\" class=\"anchor\" href=\"#project-specific-information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject Specific Information\u003c/h2\u003e\n\u003cp\u003eData resides under:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/att/nobackup/username/EVHR_requests/_deliver/EWebbRequest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin\nssh gpulogin1\nmodule load anaconda\nconda create --name slump-detection-11.1 --clone /att/nobackup/username/.conda/envs/slump-detection-11.1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-anaconda-environment\" class=\"anchor\" href=\"#anaconda-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnaconda environment\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load anaconda\nconda config --add channels conda-forge\nconda config --set channel_priority strict\nconda create -y -n slump-detection rioxarray cupy cudatoolkit=11.2 dask-cuda cudnn cutensor nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall pip dependencies\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda activate slump-detection\npip install -r requirements.txt\npip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003egit+https://github.com/facebookresearch/fvcore\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egit+https://github.com/philferriere/cocoapi.git#egg=pycocotools\u0026amp;subdirectory=PythonAPI\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ngit clone https://github.com/facebookresearch/detectron2 detectron2_repo \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e pip install -e detectron2_repo\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdding NCCL\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia\nconda create -n rapids-21.06 -c rapidsai -c nvidia -c conda-forge rapids-blazing=21.06 python=3.7 cudatoolkit=11.2 nvcc_linux-64 nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n rapids-21.06 -c rapidsai -c nvidia -c conda-forge -c pytorch rapids-blazing=21.06 python=3.7 cudatoolkit=11.1 ipykernel ipywidgets matplotlib geopandas pytorch torchvision torchaudio cudatoolkit=11.1 \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou could also enhance your kernel with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda config --add channels conda-forge\nconda config --set channel_priority strict\nconda create -y -n slump-detection-11.1 rioxarray cupy cudatoolkit=11.1 dask-cuda cudnn cutensor nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities gcc_linux-64\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install cython\npip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003egit+https://github.com/facebookresearch/fvcore\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egit+https://github.com/philferriere/cocoapi.git#egg=pycocotools\u0026amp;subdirectory=PythonAPI\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\npip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html\npip install opencv-python scikit-image\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-authors\" class=\"anchor\" href=\"#authors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eJordan Alexis Caraballo-Vega, \u003ca href=\"mailto:jordan.a.caraballo-vega@nasa.gov\"\u003ejordan.a.caraballo-vega@nasa.gov\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003e[1] Chollet, Fran\u00e7ois; et all, Keras, (2015), GitHub repository, \u003ca href=\"https://github.com/keras-team/keras\"\u003ehttps://github.com/keras-team/keras\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n\u003cp\u003e[2] Paszke, Adam; Gross, Sam; Chintala, Soumith; Chanan, Gregory; et all, PyTorch, (2016), GitHub repository, \u003ca href=\"https://github.com/pytorch/pytorch\"\u003ehttps://github.com/pytorch/pytorch\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n\u003cp\u003e[3] Google Brain Team; et all, TensorFlow, (2015), GitHub repository, \u003ca href=\"https://github.com/tensorflow/tensorflow\"\u003ehttps://github.com/tensorflow/tensorflow\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1640263903.0 + "updated_at": 1633374291.0 }, { "data_format": 2, - "description": "FabSim3_extra", + "description": "Wrapper scripts and documentation for launching database jobs to be used by Nextflow pipelines", "filenames": [ - "Singularity" + "Singularity.mysql" ], - "full_name": "kbronik2017/FabSim3_extra", + "full_name": "biocorecrg/nextflow_detached_db_wrapper", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fabsim\" class=\"anchor\" href=\"#fabsim\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFabSim\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/actions/workflows/Pytests.yml\"\u003e\u003cimg src=\"https://github.com/djgroen/FabSim3/actions/workflows/Pytests.yml/badge.svg?branch=master\" alt=\"Run Tests\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vecmafabsim3/fabsimdocker/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/15984bcb49e30e1f7e5e7b00084e0103bd4c6754edca6fbb1caa32f5dca78509/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f7665636d6166616273696d332f66616273696d646f636b65722e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/vecmafabsim3/fabsimdocker.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vecmafabsim3/fabsimdocker/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/506d3bba015b61abe07ca57664f35000afdb03531495602d97f42bb34afa35c3/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f7665636d6166616273696d332f66616273696d646f636b65722e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/automated/vecmafabsim3/fabsimdocker.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/tags\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f043c3ba40f9c2389fe1479a4488e19dfcbad1feac1fbe888c773bf0f5db411f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f646a67726f656e2f46616253696d333f7374796c653d666c6174\" alt=\"GitHub tag (latest by date)\" data-canonical-src=\"https://img.shields.io/github/v/tag/djgroen/FabSim3?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://lgtm.com/projects/g/djgroen/FabSim3/context:python\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/10419cff1f040d68ce752c6639616aaed414c6c5a7488e84662e19dee98ce77c/68747470733a2f2f696d672e736869656c64732e696f2f6c67746d2f67726164652f707974686f6e2f672f646a67726f656e2f46616253696d332e7376673f6c6f676f3d6c67746d266c6f676f57696474683d3138\" alt=\"Language grade: Python\" data-canonical-src=\"https://img.shields.io/lgtm/grade/python/g/djgroen/FabSim3.svg?logo=lgtm\u0026amp;logoWidth=18\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/djgroen/FabSim3/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/acc2a0eb223b853151fc5347101ef8574e352b40abc609e15062ccd32d937545/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f646a67726f656e2f46616253696d332e737667\" alt=\"GitHub Issues\" data-canonical-src=\"https://img.shields.io/github/issues/djgroen/FabSim3.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/djgroen/FabSim3/commits/master\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2c555714d9a4f16fd9f1c30cc71088810cb3cf12ca67e1bf9b3be68232f8fff6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f646a67726f656e2f46616253696d332e737667\" alt=\"GitHub last-commit\" data-canonical-src=\"https://img.shields.io/github/last-commit/djgroen/FabSim3.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFor the full FabSim3 documentation, please visit \u003ca href=\"https://fabsim3.readthedocs.io\" rel=\"nofollow\"\u003ehttps://fabsim3.readthedocs.io\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" href=\"#installation-and-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and usage\u003c/h2\u003e\n\u003cp\u003eFirst, user needs to install Anaconda \u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003ehttps://www.anaconda.com/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda create --name py3 python=3.6 {or any other python version \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e 3} \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate py3\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efinally for instructions on how to install and test FabSim, please go to \u003ca href=\"https://fabsim3.readthedocs.io/en/latest/installation/\" rel=\"nofollow\"\u003ehttps://fabsim3.readthedocs.io/en/latest/installation/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-easyvvuqfabmd\" class=\"anchor\" href=\"#easyvvuqfabmd\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEasyVVUQ+FabMD\u003c/h2\u003e\n\u003cp\u003eAfter updating the following files with your credentials\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e -FabSim3/deploy/machines_user.yml\n -FabSim3/deploy/machines.yml\n -FabSim3/plugins/FabMD/machines_FabMD_user.yml\n \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003erun the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - fabsim \u0026lt;remote machine name\u0026gt; lammps_init_run_analyse_campaign:fabmd_easyvvuq_InRuAn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand copy the results back to your local machine with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - fabsim \u0026lt;remote machine name\u0026gt; fetch_results\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-important\" class=\"anchor\" href=\"#important\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant\u003c/h2\u003e\n\u003cp\u003eBy default, FabSim3_extra comes with the FabDummy plugin and the FabMD plugin(fixed version!), which are available in ~/FabSim3/plugins\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow_detached_db_wrapper\" class=\"anchor\" href=\"#nextflow_detached_db_wrapper\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextflow_detached_db_wrapper\u003c/h1\u003e\n\u003cp\u003eWrapper scripts and documentation for launching database jobs to be used by Nextflow pipelines\u003c/p\u003e\n\u003cp\u003eSo far it only has been tested with SGE/Univa queues.\u003c/p\u003e\n\u003cp\u003eExample command with several options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enohup perl run_pipeline_mysql.pl -params \"-with-dag -with-report -with-timeline\" -conf params.config -nextflowver 21.04.03 -extra \"-j y -l virtual_free=4G,h_rt=372800 -N MYSQL_container -m be -cwd -V -q myqueue\" -script pipeline.nf \u0026amp;\u0026gt; log.mysql \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnly running MySQL instance. Useful for checking existing contents.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enohup perl run_pipeline_mysql.pl -conf params.config -mysqlonly -extra \"-j y -l virtual_free=4G,h_rt=372800 -N MYSQL_container -m be -cwd -V -q myqueue\" \u0026amp;\u0026gt; log.mysqlonly \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePerl (e. g., with \u003ca href=\"https://perlbrew.pl/\" rel=\"nofollow\"\u003ePerlbrew\u003c/a\u003e)\n\u003cul\u003e\n\u003cli\u003eInstall Config::Simple module: \u003ccode\u003ecpanm Config::Simple\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1641166034.0 + "updated_at": 1634748326.0 }, { "data_format": 2, "description": null, "filenames": [ - "02assembly/02long-read_assembly/lathe/singularity/Singularity.longread", - "02assembly/02long-read_assembly/lathe/singularity/Singularity.htsbox", - "02assembly/02long-read_assembly/lathe/singularity/Singularity.quickmerge" + "containers/Singularity.0.4.0", + "containers/Singularity.0.3.5", + "containers/Singularity.0.3.6", + "containers/Singularity.0.3.3", + "containers/Singularity.0.4.1" ], - "full_name": "JiaLonghao1997/MAGbenchmark", + "full_name": "LBJ-Wade/bilby", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-genome-resolved-metagenomics-using-short--long-read-and-metahic-sequencing\" class=\"anchor\" href=\"#genome-resolved-metagenomics-using-short--long-read-and-metahic-sequencing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenome-resolved metagenomics using short-, long-read and metaHiC sequencing\u003c/h1\u003e\n\u003cp\u003eIn this work, we systematically evaluated \u003cstrong\u003e26\u003c/strong\u003e distinct strategies for recovering high-quality MAGs generated from \u003cstrong\u003eeight\u003c/strong\u003e assemblers, \u003cstrong\u003etwo\u003c/strong\u003e binning strategies, and \u003cstrong\u003efour\u003c/strong\u003e sequencing technologies including both short- and long-read methods. In particular, we evaluated metagenomic high-throughput chromosomal conformation capture (metaHiC), a new technique that improves binning of assembled contigs using physically linked read-pairs within cells. To our knowledge, we are the first to evaluate the combination of long-read and metaHiC on metagenomics data.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/JiaLonghao1997/MAGbenchmark/blob/main/Figure%201_1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/JiaLonghao1997/MAGbenchmark/raw/main/Figure%201_1.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-1-preprocess\" class=\"anchor\" href=\"#1-preprocess\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Preprocess\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eTrim the adapter regions and low-quality reads: \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003e\u003cstrong\u003eTrimmomatic v.039\u003c/strong\u003e\u003c/a\u003e (using LEADING:3 TRAILING:3, SLIDINGWINDOW:4:15, MINLEN:25)\u003c/li\u003e\n\u003cli\u003eRemove human reads: Filtered reads were aligned to the human genome (NCBI, hg38) using \u003ca href=\"http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml\" rel=\"nofollow\"\u003e\u003cstrong\u003eBowtie2\u003c/strong\u003e\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-2-assemblies\" class=\"anchor\" href=\"#2-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Assemblies\u003c/h4\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-21-short-read-assemblies\" class=\"anchor\" href=\"#21-short-read-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.1 Short-read assemblies\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cs.hku.hk/~alse/idba_ud\" rel=\"nofollow\"\u003e\u003cstrong\u003eIDBA-UD\u003c/strong\u003e\u003c/a\u003e v.1.1.3 (using --pre_correction --maxk 120 --mink 20 --step 20).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/voutcn/megahit\"\u003eMEGAHIT\u003c/a\u003e\u003c/strong\u003e v.1.2.9 (using --k-list 21,29,39,59,79,99,119,141)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ablab/spades\"\u003e\u003cstrong\u003emetaSPAdes\u003c/strong\u003e\u003c/a\u003e v.3.14.1(using --meta -k 21,31,41,61,81,101,121)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-22-long-read-assemblies\" class=\"anchor\" href=\"#22-long-read-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.2 Long-read assemblies\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/marbl/canu\"\u003eCanu\u003c/a\u003e\u003c/strong\u003e v.2.0 (using genomeSize=50m/100m)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/fenderglass/Flye\"\u003emetaFlye\u003c/a\u003e\u003c/strong\u003e v. 2.7 (using \u2013meta \u2013g 100m/250m)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/ruanjue/wtdbg2\"\u003ewtdbg2\u003c/a\u003e\u003c/strong\u003e v.2.5 (using genomesize=50m/100m)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTwo long-read assembled contigs were then merged by \u003ca href=\"https://github.com/mahulchak/quickmerge\"\u003e\u003cstrong\u003equickmerge\u003c/strong\u003e\u003c/a\u003e v.0.40 as previous described in \u003cstrong\u003e\u003ca href=\"https://github.com/bhattlab/lathe\"\u003eLathe\u003c/a\u003e\u003c/strong\u003e, which is a tool for generating bacterial genomes from metagenomes with Nanopore long read sequencing.\u003c/p\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-23-hybrid-assemblies\" class=\"anchor\" href=\"#23-hybrid-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.3 Hybrid assemblies\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CSB5/OPERA-MS\"\u003e\u003cstrong\u003eOPERA-MS\u003c/strong\u003e\u003c/a\u003e v.0.9.0 (using --no-polishing)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ablab/spades\"\u003e\u003cstrong\u003ehybridSPAdes\u003c/strong\u003e\u003c/a\u003e v.3.14.1 (using --meta -k 21,31,41,61,81,101,121)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-24-polish-and-evaluation-of-metagenomic-assemblies\" class=\"anchor\" href=\"#24-polish-and-evaluation-of-metagenomic-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.4 Polish and evaluation of metagenomic assemblies\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003ePolish: \u003cstrong\u003e\u003ca href=\"https://github.com/broadinstitute/pilon\"\u003ePilon\u003c/a\u003e\u003c/strong\u003e v.1.24\u003c/li\u003e\n\u003cli\u003eEvaluation of metagenomic assemblies: \u003cstrong\u003e\u003ca href=\"http://quast.sourceforge.net/metaquast\" rel=\"nofollow\"\u003eMetaQUAST\u003c/a\u003e\u003c/strong\u003e v.5.0.2\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/elimoss/metagenomics_workflows/tree/master/assembly_comparison_circos\"\u003eCircos Assembly Comparison Visualization Workflow\u003c/a\u003e\u003c/strong\u003e are from public available scripts.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-3-binning\" class=\"anchor\" href=\"#3-binning\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Binning\u003c/h4\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-31-binning\" class=\"anchor\" href=\"#31-binning\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3.1 Binning\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://bitbucket.org/berkeleylab/metabat/src/master/\" rel=\"nofollow\"\u003e\u003cstrong\u003eMetaBAT2\u003c/strong\u003e\u003c/a\u003e v.2.15 (--minContig 2500 --minContigDepth 1 --percentIdentity 97)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/cerebis/bin3C\"\u003e\u003cstrong\u003ebin3C\u003c/strong\u003e\u003c/a\u003e v.0.1.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-32-generation-and-quality-evaluation-of-mags\" class=\"anchor\" href=\"#32-generation-and-quality-evaluation-of-mags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3.2 Generation and quality evaluation of MAGs\u003c/h5\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/elimoss/metagenomics_workflows\"\u003ebin_label_and_evaluate\u003c/a\u003e\u003c/strong\u003e is a public available Snakemake workflow for aligning, binning, classifying and evaluating a metagenomic assembly. We modified some of the scripts to make it suitable for bin3C binning.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAssembly size and contiguity: \u003cstrong\u003e\u003ca href=\"http://quast.sourceforge.net/metaquast\" rel=\"nofollow\"\u003eMetaQUAST\u003c/a\u003e\u003c/strong\u003e v.5.0.2\u003c/li\u003e\n\u003cli\u003eCompleteness and contamination: \u003ca href=\"https://ecogenomics.github.io/CheckM/\" rel=\"nofollow\"\u003e\u003cstrong\u003eCheckM\u003c/strong\u003e\u003c/a\u003e v.1.1.3\u003c/li\u003e\n\u003cli\u003eGene Content: \u003cstrong\u003e\u003ca href=\"https://github.com/tseemann/prokka\"\u003eProkka\u003c/a\u003e\u003c/strong\u003e v.1.14.6\u003c/li\u003e\n\u003cli\u003etRNA sequences: \u003ca href=\"http://www.ansikte.se/ARAGORN/\" rel=\"nofollow\"\u003e\u003cstrong\u003eAragorn\u003c/strong\u003e\u003c/a\u003e v.1.2.38\u003c/li\u003e\n\u003cli\u003eRibosomal RNA loci: \u003ca href=\"https://github.com/tseemann/barrnap\"\u003e\u003cstrong\u003eBarrnap\u003c/strong\u003e\u003c/a\u003e v.0.9\u003c/li\u003e\n\u003cli\u003eTaxonomic classification: \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003e\u003cstrong\u003eKraken2\u003c/strong\u003e\u003c/a\u003e v.2.1.1 and \u003ca href=\"https://github.com/Ecogenomics/GTDBTk\"\u003e\u003cstrong\u003eGTDB-tk\u003c/strong\u003e\u003c/a\u003e v1.4.1.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-4-trna-and-rrna\" class=\"anchor\" href=\"#4-trna-and-rrna\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. tRNA and rRNA\u003c/h4\u003e\n\u003cp\u003eThe close reference genome of MAG was determined by \u003ca href=\"https://github.com/Ecogenomics/GTDBTk\"\u003e\u003cstrong\u003eGTDB-tk\u003c/strong\u003e\u003c/a\u003e v.1.4.1.\u003c/p\u003e\n\u003cp\u003etRNA and rRNA genes of MAGs and reference genomes were identified as previously mentioned.\u003c/p\u003e\n\u003cp\u003eThen we calculated an observed-versus-expected ratio of the annotated tRNA and rRNA genes for each MAG as:\n\u003ca href=\"https://github.com/JiaLonghao1997/MAGbenchmark/blob/main/math1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/JiaLonghao1997/MAGbenchmark/raw/main/math1.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003cbr\u003e\nR_e is the expected tRNA or rRNA count of the reference genome, R_o is the observed tRNA or rRNA count of the MAG, r is the observed-versus-expected ratio.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-5-extrachromosomal-mobile-genetic-elements-emges\" class=\"anchor\" href=\"#5-extrachromosomal-mobile-genetic-elements-emges\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. extrachromosomal mobile genetic elements (eMGEs)\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003ePhages: \u003ca href=\"https://github.com/jiarong/VirSorter2\"\u003e\u003cstrong\u003eVirSorter2\u003c/strong\u003e\u003c/a\u003e v.2.1(using --min-length 1500 all) and \u003ca href=\"https://bitbucket.org/berkeleylab/checkv/src/master/\" rel=\"nofollow\"\u003e\u003cstrong\u003eCheckV\u003c/strong\u003e\u003c/a\u003e v0.8.1 (using end_to_end)\u003c/li\u003e\n\u003cli\u003ePlasmids: \u003cstrong\u003e\u003ca href=\"https://github.com/phac-nml/mob-suite\"\u003eMOB-suite\u003c/a\u003e\u003c/strong\u003e v.3.0.0\u003c/li\u003e\n\u003cli\u003eAntibiotic resistance genes: \u003ca href=\"https://www.mediterranee-infection.com/acces-ressources/base-de-donnees/arg-annot-2/\" rel=\"nofollow\"\u003e\u003cstrong\u003eARG-ANNOT\u003c/strong\u003e\u003c/a\u003e and \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs\" rel=\"nofollow\"\u003e\u003cstrong\u003eBLASTN\u003c/strong\u003e\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-6-references\" class=\"anchor\" href=\"#6-references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6. References\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eKuleshov V, Jiang C, Zhou W, Jahanbani F, Batzoglou S, Snyder M. Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome. Nat Biotechnol 2016, 34:64-69.\u003c/li\u003e\n\u003cli\u003eBishara A, Moss EL, Kolmogorov M, Parada AE, Weng Z, Sidow A, Dekas AE, Batzoglou S, Bhatt AS. High-quality genome sequences of uncultured microbes by assembly of read clouds. Nat Biotechnol 2018.\u003c/li\u003e\n\u003cli\u003eMoss EL, Maghini DG, Bhatt AS. Complete, closed bacterial genomes from microbiomes using nanopore sequencing. Nat Biotechnol 2020, 38:701-707.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1640764398.0 + "updated_at": 1633153969.0 }, { "data_format": 2, "description": null, "filenames": [ - "2.8.2/Singularity.2.8.2", - "2.11.9/Singularity" + "Singularity.STAR" ], - "full_name": "yh549848/singularity-igv", + "full_name": "izem-idem/sandboxIM", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1640802538.0 + "updated_at": 1633092971.0 }, { "data_format": 2, - "description": "Some projects in nextflow", + "description": null, "filenames": [ - "workflow/template/Singularity" + "Singularity" ], - "full_name": "lux563624348/nextflow", + "full_name": "remiolsen/dovetail-hichip-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow\" class=\"anchor\" href=\"#nextflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextflow\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dovetail-hichip-singularity\" class=\"anchor\" href=\"#dovetail-hichip-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edovetail-hichip-singularity\u003c/h1\u003e\n\u003cp\u003eSingularity dependency wrapper and containerization of Dovetail HiChiP tools - \u003ca href=\"https://hichip.readthedocs.io/en/latest/index.html\" rel=\"nofollow\"\u003ehttps://hichip.readthedocs.io/en/latest/index.html\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1640806208.0 + "updated_at": 1633078304.0 }, { "data_format": 2, - "description": "Quim\u0027s fork of fownward", + "description": "A singularity container for NodeJS, SQLite3, MongoDB and VS Code web development", "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/latest/Singularity", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06" + "Singularity" ], - "full_name": "quimortiz/downward", + "full_name": "benatuts/aip-container", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"misc/images/fast-downward.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" href=\"#tested-software-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-aip-container\" class=\"anchor\" href=\"#aip-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAIP Container\u003c/h1\u003e\n\u003cp\u003eA singularity container for NodeJS, SQLite3, MongoDB and VS Code web development.\u003c/p\u003e\n\u003cp\u003eThis is used for the subject Advanced Internet Programming (AIP) at UTS.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h1\u003e\n\u003cp\u003eConfiguration is optional. If there is no configuration file, the default settings shown below will be used.\u003c/p\u003e\n\u003cp\u003eYou can override these defaults by creating a file named ~/.config/aip_container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# The existence of base path is checked before starting the container\nBASE_PATH=\"/tmp\"\n\n# The host path is then created if it doesn\u0027t exist\n# (set BASE_PATH and HOST_PATH to be the same if you don\u0027t want directories to be created)\nHOST_PATH=\"/tmp/$USER/aip\"\n\n# This array of files is symlinked to the corresponding files in your $HOME\nSYMLINK=(\".gitconfig\" \".ssh\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that if the path /images/tmp exists and you have no configuration file, then /images/tmp will be used instead of /tmp. This is because on UTS lab computers, /images/tmp has greater capacity.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eIf you are using a lab computer, the container should already be installed for you.\u003c/p\u003e\n\u003cp\u003eTo rebuild the container using your own computer:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build aip-container_latest.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr, you can pull the pre-built image from Singularity Hub to your own computer:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://benatuts/aip-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse run_aip_singularity_container.sh to manually start the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun_aip_singularity_container.sh term # Start a gnome-terminal\nrun_aip_singularity_container.sh vscode # Start visual studio code\nrun_aip_singularity_container.sh fullterm # Start a gnome-terminal-server and gnome-terminal\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1640879253.0 + "updated_at": 1563696940.0 }, { "data_format": 2, - "description": "Metagenomic analysis of viral samples", + "description": null, "filenames": [ - "Singularity" + "latest/Singularity" ], - "full_name": "Aexbrayat/snakevir", + "full_name": "pscedu/singularity-rnaview", "latest_release": null, - "readme": "\u003cp\u003esnakevir\u003c/p\u003e\n\u003cp\u003eAuthors\u003c/p\u003e\n\u003cp\u003eAntoni Exbrayat (CIRAD) \u0026amp; Etienne Loire (CIRAD) \u0026amp; Serafin Gutierrez (CIRAD)\u003c/p\u003e\n\u003cp\u003ePurpose:\nMetagenomic analysis of viral shotgun NGS samples.\u003c/p\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://snakemake.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003esnakemake\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-step\" class=\"anchor\" href=\"#step\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCleaning\u003c/li\u003e\n\u003cli\u003eMerging\u003c/li\u003e\n\u003cli\u003eFiltering\u003c/li\u003e\n\u003cli\u003eDe novo sequence assembly\u003c/li\u003e\n\u003cli\u003eMapping\u003c/li\u003e\n\u003cli\u003eHomology search protein databases\u003c/li\u003e\n\u003cli\u003eHomology search nucleotide databases\u003c/li\u003e\n\u003cli\u003eTaxonomic annotation\u003c/li\u003e\n\u003cli\u003eTaxonomy refining\u003c/li\u003e\n\u003cli\u003eViral hosts search\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e - bioawk\n - biopython\n - blast\n - bwa\n - cap3\n - csvkit\n - cutadapt\n - diamond\n - entrez-direct\n - ete3\n - flash\n - megahit\n - pandas\n - picard\n - python\n - r-base\n - samtools\n - seqtk\n - snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe conda environment manager can be used to install python , snakemake and all the required tools and dependencies into a single environment in a way such that reproducibility is ensured.\u003c/p\u003e\n\u003cp\u003eNote: Conda must be installed on the system. For help with setting up conda, please see \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo create and activate the conda environment with the environment.yml provided , use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate snakevir\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage:\u003c/h2\u003e\n\u003cp\u003eSnakemake supports a separate configuration file for execution on a cluster. A cluster config file cluster.json is provided , it allows you to specify cluster submission parameters outside the Snakefile. The cluster config is contains all parameters with match names of rules in the Snakefile.\u003c/p\u003e\n\u003cp\u003eedit config.yaml to precise dataset and dependencies path, accomodate read files names , threads allocated to the rules (according to cluster.json).\u003c/p\u003e\n\u003cp\u003elaunch with e.g. :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -s snakefile -j 100 --cluster-config cluster.json --cluster \"sbatch -p {cluster.queue} -N {cluster.queue} -c {cluster.cpu_task} --mem {cluster.mem} -e {cluster.error} -o {cluster.log} \" --printshellcmd --rerun-incomplete --reason --dryrun\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto execute on a SLURM cluster with a maximum of 100 concurrent jobs submitted, eventually modify the command accordingly with your job scheduler.\u003c/p\u003e\n\u003cp\u003eNote : A Singularity containers image will be available soon\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca 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href=\"https://camo.githubusercontent.com/1d9776e18f2e7616bb9a34f6dafc7c0d6da72a9975f6a690c8c59599d1708961/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726e6176696577\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1d9776e18f2e7616bb9a34f6dafc7c0d6da72a9975f6a690c8c59599d1708961/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726e6176696577\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-rnaview\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-rnaview\" class=\"anchor\" href=\"#singularity-rnaview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-rnaview\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://ndbserver.rutgers.edu/ndbmodule/services/download/rnaview.html\" rel=\"nofollow\"\u003ernaview\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ernaview\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/rnaview/latest\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/rnaview\u003c/code\u003e as \u003ccode\u003elatest.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1641215049.0 + "updated_at": 1632891843.0 }, { "data_format": 2, - "description": "SCOV2-spikeScreen IMI prototype bash pipeline", + "description": "Singularity recipe for Circos.", "filenames": [ "Singularity" ], - "full_name": "IMIMF-UNILJSI/scov2-spikeScreen", + "full_name": "ArnaudBelcour/circos-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-scov2-spikescreen\" class=\"anchor\" href=\"#scov2-spikescreen\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escov2-spikeScreen\u003c/h1\u003e\n\u003cp\u003eSCOV2-spikeScreen IMI prototype bash pipeline\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-container\" class=\"anchor\" href=\"#build-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild container\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/repo/directory/buildContainer web # pull from shub\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/repo/directory/buildContainer local # build from scratch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eno argument defaults to \"web\", local requires sudo privileges. If none of the options is suitable to the user, do manual build with working parameter settings.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eCreate a working dir somewhere in your FS (preferably outside of the git dir), run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind /path/to/repo/directory:/opt/scripts,/path/to/data:/mnt /path/to/repo/directory/spikeScreenContainer.sif /opt/scripts/runPipeline runID keyword /mnt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe second argument (keyword) should be replaced with either pools/assemblies/pools_single/assemblies_single to run the appropriate analysis (self explanatory).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cleanup\" class=\"anchor\" href=\"#cleanup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCleanup\u003c/h2\u003e\n\u003cp\u003eA cleanup script is also provided (see repo directory: cleanUp), but it may not be so useful. It simply removes the contents of the work dir related to the pipeline process.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-circos-singularity\" class=\"anchor\" href=\"#circos-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCircos singularity\u003c/h1\u003e\n\u003cp\u003eA singularity recipe for Circos (inspired by the one written by \u003ca href=\"https://github.com/J35P312/CircusCircos\"\u003ehttps://github.com/J35P312/CircusCircos\u003c/a\u003e). This install all of its dependencies. The image size is around ~212 Mb.\u003c/p\u003e\n\u003cp\u003eYou can directly call \u003ccode\u003ecircos\u003c/code\u003e inside of the image like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec -c -B /shared/folder:/shared/folder circos.sif circos -conf /shared/folder/circos.conf -outputdir /shared/folder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e-c\u003c/code\u003e option isolates the container and the \u003ccode\u003e-B\u003c/code\u003e option give access to a folder outside the container for Singularity.\u003c/p\u003e\n\u003cp\u003eYou can use the path associated to \u003ccode\u003e-B\u003c/code\u003e to give access to data path in the configuration file.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1641561900.0 + "updated_at": 1632847008.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity.vcf_processing.v1.0", - "singularity/Singularity.sv_call.v1.0", - "singularity/Singularity.bcftools.v1.10.2", - "singularity/Singularity.qcbam.v1.0", - "singularity/Singularity.align_dedup.v1.0", - "singularity/Singularity.expansion_hunter.v5.0.0", - "singularity/Singularity.sv_processing.v1.0" + "DeepLearningCamelyon/0.Preparation/Singularity", + "DeepLearningCamelyon/0.Preparation/Singularity_Code_for_Prediction.sh" ], - "full_name": "edg1983/WGS_pipeline", + "full_name": "shiny0510/Camelyon_Preprocessing_tif", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-wgs-analysis-pipeline\" class=\"anchor\" href=\"#wgs-analysis-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWGS analysis pipeline\u003c/h1\u003e\n\u003cp\u003eWGS analysis pipeline. Can handle both WGS and WES data.\u003c/p\u003e\n\u003cp\u003eThe whole pipeline use singularity images and will pull images from singularity library when needed. Singularity recipes used are provided in \u003ccode\u003esingularity\u003c/code\u003e folder for reference.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-run\" class=\"anchor\" href=\"#how-to-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run\u003c/h2\u003e\n\u003cp\u003eThe pipeline can be run directly using Nextflow \u0026gt;= v20.10.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow WGS_analysis.nf -profile cluster --operation align --input input_file.txt --mode WGS --ped ped_file.ped --ref genome.fa --cohort_id cohort_name --outdir results \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe pipeline automatically infer the number of samples in the cohort from your input file and adjust the filtering accordingly. When more than one sample is present, small variants and structural variants from all samples are merged in cohort wide VCF files.\u003c/p\u003e\n\u003cp\u003eEventually update \u003ccode\u003esingularity_cachedir\u003c/code\u003e variable in \u003ccode\u003enextflow.config\u003c/code\u003e to point to a proper folder where singularity images are stored / will be downloaded\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-arguments\" class=\"anchor\" href=\"#arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArguments\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoperation : align or call_variants\nmode : WGS only supported at the moment\nref : fasta file for the genome. Note that .fai and bwa index are expected in the same location\ninput : tab-separated file describing input files. \n The exact format depends on operation requested (see below)\nped : standard PED file containing all samples\ncohort_id : a arbitrary name for the cohort files generated\noutdir : output folder for results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse \u003ccode\u003e--operation align/call_variants --help\u003c/code\u003e for more explanations.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-resources\" class=\"anchor\" href=\"#resources\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h2\u003e\n\u003cp\u003eVarious supporting files are needed and expected in the \u003ccode\u003eresources\u003c/code\u003e folder. This path can be configured by changing the parameters in \u003ccode\u003econfig/resources_GRCh37/38.conf\u003c/code\u003e. All files needed are provided in a Zenodo repository. Please refer to the README file in the resources folder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNB.\u003c/strong\u003e The available resources are based on GRCh37 with standard chromosomes \u003ccode\u003e1..22 X Y MT\u003c/code\u003e and GRCh38 using \u003ccode\u003echr1..22 chrX chrY chrM\u003c/code\u003e. Be sure the genome reference file passed with \u003ccode\u003e--ref\u003c/code\u003e matches the expected nomenclature for your genome build.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input-files-format\" class=\"anchor\" href=\"#input-files-format\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files format\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ped-file\" class=\"anchor\" href=\"#ped-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePED file\u003c/h3\u003e\n\u003cp\u003eA standard tab-separated PED file without header, describing all samples provided in the input file. All sample IDs must match between ped and input file. All samples must have sex defined.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efamily_ID individual_ID father_ID mother_ID sex(1=M,2=F) status(1=unaff,2=aff,0=unknown)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input-file\" class=\"anchor\" href=\"#input-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einput file\u003c/h3\u003e\n\u003cp\u003eNote that all files need to be specified using \u003cstrong\u003eabsolute paths\u003c/strong\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-operation-align\" class=\"anchor\" href=\"#operation-align\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOperation: align\u003c/h4\u003e\n\u003cp\u003eA 3 columns tab-separated file without header\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esampleID1 s1_lane1_R1.fastq.gz s1_lane1_R2.fastq.gz\nsampleID1 s1_lane2_R1.fastq.gz s1_lane2_R2.fastq.gz\nsampleID2 s2_lane2_R1.fastq.gz s2_lane2_R2.fastq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that if a sample has been sequenced with multiple pairs of fastq files you need to add multiple lines for each pair of fastq files using the same sampleID. The pipeline will take care of the merge.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-operation-call_variants\" class=\"anchor\" href=\"#operation-call_variants\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOperation: call_variants\u003c/h4\u003e\n\u003cp\u003eA 5 columns tab-separated file without header.\nThis file is automatically generated in the output folder when using \u003ccode\u003e--operation align\u003c/code\u003e (bam_files.txt)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esampleID1 main_bam.bam disc.bam split.bam\nsampleID2 main_bam.bam disc.bam split.bam\nsampleID3 main_bam.bam disc.bam split.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003edisc\u003c/code\u003e and \u003ccode\u003esplit\u003c/code\u003e BAM files are files containing only discordant pair and split reads like the\nones that can be obtained using Samblaster\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003eThe pipeline generates a reach set of outputs including\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ealigned deduplicated BAM files\u003c/li\u003e\n\u003cli\u003edisc/split BAM files\u003c/li\u003e\n\u003cli\u003eExtensive QC of alignements, which includes mapping stats, coverage, relatedness, ancestry\u003c/li\u003e\n\u003cli\u003eMulti sample and single sample VCFs of small variants and structural variants (variants are provided as raw calls and filtered calls)\u003c/li\u003e\n\u003cli\u003eVariants QC report for small variants\u003c/li\u003e\n\u003cli\u003eROH regions\u003c/li\u003e\n\u003cli\u003eRepeat expansions by Expansion Hunter\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline-components\" class=\"anchor\" href=\"#pipeline-components\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline components\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eAlignement and duplicate marking\n\u003cul\u003e\n\u003cli\u003eBWA-MEM + samblaster + samtools\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eQC and coverage from BAM files\n\u003cul\u003e\n\u003cli\u003efastqc: reads stats\u003c/li\u003e\n\u003cli\u003emosdepth: coverage\u003c/li\u003e\n\u003cli\u003esamtools flagstat / mapstat: alignment stats\u003c/li\u003e\n\u003cli\u003esomalier: ancestry, relatedness, sex check reports\u003c/li\u003e\n\u003cli\u003emultiqc: interactive report\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003esmall variants\n\u003cul\u003e\n\u003cli\u003edeepvariant: single sample calls\u003c/li\u003e\n\u003cli\u003eglnexus: gvcf merge\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003estructural variants\n\u003cul\u003e\n\u003cli\u003elumpy: structural variants events\u003c/li\u003e\n\u003cli\u003eCNVnator: CNV estimation\u003c/li\u003e\n\u003cli\u003esvtools: combine, merge and classify\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003erepeat expansion detection\n\u003cul\u003e\n\u003cli\u003eexpansion hunter\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eROH regions\n\u003cul\u003e\n\u003cli\u003ebcftools ROH\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-future-developments\" class=\"anchor\" href=\"#future-developments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFuture developments\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Update SV pipeline to Manta / dysgu\u003c/li\u003e\n\u003cli\u003e[ ] Add duphold for SV quality check\u003c/li\u003e\n\u003cli\u003e[ ] Variant annotation\u003c/li\u003e\n\u003cli\u003e[ ] Segregation analysis with slivar\u003c/li\u003e\n\u003cli\u003e[ ] Support for WES?\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-deeplearningcamelyon\" class=\"anchor\" href=\"#deeplearningcamelyon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeepLearningCamelyon\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-reference\" class=\"anchor\" href=\"#reference\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ereference\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/3dimaging/DeepLearningCamelyon\"\u003ehttps://github.com/3dimaging/DeepLearningCamelyon\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-file\" class=\"anchor\" href=\"#file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDeepLearningCamelyon Folder:Preprocessing (ASAP, tif), Unet Traing and prediction\u003c/li\u003e\n\u003cli\u003eannotation.py: Make mask File\u003c/li\u003e\n\u003cli\u003emain.py: tif File resize, mask File and originFile\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1642604585.0 + "updated_at": 1632725622.0 }, { "data_format": 2, - "description": null, + "description": "Run Open XDMod in a container with automated data ingest.", "filenames": [ - "Singularity" + "container/Singularity/Singularity" ], - "full_name": "genxnetwork/uk-biobank", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-federated-biobank-project\" class=\"anchor\" href=\"#federated-biobank-project\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFederated Biobank Project\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-motivation\" class=\"anchor\" href=\"#motivation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-structure\" class=\"anchor\" href=\"#structure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStructure\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003esplit\u003c/strong\u003e module generates node datasets from the whole UKB dataset based on self-reported ancestry.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eqc\u003c/strong\u003e module encapsulates node-based quality control.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003edimred\u003c/strong\u003e module performs different strategies of dimensionality reduction.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efl\u003c/strong\u003e module compares various FL strategies on selected SNPs.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "jtfrey/open-xdmod-container", + "latest_release": "v8.1.2", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-open-xdmod-container\" class=\"anchor\" href=\"#open-xdmod-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopen-xdmod-container\u003c/h1\u003e\n\u003cp\u003eRun Open XDMod in a container with automated data ingest.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1643039658.0 + "updated_at": 1632401090.0 }, { "data_format": 2, - "description": "Modified chroma code", + "description": null, "filenames": [ - "installation/chroma3.nvidia/Singularity" + "Singularity" ], - "full_name": "unlimited-name/chroma", + "full_name": "lawlessrd/SCZ", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-chroma-ultra-fast-photon-monte-carlo\" class=\"anchor\" href=\"#chroma-ultra-fast-photon-monte-carlo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChroma: Ultra-fast Photon Monte Carlo\u003c/h1\u003e\n\u003cp\u003eChroma is a high performance optical photon simulation for particle physics detectors originally written by A. LaTorre and S. Seibert. It tracks individual photons passing through a triangle-mesh detector geometry, simulating standard physics processes like diffuse and specular reflections, refraction, Rayleigh scattering and absorption.\u003c/p\u003e\n\u003cp\u003eWith the assistance of a CUDA-enabled GPU, Chroma can propagate 2.5 million photons per second in a detector with 29,000 photomultiplier tubes. This is 200x faster than the same simulation with GEANT4.\u003c/p\u003e\n\u003cp\u003eCheck out the \u003ca href=\"doc/source/chroma.pdf\"\u003eChroma whitepaper\u003c/a\u003e for information on how Chroma works.\u003c/p\u003e\n\u003cp\u003eInformation about the historical development of Chroma can be found at the \u003ca href=\"https://chroma.bitbucket.io/index.html\" rel=\"nofollow\"\u003ebitbucket repository\u003c/a\u003e this repository was forked from.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-modified-chroma-for-sbc-simulation\" class=\"anchor\" href=\"#modified-chroma-for-sbc-simulation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModified chroma for SBC simulation\u003c/h2\u003e\n\u003cp\u003eThe SBC collaboration wants to use \u003ca href=\"https://github.com/SBC-Collaboration\"\u003eSBCgeant4\u003c/a\u003e geometry in photon simulation. Chroma has a geometry interface for STL mesh, or GDML, a XML-based geometry languige. Current GDML interface is not perfect for use, and actually even has some defects. I modified the functions and classes in gdml.py to fit the need of SBC simulations.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-and-quick-use-of-chroma\" class=\"anchor\" href=\"#installation-and-quick-use-of-chroma\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and quick use of Chroma\u003c/h2\u003e\n\u003cp\u003eThe source of chroma uses \u0027Docker\u0027 for maintainance and environment controlling. However, this can cause trouble for Windows system users. To solve this problem, we choose to use Cloud platforms provided by Google and other companies, which is also stable in environments and available to anyone who wants to engage in chroma.\u003c/p\u003e\n\u003cp\u003eTo start using chroma on cloud platform, you will need to construct a VM instance including certain GPUs, using an ubuntu OS image. Google image for \u0027DEEP LEARNING\u0027 is well-constructed and worth trying.\u003c/p\u003e\n\u003cp\u003eFor any empty ubuntu image, installation of chroma can be completed in \u003ca href=\"https://github.com/unlimited-name/CloudInstallation\"\u003ebash batches\u003c/a\u003e. All the batch commands are translated and modified via the \u0027Docker Dockerfile\u0027 used by the maintainer.\n**Note you will have to mannually modify the version of CUDA installed by matching the CUDA version of host machine. **\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-subject-to-change\" class=\"anchor\" href=\"#subject-to-change\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSUBJECT TO CHANGE\u003c/h1\u003e\n", + "readme": "", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1643829901.0 + "updated_at": 1641581829.0 }, { "data_format": 2, - "description": "rstudio on RCC", + "description": null, "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "liliw-w/rstudio-server-conda_share", - "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-run-studio-server-on-rcc\" class=\"anchor\" href=\"#run-studio-server-on-rcc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun studio server on RCC\u003c/h2\u003e\n\u003cp\u003eBased on \u003ca href=\"https://github.com/grst/rstudio-server-conda\"\u003eintegrate the container with conda\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-why-this-repo\" class=\"anchor\" href=\"#why-this-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy this repo?\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eWe want to use rstudio interactively on RCC just like on our local computers. e.g. easy access to files on server, draw and check plots easily, upload and download files within rstudio, user-friendly UI.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOne way provided is through ThinLinc. But ThinLinc sometimes is slow; hard to copy-paste; not good UI, etc.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTherefore, we need another way to be able to launch rstudio on RCC.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-what-is-this-repo\" class=\"anchor\" href=\"#what-is-this-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is this repo?\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis repo implements rstudio server on RCC through a singularity container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBe able to run rstudio on computation node by sumbiting a SLURM job.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIntergrate rstudio with conda for easy package management.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-how-to-use-this-repo\" class=\"anchor\" href=\"#how-to-use-this-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use this repo?\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-git-clone-this-repo\" class=\"anchor\" href=\"#git-clone-this-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGit clone this repo\u003c/h4\u003e\n\u003cp\u003e... to your RCC folder. I store it in my \u003ccode\u003escratch\u003c/code\u003e space.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-modify-a-few-parameters\" class=\"anchor\" href=\"#modify-a-few-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModify a few parameters\u003c/h4\u003e\n\u003cp\u003eTo make it work for your own use, several parameters needed to modify. All modifications will be made in file \u003ccode\u003esingularity/run_singularity.sh\u003c/code\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSpecify the path to a conda env to parameter \u003ccode\u003e$CONDA_PREFIX\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis conda env store all packages you will need. You can use an existing conda env, or create a one as in file \u003ccode\u003econda_env_config.sh\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eSpeficy the path to the rstudio singularity container to parameter \u003ccode\u003e$CONTAINER\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the container by \u003ccode\u003esingularity pull docker://rocker/rstudio_latest\u003c/code\u003e. See \u003ca href=\"https://www.rocker-project.org/use/singularity/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for the container\u0027s info.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMove the downloaded file \u003ccode\u003erstudio_latest.sif\u003c/code\u003e to the path you assigned to \u003ccode\u003e$CONTAINER\u003c/code\u003e. I would recommend \u003ccode\u003esingularity/rstudio_latest.sif\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eSet your login password to parameter \u003ccode\u003e$USER_psw\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run\" class=\"anchor\" href=\"#run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eRun this container on login node.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebash /path/to/your_repo/singularity/run_singularity.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou will see something like highlighted in orange rectangle,\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"rstudio_contaner_login.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"rstudio_contaner_login.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eOpen the link in your browser.\u003c/p\u003e\n\u003cp\u003eUser name and password are in the figure.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun studio on computation node.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esbatch /path/to/your_repo/singularity/run_singularity.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis is to submit a slurm job. Configure the slurm resource parameters in the header of \u003ccode\u003esingularity/run_singularity.sh\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the slurm output file \u003ccode\u003erstudio-server.job\u003c/code\u003e. The content is basically the same as the above figure.\u003c/p\u003e\n\u003cp\u003eUse the info highlighted in blue rectangle.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003essh -N -L ...\u003c/code\u003e in your terminal.\u003c/li\u003e\n\u003cli\u003eOpen the link in your browser.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ref\" class=\"anchor\" href=\"#ref\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRef\u003c/h3\u003e\n\u003cp\u003eTo understand more how this works, see ref below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.rocker-project.org/use/singularity/\" rel=\"nofollow\"\u003erstudio server singularity container\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.rocker-project.org/use/singularity/\" rel=\"nofollow\"\u003emake it a SLURM sbatch script\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/grst/rstudio-server-conda\"\u003eintegrate the container with conda\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "aerval/drop", + "latest_release": "0.0.2", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" href=\"#singularity-deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"img/shpc.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" href=\"#1-template-or-fork\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Template or Fork\u003c/h3\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" href=\"#1-write-your-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" href=\"#2-update-the-version-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Update the VERSION file\u003c/h3\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/releases.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" href=\"#3-how-to-develop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How to Develop\u003c/h3\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" href=\"#4-how-to-pull\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. How to pull\u003c/h3\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1644084864.0 + "updated_at": 1641857087.0 }, { "data_format": 2, - "description": "Paired end ChIP-seq processing through alignment.", + "description": "Singularity recipe for HERA software", "filenames": [ - "Singularity.hg19v1.centos" + "Singularity.casa6_full", + "Singularity.tau", + "Singularity.casa6_modular", + "Singularity.h4c", + "Singularity.rtp", + "Singularity.validation", + "Singularity.hera1", + "Singularity.calamity", + "Singularity.mpi" ], - "full_name": "ertheisen/appalachian_centos", + "full_name": "HERA-Team/hera-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-appalachianhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#appalachianhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappalachian.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hera-singularity\" class=\"anchor\" href=\"#hera-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehera-singularity\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-notice\" class=\"anchor\" href=\"#notice\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotice\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eJuly 15, 2021\u003c/strong\u003e:\nWe are currently manually building and uploading the containers to the HERA project directory on Ilifu on an irregular basis. Please check the built dates of the container files and contact @piyanatk if you need the containers to be rebuilt. Scheduled daily re-building is being planned.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eThis repository contains recipe files of the Singularity containers for the HERA software stack.\u003c/p\u003e\n\u003cp\u003eIlifu users, please make sure to read the relevant page on the HERA wiki. A singularity container is required for computing on the Ilifu. If you need specific Python modules to be installed in the containers, please contact @piyanatk.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-about-container-and-singularity\" class=\"anchor\" href=\"#about-container-and-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout Container and Singularity\u003c/h2\u003e\n\u003cp\u003eContainers are encapsulated software environments and abstract the software and applications from the underlying operating system. This allows users to run workflows in customized environments, switch between environments, and to share these environments with colleagues and research teams.\u003c/p\u003e\n\u003cp\u003eSingularity is a free, cross-platform and open-source computer program that performs operating-system-level virtualization also known as containerization (another widely used one being Docker).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-container-content\" class=\"anchor\" href=\"#container-content\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Content\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-python-packages\" class=\"anchor\" href=\"#python-packages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython Packages\u003c/h3\u003e\n\u003cp\u003eAll containers are built with \u003ccode\u003eUbuntu 20.04\u003c/code\u003e and \u003ccode\u003eminiconda\u003c/code\u003e with \u003ccode\u003epython=3.8\u003c/code\u003e unless otherwise specify \u003ca href=\"###-Different-Between-Containers:\"\u003ebelow\u003c/a\u003e. All variances come standard with the following packages:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eData Analysis\u003c/th\u003e\n\u003cth\u003eAstronomical\u003c/th\u003e\n\u003cth\u003eHERA\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edask\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eaipy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003elinsolve\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ejupyterlab\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eastropy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003euvtools\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ematplotlib\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eastropy-healpix\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehera_qm\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003enumpy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eastroquery\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehera_cal\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epandas\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ecartopy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehera_sim\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003escipy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehealpy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehera_psepc\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003escikit-learn\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003epyuvdata\u003c/code\u003e\u003csup\u003e\u003ca href=\"#myfootnote1\"\u003e1\u003c/a\u003e\u003c/sup\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003evis_cpu\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003exarray\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003epyuvsim\u003c/code\u003e\u003csup\u003e\u003ca href=\"#myfootnote2\"\u003e2\u003c/a\u003e\u003c/sup\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eSSINS\u003c/code\u003e\u003csup\u003e\u003ca href=\"#myfootnote3\"\u003e3\u003c/a\u003e\u003c/sup\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca name=\"user-content-myfootnote1\"\u003e1\u003c/a\u003e: With CASA measurement sets, HEALPix beam, and CST beam functionalities, see \u003ca href=\"https://github.com/RadioAstronomySoftwareGroup/pyuvdata%5C\"\u003ehttps://github.com/RadioAstronomySoftwareGroup/pyuvdata\\\u003c/a\u003e\n\u003ca name=\"user-content-myfootnote2\"\u003e2\u003c/a\u003e: without line profiler and lunar capability, see \u003ca href=\"https://github.com/RadioAstronomySoftwareGroup/pyuvsim\"\u003ehttps://github.com/RadioAstronomySoftwareGroup/pyuvsim\u003c/a\u003e\n\u003ca name=\"user-content-myfootnote3\"\u003e3\u003c/a\u003e: See \u003ca href=\"https://github.com/mwilensky768/SSINS\"\u003ehttps://github.com/mwilensky768/SSINS\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-variances\" class=\"anchor\" href=\"#variances\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVariances:\u003c/h3\u003e\n\u003cp\u003eWe are currently building the following variances.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ehera1\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eInclude all packages in the table above. Intended for general-purpose computing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecasa6_full\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eEquivalent to \u003ccode\u003ehera1\u003c/code\u003e with a full installation of \u003ccode\u003ecasa-6\u003c/code\u003e, and \u003ccode\u003eAPLpy\u003c/code\u003e for visualisation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecasa6_modular\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eEquivalent to \u003ccode\u003ehera1\u003c/code\u003e with a pip-wheel installation of \u003ccode\u003ecasa-6\u003c/code\u003e, making \u003ccode\u003ecasatasks\u003c/code\u003e, \u003ccode\u003ecasatools\u003c/code\u003e, and \u003ccode\u003ecasampi\u003c/code\u003e packages (see \u003ca href=\"https://casa-pip.nrao.edu/\" rel=\"nofollow\"\u003ehttps://casa-pip.nrao.edu/\u003c/a\u003e), and \u003ccode\u003eAPLpy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBased on \u003ccode\u003ePython 3.6\u003c/code\u003e and \u003ccode\u003eUbuntu 18.04\u003c/code\u003e for casa-pip compatibility.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ertp\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eFor testing the \u003ccode\u003emakeflow\u003c/code\u003e pipeline.\u003c/li\u003e\n\u003cli\u003eEquivalent to \u003ccode\u003ehera1\u003c/code\u003e with an addition of \u003ccode\u003ehera_opm\u003c/code\u003e, \u003ccode\u003ehera_mc\u003c/code\u003e, and \u003ccode\u003ehera_notebook_templates\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ehera_pipelines\u003c/code\u003e is cloned to \u003ccode\u003e/usr/local\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eh4c\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eAlmost equivalent to \u003ccode\u003ertp\u003c/code\u003e except some specific branches on \u003ccode\u003ehera_cal\u003c/code\u003e and \u003ccode\u003epspec\u003c/code\u003e for H4C analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etau\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eThis container is \u003ccode\u003ehera1\u003c/code\u003e with extra tools for simulation, machine learning, and etc. Specifically, it contains the following additions:\n\u003cul\u003e\n\u003cli\u003eemupy (\u003ca href=\"https://github.com/nkern/emupy\"\u003ehttps://github.com/nkern/emupy\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ezreion (\u003ca href=\"https://github.com/plaplant/zreion\"\u003ehttps://github.com/plaplant/zreion\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e21cmFAST=3.1.1\u003c/li\u003e\n\u003cli\u003epowerbox\u003c/li\u003e\n\u003cli\u003etensorflow\u003c/li\u003e\n\u003cli\u003epytorch\u003c/li\u003e\n\u003cli\u003ekeras\u003c/li\u003e\n\u003cli\u003esympy\u003c/li\u003e\n\u003cli\u003enumexpr\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-python-environment\" class=\"anchor\" href=\"#python-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython Environment\u003c/h3\u003e\n\u003cp\u003eAll containers use Miniconda3, which are installed at \u003ccode\u003e/usr/local/miniconda3/\u003c/code\u003e inside the containers.\u003c/p\u003e\n\u003cp\u003eThe name of Conda environment in each container is the same as the container name, e.g. \u003ccode\u003ehera1\u003c/code\u003e, \u003ccode\u003ecasa6_full\u003c/code\u003e, and etc, The default conda environment \u003ccode\u003ebase\u003c/code\u003e is not used.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-environment-variables\" class=\"anchor\" href=\"#environment-variables\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment Variables\u003c/h3\u003e\n\u003cp\u003eThe following environment variables are also exported in all containers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCONDA_PATH=\"/usr/local/miniconda3\"\nCONDA_SH=\"$CONDA_PATH/etc/profile.d/conda.sh\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe latter is especially useful to make the \u003ccode\u003econda\u003c/code\u003e command available inside the container (see the section on \u003ca href=\"####-%60shell%60\"\u003e\u003ccode\u003esingularly shell\u003c/code\u003e usage\u003c/a\u003e below).\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ertp\u003c/code\u003e container has an additional environment variable that point to \u003ccode\u003ehera_pipelines\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eHERA_PIPELINES_PATH=\"/usr/local/hera_pipelines\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-commands\" class=\"anchor\" href=\"#singularity-commands\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Commands\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-pull\" class=\"anchor\" href=\"#pull\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003epull\u003c/code\u003e\n\u003c/h4\u003e\n\u003cp\u003eUse \u003ccode\u003esingularity pull\u003c/code\u003e to download the container from Singularity Hub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull [name_to_save_the_image_(optional)] shub://HERA-Team/hera-singularity:\u0026lt;recipe\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull rtp.sif shub://HERA-Team/hera-singularity:rtp\nINFO: Downloading shub image\n 1.98 GiB / 1.98 GiB [=======================================================] 100.00% 13.12 MiB/s 2m34s\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-shell\" class=\"anchor\" href=\"#shell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eshell\u003c/code\u003e\n\u003c/h4\u003e\n\u003cp\u003eThe \u003ccode\u003esingularity shell\u003c/code\u003e command allows you to spawn a new shell within your container and interact with it as though it were a small virtual machine.\u003c/p\u003e\n\u003cp\u003eBy default, \u003ccode\u003eshell\u003c/code\u003e invokes \u003ccode\u003e/bin/sh --norc\u003c/code\u003e, which means that \u003ccode\u003e.bashrc\u003c/code\u003e will not be executed (more on this \u003ca href=\"https://github.com/hpcng/singularity/issues/643\"\u003ehere\u003c/a\u003e) and thus Conda will not be initialized. To make the \u003ccode\u003econda\u003c/code\u003e command available, you can do one of the following:\u003c/p\u003e\n\u003cp\u003ea) Run \u003ccode\u003eexec $SHELL\u003c/code\u003e inside the singularity shell. If \u003ccode\u003e$SHELL\u003c/code\u003e is \u003ccode\u003e\\bin\\bash\u003c/code\u003e (as in our Ubuntu build), \u003ccode\u003e.bashrc\u003c/code\u003e will be read.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell rtp.sif\nSingularity\u0026gt; exec $SHELL\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb) Manually execute the conda initialization script inside singularity shell. The \u003ccode\u003eCONDA_SH\u003c/code\u003e environment variable pointing to the absolute path of the script (\u003ccode\u003e/usr/local/miniconda3/etc/profile.d/conda.sh\u003c/code\u003e), is made available for this purpose. Note that \u003ccode\u003e.\u003c/code\u003e must be used as \u003ccode\u003esource\u003c/code\u003e won\u0027t work under \u003ccode\u003esh\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell rtp.sif\nSingularity\u0026gt; . $CONDA_SH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb) Specify \u003ccode\u003e\\bin\\bash\u003c/code\u003e as a shell to use when executing the \u003ccode\u003eshell\u003c/code\u003e command, either by using the \u003ccode\u003eSINGULARITY_SHELL\u003c/code\u003e environment variable,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ SINGULARITY_SHELL=/bin/bash singularity shell hera-rtp.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor \u003ccode\u003e-s\u003c/code\u003e option,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell -s /bin/bash hera-rtp.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-exec\" class=\"anchor\" href=\"#exec\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eexec\u003c/code\u003e\n\u003c/h4\u003e\n\u003cp\u003eThe \u003ccode\u003esingularity exec\u003c/code\u003e command allows you to execute a custom command within a container by specifying the image file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec rtp.sif echo \"Hello World!\"\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat myscript.sh\nHello World!\n$ singularity exec rtp.sif bash myscript.sh\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-file-permission-and-bind-path\" class=\"anchor\" href=\"#file-permission-and-bind-path\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile Permission and Bind Path\u003c/h3\u003e\n\u003cp\u003eSingularity containers run as the user and share host services. When Singularity \u2018switch\u2019 from the host operating system to the containerized operating system, the OS-level system files on the host becomes inaccessible. (the root user on the host system is also different from the root in the container!)\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-specific-usages-for-ilifu\" class=\"anchor\" href=\"#specific-usages-for-ilifu\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecific Usages for Ilifu\u003c/h3\u003e\n\u003cp\u003ePlese see the relevant page on the HERA wiki.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 13, "topics": [], - "updated_at": 1551443298.0 + "updated_at": 1638267345.0 }, { "data_format": 2, - "description": "General container for RNA-seq sample QC, trimming, alignment and counts (STAR 2.7)", + "description": null, "filenames": [ - "Singularity.hg19v1.centos" + "Singularity" ], - "full_name": "ertheisen/wildcat_centos", + "full_name": "lkirk/nb-env", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-appalachianhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#appalachianhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappalachian.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nb-env\" class=\"anchor\" href=\"#nb-env\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enb-env\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1560527067.0 + "updated_at": 1637426544.0 }, { "data_format": 2, @@ -12511,243 +11935,245 @@ var data = "filenames": [ "Singularity" ], - "full_name": "Nemirtingas/gdown", + "full_name": "hakanyi/robust-vision-thesis", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gdownpl\" class=\"anchor\" href=\"#gdownpl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egdown.pl\u003c/h1\u003e\n\u003cp\u003eGoogle Drive direct download of big files\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003ewget\u003c/em\u003e and \u003cem\u003ePerl\u003c/em\u003e must be in the PATH.\u003cbr\u003e\n\u003cstrong\u003eWindows\u003c/strong\u003e and \u003cstrong\u003elinux\u003c/strong\u003e compatible.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eUse Google Drive shareable links, viewable by anyone:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./gdown.pl \u0027gdrive file url\u0027 [\u0027desired file name\u0027] \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-example\" class=\"anchor\" href=\"#example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h1\u003e\n\u003cp\u003eFor example, to download \u003ca href=\"https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit\" rel=\"nofollow\"\u003ethis video\u003c/a\u003e from my \u003ca href=\"https://circulosmeos.wordpress.com/2015/03/04/axolotl-a-simple-plain-text-documentation-system/\" rel=\"nofollow\"\u003eaxolotl project\u003c/a\u003e, just copy the url, and give a file name if desired:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./gdown.pl https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit axolotl.mp4 \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-resuming-a-download\" class=\"anchor\" href=\"#resuming-a-download\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResuming a download\u003c/h1\u003e\n\u003cp\u003eIf you need to resume a download, please, use \u003ca href=\"https://github.com/circulosmeos/gdown.pl/tree/with-resume\"\u003e\u003cstrong\u003egdown.pl v2.0\u003c/strong\u003e here\u003c/a\u003e.\u003cbr\u003e\nAs long as a file name is indicated as second parameter, \u003cem\u003egdown.pl v2.0\u003c/em\u003e \u003cstrong\u003ewill try to resume the partially downloaded file\u003c/strong\u003e if a local incomplete file with that name already exists.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-version\" class=\"anchor\" href=\"#version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h1\u003e\n\u003cp\u003eThis version is \u003cstrong\u003ev1.4\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-warning\" class=\"anchor\" href=\"#warning\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWarning\u003c/h3\u003e\n\u003cp\u003ePlease, note that v1.2 (available between days 12 to 31 of Jan/2019) \u003cstrong\u003eshould not be used\u003c/strong\u003e, as it contains a bug that could result in unusable downloaded files. Proceed to overwrite with v1.4 in case you have it.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003cp\u003eA simple Docker file is provided, to build a simple Docker image with gdown.pl.\u003cbr\u003e\nThis has been used for pre-pulling data from a Google Drive to Kubernetes persistent volumes.\u003cbr\u003e\nThanks @anton-khodak\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eAn example \u003ca href=\"https://sylabs.io/guides/3.2/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e file is provided.\u003cbr\u003e\nBuild the container:\n\u003ccode\u003esudo singularity build (imagename) Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRun the container:\n\u003ccode\u003esingularity run (imagename) (gdown.pl args)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThanks to @ttbrunner\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eDistributed \u003ca href=\"http://www.gnu.org/licenses/gpl-3.0.html\" rel=\"nofollow\"\u003eunder GPL 3\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h1\u003e\n\u003cp\u003eThis software is provided \"as is\", without warranty of any kind, express or implied.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-more-info\" class=\"anchor\" href=\"#more-info\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore info\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files\" rel=\"nofollow\"\u003ehttps://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eby \u003ca href=\"loopidle@gmail.com\"\u003ecirculosmeos\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-stimulus-generation-pipeline\" class=\"anchor\" href=\"#stimulus-generation-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStimulus generation pipeline\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eManually choose base and change postures from H36M dataset. The outcome of this is a \u003ccode\u003ebase_posture.txt\u003c/code\u003e file that we\u0027ll put in a folder, e.g. \u003ccode\u003esampled-lights\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e00_h36m_to_mesh.py\u003c/code\u003e to generate meshes for all of the images from \u003ccode\u003ebase_postures.txt\u003c/code\u003e and place them in the \u003ccode\u003emeshes\u003c/code\u003e folder under \u003ccode\u003esampled-lights\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e01_sample_candidates.sh\u003c/code\u003e to go through the mesh pairs and, for each, generate N base:changed-light and base:changed-pose pairs where the underlying lamp position is sampled. Record image statistics under \u003ccode\u003esampled-lights\u003c/code\u003e, but don\u0027t save images.\u003c/li\u003e\n\u003cli\u003eAnalyze the data with \u003ccode\u003e02_analyze_candidates.Rmd\u003c/code\u003e to determine a) pairs where the pixel distance due to light changes are comparable to pixel distance due to posture changes and b) out these pairs, whether the pixel distances lie in a given range. Save the filtered csv to \u003ccode\u003ecandidate_pairs.csv\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eProduce the images in \u003ccode\u003ecandidate_pairs.csv\u003c/code\u003e (incl. diff-images) to sanity-check using \u003ccode\u003e03_visualize_candidates.py\u003c/code\u003e. Place them in \u003ccode\u003ecandidate_pairs_images\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAcross all \u003ccode\u003esampled-lights-\u0026lt;x\u0026gt;\u003c/code\u003e folders, choose from the candidates and consolidate the output in a \u003ccode\u003eimage_info.csv\u003c/code\u003e in this folder.\u003c/li\u003e\n\u003cli\u003eConsolidate the images in \u003ccode\u003ecandidate_pairs.csv\u003c/code\u003e and place them in \u003ccode\u003eimages\u003c/code\u003e using \u003ccode\u003e04_collect_images.py\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e05_make_videos.py\u003c/code\u003e to produce the video stimuli for the behavioral experiment.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1645100579.0 + "updated_at": 1637252100.0 }, { "data_format": 2, - "description": "CP 2022", + "description": null, "filenames": [ - "dmc/Singularity", - "lg/Singularity" + "Singularity" ], - "full_name": "anonymizee/dper", - "latest_release": "v0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dper-dynamic-programming-for-er-ssat\" class=\"anchor\" href=\"#dper-dynamic-programming-for-er-ssat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPER (Dynamic Programming for ER-SSAT)\u003c/h1\u003e\n\u003cp\u003eDPER runs in two phases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe planning phase constructs a join tree of a CNF formula\u003c/li\u003e\n\u003cli\u003eThe execution phase computes the maximum and a maximizer from the join tree\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-cloning-this-repository\" class=\"anchor\" href=\"#cloning-this-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning this repository\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/anonymizee/dper\u003c/pre\u003e\u003c/div\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDir \u003ca href=\"lg\"\u003e\u003ccode\u003elg\u003c/code\u003e\u003c/a\u003e: DPER\u0027s planner\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"dmc\"\u003e\u003ccode\u003edmc\u003c/code\u003e\u003c/a\u003e: DPER\u0027s executor\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"experiments\"\u003e\u003ccode\u003eexperiments\u003c/code\u003e\u003c/a\u003e: empirical evaluation\u003c/li\u003e\n\u003cli\u003eFile \u003ca href=\"ACKNOWLEDGMENT.md\"\u003e\u003ccode\u003eACKNOWLEDGMENT.md\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "iferres/GTi_UY_shiny", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1645325273.0 + "updated_at": 1638368684.0 }, { "data_format": 2, "description": null, "filenames": [ - "0.1.16/Singularity" + "Singularity" ], - "full_name": "yh549848/singularity-vcftools", - "latest_release": null, + "full_name": "cmatKhan/brentlabRnaSeqTools", + "latest_release": "0.0.2", + "readme": "\u003cp\u003e\u003ca href=\"https://codecov.io/gh/cmatKhan/brentlabRnaSeqTools?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5960bfe27822500008e6c1cfcd08e981288cc2fb1c1ae70ecf9a5125057f6c7c/68747470733a2f2f636f6465636f762e696f2f67682f636d61744b68616e2f6272656e746c6162526e61536571546f6f6c732f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/cmatKhan/brentlabRnaSeqTools/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003ca href=\"https://github.com/cmatKhan/brentlabRnaSeqTools/actions\"\u003e\u003cimg src=\"https://github.com/cmatKhan/brentlabRnaSeqTools/workflows/R-CMD-check/badge.svg\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://cmatkhan.github.io/brentlabRnaSeqTools/\" rel=\"nofollow\"\u003eClick here for the online documentation\u003c/a\u003e. This is a work in progress, still. If there is documentation that you\u0027d like that doesn\u0027t exist, please make an issue report.\u003c/p\u003e\n\u003cp\u003eThe \"articles\" link in the navbar at the top of the page has some vignettes that will help with some common tasks -- please do look at those, if you are a user of this package.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation-and-updating\" class=\"anchor\" href=\"#installation-and-updating\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and updating\u003c/h1\u003e\n\u003cp\u003eThe following will both install, and update if there are changes in the repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(devtools)\n# remove build_vignettes to save time\ninstall_github(\"cmatKhan/brentlabRnaSeqTools\", dependencies = TRUE)\n\n# after you get the package installed, do this:\nlibrary(brentlabRnaSeqTools)\n\n# if you think there are changes, but install_github disagrees, try using the argument force = TRUE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI have also installed this on my htcf cluster profile like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eml miniconda # note: this will definitely load, and most likely work as expected. But it does not come with a promise. It is a cluster module I wrote. If you have issues which you suspect to be a conda problem, I suggest that you install a version of miniconda in your home profile. It will be easier to address any conda related issues that way.\n\nconda install -n brentlabRnaSeqTools # or whatever you want to call your env name\n\nconda install r r-essentials libpq\n\n$ R\n\n\u0026gt; install.packages(devtools)\n# YOU HAVE TO DO THIS! do not update RSQLite (as of 20210702 there is an install error in the boost/c++ package which is a dependency. You do not need to worry about this when you\u0027re installing)\n\u0026gt; remotes::install_version(\"RSQLite\", version = \"2.2.5\")\n\u0026gt; install_github(\"cmatKhan/brentlabRnaSeqTools\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee the bamtools vignette for examples of how to use the functions to examine bam files in an Rscript that you could run with SLURM\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-uninstall\" class=\"anchor\" href=\"#uninstall\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003euninstall\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003eremove.packages(\"brentlabRnaSeqTools\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://cmatkhan/default/brentlab_rnaseq_tools:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-issues\" class=\"anchor\" href=\"#issues\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eissues\u003c/h1\u003e\n\u003cp\u003eplease do post issues to the issues tab. Please include the full error code and the command/context that lead to the error\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-to-contribute\" class=\"anchor\" href=\"#to-contribute\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eto contribute\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003efork the repo\u003c/li\u003e\n\u003cli\u003edevelop in a branch\u003c/li\u003e\n\u003cli\u003ecreate a pull request for the branch\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis is the featureCounts/subreads homepage. In particular, has a good example of how to make mean/variance graph with voom\n\u003ca href=\"http://bioinf.wehi.edu.au/RNAseqCaseStudy/\" rel=\"nofollow\"\u003ehttp://bioinf.wehi.edu.au/RNAseqCaseStudy/\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todos\" class=\"anchor\" href=\"#todos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODOs\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eread more about packrat, add some instructions on how to use\u003c/li\u003e\n\u003cli\u003eupdate R and dependencies to R version 4\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-brentlabrnaseqtools\" class=\"anchor\" href=\"#brentlabrnaseqtools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebrentlabRnaSeqTools\u003c/h1\u003e\n\u003cp\u003eThis is a very helpful tutorial on making an R package:\u003cbr\u003e\n\u003ca href=\"https://tinyheero.github.io/jekyll/update/2015/07/26/making-your-first-R-package.html\" rel=\"nofollow\"\u003ehttps://tinyheero.github.io/jekyll/update/2015/07/26/making-your-first-R-package.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ethis github post helped with installing bioconductor dependencies (deseq2 in this case):\u003cbr\u003e\n\u003ca href=\"https://bioinformatics.stackexchange.com/a/3375\" rel=\"nofollow\"\u003ehttps://bioinformatics.stackexchange.com/a/3375\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eand this helped with installing from github:\u003cbr\u003e\n\u003ca href=\"https://cran.r-project.org/web/packages/githubinstall/vignettes/githubinstall.html\" rel=\"nofollow\"\u003ehttps://cran.r-project.org/web/packages/githubinstall/vignettes/githubinstall.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFinally, here is a nice package development cheatsheet (for R):\u003cbr\u003e\n\u003ca href=\"https://rawgit.com/rstudio/cheatsheets/master/package-development.pdf\" rel=\"nofollow\"\u003ehttps://rawgit.com/rstudio/cheatsheets/master/package-development.pdf\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1645372372.0 + "updated_at": 1636684356.0 }, { "data_format": 2, - "description": "Docker images", + "description": "This repo contains a Singularity definition file for the newest ROS2 distro", "filenames": [ - "images/sc_qc_cluster/Singularity.sc_qc_cluster" + "Singularity" ], - "full_name": "letaylor/docker-letaylor", + "full_name": "siehlema/ros2_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker-letaylor\" class=\"anchor\" href=\"#docker-letaylor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker-letaylor\u003c/h1\u003e\n\u003cp\u003eThis repo contains Docker images that are automatically built using Travis CI. It is not designed to scale to many images as each image is updated if any one image changes.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-automatically-push-images-to-docker-hub-using-travis-ci\" class=\"anchor\" href=\"#automatically-push-images-to-docker-hub-using-travis-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically push images to Docker Hub using Travis CI\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-1-edit-config-files\" class=\"anchor\" href=\"#1-edit-config-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Edit config files\u003c/h2\u003e\n\u003cp\u003eEdit the following files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e.travis.yml\u003c/code\u003e : alter \u003ccode\u003e$IMAGE_NAME\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-2-give-travis-ci-access-to-upload-to-docer-hub\" class=\"anchor\" href=\"#2-give-travis-ci-access-to-upload-to-docer-hub\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Give Travis CI access to upload to Docer Hub\u003c/h2\u003e\n\u003cp\u003eStore both \u003ccode\u003e$DOCKER_PASSWORD\u003c/code\u003e and \u003ccode\u003e$DOCKER_USERNAME\u003c/code\u003e securely in on Travis CI. These are used for authentication.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLogin to the account you want Travis to use to upload on \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eClick on your username on the top left and go to \u0027Account Settings\u0027.\u003c/li\u003e\n\u003cli\u003eOn the left hand panel, go to \u0027Security\u0027 and enter your password as requested.\u003c/li\u003e\n\u003cli\u003eNow we\u0027ll create an API token. Name it Travis CI.\u003c/li\u003e\n\u003cli\u003eCreate the token and copy it.\u003c/li\u003e\n\u003cli\u003eLogin to your account on \u003ca href=\"https://travis-ci.org\" rel=\"nofollow\"\u003etravis-ci.org\u003c/a\u003e and go to the repository that you want to add this automatic functionality to.\u003c/li\u003e\n\u003cli\u003eOn the right next to \u0027More options\u0027 go to \u0027Settings\u0027 in the hamburger menu.\u003c/li\u003e\n\u003cli\u003eAdd an environment variable with the name \u003ccode\u003eDOCKER_PASSWORD\u003c/code\u003e and give it the value of the API token that you copied from \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eAdd an environment variable with the name \u003ccode\u003eDOCKER_USERNAME\u003c/code\u003e and give it your \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e user name.\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ros2_singularity\" class=\"anchor\" href=\"#ros2_singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eros2_singularity\u003c/h1\u003e\n\u003cp\u003eThis definition file is based on the ROS2 Docker from the \u003ca href=\"https://hub.docker.com/r/osrf/ros2/dockerfile\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and the official \u003ca href=\"https://github.com/osrf/docker_images/blob/master/ros2/source/source/Dockerfile\"\u003eGithub Repo\u003c/a\u003e and adds some Singularity functionality. Singularity containers can for instance be used from SLURM workload managers on computer clusters.\u003c/p\u003e\n\u003cp\u003eIt is supposed to help new Singularity/ROS2 developers to start their projects.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to\" class=\"anchor\" href=\"#how-to\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003emeet prerequisites for demo:\n\u003cul\u003e\n\u003cli\u003eLinux environment (tested on Ubuntu 18.04)\u003c/li\u003e\n\u003cli\u003einstall Singularity (\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eGuide\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003erun all containers on one host\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eclone repo\u003c/li\u003e\n\u003cli\u003ebuild singularity container: \u003ccode\u003esudo singularity build ros2_container.simg Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eafterwards try the demo apps:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo singularity run --app example_talker ros2_container.simg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esudo singularity run --app example_listener ros2_container.simg\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ecreate your own apps on the Singularity container or copy them onto the container before building\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1611328575.0 + "updated_at": 1566384326.0 }, { "data_format": 2, - "description": "A Nextflow pipeline for processing 16S rRNA sequences using dada2", + "description": null, "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "nhoffman/dada2-nf", + "full_name": "cmatKhan/bartNP", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dada2-nextflow-pipeline\" class=\"anchor\" href=\"#dada2-nextflow-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDada2 Nextflow pipeline\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-local-execution-quickstart-for-the-truly-impatient\" class=\"anchor\" href=\"#local-execution-quickstart-for-the-truly-impatient\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal execution quickstart for the truly impatient\u003c/h2\u003e\n\u003cp\u003eInstall Docker and make sure that the Docker daemon is running.\u003c/p\u003e\n\u003cp\u003eInstall the nextflow binary in this directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget -qO- https://get.nextflow.io | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute locally, using the minimal data set.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./nextflow run main.nf -params-file params-minimal.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-execution-on-aws-batch\" class=\"anchor\" href=\"#execution-on-aws-batch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution on AWS Batch\u003c/h2\u003e\n\u003cp\u003eDetails will depend on your AWS batch configuration. General instructions TBD.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-infernal-16s-filtering\" class=\"anchor\" href=\"#infernal-16s-filtering\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInfernal 16s filtering\u003c/h3\u003e\n\u003cp\u003eCoveriance model used for Infernal sequence filtering obtained from the Rfam database:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://rfam.xfam.org/family/RF00177\" rel=\"nofollow\"\u003ehttps://rfam.xfam.org/family/RF00177\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo cite Rfam see latest web site instructions:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://rfam.xfam.org/\" rel=\"nofollow\"\u003ehttps://rfam.xfam.org/\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bartnp\" class=\"anchor\" href=\"#bartnp\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebartNP\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/cmatKhan/bartNP/actions\"\u003e\u003cimg src=\"https://github.com/cmatKhan/bartNP/workflows/R-CMD-check/badge.svg\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"https://cmatkhan.github.io/bartNP/\" rel=\"nofollow\"\u003eSee Documentation Here\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1637256255.0 + "updated_at": 1639423876.0 }, { "data_format": 2, "description": null, "filenames": [ - "QE/Singularity.QuantumESPRESSO-6.3-intel-2018b-unrrc" + "Singularity" ], - "full_name": "UNR-HPC/singularity-recipes", + "full_name": "bozmik/singularity_image", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" href=\"#singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-recipes\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1544465938.0 + "updated_at": 1552825749.0 }, { "data_format": 2, - "description": "Singularity Containers", + "description": "md5deep is a set of programs to compute MD5, SHA-1, SHA-256, Tiger, or Whirlpool message digests on an arbitrary number of files.", "filenames": [ - "Singularity.fmriprep.1.4.1rc1", - "Singularity.rclone", - "Singularity.hddm", - "Singularity.neurodebian", - "Singularity.fmriprep", - "Singularity.test.neurodebian.def" + "4.4/Singularity" ], - "full_name": "klabhub/singularity", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eKlab Singularity Containers, access them here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2510\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNeurodebian is a full install, with FSL, AFNI, datalad, etc.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-hashdeep", + "latest_release": "v4.4", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-hashdeep/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-hashdeep/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-hashdeep/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-hashdeep/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ff1122893a7ca155e9b1b0481dc029ee11528d2c1a5d57208038642526e8e646/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6861736864656570\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff1122893a7ca155e9b1b0481dc029ee11528d2c1a5d57208038642526e8e646/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6861736864656570\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-hashdeep\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f144df41de2d6e76dd24c4825a4ac826fd107f3ea3e94ec7daf3c070422137cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6861736864656570\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f144df41de2d6e76dd24c4825a4ac826fd107f3ea3e94ec7daf3c070422137cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6861736864656570\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-hashdeep\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b2bcc92aad4b69910d6a0b34f82bbe68f8529688631ce0a15fb4ddbca44fd8be/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6861736864656570\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b2bcc92aad4b69910d6a0b34f82bbe68f8529688631ce0a15fb4ddbca44fd8be/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6861736864656570\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-hashdeep\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5aa33b5c4bd7a85735363e937ad526230f325f4d70cd29a78de7f77376b0defd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6861736864656570\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5aa33b5c4bd7a85735363e937ad526230f325f4d70cd29a78de7f77376b0defd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6861736864656570\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-hashdeep\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hashdeep\" class=\"anchor\" href=\"#singularity-hashdeep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hashdeep\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/jessek/hashdeep\"\u003ehashdeep\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehashdeep\u003c/code\u003e, \u003ccode\u003esha1deep\u003c/code\u003e, \u003ccode\u003etigerdeep\u003c/code\u003e, \u003ccode\u003emd5deep\u003c/code\u003e, \u003ccode\u003esha256deep\u003c/code\u003e and \u003ccode\u003ewhirlpooldeep\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hashdeep/4.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hashdeep\u003c/code\u003e as \u003ccode\u003e4.4.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [], - "updated_at": 1637675540.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1639934583.0 }, { "data_format": 2, - "description": null, + "description": "A monitor of resources", "filenames": [ - "Singularity" + "1.0.20/Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-fidle", + "full_name": "pscedu/singularity-btop", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-container-for-fidle-httpsgricad-gitlabuniv-grenoble-alpesfrtalksfidle\" class=\"anchor\" href=\"#building-container-for-fidle-httpsgricad-gitlabuniv-grenoble-alpesfrtalksfidle\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding container for fidle (\u003ca href=\"https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle\" rel=\"nofollow\"\u003ehttps://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle\u003c/a\u003e)\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image from dockerhub registry and push to ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003cli\u003eOnly cover the software installation for cpu (\u003ca href=\"https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/Linux%20installation%20using%20conda\" rel=\"nofollow\"\u003ehttps://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/Linux%20installation%20using%20conda\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-fidle/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-fidle/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-fidle:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-fidle:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-btop/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-btop/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-btop/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-btop/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c5457f5a651b024fa69983d39c277473e069e5594ee409e53b44e391c6c15b39/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c5457f5a651b024fa69983d39c277473e069e5594ee409e53b44e391c6c15b39/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62746f70\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-btop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fed939c101ca243e5b6b049eb99d6125bc1a94e6772a69e3d1e61c7bc1dd401e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fed939c101ca243e5b6b049eb99d6125bc1a94e6772a69e3d1e61c7bc1dd401e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62746f70\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-btop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/eb5608fc1c3e2f7a3a19f5f7d22fadf1ee0714cb6c0c9d9cdc2dfd9589202a2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb5608fc1c3e2f7a3a19f5f7d22fadf1ee0714cb6c0c9d9cdc2dfd9589202a2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62746f70\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-btop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7edba1cf64e407b9a9f9fd1d76709a70797be17efa4ad0233df596ca5b65aab2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7edba1cf64e407b9a9f9fd1d76709a70797be17efa4ad0233df596ca5b65aab2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62746f70\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-btop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-btop\" class=\"anchor\" href=\"#singularity-btop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-btop\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/aristocratos/btop/raw/main/Img/logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/aristocratos/btop/raw/main/Img/logo.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/aristocratos/btop\"\u003ebtop\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebtop\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/btop/1.0.20\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/btop\u003c/code\u003e as \u003ccode\u003e1.0.20.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1637706473.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1635823834.0 }, { "data_format": 2, - "description": null, + "description": "SCG collaboration with ETA on BEAM/Atlas project", "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-fidle-gpu", + "full_name": "lbnl-science-it/atlas", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-container-for-fidle-httpsgricad-gitlabuniv-grenoble-alpesfrtalksfidle-with-a-gpu\" class=\"anchor\" href=\"#building-container-for-fidle-httpsgricad-gitlabuniv-grenoble-alpesfrtalksfidle-with-a-gpu\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding container for fidle (\u003ca href=\"https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle\" rel=\"nofollow\"\u003ehttps://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle\u003c/a\u003e) with a gpu\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image from dockerhub registry and push to ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003cli\u003eOnly cover the software installation for gpu (\u003ca href=\"https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/Linux%20installation%20using%20conda\" rel=\"nofollow\"\u003ehttps://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/Linux%20installation%20using%20conda\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-fidle-gpu/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-fidle-gpu/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-fidle-gpu:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-fidle-gpu:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-atlas\" class=\"anchor\" href=\"#atlas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eatlas\u003c/h1\u003e\n\u003cp\u003eContainer with R and necessary packages to run BEAM/Atlas vehicle simulation.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-example-running-r-script-via-docker\" class=\"anchor\" href=\"#example-running-r-script-via-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample running R script via Docker\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /global/data/transportation/ATLAS/static/urbansim/\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# R script in home dir, bind mounted to container\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eXX docker run -v /global/data/transportation/ATLAS/static/urbansim:/global/data/transportation/ATLAS/static/urbansim -it --entrypoint=Rscript ghcr.io/lbnl-science-it/atlas:main /global/data/transportation/ATLAS/static/urbansim/model_application/Model_application_hima.R \u003c/span\u003e\ndocker run -v /global/data/transportation/ATLAS/static/urbansim:/mnt -it --entrypoint=Rscript ghcr.io/lbnl-science-it/atlas:main /mnt/model_application/Model_application_hima.R \ndocker run -v /global/data/transportation/ATLAS/static/urbansim_hima_apollo:/mnt -it --entrypoint=Rscript ghcr.io/lbnl-science-it/atlas:main /mnt/model_application/Model_application_hima.R \ndocker run -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e:/mnt -it --entrypoint=Rscript ghcr.io/lbnl-science-it/atlas:main /mnt/model_application/Model_application_hima.R\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# running a bash shell, can call R from there\u003c/span\u003e\ndocker run -it --entrypoint=bash ghcr.io/lbnl-science-it/atlas:main\ndocker run -v /global/data/transportation/ATLAS/static/urbansim_hima_apollo:/mnt -it --entrypoint=bash ghcr.io/lbnl-science-it/atlas:main \n root@0b5815f5b441:/\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e export R_LIBS=/usr/local/lib/R/site-library/\u003c/span\u003e\n root@0b5815f5b441:/\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Rscript /mnt/model_application/Model_application_hima.R\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-example-running-r-script-via-singularity\" class=\"anchor\" href=\"#example-running-r-script-via-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample running R script via Singularity\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e\ncd /global/data/transportation/ATLAS/static/urbansim/\n\nsingularity pull docker://ghcr.io/lbnl-science-it/atlas:main \nsingularity exec docker://ghcr.io/lbnl-science-it/atlas:main Rscript ./model_application/Model_application_hima.R \n\n// other things to try for debug use\nsingularity shell docker://ghcr.io/lbnl-science-it/atlas:main # get bash prompt, can call R afterward\nsingularity run docker://ghcr.io/lbnl-science-it/atlas:main # get R prompt\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1637710228.0 + "subscribers_count": 0, + "topics": [ + "modeling" + ], + "updated_at": 1642141776.0 }, { "data_format": 2, - "description": "The preprocessing pipeline at ZHH", + "description": null, "filenames": [ - "HCPPipeline/Singularity.unix" + "Singularity" ], - "full_name": "argyelan/ZHHpipelines", + "full_name": "jzhanghzau/thesis_docker", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-zhhpipelines\" class=\"anchor\" href=\"#zhhpipelines\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eZHHpipelines\u003c/h1\u003e\n\u003cp\u003eThe preprocessing pipeline at ZHH\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-bids-conversion\" class=\"anchor\" href=\"#bids-conversion\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBIDS Conversion\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: dicom2bids.sh grid_num sess_num descr\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003egrid_num: subject identifier;\nsess_num: session identifier;\ndescr: study specificator (currently available: TMS)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: multiple_dicom2bids.sh info.csv\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003einfo.csv: a file with 3 columns, first subject identifier, second: session identifier, third: study type\nProgram goes over every line one by one and calls dicom2bids.sh\u003c/p\u003e\n\u003cp\u003etest 2 ....\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1637793201.0 + "updated_at": 1635770574.0 }, { "data_format": 2, - "description": null, + "description": "ENHSP Containers. This contains singularity recipes for ENHSP. ENHSP-18, ENHSP-19 and ENHSP20. More details can be found at https://sites.google.com/view/enhsp/", "filenames": [ - "Singularity.lbfs" + "2018/Singularity.2018", + "latest/Singularity", + "2019/Singularity.2019", + "2020/Singularity.2020" ], - "full_name": "ab649964207/fs", + "full_name": "hstairs/enhsp-containers", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-the-fs-functional-strips-planner\" class=\"anchor\" href=\"#the-fs-functional-strips-planner\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe FS Functional STRIPS planner\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eFS\u003c/code\u003e is a classical planner that works with the Functional STRIPS planning language \u003ca href=\"#ref-geffner-fstrips-2000\"\u003e[Geffner, 2000]\u003c/a\u003e,\na modeling language based on the quantifier-free\nfragment of first-order logic that includes constant, function and predicate symbols, but no variable symbols. The increased expressiveness\nof the Functional STRIPS language with respect to propositional languages such as standard STRIPS (which is indeed subsumed by Functional STRIPS)\noften results in problem encodings which are more compact, more readable, have fewer ground actions\nand preserve the structural properties of the problem in a manner which allows the derivation of more effective heuristics.\u003c/p\u003e\n\u003cp\u003eAlong with the core of the Functional STRIPS language, the \u003ccode\u003eFS\u003c/code\u003e planner supports certain extensions which are useful both\nfrom the expressive \u003cem\u003eand\u003c/em\u003e the computational point of view. These include \u003cem\u003eexistential quantification\u003c/em\u003e,\n\u003cem\u003estate constraints\u003c/em\u003e, a fairly large library of \u003cem\u003eglobal constraints\u003c/em\u003e, and the possibility of using \u003cem\u003eexternally-defined symbols\u003c/em\u003e\nand \u003cem\u003ebuilt-in arithmetic symbols\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThis documentation covers a number of practical issues related to the use of the \u003ccode\u003eFS\u003c/code\u003e planner. The planner, however, has\nbeen used and described in a number of academic publications that \u003ca href=\"http://gfrances.github.io/pubs/\" rel=\"nofollow\"\u003ecan be found here\u003c/a\u003e,\nthe most recent of which are \u003ca href=\"#ref-frances-modeling-2015\"\u003e[Franc\u00e8s and Geffner, 2015]\u003c/a\u003e and \u003ca href=\"#ref-frances-existential-2016\"\u003e[Franc\u00e8s and Geffner, 2016a]\u003c/a\u003e\nand \u003ca href=\"#ref-frances-effective-2016\"\u003e[Franc\u00e8s and Geffner, 2016b]\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe easiest way to use the planner is by \u003ca href=\"doc/installation.md\"\u003emanually compiling the planner source code\u003c/a\u003e.\nAlternatively, you can build and/or use a \u003ca href=\"doc/containers.md\"\u003eready-to-use image\u003c/a\u003e in some of the containerization solutions\nthat we support.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eYou can find a high-level overview of the planner usage options \u003ca href=\"doc/usage.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-credits\"\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003eFS\u003c/code\u003e planner is partially built upon the \u003ca href=\"http://www.lapkt.org\" rel=\"nofollow\"\u003eLightweight Automated Planning Toolkit\u003c/a\u003e\nand the PDDL parser from the \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003eFast Downward\u003c/a\u003e distribution.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-modeling-2015\"\u003eFranc\u00e8s, G., and Geffner, H. (2015)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2015-icaps-better-heuristics-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eModeling and Computation in Planning: Better Heuristics from More Expressive Languages\u003c/em\u003e\u003c/a\u003e, ICAPS 2015.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-existential-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016a)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-existential-quantification-planning-csp/\" rel=\"nofollow\"\u003e\u003cem\u003eE-STRIPS: Existential Quantification in Planning and Constraint Satisfaction\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-effective-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016b)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-effective-planning-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eEffective Planning with More Expressive Languages\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-geffner-fstrips-2000\"\u003eGeffner, H. (2000)\u003c/a\u003e,\n\u003ca href=\"http://www.tecn.upf.es/~hgeffner/\" rel=\"nofollow\"\u003e\u003cem\u003eFunctional STRIPS: A more flexible lan-\nguage for planning and problem solving\u003c/em\u003e\u003c/a\u003e.\nIn Minker, J., ed., Logic-Based Artificial Intelligence. Kluwer. 187\u2013205.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-enhsp-containers\" class=\"anchor\" href=\"#enhsp-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eENHSP Containers\u003c/h1\u003e\n\u003cp\u003eThis repository contains singularity recipes for ENHSP, the Expressive Numeric Heuristic Search Planner. ENHSP is an automated planning engine focused at solving planning problems with numeric state variables.\u003c/p\u003e\n\u003cp\u003eThe repository provides three versions of ENHSP, 2018, 2019 and 2020. These versions are described at \u003ca href=\"https://sites.google.com/view/enhsp/\" rel=\"nofollow\"\u003ehttps://sites.google.com/view/enhsp/\u003c/a\u003e as enhsp-18, enhsp-19, enhsp-20.\nSource code of all versions can be downloaded at: \u003ca href=\"https://gitlab.com/enricos83/ENHSP-Public\" rel=\"nofollow\"\u003ehttps://gitlab.com/enricos83/ENHSP-Public\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1637831969.0 + "updated_at": 1635518248.0 }, { "data_format": 2, - "description": "PPX protocols for open malaria", + "description": null, "filenames": [ - "src/code/Singularity" + "enricher/tests/resources/Singularity.enrichment" ], - "full_name": "bayesianbrad/openmalaria_probprog", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-todo\" class=\"anchor\" href=\"#todo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etodo\u003c/h1\u003e\n\u003cp\u003eAdd notes to all the .cpp files that we modify, to state exactly\nwhat we modified.\u003c/p\u003e\n\u003cp\u003eopenmalaria dependencies (Ubuntu):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eapt packages: xsdcxx libxerces-c-dev libgsl-dev libboost-all-dev\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHow to build wiki locally:\u003c/p\u003e\n\u003cp\u003eFirst download Gollum:\u003c/p\u003e\n\u003cp\u003eOn mac:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esudo gem install gollum\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-inputoutputs-to-the-model\" class=\"anchor\" href=\"#inputoutputs-to-the-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput/Outputs to the model\u003c/h1\u003e\n\u003cp\u003eWhat can we designate as input/output:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInputs\n\u003cul\u003e\n\u003cli\u003eMosquito nets\u003c/li\u003e\n\u003cli\u003eVaccination, type of vaccination\u003c/li\u003e\n\u003cli\u003eProphylactic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOutputs\n\u003cul\u003e\n\u003cli\u003e\"Survey measures\"\n\u003cul\u003e\n\u003cli\u003enHost\u003c/li\u003e\n\u003cli\u003enPatent\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMortality rate\u003c/li\u003e\n\u003cli\u003eProbability of seeking medical help\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-how-to-build-docker-image\" class=\"anchor\" href=\"#how-to-build-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to build Docker image:\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003edocker build . -t openmalariapp\u003c/li\u003e\n\u003cli\u003edocker run --rm -it (for interactive usage, will remove the container from memory) (-it interactive attach to terminal)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo attach a local drive / folder use\n:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edocker run --rm -it -v $PWD:/home bradleygh/openmalariapp\nConnecting docker to the external file system:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHow to run Jupyter inside Docker:\u003c/p\u003e\n\u003cp\u003eFor linux\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edocker run --rm -it -v $PWD:/home --net=host bradleygh/openmalariapp\nrun the following inside the container: jupyter notebook --allow-root\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor Mac\u003c/p\u003e\n\u003cp\u003edocker run --rm -it -p 127.0.0.1:8889:8889 -v $PWD:/home gbaydin/openmalariapp jupyter notebook --port 8889 --allow-root --ip=0.0.0.0\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-creating-an-experiment\" class=\"anchor\" href=\"#creating-an-experiment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating an experiment\u003c/h1\u003e\n\u003cp\u003eCreate a directory in your local machine, for example called examples\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ecd /home\u003c/li\u003e\n\u003cli\u003emkdir examples\u003c/li\u003e\n\u003cli\u003ecd examples\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewithin the folder add the following files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003escenario_current.xsd\u003c/li\u003e\n\u003cli\u003e\u0026lt;name_of_the_scenario_you_want_to_run\u0026gt;.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdd the openmalaria executable to the folder to, i.e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ecp /code/openmalaria/build/openMalaria examples/openMalaria\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;add_any_input_csv_or_txt_files\u0026gt;\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-simulator-once-an-experiment-has-been-created\" class=\"anchor\" href=\"#running-the-simulator-once-an-experiment-has-been-created\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the simulator once an experiment has been created\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003ecd ./examples\u003c/li\u003e\n\u003cli\u003edocker run --rm -it -v $PWD:/home/examples bradleygh/openmalariapp\u003c/li\u003e\n\u003cli\u003ecd /home/examples/\u003c/li\u003e\n\u003cli\u003e./openMalaria -s \u0026lt;name_of_the_scenario_you_want_to_run\u0026gt;.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-debugging-by-modifying-code-outside-docker-but-running-inside\" class=\"anchor\" href=\"#debugging-by-modifying-code-outside-docker-but-running-inside\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebugging by modifying code outside Docker, but running inside\u003c/h1\u003e\n\u003cp\u003edocker run --rm -it --net=host -v $PWD/examples:/home/examples -v $PWD/code/openmalaria/:/code/openmalaria bradleygh/openmalariapp\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-when-building-the-prob-prog-version\" class=\"anchor\" href=\"#when-building-the-prob-prog-version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhen building the prob prog version\u003c/h1\u003e\n\u003cp\u003eWhen openMalaria is being built it is actively looking for the current version of schema, in this case the schema\nversion is 39.0, If the main directory name is not called \"openmalaria_schema_\u0026lt;version_number\u0026gt; then the code will fail to build.\nIn addition to this, as specified by the openMalaria readme, you will have to change\nall the relevant places in the script where schema number appears before a build.\nSeems very inefficient, but that is the way in whcih the simulator is set up.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-om-simulator-with-pyprob\" class=\"anchor\" href=\"#running-om-simulator-with-pyprob\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning OM simulator with Pyprob\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-mac\" class=\"anchor\" href=\"#mac\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMac\u003c/h1\u003e\n\u003cp\u003edocker run --rm -it -p 2345:2345 -v $PWD/examples:/home/examples -v $PWD/code/openmalaria/:/code/openmalaria bradleygh/openmalariapp\u003c/p\u003e\n\u003cp\u003eadd when calling ./openmalaria\n$ ./openMalaria tcp://*:2345\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-linux\" class=\"anchor\" href=\"#linux\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinux\u003c/h1\u003e\n\u003cp\u003edocker run --rm -it -v $PWD/examples:/home/examples -v $PWD/code/openmalaria/:/code/openmalaria bradleygh/openmalariapp\u003c/p\u003e\n\u003cp\u003eadd when calling ./openmalaria\n$ ./openMalaria ipc://@\u0026lt;some_string\u0026gt;\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-using-singluarity-instead\" class=\"anchor\" href=\"#using-singluarity-instead\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singluarity instead\u003c/h1\u003e\n\u003cp\u003eTo convert a dockerfile to singularityfile run:\u003c/p\u003e\n\u003cp\u003epip install singularity\u003c/p\u003e\n\u003cp\u003eThen in the terminal / commmand line run:\u003c/p\u003e\n\u003cp\u003espython recipe Dockerfile \u0026gt;\u0026gt; Singularity\u003c/p\u003e\n\u003cp\u003eThis will convert the Dockerfile to a singularity file and save the output as Singularity.\u003c/p\u003e\n\u003cp\u003eWe can also make use of pre-built docker containers, without having to install docker, by running\nthe following:\u003c/p\u003e\n\u003cp\u003esingularity pull docker://bradleygh:openmalariapp\u003c/p\u003e\n", + "full_name": "JEstabrook/regulon-enrichment", + "latest_release": "v0.3.1a", + "readme": "\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/4fdbdfa64306659366ce76f0240a36fb0dc5e510b28870cad546175d0030658d/68747470733a2f2f7472617669732d63692e636f6d2f4a4573746162726f6f6b2f726567756c6f6e2d656e726963686d656e742e7376673f746f6b656e3d5a52445742576539735843697650314e725a7771266272616e63683d6d6173746572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4fdbdfa64306659366ce76f0240a36fb0dc5e510b28870cad546175d0030658d/68747470733a2f2f7472617669732d63692e636f6d2f4a4573746162726f6f6b2f726567756c6f6e2d656e726963686d656e742e7376673f746f6b656e3d5a52445742576539735843697650314e725a7771266272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/JEstabrook/regulon-enrichment.svg?token=ZRDWBWe9sXCivP1NrZwq\u0026amp;branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://www.python.org/downloads/release/python-367\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75b8738e1bdfe8a832711925abbc3bd449c1e7e9260c870153ec761cad8dde40/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e362b2d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/python-3.6+-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/1d48578e1082d434280fe2299efd72bcf1cba5658c8ad707406fd29a0a1a4193/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6e7269676874677265656e2e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1d48578e1082d434280fe2299efd72bcf1cba5658c8ad707406fd29a0a1a4193/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6e7269676874677265656e2e737667\" alt=\"t\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-nrightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/8416ebae4490c8fa309b7d13e3a8565f3da2ea34ce1eb8a905ce2c7195dddd63/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7374617475732d737461626c652d6e7269676874677265656e2e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8416ebae4490c8fa309b7d13e3a8565f3da2ea34ce1eb8a905ce2c7195dddd63/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7374617475732d737461626c652d6e7269676874677265656e2e737667\" alt=\"t\" data-canonical-src=\"https://img.shields.io/badge/status-stable-nrightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/31c454bdf9d517bfbf4d3eed1c768f1853de782076f85ce05b681d0017bfa7f4/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3137393735323035392e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31c454bdf9d517bfbf4d3eed1c768f1853de782076f85ce05b681d0017bfa7f4/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3137393735323035392e737667\" alt=\"t\" data-canonical-src=\"https://zenodo.org/badge/179752059.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-enrich\" class=\"anchor\" href=\"#enrich\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnrich\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eregulon-enrichment\u003c/strong\u003e is a Python module used to predict the activity of regulatory proteins from RNAseq data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eregulon-enrichment\u003c/em\u003e submodules:\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-enricherfeatures\" class=\"anchor\" href=\"#enricherfeatures\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eenricher.features\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eLoad -omic datasets\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-enricherregulon\" class=\"anchor\" href=\"#enricherregulon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eenricher.regulon\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eRegulon utilities\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eregulon-enrichment\u003c/strong\u003e requires:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- Python (\u0026gt;= 3.6)\n- scikit-learn (\u0026gt;= 0.21.3)\n- NumPy (\u0026gt;= 1.17.3)\n- SciPy (\u0026gt;= 1.3.1)\n- pandas (\u0026gt;= 0.25.3)\n- tqdm (\u0026gt;= 4.38.0)\n- dill (\u0026gt;= 0.3.1.1)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-user-installation\" class=\"anchor\" href=\"#user-installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser installation\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e\nIf you already have a working installation of numpy and scipy,\nthe easiest way to install regulon-enrichment is using ``conda`` ::\n\n conda install -c estabroj89 regulon-enrichment\n\nor ``pip``::\n\n pip install regulon-enrichment==0.0.2b0\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-overview\" class=\"anchor\" href=\"#overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h1\u003e\n\u003cp\u003eThis method leverages pathway information and gene expression data to produce regulon-based protein activity scores.\nOur method tests for positional shifts in experimental-evidence supported networks consisting of transcription factors\nand their downstream signaling pathways when projected onto a rank-sorted gene-expression signature.\u003c/p\u003e\n\u003cp\u003eThis regulon enrichment method utilizes pathway and molecular interactions and mechanisms available through Pathway\nCommons to accurately infer aberrant transcription factor activity from gene expression data.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-regulon-enrichment\" class=\"anchor\" href=\"#running-regulon-enrichment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning regulon-enrichment\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-invoking-enrich-from-the-command-line\" class=\"anchor\" href=\"#invoking-enrich-from-the-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInvoking enrich from the command line\u003c/h2\u003e\n\u003cp\u003eWhen installing the regulon-enrichment package, the set of scripts that make up to inteface to regulon-enrichment will\nautomatically be placed as an executables in your path, so that you can refer to these without modifying your shell\nenvironment. For example, if you install regulon-enrichment using conda, then enrich will become available on the path,\nand can be run as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eenrich\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-enrich-parameters\" class=\"anchor\" href=\"#enrich-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnrich parameters\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-required-parameters\" class=\"anchor\" href=\"#required-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired parameters\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecohort\u003c/code\u003e : which cohort to use; this information will be retained in the serialized Enrichment class\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eexpr\u003c/code\u003e : which tab delimited expression matrix to use shape : \u003ccode\u003e[n_features, n_samples]\u003c/code\u003e, units : \u003ccode\u003eTPM, RPKM\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eout_dir\u003c/code\u003e : output directory - directory serialized Enrichment object and enrichment.tsv will be saved to\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-optional-parameters\" class=\"anchor\" href=\"#optional-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional parameters\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eregulon\u003c/code\u003e : optional regulon containing weight interactions between regulator and\ndownstream members of its regulon shape : \u003ccode\u003e[len(Target), [\u0027Regulator\u0027,\u0027Target\u0027,\u0027MoA\u0027,\u0027likelihood\u0027]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eregulon_size\u003c/code\u003e : number of downstream interactions required for a given regulator in order to calculate enrichment score \u003ccode\u003edefault=15\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esec_intx\u003c/code\u003e : path to pre-compiled serialized secondary interaction network, \u003ccode\u003edefault=secondary_intx_regulon.pkl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escaler_type\u003c/code\u003e : scaler to normalized features/samples by: \u003ccode\u003estandard | robust | minmax | quant\u003c/code\u003e, default=\u003ccode\u003erobust\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ethresh_filter\u003c/code\u003e : Prior to normalization remove features that have a standard deviation per feature less than \u003ccode\u003e{thresh_filter}\u003c/code\u003e, \u003ccode\u003edefault=0.1\u003c/code\u003e)\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-computing-regulon-enrichment-scores\" class=\"anchor\" href=\"#computing-regulon-enrichment-scores\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComputing regulon enrichment scores\u003c/h1\u003e\n\u003cp\u003eTo quantify the regulon enrichment for a given dataset, the command line script \u003ccode\u003eenrich\u003c/code\u003e is used.\u003c/p\u003e\n\u003cp\u003eUse --help argument to view options\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eEnrich requires three positional arguments: \u003ccode\u003ecohort\u003c/code\u003e,\u003ccode\u003eexpr\u003c/code\u003e, \u003ccode\u003eout_dir\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich cohort expr out_dir [regulon] [regulon_size] [sec_intx] [scaler_type] [thresh_filter] \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt is recommended to run enrich with the default parameters.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich test tests/resources/test_expr.tsv test_enrichment_scores\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe command above will generate enrichment scores for the unittest dataset \u003ccode\u003etest_expr.tsv\u003c/code\u003e and will generate and store the output under \u003ccode\u003etest_enrichment_scores/\u003c/code\u003e. In this directory \u003ccode\u003etest_enrichment_scores/\u003c/code\u003e, both the serialized Enrichment object \u003ccode\u003etest_enrichment.pkl\u003c/code\u003e and a tsv of the enrichment scores,\u003ccode\u003etest_regulon_enrichment.tsv\u003c/code\u003e will be found.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eenrichment.tsv\u003c/code\u003e file be shaped : \u003ccode\u003e[n_samples, n_regulators]\u003c/code\u003e, where \u003ccode\u003en_samples\u003c/code\u003e refers to the original number of samples provided in \u003ccode\u003eexpr\u003c/code\u003e, while \u003ccode\u003en_regulators\u003c/code\u003e will be determined based on the overlapping features present in the \u003ccode\u003eexpr\u003c/code\u003e dataset and the \u003ccode\u003eregulon_size\u003c/code\u003e parameter.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1637861862.0 + "updated_at": 1640212972.0 }, { "data_format": 2, - "description": "Original version from: http://gki.informatik.uni-freiburg.de/tools/tfd/", + "description": "Run Rstudio-server with singularity instance", "filenames": [ - "Singularity.0.4", - "Singularity" + "Singularity.Rstudio" ], - "full_name": "roveri-marco/tfd", - "latest_release": "0.4", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-temporalfastdownward\" class=\"anchor\" href=\"#temporalfastdownward\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemporalFastDownward\u003c/h1\u003e\n\u003cp\u003eThe version of Temporal Fast Downward.\nThis version has been ported to Python3 (tested with Python3.8)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-information\" class=\"anchor\" href=\"#information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInformation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://gki.informatik.uni-freiburg.de/tools/tfd/\" rel=\"nofollow\"\u003ehttp://gki.informatik.uni-freiburg.de/tools/tfd/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e$ ./build\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003ePatrick Eyerich, Robert Mattm\u00fcller and Gabriele R\u00f6ger.\nUsing the Context-enhanced Additive Heuristic for Temporal and Numeric Planning.\nIn Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS 2009), 2009.\u003c/p\u003e\n", + "full_name": "edg1983/RStudio_server_singularity", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-run-rstudio-server-with-singularity-instance\" class=\"anchor\" href=\"#run-rstudio-server-with-singularity-instance\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Rstudio-server with singularity instance\u003c/h1\u003e\n\u003cp\u003eUsing these instructions you can run rstudio server within a singulatiry instance\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" href=\"#build-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild singularity image\u003c/h2\u003e\n\u003cp\u003eThe recipe is built with R 4.0 and r studio v1.4\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build rstudio_v1.4.sif Singularity.Rstudio\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-before-you-start\" class=\"anchor\" href=\"#before-you-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBefore you start\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1set-up-library-locations\" class=\"anchor\" href=\"#1set-up-library-locations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.Set up library locations\u003c/h3\u003e\n\u003cp\u003eAll R libraries will be installed in \u003ccode\u003e/well/brc/R_pkg/$USER\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSubfolders will be created automatically according to R version and cpu architecture so that everything stay in place and you can run correctly compiled packages according to your environment (humbug and rescomp nodes have different architectures). This means that you need to install a package for each specific environment.\u003c/p\u003e\n\u003cp\u003eThis is managed by the \u003ccode\u003eRpofile\u003c/code\u003e file\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-set-up-your-r-profile\" class=\"anchor\" href=\"#set-up-your-r-profile\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet up your R profile\u003c/h4\u003e\n\u003cp\u003eCopy the \u003ccode\u003eRprofile\u003c/code\u003e file to \u003ccode\u003e$HOME/.Rprofile\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-create-an-r-session-folder\" class=\"anchor\" href=\"#2-create-an-r-session-folder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Create an R session folder\u003c/h3\u003e\n\u003cp\u003eDuring execution the instance will create R session files. You need to create a folder where yu have access to to store these files and then bind this to the Rsession folder in the image.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-rserver\" class=\"anchor\" href=\"#run-rserver\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun rserver\u003c/h2\u003e\n\u003cp\u003eModify the variables in \u003ccode\u003estart_rstudio_instance.sh\u003c/code\u003e according to your needs and run the script. Access is secured by password you can set changing the \u003ccode\u003ePASSWORD\u003c/code\u003e variable in the script.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNB.\u003c/strong\u003e Remember to add relevant paths to the bind argument in the script WITHOUT touching the default ones. All paths you need to acces from R server must be added to \u003ccode\u003e--bind\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eDefault settings:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eaddress: 127.0.0.1\u003c/li\u003e\n\u003cli\u003eport: 9997\u003c/li\u003e\n\u003cli\u003eRsession.conf file: set rsession timeout to zero to avoid writing temp session files\u003c/li\u003e\n\u003cli\u003eRsession dir: /well/brc/Rstudio_server/$USER\u003c/li\u003e\n\u003cli\u003eRstudio session folders creaded in \u003ccode\u003e$Rsession_dir\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1638481913.0 + "updated_at": 1642779639.0 }, { "data_format": 2, - "description": "Cluster Pipeline Workflow", + "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "ftlin22/RNASeq_pipeline", + "full_name": "nickdelgrosso/crab_pipeline", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-rnaseq_pipeline\" class=\"anchor\" href=\"#rnaseq_pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRNASeq_pipeline\u003c/h1\u003e\n\u003cp\u003eCluster Pipeline Workflow\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e If using WSL and data is on a usb drive, \u003ca href=\"https://www.howtogeek.com/331053/how-to-mount-removable-drives-and-network-locations-in-the-windows-subsystem-for-linux/\" rel=\"nofollow\"\u003emount the drive on the filesystem\u003c/a\u003e first so you can access it:\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-launch-singularity-interactively\" class=\"anchor\" href=\"#launch-singularity-interactively\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch Singularity Interactively\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-if-building-a-full-sandbox-so-you-can-pip-install-during-a-session-try-out-applications-etc\" class=\"anchor\" href=\"#if-building-a-full-sandbox-so-you-can-pip-install-during-a-session-try-out-applications-etc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf building a full sandbox (so you can pip install during a session, try out applications, etc)\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --sandbox -F Singularity.sif Singularity.def\nsingularity shell --writable --bind /path/to/videos:/data/raw Singularity.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-if-just-runing-code-insteractively-or-already-have-the-container-built\" class=\"anchor\" href=\"#if-just-runing-code-insteractively-or-already-have-the-container-built\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf just runing code insteractively, or already have the container built\u003c/h3\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build\" class=\"anchor\" href=\"#build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --sandbox -F Singularity.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-run\" class=\"anchor\" href=\"#run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --bind /path/to/videos:/data/raw Singularity.sif\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-launch-jupyter-lab\" class=\"anchor\" href=\"#launch-jupyter-lab\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch Jupyter Lab\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app jupyter Singularity.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-singularity-38\" class=\"anchor\" href=\"#installing-singularity-38\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling Singularity 3.8\u003c/h2\u003e\n\u003cp\u003eThese were the best instructions!\n\u003ca href=\"https://github.com/apptainer/singularity/blob/master/INSTALL.md\"\u003ehttps://github.com/apptainer/singularity/blob/master/INSTALL.md\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-docs\" class=\"anchor\" href=\"#docs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocs\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.8/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.8/user-guide/\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1638035482.0 + "updated_at": 1642525381.0 }, { "data_format": 2, - "description": null, + "description": "Test making a Singularity-HUB image for OpenFOAM", "filenames": [ - "p9/Singularity", - "pytorch/Singularity" + "Singularity" ], - "full_name": "abergeron/bench", + "full_name": "TormodLandet/singularity-openfoam", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-openfoam\" class=\"anchor\" href=\"#singularity-openfoam\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-openfoam\u003c/h1\u003e\n\u003cp\u003eA Singularity Hub image for OpenFOAM. Not official and probably not up to date\u003c/p\u003e\n\u003cp\u003eGithub added an Apache 2.0 license (at my choice), but feel free to use the contents of this repository under any license and however you want, this is just a Singularity image bootstrap description after all\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1539805396.0 + "updated_at": 1501144397.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity/Singularity" ], - "full_name": "truatpasteurdotfr/singularity-debian11-visualstudio", + "full_name": "YadavDosieah/FYP_Simulation", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-vstudio-on-debian11-toy-system-for-singularity-for-ci\" class=\"anchor\" href=\"#building-a-vstudio-on-debian11-toy-system-for-singularity-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a vstudio on debian11 toy system for singularity for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-debian11-visualstudio/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-debian11-visualstudio/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-debian11-visualstudio:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-final-year-project\" class=\"anchor\" href=\"#final-year-project\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFinal-Year-Project\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-shepherding-and-object-clustering-using-collaborative-robots\" class=\"anchor\" href=\"#shepherding-and-object-clustering-using-collaborative-robots\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShepherding and Object Clustering using collaborative robots\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThe main code for the simulation can be found here: \u003ca href=\"https://github.com/YadavDosieah/FYP_Simulation/tree/master/enki/examples/playground\"\u003ehttps://github.com/YadavDosieah/FYP_Simulation/tree/master/enki/examples/playground\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eThe Singularity folder contains the definition file from which the Singularity container can be build\u003c/li\u003e\n\u003cli\u003eThe tracking folder contains the files used to implement the tracking system\u003c/li\u003e\n\u003cli\u003eThe MyProject folder contains the code used on the e-puck2 robot for the colour sensing\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependecies\" class=\"anchor\" href=\"#dependecies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependecies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eEnki\u003c/li\u003e\n\u003cli\u003elibcmaes\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1638371327.0 + "updated_at": 1640465576.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.latest" ], - "full_name": "cognirob/crow_vision_yolact", + "full_name": "bioexcel/biobb_cmip", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-you-only-look-at-coefficients\" class=\"anchor\" href=\"#you-only-look-at-coefficients\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eY\u003c/strong\u003eou \u003cstrong\u003eO\u003c/strong\u003enly \u003cstrong\u003eL\u003c/strong\u003eook \u003cstrong\u003eA\u003c/strong\u003et \u003cstrong\u003eC\u003c/strong\u003eoefficien\u003cstrong\u003eT\u003c/strong\u003es\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e \u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\n \u255a\u2588\u2588\u2557 \u2588\u2588\u2554\u255d\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u2550\u2588\u2588\u2554\u2550\u2550\u255d\n \u255a\u2588\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u255a\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2551 \n \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA simple, fully convolutional model for real-time instance segmentation. This is the code for our papers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1904.02689\" rel=\"nofollow\"\u003eYOLACT: Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003eYOLACT++: Better Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-yolact-v12-released-changelog\" class=\"anchor\" href=\"#yolact-v12-released-changelog\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYOLACT++ (v1.2) released! (\u003ca href=\"CHANGELOG.md\"\u003eChangelog\u003c/a\u003e)\u003c/h4\u003e\n\u003cp\u003eYOLACT++\u0027s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e (check out our journal paper \u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIn order to use YOLACT++, make sure you compile the DCNv2 code. (See \u003ca href=\"https://github.com/dbolya/yolact#installation\"\u003eInstallation\u003c/a\u003e)\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-for-a-real-time-demo-check-out-our-iccv-video\" class=\"anchor\" href=\"#for-a-real-time-demo-check-out-our-iccv-video\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor a real-time demo, check out our ICCV video:\u003c/h4\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=0pMfmo8qfpQ\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c9e061dab1a6754a417d6eb14803f1104cce54aa9231aa0d779e2523694ed23e/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f30704d666d6f38716670512f302e6a7067\" alt=\"IMAGE ALT TEXT HERE\" data-canonical-src=\"https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSome examples from our YOLACT base model (33.5 fps on a Titan Xp and 29.8 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e):\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"data/yolact_example_0.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"data/yolact_example_0.png\" alt=\"Example 0\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"data/yolact_example_1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"data/yolact_example_1.png\" alt=\"Example 1\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"data/yolact_example_2.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"data/yolact_example_2.png\" alt=\"Example 2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repository and enter it:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/dbolya/yolact.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e yolact\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eSet up the environment using one of the following methods:\n\u003cul\u003e\n\u003cli\u003eUsing \u003ca href=\"https://www.anaconda.com/distribution/\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eRun \u003ccode\u003econda env create -f environment.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eManually with pip\n\u003cul\u003e\n\u003cli\u003eSet up a Python3 environment (e.g., using virtenv).\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"http://pytorch.org/\" rel=\"nofollow\"\u003ePytorch\u003c/a\u003e 1.0.1 (or higher) and TorchVision.\u003c/li\u003e\n\u003cli\u003eInstall some other packages:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Cython needs to be installed before pycocotools\u003c/span\u003e\npip install cython\npip install opencv-python pillow pycocotools matplotlib \u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to train YOLACT, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into \u003ccode\u003e./data/coco\u003c/code\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to evaluate YOLACT on \u003ccode\u003etest-dev\u003c/code\u003e, download \u003ccode\u003etest-dev\u003c/code\u003e with this script.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO_test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you want to use YOLACT++, compile deformable convolutional layers (from \u003ca href=\"https://github.com/CharlesShang/DCNv2/tree/pytorch_1.0\"\u003eDCNv2\u003c/a\u003e).\nMake sure you have the latest CUDA toolkit installed from \u003ca href=\"https://developer.nvidia.com/cuda-toolkit\" rel=\"nofollow\"\u003eNVidia\u0027s Website\u003c/a\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e external/DCNv2\npython setup.py build develop\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-evaluation\" class=\"anchor\" href=\"#evaluation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvaluation\u003c/h1\u003e\n\u003cp\u003eHere are our YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on \u003ccode\u003etest-dev\u003c/code\u003e:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1yp7ZbbDwvMiFJEq4ptVKTYTI2VeRDXl0/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EUVpxoSXaqNIlssoLKOEoCcB1m0RpzGq_Khp5n1VX3zcUw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDarknet53-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.7\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1dukLrTzZQEuhzitGkHaGjphlmRJOjVnP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_darknet53_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/ERrao26c8llJn25dIyZPhwMBxUp2GdZTKIMUQA3t0djHLw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e29.8\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf_/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EYRWxBEoKU9DiblrWx2M89MBGFkVVB_drlRd_v5sdT3Hgg\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e700\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e23.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e31.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1lE4Lz5p25teiXV-6HdTiOJSnS7u7GBzg/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_im700_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/Eagg5RSc5hFEhp7sPtvLNyoBjhlf2feog7t8OQzHKKphjw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYOLACT++ models (released on December 16th, 2019):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1ZPu1YR2UzGHQD0o1rEqy-j5bmEm3lbyP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EcJAtMiEFlhAnVsDf00yWRIBUC4m8iE9NEEiV05XwtEoGw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e27.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.6\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/15id0Qq5eqRbkD-N3ZjDZXdCvRyIaHpFB/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EVQ62sF0SrJPrl_68onyHF8BpG7c05A8PavV4a849sZgEA\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo evalute the model, put the corresponding weights file in the \u003ccode\u003e./weights\u003c/code\u003e directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., \u003ccode\u003eyolact_base\u003c/code\u003e for \u003ccode\u003eyolact_base_54_800000.pth\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quantitative-results-on-coco\" class=\"anchor\" href=\"#quantitative-results-on-coco\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuantitative Results on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This should get 29.92 validation mask mAP last time I checked.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Output a COCOEval json to submit to the website or to use the run_coco_eval.py script.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This command will create \u0027./results/bbox_detections.json\u0027 and \u0027./results/mask_detections.json\u0027 for detection and instance segmentation respectively.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py.\u003c/span\u003e\npython run_coco_eval.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-qualitative-results-on-coco\" class=\"anchor\" href=\"#qualitative-results-on-coco\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQualitative Results on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on COCO. From here on I\u0027ll use a confidence threshold of 0.15.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --display\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-benchmarking-on-coco\" class=\"anchor\" href=\"#benchmarking-on-coco\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarking on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run just the raw model on the first 1k images of the validation set\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark --max_images=1000\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-images\" class=\"anchor\" href=\"#images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on the specified image.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process an image and save it to another file.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a whole folder of images.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-video\" class=\"anchor\" href=\"#video\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVideo\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a video in real-time. \"--video_multiframe\" will process that many frames at once for improved performance.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you want, use \"--display_fps\" to draw the FPS directly on the frame.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a video and save it to another file. This uses the same pipeline as the ones above now, so it\u0027s fast!\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs you can tell, \u003ccode\u003eeval.py\u003c/code\u003e can do a ton of stuff. Run the \u003ccode\u003e--help\u003c/code\u003e command to see everything it can do.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython eval.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-training\" class=\"anchor\" href=\"#training\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining\u003c/h1\u003e\n\u003cp\u003eBy default, we train on COCO. Make sure to download the entire dataset using the commands above.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo train, grab an imagenet-pretrained model and put it in \u003ccode\u003e./weights\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eFor Resnet101, download \u003ccode\u003eresnet101_reducedfc.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1tvqFPd4bJtakOlmn-uIA492g2qurRChj/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Resnet50, download \u003ccode\u003eresnet50-19c8e357.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1Jy3yCdbatgXa5YYIdTCRrSV0S9V5g1rn/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Darknet53, download \u003ccode\u003edarknet53.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRun one of the training commands below.\n\u003cul\u003e\n\u003cli\u003eNote that you can press ctrl+c while training and it will save an \u003ccode\u003e*_interrupt.pth\u003c/code\u003e file at the current iteration.\u003c/li\u003e\n\u003cli\u003eAll weights are saved in the \u003ccode\u003e./weights\u003c/code\u003e directory by default with the file name \u003ccode\u003e\u0026lt;config\u0026gt;_\u0026lt;epoch\u0026gt;_\u0026lt;iter\u0026gt;.pth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains using the base config with a batch size of 8 (the default).\u003c/span\u003e\npython train.py --config=yolact_base_config\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.\u003c/span\u003e\npython train.py --config=yolact_base_config --batch_size=5\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file\u0027s name.\u003c/span\u003e\npython train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the help option to see a description of all available command line arguments\u003c/span\u003e\npython train.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-multi-gpu-support\" class=\"anchor\" href=\"#multi-gpu-support\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-GPU Support\u003c/h2\u003e\n\u003cp\u003eYOLACT now supports multiple GPUs seamlessly during training:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBefore running any of the scripts, run: \u003ccode\u003eexport CUDA_VISIBLE_DEVICES=[gpus]\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3).\u003c/li\u003e\n\u003cli\u003eYou should still do this if only using 1 GPU.\u003c/li\u003e\n\u003cli\u003eYou can check the indices of your GPUs with \u003ccode\u003envidia-smi\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThen, simply set the batch size to \u003ccode\u003e8*num_gpus\u003c/code\u003e with the training commands above. The training script will automatically scale the hyperparameters to the right values.\n\u003cul\u003e\n\u003cli\u003eIf you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you\u0027re using.\u003c/li\u003e\n\u003cli\u003eIf you want to allocate the images per GPU specific for different GPUs, you can use \u003ccode\u003e--batch_alloc=[alloc]\u003c/code\u003e where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to \u003ccode\u003ebatch_size\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-logging\" class=\"anchor\" href=\"#logging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogging\u003c/h2\u003e\n\u003cp\u003eYOLACT now logs training and validation information by default. You can disable this with \u003ccode\u003e--no_log\u003c/code\u003e. A guide on how to visualize these logs is coming soon, but now you can look at \u003ccode\u003eLogVizualizer\u003c/code\u003e in \u003ccode\u003eutils/logger.py\u003c/code\u003e for help.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pascal-sbd\" class=\"anchor\" href=\"#pascal-sbd\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePascal SBD\u003c/h2\u003e\n\u003cp\u003eWe also include a config for training on Pascal SBD annotations (for rapid experimentation or comparing with other methods). To train on Pascal SBD, proceed with the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the dataset from \u003ca href=\"http://home.bharathh.info/pubs/codes/SBD/download.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. It\u0027s the first link in the top \"Overview\" section (and the file is called \u003ccode\u003ebenchmark.tgz\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eExtract the dataset somewhere. In the dataset there should be a folder called \u003ccode\u003edataset/img\u003c/code\u003e. Create the directory \u003ccode\u003e./data/sbd\u003c/code\u003e (where \u003ccode\u003e.\u003c/code\u003e is YOLACT\u0027s root) and copy \u003ccode\u003edataset/img\u003c/code\u003e to \u003ccode\u003e./data/sbd/img\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDownload the COCO-style annotations from \u003ca href=\"https://drive.google.com/open?id=1ExrRSPVctHW8Nxrn0SofU1lVhK5Wn0_S\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eExtract the annotations into \u003ccode\u003e./data/sbd/\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eNow you can train using \u003ccode\u003e--config=yolact_resnet50_pascal_config\u003c/code\u003e. Check that config to see how to extend it to other models.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI will automate this all with a script soon, don\u0027t worry. Also, if you want the script I used to convert the annotations, I put it in \u003ccode\u003e./scripts/convert_sbd.py\u003c/code\u003e, but you\u0027ll have to check how it works to be able to use it because I don\u0027t actually remember at this point.\u003c/p\u003e\n\u003cp\u003eIf you want to verify our results, you can download our \u003ccode\u003eyolact_resnet50_pascal_config\u003c/code\u003e weights from \u003ca href=\"https://drive.google.com/open?id=1yLVwtkRtNxyl0kxeMCtPXJsXFFyc_FHe\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. This model should get 72.3 mask AP_50 and 56.2 mask AP_70. Note that the \"all\" AP isn\u0027t the same as the \"vol\" AP reported in others papers for pascal (they use an averages of the thresholds from \u003ccode\u003e0.1 - 0.9\u003c/code\u003e in increments of \u003ccode\u003e0.1\u003c/code\u003e instead of what COCO uses).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-custom-datasets\" class=\"anchor\" href=\"#custom-datasets\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustom Datasets\u003c/h2\u003e\n\u003cp\u003eYou can also train on your own dataset by following these steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a COCO-style Object Detection JSON annotation file for your dataset. The specification for this can be found \u003ca href=\"http://cocodataset.org/#format-data\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Note that we don\u0027t use some fields, so the following may be omitted:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003einfo\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eliscense\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eUnder \u003ccode\u003eimage\u003c/code\u003e: \u003ccode\u003elicense, flickr_url, coco_url, date_captured\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecategories\u003c/code\u003e (we use our own format for categories, see below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a definition for your dataset under \u003ccode\u003edataset_base\u003c/code\u003e in \u003ccode\u003edata/config.py\u003c/code\u003e (see the comments in \u003ccode\u003edataset_base\u003c/code\u003e for an explanation of each field):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003emy_custom_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset_base\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecopy\u003c/span\u003e({\n \u003cspan class=\"pl-s\"\u003e\u0027name\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027My Dataset\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027train_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027train_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027valid_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027valid_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027has_gt\u0027\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027class_names\u0027\u003c/span\u003e: (\u003cspan class=\"pl-s\"\u003e\u0027my_class_id_1\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_2\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_3\u0027\u003c/span\u003e, ...)\n})\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA couple things to note:\n\u003cul\u003e\n\u003cli\u003eClass IDs in the annotation file should start at 1 and increase sequentially on the order of \u003ccode\u003eclass_names\u003c/code\u003e. If this isn\u0027t the case for your annotation file (like in COCO), see the field \u003ccode\u003elabel_map\u003c/code\u003e in \u003ccode\u003edataset_base\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf you do not want to create a validation split, use the same image path and annotations file for validation. By default (see \u003ccode\u003epython train.py --help\u003c/code\u003e), \u003ccode\u003etrain.py\u003c/code\u003e will output validation mAP for the first 5000 images in the dataset every 2 epochs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFinally, in \u003ccode\u003eyolact_base_config\u003c/code\u003e in the same file, change the value for \u003ccode\u003e\u0027dataset\u0027\u003c/code\u003e to \u003ccode\u003e\u0027my_custom_dataset\u0027\u003c/code\u003e or whatever you named the config object above. Then you can use any of the training commands in the previous section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-creating-a-custom-dataset-from-scratch\" class=\"anchor\" href=\"#creating-a-custom-dataset-from-scratch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a Custom Dataset from Scratch\u003c/h4\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/dbolya/yolact/issues/70#issuecomment-504283008\"\u003ethis nice post by @Amit12690\u003c/a\u003e for tips on how to annotate a custom dataset and prepare it for use with YOLACT.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h1\u003e\n\u003cp\u003eIf you use YOLACT or this code base in your work, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{yolact-iccv2019,\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n title = {YOLACT: {Real-time} Instance Segmentation},\n booktitle = {ICCV},\n year = {2019},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor YOLACT++, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{yolact-plus-arxiv2019,\n title = {YOLACT++: Better Real-time Instance Segmentation},\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n year = {2019},\n eprint = {1912.06218},\n archivePrefix = {arXiv},\n primaryClass = {cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eFor questions about our paper or code, please contact \u003ca href=\"mailto:dbolya@ucdavis.edu\"\u003eDaniel Bolya\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8bd09664a4dca78a8f246d76f3af7fc6da719393b3f9c6cbc6a8b291b19f3d80/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f62696f62622d636d69702f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"\" data-canonical-src=\"https://readthedocs.org/projects/biobb-cmip/badge/?version=latest\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/biobb_cmip\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a477fdb1fd9bc9eb7ffa6cae6a019c6d4c3902fd468b3126f1b78e56c7dcff83/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://quay.io/repository/biocontainers/biobb_cmip\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca418e4db0b3de91a09a5df4a59446da015b6164598a8bc255918e911484f84f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d517561792e696f2d626c7565\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docker-Quay.io-blue\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-biobb_cmip\" class=\"anchor\" href=\"#biobb_cmip\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebiobb_cmip\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eBiobb_cmip is the Biobb module collection to compute classical molecular interaction potentials.\nBiobb (BioExcel building blocks) packages are Python building blocks that\ncreate new layer of compatibility and interoperability over popular\nbioinformatics tools.\nThe latest documentation of this package can be found in our readthedocs site:\n\u003ca href=\"http://biobb-cmip.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003elatest API documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-version\" class=\"anchor\" href=\"#version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h3\u003e\n\u003cp\u003ev3.7.5 2021.4\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eUsing PIP:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eImportant:\u003c/strong\u003e PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install \"biobb_cmip\u0026gt;=3.7.5\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: \u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing ANACONDA:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda install -c bioconda \"biobb_cmip\u0026gt;=3.7.5\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: With conda installation BioBBs can be used with the \u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e and the \u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing DOCKER:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull quay.io/biocontainers/biobb_cmip:3.7.5--pyhdfd78af_0\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run quay.io/biocontainers/biobb_cmip:3.7.5--pyhdfd78af_0 \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing SINGULARITY:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMacOS users\u003c/strong\u003e: it\u0027s strongly recommended to avoid Singularity and use \u003cstrong\u003eDocker\u003c/strong\u003e as containerization system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name biobb_cmip.sif shub://bioexcel/biobb_cmip\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec biobb_cmip.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe command list and specification can be found at the \u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" href=\"#copyright--licensing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2022 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2022 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 8, "topics": [], - "updated_at": 1639151360.0 + "updated_at": 1640095100.0 }, { "data_format": 2, @@ -12767,233 +12193,231 @@ var data = "data_format": 2, "description": null, "filenames": [ - "Singularity.latest" + "Singularity" ], - "full_name": "bioexcel/biobb_cmip", + "full_name": "cognirob/crow_vision_yolact", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8bd09664a4dca78a8f246d76f3af7fc6da719393b3f9c6cbc6a8b291b19f3d80/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f62696f62622d636d69702f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"\" data-canonical-src=\"https://readthedocs.org/projects/biobb-cmip/badge/?version=latest\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/biobb_cmip\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a477fdb1fd9bc9eb7ffa6cae6a019c6d4c3902fd468b3126f1b78e56c7dcff83/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://quay.io/repository/biocontainers/biobb_cmip\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca418e4db0b3de91a09a5df4a59446da015b6164598a8bc255918e911484f84f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d517561792e696f2d626c7565\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docker-Quay.io-blue\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-biobb_cmip\" class=\"anchor\" href=\"#biobb_cmip\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebiobb_cmip\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eBiobb_cmip is the Biobb module collection to compute classical molecular interaction potentials.\nBiobb (BioExcel building blocks) packages are Python building blocks that\ncreate new layer of compatibility and interoperability over popular\nbioinformatics tools.\nThe latest documentation of this package can be found in our readthedocs site:\n\u003ca href=\"http://biobb-cmip.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003elatest API documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-version\" class=\"anchor\" href=\"#version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h3\u003e\n\u003cp\u003ev3.7.5 2021.4\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eUsing PIP:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eImportant:\u003c/strong\u003e PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install \"biobb_cmip\u0026gt;=3.7.5\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: \u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing ANACONDA:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda install -c bioconda \"biobb_cmip\u0026gt;=3.7.5\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: With conda installation BioBBs can be used with the \u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e and the \u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing DOCKER:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull quay.io/biocontainers/biobb_cmip:3.7.5--pyhdfd78af_0\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run quay.io/biocontainers/biobb_cmip:3.7.5--pyhdfd78af_0 \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing SINGULARITY:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMacOS users\u003c/strong\u003e: it\u0027s strongly recommended to avoid Singularity and use \u003cstrong\u003eDocker\u003c/strong\u003e as containerization system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name biobb_cmip.sif shub://bioexcel/biobb_cmip\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec biobb_cmip.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe command list and specification can be found at the \u003ca href=\"https://biobb-cmip.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" href=\"#copyright--licensing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2022 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2022 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-you-only-look-at-coefficients\" class=\"anchor\" href=\"#you-only-look-at-coefficients\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eY\u003c/strong\u003eou \u003cstrong\u003eO\u003c/strong\u003enly \u003cstrong\u003eL\u003c/strong\u003eook \u003cstrong\u003eA\u003c/strong\u003et \u003cstrong\u003eC\u003c/strong\u003eoefficien\u003cstrong\u003eT\u003c/strong\u003es\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e \u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\n \u255a\u2588\u2588\u2557 \u2588\u2588\u2554\u255d\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u2550\u2588\u2588\u2554\u2550\u2550\u255d\n \u255a\u2588\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u255a\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2551 \n \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA simple, fully convolutional model for real-time instance segmentation. This is the code for our papers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1904.02689\" rel=\"nofollow\"\u003eYOLACT: Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003eYOLACT++: Better Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-yolact-v12-released-changelog\" class=\"anchor\" href=\"#yolact-v12-released-changelog\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYOLACT++ (v1.2) released! (\u003ca href=\"CHANGELOG.md\"\u003eChangelog\u003c/a\u003e)\u003c/h4\u003e\n\u003cp\u003eYOLACT++\u0027s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e (check out our journal paper \u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIn order to use YOLACT++, make sure you compile the DCNv2 code. (See \u003ca href=\"https://github.com/dbolya/yolact#installation\"\u003eInstallation\u003c/a\u003e)\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-for-a-real-time-demo-check-out-our-iccv-video\" class=\"anchor\" href=\"#for-a-real-time-demo-check-out-our-iccv-video\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor a real-time demo, check out our ICCV video:\u003c/h4\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=0pMfmo8qfpQ\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c9e061dab1a6754a417d6eb14803f1104cce54aa9231aa0d779e2523694ed23e/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f30704d666d6f38716670512f302e6a7067\" alt=\"IMAGE ALT TEXT HERE\" data-canonical-src=\"https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSome examples from our YOLACT base model (33.5 fps on a Titan Xp and 29.8 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e):\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"data/yolact_example_0.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"data/yolact_example_0.png\" alt=\"Example 0\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"data/yolact_example_1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"data/yolact_example_1.png\" alt=\"Example 1\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"data/yolact_example_2.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"data/yolact_example_2.png\" alt=\"Example 2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repository and enter it:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/dbolya/yolact.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e yolact\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eSet up the environment using one of the following methods:\n\u003cul\u003e\n\u003cli\u003eUsing \u003ca href=\"https://www.anaconda.com/distribution/\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eRun \u003ccode\u003econda env create -f environment.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eManually with pip\n\u003cul\u003e\n\u003cli\u003eSet up a Python3 environment (e.g., using virtenv).\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"http://pytorch.org/\" rel=\"nofollow\"\u003ePytorch\u003c/a\u003e 1.0.1 (or higher) and TorchVision.\u003c/li\u003e\n\u003cli\u003eInstall some other packages:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Cython needs to be installed before pycocotools\u003c/span\u003e\npip install cython\npip install opencv-python pillow pycocotools matplotlib \u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to train YOLACT, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into \u003ccode\u003e./data/coco\u003c/code\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to evaluate YOLACT on \u003ccode\u003etest-dev\u003c/code\u003e, download \u003ccode\u003etest-dev\u003c/code\u003e with this script.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO_test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you want to use YOLACT++, compile deformable convolutional layers (from \u003ca href=\"https://github.com/CharlesShang/DCNv2/tree/pytorch_1.0\"\u003eDCNv2\u003c/a\u003e).\nMake sure you have the latest CUDA toolkit installed from \u003ca href=\"https://developer.nvidia.com/cuda-toolkit\" rel=\"nofollow\"\u003eNVidia\u0027s Website\u003c/a\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e external/DCNv2\npython setup.py build develop\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-evaluation\" class=\"anchor\" href=\"#evaluation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvaluation\u003c/h1\u003e\n\u003cp\u003eHere are our YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on \u003ccode\u003etest-dev\u003c/code\u003e:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1yp7ZbbDwvMiFJEq4ptVKTYTI2VeRDXl0/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EUVpxoSXaqNIlssoLKOEoCcB1m0RpzGq_Khp5n1VX3zcUw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDarknet53-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.7\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1dukLrTzZQEuhzitGkHaGjphlmRJOjVnP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_darknet53_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/ERrao26c8llJn25dIyZPhwMBxUp2GdZTKIMUQA3t0djHLw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e29.8\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf_/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EYRWxBEoKU9DiblrWx2M89MBGFkVVB_drlRd_v5sdT3Hgg\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e700\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e23.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e31.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1lE4Lz5p25teiXV-6HdTiOJSnS7u7GBzg/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_im700_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/Eagg5RSc5hFEhp7sPtvLNyoBjhlf2feog7t8OQzHKKphjw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYOLACT++ models (released on December 16th, 2019):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1ZPu1YR2UzGHQD0o1rEqy-j5bmEm3lbyP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EcJAtMiEFlhAnVsDf00yWRIBUC4m8iE9NEEiV05XwtEoGw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e27.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.6\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/15id0Qq5eqRbkD-N3ZjDZXdCvRyIaHpFB/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EVQ62sF0SrJPrl_68onyHF8BpG7c05A8PavV4a849sZgEA\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo evalute the model, put the corresponding weights file in the \u003ccode\u003e./weights\u003c/code\u003e directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., \u003ccode\u003eyolact_base\u003c/code\u003e for \u003ccode\u003eyolact_base_54_800000.pth\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quantitative-results-on-coco\" class=\"anchor\" href=\"#quantitative-results-on-coco\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuantitative Results on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This should get 29.92 validation mask mAP last time I checked.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Output a COCOEval json to submit to the website or to use the run_coco_eval.py script.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This command will create \u0027./results/bbox_detections.json\u0027 and \u0027./results/mask_detections.json\u0027 for detection and instance segmentation respectively.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py.\u003c/span\u003e\npython run_coco_eval.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-qualitative-results-on-coco\" class=\"anchor\" href=\"#qualitative-results-on-coco\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQualitative Results on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on COCO. From here on I\u0027ll use a confidence threshold of 0.15.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --display\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-benchmarking-on-coco\" class=\"anchor\" href=\"#benchmarking-on-coco\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarking on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run just the raw model on the first 1k images of the validation set\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark --max_images=1000\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-images\" class=\"anchor\" href=\"#images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on the specified image.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process an image and save it to another file.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a whole folder of images.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-video\" class=\"anchor\" href=\"#video\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVideo\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a video in real-time. \"--video_multiframe\" will process that many frames at once for improved performance.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you want, use \"--display_fps\" to draw the FPS directly on the frame.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a video and save it to another file. This uses the same pipeline as the ones above now, so it\u0027s fast!\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs you can tell, \u003ccode\u003eeval.py\u003c/code\u003e can do a ton of stuff. Run the \u003ccode\u003e--help\u003c/code\u003e command to see everything it can do.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython eval.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-training\" class=\"anchor\" href=\"#training\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining\u003c/h1\u003e\n\u003cp\u003eBy default, we train on COCO. Make sure to download the entire dataset using the commands above.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo train, grab an imagenet-pretrained model and put it in \u003ccode\u003e./weights\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eFor Resnet101, download \u003ccode\u003eresnet101_reducedfc.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1tvqFPd4bJtakOlmn-uIA492g2qurRChj/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Resnet50, download \u003ccode\u003eresnet50-19c8e357.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1Jy3yCdbatgXa5YYIdTCRrSV0S9V5g1rn/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Darknet53, download \u003ccode\u003edarknet53.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRun one of the training commands below.\n\u003cul\u003e\n\u003cli\u003eNote that you can press ctrl+c while training and it will save an \u003ccode\u003e*_interrupt.pth\u003c/code\u003e file at the current iteration.\u003c/li\u003e\n\u003cli\u003eAll weights are saved in the \u003ccode\u003e./weights\u003c/code\u003e directory by default with the file name \u003ccode\u003e\u0026lt;config\u0026gt;_\u0026lt;epoch\u0026gt;_\u0026lt;iter\u0026gt;.pth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains using the base config with a batch size of 8 (the default).\u003c/span\u003e\npython train.py --config=yolact_base_config\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.\u003c/span\u003e\npython train.py --config=yolact_base_config --batch_size=5\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file\u0027s name.\u003c/span\u003e\npython train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the help option to see a description of all available command line arguments\u003c/span\u003e\npython train.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-multi-gpu-support\" class=\"anchor\" href=\"#multi-gpu-support\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-GPU Support\u003c/h2\u003e\n\u003cp\u003eYOLACT now supports multiple GPUs seamlessly during training:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBefore running any of the scripts, run: \u003ccode\u003eexport CUDA_VISIBLE_DEVICES=[gpus]\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3).\u003c/li\u003e\n\u003cli\u003eYou should still do this if only using 1 GPU.\u003c/li\u003e\n\u003cli\u003eYou can check the indices of your GPUs with \u003ccode\u003envidia-smi\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThen, simply set the batch size to \u003ccode\u003e8*num_gpus\u003c/code\u003e with the training commands above. The training script will automatically scale the hyperparameters to the right values.\n\u003cul\u003e\n\u003cli\u003eIf you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you\u0027re using.\u003c/li\u003e\n\u003cli\u003eIf you want to allocate the images per GPU specific for different GPUs, you can use \u003ccode\u003e--batch_alloc=[alloc]\u003c/code\u003e where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to \u003ccode\u003ebatch_size\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-logging\" class=\"anchor\" href=\"#logging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogging\u003c/h2\u003e\n\u003cp\u003eYOLACT now logs training and validation information by default. You can disable this with \u003ccode\u003e--no_log\u003c/code\u003e. A guide on how to visualize these logs is coming soon, but now you can look at \u003ccode\u003eLogVizualizer\u003c/code\u003e in \u003ccode\u003eutils/logger.py\u003c/code\u003e for help.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pascal-sbd\" class=\"anchor\" href=\"#pascal-sbd\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePascal SBD\u003c/h2\u003e\n\u003cp\u003eWe also include a config for training on Pascal SBD annotations (for rapid experimentation or comparing with other methods). To train on Pascal SBD, proceed with the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the dataset from \u003ca href=\"http://home.bharathh.info/pubs/codes/SBD/download.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. It\u0027s the first link in the top \"Overview\" section (and the file is called \u003ccode\u003ebenchmark.tgz\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eExtract the dataset somewhere. In the dataset there should be a folder called \u003ccode\u003edataset/img\u003c/code\u003e. Create the directory \u003ccode\u003e./data/sbd\u003c/code\u003e (where \u003ccode\u003e.\u003c/code\u003e is YOLACT\u0027s root) and copy \u003ccode\u003edataset/img\u003c/code\u003e to \u003ccode\u003e./data/sbd/img\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDownload the COCO-style annotations from \u003ca href=\"https://drive.google.com/open?id=1ExrRSPVctHW8Nxrn0SofU1lVhK5Wn0_S\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eExtract the annotations into \u003ccode\u003e./data/sbd/\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eNow you can train using \u003ccode\u003e--config=yolact_resnet50_pascal_config\u003c/code\u003e. Check that config to see how to extend it to other models.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI will automate this all with a script soon, don\u0027t worry. Also, if you want the script I used to convert the annotations, I put it in \u003ccode\u003e./scripts/convert_sbd.py\u003c/code\u003e, but you\u0027ll have to check how it works to be able to use it because I don\u0027t actually remember at this point.\u003c/p\u003e\n\u003cp\u003eIf you want to verify our results, you can download our \u003ccode\u003eyolact_resnet50_pascal_config\u003c/code\u003e weights from \u003ca href=\"https://drive.google.com/open?id=1yLVwtkRtNxyl0kxeMCtPXJsXFFyc_FHe\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. This model should get 72.3 mask AP_50 and 56.2 mask AP_70. Note that the \"all\" AP isn\u0027t the same as the \"vol\" AP reported in others papers for pascal (they use an averages of the thresholds from \u003ccode\u003e0.1 - 0.9\u003c/code\u003e in increments of \u003ccode\u003e0.1\u003c/code\u003e instead of what COCO uses).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-custom-datasets\" class=\"anchor\" href=\"#custom-datasets\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustom Datasets\u003c/h2\u003e\n\u003cp\u003eYou can also train on your own dataset by following these steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a COCO-style Object Detection JSON annotation file for your dataset. The specification for this can be found \u003ca href=\"http://cocodataset.org/#format-data\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Note that we don\u0027t use some fields, so the following may be omitted:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003einfo\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eliscense\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eUnder \u003ccode\u003eimage\u003c/code\u003e: \u003ccode\u003elicense, flickr_url, coco_url, date_captured\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecategories\u003c/code\u003e (we use our own format for categories, see below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a definition for your dataset under \u003ccode\u003edataset_base\u003c/code\u003e in \u003ccode\u003edata/config.py\u003c/code\u003e (see the comments in \u003ccode\u003edataset_base\u003c/code\u003e for an explanation of each field):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003emy_custom_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset_base\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecopy\u003c/span\u003e({\n \u003cspan class=\"pl-s\"\u003e\u0027name\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027My Dataset\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027train_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027train_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027valid_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027valid_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027has_gt\u0027\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027class_names\u0027\u003c/span\u003e: (\u003cspan class=\"pl-s\"\u003e\u0027my_class_id_1\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_2\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_3\u0027\u003c/span\u003e, ...)\n})\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA couple things to note:\n\u003cul\u003e\n\u003cli\u003eClass IDs in the annotation file should start at 1 and increase sequentially on the order of \u003ccode\u003eclass_names\u003c/code\u003e. If this isn\u0027t the case for your annotation file (like in COCO), see the field \u003ccode\u003elabel_map\u003c/code\u003e in \u003ccode\u003edataset_base\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf you do not want to create a validation split, use the same image path and annotations file for validation. By default (see \u003ccode\u003epython train.py --help\u003c/code\u003e), \u003ccode\u003etrain.py\u003c/code\u003e will output validation mAP for the first 5000 images in the dataset every 2 epochs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFinally, in \u003ccode\u003eyolact_base_config\u003c/code\u003e in the same file, change the value for \u003ccode\u003e\u0027dataset\u0027\u003c/code\u003e to \u003ccode\u003e\u0027my_custom_dataset\u0027\u003c/code\u003e or whatever you named the config object above. Then you can use any of the training commands in the previous section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-creating-a-custom-dataset-from-scratch\" class=\"anchor\" href=\"#creating-a-custom-dataset-from-scratch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a Custom Dataset from Scratch\u003c/h4\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/dbolya/yolact/issues/70#issuecomment-504283008\"\u003ethis nice post by @Amit12690\u003c/a\u003e for tips on how to annotate a custom dataset and prepare it for use with YOLACT.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h1\u003e\n\u003cp\u003eIf you use YOLACT or this code base in your work, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{yolact-iccv2019,\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n title = {YOLACT: {Real-time} Instance Segmentation},\n booktitle = {ICCV},\n year = {2019},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor YOLACT++, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{yolact-plus-arxiv2019,\n title = {YOLACT++: Better Real-time Instance Segmentation},\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n year = {2019},\n eprint = {1912.06218},\n archivePrefix = {arXiv},\n primaryClass = {cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eFor questions about our paper or code, please contact \u003ca href=\"mailto:dbolya@ucdavis.edu\"\u003eDaniel Bolya\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 1, "topics": [], - "updated_at": 1640095100.0 + "updated_at": 1639151360.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity/Singularity" + "Singularity" ], - "full_name": "YadavDosieah/FYP_Simulation", + "full_name": "truatpasteurdotfr/singularity-debian11-visualstudio", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-final-year-project\" class=\"anchor\" href=\"#final-year-project\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFinal-Year-Project\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-shepherding-and-object-clustering-using-collaborative-robots\" class=\"anchor\" href=\"#shepherding-and-object-clustering-using-collaborative-robots\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShepherding and Object Clustering using collaborative robots\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThe main code for the simulation can be found here: \u003ca href=\"https://github.com/YadavDosieah/FYP_Simulation/tree/master/enki/examples/playground\"\u003ehttps://github.com/YadavDosieah/FYP_Simulation/tree/master/enki/examples/playground\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eThe Singularity folder contains the definition file from which the Singularity container can be build\u003c/li\u003e\n\u003cli\u003eThe tracking folder contains the files used to implement the tracking system\u003c/li\u003e\n\u003cli\u003eThe MyProject folder contains the code used on the e-puck2 robot for the colour sensing\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependecies\" class=\"anchor\" href=\"#dependecies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependecies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eEnki\u003c/li\u003e\n\u003cli\u003elibcmaes\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-vstudio-on-debian11-toy-system-for-singularity-for-ci\" class=\"anchor\" href=\"#building-a-vstudio-on-debian11-toy-system-for-singularity-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a vstudio on debian11 toy system for singularity for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-debian11-visualstudio/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-debian11-visualstudio/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-debian11-visualstudio:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1640465576.0 + "updated_at": 1638371327.0 }, { "data_format": 2, - "description": "Test making a Singularity-HUB image for OpenFOAM", + "description": null, "filenames": [ - "Singularity" + "p9/Singularity", + "pytorch/Singularity" ], - "full_name": "TormodLandet/singularity-openfoam", + "full_name": "abergeron/bench", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-openfoam\" class=\"anchor\" href=\"#singularity-openfoam\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-openfoam\u003c/h1\u003e\n\u003cp\u003eA Singularity Hub image for OpenFOAM. Not official and probably not up to date\u003c/p\u003e\n\u003cp\u003eGithub added an Apache 2.0 license (at my choice), but feel free to use the contents of this repository under any license and however you want, this is just a Singularity image bootstrap description after all\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1501144397.0 + "updated_at": 1539805396.0 }, { "data_format": 2, - "description": null, + "description": "Cluster Pipeline Workflow", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "nickdelgrosso/crab_pipeline", + "full_name": "ftlin22/RNASeq_pipeline", "latest_release": null, - "readme": "\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e If using WSL and data is on a usb drive, \u003ca href=\"https://www.howtogeek.com/331053/how-to-mount-removable-drives-and-network-locations-in-the-windows-subsystem-for-linux/\" rel=\"nofollow\"\u003emount the drive on the filesystem\u003c/a\u003e first so you can access it:\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-launch-singularity-interactively\" class=\"anchor\" href=\"#launch-singularity-interactively\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch Singularity Interactively\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-if-building-a-full-sandbox-so-you-can-pip-install-during-a-session-try-out-applications-etc\" class=\"anchor\" href=\"#if-building-a-full-sandbox-so-you-can-pip-install-during-a-session-try-out-applications-etc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf building a full sandbox (so you can pip install during a session, try out applications, etc)\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --sandbox -F Singularity.sif Singularity.def\nsingularity shell --writable --bind /path/to/videos:/data/raw Singularity.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-if-just-runing-code-insteractively-or-already-have-the-container-built\" class=\"anchor\" href=\"#if-just-runing-code-insteractively-or-already-have-the-container-built\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf just runing code insteractively, or already have the container built\u003c/h3\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build\" class=\"anchor\" href=\"#build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --sandbox -F Singularity.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-run\" class=\"anchor\" href=\"#run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --bind /path/to/videos:/data/raw Singularity.sif\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-launch-jupyter-lab\" class=\"anchor\" href=\"#launch-jupyter-lab\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch Jupyter Lab\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app jupyter Singularity.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-singularity-38\" class=\"anchor\" href=\"#installing-singularity-38\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling Singularity 3.8\u003c/h2\u003e\n\u003cp\u003eThese were the best instructions!\n\u003ca href=\"https://github.com/apptainer/singularity/blob/master/INSTALL.md\"\u003ehttps://github.com/apptainer/singularity/blob/master/INSTALL.md\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-docs\" class=\"anchor\" href=\"#docs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocs\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.8/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.8/user-guide/\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-rnaseq_pipeline\" class=\"anchor\" href=\"#rnaseq_pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRNASeq_pipeline\u003c/h1\u003e\n\u003cp\u003eCluster Pipeline Workflow\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1642525381.0 + "updated_at": 1638035482.0 }, { "data_format": 2, - "description": "Run Rstudio-server with singularity instance", + "description": "Original version from: http://gki.informatik.uni-freiburg.de/tools/tfd/", "filenames": [ - "Singularity.Rstudio" + "Singularity.0.4", + "Singularity" ], - "full_name": "edg1983/RStudio_server_singularity", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-run-rstudio-server-with-singularity-instance\" class=\"anchor\" href=\"#run-rstudio-server-with-singularity-instance\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Rstudio-server with singularity instance\u003c/h1\u003e\n\u003cp\u003eUsing these instructions you can run rstudio server within a singulatiry instance\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" href=\"#build-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild singularity image\u003c/h2\u003e\n\u003cp\u003eThe recipe is built with R 4.0 and r studio v1.4\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build rstudio_v1.4.sif Singularity.Rstudio\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-before-you-start\" class=\"anchor\" href=\"#before-you-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBefore you start\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1set-up-library-locations\" class=\"anchor\" href=\"#1set-up-library-locations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.Set up library locations\u003c/h3\u003e\n\u003cp\u003eAll R libraries will be installed in \u003ccode\u003e/well/brc/R_pkg/$USER\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSubfolders will be created automatically according to R version and cpu architecture so that everything stay in place and you can run correctly compiled packages according to your environment (humbug and rescomp nodes have different architectures). This means that you need to install a package for each specific environment.\u003c/p\u003e\n\u003cp\u003eThis is managed by the \u003ccode\u003eRpofile\u003c/code\u003e file\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-set-up-your-r-profile\" class=\"anchor\" href=\"#set-up-your-r-profile\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet up your R profile\u003c/h4\u003e\n\u003cp\u003eCopy the \u003ccode\u003eRprofile\u003c/code\u003e file to \u003ccode\u003e$HOME/.Rprofile\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-create-an-r-session-folder\" class=\"anchor\" href=\"#2-create-an-r-session-folder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Create an R session folder\u003c/h3\u003e\n\u003cp\u003eDuring execution the instance will create R session files. You need to create a folder where yu have access to to store these files and then bind this to the Rsession folder in the image.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-rserver\" class=\"anchor\" href=\"#run-rserver\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun rserver\u003c/h2\u003e\n\u003cp\u003eModify the variables in \u003ccode\u003estart_rstudio_instance.sh\u003c/code\u003e according to your needs and run the script. Access is secured by password you can set changing the \u003ccode\u003ePASSWORD\u003c/code\u003e variable in the script.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNB.\u003c/strong\u003e Remember to add relevant paths to the bind argument in the script WITHOUT touching the default ones. All paths you need to acces from R server must be added to \u003ccode\u003e--bind\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eDefault settings:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eaddress: 127.0.0.1\u003c/li\u003e\n\u003cli\u003eport: 9997\u003c/li\u003e\n\u003cli\u003eRsession.conf file: set rsession timeout to zero to avoid writing temp session files\u003c/li\u003e\n\u003cli\u003eRsession dir: /well/brc/Rstudio_server/$USER\u003c/li\u003e\n\u003cli\u003eRstudio session folders creaded in \u003ccode\u003e$Rsession_dir\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "roveri-marco/tfd", + "latest_release": "0.4", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-temporalfastdownward\" class=\"anchor\" href=\"#temporalfastdownward\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemporalFastDownward\u003c/h1\u003e\n\u003cp\u003eThe version of Temporal Fast Downward.\nThis version has been ported to Python3 (tested with Python3.8)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-information\" class=\"anchor\" href=\"#information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInformation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://gki.informatik.uni-freiburg.de/tools/tfd/\" rel=\"nofollow\"\u003ehttp://gki.informatik.uni-freiburg.de/tools/tfd/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e$ ./build\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003ePatrick Eyerich, Robert Mattm\u00fcller and Gabriele R\u00f6ger.\nUsing the Context-enhanced Additive Heuristic for Temporal and Numeric Planning.\nIn Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS 2009), 2009.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1642779639.0 + "updated_at": 1638481913.0 }, { "data_format": 2, - "description": null, + "description": "PPX protocols for open malaria", "filenames": [ - "enricher/tests/resources/Singularity.enrichment" + "src/code/Singularity" ], - "full_name": "JEstabrook/regulon-enrichment", - "latest_release": "v0.3.1a", - "readme": "\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/4fdbdfa64306659366ce76f0240a36fb0dc5e510b28870cad546175d0030658d/68747470733a2f2f7472617669732d63692e636f6d2f4a4573746162726f6f6b2f726567756c6f6e2d656e726963686d656e742e7376673f746f6b656e3d5a52445742576539735843697650314e725a7771266272616e63683d6d6173746572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4fdbdfa64306659366ce76f0240a36fb0dc5e510b28870cad546175d0030658d/68747470733a2f2f7472617669732d63692e636f6d2f4a4573746162726f6f6b2f726567756c6f6e2d656e726963686d656e742e7376673f746f6b656e3d5a52445742576539735843697650314e725a7771266272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/JEstabrook/regulon-enrichment.svg?token=ZRDWBWe9sXCivP1NrZwq\u0026amp;branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://www.python.org/downloads/release/python-367\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75b8738e1bdfe8a832711925abbc3bd449c1e7e9260c870153ec761cad8dde40/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e362b2d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/python-3.6+-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/1d48578e1082d434280fe2299efd72bcf1cba5658c8ad707406fd29a0a1a4193/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6e7269676874677265656e2e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1d48578e1082d434280fe2299efd72bcf1cba5658c8ad707406fd29a0a1a4193/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6e7269676874677265656e2e737667\" alt=\"t\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-nrightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/8416ebae4490c8fa309b7d13e3a8565f3da2ea34ce1eb8a905ce2c7195dddd63/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7374617475732d737461626c652d6e7269676874677265656e2e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8416ebae4490c8fa309b7d13e3a8565f3da2ea34ce1eb8a905ce2c7195dddd63/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7374617475732d737461626c652d6e7269676874677265656e2e737667\" alt=\"t\" data-canonical-src=\"https://img.shields.io/badge/status-stable-nrightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/31c454bdf9d517bfbf4d3eed1c768f1853de782076f85ce05b681d0017bfa7f4/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3137393735323035392e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31c454bdf9d517bfbf4d3eed1c768f1853de782076f85ce05b681d0017bfa7f4/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3137393735323035392e737667\" alt=\"t\" data-canonical-src=\"https://zenodo.org/badge/179752059.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-enrich\" class=\"anchor\" href=\"#enrich\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnrich\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eregulon-enrichment\u003c/strong\u003e is a Python module used to predict the activity of regulatory proteins from RNAseq data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eregulon-enrichment\u003c/em\u003e submodules:\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-enricherfeatures\" class=\"anchor\" href=\"#enricherfeatures\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eenricher.features\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eLoad -omic datasets\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-enricherregulon\" class=\"anchor\" href=\"#enricherregulon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eenricher.regulon\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eRegulon utilities\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eregulon-enrichment\u003c/strong\u003e requires:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- Python (\u0026gt;= 3.6)\n- scikit-learn (\u0026gt;= 0.21.3)\n- NumPy (\u0026gt;= 1.17.3)\n- SciPy (\u0026gt;= 1.3.1)\n- pandas (\u0026gt;= 0.25.3)\n- tqdm (\u0026gt;= 4.38.0)\n- dill (\u0026gt;= 0.3.1.1)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-user-installation\" class=\"anchor\" href=\"#user-installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser installation\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e\nIf you already have a working installation of numpy and scipy,\nthe easiest way to install regulon-enrichment is using ``conda`` ::\n\n conda install -c estabroj89 regulon-enrichment\n\nor ``pip``::\n\n pip install regulon-enrichment==0.0.2b0\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-overview\" class=\"anchor\" href=\"#overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h1\u003e\n\u003cp\u003eThis method leverages pathway information and gene expression data to produce regulon-based protein activity scores.\nOur method tests for positional shifts in experimental-evidence supported networks consisting of transcription factors\nand their downstream signaling pathways when projected onto a rank-sorted gene-expression signature.\u003c/p\u003e\n\u003cp\u003eThis regulon enrichment method utilizes pathway and molecular interactions and mechanisms available through Pathway\nCommons to accurately infer aberrant transcription factor activity from gene expression data.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-regulon-enrichment\" class=\"anchor\" href=\"#running-regulon-enrichment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning regulon-enrichment\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-invoking-enrich-from-the-command-line\" class=\"anchor\" href=\"#invoking-enrich-from-the-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInvoking enrich from the command line\u003c/h2\u003e\n\u003cp\u003eWhen installing the regulon-enrichment package, the set of scripts that make up to inteface to regulon-enrichment will\nautomatically be placed as an executables in your path, so that you can refer to these without modifying your shell\nenvironment. For example, if you install regulon-enrichment using conda, then enrich will become available on the path,\nand can be run as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eenrich\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-enrich-parameters\" class=\"anchor\" href=\"#enrich-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnrich parameters\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-required-parameters\" class=\"anchor\" href=\"#required-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired parameters\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecohort\u003c/code\u003e : which cohort to use; this information will be retained in the serialized Enrichment class\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eexpr\u003c/code\u003e : which tab delimited expression matrix to use shape : \u003ccode\u003e[n_features, n_samples]\u003c/code\u003e, units : \u003ccode\u003eTPM, RPKM\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eout_dir\u003c/code\u003e : output directory - directory serialized Enrichment object and enrichment.tsv will be saved to\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-optional-parameters\" class=\"anchor\" href=\"#optional-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional parameters\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eregulon\u003c/code\u003e : optional regulon containing weight interactions between regulator and\ndownstream members of its regulon shape : \u003ccode\u003e[len(Target), [\u0027Regulator\u0027,\u0027Target\u0027,\u0027MoA\u0027,\u0027likelihood\u0027]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eregulon_size\u003c/code\u003e : number of downstream interactions required for a given regulator in order to calculate enrichment score \u003ccode\u003edefault=15\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esec_intx\u003c/code\u003e : path to pre-compiled serialized secondary interaction network, \u003ccode\u003edefault=secondary_intx_regulon.pkl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escaler_type\u003c/code\u003e : scaler to normalized features/samples by: \u003ccode\u003estandard | robust | minmax | quant\u003c/code\u003e, default=\u003ccode\u003erobust\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ethresh_filter\u003c/code\u003e : Prior to normalization remove features that have a standard deviation per feature less than \u003ccode\u003e{thresh_filter}\u003c/code\u003e, \u003ccode\u003edefault=0.1\u003c/code\u003e)\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-computing-regulon-enrichment-scores\" class=\"anchor\" href=\"#computing-regulon-enrichment-scores\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComputing regulon enrichment scores\u003c/h1\u003e\n\u003cp\u003eTo quantify the regulon enrichment for a given dataset, the command line script \u003ccode\u003eenrich\u003c/code\u003e is used.\u003c/p\u003e\n\u003cp\u003eUse --help argument to view options\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eEnrich requires three positional arguments: \u003ccode\u003ecohort\u003c/code\u003e,\u003ccode\u003eexpr\u003c/code\u003e, \u003ccode\u003eout_dir\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich cohort expr out_dir [regulon] [regulon_size] [sec_intx] [scaler_type] [thresh_filter] \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt is recommended to run enrich with the default parameters.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich test tests/resources/test_expr.tsv test_enrichment_scores\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe command above will generate enrichment scores for the unittest dataset \u003ccode\u003etest_expr.tsv\u003c/code\u003e and will generate and store the output under \u003ccode\u003etest_enrichment_scores/\u003c/code\u003e. In this directory \u003ccode\u003etest_enrichment_scores/\u003c/code\u003e, both the serialized Enrichment object \u003ccode\u003etest_enrichment.pkl\u003c/code\u003e and a tsv of the enrichment scores,\u003ccode\u003etest_regulon_enrichment.tsv\u003c/code\u003e will be found.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eenrichment.tsv\u003c/code\u003e file be shaped : \u003ccode\u003e[n_samples, n_regulators]\u003c/code\u003e, where \u003ccode\u003en_samples\u003c/code\u003e refers to the original number of samples provided in \u003ccode\u003eexpr\u003c/code\u003e, while \u003ccode\u003en_regulators\u003c/code\u003e will be determined based on the overlapping features present in the \u003ccode\u003eexpr\u003c/code\u003e dataset and the \u003ccode\u003eregulon_size\u003c/code\u003e parameter.\u003c/p\u003e\n", + "full_name": "bayesianbrad/openmalaria_probprog", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-todo\" class=\"anchor\" href=\"#todo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etodo\u003c/h1\u003e\n\u003cp\u003eAdd notes to all the .cpp files that we modify, to state exactly\nwhat we modified.\u003c/p\u003e\n\u003cp\u003eopenmalaria dependencies (Ubuntu):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eapt packages: xsdcxx libxerces-c-dev libgsl-dev libboost-all-dev\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHow to build wiki locally:\u003c/p\u003e\n\u003cp\u003eFirst download Gollum:\u003c/p\u003e\n\u003cp\u003eOn mac:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esudo gem install gollum\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-inputoutputs-to-the-model\" class=\"anchor\" href=\"#inputoutputs-to-the-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput/Outputs to the model\u003c/h1\u003e\n\u003cp\u003eWhat can we designate as input/output:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInputs\n\u003cul\u003e\n\u003cli\u003eMosquito nets\u003c/li\u003e\n\u003cli\u003eVaccination, type of vaccination\u003c/li\u003e\n\u003cli\u003eProphylactic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOutputs\n\u003cul\u003e\n\u003cli\u003e\"Survey measures\"\n\u003cul\u003e\n\u003cli\u003enHost\u003c/li\u003e\n\u003cli\u003enPatent\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMortality rate\u003c/li\u003e\n\u003cli\u003eProbability of seeking medical help\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-how-to-build-docker-image\" class=\"anchor\" href=\"#how-to-build-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to build Docker image:\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003edocker build . -t openmalariapp\u003c/li\u003e\n\u003cli\u003edocker run --rm -it (for interactive usage, will remove the container from memory) (-it interactive attach to terminal)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo attach a local drive / folder use\n:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edocker run --rm -it -v $PWD:/home bradleygh/openmalariapp\nConnecting docker to the external file system:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHow to run Jupyter inside Docker:\u003c/p\u003e\n\u003cp\u003eFor linux\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edocker run --rm -it -v $PWD:/home --net=host bradleygh/openmalariapp\nrun the following inside the container: jupyter notebook --allow-root\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor Mac\u003c/p\u003e\n\u003cp\u003edocker run --rm -it -p 127.0.0.1:8889:8889 -v $PWD:/home gbaydin/openmalariapp jupyter notebook --port 8889 --allow-root --ip=0.0.0.0\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-creating-an-experiment\" class=\"anchor\" href=\"#creating-an-experiment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating an experiment\u003c/h1\u003e\n\u003cp\u003eCreate a directory in your local machine, for example called examples\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ecd /home\u003c/li\u003e\n\u003cli\u003emkdir examples\u003c/li\u003e\n\u003cli\u003ecd examples\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewithin the folder add the following files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003escenario_current.xsd\u003c/li\u003e\n\u003cli\u003e\u0026lt;name_of_the_scenario_you_want_to_run\u0026gt;.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdd the openmalaria executable to the folder to, i.e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ecp /code/openmalaria/build/openMalaria examples/openMalaria\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;add_any_input_csv_or_txt_files\u0026gt;\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-simulator-once-an-experiment-has-been-created\" class=\"anchor\" href=\"#running-the-simulator-once-an-experiment-has-been-created\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the simulator once an experiment has been created\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003ecd ./examples\u003c/li\u003e\n\u003cli\u003edocker run --rm -it -v $PWD:/home/examples bradleygh/openmalariapp\u003c/li\u003e\n\u003cli\u003ecd /home/examples/\u003c/li\u003e\n\u003cli\u003e./openMalaria -s \u0026lt;name_of_the_scenario_you_want_to_run\u0026gt;.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-debugging-by-modifying-code-outside-docker-but-running-inside\" class=\"anchor\" href=\"#debugging-by-modifying-code-outside-docker-but-running-inside\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebugging by modifying code outside Docker, but running inside\u003c/h1\u003e\n\u003cp\u003edocker run --rm -it --net=host -v $PWD/examples:/home/examples -v $PWD/code/openmalaria/:/code/openmalaria bradleygh/openmalariapp\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-when-building-the-prob-prog-version\" class=\"anchor\" href=\"#when-building-the-prob-prog-version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhen building the prob prog version\u003c/h1\u003e\n\u003cp\u003eWhen openMalaria is being built it is actively looking for the current version of schema, in this case the schema\nversion is 39.0, If the main directory name is not called \"openmalaria_schema_\u0026lt;version_number\u0026gt; then the code will fail to build.\nIn addition to this, as specified by the openMalaria readme, you will have to change\nall the relevant places in the script where schema number appears before a build.\nSeems very inefficient, but that is the way in whcih the simulator is set up.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-om-simulator-with-pyprob\" class=\"anchor\" href=\"#running-om-simulator-with-pyprob\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning OM simulator with Pyprob\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-mac\" class=\"anchor\" href=\"#mac\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMac\u003c/h1\u003e\n\u003cp\u003edocker run --rm -it -p 2345:2345 -v $PWD/examples:/home/examples -v $PWD/code/openmalaria/:/code/openmalaria bradleygh/openmalariapp\u003c/p\u003e\n\u003cp\u003eadd when calling ./openmalaria\n$ ./openMalaria tcp://*:2345\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-linux\" class=\"anchor\" href=\"#linux\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinux\u003c/h1\u003e\n\u003cp\u003edocker run --rm -it -v $PWD/examples:/home/examples -v $PWD/code/openmalaria/:/code/openmalaria bradleygh/openmalariapp\u003c/p\u003e\n\u003cp\u003eadd when calling ./openmalaria\n$ ./openMalaria ipc://@\u0026lt;some_string\u0026gt;\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-using-singluarity-instead\" class=\"anchor\" href=\"#using-singluarity-instead\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singluarity instead\u003c/h1\u003e\n\u003cp\u003eTo convert a dockerfile to singularityfile run:\u003c/p\u003e\n\u003cp\u003epip install singularity\u003c/p\u003e\n\u003cp\u003eThen in the terminal / commmand line run:\u003c/p\u003e\n\u003cp\u003espython recipe Dockerfile \u0026gt;\u0026gt; Singularity\u003c/p\u003e\n\u003cp\u003eThis will convert the Dockerfile to a singularity file and save the output as Singularity.\u003c/p\u003e\n\u003cp\u003eWe can also make use of pre-built docker containers, without having to install docker, by running\nthe following:\u003c/p\u003e\n\u003cp\u003esingularity pull docker://bradleygh:openmalariapp\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1640212972.0 + "updated_at": 1637861862.0 }, { "data_format": 2, - "description": "ENHSP Containers. This contains singularity recipes for ENHSP. ENHSP-18, ENHSP-19 and ENHSP20. More details can be found at https://sites.google.com/view/enhsp/", + "description": null, "filenames": [ - "2018/Singularity.2018", - "latest/Singularity", - "2019/Singularity.2019", - "2020/Singularity.2020" + "Singularity.lbfs" ], - "full_name": "hstairs/enhsp-containers", + "full_name": "ab649964207/fs", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-enhsp-containers\" class=\"anchor\" href=\"#enhsp-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eENHSP Containers\u003c/h1\u003e\n\u003cp\u003eThis repository contains singularity recipes for ENHSP, the Expressive Numeric Heuristic Search Planner. ENHSP is an automated planning engine focused at solving planning problems with numeric state variables.\u003c/p\u003e\n\u003cp\u003eThe repository provides three versions of ENHSP, 2018, 2019 and 2020. These versions are described at \u003ca href=\"https://sites.google.com/view/enhsp/\" rel=\"nofollow\"\u003ehttps://sites.google.com/view/enhsp/\u003c/a\u003e as enhsp-18, enhsp-19, enhsp-20.\nSource code of all versions can be downloaded at: \u003ca href=\"https://gitlab.com/enricos83/ENHSP-Public\" rel=\"nofollow\"\u003ehttps://gitlab.com/enricos83/ENHSP-Public\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-the-fs-functional-strips-planner\" class=\"anchor\" href=\"#the-fs-functional-strips-planner\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe FS Functional STRIPS planner\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eFS\u003c/code\u003e is a classical planner that works with the Functional STRIPS planning language \u003ca href=\"#ref-geffner-fstrips-2000\"\u003e[Geffner, 2000]\u003c/a\u003e,\na modeling language based on the quantifier-free\nfragment of first-order logic that includes constant, function and predicate symbols, but no variable symbols. The increased expressiveness\nof the Functional STRIPS language with respect to propositional languages such as standard STRIPS (which is indeed subsumed by Functional STRIPS)\noften results in problem encodings which are more compact, more readable, have fewer ground actions\nand preserve the structural properties of the problem in a manner which allows the derivation of more effective heuristics.\u003c/p\u003e\n\u003cp\u003eAlong with the core of the Functional STRIPS language, the \u003ccode\u003eFS\u003c/code\u003e planner supports certain extensions which are useful both\nfrom the expressive \u003cem\u003eand\u003c/em\u003e the computational point of view. These include \u003cem\u003eexistential quantification\u003c/em\u003e,\n\u003cem\u003estate constraints\u003c/em\u003e, a fairly large library of \u003cem\u003eglobal constraints\u003c/em\u003e, and the possibility of using \u003cem\u003eexternally-defined symbols\u003c/em\u003e\nand \u003cem\u003ebuilt-in arithmetic symbols\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThis documentation covers a number of practical issues related to the use of the \u003ccode\u003eFS\u003c/code\u003e planner. The planner, however, has\nbeen used and described in a number of academic publications that \u003ca href=\"http://gfrances.github.io/pubs/\" rel=\"nofollow\"\u003ecan be found here\u003c/a\u003e,\nthe most recent of which are \u003ca href=\"#ref-frances-modeling-2015\"\u003e[Franc\u00e8s and Geffner, 2015]\u003c/a\u003e and \u003ca href=\"#ref-frances-existential-2016\"\u003e[Franc\u00e8s and Geffner, 2016a]\u003c/a\u003e\nand \u003ca href=\"#ref-frances-effective-2016\"\u003e[Franc\u00e8s and Geffner, 2016b]\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe easiest way to use the planner is by \u003ca href=\"doc/installation.md\"\u003emanually compiling the planner source code\u003c/a\u003e.\nAlternatively, you can build and/or use a \u003ca href=\"doc/containers.md\"\u003eready-to-use image\u003c/a\u003e in some of the containerization solutions\nthat we support.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eYou can find a high-level overview of the planner usage options \u003ca href=\"doc/usage.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-credits\"\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003eFS\u003c/code\u003e planner is partially built upon the \u003ca href=\"http://www.lapkt.org\" rel=\"nofollow\"\u003eLightweight Automated Planning Toolkit\u003c/a\u003e\nand the PDDL parser from the \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003eFast Downward\u003c/a\u003e distribution.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-modeling-2015\"\u003eFranc\u00e8s, G., and Geffner, H. (2015)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2015-icaps-better-heuristics-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eModeling and Computation in Planning: Better Heuristics from More Expressive Languages\u003c/em\u003e\u003c/a\u003e, ICAPS 2015.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-existential-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016a)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-existential-quantification-planning-csp/\" rel=\"nofollow\"\u003e\u003cem\u003eE-STRIPS: Existential Quantification in Planning and Constraint Satisfaction\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-effective-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016b)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-effective-planning-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eEffective Planning with More Expressive Languages\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-geffner-fstrips-2000\"\u003eGeffner, H. (2000)\u003c/a\u003e,\n\u003ca href=\"http://www.tecn.upf.es/~hgeffner/\" rel=\"nofollow\"\u003e\u003cem\u003eFunctional STRIPS: A more flexible lan-\nguage for planning and problem solving\u003c/em\u003e\u003c/a\u003e.\nIn Minker, J., ed., Logic-Based Artificial Intelligence. Kluwer. 187\u2013205.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1635518248.0 + "updated_at": 1637831969.0 }, { "data_format": 2, - "description": null, + "description": "The preprocessing pipeline at ZHH", "filenames": [ - "Singularity" + "HCPPipeline/Singularity.unix" ], - "full_name": "jzhanghzau/thesis_docker", + "full_name": "argyelan/ZHHpipelines", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-zhhpipelines\" class=\"anchor\" href=\"#zhhpipelines\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eZHHpipelines\u003c/h1\u003e\n\u003cp\u003eThe preprocessing pipeline at ZHH\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-bids-conversion\" class=\"anchor\" href=\"#bids-conversion\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBIDS Conversion\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: dicom2bids.sh grid_num sess_num descr\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003egrid_num: subject identifier;\nsess_num: session identifier;\ndescr: study specificator (currently available: TMS)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: multiple_dicom2bids.sh info.csv\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003einfo.csv: a file with 3 columns, first subject identifier, second: session identifier, third: study type\nProgram goes over every line one by one and calls dicom2bids.sh\u003c/p\u003e\n\u003cp\u003etest 2 ....\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1635770574.0 + "updated_at": 1637793201.0 }, { "data_format": 2, - "description": "SCG collaboration with ETA on BEAM/Atlas project", + "description": null, "filenames": [ "Singularity" ], - "full_name": "lbnl-science-it/atlas", + "full_name": "truatpasteurdotfr/singularity-docker-fidle-gpu", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-atlas\" class=\"anchor\" href=\"#atlas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eatlas\u003c/h1\u003e\n\u003cp\u003eContainer with R and necessary packages to run BEAM/Atlas vehicle simulation.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-example-running-r-script-via-docker\" class=\"anchor\" href=\"#example-running-r-script-via-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample running R script via Docker\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /global/data/transportation/ATLAS/static/urbansim/\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# R script in home dir, bind mounted to container\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eXX docker run -v /global/data/transportation/ATLAS/static/urbansim:/global/data/transportation/ATLAS/static/urbansim -it --entrypoint=Rscript ghcr.io/lbnl-science-it/atlas:main /global/data/transportation/ATLAS/static/urbansim/model_application/Model_application_hima.R \u003c/span\u003e\ndocker run -v /global/data/transportation/ATLAS/static/urbansim:/mnt -it --entrypoint=Rscript ghcr.io/lbnl-science-it/atlas:main /mnt/model_application/Model_application_hima.R \ndocker run -v /global/data/transportation/ATLAS/static/urbansim_hima_apollo:/mnt -it --entrypoint=Rscript ghcr.io/lbnl-science-it/atlas:main /mnt/model_application/Model_application_hima.R \ndocker run -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e:/mnt -it --entrypoint=Rscript ghcr.io/lbnl-science-it/atlas:main /mnt/model_application/Model_application_hima.R\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# running a bash shell, can call R from there\u003c/span\u003e\ndocker run -it --entrypoint=bash ghcr.io/lbnl-science-it/atlas:main\ndocker run -v /global/data/transportation/ATLAS/static/urbansim_hima_apollo:/mnt -it --entrypoint=bash ghcr.io/lbnl-science-it/atlas:main \n root@0b5815f5b441:/\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e export R_LIBS=/usr/local/lib/R/site-library/\u003c/span\u003e\n root@0b5815f5b441:/\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Rscript /mnt/model_application/Model_application_hima.R\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-example-running-r-script-via-singularity\" class=\"anchor\" href=\"#example-running-r-script-via-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample running R script via Singularity\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e\ncd /global/data/transportation/ATLAS/static/urbansim/\n\nsingularity pull docker://ghcr.io/lbnl-science-it/atlas:main \nsingularity exec docker://ghcr.io/lbnl-science-it/atlas:main Rscript ./model_application/Model_application_hima.R \n\n// other things to try for debug use\nsingularity shell docker://ghcr.io/lbnl-science-it/atlas:main # get bash prompt, can call R afterward\nsingularity run docker://ghcr.io/lbnl-science-it/atlas:main # get R prompt\n\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-container-for-fidle-httpsgricad-gitlabuniv-grenoble-alpesfrtalksfidle-with-a-gpu\" class=\"anchor\" href=\"#building-container-for-fidle-httpsgricad-gitlabuniv-grenoble-alpesfrtalksfidle-with-a-gpu\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding container for fidle (\u003ca href=\"https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle\" rel=\"nofollow\"\u003ehttps://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle\u003c/a\u003e) with a gpu\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image from dockerhub registry and push to ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003cli\u003eOnly cover the software installation for gpu (\u003ca href=\"https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/Linux%20installation%20using%20conda\" rel=\"nofollow\"\u003ehttps://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/Linux%20installation%20using%20conda\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-fidle-gpu/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-fidle-gpu/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-fidle-gpu:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-fidle-gpu:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, - "topics": [ - "modeling" - ], - "updated_at": 1642141776.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1637710228.0 }, { "data_format": 2, - "description": "A monitor of resources", + "description": null, "filenames": [ - "1.0.20/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-btop", + "full_name": "truatpasteurdotfr/singularity-docker-fidle", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-btop/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-btop/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-btop/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-btop/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c5457f5a651b024fa69983d39c277473e069e5594ee409e53b44e391c6c15b39/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c5457f5a651b024fa69983d39c277473e069e5594ee409e53b44e391c6c15b39/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62746f70\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-btop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fed939c101ca243e5b6b049eb99d6125bc1a94e6772a69e3d1e61c7bc1dd401e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fed939c101ca243e5b6b049eb99d6125bc1a94e6772a69e3d1e61c7bc1dd401e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62746f70\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-btop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/eb5608fc1c3e2f7a3a19f5f7d22fadf1ee0714cb6c0c9d9cdc2dfd9589202a2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb5608fc1c3e2f7a3a19f5f7d22fadf1ee0714cb6c0c9d9cdc2dfd9589202a2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62746f70\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-btop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7edba1cf64e407b9a9f9fd1d76709a70797be17efa4ad0233df596ca5b65aab2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7edba1cf64e407b9a9f9fd1d76709a70797be17efa4ad0233df596ca5b65aab2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62746f70\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-btop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-btop\" class=\"anchor\" href=\"#singularity-btop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-btop\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/aristocratos/btop/raw/main/Img/logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/aristocratos/btop/raw/main/Img/logo.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/aristocratos/btop\"\u003ebtop\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebtop\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/btop/1.0.20\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/btop\u003c/code\u003e as \u003ccode\u003e1.0.20.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-container-for-fidle-httpsgricad-gitlabuniv-grenoble-alpesfrtalksfidle\" class=\"anchor\" href=\"#building-container-for-fidle-httpsgricad-gitlabuniv-grenoble-alpesfrtalksfidle\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding container for fidle (\u003ca href=\"https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle\" rel=\"nofollow\"\u003ehttps://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle\u003c/a\u003e)\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image from dockerhub registry and push to ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003cli\u003eOnly cover the software installation for cpu (\u003ca href=\"https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/Linux%20installation%20using%20conda\" rel=\"nofollow\"\u003ehttps://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/Linux%20installation%20using%20conda\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-fidle/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-fidle/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-fidle:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-fidle:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1635823834.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1637706473.0 }, { "data_format": 2, - "description": "md5deep is a set of programs to compute MD5, SHA-1, SHA-256, Tiger, or Whirlpool message digests on an arbitrary number of files.", + "description": "Singularity Containers", "filenames": [ - "4.4/Singularity" + "Singularity.fmriprep.1.4.1rc1", + "Singularity.rclone", + "Singularity.hddm", + "Singularity.neurodebian", + "Singularity.fmriprep", + "Singularity.test.neurodebian.def" ], - "full_name": "pscedu/singularity-hashdeep", - "latest_release": "v4.4", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-hashdeep/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-hashdeep/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-hashdeep/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-hashdeep/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ff1122893a7ca155e9b1b0481dc029ee11528d2c1a5d57208038642526e8e646/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6861736864656570\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff1122893a7ca155e9b1b0481dc029ee11528d2c1a5d57208038642526e8e646/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6861736864656570\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-hashdeep\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f144df41de2d6e76dd24c4825a4ac826fd107f3ea3e94ec7daf3c070422137cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6861736864656570\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f144df41de2d6e76dd24c4825a4ac826fd107f3ea3e94ec7daf3c070422137cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6861736864656570\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-hashdeep\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b2bcc92aad4b69910d6a0b34f82bbe68f8529688631ce0a15fb4ddbca44fd8be/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6861736864656570\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b2bcc92aad4b69910d6a0b34f82bbe68f8529688631ce0a15fb4ddbca44fd8be/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6861736864656570\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-hashdeep\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5aa33b5c4bd7a85735363e937ad526230f325f4d70cd29a78de7f77376b0defd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6861736864656570\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5aa33b5c4bd7a85735363e937ad526230f325f4d70cd29a78de7f77376b0defd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6861736864656570\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-hashdeep\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hashdeep\" class=\"anchor\" href=\"#singularity-hashdeep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hashdeep\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/jessek/hashdeep\"\u003ehashdeep\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehashdeep\u003c/code\u003e, \u003ccode\u003esha1deep\u003c/code\u003e, \u003ccode\u003etigerdeep\u003c/code\u003e, \u003ccode\u003emd5deep\u003c/code\u003e, \u003ccode\u003esha256deep\u003c/code\u003e and \u003ccode\u003ewhirlpooldeep\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hashdeep/4.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hashdeep\u003c/code\u003e as \u003ccode\u003e4.4.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "klabhub/singularity", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eKlab Singularity Containers, access them here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2510\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNeurodebian is a full install, with FSL, AFNI, datalad, etc.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1639934583.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1637675540.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "QE/Singularity.QuantumESPRESSO-6.3-intel-2018b-unrrc" ], - "full_name": "bozmik/singularity_image", + "full_name": "UNR-HPC/singularity-recipes", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" href=\"#singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-recipes\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1552825749.0 + "updated_at": 1544465938.0 }, { "data_format": 2, - "description": null, + "description": "A Nextflow pipeline for processing 16S rRNA sequences using dada2", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "cmatKhan/bartNP", + "full_name": "nhoffman/dada2-nf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bartnp\" class=\"anchor\" href=\"#bartnp\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebartNP\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/cmatKhan/bartNP/actions\"\u003e\u003cimg src=\"https://github.com/cmatKhan/bartNP/workflows/R-CMD-check/badge.svg\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"https://cmatkhan.github.io/bartNP/\" rel=\"nofollow\"\u003eSee Documentation Here\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dada2-nextflow-pipeline\" class=\"anchor\" href=\"#dada2-nextflow-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDada2 Nextflow pipeline\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-local-execution-quickstart-for-the-truly-impatient\" class=\"anchor\" href=\"#local-execution-quickstart-for-the-truly-impatient\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal execution quickstart for the truly impatient\u003c/h2\u003e\n\u003cp\u003eInstall Docker and make sure that the Docker daemon is running.\u003c/p\u003e\n\u003cp\u003eInstall the nextflow binary in this directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget -qO- https://get.nextflow.io | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute locally, using the minimal data set.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./nextflow run main.nf -params-file params-minimal.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-execution-on-aws-batch\" class=\"anchor\" href=\"#execution-on-aws-batch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution on AWS Batch\u003c/h2\u003e\n\u003cp\u003eDetails will depend on your AWS batch configuration. General instructions TBD.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-infernal-16s-filtering\" class=\"anchor\" href=\"#infernal-16s-filtering\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInfernal 16s filtering\u003c/h3\u003e\n\u003cp\u003eCoveriance model used for Infernal sequence filtering obtained from the Rfam database:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://rfam.xfam.org/family/RF00177\" rel=\"nofollow\"\u003ehttps://rfam.xfam.org/family/RF00177\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo cite Rfam see latest web site instructions:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://rfam.xfam.org/\" rel=\"nofollow\"\u003ehttps://rfam.xfam.org/\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1639423876.0 + "updated_at": 1637256255.0 }, { "data_format": 2, - "description": "This repo contains a Singularity definition file for the newest ROS2 distro", + "description": "Docker images", "filenames": [ - "Singularity" + "images/sc_qc_cluster/Singularity.sc_qc_cluster" ], - "full_name": "siehlema/ros2_singularity", + "full_name": "letaylor/docker-letaylor", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ros2_singularity\" class=\"anchor\" href=\"#ros2_singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eros2_singularity\u003c/h1\u003e\n\u003cp\u003eThis definition file is based on the ROS2 Docker from the \u003ca href=\"https://hub.docker.com/r/osrf/ros2/dockerfile\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and the official \u003ca href=\"https://github.com/osrf/docker_images/blob/master/ros2/source/source/Dockerfile\"\u003eGithub Repo\u003c/a\u003e and adds some Singularity functionality. Singularity containers can for instance be used from SLURM workload managers on computer clusters.\u003c/p\u003e\n\u003cp\u003eIt is supposed to help new Singularity/ROS2 developers to start their projects.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to\" class=\"anchor\" href=\"#how-to\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003emeet prerequisites for demo:\n\u003cul\u003e\n\u003cli\u003eLinux environment (tested on Ubuntu 18.04)\u003c/li\u003e\n\u003cli\u003einstall Singularity (\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eGuide\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003erun all containers on one host\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eclone repo\u003c/li\u003e\n\u003cli\u003ebuild singularity container: \u003ccode\u003esudo singularity build ros2_container.simg Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eafterwards try the demo apps:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo singularity run --app example_talker ros2_container.simg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esudo singularity run --app example_listener ros2_container.simg\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ecreate your own apps on the Singularity container or copy them onto the container before building\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker-letaylor\" class=\"anchor\" href=\"#docker-letaylor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker-letaylor\u003c/h1\u003e\n\u003cp\u003eThis repo contains Docker images that are automatically built using Travis CI. It is not designed to scale to many images as each image is updated if any one image changes.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-automatically-push-images-to-docker-hub-using-travis-ci\" class=\"anchor\" href=\"#automatically-push-images-to-docker-hub-using-travis-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically push images to Docker Hub using Travis CI\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-1-edit-config-files\" class=\"anchor\" href=\"#1-edit-config-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Edit config files\u003c/h2\u003e\n\u003cp\u003eEdit the following files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e.travis.yml\u003c/code\u003e : alter \u003ccode\u003e$IMAGE_NAME\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-2-give-travis-ci-access-to-upload-to-docer-hub\" class=\"anchor\" href=\"#2-give-travis-ci-access-to-upload-to-docer-hub\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Give Travis CI access to upload to Docer Hub\u003c/h2\u003e\n\u003cp\u003eStore both \u003ccode\u003e$DOCKER_PASSWORD\u003c/code\u003e and \u003ccode\u003e$DOCKER_USERNAME\u003c/code\u003e securely in on Travis CI. These are used for authentication.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLogin to the account you want Travis to use to upload on \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eClick on your username on the top left and go to \u0027Account Settings\u0027.\u003c/li\u003e\n\u003cli\u003eOn the left hand panel, go to \u0027Security\u0027 and enter your password as requested.\u003c/li\u003e\n\u003cli\u003eNow we\u0027ll create an API token. Name it Travis CI.\u003c/li\u003e\n\u003cli\u003eCreate the token and copy it.\u003c/li\u003e\n\u003cli\u003eLogin to your account on \u003ca href=\"https://travis-ci.org\" rel=\"nofollow\"\u003etravis-ci.org\u003c/a\u003e and go to the repository that you want to add this automatic functionality to.\u003c/li\u003e\n\u003cli\u003eOn the right next to \u0027More options\u0027 go to \u0027Settings\u0027 in the hamburger menu.\u003c/li\u003e\n\u003cli\u003eAdd an environment variable with the name \u003ccode\u003eDOCKER_PASSWORD\u003c/code\u003e and give it the value of the API token that you copied from \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eAdd an environment variable with the name \u003ccode\u003eDOCKER_USERNAME\u003c/code\u003e and give it your \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e user name.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1566384326.0 + "updated_at": 1611328575.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "0.1.16/Singularity" ], - "full_name": "cmatKhan/brentlabRnaSeqTools", - "latest_release": "0.0.2", - "readme": "\u003cp\u003e\u003ca href=\"https://codecov.io/gh/cmatKhan/brentlabRnaSeqTools?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5960bfe27822500008e6c1cfcd08e981288cc2fb1c1ae70ecf9a5125057f6c7c/68747470733a2f2f636f6465636f762e696f2f67682f636d61744b68616e2f6272656e746c6162526e61536571546f6f6c732f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/cmatKhan/brentlabRnaSeqTools/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003ca href=\"https://github.com/cmatKhan/brentlabRnaSeqTools/actions\"\u003e\u003cimg src=\"https://github.com/cmatKhan/brentlabRnaSeqTools/workflows/R-CMD-check/badge.svg\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://cmatkhan.github.io/brentlabRnaSeqTools/\" rel=\"nofollow\"\u003eClick here for the online documentation\u003c/a\u003e. This is a work in progress, still. If there is documentation that you\u0027d like that doesn\u0027t exist, please make an issue report.\u003c/p\u003e\n\u003cp\u003eThe \"articles\" link in the navbar at the top of the page has some vignettes that will help with some common tasks -- please do look at those, if you are a user of this package.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation-and-updating\" class=\"anchor\" href=\"#installation-and-updating\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and updating\u003c/h1\u003e\n\u003cp\u003eThe following will both install, and update if there are changes in the repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(devtools)\n# remove build_vignettes to save time\ninstall_github(\"cmatKhan/brentlabRnaSeqTools\", dependencies = TRUE)\n\n# after you get the package installed, do this:\nlibrary(brentlabRnaSeqTools)\n\n# if you think there are changes, but install_github disagrees, try using the argument force = TRUE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI have also installed this on my htcf cluster profile like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eml miniconda # note: this will definitely load, and most likely work as expected. But it does not come with a promise. It is a cluster module I wrote. If you have issues which you suspect to be a conda problem, I suggest that you install a version of miniconda in your home profile. It will be easier to address any conda related issues that way.\n\nconda install -n brentlabRnaSeqTools # or whatever you want to call your env name\n\nconda install r r-essentials libpq\n\n$ R\n\n\u0026gt; install.packages(devtools)\n# YOU HAVE TO DO THIS! do not update RSQLite (as of 20210702 there is an install error in the boost/c++ package which is a dependency. You do not need to worry about this when you\u0027re installing)\n\u0026gt; remotes::install_version(\"RSQLite\", version = \"2.2.5\")\n\u0026gt; install_github(\"cmatKhan/brentlabRnaSeqTools\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee the bamtools vignette for examples of how to use the functions to examine bam files in an Rscript that you could run with SLURM\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-uninstall\" class=\"anchor\" href=\"#uninstall\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003euninstall\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003eremove.packages(\"brentlabRnaSeqTools\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://cmatkhan/default/brentlab_rnaseq_tools:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-issues\" class=\"anchor\" href=\"#issues\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eissues\u003c/h1\u003e\n\u003cp\u003eplease do post issues to the issues tab. Please include the full error code and the command/context that lead to the error\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-to-contribute\" class=\"anchor\" href=\"#to-contribute\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eto contribute\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003efork the repo\u003c/li\u003e\n\u003cli\u003edevelop in a branch\u003c/li\u003e\n\u003cli\u003ecreate a pull request for the branch\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis is the featureCounts/subreads homepage. In particular, has a good example of how to make mean/variance graph with voom\n\u003ca href=\"http://bioinf.wehi.edu.au/RNAseqCaseStudy/\" rel=\"nofollow\"\u003ehttp://bioinf.wehi.edu.au/RNAseqCaseStudy/\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todos\" class=\"anchor\" href=\"#todos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODOs\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eread more about packrat, add some instructions on how to use\u003c/li\u003e\n\u003cli\u003eupdate R and dependencies to R version 4\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-brentlabrnaseqtools\" class=\"anchor\" href=\"#brentlabrnaseqtools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebrentlabRnaSeqTools\u003c/h1\u003e\n\u003cp\u003eThis is a very helpful tutorial on making an R package:\u003cbr\u003e\n\u003ca href=\"https://tinyheero.github.io/jekyll/update/2015/07/26/making-your-first-R-package.html\" rel=\"nofollow\"\u003ehttps://tinyheero.github.io/jekyll/update/2015/07/26/making-your-first-R-package.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ethis github post helped with installing bioconductor dependencies (deseq2 in this case):\u003cbr\u003e\n\u003ca href=\"https://bioinformatics.stackexchange.com/a/3375\" rel=\"nofollow\"\u003ehttps://bioinformatics.stackexchange.com/a/3375\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eand this helped with installing from github:\u003cbr\u003e\n\u003ca href=\"https://cran.r-project.org/web/packages/githubinstall/vignettes/githubinstall.html\" rel=\"nofollow\"\u003ehttps://cran.r-project.org/web/packages/githubinstall/vignettes/githubinstall.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFinally, here is a nice package development cheatsheet (for R):\u003cbr\u003e\n\u003ca href=\"https://rawgit.com/rstudio/cheatsheets/master/package-development.pdf\" rel=\"nofollow\"\u003ehttps://rawgit.com/rstudio/cheatsheets/master/package-development.pdf\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "yh549848/singularity-vcftools", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1636684356.0 + "updated_at": 1645372372.0 }, { "data_format": 2, - "description": null, + "description": "CP 2022", "filenames": [ - "Singularity" + "dmc/Singularity", + "lg/Singularity" ], - "full_name": "iferres/GTi_UY_shiny", - "latest_release": null, + "full_name": "anonymizee/dper", + "latest_release": "v0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dper-dynamic-programming-for-er-ssat\" class=\"anchor\" href=\"#dper-dynamic-programming-for-er-ssat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPER (Dynamic Programming for ER-SSAT)\u003c/h1\u003e\n\u003cp\u003eDPER runs in two phases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe planning phase constructs a join tree of a CNF formula\u003c/li\u003e\n\u003cli\u003eThe execution phase computes the maximum and a maximizer from the join tree\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-cloning-this-repository\" class=\"anchor\" href=\"#cloning-this-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning this repository\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/anonymizee/dper\u003c/pre\u003e\u003c/div\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDir \u003ca href=\"lg\"\u003e\u003ccode\u003elg\u003c/code\u003e\u003c/a\u003e: DPER\u0027s planner\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"dmc\"\u003e\u003ccode\u003edmc\u003c/code\u003e\u003c/a\u003e: DPER\u0027s executor\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"experiments\"\u003e\u003ccode\u003eexperiments\u003c/code\u003e\u003c/a\u003e: empirical evaluation\u003c/li\u003e\n\u003cli\u003eFile \u003ca href=\"ACKNOWLEDGMENT.md\"\u003e\u003ccode\u003eACKNOWLEDGMENT.md\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1638368684.0 + "updated_at": 1645325273.0 }, { "data_format": 2, @@ -13001,306 +12425,307 @@ var data = "filenames": [ "Singularity" ], - "full_name": "hakanyi/robust-vision-thesis", + "full_name": "Nemirtingas/gdown", "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-stimulus-generation-pipeline\" class=\"anchor\" href=\"#stimulus-generation-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStimulus generation pipeline\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eManually choose base and change postures from H36M dataset. The outcome of this is a \u003ccode\u003ebase_posture.txt\u003c/code\u003e file that we\u0027ll put in a folder, e.g. \u003ccode\u003esampled-lights\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e00_h36m_to_mesh.py\u003c/code\u003e to generate meshes for all of the images from \u003ccode\u003ebase_postures.txt\u003c/code\u003e and place them in the \u003ccode\u003emeshes\u003c/code\u003e folder under \u003ccode\u003esampled-lights\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e01_sample_candidates.sh\u003c/code\u003e to go through the mesh pairs and, for each, generate N base:changed-light and base:changed-pose pairs where the underlying lamp position is sampled. Record image statistics under \u003ccode\u003esampled-lights\u003c/code\u003e, but don\u0027t save images.\u003c/li\u003e\n\u003cli\u003eAnalyze the data with \u003ccode\u003e02_analyze_candidates.Rmd\u003c/code\u003e to determine a) pairs where the pixel distance due to light changes are comparable to pixel distance due to posture changes and b) out these pairs, whether the pixel distances lie in a given range. Save the filtered csv to \u003ccode\u003ecandidate_pairs.csv\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eProduce the images in \u003ccode\u003ecandidate_pairs.csv\u003c/code\u003e (incl. diff-images) to sanity-check using \u003ccode\u003e03_visualize_candidates.py\u003c/code\u003e. Place them in \u003ccode\u003ecandidate_pairs_images\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAcross all \u003ccode\u003esampled-lights-\u0026lt;x\u0026gt;\u003c/code\u003e folders, choose from the candidates and consolidate the output in a \u003ccode\u003eimage_info.csv\u003c/code\u003e in this folder.\u003c/li\u003e\n\u003cli\u003eConsolidate the images in \u003ccode\u003ecandidate_pairs.csv\u003c/code\u003e and place them in \u003ccode\u003eimages\u003c/code\u003e using \u003ccode\u003e04_collect_images.py\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e05_make_videos.py\u003c/code\u003e to produce the video stimuli for the behavioral experiment.\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gdownpl\" class=\"anchor\" href=\"#gdownpl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egdown.pl\u003c/h1\u003e\n\u003cp\u003eGoogle Drive direct download of big files\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003ewget\u003c/em\u003e and \u003cem\u003ePerl\u003c/em\u003e must be in the PATH.\u003cbr\u003e\n\u003cstrong\u003eWindows\u003c/strong\u003e and \u003cstrong\u003elinux\u003c/strong\u003e compatible.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eUse Google Drive shareable links, viewable by anyone:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./gdown.pl \u0027gdrive file url\u0027 [\u0027desired file name\u0027] \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-example\" class=\"anchor\" href=\"#example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h1\u003e\n\u003cp\u003eFor example, to download \u003ca href=\"https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit\" rel=\"nofollow\"\u003ethis video\u003c/a\u003e from my \u003ca href=\"https://circulosmeos.wordpress.com/2015/03/04/axolotl-a-simple-plain-text-documentation-system/\" rel=\"nofollow\"\u003eaxolotl project\u003c/a\u003e, just copy the url, and give a file name if desired:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./gdown.pl https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit axolotl.mp4 \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-resuming-a-download\" class=\"anchor\" href=\"#resuming-a-download\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResuming a download\u003c/h1\u003e\n\u003cp\u003eIf you need to resume a download, please, use \u003ca href=\"https://github.com/circulosmeos/gdown.pl/tree/with-resume\"\u003e\u003cstrong\u003egdown.pl v2.0\u003c/strong\u003e here\u003c/a\u003e.\u003cbr\u003e\nAs long as a file name is indicated as second parameter, \u003cem\u003egdown.pl v2.0\u003c/em\u003e \u003cstrong\u003ewill try to resume the partially downloaded file\u003c/strong\u003e if a local incomplete file with that name already exists.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-version\" class=\"anchor\" href=\"#version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h1\u003e\n\u003cp\u003eThis version is \u003cstrong\u003ev1.4\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-warning\" class=\"anchor\" href=\"#warning\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWarning\u003c/h3\u003e\n\u003cp\u003ePlease, note that v1.2 (available between days 12 to 31 of Jan/2019) \u003cstrong\u003eshould not be used\u003c/strong\u003e, as it contains a bug that could result in unusable downloaded files. Proceed to overwrite with v1.4 in case you have it.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003cp\u003eA simple Docker file is provided, to build a simple Docker image with gdown.pl.\u003cbr\u003e\nThis has been used for pre-pulling data from a Google Drive to Kubernetes persistent volumes.\u003cbr\u003e\nThanks @anton-khodak\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eAn example \u003ca href=\"https://sylabs.io/guides/3.2/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e file is provided.\u003cbr\u003e\nBuild the container:\n\u003ccode\u003esudo singularity build (imagename) Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRun the container:\n\u003ccode\u003esingularity run (imagename) (gdown.pl args)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThanks to @ttbrunner\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eDistributed \u003ca href=\"http://www.gnu.org/licenses/gpl-3.0.html\" rel=\"nofollow\"\u003eunder GPL 3\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h1\u003e\n\u003cp\u003eThis software is provided \"as is\", without warranty of any kind, express or implied.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-more-info\" class=\"anchor\" href=\"#more-info\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore info\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files\" rel=\"nofollow\"\u003ehttps://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eby \u003ca href=\"loopidle@gmail.com\"\u003ecirculosmeos\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1637252100.0 + "updated_at": 1645100579.0 }, { "data_format": 2, - "description": null, + "description": "General container for RNA-seq sample QC, trimming, alignment and counts (STAR 2.7)", "filenames": [ - "Singularity" + "Singularity.hg19v1.centos" ], - "full_name": "lkirk/nb-env", + "full_name": "ertheisen/wildcat_centos", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nb-env\" class=\"anchor\" href=\"#nb-env\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enb-env\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-appalachianhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#appalachianhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappalachian.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1637426544.0 + "updated_at": 1560527067.0 }, { "data_format": 2, - "description": "Singularity recipe for HERA software", + "description": "Paired end ChIP-seq processing through alignment.", "filenames": [ - "Singularity.casa6_full", - "Singularity.tau", - "Singularity.casa6_modular", - "Singularity.h4c", - "Singularity.rtp", - "Singularity.validation", - "Singularity.hera1", - "Singularity.calamity", - "Singularity.mpi" + "Singularity.hg19v1.centos" ], - "full_name": "HERA-Team/hera-singularity", + "full_name": "ertheisen/appalachian_centos", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hera-singularity\" class=\"anchor\" href=\"#hera-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehera-singularity\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-notice\" class=\"anchor\" href=\"#notice\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotice\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eJuly 15, 2021\u003c/strong\u003e:\nWe are currently manually building and uploading the containers to the HERA project directory on Ilifu on an irregular basis. Please check the built dates of the container files and contact @piyanatk if you need the containers to be rebuilt. Scheduled daily re-building is being planned.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eThis repository contains recipe files of the Singularity containers for the HERA software stack.\u003c/p\u003e\n\u003cp\u003eIlifu users, please make sure to read the relevant page on the HERA wiki. A singularity container is required for computing on the Ilifu. If you need specific Python modules to be installed in the containers, please contact @piyanatk.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-about-container-and-singularity\" class=\"anchor\" href=\"#about-container-and-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout Container and Singularity\u003c/h2\u003e\n\u003cp\u003eContainers are encapsulated software environments and abstract the software and applications from the underlying operating system. This allows users to run workflows in customized environments, switch between environments, and to share these environments with colleagues and research teams.\u003c/p\u003e\n\u003cp\u003eSingularity is a free, cross-platform and open-source computer program that performs operating-system-level virtualization also known as containerization (another widely used one being Docker).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-container-content\" class=\"anchor\" href=\"#container-content\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Content\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-python-packages\" class=\"anchor\" href=\"#python-packages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython Packages\u003c/h3\u003e\n\u003cp\u003eAll containers are built with \u003ccode\u003eUbuntu 20.04\u003c/code\u003e and \u003ccode\u003eminiconda\u003c/code\u003e with \u003ccode\u003epython=3.8\u003c/code\u003e unless otherwise specify \u003ca href=\"###-Different-Between-Containers:\"\u003ebelow\u003c/a\u003e. All variances come standard with the following packages:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eData Analysis\u003c/th\u003e\n\u003cth\u003eAstronomical\u003c/th\u003e\n\u003cth\u003eHERA\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edask\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eaipy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003elinsolve\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ejupyterlab\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eastropy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003euvtools\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ematplotlib\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eastropy-healpix\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehera_qm\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003enumpy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eastroquery\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehera_cal\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epandas\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ecartopy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehera_sim\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003escipy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehealpy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ehera_psepc\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003escikit-learn\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003epyuvdata\u003c/code\u003e\u003csup\u003e\u003ca href=\"#myfootnote1\"\u003e1\u003c/a\u003e\u003c/sup\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003evis_cpu\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003exarray\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003epyuvsim\u003c/code\u003e\u003csup\u003e\u003ca href=\"#myfootnote2\"\u003e2\u003c/a\u003e\u003c/sup\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eSSINS\u003c/code\u003e\u003csup\u003e\u003ca href=\"#myfootnote3\"\u003e3\u003c/a\u003e\u003c/sup\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca name=\"user-content-myfootnote1\"\u003e1\u003c/a\u003e: With CASA measurement sets, HEALPix beam, and CST beam functionalities, see \u003ca href=\"https://github.com/RadioAstronomySoftwareGroup/pyuvdata%5C\"\u003ehttps://github.com/RadioAstronomySoftwareGroup/pyuvdata\\\u003c/a\u003e\n\u003ca name=\"user-content-myfootnote2\"\u003e2\u003c/a\u003e: without line profiler and lunar capability, see \u003ca href=\"https://github.com/RadioAstronomySoftwareGroup/pyuvsim\"\u003ehttps://github.com/RadioAstronomySoftwareGroup/pyuvsim\u003c/a\u003e\n\u003ca name=\"user-content-myfootnote3\"\u003e3\u003c/a\u003e: See \u003ca href=\"https://github.com/mwilensky768/SSINS\"\u003ehttps://github.com/mwilensky768/SSINS\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-variances\" class=\"anchor\" href=\"#variances\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVariances:\u003c/h3\u003e\n\u003cp\u003eWe are currently building the following variances.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ehera1\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eInclude all packages in the table above. Intended for general-purpose computing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecasa6_full\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eEquivalent to \u003ccode\u003ehera1\u003c/code\u003e with a full installation of \u003ccode\u003ecasa-6\u003c/code\u003e, and \u003ccode\u003eAPLpy\u003c/code\u003e for visualisation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecasa6_modular\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eEquivalent to \u003ccode\u003ehera1\u003c/code\u003e with a pip-wheel installation of \u003ccode\u003ecasa-6\u003c/code\u003e, making \u003ccode\u003ecasatasks\u003c/code\u003e, \u003ccode\u003ecasatools\u003c/code\u003e, and \u003ccode\u003ecasampi\u003c/code\u003e packages (see \u003ca href=\"https://casa-pip.nrao.edu/\" rel=\"nofollow\"\u003ehttps://casa-pip.nrao.edu/\u003c/a\u003e), and \u003ccode\u003eAPLpy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBased on \u003ccode\u003ePython 3.6\u003c/code\u003e and \u003ccode\u003eUbuntu 18.04\u003c/code\u003e for casa-pip compatibility.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ertp\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eFor testing the \u003ccode\u003emakeflow\u003c/code\u003e pipeline.\u003c/li\u003e\n\u003cli\u003eEquivalent to \u003ccode\u003ehera1\u003c/code\u003e with an addition of \u003ccode\u003ehera_opm\u003c/code\u003e, \u003ccode\u003ehera_mc\u003c/code\u003e, and \u003ccode\u003ehera_notebook_templates\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ehera_pipelines\u003c/code\u003e is cloned to \u003ccode\u003e/usr/local\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eh4c\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eAlmost equivalent to \u003ccode\u003ertp\u003c/code\u003e except some specific branches on \u003ccode\u003ehera_cal\u003c/code\u003e and \u003ccode\u003epspec\u003c/code\u003e for H4C analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etau\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eThis container is \u003ccode\u003ehera1\u003c/code\u003e with extra tools for simulation, machine learning, and etc. Specifically, it contains the following additions:\n\u003cul\u003e\n\u003cli\u003eemupy (\u003ca href=\"https://github.com/nkern/emupy\"\u003ehttps://github.com/nkern/emupy\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ezreion (\u003ca href=\"https://github.com/plaplant/zreion\"\u003ehttps://github.com/plaplant/zreion\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e21cmFAST=3.1.1\u003c/li\u003e\n\u003cli\u003epowerbox\u003c/li\u003e\n\u003cli\u003etensorflow\u003c/li\u003e\n\u003cli\u003epytorch\u003c/li\u003e\n\u003cli\u003ekeras\u003c/li\u003e\n\u003cli\u003esympy\u003c/li\u003e\n\u003cli\u003enumexpr\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-python-environment\" class=\"anchor\" href=\"#python-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython Environment\u003c/h3\u003e\n\u003cp\u003eAll containers use Miniconda3, which are installed at \u003ccode\u003e/usr/local/miniconda3/\u003c/code\u003e inside the containers.\u003c/p\u003e\n\u003cp\u003eThe name of Conda environment in each container is the same as the container name, e.g. \u003ccode\u003ehera1\u003c/code\u003e, \u003ccode\u003ecasa6_full\u003c/code\u003e, and etc, The default conda environment \u003ccode\u003ebase\u003c/code\u003e is not used.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-environment-variables\" class=\"anchor\" href=\"#environment-variables\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment Variables\u003c/h3\u003e\n\u003cp\u003eThe following environment variables are also exported in all containers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCONDA_PATH=\"/usr/local/miniconda3\"\nCONDA_SH=\"$CONDA_PATH/etc/profile.d/conda.sh\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe latter is especially useful to make the \u003ccode\u003econda\u003c/code\u003e command available inside the container (see the section on \u003ca href=\"####-%60shell%60\"\u003e\u003ccode\u003esingularly shell\u003c/code\u003e usage\u003c/a\u003e below).\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ertp\u003c/code\u003e container has an additional environment variable that point to \u003ccode\u003ehera_pipelines\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eHERA_PIPELINES_PATH=\"/usr/local/hera_pipelines\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-commands\" class=\"anchor\" href=\"#singularity-commands\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Commands\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-pull\" class=\"anchor\" href=\"#pull\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003epull\u003c/code\u003e\n\u003c/h4\u003e\n\u003cp\u003eUse \u003ccode\u003esingularity pull\u003c/code\u003e to download the container from Singularity Hub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull [name_to_save_the_image_(optional)] shub://HERA-Team/hera-singularity:\u0026lt;recipe\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull rtp.sif shub://HERA-Team/hera-singularity:rtp\nINFO: Downloading shub image\n 1.98 GiB / 1.98 GiB [=======================================================] 100.00% 13.12 MiB/s 2m34s\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-shell\" class=\"anchor\" href=\"#shell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eshell\u003c/code\u003e\n\u003c/h4\u003e\n\u003cp\u003eThe \u003ccode\u003esingularity shell\u003c/code\u003e command allows you to spawn a new shell within your container and interact with it as though it were a small virtual machine.\u003c/p\u003e\n\u003cp\u003eBy default, \u003ccode\u003eshell\u003c/code\u003e invokes \u003ccode\u003e/bin/sh --norc\u003c/code\u003e, which means that \u003ccode\u003e.bashrc\u003c/code\u003e will not be executed (more on this \u003ca href=\"https://github.com/hpcng/singularity/issues/643\"\u003ehere\u003c/a\u003e) and thus Conda will not be initialized. To make the \u003ccode\u003econda\u003c/code\u003e command available, you can do one of the following:\u003c/p\u003e\n\u003cp\u003ea) Run \u003ccode\u003eexec $SHELL\u003c/code\u003e inside the singularity shell. If \u003ccode\u003e$SHELL\u003c/code\u003e is \u003ccode\u003e\\bin\\bash\u003c/code\u003e (as in our Ubuntu build), \u003ccode\u003e.bashrc\u003c/code\u003e will be read.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell rtp.sif\nSingularity\u0026gt; exec $SHELL\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb) Manually execute the conda initialization script inside singularity shell. The \u003ccode\u003eCONDA_SH\u003c/code\u003e environment variable pointing to the absolute path of the script (\u003ccode\u003e/usr/local/miniconda3/etc/profile.d/conda.sh\u003c/code\u003e), is made available for this purpose. Note that \u003ccode\u003e.\u003c/code\u003e must be used as \u003ccode\u003esource\u003c/code\u003e won\u0027t work under \u003ccode\u003esh\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell rtp.sif\nSingularity\u0026gt; . $CONDA_SH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb) Specify \u003ccode\u003e\\bin\\bash\u003c/code\u003e as a shell to use when executing the \u003ccode\u003eshell\u003c/code\u003e command, either by using the \u003ccode\u003eSINGULARITY_SHELL\u003c/code\u003e environment variable,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ SINGULARITY_SHELL=/bin/bash singularity shell hera-rtp.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor \u003ccode\u003e-s\u003c/code\u003e option,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell -s /bin/bash hera-rtp.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-exec\" class=\"anchor\" href=\"#exec\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eexec\u003c/code\u003e\n\u003c/h4\u003e\n\u003cp\u003eThe \u003ccode\u003esingularity exec\u003c/code\u003e command allows you to execute a custom command within a container by specifying the image file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec rtp.sif echo \"Hello World!\"\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat myscript.sh\nHello World!\n$ singularity exec rtp.sif bash myscript.sh\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-file-permission-and-bind-path\" class=\"anchor\" href=\"#file-permission-and-bind-path\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile Permission and Bind Path\u003c/h3\u003e\n\u003cp\u003eSingularity containers run as the user and share host services. When Singularity \u2018switch\u2019 from the host operating system to the containerized operating system, the OS-level system files on the host becomes inaccessible. (the root user on the host system is also different from the root in the container!)\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-specific-usages-for-ilifu\" class=\"anchor\" href=\"#specific-usages-for-ilifu\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecific Usages for Ilifu\u003c/h3\u003e\n\u003cp\u003ePlese see the relevant page on the HERA wiki.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-appalachianhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#appalachianhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappalachian.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 13, + "subscribers_count": 0, "topics": [], - "updated_at": 1638267345.0 + "updated_at": 1551443298.0 }, { "data_format": 2, - "description": null, + "description": "rstudio on RCC", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "aerval/drop", - "latest_release": "0.0.2", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" href=\"#singularity-deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"img/shpc.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" href=\"#1-template-or-fork\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Template or Fork\u003c/h3\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" href=\"#1-write-your-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" href=\"#2-update-the-version-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Update the VERSION file\u003c/h3\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/releases.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" href=\"#3-how-to-develop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How to Develop\u003c/h3\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" href=\"#4-how-to-pull\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. How to pull\u003c/h3\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", + "full_name": "liliw-w/rstudio-server-conda_share", + "latest_release": null, + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-run-studio-server-on-rcc\" class=\"anchor\" href=\"#run-studio-server-on-rcc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun studio server on RCC\u003c/h2\u003e\n\u003cp\u003eBased on \u003ca href=\"https://github.com/grst/rstudio-server-conda\"\u003eintegrate the container with conda\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-why-this-repo\" class=\"anchor\" href=\"#why-this-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy this repo?\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eWe want to use rstudio interactively on RCC just like on our local computers. e.g. easy access to files on server, draw and check plots easily, upload and download files within rstudio, user-friendly UI.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOne way provided is through ThinLinc. But ThinLinc sometimes is slow; hard to copy-paste; not good UI, etc.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTherefore, we need another way to be able to launch rstudio on RCC.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-what-is-this-repo\" class=\"anchor\" href=\"#what-is-this-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is this repo?\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis repo implements rstudio server on RCC through a singularity container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBe able to run rstudio on computation node by sumbiting a SLURM job.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIntergrate rstudio with conda for easy package management.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-how-to-use-this-repo\" class=\"anchor\" href=\"#how-to-use-this-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use this repo?\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-git-clone-this-repo\" class=\"anchor\" href=\"#git-clone-this-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGit clone this repo\u003c/h4\u003e\n\u003cp\u003e... to your RCC folder. I store it in my \u003ccode\u003escratch\u003c/code\u003e space.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-modify-a-few-parameters\" class=\"anchor\" href=\"#modify-a-few-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModify a few parameters\u003c/h4\u003e\n\u003cp\u003eTo make it work for your own use, several parameters needed to modify. All modifications will be made in file \u003ccode\u003esingularity/run_singularity.sh\u003c/code\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSpecify the path to a conda env to parameter \u003ccode\u003e$CONDA_PREFIX\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis conda env store all packages you will need. You can use an existing conda env, or create a one as in file \u003ccode\u003econda_env_config.sh\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eSpeficy the path to the rstudio singularity container to parameter \u003ccode\u003e$CONTAINER\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the container by \u003ccode\u003esingularity pull docker://rocker/rstudio_latest\u003c/code\u003e. See \u003ca href=\"https://www.rocker-project.org/use/singularity/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for the container\u0027s info.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMove the downloaded file \u003ccode\u003erstudio_latest.sif\u003c/code\u003e to the path you assigned to \u003ccode\u003e$CONTAINER\u003c/code\u003e. I would recommend \u003ccode\u003esingularity/rstudio_latest.sif\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eSet your login password to parameter \u003ccode\u003e$USER_psw\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run\" class=\"anchor\" href=\"#run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eRun this container on login node.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebash /path/to/your_repo/singularity/run_singularity.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou will see something like highlighted in orange rectangle,\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"rstudio_contaner_login.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"rstudio_contaner_login.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eOpen the link in your browser.\u003c/p\u003e\n\u003cp\u003eUser name and password are in the figure.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun studio on computation node.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esbatch /path/to/your_repo/singularity/run_singularity.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis is to submit a slurm job. Configure the slurm resource parameters in the header of \u003ccode\u003esingularity/run_singularity.sh\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the slurm output file \u003ccode\u003erstudio-server.job\u003c/code\u003e. The content is basically the same as the above figure.\u003c/p\u003e\n\u003cp\u003eUse the info highlighted in blue rectangle.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003essh -N -L ...\u003c/code\u003e in your terminal.\u003c/li\u003e\n\u003cli\u003eOpen the link in your browser.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ref\" class=\"anchor\" href=\"#ref\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRef\u003c/h3\u003e\n\u003cp\u003eTo understand more how this works, see ref below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.rocker-project.org/use/singularity/\" rel=\"nofollow\"\u003erstudio server singularity container\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.rocker-project.org/use/singularity/\" rel=\"nofollow\"\u003emake it a SLURM sbatch script\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/grst/rstudio-server-conda\"\u003eintegrate the container with conda\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1641857087.0 + "updated_at": 1644084864.0 }, { "data_format": 2, - "description": null, + "description": "Modified chroma code", "filenames": [ - "Singularity" + "installation/chroma3.nvidia/Singularity" ], - "full_name": "lawlessrd/SCZ", + "full_name": "unlimited-name/chroma", "latest_release": null, - "readme": "", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-chroma-ultra-fast-photon-monte-carlo\" class=\"anchor\" href=\"#chroma-ultra-fast-photon-monte-carlo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChroma: Ultra-fast Photon Monte Carlo\u003c/h1\u003e\n\u003cp\u003eChroma is a high performance optical photon simulation for particle physics detectors originally written by A. LaTorre and S. Seibert. It tracks individual photons passing through a triangle-mesh detector geometry, simulating standard physics processes like diffuse and specular reflections, refraction, Rayleigh scattering and absorption.\u003c/p\u003e\n\u003cp\u003eWith the assistance of a CUDA-enabled GPU, Chroma can propagate 2.5 million photons per second in a detector with 29,000 photomultiplier tubes. This is 200x faster than the same simulation with GEANT4.\u003c/p\u003e\n\u003cp\u003eCheck out the \u003ca href=\"doc/source/chroma.pdf\"\u003eChroma whitepaper\u003c/a\u003e for information on how Chroma works.\u003c/p\u003e\n\u003cp\u003eInformation about the historical development of Chroma can be found at the \u003ca href=\"https://chroma.bitbucket.io/index.html\" rel=\"nofollow\"\u003ebitbucket repository\u003c/a\u003e this repository was forked from.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-modified-chroma-for-sbc-simulation\" class=\"anchor\" href=\"#modified-chroma-for-sbc-simulation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModified chroma for SBC simulation\u003c/h2\u003e\n\u003cp\u003eThe SBC collaboration wants to use \u003ca href=\"https://github.com/SBC-Collaboration\"\u003eSBCgeant4\u003c/a\u003e geometry in photon simulation. Chroma has a geometry interface for STL mesh, or GDML, a XML-based geometry languige. Current GDML interface is not perfect for use, and actually even has some defects. I modified the functions and classes in gdml.py to fit the need of SBC simulations.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-and-quick-use-of-chroma\" class=\"anchor\" href=\"#installation-and-quick-use-of-chroma\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and quick use of Chroma\u003c/h2\u003e\n\u003cp\u003eThe source of chroma uses \u0027Docker\u0027 for maintainance and environment controlling. However, this can cause trouble for Windows system users. To solve this problem, we choose to use Cloud platforms provided by Google and other companies, which is also stable in environments and available to anyone who wants to engage in chroma.\u003c/p\u003e\n\u003cp\u003eTo start using chroma on cloud platform, you will need to construct a VM instance including certain GPUs, using an ubuntu OS image. Google image for \u0027DEEP LEARNING\u0027 is well-constructed and worth trying.\u003c/p\u003e\n\u003cp\u003eFor any empty ubuntu image, installation of chroma can be completed in \u003ca href=\"https://github.com/unlimited-name/CloudInstallation\"\u003ebash batches\u003c/a\u003e. All the batch commands are translated and modified via the \u0027Docker Dockerfile\u0027 used by the maintainer.\n**Note you will have to mannually modify the version of CUDA installed by matching the CUDA version of host machine. **\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-subject-to-change\" class=\"anchor\" href=\"#subject-to-change\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSUBJECT TO CHANGE\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1641581829.0 + "updated_at": 1643829901.0 }, { "data_format": 2, - "description": "Run Open XDMod in a container with automated data ingest.", + "description": null, "filenames": [ - "container/Singularity/Singularity" + "Singularity" ], - "full_name": "jtfrey/open-xdmod-container", - "latest_release": "v8.1.2", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-open-xdmod-container\" class=\"anchor\" href=\"#open-xdmod-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopen-xdmod-container\u003c/h1\u003e\n\u003cp\u003eRun Open XDMod in a container with automated data ingest.\u003c/p\u003e\n", + "full_name": "genxnetwork/uk-biobank", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-federated-biobank-project\" class=\"anchor\" href=\"#federated-biobank-project\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFederated Biobank Project\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-motivation\" class=\"anchor\" href=\"#motivation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-structure\" class=\"anchor\" href=\"#structure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStructure\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003esplit\u003c/strong\u003e module generates node datasets from the whole UKB dataset based on self-reported ancestry.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eqc\u003c/strong\u003e module encapsulates node-based quality control.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003edimred\u003c/strong\u003e module performs different strategies of dimensionality reduction.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efl\u003c/strong\u003e module compares various FL strategies on selected SNPs.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1632401090.0 + "updated_at": 1643039658.0 }, { "data_format": 2, "description": null, "filenames": [ - "DeepLearningCamelyon/0.Preparation/Singularity", - "DeepLearningCamelyon/0.Preparation/Singularity_Code_for_Prediction.sh" + "singularity/Singularity.vcf_processing.v1.0", + "singularity/Singularity.sv_call.v1.0", + "singularity/Singularity.bcftools.v1.10.2", + "singularity/Singularity.qcbam.v1.0", + "singularity/Singularity.align_dedup.v1.0", + "singularity/Singularity.expansion_hunter.v5.0.0", + "singularity/Singularity.sv_processing.v1.0" ], - "full_name": "shiny0510/Camelyon_Preprocessing_tif", + "full_name": "edg1983/WGS_pipeline", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-deeplearningcamelyon\" class=\"anchor\" href=\"#deeplearningcamelyon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeepLearningCamelyon\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-reference\" class=\"anchor\" href=\"#reference\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ereference\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/3dimaging/DeepLearningCamelyon\"\u003ehttps://github.com/3dimaging/DeepLearningCamelyon\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-file\" class=\"anchor\" href=\"#file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDeepLearningCamelyon Folder:Preprocessing (ASAP, tif), Unet Traing and prediction\u003c/li\u003e\n\u003cli\u003eannotation.py: Make mask File\u003c/li\u003e\n\u003cli\u003emain.py: tif File resize, mask File and originFile\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-wgs-analysis-pipeline\" class=\"anchor\" href=\"#wgs-analysis-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWGS analysis pipeline\u003c/h1\u003e\n\u003cp\u003eWGS analysis pipeline. Can handle both WGS and WES data.\u003c/p\u003e\n\u003cp\u003eThe whole pipeline use singularity images and will pull images from singularity library when needed. Singularity recipes used are provided in \u003ccode\u003esingularity\u003c/code\u003e folder for reference.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-run\" class=\"anchor\" href=\"#how-to-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run\u003c/h2\u003e\n\u003cp\u003eThe pipeline can be run directly using Nextflow \u0026gt;= v20.10.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow WGS_analysis.nf -profile cluster --operation align --input input_file.txt --mode WGS --ped ped_file.ped --ref genome.fa --cohort_id cohort_name --outdir results \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe pipeline automatically infer the number of samples in the cohort from your input file and adjust the filtering accordingly. When more than one sample is present, small variants and structural variants from all samples are merged in cohort wide VCF files.\u003c/p\u003e\n\u003cp\u003eEventually update \u003ccode\u003esingularity_cachedir\u003c/code\u003e variable in \u003ccode\u003enextflow.config\u003c/code\u003e to point to a proper folder where singularity images are stored / will be downloaded\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-arguments\" class=\"anchor\" href=\"#arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArguments\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoperation : align or call_variants\nmode : WGS only supported at the moment\nref : fasta file for the genome. Note that .fai and bwa index are expected in the same location\ninput : tab-separated file describing input files. \n The exact format depends on operation requested (see below)\nped : standard PED file containing all samples\ncohort_id : a arbitrary name for the cohort files generated\noutdir : output folder for results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse \u003ccode\u003e--operation align/call_variants --help\u003c/code\u003e for more explanations.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-resources\" class=\"anchor\" href=\"#resources\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h2\u003e\n\u003cp\u003eVarious supporting files are needed and expected in the \u003ccode\u003eresources\u003c/code\u003e folder. This path can be configured by changing the parameters in \u003ccode\u003econfig/resources_GRCh37/38.conf\u003c/code\u003e. All files needed are provided in a Zenodo repository. Please refer to the README file in the resources folder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNB.\u003c/strong\u003e The available resources are based on GRCh37 with standard chromosomes \u003ccode\u003e1..22 X Y MT\u003c/code\u003e and GRCh38 using \u003ccode\u003echr1..22 chrX chrY chrM\u003c/code\u003e. Be sure the genome reference file passed with \u003ccode\u003e--ref\u003c/code\u003e matches the expected nomenclature for your genome build.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input-files-format\" class=\"anchor\" href=\"#input-files-format\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files format\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ped-file\" class=\"anchor\" href=\"#ped-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePED file\u003c/h3\u003e\n\u003cp\u003eA standard tab-separated PED file without header, describing all samples provided in the input file. All sample IDs must match between ped and input file. All samples must have sex defined.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efamily_ID individual_ID father_ID mother_ID sex(1=M,2=F) status(1=unaff,2=aff,0=unknown)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input-file\" class=\"anchor\" href=\"#input-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einput file\u003c/h3\u003e\n\u003cp\u003eNote that all files need to be specified using \u003cstrong\u003eabsolute paths\u003c/strong\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-operation-align\" class=\"anchor\" href=\"#operation-align\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOperation: align\u003c/h4\u003e\n\u003cp\u003eA 3 columns tab-separated file without header\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esampleID1 s1_lane1_R1.fastq.gz s1_lane1_R2.fastq.gz\nsampleID1 s1_lane2_R1.fastq.gz s1_lane2_R2.fastq.gz\nsampleID2 s2_lane2_R1.fastq.gz s2_lane2_R2.fastq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that if a sample has been sequenced with multiple pairs of fastq files you need to add multiple lines for each pair of fastq files using the same sampleID. The pipeline will take care of the merge.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-operation-call_variants\" class=\"anchor\" href=\"#operation-call_variants\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOperation: call_variants\u003c/h4\u003e\n\u003cp\u003eA 5 columns tab-separated file without header.\nThis file is automatically generated in the output folder when using \u003ccode\u003e--operation align\u003c/code\u003e (bam_files.txt)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esampleID1 main_bam.bam disc.bam split.bam\nsampleID2 main_bam.bam disc.bam split.bam\nsampleID3 main_bam.bam disc.bam split.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003edisc\u003c/code\u003e and \u003ccode\u003esplit\u003c/code\u003e BAM files are files containing only discordant pair and split reads like the\nones that can be obtained using Samblaster\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003eThe pipeline generates a reach set of outputs including\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ealigned deduplicated BAM files\u003c/li\u003e\n\u003cli\u003edisc/split BAM files\u003c/li\u003e\n\u003cli\u003eExtensive QC of alignements, which includes mapping stats, coverage, relatedness, ancestry\u003c/li\u003e\n\u003cli\u003eMulti sample and single sample VCFs of small variants and structural variants (variants are provided as raw calls and filtered calls)\u003c/li\u003e\n\u003cli\u003eVariants QC report for small variants\u003c/li\u003e\n\u003cli\u003eROH regions\u003c/li\u003e\n\u003cli\u003eRepeat expansions by Expansion Hunter\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline-components\" class=\"anchor\" href=\"#pipeline-components\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline components\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eAlignement and duplicate marking\n\u003cul\u003e\n\u003cli\u003eBWA-MEM + samblaster + samtools\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eQC and coverage from BAM files\n\u003cul\u003e\n\u003cli\u003efastqc: reads stats\u003c/li\u003e\n\u003cli\u003emosdepth: coverage\u003c/li\u003e\n\u003cli\u003esamtools flagstat / mapstat: alignment stats\u003c/li\u003e\n\u003cli\u003esomalier: ancestry, relatedness, sex check reports\u003c/li\u003e\n\u003cli\u003emultiqc: interactive report\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003esmall variants\n\u003cul\u003e\n\u003cli\u003edeepvariant: single sample calls\u003c/li\u003e\n\u003cli\u003eglnexus: gvcf merge\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003estructural variants\n\u003cul\u003e\n\u003cli\u003elumpy: structural variants events\u003c/li\u003e\n\u003cli\u003eCNVnator: CNV estimation\u003c/li\u003e\n\u003cli\u003esvtools: combine, merge and classify\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003erepeat expansion detection\n\u003cul\u003e\n\u003cli\u003eexpansion hunter\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eROH regions\n\u003cul\u003e\n\u003cli\u003ebcftools ROH\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-future-developments\" class=\"anchor\" href=\"#future-developments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFuture developments\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Update SV pipeline to Manta / dysgu\u003c/li\u003e\n\u003cli\u003e[ ] Add duphold for SV quality check\u003c/li\u003e\n\u003cli\u003e[ ] Variant annotation\u003c/li\u003e\n\u003cli\u003e[ ] Segregation analysis with slivar\u003c/li\u003e\n\u003cli\u003e[ ] Support for WES?\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1632725622.0 + "updated_at": 1642604585.0 }, { "data_format": 2, - "description": "Singularity recipe for Circos.", + "description": "SCOV2-spikeScreen IMI prototype bash pipeline", "filenames": [ "Singularity" ], - "full_name": "ArnaudBelcour/circos-singularity", + "full_name": "IMIMF-UNILJSI/scov2-spikeScreen", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-circos-singularity\" class=\"anchor\" href=\"#circos-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCircos singularity\u003c/h1\u003e\n\u003cp\u003eA singularity recipe for Circos (inspired by the one written by \u003ca href=\"https://github.com/J35P312/CircusCircos\"\u003ehttps://github.com/J35P312/CircusCircos\u003c/a\u003e). This install all of its dependencies. The image size is around ~212 Mb.\u003c/p\u003e\n\u003cp\u003eYou can directly call \u003ccode\u003ecircos\u003c/code\u003e inside of the image like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec -c -B /shared/folder:/shared/folder circos.sif circos -conf /shared/folder/circos.conf -outputdir /shared/folder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e-c\u003c/code\u003e option isolates the container and the \u003ccode\u003e-B\u003c/code\u003e option give access to a folder outside the container for Singularity.\u003c/p\u003e\n\u003cp\u003eYou can use the path associated to \u003ccode\u003e-B\u003c/code\u003e to give access to data path in the configuration file.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-scov2-spikescreen\" class=\"anchor\" href=\"#scov2-spikescreen\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escov2-spikeScreen\u003c/h1\u003e\n\u003cp\u003eSCOV2-spikeScreen IMI prototype bash pipeline\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-container\" class=\"anchor\" href=\"#build-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild container\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/repo/directory/buildContainer web # pull from shub\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/repo/directory/buildContainer local # build from scratch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eno argument defaults to \"web\", local requires sudo privileges. If none of the options is suitable to the user, do manual build with working parameter settings.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eCreate a working dir somewhere in your FS (preferably outside of the git dir), run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind /path/to/repo/directory:/opt/scripts,/path/to/data:/mnt /path/to/repo/directory/spikeScreenContainer.sif /opt/scripts/runPipeline runID keyword /mnt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe second argument (keyword) should be replaced with either pools/assemblies/pools_single/assemblies_single to run the appropriate analysis (self explanatory).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cleanup\" class=\"anchor\" href=\"#cleanup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCleanup\u003c/h2\u003e\n\u003cp\u003eA cleanup script is also provided (see repo directory: cleanUp), but it may not be so useful. It simply removes the contents of the work dir related to the pipeline process.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1632847008.0 + "updated_at": 1641561900.0 }, { "data_format": 2, - "description": null, + "description": "Metagenomic analysis of viral samples", "filenames": [ - "latest/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-rnaview", + "full_name": "Aexbrayat/snakevir", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-rnaview/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rnaview/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-rnaview/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rnaview/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3a42a8171d093b99dbf3681a7fa3d291910415d6829e5b185e78e6168f87473d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726e6176696577\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3a42a8171d093b99dbf3681a7fa3d291910415d6829e5b185e78e6168f87473d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726e6176696577\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-rnaview\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca 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src=\"https://camo.githubusercontent.com/9a9e736965b0a755f4684cb670ad871f680ce0a747ca86e9cdbc0597b32e2b4e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d726e6176696577\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-rnaview\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/1d9776e18f2e7616bb9a34f6dafc7c0d6da72a9975f6a690c8c59599d1708961/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726e6176696577\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1d9776e18f2e7616bb9a34f6dafc7c0d6da72a9975f6a690c8c59599d1708961/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726e6176696577\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-rnaview\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-rnaview\" class=\"anchor\" href=\"#singularity-rnaview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-rnaview\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://ndbserver.rutgers.edu/ndbmodule/services/download/rnaview.html\" rel=\"nofollow\"\u003ernaview\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ernaview\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/rnaview/latest\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/rnaview\u003c/code\u003e as \u003ccode\u003elatest.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003esnakevir\u003c/p\u003e\n\u003cp\u003eAuthors\u003c/p\u003e\n\u003cp\u003eAntoni Exbrayat (CIRAD) \u0026amp; Etienne Loire (CIRAD) \u0026amp; Serafin Gutierrez (CIRAD)\u003c/p\u003e\n\u003cp\u003ePurpose:\nMetagenomic analysis of viral shotgun NGS samples.\u003c/p\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://snakemake.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003esnakemake\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-step\" class=\"anchor\" href=\"#step\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCleaning\u003c/li\u003e\n\u003cli\u003eMerging\u003c/li\u003e\n\u003cli\u003eFiltering\u003c/li\u003e\n\u003cli\u003eDe novo sequence assembly\u003c/li\u003e\n\u003cli\u003eMapping\u003c/li\u003e\n\u003cli\u003eHomology search protein databases\u003c/li\u003e\n\u003cli\u003eHomology search nucleotide databases\u003c/li\u003e\n\u003cli\u003eTaxonomic annotation\u003c/li\u003e\n\u003cli\u003eTaxonomy refining\u003c/li\u003e\n\u003cli\u003eViral hosts search\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e - bioawk\n - biopython\n - blast\n - bwa\n - cap3\n - csvkit\n - cutadapt\n - diamond\n - entrez-direct\n - ete3\n - flash\n - megahit\n - pandas\n - picard\n - python\n - r-base\n - samtools\n - seqtk\n - snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe conda environment manager can be used to install python , snakemake and all the required tools and dependencies into a single environment in a way such that reproducibility is ensured.\u003c/p\u003e\n\u003cp\u003eNote: Conda must be installed on the system. For help with setting up conda, please see \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo create and activate the conda environment with the environment.yml provided , use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate snakevir\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage:\u003c/h2\u003e\n\u003cp\u003eSnakemake supports a separate configuration file for execution on a cluster. A cluster config file cluster.json is provided , it allows you to specify cluster submission parameters outside the Snakefile. The cluster config is contains all parameters with match names of rules in the Snakefile.\u003c/p\u003e\n\u003cp\u003eedit config.yaml to precise dataset and dependencies path, accomodate read files names , threads allocated to the rules (according to cluster.json).\u003c/p\u003e\n\u003cp\u003elaunch with e.g. :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -s snakefile -j 100 --cluster-config cluster.json --cluster \"sbatch -p {cluster.queue} -N {cluster.queue} -c {cluster.cpu_task} --mem {cluster.mem} -e {cluster.error} -o {cluster.log} \" --printshellcmd --rerun-incomplete --reason --dryrun\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto execute on a SLURM cluster with a maximum of 100 concurrent jobs submitted, eventually modify the command accordingly with your job scheduler.\u003c/p\u003e\n\u003cp\u003eNote : A Singularity containers image will be available soon\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1632891843.0 + "updated_at": 1641215049.0 }, { "data_format": 2, - "description": "A singularity container for NodeJS, SQLite3, MongoDB and VS Code web development", + "description": "Quim\u0027s fork of fownward", "filenames": [ - "Singularity" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/latest/Singularity", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06" ], - "full_name": "benatuts/aip-container", + "full_name": "quimortiz/downward", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-aip-container\" class=\"anchor\" href=\"#aip-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAIP Container\u003c/h1\u003e\n\u003cp\u003eA singularity container for NodeJS, SQLite3, MongoDB and VS Code web development.\u003c/p\u003e\n\u003cp\u003eThis is used for the subject Advanced Internet Programming (AIP) at UTS.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h1\u003e\n\u003cp\u003eConfiguration is optional. If there is no configuration file, the default settings shown below will be used.\u003c/p\u003e\n\u003cp\u003eYou can override these defaults by creating a file named ~/.config/aip_container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# The existence of base path is checked before starting the container\nBASE_PATH=\"/tmp\"\n\n# The host path is then created if it doesn\u0027t exist\n# (set BASE_PATH and HOST_PATH to be the same if you don\u0027t want directories to be created)\nHOST_PATH=\"/tmp/$USER/aip\"\n\n# This array of files is symlinked to the corresponding files in your $HOME\nSYMLINK=(\".gitconfig\" \".ssh\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that if the path /images/tmp exists and you have no configuration file, then /images/tmp will be used instead of /tmp. This is because on UTS lab computers, /images/tmp has greater capacity.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eIf you are using a lab computer, the container should already be installed for you.\u003c/p\u003e\n\u003cp\u003eTo rebuild the container using your own computer:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build aip-container_latest.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr, you can pull the pre-built image from Singularity Hub to your own computer:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://benatuts/aip-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse run_aip_singularity_container.sh to manually start the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun_aip_singularity_container.sh term # Start a gnome-terminal\nrun_aip_singularity_container.sh vscode # Start visual studio code\nrun_aip_singularity_container.sh fullterm # Start a gnome-terminal-server and gnome-terminal\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"misc/images/fast-downward.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" href=\"#tested-software-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1563696940.0 + "updated_at": 1640879253.0 }, { "data_format": 2, - "description": null, + "description": "Some projects in nextflow", "filenames": [ - "Singularity" + "workflow/template/Singularity" ], - "full_name": "remiolsen/dovetail-hichip-singularity", + "full_name": "lux563624348/nextflow", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dovetail-hichip-singularity\" class=\"anchor\" href=\"#dovetail-hichip-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edovetail-hichip-singularity\u003c/h1\u003e\n\u003cp\u003eSingularity dependency wrapper and containerization of Dovetail HiChiP tools - \u003ca href=\"https://hichip.readthedocs.io/en/latest/index.html\" rel=\"nofollow\"\u003ehttps://hichip.readthedocs.io/en/latest/index.html\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow\" class=\"anchor\" href=\"#nextflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextflow\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1633078304.0 + "updated_at": 1640806208.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.STAR" + "2.8.2/Singularity.2.8.2", + "2.11.9/Singularity" ], - "full_name": "izem-idem/sandboxIM", + "full_name": "yh549848/singularity-igv", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1633092971.0 + "updated_at": 1640802538.0 }, { "data_format": 2, "description": null, "filenames": [ - "containers/Singularity.0.4.0", - "containers/Singularity.0.3.5", - "containers/Singularity.0.3.6", - "containers/Singularity.0.3.3", - "containers/Singularity.0.4.1" + "02assembly/02long-read_assembly/lathe/singularity/Singularity.longread", + "02assembly/02long-read_assembly/lathe/singularity/Singularity.htsbox", + "02assembly/02long-read_assembly/lathe/singularity/Singularity.quickmerge" ], - "full_name": "LBJ-Wade/bilby", + "full_name": "JiaLonghao1997/MAGbenchmark", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-genome-resolved-metagenomics-using-short--long-read-and-metahic-sequencing\" class=\"anchor\" href=\"#genome-resolved-metagenomics-using-short--long-read-and-metahic-sequencing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenome-resolved metagenomics using short-, long-read and metaHiC sequencing\u003c/h1\u003e\n\u003cp\u003eIn this work, we systematically evaluated \u003cstrong\u003e26\u003c/strong\u003e distinct strategies for recovering high-quality MAGs generated from \u003cstrong\u003eeight\u003c/strong\u003e assemblers, \u003cstrong\u003etwo\u003c/strong\u003e binning strategies, and \u003cstrong\u003efour\u003c/strong\u003e sequencing technologies including both short- and long-read methods. In particular, we evaluated metagenomic high-throughput chromosomal conformation capture (metaHiC), a new technique that improves binning of assembled contigs using physically linked read-pairs within cells. To our knowledge, we are the first to evaluate the combination of long-read and metaHiC on metagenomics data.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/JiaLonghao1997/MAGbenchmark/blob/main/Figure%201_1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/JiaLonghao1997/MAGbenchmark/raw/main/Figure%201_1.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-1-preprocess\" class=\"anchor\" href=\"#1-preprocess\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Preprocess\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eTrim the adapter regions and low-quality reads: \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003e\u003cstrong\u003eTrimmomatic v.039\u003c/strong\u003e\u003c/a\u003e (using LEADING:3 TRAILING:3, SLIDINGWINDOW:4:15, MINLEN:25)\u003c/li\u003e\n\u003cli\u003eRemove human reads: Filtered reads were aligned to the human genome (NCBI, hg38) using \u003ca href=\"http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml\" rel=\"nofollow\"\u003e\u003cstrong\u003eBowtie2\u003c/strong\u003e\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-2-assemblies\" class=\"anchor\" href=\"#2-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Assemblies\u003c/h4\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-21-short-read-assemblies\" class=\"anchor\" href=\"#21-short-read-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.1 Short-read assemblies\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cs.hku.hk/~alse/idba_ud\" rel=\"nofollow\"\u003e\u003cstrong\u003eIDBA-UD\u003c/strong\u003e\u003c/a\u003e v.1.1.3 (using --pre_correction --maxk 120 --mink 20 --step 20).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/voutcn/megahit\"\u003eMEGAHIT\u003c/a\u003e\u003c/strong\u003e v.1.2.9 (using --k-list 21,29,39,59,79,99,119,141)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ablab/spades\"\u003e\u003cstrong\u003emetaSPAdes\u003c/strong\u003e\u003c/a\u003e v.3.14.1(using --meta -k 21,31,41,61,81,101,121)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-22-long-read-assemblies\" class=\"anchor\" href=\"#22-long-read-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.2 Long-read assemblies\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/marbl/canu\"\u003eCanu\u003c/a\u003e\u003c/strong\u003e v.2.0 (using genomeSize=50m/100m)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/fenderglass/Flye\"\u003emetaFlye\u003c/a\u003e\u003c/strong\u003e v. 2.7 (using \u2013meta \u2013g 100m/250m)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/ruanjue/wtdbg2\"\u003ewtdbg2\u003c/a\u003e\u003c/strong\u003e v.2.5 (using genomesize=50m/100m)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTwo long-read assembled contigs were then merged by \u003ca href=\"https://github.com/mahulchak/quickmerge\"\u003e\u003cstrong\u003equickmerge\u003c/strong\u003e\u003c/a\u003e v.0.40 as previous described in \u003cstrong\u003e\u003ca href=\"https://github.com/bhattlab/lathe\"\u003eLathe\u003c/a\u003e\u003c/strong\u003e, which is a tool for generating bacterial genomes from metagenomes with Nanopore long read sequencing.\u003c/p\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-23-hybrid-assemblies\" class=\"anchor\" href=\"#23-hybrid-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.3 Hybrid assemblies\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CSB5/OPERA-MS\"\u003e\u003cstrong\u003eOPERA-MS\u003c/strong\u003e\u003c/a\u003e v.0.9.0 (using --no-polishing)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ablab/spades\"\u003e\u003cstrong\u003ehybridSPAdes\u003c/strong\u003e\u003c/a\u003e v.3.14.1 (using --meta -k 21,31,41,61,81,101,121)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-24-polish-and-evaluation-of-metagenomic-assemblies\" class=\"anchor\" href=\"#24-polish-and-evaluation-of-metagenomic-assemblies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.4 Polish and evaluation of metagenomic assemblies\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003ePolish: \u003cstrong\u003e\u003ca href=\"https://github.com/broadinstitute/pilon\"\u003ePilon\u003c/a\u003e\u003c/strong\u003e v.1.24\u003c/li\u003e\n\u003cli\u003eEvaluation of metagenomic assemblies: \u003cstrong\u003e\u003ca href=\"http://quast.sourceforge.net/metaquast\" rel=\"nofollow\"\u003eMetaQUAST\u003c/a\u003e\u003c/strong\u003e v.5.0.2\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://github.com/elimoss/metagenomics_workflows/tree/master/assembly_comparison_circos\"\u003eCircos Assembly Comparison Visualization Workflow\u003c/a\u003e\u003c/strong\u003e are from public available scripts.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-3-binning\" class=\"anchor\" href=\"#3-binning\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Binning\u003c/h4\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-31-binning\" class=\"anchor\" href=\"#31-binning\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3.1 Binning\u003c/h5\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://bitbucket.org/berkeleylab/metabat/src/master/\" rel=\"nofollow\"\u003e\u003cstrong\u003eMetaBAT2\u003c/strong\u003e\u003c/a\u003e v.2.15 (--minContig 2500 --minContigDepth 1 --percentIdentity 97)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/cerebis/bin3C\"\u003e\u003cstrong\u003ebin3C\u003c/strong\u003e\u003c/a\u003e v.0.1.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-32-generation-and-quality-evaluation-of-mags\" class=\"anchor\" href=\"#32-generation-and-quality-evaluation-of-mags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3.2 Generation and quality evaluation of MAGs\u003c/h5\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/elimoss/metagenomics_workflows\"\u003ebin_label_and_evaluate\u003c/a\u003e\u003c/strong\u003e is a public available Snakemake workflow for aligning, binning, classifying and evaluating a metagenomic assembly. We modified some of the scripts to make it suitable for bin3C binning.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAssembly size and contiguity: \u003cstrong\u003e\u003ca href=\"http://quast.sourceforge.net/metaquast\" rel=\"nofollow\"\u003eMetaQUAST\u003c/a\u003e\u003c/strong\u003e v.5.0.2\u003c/li\u003e\n\u003cli\u003eCompleteness and contamination: \u003ca href=\"https://ecogenomics.github.io/CheckM/\" rel=\"nofollow\"\u003e\u003cstrong\u003eCheckM\u003c/strong\u003e\u003c/a\u003e v.1.1.3\u003c/li\u003e\n\u003cli\u003eGene Content: \u003cstrong\u003e\u003ca href=\"https://github.com/tseemann/prokka\"\u003eProkka\u003c/a\u003e\u003c/strong\u003e v.1.14.6\u003c/li\u003e\n\u003cli\u003etRNA sequences: \u003ca href=\"http://www.ansikte.se/ARAGORN/\" rel=\"nofollow\"\u003e\u003cstrong\u003eAragorn\u003c/strong\u003e\u003c/a\u003e v.1.2.38\u003c/li\u003e\n\u003cli\u003eRibosomal RNA loci: \u003ca href=\"https://github.com/tseemann/barrnap\"\u003e\u003cstrong\u003eBarrnap\u003c/strong\u003e\u003c/a\u003e v.0.9\u003c/li\u003e\n\u003cli\u003eTaxonomic classification: \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003e\u003cstrong\u003eKraken2\u003c/strong\u003e\u003c/a\u003e v.2.1.1 and \u003ca href=\"https://github.com/Ecogenomics/GTDBTk\"\u003e\u003cstrong\u003eGTDB-tk\u003c/strong\u003e\u003c/a\u003e v1.4.1.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-4-trna-and-rrna\" class=\"anchor\" href=\"#4-trna-and-rrna\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. tRNA and rRNA\u003c/h4\u003e\n\u003cp\u003eThe close reference genome of MAG was determined by \u003ca href=\"https://github.com/Ecogenomics/GTDBTk\"\u003e\u003cstrong\u003eGTDB-tk\u003c/strong\u003e\u003c/a\u003e v.1.4.1.\u003c/p\u003e\n\u003cp\u003etRNA and rRNA genes of MAGs and reference genomes were identified as previously mentioned.\u003c/p\u003e\n\u003cp\u003eThen we calculated an observed-versus-expected ratio of the annotated tRNA and rRNA genes for each MAG as:\n\u003ca href=\"https://github.com/JiaLonghao1997/MAGbenchmark/blob/main/math1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/JiaLonghao1997/MAGbenchmark/raw/main/math1.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003cbr\u003e\nR_e is the expected tRNA or rRNA count of the reference genome, R_o is the observed tRNA or rRNA count of the MAG, r is the observed-versus-expected ratio.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-5-extrachromosomal-mobile-genetic-elements-emges\" class=\"anchor\" href=\"#5-extrachromosomal-mobile-genetic-elements-emges\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. extrachromosomal mobile genetic elements (eMGEs)\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003ePhages: \u003ca href=\"https://github.com/jiarong/VirSorter2\"\u003e\u003cstrong\u003eVirSorter2\u003c/strong\u003e\u003c/a\u003e v.2.1(using --min-length 1500 all) and \u003ca href=\"https://bitbucket.org/berkeleylab/checkv/src/master/\" rel=\"nofollow\"\u003e\u003cstrong\u003eCheckV\u003c/strong\u003e\u003c/a\u003e v0.8.1 (using end_to_end)\u003c/li\u003e\n\u003cli\u003ePlasmids: \u003cstrong\u003e\u003ca href=\"https://github.com/phac-nml/mob-suite\"\u003eMOB-suite\u003c/a\u003e\u003c/strong\u003e v.3.0.0\u003c/li\u003e\n\u003cli\u003eAntibiotic resistance genes: \u003ca href=\"https://www.mediterranee-infection.com/acces-ressources/base-de-donnees/arg-annot-2/\" rel=\"nofollow\"\u003e\u003cstrong\u003eARG-ANNOT\u003c/strong\u003e\u003c/a\u003e and \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs\" rel=\"nofollow\"\u003e\u003cstrong\u003eBLASTN\u003c/strong\u003e\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-6-references\" class=\"anchor\" href=\"#6-references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6. References\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eKuleshov V, Jiang C, Zhou W, Jahanbani F, Batzoglou S, Snyder M. Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome. Nat Biotechnol 2016, 34:64-69.\u003c/li\u003e\n\u003cli\u003eBishara A, Moss EL, Kolmogorov M, Parada AE, Weng Z, Sidow A, Dekas AE, Batzoglou S, Bhatt AS. High-quality genome sequences of uncultured microbes by assembly of read clouds. Nat Biotechnol 2018.\u003c/li\u003e\n\u003cli\u003eMoss EL, Maghini DG, Bhatt AS. Complete, closed bacterial genomes from microbiomes using nanopore sequencing. Nat Biotechnol 2020, 38:701-707.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1633153969.0 + "updated_at": 1640764398.0 }, { "data_format": 2, - "description": "Wrapper scripts and documentation for launching database jobs to be used by Nextflow pipelines", + "description": "FabSim3_extra", "filenames": [ - "Singularity.mysql" + "Singularity" ], - "full_name": "biocorecrg/nextflow_detached_db_wrapper", + "full_name": "kbronik2017/FabSim3_extra", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow_detached_db_wrapper\" class=\"anchor\" href=\"#nextflow_detached_db_wrapper\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextflow_detached_db_wrapper\u003c/h1\u003e\n\u003cp\u003eWrapper scripts and documentation for launching database jobs to be used by Nextflow pipelines\u003c/p\u003e\n\u003cp\u003eSo far it only has been tested with SGE/Univa queues.\u003c/p\u003e\n\u003cp\u003eExample command with several options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enohup perl run_pipeline_mysql.pl -params \"-with-dag -with-report -with-timeline\" -conf params.config -nextflowver 21.04.03 -extra \"-j y -l virtual_free=4G,h_rt=372800 -N MYSQL_container -m be -cwd -V -q myqueue\" -script pipeline.nf \u0026amp;\u0026gt; log.mysql \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnly running MySQL instance. Useful for checking existing contents.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enohup perl run_pipeline_mysql.pl -conf params.config -mysqlonly -extra \"-j y -l virtual_free=4G,h_rt=372800 -N MYSQL_container -m be -cwd -V -q myqueue\" \u0026amp;\u0026gt; log.mysqlonly \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePerl (e. g., with \u003ca href=\"https://perlbrew.pl/\" rel=\"nofollow\"\u003ePerlbrew\u003c/a\u003e)\n\u003cul\u003e\n\u003cli\u003eInstall Config::Simple module: \u003ccode\u003ecpanm Config::Simple\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fabsim\" class=\"anchor\" href=\"#fabsim\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFabSim\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/actions/workflows/Pytests.yml\"\u003e\u003cimg src=\"https://github.com/djgroen/FabSim3/actions/workflows/Pytests.yml/badge.svg?branch=master\" alt=\"Run Tests\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vecmafabsim3/fabsimdocker/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/15984bcb49e30e1f7e5e7b00084e0103bd4c6754edca6fbb1caa32f5dca78509/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f7665636d6166616273696d332f66616273696d646f636b65722e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/vecmafabsim3/fabsimdocker.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vecmafabsim3/fabsimdocker/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/506d3bba015b61abe07ca57664f35000afdb03531495602d97f42bb34afa35c3/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f7665636d6166616273696d332f66616273696d646f636b65722e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/automated/vecmafabsim3/fabsimdocker.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/tags\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f043c3ba40f9c2389fe1479a4488e19dfcbad1feac1fbe888c773bf0f5db411f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f646a67726f656e2f46616253696d333f7374796c653d666c6174\" alt=\"GitHub tag (latest by date)\" data-canonical-src=\"https://img.shields.io/github/v/tag/djgroen/FabSim3?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://lgtm.com/projects/g/djgroen/FabSim3/context:python\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/10419cff1f040d68ce752c6639616aaed414c6c5a7488e84662e19dee98ce77c/68747470733a2f2f696d672e736869656c64732e696f2f6c67746d2f67726164652f707974686f6e2f672f646a67726f656e2f46616253696d332e7376673f6c6f676f3d6c67746d266c6f676f57696474683d3138\" alt=\"Language grade: Python\" data-canonical-src=\"https://img.shields.io/lgtm/grade/python/g/djgroen/FabSim3.svg?logo=lgtm\u0026amp;logoWidth=18\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/djgroen/FabSim3/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/acc2a0eb223b853151fc5347101ef8574e352b40abc609e15062ccd32d937545/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f646a67726f656e2f46616253696d332e737667\" alt=\"GitHub Issues\" data-canonical-src=\"https://img.shields.io/github/issues/djgroen/FabSim3.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/djgroen/FabSim3/commits/master\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2c555714d9a4f16fd9f1c30cc71088810cb3cf12ca67e1bf9b3be68232f8fff6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f646a67726f656e2f46616253696d332e737667\" alt=\"GitHub last-commit\" data-canonical-src=\"https://img.shields.io/github/last-commit/djgroen/FabSim3.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFor the full FabSim3 documentation, please visit \u003ca href=\"https://fabsim3.readthedocs.io\" rel=\"nofollow\"\u003ehttps://fabsim3.readthedocs.io\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" href=\"#installation-and-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and usage\u003c/h2\u003e\n\u003cp\u003eFirst, user needs to install Anaconda \u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003ehttps://www.anaconda.com/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda create --name py3 python=3.6 {or any other python version \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e 3} \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate py3\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efinally for instructions on how to install and test FabSim, please go to \u003ca href=\"https://fabsim3.readthedocs.io/en/latest/installation/\" rel=\"nofollow\"\u003ehttps://fabsim3.readthedocs.io/en/latest/installation/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-easyvvuqfabmd\" class=\"anchor\" href=\"#easyvvuqfabmd\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEasyVVUQ+FabMD\u003c/h2\u003e\n\u003cp\u003eAfter updating the following files with your credentials\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e -FabSim3/deploy/machines_user.yml\n -FabSim3/deploy/machines.yml\n -FabSim3/plugins/FabMD/machines_FabMD_user.yml\n \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003erun the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - fabsim \u0026lt;remote machine name\u0026gt; lammps_init_run_analyse_campaign:fabmd_easyvvuq_InRuAn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand copy the results back to your local machine with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - fabsim \u0026lt;remote machine name\u0026gt; fetch_results\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-important\" class=\"anchor\" href=\"#important\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant\u003c/h2\u003e\n\u003cp\u003eBy default, FabSim3_extra comes with the FabDummy plugin and the FabMD plugin(fixed version!), which are available in ~/FabSim3/plugins\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1634748326.0 + "updated_at": 1641166034.0 }, { "data_format": 2, "description": null, "filenames": [ - "requirements/Singularity.def" + "Singularity" ], - "full_name": "nasa-cisto-ai/slump-detection", + "full_name": "porchard/snRNAseq-NextFlow", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-slump-detection\" class=\"anchor\" href=\"#slump-detection\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSlump Detection\u003c/h1\u003e\n\u003cp\u003eSlump Detection as an instance segmentation problem.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-business-case\" class=\"anchor\" href=\"#business-case\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBusiness Case\u003c/h2\u003e\n\u003cp\u003eThe following repository stores several experiments for the task of instance and semantic\nsegmentation of slumps in very high-resolution satellite imagery. Many of the instructions\nlisted below are guided towards utilizing GSFC NASA Center for Climate Simulation (NCCS)\ncomputing resources, particularly the PRISM GPU cluster.\u003c/p\u003e\n\u003cp\u003eA system with NVIDIA GPUs is required to run the scripts located in this repository.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eprojects/detectron2: utilizes the detectron2 framework for the task of instance segmentation\nleveraging MaskRCNN and Fast RCNN. The backend engine is PyTorch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-summarized-steps\" class=\"anchor\" href=\"#summarized-steps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummarized Steps\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin.nccs.nasa.gov\nssh gpulogin1\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#Logging_In\"\u003eLogging-In\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Container_Environment_Installation\"\u003eContainer Environment Installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Working_Inside_Container\"\u003eWorking Inside a Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Getting_Started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Authors\"\u003eAuthors\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#References\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-logging-in-\" class=\"anchor\" href=\"#logging-in-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogging-In \u003ca name=\"user-content-Logging_In\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eYou will need an activate NCCS account together with a PIV Card or an RSA Token. Please refer\nto the following link for instructions on setting up login or any login related questions:\n\u003ca href=\"https://www.nccs.nasa.gov/nccs-users/instructional/logging-in/bastion-host\" rel=\"nofollow\"\u003eNCCS Logging-In\u003c/a\u003e.\nOnce you are all setup, you may login to the PRISM GPU cluster.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin.nccs.nasa.gov\nssh gpulogin1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-container-environment-installation-\" class=\"anchor\" href=\"#container-environment-installation-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Environment Installation \u003ca name=\"user-content-Container_Environment_Installation\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eAll the software and scripts from this repository can be ran within a container. Containers are\nsmall versions of operating systems that are meant to speed up the process of software development.\nThese containers are simply a binary file which has all the executables needed to run the software included.\u003c/p\u003e\n\u003cp\u003eThe NCCS provides Singularity as the default container runtime tool. In order to configure your\nenvironment to run Singularity containers, you will need to setup the environment variables listed below.\nFor this, you can simply add the following lines to your ~/.bashrc file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport SINGULARITY_CACHEDIR=\u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/.singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport SINGULARITY_TMPDIR=\u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/.singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTest the environment variables with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e[username@gpulogin1 \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e]$ \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_CACHEDIR\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_TMPDIR\u003c/span\u003e\n/att/nobackup/username/.singularity /att/nobackup/username/.singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to utilize the container for this project, we first need to download the image from a container\nregistry. The image for this project is located in \u003ca href=\"https://hub.docker.com/repository/docker/nasanccs/slump-detectron2\" rel=\"nofollow\"\u003eNASA NCCS DockerHub Repository\u003c/a\u003e. Docker containers can be pulled as Singularity containers to be executed on HPC\nenvironments. The following commands allow the download of the container from DockerHub and generates a\nfile with a .sif extension. Depending on the file system, this step can take several minutes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e\nmodule load singularity\nsingularity pull docker://docker.io/nasanccs/slump-detectron2:latest\nsingularity build --sandbox slump-detectron2_latest slump-detectron2_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-working-inside-a-container-\" class=\"anchor\" href=\"#working-inside-a-container-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorking Inside a Container \u003ca name=\"user-content-Working_Inside_Container\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eEach project provides a set of Slurm scripts that will execute code inside the container without having\nto login inside the image. You may skip this step and go straight to the project README if you are only\ninterested in running scripts from outside the container. This section is meant to help users developing\nand testing code inside the container to facilitate the development process.\u003c/p\u003e\n\u003cp\u003eTo get a session in one of the PRISM GPU nodes, you can run the following command. Additional instructions\nregarding Slurm can be found in the \u003ca href=\"https://www.nccs.nasa.gov/nccs-users/instructional/adapt-instructional/using-prism\" rel=\"nofollow\"\u003eNCCS website\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esalloc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will notice that the hostname will change to something similar to gpu***. This means that you are now\nlogged into one of the GPU nodes. To access the container image, you can run the command listed below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --nv -B /att/nobackup/username:/att/nobackup/username slump-detectron2_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere username is your NASA auid. From here, you can run any command inside the container image. Note that\nfor Singularity containers to have access to other paths within the HPC environment, we need to bind\ndirectories to particular locations in the container. The command above is binding your $NOBACKUP directory\nto be visible from inside the container.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started-\" class=\"anchor\" href=\"#getting-started-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started \u003ca name=\"user-content-Getting_Started\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eThe following is a summarized set of steps to get started and running in less than 5 minutes once the container image has been downloaded.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository into your ADAPT space\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e\ngit clone https://github.com/jordancaraballo/slump-detection.git\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCopy the data into the data/ directory\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp /data/location/.tif \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/data\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eGenerate train, test, and validation datasets\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch gen_dataset.sh\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eTrain a new model\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch train_detectron2.sh\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eClassify given imagery\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch predict_detectron2.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-project-specific-information\" class=\"anchor\" href=\"#project-specific-information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject Specific Information\u003c/h2\u003e\n\u003cp\u003eData resides under:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/att/nobackup/username/EVHR_requests/_deliver/EWebbRequest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin\nssh gpulogin1\nmodule load anaconda\nconda create --name slump-detection-11.1 --clone /att/nobackup/username/.conda/envs/slump-detection-11.1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-anaconda-environment\" class=\"anchor\" href=\"#anaconda-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnaconda environment\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load anaconda\nconda config --add channels conda-forge\nconda config --set channel_priority strict\nconda create -y -n slump-detection rioxarray cupy cudatoolkit=11.2 dask-cuda cudnn cutensor nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall pip dependencies\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda activate slump-detection\npip install -r requirements.txt\npip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003egit+https://github.com/facebookresearch/fvcore\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egit+https://github.com/philferriere/cocoapi.git#egg=pycocotools\u0026amp;subdirectory=PythonAPI\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ngit clone https://github.com/facebookresearch/detectron2 detectron2_repo \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e pip install -e detectron2_repo\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdding NCCL\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia\nconda create -n rapids-21.06 -c rapidsai -c nvidia -c conda-forge rapids-blazing=21.06 python=3.7 cudatoolkit=11.2 nvcc_linux-64 nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n rapids-21.06 -c rapidsai -c nvidia -c conda-forge -c pytorch rapids-blazing=21.06 python=3.7 cudatoolkit=11.1 ipykernel ipywidgets matplotlib geopandas pytorch torchvision torchaudio cudatoolkit=11.1 \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou could also enhance your kernel with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda config --add channels conda-forge\nconda config --set channel_priority strict\nconda create -y -n slump-detection-11.1 rioxarray cupy cudatoolkit=11.1 dask-cuda cudnn cutensor nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities gcc_linux-64\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install cython\npip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003egit+https://github.com/facebookresearch/fvcore\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egit+https://github.com/philferriere/cocoapi.git#egg=pycocotools\u0026amp;subdirectory=PythonAPI\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\npip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html\npip install opencv-python scikit-image\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-authors\" class=\"anchor\" href=\"#authors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eJordan Alexis Caraballo-Vega, \u003ca href=\"mailto:jordan.a.caraballo-vega@nasa.gov\"\u003ejordan.a.caraballo-vega@nasa.gov\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003e[1] Chollet, Fran\u00e7ois; et all, Keras, (2015), GitHub repository, \u003ca href=\"https://github.com/keras-team/keras\"\u003ehttps://github.com/keras-team/keras\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n\u003cp\u003e[2] Paszke, Adam; Gross, Sam; Chintala, Soumith; Chanan, Gregory; et all, PyTorch, (2016), GitHub repository, \u003ca href=\"https://github.com/pytorch/pytorch\"\u003ehttps://github.com/pytorch/pytorch\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n\u003cp\u003e[3] Google Brain Team; et all, TensorFlow, (2015), GitHub repository, \u003ca href=\"https://github.com/tensorflow/tensorflow\"\u003ehttps://github.com/tensorflow/tensorflow\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-10x-snatac-seq-data\" class=\"anchor\" href=\"#nextflow-pipeline-for-10x-snatac-seq-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow pipeline for 10X snATAC-seq data\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eSingularity (v. 3) and NextFlow (\u0026gt;= v. 20.10.0). Containers with the software for each step are pulled from the Sylabs cloud library (\u003ca href=\"https://cloud.sylabs.io/library\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003ePaths to reference files must be included in the nextflow.config file -- check that file and change paths accordingly. These include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSTAR indices (compatible with STAR v. 2.7.9a)\u003c/li\u003e\n\u003cli\u003eGTF files\u003c/li\u003e\n\u003cli\u003eBarcode whitelist (for Chromium v3, that is the 3M-february-2018.txt file; for v2, that is the 737K-august-2016.txt file; for multiome, that is 737K-arc-v1.txt)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou\u0027ll also need to set the params.results variable -- either in the nextflow.config file itself, or on the command line when you run the pipeline (\u0027--results /path/to/results\u0027) as well as the 10X Chromium chemistry version (\u0027V2\u0027, \u0027V3\u0027, or \u0027multiome\u0027)\u003c/p\u003e\n\u003cp\u003eLastly, you\u0027ll need to include information about each RNA-seq library, including the genome to which it should be mapped, and the paths to the fastq files for each readgroup. Organize this information in a JSON file, as in library-config.json. For each readgroup, the \u00271\u0027 fastq file corresponds to the sequencing read including the UMI and the nucleus index; the \u00272\u0027 fastq file refers to the sequencing read representing the actual transcript. Also, note that the \u0027genome\u0027 attribute is given as a list (because I will be adding the ability to map to multiple genomes, in the case that nuclei from multiple species are mixed together).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eOnce you have all of the above information, you can run the pipeline as follows (in this case, indicating the path to the results on the command line):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run -params-file library-config.json --chemistry multiome --results /path/to/results /path/to/main.nf\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1633374291.0 + "updated_at": 1640263903.0 }, { "data_format": 2, - "description": "Recipe for deepspeed singularity container", + "description": "\ud83d\udc1f \ud83c\udf63 \ud83c\udf71 Highly-accurate \u0026 wicked fast transcript-level quantification from RNA-seq reads using selective alignment", "filenames": [ - "Singularity" + "1.6.0/Singularity", + "1.5.2/Singularity" ], - "full_name": "luukkonenr/deepspeed-torch-singularity", + "full_name": "pscedu/singularity-salmon", "latest_release": null, - "readme": "\u003ch3\u003e\n\u003ca id=\"user-content-note-docker-workflow-with-gh-actions-is-broken-due-to-a-broken-dependency-since-debian-git-depenceny-for-image-has-been-removed\" class=\"anchor\" href=\"#note-docker-workflow-with-gh-actions-is-broken-due-to-a-broken-dependency-since-debian-git-depenceny-for-image-has-been-removed\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTE: Docker-workflow with GH-Actions is broken due to a broken dependency, since debian-git-depenceny for image has been removed.\u003c/h3\u003e\n\u003cp\u003eTODO: update image path.\nPrevious working image is still available.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-recipe-template-for-building-deepspeed-enabled-pytorch-container\" class=\"anchor\" href=\"#singularity-recipe-template-for-building-deepspeed-enabled-pytorch-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity-recipe-template for building Deepspeed-enabled pytorch-container\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install-singularity\" class=\"anchor\" href=\"#install-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall singularity\u003c/h2\u003e\n\u003cp\u003eFollow these instructions to install singularity on a system\n\u003ca href=\"https://github.com/hpcng/singularity/blob/master/INSTALL.md\"\u003ehttps://github.com/hpcng/singularity/blob/master/INSTALL.md\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNOTE: I\u0027ve used \u003cstrong\u003eSingularity version 3.5.3\u003c/strong\u003e, newest 3.8.3 gave me some errors and I think it uses later gcc or something like that which results in build problems with some of the libraries.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-option-1-building-a-container-on-your-own-machine\" class=\"anchor\" href=\"#option-1-building-a-container-on-your-own-machine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 1: Building a container on your own machine\u003c/h2\u003e\n\u003cp\u003eYou need root-privileges (or --fakeroot) to build containers.\nYou may need to set cachedir for singularity to avoid \u0027no space left on device\u0027-errors\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir $HOME/.cache/singularity/\nexport SINGULARITY_TMPDIR=/path/ e.g $HOME/.cache/singularity/\nexport SINGULARITY_CACHEDIR=/path/ e.g $HOME/.cache/singularity/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eBUILD:\u003c/strong\u003e \u003ccode\u003esudo -E singularity build container-name Singularity\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-option-2-pulling-ready-built-image-from-ghcr\" class=\"anchor\" href=\"#option-2-pulling-ready-built-image-from-ghcr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 2: Pulling ready-built image from ghcr\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITY_TMPDIR=/path/ e.g $HOME/.cache/singularity/\nexport SINGULARITY_CACHEDIR=/path/ e.g $HOME/.cache/singularity/\nsingularity pull NAME_FOR_IMG docker://ghcr.io/luukkonenr/deepspeed-torch-singularity:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-csc-environment\" class=\"anchor\" href=\"#running-on-csc-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on CSC-environment\u003c/h2\u003e\n\u003cp\u003eIf running on Mahti make sure your $HOME/.ssh/config is looking like this\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e host c???? g???? mahti* *.mahti.csc.fi\n IdentityFile ~/.ssh/id_rsa_mahti\n StrictHostKeyChecking no\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePut the following inside your slurm-script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#Load pdsh\nmodule load pdsh/2.31\n\n#Bind directory with pdsh to /usr/local/sbin in singularity\nexport SING_FLAGS=\"$SING_FLAGS -B /appl/spack/v014/install-tree/gcc-4.8.5/pdsh-2.31-cdzt5w/bin:/usr/local/sbin\"`\nexport SING_IMAGE=/PATH/TO/CONTAINER/deepspeed.sif # This needs to match the path inside your init_node.sh\nexport SING_FLAGS=$SING_FLAGS \"--nv\" # Enable GPU\nexport SING_FLAGS=$SING_FLAGS \"--contain\" # Shadow /home/$USER/ \nexport TORCH_EXT_DIR=/path/to/some/dir/ # I f you have existing dir with some ops, may cause a hang with a msg about using this torch_ext_dir. Try removing that dir and run your job again.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing plain singularity and \u003ccode\u003e--contain\u003c/code\u003e-flag shadowing the /user/home/ to avoid possible conflicting user-packages:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEXAMPLE:\u003c/strong\u003e\n\u003ccode\u003esingularity exec --contain $SING_IMAGE python -c \u0027ds_report\u0027\u003c/code\u003e\n\u003ccode\u003esingularity exec $SING_FLAGS $SING_IMAGE python -c \u0027ds_report\u0027\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eUsing csc singularity_wrapper (\u003cstrong\u003enot preferred\u003c/strong\u003e, may lead to conflicts especially on multinode-setup) :\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRUNNING:\u003c/strong\u003e\n\u003ccode\u003esingularity_wrapper exec deepspeed DEEPSPEED_ARGUMENTS path/to/python_script.py PYTHON_ARGUMENTS\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEXAMPLE:\u003c/strong\u003e\n\u003ccode\u003esingularity_wrapper exec deepspeed --hostfile=hostfile.txt --master_addr=$MASTER_NODE /projappl/project_2004600/risto/model3multi/training/trainer.py --train_data $TRAIN_DATA \\ ... \u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-changes-to-packages\" class=\"anchor\" href=\"#changes-to-packages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChanges to packages:\u003c/h2\u003e\n\u003cp\u003eThis version has been configured to use pdsh for inter-node communications. No other runners have been tested and may need spesific configurations.\n\u003ccode\u003e/opt/conda/lib/python3.8/site-packages/deepspeed/launcher/multinode_runner.py\u003c/code\u003e has been modified to contain relevant information about running python inside the container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eadded line \"source node_init.sh\" \u003cem\u003esee node_init.sh\u003c/em\u003e to PDSH-runner-class\u003c/li\u003e\n\u003cli\u003eexec argument \u003ccode\u003epython\u003c/code\u003e changed to \u003ccode\u003esingularity exec $SING_FLAGS $SING_IMAGE python\u003c/code\u003e to PDSH-runner-class\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIMPORTANT\u003c/strong\u003e: CSC singularity_wrapper exposes user-libraries even if we use \u003ccode\u003e--contain\u003c/code\u003e-flag so using it with this container is not a good idea.\n\u003ccode\u003e--contain\u003c/code\u003e-flag prevents usage of locally installed packages. Otherwise, conflicts with different versions of packages, especially included modified Deepspeed will cause problems.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eI\u0027ve tried to test get build process working with Github Actions but during build I encounter \"no space left on device\"-error and build crashes. Will try to get this working so newest img would always be ready to get pulled. However, Docker-workflow works.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-resources\" class=\"anchor\" href=\"#resources\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://singularity-tutorial.github.io/\" rel=\"nofollow\"\u003ehttps://singularity-tutorial.github.io/\u003c/a\u003e -- Basics of singularity usage\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/\u003c/a\u003e -- Singularity docs (v.3.5)\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-salmon/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-salmon/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-salmon/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-salmon/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0b8ff9f174b15f24d56c3f8e2cf513b84784150c0375c38dc395c4b3278d6a17/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73616c6d6f6e\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0b8ff9f174b15f24d56c3f8e2cf513b84784150c0375c38dc395c4b3278d6a17/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73616c6d6f6e\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-salmon\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/2c22132440bbb02239d96119f1cf10791c102131494c8947ac8fe61fb5e02783/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73616c6d6f6e\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2c22132440bbb02239d96119f1cf10791c102131494c8947ac8fe61fb5e02783/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73616c6d6f6e\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-salmon\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/62b78a520b27fb026e274ca940aa93bf9d37fa14d817d47a533364393ecff206/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d73616c6d6f6e\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/62b78a520b27fb026e274ca940aa93bf9d37fa14d817d47a533364393ecff206/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d73616c6d6f6e\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-salmon\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7dd8453685087761442a45f4a77d6ddab1597f1cc80874ef308e2170ed183e6a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73616c6d6f6e\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7dd8453685087761442a45f4a77d6ddab1597f1cc80874ef308e2170ed183e6a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73616c6d6f6e\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-salmon\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-salmon\" class=\"anchor\" href=\"#singularity-salmon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-salmon\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/COMBINE-lab/salmon/raw/master/doc/salmon_logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg alt=\"salmon logo\" src=\"https://github.com/COMBINE-lab/salmon/raw/master/doc/salmon_logo.png\" width=\"600\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/COMBINE-lab/salmon\"\u003esalmon\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003esalmon\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/salmon/1.5.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/salmon\u003c/code\u003e as \u003ccode\u003e1.5.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1637060857.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1639902426.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "DCAN-Labs/BIDS_scripts", + "full_name": "iqbal-lab-org/triphecta", "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-bids-conversion-scripts\" class=\"anchor\" href=\"#bids-conversion-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBIDS Conversion Scripts\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eThis repository contains scripts that will assist in the conversion of raw DICOM data sets to \u003ca href=\"http://bids.neuroimaging.io/format\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e. The \"heuristics\" folder contains example scripts that have been used to convert data from DICOM to BIDS. \u003cstrong\u003eThis folder may be helpful to build your own heuristics script.\u003c/strong\u003e For additional information on how to create a heuristic script see the \u003ca href=\"https://github.com/nipy/heudiconv\"\u003eheudiconv\u003c/a\u003e github page.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-hosting\" class=\"anchor\" href=\"#container-hosting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Hosting\u003c/h3\u003e\n\u003cp\u003ePull the most recent container below, (\u003cstrong\u003eNOTE: you only have to do this once!\u003c/strong\u003e):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tjhendrickson/BIDS_scripts:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" href=\"#singularity-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Usage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/singularity/images/directory/imagename.img --help\nusage: [-h] [--output_dir OUTPUT_DIR] [--dicom_dir DICOM_DIR]\n [--study_name STUDY_NAME] [--ses_id SES_ID] [--subj_id SUBJ_ID]\n [--heuristic HEURISTIC] [--dry_run]\n\nScript that controls BIDS conversion for individual studies\n\noptional arguments:\n -h, --help show this help message and exit\n --output_dir OUTPUT_DIR\n The directory that the BIDS data will be outputted to\n --dicom_dir DICOM_DIR\n The directory that houses unzipped dicom\n directories/files.\n --study_name STUDY_NAME\n What is the shorthand name for this study?\n --ses_id SES_ID scanning session id\n --subj_id SUBJ_ID subject id\n --heuristic HEURISTIC\n Path to heuristic file, if the file is already within\n the container (i.e. within heuristics folder) you do\n not have to specify a path.\n --dry_run Dry run. A dicominfo_*.tsv file will generate within\n .heudiconv/\u0027subj_id\u0027/info directory which can be used\n to create heuristic script\n\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eThis application must be run with either the \"--heuristic\" or \"--dry_run\" argument, it will fail otherwise.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse the \"--dry_run\" argument to take a closer look at the acquistion parameters for a scanning session.\u003c/p\u003e\n\u003cp\u003eTo run a single participant with dry_run argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --dry_run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output a hidden folder (named .heudiconv) along with sub-folders based on arguments provided to \"--subj_id\" and \"--ses_id\" respectively.\nWithin the sub-folders will be a tsv file that begins with \"dicominfo\". \u003cstrong\u003eBased on the example above the path to the file will be \".heudiconv/1000/ses-10000/info/dicominfo_ses-10000.tsv\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse this tsv file to design a heuristics script to organize your eventual nifti data. \u003cstrong\u003eSee this tutorial for a how to on heuristic script creation (\u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman3\" rel=\"nofollow\"\u003eheuristics tutorial\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo run a single participant with heuristic argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir -B /path/to/heuristics/script:/heuristic.py \\\n /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --heuristic /heuristic.py\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-important-notes\" class=\"anchor\" href=\"#important-notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Notes\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gotchas\" class=\"anchor\" href=\"#gotchas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGotchas\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Bind Mounting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to run this container you will have to use \"bind mounting\", meaning you will have to link local folders/files to existing folders/files within the container with the -B flag. In the example above the local folder \"/home/timothy/sandbos_DO_NOT_DELETE/BIDS/142_CIFASD_4\" becomes \"/output_dir\" within the container as they are separated by a colon (:). \u003cstrong\u003eNotice that in both cases above the output and dicom folder and heuristic file are bound to /output_dir, /dicom_dir and /heuristic.py respectively, this is very important.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) DICOM Data Formatting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the restrictive nature of BIDS the dicom data must be in a particular format in order for the conversion to work properly. This application will copy dicom data directories by searching for either the --subj_id or --ses_id argument present within a dicom directory name, place them in a separate directory, and rearrange them. So for example if the dicom directory is named \"XYXY4776XYXY\" --subj_id 4776 the application will find the \"4776\" pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Subject ID and Session ID names\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou must use alphanumerics (i.e. letters or numbers) only (\u003cstrong\u003eno special characters\u003c/strong\u003e) with your subject IDs (subj_id) and session IDs (ses_id). \u003cstrong\u003eNote the \"--ses_id\" argument is optional\u003c/strong\u003e!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-best-practices\" class=\"anchor\" href=\"#best-practices\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBest Practices\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Initial Conversion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile testing the initial BIDS conversion it is best to start with one or two datasets and specify the \u0027--dry_run\u0027 argument (see above for an example of usage).\nThis will create a dicom_info tsv file which can be used for heuristic script creation.\nSee Step 3 of \u0027Run HeuDiConv on ses-001 scans to get the dicominfo file\u0027 within \u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman2\" rel=\"nofollow\"\u003eStanford BIDS Tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) BIDS Validator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce satisfied with an initial BIDS conversion, prior to running the conversion on an entire study first ensure that the BIDS converted dataset meets the BIDS specification by using the \u003ca href=\"http://incf.github.io/bids-validator/\" rel=\"nofollow\"\u003eBIDS validator web version\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Longitudinal Format\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen converting data to BIDS you can certainly have a cross sectonal directory format such as below:\nBIDS_output\nsub-01\nsub-02\nsub-03\nsub-04\netc\nHowever, I suggest placing data within a longitudinal directory format even if you have cross-sectional data:\nBIDS_output\nsub-01\nses-01\nses-02\nsub-02\nses-01\netc\u003c/p\u003e\n\u003cp\u003eYou can control the BIDS directory format by providing both the arguments --subj_id --ses_id for a conversion, if you only specify one of the two arguments the data will be outputted in a cross-sectional format.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/triphecta/actions/workflows/build.yaml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/triphecta/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-triphecta\" class=\"anchor\" href=\"#triphecta\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etriphecta\u003c/h1\u003e\n\u003cp\u003eUnder construction\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 3, "topics": [], - "updated_at": 1633800709.0 + "updated_at": 1639756790.0 }, { "data_format": 2, - "description": "sherlock vnc is a singularity container and job script to run xfce4 in a vnc session on the sherlock compute cluster", + "description": null, "filenames": [ "Singularity" ], - "full_name": "romxero/sherlock_vnc", - "latest_release": null, + "full_name": "AdamWilsonLab/emma_docker", + "latest_release": "0.0.605", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-emma-docker-container\" class=\"anchor\" href=\"#emma-docker-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEMMA Docker Container\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-instructions\" class=\"anchor\" href=\"#instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quickstart\" class=\"anchor\" href=\"#quickstart\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -p 8787:8787 -e PASSWORD=yourpasswordhere adamwilsonlab/emma:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eVisit \u003ccode\u003elocalhost:8787\u003c/code\u003e in your browser and log in with username rstudio and the password you set. NB: Setting a password is now REQUIRED. Container will error otherwise.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-local-machine-no-password\" class=\"anchor\" href=\"#local-machine-no-password\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal machine (no password)\u003c/h2\u003e\n\u003cp\u003eIf you are running on a local machine with other security mechanisms, you can use the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm \\\n -p 127.0.0.1:8787:8787 \\\n -e DISABLE_AUTH=true \\\n adamwilsonlab/emma:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eThere are two methods to pull the docker image into Singularity as explained below.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-set-some-useful-environment-variables\" class=\"anchor\" href=\"#set-some-useful-environment-variables\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet some useful environment variables\u003c/h3\u003e\n\u003cp\u003eIf you don\u0027t do this you\u0027re likely to run out of space because the home directory doesn\u0027t have much room.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# mount project folder inside container:\nexport PROJECT_FOLDER=\"/projects/academic/adamw/\"\n# path to singularity container file. If you want to use a different image, you\u0027ll need\n# to update this line.\nexport DOCKER_PATH=\"docker://adamwilsonlab/emma:latest\"\nexport CONTAINER_PATH=\"/panasas/scratch/grp-adamw/singularity/$USER/AdamWilsonLab-emma_docker:latest.sif\"\n# to use for ssh:\nexport SERVER_URL=\"horae.ccr.buffalo.edu\"\n# folder to hold temporary singularity files - unique for each user:\n# export SINGULARITY_LOCALCACHEDIR=\"/panasas/scratch/grp-adamw/singularity/\"$USER\nexport SINGULARITY_LOCALCACHEDIR=\"/ssd_data/singularity/\"$USER\n\n# name the resulting sif file\nexport SIF_PATH=$SINGULARITY_LOCALCACHEDIR/\"AdamWilsonLab-emma_docker-latest.sif\"\n\n# define a few more folders used by singularity\nexport SINGULARITY_CACHEDIR=$SINGULARITY_LOCALCACHEDIR\nexport SINGULARITY_TMPDIR=$SINGULARITY_LOCALCACHEDIR\n\n# Create the folders if they don\u0027t already exist\nmkdir -p $SINGULARITY_LOCALCACHEDIR/tmp\nmkdir -p $SINGULARITY_LOCALCACHEDIR/run\nmkdir -p $SINGULARITY_LOCALCACHEDIR/rstudio\n\n\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-directly-from-docker-image-locally\" class=\"anchor\" href=\"#build-directly-from-docker-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild directly from Docker image locally\u003c/h3\u003e\n\u003cp\u003eBuild the .sif directly from the docker image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# build the singularity image - note this takes about 3 hours on horae!\nnohup singularity build --force $SIF_PATH $DOCKER_PATH \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003enohup\u003c/code\u003e simply allows it to keep running if the SSH connection is broken.\u003c/p\u003e\n\u003cp\u003eThen follow the \u003ca href=\"singularity_start.sh\"\u003e\u003ccode\u003esingularity_start.sh\u003c/code\u003e\u003c/a\u003e script.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-use-the-precompiled-sif-from-github\" class=\"anchor\" href=\"#use-the-precompiled-sif-from-github\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse the precompiled .sif from Github\u003c/h3\u003e\n\u003cp\u003eA .sif file is compiled using github actions when the version number of the image is updated in this repository. These can be found \u003ca href=\"https://github.com/AdamWilsonLab/emma_docker/releases\"\u003ehere\u003c/a\u003e. However, they are only produced if turned on in the GitHub actions \u003ccode\u003ebuilder.yml\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eYou will only need to run the following once (unless the image changes).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /panasas/scratch/grp-adamw/singularity/adamw\nrm AdamWilsonLab-emma_docker-latest.sif\nwget -O $SIF_PATH https://github.com/AdamWilsonLab/emma_docker/releases/download/0.0.530/AdamWilsonLab-emma_docker-latest.sif.zip\nunzip $SIF_PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen follow the \u003ca href=\"singularity_start.sh\"\u003e\u003ccode\u003esingularity_start.sh\u003c/code\u003e\u003c/a\u003e script.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1634276991.0 + "updated_at": 1641492090.0 }, { "data_format": 2, - "description": "Trigger repo1 on repos2 release", + "description": "Generate a singularity container for XDS", "filenames": [ - "environments/illumina/Singularity" + "Singularity.xds_2021-Feb05" ], - "full_name": "sofstam/repo1", - "latest_release": "v2.1.3", - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-repo1\" class=\"anchor\" href=\"#repo1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepo1\u003c/h2\u003e\n", + "full_name": "hoangnguyen177/xds-singularity-container", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-xds-singularity-container\" class=\"anchor\" href=\"#xds-singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003exds-singularity-container\u003c/h1\u003e\n\u003cp\u003eGenerate a singularity container for XDS\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1638278004.0 + "updated_at": 1639537965.0 }, { "data_format": 2, - "description": "Singularity images for tensorflow", + "description": null, "filenames": [ - "Singularity.cuda9.0-tf1.13-with_dali", - "Singularity.cuda9.0-tf1.13-ofed4.4", - "Singularity.cuda9.0-tf1.13-ofed4.0", - "Singularity.cuda9.0-tf1.13-without-ofed" + "Singularity.speaker_tagging" ], - "full_name": "Pepitaw/singularity_tensorflow", + "full_name": "oboratav/speaker-tagging", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_tensorflow\" class=\"anchor\" href=\"#singularity_tensorflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity_tensorflow\u003c/h1\u003e\n\u003cp\u003eSingularity images for tensorflow\nUsed for 2019 APAC HPC-AI\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-red-hen-teletext-color-annotator\" class=\"anchor\" href=\"#red-hen-teletext-color-annotator\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRed Hen Teletext Color Annotator\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.redhenlab.org/home/the-cognitive-core-research-topics-in-red-hen/the-barnyard/convert-teletext-colors-to-speaker-tags\" rel=\"nofollow\"\u003eA Red Hen Lab project.\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eSome providers in certain countries use styling features available in DVB Teletext to color-code their closed captioning. These color codes can potentially be used to detect turn-taking between interlocutors.\u003c/p\u003e\n\u003cp\u003eThis program takes a \u003ccode\u003e.seg\u003c/code\u003e file, reads color tags inside it (if any), and outputs an annotated version of the same file.\u003c/p\u003e\n\u003cp\u003eThe tags it adds are in the form of:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[start]|[end]|CTG_0|[hex]/[text]\n\u003c/code\u003e\u003c/pre\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eField\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e[start]\u003c/td\u003e\n\u003ctd\u003eStarting timestamp of the annotation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e[end]\u003c/td\u003e\n\u003ctd\u003eEnding timestamp of the annotation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e[hex]\u003c/td\u003e\n\u003ctd\u003eHex color of the tag\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e[text]\u003c/td\u003e\n\u003ctd\u003eContents of the tag\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor instance:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e20200202214233.960|20200202214234.760|CTG_0|#ffff00/y nuevas pistas.\n20200202214233.960|20200202214234.760|CC1|\u0026lt;font color=\"#ffff00\"\u0026gt;y nuevas pistas.\u0026lt;/font\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003ehex/text\u003c/code\u003e pairs may repeat if more than one color tag exists in a single CC line, with each pair being separated by \u003ccode\u003e|\u003c/code\u003e like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e20200202214242.840|20200202214245.360|CTG_0|#ffff00/en busca de respuestas|#ffff00/a las nuevas tendencias.\n20200202214242.840|20200202214245.360|CC1|\u0026lt;font color=\"#ffff00\"\u0026gt;en busca de respuestas\u0026lt;/font\u0026gt; \u0026lt;font color=\"#ffff00\"\u0026gt;a las nuevas tendencias.\u0026lt;/font\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-install-and-use\" class=\"anchor\" href=\"#how-to-install-and-use\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Install and Use\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-via-docker\" class=\"anchor\" href=\"#via-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evia Docker\u003c/h3\u003e\n\u003cp\u003eInstalling and using the tool as a Docker container is by far the easiest way. Simply run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull oboratav/speaker-tagging\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd Docker will take care of the rest. To annotate a file, simply pipe it into the container, and capture its output:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat input_file.txt | docker run -i -a stdin -a stdout oboratav/speaker-tagging \u0026gt; output_file.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also use the \u003ccode\u003e-v\u003c/code\u003e flag to mount files from the local filesystem:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v /some/input/file.seg:/usr/data/input_file.seg -a stdout oboratav/speaker-tagging /usr/data/input_file.seg \u0026gt; output_file.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-directly\" class=\"anchor\" href=\"#directly\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectly\u003c/h3\u003e\n\u003cp\u003eYou can also skip Docker altogether and just clone this git repo, create a virtual environment, and install the requirements listed in \u003ccode\u003erequirements.txt\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-example-use-cases\" class=\"anchor\" href=\"#example-use-cases\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample Use Cases\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eFind occurrences of two different colors in the same line:\n\u003ccode\u003eCTG_0\\|.*([a-f0-9]{6}).*\\|(?!\\1)(?:[a-f0-9]{6})\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1634629001.0 + "updated_at": 1639347259.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "remiolsen/fast5mod-singularity", + "full_name": "piyu2181/singulariyu", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fast5mod-singularity\" class=\"anchor\" href=\"#fast5mod-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efast5mod-singularity\u003c/h1\u003e\n\u003cp\u003eSingulartized version of \u003ca href=\"https://github.com/nanoporetech/fast5mod\"\u003ehttps://github.com/nanoporetech/fast5mod\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1635176825.0 + "updated_at": 1565736075.0 }, { "data_format": 2, @@ -13308,13 +12733,13 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-busybox", + "full_name": "garciaml/BrainQCNet_CPU", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-busybox-toy-system-for-singularity-and-docker-image-for-ci\" class=\"anchor\" href=\"#building-a-busybox-toy-system-for-singularity-and-docker-image-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a busybox toy system for singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-busybox/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-busybox/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-busybox:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-busybox:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-brainqcnet-version-for-cpu--detection-of-artifacts-on-brain-t1-weighted-scans\" class=\"anchor\" href=\"#brainqcnet-version-for-cpu--detection-of-artifacts-on-brain-t1-weighted-scans\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBrainQCNet [version for CPU] : Detection of Artifacts on Brain T1-weighted Scans\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/garciaml/BrainQCNet/blob/master/T1_low_quality_2.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/garciaml/BrainQCNet/raw/master/T1_low_quality_2.jpg\" width=\"2000\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor more information about the specification of BIDS Apps see \u003ca href=\"https://docs.google.com/document/d/1E1Wi5ONvOVVnGhj21S1bmJJ4kyHFT7tkxnV3C23sjIE/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eBrainQCNet is a software that automatically detects the presence of defaults on brain structural MRI scans.\u003c/p\u003e\n\u003cp\u003eIt is based on a Deep Learning algorithm, you can find more details on how it was built on the paper \u003ca href=\"https://link-to-preprint.com\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eIn order to understand how to use BainQCNet, please go \u003ca href=\"https://github.com/garciaml/BrainQCNet\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-how-to-report-errors\" class=\"anchor\" href=\"#how-to-report-errors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to report errors\u003c/h3\u003e\n\u003cp\u003eUsers can get help using the \u003ca href=\"https://groups.google.com/g/brainqcnet-users\" rel=\"nofollow\"\u003ebrainqcnet-users mailing list\u003c/a\u003e.\nAll bugs, concerns and enhancement requests for this software can be submitted \u003ca href=\"https://github.com/garciaml/BrainQCNet_CPU/issues\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h3\u003e\n\u003cp\u003eWhen using BrainQCNet, please include the following citation:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBrainQCNet: a Deep Learning attention-based model for multi-scale detection of artifacts in brain structural MRI scans\u003c/em\u003e, Melanie Garcia, Nico Dosenbach, Clare Kelly. bioRxiv 2022.03.11.483983; doi: \u003ca href=\"https://doi.org/10.1101/2022.03.11.483983\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2022.03.11.483983\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1635194705.0 + "updated_at": 1646931972.0 }, { "data_format": 2, @@ -13322,27 +12747,26 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-stream8-ci", + "full_name": "garciaml/BrainQCNet_GPU", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-centos-stream-8-toy-system-for-singularity-and-docker-image-for-ci\" class=\"anchor\" href=\"#building-a-centos-stream-8-toy-system-for-singularity-and-docker-image-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a CentOS Stream 8 toy system for singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003estream8 system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-ci/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-ci/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-stream8-ci:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-stream8-ci:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-brainqcnet-version-for-gpu-compatible-with-cuda-cudnn--detection-of-artifacts-on-brain-t1-weighted-scans\" class=\"anchor\" href=\"#brainqcnet-version-for-gpu-compatible-with-cuda-cudnn--detection-of-artifacts-on-brain-t1-weighted-scans\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBrainQCNet [version for GPU compatible with CUDA, CuDNN] : Detection of Artifacts on Brain T1-weighted Scans\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/garciaml/BrainQCNet/blob/master/T1_low_quality_2.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/garciaml/BrainQCNet/raw/master/T1_low_quality_2.jpg\" width=\"2000\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor more information about the specification of BIDS Apps see \u003ca href=\"https://docs.google.com/document/d/1E1Wi5ONvOVVnGhj21S1bmJJ4kyHFT7tkxnV3C23sjIE/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eBrainQCNet is a software that automatically detects the presence of defaults on brain structural MRI scans.\u003c/p\u003e\n\u003cp\u003eIt is based on a Deep Learning algorithm, you can find more details on how it was built on the paper \u003ca href=\"https://link-to-preprint.com\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eIn order to understand how to use BainQCNet, please go \u003ca href=\"https://github.com/garciaml/BrainQCNet\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-how-to-report-errors\" class=\"anchor\" href=\"#how-to-report-errors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to report errors\u003c/h3\u003e\n\u003cp\u003eUsers can get help using the \u003ca href=\"https://groups.google.com/g/brainqcnet-users\" rel=\"nofollow\"\u003ebrainqcnet-users mailing list\u003c/a\u003e.\nAll bugs, concerns and enhancement requests for this software can be submitted \u003ca href=\"https://github.com/garciaml/BrainQCNet_GPU/issues\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h3\u003e\n\u003cp\u003eWhen using BrainQCNet, please include the following citation:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBrainQCNet: a Deep Learning attention-based model for multi-scale detection of artifacts in brain structural MRI scans\u003c/em\u003e, Melanie Garcia, Nico Dosenbach, Clare Kelly. bioRxiv 2022.03.11.483983; doi: \u003ca href=\"https://doi.org/10.1101/2022.03.11.483983\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2022.03.11.483983\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1635194959.0 + "updated_at": 1646931926.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "SingularityLfH.def" ], - "full_name": "truatpasteurdotfr/singularity-docker-centos8-ci", + "full_name": "LearningUAV/hallucination", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-centos-8-toy-system-for-singularity-and-docker-image-for-ci\" class=\"anchor\" href=\"#building-a-centos-8-toy-system-for-singularity-and-docker-image-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a CentOS 8 toy system for singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCentOS-8 system for github actions\u003c/li\u003e\n\u003cli\u003ebuild docker image, push to ghcr.io and re-use that docker image to create a singularity container pushed ghcr.io oras://\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-\" class=\"anchor\" href=\"#usage-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos8-ci/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-centos8-ci/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-centos8-ci:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos8-ci:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1635192721.0 + "updated_at": 1646853452.0 }, { "data_format": 2, @@ -13350,104 +12774,111 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder", + "full_name": "remiolsen/pin_hic_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-docker-stream8-warewulf4-builder-\" class=\"anchor\" href=\"#singularity-docker-stream8-warewulf4-builder-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-stream8-warewulf4-builder \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003ewarewulf4 builder container based on a CentOS Stream 8 x86_64 docker image built from github actions\u003c/p\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why\" class=\"anchor\" href=\"#why\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eprovide a minimal builder for warewulf5\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti ghcr.io/truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec oras://ghcr.io/truatpasteurdotfr/singularity-docker-stream8-warewulf4-builder:latest rpmbuild -ta warewulf-4.2.0.tar.gz \n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-pin_hic_singularity\" class=\"anchor\" href=\"#pin_hic_singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epin_hic_singularity\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1638431157.0 + "updated_at": 1647940031.0 }, { "data_format": 2, - "description": "Exploratory research using graph neural networks", + "description": null, "filenames": [ - "Singularity" + "LaMachine-master/Singularity.dev", + "LaMachine-master/Singularity" ], - "full_name": "davidhin/gnn-exploration", + "full_name": "AymanYac/Neonec-Deep-Classsifier", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-lamachine-deepclassifier--neonec-dutch-rd\" class=\"anchor\" href=\"#lamachine-deepclassifier--neonec-dutch-rd\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaMachine DeepClassifier : Neonec Dutch R\u0026amp;D\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1635288566.0 + "updated_at": 1647881800.0 }, { "data_format": 2, - "description": "DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types", + "description": "Spatially explicit individual based model that simulates Coffee Leaf Rust epidemics on a coffee farm", "filenames": [ - ".development/Singularity" + "Singularity.def" ], - "full_name": "JD2112/AlveolarCellTypeDeconvolution", - "latest_release": "v1.4.1", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-the-r-scripts-to-analyze-the-alveolar-macrophages-hla-drcd3--and-lymphocytes-cd3-specific-cell-types-from-dna-methylation-analysis\" class=\"anchor\" href=\"#the-r-scripts-to-analyze-the-alveolar-macrophages-hla-drcd3--and-lymphocytes-cd3-specific-cell-types-from-dna-methylation-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe R scripts to analyze the Alveolar macrophages (HLA-DR+/CD3-) and lymphocytes (CD3+) specific cell types from DNA methylation analysis.\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/JD2112/AlveolarCellTypeDeconvolution/actions/workflows/docker-image.yml\"\u003e\u003cimg src=\"https://github.com/JD2112/AlveolarCellTypeDeconvolution/actions/workflows/docker-image.yml/badge.svg?event=workflow_run\" alt=\"alv-decon\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-related-publication-published-in-epigenetics-2021-08-11\" class=\"anchor\" href=\"#related-publication-published-in-epigenetics-2021-08-11\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRelated publication: (Published in Epigenetics, 2021-08-11)\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eDas, J., Idh, N., Paues, J., Sikkeland, L. I. B., \u0026amp; Lerm, M.\u003c/em\u003e (2021). **DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types. \\ ** bioRxiv.\u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.03.12.435086v1\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2021.03.12.435086\u003c/a\u003e \\ \u003ca href=\"https://www.tandfonline.com/doi/full/10.1080/15592294.2021.1969499\" rel=\"nofollow\"\u003eEpigenetics link\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-create-package-and-r-script-files-according-to-the-analysis-or-result-in-the-manuscript\" class=\"anchor\" href=\"#create-package-and-r-script-files-according-to-the-analysis-or-result-in-the-manuscript\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate package and R script files according to the analysis (or Result in the manuscript).\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eDNA methylome analysis - till the normalizaed beta value calculation.\u003c/li\u003e\n\u003cli\u003eNormality calculation with Anderson\u0027s test (\u003cstrong\u003eTable 1\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003ePearson\u0027s rank correaltion analysis - Figures, Table (\u003cstrong\u003eFigure 2 - a. HLA-DR, b. CD3\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eBeanplot from the beta values of the whole dataset to describe the beta distribution over all samples (\u003cstrong\u003eFigure S1a\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eMann-Whitney test for the hypothesis - Figures, Table (F\u003cstrong\u003eigure 3a - HLA-DR and 3b. CD3\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eValidation of SI and BAL from Lung compartments (\u003cstrong\u003eFigure 4\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eTesting of 3 reference-free algorithms - algorithms testings, Venn Diagrams (\u003cstrong\u003eFigure 5a. HLA-DR and Figrue 5b. CD3\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eCell proportion analysis using the EpiDISH package (\u003cstrong\u003eFigure 6\u003c/strong\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-use-of-docker-image\" class=\"anchor\" href=\"#use-of-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse of Docker image\u003c/h2\u003e\n\u003cp\u003eDockerfile can be used for all R packages and repositories. The image file can be found here\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull jd21/alv-decon:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-functions-present-in-the-package\" class=\"anchor\" href=\"#functions-present-in-the-package\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctions present in the package\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFunctions\u003c/th\u003e\n\u003cth\u003eR scripts\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003cth\u003enotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eChAMPanalysis450K()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eChAMPanalysis.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003escript for DNA methylation using ChAMP\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eStatiscalAnalysisHLADR()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eStatisticalAnalysis.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eStatiscalAnalysisCD3()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eStatisticalAnalysis.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eValidationWithCysticFibrosis()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eValidationWithCF.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eCompareAnalysisRingh()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eStatisticalAnalysis.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003ehistogramPlot()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure2c.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003ehistogram analysis for beta values\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eAlveolarCellTypeDeconvolutionRefFreeEWAS()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eHouseman algorithm reference free analysis\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eAlveolarCellTypeDeconvolutionSVA()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eSVA analysis\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eAlveolarCellTypeDeconvolutionRefFreeCellMix()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eAlveolarCellTypeDeconvolutionTOAST()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eggplotRegression()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eFigure4.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003esFigure1()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003esupplementaryFigureS1.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003esFigure2()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003esupplementaryFigureS2.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eqqPlot()\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003esupplementaryFigureS3.R\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eQ-Q plot for compare DNA methylome data\u003c/td\u003e\n\u003ctd\u003ea sub-function can also be used; gg_qq()\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "full_name": "comses-education/coffee-leaf-rust-model", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-spatialrust\" class=\"anchor\" href=\"#spatialrust\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpatialRust\u003c/h1\u003e\n\u003cp\u003eSpatialRust is an individual based model that simulates Coffee Leaf Rust epidemics within a coffee farm.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installing-dependencies\" class=\"anchor\" href=\"#installing-dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling dependencies\u003c/h3\u003e\n\u003cp\u003eMove to this project\u0027s directory and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-julia\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eusing\u003c/span\u003e Pkg\nPkg\u003cspan class=\"pl-k\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eactivate\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\nPkg\u003cspan class=\"pl-k\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003einstantiate\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-the-model\" class=\"anchor\" href=\"#running-the-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the model\u003c/h3\u003e\n\u003cp\u003eThere are two script files available. You can run a single simulation using a fixed parameter set using \u003ccode\u003escripts/OneRun.jl\u003c/code\u003e as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia scripts/OneRun.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eResults from this single run will be saved in a \u003ccode\u003eresults\u003c/code\u003e folder as \u003ccode\u003esinglerun.csv\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe second option, \u003ccode\u003escripts/ParameterRuns.jl\u003c/code\u003e, lets you run a parameter exploration experiment. The default setup of this experiment will run 2700 simulations. To modify the parameter values to be evaluated or the replicates for each combination, open \u003ccode\u003escripts/ParameterRuns.jl\u003c/code\u003e and edit lines 11 to 14. Like the first option, you can run the script from bash:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia scripts/ParameterRuns.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eResults from this experiment will be saved in a \u003ccode\u003eresults\u003c/code\u003e folder as \u003ccode\u003eparameterexp.csv\u003c/code\u003e. Both scripts take care of creating the \u003ccode\u003eresults\u003c/code\u003e folder if it has not been created yet.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [ - "dna-methylation", - "alveolar-macrophages", - "alveolar-lymphocytes", - "hla-dr", - "cd3", - "cell-deconvolution" + "agent-based-model", + "computational-model", + "julia", + "simulation" ], - "updated_at": 1639727537.0 + "updated_at": 1654288638.0 }, { "data_format": 2, - "description": null, + "description": "Count your code, quickly.", "filenames": [ - "RStudio/Singularity", - "bc_desktop/Singularity" + "12.1.2/Singularity" ], - "full_name": "SupercomputingWales/open-ondemand-apps", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-open-ondemand-apps\" class=\"anchor\" href=\"#open-ondemand-apps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopen-ondemand-apps\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://osc.github.io/ood-documentation/latest/\" rel=\"nofollow\"\u003eOpen-Ondemand\u003c/a\u003e provides a convenient interface for users to access remote servers such as HPC systems.\u003c/p\u003e\n\u003cp\u003eThis repository will store the versions as running on \u003ca href=\"https://portal.supercomputing.wales\" rel=\"nofollow\"\u003eSupercomputing Wales\u003c/a\u003e Hawk system.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-rstudio\" class=\"anchor\" href=\"#rstudio\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRstudio\u003c/h2\u003e\n\u003cp\u003eUsing Rocker container this spins up a Rstudio session. See Singularity file.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-jupyter\" class=\"anchor\" href=\"#jupyter\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter\u003c/h2\u003e\n\u003cp\u003eUses Anaconda as installed on Hawk to provide Jupyter session. If users install jupyter in their environments installed in home directory then the kernels for their environments also appear as an option.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-bc_desktop\" class=\"anchor\" href=\"#bc_desktop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebc_desktop\u003c/h2\u003e\n\u003cp\u003eTo allow remote desktop a container was created to allow the desktop (Mate in this case from EPEL) dependencies to be isolated from host OS which doesnt allow EPEL repository. This also supports VirtualGL and TurboVNC to provide 3D interface. Requires Slurm configurationt to support spinning up Xorg and provide a desktop.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-tokei", + "latest_release": "v12.1.2", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-tokei/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-tokei/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-tokei/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-tokei/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/390a2ed4d417fdf45f007b0d91b70db9dc6ff55cff5fc8f811907b8a76d74102/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d746f6b6569\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/390a2ed4d417fdf45f007b0d91b70db9dc6ff55cff5fc8f811907b8a76d74102/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d746f6b6569\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-tokei\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7b139de678930691b4ec584c1314dc0c37bc85e1767f8a2d22394b60895ed619/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d746f6b6569\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b139de678930691b4ec584c1314dc0c37bc85e1767f8a2d22394b60895ed619/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d746f6b6569\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-tokei\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4a8856bc06400cf7ded9aef8652c3b21a9258182350126042ebd6e7a605fc4e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d746f6b6569\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4a8856bc06400cf7ded9aef8652c3b21a9258182350126042ebd6e7a605fc4e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d746f6b6569\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-tokei\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/933616d1190d66e34b2b8e2c452321c7f5aaf0eba14a74d8672c4f67db1312a0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d746f6b6569\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/933616d1190d66e34b2b8e2c452321c7f5aaf0eba14a74d8672c4f67db1312a0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d746f6b6569\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-tokei\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-tokei\" class=\"anchor\" href=\"#singularity-tokei\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-tokei\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e===============================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Language Files Lines Code Comments Blanks\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e===============================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e BASH 4 49 30 10 9\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e JSON 1 1332 1332 0 0\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Shell 1 49 38 1 10\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e TOML 2 77 64 4 9\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e-------------------------------------------------------------------------------\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Markdown 5 1355 0 1074 281\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e |- JSON 1 41 41 0 0\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e |- Rust 2 53 42 6 5\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e |- Shell 1 22 18 0 4\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e (Total) 1471 101 1080 290\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e-------------------------------------------------------------------------------\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Rust 19 3416 2840 116 460\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e |- Markdown 12 351 5 295 51\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e (Total) 3767 2845 411 511\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e===============================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Total 32 6745 4410 1506 829\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e===============================================================================\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/XAMPPRocky/tokei\"\u003etokei\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003etokei\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/tokei/12.1.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/tokei\u003c/code\u003e as \u003ccode\u003e12.1.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, - "topics": [], - "updated_at": 1635457389.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1649568351.0 }, { "data_format": 2, - "description": "Container recipes, usually related to HPC and scientific computing", + "description": "FDUPES is a program for identifying or deleting duplicate files residing within specified directories.", "filenames": [ - "cadabra/cadabra2-2.1.9-stretch/Singularity" + "2.1.2/Singularity" ], - "full_name": "jose-d/container-recipes", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-container-recipes\" class=\"anchor\" href=\"#container-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer-recipes\u003c/h1\u003e\n", + "full_name": "pscedu/singularity-fdupes", + "latest_release": "v2.1.2", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-fdupes/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fdupes/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-fdupes/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fdupes/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7700c9eafb1c45ca39d418b3ee39c83b436797c445767acae057a19d939f01dd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666475706573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7700c9eafb1c45ca39d418b3ee39c83b436797c445767acae057a19d939f01dd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666475706573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-fdupes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4e0f77fad0f89b200065773b91d9cf0199d583f2e5dcfe43a4571654d5d8da80/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666475706573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e0f77fad0f89b200065773b91d9cf0199d583f2e5dcfe43a4571654d5d8da80/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666475706573\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-fdupes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/2f63e8145296847b2c2079b4c565a9d7ad5fb9407a85cf088bd7d357b3a00201/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666475706573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2f63e8145296847b2c2079b4c565a9d7ad5fb9407a85cf088bd7d357b3a00201/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666475706573\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fdupes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0ce07a73d5d378e12e65c32c5066e0d1f9c188afcfe5184dc190f28137faf80c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666475706573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0ce07a73d5d378e12e65c32c5066e0d1f9c188afcfe5184dc190f28137faf80c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666475706573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fdupes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-fdupes\" class=\"anchor\" href=\"#singularity-fdupes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-fdupes\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/adrianlopezroche/fdupes\"\u003efdupes\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efdupes\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/fdupes/2.1.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/fdupes\u003c/code\u003e as \u003ccode\u003e2.1.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1636393528.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1633086411.0 }, { "data_format": 2, - "description": "A Strudel2 singularity container based on the code for OpenOnDemand shell application", + "description": "A visual approach to monitoring and managing the on campus HPC system known as Bender. ", "filenames": [ "Singularity" ], - "full_name": "l1ll1/terminal", + "full_name": "wrightedu/Bender-Monitor", "latest_release": null, - "readme": "\u003cp\u003eThis container runs code derived from\n\u003ca href=\"https://osc.github.io/ood-documentation/master/applications/shell.html\" rel=\"nofollow\"\u003ehttps://osc.github.io/ood-documentation/master/applications/shell.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWhen starting the program as a batch job, it simply submits a tmux new-session\nWhen connecting to the program,\nit:\u003c/p\u003e\n\u003cp\u003ea) picks an unused port\nb) generates a random token for authenticaion\nc) runs a command like ssh localhost tmux attach-session \nd) proxys that command onto the unused port\ne) watches (using lsof) for connections to the port. if its been disconnected for 5 minutes it shuts down the proxy\nf) prints out the port and token in json format\u003c/p\u003e\n\u003cp\u003eBecause the proxy is inside the container, but the tmux server is outside we have to do a bit ssh localhost\nWhen doing this we supress operations relating to SSHKnowHosts (beacuse localhost is rarely the same localhost)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-debugging\" class=\"anchor\" href=\"#debugging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebugging:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCheck that you can start a tmux session via echo \"module load singularity\\nsingularity exec term.sif /start\" | sbatch This is what strudel2 does\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFind out which node your tmux is running on, login, singularity shell term.sif\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInside the singularity shell, try executing /params. Check that it gives json output. Check that it starts node /opt/shell/tmux.js and watchdog.py\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate an SSH tunnel to the port specified. Open the URL localhost:/tmux?token=\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bender-monitor\" class=\"anchor\" href=\"#bender-monitor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBender-Monitor\u003c/h1\u003e\n\u003cp\u003eA visual approach to monitoring and managing the on campus HPC system known as Bender.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1636670270.0 + "updated_at": 1649360377.0 }, { "data_format": 2, - "description": null, + "description": "The MEME Suite allows you to discover novel motifs in collections of unaligned nucleotide or protein sequences, and to perform a wide variety of other motif-based analyses.", "filenames": [ - "Singularity.centos-7__openmpi-4.0.5__h5py" + "5.4.1/Singularity", + "5.4.0/Singularity", + "5.3.3/Singularity" ], - "full_name": "mcduta/h5py-demo", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-experimenting-with-hdf5-in-python\" class=\"anchor\" href=\"#experimenting-with-hdf5-in-python\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExperimenting with HDF5 in Python\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-material\" class=\"anchor\" href=\"#material\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaterial\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ethis README;\u003c/li\u003e\n\u003cli\u003ethe associated python files;\u003c/li\u003e\n\u003cli\u003ea Singularity container for \u003ccode\u003eminiconda\u003c/code\u003e, \u003ccode\u003empi4py\u003c/code\u003e and \u003ccode\u003eh5py\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003ccode\u003ejupyter\u003c/code\u003e notebooks to experiment with MPI are very limited in scope by the very logic of parallel execution.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-reading\" class=\"anchor\" href=\"#reading\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReading\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://twiki.cern.ch/twiki/pub/Sandbox/JaredDavidLittleSandbox/PythonandHDF5.pdf\" rel=\"nofollow\"\u003ePython and HDF5\u003c/a\u003e by Andrew Collette (O\u0027Reilly, 2014)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ntrs.nasa.gov/api/citations/20180008456/downloads/20180008456.pdf\" rel=\"nofollow\"\u003eSome notes about chunks and compression\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs.h5py.org/en/stable/mpi.html#\" rel=\"nofollow\"\u003eh5py online documentation on parallel HDF5\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-the-container\" class=\"anchor\" href=\"#the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe container\u003c/h3\u003e\n\u003cp\u003eThe Singularity container for \u003ccode\u003eminiconda\u003c/code\u003e, \u003ccode\u003empi4py\u003c/code\u003e and \u003ccode\u003eh5py\u003c/code\u003e can be directly downloaded from \u003ca href=\"https://cloud.sylabs.io/library/mcduta/default/h5py\" rel=\"nofollow\"\u003eSyLabs\u003c/a\u003e using the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://mcduta/default/h5py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, it can be generated from the recipe provided\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build --fakeroot h5py_latest.sif Singularity.centos-7__openmpi-4.0.5__h5py\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-interactive-shell\" class=\"anchor\" href=\"#interactive-shell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive Shell\u003c/h3\u003e\n\u003cp\u003eTo experiment with the parallel Python scripts, obtain an interactive shell in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell h5py_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInteract with the shell, available containerised software and the underlying files system in the normal way, just as on any linux workstation.\u003c/p\u003e\n\u003cp\u003eBasic configuration settings can be checked once in a container shell, \u003cem\u003ee.g.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eorte-info --config\nh5pcc -showconfig\nconda list h5py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBoth executables as well as the expected HDF5 tools, \u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003eh5dump\u003c/code\u003e and \u003ccode\u003eh5ls\u003c/code\u003e are already in path. The above commands shows some details of how \u003ccode\u003eh5py\u003c/code\u003e was built (\u003cem\u003ei.e.\u003c/em\u003e on top of a parallel enabled build of HDF5 itself). See also \u003ca href=\"https://docs.h5py.org/en/stable/mpi.html#building-against-parallel-hdf5\" rel=\"nofollow\"\u003eh5py notes on building HDF5\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input-and-output\" class=\"anchor\" href=\"#input-and-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput and Output\u003c/h3\u003e\n\u003cp\u003eNeither the Python scripts nor the HDF5 files generated are part of the container. The Python scripts can be anywhere in a path on DLS storage. For the purpose of experimentation for I/O performance, the HDF5 files generated can be on a path that is mounted as \u003ccode\u003egpfs\u003c/code\u003e, \u003ccode\u003enfs\u003c/code\u003e or local \u003ccode\u003eext4\u003c/code\u003e (\u003cem\u003ee.g.\u003c/em\u003e local scratch or \u003ccode\u003e/tmp\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTip\u003c/strong\u003e: an easy way to verify what a certain path is mounted as is \u003ccode\u003edf -PT /path\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eControlling input and output can be done by bind-mounting paths in the Singularity container. For example, supposing the Python files are in \u003ccode\u003e$HOME/h5pytest\u003c/code\u003e and the output is to go to \u003ccode\u003e/dls/p45/path/to/somewhere\u003c/code\u003e, the command to start the Singularity shell is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind $HOME/h5pytest:/apps/input,/dls/p45/path/to/somewhere:/apps/output h5py_latest.sif\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce in a container shell, go to the designated output path in the container and experiment, \u003cem\u003ee.g.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /apps/output/\nmpirun -np 4 python /apps/input/h5py_write_demo.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAny files written to \u003ccode\u003e/apps/output\u003c/code\u003e are \"seen\" outside the container in the path \u003ccode\u003e/dls/p45/path/to/somewhere\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAn easier alternative to the above is to have the Python scripts and output in the same path, case in which bind-mounting the current working directory is sufficient. For example, the following command lands the Singularity shell in the current directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --home $PWD h5py_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAny files generated in the container shell are visible in \u003ccode\u003e$PWD\u003c/code\u003e outside.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-cluster\" class=\"anchor\" href=\"#cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCluster\u003c/h3\u003e\n\u003cp\u003eAn interactive session on the Hamilton cluster is a good idea for a) the availability of a significant number of cores on which the \u003ccode\u003empirun\u003c/code\u003e-launched Python processes can execute and b) the availability of \u003ccode\u003egpfs\u003c/code\u003e mounted paths. An example of request for an interactive job is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqrsh -pe openmpi-savu 20 -l h_rt=01:00:00,m_mem_free=8G -P tomography\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity is available on the cluster nodes.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-h5py-experiments\" class=\"anchor\" href=\"#h5py-experiments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eh5py\u003c/code\u003e Experiments\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-1\" class=\"anchor\" href=\"#exercise-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 1\u003c/h3\u003e\n\u003cp\u003eFirst, experiment with parallel writes and reads from local disk (\u003ccode\u003eext4\u003c/code\u003e file system). Create a user writable directory in \u003ccode\u003e/tmp\u003c/code\u003e and then obtain an interactive session on Hamilton. Use the commands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /tmp/$USER\nsingularity shell --bind $PWD:/apps/input,/tmp/$USER:/apps/output h5py_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce in the container shell, run the writer \u003ccode\u003eh5py\u003c/code\u003e demo with a varying number of processes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /apps/output/\nfor np in 4 8 16; do mpirun -np ${np} python /apps/input/h5py_write_demo.py; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEdit the file \u003ccode\u003eh5py_write_demo.py\u003c/code\u003e and observe the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe HDF5 files is open using the \u003ccode\u003empio\u003c/code\u003e driver and the operation makes use of the default MPI communicator \u003ccode\u003eMPI.COMM_WORLD\u003c/code\u003e;\u003c/li\u003e\n\u003cli\u003eeach process initialises only a part of the data that is written to file;\u003c/li\u003e\n\u003cli\u003ethere is no \u003cem\u003eglobal\u003c/em\u003e (across-process) view of the data; the variable \u003ccode\u003edataset\u003c/code\u003e is a handle for the data;\u003c/li\u003e\n\u003cli\u003edata initialisation is an \u003cem\u003eindependent\u003c/em\u003e \u003ccode\u003eh5py\u003c/code\u003e operation, while file open and close are \u003cem\u003ecollective\u003c/em\u003e (see the \u003ca href=\"https://docs.h5py.org/en/stable/mpi.html#collective-versus-independent-operations\" rel=\"nofollow\"\u003eh5py notes on this\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe data size is fixed, so increasing the number of processes means each process initialises and writes less data.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-2\" class=\"anchor\" href=\"#exercise-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 2\u003c/h3\u003e\n\u003cp\u003eNow, run the reader demo, which reads the data from the file written by the writer demo. Use the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor np in 4 8 16; do mpirun -np ${np} python /apps/input/h5py_read_demo.py; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEdit the file \u003ccode\u003eh5py_read_demo.py\u003c/code\u003e and observe the similarities with the writer demo.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-3\" class=\"anchor\" href=\"#exercise-3\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 3\u003c/h3\u003e\n\u003cp\u003eIn the read demo \u003ccode\u003eh5py_read_demo.py\u003c/code\u003e, print additional information on data read by each process, \u003cem\u003ee.g.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eprint (\" iproc = {}, shape = {}, data[0,0] = {}\".format(iproc, dataproc.shape, dataproc[0,0]))\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlace this just after the last \u003ccode\u003eMPI.Wtime\u003c/code\u003e call. Rerun the demo with 4 processes and understand the output. Now replace the \"process view\" of the data \u003ccode\u003edataproc[0,0]\u003c/code\u003e with the \"global view\" \u003ccode\u003edataset[0,0]\u003c/code\u003e and rerun. What happens?\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-4\" class=\"anchor\" href=\"#exercise-4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 4\u003c/h3\u003e\n\u003cp\u003eNow repeat the write and read runs above on \u003ccode\u003egpfs\u003c/code\u003e rather than \u003ccode\u003eetx4\u003c/code\u003e. Use an interactive cluster session and an appropriate path (\u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003e/dls/p45\u003c/code\u003e) that is mounted as \u003ccode\u003egpfs\u003c/code\u003e on Hamilton nodes. How do write/read times compare with \u003ccode\u003eext4\u003c/code\u003e?\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-5\" class=\"anchor\" href=\"#exercise-5\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 5\u003c/h3\u003e\n\u003cp\u003eRepeat the same operations, on the same path as the previous exercise but this time running the containe on a linux workstation, which mounts the path as \u003ccode\u003enfs\u003c/code\u003e (check!). How do results change?\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exercise-6\" class=\"anchor\" href=\"#exercise-6\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExercise 6\u003c/h3\u003e\n\u003cp\u003eEdit the file \u003ccode\u003eh5py_serial_chunking_demo.py\u003c/code\u003e and understand what it is programmed to do. The demo is serial and can be run outside the container, using the DLS python installation, \u003cem\u003ee.g.\u003c/em\u003e using \u003ccode\u003emodule load python/3.9\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNotice how the demo writes and then reads the same amount of data (simulating a stack of images) to and from HDF5 files. The first write/read is contiguous (\u003cem\u003ei.e.\u003c/em\u003e no chunks), the second is chunked and the third is chunked and also uses compression.\u003c/p\u003e\n\u003cp\u003eRun the demo on \u003ccode\u003egpfs03\u003c/code\u003e as well as \u003ccode\u003eext4\u003c/code\u003e. The chunked reads should show increased performance over the contiguous, and compressed read even more so.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe success of chunking depends entirely on the particular read data access pattern.\u003c/li\u003e\n\u003cli\u003eThe chunks are set at dataset creation time but can be changed using command line tools, \u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003eh5repack\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "pscedu/singularity-meme-suite", + "latest_release": "v5.4.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-meme-suite/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-meme-suite/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-meme-suite/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-meme-suite/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/19fb6791fbb0d3b375a57c2d240aaaf0bcf3c3fc41c90ea1779d4b744a1b503b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/19fb6791fbb0d3b375a57c2d240aaaf0bcf3c3fc41c90ea1779d4b744a1b503b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d656d652d7375697465\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-meme-suite\" 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data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-meme-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-meme-suite\" class=\"anchor\" href=\"#singularity-meme-suite\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-meme-suite\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://meme-suite.org/meme/\" rel=\"nofollow\"\u003ememe-suite\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ememe-suite\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/meme-suite/5.4.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/meme-suite\u003c/code\u003e as \u003ccode\u003e5.4.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1639579950.0 + "updated_at": 1649276065.0 }, { "data_format": 2, @@ -13455,245 +12886,221 @@ var data = "filenames": [ "Singularity" ], - "full_name": "yimengkong/6mASCOPE", + "full_name": "truatpasteurdotfr/bioconda-perl-bioperl", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-6mascope\" class=\"anchor\" href=\"#6mascope\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6mASCOPE\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003e6mASCOPE is a toolbox to assess 6mA events in eukaryotic species using a quantitative deconvolution approach. By using a novel short-insert library (200~400bp) design with the PacBio sequencing Sequel II System, 6mASCOPE makes an effective use of the large number of circular consensus (CCS) reads to reliably capture deviations in IPD values at single molecule resolution. Taking an innovative metagenomic approach, 6mASCOPE deconvolves the DNA molecules from a gDNA sample into species and genomic regions of interests, and sources of contamination. Using a rationally designed machine learning model, 6mASCOPE enables sensitive and reliable 6mA quantification for each of the deconvolved composition.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-access\" class=\"anchor\" href=\"#access\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAccess\u003c/h2\u003e\n\u003cp\u003eThis current version is for manuscript review. Upon publication, we plan to release 6mASOCPE publically on our GitHub page \u003ca href=\"https://github.com/fanglab/6mascope\"\u003ehttps://github.com/fanglab/6mascope\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003e6mASCOPE is distributed as a fully functional image bypassing the need to install any dependencies others than the virtualization software. We recommend using Singularity, which can be installed on Linux systems and is often the preferred solution by HPC administrators (\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eQuick Start\u003c/a\u003e). \u003ccode\u003e6mASCOPE\u003c/code\u003e was tested extensively with Singularity v3.6.4.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load singularity/3.6.4 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Required only singularity/3.6.4 is a dynamic environment module. \u003c/span\u003e\nsingularity pull 6mASCOPE.sif library://yimengkong/default/6mascope:latest \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the image from cloud.sylabs.io; Make sure you have the network connection\u003c/span\u003e\nsingularity build --sandbox 6mASCOPE 6mASCOPE.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a writable container named 6mASCOPE\u003c/span\u003e\nsingularity run --no-home -w 6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start an interactive shell to use 6mASCOPE, type `exit` to leave\u003c/span\u003e\ninit_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Only required once when start using 6mASCOPE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e run_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Required every time when running 6mASCOPE\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe image retrieved from \u003ca href=\"https://cloud.sylabs.io/home\" rel=\"nofollow\"\u003eSylab Cloud\u003c/a\u003e with \u003ccode\u003esingularity pull\u003c/code\u003e (e.g. 6mASCOPE.sif) is already built and can be reused at will. Containers built with those instructions are writable meaning that results from 6mASCOPE analysis can be retrieved when the container is not running. Outputs for the following commands can be found at \u003ccode\u003e./path/to/6mASCOPE/home/6mASCOPE/\u003c/code\u003e. To re-run the same container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --no-home -w 6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Re-run container 6mASCOPE, type `exit` to leave\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e run_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Required every time when running 6mASCOPE\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tool-showcase\" class=\"anchor\" href=\"#tool-showcase\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTool showcase\u003c/h2\u003e\n\u003cp\u003eTo showcase the toolbox applications, we provide examples for the analysis of the Drosophila ~45min embryo dataset presented in our manuscript (Fig 5). The dataset can be downloaded with the following commands from within a 6mASCOPE container: \u003ccode\u003e6mASCOPE get_test_data\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-contamination-estimation\" class=\"anchor\" href=\"#contamination-estimation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContamination estimation\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-goal\" class=\"anchor\" href=\"#goal\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal\u003c/h4\u003e\n\u003cp\u003eTo get an idea about the overall contamination of a gDNA sample. This step helps users define the composition of a gDNA sample using a metagenomic approach to assign reads to different species.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-description-1\" class=\"anchor\" href=\"#description-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription:\u003c/h4\u003e\n\u003cp\u003eFor a given CCS dataset generated from short-insert library, 6mASCOPE will examine if there are contaminating species and calculate the proportion of reads mapped to the reference and top 50 contaminated species from reads that do not map to the eukaryotic species of interest.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eCCS reads file capturing all the genetic material in a gDNA sample (.fasta, pre-computed in the following example)\u003c/li\u003e\n\u003cli\u003eEukaryotic reference of genome of interest (.fasta)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs:\u003c/h4\u003e\n\u003cp\u003eFor a given CCS dataset generated from short-insert library, \u003ccode\u003e6mASCOPE\u003c/code\u003e will examine if there are contaminating species and calculate the proportion of reads mapped to the reference and top 50 contaminated species from reads that do not map to the eukaryotic species of interest.\u003c/p\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-example-of-the-output\" class=\"anchor\" href=\"#example-of-the-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample of the Output:\u003c/h5\u003e\n\u003cpre\u003e\u003ccode\u003eRemove 8491 possible inter-species chimeric reads for further analysis\n#total_CCS\tmapped_to_goi\tcontaminants\n666159\t640345 (96.1249%)\t25814 (3.87505%)\n\nTop 50 mapped species outside goi reference\n#Count\tSpecies\n 10836 Saccharomyces cerevisiae\n 2413 Acetobacter tropicalis\n 1524 Acetobacter pasteurianus\n 1479 Lactobacillus plantarum\n 882 Acetobacter sp.\n ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(Full species list can be viewed in \u003ccode\u003etest.contam.estimate.txt\u003c/code\u003e)\u003c/p\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-example-commands\" class=\"anchor\" href=\"#example-commands\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample commands:\u003c/h5\u003e\n\u003cpre\u003e\u003ccode\u003e6mASCOPE contam -c test.ccs.fasta -r test.ref.fasta -o test.contam.estimate.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this example, \u003ccode\u003etest.ccs.fasta\u003c/code\u003e includes CCS reads (674,650) from the Drosophila ~45min embryo reads dataset described in our manuscript and pre-filtered with command \u003ccode\u003e6mASCOPE ccs\u003c/code\u003e. Using 5 cores, runtime is ~12m51s. The output shows ~3.9% CCS reads come from contaminated sources other than Drosophila melanogaster, the genome of interest (goi). Please be noted, blastn is embedded within this step, which will need at least 32-64G RAM.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-6ma-analysis-using-quantitative-deconvolution\" class=\"anchor\" href=\"#6ma-analysis-using-quantitative-deconvolution\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6mA analysis using quantitative deconvolution\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-goal-1\" class=\"anchor\" href=\"#goal-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal:\u003c/h4\u003e\n\u003cp\u003eFor each source determined in \u003ccode\u003e6mASCOPE contam\u003c/code\u003e, this step will quantify the 6mA/A level and calculate the 6mA contribution (%) of each source to the total 6mA abundance in the gDNA sample.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-inputs-1\" class=\"anchor\" href=\"#inputs-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eThe same CCS reads file as explained above for Contamination Estimation (.fasta).\u003c/li\u003e\n\u003cli\u003eIPD and QV information of the CCS reads (pre-computed in the following example, ; this can be generated for new data with \u003ccode\u003e6mASCOPE ipd\u003c/code\u003e command, as explained in detailed tutorial).\u003c/li\u003e\n\u003cli\u003eUser defined groups besides the genome of interest. Examples as shown below. (Left columns: subgroup name. Right columns: contamination sources, use vertical line if multiple sources included within one subgroup).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eSaccharomyces Saccharomyces\nAcetobacter Acetobacter|Komagataeibacter\nLactobacillus Lactobacillus\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-outputs-1\" class=\"anchor\" href=\"#outputs-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs:\u003c/h4\u003e\n\u003cp\u003eA table including the following information: the proportion (%) of reads from each source out of the total number of reads; source-specific 6mA/A level with 95% confidence intervals (log10-transformed), and contribution (%) of each source to the total 6mA abundance in the gDNA sample (as presented in the manuscript Figure 5A, B, C)\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-example-commands-1\" class=\"anchor\" href=\"#example-commands-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample commands:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e6mASCOPE quant -c test.ccs.fasta -i test.IPD.out.A -o test -r test.ref.fasta -s subgroup.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this example, the file \u003ccode\u003etest.IPD.out.A\u003c/code\u003e includes the pre-calculated IPD and QV information on the CCS molecules (can be generated with \u003ccode\u003e6mASCOPE ipd\u003c/code\u003e). Only Adenines were included here to to reduce computational time and ease evaluation. \u003ccode\u003esubgroup.txt\u003c/code\u003e includes the pre-defined main contamination groups, inferred from the top mapped species and blast output from \u003ccode\u003e6mASCOPE contam\u003c/code\u003e. Using 5 cores, runtime is ~13m17s.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-example-output\" class=\"anchor\" href=\"#example-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample output:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e #Subgroup count ReadsProportion 6mAlevel(ppm) 6mAlevel(log10) UpCI DownCI subtotal(ppm) contribution(%)\n goi 640345 0.9612 2.0417 -5.69 -5.0 -6.0 1.9625 1.4431\n Saccharomyces 11011 0.0165 45.7088 -4.34 -3.9 -6.0 0.7542 0.5546\n Acetobacter 5757 0.0086 5495.4087 -2.26 -2.0 -2.5 47.2605 34.7522\n Lactobacillus 1517 0.0023 977.2372 -3.01 -2.7 -3.3 2.2476 1.6528\n others 7529 0.0113 7413.1024 -2.13 -1.9 -2.4 83.7681 61.5974\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"/docs/figures/test.6mASCOPE.ReadsProportion.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.ReadsProportion.png\" alt=\"The proportion of CCS reads from each group 6mA\" width=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n1. The % of total CCS reads mapped to different subgroups. Left: The % of CCS reads mapped to D. melanogaster (genome of interest) and contamintant subgroups. Right: The % of CCS reads mapped to different contaminant sources.\n\u003cp align=\"center\"\u003e\n \u003ca href=\"/docs/figures/test.6mASCOPE.6mAlevel.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.6mAlevel.png\" alt=\"Group-specific 6mA/A level prediction with confidence intervals 6mA\" width=\"500\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n2. 6mA quantification and 95% confidence intervals (log10-transformed) on CCS reads mapped to different subgroups. Please be noted, it is important to combine the estimated 6mA/A level with its confidence interval for reliable data interpretation. In this example, the 6mA/A level of Saccharomyces (45.7ppm) does not mean abundant 6mA events in this subgroup because it has a wide range of confidence interval (1-125ppm; -6.0 to -3.9 with log10 transformed). In the paper, an additional Sequel II run for this single species (higher yield) actually shows extremely low 6mA level (2ppm, confidence interval: 1-10ppm).\n\u003cp align=\"center\"\u003e\n \u003ca href=\"/docs/figures/test.6mASCOPE.6mAcontribution.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.6mAcontribution.png\" alt=\"Group-specific 6mA/A level prediction with confidence intervals 6mA\" width=\"300\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n3. Contribution (%) of each source to total 6mA abundance in the gDNA sample. CCS reads mapped to the D. melanogaster genome only explains 1.4% of the total 6mA events in the gDNA sample (green).\n\u003cp\u003eThese figures can be drawn with \u003ccode\u003esh ~/code/draw_example.sh test.6mASCOPE.txt\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eFor a comprehensive description of\u00a06mASCOPE including installation guide, data preprocessing and a detailed tutorial, including how to apply 6mASCOPE to your own datasets, please refer to the\u00a0\u003ca href=\"https://6mascope.readthedocs.io/en/latest/overview.html\" rel=\"nofollow\"\u003ecomplete documentation\u003c/a\u003e .\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-a-miniconda-based-container-with-bioconda-bioperl\" class=\"anchor\" href=\"#a-miniconda-based-container-with-bioconda-bioperl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea miniconda based container with bioconda-bioperl\u003c/h1\u003e\n\u003cp\u003edocker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/bioconda-perl-bioperl:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv oras://ghcr.io/truatpasteurdotfr/bioconda-perl-bioperl:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1637592376.0 + "updated_at": 1651593131.0 }, { "data_format": 2, - "description": "Scripts for building VirSorter2 Cyverse App", + "description": "CheckM provides a set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes.", "filenames": [ - "Singularity" + "1.2.0/Singularity", + "1.1.3/Singularity" ], - "full_name": "jiarong/vs2-cyverse-app", - "latest_release": null, + "full_name": "pscedu/singularity-checkm", + "latest_release": "v1.1.3", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-checkm/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-checkm/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-checkm/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-checkm/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c36d6207c7a83b2505f3c3da9648b2bde15022fb54c87c7d694d8aef86ba345/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d636865636b6d\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c36d6207c7a83b2505f3c3da9648b2bde15022fb54c87c7d694d8aef86ba345/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d636865636b6d\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-checkm\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/39b062bc9d3f1163144f8faf52a104ed79d50fb16f21cf3ab1bf888d2f31ffff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d636865636b6d\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg 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style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4e65c1f34358c555baa1e01b53582475c3621a1e12ad015e81ad9fc5dbc0a221/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d636865636b6d\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e65c1f34358c555baa1e01b53582475c3621a1e12ad015e81ad9fc5dbc0a221/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d636865636b6d\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-checkm\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-checkm\" class=\"anchor\" href=\"#singularity-checkm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-checkm\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/93019bef04184f04502c095cd35e9340a581b7ead3193c4e7b387aa845b96f59/687474703a2f2f65636f67656e6f6d6963732e6769746875622e696f2f436865636b4d2f696d672f636865636b6d2e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93019bef04184f04502c095cd35e9340a581b7ead3193c4e7b387aa845b96f59/687474703a2f2f65636f67656e6f6d6963732e6769746875622e696f2f436865636b4d2f696d672f636865636b6d2e706e67\" width=\"50%\" data-canonical-src=\"http://ecogenomics.github.io/CheckM/img/checkm.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://ecogenomics.github.io/CheckM/\" rel=\"nofollow\"\u003eCheckM\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003echeckm\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/CheckM/1.1.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/checkm\u003c/code\u003e as \u003ccode\u003e1.1.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1640407925.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1651352851.0 }, { "data_format": 2, - "description": "Operating Systems", + "description": "A repo of container definitions and CI build support", "filenames": [ - "Singularity" + "singularity/analysis/r/Singularity", + "singularity/analysis/python/Singularity", + "singularity/analysis/notebook/Singularity" ], - "full_name": "cassimpatel/COMP2211", + "full_name": "lkirk/containers", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-operating-systems-comp2211\" class=\"anchor\" href=\"#operating-systems-comp2211\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOperating Systems (COMP2211)\u003c/h1\u003e\n\u003cp\u003eNOTE: this repository does not seem to work, no source code seems to be committed or staged. Instructions to run are kept here, but find a copy of the operating system including all changes made within your UoL Linux File System in Documents\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eNote these instructions are for running on a UoL Linux terminal\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to the directory containing this README file\u003c/li\u003e\n\u003cli\u003eRun the following command: \u003ccode\u003esingularity shell xv6_tools.simg\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eThe terminal should now prompt you with \u003ccode\u003eSingularity\u0026gt; \u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eNotice you are no longer in the same folder, navigate into the \u003ccode\u003exv6-riscv\u003c/code\u003e directory\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003emake clean\u003c/code\u003e followed by \u003ccode\u003emake\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eStart up the Xv6 Operating system: \u003ccode\u003emake qemu\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOnce you are finished using the OS:\n\u003cul\u003e\n\u003cli\u003eHold \u003ccode\u003ectrl + a\u003c/code\u003e and click \u003ccode\u003ex\u003c/code\u003e to exit back to Singularity\u003c/li\u003e\n\u003cli\u003eIf you want to view new changes to the OS code: run \u003ccode\u003emake clean; make; make qemu\u003c/code\u003e again to restart the OS\u003c/li\u003e\n\u003cli\u003eTo exit Singularity: use command \u003ccode\u003eexit\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-shortcut-to-run\" class=\"anchor\" href=\"#shortcut-to-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShortcut to run\u003c/h2\u003e\n\u003cp\u003eNavigate to the top repository directory and use the commands below. Note you will have to run the first line, then the second.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell xv6_tools.simg\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Desktop/Git/COMP2211/xv6-riscv\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e make clean\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e make\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e make qemu\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1641130398.0 - }, - { - "data_format": 2, - "description": "Singularity container for RNA-Seq power analysis", - "filenames": [ - "Singularity.rnaseqpower" - ], - "full_name": "qbicsoftware/rnaseq-power-container", - "latest_release": "0.3.14", - "readme": "\u003cp\u003eCreates power or sample size matrix given different experimental parameters. Uploads created heatmaps as attachment to openBIS using attachi-cli and Dync.\u003c/p\u003e\n\u003cp\u003eUses \u003ca href=\"https://doi.org/doi:10.18129/B9.bioc.RnaSeqSampleSize\" rel=\"nofollow\"\u003eRnaSeqSampleSize\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eContainers are built using the \u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003eSingularity Deploy template\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containers\" class=\"anchor\" href=\"#containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers\u003c/h1\u003e\n\u003cp\u003eThis is my personal repo of container definitions and CI build support\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-background\" class=\"anchor\" href=\"#background\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h1\u003e\n\u003cp\u003eSince I use singularity and docker heavily in my analysis/development workflows, I needed a CI system for versioning/releasing containers.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-builds\" class=\"anchor\" href=\"#singularity-builds\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Builds\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/lkirk/analysis/r\" rel=\"nofollow\"\u003eR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/lkirk/analysis/python\" rel=\"nofollow\"\u003ePython\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/lkirk/analysis/notebook\" rel=\"nofollow\"\u003eJupyter Notebook\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003cp\u003eTools\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://quay.io/repository/lkirk/bwa\" rel=\"nofollow\"\u003eBWA\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/d8a712bb098b054f0b4be4e9e111d976dc1a1faf2dce9016f81fd76bc4d06462/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f6277612f7374617475733f746f6b656e3d38313862616539352d313133612d343762642d396561642d636630343435613137323739\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d8a712bb098b054f0b4be4e9e111d976dc1a1faf2dce9016f81fd76bc4d06462/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f6277612f7374617475733f746f6b656e3d38313862616539352d313133612d343762642d396561642d636630343435613137323739\" alt=\"\" data-canonical-src=\"https://quay.io/repository/lkirk/bwa/status?token=818bae95-113a-47bd-9ead-cf0445a17279\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://quay.io/repository/lkirk/samtools\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/c4de3955204a04af95d325f708841180e77c42bbe944739ed74609eb629450ee/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f73616d746f6f6c732f7374617475733f746f6b656e3d61316462633336612d633938352d343565312d393563642d353132333030653531383932\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c4de3955204a04af95d325f708841180e77c42bbe944739ed74609eb629450ee/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f73616d746f6f6c732f7374617475733f746f6b656e3d61316462633336612d633938352d343565312d393563642d353132333030653531383932\" alt=\"\" data-canonical-src=\"https://quay.io/repository/lkirk/samtools/status?token=a1dbc36a-c985-45e1-95cd-512300e51892\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://quay.io/repository/lkirk/bcftools\" rel=\"nofollow\"\u003eBcftools\u003c/a\u003e \u003ca href=\"https://camo.githubusercontent.com/6090ee9c9b88031c51de2fbe49de54b6c91686e8e676a2d49827af13be55f2d2/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f626366746f6f6c732f7374617475733f746f6b656e3d63363032653330612d316637392d346432622d383338372d633762656131393537366632\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6090ee9c9b88031c51de2fbe49de54b6c91686e8e676a2d49827af13be55f2d2/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6c6b69726b2f626366746f6f6c732f7374617475733f746f6b656e3d63363032653330612d316637392d346432622d383338372d633762656131393537366632\" alt=\"\" data-canonical-src=\"https://quay.io/repository/lkirk/bcftools/status?token=c602e30a-1f79-4d2b-8387-c7bea19576f2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1635346688.0 + "updated_at": 1647133221.0 }, { "data_format": 2, - "description": null, + "description": "Control + Camera code for the autonomous delivery robot developed for Albert Heijn as part of the Robotics Minor at TU Delft 2020", "filenames": [ - "bartender/Singularity" + "Gazebo/Singularity" ], - "full_name": "cory-weller/YKO-barseq", + "full_name": "Sh-Anand/delivery-fellow", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-yko-barseq\" class=\"anchor\" href=\"#yko-barseq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYKO-barseq\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-singularity-image\" class=\"anchor\" href=\"#building-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding singularity image\u003c/h2\u003e\n\u003cp\u003eOn a computer with sudo access, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e while in directory containing Singularity file\u003c/span\u003e\nsudo singularity build bartender.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-bartender-extractor\" class=\"anchor\" href=\"#running-bartender-extractor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning bartender extractor\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \\\n -B /data/SBGE/cory/YKO-barseq:/home/wellerca/ \\\n bartender/bartender.sif bartender_extractor_com \\\n -f seq/MS3059950-600V3_19109498_S7_L001_R1_001.fastq \\\n -o pre \\\n -p CGAGC[34]C -m 1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRunning bartender extractor\nbartender_extractor seq/MS3059950-600V3_19109498_S7_L001_R1_001.fastq pre 1 \"(CGAG.|CGA.C|CG.GC|C.AGC|.GAGC)([ATCGN]{34})(C)\" CGAGC C 3 1\nTotally there are 1187764 reads in seq/MS3059950-600V3_19109498_S7_L001_R1_001.fastq file!\nTotally there are 1118562 valid barcodes from seq/MS3059950-600V3_19109498_S7_L001_R1_001.fastq file\nTotally there are 1118562 valid barcodes whose quality pass the quality condition\nThe estimated sequence error from the prefix and suffix parts is 0.0311966\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-formatting-barcodes\" class=\"anchor\" href=\"#formatting-barcodes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFormatting barcodes\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003eextracted_barcode.txt\u003c/code\u003e file contains a 34-mer nucleotide sequence, but we only\nwant the 20 nucleotide barcode sequence contained within.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epython3\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eformat_barcodes\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epy\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epre_barcode\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebarcodes\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-bartender-cluster\" class=\"anchor\" href=\"#running-bartender-cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning bartender cluster\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \\\n -B /data/SBGE/cory/YKO-barseq:/home/wellerca/ \\\n bartender/bartender.sif bartender_single_com \\\n -f barcodes.txt \\\n -o barcode_clusters \\\n -d 2 \\\n -s 5\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eoutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRunning bartender\nLoading barcodes from the file\nIt takes 00:00:01 to load the barcodes from barcodes.txt\nShortest barcode length: 20\nLongest barcode length: 20\nStart to group barcode with length 20\nUsing two sample unpooled test\nTransforming the barcodes into seed clusters\nInitial number of unique reads: 64431\nThe distance threshold is 2\nClustering iteration 1\nClustering iteration 2\nClustering iteration 3\nClustering iteration 4\nIdentified 18272 barcodes with length 20\nThe clustering process takes 00:00:01\nStart to dump clusters to file with prefix barcode_clusters\nStart to remove pcr effects\n***(Overall error rate estimated from the clustering result)***\nTotal number of clusters after removing PCR effects: 18272\nThe estimated error rate is 0.00340786\nThe overall running time 00:00:05 seconds.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-take-most-abundant-seq-consensus-per-cluster-and-plot\" class=\"anchor\" href=\"#take-most-abundant-seq-consensus-per-cluster-and-plot\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTake most abundant seq (consensus) per cluster and plot\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003edata.table\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003eggplot2\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003eggrepel\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003edat\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fread(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ebarcode_clusters_barcode.csv\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003edat\u003c/span\u003e[, \u003cspan class=\"pl-smi\"\u003e.SD\u003c/span\u003e[which.max(\u003cspan class=\"pl-smi\"\u003eFrequency\u003c/span\u003e)], \u003cspan class=\"pl-v\"\u003eby\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCluster.ID\u003c/span\u003e]\n\nsetnames(\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eUnique.reads\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econsensus\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e[, \u003cspan class=\"pl-smi\"\u003eFrequency\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNULL\u003c/span\u003e]\nsetkey(\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eCluster.ID\u003c/span\u003e)\nsetkey(\u003cspan class=\"pl-smi\"\u003edat\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eCluster.ID\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003edat.merge\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e merge(\u003cspan class=\"pl-smi\"\u003edat\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003edat.merge\u003c/span\u003e[, \u003cspan class=\"pl-k\"\u003elist\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eN\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e sum(\u003cspan class=\"pl-smi\"\u003eFrequency\u003c/span\u003e)), \u003cspan class=\"pl-v\"\u003eby\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e][order(\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e)]\n\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e[, \u003cspan class=\"pl-smi\"\u003eabundance_rank\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e.N\u003c/span\u003e]\n\nfwrite(\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003efile\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econsensus_counts.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003equote\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eF\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003erow.names\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eF\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecol.names\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eT\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003esep\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e,\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\n\n\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3000\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003etext_label\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003econsensus\u003c/span\u003e]\n\n\nggplot(\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e], aes(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eabundance_rank\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e geom_point() \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\nscale_y_continuous(\u003cspan class=\"pl-v\"\u003etrans\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elog10\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003ebreaks\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003ec(\u003cspan class=\"pl-c1\"\u003e1e0\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e1\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e2\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e3\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e4\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e5\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1e6\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003elabels\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003ec(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e10\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e100\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e10000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e100000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1000000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\nlabs(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBarcodes ranked by abundance\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eAbundance\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etitle\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBarcodes with cluster counts \u0026gt;= 2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\ntheme_few(\u003cspan class=\"pl-c1\"\u003e12\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\ngeom_text_repel(aes(\u003cspan class=\"pl-v\"\u003elabel\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003etext_label\u003c/span\u003e))\n\nggplot(\u003cspan class=\"pl-smi\"\u003econsensus_counts\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10000\u003c/span\u003e], aes(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eabundance_rank\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eN\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e geom_point() \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\nlabs(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBarcodes ranked by abundance\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eAbundance\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etitle\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eBarcodes with cluster counts \u0026gt;= 2 and \u0026lt;= 100000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\ntheme_few(\u003cspan class=\"pl-c1\"\u003e12\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\ngeom_text_repel(aes(\u003cspan class=\"pl-v\"\u003elabel\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003etext_label\u003c/span\u003e))\n\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-delivery-fellow\" class=\"anchor\" href=\"#delivery-fellow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDelivery Fellow\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1631115532.0 + "updated_at": 1651228584.0 }, { "data_format": 2, - "description": "Singularity container for playing 2048", + "description": null, "filenames": [ "Singularity" ], - "full_name": "bbbbbrie/2048-container", + "full_name": "Bandit42/gdown.pl", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-2048-container\" class=\"anchor\" href=\"#2048-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2048-container\u003c/h1\u003e\n\u003cp\u003eA recipe for a Singularity container useful for playing 2048.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-construction\" class=\"anchor\" href=\"#construction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConstruction\u003c/h2\u003e\n\u003cp\u003eBuild the container with something like \u003ccode\u003esudo singularity build 2048.img Singularity\u003c/code\u003e or \u003ccode\u003ebuild-image.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-play-2048\" class=\"anchor\" href=\"#play-2048\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlay 2048!\u003c/h2\u003e\n\u003cp\u003eRun \u003ccode\u003esingularity exec 2048.img /usr/games/2048-qt\u003c/code\u003e or \u003ccode\u003eplay-2048.sh\u003c/code\u003e after building the container.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/436e8e13c282802e7481996df4fa6dc543fd7e18b520205aa794df403d2226b7/68747470733a2f2f692e696d6775722e636f6d2f64496c50474c642e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/436e8e13c282802e7481996df4fa6dc543fd7e18b520205aa794df403d2226b7/68747470733a2f2f692e696d6775722e636f6d2f64496c50474c642e706e67\" alt=\"Score: 128\" data-canonical-src=\"https://i.imgur.com/dIlPGLd.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gdownpl\" class=\"anchor\" href=\"#gdownpl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egdown.pl\u003c/h1\u003e\n\u003cp\u003eGoogle Drive direct download of big files\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003ewget\u003c/em\u003e and \u003cem\u003ePerl\u003c/em\u003e must be in the PATH.\u003cbr\u003e\n\u003cstrong\u003eWindows\u003c/strong\u003e and \u003cstrong\u003elinux\u003c/strong\u003e compatible.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eUse Google Drive shareable links, viewable by anyone:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./gdown.pl \u0027gdrive file url\u0027 [\u0027desired file name\u0027] \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-example\" class=\"anchor\" href=\"#example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h1\u003e\n\u003cp\u003eFor example, to download \u003ca href=\"https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit\" rel=\"nofollow\"\u003ethis video\u003c/a\u003e from my \u003ca href=\"https://circulosmeos.wordpress.com/2015/03/04/axolotl-a-simple-plain-text-documentation-system/\" rel=\"nofollow\"\u003eaxolotl project\u003c/a\u003e, just copy the url, and give a file name if desired:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./gdown.pl https://drive.google.com/file/d/0B1L_hFrWJfRhLUJZdXdSdTdfSWs/edit axolotl.mp4 \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-resuming-a-download\" class=\"anchor\" href=\"#resuming-a-download\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResuming a download\u003c/h1\u003e\n\u003cp\u003eIf you need to resume a download, please, use \u003ca href=\"https://github.com/circulosmeos/gdown.pl/tree/with-resume\"\u003e\u003cstrong\u003egdown.pl v2.0\u003c/strong\u003e here\u003c/a\u003e.\u003cbr\u003e\nAs long as a file name is indicated as second parameter, \u003cem\u003egdown.pl v2.0\u003c/em\u003e \u003cstrong\u003ewill try to resume the partially downloaded file\u003c/strong\u003e if a local incomplete file with that name already exists.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-version\" class=\"anchor\" href=\"#version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h1\u003e\n\u003cp\u003eThis version is \u003cstrong\u003ev1.4\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-warning\" class=\"anchor\" href=\"#warning\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWarning\u003c/h3\u003e\n\u003cp\u003ePlease, note that v1.2 (available between days 12 to 31 of Jan/2019) \u003cstrong\u003eshould not be used\u003c/strong\u003e, as it contains a bug that could result in unusable downloaded files. Proceed to overwrite with v1.4 in case you have it.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003cp\u003eA simple Docker file is provided, to build a simple Docker image with gdown.pl.\u003cbr\u003e\nThis has been used for pre-pulling data from a Google Drive to Kubernetes persistent volumes.\u003cbr\u003e\nThanks @anton-khodak\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eAn example \u003ca href=\"https://sylabs.io/guides/3.2/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e file is provided.\u003cbr\u003e\nBuild the container:\n\u003ccode\u003esudo singularity build (imagename) Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRun the container:\n\u003ccode\u003esingularity run (imagename) (gdown.pl args)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThanks to @ttbrunner\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eDistributed \u003ca href=\"http://www.gnu.org/licenses/gpl-3.0.html\" rel=\"nofollow\"\u003eunder GPL 3\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h1\u003e\n\u003cp\u003eThis software is provided \"as is\", without warranty of any kind, express or implied.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-more-info\" class=\"anchor\" href=\"#more-info\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore info\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files\" rel=\"nofollow\"\u003ehttps://circulosmeos.wordpress.com/2014/04/12/google-drive-direct-download-of-big-files\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eby \u003ca href=\"loopidle@gmail.com\"\u003ecirculosmeos\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "singularity-container", - "2048", - "2048-game", - "container" - ], - "updated_at": 1556246890.0 + "topics": [], + "updated_at": 1651087778.0 }, { "data_format": 2, "description": null, "filenames": [ - "container/Singularity.intel_am4", - "container/Singularity.intel_netcdf", - "container/Singularity.gnu" + "AttentionASR/util/Singularity.def", + "wav2vec2.0bert/util/Singularity.def" ], - "full_name": "nova0002/troubleshooting", + "full_name": "1vket/ASR", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gfdl-am4-model\" class=\"anchor\" href=\"#gfdl-am4-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGFDL AM4 Model\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/102487636\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/878db836b9000fd7d9ff531257cade7343f3a3fdf8f764b5a7f1e8ef6ccc6abe/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3130323438373633362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/102487636.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository includes the public release of the GFDL AM4 model\ncode. The AM4 model is described in the\n\u003ca href=\"https://doi.org/10.1002/2017MS001208\" rel=\"nofollow\"\u003etwo\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.1002/2017MS001209\" rel=\"nofollow\"\u003earticles\u003c/a\u003e published in the\n\u003ca href=\"https://agupubs.onlinelibrary.wiley.com/journal/19422466\" rel=\"nofollow\"\u003eJournal of Advances in Modeling Earth Systems\n(JAMES)\u003c/a\u003e.\nMore information on the model and access to the output is available on\nthe \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eAM4 data and code\nsite\u003c/a\u003e at the\n\u003ca href=\"https://www.gfdl.noaa.gov\" rel=\"nofollow\"\u003eGeophysical Fluid Dynamics Laboratory\n(GFDL)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe layout of this package includes the following directories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esrc - The source code for the AM4 model\u003c/li\u003e\n\u003cli\u003eexec - The build directory with Makefiles for building the model executable\u003c/li\u003e\n\u003cli\u003erun - Sample run script and updated files needed for running\u003c/li\u003e\n\u003cli\u003eanalysis - Sample analysis scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cloning-instructions\" class=\"anchor\" href=\"#cloning-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning Instructions\u003c/h2\u003e\n\u003cp\u003eThis repository uses \u003ca href=\"https://git-scm.com/book/en/v2/Git-Tools-Submodules\" rel=\"nofollow\"\u003egit\nsubmodules\u003c/a\u003e to\npoint to other repositories. Thus, care should be taken when cloning,\nand updating the source to ensure all source. To obtain all source,\nuse the following git command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NOAA-GFDL/AM4.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--recursive\u003c/code\u003e option to \u003ccode\u003egit clone\u003c/code\u003e instructs git to recursively\nclone all submodules. In the event the repository was not cloned\nusing the \u003ccode\u003e--recursive\u003c/code\u003e option, the following step must be taken to\nobtain all sources:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# From within the AM4 parent directory\ngit submodule update --init --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-source-code\" class=\"anchor\" href=\"#source-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource Code\u003c/h2\u003e\n\u003cp\u003eAll model source is contained in the \u003ca href=\"src\"\u003esrc\u003c/a\u003e directory. GFDL\ntracks code using the git version control system. This package\nincludes a single version of the following GFDL model components. The\ngit hash listed corresponds to the commit hash in the internal GFDL\ngit repository.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComponent\u003c/th\u003e\n\u003cth\u003eCommit Hash\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_drivers\u003c/td\u003e\n\u003ctd\u003e5ee95d6abf0879594551dd7e6635dff4004c4010\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_param\u003c/td\u003e\n\u003ctd\u003e2e94acfd8621e85216bf822c395a8c3f15a511a5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_shared\u003c/td\u003e\n\u003ctd\u003ea557d4d7bab033ef1ad1d400a62fe07a97ccb477\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eice_param\u003c/td\u003e\n\u003ctd\u003e1553c8bc4f9a66791c89367b6f327147523155ed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eice_sis\u003c/td\u003e\n\u003ctd\u003eccc7328dcd79706dd5c17c8bab660222886fc80b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eland_lad2\u003c/td\u003e\n\u003ctd\u003ea220288ecb289bf9d793d051fc5076072874ce07\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe following components are available in the\n\u003ca href=\"https://github.com/NOAA-GFDL\"\u003eNOAA-GFDL\u003c/a\u003e github organization:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/MOM6\"\u003eMOM6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/coupler\"\u003ecoupler\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/FMS\"\u003eFMS\u003c/a\u003e (as \u003ca href=\"src/shared\"\u003eshared\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/GFDL_atmos_cubed_sphere\"\u003eGFDL_atmos_cubed_sphere (tag AM4.0)\u003c/a\u003e (as \u003ca href=\"src/atmos_cubed_sphere\"\u003eatmos_cubed_sphere\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-am4\" class=\"anchor\" href=\"#building-am4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding AM4\u003c/h2\u003e\n\u003cp\u003e###Containers\nThe \u003ca href=\"container\"\u003econtainer folder\u003c/a\u003e provides example Dockerfiles and Signularity\ndefinition files to use to build AM4 containers using either GCC/GFORTAN or\nIntel oneAPI. There is a script that can be used to build the intel\nsingularity containers, and the first step of this script can be used with the\nother GFDL climate models.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-from-source\" class=\"anchor\" href=\"#from-source\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFrom source\u003c/h3\u003e\n\u003cp\u003eThe \u003ca href=\"exec\"\u003eexec\u003c/a\u003e directory contains Makefiles that can be used to\nbuild the AM4 executable. These Makefiles were generated using the\n\u003ca href=\"https://github.com/NOAA-GFDL/mkmf\"\u003eMake Makefile (mkmf)\u003c/a\u003e program.\nIncluded in the exec direcgtory is a sample make template file for the\nIntel compilers (\u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e). This make\ntemplate can be used on any system with a relatively recent version of\nthe Intel compilers, the netCDF 4 library and the MPICH2 MPI library.\nIncluded in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file are\nadditional settings that can be modified during the build.\u003c/p\u003e\n\u003cp\u003eTo run the default build (-O3 -msse2), go to the exec directory and\nenter the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you would like to change some of the compiler options, there are several different\noptions to add to the make command. For example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake ISA=-xhost BLD_TYPE=REPRO\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill replace -msse with -xhost and -O3 with -O2. The three options for\n\u003ccode\u003eBLD_TYPE\u003c/code\u003e are\u003cbr\u003e\n\u003ccode\u003ePROD\u003c/code\u003e (-O3)\u003cbr\u003e\n\u003ccode\u003eREPRO\u003c/code\u003e (-O2)\u003cbr\u003e\n\u003ccode\u003eDEBUG\u003c/code\u003e (-O0 and other traps)\u003cbr\u003e\nAll of the make line options can be\nfound in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-obtaining-the-input-data\" class=\"anchor\" href=\"#obtaining-the-input-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eObtaining the input data\u003c/h2\u003e\n\u003cp\u003eThe input data required for running the AM4 model can be found on\n\u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eGFDL\u0027s data\nportal\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eAM4.tar.gz\u003c/code\u003e contains a configured run directory to run a\nsample experiment of the AM4 model. Included in the tar file is a\nREADME.AM4_run with more instructions on how to configure the AM4 run\ndirectory.\u003c/p\u003e\n\u003cp\u003eOn Linux systems, the \u003ccode\u003ewget\u003c/code\u003e command is usually sufficient to download the data\nfile:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo ensure the file downloaded is complete and not corrupted, download one of the two files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz.sha256\nwget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz.sig\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand run the following command that corresponds to the signature file downloaded:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esha256sum -c AM4_run.tar.gz.sha256\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003egpg --verify AM4_run.tar.gz.sig\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-am4\" class=\"anchor\" href=\"#running-am4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning AM4\u003c/h2\u003e\n\u003cp\u003eIncluded in the run directory is a sample run script for reference.\nTo run the AM4 sample experiment, first download the data file\nmentioned in \u003ca href=\"#obtaining-the-input-data\"\u003eObtaining the Input data\u003c/a\u003e\nsection. Replace diag_table and input.nml in the top level of the\nuntar\u0027d directory with the corresponding files in the run directory\nof this repository. Modify the variables in the configuration section\nin the sample run script, and then run the script.\u003c/p\u003e\n\u003cp\u003eThe sample data and run script are configured to run on 216\nprocessors. To run on a different number of processors, or modify the\nexperiment, refer to the \u003ccode\u003eREADME.AM4_run\u003c/code\u003e file included in the AM4\ndata tarball.\u003c/p\u003e\n\u003cp\u003eNote: The \u003ccode\u003einput.nml\u003c/code\u003e file (found in the AM4 data tarball) contains\nFortran namelists and namelist variables that modify, at run time, the\nmodel. To learn more about the settings in the \u003ccode\u003einput.nml\u003c/code\u003e file,\nplease refer to source code where the namelist/variable are defined.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-analysis-scripts\" class=\"anchor\" href=\"#analysis-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalysis Scripts\u003c/h2\u003e\n\u003cp\u003eSome of the climate analysis scripts run at NOAA GFDL and used in the\nAM4 documentation papers are located in the analysis directory.\nWithin each analysis suite, is a \u003ca href=\"https://jupyter-notebook.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ejupyter\nnotebook\u003c/a\u003e, both\nreadable and runnable from your local jupyter environment, provided\nall dependencies are installed.\u003c/p\u003e\n\u003cp\u003eE.g.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/cjs1/radiation_atmos_av_mon/radiation_atmos_av_mon.ipynb\"\u003eRadiation processor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/bw/bw_atmos_cru_ts_a1r/bw_atmos_monthly_cru_ts.1980-2014.ipynb\"\u003eLong-term DJF seasonal mean\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/bw/bw_atmos_zm_atl_pac_a1r/bw_atmos_atl_pac.1980-2014.ipynb\"\u003eZonal_mean_zonal_wind_stress\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/pcmdimetrics/portraitPlot-AM4.AMIP.ipynb\"\u003ePCMDI Metrics Portrait Plot\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-model-output-and-other-references\" class=\"anchor\" href=\"#model-output-and-other-references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModel output and Other References\u003c/h2\u003e\n\u003cp\u003ePlease refer to the \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eAM4 data and code\nsite\u003c/a\u003e for details\nabout where to find model and OBS data used in the papers.\u003c/p\u003e\n\u003cp\u003eFor all analysis figures and pertaining data, please use the AM4\ndocumentation papers as the original reference.\u003c/p\u003e\n\u003cp\u003ePlease direct your questions and feedback to\n\u003ca href=\"mailto:gfdl.climate.model.info@noaa.gov\"\u003egfdl.climate.model.info@noaa.gov\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h2\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is\nprovided on an \u0027as is\u0027 basis and the user assumes responsibility for\nits use. DOC has relinquished control of the information and no\nlonger has responsibility to protect the integrity, confidentiality,\nor availability of the information. Any claims against the Department\nof Commerce stemming from the use of its GitHub project will be\ngoverned by all applicable Federal law. Any reference to specific\ncommercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply\ntheir endorsement, recommendation or favoring by the Department of\nCommerce. The Department of Commerce seal and logo, or the seal and\nlogo of a DOC bureau, shall not be used in any manner to imply\nendorsement of any commercial product or activity by DOC or the United\nStates Government.\u003c/p\u003e\n\u003cp\u003eThis project code is made available through GitHub but is managed by\nNOAA-GFDL at \u003ca href=\"https://gitlab.gfdl.noaa.gov\" rel=\"nofollow\"\u003ehttps://gitlab.gfdl.noaa.gov\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1631295050.0 + "updated_at": 1649148128.0 }, { "data_format": 2, - "description": null, + "description": "Definition files for singularity container", "filenames": [ - "tools/Singularity" + "Singularity.test", + "Singularity.one-point-stats", + "Singularity.reach" ], - "full_name": "psadil/meta", + "full_name": "piyanatk/singularity-containers", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" href=\"#singularity-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-containers\u003c/h1\u003e\n\u003cp\u003eDefinition files for singularity container\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1635794729.0 - }, - { - "data_format": 2, - "description": "AUGUSTUS is a program that predicts genes in eukaryotic genomic sequences.", - "filenames": [ - "3.4.0/Singularity" - ], - "full_name": "pscedu/singularity-augustus", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-augustus/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-augustus/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-augustus/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-augustus/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b009594bd55f20cf925162d0fe98bf76f1a1f741fbee0db595e0980a750bb1ae/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6175677573747573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b009594bd55f20cf925162d0fe98bf76f1a1f741fbee0db595e0980a750bb1ae/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6175677573747573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-augustus\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e5e9d7c3436fbd053be5a28483f6822d138a79c3a29716fd6d3f2fe128fae067/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6175677573747573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e5e9d7c3436fbd053be5a28483f6822d138a79c3a29716fd6d3f2fe128fae067/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6175677573747573\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-augustus\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f00d9c84f9567045a6f64ca45bb524ba8b825def2d65b2d01198055f6cba9c46/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6175677573747573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f00d9c84f9567045a6f64ca45bb524ba8b825def2d65b2d01198055f6cba9c46/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6175677573747573\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-augustus\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/955c466d3c55ba981c6418256a37775f3ba366d3272280c174ef9e6ed43f5d27/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6175677573747573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/955c466d3c55ba981c6418256a37775f3ba366d3272280c174ef9e6ed43f5d27/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6175677573747573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-augustus\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-augustus\" class=\"anchor\" href=\"#singularity-augustus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-augustus\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for AUGUSTUS.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eaugustus\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/AUGUSTUS/3.4.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/AUGUSTUS\u003c/code\u003e as \u003ccode\u003e3.4.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1631583633.0 + "updated_at": 1650540063.0 }, { "data_format": 2, "description": null, "filenames": [ - "pySCENIC-master/Singularity.0.9.18" + "Wave-U-Net-Pytorch/Singularity" ], - "full_name": "rahuldvs1904/pySCENIC-master", + "full_name": "likelian/source-separation", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-source-separation\" class=\"anchor\" href=\"#source-separation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esource-separation\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1631722647.0 + "updated_at": 1650573000.0 }, { "data_format": 2, - "description": "Class apps for CHPC OnDemand", + "description": "A toolkit for aligning multi-modal images to the Allen CCF.", "filenames": [ - "MIB2020/Singularity" + "CWLScripts/Singularity.def" ], - "full_name": "CHPC-UofU/OOD-class-apps", + "full_name": "dontminchenit/CCFAlignmentToolkit", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ood-apps\" class=\"anchor\" href=\"#ood-apps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOOD-apps\u003c/h1\u003e\n\u003cp\u003eRepository of CHPC\u0027s Class Open OnDemand apps\u003c/p\u003e\n\u003cp\u003eThis is a repository of interactive apps used at CHPC supported classes with Open OnDemand.\u003c/p\u003e\n\u003cp\u003eEach subdirectory has its own README.md which contains information about this particular app.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ccfalignmenttoolkit\" class=\"anchor\" href=\"#ccfalignmenttoolkit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCCFAlignmentToolkit\u003c/h1\u003e\n\u003cp\u003eOne-time Functions (these are functions that only need to be run once. We will run these and will provide the end results as resources)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eConstruction of fMOST atlas\nFunction: antsMultivariateTemplateConstruction2.sh\nInputs: Collection of fMOST images to be used in atlas.\nOutputs: fMOST average atlas\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSequential Registration of fMOST atlas to CCF\nFunction: AtlasToCCFSequentialRegistration.py\nInputs: Atlas \u0026amp; labels for fMOST atlas and CCF\nOutputs: Transform between fMOST atlas -\u0026gt; CCF\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUser Runtime Functions (These are functions the users will run given new images)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRegistration of new fMOST image to fMOST atlas\nFunction: fMOSTRegisterToCCF.py\nInputs: New fMOST image (downsampled) and fMOST average atlas\nOutput: Transform between new fMOST image and fMOST atlas\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e2)Applying transforms to image\nFunction: ApplyTransfromTofMOST.py\nInputs: fMOST image; transform between fMOST image-\u0026gt;fMOST atlas; transform between fMOST atlas -\u0026gt; CCF\nOutputs: new fMOST image in CCF space\u003c/p\u003e\n\u003cp\u003e3)Applying transforms to neurons\nFunction: ApplyTransfromToSWC.py\nInputs: SWC in new fMOST image space; transform between fMOST image-\u0026gt;fMOST atlas; transform between fMOST atlas-\u0026gt;CCF\nOutputs: neurons (swc) in CCF space\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1631833519.0 + "updated_at": 1649347077.0 }, { "data_format": 2, - "description": "A Singularity container Definition File for running the Tensorflow Object Detection API and a demo Python script.", + "description": "The source code for the TAAMP project", "filenames": [ - "singularity/Singularity" + "downward/misc/releases/19.06/Singularity.19.06", + "downward/misc/releases/20.06/Singularity.20.06", + "downward/misc/releases/19.12/Singularity.19.12" ], - "full_name": "cedarwarman/object_detection_singularity", + "full_name": "ScazLab/TAAMP", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-tensorflow-object-detection-in-singularity\" class=\"anchor\" href=\"#tensorflow-object-detection-in-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTensorflow Object Detection in Singularity\u003c/h1\u003e\n\u003cp\u003eThis repo contains a Singularity Definition File for making a container that runs the \u003ca href=\"https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md\"\u003eTensorflow Object Detection API\u003c/a\u003e. It also contains a Python script that runs a modified version of the API\u0027s \u003ca href=\"https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/eager_few_shot_od_training_tf2_colab.ipynb\"\u003eEager Few Shot Detector demo\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-singularity-container\" class=\"anchor\" href=\"#building-the-singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the Singularity container\u003c/h2\u003e\n\u003cp\u003eTo build the Singularity container with \u003ca href=\"https://cloud.sylabs.io/builder\" rel=\"nofollow\"\u003eRemote Builder\u003c/a\u003e, first add your credentials:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity remote login\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity -v build --remote ./singularity/tf_od.sif ./singularity/Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-demo\" class=\"anchor\" href=\"#running-the-demo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the demo\u003c/h2\u003e\n\u003cp\u003eTo run the demo with X11 forwarding and error message suppression:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv -B ~/.Xauthority ./singularity/tf_od.sif python3 ./python/eager_few_shot_od_training_tf2_singularity.py \u0026amp;\u0026gt;/dev/null \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI use this in an HPC environment, so putting it in the background and suppressing messages allows me to monitor the progress with \u003ccode\u003envtop\u003c/code\u003e or \u003ccode\u003envidia-smi\u003c/code\u003e in the same window. Adjust to suit your needs.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-task-affordance-and-motion-planning-taamppproach\" class=\"anchor\" href=\"#task-affordance-and-motion-planning-taamppproach\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTask, Affordance, And Motion Planning (TAAMP)pproach\u003c/h1\u003e\n\u003cp\u003eWe used TAAMP, which is an affordance-based TAMP approach to expedite the search on tasks with contrained environment, or tasks that are infeasible due to environmental constraints. In this approach, we checked whether the environment allow the effects of certain actions. Or in other words, whether the environment can afford these actions. This is because some constraints imposed by the context, such as a very crowded surface that does not allow more objects to be placed on top of it as shown in the image below, is independent of robot configurations (e.g., grasp poses of the object). We refer to the quality of being \"place-able\" as affordance, and each action may have different affordances.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_7_zoom_in.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_7_zoom_in.png\" height=\"150\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe build upon PDDLStream, the state-of-the-art TAMP planner. The source code of PDDLStream can be found \u003ca href=\"https://github.com/caelan/pddlstream\"\u003ehere\u003c/a\u003e, and the original readme file can be found \u003ca href=\"PDDLSTREAM_README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone --recursive git@github.com:ScazLab/Affordance-based-TAMP.git\n$ cd Affordance-based-TAMP\nAffordance-based-TAMP$ ./downward/build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstall the dependencies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install pybullet numpy scipy\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-demonstrations\" class=\"anchor\" href=\"#demonstrations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemonstrations\u003c/h2\u003e\n\u003cp\u003eThis repository contains the demonstrations in simulation that are included in the paper. There are four types of tasks: unconstrained tasks, constrained tasks 1, constrained tasks 2, and infeasible tasks. Each type of task has a demonstration without the tool and one with the tool. In these tasks, a robot should cook the \"celery\" (the green block) by first placing it on the \"sink\" (the blue surface) and then placing it on the \"stove\" (the red surface). The \"radish\" (the cyan block) is not directly related to the goal. Images of each task is shown below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_1.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_2.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_2.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_3.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_3.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_4.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_4.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_5.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_5.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_6.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_6.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_7.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_7.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/task_8.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/task_8.png\" height=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe compared our results with PDDLStream which doesn\u0027t have these affordance checks, and used them as control conditions.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preliminaries\" class=\"anchor\" href=\"#preliminaries\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreliminaries\u003c/h3\u003e\n\u003cp\u003eBefore you ran the code, you should update the directories in the urdf files in \u003ccode\u003eexamples/pybullet/utils/models\u003c/code\u003e and in \u003ccode\u003eexamples/pybullet/utils/models/drake/objects\u003c/code\u003e with the prefix \u003ccode\u003emeiying_\u003c/code\u003e. I attempted to use relative paths but the urdf cannot find the correct point cloud file. I apologize for any inconvenience.\u003c/p\u003e\n\u003cp\u003eYou also need to correct the path in the \u003ccode\u003eexamples/pybullet/utils/model/bb.json\u003c/code\u003e, \u003ccode\u003elearned_samples\\ur/simulator\\pointcloud\\tool.json\u003c/code\u003e, the \u003ccode\u003eget_package_dir()\u003c/code\u003e function in \u003ccode\u003eexamples/pybullet/utils/pybullet_tools/learn_affordance_tamp/constants.py\u003c/code\u003e. This is awarkward coding style, but I run out of time to fix it.\u003c/p\u003e\n\u003cp\u003eNote: If you would like to learn the affordances and use the generic affordance tests, you should train the tasks with TRI-STAR (steps omitted here. Please refer to the TRI-STAR readme file to see how to use the package; You also need to update the file location of the learned affordances in the function \u003ccode\u003e\\_get_goal_range\u003c/code\u003e in \u003ccode\u003emeiying_primitives.py\u003c/code\u003e).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-unconstrained-tasks\" class=\"anchor\" href=\"#unconstrained-tasks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnconstrained Tasks\u003c/h3\u003e\n\u003cp\u003eThe non-tool-use version was orginally included in \u003ca href=\"https://github.com/caelan/pddlstream/tree/main/examples/pybullet/kuka\"\u003ePDDLStream\u003c/a\u003e. We included this task to ensure that the task is friendly to the current planners. In the tool-use version, the robot should first retrieve the the green block with the L-shaped tool.\u003c/p\u003e\n\u003cp\u003eDemonstrations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-Tool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_1_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_2_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNon-Tool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_1_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_2_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can add the \u003ccode\u003e-viewer\u003c/code\u003e option to visualize the task and the solution, for example:\n\u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_1_test.run -viewer\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-constrained-tasks-1\" class=\"anchor\" href=\"#constrained-tasks-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConstrained Tasks 1\u003c/h3\u003e\n\u003cp\u003eIn these tasks, the environments are more constrainted than constrained tasks. However, the robots does not need to relocate objects that are not directly related to the goal (e.g., the cyan block). In the non-tool-use task, the robot needs to place the green block on the relatively crowded vlue surface which has limited space for the green block. In the tool-use task, the robot needs to retrieve the green block hiding under the orange tunnel with a T-shaped tool. In these tasks, the red blocks are immovable.\u003c/p\u003e\n\u003cp\u003eDemonstrations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-Tool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_3_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_4_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNon-Tool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_3_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_4_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-constrained-tasks-2\" class=\"anchor\" href=\"#constrained-tasks-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConstrained Tasks 2\u003c/h3\u003e\n\u003cp\u003eIn these tasks, the robots need to relocate objects that are not directly related to the goal (e.g., the cyan block). In the non-tool-use task, the robot needs to relocate the cyan block to make room for the green block. In the tool-use task, the robot needs to retrieve the L-shaped tool hiding under the orange tunnel with a T-shaped tool, in order to pull the green block towards itself with the T-shaped tool. In these tasks, the red blocks are also immovable.\u003c/p\u003e\n\u003cp\u003eDemonstrations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-Tool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_5_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_6_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNon-Tool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_5_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_6_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-infeasible-tasks\" class=\"anchor\" href=\"#infeasible-tasks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInfeasible Tasks\u003c/h3\u003e\n\u003cp\u003eIn these tasks, the robots cannot complete the tasks. The green block is hidding under immovable yellow contrainer, which makes it impossible to pick, pull or push the green block to retrieve it.\u003c/p\u003e\n\u003cp\u003eDemonstrations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-Tool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_7_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_8_test_learn.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNon-Tool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_7_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTool-Use Control: \u003ccode\u003eAffordance-based-TAMP$ python -m examples.pybullet.meiying_tamp_8_control.run\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-adding-new-examples\" class=\"anchor\" href=\"#adding-new-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding New Examples\u003c/h2\u003e\n\u003cp\u003eTo add a new example, one should first create a folder under \u003ccode\u003eexamples/pybullet\u003c/code\u003e. In this folder, one should create a \u003ccode\u003e__init__.py\u003c/code\u003e to initialize this folder as a package, a \u003ccode\u003edomain.pddl\u003c/code\u003e which defines the problem (e.g., the actions), a \u003ccode\u003estream.pddl\u003c/code\u003e with the streams to certify predicates or generate samples, and a \u003ccode\u003erun.py\u003c/code\u003e that defines the environment.\u003c/p\u003e\n\u003cp\u003eIf a new object is needed, one should create an urdf under \u003ccode\u003eexamples/pybullet/utils/models\u003c/code\u003e. If a pointcloud/mesh is needed, one should create an \u003ccode\u003eobj\u003c/code\u003e file, as well as a ply file with the same name for collision detection purposes.\u003c/p\u003e\n\u003cp\u003eWhen a new action is needed, the names of the correspondence affordance checks in the \u003ccode\u003estream.pddl\u003c/code\u003e should starts with the \u003ccode\u003etest\u003c/code\u003e and also include the word \u003ccode\u003efeasible\u003c/code\u003e so that these checks will be applied earlier in the search process when necessary.\u003c/p\u003e\n\u003cp\u003eWhen sampling for certain affordances are needed, and when fluents are needed (currently only support the AtPose fluent), the name of the affordance samplers should be added to \u003ccode\u003e./pddlstream/algorithms/scheduling/apply_fluents.py\u003c/code\u003e line 98. Note: this is by no means be considered as good coding style, but I did not have time to completely refactor the code. The purpose of this source code is to show the benefit of considering affordances.\u003c/p\u003e\n\u003cp\u003eNote: I only performed a minor code refactor before I upload this source code due to time constraints. I apologize for the messiness of the code.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 9, "topics": [], - "updated_at": 1632281930.0 + "updated_at": 1658517655.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "planner/symk/Singularity", + "planner/symk/misc/releases/19.06/Singularity.19.06", + "planner/symk/misc/releases/latest/Singularity", + "planner/symk/misc/releases/19.12/Singularity.19.12" ], - "full_name": "DCAN-Labs/heudiconv-helper", + "full_name": "zihangs/Janus", "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-bids-conversion-scripts\" class=\"anchor\" href=\"#bids-conversion-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBIDS Conversion Scripts\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eThis repository contains scripts that will assist in the conversion of raw DICOM data sets to \u003ca href=\"http://bids.neuroimaging.io/format\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e. The \"heuristics\" folder contains example scripts that have been used to convert data from DICOM to BIDS. \u003cstrong\u003eThis folder may be helpful to build your own heuristics script.\u003c/strong\u003e For additional information on how to create a heuristic script see the \u003ca href=\"https://github.com/nipy/heudiconv\"\u003eheudiconv\u003c/a\u003e github page.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-hosting\" class=\"anchor\" href=\"#container-hosting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Hosting\u003c/h3\u003e\n\u003cp\u003ePull the most recent container below, (\u003cstrong\u003eNOTE: you only have to do this once!\u003c/strong\u003e):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tjhendrickson/BIDS_scripts:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" href=\"#singularity-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Usage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/singularity/images/directory/imagename.img --help\nusage: [-h] [--output_dir OUTPUT_DIR] [--dicom_dir DICOM_DIR]\n [--study_name STUDY_NAME] [--ses_id SES_ID] [--subj_id SUBJ_ID]\n [--heuristic HEURISTIC] [--dry_run]\n\nScript that controls BIDS conversion for individual studies\n\noptional arguments:\n -h, --help show this help message and exit\n --output_dir OUTPUT_DIR\n The directory that the BIDS data will be outputted to\n --dicom_dir DICOM_DIR\n The directory that houses unzipped dicom\n directories/files.\n --study_name STUDY_NAME\n What is the shorthand name for this study?\n --ses_id SES_ID scanning session id\n --subj_id SUBJ_ID subject id\n --heuristic HEURISTIC\n Path to heuristic file, if the file is already within\n the container (i.e. within heuristics folder) you do\n not have to specify a path.\n --dry_run Dry run. A dicominfo_*.tsv file will generate within\n .heudiconv/\u0027subj_id\u0027/info directory which can be used\n to create heuristic script\n\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eThis application must be run with either the \"--heuristic\" or \"--dry_run\" argument, it will fail otherwise.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse the \"--dry_run\" argument to take a closer look at the acquistion parameters for a scanning session.\u003c/p\u003e\n\u003cp\u003eTo run a single participant with dry_run argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --dry_run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output a hidden folder (named .heudiconv) along with sub-folders based on arguments provided to \"--subj_id\" and \"--ses_id\" respectively.\nWithin the sub-folders will be a tsv file that begins with \"dicominfo\". \u003cstrong\u003eBased on the example above the path to the file will be \".heudiconv/1000/ses-10000/info/dicominfo_ses-10000.tsv\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse this tsv file to design a heuristics script to organize your eventual nifti data. \u003cstrong\u003eSee this tutorial for a how to on heuristic script creation (\u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman3\" rel=\"nofollow\"\u003eheuristics tutorial\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo run a single participant with heuristic argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir -B /path/to/heuristics/script:/heuristic.py \\\n /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --heuristic /heuristic.py\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-important-notes\" class=\"anchor\" href=\"#important-notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Notes\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gotchas\" class=\"anchor\" href=\"#gotchas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGotchas\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Bind Mounting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to run this container you will have to use \"bind mounting\", meaning you will have to link local folders/files to existing folders/files within the container with the -B flag. In the example above the local folder \"/home/timothy/sandbos_DO_NOT_DELETE/BIDS/142_CIFASD_4\" becomes \"/output_dir\" within the container as they are separated by a colon (:). \u003cstrong\u003eNotice that in both cases above the output and dicom folder and heuristic file are bound to /output_dir, /dicom_dir and /heuristic.py respectively, this is very important.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) DICOM Data Formatting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the restrictive nature of BIDS the dicom data must be in a particular format in order for the conversion to work properly. This application will copy dicom data directories by searching for either the --subj_id or --ses_id argument present within a dicom directory name, place them in a separate directory, and rearrange them. So for example if the dicom directory is named \"XYXY4776XYXY\" --subj_id 4776 the application will find the \"4776\" pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Subject ID and Session ID names\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou must use alphanumerics (i.e. letters or numbers) only (\u003cstrong\u003eno special characters\u003c/strong\u003e) with your subject IDs (subj_id) and session IDs (ses_id). \u003cstrong\u003eNote the \"--ses_id\" argument is optional\u003c/strong\u003e!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-best-practices\" class=\"anchor\" href=\"#best-practices\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBest Practices\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Initial Conversion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile testing the initial BIDS conversion it is best to start with one or two datasets and specify the \u0027--dry_run\u0027 argument (see above for an example of usage).\nThis will create a dicom_info tsv file which can be used for heuristic script creation.\nSee Step 3 of \u0027Run HeuDiConv on ses-001 scans to get the dicominfo file\u0027 within \u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman2\" rel=\"nofollow\"\u003eStanford BIDS Tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) BIDS Validator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce satisfied with an initial BIDS conversion, prior to running the conversion on an entire study first ensure that the BIDS converted dataset meets the BIDS specification by using the \u003ca href=\"http://incf.github.io/bids-validator/\" rel=\"nofollow\"\u003eBIDS validator web version\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Longitudinal Format\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen converting data to BIDS you can certainly have a cross sectonal directory format such as below:\nBIDS_output\nsub-01\nsub-02\nsub-03\nsub-04\netc\nHowever, I suggest placing data within a longitudinal directory format even if you have cross-sectional data:\nBIDS_output\nsub-01\nses-01\nses-02\nsub-02\nses-01\netc\u003c/p\u003e\n\u003cp\u003eYou can control the BIDS directory format by providing both the arguments --subj_id --ses_id for a conversion, if you only specify one of the two arguments the data will be outputted in a cross-sectional format.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-janus\" class=\"anchor\" href=\"#janus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJanus\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1634787198.0 + "updated_at": 1652720467.0 }, { "data_format": 2, - "description": "clone of temp_tc", + "description": "Repository for Open OnDemand Applications on Lehigh\u0027s HPC clusters", "filenames": [ - "Singularity" + "spark_r/Singularity" ], - "full_name": "JoshLorDeveloper/temp_tc_clone", + "full_name": "alexpacheco/lurc-ood-apps", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-transactive_control\" class=\"anchor\" href=\"#transactive_control\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etransactive_control\u003c/h1\u003e\n\u003cp\u003eCode meant to support and simulate the Social Game that will be launched in 2020. Elements of transactive control and behavioral engineering will be tested and designed here\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://dvc.org/doc/install\" rel=\"nofollow\"\u003edvc\u003c/a\u003e (with google drive support)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOn linux this is \u003ccode\u003epip install \u0027dvc[gdrive]\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall Docker, if you have not already.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc remote add -d gdrive gdrive://1qaTn6IYd3cpiyJegDwwEhZ3LwrujK3_x\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc pull\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./run.sh\u003c/code\u003e from the root of the repo. This will put you in a shell in the docker container with the \u003ccode\u003erl_algos/logs\u003c/code\u003e directory mounted\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 StableBaselines.py sac test_experiment\u003c/code\u003e in the docker container to start an experiment with the name \u003ccode\u003etest_experiment\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003etensorboard --logdir rl_algos/logs\u003c/code\u003e from outside the docker container to view the logs\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-12202020\" class=\"anchor\" href=\"#12202020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e12/20/2020\u003c/h3\u003e\n\u003cp\u003eThis repository has been cleaned and updated for use. It contains: (1) The OpenAI gym environment \"OfficeLearn\", in the \"gym-socialgame\" folder, and (2) implementations of Reinforcement learning algorithms in \"rl_algos\" folder. In the \"simulations\" folder are various datasets for setting up and training models associated with the gym simulation.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-912020\" class=\"anchor\" href=\"#912020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e9/1/2020\u003c/h3\u003e\n\u003cp\u003eThe most recent running of the code involves navigating to the rl_algos/ directory, then running the python command for the vanilla version:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac\u003c/p\u003e\n\u003cp\u003eAdding in the planning model can be done with the following flags:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac --planning_steps=10 --planning_model=Oracle --num_steps=10000\u003c/p\u003e\n\u003cp\u003ePlease see transactive_control/gym-socialgame/gym_socialgame/envs for files pertaining to the setup of the environment. The socialgame_env.py contains a lot of the information necessary for understanding how the agent steps through the environment. The reward.py file contains information on the variety of reward types available for testing. agents.py contains information on the deterministic people that we created for our simulation.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gym-socialgame\" class=\"anchor\" href=\"#gym-socialgame\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egym-socialgame\u003c/h3\u003e\n\u003cp\u003eOpenAI Gym environment for a social game.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-open-ondemand-applications\" class=\"anchor\" href=\"#open-ondemand-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpen OnDemand Applications\u003c/h1\u003e\n\u003cp\u003eOpen OnDemand Applications on Lehigh\u0027s HPC cluster.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMaple\u003c/li\u003e\n\u003cli\u003eMathematica\u003c/li\u003e\n\u003cli\u003eMATLAB\u003c/li\u003e\n\u003cli\u003eAbaqus\u003c/li\u003e\n\u003cli\u003eAnsys\u003c/li\u003e\n\u003cli\u003eDesktop Environment - XCFE\u003c/li\u003e\n\u003cli\u003eGNU Octave\u003c/li\u003e\n\u003cli\u003eSAS\u003c/li\u003e\n\u003cli\u003eVisualization Tools\n\u003cul\u003e\n\u003cli\u003eASE\u003c/li\u003e\n\u003cli\u003eAvogadro 2\u003c/li\u003e\n\u003cli\u003eGabedit\u003c/li\u003e\n\u003cli\u003eGaussView\u003c/li\u003e\n\u003cli\u003eParaview\u003c/li\u003e\n\u003cli\u003ePWGui\u003c/li\u003e\n\u003cli\u003ePyMol\u003c/li\u003e\n\u003cli\u003eVESTA\u003c/li\u003e\n\u003cli\u003eXCrysDen\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSTATA\u003c/li\u003e\n\u003cli\u003eDeepLabCut Desktop Application\u003c/li\u003e\n\u003cli\u003eSpyder\u003c/li\u003e\n\u003cli\u003eSpark + Jupyter\u003c/li\u003e\n\u003cli\u003eSpark + RStudio\u003c/li\u003e\n\u003cli\u003eX-Ray Crytallagraphic analysis tools - XDS, Phenix, CCP4, Cytoscape\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1636716500.0 + "updated_at": 1651619112.0 }, { "data_format": 2, - "description": "Singularity image for the presence_absence pipeline ", + "description": "High Resolution Non-Deterministic Face Aging", "filenames": [ - "Singularity" + "gdown.pl/Singularity" ], - "full_name": "vdclab/simg-PA_tools", - "latest_release": "0.0.2", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-presence_absence-tools-image\" class=\"anchor\" href=\"#singularity-presence_absence-tools-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity presence_absence tools image\u003c/h1\u003e\n\u003cp\u003eSingularity image for the presence_absence pipeline.\u003c/p\u003e\n\u003cp\u003eThis repository is created to be able to not depend on instalation or module loading for the presence abscence pipeline.\u003c/p\u003e\n\u003cp\u003eIn this Singularity container the following software and python library are installed :\u003c/p\u003e\n\u003cp\u003eSoftwares:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI BLAST+ == 2.10.1\u003c/li\u003e\n\u003cli\u003esilix == 1.2.11\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePython libraries:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003encbi-genome-download == 0.3.0\u003c/li\u003e\n\u003cli\u003eete3 == 3.1.2\u003c/li\u003e\n\u003cli\u003ematplotlib == 3.3.3\u003c/li\u003e\n\u003cli\u003epandas == 1.1.5\u003c/li\u003e\n\u003cli\u003ebiopython == 1.78\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "arshagarwal/Face-AHQ-GAN2", + "latest_release": null, + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-steps-to-run-using-docker-using-nvidia-gpus\" class=\"anchor\" href=\"#steps-to-run-using-docker-using-nvidia-gpus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to run using docker using Nvidia gpus:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall docker using \u003ca href=\"https://docs.docker.com/engine/install/ubuntu/\" rel=\"nofollow\"\u003edocker installation guide\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eInstall nvidia-docker2 using \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker\" rel=\"nofollow\"\u003envidia-docker2 installation guide\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNote: Ensure to check the latest versions of nvidia cuda runtime and coroborrate with pytorch cuda requirements , this guide was uploaded on 4/9/2020.\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild the image using \u003ccode\u003edocker build -f docker -t slimgan:gpu\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eRun the Container using \u003ccode\u003edocker run --gpus all --shm-size 10G -it slimgan:gpu\u003c/code\u003e.\n5.Run the \u003ccode\u003envidia-smi\u003c/code\u003e to check if gpus work.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003epython main.py --img_dir ../data/celeba_hq/train --iters 20000,60000,100000 --img_size 128,256,512 --batch_size 16,8,2 --gpus 0,1 --c_dim 2 --num_workers 5\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-steps-to-run-the-code-on-google-colab-just-3-lines-of-code\" class=\"anchor\" href=\"#steps-to-run-the-code-on-google-colab-just-3-lines-of-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to run the code on Google colab (just 3 lines of code):\u003c/h2\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eClone the code by running the command \u003ccode\u003e!git clone https://username:password@github.com/arshagarwal/C_slim_gan.git -b arsh_spectral\u003c/code\u003e .\n\u003cstrong\u003eReplace the username and password with your github username and password respectively.\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003ecd C_slim_gan\u003c/code\u003e to navigate to the \u003cstrong\u003eC_slim_gan\u003c/strong\u003e directory.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003e!bash import_dataset.sh\u003c/code\u003e to import the \u003cstrong\u003eBig slim dataset\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003e! python main.py --rafd_image_dir Big_slim --num_iters 20000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 \u003c/code\u003e to train on the \u003cstrong\u003eBig_slim_dataset\u003c/strong\u003e.\n\u003cstrong\u003eAlternatively to train on custom dataset replace the \u003ccode\u003eslim_dataset/Train_dataset\u003c/code\u003e string with the path of your custom dataset.\u003c/strong\u003e\u003cbr\u003e\nFor further options such as \u003cstrong\u003enumber of epochs, batch_size etc\u003c/strong\u003e refer \u003cstrong\u003emain.py\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-note-after-running-step-3-after-every-sample-stepth-iteration-generated-samples-will-be-stored-in-stargansamples-folder-for-performance-evaluation\" class=\"anchor\" href=\"#note-after-running-step-3-after-every-sample-stepth-iteration-generated-samples-will-be-stored-in-stargansamples-folder-for-performance-evaluation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNote: After running step 3 after every sample stepth iteration generated samples will be stored in stargan/samples folder for performance evaluation.\u003c/h3\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-for-continuing-training-from-20000-iteration-to-30000-iteration-follow\" class=\"anchor\" href=\"#for-continuing-training-from-20000-iteration-to-30000-iteration-follow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor Continuing training from 20000 iteration to 30000 iteration follow:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003epython main.py --rafd_image_dir Big_slim --num_iters 30000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 --resume_iters 20000\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1629657667.0 + "updated_at": 1651478504.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.main", - "Singularity.def" + "Singularity" ], - "full_name": "shubavarshini/microbiome", + "full_name": "przepiorkaGrzegorz/singularity_container", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-microbiome\" class=\"anchor\" href=\"#microbiome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emicrobiome\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1630678051.0 + "updated_at": 1660178824.0 }, { "data_format": 2, "description": null, "filenames": [ - ".ci/github/Singularity" + "Singularity" ], - "full_name": "qwert2333/CEPCSW_training", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cepcsw\" class=\"anchor\" href=\"#cepcsw\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://cepc.github.io/CEPCSW/\" rel=\"nofollow\"\u003eCEPCSW\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.travis-ci.com/cepc/CEPCSW\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/51cb592ac6435ae6b6bdc6cca7a941779434c9db16df9857df2a94e6f239971b/68747470733a2f2f7777772e7472617669732d63692e636f6d2f636570632f4345504353572e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://www.travis-ci.com/cepc/CEPCSW.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/cepc/CEPCSW/actions\"\u003e\u003cimg src=\"https://github.com/cepc/CEPCSW/workflows/CI/badge.svg?branch=master\" alt=\"CI\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCEPC offline software prototype based on \u003ca href=\"https://github.com/key4hep\"\u003eKey4hep\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start\u003c/h2\u003e\n\u003cp\u003eSSH to lxslc7 (CentOS 7).\u003c/p\u003e\n\u003cp\u003eBefore run following commands, please make sure you setup the CVMFS:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone git@github.com:cepc/CEPCSW.git\n$ cd CEPCSW\n$ git checkout master # branch name\n$ source setup.sh\n$ ./build.sh\n$ ./run.sh Examples/options/helloalg.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-packages\" class=\"anchor\" href=\"#packages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackages\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eExamples: For new comers and users\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDetector: Geometry\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenerator: Physics Generator\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSimulation: Detector Simulation\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDigitization: Digitization\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReconstruction: Reconstruction\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-conventions-for-collections\" class=\"anchor\" href=\"#conventions-for-collections\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConventions for collections\u003c/h2\u003e\n\u003cp\u003eKeep the collection names compatible between the prototype and the existing CEPC software.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMCParticle\u003c/li\u003e\n\u003cli\u003eVXDCollection\u003c/li\u003e\n\u003cli\u003eSITCollection\u003c/li\u003e\n\u003cli\u003eTPCCollection\u003c/li\u003e\n\u003cli\u003eSETCollection\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "Lipinski-B/DE-nf", + "latest_release": "v1.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-de-nf---pipeline-v10\" class=\"anchor\" aria-hidden=\"true\" href=\"#de-nf---pipeline-v10\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDE-nf : Pipeline V1.0\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-un-pipeline-nextflow-pour-r\u00e9aliser-une-analyse-dexpression-diff\u00e9rentielle-rnaseq-sur-un-ensemble-dindividus\" class=\"anchor\" aria-hidden=\"true\" href=\"#un-pipeline-nextflow-pour-r\u00e9aliser-une-analyse-dexpression-diff\u00e9rentielle-rnaseq-sur-un-ensemble-dindividus\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUn pipeline nextflow pour r\u00e9aliser une analyse d\u0027expression diff\u00e9rentielle RNAseq sur un ensemble d\u0027individus.\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/lipinskiboris/de-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5269\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\" width=\"100%\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"analyses-nf.png\"\u003e\u003cimg align=\"center\" width=\"60%\" src=\"analyses-nf.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eCe pipeline a \u00e9t\u00e9 d\u00e9velopp\u00e9 en vue de r\u00e9aliser des analyses RNAseq compl\u00e8tes \u00e0 partir de fichiers FASTA issus de s\u00e9quen\u00e7age NGS.\u003c/p\u003e\n\u003cp\u003eVoici un r\u00e9sum\u00e9 de la m\u00e9thode :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR\u00e9alisation d\u0027un index (optionnel).\u003c/li\u003e\n\u003cli\u003eAlignement des reads sur le g\u00e9nome de r\u00e9f\u00e9rence.\u003c/li\u003e\n\u003cli\u003eIntersection des fichiers SAM sur l\u0027annotation de r\u00e9f\u00e9rence.\u003c/li\u003e\n\u003cli\u003e\u00c9laboration de la matrice finale de comptage brute.\u003c/li\u003e\n\u003cli\u003eAnalyse d\u0027expression diff\u00e9rentielle sur R via le package DESeq2.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVeuillez consulter la section \"Usage\" pour tester le pipeline avec un ensemble de donn\u00e9es.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-d\u00e9pendences\" class=\"anchor\" aria-hidden=\"true\" href=\"#d\u00e9pendences\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eD\u00e9pendences\u003c/h2\u003e\n\u003cp\u003eLe pipeline est fonctionnel sous les distributions de Linux.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCe pipeline est enti\u00e8rement bas\u00e9 sur l\u0027utilisation de \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e. Il est fortement recommand\u00e9 de prendre connaissance de son \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003einstallation\u003c/a\u003e et de son utilisation avant d\u0027ex\u00e9cuter le pipeline.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSoftware \u00e0 installer :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSTAR (version 2.7.7a)\u003c/li\u003e\n\u003cli\u003eBWA (version 0.7.17-r1188)\u003c/li\u003e\n\u003cli\u003esamtools (version 1.9)\u003c/li\u003e\n\u003cli\u003efastqc (version 0.11)\u003c/li\u003e\n\u003cli\u003emultiqc (version 1.8)\u003c/li\u003e\n\u003cli\u003ehtseq-count (version 0.13.5)\u003c/li\u003e\n\u003cli\u003eR (version 4.0.3)\u003c/li\u003e\n\u003cli\u003ePackage R : DESeq2, edgeR, pheatmap, RColorBrewer, ggbeeswarm, genefilter, biomaRt, stringr, ggplot2, NMF, tidyverse.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFichier compl\u00e9mentaire n\u00e9cessaire :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFichier d\u0027annotation GTF : \u003ca href=\"https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/latest/\" rel=\"nofollow\"\u003ehg38\u003c/a\u003e ou \u003ca href=\"https://ftp.ncbi.nlm.nih.gov/genomes/all/annotation_releases/7160/102/GCF_006496715.1_Aalbo_primary.1/GCF_006496715.1_Aalbo_primary.1_genomic.gtf.gz\" rel=\"nofollow\"\u003eAedes albopictus\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFichier FNA pour l\u0027index : \u003ca href=\"https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes/\" rel=\"nofollow\"\u003ehg38\u003c/a\u003e ou \u003ca href=\"https://ftp.ncbi.nlm.nih.gov/genomes/all/annotation_releases/7160/102/GCF_006496715.1_Aalbo_primary.1/GCF_006496715.1_Aalbo_primary.1_genomic.fna.gz\" rel=\"nofollow\"\u003eAedes albopictus\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFichier XLS : M\u00e9tadonn\u00e9e (voir dossier data/ pour Aedes albopictus)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAutre :\nDes containers Docker et Singularity ont \u00e9galement \u00e9t\u00e9 \u00e9labor\u00e9 en vue de permettre aux utilisateurs de lancer le pipeline sans avoir \u00e0 installer toutes les d\u00e9pendances n\u00e9cessaires de la partie 2. Les installations des outils \u003ca href=\"https://docs.docker.com/engine/install/ubuntu/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e et \u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e sont n\u00e9cessaire au pr\u00e9alable. Voir la derni\u00e8re section de \"Usage\" pour plus de d\u00e9tails.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eFichier FASTA/FASTQ\u003c/td\u003e\n\u003ctd\u003eCorresponds aux fichiers FASTA/FASTQ d\u0027int\u00e9r\u00eat compress\u00e9s au format .gz.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-param\u00e8tres\" class=\"anchor\" aria-hidden=\"true\" href=\"#param\u00e8tres\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParam\u00e8tres\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-param\u00e8tres-obligatoires-\" class=\"anchor\" aria-hidden=\"true\" href=\"#param\u00e8tres-obligatoires-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParam\u00e8tres obligatoires :\u003c/h4\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eNom\u003c/th\u003e\n\u003cth\u003eExemple\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input\u003c/td\u003e\n\u003ctd\u003e/input/\u003c/td\u003e\n\u003ctd\u003eChemin vers le dossier o\u00f9 se trouvent les fichiers FASTA \u00e0 utiliser pour l\u0027analyse. Assurez-vous de n\u0027avoir que les fichiers FASTA d\u0027int\u00e9r\u00eats dans ce dossier et rien d\u0027autre.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output\u003c/td\u003e\n\u003ctd\u003e/output/\u003c/td\u003e\n\u003ctd\u003eChemin vers le dossier o\u00f9 se trouveront les diff\u00e9rents r\u00e9sultats issus du pipeline.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--GTF\u003c/td\u003e\n\u003ctd\u003e/data/fichier.gtf\u003c/td\u003e\n\u003ctd\u003eChemin o\u00f9 se trouve le fichier d\u0027annotation \u00e0 utiliser pour l\u0027index via STAR et l\u0027intersection via htseq-count.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-param\u00e8tres-obligatoires-compl\u00e9mentaires-pour-lindex-\" class=\"anchor\" aria-hidden=\"true\" href=\"#param\u00e8tres-obligatoires-compl\u00e9mentaires-pour-lindex-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParam\u00e8tres obligatoires compl\u00e9mentaires pour l\u0027index :\u003c/h4\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eNom\u003c/th\u003e\n\u003cth\u003eExemple\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--index\u003c/td\u003e\n\u003ctd\u003e/data/index\u003c/td\u003e\n\u003ctd\u003eChemin vers le dossier o\u00f9 se trouve l\u0027index STAR \u00e0 utiliser pour le pipeline. Si cette option n\u0027est pas utilis\u00e9e, merci de vous assurer de fournir l\u0027option --FNA en plus de l\u0027option --GTF pour r\u00e9aliser l\u0027index. Par d\u00e9faut, null.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eOu bien :\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--FNA\u003c/td\u003e\n\u003ctd\u003e/data/fichier.fna\u003c/td\u003e\n\u003ctd\u003eChemin o\u00f9 se trouve le fichier .fna \u00e0 fournir obligatoirement pour r\u00e9aliser l\u0027index si l\u0027option --index n\u0027est pas fourni.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-param\u00e8tres-optionellescompl\u00e9mentaires-\" class=\"anchor\" aria-hidden=\"true\" href=\"#param\u00e8tres-optionellescompl\u00e9mentaires-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParam\u00e8tres optionelles/compl\u00e9mentaires :\u003c/h4\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eNom\u003c/th\u003e\n\u003cth\u003eExemple\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mapper\u003c/td\u003e\n\u003ctd\u003eSTAR/BWA\u003c/td\u003e\n\u003ctd\u003eMapper \u00e0 utiliser. Par d\u00e9faut BWA (MEM).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--thread\u003c/td\u003e\n\u003ctd\u003eN\u003c/td\u003e\n\u003ctd\u003eNombre de thread \u00e0 utiliser pour le pipeline. Par d\u00e9faut 1.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--R\u003c/td\u003e\n\u003ctd\u003eon/off\u003c/td\u003e\n\u003ctd\u003eOption pour r\u00e9aliser (\"on\") ou non (\"off\") l\u0027analyse d\u0027expression diff\u00e9rentielle sur R par d\u00e9faut sur pipeline. Par d\u00e9faut, off.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--metadata\u003c/td\u003e\n\u003ctd\u003e/data/metadata.xls\u003c/td\u003e\n\u003ctd\u003eChemin o\u00f9 se trouve le fichier de m\u00e9tadonn\u00e9es \u00e0 utiliser pour l\u0027analyse d\u0027expression diff\u00e9rentielle sur R. Obligatoire si l\u0027option --R est mis sur \"on\"\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eLancement basique du pipeline, dans le cas o\u00f9 toutes les d\u00e9pendances sont install\u00e9es localement.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf --input /input/ --GTF /data/fichier.gtf --FNA /data/fichier.fna --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLa matrice de comptage r\u00e9sultant correspond au fichier finale.txt dans le dossier \"/output/merge/finale.txt\".\u003c/p\u003e\n\u003cp\u003eUn script DE.R est mis \u00e0 votre disposition dans le dossier \"bin/\" de ce r\u00e9pertoire git, afin de vous permettre de r\u00e9aliser par vous-m\u00eame l\u0027analyse de l\u0027expression diff\u00e9rentielle. Vous aurez donc besoin de la matrice finale pour terminer l\u0027analyse mais aussi d\u0027un fichier XLS r\u00e9pertoriant les m\u00e9tadonn\u00e9es des \u00e9chantillons d\u0027int\u00e9r\u00eats.\u003c/p\u003e\n\u003cp\u003eLe script DE.R se lance comme ceci :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/DE.r finale.txt /data/Metadata.xls\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eVous pouvez utiliser votre propre fichier XLS, dans ce cas il est recommand\u00e9 de suivre comme template le fichier \"Metadata.xls\" que vous trouverez dans le dossier \"data/\" de ce r\u00e9pertoire. Le but ici \u00e9tant de pouvoir permettre \u00e0 l\u0027utilisateur de r\u00e9aliser ses propres analyses exploratoires d\u0027expression diff\u00e9rentielle \u00e0 partir du template fourni dans le script DE.R\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eVous pouvez \u00e9galement lancer le pipeline avec la r\u00e9alisation d\u0027une analyse d\u0027expression diff\u00e9rentielle par d\u00e9faut sur R de fa\u00e7on automatique, via l\u0027option --R.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf --input /input/ --GTF /data/fichier.gtf --FNA /data/fichier.fna --R on --metadata /data/metadata.xls --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUn rapport sera mis \u00e0 votre disposition dans le dossier \"/output/R/\".\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eDans le cas o\u00f9 toutes les d\u00e9pendances sont install\u00e9es localement et vous souhaitez utiliser votre propre index STAR pour l\u0027analyse, vous pouvez suivre cette proc\u00e9dure. Attention pour des raisons de compatibilit\u00e9, l\u0027index ajout\u00e9 avec l\u0027option --index doit \u00eatre r\u00e9alis\u00e9 avec la m\u00eame version du mapper que celle utilis\u00e9e pour l\u0027alignement.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf --input /input/ --GTF /data/fichier.gtf --index /data/mapper_index --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eEnfin vous pouvez lancer le pipeline via l\u0027utilisation de containers Docker/Singularity via l\u0027option -profile.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf -profile docker --input /input/ --GTF /data/fichier.gtf --FNA /data/fichier.fna --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eou\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run Lipinski-B/DE-nf -profile singularity --input /input/ --GTF /data/fichier.gtf --FNA /data/fichier.fna --output /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLipinski Boris\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:boris.lipinski@etu.univ-lyon1.fr\"\u003eboris.lipinski@etu.univ-lyon1.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDevelopeur \u00e0 contacter pour support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1631168746.0 + "updated_at": 1654520921.0 }, { "data_format": 2, @@ -13701,643 +13108,609 @@ var data = "filenames": [ "Singularity" ], - "full_name": "arabnejad/FabSim4", + "full_name": "VacantiLab/LehtioDDMSQuantSearch", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fabsim4\" class=\"anchor\" href=\"#fabsim4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFabSim4\u003c/h1\u003e\n\u003cp\u003eThis the migrated version of FabSim3 to Fabric2\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-lehtiolabddamsproteomics\" class=\"anchor\" aria-hidden=\"true\" href=\"#lehtiolabddamsproteomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elehtiolab/ddamsproteomics\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eA Quantitative MS proteomics analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2e73ffd6aaca951b76d06bb9e625b9958985004799f48697d15d272d8754b96a/68747470733a2f2f7472617669732d63692e6f72672f6c656874696f6c61622f6464616d7370726f74656f6d6963732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/lehtiolab/ddamsproteomics.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/03c97559839c37998c3c1db1465217ff323c688ad1dbb4a617a90eefde35af1d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30312e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.01.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/219955514\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3cdf183590e0fd8280e9cf4762883d9e76e2b577ee19066a8d3debc07af0553/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3231393935353531342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/219955514.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3e006af0b85a286d286be1e4a41902ac68ed95f8253c038485c54ba693b93049/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c656874696f6c61622f6464616d7370726f74656f6d6963732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/lehtiolab/ddamsproteomics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThis workflow identifies peptides in mzML input data using \u003ca href=\"https://github.com/MSGFPlus/msgfplus\"\u003eMSGF+\u003c/a\u003e, and \u003ca href=\"https://github.com/percolator/percolator/\"\u003ePercolator\u003c/a\u003e, quantifies isobarically labeled samples with \u003ca href=\"https://github.com/openms/openms\"\u003eOpenMS\u003c/a\u003e, and precursor peptides with \u003ca href=\"https://github.com/fickludd/dinosaur\"\u003eDinosaur\u003c/a\u003e, and processes that output to formatted peptide and protein/gene tables using \u003ca href=\"https://github.com/lehtiolab/msstitch\"\u003eMsstitch\u003c/a\u003e. Optional PTM data is analyzed by \u003ca href=\"https://github.com/dfermin/lucxor\"\u003eLuciphor2\u003c/a\u003e, and differential expression analyses can be performed using \u003ca href=\"https://github.com/yafeng/deqms\"\u003eDEqMS\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun pipeline:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa --mods \u0027oxidation;carbamidomethylation\u0027 -profile standard,docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr for two sample sets of isobaric data you can:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa --mods \u0027oxidation;carbamidomethylation --isobaric \u0027setA:tmt10plex:126 setB:tmt10plex:127N\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more elaborate examples covering fractionation, PTMs, and more, the lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere is more extensive documentation on the options inside the main.nf file.\u003c/p\u003e\n\u003cp\u003eThe pipeline takes multiple mzML files as input and performs identification and quantification to output results and a QC report (\u003ca href=\"docs/example_qc.html\"\u003ean example can be found here\u003c/a\u003e)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003elehtiolab/ddamsproteomics was originally written by Jorrit Boekel and tries to follow the \u003ca href=\"https://nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e best practices and templates.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1630410840.0 + "updated_at": 1654280178.0 }, { "data_format": 2, - "description": "Nextflow pipeline for inference of CRISPR edits from NGS (Amplicon Sequencing) data", + "description": null, "filenames": [ - "Singularity" + "Singularity.ubuntu-20.04" ], - "full_name": "gnetsanet/crispedit", + "full_name": "zonca/singularity_github_ci", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-crispedit\" class=\"anchor\" href=\"#crispedit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecrispedit\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eNextflow pipeline for inference of CRISPR edits from NGS (Amplicon Sequencing) data\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun crispedit/crisedit.nf --in_fastq /Users/ngebremedhin/Downloads/VB26_18_3428_P1087_308811w_BF8.fastq --ref canola_badc_allgenes.fa --out_dir ${PWD}/myProject --project_name LC25 --bucket bioinformatics-analysis-netsanet --sgrna \"CCTTCTGAGCCCATGAACAAATC\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 19.04.1\nLaunching `crispedit/crisedit.nf` [hopeful_swanson] - revision: 607eaca8d5\n\n[warm up] executor \u0026gt; local\nexecutor \u0026gt; local (27)\n[a7/a397c1] process \u0026gt; mergeReads [100%] 1 of 1 \u2714\n[58/d46b33] process \u0026gt; clustering [100%] 1 of 1 \u2714\n[1d/5e8374] process \u0026gt; dereplication [100%] 1 of 1 \u2714\n[bb/cc12b3] process \u0026gt; mapHighFrequencyReads [100%] 1 of 1 \u2714\n[44/eec241] process \u0026gt; samToBam [100%] 1 of 1 \u2714\n[92/c84f30] process \u0026gt; indexBam [100%] 1 of 1 \u2714\n[d2/119094] process \u0026gt; identiyEdits [100%] 1 of 1 \u2714\n[a2/38c0af] process \u0026gt; prepForPSA [100%] 1 of 1 \u2714\n[a6/51cabd] process \u0026gt; performPSA [100%] 9 of 9 \u2714\n[bd/fd96a3] process \u0026gt; combineClustalOut [100%] 9 of 9 \u2714\n[33/0f5567] process \u0026gt; createFinalReport [100%] 1 of 1 \u2714\n\nCompleted at: 25-Jul-2019 11:37:08\nDuration : 12.4s\nCPU hours : (a few seconds)\nSucceeded : 27\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003ecrispedit is written by Netsanet Gebremedhin.\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-test-build-singularity-containers-on-github-actions\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-build-singularity-containers-on-github-actions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest build Singularity containers on Github Actions\u003c/h2\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1631673344.0 + "updated_at": 1654214816.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for centos7", "filenames": [ - "pin/conduit-binder/third-party/force-cover/Singularity" + "Singularity.dev" ], - "full_name": "mmore500/conduit-quality-of-service-writeup", + "full_name": "pndni/centos7-base", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1631675356.0 + "updated_at": 1555436901.0 }, { "data_format": 2, - "description": "ElikoPy is Python library aiming at easing the processing of diffusion imaging for microstructural analysis.", + "description": "Singularity build files for FSL and freesurfer", "filenames": [ - "Singularity_elikopy" + "Singularity.FSL-6.0.1_freesurfer-6.0.1_dev" ], - "full_name": "Hyedryn/elikopy", - "latest_release": "v0.2.2", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-elikopy\" class=\"anchor\" href=\"#elikopy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eElikoPy\u003c/h1\u003e\n\u003cp\u003eElikoPy is Python library aiming at easing the processing of diffusion imaging for microstructural analysis.\nThis Python library is based on\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDIPY, a python library for the analysis of MR diffusion imaging.\u003c/li\u003e\n\u003cli\u003eMicrostructure fingerprinting, a python library doing estimation of white matter microstructural properties from a dictionary of Monte Carlo diffusion MRI fingerprints.\u003c/li\u003e\n\u003cli\u003eFSL, a comprehensive library of analysis tools for FMRI, MRI and DTI brain imaging data.\u003c/li\u003e\n\u003cli\u003eDIAMOND, a c software that is characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion\u2010compartment imaging.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eElikoPy requires \u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython\u003c/a\u003e v3.7+ to run.\u003c/p\u003e\n\u003cp\u003eAfter cloning the repo, you can either firstly install all the python dependencies including optionnal dependency used to speed up the code:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install -r requirements.txt --user\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr you can install directly the library with only the mandatory dependencies (if you performed the previous step, you still need to perform this step):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ python3 setup.py install --user\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMicrostructure Fingerprinting is currently not avaible in the standard python repo, you can clone and install this library manually.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:rensonnetg/microstructure_fingerprinting.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e microstructure_fingerprinting\n$ python setup.py install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFSL also needs to be installed and availabe in our path if you want to perform mouvement correction or tbss.\u003c/p\u003e\n\u003cp\u003eUnfortunatly, the DIAMOND code is not publically available. If you do not have it in your possesion, you will not be able to use this algorithm. If you have it, simply add the executable to your path.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eTodo\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-development\" class=\"anchor\" href=\"#development\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h3\u003e\n\u003cp\u003eWant to contribute? Great!\u003c/p\u003e\n\u003cp\u003eDo not hesitate to open issue or pull request!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-todos\" class=\"anchor\" href=\"#todos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTodos\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eRelease a complete and accurate documentation for the library\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eFree Software, Hell Yeah!\u003c/strong\u003e\u003c/p\u003e\n", + "full_name": "pndni/FSL-and-freesurfer", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-license-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#license-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense info\u003c/h1\u003e\n\u003cp\u003eWhile the actual code in this repository is covered by the provided \u003ca href=\"LICENSE\"\u003elicense\u003c/a\u003e,\nusing freesurfer and FSL requires accepting their respective licenses. By using this\ncontainer, you must agree to these licenses.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense\" rel=\"nofollow\"\u003eFreesurfer license\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\" rel=\"nofollow\"\u003eFSL license\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou must acquire a freesurfer license from\n\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/registration.html\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/registration.html\u003c/a\u003e\nEnsure that the license file is visible from the container,\nand set the environment variable FS_LICENSE to point to it\n(or copy the file to /opt/freesurfer/license.txt from\ninside the container)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "microstructure-fingerprinting", - "fsl", - "tbss", - "python-library", - "diffusion-imaging", - "preprocessing", - "dmri", - "diamond", - "noddi", - "dti" - ], - "updated_at": 1627554863.0 + "topics": [], + "updated_at": 1554399581.0 }, { "data_format": 2, - "description": "Nextflow pipeline for inference of CRISPR edits from NGS (Amplicon Sequencing) data", + "description": "Singularity container for minc built on centos 7", "filenames": [ "Singularity" ], - "full_name": "Yield10Bio/crispedit", + "full_name": "pndni/minc-container", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-crispedit\" class=\"anchor\" href=\"#crispedit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecrispedit\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eNextflow pipeline for inference of CRISPR edits from NGS (Amplicon Sequencing) data\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun crispedit/crisedit.nf --in_fastq /Users/ngebremedhin/Downloads/VB26_18_3428_P1087_308811w_BF8.fastq --ref canola_badc_allgenes.fa --out_dir ${PWD}/myProject --project_name LC25 --bucket bioinformatics-analysis-netsanet --sgrna \"CCTTCTGAGCCCATGAACAAATC\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 19.04.1\nLaunching `crispedit/crisedit.nf` [hopeful_swanson] - revision: 607eaca8d5\n\n[warm up] executor \u0026gt; local\nexecutor \u0026gt; local (27)\n[a7/a397c1] process \u0026gt; mergeReads [100%] 1 of 1 \u2714\n[58/d46b33] process \u0026gt; clustering [100%] 1 of 1 \u2714\n[1d/5e8374] process \u0026gt; dereplication [100%] 1 of 1 \u2714\n[bb/cc12b3] process \u0026gt; mapHighFrequencyReads [100%] 1 of 1 \u2714\n[44/eec241] process \u0026gt; samToBam [100%] 1 of 1 \u2714\n[92/c84f30] process \u0026gt; indexBam [100%] 1 of 1 \u2714\n[d2/119094] process \u0026gt; identiyEdits [100%] 1 of 1 \u2714\n[a2/38c0af] process \u0026gt; prepForPSA [100%] 1 of 1 \u2714\n[a6/51cabd] process \u0026gt; performPSA [100%] 9 of 9 \u2714\n[bd/fd96a3] process \u0026gt; combineClustalOut [100%] 9 of 9 \u2714\n[33/0f5567] process \u0026gt; createFinalReport [100%] 1 of 1 \u2714\n\nCompleted at: 25-Jul-2019 11:37:08\nDuration : 12.4s\nCPU hours : (a few seconds)\nSucceeded : 27\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ebwa 0.7.17\u003c/li\u003e\n\u003cli\u003evsearch 2.18.0\u003c/li\u003e\n\u003cli\u003ebbmap 38.92\u003c/li\u003e\n\u003cli\u003esamtools=1.9\u003c/li\u003e\n\u003cli\u003eBiopython\u003c/li\u003e\n\u003cli\u003eclustalo 1.2.4\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003ecrispedit is written by Netsanet Gebremedhin.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1631063991.0 + "updated_at": 1554308962.0 }, { "data_format": 2, - "description": "Bismark is a program to map bisulfite treated sequencing reads to a genome of interest and perform methylation calls in a single step. ", + "description": "Ba\u011flant\u0131 test ara\u00e7lar\u0131 i\u00e7eren Docker imaj\u0131", "filenames": [ - "0.22.3/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-bismark", + "full_name": "gulnihalugur/testutils", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bismark/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bismark/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-bismark/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bismark/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ac6bb8f2b406618278034b709c736e772b92723686bd930d0ec0e0caaf278599/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6269736d61726b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ac6bb8f2b406618278034b709c736e772b92723686bd930d0ec0e0caaf278599/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6269736d61726b\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bismark\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5f93c1931fec2c3f8a38122749025c57f09fb930eb75c591ad412dfa911b97ae/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6269736d61726b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5f93c1931fec2c3f8a38122749025c57f09fb930eb75c591ad412dfa911b97ae/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6269736d61726b\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bismark\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f20c48b5b2226a8f0a7cff096f8cedfe80b073f0b801009d9684d408ee31ca07/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6269736d61726b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f20c48b5b2226a8f0a7cff096f8cedfe80b073f0b801009d9684d408ee31ca07/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6269736d61726b\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bismark\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/07c4815a6c07dc179ddc97de5e5faa3c7fec941cc2c81a94adaed0547946fd27/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6269736d61726b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/07c4815a6c07dc179ddc97de5e5faa3c7fec941cc2c81a94adaed0547946fd27/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6269736d61726b\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bismark\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bismark\" class=\"anchor\" href=\"#singularity-bismark\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bismark\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/FelixKrueger/Bismark\"\u003ebismark\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebismark\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bismark/0.22.3\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bismark\u003c/code\u003e as \u003ccode\u003e0.22.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003eDocker imaji: curl, wget, ping, netcat, nslookup,host, dig, psql, mysql\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-kullanim\" class=\"anchor\" aria-hidden=\"true\" href=\"#kullanim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKullanim\u003c/h2\u003e\n\u003cp\u003eKubernetes\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ekubectl run --rm utils -it --generator=run-pod/v1 --image gulnihalugur/testutils bash\n# You will be seeing a bash prompt\n$ psql -h hostname -U test -d test\n...\n$ exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDocker Engine\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker pull gulnihalugur/testutils\n$ docker run --rm -it gulnihalugur/testutils bash\n\n# konteynir icinde\n$ ping google.com\n$ ifconfig\n...\n$ exit\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629214967.0 + "subscribers_count": 0, + "topics": [], + "updated_at": 1561217122.0 }, { "data_format": 2, - "description": "Fast, reliable protein-coding gene prediction for prokaryotic genomes.", + "description": null, "filenames": [ - "2.6.3/Singularity" + "downward/misc/releases/19.12/Singularity.19.12", + "downward/misc/releases/20.06/Singularity.20.06", + "downward/misc/releases/latest/Singularity", + "downward/misc/releases/19.06/Singularity.19.06" ], - "full_name": "pscedu/singularity-prodigal", + "full_name": "aymeric75/latplan", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-prodigal/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-prodigal/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-prodigal/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-prodigal/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/dbf23f859880f70436ee9de9ee89a1528a5171defb337385614da4eee0ffeb2d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d70726f646967616c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dbf23f859880f70436ee9de9ee89a1528a5171defb337385614da4eee0ffeb2d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d70726f646967616c\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-prodigal\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f5f6ed86bbd6c79a6449e24738f02f9844e1fe9972bd1d162eb62316c06bf9dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d70726f646967616c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f5f6ed86bbd6c79a6449e24738f02f9844e1fe9972bd1d162eb62316c06bf9dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d70726f646967616c\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-prodigal\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4ef2a97630fd3ac7037f653270a9129fe20826c8bf90956c9b995c0eea86da02/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d70726f646967616c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ef2a97630fd3ac7037f653270a9129fe20826c8bf90956c9b995c0eea86da02/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d70726f646967616c\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-prodigal\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e41f95bac858eb80bd1956886531b10a1a335ab73e0c0cbdc7ab720cf22824cd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d70726f646967616c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e41f95bac858eb80bd1956886531b10a1a335ab73e0c0cbdc7ab720cf22824cd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d70726f646967616c\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-prodigal\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-prodigal\" class=\"anchor\" href=\"#singularity-prodigal\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-prodigal\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/prodigal\"\u003eprodigal\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eprodigal\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/prodigal/2.6.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/prodigal\u003c/code\u003e as \u003ccode\u003e2.6.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629226411.0 + "topics": [], + "updated_at": 1654148749.0 }, { "data_format": 2, - "description": "RAdiation SEmiconductoR ", + "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "dt-np/raser", - "latest_release": "v1.1", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-raser\" class=\"anchor\" href=\"#raser\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRASER\u003c/h1\u003e\n\u003cp\u003eRAdiation SEmiconductoR\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-build-with-singularity\" class=\"anchor\" href=\"#build-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild with Singularity\u003c/h1\u003e\n\u003cp\u003eBefore running the code, install the Singularity on your OS.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e./sinularity_build.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e./singularity_run.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eraser\u0026gt; geant4_build.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-run-with-singularity\" class=\"anchor\" href=\"#run-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun with Singularity\u003c/h1\u003e\n\u003cblockquote\u003e\n\u003cp\u003e./singularity_run.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eraser\u0026gt; ./run\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-raser-unit-test-after-you-change-some-codes\" class=\"anchor\" href=\"#raser-unit-test-after-you-change-some-codes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRaser unit test after you change some codes\u003c/h1\u003e\n\u003cblockquote\u003e\n\u003cp\u003e./singularity_run.sh\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eraser\u0026gt; ./run 0.1.5\nraser\u0026gt; ./run 0.2.5\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIf the output is \"Successful\", the code your changed is OK.\nOtherwise, you should check the code your changed.\u003c/p\u003e\n", + "full_name": "ZizZu94/covid19-ultrasound-img-prediction", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-covid19-ultrasound-image-score-prediction\" class=\"anchor\" aria-hidden=\"true\" href=\"#covid19-ultrasound-image-score-prediction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCovid19 Ultrasound image score prediction\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-models-resnet50-and-efficientnet-b0\" class=\"anchor\" aria-hidden=\"true\" href=\"#models-resnet50-and-efficientnet-b0\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModels: ResNet50 and EfficientNet-b0\u003c/h2\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1630661539.0 + "subscribers_count": 1, + "topics": [ + "classification", + "covid-19", + "deep-learning", + "efficientnet", + "neural-network", + "python", + "pytorch", + "resnet-50", + "ultrasound" + ], + "updated_at": 1654162475.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.tut0804", - "Singularity.05211526", - "Singularity", - "Singularity.386", - "Singularity.05201328", - "Singularity.sf", - "Singularity.05131402", - "Singularity.05221357", - "Singularity.1908121107", - "Singularity.cuda10" + "ext/Singularity" ], - "full_name": "timkphd/Containers", + "full_name": "dtenenba/bc_example_dan_rstudio", "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container-build-scripts\" class=\"anchor\" href=\"#singularity-container-build-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container build Scripts\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-see-httpssingularity-huborgcollections2962\" class=\"anchor\" href=\"#see-httpssingularity-huborgcollections2962\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSee \u003ca href=\"https://singularity-hub.org/collections/2962\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/2962\u003c/a\u003e\n\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, , R, MPI (intel and openMPI ), python\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.05131402\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, basic stuff, does not actually install Intel Python\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.05201328\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.05211526\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.05221357 (Built)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.1908121107 (Built)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:latest, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.386 (Built)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBasic 32 bit with Fortran, c++ make, nano,vim\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.sf (Built)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:18.04, STAR-Fusion\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.tut0804\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eubuntu:16.04, R, MPI, python, tutorial stuff\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-wip-batch-connect---osc-example-shiny-app\" class=\"anchor\" aria-hidden=\"true\" href=\"#wip-batch-connect---osc-example-shiny-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[WIP] Batch Connect - OSC Example Shiny App\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a8152a2780451d58acdca1e79b03f771d6e84ae12087e6e7a824b6759b715dc1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f6578616d706c655f7368696e792e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a8152a2780451d58acdca1e79b03f771d6e84ae12087e6e7a824b6759b715dc1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f6578616d706c655f7368696e792e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_example_shiny.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app designed for OSC OnDemand that launches a Shiny App within\nan Owens batch job.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e x.y.z+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_example_shiny/fork\"\u003ehttps://github.com/OSC/bc_osc_example_shiny/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-chpcs-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#chpcs-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCHPC\u0027s notes\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-functional-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#functional-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctional overview\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUses CHPC\u0027s R (3.6.1) which has shiny installed\u003c/li\u003e\n\u003cli\u003eTo run a webserver, use an openresty container running nginx\u003c/li\u003e\n\u003cli\u003eThe script.sh that launches the OOD app creates a nginx config file and Shiny app launcher, then runs R with the launcher, followed by looking for the Unix socket created by the R\u0027s Shiny, thich then gets used by the nginx startup\u003c/li\u003e\n\u003cli\u003eThe user shiny app path is specified in the job specs\u0027 input box\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that Shiny app can be also launched from the OOD\u0027s RStudio app by typing\nlibrary(\u0027shiny\u0027)\nrunApp(\"newdir\") - where \"newdir\" is the directory where app.R resides\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-applications-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#applications-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApplication\u0027s dependencies\u003c/h3\u003e\n\u003cp\u003eR libraries that are needed by the application need to either be installed centrally to CHPC\u0027s R libraries location, or to other shared directory location. The former approach risks potential version conflicts with other library dependencies (this is more of an issue in Python but is possible in R as well).\u003c/p\u003e\n\u003cp\u003eBest practice may be for the creator of the app to install all the dependencies to his/her home directory, and in the app modify the R library path (using the .libPaths function) to add this directory to it.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1627001875.0 + "updated_at": 1653954801.0 }, { "data_format": 2, - "description": "\u5b8f\u57fa\u56e0\u7ec4\u76f8\u5173\u4ee3\u7801", + "description": null, "filenames": [ - "scripts/lathe/singularity/Singularity.quickmerge", - "scripts/lathe/singularity/Singularity.longread", - "scripts/lathe/singularity/Singularity.htsbox" + "Singularity.zlib-1.2-centos8.def", + "Singularity.binutils-2.36.1-GCCcore-11.1.0-centos8.def" ], - "full_name": "JiaLonghao1997/Microbiome", + "full_name": "jkwmoore/centos8-eb-singularity-image", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-microbiome\" class=\"anchor\" href=\"#microbiome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMicrobiome\u003c/h1\u003e\n\u003cp\u003e\u5b8f\u57fa\u56e0\u7ec4\u76f8\u5173\u4ee3\u7801\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1626932334.0 + "updated_at": 1653574058.0 }, { "data_format": 2, - "description": "HTSlib A C library for reading/writing high-throughput sequencing data. ", + "description": "BIDS app to perform PET motion correction of dynamic data", "filenames": [ - "1.13/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-htslib", - "latest_release": "v1.13", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-htslib/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-htslib/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-htslib/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-htslib/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c19127d592d5b1774f1b776b581a4ee3e088dc8836040a4dcfb0112e233e0272/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6874736c6962\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c19127d592d5b1774f1b776b581a4ee3e088dc8836040a4dcfb0112e233e0272/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6874736c6962\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-htslib\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7eb94edba6c28c8efe671ccb26366254e3fa67b9ba22e17183a8353c985c30f7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6874736c6962\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7eb94edba6c28c8efe671ccb26366254e3fa67b9ba22e17183a8353c985c30f7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6874736c6962\" alt=\"Forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-htslib\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0c4e24ae23aaa5d0244c3ba0370b21835d696a720659445acd0879d08953dbf5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6874736c6962\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0c4e24ae23aaa5d0244c3ba0370b21835d696a720659445acd0879d08953dbf5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6874736c6962\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-htslib\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e0b439a0b671ca5e751012f4801e4febe27cb2fa88ea95ed862cca4a347af459/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6874736c6962\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e0b439a0b671ca5e751012f4801e4febe27cb2fa88ea95ed862cca4a347af459/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6874736c6962\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-htslib\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-htslib\" class=\"anchor\" href=\"#singularity-htslib\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-htslib\u003c/h2\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/samtools/htslib\"\u003ehtslib\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehtsfile\u003c/code\u003e, \u003ccode\u003etabix\u003c/code\u003e and \u003ccode\u003ebgzip\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/htslib/1.13\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/htslib\u003c/code\u003e as \u003ccode\u003e1.13.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "mnoergaard/hmcpet", + "latest_release": null, + "readme": "\u003ch2\u003e\u003ca id=\"user-content-an-example-bids-app-template-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-example-bids-app-template-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAn example BIDS App (template repository)\u003c/h2\u003e\n\u003cp\u003eEvery BIDS App needs to follow a minimal set of command arguments common across\nall of the Apps. This allows users and developers to easily use and integrate\nBIDS Apps with their environment.\u003c/p\u003e\n\u003cp\u003eThis is a minimalist example of a BIDS App consisting of a Dockerfile and a simple\nentry point script (written in this case in Python) accepting the standard BIDS\nApps command line arguments. This repository can be used as a template for new BIDS Apps.\u003c/p\u003e\n\u003cp\u003eFor more information about the specification of BIDS Apps see \u003ca href=\"https://docs.google.com/document/d/1E1Wi5ONvOVVnGhj21S1bmJJ4kyHFT7tkxnV3C23sjIE/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eThis is a placeholder for a short description explaining to the user what your App will doing.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eProvide a link to the documentation of your pipeline.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-how-to-report-errors\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-report-errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to report errors\u003c/h3\u003e\n\u003cp\u003eProvide instructions for users on how to get help and report errors.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eDescribe how would you would like users to acknowledge use of your App in their papers (citation, a paragraph that can be copy pasted, etc.)\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eThis App has the following command line arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\tusage: run.py [-h]\n\t [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]\n\t bids_dir output_dir {participant,group}\n\n\tExample BIDS App entry point script.\n\n\tpositional arguments:\n\t bids_dir The directory with the input dataset formatted\n\t according to the BIDS standard.\n\t output_dir The directory where the output files should be stored.\n\t If you are running a group level analysis, this folder\n\t should be prepopulated with the results of\n\t the participant level analysis.\n\t {participant,group} Level of the analysis that will be performed. Multiple\n\t participant level analyses can be run independently\n\t (in parallel).\n\n\toptional arguments:\n\t -h, --help show this help message and exit\n\t --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]\n\t The label(s) of the participant(s) that should be\n\t analyzed. The label corresponds to\n\t sub-\u0026lt;participant_label\u0026gt; from the BIDS spec (so it does\n\t not include \"sub-\"). If this parameter is not provided\n\t all subjects will be analyzed. Multiple participants\n\t can be specified with a space separated list.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run it in participant level mode (for one participant):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --rm \\\n\t-v /Users/filo/data/ds005:/bids_dataset:ro \\\n\t-v /Users/filo/outputs:/outputs \\\n\tbids/example \\\n\t/bids_dataset /outputs participant --participant_label 01\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter doing this for all subjects (potentially in parallel), the group level analysis\ncan be run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --rm \\\n\t-v /Users/filo/data/ds005:/bids_dataset:ro \\\n\t-v /Users/filo/outputs:/outputs \\\n\tbids/example \\\n\t/bids_dataset /outputs group\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-special-considerations\" class=\"anchor\" aria-hidden=\"true\" href=\"#special-considerations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecial considerations\u003c/h3\u003e\n\u003cp\u003eDescribe whether your app has any special requirements. For example:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMultiple map reduce steps (participant, group, participant2, group2 etc.)\u003c/li\u003e\n\u003cli\u003eUnusual memory requirements\u003c/li\u003e\n\u003cli\u003eetc.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629226143.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1653596606.0 }, { "data_format": 2, - "description": "Repo for recipes to put on singularity hub", + "description": "ffmpeg and pysoundfile in a Singularity image", "filenames": [ - "Singularity.dbspype", - "Singularity.xenial" + "Singularity" ], - "full_name": "hbraunDSP/containers", + "full_name": "rses-singularity/singularity-ubuntu-xenial-ffmpeg-pysoundfile", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containers\" class=\"anchor\" href=\"#containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n\u003cp\u003ePRIVATE repo for recipes to put on singularity hub.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ffmpeg-and-pysoundfile-in-a-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#ffmpeg-and-pysoundfile-in-a-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003effmpeg and pysoundfile in a Singularity image\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e for the Python packages installed in the image (using \u003ccode\u003epip\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1567609710.0 + "updated_at": 1503997989.0 }, { "data_format": 2, - "description": "msee is a command-line tool to read markdown file.", + "description": "Work in progress: A cookiecutter for singularity images", "filenames": [ - "0.3.5/Singularity" + "{{cookiecutter.project_name}}/Singularity" ], - "full_name": "icaoberg/singularity-msee", - "latest_release": "v0.3.5", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-msee/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-msee/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8f15c74b6b0c9b138f9da5b4933fe28294369dc8e7eb93b731dc2ce072de357e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6d736565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8f15c74b6b0c9b138f9da5b4933fe28294369dc8e7eb93b731dc2ce072de357e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6d736565\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-msee\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/dc49ece7c14937e5c2802e8ab4824bc9c4606e21bd4f04eede89efffab599a2d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6d736565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc49ece7c14937e5c2802e8ab4824bc9c4606e21bd4f04eede89efffab599a2d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6d736565\" alt=\"Forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-msee\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/bc6a9157f824c9765da6596c0beb4a82c4f71d7a27b46cec8e32781fb8c3faad/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6d736565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bc6a9157f824c9765da6596c0beb4a82c4f71d7a27b46cec8e32781fb8c3faad/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6d736565\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-msee\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5858470ac4cc23a618c46e8f874d611a5c2ba2762846b37cae4b6767e4e8784f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6d736565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5858470ac4cc23a618c46e8f874d611a5c2ba2762846b37cae4b6767e4e8784f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6d736565\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-msee\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-msee\" class=\"anchor\" href=\"#singularity-msee\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-msee\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://cloud.githubusercontent.com/assets/157338/10902801/531ba216-823d-11e5-87ac-986b8d5ea4cc.png\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://cloud.githubusercontent.com/assets/157338/10902801/531ba216-823d-11e5-87ac-986b8d5ea4cc.png\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://www.npmjs.com/package/msee\" rel=\"nofollow\"\u003emsee\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/msees/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "amanmdesai/cookiecutter-singularity", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cookiecutter-project-for-singularity-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#cookiecutter-project-for-singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCookiecutter Project for Singularity images\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/amanmdesai/cookiecutter-docker-singularity/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/486e44f0e9c09c6186d86e72c96fdfc6574e09d8885cf0fe2b912e9cdbff847e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f616d616e6d64657361692f636f6f6b69656375747465722d646f636b65722d73696e67756c6172697479\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/amanmdesai/cookiecutter-docker-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-hidden=\"true\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePurpose\u003c/h2\u003e\n\u003cp\u003eCreate Singularity image definition files\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eEasily write customized singularity images\u003c/li\u003e\n\u003cli\u003eDeploy easily to github packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstructions will be added\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-it\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning it!\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-extension\" class=\"anchor\" aria-hidden=\"true\" href=\"#extension\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtension:\u003c/h2\u003e\n\u003cp\u003eAn extension either to include docker images here, or elsewhere is foreseen.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eWORK in Progress\nContributions are welcome and can be made by opening a PR or bug report.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "singularity", - "utilties" - ], - "updated_at": 1627585753.0 + "topics": [], + "updated_at": 1663515196.0 }, { "data_format": 2, - "description": "R server within singularity container on HPC", + "description": null, "filenames": [ - "Singularity_bioc_python" + "Recipes/Singularity_spark_full", + "Recipes/Singularity_numpy", + "Recipes/Singularity_pytorch", + "Recipes/Singularity_ompi", + "Recipes/Singularity_GPU", + "Recipes/Singularity_tensorflow", + "Recipes/Singularity_Python", + "Recipes/Singularity_mpich", + "Recipes/Singularity_pytorch_full", + "Recipes/Singularity_spark", + "Recipes/Singularity_sklearn", + "Recipes/Singularity_example" ], - "full_name": "retogerber/singularity_rserver", + "full_name": "Gab0410/Cluster-HPC", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-r-server-in-singularity\" class=\"anchor\" href=\"#r-server-in-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR server in singularity\u003c/h1\u003e\n\u003cp\u003eThis workflow together with the script \u003ccode\u003esingRstudio.sh\u003c/code\u003e facilitates setting up an R server running in a singularity container on a HPC and accessing it on a local PC.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-workflow\" class=\"anchor\" href=\"#workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prepare-only-first-time\" class=\"anchor\" href=\"#prepare-only-first-time\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare (only first time)\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-on-local-pc\" class=\"anchor\" href=\"#on-local-pc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOn local PC\u003c/h3\u003e\n\u003cp\u003eSince building a singularity image requires root privilege it is often not possible to directly build on your HPC. A simple workaround is to build in on your local PC and the copy to the server.\nBuild Singularity image file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity_container.sif Singularity_bioc_python\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe given Singularity build file is just an example, to customize for your needs have a look at the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/build_a_container.html\" rel=\"nofollow\"\u003esingularity documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAfter building the image copy to server, e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escp singularity_container.sif SERVERNAME:/some/location\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively there is the possibily to build without sudo using the \u003ccode\u003e--remote\u003c/code\u003e flage. \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/cloud_library.html\" rel=\"nofollow\"\u003eSingularity documentation\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-setup\" class=\"anchor\" href=\"#setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-on-server\" class=\"anchor\" href=\"#on-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOn server\u003c/h3\u003e\n\u003cp\u003eMake sure a suitable temporary directory is available, e.g. \u003ccode\u003e~/tmp\u003c/code\u003e (the default).\u003c/p\u003e\n\u003cp\u003eDecide on the port you want to use, the default is 8788.\u003c/p\u003e\n\u003cp\u003eRun rserver with singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash singRstudio.sh -c singularity_container.sif -t ~/tmp -p 8789\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-on-local-pc-1\" class=\"anchor\" href=\"#on-local-pc-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOn local PC\u003c/h3\u003e\n\u003cp\u003eRedirect traffic from port on server to local port via ssh:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -Nf -L LOCALPORT:localhost:SERVERPORT SERVERNAME\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ereplacing \u003ccode\u003eLOCALPORT\u003c/code\u003e with the port you want to use on your local pc, \u003ccode\u003eSERVERPORT\u003c/code\u003e with the above specified port (default 8788) and \u003ccode\u003eSERVERNAME\u003c/code\u003e with the address of the server.\ne.g:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -Nf -L 8787:localhost:8788 user@myserver.com\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen open a browser and go to \u003ccode\u003ehttp://localhost:LOCALPORT\u003c/code\u003e again replacing \u003ccode\u003eLOCALPORT\u003c/code\u003e. Login with your server username and passwort (as specified with the \u003ccode\u003e-a\u003c/code\u003e argument, default: \u003ccode\u003epassword\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-other-options\" class=\"anchor\" href=\"#other-options\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther options:\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bind-local-directories-to-container\" class=\"anchor\" href=\"#bind-local-directories-to-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBind local directories to container\u003c/h3\u003e\n\u003cp\u003eTo connect directories to the container in a specific manner set the \u003ccode\u003e-b\u003c/code\u003e argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash singRstudio.sh -c singularity_container.sif -b \"local/dir/1:/absolute/container/dir/1,local/dir/2:/absolute/container/dir/2\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-local-r-library\" class=\"anchor\" href=\"#local-r-library\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elocal R library\u003c/h3\u003e\n\u003cp\u003eSince singularity containers are read-only, installing R packages is not possible. For reproducibility this is great as it is always clear what packages were used,\nbut sometimes it can be a nuissance when testing stuff. A workaround is to specify a local directory in which the packages are installed. This can be done setting\nthe \u003ccode\u003e-r\u003c/code\u003e argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash singRstudio.sh -c singularity_container.sif -r ~/my/R/library\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dry-run\" class=\"anchor\" href=\"#dry-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDry run\u003c/h3\u003e\n\u003cp\u003eTo just show the \"built\" singularity command without executing it add \u003ccode\u003e-d\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash singRstudio.sh -c singularity_container.sif -d\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-pr\u00e9-requisitos\" class=\"anchor\" aria-hidden=\"true\" href=\"#pr\u00e9-requisitos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePr\u00e9-requisitos\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sele\u00e7\u00e3o-do-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#sele\u00e7\u00e3o-do-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSele\u00e7\u00e3o do Recipe\u003c/h2\u003e\n\u003cp\u003eAdicione o caminho do \u003cem\u003esingularity recipe\u003c/em\u003e desejado na vari\u00e1vel RECIPE no github (projeto \u0026gt;\u0026gt; Settings \u0026gt;\u0026gt; Secrets \u0026gt;\u0026gt; New Secret).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-template-integra\u00e7\u00e3o-cont\u00ednua-github\" class=\"anchor\" aria-hidden=\"true\" href=\"#template-integra\u00e7\u00e3o-cont\u00ednua-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemplate Integra\u00e7\u00e3o Cont\u00ednua Github\u003c/h1\u003e\n\u003cp\u003eEsse projeto o template para uso do cluster da UFSCar, contendo integra\u00e7\u00e3o cont\u00ednua com o Google Drive e Amazon S3.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requisitos-para-google-drive\" class=\"anchor\" aria-hidden=\"true\" href=\"#requisitos-para-google-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequisitos para Google Drive\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eEntre no \u003ca href=\"https://console.developers.google.com/apis/credentials\" rel=\"nofollow\"\u003econsole de credenciais de API do Google\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSe ainda n\u00e3o houver um projeto, crie um com permiss\u00e3o para a \"Google Drive API\" (Dashboard).\u003c/li\u003e\n\u003cli\u003eEm \"Tela de consentimento OAuth\", marque \"Interno\" na primeira p\u00e1gina, preencha os campos obrigat\u00f3rios na segunda, n\u00e3o preencha nada na terceira,\u003c/li\u003e\n\u003cli\u003eClique em Credenciais \u0026gt; Criar credenciais.\u003c/li\u003e\n\u003cli\u003eSelecione \"ID do cliente do OAuth\".\u003c/li\u003e\n\u003cli\u003eEm \"Tipo de aplicativo\", selecione \"App para computador\".\u003c/li\u003e\n\u003cli\u003eD\u00ea um nome de identifica\u00e7\u00e3o para as credenciais e clique em \"criar\". V\u00e3o aparecer dois dados (\"Seu ID de cliente\" e \"Sua chave secreta de cliente\"), precisaremos dos dois no passo seguinte.\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 17 # selecione aqui o n\u00famero correspondente a op\u00e7\u00e3o \"Google Drive\"\nclient_id\u0026gt; conte\u00fado de \"Seu ID de cliente\"\nclient_secret\u0026gt; conte\u00fado de \"Sua chave secreta de cliente\"\nscope\u0026gt; 1 # Full access all files, excluding Application Data Folder\nroot_folder_id\u0026gt; deixe em branco\nservice_account_file\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/n\u0026gt; n\n\nSe j\u00e1 tiver o rclone instalado em seu computador, execute o comando exibido, caso contr\u00e1rio, o instale conforme indicado na p\u00e1gina oficial do rclone dependendo do seu sistema operacional (https://rclone.org/install/). A Seguir insira o resultado do comando exibido:\n\nconfig_token\u0026gt; c\u00f3digo fornecido pelo terminal ap\u00f3s autoriza\u00e7\u00e3o\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requisitos-para-amazon-s3\" class=\"anchor\" aria-hidden=\"true\" href=\"#requisitos-para-amazon-s3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequisitos para Amazon S3\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eEntre na \u003ca href=\"console.aws.amazon.com\"\u003eAWS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClique na seta ao lado de seu nome de usu\u00e1rio e em \"My Security Credentials\".\u003c/li\u003e\n\u003cli\u003eNa se\u00e7\u00e3o \"Access Keys\", clique em \"Create New Access Key\".\u003c/li\u003e\n\u003cli\u003eNa janela que aparece, clique em \"Show Access Key\".\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 4\nprovider\u0026gt; 1\nenv_auth\u0026gt; 1\naccess_key_id\u0026gt; conte\u00fado de \"Access Key ID\"\nsecret_access_key\u0026gt; conte\u00fado de \"Secret Access Key\"\nregion\u0026gt; 16\nendpoint\u0026gt; deixe em branco\nlocation_constraint\u0026gt; 16\nacl\u0026gt; deixe em branco\nserver_side_encryption\u0026gt; deixe em branco\nsse_kms_key_id\u0026gt; deixe em branco\nstorage_class\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1626332421.0 + "updated_at": 1663111689.0 }, { "data_format": 2, - "description": "This repository will hold: the build script to install singularity and other dependencies for it, and a definition file for the singularity container for HPC.", + "description": "Demonstration workflow with Alphafold in a Jupyter notebook", "filenames": [ - "SingularitySC", - "Singularity" + "container/Singularity.def" ], - "full_name": "perminaa/SingularityHPC", + "full_name": "parallelworks/alphafold-notebook-demo", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularityhpc\" class=\"anchor\" href=\"#singularityhpc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularityHPC\u003c/h1\u003e\n\u003cp\u003eThis repository will hold: the build script to install singularity and other dependencies for it, and a definition file for the singularity container for HPC.\u003c/p\u003e\n\u003cp\u003eTo install, run \u003ccode\u003egit clone https://github.com/perminaa/SingularityHPC.git \u0026amp;\u0026amp; cd SingularityHPC \u0026amp;\u0026amp; bash buildscript.sh\u003c/code\u003e. This will install and configure singularity\nand build a container called \u003ccode\u003eContainer.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run the container, you can use \u003ccode\u003esingularity shell Container.sif\u003c/code\u003e to run in the singularity shell or \u003ccode\u003esingularity exec Container.sif \u0026lt;command\u0026gt;\u003c/code\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-alphafold-notebook-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#alphafold-notebook-demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ealphafold-notebook-demo\u003c/h1\u003e\n\u003cp\u003eDemonstration workflow with Alphafold in a Jupyter notebook\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003cp\u003eThe following components are necessary for setting up this workflow:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eAn Alphafold Singularity container. Please see instructions in \u003ccode\u003e./container\u003c/code\u003e for how to build an Alphafold container. Currently, it is assumed that this container is available at a \u003cstrong\u003ehard coded path\u003c/strong\u003e in \u003ccode\u003e./container/run_singularity_container.py\u003c/code\u003e in this line of code:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity_image = Client.load(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/public/apps/alphafold/alphafold.sif\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eA Conda (or pip) environment that has the \u003ccode\u003eabsl-py\u003c/code\u003e and \u003ccode\u003espython\u003c/code\u003e packages to launch the container. This workflow also uses \u003ccode\u003eparsl\u003c/code\u003e (but it is not required for using the container itself). For a cluster with Conda in a module, here is an example for how to create a local environment:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load conda3\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e /gs/gsfs0/hpc01/rhel8/apps/conda3/etc/profile.d/conda.sh\nconda create -y -p /gs/gsfs0/users/gstefan/work/alphafold/env -c conda-forge absl-py==0.13.0 spython=0.1.16 parsl\nconda activate /gs/gsfs0/users/gstefan/work/alphafold/env\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e/gs/gsfs0/users/gstefan/\u003c/code\u003e is your home directory.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003ePull this workflow code into your PW environment.\n\u003cstrong\u003eTODO: Add instructions here.\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the workflow from PW.\n\u003cstrong\u003eTODO: Add instructions here.\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-interactive-runs\" class=\"anchor\" aria-hidden=\"true\" href=\"#interactive-runs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive runs\u003c/h2\u003e\n\u003cp\u003eFor the purposes of testing Alphafold, it is possible to\nstart interactive Alphafold runs (i.e. manually launch the\napplication for an instance). Instructions for launching\nan interactive run are in \u003ccode\u003e./container\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-batch-runs\" class=\"anchor\" aria-hidden=\"true\" href=\"#batch-runs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch runs\u003c/h2\u003e\n\u003cp\u003eWhen you want to run many proteins with Alphafold, there are\ntwo options:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ethe workflow form (under construction) can be used to launch a batch run or\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emain.ipynb\u003c/code\u003e, the Jupyter notebook that contains the workflow code.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWhen users opt for the first option (the workflow form), the form simply\ngrabs the code out of \u003ccode\u003emain.ipynb\u003c/code\u003e and executes it. Users can use\n\u003ccode\u003emain.ipynb\u003c/code\u003e as a template for more complicated Alphafold workflows\nand/or directly modify some of the Alphafold options that are not\navailable in the workflow form. Jupyter notebooks (\u003ccode\u003e*.ipynb\u003c/code\u003e files)\ncan be opened, edited, and run on the platform by double clicking on\nthe file in the file browser pane.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-colabfold\" class=\"anchor\" aria-hidden=\"true\" href=\"#colabfold\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eColabFold\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sokrypton/ColabFold\"\u003eColabFold\u003c/a\u003e is a community-driven\nupdate to Alphafold underpinned by \u003ca href=\"https://colabfold.mmseqs.com/\" rel=\"nofollow\"\u003enew/updated databases\u003c/a\u003e\nand the MSA search process is accelerated by \u003ca href=\"https://github.com/soedinglab/MMseqs2\"\u003eMMseqs2\u003c/a\u003e.\nPlease see the colabfold directory for more information.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1626501688.0 + "updated_at": 1659444272.0 }, { "data_format": 2, - "description": "ABySS is a de novo sequence assembler that is designed for very short reads", + "description": "SPAdes \u2013 St. Petersburg genome assembler \u2013 is intended for both standard isolates and single-cell MDA bacteria assemblies.", "filenames": [ - "2.1.5/Singularity" + "3.15.5/Singularity", + "3.15.3/Singularity", + "3.15.4/Singularity", + "3.14.1/Singularity" ], - "full_name": "pscedu/singularity-abyss", - "latest_release": "v2.1.5", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-abyss/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-abyss/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/61f7c02135e022542644bca33ec90a8b6bdc9cbb8ec4b0e3ec8b5480f035beaa/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6162797373\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/61f7c02135e022542644bca33ec90a8b6bdc9cbb8ec4b0e3ec8b5480f035beaa/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6162797373\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-abyss\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e1d01eb58e3d5d47f45f00fffe4ab16909d1541fcb3cf25e0c01bf5f9d0c8028/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6162797373\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1d01eb58e3d5d47f45f00fffe4ab16909d1541fcb3cf25e0c01bf5f9d0c8028/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6162797373\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-abyss\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b5bcaa1865f5b4c8a6901097150a4b2915c2e0a967f27a5cd175b8cd7c653ce3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6162797373\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b5bcaa1865f5b4c8a6901097150a4b2915c2e0a967f27a5cd175b8cd7c653ce3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6162797373\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-abyss\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/1d51b6d070f927bea877c0ebb5437d326390ba7459fc3a1baa9ecefc8c1f63ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6162797373\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1d51b6d070f927bea877c0ebb5437d326390ba7459fc3a1baa9ecefc8c1f63ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6162797373\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-abyss\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-abyss\" class=\"anchor\" href=\"#singularity-abyss\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-abyss\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/ABYSS\"\u003eABySS\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/ABySS/2.1.5\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/ABySS\u003c/code\u003e as \u003ccode\u003e2.1.5.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-spades", + "latest_release": "v3.15.5", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-spades/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-spades/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/11b78f1cdc09fbfab0186e430a46591d3424481741a5bf3fd4ba85e5fdf6ab64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d737061646573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11b78f1cdc09fbfab0186e430a46591d3424481741a5bf3fd4ba85e5fdf6ab64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d737061646573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-spades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4e45c3ef27ec17192287b575ac775bc38996f5ef4371b5c3d60a832ce806669e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d737061646573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e45c3ef27ec17192287b575ac775bc38996f5ef4371b5c3d60a832ce806669e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d737061646573\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-spades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1df46e77d3f98a8380250f3dc06ab84e8496724d7526816114756b5e5115363b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d737061646573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1df46e77d3f98a8380250f3dc06ab84e8496724d7526816114756b5e5115363b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d737061646573\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-spades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4819979457ec016fbbbd524e8046081eef419b6669035f155038c378d436a9dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d737061646573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4819979457ec016fbbbd524e8046081eef419b6669035f155038c378d436a9dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d737061646573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-spades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-spades\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-spades\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-spades\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/spades/3.15.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/spades\u003c/code\u003e as \u003ccode\u003e3.15.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [ "singularity", "bioinformatics" ], - "updated_at": 1628991345.0 + "updated_at": 1658280597.0 }, { "data_format": 2, - "description": "Trimmomatic performs a variety of useful trimming tasks for illumina paired-end and single ended data", + "description": "Old copy of the nf-core methylseq workflow including hacked in NuGen/Tecan support", "filenames": [ - "0.39/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-trimmomatic", - "latest_release": "0.39", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-trimmomatic/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-trimmomatic/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/886b78ddfce96f64ae6be83548b2b219652f30a8ab8e6f78413fcf5c763a5fe3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/886b78ddfce96f64ae6be83548b2b219652f30a8ab8e6f78413fcf5c763a5fe3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-trimmomatic\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f83a71da75047a61c081c63788b045654ea1befe0da1eccfc4e3d46b96c656e5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f83a71da75047a61c081c63788b045654ea1befe0da1eccfc4e3d46b96c656e5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-trimmomatic\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/42de55829ff65764902a5aa22015e62165ede512bd9503a795afe4c4815d9e61/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/42de55829ff65764902a5aa22015e62165ede512bd9503a795afe4c4815d9e61/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-trimmomatic\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/77a4e6c187943c941ff1ee8267ebe7f72b3e5d5723a03d3ac4e91e826dc35c98/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/77a4e6c187943c941ff1ee8267ebe7f72b3e5d5723a03d3ac4e91e826dc35c98/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7472696d6d6f6d61746963\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-trimmomatic\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-trimmomatic\" class=\"anchor\" href=\"#singularity-trimmomatic\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-trimmomatic\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/usadellab/Trimmomatic\"\u003eTrimmomatic\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003etrimmomatic\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/trimmomatic/0.39\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/trimmomatic\u003c/code\u003e as \u003ccode\u003e0.39.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "HPCBio/methylseq-old", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"\" class=\"anchor\" aria-hidden=\"true\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/methylseq_logo.png\"\u003e\u003cimg src=\"docs/images/methylseq_logo.png\" alt=\"nf-core/methylseq\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/methylseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1e11b31de3d567f647c562b736ad6e010ef787d1a8aa35dce459aba5b4587ed/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6d657468796c7365712e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/methylseq.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/17df893666bfa5cad487466d4476ab773ea5def560c04c50f45795017865e81c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/124913037\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/89f01223dd3cce114d92a5764aa2e589ddd0915df7208e879ab1d88a5cee4b31/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3132343931333033372e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/124913037.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/nf-core/Lobby\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75a58bd7ba3966d16ebf50e1d2d55a4d21c67fa281370f9b823c2f4e53532b03/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769747465722d2532306a6f696e253230636861742532302545322538362539322d3466623939612e737667\" alt=\"Gitter\" data-canonical-src=\"https://img.shields.io/badge/gitter-%20join%20chat%20%E2%86%92-4fb99a.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/methylseq/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fbe9131f0a48ef34c529ac997f1ac04e3b5df4586ceb45fcda42c1568a761456/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6d657468796c7365712e737667\" alt=\"Docker Container available\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/methylseq.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1091\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Container\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003enf-core/methylseq\u003c/strong\u003e is a bioinformatics best-practice analysis pipeline used for Methylation (BS-Seq) data analysis.\u003c/p\u003e\n\u003cp\u003eThe pipeline uses \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a bioinformatics workflow tool. It pre-processes raw data from FastQ inputs, aligns the reads and performs extensive quality-control on the results.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pipeline-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline Steps\u003c/h3\u003e\n\u003cp\u003eThe pipeline allows you to choose between running either \u003ca href=\"https://github.com/FelixKrueger/Bismark\"\u003eBismark\u003c/a\u003e or \u003ca href=\"https://github.com/brentp/bwa-meth\"\u003ebwa-meth\u003c/a\u003e / \u003ca href=\"https://github.com/dpryan79/methyldackel\"\u003eMethylDackel\u003c/a\u003e.\nChoose between workflows by using \u003ccode\u003e--aligner bismark\u003c/code\u003e (default) or \u003ccode\u003e--aligner bwameth\u003c/code\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eStep\u003c/th\u003e\n\u003cth\u003eBismark workflow\u003c/th\u003e\n\u003cth\u003ebwa-meth workflow\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGenerate Reference Genome Index \u003cem\u003e(optional)\u003c/em\u003e\n\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003ebwa-meth\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRaw data QC\u003c/td\u003e\n\u003ctd\u003eFastQC\u003c/td\u003e\n\u003ctd\u003eFastQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAdapter sequence trimming\u003c/td\u003e\n\u003ctd\u003eTrim Galore!\u003c/td\u003e\n\u003ctd\u003eTrim Galore!\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAlign Reads\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003ebwa-meth\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDeduplicate Alignments\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003ePicard MarkDuplicates\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eExtract methylation calls\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003eMethylDackel\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSample report\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSummary Report\u003c/td\u003e\n\u003ctd\u003eBismark\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAlignment QC\u003c/td\u003e\n\u003ctd\u003eQualimap\u003c/td\u003e\n\u003ctd\u003eQualimap\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProject Report\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/methylseq pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003eThese scripts were originally written for use at the \u003ca href=\"https://portal.scilifelab.se/genomics/\" rel=\"nofollow\"\u003eNational Genomics Infrastructure\u003c/a\u003e at \u003ca href=\"http://www.scilifelab.se/\" rel=\"nofollow\"\u003eSciLifeLab\u003c/a\u003e in Stockholm, Sweden.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMain author:\n\u003cul\u003e\n\u003cli\u003ePhil Ewels (\u003ca href=\"https://github.com/ewels/\"\u003e@ewels\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eContributors:\n\u003cul\u003e\n\u003cli\u003eRickard Hammar\u00e9n (\u003ca href=\"https://github.com/Hammarn/\"\u003e@Hammarn\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eAlexander Peltzer (\u003ca href=\"https://github.com/apeltzer/\"\u003e@apeltzer\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1628992066.0 + "subscribers_count": 5, + "topics": [], + "updated_at": 1655919157.0 }, { "data_format": 2, - "description": "Target/Integrative Genetic Element Retriever", + "description": "Standalone scripts to assist with intermediate tasks in GeoEDF workflows", "filenames": [ - "5.32.1/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-tiger", - "latest_release": "v5.32.1", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-tiger/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-tiger/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-tiger/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-tiger/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/45b51fb9658ca6d66b992d887a9258c238463b14dc9fb58c4ed17ba4e75f2efc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7469676572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b51fb9658ca6d66b992d887a9258c238463b14dc9fb58c4ed17ba4e75f2efc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7469676572\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-tiger\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/266d33a01bfbc88af8ef713b4ab3f12e4207aea6a693420544c401bcc4687384/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7469676572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/266d33a01bfbc88af8ef713b4ab3f12e4207aea6a693420544c401bcc4687384/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7469676572\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-tiger\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/cd42640e858ab21849faf7ea1ae7b80b386fea232400bb8666ab226125727f09/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7469676572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cd42640e858ab21849faf7ea1ae7b80b386fea232400bb8666ab226125727f09/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7469676572\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-tiger\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/049f66af13d64dfd0fc9fb6af0dd2b62c6d9f524419d096508747447c1c661ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7469676572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/049f66af13d64dfd0fc9fb6af0dd2b62c6d9f524419d096508747447c1c661ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7469676572\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-tiger\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-tiger\" class=\"anchor\" href=\"#singularity-tiger\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-tiger\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/tiger\"\u003etiger\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ednaStats.pl\u003c/code\u003e, \u003ccode\u003eislander.pl\u003c/code\u003e, \u003ccode\u003eresolve.pl\u003c/code\u003e, \u003ccode\u003etater.pl\u003c/code\u003e and \u003ccode\u003etiger.pl\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/tiger/4.8.25\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/tiger\u003c/code\u003e as \u003ccode\u003e4.8.25.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/tigers/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "geoedf/workflow-utils", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-geoedf-workflow-utilities\" class=\"anchor\" aria-hidden=\"true\" href=\"#geoedf-workflow-utilities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeoEDF Workflow Utilities\u003c/h1\u003e\n\u003cp\u003eStandalone scripts to assist with intermediate tasks in GeoEDF workflows\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629218294.0 + "topics": [], + "updated_at": 1655911232.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity", + "openfoam/Singularity.of-7-from-docker" ], - "full_name": "VUIIS/demo-singularity-matlab-fsl", - "latest_release": "v1.0.1", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-demo-singularity-container-for-matlab-plus-fsl\" class=\"anchor\" href=\"#demo-singularity-container-for-matlab-plus-fsl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo singularity container for Matlab plus FSL\u003c/h1\u003e\n\u003cp\u003eThis example container takes a Nifti image as input, zeroes out a hole in it of\nthe specified diameter, and saves the result to a new Nifti file. Quick,\npointless, and easy to tell whether it worked right.\u003c/p\u003e\n\u003cp\u003eThis is one way to organize a Matlab-based Singularity container -\nperhaps most easily conceived of as a series of wrappers around the main\ncodebase. Done this way, it\u0027s fairly easy to work on each piece in isolation,\nproblem-solving from the inside out.\u003c/p\u003e\n\u003cp\u003eThis container also includes an installation of FSL, which has a lot of handy\ntools including fsleyes to make the QA PDF. The FSL parts could be removed from\nthe Singularity file if FSL isn\u0027t used, to end up with a smaller container.\nContrariwise, all the Matlab parts could be removed to end up with an FSL-only\ncontainer.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity container\n| Primary entrypoint (shell script)\n| | X11 wrapper\n| | | Shell script preprocessing\n| | | Matlab processing (compiled)\n| | | | Matlab entrypoint\n| | | | Matlab main function\n| | | \\ Matlab sub-functions / codebase\n\\ \\ \\ Shell script postprocessing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDependencies in terms of the actual files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity\n src/pipeline_entrypoint.sh\n src/pipeline_main.sh\n src/copy_inputs.sh\n src/preprocessing.sh\n matlab/bin/run_matlab_entrypoint.sh\n matlab/bin/matlab_entrypoint\n / matlab/src/matlab_entrypoint.m \\ Used for compilation,\n | matlab/src/matlab_main.m | but not at container\n \\ matlab/src/* / runtime\n src/postprocessing.sh\n src/make_pdf.sh\n src/finalize.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe process of putting it together is described below. The scripts and code in\nthis repository are extensively commented, so if something isn\u0027t clear here,\nit\u0027s probably explained in the Singularity file or the example code.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-this-container-before-editing-anything\" class=\"anchor\" href=\"#building-this-container-before-editing-anything\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding this container before editing anything\u003c/h2\u003e\n\u003cp\u003eTry building this from scratch, to find any immediate issues:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eGet the installers for Matlab Compiled Runtime and FSL and place them in the\n\u003ccode\u003eexternal\u003c/code\u003e directory. URLs for these are in the \u003ccode\u003eSingularity\u003c/code\u003e file. Alternatively,\ncomment out the installer files in the \u0027%files\u0027 section and uncomment the download\nlines (\u0027wget\u0027) later - this way they will be downloaded as part of the build.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the container, following the instructions below\n\u003ca href=\"https://github.com/baxpr/demo-singularity-matlab-fsl#building-the-container\"\u003ehttps://github.com/baxpr/demo-singularity-matlab-fsl#building-the-container\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-matlab-part\" class=\"anchor\" href=\"#matlab-part\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMatlab part\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-write-the-basic-matlab-code\" class=\"anchor\" href=\"#write-the-basic-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWrite the basic Matlab code\u003c/h3\u003e\n\u003cp\u003eWrite Matlab code that does what\u0027s needed. Put it in \u003ccode\u003ematlab/src\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA popular toolbox for reading and writing Nifti files that\u0027s available on Matlab\nCentral has a lot of insidious bugs and is not being maintained. Matlab\u0027s own\nfunctions for Nifti files are quite limited. Here is an alternative, which is\nused in this example:\n\u003ca href=\"https://github.com/VUIIS/spm_readwrite_nii\"\u003ehttps://github.com/VUIIS/spm_readwrite_nii\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-write-the-matlab-entrypoint\" class=\"anchor\" href=\"#write-the-matlab-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWrite the Matlab entrypoint\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/src/matlab_entrypoint.m\u003c/code\u003e exists to take command line arguments, parse\nthem, and call the main code. A convenient way to set things up is to write a\nmain function that takes a structure as its sole input, with the structure\ncontaining whatever inputs are needed. See \u003ccode\u003ematlab/src/matlab_main.m\u003c/code\u003e for an\nexample of this.\u003c/p\u003e\n\u003cp\u003eCouple of things to note in the entrypoint code are the quit/exit sections at\nbeginning and end. The bit at the beginning allows the executable to run during\nthe container build, without actually doing anything - this is needed to extract\nthe CTF archive into the container at the only time the container is writeable\n(h/t \u003ca href=\"https://twitter.com/annash128\" rel=\"nofollow\"\u003ehttps://twitter.com/annash128\u003c/a\u003e for figuring that one out). The bit at the\nend exits matlab when the function is finished. Without it, the running Matlab\nprocess won\u0027t release execution back to the calling script when it\u0027s done.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-test-the-matlab-entrypoint\" class=\"anchor\" href=\"#test-the-matlab-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the Matlab entrypoint\u003c/h3\u003e\n\u003cp\u003eThe script \u003ccode\u003ematlab/src/test_matlab_entrypoint.m\u003c/code\u003e is an example of how to do\nthis. The appropriate Matlab must be installed on the testing computer.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-the-matlab-code\" class=\"anchor\" href=\"#compile-the-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile the Matlab code\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/compile_matlab.sh\u003c/code\u003e shows how. Many compiled executables are likely to be\ntoo large to store on github. Git LFS may be a solution.\n\u003ca href=\"https://docs.github.com/en/github/managing-large-files/working-with-large-files\"\u003ehttps://docs.github.com/en/github/managing-large-files/working-with-large-files\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-test-the-compiled-matlab-code\" class=\"anchor\" href=\"#test-the-compiled-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the compiled Matlab code\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/test_compiled_matlab.sh\u003c/code\u003e. The appropriate Matlab Runtime must be\ninstalled on the testing computer.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-shell-script-part\" class=\"anchor\" href=\"#shell-script-part\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell script part\u003c/h2\u003e\n\u003cp\u003eAll of the below procedures could be done in the matlab part, if desired,\ninstead of in shell script. If so, parsing inputs should be done following the\nexample in \u003ccode\u003ematlab/src/matlab_entrypoint.m\u003c/code\u003e. But it\u0027s often easier to move\nfiles, create the QA PDF, etc using shell script and FSL. So that\u0027s what we are\ndoing in this example. All this code is in the \u003ccode\u003esrc\u003c/code\u003e directory.\u003c/p\u003e\n\u003cp\u003eAll the shell scripts called from \u003ccode\u003esrc/pipeline_entrypoint.sh\u003c/code\u003e \"know\" the\nenvironment variables that are exported there. This is a very convenient way to\npass along the input arguments, although it isn\u0027t entirely transparent, because\nthere\u0027s no hint in the shell scripts where the variables\u0027 values are coming from\nunless we explain it in the comments.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-main-entrypoint\" class=\"anchor\" href=\"#main-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMain entrypoint\u003c/h3\u003e\n\u003cp\u003eThis is \u003ccode\u003esrc/pipeline_entrypoint.sh\u003c/code\u003e. It uses bash to parse the command line\ninputs and export them to environment variables so they\u0027re accessible. Then it\ncalls the primary shell script \u003ccode\u003esrc/pipeline_main.sh\u003c/code\u003e which in turn calls\neverything else. The main script is run in xvfb to provide a virtual display,\noften needed by matlab and required for fsleyes.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-copy-inputs\" class=\"anchor\" href=\"#copy-inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopy inputs\u003c/h3\u003e\n\u003cp\u003eWe copy input files to the output/working directory so we don\u0027t mess them up.\nThis also is an opportunity to rename them to something consistent. It\u0027s very\nconvenient to hard-code the filenames so we don\u0027t have to store and manipulate\nfilenames in environment variables or the like. Also, this makes it easy to\nproduce output files with consistent names - outputs of one pipeline may serve\nas inputs to another, and it\u0027s much easier to manage this if filenames are the\nsame for every run, or at least consistent.\u003c/p\u003e\n\u003cp\u003eWe generally assume the output directory starts out empty and will not be\ninterfered with by any other processes - this is true for XNAT/DAX, but\nimportant to be aware of in other contexts.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preprocessing\" class=\"anchor\" href=\"#preprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessing\u003c/h3\u003e\n\u003cp\u003eFor this example, there is no preprocessing before the matlab part. But initial\nFSL steps or similar could be put here: \u003ccode\u003esrc/preprocessing.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-postprocessing\" class=\"anchor\" href=\"#postprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePostprocessing\u003c/h3\u003e\n\u003cp\u003eThere isn\u0027t any postprocessing for this example either, but there could be:\n\u003ccode\u003esrc/postprocessing.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pdf-creation\" class=\"anchor\" href=\"#pdf-creation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePDF creation\u003c/h3\u003e\n\u003cp\u003eAll assessors on VUIIS XNAT require a PDF QA report of some sort. For this\nexample, a display of the segmented ROIs overlaid on the T1 is created using\nfsleyes and ImageMagick, \u003ccode\u003esrc/make_pdf.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003ePDF creation can be done in Matlab instead. It\u0027s hard to make these look good.\nAn example with some tricks, including a \u003ccode\u003e.fig\u003c/code\u003e file painstakingly made with\nMatlab\u0027s GUIDE, is\n\u003ca href=\"https://github.com/baxpr/connprep/blob/855dadc/src/connectivity_filter.m#L271\"\u003ehttps://github.com/baxpr/connprep/blob/855dadc/src/connectivity_filter.m#L271\u003c/a\u003e\nA way to show slices of functional images with a nice red/blue colormap is\n\u003ca href=\"https://github.com/baxpr/connprep/blob/855dadc/src/make_network_maps.m\"\u003ehttps://github.com/baxpr/connprep/blob/855dadc/src/make_network_maps.m\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-finalizing-the-output\" class=\"anchor\" href=\"#finalizing-the-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFinalizing the output\u003c/h3\u003e\n\u003cp\u003eAll Niftis must be compressed for storage on XNAT, and outputs can be organized\nin an easily understandable way: \u003ccode\u003esrc/finalize.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eWrite an informative README - so tedious, yet so helpful. Include the\nappropriate citations for all the methods and software you have used. Even\nessentially write the methods section for a paper that uses the pipeline. Here\u0027s\nan excellent example: \u003ca href=\"https://github.com/MASILab/PreQual\"\u003ehttps://github.com/MASILab/PreQual\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAlternatively, git-ify some documentation like this:\n\u003ca href=\"https://github.com/VUIIS/dax/tree/main/docs\"\u003ehttps://github.com/VUIIS/dax/tree/main/docs\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eto get something like this:\n\u003ca href=\"https://dax.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://dax.readthedocs.io/en/latest/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-container\" class=\"anchor\" href=\"#building-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container\u003c/h2\u003e\n\u003cp\u003eBe sure the Matlab code is newly compiled, see above. You can run\n\u003ccode\u003ematlab/check_for_compilation.sh\u003c/code\u003e first to make sure there\u0027s no source code\nnewer than the compiled executable.\u003c/p\u003e\n\u003cp\u003eThen from the root directory of the working copy of the repo, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build \u0026lt;container_name\u0026gt;.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGood practice: before you build, create a release on github (if using github) or\nat least tag the commit you are about to build. Give the container a versioned\nname like \u003ccode\u003edemo-singularity-matlab-fsl_v1.0.0.simg\u003c/code\u003e that matches the repository\nname and release version/tag.\u003c/p\u003e\n\u003cp\u003eExternal binaries such as Matlab Runtime and FSL can be included by copying\nlocal copies into the container in the Singularity file\u0027s \u003ccode\u003e%files\u003c/code\u003e section. This\ntends to be a little faster when multiple builds are needed during debugging,\nor necessary for files that are not available to download, and this is what\u0027s\nbeing done in the example Singularity file. Alternatively, binaries or install\nfiles can be downloaded from their source at build time - there are some\ncommented-out sections in the Singularity file showing how that is done. (Thanks\n\u003ca href=\"https://github.com/praitayini\"\u003ehttps://github.com/praitayini\u003c/a\u003e for exploring this in detail)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-container\" class=\"anchor\" href=\"#running-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the container\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003etest_singularity_container.sh\u003c/code\u003e for an example run command and some\nimportant info.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h3\u003e\n\u003cp\u003ePaths to files are relative to the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--t1_niigz A T1 image\n--seg_niigz Its corresponding segmentation from e.g. slant pipeline\n--diameter_mm Diameter of the hole to zero out, in mm (default 30)\n\n--label_info A label to annotate the QA PDF, e.g. info from XNAT\n\n--out_dir Where outputs will be stored (default /OUTPUTS)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePDF/holed_image.pdf QA report\nHOLED_T1/holed_t1.nii.gz T1 image with a hole in it\nHOLED_SEG/holed_seg.nii.gz Segmentation with a hole in it\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-container-with-dax\" class=\"anchor\" href=\"#running-the-container-with-dax\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the container with DAX\u003c/h2\u003e\n\u003cp\u003eWith a suitable configuration file, DAX (\u003ca href=\"https://github.com/VUIIS/dax\"\u003ehttps://github.com/VUIIS/dax\u003c/a\u003e) can run this on a cluster.\u003c/p\u003e\n\u003cp\u003eInstructions are here: \u003ca href=\"https://dax.readthedocs.io/en/latest/processors.html\" rel=\"nofollow\"\u003ehttps://dax.readthedocs.io/en/latest/processors.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn example is here:\n\u003ca href=\"https://github.com/VUIIS/dax_yaml_processor_examples/blob/master/demo-matfsl_v1.0.0_processor.yaml\"\u003ehttps://github.com/VUIIS/dax_yaml_processor_examples/blob/master/demo-matfsl_v1.0.0_processor.yaml\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "ggruszczynski/singularity_recipies", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipies\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity recipies\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4746\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOfficial Documentation:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/getting-started/recipes\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/singularityhub-docs/docs/getting-started/recipes\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-diy\" class=\"anchor\" aria-hidden=\"true\" href=\"#diy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDIY\u003c/h2\u003e\n\u003cp\u003eHow to run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to build\u003c/span\u003e\nsudo singularity build image.sif recipe.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to run \u003c/span\u003e\nsingularity shell --cleanenv lolcow_latest.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Without the --cleanenv flag, the environment on the host system will be present within the container at run time.\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e lolcow_latest.sif cowsay moo\nsingularity run lolcow_latest.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to download\u003c/span\u003e\nsingularity pull shub://ggruszczynski/singularity_recipies\nsingularity run singularity_recipies_latest.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to build from docker hub\u003c/span\u003e\nsingularity pull docker://openfoam/openfoam7-paraview56\nsingularity shell --cleanenv openfoam7-paraview56_latest.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --cleanenv cat /etc/os-release\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to build from docker file\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e https://www.nas.nasa.gov/hecc/support/kb/converting-docker-images-to-singularity-for-use-on-pleiades_643.html\u003c/span\u003e\n\n$ sudo docker build -t ood-rstudio-bio.4.1.2 - \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e Dockerfile.4.1.2\n\n$ docker images\nREPOSITORY TAG IMAGE ID CREATED SIZE\nood-rstudio-bio.4.1.2 latest 9ab18b041cba 27 minutes ago 7.05GB\n\n$ docker save 9ab18b041cba -o ood_rstudio_bio_docker_412.tar\n$ singularity build ood_rstudio_bio_singularity_412.sif docker-archive://ood_rstudio_bio_docker_412.tar\n\n$ singularity build --sandbox lolcow docker-archive://lolcow.tar\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-openfoam-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#openfoam-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenFoam notes\u003c/h3\u003e\n\u003cp\u003eOF fundation: vX versioning + third party\nOF org: vYYMM versioning\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-mpi-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#mpi-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMPI notes\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://singularity.lbl.gov/faq\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/faq\u003c/a\u003e\nWhy do we call \u2018mpirun\u2019 from outside the container (rather than inside)?\nWith Singularity, the MPI usage model is to call \u2018mpirun\u2019 from outside the container, and reference the container from your \u2018mpirun\u2019 command. Usage would look like this:\u003c/p\u003e\n\u003cp\u003e$ mpirun -np 20 singularity exec container.img /path/to/contained_mpi_prog\nBy calling \u2018mpirun\u2019 outside the container, we solve several very complicated work-flow aspects. For example, if \u2018mpirun\u2019 is called from within the container it must have a method for spawning processes on remote nodes. Historically ssh is used for this which means that there must be an sshd running within the container on the remote nodes, and this sshd process must not conflict with the sshd running on that host! It is also possible for the resource manager to launch the job and (in Open MPI\u2019s case) the Orted processes on the remote system, but that then requires resource manager modification and container awareness.\u003c/p\u003e\n\u003cp\u003eIn the end, we do not gain anything by calling \u2018mpirun\u2019 from within the container except for increasing the complexity levels and possibly losing out on some added performance benefits (e.g. if a container wasn\u2019t built with the proper OFED as the host).\u003c/p\u003e\n\u003cp\u003eSee the Singularity on HPC page for more details.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1626126045.0 + "updated_at": 1655827541.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image for the scikit-hep software ecosystem", "filenames": [ "Singularity" ], - "full_name": "baxpr/conncalc", - "latest_release": "v1.0.4", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-conncalc\" class=\"anchor\" href=\"#conncalc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econncalc\u003c/h1\u003e\n\u003cp\u003eComputes functional connectivity maps and matrices for a specified set of ROIs.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eremovegm_niigz\u003c/code\u003e, \u003ccode\u003ekeepgm_niigz\u003c/code\u003e, \u003ccode\u003emeanfmri_niigz\u003c/code\u003e. Preprocessed fMRI data from\n\u003ca href=\"https://github.com/baxpr/connprep\"\u003econnprep\u003c/a\u003e. This may be supplied in atlas space or\nsubject native space. The first two are 4D time series, the last a single 3D image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eroi_niigz\u003c/code\u003e. ROI image. This may be an image existing within the container (e.g. the\nMNI space \u0027AABHHIP_LR.nii.gz\u0027, see src/rois/README.md). Or, it may be any supplied\nimage. In the latter case, \u003ccode\u003eroilabel_csv\u003c/code\u003e must also be supplied; this file must contain\nLabel and Region columns, or may be the STATS output of a slant assessor. The ROI\nimage must be already be aligned with the T1 and the fMRI (though needn\u0027t be sampled to\nthe same voxel grid or field of view) - no coregistration or warp is performed on any\nof the images.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003et1_niigz\u003c/code\u003e. T1 image for the PDF report.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emask_niigz\u003c/code\u003e. Brain mask - will be binarized and dilated and used to exclude any clearly\nex-brain voxels in the stored connectivity maps. Supply \u0027none\u0027 to mask to the entire\nvolume (i.e. no masking).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003econnmaps_out\u003c/code\u003e. \u0027yes\u0027 or \u0027no\u0027 to choose whether to additionally store voxelwise\nconnectivity images for each ROI in the ROI image.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline\" class=\"anchor\" href=\"#pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eResample the ROI image to match the fMRI voxel sampling. It\u0027s assumed both are already\naligned.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtract mean time series from the supplied fMRI for each ROI in the ROI image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompute functional connectivity. The ROI-to-ROI connectivity matrix is computed, and also\nvoxelwise connectivity Z maps if requested.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e, the correlation coefficient\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eZ\u003c/code\u003e, the Fisher transformed correlation, \u003ccode\u003eatanh(R) * sqrt(N-3)\u003c/code\u003e where \u003ccode\u003eN\u003c/code\u003e is number of time points\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eVdf\u003c/code\u003e, \u003ccode\u003ePdf\u003c/code\u003e, \u003ccode\u003eZdf\u003c/code\u003e autocorrelation-adjusted connectivity metrics from \u003ca href=\"https://github.com/asoroosh/xDF\"\u003ehttps://github.com/asoroosh/xDF\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenerate a PDF report and organize outputs for XNAT.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "amanmdesai/singularity-scikit-hep", + "latest_release": "v1.0.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-scikit-hep\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-scikit-hep\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-scikit-hep\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/amanmdesai/singularity-scikit-hep/actions/workflows/singularity-build-deploy.yml\"\u003e\u003cimg src=\"https://github.com/amanmdesai/singularity-scikit-hep/actions/workflows/singularity-build-deploy.yml/badge.svg?branch=master\" alt=\"Singularity Build Deploy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/533611076\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fce93d17667e5605dd27f08f48424292886536d8ac123c1441b6e3a51b801dc4/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3533333631313037362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/533611076.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA singularity container for scikit-hep with python packages\u003c/p\u003e\n\u003cp\u003eTo pull this image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull oras://ghcr.io/amanmdesai/singularity-scikit-hep:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis image contains the python packages.\u003c/p\u003e\n\u003cp\u003enumpy, awkward, uproot4, scikit-hep-testdata, hist, particle, hepunits, matplotlib, boost-histogram, iminuit, zfit, vector, fastjet\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1625759471.0 + "updated_at": 1662637680.0 }, { "data_format": 2, - "description": "Singularity recipe files for multiqc (https://github.com/ewels/MultiQC)", + "description": null, "filenames": [ - "Singularity.1.6", - "Singularity.1.9", - "Singularity.1.11", - "Singularity.1.5", - "Singularity", - "Singularity.1.8", - "Singularity.1.7" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/latest/Singularity" ], - "full_name": "powerPlant/multiqc-srf", + "full_name": "silvansievers/weak-stubborn-sets", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the MultiQC tool to aggregate results from bioinformatics analyses across many samples into a single report.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1625703839.0 + "updated_at": 1659517719.0 }, { "data_format": 2, - "description": "Notebook template using Fink API for the LSST broker workshop", + "description": null, "filenames": [ - "Singularity" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/latest/Singularity" ], - "full_name": "astrolabsoftware/fink-notebook-template", + "full_name": "silvansievers/merge-strategies", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fink-broker-tutorials\" class=\"anchor\" href=\"#fink-broker-tutorials\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFink broker tutorials\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://colab.research.google.com/github/astrolabsoftware/fink-notebook-template/blob/main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains materials (notebooks \u0026amp; presentation) to explore the \u003ca href=\"https://fink-broker.org\" rel=\"nofollow\"\u003eFink broker\u003c/a\u003e alert data. As of April 2021, Fink has collected more than 80 million alerts from the ZTF public stream, and processed more than 30 millions (after quality cuts). Among these, you will find extragalatic sources (supernovae, AGN, ...), galactic sources (many classes of transients incl. variables stars from our galaxy or gravitational microlensing events, ...) and moving objects from our Solar System (asteroids, comets, and made-man objects like space-debris!). Some sources are already confirmed, many are candidates!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-materials\" class=\"anchor\" href=\"#materials\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaterials\u003c/h2\u003e\n\u003cp\u003eThe repository contains a number of notebooks focusing on the use of the Fink REST API. We shortly present different science cases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExtragalactic science: AGN \u0026amp; supernovae (\u003ca href=\"extragalactic/extragalactic.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eGalactic science: variable stars \u0026amp; microlensing (\u003ca href=\"galactic/galactic.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSolar system science: asteroids, comets \u0026amp; space debris (\u003ca href=\"sso/sso.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eMulti-messenger astronomy: searching for kilonovae (\u003ca href=\"MMA/MMA.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eBroker interfaces: presentation on the livestream service, the Science Portal and its API, and the Fink TOM module (\u003ca href=\"interfaces/README.md\"\u003esee the presentation\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese sciences are not exhaustive and we welcome new collaborations to expand them!\u003c/p\u003e\n\u003cp\u003eYou can try the notebooks using Google Colab (follow the link above). You can also clone the repo, and try it locally (very little external libraries are required).\u003c/p\u003e\n\u003cp\u003eWe also provide a Singularity script to work in a contained environment (thanks @bregeon):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBuild with \u003ccode\u003esingularity build --fakeroot fink.sif Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun with \u003ccode\u003esingularity run fink.sif\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpen the link in your browser (from the host)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-contribute\" class=\"anchor\" href=\"#how-to-contribute\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to contribute\u003c/h2\u003e\n\u003cp\u003eHow to contribute:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone (or fork) this repo, and open a new branch.\u003c/li\u003e\n\u003cli\u003eCreate a new folder with a meaningful name (e.g. \u003ccode\u003esupernovae\u003c/code\u003e, \u003ccode\u003egrb\u003c/code\u003e, ...)\u003c/li\u003e\n\u003cli\u003eRead and copy an existing notebook to get an idea of the structure of a tutorial.\u003c/li\u003e\n\u003cli\u003eOnce your notebook is finished, open a Pull Request such that we review the tutorial and merge it!\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 0, "topics": [], - "updated_at": 1625729812.0 + "updated_at": 1651653962.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/latest/Singularity" ], - "full_name": "VUIIS/demo-singularity-spm-freeview", - "latest_release": "v1.0.1", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-demo-singularity-container-with-spm12-and-freeview\" class=\"anchor\" href=\"#demo-singularity-container-with-spm12-and-freeview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo singularity container with SPM12 and Freeview\u003c/h1\u003e\n\u003cp\u003eSPM12-based pipelines require a little extra work to get them compiled and working in a\ncontainer. Freesurfer\u0027s Freeview is also included here, as it\u0027s very handy for creating\nthe PDF QA report. This example shows three different ways of creating image displays for\nthe QA PDF.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/baxpr/demo-singularity-matlab-fsl\"\u003ehttps://github.com/baxpr/demo-singularity-matlab-fsl\u003c/a\u003e for a lot of detailed info about\nputting Matlab code into singularity containers. This example uses the same structure.\u003c/p\u003e\n\u003cp\u003eA licensed Matlab installation is required to compile the Matlab code, but is not needed\nto run the compiled executable in the container.\u003c/p\u003e\n\u003cp\u003eSPM12 (\u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" rel=\"nofollow\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/a\u003e) is not in this repository and must\nbe installed separately on the compilation host. Edit \u003ccode\u003ematlab/compile_matlab.sh\u003c/code\u003e to point\nto it.\u003c/p\u003e\n\u003cp\u003eSPM requires jumping an extra hurdle at the compilation step - we use a modified version\nof SPM\u0027s own compiler function \u003ccode\u003espm_make_standalone.m\u003c/code\u003e, found at\n\u003ccode\u003ematlab/spm_make_standalone_local.m\u003c/code\u003e. This process captures a lot of dependencies that\ncould otherwise easily be left out of the executable, with the resulting symptom that it\ncompiles just fine but fails at run time with various cryptic error messages. In addition\nto SPM12, everything in the \u003ccode\u003ematlab/src\u003c/code\u003e directory is included in the path at compile time.\nIf Matlab toolboxes are used, they will need to be added to the list of included toolboxes\nin \u003ccode\u003ematlab/spm_make_standalone_local.m\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe compiled Matlab executable is stored on github using git LFS. A regular git clone will\ndownload a pointer text file instead of the executable binary. The result of building a\ncontainer from that will be a cryptic error message - so, compile it yourself. Or, if\nstoring on github, download it manually and replace the pointer text file, or include this\nstep in the Singularity file if helpful - example here:\n\u003ca href=\"https://github.com/baxpr/gf-fmri/blob/47b0552/Singularity.v1.3.4#L65\"\u003ehttps://github.com/baxpr/gf-fmri/blob/47b0552/Singularity.v1.3.4#L65\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFreesurfer requires a license to run:\n\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall#License\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall#License\u003c/a\u003e\nBest practice is to store your license file on the host that will run the container, and\nbind it to the container at runtime - NOT to include your own license file in the\ncontainer itself.\u003c/p\u003e\n", + "full_name": "silvansievers/symmetric-lookups", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1625837739.0 + "updated_at": 1659363562.0 }, { "data_format": 2, - "description": null, + "description": "Unofficial Sniffles repository for singularity container", "filenames": [ - "misc/releases/20.06/Singularity.20.06", - "misc/releases/latest/Singularity", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/19.06/Singularity.19.06" + "Singularity" ], - "full_name": "No-Diehl/FD-SAT", + "full_name": "touala/Sniffles", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fast-downward\" class=\"anchor\" href=\"#fast-downward\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast Downward\u003c/h1\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sniffles\" class=\"anchor\" aria-hidden=\"true\" href=\"#sniffles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSniffles\u003c/h1\u003e\n\u003cp\u003eUnofficial Sniffles repository for singularity container\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1625821718.0 + "updated_at": 1657955196.0 }, { "data_format": 2, - "description": "FastANI is developed for fast alignment-free computation of whole-genome Average Nucleotide Identity (ANI)", + "description": null, "filenames": [ - "1.33/Singularity" + "r4.1.3-bc3.14-cgrtextbook20200930/Singularity", + "r4.1.0-bc3.13-cgrtextbook20200930/Singularity" ], - "full_name": "pscedu/singularity-fastani", - "latest_release": "v1.3.3", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-fastani/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fastani/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8dc34ff6e6f588edb478bbdd040070223d6309ed57ff692ec669a99a4ce3c044/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66617374616e69\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8dc34ff6e6f588edb478bbdd040070223d6309ed57ff692ec669a99a4ce3c044/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66617374616e69\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-fastani\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/eee7b443f78bf774c4336377ac5dee69d66644752fb8ba1f4c38931628d44fb4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66617374616e69\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eee7b443f78bf774c4336377ac5dee69d66644752fb8ba1f4c38931628d44fb4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66617374616e69\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-fastani\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/27c0f180ef8bc2d2094b529129fd31169bf0e10b3a965f25fb6ef0d8b8311868/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66617374616e69\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/27c0f180ef8bc2d2094b529129fd31169bf0e10b3a965f25fb6ef0d8b8311868/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66617374616e69\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fastani\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/86bc49a261c15fbea5000ea905a561248a21175c8d281a5949cf47e66c9eae71/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66617374616e69\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/86bc49a261c15fbea5000ea905a561248a21175c8d281a5949cf47e66c9eae71/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66617374616e69\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fastani\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-fastani\" class=\"anchor\" href=\"#singularity-fastani\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-fastani\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"github.com/parbliss/fastani\"\u003efastANI\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efastANI\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/fastANI/1.33\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/fastANI\u003c/code\u003e as \u003ccode\u003e1.33.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "yh549848/singularity-r-notebook", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1628991664.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1637718305.0 }, { "data_format": 2, - "description": "PHYLIP is a free package of programs for inferring phylogenies.", + "description": "R package for nsphs_ml_qt", "filenames": [ - "3.697/Singularity" + "Singularity", + "scripts_local/issue_61/Singularity", + "scripts_bianca/Singularity" ], - "full_name": "pscedu/singularity-phylip-suite", - "latest_release": "v3.697", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-phylip-suite/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-phylip-suite/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-phylip-suite/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-phylip-suite/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c170f91fccaa5cf129589585cae304316c06a6c4926ef489d6ae6cec8639c69d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c170f91fccaa5cf129589585cae304316c06a6c4926ef489d6ae6cec8639c69d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-phylip-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5e02750234489a3b6681aab63cc8c7dee07d7b579324265bc6a45b0d56dad6d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e02750234489a3b6681aab63cc8c7dee07d7b579324265bc6a45b0d56dad6d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-phylip-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a77ca9fdcc58325c1ddfe94710d8ad36e21b307dbe40570153f1e80be6cac559/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a77ca9fdcc58325c1ddfe94710d8ad36e21b307dbe40570153f1e80be6cac559/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-phylip-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/50a0a1f8e6d29153411b82f9caaa4f006a72cf5b55af95c617d808d70a1588b9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/50a0a1f8e6d29153411b82f9caaa4f006a72cf5b55af95c617d808d70a1588b9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7068796c69702d7375697465\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-phylip-suite\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-phylip-suite\" class=\"anchor\" href=\"#singularity-phylip-suite\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-phylip-suite\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/b55efde725f14299bbb17335a6c6f17cde58bd9f451a128b97c0cfb8fe5b4edc/68747470733a2f2f65766f6c7574696f6e2e67656e65746963732e77617368696e67746f6e2e6564752f7068796c69702e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b55efde725f14299bbb17335a6c6f17cde58bd9f451a128b97c0cfb8fe5b4edc/68747470733a2f2f65766f6c7574696f6e2e67656e65746963732e77617368696e67746f6e2e6564752f7068796c69702e676966\" alt=\"Logo\" data-canonical-src=\"https://evolution.genetics.washington.edu/phylip.gif\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://evolution.genetics.washington.edu/phylip.html\" rel=\"nofollow\"\u003ePHYLIP\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/phylip-suite/3.697\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/phylip-suite\u003c/code\u003e as \u003ccode\u003e3.697.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "AJResearchGroup/nsphs_ml_qt", + "latest_release": "v0.3", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nsphs_ml_qt\" class=\"anchor\" aria-hidden=\"true\" href=\"#nsphs_ml_qt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ensphs_ml_qt\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/be413dbe544678e8710f09ecb2ee97d7a26b0a853e40a539069148252157700f/68747470733a2f2f636f6465636f762e696f2f67682f72696368656c62696c6465726265656b2f6e737068735f6d6c5f71742f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt?branch=develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d51e07e8b43e2d93b35336c3c0437fdb29a65ac9e5d54406d980daf81cd2567f/68747470733a2f2f636f6465636f762e696f2f67682f72696368656c62696c6465726265656b2f6e737068735f6d6c5f71742f6272616e63682f646576656c6f702f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt/branch/develop/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll code for \u003cg-emoji class=\"g-emoji\" alias=\"lock\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f512.png\"\u003e\ud83d\udd12\u003c/g-emoji\u003e \u003ca href=\"https://github.com/AJResearchGroup/article_nsphs_ml_qt\"\u003earticle_nsphs_ml_qt\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eVideo on workflow: \u003ca href=\"https://youtu.be/FSh6i0Vsf54\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"https://richelbilderbeek.nl/nsphs_ml_qt_workflow.ogv\" rel=\"nofollow\"\u003edownload\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_bianca/README.md\"\u003eWorkflow on Bianca\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_rackham/README.md\"\u003eWorkflow on Rackham\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_local/README.md\"\u003eWorkflow on local computer\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eResults: \u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt_results\"\u003esee the \u0027nsphs_ml_qt_results\u0027 repository\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/example_architecture.png\"\u003e\u003cimg src=\"man/figures/example_architecture.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/example_dimred.png\"\u003e\u003cimg src=\"man/figures/example_dimred.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/legend_HO_tiny.png\"\u003e\u003cimg src=\"man/figures/legend_HO_tiny.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "bioinformatics", - "singularity" - ], - "updated_at": 1629217939.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1657465920.0 }, { "data_format": 2, - "description": "bowtie2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences.", + "description": "FAIR+ template repository with support and scaffolding for Docker, Singularity, and the Open Science Grid", "filenames": [ - "2.4.4/Singularity", - "2.2.5/Singularity", - "2.4.1/Singularity", - "2.4.2/Singularity" + "Singularity.def" ], - "full_name": "pscedu/singularity-bowtie2", - "latest_release": "v2.4.4", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bowtie2/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bowtie2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/78e679d7d4d533686856a3aabf05836e6e4c4332ede5b3be04e448bcb0af367d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626f7774696532\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78e679d7d4d533686856a3aabf05836e6e4c4332ede5b3be04e448bcb0af367d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626f7774696532\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bowtie2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/1de77fb074acfa00a23f3b57ec7ee2899825e96179ba08e9726d43d479a8d305/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626f7774696532\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1de77fb074acfa00a23f3b57ec7ee2899825e96179ba08e9726d43d479a8d305/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626f7774696532\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bowtie2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/022a36667f17e7d4a64d83341d6f9b3ef4e8e2ca7bbb26a5e59ea5d45b8bd4ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626f7774696532\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/022a36667f17e7d4a64d83341d6f9b3ef4e8e2ca7bbb26a5e59ea5d45b8bd4ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626f7774696532\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bowtie2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/6a118105cb6b5b857541ef5a71ff99144f58396c20ec65334f65aef06d79ae43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626f7774696532\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6a118105cb6b5b857541ef5a71ff99144f58396c20ec65334f65aef06d79ae43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626f7774696532\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bowtie2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bowtie2\" class=\"anchor\" href=\"#singularity-bowtie2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bowtie2\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/bowtie2\"\u003ebowtie2\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the Perl scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bowtie2/2.4.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bowtie2\u003c/code\u003e as \u003ccode\u003e2.4.4.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "comses-education/fair-osg-template", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fair-osg-template\" class=\"anchor\" aria-hidden=\"true\" href=\"#fair-osg-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efair-osg-template\u003c/h1\u003e\n\u003cp\u003eThis template repository provides scaffolding and support for adopting the \u003ca href=\"https://doi.org/10.15497/RDA00068\" rel=\"nofollow\"\u003eFAIR4RS Principles\u003c/a\u003e and containerization support for \u003ca href=\"https://docs.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, and the \u003ca href=\"https://opensciencegrid.org/\" rel=\"nofollow\"\u003eOpen Science Grid (OSG)\u003c/a\u003e. A basic Makefile is included to be customized with basic \u003ccode\u003ebuild | deploy | clean\u003c/code\u003e targets to build container images in Docker and Singularity and copy the generated Singularity image and model files to an OSG login node.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-fair4rs-principles\" class=\"anchor\" aria-hidden=\"true\" href=\"#fair4rs-principles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAIR4RS Principles\u003c/h2\u003e\n\u003cp\u003eMore details at \u003ca href=\"https://github.com/comses-education/fair-osg-template/wiki/FAIR-Principles-for-Research-Software\"\u003ethis template repository\u0027s wiki\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003cstrong\u003eFindable\u003c/strong\u003e: create a persistent identifier for each released / published version of the software\u003c/li\u003e\n\u003cli\u003e[ ] \u003cstrong\u003eAccessible\u003c/strong\u003e: make your software open source (good start, using this!), ensure that it is well documented with descriptive metadata and narrative documentation, and make sure that this metadata remains accessible even if the software is not\u003c/li\u003e\n\u003cli\u003e[ ] \u003cstrong\u003eInteroperable\u003c/strong\u003e: your software should read, write, and exchange data using domain-relevant \u003cem\u003eopen\u003c/em\u003e community standards (e.g., netCDF, HDF, domain-specific controlled vocabularies or ontologies, etc.)*\u003c/li\u003e\n\u003cli\u003e[ ] \u003cstrong\u003eReusable\u003c/strong\u003e: Software can be executed and understood, modified, built upon, or incorporated into other software - a clear and accessible license, detailed provenance metadata, qualified persistent references to other software dependencies, domain-relevant community standards*\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] add narrative documentation in durable text formats (e.g., PDF with no special extensions, .odt OpenOffice Document file, Markdown / plaintext) about your computational model ideally with visual diagrams, flowcharts, etc., that describe expected inputs, outputs, assumptions, and consider adhering to a structured, domain-specific protocols like the \u003ca href=\"https://www.jasss.org/23/2/7.html\" rel=\"nofollow\"\u003eODD Protocol for Describing Agent-Based and other Simulation Models\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] include a README.md with a quick start for new users that addresses the following basic concerns:\u003c/li\u003e\n\u003cli\u003e[ ] What assumptions if any are embedded in the model?\u003c/li\u003e\n\u003cli\u003e[ ] Is it possible to change or extend the model?\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-containerization-and-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#containerization-and-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerization and Scripts\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] specify pinned software and system dependencies to be installed in Docker and Singularity\u003c/li\u003e\n\u003cli\u003e[ ] identify an appropriate base image. You can use base images prefixed with \u003ccode\u003eosg-\u003c/code\u003e for common platforms\nlike NetLogo, Julia, Python, and R at \u003ca href=\"https://hub.docker.com/u/comses\" rel=\"nofollow\"\u003ehttps://hub.docker.com/u/comses\u003c/a\u003e or create your own based on an OSG blessed\nimage (e.g., \u003ca href=\"https://github.com/opensciencegrid/osgvo-ubuntu-20.04\"\u003ehttps://github.com/opensciencegrid/osgvo-ubuntu-20.04\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] customize job-wrapper.sh\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-this-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-this-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run this model\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] What does this model do?\u003c/li\u003e\n\u003cli\u003e[ ] How do I run it?\u003c/li\u003e\n\u003cli\u003e[ ] What are some example inputs? What are the expected outputs for those example inputs? Where do they live?\u003c/li\u003e\n\u003cli\u003e[ ] How do I analyze or understand the outputs?\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-the-open-science-grid\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-the-open-science-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on the Open Science Grid\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-set-up-your-user-account-on-the-open-science-grid\" class=\"anchor\" aria-hidden=\"true\" href=\"#set-up-your-user-account-on-the-open-science-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet up your user account on the Open Science Grid\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://osg-htc.org/research-facilitation/accounts-and-projects/general/index.html\" rel=\"nofollow\"\u003ehttps://osg-htc.org/research-facilitation/accounts-and-projects/general/index.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou must have already gone through the OSG facilitation process with access to an Open Science Grid login node before\n\u003ccode\u003e% make deploy\u003c/code\u003e will work and you should create an alias in your \u003ccode\u003e.ssh/config\u003c/code\u003e that assigns the name \u003ccode\u003eosg\u003c/code\u003e to your OSG\nlogin node.\u003c/p\u003e\n\u003cp\u003eFor example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eHost osg\n HostName login02.osgconnect.net\n User \u0026lt;your-assigned-osg-username\u0026gt;\n IdentityFile ~/.ssh/a-private-ssh-key that you generated and added to your OSG profile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information on connecting to OSG and generating SSH keys, please see\n\u003ca href=\"https://support.opensciencegrid.org/support/solutions/articles/12000027675-generate-ssh-keys-and-activate-your-osg-login\" rel=\"nofollow\"\u003ehttps://support.opensciencegrid.org/support/solutions/articles/12000027675-generate-ssh-keys-and-activate-your-osg-login\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-customize-entry-point-scripts-and-model-metadata\" class=\"anchor\" aria-hidden=\"true\" href=\"#customize-entry-point-scripts-and-model-metadata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustomize entry point scripts and model metadata\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# user to connect to OSG as\nOSG_USERNAME := ${USER}\n# name of this computational model, used as the namespace (for singularity, Docker, and as a folder to keep things\n# organized on the OSG filesystem login node). recommend that you use all lowercase alphanumeric with - or _ to\n# separate words, e.g., chime-abm or spatial-rust-model\nMODEL_NAME := ${OSG_MODEL_NAME}\n# the directory (in the container) where the computational model source\n# code or executable can be called, e.g., main.py | netlogo-headless.sh\nMODEL_CODE_DIRECTORY := /code\n# entrypoint script to be called by job-wrapper.sh\nENTRYPOINT_SCRIPT := /srv/run.sh\n# entrypoint script language\nENTRYPOINT_SCRIPT_EXECUTABLE := bash\n# the OSG output file to be transferred\nOSG_OUTPUT_FILES := output,results\n# the submit file to be executed on OSG via `condor_submit ${OSG_SUBMIT_FILE}`\nOSG_SUBMIT_FILENAME := ${OSG_MODEL_NAME}.submit\n# the initial entrypoint for the OSG job, calls ENTRYPOINT_SCRIPT\nOSG_JOB_SCRIPT := job-wrapper.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(TODO: set data via cookiecutter and cookiecutter.json in cookiecutter project + document further)\u003c/p\u003e\n\u003cp\u003eThese can be customized in the make command.\u003c/p\u003e\n\u003cp\u003eThen run\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake OSG_USERNAME=\u0026lt;your-username\u0026gt; build\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eto build Docker + Singularity images with the model + dependencies embedded or\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake OSG_USERNAME=\u0026lt;your-username\u0026gt; clean deploy\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eto build and then copy the images to your OSG login node and public directory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input-and-output-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-and-output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput and Output Files\u003c/h2\u003e\n\u003cp\u003eOSG defaults transfer all generated output files. If your model generates all files in a given directory, say \u003ccode\u003eoutput\u003c/code\u003e and/or \u003ccode\u003eresults\u003c/code\u003e, something like\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003etransfer_output_files = output,results\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eshould work, e.g., a comma separated list of\u003c/p\u003e\n\u003cp\u003eFor more information, see \u003ca href=\"https://htcondor.readthedocs.io/en/latest/users-manual/file-transfer.html#specifying-what-files-to-transfer\" rel=\"nofollow\"\u003ehttps://htcondor.readthedocs.io/en/latest/users-manual/file-transfer.html#specifying-what-files-to-transfer\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "bioinformatics", - "singularity" - ], - "updated_at": 1628991557.0 + "subscribers_count": 4, + "topics": [], + "updated_at": 1657275644.0 }, { "data_format": 2, - "description": "HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes as well as to a single reference genome. ", + "description": "A singularity container for `fastqsplit`: https://github.com/supernifty/fastqsplit", "filenames": [ - "2.2.1/Singularity" + "Singularity.fastqsplit" ], - "full_name": "pscedu/singularity-hisat2", - "latest_release": "v2.2.1", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ea61f9228ca14a66e58889a560447e0b7c8ba73ddbaa594055242ace96eb0a84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ea61f9228ca14a66e58889a560447e0b7c8ba73ddbaa594055242ace96eb0a84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4796300b08f76b423ee0574c15e8bd8ad15b0a389a36fd3f58549b8bb5df8690/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4796300b08f76b423ee0574c15e8bd8ad15b0a389a36fd3f58549b8bb5df8690/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/25eeccddaeb5bf9a3f7053b646f9b7bf540d33b801c371db1850d745da46fc95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/25eeccddaeb5bf9a3f7053b646f9b7bf540d33b801c371db1850d745da46fc95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f14a5cb988478f36746bb17a364f669ee4d02a32a6f6fddfbccaff9dc50a8379/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f14a5cb988478f36746bb17a364f669ee4d02a32a6f6fddfbccaff9dc50a8379/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hisat\" class=\"anchor\" href=\"#singularity-hisat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hisat\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://daehwankimlab.github.io/hisat2/\" rel=\"nofollow\"\u003ehisat2\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts \u003ccode\u003ehisat2\u003c/code\u003e, \u003ccode\u003ehisat2-build\u003c/code\u003e and \u003ccode\u003ehisat2-inspect\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hisat2/2.2.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hisat2\u003c/code\u003e as \u003ccode\u003e2.2.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-singularity-definition-file\" class=\"anchor\" href=\"#building-the-image-using-the-singularity-definition-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the Singularity definition file\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "mjakobs/fastqsplit_singularity", + "latest_release": "v1.0.3", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fastqsplit-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#fastqsplit-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFastqSplit Singularity Container\u003c/h1\u003e\n\u003cp\u003eA Singularity container for \u003ccode\u003efastqsplit\u003c/code\u003e by \u003ca href=\"https://github.com/supernifty/fastqsplit\"\u003ehttps://github.com/supernifty/fastqsplit\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eBased on a template by \u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instructions-for-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#instructions-for-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions for use\u003c/h2\u003e\n\u003cp\u003eTo pull this singularity container please run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/mjakobs/fastqsplit_singularity/releases/download/v1.0.2/mjakobs-fastqsplit_singularity.fastqsplit.sif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "bioinformatics", - "singularity" - ], - "updated_at": 1629078604.0 + "topics": [], + "updated_at": 1652199670.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.openrefine" + "planner/symk/Singularity", + "planner/symk/misc/releases/19.06/Singularity.19.06", + "planner/symk/misc/releases/19.12/Singularity.19.12", + "planner/symk/misc/releases/latest/Singularity" ], - "full_name": "ternaustralia/coesra-singularity-openrefine", + "full_name": "zihangs/GRACE", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coesra-singularity-openrefine\" class=\"anchor\" href=\"#coesra-singularity-openrefine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-openrefine\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-grace\" class=\"anchor\" aria-hidden=\"true\" href=\"#grace\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGRACE\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Environment\u003c/h3\u003e\n\u003cp\u003eThe docker image can be found \u003ca href=\"https://hub.docker.com/r/suzihang/grace\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003edocker run -it -v PathToGRACE:/mnt suzihang/grace /bin/bash\u003c/p\u003e\n\u003cp\u003eThe container should contain all dependency libraries (you can install other tools into the container). Then, build the planner with all the required dependencies in the container.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "coesra" - ], - "updated_at": 1610426463.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1659585039.0 }, { "data_format": 2, - "description": "Singularity image for honggfuzz (https://github.com/google/honggfuzz)", + "description": "Dockerfile for an image containing Conda, open-ssh and java-ready for installing IBM products", "filenames": [ - "Singularity.i386", - "Singularity.1604", - "Singularity.1804", - "v21/Singularity.v21" + "Singularity" ], - "full_name": "shub-fuzz/honggfuzz", - "latest_release": "0.0.2", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/honggfuzz/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/honggfuzz/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3641\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity image for honggfuzz (\u003ca href=\"https://github.com/google/honggfuzz\"\u003ehttps://github.com/google/honggfuzz\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eusage:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name honggfuzz.sif https://github.com/shub-fuzz/honggfuzz/releases/download/0.0.2/shub-fuzz-honggfuzz.1604.sif\n\nsingularity shell honggfuzz.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003epull Ubuntu 18.04 container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name honggfuzz.1804.sif https://github.com/shub-fuzz/honggfuzz/releases/download/0.0.2/shub-fuzz-honggfuzz.1804.sif\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "cfrioux/docker_conda_ssh", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker_conda_ssh\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker_conda_ssh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker_conda_ssh\u003c/h1\u003e\n\u003cp\u003eDockerfile for an image containing Conda, open-ssh and java-ready for installing IBM products\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1623682711.0 + "updated_at": 1656509993.0 }, { "data_format": 2, - "description": "Singularity Image for AFL (https://github.com/google/AFL)", + "description": null, "filenames": [ - "Singularity.i386", - "Singularity.1604", - "Singularity.1804" + "Singularity.v1" ], - "full_name": "shub-fuzz/afl", - "latest_release": "0.0.2", - "readme": "\u003cp\u003eSingularity Image for AFL (\u003ca href=\"https://github.com/google/AFL\"\u003ehttps://github.com/google/AFL\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/afl/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/afl/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name afl.sif https://github.com/shub-fuzz/afl/releases/download/0.0.2/shub-fuzz-afl.1604.sif\n\nsingularity shell afl.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epull Ubuntu 18.04 container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name afl.1804.sif https://github.com/shub-fuzz/afl/releases/download/0.0.2/shub-fuzz-afl.1804.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epull Ubuntu 16.04 i386 container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name afl_i386.sif https://github.com/shub-fuzz/afl/releases/download/0.0.2/shub-fuzz-afl.i386.sif\n\nsingularity pull --name afl_i386.sif shub://shub-fuzz/afl:i386\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "cschu/duk_singularity", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1623682579.0 + "updated_at": 1656415847.0 }, { "data_format": 2, - "description": "Singularity image for Eclipser (https://github.com/SoftSec-KAIST/Eclipser)", + "description": null, "filenames": [ - "Singularity.1604" + "Singularity" ], - "full_name": "shub-fuzz/eclipser", - "latest_release": "0.0.2", - "readme": "\u003cp\u003eSingularity image for Eclipser (\u003ca href=\"https://github.com/SoftSec-KAIST/Eclipser\"\u003ehttps://github.com/SoftSec-KAIST/Eclipser\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/eclipser/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/eclipser/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name eclipser.sif https://github.com/shub-fuzz/eclipser/releases/download/0.0.2/shub-fuzz-eclipser.1604.sif\n\nsingularity shell eclipser.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "kirsho/yml2sing", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1623682705.0 + "updated_at": 1655971591.0 }, { "data_format": 2, - "description": "Singularity image for Ankou (https://github.com/SoftSec-KAIST/Ankou)", + "description": null, "filenames": [ - "Singularity.1604" + "Singularity" ], - "full_name": "shub-fuzz/ankou", - "latest_release": "0.0.2", - "readme": "\u003cp\u003eSingularity image for Ankou (\u003ca href=\"https://github.com/SoftSec-KAIST/Ankou\"\u003ehttps://github.com/SoftSec-KAIST/Ankou\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/ankou/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/ankou/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4173\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name ankou.sif https://github.com/shub-fuzz/ankou/releases/download/0.0.2/shub-fuzz-ankou.1604.sif\n\nsingularity shell ankou.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "VUIIS/examcardtotxt", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-examcard-conversion-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#examcard-conversion-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamcard Conversion Pipeline\u003c/h1\u003e\n\u003cp\u003eThis spider converts Philips examcards from DICOM format to PDF, HTML, and TXT formats. Special thanks goes to Sha Zhao from Manchester University.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h2\u003e\n\u003cp\u003eExamcard (.dcm)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs:\u003c/h2\u003e\n\u003cp\u003eExamcard (.pdf)\nExamcard (.html)\nExamcard (.txt)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-version-2212021\" class=\"anchor\" aria-hidden=\"true\" href=\"#version-2212021\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion 22.1.2021\u003c/h2\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1623682696.0 + "updated_at": 1655221055.0 }, { "data_format": 2, - "description": "Singularity image for Angora (https://github.com/AngoraFuzzer/Angora)", + "description": null, "filenames": [ - "Singularity.1604", - "Singularity.1804" + "Singularity" ], - "full_name": "shub-fuzz/angora", - "latest_release": "0.0.2", - "readme": "\u003cp\u003eSingularity image for Angora (\u003ca href=\"https://github.com/AngoraFuzzer/Angora\"\u003ehttps://github.com/AngoraFuzzer/Angora\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/angora/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/angora/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3645\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name angora.sif https://github.com/shub-fuzz/angora/releases/download/0.0.2/shub-fuzz-angora.1604.sif\n\nsingularity shell angora.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003einteractive session:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell angora.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003estart fuzzing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec angora.sif /start_fuzzing [[ -n \u0026lt;# instances\u0026gt; ] -t ] \u0026lt;target_path\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "touala/MUMmer", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mummer\" class=\"anchor\" aria-hidden=\"true\" href=\"#mummer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMUMmer\u003c/h1\u003e\n\u003cp\u003eAdapted from \u003ca href=\"https://forgemia.inra.fr/gafl/singularity/mummer/\" rel=\"nofollow\"\u003ehttps://forgemia.inra.fr/gafl/singularity/mummer/\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1623682691.0 + "updated_at": 1656321189.0 }, { "data_format": 2, - "description": "QSYM - Concolic Execution Engine (https://github.com/sslab-gatech/qsym)", + "description": "Spatially explicit individual based model that simulates Coffee Leaf Rust epidemics on a coffee farm", "filenames": [ - "Singularity.1604", - "Singularity.1804" + "Singularity.def" ], - "full_name": "shub-fuzz/qsym", - "latest_release": "0.0.2", - "readme": "\u003cp\u003eSingularity Image for QSYM (\u003ca href=\"https://github.com/sslab-gatech/qsym\"\u003ehttps://github.com/sslab-gatech/qsym\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/qsym/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/qsym/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3625\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eQSYM - Concolic Execution Engine (\u003ca href=\"https://github.com/sslab-gatech/qsym\"\u003ehttps://github.com/sslab-gatech/qsym\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eusage:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name qsym.sif https://github.com/shub-fuzz/qsym/releases/download/0.0.2/shub-fuzz-qsym.1604.sif\n\nsingularity shell qsym.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "comses-education/spatialrust-model", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-spatialrust-coffee-leaf-rust-epidemic-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#spatialrust-coffee-leaf-rust-epidemic-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpatialRust: Coffee Leaf Rust Epidemic Model\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/06a717d034624fa1ef05f60d027c62477e5fb10c3803b2e488c18839125fa828/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538422532302532302545322539372538422532302532302545322539372538422d6f72616e6765\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B%20%20%E2%97%8B%20%20%E2%97%8B-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/comses-education/spatialrust-model/actions/workflows/docker-image.yml\"\u003e\u003cimg src=\"https://github.com/comses-education/spatialrust-model/actions/workflows/docker-image.yml/badge.svg\" alt=\"Docker Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/comses-education/spatialrust-model/actions/workflows/singularity-image.yml\"\u003e\u003cimg src=\"https://github.com/comses-education/spatialrust-model/actions/workflows/singularity-image.yml/badge.svg\" alt=\"Singularity Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSpatialRust is an individual based model that simulates Coffee Leaf Rust epidemics within a coffee farm.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installing-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling dependencies\u003c/h3\u003e\n\u003cp\u003eMove to this project\u0027s directory and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia install.jl\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-the-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the model\u003c/h3\u003e\n\u003cp\u003eThere are two script files available. You can run a single simulation using a fixed parameter set using \u003ccode\u003escripts/OneRun.jl\u003c/code\u003e as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia scripts/OneRun.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eResults from this single run will be saved in a \u003ccode\u003eresults\u003c/code\u003e folder as \u003ccode\u003esinglerun.csv\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe second option, \u003ccode\u003escripts/ParameterRuns.jl\u003c/code\u003e, lets you run a parameter exploration experiment. The default setup of this experiment will run 2700 simulations. To modify the parameter values to be evaluated or the replicates for each combination, open \u003ccode\u003escripts/ParameterRuns.jl\u003c/code\u003e and edit lines 11 to 14. Like the first option, you can run the script from bash:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ julia scripts/ParameterRuns.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eResults from this experiment will be saved in a \u003ccode\u003eresults\u003c/code\u003e folder as \u003ccode\u003eparameterexp.csv\u003c/code\u003e. Both scripts take care of creating the \u003ccode\u003eresults\u003c/code\u003e folder if it has not been created yet.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-on-open-science-grid\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-open-science-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Open Science Grid\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eEstablish an account on Open Science Grid\n\u003ca href=\"https://osg-htc.org/research-facilitation/accounts-and-projects/general/index.html\" rel=\"nofollow\"\u003ehttps://osg-htc.org/research-facilitation/accounts-and-projects/general/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a host alias for your OSG account (\u003ca href=\"https://github.com/comses-education/fair-osg-template#set-up-your-user-account-on-the-open-science-grid\"\u003ehttps://github.com/comses-education/fair-osg-template#set-up-your-user-account-on-the-open-science-grid\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eBuild a singularity image and deploy it to your OSG \u003ccode\u003e/public/\u0026lt;username\u0026gt;\u003c/code\u003e directory via \u003ccode\u003e$ make OSG_USERNAME=\u0026lt;your-osg-username\u0026gt; deploy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003essh into the OSG login node, cd into the \u003ccode\u003espatialrust\u003c/code\u003e directory and submit the generated \u003ccode\u003espatialrust.submit\u003c/code\u003e via \u003ccode\u003e$ condor_submit spatialrust.submit\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ethis runs the ParameterRuns.jl on OSG and should drop off a \u003ccode\u003eresults.zip\u003c/code\u003e file with the data in the same directory you submitted the job script.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1623682731.0 + "subscribers_count": 5, + "topics": [ + "agent-based-model", + "computational-model", + "julia", + "simulation" + ], + "updated_at": 1655789345.0 }, { "data_format": 2, - "description": "FLAC (/fl\u00e6k/; Free Lossless Audio Codec) is an audio coding format for lossless compression of digital audio.", + "description": null, "filenames": [ - "1.3.3/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-flac", + "full_name": "kirsho/conda2sing", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-flac/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-flac/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/37fadb899e2280d332672dd4ff8c55c77b6d1da4314ad56fb36c15142413fbda/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666c6163\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37fadb899e2280d332672dd4ff8c55c77b6d1da4314ad56fb36c15142413fbda/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666c6163\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-flac\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5ccca52861f2fb4e7486645f35b923f2cbc790f1e7ed709d7422b0c9f2dc19d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666c6163\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ccca52861f2fb4e7486645f35b923f2cbc790f1e7ed709d7422b0c9f2dc19d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666c6163\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-flac\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e8f82abbc9bacec399c48512a0390d8b4d29091355a9ce463d889ecb16cd4775/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666c6163\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8f82abbc9bacec399c48512a0390d8b4d29091355a9ce463d889ecb16cd4775/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666c6163\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-flac\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5325f2a609a18c54057940e4962347ba4f1beeef88a23a92c7149e8016cf4e13/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666c6163\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5325f2a609a18c54057940e4962347ba4f1beeef88a23a92c7149e8016cf4e13/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666c6163\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-flac\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-flac\" class=\"anchor\" href=\"#singularity-flac\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-flac\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/flac\"\u003eflac\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-about-this-repository\" class=\"anchor\" href=\"#about-this-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout this repository\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/4d9e61b063851d778ce9938e3f7bb848d158ebf589dbcd68bf21c5d5eeef6d40/68747470733a2f2f6d65646961322e67697068792e636f6d2f6d656469612f31334867774773584630616947592f67697068792e6769663f6369643d6563663035653437396d61316e736b74386d786278726c323076377375656868343931687532306b6973786878636265267269643d67697068792e6769662663743d67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4d9e61b063851d778ce9938e3f7bb848d158ebf589dbcd68bf21c5d5eeef6d40/68747470733a2f2f6d65646961322e67697068792e636f6d2f6d656469612f31334867774773584630616947592f67697068792e6769663f6369643d6563663035653437396d61316e736b74386d786278726c323076377375656868343931687532306b6973786878636265267269643d67697068792e6769662663743d67\" alt=\"DANGER\" data-canonical-src=\"https://media2.giphy.com/media/13HgwGsXF0aiGY/giphy.gif?cid=ecf05e479ma1nskt8mxbxrl20v7suehh491hu20kisxhxcbe\u0026amp;rid=giphy.gif\u0026amp;ct=g\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe purpose of this repository is to highlight how to deploy a Singularity and Spack together.\u003c/li\u003e\n\u003cli\u003eAt this moment, the workflow is expected to fail as we have not found a good solution to deploying the images (yet).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCOMMENT: \u003cstrong\u003eDo not deploy on any system.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the Perl scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/flac/1.3.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/flac\u003c/code\u003e as \u003ccode\u003e1.3.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "utilities", - "singularity" - ], - "updated_at": 1628186027.0 + "topics": [], + "updated_at": 1656054479.0 }, { "data_format": 2, - "description": null, + "description": "blastfoam-CI-docker", "filenames": [ - "1.3.1/Singularity", - "1.3.3/Singularity" + "Singularity-openfoam.def" ], - "full_name": "yh549848/singularity-rsem", + "full_name": "jiaqiwang969/blastfoam-project", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/43cc1554b9e51a28dfa82673f6a9629d4b3f4151419378b68631040a1a5f52a2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/43cc1554b9e51a28dfa82673f6a9629d4b3f4151419378b68631040a1a5f52a2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bamtools\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/bb25827e818656bc2a93d95c923b954c29792611568e2828cee62c6501555455/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bb25827e818656bc2a93d95c923b954c29792611568e2828cee62c6501555455/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bamtools\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/30cf5022df93f7c848ab5284b476648b2a424ac87e287144bfdbc9460bb75256/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/30cf5022df93f7c848ab5284b476648b2a424ac87e287144bfdbc9460bb75256/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bamtools\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/72066080adaa298a3f65d9ad3d27681498685739defe4d4061e06d30f8bf5277/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72066080adaa298a3f65d9ad3d27681498685739defe4d4061e06d30f8bf5277/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bamtools\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bamtools\" class=\"anchor\" href=\"#singularity-bamtools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bamtools\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/pezmaster31/bamtools\"\u003ebamtools\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebamtools\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bamtools/2.5.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bamtools\u003c/code\u003e as \u003ccode\u003e2.5.1\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-blastfoam-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#blastfoam-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eblastfoam-project\u003c/h1\u003e\n\u003cp\u003eAim: High resolution fvm simulation using \u003ca href=\"https://github.com/synthetik-technologies/blastfoam\"\u003eblastfoam\u003c/a\u003e scheme\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emv Dockerfile.step01 Dockerfile\ndocker build jiaqiknight/openfoam-blastfoam:v1 .\nmv Dockerfile.step02 Dockerfile\ndocker build jiaqiknight/openfoam-blastfoam:v2 .\nsingularity build openfoam-blastfoam-v2012.sif Singularity-openfoam.def\nsingularity shell openfoam-blastfoam-v2012.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-action-to-dockerhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-action-to-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Action to dockerhub\u003c/h3\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1623772549.0 + "updated_at": 1655572084.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.3.0" + "Singularity" ], - "full_name": "onuryukselen/singularity", + "full_name": "anastasiadoulab/machaon", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cp\u003eDevelopment Branch\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-machaon\" class=\"anchor\" aria-hidden=\"true\" href=\"#machaon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMachaon\u003c/h1\u003e\n\u003cbr\u003e\nThis repository contains an implementation for the method presented in the paper \"Identifying and \nprofiling structural similarities between Spike of SARS-CoV-2 and other viral or host proteins with \nMachaon\".\n\u003cp\u003ePlease consult this time-saving manual before you use Machaon. It contains an in-depth explanation\u003cbr\u003e\nabout installing, setting up and using this method.\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements\u003c/h2\u003e\n\u003cbr\u003e\n\u003cp\u003eThe target system for Machaon\u0027s development is Ubuntu 20.4. Machaon has limited functionality\u003cbr\u003e\non Windows and MacOS. Some post-processing modules utilize TM-Align and DSSP which are not\u003cbr\u003e\ncross-platform implementations. DSSP data might also be used for setting the targets of constrained\u003cbr\u003e\ncomparisons, which is Machaon\u0027s default behaviour.\u003c/p\u003e\n\u003cp\u003eThe recommended ways to use Machaon is either by working inside a Docker container or a Singularity\u003cbr\u003e\ncontainer or by working in an Ubuntu 20.4 environment with Anaconda (see instructions in the \u0027Installation\u0027\u003cbr\u003e\nsection below). On Windows, you could try WSL in order to get access to a UNIX environment (not tested):\u003cbr\u003e\n\u003ca href=\"https://docs.microsoft.com/en-us/windows/wsl/install\" rel=\"nofollow\"\u003ehttps://docs.microsoft.com/en-us/windows/wsl/install\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMachaon is an I/O (input/output) intensive implementation and the performance is depended on the\u003cbr\u003e\nstorage hardware and the storage optimizations of the host operating and file systems. For every\u003cbr\u003e\nPDB file that is analyzed, there is a corresponding set of serialized data objects in the form of\u003cbr\u003e\nbinary files (pickle Python package) which hold the necessary data for the calculation of each\u003cbr\u003e\nmetric. NVMe storage is highly recommended.\u003c/p\u003e\n\u003cp\u003eMachaon is a multi-core CPU application with moderate demands on RAM memory only for\u003cbr\u003e\npost-processing and target setup for constrained comparisons due to the required alignments\u003cbr\u003e\n(especially alignments in parallel).\u003c/p\u003e\n\u003cbr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-repository-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#repository-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepository contents\u003c/h2\u003e\n\u003cbr\u003e\n\u003cul\u003e\n\u003cli\u003eassess: this folder contains scripts for Machaon\u0027s benchmarking, evaluation and assessment\u003c/li\u003e\n\u003cli\u003econfig: configuration files\u003c/li\u003e\n\u003cli\u003edocs: It contains programming-related documentation and diagrams.\n\u003cul\u003e\n\u003cli\u003edocs/classes: Extensive API documentation for all the classes of this implementation.\u003cbr\u003e\nEach class has a dedicated HTML file with thorough description.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003esetup: Scripts for downloading and preparing some (optional) related data sources.\u003c/li\u003e\n\u003cli\u003esrc: source code\u003c/li\u003e\n\u003cli\u003etest: It contains an integrity test with testing data and expected outputs.\u003c/li\u003e\n\u003cli\u003edocker-compose.yml : A file used by Docker Compose tool.\u003c/li\u003e\n\u003cli\u003eDockerfile: A file with the commands needed to set up Machaon in a Docker container.\u003c/li\u003e\n\u003cli\u003eenvironment.yml: A file used by Anaconda Python package manager.\u003c/li\u003e\n\u003cli\u003eLICENSE.md: The license of this implementation.\u003c/li\u003e\n\u003cli\u003eREADME.md: Machaon\u0027s manual (the one you are reading).\u003c/li\u003e\n\u003cli\u003eSingularity: A file used to set up a Singularity container.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e \n\u003ch2\u003e\u003ca id=\"user-content-setup-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup instructions\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-local-data-sources\" class=\"anchor\" aria-hidden=\"true\" href=\"#local-data-sources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal data sources\u003c/h3\u003e\n\u003cbr\u003e\nEnrichment and meta-analysis stages rely on external data sources. There are fallbacks in place for \nsome of them (webservice calls) but it is strongly recommended utilizing the available static resources. \nThis will minimize network activity, greatly speed up the process and protect the respective third party \nweb services from burden. Be sure to have enough available disk space (at least 30GB) for the initial \ndownloads (at least 12GB after the preparation).\n\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: You can use the \u003cb\u003e\u0027noThirdPartyData\u0027\u003c/b\u003e flag in the configuration, ending up only with the comparison\u003cbr\u003e\nresults. This mode does not require the set up of local data sources or other external data access. The metrics\u003cbr\u003e\n\u003cb\u003edo not rely on external information \u003c/b\u003e apart from the PDB file. Therefore, you only need to collect a set of\u003cbr\u003e\nPDB files to compare with your PDB of choice . However, you will miss enrichment and gene ID-based filtering\u003cbr\u003e\nof the results along with the functionality of the evaluation, meta-analysis, presentation modules.\u003cbr\u003e\nAlso, you will not able to perform the domain scanning since it requires the residue positions of the domains\u003cbr\u003e\n(information found in UniProt data).\u003c/p\u003e\n\u003cp\u003eChoose a folder that will be the root data \u0026amp; cache folder of Machaon and \u003cb\u003ecopy\u003c/b\u003e there the .sh files located\u003cbr\u003e\nin the setup folder. You can use symbolic links if you need to have some resources in separate locations\u003cbr\u003e\n(\u003ca href=\"https://en.wikipedia.org/wiki/Symbolic_link\" rel=\"nofollow\"\u003ehttps://en.wikipedia.org/wiki/Symbolic_link\u003c/a\u003e). Make sure the scripts have adequate execution permissions:\u003cbr\u003e\n\u003ccode\u003echmod 770 *.sh\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-pdb-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#pdb-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePDB files:\u003c/h4\u003e\n\u003cp\u003eThere are two ways that you can obtain multiple PDB files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.rcsb.org/downloads\" rel=\"nofollow\"\u003ehttps://www.rcsb.org/downloads\u003c/a\u003e\u003cbr\u003e\nThe downloaded files need to be decompressed and renamed (execute in the downloaded files\u0027 folder):\u003cbr\u003e\n\u003ccode\u003els *.gz | parallel gunzip\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003efor f in *.ent; do mv -- \"$f\" \"${f%.ent}.pdb\"; done\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Unix or MacOS only) \u003ca href=\"https://www.rcsb.org/docs/programmatic-access/batch-downloads-with-shell-script\" rel=\"nofollow\"\u003ehttps://www.rcsb.org/docs/programmatic-access/batch-downloads-with-shell-script\u003c/a\u003e\u003cbr\u003e\nThe downloaded files need to be decompressed (execute in the downloaded files\u0027 folder):\u003cbr\u003e\n\u003ccode\u003els *.gz | parallel gunzip\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOf course, you can use RCSB search and retrieve relevant PDB IDs by a query of choice.\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: PDB files from AlphaFold\u0027s predictions are \u003cb\u003e fully \u003c/b\u003e supported. You can download them from here:\u003cbr\u003e\n\u003ca href=\"https://alphafold.ebi.ac.uk/download\" rel=\"nofollow\"\u003ehttps://alphafold.ebi.ac.uk/download\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can manage the files as below:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emkdir AF\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003etar -xvf UP000005640_9606_HUMAN_v3.tar -C AF\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecd AF\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003erm -rf *.cif.gz\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003els *.gz | parallel gunzip\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eImportant:\u003c/b\u003e Avoid underscores in custom PDB filenames. For example, in Ubuntu you can run:\u003cbr\u003e\n\u003ccode\u003erename.ul \u0027_\u0027 \u0027\u0027 *.pdb\u003c/code\u003e and remove an underscores from every filename in the folder.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-refseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#refseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRefSeq:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExecute \u003ccode\u003e./download_refseq_resources.sh\u003c/code\u003e\u003cbr\u003e\nIf there are any errors during the downloads, you could try to run the script a while\nlater (\u003ca href=\"https://www.biostars.org/p/493656\" rel=\"nofollow\"\u003ehttps://www.biostars.org/p/493656\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003eExecute \u003ccode\u003e./download_refseq_resources.sh\u003c/code\u003e again for a final verification of the\ndownloaded files\u0027 integrity and then execute:\u003cbr\u003e\n\u003ccode\u003e./prepare_refseq_resources.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-uniprot-mapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#uniprot-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUniprot mapping:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eIt is recommended to use a dedicated FTP transferring program than a browser for the following large\u003cbr\u003e\ndownloads (e.g. FileZilla: \u003ca href=\"https://filezilla-project.org/download.php\" rel=\"nofollow\"\u003ehttps://filezilla-project.org/download.php\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eVisit the following directory : \u003ca href=\"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping\" rel=\"nofollow\"\u003ehttps://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload the following files: idmapping_selected.tab.gz, idmapping.dat.gz (Be sure to have enough space for the downloads)\u003c/li\u003e\n\u003cli\u003eExecute \u003ccode\u003e./prepare_uniprot_resources.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation (Containers)\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h4\u003e\n\u003cp\u003eIf you are going to use Docker, you only need to specify your data storage in docker-compose.yml file:\u003cbr\u003e\n\u003ccode\u003e- MY_BIG_STORAGE_PATH:/opt/storage\u003c/code\u003e\u003cbr\u003e\n(replace MY_BIG_STORAGE_PATH with your path of choice)\u003c/p\u003e\n\u003cp\u003eand run the following command to build and launch a Machaon-ready container:\u003cbr\u003e\n\u003ccode\u003esudo docker-compose up -d\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can enter into the container and start working:\u003cbr\u003e\n\u003ccode\u003esudo docker exec -it \u0026lt;container\u0027s name\u0026gt; bash\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003econda activate machaon\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecd /opt/src\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003epython run.py -h\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe folder with the configurations (config) is the shared between the host system\u003cbr\u003e\nand container for ease of use (you can read and edit configuration files outside of\u003cbr\u003e\nthe container).\u003c/p\u003e\n\u003cp\u003eAlternatively, if you plan to run it in a Cloud VM instance, you need to modify the\u003cbr\u003e\nDocker configurations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edocker-compose.yml: Set your mounts accordingly (or remove the volume directive)\u003c/li\u003e\n\u003cli\u003eDockerfile: Add the following line before WORKDIR command:\u003cbr\u003e\n\u003ccode\u003eADD ./config /opt/machaon/config\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h4\u003e\n\u003cp\u003eThese are the instructions for creating a container with Singularity (\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://sylabs.io/docs/\u003c/a\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload the latest version from here: \u003ca href=\"https://github.com/sylabs/singularity/releases\"\u003ehttps://github.com/sylabs/singularity/releases\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExecute:\u003cbr\u003e\n\u003ccode\u003esingularity build --fakeroot machaon.sif Singularity\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esingularity run machaon.sif\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003econda activate machaon\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecd /opt/src\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003epython run.py -h\u003c/code\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-manual-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#manual-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eManual Installation\u003c/h3\u003e\n\u003cbr\u003e \nThis section is a walkthrough for manual installation (please also check Dockerfile, it contains all \nneeded commands but it is recommended to execute them separately). \n\u003ch4\u003e\u003ca id=\"user-content-modified-tm-align-compilation\" class=\"anchor\" aria-hidden=\"true\" href=\"#modified-tm-align-compilation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModified TM-Align compilation\u003c/h4\u003e\n\u003cp\u003eThis well-established method is used for 3D similarity computation by the evaluation module.\u003cbr\u003e\nMachaon can run without the presence of this executable but you will miss the 3D similarity\u003cbr\u003e\nevaluation of the final candidates in the Machaon\u0027s results.\u003c/p\u003e\n\u003cp\u003eAccording to the original documentation, TM-Align is compiled as:\u003cbr\u003e\n\u003ccode\u003eg++ -static -O3 -ffast-math -lm -o TMalign TMalign.cpp\u003c/code\u003e\u003cbr\u003e\n(You might need to install g++ first: \u003ccode\u003esudo apt-get install build-essential\u003c/code\u003e )\u003cbr\u003e\nMacOS users should omit \u0027-static\u0027 option.\nFor more, you can check: \u003ca href=\"https://zhanggroup.org/TM-align\" rel=\"nofollow\"\u003ehttps://zhanggroup.org/TM-align\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-dssp\" class=\"anchor\" aria-hidden=\"true\" href=\"#dssp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDSSP\u003c/h4\u003e\n\u003cp\u003eThis well-known method is used for protein secondary structure assignment, employed in constrained\u003cbr\u003e\nsearch mode and the Gene Ontology meta-analysis process of Machaon. Alternatively, you could use\u003cbr\u003e\nprotein or hydrophobicity-focused sequences that do not require this program otherwise Machaon\u003cbr\u003e\nwill use STRIDE instead (see next section).\u003c/p\u003e\n\u003cp\u003eBelow are the steps for the compilation of DSSP 4.0 in \u003cb\u003eUbuntu 20.4\u003c/b\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCMake:\u003cbr\u003e\n\u003ccode\u003esudo apt-get install cmake\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBoost:\u003cbr\u003e\n\u003ccode\u003esudo apt-get install libboost-all-dev\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor Ubuntu versions lower than 20.04, you need to install Boost from source if your latest version is lower than 1.70:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRemove previous Boost version:\u003cbr\u003e\n\u003ccode\u003eapt remove \u0027libboost.*-dev\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload and extract the latest version from: \u003ca href=\"https://www.boost.org/\" rel=\"nofollow\"\u003ehttps://www.boost.org/\u003c/a\u003e (greater than 1.70)\u003c/li\u003e\n\u003cli\u003eInstall:\u003cbr\u003e\n\u003ccode\u003echmod +x bootstrap.sh\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003e./boostrap.sh\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esudo ./b2 link=static install\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBZIP2:\u003cbr\u003e\n\u003ccode\u003esudo apt-get install libbz2-dev\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecifpp:\nMake sure you have cmake (\u003ccode\u003esudo apt install cmake \u003c/code\u003e) and follow the instructions in \u003ca href=\"https://github.com/PDB-REDO/libcifpp\"\u003ehttps://github.com/PDB-REDO/libcifpp\u003c/a\u003e\u003cbr\u003e\nYou might need also to install this before: \u003ca href=\"https://github.com/mhekkel/mrc\"\u003ehttps://github.com/mhekkel/mrc\u003c/a\u003e (\u003ca href=\"https://github.com/PDB-REDO/dssp/issues/4\"\u003ehttps://github.com/PDB-REDO/dssp/issues/4\u003c/a\u003e)\u003cbr\u003e\nFor Ubuntu 18.04 you also need to install these first of all:\u003cbr\u003e\n\u003ccode\u003esudo add-apt-repository ppa:ubuntu-toolchain-r/test\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esudo apt update\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esudo apt install gcc-9 g++-9\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eexport CC=/usr/bin/gcc-9\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eexport CXX=/usr/bin/g++-9\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDSSP: Please follow the instructions in \u003ca href=\"https://github.com/PDB-REDO/dssp\"\u003ehttps://github.com/PDB-REDO/dssp\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cb\u003eNote:\u003c/b\u003eThere are also other options to obtain DSSP files without setting up the program: \u003ca href=\"https://swift.cmbi.umcn.nl/gv/dssp/\" rel=\"nofollow\"\u003ehttps://swift.cmbi.umcn.nl/gv/dssp/\u003c/a\u003e\u003cbr\u003e\nIn that case, you should add them in a folder named \u0027dssp_cache\u0027 located in your specified root data \u0026amp; cache folder\u003cbr\u003e\n(\u0027rootDisk\u0027 parameter, more in \u0027Execution\u0027 section) .\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-stride\" class=\"anchor\" aria-hidden=\"true\" href=\"#stride\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSTRIDE\u003c/h4\u003e\n\u003cp\u003eSTRIDE is an established method for determining the protein secondary structure from PDB files.\nIt is used as a fallback solution for custom PDB files that do not fully follow the standard PDB\nformat and lack annotations. Please follow the instructions in \u003ca href=\"http://webclu.bio.wzw.tum.de/stride/\" rel=\"nofollow\"\u003ehttp://webclu.bio.wzw.tum.de/stride/\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-using-the-executables\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-the-executables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing the executables\u003c/h4\u003e\n\u003cp\u003eAfter the compilations, you have to copy the mkdssp, stride, TM-Align executables\u003cbr\u003e\ninto the directory of Machaon and give them the required execute permissions:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd machaon/src\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecp /home/\u0026lt;user\u0026gt;/.local/bin/mkdssp .\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecp /home/\u0026lt;user\u0026gt;/.local/bin/stride .\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003echmod 770 mkdssp \u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003echmod 770 stride \u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003echmod 770 TMalign \u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-required-system-libraries\" class=\"anchor\" aria-hidden=\"true\" href=\"#required-system-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired system libraries:\u003c/h4\u003e\n\u003cp\u003eYou need the poppler library in order to export the figures in the EPS format\nwith Python plotly library:\u003cbr\u003e\n\u003ccode\u003esudo apt-get install libpoppler-cpp-dev\u003c/code\u003e\nThis a graphics related library for Open3D:\n\u003ccode\u003esudo apt-get install libgl1-mesa-dev\u003c/code\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-python-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#python-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython environment:\u003c/h4\u003e\n\u003cp\u003eAn environment setup of Anaconda Python distribution is needed : \u003ca href=\"https://www.anaconda.com\" rel=\"nofollow\"\u003ehttps://www.anaconda.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis distribution allows easy setup of all the requisites for Machaon.\u003c/p\u003e\n\u003cp\u003eOnce you have an operational Anaconda-enabled terminal, move into the setup folder and execute\u003cbr\u003e\nthe following command to install all the required packages:\u003cbr\u003e\n\u003ccode\u003econda env create -f environment.yml\u003c/code\u003e\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-testing-your-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting your installation:\u003c/h3\u003e\n\u003cp\u003eRun the test script in the /test folder:\n\u003ccode\u003epython integrity_test.py\u003c/code\u003e\u003cbr\u003e\nIf there are no differences reported at the end, than your installation should be successful.\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-execution\" class=\"anchor\" aria-hidden=\"true\" href=\"#execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h2\u003e\n\u003cbr\u003e\n\u003cp\u003eAt first, you need to activate the previously installed environment in an Anaconda-enabled terminal:\u003cbr\u003e\n\u003ccode\u003econda activate machaon\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start:\u003c/h3\u003e\n\u003cp\u003eExecute the following script which is located in the src folder: \u003ccode\u003e run.py -h\u003c/code\u003e\u003cbr\u003e\nThis will display all the available options and their descriptions.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-batch-jobs-recommended\" class=\"anchor\" aria-hidden=\"true\" href=\"#batch-jobs-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch jobs (recommended):\u003c/h3\u003e\n\u003cp\u003eEdit \u003cb\u003econfig.yaml\u003c/b\u003e file in the src folder and run \u003cb\u003e batch_run.py\u003c/b\u003e. Below is an example entry with the default\u003cbr\u003e\nvalues. You could copy it and modify it according to your needs. Configurations with \"ignore : True\" field\u003cbr\u003e\nare ignored. You could also consult with the example configurations used for the Spike protein of SARS-CoV-2.\u003c/p\u003e\n\u003cpre\u003e - rootDisk: \"\" \n referencePDBID: \"\"\n overridePDBID: \"\"\n referenceChainID: \"\"\n referenceGeneID: \"\"\n referenceSequenceLength: 0\n comparisonMode: \"\"\n pdbDatasetPath: \"\"\n outputPath: \"\"\n excludedOrganisms: []\n excludedGeneNames: []\n excludedPDBIDs: []\n isReferenceViral: False\n GOProperty: \"\"\n GOTargetProperties: []\n GOSearch: \"\"\n GOAlignmentLevel: \"secondary\"\n noThirdPartyData: False\n pdbValidation: False\n GOAnalysisOnly: False \n\u003c/pre\u003e\n\u003cp\u003eAll the options are presented below:\u003c/p\u003e\n\u003cpre\u003e\u0027rootDisk\u0027: This will also be the caching location for the extracted features.\n\u0027referencePDBID\u0027: Choose the reference PDB IDs (1 search per reference)\n\u0027overridePDBID\u0027: Override the reference PDBID for Uniprot ID retrieval (for renamed reference PDB files, e.g. 6VXX_processed.pdb)\n\u0027referenceChainID\u0027: Choose the chain of the reference PDB\n\u0027referenceGeneID\u0027: Provide the gene id (Entrez) of the reference PDB\n\u0027referenceSequenceLength\u0027: Provide the protein sequence length of the reference protein\n\u0027comparisonMode\u0027: Choose \u0027whole\u0027, \u0027domain\u0027 or \u0027segment\u0027\n\u0027alignmentLevel\u0027: Choose \u0027primary\u0027, \u0027secondary\u0027, \u0027mixed\u0027, \u0027hydrophobicity\u0027. Default is \u0027mixed\u0027. (Only from segment scans)\n\u0027pdbDatasetPath\u0027: Relative path for PDB data folder\n\u0027outputPath\u0027: The location of the outputs (can be relative or full path)\n\u0027excludedOrganisms\u0027: Filtering out structures originating from the same organism as the reference one\n\u0027excludedGeneNames\u0027: Filtering out structures originating from the same gene as the reference one\n\u0027excludedPDBIDs\u0027: Exclude PDB IDs\n\u0027isReferenceViral\u0027: Meta-analysis skips the search in viral genome data for the reference, if it is not a viral protein\n\u0027GOProperty\u0027: Choose a property type for analysis: \u0027biologicalProcess\u0027, \u0027molecularFunction\u0027, \u0027cellularComponent\u0027\n\u0027GOTargetProperties\u0027: Choose properties for analysis\n\u0027GOSearch\u0027: Choose a term to be searched in all available GO Terms belonging to the results e.g. \u0027ubiquit\u0027 (could be a stem of a word)\n\u0027GOAlignmentLevel\u0027: Choose target alignment level : [\u0027primary\u0027, \u0027secondary\u0027, \u0027mixed\u0027, \u0027hydrophobicity\u0027. Default is \u0027secondary\u0027]\n\u0027noThirdPartyData\u0027: Do not use external local or online resources. PDB data only.\n\u0027GOAnalysisOnly\u0027: Perform only GO Meta-analysis (for completed searches).\n\u0027pdbValidation\u0027: Validation for PDB files. Every file assessed as invalid is skipped from the search (very strict and slow). \n\u0027ignore\u0027: If set to True, the configuration will be ignored. Useful for storing previous job details.\n\u0027*whatever*\u0027: You can include fields of your own, like tags or notes (e.g. \u0027date\u0027 : 14-4-2003). These are not considered by the program. \n\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-constrained-mode-segments\" class=\"anchor\" aria-hidden=\"true\" href=\"#constrained-mode-segments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConstrained mode (segments):\u003c/h4\u003e\n\u003cp\u003eConstrained search on segments requires also preset about the reference segment. This is set in\u003cbr\u003e\n\u003cb\u003esegments.yaml\u003c/b\u003e file in the src folder. Below is an empty template entry. You could also consult with\u003cbr\u003e\nthe example segment definitions used for the Spike protein of SARS-CoV-2.\u003c/p\u003e\n\u003cpre\u003e- referencePDBChain: \"\"\n residues: []\n residueRanges: \"\"\n known: False\n\u003c/pre\u003e\n\u003cp\u003eAll the options are presented below:\u003c/p\u003e\n\u003cpre\u003e\u0027referencePDBChain\u0027: The reference PDB ID and chain ID separated by a dot, \u0026lt;PDB ID\u0026gt;.\u0026lt;CHAIN ID\u0026gt; e.g. \"\"6VXX.A\"\n\u0027residues\u0027: List of residue positions (one-based indexing), e.g. [1, 2, 3, 4, 5]\n\u0027residueRanges\u0027: Range definitions separated by comma, e.g. \u00271-50,70-78\u0027\n\u0027known\u0027: Select True if the segment belongs to a known site like a binding site (considered by GO Meta-analysis module).\n\u0027ignore\u0027: If set to True, the configuration will be ignored. Useful for storing past segment presets.\n\u0027*whatever*\u0027: You can include fields of your own, like tags or notes (e.g. \u0027doi\u0027 : \u0027123.3456/1234.123\u0027). These are not considered by the program. \n\nNote: \u0027residues\u0027 and \u0027residueRanges\u0027 definitions are combined, e.g. [12, 15, 59] \nand \u002713-40, 47-52\u0027 would result to the selection of residue positions from 12 to 40, \nfrom 47 to 52 and 59 (duplicate definitions are removed).\n\u003c/pre\u003e\u003cbr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data-directory-structures\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-directory-structures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData directory structures\u003c/h2\u003e\n\u003cbr\u003e \n\u003ch4\u003e\u003ca id=\"user-content-output-folder-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-folder-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput folder structure\u003c/h4\u003e\n\u003cpre\u003e (a user-specified output folder)\n |\n |__ (a user-specified top directory name)\n |\n |__metrics/ (directory for the computed metrics for all structures in the dataset)\n |\n |__candidates/ (directory for the selected final set of candidate entries, \n | the final report is saved here [HTML file])\n |\n |__plots/ (directory for plots regarding the final set)\n |\n |__go/ (directory for GO meta-analysis, mini reports and related visualizations)\n\u003c/pre\u003e\n\u003cp\u003e\u003cb\u003eNote for constrained mode search on segments\u003c/b\u003e:The corresponding output files contain a suffix\u003cbr\u003e\n\"site\u0026lt;segment index\u0026gt;\" that signify the results for a particular segment. The index comes from the\u003cbr\u003e\nconfiguration order. In the \"metrics\" folder, there is a \"*_site\u0026lt;segment index\u0026gt;-parts.csv\" file that contains\u003cbr\u003e\nthe contiguous parts of the segment as determined by the method.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-root-folder-source-data--cache-full-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#root-folder-source-data--cache-full-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRoot folder (source data \u0026amp; cache), full structure\u003c/h4\u003e\n\u003cpre\u003e (a user-specified \u003cb\u003eroot\u003c/b\u003e folder)\n |\n |--DATA_\u0026lt;PDB directory name\u0026gt;_\u0026lt;whole or domain\u0026gt;/ \n | (directory for storing the extracted features of a PDB directory)\n |\n |--domains/ (directory for caching domain information by UniProt online requests)\n |\n |--dssp_cache/ (directory for caching DSSP results)\n |\n |--enrichment/ (directory for caching data enrichment of PDB chain entries)\n |\n |__entrez/ (cache directory for NCBI Entrez online requests)\n |\n |--pdbinfo/ (directory for caching extracted PDB meta-data)\n |\n |--prot_sec/ (directory for caching PDB sequence/secondary structure data)\n |\n |__refseq/ (RefSeq resources directory)\n |\n |--rcsbenrich/ (cache directory for RCSB enrichment data) \n |\n |--(user created PDB folders, \u003cb\u003eeach folder corresponds to a target dataset for a search\u003c/b\u003e)\n |\n |__idmapping_selected.tab.gz (UniProt idmapping resources)\n\u003c/pre\u003e\n\u003cp\u003eThere is also a cache file that is generated besides the scripts in src folder (go_cache.csv) that holds\u003cbr\u003e\nGene Ontology data.\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-format\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-format\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput format\u003c/h2\u003e\n\u003cbr\u003e\nThe outputs are human interpretable CSV files with headers:\n\u003cul\u003e\n\u003cli\u003emetrics directory has comma separated CSV files\u003c/li\u003e\n\u003cli\u003ecandidates directory has tab separated CSV files\u003c/li\u003e\n\u003cli\u003eoutputs of constrained searches include columns with serialized list contents which can be parsed with eval()\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-special-cases\" class=\"anchor\" aria-hidden=\"true\" href=\"#special-cases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecial Cases\u003c/h2\u003e\n\u003cbr\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIf you want to compare a polymer as a whole structure you could use pdb-tools :\n\u003ca href=\"https://github.com/haddocking/pdb-tools\"\u003ehttps://github.com/haddocking/pdb-tools\u003c/a\u003e\u003cbr\u003e\nand combine multiple chains to one. You should remove any pre-computed features of the old PDB\u003cbr\u003e\n(*_angles.pkl, *_distances.pkl, *_triangles.pkl) and the original PDB from the dataset (you could\u003cbr\u003e\nkeep these files in a separate location as back up). You need to decide which original \u0026lt;PDB ID\u0026gt; and\u003cbr\u003e\n\u0026lt;PDB chain ID\u0026gt; you will use as a reference for the third-party resources.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn case you encounter warnings about empty chain identifiers or missing chains, use pdb_chain\u003cbr\u003e\ncommand from pdb-tools: \u003ccode\u003epdb_chain -A no_chains.pdb \u0026gt; corrected.pdb\u003c/code\u003e to put a dummy identifier\nto a problematic PDB file.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSecondary structure data cannot be extracted from PDBs that lack experimental information so you may have to\nchange the target alignment level to primary or hydrophobicity (recommended) for constrained mode search on\nsegments (default is \u0027mixed\u0027) or GO metanalysis (default is 2D).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-trivia\" class=\"anchor\" aria-hidden=\"true\" href=\"#trivia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrivia\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://en.wikipedia.org/wiki/Machaon_(mythology)\" rel=\"nofollow\"\u003ehttps://en.wikipedia.org/wiki/Machaon_(mythology)\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1623903591.0 + "updated_at": 1655488848.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.isafe", - "Singularity.breakseq", - "Singularity.pophuman", - "Singularity.abcmk" + "_profiler/Singularity" ], - "full_name": "jmurga/bgd-pic", + "full_name": "mozhgan-kch/HPC_Bootcamp", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fast-downward\" class=\"anchor\" href=\"#fast-downward\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast Downward\u003c/h1\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" href=\"#tested-software-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-hpc_bootcamp\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc_bootcamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC_Bootcamp\u003c/h1\u003e\n\u003cp\u003eThis repository contains training content for the HPC_Bootcamp materials. This repository includes the following file structure in the initial two levels:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e _advanced\n\u2502 \u251c\u2500\u2500 cuda_advanced\n\u2502 \u251c\u2500\u2500 multigpu\n\u2502 \u2514\u2500\u2500 openacc_advanced\n\u251c\u2500\u2500 _basic\n\u2502 \u251c\u2500\u2500 cuda_basic\n\u2502 \u251c\u2500\u2500 iso\n\u2502 \u251c\u2500\u2500 openacc_basic\n\u2502 \u2514\u2500\u2500 openmp\n\u251c\u2500\u2500 _profiler\n\u2502 \u251c\u2500\u2500 jupyter_notebook\n\u2502 \u251c\u2500\u2500 Presentations\n\u2502 \u2514\u2500\u2500 source_code\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 bootstrap.sh\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 _scripts\n\u2514\u2500\u2500 start_notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThe _\u003cem\u003eadvanced\u003c/em\u003e directory contains all of the advanced training materials for CUDA, OpenACC, and multiGPU.\u003c/li\u003e\n\u003cli\u003eThe _\u003cem\u003ebasic\u003c/em\u003e directory contains all of the introductory training materials for CUDA, Standard Languages, OpenMP Offloading, and OpenACC.\u003c/li\u003e\n\u003cli\u003eThe _\u003cem\u003eprofiler\u003c/em\u003e directory contains content on NVIDIA Nsight Systems and Compute.\u003c/li\u003e\n\u003cli\u003e_scripts directory contains container defintion files for each bootcamp type.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePlease note there is a container definition file for each content in \u003ccode\u003e_advanced\u003c/code\u003e, \u003ccode\u003e_basic\u003c/code\u003e, and \u003ccode\u003e_profiler\u003c/code\u003e directory and those can be used on their own without mixing with other contents. Please check the \u003ccode\u003eREADME.md\u003c/code\u003e file inside of each for more information.\u003c/p\u003e\n\u003cp\u003eYou can either clone the whole repository and isolate contents or you can only clone without any of the directories. Please follow below steps for each method.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-cloning-the-repo-with-all-the-direcotires-and-isolate-later-using-git-sparse-checkout\" class=\"anchor\" aria-hidden=\"true\" href=\"#cloning-the-repo-with-all-the-direcotires-and-isolate-later-using-git-sparse-checkout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning the repo with all the direcotires and isolate later using \u003ccode\u003egit sparse-checkout\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eYou can use the \u003ccode\u003eboostrap.sh\u003c/code\u003e script at the root of the repository to isolate the content. For example, by running \u003ccode\u003ebash ./bootstrap.sh openacc\u003c/code\u003e, your working directory will include all the content related to the OpenACC Bootcamp from basic to advanced. Now, you can run the \u003ccode\u003ebootstrap.sh\u003c/code\u003e command using one of the following pre-defined bootcamp contents: \u003ccode\u003enways-basic\u003c/code\u003e, \u003ccode\u003eopenacc\u003c/code\u003e, \u003ccode\u003eprofiling\u003c/code\u003e,\u003ccode\u003ecuda\u003c/code\u003e, \u003ccode\u003emultigpu\u003c/code\u003e. See example below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStep 1: clone the whole repository via \u003ccode\u003egit@github.com:mozhgan-kch/HPC_Bootcamp.git\u003c/code\u003e or \u003ccode\u003ehttps://github.com/mozhgan-kch/HPC_Bootcamp.git\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eStep 2: Navigate to the bootcamp folder via \u003ccode\u003ecd HPC_Bootcamp\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eStep 3: Run \u003ccode\u003ebash ./bootstrap.sh profiling\u003c/code\u003e , this example will isolate files required for the profiling material.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-cloning-the-repo-without-directories\" class=\"anchor\" aria-hidden=\"true\" href=\"#cloning-the-repo-without-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning the repo without directories\u003c/h3\u003e\n\u003cp\u003eYou can clone the repository and avoid filling in the working directory with the huge list of files by using the \u003ccode\u003e--no-checkout\u003c/code\u003e option as you clone. Try the below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --no-checkout git@github.com:mozhgan-kch/HPC_Bootcamp.git\ncd HPC_Bootcamp\ngit sparse-checkout init --cone\ngit checkout main\nbash ./bootstrap.sh profiling\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce done, navigate to \u003ccode\u003e_scripts\u003c/code\u003e via \u003ccode\u003ecd _scripts\u003c/code\u003e and build the container by following below steps.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-container-using-the-def-files-inside-the-_script-folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-container-using-the-def-files-inside-the-_script-folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container using the def files inside the \u003ccode\u003e_script\u003c/code\u003e folder\u003c/h3\u003e\n\u003cp\u003eTo build the singularity container, run:\n\u003ccode\u003esudo singularity build miniapp.simg {Name of the content}_Singularity\u003c/code\u003e , alternatively you can use \u003ccode\u003esingularity build --fakeroot miniapp.simg {Name of the content}_Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand copy the files to your local machine to make sure changes are stored locally:\n\u003ccode\u003esingularity run miniapp.simg cp -rT /labs ~/labs\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, run the container:\n\u003ccode\u003esingularity run --nv miniapp.simg jupyter-lab --notebook-dir=~/labs\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOnce inside the container, open the jupyter lab in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e, and start the lab by clicking on the \u003ccode\u003e_{Name of the content}.ipynb\u003c/code\u003e notebook. \u003ccode\u003e{Name of the content}\u003c/code\u003e can be \u003ccode\u003eprofiling\u003c/code\u003e. More alternatives will be added.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-container-using-the-def-files-inside-the-content-folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-container-using-the-def-files-inside-the-content-folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container using the def files inside the content folder\u003c/h3\u003e\n\u003cp\u003eAlternatively, you can build containers for each content by using the recipe inside of each content.\nExample : Build container for the \u003cem\u003e_profiler\u003c/em\u003e content. Navigate to \u003ccode\u003e_profiler\u003c/code\u003e directory and read the \u003ccode\u003eREADME.md\u003c/code\u003e file for more information.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1624197047.0 + "updated_at": 1662909782.0 }, { "data_format": 2, - "description": "Talking to Hinkskalle", + "description": null, "filenames": [ - "Singularity" + "SMiRL_Code/Singularity" ], - "full_name": "csf-ngs/hinkskalle-api", + "full_name": "KBoumghar/IFT4055-RL", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hinkskalle-api\" class=\"anchor\" href=\"#hinkskalle-api\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHinkskalle API\u003c/h1\u003e\n\u003cp\u003eTalking to \u003ca href=\"https://github.com/csf-ngs/hinkskalle\"\u003eHinkskalle\u003c/a\u003e made easy\u003c/p\u003e\n\u003cp\u003eUse me to\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elist available downloads\u003c/li\u003e\n\u003cli\u003edownload data\u003c/li\u003e\n\u003cli\u003eupload data\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003ehinkskalle-api provides\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea small library with a thin wrapper over the JSON API\u003c/li\u003e\n\u003cli\u003ea CLI (\u003ccode\u003ehinkli\u003c/code\u003e: short for hink-cli, get it?)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eYou will need python3 and pip. Then you can run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install git+https://github.com/csf-ngs/hinkskalle-api\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-command-line-interface\" class=\"anchor\" href=\"#command-line-interface\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand Line Interface\u003c/h3\u003e\n\u003cp\u003eGet a list of available commands and options:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehinkli --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYour first step should be logging in:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e non-VBCF.NGS users get your own instance!\u003c/span\u003e\nhinkli --base https://singularity.ngs.vbcf.ac.at/ login\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e answer prompt for username and password\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe registry and token should now be stored in \u003ccode\u003e~/.hink_api.yml\u003c/code\u003e and available for further use.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-discovering--downloading-data\" class=\"anchor\" href=\"#discovering--downloading-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscovering \u0026amp; Downloading Data\u003c/h4\u003e\n\u003cp\u003eYour most likely use case will be downloading data provided via Hinkskalle.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e shows available collections of containers\u003c/span\u003e\nhinkli list-collections\nhinkli list-containers [collection]\nhinkli list-downloads [collection]/[container]\nhinkli pull [collection]/[container]:[tag]\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e username is optional, but can be provided, too:\u003c/span\u003e\nhinkli list-collections test.hase\nhinkli list-containers test.hase/[collection]\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e etc\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBasic structure:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA Collection holds a bunch of containers (topic, type, ...)\u003c/li\u003e\n\u003cli\u003eContainers hold tagged data\u003c/li\u003e\n\u003cli\u003eEach tag points to some data (some tags point to the same data)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf Hinkskalle shows you these downloads in your container \u003ccode\u003etest.hase/example/FAQ4711\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e- \u003cspan class=\"pl-ent\"\u003efilename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ebunch_of_reads.fastq.gz\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esize\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e41.5 MB\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003etags\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ebasecalled,20210621\u003c/span\u003e\n- \u003cspan class=\"pl-ent\"\u003efilename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003erawdata.tar.gz\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esize\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e41.5 TB\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003etags\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eraw\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can use these commands to download:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e either one fetches bunch_of_reads.fastq\u003c/span\u003e\nhinkli pull example/FAQ4711:basecalled\nhinkli pull example/FAQ4711:20210621\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e fetches rawdata.tar.gz\u003c/span\u003e\nhinkli pull example/FAQ4711:raw\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHinkli will even check the sha256 checksum for you!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-api\" class=\"anchor\" href=\"#api\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAPI\u003c/h3\u003e\n\u003cp\u003eNot documented - use at your own risk!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ehinkskalle_api\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eHinkApi\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eapi\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eHinkApi\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ecollections\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eapi\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003elist_collections\u003c/span\u003e()\n\u003cspan class=\"pl-c\"\u003e# etc\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003eBy default, hinkli reads its config from \u003ccode\u003e~/.hink_api.yml\u003c/code\u003e. This file should look like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ehink_api_base\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ehttps://singularity.ngs.vbcf.ac.at\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ehink_api_key\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eyour_super_secret_token\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can use these env variables to override:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eHINK_API_BASE\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eHINK_API_KEY\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHINK_API_CFG\u003c/code\u003e - to look for the config file in a different location\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-development\" class=\"anchor\" href=\"#development\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h1\u003e\n\u003cp\u003eYou can regenerate the models from the Hinkskalle swagger/openapi definition:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install git+https://ngs.vbcf.ac.at/repo/software/swagspotta.git\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e from pkg.ngs.vbcf.ac.at production:\u003c/span\u003e\nshare/create_models.sh\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e from your local hinkskalle dev server:\u003c/span\u003e\nshare/create_models.sh http://localhost:7660/swagger\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e#IFT4055 - Journal\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-02-05-2022\" class=\"anchor\" aria-hidden=\"true\" href=\"#02-05-2022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e02-05-2022\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eLearned about MDP and Q-function (see MDP.pdf)\u003c/li\u003e\n\u003cli\u003eSMiRL paper up to page 6 (see Smirl.pdf).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eQuestions I need to answer :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAuxiliary objective, what is this exactly?\u003c/li\u003e\n\u003cli\u003eMinimizing the R.H.S to get maximum reward\u003c/li\u003e\n\u003cli\u003eEstimate of state marginal (cannot seem to find reference for that)\u003c/li\u003e\n\u003cli\u003eHow / how fast can we find the distribution that fits our p_{\\theta_t}(s)\u003c/li\u003e\n\u003cli\u003eMaximum likelihood estimation : OK. Maximum likelihood state density estimation process???\u003c/li\u003e\n\u003cli\u003eWe can\u0027t assume independence of states like what I\u0027ve seen. What is used for Maximum likelihood?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhat I (think) I need to do next :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMore reading/watching on maximum likelihood in machine learning context\u003c/li\u003e\n\u003cli\u003eRead paper about DQN algorithm : \u003ca href=\"https://arxiv.org/pdf/1312.5602.pdf\" rel=\"nofollow\"\u003ehttps://arxiv.org/pdf/1312.5602.pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRead paper about TRPO algorithm\u003c/li\u003e\n\u003cli\u003ePart with Density estimation with learned representations?\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1626991307.0 + "updated_at": 1652813085.0 }, { "data_format": 2, @@ -14345,257 +13718,248 @@ var data = "filenames": [ "Singularity" ], - "full_name": "shrutir11/lolcow", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-lolcow\" class=\"anchor\" href=\"#lolcow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elolcow\u003c/h1\u003e\n", + "full_name": "baxpr/cersuit", + "latest_release": "v2.1.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cersuit\" class=\"anchor\" aria-hidden=\"true\" href=\"#cersuit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecersuit\u003c/h1\u003e\n\u003cp\u003eCerebellar segmentation with the \u003ca href=\"http://diedrichsenlab.org/imaging/suit.htm\" rel=\"nofollow\"\u003eSUIT atlas and toolbox\u003c/a\u003e. In the container, the pipeline is installed in the \u003ccode\u003e/opt/cersuit\u003c/code\u003e directory. Matlab code is in the \u003ccode\u003esrc\u003c/code\u003e directory, and the entrypoint is \u003ccode\u003esrc/cersuit.m\u003c/code\u003e. Compiled Matlab code for use in the singularity container without a Matlab license is in \u003ccode\u003ebin\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSee the \u003ccode\u003eexternal\u003c/code\u003e directory for links, references, and license information for the underlying SPM12 and SUIT Matlab software. \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki\" rel=\"nofollow\"\u003eFSL version 6.0.2\u003c/a\u003e is also used for image file manipulation and creating the QA PDF.\u003c/p\u003e\n\u003cp\u003eThe container has a full installation of both SPM12 (compiled) and FSL.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references-for-suit\" class=\"anchor\" aria-hidden=\"true\" href=\"#references-for-suit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences for SUIT\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1016/j.neuroimage.2006.05.056\" rel=\"nofollow\"\u003eDiedrichsen, J. (2006). A spatially unbiased atlas template of the human cerebellum. Neuroimage, 33, 1, p. 127-138.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1016/j.neuroimage.2009.01.045\" rel=\"nofollow\"\u003eDiedrichsen, J., Balsters, J. H., Flavell, J., Cussans, E., \u0026amp; Ramnani, N. (2009). A probabilistic atlas of the human cerebellum. Neuroimage 46(1):39-46.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1016/j.neuroimage.2010.10.035\" rel=\"nofollow\"\u003eDiedrichsen, J., Maderwald, S., Kuper, M., Thurling, M., Rabe, K., Gizewski, E. R., et al. (2011). Imaging the deep cerebellar nuclei: A probabilistic atlas and normalization procedure. Neuroimage 54(3):1786-94\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1371/journal.pone.0133402\" rel=\"nofollow\"\u003eDiedrichsen, J. \u0026amp; Zotow, E. (2015). Surface-based display of volume-averaged cerebellar data. PLoS One, 7, e0133402.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAdjustment of the source T1 file to axial data ordering using fslreorient2std, to meet a requirement of the SUIT toolbox.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTranslation-only alignment of the supplied gray matter image to SPM12\u0027s gray matter probabilistic atlas (TPM.nii). This is accomplished by aligning the centers of mass. Rotations are not estimated, to avoid an issue with SUIT\u0027s bounding box computation. The supplied gray matter image must be in register with the supplied T1. The estimated registration is saved to file and also applied to the T1.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSUIT estimation of the affine transformation and warp of the cerebellar area of the T1 to the SUIT atlas.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eResampling of the T1 and related images to the SUIT atlas space. Gray matter and white matter images are resampled both with and without modulation by the Jacobian.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eResampling of the SUIT-supplied atlases to the original T1 native space.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eComputation of regional volumes for the Lobules_SUIT atlas in the native T1 space.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-of-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-of-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage of the singularity container\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003esingularity_examples.sh\u003c/code\u003e for examples of using the container for SUIT warp estimation, and transformation from native to SUIT space and back using an existing estimated warp. The transformations can also be done directly from matlab with the \u003ccode\u003etransform_???.m\u003c/code\u003e functions in \u003ccode\u003esrc\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters-and-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters-and-inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters and inputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;temporary-home-dir\u0026gt; Matlab will use this for temp files\n\u0026lt;tmp-dir\u0026gt; Other location for temp files \n\u0026lt;input-dir\u0026gt; Directory containing the input T1 image file\n\u0026lt;output-dir\u0026gt; Outputs will be stored here\n\u0026lt;t1-niigz-filename\u0026gt; Filename of the input T1 - expecting \u0026lt;something\u0026gt;.nii.gz\n\u0026lt;mask-threshold\u0026gt; SPM mask threshold for separating brain from background\n\u0026lt;project-name\u0026gt; Project/subject/session/scan names from XNAT, if XNAT is\n\u0026lt;subject-name\u0026gt; used. These are only used to decorate the PDF report.\n\u0026lt;session-name\u0026gt; \n\u0026lt;scan-name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cp\u003ePDF report for quality assurance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePDF cersuit.pdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTransformation from native to atlas space. Apply in this order\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRIGID coreg_t1_to_mni.mat\nAFFINE Affine_c_t1_seg1.mat\nFLOWFIELD u_a_c_t1_seg1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCropped T1 in both spaces\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eT1_CROP_NATIVE c_t1.nii.gz\nT1_CROP_SUIT wc_t1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCerebellum mask, segmented gray matter and white matter volume fraction images in native and atlas space\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMASK_NATIVE c_t1_pcereb.nii.gz\nGRAY_NATIVE c_t1_seg1.nii.gz\nWHITE_NATIVE c_t1_seg2.nii.gz\nMASK_SUIT wc_t1_pcereb.nii.gz\nGRAY_SUIT wc_t1_seg1.nii.gz\nWHITE_SUIT wc_t1_seg2.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eJacobian-modulated gray and white matter images in atlas space\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eGRAYMOD_SUIT wdc_t1_seg1.nii.gz\nWHITEMOD_SUIT wdc_t1_seg2.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSegmented regions in native and atlas space, with lookup table\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eATLASES_NATIVE SUIT-supplied atlases resampled to original T1 space\nATLASES_SUIT The SUIT-supplied atlases themselves\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eVolumetry of segmented regions, computed from native space images. The \"Total\" is the volume of the atlas region after transformation to native space. The \"Gray\" is the sum of voxel gray matter fraction within the atlas region, in native space; similar for \"White\".\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNATIVE_VOLS iw_Lobules-SUIT_u_a_c_t1_seg1-volumes.csv\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1624383824.0 + "updated_at": 1659018685.0 }, { "data_format": 2, - "description": null, + "description": "A simple utility to convert a bunch of input fastq files into their reverse complement", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "QsingularityAi/polar-pfc-master_active-crystel", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-polar-pfc-master_active-crystel\" class=\"anchor\" href=\"#polar-pfc-master_active-crystel\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epolar-pfc-master_active-crystel\u003c/h1\u003e\n", + "full_name": "sequana/revcomp", + "latest_release": "v0.9.0", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1624399268.0 + "updated_at": 1661892371.0 }, { "data_format": 2, - "description": "Massively Parallel, Portable, and Reproducible Tractography", + "description": null, "filenames": [ - "container/Singularity" + "fsl/singularity/Singularity.fsl" ], - "full_name": "LLNL/MaPPeRTrac", + "full_name": "nikhil153/brain-diff", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mappertrac\" class=\"anchor\" href=\"#mappertrac\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaPPeRTrac\u003c/h1\u003e\n\u003cp\u003eMassively Parallel, Portable, and Reproducible Tractography (MaPPeRTrac) is a brain tractography workflow for high performance computing. It incorporates novel technologies to simplify and accelerate neuroimaging research.\n\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-setup\" class=\"anchor\" href=\"#setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h3\u003e\n\u003cp\u003eRequirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.5+\u003c/li\u003e\n\u003cli\u003eSLURM job scheduling on a multi-node system\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cb\u003e1. Install NumPy and Parsl\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip3 install parsl numpy scipy\u003c/code\u003e\u003cbr\u003e\n(\u003ccode\u003epip3 install parsl numpy scipy --user\u003c/code\u003e for non-root systems)\u003c/p\u003e\n\u003cp\u003e\u003cb\u003e2. Clone repository\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone git@github.com:LLNL/MaPPeRTrac.git\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003ecd MaPPeRTrac/\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cb\u003e3. Load a Singularity container\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eRequirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity 3.0+ (\u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/guides/3.0/user-guide/\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBuilding the container:\u003cbr\u003e\ni. Obtain root access (you can copy and run the image in a non-root system afterwards).\u003cbr\u003e\nii. Place a Freesurfer \u003ccode\u003elicense.txt\u003c/code\u003e in the repo directory (\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/License\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/License\u003c/a\u003e).\u003cbr\u003e\niii. \u003ccode\u003e./container/build.sh\u003c/code\u003e\n\u003cbr\u003e\nNotes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMake sure to set \u003ccode\u003econtainer_path\u003c/code\u003e to the Singularity container\u0027s location.\u003c/li\u003e\n\u003cli\u003eIf you are having trouble building the container, try branch \u003ccode\u003eno_viz\u003c/code\u003e. This will disable render functionality.\u003c/li\u003e\n\u003cli\u003eAlternatively, download the image \u003ca href=\"https://drive.google.com/file/d/1lh0_5GO6-7qIznjvIcSMY-Ua8iBpZ4DJ/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\n\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cb\u003e4. Specify your DICOM or NIfTI data\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003ePlace your data in the same filesystem as the repository.\u003c/p\u003e\n\u003cp\u003eYou can download the example data \u003ca href=\"https://drive.google.com/file/d/1YC0QzWNohq173_zJaqZfnI5d6EPb9On2/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-launch\" class=\"anchor\" href=\"#launch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003e./s_run_all.py \u0026lt;config_json\u0026gt;\u003c/code\u003e\n\u003cbr\u003e\nSee \u003ccode\u003eexamples/dummy_config.json\u003c/code\u003e for example parameters.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-file-overview\" class=\"anchor\" href=\"#file-overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile Overview\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eTracktographyScripts/\n+- container/\n| +- build.sh\n| +- Singularity # Singularity build recipe\n|\n+- examples\n| +- dataset_description.json # Example of the BIDS dataset description\n| +- dummy_config.json # Example of the config JSON\n| +- dummy_dicom/\n| +- dummy_nifti/\n| +- dummy_subjects.json # Example of the subjects JSON\n|\n+- license.txt # Freesurfer license. NOTE: not included, required to build Singularity container\n+- LICENSE # MaPPeRTrac license.\n|\n+- lists/\n| +- connectome_idxs.txt # Brain region indices for .mat connectome files\n| +- list_edges_reduced.txt # Default edges to compute with Probtrackx and EDI (930 edges)\n| +- list_edges_all.txt # All possible edges (6643 edges)\n| +- render_targets.txt # NiFTI files to visualize with s4_render\n|\n+- README.md\n|\n+- s_run_all.py # Main script\n|\n+- subscripts/\n +- __init__.py\n +- maskseeds.py # Helper functions for s2b_freesurfer.py\n +- run_vtk.py # Helper script for s4_render.py\n +- s_debug.py # For debugging\n +- s1_dti_preproc.py\n +- s2a_bedpostx.py\n +- s2b_freesurfer.py\n +- s3_probtrackx.py\n +- s4_render.py\n +- utilities.py # General utility functions\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-overview\" class=\"anchor\" href=\"#output-overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput Overview\u003c/h3\u003e\n\u003cp\u003eThe following are the most important output files. This list is not comprehensive.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;OUTPUT DIRECTORY\u0026gt;/\n+- sourcedata/ # DICOM preprocessing data\n+- rawdata/ # BIDS-compliant NiFTI imaging data\n+- derivatives/\n +- sub-\u0026lt;SUBJECT NAME\u0026gt;\n +- [ses-\u0026lt;SESSION NAME\u0026gt;] # If session name specified, outputs will be in a session directory\n +- connectome_idxs.txt # Brain region indices for .mat connectome files\n +- connectome_#samples_oneway.txt # Oneway connectome in list form. Each edge has four columns:\n Column 1 is the source region\n Column 2 is the destination region\n Column 3 is number of fibers (NOF): the total count of successful streamlines between the two regions\n Column 4 is normalized NOF: the average density of successful streamlines the target region.\n +- connectome_#samples_twoway.txt # Twoway connectome in list form\n +- connectome_#samples_oneway_nof.mat # Oneway NOF connectome in matrix form\n +- connectome_#samples_twoway_nof.mat # Twoway NOF connectome in matrix form (should be symmetric)\n +- connectome_#samples_oneway_nof_normalized.mat # Oneway normalized NOF connectome in matrix form\n +- connectome_#samples_twoway_nof_normalized.mat # Twoway normalized NOF connectome in matrix form (should be symmetric)\n |\n +- EDI/\n | +- EDImaps/\n | +- FAtractsumsRaw.nii.gz # NiFTI image of total streamline density\n | +- FAtractsumsTwoway.nii.gz # NiFTI image of edge density (EDI). See Payabvash et al. (2019) for details.\n |\n +- log/ # Directory containing stdout and performance logs\n |\n +- render/ # Directory containing NiFTI image renders from step s4_render\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cbr\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-config-parameterscommand-line-arguments\" class=\"anchor\" href=\"#config-parameterscommand-line-arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfig Parameters/Command Line Arguments\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eRequired Parameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003esubjects_json\u003c/td\u003e\n\u003ctd\u003eJSON file with input directories for each subject\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput_dir\u003c/td\u003e\n\u003ctd\u003eThe super-directory that will contain output directories for each subject.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003escheduler_name\u003c/td\u003e\n\u003ctd\u003eScheduler to be used for running jobs. Value is \"slurm\" for LLNL, \"cobalt\" for ANL, and \"grid_engine\" for UCSF.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOptional Parameter\u003c/th\u003e\n\u003cth\u003eDefault\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003esteps\u003c/td\u003e\n\u003ctd\u003es1 s2a s2b s3 s4\u003c/td\u003e\n\u003ctd\u003eSteps to run\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egpu_steps\u003c/td\u003e\n\u003ctd\u003es2a\u003c/td\u003e\n\u003ctd\u003eSteps to enable CUDA-enabled binaries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003escheduler_bank\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eScheduler bank to charge for jobs. Required for slurm and cobalt.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003escheduler_partition\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eScheduler partition to assign jobs. Required for slurm and cobalt.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003escheduler_options\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eString to prepend to the submit script to the scheduler\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egpu_options\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eString to prepend to the submit blocks for GPU-enabled steps, such as \u0027module load cuda/8.0;\u0027\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eworker_init\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eString to run before starting a worker, such as \u2018module load Anaconda; source activate env;\u2019\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtainer_path\u003c/td\u003e\n\u003ctd\u003econtainer/image.simg\u003c/td\u003e\n\u003ctd\u003ePath to Singularity container image\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eunix_username\u003c/td\u003e\n\u003ctd\u003e[[current user]]\u003c/td\u003e\n\u003ctd\u003eUnix username for Parsl job requests\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eunix_group\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eUnix group to assign file permissions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eforce\u003c/td\u003e\n\u003ctd\u003eFalse\u003c/td\u003e\n\u003ctd\u003eForce re-compute if checkpoints already exist\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egssapi\u003c/td\u003e\n\u003ctd\u003eFalse\u003c/td\u003e\n\u003ctd\u003eUse Kerberos GSS-API authentication\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003elocal_host_only\u003c/td\u003e\n\u003ctd\u003eTrue\u003c/td\u003e\n\u003ctd\u003eRequest all jobs on local machine, ignoring other hostnames\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eparsl_path\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003ePath to Parsl binaries, if not installed in /usr/bin or /usr/sbin\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erender_list\u003c/td\u003e\n\u003ctd\u003elists/render_targets.txt\u003c/td\u003e\n\u003ctd\u003eText file list of NIfTI outputs for s4_render (relative to each subject output directory)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epbtx_sample_count\u003c/td\u003e\n\u003ctd\u003e1000\u003c/td\u003e\n\u003ctd\u003eNumber of streamlines per seed voxel in s3_probtrackx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epbtx_random_seed\u003c/td\u003e\n\u003ctd\u003e[[random number]]\u003c/td\u003e\n\u003ctd\u003eRandom seed in s3_probtrackx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epbtx_max_memory\u003c/td\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003eMaximum memory per node (in GB) for s3_probtrackx. Default value of 0 indicates unlimited memory bound\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econnectome_idx_list\u003c/td\u003e\n\u003ctd\u003elists/connectome_idxs.txt\u003c/td\u003e\n\u003ctd\u003eText file with pairs of volumes and connectome indices\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ehistogram_bin_count\u003c/td\u003e\n\u003ctd\u003e256\u003c/td\u003e\n\u003ctd\u003eNumber of bins in NiFTI image histograms\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epbtx_edge_list\u003c/td\u003e\n\u003ctd\u003elists/list_edges_reduced.txt\u003c/td\u003e\n\u003ctd\u003eText file list of edges for steps s3_probtrackx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecompress_pbtx_results\u003c/td\u003e\n\u003ctd\u003eTrue\u003c/td\u003e\n\u003ctd\u003eCompress probtrackx outputs to reduce inode and disk space usage\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edynamic_walltime\u003c/td\u003e\n\u003ctd\u003eFalse\u003c/td\u003e\n\u003ctd\u003eRequest dynamically shortened walltimes, to gain priority on job queue\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_job_time\u003c/td\u003e\n\u003ctd\u003e00:15:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s1 on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_job_time\u003c/td\u003e\n\u003ctd\u003e00:45:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s2a on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_job_time\u003c/td\u003e\n\u003ctd\u003e10:00:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s2b on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_job_time\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s3 on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_job_time\u003c/td\u003e\n\u003ctd\u003e00:15:00\u003c/td\u003e\n\u003ctd\u003eMax time to finish s4 on 1 subject with 1 node, if dynamic_walltime is true\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_cores_per_task\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s1_dti_preproc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_cores_per_task\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s2a_bedpostx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_cores_per_task\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s2b_freesurfer\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_cores_per_task\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s3_probtrackx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_cores_per_task\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of cores to assign each task for step s4_render\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s1_dti_preproc, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s2a_bedpostx, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s2b_freesurfer, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s3_probtrackx, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_hostname\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eHostname of machine to run step s4_render, if local_host_only is false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s2a\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s2b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_walltime\u003c/td\u003e\n\u003ctd\u003e23:59:00\u003c/td\u003e\n\u003ctd\u003eWalltime for step s4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(0.2 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(1.0 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s2a\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(1.0 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s2b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(1.0 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_nodes\u003c/td\u003e\n\u003ctd\u003e[[floor(0.1 * num_subjects)]]\u003c/td\u003e\n\u003ctd\u003eNode count for step s4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es1_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2a_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s2a\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es2b_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s2b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es3_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003es4_cores\u003c/td\u003e\n\u003ctd\u003e[[core count on head node]]\u003c/td\u003e\n\u003ctd\u003eCores per node for step s4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebids_json\u003c/td\u003e\n\u003ctd\u003eexamples/dummy_bids_desc.json\u003c/td\u003e\n\u003ctd\u003eDescription file dataset_description.json, as specified at \u003ca href=\"https://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html\" rel=\"nofollow\"\u003ehttps://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebids_readme\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eFree form text file describing the dataset in more detail\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebids_session_name\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eName for the session timepoint (e.g. 2weeks)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-download-mri-images-from-openneuro\" class=\"anchor\" href=\"#download-mri-images-from-openneuro\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload MRI Images from OpenNeuro\u003c/h3\u003e\n\u003cp\u003eDownload MRI images from OpenNeuro repository by providing path to install data and accession ID of the MRI image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: subscripts/download_openneuro.py [-h] [--install-directory INSTALL_DIR] [-a ACC_NUM]\n\narguments:\n -h, --help show this help message and exit\n --install-directory INSTALL_DIR\n Path where data will be installed\n -a ACC_NUM, --accession ACC_NUM\n MRI Accession ID from OpenNeuro\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRequirements:\npython package datalad, git-annex\nInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c conda-forge datalad\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eon mac:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebrew install git-annex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eon linux:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c conda-forge git-annex\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h3\u003e\n\u003cp\u003eMaPPeRTrac is distributed under the terms of the BSD-3 License.\u003c/p\u003e\n\u003cp\u003eLLNL-CODE-811655\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-brain-diff\" class=\"anchor\" aria-hidden=\"true\" href=\"#brain-diff\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebrain-diff\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-goal-brainage-prediction-with-two-timepoints\" class=\"anchor\" aria-hidden=\"true\" href=\"#goal-brainage-prediction-with-two-timepoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal: Brainage prediction with two timepoints\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-replication\" class=\"anchor\" aria-hidden=\"true\" href=\"#replication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReplication\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- [Paper](https://doi.org/10.1016/j.media.2020.101871): Accurate brain age prediction with lightweight deep neural networks Han Peng, Weikang Gong, Christian F. Beckmann, Andrea Vedaldi, Stephen M Smith Medical Image Analysis (2021)\n- Code [repo](https://github.com/ha-ha-ha-han/UKBiobank_deep_pretrain)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-datasets\" class=\"anchor\" aria-hidden=\"true\" href=\"#datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDatasets\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- UKBB: notebooks/1_ukb_follow_up.ipynb\n- ADNI: notebooks/2_adni_follow_up.ipynb\n- Simulations: notebooks/7_brain_diff_sim.ipynb\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- Brainage replication: notebooks/4_brain_age.ipynb\n- Simulation: notebooks/8_brain_diff_sim_results.ipynb\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ukb-data-wrangling\" class=\"anchor\" aria-hidden=\"true\" href=\"#ukb-data-wrangling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUKB data wrangling\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-step-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003ecopy files from squashfs on Beluga\nSes-2 (n=40681): \u0026lt;neurohub_ukbb_t1w_bids_derivatives.squashfs\u0026gt;:/neurohub/ukbb/imaging/T1\nSes-3 (n=3208): \u0026lt;neurohub_ukbb_t1w_ses3_0_derivatives.squashfs\u0026gt;:/neurohub/ukbb/imaging\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-step-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e## move them in psudo-bids\nfor i in `ls | grep sub- | grep -v json`; do \n mkdir -p ../`echo $i | cut -d \"_\" -f1`/ses-2/anat; \n mv `echo $i | cut -d \"_\" -f1`* ../`echo $i | cut -d \"_\" -f1`/ses-2/anat/; \ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-adni-data-wrangling\" class=\"anchor\" aria-hidden=\"true\" href=\"#adni-data-wrangling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eADNI data wrangling\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003euse src/generate_adni_bids.py\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun instructions\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-simulations\" class=\"anchor\" aria-hidden=\"true\" href=\"#simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimulations:\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- Simple interactive runs: notebooks/7_brain_diff_sim.ipynb\n- Batch runs: src/run_simul.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sfcn-replication\" class=\"anchor\" aria-hidden=\"true\" href=\"#sfcn-replication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSFCN replication:\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e- src/run_SFCN.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-slurm-setup-for-training-lsn\" class=\"anchor\" aria-hidden=\"true\" href=\"#slurm-setup-for-training-lsn\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eslurm setup for training LSN\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003emodule load singularity/3.8\u003c/li\u003e\n\u003cli\u003esingularity shell --overlay /project/rpp-aevans-ab/neurohub/ukbb/imaging/neurohub_ukbb_t1w_bids_derivatives.squashfs:ro --overlay /project/rpp-aevans-ab/neurohub/ukbb/imaging/neurohub_ukbb_t1w_bids_derivatives_ses3_0_bids.squashfs /home/nikhil/scratch/FastSurfer.sif\u003c/li\u003e\n\u003cli\u003e./run_LSN.sh\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1627333260.0 + "updated_at": 1654635361.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for gatk (https://github.com/broadinstitute/gatk)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.4.2.6.1" ], - "full_name": "MontrealSergiy/deformation", + "full_name": "powerPlant/gatk-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-deformation-field\" class=\"anchor\" href=\"#deformation-field\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeformation field\u003c/h1\u003e\n\u003cp\u003eThis PERL script is a wrapper that is calling sequence of commands for generating deformation fields scrips\n\u003ca href=\"https://wiki.mouseimaging.ca/display/MICePub/Generating+deformation+fields\" rel=\"nofollow\"\u003ehttps://wiki.mouseimaging.ca/display/MICePub/Generating+deformation+fields\u003c/a\u003e\nSource code for deformation pipeline and dependencies (MINC):\n\u003ca href=\"https://github.com/Mouse-Imaging-Centre/generate_deformation_fields\"\u003ehttps://github.com/Mouse-Imaging-Centre/generate_deformation_fields\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cp\u003edeformation_2.pl -input ICBM_00100_t1_final.mnc \u0026lt;\u0026lt;this could be any anatomical minc file, for a collection of minc files\u0026gt;\u0026gt; -output dummy_hoho -deformation_ratio 0.6 -coordinate 70 100 70 10 10 10 -tolerance_space 4 \u0026lt;\u0026gt; -blur_determinant 0.25 \u0026lt;\u0026gt; -error 0.00001 \u0026lt;\u0026gt; -iteration 100\u003c/p\u003e\n\u003cp\u003eThe output of running this command looks like this:\nICBM_00100_t1_final_deformed_by_0.4atROIx70-y100-z70dimx10.dimy10.dimz10.mnc. \u003c/p\u003e\n\u003cp\u003eWe will also have a directory dummy_hoho/TMP that will contain the in-between-files.\u003c/p\u003e\n\u003cp\u003e$:/dummy_hoho/TMP$ ls\u003c/p\u003e\n\u003cp\u003eblock.mnc\u003c/p\u003e\n\u003cp\u003eblurred0.25determinant_r_0.4x70-y100-z70dimx10.dimy10.dimz10.mnc\u003c/p\u003e\n\u003cp\u003eDDDDdilated.mnc\u003c/p\u003e\n\u003cp\u003eDDDDring.mnc\u003c/p\u003e\n\u003cp\u003edeterminant_r_0.4_grid.mnc\u003c/p\u003e\n\u003cp\u003edeterminant_r_0.4x70-y100-z70dimx10.dimy10.dimz10.mnc\u003c/p\u003e\n\u003cp\u003edeterminant_r_0.4.xfm\u003c/p\u003e\n\u003cp\u003emask.mnc\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for gatk (\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003ehttps://github.com/broadinstitute/gatk\u003c/a\u003e)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1623632255.0 + "updated_at": 1659582311.0 }, { "data_format": 2, - "description": "Validate and submit reads using Webin-CLI in batch.", + "description": null, "filenames": [ "Singularity" ], - "full_name": "enasequence/ena-bulk-webincli", + "full_name": "psadil/cat12", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ena-webin-cli-bulk-submission-tool\" class=\"anchor\" href=\"#ena-webin-cli-bulk-submission-tool\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eENA Webin-CLI Bulk Submission Tool\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThis tool is a wrapper to bulk submit read, un-annotated genome, targeted sequence or taxonomic reference data to the ENA using Webin-CLI.\u003c/p\u003e\n\u003cp\u003eThe tool requires an appropriate metadata spreadsheet which it uses to generate manifest files for the user and validate or submit their submission. The tool does not handle study and sample registration, therefore visit \u003ca href=\"https://ena-docs.readthedocs.io/en/latest/submit/general-guide.html\" rel=\"nofollow\"\u003eENA Submissions Documentation\u003c/a\u003e for more information on this. The documentation also provides information on manifest file fields for your type of submission (which correlate to the headers in the spreadsheet file).\u003c/p\u003e\n\u003cp\u003eAn example template spreadsheet has been provided (example_template_input.txt). This file is a tab-delimited text file, however the script also consumes spreadsheets in native MS Excel formats (e.g. .xslx) or comma-separated (.csv).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h4\u003e\n\u003cp\u003eTo ease in usage, the tool has been containerised using \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e. The only requirement is to have Docker \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003einstalled\u003c/a\u003e. Once installed, run the following commands to setup:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone the repository:\n\u003ccode\u003egit clone https://github.com/nadimm-rahman/ena-bulk-webincli.git \u0026amp;\u0026amp; cd ena-bulk-webincli\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuild the docker image:\n\u003ccode\u003edocker build --tag ena-bulk-webincli .\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eReady to go! Run the tool using docker using the following command:\n\u003ccode\u003edocker run --rm -v \u0026lt;LOCAL_DATA_DIRECTORY\u0026gt;:/data ena-bulk-webincli -h\u003c/code\u003e (for help)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026lt;LOCAL_DATA_DIRECTORY\u0026gt; is recommended to be the directory or parent directory on your machine containing your data files to submit. Below is an example command which would submit read data to the test server:\n\u003ccode\u003edocker run --rm -v pathto/data:/data ena-bulk-webincli -u Webin-XXXX -p XXXX -g reads -s example_template_read.txt -d /data -m submit -t\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNote: For data files to be submitted, relative file paths in accordance to \u003ccode\u003e\u0026lt;LOCAL_DATA_DIRECTORY\u0026gt;\u003c/code\u003e must be provided within the input spreadsheet.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-other\" class=\"anchor\" href=\"#other\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther\u003c/h4\u003e\n\u003cp\u003eTo use the tool without Docker:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone the repository:\n\u003ccode\u003egit clone https://github.com/nadimm-rahman/ena-bulk-webincli.git \u0026amp;\u0026amp; cd ena-bulk-webincli\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload the latest version of \u003ca href=\"https://github.com/enasequence/webin-cli/releases\"\u003eWebin-CLI\u003c/a\u003e installed.\u003c/li\u003e\n\u003cli\u003eDownload tool dependencies listed below.\u003c/li\u003e\n\u003cli\u003eEdit the \u0027Configuration\u0027 section at the top of bulk_webincli.py to include the full path to the Webin-CLI jar file and whether parallel processing should be carried out.\u003c/li\u003e\n\u003cli\u003eRun the tool using \u003ccode\u003epython bulk_webincli.py --help\u003c/code\u003e(for help)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe script accepts full paths to files (to be submitted e.g. fastq/fasta) within the input spreadsheet. To control location of outputs, a specific directory can be provided using the \u003ccode\u003e--directory/-d\u003c/code\u003e parameter, where the folders listed below will be generated.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eMandatory arguments include Webin submission account username and password, genetic context and metadata spreadsheet. Note that the \u003ccode\u003e--test/-t\u003c/code\u003e flag can be specified to use Webin test submission services.\u003c/p\u003e\n\u003cp\u003eBy default, the script utilises two additional directories:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u0027manifests\u0027 - which houses all generated manifest files and report files.\u003c/li\u003e\n\u003cli\u003e\u0027submissions\u0027 - housing all validation and submission related reports and files, includes analysis and receipt XMLs of submissions.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cp\u003eThe tool runs using \u003ca href=\"https://www.python.org/downloads/\" rel=\"nofollow\"\u003ePython3.6+\u003c/a\u003e and requires installation of \u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003ePython Pandas\u003c/a\u003e and \u003ca href=\"https://joblib.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ejoblib\u003c/a\u003e. This can be installed in a \u003ca href=\"https://docs.python.org/3/tutorial/venv.html\" rel=\"nofollow\"\u003evirtual environment\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cat12\" class=\"anchor\" aria-hidden=\"true\" href=\"#cat12\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecat12\u003c/h1\u003e\n\u003cp\u003eTo build, run \u003ccode\u003ebuild_singularity\u003c/code\u003e as root e.g.,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo ./build_singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that the build expects to find a few files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e./code/main\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e./CAT12.zip (zipped standalone copy of CAT12, \u003ca href=\"https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2102\u0026amp;L=SPM\u0026amp;P=R8713\" rel=\"nofollow\"\u003ehttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2102\u0026amp;L=SPM\u0026amp;P=R8713\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e./MCR_R2017b_glnxa64_installer.zip (e.g., \u003ccode\u003ewget https://ssd.mathworks.com/supportfiles/downloads/R2017b/deployment_files/R2017b/installers/glnxa64/MCR_R2017b_glnxa64_installer.zip\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe script \u003ccode\u003erun_a2cps_segment\u003c/code\u003e provides a minimal wrapper around the container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./run_a2cps_segment T1w.nii.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run cat_standalone with a different template, \u003ccode\u003e\u0026lt;template\u0026gt;\u003c/code\u003e, on T1w image, \u003ccode\u003e\u0026lt;data\u0026gt;\u003c/code\u003e, try\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --cleanenv cat12.sif -b \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etemplate\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edata\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse the \u003ccode\u003e--cleanenv\u003c/code\u003e flag may not be necessary, depending on your host. When running with host Ubuntu 20.04, there were environment variables associated with Java that interfered with MATLAB. See the Singularity documentation on \u003ca href=\"https://sylabs.io/guides/3.8/user-guide/environment_and_metadata.html?highlight=cleanenv#environment-overview\" rel=\"nofollow\"\u003eenvironment variables\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prebuilt-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#prebuilt-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrebuilt container\u003c/h2\u003e\n\u003cp\u003eA verison of the container has been prebuilt and shared on \u003ca href=\"https://cloud.sylabs.io\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io\u003c/a\u003e. To use it, replace the container definition with \u003ccode\u003elibrary://psadil/default/cat\u003c/code\u003e, e. g.,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --cleanenv library://psadil/default/cat:0.0.1 -b \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etemplate\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edata\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1625847484.0 + "updated_at": 1659538764.0 }, { "data_format": 2, "description": null, "filenames": [ - "util/PATRIC/Singularity" + "Singularity" ], - "full_name": "adamlabadorf/bf550", + "full_name": "cschu/profile_me_ci", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bf550---foundations-in-programming-data-analytics-and-machine-learning-in-python\" class=\"anchor\" href=\"#bf550---foundations-in-programming-data-analytics-and-machine-learning-in-python\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBF550 - Foundations in Programming, Data Analytics, and Machine Learning in Python\u003c/h1\u003e\n\u003cp\u003e(unofficial title: Bioinformatics Engineering)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://adamlabadorf.github.io/bf550/\" rel=\"nofollow\"\u003eGo to the Github Pages site\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1628214043.0 + "updated_at": 1636023839.0 }, { "data_format": 2, - "description": "Multi-Label Multi/Single-Class Image Segmentation", + "description": "Singularity recipe files for kraken-biom (https://github.com/smdabdoub/kraken-biom)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.1.2.0" ], - "full_name": "kbronik2017/Multi_Label_Segmentation", + "full_name": "powerPlant/kraken-biom-srf", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/UCLBrain/MSLS/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f9cab98cc8f1052f0c0096b8b462cf9b2280a706b1adc0895d8a4859f3743314/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f55434c427261696e2f4d534c53\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/network\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6c7a087c43d2a65a6ee22ec8ce2e004a29212c4b9619b117f4b2bbabf98a6c5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f55434c427261696e2f4d534c53\" alt=\"GitHub forks\" data-canonical-src=\"https://img.shields.io/github/forks/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/10fab859502fd8c9b24dde937e50fecba7449e94ea057774713238ab3ec754fc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f55434c427261696e2f4d534c53\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d525d837b03e3b7a24b6322fc2197a134fa12e6a96188e5f18d6cd92236aa47/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f55434c427261696e2f4d534c53\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-multi-label-multisingle-class-image-segmentation\" class=\"anchor\" href=\"#multi-label-multisingle-class-image-segmentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-Label Multi/Single-Class Image Segmentation\u003c/h1\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/diag.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/diag.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-publication\" class=\"anchor\" href=\"#publication\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublication\u003c/h1\u003e\n\u003cp\u003eLe Zhang, Ryutaro Tanno, Kevin Bronik, Chen Jin, Parashkev Nachev, Frederik Barkhof, Olga Ciccarelli, and Daniel C. Alexander, Learning to Segment When Experts Disagree, International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI). Springer, Cham, 2020.\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/Miccai_2020_abs.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg align=\"center\" height=\"1000\" src=\"images/Miccai_2020_abs.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\n \n \u003cp\u003eClick here to see full pdf file: \u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/MICCAI_2020.pdf\"\u003eLink to PDF\u003c/a\u003e\u003c/p\u003e\n \n\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-gui-program\" class=\"anchor\" href=\"#running-the-gui-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the GUI Program!\u003c/h1\u003e\n\u003cp\u003eFirst, user needs to install Anaconda \u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003ehttps://www.anaconda.com/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda env create -f conda_environment_Training_Inference.yml \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efinally\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python Training_Inference_GUI.py \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/GUI.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/GUI.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-program-from-the-command-line\" class=\"anchor\" href=\"#running-the-program-from-the-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Program from the command line!\u003c/h1\u003e\n\u003cp\u003eFirst\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen for training\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Training_without_GUI.py [or annotation_network_Training_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efor testing\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Inference_without_GUI.py [or annotation_network_Inference_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-testing-the-program\" class=\"anchor\" href=\"#testing-the-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting the Program!\u003c/h1\u003e\n\u003cp\u003eExamples of Training and Testing subjects can be found in: \u003ca href=\"https://github.com/UCLBrain/MSLS/tree/master/examples\"\u003ehttps://github.com/UCLBrain/MSLS/tree/master/examples\u003c/a\u003e (which will allow users to quickly and easily train and test the program)\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/bin_seg_ex.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/bin_seg_ex.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e..................................................................................................................................................................\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/multi_seg_ex.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/multi_seg_ex.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n", + "readme": "\u003cp\u003eSingularity recipe files for kraken-biom (\u003ca href=\"https://github.com/smdabdoub/kraken-biom\"\u003ehttps://github.com/smdabdoub/kraken-biom\u003c/a\u003e)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "segmentation", - "multi-label" - ], - "updated_at": 1628469613.0 + "subscribers_count": 0, + "topics": [], + "updated_at": 1659482439.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for glnexus (https://github.com/dnanexus-rnd/GLnexus)", "filenames": [ - "singularity_environment/Singularity" + "Singularity", + "Singularity.1.4.3" ], - "full_name": "cpezzato/discrete_active_inference", + "full_name": "powerPlant/glnexus-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-discrete_active_inference-for-robotics\" class=\"anchor\" href=\"#discrete_active_inference-for-robotics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ediscrete_active_inference for robotics\u003c/h1\u003e\n\u003cp\u003eRepository for active inference and behavior trees for discrete decision making. This repository relies on a TIAGo simulation in a simplified retail store. Please read the associated paper for more theorethical considerations about the algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\"Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics\"\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCorrado Pezzato, Carlos Hernandez, Stefan Bonhof, Martijn Wisse, \u003ca href=\"https://arxiv.org/abs/2011.09756\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/2011.09756\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-content\" class=\"anchor\" href=\"#content\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContent\u003c/h2\u003e\n\u003cp\u003eThis repositiry contains a Matlab examples and a ROS package for active inference for task planning and execution.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-main-files\" class=\"anchor\" href=\"#main-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMain files\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eMatlab:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eaip.m\u003c/em\u003e the active inference algorithm for decision making is illustrated in the case of heterogeneous states and actions.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eexample.m\u003c/em\u003e example of use of active inference for discrete decision making in a robotic case where conflicts and preconditions checks are required. A robot is assumed to be able to navigate to a point (MoveBase), reach a location with its end effector (Move MPC), and pick and place things. Actions have preconditions and are assumed not instantaneous\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eROS:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe other folders are related to the ROS package containing a Python implementation of active inference and behavior trees. You can run an example use case with TIAGo in a simplified retail store after installation of the package ad dependancies.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSimulation Environment\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA singularity image can be downloaded from \u003ca href=\"https://drive.google.com/drive/folders/1DYuRWgCiiHCG4ck_7Pf_Kw4Kn-ZpZ-Oy?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, you can build the singularity yourself:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ecreate a sub directory called \u0027pkgs\u0027 (in the \u003ccode\u003esingularity_environment\u003c/code\u003e directory)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e mkdir pkgs\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003euse \u003ccode\u003evcstool\u003c/code\u003e (or \u003ccode\u003ewstool\u003c/code\u003e) to clone/download the dependencies (as specified in \u003ccode\u003eretail_store_lightweight_sim.repos\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e vcs import \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e retail_store_lightweight_sim.repos pkgs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdding packages to \u003ccode\u003epkg\u003c/code\u003e will allow \u003ccode\u003erosdep\u003c/code\u003e to install all required build and run dependencies into the image, so students can then proceed to build those packages in their own workspaces (otherwise builds would fail due to missing dependencies).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Packages in \u003ccode\u003epkg\u003c/code\u003e will be installed on the image, their source will \u003cstrong\u003enot\u003c/strong\u003e be included in the image itself, so there may be some elements that are not installed. So far I\u0027ve only noticed one required change.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eModify the \u003ccode\u003eCMakeList.txt\u003c/code\u003e file from the \u003ccode\u003epal_navigation_sm\u003c/code\u003e inside the \u003ccode\u003epkgs\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eChange the \u003ccode\u003einstall\u003c/code\u003e instruction (starts at line 10) by adding some scripts as follows.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003einstall(\nPROGRAMS\n scripts/map_setup.py\n scripts/pal_navigation_main_sm.py\n scripts/navigation.sh\n scripts/base_maps_symlink.sh\n scripts/cp_maps_to_home.sh\n scripts/cp_pose_to_home.sh\n DESTINATION \u003cspan class=\"pl-smi\"\u003e${CATKIN_PACKAGE_BIN_DESTINATION}\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003echeck the \u003ccode\u003eVERSION\u003c/code\u003e variable inside the \u003ccode\u003edocker_build.sh\u003c/code\u003e, \u003ccode\u003ebuild.sh\u003c/code\u003e and \u003ccode\u003eSingularity\u003c/code\u003e files. This version should match the version of your singularity install (\u003ccode\u003esingularity -v\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun \u003ccode\u003edocker_build.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./docker_build.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter some time and a successful build, a new docker image will be created. This requires Docker to be installed and configured.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun \u003ccode\u003ebuild.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./build.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAfter some time and a successful build, a new \u003ccode\u003e.simg\u003c/code\u003e should be generated by \u003ccode\u003esingularity\u003c/code\u003e in the \u003ccode\u003ecwd\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBehavior trees library\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInstall the BT library to use this package (tested in Ubuntu 18.04 with ROS Melodic). Before proceeding, it is recommended to to install the following dependencies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install libzmq3-dev libboost-dev\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also easily install the \u003ca href=\"https://github.com/BehaviorTree/BehaviorTree.CPP\"\u003eBehavior Tree library\u003c/a\u003e with the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install ros-$ROS_DISTRO-behaviortree-cpp-v3\nsudo apt-get update \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-code\" class=\"anchor\" href=\"#running-the-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the code\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUsing the virtual environment\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccess the simngularity image by using the regular Singularity \u003ccode\u003eshell\u003c/code\u003e action:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell /path/to/discrete_ai_tiago.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse the flag for nvidia drivers if applicable to your machine:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --nv /path/to/discrete_ai_tiago.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen source \u003ccode\u003e/opt/ros/melodic/setup.bash\u003c/code\u003e to access all the TIAGo dependencies installed on the image.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e /opt/ros/melodic/setup.bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eHow to run a simple example with TIAGo\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCreate a new workspace and clone this repository in the \u003ccode\u003esrc\u003c/code\u003e folder. Build the package using \u003ccode\u003ecatkin build\u003c/code\u003e. Run the three commands below from within the singularity image after sourcing \u003ccode\u003esource/devel/setup.bash\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch retail_store_simulation tiago_simulation.launch\nrosrun discrete_ai tiago_perception.py\nrosrun discrete_ai active_inference_server.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFrom a terminal outside the singularity image run the behavior tree:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erosrun discrete_ai demo_executeBT\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe expected outcome is the following:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"tiago_sim.gif\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"tiago_sim.gif\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: The sills used in this simulation are based on standard moveBase and moveIt actions, thus robustness (especially of IK solutions) might make TIAGo fail the grasp. Aruco detection can also imprecise and will be improved over time.\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for GLnexus (\u003ca href=\"https://github.com/dnanexus-rnd/GLnexus\"\u003ehttps://github.com/dnanexus-rnd/GLnexus\u003c/a\u003e)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1623232591.0 + "updated_at": 1659481676.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.7", - "Singularity.12", - "Singularity.121", - "Singularity.11", - "Singularity.8", - "Singularity.5", - "Singularity.10", - "Singularity.9", - "Singularity.111", - "Singularity.15", - "Singularity.14", - "Singularity.6", - "Singularity.4", - "Singularity.3", - "Singularity.13" + "docker/Singularity.def" ], - "full_name": "masoudrezai/Singularity", + "full_name": "benjrise/flood-detetection", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-octopussingularity-container\" class=\"anchor\" href=\"#octopussingularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://github.com/luntergroup/octopus\"\u003eOctopus\u003c/a\u003e\n\u003ca href=\"https://github.com/hpcng/singularity\"\u003eSingularity\u003c/a\u003e container\u003c/h1\u003e\n\u003cp\u003eSylvain Schmitt\nApril 28, 2021\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBionformatics software Octopus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOctopus is a mapping-based variant caller that implements several\ncalling models within a unified haplotype-aware framework. Octopus takes\ninspiration from particle filtering by constructing a tree of haplotypes\nand dynamically pruning and extending the tree based on haplotype\nposterior probabilities in a sequential manner. This allows octopus to\nimplicitly consider all possible haplotypes at a given loci in\nreasonable time.\u003c/p\u003e\n\u003cp\u003eOctopus Version: 0.7.4\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://github.com/luntergroup/octopus\"\u003ehttps://github.com/luntergroup/octopus\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eSingularity container based on the recipe: Singularity.template.def\u003c/p\u003e\n\u003cp\u003ePackage installation using Miniconda3 V4.7.12\u003c/p\u003e\n\u003cp\u003eImage singularity (V\u0026gt;=3.3) is automatically test and built and pushed\non the registry using\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/test.yml\"\u003etest.yml\u003c/a\u003e\n\u0026amp;\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/builder.yml\"\u003ebuilder.yml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ebuild\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build octopus.sif Singularity\nsingularity run octopus.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e octopus.sif octopus -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003epull\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull https://github.com/sylvainschmitt/singularity-octopus/releases/download/0.0.1/sylvainschmitt-singularity-octopus.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esnakemake\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s1\"\u003esingularity\u003c/span\u003e: \n \u003cspan class=\"pl-s\"\u003e\"https://github.com/sylvainschmitt/singularity-octopus/releases/download/0.0.1/sylvainschmitt-singularity-octopus.latest.sif\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1623238419.0 + "updated_at": 1660656835.0 }, { "data_format": 2, - "description": "octopus Singularity container ", + "description": null, "filenames": [ "Singularity" ], - "full_name": "sylvainschmitt/singularity-octopus", - "latest_release": "0.0.1", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-octopussingularity-container\" class=\"anchor\" href=\"#octopussingularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://github.com/luntergroup/octopus\"\u003eOctopus\u003c/a\u003e\n\u003ca href=\"https://github.com/hpcng/singularity\"\u003eSingularity\u003c/a\u003e container\u003c/h1\u003e\n\u003cp\u003eSylvain Schmitt\nApril 28, 2021\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBionformatics software Octopus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOctopus is a mapping-based variant caller that implements several\ncalling models within a unified haplotype-aware framework. Octopus takes\ninspiration from particle filtering by constructing a tree of haplotypes\nand dynamically pruning and extending the tree based on haplotype\nposterior probabilities in a sequential manner. This allows octopus to\nimplicitly consider all possible haplotypes at a given loci in\nreasonable time.\u003c/p\u003e\n\u003cp\u003eOctopus Version: 0.7.4\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://github.com/luntergroup/octopus\"\u003ehttps://github.com/luntergroup/octopus\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eSingularity container based on the recipe: Singularity.template.def\u003c/p\u003e\n\u003cp\u003ePackage installation using Miniconda3 V4.7.12\u003c/p\u003e\n\u003cp\u003eImage singularity (V\u0026gt;=3.3) is automatically test and built and pushed\non the registry using\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/test.yml\"\u003etest.yml\u003c/a\u003e\n\u0026amp;\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/builder.yml\"\u003ebuilder.yml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ebuild\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build octopus.sif Singularity\nsingularity run octopus.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e octopus.sif octopus -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003epull\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull https://github.com/sylvainschmitt/singularity-octopus/releases/download/0.0.1/sylvainschmitt-singularity-octopus.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esnakemake\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s1\"\u003esingularity\u003c/span\u003e: \n \u003cspan class=\"pl-s\"\u003e\"https://github.com/sylvainschmitt/singularity-octopus/releases/download/0.0.1/sylvainschmitt-singularity-octopus.latest.sif\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "kh11kim/kstar_rev", + "latest_release": null, + "readme": "\u003ch2\u003e\u003ca id=\"user-content-welcome-to-the-page-of-k-planner----a-state-of-the-art-top-k-planner-integrating-the-k-algorithm-into-fast-downward\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcome-to-the-page-of-k-planner----a-state-of-the-art-top-k-planner-integrating-the-k-algorithm-into-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to the page of K* planner -- a state of the art Top-k planner integrating the K* algorithm into Fast Downward.\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# ./fast-downward.py \u0026lt;domain_file\u0026gt; \u0026lt;problem_file\u0026gt; --search \"kstar(heuristic,k=\u0026lt;number-of-plans\u0026gt;)\"\n\n./fast-downward.py examples/gripper/domain.pddl examples/gripper/prob01.pddl --search \"kstar(blind(),k=100)\"\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eheurisitic\u003c/em\u003e: any heuristic provided by Fast Downward\u003cbr\u003e\n(\u003ca href=\"http://www.fast-downward.org/Doc/Heuristic\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/Doc/Heuristic\u003c/a\u003e).\u003cbr\u003e\n\u003cstrong\u003eDisclaimer\u003c/strong\u003e: Optimality of K* is only guaranteed with an admissible and consistent heuristic.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h3\u003e\n\u003cp\u003eMichael Katz, Shirin Sohrabi, Octavian Udrea and Dominik Winterer\u003cbr\u003e\n\u003cstrong\u003eA Novel Iterative Approach to Top-k Planning\u003c/strong\u003e \u003ca href=\"https://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/download/17749/16971\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"/top_k.bib\"\u003e[bib]\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eIn ICAPS 2018\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h3\u003e\n\u003cp\u003eFor questions and comments please get in touch with Michael Katz (\u003ca href=\"mailto:michael.katz1@ibm.com\"\u003emichael.katz1@ibm.com\u003c/a\u003e).\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1623243296.0 + "updated_at": 1659371898.0 }, { "data_format": 2, - "description": "container for gatk tools", + "description": "code accompanying \"Nanopore sequencing of a monkeypox virus strain isolated from a pustular lesion in the Central African Republic\"", "filenames": [ "Singularity" ], - "full_name": "aseetharam/gatk", + "full_name": "mvdenbog/MPXV_NanoPoreSeq", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4700\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-container-for-the-gatk\" class=\"anchor\" href=\"#container-for-the-gatk\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer for the GATK\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tools-included\" class=\"anchor\" href=\"#tools-included\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTools included\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"http://www.htslib.org/\" rel=\"nofollow\"\u003eSamTools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://bio-bwa.sourceforge.net/\" rel=\"nofollow\"\u003eBWA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.gnu.org/software/datamash/\" rel=\"nofollow\"\u003eDatamash\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gatk.broadinstitute.org/hc/en-us\" rel=\"nofollow\"\u003eGATK\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://broadinstitute.github.io/picard/\" rel=\"nofollow\"\u003ePicard Tools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lh3/bioawk\"\u003eBioAWK\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bedtools.readthedocs.io\" rel=\"nofollow\"\u003eBedTools\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePlease be sure to cite all the programs if you use this container.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eto pull the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name gatk.sif shub://aseetharam/gatk:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto use the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec gatk.sif samtools\nsingularity exec gatk.sif bwa\nsingularity exec gatk.sif datamash\nsingularity exec gatk.sif java -jar /gatk/gatk-package-4.1.8.1-local.jar\nsingularity exec gatk.sif java -jar /picard/picard.jar\nsingularity exec gatk.sif bioawk\nsingularity exec gatk.sif bedtools\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mpxv_nanoporeseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#mpxv_nanoporeseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMPXV_NanoPoreSeq\u003c/h1\u003e\n\u003cp\u003eThis is snakefile code accompanying \"Nanopore sequencing of a monkeypox virus strain isolated from a pustular lesion in the Central African Republic\".\u003c/p\u003e\n\u003cp\u003eVandenbogaert M, Kwasiborski A, Gonofio E, Descorps-Decl\u00e8re S, Selekon B, Nkili Meyong AA, Ouilibona RS, Gessain A, Manuguerra JC, Caro V, Nakoune E, Berthet N. Nanopore sequencing of a monkeypox virus strain isolated from a pustular lesion in the Central African Republic. Sci Rep. 2022 Jun 24;12(1):10768. doi: 10.1038/s41598-022-15073-1. PMID: 35750759; PMCID: PMC9232561.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232561/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232561/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation--usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation--usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation \u0026amp; Usage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eUsing Docker/Singularity.\u003c/p\u003e\n\u003cp\u003eAll conda/python dependencies are defined in accompanying dependency files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003econda_installed_packages_base.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003econda_installed_packages_homopolish.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip38_installed_packages.txt\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe provided Singularity file is illustrative of the dependency definitions, and on building a target docker/singularity instance.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-preparation-of-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#preparation-of-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparation of data\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basecalling\" class=\"anchor\" aria-hidden=\"true\" href=\"#basecalling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasecalling\u003c/h3\u003e\n\u003cp\u003eInput data is supposed to be basecalled, prior to using the provided snakemake file.\u003c/p\u003e\n\u003cp\u003eExample basecalling instructions (below instructions are uinsg Guppy v 3.2.4, and are indicative only):\u003c/p\u003e\n\u003cp\u003eExample using CPUs:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edir=/opt/Guppy/ont-guppy-cpu_3.4.4/ont-guppy-cpu/bin\n\n${dir}/guppy_basecaller --kit ${kit} --flowcell ${flowcell} --barcode_kits ${barcode_kit} -i ${indir}/ -s ${outdir} --num_callers 4 --cpu_threads_per_caller 20 -q 4000 --qscore_filtering --min_qscore ${min_qscore} --disable_pings --trim_barcodes\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample using GPUs:\u003c/p\u003e\n\u003cp\u003eWorks on Tesla P100 only.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e${dir}/guppy_basecaller -i /data/fast5_pass/ --save_path /scratch/out/ --flowcell ${flowcell} --kit ${barcode_kit} --gpu_runners_per_device 8 -r --qscore_filtering --min_qscore 7 -x auto --disable_pings --trim_barcodes\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-organization-of-fastq-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#organization-of-fastq-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOrganization of FASTQ files\u003c/h3\u003e\n\u003cp\u003eand reference genome (here reference NC_003310).\u003c/p\u003e\n\u003cp\u003eWorking directory will be \u003ccode\u003e/scratch/\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /scratch/\nln ~/RawData/*.fastq .\nln ~/NC_003310.fasta .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-execution\" class=\"anchor\" aria-hidden=\"true\" href=\"#execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j10 -s Snakefile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eJupyter python/R notebooks for downstream analysis will be added shortly.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1623344768.0 + "updated_at": 1658744372.0 }, { "data_format": 2, - "description": "A template project to provide software to ESCAPE.", + "description": "Source code, installation, configuration and submission scripts for exascale in situ visualization with ISAAC and PIConGPU", "filenames": [ - "Singularity/Singularity" + "sources/crusher/picongpu/share/picongpu/dockerfiles/ubuntu-2004/Singularity", + "sources/summit/picongpu/share/picongpu/dockerfiles/ubuntu-2004/Singularity" ], - "full_name": "garciagenrique/template_project_escape", - "latest_release": "v0.0.3-dev", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-template_project_escape\" class=\"anchor\" href=\"#template_project_escape\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etemplate_project_escape\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.4923992\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869b462bf3a319fe4fc2ffa52fb6b0f7c8e42eb4e0ed4bc2482306b9fd5aafab/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343932333939322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4923992.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/-/commits/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a25ea71f12537b69d5aca8b409685333243d4e65ceb91c5a401dadb6eacea20e/68747470733a2f2f6769746c61622e696e3270332e66722f657363617065323032302f7770332f74656d706c6174655f70726f6a6563745f6573636170652f6261646765732f6d61737465722f706970656c696e652e737667\" alt=\"pipeline status\" data-canonical-src=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/badges/master/pipeline.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fd551ba4b042d89480347a0e74e31af63b356b2cac1116c7b80038f41b04a581/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://camo.githubusercontent.com/64f9054d866a78f16e1451647c19b22fc159a58191e004612257ce5277bd6db9/68747470733a2f2f63646e2e65736f2e6f72672f696d616765732f6c617267652f616e6e3138303834612e6a7067\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/64f9054d866a78f16e1451647c19b22fc159a58191e004612257ce5277bd6db9/68747470733a2f2f63646e2e65736f2e6f72672f696d616765732f6c617267652f616e6e3138303834612e6a7067\" width=\"640\" height=\"453\" data-canonical-src=\"https://cdn.eso.org/images/large/ann18084a.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eA simple template project to provide software to ESCAPE.\u003c/p\u003e\n\u003cp\u003eThis repository shows the \u003cstrong\u003ebasic content\u003c/strong\u003e that should be included in a project (following the\n\u003ca href=\"https://opensource.guide/starting-a-project/\" rel=\"nofollow\"\u003eopensource guide\u003c/a\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn \u003ca href=\"https://help.github.com/en/github/creating-cloning-and-archiving-repositories/licensing-a-repository#where-does-the-license-live-on-my-repository\"\u003eopen source\u003c/a\u003e\n\u003cstrong\u003elicense\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://help.github.com/en/github/getting-started-with-github/create-a-repo#commit-your-first-change\"\u003e\u003cstrong\u003eREADME\u003c/strong\u003e file\u003c/a\u003e,\nsimilar to this one.\u003c/li\u003e\n\u003cli\u003eContributing guidelines.\n\u003cul\u003e\n\u003cli\u003eSee below the general guidelines for the ESCAPE repository.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://opensource.guide/code-of-conduct/\" rel=\"nofollow\"\u003ecode of conduct\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003eCheck why is a good idea to add one.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe repository itself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt would be highly suitable to include too:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA setup file as well as the basic commands to install the library (see below).\u003c/li\u003e\n\u003cli\u003eA \u003ccode\u003e.gitignore\u003c/code\u003e file.\u003c/li\u003e\n\u003cli\u003eUnitary and integration tests, and ideally a CI pipeline.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ePlease feel free to clone / fork / template this project!\u003c/strong\u003e (For example, look to left of the\n\u003ccode\u003eClone or download\u003c/code\u003e button in the \u003ca href=\"https://github.com/garciagenrique/template_project_escape\"\u003eGitHub\u003c/a\u003e site).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor a detailed explanation of how to submit a contribution to a project / repository (Fork, create a branch, make\na pull request...), you can have a look to the \u003ca href=\"https://opensource.guide/how-to-contribute/#how-to-submit-a-contribution\" rel=\"nofollow\"\u003eopensource guide\u003c/a\u003e\nand/or the \u003ca href=\"https://git-scm.com/doc\" rel=\"nofollow\"\u003egit\u0027s documentation\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eNot that if you have login GitLab by using the \u003ccode\u003e[Shibbolenth]\u003c/code\u003e service (eduGAIN, F\u00e9d\u00e9ration d\u0027Identit\u00e9s\nRENATER), you will need to \u003ca href=\"https://gitlab.in2p3.fr/help/ssh/README#generating-a-new-ssh-key-pair\" rel=\"nofollow\"\u003eadd a SSH key\u003c/a\u003e to\nyour GitLab profile if you want to \u0027push\u0027 your changes to the server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contribute-to-the-escape-ossr\" class=\"anchor\" href=\"#contribute-to-the-escape-ossr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContribute to the ESCAPE OSSR\u003c/h1\u003e\n\u003cp\u003eIf you want to provide software to the ESCAPE repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the \u003ca href=\"https://escape2020.pages.in2p3.fr/wp3/ossr-pages/page/contribute/contribute_ossr/\" rel=\"nofollow\"\u003eESCAPE OSSR guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor ESCAPE members, follow the steps detailed in \u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/onboarding\" rel=\"nofollow\"\u003ethe onboarding project\u003c/a\u003e\nto finalise your contribution and the same onboarding process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAll the code provided should be uploaded to the \u003ca href=\"https://zenodo.org/communities/escape2020/\" rel=\"nofollow\"\u003eZenodo ESCAPE community\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the following \u003ca href=\"https://escape2020.pages.in2p3.fr/wp3/ossr-pages/page/contribute/publish_tutorial/\" rel=\"nofollow\"\u003etutorial on how to publish content in Zenodo\u003c/a\u003e,\nand how to automatise the upload of each new release of your project.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-this-project-also-includes\" class=\"anchor\" href=\"#this-project-also-includes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis project also includes\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-1-how-to-automatise-the-building-of-a-singularity-image-and-upload-it-to-zenodo-using-the-gitlab-ci\" class=\"anchor\" href=\"#1-how-to-automatise-the-building-of-a-singularity-image-and-upload-it-to-zenodo-using-the-gitlab-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. How to automatise the building of a Singularity image and upload it to Zenodo using the GitLab-CI\u003c/h2\u003e\n\u003cp\u003eA working example of how to automatise the GitLab-CI to;\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecreate a Singularity image / container of your code,\u003c/li\u003e\n\u003cli\u003emake it available as a downloadable artifact within your project and\u003c/li\u003e\n\u003cli\u003eupload it to the \u003ca href=\"https://zenodo.org/communities/escape2020\" rel=\"nofollow\"\u003eESCAPE OSSR\u003c/a\u003e,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ecan be found in the \u003ccode\u003e.singularityci\u003c/code\u003e, and \u003ccode\u003eSingularity\u003c/code\u003e directories and in the \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e file - the\n\u003ccode\u003ebuild_singularity_image\u003c/code\u003e stage. Please read carefully all the README files.\u003c/p\u003e\n\u003cp\u003eFor an easy example of how to create a Singularity receipt from scratch (and its corresponding container when executed),\nplease have a look to the \u003ccode\u003esingularity_utils\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-2-how-to-automatise-the-building-of-a-docker-container-and-upload-it-to-the-gitlab-container-registry\" class=\"anchor\" href=\"#2-how-to-automatise-the-building-of-a-docker-container-and-upload-it-to-the-gitlab-container-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. How to automatise the building of a Docker container and upload it to the GitLab Container Registry\u003c/h2\u003e\n\u003cp\u003eAn example can be found in the \u003ccode\u003eDocker\u003c/code\u003e directory and in the \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e file - the\n\u003ccode\u003ebuild_docker_image\u003c/code\u003e stage.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h1\u003e\n\u003cp\u003eExample of how to show installing instructions (and indeed the way to install this project).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e $ git clone https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape.git\n $ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e template_project_escape\n $ pip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-citing\" class=\"anchor\" href=\"#citing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCiting\u003c/h1\u003e\n\u003cp\u003eExample of citing (as well as the DOI to cite this project),\u003c/p\u003e\n\u003cp\u003eIn case of citing this repository, use the following DOI:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ev2.2 \u003ca href=\"https://doi.org/10.5281/zenodo.4923992\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869b462bf3a319fe4fc2ffa52fb6b0f7c8e42eb4e0ed4bc2482306b9fd5aafab/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343932333939322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4923992.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev2.1 \u003ca href=\"https://doi.org/10.5281/zenodo.4790629\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8f54e2f50cdf0c4d1bd5183d6472cae8b708efacd6a1319b443d8e456f41b7f/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343739303632392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4790629.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev2.0 \u003ca href=\"https://doi.org/10.5281/zenodo.3884963\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c27201272a77fc3ab3029c8c2c452e02a71736b7adaa0926bc45d3ac825598ed/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333838343936332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3884963.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev1.1 \u003ca href=\"https://doi.org/10.5281/zenodo.3743490\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbfe31d3a9bd48ef4554b414c3c2325276269a476a79ec0217aa9feccc87cecd/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333734333439302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3743490.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev1.0 \u003ca href=\"https://doi.org/10.5281/zenodo.3572655\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ec734ee4c1c793eaa8f9c2b189a6eae6d7d2ccb5f2a92adeb8af479409cc7bb/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333537323635352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3572655.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDo not forget to include your code / container into the \u003ca href=\"https://zenodo.org/communities/escape2020/\" rel=\"nofollow\"\u003eZenodo ESCAPE community\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003e\u003cstrong\u003eNote that\u003c/strong\u003e\u003c/em\u003e a DOI will be assigned in the moment create a new record/entry in Zenodo.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003ePlease check the licenses of the code within the \u003ccode\u003e.singularityci\u003c/code\u003e directory before adding this template\nto your project.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-report-an-issue--ask-a-question\" class=\"anchor\" href=\"#report-an-issue--ask-a-question\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReport an issue / Ask a question\u003c/h1\u003e\n\u003cp\u003eUse the \u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/-/issues\" rel=\"nofollow\"\u003eGitLab repository Issues\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eEmail to vuillaume [at] lapp.in2p3.fr / garcia [at] lapp.in2p3.fr.\u003c/p\u003e\n", + "full_name": "benjha/sc2022_ISAAC_artifact", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sc2022-artifact-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#sc2022-artifact-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSC\u00272022 Artifact Description\u003c/h1\u003e\n\u003cp\u003eWe reported the results of six experiments to evaluate the performance characteristics and portability of our in situ visualization solution. Three were run on Summit (\u003ccode\u003e64_gpus\u003c/code\u003e, \u003ccode\u003estrong_scaling\u003c/code\u003e, \u003ccode\u003eweak_scaling\u003c/code\u003e) and the other three on Crusher (\u003ccode\u003efirst_experiment\u003c/code\u003e, \u003ccode\u003esecond_experiment\u003c/code\u003e, \u003ccode\u003eweak_scaling\u003c/code\u003e). General simulations parameters:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eKelvin-Helmholtz instability simulation.\u003c/li\u003e\n\u003cli\u003e256x256x256 cells per GPU, additionally on Crusher: 512x512x512 cells per GPU.\u003c/li\u003e\n\u003cli\u003eFour particles per cell resulting in 134,217,728 macroparticles per GPU.\u003c/li\u003e\n\u003cli\u003eVolume, isosurface, particles and vector field visualization of three data sources. The threshold for isosurface visualization is set to the maximum of 1 for all sources to prevent any kind of early ray termination due to a valid isosurface.\u003c/li\u003e\n\u003cli\u003eTrilinear Interpolation is enabled, and the step size is set to the default of 0.5.\u003c/li\u003e\n\u003cli\u003eHalo exchange enabled.\u003c/li\u003e\n\u003cli\u003eTimings are averaged from 1440 time steps. Starting simulation time step is 10 to allow stabilization.\u003c/li\u003e\n\u003cli\u003eCamera view\u0027s animation is divided into four stages, each with 360 steps and a rotation around a different axis to cover most of the viewing angles.\u003c/li\u003e\n\u003cli\u003eISAAC streaming capabilities are disabled including image compression.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe interested reader can check the PIConGPU\u2019s documentation under this \u003ca href=\"https://picongpu.readthedocs.io\" rel=\"nofollow\"\u003elink\u003c/a\u003e for details on how to set up a simulation and a experiment. The configuration files used for the experiments are available following the next links:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSummit\n\u003cul\u003e\n\u003cli\u003e256x256x256 \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/simulation/summit/KelvinHelmholtz\"\u003eSimulation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/summit/64_gpus\"\u003e64_gpus\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/summit/strong_scaling\"\u003estrong_scaling\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/summit/weak_scaling\"\u003eweak_scaling\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCrusher\n\u003cul\u003e\n\u003cli\u003e256x256x256 \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/simulation/crusher/KelvinHelmholtz\"\u003eSimulation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e512x512x512 \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/simulation/crusher/KelvinHelmholtz_large\"\u003eSimulation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/crusher/first_experiment\"\u003efirst_experiment\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/crusher/second_experiment\"\u003esecond_experiment\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperiment: \u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/tree/main/experiments/KelvinHelmholtz/crusher/weak_scaling\"\u003eweak_scaling\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExperiments are reproduced following the instructions of the next section.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation--running-experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation--running-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation \u0026amp; Running Experiments\u003c/h1\u003e\n\u003cp\u003eWe include three scripts to deploy the experiments in Summit and Crusher systems:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/blob/main/config_vars.sh\"\u003e\u003ccode\u003econfig_vars.sh\u003c/code\u003e\u003c/a\u003e. This script includes the configuration variables that should be set by the user to install, configure and submit the experiments to the batch system. This script is modifiable by the user and is used by the \u003ccode\u003einstall.sh\u003c/code\u003e and \u003ccode\u003erun_experimen.sh\u003c/code\u003e scripts.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/blob/main/install.sh\"\u003e\u003ccode\u003einstall.sh\u003c/code\u003e\u003c/a\u003e. This script compiles and installs ISAAC, and the Kelvin-Helmholtz instability simulation. This script is only runnable by the user and should not be modified.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/benjha/sc2022_ISAAC_artifact/blob/main/run_experiment.sh\"\u003e\u003ccode\u003erun_experiment.sh\u003c/code\u003e\u003c/a\u003e. This script submits to the batch system the experiments described previously. This script is only runnable by the user and should not be modified.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe configuration variables defined in \u003ccode\u003econfig_vars.sh\u003c/code\u003e are described next:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eMAIL\u003c/code\u003e. Specifies what e-mail will receive a notification when a submitted experiment is running. This variable is optional.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePROJ_ID\u003c/code\u003e. Specifies what project id to use to submit a job. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e. Indicates the installation path of all software stack. Make sure \u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e is under \u003ccode\u003e$PROJWORK/\u0026lt;proj_id\u0026gt;/\u0026lt;user_id\u0026gt;\u003c/code\u003e. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMY_ISAAC_LOG_PATH\u003c/code\u003e. Specifies the path of the performance files generated when running the code. Make sure \u003ccode\u003eMY_ISAAC_LOG_PATH\u003c/code\u003e is under \u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMY_SIMULATIONS_PATH\u003c/code\u003e. Sets the simulations\u0027 path. Make sure it is under \u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMY_SIMULATION_NAME\u003c/code\u003e. Indicates the name of the simulation. This variable is mandatory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSYSTEM\u003c/code\u003e. Specifies the target cluster to install and execute the experiments. Available options are: \u003ccode\u003esummit\u003c/code\u003e, \u003ccode\u003ecrusher\u003c/code\u003e. This variable is mandatory\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eEXPERIMENT_NAME\u003c/code\u003e. Sets the experiment name that will be submitted to the batch system.\n\u003cul\u003e\n\u003cli\u003eOptions for summit are: \u003ccode\u003e64_gpus\u003c/code\u003e, \u003ccode\u003estrong_scaling\u003c/code\u003e, \u003ccode\u003eweak_scaling\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOptions for crusher are: \u003ccode\u003efirst_experiment\u003c/code\u003e, \u003ccode\u003esecond_experiment\u003c/code\u003e, \u003ccode\u003eweak_scaling\u003c/code\u003e. This variable is mandatory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFRAMEBUFFER\u003c/code\u003e. Sets the framebuffer resolution. This option is only used on \u003ccode\u003eSYSTEM=summit\u003c/code\u003e and \u003ccode\u003eEXPERIMENT_NAME=64_gpus\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eAvailable options: 720 , 1080 , 1440 , 2160.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eN_GPUS\u003c/code\u003e. Sets the number of GPUs for strong scaling and weak scaling experiments.\n\u003cul\u003e\n\u003cli\u003eOptions for \u003ccode\u003eSYSTEM=summit\u003c/code\u003e and \u003ccode\u003eEXPERIMENT_NAME=strong_scaling\u003c/code\u003e: 1, 2, 4, 8, 16, 32, 64, 128, 256, 512.\u003c/li\u003e\n\u003cli\u003eOptions for \u003ccode\u003eSYSTEM=summit\u003c/code\u003e and \u003ccode\u003eEXPERIMENT_NAME=weak_scaling\u003c/code\u003e: 1, 8, 64, 512, 1000, 2755, 4096, 5832, 8000, 10648, 13824.\u003c/li\u003e\n\u003cli\u003eOptions for \u003ccode\u003eSYSTEM=crusher\u003c/code\u003e and \u003ccode\u003eEXPERIMENT_NAME=weak_scaling\u003c/code\u003e: 1 , 8 , 64 , 216 , 512 , 1000.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstallation steps are as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLogin to Summit or Crusher.\u003c/li\u003e\n\u003cli\u003eClone this repository:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/benjha/sc2022_ISAAC_artifact.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eGo to \u003ccode\u003esc2022_ISAAC_artifact\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eSet executable the permissions for \u003ccode\u003einstall.sh\u003c/code\u003e and \u003ccode\u003erun_experiment.sh\u003c/code\u003e scripts:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003echmod +x install.sh\nchmod +x run_experiment.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eSet the next variables according to your preferences in config_vars.sh script:\n\u003ccode\u003eMAIL\u003c/code\u003e, \u003ccode\u003ePROJ_ID\u003c/code\u003e, \u003ccode\u003eMY_INSTALLATION_PATH\u003c/code\u003e, \u003ccode\u003eMY_ISAAC_LOG_PATH\u003c/code\u003e, \u003ccode\u003eMY_SIMULATIONS_PATH\u003c/code\u003e, \u003ccode\u003eMY_SIMULATION_NAME\u003c/code\u003e,\u003ccode\u003eSYSTEM\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport MAIL=\"mymail@myserver.com\"\nexport PROJ_ID=ABC123\nexport MY_INSTALLATION_PATH=$PROJWORK/ABC123/myuserid/sc2022_AD/summit\nexport MY_ISAAC_LOG_PATH=$MY_INSTALLATION_PATH/isaac_logs\nexport MY_SIMULATIONS_PATH=$MY_INSTALLATION_PATH/simulations\nexport MY_SIMULATION_NAME=kh_isaac_test\nexport SYSTEM=summit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote this example installs the software stack on Summit.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute the installation script only once per system:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./install.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-an-experiment\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-an-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning an experiment\u003c/h2\u003e\n\u003cp\u003eFor example, to run the weak_scaling experiment on Summit with 512 GPUs based on the previous section, follow the next steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSet the next variables in config_vars.sh script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport EXPERIMENT_NAME=weak_scaling\nexport N_GPUS=512\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun the run_experiment.sh script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./run_experiment.s\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe complete definition of variables in \u003ccode\u003econfig_vars.sh\u003c/code\u003e script for the 512 GPU weak scaling experiment on Summit is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport MAIL=\"mymail@myserver.com\"\nexport PROJ_ID=ABC123\nexport MY_INSTALLATION_PATH=$PROJWORK/ABC123/myuserid/sc2022_AD/summit\nexport MY_ISAAC_LOG_PATH=$MY_INSTALLATION_PATH/isaac_logs\nexport MY_SIMULATIONS_PATH=$MY_INSTALLATION_PATH/simulations\nexport MY_SIMULATION_NAME=kh_isaac_test\nexport SYSTEM=summit\nexport EXPERIMENT_NAME=weak_scaling\nexport N_GPUS=512\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor completeness, a \u003ccode\u003econfig_vars.sh\u003c/code\u003e script example that is used to install the software stack and run the Crusher\u0027s \u003ccode\u003esecond_experiment\u003c/code\u003e is shown next:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport MAIL=\"mymail@myserver.com\"\nexport PROJ_ID=ABC123\nexport MY_INSTALLATION_PATH=$PROJWORK/ABC123/myuserid/sc2022_AD/crusher\nexport MY_ISAAC_LOG_PATH=$MY_INSTALLATION_PATH/isaac_logs\nexport MY_SIMULATIONS_PATH=$MY_INSTALLATION_PATH/simulations\nexport MY_SIMULATION_NAME=kh_isaac_test\nexport SYSTEM=crusher\nexport EXPERIMENT_NAME=second_experiment\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1623346169.0 + "updated_at": 1649690153.0 }, { "data_format": 2, "description": null, "filenames": [ - "bc3.10--rstudio125042r362/Singularity", - "bc3.12--rstudio125042r405/Singularity" + "Singularity.def" ], - "full_name": "yh549848/singularity-rstudio-rnaseqde", + "full_name": "bsande6/fa1p1_luo", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-running-rstudio-server-in-a-conda-environment\" class=\"anchor\" href=\"#running-rstudio-server-in-a-conda-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Rstudio Server in a Conda Environment\u003c/h1\u003e\n\u003cp\u003eI usually rely on the \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003econda package manager\u003c/a\u003e to manage my environments during development. Thanks to \u003ca href=\"https://conda-forge.org/\" rel=\"nofollow\"\u003econda-forge\u003c/a\u003e and \u003ca href=\"https://bioconda.github.io/\" rel=\"nofollow\"\u003ebioconda\u003c/a\u003e most R packages are now also available through conda. For production,\nI \u003ca href=\"https://github.com/grst/containerize-conda\"\u003econvert them to containers\u003c/a\u003e as these are easier to share.\u003c/p\u003e\n\u003cp\u003eUnfortunately, there seems to be \u003ca href=\"https://community.rstudio.com/t/start-rstudio-server-session-in-conda-environment/12516/15\" rel=\"nofollow\"\u003eno straightforward way\u003c/a\u003e to use conda envs in Rstudio server. This repository provides three approaches to make rstudio server work with conda envs.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#running-rstudio-server-with-singularity\"\u003eRunning Rstudio Server in a Singularity Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-rstudio-server-with-podmandocker\"\u003eRunning Rstudio Server in a Docker/Podman Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-locally\"\u003eRunning Rstudio Server locally\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-rstudio-server-with-singularity\" class=\"anchor\" href=\"#running-rstudio-server-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Rstudio Server with Singularity\u003c/h2\u003e\n\u003cp\u003eWith this approach Rstudio Server runs in a Singularity container (based on \u003ca href=\"https://hub.docker.com/r/rocker/rstudio\" rel=\"nofollow\"\u003erocker/rstudio\u003c/a\u003e).\u003cbr\u003e\nThe conda environment gets mounted into the container - like that there\u0027s no need to rebuild the container to add a package and\n\u003ccode\u003einstall.packages\u003c/code\u003e can be used without issues. The container-based approach has the following benefits:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAuthentication works (\u003ca href=\"https://github.com/grst/rstudio-server-conda/issues/3\"\u003e#3\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSeveral separate instances of Rstudio server can run in parallel, even without the \u003cem\u003ePro\u003c/em\u003e version.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003emamba\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:grst/rstudio-server-conda.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e rstudio-server-conda/singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eActivate the target conda env or set the environment variable \u003ccode\u003eCONDA_PREFIX\u003c/code\u003e\nto point to the location of the conda env.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the \u003ccode\u003erun_singularity.sh\u003c/code\u003e script. In particular, you may need to add additional bind mounts\n(e.g. a global data directory).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExecute the \u003ccode\u003erun_singularity.sh\u003c/code\u003e script. It will automatically build the container if it is not available.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ePORT=8787 PASSWORD=notsafe ./run_singularity.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLog into Rstudio\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eopen rstudio server at \u003ccode\u003ehttp://localhost:8787\u003c/code\u003e (or whatever port you specified)\u003c/li\u003e\n\u003cli\u003elogin with your default username and the password you specified via the \u003ccode\u003ePASSWORD\u003c/code\u003e environment variable.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-rstudio-server-with-podmandocker\" class=\"anchor\" href=\"#running-rstudio-server-with-podmandocker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Rstudio Server with Podman/Docker\u003c/h2\u003e\n\u003cp\u003eThis approach is similar to \u003ca href=\"#running-rstudio-server-with-singularity\"\u003eSingularity\u003c/a\u003e, but uses\nDocker or Podman and a \u003ccode\u003edocker-compose.yml\u003c/code\u003e file instead.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-known-limitations\" class=\"anchor\" href=\"#known-limitations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown limitations\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eNo access to shared group directories (\u003ca href=\"https://github.com/grst/rstudio-server-conda/issues/14\"\u003e#14\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-prerequisites-1\" class=\"anchor\" href=\"#prerequisites-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://podman.io/\" rel=\"nofollow\"\u003ePodman\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/docker/compose\"\u003edocker-compose\u003c/a\u003e or \u003ca href=\"https://github.com/containers/podman-compose\"\u003epodman-compose\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003emamba\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage-1\" class=\"anchor\" href=\"#usage-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:grst/rstudio-server-conda.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the rstudio container (fetches the latest version of \u003ca href=\"https://hub.docker.com/r/rocker/rstudio\" rel=\"nofollow\"\u003erocker/rstudio\u003c/a\u003e and adds some custom scripts)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e rstudio-server-conda/docker\ndocker-compose build \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or podman-compose\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the docker-compose.yml file into your project directory and adjust the paths.\u003c/p\u003e\n\u003cp\u003eYou may want to add additional volumes with your data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e[...]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eports\u003c/span\u003e:\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e port on the host : port in the container (the latter is always 8787)\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e8889:8787\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003evolumes\u003c/span\u003e:\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e mount conda env into exactely the same path as on the host system - some paths are hardcoded in the env.\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/home/sturm/anaconda3/envs/R400:/home/sturm/anaconda3/envs/R400\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Share settings between rstudio instances\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/home/sturm/.local/share/rstudio/monitored/user-settings:/root/.local/share/rstudio/monitored/user-settings\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e mount the working directory containing your R project.\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/home/sturm/projects:/projects\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eenvironment\u003c/span\u003e:\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e password used for authentication\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003ePASSWORD=notsafe\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e repeat the path of the conda environment (must be identical to the path in \"volumes\")\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003eCONDAENV=/home/sturm/anaconda3/envs/R400\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun your project-specific instance of Rstudio-server\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker-compose up \u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLog into Rstudio\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOpen your server at \u003ccode\u003ehttp://localhost:8889\u003c/code\u003e (or whatever port you specified)\u003c/li\u003e\n\u003cli\u003eLogin with the user \u003ccode\u003erstudio\u003c/code\u003e (when using Docker) or \u003ccode\u003eroot\u003c/code\u003e (when using Podman) and the password you specified\nin the \u003ccode\u003edocker-compose.yml\u003c/code\u003e. If you are using Podman and login with \u003ccode\u003erstudio\u003c/code\u003e you won\u0027t have permissions to\naccess the mounted volumes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-locally\" class=\"anchor\" href=\"#running-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Locally\u003c/h2\u003e\n\u003cp\u003eWith this approach a locally installed Rstudio server is ran such that it uses the conda env.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-known-limitations-1\" class=\"anchor\" href=\"#known-limitations-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown limitations\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eno authentication (\u003ca href=\"https://github.com/grst/rstudio-server-conda/issues/3\"\u003e#3\u003c/a\u003e). Use this approach only in a secure network!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-prerequisites-2\" class=\"anchor\" href=\"#prerequisites-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rstudio.com/products/rstudio/download-server/\" rel=\"nofollow\"\u003erstudio server\u003c/a\u003e installed locally\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003emamba\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-usage-2\" class=\"anchor\" href=\"#usage-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repo\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/grst/rstudio-server-conda.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun rstudio server in the conda env\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd rstudio-server-conda/local\nconda activate my_project\n./start_rstudio_server.sh 8787 # use any free port number here. \n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConnect to Rstudio\u003c/p\u003e\n\u003cp\u003eYou should now be able to connect to rstudio server on the port you specify.\n\u003cstrong\u003eIf an R Session has previously been running, you\u0027ll need to rstart the Rsession now\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eObviously, if your env does not have a version of \u003ccode\u003eR\u003c/code\u003e installed, this will either not\nwork at all, or fall back to the system-wide R installation.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-how-it-works\" class=\"anchor\" href=\"#how-it-works\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow it works\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eRstudio server, can be started in non-daemonized mode by each user individually on a custom port (similar to a jupyter notebook). This instance can then run in a conda environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; conda activate my_project\n\u0026gt; /usr/lib/rstudio-server/bin/rserver \\\n --server-daemonize=0 \\\n --www-port 8787 \\\n --rsession-which-r=$(which R) \\\n --rsession-ld-library-path=$CONDA_PREFIX/lib\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo avoid additional problems with library paths, also \u003ccode\u003ersession\u003c/code\u003e needs to run within the conda environment. This is achieved by wrapping \u003ccode\u003ersession\u003c/code\u003e into the \u003ca href=\"https://github.com/grst/rstudio-server-conda/blob/master/local/rsession.sh\"\u003ersession.sh\u003c/a\u003e script. The path to the wrapped \u003ccode\u003ersession\u003c/code\u003e executable can be passed to \u003ccode\u003erserver\u003c/code\u003e as command line argument.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erserver # ...\n --rsession-path=rsession.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen using multiple users a unique \u003ccode\u003esecret-cookie-key\u003c/code\u003e has to be generated for each user. The path to the secret cookie key can be passed to \u003ccode\u003erserver\u003c/code\u003e as a command line parameter.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euuid \u0026gt; /tmp/rstudio-server/${USER}_secure-cookie-key\nrserver # ...\n --secure-cookie-key-file /tmp/rstudio-server/${USER}_secure-cookie-key\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fa1p1_luo\" class=\"anchor\" aria-hidden=\"true\" href=\"#fa1p1_luo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFA1p1_Luo\u003c/h1\u003e\n\u003cp\u003eRepository for Dr. Luo\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eBefore adding to this repo it is recommended to set up a .gitignore file and add the pycache folder\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-baseline-driving-network\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-baseline-driving-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun baseline driving network\u003c/h1\u003e\n\u003cp\u003eCheck that the config file for the assocated folder and configuration has the IMAGE_TRANSLATION config set to False\u003c/p\u003e\n\u003cp\u003epython coiltraine.py --gpus 0 --folder nocrash --exp resnet34imnet10S1 --single-process drive -de NocrashTraining_Town01 --docker carlagear\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-translation\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-translation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun translation\u003c/h1\u003e\n\u003cp\u003eCheck that the config file for the assocated folder and configuration has the IMAGE_TRANSLATION config set to True and the STYLE and AERIAL configs are False.\u003c/p\u003e\n\u003cp\u003eChoose translation checkpoint via the -name and --which_epoch parameters.\u003c/p\u003e\n\u003cp\u003epython coiltraine.py --gpus 0 --folder nocrash --exp resnet34imnet10S1 --single-process drive -de NocrashTraining_Town01 --docker carlagear --name finetune_fromEpoch400_episodes_1000epoch_weight2000.0 --which_epoch 200 --no_instance --n_downsample_global 2\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-translation-with-stylegan\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-translation-with-stylegan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun translation with styleGan\u003c/h1\u003e\n\u003cp\u003eThis is the model which is used for the aerial translation.\u003c/p\u003e\n\u003cp\u003eEnsure that the configuration file correctly set STYLE_TRANSLATION and AERIAL_TRANSLATION. You may also have to change these files in coil_global.py if they are not correctly adjusted.\u003c/p\u003e\n\u003cp\u003eBe sure to replace checkpoint path with the desired checkpoint\u003c/p\u003e\n\u003cp\u003epython coiltraine.py --gpus 0 --folder nocrash --exp resnet34imnet10S1 --single-process drive -de NocrashTraining_Town01 --docker carlagear --checkpoint_path pixel2style2pixel/checkpoints/carla_AtoG/checkpoints/iteration_1000000.pt\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-data_collector\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-data_collector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun data_collector\u003c/h1\u003e\n\u003cp\u003eThe data collection must be run under the old translation environment pix2pix\u003c/p\u003e\n\u003cp\u003epython multi_gpu_collection.py -pt /path/to/data/folder -d dataset_configuration_file\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1623388496.0 + "updated_at": 1668106102.0 }, { "data_format": 2, - "description": "Biobb_structure_utils is the Biobb module collection to modify or extract information from a PDB structure file, such as pulling out a particular model or chain, removing water molecules or ligands, or renumbering or sorting atoms or residues.", + "description": "Parametric face image generator for mooney faces", "filenames": [ - "Singularity.latest" + "Singularity" ], - "full_name": "bioexcel/biobb_structure_utils", + "full_name": "ShreyaKapoor18/parametric-face-image-generator", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d7c1b5de86a2921c1f759b175820fb443eba3f18bbf45e56e42f2cee72844627/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f62696f62622d7374727563747572652d7574696c732f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"\" data-canonical-src=\"https://readthedocs.org/projects/biobb-structure-utils/badge/?version=latest\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/biobb_structure_utils\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a477fdb1fd9bc9eb7ffa6cae6a019c6d4c3902fd468b3126f1b78e56c7dcff83/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://quay.io/repository/biocontainers/biobb_structure_utils\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca418e4db0b3de91a09a5df4a59446da015b6164598a8bc255918e911484f84f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d517561792e696f2d626c7565\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docker-Quay.io-blue\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3836\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-biobb_structure_utils\" class=\"anchor\" href=\"#biobb_structure_utils\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebiobb_structure_utils\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eBiobb_structure_utils is the Biobb module collection to modify or extract information from a PDB structure file, such as pulling out a particular model or chain, removing water molecules or ligands, or renumbering or sorting atoms or residues. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site:\n\u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003elatest API documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-version\" class=\"anchor\" href=\"#version\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h3\u003e\n\u003cp\u003ev3.6.1 2021.2\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eUsing PIP:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eImportant:\u003c/strong\u003e PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install \"biobb_structure_utils\u0026gt;=3.6.1\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: \u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing ANACONDA:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda install -c bioconda \"biobb_structure_utils\u0026gt;=3.6.1\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: With conda installation BioBBs can be used with the \u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e and the \u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing DOCKER:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull quay.io/biocontainers/biobb_structure_utils:3.6.1--pyhdfd78af_0\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run quay.io/biocontainers/biobb_structure_utils:3.6.1--pyhdfd78af_0 \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing SINGULARITY:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMacOS users\u003c/strong\u003e: it\u0027s strongly recommended to avoid Singularity and use \u003cstrong\u003eDocker\u003c/strong\u003e as containerization system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name biobb_structure_utils.sif shub://bioexcel/biobb_structure_utils\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec biobb_structure_utils.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe command list and specification can be found at the \u003ca href=\"https://biobb-structure-utils.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" href=\"#copyright--licensing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2021 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2021 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-acknolegements\" class=\"anchor\" href=\"#acknolegements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknolegements\u003c/h3\u003e\n\u003cp\u003eThis software uses functions to read and modify GRO files based in the \u003ca href=\"https://github.com/caizkun/gropy\"\u003eGROPY\u003c/a\u003e library created by Zhikun Cai (\u003ca href=\"mailto:caizkun@gmail.com\"\u003ecaizkun@gmail.com\u003c/a\u003e) under the \u003ca href=\"https://github.com/caizkun/gropy/blob/master/LICENSE\"\u003eMIT\u003c/a\u003e. In this project \u003ca href=\"https://github.com/caizkun/gropy\"\u003eGROPY\u003c/a\u003e has been adapted to Python 3 and our own needs.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-parametric-face-image-generator\" class=\"anchor\" aria-hidden=\"true\" href=\"#parametric-face-image-generator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eparametric-face-image-generator\u003c/h1\u003e\n\u003cp\u003eThis software enables you to generate fully parametric face images from the Basel Face Model 2017 as proposed in:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e[1] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"http://openaccess.thecvf.com/content_CVPRW_2019/papers/BEFA/Kortylewski_Analyzing_and_Reducing_the_Damage_of_Dataset_Bias_to_Face_CVPRW_2019_paper.pdf\" rel=\"nofollow\"\u003e\"Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data\"\u003c/a\u003e,\nIN: CVPRW (2019)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[2] Adam Kortylewski, Andreas Schneider, Thomas Gerig, Bernhard Egger, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"https://arxiv.org/abs/1802.05891\" rel=\"nofollow\"\u003e\"Training Deep Face Recognition Systems with Synthetic Data\"\u003c/a\u003e,\nIN: arXiv preprint (2018)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[3] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Kortylewski_Empirically_Analyzing_the_CVPR_2018_paper.pdf\" rel=\"nofollow\"\u003e\"Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems\"\u003c/a\u003e,\nIN: CVPRW (2018)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can control the variation of parameters such as pose, shape, color, camera and illumination based on your demand and application.\nThis dataset can be used for training and comparing machine learning techniques such as CNNs on a common ground as proposed in [1,3] by generating fully controlled training and test data.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rendering-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#rendering-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRendering Setup\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_0.png\"\u003e\u003cimg src=\"data/example_images/0_0.png\" alt=\"0_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1.png\"\u003e\u003cimg src=\"data/example_images/0_1.png\" alt=\"0_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2.png\"\u003e\u003cimg src=\"data/example_images/0_2.png\" alt=\"0_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/1_0.png\"\u003e\u003cimg src=\"data/example_images/1_0.png\" alt=\"1_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/1_1.png\"\u003e\u003cimg src=\"data/example_images/1_1.png\" alt=\"1_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/1_2.png\"\u003e\u003cimg src=\"data/example_images/1_2.png\" alt=\"1_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAbove you can see example face images sampled from this data generator. Each row shows different images of the same facial identity.\u003c/p\u003e\n\u003cp\u003eIn the \"controlled\" setup (top row), the model parameters are sampled at equidistant positions along a certain parameter , e.g. the yaw pose.\u003c/p\u003e\n\u003cp\u003eIn the \"random\" setup (bottom row), the model parameters are sampled randomly from a custom distribution.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rendering-different-image-modalities\" class=\"anchor\" aria-hidden=\"true\" href=\"#rendering-different-image-modalities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRendering Different Image Modalities\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1_depth.png\"\u003e\u003cimg src=\"data/example_images/0_1_depth.png\" alt=\"0_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2_depth.png\"\u003e\u003cimg src=\"data/example_images/0_2_depth.png\" alt=\"0_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_3_depth.png\"\u003e\u003cimg src=\"data/example_images/0_3_depth.png\" alt=\"0_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1_correspondence.png\"\u003e\u003cimg src=\"data/example_images/0_1_correspondence.png\" alt=\"1_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2_correspondence.png\"\u003e\u003cimg src=\"data/example_images/0_2_correspondence.png\" alt=\"1_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_3_correspondence.png\"\u003e\u003cimg src=\"data/example_images/0_3_correspondence.png\" alt=\"1_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can render different image modalities such as e.g. depth images (top row), color coded correspondence images (bottom row), normals, albedo or illumination.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rendering-face-regions\" class=\"anchor\" aria-hidden=\"true\" href=\"#rendering-face-regions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRendering Face Regions\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1_region_mask.png\"\u003e\u003cimg src=\"data/example_images/0_1_region_mask.png\" alt=\"0_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2_region_mask.png\"\u003e\u003cimg src=\"data/example_images/0_2_region_mask.png\" alt=\"0_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_3_region_mask.png\"\u003e\u003cimg src=\"data/example_images/0_3_region_mask.png\" alt=\"0_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_1_region_mask_bfm09.png\"\u003e\u003cimg src=\"data/example_images/0_1_region_mask_bfm09.png\" alt=\"0_0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_2_region_mask_bfm09.png\"\u003e\u003cimg src=\"data/example_images/0_2_region_mask_bfm09.png\" alt=\"0_1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/example_images/0_3_region_mask_bfm09.png\"\u003e\u003cimg src=\"data/example_images/0_3_region_mask_bfm09.png\" alt=\"0_2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can render different region maps, while we provide two default ones.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-facial-landmarks\" class=\"anchor\" aria-hidden=\"true\" href=\"#facial-landmarks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFacial Landmarks\u003c/h3\u003e\n\u003cp\u003eFor each face image the location and visibilty of 19 facial landmarks is written in a .tlms file in the following format:\u003c/p\u003e\n\u003cp\u003e\"facial landmark name\" \"visibility\" \"x-position\" \"y-position\"\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003einstalled \u003ca href=\"http://www.oracle.com/technetwork/java/javase/downloads/index.html\" rel=\"nofollow\"\u003eJava\u003c/a\u003e (Version 8.0 or higher recommended)\u003c/li\u003e\n\u003cli\u003edownload jar and config file under \u003ca href=\"https://github.com/unibas-gravis/parametric-face-image-generator/releases\"\u003e\u003ccode\u003erelease\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://gravis.dmi.unibas.ch/PMM/\" rel=\"nofollow\"\u003edownload\u003c/a\u003e Basel Face Model 2017\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://gravis.dmi.unibas.ch/PMM/\" rel=\"nofollow\"\u003edownload\u003c/a\u003e Basel Illumination Prior 2017\u003c/li\u003e\n\u003cli\u003eget a dataset with backgrounds, e.g. the \u003ca href=\"http://www.robots.ox.ac.uk/~vgg/data/dtd/\" rel=\"nofollow\"\u003eDescribable Textures Dataset\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eadapt paths and configuration in \u003ccode\u003edata/config_files/example_config_controlled.json\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eFor generating images in the controlled setup execute:\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ejava -Xmx2g -cp generator.jar faces.apps.ControlledFaces -c data/config_files/example_config_controlled.json\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eFor generating images in the random setup execute:\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ejava -Xmx2g -cp generator.jar faces.apps.RandomFaces -c data/config_files/example_config_random.json\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-for-developers\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor Developers:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003einstalled \u003ca href=\"http://www.oracle.com/technetwork/java/javase/downloads/index.html\" rel=\"nofollow\"\u003eJava\u003c/a\u003e (Version 8.0 or higher recommended)\u003c/li\u003e\n\u003cli\u003einstalled \u003ca href=\"http://www.scala-sbt.org/release/tutorial/Setup.html\" rel=\"nofollow\"\u003esbt\u003c/a\u003e (only for compiling from sources)\u003c/li\u003e\n\u003cli\u003eclone repository\u003c/li\u003e\n\u003cli\u003ecompile and run using \u003ccode\u003esbt run -mem 2000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ewe provide a singularity container recipe file to run the data generator directly on compute servers\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-help-needed\" class=\"anchor\" aria-hidden=\"true\" href=\"#help-needed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHelp needed\u003c/h2\u003e\n\u003cp\u003eThere is a \u003ca href=\"https://groups.google.com/forum/#!forum/scalismo-faces\" rel=\"nofollow\"\u003escalismo-faces google group\u003c/a\u003e for general questions and discussion.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-background\" class=\"anchor\" aria-hidden=\"true\" href=\"#background\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h2\u003e\n\u003cp\u003eBesides the publications listed next, we have also freely available \u003ca href=\"http://gravis.dmi.unibas.ch/PMM/lectures/overview/\" rel=\"nofollow\"\u003electures and tutorials\u003c/a\u003e. Some of the topics covered are statistical shape modeling and model-based image analysis as part of our research about \u003ca href=\"http://gravis.dmi.unibas.ch/PMM/\" rel=\"nofollow\"\u003eProbabilistic Morphable Models\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-hidden=\"true\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublications\u003c/h2\u003e\n\u003cp\u003eIf you use this software you will need the Basel Face Model 2017, the Basel Illumination Prior 2017 and a dataset of backgrounds. Please cite the following papers:\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-data-generator---random-mode\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-generator---random-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Generator - Random Mode\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e[1] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"http://openaccess.thecvf.com/content_CVPRW_2019/papers/BEFA/Kortylewski_Analyzing_and_Reducing_the_Damage_of_Dataset_Bias_to_Face_CVPRW_2019_paper.pdf\" rel=\"nofollow\"\u003e\"Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data\"\u003c/a\u003e,\nIN: CVPRW (2019)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[2] Adam Kortylewski, Andreas Schneider, Thomas Gerig, Bernhard Egger, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"https://arxiv.org/abs/1802.05891\" rel=\"nofollow\"\u003e\"Training Deep Face Recognition Systems with Synthetic Data\"\u003c/a\u003e,\nIN: arXiv preprint (2018)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-data-generator---controlled-mode\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-generator---controlled-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Generator - Controlled Mode\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e[3] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster and Thomas Vetter\n\u003ca href=\"http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Kortylewski_Empirically_Analyzing_the_CVPR_2018_paper.pdf\" rel=\"nofollow\"\u003e\"Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems\"\u003c/a\u003e,\nIN: CVPRW (2018)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basel-face-model-2017\" class=\"anchor\" aria-hidden=\"true\" href=\"#basel-face-model-2017\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasel Face Model 2017\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e[4] Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Luethi, Sandro Schoenborn and Thomas Vetter\n\u003ca href=\"https://arxiv.org/abs/1709.08398\" rel=\"nofollow\"\u003e\" Morphable Face Models - An Open Framework\"\u003c/a\u003e,\nIN: 13th IEEE Conference on Automatic Face and Gesture Recognition (FG 2018)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basel-illumination-prior-2017\" class=\"anchor\" aria-hidden=\"true\" href=\"#basel-illumination-prior-2017\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasel Illumination Prior 2017\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e[5] Bernhard Egger, Sandro Schoenborn, Andreas Schneider, Adam Kortylewski, Andreas Morel-Forster, Clemens Blumer and Thomas Vetter\n\u003ca href=\"http://gravis.dmi.unibas.ch/publications/2018/2018_Egger_IJCV.pdf\" rel=\"nofollow\"\u003e\"Occlusion-aware 3D Morphable Models and an Illumination Prior for Face Image Analysis\"\u003c/a\u003e,\nIN: International Journal of Computer Vision, 2018\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-background-dataset\" class=\"anchor\" aria-hidden=\"true\" href=\"#background-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground Dataset\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eA background dataset of your choice\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBernhard Egger\u003c/li\u003e\n\u003cli\u003eAdam Kortylewski\u003c/li\u003e\n\u003cli\u003eAndreas Morel-Forster\u003c/li\u003e\n\u003cli\u003eAndreas Schneider\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainers\" class=\"anchor\" aria-hidden=\"true\" href=\"#maintainers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainers\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eUniversity of Basel, Graphics and Vision research: \u003ca href=\"https://github.com/unibas-gravis\"\u003e@unibas-gravis\u003c/a\u003e, \u003ca href=\"http://gravis.cs.unibas.ch\" rel=\"nofollow\"\u003ehomepage\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License, Version 2.0\u003c/a\u003e, details see LICENSE\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCopyright 2017, University of Basel, Graphics and Vision Research\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 1, "topics": [], - "updated_at": 1625224033.0 + "updated_at": 1667551934.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity_fastqc", - "Singularity_multiqc", - "Singularity_trimmomatic" + "Singularity", + "Singularity_flipped", + "Singularity_test", + "Singularity_backup", + "Singularity2" ], - "full_name": "uf-icbr-bioinformatics/biocontainers", + "full_name": "mwanakijiji/rrlfe", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-biocontainers\" class=\"anchor\" href=\"#biocontainers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebiocontainers\u003c/h1\u003e\n\u003cp\u003eThis repository contains recipes for containers used to perform QC, summary statistics, and pre-processing on NGS datasets.\u003c/p\u003e\n\u003cp\u003eIn the future, we may provide the containers themselves. Stay tuned. Work in progress.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rrlfe\" class=\"anchor\" aria-hidden=\"true\" href=\"#rrlfe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003errlfe\u003c/h1\u003e\n\u003cp\u003eA code base for generating and applying calibrations for retrieving [Fe/H] from low-res spectra of RR Lyrae variable stars. See \u003ca href=\"https://rrlfe.readthedocs.io/\" rel=\"nofollow\"\u003ehttps://rrlfe.readthedocs.io/\u003c/a\u003e for documentation.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://coveralls.io/github/mwanakijiji/rrlfe?branch=main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c783f25af909dcd1dc513f24cbf780405955d2d29da614210ef15dc39a291c35/68747470733a2f2f636f766572616c6c732e696f2f7265706f732f6769746875622f6d77616e616b696a696a692f72726c66652f62616467652e7376673f6272616e63683d6d61696e\" alt=\"Coverage Status\" data-canonical-src=\"https://coveralls.io/repos/github/mwanakijiji/rrlfe/badge.svg?branch=main\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-attribution\" class=\"anchor\" aria-hidden=\"true\" href=\"#attribution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAttribution\u003c/h2\u003e\n\u003cp\u003eIf this code has been useful for your work, please cite the source in the following BibTeX entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@article{esposito2018,\n Adsurl = {},\n Author = {},\n Doi = {},\n Eid = {},\n Journal = {},\n Keywords = {},\n Month = ,\n Pages = {},\n Title = {{}},\n Volume = ,\n Year = ,\n Bdsk-Url-1 = {}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1623019187.0 + "updated_at": 1648070883.0 }, { "data_format": 2, - "description": null, + "description": "Test species and lineage calls made by mykrobe", "filenames": [ - "BlueprintPipeline/Resource/gemBS-2.1.1/Singularity" + "Python/Singularity.def" ], - "full_name": "Irfanwustl/Research_code", + "full_name": "Mykrobe-tools/mykrobe-lineage-test", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-research_code\" class=\"anchor\" href=\"#research_code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResearch_code\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mykrobe-lineage-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#mykrobe-lineage-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emykrobe-lineage-test\u003c/h1\u003e\n\u003cp\u003eThis repository contains code for testing mykrobe species and lineage calls,\nand results of the testing.\nIt is intended for mykrobe developers, for testing mykrobe species/lineage calls\nand tracking the results.\u003c/p\u003e\n\u003cp\u003eThere are two directories:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003ePython/\u003c/code\u003e: this contains the code, and a Singularity definition file that\nmakes a container with the code plus the dependencies.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAnalysis/\u003c/code\u003e: contains results of testing mykrobe species and lineage calls.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor results, please see the readme in the \u003ccode\u003eAnalysis/\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThis repository has a main script called \u003ccode\u003emlt\u003c/code\u003e (acronym for \"mykrobe lineage\ntest\", yes we are testing species calls as well but\n\"mykrobe lineage species test\" seemed like a bad name!).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eThe easiest way is to build a singularity container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd Python\nsudo singularity build mlt Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun like that, singularity will make a container file called \u003ccode\u003emlt\u003c/code\u003e.\nYou can just treat it as an normal executable, no need to run\n\u003ccode\u003esingularity exec mlt\u003c/code\u003e unless you want to.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource\u003c/h3\u003e\n\u003cp\u003eIf you want to run locally, then you will need these in your \u003ccode\u003e$PATH\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eenaDataGet\u003c/code\u003e, which is from enaBrowserTools (have a look in \u003ccode\u003eSingularity.def\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emykrobe\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e(also \u003ccode\u003efastaq\u003c/code\u003e and \u003ccode\u003encbi-genome-download\u003c/code\u003e are required, but are installed when\nyou install \u003ccode\u003emlt\u003c/code\u003e because they are in the requirements file.)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen run \u003ccode\u003epip install .\u003c/code\u003e from the \u003ccode\u003ePython/\u003c/code\u003e directory. The required python\npackages will be installed (they are in \u003ccode\u003erequirements.txt\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eAlternatively, you could not do pip install, and instead do this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=/path_to/mykrobe-lineage-test/Python /path_to/mykrobe-lineage-test/Python/mlt/__main__.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat command is equivalent to running the script \u003ccode\u003emlt\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testing-lineage-calls\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-lineage-calls\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting lineage calls\u003c/h2\u003e\n\u003cp\u003eIn short, the process is:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePut your sample info in a TSV file.\u003c/li\u003e\n\u003cli\u003eDownload reads using \u003ccode\u003emlt download_data\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun mykrobe on all samples using \u003ccode\u003emlt run_mykrobe\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMake a summary of the results using \u003ccode\u003emlt summary\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sample-tsv\" class=\"anchor\" aria-hidden=\"true\" href=\"#sample-tsv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esample TSV\u003c/h3\u003e\n\u003cp\u003eAll the commands need a TSV of sample information. The format is like\nthis (this is made up data!):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eaccession source species lineage\nSRR12345678 ena Mycobacterium_tuberculosis lineage1.2.3\nGCF_1234567 genbank Mycobacterium_tuberculosis lineage2.3.4\nXY123456 refseq Mycobacterium_tuberculosis lineage3.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou must have columns \u003ccode\u003eaccession\u003c/code\u003e, \u003ccode\u003esource\u003c/code\u003e, \u003ccode\u003especies\u003c/code\u003e and \u003ccode\u003elineage\u003c/code\u003e. They\ncan be in any order (and any extra columns are ignored). The lineage can\nbe \"NA\" if there is no lineage call and you just want to test the species\ncall.\u003c/p\u003e\n\u003cp\u003eThe source must be \u003ccode\u003eena\u003c/code\u003e, \u003ccode\u003egenbank\u003c/code\u003e, or \u003ccode\u003erefseq\u003c/code\u003e, and the \u003ccode\u003eaccession\u003c/code\u003e column\nshould have the corresponding ENA run ID, or genbank/refseq genome accession.\nSince reads are needed for mykrobe, reads are simulated from genomes using\n\u003ccode\u003efastaq to_perfect_reads\u003c/code\u003e, making perfect reads (ie no snp/indel errors)\nof length 75bp, fragment size 200, and depth 20X.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-download-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload data\u003c/h3\u003e\n\u003cp\u003eWith a TSV file of samples \u003ccode\u003esamples.tsv\u003c/code\u003e in the above format, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emlt download_data --cpus 3 samples.tsv Reads\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat example downloads 3 samples in parallel. It makes a directory called\n\u003ccode\u003eReads\u003c/code\u003e containing the downloaded data. It will (well, \u003cem\u003eshould\u003c/em\u003e) not crash\non failed downloads, but carry on and get all the samples it can. Check\nstderr to see what happened.\u003c/p\u003e\n\u003cp\u003eYou can rerun on an existing directory and it will only try to get data\nthat is missing and skip the samples that are already downloaded.\nThis also means you can do hacks like different sample TSV files run\nagainst the same directory of a superset of reads, if you\u0027re feeling\nfancy.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-mykrobe\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-mykrobe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun mykrobe\u003c/h3\u003e\n\u003cp\u003eAssuming you have a directory of downloaded reads from \u003ccode\u003emlt download_data\u003c/code\u003e\ncalled \u003ccode\u003eReads/\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emlt run_mykrobe --cpus 10 samples.tsv Reads Results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat will run 10 samples in parallel. It makes a new directory (if it\ndoesn\u0027t exit already) called \u003ccode\u003eResults\u003c/code\u003e. As for \u003ccode\u003edownload_data\u003c/code\u003e, you can\nrerun against the same directory and it will only run samples that do not\nalready have a mykrobe json file of results. It will ignore samples in the TSV\nwith no reads in \u003ccode\u003eReads/\u003c/code\u003e. It\u0027s up to you to use the right TSV file/Reads\ndirectory/results directory - there is no sanity checking. This does allow\nfor more hacking and testing of samples.\u003c/p\u003e\n\u003cp\u003eIMPORTANT: the first time a sample is run in \u003ccode\u003eResults/\u003c/code\u003e, there is no\nskeletons file. If you ask for more than one CPU, the first sample will be\nrun on its own, making the skeletons file. Then the remaining samples are\nrun using multiprocessing, since they can then all use the skeletons file,\ninstead of all trying to make one at the same time and crashing.\u003c/p\u003e\n\u003cp\u003eThere is an option \u003ccode\u003e--panels_dir\u003c/code\u003e, which will use that option with mykrobe,\nso that you can override the default panels directory and use your own.\nYou probably want this, since the point here is to test species/lineage calls.\nIt is not recommended to change the panel and then use an existing results\ndirectory because the skeletons file that is already might be used!\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-summarise-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#summarise-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummarise results\u003c/h3\u003e\n\u003cp\u003eAssuming you have a samples TSV file \u003ccode\u003esamples.tsv\u003c/code\u003e, a directory of reads\ncalled \u003ccode\u003eReads/\u003c/code\u003e, and a directory of mykrobe runs called \u003ccode\u003eResults/\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emlt summary samples.tsv Reads Results summary.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat makes a new TSV file called \u003ccode\u003esummary.tsv\u003c/code\u003e. It is the same as \u003ccode\u003esamples.tsv\u003c/code\u003e,\nbut with added columns:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecalled_species\u003c/code\u003e and \u003ccode\u003ecalled_lineage\u003c/code\u003e. These are the calls made by mykrobe.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecorrect\u003c/code\u003e: this is \u003ccode\u003etrue|false\u003c/code\u003e, showing if the both the called species and\nlineage were correct. If the expected lineage is \"NA\", then the true/false\ncall only depends on the species.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNow be good and record the results in the \u003ccode\u003eAnalysis/\u003c/code\u003e directory and push\nto github.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1627949747.0 + "updated_at": 1655217767.0 }, { "data_format": 2, - "description": "Hosts DockerFiles to build MRtrix3 containers", + "description": "Docker image for MGKit", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "MRtrix3/containers", + "full_name": "frubino/mgkit-docker-repo", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containers-for-mrtrix3\" class=\"anchor\" href=\"#containers-for-mrtrix3\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers for \u003cem\u003eMRtrix3\u003c/em\u003e\n\u003c/h1\u003e\n\u003cp\u003eHosts recipe files to build \u003cem\u003eMRtrix3\u003c/em\u003e containers\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-docker\" class=\"anchor\" href=\"#using-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Docker\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-terminal-command\" class=\"anchor\" href=\"#run-terminal-command\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun terminal command\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it mrtrix3 \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf not built locally, \u003ccode\u003edocker\u003c/code\u003e will download the latest image from DockerHub.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-gui\" class=\"anchor\" href=\"#run-gui\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun GUI\u003c/h4\u003e\n\u003cp\u003eThese instructions are for Linux.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003exhost +local:root\ndocker run --rm -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY mrtrix3 mrview\nxhost -local:root # Run this when finished.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-locally-build-docker-image\" class=\"anchor\" href=\"#locally-build-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocally build Docker image\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag mrtrix3 .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSet \u003ccode\u003eDOCKER_BUILDKIT=1\u003c/code\u003e to build parts of the Docker image in parallel, which can speed up build time.\nUse \u003ccode\u003e--build-arg MAKE_JOBS=4\u003c/code\u003e to build \u003cem\u003eMRtrix3\u003c/em\u003e with 4 processors (can substitute this with any number of processors \u0026gt; 0); if omitted, \u003cem\u003eMRtrix3\u003c/em\u003e will be built using a single thread only.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-singularity\" class=\"anchor\" href=\"#using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-build-container-natively\" class=\"anchor\" href=\"#build-container-natively\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild container natively\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build MRtrix3_\u0026lt;version\u0026gt;.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-convert-from-docker-container\" class=\"anchor\" href=\"#convert-from-docker-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConvert from Docker container\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build MRtrix3_\u0026lt;version\u0026gt;.sif docker://mrtrix/mrtrix3:\u0026lt;version\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-terminal-command-1\" class=\"anchor\" href=\"#run-terminal-command-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun terminal command\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003eMRtrix3_\u0026lt;version\u0026gt;.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-gui-1\" class=\"anchor\" href=\"#run-gui-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun GUI\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec -B /run MRtrix3_\u0026lt;version\u0026gt;.sif mrview\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-developers-update-minified-external-dependencies\" class=\"anchor\" href=\"#developers-update-minified-external-dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopers: Update minified external dependencies\u003c/h2\u003e\n\u003cp\u003eThis process can only be completed by those with write access to the \u003ca href=\"https://osf.io/5rwp3/\" rel=\"nofollow\"\u003e\"\u003cem\u003eMRtrix3\u003c/em\u003e container dependencies\" OSF project\u003c/a\u003e.\nThese files contain \"minified\" versions of external neuroimaging software package dependencies, containing only those components that are utilised by \u003cem\u003eMRtrix3\u003c/em\u003e scripts.\nThese files should only need to be updated if:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn \u003cem\u003eMRtrix3\u003c/em\u003e update introduces a new feature that invokes some new external software tool not previously utilised;\u003c/li\u003e\n\u003cli\u003eA requisite update occurs in one of these external softwares.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the \u003ccode\u003edocker\u003c/code\u003e and \u003ccode\u003eneurodocker\u003c/code\u003e Python packages.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install docker neurodocker\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the ART ACPCdetect tool from NITRC into the working directory.\u003c/p\u003e\n\u003cp\u003eThis cannot be downloaded directly via e.g. \u003ccode\u003ewget\u003c/code\u003e, as it requires logging in to NITRC; instead, visit the following link with a web browser:\n\u003ca href=\"https://www.nitrc.org/frs/download.php/10595/acpcdetect_v2.0_LinuxCentOS6.7.tar.gz\" rel=\"nofollow\"\u003e\u003ccode\u003ehttps://www.nitrc.org/frs/download.php/10595/acpcdetect_v2.0_LinuxCentOS6.7.tar.gz\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload test data necessary for minification process.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl -fL -# https://github.com/MRtrix3/script_test_data/archive/master.tar.gz | tar xz\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpdate file \u003ccode\u003eminify.Dockerfile\u003c/code\u003e to install the desired versions of external software packages.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild Docker image for \u003ccode\u003eneurodocker-minify\u003c/code\u003e, with complete installations of external packages.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eDOCKER_BUILDKIT=1 docker build --tag mrtrix3:minify --file minify.Dockerfile --build-arg MAKE_JOBS=4 .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eDOCKER_BUILDKIT=1\u003c/code\u003e enables BuildKit, which builds separate build stages in parallel.\nThis can speed up Docker build times in some circumstances.\nIn this case, ANTs and \u003cem\u003eMRtrix3\u003c/em\u003e will be compiled in parallel, and other downloads will be performed at the same time as well.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eMAKE_JOBS\u003c/code\u003e argument controls how many cores are used for compilation of ANTs and \u003cem\u003eMRtrix3\u003c/em\u003e.\nIf BuildKit is utilised, do not specify all of the available threads; specify half or fewer, so that threads are not unnecessarily split across jobs and RAM usage is not excessive.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a minified version of the Docker image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -itd --name mrtrix3 --security-opt=seccomp:unconfined --volume $(pwd)/script_test_data-master:/mnt mrtrix3:minify\nneurodocker-minify --dirs-to-prune /opt --container mrtrix3 --commands \"bash cmds-to-minify.sh\"\ndocker export mrtrix3 | docker import - mrtrix3:minified\ndocker stop mrtrix3\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenerate tarballs for each of the utilised dependencies.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p tarballs\ndocker run --rm -itd --workdir /opt --name mrtrix3 \\\n --volume $(pwd)/tarballs:/output mrtrix3:minified bash\ndocker exec mrtrix3 bash -c \"tar c art | pigz -9 \u0026gt; /output/acpcdetect_\u0026lt;version\u0026gt;.tar.gz\"\ndocker exec mrtrix3 bash -c \"tar c ants | pigz -9 \u0026gt; /output/ants_\u0026lt;version\u0026gt;.tar.gz\"\ndocker exec mrtrix3 bash -c \"tar c fsl | pigz -9 \u0026gt; /output/fsl_\u0026lt;version\u0026gt;.tar.gz\"\ndocker stop mrtrix3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor each tarball, manually replace text \"\u003ccode\u003e\u0026lt;version\u0026gt;\u003c/code\u003e\" with the version number of that particular software that was installed in the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpload these files to \u003ca href=\"https://osf.io/nfx85/\" rel=\"nofollow\"\u003eOSF\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFile \u003ccode\u003eDockerfile\u003c/code\u003e can then be modified to download the desired versions of external software packages.\nAs OSF file download links do not contain file names, which would otherwise indicate the version of each software to be downloaded, please ensure that comments within that file are updated to indicate the version of that software within the tarball.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker-image-for-mgkit\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-image-for-mgkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Image for MGKit\u003c/h1\u003e\n\u003cp\u003eThis is a new Dockerfile that allows the use of MGKit using a container. You can run the scripts directly, for example:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -i frubino/mgkit:latest sampling-utils rand_seq\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWill run the \u003ccode\u003esampling-utils rand_seq\u003c/code\u003e to create some randome FASTA sequences. Commands can be piped as well:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -i frubino/mgkit:latest sampling-utils rand_seq | docker run --rm -i frubino/mgkit:latest fasta-utils translate\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWill translate the random sequneces from the first command. Highly suggested to use an alias, such as:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ealias mgkit=\u0027docker run --rm -i frubino/mgkit:latest\u0027\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis way the above command becomes:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emgkit sampling-utils rand_seq | mgkit fasta-utils translate\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you want to run interactively a series of commands you can use \u003ccode\u003ebash\u003c/code\u003e instead of another command, but remember to add the \u003ccode\u003e-t\u003c/code\u003e option:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -it frubino/mgkit:latest bash\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-branch\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-branch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild branch\u003c/h1\u003e\n\u003cp\u003eA \u003ccode\u003efrubino/mgkit:build\u003c/code\u003e branch is present to allow the creation of Conda packages. Checkout the branch with \u003ccode\u003egit checkout build\u003c/code\u003e. A script is included to build the image and environment are used to specify output directory inside the container, the Python version to use to build and the MGKit version to use\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cblockquote\u003e\n\u003cp\u003eYou need to modify the version of MGKit manually with a tag or commit id (after the \u003ccode\u003e@\u003c/code\u003e in the \u003ccode\u003epip\u003c/code\u003e line)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThere are 2 options to use this image with \u003cem\u003eSingularity\u003c/em\u003e, 1) create a Docker image using the \u003ccode\u003eDockerfile.singularity\u003c/code\u003e and then pull it or 2) building it with \u003cem\u003eSingularity\u003c/em\u003e, for example with \u003ca href=\"https://cloud.sylabs.io/\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/\u003c/a\u003e (command \u003ccode\u003esingularity build --remote\u003c/code\u003e) if \u003ccode\u003eroot\u003c/code\u003e access is not available.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Image\u003c/h2\u003e\n\u003cp\u003eThe main difference between the 2 \u003ccode\u003eDockerfile\u003c/code\u003e is that the Singularity version removes any use of a specific user, because that is mostly done to mount a directory in the image. Also instead of using a version of MGKit in \u003ccode\u003econda\u003c/code\u003e PyPI is used instead.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest\u003c/h2\u003e\n\u003cp\u003eTry to run: \u003ccode\u003esingularity exec mgkit_0.6.0.sif sampling-utils rand_seq | singularity exec mgkit_0.6.0.sif fasta-utils info\u003c/code\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCorrect for the image name used in the build process\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, - "subscribers_count": 10, - "topics": [], - "updated_at": 1612696118.0 + "subscribers_count": 1, + "topics": [ + "mgkit", + "bioinformatics", + "metagenomics", + "metagenomic-analysis", + "evolution" + ], + "updated_at": 1635513477.0 }, { "data_format": 2, @@ -14603,120 +13967,114 @@ var data = "filenames": [ "Singularity" ], - "full_name": "baxpr/demo-singularity-spm-freeview", + "full_name": "dcgc-bfx/singularity-base", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-demo-singularity-container-with-spm12-and-freeview\" class=\"anchor\" href=\"#demo-singularity-container-with-spm12-and-freeview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo singularity container with SPM12 and Freeview\u003c/h1\u003e\n\u003cp\u003eSPM12-based pipelines require a little extra work to get them compiled and working in a\ncontainer. Freesurfer\u0027s Freeview is also included here, as it\u0027s very handy for creating\nthe PDF QA report.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/baxpr/demo-singularity-matlab-fsl\"\u003ehttps://github.com/baxpr/demo-singularity-matlab-fsl\u003c/a\u003e for a lot of detailed info about\nputting Matlab code into singularity containers. This example uses the same structure.\u003c/p\u003e\n\u003cp\u003eA licensed Matlab installation is required to compile the Matlab code, but is not needed\nto run the compiled executable in the container.\u003c/p\u003e\n\u003cp\u003eSPM12 (\u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" rel=\"nofollow\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/a\u003e) is not in this repository and must\nbe installed separately on the compilation host. Edit \u003ccode\u003ematlab/compile_matlab.sh\u003c/code\u003e to point\nto it.\u003c/p\u003e\n\u003cp\u003eSPM requires jumping an extra hurdle at the compilation step - we use a modified version\nof SPM\u0027s own compiler function \u003ccode\u003espm_make_standalone.m\u003c/code\u003e, found at\n\u003ccode\u003ematlab/spm_make_standalone_local.m\u003c/code\u003e. This process captures a lot of dependencies that\ncould otherwise easily be left out of the executable, with the resulting symptom that it\ncompiles just fine but fails at run time with various cryptic error messages. In addition\nto SPM12, everything in the \u003ccode\u003ematlab/src\u003c/code\u003e directory is included in the path at compile time.\nIf Matlab toolboxes are used, they will need to be added to the list of included toolboxes\nin \u003ccode\u003ematlab/spm_make_standalone_local.m\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe compiled Matlab executable is stored on github using git LFS. A regular git clone will\ndownload a pointer text file instead of the executable binary. The result of building a\ncontainer from that will be a cryptic error message - so, compile it yourself. Or, if\nstoring on github, download it manually and replace the pointer text file, or include this\nstep in the Singularity file if helpful - example here:\n\u003ca href=\"https://github.com/baxpr/gf-fmri/blob/master/Singularity.v1.3.4#L65\"\u003ehttps://github.com/baxpr/gf-fmri/blob/master/Singularity.v1.3.4#L65\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFreesurfer requires a license to run:\n\u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall#License\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall#License\u003c/a\u003e\nBest practice is to store your license file on the host that will run the container, and\nbind it to the container at runtime - NOT to include your own license file in the\ncontainer itself.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-dcgc-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#dcgc-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edcgc-base\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1625615789.0 + "updated_at": 1633510107.0 }, { "data_format": 2, - "description": "Singularity recipe for shellcheck", + "description": "Final year Major Project", "filenames": [ - "0.5.0/Singularity" + "gdown.pl/Singularity" ], - "full_name": "icaoberg/singularity-shellcheck", + "full_name": "arshagarwal/FA-GAN", "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n \u003ca href=\"https://camo.githubusercontent.com/762c1129f266494bbbb3faff3d673040cf7b1f19d45c6e13f49b08de12f5116a/68747470733a2f2f692e70617374652e706963732f38373031383966616466363638613935386338616163383366333865373939632e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/762c1129f266494bbbb3faff3d673040cf7b1f19d45c6e13f49b08de12f5116a/68747470733a2f2f692e70617374652e706963732f38373031383966616466363638613935386338616163383366333865373939632e706e67\" width=\"300\" align=\"left\" data-canonical-src=\"https://i.paste.pics/870189fadf668a958c8aac83f38e799c.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-pema\" class=\"anchor\" href=\"#pema\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePEMA:\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-a-flexible-pipeline-for-environmental-dna-metabarcoding-analysis-of-the-16s18s-rrna-its-and-coi-marker-genes\" class=\"anchor\" href=\"#a-flexible-pipeline-for-environmental-dna-metabarcoding-analysis-of-the-16s18s-rrna-its-and-coi-marker-genes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S rRNA, ITS and COI marker genes\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003ePEMA is reposited in\u003c/em\u003e \u003ca href=\"https://hub.docker.com/r/hariszaf/pema\" rel=\"nofollow\"\u003e\u003cem\u003eDocker Hub\u003c/em\u003e\u003c/a\u003e \u003cem\u003eas well as in\u003c/em\u003e \u003ca href=\"https://singularity-hub.org/collections/2295\" rel=\"nofollow\"\u003e\u003cem\u003eSingularity Hub\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-a-pema-tutorial-can-be-found-here\" class=\"anchor\" href=\"#a-pema-tutorial-can-be-found-here\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA PEMA tutorial can be found \u003ca href=\"https://docs.google.com/presentation/d/1lVH23DPa2NDNBhVvOTRoip8mraw8zfw8VQwbK4vkB1U/edit?fbclid=IwAR14PpWfPtxB8lLBBnoxs7UbG3IJfkArrJBS5f2kRA__kvGDUb8wiJ2Cy_s#slide=id.g57f092f54d_1_21\" rel=\"nofollow\"\u003e\u003cstrong\u003ehere\u003c/strong\u003e\u003c/a\u003e.\u003c/h4\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-for-any-troubles-you-may-have-when-running-pema-or-for-any-potential-improvevments-you-would-like-to-suggest-please-share-on-the-pema-gitter-community\" class=\"anchor\" href=\"#for-any-troubles-you-may-have-when-running-pema-or-for-any-potential-improvevments-you-would-like-to-suggest-please-share-on-the-pema-gitter-community\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor any troubles you may have when running PEMA or for any potential improvevments you would like to suggest, please share on the \u003ca href=\"https://gitter.im/pema-helpdesk/community\" rel=\"nofollow\"\u003ePEMA Gitter community\u003c/a\u003e.\u003c/h4\u003e\n\n\u003cp\u003e\u003ca href=\"https://gitter.im/pema-helpdesk/community?utm_source=badge\u0026amp;utm_medium=badge\u0026amp;utm_campaign=pr-badge\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7385c04b449351f12fb57a4bd6f9791ebd68a483493399e50a8f096fadde4246/68747470733a2f2f6261646765732e6769747465722e696d2f70656d612d68656c706465736b2f636f6d6d756e6974792e737667\" alt=\"Gitter\" data-canonical-src=\"https://badges.gitter.im/pema-helpdesk/community.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/400c4e52df43f6a0ab8a89b74b1a78d1a64da56a7848b9110c9d2991bb7c3105/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pema-biodiversity-in-all-its-different-levels\"\u003ePEMA: biodiversity in all its different levels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#a-container-based-tool\"\u003e A container-based tool\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#how-to-run-pema\"\u003eHow to run PEMA\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#parameters-file\"\u003eParameters\u0027 file\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pema-on-hpc\"\u003ePEMA on HPC\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#prerequisites-1\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installing-1\"\u003eInstalling\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-pema-1\"\u003eRunning PEMA\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#example\"\u003eExample\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pema-on-a-simple-pc\"\u003ePEMA on a simple PC\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#prerequisites\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installing\"\u003eInstalling\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-pema\"\u003eRunning PEMA\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#step-1---build-a-docker-container\"\u003eStep 1 - Build a Docker container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#step-2---run-pema\"\u003eStep 2 - Run PEMA\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#the-phyloseq-r-package\"\u003ephyloseq - for a downstream ecological analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#acknowledgments\"\u003eAcknowledgments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-diff\"\u003e\u003cpre\u003e\u003cspan class=\"pl-mi1\"\u003e\u003cspan class=\"pl-mi1\"\u003e+\u003c/span\u003e convertion of the Illumina raw data is now implemented in the framework of PEMA\u003c/span\u003e\n\u003cspan class=\"pl-mi1\"\u003e\u003cspan class=\"pl-mi1\"\u003e+\u003c/span\u003e PEMA now supports 2 extra marker genes, 18S rRNA and ITS. \u003c/span\u003e\n\u003cspan class=\"pl-mi1\"\u003e\u003cspan class=\"pl-mi1\"\u003e+\u003c/span\u003e PEMA is now available for macOS!\u003c/span\u003e\n\u003cspan class=\"pl-mi1\"\u003e\u003cspan class=\"pl-mi1\"\u003e+\u003c/span\u003e for anything feel free to contact me at: haris-zaf@hcmr.gr\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-pema-biodiversity-in-all-its-different-levels\" class=\"anchor\" href=\"#pema-biodiversity-in-all-its-different-levels\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePEMA: biodiversity in all its different levels\u003c/h1\u003e\n\u003cp\u003ePEMA supports the metabarcoding analysis of four marker genes, \u003cstrong\u003e16S rRNA\u003c/strong\u003e (Bacteria), \u003cstrong\u003eITS\u003c/strong\u003e (Fungi) as well as \u003cstrong\u003eCOI\u003c/strong\u003e and \u003cstrong\u003e18S rRNA\u003c/strong\u003e (metazoa). As input, PEMA accepts .fastq.gz files as returned by Illumina sequencing platforms.\u003c/p\u003e\n\u003cp\u003ePEMA processes the reads from each sample and \u003cstrong\u003ereturns an OTU- or an ASV-table with the taxonomies\u003c/strong\u003e of the taxa found and their abundances in each sample. It also returns statistics and a FASTQC diagram about the quality of the reads for each sample. Finally, PEMA supports \u003cstrong\u003edownstream ecological analysis\u003c/strong\u003e of the profiles retrieved, facilitated by the \u003ca href=\"http://joey711.github.io/phyloseq/index.html\" rel=\"nofollow\"\u003ephyloseq\u003c/a\u003e R package.\u003c/p\u003e\n\u003cp\u003ePEMA supports both OTU clustering (thanks to VSEARCH and CROP algorithms) and ASV inference (via SWARM) for all four marker genes.\u003c/p\u003e\n\u003cp\u003eFor the case of the 16S rRNA marker gene, PEMA includes two separate approaches for taxonomy assignment: alignment-based and phylogenetic-based. For the latter, a reference tree of 1000 taxa was created using SILVA_132_SSURef, EPA-ng and RaxML-ng.\u003c/p\u003e\n\u003cp\u003ePEMA has been implemented in \u003ca href=\"https://pcingola.github.io/BigDataScript/\" rel=\"nofollow\"\u003eBigDataScript\u003c/a\u003e programming language. BDS\u2019s ad hoc task parallelism and task synchronization, supports heavyweight computation. Thus, PEMA inherits such features and it also supports roll-back checkpoints and on-demand partial pipeline execution. In addition, PEMA takes advantage of all the computational power available on a specific machine; for example, if PEMA is executed on a personal laptop with 4 cores, it is going to use all four of them.\u003c/p\u003e\n\u003cp\u003eFinally, container-based technologies such as Docker and Singularity, make PEMA easy accessible for all operating systems.\nAs you can see in the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/help_files/GitHub%20tutorial.pdf\"\u003ePEMA_tutorial.pdf\u003c/a\u003e, once you have either Docker or Singularity on your computational environment (see below which suits your case better), running PEMA is cakewalk. You can also find the \u003ca href=\"https://docs.google.com/presentation/d/1lVH23DPa2NDNBhVvOTRoip8mraw8zfw8VQwbK4vkB1U/edit?usp=sharing\" rel=\"nofollow\"\u003e\u003cstrong\u003ePEMA tutorial\u003c/strong\u003e\u003c/a\u003e as a Google Slides file.\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-a-container-based-tool\" class=\"anchor\" href=\"#a-container-based-tool\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA container-based tool\u003c/h1\u003e\n\u003cp\u003ePEMA can run either on a HPC environment (server, cluster etc) or on a simple PC. However, we definitely suggest to run it on an HPC environment to exploit the full potential of PEMA. Running on a powerful server or a cluster can be time-saving since it would require significantly less computational time than in a common PC. However, for analyses with a small number of samples, a common PC can suffice.\u003c/p\u003e\n\u003cp\u003eThere is one \u003cstrong\u003emajor difference\u003c/strong\u003e between running PEMA on a common PC than running it on a HPC environment. In the first case, PEMA runs through \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003e\u003cstrong\u003eDocker\u003c/strong\u003e\u003c/a\u003e, while in the latter one, it runs through \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOn the following chapters, you can find how to install PEMA both in Docker and Singlularity including examples.\u003c/p\u003e\n\u003cp\u003eRunning PEMA is exactly \u003cstrong\u003ethe same\u003c/strong\u003e procedure in both of those cases.\u003c/p\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-run-pema\" class=\"anchor\" href=\"#how-to-run-pema\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run PEMA\u003c/h2\u003e\n\u003cp\u003eAssuming you have either Docker or Singularity on your system (see below how to get them).\nYou need to create a directory where you will have everything PEMA needs - we will call it \u003cem\u003e\u003cstrong\u003eanalysis directory\u003c/strong\u003e\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn this directory, you need to add the following \u003cstrong\u003emandatory\u003c/strong\u003e files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003e\u003cstrong\u003eparameters.tsv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file (you can download it from this repository and then \u003cstrong\u003ecomplete it\u003c/strong\u003e according to the needs of your analysis)\u003c/li\u003e\n\u003cli\u003ea subdirectory called \u003cem\u003e\u003cstrong\u003emydata\u003c/strong\u003e\u003c/em\u003e where your .fastq.gz files will be located \u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf your need to perform phyloseq, in the analysis directory you also need to add the following \u003cstrong\u003eoptionally\u003c/strong\u003e files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/phyloseq_in_PEMA.R\"\u003e\u003cem\u003e\u003cstrong\u003ephyloseq_in_PEMA.R\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e which you can also download from this repository and set it the way you want (that is an R script which we have implemented and has some main features that need to stay always the same in order to be executed as part of PEMA and some parts where the user can set what exactly needs to get from the phyloseq package)\u003c/li\u003e\n\u003cli\u003ethe \u003ca href=\"https://raw.githubusercontent.com/hariszaf/pema/master/analysis_directory/metadata.csv\" rel=\"nofollow\"\u003e\u003cem\u003e\u003cstrong\u003emetadata.csv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file which has to be in a \u003cstrong\u003ecomma separated\u003c/strong\u003e format (you can find an example of this file on PEMA\u0027s GitHub repository).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-attention--\" class=\"anchor\" href=\"#attention--\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eAttention!\u003c/strong\u003e \u003cbr\u003e\n\u003c/h3\u003e\n\u003cp\u003ePEMA will \u003cstrong\u003efail\u003c/strong\u003e unless you name the aforementioned files and directories \u003cstrong\u003eexactly\u003c/strong\u003e as described above.\n\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eHere is an example of how your \u003cem\u003eanalysis directory\u003c/em\u003e should be in case you do want a phyloseq analysis:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@home-PC:~/Desktop/analysis_directory$ ls\nmydata parameters.tsv phyloseq_in_PEMA.R metadata.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand in case you do not:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@home-PC:~/Desktop/analysis_directory$ ls\nmydata parameters.tsv \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/hariszaf/pema/tree/master/analysis_directory\"\u003e\u003cstrong\u003eHere\u003c/strong\u003e\u003c/a\u003e you can find an example of an \u003cem\u003eanalysis directory\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eAfter you have prepared this \u003cem\u003eanalysis directory\u003c/em\u003e you are ready to run PEMA (see below).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAn extended list with PEMA\u0027s ouput can be found \u003ca href=\"https://github.com/hariszaf/pema/blob/master/help_files/PEMA\u0027s%20output%20files.md\"\u003e\u003cstrong\u003ehere\u003c/strong\u003e\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-parameters-file\" class=\"anchor\" href=\"#parameters-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u0027 file\u003c/h1\u003e\n\u003cp\u003eThe most crucial component in running PEMA is the parameters file. This file must be located \u003cstrong\u003ein\u003c/strong\u003e the \u003cem\u003eanalysis directory\u003c/em\u003e and the user needs to fill it \u003cstrong\u003eevery time\u003c/strong\u003e PEMA is about to be called. If you need more than one analyses to run, then you need to make copies of the parameters\u0027 file and have one of those in eah of the analysis directrories you create.\u003c/p\u003e\n\u003cp\u003eSo, here is the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003e\u003cstrong\u003eparameters.tsv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file as it looks like, in a study case of our own.\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-pema-on-hpc\" class=\"anchor\" href=\"#pema-on-hpc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePEMA on HPC\u003c/h1\u003e\n\u003cp\u003ePEMA is best to run on HPC (server, cluster, cloud). Usually environmental data are quite large and the whole process has huge computational demands. To get PEMA running on your HPC you (actually your system administrator) need to install Singularity as described below.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/strong\u003e is a free, cross-platform and open-source computer program that performs operating-system-level virtualization also known as containerization. One of the main uses of Singularity is to bring containers and reproducibility to scientific computing and the high-performance computing (HPC) world.\u003c/p\u003e\n\u003cp\u003eSingularity needs a Linux/Unix system to run.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing\" class=\"anchor\" href=\"#installing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling\u003c/h2\u003e\n\u003cp\u003eAfter you install Singularity in your environment and open it, you need to download PEMA\u0027s image from Singularity Hub, by running the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull shub://hariszaf/pema:v.1.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow you have PEMA on your environment. But there is still one really \u003cstrong\u003eimportant\u003c/strong\u003e thing that you need to do! Please \u003cstrong\u003edownload\u003c/strong\u003e the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003eparameters.tsv\u003c/em\u003e\u003c/a\u003e file and move it or copy it to the same directory with your raw data.\u003c/p\u003e\n\u003cp\u003eNow you are ready to go!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-pema\" class=\"anchor\" href=\"#running-pema\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning PEMA\u003c/h2\u003e\n\u003cp\u003eSingularity permits the use of a job scheduler that allocates computional resources on clusters and at the same time, works as a queuing system, as \u003cstrong\u003e\u003ca href=\"https://slurm.schedmd.com/overview.html\" rel=\"nofollow\"\u003eSlurm\u003c/a\u003e\u003c/strong\u003e. This way you are able to create a job as you usually do in your system and after editing the parameters file as needed, run PEMA as a job on your cluster.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example\" class=\"anchor\" href=\"#example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e#SBATCH --partition=batch\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=20\n#SBATCH --mem=\n# Memory per node specification is in MB. It is optional.\n# The default limit is 3000MB per core.\n#SBATCH --job-name=\"testPema\"\n#SBATCH --output=PEMA.output\n#SBATCH --mail-user=haris-zafr@hcmr.gr\n#SBATCH --mail-type=ALL\n#SBATCH --requeue\n\n\nsingularity run -B /\u0026lt;path\u0026gt;/\u0026lt;of\u0026gt;/\u0026lt;input\u0026gt;/\u0026lt;directory\u0026gt;/:/mnt/analysis /\u0026lt;path\u0026gt;/\u0026lt;of\u0026gt;/\u0026lt;PEMA_container\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the above example, we set the cluster \"Zorba\", to run PEMA in 1 node, with 20 cores.\u003c/p\u003e\n\u003cp\u003eFor further information, you can always check \u003ca href=\"https://docs.google.com/presentation/d/1lVH23DPa2NDNBhVvOTRoip8mraw8zfw8VQwbK4vkB1U/edit?fbclid=IwAR14PpWfPtxB8lLBBnoxs7UbG3IJfkArrJBS5f2kRA__kvGDUb8wiJ2Cy_s#slide=id.g57f092f54d_1_21\" rel=\"nofollow\"\u003ePEMA\u0027s tutorial\u003c/a\u003e.\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-pema-on-a-simple-pc\" class=\"anchor\" href=\"#pema-on-a-simple-pc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePEMA on a simple PC\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites-1\" class=\"anchor\" href=\"#prerequisites-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eTo run PEMA in a simple PC on your own environment, you first need to install \u003ca href=\"https://docs.docker.com/install/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, in case you do not already have it.\u003c/p\u003e\n\u003cp\u003eYou should check your software version. A version of Docker is avalable for all Windows, Mac and Linux. If you have Windows 10 Pro or your Mac\u0027s hardware in after 2010, then you can insall Docker straightforward. Otherwise, you need to install the \u003ca href=\"https://docs.docker.com/toolbox/\" rel=\"nofollow\"\u003eDocker toolbox\u003c/a\u003e instead. You can check if your System Requirements are according to the ones mentioned below in order to be sure what you need to do.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSystem Requirements\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e**__Windows 10 64bit__**:\nPro, Enterprise or Education (1607 Anniversary Update, Build 14393 or later).\nVirtualization is enabled in BIOS. Typically, virtualization is enabled by default.\nThis is different from having Hyper-V enabled. For more detail see Virtualization must be enabled in Troubleshooting.\nCPU SLAT-capable feature.\nAt least 4GB of RAM.\n\n**__Mac__**\nMac hardware must be a 2010 or newer model, with Intel\u2019s hardware support for memory management unit (MMU)\nvirtualization, including Extended Page Tables (EPT) and Unrestricted Mode. You can check to see if your machine\nhas this support by running the following command in a terminal:\nsysctl kern.hv_support macOS El Capitan 10.11 and newer macOS releases are supported.\nWe recommend upgrading to the latest version of macOS.\nAt least 4GB of RAM\nVirtualBox prior to version 4.3.30 must NOT be installed (it is incompatible with Docker for Mac).\nIf you have a newer version of VirtualBox installed, it\u2019s fine.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-1\" class=\"anchor\" href=\"#installing-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling\u003c/h2\u003e\n\u003cp\u003eAfter you install Docker in your environment and run it, the only thing you need to do, is to download PEMA\u0027s image, by running the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull hariszaf/pema\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe PEMA image file is a quite large (~3Gb), so it will take a while until it is downloaded in your computer system.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-pema-1\" class=\"anchor\" href=\"#running-pema-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning PEMA\u003c/h2\u003e\n\u003cp\u003eRunning PEMA has two discrete steps.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-step-1---build-a-docker-container\" class=\"anchor\" href=\"#step-1---build-a-docker-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1 - Build a Docker container\u003c/h3\u003e\n\u003cp\u003eAt first, you need to let Docker have access in your dataset. To provide access you need to run the following command and specifying the path to where your data is stored, i.e. changing the \u0026lt;path_to_analysis_directory\u0026gt; accordingly:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it -v /\u0026lt;path_to_analysis_directory\u0026gt;/:/mnt/analysis hariszaf/pema\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter you run the command above, you have now built a Docker container, in which you can run PEMA!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-step-2---run-pema\" class=\"anchor\" href=\"#step-2---run-pema\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2 - Run PEMA\u003c/h3\u003e\n\u003cp\u003eNow, being inside the PEMA container, the only thing remaining to do, is to run PEMA\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./PEMA_v1.bds\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePEMA is now running. The runtime of PEMA depends on the computational features of your environment, on the size of your data, as well as the parameters you chose.\u003c/p\u003e\n\u003cp\u003ePlease, keep in mind that when you need to copy a whole directory, then you always have to put \"/\" in the end of the path that describes where the directory is located.\u003c/p\u003e\n\u003cp\u003eFinally, you will find the PEMA output in the analysis directory on your computer. \u003cbr\u003e\nAs the output directory is mounted into the built Docker container, you can copy its contents wherever you want. However, in case you want to remove it permanently, you need to do this as a sudo user.\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-the-phyloseq-r-package\" class=\"anchor\" href=\"#the-phyloseq-r-package\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe \"phyloseq\" R package\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003efor a downstream ecological analysis of OTUs/ASVs retrieved\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePEMA performs all the basic functions of the \"phyloseq\" R package. In addition, it performs certain functions of the \u003ca href=\"https://cran.r-project.org/web/packages/vegan/index.html\" rel=\"nofollow\"\u003e\u003cem\u003e\u003cstrong\u003evegan\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e R package.\u003c/p\u003e\n\u003cp\u003eWhen the user asks for a downstream analysis using the \"phyloseq\" R package, then an extra input file called \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/phyloseq_in_PEMA.R\"\u003e\u003cem\u003e\u003cstrong\u003e\"phyloseq_script.R\"\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e needs to be imported in the \"analysis_directory\". In PEMA\u0027s main repository, you can find a template of this file; this file needs to be as it would run on your own computer, as you would run \u003cem\u003ephyloseq\u003c/em\u003e in any case. PEMA will create the \u003cem\u003e\"phyloseq object\"\u003c/em\u003e automatically and then it will perform the analysis as asked. The output will be placed in an extra subfolder in the main output directory of PEMA called \u003cem\u003ephyloseq_analysis\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition, the \u003cem\u003e\u003cstrong\u003emetadata.tsv\u003c/strong\u003e\u003c/em\u003e file is also required when the phyloseq option has been selected. An example of this file you can find \u003ca href=\"https://raw.githubusercontent.com/hariszaf/pema/master/analysis_directory/metadata.csv\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" href=\"#acknowledgments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h1\u003e\n\u003cp\u003ePEMA uses a series of tools, datasets as well as Big Data Script language. We thank all the groups that developed them.\nThe tools \u0026amp; databases that PEMA uses are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBigDataScript programming language - \u003ca href=\"https://pcingola.github.io/BigDataScript/\" rel=\"nofollow\"\u003ehttps://pcingola.github.io/BigDataScript/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFASTQC - \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u03a4rimmomatic - \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003ehttp://www.usadellab.org/cms/?page=trimmomatic\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCutadapt - \u003ca href=\"https://cutadapt.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ehttps://cutadapt.readthedocs.io/en/stable/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBayesHammer - included in SPAdes - \u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003ehttp://cab.spbu.ru/software/spades/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePANDAseq - \u003ca href=\"https://github.com/neufeld/pandaseq\"\u003ehttps://github.com/neufeld/pandaseq\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOBITools - \u003ca href=\"https://pythonhosted.org/OBITools/welcome.html\" rel=\"nofollow\"\u003ehttps://pythonhosted.org/OBITools/welcome.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBLAST Command Line Applications - \u003ca href=\"https://www.ncbi.nlm.nih.gov/books/NBK52640/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK52640/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVSEARCH-2.9.1 - \u003ca href=\"https://github.com/torognes/vsearch/releases/tag/v2.9.1\"\u003ehttps://github.com/torognes/vsearch/releases/tag/v2.9.1\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSWARM - \u003ca href=\"https://github.com/torognes/swarm\"\u003ehttps://github.com/torognes/swarm\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCROP - \u003ca href=\"https://github.com/tingchenlab/CROP\"\u003ehttps://github.com/tingchenlab/CROP\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCREST - \u003ca href=\"https://github.com/lanzen/CREST\"\u003ehttps://github.com/lanzen/CREST\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRDPClassifier - \u003ca href=\"https://github.com/rdpstaff/classifier\"\u003ehttps://github.com/rdpstaff/classifier\u003c/a\u003e\n(RPDtools are required in order to execute RDPClassifier)\u003c/li\u003e\n\u003cli\u003eSILVA db - \u003ca href=\"https://www.arb-silva.de/no_cache/download/archive/current/Exports/\" rel=\"nofollow\"\u003ehttps://www.arb-silva.de/no_cache/download/archive/current/Exports/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMIDORI db - \u003ca href=\"http://reference-midori.info/index.html\" rel=\"nofollow\"\u003ehttp://reference-midori.info/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\"phat\" algorithm, from the \"gappa\" package - \u003ca href=\"https://github.com/lczech/gappa/wiki/Subcommand:-phat\"\u003ehttps://github.com/lczech/gappa/wiki/Subcommand:-phat\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMAFFT - \u003ca href=\"https://mafft.cbrc.jp/alignment/software/\" rel=\"nofollow\"\u003ehttps://mafft.cbrc.jp/alignment/software/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRAxML -ng - \u003ca href=\"https://github.com/amkozlov/raxml-ng\"\u003ehttps://github.com/amkozlov/raxml-ng\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePaPaRa - \u003ca href=\"https://cme.h-its.org/exelixis/web/software/papara/index.html\" rel=\"nofollow\"\u003ehttps://cme.h-its.org/exelixis/web/software/papara/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEPA-ng - \u003ca href=\"https://github.com/Pbdas/epa-ng\"\u003ehttps://github.com/Pbdas/epa-ng\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ephyloseq R package - \u003ca href=\"http://joey711.github.io/phyloseq/index.html\" rel=\"nofollow\"\u003ehttp://joey711.github.io/phyloseq/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003evegan R package - \u003ca href=\"https://cran.r-project.org/web/packages/vegan/index.html\" rel=\"nofollow\"\u003ehttps://cran.r-project.org/web/packages/vegan/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnd of course the container-based technologies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker - \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehttps://www.docker.com/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity - \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003ehttps://sylabs.io/singularity/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003ePEMA is under the GNU GPLv3 license (for 3rd party components separate licenses apply).\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h1\u003e\n\u003cp\u003eHaris Zafeiropoulos, Ha Quoc Viet, Katerina Vasileiadou, Antonis Potirakis, Christos Arvanitidis, Pantelis Topalis, Christina Pavloudi, Evangelos Pafilis, PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes, GigaScience, Volume 9, Issue 3, March 2020, giaa022, \u003ca href=\"https://doi.org/10.1093/gigascience/giaa022\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/gigascience/giaa022\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-official-implementation-of-the-paper-titled-fa-gan-high-resolution-face-aging\" class=\"anchor\" aria-hidden=\"true\" href=\"#official-implementation-of-the-paper-titled-fa-gan-high-resolution-face-aging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOfficial Implementation of the paper titled \u003ca href=\"https://ieeexplore.ieee.org/document/9514090\" rel=\"nofollow\"\u003eFA-GAN: High Resolution Face-Aging\u003c/a\u003e\n\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-steps-to-run-using-docker-using-nvidia-gpus\" class=\"anchor\" aria-hidden=\"true\" href=\"#steps-to-run-using-docker-using-nvidia-gpus\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to run using docker using Nvidia gpus:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall docker using \u003ca href=\"https://docs.docker.com/engine/install/ubuntu/\" rel=\"nofollow\"\u003edocker installation guide\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eInstall nvidia-docker2 using \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker\" rel=\"nofollow\"\u003envidia-docker2 installation guide\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNote: Ensure to check the latest versions of nvidia cuda runtime and coroborrate with pytorch cuda requirements , this guide was uploaded on 4/9/2020.\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild the image using \u003ccode\u003edocker build -f docker -t slimgan:gpu\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eRun the Container using \u003ccode\u003edocker run --gpus all --shm-size 10G -it slimgan:gpu\u003c/code\u003e.\n5.Run the \u003ccode\u003envidia-smi\u003c/code\u003e to check if gpus work.\u003c/li\u003e\n\u003cli\u003eRun thr command \u003ccode\u003epython main.py --rafd_image_dir Big_slim --num_iters 30000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 --resume_iters 20000 --num_workers 5\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-steps-to-run-the-code-on-google-colab-just-3-lines-of-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#steps-to-run-the-code-on-google-colab-just-3-lines-of-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to run the code on Google colab (just 3 lines of code):\u003c/h2\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eClone the code by running the command \u003ccode\u003e!git clone https://username:password@github.com/arshagarwal/C_slim_gan.git -b arsh_spectral\u003c/code\u003e .\n\u003cstrong\u003eReplace the username and password with your github username and password respectively.\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003ecd C_slim_gan\u003c/code\u003e to navigate to the \u003cstrong\u003eC_slim_gan\u003c/strong\u003e directory.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003e!bash import_dataset.sh\u003c/code\u003e to import the \u003cstrong\u003eBig slim dataset\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003e! python main.py --rafd_image_dir Big_slim --num_iters 20000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 \u003c/code\u003e to train on the \u003cstrong\u003eBig_slim_dataset\u003c/strong\u003e.\n\u003cstrong\u003eAlternatively to train on custom dataset replace the \u003ccode\u003eslim_dataset/Train_dataset\u003c/code\u003e string with the path of your custom dataset.\u003c/strong\u003e\u003cbr\u003e\nFor further options such as \u003cstrong\u003enumber of epochs, batch_size etc\u003c/strong\u003e refer \u003cstrong\u003emain.py\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-note-after-running-step-3-after-every-sample-stepth-iteration-generated-samples-will-be-stored-in-stargansamples-folder-for-performance-evaluation\" class=\"anchor\" aria-hidden=\"true\" href=\"#note-after-running-step-3-after-every-sample-stepth-iteration-generated-samples-will-be-stored-in-stargansamples-folder-for-performance-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNote: After running step 3 after every sample stepth iteration generated samples will be stored in stargan/samples folder for performance evaluation.\u003c/h3\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-continuing-training-from-20000-iteration-to-30000-iteration-follow\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-continuing-training-from-20000-iteration-to-30000-iteration-follow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor Continuing training from 20000 iteration to 30000 iteration follow:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003epython main.py --rafd_image_dir Big_slim --num_iters 30000 --sample_step 500 --c_dim 2 --log_step 100 --model_save_step 5000 --batch_size 64 --n_critic 2 --rafd_crop_size 128 --image_size 128 --resume_iters 20000\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1622859451.0 + "updated_at": 1654958671.0 }, { "data_format": 2, - "description": "Singularity recipe for ABySS", + "description": "Script allowing to convert a NIfTI file with ROIs to the DICOM SEG format.", "filenames": [ - "2.1.5/Singularity" + "Singularity.nifti-to-seg" ], - "full_name": "icaoberg/singularity-abyss", + "full_name": "roger-schaer/nifti-to-seg", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hyperfine\" class=\"anchor\" href=\"#singularity-hyperfine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hyperfine\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/hyperfine\"\u003ehyperfine\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehyperfine\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hyperfine/1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hyperfine\u003c/code\u003e as \u003ccode\u003e1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nifti-to-seg-converter\" class=\"anchor\" aria-hidden=\"true\" href=\"#nifti-to-seg-converter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNIfTI to SEG Converter\u003c/h1\u003e\n\u003cp\u003eThis project allows you to convert a NIfTI file containing\none or more non-overlapping regions-of-interest (ROIs)\ninto the DICOM Segmentation (SEG) format.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eThe following instructions will help you to perform your\nfirst NIfTI to SEG conversion.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cp\u003eYou can either run the project directly with Python, or\nuse Docker instead. If you want to run it directly with\nPython, you need to install the dependencies listed in\nrequirements.txt:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enumpy\ngit+https://github.com/roger-schaer/pydicom-seg.git#egg=pydicom-seg\nSimpleITK\npalettable\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-general-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#general-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral usage\u003c/h3\u003e\n\u003cp\u003eThe script expects the following arguments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-i, --dicom_input\u003c/code\u003e : The path of the folder with the\noriginal DICOM images (from which ROIs were extracted)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-n, --nifti_roi\u003c/code\u003e : The path of the NIfTI file containing\nthe ROI(s) to convert to DICOM SEG\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o, --output_seg\u003c/code\u003e : The path where the created DICOM SEG\nfile should be saved\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-l, --label_map\u003c/code\u003e : \u003cem\u003e(OPTIONAL)\u003c/em\u003e The path to a CSV file containing\npairs of \u003ccode\u003e\u0026lt;label_id\u0026gt;,\u0026lt;label_name\u0026gt;\u003c/code\u003e entries\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d, --match-orientation\u003c/code\u003e : \u003cem\u003e(OPTIONAL)\u003c/em\u003e No value;\npresence of argument indicates that orientation of NIfTI file will be matched to DICOM images\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-s, --match-size\u003c/code\u003e : \u003cem\u003e(OPTIONAL)\u003c/em\u003e No value;\npresence of argument indicates that size of NIfTI file will be matched to DICOM images.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo execute the script, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython nifti_to_seg.py -i /path/to/dicom/images -n /path/to/nifti.nii -o /path/to/seg.dcm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen the script is executed, it will analyze the provided\nNIfTI file to identify the various ROIs saved within. This\nis done by detecting the \u003cstrong\u003eunique\u003c/strong\u003e pixel values present in\nthe image.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-without-a-label-map-file-manual-label-name-entry\" class=\"anchor\" aria-hidden=\"true\" href=\"#without-a-label-map-file-manual-label-name-entry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWithout a label map file (manual label name entry)\u003c/h4\u003e\n\u003cp\u003eIf you have not provided a label map file path, you will then\nbe prompted to map each of these values to a string describing\nthe content of the associated ROI. To know which pixel value\ncorresponds to which ROI, you may need to refer to the software\nthat generated the NIfTI file (e.g. ITK-SNAP, which uses label\nnumbers starting from 1).\u003c/p\u003e\n\u003cp\u003eThe output looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFound X regions in the NIfTI file, please input a name for each of them.\n(1/X) - Please insert a name for the region with the assigned number N: ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the names have been input, the SEG file will be\ngenerated and saved at the path provided in the \u003ccode\u003e-o\u003c/code\u003e\nargument.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-with-a-label-map-file-bulk-processing\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-a-label-map-file-bulk-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith a label map file (bulk processing)\u003c/h4\u003e\n\u003cp\u003eInstead of inputting the label mappings manually, you can also provide\nthe \u003ccode\u003e-l\u003c/code\u003e / \u003ccode\u003e--label_map\u003c/code\u003e parameter pointing to a CSV file containing\npairs of \u003ccode\u003e\u0026lt;label_id\u0026gt;,\u0026lt;label_name\u0026gt;\u003c/code\u003e entries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE :\u003c/strong\u003e This methods requires you to know in advance the existing\npixel values in the NIfTI segmentation file. Only exhaustive files\ncontaining a label for each identified pixel value are accepted.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Docker\u003c/h2\u003e\n\u003cp\u003eTo run the script using docker, use the following syntax:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it \\\n-v /path/to/data/on/host:/data \\\nmedgift/nifti-to-seg:latest \\\n--dicom_input=/data/dicom_folder \\\n--nifti_roi=/data/seg.nii \\\n--output_seg=/data/seg.dcm \\\n--label_map=/data/labels.csv (OPTIONAL)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe parameters are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--rm\u003c/code\u003e removes the container once the script completes.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-it\u003c/code\u003e allows interacting with the container in the console.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emedgift/nifti-to-seg:latest\u003c/code\u003e is the Docker image.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-v\u003c/code\u003e maps a folder from your computer to the container (on \u003ccode\u003e/data\u003c/code\u003e).\nPut all necessary files in that folder (DICOM \u0026amp; NIfTI), and the\noutput will be written there as well.\u003c/li\u003e\n\u003cli\u003eThe other parameters are the same as for general Python usage.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Singularity\u003c/h2\u003e\n\u003cp\u003eSee the \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e pages for setup.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Image\u003c/h3\u003e\n\u003cp\u003eEnter the directory where this readme file is located.\nBuild the singularity image with name \u003cem\u003emeshtool.sif\u003c/em\u003e by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build nifti_to_seg.sif Singularity.nifti-to-seg\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-meshtool-from-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-meshtool-from-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning MeshTool from Singularity Image\u003c/h3\u003e\n\u003cp\u003eYou can enter a shell in the singularity container by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -e /path/to/nifti_to_seg.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLeave the singularity shell again with \u003ccode\u003eexit\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRoger Schaer\u003c/strong\u003e - \u003cem\u003eInitial work\u003c/em\u003e - \u003ca href=\"https://github.com/roger-schaer\"\u003eroger-schaer\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis project is licensed under the MIT License - see the \u003ca href=\"LICENSE.md\"\u003eLICENSE.md\u003c/a\u003e file for details\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/razorx89\"\u003erazorx89\u003c/a\u003e for the great work\non \u003ca href=\"https://github.com/razorx89/pydicom-seg\"\u003epydicom-seg\u003c/a\u003e,\nwhich is the core of this script\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1622730467.0 + "updated_at": 1654849338.0 }, { "data_format": 2, - "description": "Singularity recipe for methylpy", + "description": "\u62ff\u6765\u505a\u6027\u80fd\u4f18\u5316...fork from https://github.com/ot4f/stgcn_gan", "filenames": [ - "1.4.3/Singularity" + "Singularity" ], - "full_name": "icaoberg/singularity-methylpy", + "full_name": "asifreal/stgcn_gan", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"forks\" 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href=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hyperfine\" class=\"anchor\" href=\"#singularity-hyperfine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hyperfine\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/hyperfine\"\u003ehyperfine\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehyperfine\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hyperfine/1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hyperfine\u003c/code\u003e as \u003ccode\u003e1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-stgcn_gan\" class=\"anchor\" aria-hidden=\"true\" href=\"#stgcn_gan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estgcn_gan\u003c/h1\u003e\n\u003cp\u003eTraining STGCN with WGAN\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1622730662.0 + "updated_at": 1658914223.0 }, { "data_format": 2, - "description": "Singularity recipe for hyperfine", + "description": null, "filenames": [ - "1.11.0/Singularity" + "Singularity" ], - "full_name": "icaoberg/singularity-hyperfine", - "latest_release": "v1.11.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hyperfine\" class=\"anchor\" href=\"#singularity-hyperfine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hyperfine\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/hyperfine\"\u003ehyperfine\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehyperfine\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hyperfine/1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hyperfine\u003c/code\u003e as \u003ccode\u003e1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "baxpr/makerois-PMAT-fs", + "latest_release": "v1.0.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-create-study-specific-roi-image-in-mni-space\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-study-specific-roi-image-in-mni-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate study-specific ROI image in MNI space\u003c/h1\u003e\n\u003cp\u003ePMAT resting state connectivity study. Freesurfer-based ROIs for followup analysis.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h2\u003e\n\u003cp\u003eAll should be matched to the same T1 image.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eT1 image in atlas space (typically BIAS_NORM resource of cat12 assessor)\u003c/li\u003e\n\u003cli\u003eDeformation from T1 subject space to atlas space (typically DEF_FWD resource of cat12 assessor)\u003c/li\u003e\n\u003cli\u003eSUBJECT directory of Freesurfer output (typically SUBJECT resource of freesurfer_dev assessor)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003erois_PMAT_fs.nii.gz Region of interest image\nrois_PMAT_fs-labels.csv Region labels and volumes\nmakerois-PMAT-fs.pdf Visual report of final ROI image\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-regions-of-interest\" class=\"anchor\" aria-hidden=\"true\" href=\"#regions-of-interest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRegions of interest\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-visual-regions-subject-space-warped\" class=\"anchor\" aria-hidden=\"true\" href=\"#visual-regions-subject-space-warped\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisual regions (Subject space, warped)\u003c/h3\u003e\n\u003cp\u003eGenerated by Freesurfer 6. Region indices in \u003ccode\u003esrc/rois-visual-a2009s.csv\u003c/code\u003e. Method: \u003cem\u003eBruce Fischl, Andre van der Kouwe, Christophe Destrieux, Eric Halgren, Florent Segonne, David H. Salat, Evelina Busa, Larry J. Seidman, Jill Goldstein, David Kennedy, Verne Caviness, Nikos Makris, Bruce Rosen, and Anders M. Dale. Automatically Parcellating the Human Cerebral Cortex. Cerebral Cortex January 2004; 14:11-22.\u003c/em\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1624059711.0 + "topics": [], + "updated_at": 1658942371.0 }, { "data_format": 2, - "description": "Singularity recipe for dust", + "description": "Singularity recipe files for DeepVariant (https://github.com/google/deepvariant)", "filenames": [ - "0.5.4/Singularity" + "Singularity.1.0.0", + "Singularity", + "Singularity.1.4.0-gpu", + "Singularity.1.4.0" ], - "full_name": "icaoberg/singularity-dust", + "full_name": "powerPlant/deepvariant-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-dust\" class=\"anchor\" href=\"#singularity-dust\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-dust\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/bootandy/dust/raw/master/media/snap.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/bootandy/dust/raw/master/media/snap.png\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/bootandy/dust\"\u003edust\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003edust\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/dust/0.5.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/dust\u003c/code\u003e as \u003ccode\u003e0.5.4\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-singularity-recipe-files-for-deepvariant\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-files-for-deepvariant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity recipe files for Deepvariant\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/google/deepvariant\"\u003ehttps://github.com/google/deepvariant\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGenerate symlinks for executables\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec deepvariant.1.4.0.sif find /opt/deepvariant/bin -type f -executable -printf \"%f\\n\" | xargs -L1 ln -s deepvariant.1.4.0.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gpu-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#gpu-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGPU support\u003c/h2\u003e\n\u003cp\u003eSet \u003ccode\u003eSINGULARITY_NV=true\u003c/code\u003e to enable GPU support where required. Useful in environment modules, like,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Required to enable GPU\nsetenv SINGULARITY_NV true\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1622860644.0 + "subscribers_count": 0, + "topics": [], + "updated_at": 1657769856.0 }, { "data_format": 2, - "description": "Singularity recipe for graphviz", + "description": null, "filenames": [ - "2.44.0/Singularity" + "program/HiC-Pro_3.1.0/Singularity" ], - "full_name": "icaoberg/singularity-graphviz", - "latest_release": "v2.44.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-graphviz\" class=\"anchor\" href=\"#singularity-graphviz\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-graphviz\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/960789693fa68a8f442f9c6cc7d6a117639f1a792ec84c96648ad4764c385fcf/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f656e2f342f34382f477261706876697a4c6f676f2e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/960789693fa68a8f442f9c6cc7d6a117639f1a792ec84c96648ad4764c385fcf/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f656e2f342f34382f477261706876697a4c6f676f2e706e67\" alt=\"Logo\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/en/4/48/GraphvizLogo.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://graphviz.org/\" rel=\"nofollow\"\u003egraphviz \u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003egraphviz \u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/graphviz/2.44.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modules/graphviz \u003c/code\u003e as \u003ccode\u003e 2.44.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "hermanzhaozzzz/snakepipes_Hi-C", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-snakepipes_hi-c\" class=\"anchor\" aria-hidden=\"true\" href=\"#snakepipes_hi-c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esnakepipes_Hi-C\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003e\u987b\u77e5\u003c/strong\u003e\uff1a\u672c\u4ed3\u5e93\u8fd8\u5728\u6784\u5efa\u4e2d\uff0c\u6682\u65f6\u53ea\u4f5c\u53c2\u8003\uff01\uff01\u003c/p\u003e\n\u003cp\u003e\u53c2\u8003\u548c\u5f15\u7528\u4e86\u4e00\u4e9b\u003ca href=\"https://github.com/nservant/HiC-Pro\"\u003eHiC Pro\u003c/a\u003e\u7684\u4ee3\u7801\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u73af\u5883\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u73af\u5883\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u73af\u5883\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install python r-base bowtie2 samtools iced r-ggplot2 r-rcolorbrewer\nconda install -c bioconda java-jdk hicexplorer\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u6211\u7528\u7684\u7248\u672c\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e python=3.9.13\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e R=4.0.5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e bowtie2=2.4.5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e samtools=1.15.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e iced=0.5.10\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e java-jdk=1.8 # java openjdk version \"1.8.0_312\"\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e hicexplorer=3.7.2\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u7528\u6cd5\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u7528\u6cd5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u7528\u6cd5\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-0-\u6d4b\u5e8f\u8d28\u91cf\u63a7\u5236\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-0-\u6d4b\u5e8f\u8d28\u91cf\u63a7\u5236\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estep 0 \u6d4b\u5e8f\u8d28\u91cf\u63a7\u5236\u003c/h3\u003e\n\u003cp\u003e\u4f7f\u7528 \u003ca href=\"https://github.com/hermanzhaozzzz/snakepipes_fastqc-multiqc\"\u003esnakepipes_fastqc-multiqc\u003c/a\u003e\u8fdb\u884c\u8d28\u91cf\u63a7\u5236\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-\u8fd0\u884csnakemake-pipeline\u751f\u6210hi-c-contact-matrix\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-\u8fd0\u884csnakemake-pipeline\u751f\u6210hi-c-contact-matrix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estep 1 \u8fd0\u884cSnakemake Pipeline\uff0c\u751f\u6210Hi-C contact matrix\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u56de\u8d34Hi-C reads\u4ee5\u53ca\u751f\u6210RAW\u77e9\u9635ICE\u6821\u6b63\u77e9\u9635\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003evalidPairs convert to .hic file(Juicer)\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e HiC\ngit clone https://github.com/hermanzhaozzzz/snakepipes_fastqc-multiqc.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e snakepipes_fastqc-multiqc\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e use jupyterlab or runipy to run step01_generate_samples.ipynb\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e get samples.json and check it\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e dry run, rm -n to run pipeline\u003c/span\u003e\nsnakemake -pr -j 8 -s step02_run_mapping_and_generate_matrix.py -n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output as below\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e HiC|\u21d2 tree . -L 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e .\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 bam\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 fastq\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 hic_file\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 matrix\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 qc\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 quality_checks\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 snakepipes_fastqc-multiqc\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 snakepipes_Hi-C\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u251c\u2500\u2500 temp_files\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u2514\u2500\u2500 valid_pairs\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-convert-validpairs-to-juicer-hic\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-convert-validpairs-to-juicer-hic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estep 2 Convert ValidPairs to Juicer .hic\u00b6\u003c/h3\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1622859227.0 + "topics": [], + "updated_at": 1658684345.0 }, { "data_format": 2, - "description": null, + "description": "Use SNP genotype information pulled from single cell RNA-seq data to predict ancestries", "filenames": [ - "2.2.1/Singularity" + "Singularity.ancestry_prediction_scRNAseq" ], - "full_name": "icaoberg/singularity-hisat2", + "full_name": "powellgenomicslab/ancestry_prediction_scRNAseq", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hisat2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ea61f9228ca14a66e58889a560447e0b7c8ba73ddbaa594055242ace96eb0a84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ea61f9228ca14a66e58889a560447e0b7c8ba73ddbaa594055242ace96eb0a84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4796300b08f76b423ee0574c15e8bd8ad15b0a389a36fd3f58549b8bb5df8690/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4796300b08f76b423ee0574c15e8bd8ad15b0a389a36fd3f58549b8bb5df8690/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/25eeccddaeb5bf9a3f7053b646f9b7bf540d33b801c371db1850d745da46fc95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/25eeccddaeb5bf9a3f7053b646f9b7bf540d33b801c371db1850d745da46fc95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f14a5cb988478f36746bb17a364f669ee4d02a32a6f6fddfbccaff9dc50a8379/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d686973617432\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f14a5cb988478f36746bb17a364f669ee4d02a32a6f6fddfbccaff9dc50a8379/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d686973617432\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hisat2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hisat\" class=\"anchor\" href=\"#singularity-hisat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hisat\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://daehwankimlab.github.io/hisat2/\" rel=\"nofollow\"\u003ehisat2\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts \u003ccode\u003ehisat2*\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hisat2/2.2.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hisat2\u003c/code\u003e as \u003ccode\u003e2.2.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-singularity-definition-file\" class=\"anchor\" href=\"#building-the-image-using-the-singularity-definition-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the Singularity definition file\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ancestry_prediction_scrnaseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#ancestry_prediction_scrnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eancestry_prediction_scRNAseq\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1624060173.0 + "updated_at": 1661089200.0 }, { "data_format": 2, @@ -14724,893 +14082,982 @@ var data = "filenames": [ "Singularity.def" ], - "full_name": "granek/jupyter-MIC-2021", + "full_name": "roitberg-group/lammps-ani", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hts-jupyter-notebook-container\" class=\"anchor\" href=\"#hts-jupyter-notebook-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHTS Jupyter notebook container\u003c/h1\u003e\n\u003cp\u003eWe are offering a series of 6 workshops on biological assays and data analysis for HIV researchers.\nThis series is funded by an R25 grand from the National Institute of Allergies and Infectious Disease (NIAID).\nOur goal is to provide educational enrichment for HIV researchers on current assay technologies and the statistical and bioinformatic analysis techniques necessary to process such data.\u003c/p\u003e\n\u003cp\u003eThis is the source for the Docker container used to run the course Jupyter\nnotebooks.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-using-the-image\" class=\"anchor\" href=\"#using-the-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing the image\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install-docker\" class=\"anchor\" href=\"#install-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall docker\u003c/h2\u003e\n\u003cp\u003eTo run a container on your local machine or laptop, download the docker program from \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003ehttps://www.docker.com\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-image-on-your-local-computer\" class=\"anchor\" href=\"#run-image-on-your-local-computer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun image on your local computer\u003c/h2\u003e\n\u003cp\u003eOnce you have the docker program installed, open the program (you should get a terminal screen with command line). Enter the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull dukehtscourse/jupyter-hts-2019\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will pull down the course docker image from dockerhub. It may take a few minutes. Next, run the command to start a container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --name hts-course -v YOUR_DIRECTORY_WITH_COURSE_MATERIAL:/home/jovyan/work \\\n-d -p 127.0.0.1\\:9999\\:8888 \\\n-e PASSWORD=\"YOUR_CHOSEN_NOTEBOOK_PASSWORD\" \\\n-e NB_UID=1000 \\\n-t dukehtscourse/jupyter-hts-2019\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe most important parts of this verbiage are the \u003ccode\u003eYOUR_DIRECTORY_WITH_COURSE_MATERIALS\u003c/code\u003e and \u003ccode\u003eYOUR_CHOSEN_NOTEBOOK_PASSWORD\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYOUR_DIRECTORY_WITH_COURSE_MATERIALS\u003c/code\u003e (Bind mounting): The directory name is the one you extracted your course materials into. So, if you put them in your home directory, it might look something like: \u003ccode\u003e-v /home/janice/HTS2019-notebooks:/home/jovyan/work\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eYOUR_CHOSEN_NOTEBOOK_PASSWORD\u003c/code\u003e: The password is whatever you want to use to password protect your notebook. Now, this command is running the notebook so that it is only \u0027seen\u0027 by your local computer - no one else on the internet can access it, and you cannot access it remotely, so the password is a bit of overkill. Use it anyway. An example might be: \u003ccode\u003e-e PASSWORD=\"Pssst_this_is_Secret\"\u003c/code\u003e except that this is a terrible password and you should follow standard rules of not using words, include a mix of capital and lowercase and special symbols. etc.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d -p 127.0.0.1\\:9999\\:8888\u003c/code\u003e part of the command is telling docker to run the notebook so that it is only visible to the local machine. It is absolutely possible to run it as a server to be accessed across the web - but there are some security risks associated, so if you want to do this proceed with great caution and get help.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOf course, it would be better either configure HTTPS (see the options section below) or run an Nginx proxy in front of the container instance so you get https (encryption) instead of http.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-open-the-jupyter-in-your-browser\" class=\"anchor\" href=\"#open-the-jupyter-in-your-browser\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpen the Jupyter in your browser\u003c/h3\u003e\n\u003cp\u003eOpen a browser and point it to \u003ca href=\"http://127.0.0.1:9999\" rel=\"nofollow\"\u003ehttp://127.0.0.1:9999\u003c/a\u003e\nYou should get to a Jupyter screen asking for a password. This is the password you created in the docker run command.\nNow, you should be able to run anything you like from the course. Depending on your laptop\u0027s resources (RAM, cores), this might be slow, so be aware and start by testing only one file (vs the entire course data set).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-stopping-docker\" class=\"anchor\" href=\"#stopping-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStopping Docker\u003c/h3\u003e\n\u003cp\u003eThe container will continue running, even if you do not have Jupyter open in a web browser. If you don\u0027t plan to use it for a while, you might want to shut it down so it isn\u0027t using resources on your computer. Here are two ways to do that:\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-kitematic\" class=\"anchor\" href=\"#kitematic\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKitematic\u003c/h4\u003e\n\u003cp\u003eIncluded in the \u003ca href=\"https://docs.docker.com/docker-for-mac/\" rel=\"nofollow\"\u003eDocker for Mac\u003c/a\u003e and the \u003ca href=\"https://docs.docker.com/docker-for-windows/\" rel=\"nofollow\"\u003eDocker for Windows\u003c/a\u003e installations.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-commandline\" class=\"anchor\" href=\"#commandline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommandline\u003c/h4\u003e\n\u003cp\u003eYou may want to familiarize yourself with the following Docker commands.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003edocker stop\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker rm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker ps -a\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker images\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker rmi\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-windows-note\" class=\"anchor\" href=\"#windows-note\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows Note\u003c/h3\u003e\n\u003cp\u003eThese instructions have not been tested in a Windows environment. If you have problems with them, please give us feedback\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-image-on-a-server\" class=\"anchor\" href=\"#run-image-on-a-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun image on a server\u003c/h2\u003e\n\u003cp\u003eTo run on a remote server you will want to use a slightly different command from above, because you \u003cem\u003ewill need to connect remotely\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --name hts-course \\\n-v YOUR_DIRECTORY_WITH_COURSE_MATERIAL:/home/jovyan/work \\\n-d -p 8888:8888 \\\n-e USE_HTTPS=\"yes\" \\\n-e PASSWORD=\"YOUR_CHOSEN_NOTEBOOK_PASSWORD\" \\\n-e NB_UID=1000 \\\n-t dukehtscourse/jupyter-hts-2019\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-options\" class=\"anchor\" href=\"#options\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions\u003c/h2\u003e\n\u003cp\u003eYou may customize the execution of the Docker container and the Notebook server it contains with the following optional arguments.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-e PASSWORD=\"YOURPASS\"\u003c/code\u003e - Configures Jupyter Notebook to require the given password. Should be conbined with \u003ccode\u003eUSE_HTTPS\u003c/code\u003e on untrusted networks.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-e USE_HTTPS=yes\u003c/code\u003e - Configures Jupyter Notebook to accept encrypted HTTPS connections. If a \u003ccode\u003epem\u003c/code\u003e file containing a SSL certificate and key is not provided (see below), the container will generate a self-signed certificate for you.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e(v4.0.x)\u003c/strong\u003e \u003ccode\u003e-e NB_UID=1000\u003c/code\u003e - Specify the uid of the \u003ccode\u003ejovyan\u003c/code\u003e user. Useful to mount host volumes with specific file ownership.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-e GRANT_SUDO=yes\u003c/code\u003e - Gives the \u003ccode\u003ejovyan\u003c/code\u003e user passwordless \u003ccode\u003esudo\u003c/code\u003e capability. Useful for installing OS packages. \u003cstrong\u003eYou should only enable \u003ccode\u003esudo\u003c/code\u003e if you trust the user or if the container is running on an isolated host.\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-v /some/host/folder/for/work:/home/jovyan/work\u003c/code\u003e - Host mounts the default working directory on the host to preserve work even when the container is destroyed and recreated (e.g., during an upgrade).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e(v3.2.x)\u003c/strong\u003e \u003ccode\u003e-v /some/host/folder/for/server.pem:/home/jovyan/.ipython/profile_default/security/notebook.pem\u003c/code\u003e - Mounts a SSL certificate plus key for \u003ccode\u003eUSE_HTTPS\u003c/code\u003e. Useful if you have a real certificate for the domain under which you are running the Notebook server.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e(v4.0.x)\u003c/strong\u003e \u003ccode\u003e-v /some/host/folder/for/server.pem:/home/jovyan/.local/share/jupyter/notebook.pem\u003c/code\u003e - Mounts a SSL certificate plus key for \u003ccode\u003eUSE_HTTPS\u003c/code\u003e. Useful if you have a real certificate for the domain under which you are running the Notebook server.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-e INTERFACE=10.10.10.10\u003c/code\u003e - Configures Jupyter Notebook to listen on the given interface. Defaults to \u0027*\u0027, all interfaces, which is appropriate when running using default bridged Docker networking. When using Docker\u0027s \u003ccode\u003e--net=host\u003c/code\u003e, you may wish to use this option to specify a particular network interface.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-e PORT=8888\u003c/code\u003e - Configures Jupyter Notebook to listen on the given port. Defaults to 8888, which is the port exposed within the Dockerfile for the image. When using Docker\u0027s \u003ccode\u003e--net=host\u003c/code\u003e, you may wish to use this option to specify a particular port.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-course-image-with-singularity\" class=\"anchor\" href=\"#running-the-course-image-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Course Image with Singularity\u003c/h2\u003e\n\u003cp\u003eDocker requires root permissions to run, so you are unlikely to be able to run Docker on a computer that you are not fully in control of. As an alternative you can run the course image with \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, another container system. Singularity is similar to Docker, and can run Docker images, but you do not need special permissions to run Singularity images \u003cem\u003eor\u003c/em\u003e Docker images with Singularity (as long as Singularity is actually installed on the computer).\u003c/p\u003e\n\u003cp\u003eThe following command uses Singularity to start up a container from the course Jupyter image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-the-course-image-on-a-slurm-cluster\" class=\"anchor\" href=\"#running-the-course-image-on-a-slurm-cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Course Image on a SLURM cluster\u003c/h3\u003e\n\u003cp\u003eWe will use the example of the Duke Computer Cluster, but these instructions should be easily adaptable to other clusters\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFrom your computer run this to connect to DCC:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh NetID@dcc-login-03.oit.duke.edu\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOnce you are connected run this to start a tmux session:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003etmux new -s jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOnce you have started a tmux session you can start up Jupyter with this command:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun singularity exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: the first time you run this, it might take a VERY long time to download the Docker image and build the Singularity image from it\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eRunning this command will print a bunch of stuff. You can ignore everything except the last two lines, which will say something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttp://dcc-chsi-01:8889/?token=08172007896ad29bb5fbd92f6f3f516a8b2f7303ed7f1df3\nor http://127.0.0.1:8889/?token=08172007896ad29bb5fbd92f6f3f516a8b2f7303ed7f1df3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou need this information for the next few steps. For the next step you need the \u201cdcc-chsi-01:8889\u201d part.\n\u201cdcc-chsi-01\u201d is the compute node that Jupyter is running on and \u201c8889\u201d is the port it is listening on. You may get a different value every time you start the container.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eYou want to run the following command in another terminal on your computer to set up port forwarding.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh -L PORT:NODE.rc.duke.edu:PORT NetID@dcc-login-03.oit.duke.edu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this command you want to replace \u201cPORT\u201d with the value you got for port from the srun command and replace \u201cNODE\u201d with the compute node that was printed by the srun command. So for the example above, the ssh port forwarding command would be:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -L 8889:dcc-chsi-01.rc.duke.edu:8889 NetID@dcc-login-03.oit.duke.edu\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eNow you can put the last line that the srun command printed in your web browser and it should open your Jupyter instance running on DCC.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe Jupyter session keeps running until you explicitly shut it down. If the port forwarding SSH connection drops you will need to restart SSH with the same command, but you don\u2019t need to restart Jupyter.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere are two ways to explicitly shut down Jupyter:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eWithin Jupyter, click on the \u003cem\u003eJupyter\u003c/em\u003e logo in the top left to go to the main Jupyter page, then click \"Quit\" in the top right\u003c/li\u003e\n\u003cli\u003eDo control-C twice in the terminal where you started Jupyter. If this connection dropped, you can reconnect to it with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh NetID@dcc-login-03.oit.duke.edu\ntmux a -t jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter shutting down the Jupyter session you can type \u003ccode\u003eexit\u003c/code\u003e at the terminal to close the tmux session.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf you need more memory or more cpus you can use the \u003ccode\u003e--mem\u003c/code\u003e and/or \u003ccode\u003e--cpus-per-task\u003c/code\u003e arguments to in the \u201csrun\u201d, for example to request 4 CPUs and 10GB of RAM:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun --cpus-per-task=4 --mem=10G singularity exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eIf you have high priority access to a partition you can request that partition be used with the \u003ccode\u003e-A\u003c/code\u003e and \u003ccode\u003e-p\u003c/code\u003e arguments to \u003ccode\u003esrun\u003c/code\u003e:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun -A chsi -p chsi singularity exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eYou might want to access files that are outside of your home directory. Within a singularity container your access to the host computer is\nlimited: by default, from inside the container you can only access your home directory. If you want to access directories that are outside your home\ndirectory, you have to tell \u003cem\u003eSingularity\u003c/em\u003e when you start the container with the \u003ccode\u003e--bind\u003c/code\u003e command line argument. For example:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun singularity --bind /work/josh:/work/josh exec docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eYou can combine several of these command line flags:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esrun -A chsi -p chsi --cpus-per-task=4 --mem=10G singularity exec --bind /work/josh:/work/josh docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eIt is strongly recommended to set the \u003ccode\u003eSINGULARITY_CACHEDIR\u003c/code\u003e environment variables in your .bashrc or when running \u003ccode\u003esrun\u003c/code\u003e. This environment variable specifies where the Docker image (and the Singularity image built from it) are saved. If this variable is not specified, singularity will cache images in \u003ccode\u003e$HOME/.singularity/cache\u003c/code\u003e, which can fill up quickly. This is discussed in the \u003ca href=\"https://sylabs.io/guides/3.7/user-guide/build_env.html#cache-folders\" rel=\"nofollow\"\u003eSingularity Documentation\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITY_CACHEDIR=\"/work/josh/singularity_cache\"; srun -A chsi -p chsi --cpus-per-task=4 --mem=10G singularity exec --bind /work/josh:/work/josh docker://dukehtscourse/jupyter-hts-2019 /usr/local/bin/start.sh jupyter notebook --ip=0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-install-singularity\" class=\"anchor\" href=\"#install-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Singularity\u003c/h3\u003e\n\u003cp\u003eHere are instructions for installing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/2.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity version 2.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.2/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity version 3.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/singularity-desktop-macos/\" rel=\"nofollow\"\u003eSingularity Desktop for macOS (Alpha Preview)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-lammps-ani\" class=\"anchor\" aria-hidden=\"true\" href=\"#lammps-ani\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLAMMPS-ANI\u003c/h1\u003e\n\u003cp\u003eA plugin to run torchani on LAMMPS.\u003cbr\u003e\nOn hipergator, the compiled program and a working example script could be found at \u003ccode\u003e/blue/roitberg/apps/lammps-ani/examples/water/submit.sh\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirement\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirement\u003c/h2\u003e\n\u003cp\u003eRun an interactive session\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esrun --qos=roitberg --account=roitberg --nodes=1 --ntasks=2 --cpus-per-task=2 --mem=20gb --gres=gpu:2 --partition=hpg-ai -t 10:00:00 --pty /bin/bash -i\nmodule load cuda/11.4.3 gcc/9.3.0 openmpi/4.0.5 cmake/3.21.3 git/2.30.1 singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003epytorch and cudnn\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge\nconda install -c conda-forge cudnn=8.3.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity--docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity--docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity \u0026amp; Docker Container\u003c/h2\u003e\n\u003cp\u003eYou could use the pre-built \u003ca href=\"https://github.com/roitberg-group/lammps-ani/pkgs/container/lammps-ani\"\u003edocker container\u003c/a\u003e to avoid compiling the program by yourself.\u003c/p\u003e\n\u003cp\u003eSome HPCs provide Singularity instead of Docker. The following shows the instruction for Singularity usage:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive git@github.com:roitberg-group/lammps-ani.git\nsingularity pull -F docker://ghcr.io/roitberg-group/lammps-ani:master\nmkdir -p \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/singularity-home\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e exec into container\u003c/span\u003e\nSINGULARITYENV_CUDA_VISIBLE_DEVICES=\u003cspan class=\"pl-smi\"\u003e$CUDA_VISIBLE_DEVICES\u003c/span\u003e singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --cleanenv -H \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/singularity-home:/home --nv lammps-ani_master.sif /bin/bash\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e test\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e lammps-ani\nnvidia-smi \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e external/torchani_sandbox \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e python setup.py install --ext --user \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../../ \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e tests/ \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e python save_ani.py \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e ./test_all.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./build.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun example\u003c/h2\u003e\n\u003cp\u003emake sure \u003ccode\u003eLAMMPS_PLUGIN_PATH\u003c/code\u003e and \u003ccode\u003eLAMMPS_ROOT\u003c/code\u003e are set correctly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport LAMMPS_PLUGIN_PATH=/blue/roitberg/apps/lammps-ani/build/\ncd examples/water/\nmpirun -np 8 ${LAMMPS_ROOT}/build/lmp_mpi -in in.lammps\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1623704048.0 + "updated_at": 1649451939.0 }, { "data_format": 2, - "description": "Singularity recipe for bat", + "description": null, "filenames": [ - "0.17.1/Singularity" + "Singularity" ], - "full_name": "icaoberg/singularity-bat", - "latest_release": "v0.17.1", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bat\" class=\"anchor\" href=\"#singularity-bat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bat\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/7b7c397acc5b91b4c4cf7756015185fe3c5f700f70d256a212de51294a0cf673/68747470733a2f2f696d6775722e636f6d2f724773646e44652e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b7c397acc5b91b4c4cf7756015185fe3c5f700f70d256a212de51294a0cf673/68747470733a2f2f696d6775722e636f6d2f724773646e44652e706e67\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/bat\"\u003ebat\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebat\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bat/0.17.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modules/bat\u003c/code\u003e as \u003ccode\u003e0.17.1\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "ddbj/singularity_omegafold", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_omegafold\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_omegafold\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_omegafold\u003c/h1\u003e\n\u003cp\u003eUbuntu 20.04\u306bomegafold v1.1.0\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fsingularity image\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306eSingularity definition file\u3067\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-image\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" aria-hidden=\"true\" href=\"#image\u306e\u30d3\u30eb\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eimage\u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build omegafold-1.1.0.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3-login_gpuq\u3067\u306e\u5b9f\u884c\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3-login_gpuq\u3067\u306e\u5b9f\u884c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3 login_gpu.q\u3067\u306e\u5b9f\u884c\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv omegafold-1.1.0.sif python3 /opt/OmegaFold/main.py input.fasta output_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30ab\u30ec\u30f3\u30c8\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b output_dir \u304c\u4f5c\u6210\u3055\u308c\u3001\u305d\u306e\u4e2d\u306b\u7d50\u679c\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3-intelq\u3067\u306e\u5b9f\u884c\u7528\u30b8\u30e7\u30d6\u30b9\u30af\u30ea\u30d7\u30c8\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3-intelq\u3067\u306e\u5b9f\u884c\u7528\u30b8\u30e7\u30d6\u30b9\u30af\u30ea\u30d7\u30c8\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3 intel.q\u3067\u306e\u5b9f\u884c\u7528\u30b8\u30e7\u30d6\u30b9\u30af\u30ea\u30d7\u30c8\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#$ -S /bin/sh\n#$ -cwd\n#$ -l s_vmem=2G\n#$ -l mem_req=2G\n#$ -l intel\n#$ -pe def_slot 16\nN=16\nsingularity exec /home/y-okuda/singularity/omegafold/omegafold-1.1.0.sif \\\nsh -c \"\\\nexport OMP_NUM_THREADS=${N}; \\\npython3 /opt/OmegaFold/main.py \\\n--device cpu \\\n/home/y-okuda/singularity/omegafold/input.fasta \\\n/home/y-okuda/singularity/omegafold/output_dir \\\n\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eN\u306b\u8a2d\u5b9a\u3057\u305f\u6570\u306eCPU\u30b3\u30a2\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u540c\u3058\u5024\u3092 -pe def_slot \u306b\u3082\u8a2d\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1622870361.0 + "subscribers_count": 7, + "topics": [], + "updated_at": 1661924246.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "misc/releases/20.06/Singularity.20.06", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/latest/Singularity" ], - "full_name": "baxpr/demo-singularity-matlab-fsl", + "full_name": "silvansievers/pddl-symmetry-reduction", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-demo-singularity-container-for-matlab-plus-fsl\" class=\"anchor\" href=\"#demo-singularity-container-for-matlab-plus-fsl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo singularity container for Matlab plus FSL\u003c/h1\u003e\n\u003cp\u003eThis example container takes a Nifti image as input, zeroes out a hole in it of\nthe specified diameter, and saves the result to a new Nifti file. Quick,\npointless, and easy to tell whether it worked right.\u003c/p\u003e\n\u003cp\u003eThis is one way to organize a Matlab-based Singularity container -\nperhaps most easily conceived of as a series of wrappers around the main\ncodebase. Done this way, it\u0027s fairly easy to work on each piece in isolation,\nproblem-solving from the inside out.\u003c/p\u003e\n\u003cp\u003eThis container also includes an installation of FSL, which has a lot of handy\ntools including fsleyes to make the QA PDF. The FSL parts could be removed from\nthe Singularity file if FSL isn\u0027t used, to end up with a smaller container.\nContrariwise, all the Matlab parts could be removed to end up with an FSL-only\ncontainer.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity container\n| Primary entrypoint (shell script)\n| | X11 wrapper\n| | | Shell script preprocessing\n| | | Matlab processing (compiled)\n| | | | Matlab entrypoint\n| | | | Matlab main function\n| | | \\ Matlab sub-functions / codebase\n\\ \\ \\ Shell script postprocessing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDependencies in terms of the actual files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity\n src/pipeline_entrypoint.sh\n src/pipeline_main.sh\n src/copy_inputs.sh\n src/preprocessing.sh\n matlab/bin/run_matlab_entrypoint.sh\n matlab/bin/matlab_entrypoint\n / matlab/src/matlab_entrypoint.m \\ Used for compilation,\n | matlab/src/matlab_main.m | but not at container\n \\ matlab/src/* / runtime\n src/postprocessing.sh\n src/make_pdf.sh\n src/finalize.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe process of putting it together is described below. The scripts and code in\nthis repository are extensively commented, so if something isn\u0027t clear here,\nit\u0027s probably explained in the Singularity file or the example code.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-matlab-part\" class=\"anchor\" href=\"#matlab-part\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMatlab part\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-write-the-basic-matlab-code\" class=\"anchor\" href=\"#write-the-basic-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWrite the basic Matlab code\u003c/h3\u003e\n\u003cp\u003eWrite Matlab code that does what\u0027s needed. Put it in \u003ccode\u003ematlab/src\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA popular toolbox for reading and writing Nifti files that\u0027s available on Matlab\nCentral has a lot of insidious bugs and is not being maintained. Matlab\u0027s own\nfunctions for Nifti files are quite limited. Here is an alternative, which is\nused in this example:\n\u003ca href=\"https://github.com/VUIIS/spm_readwrite_nii\"\u003ehttps://github.com/VUIIS/spm_readwrite_nii\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-write-the-matlab-entrypoint\" class=\"anchor\" href=\"#write-the-matlab-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWrite the Matlab entrypoint\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/src/matlab_entrypoint.m\u003c/code\u003e exists to take command line arguments, parse\nthem, and call the main code. A convenient way to set things up is to write a\nmain function that takes a structure as its sole input, with the structure\ncontaining whatever inputs are needed. See \u003ccode\u003ematlab/src/matlab_main.m\u003c/code\u003e for an\nexample of this.\u003c/p\u003e\n\u003cp\u003eCouple of things to note in the entrypoint code are the quit/exit sections at\nbeginning and end. The bit at the beginning allows the executable to run during\nthe container build, without actually doing anything - this is needed to extract\nthe CTF archive into the container at the only time the container is writeable\n(h/t \u003ca href=\"https://twitter.com/annash128\" rel=\"nofollow\"\u003ehttps://twitter.com/annash128\u003c/a\u003e for figuring that one out). The bit at the\nend exits matlab when the function is finished. Without it, the running Matlab\nprocess won\u0027t release execution back to the calling script when it\u0027s done.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-test-the-matlab-entrypoint\" class=\"anchor\" href=\"#test-the-matlab-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the Matlab entrypoint\u003c/h3\u003e\n\u003cp\u003eThe script \u003ccode\u003ematlab/src/test_matlab_entrypoint.m\u003c/code\u003e is an example of how to do\nthis. The appropriate Matlab must be installed on the testing computer.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-the-matlab-code\" class=\"anchor\" href=\"#compile-the-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile the Matlab code\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/compile_matlab.sh\u003c/code\u003e shows how. Many compiled executables are likely to be\ntoo large to store on github. Git LFS may be a solution.\n\u003ca href=\"https://docs.github.com/en/github/managing-large-files/working-with-large-files\"\u003ehttps://docs.github.com/en/github/managing-large-files/working-with-large-files\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-test-the-compiled-matlab-code\" class=\"anchor\" href=\"#test-the-compiled-matlab-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the compiled Matlab code\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003ematlab/test_compiled_matlab.sh\u003c/code\u003e. The appropriate Matlab Runtime must be\ninstalled on the testing computer.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-shell-script-part\" class=\"anchor\" href=\"#shell-script-part\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell script part\u003c/h2\u003e\n\u003cp\u003eAll of the below procedures could be done in the matlab part, if desired,\ninstead of in shell script. If so, parsing inputs should be done following the\nexample in \u003ccode\u003ematlab/src/matlab_entrypoint.m\u003c/code\u003e. But it\u0027s often easier to move\nfiles, create the QA PDF, etc using shell script and FSL. So that\u0027s what we are\ndoing in this example. All this code is in the \u003ccode\u003esrc\u003c/code\u003e directory.\u003c/p\u003e\n\u003cp\u003eAll the shell scripts called from \u003ccode\u003esrc/pipeline_entrypoint.sh\u003c/code\u003e \"know\" the\nenvironment variables that are exported there. This is a very convenient way to\npass along the input arguments, although it isn\u0027t entirely transparent, because\nthere\u0027s no hint in the shell scripts where the variables\u0027 values are coming from\nunless we explain it in the comments.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-main-entrypoint\" class=\"anchor\" href=\"#main-entrypoint\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMain entrypoint\u003c/h3\u003e\n\u003cp\u003eThis is \u003ccode\u003esrc/pipeline_entrypoint.sh\u003c/code\u003e. It uses bash to parse the command line\ninputs and export them to environment variables so they\u0027re accessible. Then it\ncalls the primary shell script \u003ccode\u003esrc/pipeline_main.sh\u003c/code\u003e which in turn calls\neverything else. The main script is run in xvfb to provide a virtual display,\noften needed by matlab and required for fsleyes.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-copy-inputs\" class=\"anchor\" href=\"#copy-inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopy inputs\u003c/h3\u003e\n\u003cp\u003eWe copy input files to the output/working directory so we don\u0027t mess them up.\nThis also is an opportunity to rename them to something consistent. It\u0027s very\nconvenient to hard-code the filenames so we don\u0027t have to store and manipulate\nfilenames in environment variables or the like. Also, this makes it easy to\nproduce output files with consistent names - outputs of one pipeline may serve\nas inputs to another, and it\u0027s much easier to manage this if filenames are the\nsame for every run, or at least consistent.\u003c/p\u003e\n\u003cp\u003eWe generally assume the output directory starts out empty and will not be\ninterfered with by any other processes - this is true for XNAT/DAX, but\nimportant to be aware of in other contexts.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preprocessing\" class=\"anchor\" href=\"#preprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessing\u003c/h3\u003e\n\u003cp\u003eFor this example, there is no preprocessing before the matlab part. But initial\nFSL steps or similar could be put here: \u003ccode\u003esrc/preprocessing.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-postprocessing\" class=\"anchor\" href=\"#postprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePostprocessing\u003c/h3\u003e\n\u003cp\u003eThere isn\u0027t any postprocessing for this example either, but there could be:\n\u003ccode\u003esrc/postprocessing.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pdf-creation\" class=\"anchor\" href=\"#pdf-creation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePDF creation\u003c/h3\u003e\n\u003cp\u003eAll assessors on VUIIS XNAT require a PDF QA report of some sort. For this\nexample, a display of the segmented ROIs overlaid on the T1 is created using\nfsleyes and ImageMagick, \u003ccode\u003esrc/make_pdf.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003ePDF creation can be done in Matlab instead. It\u0027s hard to make these look good.\nAn example with some tricks, including a \u003ccode\u003e.fig\u003c/code\u003e file painstakingly made with\nMatlab\u0027s GUIDE, is\n\u003ca href=\"https://github.com/baxpr/connprep/blob/855dadc/src/connectivity_filter.m#L271\"\u003ehttps://github.com/baxpr/connprep/blob/855dadc/src/connectivity_filter.m#L271\u003c/a\u003e\nA way to show slices of functional images with a nice red/blue colormap is\n\u003ca href=\"https://github.com/baxpr/connprep/blob/855dadc/src/make_network_maps.m\"\u003ehttps://github.com/baxpr/connprep/blob/855dadc/src/make_network_maps.m\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-finalizing-the-output\" class=\"anchor\" href=\"#finalizing-the-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFinalizing the output\u003c/h3\u003e\n\u003cp\u003eAll Niftis must be compressed for storage on XNAT, and outputs can be organized\nin an easily understandable way: \u003ccode\u003esrc/finalize.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eWrite an informative README - so tedious, yet so helpful. Include the\nappropriate citations for all the methods and software you have used. Even\nessentially write the methods section for a paper that uses the pipeline. Here\u0027s\nan excellent example: \u003ca href=\"https://github.com/MASILab/PreQual\"\u003ehttps://github.com/MASILab/PreQual\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAlternatively, git-ify some documentation like this:\n\u003ca href=\"https://github.com/VUIIS/dax/tree/main/docs\"\u003ehttps://github.com/VUIIS/dax/tree/main/docs\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eto get something like this:\n\u003ca href=\"https://dax.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://dax.readthedocs.io/en/latest/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-container\" class=\"anchor\" href=\"#building-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container\u003c/h2\u003e\n\u003cp\u003eBe sure the Matlab code is newly compiled, see above. You can run\n\u003ccode\u003ematlab/check_for_compilation.sh\u003c/code\u003e first to make sure there\u0027s no source code\nnewer than the compiled executable.\u003c/p\u003e\n\u003cp\u003eThen from the root directory of the working copy of the repo, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build \u0026lt;container_name\u0026gt;.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGood practice: before you build, create a release on github (if using github) or\nat least tag the commit you are about to build. Give the container a versioned\nname like \u003ccode\u003edemo-singularity-matlab-fsl_v1.0.0.simg\u003c/code\u003e that matches the repository\nname and release version/tag.\u003c/p\u003e\n\u003cp\u003eExternal binaries such as Matlab Runtime and FSL can be included by copying\nlocal copies into the container in the Singularity file\u0027s \u003ccode\u003e%files\u003c/code\u003e section. This\ntends to be a little faster when multiple builds are needed during debugging,\nor necessary for files that are not available to download, and this is what\u0027s\nbeing done in the example Singularity file. Alternatively, binaries or install\nfiles can be downloaded from their source at build time - there are some\ncommented-out sections in the Singularity file showing how that is done. (Thanks\n\u003ca href=\"https://github.com/praitayini\"\u003ehttps://github.com/praitayini\u003c/a\u003e for exploring this in detail)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-container\" class=\"anchor\" href=\"#running-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the container\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003etest_singularity_container.sh\u003c/code\u003e for an example run command and some\nimportant info.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h3\u003e\n\u003cp\u003ePaths to files are relative to the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--t1_niigz A T1 image\n--seg_niigz Its corresponding segmentation from e.g. slant pipeline\n--diameter_mm Diameter of the hole to zero out, in mm (default 30)\n\n--project Labels from XNAT, used only to annotate the QA PDF\n--subject (default UNK_*)\n--session\n--scan\n\n--out_dir Where outputs will be stored (default /OUTPUTS)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePDF/holed_image.pdf QA report\nHOLED_T1/holed_t1.nii.gz T1 image with a hole in it\nHOLED_SEG/holed_seg.nii.gz Segmentation with a hole in it\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1625430481.0 + "updated_at": 1657796847.0 }, { "data_format": 2, - "description": "Simple terminal UI for git commands.", + "description": "ENIGMA CHR DTI repository", "filenames": [ - "0.28.2/Singularity", - "0.23.1/Singularity" + "singularity/Singularity.def" ], - "full_name": "icaoberg/singularity-lazygit", - "latest_release": "v0.28.2", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-lazygit/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-lazygit/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ce43f9f05edc280dcb72fb4ca8be46c0dab1ad9b88f48b7c2d9b8273288d266f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6c617a79676974\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce43f9f05edc280dcb72fb4ca8be46c0dab1ad9b88f48b7c2d9b8273288d266f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6c617a79676974\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-lazygit\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7fdf306f4f7e4fcc9fc77bc1030ff82b19deed57ab2965e952d30b70fb7b674a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6c617a79676974\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7fdf306f4f7e4fcc9fc77bc1030ff82b19deed57ab2965e952d30b70fb7b674a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6c617a79676974\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-lazygit\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e8240aa02d92e2a9a5ee84af2261f39ed2c8fc86a0c2f54f6e1f6bab629e0fe5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6c617a79676974\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8240aa02d92e2a9a5ee84af2261f39ed2c8fc86a0c2f54f6e1f6bab629e0fe5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6c617a79676974\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-lazygit\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/cef5da0642ce33fb4fb6a644a1c76721ffe638963e959e6812c008aa8c6ce693/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6c617a79676974\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cef5da0642ce33fb4fb6a644a1c76721ffe638963e959e6812c008aa8c6ce693/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6c617a79676974\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-lazygit\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-lazygit\" class=\"anchor\" href=\"#singularity-lazygit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-lazygit\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"images/screenshot.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/screenshot.png\" alt=\"Screenshot\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://github.com/jesseduffield/lazygit\"\u003elazygit\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003elazygit\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/lazygit/4.8.25\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/lazygit\u003c/code\u003e as \u003ccode\u003e4.8.25.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/lazygits/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "kcho/ENIGMA_CHR_DTI", + "latest_release": "example_dwi_data_light", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-enigma-chr-dti-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#enigma-chr-dti-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eENIGMA CHR DTI pipeline\u003c/h1\u003e\n\u003cp\u003eKevin Cho and Yoobin Kwak\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:kevincho@bwh.harvard.edu\"\u003ekevincho@bwh.harvard.edu\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:yoobinkwak@gmail.com\"\u003eyoobinkwak@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction\u003c/li\u003e\n\u003cli\u003eCitation\u003c/li\u003e\n\u003cli\u003eInstallation\u003c/li\u003e\n\u003cli\u003eArranging data for the pipeline\u003c/li\u003e\n\u003cli\u003eRunning the ENIGMA CHR DTI Pipeline\u003c/li\u003e\n\u003cli\u003eSharing outputs to other teams\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eENIGMA CHR DTI pipeline is a toolbox for analyzing diffusion weighted imaging (DWI) data developed for ENIGMA-CHR DTI project. The pipeline expects dicom files of a single DWI scan arranged in a required structure (decribed in \"Arranging data for the pipeline\") and automatically processes available data.\u003c/p\u003e\n\u003cp\u003eThe dicom files will be converted to a Nifti file, bval, and bvec file along with the BIDS sidecar json file. Then the following steps will be applied to each subject data.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGibbs unring (FSL)\u003c/li\u003e\n\u003cli\u003eFSL Eddy (6.0.4)\u003c/li\u003e\n\u003cli\u003eTensor decomposition to create fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) maps.\u003c/li\u003e\n\u003cli\u003eSkeletonization of the FA, AD, MD and RD maps using PNL-TBSS.\u003c/li\u003e\n\u003cli\u003eExtraction of mean diffusion measures in the major JHU bundles.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo increase the homogeneity of the diffusion acquisition parameters within the site, the pipeline curates the following dicom tags from all data, and highlight in the report if there is any deviation in dicom tags within a site.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSeriesDescription\u003c/li\u003e\n\u003cli\u003eImageType\u003c/li\u003e\n\u003cli\u003eAcquisitionMatrix\u003c/li\u003e\n\u003cli\u003eDeviceSerialNumber\u003c/li\u003e\n\u003cli\u003eEchoTime\u003c/li\u003e\n\u003cli\u003eFlipAngle\u003c/li\u003e\n\u003cli\u003eInPlanePhaseEncodingDirection\u003c/li\u003e\n\u003cli\u003eMagneticFieldStrength\u003c/li\u003e\n\u003cli\u003eManufacturer\u003c/li\u003e\n\u003cli\u003eManufacturerModelName\u003c/li\u003e\n\u003cli\u003eProtocolName\u003c/li\u003e\n\u003cli\u003eRepetitionTime\u003c/li\u003e\n\u003cli\u003eSequenceName\u003c/li\u003e\n\u003cli\u003eSliceThickness\u003c/li\u003e\n\u003cli\u003eSoftwareVersions\u003c/li\u003e\n\u003cli\u003eSpacingBetweenSlices\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlthough it\u0027s recommended to provide dicom data as the input to the pipeline, you can also provide diffusion files in the nifti format if your DWI data requires a specific dicom to nifti conversion or if the dicom files not available by some reason. You would need to provide DWI nifti file, bvector file, bvalue file in a structure that the pipeline expects. Pleaes make sure you are providing the raw nifti file without any preprocessing. If any of the three files is missing, the pipeline will raise an error. (See \u003ccode\u003eArranging data for the pipeline\u003c/code\u003e section.) Please let the study coordinator know your situation, and the study coordinate will guide you.\u003c/p\u003e\n\u003cp\u003eThe toolbox is deployed in a container, so as long as either Docker or Singularity is installed on the server, the toolbox should be functional regardless of the operating system.\nPlease note the pipeline does not support Apple Mac with M1 Chips yet, due to an issue with tensorflow installation on M1 Chip machines. Also, since this pipeline is specifically developed for ENIGMA-CHR DTI project, it does not support EPI distortion correction using reverse-encoding maps or field maps. If your data for ENIGMA-CHR project has multiple DWI series, blip-up / blip-down, fieldmaps, or other reverse-encoding diffusion scans, please reach out to the coordinating team.\u003c/p\u003e\n\u003cp\u003ePlease let the study coordinator know if you don\u0027t have powerful enough servers to process your diffusion data. The study coordinator will arrange a cloud server for you to run the pipeline.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eThis toolbox uses the following softwares. Please cite them if you use this pipeline in your study.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rordenlab/dcm2niix\"\u003e\u003ccode\u003edcm2niix\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pnlbwh/CNN-Diffusion-MRIBrain-Segmentation\"\u003eCNN based diffusion MRI brain segmentation tool\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://fsl.fmrib.ox.ac.uk/\" rel=\"nofollow\"\u003eFSL (and FSL unring)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ANTsX/ANTs\"\u003eANTs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pnlbwh/TBSS\"\u003ePNL TBSS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kcho/objPipe\"\u003e\u003ccode\u003eobjPipe\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pnlbwh/eddy-squeeze\"\u003e\u003ccode\u003eeddy-squeeze\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pnlbwh/nifti-snapshot\"\u003e\u003ccode\u003enifti-snapshot\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewith Docker\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eInstall and configure Docker Desktop\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.docker.com/products/docker-desktop/\" rel=\"nofollow\"\u003eDownload Docker Desktop\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003ewith at least 4 cores (12 cores preferably) and 4 GB RAM (16 GB preferably)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDownload ENIGMA CHR DTI docker image.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn terminal or power-shell, type\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker pull kcho/enigma-chr-pipeline\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewith Singularity\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity build enigma-chr-pipeline.simg docker://kcho/enigma-chr-pipeline\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\u003ca href=\"how_to_test_pipeline.md\"\u003eTest the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-arranging-data-for-the-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#arranging-data-for-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArranging data for the pipeline\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-if-you-are-providing-dicom-files-to-the-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#if-you-are-providing-dicom-files-to-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf you are providing dicom files to the pipeline\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e/Users/kc244/enigma_chr_data \u0026lt;- it could be somewhere else\n\u2514\u2500\u2500 sourcedata\n \u251c\u2500\u2500 subject_01\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610335278017249630.dcm\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610335278017249631.dcm\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610431388254021154.dcm\n \u251c\u2500\u2500 subject_02\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610335278017239630.dcm\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.2022042610335278011723631.dcm\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 MR.1.3.12.2.1107.5.2.43.166239.202204261043138825403154.dcm\n \u2514\u2500\u2500 subject_XX\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-if-you-are-providing-nifti-files-to-the-pipeline-as-the-raw-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#if-you-are-providing-nifti-files-to-the-pipeline-as-the-raw-input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf you are providing nifti files to the pipeline as the raw input\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e/Users/kc244/enigma_chr_data \u0026lt;- it could be somewhere else\n\u2514\u2500\u2500 rawdata\n \u00a0\u00a0 \u251c\u2500\u2500 subject_01\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01.nii.gz\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01.bvec\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_01.bval\n \u00a0\u00a0 \u251c\u2500\u2500 subject_02\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_02.nii.gz\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_02.bvec\n \u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_02.bval\n \u00a0\u00a0 \u251c\u2500\u2500 ...\n \u00a0\u00a0 \u2514\u2500\u2500 subject_XX\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-enigma-chr-dti-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-enigma-chr-dti-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the ENIGMA CHR DTI Pipeline\u003c/h2\u003e\n\u003cp\u003eOnce you have your dicom files arranged for each subject, run following command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ enigma_chr_dir=/Users/kc244/enigma_chr_data # set this to your data location\n$ docker run -it -v ${enigma_chr_dir}:/data kcho/enigma-chr-pipeline\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ enigma_chr_dir=/Users/kc244/enigma_chr_data # set this to your data location\n$ singularity run -e -B ${enigma_chr_dir}:/data:rw enigma-chr-pipeline.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eThe pipeline is expected to take about 2~3 hours to process a single subject data.\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-if-you-are-providing-nifti-data-to-the-pipeline-follow-the-steps-below\" class=\"anchor\" aria-hidden=\"true\" href=\"#if-you-are-providing-nifti-data-to-the-pipeline-follow-the-steps-below\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIf you are providing nifti data to the pipeline, follow the steps below.\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ enigma_chr_dir=/Users/kc244/enigma_chr_data # set this to your data location\n$ docker run -it \\\n -v ${enigma_chr_dir}:/data \\\n enigma_chr_pipeline xvfb-run -a python /opt/ENIGMA_CHR_DTI/scripts/enigma_chr_pipeline.py -b /data --nifti_input\n\n# for singularity\n$ singularity shell -e -B ${enigma_chr_dir}:/data \\\n enigma_chr_pipeline.simg xvfb-run -a python /opt/ENIGMA_CHR_DTI/scripts/enigma_chr_pipeline.py -b /data --nifti_input\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sharing-outputs-to-other-teams\" class=\"anchor\" aria-hidden=\"true\" href=\"#sharing-outputs-to-other-teams\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSharing outputs to other teams\u003c/h2\u003e\n\u003cp\u003eRun the code below to collect and compress the files to share.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -it -v ${enigma_chr_dir}:/data kcho/enigma-chr-pipeline collect_outputs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run -e -B ${enigma_chr_dir}:/data:rw enigma-chr-pipeline.simg collect_outputs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere is the list of files collected by \u003ccode\u003ecollect_outputs.py\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/Users/kc244/enigma_chr_data\n derivatives/\n \u251c\u2500\u2500 eddy_qc\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 eddy_summary.html\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_02\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 eddy_summary.html\n \u251c\u2500\u2500 screenshots\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_02\n \u251c\u2500\u2500 tbss\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 snapshots\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ENIGMA\\ Template\\ FA\\ skeleton.jpg\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ENIGMA\\ Template\\ FA.jpg\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 Mean\\ FA\\ skeleton.jpg\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 mean\\ FA.jpg\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 stats\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 AD_combined_roi.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 AD_combined_roi_avg.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 FA_combined_roi.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 FA_combined_roi_avg.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 MD_combined_roi.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 MD_combined_roi_avg.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 RD_combined_roi.csv\n \u2502\u00a0\u00a0 \u00a0\u00a0 \u2514\u2500\u2500 RD_combined_roi_avg.csv\n \u2514\u2500\u2500 web_summary\n \u251c\u2500\u2500 Study.html\n \u251c\u2500\u2500 Study.pdf\n \u251c\u2500\u2500 subject_01\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 subject_01.html\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 subject_01.pdf\n \u2514\u2500\u2500 subject_02\n \u251c\u2500\u2500 subject_02.html\n \u2514\u2500\u2500 subject_02.pdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-enter-into-the-image-shell\" class=\"anchor\" aria-hidden=\"true\" href=\"#enter-into-the-image-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnter into the image shell\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ enigma_chr_dir=/Users/kc244/enigma_chr_data # set this to your data location\n$ docker run -it \\\n -v ${enigma_chr_dir}:/data \\\n enigma_chr_pipeline /bin/bash\n\n# for singularity\n$ singularity shell -e -B ${enigma_chr_dir}:/data \\\n enigma_chr_pipeline.simg /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1625268998.0 + "topics": [], + "updated_at": 1651669505.0 }, { "data_format": 2, "description": null, "filenames": [ - "containers/Singularity" + "singularity/Singularity" ], - "full_name": "bananaeat/Cinnamon_assembly", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cinnamon\" class=\"anchor\" href=\"#cinnamon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCinnamon\u003c/h1\u003e\n\u003cp\u003eThis directory contains the code for the Cinnamon language compiler. This compiler is described in the paper:\u003c/p\u003e\n\u003cp\u003eCinnamon: A Domain-Specific Language for Binary Profiling and Monitoring,\nMahwish Arif, Ruoyu Zhou, Hsi-Ming Ho and Timothy M. Jones,\nCGO 2021\u003c/p\u003e\n\u003cp\u003ePlease cite this paper if you produce any work that builds upon this code and / or data.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-licence\" class=\"anchor\" href=\"#licence\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicence\u003c/h2\u003e\n\u003cp\u003eCinnamon is released under an Apache licence.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-cinnamon\" class=\"anchor\" href=\"#building-cinnamon\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Cinnamon\u003c/h2\u003e\n\u003cp\u003eCinnamon can currently target three different binary frameworks; Janus, Pin and Dyninst. To build the compiler:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003eexport CINNAMON_ROOT = /path/to/cinnamon-source\ncd $(CINNAMON_ROOT)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the Cinnamon backend for Janus:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003emake TARGET=janus\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the Cinnamon backend for Pin:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003emake TARGET=pin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the Cinnamon backend for Dyninst:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003emake TARGET=dyninst\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-compiling-a-sample-program\" class=\"anchor\" href=\"#compiling-a-sample-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling a sample program\u003c/h2\u003e\n\u003cp\u003eCinnamon sample programs are available in the \u003ccode\u003etests\u003c/code\u003e directory. The following commands will compile the Cinnamon program \u003ccode\u003eins.dsl\u003c/code\u003e and integrate the resulting code into one of the target frameworks. You will need to set the path to your target framework installation in the respective scripts:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003e$(CINNAMON_ROOT)/Scripts/compileToJanus.py $CINNAMON_ROOT/tests/ins.dsl\n$(CINNAMON_ROOT)/Scripts/compileToPin.py $CINNAMON_ROOT/tests/ins.dsl\n$(CINNAMON_ROOT)/Scripts/compileToDyn.py $CINNAMON_ROOT/tests/ins.dsl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter this, the final tool can be built and run using the target framework\u0027s build instructions.\u003c/p\u003e\n\u003cp\u003eIf you just want to compile the Cinnamon DSL code and not yet integrate it into a target framework, run the following command. This will generate a number of different files containing relevant code for the cinnamon program:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003ecd $CINNAMON_ROOT\n./bdc $CINNAMON_ROOT/tests/ins.dsl\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-target-frameworks\" class=\"anchor\" href=\"#target-frameworks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTarget frameworks\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-janus\" class=\"anchor\" href=\"#janus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJanus\u003c/h3\u003e\n\u003cp\u003eYou can get the Janus implementation with placeholders, templates and utility libraries for Cinnamon from the main Janus repository at \u003ca href=\"https://github.com/timothymjones/Janus.git\"\u003ehttps://github.com/timothymjones/Janus.git\u003c/a\u003e, then switch to the \u003ccode\u003ecinnamon\u003c/code\u003e branch.\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003egit clone https://github.com/timothymjones/Janus.git\ncd Janus\ngit checkout -b cinnamon origin/cinnamon\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext set \u003ccode\u003eJanusPATH\u003c/code\u003e in \u003ccode\u003ecompileToJanus.py\u003c/code\u003e to be the location that you have cloned Janus.\u003c/p\u003e\n\u003cp\u003eOnce the code for Janus has been generated and integrated (after running the \u003ccode\u003ecompileToJanus.py\u003c/code\u003e script from above), you can build the final tool using the following commands:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003e(cd build; cmake ..; make -j8)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the final tool on the target binary:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003e./janus/jdsl_run \u0026lt;target_binary\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pin\" class=\"anchor\" href=\"#pin\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePin\u003c/h3\u003e\n\u003cp\u003eEverything required for Pin is contained within the \u003ccode\u003etargets/Pin\u003c/code\u003e directory. Copy the \u003ccode\u003eMyDSLTool\u003c/code\u003e directory to \u003ccode\u003epath-to-your-pin-root-dir/source/tools\u003c/code\u003e, where \u003ccode\u003epath-to-your-pin-root\u003c/code\u003e should be self-explanatory.\u003c/p\u003e\n\u003cp\u003eNext set \u003ccode\u003ePinPATH=your-pin-root-dir/source/tools/MyDSLTool\u003c/code\u003e in \u003ccode\u003ecompileToPin.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the code for Pin has been generated and integrated (after running the \u003ccode\u003ecompileToPin.py\u003c/code\u003e script from above), you can build the final tool using the following commands:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003ecd your-pin-root-dir/source/tools/MyDSLTool\nmake obj-intel64/MyDSLTool.so\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the final tool on the target binary:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003eyour-pin-root-dir/pin -t obj-intel64/MyDSLTool.so -- \u0026lt;target_binary\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dyninst\" class=\"anchor\" href=\"#dyninst\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDyninst\u003c/h3\u003e\n\u003cp\u003eYou can obtain Dyninst version 10.1.0 as follows:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003ewget https://github.com/dyninst/dyninst/archive/v10.1.0.tar.gz``\ntar xzvf v10.1.0.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce extracted, add \u003ccode\u003ec_LoadInsn\u003c/code\u003e and \u003ccode\u003ec_StoreInsn\u003c/code\u003e into \u003ccode\u003eenum InsnCategory\u003c/code\u003e in \u003ccode\u003edyninst-10.1.0/instructionAPI/h/InstructionCategories.h\u003c/code\u003e and then build by following the Dyninst build instructions.\u003c/p\u003e\n\u003cp\u003eEverything else required for Dyninst is contained within the \u003ccode\u003etargets/Dyninst\u003c/code\u003e directory. Copy the \u003ccode\u003eMyDSLTool\u003c/code\u003e directory to \u003ccode\u003epath-to-your-dyn-root-dir/examples\u003c/code\u003e, where \u003ccode\u003epath-to-your-dyn-root-dir\u003c/code\u003e should be self-explanatory.\u003c/p\u003e\n\u003cp\u003eNext set \u003ccode\u003eDynPATH=path-to-your-dyn-root-dir/examples/MyDSLTool\u003c/code\u003e in \u003ccode\u003ecompileToDyn.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the code for Dyninst has been generated and integrated (after running the \u003ccode\u003ecompileToDyn.py\u003c/code\u003e script from above), you can build the final tool using the following commands:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003ecd path-to-your-dyn-root-dir/examples/MyDSLTool\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the final tool on the target binary:\u003c/p\u003e\n\u003cpre lang=\"shell-session\"\u003e\u003ccode\u003epath-to-your-dyn-root-dir/examples/MyDSLTool/DSLtool -m static -o \u0026lt;output_binary\u0026gt; \u0026lt;input_binary\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "sequana/variant_calling", + "latest_release": "v0.12.0", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1625671641.0 + "updated_at": 1646920385.0 }, { "data_format": 2, - "description": "Singularity recipe for singularity-term-img-cli", + "description": null, "filenames": [ - "4.1.0/Singularity" + "Singularity.cellranger" ], - "full_name": "icaoberg/singularity-term-img-cli", + "full_name": "georgia-katsoula/cellranger", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-term-img-cli\" class=\"anchor\" href=\"#singularity-term-img-cli\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-term-img-cli\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sindresorhus/term-img-cli/raw/main/screenshot.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/sindresorhus/term-img-cli/raw/main/screenshot.jpg\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sindresorhus/term-img-cli\"\u003eterm-img\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eterm-img\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/term-img/3.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/term-img\u003c/code\u003e as \u003ccode\u003e3.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/shpc.png\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-template-or-fork\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Template or Fork\u003c/h3\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-write-your-singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-update-the-version-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Update the VERSION file\u003c/h3\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/releases.png\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-how-to-develop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How to Develop\u003c/h3\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-how-to-pull\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. How to pull\u003c/h3\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1622923859.0 + "updated_at": 1661761753.0 }, { "data_format": 2, - "description": null, + "description": "This is the Singularity file for build singularity image of biomarkers module", "filenames": [ - "ext/Singularity" + "Biomarkers/Singularity" ], - "full_name": "OSC/shiny_launcher", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-wip-batch-connect---osc-shiny-app-launcher\" class=\"anchor\" href=\"#wip-batch-connect---osc-shiny-app-launcher\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[WIP] Batch Connect - OSC Shiny App Launcher\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/5e71a5a07e8e6e15f922edb76b5fb68e7ee6087e336807c38095861b8c7f5fc2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f7368696e795f6c61756e636865722e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e71a5a07e8e6e15f922edb76b5fb68e7ee6087e336807c38095861b8c7f5fc2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f7368696e795f6c61756e636865722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/shiny_launcher.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app designed for OSC OnDemand that launches a Shiny App within\nan Owens batch job.\u003c/p\u003e\n\u003cp\u003eThe Shiny app is included a submodule and each deployment can modified which\nShiny app to deploy using git config to specify the URL for the submodule. This\nway the launcher code can be reused for multiple apps but the launcher and the\napp itself can be managed separately.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e x.y.z+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eTODO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_example_shiny/fork\"\u003ehttps://github.com/OSC/bc_osc_example_shiny/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "tperezdevelopment/Singularity-Tools", + "latest_release": "1.0.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity-Tools\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/270368691\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b74d900bff6714a691edb3ec8bc54abcbf1653a66cc2dfeb1eb05e5e3f452b05/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3237303336383639312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/270368691.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eList of Singularity file to build Tools\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1569007230.0 + "updated_at": 1661186876.0 }, { "data_format": 2, - "description": "Command Line Interface and Python API for Forskalle", + "description": "Code repository for my Masters thesis with the title \"Temporal Planning for Droplet Routing on Microfluidic Biochips\".", "filenames": [ "Singularity" ], - "full_name": "csf-ngs/forskalle-api", + "full_name": "Altava/droplet-routing", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fsk-api--cli\" class=\"anchor\" href=\"#fsk-api--cli\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFSK API + cli\u003c/h1\u003e\n\u003cp\u003ePython library for Fsk3 API. Will add functionality as needed.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstall from the VBCF.NGS repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install git+https://ngs.vbcf.ac.at/repo/software/forskalle-api.git\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or github\u003c/span\u003e\npip3 install git+https://github.com/csf-ngs/forskalle-api.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-cli\" class=\"anchor\" href=\"#cli\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCLI\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003efsk-cli [command] [options] etc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePoint it at your favorite Forskalle instance either by\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esetting environment variables: \u003ccode\u003eFSK_API_BASE\u003c/code\u003e and \u003ccode\u003eFSK_API_KEY\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eusing a config file in \u003ccode\u003e~/.fsk_api.yml\u003c/code\u003e, please see \u003ca href=\"doc/\"\u003edoc/\u003c/a\u003e for an example\u003c/li\u003e\n\u003cli\u003eproviding \u003ccode\u003e--base\u003c/code\u003e and \u003ccode\u003e--key\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTry \u003ccode\u003efsk-cli --help\u003c/code\u003e for some hints!\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-examples\" class=\"anchor\" href=\"#examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h4\u003e\n\u003cp\u003eSet all sequenced samples of a multiplex to Ok:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003efsk-cli multi get M4711 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e jq \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.multiplex_samples[].sample_id\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \\\n \u003cspan class=\"pl-k\"\u003ewhile\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e sample_id\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e \n fsk-cli set-sequencing-status \u003cspan class=\"pl-smi\"\u003e$sample_id\u003c/span\u003e --status Ok\n \u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn place editing with jq and updating:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e update all request lanes to status Ready\u003c/span\u003e\nfsk-cli request get R4711 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \\\n jq \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.request_lanes[].status=\"Ready\"\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \\\n fsk-cli request update R4711\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-library\" class=\"anchor\" href=\"#library\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLibrary\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003efrom forskalle_api import FskApi\n\nfsk_api = FskApi()\nsample_json = fsk_api.get_sample(54321)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003efrom forskalle_api import FskApi\nfrom forskalle_api.auto.queryparams import IlluminaRunFilters\nfrom forskalle_api.fsk_query import FskQuery\n\nfsk_api = FskApi()\nirf = IlluminaRunFilters(sequenced_after=\"2020-05-01\")\nq = FskQuery(filters=irf)\nruns = fsk_api.get_runs_illumina(q)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere is no API-doc or similar, but we all love reading python source code!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-models\" class=\"anchor\" href=\"#models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModels\u003c/h3\u003e\n\u003cp\u003eModels and Query Parameters are autogenerated from forskalle. Return values of most api calls are thin class layers with type hints, e.g. forskalle_api.auto.models.Sample with all properties and relationships to allow easy navigation in your source code editor.\u003c/p\u003e\n\u003cp\u003eYou can also find de/serialization helpers (serializeSample from Class to dict, plainToSample from dict to Class).\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-droplet-routing\" class=\"anchor\" aria-hidden=\"true\" href=\"#droplet-routing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edroplet-routing\u003c/h1\u003e\n\u003cp\u003eCode repository for my Masters thesis with the title \"Temporal Planning for Droplet Routing on Microfluidic Biochips\".\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1625599328.0 + "updated_at": 1654011059.0 }, { "data_format": 2, - "description": null, + "description": "Eugene is an integrative genome annotation software", "filenames": [ - "volsung-cudnn8-runtime-ubuntu18.04/Singularity", - "vdt_base/Singularity" + "eugene/singularity/4.3/Singularity" ], - "full_name": "AvciRecep/chaste_nesi", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4539\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsing conventions described here.\n\u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/getting-started/naming\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/singularityhub-docs/docs/getting-started/naming\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "tschiex/eugene", + "latest_release": "v4.3a", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-welcome-to-eugene\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcome-to-eugene\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to eugene\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-an-integrative-gene-finder-for-eukaryotic-and-prokaryotic-genomes\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-integrative-gene-finder-for-eukaryotic-and-prokaryotic-genomes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAn integrative gene finder for eukaryotic and prokaryotic genomes\u003c/h2\u003e\n\u003cp\u003eThis software is OSI Certified Open Source Software. OSI Certified is\na certification mark of the Open Source Initiative. eugene is\ngoverned by the ARTISTIC LICENSE (see \u003ca href=\"http://www.opensource.org\" rel=\"nofollow\"\u003ewww.opensource.org\u003c/a\u003e). Please see\nthe file COPYING for details. For documentation, please see the files\nin the doc subdirectory. For building and installation instructions\nplease see the INSTALL file. For creating a new eugene release, please\nsee the RELEASE file.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor more information\u003c/h2\u003e\n\u003cp\u003eVisit eugene\u0027s web site at \u003ca href=\"http://eugene.toulouse.inrae.fr\" rel=\"nofollow\"\u003eINRAE\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1625528992.0 + "updated_at": 1660811863.0 }, { "data_format": 2, - "description": "METHYLPY, is an analysis pipeline for DNA methylation data.", + "description": "Recipes and definition files for building singularity", "filenames": [ - "1.4.3/Singularity" + "flameshot/Singularity", + "ansible/Singularity" ], - "full_name": "pscedu/singularity-methylpy", - "latest_release": "v1.4.3", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-methylpy/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-methylpy/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-methylpy/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-methylpy/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/68dce811b911761f8ba92aae500e0e1400720248c31724fc8f3215bead82ab11/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d657468796c7079\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/68dce811b911761f8ba92aae500e0e1400720248c31724fc8f3215bead82ab11/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d657468796c7079\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-methylpy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fe10607a3df63b8791aff64a9cb4897318e187e28b12c3a563a6a0a76bbb8f73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d657468796c7079\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe10607a3df63b8791aff64a9cb4897318e187e28b12c3a563a6a0a76bbb8f73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d657468796c7079\" alt=\"forks\" 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href=\"https://camo.githubusercontent.com/c59e0399bb825884a9fa4f45b91bdba428f86d1decc49df207e011187efa29b1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d657468796c7079\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c59e0399bb825884a9fa4f45b91bdba428f86d1decc49df207e011187efa29b1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d657468796c7079\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-methylpy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-methylpy\" class=\"anchor\" href=\"#singularity-methylpy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-methylpy\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/METHYLPY\"\u003eMETHYLPY\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the Perl scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/methylpy/1.4.3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/methylpy\u003c/code\u003e as \u003ccode\u003e1.4.3.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "serheang/singularity", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://sylabs.io/guides/3.6/user-guide/introduction.html\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to build\u003c/h2\u003e\n\u003cp\u003eThe simplest way to build a singularity container is to build from docker:\n\u003ccode\u003esingularity pull docker://centos:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eHowever, if you have a definition file like this:\ndocker.def:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: centos:7\n\n%labels\n\tAUTHOR SerTan\n\tVERSION 1.0\n\n%environment\n\texport PATH=/usr/local/bin:$PATH\n\texport LANG=en_US.UTF-8\n\texport LC_ALL=C\n\n%files\n\n%post\n\tyum -y install emacs\n\n%runscript\n\techo \"This is a container\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you can build the SIF from it:\n\u003ccode\u003esudo singularity build test.sif docker.def\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can refer to this \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html\" rel=\"nofollow\"\u003equickstart guide\u003c/a\u003e to have more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the image?\u003c/h2\u003e\n\u003cp\u003eTo run a SIF:\n\u003ccode\u003esingularity run -B $XDG_RUNTIME_DIR \u0026lt;sif file\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt require to bind $XDG_RUNTIME_DIR into the container so that we can utilize the host\u0027s X session capacity.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629218072.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1660644642.0 }, { "data_format": 2, - "description": null, + "description": "patroon with openms singularity image", "filenames": [ - "misc/releases/20.06/Singularity.20.06", - "misc/releases/latest/Singularity", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/19.06/Singularity.19.06" + "Singularity" ], - "full_name": "salome-eriksson/downward-issue751-prototype", + "full_name": "romxero/patroonOpenmsSingularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fast-downward\" class=\"anchor\" href=\"#fast-downward\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast Downward\u003c/h1\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" href=\"#tested-software-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1625214736.0 + "updated_at": 1659137230.0 }, { "data_format": 2, - "description": null, + "description": "Singularity definition files for building various software to run on HPC systems", "filenames": [ - "Singularity" + "coinfinder.def", + "octopus.def", + "demultiplex.def", + "sibeliusz.def", + "orthofinder.def", + "torstyverse.def", + "openmpibase.def", + "amiga.def", + "panx.def", + "instrain.def", + "eggnogmapper.def", + "motulizer.def", + "orthofinder_usemem.def", + "raxspectree.def", + "tychfinder.def", + "wgasuite.def", + "checkm.def", + "pheniqs.def" ], - "full_name": "mherkazandjian/ismcpak", + "full_name": "slhogle/singularity_def_files", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://cloud.sylabs.io/library/_container/5f9bd736bccfe9cf4578f166\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-quickstart\" class=\"anchor\" href=\"#quickstart\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h1\u003e\n\u003cp\u003eTo run a quick example, the following container can be used:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone -b alpha-master https://github.com/mherkazandjian/ismcpak.git ~/ismcpak\n$ cd ~/ismcpak/tests\n$ singularity exec library://mher/default/ismcpak:latest mpiexec python run_singleMesh.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is a package which implements some utilities useful for modelling and\nanalyzing simulation output of PDRs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ejupyter notebooks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo run a jupyter server inside the container with the full ismcpak environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --scratch /run/user library://mher/default/ismcpak:latest jupyter-lab\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-the-container-locally\" class=\"anchor\" href=\"#build-the-container-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the container locally\u003c/h2\u003e\n\u003cp\u003eThe following command build the singularity container on a local machine. The\nonly prerequisite is to have singularity installed and to have sudo access.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone -b alpha-master https://github.com/mherkazandjian/ismcpak.git ~/ismcpak\n$ cd ~/ismcpak\n$ sudo make singularity\n$ cd tests\n$ singularity exec ../container.sif mpiexec python run_singleMesh.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h1\u003e\n\u003cp\u003eamuse - mpich\nPyQt4\nipython\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installing-the-pdr-code\" class=\"anchor\" href=\"#installing-the-pdr-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the PDR code\u003c/h1\u003e\n\u003cp\u003eThe PDR code should be copied into:\namuse/src/amuse/community/pdr\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-compiling-the-pdr-code\" class=\"anchor\" href=\"#compiling-the-pdr-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling the PDR code\u003c/h1\u003e\n\u003cp\u003eThe PDR code can be compiled using:\n~\u0026gt; cd amuse/src/amuse/community/pdr\n~\u0026gt; make all\nThe generates the libpdr.a library\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-setting-up-the-working-environment\" class=\"anchor\" href=\"#setting-up-the-working-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetting up the working environment\u003c/h1\u003e\n\u003cp\u003eThe path to ismcpak should be added to the PYTHONPATH environment variable. For\nbash, the following line should be added:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=/PATH/TO/ismcpak:$PYTHONPATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto tcsh :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetenv PYTHONPATH /PATH/TO/ismcpak:$PYTHONPATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-code\" class=\"anchor\" href=\"#running-the-code\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the code\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-basic-test---single-model\" class=\"anchor\" href=\"#basic-test---single-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasic test - single model\u003c/h2\u003e\n\u003cp\u003eThe PDR code can only be run through the AMUSE ( \u003ca href=\"http://amusecode.org\" rel=\"nofollow\"\u003ehttp://amusecode.org\u003c/a\u003e ).\nDepending on the mpi environment installed with AMUSE, it might be\nnecessary to launch the mpd deamon before executing either:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e~\u0026gt; mpirun -np 1 python run_singleMesh.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e~\u0026gt; python run_singleMesh.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-a-grid-of-models\" class=\"anchor\" href=\"#running-a-grid-of-models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a Grid of models\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-setup-the-working-environment-variables\" class=\"anchor\" href=\"#setup-the-working-environment-variables\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetup the working environment variables\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003esource setdev\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-install-the-pdr-code-into-amuse-make-sure-the-correct\" class=\"anchor\" href=\"#install-the-pdr-code-into-amuse-make-sure-the-correct\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einstall the pdr code into amuse (make sure the correct\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-path-of-amuse-is-set-in-setenv\" class=\"anchor\" href=\"#path-of-amuse-is-set-in-setenv\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epath of amuse is set in setenv\u003c/h1\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003emake pdr_install\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-after-these-two-steps-the-tests\" class=\"anchor\" href=\"#after-these-two-steps-the-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eafter these two steps, the tests\u003c/h1\u003e\n\u003cp\u003erun_singleMesh.py\nchemical_network_pdr_code.py\nshould run without errors\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eto run a grid, use the following under ismcpak:\n~\u0026gt; ipython --pylab=qt tests/run_oneSidedGrid.py\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eafter the model data is written to\ntests/oneSidedGrid/meshes\nwe need to construct the database files .db using constructReadArchive.py\n~\u0026gt; ipython --pylab=qt constructReadArchive.py\u003c/p\u003e\n\u003cp\u003eafter the database is constructed we must have the file\nmeshes.db meshes.db.info\nin the output directory and a message\narchive integrity test passed\nmust be displayed\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eafter creating the database, a reference file must be generated which\nstores information about the parameters which have been used in\ngenerating the data. A template of this file is located under\nruns/tests/templateDir/used_params.py\nwhere the parameters used by run_oneSidedGrid.py should be filled in\nby hand. Once the values are changed :\n~\u0026gt; python used_parms.py\ngenerates the pickle file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eset the desired display parameters in analyzeArchive.py and invoke :\n~\u0026gt; ipython --pylab=qt analyzeArchive.py\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eto generate the radex databases, the bottom part of analyzeArchive.py should be enabled to\nallow radex databases to be computed and written do disk. Set the desired values of\nAv to compute and the species whose emission will be computed and re-run:\n~\u0026gt; ipython --pylab=qt analyzeArchive.py\nAs a check, the data in\ntests/oneSidedGrid/radexDbs\nshould have directories with the Avs we have set and each directory should\nhave files for each species we have specified.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eafter producing the radex database files, we can convert that data to ascii data using :\n~\u0026gt; ipython ismcpak2Ascii.py\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h1\u003e\n\u003cp\u003eTHIS SOFTWARE IS PROVIDED UNDER THE GPL LICENSE BY THE COPYRIGHT HOLDERS AND\nCONTRIBUTORS \u201cAS IS\u201d AND DOES NOT EXPRESS OR PROVIDE IMPLIED WARRANTIES,\nINCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND F\nITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\nOWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\nEXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT\nOF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\nINTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT\n, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY\nWAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH\nDAMAGE.\u003c/p\u003e\n\u003cp\u003eSee LICENSE.txt for more information about the GPL license.\u003c/p\u003e\n\u003cp\u003ePlease cite the following papers if any part of this package is used in your\nresearch.\u003c/p\u003e\n\u003cp\u003eKazandjian, M. V., Meijerink, R., Pelupessy, I., Israel, F. P., Spaans, M.,\narXiv:1403.7000\u003c/p\u003e\n\u003cp\u003eKazandjian, M. V., Meijerink, R., Pelupessy, I., Israel, F. P., Spaans, M.,\n2012, A\u0026amp;A, 542, A65, 26\u003c/p\u003e\n\u003cp\u003eMeijerink, R., Spaans, M., \u0026amp; Israel, F. P. 2007, A\u0026amp;A, 461, 793\u003c/p\u003e\n\u003cp\u003eMeijerink, R. \u0026amp; Spaans, M. 2005, A\u0026amp;A, 436, 397\u003c/p\u003e\n\u003cp\u003eIsmpak makes makes use of \"Radex\" internally to compute the line emissions. Please\nreference the RADEX paper as well:\u003c/p\u003e\n\u003cp\u003eVan der Tak, F.F.S., Black, J.H., Sch\u00f6ier, F.L., Jansen, D.J., van Dishoeck, E.F. 2007, A\u0026amp;A 468, 627\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1625261030.0 + "updated_at": 1639279998.0 }, { "data_format": 2, - "description": "Docker image", + "description": "definition files for containers used in Hanlab", "filenames": [ - "Singularity.latest" + "singularity.R.3.6.3.Bioc/R.3.6.3.Bioc.def", + "singularity.Rconda/R.3.6.3.def", + "singularity.mkl/mkl.def", + "singularity.mkl/mkl.ubuntu.def", + "singularity.R.4.0.2.Bioc/R.4.0.2.Bioc.def", + "singularity.py37.ml.openblas/py37.ml.openblas.def", + "singularity.R.3.6.3.phylo/R.3.6.3.phylo.def", + "singularity.SAD/SAD.def", + "singularity.phylo/phylo.def", + "singularity.py37.ml.mkl/py37.ml.mkl.def", + "singularity.rnaseq/rnaseq.def" ], - "full_name": "AdamWilsonLab/docker_geospatial_plus", + "full_name": "HanLabUNLV/hanlab_singularity_defs", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-geospatial-plus\" class=\"anchor\" href=\"#geospatial-plus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeospatial Plus\u003c/h1\u003e\n\u003cp\u003eBuilding on the versioned geospatial Rocker image.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-github-actions\" class=\"anchor\" href=\"#github-actions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGithub Actions\u003c/h1\u003e\n\u003cp\u003eThis repository uses GitHub Actions to test the docker image prior to making it available as a GitHub package.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1624971946.0 + "updated_at": 1648241982.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.cosmic_tagging_tf_2010" ], - "full_name": "yuma-35/wave-U-guiter", + "full_name": "maxpkatz/singularity_image_files", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-wave-u-net-pytorch\" class=\"anchor\" href=\"#wave-u-net-pytorch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWave-U-Net (Pytorch)\u003c/h1\u003e\n\u003cp\u003eImproved version of the \u003ca href=\"https://arxiv.org/abs/1806.03185\" rel=\"nofollow\"\u003eWave-U-Net\u003c/a\u003e for audio source separation, implemented in Pytorch.\u003c/p\u003e\n\u003cp\u003eClick \u003ca href=\"www.github.com/f90/Wave-U-Net\"\u003ehere\u003c/a\u003e for the original Wave-U-Net implementation in Tensorflow.\nYou can find more information about the model and results there as well.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-improvements\" class=\"anchor\" href=\"#improvements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImprovements\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eMulti-instrument separation by default, using a separate standard Wave-U-Net for each source (can be set to one model as well)\u003c/li\u003e\n\u003cli\u003eMore scalable to larger data: A depth parameter D can be set that employs D convolutions for each single convolution in the original Wave-U-Net\u003c/li\u003e\n\u003cli\u003eMore configurable: Layer type, resampling factor at each level etc. can be easily changed (different normalization, residual connections...)\u003c/li\u003e\n\u003cli\u003eFast training: Preprocesses the given dataset by saving the audio into HDF files, which can be read very quickly during training, thereby avoiding slowdown due to resampling and decoding\u003c/li\u003e\n\u003cli\u003eModular thanks to Pytorch: Easily replace components of the model with your own variants/layers/losses\u003c/li\u003e\n\u003cli\u003eBetter output handling: Separate output convolution for each source estimate with linear activation so amplitudes near 1 and -1 can be easily predicted, at test time thresholding to valid amplitude range [-1,1]\u003c/li\u003e\n\u003cli\u003eFixed or dynamic resampling: Either use fixed lowpass filter to avoid aliasing during resampling, or use a learnable convolution\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003eGPU strongly recommended to avoid very long training times.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-option-1-direct-install-recommended\" class=\"anchor\" href=\"#option-1-direct-install-recommended\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 1: Direct install (recommended)\u003c/h3\u003e\n\u003cp\u003eSystem requirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinux-based OS\u003c/li\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://mega-nerd.com/libsndfile/\" rel=\"nofollow\"\u003elibsndfile\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ffmpeg.org/\" rel=\"nofollow\"\u003effmpeg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eCUDA 10.1 for GPU usage\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eClone the repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/f90/Wave-U-Net-Pytorch.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRecommended: Create a new virtual environment to install the required Python packages into, then activate the virtual environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evirtualenv --python /usr/bin/python3.6 waveunet-env\nsource waveunet-env/bin/activate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstall all the required packages listed in the \u003ccode\u003erequirements.txt\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-option-2-singularity\" class=\"anchor\" href=\"#option-2-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 2: Singularity\u003c/h3\u003e\n\u003cp\u003eWe also provide a Singularity container which allows you to avoid installing the correct Python, CUDA and other system libraries, however we don\u0027t provide specific advice on how to run the container and so only do this if you have to or know what you are doing (since you need to mount dataset paths to the container etc.)\u003c/p\u003e\n\u003cp\u003eTo pull the container, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://f90/Wave-U-Net-Pytorch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the container from the directory where you cloned this repository to, using the commands listed further below in this readme.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-download-datasets\" class=\"anchor\" href=\"#download-datasets\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload datasets\u003c/h1\u003e\n\u003cp\u003eTo directly use the pre-trained models we provide for download to separate your own songs, now skip directly to the \u003ca href=\"#test\"\u003elast section\u003c/a\u003e, since the datasets are not needed in that case.\u003c/p\u003e\n\u003cp\u003eTo start training your own models, download the \u003ca href=\"https://sigsep.github.io/datasets/musdb.html\" rel=\"nofollow\"\u003efull MUSDB18HQ dataset\u003c/a\u003e and extract it into a folder of your choice. It should have two subfolders: \"test\" and \"train\" as well as a README.md file.\u003c/p\u003e\n\u003cp\u003eYou can of course use your own datasets for training, but for this you would need to modify the code manually, which will not be discussed here. However, we provide a loading function for the normal MUSDB18 dataset as well.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-training-the-models\" class=\"anchor\" href=\"#training-the-models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining the models\u003c/h1\u003e\n\u003cp\u003eTo train a Wave-U-Net, the basic command to use is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3.6 train.py --dataset_dir /PATH/TO/MUSDB18HQ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere the path to MUSDB18HQ dataset needs to be specified, which contains the \u003ccode\u003etrain\u003c/code\u003e and \u003ccode\u003etest\u003c/code\u003e subfolders.\u003c/p\u003e\n\u003cp\u003eAdd more command line parameters as needed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--cuda\u003c/code\u003e to activate GPU usage\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--hdf_dir PATH\u003c/code\u003e to save the preprocessed data (HDF files) to custom location PATH, instead of the default \u003ccode\u003ehdf\u003c/code\u003e subfolder in this repository\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--checkpoint_dir\u003c/code\u003e and \u003ccode\u003e--log_dir\u003c/code\u003e to specify where checkpoint files and logs are saved/loaded\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--load_model checkpoints/model_name/checkpoint_X\u003c/code\u003e to start training with weights given by a certain checkpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more config options, see \u003ccode\u003etrain.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTraining progress can be monitored by using Tensorboard on the respective \u003ccode\u003elog_dir\u003c/code\u003e.\nAfter training, the model is evaluated on the MUSDB18HQ test set, and SDR/SIR/SAR metrics are reported for all instruments and written into both the Tensorboard, and in more detail also into a \u003ccode\u003eresults.pkl\u003c/code\u003e file in the \u003ccode\u003echeckpoint_dir\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content--test-trained-models-on-songs\" class=\"anchor\" href=\"#-test-trained-models-on-songs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca name=\"user-content-test\"\u003e\u003c/a\u003e Test trained models on songs!\u003c/h1\u003e\n\u003cp\u003eWe provide the default model in a pre-trained form as download so you can separate your own songs right away.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-downloading-our-pretrained-models\" class=\"anchor\" href=\"#downloading-our-pretrained-models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownloading our pretrained models\u003c/h2\u003e\n\u003cp\u003eDownload our pretrained model \u003ca href=\"https://www.dropbox.com/s/r374hce896g4xlj/models.7z?dl=1\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nExtract the archive into the \u003ccode\u003echeckpoints\u003c/code\u003e subfolder in this repository, so that you have one subfolder for each model (e.g. \u003ccode\u003eREPO/checkpoints/waveunet\u003c/code\u003e)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-pretrained-model\" class=\"anchor\" href=\"#run-pretrained-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun pretrained model\u003c/h2\u003e\n\u003cp\u003eTo apply our pretrained model to any of your own songs, simply point to its audio file path using the \u003ccode\u003einput_path\u003c/code\u003e parameter:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3.6 predict.py --load_model checkpoints/waveunet/model --input \"audio_examples/Cristina Vane - So Easy/mix.mp3\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAdd \u003ccode\u003e--cuda \u003c/code\u003e when using a GPU, it should be much quicker\u003c/li\u003e\n\u003cli\u003ePoint \u003ccode\u003e--input\u003c/code\u003e to the music file you want to separate\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default, output is written where the input music file is located, using the original file name plus the instrument name as output file name. Use \u003ccode\u003e--output\u003c/code\u003e to customise the output directory.\u003c/p\u003e\n\u003cp\u003eTo run your own model:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePoint \u003ccode\u003e--load_model\u003c/code\u003e to the checkpoint file of the model you are using. If you used non-default hyper-parameters to train your own model, you must specify them here again so the correct model is set up and can receive the weights!\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1624842105.0 + "updated_at": 1609779903.0 }, { "data_format": 2, - "description": "Recipes for docker and singularity containers for COHERENT projects", + "description": "Singularity recipe files for sambamba (https://github.com/biod/sambamba)", "filenames": [ - "geant4/Singularity_geant4", - "geant4/Singularity" + "Singularity.0.8.0", + "Singularity" ], - "full_name": "NuTufts/coherent-containers", + "full_name": "powerPlant/sambamba-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coherent-containers\" class=\"anchor\" href=\"#coherent-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoherent-containers\u003c/h1\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for Sambamba, a high performance highly parallel robust and fast tool (and library), written in the D programming language, for working with SAM and BAM files.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1626189399.0 + "updated_at": 1613348079.0 }, { "data_format": 2, - "description": "Nextflow pipelines for a variety of bioinformatics outputs", + "description": null, "filenames": [ - "nextstrain/environments/Singularity" + "Singularity.4.4.2", + "Singularity.4.0.14" ], - "full_name": "matt-sd-watson/nextflow_for_bioinformatics", + "full_name": "sschmeier/fishtank-gpu2", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow_for_bioinformatics\" class=\"anchor\" href=\"#nextflow_for_bioinformatics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextflow_for_bioinformatics\u003c/h1\u003e\n\u003cp\u003eNextflow pipelines for routine bioinformatics analyses\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-nextstrain\" class=\"anchor\" href=\"#nextstrain\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextstrain\u003c/h2\u003e\n\u003cp\u003eThe nextstrain workflow is the most up-to-date and maintained pipeline in this repo. It can be used to generate a serie sof parallel nextstrain builds or for parameter testing. A specific README for this pipeline is provided in the named directory.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-rna-seq-and-tree_annotation\" class=\"anchor\" href=\"#rna-seq-and-tree_annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erna-seq and tree_annotation\u003c/h2\u003e\n\u003cp\u003eIn development.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fishtank-gpu2\" class=\"anchor\" aria-hidden=\"true\" href=\"#fishtank-gpu2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efishtank-gpu2\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1629726178.0 + "updated_at": 1614062687.0 }, { "data_format": 2, - "description": "FastTree infers approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences. ", + "description": "Container with Jupyter and rstudio server", "filenames": [ - "2.1.11/Singularity" + "Singularity.0.2.0", + "Singularity.0.2.1", + "Singularity", + "Singularity.0.1" ], - "full_name": "pscedu/singularity-fasttree", - "latest_release": "v2.1.11", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-fasttree/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fasttree/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-fasttree/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fasttree/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/776c7e7d72b508b47ce8bd555bc38368be7629ae529461cdfcfaf5af2920c141/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6661737474726565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/776c7e7d72b508b47ce8bd555bc38368be7629ae529461cdfcfaf5af2920c141/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6661737474726565\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-fasttree\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/6ba1353dc0bb816d3bca4dafd9f90d560fb7d5234867b2ac4b80d0557d1bdc90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6661737474726565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6ba1353dc0bb816d3bca4dafd9f90d560fb7d5234867b2ac4b80d0557d1bdc90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6661737474726565\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-fasttree\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a6aa9ae28b1371a303884250204eea3a43e273bae8eb19f063f732416f9d6bec/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6661737474726565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a6aa9ae28b1371a303884250204eea3a43e273bae8eb19f063f732416f9d6bec/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6661737474726565\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fasttree\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0cc26b73063b0fb2d5f502b2eeb83e3568bac891ce91b016300d28524b83b564/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6661737474726565\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0cc26b73063b0fb2d5f502b2eeb83e3568bac891ce91b016300d28524b83b564/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6661737474726565\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fasttree\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-fasttree\" class=\"anchor\" href=\"#singularity-fasttree\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-fasttree\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eFastTree\u003c/code\u003e, \u003ccode\u003eFastTreeMP\u003c/code\u003e and \u003ccode\u003eFastTreeDbl\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/FastTree/2.1.11\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/FastTree\u003c/code\u003e as \u003ccode\u003e2.1.11.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "dcgc-bfx/singularity-jupyter-rstudio", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/dcgc-bfx/dcgc-jupyter-rstudio/workflows/Build/badge.svg?branch=main\"\u003e\u003cimg src=\"https://github.com/dcgc-bfx/dcgc-jupyter-rstudio/workflows/Build/badge.svg?branch=main\" alt=\"Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5253\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dcgc-jupyter-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#dcgc-jupyter-rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edcgc-jupyter-rstudio\u003c/h1\u003e\n\u003cp\u003eContainer with Jupyter and rstudio server\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629226128.0 + "subscribers_count": 4, + "topics": [], + "updated_at": 1622118476.0 }, { "data_format": 2, - "description": null, + "description": "DSL 2 version of https://github.com/jhoneycuttr/nf-wgs ", "filenames": [ "Singularity" ], - "full_name": "baxpr/bedpost-singularity", - "latest_release": "v3.0.0", - "readme": "\u003cp\u003eRuns FSL\u0027s bedpostx on the input DWI data set, and creates a PDF report of the results.\nQuite simple - see /opt/src/pipeline.sh for the main script.\u003c/p\u003e\n", + "full_name": "Finterly/nf-wgs-dsl2", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-optimized-gatk4-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#optimized-gatk4-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimized GATK4 Pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-1-nextflow-dsl-2-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-1-nextflow-dsl-2-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart 1 Nextflow DSL 2 Workflow\u003c/h2\u003e\n\u003cp\u003eAdapted from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Karaniare/Optimized_GATK4_pipeline\"\u003ehttps://github.com/Karaniare/Optimized_GATK4_pipeline\u003c/a\u003e (shell script)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jhoneycuttr/nf-wgs\"\u003ehttps://github.com/jhoneycuttr/nf-wgs\u003c/a\u003e (Nextflow DSL 1)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setting-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#setting-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetting Parameters\u003c/h3\u003e\n\u003cp\u003eModify the nextflow.config file:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameters\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003einputdir\u003c/td\u003e\n\u003ctd\u003eThe folder that contains all the fastq files (default \u0027data\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutdir\u003c/td\u003e\n\u003ctd\u003eThe folder where you want the resulting data to be save (default \u0027results\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erefdir\u003c/td\u003e\n\u003ctd\u003eThe folder that contains reference genomes and bed files (default \u0027genomes\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etrimadapter\u003c/td\u003e\n\u003ctd\u003eThe adapter used for initial trimming of reads (default \u0027TruSeq3-PE.fa\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOther Parameters\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ereads\u003c/td\u003e\n\u003ctd\u003eThe fastq files in the inputdir folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eref\u003c/td\u003e\n\u003ctd\u003eThe reference genome (default \u0027Pf3D7_human.fa\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erscript\u003c/td\u003e\n\u003ctd\u003eThe r script for generating report\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdditionally, the nextflow parameter \u003ccode\u003e-profile\u003c/code\u003e can be use to target the infrastructure you wish to run the pipeline on. The different profiles are listed below, including any setup that is required.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eUnder construction \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e !\u003c/p\u003e\n\u003cp\u003eIf using singularity, please run the command below to generate the singularity image.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build nf-wgs-dsl2.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then include the \u003ccode\u003esingularity\u003c/code\u003e profile on the command line.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: you should also include executor you wish to run\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile sge,singularity -c conf/custom.config\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBelow is an example using the genome parameter:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single -w \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/work --target v4 -profile sge,singularity --genome PlasmoDB-59_Pfalciparum3D7_Genome.fasta -c conf/custom.config\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eThe pipeline can be easily run with docker and is the recommended way to run it when not using an HPC.\u003c/p\u003e\n\u003cp\u003eFollow the steps below to setup your docker image:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003edocker\u003c/a\u003e is a prerequisite.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t finterly/nf-wgs-dsl2 \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd you\u0027re done! To run the pipeline, simply add \u003ccode\u003e-profile docker\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile docker\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda\u003c/h3\u003e\n\u003cp\u003eUnder construction \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e !\u003c/p\u003e\n\u003cp\u003eTo use conda, you must first install either \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda\u003c/a\u003e. Once installed, include the \u003ccode\u003econda\u003c/code\u003e profile on the command line.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR single --target v3 -profile conda\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-customizing-for-your-institution\" class=\"anchor\" aria-hidden=\"true\" href=\"#customizing-for-your-institution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustomizing for your institution\u003c/h3\u003e\n\u003cp\u003eThere is a file named \u003ccode\u003ecustom.config\u003c/code\u003e in \u003ccode\u003econf/\u003c/code\u003e that can be used to tailor processes to your environment. By default,\nthis file is used to tailor the pipeline for Wynton HPC at UCSF. This file may be altered to fit your institution\u0027s profile.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h3\u003e\n\u003cp\u003eUnder construction \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e !\u003c/p\u003e\n\u003cp\u003ePotential ways to execute the pipeline:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e local executor\u003c/span\u003e\nnextflow run main.nf\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e with a profile (currently only supports sge)\u003c/span\u003e\nnextflow run main.nf -profile sge\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run with singularity on an HPC with specified reference sequences\u003c/span\u003e\nnextflow run main.nf --readDIR single -profile sge,singularity --refseq_fasta v4_refseq.fasta --target v4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or locally with docker\u003c/span\u003e\nnextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single/ --target v4 -profile docker --refseq_fasta v4_refseq.fasta\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e genomes can be provided in lieu of reference sequences, which will be generated with the amplicon table\u003c/span\u003e\nnextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single/ -w \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/work --target v4 -profile docker --genome PlasmoDB-59_Pfalciparum3D7_Genome.fasta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to resume after some processes were successfully executed, add -resume at the end of it\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1626106357.0 + "updated_at": 1671034942.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v1.0.0" + "Singularity" ], - "full_name": "mchugomk/cat12_long", - "latest_release": null, - "readme": "", + "full_name": "aarandad/ampseq_workflow", + "latest_release": "v0.0.4", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ampseq-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#ampseq-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAmpSeq Workflow\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setting-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#setting-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetting Parameters\u003c/h3\u003e\n\u003cp\u003eModify the nextflow.config file:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ereadDIR\u003c/td\u003e\n\u003ctd\u003eThe folder that contains all the fastq files (\u003cem\u003erequired\u003c/em\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutDIR\u003c/td\u003e\n\u003ctd\u003eThe folder where you want the resulting data to be save (default \u0027results\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esequencer\u003c/td\u003e\n\u003ctd\u003eThe sequencer used to produce your data (default \u0027nextseq\u0027)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC_only\u003c/td\u003e\n\u003ctd\u003eWhether to only run QC related workflows or all workflows\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erefseq_fasta \u003cstrong\u003eor\u003c/strong\u003e genome\u003c/td\u003e\n\u003ctd\u003ePath to reference sequences \u003cstrong\u003eor\u003c/strong\u003e path to genome (\u003cem\u003eone\u003c/em\u003e is \u003cstrong\u003erequired\u003c/strong\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdditionally, the nextflow parameter \u003ccode\u003e-profile\u003c/code\u003e can be use to target the infrastructure you wish to run the pipeline on. The different profiles are listed below, including any setup that is required.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eIf using singularity, please run the command below to generate the singularity image.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build ampseq_workflow.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then include the \u003ccode\u003esingularity\u003c/code\u003e profile on the command line.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: you should also include executor you wish to run\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR single --refseq_fasta v4_refseq.fasta --target v4 -profile sge,singularity -c conf/custom.config\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBelow is an example using the genome parameter:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single -w \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/work --target v4 -profile sge,singularity --genome PlasmoDB-59_Pfalciparum3D7_Genome.fasta -c conf/custom.config\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eThe pipeline can be easily run with docker and is the recommended way to run it when not using an HPC.\u003c/p\u003e\n\u003cp\u003eFollow the steps below to setup your docker image:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003edocker\u003c/a\u003e is a prerequisite.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t aarandad/ampseq_worfklow \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd you\u0027re done! To run the pipeline, simply add \u003ccode\u003e-profile docker\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR single --target v4-profile -profile docker\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda\u003c/h3\u003e\n\u003cp\u003eTo use conda, you must first install either \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003econda\u003c/a\u003e or \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda\u003c/a\u003e. Once installed, include the \u003ccode\u003econda\u003c/code\u003e profile on the command line.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --readDIR single --target v3 -profile conda\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-customizing-for-your-institution\" class=\"anchor\" aria-hidden=\"true\" href=\"#customizing-for-your-institution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustomizing for your institution\u003c/h3\u003e\n\u003cp\u003eThere is a file named \u003ccode\u003ecustom.config\u003c/code\u003e in \u003ccode\u003econf/\u003c/code\u003e that can be used to tailor processes to your environment. By default,\nthis file is used to tailor the pipeline for Wynton HPC at UCSF. This file may be altered to fit your institution\u0027s profile.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h3\u003e\n\u003cp\u003ePotential ways to execute the pipeline:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e local executor\u003c/span\u003e\nnextflow run main.nf --target v3\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e with a profile (currently only supports sge)\u003c/span\u003e\nnextflow run main.nf -profile sge --target v3\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run with singularity on an HPC with specified reference sequences\u003c/span\u003e\nnextflow run main.nf --readDIR single -profile sge,singularity --refseq_fasta v4_refseq.fasta --target v4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or locally with docker\u003c/span\u003e\nnextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single/ --target v4 -profile docker --refseq_fasta v4_refseq.fasta\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e genomes can be provided in lieu of reference sequences, which will be generated with the amplicon table\u003c/span\u003e\nnextflow run main.nf --readDIR \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/MAD4HATTER_example_data/single/ -w \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/work --target v4 -profile docker --genome PlasmoDB-59_Pfalciparum3D7_Genome.fasta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to resume after some processes were successfully executed, add -resume at the end of it\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1627066402.0 + "updated_at": 1659049973.0 }, { "data_format": 2, - "description": "A Nextflow pipeline for automatically running QC on Nano runs", + "description": null, "filenames": [ - "environments/illumina/Singularity" + "Singularity" ], - "full_name": "WalesGenePark/NanoSeqQC", + "full_name": "rses-singularity/tensorflow-cpu", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nanoseqqc\" class=\"anchor\" href=\"#nanoseqqc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNanoSeqQC\u003c/h1\u003e\n\u003cp\u003eA Nextflow pipeline for automatically running QC on Nano runs\u003c/p\u003e\n\u003cp\u003eWARNING - UNDER CURRENT DEVELOPMENT AND NOT FULLY FUNCTIONAL\u003c/p\u003e\n\u003cp\u003elarge sections of nextflow coding are based off the excellent ncov2019-artic-nf pipeline \u003ccode\u003econnor-lab/ncov2019-artic-nf\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h4\u003e\n\u003chr\u003e\n\u003cp\u003eThe running of this will automatically take fastq reads from a Nano sequencing read, run FastP read diagnostics and trimming before performing some comparative statistics based on library metadata such as RIN and concentration.\nAdditionally, reads will be run through Kraken2 to confirm species profile (and lack of contamination!)\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick-start\u003c/h4\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-illumina\" class=\"anchor\" href=\"#illumina\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIllumina\u003c/h5\u003e\n\u003cp\u003e\u003ccode\u003enextflow run WalesGenePark/NanoSeqQC --profile singularity,slurm --prefix \"job_output\" --directory /path/to/reads --outdir /path/to/outfile\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOptions\u003cbr\u003e\n--fastpInputVer (paired, single, merged)\u003c/p\u003e\n\u003chr\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h4\u003e\n\u003cp\u003eAn up-to-date version of Nextflow is required because the pipeline is written in DSL2. Following the instructions at \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/\u003c/a\u003e to download and install Nextflow should get you a recent-enough version.\u003c/p\u003e\n\u003cp\u003e1: git clone the repository\u003cbr\u003e\n2: chmod +x the two scripts in NanoSeqQC/scripts/\u003cbr\u003e\n3: run the singularity build\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-executor\" class=\"anchor\" href=\"#executor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecutor\u003c/h4\u003e\n\u003cp\u003eBy default, the pipeline runs locally unless specifying \u003ccode\u003e-profile slurm\u003c/code\u003e to send to a SLURM cluster.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-config\" class=\"anchor\" href=\"#config\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfig\u003c/h4\u003e\n\u003cp\u003eCommon config options are set in \u0027conf/base.config\u0027.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tensorflow-gpu-and-keras\" class=\"anchor\" aria-hidden=\"true\" href=\"#tensorflow-gpu-and-keras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTensorflow (GPU) and Keras\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1627380153.0 + "updated_at": 1542376589.0 }, { "data_format": 2, - "description": "modified version of nicMSlesions", + "description": null, "filenames": [ "Singularity" ], - "full_name": "jstutters/nicpython36", + "full_name": "amanmdesai/singularity-python-packages", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ms_cnn\" class=\"anchor\" href=\"#ms_cnn\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMS_CNN\u003c/h1\u003e\n\u003cp\u003e[This is a modified version of nicMSlesions (\u003ca href=\"https://github.com/NIC-VICOROB/nicMSlesions\"\u003ehttps://github.com/NIC-VICOROB/nicMSlesions\u003c/a\u003e)]\n\u003cbr\u003e\n\u003ca href=\"CNN.jpeg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"300\" src=\"CNN.jpeg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-this--version-support-additionally-the-following-functionalities\" class=\"anchor\" href=\"#this--version-support-additionally-the-following-functionalities\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis version support additionally the following functionalities:\u003c/h1\u003e\n\u003cdl\u003e\n \u003cdt\u003e(1) Runnable on a Mac system/computer\u003c/dt\u003e\n \u003cdt\u003e(2) Cold start and warm start support:\u003c/dt\u003e\n \u003cdd\u003e- Allowing to re-create the architecture of the model\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use the saved weights of the model\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use the training configuration and avoiding to run preprocessing again\u003c/dd\u003e\n \u003cdd\u003e- Allowing to resume training exactly where it left off(interrupting the training is \n allowed throughout the training process)\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use pretrained model\u003c/dd\u003e\n \u003cdt\u003e(3) Supporting Python 3\u003c/dt\u003e\n \u003cdt\u003e(4) Integrated Tensorborad [to provide the measurements and visualisations of TensorFlow execution (to understand, debug, and optimisation of the TensorFlow programs)]\u003c/dt\u003e\n \u003cdt\u003e(5) Checking whether a file or directory is relevant for Training and Testing\u003c/dt\u003e \n \u003cdt\u003e(6) Easy HPC (High Performance Computing) support\u003c/dt\u003e \n \u003cdt\u003e(7) Bias correction of masks using FSL\u003c/dt\u003e\n \u003cdt\u003e(8) Registration, moving all images to the Flair, T1 or Standard space\u003c/dt\u003e\n\u003c/dl\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"BR.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"500\" src=\"BR.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"note.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"100\" src=\"note.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n# Running the Program!\n\u003cp\u003eThis modified version can be run with or without a GUI (similar to original version)\u003c/p\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"GUI_NM.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"500\" src=\"GUI_NM.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003ch1\u003e\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-program-on-the-hpc-cluster-using-nvidia-gpuswithout-any-additional-librarydependency-installation\" class=\"anchor\" href=\"#running-the-program-on-the-hpc-cluster-using-nvidia-gpuswithout-any-additional-librarydependency-installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Program on the HPC cluster using NVIDIA GPUs(without any additional library/dependency installation):\u003c/h1\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"hpc.jpeg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"200\" src=\"hpc.jpeg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003eFirst, user will need to be sure that \"singularity\"\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/\u003c/a\u003e\nis available on local or remote machine.\u003c/p\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity pull docker://kbronik/ms_cnn_ucl:latest \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running the above, a singularity image using docker hub (docker://kbronik/ms_cnn_ucl:latest) will be generated:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - path to singularity//..///ms_cnn_ucl_latest.sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity run --nv (path to singularity)//..///ms_cnn_ucl_latest.sif python (path to nicpython36)/nic_train_network_batch.py (or other nic-python code)\u003c/pre\u003e\u003c/div\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"note_HPC.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"120\" src=\"note_HPC.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003ch1\u003e\n\u003ca id=\"user-content-for-an-interactive-session\" class=\"anchor\" href=\"#for-an-interactive-session\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor an interactive session:\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity shell (path to singularity)//..///ms_cnn_ucl_latest.sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate idp\n - python (path to nicpython36)/app.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-for-an-interactive-session-tensorflow-on-cpu-only\" class=\"anchor\" href=\"#for-an-interactive-session-tensorflow-on-cpu-only\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor an interactive session (TensorFlow on CPU only):\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e docker://kbronik/ms-ucl-cnn-cpu:CPU_Latest python (path to nicpython36)/app.py \u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-python-packages\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-python-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-python-packages\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1624933808.0 + "updated_at": 1673367679.0 }, { "data_format": 2, - "description": null, + "description": "Hello World image for Singularity", "filenames": [ - "Singularity.0.1.1", - "Singularity.0.1", "Singularity" ], - "full_name": "dcgc-bfx/singularity-base-conda", - "latest_release": "v0.1-alpha", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/dcgc-bfx/dcgc-base-conda/workflows/Build/badge.svg?branch=main\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/dcgc-bfx/dcgc-base-conda/workflows/Build/badge.svg?branch=main\" alt=\"Build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5252\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-dcgc-base-conda\" class=\"anchor\" href=\"#dcgc-base-conda\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edcgc-base-conda\u003c/h1\u003e\n", + "full_name": "amanmdesai/hello-world-singularity", + "latest_release": "v1.0.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hello-world-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#hello-world-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehello-world-singularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/amanmdesai/hello-world-singularity/actions/workflows/singularity-build-deploy.yml\"\u003e\u003cimg src=\"https://github.com/amanmdesai/hello-world-singularity/actions/workflows/singularity-build-deploy.yml/badge.svg?branch=master\" alt=\"Singularity Build Deploy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA simple singularity image to demonstrate how to use singularity.\u003c/p\u003e\n\u003cp\u003eTo pull this image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull oras://ghcr.io/amanmdesai/hello-world-singularity:latest\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1626686541.0 + "updated_at": 1672229206.0 }, { "data_format": 2, - "description": null, + "description": "Singularity bootstrap files inheriting from tensorflow Docker images", "filenames": [ - "docker/Singularity.snowflake" + "Singularity" ], - "full_name": "pnplab/preprocessing", + "full_name": "zhaojuanwendy/singularity-tensorflow", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-useful-utilities-for-single-cell-processing-with-alevin-fry\" class=\"anchor\" href=\"#useful-utilities-for-single-cell-processing-with-alevin-fry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful utilities for single-cell processing with alevin-fry\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/COMBINE-lab/alevin-fry\"\u003eAlevin-fry\u003c/a\u003e is a fast, accurate and memory-frugal tool for preprocessing single-cell and single-nucleus RNA-seq data. You can read more about alevin-fry in \u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.06.29.450377v1\" rel=\"nofollow\"\u003eits pre-print\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis respoistory contains scripts, functions and utilities that are useful for preparing data for processing with alevin-fry, as well as for reading alevin-fry data into other packages for downstream analysis.\u003c/p\u003e\n\u003cp\u003eThe different utilities are broken down in this repository by the language in which they are written (right now, Python, R and bash). A brief listing of\nthe available utilities currently in the repository is:\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-r-language\" class=\"anchor\" href=\"#r-language\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR language\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e \u2014 A script to build a spliced + intron (splici) ref for indexing and quantification with \u003ccode\u003ealevin-fry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esplici.R\u003c/code\u003e \u2014 Contains the \u003ccode\u003emake_splici_txome\u003c/code\u003e function, which is the function called by the \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e wrapper script. If you want to build a splici reference programatically in R code, you can use this function.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecellRangerLikeEmptyDrops.R\u003c/code\u003e \u2014 An implementation of the hybrid UMI count filtering and \u003ca href=\"https://github.com/MarioniLab/DropletUtils\"\u003e\u003ccode\u003eemptyDrops\u003c/code\u003e\u003c/a\u003e used by CellRanger (and subsequently by \u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTARsolo\u003c/a\u003e). This R implementation is a translation of the implemntation in STARsolo, which itself was reverse-engineered from CellRanger.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eload_fry.R\u003c/code\u003e \u2014 Contains a function to load \u003ccode\u003ealevin-fry\u003c/code\u003e output (including from USA mode quantification) into a \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html\" rel=\"nofollow\"\u003e\u003ccode\u003eSingleCellExperiment\u003c/code\u003e\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-python-language\" class=\"anchor\" href=\"#python-language\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython language\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eload_fry.py\u003c/code\u003e \u2014 Contains a Python function \u003ccode\u003eload_fry\u003c/code\u003e which is intended to load \u003ccode\u003ealevin-fry\u003c/code\u003e output (including from USA mode quantification) into a \u003ca href=\"https://github.com/theislab/scanpy\"\u003e\u003ccode\u003eScanpy\u003c/code\u003e\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bash\" class=\"anchor\" href=\"#bash\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBash\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eget_10x_permit_lists.sh\u003c/code\u003e \u2014 Provides a script to download the 10x chromium v2 or v3 permit lists.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esimpleaf\u003c/code\u003e \u2014 Provides a script to run the entire \u003ccode\u003esalmon -\u0026gt; alevin-fry (generate-permit-list \u0026gt; collate \u0026gt; quant)\u003c/code\u003e pipeline, though providing only a simplified set of options.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-simpleaf\" class=\"anchor\" href=\"#using-simpleaf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing simpleaf\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003esimpleaf\u003c/code\u003e script that resides in the \u003ccode\u003ebash\u003c/code\u003e subdirectory is intended to simply the running of \u003ccode\u003ealevin-fry\u003c/code\u003e in common usage scenarios. By limiting some of the different options that can be set, it provides a streamlined way to build the splici reference and index in a single command, as well as to process an experiment from raw FASTQ files to a count matrix in a single command.\u003c/p\u003e\n\u003cp\u003eTo work properly, \u003ccode\u003esimpleaf\u003c/code\u003e has a few requirements. First, it should be run from \u003cem\u003ewithin\u003c/em\u003e the \u003ccode\u003ebash\u003c/code\u003e subdirectory of this repository. This is because it currently makes assumptions about the relative paths of the scripts \u003ccode\u003eget_10x_permit_lists.sh\u003c/code\u003e and \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e. Additionally, the following environment variables are used within \u003ccode\u003esimpleaf\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eALEVIN_FRY_HOME\u003c/code\u003e \u003cstrong\u003eREQUIRED\u003c/strong\u003e \u2014 This directory will be used for persistent configuration and small file (\u0026lt;1G) storage between runs. If you provide a directory and it doesn\u0027t exist, it will be created. It is easiest to just set this in your enviornment globally so that the same home can be used over many runs without you having to provide the variable explicitly each time. A good choice for this variable might be something like \u003ccode\u003e~/.alevin_fry_home\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSALMON_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ccode\u003esalmon\u003c/code\u003e executable of version \u0026gt;= 1.5.1. If not provided, the script will assume it can simply invoke \u003ccode\u003esalmon\u003c/code\u003e in the current enviornment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eFRY_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ccode\u003ealevin-fry\u003c/code\u003e executable of version \u0026gt;= 0.4.0. If not provided, the script will assume it can simply invoke \u003ccode\u003ealevin-fry\u003c/code\u003e in the current enviornment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eTIME_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ca href=\"https://www.gnu.org/software/time/\" rel=\"nofollow\"\u003eGNU time\u003c/a\u003e executable; this is different from the shell \u003ccode\u003etime\u003c/code\u003e command, and on most linux systems exists at \u003ccode\u003e/usr/bin/time\u003c/code\u003e. If this variable is not provided, the script will assume it can use \u003ccode\u003e/usr/bin/time\u003c/code\u003e. On OSX systems, you should install GNU time explicitly. This can be done with \u003ca href=\"https://anaconda.org/conda-forge/time\" rel=\"nofollow\"\u003econda\u003c/a\u003e or homebrew.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003esimpleaf\u003c/code\u003e script has two sub-commands:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eindex\u003c/code\u003e \u2014 The \u003ccode\u003eindex\u003c/code\u003e command will take a reference genome FASTA and GTF as input, build a splici reference using the \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e script, and then build a sparse \u003ccode\u003esalmon\u003c/code\u003e index on the resulting reference. \u003cstrong\u003eNote\u003c/strong\u003e: The \u003ccode\u003eindex\u003c/code\u003e command requires the \u003ccode\u003eRscript\u003c/code\u003e executable to be in the path, as well as all of theR packages that are required by \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e. The relevant options (which you can obtain by running \u003ccode\u003e./simpleaf index -h\u003c/code\u003e) are:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eUsage: ./simpleaf index [options]\n options:\n -f, --fasta REQUIRED genome reference FASTA file\n -g, --gtf REQUIRED GTF file with gene annotations\n -l, --rlen REQUIRED the target read length the index will be built for\n -o, --output REQUIRED path to output directory (will be created if it doesn\u0027t exist)\n -s, --spliced OPTIONAL path to FASTA file with extra spliced sequence to add to the index\n -u, --unspliced OPTIONAL path to FASTA file with extra unspliced sequence to add to the index\n -d, --dedup FLAG OPTIONAL deduplicate identical sequences inside the R script when building the splici reference\n -t, --threads OPTIONAL number of threads to use when running [default: min(16, num cores)]\n -h, --help display this help message\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003equant\u003c/code\u003e \u2014 The \u003ccode\u003equant\u003c/code\u003e command takes as input the index, reads, and relevant information about the experiment (e.g. chemistry), and runs all of the steps of the \u003ccode\u003ealevin-fry\u003c/code\u003e pipeline, from mapping with \u003ccode\u003esalmon\u003c/code\u003e through \u003ccode\u003equant\u003c/code\u003e with \u003ccode\u003ealevin-fry\u003c/code\u003e. The relevant options (which you can obtain by running \u003ccode\u003e./simpleaf quant -h\u003c/code\u003e) are:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eUsage: ./simpleaf quant [options]\n options:\n -1, --r1 REQUIRED comma separated list of left reads\n -2, --r2 REQUIRED comma separated list of right reads\n -i, --index REQUIRED path to a (sparse or dense) salmon splici index\n -o, --output REQUIRED path to output directory (will be created if it doesn\u0027t exist)\n -f, --fmode REQUIRED permit list filter mode, one of {knee, k, unfilt, u}\n -c, --chem REQUIRED chemistry of experiment, one of {v2, v3}\n -r, --res REQUIRED resolution strategy for alevin-fry, one of {cr-like, cr-like-em}\n -m, --t2g REQUIRED three-column txp-to-gene file to pass to alevin-fry quant command\n -t, --threads OPTIONAL number of threads to use when running [default: min(16, num cores)]\n -h, --help display this help message\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-tensorflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-tensorflow\u003c/h1\u003e\n\u003cp\u003eStore singularity bootstrap files for tensorflow with accre mount points included.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1626495060.0 + "updated_at": 1646281475.0 }, { "data_format": 2, "description": null, "filenames": [ - "docker/Singularity.snowflake" + "Singularity.def" ], - "full_name": "nuKs/preprocessing", + "full_name": "paplessix/Recvis22", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-useful-utilities-for-single-cell-processing-with-alevin-fry\" class=\"anchor\" href=\"#useful-utilities-for-single-cell-processing-with-alevin-fry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful utilities for single-cell processing with alevin-fry\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/COMBINE-lab/alevin-fry\"\u003eAlevin-fry\u003c/a\u003e is a fast, accurate and memory-frugal tool for preprocessing single-cell and single-nucleus RNA-seq data. You can read more about alevin-fry in \u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.06.29.450377v1\" rel=\"nofollow\"\u003eits pre-print\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis respoistory contains scripts, functions and utilities that are useful for preparing data for processing with alevin-fry, as well as for reading alevin-fry data into other packages for downstream analysis.\u003c/p\u003e\n\u003cp\u003eThe different utilities are broken down in this repository by the language in which they are written (right now, Python, R and bash). A brief listing of\nthe available utilities currently in the repository is:\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-r-language\" class=\"anchor\" href=\"#r-language\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR language\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e \u2014 A script to build a spliced + intron (splici) ref for indexing and quantification with \u003ccode\u003ealevin-fry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esplici.R\u003c/code\u003e \u2014 Contains the \u003ccode\u003emake_splici_txome\u003c/code\u003e function, which is the function called by the \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e wrapper script. If you want to build a splici reference programatically in R code, you can use this function.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecellRangerLikeEmptyDrops.R\u003c/code\u003e \u2014 An implementation of the hybrid UMI count filtering and \u003ca href=\"https://github.com/MarioniLab/DropletUtils\"\u003e\u003ccode\u003eemptyDrops\u003c/code\u003e\u003c/a\u003e used by CellRanger (and subsequently by \u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTARsolo\u003c/a\u003e). This R implementation is a translation of the implemntation in STARsolo, which itself was reverse-engineered from CellRanger.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eload_fry.R\u003c/code\u003e \u2014 Contains a function to load \u003ccode\u003ealevin-fry\u003c/code\u003e output (including from USA mode quantification) into a \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html\" rel=\"nofollow\"\u003e\u003ccode\u003eSingleCellExperiment\u003c/code\u003e\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-python-language\" class=\"anchor\" href=\"#python-language\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython language\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eload_fry.py\u003c/code\u003e \u2014 Contains a Python function \u003ccode\u003eload_fry\u003c/code\u003e which is intended to load \u003ccode\u003ealevin-fry\u003c/code\u003e output (including from USA mode quantification) into a \u003ca href=\"https://github.com/theislab/scanpy\"\u003e\u003ccode\u003eScanpy\u003c/code\u003e\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bash\" class=\"anchor\" href=\"#bash\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBash\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eget_10x_permit_lists.sh\u003c/code\u003e \u2014 Provides a script to download the 10x chromium v2 or v3 permit lists.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esimpleaf\u003c/code\u003e \u2014 Provides a script to run the entire \u003ccode\u003esalmon -\u0026gt; alevin-fry (generate-permit-list \u0026gt; collate \u0026gt; quant)\u003c/code\u003e pipeline, though providing only a simplified set of options.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-simpleaf\" class=\"anchor\" href=\"#using-simpleaf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing simpleaf\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003esimpleaf\u003c/code\u003e script that resides in the \u003ccode\u003ebash\u003c/code\u003e subdirectory is intended to simply the running of \u003ccode\u003ealevin-fry\u003c/code\u003e in common usage scenarios. By limiting some of the different options that can be set, it provides a streamlined way to build the splici reference and index in a single command, as well as to process an experiment from raw FASTQ files to a count matrix in a single command.\u003c/p\u003e\n\u003cp\u003eTo work properly, \u003ccode\u003esimpleaf\u003c/code\u003e has a few requirements. First, it should be run from \u003cem\u003ewithin\u003c/em\u003e the \u003ccode\u003ebash\u003c/code\u003e subdirectory of this repository. This is because it currently makes assumptions about the relative paths of the scripts \u003ccode\u003eget_10x_permit_lists.sh\u003c/code\u003e and \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e. Additionally, the following environment variables are used within \u003ccode\u003esimpleaf\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eALEVIN_FRY_HOME\u003c/code\u003e \u003cstrong\u003eREQUIRED\u003c/strong\u003e \u2014 This directory will be used for persistent configuration and small file (\u0026lt;1G) storage between runs. If you provide a directory and it doesn\u0027t exist, it will be created. It is easiest to just set this in your enviornment globally so that the same home can be used over many runs without you having to provide the variable explicitly each time. A good choice for this variable might be something like \u003ccode\u003e~/.alevin_fry_home\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSALMON_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ccode\u003esalmon\u003c/code\u003e executable of version \u0026gt;= 1.5.1. If not provided, the script will assume it can simply invoke \u003ccode\u003esalmon\u003c/code\u003e in the current enviornment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eFRY_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ccode\u003ealevin-fry\u003c/code\u003e executable of version \u0026gt;= 0.4.0. If not provided, the script will assume it can simply invoke \u003ccode\u003ealevin-fry\u003c/code\u003e in the current enviornment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eTIME_BIN\u003c/code\u003e \u003cstrong\u003eOPTIONAL\u003c/strong\u003e \u2014 This should provide the path to a \u003ca href=\"https://www.gnu.org/software/time/\" rel=\"nofollow\"\u003eGNU time\u003c/a\u003e executable; this is different from the shell \u003ccode\u003etime\u003c/code\u003e command, and on most linux systems exists at \u003ccode\u003e/usr/bin/time\u003c/code\u003e. If this variable is not provided, the script will assume it can use \u003ccode\u003e/usr/bin/time\u003c/code\u003e. On OSX systems, you should install GNU time explicitly. This can be done with \u003ca href=\"https://anaconda.org/conda-forge/time\" rel=\"nofollow\"\u003econda\u003c/a\u003e or homebrew.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003esimpleaf\u003c/code\u003e script has two sub-commands:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eindex\u003c/code\u003e \u2014 The \u003ccode\u003eindex\u003c/code\u003e command will take a reference genome FASTA and GTF as input, build a splici reference using the \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e script, and then build a sparse \u003ccode\u003esalmon\u003c/code\u003e index on the resulting reference. \u003cstrong\u003eNote\u003c/strong\u003e: The \u003ccode\u003eindex\u003c/code\u003e command requires the \u003ccode\u003eRscript\u003c/code\u003e executable to be in the path, as well as all of theR packages that are required by \u003ccode\u003ebuild_splici_ref.R\u003c/code\u003e. The relevant options (which you can obtain by running \u003ccode\u003e./simpleaf index -h\u003c/code\u003e) are:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eUsage: ./simpleaf index [options]\n options:\n -f, --fasta REQUIRED genome reference FASTA file\n -g, --gtf REQUIRED GTF file with gene annotations\n -l, --rlen REQUIRED the target read length the index will be built for\n -o, --output REQUIRED path to output directory (will be created if it doesn\u0027t exist)\n -s, --spliced OPTIONAL path to FASTA file with extra spliced sequence to add to the index\n -u, --unspliced OPTIONAL path to FASTA file with extra unspliced sequence to add to the index\n -d, --dedup FLAG OPTIONAL deduplicate identical sequences inside the R script when building the splici reference\n -t, --threads OPTIONAL number of threads to use when running [default: min(16, num cores)]\n -h, --help display this help message\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003equant\u003c/code\u003e \u2014 The \u003ccode\u003equant\u003c/code\u003e command takes as input the index, reads, and relevant information about the experiment (e.g. chemistry), and runs all of the steps of the \u003ccode\u003ealevin-fry\u003c/code\u003e pipeline, from mapping with \u003ccode\u003esalmon\u003c/code\u003e through \u003ccode\u003equant\u003c/code\u003e with \u003ccode\u003ealevin-fry\u003c/code\u003e. The relevant options (which you can obtain by running \u003ccode\u003e./simpleaf quant -h\u003c/code\u003e) are:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eUsage: ./simpleaf quant [options]\n options:\n -1, --r1 REQUIRED comma separated list of left reads\n -2, --r2 REQUIRED comma separated list of right reads\n -i, --index REQUIRED path to a (sparse or dense) salmon splici index\n -o, --output REQUIRED path to output directory (will be created if it doesn\u0027t exist)\n -f, --fmode REQUIRED permit list filter mode, one of {knee, k, unfilt, u}\n -c, --chem REQUIRED chemistry of experiment, one of {v2, v3}\n -r, --res REQUIRED resolution strategy for alevin-fry, one of {cr-like, cr-like-em}\n -m, --t2g REQUIRED three-column txp-to-gene file to pass to alevin-fry quant command\n -t, --threads OPTIONAL number of threads to use when running [default: min(16, num cores)]\n -h, --help display this help message\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-planning-with-diffusion--\" class=\"anchor\" aria-hidden=\"true\" href=\"#planning-with-diffusion--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanning with Diffusion \u00a0\u00a0 \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eTraining and visualizing of diffusion models from \u003ca href=\"https://diffusion-planning.github.io/\" rel=\"nofollow\"\u003ePlanning with Diffusion for Flexible Behavior Synthesis\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/main\"\u003emain branch\u003c/a\u003e contains code for training diffusion models and planning via value-function guided sampling on the D4RL locomotion environments.\nThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/kuka\"\u003ekuka branch\u003c/a\u003e contains block-stacking experiments.\nThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/maze2d\"\u003emaze2d branch\u003c/a\u003e contains goal-reaching via inpainting in the Maze2D environments.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/184a6bb0418c60d778dcfa9f81dfddfdea0c0a3238b1d0cc4aa8666dd32ad3ca/68747470733a2f2f646966667573696f6e2d706c616e6e696e672e6769746875622e696f2f696d616765732f64696666757365722d636172642e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/184a6bb0418c60d778dcfa9f81dfddfdea0c0a3238b1d0cc4aa8666dd32ad3ca/68747470733a2f2f646966667573696f6e2d706c616e6e696e672e6769746875622e696f2f696d616765732f64696666757365722d636172642e706e67\" width=\"60%\" title=\"Diffuser model\" data-canonical-src=\"https://diffusion-planning.github.io/images/diffuser-card.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUpdates\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e12/09/2022: Diffuser (the RL model) has been integrated into \u003cg-emoji class=\"g-emoji\" alias=\"hugs\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f917.png\"\u003e\ud83e\udd17\u003c/g-emoji\u003e Diffusers (the Hugging Face diffusion model library)! See \u003ca href=\"https://huggingface.co/docs/diffusers/using-diffusers/rl\" rel=\"nofollow\"\u003ethese docs\u003c/a\u003e for how to run Diffuser using their pipeline.\u003c/li\u003e\n\u003cli\u003e10/17/2022: A bug in the value function scaling has been fixed in \u003ca href=\"https://github.com/jannerm/diffuser/commit/3d7361c2d028473b601cc04f5eecd019e14eb4eb\"\u003ethis commit\u003c/a\u003e. Thanks to \u003ca href=\"https://scholar.google.com/citations?user=Q6UMpRYAAAAJ\u0026amp;hl=en\" rel=\"nofollow\"\u003ePhilemon Brakel\u003c/a\u003e for catching it!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eLoad a pretrained diffusion model and sample from it in your browser with \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003escripts/diffuser-sample.ipynb\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate diffuser\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-pretrained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-pretrained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing pretrained models\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-downloading-weights\" class=\"anchor\" aria-hidden=\"true\" href=\"#downloading-weights\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownloading weights\u003c/h3\u003e\n\u003cp\u003eDownload pretrained diffusion models and value functions with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./scripts/download_pretrained.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis command downloads and extracts a \u003ca href=\"https://drive.google.com/file/d/1wc1m4HLj7btaYDN8ogDIAV9QzgWEckGy/view?usp=share_link\" rel=\"nofollow\"\u003etarfile\u003c/a\u003e containing \u003ca href=\"https://drive.google.com/drive/folders/1ie6z3toz9OjcarJuwjQwXXzDwh1XnS02?usp=sharing\" rel=\"nofollow\"\u003ethis directory\u003c/a\u003e to \u003ccode\u003elogs/pretrained\u003c/code\u003e. The models are organized according to the following structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u2514\u2500\u2500 logs/pretrained\n \u251c\u2500\u2500 ${environment_1}\n \u2502 \u251c\u2500\u2500 diffusion\n \u2502 \u2502 \u2514\u2500\u2500 ${experiment_name}\n \u2502 \u2502 \u251c\u2500\u2500 state_${epoch}.pt\n \u2502 \u2502 \u251c\u2500\u2500 sample-${epoch}-*.png\n \u2502 \u2502 \u2514\u2500\u2500 {dataset, diffusion, model, render, trainer}_config.pkl\n \u2502 \u251c\u2500\u2500 values\n \u2502 \u2502 \u2514\u2500\u2500 ${experiment_name}\n \u2502 \u2502 \u251c\u2500\u2500 state_${epoch}.pt\n \u2502 \u2502 \u2514\u2500\u2500 {dataset, diffusion, model, render, trainer}_config.pkl\n \u2502 \u2514\u2500\u2500 plans\n \u2502 \u2514\u2500\u2500 defaults\n \u2502 \u251c\u2500\u2500 0\n \u2502 \u251c\u2500\u2500 1\n \u2502 \u251c\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 149\n \u2502\n \u251c\u2500\u2500 ${environment_2}\n \u2502 \u2514\u2500\u2500 ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003estate_${epoch}.pt\u003c/code\u003e files contain the network weights and the \u003ccode\u003econfig.pkl\u003c/code\u003e files contain the instantation arguments for the relevant classes.\nThe png files contain samples from different points during training of the diffusion model.\nWithin the \u003ccode\u003eplans\u003c/code\u003e subfolders, there are the results of 150 evaluation trials for each environment using the default hyperparameters.\u003c/p\u003e\n\u003cdetails\u003e\n\u003csummary\u003eTo aggregate the results of the evaluations in the \u003ccode\u003elogs\u003c/code\u003e folder, run \u003ccode\u003epython scripts/read_results.py\u003c/code\u003e. (Expand to view the output of this command on the plans downloaded from Google Drive.)\n\u003c/summary\u003e\n\u003cpre\u003e\u003ccode\u003ehopper-medium-replay-v2 | defaults | logs/pretrained/hopper-medium-replay-v2/plans | 150 scores\n 93.6 +/- 0.37\nhopper-medium-v2 | defaults | logs/pretrained/hopper-medium-v2/plans | 150 scores\n 74.3 +/- 1.36\nhopper-medium-expert-v2 | defaults | logs/pretrained/hopper-medium-expert-v2/plans | 150 scores\n 103.3 +/- 1.30\nwalker2d-medium-replay-v2 | defaults | logs/pretrained/walker2d-medium-replay-v2/plans | 150 scores\n 70.6 +/- 1.60\nwalker2d-medium-v2 | defaults | logs/pretrained/walker2d-medium-v2/plans | 150 scores\n 79.6 +/- 0.55\nwalker2d-medium-expert-v2 | defaults | logs/pretrained/walker2d-medium-expert-v2/plans | 150 scores\n 106.9 +/- 0.24\nhalfcheetah-medium-replay-v2 | defaults | logs/pretrained/halfcheetah-medium-replay-v2/plans | 150 scores\n 37.7 +/- 0.45\nhalfcheetah-medium-v2 | defaults | logs/pretrained/halfcheetah-medium-v2/plans | 150 scores\n 42.8 +/- 0.32\nhalfcheetah-medium-expert-v2 | defaults | logs/pretrained/halfcheetah-medium-expert-v2/plans | 150 scores\n 88.9 +/- 0.25\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eTo create the table of offline RL results from the paper, run \u003ccode\u003epython plotting/table.py\u003c/code\u003e. This will print a table that can be copied into a Latex document. (Expand to view table source.)\u003c/summary\u003e\n\u003cpre\u003e\u003ccode\u003e\\definecolor{tblue}{HTML}{1F77B4}\n\\definecolor{tred}{HTML}{FF6961}\n\\definecolor{tgreen}{HTML}{429E9D}\n\\definecolor{thighlight}{HTML}{000000}\n\\newcolumntype{P}{\u0026gt;{\\raggedleft\\arraybackslash}X}\n\\begin{table*}[hb!]\n\\centering\n\\small\n\\begin{tabularx}{\\textwidth}{llPPPPPPPPr}\n\\toprule\n\\multicolumn{1}{r}{\\bf \\color{black} Dataset} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} Environment} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} BC} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} CQL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} IQL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} DT} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} TT} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MOPO} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MOReL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MBOP} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} Diffuser} \\\\ \n\\midrule\nMedium-Expert \u0026amp; HalfCheetah \u0026amp; $55.2$ \u0026amp; $91.6$ \u0026amp; $86.7$ \u0026amp; $86.8$ \u0026amp; $95.0$ \u0026amp; $63.3$ \u0026amp; $53.3$ \u0026amp; $\\textbf{\\color{thighlight}105.9}$ \u0026amp; $88.9$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.3$}} \\\\ \nMedium-Expert \u0026amp; Hopper \u0026amp; $52.5$ \u0026amp; $\\textbf{\\color{thighlight}105.4}$ \u0026amp; $91.5$ \u0026amp; $\\textbf{\\color{thighlight}107.6}$ \u0026amp; $\\textbf{\\color{thighlight}110.0}$ \u0026amp; $23.7$ \u0026amp; $\\textbf{\\color{thighlight}108.7}$ \u0026amp; $55.1$ \u0026amp; $103.3$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.3$}} \\\\ \nMedium-Expert \u0026amp; Walker2d \u0026amp; $\\textbf{\\color{thighlight}107.5}$ \u0026amp; $\\textbf{\\color{thighlight}108.8}$ \u0026amp; $\\textbf{\\color{thighlight}109.6}$ \u0026amp; $\\textbf{\\color{thighlight}108.1}$ \u0026amp; $101.9$ \u0026amp; $44.6$ \u0026amp; $95.6$ \u0026amp; $70.2$ \u0026amp; $\\textbf{\\color{thighlight}106.9}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.2$}} \\\\ \n\\midrule\nMedium \u0026amp; HalfCheetah \u0026amp; $42.6$ \u0026amp; $44.0$ \u0026amp; $\\textbf{\\color{thighlight}47.4}$ \u0026amp; $42.6$ \u0026amp; $\\textbf{\\color{thighlight}46.9}$ \u0026amp; $42.3$ \u0026amp; $42.1$ \u0026amp; $44.6$ \u0026amp; $42.8$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.3$}} \\\\ \nMedium \u0026amp; Hopper \u0026amp; $52.9$ \u0026amp; $58.5$ \u0026amp; $66.3$ \u0026amp; $67.6$ \u0026amp; $61.1$ \u0026amp; $28.0$ \u0026amp; $\\textbf{\\color{thighlight}95.4}$ \u0026amp; $48.8$ \u0026amp; $74.3$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.4$}} \\\\ \nMedium \u0026amp; Walker2d \u0026amp; $75.3$ \u0026amp; $72.5$ \u0026amp; $\\textbf{\\color{thighlight}78.3}$ \u0026amp; $74.0$ \u0026amp; $\\textbf{\\color{thighlight}79.0}$ \u0026amp; $17.8$ \u0026amp; $\\textbf{\\color{thighlight}77.8}$ \u0026amp; $41.0$ \u0026amp; $\\textbf{\\color{thighlight}79.6}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.55$}} \\\\ \n\\midrule\nMedium-Replay \u0026amp; HalfCheetah \u0026amp; $36.6$ \u0026amp; $45.5$ \u0026amp; $44.2$ \u0026amp; $36.6$ \u0026amp; $41.9$ \u0026amp; $\\textbf{\\color{thighlight}53.1}$ \u0026amp; $40.2$ \u0026amp; $42.3$ \u0026amp; $37.7$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.5$}} \\\\ \nMedium-Replay \u0026amp; Hopper \u0026amp; $18.1$ \u0026amp; $\\textbf{\\color{thighlight}95.0}$ \u0026amp; $\\textbf{\\color{thighlight}94.7}$ \u0026amp; $82.7$ \u0026amp; $\\textbf{\\color{thighlight}91.5}$ \u0026amp; $67.5$ \u0026amp; $\\textbf{\\color{thighlight}93.6}$ \u0026amp; $12.4$ \u0026amp; $\\textbf{\\color{thighlight}93.6}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.4$}} \\\\ \nMedium-Replay \u0026amp; Walker2d \u0026amp; $26.0$ \u0026amp; $77.2$ \u0026amp; $73.9$ \u0026amp; $66.6$ \u0026amp; $\\textbf{\\color{thighlight}82.6}$ \u0026amp; $39.0$ \u0026amp; $49.8$ \u0026amp; $9.7$ \u0026amp; $70.6$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.6$}} \\\\ \n\\midrule\n\\multicolumn{2}{c}{\\bf Average} \u0026amp; 51.9 \u0026amp; \\textbf{\\color{thighlight}77.6} \u0026amp; \\textbf{\\color{thighlight}77.0} \u0026amp; 74.7 \u0026amp; \\textbf{\\color{thighlight}78.9} \u0026amp; 42.1 \u0026amp; 72.9 \u0026amp; 47.8 \u0026amp; \\textbf{\\color{thighlight}77.5} \\hspace{.6cm} \\\\ \n\\bottomrule\n\\end{tabularx}\n\\vspace{-.0cm}\n\\caption{\n}\n\\label{table:locomotion}\n\\end{table*}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/diffusion-planning/diffusion-planning.github.io/blob/master/images/table.png\"\u003e\u003cimg src=\"https://github.com/diffusion-planning/diffusion-planning.github.io/raw/master/images/table.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003ch3\u003e\u003ca id=\"user-content-planning\" class=\"anchor\" aria-hidden=\"true\" href=\"#planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanning\u003c/h3\u003e\n\u003cp\u003eTo plan with guided sampling, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs/pretrained\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--logbase\u003c/code\u003e flag points the \u003ca href=\"scripts/plan_guided.py#L22-L30\"\u003eexperiment loaders\u003c/a\u003e to the folder containing the pretrained models.\nYou can override planning hyperparameters with flags, such as \u003ccode\u003e--batch_size 8\u003c/code\u003e, but the default\nhyperparameters are a good starting point.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining from scratch\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eTrain a diffusion model with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/train.py --dataset halfcheetah-medium-expert-v2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe default hyperparameters are listed in \u003ca href=\"config/locomotion.py#L22-L65\"\u003elocomotion:diffusion\u003c/a\u003e.\nYou can override any of them with flags, eg, \u003ccode\u003e--n_diffusion_steps 100\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTrain a value function with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/train_values.py --dataset halfcheetah-medium-expert-v2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"config/locomotion.py#L67-L108\"\u003elocomotion:values\u003c/a\u003e for the corresponding default hyperparameters.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003ePlan using your newly-trained models with the same command as in the pretrained planning section, simply replacing the logbase to point to your new models:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"config/locomotion.py#L110-L149\"\u003elocomotion:plans\u003c/a\u003e for the corresponding default hyperparameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeferred f-strings.\u003c/strong\u003e Note that some planning script arguments, such as \u003ccode\u003e--n_diffusion_steps\u003c/code\u003e or \u003ccode\u003e--discount\u003c/code\u003e,\ndo not actually change any logic during planning, but simply load a different model using a deferred f-string.\nFor example, the following flags:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e---horizon 32 --n_diffusion_steps 20 --discount 0.997\n--value_loadpath \u0027f:values/defaults_H{horizon}_T{n_diffusion_steps}_d{discount}\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill resolve to a value checkpoint path of \u003ccode\u003evalues/defaults_H32_T20_d0.997\u003c/code\u003e. It is possible to\nchange the horizon of the diffusion model after training (see \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for an example),\nbut not for the value function.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile . -t diffuser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTest the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm --gpus all \\\n --mount type=bind,source=$PWD,target=/home/code \\\n --mount type=bind,source=$HOME/.d4rl,target=/root/.d4rl \\\n diffuser \\\n bash -c \\\n \"export PYTHONPATH=$PYTHONPATH:/home/code \u0026amp;\u0026amp; \\\n python /home/code/scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot diffuser.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTest the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv --writable-tmpfs diffuser.sif \\\n bash -c \\\n \"pip install -e . \u0026amp;\u0026amp; \\\n python scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-azure\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-azure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Azure\u003c/h2\u003e\n\u003ch4\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eTag the Docker image (built in the \u003ca href=\"#Docker\"\u003eDocker section\u003c/a\u003e) and push it to Docker Hub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport DOCKER_USERNAME=$(docker info | sed \u0027/Username:/!d;s/.* //\u0027)\ndocker tag diffuser ${DOCKER_USERNAME}/diffuser:latest\ndocker image push ${DOCKER_USERNAME}/diffuser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eUpdate \u003ca href=\"azure/config.py\"\u003e\u003ccode\u003eazure/config.py\u003c/code\u003e\u003c/a\u003e, either by modifying the file directly or setting the relevant \u003ca href=\"azure/config.py#L47-L52\"\u003eenvironment variables\u003c/a\u003e. To set the \u003ccode\u003eAZURE_STORAGE_CONNECTION\u003c/code\u003e variable, navigate to the \u003ccode\u003eAccess keys\u003c/code\u003e section of your storage account. Click \u003ccode\u003eShow keys\u003c/code\u003e and copy the \u003ccode\u003eConnection string\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload \u003ca href=\"https://docs.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10\" rel=\"nofollow\"\u003e\u003ccode\u003eazcopy\u003c/code\u003e\u003c/a\u003e: \u003ccode\u003e./azure/download.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h4\u003e\n\u003cp\u003eLaunch training jobs with \u003ccode\u003epython azure/launch.py\u003c/code\u003e. The launch script takes no command-line arguments; instead, it launches a job for every combination of hyperparameters in \u003ca href=\"azure/launch_train.py#L36-L38\"\u003e\u003ccode\u003eparams_to_sweep\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-viewing-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#viewing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eViewing results\u003c/h4\u003e\n\u003cp\u003eTo rsync the results from the Azure storage container, run \u003ccode\u003e./azure/sync.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo mount the storage container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a blobfuse config with \u003ccode\u003e./azure/make_fuse_config.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./azure/mount.sh\u003c/code\u003e to mount the storage container to \u003ccode\u003e~/azure_mount\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo unmount the container, run \u003ccode\u003esudo umount -f ~/azure_mount; rm -r ~/azure_mount\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{janner2022diffuser,\n title = {Planning with Diffusion for Flexible Behavior Synthesis},\n author = {Michael Janner and Yilun Du and Joshua B. Tenenbaum and Sergey Levine},\n booktitle = {International Conference on Machine Learning},\n year = {2022},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe diffusion model implementation is based on Phil Wang\u0027s \u003ca href=\"https://github.com/lucidrains/denoising-diffusion-pytorch\"\u003edenoising-diffusion-pytorch\u003c/a\u003e repo.\nThe organization of this repo and remote launcher is based on the \u003ca href=\"https://github.com/jannerm/trajectory-transformer\"\u003etrajectory-transformer\u003c/a\u003e repo.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1626495005.0 + "updated_at": 1672051619.0 }, { "data_format": 2, - "description": "Singularity recipe files for bonito (https://github.com/nanoporetech/bonito)", + "description": "Gnuplot is a portable command-line driven graphing utility for Linux, OS/2, MS Windows, OSX, VMS, and many other platforms. ", "filenames": [ - "Singularity.0.3.6", - "Singularity", - "Singularity.0.4.0" + "5.4.5/Singularity", + "5.4/Singularity" ], - "full_name": "powerPlant/bonito-srf", - "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for bonito, a PyTorch Basecaller for Oxford Nanopore Reads\n\u003ca href=\"https://github.com/nanoporetech/bonito\"\u003ehttps://github.com/nanoporetech/bonito\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "pscedu/singularity-gnuplot", + "latest_release": "v5.4.5", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-gnuplot/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-gnuplot/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-gnuplot/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-gnuplot/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/23d82eb0668fe3492dd30f22157fb3d1c693125f1407bfafe285593dfe346f67/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d676e75706c6f74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/23d82eb0668fe3492dd30f22157fb3d1c693125f1407bfafe285593dfe346f67/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d676e75706c6f74\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-gnuplot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" 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src=\"https://camo.githubusercontent.com/2f454c6e98eda6f83fef993dad87279c761ee7b8c6e35dee322338d61fc7c66a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d676e75706c6f74\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-gnuplot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/74c6e2027d7219588767058a259260a20db37f4dd71ce7104696072751036512/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d676e75706c6f74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/74c6e2027d7219588767058a259260a20db37f4dd71ce7104696072751036512/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d676e75706c6f74\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-gnuplot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-gnuplot\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-gnuplot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-gnuplot\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d8469d53dc10c159651aa0f44ad8eced294e0cdb843467ee1ec27671a69f551e/687474703a2f2f676e75706c6f742e736f75726365666f7267652e6e65742f64656d6f2f616e696d617465322e312e676966\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d8469d53dc10c159651aa0f44ad8eced294e0cdb843467ee1ec27671a69f551e/687474703a2f2f676e75706c6f742e736f75726365666f7267652e6e65742f64656d6f2f616e696d617465322e312e676966\" alt=\"Plot\" data-canonical-src=\"http://gnuplot.sourceforge.net/demo/animate2.1.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://gnuplot.info/\" rel=\"nofollow\"\u003egnuplot\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003egnuplot\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/gnuplot/5.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/gnuplot\u003c/code\u003e as \u003ccode\u003e5.4.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1627353613.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1668307366.0 }, { "data_format": 2, - "description": null, + "description": "GNU Octave is software featuring a high-level programming language, primarily intended for numerical computations", "filenames": [ - "Singularity.root5", - "Singularity.17.09", - "Singularity.18.02.1", - "Singularity.18.02" + "6.3.0/Singularity", + "7.3.0/Singularity", + "6.2.0/Singularity", + "7.1.0/Singularity", + "7.2.0/Singularity", + "6.4.0/Singularity" ], - "full_name": "NuWro/builds", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nuwro-singularity-recipes\" class=\"anchor\" href=\"#nuwro-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNuWro Singularity recipes\u003c/h1\u003e\n\u003cp\u003eThis repository contains Singularity recipes for containers with \u003ca href=\"https://github.com/NuWro/nuwro\"\u003eNuWro\u003c/a\u003e releases (starting from 17.09).\u003c/p\u003e\n\u003cp\u003eThe builds can be found in \u003ca href=\"https://singularity-hub.org/collections/265\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eInstructions on how to use NuWro containers can be found in \u003ca href=\"https://nuwro.github.io/user-guide/singularity/\" rel=\"nofollow\"\u003eUser Guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor more information about Singularity please visit \u003ca href=\"http://singularity.lbl.gov/user-guide\" rel=\"nofollow\"\u003eSingularity Used Guide\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-octave", + "latest_release": "v7.2.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-octave/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-octave/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-octave/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-octave/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f0eef640ab704754409e5b3805cf764e1ae5c4a70f474b51e86e21ca7c7ce71a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6f6374617665\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f0eef640ab704754409e5b3805cf764e1ae5c4a70f474b51e86e21ca7c7ce71a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6f6374617665\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-octave\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/af95265639886aed45e4f673056ff5d595df2b5d5760eb36abe563365c430bb7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6f6374617665\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/af95265639886aed45e4f673056ff5d595df2b5d5760eb36abe563365c430bb7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6f6374617665\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-octave\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4f62994add717900b8861b5c16791ebf7c20c850bdd05ed86990f15629ef2696/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6f6374617665\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4f62994add717900b8861b5c16791ebf7c20c850bdd05ed86990f15629ef2696/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6f6374617665\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-octave\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a17a4ce476d76eb74b11af4b5598c58d76785b012ef2ed56b3eb98106a68fdab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6f6374617665\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a17a4ce476d76eb74b11af4b5598c58d76785b012ef2ed56b3eb98106a68fdab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6f6374617665\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-octave\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-octave\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-octave\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-octave\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/21d293a8bfd3418f027c62e2ef688e8549d00a26a56b2c3bb9810bc6d5adf754/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f362f36612f476e752d6f63746176652d6c6f676f2e7376672f3139323070782d476e752d6f63746176652d6c6f676f2e7376672e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/21d293a8bfd3418f027c62e2ef688e8549d00a26a56b2c3bb9810bc6d5adf754/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f362f36612f476e752d6f63746176652d6c6f676f2e7376672f3139323070782d476e752d6f63746176652d6c6f676f2e7376672e706e67\" width=\"15%\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/6/6a/Gnu-octave-logo.svg/1920px-Gnu-octave-logo.svg.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.gnu.org/software/octave/\" rel=\"nofollow\"\u003eOctave\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eoctave-cli\u003c/code\u003e, \u003ccode\u003epandoc\u003c/code\u003e and \u003ccode\u003egnuplot\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/octave/6.3.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/octave\u003c/code\u003e as \u003ccode\u003e6.3.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [], - "updated_at": 1522301666.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "numerical-computation" + ], + "updated_at": 1633062005.0 }, { "data_format": 2, - "description": "Docker image, environent, and scripts to convert dockerfiles to singularity recipes.", + "description": "mpi 4.1.4", "filenames": [ - "examples/cusignal/Singularity.def", - "examples/seti_bl/Singularity.def" + "Singularity" ], - "full_name": "jeffreyegan/docker2singularity", + "full_name": "riro3277/SimvascularSIngularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker2singularity\" class=\"anchor\" href=\"#docker2singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker2singularity\u003c/h1\u003e\n\u003cp\u003eDocker image, environent, and scripts to convert dockerfiles to singularity recipes.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eStage the \u003ccode\u003eDockerfile\u003c/code\u003e you wish to convert in the \u003ccode\u003econvert\u003c/code\u003e directory and then run the following at terminal to execute conversion to a \u003ccode\u003eSingularity.def\u003c/code\u003e output file. The output is produced int he same \u003ccode\u003econvert\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v ~/repos/docker2singularity/convert:/convert -it docker2singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1628008337.0 + "updated_at": 1664487305.0 }, { "data_format": 2, - "description": "TOMTOM docker/singularity container for scanem", + "description": null, "filenames": [ "Singularity" ], - "full_name": "jacobhepkema/scanem-motif", + "full_name": "rses-singularity/torch", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://quay.io/repository/jacobhepkema/scanem-motif\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f763ec0804bf9dcf1c8c53c453a9add6992333ec5501b757f4c23948408962c5/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6a61636f626865706b656d612f7363616e656d2d6d6f7469662f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/jacobhepkema/scanem-motif/status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-scanem-motif\" class=\"anchor\" href=\"#scanem-motif\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escanem-motif\u003c/h1\u003e\n\u003cp\u003eTOMTOM docker/singularity container for \u003ca href=\"https://github.com/jacobhepkema/scanem\"\u003e\u003cstrong\u003escanem\u003c/strong\u003e\u003c/a\u003e. Quay.io docker repo at \u003ca href=\"https://quay.io/repository/jacobhepkema/scanem-motif\" rel=\"nofollow\"\u003ehttps://quay.io/repository/jacobhepkema/scanem-motif\u003c/a\u003e (see build status above).\u003c/p\u003e\n\u003cp\u003eUsually this container is used in the Nextflow pipeline for \u003ca href=\"https://github.com/jacobhepkema/scanem\"\u003e\u003cstrong\u003escanem\u003c/strong\u003e\u003c/a\u003e. This container contains the MEME suite, which includes the Tomtom motif comparison tool\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eShobhit Gupta, JA Stamatoyannopolous, Timothy Bailey and William Stafford Noble, \"Quantifying similarity between motifs\", Genome Biology, 8(2):R24, 2007.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRun tools by prepending \u003ccode\u003e/opt/bin\u003c/code\u003e to your command, e.g. \u003ccode\u003e/opt/bin/tomtom [args]\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-torch\" class=\"anchor\" aria-hidden=\"true\" href=\"#torch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTorch\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1627983632.0 + "updated_at": 1542376613.0 }, { "data_format": 2, - "description": "Singularity recipe for RATTLE.", + "description": "A suite for benchmarking sparse format conversions using IntelMKL, SPARSKIT and TACO. The tool uses 14 sparse data sources from https://sparse.tamu.edu/ for benchmarking. It also uses a singularity conrtainer making it easy to run test on various machines.", "filenames": [ - "Singularity", - "Singularity-0.0" + "container/Singularity.experiments.def", + "container/Singularity.intel-mkl.def", + "container/Singularity.taco-experiments.def", + "container/Singularity.sparskit.def" ], - "full_name": "powerPlant/rattle-srf", + "full_name": "BoiseState-AdaptLab/Sparse_Format_Conversion_Experiments", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe for RATTLE : Reference-free reconstruction and quantification of transcriptomes from long-read sequencing\n\u003ca href=\"https://github.com/comprna/RATTLE\"\u003ehttps://github.com/comprna/RATTLE\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sparse_format_conversion_experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#sparse_format_conversion_experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSparse_Format_Conversion_Experiments\u003c/h1\u003e\n\u003cp\u003eA suite for benchmarking sparse format conversions using IntelMKL, SPARSKIT and TACO. The tool uses 14 sparse data sources from \u003ca href=\"https://sparse.tamu.edu/\" rel=\"nofollow\"\u003ehttps://sparse.tamu.edu/\u003c/a\u003e for benchmarking. It also uses a singularity conrtainer making it easy to run test on various machines.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 4, "topics": [], - "updated_at": 1627958435.0 + "updated_at": 1594315503.0 }, { "data_format": 2, - "description": null, + "description": "Open OnDemand Apps used by the ACCRE Visualization Portal", "filenames": [ - "Singularity.mpich33" + "rstudio/Singularity", + "rstudio_gpu/Singularity" ], - "full_name": "cjknight/singularity_test", + "full_name": "accre/ood_apps", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_test\" class=\"anchor\" href=\"#singularity_test\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_test\u003c/h1\u003e\n\u003cp\u003eSimple singularity example originally from here: \u003ca href=\"https://github.com/jtchilders/singularity_image_recipes\"\u003ehttps://github.com/jtchilders/singularity_image_recipes\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eTrying to replicate steps here: \u003ca href=\"https://www.alcf.anl.gov/support-center/theta/singularity-theta\" rel=\"nofollow\"\u003ehttps://www.alcf.anl.gov/support-center/theta/singularity-theta\u003c/a\u003e .\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ood_apps\" class=\"anchor\" aria-hidden=\"true\" href=\"#ood_apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eood_apps\u003c/h1\u003e\n\u003cp\u003eOpen OnDemand Apps used by the ACCRE Visualization Portal\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 11, "topics": [], - "updated_at": 1627936122.0 + "updated_at": 1663612575.0 }, { "data_format": 2, - "description": "One place for all the different container recipes", + "description": null, "filenames": [ - "uboonecode/Singularity.uboonecode", - "ubdl/Singularity.ubdldeps.u16.04_py3.6.11", - "ubdl/Singularity.ubdldev", - "ubdl/Singularity.ubdldev.python3", - "sparseconvnet/Singularity.sparseconvnet" + "Singularity" ], - "full_name": "LArbys/larbys-containers", + "full_name": "amanmdesai/singularity-python-packages-demo", "latest_release": null, - "readme": "\u003cp\u003eRepository to hold various Docker and singularity container building scripts\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-are-containers\" class=\"anchor\" href=\"#what-are-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat are containers?\u003c/h2\u003e\n\u003cp\u003eContainers according to Amazon:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eContainers provide a standard way to package your application\u0027s code, configurations, and dependencies into a single object.\nContainers share an operating system installed on the server and run as resource-isolated processes, ensuring quick,\nreliable, and consistent deployments, regardless of environment.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eAs far as our group is concerned, we use containers to be able to run the same piece of code on\nthe various compute platforms we have access to.\nThis is primary the Tufts cluster, which requires us to put our code into \u003ccode\u003eSingularity\u003c/code\u003e containers.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-whats-the-repo-for\" class=\"anchor\" href=\"#whats-the-repo-for\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat\u0027s the repo for?\u003c/h2\u003e\n\u003cp\u003eWe hold instructions on how to build particularly useful containers for our work.\nIn addition to packing up the code, containers can be built on top of another allow us to build, for example,\na container holding the common dependencies of our different software packages.\u003c/p\u003e\n\u003cp\u003eThis allows one to build a container for a specific analysis without having to repackage the whole stack of code again.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-are-you-going-to-make-me-build-all-of-these-myself\" class=\"anchor\" href=\"#are-you-going-to-make-me-build-all-of-these-myself\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAre you going to make me build all of these myself?\u003c/h2\u003e\n\u003cp\u003eNo! We keep copies of the containers on our \u003ca href=\"dockerhub\"\u003edockerhub\u003c/a\u003e and \u003ca href=\"https://www.singularity-hub.org/collections/2494\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e hub pages.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-containers-and-the-heirarchy\" class=\"anchor\" href=\"#containers-and-the-heirarchy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers (and the heirarchy)\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/232863cf1838592aed70d01a47bdf2e517095bcbf4637bbf2fccda3c9dc523ab/68747470733a2f2f672e67726176697a6f2e636f6d2f736f757263652f637573746f6d5f6d61726b31303f68747470732533412532462532467261772e67697468756275736572636f6e74656e742e636f6d2532464c41726279732532466c61726279732d636f6e7461696e6572732532466d6173746572253246636f6e7461696e65725f67726170682e646f74\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/232863cf1838592aed70d01a47bdf2e517095bcbf4637bbf2fccda3c9dc523ab/68747470733a2f2f672e67726176697a6f2e636f6d2f736f757263652f637573746f6d5f6d61726b31303f68747470732533412532462532467261772e67697468756275736572636f6e74656e742e636f6d2532464c41726279732532466c61726279732d636f6e7461696e6572732532466d6173746572253246636f6e7461696e65725f67726170682e646f74\" alt=\"Alt text\" data-canonical-src=\"https://g.gravizo.com/source/custom_mark10?https%3A%2F%2Fraw.githubusercontent.com%2FLArbys%2Flarbys-containers%2Fmaster%2Fcontainer_graph.dot\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eContainer\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescripton\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eubuntu\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003ca href=\"https://hub.docker.com/r/nvidia/cuda/\" rel=\"nofollow\"\u003envidia containers\u003c/a\u003e which include cuda and cuDNN libraries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eROOT\u003c/td\u003e\n\u003ctd align=\"left\"\u003ebuild of CERN\u0027s \u003ca href=\"https://github.com/root-project/root\"\u003eROOT\u003c/a\u003e data-analysis library\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eOpenCV\u003c/td\u003e\n\u003ctd align=\"left\"\u003eopen source \u003ca href=\"https://github.com/opencv/opencv\"\u003elibrary\u003c/a\u003e of computer vision algorithms\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ePyTorch\u003c/td\u003e\n\u003ctd align=\"left\"\u003edeep learning \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003elibrary\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eSparseConvNet\u003c/td\u003e\n\u003ctd align=\"left\"\u003eincludes submanifold convolution library for pytorch\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003edllee_unified\u003c/td\u003e\n\u003ctd align=\"left\"\u003ecurrent-gen analysis code for MicroBooNE DL low-energy excess analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eubdl\u003c/td\u003e\n\u003ctd align=\"left\"\u003erepository with next-gen LArbys tools for MicroBooNE DL-working group analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-specific-versions\" class=\"anchor\" href=\"#specific-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecific Versions\u003c/h2\u003e\n\u003cp\u003eHere we list official stack versions to be used for production and analysis studies\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eStack Name\u003c/th\u003e\n\u003cth\u003eubuntu\u003c/th\u003e\n\u003cth\u003epython\u003c/th\u003e\n\u003cth\u003eROOT\u003c/th\u003e\n\u003cth\u003eOpenCV\u003c/th\u003e\n\u003cth\u003ePyTorch\u003c/th\u003e\n\u003cth\u003eSubConvNet (nutufts-fork)\u003c/th\u003e\n\u003cth\u003edllee_unified\u003c/th\u003e\n\u003cth\u003eubdl\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003edllee_unified\u003c/td\u003e\n\u003ctd\u003e16.04 LTS+CUDA 10.0+cuDNN 7\u003c/td\u003e\n\u003ctd\u003e2.7\u003c/td\u003e\n\u003ctd\u003e6.16/00\u003c/td\u003e\n\u003ctd\u003e3.4\u003c/td\u003e\n\u003ctd\u003e1.0.1post2\u003c/td\u003e\n\u003ctd\u003etagXXXXXX\u003c/td\u003e\n\u003ctd\u003etagXXXXXXXX\u003c/td\u003e\n\u003ctd\u003en/a\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eubdl\u003c/td\u003e\n\u003ctd\u003e16.04 LTS+CUDA 10.0+cuDNN 7\u003c/td\u003e\n\u003ctd\u003e2.7\u003c/td\u003e\n\u003ctd\u003e6.16/00\u003c/td\u003e\n\u003ctd\u003e3.4\u003c/td\u003e\n\u003ctd\u003e1.0.1post2\u003c/td\u003e\n\u003ctd\u003etagXXXXXX\u003c/td\u003e\n\u003ctd\u003en/a\u003c/td\u003e\n\u003ctd\u003etagxxxx\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eubdl dependences\u003c/td\u003e\n\u003ctd\u003e16.04 LTS+CUDA 11.0+cuDNN 8\u003c/td\u003e\n\u003ctd\u003e3.6.11\u003c/td\u003e\n\u003ctd\u003e6.22/06\u003c/td\u003e\n\u003ctd\u003e3.4.11\u003c/td\u003e\n\u003ctd\u003e1.7.1\u003c/td\u003e\n\u003ctd\u003e7dfbd0f\u003c/td\u003e\n\u003ctd\u003en/a\u003c/td\u003e\n\u003ctd\u003en/a\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe \u003ccode\u003eubdl dependencies\u003c/code\u003e container is used to build the \u003ccode\u003eubdl\u003c/code\u003e repository on Tufts.\nThis provides a development environment.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-built-containers-on-tufts\" class=\"anchor\" href=\"#built-containers-on-tufts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilt containers on Tufts\u003c/h2\u003e\n\u003cp\u003eOn the Tufts Cluster you can find the containers at:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/cluster/tufts/wongjiradlab/larbys/larbys-containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-instructions\" class=\"anchor\" href=\"#instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h1\u003e\n\u003cp\u003eWe use two packages: \u003ca href=\"https://www.docker.com/why-docker\" rel=\"nofollow\"\u003edocker\u003c/a\u003e and \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTypically, we will use \u003ccode\u003edocker\u003c/code\u003e to build the containers and then convert the docker image into a \u003ccode\u003esingularity\u003c/code\u003e container.\u003c/p\u003e\n\u003cp\u003eIn the end, it is not important what tool we use to build the containers (one could use just singularity), but ultimately we must end up with a singularity container to run on the Tufts cluster. (The reason is that docker is not supported on the cluster due to security concerns with docker.)\u003c/p\u003e\n\u003cp\u003eYou can run both docker and singularity from your personal machine. You can also use lab machines at:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTufts: meitner, rubin\u003c/li\u003e\n\u003cli\u003eMIT: nudot, trex\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto build your containers.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-do-i-need-to-do-to-build-a-container\" class=\"anchor\" href=\"#what-do-i-need-to-do-to-build-a-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat do I need to do to build a container?\u003c/h2\u003e\n\u003cp\u003e(still under construction)\u003c/p\u003e\n\u003cp\u003eIn general, you just need to know the instructions you\u0027d type to install the software in question.\nYou put those instructions into a recipe file and tell docker or singularity to build the container.\u003c/p\u003e\n\u003cp\u003eAs an example, we will use the anticipated most-likely case, which is to make a container with a new version of analysis code (\u003ccode\u003eubdl\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eIn the folder \u003ccode\u003eubdl\u003c/code\u003e, there is the docker recipe file to build this container.\nIt probably looks something like the following (assuming it hasn\u0027t changed too much since the time this README was written):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFROM larbys/sparseconvnet:ubuntu16.04_latest\n\nMAINTAINER taritree.wongjirad@tufts.edu\n\n# UBDL\nRUN apt-get update -y \u0026amp;\u0026amp; apt install -y rsync \u0026amp;\u0026amp; apt-get autoremove -y \u0026amp;\u0026amp; apt-get clean -y\nRUN pip install pyyaml typing figcan zmq\nRUN cd /usr/local \u0026amp;\u0026amp; git clone --recursive https://github.com/larbys/ubdl \u0026amp;\u0026amp; \\\n cd ubdl \u0026amp;\u0026amp; chmod +x setenv.sh \u0026amp;\u0026amp; chmod +x buildall.sh \u0026amp;\u0026amp; chmod +x configure.sh\nRUN cd /usr/local/ubdl/larcv \u0026amp;\u0026amp; cp misc/FindCUDA.cmake /usr/local/share/cmake-3.13/Modules/\nRUN cd /usr/local/ubdl \u0026amp;\u0026amp; bash -c \"source /usr/local/root/build/bin/thisroot.sh \u0026amp;\u0026amp; source setenv.sh \u0026amp;\u0026amp; source configure.sh \u0026amp;\u0026amp; source buildall.sh\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first line tells docker to build off of an existing image.\nThis happens to be the \u003ccode\u003elarbys/sparseconvnet\u003c/code\u003e image,\nwhich contains the software stack up to the Sparse Convolutional Network library.\nThe SparseConvNet library is the last dependency for the \u003ccode\u003eubdl\u003c/code\u003e code.\nSo all that\u0027s left to finish the container is to build \u003ccode\u003eubdl\u003c/code\u003e into the container.\u003c/p\u003e\n\u003cp\u003eThe docker file is just the list of instructions to install \u003ccode\u003eubdl\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo build it, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t larbys/ubdl:dev . -f Dockerfile_ubuntu16.04\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e-t\u003c/code\u003e flag is to the set the \"name\" or \"tag\" of the image.\n\u003ccode\u003e.\u003c/code\u003e tells Docker where to find the docker recipe file.\nAnd \u0027-f\u0027 is what recipe file to use (in \u0027.\u0027).\u003c/p\u003e\n\u003cp\u003eWith the image with ubdl built, the next step if one wants to create a container to run\nat Tufts, is to create a singularity container.\nLike the docker build file above,\nwe list the commands we would run to configure the computer for \u003ccode\u003eubdl\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAs an example, in the \u003ccode\u003eubdl\u003c/code\u003e folder,\nyou\u0027ll see a file called \u003ccode\u003eSingularity.ubdl\u003c/code\u003e,\nwhich contains the instructions to build the \u003ccode\u003eubdl\u003c/code\u003e repository.\nIt\u0027ll look something that the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebootstrap: docker\nFrom: larbys/ubdl:latest\n\n%post\n mkdir -p /cluster/home\n mkdir -p /cluster/kappa\n mkdir -p /cluster/shared\n mkdir -p /opt/shared\n\n%environment\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-alternative-build-ubdl-outside-the-container\" class=\"anchor\" href=\"#alternative-build-ubdl-outside-the-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternative, build \u003ccode\u003eubdl\u003c/code\u003e outside the container\u003c/h2\u003e\n\u003cp\u003eHere, we of course start with the container we built with docker above, \u003ccode\u003elarbys/ubdl:latest\u003c/code\u003e.\nYou can see all we do is create four folders.\nThese folders server to provide a mount point for our container to the network storage area.\nWhen making singularity containers for the Tufts cluster,\nplease include these commands.\u003c/p\u003e\n\u003cp\u003eNote that the instructinos here were about installing \u003ccode\u003eubdl\u003c/code\u003e into the container.\nHowever, an alternative is to clone the \u003ccode\u003eubdl\u003c/code\u003e code into some folder and then compile that source\nusing the libraries found in the container.\nWe provide the \u003ccode\u003eubdl-dependencies\u003c/code\u003e container for this.\u003c/p\u003e\n\u003cp\u003eInstructions on how to do that can be found \u003ca href=\"https://github.com/LArbys/ubdl/wiki/Build-development-copy-of-UBDL-with-container\"\u003ehere\u003c/a\u003e\nas part of the \u003ccode\u003eubdl\u003c/code\u003e wiki.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-python-packages\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-python-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-python-packages\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1629120346.0 + "updated_at": 1675056243.0 + }, + { + "data_format": 2, + "description": "ABC-MK estimations", + "filenames": [ + "scripts/singularity/Singularity" + ], + "full_name": "jmurga/MKtest.jl", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-abc-mk\" class=\"anchor\" aria-hidden=\"true\" href=\"#abc-mk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eABC-MK\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://jmurga.github.io/MKtest.jl/dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/56f8252ba8e9d3f0b810769543f77823d2fe031ce560d4c2d69fb1fcad800383/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732d6c61746573742d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docs-latest-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMKtest.jl is a Julia package including a fast Approximate Bayesian Computation version of the McDonald-Kreitman test (ABC-MK) presented in \u003ca href=\"https://doi.org/10.1038/s41559-019-0890-6\" rel=\"nofollow\"\u003eUricchio et al. (2019)\u003c/a\u003e. The new ABC-MK implementation significantly improves the efficiency of the population genetics inferences. Following \u003ca href=\"https://doi.org/10.1038/s41559-019-0890-6\" rel=\"nofollow\"\u003eUricchio et al.(2019)\u003c/a\u003e, the analytical estimations were used to explore the effect of background selection and selective interference on weakly beneficial alleles. Nonetheless, we developed a more straightforward and computationally efficient ABC-based inference procedure that accounts for the DFE of deleterious and beneficial alleles and partial recombination between selected genomic elements. Our approach estimates $\\alpha$, $\\alpha_W$, $\\alpha_S$, and the Gamma distribution DFE parameters.\u003c/p\u003e\n\u003cp\u003eIn addition, the package automatizes other MK-like analyses parsing polymorphic and divergence data as well as including several extensions such as \u003ca href=\"https://doi.org/10.1371/journal.pgen.1005774\" rel=\"nofollow\"\u003eGrapes\u003c/a\u003e, \u003ca href=\"https://doi.org/10.1073/pnas.1220835110\" rel=\"nofollow\"\u003eaMK\u003c/a\u003e, \u003ca href=\"https://doi.org/10.1093/g3journal/jkac206\" rel=\"nofollow\"\u003eimputedMK\u003c/a\u003e or \u003ca href=\"https://doi.org/10.1038/4151024a\" rel=\"nofollow\"\u003efwwMK\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://jmurga.github.io/MKtest.jl/dev\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e for details.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [], + "updated_at": 1646232582.0 }, { "data_format": 2, "description": null, "filenames": [ - "util/PATRIC/Singularity" + "Singularity" ], - "full_name": "adamlabadorf/bf500", + "full_name": "rhassett-cshl/SimPolv2", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bf500---bioinformatics-engineering\" class=\"anchor\" href=\"#bf500---bioinformatics-engineering\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBF500 - Bioinformatics Engineering)\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://adamlabadorf.github.io/bf500/\" rel=\"nofollow\"\u003eGo to the Github Pages site\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-simpol--simulating-the-dynamics-of-rna-polymerase-rnap-on-dna-template\" class=\"anchor\" aria-hidden=\"true\" href=\"#simpol--simulating-the-dynamics-of-rna-polymerase-rnap-on-dna-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimPol \u2014 Simulating the dynamics of RNA Polymerase (RNAP) on DNA template\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eSimPol tracks the independent movement of RNAPs along the DNA templates of a large\nnumber of cells. It accepts several key\nuser-specified parameters, including the initiation rate, pause-escape rate,\na constant or variable elongation rate, the mean and variance of pause sites across cells,\nas well as the center-to-center spacing constraint between RNAPs,\nthe number of cells being simulated, the gene length, and the total time of transcription.\nThe simulator simply allows each RNAP to move forward or not,\nin time slices of $10^{-4}$ minutes, according to the specified position-specific\nrate parameters. It assumes that at most one movement of each RNAP can occur per\ntime slice. The simulator monitors for collisions between adjacent RNAPs, prohibiting\none RNAP to advance if it is at the boundary of the allowable distance from the next.\nAfter running for the specified time, SimPol outputs either a file in HDF5 format or files in CSV format that records all RNAP position for the last specified number of steps. See more options and details about parameters and outputs below.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/simulator.png\"\u003e\u003cimg src=\"figures/simulator.png\" alt=\"simulator\" width=\"600\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\tFig.1 Design of SimPol (\u201cSimulator of Polymerases\u201d)\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Environment\u003c/h2\u003e\n\u003cp\u003eWe provide two different approaches to set up the environment for SimPol, one with\n\u003ca href=\"https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\nand the other with \u003ca href=\"https://docs.sylabs.io/guides/latest/user-guide/quick_start.html#\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\nPre-built debug and release executables can be found in the bin folder\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\nconda activate simpol\n\nbin/simPol_Release --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot simPol.sif Singularity\n\nsingularity shell simPol.sif\n\nbin/simPol_Release --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild from source\u003c/h2\u003e\n\u003cp\u003eThere is the option to build in either debug mode with debug symbols or release mode with compiler optimizations (e.g. -O2).\u003c/p\u003e\n\u003cp\u003eTo create a build directory in release mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake -S . -B Release/ -D CMAKE_BUILD_TYPE=Release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo clean the release mode build directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake --build Release/ --target clean\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build in release mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake --build Release/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: Replace all instances of \u0027Release\u0027 with \u0027Debug\u0027 to build in Debug mode\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Locally\u003c/h2\u003e\n\u003cp\u003eSet Number of Threads [By default uses all available threads]\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport OMP_NUM_THREADS=\u0026lt;number of threads to use\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun in Release Mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Release/simPol [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun in Debug Mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Debug/simPol [options]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun program with file containing command line arguments\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./Release/simPol $(\u0026lt;simPol_arguments.dat)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-with-uge-workload-manager-within-cshl\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-with-uge-workload-manager-within-cshl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun with UGE Workload Manager (within CSHL)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -pe OpenMP 5 -cwd -b y ./Release/simPol -n 100 --csv 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote: This allocates 5 threads for the program in the OpenMP parallel environment\u003c/li\u003e\n\u003cli\u003eCheck available parallel environments: qconf -spl\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eOptions:\n\t-h, --help\n\t\tShow this help message and exit\n\n\t-k INTEGER, --tssLen=INTEGER\n\t\tdefine the mean of pause sites across cells [default 50bp]\n\n\t--kSd=DOUBLE\n\t\tdefine the standard deviation of pause sites across cells [default 0]\n\n\t--kMin=INTEGER\n\t\tupper bound of pause site allowed [default 17bp]\n\n\t--kMax=INTEGER\n\t\tlower bound of pause site allowed [default 200bp]\n\n\t--geneLen=INTEGER\n\t\tdefine the length of the whole gene [default 2000bp]\n\n\t-a DOUBLE, --alpha=DOUBLE\n\t\tinitiation rate [default 1 event per min]\n\n\t-b DOUBLE, --beta=DOUBLE\n\t\tpause release rate [default 1 event per min]\n\n\t-z DOUBLE, --zeta=DOUBLE\n\t\tthe mean of elongation rates across sites [default 2000bp per min]\n\n\t--zetaSd=DOUBLE\n\t\tthe standard deviation of elongation rates across sites [default 1000]\n\n\t--zetaMax=DOUBLE\n\t\tthe maximum of elongation rates allowed [default 2500bp per min]\n\n\t--zetaMin=DOUBLE\n\t\tthe minimum of elongation rates allowed [default 1500bp per min]\n\n\t--zetaVec=CHARACTER\n\t\ta file contains vector to scale elongation rates. All cells share the same set of parameters [default \"\"]\n\n\t-n INTEGER, --cellNum=INTEGER\n\t\tNumber of cells being simulated [default 10]\n\n\t-s INTEGER, --polSize=INTEGER\n\t\tPolymerase II size [default 33bp]\n\n\t--addSpace=INTEGER\n\t\tAdditional space in addition to RNAP size [default 17bp]\n\n\t-t DOUBLE, --time=DOUBLE\n\t\tTotal time of simulating data in a cell [default 0.1 min]\n\n\t--hdf5=INTEGER\n\t\tRecord position matrix to HDF5 file for remaining number of steps specified [default: 0 steps]\n\n\t--csv=INTEGER\n\t\tRecord position matrix to csv file for remaining number of steps specified [default: 1 step]\n\n\t-d CHARACTER, --outputDir=CHARACTER\n\t\tDirectory for saving results [default: \u0027results\u0027]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cp\u003eAfter simulation, SimPol produces multiple output files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eprobability_vector.csv\u003c/li\u003e\n\u003cli\u003epause_sites.csv\u003c/li\u003e\n\u003cli\u003ecombined_cell_data.csv: Stores the total # of polymerase at each site across all cells\u003c/li\u003e\n\u003cli\u003eposition_matrices.h5: An HDF5 file containing a group of position matrices for the last number of specified steps. The file can be viewed by importing it into the HDFView app. Download Here: \u003ca href=\"https://www.hdfgroup.org/downloads/hdfview/#download\" rel=\"nofollow\"\u003ehttps://www.hdfgroup.org/downloads/hdfview/#download\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eUsing HighFive interface to generate HDF5 file \u003ca href=\"https://github.com/BlueBrain/HighFive\"\u003ehttps://github.com/BlueBrain/HighFive\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUses chunking and ZLIB (deflate) compression at level 9\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003epositions/position_matrix_#.csv: CSV files containing the position matrix at a specified step\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sampling-nascent-rna-sequencing-read-counts\" class=\"anchor\" aria-hidden=\"true\" href=\"#sampling-nascent-rna-sequencing-read-counts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSampling nascent RNA sequencing read counts\u003c/h2\u003e\n\u003cp\u003eIf you prefer to simulate nascent RNA sequencing read counts in addition to the RNAP positions,\nyou can also follow the tutorial in \u003ccode\u003escripts/sample_read_counts.Rmd\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eZhao, Y., Liu, L. \u0026amp; Siepel, A. Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells. 2022.10.19.512929 Preprint at \u003ca href=\"https://doi.org/10.1101/2022.10.19.512929\" rel=\"nofollow\"\u003ebioRxiv\u003c/a\u003e (2022).\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1631274450.0 + "updated_at": 1673019793.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.canopy" + "analysis/assembly/containers/Singularity.canu" ], - "full_name": "ternaustralia/coesra-singularity-canopy", + "full_name": "justicengom/head_to_head_pipeline-", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coesra-singularity-canopy\" class=\"anchor\" href=\"#coesra-singularity-canopy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-canopy\u003c/h1\u003e\n\u003cp\u003eAuthor: Hoang Nguyen\nCreated: 22 July 2019\nThis will create a image with Singularity 2.5.1\u003c/p\u003e\n", + "readme": "\u003ch3\u003e\u003ca id=\"user-content-preprint\" class=\"anchor\" aria-hidden=\"true\" href=\"#preprint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprint\u003c/h3\u003e\n\u003cblockquote\u003e\n\u003cp\u003eHall, M. B. \u003cem\u003eet al\u003c/em\u003e. Nanopore sequencing for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e drug susceptibility testing and outbreak investigation. \u003cem\u003eMedrxiv\u003c/em\u003e 2022.03.04.22271870 (2022) \u003ca href=\"https://doi.org/10.1101/2022.03.04.22271870\" rel=\"nofollow\"\u003edoi:10.1101/2022.03.04.22271870\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003cp\u003eThis repository holds the pipelines/scripts used for our paper analysing Illumina and\nNanopore for \u003cem\u003eM.tuberculosis\u003c/em\u003e drug resistance calling and transmission clustering.\u003c/p\u003e\n\u003cp\u003eFor people wanting to analyse their Nanopore data in the same manner as we did in this paper, we would suggest using \u003ca href=\"https://github.com/mbhall88/tbpore\"\u003ehttps://github.com/mbhall88/tbpore\u003c/a\u003e, which is a python program that runs the drug resistance prediction and clustering (with a smaller decontamination database) components of this pipeline. It is actively maintained and much easier to use.\u003c/p\u003e\n\u003cp\u003eAll pipelines require the following dependencies to be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://snakemake.github.io/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e (and\n\u003ca href=\"https://github.com/mamba-org/mamba\"\u003eMamba\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/docs\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eThe Python library \u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003e\u003ccode\u003epandas\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee subdirectories for more specific information about different pipelines. They are\nnested according to their dependence on the outputs of each pipeline.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"data/QC\"\u003eQuality Control\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/assembly\"\u003eAssembly\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"analysis/baseline_variants\"\u003eBaseline variant analysis\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/transmission_clustering\"\u003eTransmission clustering\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/resistance_prediction\"\u003eDrug Resistance Prediction\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following pipelines are not relevant to the work in the final paper.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"data/H37Rv_PRG\"\u003eH37Rv PRG construction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/pandora_variants\"\u003ePandora variant analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-data-availability\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-availability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData availability\u003c/h1\u003e\n\u003cp\u003eAll data is submitted under the Project accession \u003cstrong\u003ePRJEB49093\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe accessions and all relevant sample metadata for this study can be found at \u003ca href=\"https://doi.org/10.6084/m9.figshare.19304648\" rel=\"nofollow\"\u003ehttps://doi.org/10.6084/m9.figshare.19304648\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe raw Nanopore data is available to download from: \u003ca href=\"https://ftp.ebi.ac.uk/pub/databases/ont_tb_eval2022/\" rel=\"nofollow\"\u003ehttps://ftp.ebi.ac.uk/pub/databases/ont_tb_eval2022/\u003c/a\u003e. See the sample metadata file for mappings between samples and the relevant Nanopore runs and barcode numbers.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "coesra" + "subscribers_count": 1, + "topics": [], + "updated_at": 1671672013.0 + }, + { + "data_format": 2, + "description": "Quantifying the life of pollen.", + "filenames": [ + "singularity/Singularity" ], - "updated_at": 1610425023.0 + "full_name": "cedarwarman/pollen_cv", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-pollen_cv\" class=\"anchor\" aria-hidden=\"true\" href=\"#pollen_cv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epollen_cv\u003c/h1\u003e\n\u003cp\u003eQuantifying the life of pollen.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [], + "updated_at": 1665173991.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.qgis" + "bc3.10-rs125042r362/Singularity", + "bc3.12-r405rs125042/Singularity", + "bc3.15-r421tv132rs2022072.576/Singularity" ], - "full_name": "ternaustralia/coesra-singularity-qgis", + "full_name": "yh549848/singularity-rstudio-methylseq", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coesra-singularity-qgis\" class=\"anchor\" href=\"#coesra-singularity-qgis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-qgis\u003c/h1\u003e\n\u003cp\u003eHoang Nguyen 24 July 2019\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1610427940.0 + "updated_at": 1665633405.0 }, { "data_format": 2, - "description": null, + "description": "Building an online mousetracking tool", "filenames": [ - "Singularity.rstudio" + "Singularity" ], - "full_name": "ternaustralia/coesra-singularity-rstudio", + "full_name": "paulstillman/Online-Mousetracking", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-coesra-singularity-rstudio\" class=\"anchor\" href=\"#coesra-singularity-rstudio\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-rstudio\u003c/h1\u003e\n\u003cp\u003eHoang Nguyen\n25 July 2019\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-online-mousetracking\" class=\"anchor\" aria-hidden=\"true\" href=\"#online-mousetracking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOnline-Mousetracking\u003c/h1\u003e\n\u003cp\u003eBuilding an online mousetracking tool\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [], + "updated_at": 1663958736.0 + }, + { + "data_format": 2, + "description": " MrBayes, a program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary models.", + "filenames": [ + "Singularity.3.2.7a-mpi" + ], + "full_name": "sghignone/MrBayes", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mrbayes\" class=\"anchor\" aria-hidden=\"true\" href=\"#mrbayes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMrBayes\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4216\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMrBayes v.3.2.7, a program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary models.\u003c/p\u003e\n\u003cp\u003eThe current release is based on MrBayes version 3.2.7a, released March 6, 2019. This version is compiled with MPI support and without the Beagle library\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [ - "coesra" + "singularity", + "container", + "bayesian-inference", + "phylogenomics", + "phylogenetics" ], - "updated_at": 1610424737.0 + "updated_at": 1663758431.0 }, { "data_format": 2, - "description": "Copy of the template_project_escape to test the GitHub CI", + "description": null, "filenames": [ - "Singularity/Singularity" + "Singularity" ], - "full_name": "escape2020/template_project_escape", - "latest_release": "v0.1.4", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-template_project_escape\" class=\"anchor\" href=\"#template_project_escape\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etemplate_project_escape\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.4923992\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869b462bf3a319fe4fc2ffa52fb6b0f7c8e42eb4e0ed4bc2482306b9fd5aafab/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343932333939322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4923992.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/-/commits/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a25ea71f12537b69d5aca8b409685333243d4e65ceb91c5a401dadb6eacea20e/68747470733a2f2f6769746c61622e696e3270332e66722f657363617065323032302f7770332f74656d706c6174655f70726f6a6563745f6573636170652f6261646765732f6d61737465722f706970656c696e652e737667\" alt=\"pipeline status\" data-canonical-src=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/badges/master/pipeline.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fd551ba4b042d89480347a0e74e31af63b356b2cac1116c7b80038f41b04a581/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://camo.githubusercontent.com/64f9054d866a78f16e1451647c19b22fc159a58191e004612257ce5277bd6db9/68747470733a2f2f63646e2e65736f2e6f72672f696d616765732f6c617267652f616e6e3138303834612e6a7067\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/64f9054d866a78f16e1451647c19b22fc159a58191e004612257ce5277bd6db9/68747470733a2f2f63646e2e65736f2e6f72672f696d616765732f6c617267652f616e6e3138303834612e6a7067\" width=\"640\" height=\"453\" data-canonical-src=\"https://cdn.eso.org/images/large/ann18084a.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eA simple template project to provide software to ESCAPE.\u003c/p\u003e\n\u003cp\u003eThis repository shows the \u003cstrong\u003ebasic content\u003c/strong\u003e that should be included in a project (following the\n\u003ca href=\"https://opensource.guide/starting-a-project/\" rel=\"nofollow\"\u003eopensource guide\u003c/a\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn \u003ca href=\"https://help.github.com/en/github/creating-cloning-and-archiving-repositories/licensing-a-repository#where-does-the-license-live-on-my-repository\"\u003eopen source\u003c/a\u003e\n\u003cstrong\u003elicense\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://help.github.com/en/github/getting-started-with-github/create-a-repo#commit-your-first-change\"\u003e\u003cstrong\u003eREADME\u003c/strong\u003e file\u003c/a\u003e,\nsimilar to this one.\u003c/li\u003e\n\u003cli\u003eContributing guidelines.\n\u003cul\u003e\n\u003cli\u003eSee below the general guidelines for the ESCAPE repository.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://opensource.guide/code-of-conduct/\" rel=\"nofollow\"\u003ecode of conduct\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003eCheck why is a good idea to add one.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe repository itself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt would be highly suitable to include too:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA setup file as well as the basic commands to install the library (see below).\u003c/li\u003e\n\u003cli\u003eA \u003ccode\u003e.gitignore\u003c/code\u003e file.\u003c/li\u003e\n\u003cli\u003eUnitary and integration tests, and ideally a CI pipeline.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ePlease feel free to clone / fork / template this project!\u003c/strong\u003e (For example, look to left of the\n\u003ccode\u003eClone or download\u003c/code\u003e button in the \u003ca href=\"https://github.com/garciagenrique/template_project_escape\"\u003eGitHub\u003c/a\u003e site).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor a detailed explanation of how to submit a contribution to a project / repository (Fork, create a branch, make\na pull request...), you can have a look to the \u003ca href=\"https://opensource.guide/how-to-contribute/#how-to-submit-a-contribution\" rel=\"nofollow\"\u003eopensource guide\u003c/a\u003e\nand/or the \u003ca href=\"https://git-scm.com/doc\" rel=\"nofollow\"\u003egit\u0027s documentation\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eNot that if you have login GitLab by using the \u003ccode\u003e[Shibbolenth]\u003c/code\u003e service (eduGAIN, F\u00e9d\u00e9ration d\u0027Identit\u00e9s\nRENATER), you will need to \u003ca href=\"https://gitlab.in2p3.fr/help/ssh/README#generating-a-new-ssh-key-pair\" rel=\"nofollow\"\u003eadd a SSH key\u003c/a\u003e to\nyour GitLab profile if you want to \u0027push\u0027 your changes to the server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contribute-to-the-escape-ossr\" class=\"anchor\" href=\"#contribute-to-the-escape-ossr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContribute to the ESCAPE OSSR\u003c/h1\u003e\n\u003cp\u003eIf you want to provide software to the ESCAPE repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the \u003ca href=\"https://escape2020.pages.in2p3.fr/wp3/ossr-pages/page/contribute/contribute_ossr/\" rel=\"nofollow\"\u003eESCAPE OSSR guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor ESCAPE members, follow the steps detailed in \u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/onboarding\" rel=\"nofollow\"\u003ethe onboarding project\u003c/a\u003e\nto finalise your contribution and the same onboarding process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAll the code provided should be uploaded to the \u003ca href=\"https://zenodo.org/communities/escape2020/\" rel=\"nofollow\"\u003eZenodo ESCAPE community\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the following \u003ca href=\"https://escape2020.pages.in2p3.fr/wp3/ossr-pages/page/contribute/publish_tutorial/\" rel=\"nofollow\"\u003etutorial on how to publish content in Zenodo\u003c/a\u003e,\nand how to automatise the upload of each new release of your project.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-this-project-also-includes\" class=\"anchor\" href=\"#this-project-also-includes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis project also includes\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-1-how-to-automatise-the-building-of-a-singularity-image-and-upload-it-to-zenodo-using-the-gitlab-ci\" class=\"anchor\" href=\"#1-how-to-automatise-the-building-of-a-singularity-image-and-upload-it-to-zenodo-using-the-gitlab-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. How to automatise the building of a Singularity image and upload it to Zenodo using the GitLab-CI\u003c/h2\u003e\n\u003cp\u003eA working example of how to automatise the GitLab-CI to;\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecreate a Singularity image / container of your code,\u003c/li\u003e\n\u003cli\u003emake it available as a downloadable artifact within your project and\u003c/li\u003e\n\u003cli\u003eupload it to the \u003ca href=\"https://zenodo.org/communities/escape2020\" rel=\"nofollow\"\u003eESCAPE OSSR\u003c/a\u003e,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ecan be found in the \u003ccode\u003e.singularityci\u003c/code\u003e, and \u003ccode\u003eSingularity\u003c/code\u003e directories and in the \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e file - the\n\u003ccode\u003ebuild_singularity_image\u003c/code\u003e stage. Please read carefully all the README files.\u003c/p\u003e\n\u003cp\u003eFor an easy example of how to create a Singularity receipt from scratch (and its corresponding container when executed),\nplease have a look to the \u003ccode\u003esingularity_utils\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-2-how-to-automatise-the-building-of-a-docker-container-and-upload-it-to-the-gitlab-container-registry\" class=\"anchor\" href=\"#2-how-to-automatise-the-building-of-a-docker-container-and-upload-it-to-the-gitlab-container-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. How to automatise the building of a Docker container and upload it to the GitLab Container Registry\u003c/h2\u003e\n\u003cp\u003eAn example can be found in the \u003ccode\u003eDocker\u003c/code\u003e directory and in the \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e file - the\n\u003ccode\u003ebuild_docker_image\u003c/code\u003e stage.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h1\u003e\n\u003cp\u003eExample of how to show installing instructions (and indeed the way to install this project).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e $ git clone https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape.git\n $ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e template_project_escape\n $ pip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-citing\" class=\"anchor\" href=\"#citing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCiting\u003c/h1\u003e\n\u003cp\u003eExample of citing (as well as the DOI to cite this project),\u003c/p\u003e\n\u003cp\u003eIn case of citing this repository, use the following DOI:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ev2.2 \u003ca href=\"https://doi.org/10.5281/zenodo.4923992\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869b462bf3a319fe4fc2ffa52fb6b0f7c8e42eb4e0ed4bc2482306b9fd5aafab/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343932333939322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4923992.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev2.1 \u003ca href=\"https://doi.org/10.5281/zenodo.4790629\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8f54e2f50cdf0c4d1bd5183d6472cae8b708efacd6a1319b443d8e456f41b7f/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343739303632392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4790629.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev2.0 \u003ca href=\"https://doi.org/10.5281/zenodo.3884963\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c27201272a77fc3ab3029c8c2c452e02a71736b7adaa0926bc45d3ac825598ed/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333838343936332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3884963.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev1.1 \u003ca href=\"https://doi.org/10.5281/zenodo.3743490\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbfe31d3a9bd48ef4554b414c3c2325276269a476a79ec0217aa9feccc87cecd/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333734333439302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3743490.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ev1.0 \u003ca href=\"https://doi.org/10.5281/zenodo.3572655\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ec734ee4c1c793eaa8f9c2b189a6eae6d7d2ccb5f2a92adeb8af479409cc7bb/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333537323635352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3572655.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDo not forget to include your code / container into the \u003ca href=\"https://zenodo.org/communities/escape2020/\" rel=\"nofollow\"\u003eZenodo ESCAPE community\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003e\u003cstrong\u003eNote that\u003c/strong\u003e\u003c/em\u003e a DOI will be assigned in the moment create a new record/entry in Zenodo.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003ePlease check the licenses of the code within the \u003ccode\u003e.singularityci\u003c/code\u003e directory before adding this template\nto your project.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-report-an-issue--ask-a-question\" class=\"anchor\" href=\"#report-an-issue--ask-a-question\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReport an issue / Ask a question\u003c/h1\u003e\n\u003cp\u003eUse the \u003ca href=\"https://gitlab.in2p3.fr/escape2020/wp3/template_project_escape/-/issues\" rel=\"nofollow\"\u003eGitLab repository Issues\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eEmail to vuillaume [at] lapp.in2p3.fr / garcia [at] lapp.in2p3.fr.\u003c/p\u003e\n", + "full_name": "rses-singularity/theano", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1631872285.0 + "updated_at": 1674934802.0 }, { "data_format": 2, - "description": "Docker recipe for building Interproscan", + "description": null, "filenames": [ - "Singularity.open", "Singularity" ], - "full_name": "biocorecrg/interproscan_docker", - "latest_release": "5.48-83.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-interproscan_docker\" class=\"anchor\" href=\"#interproscan_docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einterproscan_docker\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/150708687\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5867fa2b54b675356b6c4b17144ce558f6902bee46de35012c7bdafc38d90f88/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3135303730383638372e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/150708687.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContainer recipes for building \u003ca href=\"https://interproscan-docs.readthedocs.io\" rel=\"nofollow\"\u003eInterproscan\u003c/a\u003e. Both \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and \u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e versions are provided (the latter recomended).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to use Interproscan external privative software, these programs must be obtained first with granted academic permissions.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cbs.dtu.dk/services/SignalP/\" rel=\"nofollow\"\u003eSignalP\u003c/a\u003e \u003ccode\u003esignalp-4.1b.Linux.tar.Z\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cbs.dtu.dk/services/TMHMM/\" rel=\"nofollow\"\u003eTMHMM\u003c/a\u003e \u003ccode\u003etmhmm-2.0c.Linux.tar.gz\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://phobius.sbc.su.se/\" rel=\"nofollow\"\u003ePhobious\u003c/a\u003e \u003ccode\u003ephobius101_linux.tar.gz\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eRegarding phobius: \u003ca href=\"https://www.biostars.org/p/238642/\" rel=\"nofollow\"\u003ehttps://www.biostars.org/p/238642/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eKeep in mind that some other modifications are also needed in those programs above in advance, e. g., replacing \u003ccode\u003e/usr/bin/perl\u003c/code\u003e for \u003ccode\u003e/usr/bin/env perl\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLast software package versions of Interproscan include the whole data by default. For container performance and distribution, we don\u0027t keep Interproscan data directory.\u003c/p\u003e\n\u003cp\u003eIt is important to ensure that program and data versions match and that this is adequately reflected in \u003ccode\u003einterproscan.properties\u003c/code\u003e or \u003ccode\u003einterproscan.open.properties\u003c/code\u003e files. Otherwise Interproscan is not likely to work.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pregenerated-images\" class=\"anchor\" href=\"#pregenerated-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePregenerated images\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://biocore.crg.eu/iprscan/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/biocorecrg/interproscan\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-from-docker-recipes\" class=\"anchor\" href=\"#building-from-docker-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding from Docker recipes\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e# With privative software\ndocker build -t iprscan:5.48-83.0 -f Dockerfile .\nsudo singularity build iprscan-5.48-83.0.sif docker-daemon://iprscan:5.48-83.0\n# Without privative software\ndocker build -t iprscan-open:5.48-83.0 -f Dockerfile.open .\nsudo singularity build iprscan-5.48-83.0.open.sif docker-daemon://iprscan-open:5.48-83.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-from-singularity-recipes\" class=\"anchor\" href=\"#building-from-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding from Singularity recipes\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e# With privative software\nsudo singularity build iprscan-5.48-83.0.sif Singularity\n# Without privative software\nsudo singularity build iprscan-5.48-83.0.open.sif Singularity.open\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can avoid using \u003ccode\u003esudo\u003c/code\u003e with \u003ccode\u003e--fakeroot\u003c/code\u003e Singularity build option.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eFor running the container images, it is mandatory to mount a data directory that fits the same Interproscan version. Below some example commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Docker\ndocker run --volume /path/to/data:/usr/local/interproscan/data --volume /path/to/scratch:/scratch -t biocorecrg/interproscan:5.48-83.0 /usr/local/interproscan/interproscan.sh -i /scratch/test.fa --goterms --iprlookup --pathways -o /scratch/out_interpro -f TSV\n\n# Singularity\nsingularity exec -e iprscan-5.47-82.0.open.sif /usr/local/interproscan/interproscan.sh -i /path/to/test2.fa --goterms --iprlookup --pathways -o /path/to/out_interpro -f TSV\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTES\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eMoreover, keep into account that a user with suitable permissions may need first to index \u003ccode\u003e/usr/local/interproscan/data\u003c/code\u003e directory (e.g., with \u003ccode\u003epython3 /usr/local/interproscan/initial_setup.py\u003c/code\u003e). You can use the very container images. Details here: \u003ca href=\"https://interproscan-docs.readthedocs.io/en/5.48-83.0/HowToRun.html\" rel=\"nofollow\"\u003ehttps://interproscan-docs.readthedocs.io/en/5.48-83.0/HowToRun.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDepending on your setup, you may need to change \u003ccode\u003eSINGULARITY_TMPDIR\u003c/code\u003e (and \u003ccode\u003eSINGULARITY_CACHEDIR\u003c/code\u003e) environment variables for pointing to a location with enough space. More details at: \u003ca href=\"https://singularity.hpcng.org/admin-docs/master/installation.html\" rel=\"nofollow\"\u003ehttps://singularity.hpcng.org/admin-docs/master/installation.html\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "rses-singularity/digits", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1631532581.0 + "updated_at": 1674934803.0 }, { "data_format": 2, - "description": "Singularity image for alienpy", + "description": null, "filenames": [ "Singularity" ], - "full_name": "adriansev/alienpy.sing", + "full_name": "rses-singularity/tfgpu-theano-pytorch-keras", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-alienpysing\" class=\"anchor\" href=\"#alienpysing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ealienpy.sing\u003c/h1\u003e\n\u003cp\u003eSingularity image for alienpy\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tensorflow-gpu-theano-keras-and-pytorch-gpu-with-opencv\" class=\"anchor\" aria-hidden=\"true\" href=\"#tensorflow-gpu-theano-keras-and-pytorch-gpu-with-opencv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTensorflow (GPU), Theano, Keras and PyTorch (GPU) with OpenCV\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-listing\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-listing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware listing\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ev1\n\u003cul\u003e\n\u003cli\u003eUbuntu 16.04\u003c/li\u003e\n\u003cli\u003eCUDA 8 + cuDNN 6\u003c/li\u003e\n\u003cli\u003ePython 3.5\u003c/li\u003e\n\u003cli\u003eTheano 1.0.0\u003c/li\u003e\n\u003cli\u003eTensorflow (GPU) 1.4.1\u003c/li\u003e\n\u003cli\u003ePyTorch (GPU) 0.3.0\u003c/li\u003e\n\u003cli\u003eOpenCV 3.3.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1629840214.0 + "updated_at": 1674934803.0 }, { "data_format": 2, "description": null, "filenames": [ - "2.63/Singularity" + "recipes/single-cell-genomics/mosaic/Singularity.mosaic-v03", + "recipes/peakcallers/macs2/Singularity.macs2-2271", + "recipes/peakcallers/hiddendomains/Singularity.hiddendomains-31", + "recipes/quality-control/fastqc/Singularity.fastqc-0119cv6", + "recipes/quality-control/fastqc/Singularity.fastqc-0119cv8", + "recipes/quality-control/fastqc/Singularity.fastqc-0119cv7", + "recipes/mapping/bowtie2samtools/Singularity.bowtie2samtools-v245v115", + "recipes/mapping/bowtie2/Singularity.bowtie2-245", + "recipes/mapping/bowtie2/Singularity.bowtie2-241cv1", + "recipes/fastq-operations/parallelfastqdump/Singularity.parallelfastqdump-v063", + "recipes/fastq-operations/trimgalore/Singularity.trimgalore-v067", + "recipes/os-environments/alpine/Singularity.alpine-3160", + "recipes/rpackages/bioconductor/genomeinfodb/Singularity.genomeinfodb-1323", + "recipes/rpackages/bioconductor/genomicranges/Singularity.genomicranges-1480", + "recipes/rpackages/snakemake-pipelines/chipseq/Singularity.snakemakechipseq-v001", + "recipes/rpackages/bedtools/Singularity.bedr-107", + "recipes/image-analysis/deeplabcut/Singularity.deeplabcut-2202", + "recipes/image-analysis/cellpose/Singularity.cellpose-2.0.5", + "recipes/image-analysis/chimerax/Singularity.chimerax-1.3", + "recipes/chipseq/spikchip/Singularity.spikchip-v099", + "recipes/chipseq/spikchipcustom/Singularity.spikchipcustom-v099", + "recipes/analysissuites/picardtools/Singularity.picardtools-2221", + "recipes/analysissuites/picardtools/Singularity.picardtools-2271", + "recipes/analysissuites/deeptools/Singularity.deeptools-351", + "recipes/analysissuites/samtools/Singularity.samtools-114", + "recipes/analysissuites/samtools/Singularity.samtools-115", + "recipes/analysissuites/bedops/Singularity.bedops-2440" ], - "full_name": "yh549848/singularity-ngsplot", + "full_name": "descostesn/singularityhub-emblrome", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularityhub-embl-rome-gitlab-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularityhub-embl-rome-gitlab-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularityhub EMBL Rome (Gitlab version)\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pulling\"\u003ePulling\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThis repository aims at sharing singularity images among the EMBL community. We try to follow a strict model to provide uniformly designed singularities. Please let us know if we should modify anything.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant: Since we do not have a premium account, I tried to find some workaround. We cannot secure completely that master will stay green. Please be careful to strictly follow the instructions.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pulling\" class=\"anchor\" aria-hidden=\"true\" href=\"#pulling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePulling\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePlease read the entire section before trying to pull any singularities\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTo pull an existing singularity, first have a look at the image of interest in the list \u003ca href=\"https://git.embl.de/descoste/singularityhub-emblrome/container_registry\" rel=\"nofollow\"\u003ehere\u003c/a\u003e or in this \u003ca href=\"https://git.embl.de/descoste/singularityhub-emblrome/-/tree/main/recipes\" rel=\"nofollow\"\u003efolder\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the script below in a \u003ccode\u003edownload.sh\u003c/code\u003e file and run the command: \u003ccode\u003ebash dowload.sh username containername imagename\u003c/code\u003e. For example, \u003ccode\u003ebash download.sh descoste fastqcv0019cv8.sif \u0027fastqc:0119cv8\u0027\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/usr/bin/bash\n\nUSERNAME=$1\nCONTAINERNAME=$2\nIMAGE=$3\n\nsingularity pull --docker-username $USERNAME --docker-password $SINGULARITY_DOCKER_PASSWORD $CONTAINERNAME oras://git.embl.de:4567/descoste/singularityhub-emblrome/$IMAGE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eImportant\u003c/strong\u003e: You need to define a git token to be able to use the \u003ccode\u003e$SINGULARITY_DOCKER_PASSWORD\u003c/code\u003e variable. Follow these steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClick on your avatar at the top right of your gitlab page.\u003c/li\u003e\n\u003cli\u003eClick on \u003ccode\u003epreferences\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eClick on \u003ccode\u003eAccess Tokens\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eEnter a Token name. ex: \"singularitypull\".\u003c/li\u003e\n\u003cli\u003eIn the \u003ccode\u003eSelect scopes\u003c/code\u003e section, select \u003ccode\u003eread_registry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eClick \u003ccode\u003eCreate personal access token\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAt the beginning of the new loaded page, click on the folder icon to copy your new personal access token.\u003c/li\u003e\n\u003cli\u003eEdit your \u003ccode\u003e.bashrc\u003c/code\u003e (\u003ccode\u003eemacs -nw ~/.bashrc\u003c/code\u003e or \u003ccode\u003evim ~/.bashrc\u003c/code\u003e) by adding \u003ccode\u003eexport SINGULARITY_DOCKER_PASSWORD=\"paste_your_copied_access_token_here\"\u003c/code\u003e wherever you like.\u003c/li\u003e\n\u003cli\u003eAfter closing your editor, run \u003ccode\u003eexec bash\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eNow try to pull a particular singularity following the instructions above.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Make sure that you do use bash and not something else like zsh.\u003c/p\u003e\n\u003cp\u003eIf it does not work please do:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eAdd the remote: \u003ccode\u003esingularity remote add --no-login embl https://git.embl.de:4567\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUse the remote: \u003ccode\u003esingularity remote use embl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eLog to the remote: \u003ccode\u003esingularity remote login oras://git.embl.de:4567\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eThis repository is maintained by Nicolas Descostes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant: Since we do not have a premium account, I tried to find some workaround. We cannot secure completely that master will stay green. Please be careful to strictly follow the instructions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant Bis: Each singularity should contain a single tool. Contact me ahead if you plan otherwise.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo add a new singularity recipe, you need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone the repository: \u003ccode\u003egit clone git@git.embl.de:descoste/singularityhub-emblrome.git\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEnter the folder: \u003ccode\u003ecd singularityhub-emblrome/\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePosition yourself on the \"submission\" branch: \u003ccode\u003egit checkout submission\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eMake sure that the content of the branch is up-to-date: \u003ccode\u003egit reset --hard main\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd a singularity recipe inside \u003ccode\u003erecipes\u003c/code\u003e in the adapted folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eRespect the naming format \u003ccode\u003eSingularity.toolName-tag\u003c/code\u003e (with a upper-case S). Please use common sense to choose the folder\u003c/strong\u003e. If you are not sure, please contact me by email or by chat.\u003c/p\u003e\n\u003cp\u003eFor instance, if you want to submit fastqc version 0119cv8, your file name will be \u003ccode\u003eSingularity.fastqc-0119cv8\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eCommit and push to the repository: `git add myrecipe \u0026amp;\u0026amp; git commit -m \"initial commit\" \u0026amp;\u0026amp; git push origin submission\"\u003c/li\u003e\n\u003cli\u003eModify \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e in the \"submission area\" using the following template (replace \u003ccode\u003etoolName\u003c/code\u003e, \u003ccode\u003etag\u003c/code\u003e, and \u003ccode\u003epath_to_recipe_folder\u003c/code\u003e):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003etoolName-tag-test:\n extends: .templateTest\n variables:\n BASENAME: toolName\n TAG: tag\n RECIPE_PATH: recipes/path_to_recipe_folder_without_file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor instance, if you want to submit fastqc version 0119cv8, your rule name will be \u003ccode\u003efastqc-0119cv8-test\u003c/code\u003e and the path to the recipe \u003ccode\u003erecipes/quality-control/fastqc\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote 1:\u003c/strong\u003e There is no slash at the end of the path and the file name is \u003cstrong\u003enot\u003c/strong\u003e precised.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote 2:\u003c/strong\u003e The BASENAME and the TAG are used to create the file name (Singularity.BASENAME-TAG). Please verify that it matches.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eIn the following instruction, \u003cstrong\u003eplease add toolName-tag-test` as a commit message\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003ePush the file \u003ccode\u003e.gitlab-ci.yml\u003c/code\u003e to the repository: \u003ccode\u003egit add .gitlab-ci.yml \u0026amp;\u0026amp; git commit -m \"toolName-tag-test\" \u0026amp;\u0026amp; git push origin submission\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eVisit this \u003ca href=\"https://git.embl.de/descoste/singularityhub-emblrome/-/merge_requests\" rel=\"nofollow\"\u003epage\u003c/a\u003e to submit a merge request.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eAs title: toolName-tag-test\u003c/li\u003e\n\u003cli\u003edescription: A one-line sentence to explain what the tool is. Please precise any important information as well.\u003c/li\u003e\n\u003cli\u003eReviewer: Choose Nicolas Descostes\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBe careful:\u003c/strong\u003e Uncheck the \u003ccode\u003eDelete source branch when merge request is accepted.\u003c/code\u003e before submitting.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003eNow it is time to test the build of your singularity. You will see a gear on the right of \u003ccode\u003eDetached merge request pipeline #32160 waiting for manual action for \u003c/code\u003e. Click on it and hit the play button next to your rule.\u003c/li\u003e\n\u003cli\u003eIn the \u003ccode\u003eCI/CD \u0026gt; jobs\u003c/code\u003e (menu on the left), you can see your job running.\u003c/li\u003e\n\u003cli\u003eOnce your job passes the test (green checkmark), I will merge and deploy your singularity. I will let you know when this is done.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1629677826.0 + "updated_at": 1674643692.0 }, { "data_format": 2, "description": null, "filenames": [ - "1.0.3/Singularity" + "planner/downward/misc/releases/19.12/Singularity.19.12", + "planner/downward/misc/releases/20.06/Singularity.20.06", + "planner/downward/misc/releases/latest/Singularity", + "planner/downward/misc/releases/19.06/Singularity.19.06" ], - "full_name": "yh549848/singularity-sicer2", + "full_name": "drexlerd/downward-hffpi", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-downward-hffpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#downward-hffpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edownward-hffpi\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone recursively\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003egit clone --recursively \u0026lt;link_to_repo\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate python3 virtual environment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython3 -m venv --prompt hffpi .venv\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eActivate virtual environment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource .venv/bin/activate\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eInstall python packages (needed for experimental code)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epip install -r requirements.txt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInstall planner\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./planner/downward/build.py\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-the-planner\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-the-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the planner\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003eexperiments/experiment-hffpi.py\u003c/code\u003e on example callstrings.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-the-experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-the-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the experiments\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd experiments\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e./experiment-hffpi.py --all\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1629665658.0 + "updated_at": 1675861881.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity Recipe for accessing GPU" ], - "full_name": "baxpr/sct-fmri", + "full_name": "salammemphis/GPU-and-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-sct-fmri-processing\" class=\"anchor\" href=\"#sct-fmri-processing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSCT fMRI processing\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003etest_container.sh\u003c/code\u003e for an example run command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--fmri_niigz 4D spinal cord fMRI, fully qualified path and filename\n--masksize Size of mask to create in mm\n--label_info Text to label the PDF, e.g. from XNAT project/subject\n--out_dir Outputs directory (and working directory)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline\" class=\"anchor\" href=\"#pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003esrc/main.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003efmri0.nii.gz First volume of fMRI\n\nfmri_mask??.nii.gz Created analysis mask\n\nfmri_centerline.nii.gz Cord centerline\nfmri_centerline.csv\n\nfmri_moco.nii.gz Moco outputs\nfmri_moco_mean.nii.gz\nmoco_params.tsv\nmoco_params_x.nii.gz\nmoco_params_y.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eSingularity recipe to access GPU from host machine. It will spin up a jupyter notebook from singularity.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-cuda-110-and-tensorflow-220-and-keras-240\" class=\"anchor\" aria-hidden=\"true\" href=\"#cuda-110-and-tensorflow-220-and-keras-240\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCUDA 11.0 and tensorflow 2.2.0 and keras 2.4.0\u003c/h1\u003e\n\u003cp\u003eBootstrap: docker\n#From: tensorflow/tensorflow:latest-gpu-py3-jupyter\nFrom: nvcr.io/nvidia/tensorflow:20.08-tf2-py3\n%help\nThis container is made by Shahinur Alam (\u003ca href=\"mailto:sajonbuet@gmail.com\"\u003esajonbuet@gmail.com\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e%setup\u003c/p\u003e\n\u003cp\u003e%files\u003c/p\u003e\n\u003cp\u003e%labels\nMaintainer Shahinur\nVersion 1.0\u003c/p\u003e\n\u003cp\u003e%environment\u003c/p\u003e\n\u003cp\u003e%post\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install numpy pandas sklearn webcolors plotly matplotlib statsmodels factorial pynrrd pillow\npip install torch\npip install scikit-image medpy Tables nilearn SimpleITK h5py glob3 nibabel tifffile scipy opencv-python\npip install --upgrade keras\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e%runscript\necho \"Arguments received: $*\"\nport=$1\nroot_dir=$2\nport=\u003ccode\u003eecho $port| sed \u0027s/ *$//g\u0027\u003c/code\u003e\nroot_dir=\u003ccode\u003eecho $root_dir| sed \u0027s/ *$//g\u0027\u003c/code\u003e\necho $port\njupyter notebook --no-browser --port $port --notebook-dir $root_dir --ip 0.0.0.0\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-works-with-cuda-101\" class=\"anchor\" aria-hidden=\"true\" href=\"#works-with-cuda-101\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorks with CUDA 10.1\u003c/h1\u003e\n\u003cp\u003eBootstrap: docker\nFrom: tensorflow/tensorflow:latest-gpu-py3-jupyter\n%help\nThis container is made by Shahinur Alam (\u003ca href=\"mailto:sajonbuet@gmail.com\"\u003esajonbuet@gmail.com\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e%setup\u003c/p\u003e\n\u003cp\u003e%files\u003c/p\u003e\n\u003cp\u003e%labels\nMaintainer Shahinur\nVersion 1.0\u003c/p\u003e\n\u003cp\u003e%environment\u003c/p\u003e\n\u003cp\u003e%post\n#pip install numpy pandas sklearn webcolors plotly matplotlib statsmodels factorial pynrrd pillow\n#pip install torch\n#pip install scikit-image medpy Tables tensorflow_addons nilearn SimpleITK h5py glob3 nibabel tifffile scipy opencv-python\npip uninstall -y tensorflow tensorflow-addons tensorflow-estimator tensorflow-gpu tensorboard tensorboard-plugin-wit\npip install --upgrade keras\npip install --upgrade tensorflow\npip install tensorflow-addons==0.11.2\npip install tensorflow-estimator==2.3.0\npip install tensorflow-gpu==2.3.0\npip install tensorboard==2.3.0\npip install tensorboard-plugin-wit==1.7.0\u003c/p\u003e\n\u003cp\u003e%runscript\necho \"Arguments received: $*\"\nport=$1\nroot_dir=$2\nport=\u003ccode\u003eecho $port| sed \u0027s/ *$//g\u0027\u003c/code\u003e\nroot_dir=\u003ccode\u003eecho $root_dir| sed \u0027s/ *$//g\u0027\u003c/code\u003e\necho $port\njupyter notebook --no-browser --port $port --notebook-dir $root_dir --ip 0.0.0.0\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1629493665.0 + "updated_at": 1675440928.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "v4.7.1/Singularity" ], - "full_name": "porchard/ATACseq-NextFlow", + "full_name": "yh549848/singularity-code-server", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-atac-seq-data\" class=\"anchor\" href=\"#nextflow-pipeline-for-atac-seq-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow pipeline for ATAC-seq data\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eIf you have Singularity installed, you can use the config provided here (\u0027Singularity\u0027) to build a container with all the dependencies.\u003c/p\u003e\n\u003cp\u003eOtherwise, you\u0027ll need to have the following installed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecta\u003c/li\u003e\n\u003cli\u003ebedtools\u003c/li\u003e\n\u003cli\u003ebwa\u003c/li\u003e\n\u003cli\u003epicardtools\u003c/li\u003e\n\u003cli\u003efastqc\u003c/li\u003e\n\u003cli\u003esamtools\u003c/li\u003e\n\u003cli\u003eataqv\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis pipeline works with NextFlow versions \u0026gt;= 20.07.1\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003ePaths to various generic files (e.g., bwa indices) must be included in the nextflow.config file -- check that file and change paths accordingly. These include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBlacklist bed files for each genome\u003c/li\u003e\n\u003cli\u003eChrom size files for each genome\u003c/li\u003e\n\u003cli\u003eBWA indices\u003c/li\u003e\n\u003cli\u003eTSS files (BED6 files denoting TSS positions)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou\u0027ll also need to set the params.results variable -- either in the nextflow.config file itself, or on the command line when you run the pipeline (\u0027--results /path/to/results\u0027).\u003c/p\u003e\n\u003cp\u003eLastly, you\u0027ll need to include information about each ATAC-seq library, including the genome that each library should be mapped to and the paths to the fastq files for each readgroup. Organize this information in a JSON file, as in library-config.json.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eOnce you have all of the above information, you can run the pipeline as follows (in this case, indicating the path to the results on the command line):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run -with-singularity /path/to/Singularity.simg -with-trace -with-report -with-dag -with-timeline -params-file library-config.json --results /path/to/results /path/to/main.nf\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1629490022.0 + "updated_at": 1675855529.0 }, { "data_format": 2, - "description": "ASCIIGenome is a genome browser based on command line interface and designed for console terminals.", + "description": "Getting up to speed with Singularity", "filenames": [ - "1.16.0/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-asciigenome", - "latest_release": "v1.16.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-asciigenome/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-asciigenome/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-asciigenome/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-asciigenome/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fc6d0c140fd5a29bf338bd86c146a7c92b4c8062434faaf70ef0f6c0deb67aa6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fc6d0c140fd5a29bf338bd86c146a7c92b4c8062434faaf70ef0f6c0deb67aa6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-asciigenome\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/4ebf7a2007abd25cbc70d78eea4b3e18e84b103736a8a5edb364f1677212c427/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ebf7a2007abd25cbc70d78eea4b3e18e84b103736a8a5edb364f1677212c427/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-asciigenome\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/6e5331506177fef5e181d308bb0030987e5b6b8ed2eac29c791694f6339a6115/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6e5331506177fef5e181d308bb0030987e5b6b8ed2eac29c791694f6339a6115/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-asciigenome\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b6afd6a92e3bec558c0c64e9a5bdc6d25c18335230778d4b78f8b2f869f15e4d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6afd6a92e3bec558c0c64e9a5bdc6d25c18335230778d4b78f8b2f869f15e4d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d617363696967656e6f6d65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-asciigenome\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-asciigenome\" class=\"anchor\" href=\"#singularity-asciigenome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-asciigenome\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/fc29589526166dc2ee7ce341088c93b17e02dca1cf81bba1cf477425249fa4f3/68747470733a2f2f617363696967656e6f6d652e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6c656973686d616e69615f7472616e736372697074732e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fc29589526166dc2ee7ce341088c93b17e02dca1cf81bba1cf477425249fa4f3/68747470733a2f2f617363696967656e6f6d652e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6c656973686d616e69615f7472616e736372697074732e706e67\" width=\"50%\" data-canonical-src=\"https://asciigenome.readthedocs.io/en/latest/_images/leishmania_transcripts.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/dariober/ASCIIGenome\"\u003easciigenome\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003easciigenome\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/asciigenome/1.16.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/asciigenome\u003c/code\u003e as \u003ccode\u003e1.16.0\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "netscruff/SingularityTest", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1629217403.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1511296488.0 }, { "data_format": 2, - "description": "Container used to run IMI spikeScreen", + "description": "Nextflow workflow for benchmarking biohansel and Snippy with NCBI SRA genomes", "filenames": [ "Singularity" ], - "full_name": "IMIMF-UNILJSI/spikeScreenContainer", + "full_name": "peterk87/nf-biohansel-sra-benchmark", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-spikescreencontainer\" class=\"anchor\" href=\"#spikescreencontainer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003espikeScreenContainer\u003c/h1\u003e\n\u003cp\u003eContainer used to run IMI spikeScreen\nThis repo is meant to increase portability through automatic automatic container builds on shub.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-biohansel-sra-benchmark\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-biohansel-sra-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-biohansel-sra-benchmark\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://dev.azure.com/peterkruczkiewicz0831/nf-biohansel-sra-benchmark/_build/latest?definitionId=2\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5bffa2b25d6120420c8ab60e72373cb3d1ee278f01b086b8f0abb4d334b9bb23/68747470733a2f2f6465762e617a7572652e636f6d2f70657465726b7275637a6b69657769637a303833312f6e662d62696f68616e73656c2d7372612d62656e63686d61726b2f5f617069732f6275696c642f7374617475732f70657465726b38372e6e662d62696f68616e73656c2d7372612d62656e63686d61726b3f6272616e63684e616d653d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://dev.azure.com/peterkruczkiewicz0831/nf-biohansel-sra-benchmark/_apis/build/status/peterk87.nf-biohansel-sra-benchmark?branchName=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3444\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNextflow workflow for benchmarking \u003ca href=\"https://github.com/phac-nml/biohansel\"\u003ebiohansel\u003c/a\u003e and \u003ca href=\"https://github.com/tseemann/snippy/\"\u003eSnippy\u003c/a\u003e with NCBI SRA genomes.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pre-reqs\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-reqs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-reqs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOne or more directories each with the following files (see \u003ccode\u003eschemes/enteritidis_v1.0.7\u003c/code\u003e for an example)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eaccessions\u003c/code\u003e - List of SRA run accessions (e.g. \u003ccode\u003eSRR8820085\u003c/code\u003e) in a file (one accession per line)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme.fasta\u003c/code\u003e - biohansel scheme definition file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eref.gb\u003c/code\u003e - Genbank format reference genome\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emetadata.tsv\u003c/code\u003e tab delimited metadata file or empty file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInput scheme directory included with this repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eschemes\n\u2514\u2500\u2500 enteritidis_v1.0.7\n \u251c\u2500\u2500 accessions\n \u251c\u2500\u2500 metatadata.tsv\n \u251c\u2500\u2500 ref.gb\n \u2514\u2500\u2500 scheme.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eShow help message:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run peterk87/nf-biohansel-sra-benchmark --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eShould see something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 19.07.0-edge\nLaunching `main.nf` [drunk_dalembert] - revision: 97a449f5b6\n==================================================================\npeterk87/nf-biohansel-sra-benchmark ~ version 1.0dev\n==================================================================\n\nGit info: null - null [null]\n\nUsage:\n The typical command for running the pipeline is as follows:\n\n nextflow run peterk87/nf-biohansel-sra-benchmark \\\n --outdir results \\\n --schemesdir schemes \\\n --n_genomes 96 \\\n --iterations 10 \\\n -work workdir \\\n -profile standard\n\nOptions:\n --outdir Output directory (default: results)\n --schemesdir Directory with subtyping schemes and accessions to benchmark with biohansel (default: schemes)\n --n_genomes Number of SRA genomes to download and analyze per scheme (default: 96)\n --iterations Number of iterations per biohansel benchmark (default: 10)\n --thread_combos List of integer number of threads to test biohansel and snippy with delimited by comma (default: 1,2,4,8,16,32)\nOther options:\n -w/--work-dir The temporary directory where intermediate data will be saved (default: work)\n -profile Configuration profile to use. [singularity, conda, slurm] (default: standard)\nCluster options:\n -profile Only \"-profile slurm\" is accepted\n --slurm_queue Name of SLURM queue to submit jobs to (e.g. \"HighPriority\").\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun test profile creating Conda environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run peterk87/nf-biohansel-sra-benchmark -profile test,conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun included benchmark dataset with Singularity and default parameters (i.e. 96 genomes, 10 iterations for biohansel, run Snippy and biohansel with 1,2,4,8,16,32 threads/CPUs):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# clone/download this repo so that the scheme included with this repo can be run with the workflow\ngit clone https://github.com/peterk87/nf-biohansel-sra-benchmark.git\nnextflow run peterk87/nf-biohansel-sra-benchmark -profile singularity --schemesdir nf-biohansel-sra-benchmark/schemes\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun above on a cluster with SLURM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/peterk87/nf-biohansel-sra-benchmark.git\nnextflow run peterk87/nf-biohansel-sra-benchmark -profile singularity,slurm --slurm_queue \u0026lt;QueueName\u0026gt; --schemesdir nf-biohansel-sra-benchmark/schemes\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-run-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-run-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline run information\u003c/h2\u003e\n\u003cp\u003eWithin your output directory (e.g. \u003ccode\u003eresults/\u003c/code\u003e), you should find a \u003ccode\u003epipeline_info\u003c/code\u003e directory with runtime information about your analysis including trace information (see \u003ca href=\"https://www.nextflow.io/docs/latest/tracing.html\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/docs/latest/tracing.html\u003c/a\u003e for more info about these output files)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1629128736.0 + "updated_at": 1566494312.0 }, { "data_format": 2, - "description": "BWA is a program for aligning sequencing reads against a large reference genome (e.g. human genome). ", + "description": null, "filenames": [ - "0.7.17a/Singularity", - "0.7.3a/Singularity" + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/latest/Singularity", + "misc/releases/19.06/Singularity.19.06" ], - "full_name": "pscedu/singularity-bwa", - "latest_release": "v0.7.3a", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bwa/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bwa/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-bwa/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bwa/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/dbf644fd78a6a349b822d2b6db36a3f1ab2e4155f5d67671d27202073ac6cb6a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627761\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dbf644fd78a6a349b822d2b6db36a3f1ab2e4155f5d67671d27202073ac6cb6a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627761\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bwa\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0f4fa8203031531a1e542e55475a3668dbdc3d10e36a3ce505f5f098b465b1ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627761\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0f4fa8203031531a1e542e55475a3668dbdc3d10e36a3ce505f5f098b465b1ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627761\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bwa\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c10a09139792e339dfd18dbbab5079f2bf2027194d2dcfc6796e85d1bab3b88/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627761\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c10a09139792e339dfd18dbbab5079f2bf2027194d2dcfc6796e85d1bab3b88/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627761\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bwa\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a1977c9c08b068aecc0940ff8e8665b3e38fd423b0e217273d28b8ada424e70a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627761\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1977c9c08b068aecc0940ff8e8665b3e38fd423b0e217273d28b8ada424e70a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627761\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bwa\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bwa\" class=\"anchor\" href=\"#singularity-bwa\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bwa\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/Bwa\"\u003ebwa\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebwa\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bwa/0.7.3a\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bwa\u003c/code\u003e as \u003ccode\u003e0.7.3a.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "utop1an/rule-based-heuristic", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "bioinformatics", - "singularity" - ], - "updated_at": 1629083200.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1672794609.0 }, { "data_format": 2, - "description": "Aspera Connect helps you securely move file and folders of any size.", + "description": "libmicropython touch screen OS for nxp mxmrt 1062 and/or a souped up Teensy 4.1", "filenames": [ - "3.11.0.5/Singularity" + "ports/libmicropython/IRIDESCENT/__PYTHONMODULES/music21_deps/pygments-master/tests/examplefiles/singularity/Singularity" ], - "full_name": "pscedu/singularity-aspera-connect", + "full_name": "8888clockradio/iridescentmicropython", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-aspera-connect/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-aspera-connect/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-aspera-connect/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-aspera-connect/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/9163de09dca2f8ae61af41ff4d7baf31f1a6c6d4b86d2a361d93909e155b3ab5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9163de09dca2f8ae61af41ff4d7baf31f1a6c6d4b86d2a361d93909e155b3ab5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-aspera-connect\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5ec7de4d9f3ea32793203e28d5147d5dc6cc8b9bd7cf2ab08eb5692796de5c23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ec7de4d9f3ea32793203e28d5147d5dc6cc8b9bd7cf2ab08eb5692796de5c23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-aspera-connect\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/adf1ed132e277181fe81068629a01b5494834d8fb83cc84fc3b132e50f6e08ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/adf1ed132e277181fe81068629a01b5494834d8fb83cc84fc3b132e50f6e08ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-aspera-connect\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ca701dddc8865d68b18f35dbdd6e33054e2977f14c0409a15baeb30435b74962/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca701dddc8865d68b18f35dbdd6e33054e2977f14c0409a15baeb30435b74962/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6173706572612d636f6e6e656374\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-aspera-connect\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-aspera-connect\" class=\"anchor\" href=\"#singularity-aspera-connect\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-aspera-connect\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eascp\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/aspera-connect/3.11.0.5\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/aspera-connect\u003c/code\u003e as \u003ccode\u003e3.11.0.5.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally. As of today, Does not work on MacOSX.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003eiridescentmicropython\nANY COMMERCIAL USE OF ANY IRIDESCENT FILES REQUIRES LICENSING contact \u003ca href=\"mailto:george@georgerosar.com\"\u003egeorge@georgerosar.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eplease email \u003ca href=\"mailto:george@georgerosar.com\"\u003egeorge@georgerosar.com\u003c/a\u003e if you want to be a contributer\u003c/p\u003e\n\u003cp\u003eCopyright 2023 George Charles Rosar II\u003c/p\u003e\n\u003cp\u003eTeensy 4.1 should have at least 16MB or more of external RAM soldered into Teensy 4.1 PSRAM pads. Should either be soldered or connected to the Teensy Audio Adapter Card, also Teensy Audio Adapter Card should have an additional 2Gbit of Flash RAM soldered in the Audio Adapter.\u003c/p\u003e\n\u003cp\u003eThe MOST IMPORTANT development issue is getting micropython to recieve and send text to Serial.print() or Serial.read(), mphalport.cpp is not functioning properly.\u003c/p\u003e\n\u003cp\u003einstalling\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir iridescentBUILD; cd iridescentBUILD\ngit clone https://github.com/8888clockradio/iridescentmicropython.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eedit iridescentBUILD/iridescentmicropython/toolchain.mk\u003c/p\u003e\n\u003cp\u003echange LIBPATHFILEDROP, COMPILERPATH, TOOLSPATH and maybe also IS_WINDOWS_TOOLCHAIN_QUESTION_MARK to the proper values. Use absolute paths, works better for the tiered makefile system\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLIBPATHFILEDROP=\"/Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/lib/clang-runtimes/armv7em_hard_fpv5_d16/lib\"\nCOMPILERPATH=\"/Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/bin\"\nTOOLSPATH=\"/Applications/Teensyduino.app/Contents/Java/hardware/tools\"\nCROSSCOMPILEPREFIX = clang\nADDITIONAL_TOOLS = llvm\nCLANG_TOOLCHAIN = --config \"$(COMPILERPATH)/armv7em_hard_fpv5_d16.cfg\"\n\nIS_WINDOWS_TOOLCHAIN_QUESTION_MARK=\n#uncomment below if windows\n#IS_WINDOWS_TOOLCHAIN_QUESTION_MARK=.exe\n\nAR := \"$(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\"\nAS := \"$(COMPILERPATH)/$(ADDITIONAL_TOOLS)-as$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(ASFLAGS)\nCC := \"$(COMPILERPATH)/$(CROSSCOMPILEPREFIX)$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(CLANG_TOOLCHAIN)\nCPP := $(CC) -E\nCXX := \"$(COMPILERPATH)/$(CROSSCOMPILEPREFIX)++$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(CLANG_TOOLCHAIN)\n#GDB = $(CROSS_COMPILE)-gdb\nLD := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ld$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nOBJCOPY := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-objcopy$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nSIZE := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-size$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nSTRIP := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-strip$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nAR := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nar := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto the path of your LLVM clang and clang++ toolchain, download LLVM-embedded-toolchain-for-Arm\n\u003ca href=\"https://github.com/ARM-software/LLVM-embedded-toolchain-for-Arm/releases/download/release-15.0.2/LLVMEmbeddedToolchainForArm-15.0.2-Windows-x86_64.zip\"\u003ehttps://github.com/ARM-software/LLVM-embedded-toolchain-for-Arm/releases/download/release-15.0.2/LLVMEmbeddedToolchainForArm-15.0.2-Windows-x86_64.zip\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewindows\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\n\u003ca href=\"https://github.com/ARM-software/LLVM-embedded-toolchain-for-Arm/releases/download/release-15.0.2/LLVMEmbeddedToolchainForArm-15.0.2-Linux-x86_64.tar.gz\"\u003ehttps://github.com/ARM-software/LLVM-embedded-toolchain-for-Arm/releases/download/release-15.0.2/LLVMEmbeddedToolchainForArm-15.0.2-Linux-x86_64.tar.gz\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elinux\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor (PREFERRED)\n\u003ca href=\"https://github.com/8888clockradio/iridescentmicropython/raw/main/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64.tar.gz\"\u003ehttps://github.com/8888clockradio/iridescentmicropython/raw/main/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64.tar.gz\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emacOS x64 Intel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ekeep lib/clang-runtimes/armv7em_hard_fpv5_d16/lib in the LIBPATHFILEDROP and make sure you add /bin to COMPILERPATH\u003c/p\u003e\n\u003cp\u003echange /Applications/Teensyduino.app in TOOLSPATH if Teensyduino is installed in a non-standard location\u003c/p\u003e\n\u003cp\u003ecopy the .tar.gz file to iridescentBUILD/\nextract the .tar.gz file in iridescentBUILD/\nshould look like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLIBPATHFILEDROP=\"/Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/lib/clang-runtimes/armv7em_hard_fpv5_d16/lib\"\nCOMPILERPATH=\"/Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/bin\"\nTOOLSPATH=\"/Applications/Teensyduino.app/Contents/Java/hardware/tools\"\nCROSSCOMPILEPREFIX = clang\nADDITIONAL_TOOLS = llvm\nCLANG_TOOLCHAIN = --config \"$(COMPILERPATH)/armv7em_hard_fpv5_d16.cfg\"\n\nIS_WINDOWS_TOOLCHAIN_QUESTION_MARK=\n#uncomment below if windows\n#IS_WINDOWS_TOOLCHAIN_QUESTION_MARK=.exe\n\nAR := \"$(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\"\nAS := \"$(COMPILERPATH)/$(ADDITIONAL_TOOLS)-as$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(ASFLAGS)\nCC := \"$(COMPILERPATH)/$(CROSSCOMPILEPREFIX)$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(CLANG_TOOLCHAIN)\nCPP := $(CC) -E\nCXX := \"$(COMPILERPATH)/$(CROSSCOMPILEPREFIX)++$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\" $(CLANG_TOOLCHAIN)\n#GDB = $(CROSS_COMPILE)-gdb\nLD := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ld$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nOBJCOPY := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-objcopy$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nSIZE := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-size$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nSTRIP := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-strip$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nAR := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\nar := $(COMPILERPATH)/$(ADDITIONAL_TOOLS)-ar$(IS_WINDOWS_TOOLCHAIN_QUESTION_MARK)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou then need to copy the FPU library for heavy mathematics (specifically needed for audio, which isn\u0027t required yet\u2013 but this step is still required for linking) (THE REGULAR TEENSY LIBRARY USES SOFT FLOAT ON A HARD FLOAT BULD?! \u2013 THIS IS CORRECTED HERE)\ndownload: \u003ca href=\"https://github.com/ARM-software/CMSIS/raw/master/CMSIS/Lib/GCC/libarm_cortexM7lfdp_math.a\"\u003ehttps://github.com/ARM-software/CMSIS/raw/master/CMSIS/Lib/GCC/libarm_cortexM7lfdp_math.a\u003c/a\u003e\nand place into the $(LIBPATHFILEDROP) defined in toolchain.mk so like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp ~/Downloads/libarm_cortexM7lfdp_math.a /Users/iridescent/iridescent/iridescentCoconutSynth2/LLVMEmbeddedToolchainForArm-16.0.0-Darwin-x86_64/lib/clang-runtimes/armv7em_hard_fpv5_d16/lib/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto build:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd iridescentmicropython/ports/libmicropython\nmake submodules #only need to run make submodules once usually\nmake clean; make V=1 #you can repeat this specific command to rebuild from scratch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eif you want to get daring copy the python modules for kivy, virtual environment, numpy, intelbinhex, pygame, matplotlib, music21, et cetera :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp -R iridescentBUILD/iridescentmicropython/ports/libmicropython/modulesTakenOut/* iridescentBUILD/iridescentmicropython/ports/libmicropython/modules/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand build again\ndoubtful there\u0027s any hardware that will support it at the moment, however due to tiny flash ram size on hardware\u003c/p\u003e\n\u003cp\u003ea board is in development for this firmware/OS\u003c/p\u003e\n\u003cp\u003eif you have kdbg installed through brew\nyou can run to debug in a very basic way\nNOTE: probably doesn\u0027t work since addition of clang\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd iridescentmicropython/ports/libmicropython; ./kdbg.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTHIS PROBABLY DOESN\u0027T MATTER ANYMORE\nNOTE: need to add FLASHMEM to all micropython boot up steps and modify startup.c to run boot clock start\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/ports/libmicropython/IRIDESCENT/cores-master/teensy4/startup.c\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003egenerate extern blocks on FLASHMEM with #include \u0026lt;avr/pgmspace.h\u0026gt; from:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/ports/libmicropython/board_init.c\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAND THESE:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/system_MIMXRT1062.c\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/system_MIMXRT1062.h\niridescentBUILD/iridescentmicropython/ports/libmicropython/boards/MIMXRT1062_clock_config.c\niridescentBUILD/iridescentmicropython/ports/libmicropython/boards/MIMXRT1062_clock_config.h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy inserting in: iridescentBUILD/iridescentmicropython/ports/libmicropython/IRIDESCENT/cores-master/teensy4/startup.c in function void ResetHandler(void)\u003c/p\u003e\n\u003cp\u003eALSO THESE FILES PROBABLY NEED FLASHMEM TOO (just in .h files) on functions (plus #include \u0026lt;avr/pgmspace.h\u0026gt;):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/fsl_device_registers.h\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/drivers/fsl_gpio.h\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/drivers/fsl_iomuxc.h\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/drivers/fsl_clock.h\niridescentBUILD/iridescentmicropython/lib/nxp_driver/sdk/devices/MIMXRT1062/drivers/fsl_lpuart.h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLD Script is located:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/ports/libmicropython/IRIDESCENT/imxmrt_ld/picoimxrt1062_t41.ld\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eTHIS MATTERS THO\nMost of the desktop OS will be based off this concept, as matlibplot and kivy will work together with music21:\nSo either build GUI with matlibplot through kivy or just through kivy\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eiridescentBUILD/iridescentmicropython/ports/libmicropython/modulesTakenOut/kivy/garden/garden/matplotlib/examples\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1629217755.0 + "topics": [], + "updated_at": 1672487253.0 }, { "data_format": 2, - "description": "The ViennaRNA Package consists of a C code library and several stand-alone programs for the prediction and comparison of RNA secondary structures.", + "description": "Pulsar Timing Environments", "filenames": [ - "2.4.14/Singularity" + "containers/Singularity" ], - "full_name": "pscedu/singularity-viennarna", - "latest_release": "v2.4.14", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-viennarna/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-viennarna/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/57ccfd894f989623760a4ef3f10260f771a1fc248f5bf2218fa3c1f6a92ed7b7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/57ccfd894f989623760a4ef3f10260f771a1fc248f5bf2218fa3c1f6a92ed7b7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-viennarna\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/edfc9b50cfd2fd753e7bf402f10073da81e6ac134bb5f9d5c154d8ca266689e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/edfc9b50cfd2fd753e7bf402f10073da81e6ac134bb5f9d5c154d8ca266689e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-viennarna\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/db9a3b8ac1a85b49dcb1110778b0b6f49678043557b898cfcf02c5c8ad330245/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db9a3b8ac1a85b49dcb1110778b0b6f49678043557b898cfcf02c5c8ad330245/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-viennarna\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/09f05ae67886e7f3f5559d192fecf3d45a6bb4495adbb67e1891e55a262309d9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/09f05ae67886e7f3f5559d192fecf3d45a6bb4495adbb67e1891e55a262309d9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7669656e6e61726e61\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-viennarna\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-viennarna\" class=\"anchor\" href=\"#singularity-viennarna\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-viennarna\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://rna.tbi.univie.ac.at\" rel=\"nofollow\"\u003eviennarna\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eAnalyseDists\u003c/code\u003e, \u003ccode\u003eAnalyseSeqs\u003c/code\u003e, \u003ccode\u003eKinfold\u003c/code\u003e, \u003ccode\u003eRNA2Dfold\u003c/code\u003e, \u003ccode\u003eRNAaliduplex\u003c/code\u003e, \u003ccode\u003eRNAalifold\u003c/code\u003e, \u003ccode\u003eRNAcofold\u003c/code\u003e, \u003ccode\u003eRNAdistance\u003c/code\u003e, \u003ccode\u003eRNAduplex\u003c/code\u003e, \u003ccode\u003eRNAeval\u003c/code\u003e, \u003ccode\u003eRNAfold\u003c/code\u003e, \u003ccode\u003eRNAforester\u003c/code\u003e, \u003ccode\u003eRNAheat\u003c/code\u003e, \u003ccode\u003eRNAinverse\u003c/code\u003e, \u003ccode\u003eRNALalifold\u003c/code\u003e, \u003ccode\u003eRNALfold\u003c/code\u003e, \u003ccode\u003eRNApaln\u003c/code\u003e, \u003ccode\u003eRNApdist\u003c/code\u003e, \u003ccode\u003eRNAparconv\u003c/code\u003e, \u003ccode\u003eRNAPKplex\u003c/code\u003e, \u003ccode\u003eRNAplex\u003c/code\u003e, \u003ccode\u003eRNAplfold\u003c/code\u003e, \u003ccode\u003eRNAplot\u003c/code\u003e, \u003ccode\u003eRNAsnoop\u003c/code\u003e, \u003ccode\u003eRNAsubopt\u003c/code\u003e, \u003ccode\u003eRNAup\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/viennarna/2.4.14\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/viennarna\u003c/code\u003e as \u003ccode\u003e2.4.14.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "ipta/pulsar-env", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/IPTA/pulsar-env/actions/workflows/test_CondaEnv.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/IPTA/pulsar-env/actions/workflows/test_CondaEnv.yml/badge.svg\" alt=\"Conda Env Test\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/IPTA/pulsar-env/actions/workflows/test_Singularity.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/IPTA/pulsar-env/actions/workflows/test_Singularity.yml/badge.svg\" alt=\"Apptainer Build (Ubuntu)\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-pulsar-timing-environments\" class=\"anchor\" aria-hidden=\"true\" href=\"#pulsar-timing-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePulsar Timing Environments\u003c/h1\u003e\n\u003cp\u003eThis repository offers a centeralized location for the IPTA Pulsar Timing \u0026amp; Data Combination Teams\u0027 environment.\u003c/p\u003e\n\u003cp\u003eCurrently, this repository presents the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn Anaconda Environment for Pulsar Science (\u003ccode\u003eanaconda_env.yml\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eSingularity/Apptainer Container for HPC Resources (\u003ccode\u003econtainers/Singularity\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-of-the-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-of-the-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation of the Conda Environment\u003c/h2\u003e\n\u003cp\u003ePlease note, we highly encourage using a fresh install of \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003eMambaforge\u003c/a\u003e or \u003ca href=\"https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html\" rel=\"nofollow\"\u003eMicroMamba\u003c/a\u003eover a default install of Anaconda/Miniconda. If you must use an Anaconda/Miniconda installation, from a fresh environment install the \u003ca href=\"https://github.com/mamba-org/mamba\"\u003eMamba Environment \u0026amp; Package Handler\u003c/a\u003e via \u003ccode\u003econda install -c conda-forge mamba\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e As of \u003ccode\u003econda\u003c/code\u003e version 22.11, \u003ccode\u003elibmamba\u003c/code\u003e can be used as a solver to speed up basic Anaconda installs (though there are growing pains). You can find out more \u003ca href=\"https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community\" rel=\"nofollow\"\u003eat the official posting\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo install this environment in your flavor of Anaconda, proceed through the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone this directory: \u003ccode\u003egit clone https://github.com/ipta/pulsar-env.git\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEnter the cloned directory: \u003ccode\u003ecd pulsar-env\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUsing \u003ccode\u003emamba\u003c/code\u003e, install the environment: \u003ccode\u003emamba env create -f anaconda-env.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eActivate the environment: \u003ccode\u003emamba activate IPTA_Env\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-important-note-regarding-the-included-openmpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#important-note-regarding-the-included-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Note Regarding the Included OpenMPI\u003c/h3\u003e\n\u003cp\u003eFor Linux 64, Open MPI is built with CUDA awareness but this support is disabled by default. To enable it, please set the environment variable \u003ccode\u003eOMPI_MCA_opal_cuda_support=true\u003c/code\u003e before launching your MPI processes. Equivalently, you can set the MCA parameter in the command line: \u003ccode\u003empiexec --mca opal_cuda_support 1 ...\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIn addition, the UCX support is also built but disabled by default. To enable it, first install UCX (\u003ccode\u003econda install -c conda-forge ucx\u003c/code\u003e). Then, set the environment variables \u003ccode\u003eOMPI_MCA_pml=\"ucx\"\u003c/code\u003e and \u003ccode\u003eOMPI_MCA_osc=\"ucx\"\u003c/code\u003e before launching your MPI processes. Equivalently, you can set the MCA parameters in the command line: \u003ccode\u003empiexec --mca pml ucx --mca osc ucx ...\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNote that you might also need to set \u003ccode\u003eUCX_MEMTYPE_CACHE=n\u003c/code\u003e for CUDA awareness via UCX. Please consult UCX\u0027s documentation for detail.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1631407623.0 + "subscribers_count": 7, + "topics": [], + "updated_at": 1669905442.0 }, { "data_format": 2, - "description": "Command line ASCII boxes unlimited!", + "description": "Docker and Singularity images to run Biodiverse software", "filenames": [ - "1.3/Singularity" + "SingularityDef.def", + "SingularityDef_NoPerlbrew.def" ], - "full_name": "icaoberg/singularity-boxes", - "latest_release": "1.3", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-boxes/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-boxes/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-boxes/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-boxes/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a3f198a87deb5330effce45fa5f8a588e4f8f261b175507874e098ebd9458adf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d626f786573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3f198a87deb5330effce45fa5f8a588e4f8f261b175507874e098ebd9458adf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d626f786573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-boxes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/6d68d10803a8f101a59999b34c1be8b23084c82097bd1506f6b69eca3546ed7b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d626f786573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6d68d10803a8f101a59999b34c1be8b23084c82097bd1506f6b69eca3546ed7b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d626f786573\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-boxes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/699ba19f5376b37177283fe5acc1e075c4a846491fedbbedc3fc349b3693bc30/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d626f786573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/699ba19f5376b37177283fe5acc1e075c4a846491fedbbedc3fc349b3693bc30/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d626f786573\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-boxes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0e74e828eef6e3d5d7627cf27223de60469083957af4cdf436c4b767a7e870d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d626f786573\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e74e828eef6e3d5d7627cf27223de60469083957af4cdf436c4b767a7e870d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d626f786573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-boxes\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-boxes\" class=\"anchor\" href=\"#singularity-boxes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-boxes\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://boxes.thomasjensen.com/\" rel=\"nofollow\"\u003eboxes\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "vmikk/biodiverse-docker", + "latest_release": "v.1.0.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-biodiverse\" class=\"anchor\" aria-hidden=\"true\" href=\"#biodiverse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBiodiverse\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/vmikk/biodiverse-docker/blob/main/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1491d736cc21d494e4262c1cd8e116d4f865ff2f4bd64a2d79fa990778e324c8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f766d696b6b2f62696f646976657273652d646f636b6572\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/vmikk/biodiverse-docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vmikk/biodiverse\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ae1010d045b7a869f8b06b818b364a2ec0227e7f3d7fe3ab8cb4f280c386b732/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f486f737465642d446f636b65724875622d626c7565\" alt=\"Hosted_DockerHub\" data-canonical-src=\"https://img.shields.io/badge/Hosted-DockerHub-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/vmiks/gbif/biodiverse\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe9171fa5097d0f35af6c0988f42c6d6571880fc954aea1ee3a4259dc7603ae8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f486f737465642d53696e67756c61726974794c6962726172792d626c7565\" alt=\"Hosted_SingularityLibrary\" data-canonical-src=\"https://img.shields.io/badge/Hosted-SingularityLibrary-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains definition files the \u003ca href=\"https://shawnlaffan.github.io/biodiverse/\" rel=\"nofollow\"\u003eBiodiverse\u003c/a\u003e (Laffan et al., 2010) containers.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-docker-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker image\u003c/h1\u003e\n\u003cp\u003eTo build the \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image with Biodiverse run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag biodiverse --file Dockerfile_NoPerlbrew . \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eDockerfile_NoPerlbrew\u003c/code\u003e should be present in the current directory.\u003c/p\u003e\n\u003cp\u003eA ready-to-use image is available at \u003ca href=\"https://hub.docker.com/r/vmikk/biodiverse\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull vmikk/biodiverse:1.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity image\u003c/h1\u003e\n\u003cp\u003eTo build the \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image with Biodiverse run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build Biodiverse.sif SingularityDef_NoPerlbrew.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eSingularityDef_NoPerlbrew.def\u003c/code\u003e should be present in the current directory.\u003c/p\u003e\n\u003cp\u003eA ready-to-use image is available at the \u003ca href=\"https://cloud.sylabs.io/library/vmiks/gbif/biodiverse\" rel=\"nofollow\"\u003eSingularity Library\u003c/a\u003e and can be downloaded with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --arch amd64 library://vmiks/gbif/biodiverse:1-0-0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h1\u003e\n\u003cp\u003eLaffan SW, Lubarsky E, Rosauer DF (2010) Biodiverse, a tool for the spatial analysis of biological and related diversity. Ecography, 33: 643-647. \u003ca href=\"https://onlinelibrary.wiley.com/doi/10.1111/j.1600-0587.2010.06237.x\" rel=\"nofollow\"\u003eDOI: 10.1111/j.1600-0587.2010.06237.x\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [ - "singularity", - "utilities" + "biodiversity", + "docker", + "endemism", + "phylogenetic-diversity", + "singularity" ], - "updated_at": 1631084542.0 + "updated_at": 1650613175.0 }, { "data_format": 2, - "description": "The FASTX-Toolkit is a collection of command line tools for Short-Reads FASTA/FASTQ files preprocessing.", + "description": "Resource monitor that shows usage and stats for processor, memory, disks, network and processes.", "filenames": [ - "0.0.14/Singularity" + "1.0.68/Singularity" ], - "full_name": "pscedu/singularity-fastx-toolkit", - "latest_release": null, + "full_name": "pscedu/singularity-bpytop", + "latest_release": "v1.0.68", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bpytop/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bpytop/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bpytop/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bpytop/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6086d29389cadd3479a8523980b1ec9dd37bee0ee7f391f6e84294e38555ec6b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627079746f70\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6086d29389cadd3479a8523980b1ec9dd37bee0ee7f391f6e84294e38555ec6b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627079746f70\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bpytop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1dcdee2b45c87cb810de1b868a733f10f65a3a4a6ccf69522c58b89d6da6cae2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627079746f70\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1dcdee2b45c87cb810de1b868a733f10f65a3a4a6ccf69522c58b89d6da6cae2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627079746f70\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bpytop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/33f6a11260743e3ce7aaa7df6f4f4605ae789e6bac5ed0488e1e3b17338a5e64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627079746f70\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33f6a11260743e3ce7aaa7df6f4f4605ae789e6bac5ed0488e1e3b17338a5e64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627079746f70\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bpytop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c6693f25e42eaf97446bdc6be30ff9d42dab77028422ec377407a7c3f33c4635/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627079746f70\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6693f25e42eaf97446bdc6be30ff9d42dab77028422ec377407a7c3f33c4635/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627079746f70\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bpytop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-bpytop\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-bpytop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bpytop\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for bpytop.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebpytop\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bpytop/1.2.13\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bpytop\u003c/code\u003e as \u003ccode\u003e1.2.13.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [ "singularity", - "bioinformatics" + "utilities" ], - "updated_at": 1628888079.0 + "updated_at": 1670890527.0 }, { "data_format": 2, - "description": "Examples of Dockerfiles and Singularity recipes", + "description": null, "filenames": [ - "python-env/Singularity" + "controllers/PythonBlocks/downward/misc/releases/19.12/Singularity.19.12", + "controllers/PythonBlocks/downward/misc/releases/21.12/Singularity.21.12", + "controllers/PythonBlocks/downward/misc/releases/20.06/Singularity.20.06", + "controllers/PythonBlocks/downward/misc/releases/22.06/Singularity.22.06", + "controllers/PythonBlocks/downward/misc/releases/latest/Singularity", + "controllers/PythonBlocks/downward/misc/releases/19.06/Singularity.19.06" ], - "full_name": "kaczmarj/container-examples", + "full_name": "dylankrieg/block-stacking", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-container-examples\" class=\"anchor\" href=\"#container-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer examples\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-build-with-docker\" class=\"anchor\" href=\"#build-with-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild with docker\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-bedtoolsdockerfile\" class=\"anchor\" href=\"#bedtoolsdockerfile\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebedtools.Dockerfile\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag bedtools --file bedtools.Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-condadockerfile\" class=\"anchor\" href=\"#condadockerfile\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econda.Dockerfile\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag conda --file conda.Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-jupyter-notebook\" class=\"anchor\" href=\"#running-jupyter-notebook\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erunning jupyter notebook\u003c/h3\u003e\n\u003cp\u003eplease note that we set \u003ccode\u003e--ip 0.0.0.0\u003c/code\u003e. and we need to publish the port from the\ncontainer onto the host. otherwise, the port is only accessible inside the container\nand will not be seen by our web browser (which is outside of the container).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it --publish 8888:8888 conda --port 8888 --ip 0.0.0.0 --no-browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tensorflow24dockerfile\" class=\"anchor\" href=\"#tensorflow24dockerfile\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etensorflow24.Dockerfile\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensorflow:2.4 --file tensorflow24.Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-python-env\" class=\"anchor\" href=\"#python-env\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epython-env\u003c/h2\u003e\n\u003cp\u003ethis is an example of building a docker image for a python environment. that directory\nincludes a \u003ccode\u003erequirements.txt\u003c/code\u003e file, which lists dependencies. we copy that file into\nthe docker image when it is being built, and we install the python packages listed\nthere.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag mypyenv python-env\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-build-with-singularity\" class=\"anchor\" href=\"#build-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild with singularity\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-python-env-1\" class=\"anchor\" href=\"#python-env-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epython-env\u003c/h2\u003e\n\u003cp\u003ethis example builds a singularity image of \u003ccode\u003epython-env\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd python-env\nsudo singularity build python-env.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample of running the image. arguments after the image name are passed to the\nentrypoint. because our entrypoint is \u003ccode\u003epython\u003c/code\u003e, the command-line arguments are passed\nto that.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run python-env.sif -c \u0027import numpy; print(numpy.__version__)\u0027\n1.21.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand here\u0027s an example to show that users stay themselves in containers...\u003c/p\u003e\n\u003cp\u003eremember, just be yourself.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec python-env.sif whoami\njakub\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethis is not the case in docker. you need \u003ccode\u003esudo\u003c/code\u003e to run the containers, so inside the\ncontainer, you can be root. this is not ideal, especially on shared clusters.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1628778703.0 + "updated_at": 1668580222.0 }, { "data_format": 2, - "description": "R and bioinformatic packages Singularity container", + "description": null, "filenames": [ "Singularity" ], - "full_name": "sylvainschmitt/singularity-r-bioinfo", - "latest_release": "0.0.3", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-r-and-bioinformatic-packages-singularity-container\" class=\"anchor\" href=\"#r-and-bioinformatic-packages-singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR and bioinformatic packages Singularity container\u003c/h1\u003e\n\u003cp\u003eSylvain Schmitt\nAugust 6, 2021\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eR and bioinformatic packages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis container includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e 4.0.3\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etidyverse\u003c/code\u003e 1.3.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBiostrings\u003c/code\u003e 2.58.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evcfR\u003c/code\u003e 1.12.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evroom\u003c/code\u003e 1.3.2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecsv2sql\u003c/code\u003e 0.1.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ereshape2\u003c/code\u003e 1.4.4\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003etidyverse\u003c/code\u003e is an opinionated collection of R packages designed for\ndata science. All packages share an underlying design philosophy,\ngrammar, and data structures.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://www.tidyverse.org/\" rel=\"nofollow\"\u003ehttps://www.tidyverse.org/\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eBiostrings\u003c/code\u003e is a memory efficient string containers, string matching\nalgorithms, and other utilities, for fast manipulation of large\nbiological sequences or sets of sequences.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://bioconductor.org/packages/release/bioc/html/Biostrings.html\" rel=\"nofollow\"\u003ehttps://bioconductor.org/packages/release/bioc/html/Biostrings.html\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eThe R package \u003ccode\u003evcfR\u003c/code\u003e is a set of tools designed to read, write,\nmanipulate and analyze VCF data.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://knausb.github.io/vcfR_documentation/\" rel=\"nofollow\"\u003ehttps://knausb.github.io/vcfR_documentation/\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003evroom\u003c/code\u003e is the fastest delimited reader for R, 1.23 GB/sec.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://vroom.r-lib.org/\" rel=\"nofollow\"\u003ehttps://vroom.r-lib.org/\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ecsv2sql\u003c/code\u003e is a wrapper to convert csv files to sql database.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://github.com/kcf-jackson/csv2sql\"\u003ehttps://github.com/kcf-jackson/csv2sql\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ereshape2\u003c/code\u003e is an R package written by Hadley Wickham that makes it easy\nto transform data between wide and long formats.\u003c/p\u003e\n\u003cp\u003e[\u003ca href=\"https://seananderson.ca/2013/10/19/reshape/\" rel=\"nofollow\"\u003ehttps://seananderson.ca/2013/10/19/reshape/\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003eSingularity container based on the recipe:\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-r-bioinfo/blob/main/Singularity\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eImage singularity (V\u0026gt;=3.3) is automatically test and built and pushed\non the registry using\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/test.yml\"\u003etest.yml\u003c/a\u003e\n\u0026amp;\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template/blob/main/.github/workflows/builder.yml\"\u003ebuilder.yml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ebuild\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build Biostrings.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003epull\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull https://github.com/sylvainschmitt/singularity-r-bioinfo/releases/download/0.0.3/sylvainschmitt-singularity-r-bioinfo.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esnakemake\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s1\"\u003esingularity\u003c/span\u003e: \n \u003cspan class=\"pl-s\"\u003e\"https://github.com/sylvainschmitt/singularity-r-bioinfo/releases/download/0.0.3/sylvainschmitt-singularity-r-bioinfo.latest.sif\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "psadil/cat12_app", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cat12_app\" class=\"anchor\" aria-hidden=\"true\" href=\"#cat12_app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecat12_app\u003c/h1\u003e\n\u003cp\u003eBundle cat12 as prefect workflow\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install cat12_app\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecat12_app\u003c/code\u003e was created by Patrick Sadil. It is licensed under the terms of the MIT license.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecat12_app\u003c/code\u003e was created with \u003ca href=\"https://cookiecutter.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003e\u003ccode\u003ecookiecutter\u003c/code\u003e\u003c/a\u003e and the \u003ccode\u003epy-pkgs-cookiecutter\u003c/code\u003e \u003ca href=\"https://github.com/py-pkgs/py-pkgs-cookiecutter\"\u003etemplate\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1628683690.0 + "updated_at": 1668356057.0 }, { "data_format": 2, - "description": "Code and scripts for the bluebird bio technical exam", + "description": null, "filenames": [ - "question_1/RNAseq_DE_analysis/environments/Singularity" + "Singularity" ], - "full_name": "esha-joshi/bluebird_bio_exam", + "full_name": "pranavad/tipsytowers", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bluebird_bio_exam\" class=\"anchor\" href=\"#bluebird_bio_exam\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebluebird_bio_exam\u003c/h1\u003e\n\u003cp\u003eCode and scripts for the bluebird bio technical exam taken on 2021-07-21\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-question_1\" class=\"anchor\" href=\"#question_1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003equestion_1\u003c/h2\u003e\n\u003cp\u003eThis directory contains the Nextflow file, Singularity config files, R script for DE analysis and additional bash scripts for pre-processing for the implementation to analyze the cancer cell-lines. There is README describing the software requirements, dependencies and running of the program as well.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-question_2\" class=\"anchor\" href=\"#question_2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003equestion_2\u003c/h2\u003e\n\u003cp\u003eThis directory contains the R script for making the SQL queries to the UCSC database to generate a BED file for BRCA1 and BRCA2.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tipsytowers\" class=\"anchor\" aria-hidden=\"true\" href=\"#tipsytowers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etipsytowers\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1628654340.0 + "updated_at": 1665153756.0 }, { "data_format": 2, - "description": "nextflow pipeline for cellranger atac 10x analysis and qc", + "description": null, "filenames": [ - "container/Singularity_sc-atac-10x-builder" + "Singularity" ], - "full_name": "perllb/ctg-sc-atac-10x", + "full_name": "truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ctg-sc-atac-10x\" class=\"anchor\" href=\"#ctg-sc-atac-10x\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ectg-sc-atac-10x\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-preprocessing-of-10x-chromium-sc-atac-data-with-cellranger\" class=\"anchor\" href=\"#nextflow-pipeline-for-preprocessing-of-10x-chromium-sc-atac-data-with-cellranger\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline for preprocessing of 10x chromium sc-ATAC data with cellranger.\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDesigned to handle multiple projects in one sequencing run (but also works with only one project)\u003c/li\u003e\n\u003cli\u003eSupports mm10 and hg38 references, but can also be run with custom reference genome and annotation (must be added via nextflow.config). See custom genome below.\u003c/li\u003e\n\u003cli\u003eSupports nuclei samples\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUSAGE\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone and build the Singularity container for this pipeline: \u003ca href=\"https://github.com/perllb/ctg-sc-atac-10x/tree/master/container/ctg-sc-atac-10x\"\u003ehttps://github.com/perllb/ctg-sc-atac-10x/tree/master/container/ctg-sc-atac-10x\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit your samplesheet to match the example samplesheet. See section \u003ccode\u003eSampleSheet\u003c/code\u003e below\u003c/li\u003e\n\u003cli\u003eEdit the nextflow.config file to fit your project and system.\u003c/li\u003e\n\u003cli\u003eRun pipeline\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enohup nextflow run pipe-sc-atac-10x.nf \u0026gt; log.pipe-sc-atac-10x.txt \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input-files\" class=\"anchor\" href=\"#input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files\u003c/h2\u003e\n\u003cp\u003eThe following files must be in the runfolder to start pipeline successfully.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSamplesheet (\u003ccode\u003eCTG_SampleSheet.sc-atac-10x.csv\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-samplesheet-requirements\" class=\"anchor\" href=\"#samplesheet-requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSamplesheet requirements:\u003c/h3\u003e\n\u003cp\u003eNote: no header! only the rows shown below, starting with the column names.\nNote: Must be in comma-separated values format (.csv)\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSample_ID\u003c/th\u003e\n\u003cth\u003eindex\u003c/th\u003e\n\u003cth\u003eSample_Project\u003c/th\u003e\n\u003cth\u003eSample_Species\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSi1\u003c/td\u003e\n\u003ctd\u003eSI-GA-D9\u003c/td\u003e\n\u003ctd\u003e2021_012\u003c/td\u003e\n\u003ctd\u003ehuman\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSi2\u003c/td\u003e\n\u003ctd\u003eSI-GA-H9\u003c/td\u003e\n\u003ctd\u003e2021_012\u003c/td\u003e\n\u003ctd\u003ehuman\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSample1\u003c/td\u003e\n\u003ctd\u003eSI-GA-C9\u003c/td\u003e\n\u003ctd\u003e2021_013\u003c/td\u003e\n\u003ctd\u003emouse\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSample2\u003c/td\u003e\n\u003ctd\u003eSI-GA-C9\u003c/td\u003e\n\u003ctd\u003e2021_013\u003c/td\u003e\n\u003ctd\u003emouse\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-the-nf-pipeline-takes-the-following-columns-from-samplesheet-to-use-in-channels\" class=\"anchor\" href=\"#the-nf-pipeline-takes-the-following-columns-from-samplesheet-to-use-in-channels\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe nf-pipeline takes the following Columns from samplesheet to use in channels:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSample_ID\u003c/code\u003e : ID of sample. Sample_ID can only contain a-z, A-Z and \"_\". E.g space and hyphen (\"-\") are not allowed! If \u0027Sample_Name\u0027 is present, it will be ignored.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eindex\u003c/code\u003e : Must use index ID (10x ID) if dual index. For single index, the index sequence works too.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSample_Project\u003c/code\u003e : Project ID. E.g. 2021_033, 2021_192.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSample_Species\u003c/code\u003e : Only \u0027human\u0027/\u0027mouse\u0027/\u0027custom\u0027 are accepted. If species is not human or mouse, set \u0027custom\u0027. This custom reference genome has to be specified in the nextflow config file. See below how to edit the config file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-samplesheet-template\" class=\"anchor\" href=\"#samplesheet-template\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSamplesheet template\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eSamplesheet name \u003ccode\u003eCTG_SampleSheet.sc-atac-10x.csv\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eSample_ID,index,Sample_Project,Sample_Species \nSi1,Sn1,SI-GA-D9,2021_012,human \nSi2,Sn2,SI-GA-H9,2021_012,human \nSample1,S1,SI-GA-C9,2021_013,mouse \nSample2,S23,SI-GA-C9,2021_013,mouse\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline-steps\" class=\"anchor\" href=\"#pipeline-steps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline steps:\u003c/h2\u003e\n\u003cp\u003eCellranger version: cellranger atac v2.0.0\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eDemultiplexing\u003c/code\u003e (cellranger mkfastq): Converts raw basecalls to fastq, and demultiplex samples based on index (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/mkfastq\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/mkfastq\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFastQC\u003c/code\u003e: FastQC calculates quality metrics on raw sequencing reads (\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/a\u003e). MultiQC summarizes FastQC reports into one document (\u003ca href=\"https://multiqc.info/\" rel=\"nofollow\"\u003ehttps://multiqc.info/\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAlign\u003c/code\u003e + \u003ccode\u003eCounts\u003c/code\u003e (cellranger count): Aligns fastq files to reference genome, counts genes for each cell/barcode, perform secondary analysis such as clustering and generates the cloupe files (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAggregation\u003c/code\u003e (cellranger aggr): Automatically creates the input csv pointing to molecule_info.h5 files for each sample to be aggregated and executes aggregation (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/aggr\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/aggr\u003c/a\u003e). This is only run if there is more than one sample pr project.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCellranger count metrics\u003c/code\u003e (bin/ctg-sc-count-metrics-concat.py): Collects main count metrics (#cells and #reads/cell etc.) from each sample and collect in table\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emultiQC\u003c/code\u003e: Compile fastQC and cellranger count metrics in multiqc report\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emd5sum\u003c/code\u003e: md5sum of all generated files\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ectg-PROJ_ID-output\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eqc\u003c/code\u003e: Quality control output.\n\u003cul\u003e\n\u003cli\u003ecellranger metrics: Main metrics summarising the count / cell output\u003c/li\u003e\n\u003cli\u003efastqc output (\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003emultiqc output: Summarizing FastQC output and demultiplexing (\u003ca href=\"https://multiqc.info/\" rel=\"nofollow\"\u003ehttps://multiqc.info/\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efastq\u003c/code\u003e: Contains raw fastq files from cellranger mkfastq.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecount-cr\u003c/code\u003e: Cellranger count output. Here you find gene/cell count matrices, secondary analysis output, and more. See (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\u003c/a\u003e) for more information on the output files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esummaries\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eweb-summary files which provide an overview of essential metrics from the 10x run.\u003c/li\u003e\n\u003cli\u003ecloupe files which can be used to explore the data interactively in the Loupe browser (\u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/visualization/latest/what-is-loupe-cell-browser\" rel=\"nofollow\"\u003ehttps://support.10xgenomics.com/single-cell-atac/software/visualization/latest/what-is-loupe-cell-browser\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eaggregate\u003c/code\u003e:\n\u003cul\u003e\n\u003cli\u003eOutput from cellranger aggregation. This is only run if there is more than one sample pr project.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ectg-md5.PROJ_ID.txt\u003c/code\u003e: text file with md5sum recursively from output dir root\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-container\" class=\"anchor\" href=\"#container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-custom-genome\" class=\"anchor\" href=\"#custom-genome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustom genome\u003c/h2\u003e\n\u003cp\u003eIf custom genome (not hg38 or mm10) is used\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSet \"Sample_Species\" column to \u0027custom\u0027 in samplesheet:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSample_ID\u003c/th\u003e\n\u003cth\u003eSample_Name\u003c/th\u003e\n\u003cth\u003eindex\u003c/th\u003e\n\u003cth\u003eSample_Project\u003c/th\u003e\n\u003cth\u003eSample_Species\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSi1\u003c/td\u003e\n\u003ctd\u003eSn1\u003c/td\u003e\n\u003ctd\u003eSI-GA-D9\u003c/td\u003e\n\u003ctd\u003eproj_2021_012\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003ecustom\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSi2\u003c/td\u003e\n\u003ctd\u003eSn2\u003c/td\u003e\n\u003ctd\u003eSI-GA-H9\u003c/td\u003e\n\u003ctd\u003eproj_2021_012\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003ecustom\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eIn nextflow.config, set\n\u003ccode\u003ecustom_genome=/PATH/TO/CUSTOMGENOME\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-add-custom-genes-eg-reporters-to-cellranger-annotation\" class=\"anchor\" href=\"#add-custom-genes-eg-reporters-to-cellranger-annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdd custom genes (e.g. reporters) to cellranger annotation\u003c/h3\u003e\n\u003cp\u003eYou can use this script to add custom genes to the cellranger ref\n\u003ca href=\"https://github.com/perllb/ctg-cellranger-add2ref\"\u003ehttps://github.com/perllb/ctg-cellranger-add2ref\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003enextflow version 19.04.1.5072\u003c/li\u003e\n\u003cli\u003eSingularity (v 3.7.0-1.el7)\u003c/li\u003e\n\u003cli\u003ejava (openjdk version \"10.0.2\" 2018-07-17)\u003c/li\u003e\n\u003cli\u003eOpenJDK Runtime Environment Zulu10.3+5 (build 10.0.2+13)\u003c/li\u003e\n\u003cli\u003eOpenJDK 64-Bit Server VM Zulu10.3+5 (build 10.0.2+13, mixed mode)\u003c/li\u003e\n\u003cli\u003eSingularity container (\u003ca href=\"https://github.com/perllb/ctg-sc-atac-10x/blob/main/container/Singularity_sc-atac-10x-builder\"\u003ehttps://github.com/perllb/ctg-sc-atac-10x/blob/main/container/Singularity_sc-atac-10x-builder\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCellranger 10x ATAC or ARC references (e.g. refdata-cellranger-arc-GRCh38-2020-A-2.0.0 and refdata-cellranger-arc-mm10-2020-A-2.0.0)\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-a-miniconda-based--container-with-python-39-with-cudnn-81-cuda-112-with-tensorflow-gpu-28\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-miniconda-based--container-with-python-39-with-cudnn-81-cuda-112-with-tensorflow-gpu-28\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea miniconda based container with python 3.9 with cudnn 8.1 cuda 11.2 with tensorflow-gpu 2.8\u003c/h1\u003e\n\u003cp\u003edocker: \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv oras://ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-py39-cuda11.2-cudnn81-tf-gpu28:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1629907530.0 + "updated_at": 1667825933.0 }, { "data_format": 2, - "description": "Implements GA-DQN tuner which consists of a genetic algorithm that uses two deep Q-network agents.", + "description": null, "filenames": [ - "Singularity" + "Singularity-mpi.def", + "Singularity-test.def", + "Singularity.def" ], - "full_name": "lhutton1/ga-dqn-tuner", + "full_name": "lalilalalalu/fuchs-and-local-container", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-generating-high-performance-code-for-deep-learning-workloads-a-reinforcement-learning-based-approach\" class=\"anchor\" href=\"#generating-high-performance-code-for-deep-learning-workloads-a-reinforcement-learning-based-approach\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerating high-performance code for deep learning workloads: a reinforcement learning based approach.\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003eImplemented as part of a final year dissertation. Should not be considered for production use.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis project aims to apply reinforcement learning to auto-tuning in AutoTVM (part of the TVM machine learning compiler),\nin order to improve the experience of the end user. Currently, reinforcement learning is applied to the GATuner - a genetic algorithm\nthat repeatedly applies elitism, 2-point crossover and mutation to a population. Named \u003cstrong\u003eGA-DQN\u003c/strong\u003e, the new tuner uses two independent\ndeep Q-network (DQN)\u0027s that are applied to crossover and mutation. Crossover is completed by allowing DQN to suggest the point at\nwhich to crossover a gene, while, mutation is completed by allowing DQN to select which detail to randomly mutate. In addition, an evaluation\nframework is provided to assess the performance of GA-DQN.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/assets/ga-dqn-pipeline.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/assets/ga-dqn-pipeline.png\" alt=\"GA-DQN tuning pipeline\" title=\"GA-DQN tuning pipeline\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eTo use the tuner, TVM must be installed and visible within your python environment. Due to needing additional features not available in a released\nversion of TVM, a forked version of TVM is used which applies a small amount debugging code and a fix to the PyTorch front-end parser. A pinned\nversion is also used as TVM is mostly in a development stage and the API\u0027s used are unstable. Consequently, the GA-DQN tuner has only been tested\nwith this specific commit, along with small modifications ontop. The required version can be pulled from git like so:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive https://github.com/lhutton1/tvm.git tvm\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e tvm\ngit checkout autotvm-measure-remote-time\ngit checkout d2452502b9486a7993d9dec3d04e449efdd81cf7\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTVM also requires a number of dependencies such as: Cuda, Python3.6, LLVM, XGBoost (for the XGBTuner) and PyTorch (for the GA-DQN tuner). As such, we recommend using a containerised environment powered by Singularity. Similar to docker, an image must be built from which containers can be run based on the image. First install Singularity, then build the image using a simple script provided:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Singularity\u003c/span\u003e\nsudo wget -O- http://neuro.debian.net/lists/xenial.us-ca.full \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo tee /etc/apt/sources.list.d/neurodebian.sources.list \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\n sudo apt-key adv --recv-keys --keyserver hkp://pool.sks-keyservers.net:80 0xA5D32F012649A5A9 \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\n sudo apt-get update\n \nsudo apt-get install -y singularity-container\n \n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build image\u003c/span\u003e\n./create_image.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFrom this a container can be created and GA-DQN can be run from within this container using the presented shell:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./create_container.sh rl-tuner.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow in the shell, test your container works correctly by attempting to run the evaluation framework help prompt:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython driver.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote: This has been tested on a Ubuntu 18.04 setup and is not guaranteed to work with other operating systems. These scripts have also been tested on the University of Leeds HPC cluster, ARC.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: it is possible to build TVM and install its dependencies from scratch, although this is not recommended due to the number of packages required. The process required should be similar to that provided in \u003ccode\u003ecreate_image.sh\u003c/code\u003e script. However, it is recommended you create a new virtual environment for python in this process.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-rl-tuner\" class=\"anchor\" href=\"#rl-tuner\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRL Tuner\u003c/h2\u003e\n\u003cp\u003eGA-DQN is a tuner that combines advancements in reinforcement learning and the genetic algorithm tuner that currently exists in TVM. Two independent deep Q-network (DQN)\u0027s are used to suggest where to crossover genes and which detail of a gene to mutate.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-ga-tuner\" class=\"anchor\" href=\"#ga-tuner\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGA Tuner\u003c/h2\u003e\n\u003cp\u003eThe GA tuner is code obtained from the open source TVM compiler. It is here for convenience and to allow a small amount of debug code to be added so that it can be evaluated. This work is not my own.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-evaluation-framework-tools\" class=\"anchor\" href=\"#evaluation-framework-tools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvaluation framework (tools)\u003c/h2\u003e\n\u003cp\u003eProvides a series of tools and experiments to quickly test various tuning algorithms in AutoTVM. Use tune and benchmark commands on a series of pre-trained models to evaluate random, genetic algorithm, extreme gradient boost and GA-DQN algorithms. Use the experiment framework to evaluate various aspects of GA-DQN, with graphical monitoring.\u003c/p\u003e\n\u003cp\u003eA command line driver is provided for this framework:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython driver.py -m=tune -c=../config-example.json\npython driver.py -m=benchmark -c=../config-example.json\npython driver.py -m=experiment -c=../config-example.json\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-ga-dqn-pipeline-example\" class=\"anchor\" href=\"#ga-dqn-pipeline-example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGA-DQN pipeline example\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"/assets/ga-dqn-pipeline-example.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/assets/ga-dqn-pipeline-example.png\" alt=\"GA-DQN pipeline example\" title=\"GA-DQN pipeline example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1628544168.0 + "updated_at": 1667539488.0 }, { "data_format": 2, - "description": null, + "description": "OpenHPC recipe for NVIDIA\u0027s container maker", "filenames": [ - "Singularity" + "Singularity.def", + "container-backups/Singularity.def" ], - "full_name": "caoky8989/LVAD", + "full_name": "kaisucode/ohpc-container-recipe", "latest_release": null, + "readme": "\u003ch2\u003e\u003ca id=\"user-content-openhpc-container-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#openhpc-container-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenHPC Container Recipe\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec ohpc-recipe4.simg python /benchmark.py\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003enote: scale down the memory usage in \u003ccode\u003eVagrantfile\u003c/code\u003e if your system can\u0027t support the specified amount (4096)\u003c/p\u003e\n\u003cp\u003eThis is a container recipe for \u003ca href=\"https://github.com/NVIDIA/hpc-container-maker\"\u003eNVIDIA\u0027s HPC container maker\u003c/a\u003e. The base image is \u003ca href=\"https://quay.io/repository/ohpc/ohpc-gnu9\" rel=\"nofollow\"\u003eOpenHPC\u0027s development environment\u003c/a\u003e, with added Python, TensorFlow, and Keras support\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e[share]$ nvcc --version\nnvcc: NVIDIA (R) Cuda compiler driver\nCopyright (c) 2005-2015 NVIDIA Corporation\nBuilt on Tue_Aug_11_14:27:32_CDT_2015\nCuda compilation tools, release 7.5, V7.5.17\u003c/p\u003e\n\u003cp\u003emodule: loading \u0027cuda/7.5.18\u0027\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003etensorflow-2.6.0\ncuDNN 8.1\ncuda 11.2\u003c/p\u003e\n\u003cp\u003ein sbatch script,\nmodule load cuda/11.3.1\nmodule load cudnn/8.1.0\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.tensorflow.org/install/source\" rel=\"nofollow\"\u003ehttps://www.tensorflow.org/install/source\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage examples\u003c/h2\u003e\n\u003ch4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehpccm --recipe ohpc-recipe.py --format docker \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e Dockerfile\ndocker build -t ohpc-recipe -f Dockerfile \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/python_scripts/:/mnt/python_scripts/ -it --rm ohpc-recipe python3.7 /mnt/python_scripts/test.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h4\u003e\n\u003cp\u003eNote: For singularity builds, root access is required. If you are on MacOS or Windows, please check out the instructions \u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html#mac\" rel=\"nofollow\"\u003ehere\u003c/a\u003e on how to use Vagrant to build a Singularity virtual machine\u003c/p\u003e\n\u003cp\u003ehpccm --recipe ohpc-recipe.py --singularity-version=3.8 --format singularity \u0026gt; Singularity.def\nversion 3.8 for multistage\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehpccm --recipe ohpc-recipe.py --format singularity \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e Singularity.def\nsudo singularity build ohpc-recipe.simg Singularity.def\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --nv ohpc-recipe.simg python3 \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/python_scripts/benchmark.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn alternate solution is to build using Docker, then rebuild as singularity\n\u003ccode\u003esingularity build ohpc-recipe.simg docker://kevinhsuk/ohpc-recipe\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1628703776.0 + "updated_at": 1667364910.0 }, { "data_format": 2, - "description": "Multi-Label Multi/Single-Class Image Segmentation", + "description": null, "filenames": [ - "Singularity" + "waveunet/Singularity" ], - "full_name": "kbronik2017/Multi_Label_Segmentation_UCL", + "full_name": "bbaysal/BSS", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/UCLBrain/MSLS/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f9cab98cc8f1052f0c0096b8b462cf9b2280a706b1adc0895d8a4859f3743314/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f55434c427261696e2f4d534c53\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/network\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6c7a087c43d2a65a6ee22ec8ce2e004a29212c4b9619b117f4b2bbabf98a6c5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f55434c427261696e2f4d534c53\" alt=\"GitHub forks\" data-canonical-src=\"https://img.shields.io/github/forks/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/10fab859502fd8c9b24dde937e50fecba7449e94ea057774713238ab3ec754fc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f55434c427261696e2f4d534c53\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d525d837b03e3b7a24b6322fc2197a134fa12e6a96188e5f18d6cd92236aa47/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f55434c427261696e2f4d534c53\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/UCLBrain/MSLS\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-multi-label-multisingle-class-image-segmentation\" class=\"anchor\" href=\"#multi-label-multisingle-class-image-segmentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-Label Multi/Single-Class Image Segmentation\u003c/h1\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/diag.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/diag.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-publication\" class=\"anchor\" href=\"#publication\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublication\u003c/h1\u003e\n\u003cp\u003eLe Zhang, Ryutaro Tanno, Kevin Bronik, Chen Jin, Parashkev Nachev, Frederik Barkhof, Olga Ciccarelli, and Daniel C. Alexander, Learning to Segment When Experts Disagree, International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI). Springer, Cham, 2020.\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/Miccai_2020_abs.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg align=\"center\" height=\"1000\" src=\"images/Miccai_2020_abs.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\n \n \u003cp\u003eClick here to see full pdf file: \u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/MICCAI_2020.pdf\"\u003eLink to PDF\u003c/a\u003e\u003c/p\u003e\n \n\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-gui-program\" class=\"anchor\" href=\"#running-the-gui-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the GUI Program!\u003c/h1\u003e\n\u003cp\u003eFirst, user needs to install Anaconda \u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003ehttps://www.anaconda.com/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda env create -f conda_environment_Training_Inference.yml \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efinally\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python Training_Inference_GUI.py \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/GUI.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/GUI.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-program-from-the-command-line\" class=\"anchor\" href=\"#running-the-program-from-the-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Program from the command line!\u003c/h1\u003e\n\u003cp\u003eFirst\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen for training\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Training_without_GUI.py [or annotation_network_Training_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efor testing\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Inference_without_GUI.py [or annotation_network_Inference_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-testing-the-program\" class=\"anchor\" href=\"#testing-the-program\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting the Program!\u003c/h1\u003e\n\u003cp\u003eExamples of Training and Testing subjects can be found in: \u003ca href=\"https://github.com/UCLBrain/MSLS/tree/master/examples\"\u003ehttps://github.com/UCLBrain/MSLS/tree/master/examples\u003c/a\u003e (which will allow users to quickly and easily train and test the program)\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/bin_seg_ex.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/bin_seg_ex.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e..................................................................................................................................................................\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"images/multi_seg_ex.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"510\" src=\"images/multi_seg_ex.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-bss\" class=\"anchor\" aria-hidden=\"true\" href=\"#bss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBSS\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "segmentation", - "multi-label" - ], - "updated_at": 1628544698.0 + "topics": [], + "updated_at": 1666637248.0 }, { "data_format": 2, - "description": "Markdown Files to Explain Running anvi\u0027o in Singularity", + "description": null, "filenames": [ - "anvio-pangenomics/Singularity" + "bc3.10-rs125042r362/Singularity", + "bc3.12-r405rs125/Singularity", + "bc3.15-r421tv132rs2022072.576/Singularity" ], - "full_name": "rbartelme/anvio-singularity", + "full_name": "yh549848/singularity-rstudio-rnaseq", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-running-anvio-in-singularity-containers\" class=\"anchor\" href=\"#running-anvio-in-singularity-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning anvi\u0027o in Singularity containers\u003c/h1\u003e\n\u003cp\u003eRyan Bartelme, PhD\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-preparation\" class=\"anchor\" href=\"#preparation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparation\u003c/h2\u003e\n\u003cp\u003eIf you want to test anvi\u0027o on an HPC system, here are a few strategies:\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pulling-anvio-docker-image-into-singularity\" class=\"anchor\" href=\"#pulling-anvio-docker-image-into-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePulling anvi\u0027o docker image into Singularity\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eStart by using singularity to pull the latest version of the anvi\u0027o image from dockerhub:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull docker://meren/anvio\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAfter seeing the standard output of the docker pull command, Singularity will print out something like:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eINFO: Creating SIF file...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAnd the \u003ccode\u003e*.sif\u003c/code\u003e file should appear in the directory:\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls\nanvio_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThe latest docker image of anvi\u0027o will \u003cstrong\u003eNOT\u003c/strong\u003e have the databases configured. This is also an opportune time to create your own customized docker image from the \u003ccode\u003emeren/anvio:latest\u003c/code\u003e docker image tag.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-making-your-own-dockerfile-to-customize-your-anvio-runtime\" class=\"anchor\" href=\"#making-your-own-dockerfile-to-customize-your-anvio-runtime\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaking your own Dockerfile to customize your anvi\u0027o runtime\u003c/h2\u003e\n\u003cp\u003eSee an example: \u003ca href=\"anvio-dbconfig/Dockerfile\"\u003eDockerfile\u003c/a\u003e this runs through the database configurations for anvi\u0027o. (As of 03-25-21 this does not properly compile the 3d structure db\u0027s)\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuring-anvio-singularity-containers\" class=\"anchor\" href=\"#configuring-anvio-singularity-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguring anvi\u0027o Singularity containers\u003c/h2\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-docker-container-image-customization\" class=\"anchor\" href=\"#docker-container-image-customization\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker container image customization\u003c/h3\u003e\n\u003cp\u003eIn this case I used a \u003ca href=\"anvio-pangenomics/Dockerfile\"\u003eDockerfile\u003c/a\u003e, where I am building off the \u003ccode\u003eanvio-dbconfig\u003c/code\u003e \u003ca href=\"anvio-dbconfig/Dockerfile\"\u003eimage\u003c/a\u003e. The modifications include an installation of \u003ca href=\"https://github.com/kblin/ncbi-genome-download\"\u003encbi-genome-download\u003c/a\u003e using the anvio conda environment \u003ca href=\"https://github.com/rbartelme/anvio-singularity/blob/bacaaec5130fdb188647c4cdac72aaa275e277b8/anvio-pangenomics/Dockerfile#L4\"\u003epip\u003c/a\u003e and setting the \u003ca href=\"anvio-pangenomics/entrypoint.sh\"\u003eentrypoint\u003c/a\u003e to the conda environment of anvio for the docker runtime. Note \u003ca href=\"anvio-pangenomics/profile\"\u003eprofile\u003c/a\u003e is included to make sure the container sources the \u003ccode\u003e.bashrc\u003c/code\u003e for the conda path.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-building-singularity-images\" class=\"anchor\" href=\"#building-singularity-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Singularity images\u003c/h3\u003e\n\u003cp\u003eOur local cluster singularity version:\u003c/p\u003e\n\u003cpre lang=\"[rbartelme@gpu06\"\u003e\u003ccode\u003esingularity-ce version 3.8.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eBuilding from the Docker image above:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e \u003cem\u003eThis required \u003ccode\u003esudo su\u003c/code\u003e on our local cluster, which I have access to, this has not been tested with \u003ccode\u003e--fakeroot\u003c/code\u003e yet.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo su\u003c/code\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity build statement, using Singularity \u003ca href=\"anvio-pangenomics/Singularity\"\u003erecipe\u003c/a\u003e:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity build anvio-pangenomics.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet ownership of Singularity \u003ccode\u003e*.sif\u003c/code\u003e file and set group permissions.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo chown rbartelme:iplant-everyone anvio-pangenomics.sif\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRead up on job scheduling with your HPC\u0027s IT team documentation\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-example-with-slurm-singularity-and-snakemake\" class=\"anchor\" href=\"#example-with-slurm-singularity-and-snakemake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample with SLURM, Singularity, and Snakemake\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-snakemake-workflows-with-singularity\" class=\"anchor\" href=\"#snakemake-workflows-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakemake Workflows with Singularity\u003c/h3\u003e\n\u003cp\u003eAnvi\u0027o has awesome snakemake \u003ca href=\"\"\u003eworkflows\u003c/a\u003e built in! This is the \"end-to-end\" approach for all your HPC or cloud compute needs.\u003c/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-comparative-genomics\" class=\"anchor\" href=\"#comparative-genomics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComparative Genomics\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eExample json input for Comparative Genomics Workflow:\u003c/strong\u003e\u003c/p\u003e\n\u003chr\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1628704470.0 + "updated_at": 1665633331.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity", + "Singularity.0.4" ], - "full_name": "truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-docker-centos8-warewulf4-builder-\" class=\"anchor\" href=\"#singularity-docker-centos8-warewulf4-builder-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-centos8-warewulf4-builder \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003ewarewulf4 builder container based on a CentOS 8 x86_64 docker image built from github actions\u003c/p\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why\" class=\"anchor\" href=\"#why\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eprovide a minimal builder for warewulf4\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti ghcr.io/truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos8-warewulf4-builder:latest rpmbuild -ta warewulf-4.2.0.tar.gz \n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "Altava/tfd_time", + "latest_release": "0.4", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-temporalfastdownward\" class=\"anchor\" aria-hidden=\"true\" href=\"#temporalfastdownward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemporalFastDownward\u003c/h1\u003e\n\u003cp\u003eThe version of Temporal Fast Downward.\nThis version has been ported to Python3 (tested with Python3.8)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInformation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://gki.informatik.uni-freiburg.de/tools/tfd/\" rel=\"nofollow\"\u003ehttp://gki.informatik.uni-freiburg.de/tools/tfd/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e$ ./build\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003ePatrick Eyerich, Robert Mattm\u00fcller and Gabriele R\u00f6ger.\nUsing the Context-enhanced Additive Heuristic for Temporal and Numeric Planning.\nIn Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS 2009), 2009.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1635198514.0 + "updated_at": 1665578634.0 }, { "data_format": 2, "description": null, "filenames": [ - "rstudio_server_app/Singularity" + "Singularity", + "IHEC/Singularity.ihec" ], - "full_name": "CHPC-UofU/OOD-pe-apps", + "full_name": "pranit123-hub/gemBS", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ood-apps\" class=\"anchor\" href=\"#ood-apps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOOD-apps\u003c/h1\u003e\n\u003cp\u003eRepository of CHPC\u0027s PE Open OnDemand apps\u003c/p\u003e\n\u003cp\u003eThis is a repository of interactive apps used at CHPC Protected Environment with Open OnDemand.\u003c/p\u003e\n\u003cp\u003eEach subdirectory has its own README.md which contains information about this particular app.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-hidden=\"true\" href=\"#news\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNews\u003c/h1\u003e\n\u003cp\u003eFirst release of gemBS-rs, a complete rewrite of the gemBS pipeline (apart from the mapper) in Rust bringing increased\nstability while maintaining the high performance of gemBS: \u003ca href=\"https://github.com/heathsc/gemBS-rs.git\"\u003ehttps://github.com/heathsc/gemBS-rs.git\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-gembs\" class=\"anchor\" aria-hidden=\"true\" href=\"#gembs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egemBS\u003c/h1\u003e\n\u003cp\u003egemBS is a high performance bioinformatic pipeline designed for highthroughput analysis\nof DNA methylation data from whole genome bisulfites sequencing data\n(WGBS). It combines GEM3, a high performance read aligner and\nbs_call, a high performance variant and methyation caller, into a streamlined and efficient pipeline for\nbisulfite sueqnce analysis.\u003c/p\u003e\n\u003cp\u003eThe manuscript describing the pipeline is available \u003ca href=\"https://www.biorxiv.org/content/early/2017/10/11/201988\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-hidden=\"true\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003egemBS is licensed under GPL.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h2\u003e\n\u003cp\u003eUse \u003ccode\u003egit clone --recursive\u003c/code\u003e to retrieve the complete source code including the code from external projects such as \u003ccode\u003ebs_call\u003c/code\u003e and \u003ccode\u003egem3-mapper\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBefore starting the installation of gemBS, you will need to install\nor check the installation of several packages.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ea) gcc with development libraries\nb) python3, pip3, matplotlib, multiprocess\nc) zlib, lzma, openssl, libcurl, libncurses, wget, pigz\u003c/p\u003e\n\u003cp\u003eIf you are working on a clean (fairly recent) Ubuntu installation, you\ncan install everything required with the followiwg commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get update\nsudo apt-get install -y python3 build-essential git python3-pip wget pigz\nsudo apt-get install -y zlib1g-dev libbz2-dev\nsudo apt-get install -y libncurses5-dev liblzma-dev libssl-dev libcurl4-openssl-dev\npip3 install matplotlib multiprocess\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the gemBS distribution if you haven\u0027t already done so:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS.git\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse python install command:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo install to the standard system location (i.e., so that all users\ncan use gemBS):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e``python3 setup.py install``\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install to the user\u0027s home directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e``python3 setup.py install --user``\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-check-your-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#check-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheck your installation\u003c/h2\u003e\n\u003cp\u003eFor checking your installation follow this\n\u003ca href=\"http://statgen.cnag.cat/gemBS/v3/UserGuide/_build/html/example.html\" rel=\"nofollow\"\u003eworked example\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eDocumentation can be found at\n\u003ca href=\"http://statgen.cnag.cat/gemBS/v3/UserGuide/_build/html/index.html\" rel=\"nofollow\"\u003egemBS\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-hidden=\"true\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChangelog:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e3.5.5 Fix logging bug caused by trimming change in 3.5.3\n3.5.4 Fix bug in the output of strand specific cpg txt files (not\n encode Bed files) where the \u0027C\u0027 entry was not being printed\n3.5.3 Allow for read end specific trimming in bs_call\n3.5.3 Enable range checks and asserts in bs_call all target; add bs_call debug target\n3.5.2 Correct problems with gcc10. Move to htslib/samtools/bcftools version 1.11\n3.5.1 Check if C compiler requires --std=c99 flag for standards conformant behaviour\n3.5.1 Make sure bgzip is copied correctly during installation\n3.5.0 Make bs_call process contig pools from largest to smallest (this change alters the sqlite db format so\n if you have a previously started gemBS run you should (a) remove the .gemBS directory, (b) redo the\n \u0027gemBS prepare\u0027 step to recreate the db file and (3) run \u0027gemBS db-sync\u0027. \n3.5.0 Switch bs_call and snpxtr to use the new dbSNP index format\n3.5.0 Add ability of dbSNP to read the new JSON and VCF dbSNP format files\n that are now used for human and non-human species respectively\n3.5.0 Add multithreading to dbSNP_idx\n3.5.0 Change format of dbSNP index to allow (a) efficient loading\n of SNP data for individual contigs and (b) parallel index creation \n3.5.0 Rewrite mextr and snpxtr as standalone tools rather than\n bcftools plugins. Now multithreaded and (relatively) memoryefficient\n3.5.0 Replace bedToBigBed and wigToBigWig to reduce memory usage\n and improve speed\n3.4.5 Fix crash when using the -k (keep-mismatch) flag, and fix rare hangs at end of processing\n3.4.4 Sort input bcf files to bcftools concat stage to ensure reproducibility.\n3.4.4 Add extra sort keys when generating pools to ensure stability of pool membership in the event of multiple contigs\n having the same size\n3.4.3 Remove calculation of the goodness of filter (GOF) as this is expensive, non-standard and unreliable. Removing this\n removes the dependency on GSL.\n3.4.3 Add autodetection of output format to bs_call (unless explicitly specified on the command line)\n3.4.2 Add CRAM support (via make_cram option in configuration file)\n3.4.1 Add benchmark-mode that does not write date or program version numbers into SAM/BAM or VCF/BCF files\n Switch to samtools, bcftools and htslib v1.10\n3.4.0 Move to new bs_call version (2.1.0) which is more efficient\n in memory use and can read BAMs and write BCFs natively.\n The new bs_call requires a faidx indexed reference, so gemBS\n no creates this during indexing.\n3.4.0 Add switches to give more control to threads and memory\n usage in mapping and calling stages\n3.3.3 Remove legacy pathway for config files with no header line (fix issue \u0027error in gemBS index #65)\n3.3.2 Fix error where header line for wig files could be omitted\n3.3.2 Fix generation of non_cpg files\n3.3.1 Fix Attribute error bug due to not checking if conversion is a list\n3.3.0 Make new release for IHEC\n3.3.0 Switch conversion default in IHEC_standard configuration to 0.01,0.05 rather than auto, which can give odd results if conversion controls not present or not working correctly\n3.3.0 Fix bug where conversion parameters could be ignored\n3.2.13 Fix formatting bug in mextr with multiple samples (not triggered in normal gemBS use)\n3.2.12 Ensure that conversion statistics are correctly calculated for non-stranded or reverse conversion protocols\n3.2.11 Introduce reverse_conversion option for mapping where read 1 is G2A converted and read 2 is C2T converted\n3.2.10 Correct regex patch for single end reads\n3.2.9 Update Singularity and Dockerfile recipes to allow kemp utils to be built correctly\n3.2.9 Make setup.py and gemBS/commands.py read the version information from gemBS/version.py, so ensuring consistency\n3.2.9 Fix bug added in last version where options in config file were not being taken into account\n3.2.8 Fix mis specification errors in long options for mextr. \n3.2.8 Fix bug where mextr (methyl extract plugin for bcftools) would crash if cpg output option was not set.\n3.2.7 Apply patches for bugs in handling single end reads (suggested by I. Moghul)\n3.2.7 Changed regex for filenames to make it more general (suggested by I. Moghul)\n3.2.7 Fixed bug (reported by chhylp123) where zero arguments to some options were being ignored\n3.2.6 Cleaned up compilation and cleaning of gemBS tools\n3.2.6 Fixed python error if either the over conversion reference sequence was not defined\n3.2.6 Added check in bs_call that conversion parameters are valid (between 0 and 1)\n3.2.6 Perform more stringent sanity checking on conversion vaalues when autocomputed by gemBS\n3.2.6 Use --diasble-lzma configuration flag for samtools and bcftools as we don\u0027t need it and it removes an unneccesary dependency\n3.2.6 Add install options --disable-cuda (on by default) and --enable-cuda that affect GEM3 comppilation\n3.2.6 Bug fix with incorrect handling of duplicate reads\n3.2.5 Minor bug fix - correct error with non-paired end non-bisulfite reads\n3.2.4 Modify the bisulfite processing in gem-mapper to be more efficient (in particular for the non-stranded option)\n3.2.4 Modify gemBS to use the new conversion options for gem-mapper\n3.2.4 Switch gem-mapper to use option --underconversion-sequence and --overconversion-sequence rather than --underconversion_sequence to be consistent with other options\n3.2.3 Fixed bug if conversion parameters were not set\n3.2.2 Rework non-stranded mode so that both possible conversions are tried and the results merged\n3.2.2 Fix bug where non-stranded flag was not being passed to mapper in paired end mode\n3.2.1 Move warning message from bscall from stdout to stderr\n3.2.1 Switch Singularity build to use Ubuntu 16.04 rather than 18.04 to allow the image to work in CentOS 6 (Docker build changed as well to keep the two in sync)\n3.2.1 Fix undeclared variable bugs and missing --ignore-deps option in merge-bcfs\n3.2.1 Add default for dbSNP_index if dbSNP_files is set\n3.2.1 Add gsl-path install option\n3.2.0 Make new release\n3.1.0 Make installation process more modular. Allow for sub-installs\n3.1.0 Add support for reading config from ${index_dir}/gemBS.json if it exists\n3.1.0 Add --reference-bias option to mextr and gemBS extract\n3.1.0 Add support for non-bisulfite mapping of individual datasets\n3.1.0 Allow white space in variable values\n3.1.0 Allow fallback to gzip if pigz not present\n3.1.0 Add --dry-run, --json, --ignore-db and --ignore-dep to extract command\n3.1.0 Add --ignore-dep option to call and merge-bcfs commands\n3.1.0 Add SNP extraction function to extract command\n3.0 Make v3.0 release\n3.0 Merge with master branch.\n3.0 Bump samtools sort memory limit to 2G\n3.0 Add extra_references option for reference generation\n3.0 Allow input files to mapping to be shell commands\n3.0 Add links to documentation\n3.0 Upload new yeast example and add documentation\n3.0 Add --dir option to gemBS\n3.0 Add --ignore-db options for --dry-run / --json\n3.0 Add --json output option for dry runs\n3.0 Update help text to match new functions\n3.0 Introduce standard analysis configurations stored within distribution\n3.0 Switch gem3-mapper distribution to gembs branch on official gem3-mapper repo\n3.0 Removal of incomplete files and roll back of db in the event of pipeline failure\n3.0 Automatic removal of individual BAMs and BCFs after successful merging\n3.0 Prevent pipelines hanging in event of failure\n3.0 Generate ENCODE bed and bigbed files\n3.0 Switch to python 3\n3.0 Switch to mextr for BCF filtering\n3.0 Include fetch and build of samtools / bcftools during build process\n3.0 Add dry-run capability to map and call commands\n3.0 Introduce contig pools to automatically group small contigs\n3.0 Automatic generation of contig.size files from index build\n3.0 Allow use of in memory sqlite3 db as an option\n3.0 Allow multiple instances of gemBS (possible on different hosts) to work \n simultaneously on the same analysis\n3.0 Reduce and simply commands\n3.0 Add Dockerfile\n3.0 Add multi-threading and multi-processing options for most commands\n3.0 Use sqlite3 to track progress of analyses, file paths etc.\n3.0 Added more flexible configuration options (new csv format + new configuration file)\n3.0 Remove test dataset from distribution (distribute from web site)\n2.1.0 Ensure commands run during pipeline come from installation\n2.1.0 Added Singularity build recipe\n2.1.0 Add new command gemBS direct-mapping\n2.1.0 Fixed Makefile clean in tools\n2.0.2 Fixed bug related with the percentage of High Quality Variant in Variants summary report.\n2.0.2 Check temporary directory existence.\n2.0.2 Fixed QualityNonRefCpg sample name in png image.\n2.0.2 Fixed mapper issues related with aligning performace.\n2.0.2 Fixed arguments for Under/Over Conversion sequence name in gem3-mapper\n2.0.1 On bscall repository, fixed argument -k about discarded reads that do not form proper pairs.\n2.0 Check tmp folder before starting mapping process.\n2.0 Added Left and Right Trimming optional arguments to gemBS bscall.\n2.0 Added GC Coverage correlation value to BS Call Stats Summary.\n2.0 Fixed error when reporting complete path to not found bam files.\n2.0 Fixed iteration over sampleBams dictionary in MergeAll method.\n2.0 Updated: Avoid redo indexing when merging just one file.\n2.0 Changed conversion formula.\n2.0 Added parameter for dbSNP.\n2.0 Added threads to bscall.\n2.0 Removed CpGs reports. Already done from bscall report.\n2.0 Fixed bs_call makefile for the gcc to be used.\n2.0 New bscall version. Generates JSON report.\n2.0 Removed gemBS options snp-stats,cpg-report,cpg-stats.\n2.0 Added summary report from the bs_call json stats\n2.0 New BSCall Report. From bscall son file generates three types of reports:\n Mapping and Coverage Report\n Bs-Genotypes Calls Report\n Methylation Statistics report\n1.7 Added non stranded read conversion parameter\n1.7 Fixed SE crash when estimating overlapped bases.\n1.7 Fixed gem-index (gem3) to follow fastq and SAM specifications. \n Modified gem3-mapper repository external module.\n New external module https://github.com/heathsc/gem3-mapper.git\n1.7 Fixed threads parameter to samtools merge\n1.7 Fixed threads parameter to gem-mapper\n1.7 Removed Indels Field on Variants Report.\n1.7 Added Overlapping Bases at Mapping Report\n1.7 Modified Base Counts Overall, removed Base Counts general and Base Counts Overall\n1.7 New Dinucleotide CpGs Report\n New table dinucleotide stats\n New plots for Informative Reads and CpGs\n Methylation levels plots for different types of CpGs\n Summary Table\n1.7 New Readme file to inform about report test\n1.7 New basic statis table for Variants Report\n1.7 Removed parameter -r (reference length) parameter for mapping reports command (gemBS bsMap).\n1.6 New CpGs Density plot, include box plos, bar plot and fitting curve\n1.6 Change name at CpG report:\n \"Heterozygous\" for \"Alternative CX\"\n \"De Novo CpGs Methylation Status\" for \"Non Reference CpGs\"\n \"CpGs with SNP\" for \"SNPs (CX) at Reference CpGs\"\n1.6 CpGs Report Simplified to Q\u0026gt;20\n1.6 BigWig Default parameters for filtering CpG per a given quality and a total number of supported informative reads \n1.5 Initial Release \n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-developers\" class=\"anchor\" aria-hidden=\"true\" href=\"#developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopers\u003c/h2\u003e\n\u003cp\u003egemBS:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarcos Fernandez-Callejo - \u003ca href=\"mailto:marcos.fernandez@cnag.crg.eu\"\u003emarcos.fernandez@cnag.crg.eu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimon Heath - \u003ca href=\"mailto:simon.heath@gmail.com\"\u003esimon.heath@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003egem mapper:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSantiago Marco-Sola - \u003ca href=\"mailto:santiagomsola@gmail.com\"\u003esantiagomsola@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ebisulfite caller and filtering:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimon Heath - \u003ca href=\"mailto:simon.heath@gmail.com\"\u003esimon.heath@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1631895259.0 + "updated_at": 1664976329.0 }, { "data_format": 2, - "description": null, + "description": "This is a github MIRROR of the main ocellaris repo on bitbucket (https://bitbucket.org/ocellarisproject/ocellaris). NO pull request or issues should go to this repo, please! This repository is only here to support Singularity Hub which lacks bitbucket support. The code in this repository may be severely out of date! It is synced with bitbucket manually and may be months or years behind!", "filenames": [ - "Singularity" + "containers/Singularity" ], - "full_name": "vigo332/singularity-rstudio-r4", + "full_name": "TormodLandet/Ocellaris", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" href=\"#singularity-rstudio-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity RStudio Server\u003c/h1\u003e\n\u003cp\u003eR 4.0.3\nRStudio 1.3.1903\u003c/p\u003e\n\u003cp\u003eBased on repo \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e\nSingularity image for \u003ca href=\"https://www.rstudio.com/products/rstudio/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e. It was built on top of the base\nSingularity image \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build\" class=\"anchor\" href=\"#build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-rstudio.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-rstudio.simg rstudio.def\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-deploy\" class=\"anchor\" href=\"#deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploy\u003c/h2\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --arch amd64 library://vigo332/default/singularity-rstudio-r4:v0.01\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run\" class=\"anchor\" href=\"#run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" href=\"#rstudio-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRStudio Server\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003erserver\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app rserver singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app rserver singularity-rstudio.simg --help\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecommand-line options:\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003everify:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --verify-installation arg (=0) verify the current installation\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eserver:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-working-dir arg (=/) program working directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-user arg (=rstudio-server) program user\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-daemonize arg (=0) run program as daemon\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-app-armor-enabled arg (=1) is app armor enabled for this session\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-set-umask arg (=1) set the umask to 022 on startup\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-simple-password-authentication\" class=\"anchor\" href=\"#simple-password-authentication\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimple Password Authentication\u003c/h4\u003e\n\u003cp\u003eTo secure the RStudio Server you will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLaunch the container with the environment variable \u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e set to\na password of your choosing.\u003c/li\u003e\n\u003cli\u003eLaunch the \u003ccode\u003erserver\u003c/code\u003e command with the PAM helper script \u003ccode\u003erstudio_auth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAn example is given as:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRSTUDIO_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e singularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path=pam-helper \\\n --server-data-dir=/tmp\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow when you attempt to access the RStudio Server you will be presented with a\nlog in form. You can log in with your current user name and password you set in\n\u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-ldap-authentication----to-be-verified\" class=\"anchor\" href=\"#ldap-authentication----to-be-verified\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLDAP Authentication -- To be verified\u003c/h4\u003e\n\u003cp\u003eAnother option is using an LDAP (or Active Directory) server for\nauthentication. Configuration of the LDAP authentication script \u003ccode\u003eldap_auth\u003c/code\u003e is\nhandled through the following environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_HOST\u003c/code\u003e - the host name of the LDAP server\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_USER_DN\u003c/code\u003e - the formatted string (where \u003ccode\u003e%s\u003c/code\u003e is replaced with the\nusername supplied during log in) of the bind DN used for LDAP authentication\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e - the file containing the CA certificates used by\nthe LDAP server (default: use system CA certificates)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn example for an LDAP server with signed SSL certificate from a trusted CA:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nsingularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn example for an LDAP server with a self-signed SSL certificate:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_CERT_FILE=/ca-certs.pem\nsingularity run \\\n --bind /path/to/ca-certs.pem:/ca-certs.pem \\\n singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that we had to bind mount the CA certificates file from the host machine\ninto the container and specify the container\u0027s path in \u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e (not\nthe host\u0027s path).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-r-and-rscript\" class=\"anchor\" href=\"#r-and-rscript\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR and Rscript\u003c/h3\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e for more information on how to run \u003ccode\u003eR\u003c/code\u003e and\n\u003ccode\u003eRscript\u003c/code\u003e from within this Singularity image.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1631568351.0 + "updated_at": 1553974960.0 }, { "data_format": 2, @@ -15618,190 +15065,179 @@ var data = "filenames": [ "Singularity" ], - "full_name": "cclerget/test-wh", + "full_name": "thehyve/singularity-jupyter", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-jupyter\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1547670364.0 + "updated_at": 1674903596.0 }, { "data_format": 2, - "description": "D\u00e9mo conteneur PRECIS", + "description": " Build for docker and singularity containers for temporal lobe segmentation", "filenames": [ + "Singularity.3.1.0", "Singularity" ], - "full_name": "cclerget/demo-precis", + "full_name": "VUIIS/Temporal_Lobe_app", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-temporal_lobe_app\" class=\"anchor\" aria-hidden=\"true\" href=\"#temporal_lobe_app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemporal_Lobe_app\u003c/h1\u003e\n\u003cp\u003eThis includes everything required to build a docker and corresponding singularity container for the Temporal Lobe pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/vuiiscci/temporal_lobe/tags/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.singularity-hub.org/collections/828\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Instructions:\u003c/h1\u003e\n\u003cp\u003eJust clone and run \u003ccode\u003ebuild.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vuiiscci/Temporal_Lobe_app.git\ncd Temporal_Lobe_app/\n./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Instructions:\u003c/h1\u003e\n\u003cp\u003eFor docker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run --rm \\\n-v $(pwd)/INPUTS/:/INPUTS/ \\\n-v $(pwd)/OUTPUTS:/OUTPUTS/ \\\n--user $(id -u):$(id -g) \\\nvuiiscci/temporal_lobe\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e \\\n-B INPUTS/:/INPUTS \\\n-B OUTPUTS/:/OUTPUTS \\\nshub://vuiiscci/Temporal_Lobe_app\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1494259937.0 + "updated_at": 1592512741.0 }, { "data_format": 2, - "description": "R docker container for scanem", + "description": "Singularity recipe for NMRPipe", "filenames": [ - "Singularity" + "Singularity", + "Singularity.212_64" ], - "full_name": "jacobhepkema/scanem-r", + "full_name": "ResearchIT/NMRPipe", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://quay.io/repository/jacobhepkema/scanem-r\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/85a1c7b34a5e0ff0bab3c5a2d59f5bdb663afbcd0fecbe64eeaea4d3cb247771/68747470733a2f2f717561792e696f2f7265706f7369746f72792f6a61636f626865706b656d612f7363616e656d2d722f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/jacobhepkema/scanem-r/status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-scanem-r\" class=\"anchor\" href=\"#scanem-r\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escanem-r\u003c/h1\u003e\n\u003cp\u003eR docker/singularity container for scanem. Docker container on quay.io (see above), singularity container at \u003ccode\u003eshub://jacobhepkema/scanem-r:latest\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-for-nmrpipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-for-nmrpipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipe for NMRPipe\u003c/h1\u003e\n\u003cp\u003eThis repo contains the recipe to run \u003ca href=\"https://www.ibbr.umd.edu/nmrpipe/\" rel=\"nofollow\"\u003eNMRPipe\u003c/a\u003e\nwithin a \u003ca href=\"https://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, which can be built using \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eVersions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e212_64 - NMRPipe linux212_64 built on centos7.4\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [], - "updated_at": 1630677641.0 + "updated_at": 1523030864.0 }, { "data_format": 2, - "description": null, + "description": " Build for docker and singularity containers for FMRIQA", "filenames": [ - "Singularity" + "Singularity", + "Singularity.4.0.0" ], - "full_name": "tsgoten/multi-agent-tc", + "full_name": "VUIIS/FMRIQA_app", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-transactive_control\" class=\"anchor\" href=\"#transactive_control\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etransactive_control\u003c/h1\u003e\n\u003cp\u003eCode meant to support and simulate the Social Game that will be launched in 2020. Elements of transactive control and behavioral engineering will be tested and designed here\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://dvc.org/doc/install\" rel=\"nofollow\"\u003edvc\u003c/a\u003e (with google drive support)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOn linux this is \u003ccode\u003epip install \u0027dvc[gdrive]\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall Docker, if you have not already.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc remote add -d gdrive gdrive://1qaTn6IYd3cpiyJegDwwEhZ3LwrujK3_x\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc pull\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./run.sh\u003c/code\u003e from the root of the repo. This will put you in a shell in the docker container with the \u003ccode\u003erl_algos/logs\u003c/code\u003e directory mounted\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 StableBaselines.py sac test_experiment\u003c/code\u003e in the docker container to start an experiment with the name \u003ccode\u003etest_experiment\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003etensorboard --logdir rl_algos/logs\u003c/code\u003e from outside the docker container to view the logs\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-12202020\" class=\"anchor\" href=\"#12202020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e12/20/2020\u003c/h3\u003e\n\u003cp\u003eThis repository has been cleaned and updated for use. It contains: (1) The OpenAI gym environment \"OfficeLearn\", in the \"gym-socialgame\" folder, and (2) implementations of Reinforcement learning algorithms in \"rl_algos\" folder. In the \"simulations\" folder are various datasets for setting up and training models associated with the gym simulation.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-912020\" class=\"anchor\" href=\"#912020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e9/1/2020\u003c/h3\u003e\n\u003cp\u003eThe most recent running of the code involves navigating to the rl_algos/ directory, then running the python command for the vanilla version:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac\u003c/p\u003e\n\u003cp\u003eAdding in the planning model can be done with the following flags:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac --planning_steps=10 --planning_model=Oracle --num_steps=10000\u003c/p\u003e\n\u003cp\u003ePlease see transactive_control/gym-socialgame/gym_socialgame/envs for files pertaining to the setup of the environment. The socialgame_env.py contains a lot of the information necessary for understanding how the agent steps through the environment. The reward.py file contains information on the variety of reward types available for testing. agents.py contains information on the deterministic people that we created for our simulation.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gym-socialgame\" class=\"anchor\" href=\"#gym-socialgame\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egym-socialgame\u003c/h3\u003e\n\u003cp\u003eOpenAI Gym environment for a social game.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fmriqa_app\" class=\"anchor\" aria-hidden=\"true\" href=\"#fmriqa_app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFMRIQA_app\u003c/h1\u003e\n\u003cp\u003eThis includes everything required (except for the \"spm12r6225_with_vbm8r435_compiled\" directory and \"FMRIQA_v4_0_0\" compiled MATLAB executable, which are too large to commit) to build a docker and corresponding singularity container for the FMRIQA pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/vuiiscci/fmriqa/tags/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.singularity-hub.org/collections/920\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Instructions:\u003c/h1\u003e\n\u003cp\u003eJust clone and run \u003ccode\u003ebuild.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vuiiscci/FMRIQA_app.git\ncd FMRIQA_app/\n./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE that you must have \"spm12r6225_with_vbm8r435_compiled\" directory and \"FMRIQA_v4_0_0\" compiled MATLAB executable.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Instructions:\u003c/h1\u003e\n\u003cp\u003eFor docker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run --rm \\\n-v $(pwd)/INPUTS/:/INPUTS/ \\\n-v $(pwd)/OUTPUTS:/OUTPUTS/ \\\n--user $(id -u):$(id -g) \\\nvuiiscci/fmriqa\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e \\\n-B INPUTS/:/INPUTS \\\n-B OUTPUTS/:/OUTPUTS \\\nshub://vuiiscci/FMRIQA_app\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1630639192.0 + "updated_at": 1674914637.0 }, { "data_format": 2, - "description": null, + "description": "Singularity Recipe for High-Performance GEOS-Chem (GCHP)", "filenames": [ "Singularity" ], - "full_name": "dcgc-bfx/singularity-sc-rhapsody", + "full_name": "geoschem/Singularity_GCHP", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singlecell-sc-rhapsody\" class=\"anchor\" href=\"#singlecell-sc-rhapsody\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esinglecell-sc-rhapsody\u003c/h1\u003e\n\u003cp\u003eDCGC singularity recipe for containerized versions of the BD Rhapsody Targeted Analysis and Whole Transcriptome Analysis (WTA) pipelines (available at \u003ca href=\"https://bitbucket.org/CRSwDev/cwl/src/master/\" rel=\"nofollow\"\u003ehttps://bitbucket.org/CRSwDev/cwl/src/master/\u003c/a\u003e).\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-this-repository-is-obsolete-and-has-been-archived\" class=\"anchor\" aria-hidden=\"true\" href=\"#this-repository-is-obsolete-and-has-been-archived\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTHIS REPOSITORY IS OBSOLETE AND HAS BEEN ARCHIVED\u003c/h2\u003e\n", "stargazers_count": 0, "subscribers_count": 4, "topics": [], - "updated_at": 1630594642.0 + "updated_at": 1674873388.0 }, { "data_format": 2, - "description": null, + "description": "Singularity images for deep learning software", "filenames": [ - "Singularity" + "Singularity.py3_fast2", + "Singularity.py3_tf1gnt", + "Singularity.py3_dmda", + "Singularity.py3_trch", + "Singularity.py2_tf17", + "Singularity.py2_tf110", + "Singularity.py3_tf2gnt", + "Singularity.py3_tf" ], - "full_name": "genomic-medicine-sweden/RareDisease_RNA_workflow", + "full_name": "gnperdue/singularity_imgs", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-raredisease_rna_workflow\" class=\"anchor\" href=\"#raredisease_rna_workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRareDisease_RNA_workflow\u003c/h1\u003e\n\u003cp\u003enextflow main.nf --help\u003c/p\u003e\n\u003cp\u003erun a single sample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow main.nf -profile singularity --r1 read1.fq.gz --r2 --read2.fq.gz --sample sampleID --output output_directory -c config.conf\n\noptionally, a vcf file may be provided:\n\nnextflow main.nf -profile singularity --samplesheet sample.csv --output output_directory --vcf input.vcf -c config.conf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003erun all samples in a samplesheet:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow main.nf -profile singularity --samplesheet sample.csv --output output_directory -c config.conf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethe samplesheet is a comma-separated file with the following header:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esample,r1,r2,vcf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe sample, r1 and r2 are mandatory, the vcf column may be left empty\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-setup\" class=\"anchor\" href=\"#setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetup\u003c/h1\u003e\n\u003cp\u003eModify the config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereference_dir : specify the folder with all your references \n\nSTAR_ref_dir : the star reference index folder\n\nref :the reference fasta file (dict and fai file required)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe pipeline will automatically download and cache the latest singularity image.\u003c/p\u003e\n\u003cp\u003eAlternatively you can download the singularity collection:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://J35P312/RareDisease_RNA_workflow\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr install all dependencies, as listed in dependencies\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edependencies\u003c/h1\u003e\n\u003cp\u003eWhen using singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow\nsingularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eotherwise:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow\nsamtools\nSTAR\ngatk\nstringtie\npicard\nstar-fusion\nfusioncatcher\nArriba\t\nmultiQC\nfastQC\nBootstrapAnn (https://github.com/J35P312/BootstrapAnn)\nucsc-wigtobigwig\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eSingularity containers (with inspiration from J. Simone, \u003ca href=\"https://github.com/TomaszGolan/mlmpr\"\u003eT. Golan\u003c/a\u003e, and \u003ca href=\"https://github.com/DeepLearnPhysics/larcv2-singularity\"\u003eK. Terao\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/998\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePull, e.g. \u003ccode\u003e$ singularity pull shub://gnperdue/singularity_imgs:py2_tf17\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity.py2_tf110\u003c/code\u003e - See \u003ca href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/Dockerfile.devel-gpu\"\u003eTF\u003c/a\u003e for base package definition.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 9, - "topics": [], - "updated_at": 1630424912.0 - }, - { - "data_format": 2, - "description": "Container for R with libraries for LBNL Energy Technology Area project", - "filenames": [ - "Singularity" + "subscribers_count": 2, + "topics": [ + "singularity", + "singularity-hub", + "singularity-container" ], - "full_name": "tin6150/r4eta", - "latest_release": null, - "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1635819130.0 + "updated_at": 1593117348.0 }, { "data_format": 2, - "description": "MR preprocessing for the Healthy Brain Ageing clinic at the Thompson Institute, USC.", + "description": "for singularity biuld", "filenames": [ - "lesion-segmentation_src/Singularity", - "qatools_src/Singularity", - "deep-brain-net_src/Singularity" + "Singularity" ], - "full_name": "jakepalmer/TI-HBA-MRprep", + "full_name": "d-w-moore/singularity-icommands-4.2.1", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ti-hba-mr-preprocessing\" class=\"anchor\" href=\"#ti-hba-mr-preprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTI HBA MR Preprocessing\u003c/h1\u003e\n\u003cp\u003eThis is a basic preprocessing pipeline for MRI data from the Healthy Brain Ageing Clinic at the Thompson Institute, USC.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline-overview\" class=\"anchor\" href=\"#pipeline-overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline overview\u003c/h2\u003e\n\u003cp\u003eThese are the steps of the pipeline. These steps are explained in more detail below, along with links to helpful resources/documentation and citations.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDicoms are converted to a BIDS compliant dataset with HeuDiConv.\u003c/li\u003e\n\u003cli\u003eAutomatic QC for the T1-weighted scan using MRIQC.\u003c/li\u003e\n\u003cli\u003eSubcortical segmentation and cortical parcellation with FastSurfer (includes QC).\u003c/li\u003e\n\u003cli\u003eBrain age prediction with DeepBrainNet.\u003c/li\u003e\n\u003cli\u003eWMH segmentation with FSL\u0027s BIANCA.\u003c/li\u003e\n\u003cli\u003eDWI preprocessing with QSIprep.\u003c/li\u003e\n\u003cli\u003ersfMRI preprocessing with fMRIprep.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eEach of these steps should be cited appropriately if used in publication (citations included below).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ideas-behind-implementation\" class=\"anchor\" href=\"#ideas-behind-implementation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIdeas behind implementation\u003c/h3\u003e\n\u003cp\u003eThe pipeline was developed with the following ideas in mind:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esubmit_jobs.sh\u003c/code\u003e orchestrates the pipeline by submitting a job on the HPC for each participant. For regular use, this is the only file that should need editing, e.g. editing paths and PBS parameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun_pipeline.py\u003c/code\u003e includes the main processing pipeline and simply wraps the Singularity commands for each step.\u003c/li\u003e\n\u003cli\u003eEach step is implemented in its own container on the HPC. Containers can be built from Dockerfile/Singularity files in the \u003ccode\u003e*_src\u003c/code\u003e folders or from published containters (noted in each section below).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo setup it requires building multiple containers, but the idea was for this pipeline to remain \u0027modular\u0027 so that each processing step is independent and can be modified/removed without affecting the rest of the pipeline (with the exception of dicom to BIDS conversion being required for all subsequent steps). Similarly, the pipeline can be extended by adding a container, processing script/command and a function in the \u003ccode\u003erun_pipeline.py\u003c/code\u003e script.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-assumed-input-file-structure\" class=\"anchor\" href=\"#assumed-input-file-structure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAssumed input file structure\u003c/h2\u003e\n\u003cp\u003eThe pipeline takes dicoms as its input with the assumed file structure before processing being:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u251c\u2500\u2500 bids\n\u251c\u2500\u2500 derivatives\n\u251c\u2500\u2500 dicom\n \u251c\u2500\u2500 HBA_0001_T1\n \u251c\u2500\u2500 RESEARCH_PROTOCOLS_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 AAHEAD_SCOUT_64CH_HEAD_COIL_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 MPRAGE_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 EP2D_DIFF_QBALL96DIR_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n ...\n \u251c\u2500\u2500 HBA_0002_T1\n \u251c\u2500\u2500 RESEARCH_PROTOCOLS_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 AAHEAD_SCOUT_64CH_HEAD_COIL_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 MPRAGE_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n \u251c\u2500\u2500 EP2D_DIFF_QBALL96DIR_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n ...\n ...\n\u251c\u2500\u2500 TI-HBA-MRprep\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere...\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edicom\u003c/code\u003e = where the dicoms will be copied for each participant to be processed\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebids\u003c/code\u003e = the BIDS compliant data converted from \u003ccode\u003edicom\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ederivatives\u003c/code\u003e = the pipeline outputs\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTI-HBA-MRprep\u003c/code\u003e = the code in this repository\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-intended-usage\" class=\"anchor\" href=\"#intended-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntended usage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eMake sure directory structure exists as shown \u003ca href=\"##Assumed-input-file-structure\"\u003eabove\u003c/a\u003e in the analysis directory on the HPC.\u003c/li\u003e\n\u003cli\u003eClone this repo and move to the HPC.\u003c/li\u003e\n\u003cli\u003eCopy dicoms to process into the \u003ccode\u003edicom\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eUpdate/check the schedular parameters in \u003ccode\u003esubmit_jobs.sh\u003c/code\u003e. It might take some testing to get these right, afterwhich they most likely won\u0027t need to be changed often.\u003c/li\u003e\n\u003cli\u003eUpdate/check the file paths in \u003ccode\u003esubmit_jobs.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eWhen ready to run the pipeline, type the following in terminal:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /path/on/HPC\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e submit_jobs.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e...where \u003ccode\u003e/path/on/HPC\u003c/code\u003e is the appropriate path to the data and code on the HPC.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-detailed-processing-steps\" class=\"anchor\" href=\"#detailed-processing-steps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetailed processing steps\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e FastSurfer, QSIprep and fMRIprep require a FreeSurfer license, which can be obtained for free from \u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/License\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. The file needs to be passed to the \u003ccode\u003esubmit_jobs.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dicom-to-bids\" class=\"anchor\" href=\"#dicom-to-bids\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDicom to BIDS\u003c/h3\u003e\n\u003cp\u003eBIDS is a standard for structuring neuroimaging datasets that is being increasingly implemented that allows a consistent interface and documentation of datasets. A number of open source pipelines expect input to be in BIDS format.\u003c/p\u003e\n\u003cp\u003eHeuDiConv has been developed to automate the conversion from dicom to BIDS. It requires some setup (i.e. putting together a \u003ccode\u003eheuristic.py\u003c/code\u003e file to provide the rules for conversion), however this will generally only need to be setup once and has been done (see \u003ccode\u003eheudiconv_src/heuristic.py\u003c/code\u003e). This would need updating if the MRI sequences change. Example commands to help with the setup are included in the comments in the docstring for the \u003ccode\u003erunDcm2BIDS\u003c/code\u003e function in the \u003ccode\u003erun_pipeline.py\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eFor more info see \u003ca href=\"https://bids.neuroimaging.io/\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e and \u003ca href=\"https://heudiconv.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eHeuDiConv\u003c/a\u003e documentation, also this HeuDiConv \u003ca href=\"https://reproducibility.stanford.edu/bids-tutorial-series-part-2a/\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e and \u003ca href=\"https://github.com/bids-standard/bids-starter-kit/wiki/\"\u003ewiki\u003c/a\u003e. The HeuDiConv \u003ca href=\"https://heudiconv.readthedocs.io/en/latest/installation.html#docker\" rel=\"nofollow\"\u003econtainer\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-mriqc\" class=\"anchor\" href=\"#mriqc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMRIQC\u003c/h3\u003e\n\u003cp\u003eThis is an automated QC pipeline for T1-weighted, T2-weighted and fMRI sequences (if present in BIDS folder). It produces visual reports and a range of QC metrics that may be useful for further analysis.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://mriqc.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e, \u003ca href=\"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184661\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e and the \u003ca href=\"https://hub.docker.com/r/poldracklab/mriqc/\" rel=\"nofollow\"\u003econtainer\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-fastsurfer\" class=\"anchor\" href=\"#fastsurfer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFastSurfer\u003c/h3\u003e\n\u003cp\u003eFastSurfer is a deep learning implementation of FreeSurfer. It provides essentially the same output but is faster (as you may have guessed) and more accurate.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://deep-mi.org/research/fastsurfer/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e, \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811920304985\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e and the \u003ca href=\"https://github.com/Deep-MI/FastSurfer\"\u003egithub\u003c/a\u003e which also includes \u003ca href=\"https://github.com/Deep-MI/FastSurfer/tree/master/Docker\"\u003eDockerfiles\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-fastsurfer-qc\" class=\"anchor\" href=\"#fastsurfer-qc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFastSurfer QC\u003c/h4\u003e\n\u003cp\u003eThis is just a quick visual QC step for the output of FastSurfer and is run automatically. It produces a CSV file with some QC metrics (some of which overlap with MRIQC) and screenshots to check the segmentation and cortical parcellation.\u003c/p\u003e\n\u003cp\u003eThis is only designed for quick, preliminary visual QC and full visual QC should be completed before any statistical analysis for publication (see \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811921004511\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for discussion of QC approaches).\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://github.com/Deep-MI/qatools-python\"\u003edocumentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-deepbrainnet\" class=\"anchor\" href=\"#deepbrainnet\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeepBrainNet\u003c/h3\u003e\n\u003cp\u003eThis is a deep learning model developed for the prediction of brain age. It produces a single predicted age based on the T1-weighted input, which can then be used to calculate a difference score with chronological age.\u003c/p\u003e\n\u003cp\u003eThe model has been implemented in \u003ca href=\"https://antsx.github.io/ANTsPyNet/docs/build/html/utilities.html\" rel=\"nofollow\"\u003eANTsPyNet\u003c/a\u003e, including the preprocessing steps, which is used in \u003ccode\u003edeep-brain-net_src/run_prediction.py\u003c/code\u003e. The Dockerfile/Singularity file is also included in the \u003ccode\u003edeep-brain-net_src\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://academic.oup.com/brain/article/143/7/2312/5863667?login=true\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e for more info about the model development and interpretation and original \u003ca href=\"https://github.com/vishnubashyam/DeepBrainNet\"\u003ecode\u003c/a\u003e from authors.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-wmh-segmentation-with-bianca\" class=\"anchor\" href=\"#wmh-segmentation-with-bianca\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWMH segmentation with BIANCA\u003c/h3\u003e\n\u003cp\u003eBIANCA requires some pre/post processing. The steps used are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocess T1 and FLAIR with \u003ccode\u003efsl_anat\u003c/code\u003e (see \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/fsl_anat\" rel=\"nofollow\"\u003edocs\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a white matter mask with \u003ccode\u003emake_bianca_mask\u003c/code\u003e (see BIANCA \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BIANCA/Userguide#Data_preparation\" rel=\"nofollow\"\u003edocs\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCreate \u003ccode\u003emasterfile.txt\u003c/code\u003e as input for BIANCA\u003c/li\u003e\n\u003cli\u003eThe BIANCA output is a probability image, so apply thresholding (default to 0.9 here)\u003c/li\u003e\n\u003cli\u003eExtract the total WMH number and volume\u0027\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBIANCA also requires some manually labeled WMH masks as training data. A recent \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811921004663?via%3Dihub#bib0013\" rel=\"nofollow\"\u003epaper\u003c/a\u003e suggested the use of consistent training labels may be beneficial to avoid inter-rater variability between manual segmentations. Currently, this pipeline makes use of manual segmentations provided by those authors (included in container) for the training labels. This could be changed in future if a sample of HBA participants were manually segmented.\u003c/p\u003e\n\u003cp\u003eBIANCA \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BIANCA/Userguide#Data_preparation\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e, \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fslcourse/lectures/practicals/seg_struc/#bianca\" rel=\"nofollow\"\u003etutorial\u003c/a\u003e and \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811916303251?via%3Dihub\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e, as well as the \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1053811921004663?via%3Dihub#bib0013\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e and discussion for training labels that can be found \u003ca href=\"https://issues.dpuk.org/eugeneduff/wmh_harmonisation/-/tree/master/BIANCA_training_datasets\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-qsiprep\" class=\"anchor\" href=\"#qsiprep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQSIprep\u003c/h3\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eIMPORTANT:\u003c/strong\u003e This step has not been tested extensively. The defaults have been used for almost all options, however these should be checked before using this data in any further analysis.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eQSIprep is a BIDS app that runs preprocessing and reconstruction of DWI data. Only preprocessing is completed here but QSIprep is also an excellent tool to use for further analysis. Visual QC reports are also produced which provide and easy way to check the quality of the DWI data.\u003c/p\u003e\n\u003cp\u003eQSIprep utilises a number of software packages that should be references (as well as the QSIprep citation). Example citation information with references in produced as part of processing and can be found in the \u003ccode\u003elogs\u003c/code\u003e folder of the output.\u003c/p\u003e\n\u003cp\u003eSome steps in QSIprep (particularly eddy current correction and disortion correction with TOPUP) are resource intensive. Currently the pipeline is set to allow QSIprep\u0027s underlying workflow manager (\u003ca href=\"https://nipype.readthedocs.io/en/latest/#\" rel=\"nofollow\"\u003eNipype\u003c/a\u003e) to manage the CPU and RAM usage by detecting how many CPUs are available and using 90% of available RAM (see MultiProc section \u003ca href=\"https://miykael.github.io/nipype_tutorial/notebooks/basic_plugins.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://qsiprep.readthedocs.io/en/latest/index.html\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e, \u003ca href=\"https://www.nature.com/articles/s41592-021-01185-5\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e, Docker \u003ca href=\"https://hub.docker.com/r/pennbbl/qsiprep/\" rel=\"nofollow\"\u003eimage\u003c/a\u003e and info for using with \u003ca href=\"https://qsiprep.readthedocs.io/en/latest/installation.html#singularity-container\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-fmriprep\" class=\"anchor\" href=\"#fmriprep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efMRIprep\u003c/h3\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eIMPORTANT:\u003c/strong\u003e This step has not been tested extensively. The defaults have been used for almost all options, however these should be checked before using this data in any further analysis.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003efMRIprep is another BIDS app for preprocessing fMRI data. As for QSIprep, fMRIprep uses several software packages that should also be referenced. Visual QC reports are also produced.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://fmriprep.org/en/latest/index.html\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e, \u003ca href=\"https://www.nature.com/articles/s41592-018-0235-4\" rel=\"nofollow\"\u003ecitation\u003c/a\u003e, Docker \u003ca href=\"https://hub.docker.com/r/nipreps/fmriprep\" rel=\"nofollow\"\u003eimage\u003c/a\u003e and info for using with \u003ca href=\"https://fmriprep.org/en/latest/installation.html#containerized-execution-docker-and-singularity\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1630381276.0 + "updated_at": 1527027070.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.prc-0_8_0" ], - "full_name": "hmgu-itg/single-point-analysis-pipeline", - "latest_release": "0.0.1", - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-snakefile-order\" class=\"anchor\" href=\"#snakefile-order\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakefile order\u003c/h2\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eread-config.smk\u003c/li\u003e\n\u003cli\u003evariant-qc.smk\u003c/li\u003e\n\u003cli\u003esingle-cohort.smk\u003c/li\u003e\n\u003cli\u003emeta-analysis.smk\u003c/li\u003e\n\u003cli\u003edetect-peaks.smk\u003c/li\u003e\n\u003cli\u003epeakplot.smk\u003c/li\u003e\n\u003cli\u003ecojo.smk\u003c/li\u003e\n\u003cli\u003equery.smk\u003c/li\u003e\n\u003cli\u003egwas.smk\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-questions\" class=\"anchor\" href=\"#questions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions\u003c/h2\u003e\n\u003cp\u003eQ. Why do the \u003ccode\u003efreq\u003c/code\u003e and \u003ccode\u003efreq_geno\u003c/code\u003e column values in the \u003ccode\u003e.jma.cojo\u003c/code\u003e file differ?\nA. \u003ccode\u003efreq_geno\u003c/code\u003e column is the frequency of the \u003ccode\u003erefA\u003c/code\u003e column allele in the input bfile (you can use \u003ccode\u003eplink --freq\u003c/code\u003e to check).\nThe \u003ccode\u003efreq\u003c/code\u003e column value is the exact value extracted from the input cojofile, where the cojofile was created from the corresponding metal file.\nSo the \u003ccode\u003efreq\u003c/code\u003e column value comes from the \u003ccode\u003eAlt_Freq\u003c/code\u003e column value in the metal file, and the \u003ccode\u003eAlt_Freq\u003c/code\u003e column value is the \"weighted average of frequency for Alt allele across all studies\".\nThe \u003ccode\u003efreq_geno\u003c/code\u003e and \u003ccode\u003efreq\u003c/code\u003e column values differ because \u003ccode\u003efreq_geno\u003c/code\u003e is just the allele frequency of the variant from the genotype file (plink bfile) that was combined from all cohorts,\nwhereas \u003ccode\u003efreq\u003c/code\u003e column is the weighted average of frequency across cohorts (calculated by metal).\u003c/p\u003e\n\u003cp\u003eQ. When I try to run a rule, I get an error saying \u003ccode\u003eText file busy\u003c/code\u003e. What do I do?\nA. Delete the script and restore it using \u003ccode\u003egit restore workflow/script/problematic_script.sh\u003c/code\u003e. Your rules should run normally after doing this\u003c/p\u003e\n", + "full_name": "d-w-moore/singularity-python-irodsclient", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1636694692.0 + "updated_at": 1530133683.0 }, { "data_format": 2, - "description": "A collection of Singularity images", + "description": "Adapt the BEaST skull stripping method for 7T MRI as a BIDS app", "filenames": [ - "recipes/diffTF/Singularity.diffTF_conda", - "recipes/diffTF/Singularity.diffTF_R", - "recipes/RNA-Seq/Singularity.RNA_Seq_R", - "recipes/RNA-Seq/Singularity.RNA_seq_conda", - "recipes/RNA-Seq/Singularity.RNA_seq_fastqc", - "recipes/ATAC-Seq/Singularity.ATAC_seq_conda2", - "recipes/ATAC-Seq/Singularity.ATAC_seq_conda", - "recipes/ATAC-Seq/Singularity.ATAC_Seq_R", - "recipes/VariantCalling/Singularity.Variant-Calling_R", - "recipes/VariantCalling/Singularity.Variant-Calling_conda" + "Singularity.v0.0.1a" ], - "full_name": "chrarnold/Singularity_images", + "full_name": "Martybird/7TBEaST", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_images\" class=\"anchor\" href=\"#singularity_images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity_images\u003c/h1\u003e\n\u003cp\u003eA collection of Singularity images\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-7tbeast\" class=\"anchor\" aria-hidden=\"true\" href=\"#7tbeast\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e7TBEaST\u003c/h1\u003e\n\u003cp\u003eAdapt the BEaST skull stripping method for 7T MRI as a BIDS app\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1637101837.0 + "updated_at": 1530840788.0 }, { "data_format": 2, - "description": "Based on the original Sregistry: https://github.com/singularityhub/sregistry - Deploy the Singularity Sregistry as rootless containers with podman-compose. Also added data persistence for the PostgreSQL database and rootless setup for SSL and PAM authentication.", + "description": null, "filenames": [ - "Singularity" + "ext/Singularity" ], - "full_name": "hashkeks/sregistry-podman-compose", + "full_name": "OSC/bc_osc_rshiny", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-registry-server---podman-compose-edition\" class=\"anchor\" href=\"#singularity-registry-server---podman-compose-edition\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Registry Server - podman-compose edition\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-podman-compose\" class=\"anchor\" href=\"#what-is-podman-compose\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is podman-compose\u003c/h2\u003e\n\u003cp\u003ePodman-compose is the podman equivalent to docker-compose, using the podman container engine. It allows for the creation of rootless containers running in user namespace. For more information see \u003ca href=\"https://podman.io/\" rel=\"nofollow\"\u003ehttps://podman.io/\u003c/a\u003e and \u003ca href=\"https://github.com/containers/podman-compose\"\u003ehttps://github.com/containers/podman-compose\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-are-the-differences-to-the-original-singularity-registry-server\" class=\"anchor\" href=\"#what-are-the-differences-to-the-original-singularity-registry-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat are the differences to the original Singularity Registry Server\u003c/h2\u003e\n\u003cp\u003eThis version of the Singularity Registry Server is set-up to work in a non-root environment.\nI \u003cstrong\u003edid not\u003c/strong\u003e change the code of the applications.\nI \u003cstrong\u003edid\u003c/strong\u003e change the folder structure and the docker-compose.yml file and provide documentation to make this setup run with podman-compose.\nThis setup in it\u0027s current configuration is meant to be run with valid SSL certificates. You can change that by deactivating the corresponding settings in the docker-compose.yml and shub/settings/config.py files.\nIn the end you still have to make your configurations (like setting your services addresses, renaming your instance, enabling authentication, etc.) according to the original documentation which you can find at \u003ca href=\"https://singularityhub.github.io/sregistry/\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/sregistry/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe differences in detail:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eChanged the docker-compose.yml\n\u003cul\u003e\n\u003cli\u003eVolume paths are not taken from uwsgi directly, but are defined per service. Consquence: You don\u0027t need a nginx user on your host system anymore and don\u0027t have permissions problems after deactivating PAM again.\u003c/li\u003e\n\u003cli\u003eVolume mapping for PAM files changed.\u003c/li\u003e\n\u003cli\u003eVolume mapping for SSL certs changed.\u003c/li\u003e\n\u003cli\u003eVolume mapping for PostgreSQL database added, so it can save data persistently without initiating a backup procedure.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eA PAM folder with a \u0027shadow\u0027 file was added. You need to copy the information of configured users from your /etc/shadow into this file since rootless containers do not have access to the original /etc/shadow.\u003c/li\u003e\n\u003cli\u003eAn SSL directory with subdirectories was added to save and access cert files in the rootless environment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-to-do-besides-doing-the-usual-configuration\" class=\"anchor\" href=\"#what-to-do-besides-doing-the-usual-configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat to do besides doing the usual configuration\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eYou \u003cstrong\u003eneed\u003c/strong\u003e to change the ownership of the sregistry/minio-images folder to the user that is used inside the minio container with the UID and GID 1.\nTo do so, execute the following command inside the sregistry folder:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epodman unshare chown -R 1:1 minio-images\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will change the ownership to the UID that will be used in user namespace and represents the user with UID 1 inside the minio container.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYou can put your SSL cert and key into the according folders in the sregistry/ssl folder\u003c/li\u003e\n\u003cli\u003eYou can put your user info from /etc/shadow into sregistry/PAM/shadow\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-who-worked-on-this\" class=\"anchor\" href=\"#who-worked-on-this\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWho worked on this\u003c/h3\u003e\n\u003ctable\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/hashkeks\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/34633191?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eCedric Casper\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/hashkeks\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/kkaftan\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/74317121?v=4\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eKevin Kaftan\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/kkaftan\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-the-following-section-is-taken-from-the-original-sregistry-repo-itself-and-does-not-have-to-do-with-our-changes\" class=\"anchor\" href=\"#the-following-section-is-taken-from-the-original-sregistry-repo-itself-and-does-not-have-to-do-with-our-changes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe following section is taken from the original Sregistry repo itself and does not have to do with our changes.\u003c/h2\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-registry-server\" class=\"anchor\" href=\"#singularity-registry-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Registry Server\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"http://joss.theoj.org/papers/050362b7e7691d2a5d0ebed8251bc01e\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/77382cd0ef59a3538ed515392195d8541e46ce977b42c3838e930e6ccf221bfb/68747470733a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f30353033363262376537363931643261356430656265643832353162633031652f7374617475732e737667\" alt=\"status\" data-canonical-src=\"https://joss.theoj.org/papers/050362b7e7691d2a5d0ebed8251bc01e/status.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/singularityhub/sregistry/actions?query=branch%3Amaster+workflow%3Asregistry-ci\"\u003e\u003cimg src=\"https://github.com/singularityhub/sregistry/workflows/sregistry-ci/badge.svg?branch=master\" alt=\"GitHub actions status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.1012531\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/411f713db9ba01edfcb60386aaa1dff3e4ed4464707b95d889900a88d8f54936/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313031323533312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1012531.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2f835bc9b4458adb32cf016ec029863ab35c3b89d29ecc3a14494909424d38b5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538422532302532302545322539372538462532302532302545322539372538422d6f72616e6765\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B%20%20%E2%97%8F%20%20%E2%97%8B-orange\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"#contributors-\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/194f21da62ea53d158311e06473f9ec192dea9c1f3f6423c9c3f12aff583b546/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f616c6c5f636f6e7472696275746f72732d32302d6f72616e67652e7376673f7374796c653d666c61742d737175617265\" alt=\"All Contributors\" data-canonical-src=\"https://img.shields.io/badge/all_contributors-20-orange.svg?style=flat-square\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://singularityhub.github.io/sregistry\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\n\n\n\u003ctable\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://vsoch.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/814322?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eVanessasaurus\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=vsoch\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=vsoch\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"tschoonj.github.io\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/65736?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eTom Schoonjans\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=tschoonj\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=tschoonj\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"antoinecully.com\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/6448924?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eAntoine Cully\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=Aneoshun\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=Aneoshun\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://dctrud.sdf.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/4522799?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eDavid Trudgian\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=dctrud\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/serlophug\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/20574493?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eSergio L\u00f3pez Huguet\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=serlophug\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=serlophug\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/jbd\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/169483?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003ejbd\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=jbd\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=jbd\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"http://alex.hirzel.us/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/324152?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eAlex Hirzel\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=alhirzel\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=alhirzel\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"http://tangiblecomputationalbiology.blogspot.com\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/207407?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eSteffen M\u00f6ller\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=smoe\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=smoe\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"www.onerussian.com\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/39889?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eYaroslav Halchenko\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=yarikoptic\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=yarikoptic\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"http://sourceforge.net/u/victorsndvg/profile/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/6474985?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003evictorsndvg\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=victorsndvg\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=victorsndvg\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"arfon.org\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/4483?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eArfon Smith\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=arfon\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=arfon\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://ransomwareroundup.com\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/9367754?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eBrie Carranza\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=bbbbbrie\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=bbbbbrie\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://orcid.org/0000-0002-6178-3585\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/145659?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eDan Fornika\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=dfornika\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=dfornika\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/RonaldEnsing\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/8299064?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eRonald Ensing\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=RonaldEnsing\" title=\"Documentation\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"book\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4d6.png\"\u003e\ud83d\udcd6\u003c/g-emoji\u003e\u003c/a\u003e \u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=RonaldEnsing\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/vladdoster\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/10052309?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003evladdoster\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=vladdoster\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/pini-gh\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/1241814?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003epini-gh\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=pini-gh\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/0nebody\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/26727168?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003e0nebody\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=0nebody\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/dtrudg\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/4522799?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003edtrudg\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=dtrudg\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/craigwindell\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/44250868?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003ecraigwindell\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=craigwindell\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/hashkeks\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/34633191?v=4?s=100\" width=\"100px;\" alt=\"\" style=\"max-width:100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eCedric\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/singularityhub/sregistry/commits?author=hashkeks\" title=\"Code\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"computer\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4bb.png\"\u003e\ud83d\udcbb\u003c/g-emoji\u003e\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n\u003c/table\u003e\n\n\n\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-singularity-registry\" class=\"anchor\" href=\"#what-is-singularity-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is Singularity Registry\u003c/h2\u003e\n\u003cp\u003eSingularity Registry Server is a server to provide management and storage of\nSingularity images for an institution or user to deploy locally.\nIt does not manage building but serves endpoints to obtain and save containers.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-images-included\" class=\"anchor\" href=\"#images-included\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages Included\u003c/h2\u003e\n\u003cp\u003eSingularity Registry consists of several Docker images, and they are integrated\nto work together using \u003ca href=\"docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe images are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003evanessa/sregistry\u003c/strong\u003e: is the main uWSGI application, which serves a Django (python-based) application.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003enginx\u003c/strong\u003e: pronounced (engine-X) is the webserver. The starter application is configured for HTTP. However, you should follow our \u003ca href=\"https://singularityhub.github.io/sregistry/docs/install/server#ssl\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e to set up HTTPS properly. Note that we build a custom NGINX image that takes advantage of the \u003ca href=\"https://www.nginx.com/resources/wiki/modules/upload/\" rel=\"nofollow\"\u003enginx-upload-module\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eworker\u003c/strong\u003e: is the same uWSGI image, but with a running command for queueing jobs and processing them in the background. These jobs run via \u003ca href=\"https://github.com/rq/django-rq\"\u003edjango-rq\u003c/a\u003e backed by a\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eredis\u003c/strong\u003e: database to organize the jobs themselves.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003escheduler\u003c/strong\u003e jobs can be scheduled using the scheduler.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more information about Singularity Registry Server, please reference the\n\u003ca href=\"https://singularityhub.github.io/sregistry\" rel=\"nofollow\"\u003edocs\u003c/a\u003e. If you have any issues,\nplease \u003ca href=\"https://github.com/singularityhub/sregistry/issues\"\u003elet me know\u003c/a\u003e!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis code is licensed under the MPL 2.0 \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-wip-batch-connect---osc-shiny-app-launcher\" class=\"anchor\" aria-hidden=\"true\" href=\"#wip-batch-connect---osc-shiny-app-launcher\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[WIP] Batch Connect - OSC Shiny App Launcher\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5e71a5a07e8e6e15f922edb76b5fb68e7ee6087e336807c38095861b8c7f5fc2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f7368696e795f6c61756e636865722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e71a5a07e8e6e15f922edb76b5fb68e7ee6087e336807c38095861b8c7f5fc2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f7368696e795f6c61756e636865722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/shiny_launcher.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app designed for OSC OnDemand that launches a Shiny App within\nan Owens batch job.\u003c/p\u003e\n\u003cp\u003eThe Shiny app is included a submodule and each deployment can modified which\nShiny app to deploy using git config to specify the URL for the submodule. This\nway the launcher code can be reused for multiple apps but the launcher and the\napp itself can be managed separately.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e x.y.z+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eTODO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_example_shiny/fork\"\u003ehttps://github.com/OSC/bc_osc_example_shiny/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1637673514.0 + "updated_at": 1628181440.0 }, { "data_format": 2, - "description": "repo hosting personal example scripts and notebooks for various pieces of software by OPIG", + "description": null, "filenames": [ - "webdevel/ubuntu/.singularity.d/Singularity" + "Singularity.ubuntu" ], - "full_name": "broncio123/software_hands-on", + "full_name": "UNM-CARC/heudiconv", "latest_release": null, - "readme": "\u003cp\u003esoftware_hands-on\u003c/p\u003e\n", + "readme": "\u003cp\u003eNot much\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1638391053.0 + "updated_at": 1536784012.0 }, { "data_format": 2, - "description": "This is a repo which holds the codebase for our class project on NLP.", + "description": null, "filenames": [ - "singularity/Singularity.debian-unstable-amd64", - "singularity/Singularity.debian-unstable-i386" + "Singularity" ], - "full_name": "ravisha2396/NLPProject", + "full_name": "sbutcher/container-setc", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"/logo_assets/vowpal-wabbits-github-logo@3x.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"/logo_assets/vowpal-wabbits-github-logo@3x.png\" height=\"auto\" width=\"100%\" alt=\"Vowpal Wabbit\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://dev.azure.com/vowpalwabbit/Vowpal%20Wabbit/_build/latest?definitionId=23\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/58819a50d93dd6bfee30aecaa0f72d7e66623fd462c5ac37bdc427f3058ae723/68747470733a2f2f696d672e736869656c64732e696f2f617a7572652d6465766f70732f6275696c642f766f7770616c7761626269742f33393334313133632d396532622d346462632d383937322d3732616239623962343334322f32333f6c6162656c3d4c696e75782532306275696c64266c6f676f3d417a7572652532304465766f7073\" alt=\"Linux build status\" data-canonical-src=\"https://img.shields.io/azure-devops/build/vowpalwabbit/3934113c-9e2b-4dbc-8972-72ab9b9b4342/23?label=Linux%20build\u0026amp;logo=Azure%20Devops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://dev.azure.com/vowpalwabbit/Vowpal%20Wabbit/_build/latest?definitionId=14\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d539f0bca2e4c6aca53fbbbf2a4efb7be920f95b698171172d1af967aa5025d7/68747470733a2f2f696d672e736869656c64732e696f2f617a7572652d6465766f70732f6275696c642f766f7770616c7761626269742f33393334313133632d396532622d346462632d383937322d3732616239623962343334322f31343f6c6162656c3d57696e646f77732532306275696c64266c6f676f3d417a7572652532304465766f7073\" alt=\"Windows build status\" data-canonical-src=\"https://img.shields.io/azure-devops/build/vowpalwabbit/3934113c-9e2b-4dbc-8972-72ab9b9b4342/14?label=Windows%20build\u0026amp;logo=Azure%20Devops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://dev.azure.com/vowpalwabbit/Vowpal%20Wabbit/_build/latest?definitionId=22\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/214f203a660ee423d4694b193d4839c0bcd320402462f1030b0d25f33588b0a9/68747470733a2f2f696d672e736869656c64732e696f2f617a7572652d6465766f70732f6275696c642f766f7770616c7761626269742f33393334313133632d396532622d346462632d383937322d3732616239623962343334322f32323f6c6162656c3d4d61634f532532306275696c64266c6f676f3d417a7572652532304465766f7073\" alt=\"MacOS build status\" data-canonical-src=\"https://img.shields.io/azure-devops/build/vowpalwabbit/3934113c-9e2b-4dbc-8972-72ab9b9b4342/22?label=MacOS%20build\u0026amp;logo=Azure%20Devops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://codecov.io/gh/VowpalWabbit/vowpal_wabbit\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/32898f10a7c61069a273521aea6b4becacfc4d776e96dd0e747f03e286b1b824/68747470733a2f2f636f6465636f762e696f2f67682f566f7770616c5761626269742f766f7770616c5f7761626269742f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/VowpalWabbit/vowpal_wabbit/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://lgtm.com/projects/g/JohnLangford/vowpal_wabbit/alerts/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e389698afd7de10a602e5e1a705d05c192a37638521b67a3ca2fac8d937b69e/68747470733a2f2f696d672e736869656c64732e696f2f6c67746d2f616c657274732f672f4a6f686e4c616e67666f72642f766f7770616c5f7761626269742e7376673f6c6f676f3d6c67746d266c6f676f57696474683d3138\" alt=\"Total Alerts\" data-canonical-src=\"https://img.shields.io/lgtm/alerts/g/JohnLangford/vowpal_wabbit.svg?logo=lgtm\u0026amp;logoWidth=18\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/VowpalWabbit\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/64efc8a80a3424a0595bf90fcae3ee2ef1878436f3c22137aef60e11f4ca9126/68747470733a2f2f6261646765732e6769747465722e696d2f566f7770616c5761626269742e737667\" alt=\"Gitter chat\" data-canonical-src=\"https://badges.gitter.im/VowpalWabbit.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the \u003cem\u003eVowpal Wabbit\u003c/em\u003e fast online learning code.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-vowpal-wabbit\" class=\"anchor\" href=\"#why-vowpal-wabbit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy Vowpal Wabbit?\u003c/h2\u003e\n\u003cp\u003eVowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state of the art algorithms with performance in mind.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eInput Format.\u003c/strong\u003e The input format for the learning algorithm is substantially more flexible than might be expected. Examples can have features consisting of free form text, which is interpreted in a bag-of-words way. There can even be multiple sets of free form text in different namespaces.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSpeed.\u003c/strong\u003e The learning algorithm is fast -- similar to the few other online algorithm implementations out there. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eScalability.\u003c/strong\u003e This is not the same as fast. Instead, the important characteristic here is that the memory footprint of the program is bounded independent of data. This means the training set is not loaded into main memory before learning starts. In addition, the size of the set of features is bounded independent of the amount of training data using the hashing trick.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFeature Interaction.\u003c/strong\u003e Subsets of features can be internally paired so that the algorithm is linear in the cross-product of the subsets. This is useful for ranking problems. The alternative of explicitly expanding the features before feeding them into the learning algorithm can be both computation and space intensive, depending on how it\u0027s handled.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki\"\u003eVisit the wiki to learn more.\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eFor the most up to date instructions for getting started on Windows, MacOS or Linux \u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Getting-started\"\u003eplease see the wiki\u003c/a\u003e. This includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Getting-started\"\u003eInstalling with a package manager\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Dependencies\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Building\"\u003eBuilding\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Tutorial\"\u003eTutorial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-setc\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-setc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer-setc\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1636245564.0 + "updated_at": 1538491698.0 }, { "data_format": 2, @@ -15809,13 +15245,13 @@ var data = "filenames": [ "Singularity" ], - "full_name": "anoyaro84/snakemake_ChIPseq", + "full_name": "ResearchIT/scanindel", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-chip-seq-analysis-pipeline-based-on-snakemake\" class=\"anchor\" href=\"#chip-seq-analysis-pipeline-based-on-snakemake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChIP-seq analysis pipeline based on snakemake\u003c/h1\u003e\n\u003cp\u003eThis is an snakemake-based Peak calling pipeline used in Zwart lab at the Netherlands Cancer Institute.\nThe pipeline obtains ChIP-seq data from diverse sources (remote/local path or GEO) and process them accordingly to produce peak lists in bed format and coverage profiles in tdf format.\u003c/p\u003e\n\u003cp\u003eRoughly, the pipeline takes the following steps to produce the outcome:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownloading raw data (either bam/fastq files) from the specified locations (local, remote, or GEO) in DataList.csv\u003c/li\u003e\n\u003cli\u003eAlignment with bwa-mem (in case of fastq files)\u003c/li\u003e\n\u003cli\u003eMarking duplicate reads with picard\u003c/li\u003e\n\u003cli\u003eRemoving low-quality reads (retain reads with mapping quality \u0026gt; 20)\u003c/li\u003e\n\u003cli\u003ePeak calling with MACS1.4/MACS2/DFilter (support more than one peak callers)\u003c/li\u003e\n\u003cli\u003eTaking intersection between the peaks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that PeakPairs.csv is used to specify ChIP-seq vs input pairs, and config.yaml is used for specifiying optional parameters in softwares.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe pipeline is preliminary used in linux environment with conda/singularity available. Singularity is used only for DFilter (one of two peak callers used) within the pipeline. Currently, the pipeline is tested with conda version 4.5.4 and singularity version 2.5.1.\u003c/p\u003e\n\u003cp\u003eFor downloading repository \u0026amp; creating evnironment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/anoyaro84/snakemake_ChIPseq\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e snakemake_ChIPseq\nconda env create --file env/snakemake.yaml\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install phantompeak tools\u003c/span\u003e\ngit submodule init\ngit submodule update\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe most of softwares used in the pipeline is installed by conda or excuted in wrapper.\nOnly exception is the phantompeak, the software used for estimating the fragment length that can be used by MACS2.\nPhantompeak tools is included as a submodule, for which you can install with the last two commands.\u003c/p\u003e\n\u003cp\u003eWe recommend to run the pipeline from a different location than pipeline path, like below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake -s PATH_TO_PIPELINE/Snakefile --use-conda --use-singularity --cores=24\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWith --use-conda option, the pipeline will create environments to run rules based on .yaml files in env/.\nThe --use-singulairty option applies only to DFilter peak caller. The singularity container holds a virtual environment of Ubuntu with DFilter installed.\u003c/p\u003e\n\u003cp\u003eNote that the pipeline assumes that there is the following three files available at the location where the pipeline is executed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003econfig.yaml\u003c/li\u003e\n\u003cli\u003eDataList.csv\u003c/li\u003e\n\u003cli\u003ePeakPairs.csv\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee below for more details on how to prepare these input files.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-preparing-input-files\" class=\"anchor\" href=\"#preparing-input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparing Input files\u003c/h2\u003e\n\u003cp\u003eFor DatList.csv, it is expected to have the following structure (in csv format):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eID\u003c/th\u003e\n\u003cth\u003eSource\u003c/th\u003e\n\u003cth\u003ePath\u003c/th\u003e\n\u003cth\u003eFormat\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eIdentifier of each sequencing data\u003c/td\u003e\n\u003ctd\u003eSource of the files, can either be remote (forge), local, or GEO\u003c/td\u003e\n\u003ctd\u003e(local/remote) path to the file. (ignored if Source is GEO)\u003c/td\u003e\n\u003ctd\u003eEither fastq or bam (ignored if Source is GEO)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe pipeline will take either fastq/bam files from GEO, remote/local locations based on the table above.\u003c/p\u003e\n\u003cp\u003eFor GEO, GSM ID is required for ID, which will be used as an quiry to GEO database. For remote/local files, ID should be a part of the file name. The pipeline greps bam/fastq files with ID on the specified path. The pipeline grabs bam/fastq files with ID on the specified path. If there is none or multiple files with the specified ID on the path, it will give an error.\u003c/p\u003e\n\u003cp\u003eFor PeakPairs.csv, signal and input pairs need to be specified in the following format (in csv):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSignal\u003c/th\u003e\n\u003cth\u003eInput\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eID of ChIP-seq data\u003c/td\u003e\n\u003ctd\u003eID of Input data\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that IDs used in the PeakPairs.csv should be available in ID column of DataList.csv.\u003c/p\u003e\n\u003cp\u003eFor config.yaml, you can copy it from this repository and modify the parameters based on your need.\u003c/p\u003e\n", + "readme": "\u003ch3\u003e\u003ca id=\"user-content-scanindel-singularity-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#scanindel-singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScanIndel Singularity recipe\u003c/h3\u003e\n\u003cp\u003eScanIndel is a python program to detect indels (insertions and deletions) from NGS data by re-align and de novo assemble soft clipped reads.\u003c/p\u003e\n\u003cp\u003eOriginal repository \u003ca href=\"https://github.com/cauyrd/ScanIndel\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1534943715.0 + "updated_at": 1539032220.0 }, { "data_format": 2, @@ -15823,281 +15259,292 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-centos7-openapi-basekit", + "full_name": "melnel000/Sarek_CBIO", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker-c7-openapi-basekit-\" class=\"anchor\" href=\"#docker-c7-openapi-basekit-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker-c7-openapi-basekit \u003ca href=\"https://github.com/truatpasteurdotfr/docker-c7-openapi-basekit/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/docker-c7-openapi-basekit/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"\" class=\"anchor\" aria-hidden=\"true\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"http://sarek.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/Sarek_logo.png\" alt=\"Sarek\" title=\"Sarek\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003ch4\u003e\u003ca id=\"user-content-an-open-source-analysis-pipeline-to-detect-germline-or-somatic-variants-from-whole-genome-or-targeted-sequencing\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-open-source-analysis-pipeline-to-detect-germline-or-somatic-variants-from-whole-genome-or-targeted-sequencing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAn open-source analysis pipeline to detect germline or somatic variants from whole genome or targeted sequencing\u003c/h4\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8165e759b147d5dfd77c2603211746a0ec20eae5aaea1c6a882604a6093c564c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e7376673f6c6f676f3d646174613a696d6167652f7376672b786d6c3b6261736536342c5044393462577767646d567963326c76626a30694d5334774969426c626d4e765a476c755a7a3069565652474c54676949484e305957356b59577876626d5539496d3576496a382b50484e325a794167494868746247357a4f6d526a50534a6f644852774f6938766348567962433576636d63765a474d765a57786c6257567564484d764d5334784c7949674943423462577875637a706a597a30696148523063446f764c324e795a57463061585a6c593239746257397563793576636d6376626e4d6a49694167494868746247357a4f6e4a6b5a6a30696148523063446f764c336433647935334d793576636d63764d546b354f5338774d6938794d6931795a47597463336c75644746344c57357a497949674943423462577875637a707a646d6339496d6830644841364c79393364336375647a4d7562334a6e4c7a49774d44417663335a6e49694167494868746247357a50534a6f644852774f693876643364334c6e637a4c6d39795a7938794d4441774c334e325a7949674943423462577875637a707a623252706347396b615430696148523063446f764c334e765a476c77623252704c6e4e7664584a6a5a575a76636d646c4c6d356c64433945564551766332396b615842765a476b744d43356b644751694943416765473173626e4d366157357263324e6863475539496d6830644841364c793933643363756157357263324e686347557562334a6e4c3235686257567a6347466a5a584d766157357263324e68634755694943416764326c6b64476739496a45794c6a63354f5449794f473174496941674947686c6157646f644430694d5449754f4441304f4441356257306949434167646d6c6c64304a76654430694d434177494451314c6a4d314d5455354e4341304e53347a4e7a457a4e6a6b694943416761575139496e4e325a7a63324e54496949434167646d567963326c76626a30694d5334784969416749476c7561334e6a5958426c4f6e5a6c636e4e7062323439496a41754f544567636a457a4e7a49314969416749484e765a476c77623252704f6d52765932356862575539496d356c6548526d624739334c575a68646d6c6a62323474643268706447557563335a6e496a34674944786b5a575a7a49434167494342705a4430695a47566d637a63324e5451694943382b494341386332396b615842765a476b36626d46745a5752326157563349434167494342705a443069596d467a5a53496749434167494842685a32566a62327876636a306949325a6d5a6d5a6d5a6949674943416749474a76636d526c636d4e76624739795053496a4e6a59324e6a59324969416749434167596d39795a4756796233426859326c30655430694d53347749694167494341676157357263324e68634755366347466e5a57397759574e7064486b39496a41754d4349674943416749476c7561334e6a5958426c4f6e42685a32567a6147466b62336339496a49694943416749434270626d747a593246775a54703662323974505349334c6a6b784f5455354e546b694943416749434270626d747a593246775a54706a654430694d6a41754d54457a4d6a4d3149694167494341676157357263324e686347553659336b39496a497a4c6a45324d7a6b774f4349674943416749476c7561334e6a5958426c4f6d5276593356745a5735304c5856756158527a50534a77654349674943416749476c7561334e6a5958426c4f6d4e31636e4a6c626e5174624746355a584939496d7868655756794d5349674943416749484e6f6233646e636d6c6b50534a6d5957787a5a5349674943416749475a706443317459584a6e61573474644739775053497749694167494341675a6d6c304c573168636d6470626931735a575a305053497749694167494341675a6d6c304c573168636d6470626931796157646f644430694d4349674943416749475a706443317459584a6e61573474596d3930644739745053497749694167494341676157357263324e686347553664326c755a4739334c5864705a48526f505349784f54497749694167494341676157357263324e686347553664326c755a4739334c57686c6157646f644430694d5441784e5349674943416749476c7561334e6a5958426c4f6e6470626d5276647931345053497749694167494341676157357263324e686347553664326c755a4739334c586b39496a41694943416749434270626d747a593246775a5470336157356b623363746257463461573170656d566b5053497849694176506941675047316c6447466b5958526849434167494342705a4430696257563059575268644745334e6a5533496a34674943416750484a6b5a6a70535245592b494341674943416750474e6a4f6c6476636d73674943416749434167494342795a47593659574a76645851394969492b4943416749434167494341385a474d365a6d397962574630506d6c745957646c4c334e325a797434625777384c32526a4f6d5a76636d31686444346749434167494341674944786b597a70306558426c494341674943416749434167494342795a475936636d567a6233567959325539496d6830644841364c79397764584a734c6d39795a79396b5979396b5932317064486c775a53395464476c7362456c745957646c496941765069416749434167494341675047526a4f6e52706447786c506a77765a474d3664476c306247552b49434167494341675043396a597a705862334a7250694167494341384c334a6b5a6a70535245592b494341384c32316c6447466b5958526850694167504763674943416749476c7561334e6a5958426c4f6d7868596d567350534a4d59586c6c6369417849694167494341676157357263324e68634755365a334a76645842746232526c50534a7359586c6c636949674943416749476c6b50534a7359586c6c636a45694943416749434230636d467563325a76636d3039496e52795957357a624746305a5367784d5451754d5441304d7a63734c5451314d6934314d7a4d324e696b6950694167494341386347463061434167494341674943427a64486c735a5430695a6d6c7362446f6a5a6d5a6d5a6d5a6d49694167494341674943426b50534a74494330784d5451754d5441304d7a63734e4455314c6a51324e545979494441734f4334344e6a457a4d7941774c6a49774d7a457a4c4441754d4459774e53426a49444d754f4463794f544d734d5334784d7a6b304d79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alt=\"Nextflow version\" 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src=\"https://camo.githubusercontent.com/720a0b93892db5c772d24eb7dc2fd6fefb2b556eff92ee7ae6a2963a40a8dd5a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f5363694c6966654c61622f536172656b2e7376673f6c6f676f3d676974687562266c6f676f436f6c6f723d7768697465\" alt=\"Sarek version\" data-canonical-src=\"https://img.shields.io/github/release/SciLifeLab/Sarek.svg?logo=github\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/54024046\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2794ec0225017cde71e3ed51dd8393510fe23a950955ef03f7439d7c0f288f83/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f35343032343034362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/54024046.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.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\" alt=\"Install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?logo=data:image/png;base64,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\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/maxulysse/sarek\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bc3bec2ef3bf857d42e0bff8df09f0e81595bbd7dbc2681d0feadd729acb4bc0/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6d6178756c797373652f736172656b2e7376673f6c6f676f3d646f636b6572\" alt=\"Docker Container available\" data-canonical-src=\"https://img.shields.io/docker/automated/maxulysse/sarek.svg?logo=docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/CAW_logo.png\"\u003e\u003cimg align=\"right\" title=\"CAW\" src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/CAW_logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePreviously known as the Cancer Analysis Workflow (CAW),\nSarek is a workflow designed to run analyses on WGS data from regular samples or tumour / normal pairs, including relapse samples if required.\u003c/p\u003e\n\u003cp\u003eIt\u0027s built using \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a domain specific language for workflow building.\nSoftware dependencies are handled using \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e - container technologies that provide excellent reproducibility and ease of use.\nSingularity has been designed specifically for high-performance computing environments.\nThis means that although Sarek has been primarily designed for use with the Swedish \u003ca href=\"https://www.uppmax.uu.se\" rel=\"nofollow\"\u003eUPPMAX HPC systems\u003c/a\u003e, it should be able to run on any system that supports these two tools.\u003c/p\u003e\n\u003cp\u003eSarek was developed at the \u003ca href=\"https://ngisweden.scilifelab.se/\" rel=\"nofollow\"\u003eNational Genomics Infastructure\u003c/a\u003e and \u003ca href=\"https://www.nbis.se/\" rel=\"nofollow\"\u003eNational Bioinformatics Infastructure Sweden\u003c/a\u003e which are both platforms at \u003ca href=\"https://www.scilifelab.se/\" rel=\"nofollow\"\u003eSciLifeLab\u003c/a\u003e.\nIt is listed on the \u003ca href=\"https://bio.tools/Sarek\" rel=\"nofollow\"\u003eElixir - Tools and Data Services Registry\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-workflow-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflow-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow steps\u003c/h2\u003e\n\u003cp\u003eSarek is built with several workflow scripts.\nA wrapper script contained within the repository makes it easy to run the different workflow scripts as a single job.\nTo test your installation, follow the \u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/TESTS.md\"\u003etests documentation.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRaw FastQ files or aligned BAM files (with or without realignment \u0026amp; recalibration) can be used as inputs.\nYou can choose which variant callers to use, plus the pipeline is capable of accommodating additional variant calling software or CNV callers if required.\u003c/p\u003e\n\u003cp\u003eThe worflow steps and tools used are as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003ePreprocessing\u003c/strong\u003e - \u003ccode\u003emain.nf\u003c/code\u003e \u003cem\u003e(based on \u003ca href=\"https://software.broadinstitute.org/gatk/best-practices/\" rel=\"nofollow\"\u003eGATK best practices\u003c/a\u003e)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eMap reads to Reference\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://bio-bwa.sourceforge.net/\" rel=\"nofollow\"\u003eBWA\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMark Duplicates\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK MarkDuplicates\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBase (Quality Score) Recalibration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK BaseRecalibrator\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK ApplyBQSR\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGermline variant calling\u003c/strong\u003e - \u003ccode\u003egermlineVC.nf\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eSNVs and small indels\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK HaplotyeCaller\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka2\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStructural variants\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/manta\"\u003eManta\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSomatic variant calling\u003c/strong\u003e - \u003ccode\u003esomaticVC.nf\u003c/code\u003e \u003cem\u003e(optional)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eSNVs and small indels\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eMuTect2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ekg/freebayes\"\u003eFreebayes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka2\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStructural variants\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/manta\"\u003eManta\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSample heterogeneity, ploidy and CNVs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Crick-CancerGenomics/ascat\"\u003eASCAT\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAnnotation\u003c/strong\u003e - \u003ccode\u003eannotate.nf\u003c/code\u003e \u003cem\u003e(optional)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eVariant annotation\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://snpeff.sourceforge.net/\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ensembl.org/info/docs/tools/vep/index.html\" rel=\"nofollow\"\u003eVEP (Variant Effect Predictor)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReporting\u003c/strong\u003e - \u003ccode\u003erunMultiQC.nf\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eReporting\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe Sarek pipeline comes with documentation in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL.md\"\u003eInstallation documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL_RACKHAM.md\"\u003eInstallation documentation specific for UPPMAX \u003ccode\u003erackham\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL_BIANCA.md\"\u003eInstallation documentation specific for UPPMAX \u003ccode\u003ebianca\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/TESTS.md\"\u003eTests documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/REFERENCES.md\"\u003eReference files documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONFIG.md\"\u003eConfiguration and profiles documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INTERVALS.md\"\u003eIntervals documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/USAGE.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/PARAMETERS.md\"\u003eCommand line parameters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/USE_CASES.md\"\u003eExamples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INPUT.md\"\u003eInput files documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/PROCESS.md\"\u003eProcesses documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONTAINERS.md\"\u003eDocumentation about containers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/ASCAT.md\"\u003eMore information about ASCAT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/OUTPUT.md\"\u003eOutput documentation structure\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions--support\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions \u0026amp; Support\u003c/h2\u003e\n\u003cp\u003eIf you would like to contribute to this pipeline, please see the \u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/.github/CONTRIBUTING.md\"\u003econtributing guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor further information or help, don\u0027t hesitate to get in touch on \u003ca href=\"https://gitter.im/SciLifeLab/Sarek\" rel=\"nofollow\"\u003eGitter\u003c/a\u003e or contact us: \u003ca href=\"mailto:maxime.garcia@scilifelab.se\"\u003emaxime.garcia@scilifelab.se\u003c/a\u003e, \u003ca href=\"mailto:szilveszter.juhos@scilifelab.se\"\u003eszilveszter.juhos@scilifelab.se\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-hidden=\"true\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCHANGELOG\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/CHANGELOG.md\"\u003eCHANGELOG\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eMain authors:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MaxUlysse\"\u003eMaxime Garcia\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/szilvajuhos\"\u003eSzilveszter Juhos\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHelpful contributors:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/alneberg\"\u003eJohannes Alneberg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Sebastian-D\"\u003eSebastian DiLorenzo\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/J35P312\"\u003eJesper Eisfeldt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ewels\"\u003ePhil Ewels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gulfshores\"\u003eMax K\u00e4ller\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/malinlarsson\"\u003eMalin Larsson\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/marcelm\"\u003eMarcel Martin\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bjornnystedt\"\u003eBj\u00f6rn Nystedt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pallolason\"\u003ePall Olason\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arontommi\"\u003eAron Skaftason\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca href=\"https://www.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/SciLifeLab_logo.png\" alt=\"SciLifeLab\" title=\"SciLifeLab\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ngisweden.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/NGI_logo.png\" alt=\"NGI\" title=\"NGI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nbis.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/NBIS_logo.png\" alt=\"NBIS\" title=\"NBIS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1635355882.0 + "updated_at": 1541579046.0 }, { "data_format": 2, - "description": "Repository containing code for the paper \"Shared neural codes for visual and semantic information about familiar others in a common representational space\"", + "description": "Snakemake workflow for analysis and assembly of viral genomes from IonTorrent AmpliSeq data.", "filenames": [ - "singularity/Singularity-neurodocker" + "Singularity" ], - "full_name": "mvdoc/identity-decoding", - "latest_release": "1.0.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-shared-neural-codes-for-visual-and-semantic-information-about-familiar-faces-in-a-common-representational-space\" class=\"anchor\" href=\"#shared-neural-codes-for-visual-and-semantic-information-about-familiar-faces-in-a-common-representational-space\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShared neural codes for visual and semantic information about familiar faces in a common representational space\u003c/h1\u003e\n\u003cp\u003eThis repository contains the code for the analyses reported in \u003cem\u003eShared neural codes for visual and semantic information about familiar faces in a common representational space\u003c/em\u003e by Matteo Visconti di Oleggio Castello, James V. Haxby, \u0026amp; M. Ida Gobbini published in the \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe reference for the associated publication is\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVisconti di Oleggio Castello, M., Haxby, J. V., \u0026amp; Gobbini, M. I. Shared neural codes for visual and semantic information about familiar faces in a common representational space \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e (2021). \u003ca href=\"https://doi.org/10.1073/pnas.2110474118\" rel=\"nofollow\"\u003ehttps://doi.org/10.1073/pnas.2110474118\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis repository can be cited as\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVisconti di Oleggio Castello, M., Haxby, J. V., \u0026amp; Gobbini, M. I. (2021). mvdoc/identity-decoding. \u003cem\u003eZenodo\u003c/em\u003e. \u003ca href=\"https://doi.org/10.5281/zenodo.5645003\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.5645003\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/344613702\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31de84d904523cf98d5215b7c3dac0af54476f3416c24e0ee28469dc04ef9647/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3334343631333730322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/344613702.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-disclaimer--how-to-get-help\" class=\"anchor\" href=\"#disclaimer--how-to-get-help\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer \u0026amp; how to get help\u003c/h2\u003e\n\u003cp\u003eThese scripts are shared in a format that is suitable for archival and review. All analyses were run inside a singularity container (shared in the current repository) on a local cluster and on \u003ca href=\"https://rc.dartmouth.edu/index.php/discovery-overview/\" rel=\"nofollow\"\u003eDiscovery, Dartmouth\u0027s HPC cluster\u003c/a\u003e. The paths listed in these scripts need to be modified in order to run the scripts on a different system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIf you have any questions related to the code, please open an issue in this repository or contact us via email (see corresponding author in the publication).\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-data\" class=\"anchor\" href=\"#data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003cp\u003eThe raw data is available on OpenNeuro as the dataset \u003ccode\u003eds003834\u003c/code\u003e: \u003ca href=\"https://openneuro.org/datasets/ds003834\" rel=\"nofollow\"\u003ehttps://openneuro.org/datasets/ds003834\u003c/a\u003e.\nIf you use the data, please cite the corresponding publication:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVisconti di Oleggio Castello, M., Haxby, J. V., \u0026amp; Gobbini, M. I. Shared neural codes for visual and semantic information about familiar faces in a common representational space. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e (2021). \u003ca href=\"https://doi.org/10.5281/zenodo.5645003\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.5645003\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-repository-structure\" class=\"anchor\" href=\"#repository-structure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepository structure\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"singularity\"\u003e\u003ccode\u003esingularity\u003c/code\u003e\u003c/a\u003e contains code to generate the singularity image that was used to run all analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"src\"\u003e\u003ccode\u003esrc\u003c/code\u003e\u003c/a\u003e contains a python package (\u003ccode\u003efamfaceangles\u003c/code\u003e) containing various general functions used in the analysis scripts\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts\"\u003e\u003ccode\u003escripts\u003c/code\u003e\u003c/a\u003e contains the scripts used for the analyses reported in the manuscript\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn the following sections we describe each file in detail.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h3\u003e\n\u003cp\u003eThis folder contains the following files\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity-neurodocker\u003c/code\u003e: a singularity definition file for the image used in all analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecreate-image.sh\u003c/code\u003e: a bash script to generate the singularity image. Note that the syntax used in this script is for singularity versions 2.X. New versions of singularity will need a different syntax, and they have not been tested with this definition file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-src\" class=\"anchor\" href=\"#src\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esrc\u003c/h3\u003e\n\u003cp\u003eThis folder contains the python package \u003ccode\u003efamfaceangles\u003c/code\u003e with helper functions used in the analysis scripts. It can be installed as any other python package (e.g., \u003ccode\u003epip install -e src\u003c/code\u003e)\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-scripts\" class=\"anchor\" href=\"#scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escripts\u003c/h3\u003e\n\u003cp\u003eThis folder contains the following scripts\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-preprocessing\" class=\"anchor\" href=\"#preprocessing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessing\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/00preproc/run-fmriprep103-singularity.sh\"\u003e\u003ccode\u003e00preproc/run-fmriprep103-singularity.sh\u003c/code\u003e\u003c/a\u003e calls fmriprep to preprocess the data.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/00preproc/prepare-fsaverage6-suma.sh\"\u003e\u003ccode\u003e00preproc/prepare-fsaverage6-suma.sh\u003c/code\u003e\u003c/a\u003e prepares the \u003cem\u003efsaverage6\u003c/em\u003e surfaces to be used with SUMA.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/00preproc/make-maskmedial-fsaverage6.sh\"\u003e\u003ccode\u003e00preproc/make-maskmedial-fsaverage6.sh\u003c/code\u003e\u003c/a\u003e creates a mask in NIML format to remove medial nodes in \u003cem\u003efsaverage6\u003c/em\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-hyperalignment\" class=\"anchor\" href=\"#hyperalignment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperalignment\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/run_preproc_hpal.py\"\u003e\u003ccode\u003e01hyperalignment/run_preproc_hpal.py\u003c/code\u003e\u003c/a\u003e preprocesses the data from \u003cem\u003eThe Grand Budapest Hotel\u003c/em\u003e to be used for hyperalignment.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/run_preproc_hpal_singularity.sh\"\u003e\u003ccode\u003e01hyperalignment/run_preproc_hpal_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/run_hpal.py\"\u003e\u003ccode\u003e01hyperalignment/run_hpal.py\u003c/code\u003e\u003c/a\u003e runs the hyperalignment algorithm.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/run_hpal_singularity.sh\"\u003e\u003ccode\u003e01hyperalignment/run_hpal_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/apply_hpal.py\"\u003e\u003ccode\u003e01hyperalignment/apply_hpal.py\u003c/code\u003e\u003c/a\u003e applies the hyperalignment transformations to the input data.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/01hyperalignment/apply_hpal_singularity.sh\"\u003e\u003ccode\u003e01hyperalignment/apply_hpal_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-glm\" class=\"anchor\" href=\"#glm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGLM\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_model.py\"\u003e\u003ccode\u003e02glm/run_glm_model.py\u003c/code\u003e\u003c/a\u003e runs a GLM model for the face perception experiment using the specified model.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_blockrun_singularity.sh\"\u003e\u003ccode\u003e02glm/run_glm_blockrun_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity on anatomically-aligned data and a BLOCK model estimated within each run.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_blockrun_hpal_singularity.sh\"\u003e\u003ccode\u003e02glm/run_glm_blockrun_hpal_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity on hyperaligned data and a BLOCK model estimated within each run.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_localizer_bwsj.py\"\u003e\u003ccode\u003e02glm/run_glm_localizer_bwsj.py\u003c/code\u003e\u003c/a\u003e runs the GLM model for the hyperaligned localizer data.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/run_glm_localizer_bwsj_singularity.sh\"\u003e\u003ccode\u003e02glm/run_glm_localizer_bwsj_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/02glm/workflows.py\"\u003e\u003ccode\u003e02glm/workflows.py\u003c/code\u003e\u003c/a\u003e contains additional functions and Nipype workflows required to run the GLM models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-mvpa\" class=\"anchor\" href=\"#mvpa\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMVPA\u003c/h4\u003e\n\u003cp\u003eBetween-subject searchlight decoding\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_bwsbj.py\"\u003e\u003ccode\u003e03mvpa/run_sl_bwsbj.py\u003c/code\u003e\u003c/a\u003e runs between-subject whole-brain searchlight decoding.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_bwsbj_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_bwsbj_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity on hyperaligned data.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_bwsbj_fsaverage6_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_bwsbj_fsaverage6_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity on anatomically-aligned data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBetween-subject ROI decoding\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_bwsj_roi_v2.py\"\u003e\u003ccode\u003e03mvpa/run_bwsj_roi_v2.py\u003c/code\u003e\u003c/a\u003e runs between-subject decoding analyses within manually defined ROIs.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_bwsj_roi_v2_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_bwsj_roi_v2_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_roi.py\"\u003e\u003ccode\u003e03mvpa/run_sl_roi.py\u003c/code\u003e\u003c/a\u003e contains some additional functions needed for ROI decoding.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWithin-subject searchlight decoding\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl.py\"\u003e\u003ccode\u003e03mvpa/run_sl.py\u003c/code\u003e\u003c/a\u003e runs within-subject whole-brain searchlight decoding.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_blockrun_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_blockrun_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_blockrun_permutation_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_blockrun_permutation_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity to generate permuted maps.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCross-validated RSA\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_cvrsa.py\"\u003e\u003ccode\u003e03mvpa/run_sl_cvrsa.py\u003c/code\u003e\u003c/a\u003e runs within-subject searchlight cross-validated RSA.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_sl_cvrsa_blockrun_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_sl_cvrsa_blockrun_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_rsa_target.py\"\u003e\u003ccode\u003e03mvpa/run_rsa_target.py\u003c/code\u003e\u003c/a\u003e runs model-based RSA by comparing the cross-validated brain RDMs with model RDMs.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/03mvpa/run_rsa_target_singularity.sh\"\u003e\u003ccode\u003e03mvpa/run_rsa_target_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-statistics\" class=\"anchor\" href=\"#statistics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStatistics\u003c/h4\u003e\n\u003cp\u003ePermutation testing for between-subject MVPC\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_permtest_bootstrap.py\"\u003e\u003ccode\u003e04stat/run_permtest_bootstrap.py\u003c/code\u003e\u003c/a\u003e runs permutation testing with bootstrapping.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_permtest_bwsbj_bootstrap_singularity.sh\"\u003e\u003ccode\u003e04stat/run_permtest_bwsbj_bootstrap_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_fam-diff_bwsj_identity.sh\"\u003e\u003ccode\u003e04stat/make_fam-diff_bwsj_identity.sh\u003c/code\u003e\u003c/a\u003e creates difference maps (familiar - visual) from precomputed accuracy maps.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_permtest_famdiff_bwsbj_bootstrap_singularity.sh\"\u003e\u003ccode\u003e04stat/run_permtest_famdiff_bwsbj_bootstrap_singularity.sh\u003c/code\u003e\u003c/a\u003e runs permutation testing on the familiar - visual difference maps.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make-maskfdrval-diff-identity-bsmvpc.sh\"\u003e\u003ccode\u003e04stat/make-maskfdrval-diff-identity-bsmvpc.sh\u003c/code\u003e\u003c/a\u003e makes a mask that highlights significant nodes for the familiar - visual difference map.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThreshold-Free Cluster Enhancement for within-subject MVPC and RSA\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_tfce_fsaverage6_cosmo.m\"\u003e\u003ccode\u003e04stat/run_tfce_fsaverage6_cosmo.m\u003c/code\u003e\u003c/a\u003e runs the CoSMoMVPA TFCE code for within-subject MVPC.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_tfce_fsaverage6_cosmo_blockrun_hpalsubjs_singularity.sh\"\u003e\u003ccode\u003e04stat/run_tfce_fsaverage6_cosmo_blockrun_hpalsubjs_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_blockrun_fsaverage6-hpal.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_blockrun_fsaverage6-hpal.sh\u003c/code\u003e\u003c/a\u003e creates thresholded maps based on the TFCE values.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_blockrun_fsaverage6-hpal_all.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_blockrun_fsaverage6-hpal_all.sh\u003c/code\u003e\u003c/a\u003e calls the previous script for all comparisons of interest.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_tfce_cvrsa_fsaverage6_cosmo.m\"\u003e\u003ccode\u003e04stat/run_tfce_cvrsa_fsaverage6_cosmo.m\u003c/code\u003e\u003c/a\u003e runs the CoSMoMVPA TFCE code for within-subject cross-validated RSA.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/run_tfce_cvrsa_fsaverage6_cosmo_singularity.sh\"\u003e\u003ccode\u003e04stat/run_tfce_cvrsa_fsaverage6_cosmo_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example call with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_cvrsa_fsaverage6.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_cvrsa_fsaverage6.sh\u003c/code\u003e\u003c/a\u003e creates thresholded maps based on the TFCE values.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_cvrsa_fsaverage6_singularity.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_cvrsa_fsaverage6_singularity.sh\u003c/code\u003e\u003c/a\u003e shows an example with singularity.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/04stat/make_thresholded_avg_cvrsa_fsaverage6_all.sh\"\u003e\u003ccode\u003e04stat/make_thresholded_avg_cvrsa_fsaverage6_all.sh\u003c/code\u003e\u003c/a\u003e calls the previous script for all comparisons of interest.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-visualization\" class=\"anchor\" href=\"#visualization\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisualization\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"scripts/05viz/drive-suma-blockrun-fsaverage6-group-hpal.sh\"\u003e\u003ccode\u003e05viz/drive-suma-blockrun-fsaverage6-group-hpal.sh\u003c/code\u003e\u003c/a\u003e shows an example call to \u003ccode\u003eDriveSuma\u003c/code\u003e to generate surface plots.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" href=\"#acknowledgements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the NSF grant #1835200 to M. Ida Gobbini. We would like to thank Swaroop Guntupalli, Yaroslav Halchenko, Carlo Cipolli, and the members of the Gobbini and Haxby lab for helpful discussions during the development of this project.\u003c/p\u003e\n", + "full_name": "peterk87/viral-ampliseq-assembly", + "latest_release": "v1.0.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-snakemake-workflow-viral-ampliseq-assembly\" class=\"anchor\" aria-hidden=\"true\" href=\"#snakemake-workflow-viral-ampliseq-assembly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakemake workflow: viral-ampliseq-assembly\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://snakemake.bitbucket.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/de7b3ae9d2ddd7970750ed14a267d738217987e5635a19380de6f3b2ec3216e6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135352e352e342d627269676874677265656e2e737667\" alt=\"Snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%E2%89%A55.5.4-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/peterk87/viral-ampliseq-assembly\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9ca62ba99cb6a38032432759aa450c99bf81b9671bab9e21e2492c47bf7cf065/68747470733a2f2f7472617669732d63692e6f72672f70657465726b38372f766972616c2d616d706c697365712d617373656d626c792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/peterk87/viral-ampliseq-assembly.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3359\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://snakemake.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e workflow for analysis and assembly of viral genomes such as Classical Swine Fever Virus (\u003ca href=\"https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=11096\" rel=\"nofollow\"\u003eCSFV\u003c/a\u003e) from IonTorrent AmpliSeq data.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePreprocessing\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDuplicate reads were removed using \u003ca href=\"https://broadinstitute.github.io/picard/\" rel=\"nofollow\"\u003ePicard\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReads were trimmed with \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003eTrimmomatic\u003c/a\u003e prior to \u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003eSPAdes\u003c/a\u003e assembly\u003c/li\u003e\n\u003cli\u003eBAM file stats computed using \u003ca href=\"https://samtools.github.io/\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e (coverage depth, extent, extent per genome, # of reads mapped)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReference Genome Selection\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownloading of all Classical swine fever virus (\u003ca href=\"https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=11096\" rel=\"nofollow\"\u003eCSFV\u003c/a\u003e) (or FMDV, Ebola, Zika) virus genomes from \u003ca href=\"https://www.ncbi.nlm.nih.gov/books/NBK25501/\" rel=\"nofollow\"\u003eNCBI Entrez API\u003c/a\u003e using \u003ca href=\"https://biopython.org/\" rel=\"nofollow\"\u003eBioPython\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mash.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eMash\u003c/a\u003e screen of deduplicated reads against all reference genomes with sketch size of 10000 and sketch k-mer size of 16, sorting by Mash screen identity to find top reference genome for read mapping and variant calling\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRead Mapping \u0026amp; Variant Calling\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRead mapping with \u003ca href=\"https://github.com/lh3/bwa\"\u003eBWA MEM\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRemoval of duplicate reads with \u003ca href=\"https://samtools.github.io/\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVariant calling with \u003ca href=\"https://github.com/ekg/freebayes\"\u003eFreeBayes\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://snpeff.sourceforge.net/SnpEff.html\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e was used to predict and report variant effects using reference genome annotation\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDe Novo Assembly\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003eSPAdes\u003c/a\u003e de novo assembly of trimmed deduplicated reads.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://quast.sourceforge.net/quast.html\" rel=\"nofollow\"\u003eQUAST\u003c/a\u003e quality assessment of assemblies\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eQuality Control\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://multiqc.info/\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e interactive report of \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e, \u003ca href=\"https://samtools.github.io/\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e, \u003ca href=\"http://quast.sourceforge.net/quast.html\" rel=\"nofollow\"\u003eQUAST\u003c/a\u003e, \u003ca href=\"http://snpeff.sourceforge.net/SnpEff.html\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePhylogenetic Tree\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePhylogenetic tree constructed with \u003ca href=\"http://www.iqtree.org/\" rel=\"nofollow\"\u003eIQ-TREE\u003c/a\u003e (or \u003ca href=\"http://bioinformatics.hungry.com/clearcut/\" rel=\"nofollow\"\u003eClearcut\u003c/a\u003e if a quick and dirty tree is okay)\u003c/li\u003e\n\u003cli\u003eInteractive HTML phylogenetic tree visualization with \u003ca href=\"http://phylocanvas.org/\" rel=\"nofollow\"\u003ePhyloCanvas\u003c/a\u003e using \u003ca href=\"https://github.com/peterk87/shiptv\"\u003eshiptv\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePeter Kruczkiewicz (@peterk87)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-0-install-pre-requisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-0-install-pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 0: Install pre-requisites\u003c/h3\u003e\n\u003cp\u003eRunning this workflow with \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is recommended, but you can use \u003ca href=\"https://conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e if you prefer. The Singularity image will come with all the dependencies bundled together in a single file.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-singularity-recommended\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-singularity-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (recommended)\u003c/h4\u003e\n\u003cp\u003eFollow the instructions for installing Singularity \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/quick_start.html#quick-start\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-setup-and-activate-the-conda-environment-if-not-using-singularity-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-and-activate-the-conda-environment-if-not-using-singularity-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup and activate the \u003ca href=\"https://conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e environment if not using \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (optional)\u003c/h4\u003e\n\u003cp\u003eInstall \u003ca href=\"https://conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e if you haven\u0027t already following \u003ca href=\"https://conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ethese instructions\u003c/a\u003e and setup the \u003ca href=\"https://bioconda.github.io/user/install.html#set-up-channels\" rel=\"nofollow\"\u003eBioConda channel\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDownload or \u003ccode\u003egit clone\u003c/code\u003e this repo\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/peterk87/viral-ampliseq-assembly.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e viral-ampliseq-assembly\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e create a conda environment named \"viral-ampliseq-assembly-1.0.0\"\u003c/span\u003e\nconda env create -f environment.yml\nconda activate viral-ampliseq-assembly-1.0.0\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install snakemake into this env\u003c/span\u003e\nconda install -y snakemake\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run Snakemake on the test directory\u003c/span\u003e\nsnakemake --directory test/\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-install-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-install-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1: Install workflow\u003c/h3\u003e\n\u003cp\u003eIf you simply want to use this workflow, download and extract the \u003ca href=\"https://github.com/peterk87/viral-ampliseq-assembly/releases\"\u003elatest release\u003c/a\u003e.\nIf you intend to modify and further develop this workflow, fork this repository. Please consider providing any generally applicable modifications via a pull request.\u003c/p\u003e\n\u003cp\u003eIn any case, if you use this workflow in a paper, don\u0027t forget to give credits to the authors by citing the URL of this repository and, if available, its DOI (see above).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-configure-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-configure-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2: Configure workflow\u003c/h3\u003e\n\u003cp\u003eCreate an analysis directory, copy and modify the example \u003ccode\u003econfig.yaml\u003c/code\u003e and \u003ccode\u003esamples.tsv\u003c/code\u003e files to suit your needs.\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir ~/my-ampliseq-analysis\ncp viral-ampliseq-assembly/config.yaml ~/my-ampliseq-analysis/\ncp viral-ampliseq-assembly/samples.tsv ~/my-ampliseq-analysis/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEdit your \u003ccode\u003econfig.yaml\u003c/code\u003e as needed.\u003c/p\u003e\n\u003cp\u003eAdd sample entries to your \u003ccode\u003esamples.tsv\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esample bam_file\nSample1 bams/Sample1.bam\nSample2 bams/Sample2.bam\nSample3 bams/Sample3.bam\n... \u0026lt;more sample entries\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003ebam_file\u003c/code\u003e can be the relative or absolute path to a sample\u0027s BAM file.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-iq-tree-maximum-likelihood-or-clearcut-rnj-tree\" class=\"anchor\" aria-hidden=\"true\" href=\"#iq-tree-maximum-likelihood-or-clearcut-rnj-tree\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca href=\"http://www.iqtree.org/\" rel=\"nofollow\"\u003eIQ-TREE\u003c/a\u003e maximum-likelihood or \u003ca href=\"http://bioinformatics.hungry.com/clearcut/\" rel=\"nofollow\"\u003eClearcut\u003c/a\u003e RNJ tree\u003c/h4\u003e\n\u003cp\u003eIn your \u003ccode\u003econfig.yaml\u003c/code\u003e the \u003ccode\u003efast_tree\u003c/code\u003e parameter controls which method (ML or RNJ) is used for phylogenetic tree construction.\u003c/p\u003e\n\u003cp\u003eIf you want a quick and dirty tree, set\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003efast_tree\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ein your \u003ccode\u003econfig.yaml\u003c/code\u003e to generate a Relaxed Neighbor Joining (RNJ) tree.\u003c/p\u003e\n\u003cp\u003eOtherwise, if you want a high accuracy phylogenetic tree and are willing to wait for it, then set\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003efast_tree\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto use \u003ca href=\"http://www.iqtree.org/\" rel=\"nofollow\"\u003eIQ-TREE\u003c/a\u003e to generate a maximum-likelihood phylogenetic tree with 1000 ultrafast bootstraps (UFBoot) (see \u003ca href=\"http://dx.doi.org/10.1093/molbev/mst024\" rel=\"nofollow\"\u003eMinh et al., 2016\u003c/a\u003e for more info on UFBoot).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-3-execute-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-3-execute-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 3: Execute workflow\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eIf you do not have \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed then remove the \u003ccode\u003e--use-singularity\u003c/code\u003e flag\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTest your configuration by performing a dry-run via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity -n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the workflow locally via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity --cores $N\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eusing \u003ccode\u003e$N\u003c/code\u003e cores.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-cluster-execution\" class=\"anchor\" aria-hidden=\"true\" href=\"#cluster-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCluster execution\u003c/h4\u003e\n\u003cp\u003e\u003cem\u003eNote: You may need to install the \u003ccode\u003edrmaa\u003c/code\u003e Python library (\u003ccode\u003epip install drmaa\u003c/code\u003e)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eYou can execute the workflow on a SLURM/DRMAA cluster environment with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --directory test --use-singularity --drmaa \" -c 4 -p YourClusterQueueName --mem=4096 \" -j 8 -w 60\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the workflow on the test data in the \u003ccode\u003etest/\u003c/code\u003e directory with 4 CPUs and 4G memory per job and 8 jobs at once (\u003ccode\u003e-j 8\u003c/code\u003e) while waiting 60 seconds for output files to appear on the shared filesystem (\u003ccode\u003e-w 60\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThe cluster partition or queue to schedule jobs to is specified with \u003ccode\u003e-p YourClusterQueueName\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe above will run each rule or job with 4 CPUs and 4GB memory each, which may be way more than needed or not enough so you could create a YAML (or JSON) file to specify default and specific resource requirements for some steps:\u003c/p\u003e\n\u003cp\u003eExample \u003ccode\u003ecluster-config.yaml\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003e__default__\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003epartition\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eYourClusterQueueName\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1024\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003esamtools_index_bam_initial\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e16384\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003espades_assembly\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e16384\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ebwa_mem\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4096\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emafft_msa\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4096\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eiqtree\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ecpu\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4096\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003esnpeff\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ememory\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4096\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWith the \u003ccode\u003ecluster-config.yaml\u003c/code\u003e, run the workflow in a cluster environment via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --directory test --use-singularity --cluster-config cluster-config.yaml --drmaa \" -c {cluster.cpu} -p {cluster.partition} --mem={cluster.memory} \" -j 8 -w 60\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the above command and \u003ccode\u003ecluster-config.yaml\u003c/code\u003e, by default, a rule or step in the workflow will only use 1 CPU and request 1G of memory, while the rules like \u003ccode\u003eiqtree\u003c/code\u003e or \u003ccode\u003espades_assembly\u003c/code\u003e will request more CPUs and memory from the SLURM/DRMAA scheduler.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003eSnakemake documentation\u003c/a\u003e for further details.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting\u003c/h2\u003e\n\u003cp\u003eTests cases are in the subfolder \u003ccode\u003etest\u003c/code\u003e. They should be executed via continuous integration with Travis CI.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003eIf you were to copy the files in \u003ccode\u003etest\u003c/code\u003e (\u003ccode\u003esamples.tsv\u003c/code\u003e, \u003ccode\u003ebam/\u003c/code\u003e and \u003ccode\u003econfig.yaml\u003c/code\u003e) to a new directory \u003ccode\u003emy-analysis-directory\u003c/code\u003e and run the workflow on that directory, i.e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --directory my-analysis-directory/ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e other args\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe contents of \u003ccode\u003emy-analysis-directory\u003c/code\u003e should look like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emy-analysis-directory\n\u251c\u2500\u2500 phylogeny \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Phylogenetic Tree Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 genome-metadata.tsv\n\u2502 \u2514\u2500\u2500 tree.html\n\u251c\u2500\u2500 config.yaml \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e INPUT: Workflow Execution Config File \u003c/span\u003e\n\u251c\u2500\u2500 qc \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Quality Control Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 multiqc.html \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e MultiQC report file\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 fastqc \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e FastQC Output\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 Sample1.html\n\u2502 \u2502 \u2514\u2500\u2500 Sample1_fastqc.zip\n\u2502 \u251c\u2500\u2500 multiqc_data\n\u2502 \u2502 \u251c\u2500\u2500 [Text files]\n\u2502 \u2514\u2500\u2500 quast \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e QUAST Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 report.tex\n\u2502 \u251c\u2500\u2500 icarus_viewers\n\u2502 \u2502 \u2514\u2500\u2500 contig_size_viewer.html\n\u2502 \u251c\u2500\u2500 report.html\n\u2502 \u251c\u2500\u2500 basic_stats\n\u2502 \u2502 \u251c\u2500\u2500 [QUAST PDFs]\n\u2502 \u251c\u2500\u2500 icarus.html\n\u2502 \u251c\u2500\u2500 transposed_report.tex\n\u2502 \u251c\u2500\u2500 quast.log\n\u2502 \u251c\u2500\u2500 report.pdf\n\u2502 \u251c\u2500\u2500 report.txt\n\u2502 \u251c\u2500\u2500 .snakemake_timestamp\n\u2502 \u251c\u2500\u2500 report.tsv\n\u2502 \u251c\u2500\u2500 transposed_report.tsv\n\u2502 \u2514\u2500\u2500 transposed_report.txt\n\u251c\u2500\u2500 variant_calling \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Variant Calling Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 Sample1-filtered.vcf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Filtered variants for Sample1 in VCF format\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 Sample1.vcf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Unfiltered variants for Sample1 in VCF format\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 snpeff \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SnpEff Output\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 Sample1\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 [SnpEff specific files]\n\u2502 \u2502 \u251c\u2500\u2500 Sample1.vcf\n\u2502 \u2502 \u251c\u2500\u2500 Sample1.csv\n\u2502 \u2502 \u251c\u2500\u2500 Sample1.html \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SnpEff report for Sample1\u003c/span\u003e\n\u2502 \u2502 \u2514\u2500\u2500 Sample1.genes.txt\n\u2502 \u2514\u2500\u2500 Sample1-vcf.tsv \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SnpEff annotated variants in a tab-delimited table\u003c/span\u003e\n\u251c\u2500\u2500 mapping \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Read Mapping Output\u003c/span\u003e\n\u2502 \u2514\u2500\u2500 Sample1 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Read mapping output and summary files for Sample1\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 Sample1-extent.tsv\n\u2502 \u251c\u2500\u2500 Sample1-genome_extent.tsv\n\u2502 \u251c\u2500\u2500 Sample1-idxstats.tsv\n\u2502 \u251c\u2500\u2500 Sample1.bam\n\u2502 \u251c\u2500\u2500 Sample1-depth.tsv\n\u2502 \u251c\u2500\u2500 Sample1-idxstats-sorted.tsv\n\u2502 \u251c\u2500\u2500 Sample1-idxstats-top_mapped.txt\n\u2502 \u2514\u2500\u2500 Sample1.bam.bai\n\u251c\u2500\u2500 bam \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input directory with Sample1 BAM file specified in config.yaml\u003c/span\u003e\n\u2502 \u2514\u2500\u2500 a.bam\n\u251c\u2500\u2500 consensus \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Consensus Sequence Output\u003c/span\u003e\n\u2502 \u2514\u2500\u2500 Sample1.fasta \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Consensus sequence for Sample1 from reference mapping and variant calling\u003c/span\u003e\n\u251c\u2500\u2500 logs \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Log files for various tools\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etool name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u2502 \u2502 \u2514\u2500\u2500 Sample1.log\n\u251c\u2500\u2500 samples.tsv \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e INPUT: tab-delimited table with 2 fields: \"sample\" and \"bam_file\"\u003c/span\u003e\n\u251c\u2500\u2500 references \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Reference Genomes Downloaded From NCBI\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 Sample1 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Top Reference Genome\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 reference.gff\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.bwt\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.pac\n\u2502 \u2502 \u251c\u2500\u2500 reference.genbank\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.amb\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.ann\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta\n\u2502 \u2502 \u251c\u2500\u2500 reference-no_ambig.fasta.sa\n\u2502 \u2502 \u251c\u2500\u2500 reference.fasta\n\u2502 \u2502 \u2514\u2500\u2500 reference-no_ambig.fasta.fai\n\u2502 \u251c\u2500\u2500 csf.msh \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Mash sketch database from \"csf.fasta\"\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 csf.genbank \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e CSFV genomes downloaded from NCBI in GenBank format\u003c/span\u003e\n\u2502 \u2514\u2500\u2500 csf.fasta \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e CSFV genomes downloaded from NCBI in FASTA format\u003c/span\u003e\n\u251c\u2500\u2500 assembly \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Assembly Output\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 spades \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SPAdes assembly outputs for each input sample\u003c/span\u003e\n\u2502 \u2502 \u2514\u2500\u2500 Sample1 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SPAdes assembly output for Sample1\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 before_rr.fasta\n\u2502 \u2502 \u251c\u2500\u2500 params.txt\n\u2502 \u2502 \u251c\u2500\u2500 contigs.paths\n\u2502 \u2502 \u251c\u2500\u2500 input_dataset.yaml\n\u2502 \u2502 \u251c\u2500\u2500 \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSPAdes specific output directories\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u2502 \u2502 \u251c\u2500\u2500 scaffolds.paths\n\u2502 \u2502 \u251c\u2500\u2500 contigs.fasta\n\u2502 \u2502 \u251c\u2500\u2500 spades.log\n\u2502 \u2502 \u251c\u2500\u2500 assembly_graph.fastg\n\u2502 \u2502 \u251c\u2500\u2500 dataset.info\n\u2502 \u2502 \u251c\u2500\u2500 scaffolds.fasta\n\u2502 \u2502 \u2514\u2500\u2500 assembly_graph_with_scaffolds.gfa\n\u2502 \u2514\u2500\u2500 spades-Sample1.fasta\n\u251c\u2500\u2500 benchmarks \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Benchmark runtime info for tools in workflow\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ebenchmark tab-delimited files \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003evarious tools\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e workflow\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u251c\u2500\u2500 msa \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Multiple sequence alignment (MSA) output and IQ-TREE/Clearcut phylogenetic tree\u003c/span\u003e\n\u2502 \u251c\u2500\u2500 alignment.fasta\n\u2502 \u251c\u2500\u2500 samples-pre-aln.fasta\n\u2502 \u2514\u2500\u2500 alignment.fasta.treefile\n\u2514\u2500\u2500 preprocess \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Preprocessing Output of Input BAM Files \u003c/span\u003e\n \u251c\u2500\u2500 samtools \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Initial BAM file stats output\u003c/span\u003e\n \u2502 \u251c\u2500\u2500 depth\n \u2502 \u2502 \u251c\u2500\u2500 Sample1-extent.tsv\n \u2502 \u2502 \u251c\u2500\u2500 Sample1-genome_extent.tsv\n \u2502 \u2502 \u2514\u2500\u2500 Sample1.tsv\n \u2502 \u251c\u2500\u2500 flagstat\n \u2502 \u2502 \u2514\u2500\u2500 Sample1.flagstat\n \u2502 \u251c\u2500\u2500 index\n \u2502 \u2502 \u2514\u2500\u2500 Sample1.done\n \u2502 \u2514\u2500\u2500 idxstats\n \u2502 \u251c\u2500\u2500 Sample1-top_mapped.txt\n \u2502 \u251c\u2500\u2500 Sample1.tsv\n \u2502 \u2514\u2500\u2500 Sample1-sorted.tsv\n \u251c\u2500\u2500 fastqs \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Deduplicated reads in FASTQ format\u003c/span\u003e\n \u2502 \u2514\u2500\u2500 Sample1.fastq\n \u251c\u2500\u2500 mash \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Mash Screen results\u003c/span\u003e\n \u2502 \u251c\u2500\u2500 Sample1-screen_references-sorted.tsv\n \u2502 \u2514\u2500\u2500 Sample1-screen_references.tsv\n \u251c\u2500\u2500 trimmed_fastqs \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trimmomatic trimmed reads\u003c/span\u003e\n \u2502 \u2514\u2500\u2500 Sample1.fastq\n \u2514\u2500\u2500 dedup \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Deduplicated BAM files\u003c/span\u003e\n \u251c\u2500\u2500 Sample1.bam\n \u251c\u2500\u2500 Sample1.metrics.txt\n \u2514\u2500\u2500 Sample1.bam.bai\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1636025062.0 + "updated_at": 1566573045.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for Portcullis (https://github.com/maplesond/portcullis)", "filenames": [ - "Singularity" + "Singularity.1.1.0", + "Singularity", + "Singularity.1.1.1", + "Singularity.1.1.2" ], - "full_name": "truatpasteurdotfr/singularity-c7-openapi-basekit", + "full_name": "powerPlant/portcullis-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-c7-openapi-basekit\" class=\"anchor\" href=\"#singularity-c7-openapi-basekit\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-c7-openapi-basekit\u003c/h1\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2267\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for Portcullis, a program for PORTable CULLing of Invalid Splice junctions from pre-aligned RNA-seq data\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1635331812.0 + "updated_at": 1549336366.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for Bismark (https://github.com/FelixKrueger/Bismark)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.0.19.1", + "Singularity.0.23.0", + "Singularity.0.23.1", + "Singularity.0.20.0" ], - "full_name": "truatpasteurdotfr/singularity-docker-stream8-chrome", + "full_name": "powerPlant/bismark-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-docker-stream8-chrome\" class=\"anchor\" href=\"#singularity-docker-stream8-chrome\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-stream8-chrome\u003c/h1\u003e\n\u003cp\u003eGoogle Chrome container based on a CentOS Stream 8 x86_64 docker image built from github actions\u003c/p\u003e\n\u003cp\u003e(toy) singularity image produced by github actions available at \u003ccode\u003eghcr.io/truatpasteurdotfr/singularity-docker-stream8-chrome:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why\" class=\"anchor\" href=\"#why\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eworkaround solution when a Chrome release is not running on CentOS-7 because the required glibc is not satisfied\n(yes, I know... CentOS-7 is not on the list of approved OS).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-without-installation-\" class=\"anchor\" href=\"#running-without-installation-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning without installation: \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-chrome/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-stream8-chrome/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-docker-stream8-chrome:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building\" class=\"anchor\" href=\"#building\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-docker-stream8-chrome.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2263\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the Bismark bisulfite mapping and methylation calling program\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1638391965.0 + "updated_at": 1635284848.0 }, { "data_format": 2, - "description": "MAXCUT Simulation Code", + "description": "singularity scripts for cellprofiler", "filenames": [ - "SingularityFile.def" + "Singularity.3.1.8", + "Singularity.2.2.0", + "Singularity.3.0.0" ], - "full_name": "fenellamcandrew/aqc-maxcut", + "full_name": "arcsUVA/cellprofiler", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-adiabatic-quantum-computing-for-maxcut\" class=\"anchor\" href=\"#adiabatic-quantum-computing-for-maxcut\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdiabatic Quantum Computing for MAXCUT\u003c/h1\u003e\n\u003cp\u003eMAXCUT Simulation Code\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh -i \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.ssh/experimentr.pem ubuntu@\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eMY_IP_ADDRESS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1632103728.0 + "updated_at": 1556734065.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.1.3.1-py36", + "Singularity.1.0.0-py36" ], - "full_name": "oogasawa/singularity-img-gridengine-client", + "full_name": "arcsUVA/pytorch", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-img-ubuntu16-gridengine-client\" class=\"anchor\" href=\"#singularity-img-ubuntu16-gridengine-client\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-img-ubuntu16-gridengine-client\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epytorch\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1631971328.0 + "updated_at": 1573410610.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for vg (https://github.com/vgteam/vg)", "filenames": [ - "Singularity" + "Singularity.1.8.0", + "Singularity", + "Singularity.1.12.0", + "Singularity.1.12.1", + "Singularity.1.9.0", + "Singularity.1.11.0", + "Singularity.1.13.0", + "Singularity.1.10.0" ], - "full_name": "oogasawa/singularity-img-gridengine-master", + "full_name": "powerPlant/vg-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-img-gridengine-master\" class=\"anchor\" href=\"#singularity-img-gridengine-master\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-img-gridengine-master\u003c/h1\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2311\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the vg tools for working with genome variation graphs\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1631970804.0 + "updated_at": 1549578706.0 }, { "data_format": 2, - "description": "FLASH (Fast Length Adjustment of SHort reads) is a very fast and accurate software tool to merge paired-end reads from next-generation sequencing experiments. ", + "description": null, "filenames": [ - "1.2.11/Singularity" + "Singularity.kepler" ], - "full_name": "pscedu/singularity-flash", + "full_name": "ternaustralia/coesra-singularity-kepler", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-flash/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-flash/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-flash/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-flash/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/86a31ece843f2a6eac62c15a795deb81ca5d718c06548f2c2a47b1da60ac0398/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666c617368\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/86a31ece843f2a6eac62c15a795deb81ca5d718c06548f2c2a47b1da60ac0398/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666c617368\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-flash\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8438148a71d5669e7f72c44baa07c726adaf97954d4d37637024d1ad4ffe1838/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666c617368\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8438148a71d5669e7f72c44baa07c726adaf97954d4d37637024d1ad4ffe1838/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666c617368\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-flash\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c561b50b92370071ff56fae3c65507316dd34d582f558974abcac0f51a9fe052/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666c617368\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c561b50b92370071ff56fae3c65507316dd34d582f558974abcac0f51a9fe052/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666c617368\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-flash\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/b15c615f56761a00ff252428c0c999278f063be89c1895fc26e7d55f2a9417fc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666c617368\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b15c615f56761a00ff252428c0c999278f063be89c1895fc26e7d55f2a9417fc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666c617368\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-flash\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-flash\" class=\"anchor\" href=\"#singularity-flash\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-flash\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://ccb.jhu.edu/software/FLASH/\" rel=\"nofollow\"\u003eFLASH\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eflash\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/flash/1.2.11\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/flash\u003c/code\u003e as \u003ccode\u003e1.2.11.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-kepler\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-kepler\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-kepler\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ - "singularity", - "bioinformatics" + "coesra" ], - "updated_at": 1631930117.0 + "updated_at": 1610425796.0 }, { "data_format": 2, - "description": "BLAST-Like Alignment Tool.", + "description": "Rnnotator is an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome.", "filenames": [ - "36/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-blat", + "full_name": "sghignone/Rnnotator", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-blat/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-blat/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-blat/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-blat/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fde072f38ccaf86b9b38a5136b7663dd158f14a4e1cf1278108e06da76103d06/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626c6174\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fde072f38ccaf86b9b38a5136b7663dd158f14a4e1cf1278108e06da76103d06/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626c6174\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-blat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/af4ec22e9ffbf62e6d73e3a37e86694f53da06d4e9bb1f1340e3229c13ef3bb7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626c6174\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/af4ec22e9ffbf62e6d73e3a37e86694f53da06d4e9bb1f1340e3229c13ef3bb7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626c6174\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-blat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c1a11abb9c1ade82245064c947accad2be8fb585c711780038651614310a9e2f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626c6174\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c1a11abb9c1ade82245064c947accad2be8fb585c711780038651614310a9e2f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626c6174\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-blat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/fcaf031a92e8a29f5eef49aae1244a4d6365b34bcc206de848f3405b25751c32/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626c6174\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fcaf031a92e8a29f5eef49aae1244a4d6365b34bcc206de848f3405b25751c32/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626c6174\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-blat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-blat\" class=\"anchor\" href=\"#singularity-blat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-blat\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/djhshih/blat\"\u003eBLAT\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eblat\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/BLAT/36\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/BLAT\u003c/code\u003e as \u003ccode\u003e36.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rnnotator\" class=\"anchor\" aria-hidden=\"true\" href=\"#rnnotator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRnnotator\u003c/h1\u003e\n\u003cp\u003eRnnotator is an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome.\u003c/p\u003e\n\u003cp\u003eRnnotator must be run on a 64-bit Linux architecture. Before running Rnnotator the\nfollowing prerequisites must be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBlat v. 34 (\u003ca href=\"http://genome.ucsc.edu/FAQ/FAQblat.html#blat3\" rel=\"nofollow\"\u003ehttp://genome.ucsc.edu/FAQ/FAQblat.html#blat3\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eVelvet 1.0.15 (\u003ca href=\"http://www.ebi.ac.uk/~zerbino/velvet/\" rel=\"nofollow\"\u003ehttp://www.ebi.ac.uk/~zerbino/velvet/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eAMOS (\u003ca href=\"http://sourceforge.net/apps/mediawiki/amos/index.php\" rel=\"nofollow\"\u003ehttp://sourceforge.net/apps/mediawiki/amos/index.php\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eVmatch 2.0 (\u003ca href=\"http://www.vmatch.de/\" rel=\"nofollow\"\u003ehttp://www.vmatch.de/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003ebwa 0.5.8c (\u003ca href=\"http://bio-bwa.sourceforge.net/\" rel=\"nofollow\"\u003ehttp://bio-bwa.sourceforge.net/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eMUMmer (\u003ca href=\"http://sourceforge.net/projects/mummer/\" rel=\"nofollow\"\u003ehttp://sourceforge.net/projects/mummer/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eBioPerl (\u003ca href=\"http://www.bioperl.org\" rel=\"nofollow\"\u003ehttp://www.bioperl.org\u003c/a\u003e) -- base system\u003c/li\u003e\n\u003cli\u003ePerl modules: Parallel::ForkManager, Tree (\u003ca href=\"http://search.cpan.org/\" rel=\"nofollow\"\u003ehttp://search.cpan.org/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional prerequisites are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOases 0.1.18 (\u003ca href=\"http://www.ebi.ac.uk/~zerbino/oases/\" rel=\"nofollow\"\u003ehttp://www.ebi.ac.uk/~zerbino/oases/\u003c/a\u003e) -- DONE\u003c/li\u003e\n\u003cli\u003eBambus 2.33 (\u003ca href=\"http://www.cbcb.umd.edu/software/bambus/\" rel=\"nofollow\"\u003ehttp://www.cbcb.umd.edu/software/bambus/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSopra 1.0 (\u003ca href=\"mailto:dayarian@physics.rutgers.edu\"\u003edayarian@physics.rutgers.edu\u003c/a\u003e) x1 \u2013 x4 scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003esg\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ + "pipeline", "singularity", - "bioinformatics" + "singularity-recipe", + "rnaseq", + "docker", + "dockerfile" ], - "updated_at": 1631929745.0 + "updated_at": 1612716290.0 }, { "data_format": 2, - "description": "BEDOPS is an open-source command-line toolkit that performs highly efficient and scalable Boolean and other set operations, statistical calculations, archiving, conversion and other management of genomic data of arbitrary scale.", + "description": null, "filenames": [ - "2.4.40/Singularity", - "2.4.39/Singularity" + "singularity/Singularity_1.0.0" ], - "full_name": "pscedu/singularity-bedops", + "full_name": "daviesdrew/variantcalling", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bedops/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bedops/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-bedops/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bedops/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/046e5d2f53345f56b267b5b3fb70cf631199ba19ea8a20c754afd1fd5cae6098/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6265646f7073\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/046e5d2f53345f56b267b5b3fb70cf631199ba19ea8a20c754afd1fd5cae6098/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6265646f7073\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bedops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/1fd7fd2c301c82aa5c557690a7df3c9c3565a7badad9f11502e8fe21f7bcfbf5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6265646f7073\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1fd7fd2c301c82aa5c557690a7df3c9c3565a7badad9f11502e8fe21f7bcfbf5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6265646f7073\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bedops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/2b60ce6402987813ce63df1d7f2cdedee3f8108e37c69d00089eb9e576b81249/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6265646f7073\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b60ce6402987813ce63df1d7f2cdedee3f8108e37c69d00089eb9e576b81249/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6265646f7073\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bedops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/9e0c74991471aaa28ae5b127672f0979c5a9e3e79e590771d4df28ad0b47fad3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6265646f7073\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e0c74991471aaa28ae5b127672f0979c5a9e3e79e590771d4df28ad0b47fad3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6265646f7073\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bedops\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bedops\" class=\"anchor\" href=\"#singularity-bedops\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bedops\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/fcce2433d40cda32c9340a2c9e3b2d6c1ebd0bbb8e1361a72ae3ecd2514681ad/68747470733a2f2f6265646f70732e72656164746865646f63732e696f2f656e2f6c61746573742f5f7374617469632f6c6f676f5f776974685f6c6162656c5f76332e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fcce2433d40cda32c9340a2c9e3b2d6c1ebd0bbb8e1361a72ae3ecd2514681ad/68747470733a2f2f6265646f70732e72656164746865646f63732e696f2f656e2f6c61746573742f5f7374617469632f6c6f676f5f776974685f6c6162656c5f76332e706e67\" width=\"75%\" data-canonical-src=\"https://bedops.readthedocs.io/en/latest/_static/logo_with_label_v3.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for BEDOPS.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebedops\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/BEDOPS/2.4.40\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/BEDOPS\u003c/code\u003e as \u003ccode\u003e2.4.40.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"\" class=\"anchor\" aria-hidden=\"true\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/nf-core-illuminavariantcalling_logo.png\"\u003e\u003cimg src=\"docs/images/nf-core-illuminavariantcalling_logo.png\" alt=\"nf-core/illuminavariantcalling\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eIllumina paired end reads variant calling pipeline\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/nf-core/illuminavariantcalling/actions\"\u003e\u003cimg src=\"https://github.com/nf-core/illuminavariantcalling/workflows/nf-core%20CI/badge.svg\" alt=\"GitHub Actions CI Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/nf-core/illuminavariantcalling/actions\"\u003e\u003cimg src=\"https://github.com/nf-core/illuminavariantcalling/workflows/nf-core%20linting/badge.svg\" alt=\"GitHub Actions Linting Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1a7b876aea11f8490a824ae9376e2b0108e8b19b424effa1b67d0a7afcfe096e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413531392e31302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/illuminavariantcalling\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/609e7a6579baf2276f34ef713d9cc0b55f7fd62e2c5c7618d40423779d41fd44/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f696c6c756d696e6176617269616e7463616c6c696e672e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/illuminavariantcalling.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003ei. Install \u003ca href=\"https://nf-co.re/usage/installation\" rel=\"nofollow\"\u003e\u003ccode\u003enextflow\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eii. Install either \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003e\u003ccode\u003eDocker\u003c/code\u003e\u003c/a\u003e or \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e for full pipeline reproducibility (please only use \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003e\u003ccode\u003eConda\u003c/code\u003e\u003c/a\u003e as a last resort; see \u003ca href=\"https://nf-co.re/usage/configuration#basic-configuration-profiles\" rel=\"nofollow\"\u003edocs\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eiii. Download the pipeline and test it on a minimal dataset with a single command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/illuminavariantcalling -profile test,\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edocker/singularity/conda/institute\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePlease check \u003ca href=\"https://github.com/nf-core/configs#documentation\"\u003enf-core/configs\u003c/a\u003e to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use \u003ccode\u003e-profile \u0026lt;institute\u0026gt;\u003c/code\u003e in your command. This will enable either \u003ccode\u003edocker\u003c/code\u003e or \u003ccode\u003esingularity\u003c/code\u003e and set the appropriate execution settings for your local compute environment.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eiv. Start running your own analysis!\u003c/p\u003e\n\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/illuminavariantcalling -profile \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edocker/singularity/conda/institute\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e*_R{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --genome GRCh37\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"docs/usage.md\"\u003eusage docs\u003c/a\u003e for all of the available options when running the pipeline.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe nf-core/illuminavariantcalling pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/installation\" rel=\"nofollow\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/local_installation\" rel=\"nofollow\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/adding_own_config\" rel=\"nofollow\"\u003eAdding your own system config\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/reference_genomes\" rel=\"nofollow\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003enf-core/illuminavariantcalling was originally written by Drew Davies.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions-and-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions-and-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions and Support\u003c/h2\u003e\n\u003cp\u003eIf you would like to contribute to this pipeline, please see the \u003ca href=\".github/CONTRIBUTING.md\"\u003econtributing guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor further information or help, don\u0027t hesitate to get in touch on \u003ca href=\"https://nfcore.slack.com/channels/illuminavariantcalling\" rel=\"nofollow\"\u003eSlack\u003c/a\u003e (you can join with \u003ca href=\"https://nf-co.re/join/slack\" rel=\"nofollow\"\u003ethis invite\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\n\n\u003cp\u003eYou can cite the \u003ccode\u003enf-core\u003c/code\u003e publication as follows:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eThe nf-core framework for community-curated bioinformatics pipelines.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhilip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso \u0026amp; Sven Nahnsen.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNat Biotechnol.\u003c/em\u003e 2020 Feb 13. doi: \u003ca href=\"https://dx.doi.org/10.1038/s41587-020-0439-x\" rel=\"nofollow\"\u003e10.1038/s41587-020-0439-x\u003c/a\u003e.\u003cbr\u003e\nReadCube: \u003ca href=\"https://rdcu.be/b1GjZ\" rel=\"nofollow\"\u003eFull Access Link\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1631926426.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1593036214.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for trinityrnaseq (https://github.com/trinityrnaseq/trinityrnaseq)", "filenames": [ - "SingularityFile" + "Singularity.2.14.0", + "Singularity", + "Singularity.2.13.2", + "Singularity.2.9.0", + "Singularity.2.8.6", + "Singularity.2.9.1", + "Singularity.2.10.0" ], - "full_name": "AMarinhoSN/tutorial-cCC", + "full_name": "powerPlant/trinityrnaseq-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-tutorial-ccc\" class=\"anchor\" href=\"#tutorial-ccc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etutorial-cCC\u003c/h1\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for the Trinity RNA-Seq de novo transcriptome assembly\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1643312784.0 + "updated_at": 1645140013.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for sortmerna (https://github.com/biocore/sortmerna)", "filenames": [ - "Singularity/Singularity.v1.0", - "Singularity/Singularity.v1.1" + "Singularity", + "Singularity.4.3.2", + "Singularity.4.3.6", + "Singularity.3.0.3", + "Singularity.4.2.0", + "Singularity.4.3.4" ], - "full_name": "Monia234/IARC-fastqc", + "full_name": "powerPlant/sortmerna-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fastqc-nf\" class=\"anchor\" href=\"#fastqc-nf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efastqc-nf\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quality-control-of-raw-sequencing-reads\" class=\"anchor\" href=\"#quality-control-of-raw-sequencing-reads\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality control of raw sequencing reads\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/fastqc-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d355ed64b381b5e3e497a32c3b032d9becd558aebd39a0da28073fbe613dfd81/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f6661737471632d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/fastqc-nf.svg?style=svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/fastqc-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4559\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/IARCbioinfo/fastqc-nf/blob/master/fastqc-nf.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/IARCbioinfo/fastqc-nf/raw/master/fastqc-nf.png\" alt=\"fastqc-nf\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003ePerform quality control of Fasta files.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eFastQC: see official installation \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003cli\u003eMultiqc: see official installation \u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bam-input-files\" class=\"anchor\" href=\"#bam-input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBAM input files\u003c/h3\u003e\n\u003cp\u003eIn order to process BAM files, we convert fastq files to bam files with:\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003ca href=\"http://samtools.sourceforge.net/\" rel=\"nofollow\"\u003e\u003cem\u003esamtools\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input\" class=\"anchor\" href=\"#input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing FASTQ files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003ePath to output folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-parameters\" class=\"anchor\" href=\"#parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-optional\" class=\"anchor\" href=\"#optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eExample value\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ext\u003c/td\u003e\n\u003ctd\u003efastq.gz\u003c/td\u003e\n\u003ctd\u003eExtension of files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--multiqc_config\u003c/td\u003e\n\u003ctd\u003enone\u003c/td\u003e\n\u003ctd\u003econfig yaml file for multiqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eNumber of cpu used by fastqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e10\u003c/td\u003e\n\u003ctd\u003eSize of memory used for mapping (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-flags\" class=\"anchor\" href=\"#flags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h3\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003enextflow run IARCbioinfo/fastqc-nf -r v1.1 -profile singularity --input_folder input --output_folder results\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run the pipeline with docker or conda instead of singularity, just replace \"-profile singularity\" with \"-profile docker\" or \"-profile conda\", respectively. To run with your own local installation of softwares, just remove \"-profile singularity\"\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003emultiqc_fastqc_report.html\u003c/td\u003e\n\u003ctd\u003emultiQC report for fastQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emultiqc_fastqc_report_data\u003c/td\u003e\n\u003ctd\u003edata used for the multiQC report HTMLs\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributions\" class=\"anchor\" href=\"#contributions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@fellows.iarc.fr\"\u003eAlcalaN@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for the SortMeRNA local sequence alignment tool for filtering, mapping and clustering.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1644245739.0 + "updated_at": 1659497761.0 }, { "data_format": 2, - "description": null, + "description": "HPC-AI 2020 | Training Project NEMO - Nucleus for European Modelling of the Ocean", "filenames": [ - "Singularity/Singularity.v1.0" + "Slurm Script/Singularity.nemo.apps", + "Slurm Script/Singularity.CENTOS-7.7-NEMO-MOFED" ], - "full_name": "Monia234/IARC-imputation", + "full_name": "soycoder/nemo", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-genotyping-imputation---pipeline-v10\" class=\"anchor\" href=\"#genotyping-imputation---pipeline-v10\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenotyping imputation : Pipeline V1.0\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-a-nextflow-pipeline-to-realise-a-datasets-genotyping-imputation\" class=\"anchor\" href=\"#a-nextflow-pipeline-to-realise-a-datasets-genotyping-imputation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA nextflow pipeline to realise a dataset\u0027s genotyping imputation\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/Imputation-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e3a0e94b24410397271294a485f985c85941b292ba2c7cbf3fefb26bf1e0c76b/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f74656d706c6174652d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/template-nf.svg?style=svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/imputation-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4533\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/94193130\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5decad826d7116b1c950b6b48c06052496f141e803525b088ce3cdcaaa4d7b88/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f39343139333133302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/94193130.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"template-nf.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"template-nf.png\" alt=\"Workflow representation\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eThe pipeline used to perform the imputation of several targets datasets processed with standard input.\u003c/p\u003e\n\u003cp\u003eHere is a summary of the method :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessing of data : by using the nextflow script Preparation.nf with create a directory \"file/\" with all the dependencies.\u003c/li\u003e\n\u003cli\u003eFirst step : Origin estimation of sample from the target dataset by using admixture tools and the hapmap dataset as reference.\u003c/li\u003e\n\u003cli\u003eSecond step : Series of SNPs filters and quality checking from the target dataset before the imputation step.\u003c/li\u003e\n\u003cli\u003eThird step : VCF production\u003c/li\u003e\n\u003cli\u003eLast step : Phasing and imputation\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the Usage section to test the full pipeline with your target dataset.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eThe pipeline works under Linux distributions.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExternal software:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eLiftOver : conda install ucsc-liftover\u003c/li\u003e\n\u003cli\u003ePlink (PLINK v1.90b6.12 64-bit (28 Oct 2019)) : conda install plink\u003c/li\u003e\n\u003cli\u003eAdmixture (ADMIXTURE Version 1.3.0) : conda install admixture\u003c/li\u003e\n\u003cli\u003ePerl : conda install perl\u003c/li\u003e\n\u003cli\u003eTerm::ReadKey module : conda install perl-termreadkey\u003c/li\u003e\n\u003cli\u003eBcfTools : conda install bcftools\u003c/li\u003e\n\u003cli\u003eeagle 2.4.1 : \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-50002.2\" rel=\"nofollow\"\u003eSee instructions\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eminimac4 : conda install cmake ; pip install cget ; git clone \u003ca href=\"https://github.com/statgen/Minimac4.git\"\u003ehttps://github.com/statgen/Minimac4.git\u003c/a\u003e ; cd Minimac4 ; bash install.sh\u003c/li\u003e\n\u003cli\u003eSamtools : conda install samtools\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eFile to download :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"zzz.bwh.harvard.edu/plink/dist/hapmap_r23a.zip\"\u003eHapmap Dataset\u003c/a\u003e : as reference\u0027s dataset for admixture\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.hagsc.org/hgdp/data/hgdp.zip\" rel=\"nofollow\"\u003eHGDP Dataset\u003c/a\u003e : for the dataset\u0027s test, you have to use the toMap.py \u0026amp; toPed.py in the \u0027converstion\u0027 directory to convert files in the .map/.ped plink format. Next you have to convert this last output in the .bed/.bam/.fam plink format by using plink line command and run the imputation\u0027s pipeline.\u003c/li\u003e\n\u003cli\u003ePerl tool : \u003ca href=\"https://www.well.ox.ac.uk/~wrayner/tools/\" rel=\"nofollow\"\u003eHRC-1000G-check-bim-NoReadKey.pl\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.well.ox.ac.uk/~wrayner/tools/1000GP_Phase3_combined.legend.gz\" rel=\"nofollow\"\u003e1000GP_Phase3_combined.legend\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eLiftOver tool : \u003ca href=\"http://hgdownload.cse.ucsc.edu/goldenpath/hg19/liftOver/hg19ToHg38.over.chain.gz\" rel=\"nofollow\"\u003ehg19ToHg38.over.chain\u003c/a\u003e \u0026amp; \u003ca href=\"http://hgdownload.cse.ucsc.edu/goldenpath/hg18/liftOver/hg18ToHg38.over.chain.gz\" rel=\"nofollow\"\u003ehg18ToHg38.over.chain\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePeparation dataset tool : \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2432498/bin/pone.0002551.s003.xls\" rel=\"nofollow\"\u003epone.0002551.s003.xls\u003c/a\u003e (Convert it in .csv format)\u003c/li\u003e\n\u003cli\u003eAdmixture tool : relationships_w_pops_121708.txt\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/zhanxw/checkVCF/raw/master/checkVCF.py\"\u003eCheckVCF\u003c/a\u003e, \u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz\" rel=\"nofollow\"\u003eFasta file in V37\u003c/a\u003e \u0026amp; \u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/GRCh38_reference_genome/\" rel=\"nofollow\"\u003eFasta file in V38\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/GRCh38_positions/\" rel=\"nofollow\"\u003e1000G Reference in Hg38\u003c/a\u003e with the \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-320005.3.2\" rel=\"nofollow\"\u003edoc\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate \u003ca href=\"https://imputationserver.readthedocs.io/en/latest/create-reference-panels/#create-legend-files\" rel=\"nofollow\"\u003elegend\u003c/a\u003e, \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-320005.3.2\" rel=\"nofollow\"\u003ebcf\u003c/a\u003e \u0026amp; \u003ca href=\"https://imputationserver.readthedocs.io/en/latest/create-reference-panels/#create-m3vcf-files\" rel=\"nofollow\"\u003em3vcf\u003c/a\u003e files for the reference\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eOther to know :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSee the Usage part to create the environment to run the pipeline. All the necessary dependencies are download with the using of the script Preparation.nf. To run it, you\u0027ll need to install the next software : in2csv(1.0.5), liftOver, plink, Minimac3(2.0.1) \u0026amp; bcftools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software of the main scritp by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input\" class=\"anchor\" href=\"#input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePlink datasets\u003c/td\u003e\n\u003ctd\u003eCorresponds to the target dataset to be analysed. Composed by the following files : bed, bim \u0026amp; fam\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInput environment\u003c/td\u003e\n\u003ctd\u003ePath to your input directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-parameters\" class=\"anchor\" href=\"#parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-mandatory\" class=\"anchor\" href=\"#mandatory\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--target\u003c/td\u003e\n\u003ctd\u003emy_target\u003c/td\u003e\n\u003ctd\u003ePattern of the target dataset which do the link with the file .bed/.bim./fam for plink\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input\u003c/td\u003e\n\u003ctd\u003euser/main_data/\u003c/td\u003e\n\u003ctd\u003eThe path of the main directory where we can find 2 directory : my_target/ + files/\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output\u003c/td\u003e\n\u003ctd\u003euser/my_result/\u003c/td\u003e\n\u003ctd\u003eThe path of the main directory where you want to place your results\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optional\" class=\"anchor\" href=\"#optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--script\u003c/td\u003e\n\u003ctd\u003emy/directory/script/bin\u003c/td\u003e\n\u003ctd\u003eThe path of the bin script directory, to be able to run the annexe programme grom the pipeline\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--geno1\u003c/td\u003e\n\u003ctd\u003e0.03\u003c/td\u003e\n\u003ctd\u003eFirst genotyping call rate plink threshold, apply in the full target dataset\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--geno2\u003c/td\u003e\n\u003ctd\u003e0.03\u003c/td\u003e\n\u003ctd\u003eSecond genotyping call rate plink threshold, apply in the target dataset divide by population\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--maf\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003eMinor allele frequencies plink threshold, apply in the full target dataset\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--pihat\u003c/td\u003e\n\u003ctd\u003e0.185\u003c/td\u003e\n\u003ctd\u003eMinimum pi_hat value use for the relatedness test, 0.185 is halfway between the expected IBD for third- and second-degree relatives\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hwe\u003c/td\u003e\n\u003ctd\u003e1e-8\u003c/td\u003e\n\u003ctd\u003eHardy-Weinberg Equilibrium plink p-value threshold\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--legend\u003c/td\u003e\n\u003ctd\u003eALL.chr_GRCh38.genotypes.20170504.legend\u003c/td\u003e\n\u003ctd\u003eFile to use as .legend\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fasta\u003c/td\u003e\n\u003ctd\u003eGRCh38_full_analysis_set_plus_decoy_hla.fa\u003c/td\u003e\n\u003ctd\u003eFile to use as fasta reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--chain\u003c/td\u003e\n\u003ctd\u003ehg18ToHg38.over.chain\u003c/td\u003e\n\u003ctd\u003eFile to use as liftover conversion\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--VCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/vcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as VCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--BCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/bcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as BCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--M3VCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/m3vcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as M3VCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--conversion\u003c/td\u003e\n\u003ctd\u003ehg38/hg18/hg19\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cloud\u003c/td\u003e\n\u003ctd\u003ehg38/hg18/hg19\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--token_Michighan\u003c/td\u003e\n\u003ctd\u003epath/to/my_token.txt\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--token_TOPMed\u003c/td\u003e\n\u003ctd\u003epath/to/my_token.txt\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--QC_cloud\u003c/td\u003e\n\u003ctd\u003emy/directory/donwload_imputation_server\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-flags\" class=\"anchor\" href=\"#flags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003ePrepare the environment to run the imputation pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir data\ncd data\nnextflow run IARCbioinfo/Imputation-nf/bin/Preparation.nf --out /data/\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePaste the bim/bed/fam plink target files in a directory, and the directory in your \"data/\" directory. You have to call the plink files and your directory with the same pattern, as the following exemple : data/target/target{.bed,.bim,.fam}. So now you have 2 directories in your \"data/\" repertory :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e_ data/my_target/ : with the plink target files (my_target.bed, my_target.bim, my_target.fam).\u003c/p\u003e\n\u003cp\u003e_ data/files/ : with all the dependencies.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the imputation pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eIf you want to run the imputation in one of the server (Michigan and/or TOPMed Imputation), you need you write your token acces in a file and to give it in argument. For example :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ --cloud on --token_Michighan /folder/my_token_Michighan.txt --token_TOPMed /folder/my_token_TOPMed.txt -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce your imputation data is downloaded, you can run the end of the QC analysis :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ --QC_cloud /downloaded_imputation_server_file/ -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-detailed-description-optional-section\" class=\"anchor\" href=\"#detailed-description-optional-section\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetailed description (optional section)\u003c/h2\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" href=\"#directed-acyclic-graph\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/Imputation-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributions\" class=\"anchor\" href=\"#contributions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGabriel Aur\u00e9lie\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:gabriela@students.iarc.fr\"\u003egabriela@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLipinski Boris\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"mailto:LipinskiB@students.iarc.fr\"\u003eLipinskiB@students.iarc.fr\u003c/a\u003e / \u003ca href=\"mailto:boris.lipinski@etu.univ-lyon1.fr\"\u003eboris.lipinski@etu.univ-lyon1.fr\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references-optional\" class=\"anchor\" href=\"#references-optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences (optional)\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-faq-optional\" class=\"anchor\" href=\"#faq-optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ (optional)\u003c/h2\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-test-pipeline\" class=\"anchor\" href=\"#test-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etest-pipeline\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content--nemo---ocean\" class=\"anchor\" aria-hidden=\"true\" href=\"#-nemo---ocean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"ocean\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f30a.png\"\u003e\ud83c\udf0a\u003c/g-emoji\u003e NEMO - ocean\u003c/h1\u003e\n\u003cp\u003eHPC-AI 2020 | Training Project - NEMO: Nucleus for European Modelling of the Ocean\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content--docker-images---centos\" class=\"anchor\" aria-hidden=\"true\" href=\"#-docker-images---centos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"floppy_disk\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4be.png\"\u003e\ud83d\udcbe\u003c/g-emoji\u003e Docker Images - CentOS\u003c/h2\u003e\n\u003cp\u003eThank you for an image (\u003ca href=\"https://hub.docker.com/r/wangyoucao577/centos7-gcc7.4\" rel=\"nofollow\"\u003ewangyoucao577/centos7-gcc7.4\u003c/a\u003e)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content--tag\" class=\"anchor\" aria-hidden=\"true\" href=\"#-tag\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"bookmark\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f516.png\"\u003e\ud83d\udd16\u003c/g-emoji\u003e Tag\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/layers/soycoder/centos7/nemo-ocean/images/sha256-c7bdaa3614e1fc1bbef31bdb05ac997e64b11abff716d00315807b1b79ad13c3\" rel=\"nofollow\"\u003e:nemo-ocean\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content--environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"sunrise_over_mountains\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f304.png\"\u003e\ud83c\udf04\u003c/g-emoji\u003e Environment\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eHPC-X to build an out-of-box MPI environment\u003c/li\u003e\n\u003cli\u003eBoost library\u003c/li\u003e\n\u003cli\u003eHDF5 Parallellibrary\u003c/li\u003e\n\u003cli\u003eNETCDF Parallel library with HDF5\u003c/li\u003e\n\u003cli\u003eNETCDF-FortranParallel library with NETCDF Parallel\u003c/li\u003e\n\u003cli\u003eXIOS\u003c/li\u003e\n\u003cli\u003eGYREwith GNUgfortran + HPC-X OpenMPI\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-html-basic\"\u003e\u003cpre\u003e/usr/mpi/gcc/openmpi-3.1.1rc1/bin/mpirun \\\n-mca pml ucx -x UCX_NET_DEVICES=mlx5_0:1 \\\n-mca mpi_show_mca_params 1 -mca pml_ucx_verbose 9 \\\n/usr/mpi/gcc/openmpi-3.1.1rc1/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\n\n/usr/mpi/gcc/openmpi-3.1.1rc1/bin/mpirun -n 2 \\\n-mca pml ucx -x UCX_NET_DEVICES=mlx5_0:1 \\\n/usr/mpi/gcc/openmpi-3.1.1rc1/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\n/usr/bin/time -p mpirun -n 2 \\\n-mca pml ucx -x UCX_TLS=rc UCX_NET_DEVICES=mlx5_0:1 ./nemo\n\n/usr/bin/time -p mpirun -n 2 \\\n-mca -x UCX_TLS=rc -x UCX_NET_DEVICES=mlx5_0:1 ./nemo\n\n/usr/bin/time -p mpirun -n 2 \\\n-mca -x UCX_TLS=rc -x UCX_NET_DEVICES=ib0 \\\n/home/hpc/nemo/apps/hpcx-v2.6.0-gcc-MLNX_OFED_LINUX-4.7-1.0.0.1-redhat7.7-x86_64/ompi/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\nibstat\n\n\nNow step into the container and install MOFED:\n\n$ sudo singularity exec -w u16.04-sandbox/ bash\n(singularity)# cd MOFED/MLNX_OFED_LINUX-4.3-1.0.1.0-ubuntu16.04-x86_64\n(singularity)# ./mlnxofedinstall\n\n\n! -- (nemo) singularity exec -w nemo.sif bash\n\n\n## Run container\nTo use Singularity in Mellanox/HPCX need to load env module: `module load tools/singularity`\n.\n\nRun `osu_latency` test:\n```sh\n$ mpirun -np 2 --map-by node -mca btl self singularity exec hpcx-u16.04.simg /hpcx/ompi-a7df\nd94/tests/osu-micro-benchmarks-5.3.2/osu_latency\n# OSU MPI Latency Test v5.3.2\n# Size Latency (us)\n0 1.55\n1 1.55\n2 1.55\n4 1.55\n8 1.54\n16 1.55\n32 1.55\n64 1.65\n128 2.19\n256 2.23\n512 2.35\n1024 2.64\n2048 2.89\n4096 3.51\n8192 5.00\n16384 6.44\n32768 8.91\n65536 14.12\n131072 25.05\n262144 27.31\n524288 49.03\n1048576 92.53\n2097152 178.95\n4194304 351.24\n\n\n\n$hpcx_mpi_dir/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\ncd /home/hpc/nemo/apps/hpcx-v2.6.0-gcc-MLNX_OFED_LINUX-4.7-1.0.0.1-redhat7.7-x86_64\n\nmpirun \\\n-mca pml ucx -x UCX_NET_DEVICES=mlx5_0:1 \\\n-mca mpi_show_mca_params 1 -mca pml_ucx_verbose 9 \\\n./ompi/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\n\nmpirun \\\n-mca mpi_show_mca_params 1 -mca pml_ucx_verbose 9 \\\n./ompi/tests/osu-micro-benchmarks-5.3.2/osu_get_bw\n\n\n\n/usr/bin/time -p mpirun -np 4 \\\n--map-by core -report-bindings \\\n-mca io ompio -x UCX_NET_DEVICES=mlx5_0:1 ./nemo\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1644245707.0 + "updated_at": 1603363757.0 }, { "data_format": 2, - "description": null, + "description": "Singularity for HPC", "filenames": [ - "3.1.6/Singularity", - "2.0.19/Singularity" + "Singularity.centos7-python3.7-transformers3.0.2-ImageCrawl", + "Singularity.centos7-python3.8-transformers4.11.0-ImageCrawl", + "Singularity.centos7-python3.7-transformers2.11.0-ImageCrawl" ], - "full_name": "yh549848/singularity-q", + "full_name": "sina-ehsani/hpc-singularity", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehpc-singularity\u003c/h1\u003e\n\u003cp\u003eSingularity for HPC\u003c/p\u003e\n\u003cp\u003eMake sure the sigularity is built on \u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003ehttps://sylabs.io\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eif ready use:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull shub://sinaehsani6/hpc-singularity:centos7-python3.7-transformers3.0.2-imagecrawl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTransformer 2.11.0:\n\u003ccode\u003esingularity pull shub://sinaehsani6/hpc-singularity:centos7-python3.7-transformers2.11.0-imagecrawl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMake sure the imagecrawl is updated (latest commit)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1643962791.0 + "updated_at": 1641850034.0 }, { "data_format": 2, - "description": "singularity recipes for bioinformatic analysis", + "description": "parallel gzipper in pure python", "filenames": [ - "Singularity.vcf_processing.v1.0", - "Singularity.dysgu.v1.3.0", - "Singularity.sv_call.v1.0", - "Singularity.bcftools.v1.10.2", - "Singularity.qcbam.v1.0", - "Singularity.align_dedup.v1.0", - "Singularity.expansion_hunter.v5.0.0", - "Singularity.Rstudio", - "Singularity.pygenometracks", - "Singularity.GADO-v1.0.4", - "Singularity.HapCUT2", - "Singularity.sv_processing.v1.0", - "Singularity.expansion_hunter.v3.2.2", - "Singularity.hail", - "Singularity.V2_anno.var2reg", - "Singularity.Exomiser-v12.1.0", - "Singularity.variantstore", - "Singularity.GREEN-VARAN_v1", - "Singularity.shiny.server" + "Singularity.alpine" ], - "full_name": "edg1983/Singularity_images", + "full_name": "d-w-moore/zipit", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" href=\"#singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity recipes\u003c/h1\u003e\n\u003cp\u003eThese are singularity recipes for images used in our bionformatic analysis.\nSome images are bundled with supplementary resources for analysis.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-supporting-files\" class=\"anchor\" href=\"#supporting-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esupporting files\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-resources-folder\" class=\"anchor\" href=\"#resources-folder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eresources folder\u003c/h4\u003e\n\u003cp\u003eSome supporting files are needed for the analysis.\nSee description file in the resources folder for the list of expected files and folders\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-custom-scripts\" class=\"anchor\" href=\"#custom-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecustom scripts\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-tools-folder\" class=\"anchor\" href=\"#tools-folder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etools folder\u003c/h4\u003e\n\u003cp\u003eSome supporting scripts are included in the tools folder and are copied into the corresponding images\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-zipit\" class=\"anchor\" aria-hidden=\"true\" href=\"#zipit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ezipit\u003c/h1\u003e\n\u003cp\u003eThis repo contains two scripts useful for gzipping and checking large files\nas quickly as possible leveraging the parallelism of your machine.\u003c/p\u003e\n\u003cp\u003eThey require only that python be installed, and they depend only on modules\nincluded in the Python Standard Library -- particularly, of course, gzip.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-zipitpy\" class=\"anchor\" aria-hidden=\"true\" href=\"#zipitpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ezipit.py\u003c/h2\u003e\n\u003cp\u003eExample uses:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./zipit.py -v large.tar # =\u0026gt; Creates large.tar.gz at default level of parallelism.\n # (-v verbosely informs of the piece-wise gzip tasks)\n\n $ ./zipit.py -qm large.tar # =\u0026gt; creates large.tar.gz using all available CPU\u0027s\n\n $ some_command | ./zipit.py - \u0026gt; out.gz # =\u0026gt; gzips from the stdin stream, onto stdout\n\n $ docker export cimg | ./zipit.py \\ # =\u0026gt; export and compress the filesystem of\n -d cimg.dig - \u0026gt;cimg.tgz # a docker container\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testzippy\" class=\"anchor\" aria-hidden=\"true\" href=\"#testzippy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etestzip.py\u003c/h2\u003e\n\u003cp\u003eExample use (for context, see the final \u003ccode\u003ezipit.py\u003c/code\u003e example above):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./testzip.py cimg.tgz cimg.dig # =\u0026gt; tests the gzipped file\u0027s integrity using a digest file\n # (returns 0 if the integrity is good)\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1636544746.0 + "updated_at": 1602285708.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for REPET (https://urgi.versailles.inra.fr/Tools/REPET)", "filenames": [ + "Singularity.3.0", "Singularity" ], - "full_name": "lawlessrd/SCZ-WM-pipeline", + "full_name": "powerPlant/repet-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-scz-white-matter-pipeline\" class=\"anchor\" href=\"#scz-white-matter-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSCZ White Matter Pipeline\u003c/h1\u003e\n\u003cp\u003eThis spider will preprocess fMRI data as well as corresponding T1 data, extract mean time-courses of each predefined ROI and compute the correlation matrices between white matter ROIs and gray matter ROIs. Please see Gao\u2019s publications [1, 2] for more details. The spider will also compute FALFF, ALFF and ReHo maps.\u003c/p\u003e\n\u003cp\u003eThis XNAT spider is currently designed for three databases (ADNI_23, BLSA and OASIS-3) which are proposed to be analyzed in white matter reanalysis project (PI: Dr. Gore and Dr. Landman).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-inputs\" class=\"anchor\" href=\"#inputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h2\u003e\n\u003cp\u003efMRI (.nii.gz)\u003c/p\u003e\n\u003cp\u003eT1 (.nii.gz)\u003c/p\u003e\n\u003cp\u003eConfiguration file (.mat)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput:\u003c/h2\u003e\n\u003cp\u003ePreprocessed fMRI in MNI space:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../scz_OUTPUTS/result1_corrmatrix/FunImgARCFWD/1/Detrend_4DVolume.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTissue probability maps (gray matter and white matter) in MNI space:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../scz_OUTPUTS/result1_corrmatrix/T1ImgNewSegment/1/wc1T1.nii.gz\n\n../scz_OUTPUTS/result1_corrmatrix/T1ImgNewSegment/1/wc2T1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFunctional connectivity matrices between white matter ROIs and gray matter ROIs:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../scz_OUTPUTS/result1_corrmatrix/matr_1.mat\n\n../scz_OUTPUTS/result1_corrmatrix/matr_1.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMean time-courses of the white and gray matter ROIs:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result1_corrmatrix/tc_1.mat\t\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhole-brain ALFF maps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result2_wm_alff/ALFF_FunImgARCFWD/ALFFMap_1.nii.gz\n\n../ scz_OUTPUTS/result2_wm_alff/ALFF_FunImgARCFWD/mALFFMap_1.nii.gz\n\n../ scz_OUTPUTS/result2_wm_alff/ALFF_FunImgARCFWD/zALFFMap_1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhole-brain FALFF maps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result3_wm_falff/fALFF_FunImgARCFWD/fALFFMap_1.nii.gz\n\n../ scz_OUTPUTS/result3_wm_falff/fALFF_FunImgARCFWD/mfALFFMap_1.nii.gz\n\n../ scz_OUTPUTS/result3_wm_falff/fALFF_FunImgARCFWD/zfALFFMap_1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhole brain ReHo maps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result4_wm_reho/ReHo_FunImgARCFWD/ReHoMap_1.nii.gz\n\n../ scz_OUTPUTS/result4_wm_reho/ReHo_FunImgARCFWD/mReHoMap_1.nii.gz\n\n../ scz_OUTPUTS/result4_wm_reho/ReHo_FunImgARCFWD/zReHoMap_1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhole brain maps of degree of centrality:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/DegreeCentrality_PositiveBinarizedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/DegreeCentrality_PositiveWeightedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/mDegreeCentrality_PositiveBinarizedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/mDegreeCentrality_PositiveWeightedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/zDegreeCentrality_PositiveBinarizedSumBrainMap_1.nii.gz\n\n../ scz_OUTPUTS/result5_wm_degree_centrality/DegreeCentrality_FunImgARCFWD/zDegreeCentrality_PositiveWeightedSumBrainMap_1.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h1\u003e\n\u003cp\u003e[1] Gao Y, Sengupta A, Li M, et al. (2020) Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer\u2019s disease. PLoS ONE 15(10): e0240513. \u003ca href=\"https://doi.org/10.1371/journal.pone.0240513\" rel=\"nofollow\"\u003ehttps://doi.org/10.1371/journal.pone.0240513\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e[2] Gao Y, Li M, Huang AS. Lower functional connectivity of white matter during rest and working memory tasks is associated with cognitive impairments in schizophrenia. Schizophr Res. 2021 Jul;233:101-110. doi: 10.1016/j.schres.2021.06.013. Epub 2021 Jun 29. PMID: 34215467; PMCID: PMC8442250.\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for REPET\n(\u003ca href=\"https://urgi.versailles.inra.fr/Tools/REPET\" rel=\"nofollow\"\u003ehttps://urgi.versailles.inra.fr/Tools/REPET\u003c/a\u003e), used to detect, annotate and\nanalyse repeats in genomic sequences, specifically designed for transposable\nelements (TEs).\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1642706993.0 + "updated_at": 1602104190.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for bedops (https://github.com/bedops/bedops)", "filenames": [ - "Singularity_CPU", - "Singularity_GPU" + "Singularity", + "Singularity.2.4.39" ], - "full_name": "ddbj/singularity_alphafold", + "full_name": "powerPlant/bedops-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_alphafold\" class=\"anchor\" href=\"#singularity_alphafold\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_alphafold\u003c/h1\u003e\n\u003cp\u003eUbuntu 18.04\u306balphafold 2.1\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fsingularity image\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306eSingularity definition file\u3067\u3059\u3002GPU\u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u306fSingularity_GPU\u3001GPU\u3092\u4f7f\u7528\u3057\u306a\u3044\u5834\u5408\u306fSingularity_CPU\u3092\u4f7f\u7528\u3057\u3066image\u3092\u30d3\u30eb\u30c9\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-image\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" href=\"#image%E3%81%AE%E3%83%93%E3%83%AB%E3%83%89\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eimage\u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build alphafold-2.1-xPU.sif Singularity_xPU\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for the BEDOPS open-source command-line toolkit that performs highly efficient and scalable Boolean and other set operations, statistical calculations, archiving, conversion and other management of genomic data of arbitrary scale.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1642384956.0 + "updated_at": 1596773368.0 }, { "data_format": 2, "description": null, "filenames": [ - "ext/Singularity" + "Singularity" ], - "full_name": "clemsonciti/ood_rshiny", + "full_name": "lehtiolab/nf-deqms", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-batch-connect---example-jupyter-notebook-server-palmetto\" class=\"anchor\" href=\"#batch-connect---example-jupyter-notebook-server-palmetto\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch Connect - Example Jupyter Notebook Server Palmetto\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/3a55d969e5c049c5a679159fb5e33007b3341d832eb29fbf0419a7ea1df72b22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6578616d706c655f6a7570797465722e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3a55d969e5c049c5a679159fb5e33007b3341d832eb29fbf0419a7ea1df72b22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6578616d706c655f6a7570797465722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_example_jupyter.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/d7ea6810dec03748ecd3a16bc0028d6d3ba3f7f01daa23e53e07ed1740b54420/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6f73632f62635f6578616d706c655f6a7570797465722e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d7ea6810dec03748ecd3a16bc0028d6d3ba3f7f01daa23e53e07ed1740b54420/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6f73632f62635f6578616d706c655f6a7570797465722e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/github/license/osc/bc_example_jupyter.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app that launches a Jupyter Notebook server within a\nbatch job.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://jupyter.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eJupyter Notebook\u003c/a\u003e 4.2.3+ (earlier\nversions are untested but may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.openssl.org/\" rel=\"nofollow\"\u003eOpenSSL\u003c/a\u003e 1.0.1+ (used to hash the Jupyter Notebook\nserver password)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOptional\u003c/strong\u003e software:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e\n6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based CLI\nused to load appropriate environments within the batch job before launching\nthe Jupyter Notebook server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eThese are command line only installation directions.\u003c/p\u003e\n\u003cp\u003eWe start by downloading a zipped package of this code. This allows us to start\nwith a fresh directory that has no git history as we will be building off of\nthis.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the zip from the GitHub page\u003c/span\u003e\nwget https://github.com/OSC/bc_example_jupyter/archive/master.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a catchy directory\u003c/span\u003e\nmkdir my_jupyter_app\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Unzip the downloaded file into this directory\u003c/span\u003e\ntar xzvf master.tar.gz -C my_jupyter_app --strip-components=1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Change the working directory to this new directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e my_jupyter_app\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFrom here you will make any modifications to the code that you would like and\nversion your changes in your own repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Version our app by making a new Git repository\u003c/span\u003e\ngit init\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make all your code changes while testing them in the OnDemand Dashboard\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ...\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the files to the Git repository\u003c/span\u003e\ngit add --all\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Commit the staged files to the Git repository\u003c/span\u003e\ngit commit -m \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emy first commit\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_example_jupyter/fork\"\u003ehttps://github.com/OSC/bc_example_jupyter/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-lehtiolabnf-deqms\" class=\"anchor\" aria-hidden=\"true\" href=\"#lehtiolabnf-deqms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elehtiolab/nf-deqms\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eA small pipeline to re-run DEqMS on existing results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0fcfc6847f4944e0c46cb62bb190c0110bafa56ce455c12dd23051df8d710a4a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30312e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.01.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/lehtiolab/nf-deqms\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4068dc15ebffdfaa7d220510750dd7bcde75393d91d3fe2d05dc15190c515246/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c656874696f6c61622f6e662d6465716d732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/lehtiolab/nf-deqms.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThis workflow reruns DEqMS analysis on existing results, e.g. from the \u003ca href=\"https://github.com/lehtiolab/ddamsproteomics\"\u003elehtiolab/ddamsproteomics\u003c/a\u003e pipeline. It exists so one can use orthogonal sample groups (CTRL vs TREAT, old vs young) and rerun, or perhaps correct a mistake in the sample annotation, without having to re-search an entire set of spectra against a protein sequence database.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun pipeline:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/nf-deqms --proteins proteins.txt --peptides peptides.txt --genes genes.txt --ensg ensg.txt --sampletable samples.txt -profile standard,docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can leave out any accession that you do not have or are not interested in (e.g. \u003ccode\u003e--ensg\u003c/code\u003e in a Swissprot analysis).\u003c/p\u003e\n\u003cp\u003eThe lehtiolab/nf-deqms pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere is more extensive documentation on the options inside the main.nf file.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003elehtiolab/nf-deqms was originally written by Jorrit Boekel and tries to follow the \u003ca href=\"https://nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e best practices and templates.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1642298553.0 + "updated_at": 1605692054.0 }, { "data_format": 2, @@ -16105,94 +15552,100 @@ var data = "filenames": [ "Singularity" ], - "full_name": "biobox-info/fragpipe", + "full_name": "shreyaskamathkm/singularity_meshroom", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-fragpipe\" class=\"anchor\" href=\"#fragpipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFragpipe\u003c/h1\u003e\n\u003cp\u003eFragpipe latest version: 1.0.0\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_meshroom\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_meshroom\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_meshroom\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1642084970.0 + "updated_at": 1602807348.0 }, { "data_format": 2, - "description": "Apache Druid singularity container for holberton school student records and such", + "description": "Singularity recipe files for edta (https://github.com/oushujun/EDTA)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.1.8.3", + "Singularity.1.9.0" ], - "full_name": "romxero/Singularity_Apache_Druid", + "full_name": "powerPlant/edta-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-apache-druid-in-a-singularity-container-this-is-used-for-testing-and-for-creating-a-database-for-interactive-use-by-holberton-tulsa\" class=\"anchor\" href=\"#apache-druid-in-a-singularity-container-this-is-used-for-testing-and-for-creating-a-database-for-interactive-use-by-holberton-tulsa\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApache Druid in a singularity container. This is used for testing and for creating a database for interactive use by Holberton Tulsa.\u003c/h1\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for the Extensive de novo TE Annotator tool\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1642026259.0 + "updated_at": 1603071842.0 }, { "data_format": 2, - "description": "Playground for Julia environments to test on Milgram ", + "description": null, "filenames": [ - "Singularity" + "Singularity.FMS-gcc10-openmpi-netcdf4.6.3-ubuntu", + "Singularity", + "Singularity.FMS-gcc10-openmpi-netcdf4.6.3-ubuntu-compile" ], - "full_name": "CNCLgithub/JuliaHPCApp", + "full_name": "thomas-robinson/fms_containers", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fms_containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#fms_containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efms_containers\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1641927437.0 + "updated_at": 1604411747.0 }, { "data_format": 2, - "description": null, + "description": "Bayesian Atmospheric Radiative Transfer (BART) packaged in a Singularity container https://github.com/davecwright3/bart-singularity", "filenames": [ - "2.26.10/Singularity" + "Singularity" ], - "full_name": "yh549848/singularity-picard", + "full_name": "davecwright3/bart-singularity", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4946\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bart-singularity-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#bart-singularity-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBART Singularity Guide\u003c/h1\u003e\n\u003cp\u003eThe Singularity image has BART installed at \u003ccode\u003e/bart_dir\u003c/code\u003e. The \u003ccode\u003e$topdir\u003c/code\u003e environment variable is set to this directory inside the image. This means that the instructions for the demo listed here \u003ca href=\"https://github.com/exosports/BART/tree/master/examples/demo\"\u003ehttps://github.com/exosports/BART/tree/master/examples/demo\u003c/a\u003e still work, but we need to mount a directory for outputs into the container for two reasons:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe demo expects your output directory to be parallel to the BART directory\u003c/li\u003e\n\u003cli\u003eThe container file system is read-only (this is only a problem because of (1); being read-only is actually preferred because it helps ensure reproducible results)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eIf the output directory wasn\u0027t required to be parallel to BART, you could run the container anywhere in \u003ccode\u003e$HOME\u003c/code\u003e because Singularity mounts \u003ccode\u003e$HOME\u003c/code\u003e of the current user into the container by default\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe image has a directory parallel to BART that is meant for output at \u003ccode\u003e/bart_dir/run\u003c/code\u003e. Make a directory on your host system where you want to store results. For the sake of this guide, let\u0027s say it\u0027s under your current directory at \u003ccode\u003edemo/run\u003c/code\u003e and you have pulled the singularity image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name bart.sif shub://davecwright3/bart-singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto your current directory as well. Then start a shell in the singularity container with the bind mount specified\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B demo/run:/bart_dir/run bart.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe BART conda environment will be automatically activated. Now just \u003ccode\u003ecd $topdir/run\u003c/code\u003e and follow the instructions here \u003ca href=\"https://github.com/exosports/BART/tree/master/examples/demo\"\u003ehttps://github.com/exosports/BART/tree/master/examples/demo\u003c/a\u003e if you would like to do a demo run. You can \u003ccode\u003eexit\u003c/code\u003e the container whenever you are done, and your results will remain in your \u003ccode\u003edemo/run\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eBayesian Atmospheric Radiative Transfer (BART), a code to infer\nproperties of planetary atmospheres based on observed spectroscopic\ninformation.\u003c/p\u003e\n\u003cp\u003eThis project was completed with the support of the NASA Planetary\nAtmospheres Program, grant NNX12AI69G, held by Principal Investigator\nJoseph Harrington. Principal developers included graduate students\nPatricio E. Cubillos and Jasmina Blecic, programmer Madison Stemm, and\nundergraduates M. Oliver Bowman and Andrew S. D. Foster. The included\n\u0027transit\u0027 radiative transfer code is based on an earlier program of\nthe same name written by Patricio Rojo (Univ. de Chile, Santiago) when\nhe was a graduate student at Cornell University under Joseph\nHarrington. Statistical advice came from Thomas J. Loredo and Nate\nB. Lust.\u003c/p\u003e\n\u003cp\u003eCopyright (C) 2015-2016 University of Central Florida.\nAll rights reserved.\u003c/p\u003e\n\u003cp\u003eThis is a test version only, and may not be redistributed to any third\nparty. Please refer such requests to us. This program is distributed\nin the hope that it will be useful, but WITHOUT ANY WARRANTY; without\neven the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\nPURPOSE.\u003c/p\u003e\n\u003cp\u003eOur intent is to release this software under an open-source,\nreproducible-research license, once the code is mature and the first\nresearch paper describing the code has been accepted for publication\nin a peer-reviewed journal. We are committed to development in the\nopen, and have posted this code on github.com so that others can test\nit and give us feedback. However, until its first publication and\nfirst stable release, we do not permit others to redistribute the code\nin either original or modified form, nor to publish work based in\nwhole or in part on the output of this code. By downloading, running,\nor modifying this code, you agree to these conditions. We do\nencourage sharing any modifications with us and discussing them\nopenly.\u003c/p\u003e\n\u003cp\u003eWe welcome your feedback, but do not guarantee support. Please send\nfeedback or inquiries to:\nPatricio Cubillos \u003ca href=\"mailto:patricio.cubillos@oeaw.ac.at\"\u003epatricio.cubillos@oeaw.ac.at\u003c/a\u003e\nJasmina Blecic \u003ca href=\"mailto:jasmina@physics.ucf.edu\"\u003ejasmina@physics.ucf.edu\u003c/a\u003e\nJoseph Harrington \u003ca href=\"mailto:jh@physics.ucf.edu\"\u003ejh@physics.ucf.edu\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eor alternatively,\nJoseph Harrington, Patricio Cubillos, and Jasmina Blecic\nUCF PSB 441\n4111 Libra Drive\nOrlando, FL 32816-2385\nUSA\u003c/p\u003e\n\u003cp\u003eThank you for testing BART!\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1641914982.0 + "updated_at": 1604965509.0 }, { "data_format": 2, - "description": null, + "description": "This is the Artifact Description repository for the CGO21 paper: YaskSite \u2013 Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures", "filenames": [ - "recipes/Singularity.def" + "Singularity" ], - "full_name": "stigrj/ghcr_sandbox", - "latest_release": "v2.0.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-testing-out-ghcr-workflows\" class=\"anchor\" href=\"#testing-out-ghcr-workflows\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting out GHCR workflows\u003c/h1\u003e\n", + "full_name": "seasite-project/CGO21_YaskSite_AD", + "latest_release": "CGO21v0.3", + "readme": "\u003ch1\u003e\u003ca id=\"user-content--cgo21_yasksite_ad-\" class=\"anchor\" aria-hidden=\"true\" href=\"#-cgo21_yasksite_ad-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cins\u003e CGO21_YaskSite_AD \u003c/ins\u003e\u003c/h1\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup-phase\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-phase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup phase\u003c/h1\u003e\n\u003cp\u003eSteps 1 to 3 guide you through setting up.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-11\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-11\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1.1\u003c/h2\u003e\n\u003cp\u003eClone this repository and go to the cloned directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/seasite-project/CGO21_YaskSite_AD.git\ncd CGO21_YaskSite_AD\ngit checkout CGO21v0.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-12\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-12\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1.2\u003c/h2\u003e\n\u003cp\u003eFor the next steps we need singularity v 3.6.4 or higher.\nIf singularity is not installed, you can install singularity with the following script if you have root access.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./install_singularity.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2\u003c/h2\u003e\n\u003cp\u003eDownload the singularity container.\u003c/p\u003e\n\u003cp\u003eThe pre-build container is available under the following link \u003ca href=\"https://doi.org/10.5281/zenodo.4415558\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.4415558\u003c/a\u003e\nand can be installed using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://zenodo.org/record/4415558/files/YS_CGO.sif?download=1 -O YS_CGO.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-3\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 3\u003c/h2\u003e\n\u003cp\u003eOnce singularity image is downloaded on the benchmarking system the first step is to run the app called build.\nThis installs YaskSite. It should be done at runtime since the YaskSite does machine specific configuration\nat build time. Run the following to do this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app build YS_CGO.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-phase\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-phase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun phase\u003c/h1\u003e\n\u003cp\u003eStep 4 illustrates how to run the app to reproduce results.\nIt is recommended the settings in the paper are followed to get comparable results.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-step-4\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 4\u003c/h2\u003e\n\u003cp\u003eRun the apps corresponding to YaskSite and Offsite. There are also pre-configured apps that helps to\nreproduce data in figures of the paper. To see the list of available apps use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run-help YS_CGO.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe method to run each apps are described in corresponding app\u0027s help. For example help on how to run Fig4 app\n(reproduces results in Fig4 of the paper) can be obtained using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run-help --app Fig4 YS_CGO.sif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1641893233.0 + "updated_at": 1609764345.0 }, { "data_format": 2, - "description": null, + "description": "Singularity description files", "filenames": [ - "Singularity" + "fusorsv/Singularity", + "mousegwas/Singularity" ], - "full_name": "Mauricemonashuniversity/Epileptic-seizure-prediction", + "full_name": "asafpr/singularity", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1640038462.0 + "updated_at": 1616613441.0 }, { "data_format": 2, - "description": "singularity recipe for https://github.com/chienchi/amplicon_coverage_plot", + "description": "singularity container to run Ian Jonsen\u0027s foieGras package", "filenames": [ "Singularity" ], - "full_name": "dcgc-bfx/singularity-amplicon_coverage_plot", + "full_name": "jganong/ubuntu-bionic-R-4.0.3-foieGras", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-amplicon_coverage_plot\" class=\"anchor\" href=\"#singularity-amplicon_coverage_plot\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-amplicon_coverage_plot\u003c/h1\u003e\n\u003cp\u003esingularity recipe for \u003ca href=\"https://github.com/chienchi/amplicon_coverage_plot\"\u003ehttps://github.com/chienchi/amplicon_coverage_plot\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eImages are stored here: \u003ca href=\"https://cloud.sylabs.io/library/fabianrost84/dcgc-bfx/amplicon_coverage_plot\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/fabianrost84/dcgc-bfx/amplicon_coverage_plot\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1638796921.0 + "updated_at": 1607375064.0 }, { "data_format": 2, @@ -16200,13 +15653,12 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-debian10-visualstudio", + "full_name": "jganong/ubuntu-focal-foiegras", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-vstudio-on-debian10-toy-system-for-singularity-for-ci\" class=\"anchor\" href=\"#building-a-vstudio-on-debian10-toy-system-for-singularity-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a vstudio on debian10 toy system for singularity for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-debian10-visualstudio/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-debian10-visualstudio/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-debian10-visualstudio:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1638370657.0 + "updated_at": 1607375887.0 }, { "data_format": 2, @@ -16214,44 +15666,41 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-debian9-visualstudio", + "full_name": "marcjwilliams1/rstudio_julia", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-vstudio-on-debian9-toy-system-for-singularity-for-ci\" class=\"anchor\" href=\"#building-a-vstudio-on-debian9-toy-system-for-singularity-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a vstudio on debian9 toy system for singularity for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-debian9-visualstudio/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-debian9-visualstudio/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-debian9-visualstudio:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5054\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for R studio server with Rv4.0 + julia v1.5.3\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1638365824.0 + "updated_at": 1611507934.0 }, { "data_format": 2, - "description": "Demultiplexing and QC pipeline for Illumina and 10X Single Cell sequencing data", + "description": null, "filenames": [ - "Singularity" + "scripts/Singularity" ], - "full_name": "csawye01/nf-core-demultiplex-crick", + "full_name": "SCXsunchenxi/Auto-Pytorch", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nf-coredemultiplex\" class=\"anchor\" href=\"#nf-coredemultiplex\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/demultiplex\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eDemultiplexing pipeline for Illumina data\u003c/strong\u003e\n\u003cstrong\u003eIN PROGRESS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/demultiplex\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ae3bf24b0d68bb5e81863eb358c7f3cd3a383647e932a785a123565bf2d13391/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f64656d756c7469706c65782e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/demultiplex.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/demultiplex\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c838bd17591342d038d2a3b9de19e08588f2ae0043530f3eb082113f2651bac7/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f64656d756c7469706c65782e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/demultiplex.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003enf-core/demultiplex\u003c/strong\u003e is a bioinformatics demultiplexing pipeline used for multiple types of data input from sequencing runs.\nThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-sample-sheet-format\" class=\"anchor\" href=\"#sample-sheet-format\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSample Sheet Format\u003c/h3\u003e\n\u003cp\u003eThe sample sheet must fall into the same format as seen below to adhere to the Illumina standards with the additional column of DataAnalysisType and ReferenceGenome to ensure 10X sample will be processed correctly. Order of columns does not matter but the case of column names does.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eLane\u003c/th\u003e\n\u003cth\u003eSample_ID\u003c/th\u003e\n\u003cth\u003eUser_Sample_Name\u003c/th\u003e\n\u003cth\u003eindex\u003c/th\u003e\n\u003cth\u003eindex2\u003c/th\u003e\n\u003cth\u003eSample_Project\u003c/th\u003e\n\u003cth\u003eReferenceGenome\u003c/th\u003e\n\u003cth\u003eDataAnalysisType\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eABC11A2\u003c/td\u003e\n\u003ctd\u003eU_ABC0_BS_GL_DNA\u003c/td\u003e\n\u003ctd\u003eCGATGT\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003ePM10000\u003c/td\u003e\n\u003ctd\u003eHomo sapiens\u003c/td\u003e\n\u003ctd\u003eWhole Exome\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eSAG100A10\u003c/td\u003e\n\u003ctd\u003eSAG100A10\u003c/td\u003e\n\u003ctd\u003eSI-GA-C1\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eSC18100\u003c/td\u003e\n\u003ctd\u003eMus musculus\u003c/td\u003e\n\u003ctd\u003e10X-3prime\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e3\u003c/td\u003e\n\u003ctd\u003eCAP200A11\u003c/td\u003e\n\u003ctd\u003eUN1800_AE_6\u003c/td\u003e\n\u003ctd\u003eiCLIP\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003ePM18200\u003c/td\u003e\n\u003ctd\u003eHomo sapiens\u003c/td\u003e\n\u003ctd\u003eOther\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pipeline-summary\" class=\"anchor\" href=\"#pipeline-summary\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline summary\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eReformatting the input sample sheet\n\u003cul\u003e\n\u003cli\u003eScript looks for \u003ccode\u003eiCLIP\u003c/code\u003e in the index column of the sample sheet and collapses the iCLIP samples into one per lane.\u003c/li\u003e\n\u003cli\u003eSplits 10X single cell samples into 10X, 10X-ATAC and 10X-DNA .csv files by searching in the sample sheet column DataAnalysisType for \u003ccode\u003e10X-3prime\u003c/code\u003e, \u003ccode\u003e10X-ATAC\u003c/code\u003e and \u003ccode\u003e10X-CNV\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOutputs the results of needing to run specific processes in the pipeline (can be only 10X single cell samples, mix of 10X single cell with non single cell samples or all non single cell samples)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eChecking the sample sheet for downstream error causing samples such as:\n\u003cul\u003e\n\u003cli\u003ea mix of short and long indexes on the same lane\u003c/li\u003e\n\u003cli\u003ea mix of single and dual indexes on the same lane\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eProcesses that only run if there are issues within the sample sheet found by the sample sheet check process (CONDITIONAL):\n\u003col\u003e\n\u003cli\u003eCreates a new sample sheet with any samples that would cause an error removed and create a a txt file of a list of the removed problem samples\u003c/li\u003e\n\u003cli\u003eRun \u003ca href=\"http://emea.support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html\" rel=\"nofollow\"\u003e\u003ccode\u003ebcl2fastq\u003c/code\u003e\u003c/a\u003e on the newly created sample sheet and output the Stats.json file\u003c/li\u003e\n\u003cli\u003eParsing the Stats.json file for the indexes that were in the problem samples list.\u003c/li\u003e\n\u003cli\u003eRecheck newly made sample sheet for any errors or problem samples that did not match any indexes in the Stats.json file. If there is still an issue the pipeline will exit at this stage.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eSingle cell 10X sample processes (CONDITIONAL):\nWill run either CellRanger, CellRangerATAC, CellRangerDNA depending on the samplesheet data type\nNOTE: Must create CONFIG to point to CellRanger genome References\n\u003col\u003e\n\u003cli\u003eCell Ranger mkfastq runs only when 10X samples exist. This will run the process with \u003ca href=\"https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger\" rel=\"nofollow\"\u003e\u003ccode\u003eCellRanger\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/what-is-cell-ranger-atac\" rel=\"nofollow\"\u003e\u003ccode\u003eCellRanger ATAC\u003c/code\u003e\u003c/a\u003e, and \u003ca href=\"https://support.10xgenomics.com/single-cell-dna/software/pipelines/latest/what-is-cell-ranger-dna\" rel=\"nofollow\"\u003e\u003ccode\u003eCell Ranger DNA\u003c/code\u003e\u003c/a\u003e depending on which sample sheet has been created.\u003c/li\u003e\n\u003cli\u003eCell Ranger Count runs only when 10X samples exist. This will run the process with \u003ca href=\"https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/count\" rel=\"nofollow\"\u003e\u003ccode\u003eCell Ranger Count\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/using/count\" rel=\"nofollow\"\u003e\u003ccode\u003eCell Ranger ATAC Count\u003c/code\u003e\u003c/a\u003e, and \u003ca href=\"https://support.10xgenomics.com/single-cell-dna/software/pipelines/latest/using/cnv\" rel=\"nofollow\"\u003e\u003ccode\u003eCell Ranger DNA CNV\u003c/code\u003e\u003c/a\u003edepending on the output from Cell Ranger mkfastq. 10X reference genomes can be downloaded from the 10X site, a new config would have to be created to point to the location of these. Must add config to point Cell Ranger to genome references if used outside the Crick profile.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://emea.support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html\" rel=\"nofollow\"\u003e\u003ccode\u003ebcl2fastq\u003c/code\u003e\u003c/a\u003e (CONDITIONAL):\n\u003col\u003e\n\u003cli\u003eRuns on either the original sample sheet that had no error prone samples or on the newly created sample sheet created from the extra steps.\u003c/li\u003e\n\u003cli\u003eThis is only run when there are samples left on the sample sheet after removing the single cell samples.\u003c/li\u003e\n\u003cli\u003eThe arguments passed in bcl2fastq are changeable parameters that can be set on the command line when initiating the pipeline. Takes into account if Index reads will be made into FastQ\u0027s as well\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003e\u003ccode\u003eFastQC\u003c/code\u003e\u003c/a\u003e runs on the pooled fastq files from all the conditional processes.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/\" rel=\"nofollow\"\u003e\u003ccode\u003eFastQ Screen\u003c/code\u003e\u003c/a\u003e runs on the pooled results from all the conditional processes.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://multiqc.info/docs/\" rel=\"nofollow\"\u003e\u003ccode\u003eMultiQC\u003c/code\u003e\u003c/a\u003e runs on each projects FastQC results produced.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://multiqc.info/docs/\" rel=\"nofollow\"\u003e\u003ccode\u003eMultiQC_all\u003c/code\u003e\u003c/a\u003e runs on all FastQC results produced.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/demultiplex pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/reference_genomes.md\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003eCredits\nThe nf-core/demultiplex pipeline was written by Chelsea Sawyer of the The Bioinformatics \u0026amp; Biostatistics Group for use at The Francis Crick Institute, London.\nMany thanks to others who have helped out along the way too, including (but not limited to): \u003ca href=\"https://github.com/ChristopherBarrington\"\u003e\u003ccode\u003e@ChristopherBarrington\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://github.com/drpatelh\"\u003e\u003ccode\u003e@drpatelh\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://github.com/danielecook\"\u003e\u003ccode\u003e@danielecook\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://github.com/escudem\"\u003e\u003ccode\u003e@escudem\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://github.com/crickbabs\"\u003e\u003ccode\u003e@crickbabs\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-auto-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#auto-pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuto-PyTorch\u003c/h1\u003e\n\u003cp\u003eCopyright (C) 2019 \u003ca href=\"http://www.automl.org/\" rel=\"nofollow\"\u003eAutoML Group Freiburg\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis a very early pre-alpha version of our upcoming Auto-PyTorch.\nSo far, Auto-PyTorch supports featurized data (classification, regression) and image data (classification).\u003c/p\u003e\n\u003cp\u003eThe newest features in Auto-PyTorch for tabular data are described in the paper \u003ca href=\"https://arxiv.org/abs/2006.13799\" rel=\"nofollow\"\u003e\"Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL\"\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eClone repository\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e install/path\n$ git clone https://github.com/automl/Auto-PyTorch.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Auto-PyTorch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to contribute to this repository switch to our current develop branch\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git checkout develop\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall pytorch:\n\u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ehttps://pytorch.org/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInstall Auto-PyTorch:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat requirements.txt \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e xargs -n 1 -L 1 pip install\n$ python setup.py install\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h2\u003e\n\u003cp\u003eCode for the \u003ca href=\"https://arxiv.org/abs/2006.13799\" rel=\"nofollow\"\u003epaper\u003c/a\u003e is available under \u003ccode\u003eexamples/ensemble\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor a detailed tutorial, please refer to the jupyter notebook in \u003ca href=\"https://github.com/automl/Auto-PyTorch/tree/master/examples/basics\"\u003ehttps://github.com/automl/Auto-PyTorch/tree/master/examples/basics\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn a nutshell:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# data and metric imports\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodel_selection\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_digits\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ereturn_X_y\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\u003cspan class=\"pl-v\"\u003eX_train\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eX_test\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_test\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \\\n \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodel_selection\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etrain_test_split\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erandom_state\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# running Auto-PyTorch\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"tiny_cs\"\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# config preset\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003elog_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027info\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e300\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e90\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003evalidation_split\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.3\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ey_pred\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003epredict\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX_test\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"Accuracy score\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eaccuracy_score\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ey_test\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_pred\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMore examples with datasets:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e examples/\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003eHow to configure Auto-PyTorch for your needs:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Print all possible configuration options.\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e().\u003cspan class=\"pl-en\"\u003eprint_help\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# You can use the constructor to configure Auto-PyTorch.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elog_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027info\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e300\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e90\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# You can overwrite this configuration in each fit call.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elog_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027debug\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e900\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e150\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# You can use presets to configure the config space.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# Available presets: full_cs, medium_cs (default), tiny_cs.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# These are defined in autoPyTorch/core/presets.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# tiny_cs is recommended if you want fast results with few resources.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# full_cs is recommended if you have many resources and a very high search budget.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"full_cs\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Enable or disable components using the Auto-PyTorch config:\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enetworks\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e\"resnet\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"shapedresnet\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"mlpnet\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"shapedmlpnet\"\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# You can take a look at the search space.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# Each hyperparameter belongs to a node in Auto-PyTorch\u0027s ML Pipeline.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# The names of the hyperparameters are prefixed with the name of the node: NodeName:hyperparameter_name.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# If a hyperparameter belongs to a component: NodeName:component_name:hyperparameter_name.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# Call with the same arguments as fit.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eget_hyperparameter_search_space\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003evalidation_split\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.3\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# You can configure the search space of every hyperparameter of every component:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eHyperparameterSearchSpaceUpdates\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esearch_space_updates\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eHyperparameterSearchSpaceUpdates\u003c/span\u003e()\n\n\u003cspan class=\"pl-s1\"\u003esearch_space_updates\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enode_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"NetworkSelector\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ehyperparameter\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"shapedresnet:activation\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003evalue_range\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e\"relu\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"sigmoid\"\u003c/span\u003e])\n\u003cspan class=\"pl-s1\"\u003esearch_space_updates\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enode_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"NetworkSelector\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ehyperparameter\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"shapedresnet:blocks_per_group\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003evalue_range\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e],\n \u003cspan class=\"pl-s1\"\u003elog\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehyperparameter_search_space_updates\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003esearch_space_updates\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEnable ensemble building (for featurized data):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetEnsemble\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautoPyTorchEnsemble\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetEnsemble\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"tiny_cs\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e300\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e90\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDisable pynisher if you experience issues when using cuda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003eautoPyTorch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoNetClassification\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"tiny_cs\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elog_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027info\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_runtime\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e300\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emin_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_budget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e90\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecuda\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003euse_pynisher\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis program is free software: you can redistribute it and/or modify\nit under the terms of the Apache license 2.0 (please see the LICENSE file).\u003c/p\u003e\n\u003cp\u003eThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\u003c/p\u003e\n\u003cp\u003eYou should have received a copy of the Apache license 2.0\nalong with this program (see LICENSE file).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@incollection\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003emendoza-automlbook18a\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eHector Mendoza and Aaron Klein and Matthias Feurer and Jost Tobias Springenberg and Matthias Urban and Michael Burkart and Max Dippel and Marius Lindauer and Frank Hutter\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eTowards Automatically-Tuned Deep Neural Networks\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2018\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003emonth\u003c/span\u003e = dec,\n \u003cspan class=\"pl-s\"\u003eeditor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eHutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebooktitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eAutoML: Methods, Sytems, Challenges\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epublisher\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eSpringer\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003echapter\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e7\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epages\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e141--156\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003enote\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eTo appear.\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Previously, the name of the project was AutoNet. Since this was too generic, we changed the name to AutoPyTorch. AutoNet 2.0 in the reference mention above is indeed AutoPyTorch.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003eAuto-PyTorch is developed by the \u003ca href=\"http://www.automl.org/\" rel=\"nofollow\"\u003eAutoML Group of the University of Freiburg\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1638199013.0 + "updated_at": 1609655576.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe(s) for LSDalton.", "filenames": [ - "containers/Singularity.0.4.1", - "containers/Singularity.0.4.0", - "containers/Singularity.0.3.5", - "containers/Singularity.0.3.3", - "containers/Singularity.0.3.6" + "Singularity.latest-gcc-9.3.0" ], - "full_name": "Samanwaya1301/tidal-heating-bilby", + "full_name": "bast/lsdalton", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipes-for-lsdalton\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipes-for-lsdalton\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca href=\"https://sylabs.io/guides/latest/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e recipe(s) for \u003ca href=\"https://gitlab.com/dalton/lsdalton/\" rel=\"nofollow\"\u003eLSDalton\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5142\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/5142\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull --name lsdalton shub://bast/lsdalton:latest-gcc-9.3.0\n$ ./lsdalton myexample.dal mymolecule.mol\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1638182089.0 + "updated_at": 1612249375.0 }, { "data_format": 2, @@ -16259,39 +15708,102 @@ var data = "filenames": [ "Singularity" ], - "full_name": "talha-naveed97/orion", + "full_name": "timo-singularity/rivet", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erecipes\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1646176107.0 + "updated_at": 1622810530.0 }, { "data_format": 2, - "description": null, + "description": "Testing Singularity container and Singularity-hub", "filenames": [ "Singularity" ], - "full_name": "yhisaki/exp_pfrl", + "full_name": "kma/singularity-lab", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-use-case\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-use-case\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity use case\u003c/h1\u003e\n\u003cp\u003eCreate a reproducible container image to run a simple python program (\u003ccode\u003edata_alaysys.py\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThis code takes a csv file and plots results in two separated pdf files.\u003c/p\u003e\n\u003cp\u003eThe csv can be found \u003ca href=\"http://info.iut-bm.univ-fcomte.fr/staff/mazouzi/docs/ganglia-metrics.csv\" rel=\"nofollow\"\u003e[here]\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-create-a-container-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-a-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate a container locally\u003c/h2\u003e\n\u003cp\u003eRun \u003ccode\u003ebuild-local\u003c/code\u003e to create and bootstrap a container (This action needs root access).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity create -s 1000 mycontainer.img\n$ sudo singularity bootstrap mycontainer.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-python-code-inside-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-python-code-inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun python code inside the container\u003c/h2\u003e\n\u003cp\u003eRun \u003ccode\u003erun-local.sh\u003c/code\u003e to execute python code inside the container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ wget http://info.iut-bm.univ-fcomte.fr/staff/mazouzi/docs/ganglia-metrics.csv\n\n$ ./mycontainer.img data_analysis\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-image-container-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-image-container-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull image container from singularity-hub\u003c/h2\u003e\n\u003cp\u003eIf you don\u0027t root access, singularity-hub can create images by providing a specification file. See the \u003ca href=\"https://singularity-hub.org/faq\" rel=\"nofollow\"\u003e[documentation]\u003c/a\u003e for more details .\u003c/p\u003e\n\u003cp\u003eThe image corresponding to the \u003ccode\u003eSingularity\u003c/code\u003e file can be pulled from \u003ca href=\"https://singularity-hub.org/containers/842/\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/containers/842/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePull image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull shub://842\nOr\n$ singularity pull shub://kma/singularity-lab:master\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun python code using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kma-singularity-lab-master.img python data_analysis.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./kma-singularity-lab-master.img data_analysis.py\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1645156767.0 + "updated_at": 1493818555.0 }, { "data_format": 2, - "description": "Code related to the installation and use of the openface on PSU\u0027s ACI systems ", + "description": "Files to build Singularity images for running the Monte-Carlo event generator Sherpa", "filenames": [ - "Singularity" + "Singularity.fitting_centos6", + "Singularity.sherpa-rel-2-2-7_68ab0c9c5_Caesar", + "Singularity.sherpa-2.2.6", + "Singularity.rivet_centos6", + "Singularity.sherpa-tmp-cherrypick-ewvirt-into-master_HEAD_centos6", + "Singularity.sherpa-rel-2-2-9_HEAD_centos6", + "Singularity.sherpa-master_2dc43a3d_Asterix", + "Singularity.plotting", + "Singularity.mceg", + "Singularity.sherpa-rel-2-2-7_12338b5d_Bossix", + "Singularity.sherpa-master_HEAD_centos6", + "Singularity.plotting_centos6", + "Singularity.sherpa-openmpi.devtoolset", + "Singularity.sherpa-2.2.6_centos6", + "Singularity.rivet", + "Singularity.sherpa-rel-2-2-7_HEAD_centos6", + "Singularity.mceg_centos6" ], - "full_name": "behav/openface", + "full_name": "ebothmann/sherpa-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-openface_ics\" class=\"anchor\" href=\"#openface_ics\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopenface_ics\u003c/h1\u003e\n\u003cp\u003eCode and workflow for building \u003ca href=\"https://github.com/TadasBaltrusaitis/OpenFace\"\u003eOpenFace\u003c/a\u003e\nin Docker Hub and modifying it with Singularity Hub for use with PSU\nACI HPC clusters.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eFrom ACI, executing the following code should create an \u003ccode\u003eOpenFace\u003c/code\u003e image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://d-bohn/openface_ics:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-image-builds\" class=\"anchor\" href=\"#image-builds\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImage Builds\u003c/h2\u003e\n\u003cp\u003eThe OpenFace docker image was built from scratch on docker hub following the\n\u003ca href=\"https://github.com/TadasBaltrusaitis/OpenFace/wiki/Unix-Installation\"\u003edocumentation\u003c/a\u003e provided by it\u0027s maintainers.\u003c/p\u003e\n\u003cp\u003eThe OpenFace singularity image was built using the docker image base and\nconverting it to a singularity image via singularity hub.\u003c/p\u003e\n\u003cp\u003eSetup for linking Github with Docker Hub and Singularity Hub can be found here:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.docker.com/docker-hub/\" rel=\"nofollow\"\u003edocker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki\"\u003eSingularity Hub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003eSingularity\u003c/code\u003e file specifies creating a Singularity-compatible image\nfrom the docker image, as well as adding access to folders within ACI, specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# ACI mappings so you can access your files.\nmkdir -p /storage/home\nmkdir -p /storage/work\nmkdir -p /gpfs/group\nmkdir -p /gpfs/scratch\nmkdir -p /var/spool/torque\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe OpenFace docker image is large (\u0026gt; 6GB). It is built on Ubuntu 18.04.\nNot sure if it can be reduced in size as the executables rely on several\nlarge libraries.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSeveral important updates for \u003ccode\u003efaciallandmarkdetector\u003c/code\u003e are hosted on\nthe maintainer\u0027s cloud account. Might be prudent to download them\nseparately and/or include them in the repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSome functionality for real-time video viewing is not available\nwhen run in a container (at least not as of now).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1556740713.0 + "updated_at": 1603222289.0 + }, + { + "data_format": 2, + "description": "Run a jupyter notebook server within singularity container.", + "filenames": [ + "Singularity" + ], + "full_name": "kma/singularity-jupyter", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-jupyter\u003c/h1\u003e\n\u003cp\u003eThis example shows how to run a jupyter notebook server within singularity container.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-create-and-bootstrap-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-and-bootstrap-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate and bootstrap the container\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity create -s 1200 jupyter.img\n$ sudo singularity bootstrap jupyter.img Singularity \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-singularity-hub-to-pull-this-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-singularity-hub-to-pull-this-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse singularity-hub to pull this container\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003e$ singularity pull shub://906\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ singularity pull shub://kma/singularity-jupyter:master\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the container\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run jupyter.img\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will starts jupyter server on port 8888. The current directory will be used as the notebook direcory.\nYou can connect to the server and select the notebook file \u003ca href=\"python_heat2d.ipynb\"\u003epython_heat2d.ipynb\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [], + "updated_at": 1493997701.0 + }, + { + "data_format": 2, + "description": "local settings", + "filenames": [ + "examples/shub/Singularity", + "examples/scientific/Singularity", + "examples/arch/Singularity", + "examples/ubuntu/Singularity", + "examples/centos/Singularity", + "examples/docker/Singularity", + "examples/scratch/Singularity.busybox", + "examples/scratch/Singularity.alpine", + "examples/debian/Singularity", + "examples/self/Singularity", + "examples/busybox/Singularity", + "examples/apps/Singularity", + "examples/apps/Singularity.cowsay", + "examples/instances/Singularity", + "examples/asciinema/Singularity", + "examples/raspbian/Singularity", + "examples/library/Singularity", + "examples/multistage/Singularity", + "examples/opensuse/Singularity" + ], + "full_name": "frankwillmore/alcf-singularity", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/sylabs/singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1646c42a348a1331feb3842e34171e866c139adbae2608ba5fbd2c022c9c20f/68747470733a2f2f7472617669732d63692e6f72672f73796c6162732f73696e67756c61726974792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/sylabs/singularity.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/sylabs/singularity/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff56e7dd170e08e53c09fda12031315bb91f5b4220f2d3cfaf46044700f32fa1/68747470733a2f2f636972636c6563692e636f6d2f67682f73796c6162732f73696e67756c61726974792f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/sylabs/singularity/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://goreportcard.com/report/github.com/sylabs/singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/179d3d939b6a64c4f021860776fdc6243bc26409e966f1aa6bd7d35ca9593fea/68747470733a2f2f676f7265706f7274636172642e636f6d2f62616467652f6769746875622e636f6d2f73796c6162732f73696e67756c6172697479\" alt=\"Go Report Card\" data-canonical-src=\"https://goreportcard.com/badge/github.com/sylabs/singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eGuidelines for Contributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\".github/PULL_REQUEST_TEMPLATE.md\"\u003ePull Request Template\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"LICENSE.md\"\u003eProject License\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity is an open source container platform designed to be simple, fast, and secure. Singularity is optimized for \u003ca href=\"https://www.sylabs.io/2018/09/singularity-is-enterprise-performance-computing/\" rel=\"nofollow\"\u003eEPC\u003c/a\u003e and HPC workloads, allowing untrusted users to run untrusted containers in a trusted way.\u003c/p\u003e\n\u003cp\u003eCheck out \u003ca href=\"https://www.sylabs.io/singularity/whos-using-singularity/\" rel=\"nofollow\"\u003ewho is using Singularity\u003c/a\u003e and some \u003ca href=\"https://www.sylabs.io/category/how-tos/\" rel=\"nofollow\"\u003euse cases of Singularity\u003c/a\u003e on our website.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started with Singularity\u003c/h2\u003e\n\u003cp\u003eTo install Singularity from source, see the \u003ca href=\"INSTALL.md\"\u003einstallation instructions\u003c/a\u003e. For other installation options, see \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eour website\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor system administrators, see the \u003ca href=\"https://www.sylabs.io/guides/3.0/admin-guide/\" rel=\"nofollow\"\u003eadministrator documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor users, see the \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003euser documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing-to-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing-to-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing to Singularity\u003c/h2\u003e\n\u003cp\u003eCommunity contributions are always greatly appreciated. To start developing Singularity, check out the \u003ca href=\"CONTRIBUTING.md\"\u003eguidelines for contributing\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe also welcome contributions to our \u003ca href=\"https://github.com/sylabs/singularity-userdocs\"\u003euser docs\u003c/a\u003e and \u003ca href=\"https://github.com/sylabs/singularity-admindocs\"\u003eadmin docs\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport\u003c/h2\u003e\n\u003cp\u003eTo get help with Singularity, check out the \u003ca href=\"https://www.sylabs.io/singularity/community/\" rel=\"nofollow\"\u003eCommunity Portal\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor additional support, \u003ca href=\"https://www.sylabs.io/contact/\" rel=\"nofollow\"\u003econtact us\u003c/a\u003e to receive more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cite-as\" class=\"anchor\" aria-hidden=\"true\" href=\"#cite-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCite as:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459. https://doi.org/10.1371/journal.pone.0177459\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe also have a Zenodo citation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer, Gregory M.. (2016). Singularity 2.1.2 - Linux application and environment\ncontainers for science. 10.5281/zenodo.60736\n\nhttps://doi.org/10.5281/zenodo.60736\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eUnless otherwise noted, this project is licensed under a 3-clause BSD license found in the \u003ca href=\"LICENSE.md\"\u003elicense file\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 0, + "topics": [], + "updated_at": 1558040154.0 }, { "data_format": 2, @@ -16299,285 +15811,336 @@ var data = "filenames": [ "Singularity" ], - "full_name": "NagaComBio/singularity_gcnvplotting", - "latest_release": "v0.2.0", - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-for-gcnvplotting_v010sif\" class=\"anchor\" href=\"#for-gcnvplotting_v010sif\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor gcnvplotting_v0.1.0.sif\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/NagaComBio/singularity_gcnvplotting.git\ncd singularity_gcnvplotting\nsudo singularity build gcnvplotting_v0.1.0.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "yuechenwangwyc/topaz", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-topaz\" class=\"anchor\" aria-hidden=\"true\" href=\"#topaz\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTopaz\u003c/h1\u003e\n\u003cp\u003eA pipeline for particle detection in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Topaz also includes methods for micrograph and tomogram denoising using deep denoising models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCheck out our \u003ca href=\"https://github.com/tbepler/topaz/discussions\"\u003eDiscussion\u003c/a\u003e section for general help, suggestions, and tips on using Topaz.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v025\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v025\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.5\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eAdded Relion integration scripts\u003c/li\u003e\n\u003cli\u003eTopaz extract can now write particle coordinates to one file per input micrograph\u003c/li\u003e\n\u003cli\u003eAdded Gaussian filter option for after 3D denoising\u003c/li\u003e\n\u003cli\u003eAdded info on Topaz Workshops\u003c/li\u003e\n\u003cli\u003eTopaz GUI update\u003c/li\u003e\n\u003cli\u003eVarious bug fixes\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v024\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v024\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.4\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eAdded 3D denoising with \u003cstrong\u003etopaz denoise3d\u003c/strong\u003e and two pretrained 3D denoising models\u003c/li\u003e\n\u003cli\u003eAdded argument for setting number of threads to multithreaded commands\u003c/li\u003e\n\u003cli\u003eTopaz GUI update\u003c/li\u003e\n\u003cli\u003eVarious bug fixes\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v023\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v023\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.3\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eImprovements to the pretrained denoising models\u003c/li\u003e\n\u003cli\u003eTopaz now includes pretrained particle picking models\u003c/li\u003e\n\u003cli\u003eUpdated tutorials\u003c/li\u003e\n\u003cli\u003eUpdated GUI to include denoising commands\u003c/li\u003e\n\u003cli\u003eDenoising paper preprint is available \u003ca href=\"https://doi.org/10.1101/838920\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v022\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.2\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThe Topaz publication is out \u003ca href=\"https://doi.org/10.1038/s41592-019-0575-8\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBug fixes and GUI update\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-in-v020\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-in-v020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew in v0.2.0\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTopaz now supports the newest versions of pytorch (\u0026gt;= 1.0.0). If you have pytorch installed for an older version of topaz, it will need to be upgraded. See installation instructions for details.\u003c/li\u003e\n\u003cli\u003eAdded \u003cstrong\u003etopaz denoise\u003c/strong\u003e, a command for denoising micrographs using neural networks.\u003c/li\u003e\n\u003cli\u003eUsability improvements to the GUI.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAn Nvidia GPU with CUDA support for GPU acceleration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBasic Unix/Linux knowledge.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003e(Recommended) Click here to install \u003cem\u003eusing Anaconda\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eIf you do not have the Anaconda python distribution, \u003ca href=\"https://www.anaconda.com/download\" rel=\"nofollow\"\u003eplease install it following the instructions on their website\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe strongly recommend installing Topaz into a separate conda environment. To create a conda environment for Topaz:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n topaz python=3.6 # or 2.7 if you prefer python 2\nsource activate topaz # this changes to the topaz conda environment, \u0027conda activate topaz\u0027 can be used with anaconda \u0026gt;= 4.4 if properly configured\n# source deactivate # returns to the base conda environment\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMore information on conda environments can be found \u003ca href=\"https://conda.io/docs/user-guide/tasks/manage-environments.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-topaz\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-topaz\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Topaz\u003c/h2\u003e\n\u003cp\u003eTo install the precompiled Topaz package and its dependencies, including pytorch:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install topaz -c tbepler -c pytorch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis installs pytorch from the official channel. To install pytorch for specific cuda versions, you will need to add the \u0027cudatoolkit=X.X\u0027 package. E.g. to install pytorch for CUDA 9.0:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install cudatoolkit=9.0 -c pytorch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor combined into a single command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install topaz cudatoolkit=9.0 -c tbepler -c pytorch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for additional pytorch installation instructions.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it! Topaz is now installed in your anaconda environment.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here to install \u003cem\u003eusing Pip\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eWe strongly recommend installing Topaz into a \u003cem\u003evirtual environment\u003c/em\u003e. See \u003ca href=\"https://virtualenv.pypa.io/en/latest/installation/\" rel=\"nofollow\"\u003einstallation instructions\u003c/a\u003e and \u003ca href=\"https://virtualenv.pypa.io/en/latest/userguide/\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e for virtualenv.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-topaz-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-topaz-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Topaz\u003c/h2\u003e\n\u003cp\u003eTo install Topaz for Python 3.X\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install topaz-em\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efor Python 2.7\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install topaz-em\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for additional pytorch installation instructions, including how to install pytorch for specific CUDA versions.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it! Topaz is now installed through pip.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here to install \u003cem\u003eusing Docker\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eDo you have Docker installed? If not, \u003cem\u003eclick here\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-linuxmacos--command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#linuxmacos--command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinux/MacOS \u00a0\u00a0 \u003cem\u003e(command line)\u003c/em\u003e\n\u003c/h2\u003e\n\u003cp\u003eDownload and install Docker 1.21 or greater for \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eLinux\u003c/a\u003e or \u003ca href=\"https://store.docker.com/editions/community/docker-ce-desktop-mac\" rel=\"nofollow\"\u003eMacOS\u003c/a\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eConsider using a Docker \u0027convenience script\u0027 to install (search on your OS\u0027s Docker installation webpage).\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eLaunch docker according to your Docker engine\u0027s instructions, typically \u003ccode\u003edocker start\u003c/code\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e You must have sudo or root access to \u003cem\u003einstall\u003c/em\u003e Docker. If you do not wish to \u003cem\u003erun\u003c/em\u003e Docker as sudo/root, you need to configure user groups as described here: \u003ca href=\"https://docs.docker.com/install/linux/linux-postinstall/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/install/linux/linux-postinstall/\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-windows--gui--command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#windows--gui--command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows \u00a0\u00a0 \u003cem\u003e(GUI \u0026amp; command line)\u003c/em\u003e\n\u003c/h2\u003e\n\u003cp\u003eDownload and install \u003ca href=\"https://docs.docker.com/toolbox/toolbox_install_windows/\" rel=\"nofollow\"\u003eDocker Toolbox for Windows\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eLaunch Kitematic.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf on first startup Kitematic displays a red error suggesting that you run using VirtualBox, do so.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e \u003ca href=\"https://docs.docker.com/toolbox/toolbox_install_mac/\" rel=\"nofollow\"\u003eDocker Toolbox for MacOS\u003c/a\u003e has not yet been tested.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is Docker?\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=YFl2mCHdv24\" rel=\"nofollow\"\u003eThis tutorial explains why Docker is useful.\u003c/a\u003e\u003c/p\u003e\n\n\u003cbr\u003e\n\u003cp\u003eA Dockerfile is provided to build images with CUDA support. Build from the github repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t topaz https://github.com/tbepler/topaz.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor download the source code and build from the source directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/tbepler/topaz\ncd topaz\ndocker build -t topaz .\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here to install \u003cem\u003eusing Singularity\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eA prebuilt Singularity image for Topaz is available \u003ca href=\"https://singularity-hub.org/collections/2413\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and can be installed with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://nysbc/topaz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you can run topaz from within the singularity image with (paths must be changed appropriately):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv -B /mounted_path:/mounted_path /path/to/singularity/container/topaz_latest.sif /usr/local/conda/bin/topaz\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here to install \u003cem\u003efrom source\u003c/em\u003e\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRecommended: install Topaz into a virtual Python environment\u003c/em\u003e\u003cbr\u003e\nSee \u003ca href=\"https://conda.io/docs/user-guide/tasks/manage-environments.html\" rel=\"nofollow\"\u003ehttps://conda.io/docs/user-guide/tasks/manage-environments.html\u003c/a\u003e or \u003ca href=\"https://virtualenv.pypa.io/en/stable/\" rel=\"nofollow\"\u003ehttps://virtualenv.pypa.io/en/stable/\u003c/a\u003e for setting one up.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-the-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-the-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall the dependencies\u003c/h4\u003e\n\u003cp\u003eTested with python 3.6 and 2.7\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epytorch (\u0026gt;= 1.0.0)\u003c/li\u003e\n\u003cli\u003etorchvision\u003c/li\u003e\n\u003cli\u003epillow (\u0026gt;= 6.2.0)\u003c/li\u003e\n\u003cli\u003enumpy (\u0026gt;= 1.11)\u003c/li\u003e\n\u003cli\u003epandas (\u0026gt;= 0.20.3)\u003c/li\u003e\n\u003cli\u003escipy (\u0026gt;= 0.19.1)\u003c/li\u003e\n\u003cli\u003escikit-learn (\u0026gt;= 0.19.0)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEasy installation of dependencies with conda\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install numpy pandas scikit-learn\nconda install -c pytorch pytorch torchvision\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more info on installing pytorch for your CUDA version see \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003ehttps://pytorch.org/get-started/locally/\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-download-the-source-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-the-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload the source code\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/tbepler/topaz\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-topaz-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-topaz-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Topaz\u003c/h4\u003e\n\u003cp\u003eMove to the source code directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd topaz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, this will be the most recent version of the topaz source code. To install a specific older version, checkout that commit. For example, for v0.1.0 of Topaz:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit checkout v0.1.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that older Topaz versions may have different dependencies. Refer to the README for the specific Topaz version.\u003c/p\u003e\n\u003cp\u003eInstall Topaz into your Python path including the topaz command line interface\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install for development use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003eTopaz is also available through \u003ca href=\"https://sbgrid.org/software/titles/topaz\" rel=\"nofollow\"\u003eSBGrid\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-tutorial\" class=\"anchor\" aria-hidden=\"true\" href=\"#tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTutorial\u003c/h1\u003e\n\u003cp\u003eThe tutorials are presented in Jupyter notebooks. Please install Jupyter following the instructions \u003ca href=\"http://jupyter.org/install\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"tutorial/01_quick_start_guide.ipynb\"\u003eQuick start guide\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"tutorial/02_walkthrough.ipynb\"\u003eComplete walkthrough\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"tutorial/03_cross_validation.ipynb\"\u003eCross validation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"tutorial/04_denoising.ipynb\"\u003eMicrograph denoising\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe tutorial data can be downloaded \u003ca href=\"http://bergerlab-downloads.csail.mit.edu/topaz/topaz-tutorial-data.tar.gz\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo run the tutorial steps on your own system, you will need to install \u003ca href=\"http://jupyter.org/install\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e and \u003ca href=\"https://matplotlib.org/\" rel=\"nofollow\"\u003ematplotlib\u003c/a\u003e which is used for visualization.\u003c/p\u003e\n\u003cp\u003eWith Anaconda this can be done with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install jupyter matplotlib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you installed Topaz using anaconda, make sure these are installed into your Topaz evironment.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-user-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#user-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser guide\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here for a description of the Topaz pipeline and its commands\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe command line interface is structured as a single entry command (topaz) with different steps defined as subcommands. A general usage guide is provided below with brief instructions for the most important subcommands in the particle picking pipeline.\u003c/p\u003e\n\u003cp\u003eTo see a list of all subcommands with a brief description of each, run \u003ccode\u003etopaz --help\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-image-preprocessing\" class=\"anchor\" aria-hidden=\"true\" href=\"#image-preprocessing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImage preprocessing\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-downsampling-topaz-downsample\" class=\"anchor\" aria-hidden=\"true\" href=\"#downsampling-topaz-downsample\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownsampling (topaz downsample)\u003c/h4\u003e\n\u003cp\u003eIt is recommened to downsample and normalize images prior to model training and prediction.\u003c/p\u003e\n\u003cp\u003eThe downsample script uses the discrete Fourier transform to reduce the spacial resolution of images. It can be used as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etopaz downsample --scale={downsampling factor} --output={output image path} {input image path} \n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz downsample [-h] [-s SCALE] [-o OUTPUT] [-v] file\n\npositional arguments:\n file\n\noptional arguments:\n -h, --help show this help message and exit\n -s SCALE, --scale SCALE\n downsampling factor (default: 4)\n -o OUTPUT, --output OUTPUT\n output file\n -v, --verbose print info\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-normalization-topaz-normalize\" class=\"anchor\" aria-hidden=\"true\" href=\"#normalization-topaz-normalize\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNormalization (topaz normalize)\u003c/h4\u003e\n\u003cp\u003eThe normalize script can then be used to normalize the images. This script fits a two component Gaussian mixture model with an additional scaling multiplier per image to capture carbon pixels and account for differences in exposure. The pixel values are then adjusted by dividing each image by its scaling factor and then subtracting the mean and dividing by the standard deviation of the dominant Gaussian mixture component. It can be used as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etopaz normalize --destdir={directory to put normalized images} [list of image files]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz normalize [-h] [-s SAMPLE] [--niters NITERS] [--seed SEED]\n [-o DESTDIR] [-v]\n files [files ...]\n\npositional arguments:\n files\n\noptional arguments:\n -h, --help show this help message and exit\n -s SAMPLE, --sample SAMPLE\n pixel sampling factor for model fit (default: 100)\n --niters NITERS number of iterations to run for model fit (default:\n 200)\n --seed SEED random seed for model initialization (default: 1)\n -o DESTDIR, --destdir DESTDIR\n output directory\n -v, --verbose verbose output\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-single-step-preprocessing-topaz-preprocess\" class=\"anchor\" aria-hidden=\"true\" href=\"#single-step-preprocessing-topaz-preprocess\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingle-step preprocessing (topaz preprocess)\u003c/h4\u003e\n\u003cp\u003eBoth downsampling and normalization can be performed in one step with the preprocess script.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etopaz preprocess --scale={downsampling factor} --destdir={directory to put processed images} [list of image files]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz preprocess [-h] [-s SCALE] [-t NUM_WORKERS]\n [--pixel-sampling PIXEL_SAMPLING] [--niters NITERS]\n [--seed SEED] -o DESTDIR [-v]\n files [files ...]\n\npositional arguments:\n files\n\noptional arguments:\n -h, --help show this help message and exit\n -s SCALE, --scale SCALE\n rescaling factor for image downsampling (default: 4)\n -t NUM_WORKERS, --num-workers NUM_WORKERS\n number of processes to use for parallel image\n downsampling (default: 0)\n --pixel-sampling PIXEL_SAMPLING\n pixel sampling factor for model fit (default: 100)\n --niters NITERS number of iterations to run for model fit (default:\n 200)\n --seed SEED random seed for model initialization (default: 1)\n -o DESTDIR, --destdir DESTDIR\n output directory\n -v, --verbose verbose output\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-model-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#model-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModel training\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-file-formats\" class=\"anchor\" aria-hidden=\"true\" href=\"#file-formats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile formats\u003c/h4\u003e\n\u003cp\u003eThe training script requires a file listing the image file paths and another listing the particle coordinates. Coordinates index images from the top left. These files should be tab delimited with headers as follows:\u003c/p\u003e\n\u003cp\u003eimage file list\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eimage_name\tpath\n...\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eparticle coordinates\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eimage_name\tx_coord\ty_coord\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-train-region-classifiers-with-labeled-particles-topaz-train\" class=\"anchor\" aria-hidden=\"true\" href=\"#train-region-classifiers-with-labeled-particles-topaz-train\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrain region classifiers with labeled particles (topaz train)\u003c/h4\u003e\n\u003cp\u003eModels are trained using the \u003ccode\u003etopaz train\u003c/code\u003e command. For a complete list of training arguments, see\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etopaz train --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-segmentation-and-particle-extraction\" class=\"anchor\" aria-hidden=\"true\" href=\"#segmentation-and-particle-extraction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSegmentation and particle extraction\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-segmention-topaz-segment-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#segmention-topaz-segment-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSegmention (topaz segment, optional)\u003c/h4\u003e\n\u003cp\u003eImages can be segmented using the \u003ccode\u003etopaz segment\u003c/code\u003e command with a trained model.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz segment [-h] [-m MODEL] [-o DESTDIR] [-d DEVICE] [-v]\n paths [paths ...]\n\npositional arguments:\n paths paths to image files for processing\n\noptional arguments:\n -h, --help show this help message and exit\n -m MODEL, --model MODEL\n path to trained classifier\n -o DESTDIR, --destdir DESTDIR\n output directory\n -d DEVICE, --device DEVICE\n which device to use, \u0026lt;0 corresponds to CPU (default:\n GPU if available)\n -v, --verbose verbose mode\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-particle-extraction-topaz-extract\" class=\"anchor\" aria-hidden=\"true\" href=\"#particle-extraction-topaz-extract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParticle extraction (topaz extract)\u003c/h4\u003e\n\u003cp\u003ePredicted particle coordinates can be extracted directly from saved segmented images (see above) or images can be segmented and particles extracted in one step given a trained model using the \u003ccode\u003etopaz extract\u003c/code\u003e command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz extract [-h] [-m MODEL] [-r RADIUS] [-t THRESHOLD]\n [--assignment-radius ASSIGNMENT_RADIUS]\n [--min-radius MIN_RADIUS] [--max-radius MAX_RADIUS]\n [--step-radius STEP_RADIUS] [--num-workers NUM_WORKERS]\n [--targets TARGETS] [--only-validate] [-d DEVICE]\n [-o OUTPUT]\n paths [paths ...]\n\npositional arguments:\n paths paths to image files for processing\n\noptional arguments:\n -h, --help show this help message and exit\n -m MODEL, --model MODEL\n path to trained subimage classifier, if no model is\n supplied input images must already be segmented\n -r RADIUS, --radius RADIUS\n radius of the regions to extract\n -t THRESHOLD, --threshold THRESHOLD\n score quantile giving threshold at which to terminate\n region extraction (default: 0.5)\n --assignment-radius ASSIGNMENT_RADIUS\n maximum distance between prediction and labeled target\n allowed for considering them a match (default: same as\n extraction radius)\n --min-radius MIN_RADIUS\n minimum radius for region extraction when tuning\n radius parameter (default: 5)\n --max-radius MAX_RADIUS\n maximum radius for region extraction when tuning\n radius parameters (default: 100)\n --step-radius STEP_RADIUS\n grid size when searching for optimal radius parameter\n (default: 5)\n --num-workers NUM_WORKERS\n number of processes to use for extracting in parallel,\n 0 uses main process (default: 0)\n --targets TARGETS path to file specifying particle coordinates. used to\n find extraction radius that maximizes the AUPRC\n --only-validate flag indicating to only calculate validation metrics.\n does not report full prediction list\n -d DEVICE, --device DEVICE\n which device to use, \u0026lt;0 corresponds to CPU\n -o OUTPUT, --output OUTPUT\n file path to write\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis script uses the non maxima suppression algorithm to greedily select particle coordinates and remove nearby coordinates from the candidates list. Two additional parameters are involved in this process.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eradius: coordinates within this parameter of selected coordinates are removed from the candidates list\u003c/li\u003e\n\u003cli\u003ethreshold: specifies the score quantile below which extraction stops\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe radius parameter can be tuned automatically given a set of known particle coordinates by finding the radius which maximizes the average precision score. In this case, predicted coordinates must be assigned to target coordinates which requires an additional distance threshold (--assignment-radius).\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-choosing-a-final-particle-list-threshold-topaz-precision_recall_curve\" class=\"anchor\" aria-hidden=\"true\" href=\"#choosing-a-final-particle-list-threshold-topaz-precision_recall_curve\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoosing a final particle list threshold (topaz precision_recall_curve)\u003c/h4\u003e\n\u003cp\u003eParticles extracted using Topaz still have scores associated with them and a final particle list should be determined by choosing particles above some score threshold. The \u003ccode\u003etopaz precision_recall_curve\u003c/code\u003e command can facilitate this by reporting the precision-recall curve for a list of predicted particle coordinates and a list of known target coordinates. A threshold can then be chosen to optimize the F1 score or for specific recall/precision levels on a heldout set of micrographs.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: topaz precision_recall_curve [-h] [--predicted PREDICTED]\n [--targets TARGETS] -r ASSIGNMENT_RADIUS\n\noptional arguments:\n -h, --help show this help message and exit\n --predicted PREDICTED\n path to file containing predicted particle coordinates\n with scores\n --targets TARGETS path to file specifying target particle coordinates\n -r ASSIGNMENT_RADIUS, --assignment-radius ASSIGNMENT_RADIUS\n maximum distance between prediction and labeled target\n allowed for considering them a match\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdetails\u003e\u003csummary\u003e\u003cstrong\u003eClick here for a description of the model architectures, training methods, and training radius\u003c/strong\u003e\u003c/summary\u003e\u003cp\u003e\u003c/p\u003e\u003c/details\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-model-architectures\" class=\"anchor\" aria-hidden=\"true\" href=\"#model-architectures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModel architectures\u003c/h4\u003e\n\u003cp\u003eCurrently, there are several model architectures available for use as the region classifier\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eresnet8 [receptive field = 71]\u003c/li\u003e\n\u003cli\u003econv127 [receptive field = 127]\u003c/li\u003e\n\u003cli\u003econv63 [receptive field = 63]\u003c/li\u003e\n\u003cli\u003econv31 [receptive field = 31]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eResNet8 gives a good balance of performance and receptive field size. Conv63 and Conv31 can be better choices when less complex models are needed.\u003c/p\u003e\n\u003cp\u003eThe number of units in the base layer can be set with the --units flag. ResNet8 always doubles the number of units when the image is strided during processing. Conv31, Conv63, and Conv127 do not by default, but the --unit-scaling flag can be used to set a multiplicative factor on the number of units when striding occurs.\u003c/p\u003e\n\u003cp\u003eThe pooling scheme can be changed for the conv* models. The default is not to perform any pooling, but max pooling and average pooling can be used by specifying \"--pooling=max\" or \"--pooling=avg\".\u003c/p\u003e\n\u003cp\u003eFor a detailed layout of the architectures, use the --describe flag.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-training-methods\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining methods\u003c/h4\u003e\n\u003cp\u003eThe PN method option treats every coordinate not labeled as positive (y=1) as negative (y=0) and then optimizes the standard classification objective:\n$$ \\piE_{y=1}[L(g(x),1)] + (1-\\pi)E_{y=0}[L(g(x),0)] $$\nwhere $\\pi$ is a parameter weighting the positives and negatives, $L$ is the misclassifiaction cost function, and $g(x)$ is the model output.\u003c/p\u003e\n\u003cp\u003eThe GE-binomial method option instead treats coordinates not labeled as positive (y=1) as unlabeled (y=?) and then optimizes an objective including a generalized expectation criteria designed to work well with minibatch SGD.\u003c/p\u003e\n\u003cp\u003eThe GE-KL method option instead treats coordinates not labeled as positive (y=1) as unlabeled (y=?) and then optimizes the objective:\n$$ E_{y=1}[L(g(x),1)] + \\lambdaKL(\\pi, E_{y=?}[g(x)]) $$\nwhere $\\lambda$ is a slack parameter (--slack flag) that specifies how strongly to weight the KL divergence of the expecation of the classifier over the unlabeled data from $\\pi$.\u003c/p\u003e\n\u003cp\u003eThe PU method uses the objective function proposed by Kiryo et al. (2017)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-radius\" class=\"anchor\" aria-hidden=\"true\" href=\"#radius\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRadius\u003c/h4\u003e\n\u003cp\u003eThis sets how many pixels around each particle coordinate are treated as positive, acting as a form of data augmentation. These coordinates follow a distribution that results from which pixel was selected as the particle center when the data was labeled. The radius should be chosen to be large enough that it covers a reasonable region of pixels likely to have been selected but not so large that pixels outside of the particles are labeled as positives.\u003c/p\u003e\n\n\u003cp\u003eA user guide is also built into the \u003ca href=\"https://emgweb.nysbc.org/topaz.html\" rel=\"nofollow\"\u003eTopaz GUI\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-integration\" class=\"anchor\" aria-hidden=\"true\" href=\"#integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntegration\u003c/h1\u003e\n\u003cp\u003eTopaz also integrates with RELION, CryoSPARC, Scipion, and Appion. You can find information and tutorials here:\u003c/p\u003e\n\u003cp\u003eRELION: \u003ca href=\"https://github.com/tbepler/topaz/tree/master/relion_run_topaz\"\u003ehttps://github.com/tbepler/topaz/tree/master/relion_run_topaz\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCryoSPARC: \u003ca href=\"https://guide.cryosparc.com/processing-data/all-job-types-in-cryosparc/deep-picking/deep-picking\" rel=\"nofollow\"\u003ehttps://guide.cryosparc.com/processing-data/all-job-types-in-cryosparc/deep-picking/deep-picking\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eScipion: \u003ca href=\"https://github.com/scipion-em/scipion-em-topaz\"\u003ehttps://github.com/scipion-em/scipion-em-topaz\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-topaz-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#topaz-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTopaz\u003c/h3\u003e\n\u003cp\u003eBepler, T., Morin, A., Rapp, M., Brasch, J., Shapiro, L., Noble, A.J., Berger, B. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat Methods 16, 1153\u20131160 (2019). \u003ca href=\"https://doi.org/10.1038/s41592-019-0575-8\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41592-019-0575-8\u003c/a\u003e\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eBibtex\u003c/summary\u003e\u003cp\u003e\n\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@Article{Bepler2019,\nauthor={Bepler, Tristan\nand Morin, Andrew\nand Rapp, Micah\nand Brasch, Julia\nand Shapiro, Lawrence\nand Noble, Alex J.\nand Berger, Bonnie},\ntitle={Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs},\njournal={Nature Methods},\nyear={2019},\nissn={1548-7105},\ndoi={10.1038/s41592-019-0575-8},\nurl={https://doi.org/10.1038/s41592-019-0575-8}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003ch3\u003e\u003ca id=\"user-content-topaz-denoise\" class=\"anchor\" aria-hidden=\"true\" href=\"#topaz-denoise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTopaz-Denoise\u003c/h3\u003e\n\u003cp\u003eBepler, T., Kelley, K., Noble, A.J., Berger, B. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat Commun 11, 5208 (2020). \u003ca href=\"https://doi.org/10.1038/s41467-020-18952-1\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41467-020-18952-1\u003c/a\u003e\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eBibtex\u003c/summary\u003e\u003cp\u003e\n\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@Article{Bepler2020_topazdenoise,\nauthor={Bepler, Tristan\nand Kelley, Kotaro\nand Noble, Alex J.\nand Berger, Bonnie},\ntitle={Topaz-Denoise: general deep denoising models for cryoEM and cryoET},\njournal={Nature Communications},\nyear={2020},\nissn={2041-1723},\ndoi={10.1038/s41467-020-18952-1},\nurl={https://doi.org/10.1038/s41467-020-18952-1}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003ch1\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h1\u003e\n\u003cdetails\u003e\u003csummary\u003eTristan Bepler\u003c/summary\u003e\u003cp\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/tbepler.png\"\u003e\u003cimg src=\"images/tbepler.png\" width=\"120\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\u003c/details\u003e\n\u003cdetails\u003e\u003csummary\u003eAlex J. Noble\u003c/summary\u003e\u003cp\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/anoble.png\"\u003e\u003cimg src=\"images/anoble.png\" width=\"120\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\u003c/details\u003e\n\u003ch1\u003e\u003ca id=\"user-content-topaz-workshop\" class=\"anchor\" aria-hidden=\"true\" href=\"#topaz-workshop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTopaz Workshop\u003c/h1\u003e\n\u003cp\u003eTo request a Topaz Workshop for academic or non-academic purposes, send a request to:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;alexjnoble [at] gmail [dot] com\u0026gt;\u003c/em\u003e \u0026amp; \u003cem\u003e\u0026lt;tbepler [at] gmail [dot] com\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eTopaz is open source software released under the \u003ca href=\"https://github.com/tbepler/topaz/blob/master/LICENSE\"\u003eGNU General Public License, Version 3\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bugs--suggestions\" class=\"anchor\" aria-hidden=\"true\" href=\"#bugs--suggestions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBugs \u0026amp; Suggestions\u003c/h1\u003e\n\u003cp\u003ePlease report bugs and make specific feature requests and suggestions for improvements as a \u003ca href=\"https://github.com/tbepler/topaz/issues\"\u003eGithub issue\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor general help, questions, suggestions, tips, and installation/setup assistance, please take a look at our new \u003ca href=\"https://github.com/tbepler/topaz/discussions\"\u003eDiscussion\u003c/a\u003e section.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1640253212.0 + "updated_at": 1611916919.0 }, { "data_format": 2, - "description": "Custom implementation of neurodocker (https://github.com/ReproNim/neurodocker)", + "description": "image_preprocess", "filenames": [ "Singularity" ], - "full_name": "achennings/neurodocker", + "full_name": "lsx1980/image_preprocess", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-neurodocker\" class=\"anchor\" href=\"#neurodocker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eneurodocker\u003c/h1\u003e\n\u003cp\u003eCustom implementation of neurodocker (\u003ca href=\"https://github.com/ReproNim/neurodocker\"\u003ehttps://github.com/ReproNim/neurodocker\u003c/a\u003e)\u003c/p\u003e\n", + "readme": "\u003cp\u003e\"\"\"\nVersion: 1.5\u003c/p\u003e\n\u003cp\u003eSummary: image pre-processingfor 3D model reconstruction\u003c/p\u003e\n\u003cp\u003eAuthor: suxing liu\u003c/p\u003e\n\u003cp\u003eAuthor-email: \u003ca href=\"mailto:suxingliu@gmail.com\"\u003esuxingliu@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUSAGE:\u003c/p\u003e\n\u003cp\u003epython pipeline.py -p /path_to_image_folder/ -ft jpg\u003c/p\u003e\n\u003cp\u003eparameter list:\u003c/p\u003e\n\u003cp\u003eargument:\n(\"-p\", \"--path\", required = True, help = \"path to image file\")\n(\"-ft\", \"--filetype\", required = True, help = \"Image filetype\")\u003c/p\u003e\n\u003cp\u003esingularity build --writable image_preprocess.img Singularity\nsingularity exec image_preprocess.img python /opt/code/pipeline.py -p /path_to_image_folder/ -ft jpg\n\"\"\"\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1645031040.0 + "updated_at": 1561479834.0 }, { "data_format": 2, - "description": "The jp command is a command line interface to JMESPath, an expression language for manipulating JSON.", + "description": null, "filenames": [ - "0.2.1/Singularity" + "Singularity.nanocomp", + "Singularity.parallel", + "Singularity.pomoxis", + "Singularity.OligoMiner", + "Singularity.bedops", + "Singularity.AP_master", + "Singularity.salmon", + "Singularity.freebayes", + "Singularity.seqkit", + "Singularity.yacrd", + "Singularity.PEPPER", + "Singularity.HELEN", + "Singularity.medaka", + "Singularity.R", + "Singularity.busco", + "Singularity.slamdunk", + "Singularity.marvel", + "Singularity.medakaGPU", + "Singularity.mashmap", + "Singularity.TailfindR", + "Singularity.mosdepth", + "Singularity.cutadapt", + "Singularity.pycoQC", + "Singularity.bowtie", + "Singularity.hiC-pro", + "Singularity.ngmlr.txt", + "Singularity.deep-variant", + "Singularity.bedtools", + "Singularity.Repeatmasker", + "Singularity.filtlong", + "Singularity.samtools", + "Singularity.sratoolkit", + "Singularity.homer-tools", + "Singularity.purge_dups", + "Singularity.STAR", + "Singularity.mummer", + "Singularity.guppy", + "Singularity.nanopolish", + "Singularity.kentUtils", + "Singularity.quast", + "Singularity.albacore" ], - "full_name": "pscedu/singularity-jp", - "latest_release": "v0.2.1", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-jp/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-jp/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-jp/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-jp/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/05b134868e56bf8c8fa7dc13f4470cf13cad0b7161546e09c789aafe22deec6e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6a70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/05b134868e56bf8c8fa7dc13f4470cf13cad0b7161546e09c789aafe22deec6e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6a70\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-jp\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/92fedab816aca4c765ddc450267fdad82b3f6c32c306fde8ca397c0a1aedec59/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6a70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/92fedab816aca4c765ddc450267fdad82b3f6c32c306fde8ca397c0a1aedec59/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6a70\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-jp\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/434a811330df95d0d19571e1ec67b47ba8e8e026d81c1f24c5f0eb3fae746f35/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6a70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/434a811330df95d0d19571e1ec67b47ba8e8e026d81c1f24c5f0eb3fae746f35/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6a70\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-jp\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/9e077544905c5c3ee2d0d7b1f83444e58ecffcb7d201f604c5eb3e114c1e18b3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6a70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e077544905c5c3ee2d0d7b1f83444e58ecffcb7d201f604c5eb3e114c1e18b3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6a70\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-jp\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-jp\" class=\"anchor\" href=\"#singularity-jp\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-jp\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://jmespath.org/\" rel=\"nofollow\"\u003ejp\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ejp\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/jp/0.2.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/jp\u003c/code\u003e as \u003ccode\u003e0.2.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "dominik-handler/AP_singu", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1644903522.0 + "topics": [], + "updated_at": 1612162065.0 }, { "data_format": 2, - "description": "jq is a lightweight and flexible command-line JSON processor.", + "description": "Jupyter Miniconda Python 3 and Singularity Container", "filenames": [ - "1.6/Singularity" + "Singularity.jupyter3", + "Singularity.rstudio", + "Singularity.rbase", + "Singularity.ecmwf.odb", + "Singularity.jupyter23", + "Singularity.jupyter2rttov", + "Singularity.centos8", + "Singularity.stuff", + "Singularity.jupyter3ec", + "Singularity.centos", + "Singularity.centos.apps", + "Singularity.jedi", + "Singularity.gitlab", + "Singularity.jupyter3rttov", + "Singularity.lehre", + "Singularity.intelpy", + "Singularity.jupyter2" ], - "full_name": "pscedu/singularity-jq", - "latest_release": "v1.6", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-jq/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-jq/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-jq/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-jq/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7ba2d90cdf83d5e2c1ff43f36457bd7662a4c884cfaba4962f33517ebbcc87c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6a71\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ba2d90cdf83d5e2c1ff43f36457bd7662a4c884cfaba4962f33517ebbcc87c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6a71\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-jq\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f57604a88aedfdf5b00774448eb1e1458a22026714624a4ed775fe8f7948d252/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6a71\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f57604a88aedfdf5b00774448eb1e1458a22026714624a4ed775fe8f7948d252/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6a71\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-jq\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8f97344032bc1f53bcc9a24cb971084b86a8b67ee2c9e9cae802a9dd84cf569c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6a71\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8f97344032bc1f53bcc9a24cb971084b86a8b67ee2c9e9cae802a9dd84cf569c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6a71\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-jq\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0e1fe273a8da2bb31a820d69ebb9fce673aaed18c07e2b45b4ad319d38a46954/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6a71\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e1fe273a8da2bb31a820d69ebb9fce673aaed18c07e2b45b4ad319d38a46954/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6a71\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-jq\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-jq\" class=\"anchor\" href=\"#singularity-jq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-jq\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/2a6480b8877ea9a4e8d36292d5b1a2a918d31be6029767a0dc047dbe8c48d977/68747470733a2f2f737465646f6c616e2e6769746875622e696f2f6a712f6a712e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a6480b8877ea9a4e8d36292d5b1a2a918d31be6029767a0dc047dbe8c48d977/68747470733a2f2f737465646f6c616e2e6769746875622e696f2f6a712f6a712e706e67\" width=\"50%\" data-canonical-src=\"https://stedolan.github.io/jq/jq.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://stedolan.github.io/jq/\" rel=\"nofollow\"\u003ejq\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ejq\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/jq/1.6\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/jq\u003c/code\u003e as \u003ccode\u003e1.6.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "MBlaschek/singularity-jupyter", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3843\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-jupyter-and-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter-and-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter and Singularity\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eUpdated: 25.11.2019, new singularity version 3.5\u003c/strong\u003e\n\u003cstrong\u003eContainers are on singularity-hub now: \u003ca href=\"https://singularity-hub.org/collections/3843\" rel=\"nofollow\"\u003eMyCollections\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJupyter Miniconda Python 3 and Singularity Container\u003c/p\u003e\n\u003cp\u003eThis is an update from \u003ca href=\"https://github.com/singularityhub/jupyter\"\u003e\u003c/a\u003e the offical jupyter singularity container that requires root permissions to run:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[NEW] Only need root permissions to create the container\u003c/li\u003e\n\u003cli\u003e[NEW] Miniconda (smaller in size)\u003c/li\u003e\n\u003cli\u003e[NEW] runscript gives informaiton\u003c/li\u003e\n\u003cli\u003e[NEW] Using CentOS 6.10 not Ubuntu anymore\u003c/li\u003e\n\u003cli\u003e[NEW] GLIBC 2.12 compatibility to CentOS 6.10 (Final)\u003c/li\u003e\n\u003cli\u003e[NEW] Build NCAR WRF containers with singularity \u003ca href=\"https://github.com/NCAR/container-wrf\"\u003eNCAR WRF containers\u003c/a\u003e\nIf you haven\u0027t installed singularity, do that with \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ethese instructions\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownlaod Receipie files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.centos (Base only Centos)\u003c/li\u003e\n\u003cli\u003eSingularity.jupyter23 (Miniconda, Jupyter Python2 \u0026amp; Python 3)\u003c/li\u003e\n\u003cli\u003eSingularity.jupyter3 (Miniconda, Jupyter Python 3)\u003c/li\u003e\n\u003cli\u003eSingularity.jupyter3x (Miniconda, Jupyter Python 3, \u003ca href=\"https://confluence.ecmwf.int/display/ECC\" rel=\"nofollow\"\u003eEccodes\u003c/a\u003e, cfgrib from ECMWF)\u003c/li\u003e\n\u003cli\u003eSingualrity.jupyter3ec (Miniconda, Jupyter Python 3, Eccodes library manual build, \u003cstrong\u003edeprecated\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eSingualrity.jupyter3rttov (Miniconda, Jupyter Python 3, \u003ca href=\"https://www.nwpsaf.eu/site/software/rttov/\" rel=\"nofollow\"\u003eRTTOV\u003c/a\u003e from EUMETSAT (not included due to license))\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eClone the Repository and manually build containers:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e git clone https://github.com/MBlaschek/singularity-jupyter jupyter\n cd jupyter \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRetrieve Containers from singularity hub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull shub://MBlaschek/singularity-jupyter:[TAG]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTags are the names above (centos, jupyter23, jupyter3, ...):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull shub://MBlaschek/singularity-jupyter:centos\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-create\" class=\"anchor\" aria-hidden=\"true\" href=\"#create\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCREATE\u003c/h2\u003e\n\u003cp\u003eFirst create the CentOS container that is used by all the others.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build centos610.sif Singularity.centos\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLet\u0027s now create the notebook container:\nIf you build locally, then just edit the Recipie to use the local image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Local centos 6.10 image\nBootstrap: localimage\nFrom: centos610.sif\n# Bootstrap: shub\n# From: MBlaschek/singularity-jupyter:centos\n# most recent and debian image\n# BootStrap: docker\n# From: continuumio/miniconda3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eJupyter Python 3 Notebook Container: \u003ccode\u003eSingularity.jupyter3\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eJupyter Python 2 \u0026amp; 3 Notebook Container: \u003ccode\u003eSingularity.jupyter23\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eJupyter Python 2 \u0026amp; 3 Notebook + Eccodes Library: \u003ccode\u003eSingularity.jupyter3x\u003c/code\u003e (depends on the image from \u003ccode\u003ejupyter3.sif \u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can choose now if you prefer a writeable container (for development, installation of additional packages, ...) or a deployment container (read_only, default) \u003ca href=\"http://singularity.lbl.gov/docs-build-container\" rel=\"nofollow\"\u003eread more\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build --writeable jupyter3.sif Singularity.jupyter3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor for deployment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build jupyter3.sif Singularity.jupyter3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Notebook server Recipies include a line at the end that is quite important for jupyter to run properly:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export JUPYTER_RUNTIME_DIR=$PWD/.runtime\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis line tells jupyter to use a specific directory for its runtime. Otherwise it would try to use the default \u003ccode\u003eXDG_RUNTIME_DIR\u003c/code\u003e, which is by default set to \u003ccode\u003e/run/user/...\u003c/code\u003e and not accessable via the container.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRUN\u003c/h2\u003e\n\u003cp\u003eThen to run the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run jupyter3.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003egives Information on the container and it\u0027s apps (notebook)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity Container\n Container Centos 6.10 (docker)\n Glibc: 2.12-1.212.el6.x86_64\n Installed: wget, git, curl, bzip2 ca-certificates\n\n SCIF (Apps): notebook\n Container.Glibc : 2.12-1.212.el6.x86_64\n Container.OS : CentOS 6.10\n Definition.Author : M. Blaschek\n Definition.Author.Email : michael.blaschek@univie.ac.at\n Definition.File.Date : 5.11.2019\n Definition.File.Version : 1.0\n org.label-schema.build-date : Thursday_28_November_2019_8:49:15_UTC\n org.label-schema.schema-version : 1.0\n org.label-schema.usage : /.singularity.d/runscript.help\n org.label-schema.usage.singularity.deffile.bootstrap : shub\n org.label-schema.usage.singularity.deffile.from : MBlaschek/singularity-jupyter:centos\n org.label-schema.usage.singularity.runscript.help : /.singularity.d/runscript.help\n org.label-schema.usage.singularity.version : 3.4.2\n Bye Bye\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003elaunch the notebook:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity run jupyter3.sif notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003elaunch the console:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity run jupyter3.sif ipython\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor as a singularity instances (background server):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance start jupyter3.sif Jupy3\nsingularity run instance://Jupy3 notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor as an instance with remote access (default is just localhost):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run instance://Jupy3 notebook --ip=$(hostname) \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnyway you should see output like this:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"jupyter.png\"\u003e\u003cimg src=\"jupyter.png\" alt=\"jupyter.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe current directory is where your server starts. In your browser you should be able to navigate to the link from the console:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"jupyterweb.png\"\u003e\u003cimg src=\"jupyterweb.png\" alt=\"jupyterweb.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThere is a \u003ccode\u003e.jupyter3.log\u003c/code\u003e file that shows this output.\u003c/p\u003e\n\u003cp\u003eThe password is \u003cstrong\u003esuper-secret\u003c/strong\u003e. You can change that easily within the Singularity file.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-ipykernel-and-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#ipykernel-and-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIPYKernel and Containers\u003c/h2\u003e\n\u003cp\u003eIn order to use your container with an existing notebook server you need to register your container kernel with that server.\nOther people have done this:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/clemsonciti/singularity-in-jupyter-notebook\"\u003eTensorflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/mattpitkin/35ac19214048e96c391e948d7ec34ca5\"\u003eKernel\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo register your container, in the \u003ccode\u003e${HOME}/.local/share/jupyter/kernels\u003c/code\u003e create a new directory, e.g. myimage, and add a \u003ccode\u003ekernel.json\u003c/code\u003e file containing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"language\": \"python\",\n \"argv\": [\"/usr/bin/singularity\",\n \"exec\",\n \"/dir/to/your/image/jupyter3.sif\",\n \"/opt/conda/bin/python\",\n \"-m\",\n \"ipykernel\",\n \"-f\",\n \"{connection_file}\"\n ],\n \"display_name\": \"Python 3 (Singularity)\"\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eChange the path to your image and singularity executable. Then start a jupyter notebook with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e jupyter notebook \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand there should be a usable Python 3 (Singularity) kernel option! Check your Jupyter paths, like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e jupyter --paths\n \n config:\n /home/user/.jupyter\n /opt/anaconda2/etc/jupyter\n /usr/local/etc/jupyter\n /etc/jupyter\n data:\n /home/user/.local/share/jupyter\n /opt/anaconda2/share/jupyter\n /usr/local/share/jupyter\n /usr/share/jupyter\n runtime:\n /run/user/1000/jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand make sure the runtime directory is accessable from inside the container. In this example it isn\u0027t. There I need to change this to something like this, before I run the server again:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export JUPYTER_RUNTIME_DIR=$HOME/.local/share/jupyter/runtime\n jupyter notebook \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat should solve the issue and make your contained jupyter environment accessable via your notebook server. :)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-runtime-dir\" class=\"anchor\" aria-hidden=\"true\" href=\"#runtime-dir\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRUNTIME DIR\u003c/h4\u003e\n\u003cp\u003eI came across a few problems, which related to the \u003ccode\u003eRUNTIME_DIR\u003c/code\u003e and is quite import to run your server without root permissions.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e XDG_RUNTIME_DIR=/run/user/1000 # Default in Ubuntu/Linux (inside the container)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat is not a good path. Therefore we change it to a defined path inside the container (already in the singularity file).\nThe following shows a way around, not necessary if you use the above recipe.\u003c/p\u003e\n\u003cp\u003eThis directory \u003ccode\u003e/run/user/..\u003c/code\u003e is not accessable by default from inside the container.\nTo register your container, in the \u003ccode\u003e${HOME}/.local/share/jupyter/kernels\u003c/code\u003e create a new directory, e.g. myimage, and add a \u003ccode\u003ekernel.json\u003c/code\u003e file containing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"language\": \"python\",\n \"argv\": [\"/usr/bin/singularity\",\n \"exec\",\n \"-B\",\n \"/run/user:/run/user\",\n \"/dir/to/your/image/jupyter.img\",\n \"/opt/conda/bin/python\",\n \"-m\",\n \"ipykernel\",\n \"-f\",\n \"{connection_file}\"\n ],\n \"display_name\": \"Python 3 (Singularity)\"\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere adding the \u003ccode\u003e-B /run/user:/run/user\u003c/code\u003e option is important, which allows the container to have access.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-r-studio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#r-studio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR-Studio Server\u003c/h1\u003e\n\u003cp\u003eThis is a lightly modified version of what \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003enickjer\u003c/a\u003e has done. The Modifications allow to run the R-Studio server as an instance.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance start rserver.sif RStudio\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsually the R-Studio server runs on port 9090.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-syntax-highlighting\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-syntax-highlighting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Syntax Highlighting\u003c/h1\u003e\n\u003cp\u003eThere is a nice repo \u003ca href=\"https://github.com/singularityhub/singularity.lang\"\u003esingularity.lang\u003c/a\u003e, where this can be added for Gedit, Nano and Vim. For Atom there is a highlighting as well. Works well.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1644901477.0 + "topics": [], + "updated_at": 1662971530.0 }, { "data_format": 2, "description": null, "filenames": [ - "docker/Singularity.snowflake" + "Singularity" ], - "full_name": "nuKs/bids-preproc", + "full_name": "robomorelli/singularity_test", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_test\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_test\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1644864844.0 + "updated_at": 1610876939.0 }, { "data_format": 2, - "description": "RAxML - Randomized Axelerated Maximum Likelihood.", + "description": null, "filenames": [ - "8.2.9/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-raxml", - "latest_release": "v8.2.9", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-raxml/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-raxml/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-raxml/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-raxml/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/277233f105b68116448cd7db2faac6b9dedc5603317bc3be2789550b8554d1e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7261786d6c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/277233f105b68116448cd7db2faac6b9dedc5603317bc3be2789550b8554d1e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7261786d6c\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-raxml\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/60d3d420d133df2ca3e6346fa5baca67b3a2100e92322bef33b335d23d867cb8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7261786d6c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/60d3d420d133df2ca3e6346fa5baca67b3a2100e92322bef33b335d23d867cb8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7261786d6c\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-raxml\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/304eaca0dbef42b860c7c8d5b95de8e8e1672a13e0e5568946afa88d4f631d52/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7261786d6c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/304eaca0dbef42b860c7c8d5b95de8e8e1672a13e0e5568946afa88d4f631d52/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7261786d6c\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-raxml\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ee982fcac05c22d0d030c924d411c219e655450543d9c54220f0f105c072ede2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7261786d6c\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ee982fcac05c22d0d030c924d411c219e655450543d9c54220f0f105c072ede2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7261786d6c\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-raxml\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-raxml\" class=\"anchor\" href=\"#singularity-raxml\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-raxml\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://cme.h-its.org/exelixis/web/software/raxml\" rel=\"nofollow\"\u003eraxml\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eraxml\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/raxml/8.2.9\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/raxml\u003c/code\u003e as \u003ccode\u003e8.2.9.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "dylanturpin/shub_test", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1644856111.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1574885235.0 }, { "data_format": 2, - "description": "My collection of singularity containers recipes", + "description": null, "filenames": [ - "busco/Singularity.busco", - "Biocontainer/Singularity.Biocontainers", - "DIRT/Singularity.DIRT", - "genome-annotation/Singularity.genome-annotation" + "dockerfiles/Singularity-dota.simg", + "dockerfiles/Singularity-dotaservice.simg" ], - "full_name": "raj76/singularity", + "full_name": "bglick13/dotaservice", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cp\u003eMy collection of singularity containers recipes\n\u003ca href=\"https://singularity-hub.org/collections/611\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-dotaservice\" class=\"anchor\" aria-hidden=\"true\" href=\"#dotaservice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDotaService\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dotaservice-icon.png\"\u003e\u003cimg src=\"dotaservice-icon.png\" alt=\"dotaservice icon\" width=\"128\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eNOTE: The project that uses the dotaservice in a k8s environment is the \u003ca href=\"https://github.com/TimZaman/dotaclient\"\u003eDotaClient\u003c/a\u003e repo.\u003c/p\u003e\n\u003cp\u003eDotaService is a service to play Dota 2 through gRPC. There are first class python bindings\nand examples, so you can play dota as you would use the OpenAI gym API.\u003c/p\u003e\n\u003cp\u003eIt\u0027s fully functional and super lightweight. Starting Dota \u003ccode\u003eobs = env.reset()\u003c/code\u003e takes 5 seconds,\nand each \u003ccode\u003eobs = env.step(action)\u003c/code\u003e in the environment takes between 10 and 30 ms.\u003c/p\u003e\n\u003cp\u003eYou can even set the config of \u003ccode\u003erender=True\u003c/code\u003e and you can watch the game play live. Each game will\nhave a uuid and folder associated where there\u0027s a Dota demo (replay) and console logs.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"demo.gif\"\u003e\u003cimg src=\"demo.gif\" alt=\"demo\" width=\"600\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-dotaservice-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-dotaservice-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun DotaService Locally\u003c/h2\u003e\n\u003cp\u003eRun the DotaService so you can connect your client to it later. Only one client per server\nis supported, and only one DotaService per VM (eg local or one per docker container).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m dotaservice\n\u0026gt;\u0026gt;\u0026gt; Serving on 127.0.0.1:13337\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-dotaservice-distributed\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-dotaservice-distributed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun DotaService Distributed\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"docker/README.md\"\u003edocker/README.md\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo run two dockerservice instances, one on port \u003ccode\u003e13337\u003c/code\u003e and one on \u003ccode\u003e13338\u003c/code\u003e, f.e. run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -dp 13337:13337 ds\ndocker run -dp 13338:13337 ds\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can run as many as you want, until you run out of ports or ip addresses. If you are wearing\nyour fancy pants, use Kubernetes to deploy gazillions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-client-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#client-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClient Code\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egrpclib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eclient\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eChannel\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprotobuf\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eDotaService_grpc\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDotaServiceStub\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprotobuf\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eDotaService_pb2\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAction\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprotobuf\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eDotaService_pb2\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Connect to the DotaService.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDotaServiceStub\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eChannel\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027127.0.0.1\u0027\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e13337\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e# Get the initial observation.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eobservation\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ereset\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eConfig\u003c/span\u003e())\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erange\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e):\n \u003cspan class=\"pl-c\"\u003e# Sample an action from the action protobuf\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eaction\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAction\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eMoveToLocation\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e.., \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e.., \u003cspan class=\"pl-s1\"\u003ez\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e..)\n \u003cspan class=\"pl-c\"\u003e# Take an action, returning the resulting observation.\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eobservation\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003estep\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eaction\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis is very useful to provide an environment for reinforcement learning, and service aspect of it makes it\nespecially useful for distributed training. I am planning to provide a client python\nmodule for this (\u003ccode\u003ePyDota\u003c/code\u003e) that mimics typical OpenAI gym APIs. Maybe I won\u0027t even make PyDota\nand the gRPC client is enough.\u003c/p\u003e\n\u003cdiv\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dotaservice.png\"\u003e\u003cimg src=\"dotaservice.png\" alt=\"dotaservice connections\" width=\"680\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.7\u003c/li\u003e\n\u003cli\u003eUnix: MacOS, Ubuntu. A dockerfile is also provided see: \u003ca href=\"docker/README.md\"\u003edocker/README.md\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eInstalling from pypi:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install dotaservice\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor development; installing from source:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(Optional) Compile the protos for Python (run from repository root):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m grpc_tools.protoc -I. --python_out=. --python_grpc_out=. --grpc_python_out=. dotaservice/protos/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.proto\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h1\u003e\n\u003cp\u003eMy dev notes: \u003ca href=\"NOTES.md\"\u003eNOTES.md\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eOpenAI Dota crew\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://karpathy.github.io/2016/05/31/rl/\" rel=\"nofollow\"\u003eKarpathy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eJan Ivanecky\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Nostrademous\"\u003eNostrademous\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1518905174.0 + "updated_at": 1585923678.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.ubuntu_base" + "0.0.0.9000/Singularity.0.0.0.9000" ], - "full_name": "miquelmassot/singularity-deploy", - "latest_release": "0.0.2", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" href=\"#singularity-deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/miquelmassot/singularity-deploy/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/miquelmassot/singularity-deploy/actions/workflows/builder.yml/badge.svg\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBased on: \u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "yh549848/singularity-raptranker", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1644837961.0 + "updated_at": 1602825895.0 }, { "data_format": 2, - "description": "RNA-seq raw reads processing pipeline through alignment", + "description": null, "filenames": [ - "Singularity.hg19v1.centos" + "container/Singularity" ], - "full_name": "ertheisen/cloudsrest_centos", + "full_name": "Genomic-Medicine-Linkoping/nextflow_rnaseqfus", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-appalachianhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#appalachianhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappalachian.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 0, "topics": [], - "updated_at": 1560527292.0 + "updated_at": 1622550367.0 }, { "data_format": 2, - "description": "CBL-D (quinault) singularity and docker image for CI", + "description": "Singularity container for Samviewer", "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-quinault-ci", + "full_name": "CHPC-UofU/Singularity-ubuntu-samviewer", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-cbl-d-quinault-singularity-and-docker-image-for-ci\" class=\"anchor\" href=\"#building-a-cbl-d-quinault-singularity-and-docker-image-for-ci\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a CBL-D (quinault) singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCBL-D (Common Base Linux - Delridge)\u003c/li\u003e\n\u003cli\u003eDebian 10 based (quinault)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Azure/CloudShell\"\u003ehttps://github.com/Azure/CloudShell\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Azure/CloudShell/blob/master/linux/base.Dockerfile\"\u003ehttps://github.com/Azure/CloudShell/blob/master/linux/base.Dockerfile\u003c/a\u003e for \u003ccode\u003eFROM sbidprod.azurecr.io/quinault\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://boxofcables.dev/building-cbl-d-microsofts-other-linux-distro/\" rel=\"nofollow\"\u003ehttps://boxofcables.dev/building-cbl-d-microsofts-other-linux-distro/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eLICENSE copied verbatim from \u003ca href=\"https://raw.githubusercontent.com/Azure/CloudShell/master/LICENSE\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/Azure/CloudShell/master/LICENSE\u003c/a\u003e as of 2022/02/13\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-quinault-ci/actions/workflows/docker-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-quinault-ci/actions/workflows/docker-publish.yml/badge.svg\" alt=\"Docker build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-quinault-ci:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-quinault-ci/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-quinault-ci/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-quinault-ci:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1644758743.0 + "updated_at": 1498859914.0 }, { "data_format": 2, - "description": "Code repository for a project focused on diagnostic prediction from whole blood slides ", + "description": "Age Group Prediction in TV news (Open Source)", "filenames": [ - "pipeline_tf2/Singularity.def" + "Singularity.trial", + "Singularity.newsage" ], - "full_name": "josegcpa/wbs-prediction", + "full_name": "Xiaoyu-Lu/GSoC_2020", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-a-complete-computational-assessment-of-the-cytomorphological-determinants-of-myelodyplastic-syndromes\" class=\"anchor\" href=\"#a-complete-computational-assessment-of-the-cytomorphological-determinants-of-myelodyplastic-syndromes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA complete computational assessment of the cytomorphological determinants of myelodyplastic syndromes\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-motivation\" class=\"anchor\" href=\"#motivation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003cp\u003eThis is the repository for \u003ca href=\"\"\u003ePLACEHOLDER\u003c/a\u003e. In this work, we use the whole blood slides of \u0026gt;300 individuals with myelodyplastic syndromes and anaemias and use them to develop a method that is capable of predicting a disease and retrieving examples of cells which are relevant for each classification.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-code-map\" class=\"anchor\" href=\"#code-map\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode map\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-software\" class=\"anchor\" href=\"#software\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003epython\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esnakemake\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e (analysis and plotting)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-required-python-packages\" class=\"anchor\" href=\"#required-python-packages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired python packages\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003eopencv-python\u003c/code\u003e, \u003ccode\u003etensorflow==1.12\u003c/code\u003e, \u003ccode\u003escikit-image\u003c/code\u003e, \u003ccode\u003eh5py\u003c/code\u003e, \u003ccode\u003ealbumentations\u003c/code\u003e, \u003ccode\u003epsutil\u003c/code\u003e, \u003ccode\u003epytorch\u003c/code\u003e, \u003ccode\u003etifffile\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-project-enumeration\" class=\"anchor\" href=\"#project-enumeration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject enumeration\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003epipeline\u003c/code\u003e - contains the pipeline for WBC and RBC detection and characterisation from WBS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esimulations\u003c/code\u003e - contains simulations validating MILe-ViCe\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emile-vice\u003c/code\u003e - contains the code to train and run MILe-ViCe on the output from \u003ccode\u003epipeline\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erbc-segmentation\u003c/code\u003e - contains the code to train a predictor that filters poorly predictions for detected RBC\u003c/li\u003e\n\u003cli\u003e(STILL TESTING) \u003ccode\u003evae-characterisation\u003c/code\u003e - characterisation of blood cells using a beta-variational autoencoder\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cp\u003eGSoC 2020: Age Group Prediction in TV news\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "morphometrics", - "image-analysis", - "bioimage-analysis", - "deep-learning", - "machine-learning" - ], - "updated_at": 1641212653.0 + "topics": [], + "updated_at": 1606450294.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/student/Singularity", - "singularity/base/Singularity" + "Singularity" ], - "full_name": "UIUC-cs484/uiuccs484parallelprog", + "full_name": "CN-Healthborn/el7tf1.12gpu", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containers\" class=\"anchor\" href=\"#containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n\u003cp\u003eContainer declarations and other tools for building the containers for CS 484.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-vmfarm-setup-via-ansible\" class=\"anchor\" href=\"#vmfarm-setup-via-ansible\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVMFarm setup via ansible\u003c/h2\u003e\n\u003cp\u003eThese Ansible scripts assume CentOS_7.\u003c/p\u003e\n\u003cp\u003eInstall Ansible on your fresh VM.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo yum install ansible\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDepending on your setup, you may have a single VM, or you may have an administrative VM and several student VMs.\u003c/p\u003e\n\u003cp\u003eIn either case, you will need to create a file named \u003ccode\u003e/etc/ansible/hosts\u003c/code\u003e (or in older versions of Ansible, \u003ccode\u003e/etc/ansible/hosts/ansiblehosts\u003c/code\u003e) on the admin machine (or single machine).\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://docs.ansible.com/ansible/2.9/\" rel=\"nofollow\"\u003ehttps://docs.ansible.com/ansible/2.9/\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-single-vm\" class=\"anchor\" href=\"#single-vm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingle VM\u003c/h3\u003e\n\u003cp\u003eThe host file should look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[admin]\nlocalhost ansible_connection=local\n[all]\nlocalhost ansible_connection=local\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-multiple-vms-admin--individual-student-vms\" class=\"anchor\" href=\"#multiple-vms-admin--individual-student-vms\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMultiple VMs (admin + individual student VMs)\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e[admin]\nlocalhost ansible_connection=local\n[students]\nstudenthost1.anydomain.edu\nstudenthost2.anydomain.edu\nstudenthost3.anydomain.edu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have difficulty connecting to the student machines, please see \u003ca href=\"https://docs.ansible.com/ansible/latest/user_guide/connection_details.html\" rel=\"nofollow\"\u003ehttps://docs.ansible.com/ansible/latest/user_guide/connection_details.html\u003c/a\u003e . You may need to setup an SSH key.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-ansible-scripts\" class=\"anchor\" href=\"#running-ansible-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Ansible scripts\u003c/h3\u003e\n\u003cp\u003eSSH to the admin machine, clone this repo and run the following commands. (These take a long time, you should probably use a \u003ccode\u003escreen\u003c/code\u003e session for them.)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStart a bash terminal as root:\u003c/em\u003e \u003ccode\u003esudo bash\u003c/code\u003e .\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\nansible-playbook ./ansible/vmfarm/0_basepkgs.yml\nansible-playbook ./ansible/vmfarm/0a_disable_aslr.yml\nansible-playbook ./ansible/vmfarm/0b_mpi.yml\nansible-playbook ./ansible/vmfarm/cmake_installer.yml\nansible-playbook ./ansible/vmfarm/gtest.yml\nansible-playbook ./ansible/vmfarm/gbench.yml\nansible-playbook ./ansible/vmfarm/charm.yml\nansible-playbook ./ansible/vmfarm/hpctoolkitall.yml\n\nrm -rf /tmp/gtest /tmp/gbench /tmp/charm\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e /tmp/hpctoolkit\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n\nyum clean all \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e rm -rf /var/cache/yum\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker-container-building\" class=\"anchor\" href=\"#docker-container-building\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker container building\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eYou probably don\u0027t have to do this. Be absolutely certain beforehand.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo begin with, you shouldn\u0027t need to do this unless you have altered the Ansible scripts that build something in the container.\u003c/p\u003e\n\u003cp\u003eIf future generations of TAs decide to use a newer version of Charm or to radically change the environment for the MPs, it may be necessary to build new docker containers. Otherwise, please find working Docker containers at \u003ca href=\"https://hub.docker.com/u/uiuccs484parallelprog\" rel=\"nofollow\"\u003ehttps://hub.docker.com/u/uiuccs484parallelprog\u003c/a\u003e assignments should be done using the \u003ccode\u003euiuccs484parallelprog/cs484_student\u003c/code\u003e container.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-building-docker-containers\" class=\"anchor\" href=\"#building-docker-containers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding docker containers\u003c/h3\u003e\n\u003cp\u003eYou can build the docker containers by cloning this repo, then running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash ./docker/build.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eSTOP\u003c/em\u003e\nIf you have altered the Ansible or Docker scripts, you should increment the version number for the docker image. The version number is in the script \u003ccode\u003e./docker/build.sh\u003c/code\u003e .\u003c/p\u003e\n\u003cp\u003eIf you are logged in to docker hub and a member of the group \u003ccode\u003euiuccs484parallelprog\u003c/code\u003e, you can push these images to make them available to the world.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container-building\" class=\"anchor\" href=\"#singularity-container-building\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container building\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eHopefully you don\u0027t have to do this. If you update the docker container, then you may need to.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTODO: Write this.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nova-el7-tensorflow-gpu\" class=\"anchor\" aria-hidden=\"true\" href=\"#nova-el7-tensorflow-gpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enova-el7-tensorflow-gpu\u003c/h1\u003e\n\u003cp\u003eConfigurations for docker and singularity for making OSG-compatible CENTOS7 container with GPU-accelerated tensorflow and keras installed.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1539027275.0 + "updated_at": 1603475388.0 }, { "data_format": 2, - "description": null, + "description": "Test using singularityhub", "filenames": [ - "Singularity.salad", "Singularity", - "Singularity.pokemon" + "Singularity.centostest", + "Singularity.basic" ], - "full_name": "mwittep/EAGER", - "latest_release": "v1.92.56", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" href=\"#singularity-deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"img/shpc.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" href=\"#1-template-or-fork\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Template or Fork\u003c/h3\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" href=\"#1-write-your-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" href=\"#2-update-the-version-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Update the VERSION file\u003c/h3\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/releases.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" href=\"#3-how-to-develop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How to Develop\u003c/h3\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" href=\"#4-how-to-pull\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. How to pull\u003c/h3\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", + "full_name": "nbarlowATI/shub-test", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-shub-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#shub-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eshub-test\u003c/h1\u003e\n\u003cp\u003eTest using singularityhub\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1644482849.0 + "updated_at": 1617891470.0 }, { "data_format": 2, - "description": "Files to create singularity container for CHPC deeplearning module", + "description": null, "filenames": [ - "Singularity.deeplearning" + "Singularity" ], - "full_name": "CHPC-UofU/deeplearning-module", + "full_name": "juanca09/tgv", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-deeplearning-module\" class=\"anchor\" href=\"#deeplearning-module\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edeeplearning-module\u003c/h1\u003e\n\u003cp\u003eThis repo contains files to construct the container for the CHPC deeplearning\nmodule.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tgv\" class=\"anchor\" aria-hidden=\"true\" href=\"#tgv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etgv\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1644445691.0 + "updated_at": 1612281173.0 }, { "data_format": 2, - "description": "Nextflow pipeline for single cell analysis", + "description": "Attempt at Docker/GATK Port to Singularity for MSU HPCC", "filenames": [ "Singularity" ], - "full_name": "soulj/SkeletalVis-SingleCell", + "full_name": "msuefishlab/gatk_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-skeletalvis-singlecell\" class=\"anchor\" href=\"#skeletalvis-singlecell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSkeletalVis-SingleCell\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eSkeletalVis-SingleCell\u003c/strong\u003e is a bioinformatics pipeline for reproducible analyses of 10x Genomics single-cell RNA-sequencing data.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a portable workflow tool to run tasks across multiple compute infrastructures. This pipeline uses a singularity container containing all the software needed to run the analysis, making installation simple and the results reproducible.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pipeline-summary\" class=\"anchor\" href=\"#pipeline-summary\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline summary\u003c/h2\u003e\n\u003cp\u003eThe \u003cstrong\u003eSkeletalVis-SingleCell\u003c/strong\u003e pipeline takes a sample table and a parameter file defining the experiment as input. If not provided fastq files are automatically downloaded using the provided sample identifiers.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-features\" class=\"anchor\" href=\"#features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures:\u003c/h3\u003e\n\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Download of fastq files either directly from ENA, via conversion of sra or bam files from SRA\u003cbr\u003e\n(\u003cstrong\u003eb\u003c/strong\u003e)\tQuantification using \u003ca href=\"https://www.kallistobus.tools/\" rel=\"nofollow\"\u003e\u003ccode\u003ekallisto-bustools\u003c/code\u003e\u003c/a\u003e to produce cell x gene matrices\u003cbr\u003e\n(\u003cstrong\u003ec\u003c/strong\u003e) Flexible filtering of \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/DropletUtils.html\" rel=\"nofollow\"\u003e\u003ccode\u003eempty droplets\u003c/code\u003e\u003c/a\u003e, quality control and thresholding\u003cbr\u003e\n(\u003cstrong\u003ed\u003c/strong\u003e) Normalisation and cell cycle effect removal\u003cbr\u003e\n(\u003cstrong\u003ee\u003c/strong\u003e) Automatic cell type annotation with \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/SingleR.html\" rel=\"nofollow\"\u003e\u003ccode\u003eSingleR\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003ef\u003c/strong\u003e) Clustering and visualisation with \u003ca href=\"https://satijalab.org/seurat/\" rel=\"nofollow\"\u003e\u003ccode\u003eSeurat\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003eg\u003c/strong\u003e) Marker gene identification and pathway analysis\u003cbr\u003e\n(\u003cstrong\u003eh\u003c/strong\u003e) Cell crosstalk analysis of ligand-receptor predictions using \u003ca href=\"https://github.com/saezlab/liana\"\u003e\u003ccode\u003eliana\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003ei\u003c/strong\u003e) Sample integration and differential expression analysis between conditions with \u003ca href=\"https://github.com/MarioniLab/miloR\"\u003e\u003ccode\u003emiloR\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses are run in parallel and in result of error you can resume with the \u003ccode\u003e-resume\u003c/code\u003e parameter to re-run the pipeline starting from the previous fault.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-analyse-an-example-dataset\" class=\"anchor\" href=\"#analyse-an-example-dataset\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalyse an example dataset\u003c/h3\u003e\n\u003cp\u003eTry the pipeline on an example dataset (all inputs will be automatically downloaded): -\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html#installation\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/docs/latest/config.html\" rel=\"nofollow\"\u003e\u003ccode\u003eConfigure\u003c/code\u003e\u003c/a\u003e the resource profile for your HPC or local computer. A template for slurm schedulers is provided as an example in \u003ccode\u003enextflow.config\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the pipeline and test on the example dataset with a single command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow run soulj/SkeletalVis-SingleCell -profile slurm -params-file GSE152805.yaml -with-singularity library://jsoul/default/singlecell:latest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-analyse-your-own-data\" class=\"anchor\" href=\"#analyse-your-own-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalyse your own data\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eDefine the sampleTable\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCreate a tab seperated table with unique Sample names, SRR accession numbers (if download is needed) and any additional metadata e.g\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSample\u003c/th\u003e\n\u003cth\u003eFile\u003c/th\u003e\n\u003cth\u003eCondition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eControl_1\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eControl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eControl_2\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eControl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTreated_1\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eTreated\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTreated_2\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eTreated\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDefine the configuration\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMost parameters are set to sensible defaults within the main nextflow script, with only 5 parameters required to be altered with typical use:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eOptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eaccession\u003c/td\u003e\n\u003ctd\u003eThe GEO accession of the data - used to name output data and download fastq files\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edownloadSite\u003c/td\u003e\n\u003ctd\u003eThe site to download the raw data from if needed\u003c/td\u003e\n\u003ctd\u003eSRA, ENA, SRA_BAM\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especies\u003c/td\u003e\n\u003ctd\u003eThe species the reads originate from - used to create the kallisto bus index\u003c/td\u003e\n\u003ctd\u003ehuman, mouse\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003echemistry\u003c/td\u003e\n\u003ctd\u003eThe chemistry used for the 10x Genomics experiment\u003c/td\u003e\n\u003ctd\u003e10xv1, 10xv2, 10xv3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ereplciates\u003c/td\u003e\n\u003ctd\u003eDoes the experiment contain replicated treatments to perform differential expression analysis?\u003c/td\u003e\n\u003ctd\u003etrue, false\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eParameters should be defined within a yaml file. See \u003ccode\u003eparams/GSE152805.yaml\u003c/code\u003e for an example.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline with your own parameters\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow run soulj/SkeletalVis-SingleCell -profile slurm -params-file ownData.yaml -with-singularity library://jsoul/default/skeletalvis-singlecell\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-testing-modules\" class=\"anchor\" href=\"#testing-modules\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting modules\u003c/h3\u003e\n\u003cp\u003eModules can be tested using the \u003ca href=\"https://pypi.org/project/pytest-workflow/\" rel=\"nofollow\"\u003e\u003ccode\u003epytest-workflow\u003c/code\u003e\u003c/a\u003e framework. Module test directories within the \u003ccode\u003etests\u003c/code\u003e folder contain a nextflow script and a configuration yaml file defining the test for each module.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall pytest-workflow\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003econda install pytest-workflow\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the tests - e.g to test the GSEA module\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epytest --symlink --kwdof --tag gsea\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1644406348.0 + "updated_at": 1521034490.0 }, { "data_format": 2, - "description": "Files of FWI Paper", + "description": null, "filenames": [ - "devito/docker/Singularity.nvidia.def" + "setup/Singularity" ], - "full_name": "felipeaugustogudes/paper-fwi", - "latest_release": "v1.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-effectiveness-and-computational-efficiency-of-absorbing-boundary-conditions-for-full-waveform-inversion\" class=\"anchor\" href=\"#effectiveness-and-computational-efficiency-of-absorbing-boundary-conditions-for-full-waveform-inversion\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEffectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-authors-daiae-iglesia-dolci-felipe-a-g-silva-pedro-s-peixoto-and-ernani-v-volpe\" class=\"anchor\" href=\"#authors-daiae-iglesia-dolci-felipe-a-g-silva-pedro-s-peixoto-and-ernani-v-volpe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors: Daiae Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto and Ernani V. Volpe\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-department-of-mechanical-engineering-of-polytechnic-school-university-of-s\u00e3o-paulo\" class=\"anchor\" href=\"#department-of-mechanical-engineering-of-polytechnic-school-university-of-s%C3%A3o-paulo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDepartment of Mechanical Engineering of Polytechnic School, University of S\u00e3o Paulo\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-department-of-applied-mathematics-institute-of-mathematics-and-statistics-university-of-s\u00e3o-paulo\" class=\"anchor\" href=\"#department-of-applied-mathematics-institute-of-mathematics-and-statistics-university-of-s%C3%A3o-paulo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDepartment of Applied Mathematics, Institute of Mathematics and Statistics, University of S\u00e3o Paulo\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contacts-dolciuspbr-felipeaugustoguedesuspbr-pedrospimeuspbr-ernvolpeuspbr\" class=\"anchor\" href=\"#contacts-dolciuspbr-felipeaugustoguedesuspbr-pedrospimeuspbr-ernvolpeuspbr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContacts: \u003ca href=\"mailto:dolci@usp.br\"\u003edolci@usp.br\u003c/a\u003e, \u003ca href=\"mailto:felipe.augusto.guedes@usp.br\"\u003efelipe.augusto.guedes@usp.br\u003c/a\u003e, \u003ca href=\"mailto:pedrosp@ime.usp.br\"\u003epedrosp@ime.usp.br\u003c/a\u003e, \u003ca href=\"mailto:ernvolpe@usp.br\"\u003eernvolpe@usp.br\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eImportant Informations:\u003c/strong\u003e These codes are part of the Project Software Technologies for Modeling and Inversion (STMI) at RCGI in the University of Sao Paulo.\u003c/p\u003e\n", + "full_name": "smithhenryd/GSoC_2020_UnderrepresentedMessagesAndDemocrats-", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-gsoc_2020_underrepresentedmessagesanddemocrats\" class=\"anchor\" aria-hidden=\"true\" href=\"#gsoc_2020_underrepresentedmessagesanddemocrats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGSoC_2020_UnderrepresentedMessagesAndDemocrats:\u003c/h1\u003e\n\u003cp\u003eThe 2020 Google Summer of Code project \"Understanding Messages to Underrepresented Racial, Ethnic, Gender, and Sexual Groups on Social Media by Democratic Politicians and their Electoral Implications\" is contributed by Henry Smith with \u003ca href=\"http://www.redhenlab.org/\" rel=\"nofollow\"\u003eRed Hen Lab\u003c/a\u003e. Work on the project is completed under the mentorship of \u003ca href=\"http://home.jsjoo.com/\" rel=\"nofollow\"\u003eDr. Jungeock Joo\u003c/a\u003e and \u003ca href=\"https://bywords.github.io/\" rel=\"nofollow\"\u003eDr. Kunwoo Park\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gsoc-2020-blog\" class=\"anchor\" aria-hidden=\"true\" href=\"#gsoc-2020-blog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGSOC 2020 Blog:\u003c/h2\u003e\n\u003cp\u003eDetailed weekly updates during summer 2020 can be found at the project\u0027s \u003ca href=\"https://smithhenryd.github.io/UnderrepresentedMessagesAndDemocrats.github.io/\" rel=\"nofollow\"\u003eblog page\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-project-directory\" class=\"anchor\" aria-hidden=\"true\" href=\"#project-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject Directory:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/smithhenryd/GSoC_2020_UnderrepresentedMessagesAndDemocrats-/tree/master/background\"\u003ebackground\u003c/a\u003e details preliminary information relevant to the research project and topic. This folder currently contains the original proposal as well as a brief summary of related political science research.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/smithhenryd/GSoC_2020_UnderrepresentedMessagesAndDemocrats-/tree/master/electoral_outcomes_data\"\u003eelectoral_outcomes_data\u003c/a\u003e includes data collected from \u003ca href=\"https://ballotpedia.org/Election_results,_2018\" rel=\"nofollow\"\u003eBallotpedia\u003c/a\u003e summarizing 2018 U.S. midterm election outcomes. The current data details primary and general election outcomes in racially and ethnically diverse congressional districts, measured by the proportion of individuals that identify as people of color (POC).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/smithhenryd/GSoC_2020_UnderrepresentedMessagesAndDemocrats-/tree/master/imgs_data\"\u003eimgs_data\u003c/a\u003e contains information pertaining to the 2018 Facebook images dataset collected by Dr. Jungseock Joo and his colleagues. The dataset consists of images shared on Facebook from January 1 - November 5, 2018 by U.S. politicians who competed for the U.S. House, Senate, and state governorships during the 2018 general election.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-background-and-motivation\" class=\"anchor\" aria-hidden=\"true\" href=\"#background-and-motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground and Motivation:\u003c/h2\u003e\n\u003cp\u003eThe importance of underrepresented voters is not new to the Democratic party: a 2017 poll of registered voters by the Pew Research Institute of U.S. Politics and Policy estimated that only fifty-nine percent of self-identified Democrats/lean Democrats label themselves as white, compared to the eighty-nine percent of Republicans/lean Republicans. This figure is down from an estimated sixty-seven percent in 2007 and seventy-five percent in 1997. The same report approximates that Black voters constitute nineteen percent of this Democratic base, Hispanic voters twelve percent, and Asian together with other underrepresented racial/ethnic groups constitute ten percent [6].\u003c/p\u003e\n\u003cp\u003eMoreover, recent elections suggest the emergence of the LGBT community, which we classify as underrepresented gender and sexual individuals, as one of the most solid Democratic voting blocs. Exit polling by NBC following the 2018 midterm elections indicated that while LGBT voters constituted only six percent of the electorate, upwards of eighty-two percent of these voters supported the Democratic candidate [1].\u003c/p\u003e\n\u003cp\u003eDespite the distinct importance of these groups to the Democratic party, it is not clear that the party knows how to effectively mobilize underrepresented voters. This harrowing reality came to the forefront of the news cycle following a decade-low Black voter turnout during the 2016 election [4]. In response to this fall in turnout, to which many have attributed Democratic presidential candidate Hillary Clinton\u2019s loss, the Democratic National Committee (DNC) pledged $2.5 million for the funding of programs to increase turnout among underrepresented groups during the 2018 midterm elections [3].\u003c/p\u003e\n\u003cp\u003eOf particular interest to our research is how politicians themselves aim to mobilize these communities through social media. Past research has underscored the importance of social media as spaces for underrepresented racial, gender, and sexual groups. In conflict with the narrative that a lack of access to technology divides disadvantaged racial groups, a recent study has shown that online platforms in fact embolden social networks between these groups [2]. Likewise, it is estimated that eighty percent of LGBT adults engage on at least one social media website, which is much greater than the fifty-eight percent of the general public [5].\u003c/p\u003e\n\u003cp\u003eKeeping this in mind, we seek to answer the following questions:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eHow do Democratic politicians present themselves to underrepresented racial, gender, and sexual groups on social media platforms through visual content?\u003c/li\u003e\n\u003cli\u003eWhich traits displayed in these images are perceived most positively/negatively by underrepresented voters?\u003c/li\u003e\n\u003cli\u003eHow do visual messages predict primary election outcomes in diverse electoral districts?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eSources:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[1] Fitzsimons, T. (2018, November 08). Record LGBT support for Democrats in midterms, NBC News Exit Poll shows. NBC News. Retrieved from \u003ca href=\"https://www.nbcnews.com/feature/nbc-out/record-lgbt-support-democrats-midterms-nbc-news-exit-poll-shows-n934211\" rel=\"nofollow\"\u003ehttps://www.nbcnews.com/feature/nbc-out/record-lgbt-support-democrats-midterms-nbc-news-exit-poll-shows-n934211\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2] Amy L. Gonzales. 2015. Disadvantaged Minorities\u2019 Use of the Internet to Expand Their Social Networks. Communication Research 44, 4 (2017), 467-486.\u003c/li\u003e\n\u003cli\u003e[3] Herndon, A. W. (2018, June 21). Democrats Plan New Effort to Target Minority Voters. The New York Times. Retrieved from \u003ca href=\"https://www.nytimes.com/2018/06/21/us/politics/democrats-minority-voters-midterms.html?smtyp=cur\u0026amp;smid=tw-nytimes\" rel=\"nofollow\"\u003ehttps://www.nytimes.com/2018/06/21/us/politics/democrats-minority-voters-midterms.html?smtyp=cur\u0026amp;smid=tw-nytimes\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[4] Krogstad, J. M. and Lopez, M. H. (2017, May 12). Black voter turnout fell in 2016, even as a record number of Americans cast ballots. Pew Research Center, Washington, D.C. Retrieved from \u003ca href=\"https://www.pewresearch.org/fact-tank/2017/05/12/black-voter-turnout-fell-in-2016-even-as-a-record-number-of-americans-cast-ballots/\" rel=\"nofollow\"\u003ehttps://www.pewresearch.org/fact-tank/2017/05/12/black-voter-turnout-fell-in-2016-even-as-a-record-number-of-americans-cast-ballots/\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[5] \u201cA Survey of LGBT Americans.\u201d Pew Research Center, Washington, D.C. (2013, June 13) \u003ca href=\"https://www.pewsocialtrends.org/2013/06/13/a-survey-of-lgbt-americans/\" rel=\"nofollow\"\u003ehttps://www.pewsocialtrends.org/2013/06/13/a-survey-of-lgbt-americans/\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[6] \u201cWide Gender Gap, Growing Educational Divide in Voters\u2019 Party Identification.\u201d Pew Research Center, Washington, D.C. (2018, March 20) \u003ca href=\"https://www.people-press.org/2018/03/20/wide-gender-gap-growing-educational-divide-in-voters-party-identification/\" rel=\"nofollow\"\u003ehttps://www.people-press.org/2018/03/20/wide-gender-gap-growing-educational-divide-in-voters-party-identification/\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1644293938.0 + "topics": [ + "data-cleaning", + "statistics", + "political-science", + "political-parties", + "python", + "election-analysis", + "election-data" + ], + "updated_at": 1640627843.0 }, { "data_format": 2, - "description": null, + "description": "Singularity containers for tools using the MAGICIAN pipeline", "filenames": [ - "Singularity.0.9.18" + "drep/Singularity.drep", + "camisim_ks_fork/Singularity.cami_python2", + "bbmap_36.49_metabat2_latest/Singularity.bbmap_from_metabat" ], - "full_name": "Famingzhao/pySCENIC", + "full_name": "KatSteinke/magician-singularity-containers", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5332\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1643982275.0 + "updated_at": 1617363865.0 }, { "data_format": 2, - "description": "centos8 container to run brave ", + "description": null, "filenames": [ - "Singularity" + "Singularity.Bowtie2", + "Singularity", + "Singularity.FastQC", + "Singularity.bedtools", + "Singularity.samtools", + "Singularity.methylkit" ], - "full_name": "truatpasteurdotfr/singularity-docker-centos8-brave", + "full_name": "thakk/biobase", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-docker-centos8-brave-using-stream8-now-that-centos8-is-eoled\" class=\"anchor\" href=\"#singularity-docker-centos8-brave-using-stream8-now-that-centos8-is-eoled\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-centos8-brave (using stream8 now that centos8 is EOL\u0027ed)\u003c/h1\u003e\n\u003cp\u003ecentos8 container to run brave built from github actions\u003c/p\u003e\n\u003cp\u003eRunning without installation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos8-brave:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuilding:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-docker-centos8-brave.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload and rename:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name singularity-docker-centos8-brave.sif oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos8-brave:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning with a separate $HOME (here ~/singularity.d/home/singularity-docker-centos8-brave)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p ~/singularity.d/home/singularity-docker-centos8-brave\nsingularity run -B /run -H ~/singularity.d/home/singularity-docker-centos8-brave singularity-docker-centos8-brave.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-containers-for-bioinformatics-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-containers-for-bioinformatics-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity containers for bioinformatics tools\u003c/h1\u003e\n\u003cp\u003eBioinformatics related singularity container recipies.\u003c/p\u003e\n\u003cp\u003eBase is CentOS 8.\u003c/p\u003e\n\u003cp\u003eCurrently two containers are implemented:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebasic tools:\n\u003cul\u003e\n\u003cli\u003eSamtools\u003c/li\u003e\n\u003cli\u003eBEDTools\u003c/li\u003e\n\u003cli\u003eFastQC\u003c/li\u003e\n\u003cli\u003eBowtie2\u003c/li\u003e\n\u003cli\u003eMultiQC\u003c/li\u003e\n\u003cli\u003eCutadapt\u003c/li\u003e\n\u003cli\u003eSTAR\u003c/li\u003e\n\u003cli\u003eHisat2\u003c/li\u003e\n\u003cli\u003ePicard\u003c/li\u003e\n\u003cli\u003eTrimmomatic\u003c/li\u003e\n\u003cli\u003eSamblaster\u003c/li\u003e\n\u003cli\u003eVarScan\u003c/li\u003e\n\u003cli\u003eVcfanno\u003c/li\u003e\n\u003cli\u003ePlink\u003c/li\u003e\n\u003cli\u003eMACS2\u003c/li\u003e\n\u003cli\u003eHomer\u003c/li\u003e\n\u003cli\u003eNextFlow\u003c/li\u003e\n\u003cli\u003enf-core\u003c/li\u003e\n\u003cli\u003eMAGeCK\u003c/li\u003e\n\u003cli\u003eTrimGalore\u003c/li\u003e\n\u003cli\u003eBismark\u003c/li\u003e\n\u003cli\u003eUCSC tools\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003emethylKit (built from basic):\n\u003cul\u003e\n\u003cli\u003eR + Bioconductor\u003c/li\u003e\n\u003cli\u003emethylkit\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003esamtools (built from Alpine Linux 3.10.3)\n\u003cul\u003e\n\u003cli\u003eNote, automated Singularity Hub build does not seem to work correctly as this recipe uses multistage build to minimize container size\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-availability\" class=\"anchor\" aria-hidden=\"true\" href=\"#availability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailability\u003c/h2\u003e\n\u003cp\u003eBasic tools container is available at Singularity hub: shub://thakk/biobase\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1635199842.0 + "updated_at": 1589801761.0 }, { "data_format": 2, - "description": null, + "description": "Recipes for Singularity images used by Singularity Hub.", "filenames": [ - "Studenten/XiaoyuSun/Polygonization-by-Frame-Field-Learning/singularity/Singularity", - "Studenten/Polygonization-by-Frame-Field-Learning-master-3bandRGB/singularity/Singularity" + "Singularity.Root6.Ubuntu-18.04", + "Singularity.Root6.Geant4.OptSim.Ubuntu-18.04", + "Singularity.Root6.Geant4.Ubuntu-18.04" ], - "full_name": "vissed-kad/github_demo", - "latest_release": "v1.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-objectherkenning-met-deeplearning-technieken\" class=\"anchor\" href=\"#objectherkenning-met-deeplearning-technieken\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eObjectherkenning met Deeplearning technieken\u003c/h1\u003e\n\u003cp\u003eDeze repository bevat folders en bestanden van de projecten van het Objectherkenningsteam.\u003c/p\u003e\u003cp\u003eZie de info in de onderliggende folder(s) voor meer informatie.\u003c/p\u003e\n\u003cp\u003etest 1234\ntest 5678\u003c/p\u003e\n", + "full_name": "PPKoller/SHub", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-shub\" class=\"anchor\" aria-hidden=\"true\" href=\"#shub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSHub\u003c/h1\u003e\n\u003cp\u003eRecipes for Singularity images to be built on Singularity Hub.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4666\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-argoncube-optical-simulation--\" class=\"anchor\" aria-hidden=\"true\" href=\"#argoncube-optical-simulation--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArgonCube Optical Simulation \u003ca href=\"https://github.com/PPKoller/ArCubeOptSim\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ac28190b3bdb446d46b2760854ecec42927bd2ae802d0729c6b0e72449b56082/68747470733a2f2f6769746875622e6769746875626173736574732e636f6d2f696d616765732f6d6f64756c65732f6c6f676f735f706167652f4769744875622d4d61726b2e706e67\" width=\"30\" data-canonical-src=\"https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://argoncube.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://github.com/PPKoller/SHub/raw/master/.ArCube_Logo.png\" width=\"100\" align=\"right\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-pull-the-container-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-pull-the-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Pull the container image:\u003c/h3\u003e\n\u003cp\u003eThe optical simulation software container can be pulled directly via the Singularity command:\u003cbr\u003e\n(size ~ 1.4G)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://PPKoller/SHub:root6.geant4.optsim.ubuntu-18.04\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-image-default-checks\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-image-default-checks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Image default checks:\u003c/h3\u003e\n\u003cp\u003ePerforming the Singularity default checks should return \u003ccode\u003ePASS: (retval=0)\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emv PPKoller-SHub-master-root6.geant4.optsim.ubuntu-18.04.simg OptSim.simg\nsingularity check --tag default OptSim.simg\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-export-io-binding-paths\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-export-io-binding-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Export I/O binding paths:\u003c/h3\u003e\n\u003cp\u003eUsing the environment variable \u003ccode\u003e$SINGULARITY_BINDPATH\u003c/code\u003e there won\u0027t be any need to bind I/O paths manually later.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir input output\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_BINDPATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einput/:/input,output/:/output\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Run instructions:\u003c/h3\u003e\n\u003cp\u003eRunning the container without any arguments will return a list of the available apps including a short description on what it does and what parameters you might need to provide.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run OptSim.simg\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-5-run-apps\" class=\"anchor\" aria-hidden=\"true\" href=\"#5-run-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. Run apps:\u003c/h3\u003e\n\u003cp\u003eThere are five apps available within the container: four simulaion related apps that run the optical simulation with different levels of user defined input and one app that allows you to build the photon look-up-table using the output created by running the simulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe selected voxels will be processed sequentially. Separate container calls are needed for parallel processing.\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esim\u003c/strong\u003e\u003cbr\u003e\nRun the simulation on voxels no. 0 to 9 using the default statistics, voxel geometry and optical properties.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app sim OptSim.simg 0 10\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistics\u003c/em\u003e: 1\u0027000 events per voxel / 10\u0027000 photons per event\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVoxel geometry\u003c/em\u003e: 32 x 128 x 32 voxels / 9.460 x 9.858 x 9.692 mm\u003csup\u003e3\u003c/sup\u003e (drift x vertical x beam)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOpt. properties\u003c/em\u003e: \u003ca href=\"https://github.com/PPKoller/ArCubeOptSim/tree/LUT/resources/datafiles\"\u003ePPKoller/ArCubeOptSim/tree/LUT/resources/datafiles\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esim_usr_geo\u003c/strong\u003e\u003cbr\u003e\nRun the simulation on voxels no. 0 to 9 with user defined statistics and voxel geometry. Herefore, the file \u003ccode\u003eOptSim_LUT_voxel_table.txt\u003c/code\u003e has to be placed in the folder \u003ccode\u003einput/\u003c/code\u003e before executing the simulation.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app sim_usr_geo OptSim.simg 0 10\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eOptSim_LUT_voxel_table.txt\u003c/code\u003e can be created by the Jupyter Notebook provided \u003ca href=\"create_OptSim_LUT_voxel_table.ipynb\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esim_usr_opt\u003c/strong\u003e\u003cbr\u003e\nRun the simulation on voxels no. 0 to 9 with user defined optical properties. Herefore, a folder \u003ccode\u003edatafiles/\u003c/code\u003e containing all optical properties files has to be placed in the folder \u003ccode\u003einput/\u003c/code\u003e before executing the simulation.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app sim_usr_opt OptSim.simg 0 10\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe folder \u003ccode\u003edatafiles/\u003c/code\u003e containing the default optical properties files can be found \u003ca href=\"https://github.com/PPKoller/ArCubeOptSim/tree/LUT/resources/datafiles\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esim_usr\u003c/strong\u003e\u003cbr\u003e\nRun the simulation on voxels no. 0 to 9 with user defined statistics, voxel geometry and optical properties. (see instructions above)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app sim_usr OptSim.simg 0 10\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003elut / lut_usr\u003c/strong\u003e\u003cbr\u003e\nBuild the photon look-up-table using the output created by running the simulation. Herefore, voxel number \u00270\u0027 needs to have been processed and the respective root file \u003ccode\u003eOptSim_00000000.root\u003c/code\u003e has to be present in \u003ccode\u003eoutput/root_files/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app lut OptSim.simg\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAnd in case the simulation was run with user defined statistics and voxel geometry:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --app lut_usr OptSim.simg\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-6-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#6-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6. Output\u003c/h3\u003e\n\u003cp\u003eAfter running the optical simulation, log and error files will appear in \u003ccode\u003eoutput/log_files/\u003c/code\u003e and root files will appear in \u003ccode\u003eoutput/root_files/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAfter running the LUT builder, the photon look-up-table will apper in \u003ccode\u003eoutput/\u003c/code\u003e as \u003ccode\u003eOptSim_LUT_ArgonCube2x2.root\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[optional]\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-user-defined-tpb-thickness\" class=\"anchor\" aria-hidden=\"true\" href=\"#user-defined-tpb-thickness\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser defined TPB thickness\u003c/h4\u003e\n\u003cp\u003ePlace the file \u003ccode\u003epreinit.mac\u003c/code\u003e with custom TPB thickness in the folder \u003ccode\u003einput/\u003c/code\u003e before executing the simulation. The default \u003ccode\u003epreinit.mac\u003c/code\u003e can be found \u003ca href=\"https://github.com/PPKoller/ArCubeOptSim/tree/LUT/resources/macros/preinit.mac\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-build-and-shell-into-writable-sandbox-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-and-shell-into-writable-sandbox-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild and shell into writable sandbox image:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox OptSim OptSim.simg\nsudo singularity shell --writable OptSim\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-build-compressed-read-only-squashfs-image-from-sandbox-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-compressed-read-only-squashfs-image-from-sandbox-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild compressed read-only squashfs image from sandbox image:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build OptSim_edited.simg OptSim\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1643653877.0 + "updated_at": 1612530237.0 }, { "data_format": 2, @@ -16585,203 +16148,224 @@ var data = "filenames": [ "Singularity" ], - "full_name": "stela2502/singularityImages", + "full_name": "tomuram/singularity_recipes", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularityimages\" class=\"anchor\" href=\"#singularityimages\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularityImages\u003c/h1\u003e\n\u003cp\u003eThis git repo is a skelleton of my work I have done on singularity images.\nThese images are used on aurora-ls2 to run analyses on the blades instead of the frontend.\u003c/p\u003e\n\u003cp\u003eAll of that documention is in our Bioinformatics Slack Howto channel.\u003c/p\u003e\n\u003cp\u003eThe software I install I mainly install from within the singularity image. Hence the usage of shell.sh.\u003c/p\u003e\n\u003cp\u003eInstaling Python modules is tricky as pip3 always installs in a private path and not the global unless told otherwise.\nHence only I with my username on the computer I build the images could use the modules.\u003c/p\u003e\n\u003cp\u003eA solution could be to use some conda approach, but as this here will be a singularity image we could also try to install globaly:\u003c/p\u003e\n\u003cp\u003ePython solution:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install --prefix=/usr/local \u0026lt;package name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1643377152.0 + "updated_at": 1625019915.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for spades (git@github:powerPlant/spades-srf.git)", "filenames": [ - "Singularity" + "Singularity.cami2-submission", + "Singularity.v3.10.0", + "Singularity.v3.8.1", + "Singularity.v0.5-recomb", + "Singularity.v3.12.0", + "Singularity.v3.9.0", + "Singularity.spaligner-paper", + "Singularity.v3.11.1", + "Singularity.v3.13.0", + "Singularity.v3.8.0", + "Singularity.v3.10.1", + "Singularity.v3.14.0", + "Singularity.template", + "Singularity.cloudspades-paper", + "Singularity.v3.13.1", + "Singularity.v3.8.2", + "Singularity.v3.11.0", + "Singularity.metaplasmid-paper", + "templates/Singularity.template" ], - "full_name": "canceromics/LncPipe", + "full_name": "powerPlant/spades-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-lncpipe\" class=\"anchor\" href=\"#lncpipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/likelet/LncPipe/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/baf841111dc28f78162a4622b162743688582f72fdd1489701abaed0dbedeb6c/68747470733a2f2f696d672e736869656c64732e696f2f6175722f6c6963656e73652f79616f7572742e737667\" alt=\"AUR\" data-canonical-src=\"https://img.shields.io/aur/license/yaourt.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://nextflow.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4592602bf49949ce2bf5d14fd5d8f82ff4d9da11fcc13f9afaadaa60e0f915e0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e32342e302d627269676874677265656e2e737667\" alt=\"nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.24.0-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-overall\" class=\"anchor\" href=\"#overall\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverall\u003c/h2\u003e\n\u003cp\u003eRecently, long noncoding RNA molecules (lncRNA) captured widespread attentions for their critical\nroles in diverse biological process and important implications in variety of human diseases and\ncancers. Identification and profiling of lncRNAs is a fundamental step to advance our knowledge\non their function and regulatory mechanisms. However, RNA sequencing based lncRNA discovery is\ncurrently limited due to complicated operations and implementation of the tools involved. Therefore, we present a one-stop multi-tool integrated pipeline called \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e focused on characterizing lncRNAs from raw transcriptome sequencing data.\nThe pipeline was developed based on a popular workflow framework \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report. \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\n\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#schematic-diagram\"\u003eSchematic diagram\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation-and-quick-start\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#run-docker\"\u003eRun Docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/likelet/LncPipeTestData\"\u003eRun with example data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#interactive-reports\"\u003eInteractive reports\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#parameters\"\u003eParameters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#faq\"\u003eFAQ\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#acknowledgements\"\u003eAcknowledgements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-schematic-diagram\" class=\"anchor\" href=\"#schematic-diagram\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSchematic diagram\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e\u003cbr\u003e\nLncPipe is implemented with Nextflow pipeline management system. To run LncPipe. \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e should be pre-installed at POSIX compatible system (Linux, Solaris, OS X, etc), It requires BASH and Java 7 or higher to be installed. We do not recommend running the pipes in the Windows since most of bioinformatic tools are not supported.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start\u003c/h2\u003e\n\u003cp\u003eHere, we show step by step installation of \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e in a linux system as an example (adopted from \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003eNextFlow\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the NextFlow executable package by pasting the following command into your terminal window:\u003c/p\u003e\n\u003cp\u003ewget -qO- get.nextflow.io | bash\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIt will create the \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e main executable file in the current directory.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOptionally, move the nextflow file to a directory accessible by your \u003ccode\u003e$PATH\u003c/code\u003e variable (only required to avoid typing the full path to this file each time you need to run it). Of course, you can download the lastest binary version of NextFlow by yourself from \u003ca href=\"https://github.com/nextflow-io/nextflow/releases\"\u003ehere\u003c/a\u003e and add the path to your system environment.All those pipelines were written in \u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e commands. For more details, please see \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eDownload the LncPipe github repository by:\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/likelet/LncPipe.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eConfigure the design.file with experimental conditions and replicate info\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eConfigure your data and reference files in \u003cem\u003enextflow.config\u003c/em\u003e or \u003cem\u003edocker.config\u003c/em\u003e or \u003cem\u003esingularity.config\u003c/em\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003e\n\u003cp\u003eRun LncPipe nextflow pipeline:\u003c/p\u003e\n\u003cp\u003enextflow -c nextflow.config run LncRNAanalysisPipe.nf\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eor docker command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nextflow -c docker.config run LncRNAanalysisPipe.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor singularity command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # create image \n singularity build lncPipe.image docker://bioinformatist/lncpipe\n # run command \n nextflow -c singularity.config run LncRNAanalysisPipe.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e__7.Run with test data __ .\u003c/p\u003e\n\u003cp\u003ePlZ go to \u003ca href=\"https://github.com/likelet/LncPipeTestData\"\u003ehttps://github.com/likelet/LncPipeTestData\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-prepare-input-files\" class=\"anchor\" href=\"#prepare-input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare input files\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-references-index-and-annotation-filesmandatory\" class=\"anchor\" href=\"#references-index-and-annotation-filesmandatory\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences, index and annotation files(Mandatory).\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cg-emoji class=\"g-emoji\" alias=\"blush\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f60a.png\"\u003e\ud83d\ude0a\u003c/g-emoji\u003ePlease keep the consistency of your genome sequence,index library and annotation files (Important!): genome version, chromosome format, gtf coordinated e.g. The dependent third-party softwares may stop for any discrepencies in file-formatting.\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenome reference (genome fasta file with suffix \u003ccode\u003e.fa\u003c/code\u003e etc. )\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenome Index for alignment (hisat2 or tophat or STAR)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGENCODE gene annotation file in GTF format\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLNCipedia gene annotation file in GTF format.(set null if not available for your species)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRaw sequence file with *.fastq.gz / *.fq.gz suffixed\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-species\" class=\"anchor\" href=\"#species\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecies\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;Currently, LncPipe has been tested for detection of lncRNAs in \u0027humans\u0027 only.\nHowever, LncPipe can be manually configured to run the anlysis for other species as well and requires additional files \"known_protein_coding.gtf\" and \"known_lncRNA.gtf\" for coding probability calculations. More information on usage for non-human species can be found here. \n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eReference files for humans\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ehisat index built from Genome:\n\u003ca href=\"http://cancerbio.info/pub/hg38_hisat_index.tar.gz\" rel=\"nofollow\"\u003ehttp://cancerbio.info/pub/hg38_hisat_index.tar.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenome reference:\n\u003ca href=\"ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/GRCh38.p10.genome.fa.gz\" rel=\"nofollow\"\u003eftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/GRCh38.p10.genome.fa.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGENCODE gene annotation:\n\u003ca href=\"ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/gencode.v27.annotation.gtf.gz\" rel=\"nofollow\"\u003eftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/gencode.v27.annotation.gtf.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLNCipedia gene annotation:\n\u003ca href=\"https://lncipedia.org/downloads/lncipedia_5_0_hc_hg38.gtf\" rel=\"nofollow\"\u003ehttps://lncipedia.org/downloads/lncipedia_5_0_hc_hg38.gtf\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRaw sequence file with *.fastq.gz / *.fq.gz suffixed\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReference files for mouse\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ehisat index built from Genome\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenome reference:\u003cbr\u003e\n\u003ca href=\"ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M16/GRCm38.p5.genome.fa.gz\" rel=\"nofollow\"\u003eftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M16/GRCm38.p5.genome.fa.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGENCODE gene annotation:\n\u003ca href=\"ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M16/gencode.vM16.annotation.gtf.gz\" rel=\"nofollow\"\u003eftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M16/gencode.vM16.annotation.gtf.gz\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLNCipedia gene annotation: null\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRaw sequence file with *.fastq.gz / *.fq.gz suffixed\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-docker\" class=\"anchor\" href=\"#run-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Docker\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ePrepare input files as mentioned earlier.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eModify the \u003ccode\u003edocker.config\u003c/code\u003e in \u003ccode\u003emandatory\u003c/code\u003e section.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall docker and download the latest LncPipe build using:\n\u003ccode\u003edocker pull bioinformatist/lncpipe\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun LncPipe using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nextflow -c docker.config run LncRNAanalysisPipe.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe docker image for LncPipe is available on the docker-hub (\u003ca href=\"https://hub.docker.com/r/bioinformatist/lncpipe/tags/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/bioinformatist/lncpipe/tags/\u003c/a\u003e).\nAlternatively, nextflow can automatically pull image from docker.io. \u003ccode\u003eDockerfile\u003c/code\u003e recorded that what we have done with the image. For user from local China looking to pull the docker image can use this \u003ca href=\"https://github.com/likelet/Blogs_tips/blob/master/README.md#setting-docker-download-mirror-site\"\u003emirror site instead\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eTO Install softwares locally on your machine, please see install instructions \u003ca href=\"https://github.com/likelet/LncPipe/blob/master/InstallSoftwareLocally.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-interactive-reports\" class=\"anchor\" href=\"#interactive-reports\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive reports\u003c/h2\u003e\n\u003cp\u003eThe results of LncPipe are summarized and visualized via interactive plots by our novel R package \u003ca href=\"https://github.com/bioinformatist/LncPipeReporter\"\u003eLncPipeReporter\u003c/a\u003e. Users can also try LncPipeReporter as stand-alone for visualizing known and novel lncRNAs.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003eAs a nextflow-based analysis pipeline, LncPipe allow users edit configure file \u003ccode\u003enextflow.config\u003c/code\u003e to set the index files and default file path parameters instead of typing them into the command line.\u003c/p\u003e\n\u003cp\u003eTo configure, please go to \u003ccode\u003eparams\u003c/code\u003e line, and set the following information of various file locations and system environment settings\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-groovy\"\u003e\u003cpre\u003e params {\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e/*\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e User setting options (mandatory)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e \u003cspan class=\"pl-c\"\u003e*/\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e input file and genome reference\u003c/span\u003e\n fastq_ext \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e*_{1,2}.fq.gz\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n fasta_ref \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/data/database/hg38/genome.fa\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n design \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edesign.file\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n hisat2_index \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/data/database/hg38/hisatIndex/grch38_snp_tran/genome_snp_tran\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n cpatpath\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/opt/CPAT-1.2.3\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003ehuman gtf only\u003c/span\u003e\n gencode_annotation_gtf \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/data/database/hg38/Annotation/gencode.v24.annotation.gtf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n lncipedia_gtf \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/data/database/hg38/Annotation/lncipedia_4_0_hg38.gtf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e set \"null\" if you are going to perform analysis on other species\u003c/span\u003e\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e additional options for non-human species, else leaving them unchanged\u003c/span\u003e\n species\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehuman\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e mouse , zebrafish, fly\u003c/span\u003e\n known_coding_gtf\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n known_lncRNA_gtf\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003efor test\u003c/span\u003e\n cpatpath \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/home/zhaoqi/software/CPAT/CPAT-1.2.2/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e/*\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e User setting options (optional)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e \u003cspan class=\"pl-c\"\u003e*/\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e tools setting\u003c/span\u003e\n star_idex \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eset if star used\u003c/span\u003e\n bowtie2_index \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eset if tophat used\u003c/span\u003e\n aligner \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehisat\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e or \"star\",\"tophat\"\u003c/span\u003e\n sam_processor\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esambamba\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eor \"samtools(deprecated)\"\u003c/span\u003e\n qctools \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003efastp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e or \"afterqc\",\"fastp\",\"fastqc\"\u003c/span\u003e\n detools \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eedger\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eor \"deseq2\",\"noiseq\" not supported yet\u003c/span\u003e\n quant \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ekallisto\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e or \u0027htseq\u0027\u003c/span\u003e\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003eother setting\u003c/span\u003e\n singleEnd \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\n unstrand \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\n skip_combine \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\n lncRep_Output \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ereporter.html\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n lncRep_theme \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003enpg\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n lncRep_cdf_percent \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e\n lncRep_max_lnc_len \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10000\u003c/span\u003e\n lncRep_min_expressed_sample \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e\n mem\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e60\u003c/span\u003e\n cpu\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e\n }\n\n manifest {\n homePage \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehttps//github.com/likelet/LncPipe\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n description \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eLncPipe:a Nextflow-based Long non-coding RNA analysis PIPELINE\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n mainScript \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eLncRNAanalysisPipe.nf\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n }\n\n\n timeline {\n \u003cspan class=\"pl-c1\"\u003eenabled\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003efile\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etimeline.html\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n }\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-parameters\" class=\"anchor\" href=\"#parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThose parameters would cover the setting from \u003ccode\u003enextflow.config\u003c/code\u003e file\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eMandatory(plz configure those options in \u003cem\u003enextflow.config\u003c/em\u003e or \u003cem\u003edocker.config\u003c/em\u003e file)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth align=\"right\"\u003eExample/Default value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003e.\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003einput folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--species\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003ehuman\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eYour species, mouse, fly and zebra fish are also supported\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fastq_ext\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003e*_{1,2}.fastq.gz\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003einput raw paired reads\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--out_folder\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003e.\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eoutput folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--design\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003ccode\u003eFALSE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ea txt file that stored experimental design information, plz see details from \u003ccode\u003e--design\u003c/code\u003e section below\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003eReferences\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRequired\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--star_index/--bowtie2_index/--hisat2_index\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003ePath to STAR?bowtie2/hisat2(mutually exclusive) index(required if not set in config file)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fasta\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePath to Fasta reference(required if not set in config file)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--gencode_annotation_gtf\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAn annotation file from GENCODE database for annotating lncRNAs(required if not set in config file). e.g. gencode.v26.annotation.gtf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--lncipedia_gtf\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAn annotation file from LNCipedia database for annotating lncRNAs(required if not set in config file) e.g. \u003ca href=\"http://www.lncipedia.org/downloads/lncipedia_4_0_hc_hg38.gtf\" rel=\"nofollow\"\u003elncipedia_4_0_hc_hg38.gtf\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003esoftware path (should not setting when using docker )\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRequired\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpatpath\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eHome folder of cpat installed location\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cblockquote\u003e\n\u003cp\u003esince cpat may call model data from its home path, users should specified where the model file is located in. Especially users install cpat by themselves without our install code.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eOptional\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--singleEnd\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eFALSE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003especify that the reads are single ended\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--merged_gtf\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eFALSE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSkip mapping and assembly step by directly providing assembled merged gtf files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--unstrand\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eFALSE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003especify that library is unstrand specific\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--aligner\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003estar\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAligner for reads mapping (optional), STAR is default and supported only at present,\u003cem\u003estar\u003c/em\u003e/\u003cem\u003etophat\u003c/em\u003e/\u003cem\u003ehisat2\u003c/em\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--qctools\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003efastp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eTools for assess raw reads quality or filtered by \u003cem\u003efastp\u003c/em\u003e, \u003cem\u003efastqc\u003c/em\u003e, \u003cem\u003eafterqc\u003c/em\u003e or \u003cem\u003enone\u003c/em\u003e(skip qc step)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003eLncPipeReporter options\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--lncRep_Output\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ereporter.html\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSpecify report file name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--lncRep_theme\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003enpg\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePlot theme setting in interactive plot. Values from \u003ca href=\"https://github.com/road2stat/ggsci\"\u003eggsci\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--lncRep_min_expressed_sample\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e50\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum expressed gene allowed in each sample, 50 default. Samples not passed were filtered from analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ccode\u003e--fastq_ext\u003c/code\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eRaw fastq files are required for de-novo analysis.This parameters should be set according to your paired or singled reads file names.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Sample1_1.fq.gz\n Sample1_2.fq.gz\n Sample2_1.fq.gz\n Sample2_2.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can input pattern \u003ccode\u003e*_{1,2}.fq.gz\u003c/code\u003e to make the all paired-end file recognized by \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eFor singled reads file, file pattern should be fed with \u003ccode\u003e--singleEnd\u003c/code\u003e parameter specified\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--star_idex?--bowtie2_index/--hisat2_index\u003c/code\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThis parameter is \u003cem\u003erequired\u003c/em\u003e when not configured in nextflow.config file. It specify the star/tophat/hisat2(mutually exclusive) index folder built before running \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e .\nIf you don\u0027t know what it is?You can use \u003ccode\u003e--fasta\u003c/code\u003e to specify the reference sequence data. The index file would be built by \u003ca href=\"https://github.com/likelet/LncPipe\"\u003eLncPipe\u003c/a\u003e automatically.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ccode\u003e--design\u003c/code\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eExperimental design file matrix for differential expression analysis. Default: \u003ccode\u003enull\u003c/code\u003e\nFormat:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eWT:Sample1,Sample2,Sample3\nKO:Sample1,Sample2,Sample3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhile \u003ccode\u003eKO/WT\u003c/code\u003e represents the two experimental condition, and sample1, sample2, sample3 are replicates which should be comma-delimited in the same line .\u003c/p\u003e\n\u003cp\u003eFor sample names, it should be the sample as the prefix of fastq files which was trimmed by \u003ccode\u003e--fastq_ext\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cp\u003eif fastq file names are \u003ccode\u003eSample1_1.fq.gz, Sample1_2.fq.gz\u003c/code\u003e that comes from one sample and your \u003ccode\u003e--fastq_ext\u003c/code\u003e is set as \u003ccode\u003e*_{1,2}.fq.gz\u003c/code\u003e, the sample name\nshould be Sample1.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eResult\u003c/code\u003e folder under current path(default) or output_folder set by user. A typical structure of \u003ccode\u003eResult\u003c/code\u003e is follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Result/\n \u251c\u2500\u2500 QC\n \u2502 \u251c\u2500\u2500 N1141_1.clean_fastqc.html\n \u2502 \u251c\u2500\u2500 N1141_2.clean_fastqc.html\n \u2502 \u251c\u2500\u2500 N1177_1.clean_fastqc.html\n \u2502 \u2514\u2500\u2500 N1177_2.clean_fastqc.html\n \u251c\u2500\u2500 Identified_lncRNA\n \u2502 \u251c\u2500\u2500 all_lncRNA_for_classifier.gtf\n \u2502 \u251c\u2500\u2500 final_all.fa\n \u2502 \u251c\u2500\u2500 final_all.gtf\n \u2502 \u251c\u2500\u2500 lncRNA.fa\n \u2502 \u251c\u2500\u2500 protein_coding.fa\n \u2502 \u2514\u2500\u2500 protein_coding.final.gtf\n \u251c\u2500\u2500 LncReporter\n \u2502 \u251c\u2500\u2500 Differential_Expression_analysis.csv\n \u2502 \u2514\u2500\u2500 Report.html\n \u251c\u2500\u2500 Quantification\n \u2502 \u251c\u2500\u2500 kallisto.count.txt\n \u2502 \u2514\u2500\u2500 kallisto.tpm.txt\n \u2514\u2500\u2500 Star_alignment\n \u251c\u2500\u2500 STAR_N1141\n \u2502 \u251c\u2500\u2500 N1141Aligned.sortedByCoord.out.bam\n \u2502 \u251c\u2500\u2500 N1141Log.final.out\n \u2502 \u251c\u2500\u2500 N1141Log.out\n \u2502 \u251c\u2500\u2500 N1141Log.progress.out\n \u2502 \u2514\u2500\u2500 N1141SJ.out.tab\n \u2514\u2500\u2500 STAR_N1177\n \u251c\u2500\u2500 N1177Aligned.sortedByCoord.out.bam\n \u251c\u2500\u2500 N1177Log.final.out\n \u251c\u2500\u2500 N1177Log.out\n \u251c\u2500\u2500 N1177Log.progress.out\n \u2514\u2500\u2500 N1177SJ.out.tab\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eQC\u003c/code\u003e stored the Quality control output generated by FastQC or AfterQC software.\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eIdentified_lncRNA\u003c/code\u003e contains all assembled lncRNA and their sequences. \u003cem\u003eall_lncRNA_for_classifier.gtf\u003c/em\u003e includes both novel and known lncRNA features in \u003ca href=\"http://www.ensembl.org/info/website/upload/gff.html\" rel=\"nofollow\"\u003eGTF format\u003c/a\u003e;\n\u003cem\u003elncRNA.fa\u003c/em\u003e is all lncRNA sequences in fasta format. \u003cem\u003eprotein_coding.final.gtf\u003c/em\u003e and \u003cem\u003eprotein_coding.fa\u003c/em\u003e are protein coding information extracted from gencode annotation. \u003cem\u003efinal_all.gtf\u003c/em\u003e and \u003cem\u003efinal_all.fa\u003c/em\u003e are combined files for further analysis.\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAlignment\u003c/code\u003e are hisat/tophat/STAR aligner standard output\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eQuantification\u003c/code\u003e are estimated abundance using kallisto. \u003cem\u003ekallisto.count.txt\u003c/em\u003e stored reads count matrix and \u003cem\u003ekallisto.tpm.txt\u003c/em\u003e are tpm(Transcripts Per Kilobase Million) matrix.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLncReporter\u003c/code\u003e stored the interactive report file and differential expression matrix generated by LncPipeReporter which wrapped EdgeR.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tips\" class=\"anchor\" href=\"#tips\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTips\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cg-emoji class=\"g-emoji\" alias=\"blush\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f60a.png\"\u003e\ud83d\ude0a\u003c/g-emoji\u003ePlz keep the consistency of your genome sequence, index library and annotation files: genome version, chromosome format, gtf coordinated e.g. The third-party software may stop for any of the above reasons.\u003c/li\u003e\n\u003cli\u003e\n\u003cg-emoji class=\"g-emoji\" alias=\"confused\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f615.png\"\u003e\ud83d\ude15\u003c/g-emoji\u003eSetting your analysis parameters always in config file, differ project should corresponding to differ configurations for reproductive analysis. To rerun a project, you can just specify -c \u003ccode\u003eyour.config\u003c/code\u003e in your command, which can also help you to record analysis parameters.\u003c/li\u003e\n\u003cli\u003e\n\u003cg-emoji class=\"g-emoji\" alias=\"open_mouth\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f62e.png\"\u003e\ud83d\ude2e\u003c/g-emoji\u003eRun analysis on docker container, no much to say.\u003c/li\u003e\n\u003cli\u003e\n\u003cg-emoji class=\"g-emoji\" alias=\"grimacing\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f62c.png\"\u003e\ud83d\ude2c\u003c/g-emoji\u003eAlways use the latest version to be away from the known bugs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" href=\"#acknowledgement\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThanks to the author of \u003ca href=\"https://github.com/OpenGene/AfterQC\"\u003eAfterQC\u003c/a\u003e, Shifu Chen, for his help on providing a gzip output support to meet the require of LncPipe. Thanks to the internal test by Hongwan Zhang and Yan Wang from SYSUCC Cancer bioinformatics platform.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-faq\" class=\"anchor\" href=\"#faq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cem\u003e1. PLEK throws an error \"/data/software/PLEK.1.2/PLEK.py:line12: $\u0027\\r\u0027: can not find command\", how to fix?\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA: using the follow command as suggested in the installation section.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e perl -CD -pi -e\u0027tr/\\x{feff}//d \u0026amp;\u0026amp; s/[\\r\\n]+/\\n/\u0027 *.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cem\u003e2. IOError: [Errno 2] No such file or directory: \u0027/opt/CPAT-1.2.3/dat/Human_Hexamer.tsv\u0027?\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA: The cpat command required the \u003ccode\u003eHuman_Hexamer.tsv\u003c/code\u003e to predict lncRNA coding potential, plz check your \u003ccode\u003ecpatpath\u003c/code\u003e parameters.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cem\u003e3. When using htseq to quantify transicript, it throws \"Error occured when reading beginning of SAM/BAM file. \u0027csamtools.AlignedRead\u0027 object has no attribute \u0027reference_start\u0027 \"\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA: It\u0027s a version conflict caused by htseq and hisat generated bamfile, a possible solution for this is to install the old version of htseq\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003eFor implementation:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/likelet\"\u003eQi Zhao\u003c/a\u003e \u003ca href=\"mailto:zhaoqi@sysucc.org.cn\"\u003ezhaoqi@sysucc.org.cn\u003c/a\u003e, Sun Yat-sen University Cancer Center;\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://icannotendure.space\" rel=\"nofollow\"\u003eYu Sun\u003c/a\u003e \u003ca href=\"mailto:sun_yu@mail.nankai.edu.cn\"\u003esun_yu@mail.nankai.edu.cn\u003c/a\u003e, Nan kai University;\u003cbr\u003e\nFor project design and new feature request:\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/likelet\"\u003eQi Zhao\u003c/a\u003e \u003ca href=\"mailto:zhaoqi@sysucc.org.cn\"\u003ezhaoqi@sysucc.org.cn\u003c/a\u003e, Sun Yat-sen University Cancer Center;\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"\"\u003eZhixiang Zuo\u003c/a\u003e \u003ca href=\"mailto:zuozhx@sysucc.org.cn\"\u003ezuozhx@sysucc.org.cn\u003c/a\u003e, Sun Yat-sen University Cancer Center;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eWe strongly recommend users open new issues if they have questions or find bugs.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"LICENSE\"\u003eGPL v3 license\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eQi Zhao, Yu Sun, Dawei Wang, Hongwan Zhang, Kai Yu, Jian Zheng, Zhixiang Zuo. LncPipe: A Nextflow-based pipeline for identification and analysis of long non-coding RNAs from RNA-Seq data. Journal of Genetics and Genomics. 2018. (\u003cem\u003eIn press\u003c/em\u003e)\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for spades\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1643344466.0 + "updated_at": 1580700253.0 }, { "data_format": 2, - "description": "Singularity recipe files for sqlite-tools (http://www.sqlite.org/)", + "description": "Analysis scripts and code for Paramormyrops RNA-seq project", "filenames": [ - "Singularity.3.36.0", - "Singularity" + "trinity_singularity/Singularity" ], - "full_name": "powerPlant/sqlite-tools-srf", + "full_name": "msuefishlab/paramormyrops_rnaseq", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for sqlite-tools to provide sqldiff\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-paramormyrops_rnaseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#paramormyrops_rnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eparamormyrops_rnaseq\u003c/h1\u003e\n\u003cp\u003eAnalysis scripts and code for our research article: Losilla, M., Luecke, D.M. \u0026amp; Gallant, J.R. The transcriptional correlates of divergent electric organ discharges in Paramormyrops electric fish. BMC Evol Biol 20, 6 (2020). \u003ca href=\"https://doi.org/10.1186/s12862-019-1572-3\" rel=\"nofollow\"\u003ehttps://doi.org/10.1186/s12862-019-1572-3\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains files with the code we used in our analysis.\u003c/p\u003e\n\u003cp\u003eThe table below serves as a guide to understand the flow of the code. It details the order in which the code was executed, along with a description and comments of each step. Notes are shown in \u003cstrong\u003ebold\u003c/strong\u003e text.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e that a Singularity file is provided in the folder trinity_singularity to run on high performance computing systems. This would allow any user capable of running Singularity images to recreate the exact computing environment used for these analyses, though it is not required.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003escript/command file\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003cth\u003ecomments\u003c/th\u003e\n\u003cth\u003eadditional_outputs (These are provided in the folder named additional_files)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_01_FastQCraw.sh\u003c/td\u003e\n\u003ctd\u003eassess quality of raw reads\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_02_trim_rename_unzip.sh\u003c/td\u003e\n\u003ctd\u003etrim, rename and unzip reads\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_03_FastQCtrimmed.sh\u003c/td\u003e\n\u003ctd\u003eassess quality of trimmed reads\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eThe NCBI transcripts file we used as reference for the align and count steps was from: NCBI Paramormyrops kingsleyae Annotation Release 100, based on genome assembly PKINGS_0.1. We downloaded the transcripts file from here: ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/002/872/115/GCF_002872115.1_PKINGS_0.1 We used the file called: rna.fna.gz, and removed the sole rRNA transcript present: XR_002837744.1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecmd_generate_gene_to_trans_file.txt\u003c/td\u003e\n\u003ctd\u003egenerate a gene-to-transcript list from the NCBI transcripts file\u003c/td\u003e\n\u003ctd\u003ethis list is required by the align and count steps\u003c/td\u003e\n\u003ctd\u003egene-trans-map.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04a_RSEMindex.sh\u003c/td\u003e\n\u003ctd\u003eIndex the NCBI transcripts file\u003c/td\u003e\n\u003ctd\u003ecalls the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04a_bash.sh\u003c/td\u003e\n\u003ctd\u003eIndex the NCBI transcripts file\u003c/td\u003e\n\u003ctd\u003eexecutes commands within the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04b_RSEMperIndiv.sh\u003c/td\u003e\n\u003ctd\u003eAligns reads to NCBI transcripts file and counts reads per gene\u003c/td\u003e\n\u003ctd\u003ecalls the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04b_bash.sh\u003c/td\u003e\n\u003ctd\u003eAligns reads to NCBI transcripts file and counts reads per gene\u003c/td\u003e\n\u003ctd\u003eexecutes commands within the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04c_matrices.sh\u003c/td\u003e\n\u003ctd\u003eBuild gene expression matrices\u003c/td\u003e\n\u003ctd\u003ecalls the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_04c_bash.sh\u003c/td\u003e\n\u003ctd\u003eBuild gene expression matrices\u003c/td\u003e\n\u003ctd\u003eexecutes commands within the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eAt this point the gene expression matrices (RSEM.gene.counts.matrix and RSEM.gene.TMM.counts.matrix ) use gene names and symbols from the NCBI transcriptome. However, EntrezGeneIDs are preferred for downstream analyses. Therefore, I converted their gene names and symbols to Pkings EntrezGeneIDs with the next R code. The converted files were assigned to the original file names. The original files were first renamed to: \u0026lt;orginal name\u0026gt;_ORIG_gene_symbols\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etranslate_gene_IDs.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e\n\u003cli\u003e Replace gene names and symbols with EntrezGeneIDs in the gene expression matrices\u003c/li\u003e \u003cli\u003e generate a file with the columns Pking EntrezGeneID, gene name, gene symbol and type of gene for each of the predicted 27610 P. kingsleyae genes. This file is named Dic.PkingEntrezGeneID-to-name_symbol_type.txt \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003eThis code runs on the renamed files\u003c/td\u003e\n\u003ctd\u003eDic.PkingEntrezGeneID-to-name_symbol_type.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_05a_DE_analyses.sh\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e Data exploration - Correlation matrix, PCA \u003c/li\u003e \u003cli\u003e DGE and MA plots - all 10 possible pairwise OTU comparisons \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003ecalls the singularity container\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_05a_bash_DE_genes.sh\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e Data exploration - Correlation matrix, PCA \u003c/li\u003e \u003cli\u003e DGE and MA plots - all 10 possible pairwise OTU comparisons \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cli\u003e executes commands within the singularity container \u003c/li\u003e\n\u003cli\u003e We modified 2) to use the function estimateDisp() instead of the functions estimateCommonDisp() and estimateTagwiseDisp() \u003c/li\u003e\n\u003c/td\u003e\n\u003ctd\u003euses the samples.txt file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClustering_of_DEG_mean.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e For each phenotype pair, extract the genes that meet the expression filters (Set B groups) \u003c/li\u003e \u003cli\u003e plot expression patterns of the genes in each group from 1) \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003egenerates black \u0026amp; white and colored plots for Set B genes (These plots served informational purposes)\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egenerate_suppl_files_DEG_comparisons_and_groups.Rmd\u003c/td\u003e\n\u003ctd\u003egenerate the supplemental files with the details of the \u003col\u003e \u003cli\u003e 10 DGE comparisons and \u003c/li\u003e \u003cli\u003e Set B groups \u003c/li\u003e\n\u003c/ol\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esh_06_blastp.sh\u003c/td\u003e\n\u003ctd\u003eblast P. kingsleyae proteins to D. rerio proteins\u003c/td\u003e\n\u003ctd\u003eoutput is split into 7 files, we merged all to one file afterwards\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAnnotation_wrangling.Rmd\u003c/td\u003e\n\u003ctd\u003eFor each ontology, generate two \u0027dictionaries\u0027: \u003col\u003e \u003cli\u003e Pking Entrez Gene IDs to D. rerio GO IDs \u003c/li\u003e \u003cli\u003e D. rerio GO IDs to GO terms \u003c/li\u003e \u003c/ol\u003e\n\u003c/td\u003e\n\u003ctd\u003eFiles from 2) were not used in later scripts, they served as references\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e Dic.PkingEntrezGeneID-to-GO.{ontology}.txt \u003c/li\u003e \u003cli\u003e Dic.{ontology}.GOid_to_term.txt \u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eenrichment_on_Pkings _all_10_DGE_comparisons.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e GO enrichment on all 10 DGE comparisons \u003c/li\u003e \u003cli\u003e Horizontal bar plot significant GO terms\u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cli\u003e Xcel file from 1) is part of the supplementary files \u003c/li\u003e\n\u003cli\u003e This code also produces a file with information on each upregulated gene annotated to enriched GO terms, including how many GO terms the gene was annotated to for a given upregulated list and ontology (frequency). The file served informational purposes \u003c/li\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eenrichment_on_Pkings_clusters.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e GO enrichment on Set B groups \u003c/li\u003e \u003cli\u003e Horizontal bar plot significant GO terms \u003c/li\u003e \u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cli\u003e Xcel file from 1) is part of the supplementary files \u003c/li\u003e \u003cli\u003e the horizontal bar plot from 2) served informational purposes) \u003c/li\u003e \u003cli\u003e This code also produces a file with information on each upregulated gene annotated to enriched GO terms, including how many GO terms the gene was annotated to for a given upregulated list and ontology (frequency). The file served informational purposes \u003c/li\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eset_C.Rmd\u003c/td\u003e\n\u003ctd\u003e\u003col\u003e \u003cli\u003e Intersect upregulated genes from Sets A\u0027 and B (these intersected genes are Set C) \u003c/li\u003e \u003cli\u003e GO enrichment on Set C genes \u003c/li\u003e \u003cli\u003e plot expression patterns of Set C genes \u003c/li\u003e \u003cli\u003e Horizontal bar plot significant GO terms \u003c/li\u003e \u003c/ol\u003e\u003c/td\u003e\n\u003ctd\u003eThe outputs are: \u003col\u003e \u003cli\u003e one file per list of upregulated genes \u003c/li\u003e \u003cli\u003e one file per list of enriched GO terms \u003c/li\u003e \u003cli\u003e Xcel file with upregulated genes (consolidation of output 1) \u003c/li\u003e \u003cli\u003e Xcel file with enriched GO terms (consolidation of output 2) \u003c/li\u003e \u003cli\u003e Xcel file with information on each upregulated gene annotated to enriched GO terms, including how many GO terms the gene was annotated to for a given upregulated list and ontology (frequency). The file served informational purposes \u003c/li\u003e \u003cli\u003e Color plots for Set C genes expression patterns \u003c/li\u003e \u003cli\u003e Horizontal bar plot with enriched GO terms \u003c/li\u003e \u003c/ol\u003e \u003cli\u003e Outputs 3) and 4) are part of the supplemental files \u003c/li\u003e \u003cli\u003e Outputs 6) and 7) make up Figs. 4-6 \u003c/li\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 3, "topics": [], - "updated_at": 1643154969.0 + "updated_at": 1602949531.0 }, { "data_format": 2, - "description": "Version 3 of OnDemand apps", + "description": "Proteomics pipeline", "filenames": [ - "rstudio_server_app/Singularity", - "shiny_app/ext/Singularity" + "Singularity/singularity-master/singularity-master/examples/shub/Singularity", + "Singularity/singularity-master/singularity-master/examples/scientific/Singularity", + "Singularity/singularity-master/singularity-master/examples/arch/Singularity", + "Singularity/singularity-master/singularity-master/examples/ubuntu/Singularity", + "Singularity/singularity-master/singularity-master/examples/centos/Singularity", + "Singularity/singularity-master/singularity-master/examples/docker/Singularity", + "Singularity/singularity-master/singularity-master/examples/scratch/Singularity.busybox", + "Singularity/singularity-master/singularity-master/examples/scratch/Singularity.alpine", + "Singularity/singularity-master/singularity-master/examples/self/Singularity", + "Singularity/singularity-master/singularity-master/examples/busybox/Singularity", + "Singularity/singularity-master/singularity-master/examples/apps/Singularity", + "Singularity/singularity-master/singularity-master/examples/apps/Singularity.cowsay", + "Singularity/singularity-master/singularity-master/examples/instances/Singularity", + "Singularity/singularity-master/singularity-master/examples/asciinema/Singularity", + "Singularity/singularity-master/singularity-master/examples/sle/Singularity", + "Singularity/singularity-master/singularity-master/examples/raspbian/Singularity", + "Singularity/singularity-master/singularity-master/examples/library/Singularity", + "Singularity/singularity-master/singularity-master/examples/multistage/Singularity", + "Singularity/singularity-master/singularity-master/examples/opensuse/Singularity", + "Singularity/singularity-master/singularity-master/e2e/testdata/Singularity" ], - "full_name": "CHPC-UofU/OOD-apps-v3", + "full_name": "HayleyPrice/Pipeline", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ood-apps\" class=\"anchor\" href=\"#ood-apps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOOD-apps\u003c/h1\u003e\n\u003cp\u003eRepository of CHPC\u0027s Open OnDemand apps\u003c/p\u003e\n\u003cp\u003eVersion 3.0\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etemplated filling of job parameters\u003c/li\u003e\n\u003cli\u003edynamic filling of application versions (module files)\u003c/li\u003e\n\u003cli\u003ethe templates are in directory app-templates\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis is a repository of interactive apps used at CHPC with Open OnDemand.\u003c/p\u003e\n\u003cp\u003eEach subdirectory has its own README.md which contains information about this particular app.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1643047602.0 + "updated_at": 1645798954.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "context/ocserv-container/Singularity.def", + "context/openconnect-container/Singularity.def" ], - "full_name": "zellerlab/vortex_light", + "full_name": "cooperative-computing-lab/userlevel-vpn-tun-tap", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-vortex_light\" class=\"anchor\" href=\"#vortex_light\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evortex_light\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installing-locally-and-running-from-local-installation\" class=\"anchor\" href=\"#installing-locally-and-running-from-local-installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling locally and running from local installation\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo from GitHub.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/zellerlab/vortex_light.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate a conda environment with NextFlow, e.g. by using the provided \u003ccode\u003eenvironment.yml\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd vortex_light\nconda env create -f environment.yml\nconda activate vortex_light\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eMake a copy of the \u003ccode\u003econfig/run.config\u003c/code\u003e file and adjust it to your environment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run /path/to/vortex_light/main.nf --input_dir /path/to/input_files --output_dir /path/to/output_dir -c /path/to/run.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote: Nextflow itself requires at least \u003ccode\u003e5GB\u003c/code\u003e of memory.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-from-github\" class=\"anchor\" href=\"#running-from-github\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning from GitHub\u003c/h3\u003e\n\u003cp\u003eThis requires a local nextflow installation. If you don\u0027t have one, see Steps 1/2 above.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eMake a local copy of the \u003ca href=\"https://raw.githubusercontent.com/zellerlab/vortex_light/main/config/run.config\" rel=\"nofollow\"\u003erun configuration file\u003c/a\u003e and adjust to your environment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run zellerlab/vortex_light --input_dir /path/to/input_files --output_dir /path/to/output_dir -c /path/to/run.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote: Nextflow itself requires at least \u003ccode\u003e5GB\u003c/code\u003e of memory.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input-parameters\" class=\"anchor\" href=\"#input-parameters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput parameters\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input_dir\u003c/code\u003e should be a folder with bam files or with gzipped fastq files. For fastq files, individual samples should be separated into individual folders.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_dir\u003c/code\u003e is \u003ccode\u003evlight_out\u003c/code\u003e in the local directory by default.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--skip_\u0026lt;analysis\u0026gt;\u003c/code\u003e, \u003ccode\u003e--run_\u0026lt;analysis\u0026gt;\u003c/code\u003e skips, resp. explicitly requires execution of the specified analysis (\u003ccode\u003epathseq\u003c/code\u003e, \u003ccode\u003ebase_counts\u003c/code\u003e (read counts post pre-processing), \u003ccode\u003ekraken2\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--publishMode\u003c/code\u003e allows to switch between various modes of how results files are placed in the \u003ccode\u003eoutput_dir\u003c/code\u003e (cf. NextFlow documentation)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ekraken2\u003c/code\u003e can only run when the parameter \u003ccode\u003ekraken_database\u003c/code\u003e is set.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epathseq\u003c/code\u003e can only run when the parameter \u003ccode\u003epathseq_database\u003c/code\u003e is set.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eOutputs\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe output folder contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea subdirectory per sample (named \u003ccode\u003e\u0026lt;sample\u0026gt;\u003c/code\u003e) with\n\u003cul\u003e\n\u003cli\u003ethe kraken2 report \u003ccode\u003e\u0026lt;sample\u0026gt;.kraken2_report.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ethe library size \u003ccode\u003e\u0026lt;sample\u0026gt;.libsize.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003epathseq output\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.bam\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.bam.sgi\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.score_metrics\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.scores\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNote that by default, all files in the output folder are symlinks into the work dir! Before you delete the work dir, ensure you have dereferenced copies. Alternatively, change the --publishMode parameter to \u003ccode\u003ecopy\u003c/code\u003e or \u003ccode\u003elink\u003c/code\u003e (if the target file system supports hard links).\u003c/strong\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-userlevel-vpn-tun-tap\" class=\"anchor\" aria-hidden=\"true\" href=\"#userlevel-vpn-tun-tap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003euserlevel-vpn-tun-tap\u003c/h1\u003e\n\u003cp\u003eSetup of a virtual network interface inside a singularity container using\nnetwork namespaces. All the network traffic of the container is routed to the\nvirtual interface and then a vpn server (ocserv). The interface gets its ip\nfrom the vpn server.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pre-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-setup\u003c/h2\u003e\n\u003cp\u003eThe following is needed to allow a user to manipulate namespaces at the compute nodes:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the following line to /etc/sysctl.d/99-sysctl.conf\u003c/span\u003e\nuser.max_user_namespaces=10000\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esysctl -p\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe machine running the VPN host needs the following changes:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the following line to /etc/sysctl.d/99-sysctl.conf\u003c/span\u003e\nuser.max_user_namespaces=10000\nnet.ipv4.ip_forward=1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand similarly run \u003ccode\u003esysctl -p\u003c/code\u003e afterwards. These are the only steps that require\nroot at the execution sites.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the containers\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-singularity-image-for-the-vpn-clients\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-singularity-image-for-the-vpn-clients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the singularity image for the VPN clients:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e context/openconnect-container\n$ sudo singularity build vpncms-client.sif Singularity.def\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../..\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe build process installs openconnect and its dependencies using the\ncmssw/cms:rhel7 image as a base. It will also compile from source \u003ccode\u003evpnns\u003c/code\u003e,\n\u003ccode\u003eocproxy\u0027 and \u003c/code\u003etsocks`, the alternative programs to use openconnect without\nroot privileges.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-singularity-image-for-the-vpn-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-singularity-image-for-the-vpn-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the singularity image for the VPN server:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e context/ocserv-container\n$ sudo singularity build vpncms-server.sif Singularity.def\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../..\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-vpn-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-vpn-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the VPN server\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-without-root-privileges\" class=\"anchor\" aria-hidden=\"true\" href=\"#without-root-privileges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWithout root privileges:\u003c/h3\u003e\n\u003cp\u003eTo ensure that all processes are termianted when the singularity container\nterminates, we execute the image inside an instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./launch-vpn-server --image context/ocserv-container/vpncms-server.img --instance vpn_server --add-user myvpnuser:myvpnpasswd --port 8443\nAdded user: myvpnuser\nSERVER PIN:\npin-sha256:XXXXXXX...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe make note of the server pin printed, as we will need it when connecting the clients.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-root-privileges\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-root-privileges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith root privileges:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo ./launch-vpn-server --image context/ocserv-container/vpncms-server.img --instance vpn_server --add-user myvpnuser:myvpnpasswd --port 8443 --privileged\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-launch-some-vpn-clients\" class=\"anchor\" aria-hidden=\"true\" href=\"#launch-some-vpn-clients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch some vpn clients;\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./launch-vpn-client --image context/openconnect-container/vpncms-client.sif \\\n --server MACHINE_WHERE_OCSERV_RUNS:8443 \\\n --servercert pin-sha256:XXXXXXX... \\\n --user myvpnuser \\\n --passwd myvpnpasswd \\\n --vpn-mode ns \\\n -- /bin/bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003elaunch-vpn-client\u003c/code\u003e script simply starts/stops an instance of the singularity\ncontainer so that no openconnect services are left behind The real virtual interface\nsetup magic happens in /etc/cms-vpn/vpn-start.sh.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-adding-cvmfs-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-cvmfs-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding cvmfs support\u003c/h2\u003e\n\u003cp\u003ecvmfs can be provided using cvmfsexec via fusermount and singularity. We do\nthis by creating a self-contained cvmfsexec distribution and using it as the\nsingularity executable:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/cvmfs/cvmfsexec.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e cvmfsexec\n$ ./makedist -s -m rhel7-x86_64 osg\n$ ./makedist -s -o /tmp/singularity-cmvfsexec\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\n$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGCVMFS_REPOSITORIES=cms.cern.ch,atlas.cern.ch,oasis.opensciencegrid.org\n$ ./launch-vpn-client --image context/openconnect-container/vpncms-client.sif \\\n --server MACHINE_WHERE_OCSERV_RUNS:8443 \\\n --servercert pin-sha256:XXXXXXX... \\\n --user myvpnuser \\\n --passwd myvpnpasswd \\\n --vpn-mode ns \\\n --singularity /tmp/singularity-cmvfsexec \\\n -- ls /cvmfs/cms.cern.ch\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1642692226.0 + "updated_at": 1614364980.0 }, { "data_format": 2, - "description": "Pycharm in Singularity", + "description": "Docker Environment for running 21cmFAST", "filenames": [ "Singularity" ], - "full_name": "serheang/pycharm_singularity", - "latest_release": "pycharm", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-build-status-badge-\" class=\"anchor\" href=\"#build-status-badge-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild status badge: \u003ca href=\"https://github.com/serheang/pycharm_singularity/workflows/build-singularity-container/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/serheang/pycharm_singularity/workflows/build-singularity-container/badge.svg\" alt=\"badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-pycharm_singularity\" class=\"anchor\" href=\"#pycharm_singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epycharm_singularity\u003c/h1\u003e\n\u003cp\u003ePycharm in Singularity container.\u003c/p\u003e\n", + "full_name": "nkern/21cmfast_env", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-21cmfast_env\" class=\"anchor\" aria-hidden=\"true\" href=\"#21cmfast_env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e21cmfast_env\u003c/h1\u003e\n\u003cp\u003eDocker environment for running 21cmFAST on ubuntu\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1642676609.0 + "updated_at": 1503421722.0 }, { "data_format": 2, - "description": "A curriculum framework", + "description": "GSOC 2020 @ Red Hen \u0026 Vitrivr", "filenames": [ - "pddlgym_planners/FD/misc/releases/19.06/Singularity.19.06", - "pddlgym_planners/FD/misc/releases/latest/Singularity", - "pddlgym_planners/FD/misc/releases/19.12/Singularity.19.12", - "pddlgym_planners/FD/misc/releases/20.06/Singularity.20.06" + "openpose_singularity/Singularity.openpose_v1.60", + "openpose_singularity/Singularity.frankier_gsoc2020", + "attic/vitrivr_singularity/Singularity.adampro", + "attic/vitrivr_singularity/Singularity.cineast" ], - "full_name": "nitsan57/CDM_torch", + "full_name": "frankier/gsoc2020", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cdm_torch\" class=\"anchor\" href=\"#cdm_torch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCDM_torch\u003c/h1\u003e\n\u003cp\u003eA curriculum framework\ncheck out cdm.ipynb\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/frankier/gsoc2020/wiki\"\u003eProgress is on the wiki.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository is for small odds/ends and to point to other places where the\nactual coding has taken place including forks of other projects.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eattic\u003c/code\u003e contains old and abandoned work:\n\u003cul\u003e\n\u003cli\u003eHand pose annotation\u003c/li\u003e\n\u003cli\u003eSingularity def files for Cineast (Docker is used now)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eopenpose_singularity\u003c/code\u003e contains Singularity container for OpenPose\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingslurm\u003c/code\u003e (Snakemake SLURM profile) Run SLURM outside container by\ncommunicating over the filesystem\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eskelshop\u003c/code\u003e contains a \u003cem\u003esubmodule\u003c/em\u003e for the skelshop utility, which contains\nall the Python code/Snakemake pipelines, for skeleton dumping, tracking,\nsegmentation, and embedding pipelines\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eforks\u003c/code\u003e contains \u003cem\u003esubmodules\u003c/em\u003e with forks of existing repos:\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003evitrivr-ng\u003c/code\u003e, \u003ccode\u003ecineast\u003c/code\u003e \u0026amp; \u003ccode\u003ecottontail\u003c/code\u003e are forks of Vitrivr projects\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ejavacpp-presets-add-openpose\u003c/code\u003e: OpenPose JavaCPP binding\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eopencv_wrapper\u003c/code\u003e: Add a couple of extra methods\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eopenpose\u003c/code\u003e: Improve Python API and enable broken tracking\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evitrivr_pilot\u003c/code\u003e contains scripts to deploy pilot Vitrivr instance\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erefreeze_hand_tracking\u003c/code\u003e contains code to refreeze a pretrained hand\ndetection model\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1642618241.0 + "updated_at": 1603876660.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for truvari (https://github.com/spiralgenetics/truvari)", "filenames": [ - "Singularity.def" + "Singularity", + "Singularity.2.1.0" ], - "full_name": "mysteryresearcher/dasha", + "full_name": "powerPlant/truvari-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dasha-distributed-nonconvex-optimization-with-communication-compression-optimal-oracle-complexity-and-without-client-synchronization\" class=\"anchor\" href=\"#dasha-distributed-nonconvex-optimization-with-communication-compression-optimal-oracle-complexity-and-without-client-synchronization\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity and Without Client Synchronization\u003c/h1\u003e\n\u003cp\u003eThis repository provides the code to reproduce the experiments of the submission for The Thirty-ninth International Conference on Machine Learning (ICML 2022)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-install-singularity-optional\" class=\"anchor\" href=\"#1-install-singularity-optional\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/introduction.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (optional)\u003c/h3\u003e\n\u003cp\u003eIf you don\u0027t want to install Singularity, make sure that you have all dependecies from Singularity.def (python3, numpy, pytorch, etc.)\u003c/p\u003e\n\u003cp\u003ea. Pull an image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://k3nfalt/default/python_ml:sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb. Open a shell console of the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv ~/python_ml_sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-prepare-scripts-for-experiments\" class=\"anchor\" href=\"#2-prepare-scripts-for-experiments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Prepare scripts for experiments\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/zero_marina/config_libsvm_zero_marina.py \n--dumps_path SOME_PATH --dataset_path PATH_TO_FOLDER_WITH_DATASET --dataset mushrooms \n--experiments_name EXPERIMENT_NAME --num_nodes_list 5 \n--step_size_range -10 4 --number_of_seeds 1 --number_of_iterations 21000 \n--algorithm_names zero_marina marina --function nonconvex \n--compressors rand_k --number_of_coordinates 10 --quality_check_rate 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-execute-scripts\" class=\"anchor\" href=\"#3-execute-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Execute scripts\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esh SOME_PATH/EXPERIMENT_NAME/singularity_*.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-4-plot-results\" class=\"anchor\" href=\"#4-plot-results\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Plot results\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/plots/zero_marina/plot_marina_mushrooms_gradient.py \n--dumps_paths SOME_PATH/EXPERIMENT_NAME\n--output_path SOME_PATH_FOR_PLOTS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOne can find all other scripts \u003ca href=\"https://github.com/mysteryresearcher/dasha/blob/ac7d0dce798898fb6255e7c0ab181def8ac88f48/code/distributed_optimization_library/experiments/plots/zero_marina/script.txt#L1\"\u003ehere\u003c/a\u003e that generate experiments from the paper.\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for truvari, a Structural variant toolkit for benchmarking, annotating and more for VCFs.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1642513577.0 + "updated_at": 1613598092.0 }, { "data_format": 2, - "description": null, + "description": "proof of concept for running singularity in a singularity container", "filenames": [ "Singularity" ], - "full_name": "ddbj/singularity_guacamole_mysql", + "full_name": "lkirk/singularity-in-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_guacamole_mysql\" class=\"anchor\" href=\"#singularity_guacamole_mysql\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_guacamole_mysql\u003c/h1\u003e\n\u003cp\u003eRemote Desktop \u3084 VNC \u306e\u63a5\u7d9a\u3092 HTTP \u306b\u5909\u63db\u3057\u3066 HTML5 \u30a6\u30a7\u30d6\u30d6\u30e9\u30a6\u30b6\u3067\u8868\u793a\u3059\u308b Apache Guacamole \u3092 singularity instance \u3067\u5b9f\u884c\u3059\u308b\u305f\u3081\u306e\u30ec\u30b7\u30d4\u30d5\u30a1\u30a4\u30eb\u30fb\u521d\u671f\u5316\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u3059\u3002\u30e6\u30fc\u30b6\u30fc\u8a8d\u8a3c\u306bMySQL\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003eguacamole 1.3\u3067\u3059\u3067\u306b\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u5b9f\u884c\u3057\u3066\u3044\u308b\u5834\u5408\u306f\u3001\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4e00\u5ea6\u7d42\u4e86\u3057\u3066\u300csingularity image\u306e\u30d3\u30eb\u30c9\u300d\u3092\u5b9f\u884c\u5f8c\u3001\u300c\u30c7\u30fc\u30bf\u306e\u79fb\u884c\u300d\u307e\u3067\u9032\u3093\u3067\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-image-\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" href=\"#singularity-image-%E3%81%AE%E3%83%93%E3%83%AB%E3%83%89\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity image \u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067 singularity image \u3092\u30d3\u30eb\u30c9\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build guacamole.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMySQL, Tomcat\u306b\u3064\u3044\u3066\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u5148\u306b\u7f6e\u304b\u308c\u3066\u3044\u308b\u30d0\u30fc\u30b8\u30e7\u30f3\u304c\u9650\u5b9a\u3055\u308c\u3066\u3044\u308b\u305f\u3081\u3001\u30d5\u30a1\u30a4\u30eb\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3067\u304d\u305a\u306b\u30d3\u30eb\u30c9\u306b\u5931\u6557\u3059\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u306e\u5834\u5408\u306f\u30d5\u30a1\u30a4\u30eb\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u5148\u3092\u898b\u3066\u3001Singularity\u30d5\u30a1\u30a4\u30eb\u4e2d\u306e\u4ee5\u4e0b\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306e\u8a18\u8ff0\u3092\u9069\u5b9c\u5909\u66f4\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMYSQL_VERSION=\"5.6.51\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eTOMCAT_VERSION=\"9.0.56\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-\u521d\u671f\u8a2d\u5b9a\" class=\"anchor\" href=\"#%E5%88%9D%E6%9C%9F%E8%A8%AD%E5%AE%9A\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u521d\u671f\u8a2d\u5b9a\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067 singularity isntance \u8d77\u52d5\u306e\u305f\u3081\u306e\u521d\u671f\u8a2d\u5b9a\u3092\u884c\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u5b9f\u884c\u524d\u306b init.sh \u5185\u306e MYSQL_ROOT_PASSWD, MYSQL_GUACAMOLE_USER_PASSWD, MYSQL_PORT, GUACAMOLE_PORT, TOMCAT_SHUTDOWN_PORT, TOMCAT_PORT \u306e\u5024\u3092\u9069\u5b9c\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\"Enter current password for root (enter for none):\" \u3068\u8868\u793a\u3055\u308c\u305f\u3068\u3053\u308d\u3067\u51e6\u7406\u304c\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u306b\u306a\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u30ea\u30bf\u30fc\u30f3\u30ad\u30fc\u3092\u62bc\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u6b21\u306b\u3001\"Set root password? [Y/n]\" \u3068\u8868\u793a\u3055\u308c\u305f\u3068\u3053\u308d\u3067Y\u3092\u5165\u529b\u3057\u3001MySQL\u306eroot\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u3053\u3053\u3067\u3001init.sh\u306eMYSQL_ROOT_PASSWD\u306b\u8a2d\u5b9a\u3057\u305f\u5024\u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u4ee5\u964d\u306f\u3059\u3079\u3066Y\u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u51e6\u7406\u304c\u5b8c\u4e86\u3059\u308b\u3068\u3001data\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3068start_container.sh\u30d5\u30a1\u30a4\u30eb\u304c\u751f\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash init.sh\nexec init_mysql.sh\nperl: warning: Setting locale failed.\nperl: warning: Please check that your locale settings:\n\tLANGUAGE = \"ja_JP\",\n\tLC_ALL = (unset),\n\tLANG = \"ja_JP.UTF-8\"\n are supported and installed on your system.\nperl: warning: Falling back to the standard locale (\"C\").\nWARNING: Could not write to config file ./my.cnf: Read-only file system\n\nInstalling MySQL system tables...2021-03-17 18:46:46 0 [Warning] TIMESTAMP with implicit DEFAULT value is deprecated. Please use --explicit_defaults_for_timestamp server option (see documentation for more details).\n2021-03-17 18:46:46 0 [Note] Ignoring --secure-file-priv value as server is running with --bootstrap.\n2021-03-17 18:46:46 0 [Note] ./bin/mysqld (mysqld 5.6.51) starting as process 18851 ...\n2021-03-17 18:46:46 18851 [Note] InnoDB: Using atomics to ref count buffer pool pages\n2021-03-17 18:46:46 18851 [Note] InnoDB: The InnoDB memory heap is disabled\n2021-03-17 18:46:46 18851 [Note] InnoDB: Mutexes and rw_locks use GCC atomic builtins\n2021-03-17 18:46:46 18851 [Note] InnoDB: Memory barrier is not used\n2021-03-17 18:46:46 18851 [Note] InnoDB: Compressed tables use zlib 1.2.11\n2021-03-17 18:46:46 18851 [Note] InnoDB: Using CPU crc32 instructions\n2021-03-17 18:46:46 18851 [Note] InnoDB: Initializing buffer pool, size = 128.0M\n2021-03-17 18:46:46 18851 [Note] InnoDB: Completed initialization of buffer pool\n2021-03-17 18:46:46 18851 [Note] InnoDB: The first specified data file ./ibdata1 did not exist: a new database to be created!\n2021-03-17 18:46:46 18851 [Note] InnoDB: Setting file ./ibdata1 size to 12 MB\n2021-03-17 18:46:46 18851 [Note] InnoDB: Database physically writes the file full: wait...\n2021-03-17 18:46:46 18851 [Note] InnoDB: Setting log file ./ib_logfile101 size to 48 MB\n2021-03-17 18:46:46 18851 [Note] InnoDB: Setting log file ./ib_logfile1 size to 48 MB\n2021-03-17 18:46:47 18851 [Note] InnoDB: Renaming log file ./ib_logfile101 to ./ib_logfile0\n2021-03-17 18:46:47 18851 [Warning] InnoDB: New log files created, LSN=45781\n2021-03-17 18:46:47 18851 [Note] InnoDB: Doublewrite buffer not found: creating new\n2021-03-17 18:46:47 18851 [Note] InnoDB: Doublewrite buffer created\n2021-03-17 18:46:47 18851 [Note] InnoDB: 128 rollback segment(s) are active.\n2021-03-17 18:46:47 18851 [Warning] InnoDB: Creating foreign key constraint system tables.\n2021-03-17 18:46:47 18851 [Note] InnoDB: Foreign key constraint system tables created\n2021-03-17 18:46:47 18851 [Note] InnoDB: Creating tablespace and datafile system tables.\n2021-03-17 18:46:47 18851 [Note] InnoDB: Tablespace and datafile system tables created.\n2021-03-17 18:46:47 18851 [Note] InnoDB: Waiting for purge to start\n2021-03-17 18:46:47 18851 [Note] InnoDB: 5.6.51 started; log sequence number 0\n2021-03-17 18:46:47 18851 [Note] RSA private key file not found: /usr/local/mysql/data//private_key.pem. Some authentication plugins will not work.\n2021-03-17 18:46:47 18851 [Note] RSA public key file not found: /usr/local/mysql/data//public_key.pem. Some authentication plugins will not work.\n2021-03-17 18:46:53 18851 [Note] Binlog end\n2021-03-17 18:46:53 18851 [Note] InnoDB: FTS optimize thread exiting.\n2021-03-17 18:46:53 18851 [Note] InnoDB: Starting shutdown...\n2021-03-17 18:46:54 18851 [Note] InnoDB: Shutdown completed; log sequence number 1625977\nOK\n\nFilling help tables...2021-03-17 18:46:54 0 [Warning] TIMESTAMP with implicit DEFAULT value is deprecated. Please use --explicit_defaults_for_timestamp server option (see documentation for more details).\n2021-03-17 18:46:54 0 [Note] Ignoring --secure-file-priv value as server is running with --bootstrap.\n2021-03-17 18:46:54 0 [Note] ./bin/mysqld (mysqld 5.6.51) starting as process 18875 ...\n2021-03-17 18:46:54 18875 [Note] InnoDB: Using atomics to ref count buffer pool pages\n2021-03-17 18:46:54 18875 [Note] InnoDB: The InnoDB memory heap is disabled\n2021-03-17 18:46:54 18875 [Note] InnoDB: Mutexes and rw_locks use GCC atomic builtins\n2021-03-17 18:46:54 18875 [Note] InnoDB: Memory barrier is not used\n2021-03-17 18:46:54 18875 [Note] InnoDB: Compressed tables use zlib 1.2.11\n2021-03-17 18:46:54 18875 [Note] InnoDB: Using CPU crc32 instructions\n2021-03-17 18:46:54 18875 [Note] InnoDB: Initializing buffer pool, size = 128.0M\n2021-03-17 18:46:54 18875 [Note] InnoDB: Completed initialization of buffer pool\n2021-03-17 18:46:54 18875 [Note] InnoDB: Highest supported file format is Barracuda.\n2021-03-17 18:46:54 18875 [Note] InnoDB: 128 rollback segment(s) are active.\n2021-03-17 18:46:54 18875 [Note] InnoDB: Waiting for purge to start\n2021-03-17 18:46:55 18875 [Note] InnoDB: 5.6.51 started; log sequence number 1625977\n2021-03-17 18:46:55 18875 [Note] RSA private key file not found: /usr/local/mysql/data//private_key.pem. Some authentication plugins will not work.\n2021-03-17 18:46:55 18875 [Note] RSA public key file not found: /usr/local/mysql/data//public_key.pem. Some authentication plugins will not work.\n2021-03-17 18:46:55 18875 [Note] Binlog end\n2021-03-17 18:46:55 18875 [Note] InnoDB: FTS optimize thread exiting.\n2021-03-17 18:46:55 18875 [Note] InnoDB: Starting shutdown...\n2021-03-17 18:46:56 18875 [Note] InnoDB: Shutdown completed; log sequence number 1625987\nOK\n\nTo start mysqld at boot time you have to copy\nsupport-files/mysql.server to the right place for your system\n\nPLEASE REMEMBER TO SET A PASSWORD FOR THE MySQL root USER !\nTo do so, start the server, then issue the following commands:\n\n ./bin/mysqladmin -u root password \u0027new-password\u0027\n ./bin/mysqladmin -u root -h dbod04 password \u0027new-password\u0027\n\nAlternatively you can run:\n\n ./bin/mysql_secure_installation\n\nwhich will also give you the option of removing the test\ndatabases and anonymous user created by default. This is\nstrongly recommended for production servers.\n\nSee the manual for more instructions.\n\nYou can start the MySQL daemon with:\n\n cd . ; ./bin/mysqld_safe \u0026amp;\n\nYou can test the MySQL daemon with mysql-test-run.pl\n\n cd mysql-test ; perl mysql-test-run.pl\n\nPlease report any problems at http://bugs.mysql.com/\n\nThe latest information about MySQL is available on the web at\n\n http://www.mysql.com\n\nSupport MySQL by buying support/licenses at http://shop.mysql.com\n\nWARNING: Could not copy config file template ./support-files/my-default.cnf to\n./my.cnf, may not have access rights to do so.\nYou may want to copy the file manually, or create your own,\nit will then be used by default by the server when you start it.\n\nexec mysql_secure_installation\nINFO: instance started successfully\nperl: warning: Setting locale failed.\nperl: warning: Please check that your locale settings:\n\tLANGUAGE = \"ja_JP\",\n\tLC_ALL = (unset),\n\tLANG = \"ja_JP.UTF-8\"\n are supported and installed on your system.\nperl: warning: Falling back to the standard locale (\"C\").\n\n\n\nNOTE: RUNNING ALL PARTS OF THIS SCRIPT IS RECOMMENDED FOR ALL MySQL\n SERVERS IN PRODUCTION USE! PLEASE READ EACH STEP CAREFULLY!\n\nIn order to log into MySQL to secure it, we\u0027ll need the current\npassword for the root user. If you\u0027ve just installed MySQL, and\nyou haven\u0027t set the root password yet, the password will be blank,\nso you should just press enter here.\n\nEnter current password for root (enter for none): \nOK, successfully used password, moving on...\n\nSetting the root password ensures that nobody can log into the MySQL\nroot user without the proper authorisation.\n\nSet root password? [Y/n] Y\nNew password: \nRe-enter new password: \nPassword updated successfully!\nReloading privilege tables..\n ... Success!\n\n\nBy default, a MySQL installation has an anonymous user, allowing anyone\nto log into MySQL without having to have a user account created for\nthem. This is intended only for testing, and to make the installation\ngo a bit smoother. You should remove them before moving into a\nproduction environment.\n\nRemove anonymous users? [Y/n] Y\n ... Success!\n\nNormally, root should only be allowed to connect from \u0027localhost\u0027. This\nensures that someone cannot guess at the root password from the network.\n\nDisallow root login remotely? [Y/n] Y\n ... Success!\n\nBy default, MySQL comes with a database named \u0027test\u0027 that anyone can\naccess. This is also intended only for testing, and should be removed\nbefore moving into a production environment.\n\nRemove test database and access to it? [Y/n] Y\n - Dropping test database...\n ... Success!\n - Removing privileges on test database...\n ... Success!\n\nReloading the privilege tables will ensure that all changes made so far\nwill take effect immediately.\n\nReload privilege tables now? [Y/n] Y\n ... Success!\n\n\n\n\nAll done! If you\u0027ve completed all of the above steps, your MySQL\ninstallation should now be secure.\n\nThanks for using MySQL!\n\n\nCleaning up...\nsetup guacamole database\nWarning: Using a password on the command line interface can be insecure.\nWarning: Using a password on the command line interface can be insecure.\nWarning: Using a password on the command line interface can be insecure.\nWarning: Using a password on the command line interface can be insecure.\nWarning: Using a password on the command line interface can be insecure.\nINFO: Stopping guacamole instance of /home/okuda/singularity/ubuntu-18.04-guacamole-1.3.0-mysql/guacamole.sif (PID=18915)\ncreate server.xml\ncreate guacamole_home\nINFO: instance started successfully\nINFO: Stopping guacamole instance of /home/okuda/singularity/ubuntu-18.04-guacamole-1.3.0-mysql/guacamole.sif (PID=19214)\ncreate guacamole.properties\ncreate start_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-instance-\u306e\u8d77\u52d5\" class=\"anchor\" href=\"#singularity-instance-%E3%81%AE%E8%B5%B7%E5%8B%95\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity instance \u306e\u8d77\u52d5\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067 singularity instance \u3092\u8d77\u52d5\u3057\u307e\u3059\u3002instance \u306e\u8d77\u52d5\u5f8c\u3001instance \u5185\u3067mysqld, guacd, tomcat\u3000\u304c\u8d77\u52d5\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\nINFO: instance started successfully\nguacd[22]: INFO:\tGuacamole proxy daemon (guacd) version 1.3.0 started\nUsing CATALINA_BASE: /opt/tomcat\nUsing CATALINA_HOME: /opt/tomcat\nUsing CATALINA_TMPDIR: /opt/tomcat/temp\nUsing JRE_HOME: /usr\nUsing CLASSPATH: /opt/tomcat/bin/bootstrap.jar:/opt/tomcat/bin/tomcat-juli.jar\nUsing CATALINA_OPTS: \nTomcat started.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-\u30c7\u30fc\u30bf\u306e\u79fb\u884c\" class=\"anchor\" href=\"#%E3%83%87%E3%83%BC%E3%82%BF%E3%81%AE%E7%A7%BB%E8%A1%8C\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u30c7\u30fc\u30bf\u306e\u79fb\u884c\u003c/h2\u003e\n\u003cp\u003eguacamole 1.3\u3067\u4f5c\u6210\u6e08\u307f\u306estart_container.sh\u3092\u4f7f\u3063\u3066\u65b0\u3057\u3044\u30a4\u30e1\u30fc\u30b8\u3067\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u8d77\u52d5\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\nINFO: instance started successfully\nguacd[25]: INFO:\tGuacamole proxy daemon (guacd) version 1.4.0 started\nUsing CATALINA_BASE: /opt/tomcat\nUsing CATALINA_HOME: /opt/tomcat\nUsing CATALINA_TMPDIR: /opt/tomcat/temp\nUsing JRE_HOME: /usr/lib/jvm/java-11-openjdk-amd64\nUsing CLASSPATH: /opt/tomcat/bin/bootstrap.jar:/opt/tomcat/bin/tomcat-juli.jar\nUsing CATALINA_OPTS: \nTomcat started.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u5185\u306b\u5165\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://guacamole\nSingularity\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eguacamole-auth-jdbc-mysql-1.3.0.jar\u306e\u30b7\u30f3\u30dc\u30ea\u30c3\u30af\u30ea\u30f3\u30af\u3092guacamole-auth-jdbc-mysql-1.4.0.jar\u306e\u30b7\u30f3\u30dc\u30ea\u30c3\u30af\u30ea\u30f3\u30af\u306b\u5909\u66f4\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity\u0026gt; ls -l /etc/guacamole/extensions/\ntotal 4\nlrwxrwxrwx 1 okuda okuda 82 Mar 17 2021 guacamole-auth-jdbc-mysql-1.3.0.jar -\u0026gt; /usr/local/src/guacamole-auth-jdbc-1.3.0/mysql/guacamole-auth-jdbc-mysql-1.3.0.jar\nSingularity\u0026gt; ln -s /usr/local/src/guacamole-auth-jdbc-1.4.0/mysql/guacamole-auth-jdbc-mysql-1.4.0.jar /etc/guacamole/extensions/\nSingularity\u0026gt; ls -l /etc/guacamole/extensions/\ntotal 8\nlrwxrwxrwx 1 okuda okuda 82 Mar 17 2021 guacamole-auth-jdbc-mysql-1.3.0.jar -\u0026gt; /usr/local/src/guacamole-auth-jdbc-1.3.0/mysql/guacamole-auth-jdbc-mysql-1.3.0.jar\nlrwxrwxrwx 1 okuda okuda 82 Jan 17 12:06 guacamole-auth-jdbc-mysql-1.4.0.jar -\u0026gt; /usr/local/src/guacamole-auth-jdbc-1.4.0/mysql/guacamole-auth-jdbc-mysql-1.4.0.jar\nSingularity\u0026gt; rm /etc/guacamole/extensions/guacamole-auth-jdbc-mysql-1.3.0.jar \nSingularity\u0026gt; ls -l /etc/guacamole/extensions/\ntotal 4\nlrwxrwxrwx 1 okuda okuda 82 Jan 17 12:06 guacamole-auth-jdbc-mysql-1.4.0.jar -\u0026gt; /usr/local/src/guacamole-auth-jdbc-1.4.0/mysql/guacamole-auth-jdbc-mysql-1.4.0.jar\nSingularity\u0026gt; exit\nexit\n$\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u518d\u8d77\u52d5\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity instance stop guacamole\nINFO: Stopping guacamole instance of /home/okuda/singularity/ubuntu-18.04-guacamole-1.4.0-mysql/guacamole.sif (PID=29810)\n$ bash start_container.sh \nINFO: instance started successfully\nguacd[26]: INFO:\tGuacamole proxy daemon (guacd) version 1.4.0 started\nUsing CATALINA_BASE: /opt/tomcat\nUsing CATALINA_HOME: /opt/tomcat\nUsing CATALINA_TMPDIR: /opt/tomcat/temp\nUsing JRE_HOME: /usr/lib/jvm/java-11-openjdk-amd64\nUsing CLASSPATH: /opt/tomcat/bin/bootstrap.jar:/opt/tomcat/bin/tomcat-juli.jar\nUsing CATALINA_OPTS: \nTomcat started.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-guacamole-\u3078\u306e\u30a2\u30af\u30bb\u30b9\" class=\"anchor\" href=\"#guacamole-%E3%81%B8%E3%81%AE%E3%82%A2%E3%82%AF%E3%82%BB%E3%82%B9\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eguacamole \u3078\u306e\u30a2\u30af\u30bb\u30b9\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"http://localhost\" rel=\"nofollow\"\u003ehttp://localhost\u003c/a\u003e:\u0026lt;TOMCAT_PORT\u306e\u5024\u0026gt;/guacamole \u3092\u30a6\u30a7\u30d6\u30d6\u30e9\u30a6\u30b6\u3067\u958b\u3044\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u8d77\u52d5\u76f4\u5f8c\u306e\u30e6\u30fc\u30b6\u30fc\u540d\u3001\u30d1\u30b9\u30ef\u30fc\u30c9\u306f\u3044\u305a\u308c\u3082 guacadmin \u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-in-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-in-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity in singularity\u003c/h1\u003e\n\u003cp\u003eThis is a proof-of-concept to show that it is indeed possible to run nested singularity processes.\nMy purpose for doing this is to create containers that can run applications that are in other other containers, allowing me to decompose the containers into small, purpose-built units.\u003c/p\u003e\n\u003cp\u003eTo test this for yourself, you can do the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esudo singularity build container.sif Singularity\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esingularity shell container.sif\u003c/span\u003e\n\n# \u003cspan class=\"pl-s1\"\u003ethen, go ahead and try running\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esingularity shell container.sif\u003c/span\u003e\n# \u003cspan class=\"pl-s1\"\u003eas many \u003cspan class=\"pl-c1\"\u003etimes\u003c/span\u003e as you want\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere is an example session where I nest 3 containers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@host$ singularity shell container.sif\nSingularity\u0026gt; singularity shell container.sif\nINFO: Converting SIF file to temporary sandbox...\nSingularity\u0026gt; singularity shell container.sif\nINFO: Converting SIF file to temporary sandbox...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd the resulting process tree (reported by htop):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity runtime parent\n\u251c\u2500 /bin/bash --norc\n\u2502 \u2514\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 /bin/bash --norc\n\u2502 \u2502 \u2514\u2500 Singularity runtime parent\n\u2502 \u2502 \u251c\u2500 /bin/bash --norc\n\u2502 \u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2502 \u2514\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u251c\u2500 Singularity runtime parent\n\u2502 \u2514\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u251c\u2500 Singularity runtime parent\n\u2514\u2500 Singularity runtime parent\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you do not want to coerce conversion to a temporary sandbox on every call (it can be time intensive for large images), you can simply create the sandbox upfront:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@host$ singularity build --sandbox test container.sif\nWARNING: \u0027nodev\u0027 mount option set on /tmp, it could be a source of failure during build process\nINFO: Starting build...\nINFO: Verifying bootstrap image container.sif\nINFO: Creating sandbox directory...\nINFO: Build complete: test\nuser@host$ singularity shell container.sif\nSingularity\u0026gt; singularity shell test\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1642040783.0 + "updated_at": 1626825365.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for nanopolish (https://github.com/jts/nanopolish)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.3.8.3-1.el7" ], - "full_name": "porchard/RNAseq-NextFlow", + "full_name": "powerPlant/nanopolish-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-paired-end-rna-seq-data\" class=\"anchor\" href=\"#nextflow-pipeline-for-paired-end-rna-seq-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow pipeline for paired-end RNA-seq data\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eIf you have Singularity installed, you can use the config provided here (\u0027Singularity\u0027) to build a container with all the dependencies.\u003c/p\u003e\n\u003cp\u003eOtherwise, you\u0027ll need to have the following installed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSTAR\u003c/li\u003e\n\u003cli\u003efastqc\u003c/li\u003e\n\u003cli\u003esamtools\u003c/li\u003e\n\u003cli\u003eQoRTs\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI\u0027ve used this pipeline with NextFlow v. 19.04.1\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003ePaths to various generic files (STAR indices and chromosome size files) must be included in the nextflow.config file -- check that file and change paths accordingly.\u003c/p\u003e\n\u003cp\u003eYou\u0027ll also need to set the params.results variable -- either in the nextflow.config file itself, or on the command line when you run the pipeline (\u0027--results /path/to/results\u0027).\u003c/p\u003e\n\u003cp\u003eLastly, you\u0027ll need to include information about each RNA-seq library, including the genome to which it should be mapped, and the paths to the fastq files for each readgroup. Organize this information in a JSON file, as in library-config.json.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eOnce you have all of the above information, you can run the pipeline as follows (in this case, indicating the path to the results on the command line):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run -with-singularity /path/to/Singularity.simg -params-file library-config.json --results /path/to/results /path/to/main.nf\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for nanopolish \u003ca href=\"https://github.com/jts/nanopolish\"\u003ehttps://github.com/jts/nanopolish\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1642007112.0 + "updated_at": 1639350932.0 }, { "data_format": 2, - "description": "Tensorflow running in an Arch Linux Singularity container. Working towards JupyterHub SingularityHub Interop", + "description": null, "filenames": [ - "Singularity" + "Singularity", + "Singularity.5.28.2", + "Singularity.5.28.0", + "Singularity.5.28.1" ], - "full_name": "chiroptical/tensorflow-jupyterhub", + "full_name": "kiwiroy/singularity-perl", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-container-with-tensorflow-and-jupyter-notebook\" class=\"anchor\" href=\"#singularity-container-with-tensorflow-and-jupyter-notebook\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container with Tensorflow and Jupyter Notebook\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eIntent is to run Tensorflow on GPU compute nodes through JupyterHub\n\u003cul\u003e\n\u003cli\u003eIf you would like this built for another driver, submit an issue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBorrowed \u003ccode\u003elinks.sh\u003c/code\u003e from \u003ca href=\"https://github.com/drorlab/tf-singularity\"\u003ehttps://github.com/drorlab/tf-singularity\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eI extracted CuDNN here because the download link expires\u003c/li\u003e\n\u003cli\u003eBuilding the Singularity container:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity create -s 3072 tensorflow-jupyterhub.img\n$ sudo singularity bootstrap tensorflow-jupyterhub.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRunning local jupyter server:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run tensorflow-jupyterhub.img\n[I 21:58:36.327 NotebookApp] Serving notebooks from local directory: \u0026lt;some directory\u0026gt;\n[I 21:58:36.327 NotebookApp] 0 active kernels \n[I 21:58:36.327 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/?token=\u0026lt;some token\u0026gt;\n[I 21:58:36.327 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n[C 21:58:36.329 NotebookApp] \n \n Copy/paste this URL into your browser when you connect for the first time,\n to login with a token:\n http://localhost:8888/?token=\u0026lt;some token\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to just run a script:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec tensorflow-jupyterhub.img python hello-world.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mcburton\"\u003emcburton\u003c/a\u003e and I are working on JupyterHub\nplugins to handle Singularity Hub images cleanly.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-do\" class=\"anchor\" href=\"#to-do\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Do\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003ePossibly indicate any bloat in the image and clear it out, if possible\n\u003cul\u003e\n\u003cli\u003eTensorflow DockerHub Compressed Image with GPU is 2 GB, mine is 3 GB\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWorking on JupyterHub plugin to deploy images from SingularityHub\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2846\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-perl\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-perl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-perl\u003c/h1\u003e\n\u003cp\u003eUbuntu images with perl installed using perlbrew.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1497564620.0 + "updated_at": 1556534425.0 }, { "data_format": 2, - "description": "A symbolic generalized MaxSAT solver", - "filenames": [ - "dmc/Singularity", - "lg/Singularity" - ], - "full_name": "zzwonder/DPMS", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dpms-dynamic-programming-for-generalized-maxsat\" class=\"anchor\" href=\"#dpms-dynamic-programming-for-generalized-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPMS (Dynamic Programming for Generalized MaxSAT)\u003c/h1\u003e\n\u003cp\u003eDPMS handles generalized MaxSAT problems in an extended DIMACS format (described below)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe DPMC framework runs in two phases:\n\u003cul\u003e\n\u003cli\u003ePlanning phase: \u003ca href=\"./lg\"\u003eLG\u003c/a\u003e constructs a (graded) project-join tree of a generalized MaxSAT formula\u003c/li\u003e\n\u003cli\u003eExecution phase: \u003ca href=\"./dmc\"\u003eDMC\u003c/a\u003e computes the answer to a generalized MaxSAT formula using the (graded) project-join tree\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-linux\" class=\"anchor\" href=\"#installation-linux\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation (Linux)\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eautomake 1.16\u003c/li\u003e\n\u003cli\u003ecmake 3.16\u003c/li\u003e\n\u003cli\u003eg++ 9.3\u003c/li\u003e\n\u003cli\u003egmp 6.2\u003c/li\u003e\n\u003cli\u003emake 4.2\u003c/li\u003e\n\u003cli\u003ealready included as git submodules:\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003ecudd 3.0\u003c/a\u003e (a slightly modified version for DPMS is inlcuded)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts 2.2\u003c/a\u003e (included)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/trolando/sylvan\"\u003esylvan 1.5\u003c/a\u003e(included)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-install-submodules\" class=\"anchor\" href=\"#install-submodules\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall submodules:\u003c/h3\u003e\n\u003cp\u003eIn addmc/libraries/, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./install.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-lg-tree-builder\" class=\"anchor\" href=\"#compile-lg-tree-builder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile LG (Tree Builder)\u003c/h3\u003e\n\u003cp\u003eSee \u003ca href=\"./lg/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-dmc-executor\" class=\"anchor\" href=\"#compile-dmc-executor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile DMC (Executor)\u003c/h3\u003e\n\u003cp\u003eSee \u003ca href=\"./dmc/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-example-command-line\" class=\"anchor\" href=\"#usage-example-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage Example (Command Line)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ecnfFile=\"examples/pbtest.wbo\" \u0026amp;\u0026amp; bash -c \"lg/build/lg \u0027lg/solvers/flow-cutter-pace17/flow_cutter_pace17 -p 100\u0027\" \u0026lt; $cnfFile | dmc/dmc --cf=$cnfFile --mx=1 --mb=999\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure to use \"--mx=1\" to enable maxSAT.\u003c/p\u003e\n\u003cp\u003eChange \"999\" in \"--mb=999\" to a better upper bound of optimal cost (e.g., the result of o-line of a MaxSAT solver). For a WBO or partial MaxSAT instance, --mb is set to be the trivial bound which can be read from the instance, unless the user gives a better bound.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-problem-format-of-generalized-maxsat\" class=\"anchor\" href=\"#problem-format-of-generalized-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProblem format of Generalized MaxSAT\u003c/h2\u003e\n\u003cp\u003eSome examples of each type of problem can be found in examples/\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-generalized-maxsat-and-weighted-maxsat\" class=\"anchor\" href=\"#generalized-maxsat-and-weighted-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e(generalized) MaxSAT and weighted MaxSAT\u003c/h3\u003e\n\u003cp\u003eThe Max-CNF-SAT problems (.cnf) should use the DIMACS format: \u003ca href=\"https://www.ieee.org/conferences/publishing/templates.html\" rel=\"nofollow\"\u003ehttps://www.ieee.org/conferences/publishing/templates.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor XOR constraints, use \u0027x\u0027 at the beginning of a line\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ex1 xor x2 xor \\neg x3 =\u0026gt; x 1 2 -3 0.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor weighted MaxSAT (.cnf), use \"p wcnf nvars nclauses total-Soft-Weight\" instead of \"p cnf nvars nclauses\" in header. For each clause line, put the weight at the beginning of a line, then the first literal.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pseudo-boolean-optimization-wbo\" class=\"anchor\" href=\"#pseudo-boolean-optimization-wbo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePseudo-Boolean optimization (WBO)\u003c/h3\u003e\n\u003cp\u003eFor PB constraints (.wbo), here is an example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+1 x1 +1 x2 \u0026gt;= 1 ;\n[90] -1 x1 -1 x2 \u0026gt;= -1 ;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first constraint is a hard constraint. The second constraint is soft with weight 90.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-min-maxsat\" class=\"anchor\" href=\"#min-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMin-MaxSAT\u003c/h3\u003e\n\u003cp\u003eA Min-MaxSAT problem file is same with a MaxSAT file except that there is a \u0027vm\u0027 line indicating the min variables. Variables that do not appear in the vm line are all max variables.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1641000935.0 - }, - { - "data_format": 2, - "description": null, + "description": null, "filenames": [ "Singularity" ], - "full_name": "aminhaghparast/deep-variant", + "full_name": "juanca09/dino", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://raw.githubusercontent.com/nf-core/deepvariant/master/docs/images/deepvariant_logo.png\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/nf-core/deepvariant/master/docs/images/deepvariant_logo.png\" alt=\"deepvariant\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-nf-coredeepvariant\" class=\"anchor\" href=\"#nf-coredeepvariant\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/deepvariant\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eDeep Variant as a Nextflow pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/deepvariant\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/554b3a00bbca0efb91acd93d9efc7929d4f25be25b8c7e5a58a31906f742ac65/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6465657076617269616e742e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/deepvariant.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6478e9f9fab44bd81e58f3ac9c53bd07b4447d3ce541c677c184903c7466e52/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413531382e31302e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A518.10.1-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/nf-core/Lobby\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75a58bd7ba3966d16ebf50e1d2d55a4d21c67fa281370f9b823c2f4e53532b03/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769747465722d2532306a6f696e253230636861742532302545322538362539322d3466623939612e737667\" alt=\"Gitter\" data-canonical-src=\"https://img.shields.io/badge/gitter-%20join%20chat%20%E2%86%92-4fb99a.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/deepvariant\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b79519758c23c61efc7c090d99e6c194456d4d72c071d9fb892501ca0be4f1c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6465657076617269616e742e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/deepvariant.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Nextflow pipeline for running the \u003ca href=\"https://github.com/google/deepvariant\"\u003eGoogle DeepVariant variant caller\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-deepvariant-and-why-in-nextflow\" class=\"anchor\" href=\"#what-is-deepvariant-and-why-in-nextflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is DeepVariant and why in Nextflow?\u003c/h2\u003e\n\u003cp\u003eThe Google Brain Team in December 2017 released a \u003ca href=\"https://www.ebi.ac.uk/training/online/course/human-genetic-variation-i-introduction/variant-identification-and-analysis/what-variant\" rel=\"nofollow\"\u003eVariant Caller\u003c/a\u003e based on DeepLearning: DeepVariant.\u003c/p\u003e\n\u003cp\u003eIn practice, DeepVariant first builds images based on the BAM file, then it uses a DeepLearning image recognition approach to obtain the variants and eventually it converts the output of the prediction in the standard VCF format.\u003c/p\u003e\n\u003cp\u003eDeepVariant as a Nextflow pipeline provides several advantages to the users. It handles automatically, through \u003cstrong\u003epreprocessing steps\u003c/strong\u003e, the creation of some extra needed indexed and compressed files which are a necessary input for DeepVariant, and which should normally manually be produced by the users.\nVariant Calling can be performed at the same time on \u003cstrong\u003emultiple BAM files\u003c/strong\u003e and thanks to the internal parallelization of Nextflow no resources are wasted.\nNextflow\u0027s support of Docker allows to produce the results in a computational reproducible and clean way by running every step inside of a \u003cstrong\u003eDocker container\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFor more detailed information about Google\u0027s DeepVariant please refer to \u003ca href=\"https://github.com/google/deepvariant\"\u003egoogle/deepvariant\u003c/a\u003e or this \u003ca href=\"https://research.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e. \u003cbr\u003e\nFor more information about DeepVariant in Nextflow please refer to this \u003ca href=\"https://blog.lifebit.ai/post/deepvariant/?utm_campaign=documentation\u0026amp;utm_source=github\u0026amp;utm_medium=web\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eWarning DeepVariant can be very computationally intensive to run.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo \u003cstrong\u003etest\u003c/strong\u003e the pipeline you can run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/deepvariant -profile test,docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA typical run on \u003cstrong\u003ewhole genome data\u003c/strong\u003e looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/deepvariant --genome hg19 --bam yourBamFile --bed yourBedFile -profile standard,docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn this case variants are called on the bam files contained in the testdata directory. The hg19 version of the reference genome is used.\nOne vcf files is produced and can be found in the folder \"results\"\u003c/p\u003e\n\u003cp\u003eA typical run on \u003cstrong\u003ewhole exome data\u003c/strong\u003e looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-core/deepvariant --exome --genome hg19 --bam_folder myBamFolder --bed myBedFile -profile standard,docker\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe nf-core/deepvariant documentation is split into the following files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/reference_genomes.md\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/about.md\"\u003eMore about DeepVariant\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-more-about-the-pipeline\" class=\"anchor\" href=\"#more-about-the-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore about the pipeline\u003c/h2\u003e\n\u003cp\u003eAs shown in the following picture, the worklow both contains \u003cstrong\u003epreprocessing steps\u003c/strong\u003e ( light blue ones ) and proper \u003cstrong\u003evariant calling steps\u003c/strong\u003e ( darker blue ones ).\u003c/p\u003e\n\u003cp\u003eSome input files ar optional and if not given, they will be automatically created for the user during the preprocessing steps. If these are given, the preprocessing steps are skipped. For more information about preprocessing, please refer to the \"INPUT PARAMETERS\" section.\u003c/p\u003e\n\u003cp\u003eThe worklow \u003cstrong\u003eaccepts one reference genome and multiple BAM files as input\u003c/strong\u003e. The variant calling for the several input BAM files will be processed completely indipendently and will produce indipendent VCF result files. The advantage of this approach is that the variant calling of the different BAM files can be parallelized internally by Nextflow and take advantage of all the cores of the machine in order to get the results at the fastest.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://github.com/nf-core/deepvariant/blob/master/pics/pic_workflow.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/nf-core/deepvariant/raw/master/pics/pic_workflow.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThis pipeline was originally developed at \u003ca href=\"https://lifebit.ai/?utm_campaign=documentation\u0026amp;utm_source=github\u0026amp;utm_medium=web\" rel=\"nofollow\"\u003eLifebit\u003c/a\u003e, by @luisas, to ease and reduce cost for variant calling analyses\u003c/p\u003e\n\u003cp\u003eMany thanks to nf-core and those who have helped out along the way too, including (but not limited to): @ewels, @MaxUlysse, @apeltzer, @sven1103 \u0026amp; @pditommaso\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-dino--a-nice-dinosaurio-\" class=\"anchor\" aria-hidden=\"true\" href=\"#dino--a-nice-dinosaurio-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edino ( A nice dinosaurio )\u003c/h1\u003e\n\u003cp\u003eYou need a GitHub account\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eLog into Github\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAdd a git repository ( ex:hello )\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLog into Singularity Hub ( \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003ehttps://singularity-hub.org\u003c/a\u003e ) as the github user\u003c/p\u003e\n\u003cp\u003eIn the Hub add a new collection ( with the repository )\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone the git project\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone\n\ngit clone git@github.com:\u0026lt;USER\u0026gt;/hello.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ein the directory \"hello\" add a Singularity definition file as \"Singularity\"\u003c/p\u003e\n\u003cp\u003eEx:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap:docker\nFrom:ubuntu:16.04\n\n%labels\nMAINTAINER juanca09\nSPECIES Dinosaur\n\n %environment\nRAWR_BASE=/code\nexport RAWR_BASE\n\n %runscript\necho \"This gets run when you run the image!\" \nexec /bin/bash /code/dino.sh \"$@\"\n\n\n%post \necho \"This section happens once after bootstrap to build the image.\" \nmkdir -p /code \necho \"echo \\\"RoooAAAARRRRR !!!!\\\"\" \u0026gt;\u0026gt; /code/dino.sh\nchmod u+x /code/dino.sh \n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCommit and push the project\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1651618731.0 + "updated_at": 1613312484.0 }, { "data_format": 2, - "description": "Planning problem generation using Graph Neural Networks and Reinforcement Learning.", + "description": "Standalone Singularity file for CAMISIM fork", "filenames": [ - "src/fast-downward/misc/releases/19.06/Singularity.19.06", - "src/fast-downward/misc/releases/20.06/Singularity.20.06", - "src/fast-downward/misc/releases/21.12/Singularity.21.12", - "src/fast-downward/misc/releases/19.12/Singularity.19.12" + "Singularity.cami_python2" ], - "full_name": "ari-dasci/S-PlanningProblemGeneration", + "full_name": "KatSteinke/singularity-camisim-standalone", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1648654350.0 + "updated_at": 1618570284.0 }, { "data_format": 2, @@ -16789,509 +16373,511 @@ var data = "filenames": [ "Singularity.def" ], - "full_name": "piyu2181/singularity", + "full_name": "robomorelli/horovod_torch_nccl", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-horovod_torch_nccl\" class=\"anchor\" aria-hidden=\"true\" href=\"#horovod_torch_nccl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehorovod_torch_nccl\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1565737347.0 + "updated_at": 1616241702.0 }, { "data_format": 2, - "description": null, + "description": "singularity recipes", "filenames": [ - "singularity/Singularity" + "Singularity.tf2p4_addons", + "Singularity.tf2p1", + "Singularity.tf2p4_costum", + "Singularity.tf2_cuda", + "Singularity.skimage", + "Singularity.tf2_addons", + "Singularity.tf2", + "Singularity.tf2_cuda_pip", + "Singularity.comet", + "Singularity.pandas", + "Singularity.torch", + "Singularity.tf2p1_addons", + "Singularity..torch1p8" ], - "full_name": "Egrt/https---huggingface.co-spaces-Egrt-Luuu", + "full_name": "xiyaojin/singularity", "latest_release": null, - "readme": "\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-title-luuuemoji-colorfrom-redcolorto-purplesdk-gradiosdk_version-2812app_file-apppypinned-falselicense-apache-20\" class=\"anchor\" href=\"#title-luuuemoji-colorfrom-redcolorto-purplesdk-gradiosdk_version-2812app_file-apppypinned-falselicense-apache-20\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etitle: Luuu\nemoji: \u003cg-emoji class=\"g-emoji\" alias=\"earth_africa\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f30d.png\"\u003e\ud83c\udf0d\u003c/g-emoji\u003e\ncolorFrom: red\ncolorTo: purple\nsdk: gradio\nsdk_version: 2.8.12\napp_file: app.py\npinned: false\nlicense: apache-2.0\u003c/h2\u003e\n\u003cp\u003eCheck out the configuration reference at \u003ca href=\"https://huggingface.co/docs/hub/spaces#reference\" rel=\"nofollow\"\u003ehttps://huggingface.co/docs/hub/spaces#reference\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cp\u003esingularity recipes\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1647768660.0 + "updated_at": 1622692314.0 }, { "data_format": 2, - "description": null, + "description": "If you are going to build off of basic Empirical, this is the project for you", "filenames": [ - "Singularity" + "third-party/force-cover/Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-miniconda-quicksom", + "full_name": "EGBWright/ArbitriumSimulation", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-a-miniconda-based-quicksom-httpsgithubcombougui505quicksom-container-with-pymolpytorch\" class=\"anchor\" href=\"#a-miniconda-based-quicksom-httpsgithubcombougui505quicksom-container-with-pymolpytorch\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea miniconda based quicksom (\u003ca href=\"https://github.com/bougui505/quicksom\"\u003ehttps://github.com/bougui505/quicksom\u003c/a\u003e) container with pymol/pytorch\u003c/h1\u003e\n\u003cp\u003edocker: \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-miniconda-quicksom/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-miniconda-quicksom/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-quicksom:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv oras://ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-quicksom:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-test-the-examples\" class=\"anchor\" href=\"#test-the-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etest the examples\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/bougui505/quicksom.git\n$ cd quicksom\n$ singularity --nv -B `pwd` oras://ghcr.io/truatpasteurdotfr/singularity-docker-miniconda-quicksom:latest\nSingularity\u0026gt; dcd2npy --pdb data/2lj5.pdb --dcd data/2lj5.dcd --select \u0027name CA\u0027\nSingularity\u0026gt; time quicksom_fit -i data/2lj5.npy -o data/som_2lj5.p --n_iter 100 --batch_size 50 --periodic --alpha 0.5\nSingularity\u0026gt; quicksom_gui -i data/som_2lj5.p\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-evolutionary-algorithm\" class=\"anchor\" aria-hidden=\"true\" href=\"#evolutionary-algorithm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvolutionary Algorithm\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/anyaevostinar/evo-algo/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff63e2cc80517f7b8e8246b33025f569d757ede0ae65c7ea57418d79e5a3709d/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f25324676657273696f6e2d62616467652e6a736f6e\" alt=\"version\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fversion-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2bde10efc09d993aad000b3101be56d5410d0ced348c4c7bbedbc7ffce79d630/68747470733a2f2f696d672e736869656c64732e696f2f7472617669732f616e796165766f7374696e61722f65766f2d616c676f2e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/travis/anyaevostinar/evo-algo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://evo-algo.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2af827df5df15ff6f5c6a9fff5021e631a0049aa2274f98e983135bb66f0ed81/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f65766f2d616c676f2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/evo-algo/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://evo-algo.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e035446b6d1c7a911bd4b203bd581f2116c7873352a90d4797dc08119abbd0e/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f253246646f63756d656e746174696f6e2d636f7665726167652d62616467652e6a736f6e\" alt=\"documentation coverage\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fdocumentation-coverage-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869814fe0b90d0baadc37140ac31d6d9c0e4e00c2854a51f1030c40678bc13a4/68747470733a2f2f636f6465636f762e696f2f67682f616e796165766f7374696e61722f65766f2d616c676f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"code coverage status\" data-canonical-src=\"https://codecov.io/gh/anyaevostinar/evo-algo/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo/search?q=todo+OR+fixme\u0026amp;type=\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4455f1d1625d643fe17506cb6ad50a9a6612ce62ab4667dc60b75616241c7534/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e636f6d25324665766f2d616c676f253246646f746f2d62616467652e6a736f6e\" alt=\"dotos\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.com%2Fevo-algo%2Fdoto-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db51ac0d7785eb561dcfc0cbccfc0cfed9872513120f8f732b1301504c4eb32e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f616e796165766f7374696e61722f65766f2d616c676f2e7376673f7374796c653d666c61742d737175617265266c6f676f3d676974687562266c6162656c3d5374617273266c6f676f436f6c6f723d7768697465\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/anyaevostinar/evo-algo.svg?style=flat-square\u0026amp;logo=github\u0026amp;label=Stars\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn evolutionary algorithm\u003c/p\u003e\n\u003cp\u003eCheck out the live in-browser web app at \u003ca href=\"https://anyaevostinar.github.io/evo-algo\" rel=\"nofollow\"\u003ehttps://anyaevostinar.github.io/evo-algo\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: MIT license\u003c/li\u003e\n\u003cli\u003eDocumentation: \u003ca href=\"https://evo-algo.readthedocs.io\" rel=\"nofollow\"\u003ehttps://evo-algo.readthedocs.io\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/assets/cookie.gif\"\u003e\u003cimg src=\"docs/assets/cookie.gif\" alt=\"cookie monster example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThis package was created with \u003ca href=\"https://github.com/audreyr/cookiecutter\"\u003eCookiecutter\u003c/a\u003e and the \u003ca href=\"https://github.com/devosoft/cookiecutter-empirical-project\"\u003edevosoft/cookiecutter-empirical-project\u003c/a\u003e project template.\u003c/p\u003e\n\u003cp\u003eThis package uses \u003ca href=\"https://github.com/devosoft/Empirical#readme\"\u003eEmpirical\u003c/a\u003e, a library of tools for scientific software development, with emphasis on also being able to build web interfaces using Emscripten.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eTo install \u003ca href=\"https://github.com/devosoft/Empirical\"\u003eEmpirical\u003c/a\u003e, pull down a clone of the Empirical repository. See \u003ca href=\"https://empirical.readthedocs.io/en/latest/QuickStartGuides\" rel=\"nofollow\"\u003eQuick Start Guides\u003c/a\u003e for directions on cloning and using the library.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1647383705.0 + "updated_at": 1615402009.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for Pathway-Tools and mpwt.", "filenames": [ - "Singularity.recipe" + "Singularity" ], - "full_name": "robbieperrott/Hons", + "full_name": "ArnaudBelcour/mpwt-singularity", "latest_release": null, - "readme": "\u003cp\u003eThis repository contains\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA Jenkinsfile, which contains parameters to be fed to a Jenkins pipeline job.\u003c/li\u003e\n\u003cli\u003eA Singularity recipe file, which specifies how to build the Singularity container on the target server.\u003c/li\u003e\n\u003cli\u003eRobbie\u0027s final research paper.\u003c/li\u003e\n\u003cli\u003eA poster summarizing the contents of our paper.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOur final mark was 72 percent.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1647336586.0 + "subscribers_count": 1, + "topics": [ + "pathway-tools" + ], + "updated_at": 1643893785.0 }, { "data_format": 2, "description": null, "filenames": [ - "container/Singularity.vep-96.0" + "Singularity" ], - "full_name": "vsarsani/Genetic-Characterization-Nextflow", + "full_name": "monaghaa/mytranslator", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-genetic-characterization-of-a-phenotype-nextflow-pipeline\" class=\"anchor\" href=\"#genetic-characterization-of-a-phenotype-nextflow-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenetic Characterization of a Phenotype Nextflow-pipeline\u003c/h1\u003e\n\u003cp\u003eThis pipeline performs the following functions to do a comprehensive genetic characterization of a phenotype marker (ex: height )\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eQC of GWAS Summary Statistics files, allele matching.\u003c/li\u003e\n\u003cli\u003eTrans-ancestry meta-analysis using various approaches. ( Fixed and random effects).\u003c/li\u003e\n\u003cli\u003eIdentification of Lead Variants and gene annotation from the meta-analysis results.\u003c/li\u003e\n\u003cli\u003eConditional analysis using GCTA COJO.\u003c/li\u003e\n\u003cli\u003eDistributional and Manhanttan plots of meta-analysis and conditional analysis.\u003c/li\u003e\n\u003cli\u003eWindow-based fine-mapping around lead variants to identify causal variants.\u003c/li\u003e\n\u003cli\u003eWindow-based fine-mapping around lead variants to identify eQTL colocalization.\u003c/li\u003e\n\u003cli\u003eeQTL based summary mendelian randomization.\u003c/li\u003e\n\u003cli\u003ePRS score construction from causal variants.\u003c/li\u003e\n\u003cli\u003eEnrichment analysis.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe pipeline has a total of ten processes. The tools used for all the ten processes are containerized in the \u003ca href=\"https://github.com/vsarsani/Genetic-Characterization-Nextflow/blob/master/container/Dockerfile\"\u003edocker image \u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vsarsani/Genetic-Characterization-Nextflow.git\ncd Nextflow-pipeline\ngit checkout dev_nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-nextflow\" class=\"anchor\" href=\"#nextflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003emake install\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-docker-image-installion\" class=\"anchor\" href=\"#docker-image-installion\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker image installion\u003c/h1\u003e\n\u003cp\u003eTo install the docker image for all the process tools using Docker, run the Makefile command in the container directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd container\nmake docker-build\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-how-to-run-the-pipeline\" class=\"anchor\" href=\"#how-to-run-the-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the pipeline\u003c/h1\u003e\n\u003cp\u003eIn order to run the pipeline, you need GWAS Summary files obtained from a study or multiple studies. Please use the following command.\n\u003ccode\u003e./nextflow run main.nf -resume --gwas-files ukb_bmi.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOtherwise, you can also run the whole pipeline by using the following one liner,\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./nextflow run main.nf\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1647271167.0 + "updated_at": 1638480957.0 }, { "data_format": 2, - "description": "MLPerf Inference containers recipes", + "description": "Docker image to get DeepLabCutCore running on cloud GPUs.", "filenames": [ - "v0.5/Singularity.v0.5", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_cg_omp-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_rt_c_tbb-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_c_omp-py36-gcc75-ubuntu18-avx2", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2020.3.1_src_c_omp-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_c_omp-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_omp-py36-gcc75-ubuntu18-avx2", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_omp-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_omp-py38-gcc75-ubuntu20", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_omp-py36-gcc75-ubuntu18-sse42", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_rt_cg_tbb-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_cg_tbb-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_cg_omp-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_cg_tbb-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_c_omp-py36-gcc75-ubuntu18-sse42", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_pre-release_src_c_tbb-py36-gcc75-ubuntu18", - "v0.5/cpp/OpenVINO/singularity/Singularity.v0.5-OpenVINO-2019_R3.1_src_c_tbb-py36-gcc75-ubuntu18", - "v0.7/Singularity.v0.7", - "v0.7/python/MXNet/singularity/Singularity.v0.7-MXNet-1.7.0-py38-gcc93-ubuntu20", - "v0.7/python/MXNet/singularity/Singularity.v0.7-MXNet-1.8.0-py38-gcc93-ubuntu20", - "v0.7/python/MXNet/singularity/Singularity.v0.7-MXNet-1.7.0_6ae469a-py38-gcc93-ubuntu20", - "v0.7/cpp/TensorFlow/singularity/Singularity.v0.7-TensorFlow-v2.3.0-py38-gcc93-ubuntu20", - "v0.7/cpp/TensorFlow/singularity/Singularity.v0.7-TensorFlow-v2.3.0-py38-gcc93-ubuntu20_cl", - "v0.7/cpp/TensorFlow/singularity/Singularity.v0.7-TensorFlow-v2.3.0-py38-gcc10-ubuntu20", - "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_rt_cg_tbb-py36-gcc75-ubuntu18", - "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2021.1pre_src_c_tbb-py36-gcc75-ubuntu18", - "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_src_c_omp-py36-gcc75-ubuntu18", - "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2021.1pre_src_c_omp-py36-gcc75-ubuntu18", - "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_src_c_tbb-py36-gcc75-ubuntu18", - "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_rt_c_tbb-py36-gcc75-ubuntu18", - "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_src_cg_omp-py36-gcc75-ubuntu18", - "v0.7/cpp/OpenVINO/singularity/Singularity.v0.7-OpenVINO-2019_R3.1_src_cg_tbb-py36-gcc75-ubuntu18", - "v1.1/Singularity.v1.1", - "v1.1/cpp/OpenVINO/singularity/Singularity.v1.1-OpenVINO-2021.4_src_c_tbb-py38-gcc93-ubuntu20", - "v1.1/cpp/OpenVINO/singularity/Singularity.v1.1-OpenVINO-2021.4_src_c_omp-py38-gcc93-ubuntu20", - "v1.1/cpp/OpenVINO/singularity/Singularity.v1.1-OpenVINO-2021.1pre_src_c_omp-py36-gcc75-ubuntu18", - "v1.0/Singularity.v1.0" + "Singularity" ], - "full_name": "provarepro/mlperf_inference", - "latest_release": "0.1.9", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mlperf-inference\" class=\"anchor\" href=\"#mlperf-inference\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMLPerf Inference\u003c/h1\u003e\n\u003cp\u003eMLPerf Inference containers recipes\u003c/p\u003e\n", + "full_name": "bchaselab/DeepLabCut-HPC", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker/badge.svg\"\u003e\u003cimg src=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker%20Image%20CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker%20Image%20CI/badge.svg\" alt=\"Docker Image CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/76a9b0a79aec38ba07bbf90a456c47bbdbca1fd005e919d62ec4fbc0cb2719d4/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6663617475732f646565706c6162637574\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/76a9b0a79aec38ba07bbf90a456c47bbdbca1fd005e919d62ec4fbc0cb2719d4/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6663617475732f646565706c6162637574\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/fcatus/deeplabcut\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/62efa830e023afaea0c6e665046187228790fcc4c07825b174254f2d1694a96d/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6663617475732f646565706c6162637574\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/62efa830e023afaea0c6e665046187228790fcc4c07825b174254f2d1694a96d/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6663617475732f646565706c6162637574\" alt=\"Docker Image Size (latest by date)\" data-canonical-src=\"https://img.shields.io/docker/image-size/fcatus/deeplabcut\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-from-dockerhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#from-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFrom \u003ca href=\"https://hub.docker.com/repository/docker/fcatus/deeplabcut\" rel=\"nofollow\"\u003eDockerhub\u003c/a\u003e\n\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker pull fcatus/deeplabcut:latest\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-use-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-use-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Use With Singularity\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull docker://fcatus/deeplabcut:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor build it from a singularity file\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ vim singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eBootstrap\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edocker\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eFrom\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efcatus/deeplabcut:latest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --remote deeplabcut.sif singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-from-a-singularity-definition-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-a-singularity-definition-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild From a Singularity \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/definition_files.html\" rel=\"nofollow\"\u003eDefinition File\u003c/a\u003e\n\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the definition file\u003c/span\u003e\n$ wget https://git.io/JJvBb\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Customize the definition file (optional)\u003c/span\u003e\n$ vim dlc.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build remotely from the definition file\u003c/span\u003e\n$ singularity build --remote deeplabcut.sif dlc.def\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more information about using \u003ccode\u003esingularity build\u003c/code\u003e, see \u003ca href=\"https://sylabs.io/guides/3.1/user-guide/cli/singularity_build.html\" rel=\"nofollow\"\u003eSingularity Build\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1641476524.0 + "topics": [ + "docker", + "deeplabcut", + "clone", + "slurm", + "hpc", + "singularity" + ], + "updated_at": 1617137580.0 }, { "data_format": 2, - "description": "Repository for automatic software installation with a Singularity container containing EasyBuild. ", + "description": "Singularity recipe files for winnowmap (https://github.com/marbl/Winnowmap)", "filenames": [ - "scripts/Singularity.eb-4.5.0-Lmod-ubuntu20-LTR", - "scripts/Singularity.eb-4.5.0-Lmod-rocky8", - "scripts/Singularity.eb-4.4.2-Lmod-ubuntu20-LTR", - "scripts-23-01-2022/Singularity.eb-4.5.0-Lmod-ubuntu20-LTR", - "scripts-23-01-2022/Singularity.eb-4.5.0-Lmod-rocky8", - "scripts-23-01-2022/Singularity.eb-4.4.2-Lmod-ubuntu20-LTR", - "scripts-combined/easybuild/Singularity.eb-4.5.0-Lmod-ubuntu20-LTR", - "scripts-combined/easybuild/Singularity.eb-4.5.0-Lmod-rocky8", - "scripts-combined/easybuild/Singularity.eb-4.4.2-Lmod-ubuntu20-LTR" + "Singularity.2.0.0" ], - "full_name": "sassy-crick/software-installation", + "full_name": "powerPlant/winnowmap-srf", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-automatic-software-installation-script\" class=\"anchor\" href=\"#automatic-software-installation-script\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatic software installation script\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction:\u003c/h2\u003e\n\u003cp\u003eThe aim of the script is to install the software inside a container, and thus the so installed software is independent from the OS as much as possible, and also takes care of different architectures. The idea comes from the EESSI project and how the software is installed in there. So kudos to them!!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to\" class=\"anchor\" href=\"#how-to\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow-to:\u003c/h2\u003e\n\u003cp\u003eBefore the script can run, there are a few files which need to be adjusted.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003einstall.sh\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esoftwarelist.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esoftwarelist.yaml\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003einstall.sh\u003c/code\u003e does basically the whole magic. There are a few lines at the top which need to be changed to reflect where the software needs to go. The most important are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSOFTWARE_INSTDIR\u003c/code\u003e which is where the software tree and all the helper stuff lives\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBINDDIR\u003c/code\u003e is the directory which needs to be bound inside the container as per default Singularity does only mount \u003ccode\u003e/tmp\u003c/code\u003e and \u003ccode\u003e/home\u003c/code\u003e it seems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou also might want to look at:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINER_VERSION\u003c/code\u003e which is the name of the sif-file, i.e. the container\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eEB_VERSION\u003c/code\u003e which is the version of EasyBuild to be used for building software. If that does not exist, it should be automatically installed\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSW_LIST\u003c/code\u003e contains a simple list of the EasyConfig files to be installed. All in one line with a blank between them.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSW_YAML\u003c/code\u003econtains the software to be installed as an EasyStack file in \u003ccode\u003eyaml\u003c/code\u003e format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth the \u003ccode\u003eSW_LIST\u003c/code\u003e and the \u003ccode\u003eSW_YAML\u003c/code\u003e are independent from each other. So as long as the file got a content, it will be used.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esoftware.sh\u003c/code\u003e will be created on the fly in the right directory, using the various template files, and does contain the list of software which needs to be installed which will be pulled in by the \u003ccode\u003esoftwarelist.txt\u003c/code\u003e file. The EasyStack file, so it exists, will be places in the correct directory.\nIf you need to change any of the paths where the software will be installed, you will need to look into \u003ccode\u003esoftware.tmpl\u003c/code\u003e, the Singularity Definition file \u003ccode\u003eSingularity.eb-4.4.2-Lmod-ubuntu20-LTR\u003c/code\u003e and both the \u003ccode\u003einstall.sh\u003c/code\u003e and \u003ccode\u003einteractive-install.sh\u003c/code\u003e files.\nNote: You can mount any folder outside the container but you will need to make sure that the \u003ccode\u003eMODULEPATH\u003c/code\u003e variable are identical inside and outside the container. Thus, if you are using like in our example \u003ccode\u003e/apps/easybuild\u003c/code\u003e as the root install directory, the \u003ccode\u003eMODULEPATH\u003c/code\u003e then needs to be set to for example \u003ccode\u003e/apps/easybuild/modules/all\u003c/code\u003e inside and outside the container!\u003c/p\u003e\n\u003cp\u003eThere is currently one bad hack in the \u003ccode\u003einstall.sh\u003c/code\u003e script, which is the architecture where the container is running on is determined by a fixed-path script! That will be tidied up at one point, so please be aware of this!\nThe idea about using \u003ccode\u003earchspec.py\u003c/code\u003e is that outside the container you got different paths where to install the software, but one common path for all the source files. If you are only having one type of architecture, you can set that manually at the top of the file.\u003c/p\u003e\n\u003cp\u003eThe first time the script runs, it will create the directory structure but then stops as the Singularity container is not in place. For the full automated installation, we would download the container from somewhere. However, as this only needs to be done once, it is left for now like this.\u003c/p\u003e\n\u003cp\u003eOnce the container in the right folder we are upgrading EasyBuild to the latest version. This way, a module file is created automatically. Once that is done, the software will be installed if required.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements:\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e \u0026gt;= 2.7.x and \u003ccode\u003efusermount\u003c/code\u003e \u0026gt;= 2.9.7\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-do\" class=\"anchor\" href=\"#to-do\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Do:\u003c/h2\u003e\n\u003cp\u003eIt needs to be tested on Lustre but that does currently not work as \u003ccode\u003efusermount\u003c/code\u003e on at the current cluster is too old.\u003c/p\u003e\n\u003cp\u003eAlso, as mentioned above, the \u003ccode\u003earchpsec.py\u003c/code\u003e needs to be installed in a better way.\u003c/p\u003e\n\u003cp\u003eFinally, it somehow would be nice to include \u003ccode\u003e--cuda-compute-capabilities=8.0\u003c/code\u003e for the A100 GPU builds automatically to make it a bit more fool-proved.\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for Winnowmap, a long-read mapping algorithm optimized for mapping ONT and PacBio reads to repetitive reference sequences.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/marbl/Winnowmap\"\u003ehttps://github.com/marbl/Winnowmap\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1639737533.0 + "updated_at": 1615953867.0 }, { "data_format": 2, - "description": null, + "description": "A Singularity File for Running Trinity on the HPCC", "filenames": [ "Singularity" ], - "full_name": "raveancic/scRNAaltas_TNBC_mm", + "full_name": "msuefishlab/trinity_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-scrnaaltas_tnbc_mm\" class=\"anchor\" href=\"#scrnaaltas_tnbc_mm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escRNAaltas_TNBC_mm\u003c/h1\u003e\n\u003cp\u003eA pipeline for the scRNAseq data analysis of TNBC mouse model\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/raveancic/scRNAaltas_TNBC_mm/tree/master/cl_crt_FASTQ2countmat\"\u003eStep\u003c/a\u003e - Create the count matrix/bam file from FASTQ files. (sankemake pipeline - singularity container - PBS cluster). This step is the one published in \u003ca href=\"https://www.nature.com/articles/s41420-022-00893-x\" rel=\"nofollow\"\u003eCarpen et al., 2022, Cell Death Discovery\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 3, - "topics": [ - "scrna-seq-analysis", - "snakemake", - "snakemake-pipeline", - "cellranger", - "singularity", - "scrna", - "pbs" - ], - "updated_at": 1646907794.0 - }, - { - "data_format": 2, - "description": "Some util functions for machine learning experiments", - "filenames": [ - "Singularity" - ], - "full_name": "martinmamql/mini-tool-box", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mini-tool-box\" class=\"anchor\" href=\"#mini-tool-box\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emini-tool-box\u003c/h1\u003e\n\u003cp\u003eSome util functions for machine learning experiments\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, "topics": [], - "updated_at": 1642610373.0 + "updated_at": 1528136809.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "envs/illumina/Singularity" ], - "full_name": "talha-naveed97/orion_test", + "full_name": "here0009/SARS-Cov2_Snakemake_Pipeline", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sarscov2_snakemake_pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#sarscov2_snakemake_pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSarsCov2_Snakemake_Pipeline\u003c/h1\u003e\n\u003cp\u003eThis is a snakemake pipeline used for analyse SarsCov2 sequence data generated by illumina machine.\nThis pipelien was based on \u003ca href=\"https://github.com/artic-network/fieldbioinformatics\"\u003eARTIC network\u0027s fieldbioinformatics tools\u003c/a\u003e, \u003ca href=\"https://github.com/dridk/Sars-CoV-2-NGS-pipeline\"\u003eSars-CoV-2-NGS-pipeline\u003c/a\u003e and \u003ca href=\"https://github.com/connor-lab/ncov2019-artic-nf\"\u003encov2019-artic-nf\u003c/a\u003e with some updates:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003efastqc\u003c/code\u003e and was used to generate the qc report of input data.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003equast\u003c/code\u003e was used to generate the sequence assembly report.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/cov-lineages/pangolin\"\u003epangolin\u003c/a\u003e was used for the typing of SarsCov-2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCorGat\u003c/code\u003e was used to annotate the sequence, and generate alle frequency reports\nYou need to clone \u003ca href=\"https://github.com/matteo14c/CorGAT\"\u003eCorGat\u003c/a\u003e and specify the directory in the config files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emultiqc\u003c/code\u003e was used to generate the final report.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe workflow shows like below:\u003c/p\u003e\n\u003cp\u003eA test_data file was provided to test the pipeline.\nYou may test the pipeline by dry-run\n\u003ccode\u003esnakemake -s sars2.smk -n\u003c/code\u003e\nthen run the pipeline:\n\u003ccode\u003esnakemake -s sars2.smk -j 4 --use-conda\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWARNING - THIS REPO IS UNDER ACTIVE DEVELOPMENT AND ITS BEHAVIOUR MAY CHANGE AT \u003cstrong\u003eANY\u003c/strong\u003e TIME.\u003c/p\u003e\n\u003cp\u003ePLEASE ENSURE THAT YOU READ BOTH THE README AND THE CONFIG FILE AND UNDERSTAND THE EFFECT OF THE OPTIONS ON YOUR DATA!\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1646090180.0 + "updated_at": 1622116428.0 }, { "data_format": 2, "description": null, "filenames": [ - "RNAja/envs/Singularity.RNAja.def" + "Singularity", + "model_preprocess/Singularity" ], - "full_name": "Aucomte/RNAja", - "latest_release": "0.1.0", + "full_name": "lsx1980/3D_model_reconstruction", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-3d-root-model-reconstruction\" class=\"anchor\" aria-hidden=\"true\" href=\"#3d-root-model-reconstruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3D root model reconstruction\u003c/h1\u003e\n\u003cp\u003eThe software package was integrated as a module at PlantIT website at : \u003ca href=\"https://portnoy.cyverse.org/\" rel=\"nofollow\"\u003ehttps://portnoy.cyverse.org/\u003c/a\u003e.\n(Collaborate with Cyverse \u003ca href=\"https://www.cyverse.org/\" rel=\"nofollow\"\u003ehttps://www.cyverse.org/\u003c/a\u003e ) . Users are welcomed to registered as an user to try this package via PlantIT website.\u003c/p\u003e\n\u003cp\u003eThe software package was also available at Dockerhub (\u003ca href=\"https://hub.docker.com/r/computationalplantscience/3d-model-reconstruction\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/computationalplantscience/3d-model-reconstruction\u003c/a\u003e) for advanced users to run locally via singularity at Linux environment:\u003c/p\u003e\n\u003cp\u003eThis software can be run by docker container, users do not need to install many libraries and compile complex source files.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup-docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Docker container\u003c/h1\u003e\n\u003cp\u003eOS requirements\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTo install Docker container (https://docs.docker.com/engine/install/ubuntu/): \n\nTo install Docker Engine, you need the 64-bit version of one of these Ubuntu versions:\n\nUbuntu Groovy 20.10\nUbuntu Focal 20.04 (LTS)\nUbuntu Bionic 18.04 (LTS)\nUbuntu Xenial 16.04 (LTS)\n\nDocker Engine is supported on x86_64 (or amd64), armhf, and arm64 architectures.\n\nUninstall old versions\n$ sudo apt-get remove docker docker-engine docker.io containerd runc\n\nSet up the repository\n\nUpdate the apt package index and install packages to allow apt to use a repository over HTTPS:\n\n$ sudo apt-get update\n\n$ sudo apt-get install \\\n apt-transport-https \\\n ca-certificates \\\n curl \\\n gnupg-agent \\\n software-properties-common\n\nAdd Docker\u2019s official GPG key:\n\n$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -\n\nVerify that you now have the key with the fingerprint 9DC8 5822 9FC7 DD38 854A E2D8 8D81 803C 0EBF CD88, by searching for the last 8 characters of the fingerprint.\n\n$ sudo apt-key fingerprint 0EBFCD88\n\npub rsa4096 2017-02-22 [SCEA]\n 9DC8 5822 9FC7 DD38 854A E2D8 8D81 803C 0EBF CD88\nuid [ unknown] Docker Release (CE deb) \u0026lt;docker@docker.com\u0026gt;\nsub rsa4096 2017-02-22 [S]\n\n$ sudo add-apt-repository \\\n \"deb [arch=amd64] https://download.docker.com/linux/ubuntu \\\n $(lsb_release -cs) \\\n stable\"\n\nUpdate the apt package index, and install the latest version of Docker Engine and containerd, or go to the next step to install a specific version:\n\n$ sudo apt-get update\n$ sudo apt-get install docker-ce docker-ce-cli containerd.io\n\nVerify that Docker Engine is installed correctly by running the hello-world image.\n\n$ sudo docker run hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-this-container-by-building-it-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-this-container-by-building-it-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun this container by building it locally:\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e# Clone source code to your local path\n$ git clone https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo.git\n\n# Enter into the source code folder named as \"cd 3D_model_reconstruction_demo\"\n$ cd 3D_model_reconstruction_demo/\n\n# Build docker container locally named as \"3d_model_reconstruction\" using \"Dockerfile\" in the same folder, note: docker repository name must be lowercase.\n$ docker build -t 3d_model_reconstruction -f Dockerfile .\n\n# Run the docker container by linking docker container data path to user\u0027s image data folder local path\n# Note: please replace $path_to_image_folder as your local image data folder path, \n# Suggest to check your image folder path using \"pwd\" command\n# Example: $ docker run -v /home/suxing/example/root_images:/images -it 3d_model_reconstruction\n\n$ docker run -v /$path_to_image_folder:/images -it 3d_model_reconstruction\n\n# After launch the docker container, run \"pipeline.sh\" or \"pipeline.sh\" insider the container\n$ root@0529cde0b988:/opt/code# ./pipeline.sh\nor $ root@0529cde0b988:/opt/code# python3 pipeline.py\n\n# Get 3d model result named as \"dense.0.ply\"\n# After the container was executed successfully with image data files, user should be able to see output in your command window like this:\n\u0027\u0027\u0027\nLoading option-0000.ply, 48656 vertices ...\nSave to /images/dense.nvm ... done\nSave /images/dense.0.ply ...done\n----------------------------------------------------------------\n\u0027\u0027\u0027\nThe 3D model file was in ply format(https://en.wikipedia.org/wiki/PLY_(file_format)), it is located inside your image folder, its name is \"dense.0.ply\".\npath = \"/$path_to_image_folder/dense.0.ply\"\n\nTo visualize the 3d model file, suggest to install Meshlab(https://www.meshlab.net/) or cloudcompare(https://www.danielgm.net/cc/)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-hidden=\"true\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthor\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esuxing liu(suxingliu@gmail.com)\nWesley Paul Bonelli(wbonelli@uga.edu)\n\nReference:\nVisualSFM\n[Anders Damsgaard](mailto:adamsgaard@ucsd.edu) with contributions by Caleb Adams and Connor P Doherty.\nChangchang Wu ( wucc1130@gmail.com )\n+ Structure from Motion\n[1] Changchang Wu, \"Towards Linear-time Incremental Structure From Motion\", 3DV 2013\n[2] Changchang Wu, \"VisualSFM: A Visual Structure from Motion System\", http://ccwu.me/vsfm/, 2011\n+ Bundle Adjustment\n[3] Changchang Wu, Sameer Agarwal, Brian Curless, and Steven M. Seitz, \"Multicore Bundle Adjustment\", CVPR 2011 \n+ Feature Detection\n[4] Changchang Wu, \"SiftGPU: A GPU implementation of Scale Invaraint Feature Transform (SIFT)\", http://cs.unc.edu/~ccwu/siftgpu, 2007\n\nCOLMAP\nhttps://colmap.github.io\nAuthor: Johannes L. Schoenberger (jsch-at-demuc-dot-de)\n@inproceedings{schoenberger2016sfm,\n author={Sch\\\"{o}nberger, Johannes Lutz and Frahm, Jan-Michael},\n title={Structure-from-Motion Revisited},\n booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},\n year={2016},\n}\n\n@inproceedings{schoenberger2016mvs,\n author={Sch\\\"{o}nberger, Johannes Lutz and Zheng, Enliang and Pollefeys, Marc and Frahm, Jan-Michael},\n title={Pixelwise View Selection for Unstructured Multi-View Stereo},\n booktitle={European Conference on Computer Vision (ECCV)},\n year={2016},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDocker container was maintained by Wesley Paul Bonelli. it was deployed to Plant IT website by Wesley Paul Bonelli (\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eSingularity container overlay issues were solved by [Saravanaraj Ayyampalayam] (\u003ca href=\"https://github.com/raj76\"\u003ehttps://github.com/raj76\u003c/a\u003e) (\u003ca href=\"mailto:raj76@uga.edu\"\u003emailto:raj76@uga.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eSpecial thanks to Chris Cotter building the container recipe for testing and debugging.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-hidden=\"true\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTodo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGPU cuda version container\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eGNU Public License\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1646051177.0 + "updated_at": 1614652430.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v8", - "Singularity.v4", - "Singularity.v2", - "Singularity.v6", - "Singularity.v3", - "Singularity.va", - "Singularity.v5", - "Singularity.v1", - "Singularity.v9", - "Singularity.v7" + "external/oskar/singularity/Singularity.base-dep", + "external/oskar/singularity/Singularity.python3" ], - "full_name": "sternacht/tf_singu", + "full_name": "kernsuite-debian/everybeam", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-everybeam-library\" class=\"anchor\" aria-hidden=\"true\" href=\"#everybeam-library\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEveryBeam library\u003c/h1\u003e\n\u003cp\u003eThis package can be used to compute the beam response for a variety of\nradio telescopes, i.e.:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLOFAR\u003c/li\u003e\n\u003cli\u003eOSKAR\u003c/li\u003e\n\u003cli\u003eMWA\u003c/li\u003e\n\u003cli\u003eVLA\u003c/li\u003e\n\u003cli\u003eATCA\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis package also provides an abstract interface to a selection of beam responses for apperture arrays (LOFAR/OSKAR), and beamformed versions thereof. Currently implemented are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHamaker LOFAR model\u003c/li\u003e\n\u003cli\u003eOSKAR spherical wave model\u003c/li\u003e\n\u003cli\u003eOSKAR-dipole: work in progress\u003c/li\u003e\n\u003cli\u003eLOBEs: work in progress. A coefficient file is currently only available for a limited number of LOFAR stations. Selecting the LOBEs model defaults back to Hamaker, in case no coefficient file is available.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEveryBeam replaces the stand alone version of the LOFAR station response library (LOFARBeam).\u003c/p\u003e\n\u003cp\u003eEveryBeam is licensed under the terms of the GNU GPL3 license.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation-and-installation-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation-and-installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation and Installation Instructions\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.astron.nl/citt/EveryBeam/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e along with \u003ca href=\"https://www.astron.nl/citt/EveryBeam/build-instructions.html\" rel=\"nofollow\"\u003einstallation instructions\u003c/a\u003e can be found at the provided links.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-dp3\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-dp3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with DP3\u003c/h2\u003e\n\u003cp\u003eTo use Everybeam within \u003ca href=\"https://git.astron.nl/RD/DP3\" rel=\"nofollow\"\u003eDP3\u003c/a\u003e - the streaming visibility framework - DP3 needs to be compiled against EveryBeam. To do so, make sure DP3 can find EveryBeam by adding the EveryBeam install dir to the \u003ccode\u003eCMAKE_PREFIX_PATH\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA test measurement set is included in DP3 (\u003ccode\u003etNDP3-generic.in_MS.tgz\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eTo simulate visibilities with a certain element model, use \u003ccode\u003eDP3 DP3.parset\u003c/code\u003e with \u003ccode\u003eDP3.parset\u003c/code\u003e a parset file with the following contents:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emsin=tNDP3-generic.MS\nmsout=.\nsteps=[predict]\npredict.usebeammodel=True\npredict.elementmodel=oskardipole\npredict.sourcedb=tNDP3-generic.MS/sky # sourcedb file\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-wsclean\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-wsclean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with WSClean\u003c/h2\u003e\n\u003cp\u003eTo use EveryBeam with \u003ca href=\"https://gitlab.com/aroffringa/wsclean\" rel=\"nofollow\"\u003eWSClean\u003c/a\u003e (for A-term or primary beam corrections), WSClean needs to be compiled against EveryBeam. In order to do so, make sure WSClean can find EveryBeam by adding the EveryBeam install dir to the \u003ccode\u003eCMAKE_PREFIX_PATH\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 3, "topics": [], - "updated_at": 1560171965.0 + "updated_at": 1663586637.0 }, { "data_format": 2, - "description": "VNC Server in a Singularity container", + "description": null, "filenames": [ - "Singularity", - "Singularity.2.1.2" + "Singularity.td_base_ml" ], - "full_name": "nickjer/singularity-vncserver", + "full_name": "TurbulentDynamics/tdEnvSetup", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-vnc-server\" class=\"anchor\" href=\"#singularity-vnc-server\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity VNC Server\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/603\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://turbovnc.org/\" rel=\"nofollow\"\u003eTurboVNC\u003c/a\u003e with the inclusion of \u003ca href=\"https://github.com/novnc/websockify/\"\u003ewebsockify\u003c/a\u003e for\nconnecting to the VNC server from within your browser using \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build\" class=\"anchor\" href=\"#build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-vncserver.simg\u003c/code\u003e\nwith:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-vncserver.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-deploy\" class=\"anchor\" href=\"#deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploy\u003c/h2\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name singularity-vncserver.simg shub://nickjer/singularity-vncserver\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run\" class=\"anchor\" href=\"#run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-vncserver\" class=\"anchor\" href=\"#vncserver\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evncserver\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003evncserver\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-vncserver.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app vncserver singularity-vncserver.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app vncserver singularity-vncserver.simg\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eYou will require a password to access your desktops.\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003ePassword:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eVerify:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eWould you like to enter a view-only password (y/n)? n\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eDesktop \u0027TurboVNC: dev:1 (nickjer)\u0027 started on display dev:1\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eCreating default startup script /home/nickjer/.vnc/xstartup.turbovnc\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eStarting applications specified in /home/nickjer/.vnc/xstartup.turbovnc\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eLog file is /home/nickjer/.vnc/dev:1.log\u003c/span\u003e\n\n$ \u003cspan class=\"pl-s1\"\u003esingularity run --app vncserver singularity-vncserver.simg -kill :1\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eKilling Xvnc process ID 9738\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-vncpasswd\" class=\"anchor\" href=\"#vncpasswd\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evncpasswd\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003evncpasswd\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app vncpasswd singularity-vncserver.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emypassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e singularity run --app vncpasswd singularity-vncserver.simg -f \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e vnc_passwd\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eWarning: password truncated to the length of 8.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-websockify\" class=\"anchor\" href=\"#websockify\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewebsockify\u003c/h3\u003e\n\u003cp\u003eIn some cases you may not want to download and install a VNC client on your\nlocal machine. In those cases you can actually use the \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e client which\nruns completely in your browser.\u003c/p\u003e\n\u003cp\u003eIn order to connect to the VNC server with \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e you will need to enable\n\u003ca href=\"https://github.com/novnc/websockify/\"\u003ewebsockify\u003c/a\u003e which will translate the incoming websocket traffic from \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e\nto normal TCP traffic proxied to the listening VNC server.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ewebsockify\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app websockify singularity-vncserver.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAssuming you started a \u003ccode\u003evncserver\u003c/code\u003e above listening on port \u003ccode\u003e5901\u003c/code\u003e (display port\n\u003ccode\u003e:1\u003c/code\u003e), you will launch \u003ccode\u003ewebsockify\u003c/code\u003e on the same machine with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app websockify singularity-vncserver.simg 8000 localhost:5901\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eWebSocket server settings:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e - Listen on :8000\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e - No SSL/TLS support (no cert file)\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e - proxying from :8000 to localhost:5901\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen from your browser using the \u003ca href=\"https://kanaka.github.io/noVNC/\" rel=\"nofollow\"\u003enoVNC\u003c/a\u003e client, connect to the machine running\nthe VNC server and port \u003ccode\u003e8000\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIt is recommended you either setup SSL for a secure connection or host it\nfrom behind a reverse proxy with SSL already enabled.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-vncserver\"\u003ehttps://github.com/nickjer/singularity-vncserver\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-turbulent-dynamics\" class=\"anchor\" aria-hidden=\"true\" href=\"#turbulent-dynamics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTurbulent Dynamics\u003c/h1\u003e\n\u003cp\u003eTurbulent Dynamics developes Maching Learning, MPI and iOS applications for both High Performance Computing (Supercomputing), edge devices (Xavier, Raspberry PI, MyriadX) and MacOS. Minimising system admin workload is not trivial so this guide was created to try setup a common dominator for all projects.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#Environment-setup\"\u003eEnvironment setup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Simple-Cluster-Diagnostics\"\u003eSimple Cluster Diagnostics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Coding-Guidelines-and-Visualisations\"\u003eCoding Guidelines and Visualisations\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-environment-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment setup\u003c/h1\u003e\n\u003cp\u003eTurbulent Dynamics developes Maching Learning, MPI and iOS applications for both High Performance Computing (Supercomputing) edge devices (Xavier, Raspberry PI, MyriadX) and MacOS. Minimising system admin workload is not trivial, as different devices require a different stack, especially edge devices, and sometimes sudo is not available (on HPC systems). This drives out environment and app choices.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eAvoid sudo installs by using Brew for basic tools.\u003c/li\u003e\n\u003cli\u003eAvoid sudo and allow multiple versions of apps using Spack (also compiles all dependencies giving performance advantages).\u003c/li\u003e\n\u003cli\u003eUse containers where possible (Edge devices struggle or are unable).\u003c/li\u003e\n\u003cli\u003eUse Python Venv, for ML Tensorflow and tools.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDevice\u003c/th\u003e\n\u003cth\u003eUse Case\u003c/th\u003e\n\u003cth\u003eNotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHPC System\u003c/td\u003e\n\u003ctd\u003eTraining ML and Large Scale MPI apps 100s nodes\u003c/td\u003e\n\u003ctd\u003eSudo not available\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWorkstations (with AMD GPU)\u003c/td\u003e\n\u003ctd\u003eTraining ML\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWorkstations (with Nvidia GPU)\u003c/td\u003e\n\u003ctd\u003eTraining ML, rebuilding Xavier/Nano and MPI app testing\u003c/td\u003e\n\u003ctd\u003eNvidia SDK limits to Ubuntu 18.04\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMacOS (AMD GPU)\u003c/td\u003e\n\u003ctd\u003eVisualisations in Metal and iOS apps\u003c/td\u003e\n\u003ctd\u003eDevelop in Swift\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA Xavier/Nano\u003c/td\u003e\n\u003ctd\u003eML Inferencing\u003c/td\u003e\n\u003ctd\u003eLimited to Cuda 10.0, Tensorflow 1.14\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMyriadX (Intel Compute Stick)\u003c/td\u003e\n\u003ctd\u003eML Inferencing\u003c/td\u003e\n\u003ctd\u003eOpenVINO limits to Ubuntu 16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRaspberry Pi\u003c/td\u003e\n\u003ctd\u003eML Inferencing\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_0_basics_and_brew.md\"\u003eInstall basics and brew on both MacOS and Linux\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_1_with_spack.md\"\u003eInstall spack and some applications\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_2_python_modules.md\"\u003eInstall python modules\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_3_OpenVINO_on_Ubuntu_16_04.md\"\u003eInstall OpenVINO on Ubuntu 16.04\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_3_nvidia_for_Ubuntu_18_04.md\"\u003eInstall Nvidia CUDA and tools on Ubuntu 18.04\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_4_nvidia_docker2_base_ml_container.md\"\u003eInstall docker, nvidia-docker2 and run TD_Base_Nvidia_ML container\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_5_singularity.md.md\"\u003eInstall singularity and run TD_Base_Nvidia_ML container\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/install_6_optional_apps.md\"\u003eOptional Apps\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/spack_swift_package.py\"\u003e(WIP) Use Spack to install Swift\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"env_setup/swift_for_ubuntu.md\"\u003e(WIP) Install Swift on Ubuntu\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-simple-cluster-diagnostics\" class=\"anchor\" aria-hidden=\"true\" href=\"#simple-cluster-diagnostics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimple Cluster Diagnostics\u003c/h1\u003e\n\u003cp\u003eSimple utility to check if OpenMP, MPI and cuda are working as expected.\n\u003ca href=\"diagnostics_hello_world_mpi_openmp_gpu/README.md\"\u003eDiagnostics OpenMP, MPI, GPU\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-coding-guidelines-and-visualisations\" class=\"anchor\" aria-hidden=\"true\" href=\"#coding-guidelines-and-visualisations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCoding Guidelines and Visualisations\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"dev_info/index.md\"\u003eCoding guidelines\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://turbulentdynamics.github.io/tdEnvSetup/graphics/arrows.html\" rel=\"nofollow\"\u003eVector Identifiers\u003c/a\u003e The vectors are numbered differently than usual LBM implementations\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://turbulentdynamics.github.io/tdEnvSetup/graphics/cube.html\" rel=\"nofollow\"\u003eItem Identifiers\u003c/a\u003e The cells in the outer shell of the lattice grid has been given an identification\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://turbulentdynamics.github.io/tdEnvSetup/graphics/1000.html\" rel=\"nofollow\"\u003eVisualisation 1000 cubes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1581617600.0 + "updated_at": 1635665969.0 }, { "data_format": 2, - "description": "Recipe files used to compile SLURM (https://github.com/SchedMD/slurm) in powerPlant", + "description": "Learning temporal planning models", "filenames": [ - "21.08.8-2/centos7/singularity/Singularity.21.08.8-2-make", - "21.08.8-2/centos7/singularity/Singularity.21.08.8-2-rpm" + "planners/team1/src/Singularity" ], - "full_name": "powerPlant/slurm-build", + "full_name": "sjimenezgithub/tmodeling", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tmodeling\" class=\"anchor\" aria-hidden=\"true\" href=\"#tmodeling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etmodeling\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 0, "topics": [], - "updated_at": 1652918976.0 + "updated_at": 1574164945.0 }, { "data_format": 2, - "description": "singularity container", + "description": "Centos 8 base image for Roar", "filenames": [ - "Singularity.salad", "Singularity", - "Singularity.pokemon" + "Singularity.gpu" ], - "full_name": "dcasciotti/alexrequest", + "full_name": "willgpaik/centos8_roar", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" href=\"#singularity-deploy\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Deploy\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"img/shpc.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" href=\"#1-template-or-fork\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Template or Fork\u003c/h3\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" href=\"#1-write-your-singularity-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" href=\"#2-update-the-version-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Update the VERSION file\u003c/h3\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/releases.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" href=\"#3-how-to-develop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How to Develop\u003c/h3\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" href=\"#4-how-to-pull\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. How to pull\u003c/h3\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos8_roar\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos8_roar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos8_roar\u003c/h1\u003e\n\u003cp\u003e\u003cdel\u003eCentos\u003c/del\u003e Rocky Linux 8 base image for Roar\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-note\" class=\"anchor\" aria-hidden=\"true\" href=\"#note\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTE\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eThis recipe may include unnecessary packages for certain software installation\u003c/li\u003e\n\u003cli\u003eMore packages will be added in the future\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-updates\" class=\"anchor\" aria-hidden=\"true\" href=\"#updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUpdates\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e2020/11/13\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInitial recipe added\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e2021/03/22\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDefault Python3 is updated to Python 3.8\u003c/li\u003e\n\u003cli\u003eLapack, BLAS, OpenBLAS, ATLAS, and NetCDF are added\u003c/li\u003e\n\u003cli\u003eCMake 3.19.7, Boost 1.75.0, and R 4.0.4 are added\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e2022/10/31\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eImage changed from Centos 8 to Rocky Linux 8\u003c/li\u003e\n\u003cli\u003eDefault Python3 is updated to Python 3.9\u003c/li\u003e\n\u003cli\u003eCMake and R are removed due to later version can be installed from package repo\u003c/li\u003e\n\u003cli\u003eBoost is updated to 1.80.0\u003c/li\u003e\n\u003cli\u003e(Changes are applied to non-GPU version only)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1652978087.0 - }, - { - "data_format": 2, - "description": "Bio-Formats image file format to raw format converter.", - "filenames": [ - "0.3.0/Singularity" - ], - "full_name": "pscedu/singularity-bioformats2raw", - "latest_release": "v3.0.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-bioformats2raw/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bioformats2raw/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-bioformats2raw/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bioformats2raw/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/393a92fbeeb7f1545c3869f957dc859760052e0eff8eeb8f95b409800de2d2b2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/393a92fbeeb7f1545c3869f957dc859760052e0eff8eeb8f95b409800de2d2b2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bioformats2raw\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/444f4d60540e76ad5618bb8884c8f9d5a6a1f61fcf2a94363f379f6c6b074c5c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/444f4d60540e76ad5618bb8884c8f9d5a6a1f61fcf2a94363f379f6c6b074c5c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bioformats2raw\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/91fa772cacd77c7d5f540224712927de4888ac6dc960cd56dc1d23edeac9a1b9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/91fa772cacd77c7d5f540224712927de4888ac6dc960cd56dc1d23edeac9a1b9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bioformats2raw\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f6dea7ba6ee602447c05fc1a8d8498d7d44f22ad6f72fb6ad78b424301cd6d14/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f6dea7ba6ee602447c05fc1a8d8498d7d44f22ad6f72fb6ad78b424301cd6d14/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62696f666f726d61747332726177\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bioformats2raw\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-bioformats2raw\" class=\"anchor\" href=\"#singularity-bioformats2raw\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bioformats2raw\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/d6ac5fb6156e1b197fd547d7632a7b90cb6aac15112aff196834a376c3065baf/68747470733a2f2f7777772e676c656e636f65736f6674776172652e636f6d2f696d672f6c6f676f2e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d6ac5fb6156e1b197fd547d7632a7b90cb6aac15112aff196834a376c3065baf/68747470733a2f2f7777772e676c656e636f65736f6674776172652e636f6d2f696d672f6c6f676f2e737667\" width=\"25%\" data-canonical-src=\"https://www.glencoesoftware.com/img/logo.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/glencoesoftware/bioformats2raw\"\u003ebioformats2raw\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebioformats2raw\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bioformats2raw/3.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bioformats2raw\u003c/code\u003e as \u003ccode\u003e3.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "utilities", - "image-processing" - ], - "updated_at": 1649185211.0 + "updated_at": 1667245535.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.torch_mmf", + "Singularity.torch" ], - "full_name": "khourhin/uber_container", + "full_name": "ChunCun/container", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1653684576.0 - }, - { - "data_format": 2, - "description": "BLAST finds regions of similarity between biological sequences.", - "filenames": [ - "2.13.0/Singularity", - "2.11.0/Singularity", - "2.9.0/Singularity" - ], - "full_name": "pscedu/singularity-blast", - "latest_release": "v2.13.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-blast/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-blast/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/34775a544d028af1a76f53887556e0b47d8a40289aa78e79056c5e13dcbc48e4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626c617374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34775a544d028af1a76f53887556e0b47d8a40289aa78e79056c5e13dcbc48e4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626c617374\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-blast\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/e33eb8d62dc7df0e7a911833138fefaed18e36044c13f7a3aa53c104c1ffc719/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626c617374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e33eb8d62dc7df0e7a911833138fefaed18e36044c13f7a3aa53c104c1ffc719/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626c617374\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-blast\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c141231c49671d4a45e13d6d79f61b30ec62be1462b950fac124cfb21cc2d206/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626c617374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c141231c49671d4a45e13d6d79f61b30ec62be1462b950fac124cfb21cc2d206/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626c617374\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-blast\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0ec9871992e72eb86a829ac86878026892749bddbb4623030eb745093184def6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626c617374\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0ec9871992e72eb86a829ac86878026892749bddbb4623030eb745093184def6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626c617374\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-blast\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-blast\" class=\"anchor\" href=\"#singularity-blast\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-blast\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the other scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/blast/2.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/blast\u003c/code\u003e as \u003ccode\u003e2.11.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "bioinformatics", - "singularity" - ], - "updated_at": 1636731786.0 + "updated_at": 1605677713.0 }, { "data_format": 2, - "description": "A command-line benchmarking tool.", + "description": null, "filenames": [ - "1.13.0/Singularity", - "1.11.0/Singularity" + "hpc_files/singularity_hpc_files/Singularity.bld" ], - "full_name": "pscedu/singularity-hyperfine", - "latest_release": "v1.11.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-hyperfine/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa6ccae89f711313efdead7c3be0b796360740e33514a985a0f4968fa01cb0f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3251ba672b3bf023faefb928be7900afe28d7d2ebe6ae3a775c7e820687c6571/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c452c9170d570d496414a1d624b5d3731e36ca355843b447512b991c91ee546/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a734b24e30b49dc758633fa5394475d80c0cfe02dc89ed582ec651d630a3f19c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d687970657266696e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-hyperfine\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-hyperfine\" class=\"anchor\" href=\"#singularity-hyperfine\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-hyperfine\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88a0cb35f42e02e28b0433d4b5e0029e52e723d8feb8df753e1ed06a5161db56/68747470733a2f2f692e696d6775722e636f6d2f7a31394f5978452e676966\" alt=\"Example\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/hyperfine\"\u003ehyperfine\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehyperfine\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hyperfine/1.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hyperfine\u003c/code\u003e as \u003ccode\u003e1.11.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "ammunk/distributed-training-pytorch", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-demo-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#demo-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo scripts\u003c/h1\u003e\n\u003cp\u003eThis repository contains demo scripts for running distributed training of deep\nneural networks using PyTorch. These scripts are written according to the\ninformation found at (\u003ca href=\"https://github.com/ammunk/hpc\"\u003ehttps://github.com/ammunk/hpc\u003c/a\u003e)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1649205323.0 - }, - { - "data_format": 2, - "description": "Raw format to OME-TIFF converter.", - "filenames": [ - "3.0.0/Singularity" - ], - "full_name": "pscedu/singularity-raw2ometiff", - "latest_release": "v3.0.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-raw2ometiff/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-raw2ometiff/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-raw2ometiff/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-raw2ometiff/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/8d0d22e8f29e65a71263e2fb6bce4826c7ce3f09aed55e6dbde2928785d212f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8d0d22e8f29e65a71263e2fb6bce4826c7ce3f09aed55e6dbde2928785d212f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-raw2ometiff\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7822cb72c205ba7e407c8e3c73f1753c3051e55c850a256628b591b7640cb064/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7822cb72c205ba7e407c8e3c73f1753c3051e55c850a256628b591b7640cb064/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-raw2ometiff\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/ee493a8b01574ccdadc2be17adf48265c4d4d5b8d4dd7528eebab32317323e41/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ee493a8b01574ccdadc2be17adf48265c4d4d5b8d4dd7528eebab32317323e41/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-raw2ometiff\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/48898dd432d34088bdf217be2af09312648ecdf47ab776fb4de050c5338365bd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/48898dd432d34088bdf217be2af09312648ecdf47ab776fb4de050c5338365bd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726177326f6d6574696666\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-raw2ometiff\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-raw2ometiff\" class=\"anchor\" href=\"#singularity-raw2ometiff\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-raw2ometiff\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/d6ac5fb6156e1b197fd547d7632a7b90cb6aac15112aff196834a376c3065baf/68747470733a2f2f7777772e676c656e636f65736f6674776172652e636f6d2f696d672f6c6f676f2e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d6ac5fb6156e1b197fd547d7632a7b90cb6aac15112aff196834a376c3065baf/68747470733a2f2f7777772e676c656e636f65736f6674776172652e636f6d2f696d672f6c6f676f2e737667\" width=\"25%\" data-canonical-src=\"https://www.glencoesoftware.com/img/logo.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/glencoesoftware/raw2ometiff\"\u003eraw2ometiff\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eraw2ometiff\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/raw2ometiff/3.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/raw2ometiff\u003c/code\u003e as \u003ccode\u003e3.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "utilities", - "image-processing" - ], - "updated_at": 1633063422.0 + "topics": [], + "updated_at": 1646720319.0 }, { "data_format": 2, - "description": "Deplete Fastq files from human or other content", + "description": "This repo is intended for the tutorial in the Planning Meeting held on the 24th of October, 2018", "filenames": [ - "singularity/Singularity" + "demoPlanner/Singularity", + "runPlanningTool/planners/OPTIC-Base/Singularity", + "runPlanningTool/planners/team40/Singularity" ], - "full_name": "sequana/depletion", + "full_name": "ionut94/KCL-PlanningTutorial", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-kcl-planningtutorial\" class=\"anchor\" aria-hidden=\"true\" href=\"#kcl-planningtutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKCL-PlanningTutorial\u003c/h1\u003e\n\u003cp\u003eThis repo is intended for the tutorial in the Planning Meeting held on the 24th of October, 2018\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dev-repo-for-runplanningtool-is-here\" class=\"anchor\" aria-hidden=\"true\" href=\"#dev-repo-for-runplanningtool-is-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDev repo for runPlanningTool is \u003ca href=\"https://github.com/momartinm/runPlanningTool.git\"\u003ehere\u003c/a\u003e\n\u003c/h2\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1648819859.0 + "updated_at": 1540504981.0 }, { "data_format": 2, - "description": "This is the repository for the workshop taught at ISPW 2022 in Sydney", + "description": "ngs pipelines _ nextflow/singularity workflows", "filenames": [ - "files/daskdev/Singularity.dask" + "scATAC_cellranger/container_singularity/Singularity" ], - "full_name": "ardimirzaei/ispw2022-abm-workshop", + "full_name": "perllb/ngs_pipelines", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ispw-2022-abm-workshop\" class=\"anchor\" href=\"#ispw-2022-abm-workshop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eISPW 2022 ABm Workshop\u003c/h1\u003e\n\u003cp\u003eForked from SIH\n--Update this readme.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "abm", - "complex-systems", - "pharmacy", - "workshop" - ], - "updated_at": 1654581868.0 - }, - { - "data_format": 2, - "description": "Work with Python installed at a custom location", - "filenames": [ - "Singularity" - ], - "full_name": "richelbilderbeek/ormr", - "latest_release": "v0.6.2.1", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ormr\" class=\"anchor\" href=\"#ormr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eormr\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=master\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/ormr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80f61013497de9c4ba38bd7d37d57f2baf9ad486b3e667b76823a2fa7acb1783/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6f726d722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/ormr/coverage.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=master\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=master\" alt=\"build_singularity\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=develop\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/ormr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e40a61ddb8d3cee1a4e177f20956ab6b1887a9d5a422c8e9f9024859f4c23af/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6f726d722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/ormr/coverage.svg?branch=develop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=develop\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=develop\" alt=\"build_singularity\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"man/figures/ormr_logo_50.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"man/figures/ormr_logo_50.png\" alt=\"ormr logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWork with Python installed at a custom location.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-goal\" class=\"anchor\" href=\"#goal\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows a user to install Python packages,\ncreate a Conda environment and run Python scripts\nwith only one point of contact.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e heavily depends on \u003ccode\u003ereticulate\u003c/code\u003e, the latter being\nmore powerful and flexible. \u003ccode\u003eormr\u003c/code\u003e, however, focuses\non making it trivially simple to install Python\npackages and run Python scripts.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-install-ormr\" class=\"anchor\" href=\"#install-ormr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall \u003ccode\u003eormr\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003eAs \u003ccode\u003eormr\u003c/code\u003e is developed on the \u003ccode\u003emaster\u003c/code\u003e branch, only a release\nis tested to work:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eremotes::install_github(\"richelbilderbeek/ormr\", ref = \"v0.6.1\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee FAQ why one needs to install a release.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-examples\" class=\"anchor\" href=\"#examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e uses one point of contact, \u003ccode\u003eormr_folder_name\u003c/code\u003e.\nFor convenience, there is also a default \u003ccode\u003eormr_folder_name\u003c/code\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall a Python package\u003c/li\u003e\n\u003cli\u003eRun a Python script\u003c/li\u003e\n\u003cli\u003eRun a Python script with command-line arguments\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAlso, \u003ccode\u003eormr\u003c/code\u003e uses \u003cstrong\u003eeager loading\u003c/strong\u003e, which means that\nit will setup everything it needs for you. For example,\nif you want to run a Python script from a new \u003ccode\u003eormr_folder_name\u003c/code\u003e,\nit will create a Conda environment there for you as well.\u003c/p\u003e\n\u003cp\u003eNote that \u003ccode\u003ecreate_default_conda_env\u003c/code\u003e conveniently returns the\n\u003ccode\u003eormr_folder_name\u003c/code\u003e used to work with this environment.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-1-install-a-python-package\" class=\"anchor\" href=\"#1-install-a-python-package\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install a Python package\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall_python_package(\n \u003cspan class=\"pl-v\"\u003epackage_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003escipy\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\ninstall_python_package(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epackage_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003escipy\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-2-run-a-python-script\" class=\"anchor\" href=\"#2-run-a-python-script\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Run a Python script\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehello_world.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script(\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehello_world.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-3-run-a-python-script-with-command-line-arguments\" class=\"anchor\" href=\"#3-run-a-python-script-with-command-line-arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Run a Python script with command-line arguments\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshow_args.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script_with_args(\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworld\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshow_args.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script_with_args(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworld\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-faq\" class=\"anchor\" href=\"#faq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-the-goal-of-ormr\" class=\"anchor\" href=\"#what-is-the-goal-of-ormr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is the goal of \u003ccode\u003eormr\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows a user to install Python packages,\ncreate a Conda environment and run Python scripts\nwith only one point of contact.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-in-what-context-is-ormr-useful\" class=\"anchor\" href=\"#in-what-context-is-ormr-useful\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIn what context is \u003ccode\u003eormr\u003c/code\u003e useful?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e was written to write simpler\n\u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (a type of containerization\nsoftware, similar to Docker) scripts.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ereticulate\u003c/code\u003e is great when using its default folders on a local computer.\nHowever, for a Singularity container, it is recommended to install\nlibraries in a systems folder. In that setting, \u003ccode\u003ereticulate\u003c/code\u003e is\nharder to work with.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows to install install Python packages,\ncreate a Conda environment and run Python scripts\nin any folder easily, for example,\nin a system folder (\u003ccode\u003e/opt/ormr\u003c/code\u003e) of a Singularity container.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-not-just-use-reticulate\" class=\"anchor\" href=\"#why-not-just-use-reticulate\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy not just use \u003ccode\u003ereticulate\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e heavily depends on \u003ccode\u003ereticulate\u003c/code\u003e, the latter being\nmore powerful and flexible.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e, however, focuses\non making it trivially simple to install Python\npackages and run Python scripts,\ndue to eager loading.\nAdditionally, \u003ccode\u003eormr\u003c/code\u003e has a more extensive documentation,\nand 100% code coverage.\u003c/p\u003e\n\u003cp\u003eBeyond the domain of \u003ccode\u003eormr\u003c/code\u003e, use \u003ccode\u003ereticulate\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-do-you-mean-with-eager-loading\" class=\"anchor\" href=\"#what-do-you-mean-with-eager-loading\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat do you mean with eager loading?\u003c/h2\u003e\n\u003cp\u003eEager loading is the opposite of lazy loading.\u003c/p\u003e\n\u003cp\u003eHere, it is defined as \u0027if you want \u003ccode\u003eormr\u003c/code\u003e to do B, which depends on\nthe setup of A\u0027, \u003ccode\u003eormr\u003c/code\u003e will setup A, then do B. For example, to install\na package to a certain \u003ccode\u003eormr_folder_name\u003c/code\u003e (\u0027to do B\u0027), \u003ccode\u003eormr\u003c/code\u003e\nwill create a Conda environment for that (\u0027the setup of A\u0027).\u003c/p\u003e\n\u003cp\u003eThis means that no setup code is necessary.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-does-one-need-to-install-a-release-instead-of-just-master\" class=\"anchor\" href=\"#why-does-one-need-to-install-a-release-instead-of-just-master\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy does one need to install a release, instead of just \u003ccode\u003emaster\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003eThe development of \u003ccode\u003eormr\u003c/code\u003e takes place on the \u003ccode\u003emaster\u003c/code\u003e branch.\nHence, \u003ccode\u003emaster\u003c/code\u003e will break regularily.\nA specific release is tested to build correctly.\u003c/p\u003e\n\u003cp\u003eThe reason for this non-traditional workflow, is that the\nSingularity script always installs the \u003ccode\u003emaster\u003c/code\u003e branch,\nas it cannot detect the \u003ccode\u003egit\u003c/code\u003e branch is being built by.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" href=\"#there-is-a-feature-i-miss\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThere is a feature I miss\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" href=\"#i-want-to-collaborate\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI want to collaborate\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting code\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" href=\"#i-think-i-have-found-a-bug\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI think I have found a bug\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting bugs\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" href=\"#theres-something-else-i-want-to-say\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThere\u0027s something else I want to say\u003c/h2\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-do-i-contribute\" class=\"anchor\" href=\"#how-do-i-contribute\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do I contribute?\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-is-the-package-called-ormr\" class=\"anchor\" href=\"#why-is-the-package-called-ormr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy is the package called \u003ccode\u003eormr\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003eThis name is a pun on \u003ccode\u003ereticulate\u003c/code\u003e. \u003ccode\u003ereticulate\u003c/code\u003e is named after a\ntype of snake. \u003ccode\u003eormr\u003c/code\u003e is written in Sweden. In Swedish, \u003ccode\u003eorm\u003c/code\u003e, is a snake.\nFollowing the common tradtion of adding an \u003ccode\u003er\u003c/code\u003e to the end of an R package\nname (e.g \u003ccode\u003edplyr\u003c/code\u003e, \u003ccode\u003etidyr\u003c/code\u003e, etc) resulted in \u003ccode\u003eormr\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-about-the-logo\" class=\"anchor\" href=\"#what-about-the-logo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat about the logo?\u003c/h2\u003e\n\u003cp\u003eThe original snake image was found when searching for a\npublic domain image of a snake, using the following DuckDuckGo image seach:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttps://duckduckgo.com/?q=orm+.png\u0026amp;t=ffab\u0026amp;iar=images\u0026amp;iaf=license%3APublic%2Ctype%3Aclipart\u0026amp;iax=images\u0026amp;ia=images\u0026amp;iai=https%3A%2F%2Fcdn.pixabay.com%2Fphoto%2F2016%2F03%2F31%2F15%2F10%2Fcartoon-1293047_1280.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter that, the image was modified using KolourPaint and the R logo was added.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/search;query=ormr\" rel=\"nofollow\"\u003eFind the latest \u0027ormr\u0027 Singularity container\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-links\" class=\"anchor\" href=\"#links\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinks\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/reticulate_on_singularity\"\u003ehttps://github.com/richelbilderbeek/reticulate_on_singularity\u003c/a\u003e:\ndemo how to run \u003ccode\u003ereticulate\u003c/code\u003e within a Singularity container, without \u003ccode\u003eormr\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1639766183.0 + "updated_at": 1668091545.0 }, { "data_format": 2, - "description": "R package for nsphs_ml_qt", + "description": null, "filenames": [ - "Singularity", - "scripts_bianca/Singularity" + "deepcell-tf/Singularity.0.1", + "DS5559/Singularity-0.1", + "tensorflow/Singularity.2.0.0-py36", + "tensorflow/Singularity.1.12.0-py36", + "tensorflow/Singularity.1.6.0-py36", + "tensorflow/Singularity.1.13.1-py36", + "tensorflow/Singularity.1.12.0-py27", + "tensorflow/Singularity.1.12.3-py36", + "tensorflow/Singularity.2.1.0-py37-rs8wa", + "tensorflow/Singularity.1.6.0-py27", + "tensorflow/Singularity.2.1.0-py37", + "tensorflow/Singularity.1.14.0-py36", + "patric/Singularity.1.026", + "rhessys/Singularity.1", + "rhessys/Singularity.3.3", + "rhessys/Singularity.3", + "rhessys/Singularity.2", + "kaggle/Singularity-0.0", + "kaggle/Singularity-0.1", + "pytorch/Singularity.1.3.1-py36", + "pytorch/Singularity.1.0.0-py36", + "pytorch/Singularity.1.4.0-py37", + "cryoCARE/Singularity.0.1.0", + "danpos/Singularity.2.2.2", + "cp-analyst/Singularity.2.2.1", + "maxquant/Singularity.1.6.7.0", + "caffe2/Singularity.0.8.0", + "supernova/Singularity.2.0.0", + "anaconda/Singularity.2019.10-cuda10.0-cudnn7.6-py3.6", + "anaconda/Singularity.2019.10-cuda10.0-cudnn7.6-py3.7", + "anaconda/Singularity.2019.10-cuda9.0-cudnn7.6-py2.7", + "anaconda/Singularity.2019.10-cuda9.0-cudnn7.6-py3.6", + "anaconda/Singularity.cuda10.0-cudnn7.4-py3.6", + "anaconda/Singularity.cuda9.0-cudnn7.4-py3.6", + "lolcow/Singularity.1.0.0", + "theano/Singularity.1.0.4-py36", + "hydrator/Singularity.0.0.2", + "hydrator/Singularity.0.0.10", + "cellprofiler/Singularity.2.2.0", + "cellprofiler/Singularity.3.0.0", + "cellprofiler/Singularity.3.1.8", + "cellprofiler/Singularity.3.1.9", + "p4vasp/Singularity.0.3.30", + "anvio/Singularity.6.2-alpine", + "anvio/Singularity.6.2", + "inkscape/Singularity.0.92.3", + "sumo/Singularity.1.3.1", + "omero-client/Singularity.5.6.1", + "omero-client/Singularity.5.4.10", + "rstudio_server/Singularity.1.1.463", + "rstudio_server/Singularity.1.0.143", + "vg/Singularity.1.22.0", + "vg/Singularity.1.23.0", + "R/Singularity.3.6.0", + "R/Singularity-3.6.0", + "electron/Singularity" ], - "full_name": "richelbilderbeek/nsphs_ml_qt", - "latest_release": "v0.3", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nsphs_ml_qt\" class=\"anchor\" href=\"#nsphs_ml_qt\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ensphs_ml_qt\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=master\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/be413dbe544678e8710f09ecb2ee97d7a26b0a853e40a539069148252157700f/68747470733a2f2f636f6465636f762e696f2f67682f72696368656c62696c6465726265656b2f6e737068735f6d6c5f71742f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=develop\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/nsphs_ml_qt/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt?branch=develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d51e07e8b43e2d93b35336c3c0437fdb29a65ac9e5d54406d980daf81cd2567f/68747470733a2f2f636f6465636f762e696f2f67682f72696368656c62696c6465726265656b2f6e737068735f6d6c5f71742f6272616e63682f646576656c6f702f67726170682f62616467652e737667\" alt=\"Codecov test coverage\" data-canonical-src=\"https://codecov.io/gh/richelbilderbeek/nsphs_ml_qt/branch/develop/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll code for \u003cg-emoji class=\"g-emoji\" alias=\"lock\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f512.png\"\u003e\ud83d\udd12\u003c/g-emoji\u003e \u003ca href=\"https://github.com/AJResearchGroup/article_nsphs_ml_qt\"\u003earticle_nsphs_ml_qt\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_bianca/README.md\"\u003eWorkflow on Bianca\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_rackham/README.md\"\u003eWorkflow on Rackham\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"scripts_local/README.md\"\u003eWorkflow on local computer\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eResults: \u003ca href=\"https://github.com/richelbilderbeek/nsphs_ml_qt_results\"\u003esee the \u0027nsphs_ml_qt_results\u0027 repository\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"man/figures/example_architecture.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"man/figures/example_architecture.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"man/figures/example_dimred.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"man/figures/example_dimred.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"man/figures/legend_HO_tiny.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"man/figures/legend_HO_tiny.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "uvarc/singularity-scripts", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-scripts\u003c/h1\u003e\n\u003cp\u003eCollection of Singularity container recipe files.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1655910726.0 + "updated_at": 1614097316.0 }, { "data_format": 2, - "description": "Ancestry ", + "description": "A container for PyMultinest", "filenames": [ "Singularity" ], - "full_name": "jahaltom/RIA", + "full_name": "sysmso/singularity-multinest", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-rna-seq-inferred-ancestry-ria\" class=\"anchor\" href=\"#rna-seq-inferred-ancestry-ria\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRNA-Seq Inferred Ancestry (RIA)\u003c/h1\u003e\n\u003cp\u003eRIA is a method for infering super-population (Africa, Europe, South Asia, East Asia, and America) identity from Human RNA-seq data.\nRIA leverages data from 1000 genomes project and utilizes a machine learning approach that involves principal component analysis and support vector machine.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/jahaltom/RNA-Seq-Ancestry-Inference/blob/main/FlowChart.png?raw=true\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/jahaltom/RNA-Seq-Ancestry-Inference/raw/main/FlowChart.png?raw=true\" alt=\"alt text\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eUsing Conda 4.10.3, create the conda enviroment and activate:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate Ancestry\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor you can use the Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull ria.sif library://aseetharam/ancestry/ria:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou can access the tools inside the container by prefixing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\nsingularity exec --bind $PWD ria.sif snakemake \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-data-preparation\" class=\"anchor\" href=\"#data-preparation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Preparation\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e1000 Genomes Project:\u003c/strong\u003e\nThe snakemake script \"Prepare_1KGP\" downloads chr(1-22) level VCF files from 1000 Genomes Project phase 3 on GRCh38 (\u003ca href=\"https://www.internationalgenome.org/data-portal/data-collection/grch38\" rel=\"nofollow\"\u003ehttps://www.internationalgenome.org/data-portal/data-collection/grch38\u003c/a\u003e, \u003ca href=\"https://doi.org/10.12688/wellcomeopenres.15126.2\" rel=\"nofollow\"\u003ehttps://doi.org/10.12688/wellcomeopenres.15126.2\u003c/a\u003e) while filtering out indels. It also indexes and creates a BED for each filtered VCF file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j 22 -s Prepare_1KGP --cluster \"sbatch -t 01:00:00 -c 4 -N 1\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eGRCh38 Reference Genome\u003c/strong\u003e\nThe bash script \"Prepare_Reference_Genome\" will download the Human genome GRCh38 fasta(GCA_000001405.15_GRCh38_no_alt_plus_hs38d1_analysis_set.fna.gz) and the corresponding gtf, and will create a seqence dictionary and index file for the fasta. It also creates a STAR index.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch Prepare_Reference_Genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-raw-data-retrieval-from-sra-qc-and-star-2-pass\" class=\"anchor\" href=\"#raw-data-retrieval-from-sra-qc-and-star-2-pass\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRaw data retrieval from SRA, QC, and STAR 2-Pass\u003c/h2\u003e\n\u003cp\u003eThe snakemake script \"STAR_SRA\" takes in a list of run accession IDs \"RAids.txt\" and fetches the raw fastq files from SRA and then uses Trimgalore for QC. The reads are then ran through STAR 2-Pass mode for enhanced novel SJ detection. The SJ.out.tab file for the 2nd pass is made by combining all SJ.out.tab files from the first pass and removing SJ\u0027s that are supported by 2 or less unique mappers.\u003c/p\u003e\n\u003cp\u003eFor just 1 study, create a list of the corresponding run accession IDs \"RAids.txt\" and run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j 50 -k -s STAR_SRA --cluster \"sbatch -t 8:00:00 -c 30 -N 1\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor multiple studies, create 2 files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSRP: List of unique study accession IDs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eERP126405\nERP127339\nSRP293106\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003elist: 2 column file of study accession IDs and corresponding run accession IDs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eERP124749 ERR4777044\nERP124749 ERR4777043\nERP126405 ERR5104751\nERP126405 ERR5104750\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run STAR_SRA on all studies using this script. This will make it so each study gets its own combined SJ.out.tab file for the 2nd pass.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat SRP | while read i; do \n\tcat list | grep \"$i\" | awk \u0027{print $2}\u0027 \u0026gt; RAids.txt\n\tsnakemake -j 300 -k -s STAR_SRA --cluster \"sbatch -t 8:00:00 -c 30 -N 1 -p RM-shared\"\n\trm output/all.SJ.out.tab\ndone\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-infer-ancestry\" class=\"anchor\" href=\"#infer-ancestry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInfer Ancestry\u003c/h2\u003e\n\u003cp\u003ePerforms GATK best practices workflow for RNAseq short variant discovery (SNPs + Indels). Intersects varaint data from GATK with 1000 Genomes Project ancestry informative SNPs to gather common loci. Performs PCA on variant data via PLINK and SVM model is implemented for ancestry inference.\u003c/p\u003e\n\u003cp\u003eSplit RAids.txt so snakemake doesnt stall.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esplit -l 100 RAids.txt\n\nls *xa* | cat \u0026gt; splits\n\ncat splits | while read i; do\n\tcat $i \u0026gt; RAids.txt\n\tsnakemake -j 300 -k -s InferAncestry.py --cluster \"sbatch -t 02:00:00 -c 7 -p RM-shared\"\ndone\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-multinest\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-multinest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-multinest\u003c/h1\u003e\n\u003cp\u003eA container for PyMultinest\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1645029635.0 + "updated_at": 1602594100.0 }, { "data_format": 2, - "description": null, + "description": "If you are going to build off of basic Empirical, this is the project for you", "filenames": [ - "Singularity" + "third-party/force-cover/Singularity" ], - "full_name": "truatpasteurdotfr/singularity-cryolo-cuda10", + "full_name": "piperwelch/Basic-Empirical-Starter-carlcs361s01w21-6", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-singularity-container-for-cryolo-using-cuda-version-10\" class=\"anchor\" href=\"#building-a-singularity-container-for-cryolo-using-cuda-version-10\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a singularity container for crYOLO using CUDA version 10\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity\n\u003ca href=\"https://github.com/truatpasteurdotfr/singularity-cryolo-cuda10/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-cryolo-cuda10/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run --nv oras://ghcr.io/truatpasteurdotfr/singularity-cryolo-cuda10:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLICENSE:\nThe same as crYOLO (free for academic use, see \u003ca href=\"https://cryolo.readthedocs.io/en/stable/other/license.html\" rel=\"nofollow\"\u003ehttps://cryolo.readthedocs.io/en/stable/other/license.html\u003c/a\u003e)\ncopy retrieved from \u003ca href=\"https://raw.githubusercontent.com/MPI-Dortmund/cryolo/9ea47f694fc68bcdaedf550a128558b8e77855bc/source/other/license.rst\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/MPI-Dortmund/cryolo/9ea47f694fc68bcdaedf550a128558b8e77855bc/source/other/license.rst\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-evolutionary-algorithm\" class=\"anchor\" aria-hidden=\"true\" href=\"#evolutionary-algorithm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvolutionary Algorithm\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/anyaevostinar/evo-algo/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff63e2cc80517f7b8e8246b33025f569d757ede0ae65c7ea57418d79e5a3709d/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f25324676657273696f6e2d62616467652e6a736f6e\" alt=\"version\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fversion-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2bde10efc09d993aad000b3101be56d5410d0ced348c4c7bbedbc7ffce79d630/68747470733a2f2f696d672e736869656c64732e696f2f7472617669732f616e796165766f7374696e61722f65766f2d616c676f2e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/travis/anyaevostinar/evo-algo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca 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src=\"https://camo.githubusercontent.com/4455f1d1625d643fe17506cb6ad50a9a6612ce62ab4667dc60b75616241c7534/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e636f6d25324665766f2d616c676f253246646f746f2d62616467652e6a736f6e\" alt=\"dotos\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.com%2Fevo-algo%2Fdoto-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db51ac0d7785eb561dcfc0cbccfc0cfed9872513120f8f732b1301504c4eb32e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f616e796165766f7374696e61722f65766f2d616c676f2e7376673f7374796c653d666c61742d737175617265266c6f676f3d676974687562266c6162656c3d5374617273266c6f676f436f6c6f723d7768697465\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/anyaevostinar/evo-algo.svg?style=flat-square\u0026amp;logo=github\u0026amp;label=Stars\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn evolutionary algorithm\u003c/p\u003e\n\u003cp\u003eCheck out the live in-browser web app at \u003ca href=\"https://anyaevostinar.github.io/evo-algo\" rel=\"nofollow\"\u003ehttps://anyaevostinar.github.io/evo-algo\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: MIT license\u003c/li\u003e\n\u003cli\u003eDocumentation: \u003ca href=\"https://evo-algo.readthedocs.io\" rel=\"nofollow\"\u003ehttps://evo-algo.readthedocs.io\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/assets/cookie.gif\"\u003e\u003cimg src=\"docs/assets/cookie.gif\" alt=\"cookie monster example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThis package was created with \u003ca href=\"https://github.com/audreyr/cookiecutter\"\u003eCookiecutter\u003c/a\u003e and the \u003ca href=\"https://github.com/devosoft/cookiecutter-empirical-project\"\u003edevosoft/cookiecutter-empirical-project\u003c/a\u003e project template.\u003c/p\u003e\n\u003cp\u003eThis package uses \u003ca href=\"https://github.com/devosoft/Empirical#readme\"\u003eEmpirical\u003c/a\u003e, a library of tools for scientific software development, with emphasis on also being able to build web interfaces using Emscripten.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eTo install \u003ca href=\"https://github.com/devosoft/Empirical\"\u003eEmpirical\u003c/a\u003e, pull down a clone of the Empirical repository. See \u003ca href=\"https://empirical.readthedocs.io/en/latest/QuickStartGuides\" rel=\"nofollow\"\u003eQuick Start Guides\u003c/a\u003e for directions on cloning and using the library.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1650023541.0 + "updated_at": 1615683851.0 }, { "data_format": 2, "description": null, "filenames": [ - "2/images/Singularity.def", - "4/images/Singularity.def", - "3/images/Singularity.def", - "1/images/Singularity.def" + "Singularity" ], - "full_name": "alcidesmig/hpc-ufscar-cluster", + "full_name": "shailapar/build_container_on_shub", "latest_release": null, + "readme": "\u003cp\u003eExamples for building containers on Singularity Hub\u003c/p\u003e\n\u003cp\u003e./tutorial_steps.txt : example steps, command-by-command\u003c/p\u003e\n\u003cp\u003e./Singularity : is a recipe file for building your container\u003c/p\u003e\n\u003cp\u003e./text_translate.py is a sample python script we can run with the container\u003c/p\u003e\n\u003cp\u003e./make_git_repo.sh is a script that uploads your Singularity repository to github\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1643401257.0 + "updated_at": 1558647668.0 }, { "data_format": 2, - "description": "ShellCheck, a static analysis tool for shell scripts", + "description": "Container Library of Apptainer definition files.", "filenames": [ - "0.5.0/Singularity", - "0.8.0/Singularity" - ], - "full_name": "pscedu/singularity-shellcheck", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-shellcheck/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-shellcheck/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-shellcheck/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-shellcheck/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/405923c0ac019718e1ce7ee2305e5e1e59721f91e675a8b0905b5c8b1e2abfbc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/405923c0ac019718e1ce7ee2305e5e1e59721f91e675a8b0905b5c8b1e2abfbc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-shellcheck\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/bd63ff1e920842a88281143fb261678d5a3c13a0b9aa2b77daa4f01c294053b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bd63ff1e920842a88281143fb261678d5a3c13a0b9aa2b77daa4f01c294053b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-shellcheck\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/3501f937ae3c9e631fa7626a21c08282d81835d5df168737e339adbc9b8e6d41/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3501f937ae3c9e631fa7626a21c08282d81835d5df168737e339adbc9b8e6d41/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-shellcheck\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/f24e35c82c0a0127f9824f4cb911bf49ccdadbd7871a842b8ad8292cfbae64c6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f24e35c82c0a0127f9824f4cb911bf49ccdadbd7871a842b8ad8292cfbae64c6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7368656c6c636865636b\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-shellcheck\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-shellcheck\" class=\"anchor\" href=\"#singularity-shellcheck\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-shellcheck\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/koalaman/shellcheck.net\"\u003eshellcheck\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eshellcheck\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/shellcheck/0.8.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/shellcheck\u003c/code\u003e as \u003ccode\u003e0.8.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1649646255.0 - }, - { - "data_format": 2, - "description": "The Bootcamp of the Ghent Quantum Chemistry Group, aimed at achieving the initial competences needed in order to be able to contribute to our electronic structure method development group.", - "filenames": [ - "Singularity" - ], - "full_name": "GQCG-edu/bootcamp", - "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n\u003ca href=\"media/bootcamp.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"media/bootcamp.png\" width=\"400\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eIn this boot camp you will learn the minimal set of computer skills that are required to survive \u003ca href=\"https://gqcg.github.io/\" rel=\"nofollow\"\u003ein our computational chemistry group\u003c/a\u003e. We will first focus on acquiring high-level skills using freely available resources that run in your browser. After you have obtained these skills, we will break free from the confines of those resources and transition to running software on your local system and in the cloud. Finally, you will apply the skills you have learned by implementing Restricted Hartree-Fock using PySCF.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-schedule\" class=\"anchor\" href=\"#schedule\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSchedule\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTraining\u003c/th\u003e\n\u003cth\u003eTechnologies\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"training/browser.md\"\u003eCoding in the browser\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eGithub, LaTeX/Overleaf, SciPy-Stack/Colab\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"training/local.md\"\u003eCoding locally\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eGit, VSCode, Docker, Jupyter, VSCode: LaTeX workshop\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"training/cloud.md\"\u003eCoding in the cloud\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eHPC/modules, Singularity/Apptainer\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"project/README.md\"\u003eCapstone project\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u00a0PySCF, RHF\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", - "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "training", - "gqcg" - ], - "updated_at": 1656513940.0 - }, - { - "data_format": 2, - "description": "Research on the effects of mixing and matching dataset towards audio separation", - "filenames": [ - "museparation/waveunet/Singularity" + "Singularity.digits", + "Singularity.tensorflow", + "Singularity.theano", + "ciml/Singularity.tape-0.4", + "ciml/Singularity.sparkr-2.3.1", + "ciml/Singularity.r-3.6.1", + "ciml/Singularity.esm-0.3.1", + "ciml/Singularity.pyspark-3.1.2", + "tensorflow/Singularity.tensorflow-2.8.2-ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "tensorflow/Singularity.tensorflow-2.5.0-ubuntu-18.04-cuda-11.2-openmpi-4.0.5", + "tensorflow/Singularity.tensorflow-2.7.3-ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "tensorflow/Singularity.tensorflow-2.5.3-ubuntu-18.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "tensorflow/Singularity.tensorflow-2.5.1-ubuntu-18.04-cuda-11.2-mlnx-ofed-4.7-3.2.9.0-openmpi-4.0.5", + "tensorflow/Singularity.tensorflow-2.3.0-ubuntu-18.04-cuda-10.1.168-openmpi-3.1.4", + "hpl/Singularity.hpl-2.3-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3-openblas-0.3.18", + "hpl/Singularity.hpl-2.3-ubuntu-18.04-openmpi-4.0.5-openblas-0.3.14", + "hpl/Singularity.hpl-2.3-ubuntu-18.04-openmpi-3.1.6-openblas-0.3.10", + "hpl/Singularity.hpl-2.3-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6-openblas-0.3.18", + "hpl/Singularity.hpl-2.3-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6-openblas-0.3.18", + "hpl/Singularity.hpl-2.3-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3-openblas-0.3.18", + "hpl/Singularity.hpl-2.3-ubuntu-18.04-openmpi-3.1.4-openblas-0.3.10", + "visit/Singularity.visit-3.1.4-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "beast/Singularity.beast-1.10.4-ubuntu-18.04-cuda-10.2", + "beast/Singularity.beast-2.6.1-ubuntu-18.04-cuda-10.2", + "pytorch/Singularity.pytorch-1.8.2-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "pytorch/Singularity.pytorch-1.10.2-ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "ubuntu/Singularity.ubuntu-18.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "ubuntu/Singularity.ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "ubuntu/Singularity.ubuntu-20.04-cuda-11.2", + "ubuntu/Singularity.ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "ubuntu/Singularity.ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0", + "ubuntu/Singularity.ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "ubuntu/Singularity.ubuntu-18.04-cuda-10.2", + "ubuntu/Singularity.ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "ubuntu/Singularity.ubuntu-20.04", + "ubuntu/Singularity.ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "ubuntu/Singularity.ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "ubuntu/Singularity.ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0", + "ubuntu/Singularity.ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0", + "ubuntu/Singularity.ubuntu-18.04", + "ubuntu/Singularity.ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "ubuntu/Singularity.ubuntu-18.04-cuda-11.2", + "ubuntu/Singularity.ubuntu-20.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "ubuntu/Singularity.ubuntu-18.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0", + "ubuntu/Singularity.ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0", + "ubuntu/Singularity.ubuntu-18.04-cuda-11.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "torch/Singularity.torch-extras", + "torch/Singularity.torch", + "ior/Singularity.ior-3.3.0rc1-ubuntu-18.04-openmpi-3.1.4", + "ior/Singularity.ior-3.3.0rc1-ubuntu-18.04-openmpi-3.1.6", + "ior/Singularity.ior-3.3.0-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "ior/Singularity.ior-3.3.0-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "ior/Singularity.ior-3.3.0-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "ior/Singularity.ior-3.3.0-ubuntu-18.04-openmpi-4.0.5", + "ior/Singularity.ior-3.3.0-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "centos/Singularity.centos-7.9.2009-mvapich-2.3.2", + "centos/Singularity.centos-7.9.2009-openmpi-3.1.4", + "centos/Singularity.centos-7.9.2009", + "centos/Singularity.centos-7.7.1908-openmpi-4.0.5", + "centos/Singularity.centos-7.7.1908-cuda-11.0", + "centos/Singularity.centos-7.9.2009-cuda-10.1.168", + "centos/Singularity.centos-7.7.1908-openmpi-3.1.6", + "centos/Singularity.centos-7.7.1908", + "centos/Singularity.centos-7.7.1908-cuda-11.0-openmpi-3.1.6", + "centos/Singularity.centos-7.7.1908-cuda-11.0-openmpi-4.0.5", + "rstudio/Singularity.rstudio", + "miniconda/Singularity.miniconda3-py38-4.11.0-ubuntu-20.04", + "miniconda/Singularity.miniconda2-py27-4.8.3-ubuntu-18.04", + "miniconda/Singularity.miniconda3-py39-4.9.2-ubuntu-18.04", + "miniconda/Singularity.miniconda3-py39-4.11.0-ubuntu-20.04", + "miniconda/Singularity.miniconda3-py38-4.9.2-ubuntu-18.04", + "miniconda/Singularity.miniconda3-py37-4.9.2-ubuntu-18.04", + "miniconda/Singularity.miniconda3-py37-4.11.0-ubuntu-20.04", + "anaconda/Singularity.anaconda3-py39-2021.11-ubuntu-20.04", + "anaconda/Singularity.anaconda2-py27-2019.10-ubuntu-18.04", + "fenics/Singularity.fenics-2019.1.0-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "mxnet/Singularity.mxnet-1.7.0-ubuntu-18.04-cuda-10.1.168-openmpi-3.1.4", + "gromacs/Singularity.gromacs-2020.7-ubuntu-18.04-cuda-10.2", + "singularity/Singularity.singularity-3.7.4-ubuntu-18.04", + "keras/Singularity.keras-py3", + "keras/Singularity.keras-py2", + "stream/Singularity.stream-5.10-ubuntu-18.04", + "paraview/Singularity.paraview-5.9.0-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6-osmesa-20.1.5", + "rnaseq/Singularity.rnaseq", + "deepbench/Singularity.deepbench-da81ba7-ubuntu-18.04-cuda-11.2-mlnx-ofed-4.7-3.2.9.0-openmpi-3.1.6", + "deepbench/Singularity.deepbench-da81ba7-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "xcrysden/Singularity.xcrysden-1.6.2-ubuntu-18.04", + "spark/Singularity.spark-3.2.1-hadoop-3.2-ubuntu-20.04", + "spark/Singularity.spark-2.3.1-hadoop-2.7-ubuntu-18.04", + "spark/Singularity.spark-3.1.2-hadoop-3.2-ubuntu-18.04", + "omb/Singularity.omb-5.6.3-centos-7.9.2009-mvapich-2.3.2", + "omb/Singularity.omb-5.8-ubuntu-18.04-cuda-11.2-mlnx-ofed-4.7-3.2.9.0-openmpi-4.0.5", + "omb/Singularity.omb-5.9-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "omb/Singularity.omb-5.9-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "omb/Singularity.omb-5.7-ubuntu-18.04-openmpi-4.0.5", + "omb/Singularity.omb-5.8-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.6-1.0.1.1-openmpi-3.1.4", + "omb/Singularity.omb-5.9-ubuntu-20.04-cuda-11.4-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "omb/Singularity.omb-5.7-ubuntu-18.04-mlnx-ofed-4.7-3.2.9.0-openmpi-3.1.6", + "omb/Singularity.omb-5.7-centos-7.7.1908-openmpi-3.1.6", + "omb/Singularity.omb-5.9-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "omb/Singularity.omb-5.6.3-ubuntu-18.04-openmpi-3.1.4", + "omb/Singularity.omb-5.6.3-ubuntu-18.04-mvapich-2.3.2", + "omb/Singularity.omb-5.9-ubuntu-20.04-cuda-11.4-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "omb/Singularity.omb-5.9-ubuntu-18.04-cuda-10.2-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "omb/Singularity.omb-5.7-centos-7.7.1908-cuda-11.0-openmpi-3.1.6", + "omb/Singularity.omb-5.9-ubuntu-18.04-mlnx-ofed-4.9-4.1.7.0-openmpi-3.1.6", + "omb/Singularity.omb-5.6.3-ubuntu-18.04-cuda-10.1.168-openmpi-3.1.4", + "omb/Singularity.omb-5.7-ubuntu-18.04-cuda-11.2-openmpi-4.0.5", + "omb/Singularity.omb-5.6.3-ubuntu-18.04-openmpi-3.1.6", + "omb/Singularity.omb-5.7-ubuntu-18.04-mlnx-ofed-4.7-3.2.9.0-openmpi-4.0.5", + "omb/Singularity.omb-5.7-centos-7.7.1908-openmpi-4.0.5", + "omb/Singularity.omb-5.7-ubuntu-18.04-mlnx-ofed-4.7-3.2.9.0-mvapich-2.3.6", + "omb/Singularity.omb-5.9-ubuntu-20.04-mlnx-ofed-4.9-4.1.7.0-openmpi-4.1.3", + "omb/Singularity.omb-5.8-ubuntu-18.04-mlnx-ofed-4.6-1.0.1.1-openmpi-3.1.4", + "omb/Singularity.omb-5.6.3-centos-7.9.2009-openmpi-3.1.4" ], - "full_name": "B-lanc/Museparation", + "full_name": "acchapm1/containerlibrary", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-museparation\" class=\"anchor\" href=\"#museparation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMuseparation\u003c/h1\u003e\n\u003cp\u003eResearch on the effects of mixing and matching dataset towards audio separation\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1649044884.0 + "updated_at": 1680805708.0 }, { "data_format": 2, @@ -17299,89 +16885,91 @@ var data = "filenames": [ "Singularity" ], - "full_name": "carshadi/tiff2octree-singularity", + "full_name": "Saford91/centos7-singularity", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1651625714.0 + "updated_at": 1500478470.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container recipes for bioinformatic workflows", "filenames": [ - "Singularity.full", - "Singularity" + "Singularity", + "cellranger-atac/Singularity", + "cellranger-rna/Singularity_cellranger-rna_4.0.0" ], - "full_name": "leo-cazenille/multiAE-ME", + "full_name": "perllb/singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-multiae-me\" class=\"anchor\" href=\"#multiae-me\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emultiAE-ME\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity container recipes for bioinformatics workflows\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e-- Build container with\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esudo -E singularity build \u0026lt;.sif image file\u0026gt; \u0026lt; container recipe \u0026gt;\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1650020409.0 + "updated_at": 1604059761.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity.horovod_cpu", + "Singularity.openmpi_cuda", + "Singularity.cpu_tf2.2_torch1.5_hvd0.19", + "Singularity.cpu_tf1.14_torch1.1_hvd0.16", + "Singularity.horovod_cpu_centos", + "Singularity.julia_deps", + "Singularity.gpu", + "Singularity.test2", + "Singularity.test", + "Singularity.horovod_gpu" ], - "full_name": "Garuda-1/Thesis-2022", + "full_name": "EliseJ/kay_singularity_images", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-images-for-mldl-stack-on-kay\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-images-for-mldl-stack-on-kay\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity images for ML/DL stack on Kay\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1652887962.0 + "updated_at": 1612268805.0 }, { "data_format": 2, - "description": "compute", + "description": "Recipe for funannotate pipeline Singularity recipy for UA HPC", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "Aku02/cc", + "full_name": "dshyshlov/funannotate_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cc\" class=\"anchor\" href=\"#cc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecc\u003c/h1\u003e\n\u003cp\u003ecompute\u003c/p\u003e\n\u003cp\u003esingularity run --nv conda.sif\u003c/p\u003e\n\u003cp\u003esingularity run --nv --bind /scratch:/home/akash02 scratch/conda.sif\u003c/p\u003e\n\u003cp\u003e$ sudo singularity build --nv --nvccli --sandbox test conda.sif\u003c/p\u003e\n\u003cp\u003esingularity shell --nv --nvccli conda.sif\u003c/p\u003e\n\u003cp\u003esrun --mem=16G --cpus-per-task=2 --time=3:0:0 --gres=gpu:t4:1 --pty bash\u003c/p\u003e\n\u003cp\u003esingularity run --nv --nvccli --bind cc:/user_mnt cc/test/\u003c/p\u003e\n\u003cp\u003esudo singularity run --nv --nvccli --writable --bind cc:/root cc/product/\u003c/p\u003e\n\u003cp\u003esudo singularity run --nv banmo.sif\nsudo singularity run --nv --nvccli --writable --bind cc:/root cc/test/\u003c/p\u003e\n\u003cp\u003esudo singularity build --nv --nvccli --rocm product/ Singularity.def\u003c/p\u003e\n\u003cp\u003esudo singularity build --nv --nvccli banmo.sif --tmpdir=$SINGULARITY_TMPDIR docker-daemon://banmo:latest\u003c/p\u003e\n\u003cp\u003esudo singularity build --nv --sandbox --nvccli --rocm test/ Singularity.def\u003c/p\u003e\n\u003cp\u003eERROR conda.core.link:_execute(699): An error occurred while installing package \u0027conda-forge::cudatoolkit-dev-11.3.1-py39h3811e60_0\u0027.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1651513695.0 + "updated_at": 1602202847.0 }, { "data_format": 2, - "description": "example Singularity files", + "description": null, "filenames": [ - "cowsay/Singularity" + "Singularity" ], - "full_name": "cyverse-education/intro2singularity", + "full_name": "mmore500/tag-olympics", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-intro2singularity\" class=\"anchor\" href=\"#intro2singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eintro2singularity\u003c/h1\u003e\n\u003cp\u003eexample Singularity files\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1652622862.0 + "updated_at": 1635955138.0 }, { "data_format": 2, - "description": null, + "description": "CS 361 Evolutionary Computation and Artificial Life project. ", "filenames": [ - "Singularity.specter", - "Singularity", - "Singularity.jupyter", - "conda-cudf/Singularity.conda-cudf", - "elastic_search/Singularity", - "semantic_scholar/Singularity", - "mental-ability-proj/Singularity.mental-ability", - "vocab_comp/Singularity.vocab_comp" + "third-party/force-cover/Singularity" ], - "full_name": "ghoshmainak/singularity-recipe", + "full_name": "koellingh/empirical-p53-simulator", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-recipe\" class=\"anchor\" href=\"#singularity-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-recipe\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5061\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-this-singularity-container-contains\" class=\"anchor\" href=\"#this-singularity-container-contains\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis singularity container contains:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eConda environment\u003c/li\u003e\n\u003cli\u003eNumpy\u003c/li\u003e\n\u003cli\u003eNotebook=6.0.3\u003c/li\u003e\n\u003cli\u003ePandas\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-conda-cudf-recipe\" class=\"anchor\" href=\"#conda-cudf-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econda-cudf recipe\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/containers/15169\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is an extention of singularity-recipe. This container contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eConda environment\u003c/li\u003e\n\u003cli\u003eNotebook=6.0.3\u003c/li\u003e\n\u003cli\u003eNumpy\u003c/li\u003e\n\u003cli\u003ecudf=0.13\u003c/li\u003e\n\u003cli\u003ecudatoolkit=10.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-mental-ability-project-recipe\" class=\"anchor\" href=\"#mental-ability-project-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emental-ability-project recipe\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/containers/15485\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis container is meant for my own project on mental ability. It contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython development environment\u003c/li\u003e\n\u003cli\u003epip3\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003escipy\u003c/li\u003e\n\u003cli\u003escikit-learn\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003ejupyter\u003c/li\u003e\n\u003cli\u003ejupyterlab\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003estatmodels\u003c/li\u003e\n\u003cli\u003enltk\u003c/li\u003e\n\u003cli\u003espacy\u003c/li\u003e\n\u003cli\u003efasttext\u003c/li\u003e\n\u003cli\u003econtractions\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003ecurl\u003c/li\u003e\n\u003cli\u003enano and vim\u003c/li\u003e\n\u003cli\u003etransformers\u003c/li\u003e\n\u003cli\u003ePyTorch\u003c/li\u003e\n\u003cli\u003egit\u003c/li\u003e\n\u003cli\u003edask\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-elastic-search-recipe\" class=\"anchor\" href=\"#elastic-search-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eelastic search recipe\u003c/h1\u003e\n\u003cp\u003eIt contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython development environment\u003c/li\u003e\n\u003cli\u003epip3\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003ecurl\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003egit\u003c/li\u003e\n\u003cli\u003ejsonmerge\u003c/li\u003e\n\u003cli\u003ejsonlines\u003c/li\u003e\n\u003cli\u003eparquet\u003c/li\u003e\n\u003cli\u003eelasticsearch\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-evolutionary-algorithm\" class=\"anchor\" aria-hidden=\"true\" href=\"#evolutionary-algorithm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvolutionary Algorithm\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/anyaevostinar/evo-algo/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff63e2cc80517f7b8e8246b33025f569d757ede0ae65c7ea57418d79e5a3709d/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f25324676657273696f6e2d62616467652e6a736f6e\" alt=\"version\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fversion-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2bde10efc09d993aad000b3101be56d5410d0ced348c4c7bbedbc7ffce79d630/68747470733a2f2f696d672e736869656c64732e696f2f7472617669732f616e796165766f7374696e61722f65766f2d616c676f2e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/travis/anyaevostinar/evo-algo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://evo-algo.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2af827df5df15ff6f5c6a9fff5021e631a0049aa2274f98e983135bb66f0ed81/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f65766f2d616c676f2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/evo-algo/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://evo-algo.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e035446b6d1c7a911bd4b203bd581f2116c7873352a90d4797dc08119abbd0e/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e6769746875622e696f25324665766f2d616c676f253246646f63756d656e746174696f6e2d636f7665726167652d62616467652e6a736f6e\" alt=\"documentation coverage\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.github.io%2Fevo-algo%2Fdocumentation-coverage-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/anyaevostinar/evo-algo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/869814fe0b90d0baadc37140ac31d6d9c0e4e00c2854a51f1030c40678bc13a4/68747470733a2f2f636f6465636f762e696f2f67682f616e796165766f7374696e61722f65766f2d616c676f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"code coverage status\" data-canonical-src=\"https://codecov.io/gh/anyaevostinar/evo-algo/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo/search?q=todo+OR+fixme\u0026amp;type=\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4455f1d1625d643fe17506cb6ad50a9a6612ce62ab4667dc60b75616241c7534/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d6874747073253341253246253246616e796165766f7374696e61722e636f6d25324665766f2d616c676f253246646f746f2d62616467652e6a736f6e\" alt=\"dotos\" data-canonical-src=\"https://img.shields.io/endpoint?url=https%3A%2F%2Fanyaevostinar.com%2Fevo-algo%2Fdoto-badge.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/anyaevostinar/evo-algo\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db51ac0d7785eb561dcfc0cbccfc0cfed9872513120f8f732b1301504c4eb32e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f616e796165766f7374696e61722f65766f2d616c676f2e7376673f7374796c653d666c61742d737175617265266c6f676f3d676974687562266c6162656c3d5374617273266c6f676f436f6c6f723d7768697465\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/anyaevostinar/evo-algo.svg?style=flat-square\u0026amp;logo=github\u0026amp;label=Stars\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn evolutionary algorithm\u003c/p\u003e\n\u003cp\u003eCheck out the live in-browser web app at \u003ca href=\"https://anyaevostinar.github.io/evo-algo\" rel=\"nofollow\"\u003ehttps://anyaevostinar.github.io/evo-algo\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: MIT license\u003c/li\u003e\n\u003cli\u003eDocumentation: \u003ca href=\"https://evo-algo.readthedocs.io\" rel=\"nofollow\"\u003ehttps://evo-algo.readthedocs.io\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/assets/cookie.gif\"\u003e\u003cimg src=\"docs/assets/cookie.gif\" alt=\"cookie monster example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eThis package was created with \u003ca href=\"https://github.com/audreyr/cookiecutter\"\u003eCookiecutter\u003c/a\u003e and the \u003ca href=\"https://github.com/devosoft/cookiecutter-empirical-project\"\u003edevosoft/cookiecutter-empirical-project\u003c/a\u003e project template.\u003c/p\u003e\n\u003cp\u003eThis package uses \u003ca href=\"https://github.com/devosoft/Empirical#readme\"\u003eEmpirical\u003c/a\u003e, a library of tools for scientific software development, with emphasis on also being able to build web interfaces using Emscripten.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eTo install \u003ca href=\"https://github.com/devosoft/Empirical\"\u003eEmpirical\u003c/a\u003e, pull down a clone of the Empirical repository. See \u003ca href=\"https://empirical.readthedocs.io/en/latest/QuickStartGuides\" rel=\"nofollow\"\u003eQuick Start Guides\u003c/a\u003e for directions on cloning and using the library.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1639749845.0 + "updated_at": 1615848203.0 }, { "data_format": 2, @@ -17389,97 +16977,128 @@ var data = "filenames": [ "Singularity" ], - "full_name": "Hydroinformatics/singularity-swat681wr-main", + "full_name": "shots47s/MAGetBrain_Sinularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-soil--water-assessment-tool\" class=\"anchor\" href=\"#soil--water-assessment-tool\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoil \u0026amp; Water Assessment Tool\u003c/h1\u003e\n\u003cp\u003eThis container includes the Soil and Water Assessment Tool (\u003ca href=\"https://swat.tamu.edu/software/\" rel=\"nofollow\"\u003ehttps://swat.tamu.edu/software/\u003c/a\u003e)\nrevision 681,\nbuilt for use on amd64 Linux systems. The binary is installed at /usr/local/swat681/swat.\nAt run-time, any input files MUST be bind-mounted to /usr/local/swat681 - for example:\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-magetbrain_sinularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#magetbrain_sinularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMAGetBrain_Sinularity\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1652372813.0 + "updated_at": 1534432676.0 }, { "data_format": 2, - "description": "Uniform and Weighted Sampling using Dynamic Programming", + "description": null, "filenames": [ - "dmc/Singularity", - "lg/Singularity" + "Singularity.conda_torch", + "Singularity.torch3", + "Singularity.tf2new", + "Singularity.ubuntu_tf", + "Singularity.tf_einops", + "Singularity.ubuntu_pre", + "Singularity.centos_tf", + "Singularity.centos_torch2", + "Singularity.conda", + "Singularity.ExplainAI", + "Singularity.geometric", + "Singularity.tf23", + "Singularity.Spektral", + "Singularity.tf2", + "Singularity.ubuntu_torch", + "Singularity.torch2", + "Singularity.centos_torch", + "Singularity.tf2b1", + "Singularity.torch" ], - "full_name": "allrtaken/DPSampler", + "full_name": "alex-chunhui-yang/container", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dpmc-dynamic-programming-for-model-counting\" class=\"anchor\" href=\"#dpmc-dynamic-programming-for-model-counting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPMC (Dynamic Programming for Model Counting)\u003c/h1\u003e\n\u003cp\u003eDPMC computes weighted model counts of formulas in conjunctive normal form (CNF)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe DPMC framework runs in two phases:\n\u003cul\u003e\n\u003cli\u003ePlanning phase: \u003ca href=\"./lg\"\u003eLG\u003c/a\u003e or \u003ca href=\"./htb\"\u003eHTB\u003c/a\u003e constructs a join tree of a CNF formula\u003c/li\u003e\n\u003cli\u003eExecution phase: \u003ca href=\"./dmc\"\u003eDMC\u003c/a\u003e computes the model count of the formula using the join tree\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopers:\n\u003cul\u003e\n\u003cli\u003eJeffrey Dudek\u003c/li\u003e\n\u003cli\u003eVu Phan\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-releases\" class=\"anchor\" href=\"#releases\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://github.com/vardigroup/DPMC/releases\"\u003eReleases\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e2021/05/25: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/mc-2021\"\u003emc-2021\u003c/a\u003e \u003ca href=\"https://zenodo.org/badge/latestdoi/280443175\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a66989e99eb192ab9857e39b3f1e218d0f4b7bcd8b478436fdace72cf61b408c/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3238303434333137352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/280443175.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"./mcc\"\u003eModel Counting Competition MC-2021\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2021/05/23: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/v2.0.0\"\u003ev2.0.0\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eSAT-2021 paper: \u003cstrong\u003eProCount: Weighted Projected Model Counting with Graded Project-Join Trees\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors: Jeffrey M. Dudek, Vu H. N. Phan, Moshe Y. Vardi\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2020/07/20: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/v1.0.0\"\u003ev1.0.0\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eCP-2020 paper: \u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2008.08748\" rel=\"nofollow\"\u003eDPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors: Jeffrey M. Dudek, Vu H. N. Phan, Moshe Y. Vardi\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-example-files\" class=\"anchor\" href=\"#example-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"./examples\"\u003eExample files\u003c/a\u003e\n\u003c/h2\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" href=\"#acknowledgment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"./ACKNOWLEDGMENT.md\"\u003eAcknowledgment\u003c/a\u003e\n\u003c/h2\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1652210102.0 + "updated_at": 1617573462.0 }, { "data_format": 2, "description": null, "filenames": [ - "scripts/Singularity" + "Singularity" ], - "full_name": "waglecn/mabs", + "full_name": "jganong/singularity-test", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mabs\" class=\"anchor\" href=\"#mabs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emabs\u003c/h1\u003e\n\u003cp\u003eauthor:\u003ca href=\"mailto:nwaglechner@gmail.com\"\u003enwaglechner@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-basic-setup\" class=\"anchor\" href=\"#basic-setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasic Setup\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/waglecn/mabs.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eConda and snakemake\u003c/p\u003e\n\u003cp\u003eMiniconda available from:\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ehttps://docs.conda.io/en/latest/miniconda.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePython 3.8.3 Miniconda\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \nbash Miniconda3-latest-Linux-X86_64.sh\nconda env create --name mabs --file environment.yaml\nconda activate mabs\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e- note the version of python installed in the the mabs environment is not necessarily the same as the default miniconda python version\n- asking for ete3 in the default environment will required python 3.6 (200921)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-required-files\" class=\"anchor\" href=\"#required-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired files:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGATK3 jar file\n\u003cul\u003e\n\u003cli\u003eavailable from \u003ca href=\"https://console.cloud.google.com/storage/browser/gatk-software/package-archive/gatk\" rel=\"nofollow\"\u003ehttps://console.cloud.google.com/storage/browser/gatk-software/package-archive/gatk\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eused \u0027\u0027\u0027GenomeAnalysisTK-3.8-1-0-gf15c1c3ef.tar.bz2\u0027\u0027\u0027\u003c/li\u003e\n\u003cli\u003esee config.yaml\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eadapters for trimming - see config.yaml\n\u003cul\u003e\n\u003cli\u003elook for adapter files bundled with trimmomatic, ie.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003elocate TruSeq3-PE.fa\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eKraken database\n\u003ca href=\"ftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/\" rel=\"nofollow\"\u003eftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget ftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/minikraken_8GB_202003.tgz\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-how-to-run\" class=\"anchor\" href=\"#how-to-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --configfile config.yaml --cores 8 --use-conda --conda-prefix /path/to/.snakemake/conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse config.default.yaml as a template for other config files.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h1\u003e\n\u003cp\u003e200915\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003estrange bug causing infinite loop in snakemake downloading refseq genomes. I think this is because of the dynamic() output/input in rules. Checking this out, seeing if the bug happens if I run entire pipeline from scratch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e200917\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enoticed a bug in running shovill, increased expected memory usage. Shovill version 0.9.0 running from an older miniconda. Removed miniconda, started from scratch, and pinned Shovill 1.1.0 in shovill.yaml\u003c/li\u003e\n\u003cli\u003eafter fixing, rerunning seems to work with example data, then works after deleting the mashtree and refseq_download directories.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e210302\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eon vs masking before gubbins vs after see \u003ca href=\"https://github.com/sanger-pathogens/gubbins/issues/275\"\u003ehttps://github.com/sanger-pathogens/gubbins/issues/275\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200902\" class=\"anchor\" href=\"#todo-200902\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200902\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e[ ]download internal project data - deferred\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[X] configurable data-dir - 200914\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edownload external project data\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[X] refseq genomes - done 200904\u003c/li\u003e\n\u003cli\u003e[ ] genomes from Bryant et al, SRA\n\u003cul\u003e\n\u003cli\u003eneed to know what these are\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[X] download reference assemblies - 200908\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003efirst used all contig assemblies, changed to \u0027complete\u0027 keyword\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ereading in samples somehow, obviously this depends on how/where they are downloaded (see previous TODO item) and the data that is already downloaded\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eneed a dummy rule that requires these as input in order to define wildcards\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[X] basic Snakefile - 200905\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[X] build workflow part 1\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[X] index reference assemblies - deferred 200914\n\u003cul\u003e\n\u003cli\u003emoved to resources/alignment_references\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] pre-trim QC - done 200908\u003c/li\u003e\n\u003cli\u003e[X] trim - done 200909\n\u003cul\u003e\n\u003cli\u003especify adapter files, add variable to config\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] post-trim QC done 200909\u003c/li\u003e\n\u003cli\u003e[X] kraken check - done 200910\n\u003cul\u003e\n\u003cli\u003e[X] download kraken db automatically - deferred, added to Required files\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] genome assembly on raw reads - 200914\n\u003cul\u003e\n\u003cli\u003e[X] Erm(41) identification on assembly - 200912\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] kraken2 on assembly - 200912\u003c/li\u003e\n\u003cli\u003e[X] mashtree assembly - 200913\u003c/li\u003e\n\u003cli\u003e[X] map everything to ATCC 19977 for basic coverage - 200914\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[ ] build workflow part 2 on available assemblies\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[X] tree-guided MRCA - 200915\u003c/li\u003e\n\u003cli\u003e[X] MRCA-guided MLST - 200913\u003c/li\u003e\n\u003cli\u003e[X] MRCA-guided reference mapping - 200921\u003c/li\u003e\n\u003cli\u003e[ ] Optional: Mark duplicates with picard\u003c/li\u003e\n\u003cli\u003e[X] read filtering - see Martin et al 2018 and Lee et al 2020\n\u003cul\u003e\n\u003cli\u003e[X] filter soft clips - 200922\u003c/li\u003e\n\u003cli\u003e[X] optional GATK realignment, but see for why it was removed in 2015 for gatk4 \u003ca href=\"https://github.com/broadinstitute/gatk-docs/blob/master/blog-2012-to-2019/2016-06-21-Changing_workflows_around_calling_SNPs_and_indels.md?id=7847\"\u003ehttps://github.com/broadinstitute/gatk-docs/blob/master/blog-2012-to-2019/2016-06-21-Changing_workflows_around_calling_SNPs_and_indels.md?id=7847\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e[X] added 200923, optional 200924\u003c/li\u003e\n\u003cli\u003eintially added gatk4, got errors and followed the rabbit-hole\u003c/li\u003e\n\u003cli\u003eto follow Martin et al, added conda env with gatk3.8, since the resulting bam can be used with any downstream variant caller\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] annotate regions of interest\n\u003cul\u003e\n\u003cli\u003eremove PP/PPE regions (BED file)\n\u003cul\u003e\n\u003cli\u003e[X] identify PP/PPE - 200927\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] zero coverage of reference\u003c/li\u003e\n\u003cli\u003e[ ] remove phage, tnp, IS\u003c/li\u003e\n\u003cli\u003e[X] merge ROI BED files\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] MRCA-guided variant calling with bcftools - 200922\n\u003cul\u003e\n\u003cli\u003e[X] bcftools mpileup - 200923\u003c/li\u003e\n\u003cli\u003e[X] called variants - 200923\u003c/li\u003e\n\u003cli\u003e[X] variant filtering\n\u003cul\u003e\n\u003cli\u003e[X] basic Martin et al - 200925\u003c/li\u003e\n\u003cli\u003e[ ] density filter - see \u003ca href=\"https://github.com/c2-d2/within-host-diversity/blob/master/fastq_to_vcf_pipeline.py#L122\"\u003ehttps://github.com/c2-d2/within-host-diversity/blob/master/fastq_to_vcf_pipeline.py#L122\u003c/a\u003e line\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] variant annotation with SNPEff\u003c/li\u003e\n\u003cli\u003e[X] SNP-tree construction\n\u003cul\u003e\n\u003cli\u003e[X] SNP extraction - custom? merge vcf as per Robyn 201006\u003c/li\u003e\n\u003cli\u003e[X] - merge SNPs - 201013\u003c/li\u003e\n\u003cli\u003e[X] concatenate cSNPSs (exclude hSNPs) 201016\n\u003cul\u003e\n\u003cli\u003esnp-sites ? snippy?\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] - vcfmerge 201014\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200911\" class=\"anchor\" href=\"#todo-200911\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200911\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[X] add trimming parameters to config file - 200921\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200914\" class=\"anchor\" href=\"#todo-200914\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200914\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003esub-species type assemblies are hard-coded in scripts/tree_MRCA.py, it would be useful for these to be configurable but adds layers of complexity to snakefile\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200920\" class=\"anchor\" href=\"#todo-200920\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200920\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eAdded GATK info to REQUIREMENTS, and config.yaml\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-200926\" class=\"anchor\" href=\"#todo-200926\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 200926\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Tune variant filtering\u003c/li\u003e\n\u003cli\u003e[X] TODO big question here - use stats from part 1 to make \u003cem\u003enew\u003c/em\u003e sample_sheet with QC pass samples? No\n\u003cul\u003e\n\u003cli\u003e[X] make list to prune from SNP alignment - not needed 201012\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[X] need separate list of in-complete genomes, as MRCA-guided MLST didn\u0027t work as expected, tree has wrong structure (samples from pt 29 should be mmas) - Fixed 201006, need to convert gbff files before mashtree can read\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-201010\" class=\"anchor\" href=\"#todo-201010\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 201010\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[X] start density filter\u003c/li\u003e\n\u003cli\u003e[X] merge completed results without recalculating shovill assemblies for old samples - 201010\u003c/li\u003e\n\u003cli\u003e[X] merge 0-coverage bed files and PE_PPE bed files 201013\u003c/li\u003e\n\u003cli\u003e[X] filter merged bed from vcf\n\u003cul\u003e\n\u003cli\u003e[X] compress vcf with bcftools\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-201013\" class=\"anchor\" href=\"#todo-201013\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 201013\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[X] complete density filter - 20-11-23\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-todo-201015\" class=\"anchor\" href=\"#todo-201015\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODO 201015\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e[X] incorporate \u003ca href=\"https://github.com/phac-nml/mab_mabscessus\"\u003ehttps://github.com/phac-nml/mab_mabscessus\u003c/a\u003e 211021\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-210323\" class=\"anchor\" href=\"#210323\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e210323\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003emerging script\u003c/li\u003e\n\u003cli\u003ecopy results_folder1 and results_folder2 into results_merge folder\u003c/li\u003e\n\u003cli\u003eremove the gubbins folder\u003c/li\u003e\n\u003cli\u003eremove the SNP_phylo folder\u003c/li\u003e\n\u003cli\u003eremove the files in MRCA_ref_folder, but keep the individual reference sub-folders\u003c/li\u003e\n\u003cli\u003eremove the mashtree folder\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003erun snakemake with the following targets, in this order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emashtree/assembly_mashtree.complete.tree\u003c/li\u003e\n\u003cli\u003estage1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003etouch ./MRCA_ref_mapping/\u003cem\u003e/tempRGSC.merged.\u003c/em\u003e.sorted.bam.bai\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.intervals\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA.bam\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA.mpileup\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.vcf.gz\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.failed.vcf.gz\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.AD_failed.vcf.gz\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.hvar.vcf.gz\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA.0cov.bed\ntouch ./MRCA_ref_mapping/\u003cem\u003e/\u003c/em\u003e.RG_SC_RA_filter.hvar_DF.bed\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003estage2\u003c/li\u003e\n\u003cli\u003estage3 to generate the merged output (gubbins, SNP phylo, merged beds, etc)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1651613417.0 + "updated_at": 1606934860.0 }, { "data_format": 2, - "description": "Dynamic-programming existential-random stochastic SAT solver", + "description": "Singularity recipe files for slim (https://github.com/MesserLab/SLiM)", "filenames": [ - "lg/Singularity" + "Singularity", + "Singularity.3.4+1c85d00", + "Singularity.3.5" ], - "full_name": "vuphan314/DPER", - "latest_release": "v0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dper-dynamic-programming-existential-random-stochastic-sat-solver\" class=\"anchor\" href=\"#dper-dynamic-programming-existential-random-stochastic-sat-solver\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPER (dynamic-programming existential-random stochastic SAT solver)\u003c/h1\u003e\n\u003cp\u003eDPER runs in two phases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe planning phase constructs a graded project-join tree for a CNF formula.\u003c/li\u003e\n\u003cli\u003eThe execution phase computes the maximum and a maximizer from the constructed tree.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cloning-this-repository\" class=\"anchor\" href=\"#cloning-this-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning this repository\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vuphan314/DPER\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDir \u003ca href=\"lg\"\u003e\u003ccode\u003elg\u003c/code\u003e\u003c/a\u003e: DPER\u0027s planner\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"dmc\"\u003e\u003ccode\u003edmc\u003c/code\u003e\u003c/a\u003e: DPER\u0027s executor\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"eval\"\u003e\u003ccode\u003eeval\u003c/code\u003e\u003c/a\u003e: empirical evaluation\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" href=\"#acknowledgment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgment\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/ADDMC\"\u003eADDMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/approxmc\"\u003eBIRD\u003c/a\u003e: Soos, Meel\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cs.rochester.edu/u/kautz/Cachet\" rel=\"nofollow\"\u003eCachet\u003c/a\u003e: Sang, Beame, Kautz\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003eCUDD package\u003c/a\u003e: Somenzi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://davidkebo.com/cudd#cudd6\" rel=\"nofollow\"\u003eCUDD visualization\u003c/a\u003e: Kebo\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts\u003c/a\u003e: Beck\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/DPMC\"\u003eDPMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kit-algo/flow-cutter-pace17\"\u003eFlowCutter\u003c/a\u003e: Strasser\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mabseher/htd\"\u003ehtd\u003c/a\u003e: Abseher, Musliu, Woltran\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://reasoning.cs.ucla.edu/minic2d\" rel=\"nofollow\"\u003eminiC2D\u003c/a\u003e: Oztok, Darwiche\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mccompetition.org\" rel=\"nofollow\"\u003eModel-counting competition\u003c/a\u003e: Hecher, Fichte\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Kasekopf/SlurmQueen\"\u003eSlurmQueen\u003c/a\u003e: Dudek\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://trolando.github.io/sylvan\" rel=\"nofollow\"\u003eSylvan\u003c/a\u003e: van Dijk\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/TCS-Meiji/PACE2017-TrackA\"\u003eTamaki\u003c/a\u003e: Tamaki\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/TensorOrder\"\u003eTensorOrder\u003c/a\u003e: Dudek, Duenas-Osorio, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/WAPS\"\u003eWAPS\u003c/a\u003e: Gupta, Sharma, Roy, Meel\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "powerPlant/slim-srf", + "latest_release": null, + "readme": "\u003cp\u003eSingularity recipe files for Selection on Linked Mutations: A forward population genetic simulation for studying linkage effects, such as hitchhiking, background selection, and Hill-Robertson interference\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1652162476.0 + "updated_at": 1607459916.0 }, { "data_format": 2, - "description": "Dynamic-programming optimizer to solve exact literal-weighted SAT (Boolean MPE)", + "description": "Singularity container script for 10x Genomics SuperNova software", "filenames": [ - "lg/Singularity" + "Singularity.2.0.0" ], - "full_name": "vuphan314/DPO", - "latest_release": "v0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dpo-dynamic-programming-optimizer\" class=\"anchor\" href=\"#dpo-dynamic-programming-optimizer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPO (dynamic-programming optimizer)\u003c/h1\u003e\n\u003cp\u003eDPO runs in two phases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe planning phase constructs a project-join tree for an XOR-CNF formula.\u003c/li\u003e\n\u003cli\u003eThe execution phase computes the maximum and a maximizer from the constructed join tree.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cloning-this-repository\" class=\"anchor\" href=\"#cloning-this-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning this repository\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vuphan314/DPO\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDir \u003ca href=\"./lg/\"\u003e\u003ccode\u003elg/\u003c/code\u003e\u003c/a\u003e: DPO\u0027s planner\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"./dmc/\"\u003e\u003ccode\u003edmc/\u003c/code\u003e\u003c/a\u003e: DPO\u0027s executor\u003c/li\u003e\n\u003cli\u003eDir \u003ca href=\"./eval/\"\u003e\u003ccode\u003eeval/\u003c/code\u003e\u003c/a\u003e: empirical evaluation\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" href=\"#acknowledgment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgment\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/ADDMC\"\u003eADDMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/approxmc\"\u003eBIRD\u003c/a\u003e: Soos, Meel\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cs.rochester.edu/u/kautz/Cachet\" rel=\"nofollow\"\u003eCachet\u003c/a\u003e: Sang, Beame, Kautz\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/msoos/cryptominisat\"\u003eCryptoMiniSat\u003c/a\u003e: Soos\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003eCUDD package\u003c/a\u003e: Somenzi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://davidkebo.com/cudd#cudd6\" rel=\"nofollow\"\u003eCUDD visualization\u003c/a\u003e: Kebo\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts\u003c/a\u003e: Beck\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/DPMC\"\u003eDPMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kit-algo/flow-cutter-pace17\"\u003eFlowCutter\u003c/a\u003e: Strasser\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mabseher/htd\"\u003ehtd\u003c/a\u003e: Abseher, Musliu, Woltran\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://reasoning.cs.ucla.edu/minic2d\" rel=\"nofollow\"\u003eminiC2D\u003c/a\u003e: Oztok, Darwiche\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mccompetition.org\" rel=\"nofollow\"\u003eModel Counting Competition\u003c/a\u003e: Hecher, Fichte\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Kasekopf/SlurmQueen\"\u003eSlurmQueen\u003c/a\u003e: Dudek\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://trolando.github.io/sylvan\" rel=\"nofollow\"\u003eSylvan\u003c/a\u003e: van Dijk\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/TCS-Meiji/PACE2017-TrackA\"\u003eTamaki\u003c/a\u003e: Tamaki\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/TensorOrder\"\u003eTensorOrder\u003c/a\u003e: Dudek, Duenas-Osorio, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/WAPS\"\u003eWAPS\u003c/a\u003e: Gupta, Sharma, Roy, Meel\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "arcsUVA/supernova", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-supernova\" class=\"anchor\" aria-hidden=\"true\" href=\"#supernova\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esupernova\u003c/h1\u003e\n\u003cp\u003eSingularity container script for 10x Genomics SuperNova software\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1652124481.0 + "updated_at": 1551891095.0 }, { "data_format": 2, - "description": "Flappie singularity image =\u003e https://github.com/nanoporetech/flappie", + "description": null, "filenames": [ - "Singularity" + "os_recipes/Singularity.SuSE", + "os_recipes/Singularity.deboot.ubuntu", + "os_recipes/Singularity.centos7", + "os_recipes/Singularity.4.2.5", + "os_recipes/Singularity.archive.debian", + "os_recipes/Singularity.centos6", + "os_recipes/Singularity.base-4.2.5", + "os_recipes/Singularity.usmirror.debian", + "docs/Singularity.3_0.debian9", + "store_pw/Singularity.pw_embed", + "store_pw/Singularity.4.2.5", + "store_pw/Singularity.python-4.2.5", + "store_pw/Singularity.base-4.2.5", + "store_pw/Singularity.pw_encrypt" ], - "full_name": "romxero/flappie_singularity", + "full_name": "d-w-moore/new_d2c", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-installing-and-running-slurm-on-ubuntu-16-or-18\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-and-running-slurm-on-ubuntu-16-or-18\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling and Running SLURM on ubuntu 16 or 18\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-slurm\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall SLURM\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install slurm-wlm\ngit clone http://github.com/d-w-moore/new_d2c\ncd new_d2c\nperl process_slurm_template.pl | sudo dd of=/etc/slurm-llnl/slurm.conf\nsudo systemctl restart slurmctld slurmd\nsudo systemctl enable slurmctld slurmd\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto test:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esudo apt install bc\u003c/li\u003e\n\u003cli\u003elocate command file slurm_install_test.sh containing:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e #!/bin/bash\n bc -l \u0026lt;\u0026lt;\u0026lt;\"scale=4000;a(1)*4\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003erun the above mentioned test script using : \u003ccode\u003esbatch \u0026lt;script\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003etype: \u003ccode\u003esqueue\u003c/code\u003e and note the job present (most likely running)\u003c/li\u003e\n\u003cli\u003ewhen it disappears from queue (\u003ccode\u003ewatch -n1 squeue\u003c/code\u003e), look for \u003ccode\u003eslurm-\u0026lt;JOBNUM\u0026gt;.out\u003c/code\u003e\ncontaining the job\u0027s output\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-datacompute-automated-setup---install-irods-hook-scripts-for-slurm-prolog--epilog\" class=\"anchor\" aria-hidden=\"true\" href=\"#datacompute-automated-setup---install-irods-hook-scripts-for-slurm-prolog--epilog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData/Compute automated setup - install iRODS hook scripts for slurm prolog / epilog\u003c/h2\u003e\n\u003cp\u003eThe following command will setup prolog and epilog scripts to be run (pre- and post-,\nrespectively) for each job executed by SLURM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo ./slurm_hook_setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1555445240.0 + "updated_at": 1561308424.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity-base-ubuntu20.04-intel2021.1.1" + "Kaysera/Singularity.def" ], - "full_name": "NOAA-GFDL/HPC-ME", + "full_name": "Kaysera/test-reproducibility", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hpc-me-hpc-portable-containers-for-model-environments\" class=\"anchor\" href=\"#hpc-me-hpc-portable-containers-for-model-environments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC-ME: HPC Portable Containers for Model Environments\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contents\" class=\"anchor\" href=\"#contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#what-is-hpc-me\"\u003eWhat is HPC-ME\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#list-of-current-compilers\"\u003eList of current compilers/MPI/OS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#list-of-current-libraries\"\u003eList of current libraries\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-build\"\u003eHow to build\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-use\"\u003eHow to use\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#gfdl-example\"\u003eGFDL example\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#planned-improvements\"\u003ePlanned improvements\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-is-hpc-me\" class=\"anchor\" href=\"#what-is-hpc-me\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is HPC-ME\u003c/h2\u003e\n\u003cp\u003eHPC Portable Container - Model Environments is a set of Dockerfiles, Singularity Definition files, and containers to provide portable model environments for scientific applications that require the same set of libraries. The ultimate goal is to have a community-based list of libraries that are needed for compiling, executing, and post-processing earth science models. We all use many of the same underlying libraries, and by working together we can agree upon a community-based approach to making container usage as standardized as possible.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-list-of-current-compilersmpios\" class=\"anchor\" href=\"#list-of-current-compilersmpios\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eList of current compilers/MPI/OS\u003c/h2\u003e\n\u003cp\u003eFor each container, there is a full version that contains the programming environment and a smaller runtime environment that can be used to run compiled executables. (The runtime container definition files will be added soon.)\n#- \u003ca href=\"Dockerfile_gnu_ubuntu20.04\"\u003egcc 8/mpich/ubuntu 20.04\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"Dockerfile_gnu_rhel8\"\u003egcc 8/mpich/RHEL8\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Dockerfile_intel_ubuntu18.04\"\u003eintel oneAPI 2022.1/mpich(impi)/ubuntu 18.04\u003c/a\u003e\n#- \u003ca href=\"Dockerfile_intel_centos8\"\u003eintel oneAPI 2021.4/mpich(impi)/centos 8\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-list-of-current-libraries\" class=\"anchor\" href=\"#list-of-current-libraries\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eList of current libraries\u003c/h2\u003e\n\u003cp\u003eThis is the current list of most of the libraries used in the HPC-ME containers (We are trying to keep this up-to-date).\nThe complete lit should be found in the respective YAML file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#automake\" rel=\"nofollow\"\u003eautomake@1.16.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#bacio\" rel=\"nofollow\"\u003ebacio@2.4.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#berkeley-db\" rel=\"nofollow\"\u003eberkeley-db@18.1.40\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#bison\" rel=\"nofollow\"\u003ebison@3.7.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#bzip2\" rel=\"nofollow\"\u003ebzip2@1.0.8\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#cmake\" rel=\"nofollow\"\u003ecmake@3.21.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#crtm\" rel=\"nofollow\"\u003ecrtm@2.3.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#curl\" rel=\"nofollow\"\u003ecurl@7.78.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#diffutils\" rel=\"nofollow\"\u003ediffutils@3.7\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#esmf\" rel=\"nofollow\"\u003eesmf@8.1.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#expat\" rel=\"nofollow\"\u003eexpat@2.4.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#g2\" rel=\"nofollow\"\u003eg2@3.4.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#g2tmpl\" rel=\"nofollow\"\u003eg2tmpl@1.10.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#gdbm\" rel=\"nofollow\"\u003egdbm@1.19\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#gsl\" rel=\"nofollow\"\u003egsl@2.7\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#hdf5\" rel=\"nofollow\"\u003ehdf5@1.10.7\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#intel-mpi\" rel=\"nofollow\"\u003eintel-mpi@2019.10.317\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#ip\" rel=\"nofollow\"\u003eip@3.3.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#ip2\" rel=\"nofollow\"\u003eip2@1.1.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#jasper\" rel=\"nofollow\"\u003ejasper@2.0.32\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libbsd\" rel=\"nofollow\"\u003elibbsd@0.11.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libiconv\" rel=\"nofollow\"\u003elibiconv@1.16\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libjpeg-turbo\" rel=\"nofollow\"\u003elibjpeg-turbo@2.1.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libmd\" rel=\"nofollow\"\u003elibmd@1.0.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libpng\" rel=\"nofollow\"\u003elibpng@1.6.37\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libsigsegv\" rel=\"nofollow\"\u003elibsigsegv@2.13\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libxml2\" rel=\"nofollow\"\u003elibxml2@2.9.12\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#libyaml\" rel=\"nofollow\"\u003elibyaml@0.2.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#m4\" rel=\"nofollow\"\u003em4@1.4.19\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#nasm\" rel=\"nofollow\"\u003enasm@2.15.05\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#ncurses\" rel=\"nofollow\"\u003encurses@6.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#nemsio\" rel=\"nofollow\"\u003enemsio@2.5.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#netcdf-c\" rel=\"nofollow\"\u003enetcdf-c@4.8.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#netcdf-fortran\" rel=\"nofollow\"\u003enetcdf-fortran@4.5.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#numactl\" rel=\"nofollow\"\u003enumactl@2.0.14\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#openssl\" rel=\"nofollow\"\u003eopenssl@1.1.1l\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#parallel-netcdf\" rel=\"nofollow\"\u003eparallel-netcdf@1.12.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#perl\" rel=\"nofollow\"\u003eperl@5.34.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#pkgconf\" rel=\"nofollow\"\u003epkgconf@1.8.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#readline\" rel=\"nofollow\"\u003ereadline@8.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#sfcio\" rel=\"nofollow\"\u003esfcio@1.4.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#sigio\" rel=\"nofollow\"\u003esigio@2.3.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#sp\" rel=\"nofollow\"\u003esp@2.3.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#udunits\" rel=\"nofollow\"\u003eudunits@2.2.28\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#w3emc\" rel=\"nofollow\"\u003ew3emc@2.9.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#w3nco\" rel=\"nofollow\"\u003ew3nco@2.4.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#wrf-io\" rel=\"nofollow\"\u003ewrf-io@1.2.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#xerces-c\" rel=\"nofollow\"\u003exerces-c@3.2.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#xz\" rel=\"nofollow\"\u003exz@5.2.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#zlib\" rel=\"nofollow\"\u003ezlib@1.2.11\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#lmod\" rel=\"nofollow\"\u003elmod@8.5.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#nccmp\" rel=\"nofollow\"\u003enccmp@1.8.6.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#nco\" rel=\"nofollow\"\u003enco@4.7.9\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#cray-netcdf\" rel=\"nofollow\"\u003ecray-netcdf@4.6.3.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#cray-hdf5\" rel=\"nofollow\"\u003ecray-hdf5@1.10.5.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#uberftp\" rel=\"nofollow\"\u003euberftp\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-build\" class=\"anchor\" href=\"#how-to-build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to build\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eWe plan to make this step optional soon.\u003c/strong\u003e In order to build the Docker images, you will need access to a computer with root-like access, and either docker or singularity installed. If you do not have root-like access to a suitable machine, you can still run images that were already created (e.g. on Docker hub), and we plan on hosting runnable Docker images along with the Dockerfiles in this repository soon. If you have root-like access and docker, start by choosing one of the currently supported model environments from the list above. Then build the Docker container from the Dockerfile using docker build; for example, to build the gcc8/mpich/ubuntu18 container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --file Dockerfile_gnu_ubuntu20.04 . --tag hpc-me.ubuntu.gnu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe build process takes approximately 2-3 hours, as the packages are downloaded and compiled using Spack. After a successful build, you will see that the image was built and tagged successfully:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSuccessfully built 90a878af77b4\nSuccessfully tagged hpc-me.rhel8.gnu:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you may run the container using docker or singularity on the same host. To run the image on a different machine, pushing the image to Docker Hub is recommended. Note that you will need a DockerHub account to do this (replace USER with your Docker user ID in the examples below). For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker tag hpc-me.rhel8.gnu USER/hpc-me.rhel8.gnu\ndocker login\ndocker push USER/hpc-me.rhel8.gnu:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-use\" class=\"anchor\" href=\"#how-to-use\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use\u003c/h2\u003e\n\u003cp\u003eWe plan to make improvements on this process. Also, while we plan on making Docker images available on the GitHub container registry, currently you must build the images yourself. Please start with the \u003ca href=\"#how-to-build\"\u003eBuild instructions\u003c/a\u003e to generate a Docker image with your desired OS/compiler HPC-ME environment. Then you may run the container using docker or singularity; singularity is more likely than docker to be available on HPC environments.\u003c/p\u003e\n\u003cp\u003eThe usage documentation consists of some general notes on serial/parallel usage, files inside and outside the container, downloading the containers, and then specific usage scenarios:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#serial-applications-using-docker\"\u003eSerial applications using docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#serial-applications-using-singularity\"\u003eSerial applications using singularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#parallel-applications-using-singularity\"\u003eParallel applications using singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-serial-and-parallel-usage\" class=\"anchor\" href=\"#serial-and-parallel-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSerial and parallel usage\u003c/h3\u003e\n\u003cp\u003eHPC-ME containers are intended for both serial and parallel applications. Serial applications include compiling model executables, generating input grids, and post-processing model output. Earth system, climate, and weather models require parallelism to run efficiently, and use one of the Message Passage Interface (MPI) implementations OpenMPI, Intel MPI, or mpich. GCC-based HPC-ME containers use the mpich-based MPI library, which is widely available on most HPC sites, and the Intel-based containers contain both mpich and Intel MPI.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-notes-on-filesystems-and-writing-files\" class=\"anchor\" href=\"#notes-on-filesystems-and-writing-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes on filesystems and writing files\u003c/h3\u003e\n\u003cp\u003eWe recommend not saving or modifying files within the environment container, and instead create and modify files on your regular filesystem. To do this, you will need to connect your filesystem to your container using bind mounts.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-downloading-containers-and-managing-images-on-the-filesystem\" class=\"anchor\" href=\"#downloading-containers-and-managing-images-on-the-filesystem\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownloading containers and managing images on the filesystem\u003c/h3\u003e\n\u003cp\u003eOnce you have pushed your images to DockerHub, you will need to download them before using. In the examples below, replace USER with your Docker Hub ID. If using docker,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull USER/hpc-me.rhel8.gnu:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using singularity,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull docker://USER/hpc-me.rhel8.gnu:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using singularity, the image file (SIF format) is saved to the current working directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; ls *.sif\n-rwxr-xr-x 532M Dec 10 16:09 hpc-me.rhel8.gnu_latest.sif*\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using docker, the downloaded image is handled by the central docker service.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-serial-applications-using-docker\" class=\"anchor\" href=\"#serial-applications-using-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSerial applications using docker\u003c/h3\u003e\n\u003cp\u003eYou may activate an interactive shell within the desired HPC-ME container using docker. After running the container, the compilers and tools available within the container will be accessible in your PATH; e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; docker run -it hpc-me.rhel8.gnu:latest\n\n[root@0d2cf64e1175 /]# which nf-config\n/opt/view/bin/nf-config\n\n[root@0d2cf64e1175 /]# nf-config --version\nnetCDF-Fortran 4.5.3\n\n[root@0d2cf64e1175 /]# nf-config --cflags\n-I/opt/software/linux-rhel8-x86_64/gcc-8.4.1/netcdf-fortran-4.5.3-g5qfkdlp36unt2s4j4wyrc6heh2sa64n/include\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-serial-applications-using-singularity\" class=\"anchor\" href=\"#serial-applications-using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSerial applications using singularity\u003c/h3\u003e\n\u003cp\u003eSingularity can run Docker images and is more likely to be available on HPC environments. As with docker run, the HPC-ME tools and compilers are available in the shell, somewhat similar to loading a set of Environment Modules prepared by site administrators.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;singularity run hpc-me.rhel8.gnu_latest.sif\n\nSingularity\u0026gt; which nf-config\n/opt/view/bin/nf-config\n\nSingularity\u0026gt; nf-config --version\nnetCDF-Fortran 4.5.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-parallel-applications-using-singularity\" class=\"anchor\" href=\"#parallel-applications-using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParallel applications using singularity\u003c/h3\u003e\n\u003cp\u003eHPC-ME containers can provide the runtime environment for MPI applications. For instance, one could compile an MPI application using the instructions above using one of the HPC-ME development containers; and then run the application using the corresponding runtime HPC-ME container.\u003c/p\u003e\n\u003cp\u003ePlease note that we are continuing to improve the usability of HPC-ME containers as well as provide more usage examples.\u003c/p\u003e\n\u003cp\u003eUsually, GFDL climate models are run on gaea by submitting a runscript to the Slurm scheduler. The runscript loads needed runtime Environment Modules, prepares input directories and files, and executes the MPI executable using srun. The HPC-ME containers provide the necessary runtime environment, obviating the need for loading Environment Modules. Currently, our approach for using the HPC-ME containers is as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a new container, starting with the desired HPC-ME runtime container\u003c/li\u003e\n\u003cli\u003eAdd the MPI-compiled executable to the container filesystem\u003c/li\u003e\n\u003cli\u003eSet the MPI-compiled executable to as the container\u0027s command (so that when the container is run the MPI executable within the container runs)\u003c/li\u003e\n\u003cli\u003eRun the singularity container SIF file using srun within the runscript, replacing the traditional MPI executable.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eReplace \"srun executable.x\" with \"srun singularity run container.SIF\"\u003c/li\u003e\n\u003cli\u003eAdd --mpi=pmi2 to the srun call, which connects the system MPI to the container MPI to the singularity run call\u003c/li\u003e\n\u003cli\u003eBind the working directory so that the container has access to the input files and can write output files (singularity run -B=/path/to/workdir)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eSubmit the modified runscript to the scheduler\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe plan to provide more examples and usage scenarios, such as using the HPC-ME containers as-is (i.e. not creating a new container as described above)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-gfdl-example\" class=\"anchor\" href=\"#gfdl-example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGFDL example\u003c/h2\u003e\n\u003cp\u003eAn example of using an HPC-ME container with the GFDL FRE workflow can be found \u003ca href=\"GFDL_EXAMPLE.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-planned-improvements\" class=\"anchor\" href=\"#planned-improvements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanned improvements\u003c/h2\u003e\n\u003cp\u003eHPC-ME is a work in progress under active development, so please check back or follow the repository for more updates.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-cache\" class=\"anchor\" href=\"#build-cache\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild cache\u003c/h3\u003e\n\u003cp\u003eWe are working to create a build cache for the libraries listed so that building the containers is quick and easy.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-github-container-registry\" class=\"anchor\" href=\"#github-container-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGithub container registry\u003c/h3\u003e\n\u003cp\u003eWe are working to add CI capability to this repository, so that the containers will be automatically built and stored in the github container registry. This will make building unnecessary for most cases, though users may build the containers themselves if they wish (e.g. for custom modifications).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-more-usage-examples-and-documentation-especially-for-mpi-applications\" class=\"anchor\" href=\"#more-usage-examples-and-documentation-especially-for-mpi-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore usage examples and documentation, especially for MPI applications\u003c/h3\u003e\n\u003cp\u003eWe are still learning how to best use the HPC-ME containers with MPI appliations, so please check back.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-disclaimer\" class=\"anchor\" href=\"#disclaimer\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h3\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is provided\non an \u0027as is\u0027 basis and the user assumes responsibility for its use. DOC has\nrelinquished control of the information and no longer has responsibility to\nprotect the integrity, confidentiality, or availability of the information. Any\nclaims against the Department of Commerce stemming from the use of its GitHub\nproject will be governed by all applicable Federal law. Any reference to\nspecific commercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply their\nendorsement, recommendation or favoring by the Department of Commerce. The\nDepartment of Commerce seal and logo, or the seal and logo of a DOC bureau,\nshall not be used in any manner to imply endorsement of any commercial product\nor activity by DOC or the United States Government.\u003c/p\u003e\n\u003cp\u003eThis project code is made available through GitHub but is managed by NOAA-GFDL\nat \u003ca href=\"https://gitlab.gfdl.noaa.gov\" rel=\"nofollow\"\u003ehttps://gitlab.gfdl.noaa.gov\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-test-for-future-simd-reproducibility\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-for-future-simd-reproducibility\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest for future SIMD reproducibility\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1650907447.0 + "updated_at": 1655130007.0 }, { "data_format": 2, @@ -17487,46 +17106,45 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/miniforge3-bioconda-perl-bioperl", + "full_name": "kiwiroy/singularity-perlbrew", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-a-miniforge3-based-container-with-bioconda-bioperl\" class=\"anchor\" href=\"#a-miniforge3-based-container-with-bioconda-bioperl\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ea miniforge3 based container with bioconda-bioperl\u003c/h1\u003e\n\u003cp\u003eUsing \u003ca href=\"https://github.com/conda-forge/miniforge/\"\u003ehttps://github.com/conda-forge/miniforge/\u003c/a\u003e instead of miniconda3 from Anaconda.com\u003c/p\u003e\n\u003cp\u003edocker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/miniforge3-bioconda-perl-bioperl:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv oras://ghcr.io/truatpasteurdotfr/miniforge3-bioconda-perl-bioperl:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2845\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-perlbrew\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-perlbrew\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-perlbrew\u003c/h1\u003e\n\u003cp\u003eA simple ubuntu base with perlbrew installed. Useful as a base image for brewing\nspecific versions of perl.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1651682376.0 + "updated_at": 1556532781.0 }, { "data_format": 2, - "description": "BRAKER is a pipeline for fully automated prediction of protein coding gene structures with GeneMark-ES/ET and AUGUSTUS in novel eukaryotic genomes.", + "description": "Centos 7 base image for ACI", "filenames": [ - "2.1.5/Singularity", - "2.1.6/Singularity" + "Singularity", + "Singularity.cuda9.1", + "Singularity.gpu", + "Singularity.test" ], - "full_name": "pscedu/singularity-braker2", - "latest_release": "v2.1.6", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pscedu/singularity-braker2/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-braker2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pscedu/singularity-braker2/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-braker2/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/19dcb2c13dec2c34ca260c8ca2b2a8209ecde7a198e864340ede4eba62cb3d33/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6272616b657232\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/19dcb2c13dec2c34ca260c8ca2b2a8209ecde7a198e864340ede4eba62cb3d33/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6272616b657232\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-braker2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/0bd8da7fc9970e7e157de2eec966b6db39f4c9445336118b4feae68787406ca0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6272616b657232\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0bd8da7fc9970e7e157de2eec966b6db39f4c9445336118b4feae68787406ca0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6272616b657232\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-braker2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/1538a0ecc9226a4026e275016281ee2daead4806b53f7afec5b265acf1ff03ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6272616b657232\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1538a0ecc9226a4026e275016281ee2daead4806b53f7afec5b265acf1ff03ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6272616b657232\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-braker2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/c222f5a51b626a831c4b4d20b0afdb3b1157ec9ca835a8441f90b570f5fa7f0c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6272616b657232\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c222f5a51b626a831c4b4d20b0afdb3b1157ec9ca835a8441f90b570f5fa7f0c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6272616b657232\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-braker2\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularity-braker2\" class=\"anchor\" href=\"#singularity-braker2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-braker2\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/a84569b5f282590e3c4947c993c063920444aca07bdf99e5914d7abcc82d939a/68747470733a2f2f7777772e62696f727869762e6f72672f636f6e74656e742f62696f727869762f6561726c792f323032302f30382f31312f323032302e30382e31302e3234353133342f46312e6c617267652e6a7067\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a84569b5f282590e3c4947c993c063920444aca07bdf99e5914d7abcc82d939a/68747470733a2f2f7777772e62696f727869762e6f72672f636f6e74656e742f62696f727869762f6561726c792f323032302f30382f31312f323032302e30382e31302e3234353133342f46312e6c617267652e6a7067\" width=\"50%\" data-canonical-src=\"https://www.biorxiv.org/content/biorxiv/early/2020/08/11/2020.08.10.245134/F1.large.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/Gaius-Augustus/BRAKER\"\u003eBRAKER2\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" href=\"#installing-the-container-on-bridges-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/braker2/2.1.5\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/BRAKER2\u003c/code\u003e as \u003ccode\u003e2.1.5.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "willgpaik/centos7_aci", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7_aci\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos7_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7_aci\u003c/h1\u003e\n\u003cp\u003eCentos 7 base image for ACI Singualarity recipe\u003cbr\u003e\nThis recipe may include unnecessary packages for certain software installation.\u003cbr\u003e\nSize of CPU-only container: ~1 GB\u003cbr\u003e\nSize of GPU container: ~2.6 GB\u003c/p\u003e\n\u003cp\u003eMore packages will be added in the future\u003c/p\u003e\n\u003cp\u003e2019/2/17\n\u003cstrong\u003eCentos 7\u003c/strong\u003e with \u003cstrong\u003eGCC 8\u003c/strong\u003e\u003cbr\u003e\nTo enable GCC 8,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; source /opt/rh/devtoolset-8/enable\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e2019/3/1\u003cbr\u003e\nOpenMPI is added to \u003ccode\u003e$PATH\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e2019/3/11\u003cbr\u003e\nOpenMPI is updated to version 2.1.6\u003c/p\u003e\n\u003cp\u003e2019/4/12\u003cbr\u003e\nBoost 1.70.0 in added\u003c/p\u003e\n\u003cp\u003e2019/7/19\u003cbr\u003e\n\u003cdel\u003ePython 2 and 3 are updated to version 2.7.16 and version 3.7.4\u003c/del\u003e\u003cbr\u003e\nOpenMPI is updated to version 4.0.1\u003c/p\u003e\n\u003cp\u003e2019/7/21\u003cbr\u003e\n\u003cdel\u003eFew Python packages are added\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e2019/7/22\u003cbr\u003e\n\u003cdel\u003eFew corrections are made including Python\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e2019/7/23\u003cbr\u003e\nPythons are replaced with packages\u003cbr\u003e\nTo enable Python 2.7.16,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; source /opt/rh/python27/enable\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSystem version of python is 3.6.8\u003c/p\u003e\n\u003cp\u003e2019/7/30\u003cbr\u003e\ndevtoolset-7 GCC is added (some software can\u0027t be built with GCC 8)\u003c/p\u003e\n\u003cp\u003e2019/11/9\u003cbr\u003e\nCMake 3.15.5 is added\u003c/p\u003e\n\u003cp\u003e2019/11/22\u003cbr\u003e\nOpenMPI is downgraded to 1.10.1 to match version on ACI\u003c/p\u003e\n\u003cp\u003e2020/2/12\u003cbr\u003e\nBoost is upgraded to 1.72.0 and CMake is upgraded to 3.16.4\u003c/p\u003e\n\u003cp\u003e2020/3/2\u003cbr\u003e\nGPU version is added\u003c/p\u003e\n\u003cp\u003e2020/9/21\u003cbr\u003e\nMinor updates are made (regarding libxkb)\u003c/p\u003e\n\u003cp\u003e2020/9/28\u003cbr\u003e\nRecipe for CUDA 9.1 is added (for FSL with CUDA)\u003c/p\u003e\n\u003cp\u003e2020/10/11\u003cbr\u003e\nBoost is upgraded to 1.74.0 and CMake is upgraded to 3.18.4\u003cbr\u003e\nR 4.0.3 is added (Curl 7.72.0 and XZ 5.2.5 are added for R)\u003cbr\u003e\nVirtualGL is downgraded to 2.5.2 to match system version\u003c/p\u003e\n\u003cp\u003e2020/10/18\u003cbr\u003e\nUDUNITS 2.2.26 is added\u003c/p\u003e\n\u003cp\u003e2020/10/20\u003cbr\u003e\nTix-devel, Tx-devel, TkInter-devel, LAPACK-devel, and BLAS-devel are added\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1649280757.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1603227322.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for sex-detector-plusplus (https://gitlab.in2p3.fr/sex-det-family/sex-detector-plusplus)", "filenames": [ - "Singularity.binutils-2.36.1-GCCcore-11.1.0-centos7.def", - "Singularity.zlib-1.2-centos7.def" + "Singularity", + "Singularity.00f7d723" ], - "full_name": "jkwmoore/centos7-eb-singularity-image", + "full_name": "powerPlant/sex-detector-plusplus-srf", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7-eb-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos7-eb-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7-eb-singularity-image\u003c/h1\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for SEX-DETector, a tool for the statistical inferrence of sex-linked genes from RNA / DNA reads from a cross (parents and set of childrens)\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1653324040.0 + "updated_at": 1600917082.0 }, { "data_format": 2, @@ -17534,157 +17152,171 @@ var data = "filenames": [ "Singularity" ], - "full_name": "michalpolic/yolact", + "full_name": "lixuekai2001/brain-inversion", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-you-only-look-at-coefficients\" class=\"anchor\" aria-hidden=\"true\" href=\"#you-only-look-at-coefficients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eY\u003c/strong\u003eou \u003cstrong\u003eO\u003c/strong\u003enly \u003cstrong\u003eL\u003c/strong\u003eook \u003cstrong\u003eA\u003c/strong\u003et \u003cstrong\u003eC\u003c/strong\u003eoefficien\u003cstrong\u003eT\u003c/strong\u003es\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003e \u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\n \u255a\u2588\u2588\u2557 \u2588\u2588\u2554\u255d\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u2550\u2588\u2588\u2554\u2550\u2550\u255d\n \u255a\u2588\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u255a\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2551 \n \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA simple, fully convolutional model for real-time instance segmentation. This is the code for our papers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1904.02689\" rel=\"nofollow\"\u003eYOLACT: Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003eYOLACT++: Better Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-yolact-v12-released-changelog\" class=\"anchor\" aria-hidden=\"true\" href=\"#yolact-v12-released-changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYOLACT++ (v1.2) released! (\u003ca href=\"CHANGELOG.md\"\u003eChangelog\u003c/a\u003e)\u003c/h4\u003e\n\u003cp\u003eYOLACT++\u0027s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e (check out our journal paper \u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIn order to use YOLACT++, make sure you compile the DCNv2 code. (See \u003ca href=\"https://github.com/dbolya/yolact#installation\"\u003eInstallation\u003c/a\u003e)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-for-a-real-time-demo-check-out-our-iccv-video\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-a-real-time-demo-check-out-our-iccv-video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor a real-time demo, check out our ICCV video:\u003c/h4\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=0pMfmo8qfpQ\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c9e061dab1a6754a417d6eb14803f1104cce54aa9231aa0d779e2523694ed23e/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f30704d666d6f38716670512f302e6a7067\" alt=\"IMAGE ALT TEXT HERE\" data-canonical-src=\"https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSome examples from our YOLACT base model (33.5 fps on a Titan Xp and 29.8 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e):\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_0.png\"\u003e\u003cimg src=\"data/yolact_example_0.png\" alt=\"Example 0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_1.png\"\u003e\u003cimg src=\"data/yolact_example_1.png\" alt=\"Example 1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_2.png\"\u003e\u003cimg src=\"data/yolact_example_2.png\" alt=\"Example 2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repository and enter it:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/dbolya/yolact.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e yolact\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eSet up the environment using one of the following methods:\n\u003cul\u003e\n\u003cli\u003eUsing \u003ca href=\"https://www.anaconda.com/distribution/\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eRun \u003ccode\u003econda env create -f environment.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eManually with pip\n\u003cul\u003e\n\u003cli\u003eSet up a Python3 environment (e.g., using virtenv).\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"http://pytorch.org/\" rel=\"nofollow\"\u003ePytorch\u003c/a\u003e 1.0.1 (or higher) and TorchVision.\u003c/li\u003e\n\u003cli\u003eInstall some other packages:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Cython needs to be installed before pycocotools\u003c/span\u003e\npip install cython\npip install opencv-python pillow pycocotools matplotlib \u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to train YOLACT, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into \u003ccode\u003e./data/coco\u003c/code\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to evaluate YOLACT on \u003ccode\u003etest-dev\u003c/code\u003e, download \u003ccode\u003etest-dev\u003c/code\u003e with this script.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO_test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you want to use YOLACT++, compile deformable convolutional layers (from \u003ca href=\"https://github.com/CharlesShang/DCNv2/tree/pytorch_1.0\"\u003eDCNv2\u003c/a\u003e).\nMake sure you have the latest CUDA toolkit installed from \u003ca href=\"https://developer.nvidia.com/cuda-toolkit\" rel=\"nofollow\"\u003eNVidia\u0027s Website\u003c/a\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e external/DCNv2\npython setup.py build develop\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-hidden=\"true\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvaluation\u003c/h1\u003e\n\u003cp\u003eHere are our YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on \u003ccode\u003etest-dev\u003c/code\u003e:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1yp7ZbbDwvMiFJEq4ptVKTYTI2VeRDXl0/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EUVpxoSXaqNIlssoLKOEoCcB1m0RpzGq_Khp5n1VX3zcUw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDarknet53-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.7\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1dukLrTzZQEuhzitGkHaGjphlmRJOjVnP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_darknet53_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/ERrao26c8llJn25dIyZPhwMBxUp2GdZTKIMUQA3t0djHLw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e29.8\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf_/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EYRWxBEoKU9DiblrWx2M89MBGFkVVB_drlRd_v5sdT3Hgg\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e700\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e23.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e31.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1lE4Lz5p25teiXV-6HdTiOJSnS7u7GBzg/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_im700_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/Eagg5RSc5hFEhp7sPtvLNyoBjhlf2feog7t8OQzHKKphjw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYOLACT++ models (released on December 16th, 2019):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1ZPu1YR2UzGHQD0o1rEqy-j5bmEm3lbyP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EcJAtMiEFlhAnVsDf00yWRIBUC4m8iE9NEEiV05XwtEoGw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e27.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.6\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/15id0Qq5eqRbkD-N3ZjDZXdCvRyIaHpFB/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EVQ62sF0SrJPrl_68onyHF8BpG7c05A8PavV4a849sZgEA\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo evalute the model, put the corresponding weights file in the \u003ccode\u003e./weights\u003c/code\u003e directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., \u003ccode\u003eyolact_base\u003c/code\u003e for \u003ccode\u003eyolact_base_54_800000.pth\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quantitative-results-on-coco\" class=\"anchor\" aria-hidden=\"true\" href=\"#quantitative-results-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuantitative Results on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This should get 29.92 validation mask mAP last time I checked.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Output a COCOEval json to submit to the website or to use the run_coco_eval.py script.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This command will create \u0027./results/bbox_detections.json\u0027 and \u0027./results/mask_detections.json\u0027 for detection and instance segmentation respectively.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py.\u003c/span\u003e\npython run_coco_eval.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-qualitative-results-on-coco\" class=\"anchor\" aria-hidden=\"true\" href=\"#qualitative-results-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQualitative Results on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on COCO. From here on I\u0027ll use a confidence threshold of 0.15.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --display\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-benchmarking-on-coco\" class=\"anchor\" aria-hidden=\"true\" href=\"#benchmarking-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarking on COCO\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run just the raw model on the first 1k images of the validation set\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark --max_images=1000\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on the specified image.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process an image and save it to another file.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a whole folder of images.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-video\" class=\"anchor\" aria-hidden=\"true\" href=\"#video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVideo\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a video in real-time. \"--video_multiframe\" will process that many frames at once for improved performance.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you want, use \"--display_fps\" to draw the FPS directly on the frame.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a video and save it to another file. This uses the same pipeline as the ones above now, so it\u0027s fast!\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs you can tell, \u003ccode\u003eeval.py\u003c/code\u003e can do a ton of stuff. Run the \u003ccode\u003e--help\u003c/code\u003e command to see everything it can do.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython eval.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining\u003c/h1\u003e\n\u003cp\u003eBy default, we train on COCO. Make sure to download the entire dataset using the commands above.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo train, grab an imagenet-pretrained model and put it in \u003ccode\u003e./weights\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eFor Resnet101, download \u003ccode\u003eresnet101_reducedfc.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1tvqFPd4bJtakOlmn-uIA492g2qurRChj/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Resnet50, download \u003ccode\u003eresnet50-19c8e357.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1Jy3yCdbatgXa5YYIdTCRrSV0S9V5g1rn/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Darknet53, download \u003ccode\u003edarknet53.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRun one of the training commands below.\n\u003cul\u003e\n\u003cli\u003eNote that you can press ctrl+c while training and it will save an \u003ccode\u003e*_interrupt.pth\u003c/code\u003e file at the current iteration.\u003c/li\u003e\n\u003cli\u003eAll weights are saved in the \u003ccode\u003e./weights\u003c/code\u003e directory by default with the file name \u003ccode\u003e\u0026lt;config\u0026gt;_\u0026lt;epoch\u0026gt;_\u0026lt;iter\u0026gt;.pth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains using the base config with a batch size of 8 (the default).\u003c/span\u003e\npython train.py --config=yolact_base_config\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.\u003c/span\u003e\npython train.py --config=yolact_base_config --batch_size=5\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file\u0027s name.\u003c/span\u003e\npython train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the help option to see a description of all available command line arguments\u003c/span\u003e\npython train.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-multi-gpu-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#multi-gpu-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-GPU Support\u003c/h2\u003e\n\u003cp\u003eYOLACT now supports multiple GPUs seamlessly during training:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBefore running any of the scripts, run: \u003ccode\u003eexport CUDA_VISIBLE_DEVICES=[gpus]\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3).\u003c/li\u003e\n\u003cli\u003eYou should still do this if only using 1 GPU.\u003c/li\u003e\n\u003cli\u003eYou can check the indices of your GPUs with \u003ccode\u003envidia-smi\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThen, simply set the batch size to \u003ccode\u003e8*num_gpus\u003c/code\u003e with the training commands above. The training script will automatically scale the hyperparameters to the right values.\n\u003cul\u003e\n\u003cli\u003eIf you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you\u0027re using.\u003c/li\u003e\n\u003cli\u003eIf you want to allocate the images per GPU specific for different GPUs, you can use \u003ccode\u003e--batch_alloc=[alloc]\u003c/code\u003e where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to \u003ccode\u003ebatch_size\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-logging\" class=\"anchor\" aria-hidden=\"true\" href=\"#logging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogging\u003c/h2\u003e\n\u003cp\u003eYOLACT now logs training and validation information by default. You can disable this with \u003ccode\u003e--no_log\u003c/code\u003e. A guide on how to visualize these logs is coming soon, but now you can look at \u003ccode\u003eLogVizualizer\u003c/code\u003e in \u003ccode\u003eutils/logger.py\u003c/code\u003e for help.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pascal-sbd\" class=\"anchor\" aria-hidden=\"true\" href=\"#pascal-sbd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePascal SBD\u003c/h2\u003e\n\u003cp\u003eWe also include a config for training on Pascal SBD annotations (for rapid experimentation or comparing with other methods). To train on Pascal SBD, proceed with the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the dataset from \u003ca href=\"http://home.bharathh.info/pubs/codes/SBD/download.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. It\u0027s the first link in the top \"Overview\" section (and the file is called \u003ccode\u003ebenchmark.tgz\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eExtract the dataset somewhere. In the dataset there should be a folder called \u003ccode\u003edataset/img\u003c/code\u003e. Create the directory \u003ccode\u003e./data/sbd\u003c/code\u003e (where \u003ccode\u003e.\u003c/code\u003e is YOLACT\u0027s root) and copy \u003ccode\u003edataset/img\u003c/code\u003e to \u003ccode\u003e./data/sbd/img\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDownload the COCO-style annotations from \u003ca href=\"https://drive.google.com/open?id=1ExrRSPVctHW8Nxrn0SofU1lVhK5Wn0_S\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eExtract the annotations into \u003ccode\u003e./data/sbd/\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eNow you can train using \u003ccode\u003e--config=yolact_resnet50_pascal_config\u003c/code\u003e. Check that config to see how to extend it to other models.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI will automate this all with a script soon, don\u0027t worry. Also, if you want the script I used to convert the annotations, I put it in \u003ccode\u003e./scripts/convert_sbd.py\u003c/code\u003e, but you\u0027ll have to check how it works to be able to use it because I don\u0027t actually remember at this point.\u003c/p\u003e\n\u003cp\u003eIf you want to verify our results, you can download our \u003ccode\u003eyolact_resnet50_pascal_config\u003c/code\u003e weights from \u003ca href=\"https://drive.google.com/open?id=1yLVwtkRtNxyl0kxeMCtPXJsXFFyc_FHe\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. This model should get 72.3 mask AP_50 and 56.2 mask AP_70. Note that the \"all\" AP isn\u0027t the same as the \"vol\" AP reported in others papers for pascal (they use an averages of the thresholds from \u003ccode\u003e0.1 - 0.9\u003c/code\u003e in increments of \u003ccode\u003e0.1\u003c/code\u003e instead of what COCO uses).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-custom-datasets\" class=\"anchor\" aria-hidden=\"true\" href=\"#custom-datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCustom Datasets\u003c/h2\u003e\n\u003cp\u003eYou can also train on your own dataset by following these steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a COCO-style Object Detection JSON annotation file for your dataset. The specification for this can be found \u003ca href=\"http://cocodataset.org/#format-data\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Note that we don\u0027t use some fields, so the following may be omitted:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003einfo\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eliscense\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eUnder \u003ccode\u003eimage\u003c/code\u003e: \u003ccode\u003elicense, flickr_url, coco_url, date_captured\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecategories\u003c/code\u003e (we use our own format for categories, see below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a definition for your dataset under \u003ccode\u003edataset_base\u003c/code\u003e in \u003ccode\u003edata/config.py\u003c/code\u003e (see the comments in \u003ccode\u003edataset_base\u003c/code\u003e for an explanation of each field):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003emy_custom_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset_base\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecopy\u003c/span\u003e({\n \u003cspan class=\"pl-s\"\u003e\u0027name\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027My Dataset\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027train_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027train_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027valid_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027valid_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027has_gt\u0027\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027class_names\u0027\u003c/span\u003e: (\u003cspan class=\"pl-s\"\u003e\u0027my_class_id_1\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_2\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_3\u0027\u003c/span\u003e, ...)\n})\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA couple things to note:\n\u003cul\u003e\n\u003cli\u003eClass IDs in the annotation file should start at 1 and increase sequentially on the order of \u003ccode\u003eclass_names\u003c/code\u003e. If this isn\u0027t the case for your annotation file (like in COCO), see the field \u003ccode\u003elabel_map\u003c/code\u003e in \u003ccode\u003edataset_base\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf you do not want to create a validation split, use the same image path and annotations file for validation. By default (see \u003ccode\u003epython train.py --help\u003c/code\u003e), \u003ccode\u003etrain.py\u003c/code\u003e will output validation mAP for the first 5000 images in the dataset every 2 epochs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFinally, in \u003ccode\u003eyolact_base_config\u003c/code\u003e in the same file, change the value for \u003ccode\u003e\u0027dataset\u0027\u003c/code\u003e to \u003ccode\u003e\u0027my_custom_dataset\u0027\u003c/code\u003e or whatever you named the config object above. Then you can use any of the training commands in the previous section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-creating-a-custom-dataset-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-a-custom-dataset-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a Custom Dataset from Scratch\u003c/h4\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/dbolya/yolact/issues/70#issuecomment-504283008\"\u003ethis nice post by @Amit12690\u003c/a\u003e for tips on how to annotate a custom dataset and prepare it for use with YOLACT.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h1\u003e\n\u003cp\u003eIf you use YOLACT or this code base in your work, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{yolact-iccv2019,\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n title = {YOLACT: {Real-time} Instance Segmentation},\n booktitle = {ICCV},\n year = {2019},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor YOLACT++, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{yolact-plus-arxiv2019,\n title = {YOLACT++: Better Real-time Instance Segmentation},\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n year = {2019},\n eprint = {1912.06218},\n archivePrefix = {arXiv},\n primaryClass = {cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h1\u003e\n\u003cp\u003eFor questions about our paper or code, please contact \u003ca href=\"mailto:dbolya@ucdavis.edu\"\u003eDaniel Bolya\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1653076832.0 + "updated_at": 1652667265.0 }, { "data_format": 2, - "description": "Modified copy of GEMMA version 0.93 (Zhou and Stephens) for use with bugs", + "description": "EPACTS container", "filenames": [ "Singularity" ], - "full_name": "danny-wilson/gemma0.93b", - "latest_release": "v0.1", + "full_name": "CHPC-UofU/Singularity-ubuntu-epacts", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1653040520.0 + "updated_at": 1504217055.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "jt2gtwci/HessianScreeningRule", - "latest_release": "v0.2.0", - "readme": "\n\u003ch1\u003e\u003ca id=\"user-content-code-for-the-hessian-screening-rule\" class=\"anchor\" aria-hidden=\"true\" href=\"#code-for-the-hessian-screening-rule\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode for the Hessian Screening Rule\u003c/h1\u003e\n\n\n\u003ch2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h2\u003e\n\u003cp\u003eThe results from the simulations are stored in the \u003ca href=\"results/\"\u003eresults\nfolder\u003c/a\u003e. The figures and tables in the paper, generated from\nthese results, are stored in \u003ca href=\"figures/\"\u003e\u003ccode\u003efigures/\u003c/code\u003e\u003c/a\u003e and\n\u003ca href=\"tables/\"\u003e\u003ccode\u003etables/\u003c/code\u003e\u003c/a\u003e respectively.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reproducing-the-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#reproducing-the-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReproducing the Results\u003c/h2\u003e\n\u003cp\u003eTo reproduce the results, we recommend you use the singularity\ncontainer. See the release section on GitHub and download the container\nfrom there. To run an experiment from the singularity container, call\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --no-home --bind results:/project/results container.sif \u0026lt;script\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;script\u0026gt;\u003c/code\u003e should be a name of a script to run from the\n\u003ca href=\"experiments/\"\u003eexperiments folder\u003c/a\u003e folder, such as\n\u003ccode\u003eexperiments/simulateddata.R\u003c/code\u003e. The results will then be output to the\n\u003ccode\u003eresults\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-re-building-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#re-building-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRe-building the Singularity Container\u003c/h3\u003e\n\u003cp\u003eIf you want to re-build the singularity container (or simply want to\nclone the repo to your local drive), you can do so via the following\nsteps.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone the repository to your local hard drive. On linux, using SSH\nauthentication, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:jt2gtwci/HessianScreeningRule.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the root of the repo and build the singularity container\nby calling\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd HessianScreeningRule\nsudo singularity build container.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen proceed as in \u003ca href=\"#reproducing-the-results\"\u003eReproducing the Results\u003c/a\u003e\nto run the experiments.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-experiments-without-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-experiments-without-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Experiments without Singularity\u003c/h3\u003e\n\u003cp\u003eAlternatively, you may also reproduce the results by cloning this\nrepository, then either opening the \u003ccode\u003eHessianScreening.Rproj\u003c/code\u003e file in R\nStudio or starting R in the root directory of this folder (which will\nactivate the renv repository) and then run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erenv::restore()\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto restore the project library. Then build the R package (see below) and\nrun the simulations directly by running the scripts in the experiments\nfolder. This is not recommended, however, since it, unlike the\nSingularity container approach, does not exactly reproduce the software\nenvironment used when these simulations where originally run and may\nresult in discrepancies due to differences in for instance operating\nsystems, compilers, and BLAS/LAPACK implementations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-r-package\" class=\"anchor\" aria-hidden=\"true\" href=\"#r-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR Package\u003c/h2\u003e\n\u003cp\u003eIf you want to build and experiment with the package, you can do so by\ncalling\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e R CMD INSTALL .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003cp\u003eThe data sets used for the project are not stored on this repository and\nhave to be downloaded by running the script found in\n\u003ca href=\"data-raw/\"\u003e\u003ccode\u003edata-raw/\u003c/code\u003e\u003c/a\u003e. This does not apply when you use the\nsingularity container, however, since the data sets are stored inside it\n(and could technically be retrieved from it too).\u003c/p\u003e\n", + "full_name": "mshow34jt/analysis_container", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-analysis_container\" class=\"anchor\" aria-hidden=\"true\" href=\"#analysis_container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eanalysis_container\u003c/h1\u003e\n\u003cp\u003egit clone \u003ca href=\"http://github.com/mshow34jt/analysis_container\"\u003ehttp://github.com/mshow34jt/analysis_container\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ecd analysis_container\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eto build with Docker\u003c/h3\u003e\n\u003cp\u003edocker build -t analysis:v1 .\u003c/p\u003e\n\u003cp\u003eexecute with:\u003cbr\u003e\ndocker run --rm -d --network host --name analysis -v $PWD/log:/data/log -v $PWD/ldms:/data/ldms -v $PWD/slurm:/data/slurm -v /etc/localtime:/etc/localtime analysis:v1\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-proceed-with-singularity-as-an-alternative\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-proceed-with-singularity-as-an-alternative\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo proceed with Singularity as an alternative:\u003c/h3\u003e\n\u003cp\u003edocker save analysis:v1 \u0026gt;analysisv1.tar\u003c/p\u003e\n\u003cp\u003esingularity build analysis.sif docker-archive://analysisv1.tar\u003c/p\u003e\n\u003cp\u003ealternatively build without docker requires root or fakeroot setup\nsteps to build image (sif file) and start instance (example):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIn the wscont/ folder, as the container owner user, run ./dock2sing.sh (generates Singularity.def)\u003c/li\u003e\n\u003cli\u003eBe sure to setup \"fakeroot\" requirements first if not there already.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/cli/singularity_config_fakeroot.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/cli/singularity_config_fakeroot.html\u003c/a\u003e\nsingularity build --fakeroot analysis.sif Singularity.def\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-move-the-file-to-the-desired-host-and-there-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#move-the-file-to-the-desired-host-and-there-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMove the file to the desired host, and there run\u2026\u003c/h3\u003e\n\u003cp\u003esingularity instance start --bind /storage/nvme0n1/ncsa/eclipse/store_function_csv/spool/:/data/ldms --bind /storage/slurm/eclipse/spool-bitzer/job_detail:/data/slurm --bind /etc/localtime:/etc/localtime --bind /storage/nvme0n1/ncsa/log:/data/log analysis.sif analysis\u003c/p\u003e\n\u003cp\u003eThe first time the container is started, you will need to prime the database with test data and metadata for the metrics\u003cbr\u003e\nI do it interactively with singularity shell instance://analysis\u003cbr\u003e\ncat tests.csv |./inserttests.pl\u003cbr\u003e\ncat eclipse_md.csv |./insertmd.pl\nexit\u003c/p\u003e\n\u003cp\u003esingularity run instance://analysis /jobmon/bin/init.sh \u0026amp;\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1652962267.0 + "updated_at": 1636506455.0 }, { "data_format": 2, "description": null, "filenames": [ - "diamond-with-ncbidb/Singularity" + "Singularity", + "Singularity.test2" ], - "full_name": "AsagaKosho/containers", + "full_name": "rsm5139/singularity", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1652842419.0 + "updated_at": 1551716847.0 }, { "data_format": 2, - "description": "a Singularity recipe with openmpi 2.1.1 on base centos 7 to run on the Cineca clusters x86_64 based", + "description": null, "filenames": [ "Singularity" ], - "full_name": "simarocchi/openmpi_centos7_x86_64", + "full_name": "Freakey17/CP4TP", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-openmpi_centos7_x86_64\" class=\"anchor\" aria-hidden=\"true\" href=\"#openmpi_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopenmpi_centos7_x86_64\u003c/h1\u003e\n\u003cp\u003ea Singularity recipe with openmpi 2.1.1 on base centos 7 to run on the Cineca clusters x86_64 based\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1605098444.0 + "updated_at": 1557407944.0 }, { "data_format": 2, - "description": "WaveUnet for Saraga Dataset (Indian Carnatic Music)", + "description": "Singularity recipes for ALCF-Theta", "filenames": [ - "Singularity" + "singularity_recipes/Singularity.py36", + "singularity_recipes/Singularity.hello_world", + "singularity_recipes/Singularity.mpich33" ], - "full_name": "its-rajesh/WaveUnet", + "full_name": "Romit-Maulik/Theta_Containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-waveunet-implementation-for-saraga-dataset-indian-carnatic-music\" class=\"anchor\" aria-hidden=\"true\" href=\"#waveunet-implementation-for-saraga-dataset-indian-carnatic-music\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWaveUnet Implementation for Saraga Dataset (Indian Carnatic Music)\u003c/h1\u003e\n\u003cp\u003eActual Network: \u003ca href=\"https://github.com/f90/Wave-U-Net-Pytorch\"\u003ehttps://github.com/f90/Wave-U-Net-Pytorch\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSaraga Carnatic Dataset:\u003c/p\u003e\n\u003cp\u003eIt has five stems: Mixed, Vocal, Violin, Mrindangam Right and Mrindangam Left.\nConverting Mrindangam left and right into single audio file (mrindangam)\nExpecting Four stem output namely: Vocal, violin, mrindangam and others\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWith Bleeding (Actual Dataset)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSDR:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWithout Bleeding (Bleeding Removed Dataset)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSDR:\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers-on-theta\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers-on-theta\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers on Theta\u003c/h1\u003e\n\u003cp\u003eSingularity recipes for ALCF-Theta\u003c/p\u003e\n\u003cp\u003eSingularity hub is discontinued. One must build on dockerhub and pull on ALCF.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1665079190.0 + "updated_at": 1619207429.0 }, { "data_format": 2, - "description": "openjdk:8 based release of CANU, a PacBio assembler", + "description": "Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.", "filenames": [ - "Singularity" + "2.10.8/Singularity", + "2.10.9/Singularity", + "2.11.0/Singularity" ], - "full_name": "sghignone/canu", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-canu\" class=\"anchor\" aria-hidden=\"true\" href=\"#canu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecanu\u003c/h1\u003e\n", + "full_name": "pscedu/singularity-sra-toolkit", + "latest_release": "v2.11.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-sra-toolkit/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-sra-toolkit/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-sra-toolkit/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-sra-toolkit/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/31e3cb09d81e6fb61f9191e5ce617c6808d24498265a7a0c0f5b82303b18306d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31e3cb09d81e6fb61f9191e5ce617c6808d24498265a7a0c0f5b82303b18306d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-sra-toolkit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/829e9dae945b99db01be6a45bd00195069d38a971309a3673faa34fcc622e860/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/829e9dae945b99db01be6a45bd00195069d38a971309a3673faa34fcc622e860/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-sra-toolkit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ab0f5d5286d9642bb3b041fca0d27ceef55d8806e1f752c89fb699f6f7794e03/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ab0f5d5286d9642bb3b041fca0d27ceef55d8806e1f752c89fb699f6f7794e03/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-sra-toolkit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/753805fc4e3f4b095bd00c1a208047377e71c91f500b66d8730044e6298c3a8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/753805fc4e3f4b095bd00c1a208047377e71c91f500b66d8730044e6298c3a8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d7372612d746f6f6c6b6974\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-sra-toolkit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-sra-toolkit\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-sra-toolkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-sra-toolkit\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/ncbi/sra-tools\"\u003esra-toolkit\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003esra-toolkit\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/sra-toolkit/2.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/sra-toolkit\u003c/code\u003e as \u003ccode\u003e 2.11.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [ - "container", - "docker-container", - "dockerfile", - "dna-assembly", - "pacbio" + "singularity", + "bioinformatics" ], - "updated_at": 1662449005.0 + "updated_at": 1629226848.0 }, { "data_format": 2, - "description": "HTCondor execution point setup and configuration for using HTCondor file transfer to synchronize a directory between two hosts", + "description": "Master Thesis for Robotics Master", "filenames": [ - "Singularity.def" + "vision/src/vision/pythonClasses/deeplab/SingularityResNest", + "vision/src/vision/pythonClasses/darknet/Singularity" ], - "full_name": "brianaydemir/htcondor_file_transfer_ep", + "full_name": "GuiMateus/thesis", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-3d-volumetric-and-semantic-environment-reconstruction\" class=\"anchor\" aria-hidden=\"true\" href=\"#3d-volumetric-and-semantic-environment-reconstruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3D Volumetric and Semantic Environment Reconstruction\u003c/h1\u003e\n\u003cp\u003eThis repo contains the materials used in the Master\u0027s Thesis from Guilherme Mateus at Aalborg University. The pipeline contained in it creates 3D semantical and volumetric reconstructions of environments using Deep Learning. This implementation is done using ROS melodic as a framework of communication.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".images/gitImage.png\"\u003e\u003cimg src=\".images/gitImage.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA small description of each package is given below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eontologies\u003c/strong\u003e: Handles object ontonlogies.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eservice\u003c/strong\u003e: Consists of services files for system communication.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003erealsense-ros\u003c/strong\u003e: Gathers data using realsense camera.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003euserInterface\u003c/strong\u003e: Provides a GUI for users to control the system.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003evision\u003c/strong\u003e: Handles screw detection using YOLOv4 and DeepLabV3+.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eThe system contains YOLOv4 and DeepLabV3+. However, YOLOv4 still has to be manually built under \u003ccode\u003ecatkin_ws/src/release/vision/src/vision/pythonClasses/darknet.py\u003c/code\u003e, for that follow the instructions on the \u003ca href=\"https://github.com/AlexeyAB/darknet\"\u003erepo\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOBS: To build darknet you need to get a CMake version bigger than 3.12, which is not compatible with ROS. Do not uninstall the current version installed in the system, instead use a local CMake version.\u003c/p\u003e\n\u003cp\u003eIn case of problems with DeepLabV3+, follow the \u003ca href=\"https://github.com/jfzhang95/pytorch-deeplab-xception\"\u003erepo\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePre-trained models and configs can be found by using \u003ccode\u003e./setup.sh\u003c/code\u003e. The weights are stored under \u003ccode\u003e/opt/vision/\u003c/code\u003e, therefore to use the weights models the script needs root permissions. Alternatively the weights paths must be manually changed in \u003ccode\u003ecatkin_ws/src/release/vision/src/vision/pythonClasses/detectObjects.py\u003c/code\u003e and \u003ccode\u003ecatkin_ws/src/release/vision/src/vision/pythonClasses/segmentationInit.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf it still doesn\u0027t work, I don\u0027t know mate, ask my parrot, he might know it better than me or something like that.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h3\u003e\n\u003cp\u003eThis requires a system setup with ROS. It is recommended to use \u003ccode\u003eUbuntu 18.04\u003c/code\u003e with \u003ccode\u003eROS Melodic\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-creating-workspace-and-cloning-the-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-workspace-and-cloning-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating workspace and cloning the repository\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e create a catkin workspace\u003c/span\u003e\nmkdir -p catkin_ws/src \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e catkin_ws/src\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Clone the repository from bitbucket.\u003c/span\u003e\ngit clone --recursive https://guimateus@bitbucket.org/guimateus/thesis.git\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install dependencies\u003c/span\u003e\nsudo apt update -qq \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\nrosdep update\nrosdep install --from-paths \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --ignore-src --rosdistro melodic -y\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003einstall python catkin tools. Needed for catkin build command\u003c/span\u003e\nsudo apt-get install python-catkin-tools\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e build the workspace\u003c/span\u003e\ncatkin build\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installing-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling dependencies\u003c/h3\u003e\n\u003cp\u003eGo to Intel Realsense website and \u003ca href=\"https://www.intelrealsense.com/developers/\" rel=\"nofollow\"\u003einstall the SDK for Linux\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-launching-the-system\" class=\"anchor\" aria-hidden=\"true\" href=\"#launching-the-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunching The System\u003c/h3\u003e\n\u003cp\u003eTo launch system type the following to a terminal window.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch launch_nodes main.launch\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-reconstructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-reconstructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning reconstructions\u003c/h2\u003e\n\u003cp\u003eThis is the user interface of the system\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".images/GUI3D.png\"\u003e\u003cimg src=\".images/GUI3D.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFirst use offline reconstruction to detect static objects in the environment. Then, to perform an online reconstruction create ontological relations using the tab of the interface shown below, and select an object of interest under the \"Object Selection\" tab.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".images/ontologiesTabNew.png\"\u003e\u003cimg src=\".images/ontologiesTabNew.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe results can be visualized in \"Object Detection\", \"Object Segmentation\", and \"3D Reconstruction\".\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-future-works\" class=\"anchor\" aria-hidden=\"true\" href=\"#future-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFuture works\u003c/h2\u003e\n\u003cp\u003eSome possible future works to increase quality of the repo:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSegmentation change\u003c/strong\u003e: The qualitative results of the segmentation network are not satisfying, therefore it must be changed.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSimplifying setup\u003c/strong\u003e: Setup can be a bit convoluted, so maybe I can make it a bit easier.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eImprove ontologies framework\u003c/strong\u003e: Could be cool to have some extra functionalities in ontologies and maybe use a stochastic method.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eImprove addition of new objects\u003c/strong\u003e: Kind of hard to add custom objects to system right now, have to make the training framework easier.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eParrots\u003c/strong\u003e: This git repo critically lacks parrots.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".images/sam.jpg\"\u003e\u003cimg src=\".images/sam.jpg\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e[Guilherme Mateus Martins]\u003c/strong\u003e - \u003ca href=\"mailto:gmateu16@student.aau.dk\"\u003eemail\u003c/a\u003e - \u003ca href=\"https://bitbucket.org/%7Bba72de4e-9cb6-4e73-89db-24d4d8f12fe7%7D/\" rel=\"nofollow\"\u003eGit Profile\u003c/a\u003e - \u003ca href=\"https://www.linkedin.com/in/guilherme-mateus-346b58b5/\" rel=\"nofollow\"\u003eLinkedIn\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eAalborg university\u003c/li\u003e\n\u003cli\u003eDimitris Chrysostomou\u003c/li\u003e\n\u003cli\u003eSome other cool people\u003c/li\u003e\n\u003cli\u003eMy computer for being a real trooper and not dying after this repo made it crash several times\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1658411822.0 + "updated_at": 1641547757.0 }, { "data_format": 2, - "description": null, + "description": "Diffusion NLP project", "filenames": [ - "Testes_ate_21_10_2022/facies_classification_benchmark/my_benchmark-box/.singularity.d/Singularity", - "Testes_ate_21_10_2022/thurbridi/my_thurbridi/.singularity.d/Singularity" + "Singularity", + "Diffusion-LM/Singularity" ], - "full_name": "elis-essantos/sdumontHome", + "full_name": "mathematiguy/diffusion-nlp", "latest_release": null, - "readme": "", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-diffusion-nlp\" class=\"anchor\" aria-hidden=\"true\" href=\"#diffusion-nlp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ediffusion-nlp\u003c/h1\u003e\n\u003cp\u003eThis project attempts to reproduce the paper \"Diffusion-LM Improves Controllable Text Generation\" by Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., \u0026amp; Hashimoto, T. B. (2022), available here: \u003ca href=\"https://arxiv.org/pdf/2205.14217.pdf\" rel=\"nofollow\"\u003ehttps://arxiv.org/pdf/2205.14217.pdf\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory structure\u003c/h2\u003e\n\u003cp\u003eThere are 3 significant subfolders of this repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ediffusion_lm\u003c/code\u003e - contains code towards a from scratch reproduction of the authors\u0027 work. It includes a \u003ccode\u003emodel.py\u003c/code\u003e model definition file in PyTorch, which implements the forward pass of the model as closely as I could figure out from the paper and also by looking through their source code. It is supported by \u003ccode\u003enotebooks\u003c/code\u003e, which contains my investigations of the model design, and also \u003ccode\u003etests\u003c/code\u003e where I implemented some tests for testing the model code.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDiffusion-LM\u003c/code\u003e - contains a fork of the original source code for the paper at \u003ca href=\"https://github.com/XiangLi1999/Diffusion-LM\"\u003ehttps://github.com/XiangLi1999/Diffusion-LM\u003c/a\u003e. There I have containerized the project so it can be run reliably on other computers. The full details of the fork are documented there.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMLRC-2022-Report\u003c/code\u003e - is a latex project containing a report written by myself for the completion of a Class Project for Comp-599 Natural Language Understanding at McGill University, fall 2022 semester.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-how-to-get-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-get-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to get started\u003c/h1\u003e\n\u003cp\u003eThe only software dependencies for this repository is GNU Make and Singularity. On Ubuntu systems, make can be installed simply via \u003ccode\u003esudo apt install make\u003c/code\u003e. Instructions for how to install Singularity are available here: \u003ca href=\"https://docs.sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehttps://docs.sylabs.io/guides/3.5/user-guide/quick_start.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you are interested in running \u003ccode\u003ediffusion_lm\u003c/code\u003e, then you will need to build the singularity container in this directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Build the singularity container for this project\nmake container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen once you have done that, you can start a local Jupyterlab server via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Start local jupyterlab server\nmake jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe server will be listening at \u003ccode\u003elocalhost:8888\u003c/code\u003e and has a default password of \"jupyter\".\u003c/p\u003e\n\u003cp\u003eYou can also run other \u003ccode\u003emake\u003c/code\u003e commands, such as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Build the latex report at MLRC-2022-Report/article.pdf\nmake report\n\n# Run pytest unit tests\nmake test\n\n# Attempt to train the diffusion_lm model (not working)\nmake train\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is everything you would need to know to get around this repository. Building the singularity container does take time, so if you insist on not using it you can still install the python requirements for the project with \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e, although it is recommended to do this inside of a python environment of some sort.\u003c/p\u003e\n\u003cp\u003eYou can still run the make commands outside of the singularity container with \u003ccode\u003emake \u0026lt;command\u0026gt; RUN=\u003c/code\u003e - this suppresses the \u003ccode\u003esingularity exec\u003c/code\u003e command, but this will only work if you have the dependencies installed on your machine.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1671998603.0 + "updated_at": 1671466565.0 }, { "data_format": 2, - "description": "Mostly command-line utilities for automating cumbersome processes", + "description": "Containers for game AI", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "mfromano/utils", + "full_name": "sbutcher/game-container", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-utils\" class=\"anchor\" aria-hidden=\"true\" href=\"#utils\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eutils\u003c/h1\u003e\n\u003cp\u003eMostly command-line utilities for automating cumbersome processes\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-game-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#game-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egame-container\u003c/h1\u003e\n\u003cp\u003eContainers for game AI\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1671898445.0 + "updated_at": 1547647598.0 }, { "data_format": 2, - "description": "Collection of numerical and analytical tools for the solution of nonlinear matter-wave equation in Bose Einstein Condensates (BEC)", + "description": "Singularity recipe files for busco (https://gitlab.com/ezlab/busco)", "filenames": [ - "Singularity" + "Singularity.4.1.4", + "Singularity", + "Singularity.4.0.2", + "Singularity.4.1.0", + "Singularity.4.0.0", + "Singularity.4.0.6", + "Singularity.4.0.4", + "Singularity.5.1.2", + "Singularity.4.0.1", + "Singularity.4.0.5", + "Singularity.4.1.1", + "Singularity.5.2.2", + "Singularity.4.1.2" ], - "full_name": "lorenzifrancesco/soliton-BEC", + "full_name": "powerPlant/busco-srf", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-soliton-bec\" class=\"anchor\" aria-hidden=\"true\" href=\"#soliton-bec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esoliton-BEC\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/AshtonSBradley/FourierGPE.jl/actions\"\u003e\u003cimg src=\"https://github.com/AshtonSBradley/FourierGPE.jl/workflows/CI/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/AshtonSBradley/FourierGPE.jl\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7fe44b2d2126e2133bbad1e9b91a108b030bc57ca00f6e0e1c3b636a0811ab8e/68747470733a2f2f636f6465636f762e696f2f67682f417368746f6e53427261646c65792f466f75726965724750452e6a6c2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Coverage\" data-canonical-src=\"https://codecov.io/gh/AshtonSBradley/FourierGPE.jl/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCollection of numerical and analytical tools for the solution of nonlinear matter-wave equation in Bose Einstein Condensates (BEC)\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for the BUSCO tool for Benchmarking Universal Single-Copy Ortholog assessment\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1648714594.0 + "updated_at": 1629171754.0 }, { "data_format": 2, @@ -17692,428 +17324,439 @@ var data = "filenames": [ "Singularity" ], - "full_name": "thanhtlx/linevd2", + "full_name": "snystrom/bioconductor_docker_meme", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-linevd\" class=\"anchor\" aria-hidden=\"true\" href=\"#linevd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLineVD\u003c/h1\u003e\n\u003cp\u003eThis repository provides the code for \u003ca href=\"https://arxiv.org/pdf/2203.05181.pdf\" rel=\"nofollow\"\u003eLineVD: Statement-level Vulnerability Detection using Graph Neural Networks\u003c/a\u003e. The environment can be built using \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or by following / following the commands in the Singularity file. To start, clone the repository and navigate to the root directory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory Structure\u003c/h2\u003e\n\u003cpre lang=\"dir\"\u003e\u003ccode\u003e(main module) \u251c\u2500\u2500 sastvd\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 codebert\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 helpers\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 ivdetect\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 linevd\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 scripts\n \u251c\u2500\u2500 storage\n(memoization) \u2502\u00a0\u00a0 \u251c\u2500\u2500 cache\n(raw data) \u2502\u00a0\u00a0 \u251c\u2500\u2500 external\n(csvs) \u2502\u00a0\u00a0 \u251c\u2500\u2500 outputs\n(models) \u2502\u00a0\u00a0 \u2514\u2500\u2500 processed\n(tests) \u2514\u2500\u2500 tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training-linevd-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-linevd-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining LineVD from scratch\u003c/h2\u003e\n\u003cp\u003eBuild and initialise environment and download dataset\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build main.sif Singularity\nsingularity run main.sif -p initialise\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFeature extraction (Increase NUM_JOBS if running on HPC for speed up)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e main.sif python sastvd/scripts/prepare.py\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e main.sif python sastvd/scripts/getgraphs.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTrain model (Training takes around 1-2 hours using GTX 3060)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --nv main.sif python sastvd/scripts/train_best.py\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -H /mnt/hdd/thuonglc/linevd/ --nv --env TUNE_DISABLE_STRICT_METRIC_CHECKING=1 main.sif python sastvd/scripts/train_best.py 16\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-bioconductor-docker-with-meme-suite\" class=\"anchor\" aria-hidden=\"true\" href=\"#bioconductor-docker-with-meme-suite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBioconductor Docker with MEME Suite\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4716\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBuilds the bioconductor docker container with the \u003ca href=\"meme-suite.org\"\u003ememe-suite\u003c/a\u003e v5.1.1, using python3.7.1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e Currently only builds from the \u003ccode\u003ebioconductor_docker:devel\u003c/code\u003e container. In the future, I will support stable releases.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eBuild the Docker image from Dockerfile:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t snystrom/bioconductor_docker_meme\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePull from Dockerhub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull snystrom/bioconductor_docker_meme:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -e PASSWORD=\u0026lt;password\u0026gt; -p 8787:8787 -v \u0026lt;drive/to/mount\u0026gt;:/mnt/\u0026lt;location\u0026gt; snystrom/bioconductor_docker_meme\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhile running, go to \u003ca href=\"https://localhost:8787/\" rel=\"nofollow\"\u003ehttps://localhost:8787/\u003c/a\u003e and login with \u003ccode\u003erstudio:\u0026lt;password\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo enter the container at the commandline while running:\n\u003cstrong\u003eNOTE:\u003c/strong\u003e this will enter as \u003ccode\u003eroot\u003c/code\u003e not the \u003ccode\u003erstudio\u003c/code\u003e user\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it snystrom/bioconductor_docker_meme /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1663593135.0 + "updated_at": 1618423035.0 }, { "data_format": 2, "description": null, "filenames": [ - "install/Singularity" + "2.0.3/Singularity" ], - "full_name": "BrennanGambling/mindboogle", + "full_name": "yh549848/singularity-blastxmlparser", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1662174191.0 + "updated_at": 1645547232.0 }, { "data_format": 2, "description": null, "filenames": [ - "src/pddlstream/downward/misc/releases/20.06/Singularity.20.06", - "src/pddlstream/downward/misc/releases/19.12/Singularity.19.12", - "src/pddlstream/downward/misc/releases/19.06/Singularity.19.06", - "src/pddlstream/downward/misc/releases/latest/Singularity" + "Singularity.ExplainAI2", + "Singularity.ubuntu_tf", + "Singularity.physio", + "Singularity.centos_torch3", + "Singularity.centos_tf2", + "Singularity.ubuntu_pre", + "Singularity.centos_tf", + "Singularity.centos_torch2", + "Singularity.ExplainAI", + "Singularity.Spektral", + "Singularity.ubuntu_torch", + "Singularity.torch_mmf", + "Singularity.centos_torch", + "Singularity.jax", + "Singularity.mac_local", + "Singularity.pytorch", + "Singularity.torch" ], - "full_name": "Gaoyuan-Liu/Non-prehensile-Augmented-TAMP", + "full_name": "cyang31/containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-non-prehensile-augmented-tamp\" class=\"anchor\" aria-hidden=\"true\" href=\"#non-prehensile-augmented-tamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNon-Prehensile Augmented TAMP\u003c/h1\u003e\n\u003cp\u003eRobotic manipulation in cluttered environments requires synergistic planning among prehensile and non-prehensile actions. Previous work on sampling-based Task and Motion Planning (TAMP) algorithms, e.g. PDDLStream, provide a fast and generalizable solution for multi-modal manipulation. However, they are likely to fail in cluttered scenarios where no collision-free grasping approaches can be sampled without preliminary manipulations.\nTo extend the ability of sampling-based algorithms, we integrate a vision-based Reinforcement Learning (RL) non-prehensile procedure, namely pusher, the pushing actions generated by pusher can eliminate interlocked situations and make the problem solvable. Also, the sampling-based algorithm evaluates the pushing actions by providing rewards in the training process, thus the pusher can learn to avoid situations containing irreversible failures.\nThe proposed hybrid planning method is validated on a cluttered bin picking problem and implemented in both simulation and real world. Results show that the pusher can effectively improve the success ratio of the previous sampling-based algorithm, while the sampling-based algorithm can help the pusher to learn pushing skills.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Gaoyuan-Liu/Non-prehensile-Augmented-TAMP/blob/main/pics/intro.png\"\u003e\u003cimg src=\"https://github.com/Gaoyuan-Liu/Non-prehensile-Augmented-TAMP/raw/main/pics/intro.png\" width=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-video\" class=\"anchor\" aria-hidden=\"true\" href=\"#video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVideo\u003c/h2\u003e\n\u003cp\u003eThe method introduction and experiments:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://youtu.be/brXAh9BH_Qw\" rel=\"nofollow\"\u003e\u003cimg src=\"https://github.com/Gaoyuan-Liu/Non-prehensile-Augmented-TAMP/raw/main/pics/youtube.png\" alt=\"Watch the video\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo:\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:Gaoyuan-Liu/panda_tamp.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003eComplie DownwardFast:\n\u003cpre\u003e\u003ccode\u003ecd panda_tamp/src/pddlstream\n\n./downward/build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003eCompile IKFast:\n\u003cpre\u003e\u003ccode\u003ecd panda_tamp/src/pddlstream/examples/pybullet/utils/\n\npybullet-planning$ (cd pybullet_tools/ikfast/franka_panda; python setup.py)\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNvigate terminal to \u003ccode\u003esrc/panda_pddlstream\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate pddlstream\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun panda_pddl in pybullet:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m examples.pybullet.panda.run_pybullet -n 3 -v\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun panda_pddl with Franka:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eroslaunch panda_control franka_following.launch \n\npython -m examples.pybullet.panda.run -n 3 -v\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-trainning\" class=\"anchor\" aria-hidden=\"true\" href=\"#trainning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrainning\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRun moveit motion planner, go to to \u003ccode\u003ews_moveit\u003c/code\u003e workspace\n\u003cpre\u003e\u003ccode\u003esource devel/setup.bash\n\nroslaunch panda_moveit_config demo.launch\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003eRun trainning scripts, go to \u003ccode\u003esrc/pddlstream/examples/pybullet/panda\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-ros-interpretation\" class=\"anchor\" aria-hidden=\"true\" href=\"#ros-interpretation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eROS Interpretation\u003c/h2\u003e\n\u003cp\u003eAfter PDDLStream solve the problem, the \u003ccode\u003esolution\u003c/code\u003e after post process returns a list \u003ccode\u003ecommands\u003c/code\u003e, the elements in the list are classes defined in \u003ccode\u003epanda_primitives\u003c/code\u003e. Therefore, the main pourpose of ROS interpretation is to interpret the \u003ccode\u003epanda_primitives\u003c/code\u003e to ROS commands.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-debug\" class=\"anchor\" aria-hidden=\"true\" href=\"#debug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebug\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-general\" class=\"anchor\" aria-hidden=\"true\" href=\"#general\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eThe defaut top grasping pose is in \u003ccode\u003epanda_utils.py\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-moveit-cartesian-path\" class=\"anchor\" aria-hidden=\"true\" href=\"#moveit-cartesian-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMoveit cartesian path\u003c/h3\u003e\n\u003cp\u003eThe \u003ca href=\"https://thomasweng.com/moveit_cartesian_jump_threshold/\" rel=\"nofollow\"\u003epost\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pybullet-camera\" class=\"anchor\" aria-hidden=\"true\" href=\"#pybullet-camera\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePybullet camera\u003c/h3\u003e\n\u003cp\u003eThe \u003ca href=\"https://towardsdatascience.com/simulate-images-for-ml-in-pybullet-the-quick-easy-way-859035b2c9dd\" rel=\"nofollow\"\u003epost\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest\u003c/h3\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1662124624.0 + "updated_at": 1632080282.0 }, { "data_format": 2, - "description": "Singularity file for Cornell-MOE based off git clone https://github.com/wujian16/Cornell-MOE.git", + "description": "Singularity container with Spack", "filenames": [ - "Singularity" + "Singularity.spack-root", + "Singularity.spack-lmod", + "Singularity.spack-bowtie", + "Singularity.spack-rhel", + "Singularity.spackbase", + "Singularity.spack-fastqvalidator", + "Singularity.spack" ], - "full_name": "belledon/moe-sing", + "full_name": "baberlevi/spack-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-moe-sing\" class=\"anchor\" aria-hidden=\"true\" href=\"#moe-sing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emoe-sing\u003c/h1\u003e\n\u003cp\u003eSingularity file for Cornell-MOE based off git clone \u003ca href=\"https://github.com/wujian16/Cornell-MOE.git\"\u003ehttps://github.com/wujian16/Cornell-MOE.git\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTested on Singularity 2.4\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-work-in-progress\" class=\"anchor\" aria-hidden=\"true\" href=\"#work-in-progress\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ework in progress\u003c/h1\u003e\n\u003cp\u003eattempt to build a base singularity image with spack that can be used as the bootstrap for\nother singularity images that would perform the spack install of a particular package\u003c/p\u003e\n\u003cp\u003ecurrently having an issue with stage directory for spack attempting to write to\nthe immutable squashfs\u003c/p\u003e\n\u003cp\u003eas expected, the child container will happily install during %post since it can write\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1516305918.0 + "updated_at": 1521583740.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.ubuntu", + "Singularity.cell2location", + "Singularity.irods.4.2.8" ], - "full_name": "rses-singularity/caffe-cpu", + "full_name": "prete/singularity-recipes", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-caffe-cpu\" class=\"anchor\" aria-hidden=\"true\" href=\"#caffe-cpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaffe (CPU)\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1542376576.0 + "updated_at": 1606249308.0 }, { "data_format": 2, - "description": null, + "description": "Diamond aligner Docker image", "filenames": [ "Singularity" ], - "full_name": "smfsamir/transformer-gnn", + "full_name": "biocorecrg/diamond_docker", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-gnn-fast\" class=\"anchor\" aria-hidden=\"true\" href=\"#gnn-fast\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGNN-Fast\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003eTo make it easy for you to get started with GitLab, here\u0027s a list of recommended next steps.\u003c/p\u003e\n\u003cp\u003eAlready a pro? Just edit this README.md and make it your own. Want to make it easy? \u003ca href=\"#editing-this-readme\"\u003eUse the template at the bottom\u003c/a\u003e!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-add-your-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#add-your-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdd your files\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file\" rel=\"nofollow\"\u003eCreate\u003c/a\u003e or \u003ca href=\"https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file\" rel=\"nofollow\"\u003eupload\u003c/a\u003e files\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line\" rel=\"nofollow\"\u003eAdd files using the command line\u003c/a\u003e or push an existing Git repository with the following command:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd existing_repo\ngit remote add origin https://gitlab.com/smfsamir/gnn-fast.git\ngit branch -M main\ngit push -uf origin main\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-integrate-with-your-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#integrate-with-your-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntegrate with your tools\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://gitlab.com/smfsamir/gnn-fast/-/settings/integrations\" rel=\"nofollow\"\u003eSet up project integrations\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-collaborate-with-your-team\" class=\"anchor\" aria-hidden=\"true\" href=\"#collaborate-with-your-team\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCollaborate with your team\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/members/\" rel=\"nofollow\"\u003eInvite team members and collaborators\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html\" rel=\"nofollow\"\u003eCreate a new merge request\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically\" rel=\"nofollow\"\u003eAutomatically close issues from merge requests\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/merge_requests/approvals/\" rel=\"nofollow\"\u003eEnable merge request approvals\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html\" rel=\"nofollow\"\u003eAutomatically merge when pipeline succeeds\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-and-deploy\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-and-deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest and Deploy\u003c/h2\u003e\n\u003cp\u003eUse the built-in continuous integration in GitLab.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/ci/quick_start/index.html\" rel=\"nofollow\"\u003eGet started with GitLab CI/CD\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/application_security/sast/\" rel=\"nofollow\"\u003eAnalyze your code for known vulnerabilities with Static Application Security Testing(SAST)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/topics/autodevops/requirements.html\" rel=\"nofollow\"\u003eDeploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/user/clusters/agent/\" rel=\"nofollow\"\u003eUse pull-based deployments for improved Kubernetes management\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] \u003ca href=\"https://docs.gitlab.com/ee/ci/environments/protected_environments.html\" rel=\"nofollow\"\u003eSet up protected environments\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-editing-this-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#editing-this-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEditing this README\u003c/h1\u003e\n\u003cp\u003eWhen you\u0027re ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to \u003ca href=\"https://www.makeareadme.com/\" rel=\"nofollow\"\u003emakeareadme.com\u003c/a\u003e for this template.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-suggestions-for-a-good-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#suggestions-for-a-good-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSuggestions for a good README\u003c/h2\u003e\n\u003cp\u003eEvery project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-name\" class=\"anchor\" aria-hidden=\"true\" href=\"#name\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eName\u003c/h2\u003e\n\u003cp\u003eChoose a self-explaining name for your project.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eLet people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-badges\" class=\"anchor\" aria-hidden=\"true\" href=\"#badges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBadges\u003c/h2\u003e\n\u003cp\u003eOn some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-visuals\" class=\"anchor\" aria-hidden=\"true\" href=\"#visuals\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisuals\u003c/h2\u003e\n\u003cp\u003eDepending on what you are making, it can be a good idea to include screenshots or even a video (you\u0027ll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eWithin a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eUse examples liberally, and show the expected output if you can. It\u0027s helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport\u003c/h2\u003e\n\u003cp\u003eTell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-roadmap\" class=\"anchor\" aria-hidden=\"true\" href=\"#roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRoadmap\u003c/h2\u003e\n\u003cp\u003eIf you have ideas for releases in the future, it is a good idea to list them in the README.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eState if you are open to contributions and what your requirements are for accepting them.\u003c/p\u003e\n\u003cp\u003eFor people who want to make changes to your project, it\u0027s helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.\u003c/p\u003e\n\u003cp\u003eYou can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors-and-acknowledgment\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors-and-acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors and acknowledgment\u003c/h2\u003e\n\u003cp\u003eShow your appreciation to those who have contributed to the project.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eFor open source projects, say how it is licensed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-project-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#project-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject status\u003c/h2\u003e\n\u003cp\u003eIf you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-diamond-docker-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#diamond-docker-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiamond Docker images\u003c/h1\u003e\n\u003cp\u003eDiamond aligner Docker image\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/biocorecrg/diamond/builds/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/50336b251df61eac194e273e6751254dd983989ce3ad82bd5782d5367ad795c7/68747470733a2f2f646f636b65726275696c646261646765732e7175656c6c746578742e65752f7374617475732e7376673f6f7267616e697a6174696f6e3d62696f636f7265637267267265706f7369746f72793d6469616d6f6e64\" alt=\"Docker Build Status\" data-canonical-src=\"https://dockerbuildbadges.quelltext.eu/status.svg?organization=biocorecrg\u0026amp;repository=diamond\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1663265237.0 + "updated_at": 1567699875.0 }, { "data_format": 2, - "description": null, + "description": "code_aster containers", "filenames": [ - "haz/docker/fd/Singularity" + "Singularity.common.default", + "Singularity.salome_meca.cwa", + "Singularity.seq.default", + "Singularity.mpi.asterxx", + "Singularity.mpi.default" ], - "full_name": "FlorianPommerening/core-challenge-2022-solvers", + "full_name": "codeaster/container", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers-for-code_aster\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers-for-code_aster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers for code_aster\u003c/h1\u003e\n\u003cp\u003eThis repository provides some recipes to build containers for\n\u003ca href=\"https://www.code-aster.org/\" rel=\"nofollow\"\u003ecode_aster\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003efor \u003ca href=\"https://docs.docker.com/\" rel=\"nofollow\"\u003edocker\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003efor \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eIt should be considered as a work in progress.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor example, additional work is needed to execute a containerized version of\ncode_aster from an existing\n\u003ca href=\"https://www.code-aster.org/spip.php?article302\" rel=\"nofollow\"\u003esalome_meca\u003c/a\u003e\ninstallation.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThe repository contains recipes to build a sequential and a parallel\nversion for the development branch (\u003ccode\u003edefault\u003c/code\u003e) which refers to the \u003ccode\u003elatest\u003c/code\u003e\ntag on docker images.\nThe code_aster version is named \u003ccode\u003eunstable\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-list-of-code_aster-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#list-of-code_aster-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eList of code_aster images\u003c/h2\u003e\n\u003cp\u003eExecutable images:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecodeastersolver/codeaster-seq\u003c/code\u003e: Sequential version of code_aster.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecodeastersolver/codeaster-mpi\u003c/code\u003e: Parallel version of code_aster.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIntermediate layer with prerequisites:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecodeastersolver/codeaster-common\u003c/code\u003e: Prerequisites for the sequential and\nparallel versions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis image can also be used to build your own development version.\u003c/p\u003e\n\u003cp\u003eSingularity recipes are simple \u003cem\u003econversions\u003c/em\u003e that use the Docker images as\nbootstrap.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tags\" class=\"anchor\" aria-hidden=\"true\" href=\"#tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTags\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003elatest\u003c/code\u003e: It refers to the last head of the \u003ccode\u003edefault\u003c/code\u003e branch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eNo more for the moment...\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild images\u003c/h2\u003e\n\u003cp\u003eSee available targets:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen choose your target between \u003ccode\u003eseq\u003c/code\u003e and \u003ccode\u003empi\u003c/code\u003e, or \u003ccode\u003ebuild\u003c/code\u003e to build all:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEnvironment files added in the \u003ccode\u003eenv.d\u003c/code\u003e directory are sourced before calling\n\u003ccode\u003edocker\u003c/code\u003e/\u003ccode\u003esingularity\u003c/code\u003e builder. It may be useful for example to configure the\nenvironment to pass a proxy.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-a-shell-using-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-a-shell-using-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a shell using the image:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm -it codeastersolver/codeaster-seq:latest\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-a-testcase-using-testcase-files-embedded-in-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-a-testcase-using-testcase-files-embedded-in-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a testcase using testcase files embedded in the image:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm codeastersolver/codeaster-seq:latest as_run --nodebug_stderr --test zzzz100f\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-a-testcase-using-files-out-of-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-a-testcase-using-files-out-of-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a testcase using files out of the image:\u003c/h3\u003e\n\u003cp\u003eIn this example the data files are extracted from the \u003cem\u003eimage\u003c/em\u003e.\nIn the real life, these files are for example created from salome_meca.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e create a temporary container to access the testcase files\u003c/span\u003e\ndocker run --name astercp codeastersolver/codeaster-seq:latest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e copy files\u003c/span\u003e\nmkdir workdir\ndocker cp astercp:/scif/apps/aster/share/aster/tests/sslv155a.comm workdir/\ndocker cp astercp:/scif/apps/aster/share/aster/tests/sslv155a.mmed workdir/\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e clean the temporary container\u003c/span\u003e\ndocker rm astercp\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e create the export file\u003c/span\u003e\ndocker run --rm codeastersolver/codeaster-seq:latest as_run --get_export sslv155a --nodebug_stderr \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \\\n sed -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003es#/scif/apps/aster/share/aster/tests#.#g\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e workdir/export\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf the \u003ccode\u003eexport\u003c/code\u003e file is manually created, the version can be addressed just\nby name (\u003ccode\u003eP version unstable\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eNow, run a code_aster container using local files:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm --volume \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/workdir:/aster codeastersolver/codeaster-seq:latest \\\n as_run --nodebug_stderr /aster/export\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-validation\" class=\"anchor\" aria-hidden=\"true\" href=\"#validation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eValidation\u003c/h3\u003e\n\u003cp\u003eTo limit the size of the binary images only few testcases are available in the\ninstallation directory.\nThe 3800+ testcases can be extracted from the source tree from the\n\u003ca href=\"https://bitbucket.org/code_aster/codeaster-src\" rel=\"nofollow\"\u003eBitbucket repository\u003c/a\u003e\n(see below).\nChecking all the 3800 testcases takes about 15-20h cpu.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSome prerequisites are not yet available within the container\n(miss3d, ecrevisse, etc.). So, all the tests that are using these tools\nare currently in failure.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo execute the existing testcases, use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -t codeastersolver/codeaster-seq:latest run_testcases unstable\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to copy the result files\u003c/span\u003e\ndocker cp -a \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONTAINER\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:/home/aster/resutest \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eDESTINATION\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse the following commands to download all the 3800+ testcases from the\n\u003ca href=\"https://bitbucket.org/code_aster/codeaster-src\" rel=\"nofollow\"\u003eBitbucket repository\u003c/a\u003e and\nexecute them.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e download the testcases out of the container\u003c/span\u003e\nwget https://bitbucket.org/code_aster/codeaster-src/get/default.tar.gz\ntar xzf default.tar.gz\nmv code_aster-codeaster-src-\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/astest \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e rm -rf code_aster-codeaster-src-\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e mount \u0027astest\u0027 and run testcases in the container\u003c/span\u003e\ndocker run -t --volume \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/astest:/home/aster/tests codeastersolver/codeaster-seq:latest \\\n run_testcases --tests=/home/aster/tests unstable\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1663181341.0 + "updated_at": 1575303352.0 }, { "data_format": 2, - "description": null, + "description": "IMPICA is notoriously difficult to build, so I made this so it would build if you have docker and mount for my research use.", "filenames": [ - "IMAGES/methylator/Singularity", - "WGBS/DMT_analysis/Singularity_Methylator.def" + "singularity/Singularity" ], - "full_name": "kirsho/DASH", + "full_name": "utcs-scea/Impica-Builder", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-dash-dazl-scarlet-hygromycin\" class=\"anchor\" aria-hidden=\"true\" href=\"#dash-dazl-scarlet-hygromycin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDASH (DAzl-Scarlet-Hygromycin)\u003c/h1\u003e\n\u003cp\u003eDescription of WGBS analysis for the \u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.05.03.442415v1\" rel=\"nofollow\"\u003epreprint\u003c/a\u003e \u003cstrong\u003e\"A genome-wide knock-out screen for actors of epigenetic silencing reveals new regulators of germline genes and 2-cell like cell state\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDefossez \u003ca href=\"http://parisepigenetics.com/dmdeg/\" rel=\"nofollow\"\u003elab\u003c/a\u003e, Epigenetics \u0026amp; cell fate \u003ca href=\"http://parisepigenetics.com/fr/\" rel=\"nofollow\"\u003eUnit\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1658847725.0 + "updated_at": 1636746170.0 }, { "data_format": 2, - "description": "Batch Connect - Example Shiny App that runs on OSC OnDemand", + "description": "w2l", "filenames": [ - "ext/Singularity" + "Singularity", + "Singularity.gpu" ], - "full_name": "OSC/bc_osc_example_shiny", + "full_name": "klm122/w2l", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-wip-batch-connect---osc-example-shiny-app\" class=\"anchor\" aria-hidden=\"true\" href=\"#wip-batch-connect---osc-example-shiny-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[WIP] Batch Connect - OSC Example Shiny App\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a8152a2780451d58acdca1e79b03f771d6e84ae12087e6e7a824b6759b715dc1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f6578616d706c655f7368696e792e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a8152a2780451d58acdca1e79b03f771d6e84ae12087e6e7a824b6759b715dc1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f6578616d706c655f7368696e792e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_example_shiny.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Batch Connect app designed for OSC OnDemand that launches a Shiny App within\nan Owens batch job.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e x.y.z+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule purge\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_example_shiny/fork\"\u003ehttps://github.com/OSC/bc_osc_example_shiny/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-w2l\" class=\"anchor\" aria-hidden=\"true\" href=\"#w2l\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ew2l\u003c/h1\u003e\n\u003cp\u003ew2l\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 11, + "subscribers_count": 1, "topics": [], - "updated_at": 1527005209.0 + "updated_at": 1645905985.0 }, { "data_format": 2, - "description": null, + "description": "Set of Singularity HPC containers", "filenames": [ - "Singularity" + "fenics/Singularity" ], - "full_name": "ResearchIT/SimNIBS", + "full_name": "kma/HPC-Container", "latest_release": null, - "readme": "\u003ch3\u003e\u003ca id=\"user-content-simnibs-singularity-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#simnibs-singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimNIBS singularity recipe\u003c/h3\u003e\n\u003cp\u003eBefore building, place the SimNIBS source tarball in the /tmp directory. (recipe version 2.1.1)\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC-Container\u003c/h1\u003e\n\u003cp\u003eSet of Singularity containers\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1546981375.0 + "updated_at": 1530297750.0 }, { "data_format": 2, - "description": "test of nf-core create", + "description": "GNU Midnight Commander is a visual file manager, licensed under GNU General Public License and therefore qualifies as Free Software.", "filenames": [ - "Singularity" + "4.8.28/Singularity", + "4.8.25/Singularity", + "4.8.26/Singularity", + "4.8.29/Singularity" ], - "full_name": "czbiohub/nf-core-test", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-coretest\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-coretest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/test\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003etest of nf-core\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/test\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5656ec3ca80ae8775904761dfc7b47e3357d325de15a8d013edd4a0093630611/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f746573742e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/test.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/test\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8a74c7ad053a343b2d1b30e0ef0f86afe191999cfc823635773862aefd840fd2/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f746573742e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/test.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/test pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "pscedu/singularity-mc", + "latest_release": "v4.8.29", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-mc/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-mc/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-mc/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-mc/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/20f9b62353e523d3ab71a141bef01e7c6c9b266b5d21ca495fec6bcf5149a691/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/20f9b62353e523d3ab71a141bef01e7c6c9b266b5d21ca495fec6bcf5149a691/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6d63\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-mc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/197b32bc5699c413928a8dc98e7fc7ba78e4232c6d05026a53625842fe00f633/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/197b32bc5699c413928a8dc98e7fc7ba78e4232c6d05026a53625842fe00f633/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6d63\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-mc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d972290aeff699a37c55f27a4af658b413ceba5d7bc7697189e546d9cc80fa22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6d63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d972290aeff699a37c55f27a4af658b413ceba5d7bc7697189e546d9cc80fa22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6d63\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-mc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3210b070126fcd60c3222ffb78e07d55b8223b3eb1c9f4e7e8cb2b33fb54931c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3210b070126fcd60c3222ffb78e07d55b8223b3eb1c9f4e7e8cb2b33fb54931c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6d63\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-mc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-mc\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-mc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-mc\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/07885183f92acdf4c924ec41b91e32bf00dd0b1f3f820c477551a32ec8dfad98/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f392f39622f4d69646e696768745f436f6d6d616e6465725f342e372e302e395f6f6e5f5562756e74755f31312e30342e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/07885183f92acdf4c924ec41b91e32bf00dd0b1f3f820c477551a32ec8dfad98/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f392f39622f4d69646e696768745f436f6d6d616e6465725f342e372e302e395f6f6e5f5562756e74755f31312e30342e706e67\" alt=\"Image\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/commons/9/9b/Midnight_Commander_4.7.0.9_on_Ubuntu_11.04.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/mc\"\u003emc\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003emc\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/mc/4.8.29\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/mc\u003c/code\u003e as \u003ccode\u003e4.8.29.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [], - "updated_at": 1554245021.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1676698058.0 }, { "data_format": 2, - "description": "Nextflow workflow for assembling large, diploid, eukaryotic genomes (2 gigabases haploid size or bigger)", + "description": null, "filenames": [ - "Singularity" + "Singularity/Singularity.v1.0" ], - "full_name": "czbiohub/nf-large-assembly", + "full_name": "Monia234/IARC-RNA-seq", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-czbiohubnf-large-assembly\" class=\"anchor\" aria-hidden=\"true\" href=\"#czbiohubnf-large-assembly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eczbiohub/nf-large-assembly\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eAssemble large diploid eukaryotic genomes (2 gigabases haploid size or bigger)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/czbiohub/nf-large-assembly\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8d428dc306e8c519b4952b8239ab3eace188860f1c5dfabe1a4059c42c067a1e/68747470733a2f2f7472617669732d63692e6f72672f637a62696f6875622f6e662d6c617267652d617373656d626c792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/czbiohub/nf-large-assembly.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/nf-large-assembly\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/767f13dee3d8a1039b493b285b876f4ef216154825cb6401031b09e8d959b916/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6e662d6c617267652d617373656d626c792e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/nf-large-assembly.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe czbiohub/nf-large-assembly pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-rna-fusions\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-rna-fusions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-rna-fusions\u003c/h1\u003e\n\u003cp\u003eA nextflow pipeline to call somatic rna fusions\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1556036860.0 + "updated_at": 1644245608.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v2.2.0" + "Singularity.v1.0.1", + "Singularity.v1.0.0" ], - "full_name": "baxpr/connprep", - "latest_release": "v2.2.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-connprep\" class=\"anchor\" aria-hidden=\"true\" href=\"#connprep\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econnprep\u003c/h1\u003e\n\u003cp\u003eProduce preprocessed fMRI images ready for connectivity analysis.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eDrop initial or final volumes as specified. Default: Analyze all volumes.\u003c/li\u003e\n\u003cli\u003eGet the TR (volume acquisition time) from pixdim[4] field of the Nifti header.\u003c/li\u003e\n\u003cli\u003eSlice timing correction. Default: none.\u003c/li\u003e\n\u003cli\u003eHead motion realignment (SPM12 two-stage) and production of mean fMRI.\u003c/li\u003e\n\u003cli\u003eRigid body coregistration of mean fMRI to T1 structural.\u003c/li\u003e\n\u003cli\u003eCompute volume quality metrics FD, DVARS.\u003c/li\u003e\n\u003cli\u003eReslice realigned fMRI to native space, and also warp to MNI space using CAT12 transform.\u003c/li\u003e\n\u003cli\u003eRemove confounds from the native and MNI space fMRIs by simultaneous regression. Defaults:\n\u003cul\u003e\n\u003cli\u003e0.01 - 0.10 Hz bandpass filter\u003c/li\u003e\n\u003cli\u003e6 estimated motion parameters and their first differences\u003c/li\u003e\n\u003cli\u003e6 principal components from the white matter + CSF compartment\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRepeat the confound removal, additionally removing the mean signal of the gray matter compartment.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003enum_initial_vols_to_drop 0 Number of initial volumes to drop\nnum_vols_to_analyze all Total number of volumes to analyze\nbandpasslo_hz 0.01 Low edge of bandpass filter in Hz\nbandpasshi_hz 0.10 High edge of bandpass filter\nmot_PCs 6 Number of PCs of motion params to remove\nmotderiv_PCs 6 Same for motion derivatives\nwmcsf_PCs 6 Same for white matter/CSF compartment\nslorder none Slice timing correction, SPM12 nomenclature \nfmri_niigz fMRI images, 4D Nifti\nmt1_niigz T1 structural\ndeffwd_niigz Forward deformation of T1 to MNI\ngray_niigz Gray matter volume fraction\nwhite_niigz White matter volume fraction\ncsf_niigz CSF volume fraction\nproject XNAT project label\nsubject XNAT subject label\nsession XNAT session label\nscan XNAT scan label\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003econnprep.pdf Processing report\nrp_adfmri.txt Realignment parameters\nFD.txt Framewise displacement\nDVARS.txt Framewise noise\nfiltered_keepgm_noscrub_nadfmri.nii.gz Filtered data, native space, gray matter signal retained\nfiltered_keepgm_noscrub_wadfmri.nii.gz Filtered data, MNI space, gray matter signal retained\nfiltered_removegm_noscrub_nadfmri.nii.gz Filtered data, native space, gray matter signal removed\nfiltered_removegm_noscrub_wadfmri.nii.gz Filtered data, MNI space, gray matter signal removed\nmeanadfmri.nii.gz Mean fMRI, native space\nwmeanadfmri.nii.gz Mean fMRI, MNI space\nstats_keepgm_noscrub.txt Processing info when gray matter signal retained\nstats_removegm_noscrub.txt Processing info when gray matter signal removed\ngm_mask.nii.gz Native space gray matter mask\nwmcsf_mask.nii.gz Native space white matter/CSF mask\nconfounds_keepgm_noscrub.txt Confounds matrix when gray matter signal retained\nconfounds_removegm_noscrub.txt Confounds matrix when gray matter signal removed\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "bud42/RWML", + "latest_release": "v1.0.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rwml\" class=\"anchor\" aria-hidden=\"true\" href=\"#rwml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRWML\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1595372367.0 + "updated_at": 1612386680.0 }, { "data_format": 2, - "description": "Affinity Representing Instance Descriptors", + "description": null, "filenames": [ - "singularity/Singularity" + "Singularity.UbuntuMOE-xenial", + "Singularity.YelpMOE" ], - "full_name": "funkelab/arid", + "full_name": "aminnayebi/ContainerMOE", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1562764827.0 + "updated_at": 1554405415.0 }, { "data_format": 2, - "description": "RNA-seq analysis pipeline based on Snakemake", + "description": null, "filenames": [ - "Singularity" + "Selector/hclib/modules/bale_actor/singularity/Singularity.def" ], - "full_name": "tgac-vumc/RNA-seq", - "latest_release": "v1.0.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rna-seq-analysis-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#rna-seq-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRNA-seq analysis pipeline\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://snakemake.bitbucket.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e0a726dc69516d51067fd9fc2074a9f2dc9d44eb069ae05434a36f580af32f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b653d3d352e32352e302d627269676874677265656e2e7376673f7374796c653d666c61742d737175617265\" alt=\"Snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake==5.25.0-brightgreen.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/3066\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c9d2afb620129b7ba0f4d918b77bfdb2b91c595cd6c6d013e950ee6e3c2bbc55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d73696e67756c61726974792d2d6875622d7265642e737667\" alt=\"singularity-hub\" data-canonical-src=\"https://img.shields.io/badge/install%20with-singularity--hub-red.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e225eb3891735f81d51e8e6aa377429328cfd43656973ff807bffe9234bc28c7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d636f6e64612d677265656e2e737667\" alt=\"miniconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-conda-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a \u003ca href=\"https://snakemake.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e based pipeline for RNA-seq used in the \u003ca href=\"http://www.tgac.nl/\" rel=\"nofollow\"\u003eTumor Genome Core Analysis\u003c/a\u003e housed in the \u003ca href=\"https://www.vumc.com/departments/cancer-center-amsterdam.htm\" rel=\"nofollow\"\u003eCancer Center Amsterdam\u003c/a\u003e, at \u003ca href=\"https://www.vumc.nl/\" rel=\"nofollow\"\u003eAmsterdam UMC location VUmc\u003c/a\u003e and part of the Department of Pathology.\u003c/p\u003e\n\u003cp\u003eThe pipeline processes raw data from FastQ inputs (\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e, \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003eTrimmomatic\u003c/a\u003e), aligns the reads (\u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTAR\u003c/a\u003e), generates gene counts (\u003ca href=\"http://bioinf.wehi.edu.au/featureCounts/\" rel=\"nofollow\"\u003efeatureCounts\u003c/a\u003e) and performs quality-control on the results (\u003ca href=\"https://multiqc.info/\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e). Paired-end (PE) and single read (SR) are supported.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/RNA-seq/blob/master/DAG_RNAseq.png\"\u003e\u003cimg width=\"850\" height=\"483\" src=\"https://github.com/tgac-vumc/RNA-seq/raw/master/DAG_RNAseq.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe pipeline is preliminary used in linux environment with conda/singularity available.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Conda\u003c/h3\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-installing-miniconda-3\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-installing-miniconda-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1: Installing Miniconda 3\u003c/h3\u003e\n\u003cp\u003eFirst, please open a terminal or make sure you are logged into your Linux VM. Assuming that you have a 64-bit system, on Linux, download and install Miniconda 3 with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn MacOS X, download and install with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh\nbash Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-downloading-repository--creating-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-downloading-repository--creating-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2: Downloading repository \u0026amp; creating environment\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emkdir snakemake_RNAseq\ncd snakemake_RNAseq\ngit clone https://github.com/tgac-vumc/RNA-seq\nconda env create --name RNAseq --file env.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003c/h3\u003e\n\u003cp\u003eThe singularity container holds a virtual environment of CentOS 7 and it\u0027s available with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tgac-vumc/RNA-seq\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-path-configuration--running-the-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#path-configuration--running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePath Configuration \u0026amp; Running the pipeline\u003c/h2\u003e\n\u003cp\u003eBefore attempting to run the pipeline, please open \u003cem\u003econfig.yaml\u003c/em\u003e. Inside, you will encounter \u003cstrong\u003ePath Configuration\u003c/strong\u003e and \u003cstrong\u003eSoftware Options\u003c/strong\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOn \u003cstrong\u003ePath configuration\u003c/strong\u003e, first, you have to choose whether your data is PE or SR and after change the fastq path to the path where your fastq files are actually stored.\u003c/li\u003e\n\u003cli\u003eOn \u003cstrong\u003eSoftware Options\u003c/strong\u003e, you will find several options that can be modified by the user. Please, have a look at it before running the pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll the software used in the pipeline is installed by conda or executed in a wrapper. We recommend to run the pipeline from a different location than the pipeline path, like the example below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -s PATH_TO_PIPELINE/Snakefile --use-conda --cores=24\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith --use-conda option, the pipeline will create environments to run rules based on \u003cem\u003eenv.yaml\u003c/em\u003e.\n\u003cstrong\u003eNote\u003c/strong\u003e the pipeline assumes that \u003cem\u003econfig.yaml\u003c/em\u003e is available at the location where the pipeline is executed.\u003c/p\u003e\n", + "full_name": "youssefelmougy/tempSC", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-an-asynchronous-distributed-actor-based-approach-to-jaccard-similarity-for-genome-comparisons\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-asynchronous-distributed-actor-based-approach-to-jaccard-similarity-for-genome-comparisons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAn Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-abstract\" class=\"anchor\" aria-hidden=\"true\" href=\"#abstract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbstract\u003c/h2\u003e\n\u003cp\u003eThe computation of genome similarity is important in computational biology applications, and is assessed by calculating Jaccard similarity of DNA sequencing sets. However, it\u2019s challenging to find solutions that can compute Jaccard similarity with the efficiency and scalability needed to fully utilize capabilities of modern HPC hardware. We introduce a novel algorithm for computing Jaccard similarity for genome comparisons, founded on an actor-based programming model. Our algorithm takes advantage of fine-grained asynchronous computations, distributed/shared memory model, and the Fine-grained Asynchronous Bulk-Synchronous Parallelism execution model. Our performance results on the NERSC Perlmutter supercomputer demonstrate that this approach scales to 16,384 cores, showing an average of 3.6\u00d7 and 5.5\u00d7 improvement in execution time and hardware counters compared to a state-of-the-art baseline. Moreover, we propose a novel compiler approach enabling programmers to optionally develop distributed code using the familiar BSP-based Partitioned Global Address Space model while automatically generating Actor-based code for improved performance.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation Instructions\u003c/h2\u003e\n\u003cp\u003eThe following installation instructions are for the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-load-the-appropriate-modules-to-prepare-for-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#load-the-appropriate-modules-to-prepare-for-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLoad the appropriate modules to prepare for setup\u003c/h3\u003e\n\u003cp\u003eThis loads the modules for both Selector and GenomeAtScale to prepare for setup.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource scripts/modules.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-first-time-setup-and-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#first-time-setup-and-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFirst time setup and installation\u003c/h3\u003e\n\u003cp\u003eThis sets up and installs both the Selector and GenomeAtScale applications and their backend runtimes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource scripts/setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Instructions\u003c/h2\u003e\n\u003cp\u003eThe following running instructions are for the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC).\u003c/p\u003e\n\u003cp\u003eThe run script (\u003ccode\u003e/scripts/run.sh\u003c/code\u003e) has 4 options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e source /scripts/run.sh [selector | ctf | both] [1...inf] [1...inf] [0...5]\n \n [selector | ctf | both] Selects which application (or both) to run\n [1...inf] Selects the number of cores for the run\n [1...inf] Selects the number of nodes for the run\n [0...5] Selects the set of HWPC to collect (0:none, 1:L1DA/L1DM/L1IA/L1IM, 2:L2DR/L2DM/L2IR/L2IM, 3:TLBDM/TLBIM, 4:BRINS/BRMSP, 5:INS/CYC)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: when selecting the number of nodes for the run, please remember that GenomeAtScale uses 32 cores/node and Selector uses either 32 or 64 cores/node.\u003c/p\u003e\n\u003cp\u003eFor example, \u003ccode\u003esource /scripts/run.sh selector 1024 16 2\u003c/code\u003e will run an experiment for the Selector application using 1024 cores on 16 nodes, collecting L2 cache statistics.\u003c/p\u003e\n\u003cp\u003eThis will submit an sbatch file to the run queue at Perlmutter. At job completion, a \u003ccode\u003ejaccard_selector.out\u003c/code\u003e or \u003ccode\u003ejaccard_ctf.out\u003c/code\u003e or both will be created, showing the CMD output of the run. Moreover, if HWPC were collected, a directory with the structure \u003ccode\u003ejaccard*+pat+*\u003c/code\u003e will be created in \u003ccode\u003e/Selector/hclib/modules/bale_actor/jaccard-selector/\u003c/code\u003e or \u003ccode\u003e/GenomeAtScale/jaccard-ctf/\u003c/code\u003e or both. Please see the Output Interpretation section for instructions on how to understand these results.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-interpretation\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-interpretation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput Interpretation\u003c/h2\u003e\n\u003cp\u003eThe following instructions are for understanding the results and relating them to the results found in the paper.\u003c/p\u003e\n\u003cp\u003eAt the completion of each run, there are two outputs that are created:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejaccard_selector.out OR jaccard_ctf.out OR both Output file from submitted job\njaccard_selector+pat+* OR jaccard+pat+* OR both Output folder (in respective directory) from a CrayPat run if HWPC were collected\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e*.out\u003c/code\u003e files contain the execution times of the run for the specific version. This result directly relates to Figure 2 (q) in the paper. An example output is shown below, where \u003ccode\u003e0.06150 seconds\u003c/code\u003e would be reported as the resulting value for the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e...\nRunning jaccard on 128 threads\nK-mer Matrix is 15000x5000 and has 15248 nonzeros.\n\nJaccard Similarity Matrix is 5000x5000 and has 12497374 values.\n\nRunning Jaccard Similarity K-mers (selector): \n 0.06150 seconds\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003ejaccard*+pat+*\u003c/code\u003e folders contain information dumped by the CrayPat profiler (for more information see \u003ca href=\"https://docs.nersc.gov/tools/performance/craypat/\" rel=\"nofollow\"\u003ehttps://docs.nersc.gov/tools/performance/craypat/\u003c/a\u003e). To generate human-readable content, we run \u003ccode\u003epat_report\u003c/code\u003e on the respective directory. This will display information of interest for the specified HWPC in the run, and will directly relate to Figures 2 (a-p). An example output is shown below, where we can see the L1 cache statistics which would be reported as the resulting values for the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@perlmutter: ~\u0026gt; pat_report $PWD/Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector+pat+190420-8718377t\n CrayPat/X: Version 23.02.0 Revision a53634a72 01/11/23 17:17:09\n\n Number of PEs (MPI ranks): 128\n\n Numbers of PEs per Node: 64 PEs on each of 2 Nodes\n\n Numbers of Threads per PE: 2\n\n Number of Cores per Socket: 64\n\n Execution start time: Sun Mar 19 10:25:36 2023\n\n System name and speed: nid004836 2.552 GHz (nominal)\n\n AMD Milan CPU Family: 25 Model: 1 Stepping: 1\n\n Core Performance Boost: 256 PEs have CPB capability\n\n\n Current path to data file:\n /Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector+pat+190420-8718377t (RTS, 2 data files)\n\n ...\n ...\n\n Processing step 7 of 10\n Notes for table 5:\n ...\n ...\n ==============================================================================\n USER / #1.selector_jaccard\n ------------------------------------------------------------------------------\n Time% 2.8% \n Time 0.060836 secs\n Imb. Time 0.000013 secs\n Imb. Time% 0.0% \n Calls 16.438 /sec 1.0 calls\n PAPI_L1_DCM 0.057G/sec 2,369,390.898 misses\n PAPI_L1_DCA 2.252G/sec 110,478,052.633 refs\n Average Time per Call 0.060836 secs\n CrayPat Overhead : Time 0.0% \n perf::PERF_COUNT_HW_CACHE_L1I:ACCESS 1,214,778 \n perf::PERF_COUNT_HW_CACHE_L1I:MISS 5,868\n ==============================================================================\n\n ...\n ...\n\n Hardware performance counter events:\n PAPI_L1_DCM Level 1 data cache misses\n PAPI_L1_DCA Level 1 data cache accesses\n perf::PERF_COUNT_HW_CACHE_L1I:ACCESS Undocumented counter\n perf::PERF_COUNT_HW_CACHE_L1I:MISS Undocumented counter\n\n Estimated minimum instrumentation overhead per call of a traced function,\n which was subtracted from the data shown in this report\n (for raw data, use the option: -s overhead=include):\n Time 0.114 microsecs\n\n Number of traced functions that were called: 7\n\n (To see the list, specify: -s traced_functions=show)\nuser@perlmutter: ~\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-top-level-directory-organization\" class=\"anchor\" aria-hidden=\"true\" href=\"#top-level-directory-organization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-Level Directory Organization\u003c/h2\u003e\n\u003cp\u003eThe folder structure of this repository is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e.\n\u251c\u2500\u2500 Selector # Contains files for the Actor-based runtime and the Jaccard k-mer Selector application\n\u2502 \u251c\u2500\u2500 hclib # Contains the HClib library and the Actor-based runtime\n\u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 modules \n\u2502 \u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 bale_actor \n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 jaccard-selector # Contains the Jaccard k-mer Selector application files\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 jaccard_kmer_selector.cpp # Application code for Selector version of Jaccard similarity for genome comparisons\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 jaccard_kmer_locality_selector.cpp # Application code for locality-aware Selector version of Jaccard similarity for genome comparisons\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 kmer_matrix.mtx # K-mer matrix file for evaluation\n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 ... \n\u251c\u2500\u2500 GenomeAtScale # Contains files for the CTF library and the GenomeAtScale application\n\u2502 \u251c\u2500\u2500 ctf # Contains the CTF library\n\u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u251c\u2500\u2500 jaccard-ctf # Contains the GenomeAtScale (jaccard-ctf) files\n\u2502 \u2502 \u251c\u2500\u2500 jaccard.cxx # Application code for GenomeAtScale\n\u2502 \u2502 \u251c\u2500\u2500 kmer_matrix.zip # K-mer matrix files for evaluation\n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 ... \n\u251c\u2500\u2500 ActorCode_from_PGASOpenMP # Contains PGAS-OpenMP code and translated Actor-based code (Section 6)\n\u251c\u2500\u2500 scripts # Contains installation, running, and modules scripts and sample Perlmutter sbatch files\n\u2502 \u251c\u2500\u2500 setup.sh # Installation and build script for the system backends and application code for both the Selector application and the GenomeAtScale application\n\u2502 \u251c\u2500\u2500 run.sh # Run script for both the selector application and GenomeAtScale application\n\u2502 \u251c\u2500\u2500 modules.sh # Modules script to prepare for running experiments (only used following first time setup using setup.sh, has to be re-run everytime you login to a cluster/supercomputer)\n\u2502 \u2514\u2500\u2500 ... \n\u2514\u2500\u2500 README.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eIf you use our application in your work, please cite \u003ca href=\"\"\u003eour paper\u003c/a\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eYoussef Elmougy, Akhiro Hayashi, Jun Shirako, and Vivek Sarkar. 2023. An Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eCorresponding author: Youssef Elmougy (\u003ca href=\"mailto:yelmougy3@gatech.edu\"\u003eyelmougy3@gatech.edu\u003c/a\u003e)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), through the Advanced Graphical Intelligence Logical Computing Environment (AGILE) research program, under Army Research Office (ARO) contract number W911NF22C0083. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1625231941.0 + "updated_at": 1681587929.0 }, { "data_format": 2, - "description": "Nextflow workflow for finding conserved motifs intersecting with splice junctions", + "description": "TRACULA Pipeline", "filenames": [ - "Singularity" + "Singularity", + "Singularity.v2.0.0", + "Singularity.v2.1.1" ], - "full_name": "czbiohub/splicemotifs", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-corebedtools-intersect\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-corebedtools-intersect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/bedtools-intersect\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eIntersect lots of bed files with lots of other bed files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/bedtools-intersect\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/811368779316af4f70b4dd35fc2c24cebcc4dc194cd63234e130384ec38ac89f/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f626564746f6f6c732d696e746572736563742e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/bedtools-intersect.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/bedtools-intersect\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca7e06b0d2929a9cba14da1892e90c6d4673a695806cb07ea82e89a1cbecef92/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f626564746f6f6c732d696e746572736563742e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/bedtools-intersect.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/bedtools-intersect pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "ccmvumc/TRACULA", + "latest_release": "v2.1.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tracula\" class=\"anchor\" aria-hidden=\"true\" href=\"#tracula\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTRACULA\u003c/h1\u003e\n\u003cp\u003eTRACULA Pipeline\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1564673719.0 + "updated_at": 1621015992.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity.BlendIt.def" + "Singularity" ], - "full_name": "housw/BlendIt", + "full_name": "shots47s/cbrain-plugins-mriqc", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1623019661.0 + "updated_at": 1560259505.0 }, { "data_format": 2, - "description": "Python wrapper for submitting jobs via bsub with the option to do so in a container environment.", + "description": "Repository for \u0027Biased Exploration for Satisificing Heuristic Search\u0027 at ICAPS22", "filenames": [ - "singularity/Singularity" + "downward/misc/releases/latest/Singularity", + "downward/misc/releases/19.12/Singularity.19.12", + "downward/misc/releases/20.06/Singularity.20.06", + "downward/misc/releases/19.06/Singularity.19.06" ], - "full_name": "funkelab/funlib.run", + "full_name": "Kurorororo/biased-exploration", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-funlibrun\" class=\"anchor\" aria-hidden=\"true\" href=\"#funlibrun\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efunlib.run\u003c/h1\u003e\n\u003cp\u003ePython wrapper for submitting jobs via bsub with the option to do so in a container environment.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003emake install-full\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis creates a funlib.run config file ~/.funlib.run\nthat contains default parameters that\ncan be overwritten for each specific run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enum_gpus = 1\nmemory = 25600\nworking_directory = .\nsingularity = \"\"\nhost = \"\"\nqueue = \"normal\"\nenvironment = \"\"\nbatch = False\nmount_dirs = \"\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThere are three useful ways to use funlib.run:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDirect usage via command line arguments (overwrites config file defaults):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython run.py -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epython train.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -c 5 -g 1 -q normal -s path-to-singularity-image\n\npython run_singularity.py -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epython mknet.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -s path-to-singularity-image\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eIndirect call via another script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efunlib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003erun\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erun\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erun_singularity\u003c/span\u003e\n\n\u003cspan class=\"pl-en\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"python train.py\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_cpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_gpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003equeue\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"normal\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003erun_singularity\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"python mknet.py\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esingularity_image\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"path_to_image\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCommand creation and subsequent call:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efunlib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003erun\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erun\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erun_singularity\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esubprocess\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003echeck_call\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003erun_command\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"python train.py\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_cpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_gpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003equeue\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"normal\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003echeck_call\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003erun_command\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eshell\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003erun_singularity_command\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erun_singularity\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"python mknet.py\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esingularity_image\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"path_to_image\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003echeck_call\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003erun_singularity_command\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eshell\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-daisy\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-daisy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Daisy\u003c/h2\u003e\n\u003cp\u003eWhen used with daisy.call do not expand the cmd to a string via setting expand=False:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ecmd\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecommand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ebase_command\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003equeue\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003equeue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_gpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_cpus\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003enum_cpus\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esingularity_image\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003esingularity_container\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emount_dirs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emount_dirs\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexecute\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eexpand\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003edaisy\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecall\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecmd\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elog_out\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elog_out\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elog_err\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elog_err\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-biased-exploration-for-satisficing-heuristic-search\" class=\"anchor\" aria-hidden=\"true\" href=\"#biased-exploration-for-satisficing-heuristic-search\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBiased Exploration for Satisficing Heuristic Search\u003c/h1\u003e\n\u003cp\u003eThis repository is for our ICAPS 2022 paper, \u003ca href=\"https://tidel.mie.utoronto.ca/pubs/biased-exploration-icaps22.pdf\" rel=\"nofollow\"\u003eBiased Exploration for Satisficing Heuristic Search\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-classical-planning\" class=\"anchor\" aria-hidden=\"true\" href=\"#classical-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClassical Planning\u003c/h2\u003e\n\u003cp\u003eOur implementation is on top of \u003ca href=\"https://www.fast-downward.org/\" rel=\"nofollow\"\u003eFast Downward\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e downward\npython3 build.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eType-LAMA with Softmin-Type(h) using two type-based buckets\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehlm=lmcount(lm_factory=lm_rhw(reasonable_orders=true),transform=adapt_costs(one),pref=false)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elazy(alt([single(hff),single(hff,pref_only=true),softmin_type_based([hff,g]),single(hlm),single(hlm,pref_only=true),softmin_type_based([hlm,g])],boost=1000),preferred=[hff,hlm],cost_type=one,reopen_closed=false)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eType-LAMA with Softmin-Type(h)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehlm=lmcount(lm_factory=lm_rhw(reasonable_orders=true),transform=adapt_costs(one),pref=false)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elazy(alt([single(hff),single(hff,pref_only=true),single(hlm),single(hlm,pref_only=true),softmin_type_based([hff,g])],boost=1000),preferred=[hff,hlm],cost_type=one,reopen_closed=false)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSoftmin-Type(h) with FF\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eeager(alt([single(hff), softmin_type_based([hff, g()], ignore_size=true)]), cost_type=one)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLin-Type(h) with FF\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eeager(alt([single(hff), linear_weighted_type_based([hff, g()])]), cost_type=one)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e3-Type(h) with FF\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eeager(alt([single(hff), nth_type_based([hff, g()], n=3)]), cost_type=one)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eType(h) with FF\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 fast-downward.py domain.pddl problem.pddl --evaluator \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehff=ff(transform=adapt_costs(one))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --search \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eeager(alt([single(hff), softmin_type_based([hff, g()], ignore_size=true, ignore_weights=true)]))\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-synthetic-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#synthetic-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSynthetic Data\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 random_digraph.py -o result.json\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1635345979.0 + "updated_at": 1655238687.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.macroecodesktop" + "ploi/planning/FD/misc/releases/latest/Singularity", + "ploi/planning/FD/misc/releases/19.12/Singularity.19.12", + "ploi/planning/FD/misc/releases/20.06/Singularity.20.06", + "ploi/planning/FD/misc/releases/19.06/Singularity.19.06" ], - "full_name": "ternaustralia/coesra-singularity-macroecodesktop", + "full_name": "alestarbucks/ofappdl", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-object-filtering-in-automatic-planning-problems-using-deep-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#object-filtering-in-automatic-planning-problems-using-deep-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eObject Filtering in Automatic Planning Problems using Deep Learning\u003c/h1\u003e\n\u003cp\u003eThis README file is explicitly dedicated to serve as the guide of use of the source code associated to Alejandro \u00c1lvarez Conejo\u0027s Final Bachelor Thesis in order to run the project in any local computer. Note that these instructions are described to be applicable to Linux-based systems.\u003c/p\u003e\n\u003cp\u003eThis repository contains three main folders, which are referred to in this annex as \u003ccode\u003emodules\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003eploi\u003c/code\u003e folder contains all the code related to the execution of the main algorithm for PLOI. It includes the code related to the guiders, the planners (including Fast-Downward) and the GNN implementation, as well as the main scripts that allow the whole project to work as discussed in the main body of the thesis. Note that inside the \u003ccode\u003emodel\u003c/code\u003e folder the model and data set files for the conducted tests can be found.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003egenerators\u003c/code\u003e folder contains the scripts that were used to generate the training and test problems. Inside, there is a folder dedicated to each of the domains of study and all of their versions, including the scripts that were used for the first approach described in chapter 5.3 in the \u003ccode\u003eunconnectednoise\u003c/code\u003e subfolder.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003epddlgym\u003c/code\u003e folder, which contains all the code related to the PDDLGym module. It has to be modified in order to include the domains of study inside its existing library of domains and example problems. Note that the original code for this module was also modified in order to make it more flexible to several valid syntaxes in PDDL. These modifications are not related to the core algorithm and thus have not been thoroughly detailed but the code inside the \u003ccode\u003eparser\u003c/code\u003e file of this module can be compared to the original parser in PDDLGym\u2019s original repository in order to examine the specifics of these changes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-projects-source-code-and-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-projects-source-code-and-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the project\u2019s source code and dependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall basic dependencies: cmake, g++, make, git, Python 3.6 or higher and pip, if these are not already installed.\u003c/li\u003e\n\u003cli\u003eClone the thesis\u2019 repository using the following command:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/alestarbucks/ofappdl\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eNavigate to the \u003ccode\u003eploi\u003c/code\u003e folder and install the requirements for that module:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install -r requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRepeat the same operation for the PDDLGym module.\n4.\tAdditionally, install wandb to avoid missing dependencies:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install wandb\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eCreate a symbolic link called \u003ccode\u003evalidate\u003c/code\u003e on the machine\u2019s path, pointing to the VAL validator\u2019s binary:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo ln -s \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_ofappdl\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/ofappdl/val/bin/Validate /usr/local/bin/validate\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to check that the symbolic link is working as intended, try to enter the command \u003ccode\u003evalidate\u003c/code\u003e in the command line and expect an output showing the usage of the command.\n6.\tBuild the Fast-Downward planner by navigating to ploi/planning/fd and running the following command (it may take a few minutes):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./build.py\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eBefore the first run and every time that a new domain is added to the PDDLGym module, re-install it using the version that exists in the repository. From the root folder of the repository, run:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install -e ./pddlgym\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis command is automatically included in the provided shell script that runs the project, so it is not explicitly needed to execute this step if such script is used.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-including-a-new-domain\" class=\"anchor\" aria-hidden=\"true\" href=\"#including-a-new-domain\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIncluding a new domain\u003c/h2\u003e\n\u003cp\u003eIn order to use PLOI for the purpose of applying it to other domains, a few changes must be made inside both the \u003ccode\u003epddlgym\u003c/code\u003e module and the \u003ccode\u003eploi\u003c/code\u003e module:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFirst, add the domain. Navigate to \u003ccode\u003epddlgym/pddlgym/pddl\u003c/code\u003e and copy the domain file inside that folder.\u003c/li\u003e\n\u003cli\u003eLikewise, add the training and test problems in two separate folders called \u003ccode\u003e\u0026lt;domain name\u0026gt;\u003c/code\u003e and \u003ccode\u003e\u0026lt;domain name\u0026gt;_test\u003c/code\u003e, respectively, inside the aforementioned folder.\u003c/li\u003e\n\u003cli\u003eOpen the \u003ccode\u003e__init__.py\u003c/code\u003e file inside pddlgym/pddlgym. Locate the list of environments after line 34 (\u003ccode\u003efor env_name, kwargs in [\u003c/code\u003e) and add the following lines, completing with the same name as the domain that was added in 1:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e(\u003cspan class=\"pl-s\"\u003e\"\u0026lt;domain name\u0026gt;\"\u003c/span\u003e,\n {\u003cspan class=\"pl-s\"\u003e\"operators_as_actions\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\"dynamic_action_space\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e}\n)\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eThe domain has now been added to the PDDLGym module and now it must be included in the PLOI module. For this, open the \u003ccode\u003emain.py\u003c/code\u003e file inside the ploi module and locate the \u003ccode\u003epddlgym_env_names\u003c/code\u003e dictionary. Add an entry in which the key is the name to which the domain will be referred in the invoking command inside the PLOI module, and the value is the name of the domain inside the PDDLGym module that was used for steps 1 to 3. For clarity, using the same name for both is recommended.\u003c/li\u003e\n\u003cli\u003eIn case of using the provided shell script to run the project, set the \u003ccode\u003eDOMAIN_NAME\u003c/code\u003e variable to match the key of the previously added entry in the dictionary mentioned in step 4.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the project\u003c/h2\u003e\n\u003cp\u003eThe main command that triggers the start of the project\u2019s execution takes the following parameters:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--domain_name\u003c/code\u003e (required): The name of the domain of study to which the selected method is intended to be applied. It must be consistent and match the name chosen in the process detailed in the previous section.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--train_planner_name\u003c/code\u003e: The name of the planner used for training. In the experiments detailed in this report, this planner was fd-opt-lmcut (the optimal variant of FD).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--test_planner_name\u003c/code\u003e (required): The name of the planner used for testing. In the experiments detailed in this report, this planner was fd-lama-first (the satisficing variant of FD).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--guider_name\u003c/code\u003e (required): The name of the guider to be used, between gnn-bce-10 (GNN guider) or no-guidance (for standard planning or random score).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_seeds\u003c/code\u003e (required): The number of seeds which will be used to randomly initialize the model\u2019s weights before training. The learning phase will be repeated as many times as seeds are specified, and only the best model will be selected. Only one seed was used for the experiments in this thesis.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_train_problems\u003c/code\u003e (default to 0): The number of training problems to be considered.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_test_problems\u003c/code\u003e (required): The number of testing problems to be considered.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--do_incremental_planning\u003c/code\u003e (required): 1 or 0. Whether or not to use incremental planning, i.e., for PLOI or random scoring, whether it implements random score guidance or GNN-based guidance. For standard planning this flag must be set to 0.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--greedy_search\u003c/code\u003e (default to 0): 1 or 0. Indicates whether the greedy search algorithm is implemented in the phase of training data collection.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--timeout\u003c/code\u003e (required): Time in seconds that each test problem is dedicated before time running out and the problem being skipped. For this thesis, this time span was of 120 seconds.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_epochs\u003c/code\u003e (default 1001): Number of epochs that will constitute the learning phase.\nThe command is then executed as:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 main.py [flags]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe provided shell script called \u003ccode\u003emyrun.sh\u003c/code\u003e inside the PLOI module serves as an easy way to control the experimental process. The selected domain and method must be uncommented from the file and the script will run the appropriate command to execute the required experimental run.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "coesra" - ], - "updated_at": 1610426323.0 + "topics": [], + "updated_at": 1624570598.0 }, { "data_format": 2, - "description": "Knime", + "description": null, "filenames": [ - "Singularity.knime" + "Singularity" ], - "full_name": "ternaustralia/coesra-singularity-knime", + "full_name": "hkong1/fhirql", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-knime\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-knime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-knime\u003c/h1\u003e\n\u003cp\u003eAuthor: Hoang Nguyen\nCreated: 22 July 2019\nThis will create a image with Singularity 2.5.1\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-a-fhir-has-been-lit-on-this-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-fhir-has-been-lit-on-this-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA FHIR has been lit on this server\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-fhirql\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-fhirql\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is fhirql\u003c/h2\u003e\n\u003cp\u003eFhirql is a spring boot adaptation of hapi fhir server. This can be used as a template for extending generic FHIR server for specific use cases. See the example projects below. I have updated it to FHIR-R4 and spring-boot 2.2.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-fhir-r4-hl7-fast-healthcare-interoperability-resources-release-4\" class=\"anchor\" aria-hidden=\"true\" href=\"#fhir-r4-hl7-fast-healthcare-interoperability-resources-release-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFHIR\u00ae R4 (HL7 Fast Healthcare Interoperability Resources, Release 4)\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-other-projects-that-using-this-as-backend\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-projects-that-using-this-as-backend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther projects that using this as backend\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/E-Health/fhirform\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"fire\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f525.png\"\u003e\ud83d\udd25\u003c/g-emoji\u003e The FHIRForm framework for managing healthcare eForms\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/E-Health/drishti\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"eyes\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f440.png\"\u003e\ud83d\udc40\u003c/g-emoji\u003e Drishti | An mHealth sense-plan-act framework!\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ejava 8\u003c/li\u003e\n\u003cli\u003emaven 3\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Use:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/dermatologist/fhirql.git\nmvn spring-boot:run\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAccess UI at \u003ca href=\"http://localhost:8080/fhir\" rel=\"nofollow\"\u003ehttp://localhost:8080/fhir\u003c/a\u003e and FHIR BASE at \u003ca href=\"http://localhost:8080/fhir/fhir\" rel=\"nofollow\"\u003ehttp://localhost:8080/fhir/fhir\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-extend\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-extend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to extend\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThis uses spring boot Web.\u003c/li\u003e\n\u003cli\u003eOverride the default UI by adding files with the same name to WEB-INF/templates (Thymeleaf).\u003c/li\u003e\n\u003cli\u003eFor example this application overrides tmpl-head.html and tmpl-home-welcome.html\u003c/li\u003e\n\u003cli\u003eThe list of original templates are \u003ca href=\"https://github.com/jamesagnew/hapi-fhir/tree/master/hapi-fhir-testpage-overlay/src/main/webapp/WEB-INF/templates\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003ePre-build docker container of overlay branch is available for testing and can be deployed using the following command. Access it at \u003ca href=\"http://localhost:8080/fhirql\" rel=\"nofollow\"\u003ehttp://localhost:8080/fhirql\u003c/a\u003e\n(Docker container is for testing only.)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -d --name fhirserver -p 8080:8080 beapen/fhir\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-hidden=\"true\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthor\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://nuchange.ca\" rel=\"nofollow\"\u003eBell Eapen\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "coesra" - ], - "updated_at": 1670882548.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1603378426.0 }, { "data_format": 2, - "description": "Owncloud", + "description": "Applied nuclear physics relevant software, containerized. Including Geant4 and Root.", "filenames": [ - "Singularity.owncloud" + "Singularity" ], - "full_name": "ternaustralia/coesra-singularity-owncloud", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-owncloud\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-owncloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-owncloud\u003c/h1\u003e\n\u003cp\u003eAuthor: Hoang Nguyen\nCreated: 22 July 2019\nThis will create a image with Singularity 2.5.1\u003c/p\u003e\n", + "full_name": "peter-jansson/appnuc", + "latest_release": "0.6.3", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-appnuc-applied-nuclear-physics-relevant-software-containerized\" class=\"anchor\" aria-hidden=\"true\" href=\"#appnuc-applied-nuclear-physics-relevant-software-containerized\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappnuc: Applied nuclear physics relevant software, containerized.\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.6841830\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe0491dd1b21254f68c00e841d95cb67f03343dd15eaf13e20280daa72ec13a7/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e363834313833302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.6841830.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/peter-jansson/appnuc/actions/workflows/docker-image.yml/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/peter-jansson/appnuc/actions/workflows/docker-image.yml/badge.svg?branch=master\" alt=\"Docker build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/peter-jansson/appnuc/actions/workflows/apptainer-image.yml/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/peter-jansson/appnuc/actions/workflows/apptainer-image.yml/badge.svg?branch=master\" alt=\"Apptainer build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn Ubuntu Linux 22.04 based image/container with a bunch of standard programs that are useful for scientific work in the field of applied nuclear physics. In addition to relevant software listed \u003ca href=\"scripts/install-apt-packages.sh\"\u003ehere\u003c/a\u003e and \u003ca href=\"scripts/install-pip-packages.sh\"\u003ehere\u003c/a\u003e, the following list of software packages are installed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://geant4.web.cern.ch/\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e monte carlo framework, version 11.1.1.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://root.cern.ch/\" rel=\"nofollow\"\u003eRoot\u003c/a\u003e data analysis framework, version 6.26/10.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://dx.doi.org/10.18434/T48G6X\" rel=\"nofollow\"\u003eXCOM\u003c/a\u003e program from NIST, version 3.1.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis containerized solution can be referenced as:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePeter Jansson; \"appnuc: Applied nuclear physics relevant software, containerized\"; GitHub software repository: \u003ca href=\"https://github.com/peter-jansson/appnuc\"\u003epeter-jansson/appnuc\u003c/a\u003e; Version: 0.6.3; DOI: \u003ca href=\"https://doi.org/10.5281/zenodo.6841830\" rel=\"nofollow\"\u003e10.5281/zenodo.6841830\u003c/a\u003e; 2023-03-31\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis work is licensed under the \u003ca href=\"LICENSE\"\u003eGNU Lesser General Public License v3.0 (LGPL-3)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f400597fcdcb66eeb5702e037732d66d7eecdf94f4f363a2dde0da21c4ba9ec4/68747470733a2f2f7777772e676e752e6f72672f67726170686963732f6c67706c76332d776974682d746578742d3135347836382e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f400597fcdcb66eeb5702e037732d66d7eecdf94f4f363a2dde0da21c4ba9ec4/68747470733a2f2f7777772e676e752e6f72672f67726170686963732f6c67706c76332d776974682d746578742d3135347836382e706e67\" alt=\"LGPL-3\" data-canonical-src=\"https://www.gnu.org/graphics/lgplv3-with-text-154x68.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eA \u003ca href=\"https://docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image named \u003ccode\u003eappnuc\u003c/code\u003e can built using the Dockerfile, by the command\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker build -t appnuc:latest -t appnuc:0.6.3 .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe image can be started in a container by, e.g., the command\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -i -t appnuc bash -l\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSignificantly more information on how to mount a local file system to the container as well as other command line options is available in the \u003ca href=\"https://docs.docker.com/engine/reference/commandline/cli/\" rel=\"nofollow\"\u003eDocker documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-apptainer-former-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#apptainer-former-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApptainer (former Singularity)\u003c/h2\u003e\n\u003cp\u003eAn \u003ca href=\"http://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e file containing the same containerized software can be built using the definition file, named \u003ccode\u003eSingularity\u003c/code\u003e. E.g. using the command\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eapptainer build appnuc-0.6.3.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eto build \u003ccode\u003eappnuc-0.6.3.sif\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"http://apptainer.org/docs\" rel=\"nofollow\"\u003eApptainer documentation\u003c/a\u003e for more information.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [ - "coesra" + "applied-nuclear-physics", + "singularity", + "apptainer", + "docker", + "geant4", + "geant4-simulation", + "root", + "root-cern", + "xcom" ], - "updated_at": 1610426521.0 + "updated_at": 1671696032.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity_jlabsolidbase_devel", + "Singularity.1.0.2" ], - "full_name": "arezaii/pf_singularity_demo", + "full_name": "jlabsolid/container", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-parflow-singularity-container-demonstration\" class=\"anchor\" aria-hidden=\"true\" href=\"#parflow-singularity-container-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParFlow Singularity Container Demonstration\u003c/h1\u003e\n\u003cp\u003eThe Singularity container is built with ParFlow installed as a SCIF-app, providing access to both sequential and parallel\nbuilds of ParFlow. See additional information about \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/definition_files.html?highlight=apps#apps\" rel=\"nofollow\"\u003eApps in Singularity\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eHost OS must have Singularity installed (See \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html\" rel=\"nofollow\"\u003eInstalling Singularity\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-linux-hosts\" class=\"anchor\" aria-hidden=\"true\" href=\"#linux-hosts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinux Hosts\u003c/h2\u003e\n\u003cp\u003eVerify Singularity is installed with the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, see the Quickstart directions below\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-windowsmac-hosts\" class=\"anchor\" aria-hidden=\"true\" href=\"#windowsmac-hosts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows/Mac Hosts\u003c/h2\u003e\n\u003cp\u003eFollow the instructions to \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html#install-on-windows-or-mac\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMake sure you are ssh\u0027d into the Vagrant box before beginning the Quickstart steps below\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant ssh\nvagrant@vagrant:~$ singularity --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eSteps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/arezaii/pf_singularity_demo\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ecd to the repository directory\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd pf_singularity_demo\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003erun the shell script to execute tests for Little Washita domain on 1 processor, for 1 timestep\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./run_test.sh LW 1 1 1 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-performance-test-cases\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-performance-test-cases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Performance Test Cases\u003c/h2\u003e\n\u003cp\u003eThe shell script run_test.sh facilitates running tests on different domains.\u003c/p\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./run_test.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edomain\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eP\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eQ\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eR\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eTimeSteps\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edomain is a test domain defined below\u003c/li\u003e\n\u003cli\u003eP, Q, R are integers defining processor topology in X, Y, Z directions\u003c/li\u003e\n\u003cli\u003eTimesteps is number of timesteps to execute\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-domains\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-domains\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest Domains\u003c/h2\u003e\n\u003cp\u003eThere are several test domains for performance analysis contained in the perf_tests folder.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLW - Little Washita\u003c/li\u003e\n\u003cli\u003eclayl - ClayL\u003c/li\u003e\n\u003cli\u003econus_ru - CONUS Clip - Run off\u003c/li\u003e\n\u003cli\u003econus_tfg - CONUS Clip - Terrain Following Grid\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-little-washita\" class=\"anchor\" aria-hidden=\"true\" href=\"#little-washita\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLittle Washita\u003c/h3\u003e\n\u003cp\u003eNatural model of the Little Washita watershed in Oklahoma.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDomain Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumber of Cells: 84,050, 41x41x50 (X,Y,Z)\u003c/li\u003e\n\u003cli\u003eHorizontal Resolution: 1km\u003c/li\u003e\n\u003cli\u003eVertical Resolution: 2m\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTechnical Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCLM enabled with NLDAS Forcings\u003c/li\u003e\n\u003cli\u003eTimestep: 1hr\u003c/li\u003e\n\u003cli\u003eSuburface: Heterogeneous\u003c/li\u003e\n\u003cli\u003eInitial Condition: Pressure file from spin-up\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-clayl\" class=\"anchor\" aria-hidden=\"true\" href=\"#clayl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClayL\u003c/h3\u003e\n\u003cp\u003eSynthetic model with completely flat surface and many thin, vertical layers\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDomain Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumber of Cells: 2.4M for 1 core. Scales with processor count, 100Px100Qx240 (X,Y,Z)\u003c/li\u003e\n\u003cli\u003eHorizontal Resolution: 1m\u003c/li\u003e\n\u003cli\u003eVertical Resolution: 0.025m\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTechnical Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNo CLM, constant simulated rain on top surface @ .0008 mm/hr\u003c/li\u003e\n\u003cli\u003eTimestep 1hr\u003c/li\u003e\n\u003cli\u003eSubsurface: Homogeneous\u003c/li\u003e\n\u003cli\u003eInitial Condition: Dry\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conus-run-off\" class=\"anchor\" aria-hidden=\"true\" href=\"#conus-run-off\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCONUS Run-off\u003c/h3\u003e\n\u003cp\u003eNatural topography with an impervious surface (parking lot simulation)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDomain Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumber of Cells: 1,562,500 1250x1250x1 (X,Y,Z)\u003c/li\u003e\n\u003cli\u003eHorizontal Resolution: 1km\u003c/li\u003e\n\u003cli\u003eVertical Resolution: 0.10m\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTechnical Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNo CLM, period of 1 hour simulated rain on top surface @ .005 mm/hr, then recession for 1000 hours\u003c/li\u003e\n\u003cli\u003eTimestep: 6 minutes\u003c/li\u003e\n\u003cli\u003eSubsurface: Homogeneous\u003c/li\u003e\n\u003cli\u003eInitial Condition: Dry\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conus-terrain-following-grid\" class=\"anchor\" aria-hidden=\"true\" href=\"#conus-terrain-following-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCONUS Terrain Following Grid\u003c/h3\u003e\n\u003cp\u003eNatural topography with the terrain following grid (TFG) feature enabled\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDomain Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumber of Cells: 1,125,000 750x750x2 (X,Y,Z)\u003c/li\u003e\n\u003cli\u003eHorizontal Resolution: 1km\u003c/li\u003e\n\u003cli\u003eVertical Resolution: toplayer=1m, bottomlayer=100m\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTechnical Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNo CLM, seepage face boundary condition type on top layer, @ 0.00001\u003c/li\u003e\n\u003cli\u003eTimestep: 100000\u003c/li\u003e\n\u003cli\u003eSubsurface: Homogeneous\u003c/li\u003e\n\u003cli\u003eInitial Condition: Water Table at 45m above lower boundary\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-apps\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout Apps\u003c/h2\u003e\n\u003cp\u003eThe demo container has two apps installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epar = distributed build of ParFlow, -DPARFLOW_AMPS_SEQUENTIAL_IO=False\u003c/li\u003e\n\u003cli\u003eseq = sequential build of ParFlow, -DPARFLOW_AMPS_SEQUENTIAL_IO=True\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eapp_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e/path/to/singularity_container.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e.tcl input file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee additional information about \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/definition_files.html?highlight=apps#apps\" rel=\"nofollow\"\u003eApps in Singularity\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-build-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Build Container\u003c/h2\u003e\n\u003cp\u003eThe quickest way to build is to use a remote build service such as \u003ca href=\"https://cloud.sylabs.io/builder\" rel=\"nofollow\"\u003ecloud.sylabs.io\u003c/a\u003e\nIf a user has root access, they can build from the definition file, conventionally named Singularity.\u003c/p\u003e\n\u003cp\u003eGeneral build command is of the form:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edestination/path/to/singularity_container.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSingularity definition file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eas a specific example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/pf_singularity_demo.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-use-parflow-in-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-use-parflow-in-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Use ParFlow in Container\u003c/h2\u003e\n\u003cp\u003eExample of running the LW test case in \u003ccode\u003eparflow/test/washita/tcl_scripts\u003c/code\u003e directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app par \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/pf_singularity_demo.sif LW_Test.tcl\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-from-sylabs-cloud\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-from-sylabs-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull from Sylabs Cloud\u003c/h2\u003e\n\u003cp\u003eTo pull the pre-built image from Sylabs Cloud:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull [destination image name] library://arezaii/default/parflow_demo:master\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting\u003c/h2\u003e\n\u003cp\u003eBecause singularity containers are write protected and ParFlow tests write to disk, you must expand the image to a writable sandbox.\nThis requires super user access, similar to building a container from the definition file.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-make-container-writable\" class=\"anchor\" aria-hidden=\"true\" href=\"#make-container-writable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMake Container Writable\u003c/h3\u003e\n\u003cp\u003eFirst, create a writable sandbox from the immutable container using Singularity\u0027s build command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory_to_create_for_sandbox/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esingularity_container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eas an example, if you had pulled the parflow_ompi image from shub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox parflow_demo_master_sandbox/ parflow_demo_master.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere will now be a new directory parflow_demo_master_sandbox/ that is the root of the container.\nEditing any of the folder contents will require super user permissions.\u003c/p\u003e\n\u003cp\u003eYou can enter a console into the container now by using the Singularity shell command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell --writable \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory_to_create_for_sandbox/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Tests\u003c/h3\u003e\n\u003cp\u003eAfter making the container writable and accessing it through a shell, both documented above, running the ParFlow\ntests can be done by changing directories and exporting the PARFLOW_DIR environment variable for either distributed\nor sequential builds of ParFlow.\u003c/p\u003e\n\u003cp\u003eTake note of the ParFlow build and install directories in the container:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequential Build\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild directory: /home/parflow/build_seq\u003c/li\u003e\n\u003cli\u003einstall directory: /home/parflow/pfdir_seq\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDistributed Build\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild directory: /home/parflow/build_par\u003c/li\u003e\n\u003cli\u003einstall directory: /home/parflow/pfdir_par\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/parflow/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ebuild_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PARFLOW_DIR=/home/parflow/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003einstall_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e make \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003eContainer\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1583512107.0 - }, - { - "data_format": 2, - "description": "Singularity recipe files for pinfish (https://github.com/nanoporetech/pinfish)", - "filenames": [ - "Singularity", - "Singularity.0.1.0" - ], - "full_name": "powerPlant/pinfish-srf", - "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the pinfish collection of tools helping to make sense of long transcriptomics data (long cDNA reads, direct RNA reads)\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1583274123.0 + "updated_at": 1521236311.0 }, { "data_format": 2, - "description": "Singularity recipe files for GroIMP (http://www.grogra.de/software/groimp)", + "description": null, "filenames": [ - "Singularity", - "Singularity.1.6-jre8-cuda+sundials-2.7.0", - "Singularity.1.6-cuda", - "Singularity.1.6-jre8-cuda" + "Singularity" ], - "full_name": "powerPlant/groimp-srf", + "full_name": "mwanakijiji/rrlyrae_metallicity", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for GroIMP, a 3D-modelling platform\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rrlyrae_metallicity\" class=\"anchor\" aria-hidden=\"true\" href=\"#rrlyrae_metallicity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003errlyrae_metallicity\u003c/h1\u003e\n\u003cp\u003eThis is a package for determining metallicities from med-res RRab spectroscopy. See --- for documentation.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://coveralls.io/github/mwanakijiji/rrlyrae_metallicity?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2bb0fd2bc008af8b9f3e3838890e25c208723b50f910daa5e509bba2111d27c8/68747470733a2f2f636f766572616c6c732e696f2f7265706f732f6769746875622f6d77616e616b696a696a692f72726c797261655f6d6574616c6c69636974792f62616467652e7376673f6272616e63683d6d6173746572\" alt=\"Coverage Status\" data-canonical-src=\"https://coveralls.io/repos/github/mwanakijiji/rrlyrae_metallicity/badge.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1651533257.0 + "updated_at": 1641769814.0 }, { "data_format": 2, - "description": "The definition files for creating singularity containers that can run in the WashU HPC", + "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "humanconnectome/hcp-pipelines-singularity", + "full_name": "truatpasteurdotfr/singularity-docker-centos7-ci", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-definitions-for-hcp-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-definitions-for-hcp-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Definitions for HCP Pipelines\u003c/h1\u003e\n\u003cp\u003eThe definition files for creating singularity containers for the XNAT pipelines\nwrapper code so that it can run in the WashU HPC.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cloning-with-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#cloning-with-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloning with Submodules\u003c/h2\u003e\n\u003cp\u003eDon\u0027t forget to pull down the submodules as well, with the \u003ccode\u003e--recursive\u003c/code\u003e flag.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/humanconnectome/hcp-pipelines-singularity --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCommand\u003c/th\u003e\n\u003cth\u003eTask\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emake clean\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRemove previous container image.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emake update\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eUpdate all the git submodule repos.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emake build\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eGenerate a container image from .def file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emake upload\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eUpload the container to correct location in the HPC.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-building-a-centos7-singularity-and-docker-image-for-ci\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-a-centos7-singularity-and-docker-image-for-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a centos7 singularity and docker image for CI\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003einitial docker image project \u003ca href=\"https://github.com/truatpasteurdotfr/docker-c7-ci\"\u003ehttps://github.com/truatpasteurdotfr/docker-c7-ci\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eadding support for singularity format to be used directly\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-caveat\" class=\"anchor\" aria-hidden=\"true\" href=\"#caveat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:main\u003c/code\u003e tagged docker image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocker \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-ci/actions/workflows/docker-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-ci/actions/workflows/docker-publish.yml/badge.svg\" alt=\"Docker build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-centos7-ci:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-ci/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-ci/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-docker-centos7-ci:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1610395015.0 + "updated_at": 1635152901.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "marcjwilliams1/rstudiosrvrV4", + "full_name": "mohammadreza-sheykhmousa/FFS", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4911\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for R studio server with Rv4.0.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-polygonal-building-segmentation-by-frame-field-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#polygonal-building-segmentation-by-frame-field-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/h1\u003e\n\u003cp\u003eWe add a frame field output to an image segmentation neural network to improve segmentation quality\nand provide structural information for the subsequent polygonization step.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/frame_field_sample.png\"\u003e\u003cimg src=\"images/frame_field_sample.png\" width=\"512\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 1: Close-up of our additional frame field output on a test image.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/model_training.png\"\u003e\u003cimg src=\"images/model_training.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 2: Given an overhead image, the model outputs an edge mask, an interior mask,\n and a frame field for buildings. The total loss includes terms that align the masks and\n frame field to ground truth data as well as regularizers to enforce smoothness of the\n frame field and consistency between the outputs.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/schematic_polygonization.png\"\u003e\u003cimg src=\"images/schematic_polygonization.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 3: Given classification maps and a frame field as input, we optimize skeleton polylines to\n align to the frame field using an Active Skeleton Model (ASM) and detect corners using\n the frame field, simplifying non-corner vertices.\n\u003c/p\u003e\n\u003cp\u003eThis repository contains the official code for the paper:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://www-sop.inria.fr/members/Nicolas.Girard/\" rel=\"nofollow\"\u003eNicolas Girard\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/smirnov/\" rel=\"nofollow\"\u003eDmitriy Smirnov\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/jsolomon/\" rel=\"nofollow\"\u003eJustin Solomon\u003c/a\u003e,\n\u003ca href=\"https://www-sop.inria.fr/members/Yuliya.Tarabalka/\" rel=\"nofollow\"\u003eYuliya Tarabalka\u003c/a\u003e\u003cbr\u003e\nCVPR 2021\u003cbr\u003e\n\u003cstrong\u003e[\u003ca href=\"https://arxiv.org/abs/2004.14875\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, \u003ca href=\"https://www.youtube.com/watch?v=226pPTBsNJ8\u0026amp;t=8s\" rel=\"nofollow\"\u003evideo\u003c/a\u003e]\u003c/strong\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-git-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#git-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGit submodules\u003c/h2\u003e\n\u003cp\u003eThis project uses various git submodules that should be cloned too.\u003c/p\u003e\n\u003cp\u003eTo clone a repository including its submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive --jobs 8 \u0026lt;URL to Git repo\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you already have cloned the repository and now want to load it\u2019s submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --init --recursive --jobs 8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more about explanations about using submodules and git, see \u003ca href=\"SUBMODULES.md\"\u003eSUBMODULES.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eThe easiest way to setup environment is to use the Docker image provided in the \u003ca href=\"docker\"\u003edocker\u003c/a\u003e (see README inside the folder).\u003c/p\u003e\n\u003cp\u003eOnce the docker container is built and launched, execute the \u003ca href=\"setup.sh\"\u003esetup.sh\u003c/a\u003e script inside to install required packages.\u003c/p\u003e\n\u003cp\u003eThe environment in the container is now ready for use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda environment\u003c/h2\u003e\n\u003cp\u003eAlternatively you can install all dependencies in a conda environment.\nI provide my environment specifications in the \u003ca href=\"environment.yml\"\u003eenvironment.yml\u003c/a\u003e which you can use to create your environment own with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h1\u003e\n\u003cp\u003eSeveral datasets are used in this work.\nWe typically put all datasets in a \"data\" folder which we link to the \"/data\" folder in the container (with the \u003ccode\u003e-v\u003c/code\u003e argument when running the container).\nEach dataset has it\u0027s own sub-folder, usually named with a short version of that dataset\u0027s name.\nEach dataset sub-folder should have a \"raw\" folder inside containing all the original folders and files fo the datset.\nWhen pre-processing data, \"processed\" folders will be created alongside the \"raw\" folder.\u003c/p\u003e\n\u003cp\u003eFor example, here is an example working file structure inside the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/data \n|-- AerialImageDataset\n |-- raw\n |-- train\n | |-- aligned_gt_polygons_2\n | |-- gt\n | |-- gt_polygonized\n | |-- images\n `-- test\n |-- aligned_gt_polygons_2\n |-- images\n`-- mapping_challenge_dataset\n |-- raw\n |-- train\n | |-- images\n | |-- annotation.json\n | `-- annotation-small.json\n `-- val\n `-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf however you would like to use a different folder for the datasets (for example while not using Docker),\nyou can change the path to datasets in config files.\nYou can modify the \"data_dir_candidates\" list in the config to only include your path.\nThe training script checks this list of paths one at a time and picks the first one that exists.\nIt then appends the \"data_root_partial_dirpath\" directory to get to the dataset.\u003c/p\u003e\n\u003cp\u003eYou can find some of the data we used in this shared \"data\" folder: \u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inria-aerial-image-labeling-dataset\" class=\"anchor\" aria-hidden=\"true\" href=\"#inria-aerial-image-labeling-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInria Aerial Image Labeling Dataset\u003c/h2\u003e\n\u003cp\u003eLink to the dataset: \u003ca href=\"https://project.inria.fr/aerialimagelabeling/\" rel=\"nofollow\"\u003ehttps://project.inria.fr/aerialimagelabeling/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor the Inria dataset, the original ground truth is just a collection of raster masks.\nAs our method requires annotations to be polygons in order to compute the ground truth angle for the frame field, we made 2 versions of the dataset:\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria OSM dataset\u003c/em\u003e has aligned annotations pulled from OpenStreetMap.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria Polygonized dataset\u003c/em\u003e has polygon annotations obtained from using our frame field polygonization algorithm on the original raster masks.\nThis was done by running the \u003ccode\u003epolygonize_mask.py\u003c/code\u003e script like so:\n\u003ccode\u003epython polygonize_mask.py --run_name inria_dataset_osm_mask_only.unet16 --filepath ~/data/AerialImageDataset/raw/train/gt/*.tif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can find this new ground truth for both cases in the shared \"data\" folder (\u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-the-mainpy-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-mainpy-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the main.py script\u003c/h1\u003e\n\u003cp\u003eExecute \u003ca href=\"main.py\"\u003emain.py\u003c/a\u003e script to train a model, test a model or use a model on your own image.\nSee the help of the main script with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython main.py --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe script can be launched on multiple GPUs for multi-GPU training and evaluation.\nSimply set the \u003ccode\u003e--gpus\u003c/code\u003e argument to the number of gpus you want to use.\nHowever, for the first launch of the script on a particular dataset (when it will pre-process the data),\nit is best to leave it at 1 as I did not implement multi-GPU synchronization when pre-processing datasets.\u003c/p\u003e\n\u003cp\u003eAn example use is for training a model with a certain config file, like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained\u003c/code\u003e\nwhich will train the Unet-Resnet101 on the CrowdAI Mapping Challenge dataset.\nThe batch size can be adjusted like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained -b \u0026lt;new batch size\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen training is done, the script can be launched in eval mode, to evaluate the trained model:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval\u003c/code\u003e.\nDepending on the eval parameters of the config file, running this will output results on the test dataset.\u003c/p\u003e\n\u003cp\u003eFinally, if you wish to compute AP and AR metrics with the COCO API, you can run:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval_coco\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-launch-inference-on-one-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#launch-inference-on-one-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch inference on one image\u003c/h2\u003e\n\u003cp\u003eMake sure the run folder has the correct structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- \u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the [main.py] script like so (filling values for arguments run_name and in_filepath):\n\u003ccode\u003epython main.py --run_name \u0026lt;run_name\u0026gt; --in_filepath \u0026lt;your_image_filepath\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe outputs will be saved next to the input image\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-trained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload trained models\u003c/h2\u003e\n\u003cp\u003eWe provide already-trained models so you can run inference right away.\nDownload here: \u003ca href=\"https://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\u003c/a\u003e.\nEach model was trained in a \"run\", whose folder (named with the format \u003ccode\u003e\u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\u003c/code\u003e) you can download at the provided link.\nYou should then place those runs in a folder named \"runs\" inside the \"frame_field_learning\" folder like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard | 2020-06-02 07:57:31\n| | |-- mapping_dataset.unet_resnet101_pretrained.field_off.train_val | 2020-09-07 11:54:48\n| | |-- mapping_dataset.unet_resnet101_pretrained.train_val | 2020-09-07 11:28:51\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBecause Google Drive reformats folder names, you have to rename the run folders as above.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-hidden=\"true\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCite:\u003c/h1\u003e\n\u003cp\u003eIf you use this code for your own research, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{Girard_2021_CVPR,\n author = {Girard, Nicolas and Smirnov, Dmitriy and Solomon, Justin and Tarabalka, Yuliya},\n title = {Polygonal Building Extraction by Frame Field Learning},\n booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n month = {June},\n year = {2021},\n pages = {5891-5900}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1605122458.0 - }, - { - "data_format": 2, - "description": "Code used to generate summaries, models and figures for article \"A field-wide assessment of differential high throughput sequencing reveals widespread bias\".", - "filenames": [ - "Singularity" - ], - "full_name": "tpall/geo-htseq-paper", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tpall/geo-htseq-paper/workflows/CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/tpall/geo-htseq-paper/workflows/CI/badge.svg\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-geo-htseq-paper\" class=\"anchor\" aria-hidden=\"true\" href=\"#geo-htseq-paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeo-htseq-paper\u003c/h1\u003e\n\u003cp\u003eWe analyzed the field of expression profiling by high throughput sequencing, or RNA-seq, in terms of replicability and reproducibility, using data from the GEO (Gene Expression Omnibus) repository. Our work puts an upper bound of 56% to field-wide reproducibility, based on the types of files submitted to GEO.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting data\u003c/h2\u003e\n\u003cp\u003eGot to \u003ca href=\"https://zenodo.org/record/6795313\" rel=\"nofollow\"\u003ehttps://zenodo.org/record/6795313\u003c/a\u003e and download data archive, let\u0027s say, to your Downloads folder.\u003c/p\u003e\n\u003cp\u003eThen create new folder, e.g. \"geo-htseq\" and enter this folder\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir geo-htseq\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e geo-htseq\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCopy downloaded dataset to your working directory and uncompress:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Downloads/geo-htseq.tar.gz \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ntar -xzvf geo-htseq.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRemove tar.gz archive from working directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erm geo-htseq.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow you should have dataset in \"output\" subdirectory ready for analysis.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-workflow-graph\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflow-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow graph\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"resources/images/rulegraph.pdf\"\u003e\u003cimg src=\"resources/images/rulegraph.pdf\" alt=\"rulegraph\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1656954496.0 + "updated_at": 1636198482.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v1.0.0" + "singularity/Singularity" ], - "full_name": "baxpr/segwarp", + "full_name": "ddesvillechabrol/lora", "latest_release": null, - "readme": "\u003cp\u003eWarp SEG output of a multi-atlas assessor to MNI space using the supplied SPM warp field.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1605062943.0 + "updated_at": 1678461554.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.snowflake" ], - "full_name": "nicspalla/openmpi_centos_x86_64", + "full_name": "longgangfan/ubuntu2004uwgeo-sig", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-openmpi_centos_x86_64\" class=\"anchor\" aria-hidden=\"true\" href=\"#openmpi_centos_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eopenmpi_centos_x86_64\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ubuntu2004uwgeo-sig\" class=\"anchor\" aria-hidden=\"true\" href=\"#ubuntu2004uwgeo-sig\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eubuntu2004uwgeo-sig\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1605260984.0 + "updated_at": 1621586669.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "0.39/Singularity.0.39" ], - "full_name": "kristinebilgrav/Vep_retro_containers", + "full_name": "yh549848/singularity-trimmomatic", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-jitterbug\" class=\"anchor\" aria-hidden=\"true\" href=\"#jitterbug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJitterbug\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1617190998.0 + "updated_at": 1602826148.0 }, { "data_format": 2, @@ -18121,786 +17764,864 @@ var data = "filenames": [ "Singularity" ], - "full_name": "kristinebilgrav/Retro_files", + "full_name": "ctpelok77/ipc2018-delfi", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-retro_files\" class=\"anchor\" aria-hidden=\"true\" href=\"#retro_files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRetro_files\u003c/h1\u003e\n\u003cp\u003eContains files used to run retroseq and analyse outcome\u003c/p\u003e\n", + "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1621431178.0 + "updated_at": 1660771542.0 }, { "data_format": 2, - "description": null, + "description": "Pipeline for preprocessing fMRI data ", "filenames": [ - "Singularity" + "TheBrainPipeline/preprocessing/Singularity_Containers/Singularity", + "TheBrainPipeline/preprocessing/Singularity_Containers/.ipynb_checkpoints/Singularity-checkpoint" ], - "full_name": "juanca09/default", + "full_name": "niblunc/NIBL", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-default\" class=\"anchor\" aria-hidden=\"true\" href=\"#default\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edefault\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-neuropsychology-of-ingestive-behavior-lab\" class=\"anchor\" aria-hidden=\"true\" href=\"#neuropsychology-of-ingestive-behavior-lab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNeuropsychology of Ingestive Behavior Lab\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/niblunc/TheBrainPipeline/tree/master/TheBrainPipeline\"\u003eTheBrainPipeline\u003c/a\u003e : analysis scripts and files, such as decoding\u003cbr\u003e\n\u003ca href=\"https://github.com/niblunc/TheBrainPipeline/tree/master/OsirixFiles\"\u003eOsirix_Files\u003c/a\u003e : scripts used to prep data from OsiriX \u003cbr\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1612274393.0 + "updated_at": 1583185636.0 }, { "data_format": 2, - "description": "Computational Analysis of gene Family Evolution (CAFE)", + "description": "The GATK is the industry standard for identifying SNPs and indels in germline DNA and RNAseq data. ", "filenames": [ - "Singularity" + "4.2.0.0/Singularity", + "4.1.9.0/Singularity" ], - "full_name": "sghignone/CAFE", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cafe\" class=\"anchor\" aria-hidden=\"true\" href=\"#cafe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCAFE\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-computational-analysis-of-gene-family-evolution-cafe\" class=\"anchor\" aria-hidden=\"true\" href=\"#computational-analysis-of-gene-family-evolution-cafe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComputational Analysis of gene Family Evolution (CAFE)\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5151\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe current build is based on the bioconda distribution of the Hahn Lab CAFE v.4.2.1.\u003c/p\u003e\n\u003cp\u003eThe purpose of CAFE is to analyze changes in gene family size in a way that accounts for phylogenetic history and provides a statistical foundation for evolutionary inferences. The program uses a birth and death process to model gene gain and loss across a user-specified phylogenetic tree. The distribution of family sizes generated under this model can provide a basis for assessing the significance of the observed family size differences among taxa.\u003c/p\u003e\n\u003cp\u003eCAFE v4.2.1 is the latest in a regular series of releases to the CAFE application. The manual and various tutorials may be viewed on the website (\u003ca href=\"https://hahnlab.github.io/CAFE/\" rel=\"nofollow\"\u003ehttps://hahnlab.github.io/CAFE/\u003c/a\u003e) . This document describes how to download and use CAFE v4.2.1. (credits: \u003ca href=\"https://github.com/hahnlab/CAFE\"\u003ehttps://github.com/hahnlab/CAFE\u003c/a\u003e)\u003c/p\u003e\n", + "full_name": "pscedu/singularity-gatk", + "latest_release": "v4.2.0.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-gatk/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-gatk/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/93c4f349e621f15ffd8933b4c7d4ea8eea7b6f9156528a6b483b1b47d8064a91/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6761746b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93c4f349e621f15ffd8933b4c7d4ea8eea7b6f9156528a6b483b1b47d8064a91/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6761746b\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-gatk\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/744613aa898037bbfe237a7943e118e4fe72355964b8726f785c33e44de131e0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6761746b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/744613aa898037bbfe237a7943e118e4fe72355964b8726f785c33e44de131e0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6761746b\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-gatk\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5d9f7edad1535dfc343a82ee05a1cee751f4185de5e88e9959f1e306baf3af56/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6761746b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d9f7edad1535dfc343a82ee05a1cee751f4185de5e88e9959f1e306baf3af56/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6761746b\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-gatk\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/936434bf1d55721ba57e95e4bb99cfb8b78d330106b7ffebafb8911c75556071/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6761746b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/936434bf1d55721ba57e95e4bb99cfb8b78d330106b7ffebafb8911c75556071/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6761746b\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-gatk\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-gatk\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-gatk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-gatk\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f0a6e98fde6f4e7dd338612de8a154b1169c4572acc8ca3ffed117a81e28d4be/68747470733a2f2f7468656d652e7a646173736574732e636f6d2f7468656d655f6173736574732f323337383336302f646630383566313534333231666161633931353964646135376635303130336238376134663734332e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f0a6e98fde6f4e7dd338612de8a154b1169c4572acc8ca3ffed117a81e28d4be/68747470733a2f2f7468656d652e7a646173736574732e636f6d2f7468656d655f6173736574732f323337383336302f646630383566313534333231666161633931353964646135376635303130336238376134663734332e706e67\" alt=\"Logo\" data-canonical-src=\"https://theme.zdassets.com/theme_assets/2378360/df085f154321faac9159dda57f50103b87a4f743.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://gatk.broadinstitute.org/hc/en-us\" rel=\"nofollow\"\u003eGATK\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003egatk\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/gatk/4.1.9.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/gatk\u003c/code\u003e as \u003ccode\u003e4.1.9.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [ "singularity", - "singularity-hub", - "singularity-recipe", - "miniconda3" + "bioinformatics" ], - "updated_at": 1612624956.0 + "updated_at": 1628991719.0 }, { "data_format": 2, - "description": null, + "description": "Singularity images for everyday research work.", "filenames": [ - "Singularity" + "Singularity.deepo-cpu", + "Singularity.pymc3", + "Singularity.datasci", + "Singularity.deepo-cpu-nlp" ], - "full_name": "thomas-robinson/single-point-land", + "full_name": "hans/research-labs", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1613156529.0 + "updated_at": 1648667607.0 }, { "data_format": 2, - "description": "TransDecoder identifies candidate coding regions within transcript sequences.", + "description": "Singularity dependency container, neuroglia-core + DWI software (camino, mrtrix, unring)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.v1.4.1" ], - "full_name": "sghignone/TransDecoder", - "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-transdecoder-v550\" class=\"anchor\" aria-hidden=\"true\" href=\"#transdecoder-v550\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTransDecoder v.5.5.0\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5159\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe current build is based on the bioconda distribution of Brian Haas\u0027 transdecoder 5.5.0.\u003c/p\u003e\n\u003cp\u003eTransDecoder identifies candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to the genome using Tophat and Cufflinks.\u003c/p\u003e\n\u003cp\u003eVisit the project \u003ca href=\"https://github.com/TransDecoder/TransDecoder/wiki\"\u003ewiki\u003c/a\u003e for all TransDecoder documentation.\u003c/p\u003e\n", + "full_name": "khanlab/neuroglia-dwi", + "latest_release": "v1.5", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-neuroglia-dwi\" class=\"anchor\" aria-hidden=\"true\" href=\"#neuroglia-dwi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eneuroglia-dwi\u003c/h1\u003e\n\u003cp\u003eSingularity image for neuroimaging dependencies. Supplements \u003ca href=\"http://www.github.com/khanlab/neuroglia-core\"\u003ehttp://www.github.com/khanlab/neuroglia-core\u003c/a\u003e with additional DWI software. Includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emrtrix3\u003c/li\u003e\n\u003cli\u003ecamino\u003c/li\u003e\n\u003cli\u003eunring\u003c/li\u003e\n\u003cli\u003eDKE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCommits and pull-requests to this repository rebuild the \u003ccode\u003elatest\u003c/code\u003e version on Docker Hub, which is updated nightly to Singularity Hub. Releases on Docker Hub and Singularity Hub are built whenever a tag named \u003ccode\u003ev.*\u003c/code\u003e is committed. To avoid re-building on minor commits (e.g. changes to documentation), use \u003ccode\u003e[skip ci]\u003c/code\u003e in the commit message.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/khanlab/neuroglia-core\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6231f7b29a2b680358e7d9c865672c500cdd9b75198b457634e3cc4c3a78cb70/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f6e6575726f676c69612d6477692e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/neuroglia-dwi.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/451\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDocker:\n\u003ccode\u003edocker pull khanlab/neuroglia-dwi\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSingularity:\n\u003ccode\u003esingularity pull khanlab/neuroglia-dwi\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "miniconda3", - "singularity", - "singularity-hub", - "singularity-recipe" - ], - "updated_at": 1612624905.0 + "topics": [], + "updated_at": 1591844442.0 }, { "data_format": 2, - "description": null, + "description": "Singularity Ubuntu container with the Paraview stack", "filenames": [ - "Singularity.4.0.14", - "Singularity.4.4.2" + "Singularity" ], - "full_name": "sschmeier/container-fishtank-gpu", + "full_name": "CHPC-UofU/Singularity-ubuntu-paraview", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1624344477.0 + "updated_at": 1492111584.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.mg5_ma5_madspin" ], - "full_name": "saviodot/singularity_MACS2", + "full_name": "HenryDayHall/madspin_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_macs2\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_macs2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_MACS2\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1616690622.0 + "updated_at": 1602173663.0 }, { "data_format": 2, - "description": null, + "description": "An adaptive planner for IPC ", "filenames": [ - "Singularity.bwa", - "Singularity.gatk" + "Singularity" ], - "full_name": "mkgoita/containers", + "full_name": "zyf505/CPC0", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n", + "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCopyright (C) 2003-2016 Malte Helmert\nCopyright (C) 2008-2016 Gabriele Roeger\nCopyright (C) 2010-2016 Jendrik Seipp\nCopyright (C) 2010, 2011, 2013-2016 Silvan Sievers\nCopyright (C) 2012-2016 Florian Pommerening\nCopyright (C) 2016 Martin Wehrle\nCopyright (C) 2013, 2015 Salome Simon\nCopyright (C) 2014, 2015 Patrick von Reth\nCopyright (C) 2015 Manuel Heusner, Thomas Keller\nCopyright (C) 2009-2014 Erez Karpas\nCopyright (C) 2014 Robert P. Goldman\nCopyright (C) 2010-2012 Andrew Coles\nCopyright (C) 2010, 2012 Patrik Haslum\nCopyright (C) 2003-2011 Silvia Richter\nCopyright (C) 2009-2011 Emil Keyder\nCopyright (C) 2010, 2011 Moritz Gronbach, Manuela Ortlieb\nCopyright (C) 2011 Vidal Alc\u00e1zar Saiz, Michael Katz, Raz Nissim\nCopyright (C) 2010 Moritz Goebelbecker\nCopyright (C) 2007-2009 Matthias Westphal\nCopyright (C) 2009 Christian Muise\n\nFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1671889682.0 + "updated_at": 1613781111.0 }, { "data_format": 2, - "description": "Project for I519", + "description": "Contains the material presented at CCD lab meeting on 11/13/2019", "filenames": [ - "SingularityPRJ.def" + "examples/Singularity.pytorch-docker", + "examples/Singularity.julia", + "examples/Singularity.conda", + "examples/Singularity.fasttext" ], - "full_name": "ginnymortensen/gamortenPRJ", + "full_name": "CNCLgithub/singularity_workshop_2019", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1670041069.0 + "updated_at": 1573678647.0 }, { "data_format": 2, - "description": null, + "description": "cutadapt removes adapter sequences from sequencing reads.", "filenames": [ - "SingularityRecipe" + "2.10/Singularity" ], - "full_name": "CRC-901-On-the-Fly-Computing/executor-bootup", + "full_name": "pscedu/singularity-cutadapt", "latest_release": null, - "readme": "\u003cp\u003eThis repository contains shell scripts that are supposed to be executed within a Docker container when basic services are deployed in the Testbed.\nThe shell script downloads the source code, runs the verification, runs the compilation and finally launches the SEDE executor.\nThe Docker container that is created for basic services has the following file system structure:\u003c/p\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003cp\u003e\u251c\u2500 cpachecker\n\u251c\u2500 hooks\u003cbr\u003e\n\u251c\u2500 sede\u003cbr\u003e\n\u251c\u2500 src\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Folder src contains the C, Java or Python code of basic services. This container must contain a compile.sh for the compilation. The compile script may call another build tool like gradle or make.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Source code is downloaded from a ServiceCodeProvider repository.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Cerificates (*.proof) for C implementations must be in the same directory as the .*c file and must have a specific file name pattern: _.proof. For example, the name of the proof for the analysis sign for the C implementation service_grey_cpu.c must be service_grey_cpu_sign.proof.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; The configuration files that are necessary for the SEDE executor must be in the folder src/main/resources/config.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Folder hooks contains shell scripts for downloading the source code, running the verification, and running the compilation.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; Folder sede contains the SEDE executor logic.\u003c/p\u003e\n\u003cp\u003e-\u0026gt; The script run.sh executes all scripts in the hooks folder in alphanumerical order and starts the SEDE server in the end.\u003c/p\u003e\n\u003cp\u003eInstallation\nThe following software needs to be installed inside the Docker container:\u003c/p\u003e\n\u003cp\u003ecurl |\ngit |\njavac / gcc |\ngradle / make\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-cutadapt/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-cutadapt/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1a971ebb81368ce57d4702d1ac0fa0534e1de4e11c2f3a1fc98cb5c5203ae017/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6375746164617074\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1a971ebb81368ce57d4702d1ac0fa0534e1de4e11c2f3a1fc98cb5c5203ae017/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6375746164617074\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-cutadapt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/460062e41af5fcb53936a892aaf05699f5552d0c369d8c9d05308a15ecb18032/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6375746164617074\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/460062e41af5fcb53936a892aaf05699f5552d0c369d8c9d05308a15ecb18032/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6375746164617074\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-cutadapt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5403066544c7ca2002d9c3183ace39dbb1aabbed23f96489c23f815b84b3a31f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6375746164617074\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5403066544c7ca2002d9c3183ace39dbb1aabbed23f96489c23f815b84b3a31f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6375746164617074\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-cutadapt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d60627004d3b1d480327260e840077df4c84113f1dcd462a39d25dfc08daae2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6375746164617074\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d60627004d3b1d480327260e840077df4c84113f1dcd462a39d25dfc08daae2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6375746164617074\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-cutadapt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-cutadapt\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-cutadapt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-cutadapt\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://cutadapt.readthedocs.io/en/stable\" rel=\"nofollow\"\u003ecutadapt\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ecutadapt\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/cutadapt/2.10\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/cutadapt\u003c/code\u003e as \u003ccode\u003e2.10.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1669321582.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629217124.0 }, { "data_format": 2, - "description": null, + "description": "OpenFOAM atmospheric test cases", "filenames": [ - "Singularity.PhaGCN" + "Singularity" ], - "full_name": "cschu/phagcn_singularity", - "latest_release": null, + "full_name": "hertzsprung/AtmosTests", + "latest_release": "jshaw-thesis", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1669215943.0 + "updated_at": 1517845038.0 }, { "data_format": 2, - "description": "Material for the GPU course ML-variant", + "description": null, "filenames": [ - "singularity/Singularity.tensorflow_gpu-py3", - "singularity/Singularity.pytorch_gpu-py3", - "singularity/Singularity.tensorflow_cpu-py3" + "Singularity", + "Singularity.hpc" ], - "full_name": "mmoelle1/GPU_Cource_ML", + "full_name": "hqhv/oneapi", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1667300805.0 + "updated_at": 1611066291.0 }, { "data_format": 2, "description": null, "filenames": [ - "envs/containers/Singularity" + "Singularity" ], - "full_name": "Microbial-Ecology-Group/MHplusplus", + "full_name": "GeertvanGeest/test_shub", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-----in-development----\" class=\"anchor\" aria-hidden=\"true\" href=\"#----in-development----\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e--- In development ---\u003c/h1\u003e\n\u003ch1\u003e\u003ca id=\"user-content-mh-bioinformatic-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#mh-bioinformatic-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMH++ bioinformatic pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/FAQs.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/update_details.md\"\u003eDetails on AMR++ updates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-amr-demonstration\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ demonstration\u003c/h2\u003e\n\u003cp\u003eCreate the anaconda environment for AMR++. This will work for both the nextflow version and snakemake version.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -c conda-forge -n mamba_base mamba\nconda activate mamba_base\nmamba create -c conda-forge -c bioconda -n AMR++ snakemake nextflow git make cxx-compiler singularity\nmamba activate AMR++\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eClone the AMR++ repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/EnriqueDoster/AMRplusplus.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBrief tutorial for nextflow pipeline test run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e AMRplusplus\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run command to perform the demonstration pipeline using the singularity profile\u003c/span\u003e\nnextflow run main_AMR++.nf -profile singularity --pipeline demo\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-using-amr-to-analyze-your-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-amr-to-analyze-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing AMR++ to analyze your data\u003c/h1\u003e\n\u003cp\u003eAMR++ is customizable to suit your computing needs and analyze your data. Primarily, the \u003ccode\u003e-profile\u003c/code\u003e paramater allows you to choose between running AMR++ using a singularity container, docker container, anaconda packages, or a local installation of your software.\nAll parameters used to control how AMR++ analyzes your data can also be changed as needed in a variety of ways. For full information, review this \u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003econfiguration document.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a brief example, the default parameters were run using this command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile singularity --pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo change the reads that were analyzed, you should specify the ```--reads`` parameters. Here, we can use regular expressions to point to your samples in a different directory.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile singularity --pipeline demo --reads \"path/to/your/reads/*_R{1,2}.fastq.gz\" \u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-choosing-a-modified-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#choosing-a-modified-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoosing a modified pipeline\u003c/h2\u003e\n\u003cp\u003eAMR++ analyzes data by combining workflows that takes a set of sequencing reads through various bioinformatic software. We recommend our standard AMR++ pipeline as a comprehensive way to start from raw sequencing reads, QC assessment, host DNA removal, and resistome analysis with MEGARes. However, users might only want to replicate portions of the pipeline and have more control over their computing needs. Using the \u003ccode\u003e--pipeline\u003c/code\u003e parameter, users can change how AMR++ runs.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimple demonstration\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline fast_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR_wKraken\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\nNon-host reads \u0026gt; Microbiome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epipeline fragments\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline multiqc\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate sample QC\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline trim\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming using trimmomatic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline rmhost\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to host DNA using bwa and remove contaminants\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline resistome\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to MEGARes using bwa, perform rarefaction and resistome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline kraken\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eClassify reads taxanomically using kraken\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-test_shub\" class=\"anchor\" aria-hidden=\"true\" href=\"#test_shub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etest_shub\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1667397676.0 + "updated_at": 1618210665.0 }, { "data_format": 2, - "description": "Testing SingularityHub integration", + "description": null, "filenames": [ - "Singularity.fun" + "Singularity" ], - "full_name": "mmarinriera/Singularity_training", + "full_name": "Samip1211/MongoImage", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1551276494.0 + "updated_at": 1565456485.0 }, { "data_format": 2, "description": null, "filenames": [ - "envs/containers/Singularity" + "Singularity" ], - "full_name": "EnriqueDoster/AMRplusplus", + "full_name": "truatpasteurdotfr/singularity-docker-pytorch-a40", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-amr-bioinformatic-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-bioinformatic-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ bioinformatic pipeline\u003c/h1\u003e\n\u003cp\u003e(\u003ca href=\"https://megares.meglab.org/\" rel=\"nofollow\"\u003ehttps://megares.meglab.org/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eAMR++ is a bioinformatic pipeline meant to aid in the analysis of raw sequencing reads to characterize the profile of antimicrobial resistance genes, or resistome. AMR++ was developed to work in conjuction with the the MEGARes database which contains sequence data for approximately 9,000 hand-curated antimicrobial resistance genes accompanied by an annotation structure that is optimized for use with high throughput sequencing and metagenomic analysis. The acyclical annotation graph of MEGARes allows for accurate, count-based, hierarchical statistical analysis of resistance at the population level, much like microbiome analysis, and is also designed to be used as a training database for the creation of statistical classifiers.\u003c/p\u003e\n\u003cp\u003eThe goal of many metagenomics studies is to characterize the content and relative abundance of sequences of interest from the DNA of a given sample or set of samples. You may want to know what is contained within your sample or how abundant a given sequence is relative to another.\u003c/p\u003e\n\u003cp\u003eOften, metagenomics is performed when the answer to these questions must be obtained for a large number of targets where techniques like multiplex PCR and other targeted methods would be too cumbersome to perform. AMR++ can process the raw data from the sequencer, identify the fragments of DNA, and count them. It also provides a count of the polymorphisms that occur in each DNA fragment with respect to the reference database.\u003c/p\u003e\n\u003cp\u003eAdditionally, you may want to know if the depth of your sequencing (how many reads you obtain that are on target) is high enough to identify rare organisms (organisms with low abundance relative to others) in your population. This is referred to as rarefaction and is calculated by randomly subsampling your sequence data at intervals between 0% and 100% in order to determine how many targets are found at each depth.\u003c/p\u003e\n\u003cp\u003eWith AMR++, you will obtain alignment count files for each sample that are combined into a count matrix that can be analyzed using any statistical and mathematical techniques that can operate on a matrix of observations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/FAQs.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/update_details.md\"\u003eDetails on AMR++ updates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-amr-demonstration\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ demonstration\u003c/h2\u003e\n\u003cp\u003eCreate the anaconda environment for AMR++. This will work for both the nextflow version and snakemake version.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -c conda-forge -n mamba_base mamba\nconda activate mamba_base\nmamba create -c conda-forge -c bioconda -n AMR++ snakemake nextflow git make cxx-compiler singularity\nmamba activate AMR++\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eClone the AMR++ repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/EnriqueDoster/AMRplusplus.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBrief tutorial for nextflow pipeline test run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e AMRplusplus\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run command to perform the demonstration pipeline using the singularity profile\u003c/span\u003e\nnextflow run main_AMR++.nf -profile singularity --pipeline demo\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-using-amr-to-analyze-your-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-amr-to-analyze-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing AMR++ to analyze your data\u003c/h1\u003e\n\u003cp\u003eAMR++ is customizable to suit your computing needs and analyze your data. Primarily, the \u003ccode\u003e-profile\u003c/code\u003e paramater allows you to choose between running AMR++ using a singularity container, docker container, anaconda packages, or a local installation of your software.\nAll parameters used to control how AMR++ analyzes your data can also be changed as needed in a variety of ways. For full information, review this \u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003econfiguration document.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a brief example, the default parameters were run using this command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile singularity --pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo change the reads that were analyzed, you should specify the ```--reads`` parameters. Here, we can use regular expressions to point to your samples in a different directory.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile singularity --pipeline demo --reads \"path/to/your/reads/*_R{1,2}.fastq.gz\" \u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-choosing-a-modified-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#choosing-a-modified-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoosing a modified pipeline\u003c/h2\u003e\n\u003cp\u003eAMR++ analyzes data by combining workflows that takes a set of sequencing reads through various bioinformatic software. We recommend our standard AMR++ pipeline as a comprehensive way to start from raw sequencing reads, QC assessment, host DNA removal, and resistome analysis with MEGARes. However, users might only want to replicate portions of the pipeline and have more control over their computing needs. Using the \u003ccode\u003e--pipeline\u003c/code\u003e parameter, users can change how AMR++ runs.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimple demonstration\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline fast_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR_wKraken\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\nNon-host reads \u0026gt; Microbiome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epipeline fragments\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline multiqc\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate sample QC\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline trim\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming using trimmomatic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline rmhost\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to host DNA using bwa and remove contaminants\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline resistome\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to MEGARes using bwa, perform rarefaction and resistome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline kraken\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eClassify reads taxanomically using kraken\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-testing-image-for-a40-gpupytorch-\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-image-for-a40-gpupytorch-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etesting image for a40 gpu/pytorch \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-pytorch-a40/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-docker-pytorch-a40/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003edocker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/singularity-docker-pytorch-a40:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell oras://ghcr.io/truatpasteurdotfr/singularity-docker-pytorch-a40:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1665339720.0 + "updated_at": 1636479915.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe files for APSIM Classic (https://github.com/APSIMInitiative/APSIMClassic)", "filenames": [ - "v4.7.1/Singularity", - "v4.9.1/Singularity" + "Singularity", + "Singularity.7.10-r49ace54f9c8a670190aef9d8d0fb9d5477bb1534", + "Singularity.7.9-r4047" ], - "full_name": "yh549848/singularity-code-server-stacks", + "full_name": "powerPlant/apsim-srf", "latest_release": null, + "readme": "\u003cp\u003eSingularity recipe files for the APSIM Classic version of the Agricultural Production Systems sIMulator\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainer-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#maintainer-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainer Notes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRecipes for APSIM 7.9 use the upstream SVN repository (no longer available)\u003c/li\u003e\n\u003cli\u003ePlease see comments inside the recipes for the reasons why some upstream files are overwritten during the build process\u003c/li\u003e\n\u003cli\u003eThe Cotton Model requires a password, which needs to be obtained by the model owner and placed under \u003ccode\u003efiles/CottonPassword.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1676597534.0 + "updated_at": 1586904956.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Recipes/Singularity_pytorch", + "Recipes/Singularity_pytorch_full", + "Recipes/Singularity_spark_full", + "Recipes/Singularity_mpich", + "Recipes/Singularity_example", + "Recipes/Singularity_ompi", + "Recipes/Singularity_tensorflow", + "Recipes/Singularity_spark" ], - "full_name": "asfistonlavie/TEFLoN2", + "full_name": "Yasmim-Fernandes/Ufscar-hpc-template-ci", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-teflon2\" class=\"anchor\" aria-hidden=\"true\" href=\"#teflon2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTEFLoN2\u003c/h1\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-pr\u00e9-requisitos\" class=\"anchor\" aria-hidden=\"true\" href=\"#pr\u00e9-requisitos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePr\u00e9-requisitos\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sele\u00e7\u00e3o-do-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#sele\u00e7\u00e3o-do-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSele\u00e7\u00e3o do Recipe\u003c/h2\u003e\n\u003cp\u003eAdicione o caminho do \u003cem\u003esingularity recipe\u003c/em\u003e desejado na vari\u00e1vel RECIPE no github (projeto \u0026gt;\u0026gt; Settings \u0026gt;\u0026gt; Secrets \u0026gt;\u0026gt; New Secret).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-template-integra\u00e7\u00e3o-cont\u00ednua-github\" class=\"anchor\" aria-hidden=\"true\" href=\"#template-integra\u00e7\u00e3o-cont\u00ednua-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemplate Integra\u00e7\u00e3o Cont\u00ednua Github\u003c/h1\u003e\n\u003cp\u003eEsse projeto o template para uso do cluster da UFSCar, contendo integra\u00e7\u00e3o cont\u00ednua com o Google Drive e Amazon S3.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requisitos-para-google-drive\" class=\"anchor\" aria-hidden=\"true\" href=\"#requisitos-para-google-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequisitos para Google Drive\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eEntre no \u003ca href=\"https://console.developers.google.com/apis/credentials\" rel=\"nofollow\"\u003econsole de credenciais de API do Google\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSe ainda n\u00e3o houver um projeto, crie um com permiss\u00e3o para a \"Google Drive API\".\u003c/li\u003e\n\u003cli\u003eClique em \"Criar credenciais\".\u003c/li\u003e\n\u003cli\u003eSelecione \"ID do cliente do OAuth\".\u003c/li\u003e\n\u003cli\u003eEm \"Tipo de aplicativo\", selecione \"App para computador\".\u003c/li\u003e\n\u003cli\u003eD\u00ea um nome de identifica\u00e7\u00e3o para as credenciais e clique em \"criar\". V\u00e3o aparecer dois dados (\"Seu ID de cliente\" e \"Sua chave secreta de cliente\"), precisaremos dos dois no passo seguinte.\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 13\nclient_id\u0026gt; conte\u00fado de \"Seu ID de cliente\"\nclient_secret\u0026gt; conte\u00fado de \"Sua chave secreta de cliente\"\nscope\u0026gt; 1\nroot_folder_id\u0026gt; deixe em branco\nservice_account_file\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/n\u0026gt; n\n\nCopie o url e cole no navegador no computador local. Autorize e:\n\nEnter verification code\u0026gt; c\u00f3digo fornecido pelo navegador ap\u00f3s autoriza\u00e7\u00e3o\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requisitos-para-amazon-s3\" class=\"anchor\" aria-hidden=\"true\" href=\"#requisitos-para-amazon-s3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequisitos para Amazon S3\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eEntre na \u003ca href=\"console.aws.amazon.com\"\u003eAWS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClique na seta ao lado de seu nome de usu\u00e1rio e em \"My Security Credentials\".\u003c/li\u003e\n\u003cli\u003eNa se\u00e7\u00e3o \"Access Keys\", clique em \"Create New Access Key\".\u003c/li\u003e\n\u003cli\u003eNa janela que aparece, clique em \"Show Access Key\".\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 4\nprovider\u0026gt; 1\nenv_auth\u0026gt; 1\naccess_key_id\u0026gt; conte\u00fado de \"Access Key ID\"\nsecret_access_key\u0026gt; conte\u00fado de \"Secret Access Key\"\nregion\u0026gt; 16\nendpoint\u0026gt; deixe em branco\nlocation_constraint\u0026gt; 16\nacl\u0026gt; deixe em branco\nserver_side_encryption\u0026gt; deixe em branco\nsse_kms_key_id\u0026gt; deixe em branco\nstorage_class\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1658824620.0 + "updated_at": 1607370441.0 }, { "data_format": 2, - "description": null, + "description": "informations and configurations for OpenFLUID containerization", "filenames": [ - "singularity/Singularity" + "v2.1.3/Singularity", + "v2.1.9/Singularity", + "v1.7.2/Singularity", + "v2.1.5/Singularity", + "v2.1.4/Singularity", + "v2.1.2/Singularity", + "v2.1.8/Singularity", + "v2.1.6/Singularity", + "v2.1.7/Singularity", + "v2.0.2/Singularity", + "v2.1.10/Singularity", + "v2.1.11/Singularity" ], - "full_name": "oxfordmmm/Bugflow_DSL2", + "full_name": "OpenFLUID/openfluid-containers", "latest_release": null, + "readme": "\u003cp\u003eThis repository contains configuration files for Docker and Singularity containerization of OpenFLUID.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1676460377.0 + "updated_at": 1616516042.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "manasi-sharma/language-OG-diffuser", + "full_name": "ipelupessy/test-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-planning-with-diffusion--\" class=\"anchor\" aria-hidden=\"true\" href=\"#planning-with-diffusion--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanning with Diffusion \u00a0\u00a0 \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eTraining and visualizing of diffusion models from \u003ca href=\"https://diffusion-planning.github.io/\" rel=\"nofollow\"\u003ePlanning with Diffusion for Flexible Behavior Synthesis\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/main\"\u003emain branch\u003c/a\u003e contains code for training diffusion models and planning via value-function guided sampling on the D4RL locomotion environments.\nThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/kuka\"\u003ekuka branch\u003c/a\u003e contains block-stacking experiments.\nThe \u003ca href=\"https://github.com/jannerm/diffuser/tree/maze2d\"\u003emaze2d branch\u003c/a\u003e contains goal-reaching via inpainting in the Maze2D environments.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/184a6bb0418c60d778dcfa9f81dfddfdea0c0a3238b1d0cc4aa8666dd32ad3ca/68747470733a2f2f646966667573696f6e2d706c616e6e696e672e6769746875622e696f2f696d616765732f64696666757365722d636172642e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/184a6bb0418c60d778dcfa9f81dfddfdea0c0a3238b1d0cc4aa8666dd32ad3ca/68747470733a2f2f646966667573696f6e2d706c616e6e696e672e6769746875622e696f2f696d616765732f64696666757365722d636172642e706e67\" width=\"60%\" title=\"Diffuser model\" data-canonical-src=\"https://diffusion-planning.github.io/images/diffuser-card.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUpdates\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e12/09/2022: Diffuser (the RL model) has been integrated into \u003cg-emoji class=\"g-emoji\" alias=\"hugs\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f917.png\"\u003e\ud83e\udd17\u003c/g-emoji\u003e Diffusers (the Hugging Face diffusion model library)! See \u003ca href=\"https://huggingface.co/docs/diffusers/using-diffusers/rl\" rel=\"nofollow\"\u003ethese docs\u003c/a\u003e for how to run Diffuser using their pipeline.\u003c/li\u003e\n\u003cli\u003e10/17/2022: A bug in the value function scaling has been fixed in \u003ca href=\"https://github.com/jannerm/diffuser/commit/3d7361c2d028473b601cc04f5eecd019e14eb4eb\"\u003ethis commit\u003c/a\u003e. Thanks to \u003ca href=\"https://scholar.google.com/citations?user=Q6UMpRYAAAAJ\u0026amp;hl=en\" rel=\"nofollow\"\u003ePhilemon Brakel\u003c/a\u003e for catching it!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eLoad a pretrained diffusion model and sample from it in your browser with \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003escripts/diffuser-sample.ipynb\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate diffuser\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-pretrained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-pretrained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing pretrained models\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-downloading-weights\" class=\"anchor\" aria-hidden=\"true\" href=\"#downloading-weights\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownloading weights\u003c/h3\u003e\n\u003cp\u003eDownload pretrained diffusion models and value functions with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./scripts/download_pretrained.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis command downloads and extracts a \u003ca href=\"https://drive.google.com/file/d/1wc1m4HLj7btaYDN8ogDIAV9QzgWEckGy/view?usp=share_link\" rel=\"nofollow\"\u003etarfile\u003c/a\u003e containing \u003ca href=\"https://drive.google.com/drive/folders/1ie6z3toz9OjcarJuwjQwXXzDwh1XnS02?usp=sharing\" rel=\"nofollow\"\u003ethis directory\u003c/a\u003e to \u003ccode\u003elogs/pretrained\u003c/code\u003e. The models are organized according to the following structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u2514\u2500\u2500 logs/pretrained\n \u251c\u2500\u2500 ${environment_1}\n \u2502 \u251c\u2500\u2500 diffusion\n \u2502 \u2502 \u2514\u2500\u2500 ${experiment_name}\n \u2502 \u2502 \u251c\u2500\u2500 state_${epoch}.pt\n \u2502 \u2502 \u251c\u2500\u2500 sample-${epoch}-*.png\n \u2502 \u2502 \u2514\u2500\u2500 {dataset, diffusion, model, render, trainer}_config.pkl\n \u2502 \u251c\u2500\u2500 values\n \u2502 \u2502 \u2514\u2500\u2500 ${experiment_name}\n \u2502 \u2502 \u251c\u2500\u2500 state_${epoch}.pt\n \u2502 \u2502 \u2514\u2500\u2500 {dataset, diffusion, model, render, trainer}_config.pkl\n \u2502 \u2514\u2500\u2500 plans\n \u2502 \u2514\u2500\u2500 defaults\n \u2502 \u251c\u2500\u2500 0\n \u2502 \u251c\u2500\u2500 1\n \u2502 \u251c\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 149\n \u2502\n \u251c\u2500\u2500 ${environment_2}\n \u2502 \u2514\u2500\u2500 ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003estate_${epoch}.pt\u003c/code\u003e files contain the network weights and the \u003ccode\u003econfig.pkl\u003c/code\u003e files contain the instantation arguments for the relevant classes.\nThe png files contain samples from different points during training of the diffusion model.\nWithin the \u003ccode\u003eplans\u003c/code\u003e subfolders, there are the results of 150 evaluation trials for each environment using the default hyperparameters.\u003c/p\u003e\n\u003cdetails\u003e\n\u003csummary\u003eTo aggregate the results of the evaluations in the \u003ccode\u003elogs\u003c/code\u003e folder, run \u003ccode\u003epython scripts/read_results.py\u003c/code\u003e. (Expand to view the output of this command on the plans downloaded from Google Drive.)\n\u003c/summary\u003e\n\u003cpre\u003e\u003ccode\u003ehopper-medium-replay-v2 | defaults | logs/pretrained/hopper-medium-replay-v2/plans | 150 scores\n 93.6 +/- 0.37\nhopper-medium-v2 | defaults | logs/pretrained/hopper-medium-v2/plans | 150 scores\n 74.3 +/- 1.36\nhopper-medium-expert-v2 | defaults | logs/pretrained/hopper-medium-expert-v2/plans | 150 scores\n 103.3 +/- 1.30\nwalker2d-medium-replay-v2 | defaults | logs/pretrained/walker2d-medium-replay-v2/plans | 150 scores\n 70.6 +/- 1.60\nwalker2d-medium-v2 | defaults | logs/pretrained/walker2d-medium-v2/plans | 150 scores\n 79.6 +/- 0.55\nwalker2d-medium-expert-v2 | defaults | logs/pretrained/walker2d-medium-expert-v2/plans | 150 scores\n 106.9 +/- 0.24\nhalfcheetah-medium-replay-v2 | defaults | logs/pretrained/halfcheetah-medium-replay-v2/plans | 150 scores\n 37.7 +/- 0.45\nhalfcheetah-medium-v2 | defaults | logs/pretrained/halfcheetah-medium-v2/plans | 150 scores\n 42.8 +/- 0.32\nhalfcheetah-medium-expert-v2 | defaults | logs/pretrained/halfcheetah-medium-expert-v2/plans | 150 scores\n 88.9 +/- 0.25\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eTo create the table of offline RL results from the paper, run \u003ccode\u003epython plotting/table.py\u003c/code\u003e. This will print a table that can be copied into a Latex document. (Expand to view table source.)\u003c/summary\u003e\n\u003cpre\u003e\u003ccode\u003e\\definecolor{tblue}{HTML}{1F77B4}\n\\definecolor{tred}{HTML}{FF6961}\n\\definecolor{tgreen}{HTML}{429E9D}\n\\definecolor{thighlight}{HTML}{000000}\n\\newcolumntype{P}{\u0026gt;{\\raggedleft\\arraybackslash}X}\n\\begin{table*}[hb!]\n\\centering\n\\small\n\\begin{tabularx}{\\textwidth}{llPPPPPPPPr}\n\\toprule\n\\multicolumn{1}{r}{\\bf \\color{black} Dataset} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} Environment} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} BC} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} CQL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} IQL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} DT} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} TT} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MOPO} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MOReL} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} MBOP} \u0026amp; \\multicolumn{1}{r}{\\bf \\color{black} Diffuser} \\\\ \n\\midrule\nMedium-Expert \u0026amp; HalfCheetah \u0026amp; $55.2$ \u0026amp; $91.6$ \u0026amp; $86.7$ \u0026amp; $86.8$ \u0026amp; $95.0$ \u0026amp; $63.3$ \u0026amp; $53.3$ \u0026amp; $\\textbf{\\color{thighlight}105.9}$ \u0026amp; $88.9$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.3$}} \\\\ \nMedium-Expert \u0026amp; Hopper \u0026amp; $52.5$ \u0026amp; $\\textbf{\\color{thighlight}105.4}$ \u0026amp; $91.5$ \u0026amp; $\\textbf{\\color{thighlight}107.6}$ \u0026amp; $\\textbf{\\color{thighlight}110.0}$ \u0026amp; $23.7$ \u0026amp; $\\textbf{\\color{thighlight}108.7}$ \u0026amp; $55.1$ \u0026amp; $103.3$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.3$}} \\\\ \nMedium-Expert \u0026amp; Walker2d \u0026amp; $\\textbf{\\color{thighlight}107.5}$ \u0026amp; $\\textbf{\\color{thighlight}108.8}$ \u0026amp; $\\textbf{\\color{thighlight}109.6}$ \u0026amp; $\\textbf{\\color{thighlight}108.1}$ \u0026amp; $101.9$ \u0026amp; $44.6$ \u0026amp; $95.6$ \u0026amp; $70.2$ \u0026amp; $\\textbf{\\color{thighlight}106.9}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.2$}} \\\\ \n\\midrule\nMedium \u0026amp; HalfCheetah \u0026amp; $42.6$ \u0026amp; $44.0$ \u0026amp; $\\textbf{\\color{thighlight}47.4}$ \u0026amp; $42.6$ \u0026amp; $\\textbf{\\color{thighlight}46.9}$ \u0026amp; $42.3$ \u0026amp; $42.1$ \u0026amp; $44.6$ \u0026amp; $42.8$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.3$}} \\\\ \nMedium \u0026amp; Hopper \u0026amp; $52.9$ \u0026amp; $58.5$ \u0026amp; $66.3$ \u0026amp; $67.6$ \u0026amp; $61.1$ \u0026amp; $28.0$ \u0026amp; $\\textbf{\\color{thighlight}95.4}$ \u0026amp; $48.8$ \u0026amp; $74.3$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.4$}} \\\\ \nMedium \u0026amp; Walker2d \u0026amp; $75.3$ \u0026amp; $72.5$ \u0026amp; $\\textbf{\\color{thighlight}78.3}$ \u0026amp; $74.0$ \u0026amp; $\\textbf{\\color{thighlight}79.0}$ \u0026amp; $17.8$ \u0026amp; $\\textbf{\\color{thighlight}77.8}$ \u0026amp; $41.0$ \u0026amp; $\\textbf{\\color{thighlight}79.6}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.55$}} \\\\ \n\\midrule\nMedium-Replay \u0026amp; HalfCheetah \u0026amp; $36.6$ \u0026amp; $45.5$ \u0026amp; $44.2$ \u0026amp; $36.6$ \u0026amp; $41.9$ \u0026amp; $\\textbf{\\color{thighlight}53.1}$ \u0026amp; $40.2$ \u0026amp; $42.3$ \u0026amp; $37.7$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.5$}} \\\\ \nMedium-Replay \u0026amp; Hopper \u0026amp; $18.1$ \u0026amp; $\\textbf{\\color{thighlight}95.0}$ \u0026amp; $\\textbf{\\color{thighlight}94.7}$ \u0026amp; $82.7$ \u0026amp; $\\textbf{\\color{thighlight}91.5}$ \u0026amp; $67.5$ \u0026amp; $\\textbf{\\color{thighlight}93.6}$ \u0026amp; $12.4$ \u0026amp; $\\textbf{\\color{thighlight}93.6}$ \\scriptsize{\\raisebox{1pt}{$\\pm 0.4$}} \\\\ \nMedium-Replay \u0026amp; Walker2d \u0026amp; $26.0$ \u0026amp; $77.2$ \u0026amp; $73.9$ \u0026amp; $66.6$ \u0026amp; $\\textbf{\\color{thighlight}82.6}$ \u0026amp; $39.0$ \u0026amp; $49.8$ \u0026amp; $9.7$ \u0026amp; $70.6$ \\scriptsize{\\raisebox{1pt}{$\\pm 1.6$}} \\\\ \n\\midrule\n\\multicolumn{2}{c}{\\bf Average} \u0026amp; 51.9 \u0026amp; \\textbf{\\color{thighlight}77.6} \u0026amp; \\textbf{\\color{thighlight}77.0} \u0026amp; 74.7 \u0026amp; \\textbf{\\color{thighlight}78.9} \u0026amp; 42.1 \u0026amp; 72.9 \u0026amp; 47.8 \u0026amp; \\textbf{\\color{thighlight}77.5} \\hspace{.6cm} \\\\ \n\\bottomrule\n\\end{tabularx}\n\\vspace{-.0cm}\n\\caption{\n}\n\\label{table:locomotion}\n\\end{table*}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/diffusion-planning/diffusion-planning.github.io/blob/master/images/table.png\"\u003e\u003cimg src=\"https://github.com/diffusion-planning/diffusion-planning.github.io/raw/master/images/table.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003ch3\u003e\u003ca id=\"user-content-planning\" class=\"anchor\" aria-hidden=\"true\" href=\"#planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlanning\u003c/h3\u003e\n\u003cp\u003eTo plan with guided sampling, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs/pretrained\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--logbase\u003c/code\u003e flag points the \u003ca href=\"scripts/plan_guided.py#L22-L30\"\u003eexperiment loaders\u003c/a\u003e to the folder containing the pretrained models.\nYou can override planning hyperparameters with flags, such as \u003ccode\u003e--batch_size 8\u003c/code\u003e, but the default\nhyperparameters are a good starting point.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining from scratch\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eTrain a diffusion model with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/train.py --dataset halfcheetah-medium-expert-v2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe default hyperparameters are listed in \u003ca href=\"config/locomotion.py#L22-L65\"\u003elocomotion:diffusion\u003c/a\u003e.\nYou can override any of them with flags, eg, \u003ccode\u003e--n_diffusion_steps 100\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTrain a value function with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/train_values.py --dataset halfcheetah-medium-expert-v2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"config/locomotion.py#L67-L108\"\u003elocomotion:values\u003c/a\u003e for the corresponding default hyperparameters.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003ePlan using your newly-trained models with the same command as in the pretrained planning section, simply replacing the logbase to point to your new models:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"config/locomotion.py#L110-L149\"\u003elocomotion:plans\u003c/a\u003e for the corresponding default hyperparameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeferred f-strings.\u003c/strong\u003e Note that some planning script arguments, such as \u003ccode\u003e--n_diffusion_steps\u003c/code\u003e or \u003ccode\u003e--discount\u003c/code\u003e,\ndo not actually change any logic during planning, but simply load a different model using a deferred f-string.\nFor example, the following flags:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e---horizon 32 --n_diffusion_steps 20 --discount 0.997\n--value_loadpath \u0027f:values/defaults_H{horizon}_T{n_diffusion_steps}_d{discount}\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill resolve to a value checkpoint path of \u003ccode\u003evalues/defaults_H32_T20_d0.997\u003c/code\u003e. It is possible to\nchange the horizon of the diffusion model after training (see \u003ca href=\"https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for an example),\nbut not for the value function.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile . -t diffuser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTest the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm --gpus all \\\n --mount type=bind,source=$PWD,target=/home/code \\\n --mount type=bind,source=$HOME/.d4rl,target=/root/.d4rl \\\n diffuser \\\n bash -c \\\n \"export PYTHONPATH=$PYTHONPATH:/home/code \u0026amp;\u0026amp; \\\n python /home/code/scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot diffuser.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTest the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv --writable-tmpfs diffuser.sif \\\n bash -c \\\n \"pip install -e . \u0026amp;\u0026amp; \\\n python scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-azure\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-azure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Azure\u003c/h2\u003e\n\u003ch4\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eTag the Docker image (built in the \u003ca href=\"#Docker\"\u003eDocker section\u003c/a\u003e) and push it to Docker Hub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport DOCKER_USERNAME=$(docker info | sed \u0027/Username:/!d;s/.* //\u0027)\ndocker tag diffuser ${DOCKER_USERNAME}/diffuser:latest\ndocker image push ${DOCKER_USERNAME}/diffuser\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eUpdate \u003ca href=\"azure/config.py\"\u003e\u003ccode\u003eazure/config.py\u003c/code\u003e\u003c/a\u003e, either by modifying the file directly or setting the relevant \u003ca href=\"azure/config.py#L47-L52\"\u003eenvironment variables\u003c/a\u003e. To set the \u003ccode\u003eAZURE_STORAGE_CONNECTION\u003c/code\u003e variable, navigate to the \u003ccode\u003eAccess keys\u003c/code\u003e section of your storage account. Click \u003ccode\u003eShow keys\u003c/code\u003e and copy the \u003ccode\u003eConnection string\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload \u003ca href=\"https://docs.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10\" rel=\"nofollow\"\u003e\u003ccode\u003eazcopy\u003c/code\u003e\u003c/a\u003e: \u003ccode\u003e./azure/download.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h4\u003e\n\u003cp\u003eLaunch training jobs with \u003ccode\u003epython azure/launch.py\u003c/code\u003e. The launch script takes no command-line arguments; instead, it launches a job for every combination of hyperparameters in \u003ca href=\"azure/launch_train.py#L36-L38\"\u003e\u003ccode\u003eparams_to_sweep\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-viewing-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#viewing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eViewing results\u003c/h4\u003e\n\u003cp\u003eTo rsync the results from the Azure storage container, run \u003ccode\u003e./azure/sync.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo mount the storage container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a blobfuse config with \u003ccode\u003e./azure/make_fuse_config.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./azure/mount.sh\u003c/code\u003e to mount the storage container to \u003ccode\u003e~/azure_mount\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo unmount the container, run \u003ccode\u003esudo umount -f ~/azure_mount; rm -r ~/azure_mount\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{janner2022diffuser,\n title = {Planning with Diffusion for Flexible Behavior Synthesis},\n author = {Michael Janner and Yilun Du and Joshua B. Tenenbaum and Sergey Levine},\n booktitle = {International Conference on Machine Learning},\n year = {2022},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe diffusion model implementation is based on Phil Wang\u0027s \u003ca href=\"https://github.com/lucidrains/denoising-diffusion-pytorch\"\u003edenoising-diffusion-pytorch\u003c/a\u003e repo.\nThe organization of this repo and remote launcher is based on the \u003ca href=\"https://github.com/jannerm/trajectory-transformer\"\u003etrajectory-transformer\u003c/a\u003e repo.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1675500146.0 + "updated_at": 1522955804.0 }, { "data_format": 2, - "description": "This material contains content on how to profile and optimize simple Pytorch mnist code using NVIDIA Nsight Systems and Pytorch Profiler ", + "description": null, "filenames": [ - "Singularity" + "material/scientific/Singularity", + "material/tensorflow/Singularity", + "material/hello/Singularity", + "material/centos/Singularity", + "material/mpi/Singularity", + "material/ubuntu/Singularity" ], - "full_name": "openhackathons-org/AI-Profiler", + "full_name": "DataSystemsGroupUT/singularity-tutorial", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-optimizing-a-deep-neural-network-dnn-training-program\" class=\"anchor\" aria-hidden=\"true\" href=\"#optimizing-a-deep-neural-network-dnn-training-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimizing a Deep Neural Network (DNN) training program\u003c/h1\u003e\n\u003cp\u003eThis folder contains contents for AI training program profiling.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNVIDIA Nsight Systems\u003c/li\u003e\n\u003cli\u003ePyTorch Profiler with TensorBoard Plugin\u003c/li\u003e\n\u003cli\u003eTensorBoard Visualization\u003c/li\u003e\n\u003cli\u003eOptimization Techniques\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eTo run this tutorial you will need a machine with NVIDIA GPU.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall the latest \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eTo be able to see the profiler output, please download NVIDIA Nsight Systems\u0027 latest version from \u003ca href=\"https://developer.nvidia.com/nsight-systems\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eLinux ubuntu OS\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on containers\u003c/h2\u003e\n\u003cp\u003eTo start with, you will have to build a Docker or Singularity container.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Container\u003c/h3\u003e\n\u003cp\u003eTo build a docker container, run:\n\u003ccode\u003esudo docker build --network=host -t \u0026lt;imagename\u0026gt;:\u0026lt;tagnumber\u0026gt; .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor instance:\n\u003ccode\u003esudo docker build -t pytorch:1.0 .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe code labs have been written using Jupyter notebooks and a Dockerfile has been built to simplify deployment. In order to serve the docker instance for a student, it is necessary to expose port 8888 from the container, for instance, the following command would expose port 8888 inside the container as port 8888 on the lab machine:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -it --rm --network=host -v ~/ai_profiler/workspace:/workspace pytorch:1.0 jupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--gpus\u003c/code\u003e flag is used to enable \u003ccode\u003eall\u003c/code\u003e NVIDIA GPUs during container runtime. The \u003ccode\u003e--rm\u003c/code\u003e flag is used to clean an temporary images created during the running of the container. The \u003ccode\u003e-it\u003c/code\u003e flag enables killing the jupyter server with ctrl-c.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--ipc=host --ulimit memlock=-1 --ulimit stack=67108864\u003c/code\u003e enable sufficient memory allocation to run pytorch within the docker environment.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ejupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e command launch the jupyter notebook inside the container. The flag \u003ccode\u003e-v\u003c/code\u003e allows the mapping of working directory on your local machine \u003ccode\u003e~/ai_profiler/profiler/workspace:/workspace\u003c/code\u003e to \u003ccode\u003eworspace\u003c/code\u003e directory inside the container.\u003c/p\u003e\n\u003cp\u003eThen, open the jupyter notebook in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e\nStart working on the lab by clicking on the \u003ccode\u003estart_here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container\u003c/h3\u003e\n\u003cp\u003eTo build the singularity container, run:\n\u003ccode\u003esudo singularity build --fakeroot \u0026lt;image_name\u0026gt;.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFore example:\n\u003ccode\u003esingularity build --fakeroot pytorch.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, run the container:\n\u003ccode\u003esingularity run --nv --bind ~/ai_profiler/workspace:/workspace pytorch.simg jupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--nv\u003c/code\u003e flag is used to enable \u003ccode\u003eall\u003c/code\u003e NVIDIA GPUs during container runtime. The \u003ccode\u003e--bind\u003c/code\u003e allows the mapping of working directory on your local machine \u003ccode\u003e~/ai_profiler/profiler/workspace:/workspace\u003c/code\u003e to \u003ccode\u003eworspace\u003c/code\u003e directory inside the container.\u003c/p\u003e\n\u003cp\u003eThen, open the jupyter notebook in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e\nStart working on the lab by clicking on the \u003ccode\u003eStart_Here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-local-machine\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-local-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Local Machine\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall PyTorch \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall essentials:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e pip3 install jupyterlab\n pip3 install ipywidgets\n pip3 install torch_tb_profiler\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eInstall NVIDIA Nsight Systems version 2022.1.1 from \u003ca href=\"https://developer.nvidia.com/nsight-systems\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and set path. Please run \u003ccode\u003ensys --version\u003c/code\u003e from the terminal to ensure you are using the version 2022.1.1 or above\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e#Tutorial Duration\nThe total bootcamp material would take 2 hours.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-a-practical-guide-to-singularity---ut-data-engineering-fall-2021\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-practical-guide-to-singularity---ut-data-engineering-fall-2021\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA Practical Guide to Singularity - UT Data Engineering (Fall 2021)\u003c/h1\u003e\n\u003cp\u003eThis guide will introduce you to Singularity, a containerization system for scientific computing environments that is available on many scientific computing clusters. Containers allow you to package the environment that your code depends on inside of a portable unit. This is extremely useful for ensuring that your code can be run portably on other machines. It is also useful for installing software, packages, libraries, etc. in environments where you do not have root privileges, like an HPC account.\nThe repository contains the guide and files for the practical session of Singularity containers for the course Data Engineering at the University of Tartu.\nIt is divided in four parts and it goes from the installation process, knowing basic commands and finally a more advanced exercise.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-i-installing-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-i-installing-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart I. Installing Singularity\u003c/h2\u003e\n\u003cp\u003eYou have two options to get Singularity installed on your machine.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-option-1-the-docker-way-recommended-for-the-practice-session\" class=\"anchor\" aria-hidden=\"true\" href=\"#option-1-the-docker-way-recommended-for-the-practice-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 1: The Docker way (recommended for the practice session)\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003edocker\u003c/code\u003e and \u003ccode\u003egit\u003c/code\u003e should be installed on your machine. Then we need to create a container that has the dependencies and binary of singularity in it. The container to run uses the \u003ccode\u003ejcrm/singularity\u003c/code\u003e image that was built with a custom \u003ca href=\"./Dockerfile\"\u003eDockerfile\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDownload the contents of the repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/DataSystemsGroupUT/singularity-tutorial.git\n$ cd singularity-tutorial\n$ docker run --name singularity -v $(pwd)/material:/material -it --privileged jcrm/singularity:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTest that the installation works.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity --version\nsingularity version 3.8.0\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-option-2-the-traditional-way\" class=\"anchor\" aria-hidden=\"true\" href=\"#option-2-the-traditional-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOption 2: The traditional way\u003c/h3\u003e\n\u003cp\u003eDepending on your machine, install the dependencies and the singularity program.\nThe \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eofficial website\u003c/a\u003e provides a comprehensive manual to get it done.\u003c/p\u003e\n\u003cp\u003eTest that installation works.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity --version\nsingularity version 3.8.0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow clone the repository locally. If you have \u003ccode\u003egit\u003c/code\u003e, then just execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/DataSystemsGroupUT/singularity-tutorial.git\n$ cd singularity-tutorial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNB!\u003c/strong\u003e In the following sections we will assume that commands and examples will run under the \"Docker way\" configuration.\u003c/p\u003e\n\u003cp\u003eNow you\u0027re ready to go :)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-ii-first-steps-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-ii-first-steps-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart II. First steps with Singularity\u003c/h2\u003e\n\u003cp\u003eSingularity instantiates containers from images that define their environment. Singularity images are stored in \u003ccode\u003e.sif\u003c/code\u003e files.\nYou build a .sif file by defining your environment in a text file and providing that definition to the command singularity build.\nBuilding an image file does require root privileges, so it is most convenient to build the image on your local machine or workstation and then copy it to your HPC cluster.\nOnce you\u0027ve uploaded your image to your HPC cluster, you can submit a batch job that runs singularity exec with the image file you created and the command you want to run.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning containers\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eExample 1\u003c/strong\u003e: Latest Ubuntu image from the Docker Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run docker://ubuntu:latest\n$ docker run ubuntu:latest # Docker equivalent\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eExample 2\u003c/strong\u003e: Any image from the Docker Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run docker://godlovedc/lolcow\n$ docker run godlovedc/lolcow # Docker equivalent\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eExample 3\u003c/strong\u003e: Pre-built \u003ccode\u003e.sif\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run hello/hello.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can run containers from different sources.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e*.sif Singularity Image Format (SIF)\n*.sqsh SquashFS format. Native to Singularity 2.4+\n*.img ext3 format. Native to Singularity versions \u0026lt; 2.4\ndirectory/ sandbox format. Directory containing a valid root file\ninstance://* A local running instance of a container\nlibrary://* A SIF container hosted on a Library\ndocker://* A Docker/OCI container hosted on Docker Hub\nshub://* A container hosted on Singularity Hub\noras://* A SIF container hosted on an OCI registry\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-our-own-container-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-our-own-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding our own container image\u003c/h3\u003e\n\u003cp\u003eTo build a singularity container, we use the \u003ccode\u003ebuild\u003c/code\u003e command. The \u003ccode\u003ebuild\u003c/code\u003e command installs an OS, sets up a container\u0027s environment and installs the apps we will need.\nThe \u003ccode\u003ebuild\u003c/code\u003e command accepts a target as input and produces a container as output.\nTo use the \u003ccode\u003ebuild\u003c/code\u003e command, we need a \u003cstrong\u003erecipe file\u003c/strong\u003e (a.k.a definition file).\u003c/p\u003e\n\u003cp\u003eA Singularity recipe file is a set of instructions telling Singularity what software to install in the container.\nA Singularity Definition file is divided in two parts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eHeader :\u003c/strong\u003e Describes configuration of the base operating system within the container. The most important keyword here is \u003ccode\u003eBootstrap\u003c/code\u003e and you can find the supported options in the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/appendix.html?highlight=bootstrap\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: debootstrap\nOSVersion: trusty\nMirrorURL: http://us.archive.ubuntu.com/ubuntu/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSections :\u003c/strong\u003e Group definitions of the container. Each section is defined by the \u003ccode\u003e%\u003c/code\u003e character and a reserved keyword:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e%runscript\n echo \"This is what happens when you run the container...\"\n\n%post\n echo \"Hello from inside the container\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere we can see an overview of the valid sections. The complete reference can be found \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/definition_files.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%setup groups commands to be executed first on the host system\n%files copies files into the container\n%app* redundant to build different containers for each app\n%post installs new software and libraries, write configuration files, create new directories\n%test runs at the very end of the build process to validate the container using a method of your choice\n%environment defines environment variables used at runtime\n%startscript groups files executed when the instance start command is issued\n%runscript groups commands to be executed when the container image is run\n%labels used to add metadata to the file\n%help adds information to the metadata file in the container during the build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Singularity source code contains several example definition files in the \u003ccode\u003e/examples\u003c/code\u003e subdirectory.\nLet\u0027s take its \u003ccode\u003eubuntu\u003c/code\u003e example definition that has been copied to the \u003ccode\u003ematerial/ubuntu\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat /material/ubuntu/Singularity\nBootStrap: debootstrap\nOSVersion: trusty\nMirrorURL: http://us.archive.ubuntu.com/ubuntu/\n\n\n%runscript\n echo \"This is what happens when you run the container...\"\n\n\n%post\n echo \"Hello from inside the container\"\n sed -i \u0027s/$/ universe/\u0027 /etc/apt/sources.list\n apt-get update\n apt-get -y install vim\n apt-get clean\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow let\u0027s use this definition file as a starting point to build our \u003ccode\u003eubuntu.sif\u003c/code\u003e container. Note that the build command requires \u003ccode\u003esudo\u003c/code\u003e privileges when executing in non-docker mode.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/ubuntu\n$ singularity build ubuntu.sif Singularity\n$ singularity run ubuntu.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can also spawn a shell within the container and interact with it. For this we execute the \u003ccode\u003eshell\u003c/code\u003e command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell ubuntu.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDepending on the environment on your host system you may see your prompt change. Let\u0027s see the information of the OS running in the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity\u0026gt; cat /etc/os-release\nNAME=\"Ubuntu\"\nVERSION=\"14.04, Trusty Tahr\"\nID=ubuntu\nID_LIKE=debian\nPRETTY_NAME=\"Ubuntu 14.04 LTS\"\nVERSION_ID=\"14.04\"\nHOME_URL=\"http://www.ubuntu.com/\"\nSUPPORT_URL=\"http://help.ubuntu.com/\"\nBUG_REPORT_URL=\"http://bugs.launchpad.net/ubuntu/\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs an additional experiment, let\u0027s build the lolcow program in two different ways. These two ways will only differ in the bootstrap agent and they will contain the same definitions for the sections. This is described below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%runscript\n fortune | cowsay | lolcat\n\n%files\n install-dependencies.sh install-dependencies.sh\n\n%post\n echo \"Hello from inside the container\"\n sh -x install-dependencies.sh\n\n%environment\n export PATH=/usr/games:$PATH\n export LC_ALL=C\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first way uses the \u003ccode\u003eubuntu.sif\u003c/code\u003e image that we previously built.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: localimage\nFrom: /material/ubuntu/ubuntu.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLet\u0027s build the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/lolcow\n$ singularity build lolcow-localimage.sif lolcow-localimage.def\n$ singularity run lolcow-localimage.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe second way uses the base library, which is commonly used for Singularity containerized environments.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: library\nFrom: ubuntu:18.04\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLet\u0027s build and run the second image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/lolcow\n$ singularity build lolcow-library.sif lolcow-library.def\n$ singularity run lolcow-library.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRemember that Singularity can build containers in several different file formats. The default is to build a \u003ca href=\"https://en.wikipedia.org/wiki/SquashFS\" rel=\"nofollow\"\u003esquashfs\u003c/a\u003e image. The \u003ccode\u003esquashfs\u003c/code\u003e format is compressed and immutable making it a good choice for reproducible, production-grade containers. However, if you want to shell into a container and have more freedom with it, you should build a sandbox (which is just a directory). This is great when you are still developing your container and don\u0027t yet know what should be included in the recipe file.\nThe command would look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity --sandbox build lolcow-library.sif lolcow-library.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-iii-data-intensive-application\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-iii-data-intensive-application\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart III. Data intensive application\u003c/h2\u003e\n\u003cp\u003eFor this part we will execute a Tensorflow program (borrowed from \u003ca href=\"https://github.com/easy-tensorflow/easy-tensorflow/tree/master/3_Neural_Network\"\u003ehere\u003c/a\u003e) that trains a neural network to classify MNIST data of handwriting images. It also logs the progress of the training and saves the result into a file.\nSince we want to avoid installing all the dependencies of tensorflow in a blank Singularity image, we better use the \u003ccode\u003etensorflow/tensorflow:1.15.5\u003c/code\u003e image from the Docker Hub. Additionally we install the \u003ccode\u003ematplotlib\u003c/code\u003e dependency in the \u003ccode\u003e%post\u003c/code\u003e stage.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: tensorflow/tensorflow:1.15.5\n\n%post\n pip install matplotlib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe definition of the image can be found in \u003ca href=\"material/tensorflow/Singularity\"\u003ematerial/tensorflow/Singularity\u003c/a\u003e.\nNow we can build this definition into a \u003ccode\u003e.sif\u003c/code\u003e image file using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/tensorflow\n$ singularity build tensorflow.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis ran the commands we defined in the \u003ccode\u003e%post\u003c/code\u003e section inside a container and\nafterwards saved the state of the container in the image \u003ccode\u003etensorflow.sif\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eLet\u0027s run our Tensorflow program in a container based on the image we just built.\nBefore executing the command we have to copy the python source code files into the new container.\nWe achieve this by adding the \u003ccode\u003e--bind\u003c/code\u003e flag and specifying the source and destintation paths to mount.\nFinally we run the program using the\u003ccode\u003esh\u003c/code\u003e command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --bind /material/tensorflow/:/material tensor.sif sh -c \"cd /material \u0026amp;\u0026amp; python main.py\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis program does not take long to run. Once it finishes, it creates the file \u003ccode\u003eout.png\u003c/code\u003e with the correct and misclassified examples.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/plot.png\"\u003e\u003cimg src=\"images/plot.png\" alt=\"Plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWorth to mention that, for convenience, Singularity\n\u003ca href=\"https://www.sylabs.io/guides/3.2/user-guide/bind_paths_and_mounts.html\" rel=\"nofollow\"\u003ebinds a few important directories by default\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYour home directory\u003c/li\u003e\n\u003cli\u003eThe current working directory\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e/sys\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e/proc\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eothers (depending on the version of Singularity)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-part-iv-advanced-usage-of-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#part-iv-advanced-usage-of-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePart IV. Advanced Usage of Singularity\u003c/h2\u003e\n\u003cp\u003eFor this part it is necessary to get access to an HPC cluster or set it up locally.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-mpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#mpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMPI\u003c/h3\u003e\n\u003cp\u003eYou can run Singularity containers via MPI. You\u0027ll need to have MPI installed within the container.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf you are working on a single node, you can run MPI within a container.\u003c/li\u003e\n\u003cli\u003eHowever, more commonly you would use the MPI executable on your HPC cluster to execute Singularity containers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe key thing in order to use the system MPI to run Singularity containers is to make sure the MPI installed inside the container is compatible with the MPI installed on the HPC.\nThe easiest way to ensure this is to have the version inside the container be the same version as the MPI module you plan to use on any HPC cluster. You can see these modules with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ module load gcc # load the gcc version of interest\n$ module avail openmpi # see the MPI versions available for that gcc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere is an example of running a Singularity container via MPI. Fist we build the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /material/mpi\n$ singularity build openmpi.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will prepare the \u003ccode\u003empitest.c\u003c/code\u003e to execute MPI natively on the HPC cluster.\nThe program is simple. It ranks the completion order of MPI executors.\nFor that we launch 2 processes per node on all allocated nodes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ module load gcc openmpi\n$ mpirun -n 2 singularity run openmpi.sif /opt/mpitest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-slurm\" class=\"anchor\" aria-hidden=\"true\" href=\"#slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSLURM\u003c/h3\u003e\n\u003cp\u003eIf your target system is setup with a batch system such as SLURM, a standard way to execute MPI applications is through a batch script. The following example illustrates the context of a batch script for Slurm that aims at starting a Singularity container on each node allocated to the execution of the job. It can easily be adapted for all major batch systems available.\nHere\u0027s an example of running a Singularity container with SLURM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#SBATCH --job-name singularity-mpi\n#SBATCH -N $NNODES # total number of nodes\n#SBATCH --time=00:05:00 # Max execution time\n\nmpirun -n $NP singularity exec openmpi.sif /opt/mpitest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-gpucuda\" class=\"anchor\" aria-hidden=\"true\" href=\"#gpucuda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGPU/CUDA\u003c/h3\u003e\n\u003cp\u003eYou can easily use a Singularity container that does computation on a GPU. Singularity supports NVIDIA\u2019s CUDA GPU compute framework.\nBy using the \u003ccode\u003e--nv\u003c/code\u003e flag when running Singularity, the NVIDIA drivers in the HPC cluster are dynamically mounted into the container at run time. The container should provide the CUDA toolkit, using a version of the toolkit that is compatible with the NVIDIA driver version in the HPC.\u003c/p\u003e\n\u003cp\u003eHere\u0027s an example of running a Singularity container based on a Docker container that provides GPU-using software.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run --nv docker://pytorch/pytorch:1.6.0-cuda10.1-cudnn7-runtime\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conclusion\" class=\"anchor\" aria-hidden=\"true\" href=\"#conclusion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConclusion\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eWe have learned the necessary commands of Singularity to start producing containers that can run in HPC environments.\u003c/li\u003e\n\u003cli\u003eSingularity enables isolation, reproducibility and security in HPC environments.\u003c/li\u003e\n\u003cli\u003eIts use is mostly targeted to scientific applications with intensive performance requirements.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sylabs/singularity-userdocs/blob/master/mpi.rst\"\u003ehttps://github.com/sylabs/singularity-userdocs/blob/master/mpi.rst\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/maheshbabuadapa/Singularity-Tutorial\"\u003ehttps://github.com/maheshbabuadapa/Singularity-Tutorial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs-research-it.berkeley.edu/services/high-performance-computing/user-guide/software/using-software/using-singularity-savio\" rel=\"nofollow\"\u003ehttps://docs-research-it.berkeley.edu/services/high-performance-computing/user-guide/software/using-software/using-singularity-savio\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bdusell/singularity-tutorial\"\u003ehttps://github.com/bdusell/singularity-tutorial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1675267909.0 + "updated_at": 1637825788.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image with a selection of neuro processing packages and tools", "filenames": [ - "devops_pipeline/Singularity", - "devops_base/Singularity" + "Singularity" ], - "full_name": "ninamiolane/connect", + "full_name": "chidiugonna/nklab-neuro-tools", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-image-containing-neuroimaging-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-image-containing-neuroimaging-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity image containing Neuroimaging software\u003c/h1\u003e\n\u003cp\u003eThis Singularity image will be about 20GB when built using Singularity 2.4.2. It comes with FSL 5.10 including eddy_cuda8.0, Mrtrix 3RC2, Freesurfer 6.0.0, Afni 18.0.21, ANTS 2.2.0, MRIQC v0.1, Julia v0.6.1 and The Duke Resting State fMRI pipeline. It also has CUDA 8.0 toolkit libraries installed.\u003c/p\u003e\n\u003cp\u003eThe image can be built using Singularity build in singularity2.4.2\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Singularity Image\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have singularity 2.4 installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-tools\u003c/code\u003edirectory and check that you have a Singularity definiton file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow simply build the image as \u003ccode\u003esudo singularity build nklab-neuro-tools.simg Singularity\u003c/code\u003e - note that the image name is assumed to be \u003ccode\u003enklab-neuro-tools.simg\u003c/code\u003e but this can be changed to a more convenient label.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Singularity Image\u003c/h2\u003e\n\u003cp\u003eYou can now run commands by simply appending them to the end of \u003ccode\u003esingularity run nklab-neuro-tools.simg\u003c/code\u003e So for example to run an FSL command like flirt directly the following would be entered: \u003ccode\u003esingularity run nklab-neuro-tools.simg flirt ....\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-cuda-compatibility\" class=\"anchor\" aria-hidden=\"true\" href=\"#cuda-compatibility\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCuda Compatibility\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou can run Cuda-8.0 compatible executables by using the \u003ccode\u003e--nv\u003c/code\u003e parameter. The example provided next shows how to accomplish this with \u003ccode\u003eeddy-cuda8.0\u003c/code\u003e:\n\u003ccode\u003esingularity run --nv rsfmri.img /opt/fsl/bin/eddy_cuda8.0 --imain=G1_1_OFF_28271_cgm --mask=G1_1_OFF_28271_cgm0_brain_mask --acqp=acqparams.txt --index=index.txt --bvecs=bvecs --bvals=bvals --out=G1_1_OFF_28271_cgm_eddy\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-shell-into-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#shell-into-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell into Singularity Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou can also shell into the singularity image using: \u003ccode\u003esingularity shell nklab-neuro-tools.simg\u003c/code\u003e and then run commands at the command line within the container.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eProvided below are notes on specific aspects of the container that may be useful.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-resting-state-fmri-pipeline-nan-kuei-chenduke-university\" class=\"anchor\" aria-hidden=\"true\" href=\"#resting-state-fmri-pipeline-nan-kuei-chenduke-university\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResting State FMRI pipeline (Nan-kuei Chen/Duke University)\u003c/h2\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e for details of use.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe original python source \u003ccode\u003eresting_pipeline.py\u003c/code\u003e available at at [\u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e] has been slightly amended and is included in this repository in the folder \u003ccode\u003esrc\u003c/code\u003e. These changes are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edata1\u003c/code\u003e has been selectively converted to dtype \u003ccode\u003enumpy.float64\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eslice indices have been cast as longs in certain instances.\u003c/li\u003e\n\u003cli\u003eBXH functionality is ignored. To explicitly use BXH info pass the flag --ignorebxh=N\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sliding-window-functionality\" class=\"anchor\" aria-hidden=\"true\" href=\"#sliding-window-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSliding window functionality\u003c/h3\u003e\n\u003cp\u003eA new step has been added \u003ccode\u003e-7sw\u003c/code\u003e to enable sliding window functionality. In order to use this step you will need to use the \u003ccode\u003e--slidewin\u003c/code\u003e parameter which takes 2 numbers seperated by a comma. The 1st number is the window size in seconds and the second number is the shift in seconds between sequential windows. So for example \u003ccode\u003e--slidewin=60,3\u003c/code\u003e will use a window size of \u003ccode\u003e60\u003c/code\u003e seconds shifted by \u003ccode\u003e3\u003c/code\u003e seconds for each subsequent window. Keep in mind that the \u003ccode\u003e--tr\u003c/code\u003e (in milliseconds) parameter is required to calculate the number of volumes to use for each sliding window correlation. If you do not specify the --slidwin parameter and run step \u003ccode\u003e7sw\u003c/code\u003e then default values of \u003ccode\u003e30,3\u003c/code\u003e will be used. Sliding window files are exported to a new directory \u003ccode\u003eSlidingWindow_W_S\u003c/code\u003e and image files are consolidated into 4D volumes for viewing in FSL as a movie\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-extensions-to-slice-correction-functionality\" class=\"anchor\" aria-hidden=\"true\" href=\"#extensions-to-slice-correction-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtensions to Slice Correction functionality\u003c/h3\u003e\n\u003cp\u003eThe pipeline has been extended to accept custom slice correction timing files. A python script make_fsl_stc.py has been bundled in this container which can take .json files created by dcm2niix. This python program will create a slice correction file with timing values and one with slices in order of acquisition. It can be called as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e where fmri.json is the json output from dcm2niix. custom names for the slice order and slice time files can be provided as parameters as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake_fsl_stc.py fmri.json --slicenum=/path/num.txt --slicetime=/path/time.txt\u003c/code\u003e otherwise these files default to \u003ccode\u003esliceorder.txt\u003c/code\u003e and \u003ccode\u003eslicetimes.txt\u003c/code\u003e in the current directory.\u003c/p\u003e\n\u003cp\u003eOnce these custom files have been created then they can be provided to the resting state pipeline using the full path as input to the \u003ccode\u003e--sliceorder\u003c/code\u003e parameter\n\u003ccode\u003e--sliceorder=/path/num.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eplease note that the default custom slice file expected uses slice order. If you pass a text file with slice times then you will need to use another parameter \u003ccode\u003e--slicetimings=time\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-example-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample Commands\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-create-slice-timing-files-from-json\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-slice-timing-files-from-json\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Slice Timing files from json\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esingularity run -B $PWD:/opt/data nklab-neuro-tools.simg /opt/rsfmri_python/bin/make_fsl_stc.py /opt/data/fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --rm -B $PWD:/opt/data nklab-neuro-tools.simg /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1582874207.0 + "updated_at": 1533338985.0 }, { "data_format": 2, - "description": "pipeline for imputing snps on 1000g hg38 reference. repurposed from sceQTL-Gen for specific lab use", + "description": "A Docker/Singularity container for packaging pulsar searching software", "filenames": [ - "Singularity.Imputation" + "Singularity" ], - "full_name": "powellgenomicslab/SNP_imputation_1000g_hg38", - "latest_release": "v0.0.2", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-powell-lab-imputation-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#powell-lab-imputation-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePowell Lab Imputation Pipeline\u003c/h1\u003e\n\u003cp\u003eRepurposed pipeline from Urmo for the sceQTL-Gen Consortium. Update requirements so more suitable for more general use\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/powellgenomicslab/SNP_imputation_1000g_hg38/wiki/SNP-Genotype-Imputation-Using-1000G-hg38-Reference\"\u003eWiki\u003c/a\u003e for information on running the SNP imputation pipeline.\u003c/p\u003e\n\u003cp\u003eThese documents were put together by Drew Neavin on 16 November, 2021.\u003c/p\u003e\n", + "full_name": "federatedcloud/pulsar-pipeline-container", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker-pulsar-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-pulsar-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker-pulsar-pipeline\u003c/h1\u003e\n\u003cp\u003eA Docker/Singularity container for packaging pulsar searching software\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4541\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1648702994.0 + "updated_at": 1622819898.0 }, { "data_format": 2, - "description": "Singularity container for Python and Keras", + "description": "seqtk singulairty container", "filenames": [ "Singularity" ], - "full_name": "JasonKChow/singPyKeras", + "full_name": "phgenomics-singularity/seqtk", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singpykeras\" class=\"anchor\" aria-hidden=\"true\" href=\"#singpykeras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingPyKeras\u003c/h1\u003e\n\u003cp\u003eSingularity container for Python and Keras. Check releases for built images.\u003c/p\u003e\n\u003cp\u003eTo build:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build pyTF.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo use/test:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv pyTF.sif python kerasTest.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo get into environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv pyTF.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo get just an interactive python\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv pyTF.sif python\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1676527668.0 + "updated_at": 1576530783.0 }, { "data_format": 2, - "description": "Symbolic Bidirectional A* with Error", + "description": "R container with baySeq and riboseq libraries", "filenames": [ "Singularity" ], - "full_name": "valcazar/SymBAE", + "full_name": "callaghanmt-containers/riboseqbayseq", "latest_release": null, - "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCopyright (C) 2003-2016 Malte Helmert\nCopyright (C) 2008-2016 Gabriele Roeger\nCopyright (C) 2010-2016 Jendrik Seipp\nCopyright (C) 2010, 2011, 2013-2016 Silvan Sievers\nCopyright (C) 2012-2016 Florian Pommerening\nCopyright (C) 2016 Martin Wehrle\nCopyright (C) 2013, 2015 Salome Simon\nCopyright (C) 2014, 2015 Patrick von Reth\nCopyright (C) 2015 Manuel Heusner, Thomas Keller\nCopyright (C) 2009-2014 Erez Karpas\nCopyright (C) 2014 Robert P. Goldman\nCopyright (C) 2010-2012 Andrew Coles\nCopyright (C) 2010, 2012 Patrik Haslum\nCopyright (C) 2003-2011 Silvia Richter\nCopyright (C) 2009-2011 Emil Keyder\nCopyright (C) 2010, 2011 Moritz Gronbach, Manuela Ortlieb\nCopyright (C) 2011 Vidal Alc\u00e1zar Saiz, Michael Katz, Raz Nissim\nCopyright (C) 2010 Moritz Goebelbecker\nCopyright (C) 2007-2009 Matthias Westphal\nCopyright (C) 2009 Christian Muise\n\nFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-singularity-container-build-script-for-riboseqr-and-bayseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container-build-script-for-riboseqr-and-bayseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container build script for riboSeqR and baySeq\u003c/h2\u003e\n\u003cp\u003eBoth packages are obtained from Bioconductor and require RCurl as a prerequisite.\u003c/p\u003e\n\u003cp\u003eRCurl needs the Ubuntu \u003ccode\u003elibcurl-dev\u003c/code\u003e package which is also installed\u003c/p\u003e\n\u003cp\u003eTo build:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build riboseqbayseq.simg Singularity\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1675605071.0 + "updated_at": 1536747304.0 }, { "data_format": 2, - "description": null, + "description": "official build specifications for busybox", "filenames": [ "Singularity" ], - "full_name": "CshlSiepelLab/SimPol", + "full_name": "singularityhub/busybox", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-simpol--simulating-the-dynamics-of-rna-polymerase-rnap-on-dna-template\" class=\"anchor\" aria-hidden=\"true\" href=\"#simpol--simulating-the-dynamics-of-rna-polymerase-rnap-on-dna-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimPol \u2014 Simulating the dynamics of RNA Polymerase (RNAP) on DNA template\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eSimPol tracks the independent movement of RNAPs along the DNA templates of a large\nnumber of cells. It accepts several key\nuser-specified parameters, including the initiation rate, pause-escape rate,\na constant or variable elongation rate, the mean and variance of pause sites across cells,\nas well as the center-to-center spacing constraint between RNAPs,\nthe number of cells being simulated, the gene length, and the total time of transcription.\nThe simulator simply allows each RNAP to move forward or not,\nin time slices of $10^{-4}$ minutes, according to the specified position-specific\nrate parameters. It assumes that at most one movement of each RNAP can occur per\ntime slice. The simulator monitors for collisions between adjacent RNAPs, prohibiting\none RNAP to advance if it is at the boundary of the allowable distance from the next.\nAfter running for the specified time, SimPol outputs either a file in HDF5 format or files in CSV format that records all RNAP position for the last specified number of steps. See more options and details about parameters and outputs below.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/simulator.png\"\u003e\u003cimg src=\"figures/simulator.png\" alt=\"simulator\" width=\"600\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\tFig.1 Design of SimPol (\u201cSimulator of Polymerases\u201d)\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Environment\u003c/h2\u003e\n\u003cp\u003eWe provide two different approaches to set up the environment for SimPol, one with\n\u003ca href=\"https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\nand the other with \u003ca href=\"https://docs.sylabs.io/guides/latest/user-guide/quick_start.html#\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\nPre-built debug and release executables can be found in the bin folder\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\nconda activate simpol\n\nbin/simPol_Release --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot simPol.sif Singularity\n\nsingularity shell simPol.sif\n\nbin/simPol_Release --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild from source\u003c/h2\u003e\n\u003cp\u003eThere is the option to build in either debug mode with debug symbols or release mode with compiler optimizations (e.g. -O2).\u003c/p\u003e\n\u003cp\u003eTo create a build directory in release mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake -S . -B Release/ -D CMAKE_BUILD_TYPE=Release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo clean the release mode build directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake --build Release/ --target clean\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build in release mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake --build Release/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: Replace all instances of \u0027Release\u0027 with \u0027Debug\u0027 to build in Debug mode\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Locally\u003c/h2\u003e\n\u003cp\u003eSet Number of Threads [By default uses all available threads]\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport OMP_NUM_THREADS=\u0026lt;number of threads to use\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun in Release Mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Release/simPol [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun in Debug Mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Debug/simPol [options]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun program with file containing command line arguments\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./Release/simPol $(\u0026lt;simPol_arguments.dat)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-with-uge-workload-manager-within-cshl\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-with-uge-workload-manager-within-cshl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun with UGE Workload Manager (within CSHL)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -pe OpenMP 5 -cwd -b y ./Release/simPol -n 100 --csv 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote: This allocates 5 threads for the program in the OpenMP parallel environment\u003c/li\u003e\n\u003cli\u003eCheck available parallel environments: qconf -spl\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eOptions:\n\t-h, --help\n\t\tShow this help message and exit\n\n\t-k INTEGER, --tssLen=INTEGER\n\t\tdefine the mean of pause sites across cells [default 50bp]\n\n\t--kSd=DOUBLE\n\t\tdefine the standard deviation of pause sites across cells [default 0]\n\n\t--kMin=INTEGER\n\t\tupper bound of pause site allowed [default 17bp]\n\n\t--kMax=INTEGER\n\t\tlower bound of pause site allowed [default 200bp]\n\n\t--geneLen=INTEGER\n\t\tdefine the length of the whole gene [default 2000bp]\n\n\t-a DOUBLE, --alpha=DOUBLE\n\t\tinitiation rate [default 1 event per min]\n\n\t-b DOUBLE, --beta=DOUBLE\n\t\tpause release rate [default 1 event per min]\n\n\t-z DOUBLE, --zeta=DOUBLE\n\t\tthe mean of elongation rates across sites [default 2000bp per min]\n\n\t--zetaSd=DOUBLE\n\t\tthe standard deviation of elongation rates across sites [default 1000]\n\n\t--zetaMax=DOUBLE\n\t\tthe maximum of elongation rates allowed [default 2500bp per min]\n\n\t--zetaMin=DOUBLE\n\t\tthe minimum of elongation rates allowed [default 1500bp per min]\n\n\t--zetaVec=CHARACTER\n\t\ta file contains vector to scale elongation rates. All cells share the same set of parameters [default \"\"]\n\n\t-n INTEGER, --cellNum=INTEGER\n\t\tNumber of cells being simulated [default 10]\n\n\t-s INTEGER, --polSize=INTEGER\n\t\tPolymerase II size [default 33bp]\n\n\t--addSpace=INTEGER\n\t\tAdditional space in addition to RNAP size [default 17bp]\n\n\t-t DOUBLE, --time=DOUBLE\n\t\tTotal time of simulating data in a cell [default 0.1 min]\n\n\t--hdf5=INTEGER\n\t\tRecord position matrix to HDF5 file for remaining number of steps specified [default: 0 steps]\n\n\t--csv=INTEGER\n\t\tRecord position matrix to csv file for remaining number of steps specified [default: 1 step]\n\n\t-d CHARACTER, --outputDir=CHARACTER\n\t\tDirectory for saving results [default: \u0027results\u0027]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cp\u003eAfter simulation, SimPol produces multiple output files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eprobability_vector.csv\u003c/li\u003e\n\u003cli\u003epause_sites.csv\u003c/li\u003e\n\u003cli\u003ecombined_cell_data.csv: Stores the total # of polymerase at each site across all cells\u003c/li\u003e\n\u003cli\u003eposition_matrices.h5: An HDF5 file containing a group of position matrices for the last number of specified steps. The file can be viewed by importing it into the HDFView app. Download Here: \u003ca href=\"https://www.hdfgroup.org/downloads/hdfview/#download\" rel=\"nofollow\"\u003ehttps://www.hdfgroup.org/downloads/hdfview/#download\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eUsing HighFive interface to generate HDF5 file \u003ca href=\"https://github.com/BlueBrain/HighFive\"\u003ehttps://github.com/BlueBrain/HighFive\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUses chunking and ZLIB (deflate) compression at level 9\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003epositions/position_matrix_#.csv: CSV files containing the position matrix at a specified step\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sampling-nascent-rna-sequencing-read-counts\" class=\"anchor\" aria-hidden=\"true\" href=\"#sampling-nascent-rna-sequencing-read-counts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSampling nascent RNA sequencing read counts\u003c/h2\u003e\n\u003cp\u003eIf you prefer to simulate nascent RNA sequencing read counts in addition to the RNAP positions,\nyou can also follow the tutorial in \u003ccode\u003escripts/sample_read_counts.Rmd\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eZhao, Y., Liu, L. \u0026amp; Siepel, A. Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells. 2022.10.19.512929 Preprint at \u003ca href=\"https://doi.org/10.1101/2022.10.19.512929\" rel=\"nofollow\"\u003ebioRxiv\u003c/a\u003e (2022).\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-busybox\" class=\"anchor\" aria-hidden=\"true\" href=\"#busybox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBusyBox\u003c/h1\u003e\n\u003cp\u003eThis is a library of busybox builds for Singularity images \u003ca href=\"https://singularityhub.github.io/registry-org/singularityhub/busybox/\" rel=\"nofollow\"\u003ehosted on Singularity Static Registry\u003c/a\u003e. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003etags are supported based on the extension of the Singularity file, with an extensionless file corresponding to \"latest\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-can-i-find-here\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-can-i-find-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat can I find here?\u003c/h2\u003e\n\u003cp\u003eThe repository here serves the container under the namespace \u003ccode\u003esingularityhub/busybox\u003c/code\u003e. Specifically,\nit provides an example of using CircleCI to build and push a container to Google Storage,\nand then update manifests at \u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e.\nIf you are interested in other container build templates, see \u003ca href=\"https://github.com/singularityhub/registry/wiki/build-templates\"\u003ethis page\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-does-this-work\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-does-this-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow does this work?\u003c/h2\u003e\n\u003cp\u003eWe will submit this container to the (organizational) registry at\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e\nfor a final container uri corresponding to \u003ccode\u003ehttps://singularityhub.github.io/registry-org/singularityhub/busybox\u003c/code\u003e. Specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub/registry-org --) the organization registry\nsingularityhub/busybox --) a container collection\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethen on GitHub pages:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub.github.io/registry-org --) the registry interface\nsingularityhub.github.io/registry-org/singularityhub/busybox --) the added container\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-0-fork-the-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#0-fork-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e0. Fork the Repository\u003c/h2\u003e\n\u003cp\u003eFor the repository here to your account, and make sure to add write permissions\nfor a machine user for the repository, and the machine user\u0027s key to CircleCI.\nThis means:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eadding the machine user as a collaborator to the repository (and accepting the invitation)\u003c/li\u003e\n\u003cli\u003econnecting the repository to CircleCI\u003c/li\u003e\n\u003cli\u003enavigating to the CircleCI project page logged in as the machine user to follow the project (button in upper right)\u003c/li\u003e\n\u003cli\u003egoing to the settings -\u0026gt; Checkout SSH keys to add the machine user key.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFull instructions are provided \u003ca href=\"https://github.com/singularityhub/registry/wiki/deploy-container-storage#2-creating-a-connected-repository\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-setup-your-organizational-registry\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-setup-your-organizational-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Setup your Organizational Registry\u003c/h2\u003e\n\u003cp\u003eIf you haven\u0027t done so, follow the instructions \u003ca href=\"https://github.com/singularityhub/registry/wiki/deploy-container-storage#organizational\"\u003ehere\u003c/a\u003e to create the organizational registry. You will need to\nupdate the environment variables in the top of the \u003ca href=\".circleci/config.yml\"\u003e.circleci/config.yml\u003c/a\u003e\nto reflect your repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e environment:\n\n # The GitHub username / reponame that the container will be submit to\n - REGISTRY_BASE: singularityhub/registry-org\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should only need to do this once. The example provided here uses\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-google-storage\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-google-storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Google Storage\u003c/h2\u003e\n\u003cp\u003eWe will be interacting with Google Storage via the \u003ca href=\"https://www.github.com/singularityhub/sregistry\"\u003esregistry\u003c/a\u003e\ncommand line client.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-required-environment-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#required-environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired environment variables\u003c/h2\u003e\n\u003cp\u003eCreate a Google Project and \u003ca href=\"https://cloud.google.com/sdk/docs/authorizing#authorizing_with_a_service_account\" rel=\"nofollow\"\u003ea service account\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-download-the-service-account-key\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-download-the-service-account-key\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Download the Service Account Key\u003c/h3\u003e\n\u003cp\u003eYou should first download a service account key from the \u003ca href=\"https://console.cloud.google.com/iam-admin/serviceaccounts?_ga=2.213389911.-231410963.1512057989\" rel=\"nofollow\"\u003eservice accounts page\u003c/a\u003e. For the roles, add an admin for Google\nStorage (to store your container). If you want to use the Google Cloud Builder (a similar\nconfiguration, example at \u003ca href=\"https://www.github.com/singularityhub/nginx\"\u003enginx\u003c/a\u003e) then you can also add Google Build.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/service-account.png\"\u003e\u003cimg src=\"img/service-account.png\" alt=\"img/service-account.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOnce you add the roles, you \u003cem\u003edo not need to add users\u003c/em\u003e to the account. You can next download\nthe service account key to your local machine, and move it to the repository folder.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/create-key.png\"\u003e\u003cimg src=\"img/create-key.png\" alt=\"img/create-key.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that the .gitignore includes *.json so it won\u0027t be added to your project!\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-circle-ci-secrets\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-circle-ci-secrets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Circle CI Secrets\u003c/h3\u003e\n\u003cp\u003eOnce you have the \u003ccode\u003e\u0026lt;project-id\u0026gt;-\u0026lt;number\u0026gt;.json\u003c/code\u003e in the present working directory,\nyou can add the entire thing to your project as an encrypted environment variable.\nHere is how to copy paste the string from your terminal:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eproject-id\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdd the text output from the above to an environment variable\ncalled \u003ccode\u003eGOOGLE_APPLICATION_CREDENTIALS\u003c/code\u003e along with the following (all project secrets):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGOOGLE_COMPUTE_ZONE: the zone you want your compute builder to run in.\u003c/li\u003e\n\u003cli\u003eSREGISTRY_GOOGLE_PROJECT: the id of your project, easiest to find in the Google Project console url.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptionally, export a name for your bucket, \u003ccode\u003eSREGISTRY_GOOGLE_STORAGE_BUCKET\u003c/code\u003e\n(it will be created if it doesn\u0027t exist). It will default to your project id with sregistry- as a prefix.\nDon\u0027t forget to add the machine user to the repository, and then add its credential.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1675094116.0 + "subscribers_count": 2, + "topics": [ + "singularityhub", + "singularity", + "sregistry-org", + "static-registry", + "registry", + "registry-template" + ], + "updated_at": 1549553036.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "mysteryresearcher/dasha-partial-participation", + "full_name": "stephansmit/inkscape_containers", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-install-singularity-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/introduction.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (optional)\u003c/h3\u003e\n\u003cp\u003eIf you don\u0027t want to install Singularity, make sure that you have all dependecies from Singularity.def (python3, numpy, pytorch, etc.)\u003c/p\u003e\n\u003cp\u003ea. Pull an image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://k3nfalt/default/python_ml:sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb. Open a shell console of the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv ~/python_ml_sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-prepare-scripts-for-experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-prepare-scripts-for-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Prepare scripts for experiments\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/dasha_partial_participation/config_libsvm_dasha_partial_particiaption.py \n--dumps_path SOME_PATH --dataset_path PATH_TO_FOLDER_WITH_DATASET --dataset real-sim \n--experiments_name EXPERIMENT_NAME \n--num_nodes_list 100 --step_size_range -10 0 --number_of_seeds 1 --number_of_iterations 5000000 \n--algorithm_names zero_marina_sync_stochastic zero_marina_partial_participation_stochastic --cpus_per_task 11 \n--number_of_processes 10 --time 10 --parallel --compressors rand_k --number_of_coordinates 200 --quality_check_rate 1000 \n--oracle stochastic --mega_batch 10000 --batch_size 1 --function stochastic_logistic_regression --logistic_regression_nonconvex 0.001 \n--partial_participation_probabilities 1.0 0.5 0.1 0.01\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-execute-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-execute-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Execute scripts\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esh SOME_PATH/EXPERIMENT_NAME/singularity_*.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-plot-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-plot-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Plot results\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/plots/dasha_partial_participation/plot_vr-marina_real-sim_stochastic.py \n--dumps_paths SOME_PATH/EXPERIMENT_NAME \n--output_path SOME_PATH_FOR_PLOTS \n--ignore_methods \"VR-MARINA (online)\" \"DASHA-MVR\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOne can find all other scripts \u003ca href=\"https://github.com/mysteryresearcher/dasha-partial-participation/blob/submission_neurips2022/code/distributed_optimization_library/experiments/plots/dasha_partial_participation/script.txt\"\u003ehere\u003c/a\u003e that generate experiments from the paper.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-inkscape-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#inkscape-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInkscape containers\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-the-container-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-the-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the container locally\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build inkscape_containers_latest.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull the container\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/inkscape_containers:latest \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3588\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1650602862.0 + "updated_at": 1569690911.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for vg and toil-vg", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "mysteryresearcher/sampling-in-optimal-sgd", + "full_name": "ISU-HPC/vg-toil-vg", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sharper-rates-and-flexible-framework-for-nonconvex-sgd-with-client-and-data-sampling\" class=\"anchor\" aria-hidden=\"true\" href=\"#sharper-rates-and-flexible-framework-for-nonconvex-sgd-with-client-and-data-sampling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-install-singularity-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/introduction.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (optional)\u003c/h3\u003e\n\u003cp\u003eIf you don\u0027t want to install Singularity, make sure that you have all dependecies from Singularity.def (python3, numpy, pytorch, etc.)\u003c/p\u003e\n\u003cp\u003ea. Pull an image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://k3nfalt/default/python_ml:sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eb. Open a shell console of the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv ~/python_ml_sha256.efcd1fc038228cb7eb0f6f1942dfbaa439cd95d6463015b83ceb2dbaad9e1e98.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-prepare-scripts-for-experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-prepare-scripts-for-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Prepare scripts for experiments\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003ePYTHONPATH=./code python3 ./code/distributed_optimization_library/experiments/page_ab/config_quadratic.py \n--experiments_name EXPERIMENT_NAME --num_nodes_list 1000 \n--theretical_step_size --step_size_range -8 10 --number_of_iterations 10000 --cpus_per_task 1 \n--noise_lambdas 0.0 0.1 0.5 1.0 10.0 --dim 10 --samplings \u0027original_page\u0027 \u0027uniform_with_replacement\u0027 \u0027importance\u0027 \n--strongly_convex_constant 0.001 --generate_type worst_case --batch_size 1 10 25 50 100 500 1000 \n--dumps_path SOME_PATH --dataset_path PATH_TO_FOLDER_WITH_DATASET\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-execute-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-execute-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Execute scripts\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esh SOME_PATH/EXPERIMENT_NAME/singularity_*.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-plot-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-plot-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Plot results\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003epython3 code/distributed_optimization_library/experiments/plots/page_ab/quad_prog_plot.py \n--dumps_paths SOME_PATH/EXPERIMENT_NAME\n--output_path SOME_OUTPUT_PATH --filter_sampling importance original_page --filter_noise_lambda 0.1 --batch_experiment\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOne can find all other scripts \u003ca href=\"code/distributed_optimization_library/experiments/plots/page_ab/scripts.txt\"\u003ehere\u003c/a\u003e that generate experiments from the paper.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-vg-toil-vg\" class=\"anchor\" aria-hidden=\"true\" href=\"#vg-toil-vg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evg-toil-vg\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for vg and toil-vg\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1652883626.0 + "updated_at": 1567801955.0 }, { "data_format": 2, - "description": null, + "description": "NextFlow pipeline: fastq -\u003e SNV CNV -\u003e loqusdb", "filenames": [ - "Singularity" + "resources/Singularity" ], - "full_name": "ionut94/IPC-23-CPC", + "full_name": "Clinical-Genomics-Lund/ffpe-nextflow", "latest_release": null, - "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCopyright (C) 2003-2016 Malte Helmert\nCopyright (C) 2008-2016 Gabriele Roeger\nCopyright (C) 2010-2016 Jendrik Seipp\nCopyright (C) 2010, 2011, 2013-2016 Silvan Sievers\nCopyright (C) 2012-2016 Florian Pommerening\nCopyright (C) 2016 Martin Wehrle\nCopyright (C) 2013, 2015 Salome Simon\nCopyright (C) 2014, 2015 Patrick von Reth\nCopyright (C) 2015 Manuel Heusner, Thomas Keller\nCopyright (C) 2009-2014 Erez Karpas\nCopyright (C) 2014 Robert P. Goldman\nCopyright (C) 2010-2012 Andrew Coles\nCopyright (C) 2010, 2012 Patrik Haslum\nCopyright (C) 2003-2011 Silvia Richter\nCopyright (C) 2009-2011 Emil Keyder\nCopyright (C) 2010, 2011 Moritz Gronbach, Manuela Ortlieb\nCopyright (C) 2011 Vidal Alc\u00e1zar Saiz, Michael Katz, Raz Nissim\nCopyright (C) 2010 Moritz Goebelbecker\nCopyright (C) 2007-2009 Matthias Westphal\nCopyright (C) 2009 Christian Muise\n\nFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch3\u003e\u003ca id=\"user-content-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow\u003c/h3\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1674825486.0 + "updated_at": 1559647892.0 }, { "data_format": 2, - "description": "stable-diffusion-webui(AUTOMATIC1111\u7248) \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fsingularity image\u3092\u4f5c\u6210\u30fb\u5b9f\u884c\u3059\u308b\u305f\u3081\u306esingularity\u5b9a\u7fa9\u30d5\u30a1\u30a4\u30eb\u3068\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8", + "description": "Novel genomes can be analyzed by GeneMark-ES, an algorithm utilizing models parameterized by unsupervised training. Notably, GeneMark-ES has a special option for fungal genomes to account for fungal-specific intron organization. ", "filenames": [ - "Singularity.sdwebui", - "Singularity.repositories", - "Singularity.base" + "4.65/Singularity" ], - "full_name": "oct1971/singularity_stable_diffusion_webui", + "full_name": "pscedu/singularity-genemark-es", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_stable_diffusion_webui\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_stable_diffusion_webui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_stable_diffusion_webui\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui\"\u003estable-diffusion-webui(AUTOMATIC1111\u7248)\u003c/a\u003e \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fsingularity image\u3092\u4f5c\u6210\u30fb\u5b9f\u884c\u3059\u308b\u305f\u3081\u306esingularity\u5b9a\u7fa9\u30d5\u30a1\u30a4\u30eb\u3068\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u203binstall.py\u3092\u542b\u3080extension\u306fWebUI\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u307e\u305b\u3093\u3002\u305d\u306e\u3088\u3046\u306aextension\u306b\u3064\u3044\u3066\u306f\u3001Singularity.sdwebui\u30d5\u30a1\u30a4\u30eb\u306binstall.py\u4e2d\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u3044\u308b\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u30b3\u30de\u30f3\u30c9\u3092\u8ffd\u52a0\u3057\u3066\u30a4\u30e1\u30fc\u30b8\u3092\u518d\u751f\u6210\u3057\u3001extensions\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306bextension\u306e\u30ea\u30dd\u30b8\u30c8\u30ea\u3092git clone\u3059\u308b\u3053\u3068\u3067\u4f7f\u7528\u306f\u53ef\u80fd\u3067\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-wsl2-ubuntu2004-singularity-39\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\" class=\"anchor\" aria-hidden=\"true\" href=\"#wsl2-ubuntu2004-singularity-39\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWSL2, ubuntu20.04, singularity 3.9\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30da\u30fc\u30b8\u306e\u624b\u9806\u306b\u5f93\u3063\u3066Windows10/11\u306bWSL2, ubuntu20.04, NVIDIA driver, libnvidia-container-tools, singularity3.9\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003eLinux\u3067\u4f7f\u7528\u3059\u308b\u5834\u5408\u306fNVIDIA driver, singularity3.9\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3092\u884c\u3063\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/2022/03/wsl2-gpu/\" rel=\"nofollow\"\u003ehttps://sylabs.io/2022/03/wsl2-gpu/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u307e\u305f\u3001\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u306e\u5b9f\u884c\u7528\u306bMicrosoft Store\u304b\u3089Windows Termnal\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304a\u304f\u3053\u3068\u3092\u304a\u52e7\u3081\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u306fWSL2\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6642\u306b\u540c\u6642\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u305fUbuntu on Windows\u3084Windows Terminal\u3067\u958b\u3044\u305fubuntu\u306e\u30b7\u30a7\u30eb\u3067\u5b9f\u884c\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306eclone\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306eclone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u306eclone\u003c/h2\u003e\n\u003cp\u003eclone\u3059\u308b\u5834\u6240\u306f\u3069\u3053\u3067\u3082\u69cb\u3044\u307e\u305b\u3093\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/oct1971/singularity_stable_diffusion_webui\n$ cd singularity_stable_diffusion_webui\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-image\u306ebuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-image\u306ebuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity image\u306ebuild\u003c/h2\u003e\n\u003cp\u003esingularity image\u306ebuild\u306f\u7ba1\u7406\u8005\u6a29\u9650\u304c\u5fc5\u8981\u306a\u305f\u3081\u3001sudo\u3092\u4ed8\u3051\u3066\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u203bcudnn\u5c0e\u5165\u306e\u305f\u3081\u3001\u30d9\u30fc\u30b9\u30a4\u30e1\u30fc\u30b8\u3092 nvidia/cuda:11.3.0-cudnn8-devel-ubuntu20.04 \u306b\u5909\u66f4\u3057\u307e\u3057\u305f\u3002\u6539\u3081\u3066 base image\u306ebuild \u304b\u3089\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\uff082022-10-12\uff09\u3002\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-base-image\u306ebuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#base-image\u306ebuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebase image\u306ebuild\u003c/h3\u003e\n\u003cp\u003eubuntu 20.04\u306bpython3.10, cuda11.3, cudnn8 \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo bash build_base_image.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-repositories-image\u306ebuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#repositories-image\u306ebuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erepositories image\u306ebuild\u003c/h3\u003e\n\u003cp\u003ebase image\u306bstable-diffusion-webui\u3067\u4f7f\u7528\u3059\u308b\u30ea\u30dd\u30b8\u30c8\u30ea\u7b49\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo bash build_repositories_image.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sdwebui-image\u306ebuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#sdwebui-image\u306ebuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esdwebui image\u306ebuild\u003c/h3\u003e\n\u003cp\u003estable-diffusion-webui(AUTOMATIC1111\u7248) \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo bash build_sdwebui_image.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u66f4\u65b0\u983b\u5ea6\u306e\u9ad8\u3044stable-diffusion-webui\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3092\u5206\u96e2\u3057\u3066\u3044\u307e\u3059\u306e\u3067\u3001\u66f4\u65b0\u304c\u3042\u3063\u305f\u5834\u5408\u306f\u901a\u5e38sdwebui image\u306ebuild\u306e\u307f\u518d\u5b9f\u884c\u3057\u307e\u3059\u3002\nstable-diffusion-webui\u304c\u5185\u90e8\u3067\u4f7f\u7528\u3057\u3066\u3044\u308b\u30ea\u30dd\u30b8\u30c8\u30ea\u306e\u8ffd\u52a0\u7b49\u304c\u3042\u3063\u305f\u5834\u5408\u306f\u003ca href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs#manual-installation\"\u003eManual Installation\u003c/a\u003e\u306e\u5185\u5bb9\u3092\u53c2\u8003\u306bSingularity.repositories\u3092\u4fee\u6b63\u3057\u3001repositories.sif\u3092\u518dbuild\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u521d\u671f\u8a2d\u5b9a\u306e\u5b9f\u884c\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u521d\u671f\u8a2d\u5b9a\u306e\u5b9f\u884c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u521d\u671f\u8a2d\u5b9a\u306e\u5b9f\u884c\u003c/h2\u003e\n\u003cp\u003esingularity\u3067\u5b9f\u884c\u3055\u308c\u308b\u30b3\u30f3\u30c6\u30ca\u5185\u306f\u4e00\u90e8\u3092\u9664\u3044\u3066\u66f8\u304d\u8fbc\u307f\u7981\u6b62\u3067\u3042\u308b\u305f\u3081\u3001stable-diffusion-webui\u306e\u5b9f\u884c\u5f8c\u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u308b\u30d5\u30a1\u30a4\u30eb\u306e\u4fdd\u5b58\u5834\u6240\u306f\u30b3\u30f3\u30c6\u30ca\u5b9f\u884c\u6642\u306b\u30b3\u30f3\u30c6\u30ca\u5185\u306b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u30d0\u30a4\u30f3\u30c9\u3057\u307e\u3059\u3002\u307e\u305f\u3001\u30d5\u30a1\u30a4\u30eb\u30b5\u30a4\u30ba\u306e\u5927\u304d\u3044model\u30d5\u30a1\u30a4\u30eb\u3082\u30a4\u30e1\u30fc\u30b8\u5185\u306b\u5165\u308c\u306a\u3044\u3088\u3046\u306b\u3057\u3066\u3044\u307e\u3059\u3002\u305d\u308c\u3089\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u30fb\u30d5\u30a1\u30a4\u30eb\u306e\u6e96\u5099\u3092\u884c\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u203bdata_dir\u4ee5\u5916\u306b ~/.cache \u4ee5\u4e0b\u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u308b\u30d5\u30a1\u30a4\u30eb\u3082\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u203blattent-diffusion\u304c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u30d5\u30a1\u30a4\u30eb\u306f\u3001\u30b3\u30f3\u30c6\u30ca\u3092\u8d77\u52d5\u3057\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306brepositories\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304c\u4f5c\u6210\u3055\u308c\u3001\u305d\u306e\u4e2d\u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003estable-diffusion-webui(AUTOMATIC1111\u7248) \u306b\u3066\u753b\u50cf\u51fa\u529b\u5148\u306bmodel\u306ehash\u5024\u306e\u30b5\u30d6\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u4f7f\u3048\u308b\u3088\u3046\u306b\u306a\u3063\u305f\u305f\u3081\u3001model\u3054\u3068\u306e\u51fa\u529b\u5148\u306e\u4f5c\u6210\u304c\u4e0d\u8981\u306b\u306a\u308a\u307e\u3057\u305f\u3002init_model_integration.sh \u306fmodel\u5225\u306e\u51fa\u529b\u5148\u3092\u751f\u6210\u3057\u307e\u305b\u3093\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash init_model_integration.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-model\u306e\u914d\u7f6e\" class=\"anchor\" aria-hidden=\"true\" href=\"#model\u306e\u914d\u7f6e\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emodel\u306e\u914d\u7f6e\u003c/h2\u003e\n\u003cp\u003emodel\u30d5\u30a1\u30a4\u30eb\u306f\u5225\u9014\u7528\u610f\u3057\u3001data_dir/models/Stable-diffusion/ \u306b\u30ea\u30cd\u30fc\u30e0\u305b\u305a\u306b\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://huggingface.co/CompVis/stable-diffusion-v-1-4-original\" rel=\"nofollow\"\u003e\u672c\u5bb6model\u003c/a\u003e: sd-v1-4.ckpt\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://huggingface.co/hakurei/waifu-diffusion\" rel=\"nofollow\"\u003ewaifu-diffuion model\u003c/a\u003e: wd-v1-2-full-ema.ckpt\n\u003cul\u003e\n\u003cli\u003eOriginal PyTorch Model Download Link \u3088\u308a\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://huggingface.co/naclbit/trinart_stable_diffusion_v2\" rel=\"nofollow\"\u003etrinart2 model\u003c/a\u003e: trinart2_step60000.ckpt, trinart2_step95000.ckpt, trinart2_step115000.ckpt\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-esrgan\u306emodel\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\" class=\"anchor\" aria-hidden=\"true\" href=\"#esrgan\u306emodel\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eESRGAN\u306emodel\u306e\u914d\u7f6e\uff08\u30aa\u30d7\u30b7\u30e7\u30f3\uff09\u003c/h2\u003e\n\u003cp\u003eESRGAN\u306emodel\u306f data_dir/models/ESRGAN/ \u306b\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-swinir\u306emodel\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\" class=\"anchor\" aria-hidden=\"true\" href=\"#swinir\u306emodel\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSwinIR\u306emodel\u306e\u914d\u7f6e\uff08\u30aa\u30d7\u30b7\u30e7\u30f3\uff09\u003c/h2\u003e\n\u003cp\u003eSwinIR\u306emodel\u306f data_dir/models/SwinIR/ \u306b\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307fembedding\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307fembedding\u306e\u914d\u7f6e\u30aa\u30d7\u30b7\u30e7\u30f3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307fembedding\u306e\u914d\u7f6e\uff08\u30aa\u30d7\u30b7\u30e7\u30f3\uff09\u003c/h2\u003e\n\u003cp\u003etextual inversion\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307fembedding\u306f data_dir/embeddings/ \u306b\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-stable-diffusion-webui\u306e\u8d77\u52d5\" class=\"anchor\" aria-hidden=\"true\" href=\"#stable-diffusion-webui\u306e\u8d77\u52d5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estable-diffusion-webui\u306e\u8d77\u52d5\u003c/h2\u003e\n\u003cp\u003e\u751f\u6210\u3055\u308c\u305f\u753b\u50cf\u306foutputs\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3001\u30bb\u30fc\u30d6\u3057\u305f\u753b\u50cf\u306flog\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u4fdd\u5b58\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u203b\u3053\u306e\u5f8c\u306estable-diffusion-webui\u306e\u521d\u671f\u8a2d\u5b9a\u3067 \u0027Save images to a subdirectory\u0027, \u0027Save grids to subdirectory\u0027 \u306b\u30c1\u30a7\u30c3\u30af\u3092\u5165\u308c\u3001 \u0027Directory name pattern\u0027 \u3092 \u0027[model_hash]\u0027 \u3068\u3059\u308b\u3068\u4f7f\u7528\u3057\u3066\u3044\u308bmodel\u3054\u3068\u306b\u30b5\u30d6\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_instance.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-stable-diffusion-webui\u306e\u521d\u671f\u8a2d\u5b9a\" class=\"anchor\" aria-hidden=\"true\" href=\"#stable-diffusion-webui\u306e\u521d\u671f\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estable-diffusion-webui\u306e\u521d\u671f\u8a2d\u5b9a\u003c/h2\u003e\n\u003cp\u003eSettings\u30bf\u30d6\u3067\u4ee5\u4e0b\u306e\u8a2d\u5b9a\u3092\u884c\u3044\u3001Apply settings\u3092\u30af\u30ea\u30c3\u30af\u3057\u3066\u8a2d\u5b9a\u3092\u4fdd\u5b58\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOutput directory for txt2img images: /outputs/txt2img-images\u003c/li\u003e\n\u003cli\u003eOutput directory for img2img images: /outputs/img2img-images\u003c/li\u003e\n\u003cli\u003eOutput directory for images from extras tab: /outputs/extras-images\u003c/li\u003e\n\u003cli\u003eOutput directory for txt2img grids: /outputs/txt2img-grids\u003c/li\u003e\n\u003cli\u003eOutput directory for img2img grids: /outputs/img2img-grids\u003c/li\u003e\n\u003cli\u003eDirectory for saving images using the Save button: /log/images\u003c/li\u003e\n\u003cli\u003eFont for image grids that have text: /usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\u003c/li\u003e\n\u003cli\u003eSave images to a subdirectory: \u30c1\u30a7\u30c3\u30af\u003c/li\u003e\n\u003cli\u003eSave grids to subdirectory: \u30c1\u30a7\u30c3\u30af\u003c/li\u003e\n\u003cli\u003eDirectory name pattern: [model_hash]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u8a2d\u5b9a\u5185\u5bb9\u306f data_dir/ui-config.json, data_dir/config.json \u306b\u66f8\u304d\u8fbc\u307e\u308c\u307e\u3059\u306e\u3067\u3001Batch count\u306e\u4e0a\u9650\u5909\u66f4\u7b49\u306f\u3053\u3061\u3089\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cp\u003e\u203b\u5f53\u74b0\u5883\u3067\u306f\u3001\"Apply color correction to img2img results to match original colors.\" \u306b\u30c1\u30a7\u30c3\u30af\u304c\u5165\u3063\u3066\u3044\u308b\u3068SD upscale\u3067\u306e\u51fa\u529b\u6642\u306b\u9ed2\u305a\u3093\u3060\u8272\u306b\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u3002\u305d\u306e\u5834\u5408\u306f\u3053\u3061\u3089\u306e\u30c1\u30a7\u30c3\u30af\u3092\u5916\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-textual-inversion\u3067\u4f7f\u7528\u3059\u308b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3064\u3044\u3066\" class=\"anchor\" aria-hidden=\"true\" href=\"#textual-inversion\u3067\u4f7f\u7528\u3059\u308b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3064\u3044\u3066\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etextual inversion\u3067\u4f7f\u7528\u3059\u308b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3064\u3044\u3066\u003c/h2\u003e\n\u003cp\u003einit_model_integration.sh \u306e\u5b9f\u884c\u3067\u3001inputs \u3068 preprocessed_inputs \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u4f5c\u6210\u3057\u3066\u3042\u308a\u307e\u3059\u3002textual inversion \u306e\u753b\u9762\u3067\u3001Source directory \u306b inputs/, Destination directory \u306b preprocessed_inputs/, Dataset directory \u306b preprocessed_inputs/ \u3092\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-stable-diffusion-webui\u306e\u505c\u6b62\" class=\"anchor\" aria-hidden=\"true\" href=\"#stable-diffusion-webui\u306e\u505c\u6b62\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estable-diffusion-webui\u306e\u505c\u6b62\u003c/h2\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067\u505c\u6b62\u3055\u305b\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity instance stop sdwebui\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-windows\u306e\u30a8\u30af\u30b9\u30d7\u30ed\u30fc\u30e9\u304b\u3089stable-diffusion-webui\u3067\u751f\u6210\u3057\u305f\u753b\u50cf\u3078\u306e\u30a2\u30af\u30bb\u30b9\" class=\"anchor\" aria-hidden=\"true\" href=\"#windows\u306e\u30a8\u30af\u30b9\u30d7\u30ed\u30fc\u30e9\u304b\u3089stable-diffusion-webui\u3067\u751f\u6210\u3057\u305f\u753b\u50cf\u3078\u306e\u30a2\u30af\u30bb\u30b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows\u306e\u30a8\u30af\u30b9\u30d7\u30ed\u30fc\u30e9\u304b\u3089stable-diffusion-webui\u3067\u751f\u6210\u3057\u305f\u753b\u50cf\u3078\u306e\u30a2\u30af\u30bb\u30b9\u003c/h2\u003e\n\u003cp\u003e\u30a8\u30af\u30b9\u30d7\u30ed\u30fc\u30e9\u306e\u30a2\u30c9\u30ec\u30b9\u30d0\u30fc\u306b \u003ccode\u003e\\\\wsl\\Ubuntu\\home\\\u0026lt;\u3042\u306a\u305f\u306e\u30a2\u30ab\u30a6\u30f3\u30c8\u540d\u0026gt;\\\u0026lt;\u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u3092clone\u3057\u305f\u5834\u6240\u0026gt;\u003c/code\u003e\u3092\u5165\u529b\u3057\u3066\u958b\u3044\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-genemark-es/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-genemark-es/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-genemark-es/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-genemark-es/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/268bf28da5aff3f043cd0714535301fca5138222750e4a5728a5c25e2e832847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/268bf28da5aff3f043cd0714535301fca5138222750e4a5728a5c25e2e832847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-genemark-es\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/23ee7cd28d76bd5cef900d9662da76f2c280c918c273ee7fc6998621242d6e5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/23ee7cd28d76bd5cef900d9662da76f2c280c918c273ee7fc6998621242d6e5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-genemark-es\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a38b44e2f6b70d1fd02cecabb5e1e98970228f7141ff2c262872ebd57619e047/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a38b44e2f6b70d1fd02cecabb5e1e98970228f7141ff2c262872ebd57619e047/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-genemark-es\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/74af0a874b012a1b673b1febc99b28067b350741dc8da571c81f4e6231b52442/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/74af0a874b012a1b673b1febc99b28067b350741dc8da571c81f4e6231b52442/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d67656e656d61726b2d6573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-genemark-es\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-genemark-es\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-genemark-es\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-genemark-es\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for GeneMark-ES.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ecountFullySupportedTranscripts.py\u003c/code\u003e, \u003ccode\u003eflag_anchored_elements.py\u003c/code\u003e, \u003ccode\u003egenerateReport.py\u003c/code\u003e, \u003ccode\u003epredictionAnalysis.py\u003c/code\u003e, \u003ccode\u003eselectSupportedSubsets.py\u003c/code\u003e, \u003ccode\u003ebed_to_gff.pl\u003c/code\u003e, \u003ccode\u003ebp_seq_select.pl\u003c/code\u003e, \u003ccode\u003ebuild_mod.pl\u003c/code\u003e, \u003ccode\u003ecalc_introns_from_gtf.pl\u003c/code\u003e, \u003ccode\u003echange_path_in_perl_scripts.pl\u003c/code\u003e, \u003ccode\u003ecompare_intervals_exact.pl\u003c/code\u003e, \u003ccode\u003egc_distr.pl\u003c/code\u003e, \u003ccode\u003eget_below_gc.pl\u003c/code\u003e, \u003ccode\u003eget_sequence_from_GTF.pl\u003c/code\u003e, \u003ccode\u003egmes_petap.pl\u003c/code\u003e, \u003ccode\u003ehc_exons2hints.pl\u003c/code\u003e, \u003ccode\u003ehistogram.pl\u003c/code\u003e, \u003ccode\u003emake_nt_freq_mat.pl\u003c/code\u003e, \u003ccode\u003eparse_ET.pl\u003c/code\u003e, \u003ccode\u003eparse_by_introns.pl\u003c/code\u003e, \u003ccode\u003eparse_gibbs.pl\u003c/code\u003e, \u003ccode\u003eparse_set.pl\u003c/code\u003e, \u003ccode\u003epredict_genes.pl\u003c/code\u003e, \u003ccode\u003ereformat_gff.pl\u003c/code\u003e, \u003ccode\u003erescale_gff.pl\u003c/code\u003e, \u003ccode\u003ernaseq_introns_to_gff.pl\u003c/code\u003e, \u003ccode\u003erun_es.pl\u003c/code\u003e, \u003ccode\u003erun_hmm_pbs.pl\u003c/code\u003e, \u003ccode\u003escan_for_bp.pl\u003c/code\u003e, \u003ccode\u003estar_to_gff.pl\u003c/code\u003e and \u003ccode\u003everify_evidence_gmhmm.pl\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/GeneMark-ES/4.8.25\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/Genemark-ES\u003c/code\u003e as \u003ccode\u003e4.8.25.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/genemark-ess/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1674777883.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1631406552.0 }, { "data_format": 2, - "description": "Bin for holding recipe files", + "description": "A base Singularity container for PRESTO, including dependencies and environment, converted from docker-PRESTO", "filenames": [ - "bullseye_minio/Singularity", - "apache_gunicorn_flask/Singularity", - "nginx_gunicorn_flask/Singularity" + "Singularity" ], - "full_name": "hamrhein/containers", + "full_name": "federatedcloud/singularity-PRESTO", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n\u003cp\u003eBin for holding recipe files\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-presto\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-presto\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-PRESTO\u003c/h1\u003e\n\u003cp\u003eA base Singularity container for PRESTO, including dependencies and environment, converted from docker-PRESTO\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4510\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [], - "updated_at": 1674604619.0 + "updated_at": 1622819998.0 }, { "data_format": 2, - "description": "Container recipes for OpenVINO", + "description": null, "filenames": [ - "ubuntu18/2019/singularity/Singularity.2019_R3_c_omp-py36-gcc75-ubuntu18", - "ubuntu18/2019/singularity/Singularity.2019_R3.1_cg_omp-py36-gcc75-ubuntu18", - "ubuntu18/2019/singularity/Singularity.2019_pre-release-1_c_omp-py36-gcc75-ubuntu18", - "ubuntu18/2019/singularity/Singularity.2019_R3.1_c_omp-py36-gcc75-ubuntu18", - "ubuntu18/2019/singularity/Singularity.2019_R3.1_c_tbb-py36-gcc75-ubuntu18", - "ubuntu18/2019/singularity/Singularity.2019_R3.1_cg_tbb-py36-gcc75-ubuntu18" + "Singularity" ], - "full_name": "fenz-org/OpenVino", - "latest_release": "0.0.4", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-openvino\" class=\"anchor\" aria-hidden=\"true\" href=\"#openvino\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenVino\u003c/h1\u003e\n\u003cp\u003eContainer recipes for OpenVINO\u003c/p\u003e\n", + "full_name": "UMMS-Biocore/trinitiySing", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity\u003c/h1\u003e\n\u003cp\u003eExecutables\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1675191249.0 + "updated_at": 1519685222.0 }, { "data_format": 2, - "description": "HTCondor execution point setup and configuration for using HTCondor file transfer to synchronize a directory between two hosts", + "description": "This repository is an AI Bootcamp material that consist of a workflow for NLP", "filenames": [ - "Singularity.def" + "Singularity_riva_speech", + "Singularity_tao" ], - "full_name": "htcondor/htcondor-file-transfer", + "full_name": "openhackathons-org/End-to-End-NLP", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-end-to-end-nlp-bootcamp\" class=\"anchor\" aria-hidden=\"true\" href=\"#end-to-end-nlp-bootcamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnd-to-End NLP Bootcamp\u003c/h1\u003e\n\u003cp\u003eThis repository contains the material for the \u003cstrong\u003eEnd-To-End NLP\u003c/strong\u003e bootcamp, the goal of which is to build a complete end-to-end NLP pipeline for Question Answering application. This bootcamp will introduce participants to multiple NVIDIA\u00ae SDKs, most notably NVIDIA TAO Toolkit, NVIDIA TensorRT\u2122, and NVIDIA RIVA. Participants will also have hands-on experience in data preprocessing, model training, optimization, and deployment at scale.\u003c/p\u003e\n\u003cp\u003eThe content is structured in 3 modules, plus an introductory notebook and two challenge notebooks:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOverview of \u003cstrong\u003eEnd-To-End NLP\u003c/strong\u003e bootcamp\u003c/li\u003e\n\u003cli\u003eLab 1: Data preprocessing\u003c/li\u003e\n\u003cli\u003eLab 2: Transfer learning with NVIDIA TAO (QA training)\u003c/li\u003e\n\u003cli\u003eLab 3: Custom model deployment on RIVA\u003c/li\u003e\n\u003cli\u003eChallenge 1: building SQuAD dataset\u003c/li\u003e\n\u003cli\u003eChallenge 2: deploying custom dataset on RIVA\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tutorial-duration\" class=\"anchor\" aria-hidden=\"true\" href=\"#tutorial-duration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTutorial duration\u003c/h2\u003e\n\u003cp\u003eThe total bootcamp material would take approximately 8 hours. It is recommended to divide the teaching of the material into two days, covering Lab 1 in one session and Lab 2 \u0026amp; 3 in the next session.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning using Singularity\u003c/h2\u003e\n\u003cp\u003eUpdate coming soon\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-using-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-using-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning using Docker\u003c/h2\u003e\n\u003cp\u003eRun the material via a python virtual environment and Docker containers. Root privileges are required using \u003ccode\u003esudo\u003c/code\u003e. If you don\u0027t have root privileges on your local system, please follow the above instructions on how to run the lab using Singularity.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installing-the-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the prerequisites\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003edocker-ce\u003c/code\u003e by following the \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003eofficial instructions\u003c/a\u003e. Once you have installed docker-ce, follow the \u003ca href=\"https://docs.docker.com/engine/install/linux-postinstall/\" rel=\"nofollow\"\u003epost-installation steps\u003c/a\u003e to ensure that docker can be run without \u003ccode\u003esudo\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003envidia-container-toolkit\u003c/code\u003e by following the \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\" rel=\"nofollow\"\u003einstall-guide\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet an NGC account and API key:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGo to the \u003ca href=\"https://ngc.nvidia.com/\" rel=\"nofollow\"\u003eNGC\u003c/a\u003e website and click on \u003ccode\u003eRegister for NGC\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eClick on the \u003ccode\u003eContinue\u003c/code\u003e button where \u003ccode\u003eNVIDIA Account (Use existing or create a new NVIDIA account)\u003c/code\u003e is written.\u003c/li\u003e\n\u003cli\u003eFill in the required information and register, then proceed to log in with your new account credentials.\u003c/li\u003e\n\u003cli\u003eIn the top right corner, click on your username and select \u003ccode\u003eSetup\u003c/code\u003e in the dropdown menu.\u003c/li\u003e\n\u003cli\u003eProceed and click on the \u003ccode\u003eGet API Key\u003c/code\u003e button.\u003c/li\u003e\n\u003cli\u003eNext, you will find a \u003ccode\u003eGenerate API Key\u003c/code\u003e button in the upper right corner. After clicking on this button, a dialog box should appear and you have to click on the \u003ccode\u003eConfirm\u003c/code\u003e button.\u003c/li\u003e\n\u003cli\u003eFinally, copy the generated API key and username and save them somewhere on your local system.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall NGC CLI\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLog in with your account credentials at \u003ca href=\"https://ngc.nvidia.com/\" rel=\"nofollow\"\u003eNGC\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eIn the top right corner, click on your username and select \u003ccode\u003eSetup\u003c/code\u003e in the dropdown menu.\u003c/li\u003e\n\u003cli\u003eProceed and click on the \u003ccode\u003eDownloads\u003c/code\u003e button in the CLI panel.\u003c/li\u003e\n\u003cli\u003eSelect \u003ccode\u003eAMD64 Linux\u003c/code\u003e and follow the instructions.\u003c/li\u003e\n\u003cli\u003eOpen the terminal on your local system and log in to the NGC docker registry (\u003ccode\u003envcr.io\u003c/code\u003e) using the command \u003ccode\u003edocker login nvcr.io\u003c/code\u003e and enter \u003ccode\u003e$oauthtoken\u003c/code\u003e as Username and your \u003ccode\u003eAPI Key\u003c/code\u003e as Password.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-install-tao-toolkit-and-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-tao-toolkit-and-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall TAO Toolkit and dependencies\u003c/h3\u003e\n\u003cp\u003eTAO Toolkit is a Python pip package that is hosted on the NVIDIA PyIndex. The package uses the docker restAPI under the hood to interact with the NGC Docker registry to pull and instantiate the underlying docker containers. You must have an NGC account and an API key associated with your account.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-virtualvenwrapper-approach\" class=\"anchor\" aria-hidden=\"true\" href=\"#virtualvenwrapper-approach\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVirtualvenwrapper approach\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003envidia-container-toolkit \u0026gt; 1.3.0-1\u003c/code\u003e from \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun docker without root\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esudo groupadd docker\u003c/li\u003e\n\u003cli\u003esudo usermod -aG docker $USER\u003c/li\u003e\n\u003cli\u003enewgrp docker\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epip3 install python=3.6.9\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate virtualvenwrapper launcher\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt install python-pip python3-pip unzip\npip3 install --upgrade pip\n\npip3 install virtualenvwrapper\n\nexport VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3\nexport WORKON_HOME=/home/user/.virtualenvs\nexport PATH=/home/user/.local/bin:$PATH\nsource /home/user/.local/bin/virtualenvwrapper.sh\n\nmkvirtualenv -p /usr/bin/python3 launcher\n\nworkon launcher\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that \u003ccode\u003euser\u003c/code\u003e should be replaced with the local machine user\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003e\n\u003cp\u003eTAO and Jupyter notebook installation\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip3 install jupyterlab\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip3 install nvidia-tao\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInvoke the entrypoints using the this command \u003ccode\u003etao -h\u003c/code\u003e. You should see the following output:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eusage: tao \n {list,stop,info,augment,bpnet,classification,detectnet_v2,dssd,emotionnet,faster_rcnn,fpenet,gazenet,gesturenet,\n heartratenet,intent_slot_classification,lprnet,mask_rcnn,punctuation_and_capitalization,question_answering,\n retinanet,speech_to_text,ssd,text_classification,converter,token_classification,unet,yolo_v3,yolo_v4,yolo_v4_tiny}\n ...\n\nLauncher for TAO\n\noptional arguments:\n-h, --help show this help message and exit\n\ntasks:\n {list,stop,info,augment,bpnet,classification,detectnet_v2,dssd,emotionnet,faster_rcnn,fpenet,gazenet,gesturenet,heartratenet\n ,intent_slot_classification,lprnet,mask_rcnn,punctuation_and_capitalization,question_answering,retinanet,speech_to_text,\n ssd,text_classification,converter,token_classification,unet,yolo_v3,yolo_v4,yolo_v4_tiny}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more info, visit the \u003ca href=\"https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html\" rel=\"nofollow\"\u003eTAO Toolkit documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-other-dependencies-to-run-the-lab\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-other-dependencies-to-run-the-lab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall other dependencies to run the lab:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e pip3 install spacy-langdetect\n pip3 install -U spacy[cuda114]\n python3 -m spacy download en_core_web_sm \n pip3 install pyspellchecker\n pip3 install openpyxl\n pip3 install -U transformers==3.0.0\n pip3 install nltk\n #python3 -m nltk.downloader punkt\n #pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116\n #pip3 install Cython \n pip3 install jupyterlab\n pip3 install ipywidgets\n pip3 install gdown\n pip3 install soundfile\n \n #nemo installation\n pip install Cython\n pip install nemo_toolkit[all]\n pip3 install pynini\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-all-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-all-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun All Notebooks\u003c/h3\u003e\n\u003cp\u003eActivate virtualvenwrapper launcher \u003ccode\u003eworkon launcher\u003c/code\u003e (you may be required to export path as executed in 4. above)\u003c/p\u003e\n\u003cp\u003eYou are to run the ALL notebooks in the \u003ccode\u003elauncher\u003c/code\u003e environment.\u003c/p\u003e\n\u003cp\u003eLaunch the jupyter lab with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ejupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=~/End-to-End-NLP/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRemember to set the \u003ccode\u003e--notebook-dir\u003c/code\u003e to the location where the \u003ccode\u003eproject folder\u003c/code\u003e where this material is located.\u003c/p\u003e\n\u003cp\u003eThen, open jupyter lab in the browser at \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e and start working on the lab by clicking on the \u003ccode\u003eStart_here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cp\u003eCongratulations, you\u0027ve successfully built and deployed an end-to-end computer vision pipeline!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown issues\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tao\" class=\"anchor\" aria-hidden=\"true\" href=\"#tao\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTAO\u003c/h3\u003e\n\u003cp\u003ea. When installing the TAO Toolkit Launcher to your host machine\u2019s native python3 as opposed to the recommended route of using a virtual environment, you may get an error saying that \u003ccode\u003etao binary wasn\u2019t found\u003c/code\u003e. This is because the path to your \u003ccode\u003etao\u003c/code\u003e binary installed by pip wasn\u2019t added to the \u003ccode\u003ePATH\u003c/code\u003e environment variable in your local machine. In this case, please run the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eexport PATH=$PATH:~/.local/bin\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eb. When training, you can see an error message stating:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eResource exhausted: OOM when allocating tensor...\nERROR: Ran out of GPU memory, please lower the batch size, use a smaller input resolution, use a smaller backbone, or enable model parallelism for supported TLT architectures (see TLT documentation).\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs the error says, you ran out of GPU memory. Try playing with batch size to reduce the memory footprint.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ngc\" class=\"anchor\" aria-hidden=\"true\" href=\"#ngc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNGC\u003c/h3\u003e\n\u003cp\u003eYou can see an error message stating:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003engc: command not found ...\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can resolve this by setting the path to ngc within the conda launcher environment as:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eecho \"export PATH=\\\"\\$PATH:$(pwd)/ngc-cli\\\"\" \u0026gt;\u0026gt; ~/.bash_profile \u0026amp;\u0026amp; source ~/.bash_profile\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-riva-speech-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#riva-speech-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRiva Speech Server\u003c/h3\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1674233118.0 + "updated_at": 1683562054.0 }, { "data_format": 2, - "description": null, + "description": "Use Docker as a shell to store a Singularity image", "filenames": [ "Singularity" ], - "full_name": "DanKaptijn/souporcellCopy", + "full_name": "singularityhub/singularity-in-docker", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-souporcell\" class=\"anchor\" aria-hidden=\"true\" href=\"#souporcell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esouporcell\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/wheaton5/souporcell/blob/master/souporcell_star.png\"\u003e\u003cimg src=\"https://github.com/wheaton5/souporcell/raw/master/souporcell_star.png\" width=\"100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePreprint manuscript of this method available at \u003ca href=\"https://www.biorxiv.org/content/10.1101/699637v1\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/699637v1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003esouporcell is a method for clustering mixed-genotype scRNAseq experiments by individual.\u003c/p\u003e\n\u003cp\u003eThe inputs are just the possorted_genome_bam.bam, and barcodes.tsv as output from \u003ca href=\"https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger\" rel=\"nofollow\"\u003ecellranger\u003c/a\u003e.\nsouporcell is comprised of 6 steps with the first 3 using external tools and the final using the code provided here.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRemapping (\u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCalling candidate variants (\u003ca href=\"https://github.com/ekg/freebayes\"\u003efreebayes\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCell allele counting (\u003ca href=\"https://github.com/10XGenomics/vartrix\"\u003evartrix\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eClustering cells by genotype (souporcell.py)\u003c/li\u003e\n\u003cli\u003eCalling doublets (troublet)\u003c/li\u003e\n\u003cli\u003eCalling cluster genotypes and inferring amount of ambient RNA (consensus.py)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-easy-installation-linux-recommended\" class=\"anchor\" aria-hidden=\"true\" href=\"#easy-installation-linux-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEasy Installation (Linux) (recommended)\u003c/h2\u003e\n\u003cp\u003eDownload singularity image (1.3gb) (singularity is similar to docker but safe for clusters)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://wheaton5/souporcell\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you are running on a scientific cluster, they will likely have singularity, contact your sysadmin for more details.\nIf you are running on your own linux box you may need to install \u003ca href=\"https://www.sylabs.io/guides/3.2/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003erequires singularity \u0026gt;= 3.0\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewhich singularity\nsingularity --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should now be able to run souporcell_pipeline.py through the singularity container. Singularity automatically mounts the current working directory and directories downstream from where you run it, otherwise you would need to manually mount those directories. Therefor you can run it from a directory that is upstream of all of the inputs. Input files are the cellranger bam, cellranger barcodes file, and a reference fasta. The cellranger bam is located in the cellranger outs directory and is called possorted_genome_bam.bam. The barcodes file is located in the cellranger outs/filtered_gene_bc_matrices/\u0026lt;ref_name\u0026gt;/barcodes.tsv. The reference fasta should be of the same species but does not necessarily need to be the exact cellranger reference.\u003c/p\u003e\n\u003cp\u003eThe options for using souporcell are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec souporcell_latest.sif souporcell_pipeline.py -h\nusage: souporcell_pipeline.py [-h] -i BAM -b BARCODES -f FASTA -t THREADS -o\n OUT_DIR -k CLUSTERS [-p PLOIDY]\n [--min_alt MIN_ALT] [--min_ref MIN_REF]\n [--max_loci MAX_LOCI] [--restarts RESTARTS]\n [--common_variants COMMON_VARIANTS]\n [--known_genotypes KNOWN_GENOTYPES]\n [--known_genotypes_sample_names KNOWN_GENOTYPES_SAMPLE_NAMES [KNOWN_GENOTYPES_SAMPLE_NAMES ...]]\n [--skip_remap SKIP_REMAP] [--ignore IGNORE]\n\nsingle cell RNAseq mixed genotype clustering using sparse mixture model\nclustering with tensorflow.\n\noptional arguments:\n -h, --help show this help message and exit\n -i BAM, --bam BAM cellranger bam\n -b BARCODES, --barcodes BARCODES\n barcodes.tsv from cellranger\n -f FASTA, --fasta FASTA\n reference fasta file\n -t THREADS, --threads THREADS\n max threads to use\n -o OUT_DIR, --out_dir OUT_DIR\n name of directory to place souporcell files\n -k CLUSTERS, --clusters CLUSTERS\n number cluster, tbd add easy way to run on a range of\n k\n -p PLOIDY, --ploidy PLOIDY\n ploidy, must be 1 or 2, default = 2\n --min_alt MIN_ALT min alt to use locus, default = 10.\n --min_ref MIN_REF min ref to use locus, default = 10.\n --max_loci MAX_LOCI max loci per cell, affects speed, default = 2048.\n --restarts RESTARTS number of restarts in clustering, when there are \u0026gt; 12\n clusters we recommend increasing this to avoid local\n minima\n --common_variants COMMON_VARIANTS\n common variant loci or known variant loci vcf, must be\n vs same reference fasta\n --known_genotypes KNOWN_GENOTYPES\n known variants per clone in population vcf mode, must\n be .vcf right now we dont accept gzip or bcf sorry\n --known_genotypes_sample_names KNOWN_GENOTYPES_SAMPLE_NAMES [KNOWN_GENOTYPES_SAMPLE_NAMES ...]\n which samples in population vcf from known genotypes\n option represent the donors in your sample\n --skip_remap SKIP_REMAP\n don\u0027t remap with minimap2 (not recommended unless in\n conjunction with --common_variants\n --ignore IGNORE set to True to ignore data error assertions\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA typical command looks like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /path/to/souporcell_latest.sif souporcell_pipeline.py -i /path/to/possorted_genome_bam.bam -b /path/to/barcodes.tsv -f /path/to/reference.fasta -t num_threads_to_use -o output_dir_name -k num_clusters\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above command will run all six steps of the pipeline and it will require up to 24gb of ram for human (minimap2 bam index is high water mark for memory). For smaller genomes, fewer clusters, lower --max-loci will require less memory. Note that souporcell will require roughly 2x the amount of diskspace that the input bam file takes up. This dataset should take several hours to run on 8 threads mostly due to read processing, remapping, and variant calling.\u003c/p\u003e\n\u003cp\u003eIf you have a common snps file you may want to use the --common_variants option with or without the --skip_remap option. This option will skip conversion to fastq, remapping with minimap2, and reattaching barcodes, and the --common_variants will remove the freebayes step. Each which will save a significant amount of time, but --skip-remap isn\u0027t recommended without --common_variants.\u003c/p\u003e\n\u003cp\u003eCommon variant files from 1k genomes filtered to variants \u0026gt;= 2% allele frequency in the population and limited to SNPs can be found here for GRCh38\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download\u0026amp;confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate \u0027https://docs.google.com/uc?export=download\u0026amp;id=13aebUpEKrtjliyT9rYzRijtkNJVUk5F_\u0027 -O- | sed -rn \u0027s/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p\u0027)\u0026amp;id=13aebUpEKrtjliyT9rYzRijtkNJVUk5F_\" -O common_variants_grch38.vcf \u0026amp;\u0026amp; rm -rf /tmp/cookies.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor for hg19 here\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download\u0026amp;confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate \u0027https://docs.google.com/uc?export=download\u0026amp;id=1lw4T6d7uXsm9dt39ZtEwpuB2VTY3wK1y\u0027 -O- | sed -rn \u0027s/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p\u0027)\u0026amp;id=1lw4T6d7uXsm9dt39ZtEwpuB2VTY3wK1y\" -O common_variants_hg19.vcf \u0026amp;\u0026amp; rm -rf /tmp/cookies.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-practicetesting-data-set-demuxlet-paper-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#practicetesting-data-set-demuxlet-paper-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePractice/Testing data set: Demuxlet paper data\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://sra-pub-src-1.s3.amazonaws.com/SRR5398235/A.merged.bam.1 -O A.merged.bam\nwget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM2560nnn/GSM2560245/suppl/GSM2560245_barcodes.tsv.gz\ngunzip GSM2560245_barcodes.tsv.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if you don\u0027t have a human reference sitting around, grab one here\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget http://cf.10xgenomics.com/supp/cell-exp/refdata-cellranger-GRCh38-3.0.0.tar.gz\ntar -xzvf refdata-cellranger-GRCh38-3.0.0.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow you should be ready to test it out\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /path/to/souporcell_latest.sif souporcell_pipeline.py -i A.merged.bam -b GSM2560245_barcodes.tsv -f refdata-cellranger-GRCh38-3.0.0/fasta/genome.fa -t 8 -o demux_data_test -k 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should require about 20gb of ram mostly because of the minimap2 indexing step. I might soon host an index and reference for human to make this less painful.\u003c/p\u003e\n\u003cp\u003eThe important files are\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eclusters.tsv\u003c/li\u003e\n\u003cli\u003ecluster_genotypes.vcf\u003c/li\u003e\n\u003cli\u003eambient_rna.txt\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eclusters.tsv will look like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebarcode status assignment log_loss_singleton log_loss_doublet cluster0 cluster1\nAAACCTGAGATCCGAG-1 singlet 0 -152.7778890920112 -190.5463095948822 -43.95302689281067 -101.63377524087669\nAAACCTGAGCACCGTC-1 singlet 0 -78.56014177554212 -96.66255440088581 -20.949294849836267 -52.57478083591962\nAAACCTGAGTACGATA-1 singlet 0 -216.0188863327174 -281.3888392065457 -63.059016939362536 -159.5450834682198\nAAACCTGGTACATGTC-1 singlet 1 -47.189434469216565 -96.30865717225866 -62.652900832546955 -15.284168900754413\nAAACCTGTCTACTCAT-1 singlet 0 -129.30104434183454 -167.87811467946756 -41.09158213888751 -106.3201962010145\nAAACCTGTCTTGTCAT-1 singlet 0 -85.99781433701455 -110.81701038967158 -24.518165091815554 -60.05279033826837\nAAACGGGCACTGTTAG-1 singlet 0 -154.26595878718032 -191.05465308213363 -31.356408693487197 -81.61186496254497\nAAACGGGCATCATCCC-1 singlet 1 -46.33205678267174 -80.24152434540565 -50.78221280006256 -14.615983876840312\nAAACGGGGTAGGGTAC-1 singlet 0 -240.5237900569412 -302.91575436035924 -71.79370547349878 -154.08594135029728\nAAACGGGTCGGCATCG-1 singlet 0 -166.66827966974532 -226.56795157885028 -51.08790637893961 -148.04625123166286\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the cell barcode, singlet/doublet status, cluster, log_loss_singleton, log_loss_doublet, followed by log loss for each cluster.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ecluster_genotypes.vcf is a vcf with genotypes for each cluster for each variant in the input vcf from freebayes\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eambient_rna.txt just contains the ambient RNA percentage detected\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-hard-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#hard-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHard install\u003c/h2\u003e\n\u003cp\u003eInstead of using singularity you can install everything independently (not recommended, but shouldn\u0027t be too bad)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/wheaton5/souporcell.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eput souporcell directory on your PATH\nrequires samtools, bcftools, htslib, python3, freebayes, vartrix, minimap2 all on your PATH\nI suggest you use the conda env I have set up by using the following command if you have conda or miniconda\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f /path/to/souporcell/souporcell_env.yaml\nconda activate souporcell\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will also need Rust and to compile the two rust binaries\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl --proto \u0027=https\u0027 --tlsv1.2 -sSf https://sh.rustup.rs | sh\ncd /path/to/souporcell/souporcell \u0026amp;\u0026amp; cargo build --release\ncd /path/to/souporcell/troublet \u0026amp;\u0026amp; cargo build --release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eotherwise python packages tensorflow, pyvcf, pystan, pyfaidx, numpy, scipy are required, but as the versions change, I do recommend using the presetup env.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-through-the-pipeline-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-through-the-pipeline-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run through the pipeline script\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esouporcell_pipeline.py -i /path/to/possorted_genome_bam.bam -b /path/to/barcodes.tsv -f /path/to/reference.fasta -t num_threads_to_use -o output_dir_name -k num_clusters\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-things-step-by-step-not-through-the-pipeline-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-things-step-by-step-not-through-the-pipeline-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run things step by step not through the pipeline script\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-remapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-remapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Remapping\u003c/h3\u003e\n\u003cp\u003eWe discuss the need for remapping in our manuscript. We need to keep track of cell barcodes and and UMIs, so we first create a fastq with those items encoded in the readname.\nRequires python 3.0, modules pysam, argparse (pip install/conda install depending on environment)\nEasiest to first add the souporcell directory to your PATH variable with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PATH=/path/to/souporcell:$PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the renamer.py script to put some of the quality information in the read name. For human data this step will typically take several hours and the output fq file will be somewhat larger than the input bam\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython renamer.py --bam possorted_genome_bam.bam --barcodes barcodes.tsv --out fq.fq\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we must remap these reads using minimap2 (similar results have been seen with hisat2)\nRequires \u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e\nand add /path/to/minimap2 to your PATH. For human data the remapping will typically require more than 12 Gb memory and may take a few hours to run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eminimap2 -ax splice -t 8 -G50k -k 21 -w 11 --sr -A2 -B8 -O12,32 -E2,1 -r200 -p.5 -N20 -f1000,5000 -n2 -m20 -s40 -g2000 -2K50m --secondary=no \u0026lt;reference_fasta_file\u0026gt; \u0026lt;fastq_file\u0026gt; \u0026gt; minimap.sam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(note the -t 8 as the number of threads, change this as needed)\nNow we must retag the reads with their cell barcodes and UMIs\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython retag.py --sam minimap.sam --out minitagged.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we must sort and index our bam.\nRequires \u003ca href=\"http://www.htslib.org/\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esamtools sort minitagged.bam minitagged_sorted.bam\nsamtools index minitagged_sorted.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-calling-candidate-variants\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-calling-candidate-variants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Calling candidate variants\u003c/h3\u003e\n\u003cp\u003eYou may wish to break this into multiple jobs such as 1 job per chromosome and merge after but the basic command is the following.\nRequires \u003ca href=\"https://github.com/ekg/freebayes\"\u003efreebayes\u003c/a\u003e and add /path/to/freebayes/bin to your PATH\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efreebayes -f \u0026lt;reference_fasta\u0026gt; -iXu -C 2 -q 20 -n 3 -E 1 -m 30 --min-coverage 6 --max-coverage 100000 minitagged_sorted.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-cell-allele-counting\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-cell-allele-counting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Cell allele counting\u003c/h3\u003e\n\u003cp\u003eRequires \u003ca href=\"https://github.com/10XGenomics/vartrix\"\u003evartrix\u003c/a\u003e\nand add /path/to/vartrix to your PATH\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evartrix --umi --mapq 30 -b \u0026lt;bam file\u0026gt; -c \u0026lt;barcode tsv\u0026gt; --scoring-method coverage --threads 8 --ref-matrix ref.mtx --out-matrix alt.mtx -v \u0026lt;freebayes vcf\u0026gt; --fasta \u0026lt;fasta file used for remapping\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003enote the --threads argument and use an appropriate number of threads for your system.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-clustering-cells-by-genotype\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-clustering-cells-by-genotype\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Clustering cells by genotype\u003c/h3\u003e\n\u003cp\u003eRust required. To install rust:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://sh.rustup.rs -sSf | sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand to build souporcell clustering\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/souporcell/souporcell\ncargo build --release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd add /path/to/souporcell/souporcell/target/release to your path\nusage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esouporcell -h\nsouporcell 2.4\nHaynes Heaton \u0026lt;whheaton@gmail.com\u0026gt;\nclustering scRNAseq cells by genotype\n\nUSAGE:\n souporcell [OPTIONS] --alt_matrix \u0026lt;alt_matrix\u0026gt; --barcodes \u0026lt;barcodes\u0026gt; --num_clusters \u0026lt;num_clusters\u0026gt; --ref_matrix \u0026lt;ref_matrix\u0026gt;\n\nFLAGS:\n -h, --help Prints help information\n -V, --version Prints version information\n\nOPTIONS:\n -a, --alt_matrix \u0026lt;alt_matrix\u0026gt; alt matrix from vartrix\n -b, --barcodes \u0026lt;barcodes\u0026gt; cell barcodes\n --initialization_strategy \u0026lt;initialization_strategy\u0026gt;\n cluster initialization strategy, defaults to kmeans++, valid values are kmeans++, random_uniform,\n middle_variance, random_cell_assignment\n --known_cell_assignments \u0026lt;known_cell_assignments\u0026gt;\n tsv with barcodes and their known assignments\n\n -g, --known_genotypes \u0026lt;known_genotypes\u0026gt;\n NOT YET IMPLEMENTED population vcf/bcf of known genotypes if available.\n \n --known_genotypes_sample_names \u0026lt;known_genotypes_sample_names\u0026gt;...\n NOT YET IMPLEMENTED sample names, must be samples from the known_genotypes vcf\n\n --min_alt \u0026lt;min_alt\u0026gt;\n minimum number of cells containing the alt allele for the variant to be used for clustering\n\n --min_alt_umis \u0026lt;min_alt_umis\u0026gt; min alt umis to use locus for clustering\n --min_ref \u0026lt;min_ref\u0026gt;\n minimum number of cells containing the ref allele for the variant to be used for clustering\n\n --min_ref_umis \u0026lt;min_ref_umis\u0026gt; min ref umis to use locus for clustering\n -k, --num_clusters \u0026lt;num_clusters\u0026gt; number of clusters\n -r, --ref_matrix \u0026lt;ref_matrix\u0026gt; ref matrix from vartrix\n -r, --restarts \u0026lt;restarts\u0026gt; number of random seedings\n --seed \u0026lt;seed\u0026gt; optional random seed\n -t, --threads \u0026lt;threads\u0026gt; number of threads to use\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo generally something along the lines of\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esouporcell -a alt.mtx -r ref.mtx -b barcodes.tsv -k \u0026lt;num_clusters\u0026gt; -t 8 \u0026gt; clusters_tmp.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(note clusters_tmp.tsv output as the doublet caller outputs the final clusters file)\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-5-calling-doublets\" class=\"anchor\" aria-hidden=\"true\" href=\"#5-calling-doublets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. Calling doublets\u003c/h3\u003e\n\u003cp\u003eRust required.\nBuild troublet:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/souporcell/troublet\ncargo build --release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd add /path/to/souporcell/troublet/target/release to your path\nThe usage is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etroublet -h\ntroublet 2.4\nHaynes Heaton \u0026lt;whheaton@gmail.com\u0026gt;\nIntergenotypic doublet detection given cluster assignments and cell allele counts\n\nUSAGE:\n troublet [OPTIONS] --alts \u0026lt;alts\u0026gt; --clusters \u0026lt;clusters\u0026gt;\n\nFLAGS:\n -h, --help Prints help information\n -V, --version Prints version information\n\nOPTIONS:\n -a, --alts \u0026lt;alts\u0026gt; alt allele counts per cell in sparse matrix format out of vartrix\n -c, --clusters \u0026lt;clusters\u0026gt; cluster file output from schism\n -b, --debug \u0026lt;debug\u0026gt;... print debug info for index of cells listed\n -d, --doublet_prior \u0026lt;doublet_prior\u0026gt; prior on doublets. Defaults to 0.5\n --doublet_threshold \u0026lt;doublet_threshold\u0026gt; doublet posterior threshold, defaults to 0.90\n -r, --refs \u0026lt;refs\u0026gt; ref allele counts per cell in sparse matrix format out of vartrix\n --singlet_threshold \u0026lt;singlet_threshold\u0026gt; singlet posterior threshold, defaults to 0.90\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo generally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etroublet -a alt.mtx -r ref.mtx --clusters clusters_tmp.tsv \u0026gt; clusters.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-6-genotype-and-ambient-rna-coinference\" class=\"anchor\" aria-hidden=\"true\" href=\"#6-genotype-and-ambient-rna-coinference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6. Genotype and ambient RNA coinference\u003c/h3\u003e\n\u003cp\u003ePython3 required with modules pystan, pyvcf, pickle, math, scipy, gzip (pip install should work for each)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econsensus.py -h\nusage: consensus.py [-h] -c CLUSTERS -a ALT_MATRIX -r REF_MATRIX [-p PLOIDY]\n --soup_out SOUP_OUT --vcf_out VCF_OUT --output_dir\n OUTPUT_DIR -v VCF\n\nconsensus genotype calling and ambient RNA estimation\n\noptional arguments:\n -h, --help show this help message and exit\n -c CLUSTERS, --clusters CLUSTERS\n tsv cluster file from the troublet output\n -a ALT_MATRIX, --alt_matrix ALT_MATRIX\n alt matrix file\n -r REF_MATRIX, --ref_matrix REF_MATRIX\n ref matrix file\n -p PLOIDY, --ploidy PLOIDY\n ploidy, must be 1 or 2, defaults to 2\n --soup_out SOUP_OUT soup output\n --vcf_out VCF_OUT vcf output\n --output_dir OUTPUT_DIR\n output directory\n -v VCF, --vcf VCF vcf file from which alt and ref matrix were created\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo generally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econsensus.py -c clusters.tsv -a alt.mtx -r ref.mtx --soup_out soup.txt -v \u0026lt;freebayes vcf\u0026gt; --vcf_out cluster_genotypes.vcf --output_dir .\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-in-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-in-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity in Docker\u003c/h1\u003e\n\u003cp\u003eThis is a proof of concept for packaging a Singularity container in a Docker\nimage, only with purpose to store it in a Docker Registry for pulling later.\nOf course you\u0027d need Docker or a tool like \u003ca href=\"https://github.com/deislabs/oras\"\u003eoras\u003c/a\u003e to handle the pull.\nUse at your own risk! I don\u0027t know if there are rules against this sort of thing.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/poopies.png\"\u003e\u003cimg src=\"img/poopies.png\" alt=\"img/poopies.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eBuild the Singularity container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build busybox.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen test it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./busybox.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen build the docker container, giving the Singularity container as a build arg.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build -t vanessa/singularity-in-docker --build-arg container=busybox.sif \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMake sure it\u0027s there:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run -it vanessa/singularity-in-docker \n/ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls /\u003c/span\u003e\nbin dev home root tmp var\nbusybox.sif etc proc sys usr\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen push to wherever you like! When it\u0027s time to pull and use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker pull vanessa/singularity-in-docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can run with a different entrypoint, detached, to keep it running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run -d --rm --name squiggles vanessa/singularity-in-docker tail -f /dev/null\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen copy the Singularity container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker cp squiggles:/busybox.sif exported-busybox.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTada!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./exported-busybox.sif \nRun run run run runnnnn\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd stop your squiggles.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker stop squiggles\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1674048314.0 + "updated_at": 1567362681.0 }, { "data_format": 2, "description": null, "filenames": [ - "TFD/Singularity.0.4", - "TFD/Singularity" + "Singularity", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/latest/Singularity", + "misc/releases/22.12/Singularity.22.12", + "misc/releases/21.12/Singularity.21.12" ], - "full_name": "fromstar/Project_ASA_2022", + "full_name": "ipc2023-classical/planner8", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-project_asa_2022\" class=\"anchor\" aria-hidden=\"true\" href=\"#project_asa_2022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject_ASA_2022\u003c/h1\u003e\n\u003cp\u003eIn this project a smart house environment is simulated. In the scenario presented, the presence of people in the various rooms, the production of electricity by photovoltaic panels, the cleanliness and temperature of the rooms\nare monitored. Thanks to this information a main smart agent (HouseAgent)\nknows everything that happens in the house and is able to manage the agents\nin charge of cleaning the various rooms. Two other agents (LightAgent and\nShutterAgent) are in charge of lighting a room if a person is present. Depending on the natural brightness, it is decided whether the shutters must be opened\nor the lights must be switched on, so as to guarantee energy savings. Two last\nrobots agents are tasked with cleaning the floors of the house and are the sole\nplanning agents. The various agents can exchange information each other in\norder to perform tasks in different places.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eJavascript, Node.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003cp\u003eTo install the module use this command in the main folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enpm install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the code use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enpm run test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enode ./src/houseworld/HouseWorld.js\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-asa_assignment_3\" class=\"anchor\" aria-hidden=\"true\" href=\"#asa_assignment_3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eASA_assignment_3\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-domain\" class=\"anchor\" aria-hidden=\"true\" href=\"#domain\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDomain\u003c/h3\u003e\n\u003cp\u003eThis sample domain file uses the key-in extension which cannot be used in simulation. In the simulation, therefore, the problem is circumvented through the use of predicates with characteristics that still allow to distinguish different types.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e;; domain file: domain-robot1.pddl\n(define (domain robot1)\n (:requirements :strips :typing)\n (:types\n robot\n room \n base_station \n )\n \n (:predicates\n (is_in_room ?robot - robot ?room1 - room)\n (is_adjacent ?room1 - room ?room2 - room)\n (is_in_bs ?base_station - base_station ?robot - room)\n (is_dirty ?room - room)\n (bs_in_room ?base_station - base_station ?room - room) \n )\n \n (:action Move\n :parameters (?robot ?room1 ?room2 ?base_station)\n :precondition (and\n (is_in_room ?robot ?room1)\n (is_adjacent ?room1 ?room2)\n )\n :effect (and\n (not (is_in_room ?robot ?room1))\n (is_in_room ?robot ?room2)\n (not (is_in_bs ?base_station ?robot))\n )\n )\n \n (:action Clean\n :parameters (?room ?robot)\n :precondition (and\n (is_in_room ?robot ?room)\n (is_dirty ?room)\n )\n :effect (and\n (not (is_dirty ?room))\n )\n )\n \n (:action Charge\n :parameters (?robot ?base_station ?room)\n :precondition (and\n (is_in_room ?robot ?room)\n (bs_in_room ?base_station ?room)\n (not (is_in_bs ?base_station ?robot))\n )\n :effect (and\n (is_in_bs ?base_station ?robot)\n )\n )\n)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-problem\" class=\"anchor\" aria-hidden=\"true\" href=\"#problem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProblem\u003c/h3\u003e\n\u003cp\u003eThis sample problem file contains all the information about the environment that the agent knows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e;; problem file: problem-robot1.pddl\n(define (problem robot1)\n (:domain robot1)\n (:objects\n office - room\n tavern - room\n basement_bathroom - room\n base_station1 - base_station\n robot1 - robot\n )\n (:init\n (is_adjacent office tavern)\n (is_adjacent tavern office)\n (is_adjacent tavern basement_bathroom)\n (is_adjacent basement_bathroom tavern)\n (bs_in_room base_station1 tavern)\n (is_in_room robot1 tavern)\n (is_in_bs base_station1 robot1)\n (is_dirty tavern)\n (is_dirty office)\n )\n (:goal\n (and (not (is_dirty tavern)) (not (is_dirty basement_bathroom)) (not (is_dirty office)) (is_in_bs base_station1 robot1))\n )\n)\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-scorpion\" class=\"anchor\" aria-hidden=\"true\" href=\"#scorpion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScorpion\u003c/h1\u003e\n\u003cp\u003eScorpion is an optimal classical planner that uses saturated cost\npartitioning to combine multiple abstraction heuristics. It also contains\nimplementations of many other cost partitioning algorithms over\nabstraction and landmark heuristics. Scorpion is based on the \u003ca href=\"https://github.com/aibasel/downward\"\u003eFast\nDownward planning system\u003c/a\u003e (version 22.06),\nwhich is described below. We regularly port the latest changes from Fast Downward\nto Scorpion and also try to port Scorpion features back to Fast Downward.\u003c/p\u003e\n\u003cp\u003ePlease use the following reference when citing Scorpion:\nJendrik Seipp, Thomas Keller and Malte Helmert.\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003eSaturated Cost Partitioning for Optimal Classical Planning\u003c/a\u003e.\nJournal of Artificial Intelligence Research 67, pp. 129-167. 2020.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h2\u003e\n\u003cp\u003eAfter installing the requirements (see below), compile the planner with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand see the available options with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py --help # driver\n./fast-downward.py --search -- --help # search component\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more details (including build instructions for Windows), see the\ndocumentation about\n\u003ca href=\"https://www.fast-downward.org/ObtainingAndRunningFastDownward\" rel=\"nofollow\"\u003ecompiling\u003c/a\u003e\nand \u003ca href=\"https://www.fast-downward.org/PlannerUsage\" rel=\"nofollow\"\u003erunning\u003c/a\u003e the planner. The\n\u003ca href=\"https://jendrikseipp.github.io/scorpion\" rel=\"nofollow\"\u003eplugin documentation\u003c/a\u003e shows\nwhich plugins are available (heuristics, search algorithms, etc.) and how\nto use them.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-recommended-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#recommended-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecommended configuration\u003c/h3\u003e\n\u003cp\u003eWe recommend using the following configuration:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py \\\n --transform-task preprocess-h2 \\\n ../benchmarks/gripper/prob01.pddl \\\n --search \"astar(scp_online([\n projections(sys_scp(max_time=100, max_time_per_restart=10)),\n cartesian()],\n saturator=perimstar, max_time=1000, interval=10K, orders=greedy_orders()),\n pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003epreprocess-h2\u003c/code\u003e call prunes irrelevant operators in a preprocessing\nstep. The search configuration uses \u003ca href=\"https://ojs.aaai.org/index.php/SOCS/article/view/18535\" rel=\"nofollow\"\u003epartial order\nreduction\u003c/a\u003e and\nmaximizes over\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003ediverse\u003c/a\u003e,\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/3503\" rel=\"nofollow\"\u003esubset-saturated\u003c/a\u003e\ncost partitioning heuristics computed\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/15976/\" rel=\"nofollow\"\u003eonline\u003c/a\u003e during\nthe search. The underlying abstractions are \u003ca href=\"https://www.ijcai.org/proceedings/2019/780\" rel=\"nofollow\"\u003eSys-SCP pattern\ndatabases\u003c/a\u003e and \u003ca href=\"https://jair.org/index.php/jair/article/view/11217\" rel=\"nofollow\"\u003eCartesian\nabstractions\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e(In \u003ca href=\"https://lab.readthedocs.io/\" rel=\"nofollow\"\u003eDownward Lab\u003c/a\u003e you can use\n\u003ccode\u003eadd_algorithm(name=\"scorpion\", repo=\"path/to/repo\", rev=\"scorpion\", component_options=[], driver_options=[\"--transform-task\", \"preprocess-h2\", \"--alias\", \"scorpion\"]\u003c/code\u003e to run the recommended Scorpion configuration.)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h4\u003e\n\u003cp\u003eTo simplify the installation process, we provide an executable\n\u003ca href=\"https://github.com/hpcng/singularity\"\u003eSingularity\u003c/a\u003e container for\nScorpion. It accepts the same arguments as the \u003ccode\u003efast-downward.py\u003c/code\u003e script\n(see above).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Download the container (tested with Singularity 3.5),\nsingularity pull scorpion.sif library://jendrikseipp/default/scorpion:latest\n\n# or build the container yourself.\nsudo singularity build scorpion.sif Singularity\n\n# Then run recommended configuration (available via \"scorpion\" alias).\n./scorpion.sif --transform-task preprocess-h2 --alias scorpion PROBLEM_FILE\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ipc-2018-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#ipc-2018-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIPC 2018 version\u003c/h3\u003e\n\u003cp\u003eIf you prefer to run the Scorpion version from IPC 2018 (which uses an\nolder Fast Downward version and different abstractions), we recommend\nusing the \u003ca href=\"https://bitbucket.org/ipc2018-classical/team44/src/ipc-2018-seq-opt/\" rel=\"nofollow\"\u003eScorpion IPC\nrepo\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-differences-between-scorpion-and-fast-downward\" class=\"anchor\" aria-hidden=\"true\" href=\"#differences-between-scorpion-and-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDifferences between Scorpion and Fast Downward\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eScorpion comes with the\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/13708\" rel=\"nofollow\"\u003eh\u00b2-preprocessor\u003c/a\u003e\nby Vidal Alc\u00e1zar and \u00c1lvaro Torralba that prunes irrelevant operators.\nPass \u003ccode\u003e--transform-task preprocess-h2\u003c/code\u003e to use it.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003e--transform-task\u003c/code\u003e command allows you to run arbitrary preprocessing\ncommands that transform the SAS+ output from the translator before\npassing it to the search.\u003c/li\u003e\n\u003cli\u003eScorpion uses a\n\u003ca href=\"https://github.com/greg7mdp/parallel-hashmap\"\u003ephmap::flat_hash_set\u003c/a\u003e to check\nfor duplicate states, which often drastically reduces the peak memory usage,\ncompared to Fast Downward\u0027s \u003ccode\u003eIntHashSet\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf \u003ca href=\"https://ccache.dev/\" rel=\"nofollow\"\u003eccache\u003c/a\u003e is installed (recommended), Scorpion\nuses it to cache compilation files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-translator-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-translator-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew translator options\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUse \u003ccode\u003e--dump-predicates\u003c/code\u003e and \u003ccode\u003e--dump-static-atoms\u003c/code\u003e to write files with\ninformation that\u0027s useful for learning domain control knowledge.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-plugin-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-plugin-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew plugin options\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecegar(..., search_strategy=incremental)\u003c/code\u003e: use \u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/6667\" rel=\"nofollow\"\u003eincremental search for\nCartesian abstraction\nrefinement\u003c/a\u003e\n(default).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ehillclimbing(..., max_generated_patterns=200)\u003c/code\u003e: limit the number of\npatterns generated by hill climbing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esystematic(..., pattern_type=interesting_general)\u003c/code\u003e: compute interesting\npatterns for general cost partitioning.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-abstraction-heuristics\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-cost-partitioning-algorithms-for-abstraction-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew cost partitioning algorithms for abstraction heuristics\u003c/h3\u003e\n\u003cp\u003eWe use Cartesian abstractions in the example configurations below\n(\u003ccode\u003e[cartesian()]\u003c/code\u003e). You can also use pattern database heuristics, e.g.,\n\u003ccode\u003e[projections(systematic(2))]\u003c/code\u003e, or mix abstractions, e.g.,\n\u003ccode\u003e[projections(systematic(3)), cartesian()]\u003c/code\u003e. Some of the algorithms below\nare also part of vanilla Fast Downward, but are only implemented for PDB\nheuristics.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning:\n\u003ccode\u003eocp([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003ecanonical_heuristic([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], opportunistic=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], ..., opportunistic=true)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003egzocp([cartesian()], ...)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003escp([cartesian()], ...)\u003c/code\u003e (offline), \u003ccode\u003escp_online([cartesian()], ...)\u003c/code\u003e (online)\u003c/li\u003e\n\u003cli\u003e(Saturated) post-hoc optimization:\n\u003ccode\u003epho([cartesian()], ..., saturated={false,true})\u003c/code\u003e (offline),\n\u003ccode\u003eoperatorcounting([pho_abstraction_constraints([cartesian()], saturated={false,true})])\u003c/code\u003e (online)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can also compute the maximum over abstraction heuristics:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emaximize([cartesian()])\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe plugin documentation shows all options for \u003ca href=\"https://jendrikseipp.github.io/scorpion/Evaluator/#cost_partitioning_heuristics\" rel=\"nofollow\"\u003ecost partitioning\nheuristics\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-pattern-collection-generators\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-pattern-collection-generators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew pattern collection generators\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSystematic patterns with size limits:\n\u003ccode\u003esys_scp(max_pattern_size=X, max_pdb_size=Y, max_collection_size=Z, ..., saturate=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSys-SCP patterns:\n\u003ccode\u003esys_scp(...)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-landmark-heuristics\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-cost-partitioning-algorithms-for-landmark-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew cost partitioning algorithms for landmark heuristics\u003c/h3\u003e\n\u003cp\u003eExample using A* search and saturated cost partitioning over BJOLP\nlandmarks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--evaluator\n \"lmc=lmcount(lm_merged([lm_rhw(), lm_hm(m=1)]),\n admissible=true, cost_partitioning=suboptimal, greedy=true,\n reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\"\n--search\n \"astar(lmc, lazy_evaluator=lmc)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDifferent cost partitioning algorithms (all need \u003ccode\u003eadmissible=true\u003c/code\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=optimal)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003elmcount(..., cost_partitioning=canonical)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePost-hoc optimization:\n\u003ccode\u003elmcount(..., cost_partitioning=pho)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=true, scoring_function=min_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=false, scoring_function=max_heuristic)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-search-engines\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-search-engines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew search engines\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBreadth-first search (without overhead of the more general \u003ccode\u003eeager()\u003c/code\u003e search):\n\u003ccode\u003ebrfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDepth-first search:\n\u003ccode\u003edfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExhaustive search (useful for dumping the reachable state space of small input tasks):\n\u003ccode\u003edump_reachable_search_space()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIDA* search:\n\u003ccode\u003eidastar(cegar(cache_estimates=false))\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIterative width search:\n\u003ccode\u003eiw(width=2)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1674056102.0 + "updated_at": 1688990870.0 }, { "data_format": 2, - "description": "Code for blog Reproducibility in Tensorflow/PyTorch/JAX", + "description": null, "filenames": [ - "Singularity.recipe" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/latest/Singularity", + "misc/releases/22.12/Singularity.22.12", + "misc/releases/21.12/Singularity.21.12" ], - "full_name": "WolodjaZ/reproduc-ml-tutorial", + "full_name": "ipc2023-classical/planner25", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-reproducibility-in-tensorflowpytorchjax\" class=\"anchor\" aria-hidden=\"true\" href=\"#reproducibility-in-tensorflowpytorchjax\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReproducibility in Tensorflow/PyTorch/JAX\u003c/h1\u003e\n\u003cp\u003eThis is an example repository from my blog on \u003ca href=\"https://wolodjaz.github.io/blogs/\" rel=\"nofollow\"\u003eReproducibility in Tensorflow/PyTorch/JAX\u003c/a\u003e, so please read it first.\u003c/p\u003e\n\u003cp\u003eThe structure of this repository differs from the one in the blog due to the addition of \u003ca href=\"https://mybinder.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eBinder\u003c/a\u003e settings. The repository structure is as follows:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003ereproduc-ml-tutorial/\n workspace/ # Default location for data sets, logs, models, parameter files.\n train.yaml # Train hyper-parameters.\n .dockerignore # Docker ignore file that prevents workspace directory to be sent to docker server.\n DockerBuildfile # Docker recipe.\n environment.yml # Conda environment config file for mybinder\n index.ipynb # Example notebook from Reproducibility in Tensorflow/PyTorch/JAX part 2\n mlcube.yaml # MLCube definition file.\n train_jax.py # Python source code training simple neural network using MNIST data set with JAX.\n train_pytorch.py # Python source code training simple neural network using MNIST data set with PyTorch.\n train_tensorflow.py # Python source code training simple neural network using MNIST data set with Tensorflow.\n requirements.txt # Python project dependencies.\n run.sh # Main bash script that lunches python script based on passed argument\n Singularity.recipe # Singularity recipe.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-reproducibility-in-tensorflowpytorchjax-part-22--notebook\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-reproducibility-in-tensorflowpytorchjax-part-22--notebook\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the \"Reproducibility in Tensorflow/PyTorch/JAX Part 2/2\" Notebook\u003c/h2\u003e\n\u003cp\u003eTo run the notebook, you can pull this repository and launch \u003ccode\u003eindex.ipynb\u003c/code\u003e locally, but you can also click on the badge below to test running it on BinderHub without pulling the repository \u003cg-emoji class=\"g-emoji\" alias=\"sunglasses\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f60e.png\"\u003e\ud83d\ude0e\u003c/g-emoji\u003e:\n\u003ca href=\"https://mybinder.org/v2/gh/WolodjaZ/reproduc-ml-tutorial/HEAD?labpath=index.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/581c077bdbc6ca6899c86d0acc6145ae85e9d80e6f805a1071793dbe48917982/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-main-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#main-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMain Project\u003c/h2\u003e\n\u003cp\u003eIn addition to running the notebook, you can also run the main application where you can train MNIST datasets on a basic neural network made in Pytorch/Jax/Tensorflow. You will build a docker image or a singularity image and launch it to run the training. Everything, including logs and data, will be saved under the \u003ccode\u003eworkspace\u003c/code\u003e directory. There is also a \u003ccode\u003etrain.yaml\u003c/code\u003e file where I have defined all the parameters used for the scripts. You can check and change them if you want to.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Project\u003c/h2\u003e\n\u003cp\u003eTo run the project, we are using \u003ca href=\"https://github.com/mlcommons/mlcube\"\u003eMLCube\u003c/a\u003e, which provides the contract for our pipeline, as defined in the file \u003ccode\u003emlcube.yaml\u003c/code\u003e. Based on this file and the framework, you will need to first configure our environment by building our images. Before doing so, please install mlcube:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install mlcube\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, create our images:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prepare docker image\u003c/span\u003e\nmlcube configure --mlcube=. --platform=docker\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prepare singularity image\u003c/span\u003e\nmlcube configure --mlcube=. --platform=singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can now run our pipelines by choosing which platform and framework to use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\nmlcube run --mlcube=. --platform=docker --task=pytorch\nmlcube run --mlcube=. --platform=docker --task=tensorflow\nmlcube run --mlcube=. --platform=docker --task=jax\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\nmlcube run --mlcube=. --platform=singularity --task=pytorch\nmlcube run --mlcube=. --platform=singularity --task=tensorflow\nmlcube run --mlcube=. --platform=singularity --task=jax\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running the commands, pipeline will start the training process and the log and models will be saved under the \u003ccode\u003eworkspace\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-additional-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#additional-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional Resources:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCheck out my blog \u003ca href=\"https://wolodjaz.github.io/blogs/\" rel=\"nofollow\"\u003eReproducibility in Tensorflow/PyTorch/JAX\u003c/a\u003e for more information on the topic.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mybinder.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eBinder\u003c/a\u003e is a great tool for creating and sharing custom computing environments with others.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mlcommons/mlcube\"\u003eMLCube\u003c/a\u003e is a useful tool that provides a consistent interface for machine learning models in containers like Docker.\u003c/li\u003e\n\u003cli\u003eFor more guidance on reproducible research, check out \u003ca href=\"https://the-turing-way.netlify.app/reproducible-research/reproducible-research.html\" rel=\"nofollow\"\u003eThe Turing Way\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-and-grand-finally\" class=\"anchor\" aria-hidden=\"true\" href=\"#and-grand-finally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnd Grand Finally\u003c/h2\u003e\n\u003cp\u003eClosing comment offered by chatGPT \u003cg-emoji class=\"g-emoji\" alias=\"robot\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f916.png\"\u003e\ud83e\udd16\u003c/g-emoji\u003e\u003c/p\u003e\n\u003cp\u003eWe\u0027re so glad you\u0027ve given our project a try! Your feedback is incredibly valuable to us as we continue to improve and update the project. Whether you have questions, comments, or suggestions, please don\u0027t hesitate to reach out to us by emailing us at \u003ca href=\"mailto:vladimirzaigrajew@gmail.com\"\u003evladimirzaigrajew@gmail.com\u003c/a\u003e or by opening an issue on the GitHub repository. Thank you for your support!\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-scorpion\" class=\"anchor\" aria-hidden=\"true\" href=\"#scorpion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScorpion\u003c/h1\u003e\n\u003cp\u003eScorpion is a classical planning system that extends \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003eFast\nDownward\u003c/a\u003e. The main extensions are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enovel state-of-the-art algorithms for optimal classical planning\u003c/li\u003e\n\u003cli\u003eadditional search algorithms\u003c/li\u003e\n\u003cli\u003eseveral new plugin options and utilities\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"#differences-between-scorpion-and-fast-downward\"\u003ebelow\u003c/a\u003e for a detailed list\nof extensions. We regularly port the latest changes from Fast Downward to\nScorpion and also integrate some features from Scorpion back into Fast Downward.\u003c/p\u003e\n\u003cp\u003ePlease use the following reference when citing Scorpion:\nJendrik Seipp, Thomas Keller and Malte Helmert.\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003eSaturated Cost Partitioning for Optimal Classical Planning\u003c/a\u003e.\nJournal of Artificial Intelligence Research 67, pp. 129-167. 2020.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h2\u003e\n\u003cp\u003eInstall the dependencies (the table below lists which versions are tested):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install cmake g++ git make python3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor plugins based on linear programming (e.g., \u003ccode\u003eocp()\u003c/code\u003e, \u003ccode\u003epho()\u003c/code\u003e) you need\nto \u003ca href=\"https://www.fast-downward.org/LPBuildInstructions\" rel=\"nofollow\"\u003eadd an LP solver\u003c/a\u003e. Then\ncompile the planner with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand see the available options with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py --help # driver\n./fast-downward.py --search -- --help # search component\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more details (including build instructions for macOS and Windows), see the\ndocumentation about\n\u003ca href=\"https://www.fast-downward.org/ObtainingAndRunningFastDownward\" rel=\"nofollow\"\u003ecompiling\u003c/a\u003e\nand \u003ca href=\"https://www.fast-downward.org/PlannerUsage\" rel=\"nofollow\"\u003erunning\u003c/a\u003e the planner. The\n\u003ca href=\"https://jendrikseipp.github.io/scorpion\" rel=\"nofollow\"\u003eplugin documentation\u003c/a\u003e shows\nwhich plugins are available (heuristics, search algorithms, etc.) and how\nto use them.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-recommended-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#recommended-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecommended configuration\u003c/h3\u003e\n\u003cp\u003eWe recommend using the following configuration:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py \\\n --transform-task preprocess-h2 \\\n ../benchmarks/gripper/prob01.pddl \\\n --search \"astar(scp_online([\n projections(sys_scp(max_time=100, max_time_per_restart=10)),\n cartesian()],\n saturator=perimstar, max_time=1000, interval=10K, orders=greedy_orders()),\n pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003epreprocess-h2\u003c/code\u003e call prunes irrelevant operators in a preprocessing\nstep. The search configuration uses \u003ca href=\"https://ojs.aaai.org/index.php/SOCS/article/view/18535\" rel=\"nofollow\"\u003epartial order\nreduction\u003c/a\u003e and\nmaximizes over\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003ediverse\u003c/a\u003e,\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/3503\" rel=\"nofollow\"\u003esubset-saturated\u003c/a\u003e\ncost partitioning heuristics computed\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/15976/\" rel=\"nofollow\"\u003eonline\u003c/a\u003e during\nthe search. The underlying abstractions are \u003ca href=\"https://www.ijcai.org/proceedings/2019/780\" rel=\"nofollow\"\u003eSys-SCP pattern\ndatabases\u003c/a\u003e and \u003ca href=\"https://jair.org/index.php/jair/article/view/11217\" rel=\"nofollow\"\u003eCartesian\nabstractions\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e(In \u003ca href=\"https://lab.readthedocs.io/\" rel=\"nofollow\"\u003eDownward Lab\u003c/a\u003e you can use\n\u003ccode\u003eadd_algorithm(name=\"scorpion\", repo=\"path/to/repo\", rev=\"scorpion\", component_options=[], driver_options=[\"--transform-task\", \"preprocess-h2\", \"--alias\", \"scorpion\"]\u003c/code\u003e to run the recommended Scorpion configuration.)\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-apptainer-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#apptainer-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApptainer image\u003c/h4\u003e\n\u003cp\u003eTo simplify the installation process, we provide an executable\n\u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e container (formerly known as Singularity).\nIt accepts the same arguments as the \u003ccode\u003efast-downward.py\u003c/code\u003e script (see above).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Download the image,\napptainer pull scorpion.sif oras://ghcr.io/jendrikseipp/scorpion:latest\n\n# or build it yourself.\napptainer build scorpion.sif Apptainer\n\n# Then run recommended configuration (available via \"scorpion\" alias).\n./scorpion.sif --transform-task preprocess-h2 --alias scorpion PROBLEM_FILE\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ipc-2018-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#ipc-2018-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIPC 2018 version\u003c/h3\u003e\n\u003cp\u003eIf you prefer to run the Scorpion version from IPC 2018 (which uses an\nolder Fast Downward version and different abstractions), we recommend\nusing the \u003ca href=\"https://bitbucket.org/ipc2018-classical/team44/src/ipc-2018-seq-opt/\" rel=\"nofollow\"\u003eScorpion IPC\nrepo\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-differences-between-scorpion-and-fast-downward\" class=\"anchor\" aria-hidden=\"true\" href=\"#differences-between-scorpion-and-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDifferences between Scorpion and Fast Downward\u003c/h2\u003e\n\u003cp\u003eDiff between the latest merged version of Fast Downward and Scorpion:\n\u003ca href=\"https://github.com/jendrikseipp/scorpion/compare/main...scorpion\"\u003ehttps://github.com/jendrikseipp/scorpion/compare/main...scorpion\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScorpion comes with the\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/13708\" rel=\"nofollow\"\u003eh\u00b2-preprocessor\u003c/a\u003e\nby Vidal Alc\u00e1zar and \u00c1lvaro Torralba that prunes irrelevant operators.\nPass \u003ccode\u003e--transform-task preprocess-h2\u003c/code\u003e to use it.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003e--transform-task\u003c/code\u003e command allows you to run arbitrary preprocessing\ncommands that transform the SAS+ output from the translator before\npassing it to the search.\u003c/li\u003e\n\u003cli\u003eScorpion uses a\n\u003ca href=\"https://github.com/greg7mdp/parallel-hashmap\"\u003ephmap::flat_hash_set\u003c/a\u003e to check\nfor duplicate states, which often drastically reduces the peak memory usage,\ncompared to Fast Downward\u0027s \u003ccode\u003eIntHashSet\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf \u003ca href=\"https://ccache.dev/\" rel=\"nofollow\"\u003eccache\u003c/a\u003e is installed (recommended), Scorpion\nuses it to cache compilation files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-translator-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-translator-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew translator options\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUse \u003ccode\u003e--dump-predicates\u003c/code\u003e and \u003ccode\u003e--dump-static-atoms\u003c/code\u003e to write files with\ninformation that\u0027s useful for learning domain control knowledge.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-plugin-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-plugin-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew plugin options\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., search_strategy=incremental)\u003c/code\u003e: use \u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/6667\" rel=\"nofollow\"\u003eincremental search for\nCartesian abstraction\nrefinement\u003c/a\u003e\n(default).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., pick_flawed_abstract_state={batch_min_h, ...})\u003c/code\u003e:\nfind all current flaws, then iteratively repair the flaw that\u0027s closest to the goal\n(\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/19819\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, default=\u003ccode\u003ebatch_min_h\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., pick_split={max_cover, ...}, tiebreak_split={max_refined, ...})\u003c/code\u003e:\nsmarter strategies for splitting a flawed abstract state\n(\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/19819\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, default=\u003ccode\u003emax_cover\u003c/code\u003e\nand \u003ccode\u003emax_refined\u003c/code\u003e for tiebreaking).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar,cartesian}(..., dot_graph_verbosity={silent, write_to_console, write_to_file})\u003c/code\u003e:\nwrite intermediate abstractions as Graphviz dot files to stdout or to files (default=\u003ccode\u003esilent\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ehillclimbing(..., max_generated_patterns=200)\u003c/code\u003e: limit the number of\npatterns generated by hill climbing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esystematic(..., pattern_type=interesting_general)\u003c/code\u003e: compute interesting\npatterns for general cost partitioning.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-abstraction-heuristics\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-cost-partitioning-algorithms-for-abstraction-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew cost partitioning algorithms for abstraction heuristics\u003c/h3\u003e\n\u003cp\u003eWe use Cartesian abstractions in the example configurations below\n(\u003ccode\u003e[cartesian()]\u003c/code\u003e). You can also use pattern database heuristics, e.g.,\n\u003ccode\u003e[projections(systematic(2))]\u003c/code\u003e, or mix abstractions, e.g.,\n\u003ccode\u003e[projections(systematic(3)), cartesian()]\u003c/code\u003e. Some of the algorithms below\nare also part of vanilla Fast Downward, but are only implemented for PDB\nheuristics.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning:\n\u003ccode\u003eocp([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003ecanonical_heuristic([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], opportunistic=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], ..., opportunistic=true)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003egzocp([cartesian()], ...)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003escp([cartesian()], ...)\u003c/code\u003e (offline), \u003ccode\u003escp_online([cartesian()], ...)\u003c/code\u003e (online)\u003c/li\u003e\n\u003cli\u003e(Saturated) post-hoc optimization:\n\u003ccode\u003epho([cartesian()], ..., saturated={false,true})\u003c/code\u003e (offline),\n\u003ccode\u003eoperatorcounting([pho_abstraction_constraints([cartesian()], saturated={false,true})])\u003c/code\u003e (online)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can also compute the maximum over abstraction heuristics:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emaximize([cartesian()])\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe plugin documentation shows all options for \u003ca href=\"https://jendrikseipp.github.io/scorpion/Evaluator/#cost_partitioning_heuristics\" rel=\"nofollow\"\u003ecost partitioning\nheuristics\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-pattern-collection-generators\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-pattern-collection-generators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew pattern collection generators\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSystematic patterns with size limits:\n\u003ccode\u003esys_scp(max_pattern_size=X, max_pdb_size=Y, max_collection_size=Z, ..., saturate=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSys-SCP patterns:\n\u003ccode\u003esys_scp(...)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-landmark-heuristics\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-cost-partitioning-algorithms-for-landmark-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew cost partitioning algorithms for landmark heuristics\u003c/h3\u003e\n\u003cp\u003eExample using A* search and saturated cost partitioning over BJOLP\nlandmarks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--evaluator\n \"lmc=lmcount(lm_merged([lm_rhw(), lm_hm(m=1)]),\n admissible=true, cost_partitioning=suboptimal, greedy=true,\n reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\"\n--search\n \"astar(lmc, lazy_evaluator=lmc)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDifferent cost partitioning algorithms (all need \u003ccode\u003eadmissible=true\u003c/code\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=optimal)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003elmcount(..., cost_partitioning=canonical)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePost-hoc optimization:\n\u003ccode\u003elmcount(..., cost_partitioning=pho)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=true, scoring_function=min_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=false, scoring_function=max_heuristic)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-new-search-engines\" class=\"anchor\" aria-hidden=\"true\" href=\"#new-search-engines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNew search engines\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBreadth-first search (without overhead of the more general \u003ccode\u003eeager()\u003c/code\u003e search):\n\u003ccode\u003ebrfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDepth-first search:\n\u003ccode\u003edfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExhaustive search (useful for dumping the reachable state space of small input tasks):\n\u003ccode\u003edump_reachable_search_space()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIDA* search:\n\u003ccode\u003eidastar(cegar(cache_estimates=false))\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIterative width search:\n\u003ccode\u003eiw(width=2)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1673392331.0 + "updated_at": 1688990761.0 }, { "data_format": 2, - "description": "Singularity recipe files for dvc (https://github.com/iterative/dvc)", + "description": "lowcharts is meant to be used in those scenarios where we have numerical data in text files that we want to display in the terminal to do a basic analysis.", + "filenames": [ + "0.4.2/Singularity", + "0.5.8/Singularity", + "0.5.7/Singularity" + ], + "full_name": "pscedu/singularity-lowcharts", + "latest_release": "v0.5.8", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-lowcharts/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-lowcharts/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-lowcharts/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-lowcharts/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f7fc9ce8a0f943ba64af6c034355a9c31eecd12def757563ede07b3784c8f519/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c6f77636861727473\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f7fc9ce8a0f943ba64af6c034355a9c31eecd12def757563ede07b3784c8f519/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c6f77636861727473\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-lowcharts\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/27f2b611017dfb8fe06ab47f3a4d377ddd91d667f50f8135fc669a8abfc43a45/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c6f77636861727473\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/27f2b611017dfb8fe06ab47f3a4d377ddd91d667f50f8135fc669a8abfc43a45/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c6f77636861727473\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-lowcharts\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/619a942430a532ae89a0d9c2000a895ea8603989c6dba951ed43664c22cebb96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c6f77636861727473\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/619a942430a532ae89a0d9c2000a895ea8603989c6dba951ed43664c22cebb96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c6f77636861727473\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-lowcharts\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ebc83f1df7b32d114034b8d95b0d270b8512149b150348054f1be74c202b63d2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c6f77636861727473\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ebc83f1df7b32d114034b8d95b0d270b8512149b150348054f1be74c202b63d2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c6f77636861727473\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-lowcharts\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-lowcharts\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-lowcharts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-lowcharts\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/juan-leon/lowcharts/main/resources/histogram-example.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/juan-leon/lowcharts/main/resources/histogram-example.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/juan-leon/lowcharts\"\u003elowcharts\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003elowcharts\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/lowcharts/0.4.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/lowcharts\u003c/code\u003e as \u003ccode\u003e0.4.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 2, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1635967869.0 + }, + { + "data_format": 2, + "description": null, "filenames": [ - "Singularity.2.40.0", - "Singularity.1.11.16", - "Singularity.1.6.1", - "Singularity.2.8.2", "Singularity", - "Singularity.2.1.0", - "Singularity.2.8.1" + "spades.v3.7/Singularity", + "spades.v3.11/Singularity", + "shovill.v.1.9/Singularity" ], - "full_name": "powerPlant/dvc-srf", + "full_name": "kristyhoran/multi_assembler_singularity", + "latest_release": null, + "readme": "\u003cp\u003eA singulairty recipe which incorporates shovill and skesa\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 2, + "topics": [], + "updated_at": 1543190575.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "Singularity.v1.0.0" + ], + "full_name": "baxpr/dwi-reorder", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the DVC tool for Data Version Control\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1665710810.0 + "updated_at": 1574202643.0 }, { "data_format": 2, - "description": "Run assembler (Canu, flye, hifiasm) on a set of long read files", + "description": null, "filenames": [ - "singularity/Singularity" + "Singularity.v2.1.0" ], - "full_name": "sequana/lora", + "full_name": "baxpr/mp2rage", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mp2rage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#mp2rage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emp2rage\u003c/h1\u003e\n\u003cp\u003eReconstructs a T1-weighted image from images at multiple inversion times following Marques et al. 2009. The robust adjustment (beta factor) of O\u0027Brien 2014 is also implemented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PF, Gruetter R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage. 2010 Jan 15;49(2):1271-81. doi:10.1016/j.neuroimage.2009.10.002. PMID: 19819338.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/19819338\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/pubmed/19819338\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe large spatial inhomogeneity in transmit B(1) field (B(1)(+)) observable in human MR images at high static magnetic fields (B(0)) severely impairs image quality. To overcome this effect in brain T(1)-weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T(1)-weighted images where the result image was free of proton density contrast, T(2) contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B(1)(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T(1)-weighted images, acquired within 12 min, high-resolution 3D T(1) maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T(1) maps were validated in phantom experiments. In humans, the T(1) values obtained at 7 T were 1.15+/-0.06 s for white matter (WM) and 1.92+/-0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T(1) values obtained (0.81+/-0.03 s for WM and 1.35+/-0.05 for GM) were once again found to be in very good agreement with values in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eO\u0027Brien KR, Kober T, Hagmann P, et al. Robust T1-weighted Structural Brain Imaging and Morphometry at 7T Using MP2RAGE PLoS One. 2014;9(6):e99676. Published 2014 Jun 16. doi:10.1371/journal.pone.0099676\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/24932514/\" rel=\"nofollow\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/24932514/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePurpose: To suppress the noise, by sacrificing some of the signal homogeneity for numerical stability, in uniform T1 weighted (T1w) images obtained with the magnetization prepared 2 rapid gradient echoes sequence (MP2RAGE) and to compare the clinical utility of these robust T1w images against the uniform T1w images.\u003c/p\u003e\n\u003cp\u003eMaterials and methods: 8 healthy subjects (29.0 \u00b1 4.1 years; 6 Male), who provided written consent, underwent two scan sessions within a 24 hour period on a 7T head-only scanner. The uniform and robust T1w image volumes were calculated inline on the scanner. Two experienced radiologists qualitatively rated the images for: general image quality; 7T specific artefacts; and, local structure definition. Voxel-based and volume-based morphometry packages were used to compare the segmentation quality between the uniform and robust images. Statistical differences were evaluated by using a positive sided Wilcoxon rank test.\u003c/p\u003e\n\u003cp\u003eResults: The robust image suppresses background noise inside and outside the skull. The inhomogeneity introduced was ranked as mild. The robust image was significantly ranked higher than the uniform image for both observers (observer 1/2, p-value = 0.0006/0.0004). In particular, an improved delineation of the pituitary gland, cerebellar lobes was observed in the robust versus uniform T1w image. The reproducibility of the segmentation results between repeat scans improved (p-value = 0.0004) from an average volumetric difference across structures of \u2248 6.6% to \u2248 2.4% for the uniform image and robust T1w image respectively.\u003c/p\u003e\n\u003cp\u003eConclusions: The robust T1w image enables MP2RAGE to produce, clinically familiar T1w images, in addition to T1 maps, which can be readily used in uniform morphometry packages.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1673268784.0 + "updated_at": 1595274818.0 }, { "data_format": 2, - "description": "repository for running scripts on Orion", + "description": null, "filenames": [ - "Singularity" + "Singularity.centos7-cuda-tf1.11.0-torch0.4.1", + "Singularity.centos7-tensorflow-cpu" ], - "full_name": "LingoNMBU/DAT300-CA2-Orion", + "full_name": "apphys/hpsim_rl_singularity", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity recipe\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1667572209.0 + "updated_at": 1542320831.0 }, { "data_format": 2, - "description": "pydocbrowser website", + "description": "Run IGV in a XFCE-based Singularity container", "filenames": [ - "build/sources/pygments-2.12.0/tests/examplefiles/singularity/Singularity", - "build/sources/pygments-2.14.0/tests/examplefiles/singularity/Singularity", - "build/sources/pygments-2.11.2/tests/examplefiles/singularity/Singularity", - "build/sources/pygments-2.13.0/tests/examplefiles/singularity/Singularity" + "Singularity" ], - "full_name": "pydocbrowser/pydocbrowser.github.io", + "full_name": "bihealth/singularity-igv", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/pydocbrowser/pydocbrowser\"\u003epydocbrowser\u003c/a\u003e website\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pydocbrowser/pydocbrowser.github.io/actions/workflows/build.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pydocbrowser/pydocbrowser.github.io/actions/workflows/build.yml/badge.svg\" alt=\"build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1650700618.0 + "updated_at": 1612971331.0 }, { "data_format": 2, - "description": null, + "description": "Provides Visidata using Debian Stretch as Singularity Image", "filenames": [ - "test/core/044-singularity-nonsharedfs-minimal/image/Singularity" + "Singularity" ], - "full_name": "Jtg003/https-github.com-pegasus-isi-pegasus", + "full_name": "paulklemm/visidata-singularity", "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"doc/sphinx/images/pegasusfront-black-reduced.png\"\u003e\u003cimg src=\"doc/sphinx/images/pegasusfront-black-reduced.png\" width=\"200\" alt=\"Pegasus WMS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pegasus-workflow-management-system\" class=\"anchor\" aria-hidden=\"true\" href=\"#pegasus-workflow-management-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePegasus Workflow Management System\u003c/h2\u003e\n\u003cp align=\"left\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d86f06b102fe2b21a15c2fe7b335a1fa19d1a8e67a2086236348bcf6e2bc83b8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f706567617375732d6973692f706567617375733f636f6c6f723d626c7565266c6162656c3d4c6963656e6365\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d86f06b102fe2b21a15c2fe7b335a1fa19d1a8e67a2086236348bcf6e2bc83b8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f706567617375732d6973692f706567617375733f636f6c6f723d626c7565266c6162656c3d4c6963656e6365\" data-canonical-src=\"https://img.shields.io/github/license/pegasus-isi/pegasus?color=blue\u0026amp;label=Licence\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/eb3de5004fcc489334124e42bd6c5141eac62cd9bd5a0ac8abdc70b3abf70041/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f706567617375732d6973692f706567617375733f6c6162656c3d4c6174657374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb3de5004fcc489334124e42bd6c5141eac62cd9bd5a0ac8abdc70b3abf70041/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f706567617375732d6973692f706567617375733f6c6162656c3d4c6174657374\" data-canonical-src=\"https://img.shields.io/github/v/tag/pegasus-isi/pegasus?label=Latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/defd5997fcf06cfa5e84a7b31da92ac209152dd742f4a0f4d1ca47d7e649fc3f/68747470733a2f2f696d672e736869656c64732e696f2f707970692f646d2f706567617375732d776d733f636f6c6f723d677265656e266c6162656c3d50795049253230446f776e6c6f616473\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/defd5997fcf06cfa5e84a7b31da92ac209152dd742f4a0f4d1ca47d7e649fc3f/68747470733a2f2f696d672e736869656c64732e696f2f707970692f646d2f706567617375732d776d733f636f6c6f723d677265656e266c6162656c3d50795049253230446f776e6c6f616473\" data-canonical-src=\"https://img.shields.io/pypi/dm/pegasus-wms?color=green\u0026amp;label=PyPI%20Downloads\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6dd2629a781aaaf2b5f44f4adb568746dfc3d9601a4f93a55a752a436140e3ba/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732d616e6f6e2f706567617375732d6973692f706567617375733f636f6c6f723d677265656e266c6162656c3d436f6e7472696275746f7273\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6dd2629a781aaaf2b5f44f4adb568746dfc3d9601a4f93a55a752a436140e3ba/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732d616e6f6e2f706567617375732d6973692f706567617375733f636f6c6f723d677265656e266c6162656c3d436f6e7472696275746f7273\" data-canonical-src=\"https://img.shields.io/github/contributors-anon/pegasus-isi/pegasus?color=green\u0026amp;label=Contributors\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003ePegasus WMS is a configurable system for mapping and executing scientific\nworkflows over a wide range of computational infrastructures including laptops,\ncampus clusters, supercomputers, grids, and commercial and academic clouds.\nPegasus has been used to run workflows with up to 1 million tasks that process\ntens of terabytes of data at a time.\u003c/p\u003e\n\u003cp\u003ePegasus WMS bridges the scientific domain and the execution environment by\nautomatically mapping high-level workflow descriptions onto distributed\nresources. It automatically locates the necessary input data and computational\nresources required by a workflow, and plans out all of the required data\ntransfer and job submission operations required to execute the workflow.\nPegasus enables scientists to construct workflows in abstract terms without\nworrying about the details of the underlying execution environment or the\nparticulars of the low-level specifications required by the middleware (Condor,\nGlobus, Amazon EC2, etc.). In the process, Pegasus can $ ant dist and optimize the\nworkflow to enable efficient, high-performance execution of large\nworkflows on complex, distributed infrastructures.\u003c/p\u003e\n\u003cp\u003ePegasus has a number of features that contribute to its usability and\neffectiveness:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePortability / Reuse \u2013 User created workflows can easily be run in different\nenvironments without alteration. Pegasus currently runs workflows on top of\nCondor pools, Grid infrastructures such as Open Science Grid and XSEDE,\nAmazon EC2, Google Cloud, and HPC clusters. The same workflow can run on a\nsingle system or across a heterogeneous set of resources.\u003c/li\u003e\n\u003cli\u003ePerformance \u2013 The Pegasus mapper can reorder, group, and prioritize tasks in\norder to increase overall workflow performance.\u003c/li\u003e\n\u003cli\u003eScalability \u2013 Pegasus can easily scale both the size of the workflow, and\nthe resources that the workflow is distributed over. Pegasus runs workflows\nranging from just a few computational tasks up to 1 million. The number of\nresources involved in executing a workflow can scale as needed without any\nimpediments to performance.\u003c/li\u003e\n\u003cli\u003eProvenance \u2013 By default, all jobs in Pegasus are launched using the\nKickstart wrapper that captures runtime provenance of the job and helps in\ndebugging. Provenance data is collected in a database, and the data can be\nqueried with tools such as pegasus-statistics, pegasus-plots, or directly\nusing SQL.\u003c/li\u003e\n\u003cli\u003eData Management \u2013 Pegasus handles replica selection, data transfers and\noutput registration in data catalogs. These tasks are added to a workflow as\nauxilliary jobs by the Pegasus planner.\u003c/li\u003e\n\u003cli\u003eReliability \u2013 Jobs and data transfers are automatically retried in case of\nfailures. Debugging tools such as pegasus-analyzer help the user to debug the\nworkflow in case of non-recoverable failures.\u003c/li\u003e\n\u003cli\u003eError Recovery \u2013 When errors occur, Pegasus tries to recover when possible\nby retrying tasks, by retrying the entire workflow, by providing workflow-level\ncheckpointing, by re-mapping portions of the workflow, by trying alternative\ndata sources for staging data, and, when all else fails, by providing a rescue\nworkflow containing a description of only the work that remains to be done.\nIt cleans up storage as the workflow is executed so that data-intensive\nworkflows have enough space to execute on storage-constrained resources.\nPegasus keeps track of what has been done (provenance) including the locations\nof data used and produced, and which software was used with which parameters.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eYou can find more information about Pegasus on the \u003ca href=\"http://pegasus.isi.edu\" rel=\"nofollow\"\u003ePegasus Website\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePegasus has an extensive \u003ca href=\"http://pegasus.isi.edu/documentation/\" rel=\"nofollow\"\u003eUser Guide\u003c/a\u003e\nthat documents how to create, plan, and monitor workflows.\u003c/p\u003e\n\u003cp\u003eWe recommend you start by completing the Pegasus Tutorial from \u003ca href=\"https://pegasus.isi.edu/documentation/user-guide/tutorial.html\" rel=\"nofollow\"\u003eChapter 3 of the\nPegasus User Guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe easiest way to install Pegasus is to use one of the binary packages\navailable on the \u003ca href=\"http://pegasus.isi.edu/downloads\" rel=\"nofollow\"\u003ePegasus downloads page\u003c/a\u003e.\nConsult \u003ca href=\"https://pegasus.isi.edu/documentation/user-guide/installation.html\" rel=\"nofollow\"\u003eChapter 2 of the Pegasus User Guide\u003c/a\u003e\nfor more information about installing Pegasus from binary packages.\u003c/p\u003e\n\u003cp\u003eThere is documentation on the Pegasus website for the Python, Java and R\n\u003ca href=\"https://pegasus.isi.edu/documentation/reference-guide/api-reference.html\" rel=\"nofollow\"\u003eAbstract Workflow Generator APIs\u003c/a\u003e.\nWe strongly recommend using the Python API which is feature complete, and also\nallows you to invoke all the pegasus command line tools.\u003c/p\u003e\n\u003cp\u003eYou can use \u003cem\u003epegasus-init\u003c/em\u003e command line tool to run several examples\non your local machine. Consult \u003ca href=\"https://pegasus.isi.edu/documentation/user-guide/example-workflows.html\" rel=\"nofollow\"\u003eChapter 4 of the Pegasus\nUser Guide\u003c/a\u003e\nfor more information.\u003c/p\u003e\n\u003cp\u003eThere are also examples of how to \u003ca href=\"https://pegasus.isi.edu/documentation/user-guide/execution-environments.html\" rel=\"nofollow\"\u003eConfigure Pegasus for Different Execution\nEnvironments\u003c/a\u003e\nin the Pegasus User Guide.\u003c/p\u003e\n\u003cp\u003eIf you need help using Pegasus, please contact us. See the [contact page]\n(\u003ca href=\"http://pegasus.isi.edu/contact\" rel=\"nofollow\"\u003ehttp://pegasus.isi.edu/contact\u003c/a\u003e) on the Pegasus website for more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding from Source\u003c/h2\u003e\n\u003cp\u003ePegasus can be compiled on any recent Linux or Mac OS X system.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-source-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#source-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource Dependencies\u003c/h3\u003e\n\u003cp\u003eIn order to build Pegasus from source, make sure you have the following installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003cli\u003eJava 8 or higher\u003c/li\u003e\n\u003cli\u003ePython 3.5 or higher\u003c/li\u003e\n\u003cli\u003eR\u003c/li\u003e\n\u003cli\u003eAnt\u003c/li\u003e\n\u003cli\u003egcc\u003c/li\u003e\n\u003cli\u003eg++\u003c/li\u003e\n\u003cli\u003emake\u003c/li\u003e\n\u003cli\u003etox 3.14.5 or higher\u003c/li\u003e\n\u003cli\u003emysql (optional, required to access MySQL databases)\u003c/li\u003e\n\u003cli\u003epostgresql (optional, required to access PostgreSQL databases)\u003c/li\u003e\n\u003cli\u003ePython pyyaml\u003c/li\u003e\n\u003cli\u003ePython GitPython\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOther packages may be required to run unit tests, and build MPI tools.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-hidden=\"true\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling\u003c/h3\u003e\n\u003cp\u003eAnt is used to compile Pegasus.\u003c/p\u003e\n\u003cp\u003eTo get a list of build targets run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ant -p\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe targets that begin with \"dist\" are what you want to use.\u003c/p\u003e\n\u003cp\u003eTo build a basic binary tarball (excluding documentation), run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ant dist\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the release tarball (including documentation), run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ant dist-release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting packages will be created in the \u003ccode\u003edist\u003c/code\u003e subdirectory.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1671659562.0 + "updated_at": 1545049200.0 }, { "data_format": 2, - "description": null, + "description": "Heavy quark evolution framework in heavy-ion collisions", "filenames": [ "Singularity" ], - "full_name": "CNCLgithub/eeg-psiturk", + "full_name": "Yingru/hic_HQ", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-psiturk-experiment\" class=\"anchor\" aria-hidden=\"true\" href=\"#psiturk-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePsiturk Experiment\u003c/h1\u003e\n\u003cp\u003ePsiturk experiment used in Galileo (response slider) style experiments.\u003c/p\u003e\n\u003cp\u003eBased off of \u003ca href=\"https://github.com/CNCLgithub/rooms-psiturk\"\u003eCNCLgithub/rooms-psiturk\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-linux\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Linux\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edependencies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003esingularity\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003etar\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetup\u003c/h3\u003e\n\u003cp\u003esee help\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh --help\n./setup.sh cont data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis setup file will, by default, pull a container and data files from box.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-mac\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-mac\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup Mac\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dependencies-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edependencies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003econda\u003c/li\u003e\n\u003cli\u003etar\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-setup-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetup\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh --help\n./setup.sh data env\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-psiturk\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-psiturk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning psiturk\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda activate eeg-psiturk-env\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e psiturk/\npsiturk server on\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-api\" class=\"anchor\" aria-hidden=\"true\" href=\"#api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAPI\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-taskjs\" class=\"anchor\" aria-hidden=\"true\" href=\"#taskjs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etask.js\u003c/h3\u003e\n\u003cp\u003eThe majority of the experiment\u0027s functionality is described in \u003ccode\u003epsiturk/static/js/task.js\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe main class used to setup pages for both the experiment and instructions is defined as \u003ccode\u003ePage\u003c/code\u003e.\n\u003ccode\u003ePage\u003c/code\u003e handles both media presentation and scale setup. See the docstrings for more info.\u003c/p\u003e\n\u003cp\u003eThere are three other main elements, \u003ccode\u003eInstructionRunner\u003c/code\u003e, \u003ccode\u003eQuiz\u003c/code\u003e, and \u003ccode\u003eExperiment\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-css-and-html\" class=\"anchor\" aria-hidden=\"true\" href=\"#css-and-html\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecss and html\u003c/h3\u003e\n\u003cp\u003eThe main html files are located under \u003ccode\u003epsiturk/templates/\u003c/code\u003e and css is under \u003ccode\u003epsiturk/static/css\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNotabley, \u003ccode\u003estage.html\u003c/code\u003e describes the pages for experimental trials and \u003ccode\u003eslider.css\u003c/code\u003e describes some of the elements found in the scale.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hic_hq\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#hic_hq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehic_HQ\u003c/h1\u003e\n\u003cp\u003eA framework of heavy quark evolution in heavy-ion collisions\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#0-work-locally--make-sure-you-have-root-right-\"\u003e0. Work locally (make sure you have root right)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#1-work-with-cloud-computing-system\"\u003e1. Work with cloud computing system\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#11-install--docker--in-chameleon-instance\"\u003e1.1 Install \u003ccode\u003eDocker\u003c/code\u003e in Chameleon instance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#12a-build-a--docker--container-from--dockerfile-\"\u003e1.2a Build a \u003ccode\u003eDocker\u003c/code\u003e container from \u003cem\u003eDockerfile\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#12b-instead-of-12a--pull-a--docker--image-from--dockerhub-\"\u003e1.2b Instead of 1.2a, pull a \u003ccode\u003eDocker\u003c/code\u003e image from \u003cem\u003edockerhub\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#13-install--singularity--in-chameleon-instance\"\u003e1.3 Install \u003ccode\u003esingularity\u003c/code\u003e in Chameleon instance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#13a-pull--singularity--container-from--dockerhub-\"\u003e1.3a Pull \u003ccode\u003esingularity\u003c/code\u003e container from \u003ccode\u003edockerhub\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#13b-or-instead-13a--build--singularity--image-from-recipe\"\u003e1.3b Or instead 1.3a, build \u003ccode\u003esingularity\u003c/code\u003e image from recipe\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#2-remaining-issue--to-be-done-\"\u003e2 Remaining issue (to be done)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-0-work-locally-make-sure-you-have-root-right-or-have-the-all-the-dependencies\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#0-work-locally-make-sure-you-have-root-right-or-have-the-all-the-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e0. Work locally (make sure you have root right, or have the all the dependencies)\u003c/h2\u003e\n\u003cp\u003eprerequisites:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython3, numpy, scipy, hdf5, pip\u003c/li\u003e\n\u003cli\u003eC/C++/Fortran compilers ==\u0026gt; tested: GNU gcc/g++/gfortran 4.8.4 version\u003c/li\u003e\n\u003cli\u003ecmake (2.8+), boost (1.54+), HDF5 (1.8.11)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eor you can install all the dependencies using \u003ccode\u003einstall_software.sh\u003c/code\u003e (on a ubunut14.04 OS)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Yingru/hic_HQ.git\n\ncd hic_HQ/\nbash install_software.sh # returns a tar.gz file where contains all the modules\n\n\nmkdir test\ncp hic_HQ-osg/hic_HQ-osg.tar.gz test/\ncd test/\ntar -xzf hic_HQ-osg.tar.gz\ncp -r ../workdir/ hic_HQ-osg\ncd hic_HQ-osg/workdir\n\n\npython3 python3 run-events_cD.py args.conf 0\n# args.conf set up parameters ($\\alpha_s, \\hat{q}_{min}, \\hat{q}_{slope}, \\gamma$)\n# parameter_df.dat are diffusion parameters (particle_ID, hydro_ID, HQ list ...)\n# parameter_hd.dat are hadronization parameters (particle_ID ...)\n# HQ_sample.conf are initially sample HQ list parameters\n# vishnew.conf are hydro parameters (shear, bulk, edensity ...)\n# 0 is jobID, useful when you run parallel jobs\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-work-with-cloud-computing-system\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#1-work-with-cloud-computing-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Work with cloud computing system\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.chameleoncloud.org/\" rel=\"nofollow\"\u003e\u003cstrong\u003eChameleon\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[tutorial, get started]((\u003ca href=\"https://chameleoncloud.readthedocs.io/en/latest/getting-started/index.html\" rel=\"nofollow\"\u003ehttps://chameleoncloud.readthedocs.io/en/latest/getting-started/index.html\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCreate an account, join/create a project\u003c/li\u003e\n\u003cli\u003eLoggin in through \u003ca href=\"https://chi.uc.chameleoncloud.org/\" rel=\"nofollow\"\u003eUChicago\u003c/a\u003e or \u003ca href=\"https://chi.tacc.chameleoncloud.org/\" rel=\"nofollow\"\u003eTACC\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReserve a node, launch instance (I choosed \u003cstrong\u003eUbuntu14.04\u003c/strong\u003e), create a key pair, associate IP address\u003c/li\u003e\n\u003cli\u003eaccess your instance\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e# download the .pem key pair\nchmod 600 yx59chameleonkey.pem\nssh-add yx59chameleonkey.pem\nssh cc@ip_address\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-11-install-docker-in-chameleon-instance\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#11-install-docker-in-chameleon-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1 Install \u003ccode\u003eDocker\u003c/code\u003e in Chameleon instance\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003essh cc@192.5.87.178\n\n# check OS version\nlsb_release -a\n\n# install docker and its dependencies\n# 1. you can use the default installation, such as apt-get to install from OS repository\n# 2. install from source (17.03.2 version)\n\nmkdir Install \u0026amp;\u0026amp; cd Install\nsudo apt-get install libsystemd-journal0\nwget https://download.docker.com/linux/ubuntu/dists/trusty/pool/stable/amd64/docker-ce_17.03.2~ce-0~ubuntu-trusty_amd64.deb\nsudo dpkg -i docker-ce_17.03.2~ce-0~ubuntu-trusty_amd64.deb\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-12a-build-a-docker-container-from-dockerfile\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#12a-build-a-docker-container-from-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.2a Build a \u003ccode\u003eDocker\u003c/code\u003e container from \u003cem\u003eDockerfile\u003c/em\u003e\n\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# build the docker image\ngit clone https://github.com/Yingru/hic_HQ.git\ncd hic_HQ/\nsudo docker build -t hic_hq:v1 .\n\n# check docker images\nsudo docker images\ncd workdir/\n\n# to run the executable\n# run-events_cD.py is the pipeline script\n# args.conf changes the parameters ($alpha_s, \\hat{q}_{min}, \\hat{q}_{slope}, \\gamma$\n# 0 is the jobID (useful to run parallel events)\nsudo docker run -v `pwd`:/var/hic_HQ-osg/results hic_hq:v1 python3 run-events_cD.py args.conf 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-12b-instead-of-12a-pull-a-docker-image-from-dockerhub\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#12b-instead-of-12a-pull-a-docker-image-from-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.2b Instead of 1.2a, pull a \u003ccode\u003eDocker\u003c/code\u003e image from \u003cem\u003edockerhub\u003c/em\u003e\n\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# distinguish from previous case, dockerhub autimatically assign tag as latest\nsudo docker pull yingruxu/hic_hq:latest\nsudo docker images\ncd workdir/\nsudo docker run -v `pwd`:/var/hic_HQ-osg/results yingruxu/hic_hq:latest python3 run-events_cD.py args.conf 1\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-13-install-singularity-in-chameleon-instance\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#13-install-singularity-in-chameleon-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.3 Install \u003ccode\u003esingularity\u003c/code\u003e in Chameleon instance\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e#singularity dependencies\nsudo apt-get update\nsudo apt-get install libarchive-dev python dh-autoreconf build-essential\n\n# install the maste branch\ngit clone https://github.com/singularityware/singularity.git\ncd singularity\n\n# ERRRR, their master branch is not consistent with tutorial!\ngit checkout vault/release-2.5\n\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-13a-pull-singularity-container-from-dockerhub\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#13a-pull-singularity-container-from-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.3a Pull \u003ccode\u003esingularity\u003c/code\u003e container from \u003ccode\u003edockerhub\u003c/code\u003e\n\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# check version, better use 2.5.2 (for some reason, the older version 2.3 doesn\u0027t pull)\nsingularity --version\n\ncd workdir/\nsudo apt-get update \u0026amp;\u0026amp; sudo apt-get install squashfs-tools \nsingularity pull docker://yingruxu/hic_hq\n\n# convert this to a writable container\nsingularity build --writable hic_hq_write.img hic_hq.simg\n\n# or build from dockerhub (not sure what is the difference)\nsingularity build --writable hic_hq_write.img docker://yingruxu/hic_hq\n\n\n# doesn\u0027t work? read-only filesystem? I am not able to write? -- fixed\n# now the second question, not enough space\nsudo singularity shell --writable -B $PWD:/var/hic_HQ-osg/results hic_hq_write.img\ncd /var/hic_HQ-osg/results/\n# for some reason need to set locale?\necho \"LC_ALL=en_US.UTF-8\" \u0026gt;\u0026gt; /etc/environment\necho \"en_US.UTF-8 UTF-8\" \u0026gt;\u0026gt; /etc/locale.gen\necho \"LANG=en_US.UTF-8\" \u0026gt; /etc/locale.conf\nlocale-gen en_US.UTF-8\n\npython3 run-events_cD.py args.conf 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-13b-or-instead-13a-build-singularity-image-from-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#13b-or-instead-13a-build-singularity-image-from-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.3b Or instead 1.3a, build \u003ccode\u003esingularity\u003c/code\u003e image from recipe\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e# remember to build writable image\nsudo singularity build --writable hic_hq.img Singularity\n\n# to test singularity container interactively\nsudo singularity shell --writable -B $PWD:/var/hic_HQ-osg/results hic_hq.img\n\n\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt; HEAD\n# to run trento events\nsudo singularity exec --writable -B $PWD:/var/hic_HQ-osg/results hic_hq.img /var/hic_HQ-osg/bin/trento Pb Pb 10 --output initial.hdf5\n\n# to run full events\nsudo singularity exec --writable -B $PWD:/var/hic_HQ-osg/results hic_hq.img python3 /var/hic_HQ-osg/results/run-events_cD.py /var/hic_HQ-osg/results/args.conf 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-remaining-issue-to-be-done\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#2-remaining-issue-to-be-done\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2 Remaining issue (to be done)\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eChange the \u003cem\u003eDockerfile\u003c/em\u003e to add the \u003ccode\u003elocale\u003c/code\u003e information (it is fine with \u003ccode\u003eDocker\u003c/code\u003e container, but cause trouble when using \u003ccode\u003esingularity pull/build\u003c/code\u003e from \u003cem\u003eDockerhub\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003eRight now I still need \u003ccode\u003eroot\u003c/code\u003e privilege to be able to write in a singularity container filesystem (even though I already choose the \u003ccode\u003e--writable\u003c/code\u003e option, need to fix that\u003c/li\u003e\n\u003cli\u003eWhile running in a \u003ccode\u003esingularity\u003c/code\u003e container, the space limit is reached? (use \u003ccode\u003e--sandbox\u003c/code\u003e instead of \u003ccode\u003e--writable\u003c/code\u003e?)\n=======\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; 6c170142da31ead53fd2857f8755f37b4a68a8be\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1670977729.0 + "updated_at": 1534526792.0 }, { "data_format": 2, - "description": "Slurm Docker and Apptainer commands", + "description": "A singularity recipe for SALSA: A tool to scaffold long read assemblies with Hi-C data ", "filenames": [ - "singularity/Singularity.recipe" + "Singularity" ], - "full_name": "Yessense/slurm_ml_pipeline", + "full_name": "ISU-HPC/SALSA", "latest_release": null, + "readme": "\u003ch1 id=\"user-content-salsa\"\u003e\u003ca class=\"heading-link\" href=\"#salsa\"\u003eSALSA\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003eA singularity recipe for SALSA: A tool to scaffold long read assemblies with Hi-C data\u003c/p\u003e\n\u003cp\u003eThe executables are located in /SALSA/*.py\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1671531473.0 + "updated_at": 1526316679.0 }, { "data_format": 2, - "description": "The singularity definition file of curp container and the workflow to build and upload sif file to GHCR.", + "description": null, "filenames": [ "Singularity" ], - "full_name": "passive-radio/curp-singularity", + "full_name": "OSC/bc_osc_rstudio_server_quick", "latest_release": "v0.0.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-curp-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#curp-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecurp-singularity\u003c/h1\u003e\n\u003cp\u003eThe singularity definition file of curp container and the workflow to build and upload sif file to GHCR.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-pull-and-use-pre-built-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-pull-and-use-pre-built-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to pull and use pre-built image\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull curp_singularity.sif oras://ghcr.io/passive-radio/curp-singularity:latest\nsingularity \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e curp_singularity.sif\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1 id=\"user-content-batch-connect---osc-rstudio-server\"\u003e\u003ca class=\"heading-link\" href=\"#batch-connect---osc-rstudio-server\"\u003eBatch Connect - OSC RStudio Server\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_rstudio_server.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an RStudio Server\nwithin an Owens batch job.\u003c/p\u003e\n\u003ch2 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule restore\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job before\nlaunching the RStudio Server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ewithout Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e 3.3.2+ (earlier versions are untested but may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rstudio.com/products/rstudio-server/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e 1.0.136+ (earlier versions are untested by may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://proot-me.github.io/\" rel=\"nofollow\"\u003ePRoot\u003c/a\u003e 5.1.0+ (used to setup fake bind mount)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eor with Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e 2.4.2+\u003c/li\u003e\n\u003cli\u003eA Singularity image similar to \u003ca href=\"https://www.singularity-hub.org/collections/463\" rel=\"nofollow\"\u003enickjer/singularity-rstudio\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorresponding module to launch the above Singularity image (see\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio/blob/master/example_module/\"\u003eexample_module\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-install\"\u003e\u003ca class=\"heading-link\" href=\"#install\"\u003eInstall\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2 id=\"user-content-contributing\"\u003e\u003ca class=\"heading-link\" href=\"#contributing\"\u003eContributing\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server/fork\"\u003ehttps://github.com/OSC/bc_osc_rstudio_server/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 11, "topics": [], - "updated_at": 1670898005.0 + "updated_at": 1570733859.0 }, { "data_format": 2, - "description": "Nextflow workflow to run DPclust on a series of samples", + "description": "[read-only mirror]", "filenames": [ "Singularity" ], - "full_name": "IARCbioinfo/DPclust-nf", + "full_name": "unlhcc/bc-hcc-rstudio-server", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-name\" class=\"anchor\" aria-hidden=\"true\" href=\"#name\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eName\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-empty-template-for-nextflow-pipelines-short-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#empty-template-for-nextflow-pipelines-short-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEmpty template for nextflow pipelines (short description)\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/template-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e3a0e94b24410397271294a485f985c85941b292ba2c7cbf3fefb26bf1e0c76b/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f74656d706c6174652d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/template-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/template-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1404\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/94193130\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5decad826d7116b1c950b6b48c06052496f141e803525b088ce3cdcaaa4d7b88/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f39343139333133302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/94193130.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"template-nf.png\"\u003e\u003cimg src=\"template-nf.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eExternal software:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003einput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003einput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpecify the test files location\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param1\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param2\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param3\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param4\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--flag2\u003c/td\u003e\n\u003ctd\u003e....\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e...\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-detailed-description-optional-section\" class=\"anchor\" aria-hidden=\"true\" href=\"#detailed-description-optional-section\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetailed description (optional section)\u003c/h2\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-hidden=\"true\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/template-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib1*\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support (link to specific gitter chatroom)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib2\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib3\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eTester\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#references-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences (optional)\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#faq-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ (optional)\u003c/h2\u003e\n", + "readme": "\u003ch1 id=\"user-content-batch-connect---hcc-rstudio-server\"\u003e\u003ca class=\"heading-link\" href=\"#batch-connect---hcc-rstudio-server\"\u003eBatch Connect - HCC RStudio Server\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://git.unl.edu/hcc/bc-hcc-rstudio-server/-/commits/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eed68de7ee579cda26a799a79e6376b967f69b7e355d2eca9b2a46e88e54c904/68747470733a2f2f6769742e756e6c2e6564752f6863632f62632d6863632d7273747564696f2d7365727665722f6261646765732f6d61737465722f706970656c696e652e737667\" alt=\"pipeline status\" data-canonical-src=\"https://git.unl.edu/hcc/bc-hcc-rstudio-server/badges/master/pipeline.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an \u003ca href=\"https://www.rstudio.com/products/rstudio-server/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e\nwithin a SLURM batch job.\u003c/p\u003e\n\u003cp\u003eBased off of \u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server\"\u003ehttps://github.com/OSC/bc_osc_rstudio_server\u003c/a\u003e.\u003c/p\u003e\n\u003ch2 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule restore\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job before\nlaunching the RStudio Server.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://apptainer.org\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e 2.4.2+\u003c/li\u003e\n\u003cli\u003eAn Apptainer image similar to \u003ca href=\"https://hub.docker.com/r/rocker/rstudio\" rel=\"nofollow\"\u003erocker/rstudio\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-buildinstall\"\u003e\u003ca class=\"heading-link\" href=\"#buildinstall\"\u003eBuild/Install\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eGitlab CI will automatically build both CentOS 7 and 8 RPMs.\nThey can be installed directly via \u003ccode\u003eyum\u003c/code\u003e for testing.\u003c/p\u003e\n\u003cp\u003eFor production, add to the per-cluster common repos and require via puppet.\u003c/p\u003e\n\u003ch2 id=\"user-content-contributing\"\u003e\u003ca class=\"heading-link\" href=\"#contributing\"\u003eContributing\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://git.unl.edu/hcc/bc-hcc-rstudio-server/-/forks/new\" rel=\"nofollow\"\u003ehttps://git.unl.edu/hcc/bc-hcc-rstudio-server/-/forks/new\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocumentation, website content, and logo is licensed under\n\u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC-BY-4.0\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCode is licensed under MIT (see LICENSE.txt)\u003c/li\u003e\n\u003cli\u003eRStudio, Shiny and the RStudio logo are all registered trademarks of RStudio.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 9, "topics": [], - "updated_at": 1670593101.0 + "updated_at": 1646361327.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.harp", - "Singularity.utils" + "Singularity" ], - "full_name": "BerglandLab/HS-reconstruction-gwas", + "full_name": "Shadowphax/bc_icts_rstudio_server", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hs-reconstruction-gwas\" class=\"anchor\" aria-hidden=\"true\" href=\"#hs-reconstruction-gwas\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHS-reconstruction-gwas\u003c/h1\u003e\n\u003cp\u003eThis repository contains the scripts used to generate and process data, as well as generate figures, for the manuscript:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAccurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCory A. Weller (\u003ca href=\"mailto:caw5cv@virginia.edu\"\u003ecaw5cv@virginia.edu\u003c/a\u003e) \u0026amp; Alan O. Bergland (\u003ca href=\"mailto:aob2x@virginia.edu\"\u003eaob2x@virginia.edu\u003c/a\u003e)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eThis workflow allows for Singularity containers to process data in a reproducible manner without installing required programs and libraries. You will first need to install singularity on your system, if it is not already available. Many HPC systems already have pre-loaded \u003ccode\u003esingularity\u003c/code\u003e that can be loaded as a module.\u003c/p\u003e\n\u003cp\u003eOtherwise, install singularity 3.x following the instructions from \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003esylabs.io\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThen, you can retrieve the pre-built singularity image files from Singularity Hub.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name harp.sif shub://cory-weller/HS-reconstruction-gwas:harp\nsingularity pull --name utils.sif shub://cory-weller/HS-reconstruction-gwas:utils\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1 id=\"user-content-batch-connect---osc-rstudio-server\"\u003e\u003ca class=\"heading-link\" href=\"#batch-connect---osc-rstudio-server\"\u003eBatch Connect - OSC RStudio Server\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_rstudio_server.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an RStudio Server\nwithin an Owens batch job.\u003c/p\u003e\n\u003ch2 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule restore\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job before\nlaunching the RStudio Server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ewithout Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e 3.3.2+ (earlier versions are untested but may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rstudio.com/products/rstudio-server/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e 1.0.136+ (earlier versions are untested by may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://proot-me.github.io/\" rel=\"nofollow\"\u003ePRoot\u003c/a\u003e 5.1.0+ (used to setup fake bind mount)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eor with Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e 2.4.2+\u003c/li\u003e\n\u003cli\u003eA Singularity image similar to \u003ca href=\"https://www.singularity-hub.org/collections/463\" rel=\"nofollow\"\u003enickjer/singularity-rstudio\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorresponding module to launch the above Singularity image (see\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio/blob/master/example_module/\"\u003eexample_module\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-install\"\u003e\u003ca class=\"heading-link\" href=\"#install\"\u003eInstall\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2 id=\"user-content-contributing\"\u003e\u003ca class=\"heading-link\" href=\"#contributing\"\u003eContributing\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server/fork\"\u003ehttps://github.com/OSC/bc_osc_rstudio_server/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocumentation, website content, and logo is licensed under\n\u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC-BY-4.0\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCode is licensed under MIT (see LICENSE.txt)o\u003c/li\u003e\n\u003cli\u003eRStudio, Shiny and the RStudio logo are all registered trademarks of RStudio.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-rserver-command-line-arguements\"\u003e\u003ca class=\"heading-link\" href=\"#rserver-command-line-arguements\"\u003eRServer command line arguements\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis was the output of \u003ccode\u003e--help\u003c/code\u003e from version \u003ccode\u003e2021.09.1\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecommand-line options:\n\nverify:\n --verify-installation arg (=0) Runs verification mode to verify the \n current installation.\n\nserver:\n --server-working-dir arg (=/) The default working directory of the \n rserver process.\n --server-user arg (=rstudio-server) The user account of the rserver \n process.\n --server-daemonize arg (=0) Indicates whether or not the rserver \n process should run as a daemon.\n --server-pid-file arg (=/var/run/rstudio-server.pid)\n The path to a file where the rserver \n daemon\u0027s pid is written.\n --server-app-armor-enabled arg (=0) Indicates whether or not to enable \n AppArmor profiles for the rserver \n process.\n --server-set-umask arg (=1) If enabled, sets the rserver process \n umask to 022 on startup, which causes \n new files to have rw-r-r permissions.\n --secure-cookie-key-file arg If set, overrides the default path of \n the secure-cookie-key file used for \n encrypting cookies.\n --server-data-dir arg (=/var/run/rstudio-server)\n Path to the data directory where \n RStudio Server will write run-time \n state.\n --server-add-header arg Adds a header to all responses from \n RStudio Server. This option can be \n specified multiple times to add \n multiple headers.\n\nwww:\n --www-address arg (=0.0.0.0) The network address that RStudio Server\n will listen on for incoming \n connections.\n --www-port arg The port that RStudio Server will bind \n to while listening for incoming \n connections. If left empty, the port \n will be automatically determined based \n on your SSL settings (443 for SSL, 80 \n for no SSL).\n --www-root-path arg (=/) The path prefix added by a proxy to the\n incoming RStudio URL. This setting is \n used so RStudio Server knows what path \n it is being served from. If running \n RStudio Server behind a path-modifying \n proxy, this should be changed to match \n the base RStudio Server URL.\n --www-local-path arg (=www) The relative path from the RStudio \n installation directory, or absolute \n path where web assets are stored.\n --www-symbol-maps-path arg (=www-symbolmaps)\n The relative path from the RStudio \n installation directory, or absolute \n path, where symbol maps are stored.\n --www-use-emulated-stack arg (=0) Indicates whether or not to use GWT\u0027s \n emulated stack.\n --www-thread-pool-size arg (=2) The size of the threadpool from which \n requests will be serviced. This may be \n increased to enable more concurrency, \n but should only be done if the \n underlying hardware has more than 2 \n cores. It is recommended to use a value\n that is \u0026lt;= to the number of hardware \n cores, or \u0026lt;= to two times the number of\n hardware cores if the hardware utilizes\n hyperthreading.\n --www-proxy-localhost arg (=1) Indicates whether or not to proxy \n requests to localhost ports over the \n main server port. This should generally\n be enabled, and is used to proxy HTTP \n traffic within a session that belongs \n to code running within the session \n (e.g. Shiny or Plumber APIs)\n --www-verify-user-agent arg (=1) Indicates whether or not to verify \n connecting browser user agents to \n ensure they are compatible with RStudio\n Server.\n --www-same-site arg The value of the \u0027SameSite\u0027 attribute \n on the cookies issued by RStudio \n Server. Accepted values are \u0027none\u0027 or \n \u0027lax\u0027. The value \u0027none\u0027 should be used \n only when RStudio is hosted into an \n iFrame. For compatibility with some \n browsers (i.e. Safari 12), duplicate \n cookies will be issued by RStudio \n Server when \u0027none\u0027 is used.\n --www-frame-origin arg (=none) Specifies the allowed origin for the \n iFrame hosting RStudio if iFrame \n embedding is enabled.\n --www-enable-origin-check arg (=0) If enabled, cause RStudio to enforce \n that incoming request origins are from \n the host domain. This can be added for \n additional security. See \n https://cheatsheetseries.owasp.org/chea\n tsheets/Cross-Site_Request_Forgery_Prev\n ention_Cheat_Sheet.html#verifying-origi\n n-with-standard-headers\n --www-allow-origin arg Specifies an additional origin that \n requests are allowed from, even if it \n does not match the host domain. Used if\n origin checking is enabled. May be \n specified multiple times for multiple \n origins.\n\nrsession:\n --rsession-which-r arg The path to the main R program (e.g. \n /usr/bin/R). This should be set if no \n versions are specified in \n /etc/rstudio/r-versions and the default\n R installation is not available on the \n system path.\n --rsession-path arg (=rsession) The relative path from the RStudio \n installation directory, or absolute \n path to the rsession executable.\n --rldpath-path arg (=r-ldpath) The path to the r-ldpath script which \n specifies extra library paths for R \n versions.\n --rsession-ld-library-path arg Specifies additional LD_LIBRARY_PATHs \n to use for R sessions.\n --rsession-config-file arg If set, overrides the path to the \n /etc/rstudio/rsession.conf \n configuration file. The specified path \n may be a relative path from the RStudio\n installation directory, or an absolute \n path.\n --rsession-proxy-max-wait-secs arg (=10)\n The maximum time to wait in seconds for\n a successful response when proxying \n requests to rsession.\n --rsession-memory-limit-mb arg (=0) The limit in MB that an rsession \n process may consume.\n --rsession-stack-limit-mb arg (=0) The limit in MB that an rsession \n process may consume for its stack.\n --rsession-process-limit arg (=0) The maximum number of allowable \n rsession processes.\n\ndatabase:\n --database-config-file arg If set, overrides the path to the \n /etc/rstudio/database.conf \n configuration file.\n --db-command arg Executes the shell command specified \n injecting the current database \n configuration in the command.\n\nauth:\n --auth-none arg (=1) If set, disables multi-user \n authentication. Workbench/Pro features \n may not work in this mode.\n --auth-validate-users arg (=0) Indicates whether or not to validate \n that authenticated users exist on the \n target system. Disabling this option \n may cause issues to start or to run a \n session.\n --auth-stay-signed-in-days arg (=30) The number of days to keep a user \n signed in when using the \"Stay Signed \n In\" option. Will only take affect when \n auth-timeout-minutes is 0 (disabled).\n --auth-timeout-minutes arg (=60) The number of minutes a user will stay \n logged in while idle before required to\n sign in again. Set this to 0 (disabled)\n to enable legacy timeout \n auth-stay-signed-in-days.\n --auth-encrypt-password arg (=1) Indicates whether or not to encrypt the\n password sent from the login form. For \n security purposes, we strongly \n recommend you leave this enabled.\n --auth-login-page-html arg (=/etc/rstudio/login.html)\n The path to a file containing \n additional HTML customization for the \n login page.\n --auth-rdp-login-page-html arg (=/etc/rstudio/rdplogin.html)\n The path to a file containing \n additional HTML customization for the \n login page, as seen by RDP users.\n --auth-required-user-group arg Specifies a group that users must be in\n to be able to use RStudio.\n --auth-minimum-user-id arg (=auto) Specifies a minimum user id value. \n Users with a uid lower than this value \n may not use RStudio.\n --auth-pam-helper-path arg (=rserver-pam)\n The relative path from the RStudio \n installation directory, or absolute \n path where the PAM helper binary \n resides.\n --auth-pam-require-password-prompt arg (=1)\n Indicates whether or not to require the\n \"Password: \" prompt before sending the \n password via PAM. In most cases, this \n should be enabled. If using a custom \n PAM password prompt, you may need to \n disable this setting if PAM logins do \n not work correctly.\n --auth-pam-requires-priv arg (=1) Deprecated - will always be true.\n --auth-sign-in-throttle-seconds arg (=5)\n The minimum amount of time a user must \n wait before attempting to sign in again\n after signing out.\n --auth-revocation-list-dir arg If set, overrides the path to the \n directory which contains the revocation\n list to be used for storing expired \n tokens. As of RStudio Server 1.4, this \n has been moved to database storage, and\n so this setting is deprecated, but will\n be used to port over any existing \n file-based expired tokens.\n --auth-cookies-force-secure arg (=0) Indicates whether or not auth cookies \n should be forcefully marked as secure. \n This should be enabled if running an \n SSL terminator infront of RStudio \n Server. Otherwise, cookies will be \n marked secure if SSL is configured.\n\nmonitor:\n --monitor-interval-seconds arg (=60) The interval in seconds at which the \n monitor is probed for new data.\n\ngeneral:\n --help print help message\n --test-config test to ensure the config file is valid\n --config-file arg configuration file\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1670518878.0 + "updated_at": 1646833582.0 }, { "data_format": 2, - "description": "fastq quality assessment and filtering tool", + "description": "A Singularity Recipe to create a base CentOS container image", "filenames": [ - "Singularity-Test", - "Singularity" + "Singularity", + "cuda7.5-devel/Singularity.cu75", + "cuda-8.0-cudnn7/Singularity.cu80dnn7", + "cuda-9.1-devel/Singularity.cu91", + "cuda-8.0-devel/Singularity.cu80dnn6", + "cuda-9.0-devel/Singularity.cu90" ], - "full_name": "PaulaAlessio/FastqArazketa", + "full_name": "ISU-HPC/ml-base", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fastqpuri-an-fq-quality-control-and-filter-tool\" class=\"anchor\" aria-hidden=\"true\" href=\"#fastqpuri-an-fq-quality-control-and-filter-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFastqPuri, an fq quality control and filter tool\u003c/h1\u003e\n\u003cp\u003eSoftware and source code of \u003ccode\u003eFastqPuri\u003c/code\u003e. It creates quality reports of\n\u003ccode\u003efastq\u003c/code\u003e files and filters them removing low quality reads, reads\ncontaining too many N\u0027s or contamination reads (unwanted rRNA reads,\nimpurities coming from another organism, ...).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eClone the repository, or download the source. Make sure that\nyour system supplies the following dependencies for FastqPuri.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOS: Linux (clang, gcc), Mac OS (clang, gcc), OpenBSD (clang)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecmake\u003c/code\u003e (at least version 2.8),\u003c/li\u003e\n\u003cli\u003ea \u003ccode\u003eC\u003c/code\u003e compiler supporting the \u003ccode\u003ec11\u003c/code\u003e standard\n(change the compiler flags otherwise),\u003c/li\u003e\n\u003cli\u003epandoc (optional, see documentation in \u003ccode\u003ePANDOC.md\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eRscript\u003c/code\u003e (optional),\u003c/li\u003e\n\u003cli\u003eFollowing \u003ccode\u003eR\u003c/code\u003e packages installed (optional):\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003epheatmap\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eknitr\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ermarkdown\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e FastqPuri will work without the optional dependencies\nbut will skip creating html reports if they are not available.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cmake -H. -Bbuild/ [-DRSCRIPT=/path/to/my/R/bin/Rscript] [-DCMAKE_INSTALL_PREFIX=/path/to/my/root] ... \n$ cd build \n$ make \n$ sudo make install \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen running \u003ccode\u003ecmake\u003c/code\u003e, there are some variables you can set\nusing the option -D followed by the variable name. These variables are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCMAKE_C_COMPILER\u003c/code\u003e: \u003ccode\u003eC\u003c/code\u003e compiler (default \u003ccode\u003egcc\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCMAKE_C_FLAGS\u003c/code\u003e: compiler flags (default \u003ccode\u003e-Wall -O3 -march=native -std=c11\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCMAKE_INSTALL_PREFIX\u003c/code\u003e: root path for \u003ccode\u003emake install\u003c/code\u003e, e.g. to\nredirect to a directory with user access (default /usr/local),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePANDOC\u003c/code\u003e: \u003ccode\u003epandoc\u003c/code\u003e executable (default \u003ccode\u003epandoc\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eRSCRIPT\u003c/code\u003e: \u003ccode\u003eRscript\u003c/code\u003e executable (default \u003ccode\u003eRscript\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eREAD_MAXLEN\u003c/code\u003e: Maximum Illumina read length\u003c/li\u003e\n\u003cli\u003e(default 400),\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe executables will be created in the folder \u003ccode\u003ebin\u003c/code\u003e and installed in \u003ccode\u003e/usr/local/bin\u003c/code\u003e.\n\u003ccode\u003eR\u003c/code\u003e scripts will be installed in \u003ccode\u003e/usr/local/share/FastqPuri/R\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWARNING:\u003c/strong\u003e do not move the executables that depend on \u003ccode\u003eR\u003c/code\u003e scripts,\nanywhere else, unless you also move the corresponding \u003ccode\u003eR\u003c/code\u003e scripts respecting\nthe local folder structure.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-executables\" class=\"anchor\" aria-hidden=\"true\" href=\"#executables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecutables\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eQreport\u003c/code\u003e: creates a quality report in html format (see \u003ccode\u003eREADME_Qreport.md\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSreport\u003c/code\u003e: creates a summary report in html format on a set of samples,\nregarding either the original files or the filtering process\n(see \u003ccode\u003eREADME_Sreport.md\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emakeBloom\u003c/code\u003e: creates a bloom filter from a fasta file of a certain size,\nand stores it in a file (see \u003ccode\u003eREADME_makeBloom.md\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emakeTree\u003c/code\u003e: creates a tree of a certain depth from a fasta file and stores\nit in a file (see \u003ccode\u003eREADME_makeTree.md\u003c/code\u003e),\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etrimFilter\u003c/code\u003e: performs the filtering process for single-end data\n(see \u003ccode\u003eREADME_trimFilter.md\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etrimFilterPE\u003c/code\u003e: performs the filtering process for double stranded data\n(see \u003ccode\u003eREADME_trimFilterPE.md\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn exemplar work flow could be:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eQreport\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSreport\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emakeBloom\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etrimFilter\u003c/code\u003e or \u003ccode\u003etrimFilterPE\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eQreport\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSreport\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation-of-the-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation-of-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation of the code\u003c/h2\u003e\n\u003cp\u003eA Doxygen documentation of the code is available:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ehtml\u003c/code\u003e version under the folder \u003ccode\u003ehtml\u003c/code\u003e (open \u003ccode\u003eindex.html\u003c/code\u003e with a browser).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epdf\u003c/code\u003e version: \u003ccode\u003elatex/refman.pdf\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-a-docker-container-to-run-fastqpuri\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-a-docker-container-to-run-fastqpuri\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse a docker container to run FastqPuri\u003c/h2\u003e\n\u003cp\u003eThe file \u0027Dockerfile\u0027 documents the exact linux installation we used\nfor testing. If you have a docker installation ready on your machine,\nyou may want to use a docker container for easy installation and\ncapsulated usage of FastqPuri. After cloning this project from github\nand change to its main directory, you may install a docker container\nas follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker build -t fastqpuri .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a container based on the debian linux distribution\ncovering all dependencies including R and pandoc. As soon as such a\ncontainer is installed, you can use it either interactively:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -v $PWD:/tmp -it fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor by running a pipeline implemented in an executable bash script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -v $PWD:/tmp fastqpuri ./pipeline.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that this call generates results in the docker container\ndirectory \u003ccode\u003e/tmp\u003c/code\u003e but also keeps them after closing the docker container\nlocally where the container was started.\u003c/p\u003e\n\u003cp\u003eInstead of generating the docker container yourself with \u0027docker\nbuild\u0027, you can also pull a pre-built image from the docker hub as\nfollows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker pull clottaz/fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can run such a pre-built image with \u0027docker run\u0027 by indicating the\nimages as \u0027clottaz/fastqpuri\u0027.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-a-singularity-container-to-run-fastqpuri\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-a-singularity-container-to-run-fastqpuri\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse a singularity container to run FastqPuri\u003c/h2\u003e\n\u003cp\u003eAlternativly, if you have singularity installed on your machine, you\ncan call our docker container for FastqPuri as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell --bind .:/tmp docker://clottaz/fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis call opens a shell within the container.\nWith \u003ccode\u003e--bind\u003c/code\u003e we mount the current directory also in the container.\nThe syntax is as follows: --bind src:dest; src is the source path on\nthe host and dest is the destination path in the container, i.e. where\nyou would like to make the source path available in your container.\nNote that this destination path in your container should be an existing\ndirectory, the operation will fail if you do not create the directory first.\nHence, when we call \u003ccode\u003esingularity shell\u003c/code\u003e like this, the working directory\nin the container is \u003ccode\u003e/tmp\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, in order to execute a script from the current\ndirectory, call singularity as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run --bind .:/tmp docker://clottaz/fastqpuri /tmp/pipeline.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that \u003ccode\u003e/tmp/pipeline.sh\u003c/code\u003e relates to the call within the\ncontainer. Thus, \u003ccode\u003epipeline.sh\u003c/code\u003e is located in the directory where singularity\nrun is executed, but will be made available to the container via the \u003ccode\u003e--bind\u003c/code\u003e\nparameter.\u003c/p\u003e\n\u003cp\u003eIf you want to invoke a function of FastqPuri, you can use the \u0027exec\u0027\ncommand like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec docker://clottaz/fastqpuri Qreport -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor invoke a script located in your home directory (assuming that\nrun_ex_TREE.sh is located in your home directory):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec docker://clottaz/fastqpuri $HOME/run_ex_TREE.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity documentation can be found here: \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/docs/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-via-bioconda--under-construction\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-via-bioconda--under-construction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation via bioconda \u003cstrong\u003e-under construction\u003c/strong\u003e.\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eWe are currently working on a bioconda environment for FastqPuri.\nIf you follow the instructions below, it is quite likely that\nFastqPuri will not yet properly run from the bioconda environment.\nSorry about that and please stay tuned!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBioconda is a channel for the conda package manager specializing in\nbioinformatics software. Have a look at the reference:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBjoern Gruening, Ryan Dale, Andreas Sjoedin, Brad A. Chapman, Jillian\nRowe, Christopher H. Tomkins-Tinch, Renan Valieris, the Bioconda\nTeam, and Johannes Koester. 2018. Bioconda: Sustainable and\nComprehensive Software Distribution for the Life Sciences. Nature\nMethods, 2018.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo find out how to use bioconda, see \u003ca href=\"https://bioconda.github.io\" rel=\"nofollow\"\u003ehttps://bioconda.github.io\u003c/a\u003e.\nFor installing FastqPuri in a bioconda environment, you have to install\neither \u003ccode\u003eminiconda\u003c/code\u003e or \u003ccode\u003eanaconda\u003c/code\u003e and register channels as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda config --add channels defaults\n$ conda config --add channels bioconda\n$ conda config --add channels conda-forge\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can install \u003ccode\u003efastqpuri\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda install fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eActually, you may also want to use a specific environment for the\nsequencing quality control:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create -n qc fastqpuri\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis call installs \u003ccode\u003eFastqPuri\u003c/code\u003e directly in a separate environment.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003ePaula P\u00e9rez Rubio,\nClaudio Lottaz,\nJulia Engelmann\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eGPL v3 (see LICENSE.txt)\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ml-base\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#ml-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml-base\u003c/h1\u003e\n\u003cp\u003eA Singularity Recipe to create a base CentOS container image\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1670411214.0 + "updated_at": 1533063693.0 }, { "data_format": 2, - "description": "A small collection of programs for converting non-TIFF format images to TIFF and for manipulating and interogating the contents of TIFF images.", + "description": "Graphviz is a package of open-source tools initiated by AT\u0026T Labs Research for drawing graphs specified in DOT language scripts.", "filenames": [ - "4.2.0/Singularity" + "3.0.0/Singularity", + "2.38.0/Singularity", + "2.48.0/Singularity" ], - "full_name": "pscedu/singularity-libtiff-tools", - "latest_release": "v4.2.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-libtiff-tools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-libtiff-tools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-libtiff-tools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-libtiff-tools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/274bd4774f9c09a10655a9b440ba3c1171dc46ed6817776efaf7c5579311ba9b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/274bd4774f9c09a10655a9b440ba3c1171dc46ed6817776efaf7c5579311ba9b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-libtiff-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2cdb477414e2bc11157f8ac70f0f08f6aca89f9a77440f50e1dba8e8105dca92/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2cdb477414e2bc11157f8ac70f0f08f6aca89f9a77440f50e1dba8e8105dca92/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-libtiff-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ac7590eb9a5062fa43ca709328ec101fb9dfe119dbe72eb8825d7d9b56ce2440/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ac7590eb9a5062fa43ca709328ec101fb9dfe119dbe72eb8825d7d9b56ce2440/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-libtiff-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8ccb90532f566ba1f408e14595e4b820b5f0bfce3dbdb8ba092c5a1a937dbe4b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ccb90532f566ba1f408e14595e4b820b5f0bfce3dbdb8ba092c5a1a937dbe4b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c6962746966662d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-libtiff-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-libtiff-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-libtiff-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-libtiff-tools\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f630a7ca0123289ce19e2c391cf329e94cb29966ba21c84444284358d998749d/687474703a2f2f7777772e6c6962746966662e6f72672f696d616765732f717561642e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f630a7ca0123289ce19e2c391cf329e94cb29966ba21c84444284358d998749d/687474703a2f2f7777772e6c6962746966662e6f72672f696d616765732f717561642e6a7067\" data-canonical-src=\"http://www.libtiff.org/images/quad.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://www.libtiff.org/tools.html\" rel=\"nofollow\"\u003elibtiff-tools\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/libtiff-tools/4.2.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/libtiff-tools\u003c/code\u003e as \u003ccode\u003e4.2.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/tigers/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-graphviz", + "latest_release": "v2.44.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-graphviz/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-graphviz/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-graphviz/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-graphviz/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4f27d7f7ac7b9a86a1f0f6c45d19b90496b7c8ce89d5004d3fe96d163fe99e73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d677261706876697a\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4f27d7f7ac7b9a86a1f0f6c45d19b90496b7c8ce89d5004d3fe96d163fe99e73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d677261706876697a\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-graphviz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e6789d316f02fdfe74574853fb2870a7e4b7b6cccca76d201ff81cb1ac2adfbe/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d677261706876697a\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e6789d316f02fdfe74574853fb2870a7e4b7b6cccca76d201ff81cb1ac2adfbe/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d677261706876697a\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-graphviz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b71cbcc295d522b3323d66e9141fdec85461c9d128011383fac4956c54d95d73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d677261706876697a\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b71cbcc295d522b3323d66e9141fdec85461c9d128011383fac4956c54d95d73/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d677261706876697a\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-graphviz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5227dbb6ba22ff0c43933a4284a416f0f8d311e9972bf97e5383fe334f545102/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d677261706876697a\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5227dbb6ba22ff0c43933a4284a416f0f8d311e9972bf97e5383fe334f545102/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d677261706876697a\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-graphviz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-graphviz\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-graphviz\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-graphviz\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/960789693fa68a8f442f9c6cc7d6a117639f1a792ec84c96648ad4764c385fcf/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f656e2f342f34382f477261706876697a4c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/960789693fa68a8f442f9c6cc7d6a117639f1a792ec84c96648ad4764c385fcf/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f656e2f342f34382f477261706876697a4c6f676f2e706e67\" width=\"25%\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/en/4/48/GraphvizLogo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://graphviz.org/\" rel=\"nofollow\"\u003egraphviz\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003egraphviz\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/graphviz/2.44.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/graphviz\u003c/code\u003e as \u003ccode\u003e 2.44.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-docker-image\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build Docker image\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003edbuild.sh\u003c/code\u003e to build the Docker image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./dbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-the-cwl-workflow\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-run-the-cwl-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run the CWL workflow\u003c/h2\u003e\n\u003cp\u003eTo run the workflow, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load anaconda3\npip install --user cwl-runner cwltool udocker\ncwl-runner --singularity dot.cwl example.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ "singularity", "utilities" ], - "updated_at": 1670379411.0 + "updated_at": 1649134093.0 }, { "data_format": 2, - "description": "christmas-devcontainers-talk", + "description": "Demo recipe ", "filenames": [ - "Singularity" + "Singularity", + "Singularity.3.8.6" ], - "full_name": "ARCLeeds/christmas-devcontainers-talk", + "full_name": "ISU-HPC/singularity_demo", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-devcontainers-talk-for-christmas-conference-2022\" class=\"anchor\" aria-hidden=\"true\" href=\"#devcontainers-talk-for-christmas-conference-2022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevcontainers talk for Christmas Conference 2022\u003c/h1\u003e\n\u003cp\u003eThis is a toy repository that includes some MPI-enabled Markov chain random walks to search a 2D space for Santa \u003cg-emoji class=\"g-emoji\" alias=\"santa\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f385.png\"\u003e\ud83c\udf85\u003c/g-emoji\u003e!\u003c/p\u003e\n\u003cp\u003eIt\u0027s intention is to showcase using containers to enable portable and scalable code reuse.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eThis repository contains a Dockerfile for creating a container image and running locally.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e -t find-santa:latest\n\n$ mkdir santa-search-outputs\n\n$ docker run -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/santa-search-outputs:/app/figures find-santa:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then check inside \u003ccode\u003esanta-search-outputs\u003c/code\u003e directory to find the data visualisation plot.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-apptainer\" class=\"anchor\" aria-hidden=\"true\" href=\"#apptainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApptainer\u003c/h3\u003e\n\u003cp\u003eThis repository includes an \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e definition file that can be built using Apptainer.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ apptainer build find-santa.sif Singularity.def\n\n$ mpiexec -np 4 apptainer \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e find-santa.sif conda run -n devcontainers python /app/src/random_walk.py\n\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_demo\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity_demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_demo\u003c/h1\u003e\n\u003cp\u003eDemo recipe\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1670011770.0 + "updated_at": 1546982676.0 }, { "data_format": 2, - "description": "https://github.com/pygments/pygments.git", + "description": null, "filenames": [ - "tests/examplefiles/singularity/Singularity" + "docker/Singularity.def" ], - "full_name": "sailfishos-mirror/pygments", + "full_name": "mandarc64/RA", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mlcommons-algoperf-training-algorithms-benchmark\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#mlcommons-algoperf-training-algorithms-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMLCommons\u2122 AlgoPerf: Training Algorithms Benchmark\u003c/h1\u003e\n\u003cbr\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"#\"\u003e\u003cimg width=\"600\" src=\".assets/mlc_logo.png\" alt=\"MLCommons Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://arxiv.org/abs/2306.07179\" rel=\"nofollow\"\u003ePaper (arXiv)\u003c/a\u003e \u2022\n \u003ca href=\"/CALL_FOR_SUBMISSIONS.md\"\u003eCall for Submissions\u003c/a\u003e \u2022\n \u003ca href=\"/GETTING_STARTED.md\"\u003eGetting Started\u003c/a\u003e \u2022\n \u003ca href=\"/COMPETITION_RULES.md\"\u003eCompetition Rules\u003c/a\u003e \u2022\n \u003ca href=\"/DOCUMENTATION.md\"\u003eDocumentation\u003c/a\u003e \u2022\n \u003ca href=\"/CONTRIBUTING.md\"\u003eContributing\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/CI.yml\"\u003e\u003cimg src=\"https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/CI.yml/badge.svg\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/linting.yml\"\u003e\u003cimg src=\"https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/linting.yml/badge.svg\" alt=\"Lint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/mlcommons/algorithmic-efficiency/blob/main/LICENSE.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44bc63cdf9bc4b15dcf019006fc6e19bc712fc031f39fab78c0c4595b2967e93/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4170616368655f322e302d626c75652e737667\" alt=\"License: Apache 2.0\" data-canonical-src=\"https://img.shields.io/badge/License-Apache_2.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/google/yapf\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8bb70b59f8586325e23af2a66f72b16f21e0d8acf8cee54d403bf376e54ede6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d796170662d6f72616e6765\" alt=\"Code style: yapf\" data-canonical-src=\"https://img.shields.io/badge/code%20style-yapf-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cem\u003eAlgoPerf\u003c/em\u003e is a suite of benchmarks and competitions to measure neural network training speedups due to algorithmic improvements in both training algorithms and models. This is the repository for the \u003cem\u003eAlgoPerf: Training Algorithms benchmark\u003c/em\u003e and its associated competition. It is developed by the \u003ca href=\"https://mlcommons.org/en/groups/research-algorithms/\" rel=\"nofollow\"\u003eMLCommons Algorithms Working Group\u003c/a\u003e. This repository holds the \u003ca href=\"/COMPETITION_RULES.md\"\u003e\u003cstrong\u003ecompetition rules\u003c/strong\u003e\u003c/a\u003e, the \u003ca href=\"/DOCUMENTATION.md\"\u003e\u003cstrong\u003etechnical documentation\u003c/strong\u003e\u003c/a\u003e of the benchmark, \u003ca href=\"/GETTING_STARTED.md\"\u003e\u003cstrong\u003egetting started guides\u003c/strong\u003e\u003c/a\u003e, and the benchmark code. For a detailed description of the benchmark design, see our \u003ca href=\"https://arxiv.org/abs/2306.07179\" rel=\"nofollow\"\u003e\u003cstrong\u003epaper\u003c/strong\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!IMPORTANT]\nUpcoming Deadline:\nRegistration deadline to express non-binding intent to submit: \u003cstrong\u003eJanuary 28th, 2024\u003c/strong\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-table-of-contents-\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#table-of-contents-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents \n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#call-for-submissions\"\u003eCall for Submissions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#competition-rules\"\u003eCompetition Rules\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#technical-documentation-of-the-benchmark--faqs\"\u003eTechnical Documentation of the Benchmark \u0026amp; FAQs\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#paper-and-citing-the-algoperf-benchmark\"\u003ePaper and Citing the AlgoPerf Benchmark\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eYou can install this package and dependencies in a \u003ca href=\"/GETTING_STARTED.md#python-virtual-environment\"\u003ePython virtual environment\u003c/a\u003e or use a \u003ca href=\"/GETTING_STARTED.md#docker\"\u003eDocker/Singularity/Apptainer container\u003c/a\u003e (recommended).\nWe recommend using a Docker container (or alternatively, a Singularity/Apptainer container) to ensure a similar environment to our scoring and testing environments.\nBoth options are described in detail in the \u003ca href=\"/GETTING_STARTED.md\"\u003e\u003cstrong\u003eGetting Started\u003c/strong\u003e\u003c/a\u003e document.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTL;DR to install the Jax version for GPU run:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[pytorch_cpu]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[jax_gpu]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -f \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehttps://storage.googleapis.com/jax-releases/jax_cuda_releases.html\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[full]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTL;DR to install the PyTorch version for GPU run:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[jax_cpu]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[pytorch_gpu]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -f \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehttps://download.pytorch.org/whl/torch_stable.html\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip3 install -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.[full]\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eFor detailed instructions on developing and scoring your own algorithm in the benchmark see the \u003ca href=\"/GETTING_STARTED.md\"\u003eGetting Started\u003c/a\u003e document.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTL;DR running a JAX workload:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 submission_runner.py \\\n --framework=jax \\\n --workload=mnist \\\n --experiment_dir=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/experiments \\\n --experiment_name=my_first_experiment \\\n --submission_path=baselines/adamw/jax/submission.py \\\n --tuning_search_space=baselines/adamw/tuning_search_space.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTL;DR running a PyTorch workload:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 submission_runner.py \\\n --framework=pytorch \\\n --workload=mnist \\\n --experiment_dir=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/experiments \\\n --experiment_name=my_first_experiment \\\n --submission_path=baselines/adamw/jax/submission.py \\\n --tuning_search_space=baselines/adamw/tuning_search_space.json\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-call-for-submissions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#call-for-submissions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCall for Submissions\u003c/h2\u003e\n\u003cp\u003eThe \u003ca href=\"/CALL_FOR_SUBMISSIONS.md\"\u003eCall for Submissions\u003c/a\u003e announces the first iteration of the AlgoPerf: Training Algorithms competition based on the benchmark by the same name.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-competition-rules\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#competition-rules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompetition Rules\u003c/h3\u003e\n\u003cp\u003eThe competition rules for the \u003cem\u003eAlgoPerf: Training Algorithms\u003c/em\u003e competition can be found in the separate \u003ca href=\"/COMPETITION_RULES.md\"\u003e\u003cstrong\u003eCompetition Rules\u003c/strong\u003e\u003c/a\u003e document.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-technical-documentation-of-the-benchmark--faqs\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#technical-documentation-of-the-benchmark--faqs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTechnical Documentation of the Benchmark \u0026amp; FAQs\u003c/h3\u003e\n\u003cp\u003eWe provide additional technical documentation of the benchmark and answer frequently asked questions in a separate \u003ca href=\"/DOCUMENTATION.md\"\u003e\u003cstrong\u003eDocumentation\u003c/strong\u003e\u003c/a\u003e page. Suggestions, clarifications and questions can be raised via pull requests, creating an issue, or by sending an email to the \u003ca href=\"mailto:algorithms@mlcommons.org\"\u003eworking group\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eWe invite everyone to look through our rules, documentation, and codebase and submit issues and pull requests, e.g. for rules changes, clarifications, or any bugs you might encounter. If you are interested in contributing to the work of the working group and influence the benchmark\u0027s design decisions, please \u003ca href=\"https://mlcommons.org/en/groups/research-algorithms/\" rel=\"nofollow\"\u003ejoin the weekly meetings\u003c/a\u003e and consider becoming a member of the working group.\u003c/p\u003e\n\u003cp\u003eOur \u003ca href=\"/CONTRIBUTING.md\"\u003e\u003cstrong\u003eContributing\u003c/strong\u003e\u003c/a\u003e document provides further MLCommons contributing guidelines and additional setup and workflow instructions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe \u003cem\u003eAlgoPerf\u003c/em\u003e codebase is licensed under the \u003ca href=\"/LICENSE.md\"\u003eApache License 2.0\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-paper-and-citing-the-algoperf-benchmark\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#paper-and-citing-the-algoperf-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePaper and Citing the AlgoPerf Benchmark\u003c/h2\u003e\n\u003cp\u003eIn our paper \u003ca href=\"http://arxiv.org/abs/2306.07179\" rel=\"nofollow\"\u003e\"Benchmarking Neural Network Training Algorithms\"\u003c/a\u003e we motivate, describe, and justify the \u003cem\u003eAlgoPerf: Training Algorithms\u003c/em\u003e benchmark.\u003c/p\u003e\n\u003cp\u003eIf you are using the \u003cem\u003eAlgoPerf benchmark\u003c/em\u003e, its codebase, baselines, or workloads, please consider citing our paper:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"http://arxiv.org/abs/2306.07179\" rel=\"nofollow\"\u003eDahl, Schneider, Nado, et al.\u003cbr\u003e\n\u003cstrong\u003eBenchmarking Neural Network Training Algorithms\u003c/strong\u003e\u003cbr\u003e\n\u003cem\u003earXiv 2306.07179\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@Misc\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003eDahl2023AlgoPerf\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e{Benchmarking Neural Network Training Algorithms}\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eDahl, George E. and Schneider, Frank and Nado, Zachary and Agarwal, Naman and Sastry, Chandramouli Shama and Hennig, Philipp and Medapati, Sourabh and Eschenhagen, Runa and Kasimbeg, Priya and Suo, Daniel and Bae, Juhan and Gilmer, Justin and Peirson, Abel L. and Khan, Bilal and Anil, Rohan and Rabbat, Mike and Krishnan, Shankar and Snider, Daniel and Amid, Ehsan and Chen, Kongtao and Maddison, Chris J. and Vasudev, Rakshith and Badura, Michal and Garg, Ankush and Mattson, Peter\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2023\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003earchiveprefix\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003earXiv\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eeprint\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2306.07179\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n}\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1641488312.0 + "updated_at": 1704437124.0 }, { "data_format": 2, - "description": null, + "description": "R containers", "filenames": [ - "docker/Singularity.nvidia.def" + "Singularity.3.6.0" ], - "full_name": "guaacoelho/elastic_UMA", + "full_name": "arcsUVA/R", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-devito-fast-stencil-computation-from-symbolic-specification\" class=\"anchor\" aria-hidden=\"true\" href=\"#devito-fast-stencil-computation-from-symbolic-specification\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevito: Fast Stencil Computation from Symbolic Specification\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-core\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-core/badge.svg\" alt=\"Build Status for the Core backend\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-mpi\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-mpi/badge.svg\" alt=\"Build Status with MPI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-gpu\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-gpu/badge.svg\" alt=\"Build Status on GPU\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/devitocodes/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3371fe5bdd570d040c748fb93a3e18ce00797c85315f2d05364781a1e5b9aa53/68747470733a2f2f636f6465636f762e696f2f67682f64657669746f636f6465732f64657669746f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Code Coverage\" data-canonical-src=\"https://codecov.io/gh/devitocodes/devito/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://join.slack.com/t/devitocodes/shared_invite/zt-gtd2yxj9-Y31YKk_7lr9AwfXeL2iMFg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f0d0a8f3b06c0808c75575af15a74159d9d34f2bc02997c0f262dd916e0bf948/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636861742d6f6e253230736c61636b2d253233333643354630\" alt=\"Slack Status\" data-canonical-src=\"https://img.shields.io/badge/chat-on%20slack-%2336C5F0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://devitocodes.github.io/devito-performance\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3015b96f702ce7bd0e41f18aa7d7dfb69af77789127d64634a2223f829dbcee1/687474703a2f2f696d672e736869656c64732e696f2f62616467652f62656e63686d61726b656425323062792d6173762d626c75652e7376673f7374796c653d666c6174\" alt=\"asv\" data-canonical-src=\"http://img.shields.io/badge/benchmarked%20by-asv-blue.svg?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/249b65986b967e7268f743fa8e3face99c98762feaa8d1417d07769b1d3385bf/68747470733a2f2f62616467652e667572792e696f2f70792f64657669746f2e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/devito.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/devitocodes/devito/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/581c077bdbc6ca6899c86d0acc6145ae85e9d80e6f805a1071793dbe48917982/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.devitoproject.org\" rel=\"nofollow\"\u003eDevito\u003c/a\u003e is a Python package to implement\noptimized stencil computation (e.g., finite differences, image processing,\nmachine learning) from high-level symbolic problem definitions. Devito builds\non \u003ca href=\"http://www.sympy.org/en/index.html\" rel=\"nofollow\"\u003eSymPy\u003c/a\u003e and employs automated code\ngeneration and just-in-time compilation to execute optimized computational\nkernels on several computer platforms, including CPUs, GPUs, and clusters\nthereof.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#about-devito\"\u003eAbout Devito\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#resources\"\u003eResources\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#performance\"\u003ePerformance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#get-in-touch\"\u003eGet in touch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#interactive-jupyter-notebooks\"\u003eInteractive jupyter notebooks\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-devito\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-devito\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout Devito\u003c/h2\u003e\n\u003cp\u003eDevito provides a functional language to implement sophisticated operators that\ncan be made up of multiple stencil computations, boundary conditions, sparse\noperations (e.g., interpolation), and much more. A typical use case is\nexplicit finite difference methods for approximating partial differential\nequations. For example, a 2D diffusion operator may be implemented with Devito\nas follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGrid\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eshape\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e))\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTimeFunction\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027f\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003espace_order\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eeqn\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eEq\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edt\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elaplace\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eop\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eOperator\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eEq\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eforward\u003c/span\u003e, \u003cspan class=\"pl-en\"\u003esolve\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eeqn\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eforward\u003c/span\u003e)))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn \u003ccode\u003eOperator\u003c/code\u003e generates low-level code from an ordered collection of \u003ccode\u003eEq\u003c/code\u003e (the\nexample above being for a single equation). This code may also be compiled and\nexecuted\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003et\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etimesteps\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere is virtually no limit to the complexity of an \u003ccode\u003eOperator\u003c/code\u003e -- the Devito\ncompiler will automatically analyze the input, detect and apply optimizations\n(including single- and multi-node parallelism), and eventually generate code\nwith suitable loops and expressions.\u003c/p\u003e\n\u003cp\u003eKey features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA functional language to express finite difference operators.\u003c/li\u003e\n\u003cli\u003eStraightforward mechanisms to adjust the discretization.\u003c/li\u003e\n\u003cli\u003eConstructs to express sparse operators (e.g., interpolation), classic linear\noperators (e.g., convolutions), and tensor contractions.\u003c/li\u003e\n\u003cli\u003eSeamless support for boundary conditions and adjoint operators.\u003c/li\u003e\n\u003cli\u003eA flexible API to define custom stencils, sub-domains, sub-sampling,\nand staggered grids.\u003c/li\u003e\n\u003cli\u003eGeneration of highly optimized parallel code (SIMD vectorization, CPU and\nGPU parallelism via OpenMP and OpenACC, multi-node parallelism via MPI,\nblocking, aggressive symbolic transformations for FLOP reduction, etc.).\u003c/li\u003e\n\u003cli\u003eDistributed NumPy arrays over multi-node (MPI) domain decompositions.\u003c/li\u003e\n\u003cli\u003eInspection and customization of the generated code.\u003c/li\u003e\n\u003cli\u003eAutotuning framework to ease performance tuning.\u003c/li\u003e\n\u003cli\u003eSmooth integration with popular Python packages such as NumPy, SymPy, Dask,\nand SciPy, as well as machine learning frameworks such as TensorFlow and\nPyTorch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe easiest way to try Devito is through Docker using the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# get the code\ngit clone https://github.com/devitocodes/devito.git\ncd devito\n\n# start a jupyter notebook server on port 8888\ndocker-compose up devito\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter running the last command above, the terminal will display a URL such as\n\u003ccode\u003ehttps://127.0.0.1:8888/?token=XXX\u003c/code\u003e. Copy-paste this URL into a browser window\nto start a \u003ca href=\"https://jupyter.org/\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e notebook session where you can go\nthrough the \u003ca href=\"https://github.com/devitocodes/devito/tree/master/examples\"\u003etutorials\u003c/a\u003e\nprovided with Devito or create your own notebooks.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://devitocodes.github.io/devito/download.html\" rel=\"nofollow\"\u003eSee here\u003c/a\u003e for detailed installation\ninstructions and other options. If you encounter a problem during installation, please\nsee the\n\u003ca href=\"https://github.com/devitocodes/devito/wiki/Installation-Issues\"\u003einstallation issues\u003c/a\u003e we\nhave seen in the past.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h2\u003e\n\u003cp\u003eTo learn how to use Devito,\n\u003ca href=\"https://github.com/devitocodes/devito/blob/master/examples\"\u003ehere\u003c/a\u003e is a good\nplace to start, with lots of examples and tutorials.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.devitoproject.org/\" rel=\"nofollow\"\u003ewebsite\u003c/a\u003e also provides access to other\ninformation, including documentation and instructions for citing us.\u003c/p\u003e\n\u003cp\u003eSome FAQs are discussed \u003ca href=\"https://github.com/devitocodes/devito/wiki/FAQ\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-performance\" class=\"anchor\" aria-hidden=\"true\" href=\"#performance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePerformance\u003c/h2\u003e\n\u003cp\u003eIf you are interested in any of the following\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGeneration of parallel code (CPU, GPU, multi-node via MPI);\u003c/li\u003e\n\u003cli\u003ePerformance tuning;\u003c/li\u003e\n\u003cli\u003eBenchmarking operators;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ethen you should take a look at this\n\u003ca href=\"https://github.com/devitocodes/devito/blob/master/benchmarks/user\"\u003eREADME\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eYou may also be interested in\n\u003ca href=\"https://www.devitocodes.com/blog/thematrix\" rel=\"nofollow\"\u003eTheMatrix\u003c/a\u003e -- a cross-architecture\nbenchmarking framework showing the performance of several production-grade\nseismic operators implemented with Devito.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-get-in-touch\" class=\"anchor\" aria-hidden=\"true\" href=\"#get-in-touch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGet in touch\u003c/h2\u003e\n\u003cp\u003eIf you\u0027re using Devito, we would like to hear from you. Whether you\nare facing issues or just trying it out, join the\n\u003ca href=\"https://join.slack.com/t/devitocodes/shared_invite/zt-gtd2yxj9-Y31YKk_7lr9AwfXeL2iMFg\" rel=\"nofollow\"\u003econversation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-interactive-jupyter-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#interactive-jupyter-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive jupyter notebooks\u003c/h2\u003e\n\u003cp\u003eThe tutorial jupyter notebook are available interactively at the public \u003ca href=\"https://mybinder.org/v2/gh/devitocodes/devito/master\" rel=\"nofollow\"\u003ebinder\u003c/a\u003e jupyterhub.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eR\u003c/h1\u003e\u003ca id=\"user-content-r\" class=\"anchor\" aria-label=\"Permalink: R\" href=\"#r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eR containers\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1669732936.0 + "updated_at": 1573410996.0 }, { "data_format": 2, - "description": "This repository provides a series of Singularity recipe files used to easily deploy numerous bioinformatics softwares through containers.All these Singularity recipes are ready to be used by the bioinformatics community and have been developed to be integrated into the workflow manager TOGGLe http://toggle.southgreen.fr.", + "description": null, "filenames": [ - "Singularity.sRNA_pipeline.def" + "misc/releases/22.12/Singularity.22.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/latest/Singularity" ], - "full_name": "SouthGreenPlatform/singularityRecipeFiles", + "full_name": "silvansievers/pddl-symmetries", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/514ee51de8b3543f550d8ab786b179568b58039afa34f1f55c753c4e8045b1db/687474703a2f2f7777772e736f757468677265656e2e66722f73697465732f736f757468677265656e2e66722f7468656d65732f736f757468677265656e2f6c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/514ee51de8b3543f550d8ab786b179568b58039afa34f1f55c753c4e8045b1db/687474703a2f2f7777772e736f757468677265656e2e66722f73697465732f736f757468677265656e2e66722f7468656d65732f736f757468677265656e2f6c6f676f2e706e67\" alt=\"\" data-canonical-src=\"http://www.southgreen.fr/sites/southgreen.fr/themes/southgreen/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipe Files\u003c/h1\u003e\n\u003cp\u003eThis repository provides a series of \u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e recipe files used to easily deploy numerous bioinformatics containers.\u003cbr\u003e\nAll the singularity containers are ready to be used by the bioinformatics community and to be integrated into the \u003ca href=\"http://toggle.southgreen.fr\" rel=\"nofollow\"\u003eTOGGLe\u003c/a\u003e workflow manager.\u003c/p\u003e\n\u003cp\u003eThe images are based on either 16.04 or 18.04 Ubuntu versions. All compiled images can be found at \u003ca href=\"http://bioinfo-storage.ird.fr/SingularityImages\" rel=\"nofollow\"\u003ehttp://bioinfo-storage.ird.fr/SingularityImages\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContact : Ndomassi Tando (\u003ca href=\"mailto:ndomassi.tando@ird.fr\"\u003endomassi.tando@ird.fr\u003c/a\u003e)\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSoftware\u003c/th\u003e\n\u003cth\u003eVersion\u003c/th\u003e\n\u003cth\u003eMaintainer\u003c/th\u003e\n\u003cth\u003etested and deployed on\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"http://www.bcgsc.ca/platform/bioinfo/software/abyss\" rel=\"nofollow\"\u003eAbyss\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e1.9\u003c/td\u003e\n\u003ctd\u003eVal\u00e9rie NOEL (UMR MIVEGEC)\u003c/td\u003e\n\u003ctd\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/SouthGreenPlatform/trainings/blob/gh-pages/images/logo_ird.png\"\u003e\u003cimg src=\"https://github.com/SouthGreenPlatform/trainings/raw/gh-pages/images/logo_ird.png\" height=\"20\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (i-trop cluster)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/jdidion/atropos\"\u003eatropos\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e1.1.14\u003c/td\u003e\n\u003ctd\u003eNdomassi TANDO (UMR DIADE)\u003c/td\u003e\n\u003ctd\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/SouthGreenPlatform/trainings/blob/gh-pages/images/logo_ird.png\"\u003e\u003cimg src=\"https://github.com/SouthGreenPlatform/trainings/raw/gh-pages/images/logo_ird.png\" height=\"20\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (i-trop cluster)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://bedtools.readthedocs.io/en/latest/index.html\" rel=\"nofollow\"\u003ebedtools\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.27.1\u003c/td\u003e\n\u003ctd\u003eValentin KLEIN (UMR DIADE)\u003c/td\u003e\n\u003ctd\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/SouthGreenPlatform/trainings/blob/gh-pages/images/logo_ird.png\"\u003e\u003cimg src=\"https://github.com/SouthGreenPlatform/trainings/raw/gh-pages/images/logo_ird.png\" height=\"20\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (i-trop cluster)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"http://hannonlab.cshl.edu/fastx_toolkit/\" rel=\"nofollow\"\u003eFASTX-Toolkit\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e0.0.13\u003c/td\u003e\n\u003ctd\u003eVal\u00e9rie NOEL (UMR MIVEGEC)\u003c/td\u003e\n\u003ctd\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/SouthGreenPlatform/trainings/blob/gh-pages/images/logo_ird.png\"\u003e\u003cimg src=\"https://github.com/SouthGreenPlatform/trainings/raw/gh-pages/images/logo_ird.png\" height=\"20\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (i-trop cluster)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 10, Clang 12\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 13, - "topics": [ - "recipe-files", - "singularity-containers" - ], - "updated_at": 1580131447.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1659431272.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v0.4", - "Singularity" + "llava-container-train/Singularity", + "llava-container/Singularity" ], - "full_name": "cschu/nevermore", + "full_name": "uw-psych/llava-container", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ellava-container\u003c/h1\u003e\u003ca id=\"user-content-llava-container\" class=\"anchor\" aria-label=\"Permalink: llava-container\" href=\"#llava-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container provides a convenient way to run \u003ca href=\"https://github.com/haotian-liu/LLaVA\"\u003eLLaVA\u003c/a\u003e on Hyak.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning LLaVA on Hyak \ud83c\udf47\u003c/h2\u003e\u003ca id=\"user-content-running-llava-on-hyak-\" class=\"anchor\" aria-label=\"Permalink: Running LLaVA on Hyak \ud83c\udf47\" href=\"#running-llava-on-hyak-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you\u0027ll need to log in to Hyak. If you\u0027ve never set this up, go \u003ca href=\"https://uw-psych.github.io/compute_docs\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh your-uw-netid@klone.hyak.uw.edu\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, you\u0027ll need to request a compute node. You can do this with the \u003ccode\u003esalloc\u003c/code\u003e command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Request a GPU node with 8 CPUs, 2 GPUs, 64GB of RAM, and 1 hour of runtime:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (Note: you may need to change the account and partition)\u003c/span\u003e\nsalloc --account escience --partition gpu-a40 --mem 64G -c 8 --time 1:00:00 --gpus 2\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOne you\u0027re logged in to the compute node, you should set up your cache directories and Apptainer settings.\u003c/p\u003e\n\u003cp\u003e\ud83d\udc49 \u003cem\u003eIf you\u0027re following this tutorial, \u003cstrong\u003eyou should do this every time you\u0027re running LLaVA on Hyak!\u003c/strong\u003e This is because the default settings for Apptainer will use your home directory for caching, which will quickly fill up your home directory and cause your jobs to fail.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Do this in every session where you\u0027re running LLaVA on Hyak!\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set up cache directories:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e APPTAINER_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/.cache/apptainer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e HUGGINGFACE_HUB_CACHE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/.cache/huggingface\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${APPTAINER_CACHEDIR}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HUGGINGFACE_HUB_CACHE}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set up Apptainer:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e APPTAINER_BIND=/gscratch APPTAINER_WRITABLE_TMPFS=1 APPTAINER_NV=1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, you can run LLaVA. Let\u0027s try with the sample image on LLaVA\u0027s repository:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/046162f35e7e2d7d7ca3f2a1cde03e4be84890fb656cbeb3babc584ba669c310/68747470733a2f2f6c6c6176612d766c2e6769746875622e696f2f7374617469632f696d616765732f766965772e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/046162f35e7e2d7d7ca3f2a1cde03e4be84890fb656cbeb3babc584ba669c310/68747470733a2f2f6c6c6176612d766c2e6769746875622e696f2f7374617469632f696d616765732f766965772e6a7067\" alt=\"Sample image\" data-canonical-src=\"https://llava-vl.github.io/static/images/view.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run LLaVA:\u003c/span\u003e\napptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n llava-run \\\n --model-path liuhaotian/llava-v1.5-7b \\\n --image-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://llava-vl.github.io/static/images/view.jpg\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --query \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eWhat\u0027s going on here?\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Description of the arguments:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e llava-run: the command to run in the container\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --model-path: the name of the model to use\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --image-file: the URL of the image to use\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --query: what to ask the model\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf it\u0027s working, you should see output that looks something like this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe image features a pier extending out into a large body of water, possibly a lake or a river. The pier is made of wood and has a few benches placed on it, providing a place for people to sit and enjoy the view. The water appears calm and serene, making it an ideal spot for relaxation and contemplation.\u003c/p\u003e\n\u003cp\u003eIn the background, there are mountains visible, adding to the picturesque scenery. The pier is situated in front of a forest, creating a peaceful and natural atmosphere.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eWhen you\u0027re done, you can exit the compute node with the command \u003ccode\u003eexit\u003c/code\u003e or \u003ccode\u003eCtrl-D\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChat mode \ud83d\udde3\ufe0f\u003c/h3\u003e\u003ca id=\"user-content-chat-mode-\ufe0f\" class=\"anchor\" aria-label=\"Permalink: Chat mode \ud83d\udde3\ufe0f\" href=\"#chat-mode-\ufe0f\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor chat, just pass \u003ccode\u003e--chat\u003c/code\u003e instead of \u003ccode\u003e--query\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n llava-run \\\n --model-path liuhaotian/llava-v1.5-7b \\\n --image-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://llava-vl.github.io/static/images/view.jpg\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --chat\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning other commands \ud83c\udfc3\u003c/h3\u003e\u003ca id=\"user-content-running-other-commands-\" class=\"anchor\" aria-label=\"Permalink: Running other commands \ud83c\udfc3\" href=\"#running-other-commands-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to a different command, such as one of the commands that comes with LLaVA, you can pass it after the image name:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n python -m llava.serve.cli\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImproving startup time \ud83d\ude80\u003c/h3\u003e\u003ca id=\"user-content-improving-startup-time-\" class=\"anchor\" aria-label=\"Permalink: Improving startup time \ud83d\ude80\" href=\"#improving-startup-time-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you notice slowness when launching the container, you can try extracting the container image to a sandbox directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set up a sandbox directory:\u003c/span\u003e\nSANDBOX=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/tmp/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/sandbox/llava\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e mkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edirname \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SANDBOX}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract the container image to the sandbox:\u003c/span\u003e\napptainer build --sandbox \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SANDBOX}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e oras://ghcr.io/uw-psych/llava-container/llava-container:latest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run LLaVA by passing the sandbox directory instead of the image URL:\u003c/span\u003e\napptainer run \\\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SANDBOX}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n llava-run \\\n --model-path liuhaotian/llava-v1.5-7b \\\n --image-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://llava-vl.github.io/static/images/view.jpg\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --query \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eWhat\u0027s going on here?\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the web interface \ud83d\udd78\ufe0f\u003c/h3\u003e\u003ca id=\"user-content-running-the-web-interface-\ufe0f\" class=\"anchor\" aria-label=\"Permalink: Running the web interface \ud83d\udd78\ufe0f\" href=\"#running-the-web-interface-\ufe0f\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIncluded in the container is a wrapper script for the LLaVA web interface. To run it, you can use the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n hyak-llava-web\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis script will print out a command to set up an SSH tunnel to the web interface. You can then open the web interface by visiting \u003ccode\u003ehttp://localhost:8000\u003c/code\u003e in your browser. The output should look something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To access the gradio web server, run the following command on your local machine: \u003c/span\u003e\nssh -o StrictHostKeyChecking=no -N -L 8000:localhost:53641 -J altan@klone.hyak.uw.edu altan@g3021\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should be able to copy and paste this command into your terminal to set up the SSH tunnel. Then, you can open \u003ccode\u003ehttp://localhost:8000\u003c/code\u003e in your browser to access the web interface.\u003c/p\u003e\n\u003cp\u003eTo configure the web interface, you can set the following environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMODEL_PATHS\u003c/code\u003e: a list of model paths, quoted and separated by space (default: \"liuhaotian/llava-v1.5-7b\")\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAvailable models include, but are not limited to:\n\u003cul\u003e\n\u003cli\u003eliuhaotian/llava-v1.5-7b\u003c/li\u003e\n\u003cli\u003eliuhaotian/llava-v1.5-13b\u003c/li\u003e\n\u003cli\u003eliuhaotian/llava-v1.5-7b-lora\u003c/li\u003e\n\u003cli\u003eliuhaotian/llava-v1.5-13b-lora\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md\"\u003ehttps://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md\u003c/a\u003e for more details.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eGRADIO_CONTROLLER_PORT\u003c/code\u003e: the port number for the gradio controller (or leave it empty to use a random port)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eLOCAL_HTTP_PORT\u003c/code\u003e: the port number to print for the local HTTP server SSH tunnel command (default: 8000)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MODEL_PATHS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eliuhaotian/llava-v1.5-13b\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the 13b model instead of the 7b model\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LOCAL_HTTP_PORT=9000 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use port 9000 instead of 8000\u003c/span\u003e\napptainer run \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n hyak-llava-web\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\ud83d\udc49 \u003cem\u003eYou need to select the model from the dropdown to start. If the model doesn\u0027t appear in the dropdown, wait a few seconds and refresh the page.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ccode\u003ellava-run\u003c/code\u003e\u003c/h2\u003e\u003ca id=\"user-content-llava-run\" class=\"anchor\" aria-label=\"Permalink: llava-run\" href=\"#llava-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ellava-run.py\u003c/code\u003e script is a modification of \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/run_llava.py\"\u003e\u003ccode\u003eLLaVA/lava/eval/run_llava.py\u003c/code\u003e\u003c/a\u003e that adds support for loading 4- and 8-bit models as found in \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/cli.py\"\u003e\u003ccode\u003eLaVA/llava/serve/cli.py\u003c/code\u003e\u003c/a\u003e, as well as a chat mode that allows you to have a conversation with the model.\u003c/p\u003e\n\u003cp\u003eThe following describes the usage of \u003ccode\u003ellava-run\u003c/code\u003e:\u003c/p\u003e\n\u003cpre lang=\"plain\"\u003e\u003ccode\u003eThis container provides a convenient way to run LLaVA. In addition to the LLaVA\nmodule, it includes the commands:\n - `llava-run`, a command-line wrapper for LLaVA inference\n - `hyak-llava-web`, a wrapper to launch the gradio web interface and issue an\n SSH connection string you can copy to open a tunnel to your own computer.\n \nTo run LLaVA with the `llava-run` script, use the following command:\n apptainer run --nv --writable-tmpfs \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n llava-run [llava-run arguments]\n\nYou must pass the \"--nv\" flag to enable GPU support.\n\nDepending on your intended use, you may also want to pass the \"--bind\" flag\nto mount a directory from the host system into the container.\n\nTo specify a directory to use for the HuggingFace model cache and enable access\nto /gscratch, use the following command:\n apptainer run --nv --writable-tmpfs \\\n --env HUGGINGFACE_HUB_CACHE=/path/to/cache \\\n --bind /gscratch \\\n oras://ghcr.io/uw-psych/llava-container/llava-container:latest \\\n llava-run [llava-run arguments]\n\n\nThe following describes the usage of this script:\n\nllava-run [-h] [--model-path PATH] [--model-base PATH] --image-file\n\tIMAGE [IMAGE ...] (--query QUERY [QUERY ...] | --chat)\n\t[--json]\n\t[--conv-mode {v0,v1,vicuna_v1,llama_2,plain,v0_plain,llava_v0,v0_mmtag,llava_v1,v1_mmtag,llava_llama_2,mpt}]\n\t[--stack-sep SEP] [--temperature FLOAT] [--top_p FLOAT]\n\t[--num_beams N] [--max_new_tokens N]\n\t[--load-8bit | --load-4bit] [--device {cuda,cpu}]\n\t[--hf-cache-dir DIR]\n\noptions:\n -h, --help show this help message and exit\n --model-path PATH Model path\n --model-base PATH Model base (required for \u0027lora\u0027 models)\n --image-file IMAGE [IMAGE ...]\n Path or URL to image (provide multiple to process in\n batch; use --sep delimiter within paths to stack image\n inputs )\n --query QUERY [QUERY ...]\n Query (can be specified multiple times, e.g. --query a\n --query b)\n --chat Use chat instead of query\n --json Produce JSON output\n --conv-mode {v0,v1,vicuna_v1,llama_2,plain,v0_plain,llava_v0,v0_mmtag,llava_v1,v1_mmtag,llava_llama_2,mpt}\n Conversation mode\n --stack-sep SEP Internal separator for stacked image files (default:\n \",\")\n --temperature FLOAT Temperature (default: 0.2)\n --top_p FLOAT Top p (default: 1.0)\n --num_beams N Number of beams (default: 1)\n --max_new_tokens N Max new tokens (default: 512)\n --load-8bit Load 8bit model\n --load-4bit Load 4bit model\n --device {cuda,cpu} Device to use\n --hf-cache-dir DIR HuggingFace cache directory\n \n For details on the arguments, see the LLaVA documentation and the usage infor-\n mation for llava.eval.run_llava and llava.serve.cli.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee the \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/README.md\"\u003edocumentation\u003c/a\u003e for LLaVA or the source code for \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/run_llava.py\"\u003e\u003ccode\u003ellava/eval/run_llava.py\u003c/code\u003e\u003c/a\u003e and \u003ca href=\"https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/cli.py\"\u003e\u003ccode\u003ellava/serve/cli.py\u003c/code\u003e\u003c/a\u003e for more information on the arguments.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1639441314.0 + "updated_at": 1706071611.0 }, { "data_format": 2, - "description": "Singularity recipe files for ImageMagick (imagemagick.org)", + "description": null, "filenames": [ - "Singularity.7.1.0.52", - "Singularity" + "core_libraries/subtrees/PreQual/Singularity", + "core_libraries/subtrees/MRtrix3/Singularity" ], - "full_name": "powerPlant/imagemagick-srf", + "full_name": "Saurabh1826/CNT_Research_Template", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for ImageMagick \u003ca href=\"https://imagemagick.org/\" rel=\"nofollow\"\u003ehttps://imagemagick.org/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGenerate symlinks like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec imagemagick-7.1.0.52.sif ls -1 /opt/imagemagick/bin | xargs -L1 ln -s imagemagick-7.1.0.52.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCNT Research Repository Template\u003c/h1\u003e\u003ca id=\"user-content-cnt-research-repository-template\" class=\"anchor\" aria-label=\"Permalink: CNT Research Repository Template\" href=\"#cnt-research-repository-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ff9fd36258ece5b69dde5c086c71002d1ced07b38936b8d845c96c1531c1a0d2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e322e312d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff9fd36258ece5b69dde5c086c71002d1ced07b38936b8d845c96c1531c1a0d2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e322e312d626c7565\" alt=\"version\" data-canonical-src=\"https://img.shields.io/badge/version-0.2.1-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e3e94e150cc0334e54f7f11dadcb26c15b6661f36e71dccfeeebe76dbe7a8488/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069702e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e3e94e150cc0334e54f7f11dadcb26c15b6661f36e71dccfeeebe76dbe7a8488/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069702e737667\" alt=\"pip\" data-canonical-src=\"https://img.shields.io/pypi/v/pip.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/51cd8eed7edeb654616449db2a9bcf24c72762a19e4e9771980375413e5f4224/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f34\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/51cd8eed7edeb654616449db2a9bcf24c72762a19e4e9771980375413e5f4224/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f34\" alt=\"https://img.shields.io/pypi/pyversions/\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of this template is to consolidate shared libraries and enable consistent workflows and tests for most projects in the CNT lab. Users will be able to quickly load code from tested common libraries, or load their own personal code, in an object oriented manner.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePrerequisites\u003c/h1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use this repository, you must have access to either Python or Matlab.\u003c/p\u003e\n\u003cp\u003eWe also highly recommend the use of a virtual environment, conda environment, or similar software to manage distributions. Examples for their use can be found in the documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to install any of the common library code, we provide instructions for both Python and Matlab below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython\u003c/h2\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor python packages, python wheels and tarballs can be found in: CNT_Development/core_libraries/python/.\u003c/p\u003e\n\u003cp\u003eTo install, run:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epip install foo.whl\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eor\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epip install foo.tar.gz\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere foo is the name of the library of interest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMatlab\u003c/h2\u003e\u003ca id=\"user-content-matlab\" class=\"anchor\" aria-label=\"Permalink: Matlab\" href=\"#matlab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd37\u200d\u2640\ufe0f In development.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocumentation\u003c/h1\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis template is intended to be used as both an environment and a simple wrapper for research code. Before beginning, we highly recommend that a virtual environment (or equivalent) is created for each\nproject to ensure your dependencies and code are properly referenced. Examples for creating virtual environments is provided below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRepository Structure\u003c/h2\u003e\u003ca id=\"user-content-repository-structure\" class=\"anchor\" aria-label=\"Permalink: Repository Structure\" href=\"#repository-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA hyperlink enabled repository tree is available within the \u003ca href=\"./repository_structure.md\"\u003erepository_structure\u003c/a\u003e markdown file. We demonstrate the use of git-ginored files and folders by displaying those\nentries with a \u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e symbol.\u003c/p\u003e\n\u003cp\u003eA short description of some of the top-level directories and files are as follows:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecore_libraries\u003c/h3\u003e\u003ca id=\"user-content-core_libraries\" class=\"anchor\" aria-label=\"Permalink: core_libraries\" href=\"#core_libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains the submodules and build files that make up the core libraries used for lab-wide projects.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edata_pointers\u003c/h3\u003e\u003ca id=\"user-content-data_pointers\" class=\"anchor\" aria-label=\"Permalink: data_pointers\" href=\"#data_pointers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains pointers to data contained on Borel and Lief. Data requests should reference these data pointers to prevent duplication before downloading new data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edocuments\u003c/h3\u003e\u003ca id=\"user-content-documents\" class=\"anchor\" aria-label=\"Permalink: documents\" href=\"#documents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains various research documents associated with a project (i.e. SoPs, Pipeline diagrams, etc.) as well as code documentation (e.g.document strings) for the various libraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eexamples\u003c/h3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains example python and matlab scripts for various research tasks as well as how to use common libraries and environments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ereference_data\u003c/h3\u003e\u003ca id=\"user-content-reference_data\" class=\"anchor\" aria-label=\"Permalink: reference_data\" href=\"#reference_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains data that can be used for building targets or conducting unit tests.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esample_data\u003c/h3\u003e\u003ca id=\"user-content-sample_data\" class=\"anchor\" aria-label=\"Permalink: sample_data\" href=\"#sample_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains sample data that might be used in any of the lab-wide projects.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003escripts\u003c/h3\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-label=\"Permalink: scripts\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains user-defined scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eunit_tests\u003c/h3\u003e\u003ca id=\"user-content-unit_tests\" class=\"anchor\" aria-label=\"Permalink: unit_tests\" href=\"#unit_tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains unit tests for validating new/altered code at both the machine level and model level.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003euser_data\u003c/h3\u003e\u003ca id=\"user-content-user_data\" class=\"anchor\" aria-label=\"Permalink: user_data\" href=\"#user_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder is meant to store user data. Data in this repository is private by default and will not be uploaded to public repositories.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e.gitignore\u003c/h3\u003e\u003ca id=\"user-content-gitignore\" class=\"anchor\" aria-label=\"Permalink: .gitignore\" href=\"#gitignore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis file helps prevent certain files from being uploaded to the public repository. This can be to avoid excess data volumes, or to protect sensitive information. By default, the ignored files and\nfolders are designed for the development of a lab-wide template, and users should adjust the settings to match their own needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVirtual Environments\u003c/h1\u003e\u003ca id=\"user-content-virtual-environments\" class=\"anchor\" aria-label=\"Permalink: Virtual Environments\" href=\"#virtual-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython\u003c/h2\u003e\u003ca id=\"user-content-python-1\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConda\u003c/h3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend using the pre-built environment files provided to start your project. These files can be found in the following subfolders: core_libraries/python/*/*yml and can be installed using the following command:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda env create -f foo.yml\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere foo is the name of the environment.\u003c/p\u003e\n\u003cp\u003eFor those who wish to create their own environment, we introduced some of the basics below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreation\u003c/h4\u003e\u003ca id=\"user-content-creation\" class=\"anchor\" aria-label=\"Permalink: Creation\" href=\"#creation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda create --name myenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere myenv is the name of the environment you wish to create.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eListing environments\u003c/h4\u003e\u003ca id=\"user-content-listing-environments\" class=\"anchor\" aria-label=\"Permalink: Listing environments\" href=\"#listing-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda env list\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eActivating Environment\u003c/h4\u003e\u003ca id=\"user-content-activating-environment\" class=\"anchor\" aria-label=\"Permalink: Activating Environment\" href=\"#activating-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda activate myenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere myenv is the name of the environment you wish to activate.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDeactivating an environment\u003c/h4\u003e\u003ca id=\"user-content-deactivating-an-environment\" class=\"anchor\" aria-label=\"Permalink: Deactivating an environment\" href=\"#deactivating-an-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda deactivate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMore information\u003c/h4\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-label=\"Permalink: More information\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information, please read: \u003ca href=\"https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment\" rel=\"nofollow\"\u003ehttps://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVirtual Environment\u003c/h3\u003e\u003ca id=\"user-content-virtual-environment\" class=\"anchor\" aria-label=\"Permalink: Virtual Environment\" href=\"#virtual-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst make sure you have venv installed. If not, you can pip install it as follows: pip install venv\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreation\u003c/h4\u003e\u003ca id=\"user-content-creation-1\" class=\"anchor\" aria-label=\"Permalink: Creation\" href=\"#creation-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003epython3 -m venv /path/to/new/virtual/environment\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eListing environments\u003c/h4\u003e\u003ca id=\"user-content-listing-environments-1\" class=\"anchor\" aria-label=\"Permalink: Listing environments\" href=\"#listing-environments-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003elsvirtualenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYou may need to install virutalenvwrapper to use this command. ( pip install virtualenvwrapper. ) If it doesn\u0027t populate to your path, check the package directory for the executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eActivating Environment\u003c/h4\u003e\u003ca id=\"user-content-activating-environment-1\" class=\"anchor\" aria-label=\"Permalink: Activating Environment\" href=\"#activating-environment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esource /path/to/venv/bin/activate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDeactivating an environment\u003c/h4\u003e\u003ca id=\"user-content-deactivating-an-environment-1\" class=\"anchor\" aria-label=\"Permalink: Deactivating an environment\" href=\"#deactivating-an-environment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003edeactivate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(Type this command in your shell.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMatlab\u003c/h2\u003e\u003ca id=\"user-content-matlab-1\" class=\"anchor\" aria-label=\"Permalink: Matlab\" href=\"#matlab-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd37\u200d\u2642\ufe0f\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContact Us\u003c/h1\u003e\u003ca id=\"user-content-contact-us\" class=\"anchor\" aria-label=\"Permalink: Contact Us\" href=\"#contact-us\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny questions should be directed to the data science team. Contact information is provided below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:bjprager@seas.upenn.edu\"\u003eBrian Prager\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:asuncion@seas.upenn.edu\"\u003eJoshua Asuncion\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1667792525.0 + "updated_at": 1686633899.0 }, { "data_format": 2, - "description": "Singularity recipe files for 3D DNA (https://github.com/theaidenlab/3d-dna)", + "description": null, "filenames": [ - "Singularity", - "Singularity.180922" + "docker/Singularity.nvidia.def" ], - "full_name": "powerPlant/3d-dna-srf", + "full_name": "GeoSymCodes/devito", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2286\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the 3D de novo assembly (3D DNA) pipeline\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDevito: Fast Stencil Computation from Symbolic Specification\u003c/h1\u003e\u003ca id=\"user-content-devito-fast-stencil-computation-from-symbolic-specification\" class=\"anchor\" aria-label=\"Permalink: Devito: Fast Stencil Computation from Symbolic Specification\" href=\"#devito-fast-stencil-computation-from-symbolic-specification\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-core\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-core/badge.svg\" alt=\"Build Status for the Core backend\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-mpi\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-mpi/badge.svg\" alt=\"Build Status with MPI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/devitocodes/devito/actions?query=workflow%3ACI-gpu\"\u003e\u003cimg src=\"https://github.com/devitocodes/devito/workflows/CI-gpu/badge.svg\" alt=\"Build Status on GPU\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/devitocodes/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3341c4237b01c40446dbf572166724d26ed6e3ce3b371353f8a932b9ae54f396/68747470733a2f2f636f6465636f762e696f2f67682f64657669746f636f6465732f64657669746f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Code Coverage\" data-canonical-src=\"https://codecov.io/gh/devitocodes/devito/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://join.slack.com/t/devitocodes/shared_invite/zt-gtd2yxj9-Y31YKk_7lr9AwfXeL2iMFg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/71e4f5a15e2e4dd4c87f9f57a0c6661196ed4542f236eb982803dbd090bd99e4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636861742d6f6e253230736c61636b2d253233333643354630\" alt=\"Slack Status\" data-canonical-src=\"https://img.shields.io/badge/chat-on%20slack-%2336C5F0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://devitocodes.github.io/devito-performance\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75162422b65fe2f61b15722be747fa13ebc1d80ecfeeccbee2462ab769c89da3/687474703a2f2f696d672e736869656c64732e696f2f62616467652f62656e63686d61726b656425323062792d6173762d626c75652e7376673f7374796c653d666c6174\" alt=\"asv\" data-canonical-src=\"http://img.shields.io/badge/benchmarked%20by-asv-blue.svg?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d6895be18a87329c268ffb103d3a4541dea612dd39066dc7f6f0ec0ff0400c2/68747470733a2f2f62616467652e667572792e696f2f70792f64657669746f2e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/devito.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/devitocodes/devito/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e91e1d353a8b6acf0b42547ac3901f2c30138a3abaaa3d3c242da30b5b4f8426/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/devitocodes/devito\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fd36e0228b4c25e857e9ac2cf81d9b88dc56b5c50e75e39586fcbaa1c1a1007c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65726875622d696d616765732d696d706f7274616e742e7376673f6c6f676f3d446f636b65723f636f6c6f723d626c756576696f6c6574266c6162656c3d646f636b657226736f72743d73656d766572\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/badge/dockerhub-images-important.svg?logo=Docker?color=blueviolet\u0026amp;label=docker\u0026amp;sort=semver\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.devitoproject.org\" rel=\"nofollow\"\u003eDevito\u003c/a\u003e is a Python package to implement\noptimized stencil computation (e.g., finite differences, image processing,\nmachine learning) from high-level symbolic problem definitions. Devito builds\non \u003ca href=\"http://www.sympy.org/en/index.html\" rel=\"nofollow\"\u003eSymPy\u003c/a\u003e and employs automated code\ngeneration and just-in-time compilation to execute optimized computational\nkernels on several computer platforms, including CPUs, GPUs, and clusters\nthereof.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#about-devito\"\u003eAbout Devito\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#resources\"\u003eResources\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/devitocodes/devito/blob/master/FAQ.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#performance\"\u003ePerformance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#get-in-touch\"\u003eGet in touch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#interactive-jupyter-notebooks\"\u003eInteractive jupyter notebooks\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout Devito\u003c/h2\u003e\u003ca id=\"user-content-about-devito\" class=\"anchor\" aria-label=\"Permalink: About Devito\" href=\"#about-devito\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDevito provides a functional language to implement sophisticated operators that\ncan be made up of multiple stencil computations, boundary conditions, sparse\noperations (e.g., interpolation), and much more. A typical use case is\nexplicit finite difference methods for approximating partial differential\nequations. For example, a 2D diffusion operator may be implemented with Devito\nas follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGrid\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eshape\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e))\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTimeFunction\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027f\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egrid\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003espace_order\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eeqn\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eEq\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edt\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elaplace\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eop\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eOperator\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eEq\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eforward\u003c/span\u003e, \u003cspan class=\"pl-en\"\u003esolve\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eeqn\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eforward\u003c/span\u003e)))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn \u003ccode\u003eOperator\u003c/code\u003e generates low-level code from an ordered collection of \u003ccode\u003eEq\u003c/code\u003e (the\nexample above being for a single equation). This code may also be compiled and\nexecuted\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003et\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etimesteps\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere is virtually no limit to the complexity of an \u003ccode\u003eOperator\u003c/code\u003e -- the Devito\ncompiler will automatically analyze the input, detect and apply optimizations\n(including single- and multi-node parallelism), and eventually generate code\nwith suitable loops and expressions.\u003c/p\u003e\n\u003cp\u003eKey features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA functional language to express finite difference operators.\u003c/li\u003e\n\u003cli\u003eStraightforward mechanisms to adjust the discretization.\u003c/li\u003e\n\u003cli\u003eConstructs to express sparse operators (e.g., interpolation), classic linear\noperators (e.g., convolutions), and tensor contractions.\u003c/li\u003e\n\u003cli\u003eSeamless support for boundary conditions and adjoint operators.\u003c/li\u003e\n\u003cli\u003eA flexible API to define custom stencils, sub-domains, sub-sampling,\nand staggered grids.\u003c/li\u003e\n\u003cli\u003eGeneration of highly optimized parallel code (SIMD vectorization, CPU and\nGPU parallelism via OpenMP and OpenACC, multi-node parallelism via MPI,\nblocking, aggressive symbolic transformations for FLOP reduction, etc.).\u003c/li\u003e\n\u003cli\u003eDistributed NumPy arrays over multi-node (MPI) domain decompositions.\u003c/li\u003e\n\u003cli\u003eInspection and customization of the generated code.\u003c/li\u003e\n\u003cli\u003eAutotuning framework to ease performance tuning.\u003c/li\u003e\n\u003cli\u003eSmooth integration with popular Python packages such as NumPy, SymPy, Dask,\nand SciPy, as well as machine learning frameworks such as TensorFlow and\nPyTorch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to try Devito is through Docker using the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# get the code\ngit clone https://github.com/devitocodes/devito.git\ncd devito\n\n# start a jupyter notebook server on port 8888\ndocker-compose up devito\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter running the last command above, the terminal will display a URL such as\n\u003ccode\u003ehttps://127.0.0.1:8888/?token=XXX\u003c/code\u003e. Copy-paste this URL into a browser window\nto start a \u003ca href=\"https://jupyter.org/\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e notebook session where you can go\nthrough the \u003ca href=\"https://github.com/devitocodes/devito/tree/master/examples\"\u003etutorials\u003c/a\u003e\nprovided with Devito or create your own notebooks.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://devitocodes.github.io/devito/download.html\" rel=\"nofollow\"\u003eSee here\u003c/a\u003e for detailed installation\ninstructions and other options. If you encounter a problem during installation, please\nsee the\n\u003ca href=\"https://github.com/devitocodes/devito/wiki/Installation-Issues\"\u003einstallation issues\u003c/a\u003e we\nhave seen in the past.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResources\u003c/h2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-label=\"Permalink: Resources\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo learn how to use Devito,\n\u003ca href=\"https://github.com/devitocodes/devito/blob/master/examples\"\u003ehere\u003c/a\u003e is a good\nplace to start, with lots of examples and tutorials.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.devitoproject.org/\" rel=\"nofollow\"\u003ewebsite\u003c/a\u003e also provides access to other\ninformation, including documentation and instructions for citing us.\u003c/p\u003e\n\u003cp\u003eSome FAQs are discussed \u003ca href=\"FAQ.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePerformance\u003c/h2\u003e\u003ca id=\"user-content-performance\" class=\"anchor\" aria-label=\"Permalink: Performance\" href=\"#performance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are interested in any of the following\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGeneration of parallel code (CPU, GPU, multi-node via MPI);\u003c/li\u003e\n\u003cli\u003ePerformance tuning;\u003c/li\u003e\n\u003cli\u003eBenchmarking operators;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ethen you should take a look at this\n\u003ca href=\"https://github.com/devitocodes/devito/blob/master/benchmarks/user\"\u003eREADME\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eYou may also be interested in\n\u003ca href=\"https://www.devitocodes.com/blog/thematrix\" rel=\"nofollow\"\u003eTheMatrix\u003c/a\u003e -- a cross-architecture\nbenchmarking framework showing the performance of several production-grade\nseismic operators implemented with Devito.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet in touch\u003c/h2\u003e\u003ca id=\"user-content-get-in-touch\" class=\"anchor\" aria-label=\"Permalink: Get in touch\" href=\"#get-in-touch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re using Devito, we would like to hear from you. Whether you\nare facing issues or just trying it out, join the\n\u003ca href=\"https://join.slack.com/t/devitocodes/shared_invite/zt-gtd2yxj9-Y31YKk_7lr9AwfXeL2iMFg\" rel=\"nofollow\"\u003econversation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive jupyter notebooks\u003c/h2\u003e\u003ca id=\"user-content-interactive-jupyter-notebooks\" class=\"anchor\" aria-label=\"Permalink: Interactive jupyter notebooks\" href=\"#interactive-jupyter-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tutorial jupyter notebook are available interactively at the public \u003ca href=\"https://mybinder.org/v2/gh/devitocodes/devito/master\" rel=\"nofollow\"\u003ebinder\u003c/a\u003e jupyterhub.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1669167969.0 + "updated_at": 1693333728.0 }, { "data_format": 2, @@ -18908,309 +18629,310 @@ var data = "filenames": [ "Singularity" ], - "full_name": "JesseBrouw/Thesis", + "full_name": "darachm/singularity_grinder", "latest_release": null, - "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eThis container is for providing \u003ccode\u003egrinder\u003c/code\u003e for some bioinformatics pipelines.\u003c/p\u003e\n\u003cp\u003eThe primary reason for this is so that it flows nicely through SingularityHub\nfor Nextflow pipelines.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1668694985.0 + "updated_at": 1546565343.0 }, { "data_format": 2, - "description": "Recenta de container singularity para rodar o VASP", + "description": "collection of performance tools in centos (iperf3, perf, htop, etc)", "filenames": [ "Singularity" ], - "full_name": "natanmr/vasp-container", + "full_name": "tin6150/perf_tools", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-vasp-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#vasp-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evasp-container\u003c/h1\u003e\n\u003cp\u003eReceita de container singularity para rodar o VASP\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1668887511.0 + "updated_at": 1705619466.0 }, { "data_format": 2, - "description": "Denovo Assembly from FASTQ files", + "description": null, "filenames": [ - "singularity/Singularity" + "singularity/Singularity.example_recipe", + "singularity/Singularity.plotting", + "singularity/Singularity.align" ], - "full_name": "sequana/denovo", + "full_name": "liluacrobat/Shotgun_inStrain", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBhattlab workflows\u003c/h1\u003e\u003ca id=\"user-content-bhattlab-workflows\" class=\"anchor\" aria-label=\"Permalink: Bhattlab workflows\" href=\"#bhattlab-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputational workflows for metagenomics tasks, packaged with Snakemake, Singularity and Conda.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTable of contents\u003c/h3\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"manual/setup.md\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/running.md\"\u003eRunning a workflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eAvailable workflows\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"manual/preprocessing.md\"\u003e\u003cstrong\u003ePreprocessing\u003c/strong\u003e metagenomic data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/assembly.md\"\u003eMetagenomic \u003cstrong\u003eAssembly\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/binning.md\"\u003eMetagenomic \u003cstrong\u003eBinning\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/dRep.md\"\u003e\u003cstrong\u003eDeReplication\u003c/strong\u003e of binned genomes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/inStrain.md\"\u003e\u003cstrong\u003einStrain\u003c/strong\u003e strain-diversity aware comparison of samples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bhattlab/kraken2_classification\"\u003eMetagenomic classification with \u003cstrong\u003eKraken2\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/sourmash.md\"\u003e\u003cstrong\u003eSourmash\u003c/strong\u003e read comparison\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/download_sra.md\"\u003e\u003cstrong\u003eDownload SRA\u003c/strong\u003e data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/arg.md\"\u003e\u003cstrong\u003eARG detection\u003c/strong\u003e with RGI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/viral.md\"\u003e\u003cstrong\u003eViral\u003c/strong\u003e contig prediction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/comparative_genomics.md\"\u003eComparative microbial genomics pipelines\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuickstart\u003c/h3\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re in the Bhatt lab and working on SCG, this command is an example of how to run the workflows. Other users will need to change these options (see \u003ca href=\"manual/running.md\"\u003eRunning a workflow\u003c/a\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --configfile config_preprocessing.yaml \\\n--snakefile ~/projects/bhattlab_workflows/preprocessing/preprocessing.snakefile \\\n--profile scg --jobs 100 --use-singularity \\\n--singularity-args \u0027--bind /labs/,/oak/,/home/\u0027\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1668763760.0 - }, - { - "data_format": 2, - "description": "Glances is a cross-platform system monitoring tool written in Python.", - "filenames": [ - "3.2.3.1/Singularity", - "3.3.1/Singularity", - "3.3.0.4/Singularity" - ], - "full_name": "pscedu/singularity-glances", - "latest_release": "v3.3.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-glances/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-glances/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-glances/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-glances/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/43ec72356b9caba3c3acfed806b0652e417e6059a5b6f51dea1f5dda0835d137/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d676c616e636573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/43ec72356b9caba3c3acfed806b0652e417e6059a5b6f51dea1f5dda0835d137/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d676c616e636573\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-glances\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/95213954ba87ff8602673ef562afff42953930ec138ce96d3683c4a4536d2d84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d676c616e636573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/95213954ba87ff8602673ef562afff42953930ec138ce96d3683c4a4536d2d84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d676c616e636573\" alt=\"forks\" 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href=\"https://camo.githubusercontent.com/0d7e4849ce048818bdf2750b8463028635aa22f5a5797b1d02e5c3a24d979db0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d676c616e636573\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d7e4849ce048818bdf2750b8463028635aa22f5a5797b1d02e5c3a24d979db0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d676c616e636573\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-glances\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-glances\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-glances\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-glances\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/829c005628a650de0afbef7aa42f2aae5916381323380abb38aa97edf74873ef/68747470733a2f2f6e69636f6c6172676f2e6769746875622e696f2f676c616e6365732f7075626c69632f696d616765732f73637265656e73686f742d776964652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/829c005628a650de0afbef7aa42f2aae5916381323380abb38aa97edf74873ef/68747470733a2f2f6e69636f6c6172676f2e6769746875622e696f2f676c616e6365732f7075626c69632f696d616765732f73637265656e73686f742d776964652e706e67\" width=\"50%\" data-canonical-src=\"https://nicolargo.github.io/glances/public/images/screenshot-wide.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://nicolargo.github.io/glances/\" rel=\"nofollow\"\u003eglances\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eglances\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/glances/3.3.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/glances\u003c/code\u003e as \u003ccode\u003e3.3.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1670404321.0 + "updated_at": 1629397031.0 }, { "data_format": 2, - "description": "\u522b\u4eba\u7684", + "description": null, "filenames": [ - "W-Unet/Wave-U-Net-Pytorch-master/Wave-U-Net-Pytorch-master/Singularity" + "singularity/Singularity.minimac4" ], - "full_name": "fxd98/W-Unet", + "full_name": "h3abionet/chipimputation_evaluate_chips", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-w-unet\" class=\"anchor\" aria-hidden=\"true\" href=\"#w-unet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eW-Unet\u003c/h1\u003e\n\u003cp\u003e\u522b\u4eba\u7684\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eChip imputation evaluation Workflow h3abionet/chipimputation_evaluate_chips\u003c/h1\u003e\u003ca id=\"user-content-chip-imputation-evaluation-workflow-h3abionetchipimputation_evaluate_chips\" class=\"anchor\" aria-label=\"Permalink: Chip imputation evaluation Workflow h3abionet/chipimputation_evaluate_chips\" href=\"#chip-imputation-evaluation-workflow-h3abionetchipimputation_evaluate_chips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/h3abionet/chipimputation\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/613f3026f3cde9349d4ad1ff0e6842e170600d7473949e030192e71b08edaabc/68747470733a2f2f7472617669732d63692e6f72672f68336162696f6e65742f63686970696d7075746174696f6e2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/h3abionet/chipimputation.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/780f0e426d3a9fd5f3f54407686be63867cb8093d09e36c9bcbad58b728a111d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/h3abionet/chipimputation\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a9dd183a714011418b2104dcad694fcdbbfbf66fdca3c46b96018a56edf79026/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f63686970696d7075746174696f6e2e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/chipimputation.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is to evaluate the imputation performance and accuracy of different arrays starting from sequence data.\nIt masks non tag variants for each array, and then impute to a reference panel using Minimac.\u003cbr\u003e\nIt is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner.\u003cbr\u003e\nIt comes with singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe evaluate_chips pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and Configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eConfiguration for other clusters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup (native cluster)\u003c/h3\u003e\u003ca id=\"user-content-setup-native-cluster\" class=\"anchor\" aria-label=\"Permalink: Setup (native cluster)\" href=\"#setup-native-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHeadnode\u003c/h4\u003e\u003ca id=\"user-content-headnode\" class=\"anchor\" aria-label=\"Permalink: Headnode\" href=\"#headnode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e (can be installed as local user)\u003c/li\u003e\n\u003cli\u003eNXF_HOME needs to be set, and must be in the PATH\u003c/li\u003e\n\u003cli\u003eNote that we\u0027ve experienced problems running Nextflow when NXF_HOME is on an NFS mount.\u003c/li\u003e\n\u003cli\u003eThe Nextflow script also needs to be invoked in a non-NFS folder\u003c/li\u003e\n\u003cli\u003eJava 1.8+\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompute nodes\u003c/h4\u003e\u003ca id=\"user-content-compute-nodes\" class=\"anchor\" aria-label=\"Permalink: Compute nodes\" href=\"#compute-nodes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe compute nodes need to have singularity installed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe compute nodes need access to shared storage for input, references, output\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe following commands need to be available in PATH on the compute nodes, in case of unavailabitity of singularity.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eminimac4\u003c/code\u003e from \u003ca href=\"http://mathgen.stats.ox.ac.uk/impute/impute_v2.html\" rel=\"nofollow\"\u003eMINIMAC4\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evcftools\u003c/code\u003e from \u003ca href=\"https://vcftools.github.io/index.html\" rel=\"nofollow\"\u003eVCFtools\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebcftools\u003c/code\u003efrom \u003ca href=\"https://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebgzip\u003c/code\u003e from \u003ca href=\"http://www.htslib.org\" rel=\"nofollow\"\u003ehtslib\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eeagle\u003c/code\u003e from \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/\" rel=\"nofollow\"\u003eEagle\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython2.7\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e with the following packages ...\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1668261473.0 + "updated_at": 1630671596.0 }, { "data_format": 2, - "description": "Learning how to use the workflow called nextflow", + "description": "Code for snATAC + snRNA analyses", "filenames": [ - "nf-training/Singularity" + "eqtl/scan/scripts/Singularity", + "eqtl/susie/scripts/Singularity", + "sn_processing/rna/scripts/Singularity", + "sn_processing/atac/scripts/Singularity", + "directionality/smr/scripts/Singularity" ], - "full_name": "ayoraind/nf-training", + "full_name": "ParkerLab/sn_muscle_2023", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nextflow-training-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-training-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow Training Guide.\u003c/h1\u003e\n\u003cp\u003eWelcome to the Nextflow training repo. We are excited to have you on the path to writing reproducible and scalable scientific workflows using Nextflow. This guide complements the full Nextflow documentation - if you ever have any doubts, head over to the docs located \u003ca href=\"https://www.nextflow.io/docs/latest/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThere are two main ways to get started with Seqera\u0027s Nextflow training course.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Locally - best if you are already confident with Git and Docker, or working offline. Follow the instructions \u003ca href=\"https://training.seqera.io/#_local_installation\" rel=\"nofollow\"\u003ehere\u003c/a\u003e, section 1.1.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGitpod - (recommended), is a containerized environment with all the programs and data pre-installed. Simply click the link and login via a GitHub account to start the tutorial. The full instructions are below.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gitpod-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#gitpod-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGitpod requirements:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eA GitHub account\u003c/li\u003e\n\u003cli\u003eWeb browser (Google Chrome, Firefox)\u003c/li\u003e\n\u003cli\u003eInternet connection\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gitpod-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#gitpod-quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGitpod quick start\u003c/h2\u003e\n\u003cp\u003eTo run Gitpod:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClick the following URL:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://gitpod.io/#https://github.com/seqeralabs/nf-training-public\" rel=\"nofollow\"\u003ehttps://gitpod.io/#https://github.com/seqeralabs/nf-training-public\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e(which is our Github repository URL, prefixed with \u003ca href=\"https://gitpod.io/#\" rel=\"nofollow\"\u003ehttps://gitpod.io/#\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLog in to your Github account (and allow authorization).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce you have signed in, Gitpod should load (skip prebuild if asked).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-explore-your-gitpod-ide\" class=\"anchor\" aria-hidden=\"true\" href=\"#explore-your-gitpod-ide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExplore your Gitpod IDE\u003c/h2\u003e\n\u003cp\u003eYou should now see something similar to the following:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/asciidocs/img/gitpod.welcome.png\"\u003e\u003cimg src=\"/asciidocs/img/gitpod.welcome.png\" alt=\"PNG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe sidebar\u003c/strong\u003e allows you to customize your Gitpod environment and perform basic tasks (copy, paste, open files, search, git, etc.). Click the Explorer button to see which files are in this repository.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe terminal\u003c/strong\u003e allows you to run all the programs in the repository. For example, both \u003ccode\u003enextflow\u003c/code\u003e and \u003ccode\u003edocker\u003c/code\u003e are installed and can be executed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe main window\u003c/strong\u003e allows you to view and edit files. Clicking on a file in the explorer will open it within the main window. You should also see the nf-training material browser (\u003ca href=\"https://training.seqera.io/\" rel=\"nofollow\"\u003ehttps://training.seqera.io/\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eTo test that the environment is working correctly, type the following into the terminal:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow info\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should come up with the Nextflow version and runtime information:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eVersion: 22.04.2 build 5701\nCreated: 16-05-2022 17:52 UTC\nSystem: Linux 5.16.20-051620-generic\nRuntime: Groovy 3.0.10 on OpenJDK 64-Bit Server VM 11.0.13+8-LTS\nEncoding: UTF-8 (UTF-8)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-gitpod-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#gitpod-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGitpod resources\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eGitpod gives you up to 50 hours per month to run the environment for free.\u003c/li\u003e\n\u003cli\u003eIt includes up to 16 cpus and 30GB of workspace.\u003c/li\u003e\n\u003cli\u003eGitpod will timeout after 30 minutes. However any changes are saved for up to two week (see next section for reopening a timed out session).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"http://www.gitpod.io\" rel=\"nofollow\"\u003ewww.gitpod.io\u003c/a\u003e for more details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reopening-a-gitpod-session\" class=\"anchor\" aria-hidden=\"true\" href=\"#reopening-a-gitpod-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReopening a Gitpod session\u003c/h3\u003e\n\u003cp\u003eYou can reopen an environment by going to \u003ca href=\"https://gitpod.io/workspaces\" rel=\"nofollow\"\u003ehttps://gitpod.io/workspaces\u003c/a\u003e and finding your previous environment, then clicking the button with three dots and selecting Open.\u003c/p\u003e\n\u003cp\u003eIf you save the URL from your previous Gitpod environment, you can just paste this into your browser to open the previous environment.\u003c/p\u003e\n\u003cp\u003eAlternatively, you can start a new workspace by following the Gitpod URL:\n\u003ca href=\"https://gitpod.io/#https://github.com/seqeralabs/nf-training-public\" rel=\"nofollow\"\u003ehttps://gitpod.io/#https://github.com/seqeralabs/nf-training-public\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tutorial provides all the scripts, so don\u0027t worry if you have lost your environment. In the \u003ccode\u003enf-training\u003c/code\u003e directory, you can find the main scripts used in the tutorial.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-saving-files-from-gitpod-to-your-local-machine\" class=\"anchor\" aria-hidden=\"true\" href=\"#saving-files-from-gitpod-to-your-local-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSaving files from Gitpod to your local machine.\u003c/h3\u003e\n\u003cp\u003eTo save your files, select your file of interest from the explorer panel, then right click the file to click \u003ccode\u003eDownload\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-copyright\" class=\"anchor\" aria-hidden=\"true\" href=\"#copyright\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\" rel=\"nofollow\"\u003e\u003cimg alt=\"Creative Commons License\" src=\"https://camo.githubusercontent.com/3f6af33ec372f6eb8a74152e311d8f3ba281cbfb44b003d825de68bcbcffbe9d/68747470733a2f2f692e6372656174697665636f6d6d6f6e732e6f72672f6c2f62792d6e632d6e642f342e302f38387833312e706e67\" data-canonical-src=\"https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCopyright 2020-2022, Seqera Labs. All examples and descriptions are licensed under the \u003ca href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\" rel=\"nofollow\"\u003eCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003eCode for key analyses in the manuscript Single-nucleus chromatin and gene expression profiling across hundreds of skeletal muscle samples reveals context-specific regulation. Annotations in hg38.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo run workflow:\u003c/h1\u003e\u003ca id=\"user-content-to-run-workflow\" class=\"anchor\" aria-label=\"Permalink: To run workflow:\" href=\"#to-run-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSpecify all paths in config.yaml\u003c/h2\u003e\u003ca id=\"user-content-specify-all-paths-in-configyaml\" class=\"anchor\" aria-label=\"Permalink: Specify all paths in config.yaml\" href=\"#specify-all-paths-in-configyaml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModify scripts/nextflow.config accordingly. It currently provides configuration to run on the slurm scheduler.\u003c/h2\u003e\u003ca id=\"user-content-modify-scriptsnextflowconfig-accordingly-it-currently-provides-configuration-to-run-on-the-slurm-scheduler\" class=\"anchor\" aria-label=\"Permalink: Modify scripts/nextflow.config accordingly. It currently provides configuration to run on the slurm scheduler.\" href=\"#modify-scriptsnextflowconfig-accordingly-it-currently-provides-configuration-to-run-on-the-slurm-scheduler\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo generate the sigularity container, use the \u003ccode\u003eSingularity\u003c/code\u003e file in the scripts directory to generate one as:\u003c/h2\u003e\u003ca id=\"user-content-to-generate-the-sigularity-container-use-the-singularity-file-in-the-scripts-directory-to-generate-one-as\" class=\"anchor\" aria-label=\"Permalink: To generate the sigularity container, use the Singularity file in the scripts directory to generate one as:\" href=\"#to-generate-the-sigularity-container-use-the-singularity-file-in-the-scripts-directory-to-generate-one-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --remote container_name.sig scripts/Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the Nextflow workflow as:\u003c/h2\u003e\u003ca id=\"user-content-run-the-nextflow-workflow-as\" class=\"anchor\" aria-label=\"Permalink: Run the Nextflow workflow as:\" href=\"#run-the-nextflow-workflow-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003enextflow -C scripts/nextflow.config run scripts/main.nf -params-file config.yaml -resume -with-trace trace.txt\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1667983606.0 + "updated_at": 1699458196.0 }, { "data_format": 2, - "description": "Updated dockers for FEniCS 2019 (legacy FEniCS)", + "description": "Modified version of AMR++ ", "filenames": [ - "dockerfiles/stable/Singularity", - "dockerfiles/dev-env/Singularity" + "envs/containers/Singularity" ], - "full_name": "terjekv/fenics-docker", + "full_name": "passdan/AMR-local-mod", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker-for-fenics\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-for-fenics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker for FEniCS\u003c/h1\u003e\n\u003cp\u003eThis repository contains the scripts for building various Docker\nimages for \u003ca href=\"http://fenicsproject.org\" rel=\"nofollow\"\u003eFEniCS\u003c/a\u003e. The built images\nare available on \u003ca href=\"https://quay.io/organization/fenicsproject/\" rel=\"nofollow\"\u003equay.io\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://fenics.readthedocs.org/projects/containers/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/216da3db9027c7d6a1857be2a6ef086a77ed5dca0de68a1be21b21a464f1c7ca/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f66656e6963732d636f6e7461696e6572732f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/fenics-containers/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eTo install Docker for your platform (Windows, macOS, Linux, cloud\nplatforms, etc.), follow the instructions at\n\u003ca href=\"https://docs.docker.com/engine/getstarted/step_one/\" rel=\"nofollow\"\u003edocker.com\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOnce you have Docker installed, you can run any of the images below\nusing the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti quay.io/fenicsproject/\u0026lt;image-name\u0026gt;:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo start with you probably want to try the \u003ccode\u003estable:current\u003c/code\u003e image\nwhich includes a full stable version of FEniCS with PETSc, SLEPc,\npetsc4py and slepc4py already compiled. This image has been checked by\nthe FEniCS Project team:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti quay.io/fenicsproject/stable:current\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to share your current working directory into the container\nuse the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti -v $(pwd):/home/fenics/shared quay.io/fenicsproject/\u0026lt;image-name\u0026gt;:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to be able to view the plots in your web browser, use the following\ncommand:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti -p 127.0.0.1:8000:8000 -v $(pwd):/home/fenics/shared quay.io/fenicsproject/\u0026lt;image-name\u0026gt;:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsers with SELinux-enabled Linux distributions (Redhat, Fedora, CentOS, and others)\nwill need to add the \u003ccode\u003e:z\u003c/code\u003e flag to the volume mount, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti -v $(pwd):/home/fenics/shared:z quay.io/fenicsproject/\u0026lt;image-name\u0026gt;:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-experimental-singularity-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#experimental-singularity-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExperimental: Singularity support\u003c/h2\u003e\n\u003cp\u003eThis repository contains a script to build \u003ccode\u003edev-env\u003c/code\u003e and \u003ccode\u003estable\u003c/code\u003e\nimages that are compatible with the Singularity container runtime\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd dockerfiles\n./build-singularity-images.sh\ncd stable\nsingularity run -e stable.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease report any problems in the issue tracker.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eMore extensive documentation, including suggested workflows, is\navailable at \u003ca href=\"https://fenics-containers.readthedocs.org/\" rel=\"nofollow\"\u003ehttps://fenics-containers.readthedocs.org/\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003cp\u003eWe currently offer following end-user images. A full description of\nthe images can be found at \u003ca href=\"https://fenics-containers.readthedocs.org/\" rel=\"nofollow\"\u003ehttps://fenics-containers.readthedocs.org/\u003c/a\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eImage name\u003c/th\u003e\n\u003cth\u003eBuild status\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003estable\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/stable\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/244f27cc8397f31b11cbc2e780751c02b5e6be4fbc35b65fb734720b77f799b8/68747470733a2f2f717561792e696f2f7265706f7369746f72792f66656e69637370726f6a6563742f737461626c652f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/fenicsproject/stable/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eStable release, with PETSc and SLEPc.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edev\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003emaster\u003c/code\u003e version\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edev-env\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/dev-env\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9f60a447cec97d984a5e6d237ecb10b88e9a81054a289c509e46bd0e794561c3/68747470733a2f2f717561792e696f2f7265706f7369746f72792f66656e69637370726f6a6563742f6465762d656e762f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/fenicsproject/dev-env/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDevelopment environment with PETSc and SLEPc.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebase\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/base\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a89219955af8c2e29e3b80191c6b09fbcd0a4aec08fd3c6a796b2194eb459231/68747470733a2f2f717561792e696f2f7265706f7369746f72792f66656e69637370726f6a6563742f626173652f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/fenicsproject/base/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eBase image, not for end users.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: The \u003cem\u003eBuild status\u003c/em\u003e column refers to the latest \u003cem\u003eattempted\u003c/em\u003e\nbuild. Even if a build is marked as failed, there will still be a\nworking image available on the \u003ccode\u003elatest\u003c/code\u003e tag that you can use.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tagging-policies\" class=\"anchor\" aria-hidden=\"true\" href=\"#tagging-policies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTagging policies\u003c/h2\u003e\n\u003cp\u003eWe currently maintain tags on the \u003ccode\u003estable\u003c/code\u003e and \u003ccode\u003edev-env\u003c/code\u003e images.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-stable\" class=\"anchor\" aria-hidden=\"true\" href=\"#stable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003estable\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eYou can view the tags on the \u003ccode\u003estable\u003c/code\u003e image here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/stable?tab=tags\" rel=\"nofollow\"\u003ehttps://quay.io/repository/fenicsproject/stable?tab=tags\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe tagging policy for \u003ccode\u003estable\u003c/code\u003e image is as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003e:latest\u003c/code\u003e (default) tag refers to the latest image built by\nquay.io. The prior \u003ccode\u003e:latest\u003c/code\u003e image is automatically deleted by\nquay.io, unless it has been assigned another tag.\u003c/li\u003e\n\u003cli\u003eWe maintain a set of rolling release tags, e.g. \u003ccode\u003e:2016.1.0.r1\u003c/code\u003e,\n\u003ccode\u003e2016.1.0.r2\u003c/code\u003e that contain the \u003ccode\u003exxxx.x.x\u003c/code\u003e version of FEniCS, but\ncontain minor updates \u003ccode\u003e.rx\u003c/code\u003e to underlying dependencies (e.g. PETSc)\nand the container environment. These images have been checked\nthoroughly by the FEniCS project team.\u003c/li\u003e\n\u003cli\u003eThe latest rolling release is tagged with a \u003cem\u003emoving\u003c/em\u003e tag \u003ccode\u003e:current\u003c/code\u003e.\nThis tag is the default tag used by the \u003ccode\u003ebin/fenicsproject\u003c/code\u003e script\nwhen the user specifies \u003ccode\u003estable\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eWhen we release a new stable version of FEniCS the last rolling release\n\u003ccode\u003exxxx.x.x.rx\u003c/code\u003e of the image for the previous version will be tagged \u003ccode\u003exxxx.x.x\u003c/code\u003e for\npermanent archival. We will endeavour to keep all \u003ccode\u003exxxx.x.x.rx\u003c/code\u003e tags\nas well, but this is not guaranteed. We will always keep the last rolling\nrelease \u003ccode\u003exxxx.x.x\u003c/code\u003e tag.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dev-env\" class=\"anchor\" aria-hidden=\"true\" href=\"#dev-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003edev-env\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eYou can view the tags on the \u003ccode\u003edev-env\u003c/code\u003e image here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/fenicsproject/dev-env?tab=tags\" rel=\"nofollow\"\u003ehttps://quay.io/repository/fenicsproject/dev-env?tab=tags\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe tagging policy for the \u003ccode\u003edev-env\u003c/code\u003e image is as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003e:latest\u003c/code\u003e (default) tag refers to the latest image build by\nquay.io. The prior \u003ccode\u003e:latest\u003c/code\u003e image is automatically deleted by\nquay.io, unless it has been assigned another tag.\u003c/li\u003e\n\u003cli\u003eWhen we release a new stable version of FEniCS the last \u003ccode\u003e:latest\u003c/code\u003e image is\ntagged \u003ccode\u003exxxx.x.x\u003c/code\u003e for permanent archival. This could be useful if you\nwant to compile an old version of FEniCS.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#development-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment images\u003c/h2\u003e\n\u003cp\u003eDue to the shutdown of our Bamboo build service, \u003ccode\u003edev\u003c/code\u003e images\nare no longer produced automatically.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-process\" class=\"anchor\" aria-hidden=\"true\" href=\"#process\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcess\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003eDockerfile\u003c/code\u003es in this repository are built and distributed as\nDocker images by quay.io. For this to happen automatically on a change\nin a \u003ccode\u003eDockerfile\u003c/code\u003e we have setup a \u003ca href=\"https://docs.quay.io/guides/building.html\" rel=\"nofollow\"\u003ebuild\ntrigger\u003c/a\u003e on quay.io for\neach image (e.g. \u003ccode\u003estable\u003c/code\u003e). Setting up a trigger requires\nadministrator access on this bitbucket repository and the\n\u003ccode\u003efenicsproject\u003c/code\u003e quay.io team.\u003c/p\u003e\n\u003cp\u003eThe tagging policy is described in the section \u0027Tagging policies\u0027. To\ncreate tags you need to be an administrator on the \u003ccode\u003efenicsproject\u003c/code\u003e\nquay.io team. The procedure is described\n\u003ca href=\"https://docs.quay.io/guides/tag-operations.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Currently all\ntags are created manually via the web interface.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eJack S. Hale (\u003ca href=\"mailto:jack.hale@uni.lu\"\u003ejack.hale@uni.lu\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eLizao Li (\u003ca href=\"mailto:lzlarryli@gmail.com\"\u003elzlarryli@gmail.com\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eGarth N. Wells (\u003ca href=\"mailto:gnw20@cam.ac.uk\"\u003egnw20@cam.ac.uk\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository is a modified version of the \u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus\"\u003eAMR++ core repository\u003c/a\u003e with some notable modifications, changes, and parameterised for running on Cardiff University Biosciences compute cluster (Trinity). For usage and tutorials refer to the core AMR++ documentation.\u003c/p\u003e\n\u003cp\u003eNotable changes to original pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntegrated fastp \u0026amp; bowtie2 as QC and alignment packages\n\u003cul\u003e\n\u003cli\u003eNote: Default alignment for megares is kept as BWA consistent with original AMR++ and can be changed by parameter. Bowtie2 alignment against megaresDB has significant change on alignment rate.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBased upon modified docker image (passdan/amrplusplus-update)\u003c/li\u003e\n\u003cli\u003eSome code tweaks and fixes to work with singularity-slurm submission and repair nextflow channel bugs\u003c/li\u003e\n\u003cli\u003eBespoke job submission and result caputre to fit our specific requirements\u003c/li\u003e\n\u003cli\u003eAdded Bracken processing with new results\n\u003cul\u003e\n\u003cli\u003eNote: Two new workflow parameters: \u0027standard_AMR_wKraken_and_bracken\u0027 and \u0027kraken_and_braken\u0027 (for already filtered/host removed input)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCodebase is provided as-is and is hyper-locally modified for our infrastructure. If you are not concerned about bracken output or fastp \u0026amp; bowtie2 you probably want to work from the original AMR++ repository.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePreparing the install\u003c/h1\u003e\u003ca id=\"user-content-preparing-the-install\" class=\"anchor\" aria-label=\"Permalink: Preparing the install\" href=\"#preparing-the-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDownload the github repository with git clone and the url above\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with singularity\u003c/h2\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eThe pipeline is designed and configured to run with singularity \u0026amp; slurm. If you are using these then no further installation preparation is required.\nYou may choose to pre-build the singulariy images in advance if wanted. Note: config/singularity_slurm.conf is where singularity images can be defined.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload bowtie2 index files to remove contamination/host DNA\u003c/h2\u003e\u003ca id=\"user-content-download-bowtie2-index-files-to-remove-contaminationhost-dna\" class=\"anchor\" aria-label=\"Permalink: Download bowtie2 index files to remove contamination/host DNA\" href=\"#download-bowtie2-index-files-to-remove-contaminationhost-dna\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eEither download directly, or build your indexes to be filtered against from user supplied fasta files.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e[Recommended] download human genome indexes directly from: \u003ca href=\"https://bowtie-bio.sourceforge.net/bowtie2/index.shtml\" rel=\"nofollow\"\u003ehttps://bowtie-bio.sourceforge.net/bowtie2/index.shtml\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEdit Slurm submission scripts\u003c/h2\u003e\u003ca id=\"user-content-edit-slurm-submission-scripts\" class=\"anchor\" aria-label=\"Permalink: Edit Slurm submission scripts\" href=\"#edit-slurm-submission-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eAn example slurm script defines these parameters:\n\u003cul\u003e\n\u003cli\u003eworkdir: Location where processing will be performed (advice: use high speed location on your processing node i.e. /tmp)\u003c/li\u003e\n\u003cli\u003einstalldir: Location of this github repo on your system\u003c/li\u003e\n\u003cli\u003eresultsdir: Where do you want the main outputs to be transfered to (not the full working folders \u0026amp; outputs)\u003c/li\u003e\n\u003cli\u003erun: Name of the run for annotation and folder where the fastq files are\nDefault input read location is: \u003ccode\u003e$workdir/$run/fastq\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the pipeline\u003c/h1\u003e\u003ca id=\"user-content-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline\" href=\"#running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDefault pipeline is \u003ccode\u003estandard_AMR_wKraken_and_bracken\u003c/code\u003e which will run from raw fastqs to the endpoint. Alternatives are to use mid-process data, kraken only etc. by modifying the --pipeline parameter\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eAvailable pipelines:\n - demo: Run a demonstration of AMR++\n - standard_AMR: Run the standard AMR++ pipeline\n - fast_AMR: Run the fast AMR++ pipeline without host removal.\n - standard_AMR_wKraken: Run the standard AMR++ pipeline with Kraken\n - **NEW** standard_AMR_wKraken_and_bracken: Run the standard AMR++ pipeline with Kraken AND Bracken\n\nAvailable subworkflows:\n - eval_qc: Run FastQC analysis\n - trim_qc: Run trimming and quality control\n - rm_host: Remove host reads\n - resistome: Perform resistome analysis\n - align: Perform alignment to MEGARes database\n - kraken: Perform Kraken analysis\n - **NEW** kraken_and_bracken: Perform Kraken and Bracken analysis\n - qiime2: Perform QIIME 2 analysis\n - bam_resistome: Perform resistome analysis on BAM files\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSubmit as a slurm \u0026amp; singularity job with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch AMRplusplus_full.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou\u0027re finished!\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1668124636.0 + "updated_at": 1693998493.0 }, { "data_format": 2, - "description": null, + "description": "Study interactions hosts and microorganisms at metabolic level.", "filenames": [ - "Singularity.def" + "recipes/Singularity" ], - "full_name": "annaLtyler/CAPE_transcripts", + "full_name": "AuReMe/HoloInteract", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9aad17786c19226289cd84f82f2faf5eb0f1adc91c3e70780e7991c2a3298b60/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e392d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/python-3.9-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ba71cd0e231da422d29505443b30ee08fd26a9a935c2211fb4488ded3a7a0a2f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e31302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/python-3.10-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8279d3fba19d512de9201aa06a134661bb4283ef557ab58803fc41de8f0f479/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e31312d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/python-3.11-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a98887fbf555547fa3ddd96ce41fc24b99e450f80fcb4a45416698ef0f8499f6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d756e66696e69736865642d6f72616e67652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/documentation-unfinished-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ae131aa8ea6fc913efeff23640a7aebbe070e256be101f082c5cd0f4a68d2dd5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f77696b692d6e6f6e6578697374656e742d7265642e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/wiki-nonexistent-red.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\n\u003cstrong\u003eHoloInteract\u003c/strong\u003e - Metabolic Interaction in Holobionts\u003c/h1\u003e\u003ca id=\"user-content-holointeract---metabolic-interaction-in-holobionts\" class=\"anchor\" aria-label=\"Permalink: HoloInteract - Metabolic Interaction in Holobionts\" href=\"#holointeract---metabolic-interaction-in-holobionts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUses the \u003ca href=\"\"\u003eMetage2Metabo library\u003c/a\u003e to generate and manipulate metabolic networks.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePiPy \u003ccode\u003erequirements.txt\u003c/code\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-pipy-requirementstxt\" class=\"anchor\" aria-label=\"Permalink: PiPy requirements.txt\" href=\"#pipy-requirementstxt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epython \u0026gt;=3.9\u003c/li\u003e\n\u003cli\u003esetuptools\u0026gt;=65.5.1\u003c/li\u003e\n\u003cli\u003erich\u0026gt;=13.5.3\u003c/li\u003e\n\u003cli\u003ematplotlib\u0026gt;=3.8.0\u003c/li\u003e\n\u003cli\u003epandas\u0026gt;=1.5.3\u003c/li\u003e\n\u003cli\u003eplotly\u0026gt;=5.17.0\u003c/li\u003e\n\u003cli\u003eseaborn\u0026gt;=0.12.2\u003c/li\u003e\n\u003cli\u003eMetage2Metabo\u0026gt;=1.5.4\u003c/li\u003e\n\u003cli\u003escipy\u0026gt;=1.11.2\u003c/li\u003e\n\u003cli\u003epadmet\u0026gt;=5.0.1\u003c/li\u003e\n\u003cli\u003eete3\u0026gt;=3.1.3\u003c/li\u003e\n\u003cli\u003enumpy\u0026gt;=1.26.0\u003c/li\u003e\n\u003cli\u003estatsmodels\u0026gt;=0.14.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLocal\u003c/h4\u003e\u003ca id=\"user-content-local\" class=\"anchor\" aria-label=\"Permalink: Local\" href=\"#local\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eontosunburst : \u003ca href=\"https://github.com/PaulineGHG/Ontology_sunburst.git\"\u003ehttps://github.com/PaulineGHG/Ontology_sunburst.git\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuick install\u003c/h3\u003e\u003ca id=\"user-content-quick-install\" class=\"anchor\" aria-label=\"Permalink: Quick install\" href=\"#quick-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn HoloInteract directory :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash install_dependencies.sh\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommands\u003c/h2\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSubcommands available through \u003ccode\u003eholointeract\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMetabolic Analysis : \u003ccode\u003eholointeract metabolic_analysis\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-metabolic-analysis--holointeract-metabolic_analysis\" class=\"anchor\" aria-label=\"Permalink: Metabolic Analysis : holointeract metabolic_analysis\" href=\"#metabolic-analysis--holointeract-metabolic_analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eArguments :\u003c/h4\u003e\u003ca id=\"user-content-arguments-\" class=\"anchor\" aria-label=\"Permalink: Arguments :\" href=\"#arguments-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--comm\u003c/code\u003e, \u003ccode\u003e--community_networks\u003c/code\u003e : path to community networks in SBML\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--host\u003c/code\u003e, \u003ccode\u003e--host_networks\u003c/code\u003e : path to hosts networks in SBML\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o\u003c/code\u003e, \u003ccode\u003e--output\u003c/code\u003e : path to output directory\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-s\u003c/code\u003e, \u003ccode\u003e--seeds\u003c/code\u003e : path to seeds SBML file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e, \u003ccode\u003e--name\u003c/code\u003e : output files name (default=run)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-m\u003c/code\u003e, \u003ccode\u003e--scopes_method\u003c/code\u003e : method of scopes generation\n\u003ccode\u003e[\u0027solo\u0027, \u0027coop\u0027, \u0027full\u0027]\u003c/code\u003e (default=\u003ccode\u003e\u0027coop\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cm\u003c/code\u003e, \u003ccode\u003e--clustering_method\u003c/code\u003e : method for linkage in clustering (default=\u003ccode\u003e\u0027ward\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--max_clust\u003c/code\u003e : maximal number of cluster for the dendrogram division (default=\u003ccode\u003e10\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cpu\u003c/code\u003e : number of cpu to use (default=\u003ccode\u003e1\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTest example :\u003c/h4\u003e\u003ca id=\"user-content-test-example-\" class=\"anchor\" aria-label=\"Permalink: Test example :\" href=\"#test-example-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003eholointeract metabolic_analysis --comm small_example/inputs/community/ --host small_example/inputs/hosts/ -o small_example/outputs/ -s small_example/inputs/seeds/seeds_seawater_artefact.sbml -n test -m coop \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCoevolution Analysis : \u003ccode\u003eholointeract coevolution\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-coevolution-analysis--holointeract-coevolution\" class=\"anchor\" aria-label=\"Permalink: Coevolution Analysis : holointeract coevolution\" href=\"#coevolution-analysis--holointeract-coevolution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eArguments :\u003c/h4\u003e\u003ca id=\"user-content-arguments--1\" class=\"anchor\" aria-label=\"Permalink: Arguments :\" href=\"#arguments--1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--comm\u003c/code\u003e, \u003ccode\u003e--community_networks\u003c/code\u003e : path to community networks in SBML\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--host\u003c/code\u003e, \u003ccode\u003e--host_networks\u003c/code\u003e : path to hosts networks in SBML\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o\u003c/code\u003e, \u003ccode\u003e--output\u003c/code\u003e : path to output directory\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-s\u003c/code\u003e, \u003ccode\u003e--seeds\u003c/code\u003e : path to seeds SBML file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e, \u003ccode\u003e--name\u003c/code\u003e : output files name (default=run)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cm\u003c/code\u003e, \u003ccode\u003e--clustering_method\u003c/code\u003e : method for linkage in clustering (default=\u003ccode\u003e\u0027ward\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--max_clust\u003c/code\u003e : maximal number of cluster for the dendrogram division (default=\u003ccode\u003e10\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e, \u003ccode\u003e--phylo_tree\u003c/code\u003e : path to phylogenetic tree (Newick format) (default=\u003ccode\u003eNone\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cor\u003c/code\u003e, \u003ccode\u003e--correction\u003c/code\u003e : correction to apply to p-values\n\u003ccode\u003e[\u0027bonferroni\u0027, \u0027benjamini\u0027, None]\u003c/code\u003e (default=\u003ccode\u003eNone\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cpu\u003c/code\u003e : number of cpu to use (default=\u003ccode\u003e1\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTest example :\u003c/h4\u003e\u003ca id=\"user-content-test-example--1\" class=\"anchor\" aria-label=\"Permalink: Test example :\" href=\"#test-example--1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003eholointeract coevolution --comm small_example/inputs/community/ --host small_example/inputs/hosts/ -o small_example/outputs/ -s small_example/inputs/seeds/seeds_seawater_artefact.sbml -n test -p small_example/inputs/SpeciesTree_rooted.txt \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHelp available\u003c/h3\u003e\u003ca id=\"user-content-help-available\" class=\"anchor\" aria-label=\"Permalink: Help available\" href=\"#help-available\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eholointeract -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eholointeract \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003esubcommand\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -h\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequired Inputs\u003c/h2\u003e\u003ca id=\"user-content-required-inputs\" class=\"anchor\" aria-label=\"Permalink: Required Inputs\" href=\"#required-inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommunity Networks (\u003ccode\u003e--comm\u003c/code\u003e, \u003ccode\u003e--community_networks\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-community-networks---comm---community_networks\" class=\"anchor\" aria-label=\"Permalink: Community Networks (--comm, --community_networks)\" href=\"#community-networks---comm---community_networks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDirectory containing SBML networks files for each community organism.\u003c/p\u003e\n\u003cp\u003eEach organism must be placed in the directory named after its natural host.\u003c/p\u003e\n\u003cp\u003eHost names in community networks directory and host networks directory must coincide.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample :\u003c/h4\u003e\u003ca id=\"user-content-example-\" class=\"anchor\" aria-label=\"Permalink: Example :\" href=\"#example-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\u251c\u2500\u2500 Community_networks\n\u2502 \u251c\u2500\u2500 Host_1\n\u2502 \u2502 \u251c\u2500\u2500 Microorganism_1.sbml \n\u2502 \u2502 \u251c\u2500\u2500 Microorganism_2.sbml \n\u2502 \u251c\u2500\u2500 Host_2\n\u2502 \u2502 \u251c\u2500\u2500 Microorganism_3.sbml \n\u2502 \u2502 \u251c\u2500\u2500 Microorganism_4.sbml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHost Networks (\u003ccode\u003e--host\u003c/code\u003e, \u003ccode\u003e--host_networks\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-host-networks---host---host_networks\" class=\"anchor\" aria-label=\"Permalink: Host Networks (--host, --host_networks)\" href=\"#host-networks---host---host_networks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDirectory containing SBML networks files for each host.\u003c/p\u003e\n\u003cp\u003eHost names in community networks directory and host networks directory must coincide.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample :\u003c/h4\u003e\u003ca id=\"user-content-example--1\" class=\"anchor\" aria-label=\"Permalink: Example :\" href=\"#example--1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\u251c\u2500\u2500 Host_networks\n\u2502 \u251c\u2500\u2500 Host_1.sbml \n\u2502 \u251c\u2500\u2500 Host_2.sbml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSeeds (\u003ccode\u003e-s\u003c/code\u003e, \u003ccode\u003e--seeds\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-seeds--s---seeds\" class=\"anchor\" aria-label=\"Permalink: Seeds (-s, --seeds)\" href=\"#seeds--s---seeds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeeds are list of compounds corresponding to a common growth medium shared between all hosts.\u003c/p\u003e\n\u003cp\u003eArtefacts compounds should be added to seeds to avoid cycles + cofactors.\u003c/p\u003e\n\u003cp\u003eThe seeds file must be given at SBML format.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePhylogenetic Tree (\u003ccode\u003e-p\u003c/code\u003e, \u003ccode\u003e--phylo_tree\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-phylogenetic-tree--p---phylo_tree\" class=\"anchor\" aria-label=\"Permalink: Phylogenetic Tree (-p, --phylo_tree)\" href=\"#phylogenetic-tree--p---phylo_tree\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe phylogenetic tree is used to calculate phylogenetic distance between each pair of hosts.\u003c/p\u003e\n\u003cp\u003eThe file must be given at Newick format.\u003c/p\u003e\n\u003cp\u003eAll the hosts must be present in the tree.\u003c/p\u003e\n\u003cp\u003eAll the host names must coincide to the names in community networks directory\nand host networks directory.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1641489227.0 + "updated_at": 1696949006.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image with CharGer and R libraries for germline small variants workflow.", "filenames": [ - "Singularity.def", - "Singularity-test.def" + "Singularity" ], - "full_name": "lalilalalalu/fuchs-container", + "full_name": "NagaComBio/singularity_gSmVs", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity image with CharGer and R libraries for germline small variants workflow.\u003c/h3\u003e\u003ca id=\"user-content-singularity-image-with-charger-and-r-libraries-for-germline-small-variants-workflow\" class=\"anchor\" aria-label=\"Permalink: Singularity image with CharGer and R libraries for germline small variants workflow.\" href=\"#singularity-image-with-charger-and-r-libraries-for-germline-small-variants-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the singularity image in a cloud instance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# In a CentOS\n# If the CentOS 8.\nsudo dnf --disablerepo \u0027*\u0027 --enablerepo=extras swap centos-linux-repos centos-stream-repos\nsudo yum update\nsudo yum install git singularity\n\n# Clone the repo \ngit clone https://github.com/NagaComBio/singularity_gSmVs.git\ncd singularity_gSmVs/ \n\n#Build the image\nsudo singularity build gSmVs_${version}.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1667472176.0 + "updated_at": 1638799534.0 }, { "data_format": 2, - "description": "singularity image for gmx 2019", + "description": null, "filenames": [ - "Singularity" + "samples/Singularity/filenames/Singularity" ], - "full_name": "jmhays/singularity-gromacs", + "full_name": "0xahu/mywork", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-gromacs\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-gromacs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-gromacs\u003c/h1\u003e\n\u003cp\u003esingularity image for gmx 2019\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLinguist\u003c/h1\u003e\u003ca id=\"user-content-linguist\" class=\"anchor\" aria-label=\"Permalink: Linguist\" href=\"#linguist\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/github/linguist/actions\"\u003e\u003cimg src=\"https://github.com/github/linguist/workflows/Run%20Tests/badge.svg\" alt=\"Actions Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://codespaces.new/github-linguist/linguist\" rel=\"nofollow\"\u003e\u003cimg src=\"https://github.com/codespaces/badge.svg\" alt=\"Open in GitHub Codespaces\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis library is used on GitHub.com to detect blob languages, ignore binary or vendored files, suppress generated files in diffs, and generate language breakdown graphs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"/docs/how-linguist-works.md\"\u003eHow Linguist works\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/docs/overrides.md\"\u003eChange Linguist\u0027s behaviour with overrides\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eContributing guidelines\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the gem:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egem install github-linguist\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLinguist is a Ruby library so you will need a recent version of Ruby installed.\nThere are known problems with the macOS/Xcode supplied version of Ruby that causes problems installing some of the dependencies.\nAccordingly, we highly recommend you install a version of Ruby using Homebrew, \u003ccode\u003erbenv\u003c/code\u003e, \u003ccode\u003ervm\u003c/code\u003e, \u003ccode\u003eruby-build\u003c/code\u003e, \u003ccode\u003easdf\u003c/code\u003e or other packaging system, before attempting to install Linguist and the dependencies.\u003c/p\u003e\n\u003cp\u003eLinguist uses \u003ca href=\"https://github.com/brianmario/charlock_holmes\"\u003e\u003ccode\u003echarlock_holmes\u003c/code\u003e\u003c/a\u003e for character encoding and \u003ca href=\"https://github.com/libgit2/rugged\"\u003e\u003ccode\u003erugged\u003c/code\u003e\u003c/a\u003e for libgit2 bindings for Ruby.\nThese components have their own dependencies.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003echarlock_holmes\n\u003cul\u003e\n\u003cli\u003ecmake\u003c/li\u003e\n\u003cli\u003epkg-config\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://site.icu-project.org/\" rel=\"nofollow\"\u003eICU\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zlib.net/\" rel=\"nofollow\"\u003ezlib\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003erugged\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://curl.haxx.se/libcurl/\" rel=\"nofollow\"\u003elibcurl\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.openssl.org\" rel=\"nofollow\"\u003eOpenSSL\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou may need to install missing dependencies before you can install Linguist.\nFor example, on macOS with \u003ca href=\"http://brew.sh/\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew install cmake pkg-config icu4c\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn Ubuntu:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get install build-essential cmake pkg-config libicu-dev zlib1g-dev libcurl4-openssl-dev libssl-dev ruby-dev\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eApplication usage\u003c/h3\u003e\u003ca id=\"user-content-application-usage\" class=\"anchor\" aria-label=\"Permalink: Application usage\" href=\"#application-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLinguist can be used in your application as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ruby\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003erequire\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027rugged\u0027\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003erequire\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027linguist\u0027\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003erepo\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eRugged\u003c/span\u003e::\u003cspan class=\"pl-v\"\u003eRepository\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003enew\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u0027.\u0027\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eproject\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eLinguist\u003c/span\u003e::\u003cspan class=\"pl-v\"\u003eRepository\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003enew\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003erepo\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erepo\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ehead\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003etarget_id\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eproject\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elanguage\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e#=\u0026gt; \"Ruby\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eproject\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003elanguages\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e#=\u0026gt; { \"Ruby\" =\u0026gt; 119387 }\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand line usage\u003c/h3\u003e\u003ca id=\"user-content-command-line-usage\" class=\"anchor\" aria-label=\"Permalink: Command line usage\" href=\"#command-line-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGit Repository\u003c/h4\u003e\u003ca id=\"user-content-git-repository\" class=\"anchor\" aria-label=\"Permalink: Git Repository\" href=\"#git-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA repository\u0027s languages stats can also be assessed from the command line using the \u003ccode\u003egithub-linguist\u003c/code\u003e executable.\nWithout any options, \u003ccode\u003egithub-linguist\u003c/code\u003e will output the language breakdown by percentage and file size.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /path-to-repository\ngithub-linguist\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can try running \u003ccode\u003egithub-linguist\u003c/code\u003e on the root directory in this repository itself:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e66.84% 264519 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e24.68% 97685 C\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e6.57% 25999 Go\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.29% 5098 Lex\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.32% 1257 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.31% 1212 Dockerfile\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAdditional options\u003c/h4\u003e\u003ca id=\"user-content-additional-options\" class=\"anchor\" aria-label=\"Permalink: Additional options\" href=\"#additional-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003e\u003ccode\u003e--rev REV\u003c/code\u003e\u003c/h5\u003e\u003ca id=\"user-content---rev-rev\" class=\"anchor\" aria-label=\"Permalink: --rev REV\" href=\"#--rev-rev\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e--rev REV\u003c/code\u003e flag will change the git revision being analyzed to any \u003ca href=\"https://git-scm.com/docs/gitrevisions#_specifying_revisions\" rel=\"nofollow\"\u003egitrevisions(1)\u003c/a\u003e compatible revision you specify.\u003c/p\u003e\n\u003cp\u003eThis is useful to analyze the makeup of a repo as of a certain tag, or in a certain branch.\u003c/p\u003e\n\u003cp\u003eFor example, here is the popular \u003ca href=\"https://github.com/jekyll/jekyll\"\u003eJekyll open source project\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist jekyll\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e70.64% 709959 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e23.04% 231555 Gherkin\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e3.80% 38178 JavaScript\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.19% 11943 HTML\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.79% 7900 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.23% 2279 Dockerfile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.13% 1344 Earthly\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.10% 1019 CSS\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.06% 606 SCSS\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.02% 234 CoffeeScript\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.01% 90 Hack\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd here is Jekyll\u0027s published website, from the gh-pages branch inside their repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist jekyll --rev origin/gh-pages\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e100.00% 2568354 HTML\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003e\u003ccode\u003e--breakdown\u003c/code\u003e\u003c/h5\u003e\u003ca id=\"user-content---breakdown\" class=\"anchor\" aria-label=\"Permalink: --breakdown\" href=\"#--breakdown\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e--breakdown\u003c/code\u003e or \u003ccode\u003e-b\u003c/code\u003e flag will additionally show the breakdown of files by language.\u003c/p\u003e\n\u003cp\u003eYou can try running \u003ccode\u003egithub-linguist\u003c/code\u003e on the root directory in this repository itself:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist --breakdown\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e66.84% 264519 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e24.68% 97685 C\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e6.57% 25999 Go\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.29% 5098 Lex\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.32% 1257 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.31% 1212 Dockerfile\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eRuby:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGemfile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eRakefile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ebin/git-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ebin/github-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eext/linguist/extconf.rb\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003egithub-linguist.gemspec\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003elib/linguist.rb\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u2026\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003e\u003ccode\u003e--json\u003c/code\u003e\u003c/h5\u003e\u003ca id=\"user-content---json\" class=\"anchor\" aria-label=\"Permalink: --json\" href=\"#--json\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e--json\u003c/code\u003e or \u003ccode\u003e-j\u003c/code\u003e flag output the data into JSON format.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist --json\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e{\"Dockerfile\":{\"size\":1212,\"percentage\":\"0.31\"},\"Ruby\":{\"size\":264519,\"percentage\":\"66.84\"},\"C\":{\"size\":97685,\"percentage\":\"24.68\"},\"Lex\":{\"size\":5098,\"percentage\":\"1.29\"},\"Shell\":{\"size\":1257,\"percentage\":\"0.32\"},\"Go\":{\"size\":25999,\"percentage\":\"6.57\"}}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis option can be used in conjunction with \u003ccode\u003e--breakdown\u003c/code\u003e to get a full list of files along with the size and percentage data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist --breakdown --json\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e{\"Dockerfile\":{\"size\":1212,\"percentage\":\"0.31\",\"files\":[\"Dockerfile\",\"tools/grammars/Dockerfile\"]},\"Ruby\":{\"size\":264519,\"percentage\":\"66.84\",\"files\":[\"Gemfile\",\"Rakefile\",\"bin/git-linguist\",\"bin/github-linguist\",\"ext/linguist/extconf.rb\",\"github-linguist.gemspec\",\"lib/linguist.rb\",...]}}\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingle file\u003c/h4\u003e\u003ca id=\"user-content-single-file\" class=\"anchor\" aria-label=\"Permalink: Single file\" href=\"#single-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively you can find stats for a single file using the \u003ccode\u003egithub-linguist\u003c/code\u003e executable.\u003c/p\u003e\n\u003cp\u003eYou can try running \u003ccode\u003egithub-linguist\u003c/code\u003e on files in this repository itself:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003egithub-linguist grammars.yml\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003egrammars.yml: 884 lines (884 sloc)\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e type: Text\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e mime type: text/x-yaml\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e language: YAML\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDocker\u003c/h4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have Docker installed you can build an image and run Linguist within a container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003edocker build -t linguist \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003edocker run --rm -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e -w \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e -t linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e66.84% 264519 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e24.68% 97685 C\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e6.57% 25999 Go\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.29% 5098 Lex\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.32% 1257 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.31% 1212 Dockerfile\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003edocker run --rm -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e -w \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e -t linguist github-linguist --breakdown\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e66.84% 264519 Ruby\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e24.68% 97685 C\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e6.57% 25999 Go\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1.29% 5098 Lex\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.32% 1257 Shell\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.31% 1212 Dockerfile\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eRuby:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGemfile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eRakefile\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ebin/git-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ebin/github-linguist\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eext/linguist/extconf.rb\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003egithub-linguist.gemspec\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003elib/linguist.rb\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u2026\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease check out our \u003ca href=\"CONTRIBUTING.md\"\u003econtributing guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe language grammars included in this gem are covered by their repositories\u0027 respective licenses.\n\u003ca href=\"/vendor/README.md\"\u003e\u003ccode\u003evendor/README.md\u003c/code\u003e\u003c/a\u003e lists the repository for each grammar.\u003c/p\u003e\n\u003cp\u003eAll other files are covered by the MIT license, see \u003ca href=\"./LICENSE\"\u003e\u003ccode\u003eLICENSE\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1562360506.0 + "updated_at": 1710228788.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/latest/Singularity", + "misc/releases/22.12/Singularity.22.12", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06" ], - "full_name": "touala/rce_tools", + "full_name": "ipc2023-classical/planner28", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rce_tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#rce_tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erce_tools\u003c/h1\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIPC 2023 Apptainer Recipes\u003c/h2\u003e\u003ca id=\"user-content-ipc-2023-apptainer-recipes\" class=\"anchor\" aria-label=\"Permalink: IPC 2023 Apptainer Recipes\" href=\"#ipc-2023-apptainer-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe optimal configurations of our planner require LP support. To build\nthe Apptainer recipes, you need an installer for CPLEX at a location\navailable under $IPC_THIRD_PARTY. We use version 22.1.1 for the\ncompetition, but in theory any version of CPLEX is fine. For the\nApptainer recipe to work out of the box, the installer needst to be\nnamed as follows: cplex_studio2211.linux_x86_64.bin\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 10, Clang 12\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\nThe third party software OSI shipped within this repository is\nlicensed under the Eclipse Public License version 2.0 (EPL 2.0).\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1665495879.0 + "updated_at": 1688990783.0 }, { "data_format": 2, - "description": "singularity container for use with singularity hub", + "description": null, "filenames": [ - "Singularity" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/latest/Singularity", + "misc/releases/22.12/Singularity.22.12", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06" ], - "full_name": "sbutcher/container-R", + "full_name": "ipc2023-classical/planner4", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-r\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer-R\u003c/h1\u003e\n\u003cp\u003esingularity container for use with singularity hub\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIPC 2023 Apptainer Recipes\u003c/h2\u003e\u003ca id=\"user-content-ipc-2023-apptainer-recipes\" class=\"anchor\" aria-label=\"Permalink: IPC 2023 Apptainer Recipes\" href=\"#ipc-2023-apptainer-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe optimal configuration of our planner requires LP support. To build\nthe Apptainer recipe, you need an installer for CPLEX at a location\navailable under $IPC_THIRD_PARTY. We use version 22.1.1 for the\ncompetition, but in theory any version of CPLEX should be fine. For the\nApptainer recipe to work out of the box, the installer needs to be\nnamed as follows: cplex_studio2211.linux_x86_64.bin\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 10, Clang 12\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\nThe third party software OSI shipped within this repository is\nlicensed under the Eclipse Public License version 2.0 (EPL 2.0).\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [], - "updated_at": 1525440620.0 + "updated_at": 1688990844.0 }, { "data_format": 2, - "description": "Python from source for use with singularity", + "description": null, "filenames": [ - "Singularity" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/22.12/Singularity.22.12", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06" ], - "full_name": "sbutcher/container-python", + "full_name": "fai-saarland/fd-action-policy-testing", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-python\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainer-python\u003c/h1\u003e\n\u003cp\u003ePython from source for use with singularity\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAutomatic Metamorphic Test Oracles for Action-Policy Testing\u003c/h1\u003e\u003ca id=\"user-content-automatic-metamorphic-test-oracles-for-action-policy-testing\" class=\"anchor\" aria-label=\"Permalink: Automatic Metamorphic Test Oracles for Action-Policy Testing\" href=\"#automatic-metamorphic-test-oracles-for-action-policy-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source code for action-policy testing based on the paper \"Automatic Metamorphic Test Oracles for Action-Policy Testing\".\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{eisenhut-et-al-icaps23, \n title = {Automatic Metamorphic Test Oracles for Action-Policy Testing},\n author = {Eisenhut, Jan and Torralba, \u00c1lvaro and Christakis, Maria and Hoffmann, J\u00f6rg},\n booktitle = {Proceedings of the 33rd International Conference on Automated Planning and Scheduling ({ICAPS}\u002723), 2023},\n year = {2023}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe build on the Fast Downward fuzzing extension by Steinmetz and others (whose source code can be accessed \u003ca href=\"https://doi.org/10.5281/zenodo.6323289\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{steinmetz-et-al-icaps22, \n title = {Debugging a Policy: Automatic Action-Policy Testing in AI Planning},\n author = {Steinmetz, Marcel and Fi\u0161er, Daniel and Eniser, Hasan Ferit and Ferber, Patrick and Gros, Timo P. and Heim, Philippe and H\u00f6ller, Daniel and Schuler, Xandra and W\u00fcstholz, Valentin and Christakis, Maria and Hoffmann, J\u00f6rg},\n booktitle = {Proceedings of the 32nd International Conference on Automated Planning and Scheduling ({ICAPS}\u002722), 2022},\n doi = {10.1609/icaps.v32i1.19820}, \n url = {https://ojs.aaai.org/index.php/ICAPS/article/view/19820}, \n year = {2022}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe computation of dominance functions is based on:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{torralba-ijcai2017,\n author = {Torralba, \u00c1lvaro},\n title = {From Qualitative to Quantitative Dominance Pruning for Optimal Planning},\n booktitle = {Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, {IJCAI-17}},\n pages = {4426--4432},\n doi = {10.24963/ijcai.2017/618},\n url = {https://doi.org/10.24963/ijcai.2017/618},\n year = {2017},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the Project\u003c/h2\u003e\u003ca id=\"user-content-building-the-project\" class=\"anchor\" aria-label=\"Permalink: Building the Project\" href=\"#building-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease build the project as a part of the \u003ca href=\"https://github.com/fai-saarland/bughive/\"\u003ethe bughive framework\u003c/a\u003e using the Makefile provided there. The Makefile target is \u003ccode\u003efd-action-policy-testing\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eYou need to have \u003ca href=\"https://www.boost.org/\" rel=\"nofollow\"\u003eboost\u003c/a\u003e installed. The minimum required version is \u003ccode\u003e1.74\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCode\u003c/h2\u003e\u003ca id=\"user-content-code\" class=\"anchor\" aria-label=\"Permalink: Code\" href=\"#code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe related to policy testing is primarily located in \u003ccode\u003esrc/search/policy_testing/\u003c/code\u003e.\nThe source code for the computation of dominance functions can be found in \u003ccode\u003esrc/search/policy_testing/simulations/\u003c/code\u003e.\nWe made slight modifications to the code base since we ran the experiments for the paper, e.g., to make sure one can compile the source code using more recent compilers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheck out the test drivers in \u003ca href=\"https://github.com/fai-saarland/bughive/tree/master/test_drivers\"\u003ethe bughive repository\u003c/a\u003e to learn how to easily invoke the tool.\u003c/p\u003e\n\u003cp\u003eA possible search configuration could be \u003ccode\u003epool_fuzzer(testing_method=\u0026lt;oracle\u0026gt;)\u003c/code\u003e, where \u003ccode\u003e\u0026lt;oracle\u0026gt;\u003c/code\u003e is one of the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003earas(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eatomic_unrelaxation_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ebounded_lookahead_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecomposite_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edummy_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eestimator_based_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eiterative_improvement_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eunrelaxation_oracle(...)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003eiterative_improvement_oracle\u003c/code\u003e implements the bound maintenance oracles (BMOs), while \u003ccode\u003eatomic_unrelaxation_oracle\u003c/code\u003e and \u003ccode\u003eunrelaxation_oracle\u003c/code\u003e implement state morphing oracles (SMOs).\u003c/p\u003e\n\u003cp\u003eYou can learn about the options supported by an oracle (say \u003ccode\u003eunrelaxation_oracle\u003c/code\u003e) by calling \u003ccode\u003e./fast-downward.py --search -- --help unrelaxation_oracle\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1525427896.0 + "updated_at": 1708951067.0 }, { "data_format": 2, - "description": "Repository used to build Singularity containers of HD software", + "description": null, "filenames": [ - "Singularity" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/latest/Singularity", + "misc/releases/22.12/Singularity.22.12", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06" ], - "full_name": "faustus123/hdsingularity", + "full_name": "ipc2023-classical/planner1", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hdsingularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#hdsingularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehdsingularity\u003c/h1\u003e\n\u003cp\u003eRepository used to build Singularity containers of HD software\u003c/p\u003e\n\u003cp\u003eCheckout singularity-hub.org for details\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScorpion\u003c/h1\u003e\u003ca id=\"user-content-scorpion\" class=\"anchor\" aria-label=\"Permalink: Scorpion\" href=\"#scorpion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eScorpion is a classical planning system that extends \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003eFast\nDownward\u003c/a\u003e. The main extensions are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enovel state-of-the-art algorithms for optimal classical planning\u003c/li\u003e\n\u003cli\u003eadditional search algorithms\u003c/li\u003e\n\u003cli\u003eseveral new plugin options and utilities\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"#differences-between-scorpion-and-fast-downward\"\u003ebelow\u003c/a\u003e for a detailed list\nof extensions. We regularly port the latest changes from Fast Downward to\nScorpion and also integrate some features from Scorpion back into Fast Downward.\u003c/p\u003e\n\u003cp\u003ePlease use the following reference when citing Scorpion:\nJendrik Seipp, Thomas Keller and Malte Helmert.\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003eSaturated Cost Partitioning for Optimal Classical Planning\u003c/a\u003e.\nJournal of Artificial Intelligence Research 67, pp. 129-167. 2020.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions\u003c/h2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the dependencies (the table below lists which versions are tested):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install cmake g++ git make python3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor plugins based on linear programming (e.g., \u003ccode\u003eocp()\u003c/code\u003e, \u003ccode\u003epho()\u003c/code\u003e) you need\nto \u003ca href=\"https://www.fast-downward.org/LPBuildInstructions\" rel=\"nofollow\"\u003eadd an LP solver\u003c/a\u003e. Then\ncompile the planner with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand see the available options with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py --help # driver\n./fast-downward.py --search -- --help # search component\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more details (including build instructions for macOS and Windows), see the\ndocumentation about\n\u003ca href=\"https://www.fast-downward.org/ObtainingAndRunningFastDownward\" rel=\"nofollow\"\u003ecompiling\u003c/a\u003e\nand \u003ca href=\"https://www.fast-downward.org/PlannerUsage\" rel=\"nofollow\"\u003erunning\u003c/a\u003e the planner. The\n\u003ca href=\"https://jendrikseipp.github.io/scorpion\" rel=\"nofollow\"\u003eplugin documentation\u003c/a\u003e shows\nwhich plugins are available (heuristics, search algorithms, etc.) and how\nto use them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRecommended configuration\u003c/h3\u003e\u003ca id=\"user-content-recommended-configuration\" class=\"anchor\" aria-label=\"Permalink: Recommended configuration\" href=\"#recommended-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend using the following configuration:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py \\\n --transform-task preprocess-h2 \\\n ../benchmarks/gripper/prob01.pddl \\\n --search \"astar(scp_online([\n projections(sys_scp(max_time=100, max_time_per_restart=10)),\n cartesian()],\n saturator=perimstar, max_time=1000, interval=10K, orders=greedy_orders()),\n pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003epreprocess-h2\u003c/code\u003e call prunes irrelevant operators in a preprocessing\nstep. The search configuration uses \u003ca href=\"https://ojs.aaai.org/index.php/SOCS/article/view/18535\" rel=\"nofollow\"\u003epartial order\nreduction\u003c/a\u003e and\nmaximizes over\n\u003ca href=\"https://www.jair.org/index.php/jair/article/view/11673\" rel=\"nofollow\"\u003ediverse\u003c/a\u003e,\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/3503\" rel=\"nofollow\"\u003esubset-saturated\u003c/a\u003e\ncost partitioning heuristics computed\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/15976/\" rel=\"nofollow\"\u003eonline\u003c/a\u003e during\nthe search. The underlying abstractions are \u003ca href=\"https://www.ijcai.org/proceedings/2019/780\" rel=\"nofollow\"\u003eSys-SCP pattern\ndatabases\u003c/a\u003e and \u003ca href=\"https://jair.org/index.php/jair/article/view/11217\" rel=\"nofollow\"\u003eCartesian\nabstractions\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e(In \u003ca href=\"https://lab.readthedocs.io/\" rel=\"nofollow\"\u003eDownward Lab\u003c/a\u003e you can use\n\u003ccode\u003eadd_algorithm(name=\"scorpion\", repo=\"path/to/repo\", rev=\"scorpion\", component_options=[], driver_options=[\"--transform-task\", \"preprocess-h2\", \"--alias\", \"scorpion\"]\u003c/code\u003e to run the recommended Scorpion configuration.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eApptainer image\u003c/h4\u003e\u003ca id=\"user-content-apptainer-image\" class=\"anchor\" aria-label=\"Permalink: Apptainer image\" href=\"#apptainer-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo simplify the installation process, we provide an executable\n\u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e container (formerly known as Singularity).\nIt accepts the same arguments as the \u003ccode\u003efast-downward.py\u003c/code\u003e script (see above).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Download the image,\napptainer pull scorpion.sif oras://ghcr.io/jendrikseipp/scorpion:latest\n\n# or build it yourself.\napptainer build scorpion.sif Apptainer\n\n# Then run recommended configuration (available via \"scorpion\" alias).\n./scorpion.sif --transform-task preprocess-h2 --alias scorpion PROBLEM_FILE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIPC 2018 version\u003c/h3\u003e\u003ca id=\"user-content-ipc-2018-version\" class=\"anchor\" aria-label=\"Permalink: IPC 2018 version\" href=\"#ipc-2018-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you prefer to run the Scorpion version from IPC 2018 (which uses an\nolder Fast Downward version and different abstractions), we recommend\nusing the \u003ca href=\"https://bitbucket.org/ipc2018-classical/team44/src/ipc-2018-seq-opt/\" rel=\"nofollow\"\u003eScorpion IPC\nrepo\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDifferences between Scorpion and Fast Downward\u003c/h2\u003e\u003ca id=\"user-content-differences-between-scorpion-and-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Differences between Scorpion and Fast Downward\" href=\"#differences-between-scorpion-and-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDiff between the latest merged version of Fast Downward and Scorpion:\n\u003ca href=\"https://github.com/jendrikseipp/scorpion/compare/main...scorpion\"\u003ehttps://github.com/jendrikseipp/scorpion/compare/main...scorpion\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScorpion comes with the\n\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/13708\" rel=\"nofollow\"\u003eh\u00b2-preprocessor\u003c/a\u003e\nby Vidal Alc\u00e1zar and \u00c1lvaro Torralba that prunes irrelevant operators.\nPass \u003ccode\u003e--transform-task preprocess-h2\u003c/code\u003e to use it.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003e--transform-task\u003c/code\u003e command allows you to run arbitrary preprocessing\ncommands that transform the SAS+ output from the translator before\npassing it to the search.\u003c/li\u003e\n\u003cli\u003eScorpion uses a\n\u003ca href=\"https://github.com/greg7mdp/parallel-hashmap\"\u003ephmap::flat_hash_set\u003c/a\u003e to check\nfor duplicate states, which often drastically reduces the peak memory usage,\ncompared to Fast Downward\u0027s \u003ccode\u003eIntHashSet\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf \u003ca href=\"https://ccache.dev/\" rel=\"nofollow\"\u003eccache\u003c/a\u003e is installed (recommended), Scorpion\nuses it to cache compilation files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew translator options\u003c/h3\u003e\u003ca id=\"user-content-new-translator-options\" class=\"anchor\" aria-label=\"Permalink: New translator options\" href=\"#new-translator-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse \u003ccode\u003e--dump-predicates\u003c/code\u003e and \u003ccode\u003e--dump-static-atoms\u003c/code\u003e to write files with\ninformation that\u0027s useful for learning domain control knowledge.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew plugin options\u003c/h3\u003e\u003ca id=\"user-content-new-plugin-options\" class=\"anchor\" aria-label=\"Permalink: New plugin options\" href=\"#new-plugin-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., search_strategy=incremental)\u003c/code\u003e: use \u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/6667\" rel=\"nofollow\"\u003eincremental search for\nCartesian abstraction\nrefinement\u003c/a\u003e\n(default).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., pick_flawed_abstract_state={batch_min_h, ...})\u003c/code\u003e:\nfind all current flaws, then iteratively repair the flaw that\u0027s closest to the goal\n(\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/19819\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, default=\u003ccode\u003ebatch_min_h\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar/cartesian}(..., pick_split={max_cover, ...}, tiebreak_split={max_refined, ...})\u003c/code\u003e:\nsmarter strategies for splitting a flawed abstract state\n(\u003ca href=\"https://ojs.aaai.org/index.php/ICAPS/article/view/19819\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, default=\u003ccode\u003emax_cover\u003c/code\u003e\nand \u003ccode\u003emax_refined\u003c/code\u003e for tiebreaking).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e{cegar,cartesian}(..., dot_graph_verbosity={silent, write_to_console, write_to_file})\u003c/code\u003e:\nwrite intermediate abstractions as Graphviz dot files to stdout or to files (default=\u003ccode\u003esilent\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ehillclimbing(..., max_generated_patterns=200)\u003c/code\u003e: limit the number of\npatterns generated by hill climbing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esystematic(..., pattern_type=interesting_general)\u003c/code\u003e: compute interesting\npatterns for general cost partitioning.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew cost partitioning algorithms for abstraction heuristics\u003c/h3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-abstraction-heuristics\" class=\"anchor\" aria-label=\"Permalink: New cost partitioning algorithms for abstraction heuristics\" href=\"#new-cost-partitioning-algorithms-for-abstraction-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe use Cartesian abstractions in the example configurations below\n(\u003ccode\u003e[cartesian()]\u003c/code\u003e). You can also use pattern database heuristics, e.g.,\n\u003ccode\u003e[projections(systematic(2))]\u003c/code\u003e, or mix abstractions, e.g.,\n\u003ccode\u003e[projections(systematic(3)), cartesian()]\u003c/code\u003e. Some of the algorithms below\nare also part of vanilla Fast Downward, but are only implemented for PDB\nheuristics.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning:\n\u003ccode\u003eocp([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003ecanonical_heuristic([cartesian()])\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], opportunistic=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning:\n\u003ccode\u003eucp([cartesian()], ..., opportunistic=true)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003egzocp([cartesian()], ...)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003escp([cartesian()], ...)\u003c/code\u003e (offline), \u003ccode\u003escp_online([cartesian()], ...)\u003c/code\u003e (online)\u003c/li\u003e\n\u003cli\u003e(Saturated) post-hoc optimization:\n\u003ccode\u003epho([cartesian()], ..., saturated={false,true})\u003c/code\u003e (offline),\n\u003ccode\u003eoperatorcounting([pho_abstraction_constraints([cartesian()], saturated={false,true})])\u003c/code\u003e (online)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can also compute the maximum over abstraction heuristics:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emaximize([cartesian()])\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe plugin documentation shows all options for \u003ca href=\"https://jendrikseipp.github.io/scorpion/Evaluator/#cost_partitioning_heuristics\" rel=\"nofollow\"\u003ecost partitioning\nheuristics\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNew pattern collection generators\u003c/h2\u003e\u003ca id=\"user-content-new-pattern-collection-generators\" class=\"anchor\" aria-label=\"Permalink: New pattern collection generators\" href=\"#new-pattern-collection-generators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSystematic patterns with size limits:\n\u003ccode\u003esys_scp(max_pattern_size=X, max_pdb_size=Y, max_collection_size=Z, ..., saturate=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSys-SCP patterns:\n\u003ccode\u003esys_scp(...)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew cost partitioning algorithms for landmark heuristics\u003c/h3\u003e\u003ca id=\"user-content-new-cost-partitioning-algorithms-for-landmark-heuristics\" class=\"anchor\" aria-label=\"Permalink: New cost partitioning algorithms for landmark heuristics\" href=\"#new-cost-partitioning-algorithms-for-landmark-heuristics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExample using A* search and saturated cost partitioning over BJOLP\nlandmarks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--evaluator\n \"lmc=lmcount(lm_merged([lm_rhw(), lm_hm(m=1)]),\n admissible=true, cost_partitioning=suboptimal, greedy=true,\n reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\"\n--search\n \"astar(lmc, lazy_evaluator=lmc)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDifferent cost partitioning algorithms (all need \u003ccode\u003eadmissible=true\u003c/code\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOptimal cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=optimal)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanonical heuristic:\n\u003ccode\u003elmcount(..., cost_partitioning=canonical)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePost-hoc optimization:\n\u003ccode\u003elmcount(..., cost_partitioning=pho)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniform cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=false)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpportunistic uniform cost partitioning (part of vanilla Fast Downward):\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=true, scoring_function=min_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGreedy zero-one cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=false, scoring_function=max_heuristic)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSaturated cost partitioning:\n\u003ccode\u003elmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNew search engines\u003c/h2\u003e\u003ca id=\"user-content-new-search-engines\" class=\"anchor\" aria-label=\"Permalink: New search engines\" href=\"#new-search-engines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBreadth-first search (without overhead of the more general \u003ccode\u003eeager()\u003c/code\u003e search):\n\u003ccode\u003ebrfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDepth-first search:\n\u003ccode\u003edfs()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExhaustive search (useful for dumping the reachable state space of small input tasks):\n\u003ccode\u003edump_reachable_search_space()\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIDA* search:\n\u003ccode\u003eidastar(cegar(cache_estimates=false))\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIterative width search:\n\u003ccode\u003eiw(width=2)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1501591637.0 + "updated_at": 1688990534.0 }, { "data_format": 2, - "description": "Singularity recipes for CARC systems", + "description": "Gym Environment to simulate the Layout Problem as a Markov Decision Process to be solved by Reinforcement Learning", "filenames": [ - "Singularity.centos", - "Singularity.ubuntu-ompi", - "Singularity.ubuntu-mpich" + "Singularity.def" ], - "full_name": "UNM-CARC/singularity-test", + "full_name": "hendrikunger/factorySim", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Tests\u003c/h1\u003e\n\u003cp\u003eThis repository contains test singularity recipes for Ubuntu and CentOS repository builds for\nHPC systems at the UNM Center for Advanced Research Computing. These recipes are generally built\nusing Singularity Hub, which links to this repository, and are meant for debugging basic\ncontainer setups that are then used to develop other more complex recipes.\u003c/p\u003e\n\u003cp\u003eNote that these containers pull the CARC modules //into// the containers when they run so that\ncode compiled outside the container can run inside the container. That\u0027s rarely something you want to\ndo, as one of the main point of containers is that they\u0027re stable and reproducible.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003efactorySim\u003c/h1\u003e\u003ca id=\"user-content-factorysim\" class=\"anchor\" aria-label=\"Permalink: factorySim\" href=\"#factorysim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGym Environment to simulate the Layout Problem as a Markov Decision Process to be solved by Reinforcement Learning\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning instructions\u003c/h2\u003e\u003ca id=\"user-content-running-instructions\" class=\"anchor\" aria-label=\"Permalink: Running instructions\" href=\"#running-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse Docker host with Nvidia drivers installed.\nClone repository to Docker host.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/hendrikunger/factorySim.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e factorySim\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Docker image using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t factorysim \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun image with appropriate command e.g.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm -it --gpus all --shm-size=12gb factorysim:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eshm-size needs to be greater than 30% of RAM of Docker host\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll files from github repository are located in the default location /home/ray/factorySim. Training scripts can be run from this location as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeveloping instructions\u003c/h2\u003e\u003ca id=\"user-content-developing-instructions\" class=\"anchor\" aria-label=\"Permalink: Developing instructions\" href=\"#developing-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone Repository to your local machine or use Docker container from above\nNavigate to the factorySim/env directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/hendrikunger/factorySim.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e factorySim\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are not using docker you need to install dependecies using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapt-get update\napt-get install build-essential ibcairo2-dev pkg-config python3-dev\npip install -r requirements_factorySim.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIFC Open Shell is not in the index and needs to be installed manually.\nDownload appropriate version from \u003ca href=\"http://ifcopenshell.org/python\" rel=\"nofollow\"\u003ehttp://ifcopenshell.org/python\u003c/a\u003e and unpack into site packages directory of your Python installation.\ne.g.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget https://s3.amazonaws.com/ifcopenshell-builds/ifcopenshell-python-37-v0.6.0-517b819-linux64.zip\nunzip -q ifcopenshell-python-37-v0.6.0-517b819-linux64.zip -d \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/anaconda3/lib/python3.7/site-packages\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNavigate to the factorySim/env directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e env\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBuild a local package of factorySim using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m pip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1536783389.0 + "updated_at": 1716906355.0 }, { "data_format": 2, - "description": "FEniCS containers for CARC systems", + "description": "official build specifications for picard tools", "filenames": [ - "Singularity.docker", - "Singularity.ubuntu" + "Singularity" ], - "full_name": "UNM-CARC/FEniCS", + "full_name": "researchapps/picard", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fenics\" class=\"anchor\" aria-hidden=\"true\" href=\"#fenics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFEniCS\u003c/h1\u003e\n\u003cp\u003eThis repository contains a FEniCS container for UNM CARC high performance systems\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.docker - Singularity container built from the standard FEniCS docker container\u003c/li\u003e\n\u003cli\u003eSingularity.ubuntu - Singularity container built from the FEniCS ubuntu packages\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePicard\u003c/h1\u003e\u003ca id=\"user-content-picard\" class=\"anchor\" aria-label=\"Permalink: Picard\" href=\"#picard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official build file repository for the Picard software for Singularity Hub.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 picard.img\nsudo singularity bootstrap picard.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the picard runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./picard.img [args] ...\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1511832970.0 + "updated_at": 1484507753.0 }, { "data_format": 2, - "description": "Singularity container for samtools ", + "description": null, "filenames": [ - "Singularity", - "old/Singularity.v1.6" + "Singularity" ], - "full_name": "stevekm/singularity-samtools-demo", + "full_name": "singularityhub/redis", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h1\u003e\n\u003cp\u003eThis assumes you are building a Singularity container locally on a Mac\u003c/p\u003e\n\u003cp\u003eMake sure you\u0027ve already installed Vagrant, since its needed to run Singularity on a Mac\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebrew cask install virtualbox\nbrew cask install vagrant\nbrew cask install vagrant-manager\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have trouble install Vagrant with homebrew, try using \u003ca href=\"https://releases.hashicorp.com/vagrant/2.0.1/vagrant_2.0.1_x86_64.dmg\" rel=\"nofollow\"\u003ethis\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-creating-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating the Container\u003c/h1\u003e\n\u003cp\u003eThe workflow for creating a Singularity container on a Mac through Vagrant is saved in the included \u003ccode\u003eMakefile\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eMake the container by running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake container\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd run a test on the created container with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003eIf everything worked, the following files should be created:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esingularity-vm/image/singularity-container-samtools\u003c/code\u003e: the Singularity container file for samtools\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esingularity-vm/image/samtools-version.txt\u003c/code\u003e: the output from running samtools inside the container, should look like this:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esamtools 1.6\nUsing htslib 1.6\nCopyright (C) 2017 Genome Research Ltd.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/install-mac\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://app.vagrantup.com/singularityware/boxes/singularity-2.4\" rel=\"nofollow\"\u003ehttps://app.vagrantup.com/singularityware/boxes/singularity-2.4\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://releases.hashicorp.com/vagrant/2.0.1/vagrant_2.0.1_x86_64.dmg\" rel=\"nofollow\"\u003ehttps://releases.hashicorp.com/vagrant/2.0.1/vagrant_2.0.1_x86_64.dmg\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/docs-build-container\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/docs-build-container\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/docs-recipes\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/docs-recipes\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/qbicsoftware/qbic-singularity-samtools\"\u003ehttps://github.com/qbicsoftware/qbic-singularity-samtools\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRedis\u003c/h1\u003e\u003ca id=\"user-content-redis\" class=\"anchor\" aria-label=\"Permalink: Redis\" href=\"#redis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official library of redis builds for Singularity images hosted on Singularity Hub. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003eeach branch corresponds with a different verison, or tag.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create redis.img\nsudo singularity bootstrap redis.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity", - "singularity-container" - ], - "updated_at": 1521728818.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1484275274.0 }, { "data_format": 2, - "description": null, + "description": "Singularity Image for NLPipe", "filenames": [ "Singularity" ], - "full_name": "tanhnhn/singularityhub-sregistry", + "full_name": "vanatteveldt/nlpipe-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-registry\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Registry\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"http://joss.theoj.org/papers/050362b7e7691d2a5d0ebed8251bc01e\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4cb65855144c475cbe5584c579404a17e3e6984f958da24427dbe46b6202eb3c/687474703a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f30353033363262376537363931643261356430656265643832353162633031652f7374617475732e737667\" alt=\"status\" data-canonical-src=\"http://joss.theoj.org/papers/050362b7e7691d2a5d0ebed8251bc01e/status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.1012531\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/411f713db9ba01edfcb60386aaa1dff3e4ed4464707b95d889900a88d8f54936/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313031323533312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1012531.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://singularityhub.github.io/sregistry\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-singularity-registry\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-singularity-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is Singularity Registry\u003c/h2\u003e\n\u003cp\u003eSingularity Registry is a management and storage of Singularity images for an institution or user to deploy locally. It does not manage building, but serves endpoints to obtain and save containers. The Registry is expected to be available for use in the Fall.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-images-included\" class=\"anchor\" aria-hidden=\"true\" href=\"#images-included\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages Included\u003c/h2\u003e\n\u003cp\u003eSingularity Registry consists of several Docker images, and they are integrated to work together using \u003ca href=\"docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e. The images are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003evanessa/sregistry\u003c/strong\u003e: is the main uwsgi application, which serves a Django (python-based) application.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003enginx\u003c/strong\u003e: pronounced (engine-X) is the webserver. The starter application is configured for http, however you should follow the instructions to set up https properly.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eworker\u003c/strong\u003e: is the same uwsgi image, but with a running command that is specialized to perform tasks. The tasks are run via \u003ca href=\"http://www.celeryproject.org/\" rel=\"nofollow\"\u003ecelery\u003c/a\u003e, a distributed job queue that fits nicely into Django. The celery worker uses a\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eredis\u003c/strong\u003e: database to organize the jobs themselves.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more information about Singularity Registry, please reference the \u003ca href=\"https://singularityhub.github.io/sregistry\" rel=\"nofollow\"\u003edocs\u003c/a\u003e. If you have any issues, please \u003ca href=\"https://github.com/singularityhub/sregistry/issues\"\u003elet me know\u003c/a\u003e!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis code is licensed under the Affero GPL, version 3.0 or later \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enlpipe-singularity\u003c/h1\u003e\u003ca id=\"user-content-nlpipe-singularity\" class=\"anchor\" aria-label=\"Permalink: nlpipe-singularity\" href=\"#nlpipe-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Image for NLPipe\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBuild the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre lang=\"{sh}\"\u003e\u003ccode\u003esudo singularity build nlpipe.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun the alpino service:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre lang=\"{sh}\"\u003e\u003ccode\u003esingularity instance.start nlpipe.img nlpipe\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the worker:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre lang=\"{sh}\"\u003e\u003ccode\u003esingularity run --app worker instance://nlpipe /tmp/nlpipedata alpinocoref\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eTo test, run (in a separate shell) the client:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre lang=\"{sh}\"\u003e\u003ccode\u003esingularity run --app client instance://nlpipe /tmp/nlpipedata alpinocoref process_inline \"dit is een test\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich should print an XML file after about 10 seconds.\u003c/p\u003e\n\u003cp\u003e(Note: these are my very first steps into singularity-land, so all feedback is appreciated!)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1513562903.0 + "updated_at": 1515629557.0 }, { "data_format": 2, - "description": "Singularity Recipe for Tofu2", + "description": "Docker image for Lattice Element Method", "filenames": [ - "Singularity.v17", "Singularity" ], - "full_name": "ResearchIT/tofu2", + "full_name": "cb-geo/lem-container", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-for-tofu2\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-for-tofu2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipe for Tofu2\u003c/h1\u003e\n\u003cp\u003eThis repo contains recipes to run \u003ca href=\"https://github.com/PacificBiosciences/IsoSeq_SA3nUP/wiki/%5BBeta%5D-ToFU2:-running-and-installing-ToFU2#install\"\u003eTofu2\u003c/a\u003e\nwithin a \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eVersions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ev17 - Tofu2 installed on Ubuntu\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Use:\u003c/h2\u003e\n\u003cp\u003eRun example:\u003c/p\u003e\n\u003cp\u003esingularity run shub://ResearchIT/tofu2 run_preCluster.py --cpus=4\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-alternative-method\" class=\"anchor\" aria-hidden=\"true\" href=\"#alternative-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternative method:\u003c/h2\u003e\n\u003cp\u003euse the provided bash wrapper and module file to use the tofu2 singularity container like a standard module\n(this assumes you have a singularity/2.4 module)\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003emodule load tofu2/v17\ntofu2 run_preCluster.py --cpus=4\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker image for Lattice Element Method\u003c/h1\u003e\u003ca id=\"user-content-docker-image-for-lattice-element-method\" class=\"anchor\" aria-label=\"Permalink: Docker image for Lattice Element Method\" href=\"#docker-image-for-lattice-element-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eKrishna Kumar\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/cbgeo/lem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a191f0a1b6c4e1b41f032c2dd99bc4a5cabbc62a875f08e24da458d24bc4fc6a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f717561792d2d696d6167652d636267656f2d2d6c656d2d6666363962342e737667\" alt=\"Quay image\" data-canonical-src=\"https://img.shields.io/badge/quay--image-cbgeo--lem-ff69b4.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/cbgeo/lem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9982af777dc0e058b77b89824b8290f275c85fe153068c0cc68132431d64dabe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d2d6875622d636267656f2d2d6c656d2d6666363962342e737667\" alt=\"Docker hub\" data-canonical-src=\"https://img.shields.io/badge/docker--hub-cbgeo--lem-ff69b4.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/cb-geo/lem-container\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72966832e29d839ef2277b9d204355a9ff8c427bc681c73ce96b4fd1a443ec92/68747470733a2f2f636972636c6563692e636f6d2f67682f63622d67656f2f6c656d2d636f6e7461696e65722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/cb-geo/lem-container.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.org/cb-geo/docker-lem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9ba1a6181039c2f5cabd4a0f4b027382a31a54459e71aeacf82a64a07bea37a4/68747470733a2f2f6170692e7472617669732d63692e6f72672f63622d67656f2f646f636b65722d6c656d2e737667\" alt=\"Build status\" data-canonical-src=\"https://api.travis-ci.org/cb-geo/docker-lem.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://microbadger.com/images/cbgeo/lem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98e02b5982223671602e697fff2687b7c71a4ba023a12a4fb37767334a36e39e/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f636267656f2f6c656d2e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/cbgeo/lem.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClang 4.0\u003c/li\u003e\n\u003cli\u003eCMake\u003c/li\u003e\n\u003cli\u003eCUDA\u003c/li\u003e\n\u003cli\u003eEigen3\u003c/li\u003e\n\u003cli\u003eGCC 7\u003c/li\u003e\n\u003cli\u003eIntel TBB\u003c/li\u003e\n\u003cli\u003eIntel MKL\u003c/li\u003e\n\u003cli\u003eVim\u003c/li\u003e\n\u003cli\u003eVoro++\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing the docker image\u003c/h1\u003e\u003ca id=\"user-content-using-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Using the docker image\" href=\"#using-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe docker image can be used directly from Quay.io or Docker Hub\u003c/li\u003e\n\u003cli\u003ePull the docker image \u003ccode\u003edocker pull quay.io/cbgeo/lem\u003c/code\u003e or \u003ccode\u003edocker pull cbgeo/lem\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo launch the \u003ccode\u003ecbgeo/ca-abm\u003c/code\u003e docker container, run \u003ccode\u003edocker run -ti cbgeo/lem:latest /bin/bash\u003c/code\u003e or \u003ccode\u003edocker run -ti quay.io/cbgeo/lem:latest /bin/bash\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo login as root\u003c/h1\u003e\u003ca id=\"user-content-to-login-as-root\" class=\"anchor\" aria-label=\"Permalink: To login as root\" href=\"#to-login-as-root\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLaunching docker as root user: \u003ccode\u003edocker exec -u 0 -ti \u0026lt;containerid\u0026gt; /bin/bash\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCreating an image from the docker file\u003c/h1\u003e\u003ca id=\"user-content-creating-an-image-from-the-docker-file\" class=\"anchor\" aria-label=\"Permalink: Creating an image from the docker file\" href=\"#creating-an-image-from-the-docker-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eTo build an image from docker file run as root \u003ccode\u003edocker build -t \"cbgeo/lem\" /path/to/Dockerfile\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edocker history\u003c/code\u003e will show you the effect of each command has on the overall size of the file.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 3, "topics": [ - "tofu", - "pacbio", - "singularity" + "lem", + "container" ], - "updated_at": 1522255502.0 + "updated_at": 1518214261.0 }, { "data_format": 2, - "description": "R wrapper for bamdb", + "description": "Singularity recipe for Pandoc.", "filenames": [ - "src/bamdb/Singularity.bamdb" + "Singularity" ], - "full_name": "D-Lo/bambi", + "full_name": "robertodr/pandoc", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/mskilab/bambi\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/47c82ab2d405aa684f3a5004ed8fc79887c025105127effda9ce1d35b5568974/68747470733a2f2f7472617669732d63692e6f72672f6d736b696c61622f62616d62692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/mskilab/bambi.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/mskilab/bambi?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ccb3814df2f3f1c65e518dd49a10732518ba754f251e50546a0d42ec9fd9cdab/68747470733a2f2f696d672e736869656c64732e696f2f636f6465636f762f632f6769746875622f6d736b696c61622f62616d62692e737667\" alt=\"codecov.io\" data-canonical-src=\"https://img.shields.io/codecov/c/github/mskilab/bambi.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bambi\" class=\"anchor\" aria-hidden=\"true\" href=\"#bambi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi\u003c/h1\u003e\n\u003cp\u003eR package for querying 10x WGS and single-cell BAMs\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/gUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/bamUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-bambi-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#bambi-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi commands\u003c/h2\u003e\n\u003cp\u003eInstantiate a bambi object:\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003egrab_bx()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_bx(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_cb()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_cb(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_ub()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_ub(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003efetch_by_tag()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003efetch_by_tag(tag, tag_queries, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstantiate a \u003ccode\u003ebambi\u003c/code\u003e object\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003elibrary(bambi)\n\n\u0026gt; hcc1143_subset = bambi$new(bam_file = \"subsetHCC1143_phased_possorted0001.bam\", bamdb_path=\"subsetHCC1143_phased_possorted0001_lmdb\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCall methods\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003e\u0026gt; hcc1143_subset$grab_bx(\u0027CGACGTGTCCTCTAGC-1\u0027)\nGRanges object with 2 ranges and 11 metadata columns:\n seqnames ranges strand |\n \u0026lt;Rle\u0026gt; \u0026lt;IRanges\u0026gt; \u0026lt;Rle\u0026gt; |\n [1] chr1 [147975454, 147975580] + |\n [2] chr1 [147975675, 147975824] - |\n qname flag mapq cigar\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;character\u0026gt;\n [1] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 99 16 127M\n [2] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 147 16 150M\n rnext pnext tlen\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt;\n [1] = 147975676 371\n [2] = 147975455 -371\n seq\n \u0026lt;character\u0026gt;\n [1] ATGTCTTCTTCCTCATTATCTGGCACTGGTTAGGAAGCACTCATCTCCATGAAGTCATCTTTTGTTAATTCCTCTGGTGTGGTGTGTATTAGCTCTTAAATTCCTCCAAGATCCATATCTTGCAACC\n [2] ATCTGGACACAAATTGTACTTTTGTCCAGCACGAATTTATTGTTTTGAGTTTCATGGTTTTCTATATCAACTGATGACATCTTGAAAGGTGTAAGCCTTCCAGACTTCCATGATGTTCTCTCTATTGGGTTTCTCTTTTGCAATGTTGAC\n qual\n \u0026lt;character\u0026gt;\n [1] JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJAJFJJJJJJJJJFJJJJJJJJJJFJJJJFFFJJJFJJJJJJAAJFJJJFAFAFFFJAA\u0026lt;7F\u0026lt;\n [2] A\u0026lt;7FFFJFFFAJJAAAJJF\u0026lt;F\u0026lt;7A-\u0026lt;AA-\u0026lt;\u0026lt;\u0026lt;AFFJJJJJJJJFFJAFFAAFJFJJJAFFJJJJJJJJJJFJFAJJJJJJFJJJJJJ\u0026lt;FFJJJFJJJFJJJJJJJJJJJJJFJJJJFFJ7JJJJF\u0026lt;JJJJJJJJJJJJJJJJJJJFFAA\u0026lt;\n BX qwidth\n \u0026lt;character\u0026gt; \u0026lt;integer\u0026gt;\n [1] CGACGTGTCCTCTAGC-1 127\n [2] CGACGTGTCCTCTAGC-1 150\n -------\n seqinfo: 1 sequence from an unspecified genome; no seqlengths\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity recipe for Pandoc\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-pandoc\" class=\"anchor\" aria-label=\"Permalink: Singularity recipe for Pandoc\" href=\"#singularity-recipe-for-pandoc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4516\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/4516\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull --name pandoc.sif shub://bast/pandoc\n$ singularity run pandoc.sif --from=markdown --to=rst --output=README.rst README.md\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1531085438.0 + "updated_at": 1620331522.0 }, { "data_format": 2, - "description": null, + "description": "The Singularity file to create an image with nvBowtie and nvBWT. Available on Singularity Hub", "filenames": [ "Singularity" ], - "full_name": "markxiao/fsl", + "full_name": "KevinSayers/nvBowtie_Singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fsl\" class=\"anchor\" aria-hidden=\"true\" href=\"#fsl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efsl\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003envBowtie_Singularity\u003c/h1\u003e\u003ca id=\"user-content-nvbowtie_singularity\" class=\"anchor\" aria-label=\"Permalink: nvBowtie_Singularity\" href=\"#nvbowtie_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity file to create an image with nvBowtie and nvBWT. Available on Singularity Hub\u003c/p\u003e\n\u003cp\u003eThe Singularity file can be used to bootstrap a container with nvBowtie. This is a GPU enabled Bowtie2 implementation developed by NVIDIA.\u003c/p\u003e\n\u003cp\u003eMore information: \u003ca href=\"https://developer.nvidia.com/nvbio\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/nvbio\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1618603672.0 + "updated_at": 1503243479.0 }, { "data_format": 2, @@ -19218,125 +18940,120 @@ var data = "filenames": [ "Singularity" ], - "full_name": "markxiao/freesurfer", + "full_name": "phgenomics-singularity/mash_kmc", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-freesurfer\" class=\"anchor\" aria-hidden=\"true\" href=\"#freesurfer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efreesurfer\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1618603672.0 + "updated_at": 1576562938.0 }, { "data_format": 2, - "description": "Virtual Research Environment for Sara Server - container build scripts", + "description": "Singularity recipe for CRISPR-DAV", "filenames": [ "Singularity" ], - "full_name": "54r4/sara-server-vre", + "full_name": "ISU-HPC/crispr-dav", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sara-server-vre\" class=\"anchor\" aria-hidden=\"true\" href=\"#sara-server-vre\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esara-server-vre\u003c/h1\u003e\n\u003cp\u003eVirtual Research Environment for Sara Server - container build scripts\u003c/p\u003e\n\u003cp\u003eThis is the VRE main spec containing a Java Runtime Environment plus Eclipse\nused for the development of the SARA service.\nA local postgres database is integrated, too. The source is a docker repo\nwhich is being pulled on build time and used to locally run a postgresql\nserver using udocker.\nThis VRE has no external requirements whatsoever once the image has been built.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-prebuild-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-prebuild-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse prebuild image\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e cd /tmp\n singularity pull --name \"sara-server-vre.img\" shub://c1t4r/sara-server-vre\n ./sara-server-vre.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-local-image-singularity-23\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-local-image-singularity-23\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild local image (Singularity 2.3)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ecd /tmp\nsingularity create -s 2048 sara-server-vre.img\nsingularity bootstrap sara-server-vre.img ./Singularity\n./sara-server-vre.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-local-image-singularity-24\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-local-image-singularity-24\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild local image (Singularity 2.4)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build sara-server-vre.simg ./Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecrispr-dav\u003c/h1\u003e\u003ca id=\"user-content-crispr-dav\" class=\"anchor\" aria-label=\"Permalink: crispr-dav\" href=\"#crispr-dav\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for CRISPR-DAV\u003c/p\u003e\n\u003cp\u003eAdapted form the original Docker recipe \u003ca href=\"https://github.com/pinetree1/crispr-dav\"\u003ehttps://github.com/pinetree1/crispr-dav\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1546985098.0 + "updated_at": 1524505816.0 }, { "data_format": 2, - "description": " Build for docker and singularity containers for Multi Atlas", + "description": "Singularity container for PRIMME (PReconditioned Iterative MultiMethod Eigensolver) library", "filenames": [ - "Singularity", - "Singularity.2.1.0" + "Singularity" ], - "full_name": "VUIIS/Multi_Atlas_app", + "full_name": "twesterhout/primme-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-multi_atlas_app\" class=\"anchor\" aria-hidden=\"true\" href=\"#multi_atlas_app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti_Atlas_app\u003c/h1\u003e\n\u003cp\u003eThis includes everything required (except for the \"full-multi-atlas\" directory) to build a docker and corresponding singularity container for the Multi Atlas pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/vuiiscci/multi_atlas/tags/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/734\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Instructions:\u003c/h1\u003e\n\u003cp\u003eJust clone and run \u003ccode\u003ebuild.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vuiiscci/Multi_Atlas_app.git\ncd Multi_Atlas_app/\n./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE that you must have full-multi-atlas directory which contains atlases.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Instructions:\u003c/h1\u003e\n\u003cp\u003eFor docker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run --rm \\\n-v $(pwd)/INPUTS/:/INPUTS/ \\\n-v $(pwd)/OUTPUTS:/OUTPUTS/ \\\n--user $(id -u):$(id -g) \\\nvuiiscci/multi_atlas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e \\\n-B INPUTS/:/INPUTS \\\n-B OUTPUTS/:/OUTPUTS \\\nshub://vuiiscci/Multi_Atlas_app\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1674914637.0 + "subscribers_count": 3, + "topics": [ + "singularity-containers", + "exact-diagonalization", + "svd", + "c" + ], + "updated_at": 1597061752.0 }, { "data_format": 2, - "description": "Singularity Recipe for GEOS-Chem", + "description": null, "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "geoschem/Singularity_GC", + "full_name": "pnnlhep/p8compute", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-note-this-repository-is-obsolete-and-has-been-archived\" class=\"anchor\" aria-hidden=\"true\" href=\"#note-this-repository-is-obsolete-and-has-been-archived\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTE: THIS REPOSITORY IS OBSOLETE AND HAS BEEN ARCHIVED\u003c/h2\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "geos-chem", - "singularity-container", - "docker-image" - ], - "updated_at": 1674873388.0 + "subscribers_count": 6, + "topics": [], + "updated_at": 1539125493.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for LaTeX.", "filenames": [ - "Singularity_recipev1.0", - "Singularity_recipe_R.3.4.1", - "Singularity_add.R_packages", - "Singularity_hicpro_v1", - "Singularity.add_python_packages", - "Singularity_recipe0_part1", - "Singularity.add_g2gtools", - "Singularity_recipev1.0_addR.3.4.3", - "Singularity_recipev1.R-3-4-3", - "Singularity_recipe_MMARGE" + "Singularity" ], - "full_name": "pranithavangala/singularity", + "full_name": "robertodr/latex", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity recipe for LaTeX\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-latex\" class=\"anchor\" aria-label=\"Permalink: Singularity recipe for LaTeX\" href=\"#singularity-recipe-for-latex\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4518\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/4518\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull --name latex.sif shub://bast/latex\n$ singularity run latex.sif example.tex\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1609299433.0 + "updated_at": 1620251806.0 }, { "data_format": 2, - "description": "Nextflow + Singularity/Docker demo for CentOS 6.8 without OverlayFS", + "description": null, "filenames": [ - "containers/demo1/Singularity.demo1", - "containers/base/Singularity.base" + "Singularity" ], - "full_name": "stevekm/NYU-phoenix-docker-singularity-nextflow-demo", + "full_name": "kfox1111/p8compute", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nyu-phoenix-hpc-dockersingularity-nextflow-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#nyu-phoenix-hpc-dockersingularity-nextflow-demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNYU phoenix HPC Docker/Singularity Nextflow Demo\u003c/h1\u003e\n\u003cp\u003eDemo on how to run a Nextflow pipeline on the HPC using Singularity containers built from Docker.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003eClone this repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/stevekm/NYU-phoenix-docker-singularity-nextflow-demo.git\ncd NYU-phoenix-docker-singularity-nextflow-demo\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-remote-hpc-phoenix\" class=\"anchor\" aria-hidden=\"true\" href=\"#remote-hpc-phoenix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRemote HPC (phoenix)\u003c/h2\u003e\n\u003cp\u003eTo run this workflow on the NYU phoenix HPC system, use the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run-p\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003einstall Nextflow to the current directory\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eextract a pre-built demo Singularity image from this repository\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun the Nextflow pipeline using the Singularity image\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-local\" class=\"anchor\" aria-hidden=\"true\" href=\"#local\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal\u003c/h2\u003e\n\u003cp\u003eTo run this workflow on your local computer (Docker required), use the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run-l\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003einstall Nextflow to the current directory\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ebuild the Docker containers included in this repository\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun the Nextflow pipeline using the Docker containers\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMakefile\u003c/code\u003e: shortcuts to common actions used in the demo\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emain.nf\u003c/code\u003e: main Nextflow pipeline file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enextflow.config\u003c/code\u003e: Nextflow configuration file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebin\u003c/code\u003e: directory for scripts to use inside the Nextflow pipeline; its contents will be prepended to your \u003ccode\u003ePATH\u003c/code\u003e when pipeline tasks are executed\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003econtainers\u003c/code\u003e: directory containing Docker and Singularity container files, along with documentation on their setup \u0026amp; usage\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-software-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware Requirements\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-local--remote-hpc-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#local--remote-hpc-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elocal \u0026amp; remote HPC server\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eJava 8 (for Nextflow)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGraphViz Dot (to compile flowchart)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-local-only\" class=\"anchor\" aria-hidden=\"true\" href=\"#local-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elocal only\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDocker version 17.12.0-ce, build c97c6d6\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVagrant version 2.0.1 (for tesing Singularity containers)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-remote-hpc-server-only\" class=\"anchor\" aria-hidden=\"true\" href=\"#remote-hpc-server-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eremote HPC server only\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity version 2.4.2\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1521145930.0 + "updated_at": 1539124833.0 }, { "data_format": 2, - "description": "PreFreeSurfer-Converting Docker to Singularity (centos7-reprozip.fslbuild-centos5)", + "description": "testing running clair with a singularity image", "filenames": [ "Singularity" ], - "full_name": "soudabeh19/centos7-reprozip.fslbuild-centos5", + "full_name": "vsoch/singularity-clair", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7-reprozipfslbuild-centos5\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos7-reprozipfslbuild-centos5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7-reprozip.fslbuild-centos5\u003c/h1\u003e\n\u003cp\u003ePreFreeSurfer-Converting Docker to Singularity (centos7-reprozip.fslbuild-centos5)\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Clair\u003c/h1\u003e\u003ca id=\"user-content-singularity-clair\" class=\"anchor\" aria-label=\"Permalink: Singularity Clair\" href=\"#singularity-clair\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a test for running clair with Singularity. I haven\u0027t figured out exactly how this works yet :)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild with Makefile\u003c/h2\u003e\u003ca id=\"user-content-build-with-makefile\" class=\"anchor\" aria-label=\"Permalink: Build with Makefile\" href=\"#build-with-makefile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild with Singularity\u003c/h2\u003e\u003ca id=\"user-content-build-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Build with Singularity\" href=\"#build-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity create clair.img\nsudo singularity bootstrap clair.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the server?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run clair.img\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1521572666.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "singularity-container", + "clair", + "coreos", + "container", + "security", + "vulnerability" + ], + "updated_at": 1545323610.0 }, { "data_format": 2, - "description": null, + "description": "Singularity repo to package docker artifacts:base:latest container for running GobyWeb plugins with Singularity.", "filenames": [ "Singularity" ], - "full_name": "weatherlab/metview", + "full_name": "CampagneLaboratory/GobyWeb-Singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-metview\" class=\"anchor\" aria-hidden=\"true\" href=\"#metview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emetview\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGobyWeb-Singularity\u003c/h1\u003e\u003ca id=\"user-content-gobyweb-singularity\" class=\"anchor\" aria-label=\"Permalink: GobyWeb-Singularity\" href=\"#gobyweb-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity repo to package docker artifacts:base:latest container for running GobyWeb plugins with Singularity\u003c/p\u003e\n\u003cp\u003eTo build the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall singularity (Mac: \u003ca href=\"http://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/install-mac\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eActivate the vagrant VM\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003evagrant init singularityware/singularity-2.4\nvagrant up\nvagrant ssh\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInside the VM\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd /vagrant\ngit clone git@github.com:CampagneLaboratory/GobyWeb-Singularity.git\ncd GobyWeb-Singularity/\nsudo singularity build --writable CampagneLaboratory-GobyWeb-Singularity-master-latest.img shub://CampagneLaboratory/GobyWeb-Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eAt this point, we can exit the VM and copy the image file: CampagneLaboratory-GobyWeb-Singularity-master-latest.img\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1523286570.0 + "updated_at": 1516396334.0 }, { "data_format": 2, @@ -19344,82 +19061,79 @@ var data = "filenames": [ "Singularity" ], - "full_name": "mosoriob/pegasus_montage-workflow-v2", + "full_name": "currymj/singularity-vm-kpdmetric", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-montage-workflow-v2\" class=\"anchor\" aria-hidden=\"true\" href=\"#montage-workflow-v2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emontage-workflow-v2\u003c/h1\u003e\n\u003cp\u003eA new Python DAX generator version of the classic Montage workflow. This workflow uses the \u003ca href=\"http://montage.ipac.caltech.edu\" rel=\"nofollow\"\u003eMontage\ntoolkit\u003c/a\u003e to re-project, background correct and add astronomical\nimages into custom mosaics.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://montage.ipac.caltech.edu\" rel=\"nofollow\"\u003eMontage\u003c/a\u003e - version 4.0 or later\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.astropy.org/\" rel=\"nofollow\"\u003eAstroPy\u003c/a\u003e - version 1.0 or later\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-plan-a-montage-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#plan-a-montage-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlan a Montage Workflow\u003c/h2\u003e\n\u003cp\u003eThe \u003cem\u003e./montage-workflow.py\u003c/em\u003e Python script sets up a \u003cem\u003edata/\u003c/em\u003e directory with a Pegasus DAX,\nimage tables and region headers. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./montage-workflow.py --center \"56.7 24.0\" --degrees 2.0 \\\n --band dss:DSS2B:blue --band dss:DSS2R:green --band dss:DSS2IR:red\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a 2x2 degree mosaic centered on 56.7 24.0, with 3 bands making up the\nred, green, and blue channels for the final JPEG output. A 2 degree workflow has a lot\nof input images and thus the workflow becomes wide. I simplified version of the workflow\nlooks like:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/dax1.png?raw=true\"\u003e\u003cimg src=\"docs/images/dax1.png?raw=true\" alt=\"DAX 1\" title=\"DAX 1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h2\u003e\n\u003cp\u003eThe quickest way to get started is to use the \u003cem\u003e./example-dss.sh\u003c/em\u003e\nscript. It shows how to use the \u003cem\u003emontage-workflow.py\u003c/em\u003e DAX generator to set up and plan\n2 degree workflows as described above. Example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./example-dss.sh \n\nAdding band 1 (dss DSS2B -\u0026gt; blue)\nRunning sub command: mArchiveList dss DSS2B \"56.7 24.00\" 2.2 2.2 data/1-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 1-images.tbl region-oversized.hdr 1-raw.tbl 1-projected.tbl 1-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 1-raw.tbl 1-diffs.tbl\n[struct stat=\"OK\", count=120]\n\nAdding band 2 (dss DSS2R -\u0026gt; green)\nRunning sub command: mArchiveList dss DSS2R \"56.7 24.00\" 2.2 2.2 data/2-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 2-images.tbl region-oversized.hdr 2-raw.tbl 2-projected.tbl 2-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 2-raw.tbl 2-diffs.tbl\n[struct stat=\"OK\", count=120]\n\nAdding band 3 (dss DSS2IR -\u0026gt; red)\nRunning sub command: mArchiveList dss DSS2IR \"56.7 24.00\" 2.2 2.2 data/3-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 3-images.tbl region-oversized.hdr 3-raw.tbl 3-projected.tbl 3-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 3-raw.tbl 3-diffs.tbl\n[struct stat=\"OK\", count=120]\n2016.06.02 21:46:32.455 PDT: \n2016.06.02 21:46:32.461 PDT: ----------------------------------------------------------------------- \n2016.06.02 21:46:32.466 PDT: File for submitting this DAG to HTCondor : montage-0.dag.condor.sub \n2016.06.02 21:46:32.471 PDT: Log of DAGMan debugging messages : montage-0.dag.dagman.out \n2016.06.02 21:46:32.476 PDT: Log of HTCondor library output : montage-0.dag.lib.out \n2016.06.02 21:46:32.481 PDT: Log of HTCondor library error messages : montage-0.dag.lib.err \n2016.06.02 21:46:32.487 PDT: Log of the life of condor_dagman itself : montage-0.dag.dagman.log \n2016.06.02 21:46:32.492 PDT: \n2016.06.02 21:46:32.497 PDT: -no_submit given, not submitting DAG to HTCondor. You can do this with: \n2016.06.02 21:46:32.507 PDT: ----------------------------------------------------------------------- \n2016.06.02 21:46:33.387 PDT: Your database is compatible with Pegasus version: 4.6.1 \n2016.06.02 21:46:33.392 PDT: \n\nI have concretized your abstract workflow. The workflow has been entered \ninto the workflow database with a state of \"planned\". The next step is \nto start or execute your workflow. The invocation required is\n\npegasus-run /data/scratch/rynge/montage2/montage-workflow-v2/work/1464929190\n\n2016.06.02 21:46:33.419 PDT: Time taken to execute is 2.961 seconds \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning the workflow produces fits and jpeg mosaics for each band, as well as a combined color one:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/pleiades.jpg?raw=true\"\u003e\u003cimg src=\"docs/images/pleiades.jpg?raw=true\" alt=\"Pleiades\" title=\"Pleiades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eThis repository is what I used to build a Singularity image in order to run KPDMetric with all its dependencies on HPC Clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVagrantfile\u003c/h1\u003e\u003ca id=\"user-content-vagrantfile\" class=\"anchor\" aria-label=\"Permalink: Vagrantfile\" href=\"#vagrantfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are on a Linux system, you don\u0027t need to worry about the Vagrantfile. Singularity can only be run on Linux, so on macOS or Windows we need to set up a Linux VM to build the container in. This Vagrantfile does that. It expects the VMware provider, which is not free; you\u0027ll have to change that if you want to use VirtualBox.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis file gives the recipe to build the Singularity container. It requires a couple things:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esingscript.sh\u003c/code\u003e which does some python-related stuff (needs to be called from bash)\u003c/li\u003e\n\u003cli\u003eyou must download CPLEX for Linux and use the installer to install to the directory \u003ccode\u003e./opt\u003c/code\u003e in the same directory as this repository.\u003c/li\u003e\n\u003cli\u003eyou must build the driver code from KPDMetric as a jar and save it as Simulation1.jar -- easiest to do this once by hand in Eclipse by exporting an executable jar.\u003c/li\u003e\n\u003cli\u003eyou must have an internet connection as the build script will clone a github repository.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1535257330.0 + "updated_at": 1509658881.0 }, { "data_format": 2, - "description": "Singularity recipe for freesurfer", + "description": "PySecDec base-OS Linux Containers", "filenames": [ "Singularity" ], - "full_name": "ResearchIT/singularity-freesurfer", + "full_name": "mppmu/mppmu_pysecdec-base_img", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 4, "topics": [], - "updated_at": 1603915556.0 + "updated_at": 1538140519.0 }, { "data_format": 2, - "description": "This is singularity 2.6.0 image for PHEnix -1.4a", + "description": "A sample for Singularity container providing pynwb with h5py build with mpi support", "filenames": [ - "Singularity-2.6.0" + "Singularity" ], - "full_name": "Amjadhpc/PHEnix", + "full_name": "dandi-containers/pynwb-mpi", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1539686949.0 + "updated_at": 1571608712.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v1.0.0" + "Singularity" ], - "full_name": "baxpr/fmri_conncalc", - "latest_release": "v1.0.0-rc0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fmri_conncalc\" class=\"anchor\" aria-hidden=\"true\" href=\"#fmri_conncalc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efmri_conncalc\u003c/h1\u003e\n\u003cp\u003ePreprocessing and functional connectivity computation for fMRI\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eHere is an example for the \"jsins\" version of the processor, as described in\n\u003ca href=\"conncalc_jsins_v1.0.0.yaml\"\u003econncalc_jsins_v1.0.0.yaml\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity\n run\n --bind \u0026lt;INDIR\u0026gt;:/INPUTS\n --bind \u0026lt;OUTDIR\u0026gt;:/OUTPUTS\n baxpr-fmri_conncalc-master-v1.0.0.simg\n magick_path /usr/bin\n param_file params_JSins.csv\n wroi_file rois_JSins.nii.gz\n roi_file \u0027\u0027\n roiinfo_file rois_JSins.csv\n coregmat_file /INPUTS/coreg_mat.txt \\\n deffwd_file /INPUTS/y_deffwd.nii.gz \\\n ct1_file /INPUTS/ct1.nii.gz \\\n wgm_file /INPUTS/wgm.nii.gz \\\n wcseg_file /INPUTS/wcseg.nii.gz \\\n func_file /INPUTS/fmri.nii.gz \\\n project PROJECT_LABEL \\\n subject SUBJECT_LABEL \\\n session SESSION_LABEL \\\n scan SCAN_LABEL \\\n out_dir /OUTPUTS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe inputs \u003ccode\u003ecoregmat_file\u003c/code\u003e, \u003ccode\u003edeffwd_file\u003c/code\u003e, \u003ccode\u003ect1_file\u003c/code\u003e, \u003ccode\u003ewgm_file\u003c/code\u003e, \u003ccode\u003ewcseg_file\u003c/code\u003e would typically be obtained from the outputs of the \u003ccode\u003eMAGM_Coreg_Normalize_v2\u003c/code\u003e spider.\u003c/p\u003e\n\u003cp\u003eThe outputs are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efmri_conncalc.pdf Report\nparams.csv Parameters used in the analysis\nFD.txt Framewise displacement time series\nDVARS.txt Framewise variance time series\nbadvols.txt Scrubbed volumes indicator time series\nrp_adfunc.txt Realignment (motion) values\nwmeanadfunc.nii.gz Mean functional image in standard space\nwadfunc.nii.gz Slice time corrected and realigned functional images in standard space\nrroi_labels.nii.gz Region of interest label image\nroi_snr.nii.gz ROI SNR image\nroi_info.csv ROI info\nroi_labels.csv ROI names (if available)\n\nSeries of results repeated for each of the four processing streams\n(keep or remove mean gray matter; scrub or no scrub):\n\n confounds_removegm_noscrub.txt Confound (filter) matrix\n connectivity_matrix_R_removegm_noscrub.csv Connectivity matrix\n filtered_removegm_noscrub.nii.gz Filtered functional images\n roi_timeseries_removegm_noscrub.csv Filtered ROI time series\n stats_removegm_noscrub.txt Various statistics\n Zmap_removegm_noscrub.nii.gz Unsmoothed ROI connectivity maps\n sZmap_removegm_noscrub.nii.gz Smoothed ROI connectivity maps\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eThe built singularity container \u003ccode\u003ebaxpr-fmri_conncalc-master-v1.0.0.simg\u003c/code\u003e (URL is shub://baxpr/fmri_conncalc:v1.0.0) is stand-alone with no external dependencies. The compiled matlab \u003ca href=\"bin/run_fmri_conncalc.sh\"\u003erun_fmri_conncalc.sh\u003c/a\u003e requires only the appropriate MATLAB Runtime to execute. To build these there are two stages:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCompile the MATLAB code into a stand-alone executable, using \u003ca href=\"compile_matlab.sh\"\u003ecompile_matlab.sh\u003c/a\u003e. This requires a full MATLAB installation (R2017a, v92) and SPM12 (\u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/\" rel=\"nofollow\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/\u003c/a\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the singularity container. In addition to a few specific OS packages, this requires the MATLAB Compiled Runtime. All are specified to be downloaded during the build in the singularity recipe \u003ca href=\"Singularity.v1.0.0\"\u003eSingularity.v1.0.0\u003c/a\u003e. The container help text gives build instructions. Alternatively the built container can be obtained from singularity-hub:\n\u003ccode\u003esingularity pull shub://baxpr/fmri_conncalc:v1.0.0\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-peculiarities-of-specific-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#peculiarities-of-specific-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePeculiarities of specific pipelines\u003c/h2\u003e\n\u003cp\u003eSome critical analysis parameters are specified in the \u003ccode\u003eparam_file\u003c/code\u003e, e.g. \u003ccode\u003eparams_JSins.csv\u003c/code\u003e. This is a reference to a file that\u0027s in the built container, but these can also be viewed in the code repository e.g. \u003ca href=\"src/params/params_JSins.csv\"\u003esrc/params/params_JSins.csv\u003c/a\u003e. The parameters get as detailed as the repetition time of the fMRI scans. If the needed parameter file is not in the container already:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAdd the new parameter file in \u003ccode\u003esrc/params\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUpdate the matlab compilation code to include it with \u003ccode\u003e-a\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRecompile the matlab\u003c/li\u003e\n\u003cli\u003eCommit to github. Note that the compiled matlab executable is stored using LFS\u003c/li\u003e\n\u003cli\u003eRebuild the container (increment the patch number, e.g. 1.0.0 to 1.0.1)\u003c/li\u003e\n\u003cli\u003eCreate an updated YAML file appropriate for the parameter set\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jsins-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#jsins-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ejsins version\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"conncalc_jsins_v1.0.0.yaml\"\u003econncalc_jsins_v1.0.0.yaml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eStandard space regions of interest are used, \u003ca href=\"src/params/JS_insula/rois_JSins.nii.gz\"\u003erois_JSins.nii.gz\u003c/a\u003e, identical for every subject.\u003c/p\u003e\n\u003cp\u003eConnectivity matrix is computed (Pearson bivariate correlation R). A connectivity map is computed for each ROI (Fisher Z transform applied to Pearson bivariate correlation). Spatial smoothing is applied to the connectivity maps only.\u003c/p\u003e\n\u003cp\u003eParameter settings in \u003ca href=\"src/params/params_JSins.csv\"\u003eparams_JSins.csv\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFMRI repetition time (TR) is assumed to be 2.000 sec\u003c/li\u003e\n\u003cli\u003eUse all fMRI volumes (none dropped)\u003c/li\u003e\n\u003cli\u003eNo slice timing correction\u003c/li\u003e\n\u003cli\u003e6mm FWHM Gaussian spatial smoothing applied to connectivity maps\u003c/li\u003e\n\u003cli\u003eFilter settings (confound regressor matrix):\n\u003cul\u003e\n\u003cli\u003e0.01 Hz - 0.10 Hz bandpass filter (Fourier basis)\u003c/li\u003e\n\u003cli\u003e6 motion parameters (translation and rotation)\u003c/li\u003e\n\u003cli\u003e6 first differences of motion parameters\u003c/li\u003e\n\u003cli\u003eFirst 6 principal components of voxel time series from the eroded white matter/CSF compartment\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor scrubbed results, volumes before and after an excursion of FD \u0026gt; 0.5 are removed. DVARS is not used for scrubbing.\u003c/li\u003e\n\u003cli\u003eConnectivity maps are saved for each ROI.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-szhab-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#szhab-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eszhab version\u003c/h3\u003e\n\u003cp\u003eNo YAML available yet.\u003c/p\u003e\n\u003cp\u003eSubject-specific regions of interest are used, as described in the native space ROI image supplied as input. This image must be in the same space as the subject\u0027s native space structural.\u003c/p\u003e\n\u003cp\u003eConnectivity matrix is computed (Pearson bivariate correlation R of filtered time series). Spatial smoothing is not used.\u003c/p\u003e\n\u003cp\u003eParameter settings in \u003ccode\u003eparams_SZhab.csv\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFMRI repetition time (TR) is assumed to be 2.000 sec\u003c/li\u003e\n\u003cli\u003e5 initial volumes are dropped, and the following 60 volumes are used for the analysis\u003c/li\u003e\n\u003cli\u003eNo slice timing correction\u003c/li\u003e\n\u003cli\u003eFilter settings (confound regressor matrix):\n\u003cul\u003e\n\u003cli\u003e0.01 Hz - 0.15 Hz bandpass filter (Fourier basis)\u003c/li\u003e\n\u003cli\u003e6 motion parameters (translation and rotation)\u003c/li\u003e\n\u003cli\u003eFirst 3 principal components of voxel time series from the eroded white matter/CSF compartment\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor scrubbed results, volumes before and after an excursion of FD \u0026gt; 0.5 are removed. DVARS is not used for scrubbing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-general-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#general-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral pipeline\u003c/h2\u003e\n\u003cp\u003eOther than the above, processing proceeds as follows.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDrop functional volumes as specified.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePerform slice timing correction as specified. (SPM12 slice timing correction)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePerform motion realignment: two-stage alignment to mean image. (SPM12 realignment)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCoregister the mean functional image to the T1 weighted structural using a rigid body transform. The structural is first skull-stripped by zeroing all voxels that were not labeled by the multiatlas segmentation. The transformation is then applied to all functional volumes. (SPM12 coregistration)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eQuality parameters are computed: framewise displacement FD and framewise signal variance DVARS. Volumes exceeding scrubbing criteria are marked (\"badvols\").\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe functional and structural images are warped to standard space using the supplied nonlinear transform (forward deformation image). (SPM12 deformation tools)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe supplied standard space ROI image file is resampled to match the standard space fMRI geometry. (SPM12 reslice)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConnectivity computation. All filtering is done in a single step: a design matrix of confounds is created (see lists above), it is fit to each voxel time series, and the residuals are extracted. Then bivariate Pearson correlation is computed between ROI residual time series to produce the connectivity matrix. Fisher transformed correlation between ROIs/voxel residual time series is used to produce connectivity maps if that option is selected.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "adswa/nilearn-container", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1543615331.0 + "updated_at": 1616512270.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v2.0.0" + "Singularity" ], - "full_name": "baxpr/fmri_modularity", + "full_name": "ldynia/singularity-analyzer", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1550158474.0 + "updated_at": 1509974976.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity.petibm0.5-xenial", - "singularity/Singularity.petibm0.5.1-xenial", - "singularity/Singularity.petibm0.4.2-xenial" + "Singularity" ], - "full_name": "mesnardo/petibm-decoupledibpm", + "full_name": "eugtsa/tf_pytorch_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-decoupled-immersed-boundary-projection-method-with-petibm\" class=\"anchor\" aria-hidden=\"true\" href=\"#decoupled-immersed-boundary-projection-method-with-petibm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDecoupled Immersed Boundary Projection Method with PetIBM\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/mesnardo/petibm-decoupledibpm/raw/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ccf186e7288af6d88a1f6a930c0fcc4e7a8a9936b34e07629d815d1eab4d977/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d425344253230332d2d436c617573652d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.docker.com/u/mesnardo/repository/docker/mesnardo/petibm-decoupledibpm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b8d9674ae17bb539afa71ecc4169a1ee5a6a9242d8f9e12a10f4583093ba57c3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65722d2d6875622d696e666f726d6174696f6e616c2e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/hosted-docker--hub-informational.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3171\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-flow-over-a-stationary-circular-cylinder-re40-and-100\" class=\"anchor\" aria-hidden=\"true\" href=\"#flow-over-a-stationary-circular-cylinder-re40-and-100\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlow over a stationary circular cylinder ($Re=40$ and $100$)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/cylinder2dRe40/189_markers/figures/wz_0005000.png\"\u003e\u003cimg src=\"runs/cylinder2dRe40/189_markers/figures/wz_0005000.png\" alt=\"cylinderRe40_vorticity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Vorticity contours around the cylinder at Reynolds number $40$. (Contour levels between $-3D/U_\\infty$ and $3D/U_\\infty$ with increments of $0.4$.)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/cylinder2dRe100/189_markers/figures/wz_0020000.png\"\u003e\u003cimg src=\"runs/cylinder2dRe100/189_markers/figures/wz_0020000.png\" alt=\"cylinderRe100_vorticity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Vorticity contours around the cylinder at Reynolds number $100$ after $200$ time units of flow simulation. (Contour levels between $-3D/U_\\infty$ and $3D/U_\\infty$ with increments of $0.4$.)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/cylinder2dRe40/189_markers/figures/cp_0005000.png\"\u003e\u003cimg src=\"runs/cylinder2dRe40/189_markers/figures/cp_0005000.png\" alt=\"cylinderRe40_pressure_coefficient\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Pressure coefficient along the upper and lower surfaces of the cylinder at Reynolds number $40$. We compare with the results from Li et al. (2016).\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/cylinder2dRe100/189_markers/figures/pressure_coefficient.png\"\u003e\u003cimg src=\"runs/cylinder2dRe100/189_markers/figures/pressure_coefficient.png\" alt=\"cylinderRe100_pressure_coefficient\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Pressure coefficient along the upper and lower surfaces of the cylinder at Reynolds number $100$. We compare with the results from Li et al. (2016).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-flow-around-an-inline-oscillating-circular-cylinder-re100\" class=\"anchor\" aria-hidden=\"true\" href=\"#flow-around-an-inline-oscillating-circular-cylinder-re100\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlow around an inline oscillating circular cylinder ($Re=100$)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/algo1/figures/vorticity.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/algo1/figures/vorticity.png\" alt=\"oscillatingcylinderRe100_vorticity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Contours of the vorticity field around an inline oscillating cylinder at different phase angles ($\\phi = 2 \\pi f t$): $\\phi = 0^o$ (left) and $\\phi = 288^o$ (right). (Contour levels between $-20 U_m / D$ and $20 U_m / D$ using $30$ increments.)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/algo1/figures/pressure.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/algo1/figures/pressure.png\" alt=\"oscillatingcylinderRe100_pressure\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Contours of the pressure field around an inline oscillating cylinder at different phase angles ($\\phi = 2 \\pi f t$): $\\phi = 0^o$ (left) and $\\phi = 288^o$ (right). (Contour levels between $-1 \\rho U_m^2$ and $1 \\rho U_m^2$ using $50$ increments.)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/algo1/figures/velocity_profiles.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/algo1/figures/velocity_profiles.png\" alt=\"oscillatingcylinderRe100_velocity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Profile of the velocity components ($u$: left, $v$: right) at four locations along the centerline for various phase angles $\\phi$.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/drag_coefficient.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/drag_coefficient.png\" alt=\"oscillatingcylinderRe100_drag_coefficient\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the drag coefficient of the inline oscillating cylinder obtained using different algorithms. We also show zooms at early and developed stages.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_dt.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_dt.png\" alt=\"oscillatingcylinderRe100_drag_coefficient_dt\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_dx.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_dx.png\" alt=\"oscillatingcylinderRe100_drag_coefficient_dx\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the drag coefficient obtained with Algorithm 1 for different time-step sizes and different grid sizes.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/temporal_error.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/temporal_error.png\" alt=\"oscillatingcylinderRe100_temporal_error\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Variations of the $L_\\infty$ and $L_2$ norm errors of the streamwise velocity as a function of the computational time-step size.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/spatial_error.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/spatial_error.png\" alt=\"oscillatingcylinderRe100_temporal_error\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Variations of the $L_\\infty$ and $L_2$ norm errors of the streamwise velocity as a function of the computational grid spacing.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_lag.png\"\u003e\u003cimg src=\"runs/oscillatingcylinderRe100/figures/drag_coefficient_lag.png\" alt=\"oscillatingcylinderRe100_cd_lag\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the drag coefficient using Algorithm 3 with force-prediction scheme 3. We compared the history obtained with different Lagrangian mesh resolutions: $N_b = 500$ Lagrangian markers on the boundary and $N_b = 202$ markers (the latter one corresponding to the same resolution as the Eulerian background grid).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-flow-around-an-impulsively-started-circular-cylinder-re40\" class=\"anchor\" aria-hidden=\"true\" href=\"#flow-around-an-impulsively-started-circular-cylinder-re40\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlow around an impulsively started circular cylinder (Re=40)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/translatingcylinder2dRe40/figures/drag_coefficients.png\"\u003e\u003cimg src=\"runs/translatingcylinder2dRe40/figures/drag_coefficients.png\" alt=\"translatingcylinder2dRe40_cd\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the drag coefficient of the impulsively started cylinder. Comparison with the analytical solution of Bar-Lev \u0026amp; Yang (1997) and the numerical results from Taira \u0026amp; Colonius (2007).\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/translatingcylinder2dRe40/dt=0.0005/figures/vorticity.png\"\u003e\u003cimg src=\"runs/translatingcylinder2dRe40/dt=0.0005/figures/vorticity.png\" alt=\"translatingcylinder2dRe40_wz\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Vorticity contours around the impulsively started circular cylinder at $t=1.0$ (left) and $t=3.5$ (right). Contour levels between $-3 \\omega_z D / U_o$ and $3 \\omega_z D / U_o$ with increments of $0.4$.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/translatingcylinder2dRe40/figures/recirculation_lengths.png\"\u003e\u003cimg src=\"runs/translatingcylinder2dRe40/figures/recirculation_lengths.png\" alt=\"translatingcylinder2dRe40_lw\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e History of the recirculation length measured in the reference frame of the impulsively start cylinder at Reynolds number 40 and for different time-step sizes.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-three-dimensional-flow-around-an-inline-oscillating-sphere-re7854\" class=\"anchor\" aria-hidden=\"true\" href=\"#three-dimensional-flow-around-an-inline-oscillating-sphere-re7854\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThree-dimensional flow around an inline oscillating sphere ($Re=78.54$)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"runs/oscillatingsphere/figures/pressure.png\"\u003e\u003cimg src=\"runs/oscillatingsphere/figures/pressure.png\" alt=\"sphere_pressure\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFigure:\u003c/strong\u003e Contours of the pressure field in the $x$/$y$ at $z=0$ at three phase angles. Contour levels between $-2 p / \\rho U_m^2$ and $2 p / \\rho U_m^2$ with $30$ increments.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etf_pytorch_singularity\u003c/h1\u003e\u003ca id=\"user-content-tf_pytorch_singularity\" class=\"anchor\" aria-label=\"Permalink: tf_pytorch_singularity\" href=\"#tf_pytorch_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1581529613.0 + "updated_at": 1621430186.0 }, { "data_format": 2, @@ -19427,56 +19141,53 @@ var data = "filenames": [ "Singularity" ], - "full_name": "dfornika/nf-core-cpo", + "full_name": "tschoonj/singularity-centos6", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-corecpo\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-corecpo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/cpo\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic Analysis of Carbapenem Resistant Organisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/cpo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b4a4d26450e93f9c13ce85f059bb61ebe27051414d40e4f4ba81966ca0029a4/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f63706f2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/cpo.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/cpo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4bc4e99ea4ca2a2f9b15fda9e4d3855153c0fd74431b920ed885080d46e0cc73/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f63706f2e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/cpo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/cpo pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003ch3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003enf-core/cpo was originally written by Dan Fornika.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1544054866.0 + "updated_at": 1563792023.0 }, { "data_format": 2, "description": null, "filenames": [ - "containers/Singularity" + "Singularity" ], - "full_name": "stevekm/bwa-bench", + "full_name": "callaghanmt-containers/evolinc", "latest_release": null, - "readme": "", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eevolinc\u003c/h1\u003e\u003ca id=\"user-content-evolinc\" class=\"anchor\" aria-label=\"Permalink: evolinc\" href=\"#evolinc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1549319905.0 + "updated_at": 1556291432.0 }, { "data_format": 2, - "description": "Singularity container for Scanfold", + "description": "Singularity recipes for Docker image gedet-base", "filenames": [ - "Singularity" + "Singularity.julia06-avx2" ], - "full_name": "ResearchIT/Scanfold", + "full_name": "mppmu/gedet-base-singularity", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 4, "topics": [], - "updated_at": 1570729149.0 + "updated_at": 1516277260.0 }, { "data_format": 2, - "description": "Bioinformatic tools in a singularity container", + "description": null, "filenames": [ - "containers/Singularity", - "containers/Singularity.etoki", - "containers/Singularity.lyveset" + "Singularity" ], - "full_name": "EnriqueDoster/sing_biotools", + "full_name": "suouyang/linved", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sing_biotools\" class=\"anchor\" aria-hidden=\"true\" href=\"#sing_biotools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esing_biotools\u003c/h1\u003e\n\u003cp\u003eBioinformatic tools in a singularity container\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLineVD\u003c/h1\u003e\u003ca id=\"user-content-linevd\" class=\"anchor\" aria-label=\"Permalink: LineVD\" href=\"#linevd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository provides the code for \u003ca href=\"https://arxiv.org/pdf/2203.05181.pdf\" rel=\"nofollow\"\u003eLineVD: Statement-level Vulnerability Detection using Graph Neural Networks\u003c/a\u003e. The environment can be built using \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or by following / following the commands in the Singularity file. To start, clone the repository and navigate to the root directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory Structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory Structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre lang=\"dir\"\u003e\u003ccode\u003e(main module) \u251c\u2500\u2500 sastvd\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 codebert\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 helpers\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 ivdetect\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 linevd\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 scripts\n \u251c\u2500\u2500 storage\n(memoization) \u2502\u00a0\u00a0 \u251c\u2500\u2500 cache\n(raw data) \u2502\u00a0\u00a0 \u251c\u2500\u2500 external\n(csvs) \u2502\u00a0\u00a0 \u251c\u2500\u2500 outputs\n(models) \u2502\u00a0\u00a0 \u2514\u2500\u2500 processed\n(tests) \u2514\u2500\u2500 tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining LineVD from scratch\u003c/h2\u003e\u003ca id=\"user-content-training-linevd-from-scratch\" class=\"anchor\" aria-label=\"Permalink: Training LineVD from scratch\" href=\"#training-linevd-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild and initialise environment and download dataset\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build main.sif Singularity\nsingularity run main.sif -p initialise\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFeature extraction (Increase NUM_JOBS if running on HPC for speed up)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e main.sif python sastvd/scripts/prepare.py\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e main.sif python sastvd/scripts/getgraphs.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTrain model (Training takes around 1-2 hours using GTX 3060)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --nv main.sif python sastvd/scripts/train_best.py\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1606287922.0 + "updated_at": 1687515622.0 }, { "data_format": 2, @@ -19484,363 +19195,319 @@ var data = "filenames": [ "Singularity" ], - "full_name": "djarecka/tmp_nipype_tut", + "full_name": "rynge/osgvo-ubuntu-18.04-testing", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nipype-tutorial-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#nipype-tutorial-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNipype Tutorial Notebooks\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/miykael/nipype_tutorial/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/669c934f828c73340c0d591ed4b423ef3fa0193e787bfe385915e82dae5ed8fc/68747470733a2f2f636972636c6563692e636f6d2f67682f6d69796b61656c2f6e69707970655f7475746f7269616c2e7376673f7374796c653d736869656c64\" alt=\"CircleCi\" data-canonical-src=\"https://circleci.com/gh/miykael/nipype_tutorial.svg?style=shield\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/issues/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ea29b9a6350d6278064569a97945097dcdeedf9e93740b62ef46df808891fd37/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/pulls/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb7044b2c212e415ec4669de3bb9767f22bfed317ade3070bac8d41ea2a71529/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d70722f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub pull-requests\" data-canonical-src=\"https://img.shields.io/github/issues-pr/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://GitHub.com/miykael/nipype_tutorial/graphs/contributors/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7702816785d6120ca455fda7995bccb5bbdde3e3a92f859f27f866ad34bc55f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub contributors\" data-canonical-src=\"https://img.shields.io/github/contributors/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/commits/master\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fdcae12a957784eff34edadd6ded9a9a8cdf6354ce4d5c5b9d16727d838ecc23/68747470733a2f2f6769746875622d62617369632d6261646765732e6865726f6b756170702e636f6d2f636f6d6d6974732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub Commits\" data-canonical-src=\"https://github-basic-badges.herokuapp.com/commits/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/archive/master.zip\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fb9081bb8ee87986aea94736dd73ee86c56308df8e0b21ee9803cbe6976e3fab/68747470733a2f2f6769746875622d73697a652d62616467652e6865726f6b756170702e636f6d2f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub size\" data-canonical-src=\"https://github-size-badge.herokuapp.com/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/miykael/nipype_tutorial/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3658dcdcaf69e757f1454f83966a15fcdf8b7bcb1d3b4427ffb4226668659eb6/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d69796b61656c2f6e69707970655f7475746f7269616c2e7376673f6d61784167653d32353932303030\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/docker/pulls/miykael/nipype_tutorial.svg?maxAge=2592000\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://hits.dwyl.io/miykael/nipype_tutorial\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c19a46ac2503dae747aeea217a7a854e711a4c95b5814a8c85c59aa5c9920a61/687474703a2f2f686974732e6477796c2e696f2f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub HitCount\" data-canonical-src=\"http://hits.dwyl.io/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the Nipype Tutorial in Jupyter Notebook format. You can access the tutorial in two ways:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://miykael.github.io/nipype_tutorial/\" rel=\"nofollow\"\u003eNipype Tutorial Homepage\u003c/a\u003e: This website contains a static, read-only version of all the notebooks.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://miykael.github.io/nipype_tutorial/notebooks/introduction_docker.html\" rel=\"nofollow\"\u003eNipype Tutorial Docker Image\u003c/a\u003e: This guide explains how to use Docker to run the notebooks interactively on your own computer. The nipype tutorial docker image is the best interactive way to learn Nipype.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-feedback-help--support\" class=\"anchor\" aria-hidden=\"true\" href=\"#feedback-help--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeedback, Help \u0026amp; Support\u003c/h1\u003e\n\u003cp\u003eIf you want to help with this tutorial or have any questions, feel free to fork the repo of the \u003ca href=\"https://github.com/miykael/nipype_tutorial\"\u003eNotebooks\u003c/a\u003e or interact with other contributors on the slack channel \u003ca href=\"https://brainhack.slack.com/messages/nipype/\" rel=\"nofollow\"\u003ebrainhack.slack.com/messages/nipype/\u003c/a\u003e. If you have any questions or found a problem, open a new \u003ca href=\"https://github.com/miykael/nipype_tutorial/issues\"\u003eissue on github\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-thanks-and-acknowledgment\" class=\"anchor\" aria-hidden=\"true\" href=\"#thanks-and-acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThanks and Acknowledgment\u003c/h1\u003e\n\u003cp\u003eA huge thanks to \u003ca href=\"https://github.com/mwaskom\"\u003eMichael Waskom\u003c/a\u003e, \u003ca href=\"https://github.com/oesteban\"\u003eOscar Esteban\u003c/a\u003e, \u003ca href=\"https://github.com/chrisfilo\"\u003eChris Gorgolewski\u003c/a\u003e and \u003ca href=\"https://github.com/satra\"\u003eSatrajit Ghosh\u003c/a\u003e for their input to this tutorial! And a huge thanks to \u003ca href=\"https://github.com/djarecka/\"\u003eDorota Jarecka\u003c/a\u003e who updated this tutorial to Python 3 and is helping me with keeping this tutorial updated and running!\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eosgvo-ubuntu-18.04-testing\u003c/h1\u003e\u003ca id=\"user-content-osgvo-ubuntu-1804-testing\" class=\"anchor\" aria-label=\"Permalink: osgvo-ubuntu-18.04-testing\" href=\"#osgvo-ubuntu-1804-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOSG VO\u0027s Ubuntu 18.04 base image - testing\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1547566090.0 + "updated_at": 1543883829.0 }, { "data_format": 2, - "description": "Container to run various Game AI workloads", + "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "sbutcher/minigym-container", + "full_name": "FrancescoPesce/SE4HPC_project_part2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-minigym-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#minigym-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eminigym-container\u003c/h1\u003e\n\u003cp\u003eContainer to run various Game AI workloads\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject - part 2\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject---part-2\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject - part 2\" href=\"#se4hpcproject---part-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRepository for the second part of the second project of SE4HPC by Miotti Michele and Francesco Pesce.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe file \u003ccode\u003etest/test_matrix_multiplication.cpp\u003c/code\u003e contains the tests from part 1 that are passed by the\nnew implementation of the matrix multiplication function.\u003c/li\u003e\n\u003cli\u003eThe file \u003ccode\u003eSingularity.def\u003c/code\u003e contains a container descriptor for the program.\u003c/li\u003e\n\u003cli\u003eThe file \u003ccode\u003ejob.sh\u003c/code\u003e is used to run the containerized application using SLURM and MPI. It is supposed to be executed using sbatch, as using srun may result in issues.\u003c/li\u003e\n\u003cli\u003eThe file \u003ccode\u003e.github/workflows/ci.yml\u003c/code\u003e contains the Github actions used to execute the pipeline. At the moment of the last commit, the workflow runs with no errors.\u003c/li\u003e\n\u003cli\u003eThe files \u003ccode\u003eoutput.txt\u003c/code\u003e and \u003ccode\u003eerror.txt\u003c/code\u003e contain the expected output and error from the execution of the application. We verified that they match with the ones obtained on the cluster.\u003c/li\u003e\n\u003cli\u003eWe made minor changes to the rest of the repository to be able to use the file \u003ccode\u003esrc/matrix_mult.cpp\u003c/code\u003e in the tests.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1548062559.0 + "updated_at": 1717606364.0 }, { "data_format": 2, - "description": "Singularity recipe for Qt5 on Centos 7 and Ubuntu 16.04", + "description": "Singualrity recipe for BamM, GroopM and CheckM on Conda", "filenames": [ - "Singularity", - "Singularity.ubuntu", - "Singularity.qt5", - "dsistudio_mrtrix3/Singularity.dsi_mrtrix3", - "dsistudio_mrtrix3/Singularity.dsi_mrtrix3_ants_fsl_fmriprep", - "dsistudio_mrtrix3/Singularity.dsi_mrtrix3_fsl", - "dsistudio_mrtrix3/Singularity.dsi_mrtrix3_centos8", - "dsistudio_mrtrix3/Singularity.dsi_mrtrix3_ants" + "Singularity" ], - "full_name": "willgpaik/qt5_aci", + "full_name": "ISU-HPC/bamm-groopm-checkm", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-qt5_aci\" class=\"anchor\" aria-hidden=\"true\" href=\"#qt5_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eqt5_aci\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for Qt5 on Centos 7 and Ubuntu 16.04 For ICS\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE: DO NOT rebuild \"Singularity.dsi_mrtrix3\" image.\u003c/strong\u003e\u003cbr\u003e\n(Last successful build was Mar 12 2019)\u003c/p\u003e\n\u003cp\u003eSingularity recipe for DSI Studio and MRtrix3 is updated on \u003cstrong\u003edsistudio_mrtrix3\u003c/strong\u003e folder\u003c/p\u003e\n\u003cp\u003eIf you want to install DSI Studio and MRtrix3 on Basic Qt5 Container,\u003cbr\u003e\ndownlaod \"dsistudio_mrtrix3_install.sh\" to preferred location\nand follow commands inside Singularity environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; chmod +x dsistudio_mrtrix3_install.sh \n\u0026gt; ./dsistudio_mrtrix3_install.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e2019/2/21\u003cbr\u003e\nUnable to use \u003cstrong\u003eGCC 8.2.1\u003c/strong\u003e due to build failure =\u0026gt; Going back to \u003cstrong\u003eGCC 7.3.1\u003c/strong\u003e\u003cbr\u003e\n(Failed to resolve the issue at this moment)\u003c/p\u003e\n\u003cp\u003e\u003cdel\u003e2019/5/13\u003cbr\u003e\nUpdated dsistudio_mrtrix3_install.sh due to Qt version issue\u003cbr\u003e\n(Requires Qt 5.12.2 or above: \u003ca href=\"https://github.com/frankyeh/DSI-Studio/issues/34\"\u003ehttps://github.com/frankyeh/DSI-Studio/issues/34\u003c/a\u003e)\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e2019/5/24\u003cbr\u003e\nReverted changes made on 2019/5/13\u003c/p\u003e\n\u003cp\u003e2019/6/24\u003cbr\u003e\n\u003cdel\u003eNewer version qt5 installation recipe added (in progress)\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e2019/7/22\u003cbr\u003e\nQt is updated to 5.12 with Qt Charts (for DSI Studio)\u003c/p\u003e\n\u003cp\u003e2019/7/24\u003cbr\u003e\nQt SVG is added (for MRtrix 3)\u003cbr\u003e\n32-bit EoD graphics libraries are disable (to aviod warnings)\u003c/p\u003e\n\u003cp\u003e2019/7/29\u003cbr\u003e\nNVIDIA driver is added to DSI Studio MRtrix3 container\u003c/p\u003e\n\u003cp\u003e2019/11/10\u003cbr\u003e\nQt version 5.12.5 is used\u003c/p\u003e\n\u003cp\u003e2020/4/24\u003cbr\u003e\nUbuntu 16.04 version added with Qt 5.14.2\u003c/p\u003e\n\u003cp\u003e2020/6/20\u003cbr\u003e\nQt5 container is updated to have nvidia driver\u003c/p\u003e\n\u003cp\u003e2020/7/27\u003cbr\u003e\nUbuntu container is updated to have NVIDIA driver (Ubuntu 16.04 based)\u003c/p\u003e\n\u003cp\u003e2020/9/28\u003cbr\u003e\nQt5 container is updated to use CUDA 9.1 version (for FSL with CUDA)\u003cbr\u003e\n(Reference: \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GPU\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GPU\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e2020/10/20\u003cbr\u003e\nQt5X11Extras is added to the Qt5 recipe\u003cbr\u003e\n(Ubuntu container will not be updated unless necessary)\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebamm-groopm-checkm\u003c/h1\u003e\u003ca id=\"user-content-bamm-groopm-checkm\" class=\"anchor\" aria-label=\"Permalink: bamm-groopm-checkm\" href=\"#bamm-groopm-checkm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingualrity recipe for BamM, GroopM, CheckM and RefineM on Conda\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1618004326.0 + "updated_at": 1535561436.0 }, { "data_format": 2, - "description": "Omero client Singularity recipes.", + "description": "Singularity Bootstrap File for CERN ROOT6 built on Ubuntu 16.10", "filenames": [ - "Singularity.5.4.0", - "Singularity.5.4.10" + "Singularity" ], - "full_name": "arcsUVA/omero-client", + "full_name": "twongjirad/singularity-cern-root6-yakkety", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-omero-client\" class=\"anchor\" aria-hidden=\"true\" href=\"#omero-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eomero-client\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2227\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nOmero client Singularity recipes\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-cern-root6-yakkety\u003c/h1\u003e\u003ca id=\"user-content-singularity-cern-root6-yakkety\" class=\"anchor\" aria-label=\"Permalink: singularity-cern-root6-yakkety\" href=\"#singularity-cern-root6-yakkety\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains Bootstrap file to build singularity image\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1557760203.0 + "updated_at": 1496270730.0 }, { "data_format": 2, - "description": "sparkle planning challenge", + "description": null, "filenames": [ - "Singularity" + "Singularity.spid" ], - "full_name": "hejm37/sysu-planner", + "full_name": "romxero/spid_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sysu-planner\" class=\"anchor\" aria-hidden=\"true\" href=\"#sysu-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esysu-planner\u003c/h1\u003e\n\u003cp\u003eThe SYSU-Planner is a two-stage planner designed to solve classical planning problems. It first performs the 1-BFWS (\u003ca href=\"https://people.eng.unimelb.edu.au/nlipovetzky/papers/aaai17-BFWS-novelty-exploration.pdf\" rel=\"nofollow\"\u003eNir and Hector 2017\u003c/a\u003e) with very fast speed. If it fails to find a solution, it will then perform a modified online refinement algorithm named \u003ca href=\"http://ada.liacs.nl/events/sparkle-planning-19/documents/solver_description/SYSU-planner-description.pdf\" rel=\"nofollow\"\u003eForward-RHC\u003c/a\u003e (see also \u003ca href=\"https://ipc2018-classical.bitbucket.io/planner-abstracts/team8.pdf\" rel=\"nofollow\"\u003eMaximilian and Jorg 2018\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-and-run-with-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-and-run-with-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild and run with container\u003c/h2\u003e\n\u003cp\u003eUsing the planner with \u003ca href=\"https://sylabs.io/docs/#singularity\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is rather simple. First install Singularity following \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003ethis guide\u003c/a\u003e. Then run the following script in CLI and you will have the plan file \u003cem\u003esas_plan\u003c/em\u003e under \u003cem\u003e$RUNDIR\u003c/em\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build planner.img sysu-planner/Singularity\nmkdir rundir\ncp path/to/domain.pddl rundir\ncp path/to/problem.pddl rundir\nRUNDIR=\"$(pwd)/rundir\"\nDOMAIN=\"$RUNDIR/domain.pddl\"\nPROBLEM=\"$RUNDIR/problem.pddl\"\nPLANFILE=\"$RUNDIR/sas_plan\"\nsingularity run -C -H $RUNDIR planner.img $DOMAIN $PROBLEM $PLANFILE $COSTBOUND\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-supported-problems\" class=\"anchor\" aria-hidden=\"true\" href=\"#supported-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupported problems\u003c/h3\u003e\n\u003cp\u003eThe formulation of supported problems is the same as \u003ca href=\"https://ipc2018-classical.bitbucket.io/#pddl\" rel=\"nofollow\"\u003eIPC 2018\u003c/a\u003e. We also provide a set of supported domains and problems in \u003ca href=\"https://github.com/hejm37/benchmark-domains\"\u003ebenchmark-domains\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-notes-on-playing-with-the-source-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes-on-playing-with-the-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes on playing with the source code\u003c/h2\u003e\n\u003cp\u003eThe source code of the planner contains two part:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBFWS-public and its dependency, LAPKT-public\u003c/li\u003e\n\u003cli\u003efast-downward-conjunctions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen planner should be invoked in the fast-downward-conjunctions part (using --dual option and it will call BFWS-public/fd-version/bfws.py to perform 1-BFWS, see \u003ca href=\"https://github.com/hejm37/sysu-planner/blob/master/Singularity\"\u003ethe Singularity script\u003c/a\u003e for more details).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-potential-failures\" class=\"anchor\" aria-hidden=\"true\" href=\"#potential-failures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePotential Failures\u003c/h3\u003e\n\u003cp\u003eIf the above build has failed, it may appears to be a cmake cache fail. In this case, remove the \u003cem\u003ebuilds\u003c/em\u003e (if it exists) directory under fast-downward-conjunctions and rerun the singularity command shall solve the problem.\u003c/p\u003e\n\u003cp\u003eOr it may appears to be a scons build fail. In this case, remove all the \u003cem\u003e.sconsign.dblite\u003c/em\u003e files under the directory shall solve the problem.\u003c/p\u003e\n\u003cp\u003eBoth cases would occur if the planner was built outside a container.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eApptainer Definition file for SPID.\u003c/h1\u003e\u003ca id=\"user-content-apptainer-definition-file-for-spid\" class=\"anchor\" aria-label=\"Permalink: Apptainer Definition file for SPID.\" href=\"#apptainer-definition-file-for-spid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1563536767.0 + "updated_at": 1710191251.0 }, { "data_format": 2, - "description": "Singularity recipe files for cdo (https://www.mpimet.mpg.de/cdo/)", + "description": "Building a singularity container for PHCpack", "filenames": [ - "Singularity", - "Singularity.1.9.3", - "Singularity.1.9.5", - "Singularity.1.7.0" + "Singularity" ], - "full_name": "powerPlant/cdo-srf", + "full_name": "ARCLeeds/PHCpack_sing", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2262\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the Climate Data Operators toolset\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4512\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eA singularity container for PHCpack\u003c/h1\u003e\u003ca id=\"user-content-a-singularity-container-for-phcpack\" class=\"anchor\" aria-label=\"Permalink: A singularity container for PHCpack\" href=\"#a-singularity-container-for-phcpack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA repo containing the Singularity recipe file for build \u003ca href=\"https://github.com/janverschelde/PHCpack/\"\u003ePHCpack\u003c/a\u003e. Also builds\nthe \u003ca href=\"https://github.com/Macaulay2/M2\"\u003eMacaulay2 language\u003c/a\u003e from source.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetails:\u003c/h2\u003e\u003ca id=\"user-content-details\" class=\"anchor\" aria-label=\"Permalink: Details:\" href=\"#details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAuthor: Alex Coleman \u003ca href=\"mailto:a.coleman1@leeds.ac.uk\"\u003ea.coleman1@leeds.ac.uk\u003c/a\u003e\u003cbr\u003e\nDate: 2020-06-29\u003cbr\u003e\nOriginating request: REQTSK0356325\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTODO:\u003c/h2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO:\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] get Macauley2 built\u003c/li\u003e\n\u003cli\u003e[x] get PHCpack built\u003c/li\u003e\n\u003cli\u003e[x] create acceptable interface for user\u003c/li\u003e\n\u003cli\u003e[x] write documentation for use\u003c/li\u003e\n\u003cli\u003e[x] upload to Singularity Hub (optional)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing the container\u003c/h2\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdetails\u003e\n \u003csummary\u003eFor University of Leeds users!\u003c/summary\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIf you want to use this container on ARC3 or ARC4\u003c/h2\u003e\u003ca id=\"user-content-if-you-want-to-use-this-container-on-arc3-or-arc4\" class=\"anchor\" aria-label=\"Permalink: If you want to use this container on ARC3 or ARC4\" href=\"#if-you-want-to-use-this-container-on-arc3-or-arc4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need to load the singularity module first before running the following commands:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ module load singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cp\u003eTo download this container from Singularity Hub:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ singularity build PHCpack_sing shub://ARCLeeds/PHCpack_sing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should download a file called PHCpack_sing to your current directory which you can use in the following way:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e\n# open a Macaulay2 interactive development environment\n$ singularity run PHCpack_sing\n\n# You can also run an existing .m2 script file by doing the following:\n$ singularity exec PHCpack_sing --script name_of_script.m2\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1549335527.0 + "subscribers_count": 3, + "topics": [ + "macaulay2", + "phcpack", + "singularity-container", + "singularity-hub" + ], + "updated_at": 1593596546.0 }, { "data_format": 2, - "description": "Singularity recipe files for EddyPro Engine (https://github.com/LI-COR/eddypro-engine)", + "description": null, "filenames": [ "Singularity", - "Singularity.6.2.1", - "Singularity.5.2.1", - "Singularity.6.0.0", - "Singularity.5.2.0", - "Singularity.6.1.0", - "Singularity.5.1.1", - "Singularity.6.2.0" + "Singularity1" ], - "full_name": "powerPlant/eddypro-engine-srf", + "full_name": "DoaneAS/RforSingularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2272\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the EddyPro eddy covariance data processing software\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1549923689.0 + "updated_at": 1496472410.0 }, { "data_format": 2, - "description": "Singularity recipe files for Racon (https://github.com/isovic/racon/)", + "description": null, "filenames": [ - "Singularity", - "Singularity.1.3.2", - "Singularity.1.3.1", - "Singularity.1.4.3", - "Singularity.1.4.2", - "Singularity.1.3.3", - "Singularity.1.4.7", - "Singularity.1.3.0", - "Singularity.1.4.0" + "Singularity" ], - "full_name": "powerPlant/racon-srf", + "full_name": "vrothenbergUCSD/Salk_LSD", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2269\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the Racon consensus module for raw de novo DNA assembly of long uncorrected reads\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCOGS 185 Advanced Machine Learning - Final Project\u003c/h1\u003e\u003ca id=\"user-content-cogs-185-advanced-machine-learning---final-project\" class=\"anchor\" aria-label=\"Permalink: COGS 185 Advanced Machine Learning - Final Project\" href=\"#cogs-185-advanced-machine-learning---final-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLocal Shape Descriptors\nNeuron segmentation task\u003c/p\u003e\n\u003cp\u003eThis is a forked repository of \u003ca href=\"https://github.com/funkelab/lsd_nm_experiments\"\u003ehttps://github.com/funkelab/lsd_nm_experiments\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLSD NM experiments tutorial\u003c/h1\u003e\u003ca id=\"user-content-lsd-nm-experiments-tutorial\" class=\"anchor\" aria-label=\"Permalink: LSD NM experiments tutorial\" href=\"#lsd-nm-experiments-tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGeneral notes\u003c/h2\u003e\u003ca id=\"user-content-general-notes\" class=\"anchor\" aria-label=\"Permalink: General notes\" href=\"#general-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA tutorial for running training/inference of networks used for paper (in\nsingularity containers).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTested on Ubuntu 18.04 with Quadro P6000 (24gb gpu ram)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssuming \u003ccode\u003esingularity\u003c/code\u003e is installed and setup\n(some tutorials \u003ca href=\"https://docs.sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and\n\u003ca href=\"https://singularity-tutorial.github.io/01-installation/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssuming conda is installed and setup (helpful \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e if needed)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/funkelab/lsd_nm_experiments.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCreate simple environment for fetching containers:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n test_env python=3.8\nconda activate test_env\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you just need \u003ca href=\"https://boto3.amazonaws.com/v1/documentation/api/latest/index.html\" rel=\"nofollow\"\u003eboto3\u003c/a\u003e for fetching containers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install boto3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you also want to view data with \u003ca href=\"https://github.com/google/neuroglancer\"\u003eneuroglancer\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install boto3 neuroglancer h5py zarr\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownloading container(s)\u003c/h2\u003e\u003ca id=\"user-content-downloading-containers\" class=\"anchor\" aria-label=\"Permalink: Downloading container(s)\" href=\"#downloading-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edownload_imgs.py\u003c/code\u003e will download the singularity container used in the paper (\u003ccode\u003elsd:v0.8.img\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eWe found that sometimes this singularity container throws errors because of deprecated cuda versions that don\u0027t play well with updated drivers\u003c/li\u003e\n\u003cli\u003eWe made another image (\u003ccode\u003elsd_legacy.img\u003c/code\u003e) that should handle this. If running into \u003ccode\u003elibcublas\u003c/code\u003e or \u003ccode\u003egcc\u003c/code\u003e errors with the original container, consider using the newer one\u003c/li\u003e\n\u003cli\u003eTo download (uncomment legacy img if desired):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython download_imgs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOr building legacy container from source:\u003c/h2\u003e\u003ca id=\"user-content-or-building-legacy-container-from-source\" class=\"anchor\" aria-label=\"Permalink: Or building legacy container from source:\" href=\"#or-building-legacy-container-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNvidia has deprecated the conda packages of the cudnn / cudatoolkit versions used in the original singularity container (6.0/8.0)\u003c/li\u003e\n\u003cli\u003eThis sometimes causes problems with updated drivers (even though the point of containerization is to solve this...)\u003c/li\u003e\n\u003cli\u003eCan get around by installing these from tars, specifically these:\n\u003cpre\u003e\u003ccode\u003ehttps://anaconda.org/numba/cudatoolkit/8.0/download/osx-64/cudatoolkit-8.0-3.tar.bz2\nhttps://repo.anaconda.com/pkgs/free/linux-64/cudnn-6.0.21-cuda8.0_0.tar.bz2\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esetup.sh\u003c/code\u003e will fetch these packages and use them to install directly into the conda environment when creating the Singularity container\u003c/li\u003e\n\u003cli\u003eThe other packages are just specified in the \u003ccode\u003elsd_legacy.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo build legacy image locally:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e./setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote - the original container runs fine with \u003ccode\u003eexec\u003c/code\u003e, the legacy one uses \u003ccode\u003erun\u003c/code\u003e. To be honest, not sure what the problem is here\u003c/li\u003e\n\u003cli\u003eSo \u003ccode\u003esingularity exec --nv lsd:v0.8.img python -c \"import tensorflow\"\u003c/code\u003e will work, and\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity run --nv lsd_legacy.img python -c \"import tensorflow\"\u003c/code\u003e will work, but\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity exec --nv lsd_legacy.img python -c \"import tensorflow\"\u003c/code\u003e will cause import errors\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFetching data\u003c/h2\u003e\u003ca id=\"user-content-fetching-data\" class=\"anchor\" aria-label=\"Permalink: Fetching data\" href=\"#fetching-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to 01_data directory (\u003ccode\u003ecd 01_data\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efetch_data.py\u003c/code\u003e will download training data from the aws bucket using a json file (\u003ccode\u003edatasets.json\u003c/code\u003e) specifying the training volumes for each dataset\u003c/li\u003e\n\u003cli\u003eThe script defaults to just downloading the first volume for each dataset (one for each of zebrafinch, fib25, hemi, or three total)\u003c/li\u003e\n\u003cli\u003edownload data:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython fetch_data.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eyou could also use the singularity container to download the data, but we already have boto3 in the basic env we created anyway\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating masks\u003c/h2\u003e\u003ca id=\"user-content-creating-masks\" class=\"anchor\" aria-label=\"Permalink: Creating masks\" href=\"#creating-masks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecreate_masks.py\u003c/code\u003e will create a \u003ccode\u003elabels_mask\u003c/code\u003e that we use for training to constrain random locations\u003c/li\u003e\n\u003cli\u003eIf you installed zarr into the conda environment, you can just run with:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython create_masks.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eotherwise, using the singularity container:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../lsd:v0.8.img python create_masks.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eor \u003ccode\u003esingularity run...\u003c/code\u003e if using legacy container\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eViewing the data\u003c/h2\u003e\u003ca id=\"user-content-viewing-the-data\" class=\"anchor\" aria-label=\"Permalink: Viewing the data\" href=\"#viewing-the-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you installed neuroglancer into your environment, you can view the data with \u003ccode\u003eview_data.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ee.g \u003ccode\u003epython -i view_data.py -d funke/fib25/training/tstvol-520-1.zarr\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you are viewing remotely, you could also set the bind address with -b (defaults to localhost)\u003c/li\u003e\n\u003cli\u003eThere are some good little packages \u0026amp; tutorials for using neuroglancer differently. Examples\n\u003ca href=\"https://github.com/funkelab/funlib.show.neuroglancer\"\u003ehere\u003c/a\u003e, \u003ca href=\"https://connectomics.readthedocs.io/en/latest/external/neuroglancer.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\nand \u003ca href=\"https://github.com/google/neuroglancer/tree/master/python/examples\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample fib25 training data:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/fib25_training_data.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/fib25_training_data.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownloading network checkpoints\u003c/h2\u003e\u003ca id=\"user-content-downloading-network-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Downloading network checkpoints\" href=\"#downloading-network-checkpoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efetch_checkpoint.py\u003c/code\u003e will download a specified network checkpoint for a given dataset to the target folder in \u003ccode\u003e02_train\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eWe can start with the baseline affinities for the zebrafinch dataset just using conda boto3 (or use singularity if desired):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython fetch_checkpoint.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining a network\u003c/h2\u003e\u003ca id=\"user-content-training-a-network\" class=\"anchor\" aria-label=\"Permalink: Training a network\" href=\"#training-a-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to the zebrafinch baseline directory (\u003ccode\u003ecd ../02_train/zebrafinch/baseline\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eWe start by creating our network in \u003ccode\u003emknet.py\u003c/code\u003e (e.g placeholders to match to the trained graphs):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python mknet.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIt should print a bunch of layer names and tensor shapes (that looks like a sideways U-Net) to the command line\u003c/li\u003e\n\u003cli\u003eCheck the files in the directory (e.g \u003ccode\u003etree .\u003c/code\u003e), it should now look like:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e.\n\u251c\u2500\u2500 checkpoint\n\u251c\u2500\u2500 config.json\n\u251c\u2500\u2500 config.meta\n\u251c\u2500\u2500 mknet.py\n\u251c\u2500\u2500 predict.py\n\u251c\u2500\u2500 predict_scan.py\n\u251c\u2500\u2500 train_net_checkpoint_400000.data-00000-of-00001\n\u251c\u2500\u2500 train_net_checkpoint_400000.index\n\u251c\u2500\u2500 train_net_checkpoint_400000.meta\n\u251c\u2500\u2500 train_net.json\n\u251c\u2500\u2500 train_net.meta\n\u251c\u2500\u2500 train.py\n\u2514\u2500\u2500 view_batch.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eTrain for 1 iteration:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python train.py 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eYou\u0027ll see that gunpowder will print \u003ccode\u003eERROR:tensorflow:Couldn\u0027t match files for checkpoint ./train_net_checkpoint_500000\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis is because it checks the \u003ccode\u003echeckpoint\u003c/code\u003e file which specifies iteration 500000 (since this network was trained for longer than the optimal checkpoint)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf we view the batch, we\u0027ll see that the predictions are all grey, since it really only trained for a single iteration and didn\u0027t use the checkpoint (\u003ccode\u003epython -i view_batch.py -f snapshots/batch_1.hdf\u003c/code\u003e):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_batch_1.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_batch_1.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo fix, simply edit this \u003ccode\u003echeckpoint\u003c/code\u003e file to point to the downloaded checkpoint iteration instead (e.g 500000 -\u0026gt; 400000)\u003c/li\u003e\n\u003cli\u003eNow running the above won\u0027t do anything, because of line 27 in \u003ccode\u003etrain.py\u003c/code\u003e:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etrained_until\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emax_iteration\u003c/span\u003e:\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSo just make sure to train to the checkpoint + n, eg for 1 extra iteration:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python train.py 400001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe can then view the saved batch, e.g:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython -i view_batch.py -f snapshots/batch_400001.hdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_batch_400k.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_batch_400k.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Inference\u003c/h2\u003e\u003ca id=\"user-content-running-inference\" class=\"anchor\" aria-label=\"Permalink: Running Inference\" href=\"#running-inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFor the lsds experiments, we ran everything from inference through evaluation in a blockwise fashion using \u003ca href=\"https://github.com/funkelab/daisy\"\u003edaisy\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor inference, this meant having a blockwise prediction script that called a gunpowder predict pipeline inside each process\u003c/li\u003e\n\u003cli\u003eFor example, this \u003ca href=\"https://github.com/funkelab/lsd/blob/master/lsd/tutorial/scripts/01_predict_blockwise.py\"\u003escript\u003c/a\u003e would distribute this \u003ca href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/02_train/zebrafinch/baseline/predict.py\"\u003escript\u003c/a\u003e by using a \u003ca href=\"http://funkey.science/gunpowder/api.html?highlight=daisy#gunpowder.DaisyRequestBlocks\" rel=\"nofollow\"\u003eDaisyRequestBlocks\u003c/a\u003e gunpowder node\u003c/li\u003e\n\u003cli\u003eIf you just want to run inference on a small volume (in memory), you can instead use a \u003ca href=\"http://funkey.science/gunpowder/api.html?highlight=scan#gunpowder.Scan\" rel=\"nofollow\"\u003eScan\u003c/a\u003e node\u003c/li\u003e\n\u003cli\u003eWe added adapted all blockwise inference scripts to also use scan nodes (e.g \u003ccode\u003epredict_scan.py\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eExample run on zfinch training data:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python predict_scan.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eResulting affinities:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_preds.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_preds.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMultitask (MTLSD)\u003c/h2\u003e\u003ca id=\"user-content-multitask-mtlsd\" class=\"anchor\" aria-label=\"Permalink: Multitask (MTLSD)\" href=\"#multitask-mtlsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThis can be run exactly the same as the baseline above.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInside \u003ccode\u003e01_data/fetch_checkpoint.py\u003c/code\u003e, uncomment \u003ccode\u003emtlsd\u003c/code\u003e and run.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to \u003ccode\u003e02_train/zebrafinch/mtlsd\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate network (\u003ccode\u003e... python mknet.py\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eChange \u003ccode\u003echeckpoint\u003c/code\u003e file iteration to match downloaded checkpoint (500000 -\u0026gt; 400000)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTrain (\u003ccode\u003e... python train.py 400001\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eView batch (\u003ccode\u003e... python -i view_batch.py ...\u003c/code\u003e) -\u0026gt; will also show LSDs\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePredict (\u003ccode\u003e... python predict_scan.py\u003c/code\u003e) -\u0026gt; will write out LSDs and Affs\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis will also give us LSDs:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/mtlsd_zfinch_preds_lsds.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/mtlsd_zfinch_preds_lsds.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTip - you can view the different components of the LSDs by adjusting the shader in neuroglancer, e.g changing \u003ccode\u003e0,1,2\u003c/code\u003e to \u003ccode\u003e3,4,5\u003c/code\u003e will show the diagonal entries of the covariance component (or the direction the processes move):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003evoid main() {\n emitRGB(\n vec3(\n toNormalized(getDataValue(3)),\n toNormalized(getDataValue(4)),\n toNormalized(getDataValue(5)))\n );\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eOr to view a single channel of the 10d lsds, (e.g channel 6):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003evoid main() {\n float v = toNormalized(getDataValue(6));\n vec4 rgba = vec4(0,0,0,0);\n if (v != 0.0) {\n rgba = vec4(colormapJet(v), 1.0);\n }\n emitRGBA(rgba);\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAutocontext (ACLSD and ACRLSD)\u003c/h2\u003e\u003ca id=\"user-content-autocontext-aclsd-and-acrlsd\" class=\"anchor\" aria-label=\"Permalink: Autocontext (ACLSD and ACRLSD)\" href=\"#autocontext-aclsd-and-acrlsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThese networks rely on a pretrained LSD (raw -\u0026gt; LSDs) network, eg:\u003c/li\u003e\n\u003cli\u003eACLSD: \u003ccode\u003eRaw -\u0026gt; LSDs -\u0026gt; Affs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eACRLSD: \u003ccode\u003eRaw -\u0026gt; LSDs + cropped Raw -\u0026gt; Affs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBecause of this, they are more computationally expensive (since training requires extra context to first predict the lsds)\u003c/li\u003e\n\u003cli\u003eThey ran using 23.5 GB of available 24 GB GPU RAM when testing on quadro p6000. If you have less than that you will likely run into cuda OOM errors\u003c/li\u003e\n\u003cli\u003eIf you have access to sufficient gpu memory, to start navigate to \u003ccode\u003e01_data\u003c/code\u003e and uncomment \u003ccode\u003elsd\u003c/code\u003e + run script to get the pretrained lsd checkpoint\u003c/li\u003e\n\u003cli\u003eGo to lsd directory (\u003ccode\u003ecd ../02_train/zebrafinch/lsd\u003c/code\u003e) and follow same instructions as baseline and mtlsd nets above\u003c/li\u003e\n\u003cli\u003eOnce you have the lsd checkpoints (and \u003ccode\u003etest_prediction.zarr\u003c/code\u003e with \u003ccode\u003epred_lsds\u003c/code\u003e following prediction), start with a basic autocontext network (aclsd).\u003c/li\u003e\n\u003cli\u003eGet the aclsd checkpoint, navigate to appropriate directory, change checkpoint file as before and train network.\u003c/li\u003e\n\u003cli\u003eVisualizing the resulting batch shows us the larger raw context needed for predicting the lsds to ensure that the output affinities remain the same size:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/aclsd_training_batch.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/aclsd_training_batch.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun prediction as before - note, will not run if lsds have not been predicted first. This script could be adapted to predict the lsds on-the-fly using just the lsds and affs checkpoints\u003c/li\u003e\n\u003cli\u003eThe same can be done for the ACRLSD network (note, requires a merge provider during prediction as this network takes both lsds and raw data as input)\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTodos: add consolidated fib25/hemi nets to tutorial\u003c/h2\u003e\u003ca id=\"user-content-todos-add-consolidated-fib25hemi-nets-to-tutorial\" class=\"anchor\" aria-label=\"Permalink: Todos: add consolidated fib25/hemi nets to tutorial\" href=\"#todos-add-consolidated-fib25hemi-nets-to-tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1590711591.0 + "updated_at": 1716585661.0 }, { "data_format": 2, - "description": "Singularity recipe files for OpenDroneMap (https://www.opendronemap.org/)", + "description": null, "filenames": [ - "Singularity", - "Singularity.0.4.1", - "Singularity.0.4.0" + "Singularity" ], - "full_name": "powerPlant/opendronemap-srf", + "full_name": "marchoeppner/wgs-calling", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2266\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the OpenDroneMap Drone Mapping Software\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIKMB WGS variant calling pipeline\u003c/h1\u003e\u003ca id=\"user-content-ikmb-wgs-variant-calling--pipeline\" class=\"anchor\" aria-label=\"Permalink: IKMB WGS variant calling pipeline\" href=\"#ikmb-wgs-variant-calling--pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline generates human whole-genome variant calls from short read files.\u003c/p\u003e\n\u003cp\u003ePlease not that this code is not yet final!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation about the pipeline can be found in the \u003ccode\u003edocs/\u003c/code\u003e directory or under the links below:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/recommendations.md\"\u003eRecommendations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1549336324.0 + "updated_at": 1564730498.0 }, { "data_format": 2, - "description": "Singularity recipe files for crema (https://github.com/gbgolding/crema)", + "description": "Utilities and models for Arabic Natural Language Processing.", "filenames": [ - "Singularity", - "Singularity.fe4cf7a" + "Singularity" ], - "full_name": "powerPlant/crema-srf", + "full_name": "clu-ling/wanlp-2021", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2320\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the crema tool to classify RNAs by Ensemble Machine learning Algorithms\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003earabic-nlp\u003c/h1\u003e\u003ca id=\"user-content-arabic-nlp\" class=\"anchor\" aria-label=\"Permalink: arabic-nlp\" href=\"#arabic-nlp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModels and utilities for Arabic NLP.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDevelopment\u003c/h1\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is organized as a Python module. We recommend using \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e for development.\u003c/p\u003e\n\u003cp\u003ePlease note if you need to install additional dependencies, you\u0027ll need to alter the \u003ccode\u003erequirements.txt\u003c/code\u003e and rebuild the docker image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -f Dockerfile -t \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re only using the existing dependencies, you can simply download the published Docker image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run code interactively in the iPython interpreter:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app parsertongue/arabic-nlp:latest ipython\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run code interactively in a Jupyter notebook, run the following command and open your browser to \u003ca href=\"http://localhost:8889\" rel=\"nofollow\"\u003elocalhost:8889\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app -p 8889:9999 parsertongue/arabic-nlp:latest launch-notebook\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest\u003c/h2\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTests are written by \u003ca href=\"https://docs.python.org/3.8/library/unittest.html#unittest.TestCase\" rel=\"nofollow\"\u003eextending the \u003ccode\u003eTestCase\u003c/code\u003e class\u003c/a\u003e from the \u003ccode\u003eunittest\u003c/code\u003e module in the Python standard library. All tests can be found in the \u003ca href=\"./tests\"\u003e\u003ccode\u003etests\u003c/code\u003e\u003c/a\u003e directory.\u003c/p\u003e\n\u003cp\u003eAll tests can be run using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e test-all\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest\u003c/h2\u003e\u003ca id=\"user-content-test-1\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTests are written by \u003ca href=\"https://docs.python.org/3.8/library/unittest.html#unittest.TestCase\" rel=\"nofollow\"\u003eextending the \u003ccode\u003eTestCase\u003c/code\u003e class\u003c/a\u003e from the \u003ccode\u003eunittest\u003c/code\u003e module in the Python standard library. All tests can be foun\nd in the \u003ca href=\"./tests\"\u003e\u003ccode\u003etests\u003c/code\u003e\u003c/a\u003e directory.\u003c/p\u003e\n\u003cp\u003eAll tests can be run using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e test-all\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnit tests\u003c/h3\u003e\u003ca id=\"user-content-unit-tests\" class=\"anchor\" aria-label=\"Permalink: Unit tests\" href=\"#unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run just the unit tests, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e green -vvv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eType hints\u003c/h3\u003e\u003ca id=\"user-content-type-hints\" class=\"anchor\" aria-label=\"Permalink: Type hints\" href=\"#type-hints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code makes use of Python type hints. To perform type checking, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e mypy --ignore-missing-imports --follow-imports=skip --strict-optional /app\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning an experiment\u003c/h3\u003e\u003ca id=\"user-content-running-an-experiment\" class=\"anchor\" aria-label=\"Permalink: Running an experiment\" href=\"#running-an-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe snippet below will run an experiment using \u003ca href=\"./tests/toy-data\"\u003etoy data\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/app parsertongue/arabic-nlp:latest cdd-base-model --config /app/tests/toy-data/test-experiment-config.yml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a Singularity image from a published Docker image, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull cdd.sif docker://parsertongue/arabic-nlp:latest\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# HPC\u003c/span\u003e\n\nTo \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e the image on the UA HPC with GPU acceleration, first request an interactive session:\n\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ebash\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003eqsub -I \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-N interactive-gpu \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-W group_list=mygroupnamehere \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-q standard \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-l select=1:ncpus=2:mem=16gb:ngpus=1 \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-l cput=3:0:0 \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-l walltime=1:0:0\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe following modules are necessary:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load singularity\nmodule load cuda11\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNOTE: not all clusters have the \u003ccode\u003ecuda11\u003c/code\u003e module installed.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --nv --no-home /path/to/your.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo check that the GPU is found, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003envidia-smi\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, confirm that PyTorch can locate the GPU:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epython\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"import torch;print(f\u0027CUDA?:\u003cspan class=\"pl-cce\"\u003e\\t\u003c/span\u003e{torch.cuda.is_available()}\u0027);print(f\u0027GPU:\u003cspan class=\"pl-cce\"\u003e\\t\u003c/span\u003e{torch.cuda.get_device_name(0)}\u0027)\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLocal Singularity build\u003c/h1\u003e\u003ca id=\"user-content-local-singularity-build\" class=\"anchor\" aria-label=\"Permalink: Local Singularity build\" href=\"#local-singularity-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNOTE: These instructions assume you\u0027ve already installed Singularity (\u0026gt;= v3.7.0) locally on a Linux system.\u003c/p\u003e\n\u003cp\u003eFirst, we\u0027ll build a docker image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -f Dockerfile -t \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eparsertongue/arabic-nlp:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, we\u0027ll use the \u003ca href=\"https://sylabs.io/guides/3.7/user-guide/appendix.html#docker-daemon-archive\" rel=\"nofollow\"\u003e\u003ccode\u003edocker-daemon\u003c/code\u003e bootstrap agent\u003c/a\u003e to build a Singularity image from this docker image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build arabic-nlp.sif docker-daemon://parsertongue/arabic-nlp:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, we\u0027ll test that image works:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --nv arabic-nlp.sif train-arabert --help\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1550200930.0 + "topics": [ + "arabic-nlp" + ], + "updated_at": 1697769625.0 }, { "data_format": 2, - "description": "Singularity recipe files for pcl (https://github.com/PointCloudLibrary/pcl)", + "description": "Nextflow pipeline to analyse gene expression in plants", "filenames": [ - "Singularity", - "Singularity.1.9.1", - "Singularity.1.9.0", - "Singularity.1.8.1" + "Singularity" ], - "full_name": "powerPlant/pcl-srf", + "full_name": "lifebit-ai/Plant-RNASeq", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2329\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the pcl Point Cloud Library\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/plant-rnaseq\u003c/h1\u003e\u003ca id=\"user-content-nf-coreplant-rnaseq\" class=\"anchor\" aria-label=\"Permalink: nf-core/plant-rnaseq\" href=\"#nf-coreplant-rnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNextflow pipeline to analyse gene expression in plants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/plant-rnaseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/08cebaf367d11339b060cd29e866226fea535bdb77cd3c6ea6d27626115e8723/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f706c616e742d726e617365712e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/plant-rnaseq.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b9d943c6da4bb0a73fe6bc476304001a1e93cbc8ccadf264da8fe47716ab6c53/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/plant-rnaseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/076f241f2cae896efc7d9ebd3bd36558690eeb6574cb81c81410fc61a47d4d9c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f706c616e742d726e617365712e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/plant-rnaseq.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/plant-rnaseq pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1550093426.0 + "updated_at": 1550739422.0 }, { "data_format": 2, - "description": "Singularity recipe files for DIAMOND (https://github.com/bbuchfink/diamond)", + "description": null, "filenames": [ - "Singularity", - "Singularity.v0.9.15", - "Singularity.v0.9.18", - "Singularity.v0.9.22", - "Singularity.v0.9.16", - "Singularity.v0.9.19", - "Singularity.v0.9.21", - "Singularity.v0.9.20", - "Singularity.v0.9.23", - "Singularity.v0.9.24", - "Singularity.v0.9.17" + "Singularity" ], - "full_name": "powerPlant/diamond-srf", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2322\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the DIAMOND Accelerated BLAST compatible local sequence aligner\u003c/p\u003e\n", + "full_name": "kavonrtep/cenh3_chip_seq_pipeline", + "latest_release": "0.1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCHIP seq analysis pipeline\u003c/h1\u003e\u003ca id=\"user-content-chip-seq-analysis-pipeline\" class=\"anchor\" aria-label=\"Permalink: CHIP seq analysis pipeline\" href=\"#chip-seq-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is designed to analyze ChIP-seq data using a series of tools like epic2, Macs3 and deeptools. The pipeline is intended to be used for detection of broad peaks. The pipeline is encapsulated in a Singularity container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is required to use the container. Singularity can be installed using conda environment.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n singularity3 -c conda-forge \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esingularity\u0026gt;=3.6\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nconda activate singularity3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image (.sif file) can be downloaded from \u003ca href=\"https://github.com/kavonrtep/cenh3_chip_seq_pipeline/releases\"\u003ehttps://github.com/kavonrtep/cenh3_chip_seq_pipeline/releases\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInput files for the pipeline are provided in \u003ccode\u003econfig.yaml\u003c/code\u003e file. The file contains the following fields:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003egenome_fasta\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/path/to/genome.fasta\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003esamples\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003einput\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/path/to/input.fastq.gz\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003echip\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/path/to/chip.fastq.gz\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutput_dir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/path/to/output\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInput and ChIP could be \u003ccode\u003efastq\u003c/code\u003e or \u003ccode\u003efastq.gz\u003c/code\u003e files. To run the pipeline, execute the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /path/to/ -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e chipseq_pipeline.sif -c config.yaml -t 20\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eParameter \u003ccode\u003e-t\u003c/code\u003e specifies the number of threads to use. Singularity parameter \u003ccode\u003e-B\u003c/code\u003e is used to bind the input and output directories to the container. Without this parameter, the container will not be able to access the input and output files. File \u003ccode\u003econfig.yaml\u003c/code\u003e must be also in directory which is accessible to the container. In the example above this is the current directory \u003ccode\u003e$PWD\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf you have files in different directories, you can specify multiple \u003ccode\u003e-B\u003c/code\u003e parameters. For example if your \u003ccode\u003econfig.yaml\u003c/code\u003e is:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003egenome_fasta\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/mnt/data/genomes/genome.fasta\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003esamples\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003einput\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/mnt/data/fastq_reads/input.fastq.gz\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003echip\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/mnt/data/fastq_reads/chip.fastq.gz\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutput_dir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e/mnt/data/outputs/output\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen you can use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /mnt/data/genomes \\\n-B /mnt/data/fastq_reads -B /mnt/data/outputs \\\n-B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e chipseq_pipeline.sif -c config.yaml -t 20\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr you can use the following command to bind the whole directory \u003ccode\u003e/mnt/data\u003c/code\u003e to the container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /mnt/data -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e chipseq_pipeline.sif -c config.yaml -t 20\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput structure\u003c/h2\u003e\u003ca id=\"user-content-output-structure\" class=\"anchor\" aria-label=\"Permalink: Output structure\" href=\"#output-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline will create the following files and folders in the output directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eoutput/\n\u251c\u2500\u2500 epic2/\n\u2502 \u251c\u2500\u2500 epic2_all.bs2000.csv\n\u2502 \u251c\u2500\u2500 epic2_all.default.csv\n\u2502 \u251c\u2500\u2500 epic2_unique.bs2000.csv\n\u2502 \u2514\u2500\u2500 epic2_unique.default.csv\n\u251c\u2500\u2500 macs3/\n\u2502 \u251c\u2500\u2500 macs3_all_peaks.broadPeak\n\u2502 \u251c\u2500\u2500 macs3_all_peaks.gappedPeak\n\u2502 \u251c\u2500\u2500 macs3_all_peaks.xls\n\u2502 \u251c\u2500\u2500 macs3_unique_peaks.broadPeak\n\u2502 \u251c\u2500\u2500 macs3_unique_peaks.gappedPeak\n\u2502 \u2514\u2500\u2500 macs3_unique_peaks.xls\n\u251c\u2500\u2500 mapped_reads/\n\u2502 \u251c\u2500\u2500 chip.all.bam\n\u2502 \u251c\u2500\u2500 chip.all.sorted.bam\n\u2502 \u251c\u2500\u2500 chip.all.sorted.bam.csi\n\u2502 \u251c\u2500\u2500 chip.unique.sorted.bam\n\u2502 \u251c\u2500\u2500 chip.unique.sorted.bam.csi\n\u2502 \u251c\u2500\u2500 input.all.bam\n\u2502 \u251c\u2500\u2500 input.all.sorted.bam\n\u2502 \u251c\u2500\u2500 input.all.sorted.bam.csi\n\u2502 \u251c\u2500\u2500 input.unique.sorted.bam\n\u2502 \u2514\u2500\u2500 input.unique.sorted.bam.csi\n\u251c\u2500\u2500 chip_vs_input_all.bs2000.bw \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from bamCompare, bin witdh 2000bp\u003c/span\u003e\n\u251c\u2500\u2500 chip_vs_input_all.bs200.bw \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from bamCompare, bin witdh 200bp\u003c/span\u003e\n\u251c\u2500\u2500 chip_vs_input_unique.bs2000.bw \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from bamCompare on uniquely mapped reads, bin with 2000bp\u003c/span\u003e\n\u251c\u2500\u2500 chip_vs_input_unique.bs200.bw \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from bamCompare on uniquely mapped reads, bin with 200bp\u003c/span\u003e\n\u251c\u2500\u2500 epic2_all.bs2000.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from epic2, bin width 2000bp\u003c/span\u003e\n\u251c\u2500\u2500 epic2_all.default.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from epic2, default bin width (200bp)\u003c/span\u003e\n\u251c\u2500\u2500 epic2_unique.bs2000.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from epic2 on uniquely mapped reads, bin width 2000bp\u003c/span\u003e\n\u251c\u2500\u2500 epic2_unique.default.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from epic2 on uniquely mapped reads, default bin width (200bp)\u003c/span\u003e\n\u251c\u2500\u2500 macs3_all_peaks.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from macs3, broadPeaks as bedgraph\u003c/span\u003e\n\u2514\u2500\u2500 macs3_unique_peaks.bedgraph \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output from macs3 on uniquely mapped reads, broadPeaks as bedgraph\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container\u003c/h2\u003e\u003ca id=\"user-content-build-the-container\" class=\"anchor\" aria-label=\"Permalink: Build the container\" href=\"#build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the container, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSINGULARITY=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ewhich singularity\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\nsudo \u003cspan class=\"pl-smi\"\u003e$SINGULARITY\u003c/span\u003e build chipseq_pipeline.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1549857110.0 + "updated_at": 1719908278.0 }, { "data_format": 2, - "description": "Singularity recipe files for MAPGD (https://github.com/LynchLab/MAPGD)", + "description": "SE4HPC @POLIMI", "filenames": [ - "Singularity", - "Singularity.0.4.38-d3edee2" + "Singularity" ], - "full_name": "powerPlant/mapgd-srf", + "full_name": "fdallac/HPC-DevOps-exercise-part2", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2319\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the MAPGD series of related programs for the analysis of low coverage population genomic data or for the analysis of pooled data\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBrief description of CI/CD Github Actions Workflows\u003c/h1\u003e\u003ca id=\"user-content-brief-description-of-cicd-github-actions-workflows\" class=\"anchor\" aria-label=\"Permalink: Brief description of CI/CD Github Actions Workflows\" href=\"#brief-description-of-cicd-github-actions-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eauto-test.yaml\u003c/strong\u003e: Simple CI/CD workflow for building the executable (using CMake) and running the Unit Tests defined in \"test\\test_matrix_multiplication.cpp\"\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eauto-test-in-container.yaml\u003c/strong\u003e: CI/CD workflow for building a Docker image (from the Dockerfile in the repo) and push the image in Github Container Registry, generating a package and the artifact attestation. Then the Docker image is runned in order to launch Unit Tests, which are written in a test.log artifact (see an example in \"outputs\" folder)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eauto-test-container-on-cineca-cluster.yaml\u003c/strong\u003e: CI/CD workflow for building a Singularity container (from the Singularity file in the repo), deploy it into the CINECA cluster (Galileo100) and run the container. Output and error logs are then upload as artifacts (see examples in \"outputs\" folder)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject\" href=\"#se4hpcproject\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2 -- From build to release and manual job execution\u003c/h2\u003e\u003ca id=\"user-content-step-2----from-build-to-release-and-manual-job-execution\" class=\"anchor\" aria-label=\"Permalink: Step 2 -- From build to release and manual job execution\" href=\"#step-2----from-build-to-release-and-manual-job-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFocus now on the correct implementation of the matrix multiplication you\nfind in \u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e. This is a\nparallel implementation that uses MPI and reads the matrices to be\nmultiplied from two files, matrixA.txt and matrixB.txt. In these files\nthe first row contains the matrix dimensions (number of rows and\ncolumns), while the other rows contain the matrix itself.\u003c/p\u003e\n\u003cp\u003eYour task is to perform the following steps:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation\u003c/strong\u003e: Use the template available here\n\u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e to create your own\ngithub repository. Add to this repository the tests you have created in\nStep1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutomating the build, test and release processes\u003c/strong\u003e: Create a CI/CD\npipeline that, when someone pushes files in the repo, executes the\nbuilding and testing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContainerizing the application\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDefine a Singularity container descriptor for the matrix\nmultiplication program and push it in your repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtend the created action to create a container image from your\ndescription.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExecuting on the cluster\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a job.sh file to run your containerized application. Make\nsure that the standard output and error are mapped to txt files.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransfer on Galileo100 your job script and the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSubmit your job to the cluster and check whether it works correctly.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePush on your github repository your job.sh file and the files\nobtained from the execution of the matrix multiplication.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3 -- Automating a job submission with containerization\u003c/h2\u003e\u003ca id=\"user-content-step-3----automating-a-job-submission-with-containerization\" class=\"anchor\" aria-label=\"Permalink: Step 3 -- Automating a job submission with containerization\" href=\"#step-3----automating-a-job-submission-with-containerization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtend the action you have created at step 3 to automate completely the\nprocess from a push on the repository to the execution of the\ncontainerized software on SLURM. To do so, you will have to move your\ncontainer from the runner to the cluster. You can either use the scp\ncommand or you can publish your image on the Singularity registry and\nthen pull it from the cluster. Don\u0027t forget to handle your secrets\nproperly! You do not want to leave passwords and authentication tokens\nvisible to everybody, so you will use the \u003ca href=\"https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions?tool=cli\"\u003esecrets\nmechanism\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1549853299.0 + "updated_at": 1717595406.0 }, { "data_format": 2, - "description": "A thin Singularity image used as an alternative to Proot to wrap applications in an arbitrary file system.", + "description": "Creates graphs from problem instance pddl inputs", "filenames": [ "Singularity" ], - "full_name": "OSC/centos7-launcher", + "full_name": "JesseBrouw/GraphCreate", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7-launcher\" class=\"anchor\" aria-hidden=\"true\" href=\"#centos7-launcher\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7-launcher\u003c/h1\u003e\n\u003cp\u003eA Singularity image used wrap applications RStudio \u003ccode\u003erserver\u003c/code\u003e instances in an arbitrary file system for use with \u003ca href=\"http://openondemand.org/\" rel=\"nofollow\"\u003eOnDemand\u003c/a\u003e. Tested as compatible with Singularity 2.x and 3.x.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage:\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-2x\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-2x\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity 2.x\u003c/h3\u003e\n\u003cp\u003eTODO...\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-3x\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-3x\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity 3.x\u003c/h3\u003e\n\u003cp\u003eTODO...\u003c/p\u003e\n", + "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1550176998.0 + "updated_at": 1671531108.0 }, { "data_format": 2, - "description": "Singularity recipe files for SWAN (http://bitbucket.org/charade/swan)", + "description": "This is a read-only mirror of the git repos at https://bioconductor.org", "filenames": [ - "Singularity.3516c2f" + "Singularity" ], - "full_name": "powerPlant/swan-srf", + "full_name": "bioc/TSRchitect", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2354\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the SWAN tool for SV detection\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTSRchitect: Promoter identification from diverse types of large-scale TSS profiling data\u003c/h1\u003e\u003ca id=\"user-content-tsrchitect-promoter-identification-from-diverse-types-of-large-scale-tss-profiling-data\" class=\"anchor\" aria-label=\"Permalink: TSRchitect: Promoter identification from diverse types of large-scale TSS profiling data\" href=\"#tsrchitect-promoter-identification-from-diverse-types-of-large-scale-tss-profiling-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe TSRchitect repository encompasses an \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e\npackage developed in the \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eBrendel Group\u003c/a\u003e for analyses\nof transcription start site data.\nThe code conforms to our \u003ca href=\"https://brendelgroup.github.io/\" rel=\"nofollow\"\u003eRAMOSE\u003c/a\u003e\nphilosophy: it generates \u003cstrong\u003ereproducible\u003c/strong\u003e, \u003cstrong\u003eaccurate\u003c/strong\u003e, and \u003cstrong\u003emeaningful\u003c/strong\u003e\nresults; it is \u003cstrong\u003eopen\u003c/strong\u003e (source) and designed to be \u003cstrong\u003escalable\u003c/strong\u003e and\n\u003cstrong\u003eeasy\u003c/strong\u003e to use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start \u003ca href=\"https://singularity-hub.org/collections/1204\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-quick-start-\" class=\"anchor\" aria-label=\"Permalink: Quick Start \" href=\"#quick-start-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInput to TSRchitect will be transcription profiling read alignment data in \u003ccode\u003ebam\u003c/code\u003e\nor \u003ccode\u003ebed\u003c/code\u003e format as well as the appropriate genome annotation (if\navailable).\nOutput consists of predicted Transcription Start Sites (TSS) and Transcription\nStart Regions (TSR) as well as statistics summarizing the distribution and\ncharacteristics of identified TSSs and TSRs.\u003c/p\u003e\n\u003cp\u003eAll the TSRchitect dependencies are encapsulated in a\n\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available from\n\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\nThus, once you know what you are doing, execution could be as simple as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name tsr.simg shub://BrendelGroup/TSRchitect\nsingularity exec tsr.simg R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich will bring up an \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e console with the\nTSRchitect library and all its prerequisites available.\nFor example, in that console, you should see\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eR version 3.5.3 (2019-03-11) -- \"Great Truth\"\n...\n\u0026gt; packageVersion(\"TSRchitect\")\n[1] \u00271.13.5\u0027\n\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRealistic Start\u003c/h2\u003e\u003ca id=\"user-content-realistic-start\" class=\"anchor\" aria-label=\"Permalink: Realistic Start\" href=\"#realistic-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the\n\u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\nOnce all preparatory steps are taken care of, see the \u003ca href=\"./demo/HOWTO.md\"\u003eHOWTO\u003c/a\u003e\ndocument for examples of how to load data into TSRchitect and predict and\ncharacterize promoters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ and References\u003c/h2\u003e\u003ca id=\"user-content-faq-and-references\" class=\"anchor\" aria-label=\"Permalink: FAQ and References\" href=\"#faq-and-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see\n\u003ca href=\"https://github.com/vpbrendel/TSRchitect/wiki/FAQ\"\u003eV. Brendel\u0027s TSRchitect FAQ\u003c/a\u003e\nfor usage examples and suggestions.\u003c/p\u003e\n\u003cp\u003eIf you find \u003cem\u003eTSRchitect\u003c/em\u003e useful, you may cite:\u003c/p\u003e\n\u003cp\u003eRaborn RT, Sridharan K, Brendel VP (2017)\n\u003cem\u003eTSRchitect: Promoter identification from large-scale TSS profiling data.\u003c/em\u003e\ndoi: 10.18129/B9.bioc.TSRchitect, \u003ca href=\"https://doi.org/doi:10.18129/B9.bioc.TSRchitect\" rel=\"nofollow\"\u003ehttps://doi.org/doi:10.18129/B9.bioc.TSRchitect\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOur own publications will be linked here in due course.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease direct all comments and suggestions to\n\u003ca href=\"mailto:vbrendel@indiana.edu\"\u003eVolker Brendel\u003c/a\u003e\nat \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eIndiana University\u003c/a\u003e and\n\u003ca href=\"mailto:rtraborn@asu.edu\"\u003eTaylor Raborn\u003c/a\u003e at his current address.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1550114140.0 + "updated_at": 1689261643.0 }, { "data_format": 2, - "description": "Singularity recipe files for NovoGraph (https://github.com/NCBI-Hackathons/NovoGraph)", + "description": "transcritp assembly programs in one container", "filenames": [ - "Singularity", - "Singularity.1.0.0" + "Singularity" ], - "full_name": "powerPlant/novograph-srf", + "full_name": "aseetharam/transcript-assemblers", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2342\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the NovoGraph tool to construct a genome graph representation of long-read-based de novo sequence assemblies\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDirect Evidence Pipeline Container \u003ca href=\"https://singularity-hub.org/collections/4893\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-direct-evidence-pipeline-container-\" class=\"anchor\" aria-label=\"Permalink: Direct Evidence Pipeline Container \" href=\"#direct-evidence-pipeline-container-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis image contains all the tools required to run the direct evidence pipeline. For the complete pipeline workflow, see this \u003ca href=\"\"\u003epaste some link here\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull evidence.sif shub://aseetharam/transcript-assemblers\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a \u003ccode\u003eevidence.sif\u003c/code\u003e image, with the required programs pre-installed. All the programs required for evidence-based gene prediction are installed in this image and you can either run the pipeline by hand or use the Snakemake/Nextflow workflows to run them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisities\u003c/h3\u003e\u003ca id=\"user-content-prerequisities\" class=\"anchor\" aria-label=\"Permalink: Prerequisities\" href=\"#prerequisities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run this container you\u0027ll need \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach tool has their own usage, so please check individual tools and their requirements if you are running them by hand.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Transcript assemblers:\nsingularity run --cleanenv evidence.sif Trinity\nsingularity run --cleanenv evidence.sif class\nsingularity run --cleanenv evidence.sif strawberry\nsingularity run --cleanenv evidence.sif stringtie\nsingularity run --cleanenv evidence.sif cufflinks\n\n# ORF predictor\nsingularity run --cleanenv evidence.sif TransDecoder.LongOrfs\nsingularity run --cleanenv evidence.sif orfipy\n\n# Mikado-related\nsingularity run --cleanenv evidence.sif mikado\nsingularity run --cleanenv evidence.sif diamond\nsingularity run --cleanenv evidence.sif portcullis\nsingularity run --cleanenv evidence.sif samtools\nsingularity run --cleanenv evidence.sif prodigal\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment Variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ePATH\u003c/code\u003e Location for all the installed tools\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePYTHONDONTWRITEBYTECODE\u003c/code\u003e set to true\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTRINITY_HOME\u003c/code\u003e to utlilites to work\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample run\u003c/h2\u003e\u003ca id=\"user-content-example-run\" class=\"anchor\" aria-label=\"Permalink: Example run\" href=\"#example-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor running on your data:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewill update this section later\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eToDo\u003c/h2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: ToDo\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eAdd \u003ca href=\"https://github.com/NBISweden/AGAT\"\u003eAGAT\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd \u003ca href=\"\"\u003e\u003ccode\u003ephylostratR\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd \u003ca href=\"\"\u003eTESorter\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd \u003ca href=\"\"\u003eOrthoFinder\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd \u003ca href=\"\"\u003eBioAwk\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eArun Seetharam\u003c/strong\u003e - \u003cem\u003eauthor \u0026amp; maintainer\u003c/em\u003e - \u003ca href=\"\"\u003eWebPage\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee also the list of \u003ca href=\"https://github.com/your/repository/contributors\"\u003econtributors\u003c/a\u003e who\nparticipated in this project.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1550014659.0 + "updated_at": 1618590617.0 }, { "data_format": 2, - "description": "Singularity recipe files for RaGOO (https://github.com/malonge/RaGOO)", + "description": null, "filenames": [ - "Singularity", - "Singularity.1.02", - "Singularity.1.01" + "Singularity" ], - "full_name": "powerPlant/ragoo-srf", + "full_name": "cmaumet/nipype_tutorial", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2341\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the RaGOO tool to order and orient genome assembly contigs via Minimap2 alignments to a reference genome\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNipype Tutorial Notebooks\u003c/h1\u003e\u003ca id=\"user-content-nipype-tutorial-notebooks\" class=\"anchor\" aria-label=\"Permalink: Nipype Tutorial Notebooks\" href=\"#nipype-tutorial-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/miykael/nipype_tutorial/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/56c6ab80fc1ecc6792d64bd85b8e69e8b5992d1defa85e34eded3ab11381af28/68747470733a2f2f636972636c6563692e636f6d2f67682f6d69796b61656c2f6e69707970655f7475746f7269616c2e7376673f7374796c653d736869656c64\" alt=\"CircleCi\" data-canonical-src=\"https://circleci.com/gh/miykael/nipype_tutorial.svg?style=shield\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/issues/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/51ab5247214bfa948735d8c78ac468a02aa35e06a4cc7382d3f1c5e57406a802/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/pulls/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ccc0801d4de090929a683cd8f409c9e9f73d71b8d6b26c744b2192308ab5d092/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d70722f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub pull-requests\" data-canonical-src=\"https://img.shields.io/github/issues-pr/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://GitHub.com/miykael/nipype_tutorial/graphs/contributors/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f02cdc559a57384d13bb14ef068095850e66cfade8c0fa1acb2c454722106bb1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub contributors\" data-canonical-src=\"https://img.shields.io/github/contributors/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/commits/master\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df38a8439e58dc92bec26953fe15dbafbcae2edf29c51c8e054fc98eed3966ab/68747470733a2f2f6769746875622d62617369632d6261646765732e6865726f6b756170702e636f6d2f636f6d6d6974732f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub Commits\" data-canonical-src=\"https://github-basic-badges.herokuapp.com/commits/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/nipype_tutorial/archive/master.zip\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2829b238d51781fa9e31df4b384ba49370c5229d509e5e26cd7a96ce7d9178d9/68747470733a2f2f6769746875622d73697a652d62616467652e6865726f6b756170702e636f6d2f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub size\" data-canonical-src=\"https://github-size-badge.herokuapp.com/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/miykael/nipype_tutorial/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad2f8b06984b0f1d27d678f47c1f11518aa015202885ef2b9e812daa5c84743b/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d69796b61656c2f6e69707970655f7475746f7269616c2e7376673f6d61784167653d32353932303030\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/docker/pulls/miykael/nipype_tutorial.svg?maxAge=2592000\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://hits.dwyl.io/miykael/nipype_tutorial\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ab300fb01bfcec0b74484c2d85efe06524b363f0460c63fe55fe2e3c84bd9ae/687474703a2f2f686974732e6477796c2e696f2f6d69796b61656c2f6e69707970655f7475746f7269616c2e737667\" alt=\"GitHub HitCount\" data-canonical-src=\"http://hits.dwyl.io/miykael/nipype_tutorial.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the Nipype Tutorial in Jupyter Notebook format. You can access the tutorial in two ways:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://miykael.github.io/nipype_tutorial/\" rel=\"nofollow\"\u003eNipype Tutorial Homepage\u003c/a\u003e: This website contains a static, read-only version of all the notebooks.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://miykael.github.io/nipype_tutorial/notebooks/introduction_docker.html\" rel=\"nofollow\"\u003eNipype Tutorial Docker Image\u003c/a\u003e: This guide explains how to use Docker to run the notebooks interactively on your own computer. The nipype tutorial docker image is the best interactive way to learn Nipype.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFeedback, Help \u0026amp; Support\u003c/h1\u003e\u003ca id=\"user-content-feedback-help--support\" class=\"anchor\" aria-label=\"Permalink: Feedback, Help \u0026amp; Support\" href=\"#feedback-help--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to help with this tutorial or have any questions, feel free to fork the repo of the \u003ca href=\"https://github.com/miykael/nipype_tutorial\"\u003eNotebooks\u003c/a\u003e or interact with other contributors on the slack channel \u003ca href=\"https://brainhack.slack.com/messages/nipype/\" rel=\"nofollow\"\u003ebrainhack.slack.com/messages/nipype/\u003c/a\u003e. If you have any questions or found a problem, open a new \u003ca href=\"https://github.com/miykael/nipype_tutorial/issues\"\u003eissue on github\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThanks and Acknowledgment\u003c/h1\u003e\u003ca id=\"user-content-thanks-and-acknowledgment\" class=\"anchor\" aria-label=\"Permalink: Thanks and Acknowledgment\" href=\"#thanks-and-acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA huge thanks to \u003ca href=\"https://github.com/mwaskom\"\u003eMichael Waskom\u003c/a\u003e, \u003ca href=\"https://github.com/oesteban\"\u003eOscar Esteban\u003c/a\u003e, \u003ca href=\"https://github.com/chrisfilo\"\u003eChris Gorgolewski\u003c/a\u003e and \u003ca href=\"https://github.com/satra\"\u003eSatrajit Ghosh\u003c/a\u003e for their input to this tutorial! And a huge thanks to \u003ca href=\"https://github.com/djarecka/\"\u003eDorota Jarecka\u003c/a\u003e who updated this tutorial to Python 3 and is helping me with keeping this tutorial updated and running!\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1550774761.0 + "updated_at": 1538064645.0 }, { "data_format": 2, - "description": "Singularity image running R tidyverse + some other libraries", + "description": null, "filenames": [ - "Singularity", - "Singularity.3.6.3" + "Singularity" ], - "full_name": "tpall/singularity-tidyverse", + "full_name": "darachm/singularity_dada2", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2366\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-tidyverse\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-tidyverse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity tidyverse\u003c/h2\u003e\n\u003cp\u003eThis will run R tidyverse + some other packages, like \u003cem\u003ehere\u003c/em\u003e, \u003cem\u003ereadxl\u003c/em\u003e, \u003cem\u003elubridate\u003c/em\u003e, \u003cem\u003ebookdown\u003c/em\u003e, etc.\u003c/p\u003e\n", + "readme": "\u003cp\u003eThis container is for providing \u003ccode\u003edada2\u003c/code\u003e for some bioinformatics pipelines.\u003c/p\u003e\n\u003cp\u003eThe primary reason for this is so that it flows nicely through SingularityHub\nfor Nextflow pipelines.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1608284812.0 + "updated_at": 1546750956.0 }, { "data_format": 2, - "description": "Singularity recipe for Deformetrica on Centos 7", + "description": "BBTools is a suite of fast, multithreaded bioinformatics tools designed for analysis of DNA and RNA sequence data.", "filenames": [ "Singularity" ], - "full_name": "willgpaik/deformetrica_aci", + "full_name": "sghignone/BBTools", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-deformetrica_aci\" class=\"anchor\" aria-hidden=\"true\" href=\"#deformetrica_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edeformetrica_aci\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for Deformetrica on Centos 7 for ACI-ICS clusters\u003c/p\u003e\n\u003cp\u003e2019/2/14\u003cbr\u003e\nAnaconda3 ver. 2018.12\u003cbr\u003e\nDeformetrica 4.1\u003cbr\u003e\nGUI can be used through EoD\u003c/p\u003e\n\u003cp\u003eCommands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; source activate deformetrica \n\u0026gt; deformetrica \nOr, \n\u0026gt; deformetrica gui\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e2020/9/21\u003cbr\u003e\nGPU support is added\u003cbr\u003e\nAnaconda, Python, and Deformetrica are updated\u003c/p\u003e\n\u003cp\u003e2020/10/9\u003cbr\u003e\nPyTorch and PyKeOps are added\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBBTools\u003c/h1\u003e\u003ca id=\"user-content-bbtools\" class=\"anchor\" aria-label=\"Permalink: BBTools\" href=\"#bbtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4220\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBBTools is a suite of fast, multithreaded bioinformatics tools designed for analysis of DNA and RNA sequence data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRQCFilter2\u003c/h3\u003e\u003ca id=\"user-content-rqcfilter2\" class=\"anchor\" aria-label=\"Permalink: RQCFilter2\" href=\"#rqcfilter2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRQCFilter2 is a revised version of RQCFilter that uses a common path for all dependencies.\nThe dependencies are available at \u003ca href=\"http://portal.nersc.gov/dna/microbial/assembly/bushnell/RQCFilterData.tar\" rel=\"nofollow\"\u003ehttp://portal.nersc.gov/dna/microbial/assembly/bushnell/RQCFilterData.tar\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePerforms quality-trimming, artifact removal, linker-trimming, adapter trimming, and spike-in removal using BBDuk.\nPerforms human/cat/dog/mouse/microbe removal using BBMap.\nIt requires 40 GB RAM for mousecatdoghuman, but only 1GB or so without them.\u003c/p\u003e\n\u003cp\u003eUsage: rqcfilter2.sh in=\u0027\u0027 path=\u0027\u0027 rqcfilterdata=\u0027\u0027\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1602342657.0 + "updated_at": 1616702144.0 }, { "data_format": 2, - "description": null, + "description": "Singularity and Docker containers for day to day life", "filenames": [ "Singularity", - "Singularity.3.6.3" + "centos-from-sh/Singularity", + "centos-from-dh/Singularity", + "_xp/Singularity", + "centos-from-me/Singularity" ], - "full_name": "tpall/singularity-stan", + "full_name": "abhi18av/cli-tools", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecli-tools\u003c/h1\u003e\u003ca id=\"user-content-cli-tools\" class=\"anchor\" aria-label=\"Permalink: cli-tools\" href=\"#cli-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/806\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity and Docker containers for day to day life\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/abhi18av/cli-tools/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/abhi18av/cli-tools/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInstall Datalad\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.singularity-hub.org/collections/667\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/667\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1603529584.0 + "updated_at": 1522664549.0 }, { "data_format": 2, - "description": "Singularity recipe files for Pblat (http://icebert.github.io/pblat/)", + "description": null, "filenames": [ - "Singularity", - "Singularity.2.0", - "Singularity.2.1" + "Singularity" ], - "full_name": "powerPlant/pblat-srf", + "full_name": "photocyte/genometools_singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2380\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for Pblat, the parallelized blat with multi-threads support\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity file for \u003ca href=\"https://github.com/genometools/genometools\"\u003egenometools\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eImage building handled by singularity-hub.org\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://photocyte/genometools_singularity\nsingularity exec --cleanenv genometools_singularity_latest.sif gt -h\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1550562816.0 + "updated_at": 1591204765.0 }, { "data_format": 2, - "description": "Singularity recipe files for checkm (http://ecogenomics.github.io/CheckM)", + "description": "A Docker recipe for building a SNO+ environment for RAT. ", "filenames": [ "Singularity", - "Singularity.1.0.12", - "Singularity.1.0.13", - "Singularity.1.0.10", - "Singularity.1.0.8", - "Singularity.1.1.3", - "Singularity.1.0.11", - "Singularity.1.0.7" + "Singularity.old" ], - "full_name": "powerPlant/checkm-srf", + "full_name": "snoplus/rat-container", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2464\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the CheckM set of tools for assessing the quality of genomes recovered from isolates, single cells, or metagenomes\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erat-container\u003c/h1\u003e\u003ca id=\"user-content-rat-container\" class=\"anchor\" aria-label=\"Permalink: rat-container\" href=\"#rat-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/snoplus/rat-container\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/99f4d06cdd8e2f776dcff3a325ee95b8a490476dcc0b026dfcb7390b143580c6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65726875622d626c7565\" alt=\"https://img.shields.io/badge/hosted-dockerhub-blue\" data-canonical-src=\"https://img.shields.io/badge/hosted-dockerhub-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity and Docker recipes to build a SNO+ environment for RAT.\u003c/p\u003e\n\u003cp\u003eFor regular usage, simply download the pre-built container with the following instructions for your container platform of choice. For advanced users, see the build instructions below.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIF THE DOCKERHUB LINK STOPS WORKING, SOMEONE MAY HAVE TO BUILD AND REUPLOAD THE CONTAINER TO DOCKERHUB DUE TO A CHANGE IN THEIR POLICY\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs of \u003cem\u003e\u003cstrong\u003eNovember 1, 2020\u003c/strong\u003e\u003c/em\u003e Docker is implementing an inactive image removal policy, meaning in a free account (which is where this container is hosted) if the container is not \u003cem\u003e\u003cstrong\u003eupdated or pulled for 6 consecutive months\u003c/strong\u003e\u003c/em\u003e it will be \u003cem\u003e\u003cstrong\u003edeleted\u003c/strong\u003e\u003c/em\u003e. This isn\u0027t a huge issue, someone will just have to do the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBuild the container manually from the image file in this repository according to the instructions below\u003c/li\u003e\n\u003cli\u003eUpload it to another Dockerhub repository\u003c/li\u003e\n\u003cli\u003eUpdate the download links that reference the Dockerhub location with the new location\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFEATURES\u003c/h1\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: FEATURES\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFull RAT-compatible environment, including ROOT5 (ROOT6 version now available), GEANT4 and scons\u003c/li\u003e\n\u003cli\u003eCan build any version of RAT\u003c/li\u003e\n\u003cli\u003eGUI output support on all operating systems\u003c/li\u003e\n\u003cli\u003eTensorFlow and CppFlow (CPU-only for the time being)\u003c/li\u003e\n\u003cli\u003eSingularity and Docker compatibility\u003c/li\u003e\n\u003cli\u003e*Cluster-compatible\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e*The image can be uploaded manually, pulled directly (if the cluster firewall permits) or run from /cvmfs; however, the cvmfs\nimage is not always up-to-date with the repo version. This has been \u003ca href=\"https://github.com/snoplus/rat-container/issues/8\"\u003eidentified as an issue\u003c/a\u003e with a possible solution posed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e[PLEASE READ]\u003c/h1\u003e\u003ca id=\"user-content-please-read\" class=\"anchor\" aria-label=\"Permalink: [PLEASE READ]\" href=\"#please-read\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity and Docker are similar tools but operate slightly differently. Singularity acts more like an overlay, where\nyou have access to your filesystem as you would \u003cstrong\u003eoutside\u003c/strong\u003e the container (with the same rights as you\u0027d have outside),\nwhereas Docker provides you with an isolated virtual filesystem (meaning you \u003cstrong\u003ecan\u0027t\u003c/strong\u003e access your files from outside\nthe container). In summary, it is best to \u003cstrong\u003emount\u003c/strong\u003e whatever directories you may need when running the container, whether\nin Docker or Singularity (see the section \"\u003cstrong\u003eTo write/execute files from directories outside of RAT/launch\ndirectory\u003c/strong\u003e\" below).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRegardless of whether you download or build the container, you can use and develop RAT as you see fit as it is external\nto the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstructions to install Singularity can be found \u003ca href=\"https://github.com/sylabs/singularity/blob/master/INSTALL.md\"\u003ehere.\u003c/a\u003e For\nDocker, instructions for each platform can be found \u003ca href=\"https://docs.docker.com/install/#supported-platforms\" rel=\"nofollow\"\u003ehere.\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eFor Singularity, version 3.2+ is required\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eFor Docker, version 19.0+ is required\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eAs the DIRAC system no longer supports SL6, there is no longer a need to maintain an SL6 version when pushing new RAT releases to cvmfs. Therefore, the only image offered here is based on SL7.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTo be clear, if you wish to use the prebuilt image, then you do NOT need to clone this repo; simply follow the\ninstructions below.\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNew Video Tutorial (slightly outdated - no longer necessary to source the setup-env.sh on startup)\u003c/h1\u003e\u003ca id=\"user-content-new-video-tutorial-slightly-outdated---no-longer-necessary-to-source-the-setup-envsh-on-startup\" class=\"anchor\" aria-label=\"Permalink: New Video Tutorial (slightly outdated - no longer necessary to source the setup-env.sh on startup)\" href=\"#new-video-tutorial-slightly-outdated---no-longer-necessary-to-source-the-setup-envsh-on-startup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.snolab.ca/snoplus/private/DocDB/0062/006281/001/RAT%20container%20tutorial.mp4\" rel=\"nofollow\"\u003eAvailable here (Requires SNO+ DocDB access)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo download the pre-built container\u003c/h1\u003e\u003ca id=\"user-content-to-download-the-pre-built-container\" class=\"anchor\" aria-label=\"Permalink: To download the pre-built container\" href=\"#to-download-the-pre-built-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eIf on a shared system/cluster\u003c/strong\u003e, Singularity should be available so use the following command to obtain the latest\nversion of the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name rat-container.sif docker://snoplus/rat-container:root5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe tag (in the above command, \u003ccode\u003eroot5\u003c/code\u003e) can be replaced with the desired tag.\u003c/p\u003e\n\u003cp\u003eEnsure that the Singularity version you are using is \u003cstrong\u003e\u22653.2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the moment, certain clusters (like Cedar) have firewall rules preventing access to SingularityHub. There is a version of\nthe image located at \u003ccode\u003e/cvmfs/snoplus.egi.eu/sl7/sw/containers/rat-container.sif\u003c/code\u003e but keep in mind that it may not always be\nthe latest version (this shouldn\u0027t matter if you are simply building/running RAT).\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eIf on your own local machine\u003c/strong\u003e, Docker should be used as it is easier to install.\nThe command to obtain the latest version of the container for Docker is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull snoplus/rat-container:root5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe tag (in the above command, \u003ccode\u003eroot5\u003c/code\u003e) can be replaced with the desired tag.\u003c/p\u003e\n\u003cp\u003eDocker doesn\u0027t actually create a file in your working directory in the same way that Singularity does; rather, it\ndownloads the image layers and adds an entry to your local \u003cstrong\u003eDocker registry\u003c/strong\u003e which can be viewed by going:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker images\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis difference doesn\u0027t have an effect on how the container is actually used.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions on how to use the container with RAT\u003c/h1\u003e\u003ca id=\"user-content-instructions-on-how-to-use-the-container-with-rat\" class=\"anchor\" aria-label=\"Permalink: Instructions on how to use the container with RAT\" href=\"#instructions-on-how-to-use-the-container-with-rat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTo build RAT for the first time\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eClone RAT from GitHub (\u003cstrong\u003eNOTE\u003c/strong\u003e - If on Windows, make sure you run \u003ccode\u003egit config --global core.autocrlf input\u003c/code\u003e prior to\ncloning or else Git will automatically change the Unix line-endings to Windows (which \u003cstrong\u003ewill break the next steps\u003c/strong\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnter the following command, filling in the path to RAT with your own.\nThis will mount your RAT repo to the directory \u003ccode\u003e/rat\u003c/code\u003e inside the container:\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eSingularity\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B path/to/rat:/rat rat-container.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cem\u003eDocker\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti --init --rm -v /absolute/path/to/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e - the \u003ccode\u003e-v\u003c/code\u003e flag operates the same as \u003ccode\u003e-B\u003c/code\u003e in Singularity BUT you \u003cstrong\u003emust\u003c/strong\u003e provide it with an absolute path (one starting at /);\nrelative paths (the path from where you are now) will \u003cstrong\u003enot\u003c/strong\u003e work.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOnce in the container, Singularity users need to run the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /home/scripts/setup-env.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn \u003cstrong\u003eDocker\u003c/strong\u003e this is \u003cstrong\u003eunnecessary\u003c/strong\u003e as Docker sources it automatically on launch.\nYou may see a message about how it could not find \u003ccode\u003e/rat/env.sh\u003c/code\u003e; this is expected as you have not built RAT yet.\nIf the build is successful, you shouldn\u0027t see this message next time.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFinally, run this command to build RAT:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esource /home/scripts/build-rat.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, \u003ccode\u003escons\u003c/code\u003e can manually be called while in the \u003ccode\u003e/rat\u003c/code\u003e folder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRAT is now ready to use! Look at the instructions below for how to run it\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo exit the container (Singularity and Docker)\u003c/strong\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexit\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo run RAT\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFirst, get a shell into the container with your RAT bound into it:\n(It is \u003cstrong\u003eimportant\u003c/strong\u003e to \u003cstrong\u003emount your rat directory to /rat\u003c/strong\u003e as the build scripts look there for it!)\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eSingularity\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B path/to/rat:/rat rat-container.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cem\u003eDocker\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti --init --rm -v /absolute/path/to/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRAT is now ready for use, and you should be able to access the RAT repo itself at \u003ccode\u003e/rat\u003c/code\u003e. To use other\ndirectories, additional bind mounts are necessary (see below).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo use GUI apps like ROOT\u0027s TBrowser\u003c/strong\u003e:\n(This is based on CERN\u0027s documentation for \u003ca href=\"https://hub.docker.com/r/rootproject/root-ubuntu16/\" rel=\"nofollow\"\u003erunning ROOT with graphics\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe process is different on each OS but I will outline steps here to make it work on each. Note that these instructions\nassume that since you are on your own machine, you are using \u003cstrong\u003eDocker\u003c/strong\u003e. Singularity may work with graphics as it is, but\nthese Docker solutions are the only ones that are tested and confirmed to be working.\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003eLinux\u003c/strong\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti --init --rm --user $(id -u) -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v /absolute/path/to/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs you can see, the difference is a few extra options. As the command has gotten so large, you can \u003ca href=\"https://askubuntu.com/a/17538\" rel=\"nofollow\"\u003eset an alias in your .bashrc\u003c/a\u003e to something much shorter and more convenient.\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003eWindows 10\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAs of the new May 2020 Windows update, the Windows Subsystem for Linux (WSL) version 2 is out. Docker desktop can be\nconfigured to use this which is the recommended way to run Docker on Windows. Ensure WSL2 is enabled in the Docker Desktop\nsettings, then follow these instructions:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload and install \u003ca href=\"https://sourceforge.net/projects/xming/\" rel=\"nofollow\"\u003eXming\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eWhen Windows prompts you to allow it in the firewall, do so.\u003c/li\u003e\n\u003cli\u003eFinally, restart Xming and now run the following command in Powershell or WSL2:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --init --rm -ti -e DISPLAY=host.docker.internal:0 -v /absolute/path/to/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cstrong\u003emacOS\u003c/strong\u003e:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ca href=\"https://www.xquartz.org/\" rel=\"nofollow\"\u003eXQuartz\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOpen XQuartz, and then go XQuartz -\u0026gt; Preferences -\u0026gt; Security, and tick the box \"Allow connections from network clients\"\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003exhost + 127.0.0.1\u003c/code\u003e which will whitelist your local IP\u003c/li\u003e\n\u003cli\u003eFinally, you can run the container with the following:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm --init -ti -v /tmp/.X11-unix:/tmp/.X11-unix -v /absolute/path/to/rat:/rat -e DISPLAY=host.docker.internal:0 snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(The order \u003ccode\u003e-ti\u003c/code\u003e instead of \u003ccode\u003e-it\u003c/code\u003e seems to only matter for MacOS)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo update RAT\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eOutside of the container, \u003ccode\u003ecd\u003c/code\u003e into your RAT repo, and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit pull origin master\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThen, run the container:\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eSingularity\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B path/to/rat:/rat rat-container.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cem\u003eDocker\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -ti --init -v \"$(pwd)\"/rat:/rat snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the RAT directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /rat\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFinally, run the build script (\u003ccode\u003e/home/scripts/build-rat.sh\u003c/code\u003e) or \u003ccode\u003escons\u003c/code\u003e directly to rebuild RAT:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escons\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo write/execute files from directories outside of RAT/launch directory\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAdd additional bind mounts to your Singularity or Docker command\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eSingularity\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B path/to/rat:/rat,/other/path:/stuff rat-container.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor \u003cem\u003eDocker\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --init --rm -ti -v /absolute/path/to/rat:/rat -v /other/path:/stuff snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNow in the container, you have access to /other/path at /stuff\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo use a specific branch of RAT\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnsure you git checkout to the branch OUTSIDE the container to avoid issues, then run RAT like above\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eTo use TensorFlow/cppflow\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe libraries are already installed (tensorflow at /usr/local/lib, cppflow repo is at /home/software) and\nthe environment variables are set in the setup-env.sh script, so you should be able to just use it after sourcing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e[ADVANCED]\u003c/h1\u003e\u003ca id=\"user-content-advanced\" class=\"anchor\" aria-label=\"Permalink: [ADVANCED]\" href=\"#advanced\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo build the container\u003c/h1\u003e\u003ca id=\"user-content-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: To build the container\" href=\"#to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build, you must have \u003cstrong\u003eroot permissions\u003c/strong\u003e and \u003cstrong\u003eDocker installed on your machine\u003c/strong\u003e. Docker installation instructions can be found \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for each OS.\u003c/p\u003e\n\u003cp\u003eTo rebuild the container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate into either \u003ccode\u003e/ROOT5\u003c/code\u003e or \u003ccode\u003e/ROOT6\u003c/code\u003e depending on which you would like to build off of\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEdit \u003ccode\u003eDockerfile\u003c/code\u003e, which is the recipe on what you would like to put into your container\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOnce you are happy with your changes, navigate back to the root of the repository and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t YOUR_CONTAINER_TAG -f ROOT5/Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003eYOUR_CONTAINER_TAG\u003c/code\u003e is the name you would like to give to your container. Also, ensure you change \u003ccode\u003eROOT5\u003c/code\u003e to \u003ccode\u003eROOT6\u003c/code\u003e if using that version\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis will build your container with your tag name, which you can then use in the same way as in the above guide, but instead of\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run ... snoplus/rat-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou will now run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run ... YOUR_TAG_NAME\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e[OPTIONAL] If you would like to share or back up your container image, you can push it to Dockerhub. You can follow \u003ca href=\"https://docs.docker.com/docker-hub/repos/#pushing-a-docker-container-image-to-docker-hub\" rel=\"nofollow\"\u003ethe official documentation\u003c/a\u003e to learn how\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo run multiple RAT instances\u003c/h1\u003e\u003ca id=\"user-content-to-run-multiple-rat-instances\" class=\"anchor\" aria-label=\"Permalink: To run multiple RAT instances\" href=\"#to-run-multiple-rat-instances\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to use multiple RAT instances simultaneously, then all you have to do is run an instance of this container\nwith each version of RAT that you want; do NOT try mounting multiple RATs to the SAME instance as the image was\nnot configured for this.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo modify Geant4\u003c/h1\u003e\u003ca id=\"user-content-to-modify-geant4\" class=\"anchor\" aria-label=\"Permalink: To modify Geant4\" href=\"#to-modify-geant4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to edit Geant4 for any reason, you will have to modify the recipe file and make your changes accordingly, then\nrebuild the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eF.A.Q.\u003c/h1\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: F.A.Q.\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOn macOS I see \"docker: Error response from daemon: Mounts denied: The path ... is not shared from OS X and is not known to Docker.\"\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis happens because Docker only allows mounting from 4 locations by default to follow Apple\u0027s sandbox guidelines; these locations are:\n\u003cpre\u003e\u003ccode\u003e/Users\n/tmp\n/private\n/Volumes\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003eEnsure your RAT repository is stored in one of these locations (the easiest would be simply under \u003ccode\u003e/Users/[your username]/rat\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eI\u0027m seeing \"/usr/bin/bash: /usr/bin/bash: cannot execute binary file\" when I try to run the container\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis happens because you have \u003ccode\u003ebash\u003c/code\u003e at the end of your run command; in the new version, this is no longer necessary as it\nwill launch bash by itself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eI\u0027m seeing \"Error getting image manifest using url...\" when I try to pull the container\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis seems to happen on the clusters, most likely due to the firewall. Try pulling the container on your local machine,\nand transfer the image to your cluster with scp.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eI\u0027m seeing errors when running scons to rebuild RAT after updating to a new RAT release\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis happens when you use the GUI-enabled docker command (not the standard command) when launching the container to rebuild\nRAT. Please review the instructions for how to update RAT above for the correct way to update.\u003c/li\u003e\n\u003cli\u003eThis can also happen if you don\u0027t run \u003ccode\u003escons\u003c/code\u003e within the \u003ccode\u003e/rat\u003c/code\u003e directory as it won\u0027t be able to find the correct files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhen I try to open the TBrowser/another GUI app, it doesn\u0027t show\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis is a known issue, and happens for two reasons. If you are trying to use the Docker version on your own machine, Docker\ndoes not have access to the display by default so there is some configuration required.\u003c/li\u003e\n\u003cli\u003eThe other issue is if you are trying to do this on a cluster with the Singularity version, you will notice the same thing.\nBecause you are remotely connected, the display is not configured by default to also connect.\u003c/li\u003e\n\u003cli\u003eKnown methods for getting a GUI working are listed in a section above for each OS under Docker.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1598504920.0 + "updated_at": 1639774730.0 }, { "data_format": 2, @@ -19848,720 +19515,653 @@ var data = "filenames": [ "Singularity" ], - "full_name": "chenhongluo/horovord", + "full_name": "oogasawa/singularity_latex", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-latex\u003c/h1\u003e\u003ca id=\"user-content-singularity-latex\" class=\"anchor\" aria-label=\"Permalink: singularity-latex\" href=\"#singularity-latex\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA singularity container of LaTeX typesetting system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Singularity image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/oogasawa/singularity-latex\ncd singularity-latex\nsudo singularity build singularity-latex.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCompile a LaTeX file. (a DVI file will be generated.)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec singularity-latex.sif platex doc.tex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGenerate a PDF file from a DVI file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec singularity-latex.sif dvipdfmx doc.dvi\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1553181936.0 + "updated_at": 1622296093.0 }, { "data_format": 2, - "description": "Batch Connect - OSC RStudio Server - Pitzer", + "description": "testing container for singularity hub", "filenames": [ - "Singularity" + "Singularity", + "subfolder/Singularity.apps", + "subfolder/Singularity.tacos" ], - "full_name": "OSC/bc_osc_rstudio_server_pitzer", - "latest_release": "v0.1.5", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-batch-connect---osc-rstudio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#batch-connect---osc-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch Connect - OSC RStudio Server\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bbf138f428dd98a7b779e572caebe1d8f6c369fb4f9ba270c27f4b29282e5530/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665725f7069747a65722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bbf138f428dd98a7b779e572caebe1d8f6c369fb4f9ba270c27f4b29282e5530/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665725f7069747a65722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_rstudio_server_pitzer.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an RStudio Server\nwithin an Pitzer batch job.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deprecated-application-warning\" class=\"anchor\" aria-hidden=\"true\" href=\"#deprecated-application-warning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeprecated application warning\u003c/h2\u003e\n\u003cp\u003eThis application no longer works. It raises an exception when users attempt to submit jobs.\nThis is because we now have functionality to submit to multiple clusters and\n\u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server\"\u003ethe generic application\u003c/a\u003e now submits\nto pitzer rendering this application useless.\u003c/p\u003e\n\u003cp\u003eFor historic versions, see the last released you can still view\n\u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server_pitzer/tree/v0.3.0\"\u003ev0.3.0\u003c/a\u003e as it was the last\nworking version of this application.\u003c/p\u003e\n", + "full_name": "singularityhub/hello-world", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 5, "topics": [], - "updated_at": 1673988953.0 + "updated_at": 1560109788.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container for STACKS", "filenames": [ - "Singularity" + "Singularity", + "v2Beta9/Singularity.v2.0Beta9", + "v2.0/Singularity.v2.0" ], - "full_name": "andquintero/singularity_builds", + "full_name": "phgenomics-singularity/stacks", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_builds\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_builds\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003estacks\u003c/h1\u003e\u003ca id=\"user-content-stacks\" class=\"anchor\" aria-label=\"Permalink: stacks\" href=\"#stacks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container for STACKS\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1554218133.0 + "updated_at": 1527118463.0 }, { "data_format": 2, - "description": "Theano Singularity container scripts", + "description": "Circos is a software package for visualizing data and information.", "filenames": [ - "Singularity.1.0.4-py36" + "0.69-9/Singularity" ], - "full_name": "arcsUVA/theano", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-theano\" class=\"anchor\" aria-hidden=\"true\" href=\"#theano\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etheano\u003c/h1\u003e\n\u003cp\u003eTheano Singularity container scripts\u003c/p\u003e\n", + "full_name": "pscedu/singularity-circos", + "latest_release": "v0.69-9", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-circos/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-circos/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-circos/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-circos/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cdb759354edc534f7f5069578ef5dad67d8ec7d1242bf799e84570fd867204ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d636972636f73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cdb759354edc534f7f5069578ef5dad67d8ec7d1242bf799e84570fd867204ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d636972636f73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-circos\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c2ef2a0bed6acc8fe318bf27c8b6090c20d689daec5bb4dd2724224e5836357e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d636972636f73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c2ef2a0bed6acc8fe318bf27c8b6090c20d689daec5bb4dd2724224e5836357e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d636972636f73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-circos\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/025587ed22e26a065fbd6f9dc8981ddbb40ee487cfac9e2104c2a47796a93957/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d636972636f73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/025587ed22e26a065fbd6f9dc8981ddbb40ee487cfac9e2104c2a47796a93957/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d636972636f73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-circos\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5764b609b640af5998b1a3138590ba41649db0393f4be5b77cc884e7f45a962e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d636972636f73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5764b609b640af5998b1a3138590ba41649db0393f4be5b77cc884e7f45a962e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d636972636f73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-circos\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-circos\u003c/h1\u003e\u003ca id=\"user-content-singularity-circos\" class=\"anchor\" aria-label=\"Permalink: singularity-circos\" href=\"#singularity-circos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://circos.ca/\" rel=\"nofollow\"\u003ecircos\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ecircos\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/circos/0.69-9\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/circos\u003c/code\u003e as \u003ccode\u003e0.69-9.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCopy the data to \u003ccode\u003e/ocean\u003c/code\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-copy-the-data-to-ocean\" class=\"anchor\" aria-label=\"Permalink: Copy the data to /ocean\" href=\"#copy-the-data-to-ocean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ewget http://circos.ca/distribution/circos-current.tgz\nmkdir -p /ocean/datasets/community/genomics/circos\ntar -xvf circos-current.tgz \u0026amp;\u0026amp; rm -f circos-current.tgzmv -v circos-0.69-9/data /ocean/datasets/community/genomics/circos/\nrm -rfv circos-0.69-9\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 5, - "topics": [], - "updated_at": 1554499739.0 + "topics": [ + "singularity", + "utlities" + ], + "updated_at": 1707362994.0 }, { "data_format": 2, - "description": "Docker and Singularity images for Scanpy", + "description": "ul-fri-nlp-course-project-nhk created by GitHub Classroom", "filenames": [ - "Singularity" + "report/code/Singularity_py.def" ], - "full_name": "VIB-CBD/scanpy-images", + "full_name": "UL-FRI-NLP-2023-2024/ul-fri-nlp-course-project-nhk", "latest_release": null, - "readme": "\u003cp\u003eDependency full Scanpy Docker and Scanpy images based on Alpine.\u003c/p\u003e\n\u003cp\u003eIncludes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLoompy\u003c/li\u003e\n\u003cli\u003eLouvain\u003c/li\u003e\n\u003cli\u003eigraph\u003c/li\u003e\n\u003cli\u003eipython\u003c/li\u003e\n\u003cli\u003eJupyter\u003c/li\u003e\n\u003cli\u003eCython\u003c/li\u003e\n\u003cli\u003eMulticoreTSNE\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNatural language processing course 2023/24: \u003ccode\u003eLLM Prompt Strategies for Commonsense-Reasoning Task\u003c/code\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-natural-language-processing-course-202324-llm-prompt-strategies-for-commonsense-reasoning-task\" class=\"anchor\" aria-label=\"Permalink: Natural language processing course 2023/24: LLM Prompt Strategies for Commonsense-Reasoning Task\" href=\"#natural-language-processing-course-202324-llm-prompt-strategies-for-commonsense-reasoning-task\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAuthors:\u003c/h3\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors:\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMatic Pristavnik Vre\u0161njak\u003c/li\u003e\n\u003cli\u003eMitja Kocjan\u010di\u010d\u003c/li\u003e\n\u003cli\u003eSongeun Hong\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdvisors:\u003c/h3\u003e\u003ca id=\"user-content-advisors\" class=\"anchor\" aria-label=\"Permalink: Advisors:\" href=\"#advisors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSlavko \u017ditnik\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject File Structure\u003c/h3\u003e\u003ca id=\"user-content-project-file-structure\" class=\"anchor\" aria-label=\"Permalink: Project File Structure\" href=\"#project-file-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eroot/\n\u2502\n\u2514\u2500\u2500 report/\n \u251c\u2500\u2500 code/ # Directory containing project code\n \u2502 \u251c\u2500\u2500 promt_COT.py.py\n \u2502 \u251c\u2500\u2500 few_shot.py\n \u2502 \u2514\u2500\u2500 ... # You can see the more files in the folder\n \u2502\n \u2514\u2500\u2500 Reports_compiled_submission/ # Directory containing PDF reports\n \u251c\u2500\u2500 submisson1_report.pdf\n \u251c\u2500\u2500 submisson2_report.pdf\n \u2514\u2500\u2500 submisson3_report.pdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOverview\u003c/h3\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project explores various prompting strategies to enhance the performance of Large Language Models (LLMs) on commonsense reasoning tasks. With the increasing use of LLMs in personal and commercial domains, developing effective prompts is crucial for generating relevant and informative responses. This study provides a comprehensive comparison of different prompting strategies to determine their effectiveness and applicability.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe surge in popularity of LLMs like ChatGPT, PaLM, and Gemini has led to their widespread use. These models, often based on the transformer architecture, are capable of handling tasks that require commonsense reasoning\u2014using everyday knowledge to solve problems. Several prompting strategies have been developed to improve LLM performance on these tasks.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrompting Strategies\u003c/h3\u003e\u003ca id=\"user-content-prompting-strategies\" class=\"anchor\" aria-label=\"Permalink: Prompting Strategies\" href=\"#prompting-strategies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eZero-shot\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelies on the model being sufficiently trained with minimal prompt modifications.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eExample:\u003c/em\u003e \"Classify the text into neutral, negative, or positive. Text: I think the vacation is okay. Sentiment:\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eFew-shot\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnhances the prompt by including examples of previously solved problems.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eExample:\u003c/em\u003e \"This is awesome! //Negative This is bad! //Positive Wow that movie was rad! //Positive What a horrible show! //\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eChain-of-thought\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCombines with few-shot, adding step-by-step reasoning to the examples.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eExample:\u003c/em\u003e \"The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1. A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False.\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMethodology\u003c/h3\u003e\u003ca id=\"user-content-methodology\" class=\"anchor\" aria-label=\"Permalink: Methodology\" href=\"#methodology\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eVarious models were evaluated, including Command (Cohere AI), Mistral (Mistral AI), LLAMA3 (Meta), and T5 (Google Research).\u003c/li\u003e\n\u003cli\u003ePerformance metrics included ROUGE, BLEU, and BERT Score.\u003c/li\u003e\n\u003cli\u003eDatasets used: Winograd Schema Challenge, MultiArith, SQuAD, among others.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConclusion\u003c/h3\u003e\u003ca id=\"user-content-conclusion\" class=\"anchor\" aria-label=\"Permalink: Conclusion\" href=\"#conclusion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project offers valuable insights into the effectiveness of different prompting strategies for improving LLM performance on commonsense reasoning tasks. The comprehensive comparison highlights the potential benefits and limitations of each strategy, guiding future research and practical implementations.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1556798801.0 + "updated_at": 1716587895.0 }, { "data_format": 2, - "description": "Singularity recipe files for SqueezeMeta (https://github.com/jtamames/SqueezeMeta)", + "description": "Singularity containers to run several python scripts", "filenames": [ "Singularity", - "Singularity.1.0.0-beta", - "Singularity.0.4.4" + "Singularity.BladegeneratorQmethod", + "Singularity.InteractiveCoolProp" ], - "full_name": "powerPlant/squeezemeta-srf", + "full_name": "stephansmit/python_containers", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2930\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the SqueezeMeta fully automated metagenomics pipeline\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container for Python scripts\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-python-scripts\" class=\"anchor\" aria-label=\"Permalink: Singularity container for Python scripts\" href=\"#singularity-container-for-python-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Container\u003c/h2\u003e\u003ca id=\"user-content-build-container\" class=\"anchor\" aria-label=\"Permalink: Build Container\" href=\"#build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build python_containers.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull Image\u003c/h2\u003e\u003ca id=\"user-content-pull-image\" class=\"anchor\" aria-label=\"Permalink: Pull Image\" href=\"#pull-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/python_containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Image\u003c/h3\u003e\u003ca id=\"user-content-run-image\" class=\"anchor\" aria-label=\"Permalink: Run Image\" href=\"#run-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run python_containers test.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3414\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1557458055.0 + "updated_at": 1569418419.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v4.2.0" + "Singularity" ], - "full_name": "baxpr/fmriqa", + "full_name": "CrossR/meng_project", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-functional-mri-qa-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#functional-mri-qa-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctional MRI QA pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eTest the matlab code before compiling: \u003ccode\u003esrc/testmatlab.m\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCompile: \u003ccode\u003ecompile_matlab.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTest the compiled runtime: \u003ccode\u003ebin/test_compiled_matlab.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuild the Singularity container: \u003ccode\u003eSingularity.v4.2.0\u003c/code\u003e, \u003ca href=\"https://www.singularity-hub.org/collections/2945\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/2945\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eSee \u003ccode\u003etest_sing_container.sh\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cp\u003eThe inputs must all be provided, in the correct order. Paths are with respect to the container root.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eName of the output directory\u003c/li\u003e\n\u003cli\u003eFilename of the T1 structural image (.nii.gz)\u003c/li\u003e\n\u003cli\u003eFilename of the segmented T1 image (.nii.gz), typically the SEG output of a MultiAtlas or SLANT pipeline\u003c/li\u003e\n\u003cli\u003eFilename of the 4D fMRI (.nii.gz)\u003c/li\u003e\n\u003cli\u003eXNAT project label\u003c/li\u003e\n\u003cli\u003eXNAT subject label\u003c/li\u003e\n\u003cli\u003eXNAT session label\u003c/li\u003e\n\u003cli\u003eXNAT scan label (of the fMRI)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-processing\" class=\"anchor\" aria-hidden=\"true\" href=\"#processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcessing\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eMotion realignment and creation of mean fMRI\u003c/li\u003e\n\u003cli\u003eCoregister T1 to mean fMRI\u003c/li\u003e\n\u003cli\u003eCompute SNR and quality metrics\u003c/li\u003e\n\u003cli\u003eCarpet plots, graphical report\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003efmriqa.pdf PDF report\nrp_fmri.txt Realignment parameters (SPM12 style)\nfmriqa_stats.csv Summary stats\nfmriqa_stats_wide.csv Summary stats in wide format (XNAT/REDCap compatible)\nFD.txt Framewise displacement time series\nDVARS.txt DVARS time series\nglobal.txt Global mean time series\nmeanfmri.nii.gz Mean fMRI image after realignment\nmedian_voxel_displacement_mm.txt Framewise displacement, median over voxels\ntemporal_snr.nii.gz Temporal signal-to-noise ratio image\nvoxel_displacement_mm_95prctile.nii.gz Framewise displacement image (95th percentile over time)\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReinforcement Learning For Games\u003c/h1\u003e\u003ca id=\"user-content-reinforcement-learning-for-games\" class=\"anchor\" aria-label=\"Permalink: Reinforcement Learning For Games\" href=\"#reinforcement-learning-for-games\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall Instructions\u003c/h2\u003e\u003ca id=\"user-content-install-instructions\" class=\"anchor\" aria-label=\"Permalink: Install Instructions\" href=\"#install-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe way used throughout the project was via Singularity.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall Singularity - \u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/install-linux\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eThis must be done on a personal machine! Singularity needs \u003ccode\u003eroot\u003c/code\u003e\naccess for the initial build.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSwap to the repo folder.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003esudo singularity build starcraft.simg Singularity\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eThis is going to install everything, so takes a while.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCan be used either with:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esingularity shell -C starcraft.simg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esingularity exec starcraft.simg python run.py ${SCRIPT_ARGS}\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eIf running on a machine that has a GPU + CUDA, ensure to pass \u003ccode\u003e--nv\u003c/code\u003e\nafter \u003ccode\u003eshell\u003c/code\u003e or \u003ccode\u003eexec\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePoetry\u003c/h3\u003e\u003ca id=\"user-content-poetry\" class=\"anchor\" aria-label=\"Permalink: Poetry\" href=\"#poetry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnly used initially, so may not be fully working.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall SC2. If installed to a non-standard location set the \u003ccode\u003e$SC2PATH\u003c/code\u003e\nenvironment variable to point to the install location.\n\u003cul\u003e\n\u003cli\u003eAn example of the install and setting this variable is in the\n\u003ccode\u003eSingularity\u003c/code\u003e install file.\u003c/li\u003e\n\u003cli\u003eAlso install the maps, which are also listed in the \u003ccode\u003eSingularity\u003c/code\u003e file,\nalongside their password. They should be installed into \u003ccode\u003e$SC2PATH/Maps\u003c/code\u003e,\nwhere the maps folder may need making.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://github.com/sdispater/poetry\"\u003epoetry\u003c/a\u003e with \u003ccode\u003epip install poetry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOnce installed, use \u003ccode\u003epoetry install\u003c/code\u003e inside this repo. This will create a\nvirtualenv and install all needed packages into it.\u003c/li\u003e\n\u003cli\u003eThen you can call the scripts like so:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Basic example:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Map names can be gotten from the map file names that you just downloaded.\u003c/span\u003e\npoetry run python CNN/run.py --map_name MAP_NAME --model_name MODEL_NAME --training=False\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For example:\u003c/span\u003e\npoetry run python CNN/run.py --map_name MoveToBeacon --model_name TestModel --training=True\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To load that model back at a later date and continue training:\u003c/span\u003e\npoetry run python CNN/run.py --map_name MoveToBeacon --model_name TestModel --training=True --if_output_exists=continue\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To load that model back at a later date with no more training:\u003c/span\u003e\npoetry run python CNN/run.py --map_name MoveToBeacon --model_name TestModel --training=False\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To use that model in a secondary training phase:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This has not been tested, so its possible you\u0027ll need to instead run the initial model\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e with curriculum_num=0, since I don\u0027t remember how that code works.\u003c/span\u003e\npoetry run python CNN/run.py --map_name MoveToBeacon --model_name TestModel2 --training=True --curriculum_num=1 --previous_model=_files/models/TestModel\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis way is meant for PC dev work, so the TensorFlow version does not need a GPU.\nIf this is needed, then call \u003ccode\u003epoetry remove tensorflow\u003c/code\u003e and \u003ccode\u003epoetry add tensorflow-gpu\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eYou can also enter the poetry shell like so\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epoetry shell\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAt that point, calling \u003ccode\u003epython\u003c/code\u003e will use the project venv.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions to Run\u003c/h2\u003e\u003ca id=\"user-content-instructions-to-run\" class=\"anchor\" aria-label=\"Permalink: Instructions to Run\" href=\"#instructions-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn example script of running the CNN can be found in \u003ccode\u003eCNN\\runPySC2.sh\u003c/code\u003e, which\nuses Singularity. The paths in this script will need updating for a different\nuser.\u003c/p\u003e\n\u003cp\u003eTo run a specific pretrained CNN model, the script should be called as follows,\nwhere \u003ccode\u003eMAP_NAME\u003c/code\u003e is the map or mini-game in question and \u003ccode\u003eMODEL_NAME\u003c/code\u003e is the\nexact name of the model, as stored in \u003ccode\u003eCNN\\_files\\\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython run.py --map_name MAP_NAME --model_name MODEL_NAME --training=False\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe folders for the models should look as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCNN/_files/\n\u251c\u2500\u2500 models\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 test_model_20\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 checkpoint\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13000.data-00000-of-00001\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13000.index\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13000.meta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13500.data-00000-of-00001\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 model.ckpt-13500.index\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 model.ckpt-13500.meta\n\u2514\u2500\u2500 summaries\n \u2514\u2500\u2500 test_model_20\n \u2514\u2500\u2500 events.out.tfevents.1521839337.db12gpu1.arc3.leeds.ac.uk\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo train a new model, instead drop the \u003ccode\u003e--training=False\u003c/code\u003e. It is necessary to\nadd \u003ccode\u003e--if_output_exists=continue\u003c/code\u003e to continue training an already existing model.\nThe full set of runtime flags can be found in \u003ca href=\"CNN/run.py\"\u003erun.py\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFlags of interest are as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003etraining\u003c/code\u003e - If the model should be trained or not.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003evisualize\u003c/code\u003e - If the PyGame GUI should be shown. Useful for local running during testing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003en_envs\u003c/code\u003e - Number of games to run in parallel. \u003cstrong\u003eImportant\u003c/strong\u003e on a high powered machine to get the best performance.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esave_replays_every\u003c/code\u003e - How often a game replay should be saved, such that the training progress can be seen.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esave_permanently_every\u003c/code\u003e - Models are saved every few episodes, but are done in a rolling fashion. This flag is used to create models that will not be overwritten at any point\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecurriculum_num\u003c/code\u003e - What is the current curriculum number? Should be set for curriculum learning such that multiple models are loaded.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eprevious_model\u003c/code\u003e - Path to the previous model file, for curriculum learning.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enumber_episodes\u003c/code\u003e / \u003ccode\u003enumber_steps\u003c/code\u003e - The maximum episodes or steps to take before stopping. If either of these are met, will stop.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1558037991.0 + "updated_at": 1561411617.0 }, { "data_format": 2, - "description": "Singularity images to run on the cluster", + "description": null, "filenames": [ - "Singularity.py3_tf112_plus", - "Singularity.py3_tf114_lls", - "Singularity.py3_astro", - "Singularity.py3_tf112", - "Singularity.py3_tf115", - "Singularity.py3_tf114", - "Singularity.py3_tf113" + "Singularity" ], - "full_name": "joaocaldeira/singularity_imgs", + "full_name": "oogasawa/singularity_nodejs", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_imgs\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_imgs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_imgs\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2968\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity images to run on the cluster\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1590440777.0 + "updated_at": 1622297603.0 }, { "data_format": 2, - "description": "singularity lc builds", + "description": null, "filenames": [ "Singularity" ], - "full_name": "iapalm/lc-builds", + "full_name": "oogasawa/singularity_ubuntu18_apache2", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUbuntu18 - Apache2 Singularity\u003c/h1\u003e\u003ca id=\"user-content-ubuntu18---apache2-singularity\" class=\"anchor\" aria-label=\"Permalink: Ubuntu18 - Apache2 Singularity\" href=\"#ubuntu18---apache2-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux 18.04 (Bionic) \u306bApache2\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fSingularity\u30b3\u30f3\u30c6\u30ca\u003c/p\u003e\n\u003cp\u003eApache2\u306f\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002\u003c/p\u003e\n\u003cp\u003e\u30dd\u30a4\u30f3\u30c8\u306fhttpd.conf\u3067\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3059\u308b\u3053\u3068\u3067\u3001\u6700\u7d42\u7684\u306broot\u6a29\u9650\u306a\u3057\u3067Apache2\u3092singularity\u30b3\u30f3\u30c6\u30ca\u3067\u8d77\u52d5\u3067\u304d\u308b\u3068\u3044\u3046\u3053\u3068\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u30dd\u30fc\u30c8\u756a\u53f7\u30921024\u3088\u308a\u5927\u304d\u304f\u3059\u308b\u3002\u003c/li\u003e\n\u003cli\u003eerror_log, access_log\u3092\u30e6\u30fc\u30b6\u30fc\u306e\u30db\u30fc\u30e0\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u66f8\u304d\u51fa\u3059\u3002\u003c/li\u003e\n\u003cli\u003ePidFile\u3092\u30e6\u30fc\u30b6\u30fc\u306e\u30db\u30fc\u30e0\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u66f8\u304d\u51fa\u3059\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u524d\u63d0\u003c/h2\u003e\u003ca id=\"user-content-\u524d\u63d0\" class=\"anchor\" aria-label=\"Permalink: \u524d\u63d0\" href=\"#\u524d\u63d0\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(1) Singularity version 2.x (2.6.0\u63a8\u5968)\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\uff08Official Document)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e(2) debootstrap\u3068git\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cp\u003eUbuntu Linux\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5834\u5408\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3059\u308b\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt upgrade\nsudo apt install debootstrap git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCentOS\u3067\u306f\u73fe\u6642\u70b9\u3067\u306f\u8a66\u3057\u3066\u3044\u306a\u3044\u304c\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8a18\u4e8b\u304c\u3042\u308b\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://linuxconfig.org/how-to-debootstrap-on-centos-linux\" rel=\"nofollow\"\u003ehttps://linuxconfig.org/how-to-debootstrap-on-centos-linux\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u4f7f\u3044\u65b9\u003c/h2\u003e\u003ca id=\"user-content-\u4f7f\u3044\u65b9\" class=\"anchor\" aria-label=\"Permalink: \u4f7f\u3044\u65b9\" href=\"#\u4f7f\u3044\u65b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# sandbox\u30b3\u30f3\u30c6\u30ca\u306e\u30d3\u30eb\u30c9\ngit clone http://gitlab.ddbj.nig.ac.jp/oogasawa/singularity-ubuntu18-apache2\ncd singularity-ubuntu18-apache2\nmkdir -f ~/singularity-images\nsudo singularity build --sandbox ~/singularity-images/ubuntu18-apache2 Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u540d \u003ccode\u003e~/singularity-images/\u003c/code\u003e, \u30a4\u30e1\u30fc\u30b8\u540dubuntu18-apache2\u306f\u304a\u597d\u307f\u3067\u5909\u3048\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# sandbox\u30b3\u30f3\u30c6\u30ca\u5185\u3067\u306e\u4f5c\u696d\nsudo singularity shell --writable ~/singularity-images/ubuntu18-apache2\n\n# simg\u30d5\u30a1\u30a4\u30eb\u306b\u56fa\u3081\u308b\u3002\nsudo singularity build ~/singularity-images/ubuntu18-apache2.simg ~/singularity-images/ubuntu18-apache2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eapache\u7528singularity\u30b3\u30f3\u30c6\u30ca\u306e\u8d77\u52d5\u3068\u505c\u6b62 (exec\u30b3\u30de\u30f3\u30c9\u306b\u3088\u308b)\u003c/h3\u003e\u003ca id=\"user-content-apache\u7528singularity\u30b3\u30f3\u30c6\u30ca\u306e\u8d77\u52d5\u3068\u505c\u6b62-exec\u30b3\u30de\u30f3\u30c9\u306b\u3088\u308b\" class=\"anchor\" aria-label=\"Permalink: apache\u7528singularity\u30b3\u30f3\u30c6\u30ca\u306e\u8d77\u52d5\u3068\u505c\u6b62 (exec\u30b3\u30de\u30f3\u30c9\u306b\u3088\u308b)\" href=\"#apache\u7528singularity\u30b3\u30f3\u30c6\u30ca\u306e\u8d77\u52d5\u3068\u505c\u6b62-exec\u30b3\u30de\u30f3\u30c9\u306b\u3088\u308b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# run your container as a service.\nsingularity instance.start ~/singulariy-images/ubuntu18-apache2.simg web1\n\n# start apache2 server\nsingularity exec instance://web1 apache_start.sh\n\n# stop apache2 server\nsingularity exec instance://web1 apache_stop.sh\n\n# instance.stop \nsingularity instance.start web1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/running_services.html\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/guides/2.6/user-guide/running_services.html\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1560984106.0 + "updated_at": 1622297768.0 }, { "data_format": 2, - "description": "demo pipeline for testing different data chunking methods for MuTect2", + "description": null, "filenames": [ - "containers/annovar-150617/Singularity.annovar-150617", - "containers/variant-calling-0.0.2/Singularity.variant-calling-0.0.2" + "Singularity" ], - "full_name": "stevekm/MuTect2_target_chunking", + "full_name": "oogasawa/singularity_ubuntu1804", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mutect2-target-chunking\" class=\"anchor\" aria-hidden=\"true\" href=\"#mutect2-target-chunking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMuTect2 Target Chunking\u003c/h1\u003e\n\u003cp\u003eDemo pipeline for testing different data chunking methods for MuTect2.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://software.broadinstitute.org/gatk/documentation/tooldocs/3.8-0/org_broadinstitute_gatk_tools_walkers_cancer_m2_MuTect2.php\" rel=\"nofollow\"\u003eMuTect2\u003c/a\u003e is a common tool used for variant calling of tumor-normal pairs. However, it is limited to running only in single-threaded mode, which can lead to extremely long execution times.\u003c/p\u003e\n\u003cp\u003eThis demo pipeline uses different techniques to chunk the included list of target regions (\u003ccode\u003etargets.bed\u003c/code\u003e) into smaller segments to run in parallel, then aggregate all results for comparison to ensure that variant calls are the same across all chunking methods.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eThis pipeline comes pre-configured for usage on NYULMC\u0027s Big Purple HPC cluster using pre-built Singularity containers and pre-downloaded reference files.\u003c/p\u003e\n\u003cp\u003eIn order to use this pipeline on your system you will need to update the file paths saved in \u003ccode\u003enextflow.config\u003c/code\u003e for your system.\u003c/p\u003e\n\u003cp\u003eSingularity and Docker container recipes are included in the \u003ccode\u003econtainers\u003c/code\u003e directory.\u003c/p\u003e\n\u003cp\u003ePaths to input .bam files for tumor and normal samples are read from the file \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce correctly configured, the pipeline can be run with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUbuntu18 Singularity\u003c/h1\u003e\u003ca id=\"user-content-ubuntu18-singularity\" class=\"anchor\" aria-label=\"Permalink: Ubuntu18 Singularity\" href=\"#ubuntu18-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux 18.04 (Bionic) \u306eSingularity\u30b3\u30f3\u30c6\u30ca\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u524d\u63d0\u003c/h2\u003e\u003ca id=\"user-content-\u524d\u63d0\" class=\"anchor\" aria-label=\"Permalink: \u524d\u63d0\" href=\"#\u524d\u63d0\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux\u306bdebootstrap\u4ed6\u306e\u5fc5\u8981\u306a\u30bd\u30d5\u30c8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt upgrade\nsudo apt install build-essential libtool automake libarchive-dev debootstrap git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity\u306fversion 2.x\u7cfb\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\uff08Official Document)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u4f7f\u3044\u65b9\u003c/h2\u003e\u003ca id=\"user-content-\u4f7f\u3044\u65b9\" class=\"anchor\" aria-label=\"Permalink: \u4f7f\u3044\u65b9\" href=\"#\u4f7f\u3044\u65b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# \u30b3\u30f3\u30c6\u30ca\u306e\u30d3\u30eb\u30c9\ngit clone http://gitlab.ddbj.nig.ac.jp/oogasawa/singularity-ubuntu18\ncd singularity-ubuntu18\nmkdir $HOME/singularity-images\nsudo singularity build --sandbox $HOME/singularity-images/ubuntu18 Singularity\n\n# \u30b3\u30f3\u30c6\u30ca\u5185\u3067\u306e\u4f5c\u696d\nsudo singularity shell --write $HOME/singularity-images/ubuntu18\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [ - "nextflow", - "mutect2", - "variant-calling" - ], - "updated_at": 1562090008.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1622297682.0 }, { "data_format": 2, - "description": "Example of deployment of a Galaxy Production Instance using CVMFS with Ansible", + "description": "Repo for singularity development and test", "filenames": [ "Singularity" ], - "full_name": "MiguelJulia/GCC2019_GalaxyAnsibleDeplyoment_CVMFS", + "full_name": "mobidic/Dev-Singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-gcc2019_galaxyansibledeplyoment_cvmfs\" class=\"anchor\" aria-hidden=\"true\" href=\"#gcc2019_galaxyansibledeplyoment_cvmfs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGCC2019_GalaxyAnsibleDeplyoment_CVMFS\u003c/h1\u003e\n\u003cp\u003eExample of deployment of a Galaxy Production Instance using CVMFS with Ansible.\nFor more info, look into \u003ca href=\"https://galaxyproject.github.io/training-material/topics/admin/\" rel=\"nofollow\"\u003egalaxy admin training materials\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-deploying-a-galaxy-stance\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploying-a-galaxy-stance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploying a galaxy stance\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003eansible-playbook -i host cvmfs_playbook.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-restart-galaxy\" class=\"anchor\" aria-hidden=\"true\" href=\"#restart-galaxy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRestart galaxy\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003esudo su - galaxy\nsupervisorctl restart galaxy\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-variables-to-modify-for-quick-deployment\" class=\"anchor\" aria-hidden=\"true\" href=\"#variables-to-modify-for-quick-deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVariables to modify for quick deployment\u003c/h4\u003e\n\u003cp\u003eAdmin user name. This user is not created, still has to be registered the first time and it will automatically get admin permissions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egalaxy_config:\n galaxy:\n admin_users: admin@example.com\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBrand: Whatever appears on the banner\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egalaxy_config:\n galaxy:\n brand: \"Freiburg GCC\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-welcomehtml\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcomehtml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewelcome.html\u003c/h4\u003e\n\u003cp\u003eFrontpage is not created by default. You can find the template inside \u003ccode\u003egalaxy_root: /srv/galaxy\u003c/code\u003e, in \u003ccode\u003eserver/static/welcome.html.sample\u003c/code\u003e. Just create a \u003ccode\u003ewelcome.html\u003c/code\u003e page from this template in that same location and restart galaxy.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-deploying-your-ansible-managed-galaxy-into-a-container-not-working-yet\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploying-your-ansible-managed-galaxy-into-a-container-not-working-yet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploying your ansible-managed galaxy into a container (not working yet!)\u003c/h4\u003e\n\u003cp\u003eWe will use \u003ca href=\"https://github.com/ansible-community/ansible-bender\"\u003eansible-bender\u003c/a\u003e for this task. Your playbook will have to be adapted to this plugging standars as described in their documentation, or compare the differences between my cvmfs_playbook.yml and ansible-bender-test.yml to have a quick idea of how it has to be done.\u003c/p\u003e\n\u003cp\u003eMake sure you are running the right version of ansible, as ansible-bender only works with python3. Still, playbooks designed for python2 can still be used. You will also need to install \u003ca href=\"https://github.com/containers/buildah/blob/master/install.md\"\u003ebuildah\u003c/a\u003e and \u003ca href=\"https://github.com/containers/libpod/blob/master/install.md\"\u003epodman\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, you will need to configurate podman repo config file \u003ccode\u003e/etc/containers/registries.conf\u003c/code\u003e to tell it where to look for your containers. For example, to search in dokerhub add \u003ccode\u003e\u0027docker.io\u0027\u003c/code\u003e inside\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[registries.search]\nregistries = [\u0027docker.io\u0027]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe image is required to have python interpreter build in.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-galaxy-container-with-docker-idea---not-testet-yet\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-galaxy-container-with-docker-idea---not-testet-yet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding galaxy container with Docker (idea - not testet yet)\u003c/h4\u003e\n\u003cp\u003eUse galaxy-container \u003ca href=\"https://github.com/bgruening/docker-galaxy-stable/blob/master/galaxy/Dockerfile\"\u003eDockerfile\u003c/a\u003e as template.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDev-Singularity\u003c/h1\u003e\u003ca id=\"user-content-dev-singularity\" class=\"anchor\" aria-label=\"Permalink: Dev-Singularity\" href=\"#dev-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRepo for singularity development and test\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 4, "topics": [], - "updated_at": 1562583598.0 + "updated_at": 1528377792.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for hisat2", "filenames": [ - "Singularity.jupyter" + "Singularity" ], - "full_name": "ternaustralia/coesra-singularity-jupyter", + "full_name": "ISU-HPC/hisat2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-jupyter\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehisat2\u003c/h1\u003e\u003ca id=\"user-content-hisat2\" class=\"anchor\" aria-label=\"Permalink: hisat2\" href=\"#hisat2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for hisat2\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1610425229.0 + "updated_at": 1553692888.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.panoply" + "Singularity", + "braker/Singularity.braker" ], - "full_name": "ternaustralia/coesra-singularity-panoply", + "full_name": "aseetharam/containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-panoply\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-panoply\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-panoply\u003c/h1\u003e\n\u003cp\u003eHoang Nguyen\n25 July 2019\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainers\u003c/h1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "coesra" - ], - "updated_at": 1610426866.0 + "topics": [], + "updated_at": 1596396241.0 }, { "data_format": 2, - "description": "Singularity recipe files for paml (http://abacus.gene.ucl.ac.uk/software/paml.html)", + "description": null, "filenames": [ - "Singularity", - "Singularity.4.9i" + "Singularity" ], - "full_name": "powerPlant/paml-srf", + "full_name": "ikmb/assembly-bacteria", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3399\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the PAML tool for phylogenetic analyses of DNA or protein sequences using maximum likelihood.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIKMB Genomes - Bacterial assembly and annotation from PE reads\u003c/h1\u003e\u003ca id=\"user-content-ikmb-genomes---bacterial-assembly-and-annotation-from-pe-reads\" class=\"anchor\" aria-label=\"Permalink: IKMB Genomes - Bacterial assembly and annotation from PE reads\" href=\"#ikmb-genomes---bacterial-assembly-and-annotation-from-pe-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline takes PE genomic reads, performs trimming using trim_galore, assembles draft genomes using Spades and annotates the assembly using Dfast_core.\u003c/p\u003e\n\u003cp\u003eIn addition, reads will (optionally) be mapped back to the genome sequence to obtain stats about mapping rate, insert size, duplication rates, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the pipeline\u003c/h2\u003e\u003ca id=\"user-content-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline\" href=\"#running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is currently configured to run on RZCluster. Other execute environment may be added in the future.\u003c/p\u003e\n\u003cp\u003eTo run the pipeline, you first have to clone the repository from git:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone git@git.ikmb.uni-kiel.de:bfx-core/NF-genomes-bacteria.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you already have a local copy, make sure it is up-to-date:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit pull\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWith a local installation of the pipeline, you can next proceed to run it in a folder where you would like to generate the various result files:\u003c/p\u003e\n\u003cp\u003eLoad the environment modules for Nextflow:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emodule load IKMB Java Nextflow\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNext, move to the folder where the pipeline will be run from:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd /path/to/run_folder\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFinally, run the pipeline by providing a path to the sample info:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow -c /path/to/nextflow.config run /path/to/main.nf --samples Samples.csv\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSample file\u003c/h2\u003e\u003ca id=\"user-content-sample-file\" class=\"anchor\" aria-label=\"Permalink: Sample file\" href=\"#sample-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe file Samples is a CSV formatted list of data sets to process. An example format is included in the template subfolder. Required information include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSample name\u003c/li\u003e\n\u003cli\u003eLibrary name\u003c/li\u003e\n\u003cli\u003ePath to forward read\u003c/li\u003e\n\u003cli\u003epath to reverse read\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResfinder\u003c/h2\u003e\u003ca id=\"user-content-resfinder\" class=\"anchor\" aria-label=\"Permalink: Resfinder\" href=\"#resfinder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Pipeline can optionally run the Resfinder program to detect the presence of typical resistance-confering genes. To enable the resfinder search, use the \u003ccode\u003e--resfinder\u003c/code\u003eflag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline generates all outputs in the folder \"outputs\". For each PE data set (a library), a folder will be created containing the assembly, the annotation and the re-mapped reads for computing statistics on coverage etc.\u003c/p\u003e\n\u003cp\u003eFurthermore, trimmed reads will be jointly placed in the folder \"trimgalore\". A compilation of statistics for the various analysis is included in the older MultiQC - for library-level analytics (library_multiqc.html) and the assembly, annotation etc (sample_multiqc.html).\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1565742033.0 + "updated_at": 1695285476.0 }, { "data_format": 2, - "description": "containers", + "description": "Singularity Hub build recipe for a singularity container running R (based on https://github.com/nickjer/singularity-r).", "filenames": [ - "Singularity.py3_tfstable", - "Singularity.pyhon3" + "Singularity", + "Singularity.3.6.2" ], - "full_name": "LuisBonillaR/singularity", + "full_name": "gparadis/singularity-r", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity R\u003c/h1\u003e\u003ca id=\"user-content-singularity-r\" class=\"anchor\" aria-label=\"Permalink: Singularity R\" href=\"#singularity-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nickjer/singularity-r\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3ac61eb7693a90c7386ae808085fe4f76602fa13f140b564dfc2ff5215deef9f/68747470733a2f2f7472617669732d63692e6f72672f6e69636b6a65722f73696e67756c61726974792d722e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nickjer/singularity-r.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/462\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7edda6b40df66cdf6d87ee014ce8a73af8830d12f325162978d3b72836ea332d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-r.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-r.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name singularity-r.simg shub://nickjer/singularity-r\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR\u003c/h3\u003e\u003ca id=\"user-content-r\" class=\"anchor\" aria-label=\"Permalink: R\" href=\"#r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eR\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-r.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app R singularity-r.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app R singularity-r.simg --version\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eR version 3.4.3 (2017-11-30) -- \"Kite-Eating Tree\"\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eCopyright (C) 2017 The R Foundation for Statistical Computing\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ePlatform: x86_64-pc-linux-gnu (64-bit)\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eR is free software and comes with ABSOLUTELY NO WARRANTY.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eYou are welcome to redistribute it under the terms of the\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGNU General Public License versions 2 or 3.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eFor more information about these matters see\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ehttp://www.gnu.org/licenses/.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRscript\u003c/h3\u003e\u003ca id=\"user-content-rscript\" class=\"anchor\" aria-label=\"Permalink: Rscript\" href=\"#rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eRscript\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app Rscript singularity-r.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app Rscript singularity-r.simg --version\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eR scripting front-end version 3.4.3 (2017-11-30)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-r\"\u003ehttps://github.com/nickjer/singularity-r\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1610738330.0 + "updated_at": 1597276020.0 }, { "data_format": 2, - "description": null, + "description": "Computes and tracks the accuracy of a mechanical watch", "filenames": [ - "Singularity.1.026" + "Singularity" ], - "full_name": "arcsUVA/patric", + "full_name": "MatthewBonanni/Watch-Accuracy", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWatch-Accuracy\u003c/h1\u003e\u003ca id=\"user-content-watch-accuracy\" class=\"anchor\" aria-label=\"Permalink: Watch-Accuracy\" href=\"#watch-accuracy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputes and tracks the accuracy of a mechanical watch\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1570548606.0 + "updated_at": 1720245772.0 }, { "data_format": 2, - "description": "hackathon_intel_genci", + "description": "This program computes the cross entropy for groups of sequences that have been assigned to groups on the basis of biochemical, physiological, or other biological property. ", "filenames": [ - "Sarek/Singularity", - "Sarek/ScLifeLab/Singularity" + "1.0.0/Singularity" ], - "full_name": "larosap/hackathon_intel_genci", - "latest_release": null, + "full_name": "pscedu/singularity-gent", + "latest_release": "v1.0.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-gent/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-gent/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/dcfd71e46b35d08572edfb976a2e1b7650a33efc6ac4d731076e5546b74f2717/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d67656e74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dcfd71e46b35d08572edfb976a2e1b7650a33efc6ac4d731076e5546b74f2717/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d67656e74\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-gent\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2d09491d6d44288f5675b1f9f2d4b45e4bdb400503a96a60ff8ea2b2bc1301ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d67656e74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2d09491d6d44288f5675b1f9f2d4b45e4bdb400503a96a60ff8ea2b2bc1301ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d67656e74\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-gent\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/29113b835989f2bb185f6bdc85ce7f7acc824ba4d828617552974c7afbe674ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d67656e74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/29113b835989f2bb185f6bdc85ce7f7acc824ba4d828617552974c7afbe674ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d67656e74\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-gent\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f3dfac73d7955a4d00781ddc596da4ba22dd8ab32a636f6d31451b89188b4327/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d67656e74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f3dfac73d7955a4d00781ddc596da4ba22dd8ab32a636f6d31451b89188b4327/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d67656e74\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-gent\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-gent\u003c/h1\u003e\u003ca id=\"user-content-singularity-gent\" class=\"anchor\" aria-label=\"Permalink: singularity-gent\" href=\"#singularity-gent\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/icaoberg/gent\"\u003eGeNT\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1573750055.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1703380680.0 }, { "data_format": 2, - "description": "Singularity recipe files for plink2 (https://www.cog-genomics.org/plink/2.0/)", + "description": "Utility to prepare dicoms for conversion using BIDSKIT (https://github.com/jmtyszka/bidskit) ", "filenames": [ - "Singularity", - "Singularity.v2.00a2LM" + "Singularity" ], - "full_name": "powerPlant/plink2-srf", + "full_name": "chidiugonna/nklab-neuro-utils", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3722\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the PLINK association analysis toolset\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enklab-neuro-utils\u003c/h1\u003e\u003ca id=\"user-content-nklab-neuro-utils\" class=\"anchor\" aria-label=\"Permalink: nklab-neuro-utils\" href=\"#nklab-neuro-utils\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA number of utilities for data management. Will be updated as time goes by.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e./src/nklab-bids-convert.py\u003c/code\u003e utility to stage dicoms for conversion by \u003ccode\u003ebidskit\u003c/code\u003e (\u003ca href=\"https://github.com/jmtyszka/bidskit\"\u003ehttps://github.com/jmtyszka/bidskit\u003c/a\u003e) into BIDS format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003enklab-bids-convert.py\u003c/h2\u003e\u003ca id=\"user-content-nklab-bids-convertpy\" class=\"anchor\" aria-label=\"Permalink: nklab-bids-convert.py\" href=\"#nklab-bids-convertpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis utility will walk down the hierarchy of dicom data (\u003ccode\u003edicomdir\u003c/code\u003e) and will copy it to a new directory (\u003ccode\u003estagedir\u003c/code\u003e) in the required format for Bidskit to convert into BIDS format. It is important that the dicom data is contained within one folder for each subject. If the data has been collected in multiple sessions then the parameter \u003ccode\u003e--sessions\u003c/code\u003e can be used to prompt the tool to cluster (K-means) the dicoms based on the acquired datetime. For example \u003ccode\u003e--sessions pre post\u003c/code\u003e would copy the dicom data into 2 sessions pre and post for bidskit. In some situations the acquired datetime may be incorrect and thus lead to incorrect clustering. An exceptions file \u003ccode\u003e--exceptionlist\u003c/code\u003e may then be provided to associate a misclassified dicom with one that has the correct datetime. See \u003ccode\u003e./example/exception.json\u003c/code\u003e for an example that associates the misclassified \u003ccode\u003e3SHELL_TENSOR\u003c/code\u003e with \u003ccode\u003e3SHELL_RPE\u003c/code\u003e . Note that the string values in the exception file are substrings of the actual dicom folder names that allow for unique identification. A frozen version of bidskit is also included with this repository which has been slightly adapted for our lab\u0027s needs. Please run the tool with the flag \u003ccode\u003e--stageonly\u003c/code\u003e to avoid running this version of bidskit and to just run the dicom preparation steps described above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Singularity Image\u003c/h2\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Build Singularity Image\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have singularity 2.4 or greater installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-utils\u003c/code\u003edirectory and check that you have a Singularity definition file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow simply build the image as \u003ccode\u003esudo singularity build nklab-neuro-utils.simg Singularity\u003c/code\u003e - note that the image name is assumed to be \u003ccode\u003enklab-neuro-utils.simg\u003c/code\u003e but this can be changed to a more convenient label.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild local Docker Image\u003c/h2\u003e\u003ca id=\"user-content-build-local-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build local Docker Image\" href=\"#build-local-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAt the moment the docker image is retrievable from docker hub using \u003ccode\u003edocker pull orbisys/nklab-neuro-utils\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild local Docker Image\u003c/h2\u003e\u003ca id=\"user-content-build-local-docker-image-1\" class=\"anchor\" aria-label=\"Permalink: Build local Docker Image\" href=\"#build-local-docker-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSimply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-utils\u003c/code\u003edirectory and check that you have the Docker definition file \u003ccode\u003eDocker\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow simply build the image as \u003ccode\u003esudo docker build -t mylocaldockerimage Docker\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1572401216.0 + "updated_at": 1545416478.0 }, { "data_format": 2, - "description": "Singularity container for https://github.com/revbayes/revbayes", + "description": "Singularity image for pandoc", "filenames": [ "Singularity" ], - "full_name": "ResearchIT/revbayes-singularity", + "full_name": "golamshaifullah/SingularityPandoc", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1589324901.0 + "updated_at": 1569936107.0 }, { "data_format": 2, - "description": "Setups for various images used on the dgx.", + "description": "Using deep plant phenomics on PlantIT", "filenames": [ - "Singularity-PyTorch" + "Singularity" ], - "full_name": "uri-ai-lab/singularity-images", + "full_name": "ChenHsieh/deepplantphenomics-PlantIT", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-images\u003c/h1\u003e\n\u003cp\u003eSetups for various images used on the dgx.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edeepplantphenomics-PlantIT\u003c/h1\u003e\u003ca id=\"user-content-deepplantphenomics-plantit\" class=\"anchor\" aria-label=\"Permalink: deepplantphenomics-PlantIT\" href=\"#deepplantphenomics-plantit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo is used to serve as a flow on \u003ca href=\"http://portnoy.cyverse.org/\" rel=\"nofollow\"\u003ePlantIT\u003c/a\u003e that can conduct the semantic section pre-trained model on \u003ca href=\"https://www.plant-phenotyping.org/datasets-home\" rel=\"nofollow\"\u003eIPPN dataset\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDeep plant phenomics is a python package developed and \u003ca href=\"https://www.frontiersin.org/articles/10.3389/fpls.2017.01190/full\" rel=\"nofollow\"\u003epublished by Ubbens and Stavness\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eA truncated version of deepplantphenomics python package is in the other_module dir for the usage of docker image building recipe.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eto do list\u003c/h2\u003e\u003ca id=\"user-content-to-do-list\" class=\"anchor\" aria-label=\"Permalink: to do list\" href=\"#to-do-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e[] check how to correctly save the output\n[] build singularity image\n[] prepare other scripts\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esemantic section example USAGE\u003c/h2\u003e\u003ca id=\"user-content-semantic-section-example-usage\" class=\"anchor\" aria-label=\"Permalink: semantic section example USAGE\" href=\"#semantic-section-example-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAny image in the specified dir with suffix \u0027_rgb.png\u0027 can be read in the model.\u003c/li\u003e\n\u003cli\u003eUse -p to specify the input path dir. The -ft will be removed.\u003c/li\u003e\n\u003cli\u003eLocal testing command (script correctly run but cannot obtain output files):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/to/image/:/home/ docker://bendjamin101001/plantit_dpp:v1 python3 semantic_section_example.py -p /home/ -ft jpg\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1573772605.0 + "updated_at": 1604511244.0 }, { "data_format": 2, - "description": null, + "description": "sRNA phasing software singularity container", "filenames": [ - "Singularity.0.1.0" + "Singularity" ], - "full_name": "arcsUVA/cryoCARE", + "full_name": "seb-mueller/singularity_srna_phasing", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_srna_phasing\u003c/h1\u003e\u003ca id=\"user-content-singularity_srna_phasing\" class=\"anchor\" aria-label=\"Permalink: singularity_srna_phasing\" href=\"#singularity_srna_phasing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esRNA phasing software singularity container\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1574198978.0 + "updated_at": 1577127401.0 }, { "data_format": 2, - "description": "Singularity recipe files for MrBayes (http://nbisweden.github.io/MrBayes)", + "description": "A pagit singularity container", "filenames": [ - "Singularity", - "Singularity.3.2.7a", - "Singularity.3.2.7a-gpu" + "Singularity" ], - "full_name": "powerPlant/mrbayes-srf", + "full_name": "phgenomics-singularity/pagit", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3808\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the MrBayes program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary models\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epagit\u003c/h1\u003e\u003ca id=\"user-content-pagit\" class=\"anchor\" aria-label=\"Permalink: pagit\" href=\"#pagit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA pagit singularity container\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1574325488.0 + "updated_at": 1521839990.0 }, { "data_format": 2, - "description": "Singularity recipe files for sga (https://github.com/jts/sga)", + "description": null, "filenames": [ - "Singularity", - "Singularity.0.10.15" + "Singularity" ], - "full_name": "powerPlant/sga-srf", + "full_name": "baxpr/dticam", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3984\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the SGA tool, a de novo genome assembler based on the concept of string graphs\u003c/p\u003e\n", + "readme": "\u003cp\u003edticam\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCamino\u003c/h2\u003e\u003ca id=\"user-content-camino\" class=\"anchor\" aria-label=\"Permalink: Camino\" href=\"#camino\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://camino.cs.ucl.ac.uk/\" rel=\"nofollow\"\u003ehttp://camino.cs.ucl.ac.uk/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eP. A. Cook, Y. Bai, S. Nedjati-Gilani, K. K. Seunarine, M. G. Hall, G. J. Parker, D. C. Alexander, \"Camino: Open-Source Diffusion-MRI Reconstruction and Processing\", International Society for Magnetic Resonance in Medicine, Seattle, WA, USA, p. 2759, May 2006\u003c/p\u003e\n\u003cp\u003eCiting Camino: \u003ca href=\"http://camino.cs.ucl.ac.uk/index.php?n=Main.Citations\" rel=\"nofollow\"\u003ehttp://camino.cs.ucl.ac.uk/index.php?n=Main.Citations\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1579231330.0 + "updated_at": 1599669635.0 }, { "data_format": 2, - "description": "Singularity recipe files for hapcol (https://github.com/AlgoLab/HapCol)", + "description": "braker container generated by Nathan Weeks (USDA)", "filenames": [ - "Singularity", - "Singularity.97d4a5e" + "Singularity" ], - "full_name": "powerPlant/hapcol-srf", + "full_name": "HuffordLab-Containers/braker", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the HapCol tool, a fast and memory-efficient method for haplotype assembly from long gapless reads\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebraker\u003c/h1\u003e\u003ca id=\"user-content-braker\" class=\"anchor\" aria-label=\"Permalink: braker\" href=\"#braker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBraker container generated by \u003ca href=\"https://www.ars.usda.gov/people-locations/person?person-id=41062\" rel=\"nofollow\"\u003eNathan Weeks\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1579837367.0 + "updated_at": 1618511479.0 }, { "data_format": 2, - "description": "Singularity recipe files for getorganelle (https://github.com/Kinggerm/GetOrganelle)", + "description": null, "filenames": [ - "Singularity", - "Singularity.v1.6.2e" + "Singularity" ], - "full_name": "powerPlant/getorganelle-srf", + "full_name": "2lambda123/amazon-dsstne", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the GetOrganelle toolkit to assembly organelle genomes from genome skimming data\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAmazon DSSTNE: Deep Scalable Sparse Tensor Network Engine\u003c/h1\u003e\u003ca id=\"user-content-amazon-dsstne-deep-scalable-sparse-tensor-network-engine\" class=\"anchor\" aria-label=\"Permalink: Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine\" href=\"#amazon-dsstne-deep-scalable-sparse-tensor-network-engine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDSSTNE (pronounced \"Destiny\") is an open source software library for training and deploying recommendation\nmodels with sparse inputs, fully connected hidden layers, and sparse outputs. Models with weight matrices\nthat are too large for a single GPU can still be trained on a single host. DSSTNE has been used at Amazon\nto generate personalized product recommendations for our customers at Amazon\u0027s scale. It is designed for\nproduction deployment of real-world applications which need to emphasize speed and scale over experimental\nflexibility.\u003c/p\u003e\n\u003cp\u003eDSSTNE was built with a number of features for production recommendation workloads:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-GPU Scale\u003c/strong\u003e: Training and prediction\nboth scale out to use multiple GPUs, spreading out computation\nand storage in a model-parallel fashion for each layer.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLarge Layers\u003c/strong\u003e: Model-parallel scaling enables larger networks than\nare possible with a single GPU.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSparse Data\u003c/strong\u003e: DSSTNE is optimized for fast performance on sparse datasets, common in recommendation\nproblems. Custom GPU kernels perform sparse computation on the GPU, without filling in lots of zeroes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBenchmarks\u003c/h2\u003e\u003ca id=\"user-content-benchmarks\" class=\"anchor\" aria-label=\"Permalink: Benchmarks\" href=\"#benchmarks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003escottlegrand@ reported [near-linear scaling with multiple GPUs] on the MovieLens recommendation problem\n(\u003ca href=\"https://medium.com/@scottlegrand/first-dsstne-benchmarks-tldr-almost-15x-faster-than-tensorflow-393dbeb80c0f#.ghe74fu1q\" rel=\"nofollow\"\u003ehttps://medium.com/@scottlegrand/first-dsstne-benchmarks-tldr-almost-15x-faster-than-tensorflow-393dbeb80c0f#.ghe74fu1q\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eDirections on how to run a benchmark can be found in \u003ca href=\"benchmarks/Benchmark.md\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScaling up\u003c/h2\u003e\u003ca id=\"user-content-scaling-up\" class=\"anchor\" aria-label=\"Permalink: Scaling up\" href=\"#scaling-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://blogs.aws.amazon.com/bigdata/post/TxGEL8IJ0CAXTK/Generating-Recommendations-at-Amazon-Scale-with-Apache-Spark-and-Amazon-DSSTNE\" rel=\"nofollow\"\u003eUsing Spark in AWS EMR and Dockers in AWS ECS \u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"LICENSE\"\u003eLicense\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFollow \u003ca href=\"docs/getting_started/setup.md\"\u003eSetup\u003c/a\u003e for step by step instructions on installing and setting up DSSTNE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUser Guide\u003c/h2\u003e\u003ca id=\"user-content-user-guide\" class=\"anchor\" aria-label=\"Permalink: User Guide\" href=\"#user-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCheck \u003ca href=\"docs/getting_started/userguide.md\"\u003eUser Guide\u003c/a\u003e for detailed information about the features in DSSTNE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCheck \u003ca href=\"docs/getting_started/examples.md\"\u003eExamples\u003c/a\u003e to start trying your first Neural Network Modeling using DSSTNE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQ\u0026amp;A\u003c/h2\u003e\u003ca id=\"user-content-qa\" class=\"anchor\" aria-label=\"Permalink: Q\u0026amp;A\" href=\"#qa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"FAQ.md\"\u003eFAQ\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1579837325.0 + "updated_at": 1681407189.0 }, { "data_format": 2, - "description": "start with raw plink, end with standardized QCed plink", + "description": "Evaluation Framework and Amazon DSSTNE", "filenames": [ - "workflow/Singularity_defs.def" + "amazon-dsstne/Singularity" ], - "full_name": "pmonnahan/DataPrep", + "full_name": "fnplus/project-recSys", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-pre-imputation-qc-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-imputation-qc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-imputation QC pipeline\u003c/h1\u003e\n\u003cp\u003eThe purpose of this pipeline is to perform the following for a set of input PLINK datasets:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebasic QC (genotype/variant missingness, HWE, and minor allele frequency)\u003c/li\u003e\n\u003cli\u003eharmonize allele specifications with the GRCh37 reference genome\u003c/li\u003e\n\u003cli\u003eproduce a set of VCF files (separated by chromosome) for imputation\u003c/li\u003e\n\u003cli\u003emerge filtered datasets into a single dataset consisting only of overlapping sites.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA companion pipeline, which performs post-imputation QC, will download alongside the pre-imputation pipeline. To use the post-imputation pipeline, see the README in the postImpute directory.\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#snakemake\"\u003eSnakemake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-the-workflow\"\u003eRunning the workflow\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#other-notes\"\u003eOther Notes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#debugging-and-error-reports\"\u003eDebugging and error reports\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pipeline-overview\"\u003ePipeline Overview\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#input-data\"\u003eInput Data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#dataset-harmonization\"\u003eData Harmonization\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#reference-allele-fixing\"\u003eReference allele fixing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#basic-qc\"\u003eBasic QC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#merging-inputs-optional\"\u003eMerging Inputs (Optional)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#imputaton-preparation\"\u003eImputation Preparation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pmonnahan/DataPrep/blob/master/Pipeline_DAG.png\"\u003e\u003cimg src=\"https://github.com/pmonnahan/DataPrep/raw/master/Pipeline_DAG.png\" alt=\"Pipeline DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-snakemake\" class=\"anchor\" aria-hidden=\"true\" href=\"#snakemake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakemake\u003c/h3\u003e\n\u003cp\u003eThe pipeline is coordinated and run on an HPC (or locally) using \u003cem\u003eSnakemake\u003c/em\u003e. To install snakemake, first create a virtual environment via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load python3/3.6.3_anaconda5.0.1\nconda install -c conda-forge mamba\nmamba create -c conda-forge -c bioconda -n \u0026lt;your_environment_name\u0026gt; snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a new virtual environment and install \u003ccode\u003esnakemake\u003c/code\u003e. Then, activate this environment and perform following installations:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;your_environment_name\u0026gt;\nconda install numpy yaml pandas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnytime you need to run the pipeline, activate this environment beforehand via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;environment_name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you choose not to create an environment, you must ensure that these packages are installed and available for your python installation.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eThe installation of the individual programs used throughout this pipeline can be completely avoid by utilizing a Singularity image. This image is too large to be hosted on Github, although you can find the definitions file used to create the image \u003ca href=\"https://github.com/pmonnahan/AncInf/blob/master/singularity/Singularity_defs.def\"\u003ehere\u003c/a\u003e. Building of images is still not currently supported at MSI, so I used a Vagrant virtual machine, which comes with Singularity pre-configured/installed (\u003ca href=\"https://app.vagrantup.com/singularityware/boxes/singularity-2.4/versions/2.4\" rel=\"nofollow\"\u003ehttps://app.vagrantup.com/singularityware/boxes/singularity-2.4/versions/2.4\u003c/a\u003e). I can also share the img file directly upon request.\u003c/p\u003e\n\u003cp\u003eHowever, in order to utilize the singularity image, \u003cem\u003eSingularity\u003c/em\u003e must be installed on the HPC. Currently, the pipeline assumes that \u003cem\u003eSingularity\u003c/em\u003e will be available as a module and can be loaded into the environment via the command specified in the config.yml file, where it says \u0027singularity_module\u0027. The default setting will work for MSI at UMN.\u003c/p\u003e\n\u003cp\u003eSingularity settings in config.yml\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity:\n use_singularity: \u0027true\u0027\n image: \u0027/home/pmonnaha/pmonnaha/singularity/AncestryInference.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the workflow\u003c/h2\u003e\n\u003cp\u003eFirst, activate the virtual environment into which snakemake was installed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;environment_name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eClone the parent repository to the location where you want to store the output of the pipeline.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/pmonnahan/DataPrep.git preImputeQC\ncd preImputeQC\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe critical files responsible for executing the pipeline are contained in the \u003cem\u003e./workflow\u003c/em\u003e subdirectory contained within the cloned repo. They are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSnakefile\u003c/li\u003e\n\u003cli\u003econfig.yml\u003c/li\u003e\n\u003cli\u003ecluster.yaml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003cem\u003eSnakefile\u003c/em\u003e is the primary workhouse of snakemake, which specifies the dependencies of various parts of the pipeline and coordinates execution. No modifications to the \u003cem\u003eSnakefile\u003c/em\u003e are necessary.\u003c/p\u003e\n\u003cp\u003eIn order for the \u003cem\u003eSnakefile\u003c/em\u003e to locate all of the necessary input and correctly submit jobs to the cluster, \u003cstrong\u003eboth\u003c/strong\u003e the \u003cem\u003econfig.yaml\u003c/em\u003e and \u003cem\u003ecluster.yaml\u003c/em\u003e need to be modified. Open these files and change the required entries that are indicated with \u0027MODIFY\u0027. Other fields do not require modification, although this may be desired given the particulars of the run you wish to implement. Details on each entry in the config file (e.g. what the program expects in each entry as well as the purpose of the entry) are provided in the \u003cem\u003ePipeline Overview\u003c/em\u003e at the bottom. Note: Only use letters and numbers when naming output files or datasets as this may cause issues with the report creation.\u003c/p\u003e\n\u003cp\u003eThe entire pipeline can be executed on a local machine (not recommended) or on an HPC, and the \u003cem\u003ecluster.yaml\u003c/em\u003e file is required only for the latter. For a local run, change the \u003ccode\u003elocal_run\u003c/code\u003e entry to \u003ccode\u003etrue\u003c/code\u003e under the \u003ccode\u003erun_settings\u003c/code\u003e section of the config file, and launch snakemake from within the parent directory by the simple command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHowever, multiple steps in the pipeline have high resource demands, and so are unlikely to be able to be run locally. This option exists primarily for testing and troubleshooting, so the remainder of the documentation assumes that the pipeline will be executed on an HPC. In order to coordinate the use of the HPC, the following modifications to the snakemake command are required:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --cluster \"sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\" --cluster-config workflow/cluster_yale.yaml -j 32\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere -j specifies the number of jobs that can be submitted at once.\u003c/p\u003e\n\u003cp\u003eOne additional setting in the \u003cem\u003econfig.yml\u003c/em\u003e is needed in order to correctly submit jobs to the HPC. The relevant entries are under the \u003ccode\u003erun_settings\u003c/code\u003e section of the config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun_settings:\n local_run: \u0027false\u0027\n cluster_config: \u0027workflow/cluster_slurm.yaml\u0027\n scheduler: \u0027slurm\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere, it is necessary that the \u003ccode\u003ecluster_config\u003c/code\u003e entry is set to the path of the cluster_slurm.yaml file that will be used in the snakemake command. Also, the scheduler must correspond to the syntax used in the snakemake command and cluster.yaml file. I should point out that these additional changes are needed for responsibly using PLINK within a snakemake framework, and are not directly needed for snakemake. PLINK will attempt to auto-detect available resources upon running regardless of the resources that were requested when the job was submitted. Therefore, we have to read and parse the requested resources in the cluster config file in order for them to be communicated to PLINK from within the Snakefile.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-other-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther notes\u003c/h3\u003e\n\u003cp\u003eIt is recommended that \u003cem\u003esnakemake\u003c/em\u003e is run as an interactive session on an HPC. \u003cem\u003eSnakemake\u003c/em\u003e will launch the specified number (via the -j flag) of jobs, and then will hang and wait for them to finish. As jobs finish (and assuming no errors), \u003cem\u003esnakemake\u003c/em\u003e will launch additional jobs keeping the total running jobs at whatever -j is set for. Although \u003cem\u003esnakemake\u003c/em\u003e should not use a lot of memory, it could have long run times, which is generally not advisable on login nodes.\u003c/p\u003e\n\u003cp\u003eOne attractive feature of \u003cem\u003esnakemake\u003c/em\u003e is its ability to keep track of the progress and dependencies of the different stages of the pipeline. Specifically, if an error is encountered or the pipeline otherwise stops before the final step, \u003cem\u003esnakemake\u003c/em\u003e can resume the pipeline where it left off, avoiding redundant computation for previously completed tasks. To do so, simply resubmit the original \u003cem\u003esnakemake\u003c/em\u003e command.\u003c/p\u003e\n\u003cp\u003eTo run a specific part of the pipeline, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -R \u0026lt;rule_name\u0026gt; --cluster \"sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\" --cluster-config workflow/cluster_yale.yaml -j 20 --rerun-incomplete\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003cem\u003erule_name\u003c/em\u003e indicates the \u0027rule\u0027 (i.e. job) in the Snakefile that you wish to run. Or, you can request a specific file by providing the filename at the end of the command. You may need to include the -F (i.e. force) if the output file already exists and you want to overwrite it.\u003c/p\u003e\n\u003cp\u003eAlso, it is often very helpful to do a \u0027dry-run\u0027 of the pipeline in which the different steps and dependencies are printed to screen, but no actual jobs are executed. This can be helpful to ensure that config entries are correct, etc. To perform a dry-run, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -nrp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: It is convenient to make an alias in your ~/.bashrc file to run snakemake on the cluster without having to type the --cluster... part of the command every time. For me, it looked like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias snakeslurm=\"snakemake -k --cluster \u0027sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --job-name={cluster.job-name} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\u0027 --cluster-config workflow/cluster_slurm.yaml\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis way, I can just do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakeslurm -j 25\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo launch snakemake on the cluster.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-debugging-and-error-reports\" class=\"anchor\" aria-hidden=\"true\" href=\"#debugging-and-error-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebugging and error reports\u003c/h4\u003e\n\u003cp\u003eShould an error be encountered in a job, snakemake will halt the pipeline and indicate in the terminal that an error has occurred. The offending job will also be printed in red in the terminal window. More information on why the job failed can be found in the \u0027stdout\u0027 and \u0027stderr\u0027 files that are output to the \u003cem\u003e\u0027OandE\u0027\u003c/em\u003e directory and will be labelled with the jobname.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline Overview\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput Data\u003c/h3\u003e\n\u003cp\u003eUnder the \u0027query\u0027 section, you can specify the inputs for one or more datasets. Each dataset should be uniquely named (Note: avoid using periods or underscores when naming output files or datasets as this may cause issues with the report creation.) with values specified for the following \"keys\":\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003edata\u003c/strong\u003e: path to the PLINK files (just the PLINK prefix).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003echrom_key\u003c/strong\u003e: tab-delimited text file with 2 columns (no header). The first column contains the old chromosome names, and the second column contains the new names.\n\u003cul\u003e\n\u003cli\u003eUsed for converting to numeric names. e.g chr10 to 10.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eallele_key\u003c/strong\u003e: tab-delimited text file with 5 columns (no header). First column is snpID and following columns are: old_allele1 old_allele2 new_allele1 new_allele2.\n\u003cul\u003e\n\u003cli\u003eUsed for converting alleles with A/B specification to ACGT. Oftentimes provided in the dbGaP download. If alleles are already specified in ACGT format, this field can be set to \u0027none\u0027\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eID_key\u003c/strong\u003e: tab-delimited text file with 2 columns (no header). First column is old SNP ID and second column is new SNP ID.\n\u003cul\u003e\n\u003cli\u003eUsed for converting to rsID format. If SNP IDs are already in rs-format, this field can be set to \u0027none\u0027\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eflip_key\u003c/strong\u003e: text file with single column containing SNP rsIDs that need to be flipped in order to align strand to the hg19 reference genome.\n\u003cul\u003e\n\u003cli\u003eUsed to harmonize strand across datasets to the hg19 reference genome. Set this field to \u0027none\u0027 if all alleles are already on the same strand as the target reference genome.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEach of these fields are optional and providing \u0027none\u0027 as the entry will disable the steps associated with each key. However, these fields should only be set to \u0027none\u0027 if you are sure that they are not necessary (e.g. you have already fixed any existing strand issues across datasets).\u003c/p\u003e\n\u003cp\u003eExample of input specifications in the config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003equery:\n \"dataset1\":\n data: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n chrom_key: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n allele_key: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n ID_key: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n flip_key: \"PATH/TO/PLINK/PREFIX/FOR/DATASET1\"\n \"dataset2\":\n data: \"PATH/TO/PLINK/PREFIX/FOR/DATASET2\"\n chrom_key: \"none\"\n allele_key: \"none\"\n ID_key: \"none\"\n flip_key: \"none\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePhenotypes of the samples must be specified by a tab-delimited text file where the first column contains the sample IDs (as they appear in the imputed VCF file) and the second column contains the phenotype. The path to this file can be provided in the field labelled \u0027phenotype_file\u0027 under the \u0027phenotype_data\u0027 field in the config.yml file.\u003c/p\u003e\n\u003cp\u003eSex of the samples must also be specified in a tab-delimited text file where the first column is sample ID and the second column is the sex specification according to PLINK. The path to this file can be provided in the field labelled \u0027sex_file\u0027 under the \u0027phenotype_data\u0027 field in the config.yml file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ephenotype_data: \n pheno_file: \"none\"\n sex_file: \"/path/to/sex/file\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h3\u003e\n\u003cp\u003eThe output is a set of PLINK files in the parent directory labelled with the value provided in the \u0027outname\u0027 entry of the config file. However, if \u0027merge\u0027 is set to \u0027false\u0027 in the config file, this final merge step is skipped, and the final output would be the set of QC\u0027ed plink files within each subdirectory labelled with the dataset names. Within each of these subdirectories, there will also be a set of VCF files, which are suitable for use in either the Michigan or TOPMed imputation servers.\u003c/p\u003e\n\u003cp\u003eThe other primary output is a PDF report containing a summary of various steps in the pipeline. It is \u003cstrong\u003ehighly recommended\u003c/strong\u003e that the user carefully review this report to confirm that everything seems in order. Particular attention should be paid to whether specific steps have resulted in major loss of markers as well as whether there is a positive correlation between allele frequencies in the 1000Genomes dataset and allele frequencies in each of the query datasets. These scatter plots are provided towards the end of the report, and if a substantial subset of the points exhibit an anti-correlation, this is indicative of a preponderance of strand errors that ought to be corrected (via the \u0027flip_key\u0027) prior to proceeding.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dataset-harmonization\" class=\"anchor\" aria-hidden=\"true\" href=\"#dataset-harmonization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDataset harmonization\u003c/h3\u003e\n\u003cp\u003eThe first step(s) in the pipeline aims to harmonize the naming of chromosomes, alleles, and variant IDs. This is accomplished via the 4 keys described above. While this pipeline generally attempts to simplify the QC process, it is extremely important that the user is acquainted well enough with each individual dataset to ensure that the appropriate keys are specified (or not specified).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reference-allele-fixing\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference-allele-fixing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference allele fixing\u003c/h3\u003e\n\u003cp\u003eIn contrast to a VCF, where alleles are specified with respect to a specified reference genome (reference versus alternative alleles), PLINK-formatted files often specify alleles as major/minor alleles based on the frequency in the dataset. Furthermore, many commonly used arrays will contain a mixture of SNPs genotyped on either the + or - strand. Lastly, the default behavior of PLINK is to automatically set the minor to A1 and the major allele to A2, which can unintentionally generate inconsistencies in allele specifications across datasets.\u003c/p\u003e\n\u003cp\u003eWith respect to a reference genome, two possible types of errors can occur:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFlipped strand: The genotype is specified with respect to the opposite strand relative to the reference genome.\u003c/li\u003e\n\u003cli\u003eSwapped allele: The genotype is specified on the same strand as the reference genome, but the A1 (minor) allele has been set to equal the \u0027reference\u0027 allele when it ought to be set to equal the non-reference/\u0027alternative\u0027 allele\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo identify these errors, we use the bcftools plugin \u0027+fixref\u0027, which requires not only the reference sequence (fasta) file, but also a VCF file containing variant sites that are used to identify mismatching alleles in the query dataset. Importantly, if the program determines that no strand issues exist and that the reference/alternative alleles have simply been swapped, then program will swap the major/minor alleles to match the reference. It will not perform any strand flipping, where it converts genotypes to be specified with respect to the nucleotide on the opposite strand. Although the program will attempt to identify these strand flips, it doesn\u0027t make the correction as the authors consider this a risky move that should not be handled in an automated fashion. Thus, flip-strand mismatches are ultimately removed. If there are a large number of these, the user should attempt to understand and resolve the source of the issue and rerun this pipeline.\u003c/p\u003e\n\u003cp\u003eBy default, the pipeline will download the following files for the hg19 reference genome:\u003c/p\u003e\n\u003cp\u003eReference fasta:\nftp://ftp.ncbi.nlm.nih.gov/1000genomes/ftp/technical/reference/human_g1k_v37.fasta.gz\u003c/p\u003e\n\u003cp\u003eReference VCF (1000Genomes):\nftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/VCF/All_20170710.vcf.gz\u003c/p\u003e\n\u003cp\u003eAn indication of whether alleles are now specified correctly is to plot frequency of an allele in the query population against the frequency in the reference population and look for an obviously positive correlation. Such plots are automatically produced in the PDF report as the final step in the pipeline.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basic-qc\" class=\"anchor\" aria-hidden=\"true\" href=\"#basic-qc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasic QC\u003c/h3\u003e\n\u003cp\u003eAfter alleles have been fixed as described above, a series of basic QC steps are performed on each dataset by the script \u003cem\u003e\u0027scripts/QC.py\u0027\u003c/em\u003e, with the filtering thresholds specified in the config file (see below).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eperform_QC: \u0027true\u0027\nQC:\n vm1: \"0.2\" # Initial variant missingness filter\n gm: \"0.1\" # Individual missingness filter\n vm2: \"0.05\" # Ultimate call rate for variants after removing low-callrate samples\n maf: \"0.01\" # mimimum Minor allele frequency\n hwe: \"0.0000001\" # p-value threshold for whether site follows hardy-weinberg\n mbs: \"0.0000001\" # p-value treshold for test of whether missingness varies by sex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe first wish to identify and remove individual samples that show high missingess across markers (specified by \u0027gm\u0027). However, to identify these individuals, we first need to remove variants that imputed poorly across all individuals (specified by \u0027vm1\u0027). After removing these individuals, we then remove variants with high missingness (specified by \u0027vm2\u0027). Since poor imputation will result in missing genotypes, this missingness filter indirectly filters for low quality imputation sites. Variants are also filtered based whether or not they show significant departures from Hardy-Weinberg Equilibrium (\u0027hwe\u0027 entry) and whether there is a significant association between missingness and sex (\u0027mbs\u0027 entry). We also remove rare variants based on the \u0027maf\u0027 value. Lastly, we remove indels, duplicate SNPs, and multi-allelic variants.\u003c/p\u003e\n\u003cp\u003eNote that testing for missigness by case/control status is generally recommended as well if the user wishes to proceed straight to SNP-based analyses such as GWAS. However, if the data is to be used for ancestry inference, it may make more sense to retain these SNPs.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-merging-inputs-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#merging-inputs-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMerging inputs (Optional)\u003c/h3\u003e\n\u003cp\u003eIf multiple input datasets were provided, an optional final step is to create a single merged dataset consisting of only the sites that overlap (i.e. passed filters) across all component datasets. This behavior is controlled by the \u0027merge\u0027 entry in the config file. To enable the merging behavior, set this to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emerge: \u0027true\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-imputaton-preparation\" class=\"anchor\" aria-hidden=\"true\" href=\"#imputaton-preparation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImputaton preparation\u003c/h3\u003e\n\u003cp\u003eAnother optional, final feature is to create a set of of VCF files (parsed by chromosome) for each of the input datasets. These VCFs can be used directly as input into either the Michigan Imputation Server or the TOPMed Imputation Server. The output of the imputation servers can then be used as input into the post-imputation QC pipeline (see README.md in the \u0027postImpute\u0027 directory).\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRecSys\u003c/h1\u003e\u003ca id=\"user-content-recsys\" class=\"anchor\" aria-label=\"Permalink: RecSys\" href=\"#recsys\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEvaluation Framwork and Amazon DSSTNE\u003c/h2\u003e\u003ca id=\"user-content-evaluation-framwork-and-amazon-dsstne\" class=\"anchor\" aria-label=\"Permalink: Evaluation Framwork and Amazon DSSTNE\" href=\"#evaluation-framwork-and-amazon-dsstne\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1617574369.0 + "topics": [ + "machine-learning", + "recommendation-engine" + ], + "updated_at": 1560613253.0 }, { "data_format": 2, - "description": null, + "description": "Fork from https://github.com/dbolya/yolact", "filenames": [ - "Singularity.mash", - "Singularity.CAT_update", - "Singularity.art", - "Singularity.metawap_docker", - "Singularity.pasta", - "Singularity.cmseq_conda", - "Singularity.snakemake", - "Singularity.euk_decide", - "Singularity.dRep", - "Singularity.dbcan", - "Singularity.nanofilt", - "Singularity.metaeuk", - "Singularity.BUSCO4", - "Singularity.cmseq", - "Singularity.ploidyNGS", - "Singularity.metawrap", - "Singularity.sepp", - "Singularity.R", - "Singularity.VAMP", - "Singularity.puntseq", - "Singularity.VAMB_10.1", - "Singularity.VAMB", - "Singularity.mashmap", - "Singularity.comparem", - "Singularity.ncbi-downloader", - "Singularity.biopython", - "Singularity.spades", - "Singularity.minimap2", - "Singularity.BUSCO5", - "Singularity.bbmap", - "Singularity.sourmash", - "Singularity.raxml-ng", - "Singularity.nQuire", - "Singularity.fastani", - "Singularity.metabat2", - "Singularity.seqtk", - "Singularity.pysam", - "Singularity.krona", - "Singularity.kraken2", - "Singularity.bamm", - "Singularity.megahit", - "Singularity.ete3", - "Singularity.bioinfo", - "Singularity.trimal", - "Singularity.spades_3.13", - "Singularity.dRep3", - "Singularity.deeptools", - "Singularity.tree", - "Singularity.BUSCO414", - "Singularity.METAMVGL", - "Singularity.repeatmasker", - "Singularity.mummer", - "Singularity.iqtree", - "Singularity.eukcc_vanilla", - "Singularity.mafft", - "Singularity.bioconvert", - "Singularity.qiime2", - "Singularity.CAT", - "Singularity.bwa", - "Singularity.mmseq2", - "Singularity.famsa", - "Singularity.EukRep", - "Singularity.antismash_standalone", - "Singularity.spades_3.15" + "Singularity" ], - "full_name": "hexmek/container", + "full_name": "sokovninn/yolact-artwin", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\n\u003cstrong\u003eY\u003c/strong\u003eou \u003cstrong\u003eO\u003c/strong\u003enly \u003cstrong\u003eL\u003c/strong\u003eook \u003cstrong\u003eA\u003c/strong\u003et \u003cstrong\u003eC\u003c/strong\u003eoefficien\u003cstrong\u003eT\u003c/strong\u003es\u003c/h1\u003e\u003ca id=\"user-content-you-only-look-at-coefficients\" class=\"anchor\" aria-label=\"Permalink: You Only Look At CoefficienTs\" href=\"#you-only-look-at-coefficients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e \u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\n \u255a\u2588\u2588\u2557 \u2588\u2588\u2554\u255d\u2588\u2588\u2554\u2550\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2557\u2588\u2588\u2554\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u2550\u2588\u2588\u2554\u2550\u2550\u255d\n \u255a\u2588\u2588\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u255a\u2588\u2588\u2554\u255d \u2588\u2588\u2551 \u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2554\u2550\u2550\u2588\u2588\u2551\u2588\u2588\u2551 \u2588\u2588\u2551 \n \u2588\u2588\u2551 \u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2554\u255d\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2557\u2588\u2588\u2551 \u2588\u2588\u2551\u255a\u2588\u2588\u2588\u2588\u2588\u2588\u2557 \u2588\u2588\u2551 \n \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u255d\u255a\u2550\u255d \u255a\u2550\u255d \u255a\u2550\u2550\u2550\u2550\u2550\u255d \u255a\u2550\u255d \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA simple, fully convolutional model for real-time instance segmentation. This is the code for our papers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1904.02689\" rel=\"nofollow\"\u003eYOLACT: Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003eYOLACT++: Better Real-time Instance Segmentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eYOLACT++ (v1.2) released! (\u003ca href=\"CHANGELOG.md\"\u003eChangelog\u003c/a\u003e)\u003c/h4\u003e\u003ca id=\"user-content-yolact-v12-released-changelog\" class=\"anchor\" aria-label=\"Permalink: YOLACT++ (v1.2) released! (Changelog)\" href=\"#yolact-v12-released-changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYOLACT++\u0027s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e (check out our journal paper \u003ca href=\"https://arxiv.org/abs/1912.06218\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIn order to use YOLACT++, make sure you compile the DCNv2 code. (See \u003ca href=\"https://github.com/dbolya/yolact#installation\"\u003eInstallation\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFor a real-time demo, check out our ICCV video:\u003c/h4\u003e\u003ca id=\"user-content-for-a-real-time-demo-check-out-our-iccv-video\" class=\"anchor\" aria-label=\"Permalink: For a real-time demo, check out our ICCV video:\" href=\"#for-a-real-time-demo-check-out-our-iccv-video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=0pMfmo8qfpQ\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/70ee4ab932214f5d02c39f251a53743c6e0e8c964af4af267119e92691fb2f9c/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f30704d666d6f38716670512f302e6a7067\" alt=\"IMAGE ALT TEXT HERE\" data-canonical-src=\"https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSome examples from our YOLACT base model (33.5 fps on a Titan Xp and 29.8 mAP on COCO\u0027s \u003ccode\u003etest-dev\u003c/code\u003e):\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_0.png\"\u003e\u003cimg src=\"data/yolact_example_0.png\" alt=\"Example 0\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_1.png\"\u003e\u003cimg src=\"data/yolact_example_1.png\" alt=\"Example 1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"data/yolact_example_2.png\"\u003e\u003cimg src=\"data/yolact_example_2.png\" alt=\"Example 2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repository and enter it:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/dbolya/yolact.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e yolact\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eSet up the environment using one of the following methods:\n\u003cul\u003e\n\u003cli\u003eUsing \u003ca href=\"https://www.anaconda.com/distribution/\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eRun \u003ccode\u003econda env create -f environment.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eManually with pip\n\u003cul\u003e\n\u003cli\u003eSet up a Python3 environment (e.g., using virtenv).\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"http://pytorch.org/\" rel=\"nofollow\"\u003ePytorch\u003c/a\u003e 1.0.1 (or higher) and TorchVision.\u003c/li\u003e\n\u003cli\u003eInstall some other packages:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Cython needs to be installed before pycocotools\u003c/span\u003e\npip install cython\npip install opencv-python pillow pycocotools matplotlib \u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to train YOLACT, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into \u003ccode\u003e./data/coco\u003c/code\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you\u0027d like to evaluate YOLACT on \u003ccode\u003etest-dev\u003c/code\u003e, download \u003ccode\u003etest-dev\u003c/code\u003e with this script.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esh data/scripts/COCO_test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you want to use YOLACT++, compile deformable convolutional layers (from \u003ca href=\"https://github.com/CharlesShang/DCNv2/tree/pytorch_1.0\"\u003eDCNv2\u003c/a\u003e).\nMake sure you have the latest CUDA toolkit installed from \u003ca href=\"https://developer.nvidia.com/cuda-toolkit\" rel=\"nofollow\"\u003eNVidia\u0027s Website\u003c/a\u003e.\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e external/DCNv2\npython setup.py build develop\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEvaluation\u003c/h1\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere are our YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on \u003ccode\u003etest-dev\u003c/code\u003e:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1yp7ZbbDwvMiFJEq4ptVKTYTI2VeRDXl0/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EUVpxoSXaqNIlssoLKOEoCcB1m0RpzGq_Khp5n1VX3zcUw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eDarknet53-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28.7\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1dukLrTzZQEuhzitGkHaGjphlmRJOjVnP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_darknet53_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/ERrao26c8llJn25dIyZPhwMBxUp2GdZTKIMUQA3t0djHLw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e29.8\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf_/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EYRWxBEoKU9DiblrWx2M89MBGFkVVB_drlRd_v5sdT3Hgg\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e700\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e23.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e31.2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1lE4Lz5p25teiXV-6HdTiOJSnS7u7GBzg/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_im700_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/Eagg5RSc5hFEhp7sPtvLNyoBjhlf2feog7t8OQzHKKphjw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYOLACT++ models (released on December 16th, 2019):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eImage Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth\u003eWeights\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet50-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/1ZPu1YR2UzGHQD0o1rEqy-j5bmEm3lbyP/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_resnet50_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EcJAtMiEFlhAnVsDf00yWRIBUC4m8iE9NEEiV05XwtEoGw\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e550\u003c/td\u003e\n\u003ctd align=\"center\"\u003eResnet101-FPN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e27.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.6\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://drive.google.com/file/d/15id0Qq5eqRbkD-N3ZjDZXdCvRyIaHpFB/view?usp=sharing\" rel=\"nofollow\"\u003eyolact_plus_base_54_800000.pth\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EVQ62sF0SrJPrl_68onyHF8BpG7c05A8PavV4a849sZgEA\" rel=\"nofollow\"\u003eMirror\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo evalute the model, put the corresponding weights file in the \u003ccode\u003e./weights\u003c/code\u003e directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., \u003ccode\u003eyolact_base\u003c/code\u003e for \u003ccode\u003eyolact_base_54_800000.pth\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuantitative Results on COCO\u003c/h2\u003e\u003ca id=\"user-content-quantitative-results-on-coco\" class=\"anchor\" aria-label=\"Permalink: Quantitative Results on COCO\" href=\"#quantitative-results-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This should get 29.92 validation mask mAP last time I checked.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Output a COCOEval json to submit to the website or to use the run_coco_eval.py script.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This command will create \u0027./results/bbox_detections.json\u0027 and \u0027./results/mask_detections.json\u0027 for detection and instance segmentation respectively.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py.\u003c/span\u003e\npython run_coco_eval.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQualitative Results on COCO\u003c/h2\u003e\u003ca id=\"user-content-qualitative-results-on-coco\" class=\"anchor\" aria-label=\"Permalink: Qualitative Results on COCO\" href=\"#qualitative-results-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on COCO. From here on I\u0027ll use a confidence threshold of 0.15.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --display\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBenchmarking on COCO\u003c/h2\u003e\u003ca id=\"user-content-benchmarking-on-coco\" class=\"anchor\" aria-label=\"Permalink: Benchmarking on COCO\" href=\"#benchmarking-on-coco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run just the raw model on the first 1k images of the validation set\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark --max_images=1000\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImages\u003c/h2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display qualitative results on the specified image.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process an image and save it to another file.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a whole folder of images.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVideo\u003c/h2\u003e\u003ca id=\"user-content-video\" class=\"anchor\" aria-label=\"Permalink: Video\" href=\"#video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a video in real-time. \"--video_multiframe\" will process that many frames at once for improved performance.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you want, use \"--display_fps\" to draw the FPS directly on the frame.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0.\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Process a video and save it to another file. This uses the same pipeline as the ones above now, so it\u0027s fast!\u003c/span\u003e\npython eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs you can tell, \u003ccode\u003eeval.py\u003c/code\u003e can do a ton of stuff. Run the \u003ccode\u003e--help\u003c/code\u003e command to see everything it can do.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython eval.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTraining\u003c/h1\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, we train on COCO. Make sure to download the entire dataset using the commands above.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo train, grab an imagenet-pretrained model and put it in \u003ccode\u003e./weights\u003c/code\u003e.\n\u003cul\u003e\n\u003cli\u003eFor Resnet101, download \u003ccode\u003eresnet101_reducedfc.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1tvqFPd4bJtakOlmn-uIA492g2qurRChj/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Resnet50, download \u003ccode\u003eresnet50-19c8e357.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/1Jy3yCdbatgXa5YYIdTCRrSV0S9V5g1rn/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor Darknet53, download \u003ccode\u003edarknet53.pth\u003c/code\u003e from \u003ca href=\"https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRun one of the training commands below.\n\u003cul\u003e\n\u003cli\u003eNote that you can press ctrl+c while training and it will save an \u003ccode\u003e*_interrupt.pth\u003c/code\u003e file at the current iteration.\u003c/li\u003e\n\u003cli\u003eAll weights are saved in the \u003ccode\u003e./weights\u003c/code\u003e directory by default with the file name \u003ccode\u003e\u0026lt;config\u0026gt;_\u0026lt;epoch\u0026gt;_\u0026lt;iter\u0026gt;.pth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains using the base config with a batch size of 8 (the default).\u003c/span\u003e\npython train.py --config=yolact_base_config\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.\u003c/span\u003e\npython train.py --config=yolact_base_config --batch_size=5\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file\u0027s name.\u003c/span\u003e\npython train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the help option to see a description of all available command line arguments\u003c/span\u003e\npython train.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMulti-GPU Support\u003c/h2\u003e\u003ca id=\"user-content-multi-gpu-support\" class=\"anchor\" aria-label=\"Permalink: Multi-GPU Support\" href=\"#multi-gpu-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYOLACT now supports multiple GPUs seamlessly during training:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBefore running any of the scripts, run: \u003ccode\u003eexport CUDA_VISIBLE_DEVICES=[gpus]\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3).\u003c/li\u003e\n\u003cli\u003eYou should still do this if only using 1 GPU.\u003c/li\u003e\n\u003cli\u003eYou can check the indices of your GPUs with \u003ccode\u003envidia-smi\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThen, simply set the batch size to \u003ccode\u003e8*num_gpus\u003c/code\u003e with the training commands above. The training script will automatically scale the hyperparameters to the right values.\n\u003cul\u003e\n\u003cli\u003eIf you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you\u0027re using.\u003c/li\u003e\n\u003cli\u003eIf you want to allocate the images per GPU specific for different GPUs, you can use \u003ccode\u003e--batch_alloc=[alloc]\u003c/code\u003e where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to \u003ccode\u003ebatch_size\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLogging\u003c/h2\u003e\u003ca id=\"user-content-logging\" class=\"anchor\" aria-label=\"Permalink: Logging\" href=\"#logging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYOLACT now logs training and validation information by default. You can disable this with \u003ccode\u003e--no_log\u003c/code\u003e. A guide on how to visualize these logs is coming soon, but now you can look at \u003ccode\u003eLogVizualizer\u003c/code\u003e in \u003ccode\u003eutils/logger.py\u003c/code\u003e for help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePascal SBD\u003c/h2\u003e\u003ca id=\"user-content-pascal-sbd\" class=\"anchor\" aria-label=\"Permalink: Pascal SBD\" href=\"#pascal-sbd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe also include a config for training on Pascal SBD annotations (for rapid experimentation or comparing with other methods). To train on Pascal SBD, proceed with the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the dataset from \u003ca href=\"http://home.bharathh.info/pubs/codes/SBD/download.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. It\u0027s the first link in the top \"Overview\" section (and the file is called \u003ccode\u003ebenchmark.tgz\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eExtract the dataset somewhere. In the dataset there should be a folder called \u003ccode\u003edataset/img\u003c/code\u003e. Create the directory \u003ccode\u003e./data/sbd\u003c/code\u003e (where \u003ccode\u003e.\u003c/code\u003e is YOLACT\u0027s root) and copy \u003ccode\u003edataset/img\u003c/code\u003e to \u003ccode\u003e./data/sbd/img\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDownload the COCO-style annotations from \u003ca href=\"https://drive.google.com/open?id=1ExrRSPVctHW8Nxrn0SofU1lVhK5Wn0_S\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eExtract the annotations into \u003ccode\u003e./data/sbd/\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eNow you can train using \u003ccode\u003e--config=yolact_resnet50_pascal_config\u003c/code\u003e. Check that config to see how to extend it to other models.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI will automate this all with a script soon, don\u0027t worry. Also, if you want the script I used to convert the annotations, I put it in \u003ccode\u003e./scripts/convert_sbd.py\u003c/code\u003e, but you\u0027ll have to check how it works to be able to use it because I don\u0027t actually remember at this point.\u003c/p\u003e\n\u003cp\u003eIf you want to verify our results, you can download our \u003ccode\u003eyolact_resnet50_pascal_config\u003c/code\u003e weights from \u003ca href=\"https://drive.google.com/open?id=1yLVwtkRtNxyl0kxeMCtPXJsXFFyc_FHe\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. This model should get 72.3 mask AP_50 and 56.2 mask AP_70. Note that the \"all\" AP isn\u0027t the same as the \"vol\" AP reported in others papers for pascal (they use an averages of the thresholds from \u003ccode\u003e0.1 - 0.9\u003c/code\u003e in increments of \u003ccode\u003e0.1\u003c/code\u003e instead of what COCO uses).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCustom Datasets\u003c/h2\u003e\u003ca id=\"user-content-custom-datasets\" class=\"anchor\" aria-label=\"Permalink: Custom Datasets\" href=\"#custom-datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can also train on your own dataset by following these steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a COCO-style Object Detection JSON annotation file for your dataset. The specification for this can be found \u003ca href=\"http://cocodataset.org/#format-data\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Note that we don\u0027t use some fields, so the following may be omitted:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003einfo\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eliscense\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eUnder \u003ccode\u003eimage\u003c/code\u003e: \u003ccode\u003elicense, flickr_url, coco_url, date_captured\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecategories\u003c/code\u003e (we use our own format for categories, see below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a definition for your dataset under \u003ccode\u003edataset_base\u003c/code\u003e in \u003ccode\u003edata/config.py\u003c/code\u003e (see the comments in \u003ccode\u003edataset_base\u003c/code\u003e for an explanation of each field):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003emy_custom_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset_base\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecopy\u003c/span\u003e({\n \u003cspan class=\"pl-s\"\u003e\u0027name\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027My Dataset\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027train_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027train_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_training_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027valid_images\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_images\u0027\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027valid_info\u0027\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u0027path_to_validation_annotation\u0027\u003c/span\u003e,\n\n \u003cspan class=\"pl-s\"\u003e\u0027has_gt\u0027\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e\u0027class_names\u0027\u003c/span\u003e: (\u003cspan class=\"pl-s\"\u003e\u0027my_class_id_1\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_2\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027my_class_id_3\u0027\u003c/span\u003e, ...)\n})\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA couple things to note:\n\u003cul\u003e\n\u003cli\u003eClass IDs in the annotation file should start at 1 and increase sequentially on the order of \u003ccode\u003eclass_names\u003c/code\u003e. If this isn\u0027t the case for your annotation file (like in COCO), see the field \u003ccode\u003elabel_map\u003c/code\u003e in \u003ccode\u003edataset_base\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf you do not want to create a validation split, use the same image path and annotations file for validation. By default (see \u003ccode\u003epython train.py --help\u003c/code\u003e), \u003ccode\u003etrain.py\u003c/code\u003e will output validation mAP for the first 5000 images in the dataset every 2 epochs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFinally, in \u003ccode\u003eyolact_base_config\u003c/code\u003e in the same file, change the value for \u003ccode\u003e\u0027dataset\u0027\u003c/code\u003e to \u003ccode\u003e\u0027my_custom_dataset\u0027\u003c/code\u003e or whatever you named the config object above. Then you can use any of the training commands in the previous section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreating a Custom Dataset from Scratch\u003c/h4\u003e\u003ca id=\"user-content-creating-a-custom-dataset-from-scratch\" class=\"anchor\" aria-label=\"Permalink: Creating a Custom Dataset from Scratch\" href=\"#creating-a-custom-dataset-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/dbolya/yolact/issues/70#issuecomment-504283008\"\u003ethis nice post by @Amit12690\u003c/a\u003e for tips on how to annotate a custom dataset and prepare it for use with YOLACT.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use YOLACT or this code base in your work, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{yolact-iccv2019,\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n title = {YOLACT: {Real-time} Instance Segmentation},\n booktitle = {ICCV},\n year = {2019},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor YOLACT++, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{yolact-plus-arxiv2019,\n title = {YOLACT++: Better Real-time Instance Segmentation},\n author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},\n year = {2019},\n eprint = {1912.06218},\n archivePrefix = {arXiv},\n primaryClass = {cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContact\u003c/h1\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor questions about our paper or code, please contact \u003ca href=\"mailto:dbolya@ucdavis.edu\"\u003eDaniel Bolya\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1619700602.0 + "updated_at": 1635159103.0 }, { "data_format": 2, - "description": "Singularity recipe files for Mandalorion-Episode-II (https://github.com/rvolden/Mandalorion-Episode-II)", + "description": "official build specifications for nginx", "filenames": [ - "Singularity", - "Singularity.6219d58" + "Singularity" ], - "full_name": "powerPlant/mandalorion-episode-ii-srf", + "full_name": "singularityhub/nginx", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for Mandalorion Episode II, Attack of the Isoforms\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNginx\u003c/h1\u003e\u003ca id=\"user-content-nginx\" class=\"anchor\" aria-label=\"Permalink: Nginx\" href=\"#nginx\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container is built using Circle CI, Google Storage, and Google Cloud Build, and \u003ca href=\"https://singularityhub.github.io/registry-org/singularityhub/nginx/\" rel=\"nofollow\"\u003ehosted on Singularity Static Registry\u003c/a\u003e. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003etags are supported based on the extension of the Singularity file, with an extensionless file corresponding to \"latest\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you are interested in local usage, see \u003ca href=\"#local-usage\"\u003eLocal Usage\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can I find here?\u003c/h2\u003e\u003ca id=\"user-content-what-can-i-find-here\" class=\"anchor\" aria-label=\"Permalink: What can I find here?\" href=\"#what-can-i-find-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository here serves the container under the namespace \u003ccode\u003esingularityhub/nginx\u003c/code\u003e. Specifically,\nit provides an example of using CircleCI to build with Google Cloud Build and push a container to Google Storage,\nand then update manifests at \u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e.\nIf you are interested in other container build templates, see \u003ca href=\"https://github.com/singularityhub/registry/wiki/build-templates\"\u003ethis page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does this work?\u003c/h2\u003e\u003ca id=\"user-content-how-does-this-work\" class=\"anchor\" aria-label=\"Permalink: How does this work?\" href=\"#how-does-this-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will submit this container to the (organizational) registry at\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e\nfor a final container uri corresponding to \u003ccode\u003ehttps://singularityhub.github.io/registry-org/singularityhub/busybox\u003c/code\u003e. Specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub/registry-org --) the organization registry\nsingularityhub/nginx --) a container collection\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethen on GitHub pages:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub.github.io/registry-org --) the registry interface\nsingularityhub.github.io/registry-org/singularityhub/nginx --) the added container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions\u003c/h1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e0. Fork the Repository\u003c/h2\u003e\u003ca id=\"user-content-0-fork-the-repository\" class=\"anchor\" aria-label=\"Permalink: 0. Fork the Repository\" href=\"#0-fork-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the repository here to your account, and make sure to add write permissions\nfor a machine user for the repository, and the machine user\u0027s key to CircleCI.\nThis means:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eadding the machine user as a collaborator to the repository (and accepting the invitation)\u003c/li\u003e\n\u003cli\u003econnecting the repository to CircleCI\u003c/li\u003e\n\u003cli\u003enavigating to the CircleCI project page logged in as the machine user to follow the project (button in upper right)\u003c/li\u003e\n\u003cli\u003egoing to the settings -\u0026gt; Checkout SSH keys to add the machine user key.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFull instructions are provided \u003ca href=\"https://github.com/singularityhub/registry/wiki/deploy-container-storage#2-creating-a-connected-repository\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Setup your Organizational Registry\u003c/h2\u003e\u003ca id=\"user-content-1-setup-your-organizational-registry\" class=\"anchor\" aria-label=\"Permalink: 1. Setup your Organizational Registry\" href=\"#1-setup-your-organizational-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you haven\u0027t done so, follow the instructions \u003ca href=\"https://github.com/singularityhub/registry/wiki/deploy-container-storage#organizational\"\u003ehere\u003c/a\u003e to create the organizational registry. You will need to\nupdate the environment variables in the top of the \u003ca href=\".circleci/config.yml\"\u003e.circleci/config.yml\u003c/a\u003e\nto reflect your repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e environment:\n\n # The GitHub username / reponame that the container will be submit to\n - REGISTRY_BASE: singularityhub/registry-org\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should only need to do this once. The example provided here uses\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Google Storage\u003c/h2\u003e\u003ca id=\"user-content-2-google-storage\" class=\"anchor\" aria-label=\"Permalink: 2. Google Storage\" href=\"#2-google-storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will be interacting with Google Storage via the \u003ca href=\"https://www.github.com/singularityhub/sregistry\"\u003esregistry\u003c/a\u003e\ncommand line client.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequired environment variables\u003c/h2\u003e\u003ca id=\"user-content-required-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Required environment variables\" href=\"#required-environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a Google Project and \u003ca href=\"https://cloud.google.com/sdk/docs/authorizing#authorizing_with_a_service_account\" rel=\"nofollow\"\u003ea service account\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Download the Service Account Key\u003c/h3\u003e\u003ca id=\"user-content-1-download-the-service-account-key\" class=\"anchor\" aria-label=\"Permalink: 1. Download the Service Account Key\" href=\"#1-download-the-service-account-key\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first download a service account key from the \u003ca href=\"https://console.cloud.google.com/iam-admin/serviceaccounts?_ga=2.213389911.-231410963.1512057989\" rel=\"nofollow\"\u003eservice accounts page\u003c/a\u003e. For the roles, add an admin for Google\nStorage (to store your container), along with Storage Object Admin and Google Build Admin.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/service-account.png\"\u003e\u003cimg src=\"img/service-account.png\" alt=\"img/service-account.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOnce you add the roles, you \u003cem\u003edo not need to add users\u003c/em\u003e to the account. You can next download\nthe service account key to your local machine, and move it to the repository folder.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/create-key.png\"\u003e\u003cimg src=\"img/create-key.png\" alt=\"img/create-key.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that the .gitignore includes *.json so it won\u0027t be added to your project!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Circle CI Secrets\u003c/h3\u003e\u003ca id=\"user-content-2-circle-ci-secrets\" class=\"anchor\" aria-label=\"Permalink: 2. Circle CI Secrets\" href=\"#2-circle-ci-secrets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have the \u003ccode\u003e\u0026lt;project-id\u0026gt;-\u0026lt;number\u0026gt;.json\u003c/code\u003e in the present working directory,\nyou can add the entire thing to your project as an encrypted environment variable.\nHere is how to copy paste the string from your terminal:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eproject-id\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdd the text output from the above to an environment variable\ncalled \u003ccode\u003eGOOGLE_APPLICATION_CREDENTIALS\u003c/code\u003e along with the following (all project secrets):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGOOGLE_COMPUTE_ZONE: the zone you want your compute builder to run in.\u003c/li\u003e\n\u003cli\u003eSREGISTRY_GOOGLE_PROJECT: the id of your project, easiest to find in the Google Project console url.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptionally, export a name for your bucket, \u003ccode\u003eSREGISTRY_GOOGLE_STORAGE_BUCKET\u003c/code\u003e\n(it will be created if it doesn\u0027t exist). It will default to your project id with sregistry- as a prefix.\nDon\u0027t forget to add the machine user to the repository, and then add its credential.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLocal Usage\u003c/h2\u003e\u003ca id=\"user-content-local-usage\" class=\"anchor\" aria-label=\"Permalink: Local Usage\" href=\"#local-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to build the container locally:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e git clone https://www.github.com/singularityhub/nginx\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e nginx\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFirst, let\u0027s talk about how we would run this image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo singularity build nginx.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will see the image buildin, including downloading of Docker layers, installation of nginx. Now let\u0027s run it, and we start a webserver:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./nginx.sif\nServing HTTP on 0.0.0.0 port 9999 ...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"nginx-basic.png\"\u003e\u003cimg src=\"nginx-basic.png\" alt=\"nginx-basic.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWelp, that was easy!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does it work?\u003c/h2\u003e\u003ca id=\"user-content-how-does-it-work\" class=\"anchor\" aria-label=\"Permalink: How does it work?\" href=\"#how-does-it-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow is this working? Let\u0027s look at the spec file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Bootstrap: docker\n From: ubuntu:16.04\n\n %runscript\n\n cd /data\n exec python3 -m http.server 9999\n\n %post\n\n mkdir /data\n echo \"\u0026lt;h2\u0026gt;Hello World!\u0026lt;/h2\u0026gt;\" \u0026gt;\u0026gt; /data/index.html\n apt-get update\n apt-get -y install python3 \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Header\u003c/h3\u003e\u003ca id=\"user-content-the-header\" class=\"anchor\" aria-label=\"Permalink: The Header\" href=\"#the-header\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe First line \u003ccode\u003ebootstrap\u003c/code\u003e says that we are going to bootstrap a \u003ccode\u003edocker\u003c/code\u003e image, specifically using the (\u003ccode\u003eFrom\u003c/code\u003e field) \u003ccode\u003eubuntu:16.04\u003c/code\u003e. You couldn\u0027t choose another distribution that you like, I just happen to like Debian.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e%post\u003c/h3\u003e\u003ca id=\"user-content-post\" class=\"anchor\" aria-label=\"Permalink: %post\" href=\"#post\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePost is the section where you put commands you want to run once to create your image. This includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstallation of software\u003c/li\u003e\n\u003cli\u003ecreation of files or folders\u003c/li\u003e\n\u003cli\u003emoving data, files into the container image\u003c/li\u003e\n\u003cli\u003eanalysis things\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe list is pretty obvious, but what about the last one, analysis things? Yes, let\u0027s say that we had a script thing that we wanted to run just once to produce a result that would live in the container. In this case, we would have that thing run in %post, and then give some interactive access to the result via the \u003ccode\u003e%runscript\u003c/code\u003e. In the case that you want your image to be more like a function and run the analysis (for example, if you want your container to take input arguments, run something, and deliver a result), then this command should go in the \u003ccode\u003e%runscript\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIn our case, since we are going to serve a simple web-based thing, we create a directory to work with (\u003ccode\u003e/data\u003c/code\u003e is easy to remember), write a terribly formatted \u003ccode\u003eindex.html\u003c/code\u003e there (for those that aren\u0027t web focused, a web server by default will render a file called \u003ccode\u003eindex.html\u003c/code\u003e from a root folder). We then install python, because it has a nice command for bringing up a quick web server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e%runscript\u003c/h3\u003e\u003ca id=\"user-content-runscript\" class=\"anchor\" aria-label=\"Permalink: %runscript\" href=\"#runscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e%runscript\u003c/code\u003e is the thing executed when we run our container. For this example, we basically change directories to data, and then use python to start up a little server on port 9999 to serve that folder. Anything in that folder will then be available to our local machine on port 9999, meaning the address \u003ccode\u003elocalhost:9999\u003c/code\u003e or \u003ccode\u003e127.0.0.1:9999\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample Use Cases\u003c/h2\u003e\u003ca id=\"user-content-example-use-cases\" class=\"anchor\" aria-label=\"Permalink: Example Use Cases\" href=\"#example-use-cases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have a folder locally with some static html files or other that you want to serve, you can map a directory to data when running the container. For example, let\u0027s map the $PWD to the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B .:/data nginx-basic.img \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e.\u003c/code\u003e is a stand in for the present working directory, I could have also done:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B $PWD:/data nginx-basic.img \nsingularity run -B /path/to/singularity-web/nginx-basic:/data nginx-basic.img \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that binding the directory at runtime WILL map your specified place to the directory (and not the file we saved there before) but it does NOT overwrite the file saved to the image. In other words, if we run the image again without binding, we see the original \"Hello World!\"\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1583274107.0 + "subscribers_count": 3, + "topics": [ + "singularityhub", + "registry-template", + "static-registry", + "singularity" + ], + "updated_at": 1698809323.0 }, { "data_format": 2, - "description": "Docker images", + "description": "Singularity image for biocontainers bcftools", "filenames": [ - "images/sc_qc_cluster/Singularity.sc_qc_cluster" + "Singularity" ], - "full_name": "letaylor/docker-letaylor-travis", + "full_name": "researchapps/bcftools", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker-letaylor\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-letaylor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edocker-letaylor\u003c/h1\u003e\n\u003cp\u003eThis repo contains Docker images that are automatically built using Travis CI. It is not designed to scale to many images as each image is updated if any one image changes.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-automatically-push-images-to-docker-hub-using-travis-ci\" class=\"anchor\" aria-hidden=\"true\" href=\"#automatically-push-images-to-docker-hub-using-travis-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically push images to Docker Hub using Travis CI\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-edit-config-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-edit-config-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Edit config files\u003c/h2\u003e\n\u003cp\u003eEdit the following files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e.travis.yml\u003c/code\u003e : alter \u003ccode\u003e$IMAGE_NAME\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-give-travis-ci-access-to-upload-to-docker-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-give-travis-ci-access-to-upload-to-docker-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Give Travis CI access to upload to Docker Hub\u003c/h2\u003e\n\u003cp\u003eStore both \u003ccode\u003e$DOCKER_PASSWORD\u003c/code\u003e and \u003ccode\u003e$DOCKER_USERNAME\u003c/code\u003e securely in on Travis CI. These are used for authentication.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLogin to the account you want Travis to use to upload on \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eClick on your username on the top left and go to \u0027Account Settings\u0027.\u003c/li\u003e\n\u003cli\u003eOn the left hand panel, go to \u0027Security\u0027 and enter your password as requested.\u003c/li\u003e\n\u003cli\u003eNow we\u0027ll create an API token. Name it Travis CI.\u003c/li\u003e\n\u003cli\u003eCreate the token and copy it.\u003c/li\u003e\n\u003cli\u003eLogin to your account on \u003ca href=\"https://travis-ci.org\" rel=\"nofollow\"\u003etravis-ci.org\u003c/a\u003e and go to the repository that you want to add this automatic functionality to.\u003c/li\u003e\n\u003cli\u003eOn the right next to \u0027More options\u0027 go to \u0027Settings\u0027 in the hamburger menu.\u003c/li\u003e\n\u003cli\u003eAdd an environment variable with the name \u003ccode\u003eDOCKER_PASSWORD\u003c/code\u003e and give it the value of the API token that you copied from \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eAdd an environment variable with the name \u003ccode\u003eDOCKER_USERNAME\u003c/code\u003e and give it your \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003ehub.docker.com\u003c/a\u003e user name.\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSamtools\u003c/h1\u003e\u003ca id=\"user-content-samtools\" class=\"anchor\" aria-label=\"Permalink: Samtools\" href=\"#samtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity image to deploy samtools.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 samtools.img\nsudo singularity bootstrap samtools.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the samtools runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./samtools.img\n\nProgram: samtools (Tools for alignments in the SAM format)\nVersion: 1.3.1 (using htslib 1.3.1)\n\nUsage: samtools \u0026lt;command\u0026gt; [options]\n\nCommands:\n -- Indexing\n dict create a sequence dictionary file\n faidx index/extract FASTA\n index index alignment\n\n -- Editing\n calmd recalculate MD/NM tags and \u0027=\u0027 bases\n fixmate fix mate information\n reheader replace BAM header\n rmdup remove PCR duplicates\n targetcut cut fosmid regions (for fosmid pool only)\n addreplacerg adds or replaces RG tags\n\n -- File operations\n collate shuffle and group alignments by name\n cat concatenate BAMs\n merge merge sorted alignments\n mpileup multi-way pileup\n sort sort alignment file\n split splits a file by read group\n quickcheck quickly check if SAM/BAM/CRAM file appears intact\n fastq converts a BAM to a FASTQ\n fasta converts a BAM to a FASTA\n\n -- Statistics\n bedcov read depth per BED region\n depth compute the depth\n flagstat simple stats\n idxstats BAM index stats\n phase phase heterozygotes\n stats generate stats (former bamcheck)\n\n -- Viewing\n flags explain BAM flags\n tview text alignment viewer\n view SAM\u0026lt;-\u0026gt;BAM\u0026lt;-\u0026gt;CRAM conversion\n depad convert padded BAM to unpadded BAM\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1653062770.0 + "updated_at": 1484519232.0 }, { "data_format": 2, - "description": "This contains the latest docker and singularity images", + "description": "Pipeline for miRNA alignment and quantification", "filenames": [ - "Singularity_Ubuntu_18_04_Cuda_11_0", - "Singularity_Ubuntu_18_04_Cuda_11_1", - "Singularity_Ubuntu_18_04_Cuda_10_2", - "Singularity_Ubuntu_20_04_Cuda_11_1", - "Singularity_Ubuntu_16_04" + "Singularity" ], - "full_name": "shreyaskamathkm/Cluster_Images", + "full_name": "marchoeppner/miRNApipe", "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emiRNApipe\u003c/h1\u003e\u003ca id=\"user-content-mirnapipe\" class=\"anchor\" aria-label=\"Permalink: miRNApipe\" href=\"#mirnapipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline for miRNA alignment and quantification\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation about the pipeline can be found in the \u003ccode\u003edocs/\u003c/code\u003e directory or under the links below:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1635787927.0 + "updated_at": 1554805649.0 }, { "data_format": 2, - "description": "The purpose of this project is to map Oxford Nanopore Sequencing data down to the species level", + "description": null, "filenames": [ - "setup/Singularity" + "Singularity" ], - "full_name": "JoshLoecker/MAPT", + "full_name": "mmirko/singularitytest", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mapt\" class=\"anchor\" aria-hidden=\"true\" href=\"#mapt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMAPT\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/JoshLoecker/MAPT/wiki\"\u003ePlease view the Wiki\u003c/a\u003e for more information.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport\u003c/h3\u003e\n\u003cp\u003eIf you need help, have questions, or have feature ideas please \u003ca href=\"https://github.com/JoshLoecker/MAPT/issues\"\u003eopen a new issue\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularitytest\u003c/h1\u003e\u003ca id=\"user-content-singularitytest\" class=\"anchor\" aria-label=\"Permalink: singularitytest\" href=\"#singularitytest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1649438683.0 + "updated_at": 1605257877.0 }, { "data_format": 2, - "description": "Singularity recipe files for entrez-direct (https://ftp.ncbi.nlm.nih.gov/entrez/entrezdirect/)", + "description": "Singularity image for the EEMT project", "filenames": [ - "Singularity", - "Singularity.13.8.20200819" + "Singularity" ], - "full_name": "powerPlant/entrez-direct-srf", + "full_name": "rynge/eemt-singularity", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for Entrez Direct: E-utilities on the Unix Command Line to provide access to the NCBI\u0027s suite of interconnected databases\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eeemt-singularity\u003c/h1\u003e\u003ca id=\"user-content-eemt-singularity\" class=\"anchor\" aria-label=\"Permalink: eemt-singularity\" href=\"#eemt-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image for the EEMT project\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1598244762.0 + "updated_at": 1491087384.0 }, { "data_format": 2, - "description": "Singularity recipe files for aws-cli (https://github.com/aws/aws-cli)", + "description": "Neuroglia-core (FSL,AFNI,ANTS) + minctools", "filenames": [ + "Singularity.v1.0.1a", "Singularity", - "Singularity.2.0.43" + "Singularity.v1.0.0", + "Singularity.v1.0.1b" ], - "full_name": "powerPlant/aws-cli-srf", + "full_name": "khanlab/neuroglia-core-minc", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the AWS CLI v2 tool\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1598486009.0 + "updated_at": 1541127620.0 }, { "data_format": 2, - "description": "An implementation for solving 3SAT (Exact Cover) using the Quantum Approximate Optimization Algorithm", + "description": "XCrySDen in a Singularity container", "filenames": [ - "SingularityFile.def" + "Singularity.1.6.2", + "Singularity" ], - "full_name": "vivekkatial/qaoa-three-sat", + "full_name": "OSC/sa_singularity_xcrysden", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-qaoa-3sat--\" class=\"anchor\" aria-hidden=\"true\" href=\"#qaoa-3sat--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQAOA 3SAT \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4beb7225857c50a9391b71fbe998bc23c33b4d87ee15e3da9b7c1b7dfdc67a11/68747470733a2f2f7472617669732d63692e636f6d2f766976656b6b617469616c2f71616f612d74687265652d7361742e7376673f6272616e63683d6d6173746572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4beb7225857c50a9391b71fbe998bc23c33b4d87ee15e3da9b7c1b7dfdc67a11/68747470733a2f2f7472617669732d63692e636f6d2f766976656b6b617469616c2f71616f612d74687265652d7361742e7376673f6272616e63683d6d6173746572\" alt=\"\" data-canonical-src=\"https://travis-ci.com/vivekkatial/qaoa-three-sat.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://qaoa-three-sat.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/253d508d956ec9315fd5509c8d9cb82640904ab96c15672f2c65c9ec5c2de390/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f71616f612d74687265652d7361742f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/qaoa-three-sat/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eAn implementation for solving 3SAT (Exact Cover) using the Quantum Approximate Optimization Algorithm\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity XCrySDen\u003c/h1\u003e\u003ca id=\"user-content-singularity-xcrysden\" class=\"anchor\" aria-label=\"Permalink: Singularity XCrySDen\" href=\"#singularity-xcrysden\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4445\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7edda6b40df66cdf6d87ee014ce8a73af8830d12f325162978d3b72836ea332d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"http://www.xcrysden.org/Download.html\" rel=\"nofollow\"\u003eXCrysDen\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003excrysden.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build xcrysden.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull xcrysden.sif shub://OSC/sa_singularity_xcrysden\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart XCrysDen\u003c/h3\u003e\u003ca id=\"user-content-start-xcrysden\" class=\"anchor\" aria-label=\"Permalink: Start XCrysDen\" href=\"#start-xcrysden\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eXCrysDen is started using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run xcrysden.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as a native command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./xcrysden.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 8, "topics": [], - "updated_at": 1607596883.0 + "updated_at": 1592244254.0 }, { "data_format": 2, - "description": "launch the C++ IDE Anjuta from a Singularity container", + "description": "Singularity Hub test", "filenames": [ "Singularity" ], - "full_name": "d-w-moore/anjuta_via_singularity", + "full_name": "kav2k/fortune", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-anjuta-ide-via-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#anjuta-ide-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnjuta IDE via Singularity\u003c/h1\u003e\n\u003cp\u003eThe container includes libraries for building and debugging C++\nprograms with GCC 9, with C++17 support and Boost libraries. C/Xlib\napplications are also supported.\u003c/p\u003e\n\u003cp\u003eTo build the container under Singularity ~2.5.1 :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eget \u003ca href=\"http://sylabs.io\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e . If you\u0027re on Ubuntu/Debian,\nthe \u003ca href=\"https://neuro.debian.net\" rel=\"nofollow\"\u003eNeuroDebian\u003c/a\u003e repo can offer the\nmost up-to-date Singularity packages\u003c/li\u003e\n\u003cli\u003ein a local copy of this repo, use the build command:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build anjuta.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThe IDE can be lauched by running anjuta.simg as an executable\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./anjuta.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor via the singularity application\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run anjuta.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo alter an existing image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build --sandbox anjuta anjuta.simg\n$ sudo singularity shell --writable anjuta\nSingularity\u0026gt; apt update; apt install {custom-packages...}\nSingularity\u0026gt; exit\n$ sudo singularity build anjuta_updated.simg anjuta\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003efortune\u003c/h1\u003e\u003ca id=\"user-content-fortune\" class=\"anchor\" aria-label=\"Permalink: fortune\" href=\"#fortune\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Hub test\u003c/p\u003e\n\u003cp\u003eFortune Teller\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1600000384.0 + "updated_at": 1505266397.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image containing Freesurfer and HCP Connectome Workbench", "filenames": [ - "Singularity", - "other_images/Singularity.custom_openspiel" + "Singularity" ], - "full_name": "buregab/openspiel_singularity", + "full_name": "chidiugonna/nklab-neuro-HCP", "latest_release": null, - "readme": "\u003cp\u003eFor building openspiel singularity containers.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity image containing Freesurfer and HCP Conncectome Workbench\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-containing-freesurfer-and-hcp-conncectome-workbench\" class=\"anchor\" aria-label=\"Permalink: Singularity image containing Freesurfer and HCP Conncectome Workbench\" href=\"#singularity-image-containing-freesurfer-and-hcp-conncectome-workbench\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity image will be about 6GB when built using Singularity 2.4.2. It installs the latest development version of freesurfer and HCP Connectome Workbench\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Singularity Image\u003c/h2\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Build Singularity Image\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have at least singularity 2.4 installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-HCP\u003c/code\u003edirectory and check that you have a Singularity definiton file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow simply build the image as \u003ccode\u003esudo singularity build nklab-neuro-HCP.simg Singularity\u003c/code\u003e - note that the image name is assumed to be \u003ccode\u003enklab-neuro-HCP.simg\u003c/code\u003e but this can be changed to a more convenient label.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Singularity Image\u003c/h2\u003e\u003ca id=\"user-content-run-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Run Singularity Image\" href=\"#run-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can now run commands by simply appending them to the end of \u003ccode\u003esingularity run nklab-neuro-HCP.simg\u003c/code\u003e So for example to run \u003ccode\u003ewb_command\u003c/code\u003e simply enter \u003ccode\u003esingularity run nklab-neuro-tools.HCP wb_command ....\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1604380357.0 + "updated_at": 1533339170.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container providing OpenBLAS with OpenMP support on Alpine Linux", "filenames": [ "Singularity" ], - "full_name": "tpall/htseq-paper-singularity", + "full_name": "twesterhout/openblas-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-htseq-paper-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#htseq-paper-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehtseq-paper-singularity\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eopenblas-singularity\u003c/h1\u003e\u003ca id=\"user-content-openblas-singularity\" class=\"anchor\" aria-label=\"Permalink: openblas-singularity\" href=\"#openblas-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container providing OpenBLAS with OpenMP and 64-bit ints on Alpine\nLinux. Pre-built container is available for download from\n\u003ca href=\"https://cloud.sylabs.io/library/_container/6026ee4e1e573cd09be5c186\" rel=\"nofollow\"\u003eSingularity Container\nLibrary\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1604657436.0 + "updated_at": 1616752652.0 }, { "data_format": 2, - "description": null, + "description": "EL6/7 singularity images", "filenames": [ "Singularity" ], - "full_name": "baxpr/makerois-PMAT", - "latest_release": "v1.0.13", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-create-study-specific-roi-image-in-mni-space\" class=\"anchor\" aria-hidden=\"true\" href=\"#create-study-specific-roi-image-in-mni-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate study-specific ROI image in MNI space\u003c/h1\u003e\n\u003cp\u003ePMAT resting state connectivity study.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs:\u003c/h2\u003e\n\u003cp\u003eAll should be matched to the same T1 image.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eT1 image in atlas space (typically BIAS_NORM resource of cat12 assessor)\u003c/li\u003e\n\u003cli\u003eDeformation from T1 subject space to atlas space (typically DEF_FWD resource of cat12 assessor)\u003c/li\u003e\n\u003cli\u003eSUBJECT directory of Freesurfer output (typically SUBJECT resource of freesurfer_dev assessor)\u003c/li\u003e\n\u003cli\u003eTemporal lobe segmentation (typically SEG resource of Temporal_Lobe assessor)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003erois_PMAT.nii.gz Region of interest image\nrois_PMAT-labels.csv Region labels and volumes\nmakerois-PMAT.pdf Visual report of final ROI image\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-regions-of-interest\" class=\"anchor\" aria-hidden=\"true\" href=\"#regions-of-interest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRegions of interest\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-spheres-atlas-space\" class=\"anchor\" aria-hidden=\"true\" href=\"#spheres-atlas-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpheres (atlas space)\u003c/h3\u003e\n\u003cp\u003eSource: \u003cem\u003eLibby LA, Ekstrom AD, Ragland JD, Ranganath C. Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by high-resolution functional imaging. J Neurosci. 2012;32(19):6550-6560. doi:10.1523/JNEUROSCI.3711-11.2012\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMethod: \u003cem\u003eSchr\u00f6der TN, Haak K V., Jimenez NIZ, et al. Functional topography of the human entorhinal cortex. Elife. 2015;4(October 2016):1-17. doi:10.7554/eLife.06738\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-entorhinal-cortex-atlas-space\" class=\"anchor\" aria-hidden=\"true\" href=\"#entorhinal-cortex-atlas-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEntorhinal cortex (atlas space)\u003c/h3\u003e\n\u003cp\u003eAnterior lateral and posterior medial sections. Source and method: \u003cem\u003eSchr\u00f6der TN, Haak K V., Jimenez NIZ, et al. Functional topography of the human entorhinal cortex. Elife. 2015;4(October 2016):1-17. doi:10.7554/eLife.06738\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-temporal-lobe-subject-space-warped\" class=\"anchor\" aria-hidden=\"true\" href=\"#temporal-lobe-subject-space-warped\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTemporal lobe (Subject space, warped)\u003c/h3\u003e\n\u003cp\u003eHead for anterior hippocampus; body and tail combined for posterior hippocampus. Method: \u003cem\u003ePlassard AJ, McHugo M, Heckers S, Landman BA. Multi-Scale Hippocampal Parcellation Improves Atlas-Based Segmentation Accuracy. Proc SPIE Int Soc Opt Eng. 2017 Feb 11;10133:101332D. doi: 10.1117/12.2254425. Epub 2017 Feb 24. PMID: 28781411; PMCID: PMC5544133.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-parahippocampal-perirhinal-subject-space-warped\" class=\"anchor\" aria-hidden=\"true\" href=\"#parahippocampal-perirhinal-subject-space-warped\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParahippocampal, perirhinal (Subject space, warped)\u003c/h3\u003e\n\u003cp\u003eGenerated by Freesurfer 6. Parahippocampal (1016,2016) and perirhinal (surface patch resampled to volume, overlap with parahippocampus was assigned to perirhinal). Method: \u003cem\u003eBruce Fischl, Andre van der Kouwe, Christophe Destrieux, Eric Halgren, Florent Segonne, David H. Salat, Evelina Busa, Larry J. Seidman, Jill Goldstein, David Kennedy, Verne Caviness, Nikos Makris, Bruce Rosen, and Anders M. Dale. Automatically Parcellating the Human Cerebral Cortex. Cerebral Cortex January 2004; 14:11-22.\u003c/em\u003e\u003c/p\u003e\n", + "full_name": "khurtado/singularity-images", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-images\u003c/h1\u003e\u003ca id=\"user-content-singularity-images\" class=\"anchor\" aria-label=\"Permalink: singularity-images\" href=\"#singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1607988459.0 + "updated_at": 1528125811.0 }, { "data_format": 2, - "description": "GitHub repo for storing scripts related to simulation using JModelica. The initial focus is on simulation in HPC environments.", + "description": "Singularity", "filenames": [ - "Singularity_Recipes/Singularity_Recipe_Py2_Compilation_Simulation", - "Singularity_Recipes/Singularity_Recipe_Py3_Simulation" + "Singularity" ], - "full_name": "urbanopt/JModelica_simulation", + "full_name": "lalet/boutiques2singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-jmodelica-simulation\" class=\"anchor\" aria-hidden=\"true\" href=\"#jmodelica-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJModelica Simulation\u003c/h1\u003e\n\u003cp\u003eGitHub repo for storing scripts related to simulation of Modelica models using JModelica. The initial focus is on simulation in HPC environments.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipes\u003c/h1\u003e\n\u003cp\u003eRecipes for building Singularity containers for compilation and simulation of Modelica models using PyModelica and PyFMI. Note that the recipe that would support compilation and simulation is for use with Python2 only, while a separate recipe supports simulation in Python3.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eboutiques2singularity\u003c/h1\u003e\u003ca id=\"user-content-boutiques2singularity\" class=\"anchor\" aria-label=\"Permalink: boutiques2singularity\" href=\"#boutiques2singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1608681086.0 + "updated_at": 1507997466.0 }, { "data_format": 2, - "description": null, + "description": "Python, R, Tensorflow, etc. Put a container on it!", "filenames": [ "Singularity" ], - "full_name": "pchengi/cmorfixer_env", + "full_name": "greyspectrum/conda-tf", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-environment-for-cmor-fixer\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-for-cmor-fixer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment for cmor-fixer\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA tool to create an environment to allow easy use of the \u003ca href=\"https://github.com/EC-Earth/cmor-fixer\"\u003ecmor-fixer tool\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecmorfixer_env is a singularity container which comes with preinstalled miniconda3\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h1\u003e\n\u003cp\u003eYou need the singularity program installed. Follow the instructions here, to install singularity on your machine.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/install-linux\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-to-download-a-prebuilt-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-download-a-prebuilt-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo download a prebuilt singularity image:\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eIf you\u0027d like to use a prebuilt image, you could download from the link below; if you\u0027d rather build the container yourself, follow the build instructing in the To build section.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://esg-dn2.nsc.liu.se/virtualtestbed/cmorfixerenv.simg\" rel=\"nofollow\"\u003eLink to prebuilt image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-to-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build cmorfixerenv.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-to-initialize-container-and-optionally-mount-external-filesystems\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-initialize-container-and-optionally-mount-external-filesystems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo initialize container (and optionally mount external filesystems)\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eIf you don\u0027t have to mount any non-root filesystems, you could start the container like this:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell cmorfixerenv.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf you don\u0027t see on the container the filesystem which is accessible on the host machine, you could try this, and once inside the container, you\u0027ll be able to see the filesystem mounted on /mnt.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell --bind \u0026lt;path to filesystem you want mounted on the container\u0026gt;:/mnt cmorfixerenv.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eInside the container, do the following\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esource /etc/bashrc\nactivateminiconda3\nconda activate cmorfixer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eExecute cmorfixer (present in /opt/cmor_fixer/cmor-fixer/cmor-fixer.py, in the container)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd /root\nscript -c \u0027/opt/cmor_fixer/cmor-fixer/cmor-fixer.py --verbose --forceid --olist --npp 1 --dry /mnt/CMIP6/ScenarioMIP/EC-Earth-Consortium/EC-Earth3/ssp126/\u0027 scriptout_cmorfix_dryrun\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econda-tf\u003c/h1\u003e\u003ca id=\"user-content-conda-tf\" class=\"anchor\" aria-label=\"Permalink: conda-tf\" href=\"#conda-tf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Dockerfile will build a containerized conda environment with Python, R, and Tensorflow, among other packages.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding\u003c/h2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run this container on a high performance computing cluster, you will need to build it locally, as a Singularity image.\u003c/p\u003e\n\u003cp\u003eFirst, \u003ca href=\"https://sylabs.io/guides/3.2/user-guide/installation.html\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e on a system where you have root privileges.\u003c/p\u003e\n\u003cp\u003eThen, install \u003ca href=\"https://docs.docker.com/install/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, build a Singularity container from the Dockerfile in this repo using \u003ca href=\"https://github.com/singularityware/docker2singularity\"\u003edocker2singularity\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1611252471.0 + "updated_at": 1562875154.0 }, { "data_format": 2, - "description": "Singularity recipe files for ora (https://github.com/pseudogene/ora)", + "description": "Mumax3 application", "filenames": [ - "Singularity", - "Singularity.2.0.0" + "Singularity" ], - "full_name": "powerPlant/ora-srf", + "full_name": "SupercomputingWales/scw_ood_mumax3", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for the Bio::ORA, a featherweight object for identifying mammalian olfactory receptor genes.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/pseudogene/ora\"\u003ehttps://github.com/pseudogene/ora\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBatch Connect - Mumax3\u003c/h1\u003e\u003ca id=\"user-content-batch-connect---mumax3\" class=\"anchor\" aria-label=\"Permalink: Batch Connect - Mumax3\" href=\"#batch-connect---mumax3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLaunch Mumax3 server.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1615862307.0 + "updated_at": 1705933791.0 }, { "data_format": 2, - "description": null, + "description": "the definition of the singularity avogadro container", "filenames": [ - "Singularity.v2.0.0" + "Singularity" ], - "full_name": "baxpr/fsthalconnMNI-public", + "full_name": "mherkazandjian/singularity_avogadro", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fsthalconnmni-public\" class=\"anchor\" aria-hidden=\"true\" href=\"#fsthalconnmni-public\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efsthalconnMNI-public\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE: For this public version of the repository, the ROI images are not included due to the restrictions on the Morel set, meaning the code will not actually run.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInputs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessed fMRI data from \u003ca href=\"https://github.com/baxpr/connprep\"\u003ehttps://github.com/baxpr/connprep\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eThalamus regions of interest from \u003ca href=\"https://github.com/baxpr/freesurfer-singularity\"\u003ehttps://github.com/baxpr/freesurfer-singularity\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIncluded ROIs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\"Morel\" thalamic sub-regions from Krauth A, Blanc R, Poveda A, Jeanmonod D, Morel A, Sz\u00e9kely G. A mean three-dimensional atlas of the human thalamus: generation from multiple histological data. Neuroimage. 2010;49(3):2053\u20132062. doi:10.1016/j.neuroimage.2009.10.042. These images are copyright University of Zurich and ETH Zurich, Axel Krauth, Re\u0301mi Blanc, Alejandra Poveda, Daniel Jeanmonod, Anne Morel, Ga\u0301bor Sze\u0301kely. They may not be redistributed, or used for other than research purposes in academic institutions (see src/rois/ACDMY/Agreement.pdf).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\"ABIDE\" regions from Woodward ND, Giraldo-Chica M, Rogers B, Cascio CJ. Thalamocortical dysconnectivity in autism spectrum disorder: An analysis of the Autism Brain Imaging Data Exchange. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2(1):76\u201384. doi:10.1016/j.bpsc.2016.09.002\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNetwork maps from Yeo et al 2011 (\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174820/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174820/\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOutputs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSeed connectivity maps and matrices for all ROIs/networks specified in the roiinfo_csv file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eProcess:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eROI resampling. Freesurfer ROIs (all .mgz in the roiinfo_csv file) are already in native space aligned with the subject T1 and fMRI, so are only converted to nifti format. MNI space ROIs (all .nii.gz in roiinfo_csv are assumed to be MNI space) are warped back to native space in the T1 geometry using the supplied warp invdef_niigz.\u003c/li\u003e\n\u003cli\u003eFor each native space ROI image, the native space fMRIs (removegm_niigz and keepgm_niigz) are resampled to the ROI image geometry, and mean ROI signals are extracted.\u003c/li\u003e\n\u003cli\u003eConnectivity matrices are computed for the mean ROI signals for both the removegm and keepgm data.\u003c/li\u003e\n\u003cli\u003eThe mean ROI signals are used with the four filtered fMRI image sets (removegm_niigz, keepgm_niigz, wremovegm_niigz, wkeepgm_niigz) to compute connectivity maps for each of the four.\u003c/li\u003e\n\u003cli\u003eThe connectivity maps are smoothed by the provided fwhm.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1616091420.0 + "updated_at": 1541811097.0 }, { "data_format": 2, - "description": "Singularity recipe files for sniffles (https://github.com/fritzsedlazeck/Sniffles)", + "description": "This repo contains a simgularity recipe for setting-up and running Knet easily.", "filenames": [ - "Singularity", - "Singularity.1.0.12a" + "Singularity" ], - "full_name": "powerPlant/sniffles-srf", + "full_name": "KnetML/singularity-images", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for Sniffles, a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore).\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Image for Knet\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-for-knet\" class=\"anchor\" aria-label=\"Permalink: Singularity Image for Knet\" href=\"#singularity-image-for-knet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/686\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUSAGE\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: USAGE\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePull the base image\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name knet.simg shub://KnetML/singularity-images:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNot to loose your changes, you should create an overlay image.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity image.create --size 2048 overlay.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf you need more space, you can expand this image later\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity image.expand overlay.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eConnect to the container with the overlay image.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --overlay overlay.simg Knet.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to enable the gpu usage, you can bind your cuda and cudnn paths.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind \u0026lt;your cuda path\u0026gt;:/usr/local/cuda,\u0026lt;your cudnn path\u0026gt;:/opt/cudnn --overlay overlay.simg --nv Knet.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBuild Knet\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ejulia -e \u0027Pkg.build(\"Knet\")\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNow you can work under the \u003cem\u003e/workdir\u003c/em\u003e folder\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the image\u003c/h3\u003e\u003ca id=\"user-content-running-the-image\" class=\"anchor\" aria-label=\"Permalink: Running the image\" href=\"#running-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can also run the image directly. It starts an ipython notebook server. You can connect to this server on localhost:8888.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind \u0026lt;your cuda path\u0026gt;:/usr/local/cuda,\u0026lt;your cudnn path\u0026gt;:/opt/cudnn --overlay overlay.simg --nv Knet.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage on the kuacc cluster\u003c/h3\u003e\u003ca id=\"user-content-usage-on-the-kuacc-cluster\" class=\"anchor\" aria-label=\"Permalink: Usage on the kuacc cluster\" href=\"#usage-on-the-kuacc-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLoad necessary modules\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emodule load cuda/9.0\nmodule load cudnn/7.0.4/cuda-9.0\nmodule load singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSet SINGULARITY_CACHEDIR as your scratch\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITY_CACHEDIR=/scratch/users/username\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eOn a compute node with gpu\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind /usr/local/cuda-9.0/:/usr/local/cuda,/kuacc/apps/cudnn/v7.0.4_CUDA_9.0:/opt/cudnn --overlay overlay.img --nv Knet.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDEPENDENCIES\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: DEPENDENCIES\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"singularity.lbl.gov\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eCuda drivers and libraries (optional)\u003c/li\u003e\n\u003cli\u003eCudnn library (optinal)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1610677399.0 + "subscribers_count": 4, + "topics": [ + "singularity", + "knet", + "julia" + ], + "updated_at": 1549274984.0 }, { "data_format": 2, - "description": "Singularity Containers Developed for the University of Oslo", + "description": "Singularity container for meningotype", "filenames": [ - "trial/Singularity", - "conda/Singularity", - "conda/Singularity.conda", - "conda/Singularity.def" + "Singularity" ], - "full_name": "Economax/SingularityCo", + "full_name": "phgenomics-singularity/meningotype", "latest_release": null, "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1616284321.0 + "updated_at": 1553641042.0 }, { "data_format": 2, - "description": null, + "description": "Eddy -- a tool for correcting eddy currents and movements in diffusion data", "filenames": [ "Singularity" ], - "full_name": "fcola000/shub_test", + "full_name": "lalet/Eddy", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-shub_test\" class=\"anchor\" aria-hidden=\"true\" href=\"#shub_test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eshub_test\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEddy\u003c/h1\u003e\u003ca id=\"user-content-eddy\" class=\"anchor\" aria-label=\"Permalink: Eddy\" href=\"#eddy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEddy -- a tool for correcting eddy currents and movements in diffusion data\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1616356054.0 + "updated_at": 1533670576.0 }, { "data_format": 2, @@ -20569,97 +20169,102 @@ var data = "filenames": [ "Singularity" ], - "full_name": "fcola000/test", + "full_name": "ddbj/singularity-apache2-igvwebapp", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etest\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-apache2-igvwebapp\u003c/h1\u003e\u003ca id=\"user-content-singularity-apache2-igvwebapp\" class=\"anchor\" aria-label=\"Permalink: singularity-apache2-igvwebapp\" href=\"#singularity-apache2-igvwebapp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eigv-webapp\u3068apache2\u3092\u5b9f\u884c\u3059\u308bsingularity instance\u3092\u8d77\u52d5\u3059\u308b\u305f\u3081\u306e\u30d5\u30a1\u30a4\u30eb\u4e00\u5f0f\u3067\u3059\u3002\nsingularity image\u306f\u521d\u56de\u8d77\u52d5\u6642\u306bSylabs Cloud\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u521d\u671f\u8a2d\u5b9a\u003c/h2\u003e\u003ca id=\"user-content-\u521d\u671f\u8a2d\u5b9a\" class=\"anchor\" aria-label=\"Permalink: \u521d\u671f\u8a2d\u5b9a\" href=\"#\u521d\u671f\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ehttpd.conf\u003c/h3\u003e\u003ca id=\"user-content-httpdconf\" class=\"anchor\" aria-label=\"Permalink: httpd.conf\" href=\"#httpdconf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eServerRoot \"/usr/local/apache2\"\n\nListen 38080\nUser user1\nGroup group1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003euser1\u3092\u81ea\u5206\u306e\u30a2\u30ab\u30a6\u30f3\u30c8\u540d\u3001group1\u3092\u81ea\u5206\u306e\u30b0\u30eb\u30fc\u30d7\u540d\u300138080\u3092apache2\u304c\u4f7f\u7528\u3059\u308b\u30dd\u30fc\u30c8\u756a\u53f7\u306b\u4fee\u6b63\u3057\u307e\u3059\u3002\nnetstat\u30b3\u30de\u30f3\u30c9\u306738080\u304c\u672a\u4f7f\u7528\u306a\u3089\u5909\u66f4\u4e0d\u8981\u3067\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003epackage.json\u003c/h3\u003e\u003ca id=\"user-content-packagejson\" class=\"anchor\" aria-label=\"Permalink: package.json\" href=\"#packagejson\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"name\": \"igv-webapp\",\n \"version\": \"1.5.5\",\n \"description\": \"igv web app\",\n \"keywords\": [],\n \"author\": \"Douglass Turner and Jim Robinson\",\n \"license\": \"MIT\",\n \"scripts\": {\n \"start\": \"npx http-server -p 38081 dist\",\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e38081\u3092igv-webapp\u304c\u4f7f\u7528\u3059\u308b\u30dd\u30fc\u30c8\u756a\u53f7\u306b\u4fee\u6b63\u3057\u307e\u3059\u3002\nnetstat\u30b3\u30de\u30f3\u30c9\u306738081\u304c\u672a\u4f7f\u7528\u306a\u3089\u5909\u66f4\u4e0d\u8981\u3067\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esingularity instance\u306e\u8d77\u52d5\u003c/h2\u003e\u003ca id=\"user-content-singularity-instance\u306e\u8d77\u52d5\" class=\"anchor\" aria-label=\"Permalink: singularity instance\u306e\u8d77\u52d5\" href=\"#singularity-instance\u306e\u8d77\u52d5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u521d\u56de\u5b9f\u884c\u6642\u306b\u3001ubuntu-18.04-apache-2.4.48-igv-webapp-1.5.5_latest.sif \u304c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u307e\u3059\u3002\n\u307e\u305f\u3001cgi-bin, htdocs, logs\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002\nhtdocs\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u3001igv-webapp\u3067\u8868\u793a\u3057\u305f\u3044bam\u30d5\u30a1\u30a4\u30eb\u3068\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u30d5\u30a1\u30a4\u30eb\u3092\u914d\u7f6e\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eigv-webapp\u3078\u306e\u30a2\u30af\u30bb\u30b9\u003c/h2\u003e\u003ca id=\"user-content-igv-webapp\u3078\u306e\u30a2\u30af\u30bb\u30b9\" class=\"anchor\" aria-label=\"Permalink: igv-webapp\u3078\u306e\u30a2\u30af\u30bb\u30b9\" href=\"#igv-webapp\u3078\u306e\u30a2\u30af\u30bb\u30b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a6\u30a7\u30d6\u30d6\u30e9\u30a6\u30b6\u3067 http://\u0026lt;\u30db\u30b9\u30c8\u306eIP\u30a2\u30c9\u30ec\u30b9\u0026gt;:\u0026lt;package.json\u306b\u8a2d\u5b9a\u3057\u305f\u30dd\u30fc\u30c8\u756a\u53f7\u0026gt; \u306b\u30a2\u30af\u30bb\u30b9\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n\u30c8\u30e9\u30c3\u30af\u306e\u8ffd\u52a0\u306f\u3001Tracks\u30e1\u30cb\u30e5\u30fc\u304b\u3089URL\u3092\u9078\u3073\u3001\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ehttp://\u0026lt;\u30db\u30b9\u30c8\u306eID\u30a2\u30c9\u30ec\u30b9\u0026gt;:\u0026lt;httpd.conf\u306b\u8a2d\u5b9a\u3057\u305f\u30dd\u30fc\u30c8\u756a\u53f7\u0026gt;/\u0026lt;htdocs\u306b\u914d\u7f6e\u3057\u305fbam\u30d5\u30a1\u30a4\u30eb\u0026gt;\u003c/li\u003e\n\u003cli\u003ehttp://\u0026lt;\u30db\u30b9\u30c8\u306eID\u30a2\u30c9\u30ec\u30b9\u0026gt;:\u0026lt;httpd.conf\u306b\u8a2d\u5b9a\u3057\u305f\u30dd\u30fc\u30c8\u756a\u53f7\u0026gt;/\u0026lt;htdocs\u306b\u914d\u7f6e\u3057\u305f\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u30d5\u30a1\u30a4\u30eb\u0026gt;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u3092\u958b\u3044\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1616500401.0 + "updated_at": 1625812484.0 }, { "data_format": 2, - "description": "Singularity recipe files for badread (https://github.com/rrwick/Badread)", + "description": "Software Engineering for HPC DevOps Project Part2", "filenames": [ - "Singularity", - "Singularity.0.2.0" + "Singularity.def" ], - "full_name": "powerPlant/badread-srf", + "full_name": "supertramp277/DevOps-Project_Part2", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for badread, a long read simulator that can imitate many types of read problems.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject\" href=\"#se4hpcproject\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2 -- From build to release and manual job execution\u003c/h2\u003e\u003ca id=\"user-content-step-2----from-build-to-release-and-manual-job-execution\" class=\"anchor\" aria-label=\"Permalink: Step 2 -- From build to release and manual job execution\" href=\"#step-2----from-build-to-release-and-manual-job-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFocus now on the correct implementation of the matrix multiplication you\nfind in \u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e. This is a\nparallel implementation that uses MPI and reads the matrices to be\nmultiplied from two files, matrixA.txt and matrixB.txt. In these files\nthe first row contains the matrix dimensions (number of rows and\ncolumns), while the other rows contain the matrix itself.\u003c/p\u003e\n\u003cp\u003eYour task is to perform the following steps:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation\u003c/strong\u003e: Use the template available here\n\u003ca href=\"https://github.com/SimoneReale/SE4HPC_project_part2\"\u003ehttps://github.com/SimoneReale/SE4HPC_project_part2\u003c/a\u003e to create your own\ngithub repository. Add to this repository the tests you have created in\nStep1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutomating the build, test and release processes\u003c/strong\u003e: Create a CI/CD\npipeline that, when someone pushes files in the repo, executes the\nbuilding and testing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContainerizing the application\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDefine a Singularity container descriptor for the matrix\nmultiplication program and push it in your repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtend the created action to create a container image from your\ndescription.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExecuting on the cluster\u003c/strong\u003e: Go through the following steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a job.sh file to run your containerized application. Make\nsure that the standard output and error are mapped to txt files.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransfer on Galileo100 your job script and the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSubmit your job to the cluster and check whether it works correctly.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePush on your github repository your job.sh file and the files\nobtained from the execution of the matrix multiplication.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3 -- Automating a job submission with containerization\u003c/h2\u003e\u003ca id=\"user-content-step-3----automating-a-job-submission-with-containerization\" class=\"anchor\" aria-label=\"Permalink: Step 3 -- Automating a job submission with containerization\" href=\"#step-3----automating-a-job-submission-with-containerization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtend the action you have created at step 3 to automate completely the\nprocess from a push on the repository to the execution of the\ncontainerized software on SLURM. To do so, you will have to move your\ncontainer from the runner to the cluster. You can either use the scp\ncommand or you can publish your image on the Singularity registry and\nthen pull it from the cluster. Don\u0027t forget to handle your secrets\nproperly! You do not want to leave passwords and authentication tokens\nvisible to everybody, so you will use the \u003ca href=\"https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions?tool=cli\"\u003esecrets\nmechanism\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1613427062.0 + "updated_at": 1717622843.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity_MCR.def", + "Singularity.def" ], - "full_name": "kristinebilgrav/Containers", + "full_name": "Remi-Gau/cat12-container", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-jitter_container\" class=\"anchor\" aria-hidden=\"true\" href=\"#jitter_container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJitter_container\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCAT-12 container\u003c/h1\u003e\u003ca id=\"user-content-cat-12-container\" class=\"anchor\" aria-label=\"Permalink: CAT-12 container\" href=\"#cat-12-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker and Apptainer image for \u003ca href=\"https://neuro-jena.github.io/cat/\" rel=\"nofollow\"\u003eCAT12\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eCAT12 8.1 r2042\nSPM12, version 7771 (standalone)\nMATLAB, version 9.3.0.713579 (R2017b)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage notes\u003c/h2\u003e\u003ca id=\"user-content-usage-notes\" class=\"anchor\" aria-label=\"Permalink: Usage notes\" href=\"#usage-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample\u003c/h3\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --tag cat12\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v \u003cspan class=\"pl-smi\"\u003e${PWD}\u003c/span\u003e/tests/data/MoAEpilot:/data \\\n --rm -it cat12 \\\n /data/sub-01/anat/sub-01_T1w.nii \\\n -b cat_standalone_segment.m\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1623335528.0 + "updated_at": 1724126570.0 }, { "data_format": 2, - "description": "Singularity bootstrap which includes uboonecode, larcv3 and gallery-framework. ", + "description": "My repository for singularity hub containers.", "filenames": [ - "Singularity" + "miniconda3_mamba/Singularity.miniconda3mamba", + "rinla/Singularity.rinla" ], - "full_name": "lmlepin9/Singularity-uboonecode-gallery", + "full_name": "votti/singularity-builds", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-uboonecode-gallery\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-uboonecode-gallery\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity-uboonecode-gallery\u003c/h1\u003e\n\u003cp\u003eSingularity bootstrap which includes uboonecode, larcv3 and gallery-framework.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1617376998.0 + "updated_at": 1591370752.0 }, { "data_format": 2, - "description": null, + "description": "Trying to get Slamdunk to work on CentOS 6", "filenames": [ "Singularity" ], - "full_name": "colinsauze/Bovine_DNA_RNA", + "full_name": "FelixKrueger/SlamDunk_Shub", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSlamDunk_Shub\u003c/h1\u003e\u003ca id=\"user-content-slamdunk_shub\" class=\"anchor\" aria-label=\"Permalink: SlamDunk_Shub\" href=\"#slamdunk_shub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTrying to get SlamDunk to work on our dev server and eventually on our cluster\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1531844224.0 + "updated_at": 1538406117.0 }, { "data_format": 2, - "description": "A simple demo of singularity containers", + "description": null, "filenames": [ - "Singularity" + "Singularity.adapterremoval_2.3.1", + "Singularity.bbmap_38.86", + "Singularity.seqtk_1.3r106", + "Singularity.matlock_9fe3fdd", + "Singularity.cutadapt_2.10", + "Singularity.pysam_0.15.3" ], - "full_name": "colinsauze/singularity_example", + "full_name": "TomHarrop/seq-utils", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singualrity_example\" class=\"anchor\" aria-hidden=\"true\" href=\"#singualrity_example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingualrity_example\u003c/h1\u003e\n\u003cp\u003eA simple demo of singularity containers\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1531999980.0 + "updated_at": 1597614799.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.efficient" ], - "full_name": "upendrak/pacbio_singularity", + "full_name": "huynhngoc/CubiAI-VRU", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-running-iso-seq3-analysis-on-a-test-data-using-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-iso-seq3-analysis-on-a-test-data-using-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Iso-Seq3 analysis on a Test data using Singularity container\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003eIsoSeq3\u003c/em\u003e contains the newest tools to identify transcripts in PacBio single-molecule sequencing data. Starting in SMRT Link v6.0.0, those tools power the IsoSeq3 GUI-based analysis application. A composable workflow of existing tools and algorithms, combined with a new clustering technique, allows to process the ever-increasing yield of PacBio machines with similar performance to IsoSeq1 and IsoSeq2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e This is an example of an end-to-end cmd-line-only workflow from this \u003ca href=\"https://github.com/PacificBiosciences/IsoSeq3\"\u003etutorial\u003c/a\u003e to get from subreads to polished isoforms; timings are system dependent.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-availability\" class=\"anchor\" aria-hidden=\"true\" href=\"#availability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailability\u003c/h2\u003e\n\u003cp\u003eThe Iso-Seq3 can be run using Singualrity container hosted on Singularity hub\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Singularity\u003c/h2\u003e\n\u003cp\u003eThere are many ways to \u003ca href=\"https://www.sylabs.io/guides/2.5.1/user-guide/quick_start.html#installation\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e but this quick start guide will only cover one.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/singularityware/singularity.git\ncd singularity\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Singularity must be installed as root to function properly.\u003c/p\u003e\n\u003cp\u003eAfter installing Singularity make sure to run the --help option gives an overview of Singularity options and subcommands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity --help\n\nUSAGE: singularity [global options...] \u0026lt;command\u0026gt; [command options...] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-the-test-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-the-test-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload the test data\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eFor each cell, the \u0026lt;movie\u0026gt;.subreads.bam and \u0026lt;movie\u0026gt;.subreads.bam.pbi are needed for processing.\n\n$ mkdir tutorial \u0026amp;\u0026amp; cd tutorial\n$ wget https://downloads.pacbcloud.com/public/dataset/RC0_1cell_2017/m54086_170204_081430.subreads.bam\n$ wget https://downloads.pacbcloud.com/public/dataset/RC0_1cell_2017/m54086_170204_081430.subreads.bam.pbi\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-pull-the-singularity-container-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-pull-the-singularity-container-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation: Pull the Singularity container from Singularity hub\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name isoseq.img shub://upendrak/pacbio_singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-consensus-calling\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-consensus-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1: Consensus calling\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec isoseq.img ccs --version\nccs 3.0.0 (commit a54f14a)\n\n$ nohup singularity exec isoseq.img ccs m54086_170204_081430.subreads.bam m54086_170204_081430.ccs.bam --noPolish --minPasses 1 \u0026amp;\n\nNote: This step takes a long time. On a 6 CPU VM, it took around 5 hrs to complete\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-primer-removal-and-demultiplexing\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-primer-removal-and-demultiplexing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2: Primer removal and demultiplexing\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec isoseq.img lima --version\nlima 1.7.0 (commit v1.7.0-2-g9479065)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat primers.fasta\n\n\u0026gt;primer_5p\nAAGCAGTGGTATCAACGCAGAGTACATGGG\n\u0026gt;primer_3p\nGTACTCTGCGTTGATACCACTGCTT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec isoseq.img lima m54086_170204_081430.ccs.bam primers.fasta demux.bam --isoseq --no-pbi --dump-clips \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls demux*\ndemux.json demux.lima.counts demux.lima.report demux.lima.summary demux.primer_5p--primer_3p.bam demux.primer_5p--primer_3p.subreadset.xml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-3-clustering-and-transcript-clean-up\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-3-clustering-and-transcript-clean-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 3: Clustering and transcript clean up\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ nohup singularity exec isoseq.img isoseq3 cluster demux.primer_5p--primer_3p.bam unpolished.bam --verbose \u0026amp;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls unpolished*\nunpolished.bam unpolished.bam.pbi unpolished.cluster unpolished.fasta unpolished.flnc.bam unpolished.flnc.bam.pbi unpolished.flnc.consensusreadset.xml unpolished.transcriptset.xml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-4-polishing\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-4-polishing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 4: Polishing\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e$ nohup singularity exec isoseq.img isoseq3 polish unpolished.bam m54086_170204_081430.subreads.bam polished.bam --verbose\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls polished*\npolished.bam polished.bam.pbi polished.hq.fasta.gz polished.hq.fastq.gz polished.lq.fasta.gz polished.lq.fastq.gz polished.transcriptset.xml\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCubiAI\u003c/h1\u003e\u003ca id=\"user-content-cubiai\" class=\"anchor\" aria-label=\"Permalink: CubiAI\" href=\"#cubiai\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDeep learning can detect elbow disease in dogs screened for elbow dysplasia\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenerate dataset\u003c/h2\u003e\u003ca id=\"user-content-generate-dataset\" class=\"anchor\" aria-label=\"Permalink: Generate dataset\" href=\"#generate-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edataset_gen/gen_normal_abnormal.py\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCheck for duplication\n\u003ccode\u003edataset_gen/QA_data_splitting.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun experiments locally\u003c/h2\u003e\u003ca id=\"user-content-run-experiments-locally\" class=\"anchor\" aria-label=\"Permalink: Run experiments locally\" href=\"#run-experiments-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython experiment_binary.py config/local/pretrain.json path_to_folder/pretrain --temp_folder path_to_temp_folder/pretrain --epochs 20\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun experiemts on Orion\u003c/h2\u003e\u003ca id=\"user-content-run-experiemts-on-orion\" class=\"anchor\" aria-label=\"Permalink: Run experiemts on Orion\" href=\"#run-experiemts-on-orion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_pretrain_binary.sh config/pretrain/b0_normal_level2.json b0_normal_level2 2\nsbatch slurm_pretrain_multiclass.sh config/pretrain/b0_normal_level1_level2.json b0_normal_level1_level2 2\n\nsbatch slurm_scratch_binary.sh config/scratch/b0_normal_level2.json b0_normal_level2 2\nsbatch slurm_scratch_multiclass.sh config/scratch/b0_normal_level1_level2.json b0_normal_level1_level2 2\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1533222076.0 + "updated_at": 1724057222.0 }, { "data_format": 2, @@ -20667,43 +20272,26 @@ var data = "filenames": [ "Singularity" ], - "full_name": "vsoch/BIDShcppipelines-debug", - "latest_release": null, - "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1545323608.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "Singularity.larcv_fc", - "Singularity.larcv_cpu.txt", - "Singularity.larcv_fcAWS" - ], - "full_name": "jonmiller3/singularity_imgs", + "full_name": "compmetagen/taxies", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_imgs\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_imgs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_imgs\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1602882140.0 + "updated_at": 1544461967.0 }, { "data_format": 2, - "description": "Template repo for CircleCI DockerHub+SingularityHub continuous integration", + "description": "Custom Linux Container Build for Large Scale File Parsing in High Performance Computing Environments", "filenames": [ - "Singularity", - "Singularity.v0.3" + "base-image-ubuntu-22.04/base-image/.singularity.d/Singularity" ], - "full_name": "khanlab/template-circleci", + "full_name": "alexander-labarge/hpc-data-parser", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1503\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/khanlab/template-circleci\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c2b426ee0f066d201a83b9ac4156ab58b817dd4135c9dc90ebeac909f35a9925/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f74656d706c6174652d636972636c6563692e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/template-circleci.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-template-circleci\" class=\"anchor\" aria-hidden=\"true\" href=\"#template-circleci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etemplate-circleci\u003c/h1\u003e\n\u003cp\u003eKhanlab template repo for CircleCI DockerHub+SingularityHub continuous integration.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-functionality\" class=\"anchor\" aria-hidden=\"true\" href=\"#functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctionality:\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-every-commit\" class=\"anchor\" aria-hidden=\"true\" href=\"#every-commit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvery commit:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eBuilds Docker container\u003c/li\u003e\n\u003cli\u003eRuns tests (using built Docker container)\u003c/li\u003e\n\u003cli\u003eIf successful, pushes to Docker Hub with \u003ccode\u003elatest\u003c/code\u003e tag\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-every-night\" class=\"anchor\" aria-hidden=\"true\" href=\"#every-night\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEvery night:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eDeploys on Singularity Hub (via recipe commit, from Docker container)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-make-a-release\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-make-a-release\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo make a release:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUse Github\u0027s Draft a New Release\u003c/li\u003e\n\u003cli\u003eInclude v* in the Tag name (e.g. v0.1)\u003c/li\u003e\n\u003cli\u003eWill then automatically build, test and deploy on DockerHub and SingularityHub with v* release tag\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGithub repo in Khanlab organization\u003c/li\u003e\n\u003cli\u003eDockerfile in that repo\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-set-up-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#set-up-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet-up Instructions:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eCopy the .circleci/config.yml to your repository\u003c/li\u003e\n\u003cli\u003eLogin to circleci.com, and add the project\u003c/li\u003e\n\u003cli\u003eLogin to singularity hub, and add the project\u003c/li\u003e\n\u003cli\u003eEdit \u003ccode\u003etest\u003c/code\u003e section of .circleci/config.yml to set-up tests\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHigh-Performance Computing (HPC) File Parsing Solution - Direct Access to GPU \u0026amp; CPU Resources\u003c/h1\u003e\u003ca id=\"user-content-high-performance-computing-hpc-file-parsing-solution---direct-access-to-gpu--cpu-resources\" class=\"anchor\" aria-label=\"Permalink: High-Performance Computing (HPC) File Parsing Solution - Direct Access to GPU \u0026amp; CPU Resources\" href=\"#high-performance-computing-hpc-file-parsing-solution---direct-access-to-gpu--cpu-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis solution provides direct access to GPU and CPU resources for high-performance computing (HPC) and high-throughput computing (HTC) environments. Unlike enterprise-based container frameworks, which are designed for microservices and require root privileges to install and run applications, this solution is optimized for complex applications that require all available resources without special privileges.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTargeted Toolsets Implemented\u003c/h2\u003e\u003ca id=\"user-content-targeted-toolsets-implemented\" class=\"anchor\" aria-label=\"Permalink: Targeted Toolsets Implemented\" href=\"#targeted-toolsets-implemented\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis solution uses the following targeted toolsets:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eApache Tika\u2122 by Oracle\u003c/li\u003e\n\u003cli\u003eApptainer (formerly Singularity) by Berkeley National Laboratory\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInitial Cause for Solution Development\u003c/h2\u003e\u003ca id=\"user-content-initial-cause-for-solution-development\" class=\"anchor\" aria-label=\"Permalink: Initial Cause for Solution Development\" href=\"#initial-cause-for-solution-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe development of this solution was motivated by the need to parse 7.5 TB of digital forensics data produced and stored in a variety of non-standard formats. The parsing of all data is necessary to drive subsequent efforts wherein conjectures are made from the subsequent data parsed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhy Apptainer for HPC instead of Virtual Machines or Docker\u003c/h2\u003e\u003ca id=\"user-content-why-apptainer-for-hpc-instead-of-virtual-machines-or-docker\" class=\"anchor\" aria-label=\"Permalink: Why Apptainer for HPC instead of Virtual Machines or Docker\" href=\"#why-apptainer-for-hpc-instead-of-virtual-machines-or-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eApptainer/Singularity is a container platform created for the HPC/HTC use case and presents key concepts for the scientific community:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt\u2019s designed to execute applications with bare-metal performance while retaining a high level of security, portability, and reproducibility.\u003c/li\u003e\n\u003cli\u003eContainers run rootless to prohibit privilege escalation.\u003c/li\u003e\n\u003cli\u003eAble to Leverage GPUs, FPGAs, high-speed networks, and filesystems.\u003c/li\u003e\n\u003cli\u003eA container platform for building and running Linux containers that packages software, libraries, and runtime compilers in a self-contained environment.\n\u003cul\u003e\n\u003cli\u003eApplication portability (single image file, contain all dependencies)\u003c/li\u003e\n\u003cli\u003eReproducibility, run cross platform, provide support for legacy OS and apps.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAbility to run, and in modern systems also to be installed, without any root daemon or setuid privileges. This makes it safer for large computer centers with shared resources.\u003c/li\u003e\n\u003cli\u003ePreserves the permissions in the environment. The user outside the container can be the same user inside.\u003c/li\u003e\n\u003cli\u003eApptainer propagates most environment variables set on the host into the container, by default. Docker does not propagate any host environment variables into the container. Environment variables may change the behavior of software.\u003c/li\u003e\n\u003cli\u003eSimple integration with resource managers (SLURM in our case) and distributed computing frameworks because it runs as a regular application.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/945a382c-3488-4c65-a743-44f0a704c7a5\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/945a382c-3488-4c65-a743-44f0a704c7a5\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment Steps:\u003c/h2\u003e\u003ca id=\"user-content-development-steps\" class=\"anchor\" aria-label=\"Permalink: Development Steps:\" href=\"#development-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest Host Machine (Bare Metal):\u003c/h3\u003e\u003ca id=\"user-content-test-host-machine-bare-metal\" class=\"anchor\" aria-label=\"Permalink: Test Host Machine (Bare Metal):\" href=\"#test-host-machine-bare-metal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0c14fa9e-2508-4c80-9883-f016eb70484f\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0c14fa9e-2508-4c80-9883-f016eb70484f\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Apptainer - Build from Source/ Install Debian Packages for Dependencies\u003c/h3\u003e\u003ca id=\"user-content-step-1-apptainer---build-from-source-install-debian-packages-for-dependencies\" class=\"anchor\" aria-label=\"Permalink: Step 1: Apptainer - Build from Source/ Install Debian Packages for Dependencies\" href=\"#step-1-apptainer---build-from-source-install-debian-packages-for-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get install -y \\\n build-essential \\\n libseccomp-dev \\\n pkg-config \\\n uidmap \\\n squashfs-tools \\\n squashfuse \\\n fuse2fs \\\n fuse-overlayfs \\\n fakeroot \\\n cryptsetup \\\n curl wget git \\\n \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GOVERSION=1.20.6 OS=linux ARCH=amd64 \\\n wget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n sudo tar -C /usr/local -xzf /home/service-typhon/Downloads/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:/usr/local/go/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc \\\n curl -sSfL https://raw.githubusercontent.com/golangci/golangci-lint/master/install.sh \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sh -s -- -b \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ego env GOPATH\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/bin v1.51.1 \\\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:$(go env GOPATH)/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc \\\n git clone https://github.com/apptainer/apptainer.git \\\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e apptainer \\\n git checkout v1.2.0 \\\n ./mconfig \\\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ./builddir \\\n make \\\n sudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Create Sandbox Directory / Pull Ubuntu 22.04 - Jammy Docker Container (Base Ubuntu Build)\u003c/h3\u003e\u003ca id=\"user-content-step-2-create-sandbox-directory--pull-ubuntu-2204---jammy-docker-container-base-ubuntu-build\" class=\"anchor\" aria-label=\"Permalink: Step 2: Create Sandbox Directory / Pull Ubuntu 22.04 - Jammy Docker Container (Base Ubuntu Build)\" href=\"#step-2-create-sandbox-directory--pull-ubuntu-2204---jammy-docker-container-base-ubuntu-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7aaf3e7e-cb74-40d1-9971-24808b0885f8\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7aaf3e7e-cb74-40d1-9971-24808b0885f8\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Convert to Immutable .sif Image for Future Builds - Demonstrate Shell Access\u003c/h3\u003e\u003ca id=\"user-content-step-3-convert-to-immutable-sif-image-for-future-builds---demonstrate-shell-access\" class=\"anchor\" aria-label=\"Permalink: Step 3: Convert to Immutable .sif Image for Future Builds - Demonstrate Shell Access\" href=\"#step-3-convert-to-immutable-sif-image-for-future-builds---demonstrate-shell-access\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/88b8bf10-0e96-4ad3-8d91-6135140e9a00\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/88b8bf10-0e96-4ad3-8d91-6135140e9a00\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 4: Definition File Configuration for Building Dependencies - 1st Build Scuccessful\u003c/h3\u003e\u003ca id=\"user-content-step-4-definition-file-configuration-for-building-dependencies---1st-build-scuccessful\" class=\"anchor\" aria-label=\"Permalink: Step 4: Definition File Configuration for Building Dependencies - 1st Build Scuccessful\" href=\"#step-4-definition-file-configuration-for-building-dependencies---1st-build-scuccessful\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/ce94c114-8b0b-44b2-a7c0-33c26e4e36b7\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/ce94c114-8b0b-44b2-a7c0-33c26e4e36b7\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0d3b4389-7329-4a06-b551-392b07586abc\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0d3b4389-7329-4a06-b551-392b07586abc\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 5: Now that There is a Base Instance Working, lets create a live sandbox for testing from the image we just created:\u003c/h3\u003e\u003ca id=\"user-content-step-5-now-that-there-is-a-base-instance-working-lets-create-a-live-sandbox-for-testing-from-the-image-we-just-created\" class=\"anchor\" aria-label=\"Permalink: Step 5: Now that There is a Base Instance Working, lets create a live sandbox for testing from the image we just created:\" href=\"#step-5-now-that-there-is-a-base-instance-working-lets-create-a-live-sandbox-for-testing-from-the-image-we-just-created\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/a45a0aed-0b5b-4cc9-abdb-5178ad5ac648\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/a45a0aed-0b5b-4cc9-abdb-5178ad5ac648\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eNote: Initial Containers are limited to 64MB in size. Fix:\u003c/h4\u003e\u003ca id=\"user-content-note-initial-containers-are-limited-to-64mb-in-size-fix\" class=\"anchor\" aria-label=\"Permalink: Note: Initial Containers are limited to 64MB in size. Fix:\" href=\"#note-initial-containers-are-limited-to-64mb-in-size-fix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0f2b5e02-d8a1-42bc-995a-507b802c4c3a\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0f2b5e02-d8a1-42bc-995a-507b802c4c3a\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 6: Create a New File System Overlay/ add as a layer in SIF build:\u003c/h3\u003e\u003ca id=\"user-content-step-6-create-a-new-file-system-overlay-add-as-a-layer-in-sif-build\" class=\"anchor\" aria-label=\"Permalink: Step 6: Create a New File System Overlay/ add as a layer in SIF build:\" href=\"#step-6-create-a-new-file-system-overlay-add-as-a-layer-in-sif-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/441d3a4a-18cd-4c74-8cf8-b7ffe18167eb\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/441d3a4a-18cd-4c74-8cf8-b7ffe18167eb\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 7: Build Tika/ Configure Properly - Completed/ Success:\u003c/h3\u003e\u003ca id=\"user-content-step-7-build-tika-configure-properly---completed-success\" class=\"anchor\" aria-label=\"Permalink: Step 7: Build Tika/ Configure Properly - Completed/ Success:\" href=\"#step-7-build-tika-configure-properly---completed-success\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7c7320ab-999b-45b9-bd3c-beb8a19c1230\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7c7320ab-999b-45b9-bd3c-beb8a19c1230\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTIKA DEPENDENCY INSTALL SCRIPT IMPLEMENTED AT %POST\u003c/h4\u003e\u003ca id=\"user-content-tika-dependency-install-script-implemented-at-post\" class=\"anchor\" aria-label=\"Permalink: TIKA DEPENDENCY INSTALL SCRIPT IMPLEMENTED AT %POST\" href=\"#tika-dependency-install-script-implemented-at-post\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Java\u003c/span\u003e\napt-get update\napt-get install -y software-properties-common\napt-get install -y wget\napt-get install -y default-jre\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Tesseract OCR\u003c/span\u003e\napt-get install -y tesseract-ocr\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install ImageMagick\u003c/span\u003e\napt-get install -y imagemagick\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Poppler\u003c/span\u003e\napt-get install -y poppler-utils\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install FFmpeg\u003c/span\u003e\napt-get install -y ffmpeg\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Tika\u003c/span\u003e\nwget https://dlcdn.apache.org/tika/2.8.0/tika-app-2.8.0.jar\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Maven\u003c/span\u003e\nwget https://dlcdn.apache.org/maven/maven-3/3.9.3/binaries/apache-maven-3.9.3-bin.tar.gz\ntar -xvf apache-maven-3.9.3-bin.tar.gz \nmv apache-maven-3.9.3 /opt\nM2_HOME=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eopt/apache-maven-3.9.3/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nPATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$M2_HOME\u003c/span\u003e/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTIKA AUTOMATED TEST END OF INSTALL:\u003c/h4\u003e\u003ca id=\"user-content-tika-automated-test-end-of-install\" class=\"anchor\" aria-label=\"Permalink: TIKA AUTOMATED TEST END OF INSTALL:\" href=\"#tika-automated-test-end-of-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e \u003cspan class=\"pl-en\"\u003echeck_tika_test\u003c/span\u003e {\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eChecking Tika test...\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e grep -q \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTIKA PASSED TEST - ALEX\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e /output-files/tika-test-file.txt.json\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika test passed. FOUND STRING: TIKA PASSED TEST - ALEX in file.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e============TIKA HPC BUILD COMPLETING FINAL STEPS================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika test failed.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\n}\n\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eStarting Tika... at \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einput-files directory contents:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ncp /opt/tika-test-file.txt /input-files\nls -l /input-files/\njava -jar /tika-app-2.8.0.jar -i /input-files -o /output-files -J\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika started.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika output complete.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eoutput-files directory contents:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nls -l /output-files\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eCompleted at: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eExtracting text from files...\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eExtracted JSON OUTPUT:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract text from files \u0026amp; ignore JSON text\u003c/span\u003e\nextracted_text=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003efind /output-files -type f -exec strings {} \u003cspan class=\"pl-cce\"\u003e\\;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep -vE \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e^{.*}$\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Print extracted text\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$extracted_text\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Check Tika test\u003c/span\u003e\ncheck_tika_test\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSTEP 8: FINAL BETA BUILD SCRIPT (OTHER BASH SCRIPTS EMBEDDED)\u003c/h3\u003e\u003ca id=\"user-content-step-8-final-beta-build-script-other-bash-scripts-embedded\" class=\"anchor\" aria-label=\"Permalink: STEP 8: FINAL BETA BUILD SCRIPT (OTHER BASH SCRIPTS EMBEDDED)\" href=\"#step-8-final-beta-build-script-other-bash-scripts-embedded\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/71d64b44-a95e-484b-ad2d-082d3bc9ab60\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/71d64b44-a95e-484b-ad2d-082d3bc9ab60\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1569207854.0 + "updated_at": 1697541728.0 }, { "data_format": 2, @@ -20711,901 +20299,892 @@ var data = "filenames": [ "Singularity" ], - "full_name": "mpachkov/singularity_test", + "full_name": "oogasawa/singularity_jupyter_datascience", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity_test\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity_test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_test\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-jupyter-datascience\u003c/h1\u003e\u003ca id=\"user-content-singularity-jupyter-datascience\" class=\"anchor\" aria-label=\"Permalink: singularity-jupyter-datascience\" href=\"#singularity-jupyter-datascience\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Singularity container of Jupyter notebook for datascience,\ncreated by converting an official Docker image\n\u003ca href=\"https://hub.docker.com/r/jupyter/datascience-notebook/\" rel=\"nofollow\"\u003ejupyter/datascience-notebook\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Singularity image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/oogasawa/singularity-jupyter-datascience\ncd singularity-jupyter-datascience\nsudo singularity build . singularity-jupyter-datascience.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the server as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance start singularity-jupyter-datascience.sif sing_jupyter_ds\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEnter (attach) the Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# List the running containers.\nsingularity instance list\n\n# Attach the container\nsingularity shell instance://sing_jupyter_ds\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the Jupyter notebook (or Jupyter Lab) from within the Singularity prompt.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://sing_jupyter_ds\nSingularity\u0026gt; jupyter lab --port=50000\n[I 01:28:50.619 LabApp] JupyterLab extension loaded from /opt/conda/lib/python3.8/site-packages/jupyterlab\n[I 01:28:50.619 LabApp] JupyterLab application directory is /opt/conda/share/jupyter/lab\n[I 01:28:50.621 LabApp] Serving notebooks from local directory: /home/oogasawa/tmp3/singularity-jupyter-datascience\n[I 01:28:50.621 LabApp] The Jupyter Notebook is running at:\n[I 01:28:50.621 LabApp] http://mayfly:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n[I 01:28:50.621 LabApp] or http://127.0.0.1:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n[I 01:28:50.621 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n[C 01:28:50.624 LabApp]\n\n To access the notebook, open this file in a browser:\n\t file:///home/oogasawa/.local/share/jupyter/runtime/nbserver-25-open.html\n\tOr copy and paste one of these URLs:\n\t\thttp://mayfly:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n\t or http://127.0.0.1:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\t\t\t\t\t \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can access the Jupyter software \u003ca href=\"http://localhost:50000/\" rel=\"nofollow\"\u003ehttp://localhost:50000/\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eStop the server (and return to the bash prompt) by Ctrl-C, and stop the container as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance stop sing_jupyter_ds\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1536594044.0 + "updated_at": 1622297568.0 }, { "data_format": 2, "description": null, "filenames": [ - "SingularityRubuntu_RnBeads_FINAL", - "SingularityRRBSNF_FINAL" + "Singularity.latest", + "Singularity.ubuntu_test" ], - "full_name": "AdrianS85/RRBS", + "full_name": "EPI-APE/sing_af", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1540935055.0 + "updated_at": 1551109916.0 }, { "data_format": 2, - "description": "A singularity container for building and running the Crispred pipeline (https://dubshen.astro.su.se/wiki/index.php/CRISPRED)", + "description": null, "filenames": [ - "Singularity" + "Singularity", + "nova/Singularity.S17-12-08-maxopt" ], - "full_name": "colinsauze/crispred_singularity", + "full_name": "dingp/singularity", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1538673673.0 + "updated_at": 1519058894.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "Singularity-cpu" + "testing-with-conveyors/bale_actor/singularity/Singularity.def" ], - "full_name": "p-h/ZHAW_deep_voice", + "full_name": "singhalshubh/Conveyors-Design-Reinvented", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-zhaw-deep-voice\" class=\"anchor\" aria-hidden=\"true\" href=\"#zhaw-deep-voice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eZHAW deep voice\u003c/h1\u003e\n\u003cp\u003eThe ZHAW deep voice is a package of multiple neural networks that try resolving the speaker clustering task. The goal is to provide a uniform way of data-access, -preprocession and analysis fo the results.\u003c/p\u003e\n\u003cp\u003eNote that the suite needs the TIMIT Dataset to function at this point. This is a paid product from the LDC and can be \u003ca href=\"https://www.ldc.upenn.edu/\" rel=\"nofollow\"\u003eobtained here.\u003c/a\u003e\nThis data also needs to be processed using the \u003ca href=\"https://www.ldc.upenn.edu/language-resources/tools/sphere-conversion-tools\" rel=\"nofollow\"\u003esph2pipe tool\u003c/a\u003e and be put in the folder common/data/training/TIMIT\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-deep-voice\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-deep-voice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing deep voice\u003c/h2\u003e\n\u003cp\u003eIf you simply want to use it, you can let docker do the work for you and let it import all needed packages.\u003c/p\u003e\n\u003cp\u003eIn any way, whether you fork and pull the source code or let docker handle it for you, the whole suite is controllable over a one file interface, controller.py.\nIt can be run from console with the following calling structure:\ncontroller.py [-h] [-setup] [-n network] [-train] [-test] [-plot] [-clear] [-debug] [-best] [-val# ne]\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e-help Display the help screen you are seeing here\u003c/li\u003e\n\u003cli\u003e-setup Create all\u003c/li\u003e\n\u003cli\u003e-n specifiy which network should be used. Available:\n\u0027pairwise_lstm\u0027, \u0027pairwise_kldiv\u0027, \u0027flow_me\u0027, \u0027luvo\u0027 and \u0027all\u0027 (without the single quotes)\u003c/li\u003e\n\u003cli\u003e-train Specify to train the chosen network\u003c/li\u003e\n\u003cli\u003e-test Specify to test the chosen network\u003c/li\u003e\n\u003cli\u003e-plot Specify to plot the results of the chosen network. If network is \u0027all\u0027, all results will be displayed in one single plot\u003c/li\u003e\n\u003cli\u003e-clear Clear the folder in experiments\u003c/li\u003e\n\u003cli\u003e-debug Set the logging level of Tensorflow to Debug\u003c/li\u003e\n\u003cli\u003e-best Just the best results of the networks will be used in -plot\u003c/li\u003e\n\u003cli\u003e-val# specify which speaker number you want to use (40, 60, 80) to test the networks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAs an example, you want to train, and test but not plot the network pairwise_lstm. you would call:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003econtroller.py -n pairwise_lstm -train -test\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content-general-remarks\" class=\"anchor\" aria-hidden=\"true\" href=\"#general-remarks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral remarks\u003c/h3\u003e\n\u003cp\u003eBefore you start with your training you should run the controller once with the setup flag. This can take a while, approximately around 10 minutes.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1541411393.0 + "updated_at": 1677621757.0 }, { "data_format": 2, - "description": "Singularity Image for EPACTS", + "description": "Singularity recipe that includes git-annex, RStan, Python 3, and Snakemake", "filenames": [ "Singularity" ], - "full_name": "statgen/singularity-epacts", + "full_name": "kyleam/garps", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 8, - "topics": [], - "updated_at": 1543509361.0 + "subscribers_count": 2, + "topics": [ + "snakemake", + "singularity", + "git-annex", + "rstan" + ], + "updated_at": 1586815899.0 }, { "data_format": 2, - "description": "Singularity Image for SAIGE", + "description": "repository for singularity image of iMapSplice", "filenames": [ "Singularity" ], - "full_name": "statgen/singularity-saige", + "full_name": "cory-weller/iMapSplice.simg", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eiMapSplice.simg\u003c/h1\u003e\u003ca id=\"user-content-imapsplicesimg\" class=\"anchor\" aria-label=\"Permalink: iMapSplice.simg\" href=\"#imapsplicesimg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003erepository for singularity image of iMapSplice\u003c/p\u003e\n\u003cp\u003eput the \u003ccode\u003eSingularity\u003c/code\u003e recipe file into your directory and build an image (if you have root access):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build iMapSplice.simg ./Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, retrieve the pre-built image from singularity hub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull -n iMapSplice.simg shub://cory-weller/iMapSplice.simg\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 1, "topics": [], - "updated_at": 1543852783.0 + "updated_at": 1551883466.0 }, { "data_format": 2, - "description": "Singularity recipe for ML analysis in Python", + "description": null, "filenames": [ - "envs/Singularity.1" + "Singularity.ubuntubase:10.0-u", + "Singularity.tensorflowbase:ngc" ], - "full_name": "adswa/python-ml", + "full_name": "nckucch/singularity", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1600779264.0 + "updated_at": 1557068381.0 }, { "data_format": 2, - "description": "Singularity recipes for building mriqc container", + "description": "TensorFlow Singularity recipes.", "filenames": [ - "Singularity.0.10.4", - "Singularity.0.14.2", - "Singularity.0.11.0" + "Singularity.1.12.0-py27", + "Singularity.1.6.0-py27", + "Singularity.1.13.1-py36", + "Singularity.1.12.0-py36", + "Singularity.1.13.0-py36", + "Singularity.1.14.0-py36", + "Singularity.1.6.0-py36" ], - "full_name": "MPIB/singularity-mriqc", + "full_name": "arcsUVA/tensorflow", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-mriqc\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-mriqc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-mriqc\u003c/h1\u003e\n\u003cp\u003eSingularity recipes for building mriqc container\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUses the official docker image of mriqc as base: \u003ca href=\"https://hub.docker.com/r/poldracklab/mriqc/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/poldracklab/mriqc/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRecipes purge and reinstall libgsl2, since there were issues with it when just using the base container.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etensorflow\u003c/h1\u003e\u003ca id=\"user-content-tensorflow\" class=\"anchor\" aria-label=\"Permalink: tensorflow\" href=\"#tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2235\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nTensorFlow Singularity recipes.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 5, "topics": [], - "updated_at": 1535361688.0 + "updated_at": 1567631554.0 }, { "data_format": 2, - "description": "Singularity container with nbconvert for conversion of jupyter notebooks to other formats", + "description": null, "filenames": [ - "Singularity.latex" + "Singularity", + "__Deprecated__/Singularity_0_19" ], - "full_name": "vsoch/singularity-nbconvert", + "full_name": "daverblair/singularity_vlpi", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-latex-converter\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-latex-converter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Latex Converter\u003c/h1\u003e\n\u003cp\u003eThis container will help you to convert Jupyter notebooks to html pages.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eBefore using, make sure you have the latest version of \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pull\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull\u003c/h3\u003e\n\u003cp\u003eThe easiest thing is to pull the container from Singularity Hub where it\u0027s already built.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name latex.simg shub://vsoch/singularity-nbconvert:latex\nProgress |===================================| 100.0% \nDone. Container is at: /tmp/singularity/latex.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h3\u003e\n\u003cp\u003eThe container is a file sitting in your present working directory! To convert from Jupyter notebook (extension \u003ccode\u003e.ipynb\u003c/code\u003e) to pdf. It\u0027s primary function (called a runscript) is to perform a conversion, and that looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run latex.simg --to pdf test_notebook.ipynb\n[NbConvertApp] Converting notebook test_notebook.ipynb to pdf\n[NbConvertApp] Support files will be in test_notebook_files/\n[NbConvertApp] Making directory test_notebook_files\n[NbConvertApp] Writing 17358 bytes to notebook.tex\n[NbConvertApp] Building PDF\n[NbConvertApp] Running xelatex 3 times: [u\u0027xelatex\u0027, u\u0027notebook.tex\u0027]\n[NbConvertApp] Running bibtex 1 time: [u\u0027bibtex\u0027, u\u0027notebook\u0027]\n[NbConvertApp] WARNING | bibtex had problems, most likely because there were no citations\n[NbConvertApp] PDF successfully created\n[NbConvertApp] Writing 52431 bytes to test_notebook.pdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe call above can have any custom arguments that you would give to \u003ccode\u003ejupyter nbconvert\u003c/code\u003e. It doesn\u0027t necessarily have to convert to \u003ccode\u003e--pdf\u003c/code\u003e, and you can add other options. E.g., to see help:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run latex --help\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-exec\" class=\"anchor\" aria-hidden=\"true\" href=\"#exec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExec\u003c/h3\u003e\n\u003cp\u003eThe above command targets the nbconvert executable directly (via Jupyter), but you can also execute a custom command, the container has all of the dependencies like jupyter, nbconvert, etc. installed. For example, here I am listing the contents of the conda installation bin:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec latex ls /opt/conda/bin\n2to3\t\t infotocap\t\tpip\t\t tabs\nactivate\t jsonschema\t\tpydoc\t\t tclsh\nc_rehash\t jupyter\t\tpygmentize\t tclsh8.6\ncaptoinfo\t jupyter-kernelspec\tpython\t\t tic\nchardetect\t jupyter-migrate\tpython-config\t toe\nclear\t\t jupyter-nbconvert\tpython2\t\t tput\nconda\t\t jupyter-run\t\tpython2-config\t tset\nconda-env\t jupyter-troubleshoot\tpython2.7\t wheel\ndeactivate\t jupyter-trust\t\tpython2.7-config wish\neasy_install\t ncursesw6-config\treset\t\t wish8.6\neasy_install-2.7 openssl\t\tsmtpd.py\nidle\t\t pandoc\t\tsqlite3\ninfocmp\t\t pandoc-citeproc\tsqlite3_analyzer\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h2\u003e\n\u003cp\u003eDevelopment with Singularity is easiest when you build a \"sandbox,\" which is like building into a folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --sandbox latex/ Singularity.latex\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h3\u003e\n\u003cp\u003eYou can build the image with \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003eSingularity 2.4\u003c/a\u003e with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --writable latex.simg Singularity.latex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen it\u0027s time for a \"production\" build (squash fs image):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build latex.simg Singularity.latex\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_vlpi\u003c/h1\u003e\u003ca id=\"user-content-singularity_vlpi\" class=\"anchor\" aria-label=\"Permalink: singularity_vlpi\" href=\"#singularity_vlpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity file for VLPI project.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "nbconvert", - "singularity-container", - "singularity", - "jupyter", - "pdf" - ], - "updated_at": 1589940582.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1646980294.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "Singularity.master", - "Singularity.singularity3" + "Singularity" ], - "full_name": "stephansmit/shipyard_containers", + "full_name": "ISU-HPC/ml-intel", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-shipyard-container-to-run-containers-on-azure-shipyard\" class=\"anchor\" aria-hidden=\"true\" href=\"#shipyard-container-to-run-containers-on-azure-shipyard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShipyard container to run containers on Azure Shipyard\u003c/h1\u003e\n\u003cp\u003eContainers to run containers on \u003ca href=\"https://batch-shipyard.readthedocs.io/en/latest/00-introduction/%22\" rel=\"nofollow\"\u003eAzure Shipyard\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-the-container-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-the-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the container locally\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build su2_containers_master.sif Singularity.master\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull the container\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/shipyard_containers:master \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3377\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eml-intel\u003c/h1\u003e\u003ca id=\"user-content-ml-intel\" class=\"anchor\" aria-label=\"Permalink: ml-intel\" href=\"#ml-intel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMachine learning container with Intel python bindings\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1565710384.0 + "updated_at": 1524599924.0 }, { "data_format": 2, - "description": "example scientific filesystem to assess metrics across different solutions to a single problem, printing \"Hello World\"", + "description": "Singularity Images for neurodebian", "filenames": [ "Singularity" ], - "full_name": "sci-f/container.scif", + "full_name": "singularityhub/neurodebian", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hello-world-scientific-filesystem\" class=\"anchor\" aria-hidden=\"true\" href=\"#hello-world-scientific-filesystem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHello World Scientific Filesystem\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/containers-ftw.png\"\u003e\u003cimg src=\"img/containers-ftw.png\" alt=\"img/containers-ftw.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a Scientific Filesystem installed in a Singularity container, used to evaluate ~20 languages across different metrics for printing a simple \"Hello World,\" in dinosaur-speak of course! You can use the Makefile to build, clean, and run the container, and we will walk through the commands here. In all of these commands, we name the container based on the environment variable \u003ccode\u003e$CONTAINER\u003c/code\u003e (set in the \u003ca href=\"Makefile\"\u003eMakefile\u003c/a\u003e as \u003ccode\u003ehello-world\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eBuild the container!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build hello-world Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhat applications are available?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world apps\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the primary timing analysis.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/bin/bash test.sh hello-world\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [ - "singularity", + "neurodebian", "singularity-container", - "scif", - "scientific-filesystem" + "singularity-containers" ], - "updated_at": 1516572904.0 + "updated_at": 1487334737.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.cuda10", - "Singularity", - "Singularity.tf" + "Singularity" ], - "full_name": "callaghanmt-containers/ubuntu_cuda_cudnn_base", + "full_name": "photocyte/shournal_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ubuntu_cuda_cudnn_base\" class=\"anchor\" aria-hidden=\"true\" href=\"#ubuntu_cuda_cudnn_base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eubuntu_cuda_cudnn_base\u003c/h1\u003e\n", + "readme": "\u003cp\u003eImage building handled by singularity-hub.org\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://photocyte/shournal_singularity\nsingularity shell shournal_singularity_latest.sif \nsource /usr/share/shournal/SOURCE_ME.bash\nSHOURNAL_ENABLE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCurrently not working with error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshournal-run: shournal-run seems to lack the suid-bit (SETUID) for root. You can correct that by\nchown root shournal-run \u0026amp;\u0026amp; chmod u+s shournal-run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Singularity build does do that, but still doesn\u0027t work.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1556291490.0 + "updated_at": 1597950515.0 }, { "data_format": 2, - "description": "Singularity Bootstrap file for DL LEE SSNet using Caffe-LArbys", + "description": "A singularity recipe for AMRfinderplus", "filenames": [ "Singularity", - "SingularityTufts" + "v3.2.1/20191209/Singularity.v3.2.1_20191209", + "v3.2.1/20191106/Singularity.v3.2.1_20191106", + "v3.1.1b/20190924/Singularity.v3.1.1b_20190924" ], - "full_name": "LArbys/singularity-dllee-ssnet", + "full_name": "phgenomics-singularity/amrfinderplus", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-dllee-ssnet\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-dllee-ssnet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-dllee-ssnet\u003c/h1\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1497814537.0 + "updated_at": 1575844748.0 }, { "data_format": 2, - "description": "A client-worker proxy using ZMQ to server SSNet predictions on the Tufts Cluster", + "description": null, "filenames": [ - "container/Singularity", - "container/SingularityX", - "container/SingularityNoDrivers", - "container/Singularity390.30" + "Singularity" ], - "full_name": "LArbys/SSNetServer", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ssnet-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#ssnet-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSSNet Server\u003c/h1\u003e\n\u003cp\u003eA client-worker proxy using ZMQ to server SSNet predictions on the Tufts Cluster\u003c/p\u003e\n\u003cp\u003eThe code is a copy of the paranoid pirate proxy from the ZeroMQ Guide\u003c/p\u003e\n\u003cp\u003eThere are two goals:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a production network where many clients read data event-by-event, send to a collection of workers, receive net output, and write to disk\u003c/li\u003e\n\u003cli\u003eCreate a training network where single client app asks workers for batch data\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-classes\" class=\"anchor\" aria-hidden=\"true\" href=\"#classes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClasses\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ssnetworkerssnetclient\" class=\"anchor\" aria-hidden=\"true\" href=\"#ssnetworkerssnetclient\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSSNetWorker/SSNetClient\u003c/h3\u003e\n\u003cp\u003eThese are base classes that are meant to handle the network portion of the code.\nThey are to be inherited by child classes that handle either the reading/writing of data or the processing through a network.\u003c/p\u003e\n\u003cp\u003eNote that child client and workers are meant to be implemented together so that they understand their messages.\nWe do not enforce a standard messaging protocol.\nThis is meant to reflect the fact that different tasks usually differ in the details of the input/output data required.\nThough similar, I am not smart enough to define generic behavior.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ssnetbroker\" class=\"anchor\" aria-hidden=\"true\" href=\"#ssnetbroker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSSNetBroker\u003c/h3\u003e\n\u003cp\u003eThis class is the proxy between clients and workers.\nIt need not know anything about the data it is passing.\nIt\u0027s only job is to balance the load and keep track of connected workers (through heartbeats).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-simplelarcv1client\" class=\"anchor\" aria-hidden=\"true\" href=\"#simplelarcv1client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimpleLArCV1Client\u003c/h3\u003e\n\u003cp\u003eThis is a very basic client that reads larcv1 event images and sends it out to the SSNetBroker.\nIt only handles Image2D objects for now.\nYou can provide it a list of producer names via the \u003ccode\u003eproduct_dict\u003c/code\u003e argument of the constructor.\nIt will prepare a numpy array for each image product given. The array shapes are\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e(batchsize, number of images in event container, height, width)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe message sent to the worker is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[frame 0] \"producer name\" (string)\n[frame 1] \u0027Plane 65535 (rows,cols) = (0,0) ... Left Top (0,0) ... Right Bottom (0,0)\u0027 (the output of ImageMeta::dump())\n[frame 2] numpy array for batch\n[frame 3] \"producer name\" (string)\n[frame 4] \u0027Plane 65535 (rows,cols) = (0,0) ... Left Top (0,0) ... Right Bottom (0,0)\u0027 (the output of ImageMeta::dump())\n[frame 5] numpy array for batch\n(and so on...)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe received message is expected in the same format\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[frame 0] \"returned array name\" (string)\n[frame 1] \u0027Plane 65535 (rows,cols) = (0,0) ... Left Top (0,0) ... Right Bottom (0,0)\u0027 (the output of ImageMeta::dump())\n[frame 2] numpy array \n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe arrays in the received messages will be saved to an output larcv file.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dummylarcv1worker\" class=\"anchor\" aria-hidden=\"true\" href=\"#dummylarcv1worker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDummyLArCV1Worker\u003c/h3\u003e\n\u003cp\u003eUsed for debugging. Expects message from SimpleLArCV1Client and dumps numpy array shapes to standard out.\nReturns:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[frame 0] \"dummy\"\n[frame 1] \u0027Plane 65535 (rows,cols) = (0,0) ... Left Top (0,0) ... Right Bottom (0,0)\u0027 (the output of ImageMeta::dump())\n[frame 2] numpy array, filled with zeros, whose size is from the first received image\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-caffelarcv1clientworker\" class=\"anchor\" aria-hidden=\"true\" href=\"#caffelarcv1clientworker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaffeLArCV1Client/Worker\u003c/h3\u003e\n\u003cp\u003eWorker processes all three planes using Caffe1.\u003c/p\u003e\n\u003cp\u003eUses the same message protocol as Simple/Dummy pair above.\u003c/p\u003e\n\u003cp\u003eClient sends the images for one plane for one event as one batch. To send all three planes, 3 sets of frames are shipped together.\u003c/p\u003e\n\u003cp\u003eThe worker processes one frame at a time. It knows which plane\u0027s network to use from the meta. Because processing is frameset at a time,\nonly one network is running, while the others are idle. This could be improved by modeling the broker as a majordomo server, which\nknows how to ship different flavor of requests to different flavor of workers.\u003c/p\u003e\n\u003cp\u003eGood enough for now, though.\u003c/p\u003e\n\u003cp\u003eOn Meitner, 0.44 secs per event (read file+fill batch+roundtrip+write output)x3 planes.\nSeveral threads, but in total mem usage between 2.5 to 3 GB.\n(Will want to see mem usage in tests on separate nodes for worker and proxy.)\u003c/p\u003e\n", + "full_name": "khanlab/MRtrix3Tissue_singularity", + "latest_release": "v1.0.0", + "readme": "\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -v /data:/data MRtrix3Tissue.simg\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1569100609.0 + "updated_at": 1583525733.0 }, { "data_format": 2, - "description": "Containerized DMC application for HPCs", + "description": "Niftynet GPU singularity container", "filenames": [ - "setup/build/Singularity/Singularity.def", - "setup/build/Singularity/Singularity.ubuntu", - "setup/build/Singularity/SingularityUpdate.def", - "setup/build/Singularity/SingularityCore.def" + "Singularity.0.6.0" ], - "full_name": "McCoyGroup/RynLib", + "full_name": "yinglilu/niftynet_gpu_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rynlib\" class=\"anchor\" aria-hidden=\"true\" href=\"#rynlib\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRynLib\u003c/h1\u003e\n\u003cp\u003eThis started out as a quick layer between python and entos for running DMC\u003c/p\u003e\n\u003cp\u003eIt\u0027s grown a bit...\u003c/p\u003e\n\u003cp\u003eYou can find some documentation \u003ca href=\"https//:mccoygroup.github.io/Documentation/RynLib\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1613573199.0 + "updated_at": 1714773139.0 }, { "data_format": 2, - "description": "A singularity container for TB-profiler", + "description": "preterm birth project singularity container", "filenames": [ "Singularity" ], - "full_name": "phgenomics-singularity/tbprofiler", + "full_name": "CreRecombinase/ptb_container", "latest_release": null, "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1578274663.0 + "updated_at": 1566848645.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "Singularity.4.2.0", - "Singularity.4.3.0" + "Singularity" ], - "full_name": "MPIB/singularity-jags", + "full_name": "Raijeku/TensorOrder-tests", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1801\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://MPIB/singularity-jags\nsingularity exec singularity-jags_latest.sif jags\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-jags-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#jags-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJAGS singularity\u003c/h1\u003e\n\u003cp\u003eSingularity images containing \u003ca href=\"https://sourceforge.net/projects/mcmc-jags/\" rel=\"nofollow\"\u003eJAGS\u003c/a\u003e [1]:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebased on debian-slim\u003c/li\u003e\n\u003cli\u003edownloads and builds JAGS from: \u003ca href=\"https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Source/\" rel=\"nofollow\"\u003ehttps://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Source/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003elinks against libopenblas\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e[1]\n\u003ca href=\"https://sourceforge.net/projects/mcmc-jags/\" rel=\"nofollow\"\u003ehttps://sourceforge.net/projects/mcmc-jags/\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTensorOrder\u003c/h1\u003e\u003ca id=\"user-content-tensororder\" class=\"anchor\" aria-label=\"Permalink: TensorOrder\" href=\"#tensororder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Python 3 tool for automatically contracting tensor networks for weighted model counting on multiple CPUs and on a GPU.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with docker\u003c/h2\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running with docker\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBecause of the variety of dependencies used in the various graph decomposition tools, it is recommended to use the docker container to run TensorOrder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker container (for singlecore and multi-core) can be built with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensororder .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to leverage a GPU, you must compile the (larger) docker container with TensorFlow and gpu drivers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensororder-gpu -f Dockerfile-gpu .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container\u003c/h3\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce built, docker containers can be used as follows to run TensorOrder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i tensororder:latest python /src/tensororder.py --planner=\"line-Flow\" --weights=\"unweighted\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, this runs the tensor network contraction on all available CPU cores with numpy. One can also choose to use the GPU to perform the contraction. This requires \u003ca href=\"https://nvidia.github.io/nvidia-container-runtime/\" rel=\"nofollow\"\u003envidia-container-runtime\u003c/a\u003e to be installed.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --gpus all tensororder-gpu:latest python /src/tensororder.py --planner=\"line-Flow\" --weights=\"unweighted\" --tensor_library=\"tensorflow-gpu\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is also possible to connect to a TPU on Google Cloud to perform the tensor contractions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i tensororder:latest python /src/tensororder.py --timeout=\"1000\" --planner=\"line-Flow\" --weights=\"unweighted\" --tensor_library=\"jax-tpu\" --entry_type=\"float32\" --tpu=\"10.6.165.2\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBoth docker containers are compatible with \u003ca href=\"https://github.com/Kasekopf/Turbine\"\u003eTurbine\u003c/a\u003e to run experiments on Google Cloud.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with Singularity\u003c/h2\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere is also a \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available for TensorOrder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container-1\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity container can be built with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build tensororder Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container\u003c/h3\u003e\u003ca id=\"user-content-using-the-container-1\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce built, Singularity containers can be used as follows to run TensorOrder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./tensororder --method=\"line-Flow\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning without containers\u003c/h2\u003e\u003ca id=\"user-content-running-without-containers\" class=\"anchor\" aria-label=\"Permalink: Running without containers\" href=\"#running-without-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTensorOrder can also be used directly as a Python 3 tool. Since TensorOrder uses \u003ca href=\"https://cython.org/\" rel=\"nofollow\"\u003eCython\u003c/a\u003e, it must be compiled:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake -C src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMoreover, the various tensor methods each require additional setup. Consult the \u003ca href=\"Dockerfile\"\u003eDocker file\u003c/a\u003e for an example set of installation commands.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor KCMR-metis and KCMR-gn, METIS must be installed using the instructions \u003ca href=\"src/tensorcsp\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-Tamaki and factor-Tamaki, the tree-decomposition solver Tamaki must be compiled using the \u003ccode\u003eheuristic\u003c/code\u003e instructions \u003ca href=\"solvers/TCS-Meiji\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-Flow and factor-Flow, the tree-decomposition solver FlowCutter must be compiled using the instructions \u003ca href=\"solvers/flow-cutter-pace17\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-htd and factor-htd, the tree-decomposition solver htd must be compiled using the instructions \u003ca href=\"solvers/htd-master\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor factor-hicks, the branch-decomposition solver Hicks must be compiled using the Makefile \u003ca href=\"solvers/hicks\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-portfolio3 and line-portfolio3, all tree-decompositions solvers must be compiled, and the portfolio must be compiled using the instructions \u003ca href=\"solvers/portfolio\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce everything has been built, the primary script is located in \u003ccode\u003esrc/tensororder.py\u003c/code\u003e. Example usage is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython src/tensororder.py --method=\"line-Flow\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTensorOrder requires the following python packages (see \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e for a working set of exact version information if needed):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eclick\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enumpy\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython-igraph\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enetworkx\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecython\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ethreadpoolctl\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etensorflow\u003c/code\u003e (optional)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePublications\u003c/h3\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease cite the following article if you use our code in a publication:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://arxiv.org/abs/2006.15512\" rel=\"nofollow\"\u003eParallel Weighted Model Counting with Tensor Networks\u003c/a\u003e. Jeffrey M. Dudek and Moshe Y. Vardi. Proceedings of MCW\u002720.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1580121725.0 + "updated_at": 1706232720.0 }, { "data_format": 2, - "description": "Performance Evaluation Process Algebra", + "description": "Singularity container with Nix and OpenMPI to be used in XSEDE compute environment (currently in development)", "filenames": [ - "gpanalyser/Singularity.gpanalyser", - "pepa/Singularity.pepa", - "ipc/Singularity.ipc", - "bio-pepa/Singularity.biopepa" + "Singularity" ], - "full_name": "williamssanders/pepa", + "full_name": "XSEDE/singularity-nix-openmpi", "latest_release": null, - "readme": "{\"message\":\"API rate limit exceeded for installation ID 633759.\",\"documentation_url\":\"https://docs.github.com/rest/overview/resources-in-the-rest-api#rate-limiting\"}", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-nix-openmpi\u003c/h1\u003e\u003ca id=\"user-content-singularity-nix-openmpi\" class=\"anchor\" aria-label=\"Permalink: singularity-nix-openmpi\" href=\"#singularity-nix-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4462\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container with Nix and OpenMPI to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 16, "topics": [], - "updated_at": 1592227688.0 + "updated_at": 1637690636.0 }, { "data_format": 2, - "description": null, + "description": "test_singularity", "filenames": [ - "Singularity.v3.0.0" + "Singularity" ], - "full_name": "baxpr/ndw_wm_edat", + "full_name": "leepc12/test_singularity", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etest_singularity\u003c/h1\u003e\u003ca id=\"user-content-test_singularity\" class=\"anchor\" aria-label=\"Permalink: test_singularity\" href=\"#test_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etest_singularity\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1543615703.0 + "updated_at": 1542209843.0 }, { "data_format": 2, - "description": "A test to see if we can make images through singularity hub", + "description": "Fetch FASTQ files, clean and collapse duplicated reads", "filenames": [ "Singularity" ], - "full_name": "s-andrews/singularitytest", + "full_name": "JackCurragh/Collapse-FASTQ", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularitytest\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularitytest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularitytest\u003c/h1\u003e\n\u003cp\u003eA test to see if we can make images through singularity hub\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCollapse FASTQ Files\u003c/h1\u003e\u003ca id=\"user-content-collapse-fastq-files\" class=\"anchor\" aria-label=\"Permalink: Collapse FASTQ Files\" href=\"#collapse-fastq-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInputs\u003c/h3\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is general purpose for preparing FASTQ read files but was specifically developed to prepare data for \u003ca href=\"https://riboseq.org/\" rel=\"nofollow\"\u003eRiboSeq.Org\u003c/a\u003e.\nInputs for this pipeline may vary. Options include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStudy accession\u003c/li\u003e\n\u003cli\u003eSample accession list\u003c/li\u003e\n\u003cli\u003ePath to FASTQ directory\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutputs\u003c/h3\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCollapsed Read File\u003c/h4\u003e\u003ca id=\"user-content-collapsed-read-file\" class=\"anchor\" aria-label=\"Permalink: Collapsed Read File\" href=\"#collapsed-read-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe primary output is a gzipped collapsed read file of the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;read\u0026lt;read_number\u0026gt;_x\u0026lt;read_count\u0026gt;\nNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;read_number\u0026gt;\u003c/code\u003e signifies that this is the nth read processed.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;read_count\u0026gt;\u003c/code\u003e signifies how many times reads with this exact sequence was seen\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFastQC Report\u003c/h4\u003e\u003ca id=\"user-content-fastqc-report\" class=\"anchor\" aria-label=\"Permalink: FastQC Report\" href=\"#fastqc-report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ehtml\u003c/code\u003e report for each sample is outputted\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFastP\u003c/h4\u003e\u003ca id=\"user-content-fastp\" class=\"anchor\" aria-label=\"Permalink: FastP\" href=\"#fastp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ehtml\u003c/code\u003e report from FastP\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMultiQC Report\u003c/h4\u003e\u003ca id=\"user-content-multiqc-report\" class=\"anchor\" aria-label=\"Permalink: MultiQC Report\" href=\"#multiqc-report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCombined report for each pipeline run. Merged FastP and FastQC\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDocker\u003c/h5\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -t pipeline-image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003edocker\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027pipeline-image:latest\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eConda\u003c/h5\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a conda definition yaml file \u003ca href=\"conda/example.yml\"\u003eeg. here\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCall the pipeline directly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with all the frills\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills \u0026lt;params-file\u0026gt; \u0026lt;profile name from nextflow.config\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills example_parameters.yml standard\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1537371665.0 + "updated_at": 1723558153.0 }, { "data_format": 2, - "description": null, + "description": "Implementing the pytorch mnist example in a docker container.", "filenames": [ "Singularity" ], - "full_name": "NotTheKmers/main", + "full_name": "LBaeriswyl/pytorch-mnist-docker", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-replicate--reproduce-kmer-publication\" class=\"anchor\" aria-hidden=\"true\" href=\"#replicate--reproduce-kmer-publication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReplicate \u0026amp; Reproduce Kmer Publication\u003c/h1\u003e\n\u003cp\u003eWe have been tasked with replicating, reproducing, and extending the previous work of the \"These Are Not the K-mers You Are Looking For: Efficient Online K-mer Counting Using a Probabilistic Data Structure\" publication\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-directory\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest directory\u003c/h2\u003e\n\u003cp\u003ePut scratch and testing code in this directory\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epytorch-mnist-docker\u003c/h1\u003e\u003ca id=\"user-content-pytorch-mnist-docker\" class=\"anchor\" aria-label=\"Permalink: pytorch-mnist-docker\" href=\"#pytorch-mnist-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImplementing the pytorch MNIST example in a docker or singularity container.\u003c/p\u003e\n\u003cp\u003eTo train for a single epoch on docker:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnsure docker is installed\u003c/li\u003e\n\u003cli\u003eEnsure you are in the root directory\u003c/li\u003e\n\u003cli\u003eExecute ./run_mnist_docker_container.sh\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo train for a single epoch on singularity:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnsure singularity is installed\u003c/li\u003e\n\u003cli\u003eEnsure you are in the root directory\u003c/li\u003e\n\u003cli\u003eExecute ./run_mnist_singularity_container.sh\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1532379702.0 + "updated_at": 1700971295.0 }, { "data_format": 2, - "description": "Repository of singularity containers", + "description": "The simulation frame work for Craig Rafter\u0027s PhD research", "filenames": [ - "nanopolish/Singularity.nanopolish" + "SingularityDef" ], - "full_name": "alexiswl/singularity", + "full_name": "cbrafter/SUMO_FRAMEWORK", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSUMO Framework\u003c/h1\u003e\u003ca id=\"user-content-sumo-framework\" class=\"anchor\" aria-label=\"Permalink: SUMO Framework\" href=\"#sumo-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe simulation framework for the PhD research of Craig B. Rafter at the\nUniversity of Southampton 2015-2019.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. SUMO API\u003c/h2\u003e\u003ca id=\"user-content-1-sumo-api\" class=\"anchor\" aria-label=\"Permalink: 1. SUMO API\" href=\"#1-sumo-api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBase classes for signals and connecting to the simulation\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Models\u003c/h2\u003e\u003ca id=\"user-content-2-models\" class=\"anchor\" aria-label=\"Permalink: 2. Models\" href=\"#2-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFiles describing the road networks for SUMO\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Signal Controllers\u003c/h2\u003e\u003ca id=\"user-content-3-signal-controllers\" class=\"anchor\" aria-label=\"Permalink: 3. Signal Controllers\" href=\"#3-signal-controllers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCodes for the signal controllers used in this research\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. Simulation\u003c/h2\u003e\u003ca id=\"user-content-4-simulation\" class=\"anchor\" aria-label=\"Permalink: 4. Simulation\" href=\"#4-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCodes that run simulations using the models and signal controllers\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e5. Results Analysis\u003c/h2\u003e\u003ca id=\"user-content-5-results-analysis\" class=\"anchor\" aria-label=\"Permalink: 5. Results Analysis\" href=\"#5-results-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eScripts for analysing the SUMO outputs\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eScripts for doing useful things\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1522028262.0 + "updated_at": 1653390685.0 }, { "data_format": 2, - "description": "Singularity definition files and Dockerfiles for CentOS desktop on LURC\u0027s OOD portal", + "description": "Get Parflow built in a singularity container for distribution", "filenames": [ - "Singularity.xfce", - "Singularity.mate", - "Singularity.molgfx" + "Singularity.parflow_ompi_206", + "Singularity", + "Singularity.no_netcdf", + "base/Singularity.base", + "base/Singularity.nv_base", + "mpi/Singularity.mpich", + "mpi/Singularity.ompi", + "pf/Singularity.parflow_cuda", + "pf/Singularity.parflow", + "pf/Singularity.parflow_mpich", + "pf/Singularity.parflow_ompi", + "libs/Singularity.nv_libs", + "libs/Singularity.libs_mpich", + "libs/Singularity.libs", + "libs/Singularity.libs_ompi" ], - "full_name": "alexpacheco/lurc-ood-desktop", + "full_name": "arezaii/pf_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-lurc-ood-desktop\" class=\"anchor\" aria-hidden=\"true\" href=\"#lurc-ood-desktop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elurc-ood-desktop\u003c/h1\u003e\n\u003cp\u003eSingularity definition files and Dockerfiles for CentOS desktop on LURC\u0027s OOD portal\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParFlow Singularity Definition Files\u003c/h1\u003e\u003ca id=\"user-content-parflow-singularity-definition-files\" class=\"anchor\" aria-label=\"Permalink: ParFlow Singularity Definition Files\" href=\"#parflow-singularity-definition-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA set of singularity definition files that allow for building Singularity containers for ParFlow with\neither OMPI or MPICH mpi layers.\u003c/p\u003e\n\u003cp\u003eEach ParFlow container is built as a sci-app container, providing access to both sequential and parallel\nbuilds of ParFlow\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout Apps\u003c/h2\u003e\u003ca id=\"user-content-about-apps\" class=\"anchor\" aria-label=\"Permalink: About Apps\" href=\"#about-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epar = distributed build of ParFlow, -DPARFLOW_AMPS_SEQUENTIAL_IO=True\u003c/li\u003e\n\u003cli\u003eseq = sequential build of ParFlow, -DPARFLOW_AMPS_SEQUENTIAL_IO=False\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto run either:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eapp_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e/path/to/singularity_container.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e.tcl input file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee additional information about \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/definition_files.html?highlight=apps#apps\" rel=\"nofollow\"\u003eApps in Singularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eHost OS must have Singularity installed\u003c/li\u003e\n\u003cli\u003eTo build container from recipe file, user must have root access\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo Build Container\u003c/h2\u003e\u003ca id=\"user-content-to-build-container\" class=\"anchor\" aria-label=\"Permalink: To Build Container\" href=\"#to-build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGeneral build command is of the form:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edestination/path/to/singularity_container.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSingularity definition file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eas a specific example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/pf_singularity_ompi Singularity.parflow_ompi\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo Use ParFlow in Container\u003c/h2\u003e\u003ca id=\"user-content-to-use-parflow-in-container\" class=\"anchor\" aria-label=\"Permalink: To Use ParFlow in Container\" href=\"#to-use-parflow-in-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eexample of running the LW test case in \u003ccode\u003eparflow/test/washita/tcl_scripts\u003c/code\u003e directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app par \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/pf_singularity_ompi LW_Test.tcl\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull from Singularity Hub\u003c/h2\u003e\u003ca id=\"user-content-pull-from-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Pull from Singularity Hub\" href=\"#pull-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull shub://arezaii/pf_singularity:parflow_ompi\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen to use it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app par pf_singularity_parflow_ompi.sif LW_Test.tcl\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBecause singularity containers are immutable and ParFlow tests write to disk, you must expand the image to a writable sandbox.\nUnfortunately this requires super user access to do...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMake Container Writable\u003c/h3\u003e\u003ca id=\"user-content-make-container-writable\" class=\"anchor\" aria-label=\"Permalink: Make Container Writable\" href=\"#make-container-writable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, create a writable sandbox from the immutable container using Singularity\u0027s build command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory_to_create_for_sandbox/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esingularity_container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eas an example, if you had pulled the parflow_ompi image from shub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --sandbox pf_singularity_parflow_ompi_test/ pf_singularity_parflow_ompi.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere will now be a new directory pf_singularity_parflow_ompi_test/ that is the root of the container.\nEditing any of the folder contents will require super user permissions.\u003c/p\u003e\n\u003cp\u003eYou can enter a console into the container now by using the Singularity shell command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell --writable \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory_to_create_for_sandbox/\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Tests\u003c/h3\u003e\u003ca id=\"user-content-run-tests\" class=\"anchor\" aria-label=\"Permalink: Run Tests\" href=\"#run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter making the container writable and accessing it through a shell, both documented above, running the ParFlow\ntests can be done by changing directories and exporting the PARFLOW_DIR environment variable for either distributed\nor sequential builds of ParFlow.\u003c/p\u003e\n\u003cp\u003eTake note of the ParFlow build and install directories in the container:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequential Build\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild directory: /home/parflow/build_seq\u003c/li\u003e\n\u003cli\u003einstall directory: /home/parflow/pfdir_seq\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDistributed Build\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild directory: /home/parflow/build_par\u003c/li\u003e\n\u003cli\u003einstall directory: /home/parflow/pfdir_par\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/parflow/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ebuild_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PARFLOW_DIR=/home/parflow/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003einstall_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e make \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1614296417.0 + "updated_at": 1650137133.0 }, { "data_format": 2, - "description": "A quality control pipeline for illumina data set. This pipeline removes contaminants (e.g. Phix), performs fastqc, adapter cleaning and trimming and checks for contaminants", + "description": null, "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "sequana/quality_control", - "latest_release": "v0.10.0", + "full_name": "feilong/artful-neurodebian", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1634220788.0 + "updated_at": 1505485847.0 }, { "data_format": 2, - "description": null, + "description": "Model Evaluation Toolkit Singularity Containers", "filenames": [ - "Singularity.biodiverse" + "Singularity", + "previous/Singularity.8.0", + "previous/Singularity.8.1" ], - "full_name": "ternaustralia/coesra-singularity-biodiverse", + "full_name": "trwhitcomb/metcontainers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-coesra-singularity-biodiverse\" class=\"anchor\" aria-hidden=\"true\" href=\"#coesra-singularity-biodiverse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecoesra-singularity-biodiverse\u003c/h1\u003e\n\u003cp\u003eAuthor: Hoang Nguyen\nCreated: 22 July 2019\nThis will create a image with Singularity 2.5.1\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emetcontainers\u003c/h1\u003e\u003ca id=\"user-content-metcontainers\" class=\"anchor\" aria-label=\"Permalink: metcontainers\" href=\"#metcontainers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModel Evaluation Toolkit Singularity Containers\u003c/p\u003e\n\u003cp\u003eUnofficial Singularity version of official MET Docker containers\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "coesra" - ], - "updated_at": 1563776892.0 + "topics": [], + "updated_at": 1570823060.0 }, { "data_format": 2, - "description": "Singularity definition files and Dockerfiles for building RStudio and R packages on LURC\u0027s OOD portal", + "description": null, "filenames": [ - "Singularity.r402-lugeo", - "Singularity.r402-lubio", - "Singularity.r353", - "Singularity.r402-base", - "Singularity.r363" + "Singularity.borgbackup_1.1.13", + "Singularity.clang-upc_3.9.1" ], - "full_name": "alexpacheco/lurc-ood-rstudio", + "full_name": "TomHarrop/misc-utils", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-lurc-ood-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#lurc-ood-rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elurc-ood-rstudio\u003c/h1\u003e\n\u003cp\u003eSingularity definition files and Dockerfiles for building RStudio and R packages on LURC\u0027s OOD portal\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1615375080.0 + "updated_at": 1619659263.0 }, { "data_format": 2, - "description": "Surface morphometry BIDS app", + "description": "Python Gene Expression Spatial Toolkit", "filenames": [ - "Singularity.v0.1", - "Singularity" + "singularity/Singularity.stretch" ], - "full_name": "khanlab/surfmorph", - "latest_release": "v0.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-surfmorph\" class=\"anchor\" aria-hidden=\"true\" href=\"#surfmorph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esurfmorph\u003c/h1\u003e\n\u003cp\u003eSurface morphometry BIDS app\u003c/p\u003e\n", + "full_name": "mfschmidt/PyGEST", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1591844426.0 + "updated_at": 1611368377.0 }, { "data_format": 2, - "description": null, + "description": "Work I did for Google Summer of Code 2020", "filenames": [ - "Singularity.centos" + "Singularity.test2", + "Singularity", + "Singularity_Test/Singularity.test" ], - "full_name": "ertheisen/test", + "full_name": "timothydgreer/GSoC_2020", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGSoC_2020\u003c/h1\u003e\u003ca id=\"user-content-gsoc_2020\" class=\"anchor\" aria-label=\"Permalink: GSoC_2020\" href=\"#gsoc_2020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWork I did for Google Summer of Code 2020\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1570565785.0 + "updated_at": 1589700293.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.1.0.0", - "Singularity.1.1.0" + "Singularity.build" ], - "full_name": "pndni/freesurfer-6.0.1-container", - "latest_release": null, + "full_name": "bjorgve/hpc-build-box", + "latest_release": "0.0.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehpc-build-box\u003c/h1\u003e\u003ca id=\"user-content-hpc-build-box\" class=\"anchor\" aria-label=\"Permalink: hpc-build-box\" href=\"#hpc-build-box\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container provides an environment with key libraries and tools for high-performance computing (HPC) development. It includes MPI (Message Passing Interface), OpenMP (Open Multi-Processing), Eigen (C++ template library for linear algebra), and CMake build system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eMPI\u003c/strong\u003e: Pre-installed Open MPI for parallel computing.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOpenMP\u003c/strong\u003e: Support for multi-platform shared-memory parallel programming.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEigen\u003c/strong\u003e: Eigen 3.4 for high-level linear algebra operations.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCMake\u003c/strong\u003e: Version 3.25.0 for configuring and building your projects.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisites\u003c/h2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/guides/3.7/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed on your machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload\u003c/h2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-label=\"Permalink: Download\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo pull the latest version of the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull https://github.com/bjorgve/hpc-build-box/releases/download/0.0.2/bjorgve-hpc-build-box.build.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning CMake\u003c/h3\u003e\u003ca id=\"user-content-running-cmake\" class=\"anchor\" aria-label=\"Permalink: Running CMake\" href=\"#running-cmake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./bjorgve-hpc-build-box.build.sif cmake [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompiling Code with Make\u003c/h3\u003e\u003ca id=\"user-content-compiling-code-with-make\" class=\"anchor\" aria-label=\"Permalink: Compiling Code with Make\" href=\"#compiling-code-with-make\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./bjorgve-hpc-build-box.build.sif make [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Executables\u003c/h3\u003e\u003ca id=\"user-content-running-executables\" class=\"anchor\" aria-label=\"Permalink: Running Executables\" href=\"#running-executables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./bjorgve-hpc-build-box.build.sif ./executable [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInside the Container\u003c/h2\u003e\u003ca id=\"user-content-inside-the-container\" class=\"anchor\" aria-label=\"Permalink: Inside the Container\" href=\"#inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere\u0027s what gets installed in the container based on the \u003ccode\u003e.def\u003c/code\u003e file:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBasic build utilities (\u003ccode\u003ebuild-essential\u003c/code\u003e, \u003ccode\u003ewget\u003c/code\u003e, \u003ccode\u003egit\u003c/code\u003e, \u003ccode\u003ecurl\u003c/code\u003e, etc.)\u003c/li\u003e\n\u003cli\u003eOpenMP (\u003ccode\u003elibgomp1\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eOpen MPI (\u003ccode\u003elibopenmpi-dev\u003c/code\u003e, \u003ccode\u003eopenmpi-bin\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eBoost libraries (\u003ccode\u003elibboost-all-dev\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCMake 3.25.0\u003c/li\u003e\n\u003cli\u003eEigen 3.4\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions and Issues\u003c/h2\u003e\u003ca id=\"user-content-contributions-and-issues\" class=\"anchor\" aria-label=\"Permalink: Contributions and Issues\" href=\"#contributions-and-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFeel free to open issues or submit pull requests if you have suggestions or encounter issues.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1556655715.0 + "updated_at": 1694033038.0 }, { "data_format": 2, - "description": "Small example TI method within a docker", + "description": "C++ API \u0026 command-line toolkit for working with BAM data", "filenames": [ - "R_dynwrap/Singularity.R_dynwrap", - "python_hdf5/Singularity.python_hdf5", - "R_text/Singularity.R_text", - "python_text/Singularity.python_text", - "R_hdf5/Singularity.R_hdf5", - "R_feather/Singularity.R_feather", - "R_rds/Singularity.R_rds", - "python_feather/Singularity.python_feather" + "2.5.1/Singularity", + "2.5.2/Singularity" ], - "full_name": "dynverse/dynwrap_tester", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-creating-ti-methods-within-a-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-ti-methods-within-a-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating TI methods within a docker\u003c/h1\u003e\n\u003cp\u003eThis repository contains several examples of wrapping a TI method within a docker.\u003c/p\u003e\n\u003cp\u003eIt contains three main files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edefinition.yml\u003c/code\u003e Defining the input, output and parameters of the method\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDockerfile\u003c/code\u003e Used for building the docker, its entrypoint is used to run the method\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun.R\u003c/code\u003e Loads the data, infers a trajectory, and generates some output files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe docker image is automatically build at \u003ca href=\"https://hub.docker.com/r/dynverse/dynwrap_tester/builds/\" rel=\"nofollow\"\u003edockerhub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis method can be run directly from dockerhub using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003edynwrap\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eti_comp1\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e pull_docker_ti_method(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edynverse/dynwrap_tester\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)()\n\u003cspan class=\"pl-smi\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e infer_trajectory(\u003cspan class=\"pl-smi\"\u003etask\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eti_comp1\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "pscedu/singularity-bamtools", + "latest_release": "v2.5.2", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bamtools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/054141521af458b1312c4cb51f20a90cba03b91c428ea0010ed08382f5118785/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/054141521af458b1312c4cb51f20a90cba03b91c428ea0010ed08382f5118785/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bamtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5193a9daee0df89ed29bb24726e38247567acccb6fb574dadf1f299cf508f7c6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5193a9daee0df89ed29bb24726e38247567acccb6fb574dadf1f299cf508f7c6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bamtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/22739bec1bac08d0d267605f10cb285794413bb170278dec0274e32df1f3a5da/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22739bec1bac08d0d267605f10cb285794413bb170278dec0274e32df1f3a5da/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bamtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e9c1e69bc7ade96a1b70b1b296fc22b7df4bbb8415576d179012ecd76c87e28d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e9c1e69bc7ade96a1b70b1b296fc22b7df4bbb8415576d179012ecd76c87e28d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62616d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bamtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-bamtools\u003c/h1\u003e\u003ca id=\"user-content-singularity-bamtools\" class=\"anchor\" aria-label=\"Permalink: singularity-bamtools\" href=\"#singularity-bamtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/pezmaster31/bamtools\"\u003ebamtools\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebamtools\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bamtools/2.5.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bamtools\u003c/code\u003e as \u003ccode\u003e2.5.1\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, - "topics": [], - "updated_at": 1539684245.0 + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629217479.0 }, { "data_format": 2, - "description": "Singularity recipe for cDNA_cupcake", + "description": "official build specifications for tensorflow", "filenames": [ - "Singularity.5.8.0" + "Singularity" ], - "full_name": "ISU-HPC/cDNA_cupcake", + "full_name": "researchapps/tensorflow", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cdna_cupcake\" class=\"anchor\" aria-hidden=\"true\" href=\"#cdna_cupcake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecDNA_cupcake\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for cDNA_cupcake\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTensorflow\u003c/h1\u003e\u003ca id=\"user-content-tensorflow\" class=\"anchor\" aria-label=\"Permalink: Tensorflow\" href=\"#tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a tensorflow image developed to work on the Sherlock cluster. We start with Docker bases to make life easy.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1545169004.0 + "updated_at": 1484507796.0 }, { "data_format": 2, - "description": "Singularity Recipes for larcv3", + "description": "repo for automated processing of Ribo-Seq (and associated RNA-seq) data ", "filenames": [ - "recipes/cuda/Singularity.centos7-cuda-core", - "recipes/cuda/Singularity.centos7-cuda-core-mpich", - "recipes/cuda/torch/Singularity.centos7-cuda-torch", - "recipes/cuda/torch/Singularity.centos7-cuda-torch-mpich-larcv", - "recipes/cuda/torch/Singularity.centos7-cuda-torch-larcv", - "recipes/cuda/torch/Singularity.centos7-cuda-torch-mpich", - "recipes/cuda/tf/Singularity.centos7-cuda-tf-mpich", - "recipes/cuda/tf/Singularity.centos7-cuda-tf-mpich-larcv", - "recipes/cuda/tf/Singularity.centos7-cuda-tf", - "recipes/cuda/tf/Singularity.centos7-cuda-tf-larcv" + "Singularity" ], - "full_name": "DeepLearnPhysics/larcv3-singularity", + "full_name": "JackCurragh/riboseq_data_processing", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRibo-Seq Data Processing\u003c/h1\u003e\u003ca id=\"user-content-ribo-seq-data-processing\" class=\"anchor\" aria-label=\"Permalink: Ribo-Seq Data Processing\" href=\"#ribo-seq-data-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e[Describe here what this pipeline does]\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDocker\u003c/h5\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -t pipeline-image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003edocker\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027pipeline-image:latest\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eConda\u003c/h5\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a conda definition yaml file \u003ca href=\"conda/example.yml\"\u003eeg. here\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCall the pipeline directly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with all the frills\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills \u0026lt;params-file\u0026gt; \u0026lt;profile name from nextflow.config\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills example_parameters.yml standard\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData Processing For \u003ca href=\"riboseq.org\"\u003eRiboSeq.org\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-data-processing-for-riboseqorg\" class=\"anchor\" aria-label=\"Permalink: Data Processing For RiboSeq.org\" href=\"#data-processing-for-riboseqorg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAutomated processing of Ribo-Seq (and associated RNA-Seq) data for \u003ca href=\"https://gwips.ucc.ie/\" rel=\"nofollow\"\u003eGWIPS-Viz\u003c/a\u003e and \u003ca href=\"https://trips.ucc.ie/\" rel=\"nofollow\"\u003eTRIPS-Viz\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-automated-processing-of-ribo-seq-and-associated-rna-seq-data-for-gwips-viz-and-trips-viz\" class=\"anchor\" aria-label=\"Permalink: Automated processing of Ribo-Seq (and associated RNA-Seq) data for GWIPS-Viz and TRIPS-Viz\" href=\"#automated-processing-of-ribo-seq-and-associated-rna-seq-data-for-gwips-viz-and-trips-viz\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout Riboseq.org\u003c/h2\u003e\u003ca id=\"user-content-about-riboseqorg\" class=\"anchor\" aria-label=\"Permalink: About Riboseq.org\" href=\"#about-riboseqorg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a set of resources for the analysis and visualisation of publically available ribosome profiling data produced and maintained by various members of LAPTI lab in the School of Biochemistry and Cell Biology at Univeristy College Cork. These resources are well documented in their respective publications\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGWIPS-Viz\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://doi.org/10.1093/nar/gkx790\" rel=\"nofollow\"\u003eGWIPS-viz: 2018 update (2018).\u003c/a\u003e Nucleic Acids Res\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://doi.org/10.1002/cpbi.50\" rel=\"nofollow\"\u003eThe GWIPS-viz Browser (2018).\u003c/a\u003e Current Protocols in Bioinformatics\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://dx.doi.org/10.1002/pmic.201400603%20\" rel=\"nofollow\"\u003eGWIPS-viz as a tool for exploring ribosome profiling evidence supporting the synthesis of alternative proteoforms (2015).\u003c/a\u003e Proteomics\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://dx.doi.org/10.1093/nar/gkt1035\" rel=\"nofollow\"\u003e GWIPS-viz: development of a ribo-seq genome browser (2014).\u003c/a\u003e Nucleic Acids Res\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.1093/nar/gky842\" rel=\"nofollow\"\u003eTrips-Viz: a transcriptome browser for exploring Ribo-Seq data (2019).\u003c/a\u003e Nucleic Acids Res\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://dx.doi.org/10.1080/15476286.2016.1141862\" rel=\"nofollow\"\u003eRiboGalaxy: a browser based platform for the alignment, analysis and visualization of ribosome profiling data.\u003c/a\u003e RNA Biology-Viz\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e** Note: Ribogalaxy is being updated currently and functionality will be restored shortly (14-2-2022)**\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements-1\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003e Biopython \u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/pandas/\" rel=\"nofollow\"\u003e Pandas \u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/validators/\" rel=\"nofollow\"\u003e Validators \u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutline\u003c/h2\u003e\u003ca id=\"user-content-outline\" class=\"anchor\" aria-label=\"Permalink: Outline\" href=\"#outline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eProduce Database Of All Available Ribosome Profiling Studies\u003c/li\u003e\n\u003cli\u003eGather Metadata\u003c/li\u003e\n\u003cli\u003eFetch Files and Infer Gaps in Metadata\u003c/li\u003e\n\u003cli\u003eRun Pipeline\u003c/li\u003e\n\u003cli\u003eUpload to GWIPS \u0026amp; TRIPS\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Produce Database Of All Available Ribosome Profiling Studies\u003c/h2\u003e\u003ca id=\"user-content-1-produce-database-of-all-available-ribosome-profiling-studies\" class=\"anchor\" aria-label=\"Permalink: 1. Produce Database Of All Available Ribosome Profiling Studies\" href=\"#1-produce-database-of-all-available-ribosome-profiling-studies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn recent years the rate at which ribosome profiling studies have been published has steadily increased. When the riboseq.org resources were initiatlly developed the number of available ribo-seq datasets was managable via manual inclusion. Here we put in place a method that records the details of relevant ribosome profiling data deposited in GEO\u003c/p\u003e\n\u003cp\u003eInitially manual searching of GEO and SRA were used along with \u003ca href=\"10.3390/biology10101026\"\u003eARGEOS\u003c/a\u003e. The outputs of each of these methods were colated to find the set of unique datasets.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Gather Metadata\u003c/h2\u003e\u003ca id=\"user-content-2-gather-metadata\" class=\"anchor\" aria-label=\"Permalink: 2. Gather Metadata\" href=\"#2-gather-metadata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGEO and SRA run tables contain valuable metadata that may be important for the processing and cateloging of the datasets. In this step we use python scripts to glean what we can from the information available\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Fetch Files and Infer Gaps in Metadata\u003c/h2\u003e\u003ca id=\"user-content-3-fetch-files-and-infer-gaps-in-metadata\" class=\"anchor\" aria-label=\"Permalink: 3. Fetch Files and Infer Gaps in Metadata\" href=\"#3-fetch-files-and-infer-gaps-in-metadata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA common problem with reprocessing data for these resources is that the data is deposited in GEO and SRA with inconsistent metadata. In the stage of the process we carry out a number of steps to check for the relevant data in the provided metadata and where it is absent we infer it from the data itself. This relates to information such as cell type and treatment but also UMI position and adapter position/sequence.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. Run pipeline\u003c/h2\u003e\u003ca id=\"user-content-4-run-pipeline\" class=\"anchor\" aria-label=\"Permalink: 4. Run pipeline\" href=\"#4-run-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this stage we use nextflow to process the fetched reads following the schema below\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/JackCurragh/riboseq_data_processing/blob/main/images/pipeline.drawio.png\"\u003e\u003cimg src=\"https://github.com/JackCurragh/riboseq_data_processing/raw/main/images/pipeline.drawio.png\" alt=\"Deptiction of the data processing pipeline\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e5. Upload to GWIPS and TRIPS\u003c/h2\u003e\u003ca id=\"user-content-5-upload-to-gwips-and-trips\" class=\"anchor\" aria-label=\"Permalink: 5. Upload to GWIPS and TRIPS\" href=\"#5-upload-to-gwips-and-trips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis stage uses the metadata to upload the processed files to the web resources in an automated fashion\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1584373427.0 + "updated_at": 1685092664.0 }, { "data_format": 2, "description": null, "filenames": [ - "devops_pipeline/Singularity", - "devops_base/Singularity" + "docker/Singularity" ], - "full_name": "ninamiolane/gnetree", + "full_name": "arustamm/fwix", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1548186366.0 + "updated_at": 1722714141.0 }, { "data_format": 2, - "description": null, + "description": "Template repository for setting up new Nextflow pipelines", "filenames": [ - "Singularity.basecall_wrapper_0.0.32_albacore_2.3.3" + "Singularity" ], - "full_name": "TomHarrop/basecall_wrapper", + "full_name": "JackCurragh/Nextflow-Template", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNextflow-Template\u003c/h1\u003e\u003ca id=\"user-content-nextflow-template\" class=\"anchor\" aria-label=\"Permalink: Nextflow-Template\" href=\"#nextflow-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTemplate repository for setting up new Nextflow pipelines\u003c/p\u003e\n\u003cp\u003eThis template has been informed by many other Nextflow pipelines and templates. Some are much more advanced than this template. Please see:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nextflow-io/awesome-nextflow\"\u003eAwesome-Nextflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AustralianBioCommons/Nextflow_DSL2_template\"\u003eAustralian BioCommons Template\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/epi2me-labs/wf-template\"\u003eepi2me labs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/FredHutch/workflow-template-nextflow\"\u003eFredHutch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/search?q=Nextflow%20Template\u0026amp;type=repositories\"\u003eAnd Many More\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGeneral Setup Notes\u003c/h2\u003e\u003ca id=\"user-content-general-setup-notes\" class=\"anchor\" aria-label=\"Permalink: General Setup Notes\" href=\"#general-setup-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTower\u003c/h4\u003e\u003ca id=\"user-content-tower\" class=\"anchor\" aria-label=\"Permalink: Tower\" href=\"#tower\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etower.nf allows GUI tracking of workflow progress\nSet it up using the instructions \u003ca href=\"https://help.tower.nf/22.4/getting-started/usage/#nextflow-with-tower\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e||=====================================||\u003c/h1\u003e\u003ca id=\"\" class=\"anchor\" aria-label=\"Permalink: ||=====================================||\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e|| Template README\u003c/h1\u003e\u003ca id=\"user-content----template-readme\" class=\"anchor\" aria-label=\"Permalink: || Template README\" href=\"#---template-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e|| \u003cb\u003eDelete Everything Above\u003c/b\u003e\n\u003c/h1\u003e\u003ca id=\"user-content----delete-everything-above\" class=\"anchor\" aria-label=\"Permalink: || Delete Everything Above\" href=\"#---delete-everything-above\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e||=====================================||\u003c/h1\u003e\u003ca id=\"user-content--1\" class=\"anchor\" aria-label=\"Permalink: ||=====================================||\" href=\"#-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e[Describe here what this pipeline does]\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDocker\u003c/h5\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -t pipeline-image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003edocker\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027pipeline-image:latest\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eConda\u003c/h5\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a conda definition yaml file \u003ca href=\"conda/example.yml\"\u003eeg. here\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCall the pipeline directly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with all the frills\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills \u0026lt;params-file\u0026gt; \u0026lt;profile name from nextflow.config\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills example_parameters.yml standard\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1567650634.0 + "updated_at": 1681044968.0 }, { "data_format": 2, - "description": null, + "description": "Nextflow pipeline for running RiboMetric on Genomic BAMs (as per rdp.ucc.ie)", "filenames": [ - "Singularity.v3.3.1" + "Singularity" ], - "full_name": "baxpr/sct-singularity", + "full_name": "JackCurragh/RiboMetric-nf", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-spinal-cord-toolbox-in-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#spinal-cord-toolbox-in-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpinal Cord Toolbox in Singularity container\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cp\u003eSee \u003ccode\u003e(/opt/)fmri_pipeline/fmri_pipeline_launch.sh\u003c/code\u003e, \u003ccode\u003e(/opt/)mffe_pipeline/mffe_pipeline_launch.sh\u003c/code\u003e for a list of the inputs for each app, and \u003ccode\u003e(/opt/)test_mffe.sh\u003c/code\u003e, \u003ccode\u003e(/opt/)test_fmri.sh\u003c/code\u003e for example run scripts. Many of the inputs for the fmri app are outputs of the mffe app.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h2\u003e\n\u003cp\u003eOutput images are named as \u003ccode\u003e\u0026lt;geometry\u0026gt;_\u0026lt;contents\u0026gt;.nii.gz\u003c/code\u003e. The tag \u003ccode\u003e_template_\u003c/code\u003e indicates the image was derived from the PAM50 template; all others are derived from the subject images.\u003c/p\u003e\n\u003cp\u003eGeometries are\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efmri_ Native geometry of the fMRI\nmffe_ Native geometry of the mFFE\nt2sag_ Native geometry of the T2 sagittal.\nipmffe_ Iso-voxel padded geometry based on the native mFFE. This is used to accurately \n resample vertebral locations and ROIs between geometries.\nwarp_ Warp field between two geometries\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput contents from the mffe app are\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e_mffe Unprocessed mFFE\n\n_maskNN Registration mask, NN mm in size\n\n_cord Segmented spinal cord (\"seg\")\n_cord_labeled Vertebral label ROIs found on the t2sag\n_cord_labeled_discs Disc point labels found on the t2sag\n_cord_labeled_body Body center points from _cord_labeled\n\n_gm Segmented gray matter found on the mFFE\n_wm Segmented white matter found on the mFFE\n_csf Segmented CSF found on the mFFE\n_template_csf Atlas CSF compartment from the PAM50 template\n\n_synt2 Synthetic T2 built from the gray and white segmentations\n\nmffe_report.pdf QC report and view of results\nmffe_csa.csv Cross-sectional areas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput contents from the fmri app are\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e_fmri Unprocessed fMRI\n_fmri0 First volume of unprocessed fMRI\n_moco Motion corrected fMRI\n_moco_mean Mean of motion corrected fMRI volumes\n_regbp Filtered fMRI (confound regression and bandpass)\n\n_mffe Resampled mFFE\n\n_maskNN Registration mask, NN mm in size\n\n_cord Segmented spinal cord (\"seg\")\n_cord_labeled Vertebral label ROIs found on the t2sag\n_centerline Cord centerline\n\n_gm Segmented gray matter found on the mFFE\n_wm Segmented white matter found on the mFFE\n_csf Atlas CSF compartment from the PAM50 template\n\n_notspine \"Not spine\" region used to obtain confound signals\n\n_gmcut Gray matter cut into four horns\n_gmcutlabel Gray matter cut into four horns and marked by level\n \n_R_*_inslice Connectivity maps for within-slice seeds (R)\n_Z_*_inslice Connectivity maps for within-slice seeds (Z)\n\nfmri_report.pdf QC report and view of results\nR_inslice.csv ROI-to-ROI connectivity within slice (R)\nZ_inslice.csv ROI-to-ROI connectivity within slice (Z)\n\nfmri_gmcut.csv Label info for ROI images of same base filename\nfmri_gmcutlabel.csv\n\nphyslog_cardiac.csv Cardiac signal from physlog\nphyslog_respiratory.csv Respiratory signal from physlog\nricor.slibase.1D Physlog signals as output from RetroTS\nricor.csv Computed respiratory regressors\n\nfmri_moco_params.tsv Estimated fMRI motion parameters\nfmri_moco_params_X.nii.gz\nfmri_moco_params_Y.nii.gz\n\nvolume_acquisition_time.txt Volume acq time used for filtering (sec)\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRiboMetric-nf\u003c/h1\u003e\u003ca id=\"user-content-ribometric-nf\" class=\"anchor\" aria-label=\"Permalink: RiboMetric-nf\" href=\"#ribometric-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRuns RiboMetric on Genomic BAMs\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDocker\u003c/h5\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -t pipeline-image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003edocker\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027pipeline-image:latest\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eConda\u003c/h5\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a conda definition yaml file \u003ca href=\"conda/example.yml\"\u003eeg. here\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCall the pipeline directly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with all the frills\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills \u0026lt;params-file\u0026gt; \u0026lt;profile name from nextflow.config\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash scripts/run-w-frills example_parameters.yml standard\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e11 samtools index $file\n12 mudskipper bulk --alignment ${file} --out ${file}.trans.bam --index $MUD\n13 samtools sort ${file}.trans.bam -o ${file}.trans.bam\n14 samtools index ${file}.trans.bam\n15 RiboMetric run -b ${file}.trans.bam -a $RiboMetric -o $dir --all\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1586976537.0 + "updated_at": 1709747987.0 }, { "data_format": 2, - "description": "Singularity recipe for salmon", + "description": "Test what effect subssampling of a run has on QC", "filenames": [ - "Singularity.0.10.1", "Singularity" ], - "full_name": "ISU-HPC/salmon", + "full_name": "JackCurragh/Subsample-Effect-On-QC", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-salmon\" class=\"anchor\" aria-hidden=\"true\" href=\"#salmon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esalmon\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for salmon \u003ca href=\"https://github.com/COMBINE-lab/salmon\"\u003ehttps://github.com/COMBINE-lab/salmon\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe docker file needs to be modified as the newer versions are installed in /home in the container which is not\nrecommended in our case.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSubsample Effect On QC\u003c/h1\u003e\u003ca id=\"user-content-subsample-effect-on-qc\" class=\"anchor\" aria-label=\"Permalink: Subsample Effect On QC\" href=\"#subsample-effect-on-qc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSubsample a FASTQ to different degrees and run FASTQC on it. Output a MultiQC Report\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1528143444.0 + "updated_at": 1700500572.0 }, { "data_format": 2, - "description": "testing registry for singularity hub and singularity registry", + "description": "Minimal implementations of distributed, recurrent, deep reinforcement learning algorithms", "filenames": [ - "Singularity", - "Singularity.test", - "os/centos/Singularity", - "os/ubuntu/Singularity.14.04", - "os/ubuntu/Singularity" + "SingularityFile.def" ], - "full_name": "singularityhub/hello-registry", + "full_name": "Jjschwartz/miniDRL", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMiniDRL\u003c/h1\u003e\u003ca id=\"user-content-minidrl\" class=\"anchor\" aria-label=\"Permalink: MiniDRL\" href=\"#minidrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMinimal implementations of distributed deep reinforcement learning algorithms, with a focus on recurrent neural networks. Heavily inspired by \u003ca href=\"https://github.com/vwxyzjn/cleanrl\"\u003eCleanRL\u003c/a\u003e and \u003ca href=\"https://github.com/corl-team/CORL\"\u003eCORL\u003c/a\u003e this library provides high-quality and easy-to-follow stand-alone implementations of some distributed RL algorithms.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePrerequisites:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython \u0026gt;= 3.10 (tested with 3.10)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo install:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:Jjschwartz/miniDRL.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e miniDRL\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or to install all dependencies\u003c/span\u003e\npip install -e .[all]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun PPO on \u003ca href=\"https://gymnasium.farama.org/\" rel=\"nofollow\"\u003egymnasium\u003c/a\u003e \u003ccode\u003eCartPole-v1\u003c/code\u003e environment using four parallel workers (reduce number of workers if you have less than four cores, or feel free to increase it if you have more):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython minidrl/ppo/run_gym.py \\\n --env_id CartPole-v1 \\\n --total_timesteps 1000000 \\\n --num_workers 4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e open another terminal and run tensorboard from repo root directory\u003c/span\u003e\ntensorboard --logdir runs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo use experiment tracking with wandb, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewandb login \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e only required for the first time\u003c/span\u003e\npython minidrl/ppo/run_gym.py \\\n --env_id CartPole-v1 \\\n --total_timesteps 1000000 \\\n --num_workers 4 \\\n --track_wandb \\\n --wandb_project minidrltest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlgorithms\u003c/h2\u003e\u003ca id=\"user-content-algorithms\" class=\"anchor\" aria-label=\"Permalink: Algorithms\" href=\"#algorithms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains standalone implementations of some of the main distributed RL algorithms that support recurrent neural networks, including:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePPO - Single Machine\u003c/h3\u003e\u003ca id=\"user-content-ppo---single-machine\" class=\"anchor\" aria-label=\"Permalink: PPO - Single Machine\" href=\"#ppo---single-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://arxiv.org/abs/1707.06347\" rel=\"nofollow\"\u003epaper\u003c/a\u003e | \u003ca href=\"https://github.com/Jjschwartz/miniDRL/blob/main/minidrl/ppo/ppo.py\"\u003ecode\u003c/a\u003e | \u003ca href=\"https://github.com/Jjschwartz/miniDRL/blob/main/docs/ppo/ppo.md\"\u003edocs\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/ppo/figures/pong_vs_num_workers_wall_time.svg\"\u003e\u003cimg src=\"docs/ppo/figures/pong_vs_num_workers_wall_time.svg\" alt=\"Learning Curve by wall time vs num workers\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cem\u003eLearning curve of PPO - Single Machine on Atari Pong with different number of parallel workers\u003c/em\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR2D2\u003c/h3\u003e\u003ca id=\"user-content-r2d2\" class=\"anchor\" aria-label=\"Permalink: R2D2\" href=\"#r2d2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://openreview.net/forum?id=r1lyTjAqYX\" rel=\"nofollow\"\u003epaper\u003c/a\u003e | \u003ca href=\"https://github.com/Jjschwartz/miniDRL/tree/main/minidrl/r2d2\"\u003ecode\u003c/a\u003e | \u003ca href=\"https://github.com/Jjschwartz/miniDRL/blob/main/docs/r2d2/r2d2.md\"\u003edocs\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMaybe in the future\u003c/h3\u003e\u003ca id=\"user-content-maybe-in-the-future\" class=\"anchor\" aria-label=\"Permalink: Maybe in the future\" href=\"#maybe-in-the-future\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePPO - Multi Machine\u003c/li\u003e\n\u003cli\u003eIMPALA\u003c/li\u003e\n\u003cli\u003eR2D2 - Multi Machine\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [ - "singularity", - "container", - "testing" + "distributed-reinforcement-learning", + "ppo", + "pytorch", + "r2d2", + "reinforcement-learning", + "rnn" ], - "updated_at": 1561904313.0 + "updated_at": 1700237055.0 }, { "data_format": 2, - "description": "Singularity containers with ImageMagick", + "description": "singularity images for openmind", "filenames": [ "Singularity" ], - "full_name": "stephansmit/imagemagick_containers", + "full_name": "atacchet/om-images", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-imagemagick-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#imagemagick-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImageMagick Containers\u003c/h1\u003e\n\u003cp\u003eSingularity containers with ImageMagick\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/imagemagick_containers\nsingularity shell imagemagick_containers_latest.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\n\u003ca href=\"https://singularity-hub.org/collections/3475\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eom-images\u003c/h1\u003e\u003ca id=\"user-content-om-images\" class=\"anchor\" aria-label=\"Permalink: om-images\" href=\"#om-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity images for openmind\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1567168206.0 + "updated_at": 1493648266.0 }, { "data_format": 2, - "description": null, + "description": "Singularity for samtools", "filenames": [ "Singularity" ], - "full_name": "murphygroup/singularity-matlabmcr2017a", + "full_name": "hisplan/singularity-samtools", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-matlabmcr2017a\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-matlabmcr2017a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-matlabmcr2017a\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eWhen contributing to this repository, please first discuss the change you wish to make via issue, \u003ca href=\"mailto:cellorganizer-dev@compbio.cmu.edu\"\u003eemail\u003c/a\u003e, or any other method with the owners of this repository before making a change.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eSupport for \u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e has been provided by grants GM075205, GM090033 and GM103712 from the \u003ca href=\"http://www.nigms.nih.gov/\" rel=\"nofollow\"\u003eNational Institute of General Medical Sciences\u003c/a\u003e, grants MCB1121919 and MCB1121793 from the \u003ca href=\"http://nsf.gov/\" rel=\"nofollow\"\u003eU.S. National Science Foundation\u003c/a\u003e, by a Forschungspreis from the \u003ca href=\"http://www.humboldt-foundation.de/\" rel=\"nofollow\"\u003eAlexander von Humboldt Foundation\u003c/a\u003e, and by the \u003ca href=\"http://www.frias.uni-freiburg.de/lifenet?set_language=en\" rel=\"nofollow\"\u003eFreiburg Institute for Advanced Studies\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.mmbios.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/868693f5973d8c9980a960c4ff8b9608ae5b009bec64db9cc1b92ab5cb831892/68747470733a2f2f69312e77702e636f6d2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f4d4d42696f536c6f676f2d65313530333531373835373331332e6769663f683d3630\" alt=\"MMBioS\" data-canonical-src=\"https://i1.wp.com/www.cellorganizer.org/wp-content/uploads/2017/08/MMBioSlogo-e1503517857313.gif?h=60\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCopyright (c) 2007-2019 by the \u003ca href=\"http://murphylab.web.cmu.edu\" rel=\"nofollow\"\u003eMurphy Lab\u003c/a\u003e at the \u003ca href=\"http://www.cbd.cmu.edu\" rel=\"nofollow\"\u003eComputational Biology Department\u003c/a\u003e in \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-samtools\u003c/h1\u003e\u003ca id=\"user-content-singularity-samtools\" class=\"anchor\" aria-label=\"Permalink: singularity-samtools\" href=\"#singularity-samtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity for samtools\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity 2.2 must be installed on your system. \u003ca href=\"http://singularity.lbl.gov/docs-quick-start-installation\" rel=\"nofollow\"\u003eHere\u003c/a\u003e is the instruction.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownload \u003ccode\u003eSingularity\u003c/code\u003e file from this git repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate an empty container image of 200MB:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity create -s 200 samtools.img\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBootstrap the image using the \u003ccode\u003eSingularity\u003c/code\u003e image definition file you downloaded from the previous step:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity bootstrap samtools.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run samtools.img --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther Notes\u003c/h2\u003e\u003ca id=\"user-content-other-notes\" class=\"anchor\" aria-label=\"Permalink: Other Notes\" href=\"#other-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThis uses Alpine Linux as base image.\u003c/li\u003e\n\u003cli\u003eNote that the image definition file being used here contains a bunch of commands that downloads and compiles the source code of samtools, which is the main reason why the container image requires about 200MB. It would be nice if Singularity provides a way to shrink the image down to the only necessary size. Another workaround would be \u003ccode\u003eDockerfile\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 0, + "subscribers_count": 1, + "topics": [ + "singularity", + "samtools", + "container" + ], + "updated_at": 1486144479.0 + }, + { + "data_format": 2, + "description": "Singularity Image with EigenH5 and some other R packages", + "filenames": [ + "Singularity" + ], + "full_name": "CreRecombinase/docker-eigenh5", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edocker-eigenh5\u003c/h1\u003e\u003ca id=\"user-content-docker-eigenh5\" class=\"anchor\" aria-label=\"Permalink: docker-eigenh5\" href=\"#docker-eigenh5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Image with EigenH5 and some other R packages\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2630\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1554872376.0 + "updated_at": 1571526273.0 }, { "data_format": 2, - "description": null, + "description": "Model implementation for \"Adaptive computation as a new mechanism of human attention\"", "filenames": [ - "1.1.3/Singularity" + "env.d/Singularity" ], - "full_name": "pscedu/singularity-infernal", + "full_name": "CNCLgithub/mot", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emot\u003c/h1\u003e\u003ca id=\"user-content-mot\" class=\"anchor\" aria-label=\"Permalink: mot\" href=\"#mot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImplementation of adaptive computation and a case study on multiple object tracking (Pylyshyn \u0026amp; Storm 1988).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup and running\u003c/h2\u003e\u003ca id=\"user-content-setup-and-running\" class=\"anchor\" aria-label=\"Permalink: Setup and running\" href=\"#setup-and-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone repository \u003ccode\u003egit clone https://github.com/CNCLgithub/mot\u003c/code\u003e and \u003ccode\u003ecd mot\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./env.d/setup.sh cont_pull python julia\u003c/code\u003e to build the container and setup python and Julia.\u003c/li\u003e\n\u003cli\u003eEnter \u003ccode\u003e./env.d/run.sh julia\u003c/code\u003e to get into Julia REPL\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./env.d/setup.sh datasets\u003c/code\u003e to download the datasets used for the experiments.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis project has automatic configuration!! This configuration is defined in \u003ccode\u003edefault.conf\u003c/code\u003e.\nYou should always prepend \u003ccode\u003e./run.sh\u003c/code\u003e before any command (including running programs like \u003ccode\u003ejulia\u003c/code\u003e) to ensure consistency.\nIf you wish to have different values than \u003ccode\u003edefault.conf\u003c/code\u003e, simply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp default.conf user.conf\nvi user.conf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e edit to your liking without adding new elements\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReplication details\u003c/h2\u003e\u003ca id=\"user-content-replication-details\" class=\"anchor\" aria-label=\"Permalink: Replication details\" href=\"#replication-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe project is organized into core routines (under \u003ccode\u003esrc\u003c/code\u003e) and user scripts (under \u003ccode\u003escripts\u003c/code\u003e).\nIn order to run the adaptive computation model from scratch:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun the relevant scripts under \u003ccode\u003escripts/experiments\u003c/code\u003e (batch scripts are provided for SLURM)\u003c/li\u003e\n\u003cli\u003eAggregrate model traces using \u003ccode\u003escripts/analysis/aggregate_chains.jl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExport the produces \"csv\" files to the \u003ca href=\"https://github.com/CNCLgithub/mot-analysis\"\u003eanalysis repo\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMore details can be found in the README for each section.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMac and Window users\u003c/h3\u003e\u003ca id=\"user-content-mac-and-window-users\" class=\"anchor\" aria-label=\"Permalink: Mac and Window users\" href=\"#mac-and-window-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use singularity you must have a virtual machine running.\nAssuming you have vagrant (and something like virtualbox) setup on your host, you can follow these steps\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributing Rules\u003c/h3\u003e\u003ca id=\"user-content-contributing-rules\" class=\"anchor\" aria-label=\"Permalink: Contributing Rules\" href=\"#contributing-rules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePlace all re-used code in packages (\u003ccode\u003esrc\u003c/code\u003e or \u003ccode\u003efunctional_scenes\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePlace all interactive code in \u003ccode\u003escripts\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDo not use \"hard\" paths. Instead refer to the paths in \u003ccode\u003eSPATHS\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAdd contributions to branches derived from \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edev\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAvoid \u003ccode\u003egit add *\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDo not commit large files (checkpoints, datasets, etc). Update \u003ccode\u003esetup.sh\u003c/code\u003e accordingly.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject layout\u003c/h3\u003e\u003ca id=\"user-content-project-layout\" class=\"anchor\" aria-label=\"Permalink: Project layout\" href=\"#project-layout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package environment is managed by as defined in \u003ccode\u003esetup.sh\u003c/code\u003e (specifically \u003ccode\u003eSENV[pyenv]\u003c/code\u003e)\nLikewise, the Julia package is described under \u003ccode\u003esrc\u003c/code\u003e and \u003ccode\u003etest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAll scripts are located under \u003ccode\u003escripts\u003c/code\u003e and data/output is under \u003ccode\u003eenv.d/spaths\u003c/code\u003e as specific in the project config (\u003ccode\u003edefault.conf\u003c/code\u003e or \u003ccode\u003euser.conf\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChanging the enviroment\u003c/h3\u003e\u003ca id=\"user-content-changing-the-enviroment\" class=\"anchor\" aria-label=\"Permalink: Changing the enviroment\" href=\"#changing-the-enviroment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add new python or julia packages use the provided package managers (\u003ccode\u003epoetry add\u003c/code\u003e or \u003ccode\u003ePkg.add \u003c/code\u003e for python and julia respectively.)\u003c/p\u003e\n\u003cp\u003eFor julia you can also use \u003ccode\u003e] add \u003c/code\u003e in the REPL\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003efor more info checkout \u003ca href=\"https://python-poetry.org/docs/cli/\" rel=\"nofollow\"\u003epoetry\u003c/a\u003e and \u003ca href=\"https://julialang.github.io/Pkg.jl/v1/managing-packages/\" rel=\"nofollow\"\u003ePkg\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [], - "updated_at": 1629078386.0 + "subscribers_count": 3, + "topics": [ + "adaptive-computation", + "attention", + "julia", + "object-tracking" + ], + "updated_at": 1669660200.0 }, { "data_format": 2, - "description": null, + "description": "A singularity recipe for GPU based machine learning", "filenames": [ - "Singularity.racon-chunks_0.0.6" + "Singularity.cu80dnn6", + "Singularity", + "Singularity.cu80dnn7", + "Singularity.cu90" ], - "full_name": "TomHarrop/racon-chunks", + "full_name": "ISU-HPC/machine-learning", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emachine-learning\u003c/h1\u003e\u003ca id=\"user-content-machine-learning\" class=\"anchor\" aria-label=\"Permalink: machine-learning\" href=\"#machine-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA singularity recipe for GPU based machine learning\u003c/p\u003e\n\u003cp\u003eCurrently includes the following applications/packages\u003c/p\u003e\n\u003cp\u003eKeras\u003c/p\u003e\n\u003cp\u003eMXNET\u003c/p\u003e\n\u003cp\u003escikit-learn\u003c/p\u003e\n\u003cp\u003etensorflow\u003c/p\u003e\n\u003cp\u003epytorch\u003c/p\u003e\n\u003cp\u003elasagne\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1578525260.0 + "updated_at": 1550521785.0 }, { "data_format": 2, - "description": null, + "description": "repository for collaborating with scg4 users on Singularity containers", "filenames": [ - "Singularity.purge_haplotigs_0b9afdf", - "Singularity.circos_0.69-9", - "Singularity.busco_4.0.4", - "Singularity.racon_1.4.10", - "Singularity.ragtag_1.0.1", - "Singularity.quast_5.0.2", - "Singularity.gfatools_0.4r165", - "Singularity.agb_a41ac9e", - "Singularity.merqury_45fd3cc", - "Singularity.gffread_0.12.3" + "cbanders/Singularity" ], - "full_name": "TomHarrop/assembly-utils", + "full_name": "researchapps/scg4", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSCG4 Singularity\u003c/h1\u003e\u003ca id=\"user-content-scg4-singularity\" class=\"anchor\" aria-label=\"Permalink: SCG4 Singularity\" href=\"#scg4-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a repository for Singularity image build files to help users of SCG4 build \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. If you are a user and need help, please submit an issue and we will help you build a container! When you are happy with your container, we recommend that you add the \u003ccode\u003eSingularity\u003c/code\u003e file to a new repo, and build automatically with \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e. Generally, your workflow will look like the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAsk for help via an \u003ca href=\"https://www.github.com/researchapps/scg4/issues\"\u003eissue\u003c/a\u003e if you don\u0027t know how to start\u003c/li\u003e\n\u003cli\u003eCreate a build specification file, a text file called Singularity, for your software needs. You can start with another user\u0027s as an example.\u003c/li\u003e\n\u003cli\u003eAsk for help with your file! This is what this repo is here for. You can submit issues with questions, and we will discuss and work together on the issues.\u003c/li\u003e\n\u003cli\u003eTest your build locally.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis usually looks something like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity create --size 4000 mynewimage.img\n singularity bootstrap mynewimage.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf it has a runscript, you can run as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run mynewimage.img # or\n ./mynewimage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you are having trouble with the runscript, shell inside like this to look around. The runscript is a file at the base of the image (\u003ccode\u003e/\u003c/code\u003e) called singularity.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell mynewimage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also (on your local machine) use the \u003ccode\u003e--writable\u003c/code\u003e option to test installation of software. You should have your build file open in another window and copy down commands that work, and ensure that the entire build goes successfully from start to finish without an error. Remember, any command that you issue and don\u0027t write done is NOT reproducible!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Hub\u003c/h2\u003e\u003ca id=\"user-content-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub\" href=\"#singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThen once you are finished, and create a new repo linked to Singularity Hub, using the image on scg4 comes down to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e module load singularity/january2017\n singularity run shub://reponame/mynewimage\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1601346862.0 + "updated_at": 1488052770.0 }, { "data_format": 2, - "description": "Bayesian poissonian histogram decomposition engine for the GERDA experiment", + "description": "Container with psychopy", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "gipert/gerda-fitter", + "full_name": "mvdoc/singularity-psychopy", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".github/gerda-logo.png\"\u003e\u003cimg src=\".github/gerda-logo.png\" align=\"left\" height=\"80\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-gerda-fitter-\" class=\"anchor\" aria-hidden=\"true\" href=\"#gerda-fitter-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egerda-fitter \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/gipert/gerda-fitter/workflows/CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/gipert/gerda-fitter/workflows/CI/badge.svg\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eA fully JSON-configurable bayesian fitting engine (based on\n\u003ca href=\"https://github.com/bat/bat\"\u003eBAT\u003c/a\u003e and\n\u003ca href=\"https://github.com/root-project/root\"\u003eROOT\u003c/a\u003e) for data in the form of ROOT\nhistograms. Taylored on GERDA data and Probability Density Functions.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-compile-and-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#compile-and-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile and install\u003c/h3\u003e\n\u003cp\u003eRequirements\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/root-project/root\"\u003eROOT\u003c/a\u003e \u2265 v6.12/04\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/bat/bat\"\u003eBAT\u003c/a\u003e \u2265 v1.0.0 (with Cuba enabled)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen just \u003ccode\u003ePREFIX=/path/to/prefix make install\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, a Singularity container can be used:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esudo singularity build gerda-fitter.sif Singularity.def\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e gerda-fitter.sif gerda-fitter -h\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eUSAGE: gerda-fitter [-h|--help] json-config\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003egerda-fitter\u003c/code\u003e executable acceps a JSON config file as the only argument.\nExamples can be found in this repository under \u003ccode\u003econfig/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe JSON config file begins with some general settings:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"id\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"phIIAfterLAr\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// model name\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"logging\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"summary\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// BAT verbosity level, see manual\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"precision\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"kMedium\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// precision (number and length of Markov chains), see BAT manual\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"output-dir\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"../results\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// folder with fit results\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003esettings about the global mode search algorithm:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s\"\u003e\"global-mode-search\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"method\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"kOptMinuit\"\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// see the BAT manual to learn about the other algorithms\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003esettings about the numerical integration needed to compute the evidence:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s\"\u003e\"integration\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"enabled\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// enable/disable the integration step\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"method\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"kIntCuba\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// see the BAT manual to learn about the other algorithms\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"cuba-method\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"kCubaDivonne\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// see the Cuba manual\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"integrator-settings\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"kIntCuba\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// here you can tweak the Cuba integration settings\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"kCubaDivonne\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// here for the Divonne algorithm\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"niter-max\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e1E07\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"niter-min\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"flags\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n \"\u003cspan class=\"pl-s1\"\u003ekCubaVegas\u003c/span\u003e\" : { // here for Vegas...\n // ...\n }\n // ...\n }\n }\n },\n // ...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003esettings about the p-value determination\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s\"\u003e\"p-value\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"enabled\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// enable/disable the computation\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"iterations\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e1E07\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// play with this number until the p-value is stable\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand finally the fit configuration section \u003ccode\u003e\"fit\"\u003c/code\u003e, where everything about the data and\nthe fit components is specified in a modular fashion:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fit\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"parameters\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// define fit parameters globally\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"theoretical-expectations\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// import PDFs and associated parameters\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLet\u0027s start with the \u003ccode\u003e\"parameters\"\u003c/code\u003e section, here the fit parameters must be defined:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e\"parameters\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"alpha-slope-bege\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// unique internal name\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2E-5\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1E-4\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"long-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"#alpha-model BEGe - slope\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"units\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"cts\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"prior\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"histogram\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"priorfile.root:objname\"\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// specify prior via external TH1\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"alpha-offset-bege\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1E-1\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"long-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"#alpha-model BEGe - offset\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"units\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"cts\"\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"prior\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"TFormula\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"gaus:1,10,5\"\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// specify prior via TFormula\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"background\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fixed\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e1234\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// parameters can be fixed to a value (not fit parameters anymore)\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"long-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"Background model\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"units\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"cts\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then associated to PDFs in the \u003ccode\u003e\"theoretical-expectations\"\u003c/code\u003e section:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e\"theoretical-expectations\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// takes a list of files with data histograms\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"../data/gerda-data-bkgmodel-phaseII-v04.00-lar.root\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// takes a list of object names in the file\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"M1_enrBEGe\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// this is a 1D histogram\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"gerda-pdfs\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"../data/gerda-pdfs/v2.1\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// set here the path to the gerda-pdfs, if you want\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fit-range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e560\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2014\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2064\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5300\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// note the possibility to skip regions\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"rebin-factor\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// fixed-size rebin\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"rebin-factor\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"560:10:700,700:20:900,1000:100:5300\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// support for variable binning!\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// here you must specify a list of PDFs you want to use\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"M1_enrCoax\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"M2_enrGe\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// this is a 2D histogram\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fit-range-x\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e560\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2014\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2064\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5300\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"fit-range-y\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e700\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5300\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"rebin-factor-x\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// or just \"rebin-factor\" to rebin both axes\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"rebin-factor-y\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// here you must specify a list of PDFs you want to use\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"../data/gerda-data-bkgmodel-phaseII-v04.00-raw.root\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethe keys in the \u003ccode\u003e\"theoretical-expectations\"\u003c/code\u003e dictionary must be paths to the\nfiles that contain histograms to be fitted (the data). Then for each of these\nfiles the user must specify what histograms (ROOT objects) the program should\ntry to fit. For every data histogram a list of fit components must be provided\nin the \u003ccode\u003e\"components\"\u003c/code\u003e array. The array is filled with JSON objects that can be\nof multiple types.\u003c/p\u003e\n\u003cp\u003eAs instance, one might want to use the GERDA PDFs distributed within\n\u003ca href=\"https://github.com/mppmu/gerda-mage-sim\"\u003egerda-mage-sim\u003c/a\u003e using the following\nstructure:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"gerda-pdfs\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"../data/gerda-pdfs/v2.1\"\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// the gerda-pdfs path might be set here to override the global one\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"part\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"cables/cables_all\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"Th228-cables\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// this parameter name must be defined in the \"parameters\" section!\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"isotope\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"Tl208-larveto\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e0.3539\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"Bi212-larveto\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// specify a mixture of isotopes\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"Co60-cables\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"isotope\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Co60-run68pca\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// no mixture here\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"part\"\u003c/span\u003e: \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// you can also specify a mixture of parts!\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s2_8220\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e52183\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s2_8408\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e25337\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s2_8570\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e79868\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s3_8220\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e55438\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s3_8405\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e43433\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"calib/single_s3_8570\"\u003c/span\u003e : \u003cspan class=\"pl-c1\"\u003e24130\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e/* ... */\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor even provide manually a ROOT histogram:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"root-file\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"../data/gerda-pdfs/v2.0-RC/alphas/analytic/pdf-functions.root\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"alpha-offset\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"hist-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"flat\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor even a ROOT \u003ccode\u003eTFormula\u003c/code\u003e in the form \u003ccode\u003e\"formula:par1,par2,...\"\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"components\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"alpha-slope\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"TFormula\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"gaus:1,34,2\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLast but not least, observables that depend on the model parameters only can be\ndefined via JSON file with the following syntax:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e\"parameters\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"2nbb-half-life-bege\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// unique internal name\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"TFormula\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"1.13380E26/[2nbb-bege]\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// ROOT\u0027s TFormula\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"multiply-fit-parameter-by-pdf-integral\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// there\u0027s the possibility to multiply each parameter\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// above by the pdf integral in a range:\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// [2nbb-bege] -\u0026gt; ([2nbb-bege]*Int)\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e19\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e80\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e89\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// range for the integral\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"dataset\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"h_data\"\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e// dataset pdf refers to\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"range\"\u003c/span\u003e : \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2E-5\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1E-4\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"long-name\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"T_{1/2}^{2#nu} - BEGe\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"units\"\u003c/span\u003e : \u003cspan class=\"pl-s\"\u003e\"cts\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// ...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eModel parameters must be specified as they were a \u003ccode\u003eTFormula\u003c/code\u003e parameter,\nenclosing their name in square brackets.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-related-projects\" class=\"anchor\" aria-hidden=\"true\" href=\"#related-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRelated projects\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gipert/gerda-factory\"\u003egerda-factory\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "bayesian-statistics", - "histogram-decomposition", - "spectral-decomposition" - ], - "updated_at": 1637679682.0 + "topics": [], + "updated_at": 1508289994.0 }, { "data_format": 2, - "description": "build index for several aligners and writes a module file", + "description": "Testing Apptainer container builds via GitHub Actions", "filenames": [ - "Singularity.1.0.2", - "Singularity.1.0.1" + "dir/Singularity" ], - "full_name": "ISUGIFsingularity/genomeModules", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-genomemodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#genomemodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egenomeModules\u003c/h1\u003e\n\u003cp\u003ebuild index for several aligners and writes a module file\u003c/p\u003e\n\u003cp\u003eAfter cloning this repository\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-download-the-following-singularity-images-and-place-them-in-simg-folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-the-following-singularity-images-and-place-them-in-simg-folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload the following singularity images and place them in SIMG folder\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003esingularity pull shub://ISUGIFsingularity/genomeModules:1.0.2\u003c/li\u003e\n\u003cli\u003esingularity pull shub://ISUGIFsingularity/utilities:1.0.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-modify-the-following-environmental-variables-in-prepare_genome_modulessh\" class=\"anchor\" aria-hidden=\"true\" href=\"#modify-the-following-environmental-variables-in-prepare_genome_modulessh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModify the following environmental variables in prepare_genome_modules.sh\u003c/h4\u003e\n\u003cp\u003eUse full paths so that the module file will work correctly\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport GENMODgit=\"/pylon5/mc48o5p/severin/isugif/genomeModules\"\nGENMOD=\"/pylon5/mc48o5p/severin/isugif/\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGENMODgit is the location of this github repository.\nGENMOD is the location where you would like to store genome modules and sequence files that this script generates.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-running-the-prepare-genome-modules-command-to-generate-a-genome-module-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-prepare-genome-modules-command-to-generate-a-genome-module-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the prepare genome modules command to generate a genome module file.\u003c/h4\u003e\n\u003cp\u003eI ran this on the Seriola dorsalis genome and its corresponding GFF3 file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eprepare_genome_modules.sh serdor v2 Serdor_V2.fasta Serdor_V2.gff3\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-adding-the-modules-to-your-module-path\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-the-modules-to-your-module-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding the modules to your module path.\u003c/h4\u003e\n\u003cp\u003emodule use $GENMOD/genomes/modules/\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example-of-a-genome-module\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-of-a-genome-module\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample of a genome module\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003emodule load serdor\nmodule show serdor\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e-------------------------------------------------------------------\n/pylon5/mc48o5p/severin/genmodTest/genomes/modules//serdor/v2:\n\nmodule-whatis serdor \nunsetenv GENOME \nunsetenv GMAPDB \nunsetenv GNAME \nsetenv GENOMEDIR .//genomes/sequences/serdor/v2/ \nsetenv GENOMEFASTA .//genomes/sequences/serdor/v2/serdor_v2.fasta \nsetenv GENOMEINTERVALS .//genomes/sequences/serdor/v2/serdor_v2_100kb_coords.bed \nsetenv GNAME serdor_v2 \nsetenv GMAPDB .//genomes/sequences/serdor/v2/ \nsetenv modulefile .//genomes/modules/serdor/v2 \nsetenv VERSION v2 \nsetenv serdor_v2_genome .//genomes/sequences/serdor/v2/ \nsetenv serdor_v2_GMAPDB .//genomes/sequences/serdor/v2/serdor_v2 \nsetenv serdor_v2_GNAME serdor_v2 \nsetenv serdor_v2_intervals100k .//genomes/sequences/serdor/v2/serdor_v2_100kb_coords.bed \nsetenv serdor_v2_cdna .//genomes/sequences/serdor/v2/serdor_v2.fasta \nsetenv serdor_v2_cdna .//genomes/sequences/serdor/v2/serdor_v2.gff3 \nsetenv serdor_v2_cdna .//genomes/sequences/serdor/v2/serdor_v2.cdna.fasta \nsetenv serdor_v2_cds .//genomes/sequences/serdor/v2/serdor_v2.cds.fasta \nsetenv serdor_v2_gene .//genomes/sequences/serdor/v2/serdor_v2.gene.fasta \nsetenv serdor_v2_pep .//genomes/sequences/serdor/v2/serdor_v2.pep.fasta \nsetenv serdor_v2_upstream3000 serdor_v2.upstream3000.fasta \n-------------------------------------------------------------------\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "maouw/test-apptainer-gh", + "latest_release": "v0.0.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etest-apptainer-gh\u003c/h1\u003e\u003ca id=\"user-content-test-apptainer-gh\" class=\"anchor\" aria-label=\"Permalink: test-apptainer-gh\" href=\"#test-apptainer-gh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting Apptainer container builds via GitHub Actions\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1523035282.0 + "updated_at": 1697496824.0 }, { "data_format": 2, - "description": "Singularity recipe for ROS Indigo and Kinetic", + "description": "Container Template for the Soil and Water Assessment Toolkit", "filenames": [ - "Singularity", - "indigo/Singularity.indigo", - "kinetic/Singularity.kinetic" + "Singularity" ], - "full_name": "ISU-HPC/ros", + "full_name": "XSEDE/singularity-swat681", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ros\" class=\"anchor\" aria-hidden=\"true\" href=\"#ros\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eros\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for ROS Indigo and Kinetic\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoil \u0026amp; Water Assessment Tool\u003c/h1\u003e\u003ca id=\"user-content-soil--water-assessment-tool\" class=\"anchor\" aria-label=\"Permalink: Soil \u0026amp; Water Assessment Tool\" href=\"#soil--water-assessment-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container includes the Soil and Water Assessment Tool (\u003ca href=\"https://swat.tamu.edu/software/\" rel=\"nofollow\"\u003ehttps://swat.tamu.edu/software/\u003c/a\u003e)\nrevision 681,\nbuilt for use on amd64 Linux systems. The binary is installed at /usr/local/swat681/swat.\nAt run-time, any input files MUST be bind-mounted to /usr/local/swat681 - for example:\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 9, "topics": [], - "updated_at": 1524505755.0 + "updated_at": 1635279632.0 }, { "data_format": 2, - "description": null, + "description": "Base centos7 with miniconda installed", "filenames": [ "Singularity" ], - "full_name": "rkalyanapurdue/mpitest", + "full_name": "scleveland/centos7-miniconda", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mpitest\" class=\"anchor\" aria-hidden=\"true\" href=\"#mpitest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003empitest\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Recipe for base CentOS 7 image with Miniconda Installed\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-base-centos-7-image-with-miniconda-installed\" class=\"anchor\" aria-label=\"Permalink: Singularity Recipe for base CentOS 7 image with Miniconda Installed\" href=\"#singularity-recipe-for-base-centos-7-image-with-miniconda-installed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains recipe to install MiniConda on a base CentOS-7 singularity containteri (shub://scleveland/centos7-base-singularity)\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to Use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity run shub://scleveland\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1567614468.0 + "updated_at": 1543871839.0 }, { "data_format": 2, - "description": null, + "description": "Common tools for w3const project", "filenames": [ - "containers/Singularity", - "containers/Singularity_freesurfer_and_fastsurfer.def" + "Singularity" ], - "full_name": "neurodatascience/watts_up_compute", + "full_name": "ddbj/w3const_base", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-watts_up_compute\" class=\"anchor\" aria-hidden=\"true\" href=\"#watts_up_compute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ewatts_up_compute\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-code-repo-to-assess-compute-costs-of-neuroimaging-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#code-repo-to-assess-compute-costs-of-neuroimaging-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode repo to assess compute costs of neuroimaging pipelines\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-motivation\" class=\"anchor\" aria-hidden=\"true\" href=\"#motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIncreasing supply of large datasets and machine-learning models\u003c/li\u003e\n\u003cli\u003eGrowing demand for computational resources exceeding Moore\u2019s law [\u003ca href=\"https://openai.com/blog/ai-and-compute/\" rel=\"nofollow\"\u003e1\u003c/a\u003e, \u003ca href=\"https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/\" rel=\"nofollow\"\u003e2\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/1907.10597\" rel=\"nofollow\"\u003e3\u003c/a\u003e, \u003ca href=\"https://dl.acm.org/doi/10.1145/3442188.3445922\" rel=\"nofollow\"\u003e4\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2104.10350\" rel=\"nofollow\"\u003e5\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eEstimated carbon footprint of AI model: 284,000 Kgs of CO2 (5x lifetime emissions of a car or 300x RT-flights for single passenger between NYC and SF [\u003ca href=\"https://openai.com/blog/ai-and-compute/\" rel=\"nofollow\"\u003e1\u003c/a\u003e, \u003ca href=\"https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/\" rel=\"nofollow\"\u003e2\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/1907.10597\" rel=\"nofollow\"\u003e3\u003c/a\u003e])\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eDataset sizes\u003c/th\u003e\n\u003cth align=\"center\"\u003eModel sizes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/Fig1b.png\"\u003e\u003cimg src=\"figures/Fig1b.png\" alt=\"Drawing\" align=\"middle\" width=\"500px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/Fig1c.png\"\u003e\u003cimg src=\"figures/Fig1c.png\" alt=\"Drawing\" align=\"middle\" width=\"570px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-experiment-objectives\" class=\"anchor\" aria-hidden=\"true\" href=\"#experiment-objectives\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExperiment objectives:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBenchmark following compute cost metrics for neuroimaging pipelines:\n\u003cul\u003e\n\u003cli\u003emodel complexity (parameters, FLOPs/MACs)\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://www.bnikolic.co.uk/blog/python/flops/2019/09/27/python-counting-events.html\" rel=\"nofollow\"\u003egeneral purpose\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sovrasov/flops-counter.pytorch\"\u003epytorch:ptflops\u003c/a\u003e (Primarily used)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003emodel energy/power consumption using several carbon trackers\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Breakend/experiment-impact-tracker\"\u003eexperiment-impact-tracker\u003c/a\u003e (Primarily used)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/lfwa/carbontracker\"\u003eCarbonTracker\u003c/a\u003e (in-progress)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mlco2/codecarbon\"\u003eCodeCarbon\u003c/a\u003e (in-progress)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003emodel runtime\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eComparisons:\n\u003cul\u003e\n\u003cli\u003ehardware: cpu vs gpu\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-repo-organization-ongoing\" class=\"anchor\" aria-hidden=\"true\" href=\"#repo-organization-ongoing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepo organization (ongoing)\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/Watts_up_compute_org.jpg\"\u003e\u003cimg src=\"figures/Watts_up_compute_org.jpg\" alt=\"Drawing\" align=\"middle\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-preliminary-results-on-a-pilot-sample\" class=\"anchor\" aria-hidden=\"true\" href=\"#preliminary-results-on-a-pilot-sample\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreliminary results on a pilot sample\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDatasets: \u003ca href=\"https://www.ukbiobank.ac.uk/enable-your-research/register\" rel=\"nofollow\"\u003eUK Biobank sample\u003c/a\u003e (N=72)\u003c/li\u003e\n\u003cli\u003ePipelines: \u003ca href=\"https://surfer.nmr.mgh.harvard.edu/\" rel=\"nofollow\"\u003eFreeSurfer 6.0\u003c/a\u003e implementation with \u003ca href=\"https://nipype.readthedocs.io/en/latest/users/examples/smri_fsreconall.html\" rel=\"nofollow\"\u003eNipype\u003c/a\u003e vs. FastSurfer (deep-learning approach)\u003c/li\u003e\n\u003cli\u003eOutput: Volumetric brain segmentation and cortical thickness estimation with DKT parcellations (see figure below)\u003c/li\u003e\n\u003cli\u003eProc: CPU (Intel Xeon(R) Gold 6148 @ 2.40GHz) vs. GPU (Tesla V100-SXM2-16GB CUDA:11.0)\u003c/li\u003e\n\u003cli\u003eHPC location: Compute Canada @ Quebec, Canada (\u003ca href=\"https://en.wikipedia.org/wiki/Power_usage_effectiveness\" rel=\"nofollow\"\u003ePUE\u003c/a\u003e ~ 1.2)\u003c/li\u003e\n\u003cli\u003eCompute cost metrics\n\u003col\u003e\n\u003cli\u003eRuntime 2) Power draw 3) Carbon emissions\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eCompute cost tracker: experiment-impact-tracker\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/FreeSurfer_FastSurfer.png\"\u003e\u003cimg src=\"figures/FreeSurfer_FastSurfer.png\" alt=\"Drawing\" align=\"middle\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-compute-cost-benchmarks\" class=\"anchor\" aria-hidden=\"true\" href=\"#compute-cost-benchmarks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompute cost benchmarks:\u003c/h3\u003e\n\u003cp\u003eNote: The values in table are for processing of a single scan. A typical inference/deployment pipeline may do ~10k of these runs for a large dataset. And a model training/development pipeline may incur over 1M runs.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePipeline (single run)\u003c/th\u003e\n\u003cth\u003eRuntime (hrs): CPU\u003c/th\u003e\n\u003cth\u003eRuntime (hrs): GPU\u003c/th\u003e\n\u003cth\u003ePower (W-hrs): CPU\u003c/th\u003e\n\u003cth\u003ePower (W-hrs): GPU\u003c/th\u003e\n\u003cth\u003eCarbon Emissions (grams): CPU\u003c/th\u003e\n\u003cth\u003eCarbon Emissions (grams): GPU\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eFreeSurfer\u003c/td\u003e\n\u003ctd\u003e8.3 (1.03)\u003c/td\u003e\n\u003ctd\u003eN/A\u003c/td\u003e\n\u003ctd\u003e108.5 (19.8)\u003c/td\u003e\n\u003ctd\u003eN/A\u003c/td\u003e\n\u003ctd\u003e3.26 (0.5)\u003c/td\u003e\n\u003ctd\u003eN/A\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFastSurfer\u003c/td\u003e\n\u003ctd\u003e9.8 (0.74)\u003c/td\u003e\n\u003ctd\u003e1.6 (0.47)\u003c/td\u003e\n\u003ctd\u003e126.4 (16.1)\u003c/td\u003e\n\u003ctd\u003e26.7 (7.7)\u003c/td\u003e\n\u003ctd\u003e3.79 (0.5)\u003c/td\u003e\n\u003ctd\u003e0.80 (0.2)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ew3const_base\u003c/h1\u003e\u003ca id=\"user-content-w3const_base\" class=\"anchor\" aria-label=\"Permalink: w3const_base\" href=\"#w3const_base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCommon tools for w3const project\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHow to build the container\u003c/h1\u003e\u003ca id=\"user-content-how-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: How to build the container\" href=\"#how-to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~\ngit clone https://github.com/ddbj/w3const_base.git\nsudo singularity build constbase.sif ~/w3const_base/Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ecuratortool\u003c/em\u003e directory contains the following scripts and binaries.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003egetblastdb_ncbi.sh\u003c/h2\u003e\u003ca id=\"user-content-getblastdb_ncbish\" class=\"anchor\" aria-label=\"Permalink: getblastdb_ncbi.sh\" href=\"#getblastdb_ncbish\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload blast/db data from NCBI by using aspera connect and decompress to the blastdb directory.\u003c/p\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /home/w3const/work-kosuge/constbase.sif getblastdb_ncbi.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eVariables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDBNAME ... blast db to be downloaded.\u003c/li\u003e\n\u003cli\u003eMAXTRY ... Retry download until the times, when a downloaded file is broken.\u003c/li\u003e\n\u003cli\u003eBASE ... Base directory for running the script.\u003c/li\u003e\n\u003cli\u003eDBSRC ... URL of NCBI data resource.\u003c/li\u003e\n\u003cli\u003eDATLOC ... Usually, the latest tar.gz archives from NCBI are placed. When the downloading was failed, the tar.gz files are copied from DATLOCF directory.\u003c/li\u003e\n\u003cli\u003eDATLOCF ... Former tar.gz archives from NCBI are placed.\u003c/li\u003e\n\u003cli\u003eJSONLOC ... Manifest json files from NCBI. Each file are downloaded based on the information in the json file.\u003c/li\u003e\n\u003cli\u003eBDB ... A directory where decompressed data are placed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esendgmail_w3const.py\u003c/h2\u003e\u003ca id=\"user-content-sendgmail_w3constpy\" class=\"anchor\" aria-label=\"Permalink: sendgmail_w3const.py\" href=\"#sendgmail_w3constpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSends email by using the google account. You can specify a sender address if you have set the other email address(es) (e.g. sender alias) on the account.\u003c/p\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /home/w3const/work-kosuge/constbase.sif sendgmail_w3const.py [-h] --sj subject --to email --body file [--cc email] [--bcc email] [--att file] [--sender address]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou must prepare credential and white list files in advance.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a credential file to run the script.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -m 700 ~/.sendgmail_w3const\necho \u0027GmailAccount:ApplicationPassword\u0027 \u0026gt; ~/.sendgmail_w3const/account\nchmod 600 ~/.sendgmail_w3const/account\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate a whitelist\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003etouch ~/.sendgmail_w3const/whitelist; chmod 600 ~/.sendgmail_w3const/whitelist\nWrite an email address to the whitelist in each line.\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eExample\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd /home/w3const\nsingularity exec /home/w3const/work-kosuge/constbase.sif sendgmail_w3const.py --sj \"\u3066\u3059\u3068\u3067\u3059\" --to addr1,addr2 --body /home/w3const/work-kosuge/emailbody.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esendmail_gw1.py\u003c/h2\u003e\u003ca id=\"user-content-sendmail_gw1py\" class=\"anchor\" aria-label=\"Permalink: sendmail_gw1.py\" href=\"#sendmail_gw1py\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can send email only when sender address is \u003cem\u003enig\u003c/em\u003e and \u003cem\u003eddbj\u003c/em\u003e domain. Works only in supercomputer nodes.\u003c/p\u003e\n\u003cp\u003eYou must prepare whitelist file as mentioned above.\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /home/w3const/work-kosuge/constbase.sif sendgmail_gw1.py --sender foo@ddbj.nig.ac.jp --sj \"\u3066\u3059\u3068\u3067\u3059\" --to addr1,addr2 --body /home/w3const/work-kosuge/emailbody.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003emakeUniVec_blastdb.sh\u003c/h2\u003e\u003ca id=\"user-content-makeunivec_blastdbsh\" class=\"anchor\" aria-label=\"Permalink: makeUniVec_blastdb.sh\" href=\"#makeunivec_blastdbsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload the UniVec fasta from NCBI and replace the local file with newwer ones. The script also prepares blast databases whose names are UniVec and UniVec_Core.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBefore use\u003c/h3\u003e\u003ca id=\"user-content-before-use\" class=\"anchor\" aria-label=\"Permalink: Before use\" href=\"#before-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEdit the directory name of BASE (line 8). It is used for base directory. You need to create \"UniVec\" directory under the base directory. The blast databases for UniVec and UniVec_Core are created to the directory designated by BLASTDIR (line 12).\u003c/p\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /home/w3const/work-kosuge/constbase.sif makeUniVec_blastdb.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esplitff.sh\u003c/h2\u003e\u003ca id=\"user-content-splitffsh\" class=\"anchor\" aria-label=\"Permalink: splitff.sh\" href=\"#splitffsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeparate a huge flatfile into small-sized flat files.\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esplitff.sh -f \u0026lt;flatfile\u0026gt; -s \u0026lt;number of lines\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003egetorganismdivFF.py\u003c/h2\u003e\u003ca id=\"user-content-getorganismdivffpy\" class=\"anchor\" aria-label=\"Permalink: getorganismdivFF.py\" href=\"#getorganismdivffpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eObtain or search taxnomyc division for a target entry from the flatfile, Entrez, or local taxonomy dump file. In the case of ENV or taxid=0, taxonomic division is obtained from Entrez search or tax dump file by using the beginning of the /organism as a query.\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ee.g.1\ngetorganismdivFF.py -i \u0026lt;flatfile\u0026gt; -a \u0026lt;accession num\u0026gt;\ne.g.2\ngetorganismdivFF.py -i \u0026lt;flatfile\u0026gt; -a \u0026lt;accession num\u0026gt; -p \u0026lt;tax dump directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ejParser \u0026amp; transChecker\u003c/h2\u003e\u003ca id=\"user-content-jparser--transchecker\" class=\"anchor\" aria-label=\"Permalink: jParser \u0026amp; transChecker\" href=\"#jparser--transchecker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://ddbj.nig.ac.jp/public/ddbj-cib/MSS/\" rel=\"nofollow\"\u003ehttps://ddbj.nig.ac.jp/public/ddbj-cib/MSS/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eblast \u0026amp; matrix\u003c/h2\u003e\u003ca id=\"user-content-blast--matrix\" class=\"anchor\" aria-label=\"Permalink: blast \u0026amp; matrix\" href=\"#blast--matrix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eftp://ftp.ncbi.nih.gov/blast/executables/blast+/\nftp://ftp.ncbi.nih.gov/blast/matrices\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003evecscrnfilter.py\u003c/h2\u003e\u003ca id=\"user-content-vecscrnfilterpy\" class=\"anchor\" aria-label=\"Permalink: vecscrnfilter.py\" href=\"#vecscrnfilterpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReads vecscreen result that carried out with options -outfmt 0 -text_output, and filter the results with the degree of blast matches.\u003c/p\u003e\n\u003cp\u003eUsage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evecscrnfilter.py [-s|-m|-w] [alignment file]\n# \u0027-s\u0027 outputs only \u0027Strong match\u0027\n# \u0027-m\u0027 outputs \u0027Moderate match\u0027 \u0026amp; \u0027Strong Match\u0027\n# \u0027-w\u0027 outputs \u0027Weak match\u0027 besides \u0027Moderate match\u0027 \u0026amp; \u0027Strong match\u0027\n# They may contain \u0027Suspect origin\u0027 if included in the result\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSRA Toolkit\u003c/h2\u003e\u003ca id=\"user-content-sra-toolkit\" class=\"anchor\" aria-label=\"Permalink: SRA Toolkit\" href=\"#sra-toolkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLatest version of \u003ca href=\"https://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/\" rel=\"nofollow\"\u003ehttps://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAspera Connect\u003c/h2\u003e\u003ca id=\"user-content-aspera-connect\" class=\"anchor\" aria-label=\"Permalink: Aspera Connect\" href=\"#aspera-connect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.ibm.com/aspera/connect/\" rel=\"nofollow\"\u003ehttps://www.ibm.com/aspera/connect/\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 11, "topics": [], - "updated_at": 1639003401.0 + "updated_at": 1727936224.0 }, { "data_format": 2, - "description": "Angsd_Singularity_Install", + "description": null, "filenames": [ "Singularity" ], - "full_name": "carte731/Angsd_Singularity_Install", + "full_name": "kavonrtep/assembly_repeat_annotation_pipeline", "latest_release": null, - "readme": "\u003cp\u003eSingularity install recipe for Angsd-Wrapper program. University of Minnesota - Twin Cities, Morrell Lab.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAssembly Repeat Annotation Pipeline\u003c/h1\u003e\u003ca id=\"user-content-assembly-repeat-annotation-pipeline\" class=\"anchor\" aria-label=\"Permalink: Assembly Repeat Annotation Pipeline\" href=\"#assembly-repeat-annotation-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is required to use the container. Singularity can be installed using conda environment.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n singularity3 -c conda-forge \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esingularity\u0026gt;=3.6\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nconda activate singularity3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image (.sif file) can be downloaded from \u003ca href=\"https://github.com/kavonrtep/assembly_repeat_annotation_pipeline/releases\"\u003ehttps://github.com/kavonrtep/assembly_repeat_annotation_pipeline/releases\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFormat of \u003ccode\u003econfig.yaml\u003c/code\u003e file is as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003egenome_fasta\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edata/CEN6_ver_220406_part.fasta\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutput_dir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eoutput\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ecustom_library\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edata/pisum_custom_library.fasta\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003etandem_repeat_library\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edata/FabTR_all_sequences_210901.db.RM_format.fasta\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e posible values are : sensitive, default, quick,\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e if missing, default is used\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003erepeatmasker_sensitivity\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edefault\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e perform library size reduction, possible values are True, False, if missinf True is used\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ereduce_library\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLines specifying \u003ccode\u003ecustom_library\u003c/code\u003e and \u003ccode\u003etandem_repeat_library\u003c/code\u003e are optional. File \u003ccode\u003ecustom_library\u003c/code\u003e is\nused by RepeatMasker fo similarity based annotation. Sequences must be as FASTA with\nsequence IDs in format \u003ccode\u003e\u0026gt;repeatname#class/subclass\u003c/code\u003e\nClassification categories are:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eClass_I/LINE\nClass_I/LTR/Ty1_copia\nClass_I/LTR/Ty1_copia/Ale\nClass_I/LTR/Ty1_copia/Angela\nClass_I/LTR/Ty1_copia/Bianca\nClass_I/LTR/Ty1_copia/Ikeros\nClass_I/LTR/Ty1_copia/Ivana\nClass_I/LTR/Ty1_copia/SIRE\nClass_I/LTR/Ty1_copia/TAR\nClass_I/LTR/Ty1_copia/Tork\nClass_I/LTR/Ty1_copia/Alexandra\nClass_I/LTR/Ty1_copia/Bryana\nClass_I/LTR/Ty1_copia/Ferco\nClass_I/LTR/Ty3_gypsy\nClass_I/LTR/Ty3_gypsy/chromovirus\nClass_I/LTR/Ty3_gypsy/chromovirus/Ferney\nClass_I/LTR/Ty3_gypsy/chromovirus/CRM\nClass_I/LTR/Ty3_gypsy/chromovirus/Reina\nClass_I/LTR/Ty3_gypsy/chromovirus/Tekay\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Tatius\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Athila\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Tat\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Tat/Ogre\nClass_I/LTR/Ty3_gypsy/non-chromovirus/OTA/Tat/Retand\nClass_II/Subclass_1/TIR/EnSpm_CACTA\nClass_II/Subclass_1/TIR/hAT\nClass_II/Subclass_1/TIR/MITE\nClass_II/Subclass_1/TIR/MITE/Stowaway\nClass_II/Subclass_1/TIR/MuDR_Mutator\nClass_II/Subclass_1/TIR/PIF_Harbinger\nClass_II/Subclass_1/TIR/Tc1_Mariner\nClass_II/Subclass_2/Helitron\nrDNA_45S/18S\nrDNA_45S/25S\nrDNA_45S/5.8S\nrDNA_5S/5S\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is possible to include additional categories to repeat library. Note that \u003ccode\u003eSubclass_1\u003c/code\u003e repeats are used to clean \u003ccode\u003eClass_I/LTR\u003c/code\u003e library which is built by DANTE_LTR thus it is critical to use correct codes for \u003ccode\u003eClass_II/Subclass_1\u003c/code\u003e mobile elements in repeat library.\u003c/p\u003e\n\u003cp\u003eFile \u003ccode\u003ecustom_library\u003c/code\u003e is used by TideCluster to annotate discovered tandem repeats based\non the similarity. Format is the same as above repeat database. E.g.\n\u003ccode\u003e\u0026gt;sequence_id/Satellite/PisTR-B\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run annotation pipeline, execute following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /path/to/ -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e assembly_repeat_annotation_pipeline.sif -c config.yaml -t 20\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eParameter \u003ccode\u003e-t\u003c/code\u003e specifies the number of threads to use. Singularity parameter \u003ccode\u003e-B\u003c/code\u003e is used to bind the input and output directories to the container. Without this parameter, the container will not be able to access the input and output files. File \u003ccode\u003econfig.yaml\u003c/code\u003e must be also in directory which is accessible to the container. In the example above this is the current directory \u003ccode\u003e$PWD\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning pipeline on metacentrum\u003c/h2\u003e\u003ca id=\"user-content-running-pipeline-on-metacentrum\" class=\"anchor\" aria-label=\"Permalink: Running pipeline on metacentrum\" href=\"#running-pipeline-on-metacentrum\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse \u003ca href=\"./scripts/annotate_repeats_metacentrum.sh\"\u003e./scripts/annotate_repeats_metacentrum.sh\u003c/a\u003e script to run the pipeline on metacentrum. Adjust paths to the input files , output directory and singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput structure\u003c/h2\u003e\u003ca id=\"user-content-output-structure\" class=\"anchor\" aria-label=\"Permalink: Output structure\" href=\"#output-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container\u003c/h2\u003e\u003ca id=\"user-content-build-the-container\" class=\"anchor\" aria-label=\"Permalink: Build the container\" href=\"#build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the container, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSINGULARITY=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ewhich singularity\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\nsudo ionice -c3 \u003cspan class=\"pl-smi\"\u003e$SINGULARITY\u003c/span\u003e build images/assembly_repeat_annotation_pipeline_0.6.3.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChangelog:\u003c/h2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-label=\"Permalink: Changelog:\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ev 0.5.1 - graphical output to PDF added\u003c/li\u003e\n\u003cli\u003ev 0.5.2 - RepeatMasker sensitivity can be set\u003c/li\u003e\n\u003cli\u003ev 0.6.0 - REXdb Viridiplante v4.0, library size reduction added, RepeatMasker parallelization added, missing full LTR-RT handling added\u003c/li\u003e\n\u003cli\u003ev 0.6.1 DANTE_LTR update to 0.4.0.3 (bugfix)\u003c/li\u003e\n\u003cli\u003ev 0.6.2 bugfix in bigwig calculation\u003c/li\u003e\n\u003cli\u003ev 0.6.3 dante update to 0.2.5 - bugfix\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1566918717.0 + "updated_at": 1729236602.0 }, { "data_format": 2, - "description": null, + "description": "A repository with simple singularity recipes for tutorial purpose", "filenames": [ - "Singularity.biopython_1.78", - "Singularity.pandas_0.25.3" + "Singularity.ub16.04-step1", + "Singularity.ub16.04-step0", + "Singularity.ub16.04-step2", + "Singularity.ub16.04-step3", + "Singularity.ub16.04-step4" ], - "full_name": "TomHarrop/py-containers", + "full_name": "DeepLearnPhysics/playground-singularity", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://raw.githubusercontent.com/DeepLearnPhysics/playground-singularity/master/LICENSE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e581ac49b7e1e99fb951242be63f6fdc6ebbc20c89a97fca0de99e1f2e6ae87e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6d6173686170652f6170697374617475732e737667\" alt=\"license\" data-canonical-src=\"https://img.shields.io/github/license/mashape/apistatus.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/459\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eplayground-singularity\u003c/h1\u003e\u003ca id=\"user-content-playground-singularity\" class=\"anchor\" aria-label=\"Permalink: playground-singularity\" href=\"#playground-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA repository with simple singularity recipes for tutorial purpose. Checkout the \u003ca href=\"https://github.com/DeepLearnPhysics/playground-singularity/wiki\"\u003ewiki\u003c/a\u003e for documentation!\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1602640832.0 + "updated_at": 1536417204.0 }, { "data_format": 2, - "description": "Build recipe for a singularity container running RStudio Server.", + "description": "Singularity container with a working version of the stringr R package", "filenames": [ - "Singularity.3.6.2", "Singularity" ], - "full_name": "gparadis/singularity-rstudio", + "full_name": "richelbilderbeek/stringr_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity RStudio Server\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nickjer/singularity-rstudio\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/291de9d065fa77b739def518b0430f977c5793f78b1b4ce88d235e61c42332ee/68747470733a2f2f7472617669732d63692e6f72672f6e69636b6a65722f73696e67756c61726974792d7273747564696f2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nickjer/singularity-rstudio.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/463\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.rstudio.com/products/rstudio/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e. It was built on top of the base\nSingularity image \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-rstudio.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-rstudio.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploy\u003c/h2\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name singularity-rstudio.simg shub://nickjer/singularity-rstudio\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRStudio Server\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003erserver\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app rserver singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app rserver singularity-rstudio.simg --help\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecommand-line options:\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003everify:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --verify-installation arg (=0) verify the current installation\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eserver:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-working-dir arg (=/) program working directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-user arg (=rstudio-server) program user\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-daemonize arg (=0) run program as daemon\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-app-armor-enabled arg (=1) is app armor enabled for this session\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-set-umask arg (=1) set the umask to 022 on startup\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-simple-password-authentication\" class=\"anchor\" aria-hidden=\"true\" href=\"#simple-password-authentication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimple Password Authentication\u003c/h4\u003e\n\u003cp\u003eTo secure the RStudio Server you will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLaunch the container with the environment variable \u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e set to\na password of your choosing.\u003c/li\u003e\n\u003cli\u003eLaunch the \u003ccode\u003erserver\u003c/code\u003e command with the PAM helper script \u003ccode\u003erstudio_auth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAn example is given as:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRSTUDIO_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e singularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper rstudio_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow when you attempt to access the RStudio Server you will be presented with a\nlog in form. You can log in with your current user name and password you set in\n\u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-ldap-authentication\" class=\"anchor\" aria-hidden=\"true\" href=\"#ldap-authentication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLDAP Authentication\u003c/h4\u003e\n\u003cp\u003eAnother option is using an LDAP (or Active Directory) server for\nauthentication. Configuration of the LDAP authentication script \u003ccode\u003eldap_auth\u003c/code\u003e is\nhandled through the following environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_HOST\u003c/code\u003e - the host name of the LDAP server\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_USER_DN\u003c/code\u003e - the formatted string (where \u003ccode\u003e%s\u003c/code\u003e is replaced with the\nusername supplied during log in) of the bind DN used for LDAP authentication\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e - the file containing the CA certificates used by\nthe LDAP server (default: use system CA certificates)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn example for an LDAP server with signed SSL certificate from a trusted CA:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nsingularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn example for an LDAP server with a self-signed SSL certificate:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_CERT_FILE=/ca-certs.pem\nsingularity run \\\n --bind /path/to/ca-certs.pem:/ca-certs.pem \\\n singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that we had to bind mount the CA certificates file from the host machine\ninto the container and specify the container\u0027s path in \u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e (not\nthe host\u0027s path).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-r-and-rscript\" class=\"anchor\" aria-hidden=\"true\" href=\"#r-and-rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR and Rscript\u003c/h3\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e for more information on how to run \u003ccode\u003eR\u003c/code\u003e and\n\u003ccode\u003eRscript\u003c/code\u003e from within this Singularity image.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003estringr_singularity\u003c/h1\u003e\u003ca id=\"user-content-stringr_singularity\" class=\"anchor\" aria-label=\"Permalink: stringr_singularity\" href=\"#stringr_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/richelbilderbeek/stringr_singularity/actions/workflows/build_singularity.yaml\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/stringr_singularity/actions/workflows/build_singularity.yaml/badge.svg\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container with a working version of the \u003ccode\u003estringr\u003c/code\u003e R package.\u003c/p\u003e\n\u003cp\u003eIt does run remotely (i.e. on GitHub Actions),\nbut not on my Ubuntu 20.04 LTS laptop).\u003c/p\u003e\n\u003cp\u003eBecause I do not understand why, I\n\u003ca href=\"https://stackoverflow.com/questions/71252123/singularity-container-with-stringr-fails-only-locally-with-libicui18n-so-66-ca\" rel=\"nofollow\"\u003eposted a question on StackOverflow\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1597274627.0 + "updated_at": 1645702896.0 }, { "data_format": 2, - "description": "This is a Nextflow pipeline for generating sequencing reports for the SNP\u0026Seq Technology platform, NGI Uppsala, SciLifelab Genomics.", + "description": "Singularity container for canu", "filenames": [ - "images/Singularity.checkqc-3.6.0" + "Singularity.1.8.0" ], - "full_name": "Molmed/seqreports", - "latest_release": "v1.1.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-seqreports-snpseq-run-folder-qc-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#seqreports-snpseq-run-folder-qc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eseqreports: SNP\u0026amp;Seq Run folder QC pipeline\u003c/h1\u003e\n\u003cp\u003eThis is a Nextflow pipeline for generating sequencing reports for the SNP\u0026amp;Seq Technology platform, NGI Uppsala, SciLifelab Genomics.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-requisites\u003c/h2\u003e\n\u003cp\u003eYou need to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstall Nextflow (e.g. using conda \u003ccode\u003econda create -n nextflow-env nextflow\u003c/code\u003e or downloading from \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow.io\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://singularity.lbl.gov/install-linux#adding-the-mirror-and-installing\" rel=\"nofollow\"\u003eSingularity (version \u0026gt; 2.6)\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(currently mandatory: see known issues) Download the fastq-screen database by downloading fastq-screen from \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/fastq_screen_v0.13.0.tar.gz\" rel=\"nofollow\"\u003ehere\u003c/a\u003e, extract the archive and then run \u003ccode\u003efastq_screen --get_genomes\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-run-the-nextflow-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-run-the-nextflow-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to run the nextflow pipeline\u003c/h2\u003e\n\u003cp\u003eAwesome, you\u0027re all set! Let\u0027s try generating reports for your favourite runfolder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Using parameters supplied in a config (see below)\u003c/span\u003e\n nextflow run -c custom.config -profile snpseq,singularity main.nf\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Using parameters supplied on the command line\u003c/span\u003e\n nextflow run -profile snpseq,singularity main.nf \\\n --run_folder \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/path/to/runfolder\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n --fastqscreen_databases \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/path/to/databases\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n --checkqc_config \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/path/to/checkqc.config\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-profiles\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-profiles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable profiles\u003c/h3\u003e\n\u003cp\u003eThese are the primary config profiles:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edev\u003c/code\u003e: Run locally with low memory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eirma\u003c/code\u003e: Uppmax slurm profile for use on the cluster \u003ccode\u003eirma\u003c/code\u003e (note: The parameter \u003ccode\u003eparams.project\u003c/code\u003e must be supplied).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esnpseq\u003c/code\u003e: Run locally with greater memory available than \u003ccode\u003edev\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity\u003c/code\u003e: Enables singularity and provides container URLs.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest\u003c/code\u003e: Run the pipeline using test data\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdditional profiles:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edebug\u003c/code\u003e: prints out the \u003ccode\u003eenv\u003c/code\u003e properties before executing processes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-supplying-a-custom-config-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#supplying-a-custom-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupplying a custom config file\u003c/h3\u003e\n\u003cp\u003eCustom config files can contain all command line parameters, nextflow parameters, and overriding options.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eresume = true\nparams.run_folder = \u0027/path/to/runfolder\u0027\nparams.fastqscreen_databases = \u0027/path/to/databases\u0027\nparams.checkqc_config = \u0027/path/to/checkqc.config\u0027\nworkDir = \u0027/path/to/temporary/storage/space\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment\u003c/h2\u003e\n\u003cp\u003eThere are two primary branches of this project:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emaster\u003c/code\u003e: The stable release branch\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edev\u003c/code\u003e: The development and test branch, to which pull requests should be made.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTests are run through GitHub Actions when pushing code to the repo. See instructions below on how to reproduce it locally.\u003c/p\u003e\n\u003cp\u003eTo keep the python parts of the project nice and tidy, we enforce that code should be formatted according to \u003ca href=\"https://github.com/psf/black\"\u003eblack\u003c/a\u003e.\nTo re-format your code with black, simply run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eblack .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-tests-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-tests-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning tests locally\u003c/h3\u003e\n\u003cp\u003eAssuming you have installed all pre-requisites (except the fastq screen database: test data comes with a minimal version of it), you can run tests locally by following these steps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# create virtual environment \nvirtualenv -p python3.9 venv/ \n\n# activate venv\nsource venv/bin/activate\n\n# install dependencies\npip install -r requirements-dev.txt\n\n# run tests\npytest tests/\n\n# perform black formatter check\nblack --check .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown issues:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eUnable to download genome indicies using \u003ccode\u003efastq_screen --get_genomes\u003c/code\u003e as wget within the container does not resolve the address correctly. Fastq Screen must be installed separately (e.g. with conda) and the genomes downloaded prior to running the workflow. The path to the databases must then be given using the \u003ccode\u003eparams.fastqscreen_databases\u003c/code\u003e parameter.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "ISUGIFsingularity/canu", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container recipe and run script for the MaSuRCA assembler\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-recipe-and-run-script-for-the-masurca-assembler\" class=\"anchor\" aria-label=\"Permalink: Singularity container recipe and run script for the MaSuRCA assembler\" href=\"#singularity-container-recipe-and-run-script-for-the-masurca-assembler\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClone this repository\u003c/h3\u003e\u003ca id=\"user-content-clone-this-repository\" class=\"anchor\" aria-label=\"Permalink: Clone this repository\" href=\"#clone-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emkdir isugif\ncd isugif\ngit clone git@github.com:ISUGIFsingularity/canu.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlace singularity container into SIMG folder inside this repo\u003c/h3\u003e\u003ca id=\"user-content-place-singularity-container-into-simg-folder-inside-this-repo\" class=\"anchor\" aria-label=\"Permalink: Place singularity container into SIMG folder inside this repo\" href=\"#place-singularity-container-into-simg-folder-inside-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can pull the singularity image using these commands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd canu\nmkdir SIMG\ncd SIMG\nsingularity pull shub://ISUGIFsingularity/canu:1.8.0\nln -s ISUGIFsingularity-canu-master-1.8.0.simg ISUGIFsingularity-canu-master.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd Alias and PATH\u003c/h3\u003e\u003ca id=\"user-content-add-alias-and-path\" class=\"anchor\" aria-label=\"Permalink: Add Alias and PATH\" href=\"#add-alias-and-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlace the following into your .bashrc folder for container use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#make sure you are in the canu folder that corresponds to the Path2thisRepo\nexport canugit=`pwd`\nexport PATH=$PATH:$canugit/wrappers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlace the following into your .bashrc folder to use scripts without container (preferred method unless testing container functions)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport canugit=path2thisrepo\nexport PATH=$PATH:$canugit/canu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNotes\u003c/h3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor this to function properly had to add \u003ccode\u003e--bind $canugit:/mnt\u003c/code\u003e to the wrappers\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec --bind $canugit:/mnt --bind $PWD $canugit/SIMG/ISUGIFsingularity-canu-master.simg /mnt/canu/summary.sh\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 10, + "subscribers_count": 2, "topics": [], - "updated_at": 1644241679.0 + "updated_at": 1550868367.0 }, { "data_format": 2, - "description": null, + "description": "Haploid bacterial assembly and automatic annotation implemented using Nextflow", "filenames": [ - "Singularity.latest" + "Singularity" ], - "full_name": "bioexcel/acpype_container", + "full_name": "BU-ISCIII/bacterial_assembly-nf", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/mmbirb/acpype\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b48b2d8417bd2482df9aa6a920101731c2f1a73416deb25291044c5278738d4a/68747470733a2f2f717561792e696f2f7265706f7369746f72792f62696f636f6e7461696e6572732f62696f62625f696f2f737461747573\" alt=\"\" data-canonical-src=\"https://quay.io/repository/biocontainers/biobb_io/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3787\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-acpype-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#acpype-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eACPYPE container\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eACPYPE docker and singularity containers used for \u003ca href=\"https://github.com/bioexcel/biobb_chemistry\"\u003ebiobb_chemistry\u003c/a\u003e BioExcel Building Blocks modules.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Use\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull mmbirb/acpype:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run mmbirb/acpype:latest \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Use\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name acpype.sif shub://bioexcel/acpype_container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec acpype.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" aria-hidden=\"true\" href=\"#copyright--licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-acknolegements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknolegements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknolegements\u003c/h3\u003e\n\u003cp\u003eThe singularity container has been built through \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 3, "topics": [], - "updated_at": 1584436958.0 + "updated_at": 1650379680.0 }, { "data_format": 2, - "description": null, + "description": "official build specifications for scientific linux", "filenames": [ - "Singularity.dev" + "Singularity", + "7.0/Singularity" ], - "full_name": "pndni/minc-ants-and-fsl-container", + "full_name": "singularityhub/scientific-linux", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScientific Linux\u003c/h1\u003e\u003ca id=\"user-content-scientific-linux\" class=\"anchor\" aria-label=\"Permalink: Scientific Linux\" href=\"#scientific-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official library of scientific linux builds for Singularity images hosted on Singularity Hub. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003ethe builds are organized by folders, with one \u003ccode\u003eSingularity\u003c/code\u003e file per folder. This ensures that we can find the files programatically.\u003c/li\u003e\n\u003cli\u003ethe different image tags correspond to these folders, and the name of the tag is specified on Singularity Hub\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUnder Development\u003c/h2\u003e\u003ca id=\"user-content-under-development\" class=\"anchor\" aria-label=\"Permalink: Under Development\" href=\"#under-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese images are currently not connected to Singularity Hub, but this will be done in early 2017. The first effort will be to develop a core set of images, and then any necessary builders / templating systems that would be necessary to help with this process. If you are interested in contributing, please \u003ca href=\"http://singularity.lbl.gov/contributing-code\" rel=\"nofollow\"\u003ereach out\u003c/a\u003e!\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1555092034.0 + "updated_at": 1484506870.0 }, { "data_format": 2, - "description": "Container with xrootd for file xfer from FNAL", + "description": "container_openmpi_gnu/_centos7_x86_64", "filenames": [ "Singularity" ], - "full_name": "LArbys/singularity-xrootd", + "full_name": "CINECA-HPC/container_openmpi_gnu7_centos7_x86_64", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-for-root-with-xrootd\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-for-root-with-xrootd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity for ROOT with XROOTD\u003c/h1\u003e\n\u003cp\u003eContainer to be used for file xfer from FNAL.\nIn order to xfer files, the container must have its certificates to FNAL updated periodically.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-steps-to-using-this-container-and-image-from-scratch\" class=\"anchor\" aria-hidden=\"true\" href=\"#steps-to-using-this-container-and-image-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSteps to using this container and image (from scratch)\u003c/h1\u003e\n\u003cp\u003ePart 1: build the container\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild the container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePart 2: move the container to Tufts\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ego to the Tufts cluster (and probably to some place your user directory)\u003c/li\u003e\n\u003cli\u003eclone this repository\u003c/li\u003e\n\u003cli\u003ecopy the container to the repository folder as there are scripts we will use\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePart 3: renew your user grid certificate on a MicroBooNE machine\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elog into the microboone machine\u003c/li\u003e\n\u003cli\u003eon the UBmachine: load/renew your certificates, find your UID\u003c/li\u003e\n\u003cli\u003eback on tufts cluster: make a copy of example_setup_container_X.sh and edit it as instructed\u003c/li\u003e\n\u003cli\u003estart the container, go to the repo dir, run the script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePart 4: make a list of files to transfer. either:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emake a filelist\u003c/li\u003e\n\u003cli\u003eretrieve or setup a SAM definition\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePart 5: setup xfer_script and run\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container\u003c/h2\u003e\n\u003cp\u003eFirst find a computer that has docker and singularity (e.g. meitner). You will also need \u003ccode\u003esudo\u003c/code\u003e access to build the container.\u003c/p\u003e\n\u003cp\u003eClone this repo onto a computer:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/larbys/singularity-xrootd\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, follow the steps below to grab the required certificates from uboonebuild and copy them to your local machine\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emake sure your computer is setup to be able to get a FNAL kerberos ticket (i.e. \u003ccode\u003ekinit\u003c/code\u003e workse)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eclone this repo to your computer\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eget a kerberos ticket: \u003ccode\u003ekinit [username]@FNAL.GOV\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003emake the directory \u003ccode\u003e/tmp/$USER\u003c/code\u003e to hold certificates (must be somewhere in \u003ccode\u003e/tmp\u003c/code\u003e in order for singularity to read from outside the new container)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eif running this not for the first time, make sure \u003ccode\u003e/tmp/$USER/grid-security\u003c/code\u003e and \u003ccode\u003e/tmp/$USER/vomses\u003c/code\u003e are removed from your \u003ccode\u003e/tmp/$USER/\u003c/code\u003e folder\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003escp -r \u003ccode\u003e/etc/grid-security\u003c/code\u003e and \u003ccode\u003e/etc/vomses\u003c/code\u003e to your \u003ccode\u003e/tmp/$USER\u003c/code\u003e folder from one of the uboone gpvms.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escp -r fnal-username@ubcomputer:/etc/grid-security /tmp/$USER/\nscp fnal-username@ubcomputer:/etc/vomses /tmp/$USER/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecp \u003ccode\u003e/etc/krb5.conf\u003c/code\u003e to \u003ccode\u003e/tmp/$USER/\u003c/code\u003e or get this from the web using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://cdcvs.fnal.gov/redmine/attachments/download/9616/krb5.conf /tmp/$USER/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ego into \u003ccode\u003eSingularity\u003c/code\u003e file (this is the build instructions), and set your username at the line\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport USER=your-name-here\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, build the container using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-xrootd.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBasically what is happening is that, while being built, the container can see your system\u0027s \u003ccode\u003e/tmp\u003c/code\u003e folder.\nSo we put the required security files into \u003ccode\u003e/tmp\u003c/code\u003e and these get copied into the container\u0027s \u003ccode\u003e/etc/\u003c/code\u003e folder when it is built.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing container\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ecopy the new container to the Tuft\u0027s grid at: \u003ccode\u003e/cluster/tufts/wongjiradlab/larbys/images/singularity-xrootd\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003estart the container using \u003ccode\u003esource start_container.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eyou\u0027ll see a prompt once you are in the container. type \u003ccode\u003ebash\u003c/code\u003e to start a bash shell\u003c/li\u003e\n\u003cli\u003enavigate back to the container folder: \u003ccode\u003ecd /cluster/kappa/wongjiradlab/larbys/images/singularity-xrootd\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ein another terminal, log into one of the uboone gpvms. refresh your vomses certificates.\u003c/li\u003e\n\u003cli\u003emake a copy of \u003ccode\u003eexample_setup_container_X.sh\u003c/code\u003e, where \u003ccode\u003eX\u003c/code\u003e is your FNAL username\u003c/li\u003e\n\u003cli\u003echange XXXXXX with your FNAL username and YYYYYY with your user id\u003c/li\u003e\n\u003cli\u003erun this script\u003c/li\u003e\n\u003cli\u003eyou can test that your container now has permissions to use xrootd to access PNFS by running: \u003ccode\u003epython test_setup.py\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_openmpi_gnu7_centos7_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_openmpi_gnu7_centos7_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_openmpi_gnu7_centos7_x86_64\" href=\"#container_openmpi_gnu7_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003econtainer_openmpi_gnu/_centos7_x86_64\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1614268311.0 + "updated_at": 1605607119.0 }, { "data_format": 2, - "description": null, + "description": "Singularity port of Picard tools", "filenames": [ - "Singularity.v0.0.1" + "Singularity" ], - "full_name": "baxpr/cerconn", + "full_name": "researchapps/singularity-picard", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cerconn-cerebellar-functional-connectivity-maps\" class=\"anchor\" aria-hidden=\"true\" href=\"#cerconn-cerebellar-functional-connectivity-maps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecerconn: cerebellar functional connectivity maps\u003c/h1\u003e\n\u003cp\u003eSeed regions are the Buckner 7 set as produced by cersuit pipeline. Four sets are computed: with\nand without removal of the mean gray matter signal by regression; with and without erosion of the\nseed ROIs with a 1-voxel radius spherical kernel. Both bivariate Pearson correlation and partial\ncorrelation with respect to the other seed regions are computed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInputs\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eFrom cersuit_v2 cerebellar segmentation pipeline\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecerseg_niigz Native space Buckner7 segmentation, ATLASES_NATIVE iw_Buckner_7Networks_u_a_c_rt1_seg1.nii.gz\nwcerseg_niigz Atlas space Buckner7 segmentation, ATLASES_SUIT Buckner_7Networks.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eFrom CAT12, e.g. cat12_ss2p0_v2 pipeline\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewmt1_niigz MNI space bias corrected T1, BIAS_NORM\nfwddef_niigz Forward deformation from native to MNI, DEF_FWD\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eFrom connectivity preprocessing pipeline connprep_v2\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eremovegm_niigz Native space with mean gray signal removed, FILTERED_REMOVEGM_NATIVE\nkeepgm_niigz Native space with mean gray signal kept, FILTERED_KEEPGM_NATIVE\nmeanfmri_niigz Native space mean fmri, MEAN_FMRI_NATIVE\nwmeanfmri_niigz MNI space mean fmri, MEAN_FMRI_MNI\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eOther options\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erefimg_nii Filename of existing image for geometry reference (\u0027avg152T1.nii\u0027,\u0027mask_ICV.nii\u0027)\nfwhm Smoothing kernel for connectivity maps, in mm\nout_dir Output directory\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eInfo for PDF report title if run on XNAT\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eproject\nsubject\nsession\nscan\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eFor testing only\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efsl_dir Location of FSL installation\nmagick_dir Location of ImageMagick binaries\nsrc_dir Location of pipeline shell scripts\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eROIS Seed ROI images in native fMRI geometry (eroded and non-eroded) and list of ROI labels\n\nEROIS Eroded seed ROI images in native T1 geometry\n\nFMRIMASK Native fMRI space mask used to exclude voxels without fMRI signal from seeds\n\nCONNMAP Connectivity maps for the seed ROIs. There are a number of different types:\n\n R_* Pearson correlation\n Z_* Fisher Z transform of Pearson correlation\n pR_* Partial correlation conditioning on the the other seeds\n pZ_* Fisher transform of the partial correlation\n \n *E* Indicates eroded seed ROIs (no E indicates uneroded ROIs)\n \n REMOVEGM Mean gray matter removed during preprocessing\n KEEPGM Mean gray matter retained during preprocessing\n \n _MNI Indicates MNI space images (no _MNI indicates native space)\n\nSCONNMAP Smoothed connectivity maps. As above.\n\nCONNMAT Connectivity matrices for seed ROIs. As above.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Picard\u003c/h1\u003e\u003ca id=\"user-content-singularity-picard\" class=\"anchor\" aria-label=\"Permalink: Singularity Picard\" href=\"#singularity-picard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 picard.img\nsudo singularity bootstrap picard.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the picard runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./picard.img [args] ...\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1616681036.0 + "updated_at": 1479258335.0 }, { "data_format": 2, - "description": null, + "description": "It is for ptsim using cvmfs in singularity conitaner", "filenames": [ - "Singularity.tf-nightly", - "Singularity.compute-0-27", - "Singularity.compute-0-36" + "Singularity" ], - "full_name": "bstriner/tensorflow-cuda-10.0-cudnn7-devel-ubuntu16.04", + "full_name": "ifurther/ptsim-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tensorflow-cuda-100-cudnn7-devel-ubuntu1604\" class=\"anchor\" aria-hidden=\"true\" href=\"#tensorflow-cuda-100-cudnn7-devel-ubuntu1604\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etensorflow-cuda-10.0-cudnn7-devel-ubuntu16.04\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1560847380.0 + "updated_at": 1641846771.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.template", - "bamcmp/Singularity.bamcmp", - "star-fusion/Singularity.star-fusion", - "bcl2fastq/Singularity.bcl2fastq" + "Singularity" ], - "full_name": "BUBioinformaticsHub/bubhub-singularity-apps", + "full_name": "phgenomics-singularity/snippy", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-apps\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity apps\u003c/h1\u003e\n\u003cp\u003eThis repo contains Singularity build images for various bubhub tools\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1539704222.0 + "updated_at": 1576562320.0 }, { "data_format": 2, - "description": "Picard is a set of command line tools for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF. ", + "description": null, "filenames": [ - "2.23.2/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-picard", - "latest_release": "v2.23.2", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-picard/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-picard/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f2ee028725767bd1588c30ee90365dddfc357c08ce7b5a43ed492ec5987e19f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d706963617264\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f2ee028725767bd1588c30ee90365dddfc357c08ce7b5a43ed492ec5987e19f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d706963617264\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-picard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0011e731d3015546848fd1f5982cbad69352604dc45cd908e9ff27c2773e8107/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d706963617264\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0011e731d3015546848fd1f5982cbad69352604dc45cd908e9ff27c2773e8107/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d706963617264\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-picard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a19ffb10b86582f80f0dd57f7696abb065ebaaab0d440703423ea0f2441278ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d706963617264\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a19ffb10b86582f80f0dd57f7696abb065ebaaab0d440703423ea0f2441278ea/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d706963617264\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-picard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b6afcdc9a6fce9707e6a449e49036b6136dd0335325684f4c8605d441b992e1f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d706963617264\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6afcdc9a6fce9707e6a449e49036b6136dd0335325684f4c8605d441b992e1f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d706963617264\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-picard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-picard\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-picard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-picard\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sandialabs/PIGER\"\u003ePicard\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003epicard\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/picard/2.23.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/picard\u003c/code\u003e as \u003ccode\u003e2.23.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "callaghanmt-containers/decryptics", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edecryptics\u003c/h1\u003e\u003ca id=\"user-content-decryptics\" class=\"anchor\" aria-label=\"Permalink: decryptics\" href=\"#decryptics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "bioinformatics", - "singularity" - ], - "updated_at": 1628991999.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1553554241.0 }, { "data_format": 2, - "description": "WineHQ in a Singularity container", + "description": "Singularity images for MITgcm testing and examples", "filenames": [ - "Singularity.4.0.3", - "Singularity.5.0.0", "Singularity" ], - "full_name": "OSC/sa_singularity_winehq", + "full_name": "christophernhill/mitgcm-shub-images", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-winehq\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-winehq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity WineHQ\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3891\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.winehq.org/\" rel=\"nofollow\"\u003eWineHQ\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003ewinehq.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build winehq.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploy\u003c/h2\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull winehq.sif shub://OSC/sa_singularity_winehq\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-64-bit-windows-binary\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-64-bit-windows-binary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun 64-bit Windows binary\u003c/h3\u003e\n\u003cp\u003eWineHQ is started using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run winehq.sif /path/to/windows_64bit_exe\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as a native command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./winehq.sif /path/to/windows_64bit_exe\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-32-bit-windows-binary\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-32-bit-windows-binary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun 32-bit Windows binary\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e winehq.sif wine /path/to/windows_32bit_exe\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emitgcm-shub-images\u003c/h1\u003e\u003ca id=\"user-content-mitgcm-shub-images\" class=\"anchor\" aria-label=\"Permalink: mitgcm-shub-images\" href=\"#mitgcm-shub-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity images for MITgcm testing and examples\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esee \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003ehttps://singularity-hub.org\u003c/a\u003e and \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov\u003c/a\u003e, and also \u003ca href=\"https://hub.docker.com\" rel=\"nofollow\"\u003ehttps://hub.docker.com\u003c/a\u003e and \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003ehttps://www.docker.com\u003c/a\u003e for background on material here.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn MITgcm containers through Docker and Singluarity can provide a way to test code and maintain strong portability and interoperability and more complete workflow reproducibility. Many other projects use Docker and Singularity to share code that is neither portable or interoperable.\u003c/p\u003e\n\u003cp\u003eThere are also images here that allow MITgcm to work with example codes that are not particularly portable or interoperable. This is only possible because the core MITgcm code is widely tested on many platforms to ensure portability and interoperability. Importantly, for this to work, Singularity and/or Docker should not be a way to create MITgcm setups that unecessairily break portability, interoperability, reproducibility or ease of use by adopting narrow software stacks or user unfriendly code practices.\u003c/p\u003e\n\u003cp\u003eMainstream code in MITgcm should continue to be written with goals, as far as possible, that most model configurations\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ecan run on many platforms, including (but not limited to!) a wide array of Linux distributions, handheld devices, Windows, OSX and iOS devices, single board computers.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecan checkpoint and restart in order to run in multiple sub-steps.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esupports validating exact and statistical reproducibility, even in the presence of floating point round off\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003einclude appropriate documentation of key features and modes of use\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etries to make minimum necessary assumptions about domain geometry and/or model equation configuration(s) to which the code will be applied\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etries to maintain robust compatibility with automatice differentiation where relevant and possible\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFollowing these guidelines helps create code that can be applied to the broadest range of problems; spanning, for example, laboratory experiments to Jovian deep atmospheres to Earth system ecosystem sensitvity calculations. The guidelines also serve to make meaningful coupling to external components (such as atmospheric model suites) relatively straightforward and flexible.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1581361908.0 + "updated_at": 1517003494.0 }, { "data_format": 2, @@ -21613,1293 +21192,1382 @@ var data = "filenames": [ "Singularity" ], - "full_name": "KM3NeT/OrcaSong", + "full_name": "ricardovialle/singularity-rstudio-r4", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity RStudio Server\u003c/h1\u003e\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: Singularity RStudio Server\" href=\"#singularity-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eR 4.0.3\nRStudio 1.3.1903\u003c/p\u003e\n\u003cp\u003eBased on repo \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e\nSingularity image for \u003ca href=\"https://www.rstudio.com/products/rstudio/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e. It was built on top of the base\nSingularity image \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-rstudio.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-rstudio.simg rstudio.def\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --arch amd64 library://vigo332/default/singularity-rstudio-r4:v0.01\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRStudio Server\u003c/h3\u003e\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: RStudio Server\" href=\"#rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003erserver\u003c/code\u003e command is launched using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app rserver singularity-rstudio.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app rserver singularity-rstudio.simg --help\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecommand-line options:\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003everify:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --verify-installation arg (=0) verify the current installation\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eserver:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-working-dir arg (=/) program working directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-user arg (=rstudio-server) program user\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-daemonize arg (=0) run program as daemon\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-app-armor-enabled arg (=1) is app armor enabled for this session\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --server-set-umask arg (=1) set the umask to 022 on startup\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSimple Password Authentication\u003c/h4\u003e\u003ca id=\"user-content-simple-password-authentication\" class=\"anchor\" aria-label=\"Permalink: Simple Password Authentication\" href=\"#simple-password-authentication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo secure the RStudio Server you will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLaunch the container with the environment variable \u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e set to\na password of your choosing.\u003c/li\u003e\n\u003cli\u003eLaunch the \u003ccode\u003erserver\u003c/code\u003e command with the PAM helper script \u003ccode\u003erstudio_auth\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAn example is given as:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRSTUDIO_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e singularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path=pam-helper \\\n --server-data-dir=/tmp\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow when you attempt to access the RStudio Server you will be presented with a\nlog in form. You can log in with your current user name and password you set in\n\u003ccode\u003eRSTUDIO_PASSWORD\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLDAP Authentication -- To be verified\u003c/h4\u003e\u003ca id=\"user-content-ldap-authentication----to-be-verified\" class=\"anchor\" aria-label=\"Permalink: LDAP Authentication -- To be verified\" href=\"#ldap-authentication----to-be-verified\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnother option is using an LDAP (or Active Directory) server for\nauthentication. Configuration of the LDAP authentication script \u003ccode\u003eldap_auth\u003c/code\u003e is\nhandled through the following environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_HOST\u003c/code\u003e - the host name of the LDAP server\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_USER_DN\u003c/code\u003e - the formatted string (where \u003ccode\u003e%s\u003c/code\u003e is replaced with the\nusername supplied during log in) of the bind DN used for LDAP authentication\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e - the file containing the CA certificates used by\nthe LDAP server (default: use system CA certificates)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn example for an LDAP server with signed SSL certificate from a trusted CA:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nsingularity run singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn example for an LDAP server with a self-signed SSL certificate:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_HOST=ldap.example.com\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_USER_DN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecn=%s,dc=example,dc=com\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LDAP_CERT_FILE=/ca-certs.pem\nsingularity run \\\n --bind /path/to/ca-certs.pem:/ca-certs.pem \\\n singularity-rstudio.simg \\\n --auth-none 0 \\\n --auth-pam-helper-path ldap_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that we had to bind mount the CA certificates file from the host machine\ninto the container and specify the container\u0027s path in \u003ccode\u003eLDAP_CERT_FILE\u003c/code\u003e (not\nthe host\u0027s path).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR and Rscript\u003c/h3\u003e\u003ca id=\"user-content-r-and-rscript\" class=\"anchor\" aria-label=\"Permalink: R and Rscript\" href=\"#r-and-rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e for more information on how to run \u003ccode\u003eR\u003c/code\u003e and\n\u003ccode\u003eRscript\u003c/code\u003e from within this Singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 0, "topics": [], - "updated_at": 1625135038.0 + "updated_at": 1629302355.0 }, { "data_format": 2, - "description": "These are a collection of scripts we commonly use on the command line for exploring data and for documentation", + "description": "Numerical representation of mountains in atmospheric models", "filenames": [ - "Singularity.1.0.1", - "Singularity.1.0.0" + "Singularity" ], - "full_name": "ISUGIFsingularity/utilities", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-bioinformatic-scripts-that-we-commonly-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#bioinformatic-scripts-that-we-commonly-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBioinformatic scripts that we commonly use\u003c/h1\u003e\n\u003cp\u003eThis Singularity container is primarily for testing out how containers function. All of the functions included in the container will run without a container. Having them in a container results in a huge performance hit as singularity has to be called and these scripts do not have dependencies that could benefit from a container.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003enb\u003c/strong\u003e = notebook to add text to a notebook but not the readme file\n**readme **= readme will create a README file in the folder and put the date and text into this file along with a copy in the notebook\n\u003cstrong\u003ecreatehist.awk\u003c/strong\u003e = function that will take a binsize argument and a list of numbers and return a count of numbers within increments of binsize\n\u003cstrong\u003eintervalBins.awk\u003c/strong\u003e = modified createhist script that gives the intervals and counts of elements in the interval\n\u003cstrong\u003enew_Assemblathon.pl\u003c/strong\u003e = script that will create summary statistics from a fasta file usually used for genome assemblies (see Assemblathon2 paper)\n**seqlen.awk **= script that will take a fasta file and report the ID and the length of the sequence.\n\u003cstrong\u003ecolsum\u003c/strong\u003e = used to sum the Nth colum of a file.\n\u003cstrong\u003esummary\u003c/strong\u003e = give summary statistics of a column of numbers\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-clone-this-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#clone-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClone this repository\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emkdir isugif\ncd isugif\ngit clone git@github.com:ISUGIFsingularity/utilities.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-place-singularity-container-into-simg-folder-inside-this-repo\" class=\"anchor\" aria-hidden=\"true\" href=\"#place-singularity-container-into-simg-folder-inside-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlace singularity container into SIMG folder inside this repo\u003c/h3\u003e\n\u003cp\u003eYou can pull the singularity image using these commands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd utilities\nmkdir SIMG\ncd SIMG\nsingularity pull shub://ISUGIFsingularity/utilities:1.0.1\nln -s utilities_1.0.1.sif ISUGIFsingularity-utilities-master.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-add-alias-and-path\" class=\"anchor\" aria-hidden=\"true\" href=\"#add-alias-and-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdd Alias and PATH\u003c/h3\u003e\n\u003cp\u003ePlace the following into your .bashrc folder for container use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias UTILITIESgit=Path2thisRepo\nexport PATH=$PATH:$UTILITIESgit/wrappers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlace the following into your .bashrc folder to use scripts without container (preferred method unless testing container functions)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias UTILITIESgit=Path2thisRepo\nexport PATH=$PATH:$UTILITIESgit/utilities\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h3\u003e\n\u003cp\u003eFor this to function properly had to add \u003ccode\u003e--bind $UTILITIESgit:/mnt\u003c/code\u003e to the wrappers\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec --bind $UTILITIESgit:/mnt --bind $PWD $UTILITIESgit/SIMG/ISUGIFsingularity-utilities-master.simg /mnt/utilities/summary.sh\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "hertzsprung/thesis", + "latest_release": "jshaw-thesis-v2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNumerical representation of mountains in atmospheric models\u003c/h1\u003e\u003ca id=\"user-content-numerical-representation-of-mountains-in-atmospheric-models\" class=\"anchor\" aria-label=\"Permalink: Numerical representation of mountains in atmospheric models\" href=\"#numerical-representation-of-mountains-in-atmospheric-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling the thesis with Singularity\u003c/h2\u003e\u003ca id=\"user-content-compiling-the-thesis-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Compiling the thesis with Singularity\" href=\"#compiling-the-thesis-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e to compile the thesis. On Ubuntu 16.10 and later, \u003ccode\u003esudo apt-get install singularity-container\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce installed,\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eExecute the \u003ca href=\"https://github.com/hertzsprung/AtmosTests\"\u003eAtmosTests\u003c/a\u003e simulations\u003c/li\u003e\n\u003cli\u003eEdit \u003ca href=\"https://github.com/hertzsprung/thesis/blob/master/build.properties\"\u003e\u003ccode\u003ebuild.properties\u003c/code\u003e\u003c/a\u003e so that \u003ccode\u003eatmostests_builddir\u003c/code\u003e points to the \u003ccode\u003eAtmosTests/build\u003c/code\u003e directory\u003c/li\u003e\n\u003cli\u003eExecute \u003ccode\u003e./singularity.bootstrap.sh\u003c/code\u003e to bootstrap the Singularity image, \u003ccode\u003ethesis.img\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eExecute \u003ccode\u003e./singularity.ninja.sh\u003c/code\u003e to compile \u003ccode\u003ethesis.pdf\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1560444723.0 + "updated_at": 1537256120.0 }, { "data_format": 2, - "description": "MountainLab package with various spike sorting utilities", + "description": "bcftools \u2014 utilities for variant calling and manipulating VCFs and BCFs.", "filenames": [ - "Singularity.v0.1.7" + "1.10.2/Singularity" ], - "full_name": "magland/ml_spikeforest", + "full_name": "pscedu/singularity-bcftools", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ml_spikeforest\" class=\"anchor\" aria-hidden=\"true\" href=\"#ml_spikeforest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml_spikeforest\u003c/h1\u003e\n\u003cp\u003eSpike sorting tools\nMountainLab processor package\u003c/p\u003e\n\u003cp\u003eInstallation from conda (with python 3.6):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -c flatiron -c conda-forge ml_spikeforest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bcftools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bcftools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bcftools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bcftools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7ca9386ab21efbdc68e7ef3c00a6dc53638e07bd6b621e85510512c1273746ed/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626366746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ca9386ab21efbdc68e7ef3c00a6dc53638e07bd6b621e85510512c1273746ed/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626366746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bcftools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b2aa819c47bb92269300f234936b1a0be2eebd89037240af5c1474b7848152f0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626366746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b2aa819c47bb92269300f234936b1a0be2eebd89037240af5c1474b7848152f0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626366746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bcftools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9b54f3037e3481e0a19ad82ebd352d2138f1b42ac7e9d0495b4b99cbae77d08e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626366746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9b54f3037e3481e0a19ad82ebd352d2138f1b42ac7e9d0495b4b99cbae77d08e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626366746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bcftools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5afb6c31b9ddfeb6d0bbc5d7e455eaf2a4d8d3f63596867e32743e5cfc036d04/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626366746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5afb6c31b9ddfeb6d0bbc5d7e455eaf2a4d8d3f63596867e32743e5cfc036d04/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626366746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bcftools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-bcftools\u003c/h1\u003e\u003ca id=\"user-content-singularity-bcftools\" class=\"anchor\" aria-label=\"Permalink: singularity-bcftools\" href=\"#singularity-bcftools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/icaoberg/bcftools\"\u003ebcftools\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1537460132.0 + "subscribers_count": 5, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1629217454.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container for deploying smudgeplot", "filenames": [ - "Singularity.readfish_77c11e2", - "Singularity.readfish_14ddf60", - "Singularity.clairvoyante_1.01", - "Singularity.minionqc_1.4.2", - "Singularity.guppy_4.2.2", - "Singularity.medaka_v0.10.1", - "Singularity.chopchop_a301f2d", - "Singularity.minknow_20.10.3", - "Singularity.qcat_1.0.1", - "Singularity.guppy-cpu_4.2.2", - "Singularity.porechop_0.2.4" + "Singularity" ], - "full_name": "TomHarrop/ont-containers", + "full_name": "HuffordLab-Containers/smudgeplot", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-service-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#service-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eService installation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eChange the paths in \u003ccode\u003elaunch_server.sh\u003c/code\u003e and copy it to its location, e.g. \u003ccode\u003e/usr/local/bin\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eCopy the example systemd service and replace the paths in \u003ccode\u003eExecStart\u003c/code\u003e with path to \u003ccode\u003elaunch_server.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall the systemd service\n\u003ccode\u003esudo cp config/guppy.service /etc/systemd/user/\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eStart the service\u003cbr\u003e\n\u003ccode\u003esystemctl --user enable guppy.timer\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003esystemctl --user start guppy.timer\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esmudgeplot\u003c/h1\u003e\u003ca id=\"user-content-smudgeplot\" class=\"anchor\" aria-label=\"Permalink: smudgeplot\" href=\"#smudgeplot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container for deploying smudgeplot\u003c/p\u003e\n\u003cp\u003eOriginal source for this package is found here:\n\u003ca href=\"https://github.com/KamilSJaron/smudgeplot\"\u003ehttps://github.com/KamilSJaron/smudgeplot\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1607563658.0 + "updated_at": 1612751126.0 }, { "data_format": 2, - "description": null, + "description": "This is a snakemake/singularity pipelin for metabarcoding data processing using the OBItools and dada2.", "filenames": [ - "Singularity_recipev5.Shannon", - "Singularity_recipev2.R3.5", - "Singularity_recipev3.Rpackages", - "Singularity_recipe_4.0.3_libraries", - "Singularity_recipe_R4.0.3", - "Singularity_recipe_scenic", - "Singularity_recipev4.PyPackages" + "workflow/envs/Singularity" ], - "full_name": "elisadonnard/singularity", + "full_name": "LafontRapnouilTristan/metabarcoding_pipelino", "latest_release": null, + "readme": "\u003cp\u003eThis pipeline starts from raw foward (R1) and reverse (R2) \u003ccode\u003e.fastq\u003c/code\u003e files and a \u003ccode\u003e.tab\u003c/code\u003e ngsfilter file.\u003c/p\u003e\n\u003cp\u003eThis pipeline aims to respects the \u003ca href=\"https://www.go-fair.org/fair-principles/\" rel=\"nofollow\"\u003eFAIR\u003c/a\u003e principles using \u003ca href=\"https://snakemake.readthedocs.io/en/stable/#\" rel=\"nofollow\"\u003esnakemake\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDescription\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline for raw NGS metabarcoding data processing using a combination of the OBItools, dada2 and sumaclust.\u003c/p\u003e\n\u003cp\u003eYou\u0027ll find parameters used by the pipeline in the \u003ca href=\"config/config.yaml\"\u003econfig file\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDAG of the pipeline:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dag/dag.png\"\u003e\u003cimg src=\"dag/dag.png\" alt=\"DAG of the pipeline\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInput\u003c/h1\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequired\u003c/h2\u003e\u003ca id=\"user-content-required\" class=\"anchor\" aria-label=\"Permalink: Required\" href=\"#required\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment\u003c/h3\u003e\u003ca id=\"user-content-environment\" class=\"anchor\" aria-label=\"Permalink: Environment\" href=\"#environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run this pipeline you need \u003cstrong\u003esnakemake\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFiles\u003c/h3\u003e\u003ca id=\"user-content-files\" class=\"anchor\" aria-label=\"Permalink: Files\" href=\"#files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRaw illumina sequencing output for forward and reverse reads in \u003ccode\u003e.fastq\u003c/code\u003e format\u003c/p\u003e\n\u003cp\u003eForward file named \u003cem\u003eXXX_R1.fastq\u003c/em\u003e and reverse \u003cem\u003eXXX_R2.fastq\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, you will need a text file named \u003cem\u003eXXX_ngsfilter.tab\u003c/em\u003e as required by the \u003ca href=\"https://pythonhosted.org/OBITools/scripts/ngsfilter.html\" rel=\"nofollow\"\u003engsfilter\u003c/a\u003e command of the obitools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTree\u003c/h2\u003e\u003ca id=\"user-content-tree\" class=\"anchor\" aria-label=\"Permalink: Tree\" href=\"#tree\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is what your directory tree should look like in order to run the pipeline.\u003c/p\u003e\n\u003cp\u003eName with \u003ccode\u003e*.extension\u003c/code\u003e are file and other are folders.\u003c/p\u003e\n\u003cp\u003eThe different \u003cstrong\u003erun\u003c/strong\u003e will be independantly processed.\u003c/p\u003e\n\u003cp\u003eMake sure that you have a different folders containing associated resources.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- Snakefile\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- benchmarks\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- config\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- config.yaml\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- dag\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- log\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- report\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- resources\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run1\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run1_ngsfilter.tab\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run1_R1.fastq\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run1_R2.fastq\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run2\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run2_ngsfilter.tab\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run2_R1.fastq\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- run2_R2.fastq\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- results\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- workflow\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- envs\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- R_env.yaml\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- obi_env.yaml\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- suma_env.yaml\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- rules\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 01-pairing.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 02-sort_alignments.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 03-demultiplex.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 04-dada_prep.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 05-filterandtrim.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 06-derep.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 07-obi_clean.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 08-abbundance_filt.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 09-bimera_rm.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 10-otu_clust.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 11-merge_clust.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 12-format_out.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 12-seq_tracking.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 12-taxassign.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- 13-benchmark.smk\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- scripts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- benchmark.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- derep_dada2.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- filtandtrim_dada2.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- rm_bimera_dada2.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- seq_tracking.R\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-- taxassign_dada2.R\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline steps and tools\u003c/h1\u003e\u003ca id=\"user-content-pipeline-steps-and-tools\" class=\"anchor\" aria-label=\"Permalink: Pipeline steps and tools\" href=\"#pipeline-steps-and-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI - Pre-processing\u003c/h2\u003e\u003ca id=\"user-content-i---pre-processing\" class=\"anchor\" aria-label=\"Permalink: I - Pre-processing\" href=\"#i---pre-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1 - merging paired-end sequenced reads\u003c/h3\u003e\u003ca id=\"user-content-1---merging-paired-end-sequenced-reads\" class=\"anchor\" aria-label=\"Permalink: 1 - merging paired-end sequenced reads\" href=\"#1---merging-paired-end-sequenced-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e - split fasq for faster processing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBITools/scripts/obidistribute.html\" rel=\"nofollow\"\u003e\u003cem\u003eobidistribute\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e : number of files to split in, \u003ccode\u003enfile\u003c/code\u003e in \u003ca href=\"config/config.yaml\"\u003e\u003ccode\u003econfig\u003c/code\u003e\u003c/a\u003e. (between 2 and 1000).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e - align paired-end sequence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/illuminapairedend.html\" rel=\"nofollow\"\u003e\u003cem\u003eilluminapairedend\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e - merge output and remove temp files\u003c/p\u003e\n\u003cp\u003ebasic cat and rm UNIX commands.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2 - filtering alignments\u003c/h3\u003e\u003ca id=\"user-content-2---filtering-alignments\" class=\"anchor\" aria-label=\"Permalink: 2 - filtering alignments\" href=\"#2---filtering-alignments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obiannotate.html\" rel=\"nofollow\"\u003e\u003cem\u003eobiannotate\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-S\u003c/code\u003e : expression used for annotation, ali:\u003ccode\u003egood\u003c/code\u003e if alignment score \u0026gt; \u003ccode\u003eminscore\u003c/code\u003e in \u003ca href=\"config/config.yaml\"\u003e\u003ccode\u003econfig\u003c/code\u003e\u003c/a\u003e.\nelse \u003ccode\u003ebad\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obisplit.html\" rel=\"nofollow\"\u003e\u003cem\u003eobisplit\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-t\u003c/code\u003e : split according to a condition, here \u003ccode\u003eali = good\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-p\u003c/code\u003e : prefix of the resulting files.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3 - demultiplexing and tag/primer trimming\u003c/h3\u003e\u003ca id=\"user-content-3---demultiplexing-and-tagprimer-trimming\" class=\"anchor\" aria-label=\"Permalink: 3 - demultiplexing and tag/primer trimming\" href=\"#3---demultiplexing-and-tagprimer-trimming\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e - annotate average phred quality\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obiannotate.html\" rel=\"nofollow\"\u003e\u003cem\u003eobiannotate\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-S\u003c/code\u003e : expression used for annotation, Avgqphred:-int(math.log10(sum(sequence.quality)/len(sequence))*10)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e - demultiplex according to the ngsfilter file\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/ngsfilter.html\" rel=\"nofollow\"\u003e\u003cem\u003engsfilter\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-ngs\u003c/code\u003e : ngs filter used for the demultiplexing in a \u003ccode\u003e.tab\u003c/code\u003e format.\nCheck \u003ca href=\"##Required\"\u003einput\u003c/a\u003e for details about input format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-u\u003c/code\u003e : name of the unassigned output file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4 - prepare files for dada2\u003c/h3\u003e\u003ca id=\"user-content-4---prepare-files-for-dada2\" class=\"anchor\" aria-label=\"Permalink: 4 - prepare files for dada2\" href=\"#4---prepare-files-for-dada2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obisplit.html\" rel=\"nofollow\"\u003e\u003cem\u003eobisplit\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e : attribute to use for splitting, here \u003ccode\u003esample\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e : path to split into.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5 - sequence quality filtering and trimming\u003c/h3\u003e\u003ca id=\"user-content-5---sequence-quality-filtering-and-trimming\" class=\"anchor\" aria-label=\"Permalink: 5 - sequence quality filtering and trimming\" href=\"#5---sequence-quality-filtering-and-trimming\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003edada2\u003c/strong\u003e - \u003ca href=\"https://rdrr.io/bioc/dada2/man/filterAndTrim.html\" rel=\"nofollow\"\u003e\u003cem\u003efilterAndTrim\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etruncLen\u003c/code\u003e: 200, length at which perform trimming.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emaxN\u003c/code\u003e: 0, maximum number of accepted \u003ccode\u003eN\u003c/code\u003e nucleotides.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emaxEE\u003c/code\u003e: 2, maximum number of accepted errors.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etruncQ\u003c/code\u003e: 2,\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ematchIDs\u003c/code\u003e: TRUE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003everbose\u003c/code\u003e: TRUE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emultithread\u003c/code\u003e: 15\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e6 - sequence dereplication\u003c/h3\u003e\u003ca id=\"user-content-6---sequence-dereplication\" class=\"anchor\" aria-label=\"Permalink: 6 - sequence dereplication\" href=\"#6---sequence-dereplication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003edada2\u003c/strong\u003e - \u003ca href=\"https://rdrr.io/bioc/dada2/man/derepFastq.html\" rel=\"nofollow\"\u003e\u003cem\u003ederepFastq\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003en\u003c/code\u003e : number of sequence simutaneously processed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eII - Key processing\u003c/h2\u003e\u003ca id=\"user-content-ii---key-processing\" class=\"anchor\" aria-label=\"Permalink: II - Key processing\" href=\"#ii---key-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1 - sequencing and error elimination\u003c/h3\u003e\u003ca id=\"user-content-1---sequencing-and-error-elimination\" class=\"anchor\" aria-label=\"Permalink: 1 - sequencing and error elimination\" href=\"#1---sequencing-and-error-elimination\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obiclean.html\" rel=\"nofollow\"\u003e\u003cem\u003eobiclean\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-r\u003c/code\u003e : Threshold ratio between counts (rare/abundant counts) of two sequence records so that the less abundant one is a variant of the more abundant (default: 1, i.e. all less abundant sequences are variants)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-H\u003c/code\u003e : Select only sequences with the head status in a least one sample.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2 - Abundance filtering\u003c/h3\u003e\u003ca id=\"user-content-2---abundance-filtering\" class=\"anchor\" aria-label=\"Permalink: 2 - Abundance filtering\" href=\"#2---abundance-filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBITools/scripts/obigrep.html\" rel=\"nofollow\"\u003e\u003cem\u003eobigrep\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-s\u003c/code\u003e : Regular expression pattern to be tested against the sequence itself. The pattern is case insensitive. Here, \u003ccode\u003e\u0027^[acgt]+$\u0027\u003c/code\u003e , corresponding only to sequence containing no ambiguous nucleotids (\u003cem\u003ee.g.\u003c/em\u003e n).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e : Predicat to filter, here \u003ccode\u003ecount\u0026gt;{params.mincount}\u003c/code\u003e to filter on reads count.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIII - Post-processing\u003c/h2\u003e\u003ca id=\"user-content-iii---post-processing\" class=\"anchor\" aria-label=\"Permalink: III - Post-processing\" href=\"#iii---post-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1 - Chimera removal\u003c/h3\u003e\u003ca id=\"user-content-1---chimera-removal\" class=\"anchor\" aria-label=\"Permalink: 1 - Chimera removal\" href=\"#1---chimera-removal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003edada2\u003c/strong\u003e - \u003ca href=\"https://rdrr.io/bioc/dada2/man/removeBimeraDenovo.html\" rel=\"nofollow\"\u003e\u003cem\u003eremoveBimeraDenovo\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emultithread\u003c/code\u003e : number of thread to use for bimera detection.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2 Sequence clustering\u003c/h3\u003e\u003ca id=\"user-content-2-sequence-clustering\" class=\"anchor\" aria-label=\"Permalink: 2 Sequence clustering\" href=\"#2-sequence-clustering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esumaclust\u003c/strong\u003e - \u003ca href=\"https://git.metabarcoding.org/OBItools/sumaclust/-/wikis/home\" rel=\"nofollow\"\u003e\u003cem\u003esumaclust\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e : Score threshold for clustering (\u003cem\u003ee.g.\u003c/em\u003e 0.97).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e : Threads to use for clustering.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3 Merging Clusters\u003c/h3\u003e\u003ca id=\"user-content-3-merging-clusters\" class=\"anchor\" aria-label=\"Permalink: 3 Merging Clusters\" href=\"#3-merging-clusters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obiselect.html\" rel=\"nofollow\"\u003e\u003cem\u003eobiselect\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-c\u003c/code\u003e : Attribute used to categorize the sequence records, \u003cem\u003ei.e.\u003c/em\u003e \u003ccode\u003ecluster\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e : Indicates how many sequence records per group have to be retrieved, \u003cem\u003ei.e.\u003c/em\u003e \u003ccode\u003e1\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--merge\u003c/code\u003e : Attribute to merge, \u003cem\u003ei.e.\u003c/em\u003e \u003ccode\u003esample\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-f\u003c/code\u003e : function used to score the sequence, \u003cem\u003ei.e.\u003c/em\u003e \u003ccode\u003ecount\u003c/code\u003e to have the reads per sample.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-M\u003c/code\u003e : maximize the \u003ccode\u003e-f\u003c/code\u003e function and order sample IDs in the headers of the sequences by their reads count.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4 Output Formating\u003c/h3\u003e\u003ca id=\"user-content-4-output-formating\" class=\"anchor\" aria-label=\"Permalink: 4 Output Formating\" href=\"#4-output-formating\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOBItools\u003c/strong\u003e - \u003ca href=\"https://pythonhosted.org/OBItools/scripts/obitab.html\" rel=\"nofollow\"\u003e\u003cem\u003eobitab\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-n\u003c/code\u003e : String written in the table for the not available values (\u003cem\u003ei.e.\u003c/em\u003e NA).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d\u003c/code\u003e : Removes column containing the sequence definition in the output tab file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d\u003c/code\u003e : add column at the end of the tab for the sequence itself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5 Assign taxonomy\u003c/h3\u003e\u003ca id=\"user-content-5-assign-taxonomy\" class=\"anchor\" aria-label=\"Permalink: 5 Assign taxonomy\" href=\"#5-assign-taxonomy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003edada2\u003c/strong\u003e - \u003ca href=\"https://rdrr.io/bioc/dada2/man/assignTaxonomy.html\" rel=\"nofollow\"\u003e\u003cem\u003eassignTaxonomy\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eoptions :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003erefFasta\u003c/code\u003e : Path to the \u003ccode\u003e.fasta\u003c/code\u003e database used to assign taxonomy to the sequence table.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emultithread\u003c/code\u003e : Number of threads used to perform taxonomic assignment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIV - Workflow evaluation\u003c/h2\u003e\u003ca id=\"user-content-iv---workflow-evaluation\" class=\"anchor\" aria-label=\"Permalink: IV - Workflow evaluation\" href=\"#iv---workflow-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1 Sequence tracking\u003c/h3\u003e\u003ca id=\"user-content-1-sequence-tracking\" class=\"anchor\" aria-label=\"Permalink: 1 Sequence tracking\" href=\"#1-sequence-tracking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each step of the workflow, computes the total number of sequences and reads.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2 Benchmark\u003c/h3\u003e\u003ca id=\"user-content-2-benchmark\" class=\"anchor\" aria-label=\"Permalink: 2 Benchmark\" href=\"#2-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each step of the workflow, computes the amount of time and computing resources used and plot them.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1616106303.0 + "updated_at": 1667832079.0 }, { "data_format": 2, - "description": "VCF normalization", + "description": null, "filenames": [ - "Singularity/Singularity.v1.0", - "Singularity/Singularity.v1.1" + "Singularity" ], - "full_name": "IARCbioinfo/vcf_normalization-nf", - "latest_release": "v1.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-vcf_normalization-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#vcf_normalization-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evcf_normalization-nf\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow-pipeline-for-vcf-normalization\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-for-vcf-normalization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline for vcf normalization\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/vcf_normalization-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/972f6bfd365386090c6b3e3cd6e549a88d55259c394799f2be26101fa1495f52/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f7663665f6e6f726d616c697a6174696f6e2d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/vcf_normalization-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/vcf_normalization-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4381\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"vcf_normalization-nf.png\"\u003e\u003cimg src=\"vcf_normalization-nf.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eApply \u003ca href=\"http://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003ebcftools norm\u003c/a\u003e to decompose and normalize variants from a set of VCF (compressed with gzip/bgzip).\u003c/p\u003e\n\u003cp\u003eThis scripts takes a set of a folder containing \u003ca href=\"https://samtools.github.io/hts-specs/VCFv4.2.pdf\" rel=\"nofollow\"\u003ecompressed VCF files\u003c/a\u003e (\u003ccode\u003e*.vcf.gz\u003c/code\u003e) as an input.\nIt consists at four piped steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(optional) filtering of variants (\u003ccode\u003ebcftoolvs view -f\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003esplit multiallelic sites into biallelic records (\u003ccode\u003ebcftools norm -m -\u003c/code\u003e) and left-alignment and normalization (\u003ccode\u003e-f ref\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003esorting (\u003ccode\u003ebcftools sort \u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eduplicate removal (\u003ccode\u003ebcftools norm -d exact\u003c/code\u003e) and compression (\u003ccode\u003e-Oz\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExternal software:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCaution\u003c/strong\u003e: \u003ccode\u003ebcftools\u003c/code\u003e has to be in your $PATH. Try each of the commands \u003ccode\u003ebcftools\u003c/code\u003e and \u003ccode\u003ebgzip\u003c/code\u003e, if it returns the options this is ok.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--vcf_folder\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eFolder containing tumor zipped VCF files\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--ref\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/path/to/ref.fasta\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eReference fasta file indexed\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--output_folder\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003enormalized_VCF/\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eFolder to output resulting compressed vcf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--filter_opt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e-f PASS\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eOptions for bcftools view\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--cpu\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eNumber of cpus to use\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--mem\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003ctd\u003eSize of memory used for mapping (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that the default is to filter variants with the PASS flag. To deactivate, use \u003ccode\u003e--filter_opt \" \"\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e--help\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eSimple use case example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/vcf_normalization-nf -r v1.1 -profile singularity --vcf_folder VCF/ --ref ref.fasta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the pipeline without singularity just remove \"-profile singularity\". Alternatively, one can run the pipeline using a docker container (-profile docker) the conda receipe containing all required dependencies (-profile conda).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eVCF.gz, VCF.gz.tbi\u003c/td\u003e\n\u003ctd\u003eCompressed normalized VCF files with indexes\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:alcalan@iarc.fr\"\u003ealcalan@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:delhommet@students.iarc.fr\"\u003edelhommet@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "full_name": "murphygroup/singularity-matlabmcr2018b", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-matlabmcr2018b\u003c/h1\u003e\u003ca id=\"user-content-singularity-matlabmcr2018b\" class=\"anchor\" aria-label=\"Permalink: singularity-matlabmcr2018b\" href=\"#singularity-matlabmcr2018b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.mathworks.com/products/compiler/matlab-runtime.html\" rel=\"nofollow\"\u003eMATLAB Runtime\u003c/a\u003e is a standalone set of shared libraries that enables the execution of compiled MATLAB applications or components on computers that do not have MATLAB installed. When used together, MATLAB, MATLAB Compiler, and the MATLAB Runtime enable you to create and distribute numerical applications or software components quickly and securely.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen contributing to this repository, please first discuss the change you wish to make via issue, \u003ca href=\"mailto:cellorganizer-dev@compbio.cmu.edu\"\u003eemail\u003c/a\u003e, or any other method with the owners of this repository before making a change.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eSupport for \u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e has been provided by grants GM075205, GM090033 and GM103712 from the \u003ca href=\"http://www.nigms.nih.gov/\" rel=\"nofollow\"\u003eNational Institute of General Medical Sciences\u003c/a\u003e, grants MCB1121919 and MCB1121793 from the \u003ca href=\"http://nsf.gov/\" rel=\"nofollow\"\u003eU.S. National Science Foundation\u003c/a\u003e, by a Forschungspreis from the \u003ca href=\"http://www.humboldt-foundation.de/\" rel=\"nofollow\"\u003eAlexander von Humboldt Foundation\u003c/a\u003e, and by the \u003ca href=\"http://www.frias.uni-freiburg.de/lifenet?set_language=en\" rel=\"nofollow\"\u003eFreiburg Institute for Advanced Studies\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.mmbios.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/da21af862d75eb41d1c50d7556125597f1e9c95b7551e77d66d998b7eaf87ce5/68747470733a2f2f69312e77702e636f6d2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f4d4d42696f536c6f676f2d65313530333531373835373331332e6769663f683d3630\" alt=\"MMBioS\" data-canonical-src=\"https://i1.wp.com/www.cellorganizer.org/wp-content/uploads/2017/08/MMBioSlogo-e1503517857313.gif?h=60\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCopyright (c) 2007-2019 by the \u003ca href=\"http://murphylab.web.cmu.edu\" rel=\"nofollow\"\u003eMurphy Lab\u003c/a\u003e at the \u003ca href=\"http://www.cbd.cmu.edu\" rel=\"nofollow\"\u003eComputational Biology Department\u003c/a\u003e in \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1590414197.0 + "updated_at": 1555428752.0 }, { "data_format": 2, - "description": "Singularity recipe for ms3 mountainlab processor package", + "description": null, "filenames": [ - "Singularity.v0.0.2" + "Singularity.279", + "Singularity.conda-bpy", + "Singularity.deb", + "Singularity.pg", + "Singularity.conda", + "Singularity", + "Singularity.bpy" ], - "full_name": "magland/ml_ms3", + "full_name": "darikg/blan_singularity_def", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ml_ms3\" class=\"anchor\" aria-hidden=\"true\" href=\"#ml_ms3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml_ms3\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for ms3 mountainlab processor package\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eblan_singularity_def\u003c/h1\u003e\u003ca id=\"user-content-blan_singularity_def\" class=\"anchor\" aria-label=\"Permalink: blan_singularity_def\" href=\"#blan_singularity_def\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1535668701.0 + "updated_at": 1589238602.0 }, { "data_format": 2, - "description": "bids app wrapper for microstructure diffusion toolbox", + "description": null, "filenames": [ - "Singularity.v0.1", "Singularity" ], - "full_name": "khanlab/mdt-bids", - "latest_release": "v0.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mdt-bids\" class=\"anchor\" aria-hidden=\"true\" href=\"#mdt-bids\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emdt-bids\u003c/h1\u003e\n\u003cp\u003ebids app wrapper for microstructure diffusion toolbox\u003c/p\u003e\n\u003cp\u003ePlease see \u003ca href=\"http://github.com/cbclab/mdt\"\u003ehttp://github.com/cbclab/mdt\u003c/a\u003e for more details\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: ./run.sh \u0026lt;bids_dir\u0026gt; \u0026lt;output_dir\u0026gt; participant \u0026lt;optional arguments\u0026gt;\n\n Required arguments:\n [--in_prepdwi_dir PREPDWI_DIR]\n [--model MODEL (e.g. NODDI)]\n\n Optional arguments:\n [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL...]]\n [--model_fit_opts \"[options for mdt-model-fit]\"\n [--create_protocol_opts \"[options for mdt-create-protocol]\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor HCP WU-Minn data (e.g. HCP 1200 3T), use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--create_protocol_opts \\\"--Delta 21.8e-3 --delta 12.9e-3 --TR 8800e-3 --TE 57e-3\\\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTO DO:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eread in json files to get TR and TE\u003c/li\u003e\n\u003cli\u003eset default --maxG as 0.08 (80 mT/m for our 3T and 7T)\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "GodloveD/lolcow", + "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1591844448.0 + "updated_at": 1493671740.0 }, { "data_format": 2, "description": null, "filenames": [ - "centos/xclock_centos/Singularity", - "centos/turbo_xfce_centos/Singularity.turbo_xfce_centos", - "centos/gnome_centos/Singularity", - "centos/xfce_centos/Singularity", - "ubuntu/gnome_ubuntu/Singularity", - "ubuntu/xclock_ubuntu/Singularity", - "ubuntu/nautilus_ubuntu/Singularity" + "Singularity" ], - "full_name": "nesi/nesi-singularity-recipes", + "full_name": "Saford91/openfoam-singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4539\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1604055970.0 + "updated_at": 1525881846.0 }, { "data_format": 2, - "description": "A powerful toolset for genome arithmetic.", + "description": "Example Singularity MPI container (mpich and openmpi)", "filenames": [ - "2.30.0/Singularity", - "2.29.2/Singularity" + "Singularity.openmpi", + "Singularity.mpich" ], - "full_name": "pscedu/singularity-bedtools", - "latest_release": "v2.30.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bedtools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bedtools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bedtools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bedtools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/22f661e5e94b3846e5f54afaf85c1023c03126e1750ee8026abcc863f0440822/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626564746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22f661e5e94b3846e5f54afaf85c1023c03126e1750ee8026abcc863f0440822/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626564746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bedtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/215e7058c975fe33562a88d720a2d615ed22430b54ce020a97e7d35ae59bea21/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626564746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/215e7058c975fe33562a88d720a2d615ed22430b54ce020a97e7d35ae59bea21/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626564746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bedtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/645b58875deba1ebd7ec091eade1c85984090c2733a2582ae0eb4d5dc25ebdb8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626564746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/645b58875deba1ebd7ec091eade1c85984090c2733a2582ae0eb4d5dc25ebdb8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626564746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bedtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0f0e78a9c8323c2222fcb5d8d3acc383e7ebeef75933d388482e97b0b7ce6584/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626564746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0f0e78a9c8323c2222fcb5d8d3acc383e7ebeef75933d388482e97b0b7ce6584/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626564746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bedtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-bedtools\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-bedtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bedtools\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c477879255fc6a4ecf10f46c416998a1d4cb8bb316920074503c7cfc17b11364/687474703a2f2f7777772e616e647265772e636d752e6564752f757365722f6963616f626572672f706f73742f73696e67756c61726974792d626564746f6f6c732d7570646174652f6c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c477879255fc6a4ecf10f46c416998a1d4cb8bb316920074503c7cfc17b11364/687474703a2f2f7777772e616e647265772e636d752e6564752f757365722f6963616f626572672f706f73742f73696e67756c61726974792d626564746f6f6c732d7570646174652f6c6f676f2e706e67\" width=\"10%\" data-canonical-src=\"http://www.andrew.cmu.edu/user/icaoberg/post/singularity-bedtools-update/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebedtools\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bedtools/2.30.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bedtools\u003c/code\u003e as \u003ccode\u003e2.30.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/tigers/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "rse-ops/singularity-mpi", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Flux\u003c/h1\u003e\u003ca id=\"user-content-singularity-flux\" class=\"anchor\" aria-label=\"Permalink: Singularity Flux\" href=\"#singularity-flux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will reproduce the example \u003ca href=\"https://docs.sylabs.io/guides/3.10/user-guide/mpi.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull the container with Singularity and oras:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull oras://ghcr.io/rse-ops/singularity-mpi:mpich\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou might want an allocation (with or without userns):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ salloc --userns\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTry running the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mpirun -n 6 singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e singularity-mpi_mpich.sif /opt/mpitest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eHello, I am rank 1/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 2/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 3/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 4/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 0/6\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHello, I am rank 5/6\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then try running with flux\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ flux start mpirun -n 6 singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e singularity-mpi_mpich.sif /opt/mpitest\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, + "topics": [], + "updated_at": 1663784213.0 + }, + { + "data_format": 2, + "description": "Browsh is a fully-modern text-based browser.", + "filenames": [ + "1.6.4/Singularity" + ], + "full_name": "pscedu/singularity-browsh", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-browsh/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-browsh/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-browsh/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-browsh/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/aae04b7121c139bf037b44c4863a21114a989b54ecef4cbd4833136653229c4b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62726f777368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aae04b7121c139bf037b44c4863a21114a989b54ecef4cbd4833136653229c4b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d62726f777368\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-browsh\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3af86587ec17b775233a49c21959f73ef8bc6b15c0051db0cb1d241235635441/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62726f777368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3af86587ec17b775233a49c21959f73ef8bc6b15c0051db0cb1d241235635441/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d62726f777368\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-browsh\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1df3e071a59c704e00cf554ccca039f39a79e1af0aab077774f3667861022e67/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62726f777368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1df3e071a59c704e00cf554ccca039f39a79e1af0aab077774f3667861022e67/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d62726f777368\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-browsh\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c26a4fa48fbb99d7dec96104a642f3370e1430043143f9d36faef559894144eb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62726f777368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c26a4fa48fbb99d7dec96104a642f3370e1430043143f9d36faef559894144eb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d62726f777368\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-browsh\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-browsh\u003c/h1\u003e\u003ca id=\"user-content-singularity-browsh\" class=\"anchor\" aria-label=\"Permalink: singularity-browsh\" href=\"#singularity-browsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.brow.sh\" rel=\"nofollow\"\u003ebrowsh\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebrowsh\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/browsh/1.6.4\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/browsh\u003c/code\u003e as \u003ccode\u003e1.6.4.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 3, "topics": [ "singularity", - "bioinformatics" + "utilities" ], - "updated_at": 1668470461.0 + "updated_at": 1631148643.0 }, { "data_format": 2, - "description": "Eukaryotic Genome Annotation Pipeline", + "description": "ncdu is a disk utility for Unix systems", "filenames": [ - "1.8.15/Singularity", - "1.8.13/Singularity", - "1.8.9/Singularity" + "2.5/Singularity", + "1.17/Singularity", + "2.2.2/Singularity", + "2.3.1/Singularity", + "1.13/Singularity", + "1.16/Singularity" ], - "full_name": "pscedu/singularity-funannotate", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-funannotate/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-funannotate/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-funannotate/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-funannotate/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5db6f9744c3c7d051d19348b21768f84f789cc84db6989490b1098cdee6d38e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5db6f9744c3c7d051d19348b21768f84f789cc84db6989490b1098cdee6d38e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7ce313f3a5197862191f8b70f543e4fbba7c0c6032325bfb1113fbe4d57f88b1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ce313f3a5197862191f8b70f543e4fbba7c0c6032325bfb1113fbe4d57f88b1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ec0ba12b6cec391402dbbe6193f93d5322dcf2af0aaf708398f82c707693536f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ec0ba12b6cec391402dbbe6193f93d5322dcf2af0aaf708398f82c707693536f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/260091bdd6a589c218c92d76f07d447cf463bde01be55549ac6c70baff972a1b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/260091bdd6a589c218c92d76f07d447cf463bde01be55549ac6c70baff972a1b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66756e616e6e6f74617465\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-funannotate\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-funannotate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-funannotate\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/nextgenusfs/funannotate\"\u003efunannotate\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efunannotate\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/funannotate/1.8.15\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/funannotate\u003c/code\u003e as \u003ccode\u003e1.8.15.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-ncdu", + "latest_release": "v2.2.2", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ncdu/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ncdu/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ncdu/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ncdu/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f9e74e6eaa558f8de96b5323befbe89f15952145bcd584b1b75b07dc8f1b61d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6e636475\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f9e74e6eaa558f8de96b5323befbe89f15952145bcd584b1b75b07dc8f1b61d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6e636475\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-ncdu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e2a7e960201dfb14c0ba1a3ca02f73241aaf0e2fdd48ee298e3fa977c90e2112/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6e636475\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e2a7e960201dfb14c0ba1a3ca02f73241aaf0e2fdd48ee298e3fa977c90e2112/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6e636475\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-ncdu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/df2f5f5dec729dd2c276b41738797fc83987906652195bb30d4b10274d156bff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6e636475\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df2f5f5dec729dd2c276b41738797fc83987906652195bb30d4b10274d156bff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6e636475\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-ncdu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d5c7c6650a24f7f3338ee4f85a9f3c1a5b43212b105e88c8a22a47cfb460bea2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6e636475\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d5c7c6650a24f7f3338ee4f85a9f3c1a5b43212b105e88c8a22a47cfb460bea2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6e636475\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-ncdu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-ncdu\u003c/h1\u003e\u003ca id=\"user-content-singularity-ncdu\" class=\"anchor\" aria-label=\"Permalink: singularity-ncdu\" href=\"#singularity-ncdu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/753d93f57df7c50b16d94eddfe1805ea221ff7d1a321786617d9f12e280fc1e3/68747470733a2f2f6465762e796f7268656c2e6e6c2f696d672f6e63647568656c70322d322e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/753d93f57df7c50b16d94eddfe1805ea221ff7d1a321786617d9f12e280fc1e3/68747470733a2f2f6465762e796f7268656c2e6e6c2f696d672f6e63647568656c70322d322e706e67\" alt=\"Screenshot\" data-canonical-src=\"https://dev.yorhel.nl/img/ncduhelp2-2.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://dev.yorhel.nl/ncdu\" rel=\"nofollow\"\u003encdu\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003encdu\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/ncdu/2.3.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/ncdu\u003c/code\u003e as \u003ccode\u003e2.3.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2019-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [ - "bioinformatics", - "singularity" + "singularity", + "utilities" ], - "updated_at": 1651105066.0 + "updated_at": 1729794217.0 }, { "data_format": 2, - "description": "Singularity definition file for pycortex", + "description": "Singularity container recipe for Deep Learning with GPU for architecture x86_64 based on a CentOS 7. In the specific: centOS 7 with cuda library (10.0-devel-centos7) GNU compiler 7 Python 3.6 OpenMPI 2.1.1 (compiled with support for psm2, pmix, verbs) Tensorflow 1.14.0 GPU (pip) Py-Torch 1.4.0 GPU (pip) Torchvision 0.5.0 CPU (pip) MxNet 1.5.1 CPU (pip) Horovod 0.19.1 (compiled with Tensorflow, Pytorch, MxNet) This recipe works on in the Cineca cluster (arch x86_64): Galileo", "filenames": [ "Singularity" ], - "full_name": "mvdoc/pycortex-singularity", + "full_name": "CINECA-HPC/container_deep_learning_gpu_centos7_x86_64", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/604\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-pycortex-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#pycortex-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epycortex Singularity container\u003c/h1\u003e\n\u003cp\u003eThis repository contains a singularity definition file to create a\ncontainer with \u003ca href=\"https://gallantlab.github.io\" rel=\"nofollow\"\u003epycortex\u003c/a\u003e, FreeSurfer, and\nFSL. It installs the \u003ccode\u003eglrework-merged\u003c/code\u003e branch of pycortex. Pycortex\u0027s\nfilestore database needs to be mounted externally so that it is\npersistent, and must be pointed to \u003ccode\u003e/cortex-filestore\u003c/code\u003e inside the\ncontainer.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-build-the-container-this-assumes-singularity--242-or-pull-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-build-the-container-this-assumes-singularity--242-or-pull-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Build the container (this assumes Singularity \u0026gt;= 2.4.2), or pull from singularity hub\u003c/h3\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003esingularity build pycortex.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ealternatively, the image can be pulled from singularity-hub\u003c/p\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003esingularity pull --name pycortex.img shub://mvdoc/pycortex-singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-run-it-mounting-the-relevant-directories-eg\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-run-it-mounting-the-relevant-directories-eg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Run it mounting the relevant directories, e.g.\u003c/h3\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003esingularity run -B /path/to/my/data:/data \\\n -B /path/to/my/filestore:/cortex-filestore \\\n -e -c pycortex.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will start a shell inside the container; then one can run a jupyter\nnotebook session with\u003c/p\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003ejupyter notebook --no-browser --port=9999\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you need to use FreeSurfer, you should set the environment variable \u003ccode\u003eFS_LICENSE\u003c/code\u003e to point to your \u003ccode\u003elicense.txt\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003eexport FS_LICENSE=/path/to/license.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe container can also be used as a wrapper for commands, for example\u003c/p\u003e\n\u003cpre lang=\"terminal\"\u003e\u003ccode\u003e$ singularity run \\\n -B /path/to/my/filestore:/cortex-filestore \\\n -e -c pycortex.img \\\n \"python -c \u0027import cortex; print(cortex.__file__)\u0027\"\n\n/src/pycortex/cortex/__init__.py\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_deep_learning_gpu_centos7_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_deep_learning_gpu_centos7_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_deep_learning_gpu_centos7_x86_64\" href=\"#container_deep_learning_gpu_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer recipes for Deep Learning with GPU for architecture x86_64 based on a CentOS 7. In the specific:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCentOS 7 with cuda library (10.0-devel-centos7)\u003c/li\u003e\n\u003cli\u003eGNU compiler 7\u003c/li\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003eOpenMPI 2.1.1 (compiled with support for psm2, pmix, verbs)\u003c/li\u003e\n\u003cli\u003eTensorflow 1.14.0 GPU (pip)\u003c/li\u003e\n\u003cli\u003ePy-Torch 1.4.0 GPU (pip)\u003c/li\u003e\n\u003cli\u003eTorchvision 0.5.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003eMxNet 1.5.1 CPU (pip)\u003c/li\u003e\n\u003cli\u003eHorovod 0.19.1 (compiled with Tensorflow, Pytorch, MxNet)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis recipe works on in the Cineca cluster (arch x86_64):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGalileo\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1518741953.0 + "updated_at": 1604486860.0 }, { "data_format": 2, - "description": "singularity image for biocontainers blast (anaconda)", + "description": "Kraken 2 is the newest version of Kraken, a taxonomic classification system using exact k-mer matches to achieve high accuracy and fast classification speeds.", "filenames": [ - "Singularity" + "2.1.2/Singularity" ], - "full_name": "researchapps/blast", + "full_name": "pscedu/singularity-kraken2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-blast\" class=\"anchor\" aria-hidden=\"true\" href=\"#blast\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBlast\u003c/h1\u003e\n\u003cp\u003eThis is a singularity image to deploy blast.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install Singularity\u003c/h2\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Bootstrap the image\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 blast.img\nsudo singularity bootstrap blast.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Run commands\u003c/h2\u003e\n\u003cp\u003eHow to access the blast runtime executables?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./blast.img\n\nThis container provides the following executables:\n2to3\t\t genbrk\t\t python-config\nactivate\t gencfu\t\t python2\nblast_formatter gencnval\t\t python2.7\nblastdb_aliastool gendict\t\t rpsblast\nblastdbcheck\t gene_info_reader\t rpstblastn\nblastdbcmd\t genrb\t\t seedtop\nblastdbcp\t icu-config\t\t segmasker\nblastn\t\t icuinfo\t\t seqdb_demo\nblastp\t\t idle\t\t\t seqdb_perf\nblastx\t\t legacy_blast.pl\t smtpd.py\nc_rehash\t makeblastdb\t\t sqlite3\nconda\t\t makeconv\t\t tblastn\nconda-env\t makembindex\t\t tblastx\nconvert2blastmask makeprofiledb\t tclsh8.5\ndatatool\t openssl\t\t test_pcre\ndeactivate\t pip\t\t\t uconv\ndeltablast\t pkgdata\t\t update_blastdb.pl\nderb\t\t project_tree_builder wheel\ndustmasker\t psiblast\t\t windowmasker\neasy_install\t pydoc\t\t windowmasker_2.2.22_adapter.py\neasy_install-2.7 python\t\t wish8.5\n\nExample usage: blast.img blastn [args] [options]\n\n\n\n ./blast.img blastn\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-kraken2/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-kraken2/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-kraken2/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-kraken2/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/41387ab28ffce0d6eb470b0985070ed2f26a5482cb76c030e7808e61c6f7bff6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6b72616b656e32\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/41387ab28ffce0d6eb470b0985070ed2f26a5482cb76c030e7808e61c6f7bff6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6b72616b656e32\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-kraken2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e09d4753dfe7d898dae179b7a4fb26537ecae9ea5ef93c14d72565bbc57bdf65/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6b72616b656e32\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e09d4753dfe7d898dae179b7a4fb26537ecae9ea5ef93c14d72565bbc57bdf65/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6b72616b656e32\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-kraken2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5c939d3d6c35cabd4124398d1bd40917486d7009350ee47978428e4fbd940497/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6b72616b656e32\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c939d3d6c35cabd4124398d1bd40917486d7009350ee47978428e4fbd940497/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6b72616b656e32\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-kraken2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8aa53054e06c22dace4c851fb49c4740abe4d366f4e21895d122b09a60899b65/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6b72616b656e32\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8aa53054e06c22dace4c851fb49c4740abe4d366f4e21895d122b09a60899b65/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6b72616b656e32\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-kraken2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-kraken2\u003c/h1\u003e\u003ca id=\"user-content-singularity-kraken2\" class=\"anchor\" aria-label=\"Permalink: singularity-kraken2\" href=\"#singularity-kraken2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003ekraken2\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the other scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/kraken2/2.1.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/kraken2\u003c/code\u003e as \u003ccode\u003e2.1.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1484518249.0 + "updated_at": 1629226178.0 }, { "data_format": 2, - "description": null, + "description": "Genome Annotation Tools", "filenames": [ - "hello-world/Singularity", - "singularity-definitions/Singularity.git-session", - "singularity-definitions/Singularity.hello-world" + "Singularity.FastQC", + "Singularity.SAMtools", + "Singularity.BRAKER", + "Singularity.Diamond", + "Singularity.BLAST", + "Singularity.Augustus", + "Singularity.RepeatModeler", + "Singularity.RepeatMasker", + "Singularity.GeneMark", + "Singularity.Trimmomatic", + "Singularity.SRAToolkit", + "Singularity.STAR" ], - "full_name": "kaczmarj/container-workshop", + "full_name": "williamssanders/annotate", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-code-for-container-workshop\" class=\"anchor\" aria-hidden=\"true\" href=\"#code-for-container-workshop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode for container workshop\u003c/h1\u003e\n\u003cp\u003eSee \u003ca href=\"https://www.eventbrite.com/e/reproducible-research-in-computational-science-tickets-41433469623\" rel=\"nofollow\"\u003ethe Eventbrite page\u003c/a\u003e for more information.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eannotate\u003c/h1\u003e\u003ca id=\"user-content-annotate\" class=\"anchor\" aria-label=\"Permalink: annotate\" href=\"#annotate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenome Annotation Tools\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1532718524.0 + "updated_at": 1627567602.0 }, { "data_format": 2, - "description": null, + "description": "This repo contains the singularity container to run snpPhylo which will build a phyogenetic tree from SNPS", "filenames": [ - "Singularity" + "Singularity.1.0.0" ], - "full_name": "callaghanmt/cont_autobuild", + "full_name": "ISUGIFsingularity/snpPhylo", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cont_autobuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#cont_autobuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econt_autobuild\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esnpPhylo\u003c/h1\u003e\u003ca id=\"user-content-snpphylo\" class=\"anchor\" aria-label=\"Permalink: snpPhylo\" href=\"#snpphylo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains the singularity container to run snpPhylo which will build a phyogenetic tree from SNPS\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1582590721.0 + "updated_at": 1529509581.0 }, { "data_format": 2, "description": null, "filenames": [ - "envs/Singularity.1", - "envs/Singularity.1.2", - "envs/Singularity.1.1" + "Singularity" ], - "full_name": "adswa/test_simg", + "full_name": "marchoeppner/exome-seq", "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExome-seq Pipeline\u003c/h1\u003e\u003ca id=\"user-content-exome-seq-pipeline\" class=\"anchor\" aria-label=\"Permalink: Exome-seq Pipeline\" href=\"#exome-seq-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline offers a end-to-end workflow for exome analysis using the GATK4 toolchain\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etrimming with Fastp\u003c/li\u003e\n\u003cli\u003eread alignment with BWA\u003c/li\u003e\n\u003cli\u003eduplicate marking using Picard MarkDuplicates\u003c/li\u003e\n\u003cli\u003equality score recalibration\u003c/li\u003e\n\u003cli\u003egvcf calling\u003c/li\u003e\n\u003cli\u003ejoint variant calling\n-- variant hard-filtering [default]\n-- variant recalibration (SNPs and Indels) and filtering [optional, off by default and only recommended for \u0026gt;= 30 exomes]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe result will be a multi-sample VCF file as well as a list of VCF files for each sample.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1601186003.0 + "updated_at": 1568723827.0 }, { "data_format": 2, - "description": null, + "description": "message of the day examples for Singularity containers", "filenames": [ - "containers/Singularity.1.3.3.el7" + "graphic/Singularity", + "fortune/Singularity.lolcow", + "fortune/Singularity", + "asciiart/Singularity", + "general/Singularity", + "greeting/Singularity", + "help/Singularity" ], - "full_name": "pestoura/OpenHPC", + "full_name": "singularityhub/motd", "latest_release": null, - "readme": "\n\u003ch1\u003e\u003ca id=\"\" class=\"anchor\" aria-hidden=\"true\" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/openhpc/ohpc/blob/master/docs/recipes/install/common/figures/ohpc_logo.png\"\u003e\u003cimg src=\"https://github.com/openhpc/ohpc/raw/master/docs/recipes/install/common/figures/ohpc_logo.png\" width=\"170\" valign=\"middle\" hspace=\"5\" alt=\"OpenHPC\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-community-building-blocks-for-hpc-systems\" class=\"anchor\" aria-hidden=\"true\" href=\"#community-building-blocks-for-hpc-systems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommunity building blocks for HPC systems\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThis stack provides a variety of common, pre-built ingredients required to\ndeploy and manage an HPC Linux cluster including provisioning tools, resource\nmanagement, I/O clients, runtimes, development tools, containers, and a variety of\nscientific libraries.\u003c/p\u003e\n\u003cp\u003eThere are currently two release series: \u003ca href=\"https://github.com/openhpc/ohpc/wiki/1.3.X\"\u003e1.3.x\u003c/a\u003e and \u003ca href=\"https://github.com/openhpc/ohpc/wiki/2.x\"\u003e2.x\u003c/a\u003e,\nwhich target different major Linux OS distributions. The 1.3.x series targets\nCentOS7 and SLES12 while the 2.x series targets CentOS8 and Leap15.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h3\u003e\n\u003cp\u003eOpenHPC provides pre-built binaries via repositories for use with standard\nLinux package manager tools (e.g. \u003ccode\u003eyum\u003c/code\u003e or \u003ccode\u003ezypper\u003c/code\u003e). To get started,\nyou can enable an OpenHPC repository locally through installation of an\n\u003ccode\u003eohpc-release\u003c/code\u003e RPM which includes gpg keys for package signing and defines\nthe URL locations for [base] and [update] package repositories. Installation\nguides tailored for each supported provisioning system and resource manager\nwith detailed example instructions for installing a cluster are also available.\nCopies of the \u003ccode\u003eohpc-release\u003c/code\u003e package and installation guides along with\nmore information is available on the relevant release series pages\n(\u003ca href=\"https://github.com/openhpc/ohpc/wiki/1.3.X\"\u003e1.3.x\u003c/a\u003e or \u003ca href=\"https://github.com/openhpc/ohpc/wiki/2.x\"\u003e2.x\u003c/a\u003e).\u003c/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-questions-comments-or-bug-reports\" class=\"anchor\" aria-hidden=\"true\" href=\"#questions-comments-or-bug-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions, Comments, or Bug Reports?\u003c/h3\u003e\n\u003cp\u003eSubscribe to the \u003ca href=\"https://groups.io/g/openhpc-users\" rel=\"nofollow\"\u003eusers email list\u003c/a\u003e or see the\n\u003ca href=\"https://openhpc.community/\" rel=\"nofollow\"\u003ehttps://openhpc.community/\u003c/a\u003e page for more pointers.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-additional-software-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#additional-software-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional Software Requests?\u003c/h3\u003e\n\u003cp\u003ePlease see the component \u003ca href=\"https://github.com/openhpc/submission\"\u003esubmission page\u003c/a\u003e for more information\nregarding new software inclusion requests.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-contributing-to-openhpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing-to-openhpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing to OpenHPC\u003c/h3\u003e\n\u003cp\u003ePlease see the steps described in \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-register-your-system\" class=\"anchor\" aria-hidden=\"true\" href=\"#register-your-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRegister your system\u003c/h3\u003e\n\u003cp\u003eIf you are using elements of OpenHPC, please consider registering your system(s)\nusing the \u003ca href=\"https://drive.google.com/open?id=1KvFM5DONJigVhOlmDpafNTDDRNTYVdolaYYzfrHkOWI\" rel=\"nofollow\"\u003eSystem Registration Form\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMessage of the Day\u003c/h1\u003e\u003ca id=\"user-content-message-of-the-day\" class=\"anchor\" aria-label=\"Permalink: Message of the Day\" href=\"#message-of-the-day\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003efor Singularity containers\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/223333?speed=2\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8faea5a9de38c71271a17454687573c439d37ecfaec6d803dfcd064657aa7a5f/68747470733a2f2f61736369696e656d612e6f72672f612f3232333333332e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/223333.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that these were modified for Singularity 3.x due to a \u003ca href=\"https://github.com/singularityhub/motd/issues/2\"\u003eloss of functionality\u003c/a\u003e\nto customize the actions shell file. If you are looking for the original recipes for 2.x containers,\nsee \u003ca href=\"https://github.com/singularityhub/motd/tree/release/2.x\"\u003erelease/2.x\u003c/a\u003e. The current\nmaster should work on both.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is a message of the day?\u003c/h2\u003e\u003ca id=\"user-content-what-is-a-message-of-the-day\" class=\"anchor\" aria-label=\"Permalink: What is a message of the day?\" href=\"#what-is-a-message-of-the-day\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027ve ever logged into a linux cluster, or played a computer\ngame like Half Life or World of Warcraft, you might be greeted with some\nasciiart, or something along the lines of a \"tip of the day.\" This is more\nofficial called a \"message of the day,\" (short is \u003ca href=\"https://en.wikipedia.org/wiki/Motd_(Unix)\" rel=\"nofollow\"\u003emotd\u003c/a\u003e\nand there is a bit of \u003ca href=\"https://ownyourbits.com/2017/04/05/customize-your-motd-login-message-in-debian-and-ubuntu/\" rel=\"nofollow\"\u003ehistory behind it\u003c/a\u003e. In short, we print a message to the terminal\nfor the user to see when he or she first logs into a shell.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow can we use motd with containers?\u003c/h2\u003e\u003ca id=\"user-content-how-can-we-use-motd-with-containers\" class=\"anchor\" aria-label=\"Permalink: How can we use motd with containers?\" href=\"#how-can-we-use-motd-with-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the context of a container, we might want to give the user a friendly message\nif they shell inside. The simplest use case is to greet the user. A more useful\nuse case is to provide some help for how to interact with the container, or\nwhere to find documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do we add motd to Singularity containers?\u003c/h2\u003e\u003ca id=\"user-content-how-do-we-add-motd-to-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: How do we add motd to Singularity containers?\" href=\"#how-do-we-add-motd-to-singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf we are creating a Singularity container,\nwe can\u0027t just echo a message in the runscript, because this gets executed on\na shell \u003cem\u003eor\u003c/em\u003e a run. We need to edit the \u003ccode\u003e/.singularity.d/actions/shell\u003c/code\u003e\nscript that is executed \u003cstrong\u003eonly\u003c/strong\u003e on a shell.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity MOTDs\u003c/h1\u003e\u003ca id=\"user-content-singularity-motds\" class=\"anchor\" aria-label=\"Permalink: Singularity MOTDs\" href=\"#singularity-motds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this repository, we will provide you with a few fun examples for generating\nmessages of the day in Singularity containers.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"general\"\u003egeneral\u003c/a\u003e: will show you how to customize a message for shell, exec, run, or test.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"greeting\"\u003egreeting\u003c/a\u003e: a simple message of the day to greet the user\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"fortune\"\u003efortune\u003c/a\u003e: give the user a fortune instead, add a cow, and some color!\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"help\"\u003ehelp\u003c/a\u003e: show the container\u0027s %help section to the user when they shell inside\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"asciiart\"\u003easciiart\u003c/a\u003e: generate a greeting with awesome asciiart!\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"graphic\"\u003egraphic\u003c/a\u003e: generate a colored graphic to surprise the user with.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eClearly, many of these examples are for fun, and others are better for communicating\ninformation. I\u0027m of the firm belief that we should aspire for both - interaction\nwith containers should be both informative and fun.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1679150954.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "singularity-container", + "motd", + "message-of-the-day" + ], + "updated_at": 1639382975.0 }, { "data_format": 2, - "description": "Custom Linux Container Build for Large Scale File Parsing in High Performance Computing Environments", + "description": "Spliced Transcripts Alignment to a Reference.", "filenames": [ - "base-image-ubuntu-22.04/base-image/.singularity.d/Singularity" + "2.7.9a/Singularity", + "2.7.10b/Singularity", + "2.7.6a/Singularity" ], - "full_name": "alexander-labarge/hpc-tika-build", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-high-performance-computing-hpc-file-parsing-solution---direct-access-to-gpu--cpu-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#high-performance-computing-hpc-file-parsing-solution---direct-access-to-gpu--cpu-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHigh-Performance Computing (HPC) File Parsing Solution - Direct Access to GPU \u0026amp; CPU Resources\u003c/h1\u003e\n\u003cp\u003eThis solution provides direct access to GPU and CPU resources for high-performance computing (HPC) and high-throughput computing (HTC) environments. Unlike enterprise-based container frameworks, which are designed for microservices and require root privileges to install and run applications, this solution is optimized for complex applications that require all available resources without special privileges.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-targeted-toolsets-implemented\" class=\"anchor\" aria-hidden=\"true\" href=\"#targeted-toolsets-implemented\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTargeted Toolsets Implemented\u003c/h2\u003e\n\u003cp\u003eThis solution uses the following targeted toolsets:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eApache Tika\u2122 by Oracle\u003c/li\u003e\n\u003cli\u003eApptainer (formerly Singularity) by Berkeley National Laboratory\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-initial-cause-for-solution-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#initial-cause-for-solution-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInitial Cause for Solution Development\u003c/h2\u003e\n\u003cp\u003eThe development of this solution was motivated by the need to parse 7.5 TB of digital forensics data produced and stored in a variety of non-standard formats. The parsing of all data is necessary to drive subsequent efforts wherein conjectures are made from the subsequent data parsed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-apptainer-for-hpc-instead-of-virtual-machines-or-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-apptainer-for-hpc-instead-of-virtual-machines-or-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy Apptainer for HPC instead of Virtual Machines or Docker\u003c/h2\u003e\n\u003cp\u003eApptainer/Singularity is a container platform created for the HPC/HTC use case and presents key concepts for the scientific community:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt\u2019s designed to execute applications with bare-metal performance while retaining a high level of security, portability, and reproducibility.\u003c/li\u003e\n\u003cli\u003eContainers run rootless to prohibit privilege escalation.\u003c/li\u003e\n\u003cli\u003eAble to Leverage GPUs, FPGAs, high-speed networks, and filesystems.\u003c/li\u003e\n\u003cli\u003eA container platform for building and running Linux containers that packages software, libraries, and runtime compilers in a self-contained environment.\n\u003cul\u003e\n\u003cli\u003eApplication portability (single image file, contain all dependencies)\u003c/li\u003e\n\u003cli\u003eReproducibility, run cross platform, provide support for legacy OS and apps.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAbility to run, and in modern systems also to be installed, without any root daemon or setuid privileges. This makes it safer for large computer centers with shared resources.\u003c/li\u003e\n\u003cli\u003ePreserves the permissions in the environment. The user outside the container can be the same user inside.\u003c/li\u003e\n\u003cli\u003eApptainer propagates most environment variables set on the host into the container, by default. Docker does not propagate any host environment variables into the container. Environment variables may change the behavior of software.\u003c/li\u003e\n\u003cli\u003eSimple integration with resource managers (SLURM in our case) and distributed computing frameworks because it runs as a regular application.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/945a382c-3488-4c65-a743-44f0a704c7a5\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/945a382c-3488-4c65-a743-44f0a704c7a5\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-development-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#development-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment Steps:\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-test-host-machine-bare-metal\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-host-machine-bare-metal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest Host Machine (Bare Metal):\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0c14fa9e-2508-4c80-9883-f016eb70484f\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0c14fa9e-2508-4c80-9883-f016eb70484f\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-1-apptainer---build-from-source-install-debian-packages-for-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-1-apptainer---build-from-source-install-debian-packages-for-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 1: Apptainer - Build from Source/ Install Debian Packages for Dependencies\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get install -y \\\n build-essential \\\n libseccomp-dev \\\n pkg-config \\\n uidmap \\\n squashfs-tools \\\n squashfuse \\\n fuse2fs \\\n fuse-overlayfs \\\n fakeroot \\\n cryptsetup \\\n curl wget git \\\n \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GOVERSION=1.20.6 OS=linux ARCH=amd64 \\\n wget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n sudo tar -C /usr/local -xzf /home/service-typhon/Downloads/go\u003cspan class=\"pl-smi\"\u003e${GOVERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:/usr/local/go/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc \\\n curl -sSfL https://raw.githubusercontent.com/golangci/golangci-lint/master/install.sh \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sh -s -- -b \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ego env GOPATH\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/bin v1.51.1 \\\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:$(go env GOPATH)/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc \\\n git clone https://github.com/apptainer/apptainer.git \\\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e apptainer \\\n git checkout v1.2.0 \\\n ./mconfig \\\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ./builddir \\\n make \\\n sudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-2-create-sandbox-directory--pull-ubuntu-2204---jammy-docker-container-base-ubuntu-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-2-create-sandbox-directory--pull-ubuntu-2204---jammy-docker-container-base-ubuntu-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 2: Create Sandbox Directory / Pull Ubuntu 22.04 - Jammy Docker Container (Base Ubuntu Build)\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7aaf3e7e-cb74-40d1-9971-24808b0885f8\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7aaf3e7e-cb74-40d1-9971-24808b0885f8\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-3-convert-to-immutable-sif-image-for-future-builds---demonstrate-shell-access\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-3-convert-to-immutable-sif-image-for-future-builds---demonstrate-shell-access\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 3: Convert to Immutable .sif Image for Future Builds - Demonstrate Shell Access\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/88b8bf10-0e96-4ad3-8d91-6135140e9a00\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/88b8bf10-0e96-4ad3-8d91-6135140e9a00\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-4-definition-file-configuration-for-building-dependencies---1st-build-scuccessful\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-4-definition-file-configuration-for-building-dependencies---1st-build-scuccessful\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 4: Definition File Configuration for Building Dependencies - 1st Build Scuccessful\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/ce94c114-8b0b-44b2-a7c0-33c26e4e36b7\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/ce94c114-8b0b-44b2-a7c0-33c26e4e36b7\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0d3b4389-7329-4a06-b551-392b07586abc\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0d3b4389-7329-4a06-b551-392b07586abc\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-5-now-that-there-is-a-base-instance-working-lets-create-a-live-sandbox-for-testing-from-the-image-we-just-created\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-5-now-that-there-is-a-base-instance-working-lets-create-a-live-sandbox-for-testing-from-the-image-we-just-created\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 5: Now that There is a Base Instance Working, lets create a live sandbox for testing from the image we just created:\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/a45a0aed-0b5b-4cc9-abdb-5178ad5ac648\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/a45a0aed-0b5b-4cc9-abdb-5178ad5ac648\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-note-initial-containers-are-limited-to-64mb-in-size-fix\" class=\"anchor\" aria-hidden=\"true\" href=\"#note-initial-containers-are-limited-to-64mb-in-size-fix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNote: Initial Containers are limited to 64MB in size. Fix:\u003c/h4\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0f2b5e02-d8a1-42bc-995a-507b802c4c3a\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/0f2b5e02-d8a1-42bc-995a-507b802c4c3a\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-6-create-a-new-file-system-overlay-add-as-a-layer-in-sif-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-6-create-a-new-file-system-overlay-add-as-a-layer-in-sif-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 6: Create a New File System Overlay/ add as a layer in SIF build:\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/441d3a4a-18cd-4c74-8cf8-b7ffe18167eb\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/441d3a4a-18cd-4c74-8cf8-b7ffe18167eb\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-7-build-tika-configure-properly---completed-success\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-7-build-tika-configure-properly---completed-success\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStep 7: Build Tika/ Configure Properly - Completed/ Success:\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7c7320ab-999b-45b9-bd3c-beb8a19c1230\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/7c7320ab-999b-45b9-bd3c-beb8a19c1230\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-tika-dependency-install-script-implemented-at-post\" class=\"anchor\" aria-hidden=\"true\" href=\"#tika-dependency-install-script-implemented-at-post\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTIKA DEPENDENCY INSTALL SCRIPT IMPLEMENTED AT %POST\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Java\u003c/span\u003e\napt-get update\napt-get install -y software-properties-common\napt-get install -y wget\napt-get install -y default-jre\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Tesseract OCR\u003c/span\u003e\napt-get install -y tesseract-ocr\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install ImageMagick\u003c/span\u003e\napt-get install -y imagemagick\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Poppler\u003c/span\u003e\napt-get install -y poppler-utils\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install FFmpeg\u003c/span\u003e\napt-get install -y ffmpeg\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Tika\u003c/span\u003e\nwget https://dlcdn.apache.org/tika/2.8.0/tika-app-2.8.0.jar\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Maven\u003c/span\u003e\nwget https://dlcdn.apache.org/maven/maven-3/3.9.3/binaries/apache-maven-3.9.3-bin.tar.gz\ntar -xvf apache-maven-3.9.3-bin.tar.gz \nmv apache-maven-3.9.3 /opt\nM2_HOME=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eopt/apache-maven-3.9.3/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nPATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$M2_HOME\u003c/span\u003e/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-tika-automated-test-end-of-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#tika-automated-test-end-of-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTIKA AUTOMATED TEST END OF INSTALL:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e \u003cspan class=\"pl-en\"\u003echeck_tika_test\u003c/span\u003e {\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eChecking Tika test...\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e grep -q \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTIKA PASSED TEST - ALEX\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e /output-files/tika-test-file.txt.json\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika test passed. FOUND STRING: TIKA PASSED TEST - ALEX in file.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e============TIKA HPC BUILD COMPLETING FINAL STEPS================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e=================================================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika test failed.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\n}\n\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eStarting Tika... at \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einput-files directory contents:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ncp /opt/tika-test-file.txt /input-files\nls -l /input-files/\njava -jar /tika-app-2.8.0.jar -i /input-files -o /output-files -J\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika started.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTika output complete.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eoutput-files directory contents:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nls -l /output-files\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eCompleted at: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eExtracting text from files...\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e======================================\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eExtracted JSON OUTPUT:\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract text from files \u0026amp; ignore JSON text\u003c/span\u003e\nextracted_text=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003efind /output-files -type f -exec strings {} \u003cspan class=\"pl-cce\"\u003e\\;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep -vE \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e^{.*}$\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Print extracted text\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$extracted_text\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Check Tika test\u003c/span\u003e\ncheck_tika_test\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-step-8-final-beta-build-script-other-bash-scripts-embedded\" class=\"anchor\" aria-hidden=\"true\" href=\"#step-8-final-beta-build-script-other-bash-scripts-embedded\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSTEP 8: FINAL BETA BUILD SCRIPT (OTHER BASH SCRIPTS EMBEDDED)\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/71d64b44-a95e-484b-ad2d-082d3bc9ab60\"\u003e\u003cimg src=\"https://github.com/alexander-labarge/hpc-tika-build/assets/103531175/71d64b44-a95e-484b-ad2d-082d3bc9ab60\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "pscedu/singularity-star", + "latest_release": "v2.7.10b", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-star/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-star/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-star/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-star/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/045d3775a082afd77b840cd32fde10dd75e09c910307d3b4f7647c037b7a4ef6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73746172\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/045d3775a082afd77b840cd32fde10dd75e09c910307d3b4f7647c037b7a4ef6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73746172\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-star\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e558688ff5d4af5bf2ce91ee40e43c83deaa6078c30edf6b895a94497dcf95f7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73746172\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e558688ff5d4af5bf2ce91ee40e43c83deaa6078c30edf6b895a94497dcf95f7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73746172\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-star\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/89ccbf93867c716c4fb9024fc6d759c8722465a2221d907a0e42b806cb0478e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d73746172\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/89ccbf93867c716c4fb9024fc6d759c8722465a2221d907a0e42b806cb0478e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d73746172\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-star\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/42907d2df93df3e4e292a73cf6b96da5f0158eee37ee91309a0b50ceb8dbb50a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73746172\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/42907d2df93df3e4e292a73cf6b96da5f0158eee37ee91309a0b50ceb8dbb50a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73746172\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-star\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-star\u003c/h1\u003e\u003ca id=\"user-content-singularity-star\" class=\"anchor\" aria-label=\"Permalink: singularity-star\" href=\"#singularity-star\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/alexdobin/STAR\"\u003estar\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eSTAR\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/star/2.7.6a\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/star\u003c/code\u003e as \u003ccode\u003e2.7.6a.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1690283498.0 + "subscribers_count": 4, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1668134054.0 }, { "data_format": 2, - "description": null, + "description": "singularity image for deepribo", "filenames": [ - "code/Singularity.def", - "code/Singularity_COMMIT.def" + "Singularity" ], - "full_name": "inm7/vbc_mri_pipeline", + "full_name": "RickGelhausen/deepribo_image", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-containerized-structural-connectivity-sc-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#containerized-structural-connectivity-sc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerized structural connectivity (SC) pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREQUIREMENTS\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTo use the containerized SC pipeline, please install \u0027singularity\u0027 on your computing system: \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.3/user-guide/installation.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline uses Freesurfer. If you do not have a license, please register for Freesurfer: \u003ca href=\"https://surfer.nmr.mgh.harvard.edu/registration.html\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/registration.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEssential files\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecode/Singularity\u003c/code\u003e: Recipe file to be used with \u003ccode\u003esingularity build\u003c/code\u003e to generate a container image\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecode/input.txt\u003c/code\u003e: Example pipeline parameter specification\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecode/container_SC_pipeline_JURECA.sh\u003c/code\u003e: Example SLURM submission scripts for the JURECA HPC system\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instruction\" class=\"anchor\" aria-hidden=\"true\" href=\"#instruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eINSTRUCTION\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. ARGUMENTS\u003c/h3\u003e\n\u003cp\u003eThere are three main paths for this pipeline: working path, raw data path, and target (result) path. These paths have to be specified by the end-users based on their own computing system.\u003c/p\u003e\n\u003cp\u003eThe containerized SC pipeline consists of 4 modules: preprocessing, tractography, atlas transformation, and reconstruction. The containerized SC pipeline uses 2 arguments (module script and input file) as below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_pipeline.sh /mnt_sc/working/path/input.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also run a sigle module as below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_preprocess.sh /mnt_sc/working/path/input.txt\nsingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_tractography.sh /mnt_sc/working/path/input.txt\nsingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_atlas_transformation.sh /mnt_sc/working/path/input.txt\nsingularity exec --bind /mount/path:/mnt_sc Container_dwMRI.simg /usr/local/bin/container_SC_reconstruct.sh /mnt_sc/working/path/input.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first argument specifies a module script and the second argument specifies an input file of it.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. INPUT\u003c/h3\u003e\n\u003cp\u003eAn example of an input text file is the following.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Freesurfer license\n# ------------------\nemail=end.user@your-institute.de\ndigit=xxxxx\nline1=xxxxxxxxxxxxx\nline2=xxxxxxxxxxxxx\n\n# Input variables\n# ---------------\ngrp=INM # Name of dataset\ntract=100000 # Total number of streamlines for whole-brain tractography\natlname=atlas_prefix # Name of atlas for prefixing results\nnumparc=100 # Total number of regions in a given atlas\nshells=0,1000,2000,3000 # shells=0,1000,2000,3000 for HCP dwMRI, i.e., b-values\nnon_zero_shells=1000,2000,3000 # shells=1000,2000,3000 for HCP dwMRI\n\n# Paths setting\n# -------------\ntp=/mnt_tp # Target (result) path\nsp=/mnt_sp # Source (raw) data path\nfp=/mnt_fp # Subject\u0027s path for freesurfer\nap=/mnt_ap # Atlas path\natlas=atlas.nii.gz # Atlas on the MNI 1mm space (6th generation in FSL)\nmni=/usr/share/fsl/5.0/data/standard/MNI152_T1_1mm.nii.gz # Standard template for registration\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe parameters can be modified by the end-users. For licensing Freesurfer, they should get a license code via a registration with a license agreement and put the license code in the input text file. Input files should be prepared for each subject and each condition. For example, a process of 8 subjects with 2 conditions needs 16 input text files. All input text files should be in the working path, \u0027wp=/mount/path/to/scripts\u0027.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-data-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-data-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. DATA STRUCTURE\u003c/h3\u003e\n\u003cp\u003eThe raw data path should have a data structure (BIDS) as below (in case of /mnt_sp=/path/to/DATA_DIR, grp=INM-BIDS, and sbj=sub-01).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/mnt_sp/INM-BIDS/sub-01/anat/sub-01_T1w.json\n/mnt_sp/INM-BIDS/sub-01/anat/sub-01_T1w.nii.gz\n/mnt_sp/INM-BIDS/sub-01/dwi/sub-01_dwi.bval\n/mnt_sp/INM-BIDS/sub-01/dwi/sub-01_dwi.bvec\n/mnt_sp/INM-BIDS/sub-01/dwi/sub-01_dwi.json\n/mnt_sp/INM-BIDS/sub-01/dwi/sub-01_dwi.nii.gz\n\nDATA_DIR (/mnt_sp)\n\u251c\u2500\u2500 INM-BIDS\n\u2502 \u251c\u2500\u2500 sub-01\n\u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 anat\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sub-01_T1w.json\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 sub-01_T1w.nii.gz\n\u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 dwi\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sub-01_dwi.bval\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sub-01_dwi.bvec\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sub-01_dwi.json\n\u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 sub-01_dwi.nii.gz\n. . .\n. . .\n. . .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-4-example-script-for-the-condor\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-example-script-for-the-condor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. EXAMPLE SCRIPT FOR THE CONDOR\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n\nCPUS=\u00272\u0027\nRAM=\u00278G\u0027\nDISK=\u002790G\u0027\nLOGS_DIR=\u0027/path/to/condor/logs/directory\u0027\nVBC_DWMRI=\u0027/path/to/container/Container_SC_pipeline.simg\u0027\nDATA_DIR=\u0027/path/to/data/directory/prior/to/BIDS\u0027\nATLAS_DIR=\u0027/path/to/atlas/directory\u0027\nOUTPUT_DIR=\u0027/path/to/output/directory\u0027\nFREESURFER_OUTPUT=\u0027/path/to/freesurfer/subjects/directory\u0027\nFREESURFER_LICENSE=\u0027/opt/freesurfer/6.0/license.txt\u0027\nINPUT_PARAMETERS=\u0027/path/to/input/text/file\u0027\n\n# create the logs dir if it doesn\u0027t exist\n[ ! -d \"${LOGS_DIR}\" ] \u0026amp;\u0026amp; mkdir -p \"${LOGS_DIR}\"\n\n# print the .submit header\nprintf \"# The environment\nuniverse = vanilla\ngetenv = True\nrequest_cpus = ${CPUS}\nrequest_memory = ${RAM}\nrequest_disk = ${DISK}\n\n# Execution\ninitial_dir = \\$ENV(HOME)/htcondor-templates/vbc_dwmri\nexecutable = /usr/bin/singularity\n\\n\"\n\n# loop over all subjects\nfor sub in 110411; do\n printf \"arguments = exec --cleanenv \\\n -B ${DATA_DIR}:/mnt_sp,${OUTPUT_DIR}:/mnt_tp,${FREESURFER_OUTPUT}:/mnt_fp,${ATLAS_DIR}:/mnt_ap,${FREESURFER_LICENSE}:/opt/freesurfer/license.txt,${INPUT_PARAMETERS}:/opt/input.txt \\\n ${VBC_DWMRI} \\\n /usr/local/bin/container_SC_pipeline.sh \\\n /opt/input.txt \\\n ${CPUS} \\\n ${sub}\\n\"\n printf \"log = ${LOGS_DIR}/\\$(Cluster).\\$(Process).${sub}.log\\n\"\n printf \"output = ${LOGS_DIR}/\\$(Cluster).\\$(Process).${sub}.out\\n\"\n printf \"error = ${LOGS_DIR}/\\$(Cluster).\\$(Process).${sub}.err\\n\"\n printf \"Queue\\n\\n\"\ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-5-example-script-for-the-slurm\" class=\"anchor\" aria-hidden=\"true\" href=\"#5-example-script-for-the-slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. EXAMPLE SCRIPT FOR THE SLURM\u003c/h3\u003e\n\u003cp\u003eBased on the optimized configuration for the containerized SC pipeline on JURECA at Forschungszentrum J\u00fclich, we provide a script to run the SC pipeline, container_SC_pipeline_JURECA.sh. With a modification of three lines in it, you can use the script on JURECA. This script uses 9 arguments: a module name, 8 subject IDs.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esimg_path=/path/to/container/Container_dwMRI.simg\nwp=/mnt_sc/path/to/scripts\nmnt=/local/path/to/mount\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following example is a script for the slurm system on JURECA. You can copy the following lines and create a file for \u0027sbatch\u0027, for instance, \u0027run_sc_pipeline.sbatch\u0027, then execute like this, \u0027sbatch run_sc_pipeline.sbatch\u0027.\u003c/p\u003e\n\u003cp\u003ePrepare 8 input files for each subject in the working path (wp=/mnt_sc/path/to/scripts) as below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003einput_sub-01.txt\ninput_sub-02.txt\ninput_sub-03.txt\ninput_sub-04.txt\ninput_sub-05.txt\ninput_sub-06.txt\ninput_sub-07.txt\ninput_sub-08.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, make a script for \u0027sbatch\u0027 as below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#SBATCH -J SC_pipeline\n#SBATCH -o slurm_logs/SC_pipeline-out.%j\n#SBATCH -e slurm_logs/SC_pipeline-err.%j\n#SBATCH -A ${project_account}\n#SBATCH --nodes=1\n#SBATCH --time=16:00:00\n#SBATCH --mail-user=end.user@your-institute.de\n#SBATCH --mail-type=All\n#SBATCH --partition=batch\n\nbash container_SC_pipeline_JURECA.sh Preprocess sub-01 sub-02 sub-03 sub-04 sub-05 sub-06 sub-07 sub-08\nwait\n\nbash container_SC_pipeline_JURECA.sh Tractography sub-01 sub-02 sub-03 sub-04 sub-05 sub-06 sub-07 sub-08\nwait\n\nbash container_SC_pipeline_JURECA.sh Atlas_transformation sub-01 sub-02 sub-03 sub-04 sub-05 sub-06 sub-07 sub-08\nwait\n\nbash container_SC_pipeline_JURECA.sh Reconstruction sub-01 sub-02 sub-03 sub-04 sub-05 sub-06 sub-07 sub-08\nwait\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEach module can perform independently. For instance, if the preprocessing module was already performed for considered subjects, then you can continue to perform on the tractography module for the given subjects. An advanced version will have more parameters such as tracking algorithms, tracking steps, tracking angles, and so forth.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-troubleshoot\" class=\"anchor\" aria-hidden=\"true\" href=\"#troubleshoot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTROUBLESHOOT\u003c/h2\u003e\n\u003cp\u003eIf you have a problem to use the containerized SC pipeline. Please contact Kyesam Jung (\u003ca href=\"mailto:k.jung@fz-juelich.de\"\u003ek.jung@fz-juelich.de\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis development was supported by European Union\u2019s Horizon 2020 research and innovation programme under grant agreement \u003ca href=\"https://cordis.europa.eu/project/id/826421\" rel=\"nofollow\"\u003eVirtualBrainCloud (H2020-EU.3.1.5.3, grant no. 826421)\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1635942025.0 + "updated_at": 1580979437.0 }, { "data_format": 2, - "description": "simple login wrapper for token entry to web applications", + "description": null, "filenames": [ - "Singularity", - "docs/singularity/examples/sh_notebook/Singularity.notebook", - "docs/singularity/examples/hello-world/Singularity.helloworld", - "docs/singularity/examples/notebook/Singularity.notebook" + "Singularity" ], - "full_name": "vsoch/sh_login", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-shell-login-portal\" class=\"anchor\" aria-hidden=\"true\" href=\"#shell-login-portal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell Login Portal\u003c/h1\u003e\n\u003cp\u003eThis is an experiment to provide a general web server to wrap access to\na particular port served by nginx. We do this by having the main nginx\nroot (/) serve as a proxy for the flask application, and then the Flask\napplication expects a particular environment variable (defined at runtime)\nto check against a token provided by the user. If the token is correct,\nthe Flask response adds a header to authenticate it as so, and returns\nthe response to the user. If the response is incorrect, the user is\nreturned permission denied (403). The user cannot go to the port to\nbypass the application because of the proxy, and not exposing the port\ndirectly.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker works fairly well, as we can not expose particular ports to the host\u003c/li\u003e\n\u003cli\u003eSingularity does not, because all ports are shared\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\"docs\"\u003edocs\u003c/a\u003e for details.\u003c/p\u003e\n", + "full_name": "marchoeppner/metagenomic-profiling", + "latest_release": "1.2", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIKMB Metagenomic profiling pipeline\u003c/h1\u003e\u003ca id=\"user-content-ikmb-metagenomic-profiling-pipeline\" class=\"anchor\" aria-label=\"Permalink: IKMB Metagenomic profiling pipeline\" href=\"#ikmb-metagenomic-profiling-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipelines analyses short reads and identifies the most likely species in the respective sample.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation about the pipeline can be found in the \u003ccode\u003edocs/\u003c/code\u003e directory or under the links below:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1545323610.0 + "updated_at": 1612364252.0 }, { "data_format": 2, "description": null, "filenames": [ - "environments/Singularity.preproc" + "volsung-cudnn8-runtime-ubuntu18.04/Singularity", + "vdt_base/Singularity" ], - "full_name": "yarikoptic/demo-cifar-preproc", + "full_name": "nesi/containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-materials-for-a-basic-demo-of-datalad-functionalities\" class=\"anchor\" aria-hidden=\"true\" href=\"#materials-for-a-basic-demo-of-datalad-functionalities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaterials for a basic demo of DataLad functionalities\u003c/h1\u003e\n\u003cp\u003eTo demonstrate\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eComposition of datasets\u003c/li\u003e\n\u003cli\u003eAutomated recording of commands results\u003c/li\u003e\n\u003cli\u003ePublishing to GitHub and FigShare\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4539\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsing conventions described here.\n\u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/getting-started/naming\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/singularityhub-docs/docs/getting-started/naming\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1553541127.0 + "updated_at": 1611608990.0 }, { "data_format": 2, - "description": "Singularity recipe files for QIIME 2 (https://docs.qiime2.org/)", + "description": null, "filenames": [ - "Singularity.2019.4", - "Singularity.2020.6", - "Singularity.2021.11", - "Singularity.2018.11", - "Singularity.2021.2", - "Singularity.2022.2", - "Singularity.2019.1-picrust2", - "Singularity.2020.11-aldex2", - "Singularity.2020.11", - "Singularity.2019.10", - "Singularity.2022.8", - "Singularity.2018.2", - "Singularity.2021.4", - "Singularity.2019.7", - "Singularity.2020.2", - "Singularity.2019.7-picrust2", - "Singularity.2021.8", - "Singularity.2020.8", - "Singularity.2019.1" + "Singularity" ], - "full_name": "powerPlant/qiime2-srf", + "full_name": "callaghanmt-containers/python_jupyter", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2268\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the QIIME 2 microbiome analysis package\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epython_jupyter\u003c/h1\u003e\u003ca id=\"user-content-python_jupyter\" class=\"anchor\" aria-label=\"Permalink: python_jupyter\" href=\"#python_jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1638265189.0 + "updated_at": 1553554603.0 }, { "data_format": 2, - "description": "Tools and information for building/running the Epoch Singularity container.", + "description": "Scripts used for manuscript for Demuxafy", "filenames": [ - "Singularity/Singularity" + "files/Singularity.chord" ], - "full_name": "PlasmaFAIR/epoch_containers", - "latest_release": "v0.3.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-epoch-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#epoch-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEpoch Containers\u003c/h1\u003e\n\u003cp\u003eTools and information for building/running \u003ca href=\"https://epochpic.github.io/\" rel=\"nofollow\"\u003eEpoch\u003c/a\u003e using Docker/Singularity\ncontainers. This repository is targeted at users of the Viking HPC cluster at the\nUniversity of York, but the contents may be of use to other Epoch users.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eContainers package up software and dependencies so that code compiled on one machine\ncan be reliably run on others. When used in conjunction with scientific software, they\nallow researchers to run code without needing to build it themselves, and they make\nit much easier to share reproducible workflows.\u003c/p\u003e\n\u003cp\u003eWe provide support for two container platforms: \u003ca href=\"https://docs.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and\n\u003ca href=\"https://docs.sylabs.io/guides/3.11/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. Docker is the most widely used platform, and\nhas been used here to build a \u0027base image\u0027 of Epoch on which other tools may be built.\nSingularity is an alternative that was designed from the ground up to be useable on\nHPC systems, so unlike Docker it can be run on multi-node architectures using MPI\nwithout issue.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-epoch-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-epoch-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Epoch with Singularity\u003c/h2\u003e\n\u003cp\u003eTo run Epoch on Viking, first create a directory within \u003ccode\u003e~/scratch\u003c/code\u003e in which you\nwant to run your code:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh \u0026lt;userid\u0026gt;@viking.york.ac.uk\n$ mkdir -p ~/scratch/epoch\n$ cd ~/scratch/epoch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou\u0027ll need to ensure your \u003ccode\u003einput.deck\u003c/code\u003e file is within this directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e From your own machine\u003c/span\u003e\n$ scp input.deck \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e@viking.york.ac.uk:/users/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scratch/epoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the Singularity container, you\u0027ll need to load the following modules:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ module load tools/Singularity mpi/OpenMPI\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then run using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e library://liampattinson/epoch/epoch.sif:latest \\\n run_epoch --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis should download and cache the container, and then display some help text.\nYou can then run using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e library://liampattinson/epoch/epoch.sif:latest \\\n run_epoch -d 2 -o \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that you should only run short tests on the login nodes. Let\u0027s break this down:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity exec\u003c/code\u003e: Run a singularity container with a user provided command.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elibrary://\u003c/code\u003e: Download and run a container from \u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003esylabs.io\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eliampattinson/epoch/epoch.sif:latest\u003c/code\u003e: The specific container we want to run. This\none is a prebuilt Epoch container using the \u003ccode\u003eSingularity/Singularity\u003c/code\u003e recipe file in\nthis repo. Note that the Singularity container is built on top of the Docker\ncontainer.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun_epoch\u003c/code\u003e: The scripting entrypoint to launch an Epoch variant.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d 2\u003c/code\u003e: Run 2D epoch. Can also be 1D and 3D.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o .\u003c/code\u003e: Location of the output directory, which should container you \u003ccode\u003einput.deck\u003c/code\u003e\nfile. Ensure this is somewhere within your scratch space!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--photons\u003c/code\u003e: Optional flag that switches on QED features.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor a simplified interface, we can also use the \u003ccode\u003eepoch_singularity.py\u003c/code\u003e script within\nthis repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./epoch_singularity.py -d 2 -o \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis script mimics Epoch\u0027s behaviour of prompting the user to input their output\ndirectory after the program is running, so the following also works:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e ./epoch_singularity.py -d 2 --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run using MPI, we put the \u003ccode\u003empirun\u003c/code\u003e command \u003cem\u003ebefore\u003c/em\u003e the \u003ccode\u003esingularity\u003c/code\u003e command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mpirun -n 2 \\\n singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e library://liampattinson/epoch/epoch.sif:latest \\\n run_epoch -d 2 -o \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --photons\n$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Or...\u003c/span\u003e\n$ mpirun -n 2 ./epoch_singularity.py -d 2 -o \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen running the \u003ccode\u003eepoch_singularity.py\u003c/code\u003e script with MPI, note that we must supply the\noutput directory via the \u003ccode\u003e-o\u003c/code\u003e flag, and can\u0027t input it using \u003ccode\u003eecho output_dir |\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor real runs, we\u0027ll want to run Epoch via the Slurm scheduler. See the \u003ccode\u003e./examples\u003c/code\u003e\nfolder for an example job script \u003ccode\u003erun_sbatch.sh\u003c/code\u003e and an example \u003ccode\u003einput.deck\u003c/code\u003e. Once we\nhave a job script, we can submit a job using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sbatch run_sbatch.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can check the progress of our job using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ squeue -u \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt is also possible to pull the container from the remote repo:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull epoch.sif library://liampattinson/epoch/epoch.sif:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will download the container image to the file \u003ccode\u003eepoch.sif\u003c/code\u003e (\u003ccode\u003e.sif\u003c/code\u003e denoting a\n\u0027Singularity Image Format\u0027 file). You can then use \u003ccode\u003eepoch.sif\u003c/code\u003e in place of\n\u003ccode\u003elibrary://account/repo/container\u003c/code\u003e in any of the commands above.\u003c/p\u003e\n\u003cp\u003eTo see help text for the Singularity container, first pull it using the methods above,\nand then try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run-help epoch.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to inspect the container, it has been set up so that the following\ncommand opens a bash shell inside of it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run epoch.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTry to avoid getting \u003ccode\u003esingularity exec\u003c/code\u003e and \u003ccode\u003esingularity run\u003c/code\u003e mixed up; the\nformer lets you specify which command you want to run, while the later runs a\npre-defined script.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-analysing-code-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#analysing-code-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalysing code output\u003c/h2\u003e\n\u003cp\u003eIt is recommended to analyse Epoch output data on your own machine rather than on\nViking:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ scp \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e@viking.york.ac.uk:/users/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003euserid\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scratch/epoch/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.sdf \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need a particular Python library to read \u003ccode\u003e.sdf\u003c/code\u003e files, and this is packaged with\nEpoch itself. To install this library, try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/Warwick-Plasma/epoch\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e epoch/epoch1d\n$ make sdfutils\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that the SDF Python library is not packaged with modern best-practices in mind\n(i.e. using virtual environments, uploading packages to PyPI/conda-forge). It will\ninstall to \u003ccode\u003e~/.local/lib/python3.x/site-packages\u003c/code\u003e regardless of whether you\u0027re in a\n\u003ccode\u003evenv\u003c/code\u003e or \u003ccode\u003econda\u003c/code\u003e environment. If you feel you know what you\u0027re doing, you can manually\ncopy/move the installed files to the environment of your choice after installing, but\nit\u0027s recommended to just use the base user environment.\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://epochpic.github.io/\" rel=\"nofollow\"\u003eEpoch docs\u003c/a\u003e for info on using SDF analysis tools.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-epoch-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-epoch-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Epoch with Docker\u003c/h2\u003e\n\u003cp\u003eTo run Epoch on your own machine, you\u0027ll first need to install Docker if you don\u0027t have\nit already.\u003c/p\u003e\n\u003cp\u003eThe Epoch Docker container can be found at \u003ccode\u003eghcr.io/plasmafair/epoch:latest\u003c/code\u003e.\nTo run it, try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -v /path/to/output/dir:/output \\\n ghcr.io/plasmafair/epoch:latest -d 2 --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBreaking down each component here:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edocker run\u003c/code\u003e starts up the container and runs its \u0027entrypoint\u0027, which is the script\n\u003ccode\u003erun_epoch\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--rm\u003c/code\u003e automatically removes the container after running.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-v /path/to/output/dir:/output\u003c/code\u003e mounts the directory \u003ccode\u003e/path/to/output/dir\u003c/code\u003e on the\nhost machine to \u003ccode\u003e/output\u003c/code\u003e on the container. \u003ccode\u003e/path/to/output/dir\u003c/code\u003e should contain\nyour \u003ccode\u003einput.deck\u003c/code\u003e file before running.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eghcr.io/plasmafair/epoch:latest\u003c/code\u003e is the container to run. This will be downloaded\nthe first time you run the container, and cached for future use. It is created using\nthe file \u003ccode\u003eDocker/Dockerfile\u003c/code\u003e in this repo.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-d 2\u003c/code\u003e: Run 2D epoch. Can also be 1D and 3D.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--photons\u003c/code\u003e: Optional flag that switches on QED features.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that you shouldn\u0027t mount your current working directory. Provided the second path\nprovided to \u003ccode\u003e-v\u003c/code\u003e is \u003ccode\u003e/output\u003c/code\u003e, there\u0027s no need to provide an argument to the \u003ccode\u003e-o\u003c/code\u003e flag.\nIf you want to open an interactive shell inside the container, try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -it -v /path/to/output/dir:/output \\\n --entrypoint /bin/bash ghcr.io/plasmafair/epoch:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor a simplified interface, try using the script \u003ccode\u003eepoch_docker.py\u003c/code\u003e. To achieve the\nsame results as the call above, try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./epoch_docker.py -d 2 -o /path/to/output/dir --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that the \u003ccode\u003e-o\u003c/code\u003e flag here refers to the run location on the host machine, not the\nlocation in the docker container. If \u003ccode\u003e-o\u003c/code\u003e is not provided, this script mimics the\nbehaviour of Epoch itself by prompting the user to input their output directory after\nthe program starts:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e /path/to/output/dir \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e ./epoch_docker.py -d 2 --photons\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-docker-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-docker-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Docker images\u003c/h2\u003e\n\u003cp\u003eTo build a Docker image, enter the \u003ccode\u003eDocker\u003c/code\u003e directory and try:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e -t epoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then run the container via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -v /path/to/output/dir:/output \\\n epoch -d 2 --photons\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry\"\u003eonline docs\u003c/a\u003e to set up your GitHub account to permit pushing to\nthe GitHub Container Registry (GHCR). Once set up, you should tag your repo with the\nname it should use online:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker tag epoch ghcr.io/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_profile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/epoch:0.1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then push using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker push ghcr.io/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_profile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/epoch:0.1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-singularity-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Singularity images\u003c/h2\u003e\n\u003cp\u003eThe file \u003ccode\u003eSingularity/Singularity\u003c/code\u003e contains the definitions for an Epoch Singularity\ncontainer. As this builds on the Docker image, it doesn\u0027t do much beyond updating\nsome file access permissions.\u003c/p\u003e\n\u003cp\u003eDue to permission issues, we can\u0027t build new containers directly on Viking. However,\nwe can make use of the Sylabs remote builder. To use this, first go to\n\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003esylabs.io\u003c/a\u003e and create an account. From there, you should be able to generate\nan \u0027access token\u0027. After doing so, copy the generated token to a file \u003ccode\u003e.token\u003c/code\u003e on\nyour system. Then:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity remote login\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCopy-paste your access token when prompted. You can then build your image using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --remote epoch.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis may take some time. Once it\u0027s done, you should find the image file \u003ccode\u003eepoch.sif\u003c/code\u003e\nin your current directory. You can run this container directly using \u003ccode\u003esingularity exec\u003c/code\u003e\nas shown above.\u003c/p\u003e\n\u003cp\u003eIf you wish to share your container with others, you\u0027ll first need to sign it. This can\nbe done using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity keys newpair\n$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Fill in the prompts as they appear.\u003c/span\u003e\n$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the same email as your sylabs account.\u003c/span\u003e\n$ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can leave passwords blank\u003c/span\u003e\n$ singularity sign epoch.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can check it worked using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity verify epoch.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, we can upload it to Sylabs using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity push epoch.sif library://\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_sylabs_account\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/epoch/epoch.sif:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn addition to uploading an image with the \u003ccode\u003e:latest\u003c/code\u003e tag, we may also want to upload a\nversion with a version code like \u003ccode\u003e:1.0\u003c/code\u003e. If we add new features to the container, we\ncan then upload version \u003ccode\u003e:1.1\u003c/code\u003e etc. If we change how the container works in such a way\nthat our users must interact with it differently (e.g. we might have renamed an existing\nexecutable), we can then upload version \u003ccode\u003e:2.0\u003c/code\u003e etc.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-hidden=\"true\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003eThis repo is licensed under the GNU GPLv3 license, as it contains files from the\nsimilarly-licensed \u003ca href=\"https://github.com/Warwick-Plasma/epoch\"\u003eEpoch repository\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "powellgenomicslab/Demuxafy_manuscript", + "latest_release": "v1.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDemuxafy_manuscript\u003c/h1\u003e\u003ca id=\"user-content-demuxafy_manuscript\" class=\"anchor\" aria-label=\"Permalink: Demuxafy_manuscript\" href=\"#demuxafy_manuscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1688409808.0 + "updated_at": 1700598267.0 }, { "data_format": 2, - "description": null, + "description": "Imputation workflow with sanger impuation server, originally prepared for sceQTL-Gen consortium but copied here on 30 August, 2021 when updating to hg38 for sceQTL-Gen consortium", "filenames": [ - "Singularity.4", - "Singularity.2", - "Singularity.update", - "Singularity.0", - "Singularity.3", - "Singularity.1" + "Singularity.WGpipeline", + "Singularity.Imputation" ], - "full_name": "ddbj/singularity_R-3.6.3-CRAN-Bioconductor-packages", + "full_name": "powellgenomicslab/Imputation_pipeline", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ubuntu2004-lts--r-363--cran-packages--bioconductor-packages-\u306e-singularity\u30a4\u30e1\u30fc\u30b8\u30ec\u30b7\u30d4\u30d5\u30a1\u30a4\u30eb\" class=\"anchor\" aria-hidden=\"true\" href=\"#ubuntu2004-lts--r-363--cran-packages--bioconductor-packages-\u306e-singularity\u30a4\u30e1\u30fc\u30b8\u30ec\u30b7\u30d4\u30d5\u30a1\u30a4\u30eb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eubuntu20.04 LTS + R-3.6.3 + CRAN packages + Bioconductor packages \u306e singularity\u30a4\u30e1\u30fc\u30b8\u30ec\u30b7\u30d4\u30d5\u30a1\u30a4\u30eb\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.0 : ubuntu-20.04 LTS\u306bapt\u3067R\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u5f8c\u3001R\u3092\u524a\u9664\u003c/li\u003e\n\u003cli\u003eSingularity.1 : Singularity.0\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306bR-3.6.3\u3092\u30bd\u30fc\u30b9\u304b\u3089\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003cli\u003eSingularity.2 : Singularity.1\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306bCRAN, Bioconductor\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003cli\u003eSingularity.3 : Singularity.2\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306bCRAN\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003cli\u003eSingularity.4 : Singularity.3\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306bCRAN\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u6b8b\u308a\u3068Bioconductor\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003cli\u003eSingularity.update : Singularity.4\u3067\u4f5c\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u5185\u306eR\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u66f4\u65b0\u30fb\u65b0\u898f\u8ffd\u52a0\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u30a4\u30e1\u30fc\u30b8\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u30a4\u30e1\u30fc\u30b8\u306e\u30d3\u30eb\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u30a4\u30e1\u30fc\u30b8\u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build ubuntu-20.04-R-install-base.simg Singularity.0 2\u0026gt;\u0026amp;1 | tee log.0\n$ sudo singularity build ubuntu-20.04-R-3.6.3.simg Singularity.1 2\u0026gt;\u0026amp;1 | tee log.1\n$ sudo singularity build ubuntu-20.04-R-3.6.3-2.simg Singularity.2 2\u0026gt;\u0026amp;1 | tee log.2\n$ sudo singularity build ubuntu-20.04-R-3.6.3-CRAN-packages.simg Singularity.3 2\u0026gt;\u0026amp;1 | tee log.3\n$ sudo singularity build ubuntu-20.04-R-3.6.3-CRAN-Bioconductor-packages.simg Singularity.4 2\u0026gt;\u0026amp;1 | tee log.4\n$ sudo singularity build ubuntu-20.04-R-3.6.3-CRAN-Bioconductor-packages-update.simg Singularity.update 2\u0026gt;\u0026amp;1 | tee log.update\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity.update\u3092\u4f7f\u3063\u3066\u30a4\u30e1\u30fc\u30b8\u3092\u30d3\u30eb\u30c9\u3057\u305f\u969b\u306e\u30ed\u30b0\u3067\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u5931\u6557\u3057\u305f\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u628a\u63e1\u3059\u308b\u3002\n\u4e0d\u8db3\u3057\u3066\u3044\u308b\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3092Singularity.update\u306b\u8ffd\u52a0\u3057\u3001\u518d\u5ea6\u30a4\u30e1\u30fc\u30b8\u306e\u30d3\u30eb\u30c9\u3092\u884c\u3046\u3002\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u751f\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306e\u30b5\u30a4\u30ba\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u751f\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306e\u30b5\u30a4\u30ba\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u751f\u6210\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u306e\u30b5\u30a4\u30ba\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls -lh\n-rwxr-xr-x 1 root root 2.1G 6\u6708 5 14:29 ubuntu-20.04-R-3.6.3-2.simg\n-rwxr-xr-x 1 root root 143G 6\u6708 12 15:17 ubuntu-20.04-R-3.6.3-CRAN-Bioconductor-packages.simg\n-rwxr-xr-x 1 root root 17G 6\u6708 8 10:59 ubuntu-20.04-R-3.6.3-CRAN-packages.simg\n-rwxr-xr-x 1 root root 1.4G 5\u6708 28 14:56 ubuntu-20.04-R-3.6.3.simg\n-rwxr-xr-x 1 root root 562M 5\u6708 28 12:34 ubuntu-20.04-R-install-base.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u306a\u304b\u3063\u305fr\u30d1\u30c3\u30b1\u30fc\u30b8\" class=\"anchor\" aria-hidden=\"true\" href=\"#\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u306a\u304b\u3063\u305fr\u30d1\u30c3\u30b1\u30fc\u30b8\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u306a\u304b\u3063\u305fR\u30d1\u30c3\u30b1\u30fc\u30b8\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u306a\u3044\n\u003cul\u003e\n\u003cli\u003eBioconductor (1)\n\u003cul\u003e\n\u003cli\u003echarm : Bioconductor 3.10\u306b\u542b\u307e\u308c\u3066\u3044\u306a\u3044\u305f\u3081\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u4f9d\u5b58\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u4e0d\u8db3\n\u003cul\u003e\n\u003cli\u003eCRAN (11)\n\u003cul\u003e\n\u003cli\u003eBALD\uff1a\u003ca href=\"http://mcmc-jags.sourceforge.net\" rel=\"nofollow\"\u003eJAGS\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eBRugs\uff1a\u003ca href=\"http://www.openbugs.net/w/FrontPage\" rel=\"nofollow\"\u003eOpenBUGS\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eOpenCL\uff1aNVIDIA CUDA\u7b49\u3067\u306eOpenCL\u30e9\u30f3\u30bf\u30a4\u30e0\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eROracle\uff1aOracle Instant Client or Oracle Database Client\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eRcplex\uff1aIBM ILOG CPLEX\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eRsymphony\uff1a\u003ca href=\"https://projects.coin-or.org/SYMPHONY\" rel=\"nofollow\"\u003eSYMPHONY\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003ecplexAPI\uff1aIBM ILOG CPLEX\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003ekmcudaR\uff1aNVIDIA CUDA\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eqtbase\uff1aQt 4.x\u304c\u5fc5\u8981\u3002ubuntu 20.04\u306eapt\u30ea\u30dd\u30b8\u30c8\u30ea\u306b\u5165\u3063\u3066\u3044\u306a\u3044\u3002\u003c/li\u003e\n\u003cli\u003erLindo\uff1a\u003ca href=\"https://www.lindo.com/\" rel=\"nofollow\"\u003eLindo API\u003c/a\u003e\u304c\u5fc5\u8981\u3002LINDOAPI_HOME\u3092\u8a2d\u5b9a\u305b\u3088\u3002\u003c/li\u003e\n\u003cli\u003erunjags\uff1a\u003ca href=\"http://mcmc-jags.sourceforge.net\" rel=\"nofollow\"\u003eJAGS\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBioconductor\uff0811\uff09\n\u003cul\u003e\n\u003cli\u003eChemineOB\uff1a\u003ca href=\"http://openbabel.org/wiki/Main_Page\" rel=\"nofollow\"\u003eOpen Babel\u003c/a\u003e \u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eSharedObject\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003cli\u003emlm4omics\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003cli\u003ersbml\uff1alibsbml\u304c\u5fc5\u8981\uff08libsbml5-dev\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u304c\u9055\u3046\u3088\u3046\u3060\uff09\u3002\u003c/li\u003e\n\u003cli\u003exps\uff1a\u003ca href=\"https://root.cern.ch/releases\" rel=\"nofollow\"\u003eroot_v5.34.36\u003c/a\u003e\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eRcwl\uff1acwltool\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u304c\u3001cwlversion\u306e\u5224\u5b9a\u306b\u5931\u6557\u3057\u3066\u3044\u308b\u3002\u003c/li\u003e\n\u003cli\u003epermGPU\uff1aNVIDIA CUDA\u304c\u5fc5\u8981\u3002\u003c/li\u003e\n\u003cli\u003eMSGFplus\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003cli\u003escAlign\uff1atensorflow\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u3063\u3066tensorflow\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3002\u003c/li\u003e\n\u003cli\u003eMoonlightR\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003cli\u003eRariant\uff1a\u539f\u56e0\u4e0d\u660e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u4f9d\u5b58R\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u4e0d\u8db3 (20)\n\u003cul\u003e\n\u003cli\u003eBANOVA\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eBayesPostEst\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eIsotopeR\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003ePortfolioOptim\uff1aRsymphony\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eROI.plugin.cplex\uff1aRcplex\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eROI.plugin.symphony\uff1aRsymphony\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eRcmdrPlugin.RMTCJags\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eTreeBUGS\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003ebayescount\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003ebfw\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eora\uff1aROracle\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003epivmet\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eqtpaint\uff1aqtbase\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eBiGGR\uff1arsbml\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eMPTmultiverse\uff1aTreeBUGS, runjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eRcwlPipelines\uff1aRcwl\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eMSGFgui\uff1aMSGFplus\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eReplication\uff1arunjags\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003echarmData\uff1acharm\u304c\u5fc5\u8981\u003c/li\u003e\n\u003cli\u003eproteomics\uff1aMSGFplus\u304c\u5fc5\u8981\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWG1-pipeline-QC\u003c/h1\u003e\u003ca id=\"user-content-wg1-pipeline-qc\" class=\"anchor\" aria-label=\"Permalink: WG1-pipeline-QC\" href=\"#wg1-pipeline-qc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was built to assist with imputation of SNP genotype data. The data will be preprocessed with instructions for imputation on Sanger Imputation server and finally processing after impation.\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/powellgenomicslab/Imputation_pipeline/wiki\"\u003eWiki\u003c/a\u003e for information on running the QC pipeline.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1592213830.0 + "updated_at": 1648700971.0 }, { "data_format": 2, - "description": "Singularity container with stack for LArCV/pytorch", + "description": "SParsity-based Analysis of Reliable K-hubness (SPARK) library optimized to run on HPCs and computing clusters", "filenames": [ - "Singularity" + "for_build/containers/Singularity" ], - "full_name": "LArbys/singularity-larbys-pytorch", + "full_name": "multifunkim/spark-hpc", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-larbys-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-larbys-pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-larbys-pytorch\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003espark-hpc\u003c/h1\u003e\u003ca id=\"user-content-spark-hpc\" class=\"anchor\" aria-label=\"Permalink: spark-hpc\" href=\"#spark-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSParsity-based Analysis of Reliable K-hubness (SPARK) library optimized to run on HPCs and computing clusters\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1527174404.0 + "updated_at": 1565355677.0 }, { "data_format": 2, - "description": null, + "description": "Create a NeuroDebian Singularity image with all Python packages I need.", "filenames": [ - "diffuser/Singularity.def" + "docker/NeuroDebian/Singularity", + "docker/python2.7/Singularity", + "docker/python3.6/Singularity" ], - "full_name": "ShravanRavi2002/Diffusion_RL_RectifiedFlow", + "full_name": "feilong/neurodebian-singularity", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1680977053.0 + "updated_at": 1502647456.0 }, { "data_format": 2, - "description": "geant4 in contianer.", + "description": null, "filenames": [ + "Singularity-v0.1", "Singularity" ], - "full_name": "ifurther/geant4-docker", - "latest_release": "11.0.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-geant4-docker\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#geant4-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egeant4-docker\u003c/h1\u003e\n\u003cp\u003egeant4 in contianer.\u003c/p\u003e\n", + "full_name": "fksato/caffe2_singularity", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecaffe2_singularity\u003c/h1\u003e\u003ca id=\"user-content-caffe2_singularity\" class=\"anchor\" aria-label=\"Permalink: caffe2_singularity\" href=\"#caffe2_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCaffe2 singularity environment for facebook research video models\u003c/h2\u003e\u003ca id=\"user-content-caffe2-singularity-environment-for-facebook-research-video-models\" class=\"anchor\" aria-label=\"Permalink: Caffe2 singularity environment for facebook research video models\" href=\"#caffe2-singularity-environment-for-facebook-research-video-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecomplete installation guide:\n\u003ca href=\"https://github.com/facebookresearch/VMZ/blob/master/tutorials/Installation_guide.md\"\u003ehttps://github.com/facebookresearch/VMZ/blob/master/tutorials/Installation_guide.md\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUbuntu Version\u003c/h2\u003e\u003ca id=\"user-content-ubuntu-version\" class=\"anchor\" aria-label=\"Permalink: Ubuntu Version\" href=\"#ubuntu-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e16.04\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOfficial Nvidia docker image\u003c/h2\u003e\u003ca id=\"user-content-official-nvidia-docker-image\" class=\"anchor\" aria-label=\"Permalink: Official Nvidia docker image\" href=\"#official-nvidia-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003envidia/cuda:10.0-cudnn7-devel-ubuntu16.04\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested on\u003c/h2\u003e\u003ca id=\"user-content-tested-on\" class=\"anchor\" aria-label=\"Permalink: Tested on\" href=\"#tested-on\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGPU architecture: Pascal\u003c/p\u003e\n\u003cp\u003eCuda version: 10.0\u003c/p\u003e\n\u003cp\u003eCudnn version: 7.4.2\u003c/p\u003e\n\u003cp\u003eCompute Compatibility: 6.0 (TORCH_CUDA_ARCH_LIST)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1638461581.0 + "updated_at": 1571015501.0 }, { "data_format": 2, "description": null, "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/latest/Singularity", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06" + "nextflow/modules/SVG/Singularity.def", + "nextflow/modules/SCENVI/Singularity.def" ], - "full_name": "IBM/shortest-optimal-downward", + "full_name": "joechanlab/Xenium_lung_NE_Plasticity_2024", "latest_release": null, - "readme": "\u003ch1 id=\"user-content-a-planner-for-shortest-cost-optimal-planning-problem\"\u003e\u003ca class=\"heading-link\" href=\"#a-planner-for-shortest-cost-optimal-planning-problem\"\u003eA planner for shortest cost optimal planning problem\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003ch2 id=\"user-content-the-code-implements-two-approaches-in-two-separate-branches\"\u003e\u003ca class=\"heading-link\" href=\"#the-code-implements-two-approaches-in-two-separate-branches\"\u003eThe code implements two approaches in two separate branches\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eCost-algebraic A* in branch \u003ccode\u003eshortest-optimal-cost-algebra\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCost transformation with regular A* in branch \u003ccode\u003eshortest-optimal-cost-transformation\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCiting:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{katz-et-al-socs2022,\n title = \"On Producing Shortest Cost-Optimal Plans\",\n author = \"Michael Katz and Gabriele R{\\\"o}ger and Malte Helmert\",\n booktitle = \"Proceedings of the 15th Annual Symposium on\n Combinatorial Search (SoCS 2022)\",\n publisher = \"{AAAI} Press\",\n year = \"2022\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eXenium Lung NE Plasticity\u003c/h1\u003e\u003ca id=\"user-content-xenium-lung-ne-plasticity\" class=\"anchor\" aria-label=\"Permalink: Xenium Lung NE Plasticity\" href=\"#xenium-lung-ne-plasticity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the nextflow pipeline and scripts to analyze Xenium data for lung NE plasticity project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!NOTE]\nIf you are new to Nextflow and nf-core, please refer to \u003ca href=\"https://nf-co.re/docs/usage/installation\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e on how to set-up Nextflow. Make sure to \u003ca href=\"https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline\" rel=\"nofollow\"\u003etest your setup\u003c/a\u003e with \u003ccode\u003e-profile test\u003c/code\u003e before running the workflow on actual data.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eFirst, prepare a samplesheet with your input data that looks as follows, where each row contains the sample name, path to Xenium Ranger output.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esamplesheet.csv\u003c/code\u003e:\u003c/p\u003e\n\u003cpre lang=\"csv\"\u003e\u003ccode\u003esample, xenium\nCONTROL_REP1, xenium_output_folder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, prepare a parameter YAML file that looks as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eparams.yml\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003esamplesheet\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e../results/samplesheet.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e path to the sample sheet\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutdir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e../results/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e output folder\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eexperiment\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eXenium\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eQC\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003egene_information_file\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e../data/annotation.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e gene annotation file\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eextra_markers\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePHOX2B\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e custom markers to plot\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_memory\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e36.GB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e memory\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_cpus\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cpu\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow, you can run the pipeline. For local run on HPC with singularity installed, execute the following command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run ./main.nf \\\n -profile singularity \\\n -params-file ./params.yml \\\n -w ./work/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using MSKCC lilac, you can use the pre-defined \u003ccode\u003elilac\u003c/code\u003e profile that uses the LSF executor.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run ./main.nf \\\n -profile lilac \\\n -params-file ./params.yml \\\n -w ./work/\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1652293206.0 + "updated_at": 1728663251.0 }, { "data_format": 2, - "description": "Implementation of the Property-Directed Reachability algorithm in the Fast Downward planning system. Implementation of my masters thesis.", + "description": null, "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/latest/Singularity", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06" + "scripts/singularity_container_scripts/Singularity.def" ], - "full_name": "Tiim/fast-downward-pdr", - "latest_release": "pdr-final", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "aperonalope/TFM_computacional", + "latest_release": null, + "readme": "\u003cp\u003eThis repository contains all the scripts and files that were used for the masters thesis titled \"vscRNAFinder: A biochemical-bioinformatic pipeline to detect small circular RNAs\"\u003c/p\u003e\n\u003cp\u003eVscRNAFinder is a bioquemical-bioinformatic pipeline aimed at detecting very small circle RNAs by detecting the repetitive units of rolling circle tandem repeats and aligning them to the genome.\nIt is a nextflow pipeline that uses a singularity container\u003c/p\u003e\n\u003cp\u003eThe scipts folder contain all the scripts used either for creating the graphs present on the masters thesis manuscript or the ones employed by nextflow\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "fast-downward", - "pdr-algorithm", - "problem-solving" - ], - "updated_at": 1683295351.0 + "topics": [], + "updated_at": 1724876381.0 }, { "data_format": 2, - "description": null, + "description": "Def File of Singularity", "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/22.12/Singularity.22.12", - "misc/releases/latest/Singularity", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06" + "def/vae-mnist.def", + "def/lafin.def", + "def/stargan.def", + "def/wav2pix.def", + "def/singan.def", + "def/contextual-attention.def", + "def/edge-connect.def", + "def/sc-fegan.def" ], - "full_name": "ipc2023-classical/planner23", + "full_name": "Nahuel-Mk2/def-space", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edef-space\u003c/h1\u003e\u003ca id=\"user-content-def-space\" class=\"anchor\" aria-label=\"Permalink: def-space\" href=\"#def-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository is def-space for Singularity\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1688990723.0 + "updated_at": 1606189900.0 }, { "data_format": 2, - "description": null, + "description": "Singularity definition files for various projects", "filenames": [ - "misc/releases/21.12/Singularity.21.12", - "misc/releases/22.12/Singularity.22.12", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/22.06/Singularity.22.06" + "miniconda/Singularity", + "hauntedhouse/Singularity", + "hauntedhouse_freesurfer/Singularity" ], - "full_name": "salome-eriksson/downward-unsolvability", + "full_name": "mvdoc/singularity-def", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2023 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 10, Clang 12\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 22.1.1 and SoPlex 6.0.3+. On Ubuntu we\ntest both CPLEX and SoPlex. On Windows we currently only test CPLEX,\nand on macOS we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2023 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2023 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2023 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2023 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2023 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2023 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2023 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2023 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2018-2020, 2023 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2021-2023 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2022-2023 Remo Christen\u003c/li\u003e\n\u003cli\u003e2023 Simon Dold\u003c/li\u003e\n\u003cli\u003e2023 Claudia S. Grundke\u003c/li\u003e\n\u003cli\u003e2023 Emanuele Tirendi\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1696843043.0 + "updated_at": 1495630055.0 }, { "data_format": 2, - "description": null, + "description": "singularity def file for flair(fluka)", "filenames": [ - "anaconda2/Singularity.5.3.0", - "anaconda2/Singularity", - "anaconda3/Singularity.5.3.0", - "anaconda3/Singularity", - "gephi/Singularity.0.9.1", - "gephi/Singularity.0.9.2", - "jupyter/Singularity", - "jupyter/Singularity.4.4.0", - "rstudio/Singularity", - "rstudio/Singularity.3.5.1", - "rstudio/Singularity.3.4.4" + "flair.def", + "flair-cern.def" ], - "full_name": "OdumInstitute/singularity-dev-images", + "full_name": "ifurther/flair-def", "latest_release": null, - "readme": "\u003ch1 id=\"user-content-singularity-dev-images\"\u003e\u003ca class=\"heading-link\" href=\"#singularity-dev-images\"\u003esingularity-dev-images\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 3, "topics": [], - "updated_at": 1556725305.0 + "updated_at": 1619686613.0 }, { "data_format": 2, - "description": null, + "description": "OpenEXR in a Singularity container", "filenames": [ - "src/Singularity.def" + "Singularity.2.2", + "Singularity" ], - "full_name": "currocam/BiRC-Gaussian-graphical-models", + "full_name": "OSC/sa_singularity_openexr", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity OpenEXR\u003c/h1\u003e\u003ca id=\"user-content-singularity-openexr\" class=\"anchor\" aria-label=\"Permalink: Singularity OpenEXR\" href=\"#singularity-openexr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3586\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.openexr.com/\" rel=\"nofollow\"\u003eOpenEXR\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003eopenexr.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build openexr.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name openexr.sif shub://OSC/sa_singularity_openexr\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRender .EXR image\u003c/h3\u003e\u003ca id=\"user-content-render-exr-image\" class=\"anchor\" aria-label=\"Permalink: Render .EXR image\" href=\"#render-exr-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eexrdisplay\u003c/code\u003e command is launched using the command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e openexr.sif exrdisplay -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e openexr.sif exrdisplay rendertest_0001.exr\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 8, "topics": [], - "updated_at": 1698331009.0 + "updated_at": 1569951214.0 }, { "data_format": 2, - "description": null, + "description": "Snakemake pipeline for genome coassembly", "filenames": [ - "Singularity.latest" + "Singularity.def" ], - "full_name": "bioexcel/biobb_haddock", + "full_name": "EI-CoreBioinformatics/CoassemblyPipeline", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/59df560cf8b0622113f818a5a58a208df19e819ebe6795409cacdad4c9514fea/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f62696f62622d686164646f636b2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"\" data-canonical-src=\"https://readthedocs.org/projects/biobb-haddock/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/biobb_haddock\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a477fdb1fd9bc9eb7ffa6cae6a019c6d4c3902fd468b3126f1b78e56c7dcff83/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://quay.io/repository/biocontainers/biobb_haddock\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca418e4db0b3de91a09a5df4a59446da015b6164598a8bc255918e911484f84f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d517561792e696f2d626c7565\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docker-Quay.io-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a2157c971b7ae1deb8eb095799440551c33dcf61ea3d965d86b496a5a65df55/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-biobb_haddock\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#biobb_haddock\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebiobb_haddock\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003ebiobb_haddock is the Biobb module collection to compute information-driven flexible protein-protein docking.\nBiobb (BioExcel building blocks) packages are Python building blocks that\ncreate new layer of compatibility and interoperability over popular\nbioinformatics tools.\nThe latest documentation of this package can be found in our readthedocs site:\n\u003ca href=\"http://biobb-haddock.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003elatest API documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-version\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion\u003c/h3\u003e\n\u003cp\u003ev3.8.0 2022.1\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eUsing PIP:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eImportant:\u003c/strong\u003e PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install \"biobb_haddock\u0026gt;=3.8.0\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: \u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing ANACONDA:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda install -c bioconda \"biobb_haddock\u0026gt;=3.8.0\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage: With conda installation BioBBs can be used with the \u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/modules.html\" rel=\"nofollow\"\u003ePython API documentation\u003c/a\u003e and the \u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing DOCKER:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull quay.io/biocontainers/biobb_haddock:3.8.0--pyhdfd78af_0\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run quay.io/biocontainers/biobb_haddock:3.8.0--pyhdfd78af_0 \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsing SINGULARITY:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMacOS users\u003c/strong\u003e: it\u0027s strongly recommended to avoid Singularity and use \u003cstrong\u003eDocker\u003c/strong\u003e as containerization system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name biobb_haddock.sif shub://bioexcel/biobb_haddock\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec biobb_haddock.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe command list and specification can be found at the \u003ca href=\"https://biobb-haddock.readthedocs.io/en/latest/command_line.html\" rel=\"nofollow\"\u003eCommand Line documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#copyright--licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2022 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2022 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39c04282e694d49ea0d56c716a27845cd25b9931f791484540f625dcecf68af2/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCoassemblyPipeline\u003c/h2\u003e\u003ca id=\"user-content-coassemblypipeline\" class=\"anchor\" aria-label=\"Permalink: CoassemblyPipeline\" href=\"#coassemblypipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSnakemake pipeline to generate and QC genome (co)assemblies from single-cell (e.g., G\u0026amp;T-Seq) or metagenome data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTHIS PIPELINE DOES NOT TRIM READS. IT EXPECTS THE INPUT READS TO HAVE ALREADY BEEN ADAPTER TRIMMED\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe input to this pipeline is a comma separated file. The first line specifies the name of the coassembly, the following lines contain 4 comma separated values to specify the gDNA and cDNA libraries:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esample name\u003c/li\u003e\n\u003cli\u003elibrary type (gDNA or cDNA)\u003c/li\u003e\n\u003cli\u003epath to forward reads\u003c/li\u003e\n\u003cli\u003epath to reverse reads\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSample names should not contain periods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExample input file: \u003ccode\u003einput.csv\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCOASSEMBLY_NAME\nSample1,gDNA,Sample1_gDNA_R1.fq.gz,Sample1_gDNA_R2.fq.gz\nSample1,cDNA,Sample1_cDNA_R1.fg.gz,Sample1_cDNA_R2.fq.gz\nSample2,gDNA,Sample2_gDNA_R1.fq.gz,Sample2_gDNA_R2.fq.gz\nSample2,cDNA,Sample2_cDNA_R1.fg.gz,Sample2_cDNA_R2.fq.gz\nSample3,gDNA,Sample3_gDNA_R1.fq.gz,Sample3_gDNA_R2.fq.gz\nSample4,cDNA,Sample4_cDNA_R1.fq.gz,Sample4_cDNA_R2.fq.gz\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample config file: \u003ccode\u003econfig.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# File listing input reads, better to set this at command line with \"--config input=XXX.csv\"\n# Should use absolute paths\ninput:\n\n# Mode to run SPAdes in - \"sc\" for single-cell or \"meta\" for metagenome\nassembly_type: [sc / meta]\n\n# Optional tools to run\nrun_checkm: true\nrun_busco: true\n\n# SPAdes parameters\nkmers: \"21,33,55,77\"\nmin_scaffold_length: 1000 # for bbtools reformat\n\n# BUSCO parameters\nbusco_version: 3\nbusco_database: XXXXX\n\n# MetaBat2 parameters\nmetabat2_min_contig: 1500\n\n# Tiara parameters\ntiara_min_length: 1000\n\n# EukRep parameters\neukrep_min_length: 1000\n\n# Blobtools parameters\ndiamond_database: XXXXX\nmegablast_database: XXXXX\nnodesdb: XXXXX\nn_chunks: 4 # chunk the fasta file into N splits for the megablastn search; randomly to balance as input fasta will be sorted by sequence length\n\n# CAT parameters\ncat_database: XXXXX\ntaxonomy_dir: XXXXX\ndiamond_path: XXXXX\n\n# pr2 database\npr2_database: XXXXX\n\n# cleanup options\ncleanup_spades: true\ncleanup_cat: true\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample command to launch snakemake:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -p --config input=input.csv -j 20 --retries 1 --latency-wait 60 --snakefile CoassemblyPipeline.smk --cluster-config cluster.json --cluster \"sbatch -p {cluster.partition} -c {cluster.c} --mem={cluster.memory} --job-name={cluster.J} --time={cluster.time} --exclude={cluster.exclude} -o slurm_logs/slurm.{cluster.J}.%j.out\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eThis pipeline runs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egenome assembly using \u003ca href=\"https://github.com/ablab/spades\"\u003eSPAdes\u003c/a\u003e in single-cell or metagenome mode\u003c/li\u003e\n\u003cli\u003eannotation of rRNA genes using \u003ca href=\"https://github.com/tseemann/barrnap\"\u003ebarrnap\u003c/a\u003e, followed by classification based on comparison with the \u003ca href=\"https://github.com/pr2database/pr2database\"\u003epr2\u003c/a\u003e database\u003c/li\u003e\n\u003cli\u003etaxonomic classification using:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DRL/blobtools\"\u003eBlobtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dutilh/CAT\"\u003eCAT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/patrickwest/EukRep\"\u003eEukRep\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ibe-uw/tiara/\"\u003eTiara\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eassembly stats using \u003ca href=\"https://github.com/ablab/quast\"\u003eQUAST\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ecoverage stats based on mapping reads with \u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e and \u003ca href=\"http://qualimap.conesalab.org/\" rel=\"nofollow\"\u003eQualiMap\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003econtig binning using \u003ca href=\"https://bitbucket.org/berkeleylab/metabat\" rel=\"nofollow\"\u003eMetaBAT2\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gitlab.com/ezlab/busco\" rel=\"nofollow\"\u003eBUSCO\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Ecogenomics/CheckM\"\u003eCheckM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eOptionally, aligns RNA-Seq reads using \u003ca href=\"https://github.com/DaehwanKimLab/hisat2\"\u003ehisat2\u003c/a\u003e and assembles transcripts using \u003ca href=\"https://github.com/gpertea/stringtie\"\u003eStringTie2\u003c/a\u003e. RNA-Seq reads can come from any source (e.g., single-cell, metatranscriptome, or isolate RNA-Seq)\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003eThe Singularity definition file \u003ccode\u003eSingularity.def\u003c/code\u003e contains most of the required software. Additional software requirements not included in the definition file (due to compatbility issues):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DRL/blobtools\"\u003eblobtools (v1.1.1)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gitlab.com/ezlab/busco\" rel=\"nofollow\"\u003ebusco (v3/4/5)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/patrickwest/EukRep\"\u003eEukRep\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ibe-uw/tiara\"\u003eTiara\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eExample DAG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"example_DAG.png\"\u003e\u003cimg src=\"example_DAG.png\" alt=\"Example Snakemake DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 9, + "subscribers_count": 1, "topics": [], - "updated_at": 1654724526.0 + "updated_at": 1724235234.0 }, { "data_format": 2, - "description": "gpu image for folding at home", + "description": null, "filenames": [ - "Singularity" + "Singularity.0.8.0" ], - "full_name": "slaclab/folding-at-home-gpu", + "full_name": "arcsUVA/caffe2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-folding-at-home-gpu\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#folding-at-home-gpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efolding-at-home-gpu\u003c/h1\u003e\n\u003cp\u003egpu image for folding at home\u003c/p\u003e\n\u003cp\u003esimple merge of nvidia cl image with folding at home v7.5.1 to enable gpu processing.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecaffe2\u003c/h1\u003e\u003ca id=\"user-content-caffe2\" class=\"anchor\" aria-label=\"Permalink: caffe2\" href=\"#caffe2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 5, "topics": [], - "updated_at": 1584940583.0 + "updated_at": 1550983020.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.tensorflow_venv", - "Singularity.tensorflow-1.14" + "Singularity.def" ], - "full_name": "MuhsinFatih/singularityimages", + "full_name": "evlabwebapps/langatlas", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLanA\u003c/h1\u003e\u003ca id=\"user-content-lana\" class=\"anchor\" aria-label=\"Permalink: LanA\" href=\"#lana\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to add a new page\u003c/h2\u003e\u003ca id=\"user-content-how-to-add-a-new-page\" class=\"anchor\" aria-label=\"Permalink: How to add a new page\" href=\"#how-to-add-a-new-page\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNavigation bar is defined at \u003ccode\u003e./components/Navigation.jsx\u003c/code\u003e file. In order to add\na new page to navbar you must define a new page inside \u003ccode\u003e./pages\u003c/code\u003e and add route\nat \u003ccode\u003eroutes.js\u003c/code\u003e file. Also do not forget to export page by updating \u003ccode\u003e./pages/index.jsx\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to build and push image to DockerHub\u003c/h2\u003e\u003ca id=\"user-content-how-to-build-and-push-image-to-dockerhub\" class=\"anchor\" aria-label=\"Permalink: How to build and push image to DockerHub\" href=\"#how-to-build-and-push-image-to-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eyarn build\ndocker build --tag \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour-username\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/evlabwebapps-langatlas:latest \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker push \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour-username\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/evlabwebapps-langatlas:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOR edit and run \u003ccode\u003ebuild_push.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to deploy on the server (same as for backend)\u003c/h2\u003e\u003ca id=\"user-content-how-to-deploy-on-the-server-same-as-for-backend\" class=\"anchor\" aria-label=\"Permalink: How to deploy on the server (same as for backend)\" href=\"#how-to-deploy-on-the-server-same-as-for-backend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to enter Vagrant VM, pull Docker images and recreate containers with updated images.\u003c/p\u003e\n\u003cp\u003eOn HPC:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /om2/user/amirov/vagrant_images/evlabwebapps/\nvagrant ssh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInside VM:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker-compose pull\ndocker-compose up -d\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDocker-compose on VM that is common for frontend and backend\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eversion\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e3.5\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003eservices\u003c/span\u003e:\n\n \u003cspan class=\"pl-ent\"\u003eadmin\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eaamirov/evlab-web-apps-admin:latest\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ebuild\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e.\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eenv_file\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.env\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003evolumes\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e./assets:/app/assets\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e./backend-data:/app/data\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eports\u003c/span\u003e:\n - \u003cspan class=\"pl-c1\"\u003e8000:8000\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003ebackend\u003c/span\u003e\n\n \u003cspan class=\"pl-ent\"\u003eredis\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eredis:5-alpine\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003ebackend\u003c/span\u003e\n\n \u003cspan class=\"pl-ent\"\u003ecelery\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eaamirov/evlab-web-apps-admin:latest\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ebuild\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e.\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eenv_file\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.env\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003evolumes\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e./assets:/app/assets\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e./backend-data:/app/data\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003ebackend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ecommand\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ecelery -A src.evlabwebapps worker -l INFO\u003c/span\u003e\n\n \u003cspan class=\"pl-ent\"\u003ecelery-beat\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eaamirov/evlab-web-apps-admin:latest\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ebuild\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e.\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eenv_file\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e.env\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003ebackend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ecommand\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ecelery -A src.evlabwebapps beat -l INFO\u003c/span\u003e\n\n \u003cspan class=\"pl-ent\"\u003efrontend\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eaamirov/evlabwebapps-langatlas:latest\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eports\u003c/span\u003e:\n - \u003cspan class=\"pl-c1\"\u003e8760:8760\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003enetworks\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ebackend\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eevlabwebapps\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLearn More\u003c/h2\u003e\u003ca id=\"user-content-learn-more\" class=\"anchor\" aria-label=\"Permalink: Learn More\" href=\"#learn-more\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can learn more in the \u003ca href=\"https://facebook.github.io/create-react-app/docs/getting-started\" rel=\"nofollow\"\u003eCreate React App documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo learn React, check out the \u003ca href=\"https://reactjs.org/\" rel=\"nofollow\"\u003eReact documentation\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1565383245.0 + "updated_at": 1640121099.0 }, { "data_format": 2, - "description": "Singularity Container for SDAPS", + "description": null, "filenames": [ - "Singularity.sdaps" + "Singularity.def" ], - "full_name": "williamssanders/sdaps", + "full_name": "dome01polimi/santarsiero_ferrario_sweng_part2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sdaps_container\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#sdaps_container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esdaps_container\u003c/h1\u003e\n\u003cp\u003eSingularity Container for SDAPS\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eSingularity-Hub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://williamssanders/sdaps:sdaps\n./williamssanders-sdaps-master-sdaps.simg setup /fastscratch/ssander/sdaps/example_2 example.tex\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eBuild the container:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build -c sdaps.simg Singularity.sdaps\n./sdaps.simg setup /fastscratch/ssander/sdaps/example_1 example.tex\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject - Step 2 CI/CD Pipeline and automation\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject---step-2-cicd-pipeline-and-automation\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject - Step 2 CI/CD Pipeline and automation\" href=\"#se4hpcproject---step-2-cicd-pipeline-and-automation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe automated the build, test and release process with a CI/CD pipeline, triggering Github actions on \u0027push\u0027 events.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow file\u003c/h2\u003e\u003ca id=\"user-content-workflow-file\" class=\"anchor\" aria-label=\"Permalink: Workflow file\" href=\"#workflow-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe definition of the action workflow is defined in \u003ccode\u003e./github/workflows/c-cpp.yml\u003c/code\u003e. Here is a brief explanation of the code.\nThe job runs on the latest Ubuntu environment provided by GitHub.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall MPI: The first step updates the package list and installs MPI to run the matrix multiplication library.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheckout Repository: This step checks out the project\u0027s repository from GitHub, including any submodules.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild Application: It creates a build directory, runs CMake to generate build files, and compiles the project.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun Tests: The built application is tested by running an MPI job with two processes using mpirun.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Dependencies for Singularity: Several dependencies required to install Singularity (a container platform) are installed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Go: The Go programming language is installed, which is necessary for building Singularity.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet Up Go Environment: The Go binary path is added to the environment variables.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Singularity: Singularity is downloaded, compiled, and installed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVerify Singularity Installation: This step checks the installed version of Singularity to ensure it was installed correctly.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild Singularity Container: A Singularity container is built using a definition file (Singularity.def).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePrepare Files for Transfer to Galileo: The container file and a SLURM job script are moved into a directory (to_send) for the upcoming transfer to Galileo cluster.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSSH to Remote Server: Using sshpass for password-based SSH, the files are transferred to a remote server. The job script is executed on the remote server, and the output is printed. The server job queue status is also checked.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis workflow streamlines the process of building, testing, and deploying the C/C++ application, while also leveraging Singularity containers for consistent and portable execution environments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOn Galileo\u003c/h1\u003e\u003ca id=\"user-content-on-galileo\" class=\"anchor\" aria-label=\"Permalink: On Galileo\" href=\"#on-galileo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Galileo, cluster management and job scheduling is handled by SLURM. We run the container image \u003ccode\u003econtainer.sif\u003c/code\u003e through a script named \u003ccode\u003ejob.sh\u003c/code\u003e, both transfered previously during the workflow. \u003ccode\u003ejob.sh\u003c/code\u003e contains the command for \u003cem\u003esingularity\u003c/em\u003e to run the image and where to pipe the output, in addition to other setup variables.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1553200061.0 + "updated_at": 1717671915.0 }, { "data_format": 2, - "description": "Singularity recipes for singularity images containing ANTs (Advanced Normalization Tools).", + "description": null, "filenames": [ - "Singularity.2.2.0" + "Singularity.dev" ], - "full_name": "MPIB/singularity-ants", + "full_name": "pndni/minc-ants-fsl-and-fs-container", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-ants\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-ants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-ants\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/660\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipes for base-images containing ANTs (Advanced Normalization Tools). You can get the \u003ca href=\"https://github.com/ANTsX/ANTs\"\u003ecode and documentation for ANTs through GitHub\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eANTs is pulled from its \u003ca href=\"https://github.com/ANTsX/ANTs\"\u003egithub repository\u003c/a\u003e and build using cmake.\u003c/li\u003e\n\u003cli\u003ecmake and its dependencies are installed through the debian repositories via \u003ccode\u003eapt-get install\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAt the end of the build process the image is cleaned up by:\n\u003cul\u003e\n\u003cli\u003eremoving cmake and its dependencies through \u003ccode\u003eapt-get purge\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003edeleting the package cache,\u003c/li\u003e\n\u003cli\u003edeleting the folder containing the cloned repository.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e$ANTSPATH\u003c/code\u003e and \u003ccode\u003e$PATH\u003c/code\u003e are set according to the \u003ca href=\"https://github.com/ANTsX/ANTs/wiki/Compiling-ANTs-on-Linux-and-Mac-OS\"\u003ecompilation guide\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eANTs executable should therefore be directly available.\u003c/li\u003e\n\u003cli\u003eSuccessful build and \u003ccode\u003e$PATH\u003c/code\u003e setup is tested through calling antsRegistration with the -h flag.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1519211227.0 + "updated_at": 1555097278.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularityfile.def" + "Singularity.r_sf" ], - "full_name": "pouya-codes/singularity_create_groups", + "full_name": "callaghanmt-containers/recipes", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-create-groups\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#create-groups\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Groups\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-development-information\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#development-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment Information\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eBefore running any experiment to be sure you are using the latest commits of all modules run the following script:\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e(cd /projects/ovcare/classification/singularity_modules ; ./update_modules.sh --bcgsc-pass your/bcgsc/path)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eusage: app.py [-h] {from-experiment-manifest,from-arguments} ...\n\nSplits patches to groups by patient case and saves the path to these patches in a group file (i.e. /path/to/patient_groups.json).\nThe patient_groups.json file uses Mitch\u0027s format for groups i.e. it is a json file with the format\n\n{\n \"chunks\": [\n {\n \"id\": int,\n \"imgs\": list of paths to patches\n },\n ...DEAFULT_SEEDE || Total ||\n| Patient in Group 1 | 9 | 13 | 20 | 3 | 45 |\n| Patient in Group 2 | 9 | 13 | 20 | 3 | 45 |\n| Patient in Group 3 | 8 | 13 | 20 | 3 | 44 |\n| Whole Slide Image | 38 | 63 | 79 | 13 | 193 |\n\n|| Patch Counts || MMRD || P53ABN || P53WT || POLE || Total ||\n| Patch in Group 1 | 38659 | 43918 | 52645 | 1791 | 137013 |\n| Patch in Group 2 | 15261 | 71059 | 34979 | 17248 | 138547 |\n| Patch in Group 3 | 19431 | 51330 | 53700 | 7881 | 132342 |\n| Total | 73351 | 166307 | 141324 | 26920 | 407902 |\n\nWhat are **categories**?\n\n1) if the --is_binary flag is used, then categories=(\u0027Tumor\u0027, \u0027Normal\u0027) where \u0027Tumor\u0027 is any patch annotated as \u0027Tumor\u0027 and \u0027Normal\u0027 is any patch with annotated as \u0027Other\u0027, \u0027MucinousBorderlineTumor\u0027, \u0027Necrosis\u0027 or \u0027Stroma\u0027\n2) if the --is_binary flag is not used, then categories=subtype (i.e. CC, EC, MC, LGSC, HGSC)\n\n**balance_patches**:\n\nThe --balance_patches flag gives the following options (illustrated):\n\n1) `--balance_patches overall` sets every cell to the min cell.\nDEAFULT_SEEDll to the min cell in each group.\n\n|| Patch Counts || MMRD || P53ABN || P53WT || POLE || Total ||\n| Patch in Group 1 | 1791 | 1791 | 1791 | 1791 | 7164 |\n| Patch in Group 2 | 15261 | 15261 | 15261 | 15261 | 61044 |\n| Patch in Group 3 | 7881 | 7881 | 7881 | 7881 | 31524 |\n| Total | 24933 | 24933 | 24933 | 24933 | 99732 |\nset_random_seedup 1 | 30000 | 30000 | 30000 | 1791 | 91791 |\n| Patch in Group 2 | 15261 | 30000 | 30000 | 17248 | 92509 |\n| Patch in Group 3 | 19431 | 30000 | 30000 | 7881 | 87312 |\n| Total | 64692 | 90000 | 90000 | 26920 | 271612 |\n\n3) `--balance_patches group=cap_amt` caps the number of patches in every group by cap_amt.\n`--balance_patches group=135000`\n\n|| Patch Counts || MMRD || P53ABN || P53WT || POLE || Total ||\n| Patch in Group 1 | 38659 | 43918 | 50632 | 1791 | 135000 |\n| Patch in Group 2 | 15261 | 67512 | 34979 | 17248 | 135000 |\n| Patch in Group 3 | 19431 | 51330 | 53700 | 7881 | 132342 |\n| Total | 73351 | 162760 | 139311 | 26920 | 402342 |\n\n3) `--balance_patches category=cap_amt` caps the number of patches in every category by cap_amt.\n`--balance_patches category=100000`\n\n|| Patch Counts || MMRD || P53ABN || P53WT || POLE || Total ||\n| Patch in Group 1 | 38659 | 33333 | 33333 | 1791 | 107116 |\n| Patch in Group 2 | 15261 | 33333 | 33333 | 17248 | 99175 |\n| Patch in Group 3 | 19431 | 33333 | 33333 | 7881 | 93978 |\n| Total | 73351 | 99999 | 99999 | 26920 | 300269 |\n\n**max_patient_patches**:\n\nIf --max_patient_patches is set, then we will select max_patient_patches from each patient case, so if max_patient_patches=60 then we will select at most 60 patches across all slides belonging to the patient.\n\n (1) this will select patches uniformly across all slides belonging to the patient. For example if there are 3 slides, then we will sample 20 patches from each slide unless a few slides have \u0026lt;20 patches in which case we select \u0026gt;20 patches from the slides with enough patches until we have 60 in total.\n (2) this will select patches uniformly across all categories belonging to the patient. For example if categories=(\u0027Tumor\u0027, \u0027Normal\u0027) then we will select 30 patches each category unless one category has \u0026lt;30 slides in which case we select we select \u0026gt;30 patches in the other category until we have 60 in total.\n (3) --max_patient_patches is applied before --balance_patches if both flags are set\n\npositional arguments:\n {from-experiment-manifest,from-arguments}\n Choose whether to use arguments from experiment manifest or from commandline\n from-experiment-manifest\n Use experiment manifest\n\n from-arguments Use arguments\n\noptional arguments:\n -h, --help show this help message and exit\n\nusage: app.py from-experiment-manifest [-h] [--component_id COMPONENT_ID]\n experiment_manifest_location\n\npositional arguments:\n experiment_manifest_location\n\noptional arguments:\n -h, --help show this help message and exit\n\n --component_id COMPONENT_ID\n\nusage: app.py from-arguments [-h] [--seed SEED] [--n_groups N_GROUPS]\n [--subtypes SUBTYPES [SUBTYPES ...]]\n [--is_binary] [--is_multiscale]\n [--balance_patches BALANCE_PATCHES]\n [--patch_pattern PATCH_PATTERN]\n [--filter_labels FILTER_LABELS [FILTER_LABELS ...]]\n --out_location OUT_LOCATION\n [--min_patches MIN_PATCHES]\n [--max_patches MAX_PATCHES]\n [--max_patient_patches MAX_PATIENT_PATCHES]\n {use-extracted-patches,use-hd5} ...\n\npositional arguments:\n {use-extracted-patches,use-hd5}\n Specify how to load patches.\n There are 2 ways of loading patches: by use_extracted_patches and by use_hd5.\n use-extracted-patches\n Use extracted and saved patches\n\n use-hd5 Use hd5 files\n\noptional arguments:\n -h, --help show this help message and exit\n\n --seed SEED Seed for random shuffle.\n (default: 256)\n\n --n_groups N_GROUPS The number of groups in groups file.\n (default: 3)\n\n --subtypes SUBTYPES [SUBTYPES ...]\n Space separated words describing subtype=groupping pairs for this study. Example: if doing one-vs-rest on the subtypes MMRD vs P53ABN, P53WT and POLE then the input should be \u0027MMRD=0 P53ABN=1 P53WT=1 POLE=1\u0027\n (default: {\u0027MMRD\u0027: 0, \u0027P53ABN\u0027: 1, \u0027P53WT\u0027: 2, \u0027POLE\u0027: 3})\n\n --is_binary Whether we want to categorize patches by the Tumor/Normal category (true) or by the subtype category (false).\n (default: False)\n\n --is_multiscale Whether patches have multiple scales i.e. different magnifications. Not currently used.\n (default: False)\n\n --balance_patches BALANCE_PATCHES\n Optional method to balance patches. Can choose (1) (\u0027group\u0027, \u0027overall\u0027, \u0027category\u0027) or (2) one of (\u0027group=cap_amt\u0027, \u0027overall=cap_amt\u0027, \u0027category=cap_amt\u0027).In the case (1), we will balance out the patches in every group, category, or overall (see description for more details). In case (2), we will cap the number of patches in every group, category, or overall to the number cap_amt.\n (default: None)\n\n --patch_pattern PATCH_PATTERN\n \u0027/\u0027 separated words describing the directory structure of the patch paths. The words are (\u0027annotation\u0027, \u0027subtype\u0027, \u0027slide\u0027, \u0027patch_size\u0027, \u0027magnification\u0027). A non-multiscale patch can be contained in a directory /path/to/patch/rootdir/Tumor/MMRD/VOA-1234/1_2.png so its patch_pattern is annotation/subtype/slide. A multiscale patch can be contained in a directory /path/to/patch/rootdir/Stroma/P53ABN/VOA-1234/10/3_400.png so its patch_pattern is annotation/subtype/slide/magnification\n (default: annotation/subtype/slide)\n\n --filter_labels FILTER_LABELS [FILTER_LABELS ...]\n Space separated words describing label=value pairs to filter patch by label value. For example, if a dataset contains patche paths like path/to/patch/rootdir/Tumor/MMRD/VOA-1234/256/20/1_2.png and we want to select Tumor patches of pixel size 256 * 256 and 20x magnification then the patch_pattern is annotation/subtype/slide/patch_size/magnification and the select_labels is \u0027annotation=Tumor patch_size=256 magnification=20\u0027\n (default: {})\n\n --out_location OUT_LOCATION\n full path of the groups file (i.e. /path/to/patient_groups.json). An example is \u0027/projects/ovcare/classification/cchen/ml/data/local_ec_100/patient_groups.json\u0027\n (default: None)\n\n --min_patches MIN_PATCHES\n Only include from slides that have at least min_patches number of patches\n (default: None)\n\n --max_patches MAX_PATCHES\n Only include from slides that have at most max_patches number of patches\n (default: None)\n\n --max_patient_patches MAX_PATIENT_PATCHES\n Select at most max_patient_patches number of patches from each patient.\n (default: None)\n\nusage: app.py from-arguments use-extracted-patches [-h] --patch_location\n PATCH_LOCATION\n {use-manifest,use-origin}\n ...\n\npositional arguments:\n {use-manifest,use-origin}\n Specify how to define patient ID and slide ID:\n 1. use-manifest 2. origin\n use-manifest Use manifest file to locate slides.\n a CSV file with minimum of 4 column and maximum of 6 columns. The name of columns\n should be among [\u0027origin\u0027, \u0027patient_id\u0027, \u0027slide_id\u0027, \u0027slide_path\u0027, \u0027annotation_path\u0027, \u0027subtype\u0027].\n origin, slide_id, patient_id must be one of the columns.\n\n use-origin Use origin for detecting patient ID and slide ID.\n NOTE: It only works for German, OVCARE, and TCGA.\n\noptional arguments:\n -h, --help show this help message and exit\n\n --patch_location PATCH_LOCATION\n root directory of all patches of a study. The patch directory structure is \u0027/patch_location/patch_pattern/x_y.png\u0027. See --patch_pattern below. An example is \u0027/projects/ovcare/classification/cchen/ml/data/local_ec_100/patches_256_sorted\u0027\n (default: None)\n\nusage: app.py from-arguments use-extracted-patches use-manifest\n [-h] --manifest_location MANIFEST_LOCATION\n\noptional arguments:\n -h, --help show this help message and exit\n\n --manifest_location MANIFEST_LOCATION\n Path to manifest CSV file.\n (default: None)\n\nusage: app.py from-arguments use-extracted-patches use-origin\n [-h] [--dataset_origin DATASET_ORIGIN [DATASET_ORIGIN ...]]\n\noptional arguments:\n -h, --help show this help message and exit\n\n --dataset_origin DATASET_ORIGIN [DATASET_ORIGIN ...]\n List of the origins of the slide dataset the patches are generated from. Should be from (\u0027ovcare\u0027, \u0027tcga\u0027, \u0027german\u0027, \u0027other\u0027). (For multiple origins, works for TCGA+ovcare. Mix of Other origins must be tested.)\n (default: [\u0027ovcare\u0027])\n\nusage: app.py from-arguments use-hd5 [-h] --hd5_location HD5_LOCATION\n {use-manifest,use-origin} ...\n\npositional arguments:\n {use-manifest,use-origin}\n Specify how to define patient ID and slide ID:\n 1. use-manifest 2. origin\n use-manifest Use manifest file to locate slides.\n a CSV file with minimum of 4 column and maximum of 6 columns. The name of columns\n should be among [\u0027origin\u0027, \u0027patient_id\u0027, \u0027slide_id\u0027, \u0027slide_path\u0027, \u0027annotation_path\u0027, \u0027subtype\u0027].\n origin, slide_id, patient_id must be one of the columns.\n\n use-origin Use origin for detecting patient ID and slide ID.\n NOTE: It only works for German, OVCARE, and TCGA.\n\noptional arguments:\n -h, --help show this help message and exit\n\n --hd5_location HD5_LOCATION\n root directory of all hd5 of a study.\n (default: None)\n\nusage: app.py from-arguments use-hd5 use-manifest [-h] --manifest_location\n MANIFEST_LOCATION\n\noptional arguments:\n -h, --help show this help message and exit\n\n --manifest_location MANIFEST_LOCATION\n Path to manifest CSV file.\n (default: None)\n\nusage: app.py from-arguments use-hd5 use-origin [-h]\n [--dataset_origin DATASET_ORIGIN [DATASET_ORIGIN ...]]\n\noptional arguments:\n -h, --help show this help message and exit\n\n --dataset_origin DATASET_ORIGIN [DATASET_ORIGIN ...]\n List of the origins of the slide dataset the patches are generated from. Should be from (\u0027ovcare\u0027, \u0027tcga\u0027, \u0027german\u0027, \u0027other\u0027). (For multiple origins, works for TCGA+ovcare. Mix of Other origins must be tested.)\n (default: [\u0027ovcare\u0027])\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTODO: there is a chance --balance_patches sets empty groups. This happens if any patches for some (group, category) is zero.\nTODO: in create_groups, variables are named \u0027subtype\u0027 instead of \u0027category\u0027. That leads to confusion.\nTODO: further explain how --max_patient_patches works in description.\nTODO: make GroupCreator.group_summary() return DataFrame. Test against DataFrame output.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eARC at University of Leeds\u003c/h1\u003e\u003ca id=\"user-content-arc-at-university-of-leeds\" class=\"anchor\" aria-label=\"Permalink: ARC at University of Leeds\" href=\"#arc-at-university-of-leeds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container recipes\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/942\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSingularity.r_sf\u003c/em\u003e: Latest version of R built on top of Ubuntu 16.04. R \u0027sf\u0027 package plus associated helper libraries and packages.\u003c/p\u003e\n\u003cp\u003eCheck singularity.r_sf for details.\u003c/p\u003e\n\u003cp\u003eTo build: \u003ccode\u003esudo singularity build r_sf.simg Singularity.r_sf\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1698864845.0 + "updated_at": 1524235012.0 }, { "data_format": 2, - "description": "Convert a VCF into a MAF, where each variant is annotated to only one of all possible gene isoforms", + "description": null, "filenames": [ - "1.6.21/Singularity" + "Singularity.fun" ], - "full_name": "pscedu/singularity-vcf2maf", + "full_name": "m-novikov/singularity_training", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-vcf2maf/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-vcf2maf/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6c32b67e6bcfd7b78cca6dcfe21c4556b4fba959e13dd4612bb3e0b2846abfe7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d766366326d6166\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6c32b67e6bcfd7b78cca6dcfe21c4556b4fba959e13dd4612bb3e0b2846abfe7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d766366326d6166\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-vcf2maf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3ccc0089fe10f56f8768d722e148118222cba7cc032e7f2e3b6222aa6ec402ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d766366326d6166\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3ccc0089fe10f56f8768d722e148118222cba7cc032e7f2e3b6222aa6ec402ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d766366326d6166\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-vcf2maf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0068d09a09f0dde007625472c34b7e25f43da89ecd63871960f9fe0072f18edc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d766366326d6166\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0068d09a09f0dde007625472c34b7e25f43da89ecd63871960f9fe0072f18edc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d766366326d6166\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-vcf2maf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ff4771367a9654e511e5615d881b40f20b2ecf30679402c3517d2695b88380cf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d766366326d6166\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff4771367a9654e511e5615d881b40f20b2ecf30679402c3517d2695b88380cf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d766366326d6166\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-vcf2maf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-vcf2maf\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-vcf2maf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-vcf2maf\u003c/h2\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/mskcc/vcf2maf\"\u003evcf2maf\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003evcf2maf\u003c/code\u003e, \u003ccode\u003evcf2vcf\u003c/code\u003e, \u003ccode\u003emaf2maf\u003c/code\u003e and \u003ccode\u003emaf2vcf\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/vcf2maf/1.6.21\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/vcf2maf\u003c/code\u003e as \u003ccode\u003e1.6.21.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1653962577.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1551277834.0 }, { "data_format": 2, - "description": "Base container images using CentOS 7", + "description": "Singularity containers to run SU2", "filenames": [ + "Singularity.blackbird_v7.0.2", + "Singularity.fork_blackbird_v7.0.2", + "Singularity.forkv2_blackbird_v7.0.2", + "Singularity.fork_dev", + "Singularity.dev", "Singularity", - "Singularity.centos7-perl" + "Singularity.master" ], - "full_name": "ISU-HPC/centos7-base", + "full_name": "stephansmit/su2_containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-centos7-base\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#centos7-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecentos7-base\u003c/h1\u003e\n\u003cp\u003eBase container images using CentOS 7\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity containers for SU2\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers-for-su2\" class=\"anchor\" aria-label=\"Permalink: Singularity containers for SU2\" href=\"#singularity-containers-for-su2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainers to run \u003ca href=\"https://su2code.github.io/\" rel=\"nofollow\"\u003eSU2\u003c/a\u003e with \u003ca href=\"https://www.open-mpi.org/\" rel=\"nofollow\"\u003eOpen MPI\u003c/a\u003e version 1.10.2.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull a container\u003c/h2\u003e\u003ca id=\"user-content-pull-a-container\" class=\"anchor\" aria-label=\"Permalink: Pull a container\" href=\"#pull-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/su2_containers:master\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Local\u003c/h2\u003e\u003ca id=\"user-content-run-local\" class=\"anchor\" aria-label=\"Permalink: Run Local\" href=\"#run-local\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003empirun -np 6 singularity exec su2_containers_master.sif /SU2/bin/SU2_CFD SU2.cfg \u0026gt; log.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun SurfSara\u003c/h2\u003e\u003ca id=\"user-content-run-surfsara\" class=\"anchor\" aria-label=\"Permalink: Run SurfSara\" href=\"#run-surfsara\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#SBATCH -N 2\n#SBATCH -p normal\n#SBATCH -n 40\n\nmodule load mpi/openmpi/1.10.2\nmpirun --hostfile hostfile.txt -np 40 singularity exec su2_containers_master.sif /SU2/bin/SU2_CFD SU2.cfg \u0026gt; log.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3334\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1525366145.0 + "updated_at": 1593778647.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.rocm", - "Singularity.power9", - "Singularity.nvidia" + "Singularity.fortran", + "Singularity.mpi" ], - "full_name": "Delaunay/training-container", + "full_name": "thomas-robinson/hello_world", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehello_world\u003c/h1\u003e\u003ca id=\"user-content-hello_world\" class=\"anchor\" aria-label=\"Permalink: hello_world\" href=\"#hello_world\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity.fortran\u003c/h2\u003e\u003ca id=\"user-content-singularityfortran\" class=\"anchor\" aria-label=\"Permalink: Singularity.fortran\" href=\"#singularityfortran\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the singularity Fortran container, you can use a \u003ccode\u003esingularity build\u003c/code\u003e command. This example uses \u003cstrong\u003efakeroot\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build -f fortran.sif Singularity.fortran\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then test the functionality of the container with different commands\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run fortran.sif\n./fortran.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e fortran.sif hello.x\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITYENV_HELLO=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eyo wassup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsingularity run fortran.sif\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITYENV_HELLO=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHola mundo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n./fortran.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eif using csh\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run fortran.sif\n./fortran.sif\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e fortran.sif hello.x\nsetenv SINGULARITYENV_HELLO \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eyo wassup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsingularity run fortran.sif\nsetenv SINGULARITYENV_HELLO \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHola mundo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n./fortran.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity.mpi\u003c/h2\u003e\u003ca id=\"user-content-singularitympi\" class=\"anchor\" aria-label=\"Permalink: Singularity.mpi\" href=\"#singularitympi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the singularity MPI continer, you follow pretty much the same procedure\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build -f mpi.sif Singularity.mpi\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis container uses mpich, so you should use a compatible version of MPI (mvapich, impi, etc).\nDo not use openmpi.\u003c/p\u003e\n\u003cp\u003eYou can run the mpi.sif by using the appropriate MPI running command for your system on singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -n 10 singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e mpi.sif mpi_hello.x\nmpirun -n 10 singularity run mpi.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing slurm requires an extra argument to srun\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esrun --mpi=pmi2 -n 10 singularity run mpi.sif\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1559741876.0 + "updated_at": 1617038655.0 }, { "data_format": 2, - "description": "Collection of singularity recipes", + "description": null, "filenames": [ - "clumpak/Singularity.clumpak_v1.1", - "raisd/Singularity.raisd_v2.9", - "ngsRelate/Singularity.ngsRelate_v2.0", - "selscan/Singularity.selscan_v1.3.0", - "genespace/Singularity.genespace_v1.2.3", - "gushr/Singularity.gushr_v1.0.0", - "ohana/Singularity.ohana_vlatest", - "circos/Singularity.circos_v0.69-9", - "xpclr/Singularity.xpclr_v1.2.1", - "PCAngsd/Singularity.PCAngsd_v0.99", - "PCAngsd/Singularity.PCAngsd_vlatest", - "ngsLD/Singularity.ngsLD_v1.1.1", - "angsd/Singularity.angsd_v0.938", - "angsd/Singularity.angsd_v0.933", - "braker/Singularity.braker_v2.6.1", - "lassip/Singularity.lassip_v1.1.1", - "interproscan/Singularity.interproscan_v5.61-93.0" + "Singularity.1.0.0" ], - "full_name": "James-S-Santangelo/singularity-recipes", + "full_name": "pndni/freesurfer-5.3.0-container", "latest_release": null, - "readme": "\u003cp\u003eThis repository contains Singularity recipes for genomics tools that I have not found available through other means (e.g., Conda, Docker).\u003c/p\u003e\n\u003cp\u003eSingularity images are available on \u003ca href=\"https://cloud.sylabs.io/library/james-s-santangelo\" rel=\"nofollow\"\u003eSylab\u0027s Cloud Library\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1651280675.0 + "updated_at": 1558118428.0 }, { "data_format": 2, - "description": "Analysis code supporting the publication \"Trim66\u2019s paternal deficiency causes intrauterine overgrowth\"", + "description": "A place to keep my Singularity recipes", "filenames": [ - "snakemake-rna-seq/singularity/Singularity.qc", - "snakemake-rna-seq/singularity/Singularity.deeptools", - "snakemake-rna-seq/singularity/Singularity.samtools", - "snakemake-rna-seq/singularity/Singularity.R", - "snakemake-rna-seq/singularity/Singularity.picard", - "snakemake-rna-seq/singularity/Singularity.bedtools", - "snakemake-rna-seq/singularity/Singularity.SalmonTE", - "snakemake-rna-seq/singularity/Singularity.ncbi", - "snakemake-rna-seq/singularity/Singularity.Trimmomatic", - "snakemake-rna-seq/singularity/Singularity.alignment" - ], - "full_name": "boulardlab/trim66-testis", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-trim66s-paternal-deficiency-causes-intrauterine-overgrowth\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#trim66s-paternal-deficiency-causes-intrauterine-overgrowth\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrim66\u2019s paternal deficiency causes intrauterine overgrowth\u003c/h1\u003e\n\u003cp\u003eThis repository holds the analysis code for the paper \"Trim66\u2019s paternal deficiency causes intrauterine overgrowth\".\u003c/p\u003e\n\u003cp\u003eThe content is organized as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/\u003c/code\u003e holds scripts to reproduce most of the figures in the paper.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003enotebooks/\u003c/code\u003e directory holds both the rmarkdown and compiled version of a notebook comparing the RNA-seq datasets.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esnakemake-chip-seq/\u003c/code\u003e holds a Snakemake pipeline used to process the ChIP-seq data presented in the paper.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esnakemake-rna-seq/\u003c/code\u003e holds a Snakemake pipeline used to process the RNA-seq data presetend in the paper.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-the-snakemake-pipelines\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#running-the-snakemake-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Snakemake pipelines\u003c/h1\u003e\n\u003cp\u003eTo run the pipelines users need to \u003ca href=\"https://snakemake.readthedocs.io/en/v7.28.3/getting_started/installation.html#installation-via-conda-mamba\" rel=\"nofollow\"\u003einstall Snakemake as per the official docs\u003c/a\u003e. The pipelines were developed and run using Snakemake 7.28.3. Later version should also work.\u003c/p\u003e\n\u003cp\u003eUsers will need to download our raw data from the ENA archive following the link given in the pubblication.\u003c/p\u003e\n\u003cp\u003eWe strongly encourage users to familiarize themselves with Snakemake execution profiles, the Portable Encapsulted Projects standard and Singularity before running the pipelines.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-reproducing-figures\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#reproducing-figures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReproducing figures\u003c/h1\u003e\n\u003cp\u003eOnce the pipelines executed correctly, users can build the notebook in \u003ccode\u003enotebooks/compare_TRIM66_spermatid_libs.Rmd\u003c/code\u003e to generate Figure 4b and 4c, among the others.\u003c/p\u003e\n\u003cp\u003eIn addition, users can run the scripts in the \u003ccode\u003efigures\u003c/code\u003e directory to reproduce other figures:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/heatmap_markers.R\u003c/code\u003e produces figure S5;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/plot_sashimi.R\u003c/code\u003e produces figure 2;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/single_copy_genes_v2.R\u003c/code\u003e: produces figure S6, among other figures;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/te_v1.R\u003c/code\u003e produces figure 4a;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/scrna-seq-testis\u003c/code\u003e contains scripts to download and prepare the data for a basic Seurat analysis to generate figure 1B and S1b;\u003c/li\u003e\n\u003cli\u003eFigures 4d and 4e are generated from the Snakemake pipeline in \u003ccode\u003esnakemake-chip-seq\u003c/code\u003e;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efigures/dotplot-npeaks.R\u003c/code\u003e generates figure 4f.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-reporting-issues\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#reporting-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReporting issues\u003c/h1\u003e\n\u003cp\u003eIf users encouter problems, please use the Issue tracker to open issues, we will get back to you.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1707815581.0 - }, - { - "data_format": 2, - "description": "This is a pipeline to run basic RNA-seq analysis for single-end data.", - "filenames": [ - "envs/Singularity.deseq2_QC", - "envs/Singularity.fastqscreen", - "envs/Singularity.glimma_env", - "envs/Singularity.omic_qc_wf", - "envs/Singularity.fastqc", - "envs/Singularity.runGO", - "envs/Singularity.rseqc", - "envs/Singularity.deseq2", - "envs/Singularity.trim", - "envs/Singularity.permutation" + "Singularity.paralogfinder", + "Singularity.tetrad", + "Singularity.gatk", + "Singularity.plink", + "Singularity.ipyrad", + "Singularity.secapr", + "Singularity.yamp", + "Singularity.paup", + "Singularity.kat", + "Singularity.sra", + "Singularity.yangsmith", + "Singularity.snapper", + "Singularity.chloe", + "Singularity.clumpak", + "Singularity.hyphy", + "Singularity.hybpiper", + "Singularity.structure", + "Singularity.paragone_conda", + "Singularity.phylo", + "Singularity.gapfiller", + "Singularity.unicycler", + "Singularity.faststructure", + "Singularity.igv", + "Singularity.getorganelle", + "Singularity.hybphaser", + "Singularity.trinity", + "Singularity.samtools", + "Singularity.paragone", + "Singularity.qiime", + "Singularity.stacks", + "Singularity.quast" ], - "full_name": "ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE", + "full_name": "bmichanderson/singularity-containers", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/892af8861c721181ec79bb73e9fdeacca71296f1df88960ad9c1fc2ab1ab3067/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135352e322e312d627269676874677265656e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/892af8861c721181ec79bb73e9fdeacca71296f1df88960ad9c1fc2ab1ab3067/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135352e322e312d627269676874677265656e2e737667\" alt=\"Snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%E2%89%A55.2.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://travis-ci.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a49d71aead60190e71bf72c6971f4e53d9cb58b28bae978ab977e30e8912e1c4/68747470733a2f2f7472617669732d63692e636f6d2f6f6873752d63656461722d636f6d702d6875622f42756c6b2d524e412d7365712d706970656c696e652d53452e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bulk-rna-seq-pipeline-se\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#bulk-rna-seq-pipeline-se\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBulk-RNA-seq-pipeline-SE\u003c/h1\u003e\n\u003cp\u003ePipeline to run basic RNA-seq analysis on single-end data.\u003c/p\u003e\n\u003cp\u003eThis is a package of Python and R scripts that enable reading, processing and analysis of Omics\u0027 datasets.\nThis package implements the Snakemake management workflow system and is currently implemented to work with\nthe cluster management and job scheduling system SLURM. This snakemake workflow utilizes conda installations to download and use packages for further analysis, so please ensure that you have installed miniconda prior to use.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-questionsissues\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#questionsissues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions/issues\u003c/h1\u003e\n\u003cp\u003ePlease add an issue to the Omics-QC-pipeline repository. We would appreciate if your issue included sample code/files\n(as appropriate) so that we can reproduce your bug/issue.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eWe welcome contributors! For your pull requests, please include the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSample code/file that reproducibly causes the bug/issue\u003c/li\u003e\n\u003cli\u003eDocumented code providing fix\u003c/li\u003e\n\u003cli\u003eUnit tests evaluating added/modified methods.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-use\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse\u003c/h1\u003e\n\u003cp\u003eLocate raw files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAfter sequencing, your raw fastq files are placed in \u003ccode\u003e/path/to/sequencing/files\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd /path/to/raw/data\n$ ls -alh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck md5sum.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ md5sum \u2013c md5sum.txt \u0026gt; md5sum_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMove your files into the archive to be stored.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mv /path/to/raw/data /path/to/archive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck md5sum again to ensure your sequencing files are not corrupted.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ md5sum \u2013c md5sum.txt \u0026gt; md5sum_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eClone this Pipeline into your working directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCreate a \u003ccode\u003esamples/raw\u003c/code\u003e directory, and a \u003ccode\u003elogs\u003c/code\u003e directory in your \u003ccode\u003ewdir()\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir logs\n$ mkdir samples\n$ cd samples\n$ mkdir raw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSymbollically link the fastq files of your samples to the \u003ccode\u003ewdir/samples/raw\u003c/code\u003e directory using a bash script loop in your terminal.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003els -1 /path/to/data/LIB*gz | while read gz; do\n R=$( basename $gz | cut -d \u0027_\u0027 -f 3 | awk \u0027{print $1\".fastq.gz\"}\u0027 )\n echo $R\n ln -s ${gz} ./${R}\ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUpload your metadata file to the \u003ccode\u003edata\u003c/code\u003e directory, with the correct formatting:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eColumns should read:\n\u003ccode\u003eStudyID Column2 Column3 ...\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEach row should be a sample, with subsequent desired information provided (RNA extraction date, etc.)\u003c/li\u003e\n\u003cli\u003eEdit omic_config.yaml to include only columns included in this metadata file:\n\u003cul\u003e\n\u003cli\u003eThis includes \u003ccode\u003emeta_columns_to_plot\u003c/code\u003e and \u003ccode\u003epca labels\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAll values in this file should be tab-separated\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEdit the \u003ccode\u003eomic_config.yaml\u003c/code\u003e in your \u003ccode\u003ewdir()\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eChange the \u003ccode\u003eproject_id\u003c/code\u003e to a unique project identifier\u003c/li\u003e\n\u003cli\u003eAdd appropriate contrasts based on your samples under the \u003ccode\u003e[diffexp][contrasts]\u003c/code\u003e section\u003c/li\u003e\n\u003cli\u003eAdd the path to your metadata file for the \u003ccode\u003eomic_meta_data\u003c/code\u003e and \u003ccode\u003esamples\u003c/code\u003e parameters\u003c/li\u003e\n\u003cli\u003eChange \u003ccode\u003ebase_dir\u003c/code\u003e to your current working directory\u003c/li\u003e\n\u003cli\u003eEnsure you have the correct \u003ccode\u003eassembly\u003c/code\u003e specified\n\u003cul\u003e\n\u003cli\u003eCurrent options for this are: hg19, hg38.89 (ensembl v89) and hg38.90 (ensembl v90)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDo a dry-run of snakemake to ensure proper execution before submitting it to the cluster (in your wdir).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ snakemake -np --verbose\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce your files are symbolically linked, you can submit the job to exacloud via your terminal window.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sbatch submit_snakemake.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo see how the job is running, look at your queue.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ squeue -u your_username\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-directed-acyclic-graph-dag-of-an-example-workflow-including-two-samples\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#directed-acyclic-graph-dag-of-an-example-workflow-including-two-samples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph (DAG) of an example workflow including two samples\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE/blob/master/data/dag.png\"\u003e\u003cimg src=\"https://github.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE/raw/master/data/dag.png\" alt=\"Example Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-containers\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: singularity-containers\" href=\"#singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA place to keep my Singularity recipes.\nThis repository contains recipes in the format \"Singularity.[program]\" and is linked to Singularity Hub so that all commits trigger builds there. Since Singularity Hub is no longer automatically building, new commits are no longer built.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1582765139.0 + "updated_at": 1727856342.0 }, { "data_format": 2, - "description": "Fork of CoqGym to implement a simple ablation study for EECS595.", + "description": null, "filenames": [ - "Singularity" + "Singularity-old", + "Singularity.cuda9.0-tf1.13", + "Singularity.v8", + "Singularity.cuda10.0-tf2.0", + "Singularity.cuda9.0-tf1.14", + "Singularity.cuda-10.0", + "Singularity.cuda9.0-tf1.13-without_ofed", + "Singularity.cuda10.0-tf1.13-v2", + "Singularity.v7", + "Singularity.cuda9.0-tf1.13-v2", + "Singularity.cuda-9.0", + "Singularity.test-cuda9.0", + "Singularity.cuda9.0-tf1.13-fixed_ofed", + "Singularity.cuda9.0-tf1.13-with_ucx", + "Singularity", + "Singularity.cuda9.0-tf1.13-v3" ], - "full_name": "M-XI/CoqGym-EECS595", + "full_name": "BensonYang1999/tensorflow-gpu", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCoqGym\u003c/h1\u003e\u003ca id=\"user-content-coqgym\" class=\"anchor\" aria-label=\"Permalink: CoqGym\" href=\"#coqgym\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNOTE\u003c/h2\u003e\u003ca id=\"user-content-note\" class=\"anchor\" aria-label=\"Permalink: NOTE\" href=\"#note\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a fork of the original CoqGym repository, made in order to implement a simple ablation study of the ASTactic model below.\nThe following sections consist of the original README.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSTART OF ORIGINAL README\u003c/h2\u003e\u003ca id=\"user-content-start-of-original-readme\" class=\"anchor\" aria-label=\"Permalink: START OF ORIGINAL README\" href=\"#start-of-original-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/example_proof.jpg\"\u003e\u003cimg src=\"images/example_proof.jpg\" alt=\"Example proof\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCode for the paper:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://arxiv.org/abs/1905.09381\" rel=\"nofollow\"\u003eLearning to Prove Theorems via Interacting with Proof Assistants\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://www.cs.princeton.edu/~kaiyuy/\" rel=\"nofollow\"\u003eKaiyu Yang\u003c/a\u003e and \u003ca href=\"https://www.cs.princeton.edu/~jiadeng/\" rel=\"nofollow\"\u003eJia Deng\u003c/a\u003e\u003cbr\u003e\nInternational Conference on Machine Learning (ICML) 2019\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@inproceedings\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003eyang2019coqgym\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eLearning to Prove Theorems via Interacting with Proof Assistants\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eYang, Kaiyu and Deng, Jia\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebooktitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eInternational Conference on Machine Learning (ICML)\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2019\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor potential bugs, please open an issue. For any other questions, please ask in \u003ca href=\"https://github.com/princeton-vl/CoqGym/discussions\"\u003eDiscussions\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"#1-installing-coqgym\"\u003eInstalling CoqGym\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 1.1 \u003ca href=\"#11-dependencies\"\u003eDependencies\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 1.2 \u003ca href=\"#12-building-coq-serapi-coqhammer-and-the-coq-projects\"\u003eBuilding Coq, SerAPI, CoqHammer, and the Coq Projects\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 1.3 \u003ca href=\"#13-extracting-the-proofs-from-coq-code-optional\"\u003eExtracting the Proofs from Coq Code\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 1.4 \u003ca href=\"#14-downloading-the-pre-extracted-proofs-recommended\"\u003eDownloading the Pre-extracted Proofs\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#2-using-coqgym-in-a-container\"\u003eUsing CoqGym in a Container\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 2.1 \u003ca href=\"#21-dependencies\"\u003eDependencies\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 2.2 \u003ca href=\"#22-downloading-the-pre-built-container-image\"\u003eDownloading the Pre-built Container Image\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 2.3 \u003ca href=\"#23-using-the-container\"\u003eUsing the Container\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 2.4 \u003ca href=\"#24-building-the-container-by-yourself\"\u003eBuilding the Container by Yourself\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#3-data-format\"\u003eData Format\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 3.1 \u003ca href=\"#31-json-files\"\u003eJSON Files\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 3.2 \u003ca href=\"#32-lmdb-file\"\u003eLMDB File\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 3.3 \u003ca href=\"#33-glossary\"\u003eGloassary\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#4-data-utilities\"\u003eData Utilities\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 4.1 \u003ca href=\"#41-interacting-with-coqgym\"\u003eInteracting with CoqGym\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 4.2 \u003ca href=\"#42-parsing-coq-terms\"\u003eParsing Coq Terms\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 4.3 \u003ca href=\"#43-computing-dataset-statistics\"\u003eComputing Dataset Statistics\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#5-the-astactic-model\"\u003eThe ASTactic Model\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 5.1 \u003ca href=\"#51-prerequisites\"\u003ePrerequisites\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 5.2 \u003ca href=\"#52-extracting-proof-steps\"\u003eExtracting Proof Steps\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 5.3 \u003ca href=\"#53-training\"\u003eTraining\u003c/a\u003e\u003cbr\u003e\n\u00a0 \u00a0 5.4 \u003ca href=\"#54-testing\"\u003eTesting\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#6-credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#7-contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Installing CoqGym\u003c/h2\u003e\u003ca id=\"user-content-1-installing-coqgym\" class=\"anchor\" aria-label=\"Permalink: 1. Installing CoqGym\" href=\"#1-installing-coqgym\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow these steps to obtain the CoqGym dataset and build the environment for interacting with it.\nAlternatively, you may also use CoqGym in a \u003ca href=\"#2-using-coqgym-in-a-container\"\u003econtainer\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.1 Dependencies\u003c/h3\u003e\u003ca id=\"user-content-11-dependencies\" class=\"anchor\" aria-label=\"Permalink: 1.1 Dependencies\" href=\"#11-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://opam.ocaml.org/\" rel=\"nofollow\"\u003eOPAM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.anaconda.com/distribution/\" rel=\"nofollow\"\u003eAnaconda Python 3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://symas.com/lmdb/\" rel=\"nofollow\"\u003eLMDB\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ruby-lang.org/en/\" rel=\"nofollow\"\u003eRuby\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.2 Building Coq, SerAPI, CoqHammer, and the Coq Projects\u003c/h3\u003e\u003ca id=\"user-content-12-building-coq-serapi-coqhammer-and-the-coq-projects\" class=\"anchor\" aria-label=\"Permalink: 1.2 Building Coq, SerAPI, CoqHammer, and the Coq Projects\" href=\"#12-building-coq-serapi-coqhammer-and-the-coq-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate an OPAM switch for OCaml 4.07.1+flambda: \u003ccode\u003eopam switch create 4.07.1+flambda \u0026amp;\u0026amp; eval $(opam env)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUpgrade the installed OPAM packages (optional): \u003ccode\u003eopam upgrade \u0026amp;\u0026amp; eval $(opam env)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eClone the repository: \u003ccode\u003egit clone https://github.com/princeton-vl/CoqGym\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall Coq, SerAPI and CoqHammer: \u003ccode\u003ecd CoqGym \u0026amp;\u0026amp; source install.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuild the Coq projects (can take a while): \u003ccode\u003ecd coq_projects \u0026amp;\u0026amp; make \u0026amp;\u0026amp; cd ..\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate and activate the conda environment: \u003ccode\u003econda env create -f coq_gym.yml \u0026amp;\u0026amp; conda activate coq_gym\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: \u003ca href=\"https://github.com/coq/coq\"\u003eCoq\u003c/a\u003e, \u003ca href=\"https://github.com/ejgallego/coq-serapi\"\u003eSerAPI\u003c/a\u003e, \u003ca href=\"https://github.com/lukaszcz/coqhammer\"\u003eCoqHammer\u003c/a\u003e, and the Coq projects in \u003ca href=\"./coq_projects\"\u003ecoq_projects\u003c/a\u003e directory are indendent software projects with their own code repositories, but please follow the instructions above to build the specific versions we need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.3 Extracting the Proofs from Coq Code (Optional)\u003c/h3\u003e\u003ca id=\"user-content-13-extracting-the-proofs-from-coq-code-optional\" class=\"anchor\" aria-label=\"Permalink: 1.3 Extracting the Proofs from Coq Code (Optional)\" href=\"#13-extracting-the-proofs-from-coq-code-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe include the code for extracting CoqGym from Coq source code. However, it is not guaranteed to reproduce exactly the same data. The timeout and other miscellaneous errors during the data extraction may be machine-dependent. For example, a faster machine is likely to have fewer timeout errors and thus can extract more proofs.\nFor benchmark purpose, please download and use our pre-extracted version of CoqGym.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCheck all Coq files and locate the proofs:\u003cbr\u003e\nFor each \u003ccode\u003e*.meta\u003c/code\u003e file in \u003ccode\u003e./coq_projects/\u003c/code\u003e, run \u003ccode\u003epython check_proofs.py --file /path/to/*.meta\u003c/code\u003e\u003cbr\u003e\nNow you have generated a \u003ccode\u003e*.json\u003c/code\u003e file in \u003ccode\u003e./data/\u003c/code\u003e corresponding to each \u003ccode\u003e*.meta\u003c/code\u003e file. The \u003ccode\u003eproofs\u003c/code\u003e field of the JSON object is a list containing the proof names.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExtract the proofs:\u003cbr\u003e\nFor each \u003ccode\u003e*.meta\u003c/code\u003e file and each proof, run:\u003cbr\u003e\n\u003ccode\u003epython extract_proof.py --file /path/to/*.meta --proof $PROOF_NAME\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003epython extract_synthetic_proofs.py --file /path/to/*.meta --proof $PROOF_NAME\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePost-processing: \u003ccode\u003epython postprocess.py\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eCaveat\u003c/em\u003e: The steps above are computationally expensive. When we say \"For each XXX, run \u003ccode\u003eYYY\u003c/code\u003e\", the tasks are embarrassingly parallel, which means you can run them in parallel in any order. We do not provide the code for that because it depends on a particular HPC infrastructure.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.4 Downloading the Pre-extracted Proofs (Recommended)\u003c/h3\u003e\u003ca id=\"user-content-14-downloading-the-pre-extracted-proofs-recommended\" class=\"anchor\" aria-label=\"Permalink: 1.4 Downloading the Pre-extracted Proofs (Recommended)\" href=\"#14-downloading-the-pre-extracted-proofs-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the CoqGym dataset \u003ca href=\"https://drive.google.com/drive/folders/149m_17VkYYkl0kdSB4AI8zodCuTmPaA6?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUnzip the data and set the paths: \u003ccode\u003epython unzip_data.py\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eCaveat\u003c/em\u003e: The second step sets the absolute paths in the data. You have to re-do it whenever the absolote path of the \u003ccode\u003edata/\u003c/code\u003e directory changes (e.g. after moving the entire repo to another directory).\u003c/p\u003e\n\u003cp\u003eNow you are ready to interact with CoqGym! Run \u003ccode\u003epython eval_env.py\u003c/code\u003e to check if it terminates normally without raising an error.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Using CoqGym in a Container\u003c/h2\u003e\u003ca id=\"user-content-2-using-coqgym-in-a-container\" class=\"anchor\" aria-label=\"Permalink: 2. Using CoqGym in a Container\" href=\"#2-using-coqgym-in-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs a less painful alternative to \u003ca href=\"#1-installing-coqgym\"\u003einstalling CoqGym\u003c/a\u003e from scratch, we provide a pre-built Singularity container (There is also a 3rd-party \u003ca href=\"https://hub.docker.com/r/innochainver/coqgym\" rel=\"nofollow\"\u003edocker image\u003c/a\u003e that may be useful).\nFeel free to skip these steps if you have finished installing CoqGym.\nCurrently we do not support GPUs for the container, therefore you have to complete the installation steps manually if you want to train models on CoqGym using GPUs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.1 Dependencies\u003c/h3\u003e\u003ca id=\"user-content-21-dependencies\" class=\"anchor\" aria-label=\"Permalink: 2.1 Dependencies\" href=\"#21-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eSingularity (a.k.a. Apptainer)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.2 Downloading the Pre-built Container Image\u003c/h3\u003e\u003ca id=\"user-content-22-downloading-the-pre-built-container-image\" class=\"anchor\" aria-label=\"Permalink: 2.2 Downloading the Pre-built Container Image\" href=\"#22-downloading-the-pre-built-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container image can be downloaded \u003ca href=\"https://drive.google.com/drive/folders/13Rwa5no6W4MwSvdjRrENAdDCQqTWhcqy?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.3 Using the Container\u003c/h3\u003e\u003ca id=\"user-content-23-using-the-container\" class=\"anchor\" aria-label=\"Permalink: 2.3 Using the Container\" href=\"#23-using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eStart a shell session inside the container: \u003ccode\u003esingularity shell coq_gym.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003esource /.bashrc \u0026amp;\u0026amp; cd /CoqGym \u0026amp;\u0026amp; eval $(opam env) \u0026amp;\u0026amp; conda activate coq_gym\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou are now ready to use CoqGym! Try \u003ccode\u003epython eval_env.py\u003c/code\u003e to see if it terminates normally without raising an error.\u003cbr\u003e\nFor further instructions about how to use a Singularity container, please consult the documentation of Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.4 Building the Container by Yourself\u003c/h3\u003e\u003ca id=\"user-content-24-building-the-container-by-yourself\" class=\"anchor\" aria-label=\"Permalink: 2.4 Building the Container by Yourself\" href=\"#24-building-the-container-by-yourself\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide a \u003ca href=\"./Singularity\"\u003eSingularity recipe\u003c/a\u003e from which you can build the container by yourself.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eYou need to be on a Linux machine of which you have sudo privileges.\u003c/li\u003e\n\u003cli\u003eDownload the dataset \u003ca href=\"https://drive.google.com/drive/folders/149m_17VkYYkl0kdSB4AI8zodCuTmPaA6?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and put the files in your \u003ccode\u003eCoqGym/\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003esudo singularity build coq_gym.simg Singularity\u003c/code\u003e to build the container image \u003ccode\u003ecoq_gym.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eCaveat\u003c/em\u003e: If you run out of disk space when building the container, it may because your \u003ccode\u003e/tmp\u003c/code\u003e directory is not large enough. See \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/build_env.html#temporary-folders\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/build_env.html#temporary-folders\u003c/a\u003e for a workaround.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Data Format\u003c/h2\u003e\u003ca id=\"user-content-3-data-format\" class=\"anchor\" aria-label=\"Permalink: 3. Data Format\" href=\"#3-data-format\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe dataset contains three parts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003ccode\u003edata\u003c/code\u003e directory: \u003ccode\u003e*.json\u003c/code\u003e files corresponding to the \u003ccode\u003e*.v\u003c/code\u003e files in Coq source code, whose format is explained below. The \u003ccode\u003e*.json\u003c/code\u003e files contain all important information about the proofs: environment, local context, goals, tactics, proof trees, etc.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003ccode\u003esexp_cache\u003c/code\u003e directory: A LMDB file that serves as an index for the S-expressions in \u003ccode\u003e*.json\u003c/code\u003e files. The \u003ccode\u003e*.json\u003c/code\u003e files contain keys for querying \u003ccode\u003esexp_cache\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eprojs_split.json\u003c/code\u003e: A JSON object containing the training/validation/testing split\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.1 JSON Files\u003c/h3\u003e\u003ca id=\"user-content-31-json-files\" class=\"anchor\" aria-label=\"Permalink: 3.1 JSON Files\" href=\"#31-json-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach \u003ccode\u003e*.json\u003c/code\u003e file in \u003ccode\u003edata/\u003c/code\u003e corresponds to a Coq source file \u003ccode\u003e*.v\u003c/code\u003e in \u003ccode\u003ecoq_projects/\u003c/code\u003e. For example, \u003ccode\u003edata/StructTact/Assoc.json\u003c/code\u003e corresponds to \u003ca href=\"https://github.com/princeton-vl/CoqGym/blob/master/coq_projects/StructTact/Assoc.v\"\u003ecoq_projects/StructTact/Assoc.v\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe format of the JSON files is described below.\nThe hash codes are used as keys to query the LMDB \u003ccode\u003esexp_cache\u003c/code\u003e.\nConsult the \u003ca href=\"#33-glossary\"\u003eglossary\u003c/a\u003e for the terminology.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \u0027filename\u0027: \u0027Assoc.v\u0027, # the path of the Coq source file relative to the root directory of the Coq project\n \u0027coq_project\u0027: \u0027StructTact\u0027, # the name of the Coq project\n \u0027vernac_cmds\u0027: [ # a list of Coq commands [6] in the source file\n [\u0027Cd \"$COQGYM_ROOT/coq_projects/StructTact\".\u0027, \u0027VernacChdir\u0027, \u00273701e61f37b72b3e61788fce6317466b7bb92b55\u0027], # [raw command, command type, command AST (in hash code)]\n [\u0027Arguments a_equiv {_} {_} _ _ _.\u0027, \u0027VernacArguments\u0027, \u00276777d3c472595dae20427d0892ad03d38f70fde9\u0027],\n ...\n [\u0027Arguments a_equiv {_} {_} _ _ _.\u0027, \u0027VernacArguments\u0027, \u00276777d3c472595dae20427d0892ad03d38f70fde9\u0027],\n ],\n \u0027num_extra_cmds\u0027: 107, # the code in the original Coq file starts at vernac_cmds[num_extra_cmds]\n \u0027proofs\u0027: [ # a list of human-written proofs\n ...\n ],\n \u0027synthetic_proofs\u0027: [ # a list of synthetic proofs\n ...\n ],\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe format for a proof is as follows, taking the \u003ca href=\"https://github.com/princeton-vl/CoqGym/tree/master/coq_projects/StructTact/Assoc.v#L47\"\u003e\u003ccode\u003eget_set_same\u003c/code\u003e\u003c/a\u003e in \u003ccode\u003ecoq_projects/StructTact/Assoc.v\u003c/code\u003e as an example.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \u0027name\u0027: get_set_same, # the name of the theorem\n \u0027line_nb\u0027: 118, # the theorem is defined in $file_data[\u0027vernac_cmds\u0027][$line_nb]\n \n \u0027env_delta\u0027: { # the global environment relative to the previous proof in the same file\n \u0027add\u0027 : { # entries that should be added to the environment\n \u0027constants\u0027 : [\n {\n \u0027physical_path\u0027: \u0027coq/theories/Arith/PeanoNat.vo:Nat.mul_wd\u0027, # the unique identifier\n \u0027short_ident\u0027: \u0027PeanoNat.Nat.mul_wd\u0027, # the short identifier\n \u0027qualid\u0027: \u0027Coq.Arith.PeanoNat.Nat.mul_wd\u0027, # the qualified identifier [1]\n \u0027type\u0027: \u0027@Morphisms.Proper (forall (_ : nat) (_ : nat), nat) (@Morphisms.respectful nat (forall _ : nat, nat) (@eq nat) (@Morphisms.respectful nat nat (@eq nat) (@eq nat))) Nat.mul\u0027, # the type [7] of the constant\n \u0027sort\u0027: \u0027Prop\u0027, # the sort [2] of the constant (the type of its type) [2]\n \u0027opaque\u0027: False, # whether the constant is opaque or transparent [3]\n \u0027sexp\u0027: \u0027333b2895c8e62d21856476bf89fa9681c9058bb9\u0027 # the S-expression [4] of the constant produced by SerAPI\n }, \n ...\n ],\n \u0027inductives\u0027 : [\n {\n \u0027physical_path\u0027 : \u0027coq/theories/Init/Wf.vo:Acc\u0027,\n \u0027blocks\u0027: [ # a list of blocks in a mutual inductive definition [5]. For regular inductive definitions (most cases), the list has length 1\n {\n \u0027short_ident\u0027: \u0027Acc\u0027,\n \u0027qualid\u0027: \u0027Coq.Init.Wf.Acc\u0027,\n \u0027constructors\u0027: [\n [\u0027Acc_intro\u0027, \u0027forall (A : Type) (R : forall (_ : A) (_ : A), Prop) (x : A) (_ : forall (y : A) (_ : R y x), _UNBOUND_REL_6 A R y), _UNBOUND_REL_5 A R x\u0027], # [constructor name, constructor type]\n ...\n ]\n }\n ],\n \u0027is_record\u0027: False,\n \u0027sexp\u0027: \u002731537cb98179ad7d2de0dd2cc783b4672b34b25b\u0027\n }\n ...\n \n ],\n },\n \u0027subtract\u0027 : { # entries that should be removed from the environment\n \u0027constants\u0027 : [],\n \u0027inductives\u0027 : [],\n },\n },\n \n \u0027steps\u0027: [ # a list of proof steps\n {\n \u0027command\u0027: [\u0027induction l; intros; simpl; repeat (break_match; simpl); subst; congruence.\u0027, \u0027VernacExtend\u0027, \u0027f6d2cb314d72d23562e5f2ef2657bd2589d44794\u0027], # (raw command, command type, command AST (in hash code)) the Coq command (usually a tactic but also includes other commands such as +, -, *, etc.) \n \u0027goal_ids\u0027: { # the IDs of the goals in the current proof step\n \u0027fg\u0027: [27], # focused goals \n \u0027bg\u0027: [] . # unfocused goals\n }\n }\n ...\n ], \n \n \u0027goals\u0027 { # the goals\n 27\u0027: { # $goal_id -\u0026gt; goal\n \u0027id\u0027: 27, # the goal ID\n \u0027type\u0027: \u0027forall (k : K) (v : V) (l : list (prod K V)), @eq (option V) (assoc (assoc_set l k v) k) (@Some V v)\u0027, # the type (logical statement) of the goal\n \u0027hypotheses\u0027: [ # the local context, a list of local premises\n {\u0027idents\u0027: [\u0027K\u0027, \u0027V\u0027], \u0027term\u0027: [], \u0027type\u0027: \u0027Type\u0027, \u0027sexp\u0027: \u0027cd1531c49fce6657997962b5375a3ef0a59db34a\u0027} # {\u0027idents\u0027: [a list of identifiers of the premises (usually of length one)], \u0027term\u0027: [a list of Coq terms (usually empty)], \u0027type\u0027: the type (logical statement) of the premise}\n {\u0027idents\u0027: [\u0027K_eq_dec\u0027], \u0027term\u0027: [], \u0027type\u0027: \"forall k k\u0027 : K, sumbool (@eq K k k\u0027) (not (@eq K k k\u0027))\", \u0027sexp\u0027: \u00275f5f5bcf9e10621f8c0c4642c0eba3ff36cbfff8\u0027},\n ],\n }\n }, \n \n \u0027proof_tree\u0027 : { # the proof tree\n \u0027goal_id\u0027: 27, \u0027children\u0027: []\n }, \n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.2 LMDB File\u003c/h3\u003e\u003ca id=\"user-content-32-lmdb-file\" class=\"anchor\" aria-label=\"Permalink: 3.2 LMDB File\" href=\"#32-lmdb-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esexp_cache\u003c/code\u003e is a LMDB mapping hash codes in \u003ccode\u003e*.json\u003c/code\u003e files to their corresponding S-expressions. Below is a code snippet in Python for accessing them.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSexpCache\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esexp_cache\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSexpCache\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027sexp_cache\u0027\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esexp_cache\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e\u0027333b2895c8e62d21856476bf89fa9681c9058bb9\u0027\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.3 Glossary\u003c/h3\u003e\u003ca id=\"user-content-33-glossary\" class=\"anchor\" aria-label=\"Permalink: 3.3 Glossary\" href=\"#33-glossary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#qualified-identifiers-and-simple-identifiers\" rel=\"nofollow\"\u003equalified identifier\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#sorts\" rel=\"nofollow\"\u003esort\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#sorts\" rel=\"nofollow\"\u003eopaque, transparent\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://en.wikipedia.org/wiki/S-expression\" rel=\"nofollow\"\u003eS-expression\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#mutually-defined-inductive-types\" rel=\"nofollow\"\u003einductive definition, mutual inductive definition\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#mutually-defined-inductive-types\" rel=\"nofollow\"\u003eCoq (vernac) command\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://coq.inria.fr/distrib/current/refman/language/gallina-specification-language.html#types\" rel=\"nofollow\"\u003etype\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. Data Utilities\u003c/h2\u003e\u003ca id=\"user-content-4-data-utilities\" class=\"anchor\" aria-label=\"Permalink: 4. Data Utilities\" href=\"#4-data-utilities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe include some tools for interacting with CoqGym, but they are NOT a part of the dataset.\nYou may implement your own tools for similar purposes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.1 Interacting with CoqGym\u003c/h3\u003e\u003ca id=\"user-content-41-interacting-with-coqgym\" class=\"anchor\" aria-label=\"Permalink: 4.1 Interacting with CoqGym\" href=\"#41-interacting-with-coqgym\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eeval_env.py\u003c/code\u003e enables the interaction with the proofs in CoqGym.\nSee \u003ca href=\"ASTactic/agent.py\"\u003eASTactic/agent.py\u003c/a\u003e for examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.2 Parsing Coq Terms\u003c/h3\u003e\u003ca id=\"user-content-42-parsing-coq-terms\" class=\"anchor\" aria-label=\"Permalink: 4.2 Parsing Coq Terms\" href=\"#42-parsing-coq-terms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003egallina.py\u003c/code\u003e: a parser the S-expressions of Coq terms.\nIt may be useful for learning the embeddings of the term ASTs.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eutils.py\u003c/code\u003e: functions for iterating through all proofs or Coq files in the dataset.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.3 Computing Dataset Statistics\u003c/h3\u003e\u003ca id=\"user-content-43-computing-dataset-statistics\" class=\"anchor\" aria-label=\"Permalink: 4.3 Computing Dataset Statistics\" href=\"#43-computing-dataset-statistics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003estats/count_human_proofs.py\u003c/code\u003e: count the number of human-written proofs in CoqGym.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003estats/count_synthetic_proofs.py\u003c/code\u003e: count the number of synthetic-written proofs in CoqGym.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003estats/proofs.py\u003c/code\u003e: compute some statistics of the proofs.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e5. The ASTactic Model\u003c/h2\u003e\u003ca id=\"user-content-5-the-astactic-model\" class=\"anchor\" aria-label=\"Permalink: 5. The ASTactic Model\" href=\"#5-the-astactic-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/astactic.jpg\"\u003e\u003cimg src=\"/images/astactic.jpg\" alt=\"ASTactic\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHere we describe how to train and test the ASTactic model on CoqGym.\nThe following content is NOT a part of the CoqGym dataset, and therefore you do not need it if you only want to access the data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.1 Prerequisites\u003c/h3\u003e\u003ca id=\"user-content-51-prerequisites\" class=\"anchor\" aria-label=\"Permalink: 5.1 Prerequisites\" href=\"#51-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMake sure CoqGym has been properly installed and configured. The \u003ccode\u003ecoq_gym\u003c/code\u003e conda environment is activated, the OPAM switch is on \u003ccode\u003e4.07.1+flambda\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAutomated theorem provers: \u003ca href=\"https://vprover.github.io\" rel=\"nofollow\"\u003eVampire\u003c/a\u003e, \u003ca href=\"http://cvc4.cs.stanford.edu/\" rel=\"nofollow\"\u003eCVC4\u003c/a\u003e, \u003ca href=\"http://www.eprover.org\" rel=\"nofollow\"\u003eEprover\u003c/a\u003e, and \u003ca href=\"https://github.com/Z3Prover/z3\"\u003eZ3\u003c/a\u003e. Install all of them and make sure they are accessible in PATH, otherwise you may see a performance degradation of the hammer baseline.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e: Install the correct version for your hardware in the conda environment \u003ccode\u003ecoq_gym\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eThe instructions below assume that you are in the \u003ca href=\"./ASTactic/\"\u003eASTactic\u003c/a\u003e directory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.2 Extracting Proof Steps\u003c/h3\u003e\u003ca id=\"user-content-52-extracting-proof-steps\" class=\"anchor\" aria-label=\"Permalink: 5.2 Extracting Proof Steps\" href=\"#52-extracting-proof-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe ASTactic model is trained on individual proof steps, rather than entire proofs.\nAfter obtaining the CoqGym dataset, run \u003ccode\u003epython extract_proof_steps.py\u003c/code\u003e. This can take a while, and you have the option to run it in parallel, please see the \u003ccode\u003e--filter\u003c/code\u003e option in the source code for details.\u003c/p\u003e\n\u003cp\u003eThe extracted proof steps are in \u003ccode\u003eproof_steps/\u003c/code\u003e. You can double-check the number of proof steps to make sure everything works as expected:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDirectory\u003c/th\u003e\n\u003cth\u003e# files\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eproof_steps/train\u003c/td\u003e\n\u003ctd\u003e121,644\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eproof_steps/valid\u003c/td\u003e\n\u003ctd\u003e68,180\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.3 Training\u003c/h3\u003e\u003ca id=\"user-content-53-training\" class=\"anchor\" aria-label=\"Permalink: 5.3 Training\" href=\"#53-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train on the proof steps in training + validation set: \u003ccode\u003epython main.py --no_validation --exp_id astactic\u003c/code\u003e\u003cbr\u003e\nThe \"astactic\" above is an experiment ID, and you may change it to other IDs. Model checkpoints will be saved to \u003ccode\u003eruns/astactic/checkpoints/\u003c/code\u003e. See \u003ccode\u003eoptions.py\u003c/code\u003e for command line options.\u003c/p\u003e\n\u003cp\u003eA pre-trained model can be downloaded \u003ca href=\"https://drive.google.com/drive/folders/1AzLaEpoGS3BPMUz9Bl63MHAFRqlF4CtH?usp=sharing\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.4 Testing\u003c/h3\u003e\u003ca id=\"user-content-54-testing\" class=\"anchor\" aria-label=\"Permalink: 5.4 Testing\" href=\"#54-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAssuming you want to test the model checkpoint \u003ccode\u003eruns/astactic/checkpoints/model_003.pth\u003c/code\u003e on the proof \u003ccode\u003eget_set_same\u003c/code\u003e in \u003ccode\u003e../data/StructTact/Assoc.json\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTesting ASTactic:\u003cbr\u003e\n\u003ccode\u003epython evaluate.py ours ours-TEST --path runs/astactic/checkpoints/model_003.pth --file ../data/StructTact/Assoc.json --proof \"get_set_same\"\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTesting an automated tactic X (may be \"auto\", \"trivial\", \"easy\", \"intuition\", or \"hammer\"):\u003cbr\u003e\n\u003ccode\u003epython -u evaluate.py X X-TEST --file ../data/StructTact/Assoc.json --proof \"get_set_same\"\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTesting ASTactic+X:\u003cbr\u003e\n\u003ccode\u003epython -u evaluate.py ours+X ours+X-TEST --path runs/astactic/checkpoints/model_003.pth --file ../data/StructTact/Assoc.json --proof \"get_set_same\"\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eCaveat\u003c/em\u003e: Testing is computationally expensive, but the workloads are embarrassingly parallel, which means you can run them in parallel in any order. We do not provide the code for that because it depends on a particular HPC infrastructure.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e6. Credits\u003c/h2\u003e\u003ca id=\"user-content-6-credits\" class=\"anchor\" aria-label=\"Permalink: 6. Credits\" href=\"#6-credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe code is formatted using \u003ca href=\"https://github.com/psf/black\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7d770c433d6198d89f8c1e2f187b904a9721d176259d0e97157337741cc8e837/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e737667\" alt=\"Code style: black\" data-canonical-src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThis repo includes the codebase of \u003ca href=\"https://github.com/coq/coq\"\u003eCoq\u003c/a\u003e, \u003ca href=\"https://github.com/ejgallego/coq-serapi\"\u003eSerAPI\u003c/a\u003e, \u003ca href=\"https://github.com/lukaszcz/coqhammer\"\u003eCoqHammer\u003c/a\u003e, and the Coq projects in \u003ca href=\"./coq_projects\"\u003ecoq_projects\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e7. Contributing\u003c/h2\u003e\u003ca id=\"user-content-7-contributing\" class=\"anchor\" aria-label=\"Permalink: 7. Contributing\" href=\"#7-contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe welcome and appreciate contributions from the community. For bug fixes and relatively minor changes (such as comments, typos, etc.), feel free to submit a pull request directly. For anything beyond, please first post in \u003ca href=\"https://github.com/princeton-vl/CoqGym/discussions\"\u003eDiscussions\u003c/a\u003e before implementing.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1702431652.0 + "updated_at": 1570345087.0 }, { "data_format": 2, - "description": "CondaEnv for DM analysis pipeline", + "description": "Research code from 2018 that doesn\u0027t fit in a more specific library.", "filenames": [ - "Singularity" + "Singularity.cpu", + "Singularity.gpu" ], - "full_name": "golamshaifullah/DManalysis_condaenv", + "full_name": "dmorrill10/research2018", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eresearch2018\u003c/h1\u003e\u003ca id=\"user-content-research2018\" class=\"anchor\" aria-label=\"Permalink: research2018\" href=\"#research2018\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eResearch code from 2018 that doesn\u0027t fit in a more specific library.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1568984053.0 + "updated_at": 1604944821.0 }, { "data_format": 2, - "description": "singularity image for assembly course", + "description": null, "filenames": [ - "Singularity" + "Singularity_tf1", + "Singularity", + "tf1.12/Singularity.tf1.12", + "tf1.13/Singularity.tf1.13" ], - "full_name": "MontseTor/assembly_course", + "full_name": "mani3/tensorflow-gpu-py3-singularity", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2179\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1573119853.0 + "updated_at": 1582782591.0 }, { "data_format": 2, - "description": "Grouping of my docker recipes", + "description": null, "filenames": [ - "pschive_test/Singularity", - "psrchive_py2/Singularity_old", - "psrchive_py2/Singularity", - "gitlab_runner/Singularity", - "psrchive_Allegro/Singularity", - "psrchive_py3/Singularity_psrchive_last", - "psrchive_py3/Singularity_old", - "psrchive_py3/Singularity", - "Stable_Diffusion/Singularity" + "Singularity.def" ], - "full_name": "louisbondonneau/Docker_receipts", + "full_name": "BeatriceDRM/BoinDiRaimondoMetallo_softeng2", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Containers\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: Singularity Containers\" href=\"#singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity + Docker receipts can be found on github \u003ca href=\"https://github.com/louisbondonneau/Docker_receipts\"\u003ehttps://github.com/louisbondonneau/Docker_receipts\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDepending on the installation singularity executable can be named \"singularity\" or \"apptainer\".\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun a contaner\u003c/h2\u003e\u003ca id=\"user-content-run-a-contaner\" class=\"anchor\" aria-label=\"Permalink: Run a contaner\" href=\"#run-a-contaner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eINSTALL\u003c/th\u003e\n\u003cth align=\"left\"\u003epschive_py2\u003c/th\u003e\n\u003cth align=\"left\"\u003epschive_py3\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epsrchive\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py2)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003etempo2\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003etempo1\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epresto\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (v2.2 py2)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (v4 py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edspsr\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py2)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epsrsalsa\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eSIGPROC\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eRFICLEAN\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eGPTOOL\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003ePlotX/TransientX\u003c/td\u003e\n\u003ctd align=\"left\"\u003eNOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - nenupy\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - AntPat\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - dreamBeam\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - psrqpy\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - clean.py\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py2)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eNOK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - pyqt5\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003epylib - pyglow\u003c/td\u003e\n\u003ctd align=\"left\"\u003eNOK\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOK (py3)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRUN pschive_py2 container\u003c/h3\u003e\u003ca id=\"user-content-run-pschive_py2-container\" class=\"anchor\" aria-label=\"Permalink: RUN pschive_py2 container\" href=\"#run-pschive_py2-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /databf:/databf -B /data:/data -B /cep:/cep /cep/lofar/pulsar/Singularity/pschive_py2.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRUN pschive_py3 container\u003c/h3\u003e\u003ca id=\"user-content-run-pschive_py3-container\" class=\"anchor\" aria-label=\"Permalink: RUN pschive_py3 container\" href=\"#run-pschive_py3-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /databf:/databf -B /data:/data -B /cep:/cep /cep/lofar/pulsar/Singularity/pschive_py3.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eknown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003epsrdata, hdf5... and other things in Vlad installed used by LOFAR are not installed at this time\u003c/li\u003e\n\u003cli\u003epython installation on your home or environment variables in your bashrc can affect the operation inside the container. To avoid this, add the following lines to the beginning of your ~/.bashrc ~/.bash_profile\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Check if we are inside a Singularity container\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e [ \u003cspan class=\"pl-k\"\u003e-n\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$SINGULARITY_CONTAINER\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e ]\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If we are inside a Singularity container, exit the script here\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild a container from nothing\u003c/h2\u003e\u003ca id=\"user-content-build-a-container-from-nothing\" class=\"anchor\" aria-label=\"Permalink: Build a container from nothing\" href=\"#build-a-container-from-nothing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003einstall Go and Singularity\u003c/h3\u003e\u003ca id=\"user-content-install-go-and-singularity\" class=\"anchor\" aria-label=\"Permalink: install Go and Singularity\" href=\"#install-go-and-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapt-get update\napt-get install -y build-essential libssl-dev uuid-dev libgpgme11-dev squashfs-tools libseccomp-dev wget pkg-config git cryptsetup libglib2.0-dev\nGO_VERSION=1.20.2 OS=linux ARCH=amd64\nwget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${GO_VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${GO_VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\ntar -C /usr/local -xzf /tmp/go\u003cspan class=\"pl-smi\"\u003e${GO_VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=$PATH:/usr/local/go/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\ngit clone --recurse-submodules https://github.com/sylabs/singularity.git singularity\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\n./mconfig\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e builddir\nmake\nmake install\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ebuild python2 psrchive container\u003c/h3\u003e\u003ca id=\"user-content-build-python2-psrchive-container\" class=\"anchor\" aria-label=\"Permalink: build python2 psrchive container\" href=\"#build-python2-psrchive-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/louisbondonneau/Docker_receipts\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Docker_receipts/psrchive_py2\nsingularity build /cep/lofar/pulsar/Singularity/pschive_py2.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ebuild python3 psrchive container\u003c/h3\u003e\u003ca id=\"user-content-build-python3-psrchive-container\" class=\"anchor\" aria-label=\"Permalink: build python3 psrchive container\" href=\"#build-python3-psrchive-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/louisbondonneau/Docker_receipts\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Docker_receipts/psrchive_py3\nsingularity build /cep/lofar/pulsar/Singularity/pschive_py3.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etry a writable container\u003c/h3\u003e\u003ca id=\"user-content-try-a-writable-container\" class=\"anchor\" aria-label=\"Permalink: try a writable container\" href=\"#try-a-writable-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity run --writable-tmpfs\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etry without any interference of your personal environment\u003c/h3\u003e\u003ca id=\"user-content-try-without-any-interference-of-your-personal-environment\" class=\"anchor\" aria-label=\"Permalink: try without any interference of your personal environment\" href=\"#try-without-any-interference-of-your-personal-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity run --cleanenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003euse CUDA\u003c/h3\u003e\u003ca id=\"user-content-use-cuda\" class=\"anchor\" aria-label=\"Permalink: use CUDA\" href=\"#use-cuda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eon nancep5 there is a TESLA T4 that you can use with dspsr for example\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity run --nv ***\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e nvidia-smi\nFri May 12 12:20:25 2023\n+-----------------------------------------------------------------------------+\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e-------------------------------+----------------------+----------------------+\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e GPU Name Persistence-M\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e Bus-Id Disp.A \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e Volatile Uncorr. ECC \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e Fan Temp Perf Pwr:Usage/Cap\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e Memory-Usage \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e GPU-Util Compute M. \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e MIG M. \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e===============================+======================+======================\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 0 Tesla T4 On \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 00000000:3B:00.0 Off \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 0 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e N/A 36C P8 9W / 70W \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 4MiB / 15360MiB \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e 0% Default \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e N/A \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n+-------------------------------+----------------------+----------------------+\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTODO\u003c/h2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eajouter:\nspyder\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCONTAINER TEST\u003c/h2\u003e\u003ca id=\"user-content-container-test\" class=\"anchor\" aria-label=\"Permalink: CONTAINER TEST\" href=\"#container-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etempo1\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash /cep/lofar/pulsar/ephem_scripts/par_conv_to_tempo1.sh /databf/nenufar-pulsar/ES03/ephem/B1919+21.par\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003etempo2\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /usr/local/pulsar/tempo2/example_data\ntempo2 -f example1.par example1.tim -nofit\npsrchive_info \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Tempo2::Predictor support enabled~\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003epsrchive\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -c \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eimport psrchive\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npython /cep/lofar/pulsar/NenPlot...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003epsrcat\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epsrcat -E B1919+21\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003epsredit\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epsredit -c dm ....\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003epresto\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -c \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eimport presto\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npython /usr/local/pulsar/presto/tests/test_presto_python.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003epsrsalsa\u003c/p\u003e\n\u003cblockquote\u003e\n\u003c/blockquote\u003e\n\u003cp\u003edreamBeam\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ecalibration of a NenuFAR archive\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003edspsr\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epython -c \u0027import dspsr\u0027\ndspsr -A -L 10 -E /databf/nenufar-pulsar/ES03/ephem/B2217+47.par -b 512 -O B2217+47_D20220304T1154_59642_002110_0057_BEAM0_dspsr /databf/nenufar-pulsar/DATA/B2217+47/RAW/B2217+47_D20220304T1154_59642_002110_0057_BEAM0.0000.raw\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSE4HPCproject\u003c/h1\u003e\u003ca id=\"user-content-se4hpcproject\" class=\"anchor\" aria-label=\"Permalink: SE4HPCproject\" href=\"#se4hpcproject\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOur project satisfies all the requests correctly.\u003c/p\u003e\n\u003cp\u003eFurthermore, we have been able to upload the two files .txt (respectively output.txt and error.txt) using an artifact.\nAs a result, in txtFilesAction, you can see the two .txt created by the action \"Upload the files\".\nInside txtFiles, you can see the .txt obtained by copy.\u003c/p\u003e\n\u003cp\u003eThese two folders are both present because, in this way, we were able to make a comparison and to be sure that the result was correct.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1696427759.0 + "updated_at": 1717602739.0 }, { "data_format": 2, "description": null, "filenames": [ - "misc/releases/21.12/Singularity.21.12", - "misc/releases/latest/Singularity", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/19.12/Singularity.19.12" + "HPOBench/hpobench/container/recipes/Singularity.template", + "HPOBench/hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine", + "HPOBench/hpobench/container/recipes/surrogates/Singularity.YAHPOGymBenchmark", + "HPOBench/hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", + "HPOBench/hpobench/container/recipes/mo/Singularity.AdultBenchmark", + "HPOBench/hpobench/container/recipes/mo/Singularity.CNNBenchmark", + "HPOBench/hpobench/container/recipes/od/Singularity.ODBenchmarks", + "HPOBench/hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", + "HPOBench/hpobench/container/recipes/ml/Singularity.ml_mmfb", + "HPOBench/hpobench/container/recipes/ml/Singularity.PyBNN", + "HPOBench/hpobench/container/recipes/ml/Singularity.SupportVectorMachine", + "HPOBench/hpobench/container/recipes/ml/Singularity.XGBoostBenchmark", + "HPOBench/hpobench/container/recipes/ml/Singularity.YahpoRawBenchmark", + "HPOBench/hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark", + "HPOBench/hpobench/container/recipes/nas/Singularity.TabularBenchmarks", + "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_101", + "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_201", + "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_1shot1", + "HPOBench/hpobench/container/recipes/rl/Singularity.learnaBenchmark", + "HPOBench/hpobench/container/recipes/rl/Singularity.Cartpole" ], - "full_name": "ipc2023-classical/planner3", + "full_name": "jr2021/ManyFairHPO-AIES", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSymk \u003ca href=\"https://github.com/speckdavid/symk/actions?query=workflow%3A%22Linux+build%22\"\u003e\u003cimg src=\"https://github.com/speckdavid/symk/workflows/Linux%20build/badge.svg\" alt=\"Linux build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/speckdavid/symk/actions?query=workflow%3A%22MacOS+build%22\"\u003e\u003cimg src=\"https://github.com/speckdavid/symk/workflows/MacOS%20build/badge.svg\" alt=\"MacOS build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-symk--\" class=\"anchor\" aria-label=\"Permalink: Symk \" href=\"#symk--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSymk is a state-of-the-art classical \u003cem\u003eoptimal\u003c/em\u003e and \u003cem\u003etop-k planner\u003c/em\u003e based on symbolic search.\u003c/p\u003e\n\u003cp\u003eWith Symk, it is possible to find a \u003cem\u003esingle optimal plan\u003c/em\u003e or a \u003cem\u003eset of k different best plans\u003c/em\u003e with the lowest cost for a given planning task.\nIn addition, Symk natively supports a variety of PDDL features that are rarely supported by other planners, such as conditional effects, derived predicates with axioms, and state-dependent action costs.\nSee this readme file for more information on running Symk and the various configurations.\nWe appreciate citations when SymK is used in a scientific context (see \u003ca href=\"#references\"\u003eReferences\u003c/a\u003e for more details).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#dependencies\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#compiling-the-symk-planner\"\u003eCompiling the Symk Planner\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#apptainer-image\"\u003eApptainer Image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generating-a-single-optimal-solution\"\u003eGenerating A Single Optimal Solution\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#generating-multiple-solutions\"\u003eGenerating Multiple Solutions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#top-k-configurations\"\u003eTop-k Configurations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#top-q-configurations\"\u003eTop-q Configurations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#loopless-planning\"\u003eLoopless Planning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#other-configurations\"\u003eOther Configurations\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#plan-selection-framework\"\u003ePlan Selection Framework\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#unordered-plan-selector\"\u003eUnordered Plan Selector\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#new-plan-selector\"\u003eNew Plan Selector\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pitfalls-and-troubleshooting\"\u003ePitfalls and Troubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently we only support Linux systems. The following should install all necessary dependencies.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esudo apt-get -y install cmake g++ make python3 autoconf automake\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSymk should compile on MacOS with the GNU C++ compiler and clang with the same instructions described above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompiling the Symk Planner\u003c/h3\u003e\u003ca id=\"user-content-compiling-the-symk-planner\" class=\"anchor\" aria-label=\"Permalink: Compiling the Symk Planner\" href=\"#compiling-the-symk-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./build.py \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eApptainer Image\u003c/h3\u003e\u003ca id=\"user-content-apptainer-image\" class=\"anchor\" aria-label=\"Permalink: Apptainer Image\" href=\"#apptainer-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo simplify the installation process, we alternatively provide an executable \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e container (formerly known as Singularity). It accepts the same arguments as Symk (\u003ccode\u003efast-downward.py\u003c/code\u003e script; see below).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e# \u003cspan class=\"pl-s1\"\u003eDownload the image,\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eapptainer pull symk.sif oras://ghcr.io/speckdavid/symk:latest\u003c/span\u003e\n\n# \u003cspan class=\"pl-s1\"\u003eor build it yourself.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eapptainer build symk.sif Apptainer\u003c/span\u003e\n\n# \u003cspan class=\"pl-s1\"\u003eThen run the desired configuration (for other configurations see below).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e./symk.sif domain.pddl problem.pddl --search \"sym-bd()\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenerating A Single Optimal Solution\u003c/h2\u003e\u003ca id=\"user-content-generating-a-single-optimal-solution\" class=\"anchor\" aria-label=\"Permalink: Generating A Single Optimal Solution\" href=\"#generating-a-single-optimal-solution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend to use the following configuration which uses bidirectional search.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"sym-bd()\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOther configurations are forward or backward search: \u003ccode\u003e--search \"sym-fw()\"\u003c/code\u003e or \u003ccode\u003e--search \"sym-bw()\"\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenerating Multiple Solutions\u003c/h2\u003e\u003ca id=\"user-content-generating-multiple-solutions\" class=\"anchor\" aria-label=\"Permalink: Generating Multiple Solutions\" href=\"#generating-multiple-solutions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTop-k Configurations\u003c/h3\u003e\u003ca id=\"user-content-top-k-configurations\" class=\"anchor\" aria-label=\"Permalink: Top-k Configurations\" href=\"#top-k-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend to use the following configuration which uses bidirectional search and\nreports the best \u003cstrong\u003ek\u003c/strong\u003e plans. Note that you can also specify \u003ccode\u003enum_plans=infinity\u003c/code\u003e if you want to find all possible plans.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symk-bd(plan_selection=top_k(num_plans=**k**))\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTop-q Configurations\u003c/h3\u003e\u003ca id=\"user-content-top-q-configurations\" class=\"anchor\" aria-label=\"Permalink: Top-q Configurations\" href=\"#top-q-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend to use the following configuration which uses bidirectional search and\nreports the \u003cstrong\u003ek\u003c/strong\u003e plans with quality bound \u003cstrong\u003eq\u003c/strong\u003e. Quality \u003ccode\u003e1\u0026lt;=q\u0026lt;=infinity\u003c/code\u003e is a multiplier that is multiplied to the cost of the cheapest solution.\nFor example, \u003ccode\u003eq=1\u003c/code\u003e reports only the cheapest plans, where \u003ccode\u003equality=infinity\u003c/code\u003e corresponds to the top-k planning.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symq-bd(plan_selection=top_k(num_plans=**k**),quality=**q**)\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLoopless Planning\u003c/h3\u003e\u003ca id=\"user-content-loopless-planning\" class=\"anchor\" aria-label=\"Permalink: Loopless Planning\" href=\"#loopless-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to generate loopless/simple plans, i.e., plans that do not visit any state more than once. In general, the option to consider and generate only simple plans can be combined with any Symk search engine and with different plan selectors by setting the \u003ccode\u003esimple\u003c/code\u003e parameter to true. See the following two examples and our \u003ca href=\"https://gki.informatik.uni-freiburg.de/papers/vontschammer-etal-icaps2022.pdf\" rel=\"nofollow\"\u003eICAPS 2022 Paper\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symk-bd(simple=true,plan_selection=top_k(num_plans=**k**))\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symq-bd(simple=true,plan_selection=top_k(num_plans=**k**),quality=**q**)\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOther Configurations\u003c/h3\u003e\u003ca id=\"user-content-other-configurations\" class=\"anchor\" aria-label=\"Permalink: Other Configurations\" href=\"#other-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to run Symk also with forward or backward search instead of bidirectional search, e.g., with \u003ccode\u003e--search \"symk-fw(...)\"\u003c/code\u003e or \u003ccode\u003e--search \"symk-bw(...)\"\u003c/code\u003e. Depending on the domain, one of these configurations may be faster than bidirectional search (\u003ccode\u003e\"--search symk-bd(...)\"\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePlan Selection Framework\u003c/h2\u003e\u003ca id=\"user-content-plan-selection-framework\" class=\"anchor\" aria-label=\"Permalink: Plan Selection Framework\" href=\"#plan-selection-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to create plans until a number of plans or simply a single plan is found that meets certain requirements.\nFor this purpose it is possible to write your own plan selector. During the search, plans are created and handed over to a plan selector with an anytime behavior.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnordered Plan Selector\u003c/h3\u003e\u003ca id=\"user-content-unordered-plan-selector\" class=\"anchor\" aria-label=\"Permalink: Unordered Plan Selector\" href=\"#unordered-plan-selector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn example of a plan selector is the \u003ca href=\"src/search/symbolic/plan_selection/unordered_selector.cc\"\u003eunordered_selector\u003c/a\u003e, which considers two plans as equivalent if their action multi-sets are equivalent. In other words, plans with the same multi-set of actions form an equivalence class and only one representative plan is reported for each equivalence class.\nNote that plan selectors can be combined with the different planning configurations.\u003c/p\u003e\n\u003cp\u003eWe recommend to use the following configurations which use bidirectional search.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUnordered Top-k:\u003c/h4\u003e\u003ca id=\"user-content-unordered-top-k\" class=\"anchor\" aria-label=\"Permalink: Unordered Top-k:\" href=\"#unordered-top-k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symk-bd(plan_selection=unordered(num_plans=**k**))\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUnordered Top-q:\u003c/h4\u003e\u003ca id=\"user-content-unordered-top-q\" class=\"anchor\" aria-label=\"Permalink: Unordered Top-q:\" href=\"#unordered-top-q\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symq-bd(plan_selection=unordered(num_plans=**k**),quality=**q**)\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNew Plan Selector\u003c/h3\u003e\u003ca id=\"user-content-new-plan-selector\" class=\"anchor\" aria-label=\"Permalink: New Plan Selector\" href=\"#new-plan-selector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTwo simple examples of plan selectors are the \u003ca href=\"src/search/symbolic/plan_selection/top_k_selector.cc\"\u003etop_k_selector\u003c/a\u003e and\nthe \u003ca href=\"src/search/symbolic/plan_selection/top_k_even_selector.cc\"\u003etop_k_even_selector\u003c/a\u003e.\nFor this purpose it is possible to write your own plan selector.\nThe most important function is \u003cem\u003eadd_plan\u003c/em\u003e, in which you can specify whether a newly generated plan shall be accepted or rejected.\nTo create your own plan selector, you can copy the \u003cem\u003e.cc\u003c/em\u003e and \u003cem\u003e.h\u003c/em\u003e files of one of these two selectors and adjust them accordingly. Also add the new file name to \u003ca href=\"src/search/DownwardFiles.cmake\"\u003eDownwardFiles.cmake\u003c/a\u003e, similar to the other selection files.\nFinally, if you want to find a plan with your \u003cem\u003eawesome_selector\u003c/em\u003e selector (the name of the selector you specified for the plugin in the \u003cem\u003e.cc\u003c/em\u003e file), you can use the following command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py domain.pddl problem.pddl --search \"symk-bd(plan_selection=awesome_selector(num_plans=1))\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote, that you can also search for the best \u003cstrong\u003ek\u003c/strong\u003e plans using your selector.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePitfalls and Troubleshooting\u003c/h2\u003e\u003ca id=\"user-content-pitfalls-and-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Pitfalls and Troubleshooting\" href=\"#pitfalls-and-troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, the planner performs a relevance analysis and removes components such as variables and actions that are irrelevant to achieving the goal. Although such variables and actions can in principle lead to further (simple) plans, they are classified as irrelevant and removed when translating PDDL to SAS+. If you wish to \u003cstrong\u003eobtain all plans\u003c/strong\u003e (even the non-relevant ones), please use the following options:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e./fast-downward.py --translate --search domain.pddl problem.pddl --translate-options --keep-unimportant-variables --search-options --search \"symk-bd(plan_selection=top_k(num_plans=**k**))\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReferences\u003c/h1\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that several components of SymK have been developed and published separately.\nWe appreciate citations of these sources when used.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMain source\u003c/h3\u003e\u003ca id=\"user-content-main-source\" class=\"anchor\" aria-label=\"Permalink: Main source\" href=\"#main-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDavid Speck, Robert Mattm\u00fcller, Bernhard Nebel: Symbolic Top-k Planning. AAAI 2020: 9967-9974 \u003ca href=\"https://rlplab.com/papers/speck-et-al-aaai2020.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"https://rlplab.com/papers/speck-et-al-aaai2020.html\" rel=\"nofollow\"\u003e[bib]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLoopless Top-k planning\u003c/h3\u003e\u003ca id=\"user-content-loopless-top-k-planning\" class=\"anchor\" aria-label=\"Permalink: Loopless Top-k planning\" href=\"#loopless-top-k-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eJulian von Tschammer, Robert Mattm\u00fcller, David Speck: Loopless Top-K Planning. ICAPS 2022: 380-384 \u003ca href=\"https://rlplab.com/papers/vontschammer-et-al-icaps2022.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"https://rlplab.com/papers/vontschammer-et-al-icaps2022.html\" rel=\"nofollow\"\u003e[bib]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAxiom and derived predicate support\u003c/h3\u003e\u003ca id=\"user-content-axiom-and-derived-predicate-support\" class=\"anchor\" aria-label=\"Permalink: Axiom and derived predicate support\" href=\"#axiom-and-derived-predicate-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eDavid Speck, Florian Gei\u00dfer, Robert Mattm\u00fcller, \u00c1lvaro Torralba: Symbolic Planning with Axioms. ICAPS 2019: 464-472 \u003ca href=\"https://rlplab.com/papers/speck-et-al-icaps2019.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"https://rlplab.com/papers/speck-et-al-icaps2019.html\" rel=\"nofollow\"\u003e[bib]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eState-dependent action cost support\u003c/h3\u003e\u003ca id=\"user-content-state-dependent-action-cost-support\" class=\"anchor\" aria-label=\"Permalink: State-dependent action cost support\" href=\"#state-dependent-action-cost-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eDavid Speck: Symbolic Search for Optimal Planning with Expressive Extensions. Ph.D. thesis: University of Freiburg (2022) \u003ca href=\"https://rlplab.com/papers/speck-phd2022.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"https://rlplab.com/papers/speck-phd2022.html\" rel=\"nofollow\"\u003e[bib]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou can find examples of domains with state-dependent action cost \u003ca href=\"https://github.com/speckdavid/SDAC-Benchmarks\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge that SymK is based on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward (22.06): \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSymbolic Fast Downward: \u003ca href=\"https://people.cs.aau.dk/~alto/software.html\" rel=\"nofollow\"\u003ehttps://people.cs.aau.dk/~alto/software.html\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, SymK uses some external software, which can be found in the following folders\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"src/dd_libs/cudd-3.0.0\"\u003esrc/dd_libs/cudd-3.0.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"src/search/ext\"\u003esrc/search/ext\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"src/search/sdac_parser/boost_dependencies\"\u003esrc/search/sdac_parser/boost_dependencies\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eSymK is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nSymK is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eManyFairHPO: Many-Objective Fairness-Aware Hyperparameter Optimization\u003c/h1\u003e\u003ca id=\"user-content-manyfairhpo-many-objective-fairness-aware-hyperparameter-optimization\" class=\"anchor\" aria-label=\"Permalink: ManyFairHPO: Many-Objective Fairness-Aware Hyperparameter Optimization\" href=\"#manyfairhpo-many-objective-fairness-aware-hyperparameter-optimization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this repository you will find all source code for experiments and analyses accompanying the submission \"A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes\" to the ACM/AAAI AIES \u002724 Conference.\u003c/p\u003e\n\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"flowchart_aies_ternary.png\"\u003e\u003cimg src=\"flowchart_aies_ternary.png\" alt=\"Image Description\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall dependencies\u003c/h3\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install dependencies\" href=\"#install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHPOBench\u003c/h4\u003e\u003ca id=\"user-content-hpobench\" class=\"anchor\" aria-label=\"Permalink: HPOBench\" href=\"#hpobench\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd HPOBench\npip install -e .\ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMO-HPOBenchExperimentUtils\u003c/h4\u003e\u003ca id=\"user-content-mo-hpobenchexperimentutils\" class=\"anchor\" aria-label=\"Permalink: MO-HPOBenchExperimentUtils\" href=\"#mo-hpobenchexperimentutils\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd MO-HPOBenchExperimentUtils\npip install -e .\ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun a ManyFairHPO Experiment\u003c/h2\u003e\u003ca id=\"user-content-run-a-manyfairhpo-experiment\" class=\"anchor\" aria-label=\"Permalink: Run a ManyFairHPO Experiment\" href=\"#run-a-manyfairhpo-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython -u runner.py --objective_name f1_multi --model_name rf --dataset_name adult --seed 0 --optimizer_name nsga3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalyze ManyFairHPO Results\u003c/h2\u003e\u003ca id=\"user-content-analyze-manyfairhpo-results\" class=\"anchor\" aria-label=\"Permalink: Analyze ManyFairHPO Results\" href=\"#analyze-manyfairhpo-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe logs of our experimental results are provided on \u003ca href=\"https://drive.google.com/file/d/1TcYjjctuSEAHxL_SWopcTJ_IHRNb7tIA/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e. Please download the \u003ccode\u003eexpiriments.pkl\u003c/code\u003e file and place it in your the main project directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epickle\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epkl\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027experiments.pkl\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027rb\u0027\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eexperiments\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epkl\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e] \u003cspan class=\"pl-s1\"\u003eexperiments\u003c/span\u003e[(\u003cspan class=\"pl-s\"\u003e\u0027f1_multi\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027rf\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027adult\u0027\u003c/span\u003e)][\u003cspan class=\"pl-s\"\u003e\u0027function_values\u0027\u003c/span\u003e]\n\n \u003cspan class=\"pl-s1\"\u003eval_f1\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eval_ddsp\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eval_deod\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eval_deop\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eval_invd\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.316078\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.081204\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.026665\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.031306\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.019893\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.331510\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.075258\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.023383\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.026932\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.018170\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.323927\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.076689\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.023580\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.027098\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.019064\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.318430\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.081030\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.026330\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.031248\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.020370\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.397887\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.055827\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.007331\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.006138\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.017072\u003c/span\u003e \n... ... ... ... ... ... \n\u003cspan class=\"pl-c1\"\u003e996\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1.000000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000000\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e997\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.973703\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.001843\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.001118\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.002210\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000258\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e998\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.905509\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.006025\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.003782\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.007564\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.000940\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e999\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.405365\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.055905\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.014091\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.017897\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.013220\u003c/span\u003e \n\u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.340335\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.067798\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.017519\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.021524\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.017845\u003c/span\u003e\n\n[\u003cspan class=\"pl-c1\"\u003e10010\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecolumns\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e[\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e] \u003cspan class=\"pl-s1\"\u003eexperiments\u003c/span\u003e[(\u003cspan class=\"pl-s\"\u003e\u0027f1_multi\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027rf\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027adult\u0027\u003c/span\u003e)][\u003cspan class=\"pl-s\"\u003e\u0027archive\u0027\u003c/span\u003e]\n\n \u003cspan class=\"pl-s1\"\u003emax_depth\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emax_features\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emin_samples_leaf\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emin_samples_split\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003en_estimators\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e28\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.896547\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e15\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e78\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e109\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e22\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.367562\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e13\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e57\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e179\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e49\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.435865\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e102\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e106\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e29\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.891923\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e19\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e11\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e18\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.806194\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e17\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e175\u003c/span\u003e\n... ... ... ... ... ...\n\u003cspan class=\"pl-c1\"\u003e996\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.004743\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e127\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e167\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e997\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.580992\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e14\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e83\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e173\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e998\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.643578\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e88\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e163\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e999\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.544248\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e89\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e167\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e9\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.608166\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e69\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e173\u003c/span\u003e\n\n[\u003cspan class=\"pl-c1\"\u003e10010\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecolumns\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eManyFairHPO-AIES\u003c/h1\u003e\u003ca id=\"user-content-manyfairhpo-aies\" class=\"anchor\" aria-label=\"Permalink: ManyFairHPO-AIES\" href=\"#manyfairhpo-aies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1688990794.0 + "updated_at": 1723300161.0 }, { "data_format": 2, - "description": "Build Singularity containers to run SpaDES simulations on HPC clusters.", + "description": "Content for MAPNET Workshop in analysis of low-coverage population genomic data", "filenames": [ - "Singularity.spades_github-development", - "Singularity.spades_base", - "Singularity.spades_github-master" + "MAPGD/Singularity" ], - "full_name": "gparadis/spades-singularity", + "full_name": "MapNetNZ/Pop-Genomics-Workshop2019", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003espades-singularity\u003c/h1\u003e\u003ca id=\"user-content-spades-singularity\" class=\"anchor\" aria-label=\"Permalink: spades-singularity\" href=\"#spades-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout this project\u003c/h2\u003e\u003ca id=\"user-content-about-this-project\" class=\"anchor\" aria-label=\"Permalink: About this project\" href=\"#about-this-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project implements a scripted framework for automating the process of building Singularity containers for running SpaDES simulations on HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI am super impatient, and refuse to take the time to understand what I am doing before running any commands. Just tell me how to do the thing right now!\u003c/h2\u003e\u003ca id=\"user-content-i-am-super-impatient-and-refuse-to-take-the-time-to-understand-what-i-am-doing-before-running-any-commands-just-tell-me-how-to-do-the-thing-right-now\" class=\"anchor\" aria-label=\"Permalink: I am super impatient, and refuse to take the time to understand what I am doing before running any commands. Just tell me how to do the thing right now!\" href=\"#i-am-super-impatient-and-refuse-to-take-the-time-to-understand-what-i-am-doing-before-running-any-commands-just-tell-me-how-to-do-the-thing-right-now\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build, sign, and push the base container flavour to the cloud image repository, simply run \u003ccode\u003emake all flavour=FLAVOUR\u003c/code\u003e, where \u003ccode\u003eFLAVOUR\u003c/code\u003e is one of \u003ccode\u003ebase\u003c/code\u003e, \u003ccode\u003egithub-master\u003c/code\u003e, or \u003ccode\u003egithub-development\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNot sure which flavour to use? Read on!\u003c/p\u003e\n\u003cp\u003eNote that, if you do not have Singularity installed yet, you will need to run \u003ccode\u003emake install-singularity\u003c/code\u003e first.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity container definition files\u003c/h2\u003e\u003ca id=\"user-content-singularity-container-definition-files\" class=\"anchor\" aria-label=\"Permalink: Singularity container definition files\" href=\"#singularity-container-definition-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity container definition files follow standard Singularity definition file naming conventions (i.e., they are prefixed with \u003ccode\u003eSingularity.\u003c/code\u003e followed by a \u003cem\u003etag\u003c/em\u003e string). There are three flavours (tags) defined in this project: \u003ccode\u003ebase\u003c/code\u003e, \u003ccode\u003egithub-master\u003c/code\u003e, and \u003ccode\u003egithub-development\u003c/code\u003e. Note that the R code that installs SpaDES packages for each flavour is contained in a script named \u003ccode\u003espades-setup_flavour.R\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can also create new custom flavours by copying and modifying some files from an existing flavour. New flavours should be compatible with automated make targets (as long as you did not break the filename patterns).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBase flavour\u003c/h3\u003e\u003ca id=\"user-content-base-flavour\" class=\"anchor\" aria-label=\"Permalink: Base flavour\" href=\"#base-flavour\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe base container flavour includes the latest stable CRAN versions of core SpaDES R packages. This base can be used to run SpaDES models directly (for simpler projects, where the CRAN packages are all you need). The base image also serves as a \u003cem\u003ebootstrap\u003c/em\u003e image for other flavours. The base container flavour is implemented in \u003ccode\u003eSingularity.spades_base\u003c/code\u003e and \u003ccode\u003espades-setup_base.R\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGitHub flavours\u003c/h3\u003e\u003ca id=\"user-content-github-flavours\" class=\"anchor\" aria-label=\"Permalink: GitHub flavours\" href=\"#github-flavours\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two GitHub container flavours (\u003ccode\u003egithub-master\u003c/code\u003e, \u003ccode\u003egithub-development\u003c/code\u003e). These install core SpaDES R packages from the latest code pushed to GitHub repositories for \u003ccode\u003emaster\u003c/code\u003e and \u003ccode\u003edevelopment\u003c/code\u003e branches, respectively. The GitHub container flavours are implemented in the \u003ccode\u003eSingularity.spades-github_BRANCH\u003c/code\u003e and \u003ccode\u003espades-setup_github-BRANCH\u003c/code\u003e (where \u003ccode\u003eBRANCH\u003c/code\u003e is one of \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edevelopment\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThe GitHub container flavours are \u003cem\u003ebootstrapped\u003c/em\u003e from the base container flavour. Defintion file implementation assumes that a local base container image is available in path \u003ccode\u003ebuild/spades.sif\u003c/code\u003e, so the base container must be built first (the base container will automatically get built if not present if you run \u003ccode\u003emake build flavour=FLAVOUR\u003c/code\u003e, where \u003ccode\u003eFLAVOUR\u003c/code\u003e is any value except for \u003ccode\u003ebase\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCustom flavours\u003c/h3\u003e\u003ca id=\"user-content-custom-flavours\" class=\"anchor\" aria-label=\"Permalink: Custom flavours\" href=\"#custom-flavours\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can create a custom container flavour but copying \u003ccode\u003eSingularity.spades_github-master\u003c/code\u003e and \u003ccode\u003espades-setup_github-master.R\u003c/code\u003e---rename these to \u003ccode\u003eSingularity.spades_foo\u003c/code\u003e and \u003ccode\u003espades-setup_foo.R\u003c/code\u003e (where \u003ccode\u003efoo\u003c/code\u003e is whatever unique flavour name you want) and modify as required. Minimally, you just need to edit one line of code in the Singularity definition file to point to \u003ccode\u003espades-setup_foo.R\u003c/code\u003e, and edit the code in \u003ccode\u003espades-setup_foo.R\u003c/code\u003e to install whatever versions of SpaDES R packages you need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMakefile details\u003c/h2\u003e\u003ca id=\"user-content-makefile-details\" class=\"anchor\" aria-label=\"Permalink: Makefile details\" href=\"#makefile-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eMakefile\u003c/code\u003e implements a number of make targets.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake build flavour=FLAVOUR sandbox=true\u003c/code\u003e to build a sandbox container (in \u003ccode\u003ebuild/spades_FLAVOUR_sandbox\u003c/code\u003e). See Singularity documentation for details on sandbox containers.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake build flavour=FLAVOUR\u003c/code\u003e to build a container as a single \u003cem\u003esingularity image file\u003c/em\u003e (in \u003ccode\u003ebuild/spades_FLAVOUR.sif\u003c/code\u003e). See Singularity documentation for details on SIF containers.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake push flavour=FLAVOUR\u003c/code\u003e to sign your SIF image and push it to your Sylabs cloud image library account. See the \u003ca href=\"https:%5Ccloud.sylabs.io\"\u003eSylabs Container Library\u003c/a\u003e to create and configure your account.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake all flavour=FLAVOUR\u003c/code\u003e to build and push your image in one step.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePop-Genomics-Workshop2019\u003c/h1\u003e\u003ca id=\"user-content-pop-genomics-workshop2019\" class=\"anchor\" aria-label=\"Permalink: Pop-Genomics-Workshop2019\" href=\"#pop-genomics-workshop2019\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContent for MAPNET Workshop in analysis of low-coverage population genomic data\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LynchLab/MAPGD\"\u003ehttps://github.com/LynchLab/MAPGD\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSingularity\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/docs/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://singularityhub.github.io/\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eVagrant Box \u003ca href=\"https://app.vagrantup.com/sylabs/boxes/singularity-3.0-ubuntu-bionic64\" rel=\"nofollow\"\u003ehttps://app.vagrantup.com/sylabs/boxes/singularity-3.0-ubuntu-bionic64\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eJupyter \u003ca href=\"https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook\" rel=\"nofollow\"\u003ehttps://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMBIE TPP REPO\u003c/h2\u003e\u003ca id=\"user-content-mbie-tpp-repo\" class=\"anchor\" aria-label=\"Permalink: MBIE TPP REPO\" href=\"#mbie-tpp-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/PlantandFoodResearch/MBIE_TPP_Populations\"\u003ehttps://github.com/PlantandFoodResearch/MBIE_TPP_Populations\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReproducing the Conda Environment\u003c/h3\u003e\u003ca id=\"user-content-reproducing-the--conda-environment\" class=\"anchor\" aria-label=\"Permalink: Reproducing the Conda Environment\" href=\"#reproducing-the--conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003erun this under Linux.\u003c/li\u003e\n\u003cli\u003eassuming you have installed miniconda\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml \n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1602038529.0 + "updated_at": 1553205187.0 }, { "data_format": 2, - "description": "\ud83c\udf5d Code for the paper \"Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning.\"", + "description": "My Singularity recipe files", "filenames": [ - "misc/releases/latest/Singularity", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12" + "gerda-tgsend/Singularity.def", + "root-cern/Singularity.def", + "asciinema/Singularity.def", + "bat/Singularity.def", + "julia/Singularity.def", + "itunes/Singularity.def", + "lilypond/Singularity.def", + "texlive/Singularity.def", + "centos-base/Singularity.def", + "arch-base/Singularity.def" ], - "full_name": "yaaig-ufrgs/NeuralFastDownward-FSM", + "full_name": "gipert/Singularity.def", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNeuralFastDownward-FSM\u003c/h1\u003e\u003ca id=\"user-content-neuralfastdownward-fsm\" class=\"anchor\" aria-label=\"Permalink: NeuralFastDownward-FSM\" href=\"#neuralfastdownward-fsm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCode for the paper \"Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning\".\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNeural Fast Downward is intended to help with generating training data for\nclassical planning domains, as well as, using machine learning techniques with\nFast Downward (especially, Tensorflow and PyTorch).\u003c/p\u003e\n\u003cp\u003eNeuralFastDownward-FSM is a fork from \u003ca href=\"https://github.com/PatrickFerber/NeuralFastDownward\"\u003eFerber\u0027s Neural Fast Downward\u003c/a\u003e, which in turn derives from \u003ca href=\"https://github.com/aibasel/downward\"\u003eFast Downward\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eImportant: you can find our experiments from the paper in the \u003ccode\u003epaper-experiments\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFast Instructions\u003c/h2\u003e\u003ca id=\"user-content-fast-instructions\" class=\"anchor\" aria-label=\"Permalink: Fast Instructions\" href=\"#fast-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePre-run\u003c/h3\u003e\u003ca id=\"user-content-pre-run\" class=\"anchor\" aria-label=\"Permalink: Pre-run\" href=\"#pre-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload and extract\n\u003ca href=\"https://pytorch.org/cppdocs/installing.html\" rel=\"nofollow\"\u003e\u003ccode\u003elibtorch\u003c/code\u003e\u003c/a\u003e to a directory \u003ccode\u003ep\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ecd\u003c/code\u003e to the directory where the root of the cloned repository is located, then:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport Torch_DIR=p OR export PATH_TORCH=p\npip install -r requirements.txt\n./build.py release\n# And if interested in running FastDownward in debug mode:\n./build.py debug\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e3.1. If torch 1.9.0 is not found, install Python \u0026lt;= 3.9.10.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMessing with the neural network code\u003c/h3\u003e\u003ca id=\"user-content-messing-with-the-neural-network-code\" class=\"anchor\" aria-label=\"Permalink: Messing with the neural network code\" href=\"#messing-with-the-neural-network-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee\n\u003ca href=\"https://github.com/yaaig-ufrgs/NeuralFastDownward-FSM/tree/main/src/pytorch\"\u003e\u003ccode\u003esrc/pytorch/\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDefault arguments\u003c/h3\u003e\u003ca id=\"user-content-default-arguments\" class=\"anchor\" aria-label=\"Permalink: Default arguments\" href=\"#default-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/yaaig-ufrgs/NeuralFastDownward-FSM/tree/main/src/pytorch/utils/default_args.py\"\u003e\u003ccode\u003esrc/pytorch/utils/default_args.py\u003c/code\u003e\u003c/a\u003e and \u003ca href=\"https://github.com/yaaig-ufrgs/NeuralFastDownward-FSM/tree/main/src/pytorch/utils/parse_args.py\"\u003e\u003ccode\u003esrc/pytorch/utils/parse_args.py\u003c/code\u003e\u003c/a\u003e for lists of default argument values when invoking programs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenerating samples\u003c/h3\u003e\u003ca id=\"user-content-generating-samples\" class=\"anchor\" aria-label=\"Permalink: Generating samples\" href=\"#generating-samples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eusage: fast-sample.py [-h] [-tst-dir TEST_TASKS_DIR] [-stp STATESPACE] [-tech {rw,dfs,bfs,bfs_rw}] [-search {greedy,astar}]\n [-heur {ff,lmcut}] [-st {complete,complete_nomutex,forward_statespace}] [-max MAX_SAMPLES] [-scs SEARCHES]\n [-sscs SAMPLES_PER_SEARCH] [-rd REGRESSION_DEPTH] [-rdm REGRESSION_DEPTH_MULTIPLIER] [-s SEED]\n [-dups {all,interrollout,none}] [-ms MULT_SEED] [-c RANDOM_PERCENTAGE] [-rhg RESTART_H_WHEN_GOAL_STATE]\n [-sf {none,mutex,statespace}] [-bfsp BFS_PERCENTAGE] [-o OUTPUT_DIR] [-sai {none,partial,complete,both}]\n [-sui SUCCESSOR_IMPROVEMENT] [-suirule {supersets,subsets,samesets}] [-kd K_DEPTH] [-unit UNIT_COST]\n [-cores CORES] [-t MAX_TIME] [-m MEM_LIMIT] [-eval EVALUATOR] [-dbg DEBUG]\n instance {yaaig}\nfast-sample.py: error: the following arguments are required: instance, method\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe example below takes all the instances in the \u003ccode\u003eblocks\u003c/code\u003e directory and saves the\nsamples, facts and defaults files in the \u003ccode\u003esamples\u003c/code\u003e directory with an\nappropriate filename. In the example below, we\u0027re generating 1000 samples. Of the final sample set, 500 are generated using BFS+RW and the remaining will be randomly generated. Duplicates are only allowed between rollout, states are completed with mutexes, all h-value improvements are used and the regression depth is limited by facts/avg(eff).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-sample.py tasks/experiments/blocks yaaig --technique bfs_rw --state-representation complete --max-samples 1000 --seed 0 --allow-dups interrollout --restart-h-when-goal-state yes --sample-improvement both --statespace tasks/experiments/statespaces/statespace_blocks_probBLOCKS-7-0_hstar --successor-improvement yes --regression-depth facts_per_avg_effects --state-filtering mutex --bfs-percentage 0.1 --random-percentage 0.5 --cores 1 --output-dir samples\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTraining a neural network\u003c/h3\u003e\u003ca id=\"user-content-training-a-neural-network\" class=\"anchor\" aria-label=\"Permalink: Training a neural network\" href=\"#training-a-neural-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecuting \u003ccode\u003e./train.py -h\u003c/code\u003e will show how to use it with all\nthe possible arguments. Almost everything is modifiable, and the default neural\nnetwork is a ResNet.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: train.py [-h] [-mdl {hnn,resnet}] [-sb SAVE_BEST_EPOCH_MODEL] [-diff SAVE_GIT_DIFF] [-pte POST_TRAIN_EVAL] [-pat PATIENCE]\n [-o {regression,prefix,one-hot}] [-lo LINEAR_OUTPUT] [-f NUM_FOLDS] [-hl HIDDEN_LAYERS]\n [-hu HIDDEN_UNITS [HIDDEN_UNITS ...]] [-b BATCH_SIZE] [-lr LEARNING_RATE] [-e MAX_EPOCHS] [-t MAX_TRAINING_TIME]\n [-a {sigmoid,relu,leakyrelu}] [-w WEIGHT_DECAY] [-d DROPOUT_RATE] [-shs SHUFFLE_SEED] [-sh SHUFFLE] [-gpu USE_GPU]\n [-bi BIAS] [-tsize TRAINING_SIZE] [-spt SAMPLE_PERCENTAGE] [-us UNIQUE_SAMPLES] [-ust UNIQUE_STATES]\n [-biout BIAS_OUTPUT] [-of OUTPUT_FOLDER] [-s SEED] [-sp SCATTER_PLOT] [-spn PLOT_N_EPOCHS]\n [-wm {default,sqrt_k,1,01,xavier_uniform,xavier_normal,kaiming_uniform,kaiming_normal,rai}] [-lf {mse,rmse}]\n [-no NORMALIZE_OUTPUT] [-rst RESTART_NO_CONV] [-cdead CHECK_DEAD_ONCE] [-sibd SEED_INCREMENT_WHEN_BORN_DEAD]\n [-trd NUM_CORES] [-dnw DATA_NUM_WORKERS] [-hpred SAVE_HEURISTIC_PRED]\n [-addfn [{patience,output-layer,num-folds,hidden-layers,hidden-units,batch-size,learning-rate,max-epochs,max-training-time,activation,weight-decay,dropout-rate,shuffle-seed,shuffle,use-gpu,bias,bias-output,normalize-output,restart-no-conv,sample-percentage,training-size} [{patience,output-layer,num-folds,hidden-layers,hidden-units,batch-size,learning-rate,max-epochs,max-training-time,activation,weight-decay,dropout-rate,shuffle-seed,shuffle,use-gpu,bias,bias-output,normalize-output,restart-no-conv,sample-percentage,training-size} ...]]]\n samples\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe example below will train a neural network with a sampling file as input, utilizing seed 0 (for reproducibility), a max of 20 training epochs, ReLU activation, regression output, MSE loss function and Kaiming Uniform network initialization. The trained model will be saved in the \u003ccode\u003eresults\u003c/code\u003e folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./train.py samples/yaaig_blocks_probBLOCKS-7-0_tech-bfsrw_sui_dups-ir_sai-both_repr-complete_bnd-factseff_maxs-1000_rs-500_ss0 -s 0 -e 20 -a relu -o regression -of results -lf mse -wm kaiming_uniform\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEvaluating instances\u003c/h3\u003e\u003ca id=\"user-content-evaluating-instances\" class=\"anchor\" aria-label=\"Permalink: Evaluating instances\" href=\"#evaluating-instances\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecuting \u003ccode\u003e./test.py -h\u003c/code\u003e will show how to use it with all\nthe possible arguments.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: test.py [-h] [-tfc TRAIN_FOLDER_COMPARE] [-diff SAVE_GIT_DIFF] [-d DOMAIN_PDDL] [-a {astar,eager_greedy}]\n [-heu {nn,add,blind,ff,goalcount,hmax,lmcut,hstar}] [-hm HEURISTIC_MULTIPLIER] [-u UNARY_THRESHOLD] [-t MAX_SEARCH_TIME]\n [-m MAX_SEARCH_MEMORY] [-e MAX_EXPANSIONS] [-pt {all,best,epochs}] [-sdir SAMPLES_DIR] [-ffile FACTS_FILE]\n [-dfile DEFAULTS_FILE] [-atn AUTO_TASKS_N] [-atf AUTO_TASKS_FOLDER] [-ats AUTO_TASKS_SEED] [-dlog DOWNWARD_LOGS]\n [-unit-cost UNIT_COST]\n train_folder [problem_pddls [problem_pddls ...]]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe example below takes a network folder (the trained model is located within\nit) as the first argument and will automatically find 50 random (fixed seed as default)\ninstances of the same domain to use for testing. \u003ccode\u003e-t\u003c/code\u003e is the time limit to solve the task, \u003ccode\u003e-a\u003c/code\u003e is the search algorithm used.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./test.py results/nfd_train.yaaig_blocks_probBLOCKS-7-0_tech-bfsrw_sui_dups-ir_sai-both_repr-complete_bnd-factseff_maxs-1000_rs-500_ss0.ns0 -t 360 -a eager_greedy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning full experiments\u003c/h3\u003e\u003ca id=\"user-content-running-full-experiments\" class=\"anchor\" aria-label=\"Permalink: Running full experiments\" href=\"#running-full-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can create multiple files like \u003ccode\u003eexp_example.json\u003c/code\u003e and call \u003ccode\u003e./run.py exp_example.json\u003c/code\u003e. Batch experiments will be performed according to the content in the JSON files. All the empty/unspecified settings will be run as\ndefault, and missing sections will be ignored.\u003c/p\u003e\n\u003cp\u003eYou can find a multitude of examples in the \u003ccode\u003epaper-experiments\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eorigin/release\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity recipe files\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-files\" class=\"anchor\" aria-label=\"Permalink: Singularity recipe files\" href=\"#singularity-recipe-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sylabs/singularity\"\u003eSingularity\u003c/a\u003e containers I use the most on HPC clusters.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, - "topics": [], - "updated_at": 1690974593.0 + "topics": [ + "singularity", + "containers" + ], + "updated_at": 1587858477.0 }, { "data_format": 2, - "description": null, + "description": "Hold me closer, tiny container...", "filenames": [ - "misc/releases/latest/Singularity", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12" + "Singularity.tiny", + "Singularity" ], - "full_name": "hejia-zhang/downward", + "full_name": "singularityhub/tiny-container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFast Downward\u003c/h1\u003e\u003ca id=\"user-content-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Fast Downward\" href=\"#fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1663278115.0 + "updated_at": 1566311836.0 }, { "data_format": 2, - "description": "PlanDEM is a domain-independent planner that works with dynamically estimated action models.", + "description": null, "filenames": [ - "misc/releases/latest/Singularity", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12" + "Singularity.rabbitMQ", + "Singularity.def", + "worker/Singularity.worker" ], - "full_name": "eyal-weiss/plandem-public", + "full_name": "tahahah/py-pacman", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePlanDEM\u003c/h1\u003e\u003ca id=\"user-content-plandem\" class=\"anchor\" aria-label=\"Permalink: PlanDEM\" href=\"#plandem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlanDEM is a research project, developed at Bar-Ilan University,\nthat aims to build a domain-independent classical planning system\nwhich uses dynamically estimated action models.\nIt is based on the Fast Downward planning system,\nwith modifications that support dynamic action model estimation.\u003c/p\u003e\n\u003cp\u003eCopyright 2021--2023 PlanDEM contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlanDEM main repository: \u003ca href=\"https://github.com/eyal-weiss/plandem-public\"\u003ehttps://github.com/eyal-weiss/plandem-public\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested Software Versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested Software Versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of PlanDEM is tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to PlanDEM.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2021--2023 Eyal Weiss\u003c/li\u003e\n\u003cli\u003e2021--2023 Gal A. Kaminka\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eEyal: \u003ca href=\"mailto:eyal.weiss@biu.ac.il\"\u003eeyal.weiss@biu.ac.il\u003c/a\u003e, \u003ca href=\"https://sites.google.com/view/eyal-weiss\" rel=\"nofollow\"\u003ehttps://sites.google.com/view/eyal-weiss\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eGal: \u003ca href=\"mailto:galk@cs.biu.ac.il\"\u003egalk@cs.biu.ac.il\u003c/a\u003e, \u003ca href=\"https://u.cs.biu.ac.il/~kaminkg/\" rel=\"nofollow\"\u003ehttps://u.cs.biu.ac.il/~kaminkg/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePapers\u003c/h2\u003e\u003ca id=\"user-content-papers\" class=\"anchor\" aria-label=\"Permalink: Papers\" href=\"#papers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePlanning with Multiple Action-Cost Estimates, Eyal Weiss and Gal A. Kaminka, ICAPS 2023\u003c/li\u003e\n\u003cli\u003eA Generalization of the Shortest Path Problem to Graphs with Multiple Edge-Cost Estimates, Eyal Weiss, Ariel Felner and Gal A. Kaminka, ECAI 2023\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRelevant information appears in directories with the same name.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as Fast Downward.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as Fast Downward, but with the following choices in the run command:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo run ACE choose the search engine \"synchronic\". See documentation in plugin_synchronic_estimation.cc. To switch between estimator types, open the file synchronic_estimation_search.cc and modify the class of *estimator_ptr (currently two options: Estimator or OntarioEstimator) and the input parameters of get_estimator accordingly.\u003c/li\u003e\n\u003cli\u003eTo run BEAUTY choose the search engine \"beauty\". See documentation in plugin_beauty.cc. To run Anytime-BEAUTY choose the search engine \"anytime_beauty\". See documentation in anytime_beauty.cc.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of PlanDEM as covered by this license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePlanDEM is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nPlanDEM is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePyPacman\u003c/h1\u003e\u003ca id=\"user-content-pypacman\" class=\"anchor\" aria-label=\"Permalink: PyPacman\" href=\"#pypacman\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0c9e4d33bbb7708d007d67d3a5ef0de48b6f82ae6ba5a45524573ef512e0aeef/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c616e67756167652d707974686f6e2d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0c9e4d33bbb7708d007d67d3a5ef0de48b6f82ae6ba5a45524573ef512e0aeef/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c616e67756167652d707974686f6e2d626c7565\" alt=\"language\" data-canonical-src=\"https://img.shields.io/badge/language-python-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9d525da30058c10a3a4c3339225203b08f2e5ea08582d046ff1fd43b4dfda173/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6f72616e6765\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9d525da30058c10a3a4c3339225203b08f2e5ea08582d046ff1fd43b4dfda173/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6f72616e6765\" alt=\"license\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe classic game of Pacman built with Pygame, provided also with a Reinforcement Learning environment.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/pacman-example.gif\"\u003e\u003cimg src=\"res/pacman-example.gif\" alt=\"example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#quick-start\"\u003eQuick Start\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#game\"\u003eGame\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#rl-environment\"\u003eRL Enviroment\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuick Start\u003c/h1\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGame\u003c/h2\u003e\u003ca id=\"user-content-game\" class=\"anchor\" aria-label=\"Permalink: Game\" href=\"#game\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the requirements\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the Game with the classic maze\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython main.py -lay classic -snd\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the Game without music or sounds\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython main.py -lay classic\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the game with others option\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: main.py [-h] [-lay LAYOUT] [-snd] [-stt]\n\nArgument for the Pacman Game\n\noptional arguments:\n -h, --help show this help message and exit\n -lay LAYOUT, --layout LAYOUT\n Name of layout to load in the game\n -snd, --sound Activate sounds in the game\n -stt, --state Display the state matrix of the game\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRL Environment\u003c/h2\u003e\u003ca id=\"user-content-rl-environment\" class=\"anchor\" aria-label=\"Permalink: RL Environment\" href=\"#rl-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ePacmanEnv\u003c/code\u003e class extends the \u003ccode\u003egym.Env\u003c/code\u003e class, so if you already know how to\nuse the \u003cstrong\u003eopen ai gym\u003c/strong\u003e, the api is the same.\u003c/p\u003e\n\u003cp\u003eHere\u0027s a little example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egym\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egym\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emake\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027pacman-v0\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eframe_to_skip\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e)\n\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eepisode\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erange\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eepisodes\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ereset\u003c/span\u003e()\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erange\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emax_steps\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eaction\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eaction_space\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esample\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003eobs\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erewards\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edone\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einfo\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003estep\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eaction\u003c/span\u003e)\n \n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edone\u003c/span\u003e: \n \u003cspan class=\"pl-k\"\u003ebreak\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAgent class\u003c/h3\u003e\u003ca id=\"user-content-agent-class\" class=\"anchor\" aria-label=\"Permalink: Agent class\" href=\"#agent-class\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003esrc.env\u003c/code\u003e folder provides also an abstract class that you can use to make your own AI agent.\nYou can use it to make your own agent, train it and directly plug into the game and see\nhow will perform.\u003c/p\u003e\n\u003cp\u003eHere\u0027s how you can use it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esrc\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eenv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eagent\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAgent\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyAgent\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eAgent\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027my_agent\u0027\u003c/span\u003e\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e__init__\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003epass\u003c/span\u003e\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eact\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estate\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ekwargs\u003c/span\u003e):\n \u003cspan class=\"pl-s\"\u003e\"\"\"\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e The code that return the action to take\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \"\"\"\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003epass\u003c/span\u003e\n \n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003etrain\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ekwargs\u003c/span\u003e):\n \u003cspan class=\"pl-s\"\u003e\"\"\"\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Your code to train the agent\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \"\"\"\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003epass\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd after you\u0027re done with the training you can simply plug it into the game:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erun_agent\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003estr\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eagent\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyAgent\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003econtroller\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eController\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elayout_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eact_sound\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eact_state\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eai_agent\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eagent\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003econtroller\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_menu\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more examples check out the \u003ca href=\"./examples\"\u003e\u003ccode\u003eexamples\u003c/code\u003e\u003c/a\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTodos\u003c/h1\u003e\u003ca id=\"user-content-todos\" class=\"anchor\" aria-label=\"Permalink: Todos\" href=\"#todos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] refactor everything using ECS\u003c/li\u003e\n\u003cli\u003e[ ] implement fruit\u003c/li\u003e\n\u003cli\u003e[ ] flashing power pellet\u003c/li\u003e\n\u003cli\u003e[x] state matrix in another screen\u003c/li\u003e\n\u003cli\u003e[x] Provide an RL Environment so an AI agent can be trained\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMIT\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaolo D\u0027Elia\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1689680580.0 + "updated_at": 1730625041.0 }, { "data_format": 2, - "description": null, + "description": "This project contains build scripts, setup and how-to instructions.", "filenames": [ - "Singularity" + "hpc/simplace/Singularityfile.def", + "hpc/simplace/Singularityfile_HM.def" ], - "full_name": "stephansmit/vuenode_containers", + "full_name": "zalf-rpm/build-pipeline", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNodeJS container\u003c/h1\u003e\u003ca id=\"user-content-nodejs-container\" class=\"anchor\" aria-label=\"Permalink: NodeJS container\" href=\"#nodejs-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer with \u003ca href=\"https://vuejs.org\" rel=\"nofollow\"\u003eVue.js\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container locally\u003c/h2\u003e\u003ca id=\"user-content-build-the-container-locally\" class=\"anchor\" aria-label=\"Permalink: Build the container locally\" href=\"#build-the-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build vuenode_containers.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull the container\u003c/h2\u003e\u003ca id=\"user-content-pull-the-container\" class=\"anchor\" aria-label=\"Permalink: Pull the container\" href=\"#pull-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/vuenode_containers:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3589\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebuild-pipeline\u003c/h1\u003e\u003ca id=\"user-content-build-pipeline\" class=\"anchor\" aria-label=\"Permalink: build-pipeline\" href=\"#build-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project contains build scripts, setups, how-to instructions and examples.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1569691552.0 + "updated_at": 1731428353.0 }, { "data_format": 2, - "description": "A shovill singularity recipe", + "description": "Singularity container with R, tidyverse, and other packages", "filenames": [ - "Singularity", - "v0.9.0/Singularity.v0.9.0" + "Singularity.v0.0.14", + "Singularity.v0.0.13", + "Singularity.v0.0.11", + "Singularity.v0.0.12", + "Singularity.v0.0.10", + "Singularity.v0.0.15" ], - "full_name": "phgenomics-singularity/shovill", + "full_name": "darachm/singularity_r_for_darach", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eshovill\u003c/h1\u003e\u003ca id=\"user-content-shovill\" class=\"anchor\" aria-label=\"Permalink: shovill\" href=\"#shovill\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA shovill singularity recipe\u003c/p\u003e\n\u003cp\u003eSingularity container for Torsten Seemann\u0027s \u003ca href=\"https://github.com/tseemann/shovill\"\u003eSHOVILL\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name shovill shub://phgenomics-singularity/shovill@latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003eshovill.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eUse the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./shovill.simg --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name shovill shub://phgenomics-singularity/shovill@v0.9.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir $HOME/singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name shovill_v0.9.0 shub://phgenomics-singularity/shovill@v0.9.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eLink the image to a folder in your \u003ccode\u003e$PATH\u003c/code\u003e (e.g., \u003ccode\u003e$HOME/bin\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eln -s $HOME/singularity/shovill_v0.9.0.simg $HOME/bin/shovill\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshovill --help\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eThis container is for providing \u003ccode\u003eR\u003c/code\u003e and some packages, that Darach likes.\u003c/p\u003e\n\u003cp\u003eThe primary reason for this is so that it flows nicely through SingularityHub\nfor Nextflow pipelines.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1521520426.0 + "updated_at": 1581093246.0 + }, + { + "data_format": 2, + "description": "Singularity Recipe for scipion", + "filenames": [ + "Singularity.1.1", + "Singularity.1.1.cuda", + "Singularity.2.0", + "Singularity.2.0.cuda" + ], + "full_name": "ResearchIT/scipion", + "latest_release": null, + "stargazers_count": 0, + "subscribers_count": 7, + "topics": [ + "singularity", + "scipion" + ], + "updated_at": 1592515044.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "pong_game/singularity/team_server/Singularity.def", + "pong_game/singularity/umpire/Singularity.def" ], - "full_name": "jordiae/simpaux-release", + "full_name": "WarwickRSE/basics-of-linux-scrtp-git", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esimpaux\u003c/h1\u003e\u003ca id=\"user-content-simpaux\" class=\"anchor\" aria-label=\"Permalink: simpaux\" href=\"#simpaux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimple auxiliary network for metalearning\u003c/p\u003e\n\u003cp\u003eThe ProtoNet++ code is built on top of the \u003ca href=\"https://github.com/tristandeleu/pytorch-meta/tree/master/examples/protonet\"\u003eProtoNet example\u003c/a\u003e by @tristandeleu.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esimpaux_dev.sh\u003c/code\u003e script depends on the helper scripts for establishing persistent ssh connections \u003ca href=\"https://github.com/vmichals/ssh_persistent_connection_helpers\"\u003ehere\u003c/a\u003e. You just have to clone the repo and put the local folder in your $PATH.\u003c/p\u003e\n", + "readme": "", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1714350232.0 + "updated_at": 1731587629.0 }, { "data_format": 2, - "description": "singularity container for chlist binary", + "description": "SpecBCFuzz - Fuzzing LTL Solvers", "filenames": [ - "Singularity" + "Singularity_black_reduced_size_v92", + "Singularity_black_v052", + "Singularity_black_v092", + "Singularity_black_reduced_size_v54", + "Singularity_black_debian_v53" ], - "full_name": "ISU-HPC/chlist", + "full_name": "lzmths/SpecBCFuzz", "latest_release": null, + "readme": "\u003cp\u003eChange pom.xml and update project.baseDir\nIt should address the SpecBCFuzz project. For example, lib is a subfolder of the SpecBCFuzz project.\u003c/p\u003e\n\u003cp\u003eAfter that, you must set the Main.java (src/main/).\u003c/p\u003e\n\u003cp\u003eYou should edit:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esolverBin_version (File instance).\u003c/li\u003e\n\u003cli\u003esolver_version (SATSolver instance).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCertify that solver_version is an element of the list solvers.\u003c/p\u003e\n\u003cp\u003eAfter that, you need to provide some parameters to run SpecBCFuzz.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cp\u003e-log=/home/user/outcomes -lib=/home/user/lib -id=1 -run=1 -id=10 -sattimeout=60 -experiment=searchbased:SemSyn-format:gore -popSize=50 -maxNumOfInd=600 -specs=/home/user/specs/atm -os=mac\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elog represents the folder for the outcomes;\u003c/li\u003e\n\u003cli\u003elib represents the folder for lib. You may reference a copy of /lib present in the SpecBCFuzz repository;\u003c/li\u003e\n\u003cli\u003eid indicates a single number or name for the execution;\u003c/li\u003e\n\u003cli\u003erun is the number of executions of the differential search fuzzing;\u003c/li\u003e\n\u003cli\u003esattimeout is the timeout for each LTL solver;\u003c/li\u003e\n\u003cli\u003eexperiment indicates the type of experiment. For instance, the format of inputs;\u003c/li\u003e\n\u003cli\u003epopSize indicates the population size for the genetic algorithm;\u003c/li\u003e\n\u003cli\u003emaxNumOfInd indicates the maximum number of individuals created in the genetic algorithm;\u003c/li\u003e\n\u003cli\u003especs is the path for the specification and boundary conditions;\u003c/li\u003e\n\u003cli\u003eos refers to the operating systems;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdditional parameters and options are available in src/main/Main.java. Feel free to test them or add new ones.\u003c/p\u003e\n\u003cp\u003eYou can find specifications and boundary conditions (divergences or conflicts in the specification) in the following repository: \u003ca href=\"https://github.com/SpecBCFuzz/repo\"\u003ehttps://github.com/SpecBCFuzz/repo\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1580398633.0 + "updated_at": 1730392930.0 }, { "data_format": 2, - "description": null, + "description": "A singularity container that ships MALT, the MEGAN alignment tool.", "filenames": [ - "Singularity" + "Singularity.v0.4.0", + "Singularity.latest" ], - "full_name": "gijzelaerr/singularity-notebook", + "full_name": "qbicsoftware-archive/qbic-singularity-malt", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eqbic-singularity-malt\u003c/h1\u003e\u003ca id=\"user-content-qbic-singularity-malt\" class=\"anchor\" aria-label=\"Permalink: qbic-singularity-malt\" href=\"#qbic-singularity-malt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/641\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Singularity container with MALT, the MEGAN alignment tool (\u003ca href=\"https://ab.inf.uni-tuebingen.de/software/malt\" rel=\"nofollow\"\u003ehttps://ab.inf.uni-tuebingen.de/software/malt\u003c/a\u003e), created by \u003ca href=\"https://ab.inf.uni-tuebingen.de/people/welcome.html/huson/welcome.html\" rel=\"nofollow\"\u003eDaniel H. Huson\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor further information or help reffering the \u003cem\u003econtainer\u003c/em\u003e, please contact: \u003ca href=\"mailto:sven.fillinger@qbic.uni-tuebingen.de\"\u003esven.fillinger@qbic.uni-tuebingen.de\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBootstrap files with tags\u003c/h3\u003e\u003ca id=\"user-content-bootstrap-files-with-tags\" class=\"anchor\" aria-label=\"Permalink: Bootstrap files with tags\" href=\"#bootstrap-files-with-tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide always a bootstrap file (\u003ccode\u003eSingularity\u003c/code\u003e) tagged \u003ccode\u003e.latest\u003c/code\u003e which represents the most resent development status of the container. If you see version tags like \u003ccode\u003e.v0.4.0\u003c/code\u003e, this means that this is the recipe of a container with a stable version tag of MALT.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to build the container\u003c/h3\u003e\u003ca id=\"user-content-how-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: How to build the container\" href=\"#how-to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/qbicsoftware/qbic-singularity-malt.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e qbic-singularity-malt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSince Singularity 2.4, the build command from file looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build myContainer.simg \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSingularity file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also download the build and ready-to-use container from Singularity Hub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://qbicsoftware/qbic-singularity-malt:latest\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to run the container and calling MALT\u003c/h3\u003e\u003ca id=\"user-content-how-to-run-the-container-and-calling-malt\" class=\"anchor\" aria-label=\"Permalink: How to run the container and calling MALT\" href=\"#how-to-run-the-container-and-calling-malt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the malt-run script, you just need to\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e myContainer.simg malt-run --help\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or even shorter\u003c/span\u003e\nsingularity run myContainer.simg --help \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or even more shorter\u003c/span\u003e\n./myContainer.simg --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthor\u003c/h2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sven1103\"\u003eSven Fillinger\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [], - "updated_at": 1489998021.0 + "subscribers_count": 11, + "topics": [ + "other" + ], + "updated_at": 1600938903.0 }, { "data_format": 2, - "description": null, + "description": "Tool for gathering dicom files into organized tarballs, each containing unique study instance", "filenames": [ - "Singularity" + "singularity/Singularity.v0.0.4", + "singularity/Singularity.v.0.0.6", + "singularity/Singularity.v0.0.3", + "singularity/Singularity.v0.0.2", + "singularity/Singularity" ], - "full_name": "phgenomics-singularity/assemblers", + "full_name": "khanlab/dicom2tar", "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://circleci.com/gh/khanlab/dicom2tar/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5df563272b031151262e6399cc5983d2599e0152273767935f1b2c66cf119507/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f6469636f6d327461722f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/dicom2tar/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edicom2tar\u003c/h1\u003e\u003ca id=\"user-content-dicom2tar\" class=\"anchor\" aria-label=\"Permalink: dicom2tar\" href=\"#dicom2tar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTool for extract compressed files(if any), sort dicom files according to given rule, or tar the sorted, to a destination directory.\u003c/p\u003e\n\u003cp\u003eCheck dicom2tar.py for example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo install on graham:\u003c/h3\u003e\u003ca id=\"user-content-to-install-on-graham\" class=\"anchor\" aria-label=\"Permalink: To install on graham:\" href=\"#to-install-on-graham\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emodule unload python\nmodule load python/2\nvirtualenv ~/python_dicom2tar\nsource ~/python_dicom2tar/bin/activate\npip install dicom2tar\ndeactivate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can then run it with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource ~/python_dicom2tar/bin/activate\ndicom2tar \u0026lt;input\u0026gt; \u0026lt;output\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 7, "topics": [], - "updated_at": 1576539518.0 + "updated_at": 1666690069.0 }, { "data_format": 2, - "description": "Facial Emotion Recognition on PSU ACI", + "description": null, "filenames": [ - "Singularity" + "pipelines/0037-cell_cell_interaction/env/Singularity.cell_cell_interaction", + "pipelines/0015-preprocessing/env/Singularity.preprocessing", + "pipelines/0025-qc_cluster/env/Singularity.sc_qc_cluster" ], - "full_name": "d-bohn/emorec_aci", + "full_name": "ckrilow/dev-ckrilow", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eopenpose_aci\u003c/h1\u003e\u003ca id=\"user-content-openpose_aci\" class=\"anchor\" aria-label=\"Permalink: openpose_aci\" href=\"#openpose_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and workflow for building some emotion recognition models on PSU\nACI HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003essh\u003c/code\u003e into the PSU ACI HPC with X11 flags.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh USERID@aci-b.aci.ics.psu.edu -X -Y\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart an interactive session using \u003ccode\u003eqsub\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -A open -I -X -l walltime=24:00:00 -l nodes=5:ppn=10 -l pmem=20gb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the container, and execute the \u003ccode\u003evideo_analysis.py\u003c/code\u003e script.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n emorec_aci.simg shub://d-bohn/emorec_aci\n\nsingularity exec emorec_aci.simg /bin/bash\n\npython3 /opt/emorec/video_analysis.py VIDEOFILE OUTDIR SAVENAME\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe arguments to be passed (in order) are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eVIDEOFILE\u003c/strong\u003e: Full path to video file to be analyzed\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eOUTDIR\u003c/strong\u003e: Full path to a writable director to store\nresults (e.g., \u003ccode\u003e/storage/home\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSAVENAME\u003c/strong\u003e: File name to save the results (e.g., \u003ccode\u003edata.csv\u003c/code\u003e).\nIt will save automatically to the specified output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImage Builds\u003c/h2\u003e\u003ca id=\"user-content-image-builds\" class=\"anchor\" aria-label=\"Permalink: Image Builds\" href=\"#image-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe emorec_aci docker image was built on docker hub.\u003c/p\u003e\n\u003cp\u003eThe emorec_aci singularity image was built using the docker image base and\nconverting it to a singularity image via singularity hub.\u003c/p\u003e\n\u003cp\u003eSetup for linking Github with Docker Hub and Singularity Hub can be found here:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.docker.com/docker-hub/\" rel=\"nofollow\"\u003edocker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki\"\u003eSingularity Hub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003eSingularity\u003c/code\u003e file specifies creating a Singularity-compatible image\nfrom the docker image, as well as adding access to folders within ACI, specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# ACI mappings so you can access your files.\nmkdir -p /storage/home\nmkdir -p /storage/work\nmkdir -p /gpfs/group\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCreate command function for automatic execution of \u003ccode\u003evideo_analysis.py\u003c/code\u003e\nscript\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAdd a \u003ccode\u003eArgumentParser\u003c/code\u003e and help files.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDescription\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis README is pulled from a default template for workflows.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWorkflow template setup\u003c/h1\u003e\u003ca id=\"user-content-workflow-template-setup\" class=\"anchor\" aria-label=\"Permalink: Workflow template setup\" href=\"#workflow-template-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003elib\u003c/h2\u003e\u003ca id=\"user-content-lib\" class=\"anchor\" aria-label=\"Permalink: lib\" href=\"#lib\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003elib\u003c/code\u003e directory contains general libraries that may be referenced by multiple workflows, for instance cromwell configs and python configs. Currently nothing in this directory is used.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003epipelines\u003c/h2\u003e\u003ca id=\"user-content-pipelines\" class=\"anchor\" aria-label=\"Permalink: pipelines\" href=\"#pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEach pipeline is a full analysis. Think of it like the heading of a methods section in a paper. For instance if this were genetic summary statistics workflow, a pipeline might be \"fine-mapping\" that does both conditional and credible set analysis. Another pipeline may be \"colocalization\".\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePipelines may have numbers prior to their name (e.g., \u003ccode\u003eexample_pipeline_1\u003c/code\u003e to \u003ccode\u003e0025-example_pipeline_1\u003c/code\u003e). These numbers do not mean anything, but merely used to keep pipelines in their general order of execution. These are optional.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA pipeline consists of :\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eA workflow.\u003c/li\u003e\n\u003cli\u003eA \u003ccode\u003escripts\u003c/code\u003e directory with \u003cem\u003eall\u003c/em\u003e scripts referenced by that workflow (unless a general lib script is called). Scripts may have numbers prior to their name. These numbers do not mean anything, but merely used to keep scripts in their general order of execution. These are optional.\u003c/li\u003e\n\u003cli\u003eA \u003ccode\u003edocs\u003c/code\u003e directory that contains a documentation of the default parameters written in a style that is publishable as methods in a paper (including citations). Within the \u003ccode\u003edocs\u003c/code\u003e directory there may be a \u003ccode\u003ereference\u003c/code\u003e with any additional reference materials.\u003c/li\u003e\n\u003cli\u003eAn \u003ccode\u003eexample_runtime_setup\u003c/code\u003e directory contains files that give an example of actual config files and any other files used to run the pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003estudies\u003c/h2\u003e\u003ca id=\"user-content-studies\" class=\"anchor\" aria-label=\"Permalink: studies\" href=\"#studies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA studies directory should either exist within the workflow repo or be a separate repo that has the same name as the workflow repo, but with \u003ccode\u003estudies\u003c/code\u003e appended to it (e.g. \u003ccode\u003etemplate-workflow\u003c/code\u003e becomes \u003ccode\u003etemplate-workflow-studies\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eIf there is a standard set of plots that will always look the same way, a pipeline should generate such plots. Otherwise, all code to analyze the results of a pipeline run should be in the \u003ccode\u003estudies\u003c/code\u003e directory. For instance if this were genetic summary statistics workflow, \u003ccode\u003estudies\u003c/code\u003e may contain a \u003ccode\u003et2d\u003c/code\u003e directory and a \u003ccode\u003eweight\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eWithin a study is either an Jupyter notebook (either python or R kernel) or an R markdown file. Nearly all plots / analysis of the results of running the various pipelines should be done in the notebook / markdown file.\u003c/li\u003e\n\u003cli\u003eA study may also contain a scripts directory with scripts to aggregate data for a one off analysis (if the analysis is going to be repeated, consider making a new pipeline or adding it to an existing pipeline) or for special plots that cannot be done in the notebook / markdown file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNew workflow reminders\u003c/h1\u003e\u003ca id=\"user-content-new-workflow-reminders\" class=\"anchor\" aria-label=\"Permalink: New workflow reminders\" href=\"#new-workflow-reminders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Documentation\u003c/li\u003e\n\u003cli\u003e[ ] Environment version control\u003c/li\u003e\n\u003cli\u003e[ ] Pipeline version control\u003c/li\u003e\n\u003cli\u003e[ ] Git branches\u003c/li\u003e\n\u003cli\u003e[ ] Code review\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocumentation\u003c/h1\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBe sure to document your code!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnvironment version control\u003c/h1\u003e\u003ca id=\"user-content-environment-version-control\" class=\"anchor\" aria-label=\"Permalink: Environment version control\" href=\"#environment-version-control\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnalysis environment is controlled using conda. Each pipeline should have an \u003ccode\u003eenvironment.yml\u003c/code\u003e file with all of the packages used. If a required package or library is missing from conda (and therefore not in the \u003ccode\u003eenvironment.yml\u003c/code\u003e), it should be noted in the \u003ccode\u003eREADME.md\u003c/code\u003e of the pipeline.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda env \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e --no-builds \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep -v prefix \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep -v name \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e environment.yml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline version control\u003c/h1\u003e\u003ca id=\"user-content-pipeline-version-control\" class=\"anchor\" aria-label=\"Permalink: Pipeline version control\" href=\"#pipeline-version-control\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach pipeline within this workflow uses \u003ca href=\"https://pypi.org/project/bumpversion\" rel=\"nofollow\"\u003ebumpversion\u003c/a\u003e for automatic \u003ca href=\"https://semver.org\" rel=\"nofollow\"\u003esemantic versioning\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e bump the appropriate increment\u003c/span\u003e\nbumpversion patch --verbose --dry-run\nbumpversion minor --verbose --dry-run\nbumpversion major --verbose --dry-run\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e commit with tags\u003c/span\u003e\ngit push --tags\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGitHub forks\u003c/h1\u003e\u003ca id=\"user-content-github-forks\" class=\"anchor\" aria-label=\"Permalink: GitHub forks\" href=\"#github-forks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eForking the repository allows developers to work independently while retaining well-maintained code on the master fork. For instructions on how to fork, follow the \u003ca href=\"https://help.github.com/en/articles/fork-a-repo\"\u003eFork a repo\u003c/a\u003e instructions.\u003c/p\u003e\n\u003cp\u003eAfter forking the repo, clone the repo to your local desktop:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to use SSH\u003c/span\u003e\ngit clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/template-workflow.git\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to use Https\u003c/span\u003e\ngit clone https://github.com/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/template-workflow.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis creates a replica of the remote repository on your local desktop. \u003cem\u003eNote\u003c/em\u003e: When you create your local repository, it will also make a local clone of the remote repository (typically as \u003ccode\u003eorigin\u003c/code\u003e). So, your local master branch would simply be \u003ccode\u003emaster\u003c/code\u003e. But, your remote master branch will be \u003ccode\u003eorigin/master\u003c/code\u003e. You can also add multiple remote repositories. For instance, let us say our main repository is under the remote repository \u003ccode\u003emy_repo\u003c/code\u003e. We will want to add it as a remote repository, so we can fetch the most up-to-date code. You could add it by:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the my_repo remote repo to your local desktop -- this will allow you to pull and push to branches on the my_repo repository\u003c/span\u003e\ngit remote add my_repo git@github.com:my_repo/template-workflow.git\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGit branches\u003c/h1\u003e\u003ca id=\"user-content-git-branches\" class=\"anchor\" aria-label=\"Permalink: Git branches\" href=\"#git-branches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBranching is how git actually tracks code development. For more information, see the \u003ca href=\"https://www.atlassian.com/git/tutorials/using-branches\" rel=\"nofollow\"\u003eGit Branch Tutorial\u003c/a\u003e on Atlassian. If you want to add a new feature, pipeline, or fix a bug, a common work flow would look like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Update your local copy of the master branch to make sure you are getting the most up-to-date code\u003c/span\u003e\ngit pull\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create the branch on your local machine and switch in this branch \u003c/span\u003e\ngit checkout -b [name_of_your_new_branch]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Push the branch on github\u003c/span\u003e\ngit push origin [name_of_your_new_branch]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs you develop, you want to commit your work to your branch, so you don\u0027t lose it all if something happens!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Confirm we\u0027re on the right branch\u003c/span\u003e\ngit branch -a\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add all your work to be tracked (Note: there are many ways to add specific files, etc. See https://git-scm.com/docs/git-add for more information). The following command adds everything in your currently directory.\u003c/span\u003e\ngit add \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Commit your work to the branch with a message describing what\u0027s in the commit\u003c/span\u003e\ngit commit -m \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eCreated the scATAC-seq pipeline!\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can add a -u parameter to set-upstream for a push\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Alternatively, git will also automatically query you when you do your first push.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can also set this manually by adding a new remote for your branch:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003egit remote add [name_of_your_remote] [name_of_your_new_branch]\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Here is another push where we specify HEAD\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003egit push origin HEAD # HEAD pushes everything up to the most recent commit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCode review\u003c/h1\u003e\u003ca id=\"user-content-code-review\" class=\"anchor\" aria-label=\"Permalink: Code review\" href=\"#code-review\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a \u003ca href=\"https://help.github.com/en/articles/creating-a-pull-request\"\u003eGitHub Pull Request\u003c/a\u003e. A PR allows other developers a chance to go through and comment on lines of code they believe can be improved. In addition, it will tell you if the code you are trying to merge into the \u003ccode\u003emy_repo\u003c/code\u003e branch actually conflicts with code that already exists in the branch, so you don\u0027t overwrite someone else\u0027s work.\u003c/p\u003e\n\u003cp\u003eOnce another developer approves the PR, you have the go-ahead to merge your code! Congrats, you finished your feature!\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: There are some cases where you may just want to push directly to the my_repo fork, thereby avoiding code reviews. For instance, if you\u0027re working on a one-off project that you want people to be able to see, but no one else is necessarily working on, you can always push directly to the branches on my_repo fork. Or, you could also still go through the steps of a PR, but simply merge your own code without CR.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1557428867.0 + "updated_at": 1596817893.0 }, { "data_format": 2, - "description": "temp to troubleshoot assignment of version", + "description": "Superparameterization coupler", "filenames": [ "Singularity" ], - "full_name": "yarikoptic/neurodebian-singularity-temp", - "latest_release": null, + "full_name": "CloudResolvingClimateModeling/sp-coupler", + "latest_release": "v1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSuperparameterization of OpenIFS with DALES\u003c/h1\u003e\u003ca id=\"user-content-superparameterization-of-openifs-with-dales\" class=\"anchor\" aria-label=\"Permalink: Superparameterization of OpenIFS with DALES\" href=\"#superparameterization-of-openifs-with-dales\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.1968305\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c1d1645aa679213f22216cc946a1fab69f500e5442f02f1e947c083a061e921d/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313936383330352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1968305.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains a script for running the global atmospheric model \u003ca href=\"https://confluence.ecmwf.int/display/OIFS/OpenIFS+Home\" rel=\"nofollow\"\u003eOpenIFS\u003c/a\u003e\ncoupled to local cloud-resolving LES simulations. The LES used is \u003ca href=\"https://github.com/dalesteam/dales\"\u003eDALES\u003c/a\u003e,\nthe Dutch Atmospheric Large Eddy Simulation.\u003c/p\u003e\n\u003cp\u003eA description of the coupling procedure and simulation results are given in\u003c/p\u003e\n\u003cp\u003eJansson, F., van den Oord, G., Pelupessy, I., Gr\u00f6nqvist, J. H., Siebesma, A. P., \u0026amp; Crommelin, D. (2019). Regional superparameterization in a global circulation model using large eddy simulations. \u003ca href=\"https://doi.org/10.1029/2018MS001600\" rel=\"nofollow\"\u003eJournal of Advances in Modeling Earth Systems, 11\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInterfaces to the models are built with \u003ca href=\"https://bitbucket.org/omuse/omuse/src/default/\" rel=\"nofollow\"\u003eOMUSE\u003c/a\u003e.\nThe interfaces are documented in the \u003ca href=\"https://omuse.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eOMUSE documentation\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFredrik Jansson (TU Delft and CWI, Amsterdam),\nGijs van den Oord (Netherlands e-Science center, Amsterdam),\nInti Pelupessy (Netherlands e-Science center, Amsterdam),\nMaria Chertova (Netherlands e-Science center, Amsterdam),\nPier Siebesma (TU Delft and KNMI),\nDaan Crommelin (CWI, Amsterdam),\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code in this repository is available under the Apache 2.0 license.\u003c/p\u003e\n\u003cp\u003eDALES and OpenIFS have their own licenses.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity image\u003c/h2\u003e\u003ca id=\"user-content-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Singularity image\" href=\"#singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor easy setup of the superparameterized simulation, we provide a\nSingularity recipe. This recipe can be used to build a Singularity\ncontainer including everything required to run the simulation.\nSee the \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall singularity on a computer where you have root access.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the image. This step requires root access.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build sp.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe build procedure will ask for a user name and password for the OpenIFS git repository at ECMWF,\nto download the modified OpenIFS.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo run the simulation, launch a shell inside the container. This step does not require root access,\nand can be done on a different machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell sp.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default Singularity mounts the user\u0027s home directory inside the image. If you have the sp-coupler directory somewhere in your home directory,\nthe singularity shell will be opened there.\u003c/p\u003e\n\u003cp\u003eRun the example simulation with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./run_T21_sockets.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLimitations of the Singularity setup\u003c/h3\u003e\u003ca id=\"user-content-limitations-of-the-singularity-setup\" class=\"anchor\" aria-label=\"Permalink: Limitations of the Singularity setup\" href=\"#limitations-of-the-singularity-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s unclear whether the Singularity image supports running on multiple nodes. AMUSE launches the workers using MPI_COMM_SPAWN,\nand this may not work over multiple nodes in this setup. For large runs, we recommend a manual installation for now.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample case\u003c/h2\u003e\u003ca id=\"user-content-example-case\" class=\"anchor\" aria-label=\"Permalink: Example case\" href=\"#example-case\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains a small example case which can be run on a single workstation, with OpenIFS on a T21 grid coupled to two DALES models.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eoifs-input/\u003c/code\u003e contains the files required to run OpenIFS for the small T21 grid. This is the standard OpenIFS test case bundled with OpenIFS itself.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edales-input/\u003c/code\u003e contains files required for DALES. This is a case with 64 x 64 x 160 grid points. The horizontal resolution can easily be changed by editing the file \u003ccode\u003enamoptions.001\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun_T21_mpi.sh\u003c/code\u003e run example simulation using MPI. For simulations using one or more computer nodes.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun_T21_sockets.sh\u003c/code\u003e run example simulation using the AMUSE sockets channel. For simulations that fit within one node.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun_T21_nospawn.sh\u003c/code\u003e run example simulation with work-around for MPI that does not support spawn. Experimental, provided as-is.\u003c/p\u003e\n\u003cp\u003eIn the Singularity image, the sockets variant works immediately. The MPI variant requires the following command to load the openMPI module:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eeval `/usr/bin/modulecmd sh load mpi/openmpi-x86_64`\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe full run of 100 time steps took about 13h on a quad-core workstation (i7-4790).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel settings\u003c/h2\u003e\u003ca id=\"user-content-model-settings\" class=\"anchor\" aria-label=\"Permalink: Model settings\" href=\"#model-settings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModel settings and input data are provided in three places:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ean OpenIFS input directory, containing an initial state, and model settings in fort.4\u003c/li\u003e\n\u003cli\u003ea DALES input directory, containing model settings in namoptions.001\u003c/li\u003e\n\u003cli\u003eoptions for the model coupling, provided on the command line of the coupling script. For a list of them, run \u003ccode\u003e./spmaster.py --help\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor a small example, see \u003ccode\u003erun_T21_sockets.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll model output is organized in an output directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edales-work-nnn/ DALES work directory, one for each LES instance.\n surf_xy*.nc surface fields: liquid water path, rain water path, total water path, accumulated surface rain\n cross*.nc cross section fields of the LES volume\nles-input copy of the DALES input files.\noifs-work OpenIFS work directory, contains output from the global model, mainly in GRIB format.\nspifs.nc netCDF file containing vertical profiles and tendencies for the superparameterized columns.\ntiming.txt CPU time statistics per time step for all models.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpenIFS and DALES can be configured as usual with their respective input files, in particular the type and frequency of the output they provide.\nSee the model documentation for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFormat of spifs.nc\u003c/h3\u003e\u003ca id=\"user-content-format-of-spifsnc\" class=\"anchor\" aria-label=\"Permalink: Format of spifs.nc\" href=\"#format-of-spifsnc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe output file spifs.nc contains vertical profiles of model variables and superparameterization tendencies\nfor every superparameterized grid point and global model time step.\nThe data is organized in groups according to the grid point where the model is located,\nfor example all data for the DALES at grid point 888 is located in the group 888/ in the netCDF file.\nIn general, variables in upper case relate to the global model, and variables in lower case relate to the local model.\nForcings \u003cem\u003eon\u003c/em\u003e the global model are denoted e.g. f_T, and on the local model f_thl.\u003c/p\u003e\n\u003cp\u003eThe superparameterization coupler can also store profiles in spifs.nc for columns that are not superparameterized.\nThe data for these columns then contain only quantities for the global model, there are no forcings and no local model quantities.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eVertical coordinates\u003c/h4\u003e\u003ca id=\"user-content-vertical-coordinates\" class=\"anchor\" aria-label=\"Permalink: Vertical coordinates\" href=\"#vertical-coordinates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProfiles in the local model use \u003ccode\u003ezf\u003c/code\u003e, in the root group of the file, as vertical coordinate. These are constant in time and the same for all the local models.\nFor the global model, the vertical coordinate is \u003ccode\u003eZf\u003c/code\u003e, which depends on both the grid point and time (because the global model\u0027s\nlevels are not on a fixed height but defined by pressure, they vary in time and space).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eVariables\u003c/h4\u003e\u003ca id=\"user-content-variables\" class=\"anchor\" aria-label=\"Permalink: Variables\" href=\"#variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most important variables are summarized below.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOpenIFS Variable\u003c/th\u003e\n\u003cth\u003eUnit\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003elat, lon\u003c/td\u003e\n\u003ctd\u003edegrees\u003c/td\u003e\n\u003ctd\u003egrid point coordinates\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eU, V\u003c/td\u003e\n\u003ctd\u003em/s\u003c/td\u003e\n\u003ctd\u003evelocity components in x, y directions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eT\u003c/td\u003e\n\u003ctd\u003eK\u003c/td\u003e\n\u003ctd\u003etemperature\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSH\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003especific humidity (i.e. water vapor, not cloud condensate)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQL\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003especific cloud condensate, liquid\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQI\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003especific cloud condensate in the form of ice\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQT\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003etotal specific humidity, SH+QL+QI\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePf\u003c/td\u003e\n\u003ctd\u003ePa\u003c/td\u003e\n\u003ctd\u003epressure\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eA\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003ecloud fraction\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_U, f_V\u003c/td\u003e\n\u003ctd\u003em/s^2\u003c/td\u003e\n\u003ctd\u003eforcings on global model\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_T\u003c/td\u003e\n\u003ctd\u003eK/s\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_SH, f_QL, f_QI\u003c/td\u003e\n\u003ctd\u003ekg/kg/s\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDALES Variable\u003c/th\u003e\n\u003cth\u003eUnit\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eu, v\u003c/td\u003e\n\u003ctd\u003em/s\u003c/td\u003e\n\u003ctd\u003evelocity components in x, y directions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ethl\u003c/td\u003e\n\u003ctd\u003eK\u003c/td\u003e\n\u003ctd\u003eliquid water potential temperature\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eqt\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003etotal specific humidity\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eql\u003c/td\u003e\n\u003ctd\u003ekg/kg\u003c/td\u003e\n\u003ctd\u003econdensed water specific humidity\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewthl\u003c/td\u003e\n\u003ctd\u003eK m/s\u003c/td\u003e\n\u003ctd\u003esurface heat flux\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewqt\u003c/td\u003e\n\u003ctd\u003em/s\u003c/td\u003e\n\u003ctd\u003esurface moisture flux\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_u, f_v\u003c/td\u003e\n\u003ctd\u003em/s^2\u003c/td\u003e\n\u003ctd\u003eforcings on local model\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_thl\u003c/td\u003e\n\u003ctd\u003eK/s\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef_qt\u003c/td\u003e\n\u003ctd\u003ekg/kg/s\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSample Python script for reading spifs.nc\u003c/h4\u003e\u003ca id=\"user-content-sample-python-script-for-reading-spifsnc\" class=\"anchor\" aria-label=\"Permalink: Sample Python script for reading spifs.nc\" href=\"#sample-python-script-for-reading-spifsnc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA sample python script for extracting data from the spifs.nc file is provided in \u003ccode\u003eexamples/access-spifs-nc.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRequirements and manual installation procedure - Python 3 version\u003c/h1\u003e\u003ca id=\"user-content-requirements-and-manual-installation-procedure---python-3-version\" class=\"anchor\" aria-label=\"Permalink: Requirements and manual installation procedure - Python 3 version\" href=\"#requirements-and-manual-installation-procedure---python-3-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCartesius\u003c/h2\u003e\u003ca id=\"user-content-cartesius\" class=\"anchor\" aria-label=\"Permalink: Cartesius\" href=\"#cartesius\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emodule load 2020\nmodule load netCDF-Fortran/4.5.2-gompi-2020a\nmodule load CMake/3.16.4-GCCcore-9.3.0\nmodule load FFTW/3.3.8-gompi-2020a\nmodule load Hypre/2.18.2-foss-2020a\nmodule load Python/3.8.2-GCCcore-9.3.0\nmodule load ecCodes/2.18.0-foss-2020a-Python-3.8.2\n# OpenMPI 4.0.3\n\ngit clone https://github.com/omuse-geoscience/omuse/\ncd omuse\npython3 -m venv omuse_env_2000\nsource omuse_env_2000/bin/activate\n\npip install -e .\n\nexport DOWNLOAD_CODES=all\nexport SYST=gnu-fast\n\n# work-around for OMUSE not finding netCDF\nexport DALES_FCFLAGS=\"`nf-config --flibs` -fdefault-real-8 -cpp\"\n\n# install DALES\npython setup.py build_code --code-name dales --inplace\n\n\nexport OIFS_GRIB_API_DIR=/sw/arch/RedHatEnterpriseServer7/EB_production/2020/software/ecCodes/2.18.0-foss-2020a-Python-3.8.2\nexport OIFS_GRIB_API_LIB=\"-L$OIFS_GRIB_API_DIR/lib -leccodes_f90\"\nexport GRIB_SAMPLES_PATH=$OIFS_GRIB_API_DIR/share/eccodes/ifs_samples/grib1_mlgrib2/\nexport OIFS_LAPACK_LIB=\"-L/sw/arch/RedHatEnterpriseServer7/EB_production/2020/software/ScaLAPACK/2.1.0-gompi-2020a/lib -L/sw/arch/RedHatEnterpriseServer7/EB_production/2020/software/OpenBLAS/0.3.9-GCC-9.3.0/lib -lopenblas -lscalapack\"\n\n# install open-ifs - requires ECMWF username/password\npython setup.py build_code --code-name oifs --inplace\n\npip install scipy moviepy matplotlib h5py shapely psutil\n# ERROR: pandas 1.0.3 requires pytz\u0026gt;=2017.2, which is not installed. - ignoring this for now\n\n# install SP-coupler, this repository. \ncd \npip install scipy moviepy matplotlib h5py shapely psutil\ngit clone https://github.com/CloudResolvingClimateModeling/sp-coupler\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRequirements and manual installation procedure - Python 2 version\u003c/h1\u003e\u003ca id=\"user-content-requirements-and-manual-installation-procedure---python-2-version\" class=\"anchor\" aria-label=\"Permalink: Requirements and manual installation procedure - Python 2 version\" href=\"#requirements-and-manual-installation-procedure---python-2-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following applies to the Python2 version, year 2020 or before.\nThese instructions are becoming obsolete, since OMUSE has switched to Python 3.\u003c/p\u003e\n\u003cp\u003eFor initial tests, we recommend trying the Singularity image, since it simplifies the installation.\nThe singularity recipe in the file \u003ccode\u003eSingularity\u003c/code\u003e can also be used as instructions for a manual setup.\u003c/p\u003e\n\u003cp\u003eFor a manual setup, the following tools and libraries are required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eC and Fortran compilers, e.g. gcc and gfortran\u003c/li\u003e\n\u003cli\u003emake\u003c/li\u003e\n\u003cli\u003ecmake\u003c/li\u003e\n\u003cli\u003enetCDF4\u003c/li\u003e\n\u003cli\u003eeccodes or gribapi\u003c/li\u003e\n\u003cli\u003eMPI\u003c/li\u003e\n\u003cli\u003empi4py\u003c/li\u003e\n\u003cli\u003ethe following Python modules:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epip install --upgrade mercurial moviepy f90nml numpy scipy matplotlib nose h5py docutils netCDF4 shapely psutil\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, install the following programs, in this order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAMUSE \u003ca href=\"http://amusecode.org/\" rel=\"nofollow\"\u003ehttp://amusecode.org/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOMUSE \u003ca href=\"https://bitbucket.org/omuse/omuse\" rel=\"nofollow\"\u003ehttps://bitbucket.org/omuse/omuse\u003c/a\u003e\nThe OMUSE Makefiles downloads and builds the two models.\n\u003cul\u003e\n\u003cli\u003eOpenIFS (note: requires username/password from ECMWF)\u003c/li\u003e\n\u003cli\u003eDALES \u003ca href=\"https://github.com/CloudResolvingClimateModeling/dales\"\u003ehttps://github.com/CloudResolvingClimateModeling/dales\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that OpenIFS might require several environment variables to be set both at compilation and at runtime.\nSee \u003ca href=\"https://confluence.ecmwf.int/display/OIFS/OpenIFS+User+Guides\" rel=\"nofollow\"\u003ethe OpenIFS manual\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the above are installed, you will need to add the python modules to your PYTHONPATH:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=\u0026lt;AMUSE clone path\u0026gt;/src:\u0026lt;SP-coupler clone path\u0026gt;/splib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto run the main driver script in this repo, spmaster.py. To view all the superparametrization options and configurations (e.g. the choice of the superparametrized region), type\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./spmaster.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation notes for specific systems\u003c/h1\u003e\u003ca id=\"user-content-installation-notes-for-specific-systems\" class=\"anchor\" aria-label=\"Permalink: Installation notes for specific systems\" href=\"#installation-notes-for-specific-systems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation on Arch Linux\u003c/h2\u003e\u003ca id=\"user-content-installation-on-arch-linux\" class=\"anchor\" aria-label=\"Permalink: Installation on Arch Linux\" href=\"#installation-on-arch-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen configuring OMUSE, one must explicitly specify python2, since the default is python3.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd amuse\nPYTHON=python2 ./configure --with-netcdf=/usr/\nmake framework\n\nexport DOWNLOAD_CODES=all\n\ncd src/omuse/community/dales\nmake\n\ncd ../oifs\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation on Fedora\u003c/h2\u003e\u003ca id=\"user-content-installation-on-fedora\" class=\"anchor\" aria-label=\"Permalink: Installation on Fedora\" href=\"#installation-on-fedora\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFedora\u0027s netcdf require some extra settings, becuse the module files\nand .inc files are in different places. We specify the module path\nwith FCFLAGS: Another issue seen on Fedora is that make in the dales\ndirectory fails with \u003ccode\u003ebuild.py: error: No module named dalesreader\u003c/code\u003e. One solution is to add . to PYTHONPATH. This seems to confuse mercurial though.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFCFLAGS=-I/usr/lib64/gfortran/modules ./configure --with-netcdf=/usr\nmake framework\n\nexport DOWNLOAD_CODES=all\n\nexport PYTHONPATH=$PYTHONPATH:. # for dalesreader to be found when creating the interface code\ncd src/omuse/community/dales\nmake\n\ncd ../oifs\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation on ECMWF Cray system\u003c/h2\u003e\u003ca id=\"user-content-installation-on-ecmwf-cray-system\" class=\"anchor\" aria-label=\"Permalink: Installation on ECMWF Cray system\" href=\"#installation-on-ecmwf-cray-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInitial setup\u003c/h3\u003e\u003ca id=\"user-content-initial-setup\" class=\"anchor\" aria-label=\"Permalink: Initial setup\" href=\"#initial-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLoad modules\u003c/h4\u003e\u003ca id=\"user-content-load-modules\" class=\"anchor\" aria-label=\"Permalink: Load modules\" href=\"#load-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eprgenvswitchto intel\n\nmodule load python/2.7.12-01\nmodule load netcdf4/4.4.1\nmodule load cmake/3.12.0\nmodule load git\nmodule load eccodes\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOther settings\u003c/h4\u003e\u003ca id=\"user-content-other-settings\" class=\"anchor\" aria-label=\"Permalink: Other settings\" href=\"#other-settings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# https proxy\nexport https_proxy=proxy:2222\n\nexport AMUSE_DIR=$PERM/2019/amuse/\nexport PYTHONPATH=$PYTHONPATH:$AMUSE_DIR/src/\n\nsource $PERM/meteo/bin/activate\n\n# OpenIFS compilation options\nexport OIFS_COMP=intel\nexport OIFS_BUILD=craynomp\n\n# Cray setup: all compilers are invoked with these names:\nexport OIFS_FC=ftn\nexport OIFS_CC=cc\n\nexport OIFS_GRIB_API_DIR=$ECCODES_DIR\nexport OIFS_GRIB_API_LIB=\"-L $ECCODES_LIB_DIR -leccodes_f90 -leccodes\"\nexport OIFS_GRIB_API_INCLUDE=\"-I $ECCODES_INCLUDE_DIR\"\n\nexport FCFLAGS=\"-convert big_endian\"\n\n# On the Cray, we don\u0027t want any linking flags for Lapack\n# they are included when using the Cray compiler wrappers\nexport OIFS_LAPACK_LIB=\" \"\n\n# DALES compilation options\nexport SYST=ECMWF-intel\nexport DALES_FCFLAGS=\"-g -traceback -O3 -r8 -xHost -fpp\"\n#these flags apply to the interface only\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003evirtual Python environment\u003c/h4\u003e\u003ca id=\"user-content-virtual-python-environment\" class=\"anchor\" aria-label=\"Permalink: virtual Python environment\" href=\"#virtual-python-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epip install --user virtualenv\nPATH=$PATH:~/.local/bin/\n\ncd $PERM\nvirtualenv meteo\nsource $PERM/meteo/bin/activate\npip install --upgrade mercurial moviepy f90nml numpy scipy matplotlib nose h5py docutils netCDF4 shapely psutil\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003empi4py on ECMWF\u003c/h4\u003e\u003ca id=\"user-content-mpi4py-on-ecmwf\" class=\"anchor\" aria-label=\"Permalink: mpi4py on ECMWF\" href=\"#mpi4py-on-ecmwf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince mid-2018 the mpi4py installed with the python modules at ECMWF no longer works. It can be installed manually from source.\nThis should be done with the same set of compilers and modules loaded as used for everything else.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eactivate the virtual python environment, and with the intel compiler and our modules loaded.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd $PERM\nwget https://bitbucket.org/mpi4py/mpi4py/downloads/mpi4py-3.0.0.tar.gz -O mpi4py-3.0.0.tar.gz\ntar zxf mpi4py-3.0.0.tar.gz\ncd mpi4py-3.0.0\n\n# add an enry for the Cray system in mpi.cfg\ncat \u0026gt;\u0026gt; mpi.cfg \u0026lt;\u0026lt;EOF\n[cray]\nmpicc = cc\nmpicxx = CC\nextra_link_args = -shared\nEOF\n\npython setup.py build --mpi=cray\npython setup.py install \n\ncd $PERM\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eNotes\u003c/h5\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFJ tried to compile mpi4py with the gnu compiler (\u003ccode\u003eprgenvswitchto gnu\u003c/code\u003e). Compilation seemed OK, but python segfaulted when testing the coupled system. Compiling mpi4py with the intel compiler seems to work - no module changes needed.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://jaist-hpc.blogspot.com/2015/02/mpi4py.html\" rel=\"nofollow\"\u003eSource for mpi4py instructions\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe following instructions install omuse and amuse sibe by side in the directory $PERM/2019/.\nThen a symlink in amuse/src is created, to omuse/src/omuse, so that the path amuse/src/omuse/community still works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOMUSE\u003c/h3\u003e\u003ca id=\"user-content-omuse\" class=\"anchor\" aria-label=\"Permalink: OMUSE\" href=\"#omuse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd $PERM/2019\nhg clone --insecure https://bitbucket.org/omuse/omuse\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAmuse\u003c/h3\u003e\u003ca id=\"user-content-amuse\" class=\"anchor\" aria-label=\"Permalink: Amuse\" href=\"#amuse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/fjansson/amuse\ncd amuse\ngit checkout spawnless\n\ncd src\nln -s $PERM/2019/omuse/src/omuse omuse\n# so that the old path amuse/src/omuse/community still works\n\ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis version is our own no-spawn fork for use at ECMWF. Elsewhere, the official amuse can be used:\n\u003ca href=\"https://github.com/amusecode/amuse/\"\u003ehttps://github.com/amusecode/amuse/\u003c/a\u003e .\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#make AMUSE find the right python:\nexport PYTHON=python\n\n./configure FC=ftn CC=cc --with-netcdf=`nc-config --prefix`\n# some libraries will not be found, e.g. gsl. This is OK \n\nmake framework\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOpenIFS and DALES\u003c/h3\u003e\u003ca id=\"user-content-openifs-and-dales\" class=\"anchor\" aria-label=\"Permalink: OpenIFS and DALES\" href=\"#openifs-and-dales\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpenIFS and DALES can be cloned using the OMUSE make file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport DOWNLOAD_CODES=all\n# DOWNLOAD_CODES=all will checkout entire repo with ssh, intended for developers of the components.\n# DOWNLOAD_CODES=latest will (shallow) checkout latest revision only\n# DOWNLOAD_CODES=\u0026lt;anything else\u0026gt; will (shallow) checkout release tag spifs_v1.0.0\n\nexport AMUSE_DIR=$PERM/2019/amuse/\nexport PYTHONPATH=$PYTHONPATH:$AMUSE_DIR/src/\nexport PATH=$PATH:$AMUSE_DIR/bin/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003ecd community/dales\nmake\ncd ../..\n\ncd community/oifs\nmake\n# note: this downloads OpenIFS, which requires ECMWF credentials\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1503090511.0 + "updated_at": 1645814234.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularityfile.def" ], - "full_name": "marcoserenelli/WESADevaluator", + "full_name": "shanzewang/ros_jackal_image", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eROS-Jackal\u003c/h1\u003e\u003ca id=\"user-content-ros-jackal\" class=\"anchor\" aria-label=\"Permalink: ROS-Jackal\" href=\"#ros-jackal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the repository for the paper \"\u003ca href=\"https://arxiv.org/abs/2210.04839\" rel=\"nofollow\"\u003eBenchmarking Reinforcement Learning Techniques for Autonomous Navigation\u003c/a\u003e\".\u003c/p\u003e\n\u003cp\u003eThe results shown in the paper use Condor Cluster to distribute 100 actors for collecting trajectories. This setting can greatly speed up the training and make it feasible to finish all the experiments presented in the paper, however Condor Cluster is relatively inaccessible to most users. Instead, to guarantee reproducibility, we provide this version of repository that distributes the actors over 10 Singularity containers that can run locally on a single machine.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/ros_jackal.git\ncd ros_jackal\n\u003c/code\u003e\u003c/pre\u003e\n\u003col\u003e\n\u003cli\u003eIn your virtual environment, install the python dependencies:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\" rel=\"nofollow\"\u003ehttps://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 is \u003cstrong\u003erequired\u003c/strong\u003e to build the image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Only do following step if you really need!) The code does not require ROS installation, since the rollout happens in the container, but if you have need to develop based on our repo, running ROS and Gazebo simulation out of the container enables GUI and is easier to debug. Follow steps below to install ROS dependencies (assume \u003ccode\u003emelodic\u003c/code\u003e ROS installed already):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo and required ros packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/ros_jackal.git\ngit clone https://github.com/jackal/jackal.git --branch melodic-devel\ngit clone https://github.com/jackal/jackal_simulator.git --branch melodic-devel\ngit clone https://github.com/jackal/jackal_desktop.git --branch melodic-devel\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eInstall ROS package dependencies\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/melodic/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=melodic\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBuild the workspace\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecatkin_make\nsource devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eVerify your installation: (this script will run open-ai gym environment for 5 episodes)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePull image file (modify the \u0026lt;FOLDER_PATH_TO_SAVE_IMAGE\u0026gt; in the command, image file size ~ 3G\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \u0026lt;PATH_TO_THIS_REPO\u0026gt;/local_buffer/image:latest.sif library://zifanxu/ros_jackal_image/image:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh \u0026lt;PATH_TO_THIS_REPO\u0026gt;/local_buffer/nav_benchmark.sif python3 test_env.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrain a deep RL navigation policy\u003c/h2\u003e\u003ca id=\"user-content-train-a-deep-rl-navigation-policy\" class=\"anchor\" aria-label=\"Permalink: Train a deep RL navigation policy\" href=\"#train-a-deep-rl-navigation-policy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train a navigation policy, you just need to specify a \u003ccode\u003e.yaml\u003c/code\u003e file that includes the parameters for specific experiment. For instance,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython train.py --config configs/e2e_default_TD3.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe provide the full list of \u003ccode\u003e.yaml\u003c/code\u003e files used in our experiment in the end.\u003c/p\u003e\n\u003cp\u003eThis repo saves the collected trajectories from each actor in a local buffer folder, also actors load the recent policy from this folder. By default, buffer folder is a folder named \u003ccode\u003elocal_buffer\u003c/code\u003e in current dictionary. You can specify a new folder as \u003ccode\u003eexport BUFFER_FOLDER=/PATH/TO/YOUR/BUFFER_FOLDER\u003c/code\u003e. The logging files can be found under folder \u003ccode\u003elogging\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrain in computing cluster\u003c/h2\u003e\u003ca id=\"user-content-train-in-computing-cluster\" class=\"anchor\" aria-label=\"Permalink: Train in computing cluster\" href=\"#train-in-computing-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCluster requires a shared file system, where multiple actors load the lastest policy, rollout, and save the trajectory in the \u003ccode\u003eBUFFER_FOLDER\u003c/code\u003e. Then, a critic collects trajectories from \u003ccode\u003eBUFFER_FOLDER\u003c/code\u003e and updates the policy.\u003c/p\u003e\n\u003cp\u003eThis is asyncronized training pipeline, namely the actors might fall behind and do not generate trajectories from the latest policy.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the Singularity image\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif library://zifanxu/ros_jackal_image/image:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOn critic computing node\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport BUFFER_PATH=\u0026lt;BUFFER_PATH\u0026gt;\n./singularity_run.sh \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif python train.py --config configs/e2e_default_TD3_cluster.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOn actor computing node 0 (you need to run \u003ccode\u003e0-50\u003c/code\u003e computing nodes as defined in line 60 in \u003ccode\u003econtainer_config.yaml\u003c/code\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport BUFFER_PATH=\u0026lt;BUFFER_PATH\u0026gt;\n./singularity_run.sh \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif python actor.py --id 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSuccess rate of policies trained with different neural network architectures and history lengths in static (top) and dynamic-wall (bottom) environments.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eStatic\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e57 \u00b1 7%\u003c/td\u003e\n\u003ctd\u003e42 \u00b1 2%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e51 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e55 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e68 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e46 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eDynamic box\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e50 \u00b1 5%\u003c/td\u003e\n\u003ctd\u003e35 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e46 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e48 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 1%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e42 \u00b1 5%\u003c/td\u003e\n\u003ctd\u003e40 \u00b1 1%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e52 \u00b1 1%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e44 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eDynamic wall\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e67 \u00b1 7%\u003c/td\u003e\n\u003ctd\u003e72 \u00b1 1%\u003c/td\u003e\n\u003ctd\u003e69 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e82 \u00b1 4%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e78 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e63 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e33 \u00b1 28%\u003c/td\u003e\n\u003ctd\u003e15 \u00b1 13%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate, survival time and traversal time of policies trained with different safe-RL methods, MPC with probabilistic transition model and DWA.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSafe-RL method\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMLP\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eLagrangian\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMPC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDWA\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuccess rate\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e74 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e70 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e43%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSurvival time\u003c/td\u003e\n\u003ctd\u003e8.0 \u00b1 1.5s\u003c/td\u003e\n\u003ctd\u003e16.2 \u00b1 2.5s\u003c/td\u003e\n\u003ctd\u003e55.7 \u00b1 4.9s\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e88.6s\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTraversal time\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e7.5 \u00b1 0.3s\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e8.6 \u00b1 0.2s\u003c/td\u003e\n\u003ctd\u003e24.7 \u00b1 2.0s\u003c/td\u003e\n\u003ctd\u003e38.5s\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate of policies trained with different model-based methods and different number of transition samples\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTransition samples\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e100k\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e500k\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e2000k\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e13 \u00b1 7%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e58 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDyna-style deterministic\u003c/td\u003e\n\u003ctd\u003e8 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e30 \u00b1 10%\u003c/td\u003e\n\u003ctd\u003e66 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMPC deterministic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e21 \u00b1 10%\u003c/td\u003e\n\u003ctd\u003e62 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDyna-style probabilistic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e48 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e70 \u00b1 1%\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMPC probabilistic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e70 \u00b1 3%\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate of policies trained with different number of training environments\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eEnvironments\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e250\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuccess rate\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e54 \u00b1 8%\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e72 \u00b1 6%\u003c/td\u003e\n\u003ctd\u003e74 \u00b1 2 %\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(See below for all the config files used to reproduce the experiments)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e \u2514\u2500configs\n \u2502 \u2514\u2500safe_rl\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2502 \u2514\u2500mlp.yaml\n \u2502 \u2502 \u2514\u2500lagrangian.yaml\n \u2502 \u2514\u2500architecture_static\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_wall\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_box\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500model_based\n \u2502 \u2502 \u2514\u2500dyna.yaml\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2514\u2500generalization\n \u2502 \u2502 \u2514\u2500num_world_50.yaml\n \u2502 \u2502 \u2514\u2500num_world_5.yaml\n \u2502 \u2502 \u2514\u2500num_world_10.yaml\n \u2502 \u2502 \u2514\u2500num_world_100.yaml\n \u2502 \u2502 \u2514\u2500num_world_250.yamlconfigs\n \u2502 \u2514\u2500safe_rl\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2502 \u2514\u2500mlp.yaml\n \u2502 \u2502 \u2514\u2500lagrangian.yaml\n \u2502 \u2514\u2500architecture_static\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_wall\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_box\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500model_based\n \u2502 \u2502 \u2514\u2500dyna.yaml\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2514\u2500generalization\n \u2502 \u2502 \u2514\u2500num_world_50.yaml\n \u2502 \u2502 \u2514\u2500num_world_5.yaml\n \u2502 \u2502 \u2514\u2500num_world_10.yaml\n \u2502 \u2502 \u2514\u2500num_world_100.yaml\n \u2502 \u2502 \u2514\u2500num_world_250.yaml\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1716120220.0 + "updated_at": 1713930886.0 }, { "data_format": 2, - "description": "Singularity container for R language\u0027s current version.", + "description": null, "filenames": [ - "Singularity" + "docker/Singularity.snowflake" ], - "full_name": "romxero/R-singularity", + "full_name": "pnplab/bids-preproc", "latest_release": null, - "readme": "\u003cp\u003e#R Singularity for Sherlock\u003c/p\u003e\n\u003cp\u003eThis is an R singularity image for Sherlock using Debian SID unstable distribution\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epnplab\u0027s bids-preproc pipeline\u003c/h1\u003e\u003ca id=\"user-content-pnplabs-bids-preproc-pipeline\" class=\"anchor\" aria-label=\"Permalink: pnplab\u0027s bids-preproc pipeline\" href=\"#pnplabs-bids-preproc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn fmriprep superlayer to handle hcp/cloud or distributed scheduling for heavy longitudinal datasets.\u003c/p\u003e\n\u003cp\u003eIt does six things:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eabstract and orchestrate the preprocessing across any kind of distributed environments,\nit currently works with SLURM, but can easily be extended to any system, ie. ssh, amazon cloud, through any existing of the existing dask distributed/jobqueue implementation.\u003c/li\u003e\n\u003cli\u003eprovide an extra granularity to fmriprep at the level of session,\nas fmriprep currently only handles processing of either full dataset or of single participant at a time.\u003c/li\u003e\n\u003cli\u003ewrap tool execution calls around docker or singularity virtual containers (or none at all), with the same source code.\u003c/li\u003e\n\u003cli\u003earchive dataset with dar and only extract the relevant parts (ie. specific sessions or subjects) when needed on computing node for mutualised hypercomputing environments,\nas filesystem such as lustre, which we tested on the beluga cluster (compute canada):\n\u003cul\u003e\n\u003cli\u003ecause fmriprep to randomly hang indefinitely due to process getting stuck in D-state mode (pending kernel-level state, likely due to the network filesystem drivers)\u003c/li\u003e\n\u003cli\u003eare slow (\u003ccode\u003eseff\u003c/code\u003e averages to 2.17% of CPU utilization for 92.36% for memory usage).\u003c/li\u003e\n\u003cli\u003eare limited in the amount of file one can write (the 1M default per-user scratch file count limit is already broken out for a single dataset such as kaminati, when considering for fmriprep intermediary generated files)\nand inner compute-nodes storages are too limited (a few hundreds gigs only) to store a single dataset, or even a single subject, considering all fmriprep\u0027s intermediary generated files (for kaminati).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003emonkey patch currently broken fmriprep anatomic fast-track mechanism, which is buggy with some dataset, cf. \u003ca href=\"https://github.com/nipreps/smriprep/issues/224\"\u003ehttps://github.com/nipreps/smriprep/issues/224\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003emonitor unreachable dask workers (likely due to hypercomputer network congestion issues) and kill and reschedule their associated compute nodes, if dask+slurm is the used scheduler.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe architecture should enable easy changes of the pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eie. to partially download the dataset, such as a single session, at the relevant time instead of extracting it from dar.\u003c/li\u003e\n\u003cli\u003eie. to use a different orchestration system than slurm (for instance kubernetes, ..., basically anything, check both dask distributed and dask jobqueue documentations for most of the currently available options).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBefore use, you must:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esetup the license/freesurfer.txt file (get a license from freesurfer website and put it inside that file, cf. fmriprep doc)\u003c/li\u003e\n\u003cli\u003edownload the templateflow atlas using the script in \u003ccode\u003e./scripts/download-templateflow-data.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003edownload and build the relevant singularity images (this step is only required if singularity is used):\n\u003ccode\u003emkdir ../singularity-images/; cd ../singularity-images/; singularity build bids-validator-1.8.5.simg docker://bids/validator:v1.8.5 ; singularity build fmriprep-20.2.6.simg docker://nipreps/fmriprep:20.2.6 ; singularity build smriprep-0.8.1.simg docker://nipreps/smriprep:0.8.1\u003c/code\u003e.\nfile \u003ccode\u003econfig.py\u003c/code\u003e might have to be adapted to get the proper path.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSample usage:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003epython main.py --help\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython main.py --vm-engine docker --granularity subject --executor none --disable-mriqc \u0027/scratch/nuks/kaminati-bids\u0027 \u0027/scratch/nuks/kaminati-preproc\u0027\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e./preprocessing.sh --vm-engine singularity --granularity session --executor slurm --disable-mriqc --worker-memory-gb 64 --worker-cpu-count 16 --worker-count 7 --worker-walltime 2-12:00:00 --worker-local-dir \u0027$SLURM_TMPDIR/pnplab-kaminati\u0027 \u0027/scratch/nuks/kaminati-bids\u0027 \u0027/scratch/nuks/kaminati-preproc\u0027\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 0, "topics": [], - "updated_at": 1562008683.0 + "updated_at": 1638698896.0 }, { "data_format": 2, - "description": "https://github.com/trinityrnaseq/trinityrnaseq for Hebbe via Singularity Hub", + "description": null, "filenames": [ - "Singularity" + "Singularity.fmriprep_1.5.0_ciftify", + "Singularity.fmriprep_ciftify_short3", + "Singularity.fmriprep_1.5.0_basic", + "Singularity.repronim_fmriprep_oldconnectome", + "Singularity.repronim_fmriprep_1.5.0", + "Singularity.fmriprep_old_connectome", + "Singularity.fmriprep_ciftify_short2", + "Singularity.sing_fmriprep_ciftify", + "Singularity.fmriprep_ciftify_short6", + "Singularity.repronim_fmriprep_1.5.0_with_connectome", + "Singularity.ciftify_only", + "Singularity.fmriprep_1.5.0_no_connectome", + "Singularity.fmriprep_test", + "Singularity.fmriprep_ciftify_short5", + "Singularity.test", + "Singularity.fmriprep_ciftify", + "Singularity.fmriprep_ciftify_short", + "Singularity.fmriprep_ciftify4", + "Singularity.oldconnectome" ], - "full_name": "SysBioChalmers/trinityrnaseq-hebbe", + "full_name": "kellyuw/SingularityRecipes", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3239\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository hosts just the Singularity recipe that is used to automatically build the latest image up on Singularity Hub. A custom recipe was needed for the image to run on the Hebbe cluster.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/trinityrnaseq/trinityrnaseq\"\u003eThe original source code is here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain the \u003ccode\u003eSingularity\u003c/code\u003e file, the \u003ccode\u003eDockerfile\u003c/code\u003e from the \u003ccode\u003e2.8.5\u003c/code\u003e release was copied here and converted to a recipe with \u003ccode\u003espython\u003c/code\u003e after which it was manually tweaked. The original \u003ccode\u003erecipe.basic\u003c/code\u003e that uses the Docker image has been kept for reference.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularityRecipes\u003c/h1\u003e\u003ca id=\"user-content-singularityrecipes\" class=\"anchor\" aria-label=\"Permalink: SingularityRecipes\" href=\"#singularityrecipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCollection of Singularity recipes for neuroimaging.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1571750826.0 + "updated_at": 1572493534.0 }, { "data_format": 2, - "description": "Singularity image for (last working verison) of OpenSees", + "description": "This source code is now deprecated. For updated workflows visit", "filenames": [ - "Singularity" + "build/Singularity.beta" ], - "full_name": "researchapps/opensees", + "full_name": "glass-consortium/glasstools", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity image for GLASS workflows\u003c/h2\u003e\u003ca id=\"user-content-singularity-image-for-glass-workflows\" class=\"anchor\" aria-label=\"Permalink: Singularity image for GLASS workflows\" href=\"#singularity-image-for-glass-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003ca href=\"https://www.singularity-hub.org/collections/262\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6089d24b6232e35051c9f5d1f23d3b2440289a8bdab500934c1f194dbba13ccb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f53696e67756c61726974792d312e312e3273322d627269676874677265656e2e737667\" alt=\"Singularity 1.1.2s2\" data-canonical-src=\"https://img.shields.io/badge/Singularity-1.1.2s2-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/glasstools/keystone/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/183e5dd3d93e42f62573ab86aa911caf6c12dab38c41c6c08e2a495e8aee14f8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f446f636b65722d312e322e322d627269676874677265656e2e737667\" alt=\"Docker 1.2.2\" data-canonical-src=\"https://img.shields.io/badge/Docker-1.2.2-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/glass-consortium/glassdocs/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5874f7ca01d26ee4c18b1ba13da87158f986395e8ab9464d0a1bc0fd26477538/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f676c6173732d636f6e736f727469756d2f676c617373646f63732e737667\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/glass-consortium/glassdocs.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e18-Nov-2017\u003cbr\u003e\nv1.1.2s2\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eBuild details for \u003ccode\u003eglass-consortium/glasstools\u003c/code\u003e images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePS:\u003c/strong\u003e Documentation to run workflows is not yet ready. Visit \u003ca href=\"https://docker.glass-consortium.org\" rel=\"nofollow\"\u003ehttps://docker.glass-consortium.org\u003c/a\u003e for updates.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCurrent Build\u003c/h4\u003e\u003ca id=\"user-content-current-build\" class=\"anchor\" aria-label=\"Permalink: Current Build\" href=\"#current-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSee below on how to install Singularity version: \u003ccode\u003e2.4-install_718360bb.g718360bb\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://glass-consortium/glasstools:beta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAutomated build, when successfully built is available at Singularity Hub: \u003ca href=\"https://www.singularity-hub.org/collections/262\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/262\u003c/a\u003e with image tag: \u003ccode\u003eglass-consortium/glasstools:beta\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload using \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, v2.4 or higher.\u003c/li\u003e\n\u003cli\u003eAvoid running container as root. Singularity images does not require root privileges to run workflows.\u003c/li\u003e\n\u003cli\u003eDefault bind while running workflow is user ${HOME}.\u003c/li\u003e\n\u003cli\u003eFor better potability and disk mounts, ask your system admin to configure \u003ccode\u003e/etc/singularity/singularity.conf\u003c/code\u003e and set \u003ccode\u003eenable overlay = yes\u003c/code\u003e. Read \u003ca href=\"http://singularity.lbl.gov/docs-mount\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/docs-mount\u003c/a\u003e for details.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eManual build\u003c/h4\u003e\u003ca id=\"user-content-manual-build\" class=\"anchor\" aria-label=\"Permalink: Manual build\" href=\"#manual-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend pulling pre-built Singularity image from Singularity registry at \u003ca href=\"https://www.singularity-hub.org/collections/262\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/262\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eManual build is for improvement and debugging of current beta image, especially with reducing image size and adding shortcodes to additional GLASS workflows.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/glass-consortium/glasstools.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\n\nsingularity build glasstools_keystone_beta.simg Singularity.beta\nsingularity inspect glasstools_keystone_beta.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee file: \u003cem\u003eglasstools_keystone_beta.simg.inspect.log\u003c/em\u003e for image details.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to install Singularity\u003c/h3\u003e\u003ca id=\"user-content-how-to-install-singularity\" class=\"anchor\" aria-label=\"Permalink: How to install Singularity\" href=\"#how-to-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOne time installation, \u003cstrong\u003erequires admin privileges\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePlease ask your system administrator to install Singularity with following version. While installation should be done by IT administrator, running GLASS workflows does not require \u003ccode\u003esudo\u003c/code\u003e privilege. Also, unlike potential root escalation while running docker container, Singularity based workflows are more isolated from host environment and less vulnerable to root escalation. Visit \u003ca href=\"http://singularity.lbl.gov/user-guide#security-and-privilege-escalation\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/user-guide#security-and-privilege-escalation\u003c/a\u003e for more on security.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eGLASS workflows are using Singularity \u003ccode\u003ev2.4\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eFull version at the time of install: v2.4-install_718360bb.g718360bb\u003cbr\u003e\nCommit: \u003ca href=\"https://github.com/singularityware/singularity/commit/718360bb20b66cbafb85dd9d0a73bd6bb60c7a1f\"\u003ehttps://github.com/singularityware/singularity/commit/718360bb20b66cbafb85dd9d0a73bd6bb60c7a1f\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eFor better compatibility with pre-built GLASS image, please install Singularity from forked reposioty as follows:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eumask\u003c/span\u003e 0022\n\ngit clone https://github.com/glass-consortium/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# confirm last commit ID to be 718360bb20b66cbafb85dd9d0a73bd6bb60c7a1f for HEAD -\u0026gt; master branch\u003c/span\u003e\ngit log --name-status HEAD^..HEAD\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# fork master branch to a new branch, named install_718360bb\u003c/span\u003e\ngit checkout -b install_718360bb\ngit status\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThis will show...\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eOn branch install_718360bb\u003cbr\u003e\nnothing to commit, working tree clean\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./autogen.sh \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e ./configure --prefix=/usr/local \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e return exit code for compilation status\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$?\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# only one time, we need root privileges\u003c/span\u003e\nsudo make install\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# return to non-root user environment\u003c/span\u003e\nsudo -k\n\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${HOME}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e non-root user\u003c/span\u003e\n\nsingularity --version\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will show \u003ccode\u003e2.4-install_718360bb.g718360bb\u003c/code\u003e. If so, installation is identical to an environment used to build GLASS Singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBugs, issues\u003c/h3\u003e\u003ca id=\"user-content-bugs-issues\" class=\"anchor\" aria-label=\"Permalink: Bugs, issues\" href=\"#bugs-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReport issues related to setting up Docker/Singularity image and running workflows at \u003ca href=\"https://github.com/glass-consortium/glassdocs/issues\"\u003ehttps://github.com/glass-consortium/glassdocs/issues\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOriginal Singularity file was based on \u003ca href=\"https://github.com/jekriske/r-base\"\u003ehttps://github.com/jekriske/r-base\u003c/a\u003e by \u003ca href=\"https://github.com/jekriske\"\u003eJeff Kriske\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 3, - "topics": [ - "singularity-container", - "singularity-containers", - "opensees" - ], - "updated_at": 1486514968.0 + "topics": [], + "updated_at": 1585336352.0 }, { "data_format": 2, - "description": "Anvi\u2019o is an open-source, community-driven analysis and visualization platform for microbial \u2018omics.", + "description": null, "filenames": [ - "8/Singularity", - "7/Singularity" + "Singularity.v0.2.0", + "Singularity.v0.4.0", + "Singularity.v0.5.0", + "Singularity", + "Singularity.v0.1.0" ], - "full_name": "pscedu/singularity-anvio", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-anvio/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-anvio/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-anvio/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-anvio/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/76e66b0528620e6de223824bc0811e43da5305bc225fde5e0da2581d636a4d79/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d616e76696f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/76e66b0528620e6de223824bc0811e43da5305bc225fde5e0da2581d636a4d79/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d616e76696f\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-anvio\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b266c3fbd482290cf4c5e346f3832162d4903334e17f80734b14d5a75a2d437e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d616e76696f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b266c3fbd482290cf4c5e346f3832162d4903334e17f80734b14d5a75a2d437e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d616e76696f\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-anvio\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e125772a3252bfd4304809a815f2de28bd93d1d43e97d512f356a8db123116ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d616e76696f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e125772a3252bfd4304809a815f2de28bd93d1d43e97d512f356a8db123116ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d616e76696f\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-anvio\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7becb11640fa75f5c844770229b3132b8ed32bee6c4001e30be8dd3c86e26513/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d616e76696f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7becb11640fa75f5c844770229b3132b8ed32bee6c4001e30be8dd3c86e26513/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d616e76696f\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-anvio\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-anvio\u003c/h1\u003e\u003ca id=\"user-content-singularity-anvio\" class=\"anchor\" aria-label=\"Permalink: singularity-anvio\" href=\"#singularity-anvio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4ec10e0b03386960c0b2a8183e88162122d72b83655bd09576000f057f5bb00f/68747470733a2f2f6d6572656e6c61622e6f72672f696d616765732f616e76696f2d6e6574776f726b2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ec10e0b03386960c0b2a8183e88162122d72b83655bd09576000f057f5bb00f/68747470733a2f2f6d6572656e6c61622e6f72672f696d616765732f616e76696f2d6e6574776f726b2e706e67\" data-canonical-src=\"https://merenlab.org/images/anvio-network.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://https://merenlab.org/software/anvio/\" rel=\"nofollow\"\u003eanvio\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eanvio-*\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/anvio/8\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/anvio\u003c/code\u003e as \u003ccode\u003e8.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "darachm/singularity_runningJobs", + "latest_release": "v0.1.0", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1641398359.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1593741796.0 }, { "data_format": 2, - "description": "Singularity recipe for Braker", + "description": "Singularity containers to run Paraview", "filenames": [ - "Singularity", - "Singularity.2.1.2" + "Singularity.paraview", + "Singularity.pvbatch" ], - "full_name": "ISU-HPC/braker", + "full_name": "stephansmit/paraview_containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebraker\u003c/h1\u003e\u003ca id=\"user-content-braker\" class=\"anchor\" aria-label=\"Permalink: braker\" href=\"#braker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for Braker\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParaview_containers\u003c/h1\u003e\u003ca id=\"user-content-paraview_containers\" class=\"anchor\" aria-label=\"Permalink: Paraview_containers\" href=\"#paraview_containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity containers to run paraview\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse\u003c/h2\u003e\u003ca id=\"user-content-use\" class=\"anchor\" aria-label=\"Permalink: Use\" href=\"#use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the GUI with paraview\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec paraview_containers.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\n\u003ca href=\"https://singularity-hub.org/collections/3435\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1541795875.0 + "updated_at": 1586875597.0 }, { "data_format": 2, - "description": "This pipeline analyses rna data, deeloped for OvCa and HCC samples", + "description": "Epilepsy prediction codes running on Docker", "filenames": [ - "Singularity" + "Singularity.fea" ], - "full_name": "Krebsro/nf-core-neointrons", + "full_name": "hlya23dd/Code_evaluation_Container", "latest_release": null, - "readme": "\u003cp\u003e\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt; HEAD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/neointrons\u003c/h1\u003e\u003ca id=\"user-content-nf-coreneointrons\" class=\"anchor\" aria-label=\"Permalink: nf-core/neointrons\" href=\"#nf-coreneointrons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eThis pipeline analyses rna data, developed for OvCa and HCC data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/neointrons\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b518c18e7a0eb37dff64a817b9d7599868058ef6d149d40f2fa83d7482070f6b/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6e656f696e74726f6e732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/neointrons.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b9d943c6da4bb0a73fe6bc476304001a1e93cbc8ccadf264da8fe47716ab6c53/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/neointrons\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/454e72258d5a13e5ba99c19a69f42f9df6f888965ed224607e80a9a984bdd7dd/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6e656f696e74726f6e732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/neointrons.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/neointrons pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\n=======\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core-neointrons\u003c/h1\u003e\u003ca id=\"user-content-nf-core-neointrons\" class=\"anchor\" aria-label=\"Permalink: nf-core-neointrons\" href=\"#nf-core-neointrons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline analyses rna data, deeloped for OvCa and HCC samples\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003ef1b73feef4f24607374c98f4c1efc1f1bef016af\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1541497875.0 + "updated_at": 1598271532.0 }, { "data_format": 2, - "description": "test singularity definition", + "description": "Singularity recipe files for angsd (https://github.com/ANGSD/angsd)", "filenames": [ - "Singularity" + "Singularity.0.925", + "Singularity.0.922", + "Singularity.0.919", + "Singularity.0.917", + "Singularity.0.921", + "Singularity.0.918", + "Singularity.0.937", + "Singularity.0.940", + "Singularity", + "Singularity.0.923" ], - "full_name": "deanpettinga/alpine-singularity", + "full_name": "powerPlant/angsd-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ealpine-singularity\u003c/h1\u003e\u003ca id=\"user-content-alpine-singularity\" class=\"anchor\" aria-label=\"Permalink: alpine-singularity\" href=\"#alpine-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etest singularity definition\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2300\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the angsd program for analysing NGS data\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1571758824.0 + "updated_at": 1645138593.0 }, { "data_format": 2, - "description": "A Docker Image to run LLaMA on TACC", + "description": "singularity examples", "filenames": [ - "Singularity" + "Singularity.python", + "Singularity.MG5_alone", + "Singularity.MG5_MA5_PY8_DEL", + "Singularity.cowsay", + "Singularity.MG5_MA5_PY8_ROOT", + "Singularity.MG5_MA5_PY8", + "Singularity.MG5" ], - "full_name": "mosoriob/tacc-jupyter-llama-docker-image", + "full_name": "oliviermattelaer/singularity-recipe", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-recipe\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe\" class=\"anchor\" aria-label=\"Permalink: singularity-recipe\" href=\"#singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1702471258.0 + "updated_at": 1543410032.0 }, { "data_format": 2, - "description": null, + "description": "github actions testing", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "krafczyk/j.jcp.2016.08.012", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJulia article containerized with Docker and Singularity\u003c/h1\u003e\u003ca id=\"user-content-julia-article-containerized-with-docker-and-singularity\" class=\"anchor\" aria-label=\"Permalink: Julia article containerized with Docker and Singularity\" href=\"#julia-article-containerized-with-docker-and-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom docker hub without cloning this repo:\u003c/h3\u003e\u003ca id=\"user-content-from-docker-hub-without-cloning-this-repo\" class=\"anchor\" aria-label=\"Permalink: From docker hub without cloning this repo:\" href=\"#from-docker-hub-without-cloning-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker run -it adb16x/julia_test:fresh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen inside the container:\n\u003ccode\u003esh run.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe output is stored in \u003ccode\u003eresults.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBy cloning this repo:\u003c/h3\u003e\u003ca id=\"user-content-by-cloning-this-repo\" class=\"anchor\" aria-label=\"Permalink: By cloning this repo:\" href=\"#by-cloning-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker build --no-cache -t julia .\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker run -it julia\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen inside the container:\n\u003ccode\u003esh run.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe output is stored in \u003ccode\u003eresults.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eBuild image \u003cstrong\u003eand\u003c/strong\u003e run the code:\n\u003ccode\u003esudo singularity --debug build test.simg Singularity |\u0026amp; tee sing-build.output\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo into the image:\n\u003ccode\u003esingularity shell test.simg\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eView the results:\n\u003ccode\u003ecat /usr/local/data/results.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAny output/issues with the build can be view at: \u003ccode\u003ecat sing-build.output\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo reproduce all 6 rows, change the \u003ccode\u003enumOfRefinements\u003c/code\u003e in \u003ccode\u003e/data/examples/runExperiments.jl\u003c/code\u003e to 6. It consumes a lot of memory though.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "martinghunt/gat", + "latest_release": "v0.0.6", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egat\u003c/h1\u003e\u003ca id=\"user-content-gat\" class=\"anchor\" aria-label=\"Permalink: gat\" href=\"#gat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003egithub actions testing\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1544035761.0 + "updated_at": 1639739073.0 }, { "data_format": 2, - "description": "singularity container for nanopolish", + "description": "Simulate drug effect to get each component using GPU parallelisation", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "ISU-HPC/nanopolish", + "full_name": "kit-cml/DrugSimulationGPU", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enanopolish\u003c/h1\u003e\u003ca id=\"user-content-nanopolish\" class=\"anchor\" aria-label=\"Permalink: nanopolish\" href=\"#nanopolish\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity container for nanopolish\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1561744596.0 + "updated_at": 1720503899.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v0.0.1", - "Singularity.v0.0.3", - "Singularity", - "Singularity.v0.0.2", - "Singularity.v0.0.4" + "Singularity.def" ], - "full_name": "darachm/singularity_python3_for_darach", + "full_name": "kit-cml/ORd_dynamic_BDF", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1972\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis container is for providing a python3 environment, as Darach likes it\nfor some bioinformatics pipelines. Expect this to grow as more stuff is used.\u003c/p\u003e\n\u003cp\u003eThe primary reason for this is so that it flows nicely through SingularityHub\nfor Nextflow pipelines.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1584545946.0 + "updated_at": 1727234462.0 }, { "data_format": 2, - "description": "unofficial conversion of python from docker to singularity", + "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "tin6150/python", + "full_name": "kit-cml/DrugSimulationGPU-naver", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epython\u003c/h1\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eunofficial\ncontainerization of python,\u003c/p\u003e\n\u003cp\u003efirst intended as conversion of python from docker to singularity\u003c/p\u003e\n\u003cp\u003enow have github workflow build docker container, the convert to singularity during a pull process\u003c/p\u003e\n\u003cp\u003efor incorporation into software_module_farm\u003c/p\u003e\n\u003cp\u003eplan to be a fairly fat container with many python libraries, numby, pandas, and likely many less common one requested by researchers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eexample run command (in testing)\u003c/h1\u003e\u003ca id=\"user-content-example-run-command-in-testing\" class=\"anchor\" aria-label=\"Permalink: example run command (in testing)\" href=\"#example-run-command-in-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003edocker pull ghcr.io/tin6150/python:main\ndocker run -it --rm --entrypoint=/bin/bash ghcr.io/tin6150/python:main\ndocker run -it --rm --entrypoint=/bin/pip ghcr.io/tin6150/python:main list\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003einvoking docker from inside docker, passthru, not exactly dind (docker-in-docker)\u003c/h1\u003e\u003ca id=\"user-content-invoking-docker-from-inside-docker-passthru-not-exactly-dind-docker-in-docker\" class=\"anchor\" aria-label=\"Permalink: invoking docker from inside docker, passthru, not exactly dind (docker-in-docker)\" href=\"#invoking-docker-from-inside-docker-passthru-not-exactly-dind-docker-in-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003epushd ~/tin-gh/lbnl-science-it/atlas-run\u003c/p\u003e\n\u003cp\u003edocker run -it --rm --entrypoint=python3 -v \u003ccode\u003epwd\u003c/code\u003e:/mnt -v /var/run/docker.sock:/var/run/docker.sock \u003cbr\u003e\nghcr.io/tin6150/python:main \u003cbr\u003e\n-u ./atlas_run.py -v\n2\u0026gt;\u0026amp;1 | tee ./log_atlas_testv12.out\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ethe tee is to local fs, not inside the container\u003c/h1\u003e\u003ca id=\"user-content-the-tee-is-to-local-fs-not-inside-the-container\" class=\"anchor\" aria-label=\"Permalink: the tee is to local fs, not inside the container\" href=\"#the-tee-is-to-local-fs-not-inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\nsingularity pull --name python.sif docker://ghcr.io/tin6150/py-conda:main\n\n\n\n# historical info\n\nthis repo was started back in 2019... \n2024.0413 changed branch name from master to main\nrename used web gui, manually changing URL to \"/branches\" to get list to rename, set default.\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1714970469.0 + "updated_at": 1723195578.0 }, { "data_format": 2, - "description": "Singulary recipe for plasflow", + "description": "Terminal string styling done right", "filenames": [ - "Singularity" + "4.1.0/Singularity", + "5.0.0/Singularity", + "5.0.1/Singularity" ], - "full_name": "ISU-HPC/plasflow", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eplasflow\u003c/h1\u003e\u003ca id=\"user-content-plasflow\" class=\"anchor\" aria-label=\"Permalink: plasflow\" href=\"#plasflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingulary recipe for plasflow\u003c/p\u003e\n", + "full_name": "icaoberg/singularity-chalk-cli", + "latest_release": "v5.0.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-chalk-cli/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-chalk-cli/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-chalk-cli/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-chalk-cli/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1c66c240bc38df64015075ec6f68767bb7e4634a835398142bfc7c1999c34256/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1c66c240bc38df64015075ec6f68767bb7e4634a835398142bfc7c1999c34256/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-chalk-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/05b8d9fb69618bfe0ec53c85330e3b0f7b8c73c7a36486e22f25d2b16d12f27b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/05b8d9fb69618bfe0ec53c85330e3b0f7b8c73c7a36486e22f25d2b16d12f27b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-chalk-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a24818a83e2601459d1e387affd74934bd1718bbb3aa8380df073c768dc67828/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a24818a83e2601459d1e387affd74934bd1718bbb3aa8380df073c768dc67828/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-chalk-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/11483fce283481c4a2af404bb7eeb1692c1d85a3bb2eb4f9a22d485caad35407/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11483fce283481c4a2af404bb7eeb1692c1d85a3bb2eb4f9a22d485caad35407/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d6368616c6b2d636c69\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-chalk-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-chalk-cli\u003c/h1\u003e\u003ca id=\"user-content-singularity-chalk-cli\" class=\"anchor\" aria-label=\"Permalink: singularity-chalk-cli\" href=\"#singularity-chalk-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/chalk/chalk-cli/blob/main/screenshot.png?raw=true\"\u003e\u003cimg src=\"https://github.com/chalk/chalk-cli/raw/main/screenshot.png?raw=true\" width=\"50%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/chalk/chalk-cli\"\u003echalk-cli\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges (or similar)\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-or-similar\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges (or similar)\" href=\"#installing-the-container-on-bridges-or-similar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003echalk-cli\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/chalk-cli/4.1.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modules/chalk-cli\u003c/code\u003e as \u003ccode\u003e4.1.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample\u003c/h3\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec singularity-chalk-cli-4.1.0.sif chalk -t \u0027{red.bold Dungeons and Dragons {~bold.blue (with added fairies)}}\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/screenshot.png\"\u003e\u003cimg src=\"images/screenshot.png\" alt=\"Screenshot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlternative Installation\u003c/h2\u003e\u003ca id=\"user-content-alternative-installation\" class=\"anchor\" aria-label=\"Permalink: Alternative Installation\" href=\"#alternative-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003espack install npm\nspack load npm\nnpm install -g chalk-cli\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.andrew.cmu.edu/~icaoberg\" rel=\"nofollow\"\u003eicaoberg\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1525896053.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1653903915.0 }, { "data_format": 2, - "description": "GATK 4beta singularity", + "description": "Shared nextflow modules and assets", "filenames": [ - "Singularity" + "pipelines/tumWgs/container/Singularity" ], - "full_name": "DoaneAS/gatk", + "full_name": "Clinical-Genomics-Lund/nextflow-modules", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egatk\u003c/h1\u003e\u003ca id=\"user-content-gatk\" class=\"anchor\" aria-label=\"Permalink: gatk\" href=\"#gatk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGATK 4beta singularity\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enextflow-modules\u003c/h1\u003e\u003ca id=\"user-content-nextflow-modules\" class=\"anchor\" aria-label=\"Permalink: nextflow-modules\" href=\"#nextflow-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eShared nextflow modules and assets used at CMD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe basic structure of the pipeline is as follows\u003c/h1\u003e\u003ca id=\"user-content-the-basic-structure-of-the-pipeline-is-as-follows\" class=\"anchor\" aria-label=\"Permalink: The basic structure of the pipeline is as follows\" href=\"#the-basic-structure-of-the-pipeline-is-as-follows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 modules\n\u2502 \u251c\u2500\u2500 bwa\n\u2502 \u2502 \u2514\u2500\u2500 main.nf\n\u2502 \u251c\u2500\u2500 samtools\n\u2502 \u2502 \u2514\u2500\u2500 main.nf\n\u2502 \u2514\u2500\u2500 senteion\n\u2502 \u2514\u2500\u2500 bwa\n\u251c\u2500\u2500 pipeline\n\u2502 \u251c\u2500\u2500 micro\n\u2502 \u2502 \u251c\u2500\u2500 data\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 micro\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 SRR10490537_1.fastq.gz\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 SRR10490537_2.fastq.gz\n\u2502 \u2502 \u251c\u2500\u2500 main.nf\n\u2502 \u2502 \u2514\u2500\u2500 nextflow.config\n\u2502 \u2514\u2500\u2500 nextflow.config\n\u2514\u2500\u2500 README.md\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1499573007.0 + "updated_at": 1648196462.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.trial" ], - "full_name": "CNCLgithub/liquid_gen_model_depth_img", + "full_name": "EdOates84/Sigularity_test", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eliquid_gen_model_depth_img\u003c/h1\u003e\u003ca id=\"user-content-liquid_gen_model_depth_img\" class=\"anchor\" aria-label=\"Permalink: liquid_gen_model_depth_img\" href=\"#liquid_gen_model_depth_img\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClone the repo\u003c/h2\u003e\u003ca id=\"user-content-clone-the-repo\" class=\"anchor\" aria-label=\"Permalink: Clone the repo\" href=\"#clone-the-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, clone this repo:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone git@github.com:CNCLgithub/liquid_gen_model_depth_img.git\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet started with setting up simulation engine\u003c/h2\u003e\u003ca id=\"user-content-get-started-with-setting-up-simulation-engine\" class=\"anchor\" aria-label=\"Permalink: Get started with setting up simulation engine\" href=\"#get-started-with-setting-up-simulation-engine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get started, you need to have SPlisHSPlasH installed inside this repo. You can either go to their website and follow the instructions, as in here: \u003ca href=\"https://splishsplash.readthedocs.io/en/latest/build_from_source.html\" rel=\"nofollow\"\u003ehttps://splishsplash.readthedocs.io/en/latest/build_from_source.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOr we provide a way to install, first download this singularity container (\u003ca href=\"https://yale.box.com/shared/static/j40o27bcfgjkzzgecltoh0vj0v411ph5.sif\" rel=\"nofollow\"\u003ehttps://yale.box.com/shared/static/j40o27bcfgjkzzgecltoh0vj0v411ph5.sif\u003c/a\u003e),\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ewget https://yale.box.com/shared/static/j40o27bcfgjkzzgecltoh0vj0v411ph5.sif -O ss.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen use the following command to install\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec ${path_to_downloaded_singularity_container} bash ./setup_ss.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003ewhere you can have \u003ccode\u003e./ss.sif\u003c/code\u003e instead of \u003ccode\u003e${path_to_downloaded_singularity_container}\u003c/code\u003e if you have been following along.\u003c/p\u003e\n\u003cp\u003eRemember to install SPlisHSPlasH inside this project directory. i.e., the structure should look like \u003ccode\u003eliquid_gen_model_depth_img/SPlisHSPlasH/\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet started with the model\u003c/h2\u003e\u003ca id=\"user-content-get-started-with-the-model\" class=\"anchor\" aria-label=\"Permalink: Get started with the model\" href=\"#get-started-with-the-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003euse \u003ccode\u003e./setup.sh all\u003c/code\u003e to install all the modeling environment. Please note that if packages for Julia cannot be installed in this way, run \u003ccode\u003e./run.sh julia\u003c/code\u003e to open Julia interface and install them manually.\nIf you were to install manually, type the following command in the interface:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusing Pkg; Pkg.instantiate()\nusing Pkg; Pkg.add(\"FileIO\")\nPkg.add(\"GeometryBasics\")\nPkg.add(\"MeshIO\")\nPkg.add(\"PyCall\")\nPkg.add(\"Reexport\")\nPkg.add(\"Formatting\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun model\u003c/h2\u003e\u003ca id=\"user-content-run-model\" class=\"anchor\" aria-label=\"Permalink: Run model\" href=\"#run-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the model, use \u003ccode\u003e./run.sh julia src/exp_basic.jl ${#}/{Scene_name}\u003c/code\u003e. Please note that {#} corresponding to the number in library. [1-box, 2-boxwithahole, 3-oneobject, 4-obstacle, 5-motor, 6-wall]\n{Scene_names} corresponds to the available scene names. The scene names are combinations of scene names, which are \"box\" \"boxwithahole\" \"oneobject\" \"obstacle\" \"motor\" \"wall\", and viscosities, which are \"1016\" \"104\" \"1\" \"4\" \"16\". Connect them using an underscore, such as \u003ccode\u003ebox_1016\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAs an example, if you were to run box scene at viscosity 4, then run \u003ccode\u003e./run.sh julia src/exp_basic.jl 1/box_4\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eA pre-built singularity container can be downloaded here: \u003ca href=\"https://yale.box.com/s/7xjvx27hijjaewezso8l0mbf6hsrq3ha\" rel=\"nofollow\"\u003ehttps://yale.box.com/s/7xjvx27hijjaewezso8l0mbf6hsrq3ha\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSigularity_test\u003c/h1\u003e\u003ca id=\"user-content-sigularity_test\" class=\"anchor\" aria-label=\"Permalink: Sigularity_test\" href=\"#sigularity_test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1679446657.0 + "updated_at": 1589221154.0 }, { "data_format": 2, - "description": "Singularity container for qiime1.9.1", + "description": "Singularity container description for BigStitcher", "filenames": [ - "Singularity" + "Singularity-BigStitcher" ], - "full_name": "scleveland/qiime1.9.1-singularity", + "full_name": "PreibischLab/BigStitcher-Singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1242\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Recipe for QIIME1.9.1 For the University of Hawaii HPC system\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-qiime191-for-the-university-of-hawaii-hpc-system\" class=\"anchor\" aria-label=\"Permalink: Singularity Recipe for QIIME1.9.1 For the University of Hawaii HPC system\" href=\"#singularity-recipe-for-qiime191-for-the-university-of-hawaii-hpc-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains recipe run \u003ca href=\"https://qiime.org\" rel=\"nofollow\"\u003eqiime1.9.1\u003c/a\u003e within a\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container running the Ubuntu Xenial OS,which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to Use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity run shub://scleveland/qiime1.9.1-singularity\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBigStitcher-Singularity\u003c/h1\u003e\u003ca id=\"user-content-bigstitcher-singularity\" class=\"anchor\" aria-label=\"Permalink: BigStitcher-Singularity\" href=\"#bigstitcher-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container description that automatically creates an Uber-JAR of the current BigStitcher version (including all dependencies) using local copy of the Oracle JDK.\u003c/p\u003e\n\u003cp\u003eCan easily be deployed for example on a cluster for parallel resaving.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 11, "topics": [], - "updated_at": 1571075539.0 + "updated_at": 1584624624.0 }, { "data_format": 2, - "description": "Pipeline to run the Paintor program and its associated visualization tools on GWAS summary statistics data", + "description": "asciinema [as-kee-nuh-muh] is a free and open source solution for recording terminal sessions and sharing them on the web.", "filenames": [ - "Singularity" + "2.2.0/Singularity", + "2.4.0/Singularity", + "2.1.0/Singularity", + "2.3.0/Singularity", + "2.0.2/Singularity" ], - "full_name": "sdjebali/PaintorPipe", - "latest_release": "v2.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"files/logo_PaintorPipe.png\"\u003e\u003cimg src=\"files/logo_PaintorPipe.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePaintorPipe\u003c/h1\u003e\u003ca id=\"user-content-paintorpipe\" class=\"anchor\" aria-label=\"Permalink: PaintorPipe\" href=\"#paintorpipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ePaintorPipe\u003c/code\u003e is a pipeline that perform fine-mapping analysis, using GWAS summary statistics data and diverse functionnal annotations, implemented in Nextflow.\nThis pipeline run the Paintor program and its associated visualization tools and can be run locally or on a slurm cluster and handles containerisation using Singularity.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"files/flowchart_PaintorPipe.png\"\u003e\u003cimg src=\"files/flowchart_PaintorPipe.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#dependencies\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pull-the-pre-built-container\"\u003ePull the pre-built container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#local-machine\"\u003eLocal Machine\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-on-a-compute-cluster-with-slurm\"\u003eSlurm compute cluster\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pipeline-parameters\"\u003ePipeline parameters\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#input-options\"\u003eInput options\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output-options\"\u003eOutput options\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#nextflow-options\"\u003eNextflow options\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#example-on-a-small-dataset\"\u003eExample on a small dataset\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#gwas-summary-statistics\"\u003eGWAS summary statistics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#functionnal-annotations\"\u003eFunctionnal Annotations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDependencies\u003c/h1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use this pipeline you will need:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eNextflow\u003c/code\u003e \u0026gt;= 21.10.6\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity\u003c/code\u003e \u0026gt;= 3.7.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFirst of all, you need to install GO and singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA small dataset \u003ccode\u003eCAD_META_extract\u003c/code\u003e is provided to test this pipeline (\u003ca href=\"#example-on-a-small-dataset\"\u003eExample on a small dataset\u003c/a\u003e section). To try it out, use one of the followings commands after pulling the singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull the pre-built container\u003c/h2\u003e\u003ca id=\"user-content-pull-the-pre-built-container\" class=\"anchor\" aria-label=\"Permalink: Pull the pre-built container\" href=\"#pull-the-pre-built-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can pull the image we built for the \u003ccode\u003ePaintorPipe\u003c/code\u003e from our repository on Sylabs cloud using the command bellow :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --arch amd64 library://zgerber/paintorpipe/mainimage:0.1\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLocal Machine\u003c/h2\u003e\u003ca id=\"user-content-local-machine\" class=\"anchor\" aria-label=\"Permalink: Local Machine\" href=\"#local-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are running the pipeline on a local machine with limited resources and want to use the default configuration (at least 2 CPUs/4G mem), use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -config nextflow.config --gwasFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata/input/CAD_META_extract\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --annotationsFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata/input/annotations.txt\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --ref_genome \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehg19\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --chromosome_header \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eChr\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --pvalue_nonlead \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e1\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --snp \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e100000\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --pp_threshold \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e0.001\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile singularity -resume\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning on a Compute Cluster with Slurm\u003c/h2\u003e\u003ca id=\"user-content-running-on-a-compute-cluster-with-slurm\" class=\"anchor\" aria-label=\"Permalink: Running on a Compute Cluster with Slurm\" href=\"#running-on-a-compute-cluster-with-slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have access to a compute cluster that uses the Slurm Workload Manager and you want to utilize the resources available there (at least 22 CPUs / 60 G mem), use this command with the slurm profile:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -config nextflow.config --gwasFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata/input/CAD_META_extract\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --annotationsFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata/input/annotations.txt\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --ref_genome \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehg19\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --chromosome_header \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eChr\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --pvalue_nonlead \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e1\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --snp \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e100000\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --pp_threshold \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e0.001\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile singularity, slurm -resume\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline parameters\u003c/h1\u003e\u003ca id=\"user-content-pipeline-parameters\" class=\"anchor\" aria-label=\"Permalink: Pipeline parameters\" href=\"#pipeline-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput options\u003c/h2\u003e\u003ca id=\"user-content-input-options\" class=\"anchor\" aria-label=\"Permalink: Input options\" href=\"#input-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth width=\"200px\"\u003eOption\u003c/th\u003e\n \u003cth width=\"200px\"\u003eBy default, example\u003c/th\u003e\n \u003cth width=\"350px\"\u003eDescription\u003c/th\u003e\n \u003cth width=\"90px\"\u003eRequired\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--gwasFile\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003epath/to/GWAS_FILE\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eThe GWAS file must contains 7 required columns : Allele1, Allele2, Effect (Beta), StdErr (SE), Pvalue, CHR, BP. The order is not important, but the name of the column is (see header parameters).\u003c/td\u003e\n \u003ctd align=\"center\"\u003eRequired\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--annotationsFile\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003epath/to/ANNOTATIONS_FILE\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eThe file should contains 2 columns separeted by tabulation. The first one is the name of the annotation (of the annotation file, or the annotation type for example) and the second is the associated path to the file.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eRequired\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--ref_genome\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003ehg19\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eOnly two values are allowed : \u0027hg19\u0027 or \u0027hg38\u0027. Make sure you are using the correct reference genome for your summary statistics GWAS file, because the results of the pipeline coulb be incorrect.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--population\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eEUR\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSpecifies the name of the mainland population : AFR, AMR, EAS, EUR, SAS.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--pvalue_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003ePvalue\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003ePvalue header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--stderr_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eStdErr\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eStandard Error (SE) header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--effect_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eEffect\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eEffect (BETA) header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--chromosome_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eCHR\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eChromosome header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--effectallele_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eAllele1\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eAllele with effect header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--altallele_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eAllele2\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eAllele without effect header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--position_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eBP\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eVariant position in base pair in the chromosome header column name\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--rsID_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003ersID\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eUnique variant identifiant or markermane header\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003c/tr\u003e\n\u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--zheader_header\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eZscore\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eThe computed zscore is added in a new column, corresponding to the Effect/StdErr for each SNP, for each locus.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--kb\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e500\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSNPs selection distance in kilo bases upstream and downstream of the lead SNP during the split of the GWAS file.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--pp_treshold\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e0\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSignificant posterior probability threshold.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--snp\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e10000000\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eNumber of significant SNPs to keep.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--pvalue_lead\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e5e-08\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSignificant Pvalue threshold for lead SNP.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--pvalue_nonlead\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003e1\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eSignificant Pvalue threshold for other SNPs around the lead SNP.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput options\u003c/h2\u003e\u003ca id=\"user-content-output-options\" class=\"anchor\" aria-label=\"Permalink: Output options\" href=\"#output-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth width=\"200px\"\u003eOption\u003c/th\u003e\n \u003cth width=\"200px\"\u003eBy default, example\u003c/th\u003e\n \u003cth width=\"350px\"\u003eDescription\u003c/th\u003e\n \u003cth width=\"90px\"\u003eRequired\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_locus\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_locus\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_sorted_locus\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_sorted_locus\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_VCFandMAPfrom1000G\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_VCF_map_files\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_ld\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_ld\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_bed\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_bed\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_annotations\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_annotations\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_annotated_locus\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_annotated_locus\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_paintor\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_paintor\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_results\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_results\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_posteriorprob\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_posteriorprob\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_plot\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_plot\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e--outputDir_canvis\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edata/output_canvis\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNextflow options\u003c/h2\u003e\u003ca id=\"user-content-nextflow-options\" class=\"anchor\" aria-label=\"Permalink: Nextflow options\" href=\"#nextflow-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is written in Nextflow, which provides the following default options:\u003c/p\u003e\n\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth width=\"200px\"\u003eOption\u003c/th\u003e\n \u003cth width=\"200px\"\u003eBy default, example\u003c/th\u003e\n \u003cth width=\"350px\"\u003eDescription\u003c/th\u003e\n \u003cth width=\"90px\"\u003eRequired\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-profile\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003esingularity\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eProfile(s) to use when running the pipeline. Specify the profiles that fit your infrastructure among \u003ccode\u003esingularity\u003c/code\u003e, \u003ccode\u003eslurm\u003c/code\u003e.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eRequired\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-config\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003enextflow.config\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003e\n Configuration file tailored to your infrastructure and dataset.\n \u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-revision\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003eversion\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eVersion of the pipeline to launch.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-work-dir\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003ccode\u003edirectory\u003c/code\u003e\u003c/td\u003e\n \u003ctd\u003eWork directory where all temporary files are written.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-resume\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003c/td\u003e\n \u003ctd\u003eResume the pipeline from the last completed process.\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-with-report\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003c/td\u003e\n \u003ctd\u003eNextflow can create an HTML execution report. It is a single document that includes many useful metrics on pipeline execution\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cstrong\u003e\u003ccode\u003e-with-timeline\u003c/code\u003e\u003c/strong\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003c/td\u003e\n \u003ctd\u003eNextflow can display a timeline in HTML format for all processes performed in the pipeline\u003c/td\u003e\n \u003ctd align=\"center\"\u003eOptional\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor more Nextflow options, see \u003ca href=\"https://www.nextflow.io/docs/latest/cli.html#run\" rel=\"nofollow\"\u003eNextflow\u0027s documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExample on a small dataset\u003c/h1\u003e\u003ca id=\"user-content-example-on-a-small-dataset\" class=\"anchor\" aria-label=\"Permalink: Example on a small dataset\" href=\"#example-on-a-small-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGWAS summary statistics\u003c/h2\u003e\u003ca id=\"user-content-gwas-summary-statistics\" class=\"anchor\" aria-label=\"Permalink: GWAS summary statistics\" href=\"#gwas-summary-statistics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eCAD_META_extract\u003c/code\u003e GWAS file is an extract of the GWAS results from the latest \u003ccode\u003eCoronary Artery Disease\u003c/code\u003e (CAD) meta-analysis involving 122,733 cases and 424,528 controls (van der Harst P et al, 2018).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eunzip CAD_META_extract.zip\nhead CAD_META_extract\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eMarkerName\tAllele1\tAllele2\tFreq1\tFreqSE\tMinFreq\tMaxFreq\tEffect\tStdErr\tPvalue\tDirection\tHetISq\tHetChiSq\tHetDf\tHetPVal\trsID\tChr\tBP\n9:34486713_A_G\ta\tg\t0.9363\t0.0023\t0.9346\t0.9394\t0.0058\t0.0115\t0.6106\t+-\t0\t0.663\t1\t0.4156\trs72735241\t9\t34486713\n4:187387354_G_T\tt\tg\t0.0359\t0.0045\t0.032\t0.041\t0.009\t0.0155\t0.5604\t-+\t0\t0.847\t1\t0.3574\trs73020749\t4\t187387354\n4:76326344_C_G\tc\tg\t0.1743\t0.0062\t0.1694\t0.1823\t-3e-04\t0.0075\t0.9641\t-+\t69.2\t3.252\t1\t0.07136\trs11727982\t4\t76326344\n1:88710048_C_T\tt\tc\t0.4234\t0.008\t0.4172\t0.4337\t-0.0064\t0.0057\t0.2601\t--\t0\t0.371\t1\t0.5427\trs6428642\t1\t88710048\n1:189237277_C_T\tt\tc\t0.5217\t0.0011\t0.5209\t0.5232\t0.005\t0.0056\t0.3742\t++\t0\t0.129\t1\t0.719\trs1578705\t1\t189237277\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eCAD_META_extract\u003c/code\u003e GWAS test file provided contains the 8 required columns :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAllele1\u003c/li\u003e\n\u003cli\u003eAllele2\u003c/li\u003e\n\u003cli\u003eEffect\u003c/li\u003e\n\u003cli\u003eStdErr\u003c/li\u003e\n\u003cli\u003eCHR\u003c/li\u003e\n\u003cli\u003eBP\u003c/li\u003e\n\u003cli\u003ersID\u003c/li\u003e\n\u003cli\u003ePvalue\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe chromosome column is the only column with an incorrect header entry. We need to provide the correct version of the header: \u003ccode\u003eChr\u003c/code\u003e instead of \u003ccode\u003eCHR\u003c/code\u003e with the \u003ccode\u003e--chromosome_header\u003c/code\u003e parameter (see \u003ca href=\"#usage\"\u003eusage\u003c/a\u003e part).\u003c/p\u003e\n\u003cp\u003eThis is important that the column names are correctly written. If you have supplementary columns like in the exampe above, you can keep them, the pipeline is going to ignore them. If you don\u0027t want to change the required column names in the file, like the \u003ccode\u003eChr\u003c/code\u003ecolumn, you have to indicate the alternative names with the header arguments when launching Nextflow command. Make sure the columns are separated by tabulations.\u003c/p\u003e\n\u003cp\u003eBe careful when running the pipe, about the reference genome version (\u003ccode\u003e--ref_genome\u003c/code\u003e parameter). By default, the pipeline uses hg19 version (more used). Depending on the GWAS dataset you want to fine map, you can change by hg38 version (more recent).\u003c/p\u003e\n\u003cp\u003eAdditionally, it is important to ensure that the pvalue for non-leader SNPs is correct. Indeed, if you run \u003ccode\u003ePaintorPipe\u003c/code\u003e on a small number of variants, as in the example here, the pipeline will generate errors. We recommend in the testing phases to keep the \u003ccode\u003e--pvalue_nonlead\u003c/code\u003e parameter at 1.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFunctionnal annotations\u003c/h2\u003e\u003ca id=\"user-content-functionnal-annotations\" class=\"anchor\" aria-label=\"Permalink: Functionnal annotations\" href=\"#functionnal-annotations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConcerning the annotation library, you can use the annotations given in the Paintor github wiki or directely following this link (Warning: This is a large 6.7 GB file).\u003c/p\u003e\n\u003cp\u003eOnce the annotation bed files are downloaded, you can write the \u003ccode\u003eannotations.txt\u003c/code\u003e file, to give to the pipeline, pointing to all annotation bed files (use tabulation) looking like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egenc.exon path/to/exons.proj.bed\ngenc.intron path/to/introns.proj.bed\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first column is the name of the functionnal annotation and the second is the path to the bed file. Above, an example for a run with 2 annotations (exons \u0026amp; introns). We recommand to use no more than 4 or 5 annotations per run.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should obtain 43 loci in the \u003ccode\u003eoutput_locus\u003c/code\u003e directory. Check the \u003ccode\u003eslurm-46703827.out\u003c/code\u003e output file in the \u003ccode\u003efiles\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use \u003ccode\u003ePaintorPipe\u003c/code\u003e for your analysis, please cite the publication as follows :\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGerber, Z., Fisun, M., Aschard, H., \u0026amp; Djebali, S. (2024). PaintorPipe: a pipeline for genetic variant fine-mapping using functional annotations. Bioinformatics Advances, 4(1), vbad188.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eYou can find the publication by following this link (\u003ca href=\"https://doi.org/10.1093/bioadv/vbad188\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioadv/vbad188\u003c/a\u003e).\u003c/p\u003e\n", + "full_name": "pscedu/singularity-asciinema", + "latest_release": "v2.3.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-asciinema/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-asciinema/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-asciinema/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-asciinema/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/35478770911b6ab1d5857192d6b81d9c33caee554eca26d82a100a76f4baa7ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d61736369696e656d61\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/35478770911b6ab1d5857192d6b81d9c33caee554eca26d82a100a76f4baa7ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d61736369696e656d61\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-asciinema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/95e58d1cf98658b615e9766aa1e980ae2f20a5766700b22cf6305a04a5dbc4d9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d61736369696e656d61\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/95e58d1cf98658b615e9766aa1e980ae2f20a5766700b22cf6305a04a5dbc4d9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d61736369696e656d61\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-asciinema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9c72cbedd0caeb1d743b89c88ffd0c58127bef0190ff870d55f77558955fb4d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d61736369696e656d61\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9c72cbedd0caeb1d743b89c88ffd0c58127bef0190ff870d55f77558955fb4d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d61736369696e656d61\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-asciinema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2057263ea33bb56562217253a6b2770574d3deddcbecd54e862b8c6aa31edc90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d61736369696e656d61\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2057263ea33bb56562217253a6b2770574d3deddcbecd54e862b8c6aa31edc90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d61736369696e656d61\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-asciinema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-asciinema\u003c/h1\u003e\u003ca id=\"user-content-singularity-asciinema\" class=\"anchor\" aria-label=\"Permalink: singularity-asciinema\" href=\"#singularity-asciinema\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/232377\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/42ed19c8f85a29cee03b6168a5afbc320999af106f8af5afa28b3ae8f072cf76/68747470733a2f2f61736369696e656d612e6f72672f612f3233323337372e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/232377.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://asciinema.org/\" rel=\"nofollow\"\u003easciinema\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003easciinema\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/asciinema/2.4.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/asciinema\u003c/code\u003e as \u003ccode\u003e2.4.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1715613103.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1633086930.0 }, { "data_format": 2, - "description": null, + "description": "STAR-Fusion is a component of the Trinity Cancer Transcriptome Analysis Toolkit (CTAT).", "filenames": [ - "Singularity" + "1.9.1/Singularity", + "1.11.1/Singularity" ], - "full_name": "phgenomics-singularity/prokka", - "latest_release": null, + "full_name": "pscedu/singularity-star-fusion", + "latest_release": "v1.11.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-star-fusion/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-star-fusion/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-star-fusion/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-star-fusion/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/730795aae0a8346332ec211f8d0b3113e8a917e1b7106ffed407c7ebbb9102b2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/730795aae0a8346332ec211f8d0b3113e8a917e1b7106ffed407c7ebbb9102b2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-star-fusion\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/25696986acf6b6f3651c739b24804afb212695c2c561a2734e7e57862f1f7caf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/25696986acf6b6f3651c739b24804afb212695c2c561a2734e7e57862f1f7caf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-star-fusion\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5fe0c9ec591212ad970580635f0e0e0b0c9ce57201207253ba90659c15e8727f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5fe0c9ec591212ad970580635f0e0e0b0c9ce57201207253ba90659c15e8727f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d737461722d667573696f6e\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-star-fusion\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f9d2339d9895863d2b09eabb921f4fedbb665f04823e79c1f16ad23296d0b609/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d737461722d667573696f6e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f9d2339d9895863d2b09eabb921f4fedbb665f04823e79c1f16ad23296d0b609/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d737461722d667573696f6e\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-star-fusion\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-star-fusion\u003c/h1\u003e\u003ca id=\"user-content-singularity-star-fusion\" class=\"anchor\" aria-label=\"Permalink: singularity-star-fusion\" href=\"#singularity-star-fusion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/STAR-Fusion/STAR-Fusion\"\u003eSTAR-Fusion\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/STAR-Fusion/1.11.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/STAR-Fusion\u003c/code\u003e as \u003ccode\u003e1.11.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [], - "updated_at": 1564451005.0 + "subscribers_count": 4, + "topics": [ + "bioinformatics", + "singularity" + ], + "updated_at": 1668127864.0 + }, + { + "data_format": 2, + "description": "Build amazing TUIs (Text User Interfaces) with this innovative Python framework.", + "filenames": [ + "1.8.0/Singularity" + ], + "full_name": "pscedu/singularity-rich-cli", + "latest_release": "v1.8.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rich-cli/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rich-cli/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rich-cli/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rich-cli/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bcc09e15c6913a24778356e114f64ed669e51406eccbf8671e3dc3ac18f5610f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726963682d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bcc09e15c6913a24778356e114f64ed669e51406eccbf8671e3dc3ac18f5610f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d726963682d636c69\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-rich-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2a1d9bdee597dea864b05afd5814685a72bdb6f36f2b6383a6734c8296644b3d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d726963682d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a1d9bdee597dea864b05afd5814685a72bdb6f36f2b6383a6734c8296644b3d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d726963682d636c69\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-rich-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6b3c80e5ab41877d9e48d6b59f00c710c486977b5ce9bdc53cfe8c03b8a3213a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d726963682d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b3c80e5ab41877d9e48d6b59f00c710c486977b5ce9bdc53cfe8c03b8a3213a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d726963682d636c69\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-rich-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cbd43977ff4ce22f0fd3f1e8a332320dfe3f28bf48e8d503ed4329ce3f66b82e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726963682d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbd43977ff4ce22f0fd3f1e8a332320dfe3f28bf48e8d503ed4329ce3f66b82e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d726963682d636c69\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-rich-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-rich-cli\u003c/h1\u003e\u003ca id=\"user-content-singularity-rich-cli\" class=\"anchor\" aria-label=\"Permalink: singularity-rich-cli\" href=\"#singularity-rich-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n \u003csource type=\"video/mp4\"\u003e\n\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.textualize.io/\" rel=\"nofollow\"\u003erich-cli\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003erich\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/rich-cli/1.8.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/rich-cli\u003c/code\u003e as \u003ccode\u003e1.8.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 4, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1660873798.0 }, { "data_format": 2, @@ -22907,423 +22575,458 @@ var data = "filenames": [ "Singularity" ], - "full_name": "phgenomics-singularity/iqtree", + "full_name": "rses-singularity/fsl-debian-stretch-singularity", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity image definition for FSL\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-definition-for-fsl\" class=\"anchor\" aria-label=\"Permalink: Singularity image definition for FSL\" href=\"#singularity-image-definition-for-fsl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMaking it easier to start using \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\" rel=\"nofollow\"\u003eFSL\u003c/a\u003e on e.g. HPC.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2514\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"LICENSE.txt\"\u003eLICENSE.txt\u003c/a\u003e, particularly the conditions regarding commercial use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning FSL within a Singularity container using this Singularity image definition\u003c/h2\u003e\u003ca id=\"user-content-running-fsl-within-a-singularity-container-using-this-singularity-image-definition\" class=\"anchor\" aria-label=\"Permalink: Running FSL within a Singularity container using this Singularity image definition\" href=\"#running-fsl-within-a-singularity-container-using-this-singularity-image-definition\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest way to start using FSL via this Singularity image is to\npull the image from the \u003ca href=\"http://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularityHub\u003c/a\u003e on-line repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/home/\u003cspan class=\"pl-smi\"\u003e$USER\u003c/span\u003e/singularity_cache\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SINGULARITY_CACHEDIR}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsingularity pull --name fsl-debian-stretch-singularity-latest.sif shub://rses-singularity/fsl-debian-stretch-singularity:latest \nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${SINGULARITY_CACHEDIR}\u003c/span\u003e/fsl-debian-stretch-singularity-latest.sif\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e /bin/bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running \u003ccode\u003esingularity exec\u003c/code\u003e you are then able to run commands \u0027within\u0027 a FSL \u0027container\u0027 e.g.\n\u003ccode\u003efsl-selftest\u003c/code\u003e or \u003ccode\u003efsl5.0-gps\u003c/code\u003e. Note that most FSL commands start with \u003ccode\u003efsl5.0-\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA note re SingularityHub: the \u003ca href=\"https://www.singularity-hub.org/collections/2514\" rel=\"nofollow\"\u003eFSL image provided via SingularityHub\u003c/a\u003e is\nrebuilt whenever there is a push to this GitHub repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding a Singularity image and running a FSL container \u003cem\u003ewithout\u003c/em\u003e using SingularityHub\u003c/h2\u003e\u003ca id=\"user-content-building-a-singularity-image-and-running-a-fsl-container-without-using-singularityhub\" class=\"anchor\" aria-label=\"Permalink: Building a Singularity image and running a FSL container without using SingularityHub\" href=\"#building-a-singularity-image-and-running-a-fsl-container-without-using-singularityhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to use the SingularityHub-built image then you can build it yourself \u003cstrong\u003eon your own machine\u003c/strong\u003e (not HPC):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMake sure you have Singularity installed.\u003c/li\u003e\n\u003cli\u003eEnsure you\u0027re read the \u003ca href=\"LICENSE.txt\"\u003eFSL license\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eInspect the \u003ca href=\"Singularity\"\u003eSingularity image definition in this repo\u003c/a\u003e; this includes steps to:\n\u003cul\u003e\n\u003cli\u003eInstall FSL.\u003c/li\u003e\n\u003cli\u003eInstall the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEEDS\" rel=\"nofollow\"\u003eFSL Evaluation and Example Data Suite (FEEDS)\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStart building an image file:\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo SINGULARITY_TMPDIR=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.cache/singularity singularity build ./fsl-debian-stretch-singularity.sif ./Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo start a FSL container using this image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e ./fsl-debian-stretch-singularity.sif /bin/bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen from the resulting shell start the FSL command you want to use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting FSL inside a container\u003c/h2\u003e\u003ca id=\"user-content-testing-fsl-inside-a-container\" class=\"anchor\" aria-label=\"Permalink: Testing FSL inside a container\" href=\"#testing-fsl-inside-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003efsl-selftest\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1576535464.0 + "updated_at": 1552339344.0 }, { "data_format": 2, - "description": "singularity container for edgeR", + "description": null, "filenames": [ - "Singularity", - "Singularity.1" + "containers/Singularity.omnia.rocm" ], - "full_name": "deanpettinga/edger-singularity", + "full_name": "PhilipVinc/uvtest", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Container: EdgeR\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-edger\" class=\"anchor\" aria-label=\"Permalink: Singularity Container: EdgeR\" href=\"#singularity-container-edger\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3573\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOmnia\u003c/h1\u003e\u003ca id=\"user-content-omnia\" class=\"anchor\" aria-label=\"Permalink: Omnia\" href=\"#omnia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a single repository containing all working codes of the group.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStructure\u003c/h2\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e/packages # contains packages used by multiple users\n # This contains both internal versions of public packages\n # (netket) and private packages.\n```\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1569864443.0 + "updated_at": 1732149584.0 }, { "data_format": 2, - "description": "Singularity GENIE image built on Ubuntu", + "description": null, "filenames": [ "Singularity" ], - "full_name": "twongjirad/singularity-genie-ubuntu", + "full_name": "researchapps/fasta-utilities", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-genie-ubuntu\u003c/h1\u003e\u003ca id=\"user-content-singularity-genie-ubuntu\" class=\"anchor\" aria-label=\"Permalink: singularity-genie-ubuntu\" href=\"#singularity-genie-ubuntu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComing soon.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFasta Utilities\u003c/h1\u003e\u003ca id=\"user-content-fasta-utilities\" class=\"anchor\" aria-label=\"Permalink: Fasta Utilities\" href=\"#fasta-utilities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a Singularity build file for the \u003ca href=\"https://github.com/jimhester/fasta_utilities\"\u003efasta-utilities\u003c/a\u003e library.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Download this repo\u003c/h2\u003e\u003ca id=\"user-content-2-download-this-repo\" class=\"anchor\" aria-label=\"Permalink: 2. Download this repo\" href=\"#2-download-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/singularituhub/fasta-utilities\ncd fasta-utilities\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-3-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 3. Bootstrap the image\" href=\"#3-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create fasta-utils.img\nsudo singularity bootstrap fasta-utils.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. Run commands\u003c/h2\u003e\u003ca id=\"user-content-4-run-commands\" class=\"anchor\" aria-label=\"Permalink: 4. Run commands\" href=\"#4-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhat commands are in bin?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./fasta-utils.img ls\n2big.pl\t\t\t fetch_entrez.pl\t pairs_unsorted.pl\nCpG_count.pl\t\t fetch_gi.pl\t\t percent_GC.pl\nabsolute_coordinates.pl fetch_sra.pl\t\t regex_fasta.pl\nadd_type.pl\t\t filter_align.pl\t remap_file.pl\nalign_progress.pl\t filter_bam.pl\t\t remove_ambiguous.pl\nascii2csv.pl\t\t filter_reads.pl\t rename_script.pl\navg_coverage.pl\t\t fix_headers.pl\t\t reverse_complement.pl\nbed2fasta.pl\t\t generate_fasta.pl\t sam2fastq.pl\nbed2igv.pl\t\t generate_map.pl\t sam_lengths.pl\nbisulfite_convert.pl\t get_fasta.pl\t\t sequence_counts.pl\nblast_information.pl\t gff2bed.pl\t\t size.pl\ncalcN.pl\t\t gff2data_frame.pl\t size_select.pl\ncollapse_duplicates.pl\t grep.pl\t\t sort.pl\ncombine_bed.pl\t\t in_list.pl\t\t splice.pl\ncommify.pl\t\t lengths.pl\t\t split_fasta.pl\nconsensus.pl\t\t maf2bed.pl\t\t standardize_names.pl\ndistances.pl\t\t mate_pair2paired_end.pl subset_fasta.pl\nfasta2fastq.pl\t\t merge_records.pl\t trans_fasta.pl\nfasta_head.pl\t\t mpileup_consensus.pl\t trim_fasta.pl\nfasta_tail.pl\t\t mpileup_counts.pl\t unique_headers.pl\nfastq2fasta.pl\t\t pairs_sorted.pl\t wrap.pl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun a command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./fasta-utils.img perl add_type.pl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMount the data directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run -b /path/to/data:/data/ fasta-utils.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo specify inputs and outputs, and run with data (not tested)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run -b /path/to/data:/data/ fasta-utils.img perl in_list.pl [args]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eShell into a (writable) container to test changes (that you should then add to the build file \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell --writable fasta-utils.img\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1496374870.0 + "updated_at": 1484506830.0 }, { "data_format": 2, - "description": null, + "description": "Singularity example 1: Hello World", "filenames": [ "Singularity" ], - "full_name": "aseetharam/class2", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eclass2\u003c/h1\u003e\u003ca id=\"user-content-class2\" class=\"anchor\" aria-label=\"Permalink: class2\" href=\"#class2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "full_name": "richelbilderbeek/singularity_example_1", + "latest_release": "v2.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_example_1\u003c/h1\u003e\u003ca id=\"user-content-singularity_example_1\" class=\"anchor\" aria-label=\"Permalink: singularity_example_1\" href=\"#singularity_example_1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/singularity_example_1/actions\"\u003e\u003cimg src=\"GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/singularity_example_1/workflows/build_singularity/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/singularity_example_1/workflows/build_singularity/badge.svg?branch=master\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/singularity_example_1/workflows/build_singularity/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/singularity_example_1/workflows/build_singularity/badge.svg?branch=develop\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSingularity example 1: Hello World.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e (a script) to see what the container does.\u003c/p\u003e\n\u003cp\u003eThis repo builds the container, runs it and uploads it.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1599421677.0 + "updated_at": 1627290514.0 }, { "data_format": 2, - "description": "Singularity containers to run Gmsh", + "description": null, "filenames": [ - "Singularity.paraview", - "Singularity", - "Singularity.pvbatch" + "Singularity" ], - "full_name": "stephansmit/gmsh_containers", + "full_name": "nglinhbao/EU-AI-Act-Assessment", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container for gmsh\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-gmsh\" class=\"anchor\" aria-label=\"Permalink: Singularity container for gmsh\" href=\"#singularity-container-for-gmsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container with \u003ca href=\"http://gmsh.info/\" rel=\"nofollow\"\u003eGmsh\u003c/a\u003e and \u003ca href=\"http://www.coolprop.org/\" rel=\"nofollow\"\u003eCoolProp\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Container\u003c/h2\u003e\u003ca id=\"user-content-build-container\" class=\"anchor\" aria-label=\"Permalink: Build Container\" href=\"#build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build gmsh_containers.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull Image\u003c/h2\u003e\u003ca id=\"user-content-pull-image\" class=\"anchor\" aria-label=\"Permalink: Pull Image\" href=\"#pull-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/gmsh_containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Image\u003c/h3\u003e\u003ca id=\"user-content-run-image\" class=\"anchor\" aria-label=\"Permalink: Run Image\" href=\"#run-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run gmsh_containers test.geo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHosted on Singularity Hub:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3419\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"h://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEU AI Act Assessment\u003c/h1\u003e\u003ca id=\"user-content-eu-ai-act-assessment\" class=\"anchor\" aria-label=\"Permalink: EU AI Act Assessment\" href=\"#eu-ai-act-assessment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is designed to classify AI systems according to the \u003cstrong\u003eEU AI Act\u003c/strong\u003e using the \u003cstrong\u003eLLaMA 2 (7B)\u003c/strong\u003e model. The AI system is evaluated through several stages, including \u003cstrong\u003eInitial Risk Assessment\u003c/strong\u003e, \u003cstrong\u003eLevel-Based Risk Assessment\u003c/strong\u003e, and \u003cstrong\u003eRisk Categorization\u003c/strong\u003e. The purpose is to ensure that AI systems comply with regulations and are categorized according to their risk levels.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#project-description\"\u003eProject Description\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#features\"\u003eFeatures\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ai-risk-assessment-workflow\"\u003eAI Risk Assessment Workflow\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProject Description\u003c/h2\u003e\u003ca id=\"user-content-project-description\" class=\"anchor\" aria-label=\"Permalink: Project Description\" href=\"#project-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project evaluates AI systems to assess their compliance with the \u003cstrong\u003eEU AI Act\u003c/strong\u003e. The system follows a tree-based workflow, beginning with an \u003cstrong\u003eInitial Risk Assessment\u003c/strong\u003e. Based on the outcomes, it moves to \u003cstrong\u003eLevel-Based Risk Assessment\u003c/strong\u003e or determines whether the AI system poses an \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e. The core components of the system include structured prompts designed for different phases of risk assessment and a fine-tuned \u003cstrong\u003eLLaMA 2\u003c/strong\u003e model to generate responses.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAI Risk Categorization\u003c/strong\u003e based on \u003cstrong\u003eEU AI Act\u003c/strong\u003e principles.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLLaMA 2 (7B)\u003c/strong\u003e model integration for automated risk assessments.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCSV-driven prompt system\u003c/strong\u003e for systematic AI evaluation.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-step automated workflow\u003c/strong\u003e: Initial risk, level-based assessment, and ongoing monitoring.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisites\u003c/h3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEnsure you have the following installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.8+.\u003c/li\u003e\n\u003cli\u003eConda environment (optional but recommended).\u003c/li\u003e\n\u003cli\u003eHugging Face API Token (required to access the LLaMA 2 model).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClone the Repository\u003c/h3\u003e\u003ca id=\"user-content-clone-the-repository\" class=\"anchor\" aria-label=\"Permalink: Clone the Repository\" href=\"#clone-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/nglinhbao/AI-EU-Act-Assessment.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e AI-EU-Act-Assessment\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSet Up Virtual Environment (Optional)\u003c/h3\u003e\u003ca id=\"user-content-set-up-virtual-environment-optional\" class=\"anchor\" aria-label=\"Permalink: Set Up Virtual Environment (Optional)\" href=\"#set-up-virtual-environment-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create --name AI-Act python=3.10\nconda activate AI-Act\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall Required Packages\u003c/h3\u003e\u003ca id=\"user-content-install-required-packages\" class=\"anchor\" aria-label=\"Permalink: Install Required Packages\" href=\"#install-required-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install -r requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAuthenticate Hugging Face\u003c/h3\u003e\u003ca id=\"user-content-authenticate-hugging-face\" class=\"anchor\" aria-label=\"Permalink: Authenticate Hugging Face\" href=\"#authenticate-hugging-face\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEnsure you have a Hugging Face token to access the LLaMA 2 model.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehuggingface-cli login\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, you can export the token directly in your environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e HUGGINGFACEHUB_API_TOKEN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eyour_token_here\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSteps to Run the Assessment:\u003c/h3\u003e\u003ca id=\"user-content-steps-to-run-the-assessment\" class=\"anchor\" aria-label=\"Permalink: Steps to Run the Assessment:\" href=\"#steps-to-run-the-assessment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrepare the AI System Description\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a \u003ccode\u003e.csv\u003c/code\u003e file containing the description of the AI systems that you wish to assess.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./sample.csv\"\u003eExample\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRun the Assessment Script\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eTo start the AI system classification process, make sure you have the system description \u003ccode\u003e.csv\u003c/code\u003e file ready, and execute the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 main.py\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrompts and AI System Evaluation\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe system reads prompts from the \u003ccode\u003eCSV\u003c/code\u003e file (\u003ccode\u003eai_risk_prompts.csv\u003c/code\u003e) and evaluates the AI system based on its description.\u003c/li\u003e\n\u003cli\u003eIf the AI system poses an \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e, the process halts and returns the result. Otherwise, it proceeds through all stages to classify the system as \u003cstrong\u003eHigh Risk\u003c/strong\u003e, \u003cstrong\u003eLimited Risk\u003c/strong\u003e, or \u003cstrong\u003eMinimal Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eOutput\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe system provides a risk categorization based on the predefined prompts in the CSV file.\u003c/li\u003e\n\u003cli\u003eExample outcomes include: \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e, \u003cstrong\u003eHigh Risk\u003c/strong\u003e, \u003cstrong\u003eLimited Risk\u003c/strong\u003e, or \u003cstrong\u003eMinimal Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Output:\u003c/h3\u003e\u003ca id=\"user-content-example-output\" class=\"anchor\" aria-label=\"Permalink: Example Output:\" href=\"#example-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is an example of how the system would respond to various prompts:\u003c/p\u003e\n\u003cpre lang=\"plaintext\"\u003e\u003ccode\u003e(AI-Act) User:~/EU-AI-Act$ python3 main.py \nLoading checkpoint shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:02\u0026lt;00:00, 1.50s/it]\nAccuracy: 0.30\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAI Risk Assessment Workflow with Scoring System\u003c/h2\u003e\u003ca id=\"user-content-ai-risk-assessment-workflow-with-scoring-system\" class=\"anchor\" aria-label=\"Permalink: AI Risk Assessment Workflow with Scoring System\" href=\"#ai-risk-assessment-workflow-with-scoring-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./workflow.png\"\u003e\u003cimg src=\"./workflow.png\" alt=\"Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment follows these key stages, evaluated sequentially from top to bottom. Each prompt at every risk level is scored on a scale of \u003cstrong\u003e1 to 5\u003c/strong\u003e, where:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e1\u003c/strong\u003e means the system absolutely does not meet the criteria,\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e5\u003c/strong\u003e means the system absolutely meets the criteria.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each stage, if the \u003cstrong\u003emean score\u003c/strong\u003e is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system will be classified under that risk category.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e:\u003c/h3\u003e\u003ca id=\"user-content-1-unacceptable-risk\" class=\"anchor\" aria-label=\"Permalink: 1. Unacceptable Risk:\" href=\"#1-unacceptable-risk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe system is first evaluated against prompts to identify if it violates fundamental human rights, Union values, or carries unacceptable risks.\u003c/li\u003e\n\u003cli\u003eEach prompt at this stage receives a score from \u003cstrong\u003e1 to 5\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eIf the \u003cstrong\u003emean score\u003c/strong\u003e of all prompts in this stage is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system is classified as \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e, and the assessment halts. No further evaluation is carried out.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. \u003cstrong\u003eHigh Risk\u003c/strong\u003e:\u003c/h3\u003e\u003ca id=\"user-content-2-high-risk\" class=\"anchor\" aria-label=\"Permalink: 2. High Risk:\" href=\"#2-high-risk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf the system passes the \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e stage, it is assessed for \u003cstrong\u003eHigh Risk\u003c/strong\u003e factors such as:\n\u003cul\u003e\n\u003cli\u003eImpact on human life,\u003c/li\u003e\n\u003cli\u003eHandling of sensitive personal data,\u003c/li\u003e\n\u003cli\u003eAutonomous decision-making without human oversight.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach prompt in this stage is scored from \u003cstrong\u003e1 to 5\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eIf the \u003cstrong\u003emean score\u003c/strong\u003e for the prompts in this stage is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system is classified as \u003cstrong\u003eHigh Risk\u003c/strong\u003e, and further evaluation stops.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. \u003cstrong\u003eLimited Risk\u003c/strong\u003e:\u003c/h3\u003e\u003ca id=\"user-content-3-limited-risk\" class=\"anchor\" aria-label=\"Permalink: 3. Limited Risk:\" href=\"#3-limited-risk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf the system does not qualify as \u003cstrong\u003eHigh Risk\u003c/strong\u003e, it is evaluated for \u003cstrong\u003eLimited Risk\u003c/strong\u003e factors like:\n\u003cul\u003e\n\u003cli\u003eBias detection mechanisms,\u003c/li\u003e\n\u003cli\u003eSystem transparency,\u003c/li\u003e\n\u003cli\u003eFairness in decision-making processes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach prompt in this stage is scored from \u003cstrong\u003e1 to 5\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eIf the \u003cstrong\u003emean score\u003c/strong\u003e is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system is classified as \u003cstrong\u003eLimited Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. \u003cstrong\u003eMinimal Risk\u003c/strong\u003e:\u003c/h3\u003e\u003ca id=\"user-content-4-minimal-risk\" class=\"anchor\" aria-label=\"Permalink: 4. Minimal Risk:\" href=\"#4-minimal-risk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf none of the previous stages result in a classification, the system is evaluated for \u003cstrong\u003eMinimal Risk\u003c/strong\u003e, including:\n\u003cul\u003e\n\u003cli\u003eProper handling of non-sensitive data,\u003c/li\u003e\n\u003cli\u003eMinimal impact on users and society,\u003c/li\u003e\n\u003cli\u003eLow potential for bias or unfair treatment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach prompt in this stage is scored from \u003cstrong\u003e1 to 5\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eIf the \u003cstrong\u003emean score\u003c/strong\u003e is greater than \u003cstrong\u003e3\u003c/strong\u003e, the system is classified as \u003cstrong\u003eMinimal Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Workflow (Scoring):\u003c/h3\u003e\u003ca id=\"user-content-example-workflow-scoring\" class=\"anchor\" aria-label=\"Permalink: Example Workflow (Scoring):\" href=\"#example-workflow-scoring\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eUnacceptable Risk Prompts\u003c/strong\u003e: Scores [5, 4, 2, 3].\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMean score = (5 + 4 + 2 + 3) / 4 = 3.5 \u2192 \u003cstrong\u003eUnacceptable Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eClassification stops.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eHigh Risk Prompts\u003c/strong\u003e: Scores [2, 3, 4, 4].\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMean score = (2 + 3 + 4 + 4) / 4 = 3.25 \u2192 \u003cstrong\u003eHigh Risk\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eLimited Risk Prompts\u003c/strong\u003e: Scores [2, 2, 3].\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMean score = (2 + 2 + 3) / 3 = 2.33 \u2192 Not classified as Limited Risk.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1567069655.0 + "updated_at": 1732090527.0 }, { "data_format": 2, - "description": "The BARI RStudio Singularity container, for the Discovery cluster", + "description": "An agent for Azure Pipelines using a Singularity image", "filenames": [ "Singularity" ], - "full_name": "BARIBoston/bari_singularity_rstudio", + "full_name": "basnijholt/azure-singularity-agent", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebari_singularity_rstudio\u003c/h1\u003e\u003ca id=\"user-content-bari_singularity_rstudio\" class=\"anchor\" aria-label=\"Permalink: bari_singularity_rstudio\" href=\"#bari_singularity_rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source code for the BARI Singularity image, containing R packages for geospatial analysis. The full documentation for Singularity can be found on the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/index.html\" rel=\"nofollow\"\u003esyslabs.io documentation site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguring installed packages\u003c/h2\u003e\u003ca id=\"user-content-configuring-installed-packages\" class=\"anchor\" aria-label=\"Permalink: Configuring installed packages\" href=\"#configuring-installed-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe complete image build process is outlined in the \u003ca href=\"Singularity\"\u003eSingularity definition file\u003c/a\u003e. Most likely, you will want to edit the \u003ccode\u003ePACKAGES\u003c/code\u003e array in the \u003ccode\u003e%post\u003c/code\u003e section of the definition file.\u003c/p\u003e\n\u003cp\u003eFor full documentation, see the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/definition_files.html\" rel=\"nofollow\"\u003eThe Definition File\u003c/a\u003e section of the syslabs.io documentation site.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding\u003c/h2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo quickly build the BARI Singularity image, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build bari.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen prototyping, though, it will likely be more useful to use a temporary sandbox directory to iterate through commands before writing the final procedure to the Singularity definition file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build --sandbox bari-dev/ Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can then access a root shell that will persist changes as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell --writable bari-dev/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce you are finished prototyping, it is best to edit the definition file so that future builds will reflect your updated process and you can see if it is working as intended from a clean base image, but if you are impatient, you can build an image directly from the temporary sandbox:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build bari.img bari-dev/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor full documentation, see the \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/build_a_container.html\" rel=\"nofollow\"\u003eBuilding a container\u003c/a\u003e section of the syslabs.io documentation site.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWIP: azure-singularity-agent\u003c/h1\u003e\u003ca id=\"user-content-wip-azure-singularity-agent\" class=\"anchor\" aria-label=\"Permalink: WIP: azure-singularity-agent\" href=\"#wip-azure-singularity-agent\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn agent for Azure Pipelines using a Singularity image\u003c/p\u003e\n\u003cp\u003eBuild with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build azure-singularity-agent.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eenv AZP_URL=https://dev.azure.com/\u0026lt;organization\u0026gt; AZP_TOKEN=\u0026lt;PAT token\u0026gt; AZP_AGENT_NAME=mydockeragent singularity run azure-singularity-agent.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSeems to not work because the resulting \u003ccode\u003esif\u003c/code\u003e is read-only.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1594835811.0 + "updated_at": 1583410819.0 }, { "data_format": 2, - "description": "Docker and Singularity images for FSL 5.11 and Resting State FMRI pipeline (Nan-kuei Chen/Duke University)", + "description": "Docker / Singularity image for using the iRODS client commands on HPC systems", "filenames": [ "Singularity" ], - "full_name": "chidiugonna/nklab-neuro-fsl", + "full_name": "SystemsGenetics/irods-docker", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker and Singularity images for FSL 5.11 and Resting State FMRI pipeline (Nan-kuei Chen/Duke University)\u003c/h1\u003e\u003ca id=\"user-content-docker-and-singularity-images-for-fsl-511-and-resting-state-fmri-pipeline-nan-kuei-chenduke-university\" class=\"anchor\" aria-label=\"Permalink: Docker and Singularity images for FSL 5.11 and Resting State FMRI pipeline (Nan-kuei Chen/Duke University)\" href=\"#docker-and-singularity-images-for-fsl-511-and-resting-state-fmri-pipeline-nan-kuei-chenduke-university\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e for details of use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSummary\u003c/h1\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-label=\"Permalink: Summary\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the build scripts for Docker and Singularity images of the Duke resting pipeline that perform processing of resting state data using FSL (Jenkinson et al. 2012) tools and custom scripts.\u003c/p\u003e\n\u003cp\u003eversion information can be obtained as \u003ccode\u003edocker run --rm orbisys/nklab-neuro-fsl -V\u003c/code\u003e and \u003ccode\u003esingularity run orbisys/nklab-neuro-fsl -V\u003c/code\u003e\nhelp information can be obtained as \u003ccode\u003edocker run --rm orbisys/nklab-neuro-fsl -h\u003c/code\u003e and \u003ccode\u003esingularity run orbisys/nklab-neuro-fsl -h\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe Docker image will be quite large when built. It comes with version 5.11 of FSL and also includes gpu versions of eddy, probtrackx and bedpostx. It is recommended if possible to use the Singularity version of this container.\u003c/p\u003e\n\u003cp\u003eAlternatively if you do not want to build the docker image locally you can pull it from the Docker hub using the command \u003ccode\u003edocker run -it --rm -v $PWD:/opt/data orbisys/nklab-neuro-fsl\u003c/code\u003e or \u003ccode\u003edocker pull orbisys/nklab-neuro-fsl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe Singularity image will be about 6GB when built. It comes with version 5.11 of FSL and also includes gpu versions of eddy, probtrackx and bedpostx. Again if you prefer not to build this locally then a sister version of this singularity image can be downloaded as \u003ccode\u003eSingularity pull shub://chidiugonna/nklab-neuro-fsl\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe original python source \u003ccode\u003eresting_pipeline.py\u003c/code\u003e available at at [\u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e] has been slightly amended. These changes are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edata1\u003c/code\u003e has been selectively converted to dtype \u003ccode\u003enumpy.float64\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eslice indices have been cast as longs in certain instances.\u003c/li\u003e\n\u003cli\u003eBXH functionality is ignored. To explicitly use BXH info pass the flag --ignorebxh=N\u003c/li\u003e\n\u003cli\u003eChanges have been made in step 8 to force the diagonals of the correlation matrix to zero to prevent inconsistencies due to NaNs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSliding window functionality\u003c/h3\u003e\u003ca id=\"user-content-sliding-window-functionality\" class=\"anchor\" aria-label=\"Permalink: Sliding window functionality\" href=\"#sliding-window-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA new step has been added \u003ccode\u003e-7sw\u003c/code\u003e to enable sliding window functionality. In order to use this step you will need to use the \u003ccode\u003e--slidewin\u003c/code\u003e parameter which takes 2 numbers seperated by a comma. The 1st number is the window size in seconds and the second number is the shift in seconds between sequential windows. So for example \u003ccode\u003e--slidewin=60,3\u003c/code\u003e will use a window size of \u003ccode\u003e60\u003c/code\u003e seconds shifted by \u003ccode\u003e3\u003c/code\u003e seconds for each subsequent window. Keep in mind that the \u003ccode\u003e--tr\u003c/code\u003e (in milliseconds) parameter is required to calculate the number of volumes to use for each sliding window correlation. If you do not specify the --slidwin parameter and run step \u003ccode\u003e7sw\u003c/code\u003e then default values of \u003ccode\u003e30,3\u003c/code\u003e will be used. Sliding window files are exported to a new directory \u003ccode\u003eSlidingWindow_W_S\u003c/code\u003e and image files are consolidated into 4D volumes for viewing in FSL as a movie\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExtensions to Slice Correction functionality\u003c/h3\u003e\u003ca id=\"user-content-extensions-to-slice-correction-functionality\" class=\"anchor\" aria-label=\"Permalink: Extensions to Slice Correction functionality\" href=\"#extensions-to-slice-correction-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline has been extended to accept custom slice correction timing files. A python script \u003ccode\u003emake_fsl_stc.py\u003c/code\u003e has been bundled in this container which can take .json files created by dcm2niix. This python program will create a slice correction file with timing values and one with slices in order of acquisition. It can be called as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e where fmri.json is the json output from dcm2niix. custom names for the slice order and slice time files can be provided as parameters as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake_fsl_stc.py fmri.json --slicenum=/path/num.txt --slicetime=/path/time.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOtherwise these files default to \u003ccode\u003esliceorder.txt\u003c/code\u003e and \u003ccode\u003eslicetimes.txt\u003c/code\u003e in the current directory.\u003c/p\u003e\n\u003cp\u003eIf \u003ccode\u003e--slicetime\u003c/code\u003e is provided and --sliceorder is not then only the slicetimes textfile is created. The opposite is true if \u003ccode\u003e--slicenum\u003c/code\u003e is provided.\u003c/p\u003e\n\u003cp\u003eOnce these custom files have been created then they can be provided to the resting state pipeline using the full path as input to the \u003ccode\u003e--sliceorder\u003c/code\u003e parameter\n\u003ccode\u003e--sliceorder=/path/num.txt\u003c/code\u003e as follows \u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eplease note that the default custom slice file expected uses slice order. If you pass a text file with slice times then you will need to use another parameter \u003ccode\u003e--slicetimings=time\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Docker Image\u003c/h3\u003e\u003ca id=\"user-content-build-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build Docker Image\" href=\"#build-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have docker installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003erest-state-fmri\u003c/code\u003edirectory and check that have a Docker file \u003ccode\u003eDockerfile\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and the \u003ccode\u003esrc/resting_pipeline.py\u003c/code\u003e file have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow build the image as follows \u003ccode\u003esudo docker build -t orbisys/nklab-neuro-fsl .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Docker Image\u003c/h3\u003e\u003ca id=\"user-content-run-docker-image\" class=\"anchor\" aria-label=\"Permalink: Run Docker Image\" href=\"#run-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWithin Shell\u003c/h4\u003e\u003ca id=\"user-content-within-shell\" class=\"anchor\" aria-label=\"Permalink: Within Shell\" href=\"#within-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to a directory with a test NIFTII image and enter \u003ccode\u003edocker run -it --rm -v $PWD:/opt/data --entrypoint /bin/bash orbisys/nklab-neuro-fsl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe docker image should run and automatically start in \u003ccode\u003e/opt/data\u003c/code\u003e directory which is mapped to the original directory from which you ran the image. The prompt should look something like below:\n\u003ccode\u003eroot@62e040b47368:/opt/data#\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eYou can now run the pipeline with the shell as follows: \u003ccode\u003eresting_pipeline.py --func PBIA6_26386_20140402_045154_93696_magnitude.nii --throwaway=4 --steps=2,3,4,5,6,7 -o PBIA6_26386_20140402_045154_93696 --sliceorder=odd --tr=5000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAs a one line command\u003c/h4\u003e\u003ca id=\"user-content-as-a-one-line-command\" class=\"anchor\" aria-label=\"Permalink: As a one line command\" href=\"#as-a-one-line-command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to a directory with a test NIFTII image and enter:\n\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func moco14a0001.nii.gz --steps=1,2,3,4,5,6,7,8 -o 14a0001 --sliceorder=\"even\" --tr=3000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning Gui within docker\u003c/h4\u003e\u003ca id=\"user-content-running-gui-within-docker\" class=\"anchor\" aria-label=\"Permalink: Running Gui within docker\" href=\"#running-gui-within-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo access GUI interaces of programs in the docker image then use the construct shown next (Courtesy of work by Fabio Rehm [\u003ca href=\"https://fabiorehm.com/blog/2014/09/11/running-gui-apps-with-docker/\" rel=\"nofollow\"\u003ehttps://fabiorehm.com/blog/2014/09/11/running-gui-apps-with-docker/\u003c/a\u003e] ). For example to run FSL as GUI then perform the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v $HOME/.Xauthority:/home/developer/.Xauthority -it --net=host --pid=host --ipc=host orbisys/rest-state-fmri fsl\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Commands\u003c/h3\u003e\u003ca id=\"user-content-example-commands\" class=\"anchor\" aria-label=\"Permalink: Example Commands\" href=\"#example-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreate Slice Timing files from json\u003c/h4\u003e\u003ca id=\"user-content-create-slice-timing-files-from-json\" class=\"anchor\" aria-label=\"Permalink: Create Slice Timing files from json\" href=\"#create-slice-timing-files-from-json\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/nklab-neuro-fsl /opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\" class=\"anchor\" aria-label=\"Permalink: Run pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/nklab-neuro-fsl /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity Image\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Build Singularity Image\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have singularity 2.4 or greater installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003enklab-neuro-fsl\u003c/code\u003edirectory and check that you have a Singularity definiton file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow build the image as follows \u003ccode\u003esudo singularity build nklab-neuro-fsl.simg Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Singularity Image\u003c/h3\u003e\u003ca id=\"user-content-run-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Run Singularity Image\" href=\"#run-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou can now run the pipeline as follows: \u003ccode\u003esingularity run nklab-neuro-fsl.simg /opt/rsfmri_python/bin/resting_pipeline.py --func PBIA6_26386_20140402_045154_93696_magnitude.nii --throwaway=4 --steps=2,3,4,5,6,7 -o PBIA6_26386_20140402_045154_93696 --sliceorder=odd --tr=5000\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eYou can also run FSL commands (e.g. flirt) directly as follows: \u003ccode\u003esingularity run --nv rest-state-fmri.simg /opt/fsl/bin/flirt ....\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShell into Singularity Image\u003c/h3\u003e\u003ca id=\"user-content-shell-into-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Shell into Singularity Image\" href=\"#shell-into-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou can shell into the singularity image using: \u003ccode\u003esingularity shell nklab-neuro-fsl.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Commands\u003c/h3\u003e\u003ca id=\"user-content-example-commands-1\" class=\"anchor\" aria-label=\"Permalink: Example Commands\" href=\"#example-commands-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreate Slice Timing files from json\u003c/h4\u003e\u003ca id=\"user-content-create-slice-timing-files-from-json-1\" class=\"anchor\" aria-label=\"Permalink: Create Slice Timing files from json\" href=\"#create-slice-timing-files-from-json-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esingularity run -B $PWD:/opt/data nklab-neuro-fsl.simg /opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file-1\" class=\"anchor\" aria-label=\"Permalink: Run pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --rm -B $PWD:/opt/data nklab-neuro-fsl.simg /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eM. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eirods-docker\u003c/h1\u003e\u003ca id=\"user-content-irods-docker\" class=\"anchor\" aria-label=\"Permalink: irods-docker\" href=\"#irods-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the files for building a Docker or Singularity image of the iRODS client commands, as well the files to create an Environment Module (or Lmod module) for the client commands, for use on an HPC system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity documentation: \u003ca href=\"https://www.sylabs.io/guides/2.5/user-guide/index.html\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/guides/2.5/user-guide/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eiRODS documentation: \u003ca href=\"https://docs.irods.org/4.1.12/\" rel=\"nofollow\"\u003ehttps://docs.irods.org/4.1.12/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEnvironment Modules: \u003ca href=\"http://modules.sourceforge.net/\" rel=\"nofollow\"\u003ehttp://modules.sourceforge.net/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eLmod: \u003ca href=\"https://lmod.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://lmod.readthedocs.io/en/latest/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must have Singularity installed on a local machine and your HPC system. It is recommended that you use Singularity 2.4 or newer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build irods.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run an icommand:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec irods.simg \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that you need admin privileges to build the Singularity image, so you will most likely have to build the image on a local machine and then transfer the image to your HPC system.\u003c/p\u003e\n\u003cp\u003eOnce you\u0027ve built the image, you can use the icommands \"out-of-the-box\" by creating aliases for each icommand, for example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias iinit=\"singularity exec irods.simg iinit\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe scripts \u003ccode\u003einstall-irods-lmod.sh\u003c/code\u003e and \u003ccode\u003einstall-irods-tmod.sh\u003c/code\u003e respectively create an Lmod module or Environment Module which provides these aliases automatically. You may need to edit these scripts to work for your particular environment.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 8, "topics": [], - "updated_at": 1533338767.0 + "updated_at": 1550696838.0 }, { "data_format": 2, - "description": "Container configuration file for GPU+Singularity tests.", + "description": "BIRN Quality Assurance Tools", "filenames": [ - "Singularity", - "Singularity.1404", - "Singularity.1604" + "Singularity" ], - "full_name": "ruycastilho/GPUtest", + "full_name": "NenckaLab/BXH-XCEDE-QA-TOOLS", "latest_release": null, + "readme": "\u003cp\u003e\u003cem\u003eSingularity instance based on \u003ca href=\"https://github.com/flywheel-apps/bxh-xcede-tools-qa\"\u003eflywheel-apps/bxh-xcede-tools-qa\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDESCRIPTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUses the \u003ca href=\"https://www.nitrc.org/projects/bxh_xcede_tools/\" rel=\"nofollow\"\u003eNITRC BXH/XCEDE Tools\u003c/a\u003e to gather QA data, both fmriqa_phantomqa.pl and fmriqa_generate.pl show an HTML page with images, graphs, and other data.\u003c/p\u003e\n\u003cp\u003efmriqa_phantomqa.pl is used for the BIRN stability phantom and is the default use for this container\u003c/p\u003e\n\u003cp\u003efmriqa_generate.pl can be used for human fMRI data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINPUTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA directory of DICOM images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOUTPUT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn HTML file, an XML Summary, and graphs/images. The HTML file contains the relevant information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOPTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOptions are found in manifest.json. Includes whether to use phantom or human fMRI and the various choices for outputs.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1504295844.0 + "updated_at": 1688053174.0 }, { "data_format": 2, - "description": null, + "description": "Simple Apptainer for TrajOptKP repository. Can be used to run TrajOptKP easily without needing to download packages manually. Also used for setting up GitHub actions.", "filenames": [ + "Singularity_static", "Singularity" ], - "full_name": "StefReck/OrcaNet", + "full_name": "DMackRus/Apptainer_TrajOptKP", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eApptainer_TrajOptKP\u003c/h1\u003e\u003ca id=\"user-content-apptainer_trajoptkp\" class=\"anchor\" aria-label=\"Permalink: Apptainer_TrajOptKP\" href=\"#apptainer_trajoptkp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimple Apptainer for TrajOptKP repository. Can be used to run TrajOptKP easily without needing to download packages manually. Also used for setting up GitHub actions.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1628086621.0 + "updated_at": 1730815636.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity", - "v0.8.13/DB20190619/Singularity.v0.8.13_DB20190619", - "v0.8/DB20180717/Singularity.v0.8_DB20180717", - "v0.8/DB20190618/Singularity.v0.8_DB20190618" + "Singularity" ], - "full_name": "phgenomics-singularity/abricate_k", + "full_name": "rynge/hub-test", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbricate --- A Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-abricate-----a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Abricate --- A Singularity Container\" href=\"#abricate-----a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1288\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for \u003ca href=\"https://github.com/tseemann/abricate\"\u003eAbricate\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ePull the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name TMP_DIRECTORY shub://phgenomics-singularity/Abricate@latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003eAbricate.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./Abricate.simg --help\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name mlst shub://phgenomics-singularityAbricate@VERSION.NUMBER\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir HOME/singularity\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name Abricate shub://phgenomics-singularity/Abricate@latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLink the image to a folder in your \u003ccode\u003ePATH\u003c/code\u003e (e.g., \u003ccode\u003eHOME/bin\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eln -s HOME/singularity/Abricate.simg HOME/bin/Abricate\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e abricate --help\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdating the DB\u003c/h2\u003e\u003ca id=\"user-content-updating-the-db\" class=\"anchor\" aria-label=\"Permalink: Updating the DB\" href=\"#updating-the-db\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eRun the \u003ccode\u003eupdate_db.py\u003c/code\u003e script (default version is 0.8 at the moment)\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehub-test\u003c/h1\u003e\u003ca id=\"user-content-hub-test\" class=\"anchor\" aria-label=\"Permalink: hub-test\" href=\"#hub-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1576546683.0 + "updated_at": 1501705378.0 }, { "data_format": 2, - "description": null, + "description": "Jupyterlab plugin to interface with the EVN Archive", "filenames": [ "Singularity" ], - "full_name": "CNCLgithub/psiturk-sing", - "latest_release": "v1.1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePsiturk singularity container\u003c/h1\u003e\u003ca id=\"user-content-psiturk-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Psiturk singularity container\" href=\"#psiturk-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe definition file describes a lightweight, apline-based container to run psiturk\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild or pull the container\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePulling\u003c/h3\u003e\u003ca id=\"user-content-pulling\" class=\"anchor\" aria-label=\"Permalink: Pulling\" href=\"#pulling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container is hosted at \u003ca href=\"https://cloud.sylabs.io/library/mebelledonne/default/psiturk-apline\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/mebelledonne/default/psiturk-apline\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCan be pulled using the following commands (see link above for up-to-date info)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Pull with Singularity\u003c/span\u003e\n$ singularity pull --arch amd64 library://mebelledonne/default/psiturk-apline:1.0.0\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Pull by unique ID (reproducible even if tags change)\u003c/span\u003e\n$ singularity pull library://mebelledonne/default/psiturk-apline:sha256.4fd46933f838844df2dc14e09651f07d3d322a13c7a0458942028e8347f2addb\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell psiturk.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e now inside the container\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /where/your/project/is/psiturk\npsiturk \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e + any addition args you might want to pass \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "aardk/EVN-Archive", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1601405204.0 + "updated_at": 1727784318.0 }, { "data_format": 2, - "description": "Repository for a singularity container to run the prob_mbrl code", + "description": "This is an openxdmod container for the bruno super computer @ biohub sf", "filenames": [ "Singularity" ], - "full_name": "juancamilog/prob_mbrl_container", + "full_name": "romxero/czbiohub_openxdmod", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eprob_mbrl_container\u003c/h1\u003e\u003ca id=\"user-content-prob_mbrl_container\" class=\"anchor\" aria-label=\"Permalink: prob_mbrl_container\" href=\"#prob_mbrl_container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1568145713.0 + "updated_at": 1720476769.0 }, { "data_format": 2, - "description": "RepeatMasker singularity image recipe", + "description": "Tools for monitoring HTCondor and other things", "filenames": [ - "Singularity", - "Singularity.4.0.8" + "Singularity" ], - "full_name": "tpall/repeatmasker-singularity", - "latest_release": "v0.9", - "readme": "\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.2671673\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/05b4f48cf033550d0ad2dce2ad5b66ea091ef2b8baff1ff6ca0ed9c41194328f/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e323637313637332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.2671673.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://travis-ci.org/tpall/repeatmasker-singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2ac9880bdf7d2fd57847145748defa6ac26f6aaf31cc4fe84e4668d2846e890a/68747470733a2f2f7472617669732d63692e6f72672f7470616c6c2f7265706561746d61736b65722d73696e67756c61726974792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/tpall/repeatmasker-singularity.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRepeatMasker singularity image\u003c/h1\u003e\u003ca id=\"user-content-repeatmasker-singularity-image\" class=\"anchor\" aria-label=\"Permalink: RepeatMasker singularity image\" href=\"#repeatmasker-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains singularity image recipes for RepeatMasker 4.0.8 and 4.0.9(p2) together with RepBase RepeatMasker Edition database v20181026. RepBase RM database was installed from the private copy downloaded from \u003ca href=\"https://www.girinst.org/repbase\" rel=\"nofollow\"\u003egirinst.org\u003c/a\u003e before them fully switching to a \u003ca href=\"https://www.girinst.org/repbase/sub_announcement.html\" rel=\"nofollow\"\u003esubscription-based funding model\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBuilt RM-4.0.9p2 image can be downloaded from Zenodo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCommand line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /path/to/repeatmasker409.simg /usr/local/bin/RepeatMasker test/seqs/small-1.fa\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "WIPACrepo/monitoring-scripts", + "latest_release": "0.4.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emonitoring-scripts\u003c/h1\u003e\u003ca id=\"user-content-monitoring-scripts\" class=\"anchor\" aria-label=\"Permalink: monitoring-scripts\" href=\"#monitoring-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome scripts for sending data to ES, or plotting it, or other misc activities.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1557682456.0 + "updated_at": 1730332969.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "containers/Singularity.omnia.rocm" ], - "full_name": "oogasawa/singularity_postgresql-12.0", + "full_name": "PhilipVinc/uvtest3", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_postgresql\u003c/h1\u003e\u003ca id=\"user-content-singularity_postgresql\" class=\"anchor\" aria-label=\"Permalink: singularity_postgresql\" href=\"#singularity_postgresql\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3\u3067\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306esingularity image\u306e\u4f7f\u3044\u65b9\u003c/p\u003e\n\u003cp\u003e\u5bfe\u5fdc\u3059\u308bPostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e9.6.15\u003c/li\u003e\n\u003cli\u003e10.10\u003c/li\u003e\n\u003cli\u003e11.5\u003c/li\u003e\n\u003cli\u003e12.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u751f\u6210\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u751f\u6210\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u751f\u6210\" href=\"#image\u306e\u751f\u6210\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u81ea\u5206\u306e\u74b0\u5883\u3067image\u3092build\u3059\u308b\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u306f9.6.15, 10.10, 11.5, 12.0\u306e\u3044\u305a\u308c\u304b\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ddbj/singularity_postgresql.git\n$ cd singularity_postgresql\n$ sudo singularity build ubuntu-18.04-postgresql-\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;.simg Singularity.\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" href=\"#image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Hub\u306b\u767b\u9332\u3055\u308c\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u306f9.6.15, 10.10, 11.5, 12.0\u306e\u3044\u305a\u308c\u304b\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ddbj/singularity_postgresql.git\n$ cd singularity_postgresql\n$ singularity pull --name ubuntu-18.04-postgresql-\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;.simg shub://ddbj/singularity_postgresql:\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u751f\u6210\u307e\u305f\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3060\u3051\u3067\u306fPostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u5b9f\u884c\u3067\u304d\u307e\u305b\u3093\u3002 start_container.sh\u3092\u5b9f\u884c\u3057\u3066singularity instance\u3092\u8d77\u52d5\u3057\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\u3092\u884c\u3044\u307e\u3059\u3002\n\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u524d\u306b\u3001\u81ea\u5206\u306e\u74b0\u5883\u306b\u5408\u308f\u305b\u3066 start_container.sh \u306e CONTAINER_HOME, IMAGE, INSTANCE, PORT\u5909\u6570\u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCONTAINER_HOME\u306b\u306fgit clone\u3067\u3067\u304d\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u3092\u8a18\u8f09\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003cli\u003eIMAGE\u306b\u306f\u3001image\u751f\u6210\u307e\u305f\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u306e\u969b\u306b\u6307\u5b9a\u3057\u305f\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u8a18\u8f09\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003cli\u003ePORT\u5909\u6570\u306f5000\u4ee5\u4e0a\u3067\u4efb\u610f\u306e\u6574\u6570\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3059\u308b\u3068\u3001\u521d\u56de\u5b9f\u884c\u6642\u306b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u521d\u671f\u5316\u304c\u884c\u308f\u308c\u305f\u5f8c\u3067\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b5\u30fc\u30d0\u304c\u8d77\u52d5\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\nThe files belonging to this database system will be owned by user \"\u0026lt;\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u30e6\u30fc\u30b6\u30fc\u0026gt;\".\nThis user must also own the server process.\n\nThe database cluster will be initialized with locale \"C\".\nThe default text search configuration will be set to \"english\".\n\nData page checksums are disabled.\n\nfixing permissions on existing directory /usr/local/pgsql12/data ... ok\ncreating subdirectories ... ok\nselecting dynamic shared memory implementation ... posix\nselecting default max_connections ... 100\nselecting default shared_buffers ... 128MB\nselecting default time zone ... Japan\ncreating configuration files ... ok\nrunning bootstrap script ... ok\nperforming post-bootstrap initialization ... ok\nsyncing data to disk ... ok\n\ninitdb: warning: enabling \"trust\" authentication for local connections\nYou can change this by editing pg_hba.conf or using the option -A, or\n--auth-local and --auth-host, the next time you run initdb.\n\nSuccess. You can now start the database server using:\n\n pg_ctl -D /usr/local/pgsql12/data -l logfile start\n\nStopping pgsql instance of /gpfs1/lustre2/home/y-okuda/git/singularity_postgresql/ubuntu-18.04-postgresql-12.0.simg (PID=36513)\nwaiting for server to start.... done\nserver started\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity instance\u3092\u8d77\u52d5\u3057\u305f\u30e6\u30fc\u30b6\u30fc\uff08initdb\u3092\u5b9f\u884c\u3057\u305f\u30e6\u30fc\u30b6\u30fc\uff09\u304cPostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec instance://pgsql psql -d postgres -p 55432\npsql (12.0)\nType \"help\" for help.\n\npostgres=# alter role \"\u0026lt;\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u30e6\u30fc\u30b6\u30fc\u0026gt;\" with password \u0027\u0026lt;\u30d1\u30b9\u30ef\u30fc\u30c9\u0026gt;\u0027;\nALTER ROLE\npostgres=# \\q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30d1\u30b9\u30ef\u30fc\u30c9\u306e\u8a2d\u5b9a\u306b\u3088\u308asingularity instance\u306e\u5916\u304b\u3089\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u30a2\u30af\u30bb\u30b9\u306e\u969b\u306f-h\u30aa\u30d7\u30b7\u30e7\u30f3\u3067singularity instance\u3092\u5b9f\u884c\u3057\u3066\u3044\u308b\u30db\u30b9\u30c8\u540d\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ psql -d postgres -p 55432 -h at043\n\u30d1\u30b9\u30ef\u30fc\u30c9: \npsql (9.2.24, \u30b5\u30fc\u30d0\u30fc 12.0)\n\u6ce8\u610f\uff1a psql \u30d0\u30fc\u30b8\u30e7\u30f3 9.2, \u30b5\u30fc\u30d0\u30fc\u30d0\u30fc\u30b8\u30e7\u30f3 12.0.\n psql \u306e\u6a5f\u80fd\u306e\u4e2d\u3067\u3001\u52d5\u4f5c\u3057\u306a\u3044\u3082\u306e\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\n\"help\" \u3067\u30d8\u30eb\u30d7\u3092\u8868\u793a\u3057\u307e\u3059.\n\npostgres=# \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u5225\u30ce\u30fc\u30c9\u304b\u3089\u306e\u30a2\u30af\u30bb\u30b9\u3082\u53ef\u80fd\u3067\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh at044\nLast login: Fri Nov 1 20:25:26 2019 from at043\n$ psql -d postgres -p 55432 -h at043\n\u30d1\u30b9\u30ef\u30fc\u30c9: \npsql (9.2.24, \u30b5\u30fc\u30d0\u30fc 12.0)\n\u6ce8\u610f\uff1a psql \u30d0\u30fc\u30b8\u30e7\u30f3 9.2, \u30b5\u30fc\u30d0\u30fc\u30d0\u30fc\u30b8\u30e7\u30f3 12.0.\n psql \u306e\u6a5f\u80fd\u306e\u4e2d\u3067\u3001\u52d5\u4f5c\u3057\u306a\u3044\u3082\u306e\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\n\"help\" \u3067\u30d8\u30eb\u30d7\u3092\u8868\u793a\u3057\u307e\u3059.\n\npostgres=# \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u63d0\u4f9b\u3057\u3066\u3044\u308bpg_hba.conf\u306e\u8a18\u8ff0\u3067\u306f\u30a2\u30af\u30bb\u30b9\u53ef\u80fd\u306aIP\u30a2\u30c9\u30ec\u30b9\u306b\u5236\u9650\u304c\u304b\u304b\u3063\u3066\u3044\u307e\u305b\u3093\u3002\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOmnia\u003c/h1\u003e\u003ca id=\"user-content-omnia\" class=\"anchor\" aria-label=\"Permalink: Omnia\" href=\"#omnia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a single repository containing all working codes of the group.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStructure\u003c/h2\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e/packages # contains packages used by multiple users\n # This contains both internal versions of public packages\n # (netket) and private packages.\n```\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1618363302.0 + "updated_at": 1732150434.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "containers/Singularity.omnia.rocm" ], - "full_name": "yiwan-rl/singularity_recipes", + "full_name": "PhilipVinc/uvtest2", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOmnia\u003c/h1\u003e\u003ca id=\"user-content-omnia\" class=\"anchor\" aria-label=\"Permalink: Omnia\" href=\"#omnia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a single repository containing all working codes of the group.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStructure\u003c/h2\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e/packages # contains packages used by multiple users\n # This contains both internal versions of public packages\n # (netket) and private packages.\n```\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1615835305.0 + "updated_at": 1732149899.0 }, { "data_format": 2, - "description": "CentOS 7 Base Singularity Image", + "description": "Singularity container for dropSeqPipe", "filenames": [ + "Singularity.v04", "Singularity" ], - "full_name": "scleveland/centos7-base-singularity", + "full_name": "seb-mueller/singularity_dropSeqPipe", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1254\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Recipe for base CentOS 7 image For the University of Hawaii HPC system\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-base-centos-7-image-for-the-university-of-hawaii-hpc-system\" class=\"anchor\" aria-label=\"Permalink: Singularity Recipe for base CentOS 7 image For the University of Hawaii HPC system\" href=\"#singularity-recipe-for-base-centos-7-image-for-the-university-of-hawaii-hpc-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains recipe to create a base CentOS-7 singularity containter\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to Use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity run shub://scleveland/centos7-base-singularity\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1531340029.0 + "updated_at": 1569595505.0 }, { "data_format": 2, - "description": "Detect germline or somatic variants from normal or tumour/normal whole-genome or targeted sequencing", + "description": "Singularity Container Handle", "filenames": [ - "Singularity" + "Singularity.1_4", + "Singularity.old", + "Singularity.1_11" ], - "full_name": "UCL-BLIC/Sarek_v2.3.FIX1", + "full_name": "jrenslo/singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\u003ca href=\"http://sarek.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/Sarek_logo.png\" alt=\"Sarek\" title=\"Sarek\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\u003ca id=\"\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAn open-source analysis pipeline to detect germline or somatic variants from whole genome or targeted sequencing\u003c/h4\u003e\u003ca id=\"user-content-an-open-source-analysis-pipeline-to-detect-germline-or-somatic-variants-from-whole-genome-or-targeted-sequencing\" class=\"anchor\" aria-label=\"Permalink: An open-source analysis pipeline to detect germline or somatic variants from whole genome or targeted sequencing\" href=\"#an-open-source-analysis-pipeline-to-detect-germline-or-somatic-variants-from-whole-genome-or-targeted-sequencing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a5d620fbf501842bc77646b936f0473db29f72acd364b8f0e158b2619fa910a6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e7376673f6c6f676f3d646174613a696d6167652f7376672b786d6c3b6261736536342c5044393462577767646d567963326c76626a30694d5334774969426c626d4e765a476c755a7a3069565652474c54676949484e305957356b59577876626d5539496d3576496a382b50484e325a794167494868746247357a4f6d526a50534a6f644852774f6938766348567962433576636d63765a474d765a57786c6257567564484d764d5334784c7949674943423462577875637a706a597a30696148523063446f764c324e795a57463061585a6c593239746257397563793576636d6376626e4d6a49694167494868746247357a4f6e4a6b5a6a30696148523063446f764c336433647935334d793576636d63764d546b354f5338774d6938794d6931795a47597463336c75644746344c57357a497949674943423462577875637a707a646d6339496d6830644841364c79393364336375647a4d7562334a6e4c7a49774d44417663335a6e49694167494868746247357a50534a6f644852774f693876643364334c6e637a4c6d39795a7938794d4441774c334e325a7949674943423462577875637a707a623252706347396b615430696148523063446f764c334e765a476c77623252704c6e4e7664584a6a5a575a76636d646c4c6d356c64433945564551766332396b615842765a476b744d43356b644751694943416765473173626e4d366157357263324e6863475539496d6830644841364c793933643363756157357263324e686347557562334a6e4c3235686257567a6347466a5a584d766157357263324e68634755694943416764326c6b64476739496a45794c6a63354f5449794f473174496941674947686c6157646f644430694d5449754f4441304f4441356257306949434167646d6c6c64304a76654430694d434177494451314c6a4d314d5455354e4341304e53347a4e7a457a4e6a6b694943416761575139496e4e325a7a63324e54496949434167646d567963326c76626a30694d5334784969416749476c7561334e6a5958426c4f6e5a6c636e4e7062323439496a41754f544567636a457a4e7a49314969416749484e765a476c77623252704f6d52765932356862575539496d356c6548526d624739334c575a68646d6c6a62323474643268706447557563335a6e496a34674944786b5a575a7a49434167494342705a4430695a47566d637a63324e5451694943382b494341386332396b615842765a476b36626d46745a5752326157563349434167494342705a443069596d467a5a53496749434167494842685a32566a62327876636a306949325a6d5a6d5a6d5a6949674943416749474a76636d526c636d4e76624739795053496a4e6a59324e6a59324969416749434167596d39795a4756796233426859326c30655430694d53347749694167494341676157357263324e68634755366347466e5a57397759574e7064486b39496a41754d4349674943416749476c7561334e6a5958426c4f6e42685a32567a6147466b62336339496a49694943416749434270626d747a593246775a54703662323974505349334c6a6b784f5455354e546b694943416749434270626d747a593246775a54706a654430694d6a41754d54457a4d6a4d3149694167494341676157357263324e686347553659336b39496a497a4c6a45324d7a6b774f4349674943416749476c7561334e6a5958426c4f6d5276593356745a5735304c5856756158527a50534a77654349674943416749476c7561334e6a5958426c4f6d4e31636e4a6c626e5174624746355a584939496d7868655756794d5349674943416749484e6f6233646e636d6c6b50534a6d5957787a5a5349674943416749475a706443317459584a6e61573474644739775053497749694167494341675a6d6c304c573168636d6470626931735a575a305053497749694167494341675a6d6c304c573168636d6470626931796157646f644430694d4349674943416749475a706443317459584a6e61573474596d3930644739745053497749694167494341676157357263324e686347553664326c755a4739334c5864705a48526f505349784f54497749694167494341676157357263324e686347553664326c755a4739334c57686c6157646f644430694d5441784e5349674943416749476c7561334e6a5958426c4f6e6470626d5276647931345053497749694167494341676157357263324e686347553664326c755a4739334c586b39496a41694943416749434270626d747a593246775a5470336157356b623363746257463461573170656d566b5053497849694176506941675047316c6447466b5958526849434167494342705a4430696257563059575268644745334e6a5533496a34674943416750484a6b5a6a70535245592b494341674943416750474e6a4f6c6476636d73674943416749434167494342795a47593659574a76645851394969492b4943416749434167494341385a474d365a6d397962574630506d6c745957646c4c334e325a797434625777384c32526a4f6d5a76636d31686444346749434167494341674944786b597a70306558426c494341674943416749434167494342795a475936636d567a6233567959325539496d6830644841364c79397764584a734c6d39795a79396b5979396b5932317064486c775a53395464476c7362456c745957646c496941765069416749434167494341675047526a4f6e52706447786c506a77765a474d3664476c306247552b49434167494341675043396a597a705862334a7250694167494341384c334a6b5a6a70535245592b494341384c32316c6447466b5958526850694167504763674943416749476c7561334e6a5958426c4f6d7868596d567350534a4d59586c6c6369417849694167494341676157357263324e68634755365a334a76645842746232526c50534a7359586c6c636949674943416749476c6b50534a7359586c6c636a45694943416749434230636d467563325a76636d3039496e52795957357a624746305a5367784d5451754d5441304d7a63734c5451314d6934314d7a4d324e696b6950694167494341386347463061434167494341674943427a64486c735a5430695a6d6c7362446f6a5a6d5a6d5a6d5a6d49694167494341674943426b50534a74494330784d5451754d5441304d7a63734e4455314c6a51324e545979494441734f4334344e6a457a4d7941774c6a49774d7a457a4c4441754d4459774e53426a49444d754f4463794f544d734d5334784d7a6b304d79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alt=\"Nextflow version\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg?logo=data:image/svg+xml;base64,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\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.org/SciLifeLab/Sarek\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d304fd5e83cd3987fc679ac8cc4b38f941c110391bbf4aa01042a516e3eac68d/68747470733a2f2f696d672e736869656c64732e696f2f7472617669732f5363694c6966654c61622f536172656b2e7376673f6c6f676f3d747261766973\" alt=\"Travis build status\" data-canonical-src=\"https://img.shields.io/travis/SciLifeLab/Sarek.svg?logo=travis\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/SciLifeLab/Sarek\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1bfcab243c9072ceb2cf58c2015147783c497c2680ae5bfa52173f475c6273dd/68747470733a2f2f696d672e736869656c64732e696f2f6769747465722f726f6f6d2f5363694c6966654c61622f536172656b2e7376673f6c6f676f3d676974746572266c6f676f436f6c6f723d776869746526636f6c6f72423d346662393961\" alt=\"Join the chat on https://gitter.im/SciLifeLab/Sarek\" data-canonical-src=\"https://img.shields.io/gitter/room/SciLifeLab/Sarek.svg?logo=gitter\u0026amp;logoColor=white\u0026amp;colorB=4fb99a\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34e3a02739fea50d64339fc189488413a41ade31ef5323543cb4b2b74eb0d54a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f5363694c6966654c61622f536172656b2e737667\" alt=\"MIT License\" data-canonical-src=\"https://img.shields.io/github/license/SciLifeLab/Sarek.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/SciLifeLab/Sarek/releases/latest\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6601edf424039dadb758444948fd35a899c0e55d1d38c24e2b00d7960710d180/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f5363694c6966654c61622f536172656b2e7376673f6c6f676f3d676974687562266c6f676f436f6c6f723d7768697465\" alt=\"Sarek version\" data-canonical-src=\"https://img.shields.io/github/release/SciLifeLab/Sarek.svg?logo=github\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/54024046\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8306a87ffc25c6750b3f82dcba1992ba2bab5802fd9cd64cf194612ec66f9a1/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f35343032343034362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/54024046.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.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\" alt=\"Install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?logo=data:image/png;base64,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\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/maxulysse/sarek\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/331acbc5ce8eb54738019206937dfb7d73368e38ab28d073f3bb1e9e13f01e74/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6d6178756c797373652f736172656b2e7376673f6c6f676f3d646f636b6572\" alt=\"Docker Container available\" data-canonical-src=\"https://img.shields.io/docker/automated/maxulysse/sarek.svg?logo=docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/CAW_logo.png\"\u003e\u003cimg align=\"right\" title=\"CAW\" src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/CAW_logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePreviously known as the Cancer Analysis Workflow (CAW),\nSarek is a workflow designed to run analyses on WGS data from regular samples or tumour / normal pairs, including relapse samples if required.\u003c/p\u003e\n\u003cp\u003eIt\u0027s built using \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a domain specific language for workflow building.\nSoftware dependencies are handled using \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e - container technologies that provide excellent reproducibility and ease of use.\nSingularity has been designed specifically for high-performance computing environments.\nThis means that although Sarek has been primarily designed for use with the Swedish \u003ca href=\"https://www.uppmax.uu.se\" rel=\"nofollow\"\u003eUPPMAX HPC systems\u003c/a\u003e, it should be able to run on any system that supports these two tools.\u003c/p\u003e\n\u003cp\u003eSarek was developed at the \u003ca href=\"https://ngisweden.scilifelab.se/\" rel=\"nofollow\"\u003eNational Genomics Infastructure\u003c/a\u003e and \u003ca href=\"https://www.nbis.se/\" rel=\"nofollow\"\u003eNational Bioinformatics Infastructure Sweden\u003c/a\u003e which are both platforms at \u003ca href=\"https://www.scilifelab.se/\" rel=\"nofollow\"\u003eSciLifeLab\u003c/a\u003e.\nIt is listed on the \u003ca href=\"https://bio.tools/Sarek\" rel=\"nofollow\"\u003eElixir - Tools and Data Services Registry\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow steps\u003c/h2\u003e\u003ca id=\"user-content-workflow-steps\" class=\"anchor\" aria-label=\"Permalink: Workflow steps\" href=\"#workflow-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSarek is built with several workflow scripts.\nA wrapper script contained within the repository makes it easy to run the different workflow scripts as a single job.\nTo test your installation, follow the \u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/TESTS.md\"\u003etests documentation.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRaw FastQ files or aligned BAM files (with or without realignment \u0026amp; recalibration) can be used as inputs.\nYou can choose which variant callers to use, plus the pipeline is capable of accommodating additional variant calling software or CNV callers if required.\u003c/p\u003e\n\u003cp\u003eThe worflow steps and tools used are as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003ePreprocessing\u003c/strong\u003e - \u003ccode\u003emain.nf\u003c/code\u003e \u003cem\u003e(based on \u003ca href=\"https://software.broadinstitute.org/gatk/best-practices/\" rel=\"nofollow\"\u003eGATK best practices\u003c/a\u003e)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eMap reads to Reference\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://bio-bwa.sourceforge.net/\" rel=\"nofollow\"\u003eBWA\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMark Duplicates\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK MarkDuplicates\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBase (Quality Score) Recalibration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK BaseRecalibrator\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK ApplyBQSR\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGermline variant calling\u003c/strong\u003e - \u003ccode\u003egermlineVC.nf\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eSNVs and small indels\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eGATK HaplotyeCaller\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka2\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStructural variants\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/manta\"\u003eManta\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSomatic variant calling\u003c/strong\u003e - \u003ccode\u003esomaticVC.nf\u003c/code\u003e \u003cem\u003e(optional)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eSNVs and small indels\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/broadinstitute/gatk\"\u003eMuTect2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ekg/freebayes\"\u003eFreebayes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka2\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eStructural variants\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Illumina/manta\"\u003eManta\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSample heterogeneity, ploidy and CNVs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Crick-CancerGenomics/ascat\"\u003eASCAT\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAnnotation\u003c/strong\u003e - \u003ccode\u003eannotate.nf\u003c/code\u003e \u003cem\u003e(optional)\u003c/em\u003e\n\u003cul\u003e\n\u003cli\u003eVariant annotation\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://snpeff.sourceforge.net/\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ensembl.org/info/docs/tools/vep/index.html\" rel=\"nofollow\"\u003eVEP (Variant Effect Predictor)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReporting\u003c/strong\u003e - \u003ccode\u003erunMultiQC.nf\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eReporting\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Sarek pipeline comes with documentation in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL.md\"\u003eInstallation documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL_RACKHAM.md\"\u003eInstallation documentation specific for UPPMAX \u003ccode\u003erackham\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INSTALL_BIANCA.md\"\u003eInstallation documentation specific for UPPMAX \u003ccode\u003ebianca\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/TESTS.md\"\u003eTests documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/REFERENCES.md\"\u003eReference files documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONFIG.md\"\u003eConfiguration and profiles documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INTERVALS.md\"\u003eIntervals documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/USAGE.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONDA.md\"\u003eRunning the pipeline using Conda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/PARAMETERS.md\"\u003eCommand line parameters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/USE_CASES.md\"\u003eExamples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/INPUT.md\"\u003eInput files documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/PROCESS.md\"\u003eProcesses documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/CONTAINERS.md\"\u003eDocumentation about containers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/ASCAT.md\"\u003eComplementary information about ASCAT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/ANNOTATION.md\"\u003eComplementary information about annotations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/docs/OUTPUT.md\"\u003eOutput documentation structure\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions \u0026amp; Support\u003c/h2\u003e\u003ca id=\"user-content-contributions--support\" class=\"anchor\" aria-label=\"Permalink: Contributions \u0026amp; Support\" href=\"#contributions--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you would like to contribute to this pipeline, please see the \u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/.github/CONTRIBUTING.md\"\u003econtributing guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor further information or help, don\u0027t hesitate to get in touch on \u003ca href=\"https://gitter.im/SciLifeLab/Sarek\" rel=\"nofollow\"\u003eGitter\u003c/a\u003e or contact us: \u003ca href=\"mailto:maxime.garcia@scilifelab.se\"\u003emaxime.garcia@scilifelab.se\u003c/a\u003e, \u003ca href=\"mailto:szilveszter.juhos@scilifelab.se\"\u003eszilveszter.juhos@scilifelab.se\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCHANGELOG\u003c/h2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-label=\"Permalink: CHANGELOG\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SciLifeLab/Sarek/blob/master/CHANGELOG.md\"\u003eCHANGELOG\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMain authors:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MaxUlysse\"\u003eMaxime Garcia\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/szilvajuhos\"\u003eSzilveszter Juhos\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHelpful contributors:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/alneberg\"\u003eJohannes Alneberg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Sebastian-D\"\u003eSebastian DiLorenzo\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/J35P312\"\u003eJesper Eisfeldt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ewels\"\u003ePhil Ewels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gulfshores\"\u003eMax K\u00e4ller\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/malinlarsson\"\u003eMalin Larsson\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/marcelm\"\u003eMarcel Martin\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bjornnystedt\"\u003eBj\u00f6rn Nystedt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pallolason\"\u003ePall Olason\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arontommi\"\u003eAron Skaftason\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nilesh-tawari\"\u003eNilesh Tawari\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca href=\"https://www.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/SciLifeLab_logo.png\" alt=\"SciLifeLab\" title=\"SciLifeLab\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ngisweden.scilifelab.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/NGI_logo.png\" alt=\"NGI\" title=\"NGI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nbis.se/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/SciLifeLab/Sarek/master/docs/images/NBIS_logo.png\" alt=\"NBIS\" title=\"NBIS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Container Handle\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1633704576.0 + "updated_at": 1542304210.0 }, { "data_format": 2, - "description": "Small tools/scripts written in python", + "description": "xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed limit.", "filenames": [ - "Singularity" + "0.8.0/Singularity", + "0.8.1/Singularity" ], - "full_name": "MDU-PHL/mdu-pytools", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMDU Python Tools\u003c/h1\u003e\u003ca id=\"user-content-mdu-python-tools\" class=\"anchor\" aria-label=\"Permalink: MDU Python Tools\" href=\"#mdu-python-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/MDU-PHL/mdu-pytools\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/beff6ea1fbb945167b46572357722ddea3d563f13997f1a5c83a2f7870d3e87e/68747470733a2f2f636972636c6563692e636f6d2f67682f4d44552d50484c2f6d64752d7079746f6f6c732e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/MDU-PHL/mdu-pytools.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/17430b1f952dff83ad16be3bc6d7bc1b28e5256448bd1b13cf569a42088dc70d/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f6d64752d7079746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/17430b1f952dff83ad16be3bc6d7bc1b28e5256448bd1b13cf569a42088dc70d/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f6d64752d7079746f6f6c73\" alt=\"PyPI - Python Version\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/mdu-pytools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/770d75608dabaa929e1fcf0af23b0afb832d96ab70b51c9e6e34cfed3c69b6dc/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d64752d7079746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/770d75608dabaa929e1fcf0af23b0afb832d96ab70b51c9e6e34cfed3c69b6dc/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d64752d7079746f6f6c73\" alt=\"PyPI\" data-canonical-src=\"https://img.shields.io/pypi/v/mdu-pytools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/47b997d6848a3598820db5da51efb22983fc9e454a83c40caca5457c3f96ab45/68747470733a2f2f696d672e736869656c64732e696f2f707970692f6c2f6d64752d7079746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/47b997d6848a3598820db5da51efb22983fc9e454a83c40caca5457c3f96ab45/68747470733a2f2f696d672e736869656c64732e696f2f707970692f6c2f6d64752d7079746f6f6c73\" alt=\"PyPI - License\" data-canonical-src=\"https://img.shields.io/pypi/l/mdu-pytools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#mdu-python-tools\"\u003eMDU Python Tools\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#background\"\u003eBackground\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#tools\"\u003eTools\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#mdu-merge-ngs-lanes\"\u003emdu-merge-ngs-lanes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#mdu-sra-uploads\"\u003emdu-sra-uploads\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#environmental-variables-that-can-be-used-to-set-options\"\u003eEnvironmental variables that can be used to set options\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#development\"\u003eDevelopment\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#development-environment\"\u003eDevelopment environment\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBackground\u003c/h2\u003e\u003ca id=\"user-content-background\" class=\"anchor\" aria-label=\"Permalink: Background\" href=\"#background\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome simple tools in python for MDU\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emdu-merge-ngs-lanes\u003c/h3\u003e\u003ca id=\"user-content-mdu-merge-ngs-lanes\" class=\"anchor\" aria-label=\"Permalink: mdu-merge-ngs-lanes\" href=\"#mdu-merge-ngs-lanes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse it to correctly merge lanes from an Illumina run into the a single FASTQ.\u003c/p\u003e\n\u003cp\u003eGet help:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emdu-merge-ngs-lanes --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBasic usage:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emdu-merge-ngs-lanes -i /path/to/fastq_folder -o /path/to/output \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e cmd.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdvanced usage:\u003c/p\u003e\n\u003cp\u003eYou can split the output to muliple subfolders of the output folder by adding \u003ccode\u003e--subfolder\u003c/code\u003e\nto the command line. The option can be used multiple times, and takes two space separated values as input:\n\u003ccode\u003epath\u003c/code\u003e \u003ccode\u003eregex\u003c/code\u003e. The \u003ccode\u003epath\u003c/code\u003e gives a name of the subfolder in the output folder, and the \u003ccode\u003eregex\u003c/code\u003e expression\ndetermines which samples go in that subfolder.\u003c/p\u003e\n\u003cp\u003eFor instance, the command below will split samples starting the NTC in to a subfolder called \u003ccode\u003entc\u003c/code\u003e,\nwhile all other samples will be added to a subfolder called \u003ccode\u003edata\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emdu-merge-ngs-lanes -i /path/to/fastq -o /path/to/output --subfolder \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edata\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e(?!NTC).*\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e --subfolder \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003entc\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e(?\u0026lt;=NTC).*\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e cmd.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emdu-sra-uploads\u003c/h3\u003e\u003ca id=\"user-content-mdu-sra-uploads\" class=\"anchor\" aria-label=\"Permalink: mdu-sra-uploads\" href=\"#mdu-sra-uploads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse to it to upload FASTQ data to NCBI SRA.\u003c/p\u003e\n\u003cp\u003eRequires a file with tab-separated values of \u003ccode\u003eMDU ID\u003c/code\u003e and \u003ccode\u003eAUSMDUID\u003c/code\u003e. For example:\u003c/p\u003e\n\u003cp\u003emdu1\\tausmdu1\u003c/p\u003e\n\u003cp\u003emdu2\\tausmdu2\u003c/p\u003e\n\u003cp\u003eGetting help:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emdu-sra-uploads --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eUsage: mdu-sra-upload [OPTIONS] ISOLATES\n\nOptions:\n -f, --folder TEXT Folder on NCBI to upload. Used to find the reads\n when submitting via the SRA portal. [default:\n mdu]\n -r, --reads-folder TEXT Where reads are located (uses MDU_READS env\n variable \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e available).\n -k, --ascp-key TEXT Path to ascp ssh upload key (uses ASCP_UPLOAD_KEY\n env variable \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e available). This can be obtained\n from the SRA Submission Portal.\n -s, --sra-subfolder TEXT SRA subfolder owned by you where data will copied\n to (uses SRA_SUBFOLDER env variable is available).\n --help Show this message and exit.\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBasic usage:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /path/for/upload\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e copy paste isolates.txt\u003c/span\u003e\nmdu-sra-uploads isolates.txt\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e when completing the submission, search for pre-uploaded files in the folder called mdu\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eEnvironmental variables that can be used to set options\u003c/h4\u003e\u003ca id=\"user-content-environmental-variables-that-can-be-used-to-set-options\" class=\"anchor\" aria-label=\"Permalink: Environmental variables that can be used to set options\" href=\"#environmental-variables-that-can-be-used-to-set-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eMDU_READS\u003c/code\u003e: full path to where FASTQ data is stored\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eASCP_UPLOAD_KEY\u003c/code\u003e: full path to where your Aspera NCBI upload key is located (obtain one from the SRA submission portal under the Aspera command line instructions)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSRA_FOLDER\u003c/code\u003e: path to your folder at SRA. Usually composed by your \u003ccode\u003eemail\u003c/code\u003e plus an \"_\" and some random alphanumeric characters. This can be obtained from SRA submission portal under the Aspera command line instructions (e.g., \u003ccode\u003ejohn.doe@doe.industries.com_qEWo9\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDevelopment environment\u003c/h3\u003e\u003ca id=\"user-content-development-environment\" class=\"anchor\" aria-label=\"Permalink: Development environment\" href=\"#development-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo develop with the same environment use \u003ccode\u003evagrant\u003c/code\u003e and \u003ccode\u003evirtualbox\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\nvagrant ssh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce logged in to the VM, the shared folder is in \u003ccode\u003e/vagrant\u003c/code\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-xxhash", + "latest_release": "v0.8.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-xxhash/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-xxhash/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/38e6751944f979b39f22301ecf8a0769e81dc664fd9356a0b3b91ba35f7601a4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d787868617368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/38e6751944f979b39f22301ecf8a0769e81dc664fd9356a0b3b91ba35f7601a4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d787868617368\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-xxhash\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7b513d4b9426f73a45bd6cef405c18ba52dcbce34d97cf4359f5ed52b501ad7b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d787868617368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b513d4b9426f73a45bd6cef405c18ba52dcbce34d97cf4359f5ed52b501ad7b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d787868617368\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-xxhash\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e10a48f4a1caa69da93117716e45f60bb3ed92a40e52c3e9d0398bbed6539585/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d787868617368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e10a48f4a1caa69da93117716e45f60bb3ed92a40e52c3e9d0398bbed6539585/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d787868617368\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-xxhash\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/eb2962cd520a014471cc5af4a22bde6bc6af147099c98ef8651a90eac25190d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d787868617368\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb2962cd520a014471cc5af4a22bde6bc6af147099c98ef8651a90eac25190d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d787868617368\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-xxhash\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-xxhash\u003c/h1\u003e\u003ca id=\"user-content-singularity-xxhash\" class=\"anchor\" aria-label=\"Permalink: singularity-xxhash\" href=\"#singularity-xxhash\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://xxhash.com/\" rel=\"nofollow\"\u003exxhash\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003exxh128sum\u003c/code\u003e, \u003ccode\u003exxh32sum\u003c/code\u003e, \u003ccode\u003exxh64sum\u003c/code\u003e and \u003ccode\u003exxhsum\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/xxhash/0.8.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/xxhash\u003c/code\u003e as \u003ccode\u003e0.8.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 5, "topics": [ - "bioinformatics", - "scripts", - "microbial-genomics" + "singularity", + "utilities" ], - "updated_at": 1568675585.0 + "updated_at": 1633063373.0 }, { "data_format": 2, - "description": "Behavioral experiments for \"Adaptive computation as a new mechanism of human attention\"", + "description": null, "filenames": [ - "Singularity" + "anaconda3/Singularity.5.3.0", + "anaconda3/Singularity", + "rstudio/Singularity.3.5.1", + "rstudio/Singularity.3.4.4", + "rstudio/Singularity", + "gephi/Singularity.0.9.1", + "gephi/Singularity.0.9.2", + "jupyter/Singularity.4.4.0", + "jupyter/Singularity", + "anaconda2/Singularity.5.3.0", + "anaconda2/Singularity" ], - "full_name": "CNCLgithub/mot-psiturk", - "latest_release": "0.1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMOT Psiturk\u003c/h1\u003e\u003ca id=\"user-content-mot-psiturk\" class=\"anchor\" aria-label=\"Permalink: MOT Psiturk\" href=\"#mot-psiturk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrent MOT psiturk for probe experiments\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003esingularity\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003etar\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esetup\u003c/h3\u003e\u003ca id=\"user-content-setup-1\" class=\"anchor\" aria-label=\"Permalink: setup\" href=\"#setup-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esee help\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis setup file will, by default, pull a container and data files from dropbox.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning psiturk\u003c/h2\u003e\u003ca id=\"user-content-running-psiturk\" class=\"anchor\" aria-label=\"Permalink: Running psiturk\" href=\"#running-psiturk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x start_psiturk.sh\n./start_psiturk.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAPI\u003c/h2\u003e\u003ca id=\"user-content-api\" class=\"anchor\" aria-label=\"Permalink: API\" href=\"#api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etask.js\u003c/h3\u003e\u003ca id=\"user-content-taskjs\" class=\"anchor\" aria-label=\"Permalink: task.js\" href=\"#taskjs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe majority of the experiment\u0027s functionality is described in \u003ccode\u003epsiturk/static/js/task.js\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe main class used to setup pages for both the experiment and instructions is defined as \u003ccode\u003ePage\u003c/code\u003e.\n\u003ccode\u003ePage\u003c/code\u003e handles both media presentation and scale setup. See the docstrings for more info.\u003c/p\u003e\n\u003cp\u003eThere are three other main elements, \u003ccode\u003eInstructionRunner\u003c/code\u003e, \u003ccode\u003eQuiz\u003c/code\u003e, and \u003ccode\u003eExperiment\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecss and html\u003c/h3\u003e\u003ca id=\"user-content-css-and-html\" class=\"anchor\" aria-label=\"Permalink: css and html\" href=\"#css-and-html\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main html files are located under \u003ccode\u003epsiturk/templates/\u003c/code\u003e and css is under \u003ccode\u003epsiturk/static/css\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNotabley, \u003ccode\u003estage.html\u003c/code\u003e describes the pages for experimental trials and \u003ccode\u003eslider.css\u003c/code\u003e describes some of the elements found in the scale.\u003c/p\u003e\n", + "full_name": "uncch-rdmc/singularity-dev-images", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-dev-images\u003c/h1\u003e\u003ca id=\"user-content-singularity-dev-images\" class=\"anchor\" aria-label=\"Permalink: singularity-dev-images\" href=\"#singularity-dev-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 5, + "topics": [], + "updated_at": 1556725305.0 + }, + { + "data_format": 2, + "description": "Use ImageMagick\u00ae to create, edit, compose, or convert digital images.", + "filenames": [ + "7.1.0-61/Singularity", + "7.0.10-48/Singularity", + "7.1.0-2/Singularity", + "7.1.1-15/Singularity", + "6.9.11-60/Singularity", + "7.1.1-39/Singularity" + ], + "full_name": "pscedu/singularity-imagemagick", + "latest_release": "v7.1.1-39", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-imagemagick/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-imagemagick/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-imagemagick/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-imagemagick/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5528404bef0c0f51ff4a1ccdf773e7a55f5ef21a4f7d406e5cabb7372308c736/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5528404bef0c0f51ff4a1ccdf773e7a55f5ef21a4f7d406e5cabb7372308c736/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-imagemagick\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3ecad539fb45c06305ec52e667d3ef8ca3eee70fe238910c7dcb23762cb0a5c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3ecad539fb45c06305ec52e667d3ef8ca3eee70fe238910c7dcb23762cb0a5c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-imagemagick\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1f17716ff11baefd5fc71f673ef822f24f490b32808190a8f18d27991459a89a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1f17716ff11baefd5fc71f673ef822f24f490b32808190a8f18d27991459a89a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d696d6167656d616769636b\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-imagemagick\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3ffdcfa0d7b9b3f63e1a8d0e85f1deef8abe74f43040c727749fadc5ea1b9558/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d696d6167656d616769636b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3ffdcfa0d7b9b3f63e1a8d0e85f1deef8abe74f43040c727749fadc5ea1b9558/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d696d6167656d616769636b\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-imagemagick\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eImageMagick\u003c/h1\u003e\u003ca id=\"user-content-imagemagick\" class=\"anchor\" aria-label=\"Permalink: ImageMagick\" href=\"#imagemagick\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e2097f450d2fe8919131656de2541415e0cb72d469c16b0211e5c5fbb0e129b6/68747470733a2f2f65787465726e616c2d636f6e74656e742e6475636b6475636b676f2e636f6d2f69752f3f753d6874747025334125324625324679656e7061692e696469732e636f6d2e747725324677702d636f6e74656e7425324675706c6f616473253246323031322532463131253246696d6167656d616769636b5f77697a6172645f7468756d622e6a706726663d31266e6f66623d31\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e2097f450d2fe8919131656de2541415e0cb72d469c16b0211e5c5fbb0e129b6/68747470733a2f2f65787465726e616c2d636f6e74656e742e6475636b6475636b676f2e636f6d2f69752f3f753d6874747025334125324625324679656e7061692e696469732e636f6d2e747725324677702d636f6e74656e7425324675706c6f616473253246323031322532463131253246696d6167656d616769636b5f77697a6172645f7468756d622e6a706726663d31266e6f66623d31\" alt=\"Logo\" data-canonical-src=\"https://external-content.duckduckgo.com/iu/?u=http%3A%2F%2Fyenpai.idis.com.tw%2Fwp-content%2Fuploads%2F2012%2F11%2Fimagemagick_wizard_thumb.jpg\u0026amp;f=1\u0026amp;nofb=1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://imagemagick.org/index.php\" rel=\"nofollow\"\u003eImageMagick\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eUse ImageMagick\u00ae to create, edit, compose, or convert bitmap images. It can read and write images in a variety of formats (over 200) including PNG, JPEG, GIF, HEIC, TIFF, DPX, EXR, WebP, Postscript, PDF, and SVG. Use ImageMagick to resize, flip, mirror, rotate, distort, shear and transform images, adjust image colors, apply various special effects, or draw text, lines, polygons, ellipses and B\u00e9zier curves.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 5, "topics": [ - "attention", - "behavior", - "psiturk" + "singularity", + "utilities", + "image-processing" ], - "updated_at": 1669660072.0 + "updated_at": 1729796703.0 }, { "data_format": 2, - "description": "Singularity container containing an environment for Ringer users", + "description": null, "filenames": [ - "Singularity" + "Singularity.latest" ], - "full_name": "gabriel-milan/atlas-singularity", + "full_name": "bioexcel/zip_container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eATLAS Singularity Environment\u003c/h1\u003e\u003ca id=\"user-content-atlas-singularity-environment\" class=\"anchor\" aria-label=\"Permalink: ATLAS Singularity Environment\" href=\"#atlas-singularity-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4521\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository provides \u003ccode\u003egabriel-milan/atlas-singularity\u003c/code\u003e, a singularity container containing an environment for Ringer users.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/mmbirb/zip\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/339e4f315da6f622b2972753d519e70d7d9bd259f370f9e8ddc90738339303d7/68747470733a2f2f717561792e696f2f7265706f7369746f72792f62696f636f6e7461696e6572732f62696f62625f696f2f737461747573\" alt=\"\" data-canonical-src=\"https://quay.io/repository/biocontainers/biobb_io/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4075\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eZIP container\u003c/h1\u003e\u003ca id=\"user-content-zip-container\" class=\"anchor\" aria-label=\"Permalink: ZIP container\" href=\"#zip-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eZIP docker and singularity containers used for \u003ca href=\"https://github.com/bioexcel/biobb_template\"\u003ebiobb_template\u003c/a\u003e BioExcel Building Blocks modules.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker Use\u003c/h3\u003e\u003ca id=\"user-content-docker-use\" class=\"anchor\" aria-label=\"Permalink: Docker Use\" href=\"#docker-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull mmbirb/zip:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run mmbirb/zip:latest \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Use\u003c/h3\u003e\u003ca id=\"user-content-singularity-use\" class=\"anchor\" aria-label=\"Permalink: Singularity Use\" href=\"#singularity-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name zip.sif shub://bioexcel/zip_container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec zip.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" aria-label=\"Permalink: Copyright \u0026amp; Licensing\" href=\"#copyright--licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknolegements\u003c/h3\u003e\u003ca id=\"user-content-acknolegements\" class=\"anchor\" aria-label=\"Permalink: Acknolegements\" href=\"#acknolegements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe singularity container has been built through \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5aaf032aade8af7cf9f6e16cb3c7ba70c927ecd783c1cfff82dc0ed8062560df/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5aaf032aade8af7cf9f6e16cb3c7ba70c927ecd783c1cfff82dc0ed8062560df/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 9, "topics": [], - "updated_at": 1593796964.0 + "updated_at": 1584437236.0 }, { "data_format": 2, - "description": null, + "description": "An Apptainer/Singularity container for using MuJoCo-mjx on GPU. ", "filenames": [ "Singularity" ], - "full_name": "mike-dixon/build_container_on_shub", + "full_name": "DMackRus/Apptainer_mujoco-mjx", "latest_release": null, - "readme": "\u003cp\u003eExamples for building containers on Singularity Hub\u003c/p\u003e\n\u003cp\u003e./tutorial_steps.txt : example steps, command-by-command\u003c/p\u003e\n\u003cp\u003e./Singularity : is a recipe file for building your container\u003c/p\u003e\n\u003cp\u003e./text_translate.py is a sample python script we can run with the container\u003c/p\u003e\n\u003cp\u003e./make_git_repo.sh is a script that uploads your Singularity repository to github\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eApptainer_RL\u003c/h1\u003e\u003ca id=\"user-content-apptainer_rl\" class=\"anchor\" aria-label=\"Permalink: Apptainer_RL\" href=\"#apptainer_rl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple container for using MuJoCo-mjx, and Jax on the GPU of your PC. Installs Cuda and CudNN as well as other python dependancies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe only requirment for using this repository is \u003ca href=\"https://apptainer.org/admin-docs/master/installation.html\" rel=\"nofollow\"\u003eSingularity/Apptainer\u003c/a\u003e Version \u0026gt;= 3.5.\u003c/p\u003e\n\u003cp\u003eOnce Singularity/Apptainer has been installed, you can simply build the container using /.build.sh (This will take a while depending on your machine and wifi connection, approximately 30 minutes) and then you can launch the container using /.run.sh.\u003c/p\u003e\n\u003cp\u003eIf you need to admin priveledges inside the container to isntall something, you can use /.write.sh.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCurrent Issues\u003c/h2\u003e\u003ca id=\"user-content-current-issues\" class=\"anchor\" aria-label=\"Permalink: Current Issues\" href=\"#current-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eRepository is hard coded to download Cuda 12.4 and Cudnn 9.5. I will make this flexible once I figure that out.\u003c/li\u003e\n\u003cli\u003eSome possible issue with jax installation. It seems to be using the GPU but there is a warning about a version mismatch between cuda and XLA.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eToDo\u003c/h1\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: ToDo\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAdd paths in post script\u003c/li\u003e\n\u003cli\u003eMake CUDA and CuDDn installations dynamic.\u003c/li\u003e\n\u003cli\u003eLook into fixing XLA and Jax mismatch?\u003c/li\u003e\n\u003cli\u003eMake some nice mjx-examples and have this repo clone them automatically.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [], - "updated_at": 1537307784.0 + "topics": [ + "apptainer", + "mujoco", + "mujoco-environments", + "mujoco-mjx", + "reinforcement-learning", + "singularity" + ], + "updated_at": 1729862068.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe that can be used to build a container for BRER simulations.", "filenames": [ - "Singularity" + "Singularity.brer-cuda-10.0-ubuntu-18.04", + "Singularity.trainA", + "Singularity.nogpu", + "Singularity.comet", + "Singularity.0_0_7", + "Singularity.0_0_6" ], - "full_name": "deanpettinga/bbcRNA-singularity", + "full_name": "kassonlab/singularity-brer", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebbcRNA-singularity\u003c/h1\u003e\u003ca id=\"user-content-bbcrna-singularity\" class=\"anchor\" aria-label=\"Permalink: bbcRNA-singularity\" href=\"#bbcrna-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3616\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBRER: Singularity\u003c/h1\u003e\u003ca id=\"user-content-brer-singularity\" class=\"anchor\" aria-label=\"Permalink: BRER: Singularity\" href=\"#brer-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the Singularity recipe used to build containers for BRER simulations.\nA pre-built image is hosted on Sylabs Singularity \u003ca href=\"https://cloud.sylabs.io/library/kassonlab/default/brer\" rel=\"nofollow\"\u003elibrary\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe main project for running these simulations is hosted at \u003ca href=\"https://github.com/kassonlab/run_brer\"\u003ehttps://github.com/kassonlab/run_brer\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting the container\u003c/h2\u003e\u003ca id=\"user-content-getting-the-container\" class=\"anchor\" aria-label=\"Permalink: Getting the container\" href=\"#getting-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull directly from singularity library (recommended):\u003c/p\u003e\n\u003cpre lang=\"angular2html\"\u003e\u003ccode\u003esingularity pull --name singularity-brer.sif library://kassonlab/default/brer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor build it yourself:\u003c/p\u003e\n\u003cpre lang=\"angular2html\"\u003e\u003ccode\u003esudo singularity build singularity-brer.sif deffile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003edeffile\u003c/code\u003e is one of the recipe files in this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOnce you\u0027ve got the container\u003c/h2\u003e\u003ca id=\"user-content-once-youve-got-the-container\" class=\"anchor\" aria-label=\"Permalink: Once you\u0027ve got the container\" href=\"#once-youve-got-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe GROMACS build in this container is GPU-compatible (built with CUDA). In order to take advantage of this, use\nthe Singularity \u003ccode\u003eexec\u003c/code\u003e command with the \u003ccode\u003e--nv\u003c/code\u003e option:\u003c/p\u003e\n\u003cpre lang=\"angular2html\"\u003e\u003ccode\u003esingularity exec --nv singularity-brer.sif python3 my_run_script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003e--nv\u003c/code\u003e will bind the host nvidia drivers to the container, so be sure that your drivers are compatible with the CUDA version in the container (default CUDA 10.1).\u003c/p\u003e\n\u003cp\u003eAn example run script is provided on the \u003ca href=\"https://github.com/kassonlab/run_brer\"\u003emain project website\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eNote: There is no Python 2 installed in the container (\u003ccode\u003e/usr/bin/python\u003c/code\u003e is actually Python3). Any Python scripts you write that you wish to run in the container\nmust be compatible with Python 3.X\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMiscellaneous\u003c/h4\u003e\u003ca id=\"user-content-miscellaneous\" class=\"anchor\" aria-label=\"Permalink: Miscellaneous\" href=\"#miscellaneous\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e: Use \u003ccode\u003esingularity exec ...\u003c/code\u003e, not \u003ccode\u003esingularity run ...\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1571763045.0 + "updated_at": 1594306038.0 }, { "data_format": 2, - "description": "Singularity image for biocontainers bamtools", + "description": "Testing container for playing with singularity", "filenames": [ - "Singularity" + "Hello-World/Singularity", + "01-Building/Singularity.build" ], - "full_name": "researchapps/bamtools", + "full_name": "Deadlyelder/Singularity-hello-world", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePicard\u003c/h1\u003e\u003ca id=\"user-content-picard\" class=\"anchor\" aria-label=\"Permalink: Picard\" href=\"#picard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity image to deploy the bamtools software.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 bamtools.img\nsudo singularity bootstrap bamtools.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the bamtools runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bamtools.img [args] ...\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity-hello-world\u003c/h1\u003e\u003ca id=\"user-content-singularity-hello-world\" class=\"anchor\" aria-label=\"Permalink: Singularity-hello-world\" href=\"#singularity-hello-world\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting container for playing with singularity.\u003c/p\u003e\n\u003cp\u003eUsed for testing on HPC and pushing on the \u003ca href=\"https://www.singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity hub\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1484507858.0 + "updated_at": 1533107262.0 }, { "data_format": 2, - "description": "The codebase for the DI-LSTM neural network framework. ", + "description": "Singularity Image for GENIE dependencies built with ROOT5 on Ubuntu 14.04", "filenames": [ "Singularity" ], - "full_name": "Randers114/DI-LSTM", + "full_name": "twongjirad/singularity-genie-deps-root5-ubuntu14.04", "latest_release": null, - "readme": "\u003cp\u003eTo run the model execute \"python -m models.model_tester\".\u003c/p\u003e\n\u003cp\u003eFor more information, the project can be found at \"\u003ca href=\"https://projekter.aau.dk/projekter/da/studentthesis/estimating-travel-cost-distributions-of-paths-in-road-networks-using-dualinput-lstms(a9e50268-29d3-4123-9ef1-766bf20307b0).html\" rel=\"nofollow\"\u003ehttps://projekter.aau.dk/projekter/da/studentthesis/estimating-travel-cost-distributions-of-paths-in-road-networks-using-dualinput-lstms(a9e50268-29d3-4123-9ef1-766bf20307b0).html\u003c/a\u003e\" if you have AAU access.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-cern-root5-ubuntu14.04\u003c/h1\u003e\u003ca id=\"user-content-singularity-cern-root5-ubuntu1404\" class=\"anchor\" aria-label=\"Permalink: singularity-cern-root5-ubuntu14.04\" href=\"#singularity-cern-root5-ubuntu1404\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Image for GENIE dependencies built with ROOT5 on Ubuntu 14.04\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1594109503.0 + "updated_at": 1496352524.0 }, { "data_format": 2, - "description": "RNA-seq analysis for Kannan et al. 2019", + "description": null, "filenames": [ - "Singularity", - "Singularity.Bioc3.8" + "Recipes/Singularity_example", + "Recipes/Singularity_ompi", + "Recipes/Singularity_pytorch", + "Recipes/Singularity_mpich", + "Recipes/Singularity_spark_full", + "Recipes/Singularity_spark", + "Recipes/Singularity_tensorflow", + "Recipes/Singularity_pytorch_full" ], - "full_name": "winni2k/kannan_et_al_2019", + "full_name": "souzaitor/HPC-projects", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePr\u00e9-requisitos\u003c/h1\u003e\u003ca id=\"user-content-pr\u00e9-requisitos\" class=\"anchor\" aria-label=\"Permalink: Pr\u00e9-requisitos\" href=\"#pr\u00e9-requisitos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSele\u00e7\u00e3o do Recipe\u003c/h2\u003e\u003ca id=\"user-content-sele\u00e7\u00e3o-do-recipe\" class=\"anchor\" aria-label=\"Permalink: Sele\u00e7\u00e3o do Recipe\" href=\"#sele\u00e7\u00e3o-do-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdicione o caminho do \u003cem\u003esingularity recipe\u003c/em\u003e desejado na vari\u00e1vel RECIPE no github (projeto \u0026gt;\u0026gt; Settings \u0026gt;\u0026gt; Secrets \u0026gt;\u0026gt; New Secret).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTemplate Integra\u00e7\u00e3o Cont\u00ednua Github\u003c/h1\u003e\u003ca id=\"user-content-template-integra\u00e7\u00e3o-cont\u00ednua-github\" class=\"anchor\" aria-label=\"Permalink: Template Integra\u00e7\u00e3o Cont\u00ednua Github\" href=\"#template-integra\u00e7\u00e3o-cont\u00ednua-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEsse projeto o template para uso do cluster da UFSCar, contendo integra\u00e7\u00e3o cont\u00ednua com o Google Drive e Amazon S3.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequisitos para Google Drive\u003c/h2\u003e\u003ca id=\"user-content-requisitos-para-google-drive\" class=\"anchor\" aria-label=\"Permalink: Requisitos para Google Drive\" href=\"#requisitos-para-google-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eEntre no \u003ca href=\"https://console.developers.google.com/apis/credentials\" rel=\"nofollow\"\u003econsole de credenciais de API do Google\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSe ainda n\u00e3o houver um projeto, crie um.\u003c/li\u003e\n\u003cli\u003eEm \"Ativar API e Servi\u00e7os\", busque por \"Google Drive\" e ative a permiss\u00e3o.\u003c/li\u003e\n\u003cli\u003eClique em \"Criar credenciais\".\u003c/li\u003e\n\u003cli\u003eSelecione \"ID do cliente do OAuth\".\u003c/li\u003e\n\u003cli\u003eEm \"Tipo de aplicativo\", selecione \"App para computador\".\u003c/li\u003e\n\u003cli\u003eD\u00ea um nome de identifica\u00e7\u00e3o para as credenciais e clique em \"criar\". V\u00e3o aparecer dois dados (\"Seu ID de cliente\" e \"Sua chave secreta de cliente\"), precisaremos dos dois no passo seguinte.\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 17 # selecione aqui o n\u00famero correspondente a op\u00e7\u00e3o \"Google Drive\"\nclient_id\u0026gt; conte\u00fado de \"Seu ID de cliente\"\nclient_secret\u0026gt; conte\u00fado de \"Sua chave secreta de cliente\"\nscope\u0026gt; 1 # Full access all files, excluding Application Data Folder\nroot_folder_id\u0026gt; deixe em branco\nservice_account_file\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/n\u0026gt; n\n\nSe j\u00e1 tiver o rclone instalado em seu computador, execute o comando exibido, caso contr\u00e1rio, o instale conforme indicado na p\u00e1gina oficial do rclone dependendo do seu sistema operacional (https://rclone.org/install/). A Seguir insira o resultado do comando exibido:\n\nconfig_token\u0026gt; c\u00f3digo fornecido pelo terminal ap\u00f3s autoriza\u00e7\u00e3o\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequisitos para Amazon S3\u003c/h2\u003e\u003ca id=\"user-content-requisitos-para-amazon-s3\" class=\"anchor\" aria-label=\"Permalink: Requisitos para Amazon S3\" href=\"#requisitos-para-amazon-s3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eEntre na \u003ca href=\"console.aws.amazon.com\"\u003eAWS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClique na seta ao lado de seu nome de usu\u00e1rio e em \"My Security Credentials\".\u003c/li\u003e\n\u003cli\u003eNa se\u00e7\u00e3o \"Access Keys\", clique em \"Create New Access Key\".\u003c/li\u003e\n\u003cli\u003eNa janela que aparece, clique em \"Show Access Key\".\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 4\nprovider\u0026gt; 1\nenv_auth\u0026gt; 1\naccess_key_id\u0026gt; conte\u00fado de \"Access Key ID\"\nsecret_access_key\u0026gt; conte\u00fado de \"Secret Access Key\"\nregion\u0026gt; 16\nendpoint\u0026gt; deixe em branco\nlocation_constraint\u0026gt; 16\nacl\u0026gt; deixe em branco\nserver_side_encryption\u0026gt; deixe em branco\nsse_kms_key_id\u0026gt; deixe em branco\nstorage_class\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1551895749.0 + "updated_at": 1672685576.0 }, { "data_format": 2, - "description": "Container for SLEAP", + "description": "pacbio tools", "filenames": [ - "Singularity" + "singularity/Singularity.v2", + "singularity/Singularity.v3", + "singularity/Singularity.v1" ], - "full_name": "maouw/sleap-container", - "latest_release": "0.9.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esleap-container\u003c/h1\u003e\u003ca id=\"user-content-sleap-container\" class=\"anchor\" aria-label=\"Permalink: sleap-container\" href=\"#sleap-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis a \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003econtainer\u003c/a\u003e to run \u003ca href=\"https://sleap.ai\" rel=\"nofollow\"\u003eSLEAP\u003c/a\u003e jobs on the University of Washington \u003ca href=\"https://hyak.uw.edu\" rel=\"nofollow\"\u003eHyak\u003c/a\u003e cluster. You can use this container to run SLEAP training and prediction jobs on Hyak in a GPU-accelerated environment.\ns, which provide reproducible environments that can run anywhere and be shared with other researchers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore running this container, you\u0027ll need the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA Linux, macOS, or Windows machine\u003c/li\u003e\n\u003cli\u003eAn SSH client (usually included with Linux and macOS, and available for Windows through the built-in SSH client on Windows 10+, \u003ca href=\"https://learn.microsoft.com/en-us/windows/wsl/install\" rel=\"nofollow\"\u003eWSL2\u003c/a\u003e or \u003ca href=\"https://www.cs.odu.edu/~zeil/cs252/latest/Public/loggingin/cygwin.mmd.html\" rel=\"nofollow\"\u003eCygwin\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://hyak.uw.edu\" rel=\"nofollow\"\u003eHyak\u003c/a\u003e Klone access with compute resources\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFollow the instructions below to set up your machine correctly:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstalling SSH\u003c/h3\u003e\u003ca id=\"user-content-installing-ssh\" class=\"anchor\" aria-label=\"Permalink: Installing SSH\" href=\"#installing-ssh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLinux\u003c/h4\u003e\u003ca id=\"user-content-linux\" class=\"anchor\" aria-label=\"Permalink: Linux\" href=\"#linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using Linux, OpenSSH is probably installed already -- if not, you can install it via \u003ccode\u003eapt-get install openssh-client\u003c/code\u003e on Debian/Ubuntu or \u003ccode\u003eyum install openssh-clients\u003c/code\u003e on RHEL/CentOS/Rocky/Fedora. To open a terminal window, search for \"Terminal\" in your desktop environment\u0027s application launcher.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003emacOS\u003c/h4\u003e\u003ca id=\"user-content-macos\" class=\"anchor\" aria-label=\"Permalink: macOS\" href=\"#macos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re on macOS, OpenSSH will already be installed. To open a terminal window, open \u003ccode\u003e/Applications/Utilities/Terminal.app\u003c/code\u003e or search for \"Terminal\" in Launchpad or Spotlight.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWindows\u003c/h4\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Windows 10+, you can \u003ca href=\"https://learn.microsoft.com/en-us/windows/terminal/tutorials/ssh\" rel=\"nofollow\"\u003euse the built-in SSH client\u003c/a\u003e. You may also install a SSH client through \u003ca href=\"https://learn.microsoft.com/en-us/windows/wsl/install\" rel=\"nofollow\"\u003eWSL2\u003c/a\u003e or \u003ca href=\"https://www.cs.odu.edu/~zeil/cs252/latest/Public/loggingin/cygwin.mmd.html\" rel=\"nofollow\"\u003eCygwin\u003c/a\u003e (not recommended, needs additional setup). See the links for instructions on how to install these. You can start a terminal window by searching for \"Terminal\" in the Start menu.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetting up SSH keys to connect to Hyak compute nodes\u003c/h3\u003e\u003ca id=\"user-content-setting-up-ssh-keys-to-connect-to-hyak-compute-nodes\" class=\"anchor\" aria-label=\"Permalink: Setting up SSH keys to connect to Hyak compute nodes\" href=\"#setting-up-ssh-keys-to-connect-to-hyak-compute-nodes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore you are allowed to connect to a compute node where your SLEAP job will be running, you must add your SSH public key to the authorized keys on the login node of the Hyak Klone cluster.\u003c/p\u003e\n\u003cp\u003eIf you don\u0027t, you will receive an error like this when you try to connect to the compute node:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003ePermission denied (publickey,gssapi-keyex,gssapi-with-mic)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo set this up quickly on Linux, macOS, or Windows (WSL2/Cygwin), open a new terminal window \u003cstrong\u003eon your machine\u003c/strong\u003e and enter the following 2 commands before you try again. Replace \u003ccode\u003eyour-uw-netid\u003c/code\u003e with your UW NetID:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e[ \u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e-r\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.ssh/id_rsa ] \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e ssh-keygen -t rsa -b 4096 -N \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -C \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eyour-uw-netid@uw.edu\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -f \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.ssh/id_rsa\nssh-copy-id -o StrictHostKeyChecking=no -i \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.ssh/id_rsa your-uw-netid@klone.hyak.uw.edu\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"https://hyak.uw.edu/docs/setup/intracluster-keys\" rel=\"nofollow\"\u003eHyak documentation\u003c/a\u003e for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSet up the Apptainer cache directory on Hyak \u003ccode\u003eklone\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-set-up-the-apptainer-cache-directory-on-hyak-klone\" class=\"anchor\" aria-label=\"Permalink: Set up the Apptainer cache directory on Hyak klone\" href=\"#set-up-the-apptainer-cache-directory-on-hyak-klone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eApptainer containers can take up several gigabytes of space each. By default, Apptainer will store cached containers in your home directory (\u003ccode\u003e~\u003c/code\u003e), under \u003ccode\u003e~/.cache/apptainer\u003c/code\u003e. However, because home directory space on Hyak is limited to 10 GiB per user, you may want to set up a different cache directory.\u003c/p\u003e\n\u003cp\u003eWe advise setting up a cache directory under the \u003ccode\u003e/tmp\u003c/code\u003e directory or in the \u003ca href=\"https://hyak.uw.edu/docs/storage/gscratch/\" rel=\"nofollow\"\u003escrubbed\u003c/a\u003e directory, under \u003ccode\u003e/gscratch/scrubbed/your-uw-netid\u003c/code\u003e. To set this up, first connect to \u003ccode\u003eklone.hyak.uw.edu\u003c/code\u003e via SSH:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh your-uw-netid@klone.hyak.uw.edu \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Replace your-uw-netid with your UW NetID\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce you\u0027re logged in, create a directory for the cache and set the \u003ccode\u003eAPPTAINER_CACHEDIR\u003c/code\u003e environment variable to point to it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e$USER\u003c/span\u003e/apptainer-cache\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e APPTAINER_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e$USER\u003c/span\u003e/apptainer-cache\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, add the following line to your \u003ccode\u003e~/.bashrc\u003c/code\u003e file (or \u003ccode\u003e~/.zshrc\u003c/code\u003e if you use ZSH) to retain this setting across multiple logins:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport APPTAINER_CACHEDIR=\u003cspan class=\"pl-cce\"\u003e\\\"\u003c/span\u003e/gscratch/scrubbed/\u003cspan class=\"pl-smi\"\u003e$USER\u003c/span\u003e/apptainer-cache\u003cspan class=\"pl-cce\"\u003e\\\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis guide assumes that you are running SLEAP on your own machine, with an open SLEAP project that you are ready to start training on. If you need help creating a SLEAP project, consult the \u003ca href=\"https://sleap.ai/tutorials/tutorial.html\" rel=\"nofollow\"\u003eSLEAP documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo start training your model on the cluster, you must first create a \u003cem\u003etraining package\u003c/em\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA self-contained \u003cstrong\u003etraining job package\u003c/strong\u003e contains a .slp file with labeled data and images which will be used for training, as well as .json training configuration file(s). \u003ca href=\"https://sleap.ai/notebooks/Training_and_inference_using_Google_Drive.html\" rel=\"nofollow\"\u003e*\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExporting a training package\u003c/h3\u003e\u003ca id=\"user-content-exporting-a-training-package\" class=\"anchor\" aria-label=\"Permalink: Exporting a training package\" href=\"#exporting-a-training-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can create a training job package in the \u003ccode\u003esleap-label\u003c/code\u003e GUI by following the \u003ccode\u003eRun Training...\u003c/code\u003e option under the \u003ccode\u003ePredict\u003c/code\u003e menu:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/screenshots/01-main_dropdown_predict_run_training.png\"\u003e\u003cimg src=\"./docs/screenshots/01-main_dropdown_predict_run_training.png\" alt=\"SLEAP GUI: Main Window Run Training\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSet the parameters for your training job (refer to \u003ca href=\"https://sleap.ai/tutorials/initial-training.html\" rel=\"nofollow\"\u003eSLEAP documentation\u003c/a\u003e if you\u0027re not sure), and click \u003ccode\u003eExport training job package\u003c/code\u003e once you\u0027re done:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/screenshots/02-run_training_dialog.png\"\u003e\u003cimg src=\"./docs/screenshots/02-run_training_dialog.png\" alt=\"SLEAP GUI: Run Training Dialog\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNext, you should see a dialog that says, \u003ccode\u003eCreated training job package.\u003c/code\u003e Click \u003ccode\u003eShow Details...\u003c/code\u003e:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/screenshots/03-created_training_job_package.png\"\u003e\u003cimg src=\"./docs/screenshots/03-created_training_job_package.png\" alt=\"SLEAP GUI: Created training job package\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe full file path to the training package will be displayed (e.g., \u003ccode\u003e/home/me/sleap/my_training_job.zip\u003c/code\u003e). Select and copy this path:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/screenshots/04-created_training_job_package_details.png\"\u003e\u003cimg src=\"./docs/screenshots/04-created_training_job_package_details.png\" alt=\"SLEAP GUI: Run Training Dialog\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUploading a training package to the cluster\u003c/h3\u003e\u003ca id=\"user-content-uploading-a-training-package-to-the-cluster\" class=\"anchor\" aria-label=\"Permalink: Uploading a training package to the cluster\" href=\"#uploading-a-training-package-to-the-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow you must use the terminal on your computer to upload the training package to the Hyak cluster. You can find instructions on how to set up your terminal to access Hyak \u003ca href=\"https://uw-psych.github.io/compute_docs/hyak/start/connect-ssh.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOpen a terminal window \u003cstrong\u003eon your computer\u003c/strong\u003e and enter the following command to copy the training package to your home directory (\u003ccode\u003e~\u003c/code\u003e) on the cluster:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escp /home/me/sleap/my_training_job.zip your-uw-netid@klone.hyak.uw.edu: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Replace your-uw-netid with your UW NetID\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNOTE: You may need to log in with your UW NetID and two-factor authentication.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunnning the training package on the cluster\u003c/h3\u003e\u003ca id=\"user-content-runnning-the-training-package-on-the-cluster\" class=\"anchor\" aria-label=\"Permalink: Runnning the training package on the cluster\" href=\"#runnning-the-training-package-on-the-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the file has been copied, log in to the cluster via SSH:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh your-uw-netid@klone.hyak.uw.edu \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Replace your-uw-netid with your UW NetID\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExtracting the training package\u003c/h4\u003e\u003ca id=\"user-content-extracting-the-training-package\" class=\"anchor\" aria-label=\"Permalink: Extracting the training package\" href=\"#extracting-the-training-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe training package should be located in your home directory on \u003ccode\u003eklone\u003c/code\u003e. You can check by running \u003ccode\u003els\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003els \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.zip \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Should display all ZIP files in directory, including `my_training_job.zip`\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUnzip the package file to a new directory. Let\u0027s call it \u003ccode\u003etraining_job\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eunzip my_training_job.zip -d training_job\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAllocating a node on the cluster\u003c/h4\u003e\u003ca id=\"user-content-allocating-a-node-on-the-cluster\" class=\"anchor\" aria-label=\"Permalink: Allocating a node on the cluster\" href=\"#allocating-a-node-on-the-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are almost ready to launch the container. First, though, we need to allocate a job on the cluster. We will use the \u003ccode\u003esalloc\u003c/code\u003e command to do this.\u003c/p\u003e\n\u003cp\u003eThe following command will allocate a job on one node with 4 GPUs, 64 GB of memory, and 8 CPUs for 24 hours on the \u003ccode\u003egpu-a40\u003c/code\u003e partition available to the \u003ccode\u003eescience\u003c/code\u003e account. You can adjust these parameters as needed. For more information on the \u003ccode\u003esalloc\u003c/code\u003e command, see this page and the \u003ca href=\"https://slurm.schedmd.com/salloc.html\" rel=\"nofollow\"\u003esalloc documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esalloc --job-name sleap-train-test \\\n --account escience \\\n --partition gpu-a40 \\\n --gpus 4 \\\n --ntasks 1 \\\n --gpus-per-task=4 \\\n --mem 64G \\\n --cpus-per-task 4 \\\n --time 24:00:00\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen the allocation is ready, you will automatically connect to the compute node. When you exit this session, the allocation will automatically be released.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning SLEAP\u003c/h4\u003e\u003ca id=\"user-content-running-sleap\" class=\"anchor\" aria-label=\"Permalink: Running SLEAP\" href=\"#running-sleap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eVerifying GPU access\u003c/h5\u003e\u003ca id=\"user-content-verifying-gpu-access\" class=\"anchor\" aria-label=\"Permalink: Verifying GPU access\" href=\"#verifying-gpu-access\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you are connected to the node, you can verify that the SLEAP container has access to the GPUs by running the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run --nv --bind /gscratch oras://ghcr.io/maouw/sleap-container:latest python -c \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eimport sleap; sleap.system_summary()\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should get output that looks something like this:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eGPUs: 4/4 available\n Device: /physical_device:GPU:0\n Available: True\n Initalized: False\n Memory growth: None\n Device: /physical_device:GPU:1\n Available: True\n Initalized: False\n Memory growth: None\n Device: /physical_device:GPU:2\n Available: True\n Initalized: False\n Memory growth: None\n Device: /physical_device:GPU:3\n Available: True\n Initalized: False\n Memory growth: None\n6.40s user 6.24s system 88% cpu 14.277s total\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eTraining the model\u003c/h5\u003e\u003ca id=\"user-content-training-the-model\" class=\"anchor\" aria-label=\"Permalink: Training the model\" href=\"#training-the-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow, navigate to the directory where you unzipped the training package:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/training_job\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe next step is to launch the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapptainer run --nv --bind /gscratch oras://ghcr.io/maouw/sleap-container:latest bash train-script.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eApptainer will download the container image from GitHub and launch it on the node. The option \u003ccode\u003e--nv\u003c/code\u003e enables Nvidia GPU support. Once the container has launched, it will instruct \u003ccode\u003ebash\u003c/code\u003e to run the script \u003ccode\u003etrain-script.sh\u003c/code\u003e. This script will start the training job.\u003c/p\u003e\n\u003cp\u003eDuring training, you will see a lot of output in the terminal. After some time, if training is successful, the last of the output should look something similar to this:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eINFO:sleap.nn.evals:Saved predictions: models/231009_165437.centered_instance/labels_pr.train.slp\nINFO:sleap.nn.evals:Saved metrics: models/231009_165437.centered_instance/metrics.train.npz\nINFO:sleap.nn.evals:OKS mAP: 0.205979\nPredicting... \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% ETA: 0:00:00 3.3 FPS\nINFO:sleap.nn.evals:Saved predictions: models/231009_165437.centered_instance/labels_pr.val.slp\nINFO:sleap.nn.evals:Saved metrics: models/231009_165437.centered_instance/metrics.val.npz\nINFO:sleap.nn.evals:OKS mAP: 0.064026\n229.63s user 44.64s system 77% cpu 5:53.45s total\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce training finishes, you\u0027ll see a new directory (or two new directories for top-down training pipeline) containing all the model files SLEAP needs to use for inference:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003els models/\u003c/pre\u003e\u003c/div\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e231009_165437.centered_instance 231009_165437.centroid\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can use these model files to run inference on your own computer, or you can run inference on the cluster (consult the \u003ca href=\"https://sleap.ai/guides/remote.html\" rel=\"nofollow\"\u003eSLEAP documentation\u003c/a\u003e for more information).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownloading the model\u003c/h3\u003e\u003ca id=\"user-content-downloading-the-model\" class=\"anchor\" aria-label=\"Permalink: Downloading the model\" href=\"#downloading-the-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo copy the model files back to your computer, in a terminal where you are logged into \u003ccode\u003eklone.hyak.uw.edu\u003c/code\u003e, compress the model directory with \u003ccode\u003ezip\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/training_job\nzip -r trained_models.zip models\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, in a new terminal window \u003cem\u003eon your own computer\u003c/em\u003e, use the \u003ccode\u003escp\u003c/code\u003e command to copy the model files from \u003ccode\u003eklone\u003c/code\u003e to your computer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escp your-uw-netid@klone.hyak.uw.edu:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/training_job/trained_models.zip \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Replace your-uw-netid with your UW NetID\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will copy the file \u003ccode\u003etrained_models.zip\u003c/code\u003e to your current directory. You can then unzip the file and use the model files for inference on your own computer. Consult the \u003ca href=\"https://sleap.ai/guides/remote.html\" rel=\"nofollow\"\u003eSLEAP documentation\u003c/a\u003e for more information on running inference with a trained model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnding the cluster job\u003c/h3\u003e\u003ca id=\"user-content-ending-the-cluster-job\" class=\"anchor\" aria-label=\"Permalink: Ending the cluster job\" href=\"#ending-the-cluster-job\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eBe sure to end your cluster job when you are done!\u003c/strong\u003e This will free up resources for other users and potentially prevent you from being charged for time you are not using.\u003c/p\u003e\n\u003cp\u003eTo do this, go back to the terminal where you were running SLEAP on the cluster. (If you closed the terminal, you can log back in to the cluster with \u003ccode\u003essh klone.hyak.uw.edu\u003c/code\u003e.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIf you\u0027re still logged in to the compute node\u003c/strong\u003e, exit:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCancel the job allocation with the \u003ccode\u003escancel\u003c/code\u003e command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escancel --me --jobname sleap-train-test\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, exit the cluster:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSLEAP well!\u003c/p\u003e\n", + "full_name": "cokelaer/pacbio4all", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epacbio4all\u003c/h1\u003e\u003ca id=\"user-content-pacbio4all\" class=\"anchor\" aria-label=\"Permalink: pacbio4all\" href=\"#pacbio4all\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA container with some of the pacbio tools. This is for Singularity 2.4 at least !\u003c/p\u003e\n\u003cp\u003e::\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name pacbio.img shub://cokelaer/pacbio4all:v2\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1719879342.0 + "updated_at": 1508516491.0 }, { "data_format": 2, - "description": "Detects copy number variants in exomes and gene panels using high throughput DNA sequencing data", + "description": "An example repository to deploy multiple containers to a Singularity Registry Server from CircleCI", "filenames": [ - "Singularity" + "vanessa/greeting/Singularity.tag", + "vanessa/greeting/Singularity" ], - "full_name": "PhilPalmer/exomedepth", + "full_name": "singularityhub/circle-ci-sregistry", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/exomedepth\u003c/h1\u003e\u003ca id=\"user-content-nf-coreexomedepth\" class=\"anchor\" aria-label=\"Permalink: nf-core/exomedepth\" href=\"#nf-coreexomedepth\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDetects copy number variants in exomes and gene panels using high throughput DNA sequencing data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/exomedepth\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/015ae7651ea4b7da42335298fa163b8df51591214941b98882663f4bc8d97b3a/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f65786f6d6564657074682e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/exomedepth.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/780f0e426d3a9fd5f3f54407686be63867cb8093d09e36c9bcbad58b728a111d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/exomedepth\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/36aed7cd499155ccd53712f856d14059fbb3a99b7e042f8624b3ed424e18c3d3/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f65786f6d6564657074682e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/exomedepth.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/exomedepth pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Builder Circle-CI\u003c/h1\u003e\u003ca id=\"user-content-singularity-builder-circle-ci\" class=\"anchor\" aria-label=\"Permalink: Singularity Builder Circle-CI\" href=\"#singularity-builder-circle-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".circleci/sregistry-circle.png\"\u003e\u003cimg src=\".circleci/sregistry-circle.png\" alt=\".circleci/sregistry-circle.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a simple example of how you can achieve:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eversion control of your recipes\u003c/li\u003e\n\u003cli\u003eversioning to include image hash \u003cem\u003eand\u003c/em\u003e commit id\u003c/li\u003e\n\u003cli\u003ebuild of associated container and\u003c/li\u003e\n\u003cli\u003epush to a storage endpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003efor a reproducible build workflow. Specifically, this example will use a \u003cem\u003esingle repository\u003c/em\u003e\nas a base to build \u003cem\u003emultiple containers\u003c/em\u003e and push to a shared \u003ca href=\"https://www.github.com/singularityhub/sregistry\"\u003eSingularity Registry Server\u003c/a\u003e based on the namespace organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should this be managed via Github?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGithub, by way of easy integration with continuous integration, is an easy way\nto have a workflow set up where multiple people can collaborate on a container recipe,\nthe recipe can be tested (with whatever testing you need), discussed in pull requests,\nand then finally pushed to your storage of choice or Singularity Registry.\nImportantly, you don\u0027t need to give your entire team manager permissions\nto the registry. An encrypted credential that only is accessible to\nadministrators can do the push upon merge of a discussed change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should I use this instead of a service?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou could use a remote builder, but if you do the build in a continuous integration\nservice you get complete control over it. This means everything from the version of\nSingularity to use, to the tests that you run for your container. You have a lot more\nfreedom in the rate of building, and organization of your repository, because it\u0027s you\nthat writes the configuration. Although the default would work for most, you can\nedit the build, setup, and circle configuration file in the\n\u003ca href=\".circleci\"\u003e.circleci\u003c/a\u003e folder to fit your needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/singularityhub/circle-ci-sregistry\"\u003ecircle-ci-sregistry\u003c/a\u003e repository is\nan example repository that will allow you to store multiple recipes within, and then deploy\nto a \u003ca href=\"https://www.github.com/singularityhub/sregistry\"\u003eSingularity Registey Server\u003c/a\u003e.\nWe use CircleCI to build and push to your Singularity Registry. You have the freedom\nto store as many recipes in one repository as you please, with the understanding that one\nrepository maps to one builder on CircleCI (in terms of time allowed). However, you should\nalso realize that since the build and deploy happens with pull requests, you can have the bulids\ngoing in parallel (up to the time limit, of course). You are also free to have multiple repositories\nto deploy separate containers, but you would then need to ensure that the namespaces (the folders\nnamed inside that map to collection names) do not overlap.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Setup\u003c/h3\u003e\u003ca id=\"user-content-1-setup\" class=\"anchor\" aria-label=\"Permalink: 1. Setup\" href=\"#1-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo deploy this template for your registry you can:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFork or download \u003ca href=\"https://www.github.com/singularityhub/circle-ci-sregistry\"\u003esingularityhub/circle-ci-sregistry\u003c/a\u003e to your own GitHub account. Since the container namespace comes from the folders within, the name of the repository itself is not incredibly important.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://circleci.com/docs/2.0/getting-started/#setting-up-your-build-on-circleci\" rel=\"nofollow\"\u003eConnect your repository\u003c/a\u003e to CircleCI\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Adding Containers\u003c/h3\u003e\u003ca id=\"user-content-2-adding-containers\" class=\"anchor\" aria-label=\"Permalink: 2. Adding Containers\" href=\"#2-adding-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow does building work? Each folder represents a namespace. For example, the folder \u003ccode\u003evanessa/greeting\u003c/code\u003e maps to a container collection \u003ccode\u003evanessa/greeting\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAdd a New Container\u003c/h4\u003e\u003ca id=\"user-content-add-a-new-container\" class=\"anchor\" aria-label=\"Permalink: Add a New Container\" href=\"#add-a-new-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis means that to add a new container collection namespace, just create a folder for it.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir -p vanessa/greeting\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHow do tags work? The tags within the folder correspond to the tags for the container namespace. For example, here\nis how to create the tag \"pancakes\" for the container collection \"vanessa/greeting.\"\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ touch vanessa/greeting/Singularity.pancakes\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity file without any tags maps to the tag \"latest\"\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ touch vanessa/greeting/Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThat\u0027s it! Write your recipe there, and then open a pull request to build the container. Once the container is built, you need to approve the Hold in the continuous integration, and then the container will be pushed.\nMerging (or generally pushing to master) doesn\u0027t do any deployment. All deployments must happen\nthrough this pull request and approve process.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFreezing a Container\u003c/h4\u003e\u003ca id=\"user-content-freezing-a-container\" class=\"anchor\" aria-label=\"Permalink: Freezing a Container\" href=\"#freezing-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want a container collection to build, just put a .frozen file in the collection folder.\nIf you want to freeze the entire collection namespace, just put the .frozen file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003etouch vanessa/greeting/.frozen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to freeze a particular container, add an equivalently named empty file with frozen as\nan extension.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003etouch vanessa/greeting/Singularity.pancakes.frozen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s a very manual way of doing it, but importantly, the status of your building is\nreflected in the repository (version controlled!).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCustom Build for a Container\u003c/h4\u003e\u003ca id=\"user-content-custom-build-for-a-container\" class=\"anchor\" aria-label=\"Permalink: Custom Build for a Container\" href=\"#custom-build-for-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to custom build a container, just add a build.sh file to the directory with the recipe.\nIt will be used instead of the default build.sh provided with the repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Connect to CircleCI\u003c/h3\u003e\u003ca id=\"user-content-3-connect-to-circleci\" class=\"anchor\" aria-label=\"Permalink: 3. Connect to CircleCI\" href=\"#3-connect-to-circleci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you go to your \u003ca href=\"https://circleci.com/dashboard\" rel=\"nofollow\"\u003eCircle Dashboard\u003c/a\u003e you can usually select a Github organization (or user) and then the repository, and then click the toggle button to activate it to build on commit --\u0026gt; push.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. CircleCI Environment\u003c/h3\u003e\u003ca id=\"user-content-4-circleci-environment\" class=\"anchor\" aria-label=\"Permalink: 4. CircleCI Environment\" href=\"#4-circleci-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to communicate with your Singularity Registry Server, you should generate a\ntoken (a credential to push) in your $HOME/.sregistry file. Then you should add the entire\ncontents of this file to an encrypted CircleCI environment variable (just copy paste in the entire thing)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat $HOME/.sregistry\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewrite this to the environment variable \u003ccode\u003eSREGISTRY_CREDENTIALS\u003c/code\u003e in CircleCI.\nIf you don\u0027t define this variable, the builds will happen, but the deploy will\nbe skipped.\u003c/p\u003e\n\u003cp\u003eThat should be it! You should then open pull requests to build containers,\nand then approve the Holds in the CircleCI interface to push to your registry. For example,\nhere is the workflow view right after a hold was approved (notice that the deploy step is\nrunning):\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".circleci/hold.png\"\u003e\u003cimg src=\".circleci/hold.png\" alt=\".circleci/hold.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAnd here is when the deploy is done!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".circleci/deploy.png\"\u003e\u003cimg src=\".circleci/deploy.png\" alt=\".circleci/deploy.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can check what will be deployed (and the command used) in the Build step, it will\nlook something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSREGISTRY_CLIENT=registry sregistry push --name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evanessa/greeting:latest\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/home/circleci/repo/vanessa/greeting/Singularity.sif\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nSREGISTRY_CLIENT=registry sregistry push --name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evanessa/greeting:tag\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/home/circleci/repo/vanessa/greeting/Singularity.tag.sif\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNotice how the container namespace reflects the folder organization provided in\nthe repository here!\u003c/p\u003e\n\u003cp\u003eIf you are interested in learning more about CircleCI (extra features!) continue\nreading below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExtra: Get to Know CircleCi\u003c/h3\u003e\u003ca id=\"user-content-extra-get-to-know-circleci\" class=\"anchor\" aria-label=\"Permalink: Extra: Get to Know CircleCi\" href=\"#extra-get-to-know-circleci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs we are working with \u003ca href=\"https://www.circleci.com\" rel=\"nofollow\"\u003eCircle CI\u003c/a\u003e, here are some other features\nthat might be of interest.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCircle offers \u003ca href=\"https://support.circleci.com/hc/en-us/articles/115015481128-Scheduling-jobs-cron-for-builds-\" rel=\"nofollow\"\u003escheduled builds\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eCircleCI also offers \u003ca href=\"https://circleci.com/docs/enterprise/gpu-configuration/\" rel=\"nofollow\"\u003eGPU Builders\u003c/a\u003e if you want/need that sort of thing.\u003c/li\u003e\n\u003cli\u003eIf you don\u0027t want to use the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry\u003c/a\u003e to push to Google Storage, Drive, Globus, Dropbox, or your personal Singularity Registry, CircleCI will upload your artifacts directly to your \u003ca href=\"https://circleci.com/docs/2.0/deployment-integrations/#section=deployment\" rel=\"nofollow\"\u003edeployment\u003c/a\u003e location of choice.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1576776775.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "sregistry", + "builder-repository" + ], + "updated_at": 1550648627.0 }, { "data_format": 2, - "description": "Singularity container with Alpine OS, and miniconda3 installed in /opt/miniconda. ", + "description": "Prokka: rapid prokaryotic genome annotation.", "filenames": [ - "Singularity" + "1.14.5/Singularity" ], - "full_name": "carissableker/alpine-miniconda-singularity", + "full_name": "pscedu/singularity-prokka", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAlpine Miniconda Singularity\u003c/h1\u003e\u003ca id=\"user-content-alpine-miniconda-singularity\" class=\"anchor\" aria-label=\"Permalink: Alpine Miniconda Singularity\" href=\"#alpine-miniconda-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container with Alpine OS, and miniconda3 installed in /opt/miniconda.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot miniconda.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSylabs Container Library\u003c/h2\u003e\u003ca id=\"user-content-sylabs-container-library\" class=\"anchor\" aria-label=\"Permalink: Sylabs Container Library\" href=\"#sylabs-container-library\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull library://cbleker/dev/alpine-miniconda-singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInspired by:\u003c/h2\u003e\u003ca id=\"user-content-inspired-by\" class=\"anchor\" aria-label=\"Permalink: Inspired by:\" href=\"#inspired-by\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/layers/continuumio/miniconda3/4.8.2-alpine/images/sha256-b9c9f2c7748abdb3291ef2e9b04a8ef3e355f0d7e8030e7a07b8f26c11ed88be?context=explore\" rel=\"nofollow\"\u003econtinuumio/miniconda3:4.8.2-alpine\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Docker-Hub-frolvlad/docker-alpine-miniconda3\"\u003ehttps://github.com/Docker-Hub-frolvlad/docker-alpine-miniconda3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/show0k/alpine-jupyter-docker\"\u003ehttps://github.com/show0k/alpine-jupyter-docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcng/singularity/issues/5075\"\u003ehttps://github.com/hpcng/singularity/issues/5075\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, - "topics": [], - "updated_at": 1601374050.0 - }, - { - "data_format": 2, - "description": "Pandoc is a free and open-source document converter, widely used as a writing tool and as a basis for publishing workflows.", - "filenames": [ - "3.1.1/Singularity", - "2.18/Singularity", - "2.2.1/Singularity" - ], - "full_name": "pscedu/singularity-pandoc", - "latest_release": "v2.18", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-pandoc/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-pandoc/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-pandoc/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-pandoc/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4346713e9da7ba810ef27b2a44f8b33e47b02c264afc5c5e106fb984a26ad669/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d70616e646f63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4346713e9da7ba810ef27b2a44f8b33e47b02c264afc5c5e106fb984a26ad669/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d70616e646f63\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-pandoc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/03f7d32b0cccc34d047656666fe6bfaff4953ac365e41e0c4349d20c2c1bcb1a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d70616e646f63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/03f7d32b0cccc34d047656666fe6bfaff4953ac365e41e0c4349d20c2c1bcb1a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d70616e646f63\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-pandoc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ab81f0905c8940279814f23c661f2f0dcbed9e252ec51aef7e19e226a841b9aa/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d70616e646f63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ab81f0905c8940279814f23c661f2f0dcbed9e252ec51aef7e19e226a841b9aa/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d70616e646f63\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-pandoc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4dca258df814adeb29b104b0a0b9d77357a405889c2b22690fbd8b7ecea496f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d70616e646f63\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4dca258df814adeb29b104b0a0b9d77357a405889c2b22690fbd8b7ecea496f2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d70616e646f63\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-pandoc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-pandoc\u003c/h1\u003e\u003ca id=\"user-content-singularity-pandoc\" class=\"anchor\" aria-label=\"Permalink: singularity-pandoc\" href=\"#singularity-pandoc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://pandoc.org/\" rel=\"nofollow\"\u003epandoc\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003epandoc\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/pandoc/2.2.1\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/pandoc\u003c/code\u003e as \u003ccode\u003e2.2.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [ "singularity", - "utilities" + "bioinformatics" ], - "updated_at": 1715636760.0 + "updated_at": 1624982164.0 }, { "data_format": 2, @@ -23331,2287 +23034,2256 @@ var data = "filenames": [ "Singularity" ], - "full_name": "SciNetHPC/singularity-CRISPRCasFinder", - "latest_release": null, + "full_name": "cfe-lab/proviral", + "latest_release": "v2.3.4", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReadme\u003c/h2\u003e\u003ca id=\"user-content-readme\" class=\"anchor\" aria-label=\"Permalink: Readme\" href=\"#readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eminimap2 (\u003ca href=\"https://github.com/lh3/minimap2\"\u003ehttps://github.com/lh3/minimap2\u003c/a\u003e) (must be available via commandline)\u003c/li\u003e\n\u003cli\u003eblast tools (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)\u003c/li\u003e\n\u003cli\u003eR and RSCRIPT (\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003ehttps://www.r-project.org/\u003c/a\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity builds\u003c/h3\u003e\u003ca id=\"user-content-singularity-builds\" class=\"anchor\" aria-label=\"Permalink: Singularity builds\" href=\"#singularity-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBuild all singularity images inside of the \u003ccode\u003esimages\u003c/code\u003e folder\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFiltering\u003c/h3\u003e\u003ca id=\"user-content-filtering\" class=\"anchor\" aria-label=\"Permalink: Filtering\" href=\"#filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAt the core of the proviral pipeline, data is read from \u003ccode\u003econtigs.csv\u003c/code\u003e and \u003ccode\u003econseqs.csv\u003c/code\u003e files produced by MiCall\u003c/li\u003e\n\u003cli\u003eFirst the pipeline reads through all of the contigs, then the contigs\u003c/li\u003e\n\u003cli\u003eWhen it does this (see the \u003ccode\u003efind_primers()\u003c/code\u003e function) it applies the following logic in this order for filtering/tagging:\n\u003col\u003e\n\u003cli\u003eIf a sample is not proviral, skip it. Do not attempt to find primers or anything, just log a message saying \u003ccode\u003esample X was skipped because it was non-proviral\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf a sample has 0 in the remap column of the \u003ccode\u003ecascade.csv\u003c/code\u003e file, tag that sequence with an error: \u003ccode\u003eNo contig/conseq constructed\u003c/code\u003e, do not analyze it or try to find primers, and write it to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file (which records all failures)\u003c/li\u003e\n\u003cli\u003eIf the \u003ccode\u003econsensus-percent-cutoff\u003c/code\u003e is NOT \u003ccode\u003eMAX\u003c/code\u003e, tag it with an error: \u003ccode\u003econtig not MAX\u003c/code\u003e and skip the sequence (do not try to find primers)\u003c/li\u003e\n\u003cli\u003eIf the reference of the sample is \u003ccode\u003eHIV1-CON-XX-Consensus-seed\u003c/code\u003e tag that sequence with an error: \u003ccode\u003eis V3 sequence\u003c/code\u003e, skip the sequence (do not try to find primers), and write it to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eIf there is an \u003ccode\u003eX\u003c/code\u003e in the middle of the sequence, tag that sequence with an error: \u003ccode\u003elow internal read coverage\u003c/code\u003e, skip the sequence (do not try to find primers), and write it to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eIf there are ANY non-TCGA characters in the sequence, tag that sequence with an error: \u003ccode\u003econtig sequence contained non-TCGA/gap\u003c/code\u003e, skip the sequence (do not try to find primers), and write it to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eFor each end (5\u0027 (fwd), 3\u0027 (rev)) of the sequence:\n\u003col\u003e\n\u003cli\u003eIf there are \u003ccode\u003eX\u003c/code\u003e characters found, try to remove them (if they are clustered) and if not possible to remove tag the fwd/rev end with a fwd/rev primer error: \u003ccode\u003elow read coverage in primer region\u003c/code\u003e, skip the fwd/rev end (do not try to find primers)\u003c/li\u003e\n\u003cli\u003eIf fwd/rev end has zero nucleotides found for primer, tag the fwd/rev end with a fwd/rev primer error: \u003ccode\u003eprimer was not found\u003c/code\u003e, skip to the next end if any\u003c/li\u003e\n\u003cli\u003eIf the fwd/rev primer is deemed not valid, tag the fwd/rev end with a fwd/rev primer error: \u003ccode\u003eprimer failed secondary validation\u003c/code\u003e, skip to the next end if any\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eWrite the sequence to the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e file regardless of tagged errors in any error column\u003c/li\u003e\n\u003cli\u003eLoad the \u003ccode\u003e*primer_analysis.csv\u003c/code\u003e files for both contigs and conseqs and for both of them apply the following filters in order:\n\u003col\u003e\n\u003cli\u003eRemove all rows where either the \u003ccode\u003eerror\u003c/code\u003e, \u003ccode\u003efwd_error\u003c/code\u003e, or \u003ccode\u003erev_error\u003c/code\u003e is tagged\u003c/li\u003e\n\u003cli\u003eRemove the primers from the sequences (for hivseqinr)\u003c/li\u003e\n\u003cli\u003eRemove rows where sample name appears twice (duplicates)\u003c/li\u003e\n\u003cli\u003eRemove rows where the reference contains \u003ccode\u003eunknown\u003c/code\u003e or \u003ccode\u003ereverse\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eFinally merge the filtered contigs and conseqs and write the final \u003ccode\u003e*filtered.csv\u003c/code\u003e file with conseqs taking precedence over contigs\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 8, "topics": [], - "updated_at": 1542826836.0 + "updated_at": 1655928986.0 }, { "data_format": 2, - "description": null, + "description": "metarepo for tidying up container recipes, currently Singularity", "filenames": [ - "Singularity" + "ubuntu/Singularity.ubuntu2004", + "pacbio/Singularity.pacbio", + "base/Singularity.base", + "r/Singularity.r", + "r/Singularity.r-plus", + "starcode/Singularity.starcode-v0.1.1", + "jupyter/Singularity.jupyter-plus-alignparse", + "jupyter/Singularity.jupyter-plus-bioconda", + "jupyter/Singularity.jupyter-plus", + "jupyter/Singularity.jupyter-plus-tensorflow-v2.5.0-compiled", + "jupyter/Singularity.jupyter", + "jupyter/Singularity.jupyter-plus-tensorflow-v2.2.0-compiled", + "jupyter/Singularity.jupyter-plus-tensorflow-v2.4.0-rc4-compiled", + "shell/Singularity.shell-plus", + "tensorflow/Singularity.tensorflow-v2.5.0-compiled", + "tensorflow/Singularity.tensorflow-v2.0.3-compiled", + "tensorflow/Singularity.tensorflow-v1.15.4-compiled-partial", + "tensorflow/Singularity.tensorflow-v2.4.0-rc4-compiled", + "tensorflow/Singularity.tensorflow-v2.2.0-compiled", + "bioinfmunger/Singularity.bioinfmunger", + "lh3-aligners/Singularity.lh3-aligners", + "bioconda/Singularity.bioconda" ], - "full_name": "truatpasteurdotfr/singularity-d12-scipion-runtime-cuda123", + "full_name": "darachm/containers2", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebuilding a debian12 based singularity/apptainer container for scipion3 (using cuda 12.3 which is the oldest cuda version for debian12 as of 2024/07/08)\u003c/h1\u003e\u003ca id=\"user-content-building-a-debian12-based-singularityapptainer-container-for-scipion3-using-cuda-123-which-is-the-oldest-cuda-version-for-debian12-as-of-20240708\" class=\"anchor\" aria-label=\"Permalink: building a debian12 based singularity/apptainer container for scipion3 (using cuda 12.3 which is the oldest cuda version for debian12 as of 2024/07/08)\" href=\"#building-a-debian12-based-singularityapptainer-container-for-scipion3-using-cuda-123-which-is-the-oldest-cuda-version-for-debian12-as-of-20240708\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhy ?\u003c/h2\u003e\u003ca id=\"user-content-why-\" class=\"anchor\" aria-label=\"Permalink: Why ?\" href=\"#why-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eready to use runtime environment for scipion3\u003c/li\u003e\n\u003cli\u003ejust add miniconda and scipion3 (use a writable bind mount for /opt)\u003c/li\u003e\n\u003cli\u003eallow easy snapshoting/replacement for /opt and upgrade/downgrade\u003c/li\u003e\n\u003cli\u003eshare scipion with your co-worker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to:\u003c/h2\u003e\u003ca id=\"user-content-how-to\" class=\"anchor\" aria-label=\"Permalink: How to:\" href=\"#how-to\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ebuild the container\u003c/li\u003e\n\u003cli\u003euse the container with a mounted /opt to\n\u003cul\u003e\n\u003cli\u003einstall miniconda and scipion-installer\u003c/li\u003e\n\u003cli\u003einstall any plugin/binary\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003euse mksquashfs \"freeze\" your scipion and share it with the debian runtime\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ehelper scripts (read and modify to your needs especially \u003ccode\u003e_S\u003c/code\u003e and \u003ccode\u003e_B\u003c/code\u003e)\u003c/h2\u003e\u003ca id=\"user-content-helper-scripts-read-and-modify-to-your-needs-especially-_s-and-_b\" class=\"anchor\" aria-label=\"Permalink: helper scripts (read and modify to your needs especially _S and _B)\" href=\"#helper-scripts-read-and-modify-to-your-needs-especially-_s-and-_b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esetup.sh\u003c/code\u003e build the container and install scipion3 conda environment\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escipion3.sh\u003c/code\u003e run scipion3 from the container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003euse the artefact produced on github instead of building your own:\u003c/h2\u003e\u003ca id=\"user-content-use-the-artefact-produced-on-github-instead-of-building-your-own\" class=\"anchor\" aria-label=\"Permalink: use the artefact produced on github instead of building your own:\" href=\"#use-the-artefact-produced-on-github-instead-of-building-your-own\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eapptainer build ghrc-io-singularity-d12-scipion-runtime-cuda123.sif oras://ghcr.io/truatpasteurdotfr/singularity-d12-scipion-runtime-cuda123:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://scipion.i2pc.es/\" rel=\"nofollow\"\u003ehttps://scipion.i2pc.es/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCaveat\u003c/h2\u003e\u003ca id=\"user-content-caveat\" class=\"anchor\" aria-label=\"Permalink: Caveat\" href=\"#caveat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003eThis is for tracking, hosting recipes for Singularity containers, such that\nit can get mirrored on Github and singularity-hub can get it.\u003c/p\u003e\n\u003cp\u003eOrganzation copied from \u003ca href=\"https://github.com/jlboat/BioinfoContainers\"\u003ejlboat\u003c/a\u003e.\n(Of course, makes total sense to just use tags to organize things!)\u003c/p\u003e\n\u003cp\u003eSome recipes are for individual tools, some are for workflows and so are\ncombos.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1720732063.0 + "updated_at": 1641869104.0 }, { "data_format": 2, - "description": "They keep messing up their docker repository", + "description": "Build Singularity containers to run SpaDES simulations on HPC clusters.", "filenames": [ - "Singularity" + "Singularity.spades_base", + "Singularity.spades_github-development", + "Singularity.spades_github-master" ], - "full_name": "ISU-HPC/Roary", + "full_name": "UBC-FRESH/spades-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRoary\u003c/h1\u003e\u003ca id=\"user-content-roary\" class=\"anchor\" aria-label=\"Permalink: Roary\" href=\"#roary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThey keep messing up their docker repository\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003espades-singularity\u003c/h1\u003e\u003ca id=\"user-content-spades-singularity\" class=\"anchor\" aria-label=\"Permalink: spades-singularity\" href=\"#spades-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout this project\u003c/h2\u003e\u003ca id=\"user-content-about-this-project\" class=\"anchor\" aria-label=\"Permalink: About this project\" href=\"#about-this-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project implements a scripted framework for automating the process of building Singularity containers for running SpaDES simulations on HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI am super impatient, and refuse to take the time to understand what I am doing before running any commands. Just tell me how to do the thing right now!\u003c/h2\u003e\u003ca id=\"user-content-i-am-super-impatient-and-refuse-to-take-the-time-to-understand-what-i-am-doing-before-running-any-commands-just-tell-me-how-to-do-the-thing-right-now\" class=\"anchor\" aria-label=\"Permalink: I am super impatient, and refuse to take the time to understand what I am doing before running any commands. Just tell me how to do the thing right now!\" href=\"#i-am-super-impatient-and-refuse-to-take-the-time-to-understand-what-i-am-doing-before-running-any-commands-just-tell-me-how-to-do-the-thing-right-now\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build, sign, and push the base container flavour to the cloud image repository, simply run \u003ccode\u003emake all flavour=FLAVOUR\u003c/code\u003e, where \u003ccode\u003eFLAVOUR\u003c/code\u003e is one of \u003ccode\u003ebase\u003c/code\u003e, \u003ccode\u003egithub-master\u003c/code\u003e, or \u003ccode\u003egithub-development\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNot sure which flavour to use? Read on!\u003c/p\u003e\n\u003cp\u003eNote that, if you do not have Singularity installed yet, you will need to run \u003ccode\u003emake install-singularity\u003c/code\u003e first.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity container definition files\u003c/h2\u003e\u003ca id=\"user-content-singularity-container-definition-files\" class=\"anchor\" aria-label=\"Permalink: Singularity container definition files\" href=\"#singularity-container-definition-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity container definition files follow standard Singularity definition file naming conventions (i.e., they are prefixed with \u003ccode\u003eSingularity.\u003c/code\u003e followed by a \u003cem\u003etag\u003c/em\u003e string). There are three flavours (tags) defined in this project: \u003ccode\u003ebase\u003c/code\u003e, \u003ccode\u003egithub-master\u003c/code\u003e, and \u003ccode\u003egithub-development\u003c/code\u003e. Note that the R code that installs SpaDES packages for each flavour is contained in a script named \u003ccode\u003espades-setup_flavour.R\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can also create new custom flavours by copying and modifying some files from an existing flavour. New flavours should be compatible with automated make targets (as long as you did not break the filename patterns).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBase flavour\u003c/h3\u003e\u003ca id=\"user-content-base-flavour\" class=\"anchor\" aria-label=\"Permalink: Base flavour\" href=\"#base-flavour\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe base container flavour includes the latest stable CRAN versions of core SpaDES R packages. This base can be used to run SpaDES models directly (for simpler projects, where the CRAN packages are all you need). The base image also serves as a \u003cem\u003ebootstrap\u003c/em\u003e image for other flavours. The base container flavour is implemented in \u003ccode\u003eSingularity.spades_base\u003c/code\u003e and \u003ccode\u003espades-setup_base.R\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGitHub flavours\u003c/h3\u003e\u003ca id=\"user-content-github-flavours\" class=\"anchor\" aria-label=\"Permalink: GitHub flavours\" href=\"#github-flavours\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two GitHub container flavours (\u003ccode\u003egithub-master\u003c/code\u003e, \u003ccode\u003egithub-development\u003c/code\u003e). These install core SpaDES R packages from the latest code pushed to GitHub repositories for \u003ccode\u003emaster\u003c/code\u003e and \u003ccode\u003edevelopment\u003c/code\u003e branches, respectively. The GitHub container flavours are implemented in the \u003ccode\u003eSingularity.spades-github_BRANCH\u003c/code\u003e and \u003ccode\u003espades-setup_github-BRANCH\u003c/code\u003e (where \u003ccode\u003eBRANCH\u003c/code\u003e is one of \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edevelopment\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThe GitHub container flavours are \u003cem\u003ebootstrapped\u003c/em\u003e from the base container flavour. Defintion file implementation assumes that a local base container image is available in path \u003ccode\u003ebuild/spades.sif\u003c/code\u003e, so the base container must be built first (the base container will automatically get built if not present if you run \u003ccode\u003emake build flavour=FLAVOUR\u003c/code\u003e, where \u003ccode\u003eFLAVOUR\u003c/code\u003e is any value except for \u003ccode\u003ebase\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCustom flavours\u003c/h3\u003e\u003ca id=\"user-content-custom-flavours\" class=\"anchor\" aria-label=\"Permalink: Custom flavours\" href=\"#custom-flavours\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can create a custom container flavour but copying \u003ccode\u003eSingularity.spades_github-master\u003c/code\u003e and \u003ccode\u003espades-setup_github-master.R\u003c/code\u003e---rename these to \u003ccode\u003eSingularity.spades_foo\u003c/code\u003e and \u003ccode\u003espades-setup_foo.R\u003c/code\u003e (where \u003ccode\u003efoo\u003c/code\u003e is whatever unique flavour name you want) and modify as required. Minimally, you just need to edit one line of code in the Singularity definition file to point to \u003ccode\u003espades-setup_foo.R\u003c/code\u003e, and edit the code in \u003ccode\u003espades-setup_foo.R\u003c/code\u003e to install whatever versions of SpaDES R packages you need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMakefile details\u003c/h2\u003e\u003ca id=\"user-content-makefile-details\" class=\"anchor\" aria-label=\"Permalink: Makefile details\" href=\"#makefile-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eMakefile\u003c/code\u003e implements a number of make targets.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake build flavour=FLAVOUR sandbox=true\u003c/code\u003e to build a sandbox container (in \u003ccode\u003ebuild/spades_FLAVOUR_sandbox\u003c/code\u003e). See Singularity documentation for details on sandbox containers.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake build flavour=FLAVOUR\u003c/code\u003e to build a container as a single \u003cem\u003esingularity image file\u003c/em\u003e (in \u003ccode\u003ebuild/spades_FLAVOUR.sif\u003c/code\u003e). See Singularity documentation for details on SIF containers.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake push flavour=FLAVOUR\u003c/code\u003e to sign your SIF image and push it to your Sylabs cloud image library account. See the \u003ca href=\"https:%5Ccloud.sylabs.io\"\u003eSylabs Container Library\u003c/a\u003e to create and configure your account.\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003emake all flavour=FLAVOUR\u003c/code\u003e to build and push your image in one step.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1540592377.0 - }, - { - "data_format": 2, - "description": "Simple example container with Nix and Python", - "filenames": [ - "Singularity" - ], - "full_name": "XSEDE/nix-container-python-mandle", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-python-mandle\u003c/h1\u003e\u003ca id=\"user-content-nix-container-python-mandle\" class=\"anchor\" aria-label=\"Permalink: nix-container-python-mandle\" href=\"#nix-container-python-mandle\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis directory is a full example of using the docker-centos-nix-python template to\ncontainerize a very simple Python3 app.\u003c/p\u003e\n\u003cp\u003eThis app allows you to create a GIF file with a straight-line zoom-in of the Mandlebrot set.\nRunning the bare container will show the various commandline options available, which\nmay be confusing, as this was written immediately following in-depth perusal of\n\u003ca href=\"https://en.wikipedia.org/wiki/Mandelbrot_set\" rel=\"nofollow\"\u003eThe Wikipedia article on the Mandlebrot Set\u003c/a\u003e.\nIf you have some time available and are interested in this sort of thing, please go down\nthe rabbithole, but otherwise view this as a somewhat helpful example.\u003c/p\u003e\n\u003cp\u003eThe following steps should allow you to test this out on a system with docker and singularity installed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker build -t $USER/python-mandle .\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003edocker run -v $PWD:$PWD -it $USER/python-mandle $PWD/mandle_ex.gif\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build mandle.sif mandle.def\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esingularity run mandle.sif -n 2 sing_mandle_ex.gif\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor submission on an HPC system using SLURM, you could use the following:\n(Assuming you\u0027ve uploaded this .sif file locally to APPDIR)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#/bin/bash\n#SBATCH -N 1\n#SBATCH -n 24\n#SBATCH -o mandle_%A.out\n\nmodule load singularity/3.5 #Versions above 3.6 are incompatible with lower versions!\n\nWORKDIR=/scratch/myuser\nAPPDIR=/home/myuser/images/\n\nsingularity run $APPDIR/mandle.sif -n 24 $WORKDIR/my_mandle.gif\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 0, - "subscribers_count": 16, - "topics": [], - "updated_at": 1628539859.0 + "updated_at": 1716933506.0 }, { "data_format": 2, - "description": "centos7 singularity container for HPC", + "description": "Singularity recipe to build https://github.com/brentp/smoove", "filenames": [ "Singularity" ], - "full_name": "DoaneAS/dijon", + "full_name": "lorenzgerber/smoove-singularity", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esmoove-singularity\u003c/h1\u003e\u003ca id=\"user-content-smoove-singularity\" class=\"anchor\" aria-label=\"Permalink: smoove-singularity\" href=\"#smoove-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1493118640.0 + "updated_at": 1544424250.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "containers/Singularity.0.3.5", + "containers/Singularity.0.3.3", + "containers/Singularity.0.4.1", + "containers/Singularity.0.3.6", + "containers/Singularity.0.4.0" ], - "full_name": "callaghanmt-containers/r_rgdal_tmap_sf", + "full_name": "tdalford/bilby_relative_binning", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003er_rgdal_tmap_sf\u003c/h1\u003e\u003ca id=\"user-content-r_rgdal_tmap_sf\" class=\"anchor\" aria-label=\"Permalink: r_rgdal_tmap_sf\" href=\"#r_rgdal_tmap_sf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1556721577.0 + "updated_at": 1601504336.0 }, { "data_format": 2, - "description": "Sngularity recipe for jupyter lab on R studio", + "description": "Demuxlet workflows for snRNA and snATAC", "filenames": [ - "Singularity" + "containers/general/Singularity", + "containers/demuxlet/Singularity" ], - "full_name": "ISU-HPC/jupyter", + "full_name": "arushiv/sn_demuxlet", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejupyter\u003c/h1\u003e\u003ca id=\"user-content-jupyter\" class=\"anchor\" aria-label=\"Permalink: jupyter\" href=\"#jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSngularity recipe for jupyter lab on R studio\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNextFlow pipeline for RNA Demuxlet in demuxlet_rna; ATAC demuxlet in demuxlet_atac\u003c/h2\u003e\u003ca id=\"user-content-nextflow-pipeline-for-rna-demuxlet-in-demuxlet_rna-atac-demuxlet-in-demuxlet_atac\" class=\"anchor\" aria-label=\"Permalink: NextFlow pipeline for RNA Demuxlet in demuxlet_rna; ATAC demuxlet in demuxlet_atac\" href=\"#nextflow-pipeline-for-rna-demuxlet-in-demuxlet_rna-atac-demuxlet-in-demuxlet_atac\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Demuxlet workflows take as input pruned bam files along with the QC metrics generated from the snRNA or snATAC workflows. Bam files are split into chunks of 1000 nuclei to expedite the Demuxlet run (can change this in the main.nf). Vcf files are prepped by selecting SNPs to be tested and samples to be kept. For RNA I\u0027ve used gencode v19 gene introns+exons - ENCODE blacklist regions (this bed file is in the data folder). For ATAC I\u0027ve used gencode introns+exons - ENCODE blacklist regions + ATAC-seq peaks in the bulk/previously available snATAC cell types from the tissue of interest. This might need to be updated according to your needs.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eContainers general and demuxlet carry the software to run different processes.\u003c/li\u003e\n\u003cli\u003eRNA Demuxlet requires pruned bam files and qc files from the \u003ca href=\"https://github.com/porchard/snRNAseq-NextFlow\"\u003eRNA workflow\u003c/a\u003e as input. One way to do this is to provide the directory paths of the snRNA pruned bam directory and the list of library names so the workflow fetches bam files of the form \u003ccode\u003e${pruned_bam_dir_path}/${library}-hg19.before-dedup.bam\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eATAC Demuxlet requires pruned bam files and qc files from the \u003ca href=\"https://github.com/porchard/snATACseq-NextFlow\"\u003eATAC workflow\u003c/a\u003e as input. One way to do this is to provide the directory paths of the snATAC pruned bam directory and the list of library names so the workflow fetches bam files of the form \u003ccode\u003e${params.pruned_bam_dir}/${library}-hg19.pruned.bam\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eEdit the \u003ccode\u003enextflow.config\u003c/code\u003e that has the config parameters such as executor, container paths etc. to suit your system.\u003c/li\u003e\n\u003cli\u003eUpdate the \u003ccode\u003elibrary-config.json\u003c/code\u003e file with information about the individual libraries.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun.sh\u003c/code\u003e includes an example run command.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1527790762.0 + "updated_at": 1586194289.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "famishedrover/pyhipop-planner", + "full_name": "nilsec/mtrack", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHiPOP -- Hierarchical Partial-Order Planning\u003c/h1\u003e\u003ca id=\"user-content-hipop----hierarchical-partial-order-planning\" class=\"anchor\" aria-label=\"Permalink: HiPOP -- Hierarchical Partial-Order Planning\" href=\"#hipop----hierarchical-partial-order-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAuthors:\u003c/p\u003e\n\u003cp\u003eCharles Lesire, Alexandre Albore,\u003c/p\u003e\n\u003cp\u003eONERA/DTIS, Univ. of Toulouse, France\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMTrack\u003c/h1\u003e\u003ca id=\"user-content-mtrack\" class=\"anchor\" aria-label=\"Permalink: MTrack\" href=\"#mtrack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAutomatic extraction of microtubules in electron microscopy volumes of neural tissue.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1700254560.0 + "updated_at": 1565039518.0 }, { "data_format": 2, - "description": "A GPU enabled Singularity image for the BarraCUDA short read aligner", + "description": "A simple template for future projects", "filenames": [ "Singularity" ], - "full_name": "KevinSayers/BarraCUDA_Singularity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBarraCUDA_Singularity\u003c/h1\u003e\u003ca id=\"user-content-barracuda_singularity\" class=\"anchor\" aria-label=\"Permalink: BarraCUDA_Singularity\" href=\"#barracuda_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA GPU enabled Singularity image for the BarraCUDA short read aligner\u003c/p\u003e\n\u003cp\u003eNote: The nvidia/cuda Docker container that is used for bootstrapping must be a devel version. The others lack the nvcc compiler.\u003c/p\u003e\n\u003cp\u003eBarraCUDA paper: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278344/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278344/\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1501794254.0 - }, - { - "data_format": 2, - "description": "Singularity recipe for keras", - "filenames": [ - "Singularity1.2", - "Singularity1.0", - "Singularity1.1" - ], - "full_name": "ResearchIT/singularity-keras", + "full_name": "mathematiguy/minimal-project", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingulairty Recipe for keras\u003c/h1\u003e\u003ca id=\"user-content-singulairty-recipe-for-keras\" class=\"anchor\" aria-label=\"Permalink: Singulairty Recipe for keras\" href=\"#singulairty-recipe-for-keras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains the recipe for a basic keras install with gpu support.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersions:\u003c/h2\u003e\u003ca id=\"user-content-versions\" class=\"anchor\" aria-label=\"Permalink: Versions:\" href=\"#versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e1.0 - Initial effort\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eminimal-project\u003c/h1\u003e\u003ca id=\"user-content-minimal-project\" class=\"anchor\" aria-label=\"Permalink: minimal-project\" href=\"#minimal-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple template for future projects\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 3, "topics": [], - "updated_at": 1531950701.0 + "updated_at": 1723292824.0 }, { "data_format": 2, - "description": "Label Me Application for PTdatax", + "description": null, "filenames": [ "Singularity" ], - "full_name": "In-For-Disaster-Analytics/labelme", + "full_name": "mathematiguy/star-stuff", "latest_release": null, - "readme": "\u003cp\u003eThis template is the first in a \u003ca href=\"#next-templates\"\u003eseries of templates\u003c/a\u003e that will guide you through the process of creating a cookbook and running it on TACC systems. From simple ones that run a command to more complex ones that run a Python script using conda or a Jupyter Notebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA GitHub account\u003c/li\u003e\n\u003cli\u003eTACC account. If you don\u0027t have one, you can request one \u003ca href=\"https://accounts.tacc.utexas.edu/register\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eTo access TACC systems, you should have an \u003ca href=\"https://tacc.utexas.edu/use-tacc/allocations/\" rel=\"nofollow\"\u003eallocation\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003eYou can see your allocations \u003ca href=\"https://ptdatax.tacc.utexas.edu/workbench/allocations/approved\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eIf you don\u0027t have an allocation, you can request one \u003ca href=\"https://portal.tacc.utexas.edu/allocation-request\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTemplate Overview\u003c/h2\u003e\u003ca id=\"user-content-template-overview\" class=\"anchor\" aria-label=\"Permalink: Template Overview\" href=\"#template-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis template creates a simple Python script that will be used to demonstrate how to run a cookbook on a TACC cluster and obtain the output using a UI. The cookbook will use a CSV file stored on TACC storage and run a Python script that reads it, calculates the average of the values in the first column, and writes the result to a file.\u003c/p\u003e\n\u003cp\u003eIn this case, the file is small for demonstration purposes. However, you can use the same process to analyze large files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow does it work?\u003c/h3\u003e\u003ca id=\"user-content-how-does-it-work\" class=\"anchor\" aria-label=\"Permalink: How does it work?\" href=\"#how-does-it-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"app.json\"\u003e\u003ccode\u003eapp.json\u003c/code\u003e\u003c/a\u003e file: contains the definition of the Tapis application, including the application\u0027s name, description, Docker image, input files, and advanced options.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Dockerfile\"\u003e\u003ccode\u003eDockerfile\u003c/code\u003e\u003c/a\u003e: a Docker image is built from the \u003ca href=\"./Dockerfile\"\u003e\u003ccode\u003eDockerfile\u003c/code\u003e\u003c/a\u003e. The Docker image defines the runtime environment for the application and the files that will be used by the application.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"run.sh\"\u003e\u003ccode\u003erun.sh\u003c/code\u003e\u003c/a\u003e: contains all the commands that will be executed on the TACC cluster.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUpload files to TACC storage\u003c/h3\u003e\u003ca id=\"user-content-upload-files-to-tacc-storage\" class=\"anchor\" aria-label=\"Permalink: Upload files to TACC storage\" href=\"#upload-files-to-tacc-storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne of the goals of the template is to demonstrate how to use the TACC storage system to store the input and output files. So, you should upload the CSV file to the TACC storage system.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGo to the \u003ca href=\"https://portal.tacc.utexas.edu\" rel=\"nofollow\"\u003eTACC Portal\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eClick on the \"Data Files\" tab.\u003c/li\u003e\n\u003cli\u003eClick on the \"Add +\" button.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image.png\"\u003e\u003cimg src=\"images/image.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick on the \"Upload\" button.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image-1.png\"\u003e\u003cimg src=\"images/image-1.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSelect the file you want to upload and click \u003ccode\u003eUpload Selected\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eModify the Dockerfile\u003c/h3\u003e\u003ca id=\"user-content-modify-the-dockerfile\" class=\"anchor\" aria-label=\"Permalink: Modify the Dockerfile\" href=\"#modify-the-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eDockerfile\u003c/code\u003e is used to create a Docker image that will be used to run the Python script. In this case, the Docker image is created using the \u003ccode\u003emicroconda\u003c/code\u003e base image, which is a minimal image that contains conda.\u003c/p\u003e\n\u003cp\u003eFor example, the Dockerfile below installs \u003ccode\u003ecurl\u003c/code\u003e using \u003ccode\u003eapt-get\u003c/code\u003e. This is useful if you need to install packages that are not available in conda.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-dockerfile\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eRUN\u003c/span\u003e apt-get update \u0026amp;\u0026amp; apt-get install -y \\\n curl \\\n \u0026amp;\u0026amp; rm -rf /var/lib/apt/lists/*\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDefine conda dependencies using \u003ccode\u003eenvironment.yaml\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-define-conda-dependencies-using-environmentyaml\" class=\"anchor\" aria-label=\"Permalink: Define conda dependencies using environment.yaml\" href=\"#define-conda-dependencies-using-environmentyaml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eenvironment.yaml\u003c/code\u003e file is used to define the conda environment that will be used to run the Python script. In this case, the \u003ccode\u003eenvironment.yaml\u003c/code\u003e file contains the dependencies needed to run the Python script.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ebase\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003echannels\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003econda-forge\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003edependencies\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003epython=3.9.1\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003epandas=1.2.1\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJob run script\u003c/h3\u003e\u003ca id=\"user-content-job-run-script\" class=\"anchor\" aria-label=\"Permalink: Job run script\" href=\"#job-run-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003erun.sh\u003c/code\u003e file is used to run the Python script. It activates the conda environment and runs the Python script.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e -xe\n\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${_tapisExecSystemInputDir}\u003c/span\u003e\npython /code/main.py billing.csv \u003cspan class=\"pl-smi\"\u003e${_tapisExecSystemOutputDir}\u003c/span\u003e/output.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003erun.sh\u003c/code\u003e has two variables that are used to define the input and output directories. These variables are \u003ccode\u003e_tapisExecSystemInputDir\u003c/code\u003e and \u003ccode\u003e_tapisExecSystemOutputDir\u003c/code\u003e which are automatically set by the Tapis system.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e_tapisExecSystemInputDir: The directory where the input files are staged\u003c/li\u003e\n\u003cli\u003e_tapisExecSystemOutputDir: The directory where the application writes the output files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate your cookbook\u003c/h2\u003e\u003ca id=\"user-content-create-your-cookbook\" class=\"anchor\" aria-label=\"Permalink: Create your cookbook\" href=\"#create-your-cookbook\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use this repository as a template to create your cookbook. Follow the steps below to create your cookbook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate a new repository\u003c/h3\u003e\u003ca id=\"user-content-create-a-new-repository\" class=\"anchor\" aria-label=\"Permalink: Create a new repository\" href=\"#create-a-new-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClick on the \"Use this template\" button to create a new repository\u003c/li\u003e\n\u003cli\u003eFill in the form with the information for your new repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild the Docker image\u003c/h3\u003e\u003ca id=\"user-content-build-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build the Docker image\" href=\"#build-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the repository\u003c/li\u003e\n\u003cli\u003eBuild the Docker image using the command below\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t cookbook-python \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003ePush the Docker image to a container registry\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker tag cookbook-python \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour-registry\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/cookbook-python\ndocker push \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour-registry\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/cookbook-python\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eModify the \u003ccode\u003eapp.json\u003c/code\u003e file\u003c/h3\u003e\u003ca id=\"user-content-modify-the-appjson-file\" class=\"anchor\" aria-label=\"Permalink: Modify the app.json file\" href=\"#modify-the-appjson-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach app has a unique \u003ccode\u003eid\u003c/code\u003e and \u003ccode\u003edescription\u003c/code\u003e. So, you should change these fields to match your app\u0027s name and description.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the \u003ccode\u003eapp.json\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eChange the values \u003ccode\u003eid\u003c/code\u003e and \u003ccode\u003edescription\u003c/code\u003e fields with the name and description as you wish.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate a New Application on the Cookbook UI\u003c/h3\u003e\u003ca id=\"user-content-create-a-new-application-on-the-cookbook-ui\" class=\"anchor\" aria-label=\"Permalink: Create a New Application on the Cookbook UI\" href=\"#create-a-new-application-on-the-cookbook-ui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eGo to \u003ca href=\"https://in-for-disaster-analytics.github.io/cookbooks-ui/#/apps\" rel=\"nofollow\"\u003eCookbook UI\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick on the \"Create Application\" button\u003c/li\u003e\n\u003cli\u003eFill in the form with the information from your \u003ccode\u003eapp.json\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eClick \"Create Application\"\u003c/li\u003e\n\u003cli\u003eA new application will be created, and you will be redirected to the application\u0027s page\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun your Cookbook\u003c/h3\u003e\u003ca id=\"user-content-run-your-cookbook\" class=\"anchor\" aria-label=\"Permalink: Run your Cookbook\" href=\"#run-your-cookbook\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eGo to the application\u0027s page on the Cookbook UI, if you are not already there\u003c/li\u003e\n\u003cli\u003eClick on the \"Run\" button on the right side of the page. This will open the Portal UI\u003c/li\u003e\n\u003cli\u003eClick on the \"Select\" button to choose the input file\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image-2.png\"\u003e\u003cimg src=\"images/image-2.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick \"Run\"\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCheck the output\u003c/h3\u003e\u003ca id=\"user-content-check-the-output\" class=\"anchor\" aria-label=\"Permalink: Check the output\" href=\"#check-the-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eAfter the job finishes, you can check the output by clicking on the \"Output location\" link on the job\u0027s page\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/job-finished.png\"\u003e\u003cimg src=\"images/job-finished.png\" alt=\"Show a job finished \" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eYou will be redirected to the output location, where you can see the output files generated by the job\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image-4.png\"\u003e\u003cimg src=\"images/image-4.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick on a file to see its content. In this case, the file is named \u003ccode\u003eoutput.txt\u003c/code\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/image-3.png\"\u003e\u003cimg src=\"images/image-3.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNext templates\u003c/h2\u003e\u003ca id=\"user-content-next-templates\" class=\"anchor\" aria-label=\"Permalink: Next templates\" href=\"#next-templates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/In-For-Disaster-Analytics/Cookbook-Docker-Template\"\u003eRunning a command\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/In-For-Disaster-Analytics/Cookbook-Conda-Template\"\u003eRunning a Python script using conda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/In-For-Disaster-Analytics/Cookbook-Jupyter-Template\"\u003eRunning a Jupyter Notebook\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWilliam Mobley - \u003ca href=\"mailto:wmobley@tacc.utexas.edu\"\u003ewmobley@tacc.utexas.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMaximiliano Osorio - \u003ca href=\"mailto:maxiosorio@gmail.com\"\u003emaxiosorio@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eminimal-project\u003c/h1\u003e\u003ca id=\"user-content-minimal-project\" class=\"anchor\" aria-label=\"Permalink: minimal-project\" href=\"#minimal-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple template for future projects\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1718223396.0 + "updated_at": 1723292845.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.latest" ], - "full_name": "oogasawa/singularity_jupyter_typescript", + "full_name": "brentritzema/senior-project", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-jupyter-typescript\u003c/h1\u003e\u003ca id=\"user-content-singularity-jupyter-typescript\" class=\"anchor\" aria-label=\"Permalink: singularity-jupyter-typescript\" href=\"#singularity-jupyter-typescript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Singularity container of Jupyter notebook for datascience + typescript,\ncreated by converting an official Docker image\n\u003ca href=\"https://hub.docker.com/r/jupyter/datascience-notebook/\" rel=\"nofollow\"\u003ejupyter/datascience-notebook\u003c/a\u003e,\nand \u003ca href=\"https://github.com/yunabe/tslab\"\u003eyunabe/tslab Jupyter kernel\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Singularity image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/oogasawa/singularity-jupyter-typescript\ncd singularity-jupyter-typescript\nsudo singularity build . singularity-jupyter-typescript.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the server as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance start singularity-jupyter-typescript.sif sing_typescript\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEnter (attach) the Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# List the running containers.\nsingularity instance list\n\n# Attach the container\nsingularity shell instance://sing_typescript\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart the Jupyter notebook (or Jupyter Lab) from within the Singularity prompt.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://sing_typescript\nSingularity\u0026gt; jupyter lab --port=50000\n[I 01:28:50.619 LabApp] JupyterLab extension loaded from /opt/conda/lib/python3.8/site-packages/jupyterlab\n[I 01:28:50.619 LabApp] JupyterLab application directory is /opt/conda/share/jupyter/lab\n[I 01:28:50.621 LabApp] Serving notebooks from local directory: /home/oogasawa/tmp3/singularity-jupyter-typescript\n[I 01:28:50.621 LabApp] The Jupyter Notebook is running at:\n[I 01:28:50.621 LabApp] http://mayfly:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n[I 01:28:50.621 LabApp] or http://127.0.0.1:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n[I 01:28:50.621 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n[C 01:28:50.624 LabApp]\n\n To access the notebook, open this file in a browser:\n\t file:///home/oogasawa/.local/share/jupyter/runtime/nbserver-25-open.html\n\tOr copy and paste one of these URLs:\n\t\thttp://mayfly:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\n\t or http://127.0.0.1:50000/?token=056ea4ee0e654429927ef2a0696a10100c623fbc7b4a8ee4\t\t\t\t\t \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can access the Jupyter software \u003ca href=\"http://localhost:50000/\" rel=\"nofollow\"\u003ehttp://localhost:50000/\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eStop the server (and return to the bash prompt) by Ctrl-C, and stop the container as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity instance stop sing_typescript\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRoy and Brent\u0027s Senior Project\u003c/h1\u003e\u003ca id=\"user-content-roy-and-brents-senior-project\" class=\"anchor\" aria-label=\"Permalink: Roy and Brent\u0027s Senior Project\" href=\"#roy-and-brents-senior-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1622297530.0 + "updated_at": 1549999922.0 }, { "data_format": 2, - "description": null, + "description": "Achab In Singularity, a Singularity Container for Captain Achab (annotation workflow)", "filenames": [ "Singularity" ], - "full_name": "darachm/singularity_csvkit", + "full_name": "mobidic/Achabilarity", "latest_release": null, - "readme": "\u003cp\u003eThis container is for providing \u003ccode\u003ecsvkit\u003c/code\u003e for a nextflow pipeline.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eACHABILARITY\u003c/h1\u003e\u003ca id=\"user-content-achabilarity\" class=\"anchor\" aria-label=\"Permalink: ACHABILARITY\" href=\"#achabilarity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAchabInsinguLARITY, a container to use captainAchab workflow easier !\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/achab_logo.png\"\u003e\u003cimg src=\"img/achab_logo.png\" width=\"350\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGoals\u003c/h2\u003e\u003ca id=\"user-content-goals\" class=\"anchor\" aria-label=\"Permalink: Goals\" href=\"#goals\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse a Singularity container which already has all tools to run captainAchab workflow.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/img/captainAchab.svg\"\u003e\u003cimg src=\"/img/captainAchab.svg\" alt=\"captain achab workflow description\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eFirst, build\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename.simg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e Singularity \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eThen run\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e/PATH/TO/ANNOVAR\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename.simg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -i workflow_inputs.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity help\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename.simg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptions\u003c/h2\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: Options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e-c | --conf \u0026lt;file.conf\u0026gt;\u003c/strong\u003e : To add a conf file\u003cbr\u003e\n\u003cstrong\u003e-o | --option \u0026lt;option.json\u0026gt;\u003c/strong\u003e : To add an option file\u003cbr\u003e\n\u003cstrong\u003e-v | --verbosity \u0026lt;1, 2, 3 or 4\u0026gt;\u003c/strong\u003e : To set verbosity level (ERROR : 1 | WARNING : 2 | INFO [default] : 3 | DEBUG : 4)\u003cbr\u003e\n\u003cstrong\u003e-h | --help\u003c/strong\u003e : Print help message in terminal and close the script (Help provided by -h concerns wrapper using)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMore informations\u003c/h2\u003e\u003ca id=\"user-content-more-informations\" class=\"anchor\" aria-label=\"Permalink: More informations\" href=\"#more-informations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAchabilarity is currently using a part of \u003ca href=\"https://github.com/mobidic/MobiDL\"\u003eMobiDL\u003c/a\u003e which is \u003ca href=\"https://github.com/mobidic/Captain-ACHAB\"\u003eCaptainAchab\u003c/a\u003e workflow.\u003cbr\u003e\nThis Singularity contains CentOS environnement and all requirements to run Captain Achab workflow (MPA, Phenolyzer, Achab) and few others (BCFTools, GATK4 ...).\u003cbr\u003e\n\u003cstrong\u003eMake sure you already have Annovar (and its database) to bind it. It is not include in this container.\u003c/strong\u003e\nBinding of ANNOVAR and data folder (inputs) will look like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /path/to/annovar/:/media -B /path/to/data/:/mnt achabilarity.simg -c /path/to/conf -i /path/to/json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe container will execute specific wrapper of cromwell (\u003ca href=\"https://github.com/mobidic/Crom-wellWrapped\"\u003eCrom-wellWrapped\u003c/a\u003e) which will generate the right cromwell command depending on options and arguments.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eMontpellier Bioinformatique pour le Diagnostique Clinique (MoBiDiC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCHU de Montpellier\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/logo-mobidic.png\"\u003e\u003cimg src=\"img/logo-mobidic.png\" alt=\"MoBiDiC\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://neuro-2.iurc.montp.inserm.fr/mobidic/\" rel=\"nofollow\"\u003eVisit our website\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1543722869.0 + "updated_at": 1617369638.0 }, { "data_format": 2, - "description": "The Common Workflow Language (CWL) is an open standard for describing analysis workflows and tools in a way that makes them portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. ", + "description": "Seeing scenes with multi-granularity", "filenames": [ - "3.1.20211020155521/Singularity", - "3.1.20220210171524/Singularity" + "env.d/Singularity.blender", + "env.d/Singularity.base" ], - "full_name": "pscedu/singularity-cwltool", - "latest_release": "v3.1.20211020155521", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-cwltool/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-cwltool/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-cwltool/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-cwltool/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0713bada75749349dcdd345d4398a0394c60a68411e755ae4cc4de586633d366/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d63776c746f6f6c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0713bada75749349dcdd345d4398a0394c60a68411e755ae4cc4de586633d366/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d63776c746f6f6c\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-cwltool\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/06c1ad52de3c2984a480093626061fe1e2abf5399e3cf525379c8a0731f7b66b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d63776c746f6f6c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/06c1ad52de3c2984a480093626061fe1e2abf5399e3cf525379c8a0731f7b66b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d63776c746f6f6c\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-cwltool\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c19f9097b3a5bd623b4adaea7d7bf114d414cbe08983fb427ec9b51c1199cf8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d63776c746f6f6c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c19f9097b3a5bd623b4adaea7d7bf114d414cbe08983fb427ec9b51c1199cf8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d63776c746f6f6c\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-cwltool\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6f169d24efbee5cd194f8ecf9395464851313f0c36e4c8041d21e801a50b53b0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d63776c746f6f6c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6f169d24efbee5cd194f8ecf9395464851313f0c36e4c8041d21e801a50b53b0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d63776c746f6f6c\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-cwltool\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-cwltool\u003c/h1\u003e\u003ca id=\"user-content-singularity-cwltool\" class=\"anchor\" aria-label=\"Permalink: singularity-cwltool\" href=\"#singularity-cwltool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/72df1b7e3dc95fb0111902a35b64a124d254b58a0058c67416ae0a95d09f10bc/68747470733a2f2f7777772e636f6d6d6f6e776c2e6f72672f43574c2d4c6f676f2d4865616465722e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72df1b7e3dc95fb0111902a35b64a124d254b58a0058c67416ae0a95d09f10bc/68747470733a2f2f7777772e636f6d6d6f6e776c2e6f72672f43574c2d4c6f676f2d4865616465722e706e67\" data-canonical-src=\"https://www.commonwl.org/CWL-Logo-Header.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.commonwl.org/\" rel=\"nofollow\"\u003ecwltool\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ecwltool\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/cwltool/3.1.20211020155521\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/cwltool\u003c/code\u003e as \u003ccode\u003e3.1.20211020155521.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "CNCLgithub/GranularScenes", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGranularScenes\u003c/h1\u003e\u003ca id=\"user-content-granularscenes\" class=\"anchor\" aria-label=\"Permalink: GranularScenes\" href=\"#granularscenes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSeeing scenes with multi-granularity!\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup and running\u003c/h2\u003e\u003ca id=\"user-content-setup-and-running\" class=\"anchor\" aria-label=\"Permalink: Setup and running\" href=\"#setup-and-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./setup.sh cont_build python julia\u003c/code\u003e to build the container and setup enviroment\u003c/li\u003e\n\u003cli\u003eEnter \u003ccode\u003e./run.sh julia\u003c/code\u003e to get into Julia REPL\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNOTE: when setting up Julia, if \u003ccode\u003ePkg\u003c/code\u003e complains about some packages not being registered, you can install them manually via \u003ccode\u003ePkg.add(git url)\u003c/code\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis project has automatic configuration!! This configuration is defined in \u003ccode\u003edefault.conf\u003c/code\u003e.\nYou should always prepend \u003ccode\u003e./run.sh\u003c/code\u003e before any command (including running programs like \u003ccode\u003ejulia\u003c/code\u003e) to ensure consistency.\nIf you wish to have different values than \u003ccode\u003edefault.conf\u003c/code\u003e, simply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp default.conf user.conf\nvi user.conf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e edit to your liking without adding new elements\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMac and Window users\u003c/h2\u003e\u003ca id=\"user-content-mac-and-window-users\" class=\"anchor\" aria-label=\"Permalink: Mac and Window users\" href=\"#mac-and-window-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use singularity you must have a virtual machine running.\nAssuming you have vagrant (and something like virtualbox) setup on your host, you can follow these steps\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing \u003ccode\u003esetup.sh\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-setupsh\" class=\"anchor\" aria-label=\"Permalink: Using setup.sh\" href=\"#using-setupsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing \u003ccode\u003erun.sh\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-runsh\" class=\"anchor\" aria-label=\"Permalink: Using run.sh\" href=\"#using-runsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvision the virtual machine defined in \u003ccode\u003eVagrantfile\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCreate a \u003ccode\u003euser.conf\u003c/code\u003e as described above\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: git will not track \u003ccode\u003euser.conf\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eModify \u003ccode\u003euser.conf\u003c/code\u003e such that \u003ccode\u003epath\u003c/code\u003e is set to route through vagrant\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e[ENV]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003epath:vagrant ssh -c singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributing rules\u003c/h3\u003e\u003ca id=\"user-content-contributing-rules\" class=\"anchor\" aria-label=\"Permalink: Contributing rules\" href=\"#contributing-rules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eplace all re-used code in packages (\u003ccode\u003esrc\u003c/code\u003e or \u003ccode\u003epydeps\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eplace all interactive code in \u003ccode\u003escripts\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003enot use \"hard\" paths. Instead update \u003ccode\u003ePATHS\u003c/code\u003e in the config.\u003c/li\u003e\n\u003cli\u003eadd contributions to branches derived from \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edev\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003enot use \u003ccode\u003egit add *\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003enot commit large files (checkpoints, datasets, etc). Update \u003ccode\u003esetup.sh\u003c/code\u003e accordingly.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject layout\u003c/h3\u003e\u003ca id=\"user-content-project-layout\" class=\"anchor\" aria-label=\"Permalink: Project layout\" href=\"#project-layout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package environment is located under \u003ccode\u003epydeps\u003c/code\u003e and can be imported using \u003ccode\u003eimport pydepts\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eLikewise, the Julia package is described under \u003ccode\u003esrc\u003c/code\u003e and \u003ccode\u003etest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAll scripts are located under \u003ccode\u003escripts\u003c/code\u003e and data/output is under \u003ccode\u003eoutput\u003c/code\u003e as specific in the project config (\u003ccode\u003edefault.conf\u003c/code\u003e or \u003ccode\u003euser.conf\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChanging the enviroment\u003c/h3\u003e\u003ca id=\"user-content-changing-the-enviroment\" class=\"anchor\" aria-label=\"Permalink: Changing the enviroment\" href=\"#changing-the-enviroment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add new python or julia packages use the provided package managers. The Python dependencies are listed under \u003ccode\u003eenv.d/requirements.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor julia you can also use \u003ccode\u003e] add \u003c/code\u003e in the REPL\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRelated works\u003c/h2\u003e\u003ca id=\"user-content-related-works\" class=\"anchor\" aria-label=\"Permalink: Related works\" href=\"#related-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eManuscript pending!\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConference submissions\u003c/h3\u003e\u003ca id=\"user-content-conference-submissions\" class=\"anchor\" aria-label=\"Permalink: Conference submissions\" href=\"#conference-submissions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBelledonne, M., Bao, Y., \u0026amp; Yildirim, I. (2022). Navigational affordances are automatically computed during scene perception: Evidence from behavioral change blindness and a computational model of active attention. \u003cem\u003eJournal of Vision, 22\u003c/em\u003e(14), 4128-4128.\u003c/p\u003e\n\u003cp\u003eBelledonne, M., \u0026amp; Yildirim, I. (2021). Automatic computation of navigational affordances explains selective processing of geometry in scene perception: Behavioral and computational evidence. In \u003cem\u003eProceedings of the Annual Meeting of the Cognitive Science Society\u003c/em\u003e (Vol. 43, No. 43).\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1635309195.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1727729194.0 }, { "data_format": 2, "description": null, "filenames": [ - "Containers/First experiments/not-used/Singularity def files/ubuntu-openmpi-master/Singularity", - "Containers/First experiments/not-used/Singularity def files/ubuntu-mvapich-master/Singularity", - "Containers/First experiments/not-used/Singularity def files/centos-mvapich-master/Singularity", - "Containers/First experiments/not-used/Singularity def files/centos-master/Singularity", - "Containers/First experiments/not-used/Singularity def files/ubuntu-master/Singularity", - "Containers/First experiments/not-used/Singularity def files/centos-openmpi-master/Singularity" + "Singularity.dropbox" ], - "full_name": "radical-group/koubbe", + "full_name": "ternaustralia/coesra-singularity-dropbox", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ekoubbe\u003c/h1\u003e\u003ca id=\"user-content-koubbe\" class=\"anchor\" aria-label=\"Permalink: koubbe\" href=\"#koubbe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBelow you have a brief summary of the main work that I have been doing during my time in \u003ca href=\"http://radical.rutgers.edu\" title=\"Radical-Lab\" rel=\"nofollow\"\u003eRadical-Lab\u003c/a\u003e at Rutgers Universiry. For detailed information (descriptions, instructions, source code, results, etc.), please visit each section\u0027s topic.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#radical-cybertools-rct\"\u003eRadical-Cybertools (RCT)\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#hyperparameter-optimization-hpo\"\u003eHyperparameter Optimization (HPO)\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#containers\"\u003eContainers\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#facts\"\u003eFACTS\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#misc\"\u003eMisc\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#installation-of-stress-ng-executable\"\u003eInstallation of stress-ng executable\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#installation-of-mpi4py-on-xsede-bridges-using-gcc-compiler\"\u003eInstallation of mpi4py on XSEDE Bridges using GCC compiler\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/radical-group/koubbe/blob/master/README.md#reference\"\u003eReference\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRadical-Cybertools (RCT)\u003c/h2\u003e\u003ca id=\"user-content-radical-cybertools-rct\" class=\"anchor\" aria-label=\"Permalink: Radical-Cybertools (RCT)\" href=\"#radical-cybertools-rct\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload RCT stack as per instructed on \u003ca href=\"https://radicalentk.readthedocs.io/en/latest/install.html\" rel=\"nofollow\"\u003eEnTK installation website\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ virtualenv -p python3.7 \\\u0026lt;VE name\\\u0026gt; \n$ source \\\u0026lt;path-to-VE\\\u0026gt;/bin/activate \n$ pip install radical.entk \n$ pip install radical.analytics\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSimple RP exercise\u003c/h3\u003e\u003ca id=\"user-content-simple-rp-exercise\" class=\"anchor\" aria-label=\"Permalink: Simple RP exercise\" href=\"#simple-rp-exercise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere I ran the getting started example provided with RP and verified correct functionality:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd \\\u0026lt;path-to-VE\\\u0026gt;/radical.pilot/examples \n$ python 00_getting_started.py xsede.bridges\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSimple EnTK exercise\u003c/h3\u003e\u003ca id=\"user-content-simple-entk-exercise\" class=\"anchor\" aria-label=\"Permalink: Simple EnTK exercise\" href=\"#simple-entk-exercise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere I wrote three suggested applications to get familiar with EnTK (the duration of the tasks can be arbitrary short):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e128 tasks concurrently, where each task is 1 core\u003c/li\u003e\n\u003cli\u003e8 tasks where each task is 16 cores\u003c/li\u003e\n\u003cli\u003e16 concurrent batches of 8 tasks (each of 1 core, but where in each batch each task runs sequentially i.e., one after the other.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe results of these applications are posted \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/RCT/First%20Example%20on%20EnTK/results/results.pdf\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHyperparameter Optimization (HPO)\u003c/h2\u003e\u003ca id=\"user-content-hyperparameter-optimization-hpo\" class=\"anchor\" aria-label=\"Permalink: Hyperparameter Optimization (HPO)\" href=\"#hyperparameter-optimization-hpo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to see my Initial Presentation on HPO, please visit \u003ca href=\"https://docs.google.com/presentation/d/12yYCymB0-m4qGEPdgg0XKipuziSUmEoVhI32XXhDOtc/edit?usp=sharing\" rel=\"nofollow\"\u003eHPO Initial Presentation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo install HyperSpace (on Bridges login node, make sure MPICH or OpenMPI is available):\u003c/p\u003e\n\u003cp\u003eIf Anaconda (or Miniconda) not installed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \n$ bash Miniconda3-latest-Linux-x86_64.sh \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eElse:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create --name \\\u0026lt;VE name\\\u0026gt; python=3.7 \n$ conda activate \\\u0026lt;VE name\\\u0026gt; \n$ pip install mpi4py \n$ git clone https://github.com/yngtodd/hyperspace.git \n$ cd hyperspace \n$ pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFirst thing I did was to reproduce results for the HyperSpace Styblinski-Tang benchmark (on Bridges compute node):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd benchmarks/styblinskitang/hyperdrive \n$ mpirun -n 4 python3 benchmark.py --ndims 2 --results \\\u0026lt;/path/to/save/results\\\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to visualize the results, install HyperSpace on your local machine this time and follow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda install mpi4py (through conda this time so MPI packages get installed as well) \n$ conda install scikit-learn seaborn \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollow the Jupyter Notebook located in the repo \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/HyperSpace/First%20benchmark/results/vis_results.ipynb\"\u003ehere\u003c/a\u003e in order to visualize results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePerforming HPO for the CHEERS project\u003c/h3\u003e\u003ca id=\"user-content-performing-hpo-for-the-cheers-project\" class=\"anchor\" aria-label=\"Permalink: Performing HPO for the CHEERS project\" href=\"#performing-hpo-for-the-cheers-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a brief overview of what the CHEERS project is, as well as experiments design and results, please visit the \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/docs/First%20approach.pdf\"\u003eCHEERS First Approach document\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eParallel Bayesian SMBO vs Grid Search\u003c/h4\u003e\u003ca id=\"user-content-parallel-bayesian-smbo-vs-grid-search\" class=\"anchor\" aria-label=\"Permalink: Parallel Bayesian SMBO vs Grid Search\" href=\"#parallel-bayesian-smbo-vs-grid-search\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter playing around with HyperSpace and managing to get a working hyperparameter optimization code, the first thing that I did was a comparison of this approach (parallel Bayesian SMBO) against the already existing Grid Search one. You can find it here: \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/hyperparams-opt/code/NIRONE2-5/Andy_comparison_3params.ipynb\"\u003eAndy_comparison_3params.ipynb\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOf course, you need to have HyperSpace installed beforehand:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eEasy HyperSpace install on XSEDE Comet with mvapich2:\n\n$ pip3 install virtualenv --user\n$ add virtualenv to .bashrc:\n\texport PATH=\"/home/karahbit/.local/bin:$PATH\"\n$ source .bashrc\n$ virtualenv -p python3 ve-cheers\n$ module load mpi4py\n$ source ve-cheers/bin/activate\n$ pip install seaborn scikit-optimize==0.5.2\n$ git clone https://github.com/yngtodd/hyperspace.git\n$ cd ~/hyperspace\n$ pip install .\n$ export MV2_ENABLE_AFFINITY=0\n$ srun --partition=debug --pty --nodes=2 --ntasks-per-node=24 -t 00:30:00 --wait=0 --export=ALL /bin/bash\n$ mpirun -n 4 python benchmarks/styblinskitang/hyperdrive/benchmark.py --ndims 2 --results /home/karahbit/hyperspace_results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWeak Scaling experiment\u003c/h4\u003e\u003ca id=\"user-content-weak-scaling-experiment\" class=\"anchor\" aria-label=\"Permalink: Weak Scaling experiment\" href=\"#weak-scaling-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs a natural next step, I went ahead and performed weak scaling experiments by running the following on local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./cheers_hyperspace_entk.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003enote: cheers_hyperspace_entk.sh is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/hyperparams-opt/code/NIRONE2-5/cheers_hyperspace_entk.sh\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003enote2: modify \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/hyperparams-opt/code/NIRONE2-5/cheers_hyperspace_entk.py\"\u003echeers_hyperspace_entk.py\u003c/a\u003e according to your needs (e.g. which dataset, # of hyperparams, which remote cluster, etc.).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eStrong Scaling experiment\u003c/h4\u003e\u003ca id=\"user-content-strong-scaling-experiment\" class=\"anchor\" aria-label=\"Permalink: Strong Scaling experiment\" href=\"#strong-scaling-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHyperSpace as it is has a method called \u201chyperdrive\u201d which runs each subspace/optimization on its own single rank/core. There is also \u201cdualdrive\u201d which runs 2 subspaces/optimizations per rank/core.\u003c/p\u003e\n\u003cp\u003eIn order to perform strong scaling, we would need to create more of these functions, e.g. quadrive, octadrive, etc (I made those names up), so we can run 4, 8, 16, etc. optimizations per MPI rank respectively.. Eventually, we would like to name this function something like \u201cmultidrive\u201d, and specify the number of optimizations we would like per rank/core.\u003c/p\u003e\n\u003cp\u003eThis requires new development, thus more time. I already started experimenting with \u201cdualdrive\u201d, but we can\u2019t perform strong scaling until this is done.\u003c/p\u003e\n\u003cp\u003eYou can find an issue created specifically for this purpose in the HyperSpace GitHub repo:\n\u003ca href=\"https://github.com/yngtodd/hyperspace/issues/31\"\u003ehttps://github.com/yngtodd/hyperspace/issues/31\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAs said before, you can see the results for both experiments in \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/CHEERS/docs/First%20approach.pdf\"\u003eCHEERS First Approach document\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainers\u003c/h2\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to see my Initial Presentation on Containers, please visit \u003ca href=\"https://docs.google.com/presentation/d/1ZA0dlyVj5jCw4b_unFurkM9Q9E7sMrNNn_DfLtdanfA/edit?usp=sharing\" rel=\"nofollow\"\u003eContainers Initial Presentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo see my final paper regarding containerization, please visit \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Misc/Technical%20Report/GeorgeKoubbe_Report.pdf\"\u003eCase Studies of executing containerized scientific applications on High-Performance Computing Platforms using RADICAL-Cybertools\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eMoreover, to see a step-by-step walkthrough of how to create and use Singularity containers on remote clusters (e.g. Bridges) using RCT, go to the following \u003ca href=\"https://github.com/radical-cybertools/radical.pilot/wiki/Singularity-Containers\"\u003ewiki\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe experiments design is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/docs/First%20Container%20Experiments%20Design%20Dec%2012%2C%202019.pdf\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExperiment 1\u003c/h3\u003e\u003ca id=\"user-content-experiment-1\" class=\"anchor\" aria-label=\"Permalink: Experiment 1\" href=\"#experiment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run experiment 1, make sure stress-ng executable is installed on Bridges and radical stack is installed on local machine. Then, execute on local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./stress_rp.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003enote: stress_rp.sh is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp1/stress_rp.sh\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003enote2: modify \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp1/stress_rp.py\"\u003estress_rp.py\u003c/a\u003e accordingly to run via RP the executable or the containerized executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExperiment 2\u003c/h3\u003e\u003ca id=\"user-content-experiment-2\" class=\"anchor\" aria-label=\"Permalink: Experiment 2\" href=\"#experiment-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are going to run a Singularity containerized MPI executable on Bind mode \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/mpi.html\" rel=\"nofollow\"\u003e(what is Bind mode?)\u003c/a\u003e. Same as with experiment 1, we are going to execute on local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./mpi_rp.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOn Bridges:\u003c/h5\u003e\u003ca id=\"user-content-on-bridges\" class=\"anchor\" aria-label=\"Permalink: On Bridges:\" href=\"#on-bridges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003enote: For further instructions on how to build the container and install/compile the executable, go \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Bridges/Bind-Intel19.5/instructions.txt\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003enote2: mpi_rp.sh is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Bridges/Bind-Intel19.5/mpi_rp.sh\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003enote3: modify \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Bridges/Bind-Intel19.5/mpi_rp.py\"\u003empi_rp.py\u003c/a\u003e accordingly to run via RP the executable or the containerized executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOn Comet:\u003c/h5\u003e\u003ca id=\"user-content-on-comet\" class=\"anchor\" aria-label=\"Permalink: On Comet:\" href=\"#on-comet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003enote: For further instructions on how to build the container and install/compile the executable, go \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Comet/Bind-IntelMPI/instructions.txt\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003enote2: mpi_rp.sh is located \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Comet/Bind-IntelMPI/mpi_rp.sh\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003enote3: modify \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/src/exp2/Comet/Bind-IntelMPI/mpi_rp.py\"\u003empi_rp.py\u003c/a\u003e accordingly to run via RP the executable or the containerized executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFACTS\u003c/h2\u003e\u003ca id=\"user-content-facts\" class=\"anchor\" aria-label=\"Permalink: FACTS\" href=\"#facts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGetting started\u003c/h3\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMy initial work consisted on helping out in running simple FACTS \"modules\" on XSEDE Bridges and verifying correct functionality.\u003c/p\u003e\n\u003cp\u003eAfter this testing was done, I proceeded to package the \u003ca href=\"https://github.com/radical-collaboration/facts\"\u003eFACTS repo\u003c/a\u003e into a python pip package and uploaded it to the pip server for easy download of general users.\u003c/p\u003e\n\u003cp\u003eLastly, I was tasked with the containerization of the FACTS framework. As it is right now, automation is achieved by creating a virtual environment and installing FACTS along with its dependencies through PIP. This framework will launch the executables for the required modules on a remote machine, being an HPC cluster, etc.\u003c/p\u003e\n\u003cp\u003eSo, why do we need containers? What is the benefit that containers are going to bring to FACTS?\u003c/p\u003e\n\u003cp\u003eWe envision this at two levels:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eWe containerize at the framework level. This will allow us to take FACTS apart into individual modules, completely independent from one another, with their own container each. The end user won\u2019t have to know about anything else, no virtual environment, no dependencies, no other steps. We would take full advantage of the portability and reproducibility benefits of containers. Therefore, the end user can simply execute the containerized module on the local machine. We can use Docker for this purpose.\u003c/li\u003e\n\u003cli\u003eWe containerize at the executable level. There is a growing number of modules inside FACTS. Each module has 4 stages: pre-processing, fit, project, post-processing. Each stage has one executable (python3 script). We can use Singularity for this purpose.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFew notes to keep in mind:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInput data is not going to be included in the container. We can integrate (bind mount) it to the Docker container at the time of execution. Singularity already offers integration features that make this easier.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhere are we going to obtain the containers from?\tAs said before, each container would be representing a FACTS module. The containers can be downloaded from Docker Hub or the Singularity equivalent, for example, with every container being specific to the application and remote resource. Lastly, the end user would just need to execute the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainerization at the executable level\u003c/h3\u003e\u003ca id=\"user-content-containerization-at-the-executable-level\" class=\"anchor\" aria-label=\"Permalink: Containerization at the executable level\" href=\"#containerization-at-the-executable-level\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs an initial approach, I started containerizing at the executable level (Singularity) on Comet with the kopp14 module and data that Greg sent me. Once done, I characterized performance and looked for any overheads. You can read how to run the container from the following file: \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/FACTS/Containerizing%20FACTS/Executable%20level/src/Comet/facts/facts_re.sh\"\u003efacts_re.sh\u003c/a\u003e. You can find the results in the last slide of the presentation \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/Containers/First%20experiments/docs/Containers%20Initial%20Presentation.pdf\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003enote: keep in mind that you would have to build the Singularity container from the definition file I provided by running the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build ./modules/kopp14/landwaterstorage/kopp14_landwaterstorage.sif kopp14_landwaterstorage.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainerization at the framework level\u003c/h3\u003e\u003ca id=\"user-content-containerization-at-the-framework-level\" class=\"anchor\" aria-label=\"Permalink: Containerization at the framework level\" href=\"#containerization-at-the-framework-level\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis was not a requirement at the moment, but for fun I proceeded to create a Dockerfile containerizing FACTS at the framework level. You can find the file \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/FACTS/Containerizing%20FACTS/Framework%20level/Dockerfile\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMisc\u003c/h2\u003e\u003ca id=\"user-content-misc\" class=\"anchor\" aria-label=\"Permalink: Misc\" href=\"#misc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere you have general information about my work, readings, meetings, weekly summaries, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation of stress-ng executable\u003c/h2\u003e\u003ca id=\"user-content-installation-of-stress-ng-executable\" class=\"anchor\" aria-label=\"Permalink: Installation of stress-ng executable\" href=\"#installation-of-stress-ng-executable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install stress-ng on Bridges login node:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ wget http://kernel.ubuntu.com/~cking/tarballs/stress-ng/stress-ng-0.09.34.tar.xz \n$ tar xvf stress-ng-0.09.34.tar.xz \n$ cd stress-ng-0.09.34 \n$ make \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRequest 1 node, 4 cores on RM partition for 8 hours:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ interact -p RM -N 1 -n 4 -t 8:00:00 \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMeasure Total Time of Execution of stress-ng python script through MPI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/usr/bin/time -v mpirun -n 2 python3 helloworld.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo see core usage on each node:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh r001 \n$ htop\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003enote: helloworld.py is located in the repo \u003ca href=\"https://github.com/radical-group/koubbe/blob/master/HPO/HyperSpace/First%20benchmark/docs/Guides/stress-ng/helloworld.py\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation of mpi4py on XSEDE Bridges using GCC compiler\u003c/h2\u003e\u003ca id=\"user-content-installation-of-mpi4py-on-xsede-bridges-using-gcc-compiler\" class=\"anchor\" aria-label=\"Permalink: Installation of mpi4py on XSEDE Bridges using GCC compiler\" href=\"#installation-of-mpi4py-on-xsede-bridges-using-gcc-compiler\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf Anaconda (or Miniconda) not installed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \n$ bash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eElse:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create --name \\\u0026lt;VE name\\\u0026gt; python=3.7 \n$ conda activate \\\u0026lt;VE name\\\u0026gt; \n$ wget https://bitbucket.org/mpi4py/mpi4py/downloads/mpi4py-3.0.3.tar.gz \n$ tar -zxf mpi4py-3.0.3.tar.gz \u0026amp;\u0026amp; rm mpi4py-3.0.3.tar.gz\n$ cd mpi4py-3.0.3 \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003emodify mpi.cfg as instructed in \u003ca href=\"https://mpi4py.readthedocs.io/en/stable/install.html#using-pip-or-easy-install\" rel=\"nofollow\"\u003empi4py installation\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Open MPI example \n# ---------------- \n[openmpi] \nmpi_dir = /usr/mpi/gcc/openmpi-2.1.2-hfi \nmpicc = %(mpi_dir)s/bin/mpicc \nmpicxx = %(mpi_dir)s/bin/mpicxx \n#include_dirs = %(mpi_dir)s/include \n#libraries = mpi \nlibrary_dirs = %(mpi_dir)s/lib64:/opt/packages/gcc/9.2.0/bin/gcc \nruntime_library_dirs = %(library_dirs)s \n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ python setup.py build --mpi=openmpi \n$ python setup.py install \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReference\u003c/h2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-label=\"Permalink: Reference\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe local machine used throughout the proyects is a virtual machine with Ubuntu 16.04.6 LTS.\u003c/p\u003e\n\u003cp\u003eThe radical-stack used is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e python : 3.7.6\n pythonpath : \n virtualenv : /home/karahbit/ve-rct3\n\n radical.analytics : 0.90.7\n radical.entk : 1.0.2\n radical.pilot : 1.3.0\n radical.saga : 1.3.0\n radical.utils : 1.3.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor specific references, please visit each section\u0027s topic.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"http://radical.rutgers.edu\" rel=\"nofollow\"\u003ehttp://radical.rutgers.edu\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://radical-cybertools.github.io\" rel=\"nofollow\"\u003ehttp://radical-cybertools.github.io\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.psc.edu/bridges/user-guide\" rel=\"nofollow\"\u003ehttps://www.psc.edu/bridges/user-guide\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sdsc.edu/support/user_guides/comet.html\" rel=\"nofollow\"\u003ehttps://www.sdsc.edu/support/user_guides/comet.html\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://radicalpilot.readthedocs.io/en/stable\" rel=\"nofollow\"\u003ehttps://radicalpilot.readthedocs.io/en/stable\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://radicalentk.readthedocs.io/en/latest\" rel=\"nofollow\"\u003ehttps://radicalentk.readthedocs.io/en/latest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hyperspace.readthedocs.io/en/latest\" rel=\"nofollow\"\u003ehttps://hyperspace.readthedocs.io/en/latest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://containers-at-tacc.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://containers-at-tacc.readthedocs.io/en/latest/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.open-mpi.org\" rel=\"nofollow\"\u003ehttps://www.open-mpi.org\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://wiki.ubuntu.com/Kernel/Reference/stress-ng\" rel=\"nofollow\"\u003ehttps://wiki.ubuntu.com/Kernel/Reference/stress-ng\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAuthor: \u003ca href=\"https://github.com/karahbit\"\u003eGeorge Koubbe\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecoesra-singularity-dropbox\u003c/h1\u003e\u003ca id=\"user-content-coesra-singularity-dropbox\" class=\"anchor\" aria-label=\"Permalink: coesra-singularity-dropbox\" href=\"#coesra-singularity-dropbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 2, "topics": [], - "updated_at": 1595518122.0 + "updated_at": 1670904307.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.samtools_1.10", - "Singularity.syri_2aff3ba", - "Singularity.blast_2.2.31", - "Singularity.salmontools_23eac84", - "Singularity.star_2.7.6a", - "Singularity.ngmlr_8d76779", - "Singularity.muscle_3.8.1551", - "Singularity.minimap2_2.17r941", - "Singularity.samblaster_0.1.24" + "singularity/Singularity.anaconda3-dask-numba" ], - "full_name": "TomHarrop/align-utils", + "full_name": "zonca/Python_HPC_2022", "latest_release": null, "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1603929017.0 + "updated_at": 1657276145.0 }, { "data_format": 2, - "description": "Singularity container", + "description": null, "filenames": [ - "Singularity.def" + "chroma/Singularity.chroma.tog4", + "chroma/Singularity.chroma.base", + "chroma/Singularity.chroma.all-but-chroma", + "chroma/Singularity.chroma.chroma-docker", + "chroma/Singularity.chroma.chroma-only", + "chroma/Singularity.chroma.chroma" ], - "full_name": "ParthDoshi97/bioinformatic_tools_container", + "full_name": "wkcwells/singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Container for Bioinformatics Tools\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-bioinformatics-tools\" class=\"anchor\" aria-label=\"Permalink: Singularity Container for Bioinformatics Tools\" href=\"#singularity-container-for-bioinformatics-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity container is designed to provide a comprehensive suite of bioinformatics tools for quality control, read trimming, alignment, and post-alignment analysis. The container is based on Ubuntu 20.04 and includes commonly used tools such as FastQC, MultiQC, Trimmomatic, STAR, Bowtie2, HISAT2, SAMtools, PICARD, HOMER, MACS2, and MEME Suite.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Control Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eFastQC\u003c/strong\u003e: A quality control tool for high throughput sequence data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMultiQC\u003c/strong\u003e: Aggregates results from bioinformatics analyses into a single report.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRead Trimming and Filtering Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTrimmomatic\u003c/strong\u003e: A flexible read trimming tool for Illumina NGS data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFastx Toolkit\u003c/strong\u003e: A collection of command line tools for Short-Reads FASTA/FASTQ files preprocessing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAlignment Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSTAR\u003c/strong\u003e: A fast RNA-seq read mapper.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBowtie2\u003c/strong\u003e: A fast and sensitive gapped read aligner.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHISAT2\u003c/strong\u003e: A fast and sensitive alignment program for mapping next-generation sequencing reads.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePost-Alignment Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSAMtools\u003c/strong\u003e: A suite of programs for interacting with high-throughput sequencing data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePICARD\u003c/strong\u003e: A set of command line tools for manipulating high-throughput sequencing data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHOMER\u003c/strong\u003e: A suite of tools for Motif Discovery and next-generation sequencing analysis.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMACS2\u003c/strong\u003e: Model-based Analysis of ChIP-Seq data.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMEME Suite\u003c/strong\u003e: A collection of tools for the discovery and analysis of sequence motifs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the Singularity container, use the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build bioinformatics.sif Singularity.def\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the container, use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e bioinformatics.sif \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecommand\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor example, to run FastQC on a file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e bioinformatics.sif fastqc sample.fastq\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIncluded Tools and Versions\u003c/h2\u003e\u003ca id=\"user-content-included-tools-and-versions\" class=\"anchor\" aria-label=\"Permalink: Included Tools and Versions\" href=\"#included-tools-and-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eUbuntu\u003c/strong\u003e: 20.04\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePerl\u003c/strong\u003e: 5.40.0\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFastQC\u003c/strong\u003e: 0.12.1\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMultiQC\u003c/strong\u003e: Latest via pip\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTrimmomatic\u003c/strong\u003e: 0.39\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFastx Toolkit\u003c/strong\u003e: 0.0.13\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSTAR\u003c/strong\u003e: 2.7.11b\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBowtie2\u003c/strong\u003e: 2.4.2\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHISAT2\u003c/strong\u003e: Latest from GitHub\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSAMtools\u003c/strong\u003e: 1.10\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePICARD\u003c/strong\u003e: 2.23.9\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHOMER\u003c/strong\u003e: Latest via Perl script\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMACS2\u003c/strong\u003e: Latest via pip\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMEME Suite\u003c/strong\u003e: 5.5.5\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnvironment Variables\u003c/h2\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container sets the following environment variables:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e DEBIAN_FRONTEND=noninteractive\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/opt/STAR-2.7.11b/bin/Linux_x86_64_static:/opt/hisat2:/opt/bowtie2:/opt/samtools-1.10:/usr/local/bin:/opt/FastQC:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/root/homer/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/opt/local/bin:/opt/local/libexec/meme-5.5.5:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthor\u003c/h2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eParth Doshi\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersion\u003c/h2\u003e\u003ca id=\"user-content-version\" class=\"anchor\" aria-label=\"Permalink: Version\" href=\"#version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container includes tools for quality control, read trimming, alignment, and post-alignment analysis in bioinformatics.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLabels\u003c/h2\u003e\u003ca id=\"user-content-labels\" class=\"anchor\" aria-label=\"Permalink: Labels\" href=\"#labels\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAuthor\u003c/strong\u003e: Parth Doshi\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVersion\u003c/strong\u003e: 1.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e\nThis README provides a clear and concise overview of the container, its features, installation instructions, usage examples, included tools and versions, environment variables, and author information. Adjust the placeholders such as `YourName` and any other details as necessary.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1721847941.0 - }, - { - "data_format": 2, - "description": "FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. ", - "filenames": [ - "0.11.9/Singularity", - "0.12.1/Singularity" - ], - "full_name": "pscedu/singularity-fastqc", - "latest_release": "v0.12.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-FastQC/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-FastQC/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-FastQC/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-FastQC/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c8d644d0b2e64f21fb4cc1192ab1921c4633b7d5c7390c6bbeabe858d9b378fb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666173747163\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8d644d0b2e64f21fb4cc1192ab1921c4633b7d5c7390c6bbeabe858d9b378fb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d666173747163\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-fastqc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9257c3dddaa94762774ea298578d7b41acec132a7c614be38c5dfddcc41bf884/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666173747163\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9257c3dddaa94762774ea298578d7b41acec132a7c614be38c5dfddcc41bf884/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d666173747163\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-fastqc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c64e867a78719d38b0be4bd1d4fa1fa248f47748a1f94a50a34663859db5cbc4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666173747163\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c64e867a78719d38b0be4bd1d4fa1fa248f47748a1f94a50a34663859db5cbc4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d666173747163\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fastqc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bb023ccb6e3a745b5603ada1d21ff3936a69a2654e09c85b2cf69a041f4e0d1b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666173747163\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bb023ccb6e3a745b5603ada1d21ff3936a69a2654e09c85b2cf69a041f4e0d1b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d666173747163\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fastqc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-fastqc\u003c/h1\u003e\u003ca id=\"user-content-singularity-fastqc\" class=\"anchor\" aria-label=\"Permalink: singularity-fastqc\" href=\"#singularity-fastqc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cad1f53832db2e5156a4bf58563606936b992127e490293214ae663ce59933f0/68747470733a2f2f65787465726e616c2d636f6e74656e742e6475636b6475636b676f2e636f6d2f69752f3f753d68747470732533412532462532467777772e62696f696e666f726d61746963732e626162726168616d2e61632e756b25324670726f6a656374732532466661737471632532466661737471632e706e6726663d31266e6f66623d31\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cad1f53832db2e5156a4bf58563606936b992127e490293214ae663ce59933f0/68747470733a2f2f65787465726e616c2d636f6e74656e742e6475636b6475636b676f2e636f6d2f69752f3f753d68747470732533412532462532467777772e62696f696e666f726d61746963732e626162726168616d2e61632e756b25324670726f6a656374732532466661737471632532466661737471632e706e6726663d31266e6f66623d31\" alt=\"Screenshot\" data-canonical-src=\"https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fwww.bioinformatics.babraham.ac.uk%2Fprojects%2Ffastqc%2Ffastqc.png\u0026amp;f=1\u0026amp;nofb=1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efastqc\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/FastQC/0.11.9\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/FastQC\u003c/code\u003e as \u003ccode\u003e0.11.9.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1649274180.0 + "updated_at": 1556152675.0 }, { "data_format": 2, - "description": "Singularity container for MitoGraph.", + "description": null, "filenames": [ - "Singularity" + "deps/DPMC/lg/Singularity", + "deps/DPMC/DMC/Singularity", + "deps/DPMC/tensor/Singularity", + "deps/DPMC/HTB/Singularity" ], - "full_name": "mcw-rcc/mitograph", + "full_name": "dilkas/wmc-without-parameters", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emitograph\u003c/h1\u003e\u003ca id=\"user-content-mitograph\" class=\"anchor\" aria-label=\"Permalink: mitograph\" href=\"#mitograph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe and modulefile for running Mitograph on a Linux system. This requires Singularity to build the container file. You can adapt the modulefile for your needs or run the analysis from the command line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Mitograph container\u003c/h2\u003e\u003ca id=\"user-content-run-mitograph-container\" class=\"anchor\" aria-label=\"Permalink: Run Mitograph container\" href=\"#run-mitograph-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec mitograph.sif input_file\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eThis repository holds all source code (that I am legally allowed to distribute) related to the following publications:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDilkas P., Belle V. \u003cstrong\u003eWeighted Model Counting with Conditional Weights for Bayesian Networks\u003c/strong\u003e. UAI 2021.\u003c/li\u003e\n\u003cli\u003eDilkas P., Belle V. \u003cstrong\u003eWeighted Model Counting Without Parameter Variables\u003c/strong\u003e. SAT 2021.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "singularity-container" - ], - "updated_at": 1699631258.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1656402559.0 }, { "data_format": 2, - "description": "T1 mapping BIDS app", + "description": null, "filenames": [ "Singularity" ], - "full_name": "khanlab/despot1", + "full_name": "ISU-HPC/big-scape", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edespot1: DESPOT1-HIFI T1 mapping BIDS app\u003c/h1\u003e\u003ca id=\"user-content-despot1--despot1-hifi-t1-mapping-bids-app\" class=\"anchor\" aria-label=\"Permalink: despot1: DESPOT1-HIFI T1 mapping BIDS app\" href=\"#despot1--despot1-hifi-t1-mapping-bids-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs:\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs:\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExample BIDS-like naming (acq- flag and DESPOT suffix are \u003cem\u003erequired\u003c/em\u003e):\nsub-01/anat/sub-01_acq-SPGR_flip-1_DESPOT.nii.gz\nsub-01/anat/sub-01_acq-SPGR_flip-2_DESPOT.nii.gz\nsub-01/anat/sub-01_acq-IRSPGR_DESPOT.nii.gz\u003c/p\u003e\n\u003cp\u003eThe SPGR json files must include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRepetitionTime\u003c/li\u003e\n\u003cli\u003eFlipAngle\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe IRSPGR json files must include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRepetitionTime\u003c/li\u003e\n\u003cli\u003eInversionTime\u003c/li\u003e\n\u003cli\u003eFlipAngle\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following variables are hardcoded in run.sh (need to be modified if they are different for your sequence):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enpulse=78 #readout pulses following inversion\nfield=3 #field strength\ninvmode=2 #number of inversions per slice\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverall pipeline:\u003c/h2\u003e\u003ca id=\"user-content-overall-pipeline\" class=\"anchor\" aria-label=\"Permalink: Overall pipeline:\" href=\"#overall-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eParse and extract parameters from JSON files\u003c/li\u003e\n\u003cli\u003eRigidly register images to the first SPGR (flirt)\u003c/li\u003e\n\u003cli\u003ePre-process images prior to C code (nifti -\u0026gt; analyze)\u003c/li\u003e\n\u003cli\u003eRun DESPOT1-HIFI fitting using pre-compiled C code (src from Sean Deoni)\u003c/li\u003e\n\u003cli\u003ePost-process T1map, B1map, M0map images after C code (analyze -\u0026gt; nifti)\u003c/li\u003e\n\u003cli\u003eGenerate synthetic T1w map (simple octave script)\u003c/li\u003e\n\u003cli\u003eGenerate hard and soft brain masks from M0map\n\u003cul\u003e\n\u003cli\u003eHard mask from bet\u003c/li\u003e\n\u003cli\u003eSoft mask generated by N4 on bet M0map, then 10th percentile cutoff (hard above, 0-1 below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eApply soft mask to T1map, T1w, and B1map (to downweight high-intensity boundary voxels), and hard mask to M0map\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput:\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\u2500\u2500 OUT_FOLDER\n \u2514\u2500\u2500 sub-P004\n \u2514\u2500\u2500 anat\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_B1map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_M0map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_proc-masked_B1map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_proc-masked_M0map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_proc-masked_T1map.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_proc-masked_T1w.nii.gz\n \u251c\u2500\u2500 sub-P004_acq-DESPOT_T1map.nii.gz\n \u2514\u2500\u2500 sub-P004_acq-DESPOT_T1w.nii.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn \u003ccode\u003ework\u003c/code\u003e folder with intermediate files will also be generated\u003c/p\u003e\n\u003cp\u003eTo do:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esimilar app for DESPOT2-FM mapping..\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebig-scape\u003c/h1\u003e\u003ca id=\"user-content-big-scape\" class=\"anchor\" aria-label=\"Permalink: big-scape\" href=\"#big-scape\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for big-scape\u003c/p\u003e\n\u003cp\u003eAdapted from \u003ca href=\"https://git.wageningenur.nl/medema-group/BiG-SCAPE/blob/master/Dockerfile\" rel=\"nofollow\"\u003ehttps://git.wageningenur.nl/medema-group/BiG-SCAPE/blob/master/Dockerfile\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, "topics": [], - "updated_at": 1591844418.0 + "updated_at": 1547836893.0 }, { "data_format": 2, - "description": "Testing conversion", + "description": "My software stack.", "filenames": [ - "Singularity" + "Singularity.cuda8-comet", + "Singularity.comet", + "Singularity.cuda8", + "Singularity.cuda8-bridges", + "Singularity", + "Singularity.bridges", + "Singularity.latest", + "Singularity.cuda8-flux", + "Singularity.flux" ], - "full_name": "lalet/Singularity-hcp-prefreesurfer", + "full_name": "csadorf/software", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity-hcp-prefreesurfer\u003c/h1\u003e\u003ca id=\"user-content-singularity-hcp-prefreesurfer\" class=\"anchor\" aria-label=\"Permalink: Singularity-hcp-prefreesurfer\" href=\"#singularity-hcp-prefreesurfer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting conversion\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "containers", - "singularity", - "docker" - ], - "updated_at": 1521423231.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1546998866.0 }, { "data_format": 2, - "description": "Getting YOLO up and running on ACI", + "description": null, "filenames": [ - "Singularity" + "1.58.1/Singularity", + "1.75.0/Singularity" ], - "full_name": "d-bohn/yolo_aci", + "full_name": "pscedu/singularity-rust", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eYOLO_aci\u003c/h1\u003e\u003ca id=\"user-content-yolo_aci\" class=\"anchor\" aria-label=\"Permalink: YOLO_aci\" href=\"#yolo_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and workflow for building \u003ca href=\"https://pjreddie.com/darknet/yolo/\" rel=\"nofollow\"\u003eYOLO\u003c/a\u003e\nin Docker Hub and modifying it with Singularity Hub for use with PSU\nACI HPC clusters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand line\u003c/h3\u003e\u003ca id=\"user-content-command-line\" class=\"anchor\" aria-label=\"Permalink: Command line\" href=\"#command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eImages\u003c/h4\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eVideos\u003c/h4\u003e\u003ca id=\"user-content-videos\" class=\"anchor\" aria-label=\"Permalink: Videos\" href=\"#videos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights \u0026lt;video file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython3\u003c/h3\u003e\u003ca id=\"user-content-python3\" class=\"anchor\" aria-label=\"Permalink: Python3\" href=\"#python3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eImages\u003c/h4\u003e\u003ca id=\"user-content-images-1\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython yolo.py --image /path/to/image.jpg --yolo yolo-coco\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eVideo\u003c/h4\u003e\u003ca id=\"user-content-video\" class=\"anchor\" aria-label=\"Permalink: Video\" href=\"#video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython3 yolo_video.py --input /path/to/vid.mp4 \\\n\t--output /path/to/output/save.avi \\\n\t--write_res /path/to/write/results.txt \\\n\t--yolo yolo-coco\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rust/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rust/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rust/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rust/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cf560daa2034e21d03639b214b57dc77409c3c04cd700171ffeea3d55a611e92/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d72757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cf560daa2034e21d03639b214b57dc77409c3c04cd700171ffeea3d55a611e92/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d72757374\" 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href=\"https://camo.githubusercontent.com/eee6f13f6796e4d3e45b0f400ba03ea0c7775f9021705157877a4007fa1fbeb3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d72757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eee6f13f6796e4d3e45b0f400ba03ea0c7775f9021705157877a4007fa1fbeb3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d72757374\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-rust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c719c29ce3719447d26c7ef3c2f8a4fbda921774edb12d766d211af0720ba9e0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d72757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c719c29ce3719447d26c7ef3c2f8a4fbda921774edb12d766d211af0720ba9e0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d72757374\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-rust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-rust\u003c/h1\u003e\u003ca id=\"user-content-singularity-rust\" class=\"anchor\" aria-label=\"Permalink: singularity-rust\" href=\"#singularity-rust\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7174f54056f65968a2515417952a46695bb1eb3d588756dd31d57cdb9fe2b0b0/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f642f64352f527573745f70726f6772616d6d696e675f6c616e67756167655f626c61636b5f6c6f676f2e7376672f3132303070782d527573745f70726f6772616d6d696e675f6c616e67756167655f626c61636b5f6c6f676f2e7376672e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7174f54056f65968a2515417952a46695bb1eb3d588756dd31d57cdb9fe2b0b0/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f642f64352f527573745f70726f6772616d6d696e675f6c616e67756167655f626c61636b5f6c6f676f2e7376672f3132303070782d527573745f70726f6772616d6d696e675f6c616e67756167655f626c61636b5f6c6f676f2e7376672e706e67\" width=\"25%\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/d/d5/Rust_programming_language_black_logo.svg/1200px-Rust_programming_language_black_logo.svg.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.rust-lang.org/\" rel=\"nofollow\"\u003erust\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003erust\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/rust/1.58.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/rust\u003c/code\u003e as \u003ccode\u003e1.58.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 0, + "subscribers_count": 4, "topics": [], - "updated_at": 1561693683.0 + "updated_at": 1645584492.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "diplomacy_research/containers/tensorflow-serving/Singularity", + "diplomacy_research/containers/research/Singularity", + "diplomacy_research/containers/ubuntu-cuda10/Singularity", + "diplomacy_research/containers/redis/Singularity", + "diplomacy_research/containers/albert-ai/Singularity" ], - "full_name": "CINECA-HPC/container_openmpi420_gnu930__spack160_ubuntu2004_x86_64", + "full_name": "tanushreebanerjee/paquette_2019", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_openmpi420_gnu930__spack160_ubuntu2004_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_openmpi420_gnu930__spack160_ubuntu2004_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_openmpi420_gnu930__spack160_ubuntu2004_x86_64\" href=\"#container_openmpi420_gnu930__spack160_ubuntu2004_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSupervised and RL Models for No Press Diplomacy\u003c/h1\u003e\u003ca id=\"user-content-supervised-and-rl-models-for-no-press-diplomacy\" class=\"anchor\" aria-label=\"Permalink: Supervised and RL Models for No Press Diplomacy\" href=\"#supervised-and-rl-models-for-no-press-diplomacy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source code used to develop a supervised and RL agent that can play the No Press version of Diplomacy.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/map_overview.png\"\u003e\u003cimg width=\"500\" src=\"docs/images/map_overview.png\" alt=\"Diplomacy Map Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the MIT License - see the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file for details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRestrictions: The trained weights provided with this repository are for research purposes only and cannot be used to power any bots on any website without my prior written consent, which may be withheld without reasons.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe data provider also prevents using its data to train any bots accessible on any website.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYou can play against the trained model by playing against \"KestasBot\" on webdiplomacy.net\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataset\u003c/h2\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe model was trained by using a dataset of 156,468 games (diplomacy-v1-27k-msgs.zip), which consists of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e16,633 games on non-standard maps (e.g. modern and ancmed) (other_maps.jsonl)\u003c/li\u003e\n\u003cli\u003e33,279 no-press games on the standard map (standard_no_press.jsonl)\u003c/li\u003e\n\u003cli\u003e50 press games on the standard map with messages (standard_press_with_msgs.jsonl)\u003c/li\u003e\n\u003cli\u003e106,456 press games on the standard map without messages (standard_press_without_msgs.jsonl)\u003c/li\u003e\n\u003cli\u003e50 public press games on the standard map with messages (standard_public_press.jsonl)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA dataset of 156,458 games with 13,469,536 messages is also being prepared, but it is not yet available.\u003c/p\u003e\n\u003cp\u003eAccess to the dataset used to train the model can be requested by sending an email to \u003ca href=\"mailto:webdipmod@gmail.com\"\u003ewebdipmod@gmail.com\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository can be installed in a conda environment with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecreate\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003en\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eactivate\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements_dev\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis package depends on Redis and singularity 3+. Singularity can be installed with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing Singularity v3.2.0\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e VERSION=v3.2.0\nsudo apt-get update -y\nsudo apt-get install -y build-essential libssl-dev uuid-dev libgpgme11-dev libseccomp-dev pkg-config squashfs-tools\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing GO 1.12.5\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GO_VERSION=1.12.5 OS=linux ARCH=amd64\nwget -nv https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\nsudo tar -C /usr/local -xzf go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\nrm -f go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GOPATH=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.go\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=/usr/local/go/bin:\u003cspan class=\"pl-smi\"\u003e${PATH}\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e${GOPATH}\u003c/span\u003e/bin\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e\ngo get github.com/golang/dep/cmd/dep\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Building from source\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e/src/github.com/sylabs\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e/src/github.com/sylabs\ngit clone https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\ngit checkout \u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\n./mconfig -p /usr/local\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ./builddir\nmake\nsudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe package is compatible with Python 3.5, 3.6, and 3.7.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTraining models\u003c/h3\u003e\u003ca id=\"user-content-training-models\" class=\"anchor\" aria-label=\"Permalink: Training models\" href=\"#training-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train a model:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e WORKING_DIR=/path/to/some/directory\n$ cp diplomacy-v1-27k-msgs.zip \u003cspan class=\"pl-smi\"\u003e$WORKING_DIR\u003c/span\u003e\n$ conda activate diplomacy\n$ python diplomacy_research/scripts/build_dataset.py\n$ python diplomacy_research/models/policy/order_based/train.py --model_id 12\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlaying against the SL and RL agents\u003c/h3\u003e\u003ca id=\"user-content-playing-against-the-sl-and-rl-agents\" class=\"anchor\" aria-label=\"Permalink: Playing against the SL and RL agents\" href=\"#playing-against-the-sl-and-rl-agents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to play against the published results by using the \u003ccode\u003eDipNetSLPlayer\u003c/code\u003e and \u003ccode\u003eDipNetRLPlayer\u003c/code\u003e players in \u003ccode\u003ediplomacy_research.players.benchmark_player\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThese players will automatically download a singularity container with the trained weights, and then launch a TF serving server to handle the requests.\u003c/p\u003e\n\u003cp\u003eA simple example on how to play a 7 bots game is:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etornado\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egen\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eujson\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGame\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexport\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eto_saved_game_format\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy_research\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eplayers\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ebenchmark_player\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDipNetSLPlayer\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy_research\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecluster\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003estart_io_loop\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estop_io_loop\u003c/span\u003e\n\n\u003cspan class=\"pl-en\"\u003e@\u003cspan class=\"pl-s1\"\u003egen\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecoroutine\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emain\u003c/span\u003e():\n \u003cspan class=\"pl-s\"\u003e\"\"\" Plays a local game with 7 bots \"\"\"\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eplayer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDipNetSLPlayer\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGame\u003c/span\u003e()\n\n \u003cspan class=\"pl-c\"\u003e# Playing game\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ewhile\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enot\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eis_game_done\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eorders\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eyield\u003c/span\u003e {\u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eplayer\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eget_orders\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epowers\u003c/span\u003e}\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_orders\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eorders\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eset_orders\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_orders\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eprocess\u003c/span\u003e()\n\n \u003cspan class=\"pl-c\"\u003e# Saving to disk\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027game.json\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027w\u0027\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edumps\u003c/span\u003e(\u003cspan class=\"pl-en\"\u003eto_saved_game_format\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e)))\n \u003cspan class=\"pl-en\"\u003estop_io_loop\u003c/span\u003e()\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e__name__\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027__main__\u0027\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003estart_io_loop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emain\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlaying against a model\u003c/h3\u003e\u003ca id=\"user-content-playing-against-a-model\" class=\"anchor\" aria-label=\"Permalink: Playing against a model\" href=\"#playing-against-a-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is also possible for humans to play against bots using the web interface. The player can be changed in \u003ccode\u003ediplomacy_research.scripts.launch_bot\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In a terminal window or tab - Launch React server (from diplomacy/diplomacy)\u003c/span\u003e\nnpm start\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In another terminal window or tab - Launch diplomacy server\u003c/span\u003e\npython -m diplomacy.server.run\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In a third terminal window or tab - Launch the bot script\u003c/span\u003e\npython diplomacy_research/scripts/launch_bot.py\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTrained weights and experiment logs\u003c/h3\u003e\u003ca id=\"user-content-trained-weights-and-experiment-logs\" class=\"anchor\" aria-label=\"Permalink: Trained weights and experiment logs\" href=\"#trained-weights-and-experiment-logs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo facilitate reproducibility, the experiments can be downloaded using the following links. These include hyperparameters, tensorboard graphs, output logs, and weights for each epoch.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOrder based LSTM model (order-based v12 - Accuracy of 61.3% - \u003cstrong\u003eDipNet SL\u003c/strong\u003e) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/order-based-lstm.zip\" rel=\"nofollow\"\u003eDownload - 5.4GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOrder based Transformer model (order-based v15 - Accuracy of 60.7%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/order-based-trsf.zip\" rel=\"nofollow\"\u003eDownload - 8.2GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eToken based LSTM model (token-based v10 - Accuracy of 60.3%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/token-based-lstm.zip\" rel=\"nofollow\"\u003eDownload - 6.0GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eToken based Transformer model (token-based v11 - Accuracy of 58.9%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/token-based-trsf.zip\" rel=\"nofollow\"\u003eDownload - 3.5GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRL Model (Bootstrapped from order-based v12 and value v1 - \u003cstrong\u003eDipNet RL\u003c/strong\u003e) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/rl-model.zip\" rel=\"nofollow\"\u003eDownload - 11.1GB\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGames against Albert (DAIDE)\u003c/h3\u003e\u003ca id=\"user-content-games-against-albert-daide\" class=\"anchor\" aria-label=\"Permalink: Games against Albert (DAIDE)\" href=\"#games-against-albert-daide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe 1v6 and 6v1 games played between DipNet SL and Albert (DAIDE) can be downloaded below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eList of games with power assignments \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/daide_albert_results.xlsx\" rel=\"nofollow\"\u003eDownload - 53KB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVisualisation of each game (svg and json) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/daide_albert_games.zip\" rel=\"nofollow\"\u003eDownload - 2.3GB\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1614275862.0 + "updated_at": 1671746273.0 }, { "data_format": 2, - "description": "Singularity Container for PyTorch", + "description": "try adding press to DipNet", "filenames": [ - "Singularity", - "Singularity.0.5.0" + "diplomacy_research/containers/tensorflow-serving/Singularity", + "diplomacy_research/containers/research/Singularity", + "diplomacy_research/containers/ubuntu-cuda10/Singularity", + "diplomacy_research/containers/redis/Singularity", + "diplomacy_research/containers/albert-ai/Singularity" ], - "full_name": "vansky/pytorch", + "full_name": "wwongkamjan/dipnet_press", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePyTorch\u003c/h1\u003e\u003ca id=\"user-content-pytorch\" class=\"anchor\" aria-label=\"Permalink: PyTorch\" href=\"#pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/312\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePyTorch Version: 0.5.0\u003c/p\u003e\n\u003cp\u003eMARCC NVidia GPU and installed drivers at testing time were: K80 \u0026amp; 396.26.\u003c/p\u003e\n\u003cp\u003eHere is a display of the job submission script: \u003ccode\u003epytorch_job.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#SBATCH -N 1\n#SBATCH -n 6\n#SBATCH -p gpu\n#SBATCH --gres=gpu:1\n#SBATCH -t 1:0:0\n\nmodule load cuda/9.0\nmodule load singularity\n\ncd mnist\n\n# redefine SINGULARITY_HOME to mount current working directory to base $HOME\nexport SINGULARITY_HOME=$PWD:/home/$USER\n\nsingularity pull --name pytorch.simg shub://marcc-hpc/pytorch\nsingularity exec --nv ./pytorch.simg python main.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eClone this repository and submit a job, for example on MARCC Blue Crab:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /scratch/users/$USER\ngit clone https://github.com/marcc-hpc/pytorch.git\ncd pytorch\nsbatch pytorch_job.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease open Github issues if you interested in correcting typos, adding\nexamples, or just providing feedback!\u003c/p\u003e\n\u003cp\u003eExample taken from \u003ca href=\"https://github.com/pytorch/examples\"\u003ehttps://github.com/pytorch/examples\u003c/a\u003e (examples/mnist) LICENSE\nis reflected to show attribution; the only modifications are to point to an\nempty data folder inside the mnist folder.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSupervised and RL Models for No Press Diplomacy + press\u003c/h1\u003e\u003ca id=\"user-content-supervised-and-rl-models-for-no-press-diplomacy--press\" class=\"anchor\" aria-label=\"Permalink: Supervised and RL Models for No Press Diplomacy + press\" href=\"#supervised-and-rl-models-for-no-press-diplomacy--press\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source code used to develop a supervised and RL agent that can play the No Press version of Diplomacy.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/map_overview.png\"\u003e\u003cimg width=\"500\" src=\"docs/images/map_overview.png\" alt=\"Diplomacy Map Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the MIT License - see the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file for details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRestrictions: The trained weights provided with this repository are for research purposes only and cannot be used to power any bots on any website without my prior written consent, which may be withheld without reasons.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe data provider also prevents using its data to train any bots accessible on any website.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYou can play against the trained model by playing against \"KestasBot\" on webdiplomacy.net\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataset\u003c/h2\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe model was trained by using a dataset of 156,468 games (diplomacy-v1-27k-msgs.zip), which consists of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e16,633 games on non-standard maps (e.g. modern and ancmed) (other_maps.jsonl)\u003c/li\u003e\n\u003cli\u003e33,279 no-press games on the standard map (standard_no_press.jsonl)\u003c/li\u003e\n\u003cli\u003e50 press games on the standard map with messages (standard_press_with_msgs.jsonl)\u003c/li\u003e\n\u003cli\u003e106,456 press games on the standard map without messages (standard_press_without_msgs.jsonl)\u003c/li\u003e\n\u003cli\u003e50 public press games on the standard map with messages (standard_public_press.jsonl)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA dataset of 156,458 games with 13,469,536 messages is also being prepared, but it is not yet available.\u003c/p\u003e\n\u003cp\u003eAccess to the dataset used to train the model can be requested by sending an email to \u003ca href=\"mailto:webdipmod@gmail.com\"\u003ewebdipmod@gmail.com\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository can be installed in a conda environment with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003econda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecreate\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003en\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eactivate\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements_dev\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis package depends on Redis and singularity 3+. Singularity can be installed with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing Singularity v3.2.0\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e VERSION=v3.2.0\nsudo apt-get update -y\nsudo apt-get install -y build-essential libssl-dev uuid-dev libgpgme11-dev libseccomp-dev pkg-config squashfs-tools\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing GO 1.12.5\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GO_VERSION=1.12.5 OS=linux ARCH=amd64\nwget -nv https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\nsudo tar -C /usr/local -xzf go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\nrm -f go\u003cspan class=\"pl-smi\"\u003e$GO_VERSION\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e$OS\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e$ARCH\u003c/span\u003e.tar.gz\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GOPATH=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.go\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=/usr/local/go/bin:\u003cspan class=\"pl-smi\"\u003e${PATH}\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e${GOPATH}\u003c/span\u003e/bin\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e\ngo get github.com/golang/dep/cmd/dep\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Building from source\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e/src/github.com/sylabs\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$GOPATH\u003c/span\u003e/src/github.com/sylabs\ngit clone https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\ngit checkout \u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\n./mconfig -p /usr/local\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ./builddir\nmake\nsudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe package is compatible with Python 3.5, 3.6, and 3.7.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTraining models\u003c/h3\u003e\u003ca id=\"user-content-training-models\" class=\"anchor\" aria-label=\"Permalink: Training models\" href=\"#training-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train a model:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e WORKING_DIR=/path/to/some/directory\n$ cp diplomacy-v1-27k-msgs.zip \u003cspan class=\"pl-smi\"\u003e$WORKING_DIR\u003c/span\u003e\n$ conda activate diplomacy\n$ python diplomacy_research/scripts/build_dataset.py\n$ python diplomacy_research/models/policy/order_based/train.py --model_id 12\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlaying against the SL and RL agents\u003c/h3\u003e\u003ca id=\"user-content-playing-against-the-sl-and-rl-agents\" class=\"anchor\" aria-label=\"Permalink: Playing against the SL and RL agents\" href=\"#playing-against-the-sl-and-rl-agents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to play against the published results by using the \u003ccode\u003eDipNetSLPlayer\u003c/code\u003e and \u003ccode\u003eDipNetRLPlayer\u003c/code\u003e players in \u003ccode\u003ediplomacy_research.players.benchmark_player\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThese players will automatically download a singularity container with the trained weights, and then launch a TF serving server to handle the requests.\u003c/p\u003e\n\u003cp\u003eA simple example on how to play a 7 bots game is:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etornado\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egen\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eujson\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGame\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexport\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eto_saved_game_format\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy_research\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eplayers\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ebenchmark_player\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDipNetSLPlayer\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ediplomacy_research\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecluster\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003estart_io_loop\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estop_io_loop\u003c/span\u003e\n\n\u003cspan class=\"pl-en\"\u003e@\u003cspan class=\"pl-s1\"\u003egen\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecoroutine\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emain\u003c/span\u003e():\n \u003cspan class=\"pl-s\"\u003e\"\"\" Plays a local game with 7 bots \"\"\"\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eplayer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDipNetSLPlayer\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGame\u003c/span\u003e()\n\n \u003cspan class=\"pl-c\"\u003e# Playing game\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ewhile\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enot\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eis_game_done\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eorders\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eyield\u003c/span\u003e {\u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eplayer\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eget_orders\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epowers\u003c/span\u003e}\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_orders\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eorders\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eset_orders\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003epower_name\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epower_orders\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eprocess\u003c/span\u003e()\n\n \u003cspan class=\"pl-c\"\u003e# Saving to disk\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027game.json\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027w\u0027\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ejson\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edumps\u003c/span\u003e(\u003cspan class=\"pl-en\"\u003eto_saved_game_format\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003egame\u003c/span\u003e)))\n \u003cspan class=\"pl-en\"\u003estop_io_loop\u003c/span\u003e()\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e__name__\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u0027__main__\u0027\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003estart_io_loop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emain\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlaying against a model\u003c/h3\u003e\u003ca id=\"user-content-playing-against-a-model\" class=\"anchor\" aria-label=\"Permalink: Playing against a model\" href=\"#playing-against-a-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is also possible for humans to play against bots using the web interface. The player can be changed in \u003ccode\u003ediplomacy_research.scripts.launch_bot\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In a terminal window or tab - Launch React server (from diplomacy/diplomacy)\u003c/span\u003e\nnpm start\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In another terminal window or tab - Launch diplomacy server\u003c/span\u003e\npython -m diplomacy.server.run\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e In a third terminal window or tab - Launch the bot script\u003c/span\u003e\npython diplomacy_research/scripts/launch_bot.py\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTrained weights and experiment logs\u003c/h3\u003e\u003ca id=\"user-content-trained-weights-and-experiment-logs\" class=\"anchor\" aria-label=\"Permalink: Trained weights and experiment logs\" href=\"#trained-weights-and-experiment-logs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo facilitate reproducibility, the experiments can be downloaded using the following links. These include hyperparameters, tensorboard graphs, output logs, and weights for each epoch.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOrder based LSTM model (order-based v12 - Accuracy of 61.3% - \u003cstrong\u003eDipNet SL\u003c/strong\u003e) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/order-based-lstm.zip\" rel=\"nofollow\"\u003eDownload - 5.4GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOrder based Transformer model (order-based v15 - Accuracy of 60.7%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/order-based-trsf.zip\" rel=\"nofollow\"\u003eDownload - 8.2GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eToken based LSTM model (token-based v10 - Accuracy of 60.3%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/token-based-lstm.zip\" rel=\"nofollow\"\u003eDownload - 6.0GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eToken based Transformer model (token-based v11 - Accuracy of 58.9%) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/token-based-trsf.zip\" rel=\"nofollow\"\u003eDownload - 3.5GB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRL Model (Bootstrapped from order-based v12 and value v1 - \u003cstrong\u003eDipNet RL\u003c/strong\u003e) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/rl-model.zip\" rel=\"nofollow\"\u003eDownload - 11.1GB\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGames against Albert (DAIDE)\u003c/h3\u003e\u003ca id=\"user-content-games-against-albert-daide\" class=\"anchor\" aria-label=\"Permalink: Games against Albert (DAIDE)\" href=\"#games-against-albert-daide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe 1v6 and 6v1 games played between DipNet SL and Albert (DAIDE) can be downloaded below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eList of games with power assignments \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/daide_albert_results.xlsx\" rel=\"nofollow\"\u003eDownload - 53KB\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVisualisation of each game (svg and json) \u003ca href=\"https://f002.backblazeb2.com/file/ppaquette-public/benchmarks/experiments/daide_albert_games.zip\" rel=\"nofollow\"\u003eDownload - 2.3GB\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1590676888.0 + "updated_at": 1645489677.0 }, { "data_format": 2, - "description": "MXE", + "description": null, "filenames": [ "Singularity" ], - "full_name": "richelbilderbeek/singularity_example_9", + "full_name": "ISU-HPC/orthomcl", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_example_5\u003c/h1\u003e\u003ca id=\"user-content-singularity_example_5\" class=\"anchor\" aria-label=\"Permalink: singularity_example_5\" href=\"#singularity_example_5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://travis-ci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"pics/TravisCI.png\" alt=\"Travis CI logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://travis-ci.org/richelbilderbeek/singularity_example_5\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/81763108d8ea59e0425cb30af7c68be2e5b88c0872592f0cab297d542fb2d83a/68747470733a2f2f7472617669732d63692e6f72672f72696368656c62696c6465726265656b2f73696e67756c61726974795f6578616d706c655f352e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/richelbilderbeek/singularity_example_5.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSingularity example 5: Ubuntu 19.04 (disco)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3310\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eorthomcl\u003c/h1\u003e\u003ca id=\"user-content-orthomcl\" class=\"anchor\" aria-label=\"Permalink: orthomcl\" href=\"#orthomcl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1564746746.0 + "updated_at": 1524255245.0 }, { "data_format": 2, - "description": "Singularity container for MaxQuant in CentOS 7.", + "description": "dockerize bidskit for TACC usage", "filenames": [ + "Singularity.TACC", "Singularity" ], - "full_name": "bihealth/singularity-maxquant", + "full_name": "jungheejung/docker-bidskit", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMaxQuant in Singularity\u003c/h1\u003e\u003ca id=\"user-content-maxquant-in-singularity\" class=\"anchor\" aria-label=\"Permalink: MaxQuant in Singularity\" href=\"#maxquant-in-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload MaxQuant ZIP into this directory.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esudo singularity build maxquant-2.0.3.0.sif Singularity\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edocker-bidskit\u003c/h1\u003e\u003ca id=\"user-content-docker-bidskit\" class=\"anchor\" aria-label=\"Permalink: docker-bidskit\" href=\"#docker-bidskit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003edockerize bidskit for TACC usage\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1640476469.0 + "updated_at": 1594676027.0 }, { "data_format": 2, - "description": "OSGVO image for blaylockbk", + "description": "Singularity images and recipes", "filenames": [ - "Singularity" + "lammps/Singularity.lammps_ase_kim", + "lammps/Singularity.lammps_prophet", + "lammps/Singularity.lammps_ase", + "lammps/Singularity.lammps", + "deal.II/Singularity.deal", + "acroread/Singularity.acroread", + "ubuntu/Singularity.1804", + "ubuntu/Singularity.2004", + "tesseract/Singularity.tesseract", + "nut/Singularity.nut", + "SLURM/Singularity.slurm", + "ase-twistd/Singularity.ase-twistd", + "mariadb/Singularity.mariadb", + "jmol/Singularity.jmol", + "kmos/Singularity.kmos3_9", + "kmos/Singularity.kmos", + "Atom/Singularity.atom", + "fuse-overlayfs/Singularity.fuse-overlayfs", + "mongodb/Singularity.mongodb", + "gdis-git/Singularity-slim.gdis", + "gdis-git/Singularity.gdis", + "AMPE/Singularity.ampe", + "pp/Singularity.pp2", + "tools/Singularity.mc", + "tools/Singularity.gnuplot", + "tools/Singularity.vim", + "tools/Singularity.ncdu", + "tools/Singularity.gawk", + "tools/Singularity.meld", + "jupyter/Singularity.jupyter", + "graphics/Singularity.graphics", + "graphics/Singularity.gnuplot_5.4a", + "graphics/Singularity.gnuplot_5.4", + "graphics/Singularity.gnuplot_4.6", + "graphics/Singularity.gnuplot_alpine", + "graphics/Singularity.gnuplot_4.6a", + "texlive/Singularity.texlive", + "VESTA/Singularity.vesta", + "obabel/Singularity.obabel", + "cuda/Singularity.u18.04_cuda9.2", + "qemu/Singularity.qemu-utils", + "MD2-lab/Singularity.md2-lab", + "emacs/Singularity.emacs", + "gdis/Singularity.gdis", + "aria2/Singularity.aria2c", + "mkdocs-serve/Singularity.mkdocs-serve", + "clease/Singularity.clease", + "rstudio-server/Singularity.rstudio-server", + "rstudio-server/Singularity.rstudio-desktop", + "rstudio-server/Singularity-22.04.rstudio-server", + "gromacs/Singularity.gromacs", + "xcrysden/Singularity.xcrysden", + "xcrysden/Singularity.xcrysden_1.5.60" ], - "full_name": "opensciencegrid/osgvo-blaylockbk", + "full_name": "pmitev/Teoroo-singularity", "latest_release": null, - "readme": "", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2338\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTeoroo-singularity\u003c/h1\u003e\u003ca id=\"user-content-teoroo-singularity\" class=\"anchor\" aria-label=\"Permalink: Teoroo-singularity\" href=\"#teoroo-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 16, - "topics": [], - "updated_at": 1498768856.0 + "subscribers_count": 2, + "topics": [ + "singularity", + "singularity-containers" + ], + "updated_at": 1730474659.0 }, { "data_format": 2, - "description": "Ticket 297240", + "description": "Singularity container for Gate ", "filenames": [ - "Singularity_approach_5", - "Singularity_approach_4", - "Singularity", - "Singularity_approach_3" + "geant4/Singularity" ], - "full_name": "UPPMAX/ticket_297240", + "full_name": "tfunck/gate", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eticket_297240\u003c/h1\u003e\u003ca id=\"user-content-ticket_297240\" class=\"anchor\" aria-label=\"Permalink: ticket_297240\" href=\"#ticket_297240\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTicket 297240\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApproach 5: Use Singularity from BZL file\u003c/h2\u003e\u003ca id=\"user-content-approach-5-use-singularity-from-bzl-file\" class=\"anchor\" aria-label=\"Permalink: Approach 5: Use Singularity from BZL file\" href=\"#approach-5-use-singularity-from-bzl-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDue to file that 10x uses \u003ca href=\"https://github.com/10XGenomics/cellranger/blob/main/conda_spec.bzl\"\u003ehttps://github.com/10XGenomics/cellranger/blob/main/conda_spec.bzl\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHere is a Docker file: \u003ca href=\"https://hub.docker.com/r/chainguard/bazel\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/chainguard/bazel\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDocker pull command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull chainguard/bazel:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003ebazel build conda_spec.bzl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuild Singularity file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: chainguard/bazel:latest\n\n%post\n # From https://github.com/brucemoran/Singularity/blob/8eb44591284ffb29056d234c47bf8b1473637805/shub/bases/recipe.CentOs7-R_3.5.2#L21\n echo \u0027export LANG=en_US.UTF-8 LANGUAGE=C LC_ALL=C LC_CTYPE=C LC_COLLATE=C LC_TIME=C LC_MONETARY=C LC_PAPER=C LC_MEASUREMENT=C\u0027 \u0026gt;\u0026gt; $SINGULARITY_ENVIRONMENT\n\n cd /opt\n git clone https://github.com/10XGenomics/cellranger\n cd cellranger\n\n bazel build conda_spec.bzl\n\n%runscript\npython \"$@\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuild:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eError:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eERROR: The \u0027build\u0027 command is only supported from within a workspace (below a directory having a WORKSPACE file).\nSee documentation at https://bazel.build/concepts/build-ref#workspace\nFATAL: While performing build: while running engine: exit status 2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse tip from \u003ca href=\"https://stackoverflow.com/q/61869719\" rel=\"nofollow\"\u003ehttps://stackoverflow.com/q/61869719\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGives error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+ touch WORKSPACE\n+ bazel build conda_spec.bzl\nExtracting Bazel installation...\nStarting local Bazel server and connecting to it...\nWARNING: --enable_bzlmod is set, but no MODULE.bazel file was found at the workspace root. Bazel will create an empty MODULE.bazel file. Please consider migrating your external dependencies from WORKSPACE to MODULE.bazel. For more details, please refer to https://github.com/bazelbuild/bazel/issues/18958.\nWARNING: Target pattern parsing failed.\nERROR: Skipping \u0027conda_spec.bzl\u0027: couldn\u0027t determine target from filename \u0027conda_spec.bzl\u0027\nERROR: couldn\u0027t determine target from filename \u0027conda_spec.bzl\u0027\nINFO: Elapsed time: 7.913s\nINFO: 0 processes.\nERROR: Build did NOT complete successfully\nFATAL: While performing build: while running engine: exit status 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdded tip from \u003ca href=\"https://raw.githubusercontent.com/10XGenomics/cellranger/main/conda_spec.bzl\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/10XGenomics/cellranger/main/conda_spec.bzl\u003c/a\u003e to add content to WORKSPACE.\u003c/p\u003e\n\u003cp\u003eBuild again:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+ echo \u0027load(\":conda_spec.bzl\", \"anaconda_workspace\")\u0027\n+ echo \u0027anaconda_workspace()\u0027\n+ bazel build conda_spec.bzl\nExtracting Bazel installation...\nStarting local Bazel server and connecting to it...\nWARNING: --enable_bzlmod is set, but no MODULE.bazel file was found at the workspace root. Bazel will create an empty MODULE.bazel file. Please consider migrating your external dependencies from WORKSPACE to MODULE.bazel. For more details, please refer to https://github.com/bazelbuild/bazel/issues/18958.\nERROR: Error computing the main repository mapping: Every .bzl file must have a corresponding package, but \u0027//:conda_spec.bzl\u0027 does not have one. Please create a BUILD file in the same or any parent directory. Note that this BUILD file does not need to do anything except exist.\nComputing main repo mapping: \nFATAL: While performing build: while running engine: exit status 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOK, create an empty BUILD file ...\u003c/p\u003e\n\u003cp\u003eWith content in the BUILD file:\nRun again, new error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+ touch BUILD\n+ bazel build conda_spec.bzl\nExtracting Bazel installation...\nStarting local Bazel server and connecting to it...\nWARNING: --enable_bzlmod is set, but no MODULE.bazel file was found at the workspace root. Bazel will create an empty MODULE.bazel file. Please consider migrating your external dependencies from WORKSPACE to MODULE.bazel. For more details, please refer to https://github.com/bazelbuild/bazel/issues/18958.\nERROR: Failed to load Starlark extension \u0027@@tenx_bazel_rules//rules:conda_package_repository.bzl\u0027.\nCycle in the workspace file detected. This indicates that a repository is used prior to being defined.\nThe following chain of repository dependencies lead to the missing definition.\n - @@tenx_bazel_rules\nThis could either mean you have to add the \u0027@@tenx_bazel_rules\u0027 repository with a statement like `http_archive` in your WORKSPACE file (note that transitive dependencies are not added automatically), or move an existing definition earlier in your WORKSPACE file.\nERROR: Error computing the main repository mapping: cycles detected during computation of main repo mapping\nComputing main repo mapping: \nFATAL: While performing build: while running engine: exit status 37\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApproach 4: Use Singularity from conda\u003c/h2\u003e\u003ca id=\"user-content-approach-4-use-singularity-from-conda\" class=\"anchor\" aria-label=\"Permalink: Approach 4: Use Singularity from conda\" href=\"#approach-4-use-singularity-from-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple Singularity file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: mcr.microsoft.com/devcontainers/anaconda\n# From: pnnlmiscscripts/anaconda:latest\n\n\n%post\n # From https://github.com/brucemoran/Singularity/blob/8eb44591284ffb29056d234c47bf8b1473637805/shub/bases/recipe.CentOs7-R_3.5.2#L21\n echo \u0027export LANG=en_US.UTF-8 LANGUAGE=C LC_ALL=C LC_CTYPE=C LC_COLLATE=C LC_TIME=C LC_MONETARY=C LC_PAPER=C LC_MEASUREMENT=C\u0027 \u0026gt;\u0026gt; $SINGULARITY_ENVIRONMENT\n\n conda install hcc::cellranger\n\n%runscript\npython \"$@\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuild:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+ conda install hcc::cellranger\nChannels:\n - defaults\n - hcc\nPlatform: linux-64\nCollecting package metadata (repodata.json): done\nSolving environment: failed\n\nLibMambaUnsatisfiableError: Encountered problems while solving:\n - nothing provides bcftools 1.9.* needed by cellranger-3.0.2-py27_1\n\nCould not solve for environment specs\nThe following package could not be installed\n\u2514\u2500 cellranger is not installable because it requires\n \u2514\u2500 bcftools 1.9.* , which does not exist (perhaps a missing channel).\n\nFATAL: While performing build: while running engine: exit status 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eApproach 5 seems easier.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApproach 3: Use conda from module\u003c/h2\u003e\u003ca id=\"user-content-approach-3-use-conda-from-module\" class=\"anchor\" aria-label=\"Permalink: Approach 3: Use conda from module\" href=\"#approach-3-use-conda-from-module\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLoading the conda module:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWorks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham1 ~]$ module load conda\nThe variable CONDA_ENVS_PATH contains the location of your environments. Set it to your project\u0027s environments folder if you have one.\nOtherwise, the default is ~/.conda/envs. Remember to export the variable with export CONDA_ENVS_PATH=/proj/...\n\nYou may run \"source conda_init.sh\" to initialise your shell to be able\nto run \"conda activate\" and \"conda deactivate\" etc.\nJust remember that this command adds stuff to your shell outside the scope of the module system.\n\nREMEMBER TO USE \u0027conda clean -a\u0027 once in a while\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNaive install fails:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install cellranger\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham1 ~]$ conda install cellranger\nRetrieving notices: ...working... done\nChannels:\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - conda-forge\nPlatform: linux-64\nCollecting package metadata (repodata.json): - subdir mismatch\n\\ subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\n- subdir mismatch\n\\ subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\ndone\nSolving environment: failed\n\nPackagesNotFoundError: The following packages are not available from current channels:\n\n - cellranger\n\nCurrent channels:\n\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - https://conda.anaconda.org/conda-forge/linux-64\n - https://conda.anaconda.org/conda-forge/noarch\n\nTo search for alternate channels that may provide the conda package you\u0027re\nlooking for, navigate to\n\n https://anaconda.org\n\nand use the search bar at the top of the page.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDo conda install from the doc at \u003ca href=\"https://anaconda.org/hcc/cellranger\" rel=\"nofollow\"\u003ehttps://anaconda.org/hcc/cellranger\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install hcc::cellranger\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFails too:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham1 ~]$ conda install hcc::cellranger\nChannels:\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - hcc\n - conda-forge\nPlatform: linux-64\nCollecting package metadata (repodata.json): / subdir mismatch\n- subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\n/ subdir mismatch\n- subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\ndone\nSolving environment: / warning libmamba Added empty dependency for problem type SOLVER_RULE_UPDATE\nfailed\n\nPackagesNotFoundError: The following packages are not available from current channels:\n\n - hcc::cellranger\n\nCurrent channels:\n\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - https://conda.anaconda.org/hcc\n - https://conda.anaconda.org/conda-forge/linux-64\n - https://conda.anaconda.org/conda-forge/noarch\n\nTo search for alternate channels that may provide the conda package you\u0027re\nlooking for, navigate to\n\n https://anaconda.org\n\nand use the search bar at the top of the page.\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing -c:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham1 ~]$ conda install -c hcc cellranger\nChannels:\n - hcc\n - file:///sw/apps/conda/latest/rackham/local_repo/conda-forge\n - file:///sw/apps/conda/latest/rackham/local_repo/scilifelab-lts\n - file:///sw/apps/conda/latest/rackham/local_repo/r\n - file:///sw/apps/conda/latest/rackham/local_repo/main\n - file:///sw/apps/conda/latest/rackham/local_repo/bioconda\n - file:///sw/apps/conda/latest/rackham/local_repo/free\n - file:///sw/apps/conda/latest/rackham/local_repo/pro\n - file:///sw/apps/conda/latest/rackham/local_repo/qiime2\n - file:///sw/apps/conda/latest/rackham/local_repo/biocore\n - file:///sw/apps/conda/latest/rackham/local_repo/dranew\n - file:///sw/apps/conda/latest/rackham/local_repo/r2018.11\n - file:///sw/apps/conda/latest/rackham/local_repo/nvidia\n - file:///sw/apps/conda/latest/rackham/local_repo/pytorch\n - file:///sw/apps/conda/latest/rackham/local_repo/anaconda\n - defaults\n - conda-forge\nPlatform: linux-64\nCollecting package metadata (repodata.json): | subdir mismatch\n/ subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\n- subdir mismatch\n\\ subdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\nsubdir mismatch\ndone\nSolving environment: / warning libmamba Added empty dependency for problem type SOLVER_RULE_UPDATE\nfailed\n\nLibMambaUnsatisfiableError: Encountered problems while solving:\n - nothing provides openssl \u0026gt;=1.1.1,\u0026lt;1.1.2.0a0 needed by python-2.7.15-h9bab390_2\n\nCould not solve for environment specs\nThe following packages are incompatible\n\u251c\u2500 cellranger is installable with the potential options\n\u2502 \u251c\u2500 cellranger 3.0.2 would require\n\u2502 \u2502 \u2514\u2500 python \u0026gt;=2.7,\u0026lt;2.8.0a0 with the potential options\n\u2502 \u2502 \u251c\u2500 python [2.7.10|2.7.11|...|2.7.9], which can be installed;\n\u2502 \u2502 \u2514\u2500 python 2.7.15 would require\n\u2502 \u2502 \u2514\u2500 openssl \u0026gt;=1.1.1,\u0026lt;1.1.2.0a0 , which does not exist (perhaps a missing channel);\n\u2502 \u2514\u2500 cellranger 3.0.2 would require\n\u2502 \u2514\u2500 python \u0026lt;3 with the potential options\n\u2502 \u251c\u2500 python [2.7.10|2.7.11|...|2.7.9], which can be installed;\n\u2502 \u251c\u2500 python 2.7.15, which cannot be installed (as previously explained);\n\u2502 \u2514\u2500 python [1.0.1|1.2|...|2.6.9], which can be installed;\n\u2514\u2500 pin-1 is not installable because it requires\n \u2514\u2500 python 3.12.* , which conflicts with any installable versions previously reported.\n\nPins seem to be involved in the conflict. Currently pinned specs:\n - python 3.12.* (labeled as \u0027pin-1\u0027)\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSeems unsolvable on Rackham\u003c/p\u003e\n\u003cp\u003eFrom \u003ca href=\"https://anaconda.org/hcc/repo/installers\" rel=\"nofollow\"\u003ehttps://anaconda.org/hcc/repo/installers\u003c/a\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTo install a conda package from this channel, run:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003econda install --channel \"HCC\" package\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTakes us back to the earlier tried:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./conda install --channel \"HCC\" cellranger\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewith error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erichel@richel-N141CU:~/anaconda3/bin$ ./conda install --channel \"HCC\" cellranger\nChannels:\n - HCC\n - defaults\nPlatform: linux-64\nCollecting package metadata (repodata.json): done\nSolving environment: failed\n\nLibMambaUnsatisfiableError: Encountered problems while solving:\n - nothing provides bcftools 1.9.* needed by cellranger-3.0.2-py27_1\n\nCould not solve for environment specs\nThe following package could not be installed\n\u2514\u2500 cellranger is not installable because it requires\n \u2514\u2500 bcftools 1.9.* , which does not exist (perhaps a missing channel).\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e[ABANDON] Approach 2: Use conda from local install\u003c/h2\u003e\u003ca id=\"user-content-abandon-approach-2-use-conda-from-local-install\" class=\"anchor\" aria-label=\"Permalink: [ABANDON] Approach 2: Use conda from local install\" href=\"#abandon-approach-2-use-conda-from-local-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/hcc/cellranger\" rel=\"nofollow\"\u003ehttps://anaconda.org/hcc/cellranger\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e[FAILS] Approach 1: use Python files on clusters\u003c/h2\u003e\u003ca id=\"user-content-fails-approach-1-use-python-files-on-clusters\" class=\"anchor\" aria-label=\"Permalink: [FAILS] Approach 1: use Python files on clusters\" href=\"#fails-approach-1-use-python-files-on-clusters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e module load bioinfo-tools lz4/1.9.2 cellranger/8.0.1\npython\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eimport sys\nsys.path.append(\u0027/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/\u0027)\nfrom cellranger import *\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWith \u003ccode\u003emodule load lz4/1.9.2\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-with-module-load-lz4192\" class=\"anchor\" aria-label=\"Permalink: With module load lz4/1.9.2\" href=\"#with-module-load-lz4192\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e module load bioinfo-tools lz4/1.9.2 cellranger/8.0.1\npython\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eimport sys\nsys.path.append(\u0027/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/\u0027)\nfrom cellranger import *\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;\u0026gt;\u0026gt; import cellranger.matrix as cr_matrix\nTraceback (most recent call last):\n File \"\u0026lt;stdin\u0026gt;\", line 1, in \u0026lt;module\u0026gt;\n File \"/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/cellranger/matrix.py\", line 19, in \u0026lt;module\u0026gt;\n import cellranger.cr_io as cr_io\n File \"/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/cellranger/cr_io.py\", line 17, in \u0026lt;module\u0026gt;\n import lz4.frame as lz4\nModuleNotFoundError: No module named \u0027lz4\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHowever, there is no Python code for \u003ccode\u003elz4\u003c/code\u003e on the cluster:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[richel@rackham2 1.9.2]$ pwd\n/sw/libs/lz4/1.9.2\n\n[richel@rackham2 1.9.2]$ find . | grep py\n./rackham/src/contrib/debian/copyright\n./rackham/src/contrib/meson/GetLz4LibraryVersion.py\n./rackham/src/contrib/meson/InstallSymlink.py\n./rackham/src/tests/test-lz4-list.py\n./rackham/src/tests/test-lz4-speed.py\n./rackham/src/tests/test-lz4-versions.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNo module load lz4\u003c/h3\u003e\u003ca id=\"user-content-no-module-load-lz4\" class=\"anchor\" aria-label=\"Permalink: No module load lz4\" href=\"#no-module-load-lz4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e module load bioinfo-tools cellranger/8.0.1\npython\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eimport sys\nsys.path.append(\u0027/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/\u0027)\nfrom cellranger import *\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;\u0026gt;\u0026gt; import cellranger.matrix as cr_matrix\nTraceback (most recent call last):\n File \"\u0026lt;stdin\u0026gt;\", line 1, in \u0026lt;module\u0026gt;\n File \"/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/cellranger/matrix.py\", line 19, in \u0026lt;module\u0026gt;\n import cellranger.cr_io as cr_io\n File \"/sw/bioinfo/Chromium-cellranger/8.0.1/bianca/lib/python/cellranger/cr_io.py\", line 17, in \u0026lt;module\u0026gt;\n import lz4.frame as lz4\nModuleNotFoundError: No module named \u0027lz4\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThanks for the help 10x!\u003c/h2\u003e\u003ca id=\"user-content-thanks-for-the-help-10x\" class=\"anchor\" aria-label=\"Permalink: Thanks for the help 10x!\" href=\"#thanks-for-the-help-10x\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom \u003ca href=\"https://github.com/10XGenomics/cellranger/tree/main\"\u003ehttps://github.com/10XGenomics/cellranger/tree/main\u003c/a\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePlease note that this source code is made available only for informational\npurposes. 10x does not provide support for interpreting,\nmodifying, building, or running this code.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThanks 10x!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommunication\u003c/h2\u003e\u003ca id=\"user-content-communication\" class=\"anchor\" aria-label=\"Permalink: Communication\" href=\"#communication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2024-08-26\u003c/h3\u003e\u003ca id=\"user-content-2024-08-26\" class=\"anchor\" aria-label=\"Permalink: 2024-08-26\" href=\"#2024-08-26\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo me to 10X:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eI do support like you, and a user wants to use the Python code of Cell Ranger.\u003c/p\u003e\n\u003cp\u003eI\u0027ve had multiple approaches to get these Python modules to work (notes at \u003ca href=\"https://github.com/UPPMAX/ticket_297240\"\u003ehttps://github.com/UPPMAX/ticket_297240\u003c/a\u003e ), such as using the Bazel file nicely provided at \u003ca href=\"https://github.com/10XGenomics/cellranger/blob/main/conda_spec.bzl\"\u003ehttps://github.com/10XGenomics/cellranger/blob/main/conda_spec.bzl\u003c/a\u003e , yet always I got stranded at a dead end/\u003c/p\u003e\n\u003cp\u003eHow do I use the Cell Ranger Python modules? Could there be a guide published to? Or a GitHub Actions build script on the GitHub repo?\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1724654624.0 + "updated_at": 1557681223.0 }, { "data_format": 2, - "description": "A Singularity recipe for sistr --- a tool for Salmonella typing", + "description": "Core repository for neuroglia singularity image", "filenames": [ - "Singularity", - "v1.0.2/Singularity.v1.0.2" + "Singularity.v1.4", + "Singularity.v1.5", + "Singularity" ], - "full_name": "phgenomics-singularity/sistr", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esistr --- A Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-sistr-----a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: sistr --- A Singularity Container\" href=\"#sistr-----a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1206\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for \u003ca href=\"https://github.com/peterk87/sistr_cmd\"\u003esistr\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name TMP_DIRECTORY shub://phgenomics-singularity/sistr@latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003esistr.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eUse the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./sistr.simg --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name mlst shub://phgenomics-singularitysistr@VERSION.NUMBER\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir HOME/singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name sistr shub://phgenomics-singularity/sistr@latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eLink the image to a folder in your \u003ccode\u003ePATH\u003c/code\u003e (e.g., \u003ccode\u003eHOME/bin\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eln -s HOME/singularity/sistr.simg HOME/bin/sistr\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esistr --help\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "khanlab/neuroglia-core", + "latest_release": "v1.5", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eneuroglia-core\u003c/h1\u003e\u003ca id=\"user-content-neuroglia-core\" class=\"anchor\" aria-label=\"Permalink: neuroglia-core\" href=\"#neuroglia-core\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image for neuroimaging dependencies. Base image for khanlab apps and containers. Includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNeurodebian\u003c/li\u003e\n\u003cli\u003eOctave\u003c/li\u003e\n\u003cli\u003eNipype\u003c/li\u003e\n\u003cli\u003eFSL\u003c/li\u003e\n\u003cli\u003eAFNI\u003c/li\u003e\n\u003cli\u003eC3D\u003c/li\u003e\n\u003cli\u003eFreesurfer\u0027s mri_convert and mris_convert\u003c/li\u003e\n\u003cli\u003eANTS\u003c/li\u003e\n\u003cli\u003edcm2niix\u003c/li\u003e\n\u003cli\u003eheudiconv\u003c/li\u003e\n\u003cli\u003ebids-validator\u003c/li\u003e\n\u003cli\u003eNiftyReg\u003c/li\u003e\n\u003cli\u003egradunwarp\u003c/li\u003e\n\u003cli\u003edcmstack\u003c/li\u003e\n\u003cli\u003eConnectome Workbench\u003c/li\u003e\n\u003cli\u003eDatalad-osf\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCommits and pull-requests to this repository rebuild the \u003ccode\u003elatest\u003c/code\u003e version on Docker Hub, which is updated nightly to Singularity Hub. Releases on Docker Hub and Singularity Hub are built whenever a tag named \u003ccode\u003ev.*\u003c/code\u003e is committed. To avoid re-building on minor commits (e.g. changes to documentation), use \u003ccode\u003e[skip ci]\u003c/code\u003e in the commit message.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/khanlab/neuroglia-core\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/835cd5c934ef218fbd0ef5bdc983afeca64e7f868c316e16de506c0216eada12/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f6e6575726f676c69612d636f72652e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/neuroglia-core.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/393\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDocker:\n\u003ccode\u003edocker pull khanlab/neuroglia-core\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSingularity:\n\u003ccode\u003esingularity pull khanlab/neuroglia-core\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 8, "topics": [], - "updated_at": 1530577180.0 + "updated_at": 1591844429.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "singularity/e2e/testdata/Singularity", + "singularity/examples/busybox/Singularity", + "singularity/examples/ubuntu/Singularity", + "singularity/examples/asciinema/Singularity", + "singularity/examples/apps/Singularity.cowsay", + "singularity/examples/apps/Singularity", + "singularity/examples/centos/Singularity", + "singularity/examples/shub/Singularity", + "singularity/examples/raspbian/Singularity", + "singularity/examples/arch/Singularity", + "singularity/examples/opensuse/Singularity", + "singularity/examples/debian/Singularity", + "singularity/examples/docker/Singularity", + "singularity/examples/sle/Singularity", + "singularity/examples/instances/Singularity", + "singularity/examples/multistage/Singularity", + "singularity/examples/scratch/Singularity.busybox", + "singularity/examples/scratch/Singularity.alpine", + "singularity/examples/library/Singularity", + "singularity/examples/scientific/Singularity", + "singularity/examples/self/Singularity" ], - "full_name": "oogasawa/singularity_ubuntu18", + "full_name": "DeepLearningItalia/NLP-HandsOn-2", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUbuntu18 Singularity\u003c/h1\u003e\u003ca id=\"user-content-ubuntu18-singularity\" class=\"anchor\" aria-label=\"Permalink: Ubuntu18 Singularity\" href=\"#ubuntu18-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux 18.04 (Bionic) + \u89e3\u6790\u7528\u30c4\u30fc\u30eb\u5168\u90e8\u5165\u308a\u306e Singularity\u30b3\u30f3\u30c6\u30ca\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u524d\u63d0\u003c/h2\u003e\u003ca id=\"user-content-\u524d\u63d0\" class=\"anchor\" aria-label=\"Permalink: \u524d\u63d0\" href=\"#\u524d\u63d0\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUbuntu Linux\u306bdebootstrap\u4ed6\u306e\u5fc5\u8981\u306a\u30bd\u30d5\u30c8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt upgrade\nsudo apt install build-essential libtool automake libarchive-dev debootstrap git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSingularity\u306fversion 2.x\u7cfb\u3067\u30823.x\u7cfb\u3067\u3082\u3088\u3044\u3002\uff08\u4eca\u306f\u3082\u30463.x\u7cfb\u63a8\u5968\uff09\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.5/admin-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\uff08Official Document)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u4f7f\u3044\u65b9\u003c/h2\u003e\u003ca id=\"user-content-\u4f7f\u3044\u65b9\" class=\"anchor\" aria-label=\"Permalink: \u4f7f\u3044\u65b9\" href=\"#\u4f7f\u3044\u65b9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# \u30b3\u30f3\u30c6\u30ca\u306e\u30d3\u30eb\u30c9\ngit clone http://gitlab.ddbj.nig.ac.jp/oogasawa/singularity-ubuntu18\ncd singularity-ubuntu18\nmkdir $HOME/singularity-images\nsudo singularity build --sandbox $HOME/singularity-images/ubuntu18 Singularity\n\n# \u30b3\u30f3\u30c6\u30ca\u5185\u3067\u306e\u4f5c\u696d\nsudo singularity shell --write $HOME/singularity-images/ubuntu18\n\n# *.sif\u30d5\u30a1\u30a4\u30eb\u306b\u56fa\u3081\u3066\u30b9\u30d1\u30b3\u30f3\u306a\u3069\u5171\u7528\u8a08\u7b97\u6a5f\u306b\u30b3\u30d4\u30fc\nsudo singularity build ubuntu18.sif $HOME/singularity-images/ubuntu18\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1622297627.0 + "updated_at": 1628067602.0 }, { "data_format": 2, - "description": "Tidyverse singularity container", + "description": "Practicas de la asignatura", "filenames": [ - "Singularity" - ], - "full_name": "richelbilderbeek/tidyverse_singularity", - "latest_release": "v1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etidyverse_singularity\u003c/h1\u003e\u003ca id=\"user-content-tidyverse_singularity\" class=\"anchor\" aria-label=\"Permalink: tidyverse_singularity\" href=\"#tidyverse_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/richelbilderbeek/tidyverse_singularity/actions/workflows/build_singularity.yaml\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/tidyverse_singularity/actions/workflows/build_singularity.yaml/badge.svg\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container with a working version of the \u003ccode\u003esingularity\u003c/code\u003e R package.\u003c/p\u003e\n\u003cp\u003eIt does run remotely (i.e. on GitHub Actions),\nbut not on my Ubuntu 20.04 LTS laptop).\u003c/p\u003e\n\u003cp\u003eThis is a follow-up of a question I \u003ca href=\"https://stackoverflow.com/questions/71252123/singularity-container-with-singularity-fails-only-locally-with-libicui18n-so-66-ca\" rel=\"nofollow\"\u003eposted a question on StackOverflow\u003c/a\u003e.\u003c/p\u003e\n", + "Practica1/compiladores/singularity-ce-3.9.5/e2e/testdata/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/busybox/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/ubuntu/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/asciinema/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/centos-arm64/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/apps/Singularity.cowsay", + "Practica1/compiladores/singularity-ce-3.9.5/examples/apps/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/centos/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/shub/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/raspbian/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/arch/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/opensuse-arm64/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/opensuse/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/debian/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/docker/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/sle/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/instances/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/multistage/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/scratch/Singularity.busybox", + "Practica1/compiladores/singularity-ce-3.9.5/examples/scratch/Singularity.alpine", + "Practica1/compiladores/singularity-ce-3.9.5/examples/library/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/scientific/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/examples/self/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/e2e/testdata/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/busybox/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/ubuntu/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/asciinema/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/centos-arm64/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/apps/Singularity.cowsay", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/apps/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/centos/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/shub/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/raspbian/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/arch/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/opensuse-arm64/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/opensuse/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/debian/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/docker/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/sle/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/instances/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/multistage/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/scratch/Singularity.busybox", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/scratch/Singularity.alpine", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/library/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/scientific/Singularity", + "Practica1/compiladores/singularity-ce-3.9.5/singularity-ce-3.9.5/examples/self/Singularity" + ], + "full_name": "EstebanGomez1/Planificacion-Automatica", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1645771472.0 + "updated_at": 1716230355.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image recipe", "filenames": [ - "Singularity" + "tensorflow/Singularity.tf-1.14.0-gpu-py3", + "tensorflow/Singularity.tf-2.3.0-gpu", + "tensorflow/Singularity.tf-2.5.1-gpu", + "tensorflow/Singularity.tf-1.15.5-gpu", + "tensorflow/Singularity.tf-2.1.0-gpu-py3", + "tensorflow/Singularity.tf-2.0.0-gpu-py3", + "tensorflow/Singularity.tf-1.12.0-gpu-mlflow-py3", + "tensorflow/Singularity.tf-2.3.1-gpu", + "tensorflow/Singularity.tf-1.15.2-gpu-py3", + "tensorflow/Singularity.tf-1.12.0-gpu-py3", + "tensorflow/Singularity.tf-1.13.1-gpu-py3", + "tensorflow/Singularity.tf-2.4.3-gpu", + "tensorflow/Singularity.tf-2.2.0-gpu" ], - "full_name": "J-Andy/eQTL-analysis", + "full_name": "myzkyuki/singularity_recipe", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGene Knockout and Mutation Analysis\u003c/h1\u003e\u003ca id=\"user-content-gene-knockout-and-mutation-analysis\" class=\"anchor\" aria-label=\"Permalink: Gene Knockout and Mutation Analysis\" href=\"#gene-knockout-and-mutation-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project analyzes the association between specific cancer mutations and gene knockout experiments using Welch\u0027s t-test in R. The analysis is encapsulated in a Singularity container, which includes all necessary dependencies and R scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eComment: Extending the Analysis with Advanced Models\u003c/h2\u003e\u003ca id=\"user-content-comment-extending-the-analysis-with-advanced-models\" class=\"anchor\" aria-label=\"Permalink: Comment: Extending the Analysis with Advanced Models\" href=\"#comment-extending-the-analysis-with-advanced-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhile Welch\u2019s t-test is suitable for this toy example, more advanced methods would be necessary for larger datasets or when controlling for additional covariates. If we had more data or multiple covariates, I would be using linear regression or ANOVA. Linear regression would help model the relationship between mutation status and knockout outcomes while accounting for factors such as tissue type or growth conditions.\nFor efficiently handling large-scale data with many gene-mutation pairs, I would use the MatrixEQTL package in R (\u003ca href=\"https://github.com/andreyshabalin/MatrixEQTL\"\u003ehttps://github.com/andreyshabalin/MatrixEQTL\u003c/a\u003e). MatrixEQTL allows the use of linear models and is optimized for large-scale genomic data, automatically performs False Discovery Rate (FDR) correction and has some model customization options.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Container\u003c/h2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity Container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container runs the R script \u003ccode\u003emain.R\u003c/code\u003e which:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLoads mutation data (\u003ccode\u003eMutations.tsv\u003c/code\u003e) and gene knockout data (\u003ccode\u003eGene_KOs.tsv\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003ePerforms Welch\u2019s t-tests for each mutation-gene pair.\u003c/li\u003e\n\u003cli\u003eOutputs results with False Discovery Rate (FDR) correction.\u003c/li\u003e\n\u003cli\u003eGenerates Q-Q plots, histograms, and volcano plots.\u003c/li\u003e\n\u003cli\u003eSaves significant results and all plots in the output directory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation Instructions\u003c/h2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-label=\"Permalink: Installation Instructions\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Clone the repository\u003c/h3\u003e\u003ca id=\"user-content-1-clone-the-repository\" class=\"anchor\" aria-label=\"Permalink: 1. Clone the repository\" href=\"#1-clone-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the repository containing the \u003ccode\u003eSingularity\u003c/code\u003e definition file, R scripts, and data files:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/J-Andy/eQTL-analysis.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e eQTL-analysis\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Build the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-2-build-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: 2. Build the Singularity image\" href=\"#2-build-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build the Singularity image locally using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build r-analysis.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t have root access on the system, like me, you can use the remote build service:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build --remote r-analysis.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Running the Analysis\u003c/h3\u003e\u003ca id=\"user-content-3-running-the-analysis\" class=\"anchor\" aria-label=\"Permalink: 3. Running the Analysis\" href=\"#3-running-the-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter building the container, run the analysis using this script:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./run_analysis.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMake sure the \u003ccode\u003erun_analysis.sh\u003c/code\u003e script is executable\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e chmod +x run_analysis.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe shell script will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate an output directory in your current directory.\u003c/li\u003e\n\u003cli\u003eRun the Singularity container and bind the local output directory to the container\u2019s /usr/src/app/output.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Running Unit Tests\u003c/h3\u003e\u003ca id=\"user-content-4-running-unit-tests\" class=\"anchor\" aria-label=\"Permalink: 4. Running Unit Tests\" href=\"#4-running-unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the unit tests, use \u003ccode\u003erun_tests.sh \u003c/code\u003e script.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUnit Test Details\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eData Alignment Test:\nEnsures that the mutation data (Mutations.tsv) and gene knockout data (Gene_KOs.tsv) are properly aligned by common models (cell lines).\nThe test verifies that the aligned datasets share the same number of common cell lines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eT-test Validation:\nChecks that Welch\u2019s t-tests return valid p-values when comparing mutation presence with gene knockout outcomes.\nThe test simulates mutation and fold change data and ensures the p-value is numeric and less than 1.\nTest Output:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf all tests pass, the script will print a success message: \"All tests passed successfully!\"\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Outputs\u003c/h3\u003e\u003ca id=\"user-content-5-outputs\" class=\"anchor\" aria-label=\"Permalink: 5. Outputs\" href=\"#5-outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe results and plots generated by the analysis are saved in the output directory. These include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esignificant_mutation_gene_associations.tsv: A table of significant mutation-gene associations after FDR correction.\u003c/li\u003e\n\u003cli\u003eqqplot_pvalues.png: A Q-Q plot for visualizing the distribution of p-values.\u003c/li\u003e\n\u003cli\u003ehistogram_pvalues.png: A histogram displaying the p-value distribution.\u003c/li\u003e\n\u003cli\u003evolcano_plot.png: A volcano plot of mutation-gene associations.\u003c/li\u003e\n\u003cli\u003eBoxplots: Individual PNG files for each significant mutation-gene association.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload the Prebuilt Singularity Image\u003c/h3\u003e\u003ca id=\"user-content-download-the-prebuilt-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Download the Prebuilt Singularity Image\" href=\"#download-the-prebuilt-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you prefer not to build the container yourself, you can download the prebuilt Singularity image (\u003ccode\u003er-analysis.sif\u003c/code\u003e) from the following link:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://drive.google.com/file/d/17zTK5rTlSquPqw5A8xGvv1kwUP1szQDB/view?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/file/d/17zTK5rTlSquPqw5A8xGvv1kwUP1szQDB/view?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMachine Learning Models for Predicting CRISPR Knockout Effects\u003c/h1\u003e\u003ca id=\"user-content-machine-learning-models-for-predicting-crispr-knockout-effects\" class=\"anchor\" aria-label=\"Permalink: Machine Learning Models for Predicting CRISPR Knockout Effects\" href=\"#machine-learning-models-for-predicting-crispr-knockout-effects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClassical ML Approach\u003c/h3\u003e\u003ca id=\"user-content-classical-ml-approach\" class=\"anchor\" aria-label=\"Permalink: Classical ML Approach\" href=\"#classical-ml-approach\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each gene:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDefine the features as the binary mutations in the cell lines.\u003c/li\u003e\n\u003cli\u003eDefine the target: the target is a continuous variable \"knock-out effect\".\u003c/li\u003e\n\u003cli\u003eTrain a separate Random Forest regression model for each gene: each model is trained on the same mutation data (features), but the target variable changes based on the gene whose knock-out effect we are trying to predict.\u003c/li\u003e\n\u003cli\u003eI would then test several other models that people previously used in the literature, like Elastic Net Regression and Gradient Boosting Machines.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMulti-Output Models\u003c/h3\u003e\u003ca id=\"user-content-multi-output-models\" class=\"anchor\" aria-label=\"Permalink: Multi-Output Models\" href=\"#multi-output-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the current approach, I train separate models for each gene. However, there is an opportunity to take advantage of the correlation between gene knockouts by predicting multiple gene knockout effects simultaneously using a Multi-Output Random Forest or indeed multi-task learning with Convolutional Neural Networks (becasue neural network can be set up to have multiple output neurons, one for each target variable).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMulti-Task Linear Regression\u003c/h3\u003e\u003ca id=\"user-content-multi-task-linear-regression\" class=\"anchor\" aria-label=\"Permalink: Multi-Task Linear Regression\" href=\"#multi-task-linear-regression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn even more advanced approach would be to apply a Bayesian multi-task linear regression model or multi-task learning with CNNs to solve this problem. For example, see here: \u003ca href=\"https://www.sciencedirect.com/science/article/abs/pii/S1046202323002128?via%3Dihub\" rel=\"nofollow\"\u003ehttps://www.sciencedirect.com/science/article/abs/pii/S1046202323002128?via%3Dihub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImplementation\u003c/h3\u003e\u003ca id=\"user-content-implementation\" class=\"anchor\" aria-label=\"Permalink: Implementation\" href=\"#implementation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI implemented a simple RF model, for a single gene, in the script \u003ccode\u003erandom_forest_model_for_gene_knockout_prediction_using_mutation_data.py\u003c/code\u003e\nYou can also run it on Colab: \u003ca href=\"https://colab.research.google.com/drive/1aIjK0wWs3UwiVI_bn-OcuUrHaQCpnZZo?usp=sharing\" rel=\"nofollow\"\u003ehttps://colab.research.google.com/drive/1aIjK0wWs3UwiVI_bn-OcuUrHaQCpnZZo?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGeneralization to Unseen Data\u003c/h1\u003e\u003ca id=\"user-content-generalization-to-unseen-data\" class=\"anchor\" aria-label=\"Permalink: Generalization to Unseen Data\" href=\"#generalization-to-unseen-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInitial Model Build\u003c/h2\u003e\u003ca id=\"user-content-initial-model-build\" class=\"anchor\" aria-label=\"Permalink: Initial Model Build\" href=\"#initial-model-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe generalization of the model will primarily depend the diversity and size of training data and regularization techniques (i.e. feature selection) we used. We can assess the initial generalizability with k-fold cross-validation and bootstrapping.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePan-Cancer vs. Tissue-Specific Models\u003c/strong\u003e\u003c/em\u003e\nIn a broader context, models could be trained in two ways:\u003c/p\u003e\n\u003cp\u003e\u2022\tPan-cancer models pool data from multiple cancer types, treating the knockouts as a universal phenomenon across all cell lines. This assumes mutation-response relationships are similar across different tissue types.\u003c/p\u003e\n\u003cp\u003e\u2022\tTissue-specific models account for lineage-specific effects by training separate models for individual cancer types.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel in Production\u003c/h2\u003e\u003ca id=\"user-content-model-in-production\" class=\"anchor\" aria-label=\"Permalink: Model in Production\" href=\"#model-in-production\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the models are deployed to production two issues are likely to occur \u003cstrong\u003edata drift\u003c/strong\u003e (changing mutation patterns) and \u003cstrong\u003econcept drift\u003c/strong\u003e (shifting biological relationships/interpretation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Data Drift:\u003c/strong\u003e\nThis will occur when the distribution of mutations in the new cell lines is significantly different from the training data. For instance, if the model was trained on cell lines that mostly contained mutations in specific genes, but new cell lines have mutations in entirely different genes or exhibit new mutation patterns, the model\u0027s performance will degrade.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSuggested Solution:\u003c/strong\u003e\u003c/em\u003e To detect data drift, we could monitor the distribution of mutations (input features) in new cell lines and compare them with the original training data. We can use statistical tests like Kolmogorov-Smirnov test or domain classifiers (which distinguish between old and new data) to detect these changes. A significant drift will require model retraining.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Concept Drift:\u003c/strong\u003e\nThis I believe will be less of a concern. Concept drift refers to a change in the underlying relationship between the input features and the target variable over time. In our context this could happen when we introduce new experimental conditions, such as different cell environments or evolve resistant cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSuggested Solution:\u003c/strong\u003e\u003c/em\u003e I think detecting this type of drift in our context would be difficult and would have to be informed by wet-lab scientists (are they doing anything different?). We could monitor the model\u2019s predictions and their accuracy on new cell lines. If the KO effect predictions are consistently inaccurate for new data even after retraining that to me would indicate concept drift.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_recipe\u003c/h1\u003e\u003ca id=\"user-content-singularity_recipe\" class=\"anchor\" aria-label=\"Permalink: singularity_recipe\" href=\"#singularity_recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1726235293.0 + "updated_at": 1644473010.0 }, { "data_format": 2, - "description": null, + "description": "Singularity images for Jupyter (based on minconda3 Docker image)", "filenames": [ "Singularity" ], - "full_name": "aswinnarayanan/ci-dev", + "full_name": "bihealth/singularity-jupyter", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eci-dev\u003c/h1\u003e\u003ca id=\"user-content-ci-dev\" class=\"anchor\" aria-label=\"Permalink: ci-dev\" href=\"#ci-dev\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Image with Jupyter\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-with-jupyter\" class=\"anchor\" aria-label=\"Permalink: Singularity Image with Jupyter\" href=\"#singularity-image-with-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1598406354.0 + "updated_at": 1591308299.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for installing NEMO prerequisites, and scripts for configuring and running AMM7 model", "filenames": [ "Singularity" ], - "full_name": "thiago-rissi/time_encoding_experiment", + "full_name": "swarder/NEMO-AMM7-recipe", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etime_encoder\u003c/h1\u003e\u003ca id=\"user-content-time_encoder\" class=\"anchor\" aria-label=\"Permalink: time_encoder\" href=\"#time_encoder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNEMO-AMM7-recipe\u003c/h1\u003e\u003ca id=\"user-content-nemo-amm7-recipe\" class=\"anchor\" aria-label=\"Permalink: NEMO-AMM7-recipe\" href=\"#nemo-amm7-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for installing NEMO prerequisites, and scripts for configuring and running AMM7 model\u003c/p\u003e\n\u003cp\u003eScripts installing prerequisites and downloading NEMO source code are modified from \u003ca href=\"https://github.com/rcaneill/NEMO-installs\"\u003ehttps://github.com/rcaneill/NEMO-installs\u003c/a\u003e (Copyright (c) 2019 Romain Caneill)\nModified here under MIT licence \u003ca href=\"https://github.com/rcaneill/NEMO-installs/blob/master/LICENSE\"\u003ehttps://github.com/rcaneill/NEMO-installs/blob/master/LICENSE\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAMM7 configuration based on \u003ca href=\"https://zenodo.org/record/4022310\" rel=\"nofollow\"\u003ehttps://zenodo.org/record/4022310\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe pre-built image can be pulled from Singularity Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://swarder/NEMO-AMM7-recipe:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, the recipe can be built locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build NEMO_AMM7.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce pulled or built, launch the shell (replace file name as appropriate):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell NEMO-AMM7-recipe_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDefine working directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport WORKDIR=/home/$USER/nemo_workdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen configure AMM7 within container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd $WORKDIR\ncp /nemo/installations/configure_amm7.sh .\n./configure_amm7.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, run NEMO\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd $WORKDIR/NEMOGCM/CONFIG/AMM7_SURGE/EXP_tideonly\nmpirun -np 6 ./opa : -np 1 ./xios_server.exe\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1724562717.0 + "updated_at": 1610120713.0 }, { "data_format": 2, - "description": "Container image with signalp and targetp programs for functional analysis pipelines", + "description": "get openpose on PSU ACI", "filenames": [ - "Singularity" + "Singularity.gpu" ], - "full_name": "biocorecrg/sigtarp_docker", - "latest_release": "5.0b", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esigtarp_docker\u003c/h1\u003e\u003ca id=\"user-content-sigtarp_docker\" class=\"anchor\" aria-label=\"Permalink: sigtarp_docker\" href=\"#sigtarp_docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/152766566\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9fb76abe902d36be7979eb4e057c173eb23157d522a904a478f52585e65ca0da/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3135323736363536362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/152766566.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContainer image with \u003ca href=\"http://www.cbs.dtu.dk/services/SignalP/\" rel=\"nofollow\"\u003esignalP\u003c/a\u003e, \u003ca href=\"http://www.cbs.dtu.dk/services/TargetP/\" rel=\"nofollow\"\u003etargetP\u003c/a\u003e programs for functional analysis pipelines.\u003c/p\u003e\n\u003cp\u003eCreate a directory named \u003ccode\u003eexternal\u003c/code\u003e and place 2 directories with its associated files and binaries as downloaded from the links above. You need to be granted an academic license permission first.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esignalp (5.0b)\u003c/li\u003e\n\u003cli\u003etargetp (2.0)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eContainer recipes will grab the necessary files from these directories.\u003c/p\u003e\n\u003cp\u003eBuilding with \u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build sigtarp.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can avoid using sudo with \u003ccode\u003e--fakeroot\u003c/code\u003e Singularity build option.\u003c/p\u003e\n", + "full_name": "d-bohn/openpose_aci", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eopenpose_aci\u003c/h1\u003e\u003ca id=\"user-content-openpose_aci\" class=\"anchor\" aria-label=\"Permalink: openpose_aci\" href=\"#openpose_aci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and workflow for building \u003ca href=\"https://github.com/CMU-Perceptual-Computing-Lab/openpose\"\u003eOpenPose\u003c/a\u003e\nin Docker Hub and modifying it with Singularity Hub for use with PSU\nACI HPC clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: This is the GPU version of OpenPose, for the CPU-only version please\nrefer to the appropriately labelled branch.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003essh\u003c/code\u003e into the PSU ACI HPC with X11 flags.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh USERID@aci-b.aci.ics.psu.edu -X -Y\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStart an interactive session using \u003ccode\u003eqsub\u003c/code\u003e. We need a lot of memory for\nthe CPU version of OpenPose\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqsub -A open -I -X -l walltime=24:00:00 -l nodes=5:ppn=10 -l pmem=20gb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom ACI pull the OpenPose image and shell into it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n openpose_aci.simg shub://d-bohn/openpose_aci\n\nsingularity exec -n openpose_aci.simg /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, once inside the image you can run the example utilizing the following\ncode:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /opt/openpose\nmkdir data \u0026amp;\u0026amp; mkdir data/poses\n\n./build/examples/openpose/openpose.bin --video examples/media/video.avi --write_video ./data/result.avi --write_json ./data/poses --display 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImage Builds\u003c/h2\u003e\u003ca id=\"user-content-image-builds\" class=\"anchor\" aria-label=\"Permalink: Image Builds\" href=\"#image-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe OpenPose docker image was built on docker hub.\u003c/p\u003e\n\u003cp\u003eThe OpenPose singularity image was built using the docker image base and\nconverting it to a singularity image via singularity hub.\u003c/p\u003e\n\u003cp\u003eSetup for linking Github with Docker Hub and Singularity Hub can be found here:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.docker.com/docker-hub/\" rel=\"nofollow\"\u003edocker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki\"\u003eSingularity Hub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003eSingularity\u003c/code\u003e file specifies creating a Singularity-compatible image\nfrom the docker image, as well as adding access to folders within ACI, specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# ACI mappings so you can access your files.\nmkdir -p /storage/home\nmkdir -p /storage/work\nmkdir -p /gpfs/group\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe OpenFace docker image is large (\u0026gt; 3.7GB). It is built on Ubuntu 16.04.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe current image is built with only CPU support, but can easily be adapted to\ninclude GPU support when that is available (see first two \u003ccode\u003emake\u003c/code\u003e flags in \u003ccode\u003eDockerfile\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe CPU version is SLOW. The example above takes several minutes to\nexecute. Runs at between 0.3 and 0.1 frames/second.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1638960392.0 + "updated_at": 1578090055.0 }, { "data_format": 2, - "description": "Container recipe for NECAT available here: https://github.com/xiaochuanle/NECAT", + "description": null, "filenames": [ - "Singularity" + "Singularity.PGDSpider_2.1.1.5", + "Singularity.longshot", + "Singularity.FastQC_0.11.5", + "Singularity.plink_1.9", + "Singularity.minimap2_2.17", + "Singularity.BCFtools_1.9", + "Singularity.R_3.5.0", + "Singularity.AdapterRemoval", + "Singularity.plink_2.0", + "Singularity.STAR_2.7.1a", + "Singularity.BayeScan_2.1", + "Singularity.R_3.6.0", + "Singularity.BBMap_37.92", + "Singularity.vcflib", + "Singularity.samtools_1.9", + "Singularity.fastsimcoal_2.6", + "Singularity.VCFtools_0.1.17", + "Singularity.gatk_3.8.0" ], - "full_name": "HuffordLab-Containers/necat", + "full_name": "MarissaLL/singularity-containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enecat\u003c/h1\u003e\u003ca id=\"user-content-necat\" class=\"anchor\" aria-label=\"Permalink: necat\" href=\"#necat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003eRecipes for Singularity containers, which are hosted on SingularityHub at\n\u003ca href=\"https://www.singularity-hub.org/collections/1290\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/1290\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1614611989.0 + "updated_at": 1629949124.0 }, { "data_format": 2, - "description": "containers-ftw version of https://www.kaggle.com/c/flavours-of-physics", + "description": "du + rust = dust. Like du but more intuitive.", "filenames": [ - "Singularity" + "0.8.3/Singularity", + "0.7.0/Singularity", + "0.6.0/Singularity", + "0.8.6/Singularity", + "0.6.1/Singularity", + "0.8.0/Singularity", + "0.5.4/Singularity", + "0.8.4/Singularity" ], - "full_name": "sci-f/flavours-of-physics.scif", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMu Tau Tau Tau ContainersFTW\u003c/h1\u003e\u003ca id=\"user-content-mu-tau-tau-tau-containersftw\" class=\"anchor\" aria-label=\"Permalink: Mu Tau Tau Tau ContainersFTW\" href=\"#mu-tau-tau-tau-containersftw\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/containers-ftw.png\"\u003e\u003cimg src=\"img/containers-ftw.png\" alt=\"img/containers-ftw.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.org/containers-ftw/flavours-of-physics-ftw\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ed5f9af035d26113e1a9dff5169a0af2fb904250e307d127a4a8497c23d0b714/68747470733a2f2f7472617669732d63692e6f72672f636f6e7461696e6572732d6674772f666c61766f7572732d6f662d706879736963732d6674772e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/containers-ftw/flavours-of-physics-ftw.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a competition orginally \u003ca href=\"https://www.kaggle.com/c/flavours-of-physics/data\" rel=\"nofollow\"\u003ehosted on Kaggle\u003c/a\u003e, reproduced here to encourage containerization of submissions by way of \u003ca href=\"https://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. If you aren\u0027t familiar with Singularity, it\u0027s a container (like Docker) that can be run securely on HPC architectures.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClone\u003c/h2\u003e\u003ca id=\"user-content-clone\" class=\"anchor\" aria-label=\"Permalink: Clone\" href=\"#clone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst fork the repo to your own username. For example, if my user name is \u003ccode\u003evsoch\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/vsoch/flavours-of-physics-ftw\ncd flavours-of-physics-ftw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity create --size 8000 container.ftw \nsudo singularity bootstrap container.ftw Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B data/input:/data/input -B analysis:/code --pwd /code container.ftw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow edit \u003ccode\u003emain.py\u003c/code\u003e, do better, and submit a PR to the contest repo for your entry. Want more details? keep reading!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompetition Overview\u003c/h2\u003e\u003ca id=\"user-content-competition-overview\" class=\"anchor\" aria-label=\"Permalink: Competition Overview\" href=\"#competition-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eGoals\u003c/strong\u003e: Read about the \u003ca href=\"https://www.kaggle.com/c/flavours-of-physics/data\" rel=\"nofollow\"\u003edata\u003c/a\u003e to get a breakdown of the data provided, and the \u003ca href=\"https://www.kaggle.com/c/flavours-of-physics\" rel=\"nofollow\"\u003ebackground and goals\u003c/a\u003e of the competition is beautifully described and shown on Kaggle.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBuild\u003c/strong\u003e: Build your container (see build section below), which will install dependencies and prepare data for you. If you find that you need any more, or any additional software or libraries, you can add them to the \u003ccode\u003e%post\u003c/code\u003e section of the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCode\u003c/strong\u003e: Once you have your container built, you can use it to develop and test your submission.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSubmit\u003c/strong\u003e: A submission to the competition means submitting a PR (pull request)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGoals\u003c/h3\u003e\u003ca id=\"user-content-goals\" class=\"anchor\" aria-label=\"Permalink: Goals\" href=\"#goals\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEvaluation for this competition is based on AUC (area under the curve), defined as area under the curve, which broadly gets at the ratio of false positives to false negatives for your model. In addition to this criteria, the \u003ca href=\"metrics.py\"\u003emetrics\u003c/a\u003e file includes multiple checks that physicists do to make sure that results are unbiased.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild\u003c/h3\u003e\u003ca id=\"user-content-build-1\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen you are ready to start your submission, you should fork the repo to your branch, and then clone the fork. For example, if my username on Github was \u003ccode\u003evsoch\u003c/code\u003e, I would fork and then do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/vsoch/flavours-of-physics-ftw\ncd flavours-of-physics-ftw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can build your image. You will need one dependency, that \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003eSingularity is installed\u003c/a\u003e. Building comes down to creating an image and then using \u003ccode\u003ebootstrap\u003c/code\u003e to build from the container recipe, \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity create --size 8000 container.ftw \nsudo singularity bootstrap container.ftw Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWork in your Container\u003c/h2\u003e\u003ca id=\"user-content-work-in-your-container\" class=\"anchor\" aria-label=\"Permalink: Work in your Container\" href=\"#work-in-your-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo shell into your container, you will want to mount the analysis folder, and the external data. You can do that like this. Note that we are making the present working directory (\u003ccode\u003epwd\u003c/code\u003e) our folder with analysis scripts:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell -B data/input:/data/input -B analysis:/code --pwd /code container.ftw\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen you shell into your container, it probably will look the same, but if you do \u003ccode\u003els /\u003c/code\u003e you will see a file called \u003ccode\u003esingularity\u003c/code\u003e and root folders \u003ccode\u003e/data\u003c/code\u003e and \u003ccode\u003e/code\u003c/code\u003e that aren\u0027t on your host. If you look inside, you will see the data and\nanalysis scripts mounted!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003els /code\nREADME.md helpers main.py metrics.py results tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTry creating a file on the host, and you will see it change in the container, or vice versa. Thus, your general workflow will be the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erun things from within the container, using the python or ipython located at \u003ccode\u003e/opt/conda/bin\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eedit code in your editor of choice on your host machine\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment\u003c/h3\u003e\u003ca id=\"user-content-environment\" class=\"anchor\" aria-label=\"Permalink: Environment\" href=\"#environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to ever find data or results locations, these have been provided for you via environment variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINERSFTW_DATA\u003c/code\u003e: The base folder with data\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINERSFTW_RESULT\u003c/code\u003e: The folder where results are written to\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINERSFTW_WORK\u003c/code\u003e: The folder where your scripts live.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt\u0027s definitely a good idea if you are interested to shell around the container to understand where things are located, and test the variables to confirm they are the same:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eecho $CONT\n$CONTAINERSFTW_RESULT $CONTAINERSFTW_DATA $CONTAINERSFTW_WORK \necho $CONTAINERSFTW_DATA\n/data/input\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCode\u003c/h3\u003e\u003ca id=\"user-content-code\" class=\"anchor\" aria-label=\"Permalink: Code\" href=\"#code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can work from inside the container, or comfortable from the host in the \u003ccode\u003eanalysis\u003c/code\u003e folder (mapped to \u003ccode\u003e/code\u003c/code\u003e in the container). Your main work is going to be located at \u003ccode\u003e/code/main.py\u003c/code\u003e in the container, which is \u003ccode\u003eanalysis/main.py\u003c/code\u003e on the host. If you open up this file, you can start interactively working in an ipython terminal in the container to test commands. For example, from \u003ccode\u003e/code\u003c/code\u003e let\u0027s try loading the data in \u003ccode\u003eipython\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efrom sklearn.ensemble import GradientBoostingClassifier\nfrom helpers.data import load_data\nfrom helpers.logger import bot\n\ntrain = load_data(name=\"training\")\nDEBUG Loading training : /data/input/training.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand if you proceed through the rest of the script, you will produce an example result. You can also run the entire example without shelling into the container at all:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B data/input:/data/input -B analysis:/code --pwd /code container.ftw\nDEBUG Loading training : /data/input/training.csv\n\nChecking Agreement:\nDEBUG Loading check_agreement : /data/input/check_agreement.csv\nKS metric 0.0681705596239 True\n\nChecking Correlation:\nDEBUG Loading check_correlation : /data/input/check_correlation.csv\nCvM metric 0.000981509354914 True\n\nChecking AUC:\nAUC 0.834346382383\nDEBUG Loading test : /data/input/test.csv\nDEBUG submission : /code/results/submission.csv\nLOG Result saved to /code/results/submission.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe result file is what gets tested in the continuous integration.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdding Dependencies\u003c/h3\u003e\u003ca id=\"user-content-adding-dependencies\" class=\"anchor\" aria-label=\"Permalink: Adding Dependencies\" href=\"#adding-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you add dependencies (another python module, additional data that conforms to competition rules, etc) you should update the Singularity recipe, for example, we have marked in \u003ccode\u003e%post\u003c/code\u003e where you can add installation steps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e #########################################################\n # Install additional software / libraries here\n #########################################################\n\n pip install -y pokemon\n\n #########################################################\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eDo I have to use Python?\u003c/strong\u003e: Of course not! The base template image given to use is based on a choice by the creator (for example, lots of people use \u003ccode\u003escikit-learn\u003c/code\u003e in python for machine learning). At the end of the day, the evaluation is done over the text file in \u003ccode\u003e/analysis/results/submission\u003c/code\u003e and is ambivalent to how it is generated. Your submission (the container image) must simply run to generate it, and you are good.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor now, for additional FAQ please see our \u003ca href=\"https://containers-ftw.github.io\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "pscedu/singularity-dust", + "latest_release": "v0.8.4", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-dust/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-dust/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-dust/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-dust/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/61adf552b3234b2843e4c95d2a40467bcf6ed674755f53c0298c3fef098cefcf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d64757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/61adf552b3234b2843e4c95d2a40467bcf6ed674755f53c0298c3fef098cefcf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d64757374\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-dust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8423ec0eae69f2f03ad7b0b727465c03df5d0a7f33fb5249b5190e6297681242/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d64757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8423ec0eae69f2f03ad7b0b727465c03df5d0a7f33fb5249b5190e6297681242/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d64757374\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-dust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7b19576388c609249f90940002caee3d06bfc3a99c7d4613e3f12d95d8d755cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d64757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b19576388c609249f90940002caee3d06bfc3a99c7d4613e3f12d95d8d755cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d64757374\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-dust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d9c4b5e46fea27c4a179d1ae397e785209ca6251d122ffd936a03692f6c1c471/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d64757374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d9c4b5e46fea27c4a179d1ae397e785209ca6251d122ffd936a03692f6c1c471/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d64757374\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-dust\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-dust\u003c/h1\u003e\u003ca id=\"user-content-singularity-dust\" class=\"anchor\" aria-label=\"Permalink: singularity-dust\" href=\"#singularity-dust\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bootandy/dust/raw/master/media/snap.png\"\u003e\u003cimg src=\"https://github.com/bootandy/dust/raw/master/media/snap.png\" alt=\"Example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/bootandy/dust\"\u003edust\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003edust\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/dust/0.8.6\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/dust\u003c/code\u003e as \u003ccode\u003e0.8.6.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [ - "containers-ftw", - "mu", - "tau", - "physics", - "competition", - "containers", - "singularity-containers" + "singularity", + "utilities" ], - "updated_at": 1516487103.0 + "updated_at": 1729743820.0 }, { "data_format": 2, - "description": "CachingOnDemand provides recipes and PaaS description templates for an end to end deployment of an XCache cluster", + "description": null, "filenames": [ - "singularity/Singularity" + "vrep/Singularity+", + "vrep/Singularity-without-conda+", + "vrep/Singularity-without-conda", + "vrep/Singularity", + "vrep/Singularity-cupy" ], - "full_name": "Cloud-PG/CachingOnDemand", - "latest_release": "v2.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOn-Demand XCache cluster\u003c/h1\u003e\u003ca id=\"user-content-on-demand-xcache-cluster\" class=\"anchor\" aria-label=\"Permalink: On-Demand XCache cluster\" href=\"#on-demand-xcache-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1e34aacbd16043130fdc18f969404611342511683f9225d581c4d58856802f51/68747470733a2f2f7472617669732d63692e6f72672f436c6f75642d50472f43616368696e674f6e44656d616e642e7376673f6272616e63683d6d6173746572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e34aacbd16043130fdc18f969404611342511683f9225d581c4d58856802f51/68747470733a2f2f7472617669732d63692e6f72672f436c6f75642d50472f43616368696e674f6e44656d616e642e7376673f6272616e63683d6d6173746572\" alt=\"travis build of ansible\" data-canonical-src=\"https://travis-ci.org/Cloud-PG/CachingOnDemand.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://cloud-pg.github.io/CachingOnDemand/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "takuma-yoneda/singularity-envs", + "latest_release": null, "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1574268938.0 + "updated_at": 1597803674.0 }, { "data_format": 2, - "description": "Build a Singularity container for Photoscan", + "description": null, "filenames": [ "Singularity" ], - "full_name": "ucb-rit/singularity-photoscan", + "full_name": "soulj/OAModelmicroRNA", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuild a Singularity container that includes Photoscan, using a Singularity recipe file\u003c/h1\u003e\u003ca id=\"user-content-build-a-singularity-container-that-includes-photoscan-using-a-singularity-recipe-file\" class=\"anchor\" aria-label=\"Permalink: Build a Singularity container that includes Photoscan, using a Singularity recipe file\" href=\"#build-a-singularity-container-that-includes-photoscan-using-a-singularity-recipe-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild a Photoscan container using Singularity to run on Jetstream, Savio, or anyehwere else the Singularity container runtime is installed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThese instructions are current as of 08 August 2018, for Photoscan v1.4.3; as of this date, the \u003ccode\u003eSingularity\u003c/code\u003e file in this repo folder builds this version of Photoscan. It\u0027s a good idea to include the Photoscan version used in the name the Singularity container, as in \u003ccode\u003ephotoscan_1_4_3.simg\u003c/code\u003e, below.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/ucberkeley/brc-cyberinfrastructure\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e brc-cyberinfrastructure/photoscancontainer\n$ sudo singularity build photoscan_1_4_3.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor a simple test that Photoscan runs in the container, it is not necessary to have a license:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /home/masover/:/opt photoscan_1_4_3.simg /usr/local/photoscan-pro/photoscan.sh --version -platform offscreen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe smoke-test passes if you get something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eAgisoft PhotoScan Professional Version: 1.4.3 build 6185 (64 bit)\nCopyright (C) 2017 Agisoft LLC.\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA simple functional test (which requires access to a license server that serves a valid Photoscan license) is included in the subdirectory \"container-test\" (a Python script and a set of images that the script references). Invocation of the script would look something like this (replacing, as appropriate: the /global/scratch... paths; as well as the reference to a license server on UC Berkeley\u0027s shared HPC cluster, Savio, with another license server):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSINGULARITYENV_RLM_LICENSE=5053@lmgr0@brc.berkeley.edu singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /global/scratch/username/photoscan/:/opt /global/scratch/username/containers/photoscan_1_4_3.simg /usr/local/photoscan-pro/photoscan.sh -r /opt/photoscan-test.py -platform offscreen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA successful response will look something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eAddPhotos: filenames = /opt/images/coffeecup-1.jpg\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e/opt/images/coffeecup-2.jpg\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e/opt/images/coffeecup-3.jpg\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e/opt/images/coffeecup-4.jpg\nSaveProject: chunks = 0, path = /opt/photoscan-test.psz\nsaved project \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e 0.014898 sec\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDeeper exercise of Photoscan functionality should probably not use the sample images of a coffee cup. Instead, use images provided by the Photoscan vendor (Agisoft) at \u003ca href=\"http://www.agisoft.com/downloads/sample-data/\" rel=\"nofollow\"\u003ehttp://www.agisoft.com/downloads/sample-data/\u003c/a\u003e with appropriate changes to the/a test script.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003cem\u003eThese instructions supercede a previous set that involved building a Docker container and pulling it into Singularity. As of Singularity v2.6.0 and Photoscan 1.4.3, the method given here is the one that works (and is faster besides). This repository was replicated from \u003ca href=\"https://github.com/ucberkeley/brc-cyberinfrastructure/tree/master/photoscancontainer\"\u003ehttps://github.com/ucberkeley/brc-cyberinfrastructure/tree/master/photoscancontainer\u003c/a\u003e on 2019-02-15.\u003c/em\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eACL model of osteoarthritis mRNA and miRNA analysis\u003c/h1\u003e\u003ca id=\"user-content-acl-model-of-osteoarthritis-mrna-and-mirna-analysis\" class=\"anchor\" aria-label=\"Permalink: ACL model of osteoarthritis mRNA and miRNA analysis\" href=\"#acl-model-of-osteoarthritis-mrna-and-mirna-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/soulj/OAModelmicroRNA/blob/main/figures/Fig2C_MAPlot.png\"\u003e\u003cimg src=\"https://github.com/soulj/OAModelmicroRNA/raw/main/figures/Fig2C_MAPlot.png\" width=\"40%\" height=\"40%\" align=\"center\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following R notebooks can be used to generate the bioinformatics figures and tables shown in the paper:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e01_ACLmRNA.qmd - DESeq2 analysis of the ACL rupture model mRNA-seq\u003c/li\u003e\n\u003cli\u003e02_ACLmiRNA.qmd - DESeq2 analysis of the ACL rupture model smallRNA-seq\u003c/li\u003e\n\u003cli\u003e03_mir199DiffExp.qmd - RNA-seq Differential expression, gene ontology and target analysis of mir199 inhibited HACs\u003c/li\u003e\n\u003cli\u003e04_DMMDiffExp.qmd - DESeq2 analysis of the DMM OA model mRNA-seq\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the analysis\u003c/h2\u003e\u003ca id=\"user-content-running-the-analysis\" class=\"anchor\" aria-label=\"Permalink: Running the analysis\" href=\"#running-the-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReproducibly with singularity\u003c/h3\u003e\u003ca id=\"user-content-reproducibly-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Reproducibly with singularity\" href=\"#reproducibly-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter cloning/downloading this repository.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://docs.sylabs.io/guides/3.8/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload and run the singularity container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e singularity run https://pgb.liv.ac.uk/~jsoul/OAModelmicroRNA/analysis.img\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild the singularity container:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e sudo singularity build runAnalysis.img Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the analysis and render tables and figures with a single command:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e ./runAnalysis.img\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAlternatively using RScript\u003c/h3\u003e\u003ca id=\"user-content-alternatively-using-rscript\" class=\"anchor\" aria-label=\"Permalink: Alternatively using RScript\" href=\"#alternatively-using-rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall the needed R packages\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e RScript install/install.R\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the analysis and render the html notebooks\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e RScript runAnalysis.R\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRaw data processing\u003c/h2\u003e\u003ca id=\"user-content-raw-data-processing\" class=\"anchor\" aria-label=\"Permalink: Raw data processing\" href=\"#raw-data-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the smallRNA-seq data the nextflow core smrnaseq v1.1.0\nwas run using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run nf-core/smrnaseq -r 1.1.0 --input \"fastqFiles/*.fastq.gz\" --genome GRCm38 --protocol \u0027custom\u0027 --three_prime_adapter AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC --mirtrace_protocol illumina --max_cpus 6 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSkeletalvis pipeline was used to process the RNA-seq data (github.com/soulj/SkeletalVis-Pipeline)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 17, + "subscribers_count": 1, "topics": [], - "updated_at": 1550266906.0 + "updated_at": 1683907187.0 }, { "data_format": 2, - "description": "Singularity ML Box with PyTorch, Keras, Tensorflow", + "description": "Comp-550 Fall 2023 term group project", "filenames": [ - "Singularity.ubuntu-bionic-cuda10", - "Singularity.ubuntu-xenial-cuda10", - "Singularity.ubuntu-bionic-cuda92", - "Singularity.ubuntu-xenial-cuda92" + "Singularity" ], - "full_name": "jeffacce/singularity-ml-box", + "full_name": "mathematiguy/enron-nlp-analysis", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-ml-box\u003c/h1\u003e\u003ca id=\"user-content-singularity-ml-box\" class=\"anchor\" aria-label=\"Permalink: singularity-ml-box\" href=\"#singularity-ml-box\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity ML Box with PyTorch 1.0, Keras, Tensorflow, CUDA 10, Ubuntu 18.04 LTS\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eenron-nlp-analysis\u003c/h1\u003e\u003ca id=\"user-content-enron-nlp-analysis\" class=\"anchor\" aria-label=\"Permalink: enron-nlp-analysis\" href=\"#enron-nlp-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComp-550 Fall 2023 term group project\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProject Setup and Execution Guide\u003c/h1\u003e\u003ca id=\"user-content-project-setup-and-execution-guide\" class=\"anchor\" aria-label=\"Permalink: Project Setup and Execution Guide\" href=\"#project-setup-and-execution-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis README provides instructions for setting up and running a project that uses Data Version Control (DVC). DVC is an open-source tool for data science and machine learning projects. It allows for tracking and versioning of datasets and machine learning models, making it easier to share and reproduce experiments and analyses.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore you start, ensure you have the following installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython (version as per project requirements)\u003c/li\u003e\n\u003cli\u003epip (Python package manager)\u003c/li\u003e\n\u003cli\u003evirtualenv (Python environment management tool)\u003c/li\u003e\n\u003cli\u003eDVC (Data Version Control)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup Instructions\u003c/h2\u003e\u003ca id=\"user-content-setup-instructions\" class=\"anchor\" aria-label=\"Permalink: Setup Instructions\" href=\"#setup-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Clone the Project Repository\u003c/h3\u003e\u003ca id=\"user-content-1-clone-the-project-repository\" class=\"anchor\" aria-label=\"Permalink: 1. Clone the Project Repository\" href=\"#1-clone-the-project-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the project from the provided source and navigate to the project directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Create and Activate a Python Virtual Environment\u003c/h3\u003e\u003ca id=\"user-content-2-create-and-activate-a-python-virtual-environment\" class=\"anchor\" aria-label=\"Permalink: 2. Create and Activate a Python Virtual Environment\" href=\"#2-create-and-activate-a-python-virtual-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython -m venv venv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn Windows: \u003ccode\u003evenv\\Scripts\\activate\u003c/code\u003e\nmacOS and Linux: \u003ccode\u003esource venv/bin/activate\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Install Project Dependencies\u003c/h3\u003e\u003ca id=\"user-content-3-install-project-dependencies\" class=\"anchor\" aria-label=\"Permalink: 3. Install Project Dependencies\" href=\"#3-install-project-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Download NLTK Dependencies\u003c/h3\u003e\u003ca id=\"user-content-4-download-nltk-dependencies\" class=\"anchor\" aria-label=\"Permalink: 4. Download NLTK Dependencies\" href=\"#4-download-nltk-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the following in the terminal:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m nltk.downloader -d data/nltk_data all\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Install spaCy Language Model\u003c/h3\u003e\u003ca id=\"user-content-5-install-spacy-language-model\" class=\"anchor\" aria-label=\"Permalink: 5. Install spaCy Language Model\" href=\"#5-install-spacy-language-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m spacy download en_core_web_sm\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the Project\u003c/h2\u003e\u003ca id=\"user-content-running-the-project\" class=\"anchor\" aria-label=\"Permalink: Running the Project\" href=\"#running-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eData Version Control (DVC)\u003c/h3\u003e\u003ca id=\"user-content-data-version-control-dvc\" class=\"anchor\" aria-label=\"Permalink: Data Version Control (DVC)\" href=\"#data-version-control-dvc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun dvc repro to execute the DVC pipeline. DVC manages the data processing stages as per the dvc.yaml file.\u003c/p\u003e\n\u003cp\u003eTo run the project, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edvc repro\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the entire pipeline from start to finish. If you wanna see the dag for the project, run \u003ccode\u003edvc dag\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1551987453.0 + "updated_at": 1702773276.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.R_3.6.0", - "Singularity.bioconductor_3.9", - "Singularity.R-Mfuzz_2.38.0" + "container/Singularity", + "container/single_container/Singularity" ], - "full_name": "TomHarrop/r-singularity", + "full_name": "Clinical-Genomics-Lund/SomaticPanelPipeline", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1568680401.0 + "updated_at": 1732607811.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity", + "SingularityFedora" ], - "full_name": "rkalyanapurdue/geoedf-connector", + "full_name": "jolars/HessianScreening", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egeoedf-connector\u003c/h1\u003e\u003ca id=\"user-content-geoedf-connector\" class=\"anchor\" aria-label=\"Permalink: geoedf-connector\" href=\"#geoedf-connector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCode for the Hessian Screening Rule\u003c/h1\u003e\u003ca id=\"user-content-code-for-the-hessian-screening-rule\" class=\"anchor\" aria-label=\"Permalink: Code for the Hessian Screening Rule\" href=\"#code-for-the-hessian-screening-rule\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://arxiv.org/abs/2104.13026\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6388ff4fabd00a51d4bfac35fb9ed96aa426dd928c7525234fe887624a148bab/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d313233342e35363738392d6233316231622e737667\" alt=\"arXiv\" data-canonical-src=\"https://img.shields.io/badge/arXiv-1234.56789-b31b1b.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe results from the simulations, which were run on a dedicated HPC\ncluster, are stored in the \u003ca href=\"results/\"\u003eresults folder\u003c/a\u003e. The figures and\ntables in the paper, generated from these results, are stored in\n\u003ca href=\"figures/\"\u003e\u003ccode\u003efigures/\u003c/code\u003e\u003c/a\u003e and \u003ca href=\"tables/\"\u003e\u003ccode\u003etables/\u003c/code\u003e\u003c/a\u003e respectively.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReproducing the Results\u003c/h2\u003e\u003ca id=\"user-content-reproducing-the-results\" class=\"anchor\" aria-label=\"Permalink: Reproducing the Results\" href=\"#reproducing-the-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe results from our paper were run through a singularity container.\nCheck the releases for pre-built singularity containers that you can\ndownload and use.\u003c/p\u003e\n\u003cp\u003eTo reproduce the results, \u003cstrong\u003ealways\u003c/strong\u003e use the singularity container. To\nrun an experiment from the singularity container, call\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind results:/project/results container.sif \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003escript\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;script\u0026gt;\u003c/code\u003e should be a name of a script in the \u003ca href=\"experiments/\"\u003eexperiments\nfolder\u003c/a\u003e, such as \u003ccode\u003esimulateddata.R\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRe-building the Singularity Container\u003c/h3\u003e\u003ca id=\"user-content-re-building-the-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Re-building the Singularity Container\" href=\"#re-building-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to re-build the singularity container from scratch (or\nsimply want to clone the repo to your local drive), you can do so via\nthe following steps.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone the repository to your local hard drive. On linux, using SSH\nauthentication, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:jolars/HessianScreening.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the root of the repo and download\nthe data sets by running the following commands.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e HessianScreening\nRscript data-raw/arcene.R\nRscript data-raw/breheny-data.R\nRscript data-raw/libsvm-data.R\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the singularity container by calling\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build container.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen proceed as in \u003ca href=\"#reproducing-the-results\"\u003eReproducing the Results\u003c/a\u003e\nto run the experiments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Experiments without Singularity (Not Recommended!)\u003c/h3\u003e\u003ca id=\"user-content-running-experiments-without-singularity-not-recommended\" class=\"anchor\" aria-label=\"Permalink: Running Experiments without Singularity (Not Recommended!)\" href=\"#running-experiments-without-singularity-not-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, you may also reproduce the results by cloning this\nrepository, then either opening the \u003ccode\u003eHessianScreening.Rproj\u003c/code\u003e file in R\nStudio or starting R in the root directory of this folder (which will\nactivate the renv repository) and then run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003erenv\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003erestore()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto restore the project library. Then build the R package (see below) and\nrun the simulations directly by running the scripts in the experiments\nfolder. This is \u003cstrong\u003enot recommended\u003c/strong\u003e, however, since it, unlike the\nSingularity container approach, does not exactly reproduce the software\nenvironment used when these simulations where originally run and may\nresult in discrepancies due to differences in for instance operating\nsystems, compilers, BLAS/LAPACK implementations, and most\ncritically, the version of R.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eR Package\u003c/h2\u003e\u003ca id=\"user-content-r-package\" class=\"anchor\" aria-label=\"Permalink: R Package\" href=\"#r-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to build and experiment with the package, you can do so by\ncalling\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e R CMD INSTALL \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eprovided you have \u003ccode\u003ecd\u003c/code\u003eed to the root folder of this repository. First\nensure, however, that you have enabled the renv project library by\ncalling \u003ccode\u003erenv::restore()\u003c/code\u003e (see the section above).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData\u003c/h2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-label=\"Permalink: Data\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe datasets used in these simulations are stored in the \u003ca href=\"data/\"\u003edata\nfolder\u003c/a\u003e. Scripts to retrieve these datasets from their original\nsources can be found in \u003ca href=\"data-raw/\"\u003e\u003ccode\u003edata-raw/\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1592580440.0 - }, - { - "data_format": 2, - "description": "HMMER is used for searching sequence databases for sequence homologs, and for making sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs).", - "filenames": [ - "3.3.1/Singularity", - "3.3.2/Singularity" - ], - "full_name": "pscedu/singularity-hmmer", - "latest_release": "v3.3.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-hmmer/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-hmmer/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-hmmer/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-hmmer/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/799816fd70c8377213a151f0eaae10e558bb65c45e9abb0d00faf2b3cfb6250a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d686d6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/799816fd70c8377213a151f0eaae10e558bb65c45e9abb0d00faf2b3cfb6250a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d686d6d6572\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-hmmer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/99a5788907342c6b3f1efdb2bd7b133bc109d02d875ead69c068fca8e20f1044/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d686d6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/99a5788907342c6b3f1efdb2bd7b133bc109d02d875ead69c068fca8e20f1044/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d686d6d6572\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-hmmer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/fa9dcf57e459cf613f3f62f13cda857ba73eaf63a5c8e324654c90b8aad6e344/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d686d6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fa9dcf57e459cf613f3f62f13cda857ba73eaf63a5c8e324654c90b8aad6e344/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d686d6d6572\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-hmmer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6edb3fb4ea40a9e47df0faa9e813a800869971e1d990e660a609031ad4d81a18/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d686d6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6edb3fb4ea40a9e47df0faa9e813a800869971e1d990e660a609031ad4d81a18/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d686d6d6572\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-hmmer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-hmmer\u003c/h1\u003e\u003ca id=\"user-content-singularity-hmmer\" class=\"anchor\" aria-label=\"Permalink: singularity-hmmer\" href=\"#singularity-hmmer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/EddyRivasLab/hmmer\"\u003ehmmer\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ealimask\u003c/code\u003e, \u003ccode\u003ehmmbuild\u003c/code\u003e, \u003ccode\u003ehmmemit\u003c/code\u003e, \u003ccode\u003ehmmpgmd\u003c/code\u003e, \u003ccode\u003ehmmscan\u003c/code\u003e, \u003ccode\u003ehmmstat\u003c/code\u003e, \u003ccode\u003ephmmer\u003c/code\u003e, \u003ccode\u003ehmmc2\u003c/code\u003e, \u003ccode\u003ehmmfetch\u003c/code\u003e, \u003ccode\u003ehmmpgmd_shard\u003c/code\u003e, \u003ccode\u003ehmmsearch\u003c/code\u003e, \u003ccode\u003ejackhmmer\u003c/code\u003e, \u003ccode\u003enhmmer\u003c/code\u003e, \u003ccode\u003ehmmalign\u003c/code\u003e, \u003ccode\u003ehmmconvert\u003c/code\u003e, \u003ccode\u003ehmmlogo\u003c/code\u003e, \u003ccode\u003ehmmpress\u003c/code\u003e, \u003ccode\u003ehmmsim\u003c/code\u003e, \u003ccode\u003emakehmmerdb\u003c/code\u003e, \u003ccode\u003enhmmscan\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/hmmer/3.3.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/hmmer\u003c/code\u003e as \u003ccode\u003e3.3.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 3, - "topics": [ - "bioinformatics", - "singularity" - ], - "updated_at": 1653937435.0 + "updated_at": 1718911019.0 }, { "data_format": 2, - "description": "Test Singularity-Hub.org", + "description": "Review how to write a singularity image", "filenames": [ "Singularity" ], - "full_name": "mandelkow/SgTest", + "full_name": "j23414/singularity_event", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSgTest\u003c/h1\u003e\u003ca id=\"user-content-sgtest\" class=\"anchor\" aria-label=\"Permalink: SgTest\" href=\"#sgtest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTest Singularity-Hub.org\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_event\u003c/h1\u003e\u003ca id=\"user-content-singularity_event\" class=\"anchor\" aria-label=\"Permalink: singularity_event\" href=\"#singularity_event\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4858\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eReview how to write a singularity image\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1530825852.0 + "updated_at": 1602528896.0 }, { "data_format": 2, - "description": null, + "description": "My software stack of singularity images.", "filenames": [ - "Singularity.itermae", - "Singularity.test", - "Singularity.test_base", - "Singularity.itermae-plus", - "Singularity.latest" + "Singularity.cuda8-comet", + "Singularity.cuda8-openmpi3.0", + "Singularity.cuda8-ml", + "Singularity.comet", + "Singularity.cuda8", + "Singularity.cuda8-bridges", + "Singularity.bridges", + "Singularity.cuda8-flux", + "Singularity.flux" ], - "full_name": "darachm/itermae", + "full_name": "csadorf/singularity-recipes", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eitermae\u003c/h1\u003e\u003ca id=\"user-content-itermae\" class=\"anchor\" aria-label=\"Permalink: itermae\" href=\"#itermae\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"https://darachm.gitlab.io/itermae/concept.html\" rel=\"nofollow\"\u003econcept here\u003c/a\u003e and\n\u003ca href=\"https://darachm.gitlab.io/itermae/usage/tutorial.html\" rel=\"nofollow\"\u003etutorial here\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eitermae\u003c/code\u003e is a command-line utility to recognize patterns in input sequences\nand generate outputs from groups recognized. Basically, it uses fuzzy regular\nexpression operations to (primarily) DNA sequence for purposes of DNA\nbarcode/tag/UMI parsing, sequence and quality -based filtering,\nand general output re-arrangment.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a09342327230d778e1e997dbd43b10e60ec7ce50b0b15deebe5a3a219d1d44b1/68747470733a2f2f6461726163686d2e6769746c61622e696f2f697465726d61652f5f696d616765732f70617273655f6469616772616d5f312e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a09342327230d778e1e997dbd43b10e60ec7ce50b0b15deebe5a3a219d1d44b1/68747470733a2f2f6461726163686d2e6769746c61622e696f2f697465726d61652f5f696d616765732f70617273655f6469616772616d5f312e737667\" alt=\"itermae diagram\" data-canonical-src=\"https://darachm.gitlab.io/itermae/_images/parse_diagram_1.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eitermae\u003c/code\u003e reads and makes FASTQ, FASTA, text-file, and SAM (tab-delimited)\nfiles using \u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003e\u003ccode\u003eBiopython\u003c/code\u003e\u003c/a\u003e sequence records\nto represent slice, and read/output formats.\nPattern matching uses the \u003ca href=\"https://pypi.org/project/regex/\" rel=\"nofollow\"\u003e\u003ccode\u003eregex\u003c/code\u003e\u003c/a\u003e library,\nand the tool is designed to function in command-line pipes from tools like\n\u003ca href=\"https://www.gnu.org/software/parallel/\" rel=\"nofollow\"\u003eGNU \u003ccode\u003eparallel\u003c/code\u003e\u003c/a\u003e\nto permit light-weight parallelization.\u003c/p\u003e\n\u003cp\u003eIt\u0027s usage might look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ezcat seq_data.fastqz | itermae --config my_config.yml -v \u0026gt; output.sam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ezcat seq_data.fastqz \\\n | parallel --quote --pipe -l 4 --keep-order -N 10000 \\\n itermae --config my_config.yml -v \u0026gt; output.sam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewith a \u003ccode\u003emy_config.yml\u003c/code\u003e file that may look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematches:\n - use: input\n pattern: NNNNNGTCCTCGAGGTCTCTNNNNNNNNNNNNNNNNNNNNCGTACGCTGCAGGTC\n marking: aaaaaBBBBBBBBBBBBBBBccccccccccccccccccccDDDDDDDDDDDDDDD\n marked_groups:\n a:\n name: sampleIndex\n repeat: 5\n B:\n allowed_errors: 2\n c:\n name: barcode\n repeat_min: 18\n repeat_max: 22\n D:\n allowed_insertions: 1\n allowed_deletions: 2\n allowed_substititions: 2\noutput_list:\n - name: \u0027barcode\u0027\n description: \u0027description+\" sample=\"+sampleIndex\u0027\n seq: \u0027barcode\u0027\n filter: \u0027statistics.median(barcode.quality) \u0026gt;= 35\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAvailability, installation, \u0027installation\u0027\u003c/h1\u003e\u003ca id=\"user-content-availability-installation-installation\" class=\"anchor\" aria-label=\"Permalink: Availability, installation, \u0027installation\u0027\" href=\"#availability-installation-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOptions:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eUse pip to install \u003ccode\u003eitermae\u003c/code\u003e, so\u003c/p\u003e\n\u003cp\u003epython3 -m pip install itermae\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou can clone this repo, and install it locally. Dependencies are in\n\u003ccode\u003erequirements.txt\u003c/code\u003e, so\n\u003ccode\u003epython3 -m pip install -r requirements.txt\u003c/code\u003e will install those.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou can use \u003ca href=\"https://syslab.org\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e to pull and run a\n\u003ca href=\"https://singularity-hub.org/collections/4537\" rel=\"nofollow\"\u003eSingularity image of itermae.py\u003c/a\u003e,\nwhere everything is already installed.\nThis is the recommended usage.\u003c/p\u003e\n\u003cp\u003eThis image is built with a few other tools,\nlike g/mawk, perl, and parallel, to make command line munging easier.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eitermae\u003c/code\u003e is envisioned to be used in a pipe-line where you just got your\nDNA sequencing FASTQ reads back, and you want to parse them.\nThe recommended interface is the YAML config file, as demonstrated\nin \u003ca href=\"https://darachm.gitlab.io/itermae/usage/tutorial.html\" rel=\"nofollow\"\u003ethe tutorial\u003c/a\u003e\nand detailed again in the\n\u003ca href=\"https://darachm.gitlab.io/itermae/usage/config.html\" rel=\"nofollow\"\u003econfiguration details\u003c/a\u003e.\nYou can also use a command-line argument interface as detailed more\n\u003ca href=\"https://darachm.gitlab.io/itermae/usage/examples.html\" rel=\"nofollow\"\u003ein the examples\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eI recommend you test this on small batches of data,\nthen stick it behind GNU \u003ccode\u003eparallel\u003c/code\u003e and feed the whole FASTQ file via\n\u003ccode\u003ezcat\u003c/code\u003e in on standard input.\nThis parallelizes with a small memory footprint, then\nyou write it out to disk (or stream into another tool).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThanks\u003c/h1\u003e\u003ca id=\"user-content-thanks\" class=\"anchor\" aria-label=\"Permalink: Thanks\" href=\"#thanks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAgain, the tool is built upon on the excellent work of\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/regex/\" rel=\"nofollow\"\u003e\u003ccode\u003eregex\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003e\u003ccode\u003eBiopython\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.gnu.org/software/parallel/\" rel=\"nofollow\"\u003e\u003ccode\u003eparallel\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDevelopment, helping\u003c/h1\u003e\u003ca id=\"user-content-development-helping\" class=\"anchor\" aria-label=\"Permalink: Development, helping\" href=\"#development-helping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny issues or advice are welcome as an\n\u003ca href=\"https://gitlab.com/darachm/itermae/-/issues\" rel=\"nofollow\"\u003eissue on the gitlab repo\u003c/a\u003e.\nComplaints are especially welcome.\u003c/p\u003e\n\u003cp\u003eFor development, see the\n\u003ca href=\"https://darachm.gitlab.io/itermae/package.html\" rel=\"nofollow\"\u003edocumentation as rendered from docstrings\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eA set of tests is written up with \u003ccode\u003epytest\u003c/code\u003e module, and can be run from inside\nthe cloned repo with \u003ccode\u003emake test\u003c/code\u003e.\nSee \u003ccode\u003emake help\u003c/code\u003e for more options, such as building, installing, and uploading.\u003c/p\u003e\n\u003cp\u003eThere\u0027s also a bash script with some longer runs in\n\u003ccode\u003eprofiling_tests\u003c/code\u003e, these generate longer runs for profiling purposes\nwith \u003ccode\u003ecProfile\u003c/code\u003e and \u003ccode\u003esnakeviz\u003c/code\u003e.\nBut is out of date. Todo is to re-configure and retest that for speed.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMy software stack of singularity images.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1619137884.0 + "updated_at": 1546983060.0 }, { "data_format": 2, - "description": "Guppy is a data processing toolkit that contains the Oxford Nanopore Technologies\u2019 basecalling algorithms.", + "description": null, "filenames": [ - "6.0.0/Singularity" + "docker/Singularity", + "docker/railrl_v9_cuda10-1_mj1-50-1-59_torch0-4-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_hand_v2/Singularity", + "docker/railrl_hand_v2/Singularity_cpu", + "docker/railrl_gpu_mujoco1-5-v4/singularity/Singularity", + "docker/railrl_v8_cuda10-1/Singularity", + "docker/railrl_v6_cuda9/Singularity", + "docker/railrl_v11_cuda10-1_mj2-0-2-2_torch0-3-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v7/Singularity", + "docker/railrl_v5/singularity/Singularity", + "docker/railrl_hand_v1/Singularity", + "docker/railrl_hand_v1/Singularity_cpu", + "docker/railrl_v10_cuda10-1_mj2-0-2-2_torch0-4-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v6_cuda8/Singularity", + "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", + "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", + "docker/railrl_ray/Singularity", + "docker/railrl_v9-5_cuda10-1_mj1-50-1-59_torch1-1-0_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v7_cuda8/Singularity", + "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch_cuda8", + "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch", + "experiments/ashvin/icml2020/singularity/Singularity" ], - "full_name": "pscedu/singularity-guppy", + "full_name": "Asap7772/rail-rl-franka-eval", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-guppy/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-guppy/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-guppy/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-guppy/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/acd271ee64be7e03cce0aa20980a61a37f2a803c6c21475e4f5e2e750f91b847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/acd271ee64be7e03cce0aa20980a61a37f2a803c6c21475e4f5e2e750f91b847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ce3bd0aacdb7b3b72cb6bf39ce61e5bb1cc29a5784ded8bb4f63d60ba37d4e96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce3bd0aacdb7b3b72cb6bf39ce61e5bb1cc29a5784ded8bb4f63d60ba37d4e96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b6e3ded696588df6d0e3326ff98a3915f0822c93b919f7af672f1bbb878a4fac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6e3ded696588df6d0e3326ff98a3915f0822c93b919f7af672f1bbb878a4fac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3fe9defc0e02f64e7c2aa7b7bb20b84f9a2e6f0239ee4e213edd1a550157e8c0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3fe9defc0e02f64e7c2aa7b7bb20b84f9a2e6f0239ee4e213edd1a550157e8c0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6775707079\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-guppy\u003c/h1\u003e\u003ca id=\"user-content-singularity-guppy\" class=\"anchor\" aria-label=\"Permalink: singularity-guppy\" href=\"#singularity-guppy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://community.nanoporetech.com/protocols/Guppy-protocol/v/gpb_2003_v1_revac_14dec2018/linux-guppy\" rel=\"nofollow\"\u003eguppy\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/guppy/6.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/guppy\u003c/code\u003e as \u003ccode\u003e6.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003eREADME last updated on: 01/24/2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erailrl\u003c/h1\u003e\u003ca id=\"user-content-railrl\" class=\"anchor\" aria-label=\"Permalink: railrl\" href=\"#railrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReinforcement learning framework.\nSome implemented algorithms:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/ddpg.py\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/sac.py\"\u003eSoft Actor Critic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/dqn_and_double_dqn.py\"\u003e(Double) Deep Q-Network (DQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/her.py\"\u003eHindsight Experience Replay (HER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/model_based_dagger.py\"\u003eMPC with Neural Network Model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/naf.py\"\u003eNormalized Advantage Function (NAF)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eWARNING: I haven\u0027t tested this NAF implementation much, so it may not match the paper\u0027s performance. I\u0027m pretty confident about the other two implementations though.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, checkout the example scripts, linked above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSome dependancies\u003c/h3\u003e\u003ca id=\"user-content-some-dependancies\" class=\"anchor\" aria-label=\"Permalink: Some dependancies\" href=\"#some-dependancies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo apt-get install swig\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate Conda Env\u003c/h3\u003e\u003ca id=\"user-content-create-conda-env\" class=\"anchor\" aria-label=\"Permalink: Create Conda Env\" href=\"#create-conda-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall and use the included ananconda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda env create -f docker/railrl/railrl-env.yml\n$ source activate railrl-env\n(railrl-env) $ # Ready to run examples/ddpg_cheetah_no_doodad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if you want you can use the docker image included.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Simulation Env Code\u003c/h3\u003e\u003ca id=\"user-content-download-simulation-env-code\" class=\"anchor\" aria-label=\"Permalink: Download Simulation Env Code\" href=\"#download-simulation-env-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vitchyr/multiworld\"\u003emultiworld\u003c/a\u003e (contains environments):\u003ccode\u003egit clone https://github.com/vitchyr/multiworld\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e(Optional) Install doodad\u003c/h3\u003e\u003ca id=\"user-content-optional-install-doodad\" class=\"anchor\" aria-label=\"Permalink: (Optional) Install doodad\" href=\"#optional-install-doodad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI recommend installing \u003ca href=\"https://github.com/justinjfu/doodad\"\u003edoodad\u003c/a\u003e to\nlaunch jobs. Some of its nice features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEasily switch between running code locally, on a remote compute with\nDocker, on EC2 with Docker\u003c/li\u003e\n\u003cli\u003eEasily add your dependencies that can\u0027t be installed via pip (e.g. you\nborrowed someone\u0027s code)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you install doodad, also modify \u003ccode\u003eCODE_DIRS_TO_MOUNT\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePath to rllab directory\u003c/li\u003e\n\u003cli\u003ePath to railrl directory\u003c/li\u003e\n\u003cli\u003ePath to other code you want to juse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou\u0027ll probably also need to update the other variables besides the docker\nimages/instance stuff.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup Config File\u003c/h3\u003e\u003ca id=\"user-content-setup-config-file\" class=\"anchor\" aria-label=\"Permalink: Setup Config File\" href=\"#setup-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must setup the config file for launching experiments, providing paths to your code and data directories. Inside \u003ccode\u003erailrl/config/launcher_config.py\u003c/code\u003e, fill in the appropriate paths. You can use \u003ccode\u003erailrl/config/launcher_config_template.py\u003c/code\u003e as an example reference.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecp railrl/launchers/config-template.py railrl/launchers/config.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a policy and seeing results\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-policy-and-seeing-results\" class=\"anchor\" aria-label=\"Permalink: Visualizing a policy and seeing results\" href=\"#visualizing-a-policy-and-seeing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDuring training, the results will be saved to a file called under\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e is the directory set by \u003ccode\u003erailrl.launchers.config.LOCAL_LOG_DIR\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;exp_prefix\u0026gt;\u003c/code\u003e is given either to \u003ccode\u003esetup_logger\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;foldername\u0026gt;\u003c/code\u003e is auto-generated and based off of \u003ccode\u003eexp_prefix\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003einside this folder, you should see a file called \u003ccode\u003eparams.pkl\u003c/code\u003e. To visualize a policy, run\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e(railrl) $ python scripts/sim_policy LOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;/params.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have rllab installed, you can also visualize the results\nusing \u003ccode\u003erllab\u003c/code\u003e\u0027s viskit, described at\nthe bottom of \u003ca href=\"http://rllab.readthedocs.io/en/latest/user/cluster.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython rllab/viskit/frontend.py LOCAL_LOG_DIR/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eexp_prefix\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd paths\u003c/h3\u003e\u003ca id=\"user-content-add-paths\" class=\"anchor\" aria-label=\"Permalink: Add paths\" href=\"#add-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=$PYTHONPATH:/path/to/multiworld/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/doodad/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/viskit/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/railrl-private/repo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredit\u003c/h2\u003e\u003ca id=\"user-content-credit\" class=\"anchor\" aria-label=\"Permalink: Credit\" href=\"#credit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA lot of the coding infrastructure is based on \u003ca href=\"https://github.com/rll/rllab\"\u003erllab\u003c/a\u003e.\nAlso, the serialization and logger code are basically a carbon copy.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 4, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1644268950.0 - }, - { - "data_format": 2, - "description": "recipe for containers", - "filenames": [ - "NucleoATAC/Singularity", - "RGT/Singularity", - "FitHiChIP/Singularity.FitHiChIP", - "Homer/Singularity" - ], - "full_name": "Tuteja-Lab/containers", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainers\u003c/h1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003erecipe for containers\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 1, "topics": [], - "updated_at": 1706042876.0 + "updated_at": 1605834321.0 }, { "data_format": 2, - "description": "testing container for pushing to singularity-hub", + "description": "robot learning repository for IRIS robots. ", "filenames": [ - "Singularity" + "docker/Singularity", + "docker/railrl_v9_cuda10-1_mj1-50-1-59_torch0-4-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_hand_tf_v1/Singularity", + "docker/railrl_hand_tf_v1/Singularity_cpu", + "docker/railrl_hand_v2/Singularity", + "docker/railrl_hand_v2/Singularity_cpu", + "docker/railrl_hand_v3/Singularity", + "docker/railrl_hand_v3/Singularity_cpu", + "docker/railrl_gpu_mujoco1-5-v4/singularity/Singularity", + "docker/railrl_v8_cuda10-1/Singularity", + "docker/railrl_v6_cuda9/Singularity", + "docker/railrl_v11_cuda10-1_mj2-0-2-2_torch0-3-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v7/Singularity", + "docker/railrl_v5/singularity/Singularity", + "docker/railrl_hand_v1/Singularity", + "docker/railrl_hand_v1/Singularity_cpu", + "docker/railrl_v10_cuda10-1_mj2-0-2-2_torch0-4-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v6_cuda8/Singularity", + "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", + "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", + "docker/railrl_ray/Singularity", + "docker/railrl_v9-5_cuda10-1_mj1-50-1-59_torch1-1-0_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v7_cuda8/Singularity", + "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch_cuda8", + "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch", + "docker/vitchyr/railrl_v15_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", + "docker/vitchyr/railrl_v15_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", + "experiments/ashvin/icml2020/singularity/Singularity" ], - "full_name": "vsoch/hello-world", + "full_name": "JonathanYang0127/iris_robot_learning", "latest_release": null, + "readme": "\u003cp\u003eREADME last updated on: 01/24/2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erailrl\u003c/h1\u003e\u003ca id=\"user-content-railrl\" class=\"anchor\" aria-label=\"Permalink: railrl\" href=\"#railrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReinforcement learning framework.\nSome implemented algorithms:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/ddpg.py\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/sac.py\"\u003eSoft Actor Critic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/dqn_and_double_dqn.py\"\u003e(Double) Deep Q-Network (DQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/her.py\"\u003eHindsight Experience Replay (HER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/model_based_dagger.py\"\u003eMPC with Neural Network Model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/naf.py\"\u003eNormalized Advantage Function (NAF)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eWARNING: I haven\u0027t tested this NAF implementation much, so it may not match the paper\u0027s performance. I\u0027m pretty confident about the other two implementations though.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, checkout the example scripts, linked above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSome dependancies\u003c/h3\u003e\u003ca id=\"user-content-some-dependancies\" class=\"anchor\" aria-label=\"Permalink: Some dependancies\" href=\"#some-dependancies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo apt-get install swig\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate Conda Env\u003c/h3\u003e\u003ca id=\"user-content-create-conda-env\" class=\"anchor\" aria-label=\"Permalink: Create Conda Env\" href=\"#create-conda-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall and use the included ananconda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda env create -f docker/railrl/railrl-env.yml\n$ source activate railrl-env\n(railrl-env) $ # Ready to run examples/ddpg_cheetah_no_doodad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if you want you can use the docker image included.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Simulation Env Code\u003c/h3\u003e\u003ca id=\"user-content-download-simulation-env-code\" class=\"anchor\" aria-label=\"Permalink: Download Simulation Env Code\" href=\"#download-simulation-env-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vitchyr/multiworld\"\u003emultiworld\u003c/a\u003e (contains environments):\u003ccode\u003egit clone https://github.com/vitchyr/multiworld\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting\u003c/h3\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWriting more tests in progress. Run with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enose2 -v -B -s tests/regression\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e(Optional) Install doodad\u003c/h3\u003e\u003ca id=\"user-content-optional-install-doodad\" class=\"anchor\" aria-label=\"Permalink: (Optional) Install doodad\" href=\"#optional-install-doodad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI recommend installing \u003ca href=\"https://github.com/justinjfu/doodad\"\u003edoodad\u003c/a\u003e to\nlaunch jobs. Some of its nice features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEasily switch between running code locally, on a remote compute with\nDocker, on EC2 with Docker\u003c/li\u003e\n\u003cli\u003eEasily add your dependencies that can\u0027t be installed via pip (e.g. you\nborrowed someone\u0027s code)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you install doodad, also modify \u003ccode\u003eCODE_DIRS_TO_MOUNT\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePath to rllab directory\u003c/li\u003e\n\u003cli\u003ePath to railrl directory\u003c/li\u003e\n\u003cli\u003ePath to other code you want to juse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou\u0027ll probably also need to update the other variables besides the docker\nimages/instance stuff.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup Config File\u003c/h3\u003e\u003ca id=\"user-content-setup-config-file\" class=\"anchor\" aria-label=\"Permalink: Setup Config File\" href=\"#setup-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must setup the config file for launching experiments, providing paths to your code and data directories. Inside \u003ccode\u003erailrl/config/launcher_config.py\u003c/code\u003e, fill in the appropriate paths. You can use \u003ccode\u003erailrl/config/launcher_config_template.py\u003c/code\u003e as an example reference.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecp railrl/launchers/config-template.py railrl/launchers/config.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a policy and seeing results\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-policy-and-seeing-results\" class=\"anchor\" aria-label=\"Permalink: Visualizing a policy and seeing results\" href=\"#visualizing-a-policy-and-seeing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDuring training, the results will be saved to a file called under\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e is the directory set by \u003ccode\u003erailrl.launchers.config.LOCAL_LOG_DIR\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;exp_prefix\u0026gt;\u003c/code\u003e is given either to \u003ccode\u003esetup_logger\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;foldername\u0026gt;\u003c/code\u003e is auto-generated and based off of \u003ccode\u003eexp_prefix\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003einside this folder, you should see a file called \u003ccode\u003eparams.pkl\u003c/code\u003e. To visualize a policy, run\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e(railrl) $ python scripts/sim_policy LOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;/params.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have rllab installed, you can also visualize the results\nusing \u003ccode\u003erllab\u003c/code\u003e\u0027s viskit, described at\nthe bottom of \u003ca href=\"http://rllab.readthedocs.io/en/latest/user/cluster.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython rllab/viskit/frontend.py LOCAL_LOG_DIR/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eexp_prefix\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd paths\u003c/h3\u003e\u003ca id=\"user-content-add-paths\" class=\"anchor\" aria-label=\"Permalink: Add paths\" href=\"#add-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=$PYTHONPATH:/path/to/multiworld/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/doodad/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/viskit/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/railrl-private/repo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredit\u003c/h2\u003e\u003ca id=\"user-content-credit\" class=\"anchor\" aria-label=\"Permalink: Credit\" href=\"#credit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository was initially developed primarily by \u003ca href=\"https://github.com/vitchyr\"\u003eVitchyr\nPong\u003c/a\u003e, until July 2021, at which point it was\ntransferred to the RAIL Berkeley organization and is primarily maintained\nby \u003ca href=\"https://github.com/anair13\"\u003eAshvin Nair\u003c/a\u003e.\nOther major collaborators and contributions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mdalal2020\"\u003eMurtaza Dalal\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/stevenlin1111\"\u003eSteven Lin\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA lot of the coding infrastructure is based on\n\u003ca href=\"https://github.com/rll/rllab\"\u003erllab\u003c/a\u003e.\nThe serialization and logger code are basically a carbon copy of the rllab\nversions.\u003c/p\u003e\n\u003cp\u003eThe Dockerfile is based on the \u003ca href=\"https://github.com/openai/mujoco-py/blob/master/Dockerfile\"\u003eOpenAI mujoco-py\nDockerfile\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe SMAC code builds off of the \u003ca href=\"https://github.com/katerakelly/oyster\"\u003ePEARL\ncode\u003c/a\u003e, which built off of an older\nRLKit version.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1545323609.0 + "updated_at": 1697423014.0 }, { "data_format": 2, - "description": "Pipeline used to detect ORFs from Mouse Ribo-Seq during time at EBI/Gencode", + "description": null, "filenames": [ - "Singularity" + "Singularity.bioconductor_3.20.def", + "Singularity.bioconductor_3.14.def", + "Singularity.bioconductor_3.19.def old", + "Singularity.Muscle_5.1.0.def", + "Singularity.gemma_0.98.5.def", + "Singularity.BBMap_39.01.def", + "Singularity.plink_2.0.def", + "Singularity.redDog.def", + "Singularity.bioconductor_3.19.def", + "Singularity.bioconductor_3.12.def", + "Singularity.viralFlye_0.2.def", + "Singularity.bioconductor_3.18.def", + "Singularity.diamond_2.1.6.def", + "Singularity.circos-0.69-9.def", + "Singularity.masurca_4.0.9.def" ], - "full_name": "JackCurragh/gencode-mouse-orfs", + "full_name": "sarahinwood/singularity-containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGencode Mouse ORF prediciton Pipeline\u003c/h1\u003e\u003ca id=\"user-content-gencode-mouse-orf-prediciton-pipeline\" class=\"anchor\" aria-label=\"Permalink: Gencode Mouse ORF prediciton Pipeline\" href=\"#gencode-mouse-orf-prediciton-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline predicts Mouse ORFs from Ribo-Seq data using RiboSeq.Org Data Portal\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run using each of the following container methods\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eInstructions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003edocs.syslabs.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003edocs.docker.com\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConda\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003edocs.conda.io\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSingularity\u003c/h5\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity/pipeline Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen as the profile \u003ccode\u003esingularity\u003c/code\u003e specifies \u003ccode\u003econtainer = \u0027singularity/pipeline\u0027\u003c/code\u003e use the following to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1700502150.0 + "updated_at": 1732498193.0 }, { "data_format": 2, - "description": "Knime build in Singularity Hub", + "description": null, "filenames": [ - "Singularity" + "Palma-II/Singularity.1.0.0" ], - "full_name": "tin6150/knime", + "full_name": "PointCloudSegementationOnALSandMLS/DataProcessingAndEvaluation", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDataProcessingAndEvaluation\u003c/h1\u003e\u003ca id=\"user-content-dataprocessingandevaluation\" class=\"anchor\" aria-label=\"Permalink: DataProcessingAndEvaluation\" href=\"#dataprocessingandevaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains a collection of scripts that wer used for the thesis: Point Cloud Segmentation of Urban Architectural Structures from Aerial and Mobile LiDAR Scans using Neural Networks. For further information, please refer to the organisation \u003ca href=\"https://github.com/PointCloudSegementationOnALSandMLS\"\u003ePointCloudSegementationOnALSandMLS\u003c/a\u003e. In this repository we provide differnt data preperation, visualiztion and scripts. Using, this data still needs adjusting data paths and configurations for the differnt models. Further, it currently misses some documentation, which will be added soon.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThis repository consits of the following folders and scripts:\u003c/h3\u003e\u003ca id=\"user-content-this-repository-consits-of-the-following-folders-and-scripts\" class=\"anchor\" aria-label=\"Permalink: This repository consits of the following folders and scripts:\" href=\"#this-repository-consits-of-the-following-folders-and-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePALMA-II: Since the most computation were conducted on th PALMA-II HPC cluster of the University of M\u00fcnster. These scipts mainly bash scripts, which allow to start the calculations on the cluster. Further, it consists off the Apptainer/Singularity image used on the Cluster.\u003c/li\u003e\n\u003cli\u003echange_class_divisions: As we tested differen class divisions inside our training data, we here provide the scipts, which restructured the class labeling in the point clouds\u003c/li\u003e\n\u003cli\u003eclusters: In this folder we provide a method to cluster vertical objects in the point clouds. Further we provide the results in this folder. For further information, please read the Readme inside this folder.\u003c/li\u003e\n\u003cli\u003eevaluation: The scripts in this folder are used to evaluate the model performances. Please read the Readme inside the folder for further information.\u003c/li\u003e\n\u003cli\u003ehelperFunctions: In this folder we just provide one script with different helper and test functions we used.\u003c/li\u003e\n\u003cli\u003epreprocessing: In order to be able to start the training data, we had to perform several pre processing steps. Please read the Readme in this folder for further information.\u003c/li\u003e\n\u003cli\u003evisualization: This folder contains different visualization scripts. For more information please stick to te Readme in this folder\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1504507455.0 + "updated_at": 1708817415.0 }, { "data_format": 2, - "description": "Singularity container for the Dartmouth 2017 MIND Summer School", + "description": null, "filenames": [ "Singularity" ], - "full_name": "mvdoc/mind-tools-singularity", + "full_name": "alejandrox1/singularity-test", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity definition file for mind-tools\u003c/h1\u003e\u003ca id=\"user-content-singularity-definition-file-for-mind-tools\" class=\"anchor\" aria-label=\"Permalink: Singularity definition file for mind-tools\" href=\"#singularity-definition-file-for-mind-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can pull directly this image from singularity hub with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e singularity pull shub://mvdoc/mind-tools-singularity\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1090\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTesting Singularity\u003c/h1\u003e\u003ca id=\"user-content-testing-singularity\" class=\"anchor\" aria-label=\"Permalink: Testing Singularity\" href=\"#testing-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo is designed to have a small test case for the usage of an OpenMPI\nexecutable on the Stampede2 supercomputer.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"src/\"\u003esrc\u003c/a\u003e contains the code necessary to build an executable.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"module/\"\u003emodule\u003c/a\u003e Stampede2 currently has version 2.3.1 installed as the\nmodule \u003ccode\u003etacc-singularity\u003c/code\u003e. This is an atempt to install Singularity v2.5.1\non the stampede2 supercomputer, along with its dependencies (there were\nsignificant changes to the API and Singularity itself between versions 2.3.X\nand 2.4.X). This still needs work...\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTODO\u003c/strong\u003e:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFinish installation of \u003ccode\u003esquash-tools\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eMake modules for \u003ccode\u003elibarchive-dev\u003c/code\u003e and \u003ccode\u003esquash-tools\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes:\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes:\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e./src/mpi_hello_world\u003c/code\u003e hangs when executed on a container based off \u003ccode\u003eubuntu 16.04\u003c/code\u003e. When checking the system resources, \u003ccode\u003empirun singularity exec ubuntu mpi_hello_world\u003c/code\u003e is indeed creating MPI tasks, however processes hang\nindefinetely alternating between \u003ccode\u003eS\u003c/code\u003e and \u003ccode\u003eR\u003c/code\u003e states.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMPICH\u003c/code\u003e doesn\u0027t seem to work - hence the use of \u003ccode\u003eOpenMPI\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWork around for mounting PWD on stampede: \u003ccode\u003emkdir /work /scratch\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1502763490.0 + "updated_at": 1528214682.0 }, { "data_format": 2, - "description": "work with RAR archives with tools in a Singularity container", + "description": "BUSCO is a tool to assess completeness of genome assembly, gene set, and transcriptome.", "filenames": [ - "Singularity" + "5.0.0_cv1/Singularity", + "5.2.2/Singularity" ], - "full_name": "singularityhub/rar", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRar\u003c/h1\u003e\u003ca id=\"user-content-rar\" class=\"anchor\" aria-label=\"Permalink: Rar\" href=\"#rar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1080\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a tutorial brought to you by the \u003ca href=\"https://www.github.com/vsoch\"\u003edebugger dinosaur\u003c/a\u003e of \u003ca href=\"https://srcc.stanford.edu\" rel=\"nofollow\"\u003eStanford Research Computing\u003c/a\u003e and is part of the \u003ca href=\"https://vsoch.github.io/lessons/\" rel=\"nofollow\"\u003eResearch Computing Lessons\u003c/a\u003e series.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/98dfd6c0463046b3bbd40debb29f3dfb8850ed286459aaff417080c5787a4b56/68747470733a2f2f76736f63682e6769746875622e696f2f6c6573736f6e732f6173736574732f696d672f6c6f676f2d626f6f6b2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98dfd6c0463046b3bbd40debb29f3dfb8850ed286459aaff417080c5787a4b56/68747470733a2f2f76736f63682e6769746875622e696f2f6c6573736f6e732f6173736574732f696d672f6c6f676f2d626f6f6b2e706e67\" alt=\"\" width=\"200\" data-canonical-src=\"https://vsoch.github.io/lessons/assets/img/logo-book.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor complete instructions and documentation for using the container, please \u003ca href=\"https://vsoch.github.io/lessons/unrar-python/#rar-ing-with-a-container\" rel=\"nofollow\"\u003eread the lesson\u003c/a\u003e. If you need help, post an issue on this repository, or to the \u003ca href=\"https://github.com/vsoch/lessons\"\u003elessons repository\u003c/a\u003e directly! You can also request a tutorial or lesson to be added. The debugger dinosaur and Research Computing are here for you!\u003c/p\u003e\n", + "full_name": "pscedu/singularity-busco", + "latest_release": "v5.2.2", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-busco/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-busco/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-busco/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-busco/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/425d93185f3da1c8104cf1995eea245773a65051714696e18a8c4fa9f8f6f510/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627573636f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/425d93185f3da1c8104cf1995eea245773a65051714696e18a8c4fa9f8f6f510/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d627573636f\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-busco\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f66f1a3e9208a90e17fbcef3dda2d1d93c5a50781dc9b2ff48fb557c3a87e75e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627573636f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f66f1a3e9208a90e17fbcef3dda2d1d93c5a50781dc9b2ff48fb557c3a87e75e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d627573636f\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-busco\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/69480c6012aa57c6cc42f91e786ec416d33c47877919882b87cf603674fbb454/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627573636f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/69480c6012aa57c6cc42f91e786ec416d33c47877919882b87cf603674fbb454/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d627573636f\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-busco\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/22741fcd23b2c0a3194bce34abf262f6d082a68307f196a7c37c94cf155c56c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627573636f\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22741fcd23b2c0a3194bce34abf262f6d082a68307f196a7c37c94cf155c56c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d627573636f\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-busco\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-busco\u003c/h1\u003e\u003ca id=\"user-content-singularity-busco\" class=\"anchor\" aria-label=\"Permalink: singularity-busco\" href=\"#singularity-busco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/aa7bee9ba2fc3541e26ef39aa126247cb4bd0e759d745d742d78f1c538a92813/68747470733a2f2f627573636f2e657a6c61622e6f72672f686f6d652f627573636f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa7bee9ba2fc3541e26ef39aa126247cb4bd0e759d745d742d78f1c538a92813/68747470733a2f2f627573636f2e657a6c61622e6f72672f686f6d652f627573636f2e706e67\" width=\"40%\" data-canonical-src=\"https://busco.ezlab.org/home/busco.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://busco.ezlab.org\" rel=\"nofollow\"\u003ebusco\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebusco\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/busco/5.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/busco\u003c/code\u003e as \u003ccode\u003e5.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [ - "rar", - "archive", "singularity", - "singularity-container", - "cluster", - "hpc", - "srcc", - "srcc-lessons" + "bioinformatics" ], - "updated_at": 1527981066.0 + "updated_at": 1639864357.0 }, { "data_format": 2, - "description": "Work with reticulate on Singularity", + "description": "ncview is a visual browser for netCDF format files.", "filenames": [ - "Singularity" + "2.1.8/Singularity" ], - "full_name": "richelbilderbeek/reticulate_on_singularity", - "latest_release": "v0.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ereticulate_on_singularity\u003c/h1\u003e\u003ca id=\"user-content-reticulate_on_singularity\" class=\"anchor\" aria-label=\"Permalink: reticulate_on_singularity\" href=\"#reticulate_on_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo shows how to work with the R package \u003ccode\u003ereticulate\u003c/code\u003e\nto run a Python script on Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSteps\u003c/h2\u003e\u003ca id=\"user-content-steps\" class=\"anchor\" aria-label=\"Permalink: Steps\" href=\"#steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e \u003ccode\u003e%post\u003c/code\u003e section contains the build\u003c/li\u003e\n\u003cli\u003eThe \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e \u003ccode\u003e%test\u003c/code\u003e section contains the test\u003c/li\u003e\n\u003cli\u003eThe \u003ca href=\".github/workflows/build_sandbox.yaml\"\u003e.github/workflows/build_sandbox.yaml\u003c/a\u003e\nand \u003ca href=\".github/workflows/build_singularity.yaml\"\u003e.github/workflows/build_singularity.yaml\u003c/a\u003e\nshow the final usage\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "pscedu/singularity-ncview", + "latest_release": "v2.1.8", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ncview/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ncview/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ncview/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ncview/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f299218f6bd16993a0a59184094020eaab425fa4df557e96c4204187ad7fb9f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6e6376696577\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f299218f6bd16993a0a59184094020eaab425fa4df557e96c4204187ad7fb9f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6e6376696577\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-ncview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8cab69331f2a79e6d4684003fe63745d10180043acfac10977a8b1718cd54409/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6e6376696577\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8cab69331f2a79e6d4684003fe63745d10180043acfac10977a8b1718cd54409/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6e6376696577\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-ncview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/82b02b9759f899d2ef35be705c342dafe5ef14ba5aff88e176f1993ec2a87b8b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6e6376696577\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/82b02b9759f899d2ef35be705c342dafe5ef14ba5aff88e176f1993ec2a87b8b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6e6376696577\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-ncview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e3ad3ddea45d0e7f1bacbb407e530eeaa1e847488bf9e5f1d5acdcfb7bfa147c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6e6376696577\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e3ad3ddea45d0e7f1bacbb407e530eeaa1e847488bf9e5f1d5acdcfb7bfa147c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6e6376696577\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-ncview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-ncview\u003c/h1\u003e\u003ca id=\"user-content-singularity-ncview\" class=\"anchor\" aria-label=\"Permalink: singularity-ncview\" href=\"#singularity-ncview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/63f21827549552a28b8a2897d85328e059db374c4f4d631be32eea2f935d8c67/687474703a2f2f6369727275732e756373642e6564752f7e7069657263652f736f6674776172652f6e63766965772f636f6e74726f6c5f322e676966\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/63f21827549552a28b8a2897d85328e059db374c4f4d631be32eea2f935d8c67/687474703a2f2f6369727275732e756373642e6564752f7e7069657263652f736f6674776172652f6e63766965772f636f6e74726f6c5f322e676966\" data-canonical-src=\"http://cirrus.ucsd.edu/~pierce/software/ncview/control_2.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.hpc.cineca.it/software/ncview#:~:text=Ncview%20is%20a%20visual%20browser,%2C%20invert%20the%20data%2C%20etc\" rel=\"nofollow\"\u003encview\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003encview\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/ncview/0.17.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/ncview\u003c/code\u003e as \u003ccode\u003e0.17.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1638270295.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "Singularity" + "subscribers_count": 4, + "topics": [ + "singularity", + "oceanography" ], - "full_name": "Cloud-PG/CachingOnDemand_singularity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity container for XRootD proxy server\u003c/h1\u003e\u003ca id=\"user-content-singularity-container-for-xrootd-proxy-server\" class=\"anchor\" aria-label=\"Permalink: Singularity container for XRootD proxy server\" href=\"#singularity-container-for-xrootd-proxy-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSet up log directory\u003c/h2\u003e\u003ca id=\"user-content-set-up-log-directory\" class=\"anchor\" aria-label=\"Permalink: Set up log directory\" href=\"#set-up-log-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe log directory should have the correct permisions:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo chown -R 998:996 /var/log/xrootd\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStart the service from local image\u003c/h2\u003e\u003ca id=\"user-content-start-the-service-from-local-image\" class=\"anchor\" aria-label=\"Permalink: Start the service from local image\" href=\"#start-the-service-from-local-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the image from github repo:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Cloud-PG/CachingOnDemand_singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e CachingOnDemand_singularity\n\nsudo singularity build xrd_proxy.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen start the service:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo REMOTE_HOST=XXX.XXX.XXX.XX PROXY_PORT=1124 REMOTE_PORT=31094 singularity instance start -B /var/log/xrootd/:/var/log/xrootd/ xrd_proxy.sif myproxy\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStart the service with SingularityHub image\u003c/h2\u003e\u003ca id=\"user-content-start-the-service-with-singularityhub-image\" class=\"anchor\" aria-label=\"Permalink: Start the service with SingularityHub image\" href=\"#start-the-service-with-singularityhub-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart the service with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo REMOTE_HOST=XXX.XXX.XXX.XX PROXY_PORT=1124 REMOTE_PORT=31094 singularity instance start -B /var/log/xrootd/:/var/log/xrootd/ shub://Cloud-PG/CachingOnDemand_singularity:latest myproxy\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 0, - "subscribers_count": 7, - "topics": [], - "updated_at": 1574258154.0 + "updated_at": 1649398866.0 }, { "data_format": 2, - "description": "Docker file for building MiCall execution environment to run under Kive", + "description": "Zork", "filenames": [ "Singularity" ], - "full_name": "cfe-lab/kive-default-docker", - "latest_release": "v1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ekive-default-docker\u003c/h1\u003e\u003ca id=\"user-content-kive-default-docker\" class=\"anchor\" aria-label=\"Permalink: kive-default-docker\" href=\"#kive-default-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker file for building default execution environment to run Kive pipelines\u003c/p\u003e\n", + "full_name": "richelbilderbeek/singularity_example_8", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_example_8\u003c/h1\u003e\u003ca id=\"user-content-singularity_example_8\" class=\"anchor\" aria-label=\"Permalink: singularity_example_8\" href=\"#singularity_example_8\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://travis-ci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"pics/TravisCI.png\" alt=\"Travis CI logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://travis-ci.org/richelbilderbeek/singularity_example_8\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75bd9e451bb113ffdd02e04479079eb460b56ab2170c3c1742e4c9ec399b83f1/68747470733a2f2f7472617669732d63692e6f72672f72696368656c62696c6465726265656b2f73696e67756c61726974795f6578616d706c655f382e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/richelbilderbeek/singularity_example_8.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSingularity example 8: \u003ca href=\"https://github.com/richelbilderbeek/Zork\"\u003eZork\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 11, + "subscribers_count": 3, "topics": [], - "updated_at": 1538429377.0 + "updated_at": 1565352003.0 }, { "data_format": 2, - "description": "Small utilities for working with fastq sequence files.", + "description": "Samtools is a suite of programs for interacting with high-throughput sequencing data.", "filenames": [ - "0.8/Singularity" + "1.11.0/Singularity", + "1.13.0/Singularity", + "1.15.1/Singularity", + "1.10.0/Singularity" ], - "full_name": "pscedu/singularity-fastq-tools", - "latest_release": "v0.8", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-fastq-tools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fastq-tools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-fastq-tools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-fastq-tools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c7b7a1fdf0e2c8b7598b24e1f68857d5983b78a67a4972bc8a40bfa100030283/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c7b7a1fdf0e2c8b7598b24e1f68857d5983b78a67a4972bc8a40bfa100030283/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-fastq-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a37739f85a6b18346dfaecdc54fee862893d98cb9580108c32e41972a5d908b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a37739f85a6b18346dfaecdc54fee862893d98cb9580108c32e41972a5d908b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-fastq-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bcbd2e3a37265324da540a52ea3c2beb2303fe9fd469e9866de39cc7eb2506da/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bcbd2e3a37265324da540a52ea3c2beb2303fe9fd469e9866de39cc7eb2506da/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-fastq-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/34aaa5fff834d641182221cb6e6f46fdbe6285629013261f7298219aca1ce234/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34aaa5fff834d641182221cb6e6f46fdbe6285629013261f7298219aca1ce234/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66617374712d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-fastq-tools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-fastq-tools\u003c/h1\u003e\u003ca id=\"user-content-singularity-fastq-tools\" class=\"anchor\" aria-label=\"Permalink: singularity-fastq-tools\" href=\"#singularity-fastq-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/dcjones/fastq-tools\"\u003efastq-tools\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003efastq\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/fastq-tools/0.8\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/fastq-tools\u003c/code\u003e as \u003ccode\u003e0.8.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-samtools", + "latest_release": "v1.15.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-samtools/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-samtools/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-samtools/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-samtools/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a0eadf22f405a09437137ff0c6ec856f6babc4249d9dddd5da94a5eb3629156e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a0eadf22f405a09437137ff0c6ec856f6babc4249d9dddd5da94a5eb3629156e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-samtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e475e80b6537836f51d6ad5734082aa59be755d4c096eda82ed4d7c0e41a8c87/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e475e80b6537836f51d6ad5734082aa59be755d4c096eda82ed4d7c0e41a8c87/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-samtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/eb062fd4efe27dbb4a365c73d20fb06bea9c69c114703f5d14171090c87bc35e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb062fd4efe27dbb4a365c73d20fb06bea9c69c114703f5d14171090c87bc35e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d73616d746f6f6c73\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-samtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bb47bf9c1b1dbe0f0e158a4721b1cdb1dba3dd2b2e9596626f2610b4d0058515/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73616d746f6f6c73\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bb47bf9c1b1dbe0f0e158a4721b1cdb1dba3dd2b2e9596626f2610b4d0058515/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d73616d746f6f6c73\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-samtools\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-samtools\u003c/h1\u003e\u003ca id=\"user-content-singularity-samtools\" class=\"anchor\" aria-label=\"Permalink: singularity-samtools\" href=\"#singularity-samtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://www.htslib.org/\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eace2sam\u003c/code\u003e, \u003ccode\u003eblast2sam.pl\u003c/code\u003e \u003ccode\u003ebowtie2sam.pl\u003c/code\u003e, \u003ccode\u003eexport2sam.pl\u003c/code\u003e, \u003ccode\u003efasta-sanitize.pl\u003c/code\u003e, \u003ccode\u003egenerate_binaries.sh\u003c/code\u003e, \u003ccode\u003einterpolate_sam.pl\u003c/code\u003e, \u003ccode\u003emaq2sam-long\u003c/code\u003e, \u003ccode\u003emaq2sam-short\u003c/code\u003e, \u003ccode\u003emd5fa\u003c/code\u003e, \u003ccode\u003emd5sum-lite\u003c/code\u003e, \u003ccode\u003enovo2sam.pl\u003c/code\u003e, \u003ccode\u003eplot-ampliconstats\u003c/code\u003e, \u003ccode\u003eplot-bamstats\u003c/code\u003e, \u003ccode\u003epsl2sam.pl\u003c/code\u003e, \u003ccode\u003esam2vcf.pl\u003c/code\u003e, \u003ccode\u003esamtools\u003c/code\u003e, \u003ccode\u003esamtools.pl\u003c/code\u003e, \u003ccode\u003eseq_cache_populate.pl\u003c/code\u003e, \u003ccode\u003esoap2sam.pl\u003c/code\u003e, \u003ccode\u003ewgsim\u003c/code\u003e, \u003ccode\u003ewgsim_eval.pl\u003c/code\u003e and \u003ccode\u003ezoom2sam.pl\u003c/code\u003e scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/samtools/1.13.0\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/samtools\u003c/code\u003e as \u003ccode\u003e1.13.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [ - "bioinformatics", - "singularity" + "singularity", + "bioinformatics" ], - "updated_at": 1650574199.0 + "updated_at": 1649388765.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "vowpal_wabbit-master/singularity/Singularity.debian-unstable-i386", + "vowpal_wabbit-master/singularity/Singularity.debian-unstable-amd64" ], - "full_name": "raffenet/singularity-images", + "full_name": "maaz-kang-92/vowpal_wabbit-master", "latest_release": null, "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1598301904.0 + "updated_at": 1722259857.0 }, { "data_format": 2, - "description": "Nextflow pipeline for microarray and RNA-seq", + "description": "Scibian packaging for: singularity-container", "filenames": [ - "Singularity" + "e2e/testdata/Singularity", + "examples/busybox/Singularity", + "examples/ubuntu/Singularity", + "examples/asciinema/Singularity", + "examples/almalinux/Singularity", + "examples/centos-arm64/Singularity", + "examples/fedora-arm64/Singularity", + "examples/apps/Singularity.cowsay", + "examples/apps/Singularity", + "examples/fedora/Singularity", + "examples/centos/Singularity", + "examples/shub/Singularity", + "examples/almalinux-arm64/Singularity", + "examples/raspbian/Singularity", + "examples/arch/Singularity", + "examples/opensuse-arm64/Singularity", + "examples/opensuse/Singularity", + "examples/docker/Singularity", + "examples/sle/Singularity", + "examples/instances/Singularity", + "examples/multistage/Singularity", + "examples/scratch/Singularity.busybox", + "examples/scratch/Singularity.alpine", + "examples/library/Singularity", + "examples/scientific/Singularity", + "examples/self/Singularity" ], - "full_name": "CBFLivUni/SkeletalVis-Transcriptomics", + "full_name": "scibian/singularity-container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSkeletalVis-Transcriptomics\u003c/h1\u003e\u003ca id=\"user-content-skeletalvis-transcriptomics\" class=\"anchor\" aria-label=\"Permalink: SkeletalVis-Transcriptomics\" href=\"#skeletalvis-transcriptomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSkeletalVis-Transcriptomics\u003c/strong\u003e is a bioinformatics pipeline for reproducible analyses of microarray and RNA-seq data.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a portable workflow tool to run tasks across multiple compute infrastructures. This pipeline uses a singularity container containing all the software needed to run the analysis, making installation simple and the results reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline summary\u003c/h2\u003e\u003ca id=\"user-content-pipeline-summary\" class=\"anchor\" aria-label=\"Permalink: Pipeline summary\" href=\"#pipeline-summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003cstrong\u003eSkeletalVis-Transcriptomics\u003c/strong\u003e pipeline takes a sample table and a parameter file defining the experiment as input. If not provided microarray data and fastq files are automatically downloaded using the provided accession numbers/sample identifiers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFeatures:\u003c/h3\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features:\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Download of raw microarray data from GEO, fastq files either directly from ENA, via conversion of sra files from SRA\u003cbr\u003e\n(\u003cstrong\u003eb\u003c/strong\u003e) Microarray with affyQCReport and RNA-seq read quality trimming with trimmomatic, QC reports with fastqc and multiQC\u003cbr\u003e\n(\u003cstrong\u003ec\u003c/strong\u003e)\tRNA-seq Quantification using \u003ca href=\"https://pachterlab.github.io/kallisto/\" rel=\"nofollow\"\u003e\u003ccode\u003ekallisto\u003c/code\u003e\u003c/a\u003e and processing with tximport to produce a sample x gene expression table\u003cbr\u003e\n(\u003cstrong\u003ed\u003c/strong\u003e) Differential expression analysis with limma, DESeq2 and Characteristic Direction\u003cbr\u003e\n(\u003cstrong\u003ee\u003c/strong\u003e) Pathway and gene ontology enrichment analysis with goseq\u003ca href=\"https://bioconductor.org/packages/release/bioc/html/goseq.html\" rel=\"nofollow\"\u003e\u003ccode\u003egoseq\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003ef\u003c/strong\u003e) Active subnetwork identification with \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/15272936/\" rel=\"nofollow\"\u003e\u003ccode\u003eGIGA\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\n(\u003cstrong\u003ei\u003c/strong\u003e) Identify transcription factors potenitally driving differential expression with \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/31114921/\" rel=\"nofollow\"\u003e\u003ccode\u003eCHEA3\u003c/code\u003e\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses are run in parallel and in result of error you can resume with the \u003ccode\u003e-resume\u003c/code\u003e parameter to re-run the pipeline starting from the previous fault.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAnalyse an example dataset\u003c/h3\u003e\u003ca id=\"user-content-analyse-an-example-dataset\" class=\"anchor\" aria-label=\"Permalink: Analyse an example dataset\" href=\"#analyse-an-example-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTry the pipeline on an example dataset (all inputs will be automatically downloaded): -\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html#installation\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the pipeline\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow clone CBFLivUni/SkeletalVis-Transcriptomics\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/docs/latest/config.html\" rel=\"nofollow\"\u003e\u003ccode\u003eConfigure\u003c/code\u003e\u003c/a\u003e the resource profile for your HPC or local computer. A template for slurm schedulers is provided as an example in \u003ccode\u003enextflow.config\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThere is a utility function provided to help replace paths within the config text files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e```console\n bash scripts/install/replacePath.sh nextflow.config /mnt/hc-storage/groups/cbf/Nextflow/SkeletalVis-Transcriptomics `pwd -P`\n```\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003e\n\u003cp\u003eTest on the example dataset:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow run main.nf -profile slurm -params-file params/GSE152805.yaml -with-singularity library://jsoul/default/skeletalvis-transcriptomics:latest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAnalyse your own data\u003c/h3\u003e\u003ca id=\"user-content-analyse-your-own-data\" class=\"anchor\" aria-label=\"Permalink: Analyse your own data\" href=\"#analyse-your-own-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDefine the sampleTable for RNA-seq data\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCreate a tab seperated table with unique Sample names, SRR accession numbers (if download is needed) and any additional metadata e.g\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSample\u003c/th\u003e\n\u003cth\u003eFile\u003c/th\u003e\n\u003cth\u003eCondition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eControl_1\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eControl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eControl_2\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eControl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTreated_1\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eTreated\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTreated_2\u003c/td\u003e\n\u003ctd\u003eSRRXXX\u003c/td\u003e\n\u003ctd\u003eTreated\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote for microarray data the metadata is retrived directly from GEO and we instead just need to specify the columns of interest that define variables to compare.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDefine the configuration\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMost parameters are set to sensible defaults within the main nextflow script, with only a few parameters required to be altered with typical use. Note the use of Groovy, python and R booleans.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eOptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eaccession\u003c/td\u003e\n\u003ctd\u003eA unique identifier for the experiment to be analysed e.g the GEO accession of the data - used to name output data and download fastq files\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especies\u003c/td\u003e\n\u003ctd\u003eThe species the reads originate from - used to create the kallisto index\u003c/td\u003e\n\u003ctd\u003eHuman, Mouse, Rat, Cow, Pig\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esingle\u003c/td\u003e\n\u003ctd\u003eIs the data single ended RNA-seq?\u003c/td\u003e\n\u003ctd\u003etrue, false\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebatchCorrect\u003c/td\u003e\n\u003ctd\u003eShould batch effect correction (sva) be used?\u003c/td\u003e\n\u003ctd\u003eTRUE, FALSE\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eskipTrimming\u003c/td\u003e\n\u003ctd\u003eShould read trimming be skipped?\u003c/td\u003e\n\u003ctd\u003efalse (default), true\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eParameters should be defined within a yaml file. See \u003ccode\u003eparams/GSE152805.yaml\u003c/code\u003e for an example.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eaccession\u003c/code\u003e parameter defines the default search path for fastq.gz files (data/\u003ccode\u003eaccession\u003c/code\u003e/fastqFiles/). Trimmed unpaired reads e.g \"*_R0.fastq.gz\" are skipped by default. If fastq files are not found locally the data will be downloaded using the provided \u003ccode\u003eaccession\u003c/code\u003e number.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline with your own parameters\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e nextflow run soulj/SkeletalVis-Transcriptomics -profile slurm -params-file ownData.yaml -with-singularity library://jsoul/default/skeletalvis-transcriptomics\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting modules\u003c/h3\u003e\u003ca id=\"user-content-testing-modules\" class=\"anchor\" aria-label=\"Permalink: Testing modules\" href=\"#testing-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModules can be tested using the \u003ca href=\"https://pypi.org/project/pytest-workflow/\" rel=\"nofollow\"\u003e\u003ccode\u003epytest-workflow\u003c/code\u003e\u003c/a\u003e framework. Module test directories within the \u003ccode\u003etests\u003c/code\u003e folder contain a nextflow script and a configuration yaml file defining the test for each module.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall pytest-workflow\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003econda install pytest-workflow\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the tests - e.g to test the pathway enrichment module\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epytest --symlink --kwdof --tag pathwayEnrichment\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularityCE\u003c/h1\u003e\u003ca id=\"user-content-singularityce\" class=\"anchor\" aria-label=\"Permalink: SingularityCE\" href=\"#singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/sylabs/singularity/tree/main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cb144ed26ca93a9146b0669951f71e8538e59a23370f81a5f44a3a39f59cbecc/68747470733a2f2f636972636c6563692e636f6d2f67682f73796c6162732f73696e67756c61726974792f747265652f6d61696e2e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/sylabs/singularity/tree/main.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Links\u003c/h2\u003e\u003ca id=\"user-content-quick-links\" class=\"anchor\" aria-label=\"Permalink: Quick Links\" href=\"#quick-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#support\"\u003eGetting Support\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sylabs/singularity/discussions/categories/community-call\"\u003eMonthly Community Call\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sylabs/singularity/discussions/categories/roadmap\"\u003eRoadmap\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"LICENSE.md\"\u003eProject License\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eGuidelines for Contributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"CODE_OF_CONDUCT.md\"\u003eCode of Conduct\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is SingularityCE?\u003c/h2\u003e\u003ca id=\"user-content-what-is-singularityce\" class=\"anchor\" aria-label=\"Permalink: What is SingularityCE?\" href=\"#what-is-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularityCE is the Community Edition of Singularity, an open source container\nplatform designed to be simple, fast, and secure. Many container platforms are\navailable, but SingularityCE is designed for ease-of-use on shared systems and in\nhigh performance computing (HPC) environments. It features:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn immutable single-file container image format, supporting cryptographic\nsignatures and encryption.\u003c/li\u003e\n\u003cli\u003eIntegration over isolation by default. Easily make use of GPUs, high speed\nnetworks, parallel filesystems on a cluster or server.\u003c/li\u003e\n\u003cli\u003eMobility of compute. The single file SIF container format is easy to transport\nand share.\u003c/li\u003e\n\u003cli\u003eA simple, effective security model. You are the same user inside a container\nas outside, and cannot gain additional privilege on the host system by\ndefault.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularityCE is open source software, distributed under the \u003ca href=\"LICENSE.md\"\u003eBSD License\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started with SingularityCE\u003c/h2\u003e\u003ca id=\"user-content-getting-started-with-singularityce\" class=\"anchor\" aria-label=\"Permalink: Getting Started with SingularityCE\" href=\"#getting-started-with-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install SingularityCE from source, see the\n\u003ca href=\"INSTALL.md\"\u003einstallation instructions\u003c/a\u003e. For other installation options, see\n\u003ca href=\"https://www.sylabs.io/guides/latest/admin-guide/\" rel=\"nofollow\"\u003eour guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSystem administrators can learn how to configure SingularityCE, and get an\noverview of its architecture and security features in the\n\u003ca href=\"https://www.sylabs.io/guides/latest/admin-guide/\" rel=\"nofollow\"\u003eadministrator guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor users, see the \u003ca href=\"https://www.sylabs.io/guides/latest/user-guide/\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e\nfor details on how to run and build containers with SingularityCE.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing to SingularityCE\u003c/h2\u003e\u003ca id=\"user-content-contributing-to-singularityce\" class=\"anchor\" aria-label=\"Permalink: Contributing to SingularityCE\" href=\"#contributing-to-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCommunity contributions are always greatly appreciated. To start developing\nSingularityCE, check out the \u003ca href=\"CONTRIBUTING.md\"\u003eguidelines for contributing\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease note we have a \u003ca href=\"CODE_OF_CONDUCT.md\"\u003ecode of conduct\u003c/a\u003e. Please follow it in\nall your interactions with the project members and users.\u003c/p\u003e\n\u003cp\u003eOur roadmap, other documents, and user/developer meeting information can be\nfound in \u003ca href=\"https://github.com/sylabs/singularity/discussions/\"\u003eGitHub Discussions\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe also welcome contributions to our\n\u003ca href=\"https://github.com/sylabs/singularity-userdocs\"\u003euser guide\u003c/a\u003e and\n\u003ca href=\"https://github.com/sylabs/singularity-admindocs\"\u003eadmin guide\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get help with SingularityCE, check out the community spaces detailed at our\n\u003ca href=\"https://sylabs.io/singularity#community\" rel=\"nofollow\"\u003eCommunity Portal\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSee also our \u003ca href=\"SUPPORT.md\"\u003eSupport Guidelines\u003c/a\u003e for further information about the\nbest place, and how, to raise different kinds of issues and questions.\u003c/p\u003e\n\u003cp\u003eFor additional support, \u003ca href=\"https://sylabs.io/contact-us\" rel=\"nofollow\"\u003econtact Sylabs\u003c/a\u003e to receive\nmore information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommunity Calls \u0026amp; Roadmap\u003c/h2\u003e\u003ca id=\"user-content-community-calls--roadmap\" class=\"anchor\" aria-label=\"Permalink: Community Calls \u0026amp; Roadmap\" href=\"#community-calls--roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe maintain our roadmap on \u003ca href=\"https://github.com/sylabs/singularity/discussions/categories/roadmap\"\u003eGitHub\nDiscussions\u003c/a\u003e,\nso that it\u0027s easy to collect ideas for new features, and discuss which should be\nprioritized for the next release.\u003c/p\u003e\n\u003cp\u003eRegular community calls are held for the project, on the first Thursday of each\nmonth, via Zoom. The agenda for each call includes a demonstration of new\nfeatures, or a project / workflow related to SingularityCE. This is followed by\ndevelopment updates \u0026amp; discussion, before open questions. Meeting details are\nposted in \u003ca href=\"https://github.com/sylabs/singularity/discussions/categories/community-call\"\u003eGithub\nDiscussions\u003c/a\u003e,\nand recordings made available at the \u003ca href=\"https://www.youtube.com/c/SylabsInc/videos\" rel=\"nofollow\"\u003eSylabs YouTube\nChannel\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you work on a project related to Singularity, or use Singularity in an\ninteresting workflow, \u003ca href=\"mailto:community@sylabs.io\"\u003elet us know\u003c/a\u003e if you\u0027d like to\npresent to the community!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGo Version Compatibility\u003c/h2\u003e\u003ca id=\"user-content-go-version-compatibility\" class=\"anchor\" aria-label=\"Permalink: Go Version Compatibility\" href=\"#go-version-compatibility\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularityCE aims to maintain support for the two most recent stable versions\nof Go. This corresponds to the Go\n\u003ca href=\"https://github.com/golang/go/wiki/Go-Release-Cycle#release-maintenance\"\u003eRelease Maintenance Policy\u003c/a\u003e\nand \u003ca href=\"https://golang.org/security\" rel=\"nofollow\"\u003eSecurity Policy\u003c/a\u003e, ensuring critical bug\nfixes and security patches are available for all supported language versions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting Singularity\u003c/h2\u003e\u003ca id=\"user-content-citing-singularity\" class=\"anchor\" aria-label=\"Permalink: Citing Singularity\" href=\"#citing-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe SingularityCE software may be cited using our Zenodo DOI \u003ccode\u003e10.5281/zenodo.5564905\u003c/code\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSingularityCE Developers (2021) SingularityCE. 10.5281/zenodo.5564905\n\u003ca href=\"https://doi.org/10.5281/zenodo.5564905\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.5564905\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis is an \u0027all versions\u0027 DOI for referencing SingularityCE in a manner that is\nnot version-specific. You may wish to reference the particular version of\nSingularityCE used in your work. Zenodo creates a unique DOI for each release,\nand these can be found in the \u0027Versions\u0027 sidebar on the \u003ca href=\"https://doi.org/10.5281/zenodo.5564905\" rel=\"nofollow\"\u003eZenodo record page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease also consider citing the original publication describing Singularity:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eKurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for\nmobility of compute. PLoS ONE 12(5): e0177459.\n\u003ca href=\"https://doi.org/10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003ehttps://doi.org/10.1371/journal.pone.0177459\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eUnless otherwise noted, this project is licensed under a 3-clause BSD license\nfound in the \u003ca href=\"LICENSE.md\"\u003elicense file\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [ - "microarray", - "nextflow", - "pathway-analysis", - "rna-seq", - "transcriptomics" - ], - "updated_at": 1700837110.0 + "subscribers_count": 5, + "topics": [], + "updated_at": 1696519487.0 }, { "data_format": 2, - "description": "A BIDSapp for automated mixed model analyses of mindboggle outputs.", + "description": null, "filenames": [ - "Singularity" + ".OLD/Singularity.Seurat_monocle2", + ".OLD/Singularity.Seurat_monocle", + ".OLD/Singularity.ChIPseq" ], - "full_name": "C0C0AN/Mindboggle-MixedModels", + "full_name": "dfernandezperez/Docker", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker\u003c/h1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity (old) and docker recipies for bioinformatic pipelines\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1580230589.0 + "updated_at": 1661190588.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "cme-lab/Singularity.hoomd", + "cme-lab/Singularity.cuda91", + "cme-lab/Singularity.cuda80", + "cme-lab/Singularity.base", + "cme-lab/Singularity.mbuild", + "cme-lab/Singularity.cuda92" ], - "full_name": "aces/simulation_toolkit_singularity", + "full_name": "mikemhenry/cme-lab-images", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSimulation Toolkit for Coticometry Pipeline\u003c/h1\u003e\u003ca id=\"user-content-simulation-toolkit-for-coticometry-pipeline\" class=\"anchor\" aria-label=\"Permalink: Simulation Toolkit for Coticometry Pipeline\" href=\"#simulation-toolkit-for-coticometry-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTools in this repository can be used to simulate artificial lesions in the brain in order to estimate the sensitivity and specificity of lesion\ndetection, using different automated corticometry pipelines.\u003c/p\u003e\n\u003cp\u003eTo set up software you need the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the software packages needed to run the deformation-2.pl script. Please follow steps in: \u003ca href=\"https://github.com/aces/simulation_toolkit_singularity/blob/main/Singularity\"\u003ehttps://github.com/aces/simulation_toolkit_singularity/blob/main/Singularity\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain data from\n\u003ca href=\"https://ida.loni.usc.edu/collaboration/access/appLicense.jsp;jsessionid=B0278AF5FD413E9AC14512DF841FFCA4/\" rel=\"nofollow\"\u003ehttps://ida.loni.usc.edu/collaboration/access/appLicense.jsp;jsessionid=B0278AF5FD413E9AC14512DF841FFCA4/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun deformation pipeline\"\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUsage\nUsage: deformation.pl -input \u0026lt;.mnc\u0026gt; -output [options]\u003c/p\u003e\n\u003cp\u003eMandatory options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-deformation_ratio provide the ratio of deformation, values must be between 0.1 [shrinkage] to 1.50 [expansion] [e.g. 0.1,1.2,0.6,\u2026]\n\n-mask Specify a tolerance map file (.mnc) indicating voxels that have a different amount of error allowed e.g., CSF, background [e.g. your-mask.mnc]\n\n-coordinate Specify a hyperslab starting at \u0026lt;x\u0026gt; \u0026lt;y\u0026gt; \u0026lt;z\u0026gt; and extending in respective directions by \u0026lt;sizex\u0026gt; \u0026lt;sizey\u0026gt; \u0026lt;sizez\u0026gt; [e.g. 70 100 80 5 5 5]\n\n-tolerance_space Define the buffer area around the deformation region [default = 4]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOther options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-blur_determinant Blurring kernel size for blurring deformation determinant blurring kernel 0-1\n\n-error Specify the amount of error that is allowed between the specified determinant and the final determinant (per voxel) [default =0.00001]\n\n-iteration Specify the maximum number of iterations to update the deformations field (-1 means until convergence) [default 1000]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cp\u003e./deformation.pl -input ICBM_00100_t1_final.mnc -output Debugging_Folder -deformation_ratio 0.6 -coordinate 70 100 70 10 10 10 -tolerance_space 4 -blur_determinant 0.25 -error 0.00001 -iteration 100\u003c/p\u003e\n\u003cp\u003eThe locally-deformed output file name includes input parameters to simplify creating GLM matrices for statistical analysis.\u003c/p\u003e\n\u003cp\u003eICBM_00100_t1_final_deformed_by_0.4atROIx70-y100-z70dimx10.dimy10.dimz10.mnc.\u003c/p\u003e\n\u003cp\u003eThere following intermediate files are generated to help you do quality control and can be deleted:\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/block.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/blurred0.25determinant_r_0.4x70-y100-z70dimx10.dimy10.dimz10.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/DDDDdilated.mnc \u0026lt;\u0026lt;number of D\u0027s corresponds to the number of times the tolerance space (defined to be 4 in the commandline) is dilated\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/DDDDring.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/determinant_r_0.4_grid.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/determinant_r_0.4x70-y100-z70dimx10.dimy10.dimz10.mnc\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMP/determinant_r_0.4.xfm\u003c/p\u003e\n\u003cp\u003e/Debugging_Folder/TMPmask.mnc\u003c/p\u003e\n\u003cp\u003eALTERNATIVELY: If you don\u0027t want to use this Perl wrapper, then follow the instructions for creating your own deformations:\n\u003ca href=\"https://wiki.mouseimaging.ca/display/MICePub/Generating+deformation+fields\" rel=\"nofollow\"\u003ehttps://wiki.mouseimaging.ca/display/MICePub/Generating+deformation+fields\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSource code for deformation pipeline and dependencies (MINC):\n\u003ca href=\"https://github.com/Mouse-Imaging-Centre/generate_deformation_fields\"\u003ehttps://github.com/Mouse-Imaging-Centre/generate_deformation_fields\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eExample Data, Scripts and Statistical analysis used in our Frontier\u0027s Paper can be found here: \u003ca href=\"https://github.com/aces/simulation_toolkit_statistics\"\u003ehttps://github.com/aces/simulation_toolkit_statistics\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAll these tools and data needed will be made available via CBRAIN. To learn more, please contact us at \u003ca href=\"mailto:cbrain-support.mni@mcgill.ca\"\u003ecbrain-support.mni@mcgill.ca\u003c/a\u003e. In the subject line, pleasee be sure to write SIMULATION TOOLKIT.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecme-lab-images\u003c/h1\u003e\u003ca id=\"user-content-cme-lab-images\" class=\"anchor\" aria-label=\"Permalink: cme-lab-images\" href=\"#cme-lab-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1188\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWork in progress\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, + "subscribers_count": 3, "topics": [], - "updated_at": 1626116668.0 + "updated_at": 1530915345.0 }, { "data_format": 2, - "description": "Scripts to build a docker and singularity with uboonecode", + "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "NuTufts/uboonecode-container", + "full_name": "NatoNathan/setapDocker", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003euboonecode-container\u003c/h1\u003e\u003ca id=\"user-content-uboonecode-container\" class=\"anchor\" aria-label=\"Permalink: uboonecode-container\" href=\"#uboonecode-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker and singularity file to build uboonecode containers.\u003c/p\u003e\n\u003cp\u003eThis takes advantage of scisoft and an utility script, \u003ccode\u003epullProducts\u003c/code\u003e. The code is built on top of Scientific Linux 6.\u003c/p\u003e\n\u003cp\u003eGiven a tagged-release version of uboonecode, pullProducts, untars all dependencies into the \u003ccode\u003e/products\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eOnce built, one can go into the container and setup UPS, which is the utilty to setup different software products, by sourcing\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /products/setup\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, one can then list the version of uboonecode stored in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eups list -aK+ uboonecode\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Docker image\u003c/h2\u003e\u003ca id=\"user-content-build-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build Docker image\" href=\"#build-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOf course, you need to install docker.\u003c/p\u003e\n\u003cp\u003eTo build the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker build -t [container_name:tag] .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the last step of the Dockerfile installs LArCV2.\nThis is used by our group to make truth-labeled training data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the Docker image\u003c/h2\u003e\u003ca id=\"user-content-run-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Run the Docker image\" href=\"#run-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it [container_name:tag] bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSetup uboonecode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetup uboonecode [version] [qualifier]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun larsoft\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elar -c [fcl file] -s [input file]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild a singularity image, based on the docker image\u003c/h2\u003e\u003ca id=\"user-content-build-a-singularity-image-based-on-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Build a singularity image, based on the docker image\" href=\"#build-a-singularity-image-based-on-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe use singularity\u0027s feature of importing a docker image.\nTo do that, we have to push the docker image to dockerhub.\nYou\u0027ll need a dockerhub account.\u003c/p\u003e\n\u003cp\u003eOnce you get an account, push to dockerhub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker login\ndocker push [container_name:tag]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen build the singularity image. Note that the build file has extra folders created to allow\nnetwork folders to be mounted on the Tufts Cluster.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build [output_image_name] Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the singularity image\u003c/h2\u003e\u003ca id=\"user-content-run-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Run the singularity image\" href=\"#run-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart the singularity container by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell [container image]\nprompt\u0026gt; bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen follow the instructions for the docker image to run \u003ccode\u003elar\u003c/code\u003e or build a development copy.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup and build a development copy\u003c/h2\u003e\u003ca id=\"user-content-setup-and-build-a-development-copy\" class=\"anchor\" aria-label=\"Permalink: Setup and build a development copy\" href=\"#setup-and-build-a-development-copy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHave confirmed that one can also setup and build a development version as well.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /products/setup\nsetup mrb\nmkdir workdir\ncd workdir\nsetup uboonecode [version] [qualifier]\nexport MRB_PROJECT=larsoft\nmrb newDev\nsource localProducts-XXXX/setup\ncd srcs\nmrb g uboonecode\nmrbsetenv\nmrb i\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHelper scripts\u003c/h3\u003e\u003ca id=\"user-content-helper-scripts\" class=\"anchor\" aria-label=\"Permalink: Helper scripts\" href=\"#helper-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repo also contains scripts to launch a job to build a development setup on the Tufts cluster.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esetapDocker\u003c/h1\u003e\u003ca id=\"user-content-setapdocker\" class=\"anchor\" aria-label=\"Permalink: setapDocker\" href=\"#setapdocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1532097120.0 + "updated_at": 1696674218.0 }, { "data_format": 2, - "description": "Methylation and mqtl analysis of the developing knee", + "description": "PaCBAM is a C command line tool for the complete characterization of genomic regions and single nucleotide positions from next-generation sequencing data.", "filenames": [ - "Singularity" + "containers/Singularity" ], - "full_name": "CBFLivUni/Epigenetics-OA-Risk-Human-Skeletal-Dev", + "full_name": "gerbenvoshol/pacbam", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDifferential methylation and mQTL analysis of cartilage tissue in the developing knee joint\u003c/h1\u003e\u003ca id=\"user-content-differential-methylation-and-mqtl-analysis-of-cartilage-tissue-in-the-developing-knee-joint\" class=\"anchor\" aria-label=\"Permalink: Differential methylation and mQTL analysis of cartilage tissue in the developing knee joint\" href=\"#differential-methylation-and-mqtl-analysis-of-cartilage-tissue-in-the-developing-knee-joint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/CBFLivUni/SarahRice_mQTL/assets/8311721/7200daa9-99a6-4db5-8f9a-6c48971f405b\"\u003e\u003cimg src=\"https://github.com/CBFLivUni/SarahRice_mQTL/assets/8311721/7200daa9-99a6-4db5-8f9a-6c48971f405b\" width=\"70%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow\u003c/h2\u003e\u003ca id=\"user-content-workflow\" class=\"anchor\" aria-label=\"Permalink: Workflow\" href=\"#workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code required to reproduce the bioinformatics analysis of the methylation and genotyping arrays.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e01_Methyl_Preprocess.Rmd\u003c/strong\u003e\u003cbr\u003e\nQuality control and normalisation of the EPIC methylation arrays\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e02_Methyl_EDA.Rmd\u003c/strong\u003e\u003cbr\u003e\nPrincipal component analysis of the methylation data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e03_Methyl_Differential.Rmd\u003c/strong\u003e\u003cbr\u003e\nDifferential methylated site and region analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e04_DMR_mFuzzClustering.Rmd\u003c/strong\u003e\u003cbr\u003e\nMfuzz clustering of CpGs and enrichment analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e05_processROADMAP.Rmd\u003c/strong\u003e\u003cbr\u003e\nOverlap of DMRs with ROADMAP chondrocyte chromatin states and ATAC-seq data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e06_DMR_TFMotifs.qmd\u003c/strong\u003e\u003cbr\u003e\nEnrichment anaylsis of transcription factor motifs in DMRs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e07_genotype_arrays.Rmd\u003c/strong\u003e\u003cbr\u003e\nGenotyping array processing (using bash scripts), quality control and identification of GWAS OA SNP LD proxies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e08_matrixQTL_prep.Rmd\u003c/strong\u003e\u003cbr\u003e\nPreparation of methylation and genotyping data for mQTL analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e09_matrixQTL_analysis.Rmd\u003c/strong\u003e\u003cbr\u003e\nIdentification and plotting of mQTLs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10_mQTLComparisons.qmd\u003c/strong\u003e\u003cbr\u003e\nComparison of the mQTLs against existing mQTLs from foetal brain and comparison of CpGs in the developmental DMRs vs the mQTLs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e11_Colocalisation.qmd\u003c/strong\u003e\u003cbr\u003e\nColocalisation of the mQTL SNPs with osteoarthritis GWAS data\u003c/p\u003e\n\u003cp\u003eAll \u003ccode\u003eRmd\u003c/code\u003e and \u003ccode\u003eqmd\u003c/code\u003e files can be rendered within RStudio.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the needed R packages\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRScript install/installRLibs.R\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNON OFFICIAL REPOSITORY!! See \u003ca href=\"https://bitbucket.org/CibioBCG/pacbam/src/master/\" rel=\"nofollow\"\u003ehttps://bitbucket.org/CibioBCG/pacbam/src/master/\u003c/a\u003e for the official repository\u003c/h1\u003e\u003ca id=\"user-content-non-official-repository-see-httpsbitbucketorgcibiobcgpacbamsrcmaster-for-the-official-repository\" class=\"anchor\" aria-label=\"Permalink: NON OFFICIAL REPOSITORY!! See https://bitbucket.org/CibioBCG/pacbam/src/master/ for the official repository\" href=\"#non-official-repository-see-httpsbitbucketorgcibiobcgpacbamsrcmaster-for-the-official-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePaCBAM\u003c/h1\u003e\u003ca id=\"user-content-pacbam\" class=\"anchor\" aria-label=\"Permalink: PaCBAM\" href=\"#pacbam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaCBAM is a C command line tool for the complete characterization of genomic regions and single nucleotide positions from next-generation sequencing data.\u003cbr\u003e\nPaCBAM implements a fast and scalable multi-core computational engine, generates exhaustive output files for downstream analysis, introduces an innovative on-the-fly read duplicates filtering strategy and provides comprehensive visual reports.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompilation from source code\u003c/h2\u003e\u003ca id=\"user-content-compilation-from-source-code\" class=\"anchor\" aria-label=\"Permalink: Compilation from source code\" href=\"#compilation-from-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install PaCBAM clone the repository and compile the C source code.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/gerbenvoshol/pacbam.git \n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pacbam\nmake -f Makefile.linux\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse instead Makefile.macos and Makefile.mingw to compile PaCBAM on, respectively, macOS and Windows systems.\u003cbr\u003e\nSamtools library \u003ccode\u003elibbam.a\u003c/code\u003e has been generated for GNU/Linux, Windows and macOS systems.\u003cbr\u003e\nFor compilation on Windows we have added also \u003ccode\u003elibz.a\u003c/code\u003e library, while compilation on Linux/macOS requires the installation of the development \u003ccode\u003ezlib\u003c/code\u003e package.\u003cbr\u003e\nLibraries can be found in \u003ccode\u003e./lib\u003c/code\u003e directory.\u003cbr\u003e\nWindows libraries have been generated using MinGW.\u003cbr\u003e\nIf libraries are not working we suggest to download/recompile them again.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaCBAM expects as input a sorted and indexed BAM file, a BED file with the coordinates of the genomic regions of interest (namely the target, e.g. captured regions of a WES experiment), a VCF file specifying a list of SNPs within the target and a reference genome FASTA file.\u003cbr\u003e\nDifferent running modes and filtering/computation options are available.\u003cbr\u003e\nRunning PaCBAM executable will list all usage options.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: \n ./pacbam bam=string bed=string vcf=string fasta=string [mode=int] [threads=int] [mbq=int] [mrq=int] [mdc=int] [out=string]\n [dedup] [dedupwin=int] [regionperc=float] [strandbias]\n\nbam=string \n NGS data file in BAM format \nbed=string \n List of target captured regions in BED format \nvcf=string \n List of SNP positions in VCF format (no compressed files are admitted)\nfasta=string \n Reference genome FASTA format file \nmode=string \n Execution mode [0=RC+SNPs+SNVs|1=RC+SNPs+SNVs+PILEUP(not including SNPs)|2=SNPs|3=RC|4=PILEUP|6=BAMCOUNT]\n (default 6)\ndedup \n On-the-fly duplicates filtering\ndedupwin=int \n Flanking region around captured regions to consider in duplicates filtering [default 1000]\nthreads=int \n Number of threads used (if available) for the pileup computation\n (default 1)\nregionperc=float \n Fraction of the captured region to consider for maximum peak signal characterization\n (default 0.5)\nmbq=int \n Min base quality\n (default 20)\nmrq=int \n Min read quality\n (default 1)\nmdc=int \n Min depth of coverage that a position should have to be considered in the output\n (default 0)\nstrandbias \n Print strand bias count information\ngenotype \n Print genotype calls for input SNPs using a strategy based on an allelic fraction cutoff threshold at 20%\ngenotypeBT \n Print genotype calls for input SNPs using a strategy based on a binomial test with significance at 1%)\nout=string \n Path of output directory (default is the current directory)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFolder \u003ccode\u003eexamples\u003c/code\u003e contains a small example of a BAM file and correspoding target regions in BED format and a SNPs in target regions in VCF format.\u003cbr\u003e\nThe following command executes PaCBAM with mode 1, generating 4 output files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e../pacbam bam=NGSData.bam bed=TargetRegions.bed vcf=SNPsInTargetRegions.vcf fasta=/path-to-reference-genome/human_g1k_v37.fasta mode=1 out=./\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe reference genome to use in this example can be downloaded at\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eftp://ftp.ncbi.nlm.nih.gov/1000genomes/ftp/technical/reference/human_g1k_v37.fasta.gz\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDuplicates filtering\u003c/h4\u003e\u003ca id=\"user-content-duplicates-filtering\" class=\"anchor\" aria-label=\"Permalink: Duplicates filtering\" href=\"#duplicates-filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo activate the \u003cem\u003eon-the-fly read duplicates filtering\u003c/em\u003e add to the command \u003ccode\u003ededup\u003c/code\u003e. To enlarge the genomic window (default 1000) used at captured regions to find duplicated reads use \u003ccode\u003ededupwin=N\u003c/code\u003e with \u003ccode\u003eN\u003c/code\u003e integer number.\nWhen single end reads are used you can set \u003ccode\u003eW=0\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput files\u003c/h2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-label=\"Permalink: Output files\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach execution mode computes and generates a combination of the following files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDepth of coverage characterization of all genomic regions\u003c/h4\u003e\u003ca id=\"user-content-depth-of-coverage-characterization-of-all-genomic-regions\" class=\"anchor\" aria-label=\"Permalink: Depth of coverage characterization of all genomic regions\" href=\"#depth-of-coverage-characterization-of-all-genomic-regions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each region provides the mean depth of coverage, the GC content and the mean depth of coverage of the subregion (user specified, default 0.5 fraction) that maximizes the coverage peak signal (\u003ccode\u003ercS\u003c/code\u003e and corresponding genomic coordinates \u003ccode\u003efromS\u003c/code\u003e and \u003ccode\u003etoS\u003c/code\u003e), to account for the reduced coverage depth due to incomplete match of reads to the captured regions.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tfrom\tto\tfromS\ttoS\trc\trcS\tgc\n20\t68348\t68410\t68348\t68378\t130.40\t129.68\t0.48\n20\t76643\t77060\t76845\t77052\t81.18\t111.99\t0.41\n20\t123267\t123329\t123293\t123323\t93.00\t99.81\t0.50\n20\t126053\t126335\t126100\t126240\t32.55\t54.73\t0.44\n20\t138183\t138236\t138210\t138235\t78.08\t99.92\t0.51\n20\t139412\t139667\t139510\t139636\t117.86\t125.38\t0.39\n20\t168524\t168761\t168524\t168641\t69.79\t91.03\t0.39\n20\t170213\t170266\t170213\t170238\t13.91\t18.69\t0.40\n20\t207927\t207989\t207958\t207988\t96.40\t106.65\t0.48\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingle-base resolution pileup\u003c/h4\u003e\u003ca id=\"user-content-single-base-resolution-pileup\" class=\"anchor\" aria-label=\"Permalink: Single-base resolution pileup\" href=\"#single-base-resolution-pileup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each genomic position in the target provides the read depth of the 4 possible bases A, C, G and T, the total depth of coverage, the variants allelic fraction (VAF), the strand bias information for each base, the unique identifier (e.g. dbsnp id) if available.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\tref\tA\tC\tG\tT\taf\tcov\n20\t68348\tG\t0\t0\t129\t0\t0.000000\t129\n20\t68349\tC\t0\t130\t0\t0\t0.000000\t130\n20\t68350\tC\t0\t130\t0\t0\t0.000000\t130\n20\t68352\tT\t0\t0\t0\t130\t0.000000\t130\n20\t68353\tG\t0\t0\t130\t0\t0.000000\t130\n20\t68354\tA\t130\t0\t0\t0\t0.000000\t130\n20\t68355\tA\t130\t0\t0\t0\t0.000000\t130\n20\t68356\tT\t0\t0\t0\t130\t0.000000\t130\n20\t68357\tA\t130\t0\t0\t0\t0.000000\t130\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingle-base resolution pileup (mode 6: BAMCOUNT)\u003c/h4\u003e\u003ca id=\"user-content-single-base-resolution-pileup-mode-6-bamcount\" class=\"anchor\" aria-label=\"Permalink: Single-base resolution pileup (mode 6: BAMCOUNT)\" href=\"#single-base-resolution-pileup-mode-6-bamcount\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each genomic position in the target provides the read depth of the 4 possible bases A, C, G and T, the total depth of coverage, the allelic fraction (e.g. FracA), and the strand bias information for each base (e.g. StrandA).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr pos ref cov CountA FracA StrandA CountC FracC StrandC CountG FracG StrandG CountT FracT StrandT\n20 68348 G 129 0 0.0000 0.00 0 0.0000 0.00 129 0.0000 1.00 0 0.0000 0.00\n20 76643 C 19 0 0.0000 0.00 19 1.0000 0.79 0 0.0000 0.00 0 0.0000 0.00\n20 76644 A 19 19 1.0000 0.79 0 0.0000 0.00 0 1.0000 0.00 0 0.0000 0.00\n20 76645 G 19 0 0.0000 0.00 0 0.0000 0.00 19 0.0000 0.79 0 0.0000 0.00\n20 76646 G 19 0 0.0000 0.00 0 0.0000 0.00 19 0.0000 0.79 0 0.0000 0.00\n20 76647 T 15 0 0.0000 0.00 0 0.0000 0.00 0 0.0000 0.00 15 1.0000 1.00\n20 76648 A 15 15 1.0000 1.00 0 0.0000 0.00 0 1.0000 0.00 0 0.0000 0.00\n20 76649 G 15 0 0.0000 0.00 0 0.0000 0.00 15 0.0000 1.00 0 0.0000 0.00\n20 76650 C 15 0 0.0000 0.00 15 1.0000 1.00 0 0.0000 0.00 0 0.0000 0.00\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePositions with reads support for alternative base\u003c/h4\u003e\u003ca id=\"user-content-positions-with-reads-support-for-alternative-base\" class=\"anchor\" aria-label=\"Permalink: Positions with reads support for alternative base\" href=\"#positions-with-reads-support-for-alternative-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvides pileup information only for position with positive VAF, computed using the alternative base with highest read depth (if any).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\tref\talt\tA\tC\tG\tT\taf\tcov\n20\t76953\tG\tA\t1\t0\t99\t0\t0.010000\t100\n20\t126263\tC\tT\t0\t26\t0\t1\t0.037037\t27\n20\t139484\tA\tG\t156\t0\t1\t0\t0.006369\t157\n20\t139557\tA\tG\t99\t0\t1\t0\t0.010000\t100\n20\t139570\tC\tA\t1\t171\t0\t0\t0.005814\t172\n20\t139622\tC\tA\t1\t135\t0\t0\t0.007353\t136\n20\t168728\tT\tA\t56\t0\t0\t0\t1.000000\t56\n20\t209986\tA\tT\t227\t0\t0\t2\t0.008734\t229\n20\t210097\tC\tT\t0\t82\t0\t1\t0.012048\t83\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhen \u003ccode\u003estrandbias\u003c/code\u003e option is used, the output format is the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\tref\talt\tA\tC\tG\tT\taf\tcov\tArs\tCrs\tGrs\tTrs\n20\t76953\tG\tA\t1\t0\t99\t0\t0.010000\t100\t1\t0\t80\t0\n20\t126263\tC\tT\t0\t26\t0\t1\t0.037037\t27\t0\t0\t0\t0\n20\t139484\tA\tG\t156\t0\t1\t0\t0.006369\t157\t111\t0\t1\t0\n20\t139557\tA\tG\t99\t0\t1\t0\t0.010000\t100\t39\t0\t0\t0\n20\t139570\tC\tA\t1\t171\t0\t0\t0.005814\t172\t0\t91\t0\t0\n20\t139622\tC\tA\t1\t135\t0\t0\t0.007353\t136\t0\t67\t0\t0\n20\t168728\tT\tA\t56\t0\t0\t0\t1.000000\t56\t19\t0\t0\t0\n20\t209986\tA\tT\t227\t0\t0\t2\t0.008734\t229\t106\t0\t0\t1\n20\t210097\tC\tT\t0\t82\t0\t1\t0.012048\t83\t0\t37\t0\t0\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLast four columns represent the number of reads, for each base, that are on the reverse strand. This information can be used to compute strand bias at base-specific resolution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSNPs pileup\u003c/h4\u003e\u003ca id=\"user-content-snps-pileup\" class=\"anchor\" aria-label=\"Permalink: SNPs pileup\" href=\"#snps-pileup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvides pileup information for all positions specified in the input VCF and uses the alternative alleles specified in the VCF file for the VAFs calculations.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\trsid\tref\talt\tA\tC\tG\tT\taf\tcov\n20\t68351\trs757428359\tA\tG\t130\t0\t0\t0\t0.000000\t130\n20\t68363\trs200192457\tA\tT\t129\t0\t0\t0\t0.000000\t129\n20\t68373\trs745889706\tT\tC\t0\t0\t0\t130\t0.000000\t130\n20\t68375\trs754912258\tA\tG\t54\t0\t50\t0\t0.480769\t104\n20\t68396\trs138777928\tC\tT\t0\t141\t0\t0\t0.000000\t141\n20\t68397\trs748102612\tG\tA\t0\t0\t141\t0\t0.000000\t141\n20\t68406\trs771803424\tA\tG\t140\t0\t0\t0\t0.000000\t140\n20\t76654\trs564320474\tG\tT\t0\t0\t31\t0\t0.000000\t31\n20\t76658\trs745496891\tC\tA\t0\t49\t0\t0\t0.000000\t49\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhen \u003ccode\u003egenotype\u003c/code\u003e or \u003ccode\u003egenotypeBT\u003c/code\u003e option is used, the output format is the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echr\tpos\trsid\tref\talt\tA\tC\tG\tT\taf\tcov\tgenotype\n20\t68351\trs757428359\tA\tG\t130\t0\t0\t0\t0.000000\t130\t0/0\n20\t68363\trs200192457\tA\tT\t129\t0\t0\t0\t0.000000\t129\t0/0\n20\t68373\trs745889706\tT\tC\t0\t0\t0\t130\t0.000000\t130\t0/0\n20\t68375\trs754912258\tA\tG\t54\t0\t50\t0\t0.480769\t104\t0/1\n20\t68396\trs138777928\tC\tT\t0\t141\t0\t0\t0.000000\t141\t0/0\n20\t68397\trs748102612\tG\tA\t0\t0\t141\t0\t0.000000\t141\t0/0\n20\t68406\trs771803424\tA\tG\t140\t0\t0\t0\t0.000000\t140\t0/0\n20\t76654\trs564320474\tG\tT\t0\t0\t31\t0\t0.000000\t31\t0/0\n20\t76658\trs745496891\tC\tA\t0\t49\t0\t0\t0.000000\t49\t0/0\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e0/0\u003c/code\u003e, \u003ccode\u003e0/1\u003c/code\u003e and \u003ccode\u003e1/1\u003c/code\u003e represent, respectively, the reference base homozygous genotype, the heterozygous genotype and the alternative base homozygous genotype.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003egenotype\u003c/code\u003e option implements an allelic fraction cutoff method where heterozygous genotype is assigned when the position allelic fraction is in the range (0.2,0.8). The \u003ccode\u003egenotypeBT\u003c/code\u003e option, instead, implements a Binomial Test statistics at significance of 1% and with probabilities p=0.55 (reference) and q=45 (alternative) to account for the reference mapping bias.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisual reports\u003c/h2\u003e\u003ca id=\"user-content-visual-reports\" class=\"anchor\" aria-label=\"Permalink: Visual reports\" href=\"#visual-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaCBAM includes a script to generate visual data reports written in python.\u003cbr\u003e\nIt provides different graphs for every output file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erc: gc content and region coverage distributions \nsnps: total SNPs count, total distribution and quantile distributions of alternative heterozygous and alternative homozygous SNPs \npabs: base modification count and strand bias distribution \npileup: cumulative coverage and allelic fraction distributions \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRequirements\u003c/h4\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePython 3.6.8\u003cbr\u003e\nNumpy 1.17.3\u003cbr\u003e\nmatplotlib 3.1.1\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsage\u003c/h4\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe report scripts expect as input the prefix of the output files from PaCBAM and the mode in which it was runned.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage:\n ./pacbam_report.py -i/--input string -m/--mode int [-o/--output string] [-s/--strandBias]\n\n-i INPUT, --input INPUT\n\tSpecify the input file prefix\n-m MODE, --mode MODE\n\tSpecify the mode used\n-o OUTPUT, --output OUTPUT\n\tSpecify the output file name (Default input.pdf)\n-s, --strandBias\n\tPlots the strand bias distribution \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMode option:\u003cbr\u003e\n0 Files: .rc, .snps and .pabs\n1 Files: .rc, .snps, .pabs and .pileup\u003cbr\u003e\n2 Files: .snps\u003cbr\u003e\n3 Files: .rc\u003cbr\u003e\n4 Files: .pileup\u003c/p\u003e\n\u003cp\u003eStrandBias reporting is available only in modes 0 and 1.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample\u003c/h4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following command computes the visual reports for the example data.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./pacbam_report.py -i example/NGSData -m 1 -o reports/reports.pdf\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you are using a container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run cibiobcg/pacbam:latest pacbam_report.py -i example/NGSData -m 1 -o reports/reports.pdf\n\nsingularity run pacbam.simg pacbam_report.py -i example/NGSData -m 1 -o reports/reports.pdf\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput file\u003c/h4\u003e\u003ca id=\"user-content-output-file\" class=\"anchor\" aria-label=\"Permalink: Output file\" href=\"#output-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe report script produces a single pdf file with all the graphs of the choosen mode.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/987756c702d790521a0a71c2de2cd65a8b3885de017ec1cd6c377299f52d3bf2/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f63756d756c6174697665436f7665726167652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/987756c702d790521a0a71c2de2cd65a8b3885de017ec1cd6c377299f52d3bf2/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f63756d756c6174697665436f7665726167652e706e67\" alt=\"cumulativeCoverage\" data-canonical-src=\"https://bitbucket.org/CibioBCG/pacbam/raw/master/reports/cumulativeCoverage.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eExample of PaCBAM reporting the cumulative coverage distribution for all positions reported in the PaCBAM pileup output file.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/09d7d883d14f5aa9d81994decdb7ac3f2e800f9d9042008ff983726fa5f27847/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f534e507354797065732e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/09d7d883d14f5aa9d81994decdb7ac3f2e800f9d9042008ff983726fa5f27847/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f534e507354797065732e706e67\" alt=\"SNPsTypes\" data-canonical-src=\"https://bitbucket.org/CibioBCG/pacbam/raw/master/reports/SNPsTypes.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eExample of PaCBAM reporting on allelic fraction (AF) distribution of all positions contained in the PaCBAM SNPs output file. SNPs are classified as heterozygous or alternative homozygous based on standard AF thresholds. Classification is also reported stratified by coverage quartiles.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8a805b120be3aa27b58d3caa76b7aa80be5445c36cca7f4eaea709f473ceeccd/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f626173654d6f64696669636174696f6e2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8a805b120be3aa27b58d3caa76b7aa80be5445c36cca7f4eaea709f473ceeccd/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f626173654d6f64696669636174696f6e2e706e67\" alt=\"baseModification\" data-canonical-src=\"https://bitbucket.org/CibioBCG/pacbam/raw/master/reports/baseModification.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eExample of PaCBAM reporting on distribution of alternative bases found for each reference base across all positions reported in the PABS PaCBAM output file (i.e. all positions with non-zero variant allelic fraction).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2ec17742ed0fe43ceb795540d19cdd3f5c0238263a77b1b6f4f6278ce5c40ba9/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f726567696f6e436f7665726167652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2ec17742ed0fe43ceb795540d19cdd3f5c0238263a77b1b6f4f6278ce5c40ba9/68747470733a2f2f6269746275636b65742e6f72672f436962696f4243472f70616362616d2f7261772f6d61737465722f7265706f7274732f726567696f6e436f7665726167652e706e67\" alt=\"regionCoverage\" data-canonical-src=\"https://bitbucket.org/CibioBCG/pacbam/raw/master/reports/regionCoverage.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eExample of PaCBAM reporting on mean depth of coverage distribution computed across all regions reported in the genomic regions of the PaCBAM output file. Distribution is reported both for regions overall mean coverage and for regions fractions maximizing mean coverage.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicence\u003c/h2\u003e\u003ca id=\"user-content-licence\" class=\"anchor\" aria-label=\"Permalink: Licence\" href=\"#licence\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaCBAM is released under \u003ca href=\"https://bitbucket.org/CibioBCG/pacbam/src/master/COPYING\" rel=\"nofollow\"\u003eMIT\u003c/a\u003e licence.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCite\u003c/h2\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-label=\"Permalink: Cite\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSamuel Valentini, Tarcisio Fedrizzi, Francesca Demichelis, Alessandro Romanel. \u003cstrong\u003ePaCBAM: fast and scalable processing of whole exome and targeted sequencing data\u003c/strong\u003e. \u003cem\u003eBMC Genomics\u003c/em\u003e, 20:1018, 2019.\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "colocalization", - "genotyping", - "methylation", - "mqtl", - "osteoarthritis" - ], - "updated_at": 1723046147.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "Singularity_custom", - "Singularity" - ], - "full_name": "h1-the-swan/shortest_path_wos_disciplines", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCalculate shortest path citation distance between pairs of disciplines in Web of Science (WoS)\u003c/h1\u003e\u003ca id=\"user-content-calculate-shortest-path-citation-distance-between-pairs-of-disciplines-in-web-of-science-wos\" class=\"anchor\" aria-label=\"Permalink: Calculate shortest path citation distance between pairs of disciplines in Web of Science (WoS)\" href=\"#calculate-shortest-path-citation-distance-between-pairs-of-disciplines-in-web-of-science-wos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJason Portenoy 2019\u003c/p\u003e\n\u003cp\u003eFor a set of disciplines in WoS, calculate the pairwise shortest path citation distance between them by calculating the average distance between a sample of papers from each discipline.\u003c/p\u003e\n\u003cp\u003eUse \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e for containerization of python graph-tool and to run on HPC.\u003c/p\u003e\n\u003cp\u003eFirst, clean the input data using \u003ca href=\"src/data/clean_samples.py\"\u003esrc/data/clean_samples.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eNext, convert to JSON using \u003ca href=\"src/data/make_json.py\"\u003esrc/data/make_json.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe analysis can be run on HPC with Singularity using the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# example command\n\nsingularity exec graph_tool.sif python3 -m src.process.calc_shortest_path_distances \u0026lt;PATH_TO_CITATIONS_TSV\u0026gt; \u0026lt;PATH_TO_SAMPLES_JSON\u0026gt; ./data/processed/discipline000 --discipline-index 0 --undirected --debug\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will calculate shortest path distances for one discipline. It will, for each separate paper ID, calculate shortest path distances between that paper and all of the others in the JSON file. It will output this to a separate file in the output directory (\u003ccode\u003e./data/processed/discipline000\u003c/code\u003e in the example above). In the example command above, it will run this analysis for the first discipline (discipline 0) in the JSON file.\u003c/p\u003e\n\u003cp\u003eOn HPC, these jobs can run in the backfill queue on nodes with 200GB RAM. These jobs have a maximum of 4 hours. The command above will ignore everything that has already been calculated (in the output directory), and process as many IDs as it can before the time runs out. So, it may be necessary to run each discipline multiple times, until all the IDs have been processed.\u003c/p\u003e\n\u003cp\u003eThe script \u003ca href=\"src/modify_slurm_script.py\"\u003esrc/modify_slurm_script.py\u003c/a\u003e can generate multiple scripts to run on the Slurm scheduler on HPC. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m src.modify_slurm_script scripts/201910040930/calc_shortest_path_slurm.sh --start=0 --end=35 --log-dir logs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(In this example, there are 36 disciplines. The file \u003ccode\u003ecalc_shortest_path_slurm.sh\u003c/code\u003e is a template Slurm script.)\u003c/p\u003e\n\u003cp\u003eThen, the jobs can all be submitted at once with (for example):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor file in scripts/201910040930/calc_shortest_paths_discipline*.sh; do sbatch -p ckpt -A stf-ckpt --mail-user $EMAIL $file; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(\u003ccode\u003e$EMAIL\u003c/code\u003e is an environment variable storing your email address. You will be notified when the job starts and ends.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is a containerization tool. It is good for shared supercomputers because, unlike Docker, it does not require admin/root privileges and does not give access to others\u0027 files.\u003c/p\u003e\n\u003cp\u003eIt does, however, require root privileges to build the images. Therefore, you should build on a system for which you have root privileges. This will output a (large) Singularity image file (\u003ccode\u003e.sif\u003c/code\u003e), which can be uploaded to a different system and used without root privileges.\u003c/p\u003e\n\u003cp\u003eImages are built from a Singularity definition file (e.g., \u003ca href=\"./Singularity\"\u003e./Singularity\u003c/a\u003e). These can be built off of Docker containers. This is the case for the \u003ccode\u003e./Singularity\u003c/code\u003e definition file in this repo. There is an existing Docker image for \u003ccode\u003epython-graph-tool\u003c/code\u003e, which is used as the base for the image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build graph_tool.sif ./Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere is another alternative definition file: \u003ca href=\"./Singularity_custom\"\u003e./Singularity_custom\u003c/a\u003e. This is basically copying the Dockerfile from the \u003ccode\u003epython-graph-tool\u003c/code\u003e Docker image, into Singularity format. (This takes a while to build.)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build graph_tool_custom.sif ./Singularity_custom\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eTODO\u003c/h5\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eParallelize the distance calculations, after the graph data is loaded (using multiprocessing)\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 0, - "subscribers_count": 3, "topics": [], - "updated_at": 1571469085.0 + "updated_at": 1643639323.0 }, { "data_format": 2, - "description": "Container for AnnotSV software. ", + "description": "Singularity support for Wire-Cell toolkit", "filenames": [ - "Singularity_2.2", - "Singularity" + "Singularity.sl7big", + "Singularity.sl7kc", + "Singularity.wclsdev", + "Singularity.sl7wclsdev", + "Singularity.sl7", + "Singularity.sl7mvp", + "Singularity.wct0.8.0-ub1804", + "Singularity.artdaq", + "Singularity.externals", + "Singularity.wctdev" ], - "full_name": "Clinical-Genomics-Lund/annotsv_container", + "full_name": "WireCell/wire-cell-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnnotSV container for WGS pipeline\u003c/h2\u003e\u003ca id=\"user-content-annotsv-container-for-wgs-pipeline\" class=\"anchor\" aria-label=\"Permalink: AnnotSV container for WGS pipeline\" href=\"#annotsv-container-for-wgs-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity recipe for AnnotSV 2.3 and 2.2.\u003c/p\u003e\n\u003cp\u003esudo singularity build annotsv2.3.sif Singularity\u003c/p\u003e\n", "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1618833570.0 - }, - { - "data_format": 2, - "description": "Testing the use of Github Actions to deploy singularity images", - "filenames": [ - "Singularity" - ], - "full_name": "bailey-lab/deploy-singularity-testing", - "latest_release": "v0.1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edeploy-singularity-testing\u003c/h1\u003e\u003ca id=\"user-content-deploy-singularity-testing\" class=\"anchor\" aria-label=\"Permalink: deploy-singularity-testing\" href=\"#deploy-singularity-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting the use of Github Actions to deploy singularity images\u003c/p\u003e\n", - "stargazers_count": 0, - "subscribers_count": 6, - "topics": [], - "updated_at": 1635190346.0 + "updated_at": 1585692767.0 }, { "data_format": 2, - "description": "IQmol in a Singularity container", + "description": "Container for the BraTS 2023 challenge using the TCuPGAN model", "filenames": [ - "Singularity.2.13b", - "Singularity", - "Singularity.2.14", - "Singularity.2.11.2" + "project/Singularity.recipe" ], - "full_name": "OSC/sa_singularity_iqmol", + "full_name": "ramanakumars/brats2023-tcupgan", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity IQmol\u003c/h1\u003e\u003ca id=\"user-content-singularity-iqmol\" class=\"anchor\" aria-label=\"Permalink: Singularity IQmol\" href=\"#singularity-iqmol\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3599\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"http://iqmol.org/index.html\" rel=\"nofollow\"\u003eIQmol\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e or CentOS image \u003ca href=\"https://hub.docker.com/_/centos\" rel=\"nofollow\"\u003ecentos\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003eiqmol.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build iqmol.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull iqmol.sif shub://OSC/sa_singularity_iqmol\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart IQmol\u003c/h3\u003e\u003ca id=\"user-content-start-iqmol\" class=\"anchor\" aria-label=\"Permalink: Start IQmol\" href=\"#start-iqmol\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIQmol is started using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run iqmol.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as a native command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./iqmol.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebrats2023-tcupgan\u003c/h1\u003e\u003ca id=\"user-content-brats2023-tcupgan\" class=\"anchor\" aria-label=\"Permalink: brats2023-tcupgan\" href=\"#brats2023-tcupgan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer for the BraTS 2023 challenge using the TCuPGAN model\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 2, "topics": [], - "updated_at": 1599018735.0 + "updated_at": 1689367513.0 }, { "data_format": 2, - "description": "Metabobank tools", + "description": null, "filenames": [ - "Singularity" + "Singularity.PhaGCN", + "Singularity.smurf.v0.1", + "Singularity.mtags.v1.1_cs", + "Singularity.mongodb.v0.1", + "Singularity.qiime2-smurf.v0.1", + "Singularity.samestr", + "Singularity.fish_probes.v0.1", + "Singularity.mapseq.v.2.0.1alpha", + "Singularity.vknight.v0.12", + "Singularity.carveme", + "Singularity.reCOGnise.v0.1", + "Singularity.gffread", + "Singularity.prokka", + "Singularity.humann3", + "Singularity.vknight.v0.14_with_idtaxa", + "Singularity.reCOGnise.0.4.5", + "Singularity.bbmap", + "Singularity.vknight.v0.6.5", + "Singularity.vknight.v0.13_collate" ], - "full_name": "ddbj/metabobank_tools", + "full_name": "cschu/container-forge", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMetabobank tools\u003c/h1\u003e\u003ca id=\"user-content-metabobank-tools\" class=\"anchor\" aria-label=\"Permalink: Metabobank tools\" href=\"#metabobank-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003embexcel2idfsdrf.rb\u003c/h2\u003e\u003ca id=\"user-content-mbexcel2idfsdrfrb\" class=\"anchor\" aria-label=\"Permalink: mbexcel2idfsdrf.rb\" href=\"#mbexcel2idfsdrfrb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMetaboBank metadata excel \u304b\u3089 IDF/SDRF tsv \u30d5\u30a1\u30a4\u30eb\u3092\u751f\u6210\u003c/p\u003e\n\u003cp\u003eOptions\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e-i: MetaboBank metadata excel\u003c/li\u003e\n\u003cli\u003e-f: base filename (\u6307\u5b9a\u3057\u306a\u3044\u5834\u5408 .xlsx \u3092\u9664\u3044\u305f\u30a8\u30af\u30bb\u30eb\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u4f7f\u7528)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eruby mbexcel2idfsdrf.rb -i MBS-22_1_LC-MS_metadata.xlsx -f test\n\ntest.idf.txt\ntest.sdrf.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eruby mbexcel2idfsdrf.rb -i MBS-22_1_LC-MS_metadata.xlsx\n\nMBS-22_1_LC-MS_metadata.idf.txt\nMBS-22_1_LC-MS_metadata.sdrf.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ebase filename \u3092\u30d5\u30a1\u30a4\u30eb\u540d\u3068\u3057\u3066 IDF/SDRF \u30d5\u30a1\u30a4\u30eb\u304c\u751f\u6210\u3055\u308c\u308b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[base filename].idf.txt\u003c/li\u003e\n\u003cli\u003e[base filename].sdrf.txt\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIDF \u306f MAGE-TAB 1.1 \u4ee5\u964d\u3092\u3001SDRF \u306f Source Name \u4ee5\u964d\u3092\u51fa\u529b\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 9, + "subscribers_count": 2, "topics": [], - "updated_at": 1720571144.0 + "updated_at": 1658328247.0 }, { "data_format": 2, - "description": "Singularity container for restrained-ensemble simulations using gmxapi", + "description": "Singularity containers to run Pointwise 18.0", "filenames": [ + "Singularity.template", + "Singularity.local", "Singularity" ], - "full_name": "jmhays/singularity-restrained-ensemble", + "full_name": "stephansmit/pointwise_containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-restrained-ensemble\u003c/h1\u003e\u003ca id=\"user-content-singularity-restrained-ensemble\" class=\"anchor\" aria-label=\"Permalink: singularity-restrained-ensemble\" href=\"#singularity-restrained-ensemble\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity container for restrained-ensemble simulations using gmxapi\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity containers for Pointwise\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers-for-pointwise\" class=\"anchor\" aria-label=\"Permalink: Singularity containers for Pointwise\" href=\"#singularity-containers-for-pointwise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainers to run \u003ca href=\"https://www.pointwise.com/\" rel=\"nofollow\"\u003ePointwise\u003c/a\u003e version 18.0.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container\u003c/h2\u003e\u003ca id=\"user-content-build-the-container\" class=\"anchor\" aria-label=\"Permalink: Build the container\" href=\"#build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLocal build\u003c/h3\u003e\u003ca id=\"user-content-local-build\" class=\"anchor\" aria-label=\"Permalink: Local build\" href=\"#local-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build pointwise_containers.sif Singularity.local\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Hub build\u003c/h3\u003e\u003ca id=\"user-content-singularity-hub-build\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub build\" href=\"#singularity-hub-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUpload the installer to a temporary location via \u003ca href=\"https://www.file.io/\" rel=\"nofollow\"\u003efile.io\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./upload_files.sh \u0026lt;Installer_Dir\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFill in the links in the recipe\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./make_recipe.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePush the image to github\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit add Singularity; git commit -m \"latest image\"; git push;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTrigger the build on \u003ca href=\"https://singularity-hub.org/collections/3396\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull a container\u003c/h2\u003e\u003ca id=\"user-content-pull-a-container\" class=\"anchor\" aria-label=\"Permalink: Pull a container\" href=\"#pull-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://stephansmit/pointwise_containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecute Pointwise script\u003c/h2\u003e\u003ca id=\"user-content-execute-pointwise-script\" class=\"anchor\" aria-label=\"Permalink: Execute Pointwise script\" href=\"#execute-pointwise-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo execute a pointwise script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSINGULARITYENV_pwid_LICENSE=\u0026lt;port\u0026gt;@\u0026lt;host\u0026gt; singularity exec pointwise_containers.sif /opt/pointwise/pointwise -b \u0026lt;script-name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;port\u0026gt;\u003c/code\u003e and \u003ccode\u003e\u0026lt;host\u0026gt;\u003c/code\u003e point to the license server\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3396\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1540327715.0 + "updated_at": 1596800030.0 }, { "data_format": 2, - "description": "Containers for arch x86_64 based on Centos 7 and 8 with GNU 7 and 8 compiler and different versions of Spack 0.15.4 and 0.16.0", + "description": null, "filenames": [ - "Singularity" + "Singularity.whatshap_491ec8e", + "Singularity.shapeit_v2.r904", + "Singularity.vcftools_0.1.16", + "Singularity.stacks_2.53", + "Singularity.easysfs_c2b26c5", + "Singularity.vcflib_1.0.1", + "Singularity.transindel_7098bd6", + "Singularity.sniffles_f958698", + "Singularity.bayescan_2.1", + "Singularity.deepvariant_0.9.0", + "Singularity.freebayes_1.3.1", + "Singularity.deepvariant_0.9.0-gpu" ], - "full_name": "CINECA-HPC/container_spack_centos_x86_64", + "full_name": "TomHarrop/variant-utils", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_spack_centos_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_spack_centos_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_spack_centos_x86_64\" href=\"#container_spack_centos_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainers for arch x86_64 based on Centos 7 with GNU 7 compiler and different versions of Spack\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e15.4\u003c/li\u003e\n\u003cli\u003e16.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIMPORTANT (NOT NECESSARY IF YOU START FROM A DOCKER IMAGE): When you are going to work inside the container remember to source these 2 file in order to set the proper module environment with spack and Lmod\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esource /opt/spack/share/spack/setup-env.sh\u003c/li\u003e\n\u003cli\u003esource /usr/share/lmod/8.2.7/init/sh\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 2, - "topics": [ - "spack" - ], - "updated_at": 1614178565.0 + "topics": [], + "updated_at": 1606169142.0 }, { "data_format": 2, - "description": " Molecular graphics systems in a Singularity container", + "description": "selective regression testing on method level for node.js apps", "filenames": [ - "Singularity.1.0", - "Singularity" + "docker/Singularity.snowflake" ], - "full_name": "OSC/sa_singularity_molgfx", + "full_name": "charlie-cyf/nodeSRT", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Molgfx\u003c/h1\u003e\u003ca id=\"user-content-singularity-molgfx\" class=\"anchor\" aria-label=\"Permalink: Singularity Molgfx\" href=\"#singularity-molgfx\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4301\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://github.com/OpenChemistry\"\u003eOpen Chemistry\u003c/a\u003e, Gabedit and Jmol. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003emolgfx.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build molgfx.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull molgfx.sif shub://OSC/sa_singularity_molgfx\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFind versions of molecular graphics systems\u003c/h3\u003e\u003ca id=\"user-content-find-versions-of-molecular-graphics-systems\" class=\"anchor\" aria-label=\"Permalink: Find versions of molecular graphics systems\" href=\"#find-versions-of-molecular-graphics-systems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity inspect -H molgfx.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart Avogadro2\u003c/h3\u003e\u003ca id=\"user-content-start-avogadro2\" class=\"anchor\" aria-label=\"Permalink: Start Avogadro2\" href=\"#start-avogadro2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAvogadro2 is started using the default exec command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e molgfx.sif avogadro2\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSelective Regression Testing for Node.js\u003c/h1\u003e\u003ca id=\"user-content-selective-regression-testing-for-nodejs\" class=\"anchor\" aria-label=\"Permalink: Selective Regression Testing for Node.js\" href=\"#selective-regression-testing-for-nodejs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003einstall:\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: install:\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# install dependencies\nnpm i\n\n# install NodeSRT package\ncd nodeSRT\nsudo npm i .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eusage:\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003euse \u003ccode\u003enodeSRT --help\u003c/code\u003e to get help \u003cbr\u003e\nuse this command to run nodeSRT\n\u003ccode\u003enodeSRT -b \u0026lt;codebase dir\u0026gt; -d \u0026lt;diff patch dir\u0026gt; \u0026lt;options\u0026gt;\u003c/code\u003e \u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eoptions\u003c/h3\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e-b, --basefolder \u0026lt;path\u0026gt;\u003c/code\u003e the path to codebase to be analyzed \u003cbr\u003e\n\u003ccode\u003e-d --diffFile \u0026lt;path\u0026gt;\u003c/code\u003e path to diff file \u003cbr\u003e\n\u003ccode\u003e--config \u0026lt;path\u0026gt;\u003c/code\u003e path to JSON config file \u003cbr\u003e\n\u003ccode\u003e--e2e \u0026lt;boolean\u0026gt;\u003c/code\u003e run analyze on e2e tests \u003cbr\u003e\n\u003ccode\u003e--skipGetDependency \u0026lt;boolean\u0026gt;\u003c/code\u003e skip get denpendency graph step, has to set \u003ccode\u003e--callGraph\u003c/code\u003e \u003ccode\u003e--fileDependencyGraph\u003c/code\u003e \u003ccode\u003e--E2EdenpendencyGraph\u003c/code\u003e as well \u003cbr\u003e\n\u003ccode\u003e--callGraph \u0026lt;path\u0026gt;\u003c/code\u003e path to callgraph \u003cbr\u003e\n\u003ccode\u003e--fileDependencyGraph \u0026lt;path\u0026gt;\u003c/code\u003e path to file denpendency graph \u003cbr\u003e\n\u003ccode\u003e--E2EdenpendencyGraph \u0026lt;path\u0026gt;\u003c/code\u003e dir to E2e dependencyGraph \u003cbr\u003e\n\u003ccode\u003e--excepts \u0026lt;array\u0026gt;\u003c/code\u003e array of files to be excluded when injection \u003cbr\u003e\n\u003ccode\u003e--docker \u0026lt;boolean\u0026gt;\u003c/code\u003e whether running in docker or ci environment, if it is then skip user inputs\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eoptions only in configuration files only: \u003cbr\u003e\n\u003ccode\u003eexcepts: \u0026lt;array of file paths\u0026gt;\u003c/code\u003e files to be excepted in injection step \u003cbr\u003e\n\u003ccode\u003ee2eTestSuite: \u0026lt;object\u0026gt;\u003c/code\u003e e2e configuration object, includes \u003ccode\u003especs\u003c/code\u003e and \u003ccode\u003eexclude\u003c/code\u003e to specify e2e test suite \u003cbr\u003e\n\u003ccode\u003eincludesE2E: \u0026lt;boolean\u0026gt;\u003c/code\u003e run analyze on e2e tests \u003cbr\u003e\n\u003ccode\u003eonlyE2E: \u0026lt;boolean\u0026gt;\u003c/code\u003e only run e2e analysis, skip unit tests \u003cbr\u003e\n\u003ccode\u003erunUnitTestsInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to run unit test. e.g. \u003ccode\u003enpm run test:unit\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003eE2EprerunInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to setup e2e tests \u003cbr\u003e\n\u003ccode\u003eE2EpostrunInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to run after e2e tests finished \u003cbr\u003e\n\u003ccode\u003ebuildE2EInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to build code for e2e tests \u003cbr\u003e\n\u003ccode\u003erunE2EInstr: \u0026lt;string\u0026gt;\u003c/code\u003e instructions to run e2e tests \u003cbr\u003e\n\u003ccode\u003eE2Edir: \u0026lt;string\u0026gt;\u003c/code\u003e path E2E test folder \u003cbr\u003e\n\u003ccode\u003eE2Etemp: \u0026lt;string\u0026gt;\u003c/code\u003e test folder to be copied to E2E test folder if you want to use your own E2E test folder \u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esince each End-to-end test suite set up differently, you might want to customize \u003ca href=\"https://github.com/charlie-cyf/nodeSRT/tree/master/e2eTestsHandler\"\u003ee2e test handler\u003c/a\u003e for your e2e test suite\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eexamples\u003c/h3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esample nodeSRT command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enodeSRT -b ./simorgh -d ./diff.patch --config ./nodeSRT/example/dockerConfig.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou can find some sample configuration files in \u003ca href=\"https://github.com/charlie-cyf/nodeSRT/tree/master/example\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow it works\u003c/h2\u003e\u003ca id=\"user-content-how-it-works\" class=\"anchor\" aria-label=\"Permalink: How it works\" href=\"#how-it-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSteps: \u003cbr\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-steps-\" class=\"anchor\" aria-label=\"Permalink: Steps: \" href=\"#steps-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003estatic analysis on each js file, generate dependency graph on functions and fields\u003c/li\u003e\n\u003cli\u003einject dynamic analysis code to each function\u003c/li\u003e\n\u003cli\u003erun tests one by one, get entities influenced by each tests. Store as json file.\u003c/li\u003e\n\u003cli\u003eparse diff file, identify changes.\u003c/li\u003e\n\u003cli\u003egenerate a list of tests on test-entities relationship.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edesign decisions\u003c/h3\u003e\u003ca id=\"user-content-design-decisions\" class=\"anchor\" aria-label=\"Permalink: design decisions\" href=\"#design-decisions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003esince the selection is at method level, if a modification is outsid of the method, we will run all tests have dependencies on this file.\u003c/li\u003e\n\u003cli\u003eAlthough astring can handle comments, some special comments are misplaced after injection. e.g. @ts-ignore Therefore, they need to be ignore when doing code injection. Otherwise we will have a build error.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003edocker comamnd\u003c/h2\u003e\u003ca id=\"user-content-docker-comamnd\" class=\"anchor\" aria-label=\"Permalink: docker comamnd\" href=\"#docker-comamnd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edocker build -t srtdock:latest .\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003edocker run --rm -ti srtdock\u003c/code\u003e \u003cbr\u003e\u003c/p\u003e\n\u003cp\u003epush to docker hub: \u003ca href=\"https://ropenscilabs.github.io/r-docker-tutorial/04-Dockerhub.html\" rel=\"nofollow\"\u003ehttps://ropenscilabs.github.io/r-docker-tutorial/04-Dockerhub.html\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker login\ndocker tag f645182d6e68 charlie9731/srtdock:v2\ndocker push charlie9731/srtdock\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build -s srtSingular.sif docker://charlie9731/srtdock:v2\nsingularity run -w -e srtSingular.sif \u0026lt;commit id\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRHEL install docker script\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo dnf config-manager --add-repo=https://download.docker.com/linux/centos/docker-ce.repo\nsudo dnf install -y vim git curl\nsudo dnf install --nobest docker-ce\nsudo groupadd docker\nsudo usermod -aG docker ec2-user\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eexperiment data\u003c/h2\u003e\u003ca id=\"user-content-experiment-data\" class=\"anchor\" aria-label=\"Permalink: experiment data\" href=\"#experiment-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eyou can find experiment data in \u003ca href=\"https://github.com/charlie-cyf/nodeSRT/tree/master/experiment\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1588619360.0 + "updated_at": 1621883262.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.latest" ], - "full_name": "ddbj/singularity_centos7_nginx", + "full_name": "EPI-APE/simu_IV", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enginx\u3092\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067\u5b9f\u884c\u3059\u308bsingularity image\u003c/h1\u003e\u003ca id=\"user-content-nginx\u3092\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067\u5b9f\u884c\u3059\u308bsingularity-image\" class=\"anchor\" aria-label=\"Permalink: nginx\u3092\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067\u5b9f\u884c\u3059\u308bsingularity image\" href=\"#nginx\u3092\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067\u5b9f\u884c\u3059\u308bsingularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u30d3\u30eb\u30c9\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u30d3\u30eb\u30c9\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u30d3\u30eb\u30c9\" href=\"#image\u306e\u30d3\u30eb\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build centos7_nginx.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003enginx\u306e\u8a2d\u5b9a\u003c/h2\u003e\u003ca id=\"user-content-nginx\u306e\u8a2d\u5b9a\" class=\"anchor\" aria-label=\"Permalink: nginx\u306e\u8a2d\u5b9a\" href=\"#nginx\u306e\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u7f6e\u3044\u3066\u3042\u308bnginx.conf\u306fDDBJ nginx\u3092\u30ea\u30d0\u30fc\u30b9\u30d7\u30ed\u30ad\u30b7\u3068\u3057\u3066\u4f7f\u3046\u8a2d\u5b9a\u306b\u306a\u3063\u3066\u3044\u308b\u3002\u9069\u5b9c\u4fee\u6b63\u3059\u308b\u3053\u3068\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003einstance\u306e\u8d77\u52d5\u003c/h2\u003e\u003ca id=\"user-content-instance\u306e\u8d77\u52d5\" class=\"anchor\" aria-label=\"Permalink: instance\u306e\u8d77\u52d5\" href=\"#instance\u306e\u8d77\u52d5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1581482569.0 + "updated_at": 1566030551.0 }, { "data_format": 2, - "description": "Multimodal Barlow Twins architecture trained via self-supervision using feature based augmentation.", + "description": null, "filenames": [ - "slp/Singularity" + "singularity/Singularity.PyTorch", + "singularity/Singularity.PyTensorflow" ], - "full_name": "Poulinakis-Konstantinos/Multimodal-Barlow-Twins", + "full_name": "huynhngoc/orion-slurm-gpu", "latest_release": null, - "readme": "\u003cp\u003eMaster Thesis for M.Sc. in Data Science and Machine Learning @ NTUA\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://dspace.lib.ntua.gr/xmlui/bitstream/handle/123456789/58314/Poulinakis_Konstantinos-Master_Thesis-2023-ML.pdf?sequence=1\u0026amp;isAllowed=y\" rel=\"nofollow\"\u003e\"Self Supervised Multimodal Learning for Emotion recognition\"\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Poulinakis-Konstantinos/Multimodal-Barlow-Twins/assets/75034778/7b0da713-da02-4dee-a05e-f65583632448\"\u003e\u003cimg src=\"https://github.com/Poulinakis-Konstantinos/Multimodal-Barlow-Twins/assets/75034778/7b0da713-da02-4dee-a05e-f65583632448\" alt=\"proposed architecture\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1702207569.0 + "updated_at": 1667480068.0 }, { "data_format": 2, - "description": null, + "description": "Container (SIMG,Docker) recipes for a number of projects", "filenames": [ - "Singularity" + "adni_simg/Singularity.adni_lashis_simg", + "grad_unwarp/Singularity.gradient_unwarp_singularity", + "NeuroImaging/Singularity.ants_fsl_robex", + "NeuroImaging/Singularity.ashs", + "NeuroImaging/Singularity.spm_fsl_mrtrix", + "NeuroImaging/Singularity.cpac", + "NeuroImaging/Singularity.freesurfer-6.0", + "NeuroImaging/Singularity.mrtrix", + "NeuroImaging/Singularity.adni_lashis_simg", + "NeuroImaging/Singularity.fsl_robex", + "DEEPSEACAT/Singularity.deepseacat_singularity" ], - "full_name": "bstriner/tensorflow-xla-cuda-10.1-cudnn7-devel-ubuntu16.04", + "full_name": "thomshaw92/container_recipes", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etensorflow-xla-cuda-10.1-cudnn7-devel-ubuntu16.04\u003c/h1\u003e\u003ca id=\"user-content-tensorflow-xla-cuda-101-cudnn7-devel-ubuntu1604\" class=\"anchor\" aria-label=\"Permalink: tensorflow-xla-cuda-10.1-cudnn7-devel-ubuntu16.04\" href=\"#tensorflow-xla-cuda-101-cudnn7-devel-ubuntu1604\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContainer Recipes\u003c/h1\u003e\u003ca id=\"user-content-container-recipes\" class=\"anchor\" aria-label=\"Permalink: Container Recipes\" href=\"#container-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer (SIMG,Docker) recipes for a number of projects\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1560510385.0 + "updated_at": 1585190745.0 }, { "data_format": 2, - "description": "Singularity container recipe for Quantum Espresso and Yambo for cpu, architecture x86_64 based on CentOS 7. ", + "description": "Simple high quality GIF encoding", "filenames": [ - "Singularity" + "1.2.0/Singularity" ], - "full_name": "CINECA-HPC/container_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64\" href=\"#container_quantum_espresso_yambo_cpu_openmpi_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer recipes for Quantum Espresso and Yambo for cpu, architecture x86_64 based on CentOS 7. In the specific:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ecentOS 7\u003c/li\u003e\n\u003cli\u003eGNU compiler 7.3.1\u003c/li\u003e\n\u003cli\u003ePython 2.7\u003c/li\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003eOpenmpi 2.1.1\u003c/li\u003e\n\u003cli\u003eQuantum Espresso 6.5 ( with --enable-openmp=yes --enable-parallel=yes)\u003c/li\u003e\n\u003cli\u003eYambo 4.5.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis recipe works in the Cineca cluster (arch x86_64):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarconi Skylake\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "icaoberg/singularity-gifgen", + "latest_release": "v1.2.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-gifgen\u003c/h1\u003e\u003ca id=\"user-content-singularity-gifgen\" class=\"anchor\" aria-label=\"Permalink: singularity-gifgen\" href=\"#singularity-gifgen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-gifgen/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-gifgen/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/icaoberg/singularity-gifgen/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-gifgen/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ddcb279561c5f18a20a4ba52ac610c17ff1211eb392eb5e0018b36cec8351abb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d67696667656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ddcb279561c5f18a20a4ba52ac610c17ff1211eb392eb5e0018b36cec8351abb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d67696667656e\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-gifgen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/da84737ceaa96074c0f93f7e597a610cf976277838f95500a1b152e68829e579/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d67696667656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/da84737ceaa96074c0f93f7e597a610cf976277838f95500a1b152e68829e579/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d67696667656e\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-gifgen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/11fa14457c5a8dcd4494066d37ab61b76bc998132ef9e8ce5bd91cb952c692ed/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d67696667656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11fa14457c5a8dcd4494066d37ab61b76bc998132ef9e8ce5bd91cb952c692ed/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d67696667656e\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-gifgen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a1cb8f35430cad386966f91019470b5e6b32c19e02067af5a14f31274ac16c69/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d67696667656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1cb8f35430cad386966f91019470b5e6b32c19e02067af5a14f31274ac16c69/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d67696667656e\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-gifgen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./images/joaquin_sabina-19dias_y_500noches.gif\"\u003e\u003cimg src=\"./images/joaquin_sabina-19dias_y_500noches.gif\" width=\"50%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003e\u003ca href=\"https://www.youtube.com/watch?v=NY_EOhHRTdo\" rel=\"nofollow\"\u003eJoaqu\u00edn Sabina - 19 d\u00edas y 500 noches\u003c/a\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, - "topics": [], - "updated_at": 1604598436.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1697611081.0 }, { "data_format": 2, - "description": ":whale: Script to build a Singularity image for CellOrganizer", + "description": "Singularity recipes for base-images containing mrtrix3.", "filenames": [ - "Singularity" + "Singularity.3.0_RC3", + "Singularity.3.0_RC2" ], - "full_name": "murphygroup/singularity-cellorganizer", + "full_name": "MPIB/singularity-mrtrix3", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-cellorganizer\u003c/h1\u003e\u003ca id=\"user-content-singularity-cellorganizer\" class=\"anchor\" aria-label=\"Permalink: singularity-cellorganizer\" href=\"#singularity-cellorganizer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://cloud.sylabs.io/library/icaoberg/default/cellorganizer\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/804b7bdd4c5bf1cd2bf36b4c08742c95c2876b82d7734f4643ea80b9b415d1c1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d73796c6162732e696f2d677265656e2e737667\" alt=\"Hosted\" data-canonical-src=\"https://img.shields.io/badge/hosted-sylabs.io-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://www.cellorganizer.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5a654b31b83404ad847088d2645b299c92b54f8ca8be41fc57f262a6e3854ac7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72656c656173652d76322e382e312d7265642e737667\" alt=\"Release\" data-canonical-src=\"https://img.shields.io/badge/release-v2.8.1-red.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-cellorganizer/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8d054afb0990477a5bfd0e7e3deb8d3decc96b775aec8459a41885f03d351d52/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d63656c6c6f7267616e697a65722e737667\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-cellorganizer.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-cellorganizer/network\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f5f8af960220ab57f10b2ea754be5ca79f5b9552135d71eb05aec0f8818a01e4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d63656c6c6f7267616e697a65722e737667\" alt=\"GitHub forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-cellorganizer.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-cellorganizer/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/de0d7005345076385e9b0ec0b0c3bc063f5fd124de03c74337b296565715c87f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d63656c6c6f7267616e697a65722e737667\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-cellorganizer.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.gnu.org/licenses/quick-guide-gplv3.en.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fcf50c1f3a4879682b94be5dd325b2dec1661a159904a2a45493e553c8ccf175/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d47504c76332d626c75652e737667\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/badge/license-GPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout CellOrganizer\u003c/h2\u003e\u003ca id=\"user-content-about-cellorganizer\" class=\"anchor\" aria-label=\"Permalink: About CellOrganizer\" href=\"#about-cellorganizer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/19100b052bbc72a5acb1f431bae9b7f7b1bf42f54f89c0c78d767e674b98bd8f/687474703a2f2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f43656c6c4f7267616e697a65724c6f676f322d3235302e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/19100b052bbc72a5acb1f431bae9b7f7b1bf42f54f89c0c78d767e674b98bd8f/687474703a2f2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f43656c6c4f7267616e697a65724c6f676f322d3235302e6a7067\" alt=\"CellOrganizer Logo\" data-canonical-src=\"http://www.cellorganizer.org/wp-content/uploads/2017/08/CellOrganizerLogo2-250.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e project provides tools for\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elearning generative models of cell organization directly from images\u003c/li\u003e\n\u003cli\u003estoring and retrieving those models\u003c/li\u003e\n\u003cli\u003esynthesizing cell images (or other representations) from one or more models\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eModel learning captures variation among cells in a collection of images. Images used for model learning and instances synthesized from models can be two- or three-dimensional static images or movies.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e can learn models of\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ecell shape\u003c/li\u003e\n\u003cli\u003enuclear shape\u003c/li\u003e\n\u003cli\u003echromatin texture\u003c/li\u003e\n\u003cli\u003evesicular organelle size, shape and position\u003c/li\u003e\n\u003cli\u003emicrotubule distribution.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese models can be conditional upon each other. For example, for a given synthesized cell instance, organelle position is dependent upon the cell and nuclear shape of that instance.\u003c/p\u003e\n\u003cp\u003eCell types for which generative models for at least some organelles have been built include human HeLa cells, mouse NIH 3T3 cells, and Arabidopsis protoplasts. Planned projects include mouse T lymphocytes and rat PC12 cells.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisites\u003c/h2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity v3.5.+\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCellOrganizer v2.8.1\u003c/h3\u003e\u003ca id=\"user-content-cellorganizer-v281\" class=\"anchor\" aria-label=\"Permalink: CellOrganizer v2.8.1\" href=\"#cellorganizer-v281\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFixes\u003c/h4\u003e\u003ca id=\"user-content-fixes\" class=\"anchor\" aria-label=\"Permalink: Fixes\" href=\"#fixes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDisplay shape space when dataset field is not present or empty.\u003c/li\u003e\n\u003cli\u003eGeneration of watertight SBML Spatial output has been corrected for translation errors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOther\u003c/h4\u003e\u003ca id=\"user-content-other\" class=\"anchor\" aria-label=\"Permalink: Other\" href=\"#other\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe following models have been rebuilt using this version of CellOrganizer. Updated models can be found in the model repository.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2D HeLa diffeomorphic framework\u003c/li\u003e\n\u003cli\u003e2D HeLa PCA framework\u003c/li\u003e\n\u003cli\u003e2D HeLa classic framework\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCellOrganizer for Galaxy now supports Galaxy server v19.05.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCellOrganizer v2.8.0\u003c/h3\u003e\u003ca id=\"user-content-cellorganizer-v280\" class=\"anchor\" aria-label=\"Permalink: CellOrganizer v2.8.0\" href=\"#cellorganizer-v280\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFeatures\u003c/h4\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAdded improved model for generating protein distributions during T cell synapse formation that only requires annotation of cell couples at a single time point model and improves synapse alignment. Includes training, synthesis and info demos.\u003c/li\u003e\n\u003cli\u003eAdded outline PCA model for 2D cell and nuclear shapes. Includes training, synthesis and info demos.\u003c/li\u003e\n\u003cli\u003eAdded SPHARM-RPDM model for 3D cell and nuclear shapes (see \u003ca href=\"https://doi.org/10.1093/bioinformatics/bty983\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/bty983\u003c/a\u003e). Includes training, synthesis and info demos.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFixes\u003c/h4\u003e\u003ca id=\"user-content-fixes-1\" class=\"anchor\" aria-label=\"Permalink: Fixes\" href=\"#fixes-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFixed issues with options.train.flag. Valid options should be nuclear, cell, framework, and protein.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eEnhancements\u003c/h4\u003e\u003ca id=\"user-content-enhancements\" class=\"anchor\" aria-label=\"Permalink: Enhancements\" href=\"#enhancements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eModularized and cleaned up img2slml.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning on Singularity\u003c/h2\u003e\u003ca id=\"user-content-running-on-singularity\" class=\"anchor\" aria-label=\"Permalink: Running on Singularity\" href=\"#running-on-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCellOrganizer v2.8.*\u003c/h2\u003e\u003ca id=\"user-content-cellorganizer-v28\" class=\"anchor\" aria-label=\"Permalink: CellOrganizer v2.8.*\" href=\"#cellorganizer-v28\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreating the container\u003c/h3\u003e\u003ca id=\"user-content-creating-the-container\" class=\"anchor\" aria-label=\"Permalink: Creating the container\" href=\"#creating-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo create the container, run this command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; bash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAccessing the container\u003c/h3\u003e\u003ca id=\"user-content-accessing-the-container\" class=\"anchor\" aria-label=\"Permalink: Accessing the container\" href=\"#accessing-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo access the container, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; singularity shell cellorganizer.sif\n\nSingularity: Invoking an interactive shell within container...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo list the possible apps, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity cellorganizer.img:~/singularity-cellorganizer\u0026gt; ls -lt /opt/cellorganizer-binaries/\n\ntotal 111821\n-rwxr-xr-x 1 14246 users 12699470 Mar 29 14:25 slml2report\n-rwxr-xr-x 1 14246 users 12471747 Mar 29 14:25 slml2info\n-rwxr-xr-x 1 14246 users 40728639 Mar 29 14:25 slml2img\n-rwxr-xr-x 1 14246 users 48604048 Mar 29 14:25 img2slml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Demos\u003c/h3\u003e\u003ca id=\"user-content-running-demos\" class=\"anchor\" aria-label=\"Permalink: Running Demos\" href=\"#running-demos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run a specific demo\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt; cd demos/2D/demo2D**/\n\u0026gt; singularity run ~/path/to/cellorganizer.simg demo2D**.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen contributing to this repository, please first discuss the change you wish to make via issue, \u003ca href=\"mailto:cellorganizer-dev@compbio.cmu.edu\"\u003eemail\u003c/a\u003e, or any other method with the owners of this repository before making a change.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eSupport for \u003ca href=\"http://cellorganizer.org/\" rel=\"nofollow\"\u003eCellOrganizer\u003c/a\u003e has been provided by grants GM075205, GM090033 and GM103712 from the \u003ca href=\"http://www.nigms.nih.gov/\" rel=\"nofollow\"\u003eNational Institute of General Medical Sciences\u003c/a\u003e, grants MCB1121919 and MCB1121793 from the \u003ca href=\"http://nsf.gov/\" rel=\"nofollow\"\u003eU.S. National Science Foundation\u003c/a\u003e, by a Forschungspreis from the \u003ca href=\"http://www.humboldt-foundation.de/\" rel=\"nofollow\"\u003eAlexander von Humboldt Foundation\u003c/a\u003e, and by the \u003ca href=\"http://www.frias.uni-freiburg.de/lifenet?set_language=en\" rel=\"nofollow\"\u003eFreiburg Institute for Advanced Studies\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.mmbios.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/da21af862d75eb41d1c50d7556125597f1e9c95b7551e77d66d998b7eaf87ce5/68747470733a2f2f69312e77702e636f6d2f7777772e63656c6c6f7267616e697a65722e6f72672f77702d636f6e74656e742f75706c6f6164732f323031372f30382f4d4d42696f536c6f676f2d65313530333531373835373331332e6769663f683d3630\" alt=\"MMBioS\" data-canonical-src=\"https://i1.wp.com/www.cellorganizer.org/wp-content/uploads/2017/08/MMBioSlogo-e1503517857313.gif?h=60\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCopyright (c) 2007-2020 by the \u003ca href=\"http://murphylab.web.cmu.edu\" rel=\"nofollow\"\u003eMurphy Lab\u003c/a\u003e at the \u003ca href=\"http://www.cbd.cmu.edu\" rel=\"nofollow\"\u003eComputational Biology Department\u003c/a\u003e in \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-mrtrix3\u003c/h1\u003e\u003ca id=\"user-content-singularity-mrtrix3\" class=\"anchor\" aria-label=\"Permalink: singularity-mrtrix3\" href=\"#singularity-mrtrix3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/729\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipes for base-images containing mrtrix3.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emrtrix3 is pulled from its \u003ca href=\"https://github.com/MRtrix3/mrtrix3\"\u003egithub repository\u003c/a\u003e and build using cmake.\u003c/li\u003e\n\u003cli\u003ecmake and the build dependencies are installed through the debian repositories via \u003ccode\u003eapt-get install\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAt the end of the build process the image is cleaned up by:\n\u003cul\u003e\n\u003cli\u003eremoving cmake and build dependencies through \u003ccode\u003eapt-get purge\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003edeleting the package cache.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 5, - "topics": [ - "cellorganizer", - "container", - "virtualization", - "bioimage-informatics", - "modeling-tools" - ], - "updated_at": 1587470789.0 + "subscribers_count": 4, + "topics": [], + "updated_at": 1528782437.0 }, { "data_format": 2, "description": null, "filenames": [ - "Dockerfile/Singularity" + "containers/2024/methylKitDMRs/Singularity", + "containers/2024/dmrseq/Singularity", + "containers/2024/methylKitAvgMethylOverRegions/Singularity" ], - "full_name": "namzoo99/ecNAPP", + "full_name": "BenGSt/repo_for_reizel_lab", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLet\u0027s look for neoantigens!\u003c/h1\u003e\u003ca id=\"user-content-lets-look-for-neoantigens\" class=\"anchor\" aria-label=\"Permalink: Let\u0027s look for neoantigens!\" href=\"#lets-look-for-neoantigens\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecreated by Harold and Mary of CBM LAB\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow of ecNAPP\u003c/h2\u003e\u003ca id=\"user-content-workflow-of-ecnapp\" class=\"anchor\" aria-label=\"Permalink: Workflow of ecNAPP\" href=\"#workflow-of-ecnapp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/86759935/198952280-ea38ed73-16d7-484f-af9a-475aa0b6af09.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/86759935/198952280-ea38ed73-16d7-484f-af9a-475aa0b6af09.png\" alt=\"ing\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFor the use of this script, please prepare\u003c/h2\u003e\u003ca id=\"user-content-for-the-use-of-this-script-please-prepare\" class=\"anchor\" aria-label=\"Permalink: For the use of this script, please prepare\" href=\"#for-the-use-of-this-script-please-prepare\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003cli\u003emosek license and reference data for AA-suite, svaba\u003c/li\u003e\n\u003cli\u003ecsv file consisted of:\u003c/li\u003e\n\u003c/ol\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ecolnames\u003c/th\u003e\n\u003cth\u003edefinition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eproject\u003c/td\u003e\n\u003ctd\u003eproject name\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebarcode\u003c/td\u003e\n\u003ctd\u003esample barcode\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edocker_bind_path\u003c/td\u003e\n\u003ctd\u003epath where docker will bind to (docker -v)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003einput\u003c/td\u003e\n\u003ctd\u003einput file directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eref\u003c/td\u003e\n\u003ctd\u003ereference genome build\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eworkdir\u003c/td\u003e\n\u003ctd\u003eworking directory where the pipeline will be at\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutdir\u003c/td\u003e\n\u003ctd\u003eoutput directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esvaba_ref\u003c/td\u003e\n\u003ctd\u003edirectory of reference for svaba (genome)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDBSNP\u003c/td\u003e\n\u003ctd\u003edirectory of reference for svaba (dbsnp)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emoseklic\u003c/td\u003e\n\u003ctd\u003edirectory of MOSEK license\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAArepo\u003c/td\u003e\n\u003ctd\u003edirectory of AA repo\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ethe \u003ccode\u003edocker_bind_path\u003c/code\u003e must be the parent folder of \u003ccode\u003einput\u003c/code\u003e, \u003ccode\u003eworkdir\u003c/code\u003e, \u003ccode\u003eoutdir\u003c/code\u003e, \u003ccode\u003esvaba_ref\u003c/code\u003e, and \u003ccode\u003eDBSNP\u003c/code\u003e. Check our \u003ca href=\"https://github.com/skadbswn/ecNAPP/blob/main/example.csv\"\u003eexample.csv\u003c/a\u003e for more info.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. \u003ca href=\"https://github.com/jluebeck/AmpliconSuite-pipeline\"\u003eAmpliconSuite-pipeline\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-1-ampliconsuite-pipeline\" class=\"anchor\" aria-label=\"Permalink: 1. AmpliconSuite-pipeline\" href=\"#1-ampliconsuite-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe arguments are absed on the \u003ccode\u003eHL-NF:AmpliconArchitect\u003c/code\u003e, which are \u003ccode\u003e--AA_extendmode EXPLORE --AA_runmode FULL\u003c/code\u003e.\nTo download MOSEK liscence(mosek.lic), visit \u003ca href=\"https://www.mosek.com/products/academic-licenses/\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor AA_DATA_REPO, visit \u003ca href=\"https://datasets.genepattern.org/?prefix=data/module_support_files/AmpliconArchitect/\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eGenome build should be downloaded with \u003ccode\u003e_indexed\u003c/code\u003e files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. \u003ca href=\"https://github.com/walaj/svaba\"\u003eSVABA\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-2-svaba\" class=\"anchor\" aria-label=\"Permalink: 2. SVABA\" href=\"#2-svaba\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe used docker image for our pipeline. Since \u003ccode\u003eSVABA\u003c/code\u003e does not have output argument, the \u003ccode\u003eBAM\u003c/code\u003e files need to be placed where the output should be placed using symlink.\u003c/p\u003e\n\u003cp\u003eAfter the run, script automatically removes the symlink.\u003c/p\u003e\n\u003cp\u003eFor the additional info of reference(\u003ccode\u003eDBSNP\u003c/code\u003e), please visit the official svaba github(\u003ca href=\"https://github.com/walaj/svaba\"\u003eHERE\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. \u003ca href=\"https://hub.docker.com/r/sachet/polysolver\" rel=\"nofollow\"\u003ePOLYSOLVER\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-3-polysolver\" class=\"anchor\" aria-label=\"Permalink: 3. POLYSOLVER\" href=\"#3-polysolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe used docker image for polysolver. Since it has its own reference inside the image, we can choose genome build by argument, \u003ccode\u003ehg19\u003c/code\u003e or \u003ccode\u003ehg38\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003ePolysolver gives output with fixed name \u003ccode\u003ewinners.hla.txt\u003c/code\u003e, so the output is created under barcode folder.\u003c/p\u003e\n\u003cp\u003eDon\u0027t worry, the input hla will have it\u0027s own name while going through the next process.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. \u003ca href=\"https://services.healthtech.dtu.dk/service.php?NetMHCpan-4.1\" rel=\"nofollow\"\u003enetMHCpan\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-4-netmhcpan\" class=\"anchor\" aria-label=\"Permalink: 4. netMHCpan\" href=\"#4-netmhcpan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003efor the final output of neoantigens, we are using \u003ccode\u003enetMHCpan4.1b\u003c/code\u003e to find peptides binding with MHC class I.\u003c/p\u003e\n\u003cp\u003ethe final output will be under this header:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003ePos\u003c/th\u003e\n\u003cth align=\"center\"\u003eMHC\u003c/th\u003e\n\u003cth align=\"center\"\u003ePeptide\u003c/th\u003e\n\u003cth align=\"center\"\u003eCore\u003c/th\u003e\n\u003cth align=\"center\"\u003eOf\u003c/th\u003e\n\u003cth align=\"center\"\u003eGp\u003c/th\u003e\n\u003cth align=\"center\"\u003eGl\u003c/th\u003e\n\u003cth align=\"center\"\u003eIp\u003c/th\u003e\n\u003cth align=\"center\"\u003eIl\u003c/th\u003e\n\u003cth align=\"center\"\u003eIcore\u003c/th\u003e\n\u003cth align=\"center\"\u003eidentity\u003c/th\u003e\n\u003cth align=\"center\"\u003eScore_EL\u003c/th\u003e\n\u003cth align=\"center\"\u003e%Rank_EL\u003c/th\u003e\n\u003cth align=\"center\"\u003eBindLevel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e1\u003c/td\u003e\n\u003ctd align=\"center\"\u003eHLA-B*40:01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNETQRLLLL\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNETQRLLLL\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNETQRLLLL\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePEPLIST\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7000450\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.237\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u0026lt;= SB\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003ewhere:\n\u003cul\u003e\n\u003cli\u003ePos: Residue number (starting from 0) of the peptide in the protein sequence.\u003c/li\u003e\n\u003cli\u003eHLA: Specified MHC molecule / Allele name.\u003c/li\u003e\n\u003cli\u003ePeptide: Amino acid sequence of the potential ligand.\u003c/li\u003e\n\u003cli\u003eCore: The minimal 9 amino acid binding core directly in contact with the MHC.\u003c/li\u003e\n\u003cli\u003eOf: The starting position of the Core within the Peptide (if \u0026gt; 0, the method predicts a N-terminal protrusion).\u003c/li\u003e\n\u003cli\u003eGp: Position of the deletion, if any.\u003c/li\u003e\n\u003cli\u003eGl: Length of the deletion, if any.\u003c/li\u003e\n\u003cli\u003eIp: Position of the insertion, if any.\u003c/li\u003e\n\u003cli\u003eIl: Length of the insertion, if any.\u003c/li\u003e\n\u003cli\u003eIcore: Interaction core. This is the sequence of the binding core including eventual insertions of deletions.\u003c/li\u003e\n\u003cli\u003eIdentity: Protein identifier, i.e. the name of the FASTA entry.\u003c/li\u003e\n\u003cli\u003eScore: The raw prediction score.\u003c/li\u003e\n\u003cli\u003e%Rank: Rank of the predicted binding score compared to a set of random natural peptides. This measure is not affected by inherent bias of certain molecules towards higher or lower mean predicted affinities. Strong binders are defined as having %rank\u0026lt;0.5, and weak binders with %rank\u0026lt;2. We advise to select candidate binders based on %Rank rather than Score\u003c/li\u003e\n\u003cli\u003eBindLevel: (SB: Strong Binder, WB: Weak Binder). The peptide will be identified as a strong binder if the %Rank is below the specified threshold for the strong binders (by default, 0.5%). The peptide will be identified as a weak binder if the %Rank is above the threshold of the strong binders but below the specified threshold for the weak binders (by default, 2%).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBen Steinberg\u0027s Repository for Dr. Reizel\u0027s lab (The Epigenetic Editing Research Lab, Technion, Israel)\u003c/h1\u003e\u003ca id=\"user-content-ben-steinbergs-repository-for-dr-reizels-lab-the-epigenetic-editing-research-lab-technion-israel\" class=\"anchor\" aria-label=\"Permalink: Ben Steinberg\u0027s Repository for Dr. Reizel\u0027s lab (The Epigenetic Editing Research Lab, Technion, Israel)\" href=\"#ben-steinbergs-repository-for-dr-reizels-lab-the-epigenetic-editing-research-lab-technion-israel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains varius scripts for running the following genomic data analysis pipelines:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWGBS\u003c/li\u003e\n\u003cli\u003eRRBS (nugen ovation kit)\u003c/li\u003e\n\u003cli\u003eDMR finding + heatmaps\u003c/li\u003e\n\u003cli\u003eRNA-SEQ DEG\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, "topics": [], - "updated_at": 1666414969.0 + "updated_at": 1733087762.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v1.0", - "Singularity.latest" + "singularity/Singularity", + "singularity/docker-to-singularity/Singularity" ], - "full_name": "wkpalan/singularity-snpeff-snpsift", + "full_name": "snic-nsc/nscjekyllsetup", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-snpeff-snpsft\u003c/h1\u003e\u003ca id=\"user-content-singularity-snpeff-snpsft\" class=\"anchor\" aria-label=\"Permalink: singularity-snpeff-snpsft\" href=\"#singularity-snpeff-snpsft\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a duplicate project with access to snpeff and snpsift as apps within a singularity container.\u003c/p\u003e\n\u003cp\u003eThis project is an updated format code available from qbicsoftware \u003ca href=\"https://github.com/qbicsoftware/qbic-singularity-snpeff\"\u003esnpEff\u003c/a\u003e container\u003c/p\u003e\n\u003cp\u003eThis is a containerized version of the genetic variant annotation tool \u003ca href=\"http://snpeff.sourceforge.net/\" rel=\"nofollow\"\u003eSnpEff\u003c/a\u003e. We use \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e as container technology.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootstrap files with tags\u003c/h2\u003e\u003ca id=\"user-content-bootstrap-files-with-tags\" class=\"anchor\" aria-label=\"Permalink: Bootstrap files with tags\" href=\"#bootstrap-files-with-tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide always a bootstrap file (\u003ccode\u003eSingularity\u003c/code\u003e) tagged \u003ccode\u003e.latest\u003c/code\u003e which represents the most resent development status of the container. If you see version tags like \u003ccode\u003e.v1.0\u003c/code\u003e, this means that this is the recipe of a container with a stable version tag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to build the container\u003c/h2\u003e\u003ca id=\"user-content-how-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: How to build the container\" href=\"#how-to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/qbicsoftware/qbic-singularity-snpeff.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e qbic-singularity-snpeff\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSince Singularity 2.4, the build command from file looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build myContainer.simg \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSingularity file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also download the build and ready-to-use container from Singularity Hub:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://qbicsoftware/qbic-singularity-snpeff:latest\nsingularity pull shub://qbicsoftware/qbic-singularity-snpeff:v1.0\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run the container and calling SnpEff\u003c/h2\u003e\u003ca id=\"user-content-how-to-run-the-container-and-calling-snpeff\" class=\"anchor\" aria-label=\"Permalink: How to run the container and calling SnpEff\" href=\"#how-to-run-the-container-and-calling-snpeff\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the container and calling SnpEff you just need to\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e myContainer.simg snpEff [arguments]\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e myContainer.simg snpEff -h\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDefining the reference genome\u003c/h2\u003e\u003ca id=\"user-content-defining-the-reference-genome\" class=\"anchor\" aria-label=\"Permalink: Defining the reference genome\" href=\"#defining-the-reference-genome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProviding them inside of the container would make the container big, so we think it is a better idea to mount the reference genome into the right folder inside the container, where snpEff automatically searches for reference genome databases.\u003c/p\u003e\n\u003cp\u003eYou can simple download the databases, unzip them on your filesystem, and bind its \u003ccode\u003edata\u003c/code\u003e directory into the container. If you use snpEff\u0027s \u003ccode\u003e-v\u003c/code\u003e verbose output option, you will see that it will find the pre-installed databases and will not try to download it.\u003c/p\u003e\n\u003cp\u003eHere is an example, where we downloaded the \u003cstrong\u003ehg19\u003c/strong\u003e reference genome with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_hg19.zip\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eon the host filesystem, unzipped it and bound it during the container execution.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B ./data/:/usr/local/lib/snpEff/data snpEff.simg snpEff -v hg19 myVCF.vcf\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthor\u003c/h2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/wkpalan\"\u003eKokulapala (Gokul) Wimalanathan\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWhat is included\u003c/h1\u003e\u003ca id=\"user-content-what-is-included\" class=\"anchor\" aria-label=\"Permalink: What is included\" href=\"#what-is-included\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe Dockerfile in this repo is used to build a docker container with Jekyll 2.1.1, under rbenv (v2.4.1), with all the required gem files, to run the NSC webpages.\u003c/li\u003e\n\u003cli\u003eA second rbenv (v2.4.0) is also installed and setup with Jekyll 3.4.2, and can be used to test code requiring a more current Jekyll.\u003c/li\u003e\n\u003cli\u003eThere is a script (compile.sh) which can be used if you want to generate html code for the webpage, without actually logging onto the container.\u003c/li\u003e\n\u003cli\u003eThere\u0027s also a Singularity recipe, to build a singularity container.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Installation\u003c/h2\u003e\u003ca id=\"user-content-docker-installation\" class=\"anchor\" aria-label=\"Permalink: Docker Installation\" href=\"#docker-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe prebuilt container is also available on the Docker hub, and can be pulled down.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull pchengi/nscjekyll\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild locally from Dockerfile\u003c/h2\u003e\u003ca id=\"user-content-build-locally-from-dockerfile\" class=\"anchor\" aria-label=\"Permalink: Build locally from Dockerfile\" href=\"#build-locally-from-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou can also build the docker container yourself.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e git clone https://github.com/snic-nsc/nscjekyllsetup.git\n cd nscjekyllsetup\n sudo docker build -t nscjekyll .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStarting the docker container\u003c/h2\u003e\u003ca id=\"user-content-starting-the-docker-container\" class=\"anchor\" aria-label=\"Permalink: Starting the docker container\" href=\"#starting-the-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e sudo docker run --rm -i -d -v \u0026lt;path to checked out nscweb repo\u0026gt;:/mnt -p 4000:4000 --name nscjekyll nscjekyll bash\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThe above command starts the container, and mounts your checked out nscweb directory onto /mnt directory on the container; it also proxies port 4000 on the container onto your host machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScripted html code generation\u003c/h2\u003e\u003ca id=\"user-content-scripted-html-code-generation\" class=\"anchor\" aria-label=\"Permalink: Scripted html code generation\" href=\"#scripted-html-code-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou can generate the html code for the files in the nscweb repo, without having to login into the container, using the compile.sh script on the container. It\u0027ll write the generated files to the _site directory, within your repo. It will output the compilation message(s) onto the terminal, and also return the exit code returned by jekyll, which can be used to test if the compilation was successful. Note that the \u003ccode\u003ecompile.sh\u003c/code\u003e script takes an argument; if \u003ccode\u003ensc\u003c/code\u003e is specified, it uses \u003ccode\u003ejekyll 2.1.1\u003c/code\u003e, else it will use a more current version of Jekyll, \u003ccode\u003ejekyll 3.5.2\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker exec -it nscjekyll bash /home/nscuser/compile.sh nsc\nConfiguration file: /home/nscuser/mnt/_config.yml\n Source: /home/nscuser/mnt\n Destination: /home/nscuser/mnt/_site\n Generating... \n done.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eServing the contents using Jekyll\u003c/h2\u003e\u003ca id=\"user-content-serving-the-contents-using-jekyll\" class=\"anchor\" aria-label=\"Permalink: Serving the contents using Jekyll\" href=\"#serving-the-contents-using-jekyll\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIn order to serve the file contents using Jekyll, simply do the following:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker exec -it nscjekyll bash\nsource rubyenv nsc\ncd mnt\njekyll serve --watch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAt this point, if you don\u0027t see errors on the console, you should be able to point the browser on your host machine to localhost:4000 and view the pages.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity installation\u003c/h2\u003e\u003ca id=\"user-content-singularity-installation\" class=\"anchor\" aria-label=\"Permalink: Singularity installation\" href=\"#singularity-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe singularity build recipe is found in the singularity directory, in this repo.\u003c/li\u003e\n\u003cli\u003eTo build:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd singularity\nsudo singularity build nscjekyll.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eTo simply compile pages (such as via a script)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind \u0026lt;checked-out nscweb directory\u0026gt;:/mnt nscjekyll.simg bash /usr/local/src/nscjekyllsetup/compile.sh nsc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRun the jekyll web server, to serve pages, you could do one of the following:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind \u0026lt;checked-out nscweb directory\u0026gt;:/mnt nscjekyll.simg bash\nsource /usr/local/src/nscjekyllsetup/rubyenv nsc\ncd /mnt\njekyll serve --watch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell nscjekyll.simg\nsource /usr/local/src/nscjekyllsetup/rubyenv nsc\ncd \u0026lt;checked-out nscweb directory\u0026gt;\njekyll serve --watch\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConverting Docker to Singularity\u003c/h2\u003e\u003ca id=\"user-content-converting-docker-to-singularity\" class=\"anchor\" aria-label=\"Permalink: Converting Docker to Singularity\" href=\"#converting-docker-to-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you don\u0027t wish to build a singularity container from scratch, using the recipe, you can convert it from a prebuilt docker image.\u003c/li\u003e\n\u003cli\u003eTo do this, execute the build.sh script in docker-to-singularity folder, under `singularity\u0027.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1523929389.0 + "updated_at": 1534270572.0 }, { "data_format": 2, - "description": "Nextflow workflow for automated IGV snapshots", + "description": "dot and other graphviz executable in a simple singularity container", "filenames": [ - "containers/IGV/Singularity.IGV" + "singularity/Singularity.v1" ], - "full_name": "stevekm/IGV-snapshot-nf", + "full_name": "cokelaer/graphviz4all", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIGV-snapshot-nf\u003c/h1\u003e\u003ca id=\"user-content-igv-snapshot-nf\" class=\"anchor\" aria-label=\"Permalink: IGV-snapshot-nf\" href=\"#igv-snapshot-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn example Nextflow workflow for creating automated IGV snapshots of .bam files based on a list of target regions.\u003c/p\u003e\n\u003cp\u003eThis workflow is designed to show how to integrate \u003ca href=\"https://github.com/stevekm/IGV-snapshot-automator\"\u003eautomated IGV snapshotting\u003c/a\u003e into a Nextflow workflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, clone this repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/stevekm/IGV-snapshot-automator.git\ncd IGV-snapshot-automator\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainers\u003c/h3\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker and/or Singularity containers are used to package IGV, X11, and \u003ccode\u003exvfb\u003c/code\u003e required for functionality. Docker is required to build Singularity containers\u003c/p\u003e\n\u003cp\u003eTo build the Docker container for IGV:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd containers\nmake docker-build VAR=IGV\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo test out the IGV Docker container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-test VAR=IGV\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(optional) To build a Singuarity container for IGV, first build the Singularity Docker container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-build VAR=Singularity-2.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThis container is used to build Singularity containers\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo build the Singularity container for IGV:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-build VAR=IGV\n\n# test the container:\nmake singularity-test VAR=IGV\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThe Singularity container will be saved to \u003ccode\u003econtainers/IGV/IGV.simg\u003c/code\u003e, which you can upload to your remote server for usage\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the included demo workflow (from the parent repo directory):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eShould look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eIGV-snapshot-nf$ make run\n./nextflow run main.nf -profile \"docker\"\nN E X T F L O W ~ version 19.04.1\nLaunching `main.nf` [kickass_cray] - revision: 1823b32e4f\n~~~~~~~ IGV Pipeline ~~~~~~~\n* Project dir: /Users/steve/projects/IGV-snapshot-nf\n* Launch dir: /Users/steve/projects/IGV-snapshot-nf\n* Work dir: /Users/steve/projects/IGV-snapshot-nf/work\n* Profile: docker\n* Script name: main.nf\n* Script ID: 1823b32e4f4fbc1caa63b0c12b2d4340\n* Container engine: docker\n* Workflow session: 843f9541-9cc2-46c8-9005-89659c67ed80\n* Nextflow run name: kickass_cray\n* Nextflow version: 19.04.1, build 5072 (03-05-2019 12:29 UTC)\n* Launch command:\nnextflow run main.nf -profile docker\n[warm up] executor \u0026gt; local\nexecutor \u0026gt; local (1)\n[91/852794] process \u0026gt; run_IGV [100%] 1 of 1 \u2714\nCompleted at: 22-May-2019 15:27:46\nDuration : 1m 20s\nCPU hours : (a few seconds)\nSucceeded : 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample snapshot output:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/stevekm/IGV-snapshot-nf/output/output/snapshots/chr13_113976596_113976736.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/stevekm/IGV-snapshot-nf/output/output/snapshots/chr13_113976596_113976736.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoftware\u003c/h1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-label=\"Permalink: Software\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTested with macOS 10.12.6 and RHEL 7\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNextflow (requires Java 8+ and \u003ccode\u003ebash\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIGV 2.4.10\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePython\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egraphviz4all\u003c/h1\u003e\u003ca id=\"user-content-graphviz4all\" class=\"anchor\" aria-label=\"Permalink: graphviz4all\" href=\"#graphviz4all\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDEPRECATED, Aug 2020\u003c/strong\u003e: This is now part of \u003ca href=\"https://damona.readthedocs.io\" rel=\"nofollow\"\u003ehttps://damona.readthedocs.io\u003c/a\u003e project.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edamona install graphviz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA container with graphviz (\u003ca href=\"http://www.graphviz.org/\" rel=\"nofollow\"\u003ehttp://www.graphviz.org/\u003c/a\u003e) executables (dot, circo, etc).\u003c/p\u003e\n\u003cp\u003eThis is for Singularity 2.4 at least and is available on singularity-hub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name graphviz.img shub://cokelaer/graphviz4all:v1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eConversion of the dot file into SVG conterpart:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./graphviz.img dot -Tsvg test.dot -o test.svg\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ - "igv", - "nextflow" + "dot", + "circo", + "graphviz", + "singularity" ], - "updated_at": 1558554996.0 + "updated_at": 1597173467.0 }, { "data_format": 2, - "description": "Singularity definition files for projects.", + "description": null, "filenames": [ - "Singularity.gvfn", - "Singularity.explorer" + "Singularity" ], - "full_name": "qlan3/singularity-deffile", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-deffile\u003c/h1\u003e\u003ca id=\"user-content-singularity-deffile\" class=\"anchor\" aria-label=\"Permalink: singularity-deffile\" href=\"#singularity-deffile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3126\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity definition files for projects.\u003c/p\u003e\n", + "full_name": "baxpr/mniconn", + "latest_release": "v3.3.0-beta2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emniconn\u003c/h1\u003e\u003ca id=\"user-content-mniconn\" class=\"anchor\" aria-label=\"Permalink: mniconn\" href=\"#mniconn\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputes functional connectivity maps and matrices for a specified set of ROIs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ewremovegm_niigz\u003c/code\u003e, \u003ccode\u003ewkeepgm_niigz\u003c/code\u003e, \u003ccode\u003ewmeanfmri_niigz\u003c/code\u003e. Preprocessed fMRI data from \u003ca href=\"https://github.com/baxpr/connprep\"\u003econnprep\u003c/a\u003e. This may be supplied in atlas space or subject native space, as long as the ROI image is in the same space.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ewroi_niigz\u003c/code\u003e. ROI image. This may be an image existing within the container (e.g. the MNI space \u0027AABHHIP_LR.nii.gz\u0027). Or, it may be any supplied image. In the latter case, \u003ccode\u003ewroilabel_csv\u003c/code\u003e must also be supplied; this file must contain Label and Region columns, or may be the STATS output of a slant assessor.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ewt1_niigz\u003c/code\u003e. T1 image for the PDF report.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline\u003c/h2\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-label=\"Permalink: Pipeline\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eResample the ROI image to match the fMRI. It\u0027s assumed both are already aligned and in the same space as the ROI image.\u003c/li\u003e\n\u003cli\u003eExtract mean time series from the supplied fMRI for each ROI in the ROI image.\u003c/li\u003e\n\u003cli\u003eCompute functional connectivity: \u003ccode\u003eR\u003c/code\u003e, the correlation coefficient; and \u003ccode\u003eZ\u003c/code\u003e, the Fisher transformed correlation, \u003ccode\u003eatanh(R) * sqrt(N-3)\u003c/code\u003e where \u003ccode\u003eN\u003c/code\u003e is number of time points. The ROI-to_ROI matrix is computed, and also voxelwise connectivity maps.\u003c/li\u003e\n\u003cli\u003eGenerate a PDF report and organize outputs for XNAT.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, "subscribers_count": 1, - "topics": [ - "singularity", - "singularity-hub", - "singularity-container" - ], - "updated_at": 1564610770.0 + "topics": [], + "updated_at": 1625437966.0 }, { "data_format": 2, - "description": "Singularity containers for running COVISE, OpenCOVER, and Vistle", + "description": "Singularity definitions for agalma", "filenames": [ - "Singularity.centos7", - "Singularity.vistle-client", - "Singularity.vistle-server", - "Singularity.covise-deps", - "Singularity.covise" + "Singularity.latest", + "versions/Singularity.1.0.1", + "versions/Singularity.1.0.0" ], - "full_name": "vistle/singularity", + "full_name": "brevans/agalma", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity containers for COVISE, OpenCOVER and Vistle\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers-for-covise-opencover-and-vistle\" class=\"anchor\" aria-label=\"Permalink: Singularity containers for COVISE, OpenCOVER and Vistle\" href=\"#singularity-containers-for-covise-opencover-and-vistle\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains definition files for building \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e containers\nfor \u003ca href=\"https://www.hlrs.de/covise\" rel=\"nofollow\"\u003eCOVISE\u003c/a\u003e, \u003ca href=\"https://www.hlrs.de/opencover\" rel=\"nofollow\"\u003eOpenCOVER\u003c/a\u003e, and \u003ca href=\"https://vistle.io\" rel=\"nofollow\"\u003eVistle\u003c/a\u003e.\nThey are based on \u003ca href=\"https://www.centos.org\" rel=\"nofollow\"\u003eCentos 7\u003c/a\u003e.\nCOVISE and OpenCOVER are built within the same container, and Vistle builds on\ntop of this.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding\u003c/h2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003einstall singularity\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo make\u003c/code\u003e inside this directory (super user access is required for building Singularity containers)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing\u003c/h2\u003e\u003ca id=\"user-content-using\" class=\"anchor\" aria-label=\"Permalink: Using\" href=\"#using\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003erunning COVISE\n\u003ccode\u003esingularity run --nv covise.sif\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003erunning OpenCOVER\n\u003ccode\u003esingularity exec --nv covise.sif /usr/bin/opencover\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003erunning Vistle\n\u003ccode\u003esingularity run --nv vistle-client.sif\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you do not use the proprietary NVidia driver, you should omit \u003ccode\u003e--nv\u003c/code\u003e from the command lines.\nIn all three cases, you can append files to be opened, to the command line.\nAlternatively, you can just execute the containers directly, e.g. \u003ccode\u003e./vistle-client.sif\u003c/code\u003e.\nEditing your \u003ccode\u003erun-singularity\u003c/code\u003e script will allow to change default parameters.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity \u0026amp; agalma\u003c/h1\u003e\u003ca id=\"user-content-singularity--agalma\" class=\"anchor\" aria-label=\"Permalink: Singularity \u0026amp; agalma\" href=\"#singularity--agalma\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e definition file is meant to closely mirror the dockerfile for agalma. Singularity containers are well suited for running docker-like workflows in multi-user contexts, such as HPC clusters. Please see the \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003elinux\u003c/a\u003e or \u003ca href=\"http://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003eMacOS\u003c/a\u003e install instructions to get singularity.\u003c/p\u003e\n\u003cp\u003eTo build this Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo $(which singularity) build agalma.simg Singularity.latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo pull this Singularity image from singularity-hub and run the agalma tests in current directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run shub://brevans/agalma:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 6, - "topics": [ - "singularity-containers", - "visualization", - "hpc", - "hlrs", - "vistle", - "covise" - ], - "updated_at": 1600420965.0 + "subscribers_count": 7, + "topics": [], + "updated_at": 1512665600.0 }, { "data_format": 2, "description": null, "filenames": [ - "r01/Singularity.def" + "tests/Singularity.py3.6.8_biopython1.73_mod", + "tests/Singularity.py3.6.3_biopython1.73", + "tests/Singularity.py3.7.1_biopython1.73_mod", + "tests/Singularity.py3.7.3_biopython1.73_mod", + "tests/Singularity.py3.7.1_biopython1.73", + "tools/Singularity.deepvariant_0.8.0", + "tools/Singularity.salmon_0.14.1", + "tools/Singularity.vcftools_0.1.16", + "tools/Singularity.krakenuniq_0.5.8", + "tools/Singularity.vcflib_1.0.0-rc2", + "tools/Singularity.plink_1.90beta5", + "tools/Singularity.meraculous_2.2.6", + "tools/Singularity.sambamba_0.6.9", + "tools/Singularity.stacks_2.3e", + "tools/Singularity.biopython_1.73", + "tools/Singularity.deepbinner_0.2.0", + "tools/Singularity.star_2.7.0c", + "tools/Singularity.mummer_4.0.0beta2", + "tools/Singularity.shinotate_1.5.8.918", + "tools/Singularity.scrmshaw_20180523", + "tools/Singularity.cutadapt_2.6", + "tools/Singularity.bbmap_38.50b", + "tools/Singularity.blobtools_1.0.1", + "tools/Singularity.gatk_4.1.0.0", + "tools/Singularity.trinity_2.8.4", + "tools/Singularity.flye_2.5", + "tools/Singularity.stacks_2.0Beta9", + "tools/Singularity.ensemble-vep_96.1", + "tools/Singularity.hmmer_3.2.1", + "tools/Singularity.spades_3.13.0", + "tools/Singularity.bracken_2.2", + "tools/Singularity.R_3.6.0", + "tools/Singularity.swarm_2.2.2", + "tools/Singularity.bioconductor_3.9", + "tools/Singularity.freebayes_1.2.0", + "tools/Singularity.vt_0.57721", + "tools/Singularity.pychopper_0.6.1", + "tools/Singularity.apollo_2.2.0", + "tools/Singularity.mothur_1.40.5", + "tools/Singularity.racon_1.4.7", + "tools/Singularity.minimap2_2.17r941", + "tools/Singularity.sra_2.9.2", + "tools/Singularity.BUSCO_3.0.2", + "tools/Singularity.kraken_2.0.8beta", + "tools/Singularity.transdecoder_5.3.0", + "tools/Singularity.bwa_0.7.17", + "tools/Singularity.last_973", + "tools/Singularity.kollector_1.0.1", + "tools/Singularity.quast_5.0.2", + "tools/Singularity.clustalo_1.2.4", + "tools/Singularity.R-Mfuzz_2.38.0", + "tools/Singularity.borgbackup_1.1.6", + "tools/Singularity.purge_haplotigs_20181203", + "pipelines/Singularity.five-accessions", + "pipelines/Singularity.racon-chunks_0.0.4", + "pipelines/Singularity.basecall_wrapper_0.0.32_albacore_2.3.3", + "pipelines/Singularity.racon-chunks_py36", + "pipelines/Singularity.pinfish", + "utils/Singularity.pigz_2.4.0", + "utils/Singularity.optaweb-employee-rostering", + "utils/Singularity.samtools_1.9", + "utils/Singularity.optaplanner_7.23.0", + "utils/Singularity.openshift" ], - "full_name": "7yl4r/thiago_spoccupancy", + "full_name": "TomHarrop/singularity-containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003esetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003estarting from ubuntu 24 with r-base installed\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esudo apt install -y cmake libssl-dev libudunits2-dev libgdal-dev\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003emight also need \u003ccode\u003egdal-bin\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRscript -e \"install.packages(\"spOccupancy\")\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRscript -e \"install.packages(\"stars\")\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRscript -e \"install.packages(\"ggplot2\")\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003erun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eRscript 5...\u003c/code\u003e5.Analysis_spOccupancy_MultiSp_SpatInteg_Summer.R\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is used to package the application for a SLURM supercomputer.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build `run.sif` from `Singularity.def` on local machine with sudo:\u003c/span\u003e\nsudo singularity build run.sif Singularity.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e transfer .sif to supercomputer\u003c/span\u003e\nrsync -hazv run.sif tylarmurray@circe.rc.usf.edu:.\n\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSLURM\u003c/h3\u003e\u003ca id=\"user-content-slurm\" class=\"anchor\" aria-label=\"Permalink: SLURM\" href=\"#slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eset up new run\u003c/h4\u003e\u003ca id=\"user-content-set-up-new-run\" class=\"anchor\" aria-label=\"Permalink: set up new run\" href=\"#set-up-new-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003emkdir\u003c/code\u003e run##, \u003ccode\u003ecd\u003c/code\u003e into it, then:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eTEMPLATE=run03\nmkdir old-slurm\ncat \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enew run from template \u003cspan class=\"pl-smi\"\u003e$TEMPLATE\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e README.md\n\nln -s ../data/Shapes Shapes\nln -s ../data/spOccupancy_MultiSpp_FullArea spOccupancy_MultiSpp_FullArea\nln -s ../data/Grid_OccEnv_Seasonal.txt Grid_OccEnv_Seasonal.txt\n\ncp ../\u003cspan class=\"pl-smi\"\u003e$TEMPLATE\u003c/span\u003e/5.Analysis_spOccupancy_MultiSp_SpatInteg_Summer.R \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ncp ../\u003cspan class=\"pl-smi\"\u003e$TEMPLATE\u003c/span\u003e/submit.sh \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\ngit add README.md 5.Analysis_spOccupancy_MultiSp_SpatInteg_Summer.R submit.sh\ngit add -f Grid_OccEnv_Seasonal.txt Shapes spOccupancy_MultiSpp_FullArea\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNOTE: for *winter runs, the file is still named \u003ccode\u003e*_Summer.R\u003c/code\u003e, just use a different \u003ccode\u003eTEMPLATE\u003c/code\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity containers\u003c/h1\u003e\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: Singularity containers\" href=\"#singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/996\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipelines\u003c/h2\u003e\u003ca id=\"user-content-pipelines\" class=\"anchor\" aria-label=\"Permalink: Pipelines\" href=\"#pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tomharrop/5acc/\"\u003efive-accessions\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://jgi.doe.gov/data-and-tools/bbtools/\" rel=\"nofollow\"\u003eBBMap 38.00\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bioconductor.org/help/docker/\" rel=\"nofollow\"\u003eBioconductor 3.7\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://biopython.org/\" rel=\"nofollow\"\u003eBiopython 1.72\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/borgbackup/borg\"\u003eborgbackup 1.1.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://busco.ezlab.org/\" rel=\"nofollow\"\u003eBUSCO 3.0.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.clustal.org/omega/\" rel=\"nofollow\"\u003eClustal Omega 1.2.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ekg/freebayes\"\u003efreebayes 1.2.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DerrickWood/kraken2\"\u003ekraken2 2.0.7-beta\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.hmmer.org/\" rel=\"nofollow\"\u003eHMMER 3.2.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://jgi.doe.gov/data-and-tools/meraculous/\" rel=\"nofollow\"\u003emeraculous 2.2.6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2 2.11 r797\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.mothur.org/wiki/Main_Page\" rel=\"nofollow\"\u003eMothur 1.40.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://mummer4.github.io/\" rel=\"nofollow\"\u003emummer 4.0.0beta2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cog-genomics.org/plink/1.9/\" rel=\"nofollow\"\u003eplink 1.09 beta 5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rrwick/Porechop\"\u003ePorechop 0.2.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://r-project.org/\" rel=\"nofollow\"\u003eR 3.5.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://halfonlab.ccr.buffalo.edu/scrmshaw.html\" rel=\"nofollow\"\u003eSCRMshaw 05142018\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/COMBINE-lab/salmon/releases\"\u003eSalmon 0.11.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://catchenlab.life.illinois.edu/stacks/\" rel=\"nofollow\"\u003eStacks 2.0b\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003eSpades 3.12.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/torognes/swarm\"\u003eSwarm 2.2.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TransDecoder/TransDecoder/wiki\"\u003eTransDecoder 5.3.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/trinityrnaseq/trinityrnaseq\"\u003eTrinity 2.6.6\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 0, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1732456909.0 + "updated_at": 1574294473.0 }, { "data_format": 2, - "description": "A complete, cross-platform solution to record, convert and stream audio and video.", + "description": "Singularity image for AFLGo (https://github.com/aflgo/aflgo)", "filenames": [ - "4.4.1-r4/Singularity", - "6.0-r26/Singularity", - "4.4.1-r3/Singularity", - "4.3.1/Singularity", - "6.1.1/Singularity", - "5.0.1/Singularity", - "5.0-r1/Singularity" + "Singularity.1604" ], - "full_name": "pscedu/singularity-ffmpeg", - "latest_release": "v6.1.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ffmpeg/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ffmpeg/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-ffmpeg/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-ffmpeg/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/dab232334f74c7ae2794e7c211bd1ea6b661fc8702ecd7f97bfcfe0c4604c2af/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66666d706567\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dab232334f74c7ae2794e7c211bd1ea6b661fc8702ecd7f97bfcfe0c4604c2af/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d66666d706567\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-ffmpeg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/007f76ba939ab1d9a9b8ef43f7fde47e28c7eca5f8e998621a845ba1ab7da75e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66666d706567\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/007f76ba939ab1d9a9b8ef43f7fde47e28c7eca5f8e998621a845ba1ab7da75e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d66666d706567\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-ffmpeg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ad31dc0aac0e49620585afad5cf6d6ddbcd3bdff0f47163132dfc776b36c48bb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66666d706567\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad31dc0aac0e49620585afad5cf6d6ddbcd3bdff0f47163132dfc776b36c48bb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d66666d706567\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-ffmpeg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7cf0d28924b6b4cd8a1ec7b8d3b53549bee40a1fe61baa24cf5d1d7a436ef1cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66666d706567\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7cf0d28924b6b4cd8a1ec7b8d3b53549bee40a1fe61baa24cf5d1d7a436ef1cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d66666d706567\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-ffmpeg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2023 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "shub-fuzz/aflgo", + "latest_release": "0.0.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAFLGo\u003c/h1\u003e\u003ca id=\"user-content-aflgo\" class=\"anchor\" aria-label=\"Permalink: AFLGo\" href=\"#aflgo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/aflgo/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/aflgo/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5085\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for AFLGo (\u003ca href=\"https://github.com/aflgo/aflgo\"\u003ehttps://github.com/aflgo/aflgo\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name aflgo.sif https://github.com/shub-fuzz/aflgo/releases/download/0.0.2/shub-fuzz-aflgo.1604.sif\n\nsingularity shell aflgo.sif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 0, - "subscribers_count": 4, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1649191357.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1623682654.0 }, { "data_format": 2, - "description": null, + "description": "Mycobacterial pre-processing pipeline", "filenames": [ - "Singularity.latest" + "singularity/Singularity.ppBedtools", + "singularity/Singularity.ppKraken2", + "singularity/Singularity.ppBowtie2", + "singularity/Singularity.ppFastp", + "singularity/Singularity.ppMykrobe", + "singularity/Singularity.ppFastqc", + "singularity/Singularity.ppPerljson", + "singularity/Singularity.ppBwa", + "singularity/Singularity.ppFqtools" ], - "full_name": "bioexcel/pmx_container", + "full_name": "oxfordmmm/preprocessing", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b26407ffc70e9b6a282c6c8fb352ee6ffc8d0c07ce393e6a4ae150283c0951c4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d6875622d626c7565\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/docker-hub-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePMX container\u003c/h1\u003e\u003ca id=\"user-content-pmx-container\" class=\"anchor\" aria-label=\"Permalink: PMX container\" href=\"#pmx-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePMX (python 3 version) docker and singularity containers used for \u003ca href=\"https://github.com/bioexcel/biobb_pmx\"\u003ebiobb_pmx\u003c/a\u003e BioExcel Building Blocks modules.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker Use\u003c/h3\u003e\u003ca id=\"user-content-docker-use\" class=\"anchor\" aria-label=\"Permalink: Docker Use\" href=\"#docker-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull mmbirb/pmx_biobb:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run mmbirb/pmx_biobb:latest \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Use\u003c/h3\u003e\u003ca id=\"user-content-singularity-use\" class=\"anchor\" aria-label=\"Permalink: Singularity Use\" href=\"#singularity-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstallation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull --name pmx_biobb.sif shub://bioexcel/pmx_biobb_container\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsage:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec pmx_biobb.sif \u0026lt;command\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCopyright \u0026amp; Licensing\u003c/h3\u003e\u003ca id=\"user-content-copyright--licensing\" class=\"anchor\" aria-label=\"Permalink: Copyright \u0026amp; Licensing\" href=\"#copyright--licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software has been developed in the \u003ca href=\"http://mmb.irbbarcelona.org\" rel=\"nofollow\"\u003eMMB group\u003c/a\u003e at the \u003ca href=\"http://www.bsc.es/\" rel=\"nofollow\"\u003eBSC\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eIRB\u003c/a\u003e for the \u003ca href=\"http://bioexcel.eu/\" rel=\"nofollow\"\u003eEuropean BioExcel\u003c/a\u003e, funded by the European Commission (EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/823830\" rel=\"nofollow\"\u003e823830\u003c/a\u003e, EU H2020 \u003ca href=\"http://cordis.europa.eu/projects/675728\" rel=\"nofollow\"\u003e675728\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.bsc.es/\" rel=\"nofollow\"\u003eBarcelona Supercomputing Center\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(c) 2015-2020 \u003ca href=\"https://www.irbbarcelona.org/\" rel=\"nofollow\"\u003eInstitute for Research in Biomedicine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLicensed under the\n\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache License 2.0\u003c/a\u003e, see the file LICENSE for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknolegements\u003c/h3\u003e\u003ca id=\"user-content-acknolegements\" class=\"anchor\" aria-label=\"Permalink: Acknolegements\" href=\"#acknolegements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe singularity container has been built through \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5aaf032aade8af7cf9f6e16cb3c7ba70c927ecd783c1cfff82dc0ed8062560df/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5aaf032aade8af7cf9f6e16cb3c7ba70c927ecd783c1cfff82dc0ed8062560df/68747470733a2f2f62696f657863656c2e65752f77702d636f6e74656e742f75706c6f6164732f323031392f30342f42696f657863656c6c5f6c6f676f5f3130383070785f7472616e73702e706e67\" alt=\"\" title=\"Bioexcel\" data-canonical-src=\"https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMycobacterial Pre-processing Pipeline\u003c/h1\u003e\u003ca id=\"user-content-mycobacterial-pre-processing-pipeline\" class=\"anchor\" aria-label=\"Permalink: Mycobacterial Pre-processing Pipeline\" href=\"#mycobacterial-pre-processing-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCleans and QCs reads with fastp and FastQC, classifies with Kraken2 \u0026amp; Mykrobe, removes non-bacterial content, and - by alignment to any minority genomes - disambiguates mixtures of bacterial reads.\u003c/p\u003e\n\u003cp\u003eTakes as input one directory containing pairs of fastq(.gz) or bam files.\nProduces as output one directory per sample, containing the relevant reports \u0026amp; a pair of cleaned fastqs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow is designed to run with either docker \u003ccode\u003e-profile docker\u003c/code\u003e or singularity \u003ccode\u003e-profile singularity\u003c/code\u003e. Before running the workflow using singularity, the singularity images for the workflow will need to be built by running \u003ccode\u003esingularity/singularity_pull.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eE.g. to run the workflow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity --filetype fastq --input_dir fq_dir --pattern \"*_R{1,2}.fastq.gz\" --unmix_myco yes \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\nnextflow run main.nf -profile docker --filetype bam --input_dir bam_dir --unmix_myco no \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParams\u003c/h2\u003e\u003ca id=\"user-content-params\" class=\"anchor\" aria-label=\"Permalink: Params\" href=\"#params\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following parameters should be set in \u003ccode\u003enextflow.config\u003c/code\u003e or specified on the command line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003einput_dir\u003c/strong\u003e\u003cbr\u003e\nDirectory containing fastq OR bam files\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efiletype\u003c/strong\u003e\u003cbr\u003e\nFile type in input_dir. Either \"fastq\" or \"bam\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003epattern\u003c/strong\u003e\u003cbr\u003e\nRegex to match fastq files in input_dir, e.g. \"*_R{1,2}.fq.gz\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eoutput_dir\u003c/strong\u003e\u003cbr\u003e\nOutput directory\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eunmix_myco\u003c/strong\u003e\u003cbr\u003e\nDo you want to disambiguate mixed-mycobacterial samples by read alignment? Either \"yes\" or \"no\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003especies\u003c/strong\u003e\u003cbr\u003e\nPrincipal species in each sample, assuming genus Mycobacterium. Default \u0027null\u0027. If parameter used, takes 1 of 10 values: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ekraken_db\u003c/strong\u003e\u003cbr\u003e\nDirectory containing \u003ccode\u003e*.k2d\u003c/code\u003e Kraken2 database files (obtain from \u003ca href=\"https://benlangmead.github.io/aws-indexes/k2\" rel=\"nofollow\"\u003ehttps://benlangmead.github.io/aws-indexes/k2\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie2_index\u003c/strong\u003e\u003cbr\u003e\nDirectory containing Bowtie2 index (obtain from ftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/hg19_1kgmaj_bt2.zip). The specified path should NOT include the index name\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie_index_name\u003c/strong\u003e\u003cbr\u003e\nName of the bowtie index, e.g. hg19_1kgmaj\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp\u003eFor more information on the parameters run \u003ccode\u003enextflow run main.nf --help\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCheckpoints\u003c/h2\u003e\u003ca id=\"user-content-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Checkpoints\" href=\"#checkpoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheckpoints used throughout this workflow to fail a sample/issue warnings:\u003c/p\u003e\n\u003cp\u003eprocesses preprocessing_checkFqValidity or preprocessing_checkBamValidity\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e(Fail) If sample does not pass fqtools \u0027validate\u0027 or samtools \u0027quickcheck\u0027, as appropriate.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eprocess preprocessing_countReads\u003cbr\u003e\n2. (Fail) If sample contains \u0026lt; 100k pairs of raw reads.\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_fastp\u003cbr\u003e\n3. (Fail) If sample contains \u0026lt; 100k pairs of cleaned reads, required to all be \u0026gt; 50bp (cleaning using fastp with --length_required 50 --average_qual 10 --low_complexity_filter --correction --cut_right --cut_tail --cut_tail_window_size 1 --cut_tail_mean_quality 20).\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_kraken2\u003cbr\u003e\n4. (Fail) If the top family hit is not Mycobacteriaceae\u003cbr\u003e\n5. (Fail) If there are fewer than 100k reads classified as Mycobacteriaceae \u003cbr\u003e\n6. (Warn) If the top family classification is mycobacterial, but this is not consistent with top genus and species classifications\u003cbr\u003e\n7. (Warn) If the top family is Mycobacteriaceae but no G1 (species complex) classifications meet minimum thresholds of \u0026gt; 5000 reads or \u0026gt; 0.5% of the total reads (this is not necessarily a concern as not all mycobacteria have a taxonomic classification at this rank) \u003cbr\u003e\n8. (Warn) If sample is mixed or contaminated - defined as containing reads \u0026gt; the 5000/0.5% thresholds from multiple non-human species\u003cbr\u003e\n9. (Warn) If sample contains multiple classifications to mycobacterial species complexes, each meeting the \u0026gt; 5000/0.5% thresholds\u003cbr\u003e\n10. (Warn) If no species classification meets the 5000/0.5% thresholds\u003cbr\u003e\n11. (Warn) If no genus classification meets the 5000/0.5% thresholds\u003cbr\u003e\n12. (Fail) If no family classification meets the 5000/0.5% thresholds (redundant given point 5)\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_identifyBacterialContaminants\u003cbr\u003e\n13. (Fail) If the sample is not contaminated and the top species hit is not one of the 10 supported Mycobacteria:\\ abscessus|africanum|avium|bovis|chelonae|chimaera|fortuitum|intracellulare|kansasii|tuberculosis\u003cbr\u003e\n14. (Fail) If the sample is not contaminated and the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003cbr\u003e\n15. (Warn) If the top species hit is supported by \u0026lt; 75% coverage\u003cbr\u003e\n16. (Warn) If the top species hit has a median coverage depth \u0026lt; 10-fold\u003cbr\u003e\n17. (Warn) If we are unable to associate an NCBI taxon ID to any given contaminant species, which means we will not be able to locate its genome, and thereby remove it as a contaminant\u003cbr\u003e\n18. (Warn) If we are unable to determine a URL for the latest RefSeq genome associated with a contaminant species\u0027 taxon ID\u003cbr\u003e\n19. (Warn) If no complete genome could be found for a contaminant species. The workflow will proceed with alignment-based contaminant removal, but you\u0027re warned that there\u0027s reduced confidence in detecting reads from this species\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_downloadContamGenomes\u003cbr\u003e\n20. (Fail) If a contaminant is detected but we are unable to download a representative genome, and thereby remove it\u003c/p\u003e\n\u003cp\u003eprocess preprocessing_summarise\u003cbr\u003e\n21. (Fail) If after having taken an alignment-based approach to decontamination, Kraken still detects a contaminant species\u003cbr\u003e\n22. (Fail) If after having taken an alignment-based approach to decontamination, the top species hit is not one of the 10 supported Mycobacteria\u003cbr\u003e\n23. (Fail) If, after successfully removing contaminants, the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 8, + "subscribers_count": 2, "topics": [], - "updated_at": 1601384286.0 + "updated_at": 1665133955.0 + }, + { + "data_format": 2, + "description": "a collection of lmod modules", + "filenames": [ + "singularity/macs2/2.1.2.1r0/Singularity", + "singularity/pindel/cgpPindel_2.0.1/Singularity", + "singularity/picard/2.18.17r0/Singularity", + "singularity/trimgalore/0.5.0r0/Singularity", + "singularity/repenrich2/20190521r0/Singularity", + "singularity/bedops/2.4.35r0/Singularity", + "singularity/mfold/3.6r0/Singularity", + "singularity/gatsby.js/Singularity", + "singularity/iclipro/0.1.1r0/Singularity", + "singularity/rmats/4.0.2r0/Singularity", + "singularity/rseqc/3.0.0r0/Singularity", + "singularity/cutadapt/1.18r0/Singularity", + "singularity/bedtools/2.28.0r0/Singularity", + "singularity/bedtools/2.27.1r0/Singularity", + "singularity/meme/5.0.2r0/Singularity", + "singularity/fastqc/0.11.8r0/Singularity", + "singularity/bowtie2/2.3.5.1r0/Singularity", + "singularity/bowtie2/2.3.4.3r0/Singularity", + "singularity/subread/1.6.3r0/Singularity", + "singularity/samtools/1.9r0/Singularity", + "singularity/samtools/1.9r1/Singularity", + "singularity/star/2.6.1dr0/Singularity", + "singularity/star/2.7.0fr0/Singularity", + "singularity/fastp/0.19.5r0/Singularity", + "singularity/fastp/0.20.0r0/Singularity", + "singularity/R/Bioconductor_3.11/Singularity", + "singularity/R/3.6.0r0/Singularity", + "singularity/openjdk/7u211r0/Singularity", + "singularity/openjdk/8u181r0/Singularity", + "singularity/openjdk/7u181r0/Singularity", + "singularity/openjdk/8u212r0/Singularity", + "singularity/hisat2/2.1.0r0/Singularity", + "singularity/flexbar/3.5.0r0/Singularity", + "singularity/flexbar/3.4.0r0/Singularity", + "singularity/crossmap/0.3.1r0/Singularity", + "singularity/crossmap/0.3.2r0/Singularity", + "singularity/deeptools/3.1.2r0/Singularity" + ], + "full_name": "imbforge/sysops", + "latest_release": null, + "readme": "\u003cp\u003eA collection of stuff to keep the systems up and running\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 4, + "topics": [], + "updated_at": 1599664216.0 + }, + { + "data_format": 2, + "description": "Scripts to run Numerical Weather Prediction procedures, integrating with nwpconf and ecFlow", + "filenames": [ + "Singularity.nwprun_f36", + "Singularity.nwprun_r8", + "Singularity.bufr2netcdf_r8", + "Singularity.simc_tools_r8", + "Singularity.simc_tools_debug_r8" + ], + "full_name": "ARPA-SIMC/nwprun", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNwprun\u003c/h1\u003e\u003ca id=\"user-content-nwprun\" class=\"anchor\" aria-label=\"Permalink: Nwprun\" href=\"#nwprun\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNwprun combines the configuration and scripting framework\n\u003ca href=\"https://github.com/ARPA-SIMC/nwpconf\"\u003enwpconf\u003c/a\u003e with the ECMWF\n\u003ca href=\"https://software.ecmwf.int/wiki/display/ECFLOW/\" rel=\"nofollow\"\u003eecFlow\u003c/a\u003e workflow\nmanager to create complete suites running Numerical Weather Prediction\nmodels on HPC systems.\u003c/p\u003e\n\u003cp\u003eIt is targeted at the generation and management of operational model\nsuites contaning the typical tasks involved in continuous and\nintermittent atmospheric data assimilation (using various techniques\nincluding ensemble data assimilation), and forecasting (both in\ndeterministic and in ensemble modes). The main target is real time\nsuites, but there are options for applying the system to long-period\nresearch and reanalysis suites.\u003c/p\u003e\n\u003cp\u003eNwprun includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea set of job templates for performing the different parts of the\necFlow workflow using the nwpconf framework\u003c/li\u003e\n\u003cli\u003ea set of ecFlow include files to be used by the jobs, targeted at\nslurm and pbs schedulers\u003c/li\u003e\n\u003cli\u003ea generic python module for generating ecFlow suites\u003c/li\u003e\n\u003cli\u003esome python suite generators, using the indicated module for\ngenerating specifical suite definitions\u003c/li\u003e\n\u003cli\u003ea set of configuration trees for a number of NWP suites using the\nnwpconf framework\u003c/li\u003e\n\u003cli\u003ea set of shell script to be run as cron jobs for performing\nancillary operations related to operational NWP, mainly access to\ninput data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe practical configuration files and python suite generators included\nin the package are used in the Italian LAMI modelling suites both on\n\u003ca href=\"https://www.cineca.it/\" rel=\"nofollow\"\u003eCineca\u003c/a\u003e and on\n\u003ca href=\"https://www.arpae.it/sim\" rel=\"nofollow\"\u003eArpae-SIMC\u003c/a\u003e HPC systems.\u003c/p\u003e\n", + "stargazers_count": 0, + "subscribers_count": 12, + "topics": [ + "ecflow", + "nwp", + "workflow" + ], + "updated_at": 1732203799.0 }, { "data_format": 2, "description": null, "filenames": [ - "src/llama/Singularity.def", - "src/translation/Singularity.def" + "Singularity.mathematica" ], - "full_name": "timopetric/LLaMA-T5-Slovene-Paraphrasing", + "full_name": "uit-no/apptainer-mathematica", + "latest_release": "0.0.3", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLocal Mathematica via Apptainer/Singularity\u003c/h1\u003e\u003ca id=\"user-content-local-mathematica-via-apptainersingularity\" class=\"anchor\" aria-label=\"Permalink: Local Mathematica via Apptainer/Singularity\" href=\"#local-mathematica-via-apptainersingularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eApptainer or Singularity\u003c/li\u003e\n\u003cli\u003eA valid Mathematica license file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Download the container image\u003c/h3\u003e\u003ca id=\"user-content-step-1-download-the-container-image\" class=\"anchor\" aria-label=\"Permalink: Step 1: Download the container image\" href=\"#step-1-download-the-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, pull the image from the release page:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/uit-no/apptainer-mathematica/releases/download/0.0.1/mathematica.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ apptainer pull https://github.com/uit-no/apptainer-mathematica/releases/download/0.0.1/mathematica.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Locate your Mathematica license file\u003c/h3\u003e\u003ca id=\"user-content-step-2-locate-your-mathematica-license-file\" class=\"anchor\" aria-label=\"Permalink: Step 2: Locate your Mathematica license file\" href=\"#step-2-locate-your-mathematica-license-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEnsure you have a valid Mathematica license file accessible on your local machine. This is required to run the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning a Mathematica Script (.wl)\u003c/h3\u003e\u003ca id=\"user-content-running-a-mathematica-script-wl\" class=\"anchor\" aria-label=\"Permalink: Running a Mathematica Script (.wl)\" href=\"#running-a-mathematica-script-wl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run your Mathematica script, use the following command with singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run --bind path_to_license_file:/root/.WolframEngine/Licensing/mathpass mathematica.sif your_script.wl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor with Apptainer\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ apptainer run --bind path_to_license_file:/root/.WolframEngine/Licensing/mathpass mathematica.sif your_script.wl\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 0, + "subscribers_count": 4, + "topics": [], + "updated_at": 1692957745.0 + }, + { + "data_format": 2, + "description": "A package that sets up everything you need to run the simulator.", + "filenames": [ + "Singularity" + ], + "full_name": "abersailbot/simulator", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLLaMA-T5-Slovene-Paraphrasing\u003c/h1\u003e\u003ca id=\"user-content-llama-t5-slovene-paraphrasing\" class=\"anchor\" aria-label=\"Permalink: LLaMA-T5-Slovene-Paraphrasing\" href=\"#llama-t5-slovene-paraphrasing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNatural Language Processing course 2022-23 at Faculty of Computer and Information Science at University of Ljubljana\u003c/h3\u003e\u003ca id=\"user-content-natural-language-processing-course-2022-23-at-faculty-of-computer-and-information-science-at-university-of-ljubljana\" class=\"anchor\" aria-label=\"Permalink: Natural Language Processing course 2022-23 at Faculty of Computer and Information Science at University of Ljubljana\" href=\"#natural-language-processing-course-2022-23-at-faculty-of-computer-and-information-science-at-university-of-ljubljana\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a NLP course project in which we explored ways of Slovene language sentence paraphrisation.\nThe best approach turned out to be traslating original Slovene sentence into English, use Vicuna/LLaMA (1st gen) to paraphrase the sentence and then translate the results back into Slovene.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe \u003ca href=\"report/NLP_project_Paraphrasing_Sentences.pdf\"\u003ePDF report describing the methodology is located here\u003c/a\u003e.\u003c/h3\u003e\u003ca id=\"user-content-the-pdf-report-describing-the-methodology-is-located-here\" class=\"anchor\" aria-label=\"Permalink: The PDF report describing the methodology is located here.\" href=\"#the-pdf-report-describing-the-methodology-is-located-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTeam members:\u003c/h3\u003e\u003ca id=\"user-content-team-members\" class=\"anchor\" aria-label=\"Permalink: Team members:\" href=\"#team-members\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eMatej Kranjec\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eTimotej Petri\u010d\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eDomen Vilar\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003c/h4\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEvaluation\u003c/h2\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eplot_distribution_scores.py\u003c/h4\u003e\u003ca id=\"user-content-plot_distribution_scorespy\" class=\"anchor\" aria-label=\"Permalink: plot_distribution_scores.py\" href=\"#plot_distribution_scorespy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script plots the distribution of scores and writes the best paraphrases to a file based on the provided scores and paraphrases directories.\u003c/p\u003e\n\u003cp\u003eArguments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--scores_path\u003c/code\u003e: Path to the directory containing the scores files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--paraphrases_path\u003c/code\u003e: Path to the directory containing the paraphrases files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_directory\u003c/code\u003e: Output directory for the best paraphrases.\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe best paraphrases will be written to a file named \"best_paraphrases_euparl_t5.txt\" in the specified output directory.\u003c/li\u003e\n\u003cli\u003eTwo distribution plots will be generated: \"maximum_values.png\" and \"first_values.png\" in the current directory where script will run.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003emanual_evaluation.py\u003c/h4\u003e\u003ca id=\"user-content-manual_evaluationpy\" class=\"anchor\" aria-label=\"Permalink: manual_evaluation.py\" href=\"#manual_evaluationpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script is used for manual evaluation of the best paraphrases.\u003c/p\u003e\n\u003cp\u003eArguments:\u003c/p\u003e\n\u003cp\u003eThe script accepts the following arguments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--file1_path\u003c/code\u003e: Path to the first file containing paraphrases.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--file2_path\u003c/code\u003e: Path to the second file containing paraphrases.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--file_original_path\u003c/code\u003e: Path to the file containing original sentences.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--scores_folder_path\u003c/code\u003e: Path to the folder where the evaluation scores will be stored.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote: If the specified \u003ccode\u003escores_folder_path\u003c/code\u003e does not exist, the script will create it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eevaluation/run.sh\u003c/h4\u003e\u003ca id=\"user-content-evaluationrunsh\" class=\"anchor\" aria-label=\"Permalink: evaluation/run.sh\" href=\"#evaluationrunsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach dataset block in the run.sh file contains the necessary variables for the evaluation. Make sure to modify these variables according to your dataset paths and filenames.\u003c/p\u003e\n\u003cp\u003eFor example, if you want to evaluate the \u003ccode\u003eeuroparl-llama\u003c/code\u003e dataset, uncomment the block of code for that dataset and update the following variables:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eDATASET_PATH\u003c/code\u003e variable represents the path to the dataset you want to evaluate.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDATASET_ORIG_SENTS_FILE\u003c/code\u003e variable represents the file containing the original sentences of the dataset.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDATASET_TRAN_SENTS_FILE\u003c/code\u003e variable represents the file containing the paraphrased sentences of the dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce you have made the necessary modifications, you can run the run.sh script. It will execute the evaluate.py script with the provided dataset variables.\u003c/p\u003e\n\u003cp\u003eIf you prefer to run the evaluation separately without using the run.sh script, you can directly execute the evaluate.py script and pass the dataset variables as command-line arguments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the T5 model\u003c/h2\u003e\u003ca id=\"user-content-run-the-t5-model\" class=\"anchor\" aria-label=\"Permalink: Run the T5 model\" href=\"#run-the-t5-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e*You have to have \u003ccode\u003etransformers\u003c/code\u003e python library installed. Preferrably the one with GPU/CUDA support.\n*The Singularity (Docker) image with the prepared env is already set up in the shared location \u003ccode\u003e/d/hpc/projects/FRI/tp1859/nlp_project8/lma/containers/hf.sif\u003c/code\u003e on \u003cem\u003eArnes HPC\u003c/em\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMove to \u003ccode\u003esrc/paraphrasing\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003eDownload the \u003ccode\u003efinetune_t5-sl-small_v0.0.4-Euparl600k_ensl_b4_lr3E-05_g16_j38753698\u003c/code\u003e model from:\n\u003ca href=\"https://unilj-my.sharepoint.com/:f:/g/personal/tp1859_student_uni-lj_si/Eie-WJrrsIVAiJCFNQ8r28UBhKVq6vxhvNcud7RgXTr0tw?e=Xot15v\" rel=\"nofollow\"\u003ehttps://unilj-my.sharepoint.com/:f:/g/personal/tp1859_student_uni-lj_si/Eie-WJrrsIVAiJCFNQ8r28UBhKVq6vxhvNcud7RgXTr0tw?e=Xot15v\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRename the downloaded folder to \u003ccode\u003emodels/t5model\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003echange \u003ccode\u003eOUT_MODEL_CHECKPOINTS_DIR\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to \u003ccode\u003emodels\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003echange \u003ccode\u003eMODEL_CHECKPOINT_FIN_GLOB\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to \u003ccode\u003et5*\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003epython3 inference.py\u003c/code\u003e or \u003ccode\u003esbatch run.sbatch\u003c/code\u003e if running on \u003cem\u003eArnes HPC\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the LLaMA/Vicuna based model\u003c/h2\u003e\u003ca id=\"user-content-run-the-llamavicuna-based-model\" class=\"anchor\" aria-label=\"Permalink: Run the LLaMA/Vicuna based model\" href=\"#run-the-llamavicuna-based-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e*You have to have \u003ccode\u003etransformers\u003c/code\u003e python library installed. Preferrably the one with GPU/CUDA support.\n*The Singularity (Docker) image with the prepared env is already set up in the shared location \u003ccode\u003e/d/hpc/projects/FRI/tp1859/nlp_project8/lma/containers/hf.sif\u003c/code\u003e on \u003cem\u003eArnes HPC\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eSince the model weights setup is quite difficult to do and the weights transformations consume large amounts of disk (at least 60 GB with intermediate cleanups) and RAM (60GB) we have prepared the already converted weights and uploaded them to the shared \u003cem\u003eArnes HPC\u003c/em\u003e space at path: \u003ccode\u003e/d/hpc/projects/FRI/tp1859/nlp_project8/lma/model_hf_vicuna\u003c/code\u003e. The Vicuna/LLaMA 13B model is in half precision and takes about 25 GB of VRAM on the GPU.\u003c/p\u003e\n\u003cp\u003eYou can then run the paraphrase generation by modifying the \u003ccode\u003erun_llama.sbatch\u003c/code\u003e script in \u003ccode\u003esrc/llama\u003c/code\u003e directory. You should set \u003ccode\u003e--model-path\u003c/code\u003e to the above LLaMA model wights directory.\nThen change the \u003ccode\u003e--corpus-name\u003c/code\u003e to some string and set \u003ccode\u003e--file-in\u003c/code\u003e to a file containing 1 english sentence per line. Then you can run the inference by running \u003ccode\u003esbatch run_llama.sbatch\u003c/code\u003e. Output files will be in \u003ccode\u003eprocessed\u003c/code\u003e directory, logs in \u003ccode\u003elogs\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTranslation\u003c/h3\u003e\u003ca id=\"user-content-translation\" class=\"anchor\" aria-label=\"Permalink: Translation\" href=\"#translation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can run translation with NEmO model simillarly as described in T5 / Vicuna section. Needed files like \u003ccode\u003erun_translation.sbatch\u003c/code\u003e are in \u003ccode\u003esrc/translation\u003c/code\u003e dir.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReport\u003c/h3\u003e\u003ca id=\"user-content-report\" class=\"anchor\" aria-label=\"Permalink: Report\" href=\"#report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReport is located in folder \u003ccode\u003ereport\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSimulator Instructions\u003c/h1\u003e\u003ca id=\"user-content-simulator-instructions\" class=\"anchor\" aria-label=\"Permalink: Simulator Instructions\" href=\"#simulator-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package uses the sails simulator and boatd to simulate a robot sailing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePre-requisites\u003c/h3\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003elibjansson-dev\u003c/p\u003e\n\u003cp\u003ePython 2.7 or 3.x\u003c/p\u003e\n\u003cp\u003eFor sails-ui\u003c/p\u003e\n\u003cp\u003elibgirepository1.0-dev\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCheckout Code\u003c/h3\u003e\u003ca id=\"user-content-checkout-code\" class=\"anchor\" aria-label=\"Permalink: Checkout Code\" href=\"#checkout-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheckout this repository and its submodules\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone --recursive https://github.com/abersailbot/simulator\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompile sails\u003c/h3\u003e\u003ca id=\"user-content-compile-sails\" class=\"anchor\" aria-label=\"Permalink: Compile sails\" href=\"#compile-sails\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ecd sailsd\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall python dependencies\u003c/h3\u003e\u003ca id=\"user-content-install-python-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install python dependencies\" href=\"#install-python-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsing Anaconda (optional)\u003c/h4\u003e\u003ca id=\"user-content-using-anaconda-optional\" class=\"anchor\" aria-label=\"Permalink: Using Anaconda (optional)\" href=\"#using-anaconda-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall Anaconda from \u003ca href=\"http://www.anaconda.org\" rel=\"nofollow\"\u003ewww.anaconda.org\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAnaconda has its own copy of Python (and many other packages), its huge but probably has more up to date packages than your OS.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda create -n boatd python=3.7 anaconda\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda activate boatd\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-forge jansson\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-forge pygobject\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsing Virtualenv (optional)\u003c/h4\u003e\u003ca id=\"user-content-using-virtualenv-optional\" class=\"anchor\" aria-label=\"Permalink: Using Virtualenv (optional)\" href=\"#using-virtualenv-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e*** Don\u0027t do this if you are using Anaconda. ***\u003c/p\u003e\n\u003cp\u003eUsing a virtual env is a lighter weight method of isolating your Python configuration.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m venv simulator-env\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esource simulator-env/bin/activate\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eor for python2\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython -m virtualenv simulator-env\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esource simulator-env/bin/activate\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstalling Packages\u003c/h4\u003e\u003ca id=\"user-content-installing-packages\" class=\"anchor\" aria-label=\"Permalink: Installing Packages\" href=\"#installing-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003epip install python-boatdclient python-sailsd gobject PyGObject\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall boatd as a python package\u003c/h3\u003e\u003ca id=\"user-content-install-boatd-as-a-python-package\" class=\"anchor\" aria-label=\"Permalink: Install boatd as a python package\" href=\"#install-boatd-as-a-python-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ecd boatd\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython setup.py install\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfigure boatd port\u003c/h3\u003e\u003ca id=\"user-content-configure-boatd-port\" class=\"anchor\" aria-label=\"Permalink: Configure boatd port\" href=\"#configure-boatd-port\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBoatd\u0027s default port is 2222, but this config uses 2223 (because i\u0027ve got an SSH tunnel using 2222).\nChange this by editing boatd.yml and boatd_client.py in the boatdclient Python package.\nThe script set_port.sh will read the config file and automatically set\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning\u003c/h2\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThree components must be launched:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe sails simulator\u003c/li\u003e\n\u003cli\u003eBoatd\u003c/li\u003e\n\u003cli\u003eThe behaviour to control the simulated boat via boatd\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOptionally you can launch the sails-ui graphical interface.\u003c/p\u003e\n\u003cp\u003eThe script run.sh will launch all four of these.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Singularity\u003c/h3\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall Singularity, see \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e for instructions.\u003c/p\u003e\n\u003cp\u003eDownload the container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull shub://abersailbot/simulator:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRunning the container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run abersailbot-simulator-master-latest.simg\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can either run the behaviour inside another container:\u003c/p\u003e\n\u003cp\u003esingularity exec abersailbot-simulator-master-latest.simg /opt/simulator/simulator-behaviour/waypoint-behaviour\u003c/p\u003e\n\u003cp\u003eOr execute your own behaviour outside the container. Note you\u0027ll have to change boatd-client to use port 2223 by editing\u003c/p\u003e\n\u003cp\u003eEdit boatd_client.py in your Python library directory and change:\u003c/p\u003e\n\u003cp\u003eclass Boatd(object):\u003cbr\u003e\ndef \u003cstrong\u003einit\u003c/strong\u003e(self, host=\u0027localhost\u0027, port= 2222):\u003c/p\u003e\n\u003cp\u003eto\u003c/p\u003e\n\u003cp\u003eclass Boatd(object):\u003cbr\u003e\ndef \u003cstrong\u003einit\u003c/strong\u003e(self, host=\u0027localhost\u0027, port= 2223):\u003c/p\u003e\n\u003cp\u003eOr run the fix_port.sh script in the root directory of this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Docker\u003c/h3\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running with Docker\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStopping everything\u003c/h2\u003e\u003ca id=\"user-content-stopping-everything\" class=\"anchor\" aria-label=\"Permalink: Stopping everything\" href=\"#stopping-everything\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script stop.sh\u003c/p\u003e\n", "stargazers_count": 0, - "subscribers_count": 2, + "subscribers_count": 11, "topics": [], - "updated_at": 1709121666.0 + "updated_at": 1588465186.0 }, { "data_format": 2, - "description": "https://www.synapse.org/modulechallenge", + "description": "Snakemake pipeline for QC of low-coverage skim-sequencing of G\u0026T seq data", "filenames": [ - "containers/K1/singularity/Singularity", - "containers/R1/singularity/Singularity", - "containers/M1/singularity/Singularity" + "Singularity.def" ], - "full_name": "mattiat/DREAM_DMI_Tool", + "full_name": "EI-CoreBioinformatics/SkimSeqQC", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDreamDMI\u003c/h1\u003e\u003ca id=\"user-content-dreamdmi\" class=\"anchor\" aria-label=\"Permalink: DreamDMI\" href=\"#dreamdmi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository holds the source code for \u003cstrong\u003eDreamDMI\u003c/strong\u003e, a Linux/macOS command-line tool for Disease Module Identification in molecular networks, leveraging the top performing methods of the \u003cstrong\u003eDisease Module Identification (DMI) DREAM Challenge\u003c/strong\u003e (\u003ca href=\"https://www.synapse.org/modulechallenge\" rel=\"nofollow\"\u003ehttps://www.synapse.org/modulechallenge\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMethods\u003c/h2\u003e\u003ca id=\"user-content-methods\" class=\"anchor\" aria-label=\"Permalink: Methods\" href=\"#methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eK1\u003c/strong\u003e: Kernel clustering optimisation algorithm, \u003ca href=\"https://www.synapse.org/#!Synapse:syn7349492/wiki/407359\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7349492/wiki/407359\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eM1\u003c/strong\u003e: Modularity optimization algorithm, \u003ca href=\"https://www.synapse.org/#!Synapse:syn7352969/wiki/407384\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7352969/wiki/407384\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eR1\u003c/strong\u003e: Random-walk-based algorithm, \u003ca href=\"https://www.synapse.org/#!Synapse:syn7286597/wiki/406659\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7286597/wiki/406659\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSOURCE CODE\u003c/h2\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-label=\"Permalink: SOURCE CODE\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe source code is hosted at: \u003ca href=\"https://github.com/mattiat/DREAM_DMI_Tool\"\u003ehttps://github.com/mattiat/DREAM_DMI_Tool\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePREREQUISITES\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: PREREQUISITES\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEither \u003ccode\u003edocker\u003c/code\u003e or \u003ccode\u003esingularity\u003c/code\u003e must be installed. Please visit \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003ehttps://www.docker.com\u003c/a\u003e or \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSome of the Methods may require large amount of resources, depending on your input.\u003c/p\u003e\n\u003cp\u003eThe tool was tested on \u003cem\u003eUbuntu Linux 18.04\u003c/em\u003e, \u003cem\u003eCentOS Linux 7.5\u003c/em\u003e and \u003cem\u003emacOS Sierra\u003c/em\u003e Version 10.12.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eINSTALLATION\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: INSTALLATION\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install: \u003ccode\u003e./install\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo uninstall: \u003ccode\u003e./uninstall\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRUNNING\u003c/h2\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: RUNNING\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run, invoke, from any location: \u003ccode\u003edream_dmi --help\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eINPUT\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: INPUT\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe format for the input network is the following: a tab-separated file containing one line for each edge. If an edge is connecting two nodes, nodeA and nodeB, with weight weight_AB, the file will contain the entry:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e[nodeA]\t[nodeB]\t[weight_AB]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003enodeA and nodeB are of type \u003cem\u003einteger\u003c/em\u003e, weight_AB is of type \u003cem\u003efloat\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eFor an example, see the contents of test/system_test/input/.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBENCHMARKING\u003c/h2\u003e\u003ca id=\"user-content-benchmarking\" class=\"anchor\" aria-label=\"Permalink: BENCHMARKING\" href=\"#benchmarking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esee test/benchmarking\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePUBLICATION\u003c/h2\u003e\u003ca id=\"user-content-publication\" class=\"anchor\" aria-label=\"Permalink: PUBLICATION\" href=\"#publication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpen Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases\u003c/p\u003e\n\u003cp\u003eSarvenaz Choobdar, Mehmet E. Ahsen, Jake Crawford, Mattia Tomasoni, David Lamparter, Junyuan Lin, Benjamin Hescott, Xiaozhe Hu, Johnathan Mercer, Ted Natoli, Rajiv Narayan, The DREAM Module Identification Challenge Consortium, Aravind Subramanian, Gustavo Stolovitzky, Zolt\u00e1n Kutalik, Kasper Lage, Donna K. Slonim, Julio Saez-Rodriguez, Lenore J. Cowen, Sven Bergmann, Daniel Marbach.\nbioRxiv 265553 (2018). doi: \u003ca href=\"https://doi.org/10.1101/265553\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/265553\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 1, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSkimSeqQC Pipeline\u003c/h2\u003e\u003ca id=\"user-content-skimseqqc-pipeline\" class=\"anchor\" aria-label=\"Permalink: SkimSeqQC Pipeline\" href=\"#skimseqqc-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSnakemake pipeline for QC of low-coverage skim-sequencing of G\u0026amp;T seq data\u003c/p\u003e\n\u003cp\u003eExpects reads to be in a directory called \u003ccode\u003eREADS\u003c/code\u003e with filenames in the format: \u003ccode\u003e{sample}_cDNA_R[12].fastq.gz\u003c/code\u003e and \u003ccode\u003e{sample}_gDNA_R[12].fastq.gz\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eModify the config file \u003ccode\u003econfig.yaml\u003c/code\u003e to set run_name and choose min number of gDNA and cDNA reads (see example below).\u003c/p\u003e\n\u003cp\u003eFirst run the \u003ccode\u003eGenerateSamplesheet.smk\u003c/code\u003e pipeline which just runs fastp on the raw reads to count reads and generate a \"samplesheet\":\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j 100 -p --snakefile GenerateSamplesheet.smk --cluster-config cluter.json --latency-wait 60 --cluster \"sbatch -p {cluster.partition} -c {cluster.c} --mem={cluster.memory} --job-name={cluster.J} --time={cluster.time}\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the full pipeline:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -j 100 -p --snakefile SkimSeqQC.smk --cluster-config cluster.json --latency-wait 60 --cluster \"sbatch -p {cluster.partition} -c {cluster.c} --mem={cluster.memory} --job-name={cluster.J} --time={cluster.time}\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample \u003ccode\u003econfig.yaml\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun_name: PLATE_NAME\nsamplesheet: samplesheet.tsv\nfastq_dir: \"READS\"\n\n# Minimum gDNA read count to be passed to SPAdes\nmin_gDNA_read_count: 70000\n\n# Minimum cDNA read count to be passed to Trinity\nmin_cDNA_read_count: 50000\n\n# Minimum rRNA length to report in top blast hits vs pr2 DB\n# This is the length of the blast alignment not the annotated gene\nmin_rRNA_blast_length: 500\n\n# Interval to use for calculating random kmer uniquess (not currently reported in summary)\nrku_gDNA_interval: 25000\nrku_cDNA_interval: 25000\n\n# Map gDNA reads against the corresponding genome assembly\nmap_gDNA: True\n\n# Map cDNA reads against the corresponding genome assembly\nmap_cDNA: True\n\n# Identity threshold for clustering transcriptome assembly\ncdhit_identity_threshold: 0.98\n\n# How many reads to classify using centrifuge [-u/--upto \u0026lt;int\u0026gt; stop after first \u0026lt;int\u0026gt; reads/pairs (no limit)]\ncentrifuge_upto: 1000000\n\n# Paths to databases\nbbduk_adapters: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/bbmap/resources/adapters.fa\npr2_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/pr2/5.0.0/pr2_version_5.0.0_SSU_taxo_long.fasta\ndiamond_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/diamond/reference_proteomes.dmnd\ntaxonomy_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/taxdump/fullnamelineage.dmp\nkraken2_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/kraken2/\ncentrifuge_database: /ei/projects/e/e5f1ee13-d3bf-4fec-8be8-38c6ad26aac3/data/results/CB-GENANNO-476_DToL_Protists/Reference/databases/centrifuge/centrifugedb\ncentrifuge_NT_database: /ei/public/databases/centrifuge/ncbi/nt_20200707/nt\n\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 0, "subscribers_count": 3, "topics": [], - "updated_at": 1558215673.0 + "updated_at": 1678468349.0 }, { "data_format": 2, - "description": "Barcode/amplicon sequencing bioinformatics tool, chops up FASTQ reads into capture groups using fuzzy regular expressions instead of base-positions. Very flexible, very parallelized, order now !", + "description": "Container files for sc-benchmark in Docker and Singularity with Nix", "filenames": [ - "Singularity.v0.3.0-alpha", - "Singularity.v0.5.0-alpha", - "Singularity.v0.5.1-alpha" + "Singularity" ], - "full_name": "darachm/slapchop", - "latest_release": "v0.2.0-alpha", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSLAPCHOP\u003c/h1\u003e\u003ca id=\"user-content-slapchop\" class=\"anchor\" aria-label=\"Permalink: SLAPCHOP\" href=\"#slapchop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSLAPCHOP.py parses Illumina reads using patterns to extract barcodes.\u003c/p\u003e\n\u003cp\u003eBy using fuzzy regular expressions we can chop robustly barcodes from\nindeterminate positions, filter the results based on sequence or match\nproperties, and reassemble a fastq record from the results.\u003c/p\u003e\n\u003cp\u003eAvailable as\n\u003ca href=\"https://www.singularity-hub.org/collections/1361\" rel=\"nofollow\"\u003ea singularity containter\u003c/a\u003e!\nSo you if you have\n\u003ca href=\"https://github.com/sylabs/singularity/releases\"\u003eSingularity\u003c/a\u003e\ninstalled you can just use it (without worrying about dependencies) with:\n\u003ccode\u003esingularity run shub://darachm/slapchop:latest -h\u003c/code\u003e (to download and show the\nargument help message for example). Then you use it like\n\u003ccode\u003esingularity run shub://darachm/slapchop:latest whatever.fastq arguments added\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMore completely, this tool is a python script. You give it a FASTQ(Z) file and\nsome operations to do, and it\u0027ll do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- read chunks of Illumina-format sequencing reads\n- apply a series of operations:\n - match fuzzy regular expression to original sequence or previous\n capture groups\n - extract capture groups and start next operation\n- apply pythonic filters (pass/fail) on sequence or quality properties \n (like average quality or group length)\n- apply pythonic constructors to construct new FASTQ read from the capture\n groups (so ID plus the last four bases of the UMI plus length of whatever)\n- write out these reads to new files of pass and fail\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor tuning/debugging/designing it has some verbosity modes to spill the gory\ndetails of each operation in stats files, and should still have some memory\nprofiling functionality to debug memory leaks (fixed that one).\u003c/p\u003e\n\u003cp\u003eFor a very very verbose example of a debugging run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./slapchop.py \\\n input.fastqz -z \\\n output_basename \\\n --bite-size 10 --processes 3 \\\n --write-report --limit 10000 \\\n -o \"Sample: input \u0026gt; (?P\u0026lt;sample\u0026gt;[ATCG]{5})(?P\u0026lt;fixed1\u0026gt;GTCCACGAGGTC){e\u0026lt;=1}(?P\u0026lt;rest\u0026gt;TCT.*){e\u0026lt;=1}\" \\\n -o \"Strain: rest \u0026gt; (?P\u0026lt;tag\u0026gt;TCT){e\u0026lt;=1}(?P\u0026lt;strain\u0026gt;[ATCG]{10,26})CGTACGCTGCAGGTCGAC\" \\\n -o \"UMITail: rest \u0026gt; (?P\u0026lt;fixed2\u0026gt;CGTACGCTGCAGGTC)(?\u0026lt;UMItail\u0026gt;GAC[ATCG]G[ATCG]A[ATCG]G[ATCG]G[ATCG]G[ATCG]GAT){s\u0026lt;=2}\" \\\n -o \"UMI: UMItail \u0026gt; (GAC(?P\u0026lt;umi1\u0026gt;[ATCG])G(?\u0026lt;umi2\u0026gt;[ATCG])A(?\u0026lt;umi3\u0026gt;[ATCG])G(?\u0026lt;umi4\u0026gt;[ATCG])G(?\u0026lt;umi5\u0026gt;[ATCG])G(?\u0026lt;umi6\u0026gt;[ATCG])G){e\u0026lt;=2}\" \\\n --output-seq \"strain\" \\\n --output-id \"input.id+\u0027_umi=\u0027+umi1.seq+umi2.seq+umi3.seq+ \\\n umi4.seq+umi5.seq+umi6.seq+\u0027_sample=\u0027+sample.seq\" \\\n --filter \"sample_length == 5 and rest_start \u0026gt;= 16 and ( min(strain.letter_annotations[\u0027phred_quality\u0027]) \u0026gt;= 30 )\"\\\n --verbose --verbose --verbose\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat invocation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- Takes records from the `input.fastq`\n- Starts three processes that each take bites of 10 records\n- Applies the four operations to cut up the read\n- Writes the full detailed report including json reports for \n each read, so we limit it to the first 10,000 bytes\n of the file (about 50 records). This is for debugging.\n- Filters the records on having a `sample` barcode of 5 bases \n and having the `rest` sequence match starting at least past\n index 16 (so base 15 in english).\n- Re-assembles the records that pass this filter, making the ID\n of the fastq record having the original ID plus a UMI \n sequence and the sample barcode, then the sequence is just\n the match to the strain barcode context. This is suitable for\n feeding into `bwa` for example.\n- We\u0027ve got three levels of verbosity, so a per-record verbosity\n for debugging purposes.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the output formatting is a bit funny. This is directly evaluated\n(because security is what?) on BioPython SequenceRecords, so you need to specify\njust the name of the capture group(s) for the outputs so it can access the\n\u003ccode\u003e.seq\u003c/code\u003e and qualities. For the ID, etc, you can access \u003ccode\u003e.seq\u003c/code\u003e or \u003ccode\u003e.id\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThen if we like our thresholds we\u0027d re-run, and drop the \u003ccode\u003e--limit\u003c/code\u003e\nand \u003ccode\u003e--write-report\u003c/code\u003e flags. This will turn records like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@NB501157:100:H5J5LBGX2:1:11101:10000:6068 1:N:0:\nCTACTGTCCACGAGGTCTCTGCAGATAATACACTGTCACCCGTACGCTGCAGGTCGACCGTAGGAGGGAGATGTG\n+\nAAAAAEEEE/AEE\u0026lt;EEEEEEEEAEEAEEAEEEEE/EEE/EEEEEEEEE/EEEEEEEEEEEEE/EEEEEEEEEEEE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003einto records like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@NB501157:100:H5J5LBGX2:1:11101:10000:6068_umi=CTGAGA_sample=CTACT\nGCAGATAATACACTGTCACC\n+\nEEAEEAEEAEEEEE/EEE/E\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe sample barcode is the first five, the strain barcode starts after\nthe \u003ccode\u003eTCT\u003c/code\u003e, and the UMI is interspersed downstream. This is modified\nyeast BarSeq, btw.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eThis script depends strongly upon (uses) the work of\n\u003ca href=\"https://pypi.org/project/regex/\" rel=\"nofollow\"\u003eregex\u003c/a\u003e\nand\n\u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003eBiopython\u003c/a\u003e. Thanks! Check them out...\u003c/p\u003e\n", + "full_name": "XSEDE/nix-container-sc-benchmark", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-sc-benchmark\u003c/h1\u003e\u003ca id=\"user-content-nix-container-sc-benchmark\" class=\"anchor\" aria-label=\"Permalink: nix-container-sc-benchmark\" href=\"#nix-container-sc-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5358\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container with Nix to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 15, "topics": [], - "updated_at": 1711268013.0 + "updated_at": 1628542024.0 }, { "data_format": 2, - "description": "Automatically build Apptainer images for APSIM", + "description": null, "filenames": [ - "templates/Singularity.template" + "GPU_example_2/Singularity.def" ], - "full_name": "JBris/auto-apsim-singularity", + "full_name": "KempnerInstitute/intro-compute-march-2024", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eauto-apsim-singularity\u003c/h1\u003e\u003ca id=\"user-content-auto-apsim-singularity\" class=\"anchor\" aria-label=\"Permalink: auto-apsim-singularity\" href=\"#auto-apsim-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAutomatically build Apptainer images for APSIM\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eKempner Institute Spring 2024 Compute Workshop Supplemental Material\u003c/h1\u003e\u003ca id=\"user-content-kempner-institute-spring-2024-compute-workshop-supplemental-material\" class=\"anchor\" aria-label=\"Permalink: Kempner Institute Spring 2024 Compute Workshop Supplemental Material\" href=\"#kempner-institute-spring-2024-compute-workshop-supplemental-material\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see the \u003ca href=\"TBD\"\u003eWorkshop Page\u003c/a\u003e for more information.\u003c/p\u003e\n\u003cp\u003eThe supplementary material contains files for each run scenario. Below is a list of the folders along with a brief description of their purposes. In all models, we modified the first and the last layers of the ResNet-18 model to fit the CIFAR-10 dataset.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSLURM Examples\u003c/h2\u003e\u003ca id=\"user-content-slurm-examples\" class=\"anchor\" aria-label=\"Permalink: SLURM Examples\" href=\"#slurm-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSLURM Example 1\u003c/strong\u003e:\n\u003cul\u003e\n\u003cli\u003eUsefule SLURM commands\u003c/li\u003e\n\u003cli\u003eTest Only Jobs\u003c/li\u003e\n\u003cli\u003eEstimating job efficiency (CPU and Memory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGPU Examples\u003c/h2\u003e\u003ca id=\"user-content-gpu-examples\" class=\"anchor\" aria-label=\"Permalink: GPU Examples\" href=\"#gpu-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 1\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-18\u003c/li\u003e\n\u003cli\u003eDataset: CIFAR-10\u003c/li\u003e\n\u003cli\u003eResources: 1 GPU (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Interactive\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 2\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-18\u003c/li\u003e\n\u003cli\u003eDataset: CIFAR-10\u003c/li\u003e\n\u003cli\u003eResources: 1 GPU (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Batch\u003c/li\u003e\n\u003cli\u003eIntegration: Weights \u0026amp; Biases (wandb.ai)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 3\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-18\u003c/li\u003e\n\u003cli\u003eDataset: CIFAR-10\u003c/li\u003e\n\u003cli\u003eResources: 1 GPU (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Batch\u003c/li\u003e\n\u003cli\u003eIntegration: Weights \u0026amp; Biases (wandb.ai)\u003c/li\u003e\n\u003cli\u003eJob Arrays\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 4\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-50\u003c/li\u003e\n\u003cli\u003eDataset: CIFAR-10\u003c/li\u003e\n\u003cli\u003eResources: 2, 4 GPUs (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Batch\u003c/li\u003e\n\u003cli\u003eIntegration: Weights \u0026amp; Biases (wandb.ai)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 5\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-50\u003c/li\u003e\n\u003cli\u003eDataset: Subset of imagenet\u003c/li\u003e\n\u003cli\u003eResources: 4 GPUs, 8 GPUs (two node) (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Batch\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, - "topics": [ - "apptainer", - "apptainer-container", - "apsim", - "apsimx", - "automation", - "singularity", - "singularity-container", - "singularity-containers" + "subscribers_count": 6, + "topics": [], + "updated_at": 1716485818.0 + }, + { + "data_format": 2, + "description": "A tool for semi-automatic containerization, deployment, and execution of scientific applications and workflows on cloud computing and supercomputing platforms. ", + "filenames": [ + "testcases/BiasNet/Basil/Singularity.def", + "testcases/LigandNet/Manual/Singularity.def", + "testcases/Sumo/Basil/Singularity.def", + "testcases/Sumo/Manual/Singularity.def", + "testcases/OpenDMPK/Basil/Singularity.def" ], - "updated_at": 1679641238.0 + "full_name": "ritua2/Basil", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBasil\u003c/h1\u003e\u003ca id=\"user-content-basil\" class=\"anchor\" aria-label=\"Permalink: Basil\" href=\"#basil\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBasil is a tool for semi-automatic containerization, deployment, and execution of scientific applications and workflows on cloud computing and supercomputing platforms.\u003c/p\u003e\n\u003cp\u003eThe \"containerization\" of software applications not only future-proofs them and helps in their long-term preservation but also makes them portable across different hardware platforms, ensures reproducible results, and makes them convenient to disseminate. Docker and Singularity are two popular software technologies for containerizing scientific applications and are widely supported on different hardware platforms. However, their adoption involves a learning curve, especially when it comes to developing secure and optimized images of the applications of interest. A large number of domain-scientists and scholars are usually not formally trained at containerizing their applications with Docker and Singularity, and spend a significant amount of their time in porting their applications to different cloud computing and supercomputing platforms. The process of porting the applications having multiple software dependencies and sensitivities to specific software versions can be especially arduous for such users, and to assist them, this project will develop Basil - a tool for semi-automatically containerizing the scientific applications, frameworks, and workflows. This project will deliver Basil through a web portal, as a command-line tool, and through APIs.\u00a0Basil has a broad applicability across multiple domains of deep societal impact such as artificial intelligence, drug discovery, and earthquake engineering. By enabling the preservation of valuable legacy software and making them usable for several years in future, Basil will save cost and time in software rewriting and software installations, and thus contribute towards advancing the prosperity of the society. The project will result in educational content on \u201cIntroduction to Containerization\u201d and students engaged in the project will develop valuable skills in the areas of national interest such as\u00a0supercomputing/High Performance Computing (HPC) and cloud computing.\u003c/p\u003e\n\u003cp\u003eBasil will be useful across diverse domains in disseminating the research outcomes in the form of ready-to-use Docker/Singularity images that can contain input, source code, and output related to the research all packaged together in a single image where possible.\u00a0Users will provide the recipes for building their applications/workflows in one of the following forms (1) Makefiles/CMakefiles, (2) scripts, (3) commands, or (4) a text-file with predefined keywords and notations (templates for which will be provided by the project team). These recipes (e.g., in Makefiles or text-files) will be parsed, and Dockerfiles or Singularity definition files will be generated. Using a generated Dockerfile or Singularity definition file, a Docker or Singularity image will be built. Next, the image will be scanned for any vulnerabilities, signed, and if the user desires, released in public\u00a0registries\u00a0with appropriate licenses. These container images can be tested using the Basil web portal, and can be pulled to run or deploy on diverse hardware platforms on-prem or in the cloud.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProject Web Portal\u003c/h1\u003e\u003ca id=\"user-content-project-web-portal\" class=\"anchor\" aria-label=\"Permalink: Project Web Portal\" href=\"#project-web-portal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollowing is the link to the project\u0027s web portal : \u003ca href=\"https://icompute.us\" rel=\"nofollow\"\u003ehttps://icompute.us\u003c/a\u003e . This web portal is under iterative development and is available for use for generating and testing Docker and Singularity images generated by Basil.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAcknowledgement\u003c/h1\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project has been generously funded by the National Science Foundation (NSF) through award # \u003ca href=\"https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209946\u0026amp;HistoricalAwards=false\" rel=\"nofollow\"\u003e2209946\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 1, + "subscribers_count": 2, + "topics": [], + "updated_at": 1719993394.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container for MLST", "filenames": [ - "Singularity.qc", - "Singularity.count", - "Singularity.mapping", - "Singularity.renv" + "Singularity", + "v2.15.2/20181216/Singularity.v2.15.2_20181216", + "v2.15.2/20181211/Singularity.v2.15.2_20181211", + "v2.15.2/20181210/Singularity.v2.15.2_20181210", + "v2.8/20181216/Singularity.v2.8_20181216", + "v2.10/Singularity.v2.10", + "v2.9/Singularity.v2.9" ], - "full_name": "chaetognatha/singularity_library", + "full_name": "phgenomics-singularity/mlst", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emlst --- Scan contig files against PubMLST typing schemes\u003c/h1\u003e\u003ca id=\"user-content-mlst-----scan-contig-files-against-pubmlst-typing-schemes\" class=\"anchor\" aria-label=\"Permalink: mlst --- Scan contig files against PubMLST typing schemes\" href=\"#mlst-----scan-contig-files-against-pubmlst-typing-schemes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/576\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for Torsten Seemann\u0027s \u003ca href=\"https://github.com/tseemann/mlst\"\u003eMLST\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name mlst shub://phgenomics-singularity/mlst@latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003emlst.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eUse the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./mlst.simg --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name mlst shub://phgenomics-singularity/mlst@v2.9\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir $HOME/singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name mlst_v2.10 shub://phgenomics-singularity/mlst@v2.10\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eLink the image to a folder in your \u003ccode\u003e$PATH\u003c/code\u003e (e.g., \u003ccode\u003e$HOME/bin\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eln -s $HOME/singularity/mlst_v2.10.simg $HOME/bin/mlst\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emlst --help\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1657573666.0 + "updated_at": 1698091027.0 + }, + { + "data_format": 2, + "description": "Quality Control plots and data normalisation for Microarray data", + "filenames": [ + "Singularity" + ], + "full_name": "qbicsoftware-archive/microarray-qc-workflow", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWork in progress\u003c/h1\u003e\u003ca id=\"user-content-work-in-progress\" class=\"anchor\" aria-label=\"Permalink: Work in progress\" href=\"#work-in-progress\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emicroarray-qc-workflow\u003c/h1\u003e\u003ca id=\"user-content-microarray-qc-workflow\" class=\"anchor\" aria-label=\"Permalink: microarray-qc-workflow\" href=\"#microarray-qc-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTakes .cel files and creates qc plots as well as normalising the data\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/qbicsoftware/microarray-qc-workflow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe6ac9fe50a390fce8ab5d166033ccf9da248c6e07f04a46a10ef3aa151bba5a/68747470733a2f2f7472617669732d63692e6f72672f71626963736f6674776172652f6d6963726f61727261792d71632d776f726b666c6f772e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/qbicsoftware/microarray-qc-workflow.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c42e6080d15f532168bbb6d26fb300c5ed31e7f5e8648abbe9d5f6d8d4285475/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/microarray-qc-workflow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/03e3c878703a868edafe988884a1b52d3631ffc9fb199cc091a3421f122f1618/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6d6963726f61727261792d71632d776f726b666c6f772e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/microarray-qc-workflow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003emicroarray-qc-workflow: Takes .cel files and creates qc plots as well as normalising the data\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe microarray-qc-workflow pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was written by Timo Lucas (\u003ca href=\"https://github.com/lucass122\"\u003elucass122\u003c/a\u003e) at \u003ca href=\"http://www.qbic.uni-tuebingen.de/\" rel=\"nofollow\"\u003eQBiC T\u00fcbingen\u003c/a\u003e.\nR script based on script by Stefan Czemmel [qbicStefanC]:\n\u003ca href=\"https://github.com/qbicsoftware/qbic-wf-microarrayQC\"\u003ehttps://github.com/qbicsoftware/qbic-wf-microarrayQC\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 1, + "subscribers_count": 5, + "topics": [], + "updated_at": 1632720523.0 }, { "data_format": 2, "description": null, "filenames": [ - "recipes/Singularity.fastqc__0.11.8__1", - "recipes/Singularity.DEAGO__1.0.0", - "recipes/Singularity.seqtk__1.3", - "recipes/Singularity.BAGEL__0.9", - "recipes/Singularity.fqtools__2.0__hf50d5a6_4", - "recipes/Singularity.kallisto__0.46.0__hb6a4e58_0", - "recipes/Singularity.multiqc__1.7__py_2", - "recipes/Singularity.fqtools__2.2", - "recipes/Singularity.nextflow__19.01.0__ha4d7672_4", - "recipes/Singularity.mageck__0.5.8__py36h3e44d54_0", - "recipes/Singularity.STAR-Fusion__1.6.0", - "recipes/Singularity.jq__1.6_0", - "recipes/R/Singularity.R-3.6.0.subclonal_reconstruction-1.0.0", - "recipes/R/Singularity.R-3.6.0.base-1.0.1", - "recipes/R/Singularity.R-3.6.0.methylation-1.0.0", - "recipes/R/Singularity.R-3.6.0.base-1.0.0" + "Singularity" ], - "full_name": "team113sanger/t113-singularity", + "full_name": "IARCbioinfo/qualimap-nf", + "latest_release": "v1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuality control of WGS/WES/target alignment data\u003c/h1\u003e\u003ca id=\"user-content-quality-control-of-wgswestarget-alignment-data\" class=\"anchor\" aria-label=\"Permalink: Quality control of WGS/WES/target alignment data\" href=\"#quality-control-of-wgswestarget-alignment-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/qualimap-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/14531b9b6bc782d5ceaaba38d5daadb07ad3564e31008fc0c0eab83b4315d172/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f7175616c696d61702d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/qualimap-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/qualimap-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1623\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/145996279\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4d1b67cf1a9bdf91dcf8186c534a74ecd9de21cb1c7edcc8d6e0d4b610f7e8f5/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3134353939363237392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/145996279.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ImaneLboukili/WGS_analysis/blob/master/Qualimap/Qualimap-nf.png\"\u003e\u003cimg src=\"https://github.com/ImaneLboukili/WGS_analysis/raw/master/Qualimap/Qualimap-nf.png\" alt=\"Image Qualimap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePerform quality control of WGS/WES/target alignment data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003esamtools: see official installation \u003ca href=\"http://www.htslib.org\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003cli\u003eQualimap: see official installation \u003ca href=\"http://qualimap.bioinfo.cipf.es\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003cli\u003eMultiqc: see official installation \u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing Fasta files\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOptional\u003c/h3\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDefault value\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003e.\u003c/td\u003e\n\u003ctd\u003ePath to output folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--qualimap\u003c/td\u003e\n\u003ctd\u003e/usr/bin/qualimap\u003c/td\u003e\n\u003ctd\u003ePath to Qualimap installation directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--feature_file\u003c/td\u003e\n\u003ctd\u003emyfeatures.txt\u003c/td\u003e\n\u003ctd\u003eQualimap feature file for coverage analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--multiqc_config\u003c/td\u003e\n\u003ctd\u003enull\u003c/td\u003e\n\u003ctd\u003econfig yaml file for multiqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003eINTEGER\u003c/td\u003e\n\u003ctd\u003eNumber of cpus to be used\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFlags\u003c/h3\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload test data set\u003c/h2\u003e\u003ca id=\"user-content-download-test-data-set\" class=\"anchor\" aria-label=\"Permalink: Download test data set\" href=\"#download-test-data-set\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/iarcbioinfo/data_test\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003enextflow run iarcbioinfo/Qualimap-nf --qualimap /path/to/qualimap --input_folder /path/to/bam --output_folder /path/to/output\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHTMLs\u003c/td\u003e\n\u003ctd\u003eAn html file for each analysed BAM file, and one containing the aggregated multiQC results\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:Delhommet@students.iarc.fr\"\u003eDelhommet@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003edeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaxime Vallee\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:Valleem@iarc.fr\"\u003eValleem@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003edeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMatthieu Foll\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:Follm@iarc.fr\"\u003eFollm@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003edeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@fellows.iarc.fr\"\u003eAlcalaN@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "stargazers_count": 1, + "subscribers_count": 5, + "topics": [], + "updated_at": 1720620729.0 + }, + { + "data_format": 2, + "description": "A container for running the perfzero benchmark in an XSEDE environment", + "filenames": [ + "Singularity" + ], + "full_name": "XSEDE/nix-container-perfzero", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2811\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-perfzero\u003c/h1\u003e\u003ca id=\"user-content-nix-container-perfzero\" class=\"anchor\" aria-label=\"Permalink: nix-container-perfzero\" href=\"#nix-container-perfzero\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003eSingularity container with Nix to be used in XSEDE compute environment (\u003cstrong\u003ecurrently in development\u003c/strong\u003e)\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 16, "topics": [], - "updated_at": 1570094851.0 + "updated_at": 1623176998.0 }, { "data_format": 2, - "description": "UPPMAX Singularity builds", + "description": "Iterative approach to relevant top-k planning", "filenames": [ - "IMAP/Singularity.IMAP", - "bonito/Singularity.bonito", - "metaWRAP/Singularity.metaWRAP", - "metaWRAP/Singularity.metaWRAP-deps-only", - "metaWRAP/Singularity.metaWRAP-deps-only-ubuntu", - "samtools++/Singularity.samtools", - "video-tools/Singularity.tools", - "MitoZ/Singularity.v2.3-pm", - "zsim/Singularity.zsim", - "gapseq/Singularity.gapseq", - "VESPA/Singularity.VESPA", - "ORFfinder/Singularity.ORFfinder", - "arcasHLA/Singularity.arcasHLA", - "numSCAL/Singularity.numSCAL", - "parautomatik/Singularity.parautomatik", - "gfaestus/Singularity.vulkan-u", - "gfaestus/Singularity.vulkan", - "miniconda3-rw/Singularity.conda", - "UniteM/Singularity.UniteM", - "gromacs/Singularity.gromacs-apt", - "gromacs/Singularity.gromacs-apt18", - "gromacs/Singularity.gromacs", - "HiCExplorer/Singularity.HiCExplorer" + "Singularity" ], - "full_name": "pmitev/UPPMAX-Singularity", + "full_name": "mtabernerop/relevant-forbiditerative", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUPPMAX-Singularity\u003c/h1\u003e\u003ca id=\"user-content-uppmax-singularity\" class=\"anchor\" aria-label=\"Permalink: UPPMAX-Singularity\" href=\"#uppmax-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUPPMAX Singularity builds\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFiltering Top-k Relevant Plans\u003c/h1\u003e\u003ca id=\"user-content-filtering-top-k-relevant-plans\" class=\"anchor\" aria-label=\"Permalink: Filtering Top-k Relevant Plans\" href=\"#filtering-top-k-relevant-plans\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository proposes an interative approach to filter plans in top-k planning tasks under perfect justification criteria. Its implementation is based on Forbid-Iterative (FI) Planner, an Automated PDDL based planner that includes planners for top-k, top-quality, and diverse computational tasks, developed by Michael Katz et al.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe planner is based on the idea of obtaining multiple solutions by iteratively reformulating planning tasks to restrict the set of valid plans, forbidding previously found ones. Solution plans are required to be relevant (perfectly-justified).\u003c/h2\u003e\u003ca id=\"user-content-the-planner-is-based-on-the-idea-of-obtaining-multiple-solutions-by-iteratively-reformulating-planning-tasks-to-restrict-the-set-of-valid-plans-forbidding-previously-found-ones-solution-plans-are-required-to-be-relevant-perfectly-justified\" class=\"anchor\" aria-label=\"Permalink: The planner is based on the idea of obtaining multiple solutions by iteratively reformulating planning tasks to restrict the set of valid plans, forbidding previously found ones. Solution plans are required to be relevant (perfectly-justified).\" href=\"#the-planner-is-based-on-the-idea-of-obtaining-multiple-solutions-by-iteratively-reformulating-planning-tasks-to-restrict-the-set-of-valid-plans-forbidding-previously-found-ones-solution-plans-are-required-to-be-relevant-perfectly-justified\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding\u003c/h1\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor building the code please use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning\u003c/h1\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./plan_topk.sh \u0026lt;domain\u0026gt; \u0026lt;problem\u0026gt; \u0026lt;number-of-plans\u0026gt; \u0026lt;overall-time-limit\u0026gt; \u0026lt;plan-reorderings\u0026gt; \u0026lt;structural-symmetries\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample of running command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./plan_topk.sh examples/blocks/domain.pddl examples/blocks/instances/p0.pddl 5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting\u003c/h2\u003e\u003ca id=\"user-content-citing\" class=\"anchor\" aria-label=\"Permalink: Citing\" href=\"#citing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTop-k Planning\u003c/h3\u003e\u003ca id=\"user-content-top-k-planning\" class=\"anchor\" aria-label=\"Permalink: Top-k Planning\" href=\"#top-k-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{katz-et-al-icaps2018,\n title = \"A Novel Iterative Approach to Top-k Planning\",\n author = \"Michael Katz and Shirin Sohrabi and Octavian Udrea and Dominik Winterer\",\n booktitle = \"Proceedings of the Twenty-Eighth International Conference on\n Automated Planning and Scheduling (ICAPS 2018)\",\n publisher = \"{AAAI} Press\",\n pages = \"132--140\",\n year = \"2018\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRelevant Top-k Planning\u003c/h3\u003e\u003ca id=\"user-content-relevant-top-k-planning\" class=\"anchor\" aria-label=\"Permalink: Relevant Top-k Planning\" href=\"#relevant-top-k-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{salerno22filtering,\n title = \"{F}iltering {T}op-k {R}elevant {P}lans\",\n author = \"Salerno, Mauricio and Tabernero, Miguel and Fuentetaja, Raquel and Pozanco, Alberto\",\n year = \"2022\",\n booktitle = \"Proceedings of the 32nd International Conference on Automated Planning and Scheduling\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicensing\u003c/h2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-label=\"Permalink: Licensing\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eForbid-Iterative (FI) Planner is an Automated PDDL based planner that\nincludes planners for top-k, top-quality, and diverse computational\ntasks. Copyright (C) 2019 Michael Katz, IBM Research, USA.\nThe code extends the Fast Downward planning system. The license for the\nextension is specified in the LICENSE file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFast Downward\u003c/h2\u003e\u003ca id=\"user-content-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Fast Downward\" href=\"#fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward-logo.png\"\u003e\u003cimg src=\"misc/images/fast-downward-logo.png\" width=\"500\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 2, - "topics": [], - "updated_at": 1732273448.0 + "topics": [ + "artificial-intelligence", + "automated-planning", + "perfect-justification", + "plan-filtering", + "bachelor-thesis" + ], + "updated_at": 1679787208.0 }, { "data_format": 2, - "description": "Hyperparameter Optimization via Interacting with Probabilistic Circuits", + "description": null, "filenames": [ - "HPOBench/hpobench/container/recipes/Singularity.template", - "HPOBench/hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine", - "HPOBench/hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", - "HPOBench/hpobench/container/recipes/od/Singularity.ODBenchmarks", - "HPOBench/hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", - "HPOBench/hpobench/container/recipes/ml/Singularity.ml_mmfb", - "HPOBench/hpobench/container/recipes/ml/Singularity.PyBNN", - "HPOBench/hpobench/container/recipes/ml/Singularity.SupportVectorMachine", - "HPOBench/hpobench/container/recipes/ml/Singularity.XGBoostBenchmark", - "HPOBench/hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark", - "HPOBench/hpobench/container/recipes/nas/Singularity.TabularBenchmarks", - "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_101", - "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_201", - "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_1shot1", - "HPOBench/hpobench/container/recipes/rl/Singularity.learnaBenchmark", - "HPOBench/hpobench/container/recipes/rl/Singularity.Cartpole" + "Singularity" ], - "full_name": "ml-research/ibo-hpc", + "full_name": "rkalyanapurdue/geoedf-cont", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHyperparameter Optimization via Interacting with Probabilistic Circuits\u003c/h1\u003e\u003ca id=\"user-content-hyperparameter-optimization-via-interacting-with-probabilistic-circuits\" class=\"anchor\" aria-label=\"Permalink: Hyperparameter Optimization via Interacting with Probabilistic Circuits\" href=\"#hyperparameter-optimization-via-interacting-with-probabilistic-circuits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code of the paper titled \u003ccode\u003eHyperparameter Optimization via Interacting with Probabilistic Circuits\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbstract\u003c/h2\u003e\u003ca id=\"user-content-abstract\" class=\"anchor\" aria-label=\"Permalink: Abstract\" href=\"#abstract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDespite the growing interest in designing truly interactive hyperparameter optimization (HPO) methods, only a few allow human feedback to be included. However, these methods add friction to the interactive process, rigidly requiring users to define prior distributions ex ante and often imposing additional constraints on the optimization framework. This hinders flexible incorporation of expertise and valuable knowledge of domain experts, who might provide partial feedback at any time during optimization. To overcome these limitations, we introduce a novel Bayesian optimization approach leveraging probabilistic circuits (PCs) as a surrogate model. PCs encode a tractable joint distribution over the hybrid hyperparameter space and evaluation scores. They enable tractable and exact conditional inference and sampling, allowing users to provide beliefs interactively and generate configurations adhering to their feedback. We demonstrate the benefits of the resulting interactive HPO through an extensive empirical evaluation of diverse benchmarks, including the challenging setting of neural architecture search.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend to run the code in a \u003ccode\u003eDocker\u003c/code\u003e container, we don\u0027t guarantee a working version without Docker.\u003c/p\u003e\n\u003cp\u003eTo setup the code and run experiments do the following instructions:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eNavigate to the root directory of the repository\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003edocker build --build-arg baseimage=[BASE_IMAGE] --no-cache --tag ihpo .\u003c/code\u003e where \u003ccode\u003eBASE_IMAGE=python:3.9-slim\u003c/code\u003e depending on whether you\u0027d like to use GPU or not.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003edocker run -v .:/app/ -it ihpo\u003c/code\u003e to enter the Docker container.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython main.py --exp nas101 --optimizer pc\u003c/code\u003e to run the code (change options accordingly).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractions\u003c/h2\u003e\u003ca id=\"user-content-interactions\" class=\"anchor\" aria-label=\"Permalink: Interactions\" href=\"#interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo simulate user interactions we allow you to put json-files into the \u003ccode\u003einterventions\u003c/code\u003e directory. The json-files should have the following structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[\n {\n \"type\": \"good\" or \"bad\",\n \"kind\": \"point\" or \"dist\"\n \"intervention\": {YOUR INTERVENTION},\n \"iteration\" ITERATION TO TRIGGER INTERACTION\n },\n ...\n]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can put as many interactions as you like into the json-file. The interactions will be evaluated top down, i.e. the first appearing in the list first and so on.\nThe \u003ccode\u003eiteration\u003c/code\u003e field only holds information for the optimizer when to use the interaction but it has no effect on the order of how the interactions are triggered. This means that \u003cstrong\u003einteractions have to be ordered correctly in the list!\u003c/strong\u003e Otherwise this might cause errors or unexpected behavior.\u003c/p\u003e\n\u003cp\u003eThe \"type\" is only meta-data and specifies whether an interaction is intended to be beneficial or harmful. The \"kind\" field defines whether the interaction is a point-interaction, i.e. the user fixes a certain hyperparameter to a certain value or whether the interaction is a distribution, i.e. the user specifies a distribution over possible interactions. The \"intervention\" field then specifies the exact interaction as a dictionary where {\"hyperparameter\": VALUE or {\"dist:\" [\"uniform, cat, gauss\"], \"parameters\": [parameters]}}\" is the syntax. The \"iteration\" field specifies the iteration the interaction is applied. See the existing json-files in the \u003ccode\u003einterventions\u003c/code\u003e-directory for examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePlotting\u003c/h2\u003e\u003ca id=\"user-content-plotting\" class=\"anchor\" aria-label=\"Permalink: Plotting\" href=\"#plotting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo plot the results of the optimization runs, swith to the directory \u003ccode\u003eihpo/utils\u003c/code\u003e. Here you can execute \u003ccode\u003epython plot.py\u003c/code\u003e with several arguments (described in the argument definitions). Generating some plots can take a while (the regret_vs_cost up to 5-10 minutes).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTroubleshooting\u003c/h2\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSometimes you might encounter an error of scikit-learn stating that some metric(s) cannot be imported.\nThere is a hacky workaround that resolves this issue:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003epip uninstall scikit-learn pandas\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip install scikit-learn pandas\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip uninstall scikit-learn\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip install scikit-learn==1.1.3\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis forces pip to downgrade to \u003ccode\u003escikit-learn==1.1.3.\u003c/code\u003e while keeping the other packages at higher versions (might cause warnings by pip). The code should work after that.\u003c/p\u003e\n\u003cp\u003eIf the automatic setup of the dataset fails, try to manually run the following command in your docker container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egdown 1KSjP3-5x1O0_2-DuQJgcXG3_h_AiYh0j --folder -O /app/data/\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf it does not succeed, try to download the contents of the Google Drive directory with the given ID (\u003ccode\u003e1KSjP3-5x1O0_2-DuQJgcXG3_h_AiYh0j\u003c/code\u003e) manually one by one and place it in the \u003ccode\u003e/app/data/\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you find this code useful in your research, please consider citing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@incollection{seng2024tpm,\n title = {Hyperparameter Optimization via Interacting with Probabilistic Circuits},\n author = {Jonas Seng and Fabrizio Ventola and Zhongjie Yu and Kristian Kersting},\n booktitle = {7th Workshop on Tractable Probabilistic Modeling at UAI (TPM 2024)},\n year = {2024}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was supported by the National High-Performance Computing Project for Computational Engineering Sciences (NHR4CES) and the Federal Ministry of Education and Research (BMBF) Competence Center for AI and Labour (\"KompAKI\", FKZ 02L19C150). Furthermore, this work benefited from the cluster project \"The Third Wave of AI\".\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egeoedf-cont\u003c/h1\u003e\u003ca id=\"user-content-geoedf-cont\" class=\"anchor\" aria-label=\"Permalink: geoedf-cont\" href=\"#geoedf-cont\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1726572019.0 + "updated_at": 1575383900.0 }, { "data_format": 2, - "description": null, + "description": "A container that looks like uppmax but can be run completely offline.", "filenames": [ - "singularity/Singularity.2020.10.21.5755", - "singularity/Singularity.2020.04.09.5012", - "singularity/Singularity.2019.01.30.3436", - "singularity/Singularity.2023.10.7317.0", - "singularity/Singularity.2019.06.05.3920", - "singularity/Singularity.2022.06.7046.0", - "singularity/Singularity.2021.04.15.6139", - "singularity/Singularity.2018.01.30.2253", - "singularity/Singularity.2019.10.04.4236", - "singularity/Singularity.2022.12.7130.0", - "singularity/Singularity.2022.09.7085.0", - "singularity/Singularity.2020.08.04.5350", - "singularity/Singularity.2019.04.03.3693", - "singularity/Singularity.2019.07.18.4025", - "singularity/Singularity.2021.10.8.6835", - "singularity/Singularity.2023.09.7308.0", - "singularity/Singularity.2018.09.28.3099", - "singularity/Singularity.2021.9.21.6799", - "singularity/Singularity.2020.11.27.5887", - "singularity/Singularity.2022.04.7021.0", - "singularity/Singularity.2024.05.7470.0", - "singularity/Singularity.2020.09.17.5665", - "singularity/Singularity.2019.01.08.3392" + "Singularity.ngsintro", + "Singularity.default" ], - "full_name": "APSIMInitiative/APSIM.HPC", + "full_name": "UPPMAX/offline-uppmax-env", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAPSIM.HPC\u003c/h1\u003e\u003ca id=\"user-content-apsimhpc\" class=\"anchor\" aria-label=\"Permalink: APSIM.HPC\" href=\"#apsimhpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains Singularity recipes for building and running APSIM within Singularity containers. The principal motivation for using these Singularity recipes is to execute APSIM within a high-performance computing (HPC) environment.\u003c/p\u003e\n\u003cp\u003ePlease refer to the official Singularity documentation for more information: \u003ca href=\"https://docs.sylabs.io/guides/latest/user-guide/\" rel=\"nofollow\"\u003ehttps://docs.sylabs.io/guides/latest/user-guide/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample\u003c/h2\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the following to build and execute a Singularity container for APSIM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl -o apsim.Singularity https://raw.githubusercontent.com/APSIMInitiative/APSIM.HPC/main/singularity/Singularity.2022.12.7130.0\nsingularity build apsim.sif apsim.Singularity\n./apsim.sif \n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eoffline-uppmax-env\u003c/h1\u003e\u003ca id=\"user-content-offline-uppmax-env\" class=\"anchor\" aria-label=\"Permalink: offline-uppmax-env\" href=\"#offline-uppmax-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA container that has the same operating system, same packages installed, and a copy of the module system (not the actual software though) at UPPMAX. The script \u003ccode\u003esoftware_packer.sh\u003c/code\u003e can be run at UPPMAX to create a tarball of the software you wish to include in container at build time. If any data needs to be accessed from inside the container it can be mounted at runtime.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTLDR\u003c/h1\u003e\u003ca id=\"user-content-tldr\" class=\"anchor\" aria-label=\"Permalink: TLDR\" href=\"#tldr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# ON UPPMAX\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e package the software you want to have in your image\u003c/span\u003e\ngit clone https://github.com/UPPMAX/offline-uppmax-env.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e offline-uppmax-env\nbash software_packer.sh bwa star samtools\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# ON LOCAL COMPUTER\u003c/span\u003e\ngit clone https://github.com/UPPMAX/offline-uppmax-env.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e offline-uppmax-env\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e download the created software.package.tar.gz to the package/ folder\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e using Docker\u003c/span\u003e\ndocker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run \\\n-v offline-uppmax-env-proj:/proj \\\n-v /any/host/data/you/want/access/to:/path/inside/container \\\n-it \\\nuppmax/offline-uppmax-env:latest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e using singularity\u003c/span\u003e\nsingularity build offline-uppmax-env.sif Singularity\nsingularity shell \\\n-b /host/path/to/persistent/projfolder:/proj \\\n-b /any/host/data/you/want/access/to:/path/inside/container \\\noffline-uppmax-env.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eWhat you get\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCentOS 7\u003c/li\u003e\n\u003cli\u003eAll yum packages installed at UPPMAX\u003c/li\u003e\n\u003cli\u003eA copy of the module files at UPPMAX (not the programs themselves)\u003c/li\u003e\n\u003cli\u003eThe option to include any of the installed programs at UPPMAX, requires you to rebuild the image.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat you don\u0027t get\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eShared libraries, these would bloat the image quite a bit. These are solvable on a case by case basis, more on that further down.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse case\u003c/h2\u003e\u003ca id=\"user-content-use-case\" class=\"anchor\" aria-label=\"Permalink: Use case\" href=\"#use-case\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo was created to make a offline replacement for UPPMAX for courses, in case there is some kind of problem making UPPMAX unusable at the time the course is given. If UPPMAX suddenly disappears we can just tell the students to start up a container and all data and software needed would be included, making it possible to continue the course. This will require us to build our own version of this image where we include the software we want to be installed and to provide any data we want to be accessible to the students.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to create a course specific image\u003c/h2\u003e\u003ca id=\"user-content-how-to-create-a-course-specific-image\" class=\"anchor\" aria-label=\"Permalink: How to create a course specific image\" href=\"#how-to-create-a-course-specific-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding off the base image in Dockerhub\u003c/h3\u003e\u003ca id=\"user-content-building-off-the-base-image-in-dockerhub\" class=\"anchor\" aria-label=\"Permalink: Building off the base image in Dockerhub\" href=\"#building-off-the-base-image-in-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe base image will just have the OS and packages of UPPMAX, and the \u003ccode\u003euppmax\u003c/code\u003e and \u003ccode\u003ebioinfo-tools\u003c/code\u003e module. To include the software you want to have access to you will have to login to UPPMAX and run \u003ccode\u003esoftware_packer.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run on uppmax\u003c/span\u003e\nbash software_packer.sh bwa star R GATK\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will package everything needed to load these modules into a file called \u003ccode\u003esoftware.package.tar.gz\u003c/code\u003e. Download this file to your computer, put it in the \u003ccode\u003epackages\u003c/code\u003e folder and build the Dockerfile in that folder (replace \u003ccode\u003erepo/name:version\u003c/code\u003e with whatever you want to name it on Dockerhub, or remove it to have it untagged). The dockerfile will copy all files in \u003ccode\u003epackages/\u003c/code\u003e and unzip all files named \u003ccode\u003e*.package.tar.gz\u003c/code\u003e, so feel free to put additional files there following this naming pattern.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run locally\u003c/span\u003e\ndocker build -t repo/name:version \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding your own base image\u003c/h3\u003e\u003ca id=\"user-content-building-your-own-base-image\" class=\"anchor\" aria-label=\"Permalink: Building your own base image\" href=\"#building-your-own-base-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf the base image on Dockerhub is too old for your liking you can rebuild it yourself. Follow the same steps as above, but put the \u003ccode\u003esoftware.package.tar.gz\u003c/code\u003e you created on UPPMAX in the \u003ccode\u003ebase/packages\u003c/code\u003e folder instead. The dockerfile will copy all files in \u003ccode\u003epackages/\u003c/code\u003e and unzip all files named \u003ccode\u003e*.package.tar.gz\u003c/code\u003e, so feel free to put additional files there following this naming pattern.\u003c/p\u003e\n\u003cp\u003eTo update the list of packages installed by \u003ccode\u003eyum\u003c/code\u003e, run the following line on UPPMAX:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e list all installed packages and print the all on a single line\u003c/span\u003e\nyum list installed \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e cut -f 1 -d \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e cut -f 1 -d \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sort \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e awk -vORS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e{ print $1 }\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand replace the list of packages in the \u003ccode\u003eDockerfile\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThen build the Dockerfile in the \u003ccode\u003ebase\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e base\ndocker build -t repo/name:version \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis build will download and install all the yum packages from scratch so the image will be completely up-to-date, but it will take about an hour to build it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run the image once it is built\u003c/h2\u003e\u003ca id=\"user-content-how-to-run-the-image-once-it-is-built\" class=\"anchor\" aria-label=\"Permalink: How to run the image once it is built\" href=\"#how-to-run-the-image-once-it-is-built\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a named volume called \u003ccode\u003eoffline-uppmax-env-proj\u003c/code\u003e which will be mounted to \u003ccode\u003e/proj\u003c/code\u003e inside the container. All data put in there will persist between restarts of the container, i.e. this is where the students should put their lab work. The data used in the labs are usually so big (10+gb) that it does not make sens to put it inside the image. It\u0027s better to download it separately and mount it when starting the container.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDocker\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run \\\n-v offline-uppmax-env-proj:/proj \\\n-v /host/path/to/data:/container/path/to/data \\\n-it \\\nrepo/name:version\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e example\u003c/span\u003e\ndocker run \\\n-v offline-uppmax-env-proj:/proj \\\n-v /home/user/ngsintro_data:/sw/courses/ngsintro \\\n-it \\\nuppmax/offline-uppmax-env:latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter the container is running it should be just like working on uppmax. \u003ccode\u003emodule load\u003c/code\u003e should behave the same way and all modules you packed with \u003ccode\u003esoftware_packer.sh\u003c/code\u003e should be available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\nTo get the module system to work in Singularity you have to build the Singularity file as sudo and everything should work. Package the software you need on UPPMAX like in the Docker approach, put the downloaded tarball in the \u003ccode\u003epackages/\u003c/code\u003e folder just like with Docker, and then build it with Singularity.\u003c/p\u003e\n\u003cp\u003eJust building from Dockerhub (uppmax/offline-uppmax-env:latest) will give you a container with only the \u003ccode\u003euppmax\u003c/code\u003e and \u003ccode\u003ebioinfo-tools\u003c/code\u003e in it, and the \u003ccode\u003emodule\u003c/code\u003e command will not work since it is a function that is not inherited properly when being converted by Singularity. You can get around this by manually typing \u003ccode\u003esource /etc/bashrc.module_env\u003c/code\u003e every time the container starts.\u003c/p\u003e\n\u003cp\u003eIf you build your own Docker image with the software your want, push it to Dockerhub, and convert it to Singularity, you will still have the problem of the \u003ccode\u003emodule\u003c/code\u003e command not working. The solution is the same, manually type \u003ccode\u003esource /etc/bashrc.module_env\u003c/code\u003e when the container starts and it should start working. Building the Singularity file instead will not have this problem.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTroubleshooting\u003c/h1\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMissing shared libraries\u003c/h3\u003e\u003ca id=\"user-content-missing-shared-libraries\" class=\"anchor\" aria-label=\"Permalink: Missing shared libraries\" href=\"#missing-shared-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUnfortunately I could not find an easy way to automatically pull all the shared libraries needed by programs. I had a problem with STAR, that it needed a newer version of GCC. I could get around it by running \u003ccode\u003eldd $(which star)\u003c/code\u003e on uppmax and see that the file uses was \u003ccode\u003e/sw/comp/gcc/8.3.0_rackham/lib64/libstdc++.so.6\u003c/code\u003e. I put this file in a tar file,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003etar -chzvf libs.package.tar.gz /sw/comp/gcc/8.3.0_rackham/lib64/libstdc++.so.6 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e note the -h option, will dereference symbolic links\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand put the \u003ccode\u003elibs.package.tar.gz\u003c/code\u003e file in the \u003ccode\u003epackages\u003c/code\u003e folder, build the image, and it worked after that.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTodos\u003c/h1\u003e\u003ca id=\"user-content-todos\" class=\"anchor\" aria-label=\"Permalink: Todos\" href=\"#todos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eTest if it works.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 7, "topics": [], - "updated_at": 1725169910.0 + "updated_at": 1604866356.0 }, { "data_format": 2, - "description": null, + "description": "Example of Singularity container", "filenames": [ - "docker/Singularity", - "docker/railrl_v9_cuda10-1_mj1-50-1-59_torch0-4-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_hand_tf_v1/Singularity", - "docker/railrl_hand_tf_v1/Singularity_cpu", - "docker/railrl_hand_v2/Singularity", - "docker/railrl_hand_v2/Singularity_cpu", - "docker/railrl_hand_v3/Singularity", - "docker/railrl_hand_v3/Singularity_cpu", - "docker/railrl_gpu_mujoco1-5-v4/singularity/Singularity", - "docker/railrl_v8_cuda10-1/Singularity", - "docker/railrl_v6_cuda9/Singularity", - "docker/railrl_v11_cuda10-1_mj2-0-2-2_torch0-3-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v7/Singularity", - "docker/railrl_v5/singularity/Singularity", - "docker/railrl_hand_v1/Singularity", - "docker/railrl_hand_v1/Singularity_cpu", - "docker/railrl_v10_cuda10-1_mj2-0-2-2_torch0-4-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v6_cuda8/Singularity", - "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", - "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", - "docker/railrl_ray/Singularity", - "docker/metac_railrl_v12_cuda10-1_mj2-0-2-2_torch1-4-0_gym0-12-5_py3-6-5/Singularity", - "docker/metac_railrl_v12_cuda10-1_mj2-0-2-2_torch1-4-0_gym0-12-5_py3-6-5/Singularity_cpu", - "docker/railrl_v9-5_cuda10-1_mj1-50-1-59_torch1-1-0_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v7_cuda8/Singularity", - "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch_cuda8", - "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch" + "Singularity" ], - "full_name": "jcoreyes/erl", + "full_name": "breakfast-carpentry/diamondsingularity", "latest_release": null, - "readme": "\u003cp\u003eREADME last updated on: 01/24/2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erailrl\u003c/h1\u003e\u003ca id=\"user-content-railrl\" class=\"anchor\" aria-label=\"Permalink: railrl\" href=\"#railrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReinforcement learning framework.\nSome implemented algorithms:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/ddpg.py\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/sac.py\"\u003eSoft Actor Critic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/dqn_and_double_dqn.py\"\u003e(Double) Deep Q-Network (DQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/her.py\"\u003eHindsight Experience Replay (HER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/model_based_dagger.py\"\u003eMPC with Neural Network Model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/naf.py\"\u003eNormalized Advantage Function (NAF)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eWARNING: I haven\u0027t tested this NAF implementation much, so it may not match the paper\u0027s performance. I\u0027m pretty confident about the other two implementations though.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, checkout the example scripts, linked above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSome dependancies\u003c/h3\u003e\u003ca id=\"user-content-some-dependancies\" class=\"anchor\" aria-label=\"Permalink: Some dependancies\" href=\"#some-dependancies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo apt-get install swig\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate Conda Env\u003c/h3\u003e\u003ca id=\"user-content-create-conda-env\" class=\"anchor\" aria-label=\"Permalink: Create Conda Env\" href=\"#create-conda-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall and use the included ananconda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda env create -f docker/railrl/railrl-env.yml\n$ source activate railrl-env\n(railrl-env) $ # Ready to run examples/ddpg_cheetah_no_doodad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if you want you can use the docker image included.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Simulation Env Code\u003c/h3\u003e\u003ca id=\"user-content-download-simulation-env-code\" class=\"anchor\" aria-label=\"Permalink: Download Simulation Env Code\" href=\"#download-simulation-env-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vitchyr/multiworld\"\u003emultiworld\u003c/a\u003e (contains environments):\u003ccode\u003egit clone https://github.com/vitchyr/multiworld\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e(Optional) Install doodad\u003c/h3\u003e\u003ca id=\"user-content-optional-install-doodad\" class=\"anchor\" aria-label=\"Permalink: (Optional) Install doodad\" href=\"#optional-install-doodad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI recommend installing \u003ca href=\"https://github.com/justinjfu/doodad\"\u003edoodad\u003c/a\u003e to\nlaunch jobs. Some of its nice features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEasily switch between running code locally, on a remote compute with\nDocker, on EC2 with Docker\u003c/li\u003e\n\u003cli\u003eEasily add your dependencies that can\u0027t be installed via pip (e.g. you\nborrowed someone\u0027s code)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you install doodad, also modify \u003ccode\u003eCODE_DIRS_TO_MOUNT\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePath to rllab directory\u003c/li\u003e\n\u003cli\u003ePath to railrl directory\u003c/li\u003e\n\u003cli\u003ePath to other code you want to juse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou\u0027ll probably also need to update the other variables besides the docker\nimages/instance stuff.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup Config File\u003c/h3\u003e\u003ca id=\"user-content-setup-config-file\" class=\"anchor\" aria-label=\"Permalink: Setup Config File\" href=\"#setup-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must setup the config file for launching experiments, providing paths to your code and data directories. Inside \u003ccode\u003erailrl/config/launcher_config.py\u003c/code\u003e, fill in the appropriate paths. You can use \u003ccode\u003erailrl/config/launcher_config_template.py\u003c/code\u003e as an example reference.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecp railrl/launchers/config-template.py railrl/launchers/config.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a policy and seeing results\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-policy-and-seeing-results\" class=\"anchor\" aria-label=\"Permalink: Visualizing a policy and seeing results\" href=\"#visualizing-a-policy-and-seeing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDuring training, the results will be saved to a file called under\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e is the directory set by \u003ccode\u003erailrl.launchers.config.LOCAL_LOG_DIR\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;exp_prefix\u0026gt;\u003c/code\u003e is given either to \u003ccode\u003esetup_logger\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;foldername\u0026gt;\u003c/code\u003e is auto-generated and based off of \u003ccode\u003eexp_prefix\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003einside this folder, you should see a file called \u003ccode\u003eparams.pkl\u003c/code\u003e. To visualize a policy, run\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e(railrl) $ python scripts/sim_policy LOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;/params.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have rllab installed, you can also visualize the results\nusing \u003ccode\u003erllab\u003c/code\u003e\u0027s viskit, described at\nthe bottom of \u003ca href=\"http://rllab.readthedocs.io/en/latest/user/cluster.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython rllab/viskit/frontend.py LOCAL_LOG_DIR/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eexp_prefix\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd paths\u003c/h3\u003e\u003ca id=\"user-content-add-paths\" class=\"anchor\" aria-label=\"Permalink: Add paths\" href=\"#add-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=$PYTHONPATH:/path/to/multiworld/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/doodad/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/viskit/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/railrl-private/repo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredit\u003c/h2\u003e\u003ca id=\"user-content-credit\" class=\"anchor\" aria-label=\"Permalink: Credit\" href=\"#credit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA lot of the coding infrastructure is based on \u003ca href=\"https://github.com/rll/rllab\"\u003erllab\u003c/a\u003e.\nAlso, the serialization and logger code are basically a carbon copy.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Example\u003c/h1\u003e\u003ca id=\"user-content-singularity-example\" class=\"anchor\" aria-label=\"Permalink: Singularity Example\" href=\"#singularity-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions to create the Singularity image and run the container!\u003c/h2\u003e\u003ca id=\"user-content-instructions-to-create-the-singularity-image-and-run-the-container\" class=\"anchor\" aria-label=\"Permalink: Instructions to create the Singularity image and run the container!\" href=\"#instructions-to-create-the-singularity-image-and-run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePull this Github repo\nOn a Jetstream instance,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/breakfast-carpentry/diamondsingularity.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGo into the repo,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd diamondsingularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eBuild the Singularity image\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build diamond.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the python script with the container.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also go into the container and play with it interactively\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity shell diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can exit out of the shell by typing \u003ccode\u003eexit\u003c/code\u003e inside the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity diamond.simg:~\u0026gt; exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of what we did morning 7/19/18\u003c/h2\u003e\u003ca id=\"user-content-overview-of-what-we-did-morning-71918\" class=\"anchor\" aria-label=\"Permalink: Overview of what we did morning 7/19/18\" href=\"#overview-of-what-we-did-morning-71918\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis morning our goal was to build and run a Singularity image with our own script and data and get output from our code.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWe first used Jetstream (because we have root access there).\u003c/li\u003e\n\u003cli\u003eWe started with our diamond directory from yesterday - in my case \u003ccode\u003e/home/aglad/diamond_blast\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eI wrote a toy python script, \u003ccode\u003escript.py\u003c/code\u003e, which just prints \"hello\":\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eprint(\u0027Hello\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe then started to write our Singularity recipe. We started with only an empty ubuntu image just to check that we could create that simple image correctly. (to write the Singularity recipe I used a Jupyter text editor so I wouldn\u0027t need to use a command line editor)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: ubuntu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe built the Singularity image and called it diamond.simg.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build diamond.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe ran the diamond.simg container interactively so that we could test our setup\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity shell diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAfter we installed python and checked that it worked, we copied those commands into our Singularity recipe:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: ubuntu\n\n%post\n apt-get update\n apt-get install -y python3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eFinally we ran our python script with our Singularity container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec diamond.simg python3 script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of what we did morning 7/24/18\u003c/h2\u003e\u003ca id=\"user-content-overview-of-what-we-did-morning-72418\" class=\"anchor\" aria-label=\"Permalink: Overview of what we did morning 7/24/18\" href=\"#overview-of-what-we-did-morning-72418\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis morning our goal was to run a Singularity container with multiple apps with input data and output data.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWe started on Jetstream.\u003c/li\u003e\n\u003cli\u003eWe cloned the repo following the instructions above.\u003c/li\u003e\n\u003cli\u003eWe edited the Singularity recipe:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: ubuntu\n\n%post\n apt-get update\n apt-get install -y python3\n echo \"I\u0027m in post\"\n\n%files\n script.py /tmp\n\n%runscript\n echo \"Containers are cool\"\n python3 /tmp/script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eWe explored the difference between \u003ccode\u003esingularity run\u003c/code\u003e and \u003ccode\u003esingularity exec\u003c/code\u003e.\nIf we run,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Singularity container runs the runscript from the recipe and we see,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eContainers are cool\nhello\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn the other hand, if we run,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Singularity container runs the command after \u003ccode\u003ediamond.simg\u003c/code\u003e (which in the above case is nothing), so we get an error,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eERROR : No program name to exec\nABORT : Retval = 255\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can run,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec diamond.simg python3 script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis gives,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehello\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr we can run,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec diamond.simg echo \"I am echoing\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis gives,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eI am echoing\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe shared Singularity containers with other people.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe ran apps.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIn order to run multiple things in one container we can use apps.\u003c/li\u003e\n\u003cli\u003eWe created a new script, \u003ccode\u003eotherscript.py\u003c/code\u003e,\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eprint(\u0027Goodbye\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe added the app other to the Singularity recipe\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun other\n python3 /tmp/otherscript.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThen, we built the Singularity container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build diamond.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf we \u003ccode\u003erun\u003c/code\u003e or \u003ccode\u003eexec\u003c/code\u003e as above, we will still get the same outputs.\u003c/li\u003e\n\u003cli\u003eTo check what apps we included in the Singularity container,\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity apps diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis lists our app,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eother\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe can run our app,\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app other diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis gives,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eGoodbye\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe could also have multiple apps that do different things.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHomework\u003c/h3\u003e\u003ca id=\"user-content-homework\" class=\"anchor\" aria-label=\"Permalink: Homework\" href=\"#homework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a new app in the Singularity file called \u003ccode\u003edownload\u003c/code\u003e that downloads the following csv file to the \u003ccode\u003e/tmp/\u003c/code\u003e directory: \u003ca href=\"https://gist.githubusercontent.com/julianpistorius/e380eee2743a59e8aad2cb66e307736b/raw/216071913bc377d2d2153e4b4c6bf1770320f5a7/real_data.csv\" rel=\"nofollow\"\u003ehttps://gist.githubusercontent.com/julianpistorius/e380eee2743a59e8aad2cb66e307736b/raw/216071913bc377d2d2153e4b4c6bf1770320f5a7/real_data.csv\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a new app called \u003ccode\u003etotal_lines\u003c/code\u003e that prints the total number of lines in the above file. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app total_lines diamond.simg\n7\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a new app called \u003ccode\u003efilter\u003c/code\u003e that prints out the lines that contain a string (passed as a parameter). For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app filter diamond.simg piano\nSamantha,piano,Kit\nMark,piano,Cranky\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a new app called \u003ccode\u003enotebook\u003c/code\u003e that starts a Jupyter notebook. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run notebook\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1603485789.0 + "updated_at": 1631722780.0 }, { "data_format": 2, - "description": null, + "description": "Dockerfile for HAND-TauDEM", "filenames": [ - "docker/Singularity", - "docker/railrl_v9_cuda10-1_mj1-50-1-59_torch0-4-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_hand_tf_v1/Singularity", - "docker/railrl_hand_tf_v1/Singularity_cpu", - "docker/railrl_hand_v2/Singularity", - "docker/railrl_hand_v2/Singularity_cpu", - "docker/railrl_hand_v3/Singularity", - "docker/railrl_hand_v3/Singularity_cpu", - "docker/railrl_gpu_mujoco1-5-v4/singularity/Singularity", - "docker/railrl_v8_cuda10-1/Singularity", - "docker/railrl_v6_cuda9/Singularity", - "docker/railrl_v11_cuda10-1_mj2-0-2-2_torch0-3-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v7/Singularity", - "docker/railrl_v5/singularity/Singularity", - "docker/railrl_hand_v1/Singularity", - "docker/railrl_hand_v1/Singularity_cpu", - "docker/railrl_v10_cuda10-1_mj2-0-2-2_torch0-4-1_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v6_cuda8/Singularity", - "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", - "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", - "docker/railrl_ray/Singularity", - "docker/railrl_v9-5_cuda10-1_mj1-50-1-59_torch1-1-0_gym0-10-5_py3-5-2/Singularity", - "docker/railrl_v7_cuda8/Singularity", - "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch_cuda8", - "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch", - "experiments/ashvin/icml2020/singularity/Singularity" + "Singularity" ], - "full_name": "Asap7772/railrl_evalsawyer", + "full_name": "dhardestylewis/hand_taudem_docker", "latest_release": null, - "readme": "\u003cp\u003eREADME last updated on: 01/24/2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erailrl\u003c/h1\u003e\u003ca id=\"user-content-railrl\" class=\"anchor\" aria-label=\"Permalink: railrl\" href=\"#railrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReinforcement learning framework.\nSome implemented algorithms:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/ddpg.py\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/sac.py\"\u003eSoft Actor Critic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/dqn_and_double_dqn.py\"\u003e(Double) Deep Q-Network (DQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/her.py\"\u003eHindsight Experience Replay (HER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/model_based_dagger.py\"\u003eMPC with Neural Network Model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/naf.py\"\u003eNormalized Advantage Function (NAF)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eWARNING: I haven\u0027t tested this NAF implementation much, so it may not match the paper\u0027s performance. I\u0027m pretty confident about the other two implementations though.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, checkout the example scripts, linked above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSome dependancies\u003c/h3\u003e\u003ca id=\"user-content-some-dependancies\" class=\"anchor\" aria-label=\"Permalink: Some dependancies\" href=\"#some-dependancies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo apt-get install swig\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate Conda Env\u003c/h3\u003e\u003ca id=\"user-content-create-conda-env\" class=\"anchor\" aria-label=\"Permalink: Create Conda Env\" href=\"#create-conda-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall and use the included ananconda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda env create -f docker/railrl/railrl-env.yml\n$ source activate railrl-env\n(railrl-env) $ # Ready to run examples/ddpg_cheetah_no_doodad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if you want you can use the docker image included.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Simulation Env Code\u003c/h3\u003e\u003ca id=\"user-content-download-simulation-env-code\" class=\"anchor\" aria-label=\"Permalink: Download Simulation Env Code\" href=\"#download-simulation-env-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vitchyr/multiworld\"\u003emultiworld\u003c/a\u003e (contains environments):\u003ccode\u003egit clone https://github.com/vitchyr/multiworld\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e(Optional) Install doodad\u003c/h3\u003e\u003ca id=\"user-content-optional-install-doodad\" class=\"anchor\" aria-label=\"Permalink: (Optional) Install doodad\" href=\"#optional-install-doodad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI recommend installing \u003ca href=\"https://github.com/justinjfu/doodad\"\u003edoodad\u003c/a\u003e to\nlaunch jobs. Some of its nice features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEasily switch between running code locally, on a remote compute with\nDocker, on EC2 with Docker\u003c/li\u003e\n\u003cli\u003eEasily add your dependencies that can\u0027t be installed via pip (e.g. you\nborrowed someone\u0027s code)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you install doodad, also modify \u003ccode\u003eCODE_DIRS_TO_MOUNT\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePath to rllab directory\u003c/li\u003e\n\u003cli\u003ePath to railrl directory\u003c/li\u003e\n\u003cli\u003ePath to other code you want to juse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou\u0027ll probably also need to update the other variables besides the docker\nimages/instance stuff.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup Config File\u003c/h3\u003e\u003ca id=\"user-content-setup-config-file\" class=\"anchor\" aria-label=\"Permalink: Setup Config File\" href=\"#setup-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must setup the config file for launching experiments, providing paths to your code and data directories. Inside \u003ccode\u003erailrl/config/launcher_config.py\u003c/code\u003e, fill in the appropriate paths. You can use \u003ccode\u003erailrl/config/launcher_config_template.py\u003c/code\u003e as an example reference.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecp railrl/launchers/config-template.py railrl/launchers/config.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a policy and seeing results\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-policy-and-seeing-results\" class=\"anchor\" aria-label=\"Permalink: Visualizing a policy and seeing results\" href=\"#visualizing-a-policy-and-seeing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDuring training, the results will be saved to a file called under\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e is the directory set by \u003ccode\u003erailrl.launchers.config.LOCAL_LOG_DIR\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;exp_prefix\u0026gt;\u003c/code\u003e is given either to \u003ccode\u003esetup_logger\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;foldername\u0026gt;\u003c/code\u003e is auto-generated and based off of \u003ccode\u003eexp_prefix\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003einside this folder, you should see a file called \u003ccode\u003eparams.pkl\u003c/code\u003e. To visualize a policy, run\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e(railrl) $ python scripts/sim_policy LOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;/params.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have rllab installed, you can also visualize the results\nusing \u003ccode\u003erllab\u003c/code\u003e\u0027s viskit, described at\nthe bottom of \u003ca href=\"http://rllab.readthedocs.io/en/latest/user/cluster.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython rllab/viskit/frontend.py LOCAL_LOG_DIR/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eexp_prefix\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd paths\u003c/h3\u003e\u003ca id=\"user-content-add-paths\" class=\"anchor\" aria-label=\"Permalink: Add paths\" href=\"#add-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=$PYTHONPATH:/path/to/multiworld/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/doodad/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/viskit/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/railrl-private/repo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredit\u003c/h2\u003e\u003ca id=\"user-content-credit\" class=\"anchor\" aria-label=\"Permalink: Credit\" href=\"#credit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA lot of the coding infrastructure is based on \u003ca href=\"https://github.com/rll/rllab\"\u003erllab\u003c/a\u003e.\nAlso, the serialization and logger code are basically a carbon copy.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDockerfile for HAND-TauDEM\u003c/h1\u003e\u003ca id=\"user-content-dockerfile-for-hand-taudem\" class=\"anchor\" aria-label=\"Permalink: Dockerfile for HAND-TauDEM\" href=\"#dockerfile-for-hand-taudem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1679763993.0 + "updated_at": 1633656787.0 }, { "data_format": 2, - "description": "Comp 550 Fall 2023 - Natural Language Processing with Jackie Cheung", + "description": "Singularity container recipe for Deep Learning for architecture x86_64 based on a CentOS 7. In the specific: centos7.8.2003 - GNU compiler 7 - Python 3.6 - OpenMPI 2.1.1 (compiled with support for psm2, pmix, verbs) - Tensorflow 1.14.0 CPU (pip) - Py-Torch 1.4.0 CPU (pip) - Torchvision 0.5.0 CPU (pip) - MxNet 1.6.0 CPU (pip) - Horovod 0.19.1 (compiled with Tensorflow, Pytorch, MxNet)", "filenames": [ - "reading-assignment-4/Singularity", - "assignment-1/Singularity", - "reading-assignment-2/Singularity", - "reading-assignment-3/Singularity", - "assignment-2/Singularity" + "Singularity" ], - "full_name": "mathematiguy/comp-550", + "full_name": "CINECA-HPC/container_deep_learning_cpu_centos7_x86_64", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecomp-550\u003c/h1\u003e\u003ca id=\"user-content-comp-550\" class=\"anchor\" aria-label=\"Permalink: comp-550\" href=\"#comp-550\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComp 550 Fall 2023 - Natural Language Processing with Jackie Cheung\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_deep_learning_cpu_centos7_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_deep_learning_cpu_centos7_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_deep_learning_cpu_centos7_x86_64\" href=\"#container_deep_learning_cpu_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer recipes for Deep Learning for architecture x86_64 based on a CentOS 7.\nIn the specific:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ecentos7.8.2003\u003c/li\u003e\n\u003cli\u003eGNU compiler 7\u003c/li\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003eOpenMPI 2.1.1 (compiled with support for psm2, pmix, verbs)\u003c/li\u003e\n\u003cli\u003eTensorflow 1.14.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003ePy-Torch 1.4.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003eTorchvision 0.5.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003eMxNet 1.6.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003eHorovod 0.19.1 (compiled with Tensorflow, Pytorch, MxNet)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis recipe works on both clusters in Cineca (arch x86_64):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarconi Skylake,\u003c/li\u003e\n\u003cli\u003eGalileo\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1705846634.0 + "updated_at": 1644835746.0 }, { "data_format": 2, - "description": null, + "description": "QGIS in a Singularity container", "filenames": [ - "containers/DL/published/TensorFlow/2.1/cpu/src/Singularity.tensorflow-2.1-cpu-src", - "containers/DL/published/TensorFlow/2.1/cpu/pip/Singularity.tensorflow-2.1-cpu-pip", - "containers/DL/published/TensorFlow/2.1/gpu/Singularity.tensorflow-2.1-gpu-src", - "containers/DL/published/TensorFlow/2.1/gpu/src/Singularity.tensorflow-2.1-gpu-src", - "containers/DL/published/TensorFlow/2.1/gpu/pip/Singularity.tensorflow-2.1-gpu-pip", - "containers/DL/published/TensorFlow/ngraph/Singularity.tensorflow-ngraph", - "containers/DL/published/TensorFlow/latest/cpu/src/Singularity.tensorflow-latest-cpu-src", - "containers/DL/published/TensorFlow/latest/cpu/pip/Singularity.tensorflow-latest-cpu-pip", - "containers/DL/published/TensorFlow/latest/gpu/Singularity.tensorflow-latest-gpu-src", - "containers/DL/published/TensorFlow/latest/gpu/src/Singularity.tensorflow-latest-gpu-src", - "containers/DL/published/TensorFlow/latest/gpu/pip/Singularity.tensorflow-latest-gpu-pip", - "containers/DL/published/PyTorch/1.5/cpu/src/Singularity.pytorch-1.5-cpu-src", - "containers/DL/published/PyTorch/1.5/cpu/src/glow/Singularity.pytorch-1.5-cpu-src-glow", - "containers/DL/published/PyTorch/1.5/cpu/pip/Singularity.pytorch-1.5-cpu-pip", - "containers/DL/published/PyTorch/1.5/gpu/src/Singularity.pytorch-1.5-gpu-src", - "containers/DL/published/PyTorch/1.5/gpu/pip/Singularity.pytorch-1.5-gpu-pip", - "containers/DL/published/PyTorch/latest/cpu/src/Singularity.pytorch-1.5-cpu-src", - "containers/DL/published/PyTorch/latest/cpu/src/glow/Singularity", - "containers/DL/published/PyTorch/latest/cpu/pip/Singularity.pytorch-latest-cpu-pip", - "containers/DL/published/PyTorch/latest/gpu/src/Singularity.pytorch-latest-gpu-src", - "containers/DL/published/PyTorch/latest/gpu/pip/Singularity.pytorch-latest-gpu-pip" + "Singularity.3.4.12", + "Singularity" ], - "full_name": "SODALITE-EU/application-optimisation", - "latest_release": "M36Release", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSODALITE Application Optimizer (MODAK)\u003c/h1\u003e\u003ca id=\"user-content-sodalite-application-optimizer-modak\" class=\"anchor\" aria-label=\"Permalink: SODALITE Application Optimizer (MODAK)\" href=\"#sodalite-application-optimizer-modak\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe MODAK (Model Optimized Deployment of Applications in Containers) package, a software-defined optimization framework for containerized MPI and DL applications, is the SODALITE component responsible for enabling the static optimization of applications before deployment. It aims to optimize the performance of application deployment to infrastructure in a software-defined way. Automation in application optimization is enabled using performance modeling and container technology. Containers provide an optimized runtime for application deployment based on the target hardware and along with any software dependencies and libraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains all components for the Application Optimizer:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"MODAK\"\u003eMODAK\u003c/a\u003e - the main application optimizer\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Performance-Model\"\u003ePerformance-Model\u003c/a\u003e - Infrastructure and Application Performance model\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"containers\"\u003econtainers\u003c/a\u003e - Tests to optimize containers for DL applications\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"use-cases\"\u003euse-cases\u003c/a\u003e - SODALITE Use Case applications\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor development you need the \u003ccode\u003epre-commit\u003c/code\u003e tools.\nThis registers the \u003ccode\u003epre-commit\u003c/code\u003e hooks for the current git checkout such\nthat tools like \u003ccode\u003eblack\u003c/code\u003e or \u003ccode\u003eflake8\u003c/code\u003e are run automatically on commit.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003epip install pre-commit\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003epre-commit install --install-hooks\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo manually check that the current tree is clean:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003epre-commit run -a\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use MODAK\u003c/h2\u003e\u003ca id=\"user-content-how-to-use-modak\" class=\"anchor\" aria-label=\"Permalink: How to use MODAK\" href=\"#how-to-use-modak\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease follow the instructions in the \u003ca href=\"MODAK\"\u003eMODAK\u003c/a\u003e directory.\u003c/p\u003e\n", + "full_name": "OSC/sa_singularity_qgis", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity QGIS\u003c/h1\u003e\u003ca id=\"user-content-singularity-qgis\" class=\"anchor\" aria-label=\"Permalink: Singularity QGIS\" href=\"#singularity-qgis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3587\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://qgis.org/en/site/index.html\" rel=\"nofollow\"\u003eQGIS\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e. Packages installed: \u003ccode\u003eqgis qgis-plugin-grass\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003eqgis.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build qgis.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull qgis.sif shub://OSC/sa_singularity_qgis\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart QGIS\u003c/h3\u003e\u003ca id=\"user-content-start-qgis\" class=\"anchor\" aria-label=\"Permalink: Start QGIS\" href=\"#start-qgis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eQGIS is started using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run qgis.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as a native command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./qgis.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 8, "topics": [], - "updated_at": 1693990969.0 + "updated_at": 1669143061.0 }, { "data_format": 2, - "description": "a framework for automatic and comprehensive knowledge extraction based on mutational data from sequenced tumor samples from patients. ", + "description": "the barn challenge submission", "filenames": [ - "build/containers/mutpanning/Singularity", - "build/containers/signature/Singularity", - "build/containers/transvar/Singularity", - "build/containers/smregions/Singularity", - "build/containers/oncodrivefml/Singularity", - "build/containers/hotmaps/Singularity", - "build/containers/deconstructsig/Singularity", - "build/containers/oncodriveclustl/Singularity", - "build/containers/cbase/Singularity", - "build/containers/combination/Singularity", - "build/containers/core/Singularity", - "build/containers/dndscv/Singularity" + "Singularityfile.def" ], - "full_name": "bbglab/intogen-plus", - "latest_release": "v2024", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIntOGen\u003c/h1\u003e\u003ca id=\"user-content-intogen\" class=\"anchor\" aria-label=\"Permalink: IntOGen\" href=\"#intogen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!CAUTION]\nPlease note that IntOGen needs a lot of resources. We strongly suggest to run it in a cluster environment!\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall requirements\u003c/h3\u003e\u003ca id=\"user-content-install-requirements\" class=\"anchor\" aria-label=\"Permalink: Install requirements\" href=\"#install-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e (the pipeline has been tested\nwith version 2.x)\u003c/li\u003e\n\u003cli\u003eInstall nextflow (You can use \u003ccode\u003econda install nextflow==22.10.6\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload and build prerequisites\u003c/h3\u003e\u003ca id=\"user-content-download-and-build-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Download and build prerequisites\" href=\"#download-and-build-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe IntOGen pipeline requires a collection of datasets and\nSingularity containers in order to run.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!WARNING]\nThe pipeline has been tested with \u003cstrong\u003ehg38\u003c/strong\u003e and \u003cstrong\u003evep111\u003c/strong\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eSee the README.rst file in the \u003ccode\u003ebuild\u003c/code\u003e folder for further details.\u003c/p\u003e\n\u003cp\u003eThen you can build all the datasets from the original sources.\nNote that this process can take a very long time and it might fail if the\noriginal sources had changed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun the pipeline\u003c/h3\u003e\u003ca id=\"user-content-run-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Run the pipeline\" href=\"#run-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe IntOGen pipeline is built on top of \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e.\nIn order to execute the pipeline, you only need to execute:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run intogen.nf -resume -profile \u003cspan class=\"pl-k\"\u003elocal\u003c/span\u003e --input test/ --output ./output\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor further details, please check our documentation: \u003ca href=\"http://intogen-plus.rtfd.io/\" rel=\"nofollow\"\u003ehttp://intogen-plus.rtfd.io/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo avoid stopping the pipeline execution for one or a few incorrect\ninput, we have decided to ignore the errors of the steps by default.\nHowever, we advise to review each of them carefully to\nunderstand the underlying reasons.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLicensing\u003c/h3\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-label=\"Permalink: Licensing\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIntoGEn uses a variety of software tools and datasets that\nare released under a variety of licenses.\nIn order to accommodate all of them, the pipeline itself is released\nunder GNU General Public License version 3.\u003c/p\u003e\n\u003cp\u003eIf you are using it for research/academic purposes it should be\nfine. For commercial usage, you need to revise the license of the\ndifferent software pieces and datasets used, as some of them\n(e.g. CADD or CGC) are restricted for commercial usage.\u003c/p\u003e\n", + "full_name": "shanzewang/barn-EIT-NUS", + "latest_release": null, + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/BARN_Challenge.png\"\u003e\u003cimg width=\"100%\" src=\"res/BARN_Challenge.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eICRA 2024 BARN Challenge\u003c/h1\u003e\u003ca id=\"user-content-icra-2024-barn-challenge\" class=\"anchor\" aria-label=\"Permalink: ICRA 2024 BARN Challenge\" href=\"#icra-2024-barn-challenge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you run it on a local machine without containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eROS version at least Kinetic\u003c/li\u003e\n\u003cli\u003eCMake version at least 3.0.2\u003c/li\u003e\n\u003cli\u003ePython version at least 3.6\u003c/li\u003e\n\u003cli\u003ePython packages: defusedxml, rospkg, netifaces, numpy\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run it in Singularity containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGo version at least 1.13\u003c/li\u003e\n\u003cli\u003eSingularity version at least 3.6.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe requirements above are just suggestions. If you run into any issue, please contact organizers for help (\u003ca href=\"mailto:zfxu@utexas.edu\"\u003ezfxu@utexas.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow the instructions below to run simulations on your local machines. (You can skip 1-6 if you only use Singularity container)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a virtual environment (we show examples with python venv, you can use conda instead)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eapt -y update; apt-get -y install python3-venv\npython3 -m venv /\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge\nexport PATH=\"/\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge/bin:$PATH\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eInstall Python dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install defusedxml rospkg netifaces numpy\npip3 install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eClone this repo and required ros packages\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/shanzewang/barn-EIT-NUS.git\ngit clone https://github.com/jackal/jackal.git\ngit clone https://github.com/jackal/jackal_simulator.git\ngit clone https://github.com/jackal/jackal_desktop.git\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInstall ROS package dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/\u0026lt;YOUR_ROS_VERSION\u0026gt;/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=melodic\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eBuild the workspace\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esource devel/setup.bash\ncatkin_make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollow the instruction below to run simulations in Singularity containers.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 is required to successfully build the image!\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repo\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/shanzewang/barn-EIAS-NUS.git\ncd barn-EIAS-NUS\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild Singularity image (sudo access required)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --notest nav_competition_image.sif Singularityfile.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Simulations\u003c/h2\u003e\u003ca id=\"user-content-run-simulations\" class=\"anchor\" aria-label=\"Permalink: Run Simulations\" href=\"#run-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNavigate to the folder of this repo. Below is the example to run move_base with DWA as local planner.\u003c/p\u003e\n\u003cp\u003eIf you run it on your local machines: (the example below runs \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e with DWA local planner in world 0)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource ../../devel/setup.sh\npython3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you run it in a Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA successful run should print the episode status (collided/succeeded/timeout) and the time cost in second:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation collided with time 27.2930 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation succeeded with time 29.4610 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation timeout with time 100.0000 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest your own navigation stack\u003c/h2\u003e\u003ca id=\"user-content-test-your-own-navigation-stack\" class=\"anchor\" aria-label=\"Permalink: Test your own navigation stack\" href=\"#test-your-own-navigation-stack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe currently don\u0027t provide a lot of instructions or a standard API for implementing the navigation stack, but we might add more in this section depending on people\u0027s feedback. If you are new to the ROS or mobile robot navigation, we suggest checking \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e which provides basic interface to manipulate a robot.\u003c/p\u003e\n\u003cp\u003eThe suggested work flow is to edit section 1 in \u003ccode\u003erun.py\u003c/code\u003e file (line 89-109) that initialize your own navigation stack. You should not edit other parts in this file. We provide a bash script \u003ccode\u003etest.sh\u003c/code\u003e to run your navigation stack on 50 uniformly sampled BARN worlds with 10 runs for each world. Once the tests finish, run \u003ccode\u003epython report_test.py --out_path /path/to/out/file\u003c/code\u003e to report the test. Below is an example of DWA:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython report_test.py --out_path res/dwa_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see the report as this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAvg Time: 39.5296, Avg Metric: 0.1577, Avg Success: 0.8500, Avg Collision: 0.0560, Avg Timeout: 0.0940\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSubmission\u003c/h2\u003e\u003ca id=\"user-content-submission\" class=\"anchor\" aria-label=\"Permalink: Submission\" href=\"#submission\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSubmit a link that downloads your customized repository to this \u003ca href=\"https://docs.google.com/forms/d/e/1FAIpQLScGCMwVm-Kzg2c3kkXd_IUNwHw8D3s06ydCg4lPgOJYkEy8aQ/viewform?usp=sf_link\" rel=\"nofollow\"\u003eGoogle form\u003c/a\u003e. Your navigation stack will be tested in the Singularity container on 50 hold-out BARN worlds sampled from the same distribution as the 300 BARN worlds. In the repository, make sure the \u003ccode\u003erun.py\u003c/code\u003e runs your navigation stack and \u003ccode\u003eSingularityfile.def\u003c/code\u003e installs all the dependencies of your repo. We suggest to actually build an image and test it with \u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1732279601.0 + "updated_at": 1725259274.0 }, { "data_format": 2, - "description": "Singularity Container Recipes", + "description": null, "filenames": [ - "images/Singularity.lsf", - "images/imod/Singularity", - "images/imod/4.9.11/Singularity", - "images/imod/4.9.12/Singularity", - "images/imod/4.9.10/Singularity", - "images/imod/4.10.38/Singularity", - "images/imod/4.10.42/Singularity", - "images/appion-protomo/Singularity", - "images/icon-gpu/Singularity", - "images/protomo/Singularity", - "images/topaz/0.2.4/Singularity", - "images/topaz/0.2.2/Singularity", - "images/resmap/Singularity", - "images/cryosparc/2.14.2/Singularity", - "images/cryosparc/2.12.4/Singularity", - "images/cryosparc/2.13.2/Singularity", - "images/scipion/Singularity", - "images/rclone/Singularity", - "images/ctffind/Singularity", - "images/ctffind/4.1.13/Singularity", - "images/ctffind/4.1.10/Singularity", - "images/ctffind/4.1.12/Singularity", - "images/chimera/Singularity", - "images/openmbir/2.3.5/Singularity", - "images/motioncor2/Singularity", - "images/motioncor2/1.3.2/Singularity", - "images/motioncor2/1.2.6/Singularity", - "images/motioncor2/1.2.3-intpix/Singularity", - "images/motioncor2/1.2.3/Singularity", - "images/motioncor2/1.2.2/Singularity", - "images/motioncor2/1.2.1/Singularity", - "images/motioncor2/1.3.0/Singularity", - "images/cdms-jupyterlab/Singularity", - "images/amira/6.7.0/Singularity", - "images/pymol/Singularity", - "images/emClarity/Singularity", - "images/matlab/R2020a/Singularityfile", - "images/fah/7.5.1/Singularity", - "images/eman2/Singularity", - "images/eman2/20190603/Singularity", - "images/eman2/20190805/Singularity", - "images/eman2/20200419/Singularity", - "images/eman2/20190418/Singularity", - "images/eman2/2.31/Singularity", - "images/eman2/20190917/Singularity", - "images/eman2/20200330/Singularity", - "images/eman2/20190324/Singularity", - "images/eman2/20200319.0/Singularity", - "images/relion/Singularity.docker", - "images/relion/Singularity.old", - "images/relion/Singularity", - "images/relion/ver3.1/Singularity.docker", - "images/relion/3.0.2/Singularity.docker", - "images/relion/3.0.2/Singularity", - "images/relion/3.0.7/Singularity.orig", - "images/relion/3.0.7/Singularity", - "images/relion/2.1/Singularity.docker", - "images/relion/2.1/Singularity", - "images/relion/3.0.4/Singularity.docker", - "images/relion/3.0.4/Singularity", - "images/relion/3.1.0-beta/Singularity.docker", - "images/relion/3.1.0-beta/Singularity", - "images/relion/3.0.8/Singularity.docker", - "images/relion/3.0.6/Singularity.docker", - "images/relion/3.0.6/Singularity", - "images/phenix/Singularity", - "images/imagemagick/Singularity", - "images/xds/Singularity", - "images/openmpi/Singularity.centos7", - "images/openmpi/Singularity.ubuntu1804", - "images/openmpi/Singularity", - "images/openmpi/Singularity.ubuntu1810", - "images/rosetta/Singularity", - "images/rosetta/2018.48/Singularity", - "images/git/Singularity", - "images/tem-simulator/Singularity", - "images/slac-ml/Singularity", - "images/slac-ml/20190712.2/Singularity", - "images/slac-ml/20200211.0/Singularity", - "images/slac-ml/20200227.0/Singularity", - "images/slac-ml/20200618.0/Singularity", - "images/cryolo/1.5.4/Singularity" + "Singularity.def" ], - "full_name": "slaclab/singularity-modules", + "full_name": "HliasGit/sweng_proj_pt2", "latest_release": null, "stargazers_count": 1, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1646881460.0 + "updated_at": 1717593518.0 }, { "data_format": 2, - "description": null, + "description": "Singularity base images that will be build on singularity hub and can be used to build other images", "filenames": [ - "squeakr/Singularity.squeakr", - "ubuntu/Singularity.ubuntu2004", - "pacbio/Singularity.pacbio", - "python3/Singularity.python3-extra", - "python3/Singularity.python3", - "bcl2fastq/Singularity.bcl2fastq", - "t-coffee/Singularity.t-coffee", - "miniasm/Singularity.miniasm", - "racon/Singularity.bioinf-racon", - "umi-tools/Singularity.umi-tools", - "seq-qc/Singularity.seq-qc", - "rr/Singularity.r-base", - "rr/Singularity.r-tidy-extra", - "rr/Singularity.r-tidy-some", - "rr/Singularity.r-tidy", - "mummer4/Singularity.mummer4", - "enrich2/Singularity.enrich22", - "enrich2/Singularity.enrich2", - "nanopore/Singularity.guppy-cpu", - "nanopore/Singularity.flye", - "nanopore/Singularity.medaka_hack", - "nanopore/Singularity.medaka", - "nanopore/Singularity.guppy-gpu", - "nanopore/Singularity.canu", - "nanopore/Singularity.pycoqc", - "nanopore/Singularity.ont-polishing", - "jupyter/Singularity.jupyter-plus-alignparse", - "jupyter/Singularity.jupyter-plus-bioconda", - "jupyter/Singularity.jupyter-plus", - "jupyter/Singularity.jupyter-plus-tensorflow-v2.5.0-compiled", - "jupyter/Singularity.cuda-tensorflow-v2.6.0-jupyter-plus", - "jupyter/Singularity.jupyter-plus-tensorflow-v2.5.0-compiled-patch", - "jupyter/Singularity.jupyter", - "jupyter/Singularity.jupyter-plus-tensorflow-v2.2.0-compiled", - "jupyter/Singularity.jupyter-plus-tensorflow", - "jupyter/Singularity.jupyter-plus-tensorflow-v2.4.0-rc4-compiled", - "jupyter/Singularity.plus-jupyter", - "bioinf/Singularity.bioinfmunger", - "ncbi-blast/Singularity.ncbi-blast", - "itermae/Singularity.itermae-plus", - "kalign/Singularity.kalign2", - "kalign/Singularity.kalign3", - "fitseq/Singularity.fitseq-dev", - "fitseq/Singularity.fitseq", - "cuda-tensorflow/Singularity.tensorflow-v2.5.0-compiled", - "cuda-tensorflow/Singularity.tensorflow-v2.0.3-compiled", - "cuda-tensorflow/Singularity.tensorflow-v1.15.4-compiled", - "alignparse/Singularity.alignparse", - "bioconda/Singularity.bioconda" + "Singularity.R363_python368", + "Singularity.DoubletDetection", + "Singularity.DemultiplexingSoftwares", + "Singularity.Anne_demultiplexing_test", + "Singularity.AllSoftwares", + "Singularity.TxnDoubletDetection", + "Singularity.R4_python368" ], - "full_name": "darachm/containers", + "full_name": "powellgenomicslab/SingularityBaseImages", "latest_release": null, - "readme": "\u003cp\u003eThis is a repo for Darach to track and host containers for doing\nbioinf/research.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe main intention is to run as Singularity containers on linux systems,\nbut they\u0027re written as Docker for compatibility (thanks Mohammed Kahlfan\nfor the tip).\u003c/li\u003e\n\u003cli\u003eThere\u0027s a hope that this can get built and hosted on GitHub\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOrganzation copied from \u003ca href=\"https://github.com/jlboat/BioinfoContainers\"\u003ejlboat\u003c/a\u003e.\n(Of course, makes total sense to just use tags to organize things!)\u003c/p\u003e\n\u003cp\u003eSome recipes are for individual tools, some are for workflows and so are\ncombos. Trying to figure out the ontology of this.\u003c/p\u003e\n\u003cp\u003eTodo:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker containers for:\n\u003cul\u003e\n\u003cli\u003eseq qc\u003c/li\u003e\n\u003cli\u003emunging and sed/awk-fu and shell\u003c/li\u003e\n\u003cli\u003estarcode and requisite munging\u003c/li\u003e\n\u003cli\u003ebartender I guess\u003c/li\u003e\n\u003cli\u003er\u003c/li\u003e\n\u003cli\u003ejupyter\u003c/li\u003e\n\u003cli\u003epython3\u003c/li\u003e\n\u003cli\u003elh3\u003c/li\u003e\n\u003cli\u003epacbio\u003c/li\u003e\n\u003cli\u003enanopore\u003c/li\u003e\n\u003cli\u003ealignparse\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularityBaseImages\u003c/h1\u003e\u003ca id=\"user-content-singularitybaseimages\" class=\"anchor\" aria-label=\"Permalink: SingularityBaseImages\" href=\"#singularitybaseimages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA repo for singularity images. This is linked to singularity hub and all results can be pulled from there.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Hub Images\u003c/h2\u003e\u003ca id=\"user-content-singularity-hub-images\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub Images\" href=\"#singularity-hub-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.R363_python368\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo pull: \u003ccode\u003esingularity pull shub://drneavin/SingularityBaseImages:r363_python368\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eContains:\n\u003cul\u003e\n\u003cli\u003eR 3.6.3\u003c/li\u003e\n\u003cli\u003epython 3.6.8\u003c/li\u003e\n\u003cli\u003econda\u003c/li\u003e\n\u003cli\u003eSome basic R packages (see the definition file to see all installed)\u003c/li\u003e\n\u003cli\u003eSome basic python package (see the definition file to see all installed)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.R4_python368\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo pull: \u003ccode\u003esingularity pull shub://drneavin/SingularityBaseImages:r4_python368\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eContains:\n\u003cul\u003e\n\u003cli\u003eR 4.0.3\u003c/li\u003e\n\u003cli\u003epython 3.6.8\u003c/li\u003e\n\u003cli\u003econda\u003c/li\u003e\n\u003cli\u003eSome basic R packages (see the definition file to see all installed)\u003c/li\u003e\n\u003cli\u003eSome basic python package (see the definition file to see all installed)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.TxnDoubletDetection\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo pull: \u003ccode\u003esingularity pull shub://drneavin/SingularityBaseImages:txndoubletdetection\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuilt on top of \u003ccode\u003eSingularity.R4_python368\u003c/code\u003e image\u003c/li\u003e\n\u003cli\u003eAlso contains:\n\u003cul\u003e\n\u003cli\u003eDoubletDetection\u003c/li\u003e\n\u003cli\u003eDoubletDecon\u003c/li\u003e\n\u003cli\u003eDoubletFinder\u003c/li\u003e\n\u003cli\u003escds\u003c/li\u003e\n\u003cli\u003escrublet\u003c/li\u003e\n\u003cli\u003escDoubletFinder\u003c/li\u003e\n\u003cli\u003esolo\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1646881771.0 + "updated_at": 1611115640.0 }, { "data_format": 2, - "description": null, + "description": "Container with NAMD 2.14 built with CUDA 10.2 support using Nix.", "filenames": [ - "arxiv/Singularity.ub16.04-tf1.10.1-torch0.4.1-root6.14.04", - "arxiv/Singularity.ubuntu16.04-larcv_develop", - "arxiv/Singularity.ub16.04-tf1.10.1-torch0.4.1", - "arxiv/Singularity.ubuntu16.04-gpu", - "arxiv/Singularity.ubuntu16.04-basic", - "arxiv/Singularity.ubuntu16.04-gpu-larcv_develop", - "arxiv/Singularity.ub16.04-tf1.11.0-torch0.4.1-root6.14.04", - "arxiv/Singularity.ub16.04-tf1.7-torch0.4", - "arxiv/Singularity.ub16.04-tf1.11.0-torch0.4.1", - "arxiv/Singularity.ubuntu16.04-gpu-py3", - "recipes/Singularity.ub18.04-cuda100-py3-pytorch1.1.0-scn", - "recipes/Singularity.ub18.04-gpu-ana0-ml", - "recipes/Singularity.ub16.04-cuda90-pytorch1.0.0-scn", - "recipes/Singularity.ub18.04-cuda100-py3-pytorch1.0.1-scn", - "recipes/Singularity.ub18.04-cpu-ana0", - "recipes/Singularity.ub18.04-gpu", - "recipes/Singularity.ub18.04-gpu-ana0-mn", - "recipes/Singularity.ub18.04-cpu-ana0-larcv2", - "recipes/Singularity.ub16.04-cuda90-pytorch0.4.1", - "recipes/Singularity.ub18.04-gpu-ana0", - "recipes/Singularity.ub18.04-gpu-ana0-ml-larcv2", - "recipes/Singularity.ub18.04-cpu", - "recipes/Singularity.ub16.04-cuda100-pytorch1.0.0-scn", - "recipes/Singularity.ub16.04-cuda100-pytorchdev20181215", - "recipes/Singularity.ub18.04-cuda100-py3-pytorch1.1.0-scn-docker", - "recipes/Singularity.ub16.04-cuda90-pytorchdev20181015", - "recipes/Singularity.ub16.04-cuda90-tf1.12.0", - "recipes/Singularity.HKMLWorkshop", - "recipes/Singularity.ub16.04-cuda90-py3-pytorch1.0.1-scn", - "recipes/Singularity", - "recipes/Singularity.ub18.04-cuda10.1-ml-larcv2", - "recipes/Singularity.ub18.04-cuda10.2-extra" + "Singularity" ], - "full_name": "DeepLearnPhysics/larcv2-singularity", + "full_name": "XSEDE/nix-container-namd2.14-cuda10.2", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://raw.githubusercontent.com/DeepLearnPhysics/larcv2-singularity/master/LICENSE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e581ac49b7e1e99fb951242be63f6fdc6ebbc20c89a97fca0de99e1f2e6ae87e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6d6173686170652f6170697374617475732e737667\" alt=\"license\" data-canonical-src=\"https://img.shields.io/github/license/mashape/apistatus.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/459\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003elarcv2-singularity\u003c/h1\u003e\u003ca id=\"user-content-larcv2-singularity\" class=\"anchor\" aria-label=\"Permalink: larcv2-singularity\" href=\"#larcv2-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity build scripts for \u003ca href=\"https://www.singularity-hub.org/collections/459\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e. You can learn about Singularity in \u003ca href=\"https://github.com/DeepLearnPhysics/playground-singularity/wiki\"\u003eour wiki\u003c/a\u003e or \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eofficial doc\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo \u003ccode\u003epull\u003c/code\u003e the container, simply try\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTAG=latest\nsingularity pull -n local.img shub://DeepLearnPhysics/larcv2-singularity:$TAG\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more fun things to do, you can read \u003ca href=\"https://github.com/DeepLearnPhysics/playground-singularity/wiki\"\u003eour wiki\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat\u0027s in the build?\u003c/h2\u003e\u003ca id=\"user-content-whats-in-the-build\" class=\"anchor\" aria-label=\"Permalink: What\u0027s in the build?\" href=\"#whats-in-the-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll builds are based on \u003cstrong\u003eUbuntu16.04 LTS\u003c/strong\u003e with some highlighted packages below\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython packages: \u003ccode\u003epip\u003c/code\u003e \u003ccode\u003enumpy\u003c/code\u003e \u003ccode\u003escipy\u003c/code\u003e \u003ccode\u003escikit\u003c/code\u003e \u003ccode\u003eopencv-python\u003c/code\u003e \u003ccode\u003eh5py\u003c/code\u003e \u003ccode\u003etables\u003c/code\u003e \u003ccode\u003epandas\u003c/code\u003e \u003ccode\u003ematplotlib\u003c/code\u003e \u003ccode\u003eipython\u003c/code\u003e \u003ccode\u003ejupyter notebook\u003c/code\u003e \u003ccode\u003epyyaml\u003c/code\u003e \u003ccode\u003ezmq\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopment kit: \u003ccode\u003eg++\u003c/code\u003e/\u003ccode\u003egcc\u003c/code\u003e \u003ccode\u003elibqt4-dev\u003c/code\u003e \u003ccode\u003epython-dev\u003c/code\u003e \u003ccode\u003ecuda-9.0\u003c/code\u003e \u003ccode\u003ecudnn-7\u003c/code\u003e \u003ccode\u003ecython\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUtility kit : \u003ccode\u003egit\u003c/code\u003e \u003ccode\u003ewget\u003c/code\u003e \u003ccode\u003eemacs\u003c/code\u003e \u003ccode\u003evim\u003c/code\u003e \u003ccode\u003easciinema\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe build 3 types of images.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eBase\u003c/em\u003e image\n\u003cul\u003e\n\u003cli\u003eLatest tag: \u003cstrong\u003eub16.04-tf1.10.1-torch0.4.1\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etensorflow-gpu\u003c/code\u003e 1.10.1, \u003ccode\u003epytorch\u003c/code\u003e 0.4.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n local.img shub://DeepLearnPhysics/larcv2-singularity:ub16.04-tf1.10.1-torch0.4.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eROOT\u003c/em\u003e image (include \u003cem\u003eBase\u003c/em\u003e)\n\u003cul\u003e\n\u003cli\u003eLatest tag: \u003cstrong\u003eub16.04-tf1.10-torch0.4.1-root6.14.04\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eROOT\u003c/code\u003e 6.14.04, additional python package \u003ccode\u003eroot_numpy\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n local.img shub://DeepLearnPhysics/larcv2-singularity:ub16.04-tf1.10.1-torch0.4.1-root6.14.04\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eLArCV\u003c/em\u003e image (include \u003cem\u003eROOT\u003c/em\u003e)\n\u003cul\u003e\n\u003cli\u003eTag: \u003cstrong\u003elatest\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eAdditional python package \u003ccode\u003elarcv\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n local.img shub://DeepLearnPhysics/larcv2-singularity:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker images?\u003c/h1\u003e\u003ca id=\"user-content-docker-images\" class=\"anchor\" aria-label=\"Permalink: Docker images?\" href=\"#docker-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheckout built images on our \u003ca href=\"https://hub.docker.com/u/deeplearnphysics/dashboard/\" rel=\"nofollow\"\u003edocker hub\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-namd2.14-cuda10.2\u003c/h1\u003e\u003ca id=\"user-content-nix-container-namd214-cuda102\" class=\"anchor\" aria-label=\"Permalink: nix-container-namd2.14-cuda10.2\" href=\"#nix-container-namd214-cuda102\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5362\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContainer with Nix, namd, and CUDA to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, - "topics": [ - "larcv", - "singularity", - "singularity-hub" - ], - "updated_at": 1692637450.0 + "subscribers_count": 16, + "topics": [], + "updated_at": 1628541599.0 }, { "data_format": 2, - "description": "Files to support building and maintenance of Singularity containers for Hall D", + "description": null, "filenames": [ - "recipes/Singularity.centos-8.2.2004", - "recipes/Singularity.rockylinux-8.6.20220707", - "recipes/Singularity.centos-8.3.2011", - "recipes/Singularity.2.0.11-centos8", - "recipes/Singularity.centos-7.7.1908", - "recipes/Singularity.centos-6.10", - "recipes/Singularity.markito3.gluex_docker_devel", - "recipes/Singularity.fedora-34", - "recipes/Singularity.fedora-35", - "recipes/Singularity.markito3-gluex_docker_prod", - "recipes/Singularity.almalinux-9.2", - "recipes/Singularity.fedora-33", - "recipes/Singularity.ubuntu.xenial-20210114", - "recipes/Singularity.fedora-32", - "recipes/Singularity.centos-8.4.2105", - "recipes/Singularity.ubuntu.bionic-20210222", - "recipes/Singularity.ubuntu.focal-20200925", - "recipes/Singularity.centos-3.0.6-stream8" + "Singularity" ], - "full_name": "JeffersonLab/hd_singularity", + "full_name": "JeffersonLab/jleic", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehd_singularity\u003c/h1\u003e\u003ca id=\"user-content-hd_singularity\" class=\"anchor\" aria-label=\"Permalink: hd_singularity\" href=\"#hd_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFiles to support building and maintenance of Singularity containers for Hall D.\u003c/p\u003e\n\u003cp\u003eContains scripts and recipes for creating Singularity containers from scratch.\u003c/p\u003e\n\u003cp\u003eThe main script is scripts/create_gluex_container.sh. Its usage message is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: create_gluex_container.sh [-h] -r \u0026lt;recipe-file\u0026gt; -p \u0026lt;prereqs-script\u0026gt; \\\n [-d DIRECTORY] [-t STRING]\n\nNote: must be run as root\n\nOptions:\n -h print this usage message\n -r Singularity recipe file\n -p script that installs gluex software\n -d output directory for containers (default: current working directory)\n -t token to be used to name containers (default = extension in \"Singularity.ext\")\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejleic: Singularity recipe for jleic docker container\u003c/h1\u003e\u003ca id=\"user-content-jleic-singularity-recipe-for-jleic-docker-container\" class=\"anchor\" aria-label=\"Permalink: jleic: Singularity recipe for jleic docker container\" href=\"#jleic-singularity-recipe-for-jleic-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 57, + "subscribers_count": 6, "topics": [], - "updated_at": 1704073259.0 + "updated_at": 1513099325.0 }, { "data_format": 2, - "description": "Collection of SNP pipelines to analyze assembled genomes", + "description": "Container Template using Nix with Python3 , and examples", "filenames": [ - "containers/Singularity.kSNP3_cfsansnp", - "containers/Singularity.tree_analysis", - "containers/Singularity.lyveset", - "containers/Singularity.cfsansnp", - "containers/Singularity.peppa", - "containers/Singularity.atlas", - "containers/Singularity.lyveset1", - "containers/Singularity.etoki", - "containers/Singularity" + "Singularity" ], - "full_name": "TheNoyesLab/WGS_SNP_pipelines", + "full_name": "XSEDE/nix-container-python", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTitle of Proposal:\u003c/h1\u003e\u003ca id=\"user-content-title-of-proposal\" class=\"anchor\" aria-label=\"Permalink: Title of Proposal:\" href=\"#title-of-proposal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does analytic approach impact pathogen population structure when analyzing whole genome sequence data?\u003c/h2\u003e\u003ca id=\"user-content-how-does-analytic-approach-impact-pathogen-population-structure-when-analyzing-whole-genome-sequence-data\" class=\"anchor\" aria-label=\"Permalink: How does analytic approach impact pathogen population structure when analyzing whole genome sequence data?\" href=\"#how-does-analytic-approach-impact-pathogen-population-structure-when-analyzing-whole-genome-sequence-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cp\u003econctact \u003ca href=\"mailto:enriquedoster@gmail.com\"\u003eenriquedoster@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe overall goal of this project is to support an accurate, reproducible, transparent and uniform approach to whole-genome sequence (WGS) analysis for purposes of outbreak detection and pathogen surveillance.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe overarching objective is to demonstrate how different analytic approaches to whole-genome sequence analysis can impact analysis results.\u003c/li\u003e\n\u003cli\u003eSupporting objectives are to evaluate the impacts:\n\u003cul\u003e\n\u003cli\u003edataset\u003c/li\u003e\n\u003cli\u003ecore- vs. pan-genome inclusion\u003c/li\u003e\n\u003cli\u003egenome comparison approach (i.e., using SNPs, k-mers, gene-by-gene alleles, or functional domains).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAdditionally, we wil provide information regarding the usability of different WGS pipelines and NCBI\u0027s pathogen genome database.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Study_design.md\"\u003eStudy design\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/WGS_tool_descriptions.md\"\u003eWGS tool descriptions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Installing_WGS_tools.md\"\u003eInstalling WGS tools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Accessing_NCBI_pathogen_genomes.md\"\u003eAccessing NCBI\u0027s pathogen genomes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Questions.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWGS analysis tools\u003c/h2\u003e\u003ca id=\"user-content-wgs-analysis-tools\" class=\"anchor\" aria-label=\"Permalink: WGS analysis tools\" href=\"#wgs-analysis-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/WGS_tool_descriptions.md\"\u003eTools included in the study\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCommand-line tools\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lskatz/lyve-SET\"\u003eLYVE-SET\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CFSAN-Biostatistics/snp-pipeline\"\u003eCFSAN-SNP\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sourceforge.net/projects/ksnp/files/\" rel=\"nofollow\"\u003eKSNP3\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eGUI tools included in the study:\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/zheminzhou/EToKi\"\u003eENTEROBASE\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eHas \"EBEis\" in silico serotype prediction for Escherichia coli and Shigella spp.\u003c/li\u003e\n\u003cli\u003eAlso has \"isCRISPOL\" in silico prediction of CRISPOL array for Salmonella enterica serovar Typhimurium\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.applied-maths.com/applications/wgmlst\" rel=\"nofollow\"\u003ewgMLST/BioNumerics\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOther tools to explore\n\u003cul\u003e\n\u003cli\u003eSimultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks - \u003ca href=\"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005495\" rel=\"nofollow\"\u003ehttps://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005495\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eNucleotide-resolution bacterial pan-genomics with reference graphs - \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.11.12.380378v2\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/2020.11.12.380378v2\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/staphb/lyveset\" rel=\"nofollow\"\u003eRepository of useful docker containers\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNCBI\u0027s pathogen database and genome analysis\u003c/h2\u003e\u003ca id=\"user-content-ncbis-pathogen-database-and-genome-analysis\" class=\"anchor\" aria-label=\"Permalink: NCBI\u0027s pathogen database and genome analysis\" href=\"#ncbis-pathogen-database-and-genome-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Questions.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Accessing_NCBI_pathogen_genomes.md\"\u003eNCBI pathogen database\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Using_common_WGS_tools.md\"\u003eUsing Common WGS tools\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the pipelines with this repository\u003c/h1\u003e\u003ca id=\"user-content-running-the-pipelines-with-this-repository\" class=\"anchor\" aria-label=\"Permalink: Running the pipelines with this repository\" href=\"#running-the-pipelines-with-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cul\u003e\n\u003cli\u003eThis pipeline requires singularity to be installed and available in your $PATH.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample commands\u003c/h3\u003e\u003ca id=\"user-content-example-commands\" class=\"anchor\" aria-label=\"Permalink: Example commands\" href=\"#example-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Download genomes from ncbi\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e On MSI, you can use the scripts in the \"bin\" directory to download SRA files.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make a text file with one SRA value per row.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Modify the \"prefetch_SRA_from_file.sh\" to point to your file with SRA values. NB. This will download prefetch values to your default location (usually in $HOME). Run this script using bash, or submit as a job to MSI by removing the first \"#\" in the first few rows of the script.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Modify the \"fastq_dump_from_sra.sh\" to point to the location of the SRA files and to output the fastq files into your desired output directory.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Main steps\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Load singularity module \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make sure nextflow is installed and in your $PATH\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download this git repository, navigate inside it and modify the commands below to suit your data\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Remember to change the \"species\" flag. Options are: escherichia_coli, salmonella_enterica, and listeria_monocytogenes\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Main combined pipeline\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input is paired end reads\u003c/span\u003e\nnextflow run main_combined_pipeline.nf --species \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esalmonella_enterica\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --reference_genome /scratch.global/Salmonella_WGS/ref_L_monocytogenes_NC_003210.fasta --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/scratch.global/Salmonella_WGS/List_test_genomes/*_{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -profile singularity --output /scratch.global/Salmonella_WGS/test_GenomeTrakr_L_monocytogenes_WGS_results --threads 20 -w /scratch.global/Salmonella_WGS/work_test_qsub_l_latest -resume -with-report test_250_Listeria_WGS_tools.report -with-trace -with-timeline\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Lyveset\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input is interleaved fastq files\u003c/span\u003e\nnextflow run main_LYVESET.nf --reference_genome /scratch.global/Salmonella_WGS/ref_L_monocytogenes_NC_003210.fasta --interleaved_fastq \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/scratch.global/Salmonella_WGS/test_GenomeTrakr_L_monocytogenes_WGS_results/Interleaved_fasta/interleaved_reads/*.fastq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -profile singularity --output /scratch.global/Salmonella_WGS/test_LYVESET_250_GenomeTrakr_L_monocytogenes_WGS_results --threads 3 -w /scratch.global/Salmonella_WGS/work_250_lyveset_qsub_l_latest -resume -with-report 250_Listeria_WGS_tools.report -with-trace -with-timeline --singleEnd \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e lyveset sometimes requires\u003c/span\u003e\n/home/noyes046/shared/tools/lyve-SET-1.1.4g/scripts/mergeVcf.sh -o msa/out.p\nooled.vcf.gz vcf/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.vcf.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cfsan_snp\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input file is a directory containing one directory per sample with the corresponding paired reads\u003c/span\u003e\nnextflow run main_CFSAN_snp.nf --reference_genome /scratch.global/Salmonella_WGS/ref_L_monocytogenes_NC_003210.fasta --fastq_dir_path \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/scratch.global/Salmonella_WGS/test_GenomeTrakr_L_monocytogenes_WGS_results/Interleaved_fasta/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile singularity --output /scratch.global/Salmonella_WGS/test_250_GenomeTrakr_L_monocytogenes_WGS_results --threads 128 -w /scratch.global/Salmonella_WGS/work_250_qsub_l_latest -resume -with-report 250_Listeria_WGS_tools.report -with-trace -with-timeline\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e KSNP3\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input file is \".tsv\" file containing a column with an absolute path to each sample file and it\u0027s sample ID\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Example below\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e /path/to/file/SRR10001252.fasta SRR10001252\u003c/span\u003e\nnextflow run main_kSNP3.nf --reference_genome /scratch.global/Salmonella_WGS/ref_L_monocytogenes_NC_003210.fasta --genomes /scratch.global/Salmonella_WGS/WGS_SNP_pipelines/Listeria_genome_location.tsv -profile singularity_pbs --output /scratch.global/Salmonella_WGS/kSNP3_GenomeTrakr_L_monocytogenes_WGS_results --threads 128 -w /scratch.global/Salmonella_WGS/work_kSNP3_l_latest -resume -with-report kSNP3_Listeria_WGS_tools.report -with-trace -with-timeline\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Enterobase\u003c/span\u003e\nnextflow run main_enterobase.nf --reference_genome /tempalloc/noyes042/FMPRE_clean/Host_genomes/Senterica_LT2_ref_genome.fasta --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/tempalloc/noyes042/FMPRE_clean/Raw_datasets/Outbreak_genomes/genomes_final_salmonella_outbreak/*_{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile singularity --output /tempalloc/noyes042/FMPRE_clean/ALL_results/temp_results/Salmonella_MLST_OUTBREAK_WGS_results --threads 15 -w /tempalloc/noyes042/FMPRE_clean/ALL_results/temp_results/work_salm_outbreak_MLST -resume -with-report Salm_MLST_outbreak_WGS_tools.report -with-trace -with-timeline --species salmonella_enterica --allele_fasta data/7gene_MLST_schemes/Salmonella_7gene_Achtman_MLST.fasta.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocuments\u003c/h1\u003e\u003ca id=\"user-content-documents\" class=\"anchor\" aria-label=\"Permalink: Documents\" href=\"#documents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWGS and regulations\u003c/h2\u003e\u003ca id=\"user-content-wgs-and-regulations\" class=\"anchor\" aria-label=\"Permalink: WGS and regulations\" href=\"#wgs-and-regulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.efsa.europa.eu/en/consultations/call/public-consultation-efsa-statement-requirements-whole-genome\" rel=\"nofollow\"\u003ehttps://www.efsa.europa.eu/en/consultations/call/public-consultation-efsa-statement-requirements-whole-genome\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMisc resources\u003c/h2\u003e\u003ca id=\"user-content-misc-resources\" class=\"anchor\" aria-label=\"Permalink: Misc resources\" href=\"#misc-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDifferences in results by analytic method\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.nature.com/articles/d41586-020-01282-z?utm_source=twitter\u0026amp;utm_medium=social\u0026amp;utm_content=organic\u0026amp;utm_campaign=NGMT_USG_JC01_GL_Nature\" rel=\"nofollow\"\u003eNeuroimaging results altered by varying analysis pipelines\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWays to compare the results from WGS pipelines\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lskatz/Lyve-SET-paper/blob/master/compareSnps.sh\"\u003ehttps://github.com/lskatz/Lyve-SET-paper/blob/master/compareSnps.sh\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://academic.oup.com/mbe/article/33/8/2163/2579233\" rel=\"nofollow\"\u003ePhylo.io\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eTimme et. al. 2019 \u003ca href=\"https://jcm.asm.org/content/57/5/e01816-18\" rel=\"nofollow\"\u003ePhylogenomic Pipeline Validation for Foodborne Pathogen Disease Surveillance\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWGS tools to consider:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz795/5607735?rss=1\" rel=\"nofollow\"\u003eKalign 3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nmquijada/tormes\"\u003eTormes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/25201145\" rel=\"nofollow\"\u003eSPANDx\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ewgMLST \u003ca href=\"https://anaconda.org/bioconda/chewbbaca\" rel=\"nofollow\"\u003echewBBACA\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMLST \u003ca href=\"https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2887-1\" rel=\"nofollow\"\u003eSTRAIN\u003c/a\u003e R package\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWGS analysis\n\u003cul\u003e\n\u003cli\u003eCore genome\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.pnas.org/content/102/39/13950\" rel=\"nofollow\"\u003ehttps://www.pnas.org/content/102/39/13950\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePan-genome\u003c/li\u003e\n\u003cli\u003eMLST\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pubmlst.org/\" rel=\"nofollow\"\u003ehttps://pubmlst.org/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eEnterobase\u003c/li\u003e\n\u003cli\u003eBioNumerics\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePhylogenetic trees\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://academic.oup.com/sysbio/article/64/2/205/1630737\" rel=\"nofollow\"\u003ePractical Performance of Tree Comparison Metrics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nature.com/articles/s41576-020-0233-0\" rel=\"nofollow\"\u003eReview: Phylogenetic tree building in the genomic age (Kapli et. al. 2020)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOther useful links:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jhcepas\"\u003ehttps://github.com/jhcepas\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-python\u003c/h1\u003e\u003ca id=\"user-content-nix-container-python\" class=\"anchor\" aria-label=\"Permalink: nix-container-python\" href=\"#nix-container-python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains files necessary to build a container with Nix and Python to be used in the XSEDE compute environment. Both Docker and Singularity are represented.\u003c/p\u003e\n\u003cp\u003eThis repo can be cloned as a template and used as a starting point for containerizing\napplications that require Python.\u003c/p\u003e\n\u003cp\u003eFor a \u003cem\u003every\u003c/em\u003e simple example, see: \u003ca href=\"https://github.com/XSEDE/nix-container-python-mandle\"\u003ehttps://github.com/XSEDE/nix-container-python-mandle\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA more complex example is available at: \u003ca href=\"https://github.com/XSEDE/nix-container-perfzero\"\u003ehttps://github.com/XSEDE/nix-container-perfzero\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn example containing both Python and R dependencies is available at:\n\u003ca href=\"https://github.com/XSEDE/nix-container-sc-benchmark\"\u003ehttps://github.com/XSEDE/nix-container-sc-benchmark\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo include more Python libraries, edit prod-env.nix to pull these in, as demonstrated\nwith the commented-out lines installing Pillow, numpy, and the attendant system-level\ndependencies.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 16, "topics": [], - "updated_at": 1615365486.0 + "updated_at": 1632511274.0 }, { "data_format": 2, - "description": "Hosting recipes for Singularity Hub", + "description": "Singularity image for biocontainers samtools", "filenames": [ - "Singularity.nompi", - "Singularity.v1.0.1-nompi", - "Singularity.v1.0.0-nompi", - "Singularity.v1.0.0-openmpi4.0.5", - "Singularity.v1.0.1-openmpi4.0.5" + "Singularity" ], - "full_name": "MRChemSoft/mrchem-singularity", + "full_name": "researchapps/samtools", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4912\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84beb2851e273df815be30c84cb993af8267097b92ef4e7c7a60a256c72e9c83/68747470733a2f2f73696e67756c61726974796875622e6769746875622e696f2f73696e67756c61726974796875622d646f63732f6173736574732f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://singularityhub.github.io/singularityhub-docs/assets/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://singularityhub.github.io/singularityhub-docs/assets/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenerate new recipes using HPC Container Maker (HPCCM)\u003c/h3\u003e\u003ca id=\"user-content-generate-new-recipes-using-hpc-container-maker-hpccm\" class=\"anchor\" aria-label=\"Permalink: Generate new recipes using HPC Container Maker (HPCCM)\" href=\"#generate-new-recipes-using-hpc-container-maker-hpccm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ hpccm --recipe \u0026lt;recipe_name\u0026gt;.py --format singularity --singularity-version=3.2 \u0026gt; recipes/Singularity.\u0026lt;version-tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity image locally\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-image-locally\" class=\"anchor\" aria-label=\"Permalink: Build Singularity image locally\" href=\"#build-singularity-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$\u00a0sudo singularity build \u0026lt;image-name\u0026gt;.sif recipes/Singularity.\u0026lt;version-tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity image remotely on Singularity Hub\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-image-remotely-on-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Build Singularity image remotely on Singularity Hub\" href=\"#build-singularity-image-remotely-on-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ git add recipes/Singularity.\u0026lt;version-tag\u0026gt;\n$ git commit -m \"Add recipe for \u0026lt;version-tag\u0026gt;\"\n$ git push\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePull Singularity image from Singularity Hub\u003c/h3\u003e\u003ca id=\"user-content-pull-singularity-image-from-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Pull Singularity image from Singularity Hub\" href=\"#pull-singularity-image-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull --name \u0026lt;image-name\u0026gt;.sif shub://MRChemSoft/mrchem-singularity:\u0026lt;version-tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Singularity container (non MPI)\u003c/h3\u003e\u003ca id=\"user-content-run-singularity-container-non-mpi\" class=\"anchor\" aria-label=\"Permalink: Run Singularity container (non MPI)\" href=\"#run-singularity-container-non-mpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec \u0026lt;image-name\u0026gt;.sif mrchem molecule\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Singularity container (MPI)\u003c/h3\u003e\u003ca id=\"user-content-run-singularity-container-mpi\" class=\"anchor\" aria-label=\"Permalink: Run Singularity container (MPI)\" href=\"#run-singularity-container-mpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec \u0026lt;image-name\u0026gt;.sif mrchem -D molecule\n$ mpirun singularity exec \u0026lt;image-name\u0026gt;.sif mrchem.x molecule.json\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBCFTools\u003c/h1\u003e\u003ca id=\"user-content-bcftools\" class=\"anchor\" aria-label=\"Permalink: BCFTools\" href=\"#bcftools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity image to deploy bcftools.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 bcftools.img\nsudo singularity bootstrap bcftools.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the bcftools runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bcftools.img\n\nProgram: bcftools (Tools for variant calling and manipulating VCFs and BCFs)\nVersion: 1.3.1 (using htslib 1.3.1)\n\nUsage: bcftools [--version|--version-only] [--help] \u0026lt;command\u0026gt; \u0026lt;argument\u0026gt;\n\nCommands:\n\n -- Indexing\n index index VCF/BCF files\n\n -- VCF/BCF manipulation\n annotate annotate and edit VCF/BCF files\n concat concatenate VCF/BCF files from the same set of samples\n convert convert VCF/BCF files to different formats and back\n isec intersections of VCF/BCF files\n merge merge VCF/BCF files files from non-overlapping sample sets\n norm left-align and normalize indels\n plugin user-defined plugins\n query transform VCF/BCF into user-defined formats\n reheader modify VCF/BCF header, change sample names\n view VCF/BCF conversion, view, subset and filter VCF/BCF files\n\n -- VCF/BCF analysis\n call SNP/indel calling\n consensus create consensus sequence by applying VCF variants\n cnv HMM CNV calling\n filter filter VCF/BCF files using fixed thresholds\n gtcheck check sample concordance, detect sample swaps and contamination\n roh identify runs of autozygosity (HMM)\n stats produce VCF/BCF stats\n\n Most commands accept VCF, bgzipped VCF, and BCF with the file type detected\n automatically even when streaming from a pipe. Indexed VCF and BCF will work\n in all situations. Un-indexed VCF and BCF and streams will work in most but\n not all situations.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 7, + "subscribers_count": 3, "topics": [], - "updated_at": 1641394651.0 + "updated_at": 1488063681.0 }, { "data_format": 2, - "description": null, + "description": "testing an enzo simulation with MPI, enzo, and Singularity", "filenames": [ "Singularity" ], - "full_name": "SupercomputingWales/TensorFlow-GPU-Example-Singularity", + "full_name": "vsoch/enzo-simulation", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTensorFlow-GPU-Example\u003c/h1\u003e\u003ca id=\"user-content-tensorflow-gpu-example\" class=\"anchor\" aria-label=\"Permalink: TensorFlow-GPU-Example\" href=\"#tensorflow-gpu-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn simple example of how to use Tensorflow with Anaconda, Python and GPU on Super Computing Wales.\u003c/p\u003e\n\u003cp\u003eNote that this example sets up an Anaconda environment which takes around 40,000 files. The default quota on Super Computing Wales is only 100,000 files, please delete or achive some files before running this if you have more than 60,000 files already.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/SupercomputingWales/TensorFlow-GPU-Example\ncd TensorFlow-GPU-Example\n./tensorflow_setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the example\u003c/h2\u003e\u003ca id=\"user-content-running-the-example\" class=\"anchor\" aria-label=\"Permalink: Running the example\" href=\"#running-the-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esbatch TensorFlow-GPU-Example.slurm\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis should result in tensorflow_gpu_demo.out.XXXXX and tensorflow_gpu_demo.err.XXXXX (where XXXXX is the job number) files being created. The output file should contain something like:\u003c/p\u003e\n\u003cpre lang=\"Extracting\"\u003e\u003ccode\u003eExtracting /home/a.cos/neuralnet_data/train-labels-idx1-ubyte.gz\nExtracting /home/a.cos/neuralnet_data/t10k-images-idx3-ubyte.gz\nExtracting /home/a.cos/neuralnet_data/t10k-labels-idx1-ubyte.gz\nEpoch: 0001 cost=316.534357009\nEpoch: 0002 cost=99.598349252\nEpoch: 0003 cost=72.017426792\nEpoch: 0004 cost=57.256541054\nEpoch: 0005 cost=48.566505551\nEpoch: 0006 cost=42.126385042\nEpoch: 0007 cost=38.068715960\nEpoch: 0008 cost=33.654687686\nEpoch: 0009 cost=30.905442436\nEpoch: 0010 cost=28.964738740\nEpoch: 0011 cost=27.089578144\nEpoch: 0012 cost=25.210279087\nEpoch: 0013 cost=24.343561058\nEpoch: 0014 cost=22.425776573\nEpoch: 0015 cost=21.565193366\nOptimization Finished!\nAccuracy: 0.8936\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVerifying the GPU was used\u003c/h2\u003e\u003ca id=\"user-content-verifying-the-gpu-was-used\" class=\"anchor\" aria-label=\"Permalink: Verifying the GPU was used\" href=\"#verifying-the-gpu-was-used\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe error log should contain a line similar to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCreated TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14873 MB memory) -\u0026gt; physical GPU (device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:3b:00.0, compute capability: 7.0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also SSH to the node and run the nvidia-smi command.\nThis can be done in one command with:\n\u003ccode\u003essh $(squeue -u $USER --format=%N | tail -1) nvidia-smi\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis should show something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 396.37 Driver Version: 396.37 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n|===============================+======================+======================|\n| 0 Tesla V100-PCIE... On | 00000000:3B:00.0 Off | 0 |\n| N/A 36C P0 45W / 250W | 15431MiB / 16160MiB | 30% Default |\n+-------------------------------+----------------------+----------------------+\n| 1 Tesla V100-PCIE... On | 00000000:D8:00.0 Off | 0 |\n| N/A 37C P0 27W / 250W | 0MiB / 16160MiB | 0% Default |\n+-------------------------------+----------------------+----------------------+\n \n+-----------------------------------------------------------------------------+\n| Processes: GPU Memory |\n| GPU PID Type Process name Usage |\n|=============================================================================|\n| 0 431923 C python 15420MiB |\n+-----------------------------------------------------------------------------+\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePotential Problems\u003c/h2\u003e\u003ca id=\"user-content-potential-problems\" class=\"anchor\" aria-label=\"Permalink: Potential Problems\" href=\"#potential-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSCW_TPN_OVERRIDE error\u003c/h3\u003e\u003ca id=\"user-content-scw_tpn_override-error\" class=\"anchor\" aria-label=\"Permalink: SCW_TPN_OVERRIDE error\" href=\"#scw_tpn_override-error\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHawk will not let you submit a single core job to the GPU partition without first running the commmand:\n\u003ccode\u003eexport SCW_TPN_OVERRIDE=1\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis shouldn\u0027t be required on Sunbird.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnzo in Singularity\u003c/h1\u003e\u003ca id=\"user-content-enzo-in-singularity\" class=\"anchor\" aria-label=\"Permalink: Enzo in Singularity\" href=\"#enzo-in-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall Singularity\u003c/h2\u003e\u003ca id=\"user-content-install-singularity\" class=\"anchor\" aria-label=\"Permalink: Install Singularity\" href=\"#install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst you should install Singularity, and we will use development branch for latest features.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone -b development https://www.github.com/singularityware/singularity.git\ncd singularity\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate Enzo Image\u003c/h2\u003e\u003ca id=\"user-content-create-enzo-image\" class=\"anchor\" aria-label=\"Permalink: Create Enzo Image\" href=\"#create-enzo-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity file will download the enzo package from bitbucket, and install/configure for ubuntu 14.04. The scripts for the running are included in this repo under \u003ca href=\"scripts\"\u003escripts\u003c/a\u003e. To create the image, do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vsoch/enzo-simulation\ncd enzo-simulation\nsingularity create --size 4000 enzo.img\nsudo singularity bootstrap enzo.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLooking around\u003c/h3\u003e\u003ca id=\"user-content-looking-around\" class=\"anchor\" aria-label=\"Permalink: Looking around\" href=\"#looking-around\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can then shell into the image to look around, here I am using sudo with \u003ccode\u003e--writable\u003c/code\u003e so I can make changes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity shell --writable enzo.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above is useful mostly during the development time of the image, when you have it locally (and thus have sudo) and want to try small tweaks to the image to test running it. It is best practice generally to represent all changes/tweaks in the build file (\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e) so that they are reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Runscript\u003c/h3\u003e\u003ca id=\"user-content-the-runscript\" class=\"anchor\" aria-label=\"Permalink: The Runscript\" href=\"#the-runscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \"runscript\" of the container is \"the thing the container does when you execute it.\" We can look at this runscript, which is linked at \u003ccode\u003e/singularity\u003c/code\u003e. Below we are using \u003ccode\u003eexec\u003c/code\u003e (execute) to issue a command to cat the file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec enzo.img cat /singularity\n\n\n# Get job number\nif [ $# -lt 1 ]; then\n echo \"You must pass the job number.\"\n echo \"Usage: singularity run -B /scratch/data:/data enzo.img 10\"\n exit 1\nelse\n num=\"$1\"\n case ${num} in\n -h|--help|-H)\n echo \"Usage: singularity run -B /host/data:/data enzo.img [num]\n echo \"e.g., singularity run -B /scratch/data:/data enzo.img 10\"\n exit 1;\n \u0027\u0027|*[!0-9]*) \n echo \"You must pass a number as the first argument.\"; exit 2;;\n *) ;;\n esac\nfi;\n\n# Create the job directory, if doesn\u0027t exist\njobdir=\"/data/jobs/${num}\"\nif [ ! -d \"${jobdir}\" ]; then\n echo \"Creating job directory ${num}\"\n cp -r /code/scripts/Init \"${jobdir}\"\nfi\n\ncd $jobdir\n/bin/bash /code/scripts/job.pbs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that although we are looking at \u003ccode\u003e/singularity\u003c/code\u003e, this is a link to the actual file in the container metadata folder at \u003ccode\u003e/.singularity.d/runscript\u003c/code\u003e. Above, we see that the runscript is going to check for a numerical argument to indicate the run number. If the user has specified a flag for help (any in \u003ccode\u003e-H|--help|-h\u003c/code\u003e) then it will print out usage. If all goes well, then we set up the job output folder (copying files to \u003ccode\u003ejobdir\u003c/code\u003e from \u003ca href=\"scripts/Init\"\u003escripts/Init\u003c/a\u003e and then launching the job by calling \u003ca href=\"scripts/job.pbs\"\u003ethis file\u003c/a\u003e. Note that these files are all added to the container, and the specific job script is also executed and inside the container. The only thing the user has to do is load the singularity module and then write some loop to execute N jobs. Doing this, and running, is discussed next.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun The Container\u003c/h2\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-label=\"Permalink: Run The Container\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou next need to move your image onto the cluster where it is intended to be. The basic workflow is going to be to bind some data folder on my cluster (in the example, \u003ccode\u003e/scratch/users/vsochat/DATA/physics\u003c/code\u003e to where the container expects it\u0027s data directory to be (in this case, \u003ccode\u003e/data\u003c/code\u003e). The MPI run is going to work as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe container acts as an executable, and manages the entire lifecycle of one job. This includes:\n\u003cul\u003e\n\u003cli\u003ecreating the job directory\u003c/li\u003e\n\u003cli\u003elaunching the job (\u003ccode\u003e.pbs\u003c/code\u003e) script that loads required modules and issues the mpi command.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe container could also do a different / custom command, and in this case you would run \u003ccode\u003eexec\u003c/code\u003e and specify a script (on the host or in container) to run.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOn your cluster, first load singularity (or have it on your path if your cluster doesn\u0027t use modules.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo start, we can be silly and just run the container, as most users would do. The following commands are equivalent:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./enzo.img \nYou must pass the job number.\nUsage: singularity run -B /scratch/data:/data enzo.img 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd every good executable will have a \u003ccode\u003e--help\u003c/code\u003e argument. We can do any of the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run enzo.img --help\nsingularity run enzo.img -H\nsingularity run enzo.img -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor just execute the image itself!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./enzo.img --help\n./enzo.img -H\n./enzo.img -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ein all cases above, we see the help prompt:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: singularity run -B /host/data:/data enzo.img [num]\ne.g., singularity run -B /scratch/data:/data enzo.img 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Enzo Jobs\u003c/h2\u003e\u003ca id=\"user-content-run-enzo-jobs\" class=\"anchor\" aria-label=\"Permalink: Run Enzo Jobs\" href=\"#run-enzo-jobs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow we are again sitting on our cluster, and remember that we have singularity loaded or on the path. Make sure this does not return empty!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewhich singularity\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow let\u0027s say we want to run 10 jobs. We want the container to use \u003ccode\u003e/scratch/users/vsochat/DATA/physics\u003c/code\u003e as our data directory (meaning the folder \u003ccode\u003ejobs\u003c/code\u003e with our enzo analyses, directories labeled 0-N) will be created inside. This is an important distinction - inside the container, operations will run relative to \u003ccode\u003e/data\u003c/code\u003e. However, from outside the container we have this directory represented as \u003ccode\u003e/scratch/users/vsochat/DATA/physics.\u003c/code\u003e Also note that our container is located one level above that at \u003ccode\u003e/scratch/users/vsochat/DATA\u003c/code\u003e. Thus, to run our job for N=10, we want to have the following scripts on our cluster, along with the image itself (enzo.img) that we built above:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"scripts/launch_jobs.sh\"\u003elaunch_jobs.sh\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"scripts/run_jobs.sh\"\u003erun_job.sh\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe first is basically a for loop to send a bunch of commands to the batch manager with qsub, each submitting a script to run the file \u003ca href=\"scripts/run_jobs.sh\"\u003erun_jobs.sh\u003c/a\u003e. You don\u0027t need to use bash scripts, or even this approach, the general idea is that you want to send the command our to your cluster nodes to load the modules on the host, load singularity, and then run the container. The script \u003ca href=\"scripts/launch_jobs.sh\"\u003escripts/launch_jobs.sh\u003c/a\u003e is an example of the \"for loop\" part of this, run on the host:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n\n# Get number of jobs\nif [ $# -lt 1 ]; then\n echo \"You need to pass the number of jobs.\"\n exit 1\nfi;\n\nnum=\"$1\"\n\n#User input validation\ncase ${num} in\n \u0027\u0027|*[!0-9]*) echo \"You must pass a number as the first argument.\"; exit 2;;\n *) ;;\nesac\n\nhost_data_dir=\"/scratch/users/vsochat/DATA/physics\"\ncompiler_version=\"1.10.2\"\ncompiler_vendor=\"gcc\"\noptimization_level=\"high\"\nexport host_data_dir compiler_vendor compiler_version optimization_level\n\njob=0\nwhile [ ${job} -lt ${num} ]; do\n job_dir=\"${host_data_dir}/jobs/${job}\"\n if [ ! -e \"${job_dir}/RunFinished\" ]; then\n echo \"Submitting job ${job_num}...\"\n qsub -N \"${compiler_vendor}_${compiler_version}_${optimization_level}_${job_num}\" -o ${job_dir} run_job.sh $job\n\tfi;\n\tjob=$(expr ${job} + 1)\ndone;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the script \u003ca href=\"scripts/run_job.sh\"\u003erun_job.sh\u003c/a\u003e is located outside the image, and called by qsub. It is this script that loads required modules (on the host) and then executes the container via a call to singularity, which has the main job script (job.pbs) inside. We bind the host data directory to \u003ccode\u003e/data\u003c/code\u003e in the container using the command \u003ccode\u003e-B\u003c/code\u003e, and we do this so that it is writable.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n\nmodule load singularity\nmodule unload gcc\nmodule load gcc/4.8.1\nmodule load openmpi/1.10.2/gcc\n\njob_num=$1\n\nhost_data=\"/scratch/users/vsochat/DATA/physics\"\nenzo=\"/scratch/users/vsochat/DATA/enzo.img\"\n\nsingularity run -B $host_data:/data $enzo $job_num\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 3, "topics": [], - "updated_at": 1653388334.0 + "updated_at": 1545323609.0 }, { "data_format": 2, - "description": "Singularity base container with Nix to be used in XSEDE compute environment (currently in development)", + "description": null, "filenames": [ "Singularity" ], - "full_name": "XSEDE/singularity-nix-base", + "full_name": "MASILab/DeepN4", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-nix-base\u003c/h1\u003e\u003ca id=\"user-content-singularity-nix-base\" class=\"anchor\" aria-label=\"Permalink: singularity-nix-base\" href=\"#singularity-nix-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4462\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity base container with Nix to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDeepN4\u003c/h1\u003e\u003ca id=\"user-content-deepn4\" class=\"anchor\" aria-label=\"Permalink: DeepN4\" href=\"#deepn4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePytorch implementation of DeepN4, from the paper: DeepN4: Learning ITKN4 Bias Field Correction for T1 weighted Images.\nWe propose \u003cstrong\u003eDeepN4\u003c/strong\u003e, a 3D UNet to generate Bias Field for T1w images and inturn correct T1w images for inhomogeneities.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/fig.png\"\u003e\u003cimg src=\"figures/fig.png\" width=\"100%\" height=\"40%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrequisite installation\u003c/h2\u003e\u003ca id=\"user-content-prequisite-installation\" class=\"anchor\" aria-label=\"Permalink: Prequisite installation\" href=\"#prequisite-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease look into \u003ccode\u003erequirements.txt\u003c/code\u003e for libraries and versions used in the virutal environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining and Testing\u003c/h2\u003e\u003ca id=\"user-content-training-and-testing\" class=\"anchor\" aria-label=\"Permalink: Training and Testing\" href=\"#training-and-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInput train/test/val format: csv file with each row with \u003ccode\u003e/path/to/corrected_T1.nii.gz,/path/to/input.nii.gz,/path/to/bias_field.nii.gz\u003c/code\u003e for each subject\u003c/li\u003e\n\u003cli\u003eRun training\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython main.py train 0 Synbo_UNet3D False /path/to/save/model /path/to/tensorboard/output \\\n/path/to/save/predictions checkpoint_epoch_# guass \\\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun testing\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython main.py pred 0 Synbo_UNet3D False /path/to/saved/model /path/to/tensorboard/output \\ \n/path/to/save/predictions checkpoint_epoch_# guass \\\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExternal evaluation\u003c/h2\u003e\u003ca id=\"user-content-external-evaluation\" class=\"anchor\" aria-label=\"Permalink: External evaluation\" href=\"#external-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEfficient evaulation can be performed for the public datasets as in \u003ccode\u003eEvaluation_deepN4.ipynb\u003c/code\u003e. The example data used is in \u003ccode\u003edata/\u003c/code\u003e\nThe trained weights and the singualrity image can be downloaded from \u003ca href=\"https://drive.google.com/drive/folders/1mdBsV0kHRRV_Alu1QJrTT7N0GGNJDuiu?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1mdBsV0kHRRV_Alu1QJrTT7N0GGNJDuiu?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo containize the source code\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build deepn4v1.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run singularity\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingaulrity run\n--contain -e\n-B /path/to/inputs/directory/:/INPUTS\n-B /path/to/outputs/directory/:/OUTPUTS\n-B /tmp:/tmp\n-B /path/to/freesurfer/license.txt:/APPS/freesurfer/license.txt\ndeepn4v1.simg\n[options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInputs and arguments for singularity\nInput Directory: one T1 image named as t1.nii.gz\nOutput Directory: results will be saved as t1processed.nii.gz and (if the option below is on) bias.nii.gz\nOptions: \"--bias_file\" to save the bias field field\u003c/p\u003e\n\u003chr\u003e\n", "stargazers_count": 1, - "subscribers_count": 16, - "topics": [ - "nix", - "singularity", - "singularity-nix" - ], - "updated_at": 1715213020.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1723393954.0 }, { "data_format": 2, - "description": "Examples of bioinformatics pipeline languages: Nextflow, Snakemake, and CWL", + "description": "singularity to register an atlas to the diffusion space of a subject", "filenames": [ - "containers/Singularity" + "Singularity" ], - "full_name": "CSUBioinformatics/pipeline-examples", + "full_name": "MASILab/AtlasToDiffusionReg", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epipeline-examples\u003c/h1\u003e\u003ca id=\"user-content-pipeline-examples\" class=\"anchor\" aria-label=\"Permalink: pipeline-examples\" href=\"#pipeline-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExamples of bioinformatics pipeline languages: Nextflow, Snakemake, and CWL\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAtlasToDiffusionReg v1.1\u003c/h1\u003e\u003ca id=\"user-content-atlastodiffusionreg-v11\" class=\"anchor\" aria-label=\"Permalink: AtlasToDiffusionReg v1.1\" href=\"#atlastodiffusionreg-v11\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity to register a structural atlas to the diffusion space of a subject\u003c/p\u003e\n\u003cp\u003eauthor: Michael Kim\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNOTE:\u003c/h3\u003e\u003ca id=\"user-content-note\" class=\"anchor\" aria-label=\"Permalink: NOTE:\" href=\"#note\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDepending on the version of numpy, you might get the error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\tnext_arr = _load_from_filelike(\nTypeError: control character \u0027delimiter\u0027 cannot be a newline (`\\r` or `\\n`).\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo alleviate, downgrade to an earlier version of numpy\u003c/p\u003e\n\u003cp\u003eEDIT: this should be fixed now\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTO DOs:\u003c/h1\u003e\u003ca id=\"user-content-to-dos\" class=\"anchor\" aria-label=\"Permalink: TO DOs:\" href=\"#to-dos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e-QA of the registered atlases into diffusion space\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-overlay of atlases on an FA map\n\n-perhaps something else as well\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e-Possibly options\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-just calculating the transforms and not applying them\n\n-only keeping certain outputs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e-have the outputs more organized than just in one outputs folder\u003c/p\u003e\n\u003cp\u003e-checks to make sure that the inputs are correct and the output directory is valid as well\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTO RUN:\u003c/h1\u003e\u003ca id=\"user-content-to-run\" class=\"anchor\" aria-label=\"Permalink: TO RUN:\" href=\"#to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /path/to/inputs/:/INPUTS -B /path/to/outputs:/OUTPUTS WMAtlas.simg (or whatever the singularity image is called)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eINPUTS:\u003c/h1\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: INPUTS:\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAtlases you want to register\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-must have \"Atlas_\" at the beginning of the filename, followed by the atlas name\n\n\t-i.e. \"Atlas_JHU_MNI_WMPM_Type_I.nii.gz\"\n\t\n\t\t-Name of the Atlas is then \"JHU_MNI_WMPM_Type_I\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLabels for each Atlas in the inputs\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-the corresponding labels must be named: \"Labels_\u0026lt;Atlas_Name\u0026gt;.txt\"\n\n\t-if we use the atlas above, the label file should be called \"Labels_JHU_MNI_WMPM_Type_I.txt\"\n\t\n-the label files should have the following structure:\n\n\t#INTENSITY/Integer_label Region_Of_Interest_Name\n\n\t0 Background\t\t\t\n\t1 Superior_Parietal_Lobule_left\n\t2 Cingulate_Gyrus_left\n\t3 Middle_Frontal_Gyrus_left\n\t...\n\t\n- In other words, each line should have an intensity value of the labelmap and the corresponding name of the label delimited by a space\n\n- The first line of the text file should be the background with intensity of zero\n\n- The names if the ROIs in the labelmap should not contain any spaces: the only spaces should be between the intensity and corresponding ROI name\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStructural Template that the atlases are in the space of\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-needs to be named \"template.nii.gz\"\n\n\t-if it is not named this you can specify so by adding the following option in the singularity call:\n\t\n\t\tsingularity run -B ...:/INPUTS -B ...:/OUTPUTS -B /path/to/the/template/:/INPUTS/template.nii.gz WMAtlas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStructural T1 scan of the subject\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-needs to be named \"t1.nii.gz\"\n\n\t-like with the template, can specify an additional line if it is not named so:\n\t\n\t\t\"-B /path/to/t1:/INPUTS/t1.nii.gz\"\n\t\t\n-CANNOT already be skull stripped\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDiffusion data for the subject (dwmri scan, bval, bvec)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-Name can be whatever you want, but they must all have the same name\n\n\t-i.e if the name you want to give it is \"dwmri\"\n\t\n\t\tdwmri.nii.gz\n\t\tdwmri.bval\n\t\tdwmri.bvec\n\t\t\n\t-note that all outputs will use this name you provide\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eT1 segmentation from SLANT (optional)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-needs to be named \"T1_seg.nii.gz\"\n\n-can technically also be a brain mask\n\n-like with the template, can specify an additional line if it is not named so:\n\n\t\"-B /path/to/t1:/INPUTS/T1_seg.nii.gz\"\n\t\n-If this input is not included, then fsl\u0027s bet will be used for the brain extraction and the mask (not recommended)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOUTPUTS are:\u003c/h1\u003e\u003ca id=\"user-content-outputs-are\" class=\"anchor\" aria-label=\"Permalink: OUTPUTS are:\" href=\"#outputs-are\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e- the transformations\n\n\t- see below in Notes for how to use them yourself\n\t\n-the registered atlases\n\n\t- \u0026lt;dwiname\u0026gt;%\u0026lt;atlas_name\u0026gt;.nii.gz\n\t\n-skull stripped t1 and the extracted b0\n\n-diffusion scalar maps\n\n\t-e.g. \u0026lt;dwiname\u0026gt;%fa.nii.gz\n\t\n-the single shell dwi and its bval/bvec files\n\n-the csv file containing the calculations\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSteps:\u003c/h1\u003e\u003ca id=\"user-content-steps\" class=\"anchor\" aria-label=\"Permalink: Steps:\" href=\"#steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eT1 is registered to the template using ANTs to obtain both the affine and nonlinear transformations\u003c/li\u003e\n\u003cli\u003eT1 is skull stripped and b0 is extracted\u003c/li\u003e\n\u003cli\u003eTransformation from diffusion to t1 space is calculated using FSL\u003c/li\u003e\n\u003cli\u003eFSL transformations are converted to ANTs format using c3d\u003c/li\u003e\n\u003cli\u003eAtlases are registered to diffusion space using the transforms\u003c/li\u003e\n\u003cli\u003eThe first shell diffusion scans are extracted from the dwi file\u003c/li\u003e\n\u003cli\u003eFA, MD, AD, RD maps are calculated from the first shell\u003c/li\u003e\n\u003cli\u003eMean and std dev are calculated for the diffusion metrics for each ROI for each atlas and placed in a csv file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe singularity assumptions:\u003c/h1\u003e\u003ca id=\"user-content-the-singularity-assumptions\" class=\"anchor\" aria-label=\"Permalink: The singularity assumptions:\" href=\"#the-singularity-assumptions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e-all imaging files provided are gzpied niftis (.nii.gz)\u003c/p\u003e\n\u003cp\u003e-the bvals/bvecs are iun FSL format (can be otherwise, but not guaranteed to work)\u003c/p\u003e\n\u003cp\u003e-the dwi data have already been preprocessed for distortion correction, to remove noise, artifacts, etc.\u003c/p\u003e\n\u003cp\u003e-the t1 is NOT skull stripped\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNotes\u003c/h1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eif you would like to apply the transformations yourself, this is the following ANTs command to do so:\u003c/p\u003e\n\u003cp\u003eantsApplyTransforms -d 3 -i \u0026lt;atlas_file\u0026gt; -r \u0026lt;b0_file\u0026gt; -n NearestNeighbor \u003cbr\u003e\n-t \u0026lt;t1_to_b0_transform\u0026gt; -t [\u0026lt;t1_to_template_affine_transform\u0026gt;,1] \u003cbr\u003e\n-t \u0026lt;t1_to_template_inv_warp\u0026gt; -o \u0026lt;output_file_name\u0026gt;\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e -t1_to_b0 tranform is called \t\t\t...%ANTS_t1tob0.txt\n -t1_to_template_affine is called\t\t...%0GenericAffine.mat\n -t1_to_template_inv_warp is called\t\t...%1InverseWarp.nii.gz\n -do not have to use the b0 as reference, can use anything in the diffusion space (like the FA map)\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eto move something from diffusion space into the template space, use a similar call, but reverse the order of the transformations\u003c/p\u003e\n\u003cp\u003e-additionally, you must use the inverse of the transformations applied\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1724128150.0 + "updated_at": 1720041531.0 }, { "data_format": 2, - "description": "Simulation Science research team repo for probabilistic record linkage project", + "description": "Build files for creating singularity containers containing KERN packages.", "filenames": [ - "linkage/person_linkage_case_study/Singularity", - "linkage/ecosystem_dummy_containers/python_pandas/Singularity", - "linkage/ecosystem_dummy_containers/r/Singularity", - "linkage/ecosystem_dummy_containers/python_pyspark/Singularity" + "Singularity" ], - "full_name": "ihmeuw/vivarium_research_prl", + "full_name": "kernsuite/singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003evivarium_research_prl\u003c/h1\u003e\u003ca id=\"user-content-vivarium_research_prl\" class=\"anchor\" aria-label=\"Permalink: vivarium_research_prl\" href=\"#vivarium_research_prl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode used by the IHME Simulation Science reasearch team for the US Census entity resolution/probabilistic record linkage project.\u003c/p\u003e\n\u003cp\u003eOnce you have cloned the repository and activated a conda environment, you can run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efrom within the root directory \u003ccode\u003evivarium_research_prl\u003c/code\u003e in order to get an \u003ca href=\"https://stackoverflow.com/questions/35064426/when-would-the-e-editable-option-be-useful-with-pip-install\" rel=\"nofollow\"\u003eeditable pip installation\u003c/a\u003e for local development.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eKERN suite singularity container\u003c/h1\u003e\u003ca id=\"user-content-kern-suite-singularity-container\" class=\"anchor\" aria-label=\"Permalink: KERN suite singularity container\" href=\"#kern-suite-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is source repo for creating a Singularity container populated with\nKERN suite software. KERN is a bi-annually released set of radio astronomical\nsoftware packages. It should contain most of the tools that a radio astronomer\nneeds to work with radio telescope data.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://kernsuite.info/\" rel=\"nofollow\"\u003ehttp://kernsuite.info/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ebuild your own\u003c/h2\u003e\u003ca id=\"user-content-build-your-own\" class=\"anchor\" aria-label=\"Permalink: build your own\" href=\"#build-your-own\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003erun make:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ make\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1730742571.0 + "updated_at": 1563189833.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.11.5", - "Singularity.12.0", - "Singularity.9.6.15", - "Singularity.10.10" + "Singularity" ], - "full_name": "ddbj/singularity_postgresql", + "full_name": "CNCLgithub/rooms-psiturk", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_postgresql\u003c/h1\u003e\u003ca id=\"user-content-singularity_postgresql\" class=\"anchor\" aria-label=\"Permalink: singularity_postgresql\" href=\"#singularity_postgresql\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3\u3067\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306esingularity image\u306e\u4f7f\u3044\u65b9\u003c/p\u003e\n\u003cp\u003e\u5bfe\u5fdc\u3059\u308bPostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e9.6.15\u003c/li\u003e\n\u003cli\u003e10.10\u003c/li\u003e\n\u003cli\u003e11.5\u003c/li\u003e\n\u003cli\u003e12.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u751f\u6210\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u751f\u6210\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u751f\u6210\" href=\"#image\u306e\u751f\u6210\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u81ea\u5206\u306e\u74b0\u5883\u3067image\u3092build\u3059\u308b\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u306f9.6.15, 10.10, 11.5, 12.0\u306e\u3044\u305a\u308c\u304b\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ddbj/singularity_postgresql.git\n$ cd singularity_postgresql\n$ sudo singularity build ubuntu-18.04-postgresql-\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;.simg Singularity.\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" href=\"#image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Hub\u306b\u767b\u9332\u3055\u308c\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u306f9.6.15, 10.10, 11.5, 12.0\u306e\u3044\u305a\u308c\u304b\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ddbj/singularity_postgresql.git\n$ cd singularity_postgresql\n$ singularity pull --name ubuntu-18.04-postgresql-\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;.simg shub://ddbj/singularity_postgresql:\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u751f\u6210\u307e\u305f\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3060\u3051\u3067\u306fPostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u5b9f\u884c\u3067\u304d\u307e\u305b\u3093\u3002 start_container.sh\u3092\u5b9f\u884c\u3057\u3066singularity instance\u3092\u8d77\u52d5\u3057\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\u3092\u884c\u3044\u307e\u3059\u3002\n\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u524d\u306b\u3001\u81ea\u5206\u306e\u74b0\u5883\u306b\u5408\u308f\u305b\u3066 start_container.sh \u306e CONTAINER_HOME, IMAGE, INSTANCE, PORT\u5909\u6570\u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCONTAINER_HOME\u306b\u306fgit clone\u3067\u3067\u304d\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u3092\u8a18\u8f09\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003cli\u003eIMAGE\u306b\u306f\u3001image\u751f\u6210\u307e\u305f\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u306e\u969b\u306b\u6307\u5b9a\u3057\u305f\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u8a18\u8f09\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003cli\u003ePORT\u5909\u6570\u306f5000\u4ee5\u4e0a\u3067\u4efb\u610f\u306e\u6574\u6570\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3059\u308b\u3068\u3001\u521d\u56de\u5b9f\u884c\u6642\u306b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u521d\u671f\u5316\u304c\u884c\u308f\u308c\u305f\u5f8c\u3067\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b5\u30fc\u30d0\u304c\u8d77\u52d5\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\nThe files belonging to this database system will be owned by user \"\u0026lt;\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u30e6\u30fc\u30b6\u30fc\u0026gt;\".\nThis user must also own the server process.\n\nThe database cluster will be initialized with locale \"C\".\nThe default text search configuration will be set to \"english\".\n\nData page checksums are disabled.\n\nfixing permissions on existing directory /usr/local/pgsql12/data ... ok\ncreating subdirectories ... ok\nselecting dynamic shared memory implementation ... posix\nselecting default max_connections ... 100\nselecting default shared_buffers ... 128MB\nselecting default time zone ... Japan\ncreating configuration files ... ok\nrunning bootstrap script ... ok\nperforming post-bootstrap initialization ... ok\nsyncing data to disk ... ok\n\ninitdb: warning: enabling \"trust\" authentication for local connections\nYou can change this by editing pg_hba.conf or using the option -A, or\n--auth-local and --auth-host, the next time you run initdb.\n\nSuccess. You can now start the database server using:\n\n pg_ctl -D /usr/local/pgsql12/data -l logfile start\n\nStopping pgsql instance of /gpfs1/lustre2/home/y-okuda/git/singularity_postgresql/ubuntu-18.04-postgresql-12.0.simg (PID=36513)\nwaiting for server to start.... done\nserver started\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity instance\u3092\u8d77\u52d5\u3057\u305f\u30e6\u30fc\u30b6\u30fc\uff08initdb\u3092\u5b9f\u884c\u3057\u305f\u30e6\u30fc\u30b6\u30fc\uff09\u304cPostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec instance://pgsql psql -d postgres -p 55432\npsql (12.0)\nType \"help\" for help.\n\npostgres=# alter role \"\u0026lt;\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u30e6\u30fc\u30b6\u30fc\u0026gt;\" with password \u0027\u0026lt;\u30d1\u30b9\u30ef\u30fc\u30c9\u0026gt;\u0027;\nALTER ROLE\npostgres=# \\q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30d1\u30b9\u30ef\u30fc\u30c9\u306e\u8a2d\u5b9a\u306b\u3088\u308asingularity instance\u306e\u5916\u304b\u3089\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u30a2\u30af\u30bb\u30b9\u306e\u969b\u306f-h\u30aa\u30d7\u30b7\u30e7\u30f3\u3067singularity instance\u3092\u5b9f\u884c\u3057\u3066\u3044\u308b\u30db\u30b9\u30c8\u540d\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ psql -d postgres -p 55432 -h at043\n\u30d1\u30b9\u30ef\u30fc\u30c9: \npsql (9.2.24, \u30b5\u30fc\u30d0\u30fc 12.0)\n\u6ce8\u610f\uff1a psql \u30d0\u30fc\u30b8\u30e7\u30f3 9.2, \u30b5\u30fc\u30d0\u30fc\u30d0\u30fc\u30b8\u30e7\u30f3 12.0.\n psql \u306e\u6a5f\u80fd\u306e\u4e2d\u3067\u3001\u52d5\u4f5c\u3057\u306a\u3044\u3082\u306e\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\n\"help\" \u3067\u30d8\u30eb\u30d7\u3092\u8868\u793a\u3057\u307e\u3059.\n\npostgres=# \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u5225\u30ce\u30fc\u30c9\u304b\u3089\u306e\u30a2\u30af\u30bb\u30b9\u3082\u53ef\u80fd\u3067\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh at044\nLast login: Fri Nov 1 20:25:26 2019 from at043\n$ psql -d postgres -p 55432 -h at043\n\u30d1\u30b9\u30ef\u30fc\u30c9: \npsql (9.2.24, \u30b5\u30fc\u30d0\u30fc 12.0)\n\u6ce8\u610f\uff1a psql \u30d0\u30fc\u30b8\u30e7\u30f3 9.2, \u30b5\u30fc\u30d0\u30fc\u30d0\u30fc\u30b8\u30e7\u30f3 12.0.\n psql \u306e\u6a5f\u80fd\u306e\u4e2d\u3067\u3001\u52d5\u4f5c\u3057\u306a\u3044\u3082\u306e\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\n\"help\" \u3067\u30d8\u30eb\u30d7\u3092\u8868\u793a\u3057\u307e\u3059.\n\npostgres=# \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u63d0\u4f9b\u3057\u3066\u3044\u308bpg_hba.conf\u306e\u8a18\u8ff0\u3067\u306f\u30a2\u30af\u30bb\u30b9\u53ef\u80fd\u306aIP\u30a2\u30c9\u30ec\u30b9\u306b\u5236\u9650\u304c\u304b\u304b\u3063\u3066\u3044\u307e\u305b\u3093\u3002\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePsiturk Experiment`\u003c/h1\u003e\u003ca id=\"user-content-psiturk-experiment\" class=\"anchor\" aria-label=\"Permalink: Psiturk Experiment`\" href=\"#psiturk-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePsiturk experiment used in Galileo (response slider) style experiments\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003esingularity\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003etar\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esetup\u003c/h3\u003e\u003ca id=\"user-content-setup-1\" class=\"anchor\" aria-label=\"Permalink: setup\" href=\"#setup-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esee help\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh --help\n./setup.sh cont data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis setup file will, by default, pull a container and data files from box.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning psiturk\u003c/h2\u003e\u003ca id=\"user-content-running-psiturk\" class=\"anchor\" aria-label=\"Permalink: Running psiturk\" href=\"#running-psiturk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x start_psiturk.sh\n./start_psiturk.sh on\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAPI\u003c/h2\u003e\u003ca id=\"user-content-api\" class=\"anchor\" aria-label=\"Permalink: API\" href=\"#api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etask.js\u003c/h3\u003e\u003ca id=\"user-content-taskjs\" class=\"anchor\" aria-label=\"Permalink: task.js\" href=\"#taskjs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe majority of the experiment\u0027s functionality is described in \u003ccode\u003epsiturk/static/js/task.js\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe main class used to setup pages for both the experiment and instructions is defined as \u003ccode\u003ePage\u003c/code\u003e.\n\u003ccode\u003ePage\u003c/code\u003e handles both media presentation and scale setup. See the docstrings for more info.\u003c/p\u003e\n\u003cp\u003eThere are three other main elements, \u003ccode\u003eInstructionRunner\u003c/code\u003e, \u003ccode\u003eQuiz\u003c/code\u003e, and \u003ccode\u003eExperiment\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecss and html\u003c/h3\u003e\u003ca id=\"user-content-css-and-html\" class=\"anchor\" aria-label=\"Permalink: css and html\" href=\"#css-and-html\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main html files are located under \u003ccode\u003epsiturk/templates/\u003c/code\u003e and css is under \u003ccode\u003epsiturk/static/css\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNotabley, \u003ccode\u003estage.html\u003c/code\u003e describes the pages for experimental trials and \u003ccode\u003eslider.css\u003c/code\u003e describes some of the elements found in the scale.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 7, + "subscribers_count": 3, "topics": [], - "updated_at": 1586795356.0 + "updated_at": 1666800050.0 }, { "data_format": 2, - "description": "Containerized image processing tools for JACS", + "description": "Singularity bootstrap file for uboonecode container", "filenames": [ - "aligner_jrc2018_20x_40x/Singularity", - "aligner_vnc2018_20x_40x/Singularity", - "aligner_jrc2018_20x_gen1/Singularity", - "aligner_jrc2018_63x/Singularity", - "aligner_yuy_20x63xpair/Singularity", - "aligner_yuy/Singularity", - "aligner_yuy_legacy/Singularity", - "aligner_vnc2017_20x/Singularity", - "aligner_vnc2018_63x/Singularity" + "Singularity" ], - "full_name": "JaneliaSciComp/jacs-tools", + "full_name": "LArbys/singularity-sl6-uboonecode", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJACS Tools\u003c/h1\u003e\u003ca id=\"user-content-jacs-tools\" class=\"anchor\" aria-label=\"Permalink: JACS Tools\" href=\"#jacs-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains tools which JACS runs on the cluster to process data.\u003c/p\u003e\n\u003cp\u003eEach sub directory contains a single tool which can be built into a Singularity container.\u003c/p\u003e\n\u003cp\u003eFor information on how to create a new tool, read about \u003ca href=\"CONTRIBUTING.md\"\u003eContributing\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build one or more tools:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage.sh build [tool1] [tool2] [tool3] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis creates a set of corresponding img files in the build directory which can be run with Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShell\u003c/h2\u003e\u003ca id=\"user-content-shell\" class=\"anchor\" aria-label=\"Permalink: Shell\" href=\"#shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo open a shell into a built container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage.sh shell [tool]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRegression Tests\u003c/h2\u003e\u003ca id=\"user-content-regression-tests\" class=\"anchor\" aria-label=\"Permalink: Regression Tests\" href=\"#regression-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRegression tests can be added to each container under the \"test\" directory. Each sub-directory in the \"tests\" directory is\nconsidered to be a standalone test. A file called test.sh must be placed in each test directory. To run tests:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage test [tool1] [tool2] [tool3] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo deploy a built container to another location, you must first define the target location in your environment,\ne.g. in your ~/.bashrc file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport JACS_SINGULARITY_DIR=/groups/jacs/jacsDev/servers/jacs-data/executables/singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage.sh deploy [tool1] [tool2] [tool3] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClean\u003c/h2\u003e\u003ca id=\"user-content-clean\" class=\"anchor\" aria-label=\"Permalink: Clean\" href=\"#clean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can delete existing builds for one or more containers with the \u003ccode\u003eclean\u003c/code\u003e command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage.sh clean [tool1] [tool2] [tool3] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersioning\u003c/h2\u003e\u003ca id=\"user-content-versioning\" class=\"anchor\" aria-label=\"Permalink: Versioning\" href=\"#versioning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer versioning is done in the Singularity build file. When making changes to a container, make sure to increment the\nVERSION variable in the Singularity file before building or deploying that container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use container named \u0026lt;container.img\u0026gt; which contains an app called you can invoke Singularity as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eB1=/external/path1\nB2=/external/path2\nsingularity run -B $B1 -B $B2 --app appName container.img -i $B1 -o $B2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll external paths which the container needs to access must be mounted with -B flags.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.janelia.org/open-science/software-licensing\" rel=\"nofollow\"\u003eJanelia Open Source License\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-sl6-uboonecode\u003c/h1\u003e\u003ca id=\"user-content-singularity-sl6-uboonecode\" class=\"anchor\" aria-label=\"Permalink: singularity-sl6-uboonecode\" href=\"#singularity-sl6-uboonecode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains a script for building a uboonecode container.\u003c/p\u003e\n\u003cp\u003eIt uses \u003ccode\u003epullProducts\u003c/code\u003e, a script that untars binaries for a specific uboonecode version into a folder in the container, \u003ccode\u003e/products\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo build the version of your choice, go into the file, \u003ccode\u003eSingularity\u003c/code\u003e, and change the line\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./pullProducts /products slf6 uboone-v06_26_01_14 e10 prof\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote, this applies to other sci-soft products as well. List of products \u003ca href=\"http://scisoft.fnal.gov/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOnce in the container, you can set it up and run \u003ccode\u003elar\u003c/code\u003e by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /products/setup\nsetup uboonecode [version] -q [qualifier]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also develop with the container!!!\u003c/p\u003e\n\u003cp\u003eTo setup an MRB build environment for the first time (for uboonecode):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /products/setup\nsetup uboonecode [version] -q [qualifier]\n\nmkdir [my build dir]\ncd [my build dir]\n\nsetup mrb\nexport MRB_PROJECT=larsoft\nmrb newDev\nsource localProducts-xxxxxx/setup\ncd srcs\nmrb g uboonecode\n\nmrbsetenv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNotes\u003c/h3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAs of 4/23/18, building a uboonecode image was about 3.9 GB.\u003c/li\u003e\n\u003cli\u003eeventdump.fcl works\u003c/li\u003e\n\u003cli\u003erunning DL_br branch to make larcv files work!\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 8, + "subscribers_count": 3, "topics": [], - "updated_at": 1705157162.0 + "updated_at": 1617400552.0 }, { "data_format": 2, - "description": "Implementation of the Iterative Boltzmann Inversion for statistical analysis.", + "description": "One metagenome pipeline to rule them all - this nextflow pipeline is a complete end-to-end metagenomic solution creating visualisations of taxonomic data from fastq input files, by utilising mmseq", "filenames": [ - "iterboltz-container/Singularity.def" + "Singularity" ], - "full_name": "2bys/ibi", + "full_name": "lifebit-ai/onemetagenome", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIterative Boltzmann Inversion\u003c/h1\u003e\u003ca id=\"user-content-iterative-boltzmann-inversion\" class=\"anchor\" aria-label=\"Permalink: Iterative Boltzmann Inversion\" href=\"#iterative-boltzmann-inversion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImplementation of the Iterative Boltzmann Inversion for statistical analysis.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run the code?\u003c/h2\u003e\u003ca id=\"user-content-how-to-run-the-code\" class=\"anchor\" aria-label=\"Permalink: How to run the code?\" href=\"#how-to-run-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a config file (see example files).\u003c/li\u003e\n\u003cli\u003eGo to folder \u0027iterboltz-container\u0027 build singularity container by, e.g.,\nsingularity build portable Singulartiy.def\u003c/li\u003e\n\u003cli\u003eRun all config files in config folder with\npython3 manage_runs.py\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/onemetagenome\u003c/h1\u003e\u003ca id=\"user-content-nf-coreonemetagenome\" class=\"anchor\" aria-label=\"Permalink: nf-core/onemetagenome\" href=\"#nf-coreonemetagenome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOne metagenome pipeline to rule them all - this nextflow pipeline is a complete end-end metagenomic solution creating visulations of taxonomic data from fastq input files, by utilising plass and mmseq tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/onemetagenome\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6cf9eeca9afa6d30ca877f3deeabde9cf8d075b26065a1dfeea39db0904e44b5/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6f6e656d65746167656e6f6d652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/onemetagenome.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/780f0e426d3a9fd5f3f54407686be63867cb8093d09e36c9bcbad58b728a111d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/onemetagenome\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/326409a97c5cebd433effd380b14db006172c51f8130eb0f595df496e62cd4a5/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6f6e656d65746167656e6f6d652e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/onemetagenome.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/onemetagenome pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1689353097.0 + "updated_at": 1552370066.0 }, { "data_format": 2, - "description": "Singularity recipes for images containing R", + "description": null, "filenames": [ - "Singularity.3.5.1", - "Singularity.3.6.0", - "Singularity.3.4.4", - "Singularity.3.5.0", - "Singularity", - "Singularity.2.15.3", - "Singularity.3.3.3" + "Singularity" ], - "full_name": "MPIB/singularity-r", + "full_name": "marchoeppner/clinical-metagenomics", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-r\u003c/h1\u003e\u003ca id=\"user-content-singularity-r\" class=\"anchor\" aria-label=\"Permalink: singularity-r\" href=\"#singularity-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/623\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipes for base-images containing R.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR is installed from source which is downloaded from \u003ca href=\"https://cran.r-project.org/\" rel=\"nofollow\"\u003eCRAN\u003c/a\u003e (Comprehensive R Archive Network).\u003c/li\u003e\n\u003cli\u003ePackages needed to build R and those needed to run R like gfortran, g++ and gcc are installed through the debian repositories via \u003ccode\u003eapt-get install\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAt the end of the build process the images is cleaned up by:\n\u003cul\u003e\n\u003cli\u003eremoving packages only needed for the build,\u003c/li\u003e\n\u003cli\u003eremoving the package cache,\u003c/li\u003e\n\u003cli\u003eremoving downloaded files used for the build.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIKMB Metagenomic profiling pipeline\u003c/h1\u003e\u003ca id=\"user-content-ikmb-metagenomic-profiling-pipeline\" class=\"anchor\" aria-label=\"Permalink: IKMB Metagenomic profiling pipeline\" href=\"#ikmb-metagenomic-profiling-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipelines analyses short reads and identifies the most likely species in the respective sample.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecuting the pipeline\u003c/h2\u003e\u003ca id=\"user-content-executing-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Executing the pipeline\" href=\"#executing-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline requires Nextflow \u0026gt;= 0.30.2. All dependencies are provided through Bioconda. Please make sure that conda/miniconda2 are available before starting the pipeline.\u003c/p\u003e\n\u003cp\u003ePlease note that Pathoscope, Metaphlan and Kaiju require reference databases which are \u003cem\u003enot\u003c/em\u003e included with the Bioconda packages. On RZCluster, these are\navailable automatically through the included config file.\u003c/p\u003e\n\u003cp\u003eThis pipeline uses a CSV formatted file as input, using the following columns (separated by \u0027;\u0027):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epatientID\u003c/li\u003e\n\u003cli\u003esampleID\u003c/li\u003e\n\u003cli\u003esample type (e.g. Blood)\u003c/li\u003e\n\u003cli\u003eread type (e.g. 2x150bp)\u003c/li\u003e\n\u003cli\u003eplatform (e.g. HiSeq 4000)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA simple script to create such a basic input format from a folder of FastQ files is included in the bin/ subfolder.\u003c/p\u003e\n\u003cp\u003eTo run the pipeline, do:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow -c nextflow.config run main.nf --samples Samples.csv\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhere nextflow.config and main.nf need to be passed with their full path to the local git clone.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1581391833.0 + "updated_at": 1603659103.0 }, { "data_format": 2, - "description": "Singularity containers", + "description": "An abricate singularity recipe", "filenames": [ - "Singularity.py35", - "Singularity.py37_mcts~", - "Singularity.py35_mcts", - "Singularity.py36_pybullet_pybox2d_pytorch", - "Singularity.py37_pybullet~", - "Singularity.py37_mcts", - "Singularity.py37_pybullet" + "Singularity", + "v0.8/DB20180717/Singularity.v0.8_DB20180717" ], - "full_name": "MicroSTM/singularity_containers", + "full_name": "phgenomics-singularity/abricate", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity containers\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbricate --- A Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-abricate-----a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Abricate --- A Singularity Container\" href=\"#abricate-----a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1288\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for \u003ca href=\"https://github.com/tseemann/abricate\"\u003eAbricate\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ePull the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name TMP_DIRECTORY shub://phgenomics-singularity/Abricate@latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003eAbricate.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./Abricate.simg --help\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name mlst shub://phgenomics-singularityAbricate@VERSION.NUMBER\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir HOME/singularity\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name Abricate shub://phgenomics-singularity/Abricate@latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLink the image to a folder in your \u003ccode\u003ePATH\u003c/code\u003e (e.g., \u003ccode\u003eHOME/bin\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eln -s HOME/singularity/Abricate.simg HOME/bin/Abricate\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e abricate --help\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdating the DB\u003c/h2\u003e\u003ca id=\"user-content-updating-the-db\" class=\"anchor\" aria-label=\"Permalink: Updating the DB\" href=\"#updating-the-db\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eRun the \u003ccode\u003eupdate_db.py\u003c/code\u003e script (default version is 0.8 at the moment)\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1628067083.0 + "updated_at": 1564581420.0 }, { "data_format": 2, - "description": "Research template repository for the University of Pennsylvania CNT lab", + "description": "A test of singularity with conda", "filenames": [ - "core_libraries/subtrees/MRtrix3/Singularity", - "core_libraries/subtrees/PreQual/Singularity" + "Singularity" ], - "full_name": "UPennBJPrager/CNT_Research_Template", + "full_name": "s-andrews/singularityconda", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCNT Research Repository Template\u003c/h1\u003e\u003ca id=\"user-content-cnt-research-repository-template\" class=\"anchor\" aria-label=\"Permalink: CNT Research Repository Template\" href=\"#cnt-research-repository-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/004a49ac296a3a07d489192d357f2eb3bf4f113d9e09beda8ef0145c528f0d62/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e322e312d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/004a49ac296a3a07d489192d357f2eb3bf4f113d9e09beda8ef0145c528f0d62/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e322e312d626c7565\" alt=\"version\" data-canonical-src=\"https://img.shields.io/badge/version-0.2.1-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/08b2323eca8d03dc78847012206c948114fdcfa0eea71a46bcc3ffd71ac45cc9/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069702e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/08b2323eca8d03dc78847012206c948114fdcfa0eea71a46bcc3ffd71ac45cc9/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069702e737667\" alt=\"pip\" data-canonical-src=\"https://img.shields.io/pypi/v/pip.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c7b947b8ec2d00f5a40dcfee4cf551b025fba56093f7d011e4422126eafbccce/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f34\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c7b947b8ec2d00f5a40dcfee4cf551b025fba56093f7d011e4422126eafbccce/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f34\" alt=\"https://img.shields.io/pypi/pyversions/\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of this template is to consolidate shared libraries and enable consistent workflows and tests for most projects in the CNT lab. Users will be able to quickly load code from tested common libraries, or load their own personal code, in an object oriented manner.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePrerequisites\u003c/h1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use this repository, you must have access to either Python or Matlab.\u003c/p\u003e\n\u003cp\u003eWe also highly recommend the use of a virtual environment, conda environment, or similar software to manage distributions. Examples for their use can be found in the documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to install any of the common library code, we provide instructions for both Python and Matlab below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython\u003c/h2\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor python packages, python wheels and tarballs can be found in: CNT_Development/core_libraries/python/.\u003c/p\u003e\n\u003cp\u003eTo install, run:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epip install foo.whl\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eor\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epip install foo.tar.gz\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere foo is the name of the library of interest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMatlab\u003c/h2\u003e\u003ca id=\"user-content-matlab\" class=\"anchor\" aria-label=\"Permalink: Matlab\" href=\"#matlab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd37\u200d\u2640\ufe0f In development.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocumentation\u003c/h1\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis template is intended to be used as both an environment and a simple wrapper for research code. Before beginning, we highly recommend that a virtual environment (or equivalent) is created for each\nproject to ensure your dependencies and code are properly referenced. Examples for creating virtual environments is provided below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRepository Structure\u003c/h2\u003e\u003ca id=\"user-content-repository-structure\" class=\"anchor\" aria-label=\"Permalink: Repository Structure\" href=\"#repository-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA hyperlink enabled repository tree is available within the \u003ca href=\"./repository_structure.md\"\u003erepository_structure\u003c/a\u003e markdown file. We demonstrate the use of git-ginored files and folders by displaying those\nentries with a \u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e symbol.\u003c/p\u003e\n\u003cp\u003eA short description of some of the top-level directories and files are as follows:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecore_libraries\u003c/h3\u003e\u003ca id=\"user-content-core_libraries\" class=\"anchor\" aria-label=\"Permalink: core_libraries\" href=\"#core_libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains the submodules and build files that make up the core libraries used for lab-wide projects.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edata_pointers\u003c/h3\u003e\u003ca id=\"user-content-data_pointers\" class=\"anchor\" aria-label=\"Permalink: data_pointers\" href=\"#data_pointers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains pointers to data contained on Borel and Lief. Data requests should reference these data pointers to prevent duplication before downloading new data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edocuments\u003c/h3\u003e\u003ca id=\"user-content-documents\" class=\"anchor\" aria-label=\"Permalink: documents\" href=\"#documents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains various research documents associated with a project (i.e. SoPs, Pipeline diagrams, etc.) as well as code documentation (e.g.document strings) for the various libraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eexamples\u003c/h3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains example python and matlab scripts for various research tasks as well as how to use common libraries and environments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ereference_data\u003c/h3\u003e\u003ca id=\"user-content-reference_data\" class=\"anchor\" aria-label=\"Permalink: reference_data\" href=\"#reference_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains data that can be used for building targets or conducting unit tests.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esample_data\u003c/h3\u003e\u003ca id=\"user-content-sample_data\" class=\"anchor\" aria-label=\"Permalink: sample_data\" href=\"#sample_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains sample data that might be used in any of the lab-wide projects.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003escripts\u003c/h3\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-label=\"Permalink: scripts\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains user-defined scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eunit_tests\u003c/h3\u003e\u003ca id=\"user-content-unit_tests\" class=\"anchor\" aria-label=\"Permalink: unit_tests\" href=\"#unit_tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains unit tests for validating new/altered code at both the machine level and model level.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003euser_data\u003c/h3\u003e\u003ca id=\"user-content-user_data\" class=\"anchor\" aria-label=\"Permalink: user_data\" href=\"#user_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder is meant to store user data. Data in this repository is private by default and will not be uploaded to public repositories.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e.gitignore\u003c/h3\u003e\u003ca id=\"user-content-gitignore\" class=\"anchor\" aria-label=\"Permalink: .gitignore\" href=\"#gitignore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis file helps prevent certain files from being uploaded to the public repository. This can be to avoid excess data volumes, or to protect sensitive information. By default, the ignored files and\nfolders are designed for the development of a lab-wide template, and users should adjust the settings to match their own needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVirtual Environments\u003c/h1\u003e\u003ca id=\"user-content-virtual-environments\" class=\"anchor\" aria-label=\"Permalink: Virtual Environments\" href=\"#virtual-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython\u003c/h2\u003e\u003ca id=\"user-content-python-1\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConda\u003c/h3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend using the pre-built environment files provided to start your project. These files can be found in the following subfolders: core_libraries/python/*/*yml and can be installed using the following command:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda env create -f foo.yml\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere foo is the name of the environment.\u003c/p\u003e\n\u003cp\u003eFor those who wish to create their own environment, we introduced some of the basics below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreation\u003c/h4\u003e\u003ca id=\"user-content-creation\" class=\"anchor\" aria-label=\"Permalink: Creation\" href=\"#creation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda create --name myenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere myenv is the name of the environment you wish to create.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eListing environments\u003c/h4\u003e\u003ca id=\"user-content-listing-environments\" class=\"anchor\" aria-label=\"Permalink: Listing environments\" href=\"#listing-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda env list\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eActivating Environment\u003c/h4\u003e\u003ca id=\"user-content-activating-environment\" class=\"anchor\" aria-label=\"Permalink: Activating Environment\" href=\"#activating-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda activate myenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere myenv is the name of the environment you wish to activate.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDeactivating an environment\u003c/h4\u003e\u003ca id=\"user-content-deactivating-an-environment\" class=\"anchor\" aria-label=\"Permalink: Deactivating an environment\" href=\"#deactivating-an-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda deactivate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMore information\u003c/h4\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-label=\"Permalink: More information\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information, please read: \u003ca href=\"https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment\" rel=\"nofollow\"\u003ehttps://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVirtual Environment\u003c/h3\u003e\u003ca id=\"user-content-virtual-environment\" class=\"anchor\" aria-label=\"Permalink: Virtual Environment\" href=\"#virtual-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst make sure you have venv installed. If not, you can pip install it as follows: pip install venv\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreation\u003c/h4\u003e\u003ca id=\"user-content-creation-1\" class=\"anchor\" aria-label=\"Permalink: Creation\" href=\"#creation-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003epython3 -m venv /path/to/new/virtual/environment\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eListing environments\u003c/h4\u003e\u003ca id=\"user-content-listing-environments-1\" class=\"anchor\" aria-label=\"Permalink: Listing environments\" href=\"#listing-environments-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003elsvirtualenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYou may need to install virutalenvwrapper to use this command. ( pip install virtualenvwrapper. ) If it doesn\u0027t populate to your path, check the package directory for the executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eActivating Environment\u003c/h4\u003e\u003ca id=\"user-content-activating-environment-1\" class=\"anchor\" aria-label=\"Permalink: Activating Environment\" href=\"#activating-environment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esource /path/to/venv/bin/activate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDeactivating an environment\u003c/h4\u003e\u003ca id=\"user-content-deactivating-an-environment-1\" class=\"anchor\" aria-label=\"Permalink: Deactivating an environment\" href=\"#deactivating-an-environment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003edeactivate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(Type this command in your shell.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMatlab\u003c/h2\u003e\u003ca id=\"user-content-matlab-1\" class=\"anchor\" aria-label=\"Permalink: Matlab\" href=\"#matlab-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd37\u200d\u2642\ufe0f\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContact Us\u003c/h1\u003e\u003ca id=\"user-content-contact-us\" class=\"anchor\" aria-label=\"Permalink: Contact Us\" href=\"#contact-us\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny questions should be directed to the data science team. Contact information is provided below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:bjprager@seas.upenn.edu\"\u003eBrian Prager\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:asuncion@seas.upenn.edu\"\u003eJoshua Asuncion\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1684488681.0 + "updated_at": 1548255424.0 }, { "data_format": 2, - "description": "Collection of Dockerfiles and Singularity recipes for PetIBM", + "description": "singularity container for software development", "filenames": [ - "singularity/Singularity.0.5.4-HPCX207-CUDA102-bionic", - "singularity/Singularity.0.5.2-GPU-OpenMPI-centos7", - "singularity/Singularity.0.5.1-GPU-OpenMPI-xenial", - "singularity/Singularity.0.4.2-GPU-OpenMPI-xenial", - "singularity/Singularity.0.5.3-GPU-OpenMPI-focal", - "singularity/Singularity.0.5-GPU-OpenMPI-xenial" + "Singularity" ], - "full_name": "barbagroup/petibm-recipes", + "full_name": "cshelton/devcontainer", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker and Singularity recipes for PetIBM\u003c/h1\u003e\u003ca id=\"user-content-docker-and-singularity-recipes-for-petibm\" class=\"anchor\" aria-label=\"Permalink: Docker and Singularity recipes for PetIBM\" href=\"#docker-and-singularity-recipes-for-petibm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/barbagroup/petibm-recipes/raw/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/439fd5ffe2ac21b3daa03ac6474978163be089da5fbba2790769ae2275464e35/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d425344253230332d2d436c617573652d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.docker.com/u/barbagroup/repository/docker/barbagroup/petibm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e94e378ff6380fa9aff7c21b9318eea65a0c47459473fafade5317993ff7526/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65722d2d6875622d696e666f726d6174696f6e616c2e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/hosted-docker--hub-informational.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3692\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains Dockerfiles and Singularity recipes used to build and share images of the runtime environment required for \u003ca href=\"https://github.com/barbagroup/PetIBM\"\u003ePetIBM\u003c/a\u003e.\nDockerfiles are located in the \u003ccode\u003edocker\u003c/code\u003e folder; Singularity recipes are in the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eDocker and Singularity images are available for:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePetIBM (0.4.2, 0.5, 0.5.1, 0.5.2, 0.5.3, 0.5.4)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo report bugs, submit questions, or offer suggestions, please use the GitHub issue tracking system.\nWe also welcome pull-requests.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edevcontainer\u003c/h1\u003e\u003ca id=\"user-content-devcontainer\" class=\"anchor\" aria-label=\"Permalink: devcontainer\" href=\"#devcontainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity container for software development\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1647661524.0 + "updated_at": 1693145631.0 }, { "data_format": 2, - "description": "UNDER CONSTRUCTION - Scripts for the workshop on OpenFOAM containers", + "description": null, "filenames": [ - "04_buildingAnOpenFOAMContainer/openfoam-2.4.x/02_PortingToSingularity/Singularity.def" + "Singularity" ], - "full_name": "PawseySC/containers-openfoam-workshop-scripts", + "full_name": "ReproducibilityInPublishing/j.jcp.2016.08.012", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWorkshop on the usage of OpenFOAM containers at Pawsey\u003c/h1\u003e\u003ca id=\"user-content-workshop-on-the-usage-of-openfoam-containers-at-pawsey\" class=\"anchor\" aria-label=\"Permalink: Workshop on the usage of OpenFOAM containers at Pawsey\" href=\"#workshop-on-the-usage-of-openfoam-containers-at-pawsey\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOrganisers\u003c/strong\u003e: Alexis Espinosa (PawseySC) and Marco De La Pierre (PawseySC)\u003c/p\u003e\n\u003cp\u003eThe use of containers has become an attractive framework for several areas of research supported by Pawsey (including bioinformatics and machine learning, among others).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNow, Pawsey supports the usage of OpenFOAM containers.\u003c/strong\u003e For the most recent versions of OpenFOAM (and some others), Pawsey have prebuilt and tested Singularity containers.\u003c/p\u003e\n\u003cp\u003eThis repository contains material for the exercises of the workshop.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep-by-step guide\u003c/strong\u003e: \u003ca href=\"https://pawseysc.github.io/containers-openfoam-workshop\" rel=\"nofollow\"\u003ehttps://pawseysc.github.io/containers-openfoam-workshop\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eA Fast Marching Algorithm for the Factored Eikonal Equation\u003c/h1\u003e\u003ca id=\"user-content-a-fast-marching-algorithm-for-the-factored-eikonal-equation\" class=\"anchor\" aria-label=\"Permalink: A Fast Marching Algorithm for the Factored Eikonal Equation\" href=\"#a-fast-marching-algorithm-for-the-factored-eikonal-equation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a Reproduction Package as described in the manuscript \"Three Empirical Principles\nfor Computational Reproducibility and their Implementation: \u003cem\u003eThe Reproduction Package\u003c/em\u003e\"\nby M. S. Krafczyk, A. Shi, A. Bhaskar, D. Marinov, \u0026amp; V. Stodden.\u003c/p\u003e\n\u003cp\u003eThis Reproduction Package includes the directory \u003ccode\u003eexpected_figures\u003c/code\u003e as well as \u003ccode\u003eexpected_output\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://dx.doi.org/10.1016/j.jcp.2016.08.012\" rel=\"nofollow\"\u003e\"A fast marching algorithm for the factored eikonal\nequation\"\u003c/a\u003e discusses an algorithm\nfor solving the factored eikonal equation. Practically, this can be used to\nperform a kind of slowness tomography whereby travel times from some source\nwithin a material can be used to determine the slowness of an object with\nrespect to position throughout the object.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Instructions\u003c/h2\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-label=\"Permalink: Build Instructions\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the software in this package, all you need is an appropriate Docker installation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions were tested using\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker version 18.06.0-ce, build 0ffa825, on Ubuntu 16.04.5 LTS.\u003c/li\u003e\n\u003cli\u003eDocker version 19.03.1, build 74b1e89, on Mac OS Mojave (10.14.6)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding with Docker\u003c/h3\u003e\u003ca id=\"user-content-building-with-docker\" class=\"anchor\" aria-label=\"Permalink: Building with Docker\" href=\"#building-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Dockerfile handles installation of all necessary dependencies. Simply execute the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t ${DOCKER_IMAGE_NAME} .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Instructions\u003c/h2\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-label=\"Permalink: Run Instructions\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the Docker container\u003c/h3\u003e\u003ca id=\"user-content-running-the-docker-container\" class=\"anchor\" aria-label=\"Permalink: Running the Docker container\" href=\"#running-the-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo start a container for the Docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm -v $(pwd):/Scratch ${DOCKER_IMAGE_NAME}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInitialize the Julia environment\u003c/h3\u003e\u003ca id=\"user-content-initialize-the-julia-environment\" class=\"anchor\" aria-label=\"Permalink: Initialize the Julia environment\" href=\"#initialize-the-julia-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore running the experiments, you must first run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./algo.sh init\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis downloads necessary Julia packages.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun the experiments\u003c/h3\u003e\u003ca id=\"user-content-run-the-experiments\" class=\"anchor\" aria-label=\"Permalink: Run the experiments\" href=\"#run-the-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run the experiments, you use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./algo.sh run \u0026lt;num 2d refinements\u0026gt; \u0026lt;num 3d refinements\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput will be written to \u003ccode\u003eresults/results_\u0026lt;x\u0026gt;_\u0026lt;y\u0026gt;.txt\u003c/code\u003e where \u003ccode\u003ex\u003c/code\u003e is \u003ccode\u003enum 2d refinements\u003c/code\u003e and \u003ccode\u003ey\u003c/code\u003e is \u003ccode\u003enum 3d refinements\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis command also creates the figures for the experiment. They can be found in \u003ccode\u003efigures/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCheck the experiment results\u003c/h3\u003e\u003ca id=\"user-content-check-the-experiment-results\" class=\"anchor\" aria-label=\"Permalink: Check the experiment results\" href=\"#check-the-experiment-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to check the results of the experiment, you use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./algo.sh check \u0026lt;num 2d refinements\u0026gt; \u0026lt;num 3d refinements\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe expected output to be compared against is found in \u003ccode\u003eexpected_output/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe expected figures are found in \u003ccode\u003eexpected_figures/\u003c/code\u003e, however the script does not compare these with the generated figures.\u003c/p\u003e\n\u003cp\u003eThis command will let you know if the results match what is expected or not.\u003c/p\u003e\n\u003cp\u003eThe script that checks equality to within a reasonable degree is \u003ccode\u003eexamples/check_results.py\u003c/code\u003e. If you wish to tighten or loosen the margin of error, modify that file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Everything\u003c/h3\u003e\u003ca id=\"user-content-run-everything\" class=\"anchor\" aria-label=\"Permalink: Run Everything\" href=\"#run-everything\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo initilize, run the experiments, and check the results with one command, use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./algo.sh all \u0026lt;num 2d refinements\u0026gt; \u0026lt;num 3d refinements\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease be aware of computational efforts for the scripts. More details can be found \u003ca href=\"COMPUTATIONAL_EFFORTS.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNotes about Running All Rows\u003c/h3\u003e\u003ca id=\"user-content-notes-about-running-all-rows\" class=\"anchor\" aria-label=\"Permalink: Notes about Running All Rows\" href=\"#notes-about-running-all-rows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo reproduce all 6 rows from the article, use \u003ccode\u003e6\u003c/code\u003e as both \u003ccode\u003enum 2d refinements\u003c/code\u003e and \u003ccode\u003enum 3d refinements\u003c/code\u003e. Note that it does consume a lot of memory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReproduction Notes\u003c/h2\u003e\u003ca id=\"user-content-reproduction-notes\" class=\"anchor\" aria-label=\"Permalink: Reproduction Notes\" href=\"#reproduction-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe kept track of our progress and issues inside \u003ccode\u003enotes.txt\u003c/code\u003e. We also have an\njupyter notebook showing this progress over time \u003ccode\u003eReproducibilityPlot.ipynb\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe want acknowledge the authors for their fine work on this experiment. We\nsucceeded with this project where many others had failed. The authors should be commended on putting together high quality work.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGrant Acknowledgement\u003c/h2\u003e\u003ca id=\"user-content-grant-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Grant Acknowledgement\" href=\"#grant-acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was partially funded by NSF grants OAC-1839010 and CNS-1646305.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1638345755.0 + "updated_at": 1625533090.0 }, { "data_format": 2, "description": null, "filenames": [ - "SingularityRecipe" + "Singularity" ], - "full_name": "mwever/tpami-automlc", + "full_name": "lifebit-ai/rnaseq", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAutoML for Multi-Label Classification: Overview and Empirical Evaluation\u003c/h1\u003e\u003ca id=\"user-content-automl-for-multi-label-classification-overview-and-empirical-evaluation\" class=\"anchor\" aria-label=\"Permalink: AutoML for Multi-Label Classification: Overview and Empirical Evaluation\" href=\"#automl-for-multi-label-classification-overview-and-empirical-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project provides a platform for benchmarking different optimizers for the task of automated machine learning ensuring all optimizers to work on the same set of potential solution candidates. The implementation is based on the Java open-source library \u003ca href=\"https://github.com/starlibs/AILibs\"\u003eAILibs\u003c/a\u003e, providing the basic technical support for describing search spaces, HTN planning and heuristic search algorithms, as well as the infrastructure for synchronizing the execution of a benchmarking suite on a distributed system.\u003c/p\u003e\n\u003cp\u003eThe benchmark distinguishes itself from previously published benchmark in the way how the optimizers are integrated with the benchmarking system. While all optimizers share the same routine for executing candidate solutions, the benchmark works in a cross-platform fashion, i.e. although the benchmark and the execution of candidate solutions is implemented in Java, optimizers available in Python can be benchmarked within the system. The search space and recursive structures of the search space are automatically translated into a format understandable to the respective optimizers. The inter-platform communication is done via the \u003ca href=\"https://developers.google.com/protocol-buffers\" rel=\"nofollow\"\u003eGoogle ProtoBuf\u003c/a\u003e library which offers interfaces for various platforms. Thereby, the communication link only transfers the execution request to the benchmarking system allowing to share the same evaluation routine for all the optimizers. Another advantage is that it also allows for live-tracking the performance of the optimizers, logging each evaluated candidate and monitoring the current incumbent at any point in time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"#quickstart---setup\"\u003eQuickstart - Setup\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#preparing-the-singularity-container\"\u003ePreparing the Singularity Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#without-a-singularity-container\"\u003eWithout a Singularity Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#test-your-setup\"\u003eTest your Setup\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-the-benchmark\"\u003eRunning the Benchmark\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#hardware-requirements\"\u003eHardware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#initialize-the-database-server\"\u003eInitialize the Database Server\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-a-worker-client\"\u003eRunning a Worker Client\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#post-processing-for-anytime-test-performances\"\u003ePost-Processing for Anytime Test Performances\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#visualizing-benchmark-data\"\u003eVisualizing Benchmark Data\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"#inspecting-the-search-space\"\u003eInspecting the Search Space\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#list-algorithms-contained-in-the-search-space\"\u003eList Algorithms Contained in the Search Space\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#export-to-gephi-to-visualize-the-search-space-as-a-dag\"\u003eExport to Gephi to visualize the search space as a DAG\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generate-html-overview\"\u003eGenerate HTML Overview\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#evaluation-results\"\u003eEvaluation Results\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#generate-one-vs-rest-scatter-plots\"\u003eGenerate One-VS-Rest Scatter Plots\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generate-anytime-average-rank-plots\"\u003eGenerate Anytime Average Rank Plots\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generate-result-tables\"\u003eGenerate Result Tables\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generate-incumbent-frequency-statistics\"\u003eGenerate Incumbent Frequency Statistics\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuickstart - Setup\u003c/h2\u003e\u003ca id=\"user-content-quickstart---setup\" class=\"anchor\" aria-label=\"Permalink: Quickstart - Setup\" href=\"#quickstart---setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePrerequisites: Due to certain dependencies requiring a Linux operating system, the execution of optimizers in Python is not supported for Windows nor MacOS. However, optimizers available in Java can still be executed on Windows or MacOS. Running a Linux OS, you may execute Python optimizers as well. To this end, please use the SingularityRecipe to build a container in order to ensure all the dependencies necessary for running the benchmark are available.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePreparing the Singularity Container\u003c/h3\u003e\u003ca id=\"user-content-preparing-the-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Preparing the Singularity Container\" href=\"#preparing-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to set up a Singularity Container, please ensure that you have administrator permissions and installed the \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/\" rel=\"nofollow\"\u003eSingularity Container\u003c/a\u003e software on your computer. (Hint: On Ubuntu you can simply install it via \u003ccode\u003esudo apt install singularity-container\u003c/code\u003e). Once you have Singularity Container installed on your system, follow these steps in order to create the container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBuild a singularity container from the provided recipe file: \u003ccode\u003esudo singularity build automlc.sif SingularityRecipe\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOnce the container is built, you can now proceed to run a shell within the Singularity container like this: \u003ccode\u003esingularity shell automlc.sif\u003c/code\u003e (No sudo this time).\u003c/li\u003e\n\u003cli\u003ePlease make sure that your current folder is mounted into the Singularity container. You may prove this by typing \u003ccode\u003edir\u003c/code\u003e. If the files of the project\u0027s root directory are printed on your command line, everything should be fine.\u003c/li\u003e\n\u003cli\u003eYou are now prepared to run the tasks via the gradle wrapper. For further steps please have a look at the subsequent documentation.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWithout a Singularity Container\u003c/h3\u003e\u003ca id=\"user-content-without-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Without a Singularity Container\" href=\"#without-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are running Linux you also have the possibility to run the benchmark directly on your system, without creating any Singularity container.\nTo this end, we have prepared a \u003ccode\u003erequirements.txt\u003c/code\u003e file so that you can install the required dependencies for the Python environment with ease.\nHowever, since we mainly work with Singularity containers to have a clearly distinct system running, preventing interferences with inappropriate python versions and compatibility conflicts in general, we do not officially support the setup variant without creating a Singularity container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest your Setup\u003c/h3\u003e\u003ca id=\"user-content-test-your-setup\" class=\"anchor\" aria-label=\"Permalink: Test your Setup\" href=\"#test-your-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to test your setup we have prepared a test runner that will work out-of-the-box if everything has been setup correctly.\nMore precisely, you can test whether each of the optimizers can be run for a specific dataset split with short timeouts of 1 minute for the entire optimization run and 45 seconds for a single evaluation.\u003c/p\u003e\n\u003cp\u003eYou can test to run each optimizer individually via the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew testHTNBF\n./gradlew testBOHB\n./gradlew testHB\n./gradlew testSMAC\n./gradlew testRandomSearch\n./gradlew testGGP\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs a shortcut you can also simply test all the optimizers as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew testAllOptimizers\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the Benchmark\u003c/h2\u003e\u003ca id=\"user-content-running-the-benchmark\" class=\"anchor\" aria-label=\"Permalink: Running the Benchmark\" href=\"#running-the-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe benchmark implemented in this repository is meant to be run in a distributed way.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Requirements\u003c/h3\u003e\u003ca id=\"user-content-hardware-requirements\" class=\"anchor\" aria-label=\"Permalink: Hardware Requirements\" href=\"#hardware-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor running a benchmark suite you need the following resources:\nA central database server managing the experiments to be executed and worker clients meeting the hardware requirements.\nIn the paper we used worker clients each equipped with \u003ccode\u003e8 CPU cores\u003c/code\u003e and \u003ccode\u003e32GB RAM\u003c/code\u003e.\nThe recommended hardware specifications for the database server depends on the degree of parallelization and how you set the parameters for the experiments.\nThe latter point is for instance depending what evaluation timeouts you choose for assessing the performance of single candidates.\nThe smaller the timeout the more intermediate evaluation results will be logged in the database.\nAs a consequence there will be a higher load on the respective database server.\nIn our study and with the timeout configuration proposed in the paper, we found that a configuration of \u003ccode\u003e4 CPU cores\u003c/code\u003e and \u003ccode\u003e16GB RAM\u003c/code\u003e is sufficient to deal with up to 200 worker clients.\nFor the database, we tested only a MySQL database. In principle other drivers are usable, but may require the inclusion of additional dependencies for the project.\nOfficially, we only support MySQL databases.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInitialize the Database Server\u003c/h3\u003e\u003ca id=\"user-content-initialize-the-database-server\" class=\"anchor\" aria-label=\"Permalink: Initialize the Database Server\" href=\"#initialize-the-database-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to initialize the database server, first of all, you need to fill in the connection details in the \u003ccode\u003eautomlc-setup.properties\u003c/code\u003e file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e...\n\u003cspan class=\"pl-k\"\u003edb.driver\u003c/span\u003e = mysql\n\u003cspan class=\"pl-k\"\u003edb.host\u003c/span\u003e = \u0026lt;YOUR DB HOST\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.username\u003c/span\u003e = \u0026lt;YOUR DB USER\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.password\u003c/span\u003e = \u0026lt;YOUR DB PASSWORD\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.database\u003c/span\u003e = \u0026lt;YOUR DATABASE NAME\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.table\u003c/span\u003e = \u0026lt;YOUR JOBS TABLE NAME\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.ssl\u003c/span\u003e = \u0026lt;FLAG WHETHER TO USE SSL\u0026gt;\n\u003cspan class=\"pl-k\"\u003ecandidate_eval_table\u003c/span\u003e = \u0026lt;YOUR INTERMEDIATE EVALUATIONS TABLE NAME\u0026gt;\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe specifics of the benchmark are then given with the following properties in the same files:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003emem.max\u003c/span\u003e = 32768 // maximum available memory\n\u003cspan class=\"pl-k\"\u003ecpu.max\u003c/span\u003e = 8 // number of cores\n\n... // database connection properties and AILibs experimenter specific properties \n\n\n\u003cspan class=\"pl-k\"\u003ealgorithm\u003c/span\u003e = bf,random,hb,bohb,smac,ggp // optimizers to consider\n\u003cspan class=\"pl-k\"\u003edataset\u003c/span\u003e = arts1,bibtex,birds,bookmarks,business1,computers1,education1,emotions,enron-f,entertainment1,flags,genbase,health1,llog-f,mediamill,medical,recreation1,reference1,scene,science1,social1,society1,tmc2007,yeast // datasets to consider\n\u003cspan class=\"pl-k\"\u003emeasure\u003c/span\u003e = FMacroAvgD,FMacroAvgL,FMicroAvg // performance measures to consider\n\u003cspan class=\"pl-k\"\u003esplit\u003c/span\u003e = 0,1,2,3,4,5,6,7,8,9 // split indices to consider\n\u003cspan class=\"pl-k\"\u003eseed\u003c/span\u003e = 42 // seed of the dataset splitter to consider\n\u003cspan class=\"pl-k\"\u003eglobalTimeout\u003c/span\u003e=86400 // timeout for an entire optimization run\n\u003cspan class=\"pl-k\"\u003eevaluationTimeout\u003c/span\u003e=1800 // timeout for a single candidate evaluation\n\n... // constant properties for the experiment runner\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBased on this specification, the benchmark will compute all possible combinations of entires given via the fields \u003ccode\u003ealgorithm\u003c/code\u003e, \u003ccode\u003edataset\u003c/code\u003e, \u003ccode\u003emeasure\u003c/code\u003e, \u003ccode\u003esplit\u003c/code\u003e, \u003ccode\u003eseed\u003c/code\u003e, \u003ccode\u003eglobalTimeout\u003c/code\u003e, \u003ccode\u003eevaluationTimeout\u003c/code\u003e.\nIn principle it is also possible to configure multiple seeds, globalTimeouts or evaluationTimeouts in the same style as it is done e.g. for the algorithm field, i.e. simply by seperating multiple values by a comma.\u003c/p\u003e\n\u003cp\u003eOnce the database connection is configured and the property values for all the benchmark suite specific parameters have been set, you can proceed by initializing the database server centrally managing the experiment conduction with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew initializeExperimentsInDatabase\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will automatically create a table with the name specified in the \u003ccode\u003edb.table\u003c/code\u003e property which will then specify all the experiments to be executed which have been computed via taking the cross-product of all possible combinations of the properties describing the benchmark suite.\u003c/p\u003e\n\u003cp\u003eFor cleaning this table, i.e., removing all of its entries, you can run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew cleanExperimentsInDatabase\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eCaution:\u003c/strong\u003e This functionality will also remove all results that are stored in this table after running an experiment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePreparing Dataset Splits\u003c/h3\u003e\u003ca id=\"user-content-preparing-dataset-splits\" class=\"anchor\" aria-label=\"Permalink: Preparing Dataset Splits\" href=\"#preparing-dataset-splits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe original datasets from which the train and test splits have been derived are provided via this repository as well.\nThe datasets are located in the \u003ccode\u003eoriginal_datasets/\u003c/code\u003e directory.\nIn order to derive the train and test dataset splits via a 10-fold cross-validation, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew generateDatasetSplits\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a directory \u003ccode\u003edatasets/\u003c/code\u003e, where the generated train and test splits are stored in seperated files.\nAs this procedure is done for all datasets contained in the \u003ccode\u003eoriginal_datasets/\u003c/code\u003e directory and seed combinations, this probably takes some time and disk space.\nThe generated files follow the name schema \u003ccode\u003e\u0026lt;DATASET\u0026gt;_\u0026lt;SEED\u0026gt;_\u0026lt;SPLIT INDEX\u0026gt;_{train|test}.arff\u003c/code\u003e. As the worker client relies on this naming, the schema must not be changed.\nUnfortunately, we cannot provide the dataset splits used in our study directly, as this would dramatically increase the size of the repository and lead to unreasonable download times for cloning.\nHowever, on request we can of course provide the original dataset splits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: We assume that all worker clients either have all the dataset splits locally available or share a network hard drive, centrally providing access to the respective dataset splits. The dataset folder can be configured in the \u003ccode\u003eautomlc-setup.properties\u003c/code\u003e file via the property \u003ccode\u003edatasetFolder\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning a Worker Client\u003c/h3\u003e\u003ca id=\"user-content-running-a-worker-client\" class=\"anchor\" aria-label=\"Permalink: Running a Worker Client\" href=\"#running-a-worker-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce everything is set up correctly, you may run a worker client via the command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew runExperimentEvaluationWorker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe execution of the worker will also rely on the database connection configured in the \u003ccode\u003eautomlc-setup.properties\u003c/code\u003e.\nHowever, there is nothing specific you need to configure when deploying the worker client in a distributed way.\nIn fact, you can simply run the same command multiple times (on different nodes) in order to parallelize the processing of the benchmark.\u003c/p\u003e\n\u003cp\u003eThe central database server will take care that each of the specified experiments will be executed only once at maximum, i.e. it will prevent the same experiment from being conducted twice.\nSince one worker client will also only take care of running a single experiment, you need to deploy as many worker clients as there are rows in the jobs table (named as you configured it in the properties file). In addition to the final results, the worker clients will also store intermediate evaluation results, i.e., candidates that have been requested for evaluation by the respective optimizer together with the measured performance value.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePost-Processing for Anytime Test Performances\u003c/h3\u003e\u003ca id=\"user-content-post-processing-for-anytime-test-performances\" class=\"anchor\" aria-label=\"Permalink: Post-Processing for Anytime Test Performances\" href=\"#post-processing-for-anytime-test-performances\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince assessing the test performances on-line, i.e. during an optimizer run, would distort the overall perception of the optimization performance, all the test performances for later on compiling anytime plots etc. have been estimated via a post-processing step based on the log of intermediate candidate evaluations.\nThe post-processing is also implemented in a distributed way, analoguous to the already explained setup for running the actual benchmark of optimizers. In contrast to the latter, for the post-processing the \u003ccode\u003etest-eval.properties\u003c/code\u003e serves as a configuration.\nObviously, the jobs table for the post-processing needs a different name than the one specified for the benchmark itself. Furthermore, it will also use the \u003ccode\u003eautomlc-setup.properties\u003c/code\u003e file for accessing the logged data and filtering the evaluated candidates.\nAdditionally, the post-processing requires access to the datasets directory.\u003c/p\u003e\n\u003cp\u003eAs before you can configure the corresponding properties, for which optimizers,datasets, measures, etc. you want to run the post-processing. Furthermore you can setup the jobs table for distributing the workload on a cluster etc. as already described before for the benchmark via the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew initializePostProcessingsInDatabase\n./gradlew cleanPostProcessingsInDatabase\n./gradlew runPostProcessingWorker\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing Benchmark Data\u003c/h2\u003e\u003ca id=\"user-content-visualizing-benchmark-data\" class=\"anchor\" aria-label=\"Permalink: Visualizing Benchmark Data\" href=\"#visualizing-benchmark-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThroughout the paper, several visualization of the results have been presented. A pre-processed version of the logged data is contained in the directory \u003ccode\u003eresults/data/\u003c/code\u003e.\nFrom this data, you can further process the data to derive the numbers presented in the paper. Plots that have been generated via LaTeX\u0027s \u003ccode\u003etikz\u003c/code\u003e or \u003ccode\u003epgfplots\u003c/code\u003e, you can even generate the corresponding LaTeX code in the following.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInspecting the Search Space\u003c/h3\u003e\u003ca id=\"user-content-inspecting-the-search-space\" class=\"anchor\" aria-label=\"Permalink: Inspecting the Search Space\" href=\"#inspecting-the-search-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe configuration files containing the specification of the search space are contained in the folder \u003ccode\u003esearchspace/\u003c/code\u003e. For this, the root file is \u003ccode\u003esearchspace/meka-all.json\u003c/code\u003e and (recursively) includes the remaining configuration files\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eList Algorithms Contained in the Search Space\u003c/h4\u003e\u003ca id=\"user-content-list-algorithms-contained-in-the-search-space\" class=\"anchor\" aria-label=\"Permalink: List Algorithms Contained in the Search Space\" href=\"#list-algorithms-contained-in-the-search-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you only want to obtain a list of algorithms ordered by algorithm type that are contained in the search space including their abbreviation as given in the paper, you can use the following short cut:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew exportAlgorithmsInSearchSpace\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a txt file in the \u003ccode\u003e/results\u003c/code\u003e directory with the name \u003ccode\u003esearchspace-algorithms-in-space.txt\u003c/code\u003e listing all the algorithm types together with the algorithms belonging to these types.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExport to Gephi to visualize the search space as a DAG\u003c/h4\u003e\u003ca id=\"user-content-export-to-gephi-to-visualize-the-search-space-as-a-dag\" class=\"anchor\" aria-label=\"Permalink: Export to Gephi to visualize the search space as a DAG\" href=\"#export-to-gephi-to-visualize-the-search-space-as-a-dag\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://gephi.org/\" rel=\"nofollow\"\u003eGephi\u003c/a\u003e is a graph modeling and visualization tool. You can export the search space as described in the \u003ccode\u003esearchspace/\u003c/code\u003e folder to the Gephi graph format to be loaded and visualized in Gephi. By running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew exportSearchSpacesToGephiFormat\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eyou will find a folder \u003ccode\u003eresults/gephi-export\u003c/code\u003e, containing three files: \u003ccode\u003eslc-only.gephi\u003c/code\u003e, \u003ccode\u003emlc-only.gephi\u003c/code\u003e, and \u003ccode\u003emlc-complete.gephi\u003c/code\u003e. These three files contain a Gephi specification of a directed acyclic graph (DAG) where each node corresponds to one algorithm in the search space and an edge a dependency relation from one to another algorithm indicating that the algorithm represented by the parent node can be configured with the child node as a base learner/kernel. With these graph visualizations one can easily see the exponential growth of the search space when combining the single-label classification algorithms with the multi-label classification algorithms.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate HTML Overview\u003c/h4\u003e\u003ca id=\"user-content-generate-html-overview\" class=\"anchor\" aria-label=\"Permalink: Generate HTML Overview\" href=\"#generate-html-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate an HTML document summarizing the search space in terms of some statistics, listing all included algorithms together with their hyper-parameters (including their types, domains, and defaults) and recursive dependencies on other algorithms. The possible choices for each dependency are listed and linked within the document accordingly.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew generateMultiLabelSearchSpaceDescription\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will generate a file \u003ccode\u003eresults/searchspace-meka.html\u003c/code\u003e. If you want to generate the same type of description for the single-label classification (WEKA) search space use the following.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew generateSingleLabelSearchSpaceDescription\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will generate a file \u003ccode\u003eresults/searchspace-weka.html\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe bar charts comparing the two search spaces in the paper have been generated from the statistical values contained on the top of these HTML documents. However, since the plots have been generated using \u003ccode\u003ematplotlibs\u003c/code\u003e in Python, please refer to the Jupyter notebook \u003ccode\u003eTPAMI Plots.ipynb\u003c/code\u003e for deriving the bar charts from these statistics.\nTherewith, you can obtain the comparison figure for the different search spaces.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEvaluation Results\u003c/h3\u003e\u003ca id=\"user-content-evaluation-results\" class=\"anchor\" aria-label=\"Permalink: Evaluation Results\" href=\"#evaluation-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe data obtained by running the benchmark across various datasets and folds can be found in \u003ccode\u003eresults/data\u003c/code\u003e. From this data you can generate several statistics, summaries, and plots. How these can be generated is explained in the following.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate One-VS-Rest Scatter Plots\u003c/h4\u003e\u003ca id=\"user-content-generate-one-vs-rest-scatter-plots\" class=\"anchor\" aria-label=\"Permalink: Generate One-VS-Rest Scatter Plots\" href=\"#generate-one-vs-rest-scatter-plots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the paper we compared the different optimizers in a one-vs-rest fashion plotting their performances against each other facilitating the analysis which approach performs preferably over the rest. The plots can be generated with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew compileScatterPlots\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe command will produce its output in the folder \u003ccode\u003eresults/scatter-plots\u003c/code\u003e, where afterwards you will find for each optimizer and performance measure (instance-wise F-measure, label-wise F-measure, and micro F-measure) you will find a LaTeX file named like this: \u003ccode\u003escatter-\u0026lt;optimizer\u0026gt;VSrest-\u0026lt;measure\u0026gt;.tex\u003c/code\u003e. Furthermore you can find a \u003ccode\u003emain.tex\u003c/code\u003e which will include all packages and scatter plots to compile them into a PDF document.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Anytime Average Rank Plots\u003c/h4\u003e\u003ca id=\"user-content-generate-anytime-average-rank-plots\" class=\"anchor\" aria-label=\"Permalink: Generate Anytime Average Rank Plots\" href=\"#generate-anytime-average-rank-plots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFurthermore, we have presented anytime average rank plots comparing the different optimizers across the time dimension. You can compile the result data into these anytime plots by executing the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew compileAnytimeAverageRankPlots\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will generate several \u003ccode\u003e.tex\u003c/code\u003e-files in the directory \u003ccode\u003eresults/anytime-plots/\u003c/code\u003e.\nIn fact, there are even two different types of plots: First following the naming schema \u003ccode\u003eavgrank-\u0026lt;MEASURE\u0026gt;.tex\u003c/code\u003e you will find the average rank plots as presented in the paper.\nHowever, for each combination of measure and dataset you can also find the actual average anytime performance of the different optimizers contained in the files named after the schema \u003ccode\u003e\u0026lt;MEASURE\u0026gt;_\u0026lt;DATASET\u0026gt;.tex\u003c/code\u003e.\nSince presenting those would have required lots of space, these plots have not been included in the paper but are made available here as a kind of supplementary material.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Result Tables\u003c/h4\u003e\u003ca id=\"user-content-generate-result-tables\" class=\"anchor\" aria-label=\"Permalink: Generate Result Tables\" href=\"#generate-result-tables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a comparison of the performance of eventually returned incumbents, we presented tables for each measure individually.\nYou can generate these tables yourself once again by running the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew compileResultTables\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will again generate \u003ccode\u003e.tex\u003c/code\u003e-files containing the LaTeX code to represent the corresponding table data. In addition to the average performances per dataset and optimizer, a Wilcoxon signed-rank test is conducted with a threshold for the p-value of 0.05. The best results, significant improvements and degradations are highlighted as described in the paper. Furthermore, in the last row of the tables, an average rank for each optimizer across all datasets is given.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Incumbent Frequency Statistics\u003c/h4\u003e\u003ca id=\"user-content-generate-incumbent-frequency-statistics\" class=\"anchor\" aria-label=\"Permalink: Generate Incumbent Frequency Statistics\" href=\"#generate-incumbent-frequency-statistics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnother figure in the paper shows which algorithms have been chosen with what frequency by which optimizer. Since these plots have been generated with the help of matplotlibs, here, we will only compile the necessary statistics from the data, necessary to produce the figures.\nIn order to compile the statistics from the result data, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew compileIncumbentFrequencyStatistics\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will generate a txt file \u003ccode\u003eresults/incumbent-frequencies.txt\u003c/code\u003e containing JSON arrays that can be copied and pasted into a Jupyter notebook which is also available via this repository. Please refer to the \u003ccode\u003eTPAMI Plots.ipynb\u003c/code\u003e notebook file for further processing of the compiled raw data into the nested donut charts.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/nfcore-rnaseq_logo.png\"\u003e\u003cimg src=\"docs/images/nfcore-rnaseq_logo.png\" alt=\"nfcore/rnaseq\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\u003ca id=\"\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/rnaseq\" rel=\"nofollow\"\u003e\u003cimg 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rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90aa4d797a234aa5664e291671dac103ea72e08cdb05f0a0dd7159c34b9e8b68/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f726e617365712e737667\" alt=\"Docker Container available\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/rnaseq.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003enfcore/rnaseq\u003c/strong\u003e is a bioinformatics analysis pipeline used for RNA sequencing data.\u003c/p\u003e\n\u003cp\u003eThe workflow processes raw data from FastQ inputs (\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e, \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/\" rel=\"nofollow\"\u003eTrim Galore!\u003c/a\u003e), aligns the reads (\u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTAR\u003c/a\u003e or \u003ca href=\"https://ccb.jhu.edu/software/hisat2/index.shtml\" rel=\"nofollow\"\u003eHiSAT2\u003c/a\u003e), generates gene counts (\u003ca href=\"http://bioinf.wehi.edu.au/featureCounts/\" rel=\"nofollow\"\u003efeatureCounts\u003c/a\u003e, \u003ca href=\"https://ccb.jhu.edu/software/stringtie/\" rel=\"nofollow\"\u003eStringTie\u003c/a\u003e) and performs extensive quality-control on the results (\u003ca href=\"http://rseqc.sourceforge.net/\" rel=\"nofollow\"\u003eRSeQC\u003c/a\u003e, \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/dupRadar.html\" rel=\"nofollow\"\u003edupRadar\u003c/a\u003e, \u003ca href=\"http://smithlabresearch.org/software/preseq/\" rel=\"nofollow\"\u003ePreseq\u003c/a\u003e, \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/edgeR.html\" rel=\"nofollow\"\u003eedgeR\u003c/a\u003e, \u003ca href=\"http://multiqc.info/\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e). See the \u003ca href=\"docs/output.md\"\u003eoutput documentation\u003c/a\u003e for more details of the results.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a bioinformatics workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nfcore/rnaseq pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/aws.md\"\u003eAmazon Web Services (aws)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/uppmax.md\"\u003eSwedish UPPMAX clusters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/c3se.md\"\u003eSwedish cs3e Hebbe cluster\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/qbic.md\"\u003eT\u00fcbingen QBiC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/ccga.md\"\u003eCCGA Kiel\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese scripts were originally written for use at the \u003ca href=\"https://portal.scilifelab.se/genomics/\" rel=\"nofollow\"\u003eNational Genomics Infrastructure\u003c/a\u003e, part of \u003ca href=\"http://www.scilifelab.se/\" rel=\"nofollow\"\u003eSciLifeLab\u003c/a\u003e in Stockholm, Sweden, by Phil Ewels (\u003ca href=\"https://github.com/ewels\"\u003e@ewels\u003c/a\u003e) and Rickard Hammar\u00e9n (\u003ca href=\"https://github.com/Hammarn\"\u003e@Hammarn\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eMany thanks to other who have helped out along the way too, including (but not limited to):\n\u003ca href=\"https://github.com/Galithil\"\u003e@Galithil\u003c/a\u003e,\n\u003ca href=\"https://github.com/pditommaso\"\u003e@pditommaso\u003c/a\u003e,\n\u003ca href=\"https://github.com/orzechoj\"\u003e@orzechoj\u003c/a\u003e,\n\u003ca href=\"https://github.com/apeltzer\"\u003e@apeltzer\u003c/a\u003e,\n\u003ca href=\"https://github.com/colindaven\"\u003e@colindaven\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 28, "topics": [], - "updated_at": 1706803145.0 + "updated_at": 1552290431.0 }, { "data_format": 2, - "description": "A Singularity recipe for machine learning packages for use with gpus", + "description": "Dockerized and singularity-friendly cancer genomics tools ", "filenames": [ - "Singularity20210927", - "Singularity20220919", - "Singularity20210202", - "Singularity20210319", - "Singularity20220928", - "Singularity20220603", - "Singularity20190305", - "Singularity20210616", - "Singularity20190917", - "Singularity20220804", - "Singularity20210222", - "Singularity20210901", - "Singularity20210428", - "Singularity20190715", - "Singularity20200210", - "Singularity20200902", - "Singularity20210730", - "Singularity20200413" + "sclust/Singularity.sclust", + "vcf2maf/Singularity.vcf2maf", + "gistic2/Singularity.gistic2" ], - "full_name": "ResearchIT/singularity-ml", + "full_name": "rdmorin/cancer_docker_singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingulairty Recipe for keras\u003c/h1\u003e\u003ca id=\"user-content-singulairty-recipe-for-keras\" class=\"anchor\" aria-label=\"Permalink: Singulairty Recipe for keras\" href=\"#singulairty-recipe-for-keras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains the recipe for a general purpose gpu enabled machine learning container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersions:\u003c/h2\u003e\u003ca id=\"user-content-versions\" class=\"anchor\" aria-label=\"Permalink: Versions:\" href=\"#versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e1.0 - Initial effort\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecancer_docker_singularity\u003c/h1\u003e\u003ca id=\"user-content-cancer_docker_singularity\" class=\"anchor\" aria-label=\"Permalink: cancer_docker_singularity\" href=\"#cancer_docker_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDockerized and singularity-friendly cancer genomics tools\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1670046313.0 + "updated_at": 1583188383.0 }, { "data_format": 2, - "description": null, + "description": "RepeatExplorer2 singularity definition", "filenames": [ - "Singularity.harp", - "Singularity.utils" + "Singularity.0.3.8.dbaa07f", + "Singularity", + "Singularity.0.3.7.5996768" ], - "full_name": "cory-weller/HS-reconstruction-gwas", - "latest_release": "v1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHS-reconstruction-gwas\u003c/h1\u003e\u003ca id=\"user-content-hs-reconstruction-gwas\" class=\"anchor\" aria-label=\"Permalink: HS-reconstruction-gwas\" href=\"#hs-reconstruction-gwas\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the scripts used to generate and process data, as well as generate figures, for the manuscript:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAccurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCory A. Weller (\u003ca href=\"mailto:caw5cv@virginia.edu\"\u003ecaw5cv@virginia.edu\u003c/a\u003e) \u0026amp; Alan O. Bergland (\u003ca href=\"mailto:aob2x@virginia.edu\"\u003eaob2x@virginia.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow allows for Singularity containers to process data in a reproducible manner without installing required programs and libraries. You will first need to install singularity on your system, if it is not already available. Many HPC systems already have pre-loaded \u003ccode\u003esingularity\u003c/code\u003e that can be loaded as a module.\u003c/p\u003e\n\u003cp\u003eOtherwise, install singularity 3.x following the instructions from \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003esylabs.io\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThen, you can retrieve the pre-built singularity image files from Singularity Hub.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name harp.sif shub://cory-weller/HS-reconstruction-gwas:harp\nsingularity pull --name utils.sif shub://cory-weller/HS-reconstruction-gwas:utils\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "repeatexplorer/repex_tarean", + "latest_release": "0.3.8.2", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1649753066.0 + "updated_at": 1701766150.0 }, { "data_format": 2, - "description": "Example container with NAMD3 built in using Nix", + "description": "Pipleine code for \"Sensitive identification of bacterial DNA in clinical specimens by broad range 16S rRNA enrichment\"", "filenames": [ - "Singularity" + "singularity/Singularity_ea-utils", + "singularity/Singularity_gappa", + "singularity/Singularity_krona", + "singularity/Singularity_htstream", + "singularity/Singularity_epa" ], - "full_name": "XSEDE/nix-container-namd3", + "full_name": "nhoffman/16s-capture", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edocker-centos-nix-openmpi\u003c/h1\u003e\u003ca id=\"user-content-docker-centos-nix-openmpi\" class=\"anchor\" aria-label=\"Permalink: docker-centos-nix-openmpi\" href=\"#docker-centos-nix-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker container with Nix and OpenMPI to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 10, + "subscribers_count": 2, "topics": [], - "updated_at": 1618581042.0 + "updated_at": 1677049221.0 }, { "data_format": 2, - "description": "Singularity container build repository for pgfem-3d", + "description": "Singularity container for OpenMS 2.3 with Thirdparty tools", "filenames": [ - "Singularity.v2.1", - "Singularity.v2.0", - "Singularity", - "Singularity.latest" + "Singularity.2.2.0+", + "Singularity.2.3.0+", + "Singularity.dependencies", + "Singularity.contrib", + "Singularity" ], - "full_name": "C-SWARM/pgfem-3d-singularity", + "full_name": "mafreitas/singularity-openms", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePGFem_3D Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-pgfem_3d-singularity-container\" class=\"anchor\" aria-label=\"Permalink: PGFem_3D Singularity Container\" href=\"#pgfem_3d-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the build scripts necessary in order build a deployable \u003ccode\u003esingularity\u003c/code\u003e image of \u003ca href=\"https://github.com/C-SWARM/pgfem-3d\"\u003ePGFem_3D\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAccess to a machine with singularity to pull image \u003cem\u003eor\u003c/em\u003e root access to a machine to build custom image\u003c/li\u003e\n\u003cli\u003eSingularity installed as root (tested with 3.5.2)\u003c/li\u003e\n\u003cli\u003eAt least 2.3 GB storage to hold resulting container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eObtain the Container\u003c/h2\u003e\u003ca id=\"user-content-obtain-the-container\" class=\"anchor\" aria-label=\"Permalink: Obtain the Container\" href=\"#obtain-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 1: Use prebuilt container\u003c/h3\u003e\u003ca id=\"user-content-option-1-use-prebuilt-container\" class=\"anchor\" aria-label=\"Permalink: Option 1: Use prebuilt container\" href=\"#option-1-use-prebuilt-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThrough Singularity-Hub, a portable image built from this repository\u0027s \u003ccode\u003eSingularity\u003c/code\u003e build specification can be downloaded\nanywhere \u003ccode\u003esingularity\u003c/code\u003e is supported. This container will be matched with the latest change to this repository\u0027s\n\u003ccode\u003eSingularity\u003c/code\u003e file. Note that this container has \u003ccode\u003ePGFem_3D\u003c/code\u003e built with MVAPICH2-2.2. If a different version is needed\nfor infiniband support, a custom container must be built following the instructions in \u003ca href=\"#using-infiniband\"\u003eUsing infiniband\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo pull the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull shub://C-SWARM/pgfem-3d-singularity\n$ mv C-SWARM-pgfem-3d-singularity-master-latest.simg pgfem-3d.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe result from a \u003ccode\u003esingularity pull\u003c/code\u003e will be a container named \u003ccode\u003eC-SWARM-pgfem-3d-singularity-master-latest.simg\u003c/code\u003e due to\nSingularity-Hub naming conventions. It may be best to rename the container to something simple.\u003c/p\u003e\n\u003cp\u003eOnce the image is pulled, it can executed to run \u003ccode\u003ePGFem_3D\u003c/code\u003e seen in \u003ca href=\"#executing-the-container\"\u003eExecuting the Container\u003c/a\u003e.\nIf an MPI implementation other than \u003ccode\u003eMVAPICH2-2.2\u003c/code\u003e is desired, it is best to build a custom container with the desired MPI. Instructions for building a container are below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 2: Build the container on own machine\u003c/h3\u003e\u003ca id=\"user-content-option-2-build-the-container-on-own-machine\" class=\"anchor\" aria-label=\"Permalink: Option 2: Build the container on own machine\" href=\"#option-2-build-the-container-on-own-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis method requires root access to a machine with \u003ccode\u003esingularity\u003c/code\u003e installed. If \u003ccode\u003emvapich2-2.2\u003c/code\u003e is satisfactory, it is recommended to\nuse the prebuilt image using the instructions above as building a container takes time and space. The following instructions are for\nbuilding your own container when the \u003ccode\u003esingularity-hub\u003c/code\u003e image will not suffice.\u003c/p\u003e\n\u003cp\u003eClone this directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/C-SWARM/pgfem-3d-singularity.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pgfem-3d-singularity/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMake any changes necessary to the \u003ccode\u003eSingularity\u003c/code\u003e build file or the \u003ccode\u003ebuild.sh\u003c/code\u003e file where each software component will be compiled.\nBuild the container using the \u003ccode\u003ebuild\u003c/code\u003e command as super user / root. This can take 10-20 minutes depending on machine specs.\nA faster build may be achieved by increasing the make workers, replacing \u003ccode\u003emake\u003c/code\u003e with \u003ccode\u003emake -j 4\u003c/code\u003e for example.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esu -\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ePassword:\u003c/span\u003e\n# \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /path/to/this/repo\u003c/span\u003e\n# \u003cspan class=\"pl-s1\"\u003esingularity build pgfem3d.simg Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA large amount of text will appear on the screen during the build process. Once completed, a container will be created\nnamed \u003ccode\u003epgfem3d.simg\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing infiniband\u003c/h2\u003e\u003ca id=\"user-content-using-infiniband\" class=\"anchor\" aria-label=\"Permalink: Using infiniband\" href=\"#using-infiniband\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy using the host\u0027s shared libraries it is possible to utilize infiniband. In order to properly communicate, within\nthe container it is best to build the version of MPI library normally used on the host to communicate over infiniband.\nIn the current singularity container defined by the \u003ccode\u003eSingularity\u003c/code\u003e specification file and the hosted on\n\u003ccode\u003eSingularity-Hub\u003c/code\u003e, \u003ccode\u003emvapich2-2.2\u003c/code\u003e is built and configured with \u003ccode\u003e--disable-wrapper-rpath\u003c/code\u003e. This allows the container\u0027s\n\u003ccode\u003elibmpi.so\u003c/code\u003e to be swapped to utilize the host\u0027s library. If a targeted cluster requires a different version of MVAPICH\nor a different implementation of MPI, replace the current download and build of \u003ccode\u003eMVAPICH\u003c/code\u003e\nwith the desired version within the \u003ccode\u003eSingularity\u003c/code\u003e build file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MVAPICH=mvapich2-2.2.tar.gz\ncurl -O http://mvapich.cse.ohio-state.edu/download/mvapich/mv2/\u003cspan class=\"pl-smi\"\u003e$MVAPICH\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ntar -xf \u003cspan class=\"pl-smi\"\u003e$MVAPICH\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${MVAPICH\u003cspan class=\"pl-k\"\u003e%\u003c/span\u003e.tar.gz}\u003c/span\u003e\n./configure --prefix=/mvapich --disable-wrapper-rpath\nmake -j 4 install\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:/mvapich/bin\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e:/mvapich/lib\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce the matching version of MPI is built into the container, \u003ccode\u003epgfem_3d\u003c/code\u003e should be compiled with this version. \u003ccode\u003epgfem_3d\u003c/code\u003e is\nbuilt within the \u003ccode\u003ebuild.sh\u003c/code\u003e helper script. The container can then be built, instrcutions can be found above at Building the container on own machine\u003c/p\u003e\n\u003cp\u003eWhile running on the targeted host, it is necessary to \u003ca href=\"#library-swapping\"\u003eSwap libraries\u003c/a\u003e in order to properly utilize infiniband.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLibrary swapping\u003c/h3\u003e\u003ca id=\"user-content-library-swapping\" class=\"anchor\" aria-label=\"Permalink: Library swapping\" href=\"#library-swapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the container is built and transferred over to a host, a job script should be built with the following to pass host\nlibraries and paths into the container. If the container and necessary files to run live in a FS space other than the\ncurrent user\u0027s home space, it will be necessary to pass that along below as well within the \u003ccode\u003eSINGULARITY_BINDPATH\u003c/code\u003e variable.\nThis is an example of a partial script on \u003ca href=\"https://hpc.llnl.gov/hardware/platforms/Quartz\" rel=\"nofollow\"\u003eQuartz at LLNL\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load mvapich2/2.2\nmodule load mkl/2018.0\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Passing dynamic libraries\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITYENV_LD_LIBRARY_PATH=\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Passing FS paths for host MVAPICH and where the container is stored\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_BINDPATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/usr/tce/packages/mvapich2/mvapich2-2.2-gcc-7.1.0/lib,/p/lscratchh/USERNAME\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /p/lscratchh/USERNAME/pgfem-3d-examples\n./run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecuting the Container\u003c/h2\u003e\u003ca id=\"user-content-executing-the-container\" class=\"anchor\" aria-label=\"Permalink: Executing the Container\" href=\"#executing-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce finished building or pulling, the container can be executed to run PGFem_3D, passing in any necessary parameters.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./pgfem3d.simg -SS -help\n\u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e Parsing options from: -help \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e\n _______ ______ ________ ______ _______ \n/ \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e / \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e/ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e / \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e/ \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$$$$$\u003c/span\u003e/______ _____ ____ /\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e__\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e _\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e__ / \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e/ \u003cspan class=\"pl-cce\"\u003e\\/\u003c/span\u003e \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e ___\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e/ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e / \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$\u003c/span\u003e$/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e _\u003cspan class=\"pl-smi\"\u003e$$$$\u003c/span\u003e$ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-cce\"\u003e\\_\u003c/span\u003e_\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$$$$$$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e/ \u003cspan class=\"pl-cce\"\u003e\\_\u003c/span\u003e_\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e__\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$/ \n\nSS_USAGE: mpirun -np [NP] PGFem3D -SS [options] input output\nMS_USAGE: mpirun -np [NP] PGFem3D -MS [network] -macro-np [P] -micro-group-size [S] [macro OPTION_BLK] [micro OPTION_BLK]\nOPTION_BLK: -[scale]-start [options] input output -[scale]-end\n\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf running on an HPC system, it is best to use \u003ccode\u003empirun\u003c/code\u003e or an equivalent \u003cem\u003eoutside\u003c/em\u003e the container. This would require\nthe proper module or software in place, such as \u003ccode\u003emodule load mvapich2/2.2\u003c/code\u003e for example. If you are intending to\nrun using infiniband technologies, see \u003ca href=\"#using-infiniband\"\u003eUsing Infiniband\u003c/a\u003e above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning pgfem-3d-examples\u003c/h2\u003e\u003ca id=\"user-content-running-pgfem-3d-examples\" class=\"anchor\" aria-label=\"Permalink: Running pgfem-3d-examples\" href=\"#running-pgfem-3d-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity can utilize the host\u0027s native file system, allowing the following commands to be performed outside\nthe container on the machine targeted to run on. Be sure to transfer the container to the targeted machine in order\nto execute it.\u003c/p\u003e\n\u003cp\u003eClone the examples to obtain the source:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/C-SWARM/pgfem-3d-examples.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pgfem-3d-examples\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eReplace the executable within run.sh with the singularity image.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PGFEM_SIMG=/path/to/pgfem_3d.simg\n$ sed -i \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003es|/opt/pgfem-3d/bin/PGFem3D|\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${PGFEM_SIMG}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e|\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFrom here follow the directions supplied within pgfem-3d-examples:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./local_makeset.pl -np 4\n$ ./run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create 2 files within the pgfem-3d-examples directory: \u003ccode\u003eparview_displacement_y.pvsm\u003c/code\u003e and \u003ccode\u003eparview_displacement_z.pvsm\u003c/code\u003e.\nThese files can be opened using \u003ccode\u003eParaView\u003c/code\u003e outside of the container and examined by the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClick \u003ccode\u003eFile -\u0026gt; Load State -\u0026gt; \u003c/code\u003e, select either parview_displacement_y.pvsm or parview_displacement_z.pvsm and click \u003ccode\u003eOK\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIn the next window, browse to: \u003ccode\u003eout -\u0026gt; box_4 -\u0026gt; VTK -\u0026gt; box_../pvtu\u003c/code\u003e and click \u003ccode\u003eOK\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePress the play button towards the top middle of the screen.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor any technical assistance, please contact:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCody Kankel \u003ca href=\"mailto:ckankel@nd.edu\"\u003eckankel@nd.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEzra Kissel \u003ca href=\"mailto:ezkissel@indiana.edu\"\u003eezkissel@indiana.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eKamal K Saha \u003ca href=\"mailto:ksaha@nd.edu\"\u003eksaha@nd.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eLuke D\u0027Alessandro \u003ca href=\"mailto:ldalessa@uw.edu\"\u003eldalessa@uw.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-openms\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-openms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-openms\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/601\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eReceipes for Singularity containers that can run OpenMS (2.2 and 2.3 with thirdparty tools).\u003c/p\u003e\n\u003cp\u003eThese are early builds for testing only.\u003c/p\u003e\n\u003cp\u003eThe receipes are based on OpenMS docker containers available from the OpenMS team. For examples checkout\n\u003ca href=\"https://hub.docker.com/u/hroest/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/u/hroest/\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-the-container-to-your-machine-and-optionally-name-custom-or-by-hashcommit\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#pull-the-container-to-your-machine-and-optionally-name-custom-or-by-hashcommit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull the container to your machine (and optionally name custom, or by hash/commit:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://mafreitas/singularity-openms\nsingularity pull --name customname.img shub://mafreitas/singularity-openms\nsingularity pull --commit shub://mafreitas/singularity-openms\nsingularity pull --hash shub://mafreitas/singularity-openms\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-shell-into-the-container\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell into the container:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell shub://mafreitas/singularity-openms\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the container:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run shub://mafreitas/singularity-openms\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-using-as-a-base\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#build-using-as-a-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild using as a base:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-openms.simg shub://mafreitas/singularity-openms\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eLatest \u0026amp; 2.3+ - contain the latest release (2.3.0) and thirdparty tools.\u003c/li\u003e\n\u003cli\u003e2.2+ - contains 2.2.0 and thirdparty tools .\u003c/li\u003e\n\u003cli\u003econtrib - contains the dependencies and contributing libraries.\u003c/li\u003e\n\u003cli\u003edependencies - contains the base image with all build dependencies.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo pull a specific container add the appropriate tag.\u003cbr\u003e\nFor example to pull the 2.2+ container use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://mafreitas/singularity-openms:2.2+\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1582925466.0 + "updated_at": 1519702192.0 }, { "data_format": 2, - "description": "circos.ca in a singularity container", + "description": null, "filenames": [ - "Singularity" + "src/linker/steps/dev/python_pandas/Singularity" ], - "full_name": "tin6150/circos", + "full_name": "ihmeuw/linker", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-linker\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#linker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elinker\u003c/h1\u003e\n\u003cp\u003eNOTE: \"linker\" is a temporary name and will change when the official one is\ndecided on.\u003c/p\u003e\n\u003cp\u003elinker is a framework that allows users to build and run highly configurable\nentity resolution (ER) pipelines.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall docker\n\u003cul\u003e\n\u003cli\u003eMac: \u003ca href=\"https://docs.docker.com/desktop/install/mac-install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/desktop/install/mac-install/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eWindows: \u003ca href=\"https://docs.docker.com/desktop/install/windows-install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/desktop/install/windows-install/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eClone and install this repo\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd \u0026lt;path/to/repositories/\u0026gt;\n$ git clone git@github.com:ihmeuw/linker.git\n$ # OR `git clone https://github.com/ihmeuw/linker.git`\n$ cd linker\n$ pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-a-pipeline\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#running-a-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a pipeline\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ linker run \u0026lt;PIPELINE-SPECIFICATION\u0026gt;\n$ # e.g. `linker run ~/repos/linker/src/linker/pipelines/pipeline.yaml`\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor help, please use \u003ccode\u003elinker --help\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-creating-a-docker-image-to-be-shared\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#creating-a-docker-image-to-be-shared\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a docker image to be shared\u003c/h2\u003e\n\u003cp\u003eDocker image binaries can be built from a Dockerfile. For example, to create a\ncompressed image .tar.gz file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd \u0026lt;PATH-TO-DOCKERFILE-PARENT-DIRECTORY\u0026gt;\n$ # build the image\n$ sudo docker build -t linker:\u0026lt;IMAGE-NAME\u0026gt; .\n$ # save as compressed tarball\n$ sudo docker save linker:\u0026lt;IMAGE-NAME\u0026gt; | gzip \u0026gt; \u0026lt;IMAGE-NAME\u0026gt;.tar.gz\n$ # remove the image\n$ sudo docker rmi linker:\u0026lt;IMAGE-NAME\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can use the \u003ccode\u003e-f\u003c/code\u003e option to build a dockerfile from a different location\n(including a different filename than \u0027Dockerfile\u0027):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker build -t linker:\u0026lt;IMAGE-NAME\u0026gt; \u0026lt;PATH-TO-DOCKERFILE\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should now have an image file named \u003ccode\u003e\u0026lt;IMAGE-NAME\u0026gt;.tar.gz\u003c/code\u003e alongside the Dockerfile which can be used to spin up the container.\u003c/p\u003e\n\u003cp\u003eNote that it may be occasionally required to clean up unused data to make room for building\nimages: \u003ccode\u003esudo docker system prune\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-creating-a-singularity-image-to-be-shared\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#creating-a-singularity-image-to-be-shared\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a singularity image to be shared\u003c/h2\u003e\n\u003cp\u003eSingularity image files can be created from a Singularity file. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd \u0026lt;PATH-TO-SINGULARITY-FILE-PARENT-DIRECTORY\u0026gt;\n$ # build the image\n$ singularity build --force \u0026lt;IMAGE-NAME\u0026gt;.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, a Docker binary can be converted to a Singularity image file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity build --force \u0026lt;IMAGE-NAME\u0026gt;.sif docker-archive://$(pwd)/\u0026lt;IMAGE-NAME\u0026gt;.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 8, "topics": [], - "updated_at": 1715723731.0 + "updated_at": 1694131143.0 }, { "data_format": 2, - "description": null, + "description": "A cat(1) clone with syntax highlighting and Git integration.", "filenames": [ - "Singularity.casacore.gpuvmem.10.0.ubuntu1604", - "Singularity.casacore.gpuvmem.10.0", - "Singularity.HPC", - "Singularity.casacore.gpuvmem.11.0", - "Singularity.casacore.gpuvmem.9.2.ubuntu1604", - "Singularity.casacore.gpuvmem.9.2", - "Singularity" + "0.18.1/Singularity", + "0.21.0/Singularity", + "0.17.1/Singularity", + "0.23.0/Singularity", + "0.22.1/Singularity", + "0.18.3/Singularity" ], - "full_name": "miguelcarcamov/container_docker", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_docker\u003c/h1\u003e\u003ca id=\"user-content-container_docker\" class=\"anchor\" aria-label=\"Permalink: container_docker\" href=\"#container_docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUseful containers to work with radio astronomical data\u003c/p\u003e\n\u003cp\u003eMiguel C\u00e1rcamo.\u003c/p\u003e\n", + "full_name": "pscedu/singularity-bat", + "latest_release": "v0.23.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bat/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bat/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bat/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bat/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/80ce266ea551486e532b8479474ece87011121c1b177e0e067f4d8022ad2f52c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626174\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80ce266ea551486e532b8479474ece87011121c1b177e0e067f4d8022ad2f52c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626174\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b6d53daf33b347fa4d1a74ab0a30fd631965db08d1237e10991c7e7230ed671f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626174\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6d53daf33b347fa4d1a74ab0a30fd631965db08d1237e10991c7e7230ed671f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626174\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d23d5a1f67b897b9eeaf3e805efa8c4c8562272babd67bc8d1da9c8ca70d6259/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626174\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d23d5a1f67b897b9eeaf3e805efa8c4c8562272babd67bc8d1da9c8ca70d6259/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626174\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bf0a53aac9fa2abb057c22f8fb00c5d07141c61f15c27983fd073a5445c15421/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626174\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bf0a53aac9fa2abb057c22f8fb00c5d07141c61f15c27983fd073a5445c15421/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626174\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-bat\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-bat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bat\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7b7c397acc5b91b4c4cf7756015185fe3c5f700f70d256a212de51294a0cf673/68747470733a2f2f696d6775722e636f6d2f724773646e44652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b7c397acc5b91b4c4cf7756015185fe3c5f700f70d256a212de51294a0cf673/68747470733a2f2f696d6775722e636f6d2f724773646e44652e706e67\" alt=\"Example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/bat\"\u003ebat\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebat\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bat/0.17.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bat\u003c/code\u003e as \u003ccode\u003e0.17.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, - "topics": [], - "updated_at": 1636977837.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1633086539.0 }, { "data_format": 2, - "description": "Additional CI and a dashboard to check health status of DataLad et al.", + "description": null, "filenames": [ - "containers/Singularity.buildenv-git-annex-buster" + "containers/calculateSnPrecision/Singularity", + "containers/assessmentRfHeatmap/Singularity", + "containers/checkFormat/Singularity", + "containers/robinsonFouldsMetric/Singularity", + "containers/getQueryIds/Singularity" ], - "full_name": "datalad/datalad-extensions", + "full_name": "BU-ISCIII/openebench_gmi", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDataLad healthchecks\u003c/h1\u003e\u003ca id=\"user-content-datalad-healthchecks\" class=\"anchor\" aria-label=\"Permalink: DataLad healthchecks\" href=\"#datalad-healthchecks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a \"dashboard\" of various CIs of DataLad, its extensions, and underlying\n3-rd party projects like git-annex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis README.md is autogenerated - do not edit.\nSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e for more information.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGit-annex Status\u003c/h2\u003e\u003ca id=\"user-content-git-annex-status\" class=\"anchor\" aria-label=\"Permalink: Git-annex Status\" href=\"#git-annex-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eConda: \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/fdc1b5de2056ba6e9fe88f7ce41a97cb6ea2852cae9cc715b0fce1ee307a37d1/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f76657273696f6e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fdc1b5de2056ba6e9fe88f7ce41a97cb6ea2852cae9cc715b0fce1ee307a37d1/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f76657273696f6e2e737667\" alt=\"Conda?\" data-canonical-src=\"https://anaconda.org/conda-forge/git-annex/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/26fc1d266decec815b3d821b55616af8b92f4093f7cf2547d3e312cbe89a0530/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f6c61746573745f72656c656173655f72656c61746976655f646174652e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/26fc1d266decec815b3d821b55616af8b92f4093f7cf2547d3e312cbe89a0530/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f6c61746573745f72656c656173655f72656c61746976655f646174652e737667\" alt=\"Updated\" data-canonical-src=\"https://anaconda.org/conda-forge/git-annex/badges/latest_release_relative_date.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/35ed3f186127909c18aa701be3c9b3e65a91b57ac1bc676881005a5346085d4c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f706c6174666f726d732e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/35ed3f186127909c18aa701be3c9b3e65a91b57ac1bc676881005a5346085d4c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f706c6174666f726d732e737667\" alt=\"Platforms?\" data-canonical-src=\"https://anaconda.org/conda-forge/git-annex/badges/platforms.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCurrent snapshot build/tests + DataLad tests:\n\u003ca href=\"https://github.com/datalad/git-annex/actions?query=workflow%3A%22Build+git-annex+on+Ubuntu%22\"\u003e\u003cimg src=\"https://github.com/datalad/git-annex/workflows/Build%20git-annex%20on%20Ubuntu/badge.svg\" alt=\"Ubuntu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/datalad/git-annex/actions?query=workflow%3A%22Build+git-annex+on+macOS%22\"\u003e\u003cimg src=\"https://github.com/datalad/git-annex/workflows/Build%20git-annex%20on%20macOS/badge.svg\" alt=\"macOS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/datalad/git-annex/actions?query=workflow%3A%22Build+git-annex+on+Windows%22\"\u003e\u003cimg src=\"https://github.com/datalad/git-annex/workflows/Build%20git-annex%20on%20Windows/badge.svg\" alt=\"Windows\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/datalad/git-annex#client-tests\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/datalad/git-annex-ci-client-jobs/master/badges/.all-clients.svg\" alt=\"Clients\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataLad Status\u003c/h2\u003e\u003ca id=\"user-content-datalad-status\" class=\"anchor\" aria-label=\"Permalink: DataLad Status\" href=\"#datalad-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDistributions:\n\u003ca href=\"https://GitHub.com/datalad/datalad/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b773fad9b338ee62ab4e42ac766e5ac5444d6f8f60235cfcd565b91952197318/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642e737667\" alt=\"DataLad GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a240f2c8b107fef21725258e78e2ca5664af24658cd4d99a710c4d2d45dff939/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642f6261646765732f76657273696f6e2e737667\" alt=\"Anaconda\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://repology.org/project/datalad/versions\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e787793a206ab2cf5491c41559db9425728e0535e0948b09f30f279ee54aaa8/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f76657273696f6e2d666f722d7265706f2f6175722f646174616c61642e7376673f6865616465723d41726368253230253238253431253535253532253239\" alt=\"Arch (AUR)\" data-canonical-src=\"https://repology.org/badge/version-for-repo/aur/datalad.svg?header=Arch%20%28%41%55%52%29\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://packages.debian.org/stable/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e1338610f5b64a32bc928206f288bba3cb4cfbd1dc93e16c4f6cccec1a20050/68747470733a2f2f6261646765732e64656269616e2e6e65742f6261646765732f64656269616e2f737461626c652f646174616c61642f76657273696f6e2e737667\" alt=\"Debian Stable\" data-canonical-src=\"https://badges.debian.net/badges/debian/stable/datalad/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://packages.debian.org/unstable/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9163382edda1888f23ea915f38860e726a0d4465e6a35b4dc5fcc028c2ae1649/68747470733a2f2f6261646765732e64656269616e2e6e65742f6261646765732f64656269616e2f756e737461626c652f646174616c61642f76657273696f6e2e737667\" alt=\"Debian Unstable\" data-canonical-src=\"https://badges.debian.net/badges/debian/unstable/datalad/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://repology.org/project/datalad/versions\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6eac67dd1e795963aacd41219a8c4500427016f76728a5a71d71b4c8b7d5fea0/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f76657273696f6e2d666f722d7265706f2f6665646f72615f726177686964652f646174616c61642e7376673f6865616465723d4665646f726125323025323872617768696465253239\" alt=\"Fedora Rawhide package\" data-canonical-src=\"https://repology.org/badge/version-for-repo/fedora_rawhide/datalad.svg?header=Fedora%20%28rawhide%29\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://repology.org/project/datalad/versions\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/62e73ef0b609cf084e9b5cb2861ed9e83db8efacac4a791f39a2784c94d5a866/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f76657273696f6e2d666f722d7265706f2f67656e746f6f5f6f766c5f736369656e63652f646174616c61642e7376673f6865616465723d47656e746f6f253230253238253341253341736369656e6365253239\" alt=\"Gentoo (::science)\" data-canonical-src=\"https://repology.org/badge/version-for-repo/gentoo_ovl_science/datalad.svg?header=Gentoo%20%28%3A%3Ascience%29\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://repology.org/project/datalad/versions\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/064297c8aaeb3b31b9e1d55e747bccaed7c90858753de665ae10db089f30d2e6/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f76657273696f6e2d666f722d7265706f2f707970692f646174616c61642e7376673f6865616465723d50795049\" alt=\"PyPI package\" data-canonical-src=\"https://repology.org/badge/version-for-repo/pypi/datalad.svg?header=PyPI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCI:\n\u003ca href=\"https://app.travis-ci.com/datalad/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/347775cd48a24cb02f61394b422f8b0011f87f25b274c6fc30fd2d3fc7fa2c82/68747470733a2f2f6170702e7472617669732d63692e636f6d2f646174616c61642f646174616c61642e7376673f6272616e63683d6d61696e74\" alt=\"Travis maint\" data-canonical-src=\"https://app.travis-ci.com/datalad/datalad.svg?branch=maint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://app.travis-ci.com/datalad/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d46614c62a98d2638c88db090d11c0cc889a8028fdd5641ea907db40c3f870b5/68747470733a2f2f6170702e7472617669732d63692e636f6d2f646174616c61642f646174616c61642e7376673f6272616e63683d6d6173746572\" alt=\"Travis master\" data-canonical-src=\"https://app.travis-ci.com/datalad/datalad.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ci.appveyor.com/project/mih/datalad/branch/maint\" rel=\"nofollow\"\u003e\u003cimg 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src=\"https://github.com/datalad/datalad/actions/workflows/lint.yml/badge.svg\" alt=\"Linters\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMisc:\n\u003ca href=\"https://codecov.io/github/datalad/datalad?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4142fd6c5cb95221439a86d0baece98a6a4a19c9a7f65fc8ef7006601f65942f/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataLad Extensions Status\u003c/h2\u003e\u003ca id=\"user-content-datalad-extensions-status\" class=\"anchor\" aria-label=\"Permalink: DataLad Extensions Status\" href=\"#datalad-extensions-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRelease\u003c/th\u003e\n\u003cth\u003ePyPI Release\u003c/th\u003e\n\u003cth\u003eConda Release\u003c/th\u003e\n\u003cth\u003eCI: Released + DL master\u003c/th\u003e\n\u003cth\u003eCI: Released + DL maint\u003c/th\u003e\n\u003cth\u003eCI: develop + DL Release\u003c/th\u003e\n\u003cth\u003eCodecov\u003c/th\u003e\n\u003cth\u003eIssue Resolution\u003c/th\u003e\n\u003cth\u003eOpen Issues\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-catalog\"\u003edatalad_catalog\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-catalog/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df39f3f96ee7917f94cc2e1fef2862b3d07aa63b1f5883dc12b2e837eb95f416/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d636174616c6f672e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-catalog.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-catalog/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/73e3a037f24365d85c4af4000581d32169e696370de6c2e8052a4ba386455d19/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d636174616c6f672e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-catalog.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-container\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9908f3665be9a3add37dd30f2d86ad2b098d1d1557b3d177d2bab87394e21588/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d636f6e7461696e65722e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-container.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-container\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8a0bc1decd25401c4b6096b0d2861906a7e1dab7f889fed0050ae339ce8359f7/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d636f6e7461696e65722e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-container.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-crawler\"\u003edatalad_crawler\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-crawler/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ec87de091a1ae304a18afef0c2fd1a119233df025e83499b7f45bed26e1fdad8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d637261776c65722e737667\" alt=\"?\" 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src=\"https://camo.githubusercontent.com/a1a23f88e9475031f20e538a48a3cb4da575ab90a0dcbb21188726478399206b/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d637261776c65722f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-crawler/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_crawler-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_crawler-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_crawler-maint\"\u003e\u003cimg 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src=\"https://camo.githubusercontent.com/e6d4c3bac372feb3a3019a9e1ad930391d5bbb236e4c2aa5312eb700c28b2846/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6461746176657273652e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-dataverse.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-dataverse/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6c4535f1b60ab2c5f9288ebd4e19af751be7d28723806e8652768d2a522de49/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d6461746176657273652e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-dataverse.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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data-canonical-src=\"https://codecov.io/github/datalad/datalad-deprecated/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-deprecated\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3483ab70f13a97ef5126f8fd073b9c7179cbc646606bd4cb9162450d55331f4b/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d646570726563617465642e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-deprecated.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-deprecated\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d8b0c2805e52ce84301f20b1cf1f0eebee4162143b4085d639fbfed1b82f9948/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d646570726563617465642e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-deprecated.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-fuse\"\u003edatalad_fuse\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-fuse/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98f8e867b8a30b9f3648d756ce5df718eea322e4c75444ddb17784830a018b69/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d667573652e737667\" alt=\"?\" 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src=\"https://camo.githubusercontent.com/a8296fc3a80ad88888b3acef09a8aa1ce8105971d77e60cb6fa5eb250b535224/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d667573652f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-fuse/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_fuse-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_fuse-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_fuse-maint\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_fuse-maint.yaml/badge.svg\" alt=\"Released+DataLad maint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-fuse/actions/workflows/test.yml?query=branch%3Amaster\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-fuse/actions/workflows/test.yml/badge.svg?branch=master\" alt=\"develop+Released Datalad\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/datalad/datalad-fuse?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a693b0d530a6446473815ca701c9de18dee953938fb1b16d937de89ad2323fb8/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d667573652f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-fuse/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-fuse\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d581fa7439b0eeb8e8ccfc363fb3b4919627ee56189de62a4f8ba09e627ffcbf/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d667573652e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-fuse.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-fuse\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe5511ce2b40d649d050bdbb197ada71435d239aa528120651035b34738fd4ae/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d667573652e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-fuse.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-metalad\"\u003edatalad_metalad\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-metalad/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3411710c4784c25a4125e6fec11d026d038eaa5f7b201e73760992d78e30cace/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6d6574616c61642e737667\" alt=\"?\" 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src=\"https://camo.githubusercontent.com/3ede67c44c33cb38d3cbefd530a6c5d099087719b8e7cc5971e93a3d4053b0f0/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d6d6574616c61642f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-metalad/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_metalad-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_metalad-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_metalad-maint\"\u003e\u003cimg 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style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-metalad\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/61f8c01570d1872076b3a011fa5432c1dd2e9e062c2bb1d31663a9d8ca7cdf92/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d6d6574616c61642e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-metalad.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-neuroimaging\"\u003edatalad_neuroimaging\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-neuroimaging/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3b46b08339d91628c7de50a03f6a444b1917f024a808651064938dfe01663b8b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-neuroimaging/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4466cdd0dfd7c35e5746d86fadfb58edabac888c4b411d77bdd6ed0d324502b4/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/datalad/datalad-neuroimaging?branch=master\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/datalad/datalad-neuroimaging?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31b8a3e8dada4f28be012c0bcf21f29445d8b0ffd53ee7e11e245e53b1ef46c6/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d6e6575726f696d6167696e672f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-neuroimaging/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-neuroimaging\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg 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data-canonical-src=\"https://anaconda.org/conda-forge/datalad-next/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_next-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_next-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_next-maint\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_next-maint.yaml/badge.svg\" alt=\"Released+DataLad maint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/mih/datalad-next/branch/main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2d94e42e168217e700e94379a10d3c0232532e9022232b1795131cd04f07b2e2/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f646174616c61642f646174616c61642d6e6578743f6272616e63683d6d61696e267376673d74727565\" alt=\"develop+Released Datalad\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/datalad/datalad-next?branch=main\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/datalad/datalad-next?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb469856ff2e3414149de0ea8f239f804e6f61524074763ff605c6fb4491be16/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d6e6578742f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-next/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-next\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4362675ebd9a84db84c686e5da70eb9cf656d0aa171fb4a09c1d0514a264d447/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d6e6578742e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-next.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-next\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3a02d4c829ae392cc1ebda529f24c9a55fbd6a7ec73d4e3365969f8884157596/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d6e6578742e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-next.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-osf\"\u003edatalad_osf\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-osf/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7501bb4ffaafd7775babaf1be0c68f5fb194beff761d18d952c9162f6ddf7581/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6f73662e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-osf.svg\" 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src=\"https://camo.githubusercontent.com/e30df860973991421a79372aba47e3e96ed2412ef81a1bb5dc737eb55517efd0/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d6f73662f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-osf/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_osf-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_osf-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_osf-maint\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_osf-maint.yaml/badge.svg\" 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100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-osf\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d901b86649a36a27954ed0bd6f79a5e1e2f752885269559a09469aa0d5122d1/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d6f73662e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-osf.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-ukbiobank\"\u003edatalad_ukbiobank\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-ukbiobank/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7d7a4590cc23a39f957a017fc24f10e1ba401fe4007cf1bce705de5397966ff8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d756b62696f62616e6b2e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-ukbiobank.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-ukbiobank/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8014c7f87d1ffc47e00372727dfa38872b386b33d35867364f0830cbc9dd36f3/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d756b62696f62616e6b2e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-ukbiobank.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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src=\"https://camo.githubusercontent.com/0149545a3d0116df9f6b1cf8ad2967ca28267791b0c7cb640f1a8b25ba67a083/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d756b62696f62616e6b2e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-ukbiobank.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-ukbiobank\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98850f05b1ccce5ebf101d96fa52fc7096a8f81336fb36e2ea0932f04bcbb721/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d756b62696f62616e6b2e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-ukbiobank.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-xnat\"\u003edatalad_xnat\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-xnat/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a30c68966fda3a2bf782320d2e5aa750f4899544840a7f6419799921589200b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d786e61742e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-xnat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-xnat/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8517e485da5e49b590b24a28e26e001cfaec4abce0160433255e7c4a55b4d7ad/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d786e61742e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-xnat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/10447a12c5dbb7924338da8c9b54c04f6c6f05debf2b2acba92134a335a69b08/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d786e61742f6261646765732f76657273696f6e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/10447a12c5dbb7924338da8c9b54c04f6c6f05debf2b2acba92134a335a69b08/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d786e61742f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-xnat/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_xnat-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_xnat-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_xnat-maint\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_xnat-maint.yaml/badge.svg\" alt=\"Released+DataLad maint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/mih/datalad-xnat/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c75dff9a139b899cdab10ff9bbdc5d7eaa4b8240c2f9fe772f755dd1ff94b3e8/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f646174616c61642f646174616c61642d786e61743f6272616e63683d6d6173746572267376673d74727565\" alt=\"develop+Released Datalad\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/datalad/datalad-xnat?branch=master\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/datalad/datalad-xnat?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/83cc5a7741aaef0d03aab1901a2851f2fbe06f01fc38ff4a6f8b2777b6f9d833/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d786e61742f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-xnat/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-xnat\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/847b7b746ca3b982db6c6e2efa1afad725ff9655ebafde3beaf6c9ef6c2bd36e/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d786e61742e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-xnat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-xnat\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/55ac571f30d87ed22e4b8a07a6a9373a65903592c5dd9b2f3683ea8d0799af3f/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d786e61742e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-xnat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e54064fb698af20a2b6089b4f16ec3e31f31f72b47f15a5bb215bfd2e41d1b2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1ee06357ac79da293d08136619bdf903a80f520229e0916813d4a6eca768a963/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f46696c6573797374656d2d536369656e74696669632d627269676874677265656e2e737667\" alt=\"Scif\" data-canonical-src=\"https://img.shields.io/badge/Filesystem-Scientific-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-nextflow-pipeline-using-containers-for-an-outbreak-detection-challenge-using-openebench-platform\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-using-containers-for-an-outbreak-detection-challenge-using-openebench-platform\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline using containers for an Outbreak detection challenge using OpenEbench platform\u003c/h1\u003e\n\u003cp\u003eThis repository intends to be a nextflow + container implementation of OpenEbench workflow for an Outbreak detection challenge.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use-it\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use it\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/BU-ISCIII/openebench_gmi.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e openebench_gmi.git\ngit submodule init\ngit submodule update\nnextflow run main.nf -profile docker \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eParameters available:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --help\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eUsage:\nnextflow run BU-ISCIII/openebench_gmi --tree_test {test.newick.file} --goldstandard_dir {golden.folder.path} --assess_dir {assessment.path} --public_ref_dir {path.to.info.ref.dataset} --event_id {event.id}\n\nMandatory arguments:\n --tree_test Path to input data (must be surrounded with quotes).\n --goldstandard_dir Path to reference data. Golden datasets.\n --public_ref_dir Path where public dataset info is stored for validation.\n --assess_dir Path where benchmark data is stored.\n --event_id Event identifier.\n --participant_id Participant identifier.\n --tree_format Format tree [\"nexus\",\"newick\"].\n\nOther options:\n --outdir The output directory where the results will be saved\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-datasets\" class=\"anchor\" aria-hidden=\"true\" href=\"#datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDatasets\u003c/h2\u003e\n\u003cp\u003eFirst of all, needed datasets have been collected in: \u003ca href=\"datasets\"\u003edatasets folder\u003c/a\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eInput dataset:\u003c/strong\u003e fastq input data obtained from \u003ca href=\"https://github.com/globalmicrobialidentifier-WG3/datasets\"\u003eGMI WGS standards and benchmarks repository\u003c/a\u003e. \u003ca href=\"datasets/inputDataset/Readme.me\"\u003eHere\u003c/a\u003e you can find instructions for download.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGold standard dataset:\u003c/strong\u003e confirmed phylogeny for the outbreak being investigated.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eInput dataset ids:\u003c/strong\u003e input dataset ids in .txt and .json format.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTest dataset:\u003c/strong\u003e a test tree for comparing with gold standard result. In this case just the same golden dataset. Robinson-Foulds metrics must be 0.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebenchmark_data\u003c/strong\u003e: path where benchmark results are stored.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow-pipeline-and-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-and-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline and containers\u003c/h2\u003e\n\u003cp\u003eSecond, a pipeline has been developed which is splitted in three steps following OpenEbench specifications following this \u003ca href=\"https://github.com/inab/opeb-submission\"\u003erepo\u003c/a\u003e as an example:\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nextflow-processes\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-processes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow processes\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eValidation and data preprocessing:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eCheck results format:\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTree input: User input tree format is validated, nexus and newick formats are allowed being newick the canonical format. If format validated, a tree is outputted in the canonical format (.nwk).\u003c/li\u003e\n\u003cli\u003eVCF input:\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eGet query ids:\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTree input: ids are extracted for user input tree in newick or nexus format. IDs are writed in: queryids.json\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eValidate query ids:\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTree input: query ids are validated against ref input ids.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMetrics:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cem\u003ePrecision/Recall calculation:\u003c/em\u003e common (TP), source (FP) and ref(FN) edges are calculated in the comparison of ref and test tree topologies. Recall and precision are calculated using this values and stored in a json file called {participant_id}_snprecision.json.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eRobinson-Foulds metric calculation:\u003c/em\u003e Normalized Robinson-Foulds test is performed between user tree and every participant tree already analyzed and stored in the benchmark_data folder in order to compare their topologies. Result value is writted to participant_matrix.json file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eData visualization and consolidation:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePrecision/Recall graph is created, classifying each participant inside a quartile.\u003c/li\u003e\n\u003cli\u003eA all participant vs all participant heatmap is created usign normalized robinson-foulds matrix.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-containers-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers info\u003c/h3\u003e\n\u003cp\u003eEach step runs in its own container. Containers are built using a Dockerfile recipe which makes use of \u003ca href=\"https://sci-f.github.io/\" rel=\"nofollow\"\u003eSCI-F\u003c/a\u003e recipes for software installation. All scif recipes are available in \u003ca href=\"https://github.com/BU-ISCIII/scif_app_recipes\"\u003escif_app_recipes repository\u003c/a\u003e. Singularity recipes are also provided (Not yet adapted in nextflow pipeline).\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1729006991.0 + "updated_at": 1559826274.0 }, { "data_format": 2, "description": null, "filenames": [ - "devops_pipeline/Singularity", - "devops_pipeline/Singularity~", - "devops_processing/Singularity", - "devops_base/Singularity" + "Singularity.compute-0-36", + "Singularity.tf-nightly", + "Singularity.compute-0-27" ], - "full_name": "ninamiolane/vaetree", + "full_name": "bstriner/tensorflow-cuda-10.1-cudnn7-devel-ubuntu16.04", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tensorflow-cuda-101-cudnn7-devel-ubuntu1604\" class=\"anchor\" aria-hidden=\"true\" href=\"#tensorflow-cuda-101-cudnn7-devel-ubuntu1604\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etensorflow-cuda-10.1-cudnn7-devel-ubuntu16.04\u003c/h1\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1592561461.0 + "updated_at": 1575469838.0 }, { "data_format": 2, @@ -25619,1675 +25291,1506 @@ var data = "filenames": [ "Singularity" ], - "full_name": "vaofford/Bio-Deago", - "latest_release": "v1.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBio-Deago\u003c/h1\u003e\u003ca id=\"user-content-bio-deago\" class=\"anchor\" aria-label=\"Permalink: Bio-Deago\" href=\"#bio-deago\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate user-friendly HTML reports from differential expression and GO term enrichment analysis.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/sanger-pathogens/Bio-Deago\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5674903bd465390785be9dbb45993d05faed8090f3c6986fe3bc719a0c1fa98a/68747470733a2f2f7472617669732d63692e6f72672f73616e6765722d706174686f67656e732f42696f2d446561676f2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/sanger-pathogens/Bio-Deago.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/sanger-pathogens/Bio-Deago/blob/master/GPL-LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b431b095e9c63c93a8954655294a66412158aa1d5dc92a1cd41e1a8b5515bc37/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d627269676874677265656e2e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://codecov.io/gh/sanger-pathogens/bio-deago\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8f39fe02137ebdff6bfa8ea47fc8744a902f2f96bce8046b1aa40e4f2fd66ad7/68747470733a2f2f636f6465636f762e696f2f67682f73616e6765722d706174686f67656e732f62696f2d646561676f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/sanger-pathogens/bio-deago/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3450\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#required-dependencies\"\u003eRequired dependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#environment-variables\"\u003eEnvironment variables\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#input-data\"\u003eInput data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#quick-start\"\u003eQuick start\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#qc-only\"\u003eQC only\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#convert-biomart-annotation\"\u003eConvert BioMart annotation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#go-term-enrichment-analysis\"\u003eGO term enrichment analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output-files\"\u003eOutput files\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#feedbackissues\"\u003eFeedback/Issues\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#further-information\"\u003eFurther Information\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDEAGO generates a user-friendly HTML report from differential expression (\u003ca href=\"https://bioconductor.org/packages/release/bioc/html/DESeq2.html\" rel=\"nofollow\"\u003eDESeq2\u003c/a\u003e) and GO term enrichment (\u003ca href=\"http://bioconductor.org/packages/release/bioc/html/topGO.html\" rel=\"nofollow\"\u003etopGO\u003c/a\u003e) analysis of RNA-Seq data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDEAGO has the following dependencies:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequired dependencies\u003c/h3\u003e\u003ca id=\"user-content-required-dependencies\" class=\"anchor\" aria-label=\"Permalink: Required dependencies\" href=\"#required-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://pandoc.org/installing.html\" rel=\"nofollow\"\u003epandoc\u003c/a\u003e \u0026gt;= 1.12.3\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org\" rel=\"nofollow\"\u003eR\u003c/a\u003e \u0026gt;= 3.4.0\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sanger-pathogens/deago\"\u003edeago\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDetails to install DEAGO are provided below. If you encounter an issue when installing DEAGO please contact your local system administrator. If you encounter a bug please log it \u003ca href=\"https://github.com/sanger-pathogens/bio-deago/issues\"\u003ehere\u003c/a\u003e or email us at \u003ca href=\"mailto:path-help@sanger.ac.uk\"\u003epath-help@sanger.ac.uk\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom Source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From Source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake sure you have installed all dependencies, then clone the repository:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/sanger-pathogens/Bio-Deago.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMove into the directory and install all perl dependencies using \u003ca href=\"http://dzil.org/\" rel=\"nofollow\"\u003eDistZilla\u003c/a\u003e and \u003ca href=\"https://github.com/miyagawa/cpanminus\"\u003ecpanm\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd Bio-Deago\ndzil authordeps --missing | cpanm\ndzil listdeps --missing | cpanm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the tests:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edzil test\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf the tests pass, install Bio-Deago:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edzil install\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, DEAGO will look for R in your \u003ccode\u003e$PATH\u003c/code\u003e and for the associated R libraries in \u003ccode\u003e$R_LIBS\u003c/code\u003e. Where there are multiple R versions or libraries installed, setting the environment variables below will enable you to overwrite this default behaviour.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eDependency\u003c/th\u003e\n\u003cth align=\"left\"\u003eEnvironment variable\u003c/th\u003e\n\u003cth\u003eExample\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eR bin\u003c/td\u003e\n\u003ctd align=\"left\"\u003eDEAGO_R\u003c/td\u003e\n\u003ctd\u003e/path/to/R-3.4.0/bin\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eR library\u003c/td\u003e\n\u003ctd align=\"left\"\u003eDEAGO_R_LIBS\u003c/td\u003e\n\u003ctd\u003e/path/to/personal/rlib\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: deago [options]\nRNA-Seq differential expression qc and analysis\n\nMain options:\n --output_directory (-o) output directory [.]\n --convert_annotation convert annotation for use with deago (requires -a)\n --annotation_delim annotation file delimiter [\\t]\n --build_config build configuration file from command line arguments (see configuration options)\n --config_file configuration filename or output filename for configuration file if building [./deago.config]\n --markdown_file output filename for markdown file [./deago_markdown.Rmd]\n --html_file output filename for html file [./deago_markdown.html]\n -v verbose output to STDOUT\n -w print version and exit\n -h print help message and exit\n\nConfiguration options (required):\n -c STR directory containing count files (absolute path)\n -t STR targets filename (absolute path)\n\n Configuration options (optional):\n -r STR results directory [current working directory]\n -a STR annotation filename (absolute path)\n -q NUM qvalue (DESeq2) [0.05]\n --control name of control condition (must be present in targets file)\n --keep_images keep images used in report\n --qc QC only\n --go GO term enrichment\n --go_levels BP only, MF only or all [BP|MF|all]\n --count_type type of count file [expression|featurecounts]\n --count_column number of column containing count values\n --skip_lines number of lines to skip in count file\n --count_delim count file delimiter\n --gene_ids name of column containing gene ids\n\nDEAGO takes in a configuration file containing key/value pairs [default: ./deago.config]. You can\nuse your own configuration file with --config_file or specify parameters and let DEAGO build a\nconfiguration file with --build_config (and --config_file if you don\u0027t want the default\nconfiguration filename). For more information on configuration parameters run: build_deago_config -h.\n\nDEAGO will then build a master R markdown file (--markdown_file if you don\u0027t want the default\nmarkdown filename) from templates which utilize the companion DEAGO R package and the key/value\npairs set out in the configuration file. The R markdown will be processed and used to generate a\nHTML report (--html_file if you don\u0027t want the default html filename).\n\nTo use custom gene names and for GO term enrichment (--go) and annotation file must be provided\n(-a). Annotations downloaded from BioMart or those in a similar format can be converted for use\nwith DEAGO. For more information run: mart_to_deago -h.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput data\u003c/h3\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-label=\"Permalink: Input data\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run DEAGO, the user must provide a path to the read count matrices for each sample and a sample/condition mapping file. For GO term enrichment analysis, an annotation file must also be provided.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eInput\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003ecount data directory (required)\u003c/td\u003e\n\u003ctd align=\"left\"\u003epath to directory containing count matrix files (one per sample)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003etargets file (required)\u003c/td\u003e\n\u003ctd align=\"left\"\u003esample to experimental condition mappings\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eannotation file (optional)\u003c/td\u003e\n\u003ctd align=\"left\"\u003erequired for gene name annotation and GO term enrichment analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePlease read the \u003ca href=\"https://github.com/sanger-pathogens/deago/wiki\"\u003eDEAGO wiki\u003c/a\u003e for more information on the required file formats.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuick start\u003c/h3\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run QC and differential expression analyses:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edeago --build_config -c \u0026lt;path/to/count/data\u0026gt; -t \u0026lt;sample_mapping.txt\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQC only\u003c/h3\u003e\u003ca id=\"user-content-qc-only\" class=\"anchor\" aria-label=\"Permalink: QC only\" href=\"#qc-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo only generate QC plots:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edeago --build_config -c \u0026lt;path/to/count/data\u0026gt; -t \u0026lt;sample_mapping.txt\u0026gt; --qc\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConvert BioMart annotation\u003c/h3\u003e\u003ca id=\"user-content-convert-biomart-annotation\" class=\"anchor\" aria-label=\"Permalink: Convert BioMart annotation\" href=\"#convert-biomart-annotation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo convert a delimited annotation file (e.g. BioMart):\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edeago --build_config -c \u0026lt;path/to/count/data\u0026gt; -t \u0026lt;sample_mapping.txt\u0026gt; --convert_annotation -a \u0026lt;annotation.txt\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGO term enrichment analysis\u003c/h3\u003e\u003ca id=\"user-content-go-term-enrichment-analysis\" class=\"anchor\" aria-label=\"Permalink: GO term enrichment analysis\" href=\"#go-term-enrichment-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGO term enrichment analysis requires an annotation file mapping the gene identifiers in the count matrices with their associated GO terms.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edeago --build_config -c \u0026lt;path/to/count/data\u0026gt; -t \u0026lt;sample_mapping.txt\u0026gt; -a \u0026lt;annotation_file.txt\u0026gt; --go\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput files\u003c/h3\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-label=\"Permalink: Output files\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDEAGO generates a user-friendly HTML report of the analysis (\u003cstrong\u003edeago_markdown.html\u003c/strong\u003e). The markdown file used to run the analysis and knit the report is also provided (\u003cstrong\u003edeago_markdown.Rmd\u003c/strong\u003e) along with a log file containing the STDOUT from the conversion. If \u003ccode\u003e--build-config\u003c/code\u003e was used instead of providing a configuration file, then a configuration file will be generated (\u003cstrong\u003edeago.config\u003c/strong\u003e).\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eFile(s)\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edeago.config (optional)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eKey/value parameters to define the analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edeago_markdown.Rmd\u003c/td\u003e\n\u003ctd align=\"left\"\u003eR markdown file of analysis commands\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edeago_markdown.html\u003c/td\u003e\n\u003ctd align=\"left\"\u003eHTML report knitted from R markdown file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edeago.rlog\u003c/td\u003e\n\u003ctd align=\"left\"\u003eLog file of STDOUT from R markdown conversion\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDifferential expression and GO term enrichment analysis results are written to tab-delimited files within a timestamped results directory (results_). The corresponding results directory can be found in the \u003cstrong\u003ePipeline configuration\u003c/strong\u003e section of the HTML report.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eFile(s)\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026lt;contrast\u0026gt;_\u0026lt;alpha\u0026gt;.txt\u003c/td\u003e\n\u003ctd align=\"left\"\u003eDifferential expression analysis results and normalised counts\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026lt;contrast\u0026gt;_\u0026lt;go_level\u0026gt;.tsv\u003c/td\u003e\n\u003ctd align=\"left\"\u003eGO term enrichment analysis results\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDEAGO is free software, licensed under \u003ca href=\"https://github.com/sanger-pathogens/Bio-Deago/blob/master/GPL-LICENSE\"\u003eGPLv3\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeedback/Issues\u003c/h2\u003e\u003ca id=\"user-content-feedbackissues\" class=\"anchor\" aria-label=\"Permalink: Feedback/Issues\" href=\"#feedbackissues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease report any issues to the \u003ca href=\"https://github.com/sanger-pathogens/Bio-Deago/issues\"\u003eissues page\u003c/a\u003e or email \u003ca href=\"mailto:path-help@sanger.ac.uk\"\u003epath-help@sanger.ac.uk\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFurther Information\u003c/h2\u003e\u003ca id=\"user-content-further-information\" class=\"anchor\" aria-label=\"Permalink: Further Information\" href=\"#further-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information, please go to the \u003ca href=\"https://github.com/sanger-pathogens/deago/wiki\"\u003eDEAGO wiki\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "jolars/LookAheadScreening", + "latest_release": "v0.2.0", + "readme": "\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-github_document\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-github_document\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eoutput: github_document\u003c/h2\u003e\n\n\u003ch1\u003e\u003ca id=\"user-content-code-for-the-hessian-screening-rule\" class=\"anchor\" aria-hidden=\"true\" href=\"#code-for-the-hessian-screening-rule\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode for the Hessian Screening Rule\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/jolars/HessianScreening/actions\"\u003e\u003cimg src=\"https://github.com/jolars/LookAheadScreening/workflows/R-CMD-check/badge.svg\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h2\u003e\n\u003cp\u003eThe results from the simulations, which were run\non a dedicated HPC cluster, are stored in the \u003ca href=\"results/\"\u003eresults folder\u003c/a\u003e.\nThe source code for the actual paper, including figures,\nis found in \u003ca href=\"paper/\"\u003e\u003ccode\u003epaper/\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reproducing-the-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#reproducing-the-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReproducing the Results\u003c/h2\u003e\n\u003cp\u003eThe results from our paper were run through a singularity container. Check\nthe releases for pre-built singularity containers that you can download and use.\u003c/p\u003e\n\u003cp\u003eTo reproduce the results, \u003cstrong\u003ealways\u003c/strong\u003e use the\nsingularity container. To run an experiment from the\nsingularity container, call\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind results:/Project/results container.sif \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003escript\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;script\u0026gt;\u003c/code\u003e should be a name of a script in the\n\u003ca href=\"experiments/\"\u003eexperiments folder\u003c/a\u003e, such as \u003ccode\u003eexperiments/simulateddata.R\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-re-building-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#re-building-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRe-building the Singularity Container\u003c/h3\u003e\n\u003cp\u003eIf you want to re-build the singularity container from scratch (or\nsimply want to clone the repo to your local drive), you can\ndo so via the following steps.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone the repository to your local hard drive. On linux, using\nSSH authentication, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:jolars/LookAheadScreening.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the root of the repo and\nbuild the singularity container by calling\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e LookAheadScreening\nsudo singularity build container.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen proceed as in \u003ca href=\"#reproducing-the-results\"\u003eReproducing the Results\u003c/a\u003e\nto run the experiments.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-experiments-without-singularity-not-recommended\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-experiments-without-singularity-not-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Experiments without Singularity (Not Recommended!)\u003c/h3\u003e\n\u003cp\u003eAlternatively, you may also reproduce the results by cloning this repository\nand starting\nR in the root directory of this folder (which will activate the renv\nrepository) and then run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003erenv\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003erestore()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto restore the project library. Then build the R package (see below) and run the\nsimulations directly by running the scripts in the experiments folder. This\nis \u003cstrong\u003enot recommended\u003c/strong\u003e, however, since it, unlike the Singularity\ncontainer approach, does not exactly\nreproduce the software environment\nused when these simulations where originally run and may result in\ndiscrepancies due to differences in for instance operating systems,\ncompilers, and BLAS/LAPACK implementations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-r-package\" class=\"anchor\" aria-hidden=\"true\" href=\"#r-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR Package\u003c/h2\u003e\n\u003cp\u003eIf you want to build\nand experiment with the package, you can do so by calling\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e R CMD INSTALL \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eprovided you have \u003ccode\u003ecd\u003c/code\u003eed to the root folder of this repository. First\nensure, however, that you have enabled the renv project library by calling\n\u003ccode\u003erenv::restore()\u003c/code\u003e (see the section above).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003cp\u003eThe datasets used in these simulations are stored in the \u003ca href=\"data/\"\u003edata folder\u003c/a\u003e.\nScripts to retrieve these datasets from their original\nsources can be found in \u003ca href=\"data-raw/\"\u003e\u003ccode\u003edata-raw/\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, - "topics": [ - "genomics", - "sequencing", - "next-generation-sequencing", - "research", - "bioinformatics", - "bioinformatics-pipeline", - "global-health", - "infectious-diseases", - "pathogen" - ], - "updated_at": 1567577290.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1624959145.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "HaitaoMao/GraphSSLScaling", + "full_name": "aseetharam/maker", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCode Base for \"Graph SSL Scaling\"\u003c/h1\u003e\u003ca id=\"user-content-code-base-for-graph-ssl-scaling\" class=\"anchor\" aria-label=\"Permalink: Code Base for \u0026quot;Graph SSL Scaling\u0026quot;\" href=\"#code-base-for-graph-ssl-scaling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall the dependencies via Dockerfile or \u0027pip install -r requirements.txt\u0027\u003c/li\u003e\n\u003cli\u003eExecute data_scaling.sh or model_scaling.sh to use the pipeline for experiments.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall via Dockerfile\u003c/h2\u003e\u003ca id=\"user-content-install-via-dockerfile\" class=\"anchor\" aria-label=\"Permalink: Install via Dockerfile\" href=\"#install-via-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/HaitaoMao/GraphSSLScaling.git ./GraphSSLScaling\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e GraphSSLScaling\ndocker build --no-cache --tag ssl:latest \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -it --gpus all --ipc host --name ssl -v /home:/home ssl:latest bash\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e upon completion, you should be at /GraphSSLScaling inside the container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall via Singularity\u003c/h2\u003e\u003ca id=\"user-content-install-via-singularity\" class=\"anchor\" aria-label=\"Permalink: Install via Singularity\" href=\"#install-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/HaitaoMao/GraphSSLScaling.git ./GraphSSLScaling\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e GraphSSLScaling\nsingularity build ssl.sif Singularity.def\nsingularity shell --nv -B /home:/home ssl.sif\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e upon completion, you should be at /GraphSSLScaling inside the container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions\u003c/h2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhy we build this pipeline?\u003c/h3\u003e\u003ca id=\"user-content-why-we-build-this-pipeline\" class=\"anchor\" aria-label=\"Permalink: Why we build this pipeline?\" href=\"#why-we-build-this-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThere is a lot of parameters to set. It\u0027s not practical to set all of them manually in main function.\u003c/li\u003e\n\u003cli\u003eThere is a lot of results to record. It\u0027s necessary to record the results with corresponding settings.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow can this pipeline help us?\u003c/h3\u003e\u003ca id=\"user-content-how-can-this-pipeline-help-us\" class=\"anchor\" aria-label=\"Permalink: How can this pipeline help us?\" href=\"#how-can-this-pipeline-help-us\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003cstrong\u003eConfigParser\u003c/strong\u003e will read the config file of your settings(e.g., learning rate,hidden_size) or default settings and set them for datasets or the corresponding executor of the model chosed by you.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003cstrong\u003e./libgptb/log folder\u003c/strong\u003e will restore the config settings and the \u003cstrong\u003e./libgptb/cache folder\u003c/strong\u003e will restore the evaluate results with the exp_id. Then you can easily collect them.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow is this pipeline organised?\u003c/h3\u003e\u003ca id=\"user-content-how-is-this-pipeline-organised\" class=\"anchor\" aria-label=\"Permalink: How is this pipeline organised?\" href=\"#how-is-this-pipeline-organised\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe whole process is outlined in \u003ccode\u003elibgptb/pipeline/pipeline.py\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIn brief the whole process will be like\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eConfigParser\u003c/strong\u003e will load the config file and default config file for the \u003cstrong\u003eDataset\u003c/strong\u003e and \u003cstrong\u003eExecutor\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDataset\u003c/strong\u003e will load the data and get some features(e.g., input feature dims)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExecutor\u003c/strong\u003e will load the chosen model and the model will be trained and evaluated with stored evaluation results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe default config\u003c/h3\u003e\u003ca id=\"user-content-the-default-config\" class=\"anchor\" aria-label=\"Permalink: The default config\" href=\"#the-default-config\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou check the default configs for our \u003cstrong\u003eExecutor\u003c/strong\u003e and \u003cstrong\u003eModel\u003c/strong\u003e. Check \u003ccode\u003elibgptb/config/executors/XXXExecutor.json\u003c/code\u003e and \u003ccode\u003elibgptb/config/model/SSGCL/XXX.json\u003c/code\u003e .\u003c/p\u003e\n\u003cp\u003eThe config parameters specified by the user have the highest priority, which can be included in a json file. In other words the default config will be overwrittern.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe execution environment\u003c/h2\u003e\u003ca id=\"user-content-the-execution-environment\" class=\"anchor\" aria-label=\"Permalink: The execution environment\" href=\"#the-execution-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocerfile_x86\u003c/h3\u003e\u003ca id=\"user-content-docerfile_x86\" class=\"anchor\" aria-label=\"Permalink: Docerfile_x86\" href=\"#docerfile_x86\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-dockerfile\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eFROM\u003c/span\u003e pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel\n\u003cspan class=\"pl-k\"\u003eCOPY\u003c/span\u003e requirements_docker.txt /opt\n\u003cspan class=\"pl-k\"\u003eRUN\u003c/span\u003e pip install -r /opt/requirements.txt \\\n \u0026amp;\u0026amp; pip install tensorboard \\\n \u0026amp;\u0026amp; pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cu116.html \\\n \u0026amp;\u0026amp; pip install torch_geometric\\\n \u0026amp;\u0026amp; pip install dgl -f https://data.dgl.ai/wheels/cu116/repo.html \\\n \u0026amp;\u0026amp; pip install dglgo -f https://data.dgl.ai/wheels-test/repo.html\n\u003cspan class=\"pl-k\"\u003eENV\u003c/span\u003e PATH=$PATH:/usr/local/cuda-11.6/bin LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.6/lib64:/usr/lib/x86_64-linux-gnu\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eYou can build from source with command \u003ccode\u003edocker build -f $Dockerfile path -t $YourImageName\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExecute the following command in the parent path of the project.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v ./GraphSSLScaling/:/GraphSSLScaling -w /GraphSSLScaling --gpus all \u003cspan class=\"pl-smi\"\u003e$YourImageName\u003c/span\u003e /bin/bash /GraphSSLScaling/command.sh\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -v $HOST_PATH:$Container_Path means mounting the localfile to the container. \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -w means setting the working dir\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --gpus all means using all GPU\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e /bin/bash /GraphSSLScaling/command.sh means using the /bin/bash of Container to execute the customized command.sh. You could combine it with the model_scaling.sh or data_scaling.sh\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity.def file\u003c/h3\u003e\u003ca id=\"user-content-singularitydef-file\" class=\"anchor\" aria-label=\"Permalink: Singularity.def file\" href=\"#singularitydef-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eBootstrap is used to specify the agent,where the base image from,here localimage means to build from a local image\u003c/span\u003e\nBootstrap: localimage\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# This is something like \u0027From\u0027 in DOCKERFILE to indicate the base image\u003c/span\u003e\nFrom: ./pytorch_1.13.1-cuda11.6-cudnn8-devel.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e %files can be used to copy files from host into the image\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e like \u0027COPY\u0027 in DOCKERFILE\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Here we copy the requirements.txt into the image, then we can use it to install the required dependencies.\u003c/span\u003e\n%files\n requirements_docker.txt /opt\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e %post is used to build the new image\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Usage is same to shell.Here we used pip to install dependencies.\u003c/span\u003e\n%post\n pip install -r /opt/requirements.txt\n pip install tensorboard\n pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cu116.html\n pip install dgl -f https://data.dgl.ai/wheels/cu116/repo.html \n pip install dglgo -f https://data.dgl.ai/wheels-test/repo.html \n\n \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e% environment is used to set env_variables once the image starts\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e These lines are necessary to load cuda\u003c/span\u003e\n%environment\n \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:/usr/local/cuda-11.6/bin\n \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e:/usr/local/cuda-11.6/lib64:/usr/lib/x86_64-linux-gnu\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The usage of singularity is similar to docker\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can use \u0027singularity exec --writable-tmpfs --nv --nvccli\u0027 to enable the NVIDIA gpu for executing with sigularity \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1729774886.0 + "updated_at": 1601247148.0 }, { "data_format": 2, - "description": "Definition (recipe) files for singularity containers.", + "description": "Nextflow pipeline to detect matched BAMs with NGSCheckMate", "filenames": [ - "cytof-deep-cnn/Singularity.def", - "somascan-power-tool/Singularity.def", - "H5N1/Singularity_R_3.6.def", - "H5N1/Singularity.def", - "bittersweet/Singularity.def", - "cite-seq/Singularity_rocker.def", - "cite-seq/Singularity_xenial.def", - "cite-seq/Singularity_publish.def", - "cite-seq/Singularity_AJM_COVID.def", - "cite-seq/Singularity_3.def", - "tf-gpu-chemistry/Singularity", - "generic/Singularity.def", - "cytof-workflow-v4/Singularity.def", - "cytof-workflow-v3/Singularity_SCS.def", - "cytof-workflow-v3/Singularity.def", - "comet/Singularity.def" + "Singularity/Singularity.v1.1" ], - "full_name": "rohitfarmer/singularity-defs", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDefinition/Recipe Files for Singularity Containers\u003c/h1\u003e\u003ca id=\"user-content-definitionrecipe-files-for-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: Definition/Recipe Files for Singularity Containers\" href=\"#definitionrecipe-files-for-singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome of the containers are available to download from \u003ca href=\"https://cloud.sylabs.io/library/rohitfarmer\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/rohitfarmer\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor feedback and collaboration write to me at \u003ca href=\"mailto:rohit.farmer@gmail.com\"\u003erohit.farmer@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall Singularity on Linux\u003c/h1\u003e\u003ca id=\"user-content-install-singularity-on-linux\" class=\"anchor\" aria-label=\"Permalink: Install Singularity on Linux\" href=\"#install-singularity-on-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Version 3.4\u003c/h2\u003e\u003ca id=\"user-content-singularity-version-34\" class=\"anchor\" aria-label=\"Permalink: Singularity Version 3.4\" href=\"#singularity-version-34\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow the instructions on \u003ca href=\"https://sylabs.io/guides/3.4/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.4/user-guide/quick_start.html#quick-installation-steps\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding a Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-building-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Building a Singularity Container\" href=\"#building-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReadonly Container\u003c/h2\u003e\u003ca id=\"user-content-readonly-container\" class=\"anchor\" aria-label=\"Permalink: Readonly Container\" href=\"#readonly-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a read-only SquashFS Singularity container on a local machine using a recipe/definition file.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build \u0026lt;container-name.sif\u0026gt; \u0026lt;Singularity.def\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo set a different temporary directory than the default \u003ccode\u003e/tmp\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003esudo -E SINGULARITY_TMPDIR=/home/rohit/tmp singularity build \u0026lt;container.sif\u0026gt; \u0026lt;container.def\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWritable Sandbox.\u003c/h2\u003e\u003ca id=\"user-content-writable-sandbox\" class=\"anchor\" aria-label=\"Permalink: Writable Sandbox.\" href=\"#writable-sandbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a writable sandbox (essentially a folder) on a local machine using a recipe/definition file.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build --sandbox \u0026lt;sandbox-folder-name/\u0026gt; \u0026lt;Singularity.def\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: The advantage of building a writable sandbox is that it can be used to install and configure packages as you go, and once you are satisfied with the requirements, the sandbox can be converted into a read-only SquashFS container. To build a sandbox quickly, it\u0027s better to install a minimal set of packages via the definition file.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall/Configure Packages in a Writable Sandbox\u003c/h3\u003e\u003ca id=\"user-content-installconfigure-packages-in-a-writable-sandbox\" class=\"anchor\" aria-label=\"Permalink: Install/Configure Packages in a Writable Sandbox\" href=\"#installconfigure-packages-in-a-writable-sandbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce a writable sandbox is created to execute it to invoke the shell of the operating installed in the container in the \"writable\" mode. If the shell is not invoked in the \"writable\" mode, all the changes will be lost once you exit from the container environment.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity shell --writable \u0026lt;sandbox-folder-name/\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eInstall packages as you would, for example, in Ubuntu from the command line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConvert a Writable Sandbox to a Readonly Container\u003c/h2\u003e\u003ca id=\"user-content-convert-a-writable-sandbox-to-a-readonly-container\" class=\"anchor\" aria-label=\"Permalink: Convert a Writable Sandbox to a Readonly Container\" href=\"#convert-a-writable-sandbox-to-a-readonly-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build \u0026lt;container-name.sif\u0026gt; \u0026lt;sandbox-folder-name/\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExecute a Container\u003c/h1\u003e\u003ca id=\"user-content-execute-a-container\" class=\"anchor\" aria-label=\"Permalink: Execute a Container\" href=\"#execute-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInvoke a shell\u003c/h2\u003e\u003ca id=\"user-content-invoke-a-shell\" class=\"anchor\" aria-label=\"Permalink: Invoke a shell\" href=\"#invoke-a-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe command below can be used for both read-only/writable containers/sandbox.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity shell \u0026lt;container-name.sif\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: By default, Singularity binds to your current working and home directory. Therefore, you do not need to do anything else to execute a script that is in your current working directory. It can also pull, for example, Vim settings from the .vimrc file in your home directory. Therefore, if Vim installed in the container, it can be used with the same settings from inside the container as it would from outside.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecute a Command via Container\u003c/h2\u003e\u003ca id=\"user-content-execute-a-command-via-container\" class=\"anchor\" aria-label=\"Permalink: Execute a Command via Container\" href=\"#execute-a-command-via-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec \u0026lt;container-name.sif\u0026gt; \u0026lt;command\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor example: \u003ccode\u003esingularity exec \u0026lt;container-name.sif\u0026gt; Rscript --vanilla hello.R\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning Jupyter Notebooks from Within a Container\u003c/h1\u003e\u003ca id=\"user-content-running-jupyter-notebooks-from-within-a-container\" class=\"anchor\" aria-label=\"Permalink: Running Jupyter Notebooks from Within a Container\" href=\"#running-jupyter-notebooks-from-within-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section is for containers that have Jupyter notebook installed (e.g. cite-seq).\u003c/p\u003e\n\u003cp\u003eA generic command that should work on a personal computer. \u003ccode\u003esingularity exec container-name.sif jupyter notebook --no-browser --ip=127.0.0.1 --port=8888\u003c/code\u003e\u003cbr\u003e\n\u003cem\u003eNote: The IP address and the port number mentioned in the command are the jupyter defaults. They can be changed as per need.\u003c/em\u003e\u003cbr\u003e\nCopy the URL generated by jupyter daemon and paste it in your browser; this should open Jupyter with the list of the files in your current working directory on the host computer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with R as a Kernel\u003c/h2\u003e\u003ca id=\"user-content-running-with-r-as-a-kernel\" class=\"anchor\" aria-label=\"Permalink: Running with R as a Kernel\" href=\"#running-with-r-as-a-kernel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSometimes if you already have an R kernel installed in your home directory, it conflicts with what you have inside the container. Therefore, it would require you to re-install the kernel specs in your home directory via the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec container-name.sif R --quiet --slave -e \u0027IRkernel::installspec()\u0027\n\n# Screen log.\n# [InstallKernelSpec] Removing existing kernelspec in /home/user/.local/share/jupyter/kernels/ir\n# [InstallKernelSpec] Installed kernelspec ir in /home/user/.local/share/jupyter/kernels/ir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning on an HPC\u003c/h2\u003e\u003ca id=\"user-content-running-on-an-hpc\" class=\"anchor\" aria-label=\"Permalink: Running on an HPC\" href=\"#running-on-an-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eSSH to the HPC.\u003c/li\u003e\n\u003cli\u003eClaim an interactive node.\u003c/li\u003e\n\u003cli\u003eNavigate to your project directory. Singularity container should be in your project directory.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esingularity exec container-name.sif jupyter notebook --no-browser --ip=\u00270.0.0.0\u0027\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eKeep the SSH session and Jupyter notebook session running. Copy the URL on your local browser.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eNote: On some HPCs, you may have to initiate an additional SSH tunnel connecting your local machine to the interactive node on the HPC. In that case, follow some generic instructions here \u003ca href=\"https://rohitfarmer.github.io/docs/docs/HPC/jupyter/\" rel=\"nofollow\"\u003ehttps://rohitfarmer.github.io/docs/docs/HPC/jupyter/\u003c/a\u003e or ask your system administrator.\u003c/em\u003e\u003c/p\u003e\n", + "full_name": "IARCbioinfo/NGSCheckMate-nf", + "latest_release": "v1.1a", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ngscheckmate\" class=\"anchor\" aria-hidden=\"true\" href=\"#ngscheckmate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNGSCheckMate\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow-pipeline-to-detect-matched-bams-with-ngscheckmate\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-to-detect-matched-bams-with-ngscheckmate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline to detect matched BAMs with \u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003eNGSCheckMate\u003c/a\u003e.\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/NGSCheckMate-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/46874680377559d9fb7b208025569f78feddc56fd2db72390df01be75534adef/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f4e4753436865636b4d6174652d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/NGSCheckMate-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/repository/docker/iarcbioinfo/ngscheckmate-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4613\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"NGSCheckMate-nf.png?raw=true\"\u003e\u003cimg src=\"NGSCheckMate-nf.png?raw=true\" alt=\"Workflow representation\" title=\"Scheme of NGSCheckMate Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eImplementation of NGSCheckMate and its underlying subset calling, distibuted per sample.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eNextflow : for common installation procedures see the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003eNGSCheckMate\u003c/a\u003e (follow instructions, especially setting up \u003ccode\u003e$NCM_HOME\u003c/code\u003e variable)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.htslib.org/download/\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.htslib.org/download/\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAdditionally, the graph output option requires \u003ca href=\"https://cran.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e; see details below about this option.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input\u003c/td\u003e\n\u003ctd\u003eyour input BAM file(s) (do not forget the quotes e.g. \u003ccode\u003e--input \"test_*.bam\"\u003c/code\u003e). Warning : your BAM file(s) must be indexed, and the \u003ccode\u003etest_*.bai\u003c/code\u003e should be in the same folder.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder with BAM files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_file\u003c/td\u003e\n\u003ctd\u003eInput file (comma-separated) with 3 columns: ID (individual ID), suffix (suffix for sample names; e.g. RNA), and bam (path to bam file).\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA nextflow.config is also included, please modify it for suitability outside our pre-configured clusters (\u003ca href=\"https://www.nextflow.io/docs/latest/config.html#configuration-file\" rel=\"nofollow\"\u003esee Nexflow configuration\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eNote that the input_file format is tab-delimited text file; this file is used both to provide input bam file locations but also for the generation of the graphs. The ID field must be unique to a subject (e.g. both tumor and normal samples from the same individual must have the same individual identifier). The bam field must be unique to a file name. For example, the following is a valid file:\u003c/p\u003e\n\u003cp\u003eID suffix bam\nNA06984 _RNA NA06984_T_transcriptome.bam\u003cbr\u003e\nNA06984 _WGS NA06984_T_genome.bam\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003eresults\u003c/td\u003e\n\u003ctd\u003ethe folder that will contain NGSCheckMate folder with all results in text files.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd\u003eref.fasta\u003c/td\u003e\n\u003ctd\u003eyour reference in FASTA\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bed\u003c/td\u003e\n\u003ctd\u003eSNP_GRCh38.bed\u003c/td\u003e\n\u003ctd\u003ePanel of SNP bed file from \u003ca href=\"https://github.com/parklab/NGSCheckMate/tree/master/SNP\"\u003eNGSCheckMate\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that a bed file SNP_GRCh38.bed is provided, which is a liftOver of the files at \u003ca href=\"https://github.com/parklab/NGSCheckMate/tree/master/SNP\"\u003ehttps://github.com/parklab/NGSCheckMate/tree/master/SNP\u003c/a\u003e. To use other references, you can provide your own bedfile.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e16\u003c/td\u003e\n\u003ctd\u003eMemory requested (in GB) for calling and NGSCheckmate run\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003eNumber of threads for germline calling\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bai_ext\u003c/td\u003e\n\u003ctd\u003e.bam.bai\u003c/td\u003e\n\u003ctd\u003eExtenstion of bai files\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run NGSCheckMate-nf/ -r v1.1 -profile singularity --ref ref.fasta --input_folder BAM/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the pipeline without singularity just remove \"-profile singularity\". Alternatively, one can run the pipeline using a docker container (-profile docker) the conda receipe containing all required dependencies (-profile conda).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003evcfs\u003c/td\u003e\n\u003ctd\u003ea folder with the vcfs used for the matching\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNCM_output/output*.txt\u003c/td\u003e\n\u003ctd\u003eNGSCheckmate output files with matches between files (see \u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003ehttps://github.com/parklab/NGSCheckMate\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNCM_output/output.pdf\u003c/td\u003e\n\u003ctd\u003ehierarchical clustering plot from \u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003ehttps://github.com/parklab/NGSCheckMate\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNCM_output/NCM_graph_wrongmatch.xgmml\u003c/td\u003e\n\u003ctd\u003egraph with only the samples without a match (adapted from \u003ca href=\"https://github.com/parklab/NGSCheckMate/blob/master/graph/ngscheckmate2xgmml.R\"\u003ehttps://github.com/parklab/NGSCheckMate/blob/master/graph/ngscheckmate2xgmml.R\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNCM_output/NCM_graph.xgmml\u003c/td\u003e\n\u003ctd\u003egraph with all samples (adapted from \u003ca href=\"https://github.com/parklab/NGSCheckMate/blob/master/graph/ngscheckmate2xgmml.R\"\u003ehttps://github.com/parklab/NGSCheckMate/blob/master/graph/ngscheckmate2xgmml.R\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that we recommend \u003ca href=\"https://cytoscape.org/\" rel=\"nofollow\"\u003eCytoscape\u003c/a\u003e to visualize the .xgmml graphs.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-for-cobalt-cluster\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-for-cobalt-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage for Cobalt cluster\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run iarcbioinfo/NGSCheckMate -profile cobalt --input \"/data/test_*.bam\" --output_dir /data/cohort_output --ref_fasta /ref/Homo_sapiens_assembly38.fasta --bed /home/user/bin/NGSCheckMate/SNP/SNP_GRCh38.bed\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-hidden=\"true\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-why-are-some-files-not-included-although-the-are-in-the-intput_folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-are-some-files-not-included-although-the-are-in-the-intput_folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy are some files not included although the are in the intput_folder?\u003c/h3\u003e\n\u003cp\u003ebe careful that if bai files are missing for some bam files, the bam files will be ignored without the workflow returning an error\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-what-modifications-have-been-done-to-the-original-ngscheckmate-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-modifications-have-been-done-to-the-original-ngscheckmate-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat modifications have been done to the original NGSCheckMate code?\u003c/h3\u003e\n\u003cp\u003eWe provide a modified version of the graph/ngscheckmate2xgmml.R R script from \u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003ehttps://github.com/parklab/NGSCheckMate\u003c/a\u003e to output graphs in .xgmml format. The modifications allow to represent all samples, even those that match, and improve a small glitch in the color palette.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@iarc.fr\"\u003eAlcalaN@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaxime Vall\u00e9e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 1, - "subscribers_count": 0, + "subscribers_count": 5, "topics": [], - "updated_at": 1724962082.0 + "updated_at": 1626965540.0 }, { "data_format": 2, - "description": "Singularity image for the CORAL group at Washington University School of Medicine", + "description": null, "filenames": [ - "Singularity.1p13p1", - "Singularity.scikit", - "Singularity.tf2", - "Singularity.tf2_nightly", - "Singularity.SimpleITK", - "Singularity" + "SingularitY/Singularity_fenics2017_msucompbiomechlab" ], - "full_name": "gdhugo/coral_singularity", + "full_name": "MJ0706/HCM-project", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecoral_singularity\u003c/h1\u003e\u003ca id=\"user-content-coral_singularity\" class=\"anchor\" aria-label=\"Permalink: coral_singularity\" href=\"#coral_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image for the CORAL group at Washington University School of Medicine\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/687\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo run on the CHPC cluster:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLogin and then load singularity. No need to load cuda as only the driver is required on the host. Cuda will be installed in the singularity image. This is nice, because then we can install any cuda version we need (as long as it is compatible with the driver), and it will work.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eload singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePull the singularity image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://gdhugo/coral_singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to pull a specific image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://gdhugo/coral_singularity:scikit\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInvoke with GPU tools (--nv switch):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv gdhugo-coral_singularity-master-latest.simg\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hypertrophic-cardiomyopathy-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#hypertrophic-cardiomyopathy-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHypertrophic cardiomyopathy project\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-the-mathematical-details-data-simulation-protocols-and-results-are-explained-in-the-manuscript-if-you-have-access-to-the-manuscript-please-follow-it-first\" class=\"anchor\" aria-hidden=\"true\" href=\"#the-mathematical-details-data-simulation-protocols-and-results-are-explained-in-the-manuscript-if-you-have-access-to-the-manuscript-please-follow-it-first\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe mathematical details, data, simulation protocols and results are explained in the manuscript. If you have access to the manuscript, please, follow it first.\u003c/h3\u003e\n\u003cp\u003eSimulator of mechanics in the heart based on \u003ca href=\"https://fenicsproject.org/\" rel=\"nofollow\"\u003eFEniCS\u003c/a\u003e library.\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#installation-and-running-the-code\"\u003eInstallation and Running the code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#organization-of-the-code\"\u003eOrganization of the code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#simulation-protocols\"\u003eSimulation protocols\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch3\u003e\u003ca id=\"user-content-installation-and-running-the-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-and-running-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and Running the code\u003c/h3\u003e\n\u003cp\u003eA singularity \"build\" \u003ca href=\"./SingularitY/Singularity_fenics2017_msucompbiomechlab\"\u003efile\u003c/a\u003e is provided that will install all the libraries required to run the code.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall singularity by following the instruction in \u003ca href=\"https://sylabs.io/guides/3.5/admin-guide/installation.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild a singularity container using the \"build\" \u003ca href=\"./SingularitY/Singularity_fenics2017_msucompbiomechlab\"\u003efile\u003c/a\u003e with\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build \u0026lt;container_name\u0026gt;.img Singularity_fenics2017_msucompbiomechlab\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOnce the container is built, you can launch the Singularity container by\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run \u0026lt;container_name\u0026gt;.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eThe code can be run within the singularity container. For example, for the code \u003ca href=\"./ed_mesh_create/Patient_1/createLV_refine.py\"\u003ecreateLV_refine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython createLV_refine.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor in parallel\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003empirun.mpich -np \u0026lt;# processors\u0026gt; python createLV_refine.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-organization-of-the-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#organization-of-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOrganization of the code\u003c/h3\u003e\n\u003cp\u003eThe code is organized as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"./src2/mechanics\"\u003emechanics module\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./src2/sim_protocols/README.md\"\u003esimulation protocols\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./src2/utils\"\u003eutilities\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./src2/bmark_analytical\"\u003ebenchmark analytical solution\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./src2/postprocessing\"\u003epostprocessing\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDemo python scripts are also provided to simulate\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate end diastole mesh file\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./ed_mesh_create/Patient_1/createLV_refine.py\"\u003ePatient_1\u003c/a\u003e : Control Patient\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./ed_mesh_create/Patient_2/createLV_refine.py\"\u003ePatient_2\u003c/a\u003e : Non-obstructive HCM patient\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./ed_mesh_create/Patinet_3/createLV_refine.py\"\u003ePatient_3\u003c/a\u003e : Obstructive HCM patient\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate Hdf5 file to run simuations using \u003ca href=\"./ed_mesh_create/create_baselinegeo_animal.py\"\u003ecreate_baselinegeo_animal.py\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003emake sure the directory is correct for specific patient at \u003ca href=\"./ed_mesh_create/create_baselinegeo_animal.py\"\u003ecreate_baselinegeo_animal.py\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulation protocol \u0026amp; post-processing\n\u003cul\u003e\n\u003cli\u003eDetail of the code is explained in patient specific code.\n\u003cul\u003e\n\u003cli\u003eControl patient \u003ca href=\"./main/1.Control.py\"\u003e1.Control.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eNonobstructive HCM patient \u003ca href=\"./main/2.Nonobstructive_main.py\"\u003e2. Nonobstructive_main.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eObstructive HCM patient \u003ca href=\"./main/3.Obstructive_main.py\"\u003e3. Obstructive_main.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eThe simulation cases with various degree of dispersion for non-obstructive HCM patient are:\n\u003cul\u003e\n\u003cli\u003eFor kappa = 0.07, \u003ca href=\"./main/2.Nonobstructive_k1.py\"\u003e2.Nonobstructive_k1.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.1, \u003ca href=\"./main/2.Nonobstructive_k2.py\"\u003e2.Nonobstructive_k2.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.14, \u003ca href=\"./main/2.Nonobstructive_k3.py\"\u003e2.Nonobstructive_k3.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.18, \u003ca href=\"./main/2.Nonobstructive_k4.py\"\u003e2.Nonobstructive_k4.py\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe simulation cases with various degree of dispersion for Obstructive HCM patient are:\n\u003cul\u003e\n\u003cli\u003eFor kappa = 0.07, \u003ca href=\"./main/2.Obstructive_k1.py\"\u003e2.Obstructive_k1.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.1, \u003ca href=\"./main/2.Obstructive_k2.py\"\u003e2.Obstructive_k2.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.14, \u003ca href=\"./main/2.Obstructive_k3.py\"\u003e2.Obstructive_k3.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.18, \u003ca href=\"./main/2.Obstructive_k4.py\"\u003e2.Obstructive_k4.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.22, \u003ca href=\"./main/2.Obstructive_k5.py\"\u003e2.Obstructive_k5.py\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePostprocessing of the code is explained at last 4 lines (that are commented) in codes of \u003ca href=\"./main/13.Postprocessing.py\"\u003e13.Postprocessing.py\u003c/a\u003e. Make sure you run each step at a time using single processor.\u003c/li\u003e\n\u003cli\u003eKlotz plot will be plotted using \u003ca href=\"/main/4.KlotzPlot.py\"\u003e4. KlotzPlot.py\u003c/a\u003e. Make sure, only passive simulation results are used to plot the Klotz curve using this code. Please, check the references in the manuscript to learn more about Klotz curve.\u003c/li\u003e\n\u003cli\u003ePV plot for without disarray case will be outlined using \u003ca href=\"./main/5.plot_data_WithoutDisarray.py\"\u003e 5. plot_data_WithoutDisarray.py\u003c/a\u003e. Make sure the input directory is correct while running this code.\u003c/li\u003e\n\u003cli\u003ePV plot for disarray case will be outlined using \u003ca href=\"./main/6.plot_data_P2_WithDisarray.py\"\u003e6. plot_data_P2_WithDisarray.py\u003c/a\u003e for non-obstructive patient and \u003ca href=\"./main/8.plot_data_P3_WithDisarray.py\"\u003e8. plot_data_P3_WithDisarray.py\u003c/a\u003e for obstructive patient. Make sure the input directory is correct while running this code.\u003c/li\u003e\n\u003cli\u003eError bar plot will be plotted by \u003ca href=\"./main/9.plot_data_errorplot.py\"\u003e9. plot_data_errorplot.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAHA plot for without disarray cases can be outlined by \u003ca href=\"./main/10.ahaplot_WithoutDisarray.py\"\u003e10. ahaplot_WithoutDisarray.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAHA plot with disarray cases can be outlined for \u003ca href=\"./main/11.ahaplot_With_disarray_nonobstructive.py\"\u003eNon-obstructive\u003c/a\u003e and \u003ca href=\"./main/11.ahaplot_With_disarray_obstructive.py\"\u003eObstructive\u003c/a\u003e patient for various degree of disarray.\u003c/li\u003e\n\u003cli\u003eDeformation can be extracted using \u003ca href=\"/main/12.extract_deformation.py\"\u003e12. extract_deformation.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you face any issues running with code, email (\u003ca href=\"mailto:mojumder@msu.edu\"\u003emojumder@msu.edu\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1575649682.0 + "updated_at": 1664856512.0 }, { "data_format": 2, - "description": "PhD thesis in Computer Science at Rice University", + "description": null, "filenames": [ - "lg/Singularity", - "tensor/Singularity" + "Singularity.def" ], - "full_name": "vuphan314/phd-thesis", - "latest_release": "v0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuantitative Reasoning on Hybrid Formulas with Dynamic Programming\u003c/h1\u003e\u003ca id=\"user-content-quantitative-reasoning-on-hybrid-formulas-with-dynamic-programming\" class=\"anchor\" aria-label=\"Permalink: Quantitative Reasoning on Hybrid Formulas with Dynamic Programming\" href=\"#quantitative-reasoning-on-hybrid-formulas-with-dynamic-programming\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThis repository supplements Vu Phan\u0027s \u003ca href=\"https://repository.rice.edu/items/2e464125-244d-431b-b998-612f0dc2b41a\" rel=\"nofollow\"\u003ePhD thesis\u003c/a\u003e in Computer Science at Rice University.\u003c/li\u003e\n\u003cli\u003eWe provide four exact solvers that support XOR-CNF formulas.\n\u003cul\u003e\n\u003cli\u003eDPMC solves \u003cem\u003eweighted model counting (WMC)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eProCount solves \u003cem\u003eweighted projected model counting (WPMC)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDPO solves \u003cem\u003eweighted SAT (WSAT)\u003c/em\u003e, also called Boolean MPE.\u003c/li\u003e\n\u003cli\u003eDPER solves \u003cem\u003eexist-random SAT (ERSAT)\u003c/em\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach of these four solvers is a combination of a planner and an executor.\n\u003cul\u003e\n\u003cli\u003eA planner produces a \u003cstrong\u003eproject-join tree\u003c/strong\u003e \u003ccode\u003eT\u003c/code\u003e from an XOR-CNF formula \u003ccode\u003eF\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAn executor traverses \u003ccode\u003eT\u003c/code\u003e to computes a solution of \u003ccode\u003eF\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eFor WPMC and ERSAT, \u003ccode\u003eT\u003c/code\u003e must be \u003cstrong\u003egraded\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTwo planners are available.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./htb/\"\u003eHTB\u003c/a\u003e uses constraint-programming heuristics.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./lg/\"\u003eLG\u003c/a\u003e uses tree decomposers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTwo executors are available.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./dmc/\"\u003eDMC\u003c/a\u003e uses \u003cem\u003ealgebraic decision diagrams (ADDs)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./tensor/\"\u003eTensor\u003c/a\u003e uses tensors and only solves WMC on pure CNF.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopers\u003c/h2\u003e\u003ca id=\"user-content-developers\" class=\"anchor\" aria-label=\"Permalink: Developers\" href=\"#developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eVu Phan: HTB and DMC\u003c/li\u003e\n\u003cli\u003eJeffrey Dudek: LG and Tensor\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning this repository\u003c/h2\u003e\u003ca id=\"user-content-cloning-this-repository\" class=\"anchor\" aria-label=\"Permalink: Cloning this repository\" href=\"#cloning-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vuphan314/phd-thesis\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"./eval/\"\u003eEvaluation\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"./examples/\"\u003eExamples\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgment\u003c/h2\u003e\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" aria-label=\"Permalink: Acknowledgment\" href=\"#acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/ADDMC\"\u003eADDMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/approxmc\"\u003eBIRD\u003c/a\u003e: Soos, Meel\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cs.rochester.edu/u/kautz/Cachet\" rel=\"nofollow\"\u003eCachet\u003c/a\u003e: Sang, Beame, Kautz\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/msoos/cryptominisat\"\u003eCryptoMiniSat\u003c/a\u003e: Soos\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003eCUDD package\u003c/a\u003e: Somenzi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://davidkebo.com/cudd#cudd6\" rel=\"nofollow\"\u003eCUDD visualization\u003c/a\u003e: Kebo\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts\u003c/a\u003e: Beck\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/DPMC\"\u003eDPMC/ProCount/DPO/DPER\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kit-algo/flow-cutter-pace17\"\u003eFlowCutter\u003c/a\u003e: Strasser\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mabseher/htd\"\u003ehtd\u003c/a\u003e: Abseher, Musliu, Woltran\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://reasoning.cs.ucla.edu/minic2d\" rel=\"nofollow\"\u003eminiC2D\u003c/a\u003e: Oztok, Darwiche\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mccompetition.org\" rel=\"nofollow\"\u003eModel Counting Competition\u003c/a\u003e: Hecher, Fichte\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cril.univ-artois.fr/KC/pmc.html\" rel=\"nofollow\"\u003epmc\u003c/a\u003e: Lagniez, Marquis\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Kasekopf/SlurmQueen\"\u003eSlurmQueen\u003c/a\u003e: Dudek\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://trolando.github.io/sylvan\" rel=\"nofollow\"\u003eSylvan\u003c/a\u003e: van Dijk\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/TCS-Meiji/PACE2017-TrackA\"\u003eTamaki\u003c/a\u003e: Tamaki\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/TensorOrder\"\u003eTensorOrder\u003c/a\u003e: Dudek, Duenas-Osorio, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/WAPS\"\u003eWAPS\u003c/a\u003e: Gupta, Sharma, Roy, Meel\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "SETAP2021/setapDocker", + "latest_release": "latest", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-setapdocker\" class=\"anchor\" aria-hidden=\"true\" href=\"#setapdocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetapDocker\u003c/h1\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1721233440.0 + "updated_at": 1641570752.0 }, { "data_format": 2, - "description": null, + "description": "A repo containing code demonstrating how the CUDA accelerated TVL1 in OpenCV 4.X is much slower than in OpenCV 2.x", "filenames": [ - "Singularity" + "opencv2/src/Singularity", + "opencv4/src/Singularity" ], - "full_name": "bstriner/cuda-10.1-cudnn7-devel-ubuntu16.04", + "full_name": "willprice/opencv-tvl1-performance-regression-demo", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecuda-10.1-cudnn7-devel-ubuntu16.04\u003c/h1\u003e\u003ca id=\"user-content-cuda-101-cudnn7-devel-ubuntu1604\" class=\"anchor\" aria-label=\"Permalink: cuda-10.1-cudnn7-devel-ubuntu16.04\" href=\"#cuda-101-cudnn7-devel-ubuntu1604\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-opencv-optical-flow-speed-comparison\" class=\"anchor\" aria-hidden=\"true\" href=\"#opencv-optical-flow-speed-comparison\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenCV optical flow speed comparison\u003c/h1\u003e\n\u003cp\u003eI\u0027ve found OpenCV\u0027s GPU TVL1 implementation to be much slower in v4 than in v2.\nThis repository serves as an example demonstrating the issue.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-set-up\" class=\"anchor\" aria-hidden=\"true\" href=\"#set-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet up\u003c/h2\u003e\n\u003cp\u003eEnsure you have docker 19.03+ with an NVIDIA card present on your system. Build\nthe docker images (this handles building the base OpenCV images + the optical\nflow demo application)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003emake flow-images\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDownload test media and extract frames:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003effmpeg -i \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://github.com/MarkAYoder/esc-media/raw/master/BigBuckBunny_640x360.m4v\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -t 00:00:10 -qscale 2 frames/frame_%010d.jpg\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-speed-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-speed-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun speed test\u003c/h2\u003e\n\u003cp\u003eDiscard the first results as they will include the time spent by the nvidia\ndriver generating binaries for the current GPU from the PTX files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-k\"\u003etime\u003c/span\u003e docker run \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --gpus \u0027\"device=0\"\u0027 \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --rm -it \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/frames:/input \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/flow/opencv2:/output \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/.cache-opencv2:/cache/nv \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e willprice/furnari-flow:opencv2 \u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.03user 0.02system 0:14.57elapsed 0%CPU (0avgtext+0avgdata 63544maxresident)k\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0inputs+0outputs (0major+7956minor)pagefaults 0swaps\u003c/span\u003e\n\n$ \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-k\"\u003etime\u003c/span\u003e docker run \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --gpus \u0027\"device=0\"\u0027 \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --rm -it \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/frames:/input \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/flow/opencv4:/output \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/.cache-opencv4:/cache/nv \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e willprice/furnari-flow:opencv4\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.04user 0.02system 2:31.88elapsed 0%CPU (0avgtext+0avgdata 63404maxresident)k\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0inputs+0outputs (0major+7877minor)pagefaults 0swaps\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1599494490.0 + "updated_at": 1666537823.0 }, { "data_format": 2, - "description": "A LaTeX Beamer template for presentations using the Metropolis theme.", + "description": "Containerisation of NEMO Employing Singularity", "filenames": [ - "Singularity" + "Singularity.nemo", + "base_def/Singularity.nemo_baseOS" ], - "full_name": "mmore500/presentation-template", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePresentation Template\u003c/h2\u003e\u003ca id=\"user-content-presentation-template\" class=\"anchor\" aria-label=\"Permalink: Presentation Template\" href=\"#presentation-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1774\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/mmore500/presentation-template\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ba384827ddaf20d52b67fbbd58c3f6c187cfc6a31fac82c3b6765799d19d374d/68747470733a2f2f636972636c6563692e636f6d2f67682f6d6d6f72653530302f70726573656e746174696f6e2d74656d706c6174652e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/mmore500/presentation-template.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA LaTeX Beamer template for presentations using the Metropolis theme.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to build the container, after cloning this repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t presentation-template \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo generate your pdf, you should bind the directory with main.tex to \u003ccode\u003e/data\u003c/code\u003e\nin the container, and provide a prefix for your output. That looks like this, and\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data presentation-template mypdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter this, the files will be in your present working directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls my\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\nmypdf.aux mypdf.bbl mypdf.blg mypdf.fdb_latexmk mypdf.fls mypdf.log mypdf.nav mypdf.out mypdf.pdf mypdf.snm mypdf.toc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to build the container (it takes quite some time) this development\ncontainer is provided at \u003ca href=\"https://hub.docker.com/r/mmore500/presentation-template/\" rel=\"nofollow\"\u003emmore500/presentation-template\u003c/a\u003e. You can run it as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://www.github.com/mmore500/presentation-template\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e presentation-template\ndocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data mmore500/presentation-template mypdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn \u003ca href=\"example\"\u003eexample\u003c/a\u003e output is provided. Have fun!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, build the container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build presentation-template.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, run it and bind the present working directory to data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data presentation-template.simg mypdf\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should build the container with the version provided as a \u003ccode\u003e--build-arg\u003c/code\u003e\nas follows. For example, to build the version \u003ccode\u003e1.0.1-rc\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build -t mmore500/presentation-template:1.0.1-rc --build-arg Version=1.0.1-rc \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n$ docker push mmore500/presentation-template:1.0.1-rc\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow To \u0026amp; What You Get\u003c/h3\u003e\u003ca id=\"user-content-how-to--what-you-get\" class=\"anchor\" aria-label=\"Permalink: How To \u0026amp; What You Get\" href=\"#how-to--what-you-get\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe original post for the package is \u003ca href=\"https://twitter.com/MorenoMatthewA/status/1048676082952626177\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAuthorship\u003c/h3\u003e\u003ca id=\"user-content-authorship\" class=\"anchor\" aria-label=\"Permalink: Authorship\" href=\"#authorship\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMatthew Andres Moreno\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ematthew.andres.moreno@gmail.com\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributors\u003c/h3\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.github.com/vsoch\"\u003e@vsoch\u003c/a\u003e contributed Dockerfile and build / run instructions, continuous integration\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "NOC-MSM/CoNES", + "latest_release": "0.0.2", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-containerisation-of-nemo-employing-singularity-cones\" class=\"anchor\" aria-hidden=\"true\" href=\"#containerisation-of-nemo-employing-singularity-cones\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerisation of NEMO Employing Singularity (CoNES)\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://cones.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/be5581c539aaf212df6fa28589f126444a52feca29ebc442ea146c24334cc87d/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f636f6e65732f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/cones/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eThe CoNES repository was templated from \u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003eSingularity Deploy\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo generate a NEMO/XIOS Singularity Container please read the \u003ca href=\"https://cones.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003edocumentaion\u003c/a\u003e. What follows is a simplified quick-start guide:\u003c/p\u003e\n\u003cp\u003eIf building locally is not an option then it is also possible to build and\nrelease Singularity containers using \u003ca href=\"https://github.com/features/actions\"\u003eGitHub Actions\u003c/a\u003e.\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003eSingularity Deploy\u003c/a\u003e\ndeveloped by \u003ca href=\"https://github.com/vsoch\"\u003eVanessa Sochat\u003c/a\u003e has been modified\nto allow users to fork the \u003ca href=\"https://github.com/NOC-MSM/CoNES\"\u003eGitHub CoNES repository\u003c/a\u003e\nand, using \u003ca href=\"https://github.com/features/actions\"\u003eGitHub Actions\u003c/a\u003e, build and\nrelease a \u003cem\u003ebespoke\u003c/em\u003e NEMO singularity container.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eCoNES\u003c/code\u003e repository has been set up such that:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe container is updated/developed via a branch\u003c/li\u003e\n\u003cli\u003ethe container build will be tested on a pull request\u003c/li\u003e\n\u003cli\u003ea release will be triggered on merge into main\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis workflow can easily be modified by altering:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e.github/workflows/test.yml\u003c/code\u003e for the testing of builds\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e.github/workflows/builder.yml\u003c/code\u003e for the container release\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn individual NEMO SIF build can be created using the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFork the \u003ccode\u003eCoNES\u003c/code\u003e repository under \u003ccode\u003eUSER\u003c/code\u003e account (main branch only is fine) \u003cbr\u003e\nUnder the \u003ccode\u003eActions\u003c/code\u003e tab enable workflows \u003cbr\u003e\nUnder the \u003ccode\u003eSettings\u003c/code\u003e tab click through \u003ccode\u003eactions\u003c/code\u003e -\u0026gt; \u003ccode\u003egeneral\u003c/code\u003e and set \u003ccode\u003eworkflow permissions\u003c/code\u003e to r+w and save \u003cbr\u003e\nReturn to the \u003ccode\u003ecode\u003c/code\u003e tab\u003c/li\u003e\n\u003cli\u003eCreate a new branch\u003c/li\u003e\n\u003cli\u003eEdit the \u003ccode\u003eVERSION\u003c/code\u003e file to something approprate (e.g. 0.0.3)\u003cbr\u003e\n[Optional] Edit the \u003ccode\u003einputs/NEMO_in\u003c/code\u003e namelist for NEMO version number, MPI choice etc.\u003c/li\u003e\n\u003cli\u003eCreate a \u003cem\u003ePull Request\u003c/em\u003e from that branch to main. \u003cstrong\u003eMake sure this is from \u003ccode\u003eUSER/branch\u003c/code\u003e to \u003ccode\u003eUSER/main\u003c/code\u003e and not to \u003ccode\u003eNOC-MSM/main\u003c/code\u003e.\u003c/strong\u003e\u003cbr\u003e\nAt this point a test build will be triggered, which can take ~15 minutes per MPI build requested\u003c/li\u003e\n\u003cli\u003eIf successful the \u003cem\u003emerge\u003c/em\u003e will be available. Click merge and ...\u003c/li\u003e\n\u003cli\u003eA NEMO SIF will be built and released under the \u003cem\u003eversion\u003c/em\u003e specified (again this can take ~15 minutes per MPI build requested).\u003c/li\u003e\n\u003cli\u003eThe NEMO SIF and asscoiated assets will appear under the \u003ccode\u003eReleases\u003c/code\u003e tab.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe branch can now either be deleted or held open for further changes to \u003ccode\u003eNEMO_in\u003c/code\u003e and subsequent releases.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIf the tag in the \u003ccode\u003eVERSION\u003c/code\u003e file is not incremented then a new release is not built.\u003c/p\u003e\n\u003cp\u003eTo download the released NEMO SIF either use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e wget -c https://github.com/MY_CoNES/releases/download/$VERSION/MY_CoNES.nemo.sif -o nemo.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor Singularity can also \u003cem\u003epull\u003c/em\u003e just knowing the URL. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull https://github.com/MY_CONES/releases/download/$VERSION/MY_CONES.nemo.sif\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, - "topics": [ - "latex-beamer", - "latex-beamer-template", - "sans-forgetica", - "singularity-container", - "docker-container" - ], - "updated_at": 1596213882.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1678184977.0 }, { "data_format": 2, - "description": "singularity recipe for openmm", + "description": "Our Run scripts for calling SNPS using their Best practices", "filenames": [ - "Singularity_version.def", - "Singularity_11.def", - "Singularity_12.def", - "Singularity.def", - "mpi/Singularity_version.def" + "Singularity.1.0.4", + "Singularity.1.0.7a", + "Singularity.1.0.6", + "Singularity.1.0.2", + "Singularity.1.0.7b", + "Singularity.1.0.5", + "Singularity.1.0.3", + "Singularity.1.0.7" ], - "full_name": "edoapra/openmm-singularity", + "full_name": "ISUGIFsingularity/GATK", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eopenmm-singularity\u003c/h1\u003e\u003ca id=\"user-content-openmm-singularity\" class=\"anchor\" aria-label=\"Permalink: openmm-singularity\" href=\"#openmm-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eApptainer/Singularity recipe for openmm focused on Nvidia GPUS\u003cbr\u003e\nand tested on\u003cbr\u003e\nEMSL Tahoma \u003ca href=\"https://www.emsl.pnnl.gov/MSC/UserGuide/tahoma/tahoma_overview.html\" rel=\"nofollow\"\u003ehttps://www.emsl.pnnl.gov/MSC/UserGuide/tahoma/tahoma_overview.html\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-gatk\" class=\"anchor\" aria-hidden=\"true\" href=\"#gatk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGATK\u003c/h1\u003e\n\u003cp\u003eOur Run scripts for calling SNPS using their Best practices\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1730500215.0 + "updated_at": 1646016824.0 }, { "data_format": 2, - "description": null, + "description": "Pipeline to preprocess raw T1w and fMRI data, normalize it to the standard MNI152 space and extract the blood-oxygenation level dependent (BOLD) signals and corresponding functional connectivity (FC).", "filenames": [ - "Singularity.v0.1_rocker", - "Singularity.vkR", - "Singularity", - "Singularity.motus2.6" + "code/Singularity" ], - "full_name": "cschu/vortex_knight", - "latest_release": "v0.14.4", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003evortex_knight\u003c/h1\u003e\u003ca id=\"user-content-vortex_knight\" class=\"anchor\" aria-label=\"Permalink: vortex_knight\" href=\"#vortex_knight\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstalling locally and running from local installation\u003c/h3\u003e\u003ca id=\"user-content-installing-locally-and-running-from-local-installation\" class=\"anchor\" aria-label=\"Permalink: Installing locally and running from local installation\" href=\"#installing-locally-and-running-from-local-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo from GitHub.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/cschu/vortex_knight.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate a conda environment with NextFlow, e.g. by using the provided \u003ccode\u003eenvironment.yml\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd vortex_knight\nconda env create -f environment.yml\nconda activate vortex_knight\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eMake a copy of the \u003ccode\u003econfig/run.config\u003c/code\u003e file and adjust it to your environment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run /path/to/vortex_knight/main.nf --input_dir /path/to/input_files --output_dir /path/to/output_dir -c /path/to/run.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote: Nextflow itself requires at least \u003ccode\u003e5GB\u003c/code\u003e of memory.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning from GitHub\u003c/h3\u003e\u003ca id=\"user-content-running-from-github\" class=\"anchor\" aria-label=\"Permalink: Running from GitHub\" href=\"#running-from-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis requires a local nextflow installation. If you don\u0027t have one, see Steps 1/2 above.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eMake a local copy of the \u003ca href=\"https://raw.githubusercontent.com/cschu/vortex_knight/main/nextflow/run.config\" rel=\"nofollow\"\u003erun configuration file\u003c/a\u003e and adjust to your environment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run cschu/vortex_knight --input_dir /path/to/input_files --output_dir /path/to/output_dir -c /path/to/run.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote: Nextflow itself requires at least \u003ccode\u003e5GB\u003c/code\u003e of memory.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput parameters\u003c/h3\u003e\u003ca id=\"user-content-input-parameters\" class=\"anchor\" aria-label=\"Permalink: Input parameters\" href=\"#input-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input_dir\u003c/code\u003e should be a folder with bam files or with gzipped fastq files. For fastq files, individual samples should be separated into individual folders.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_dir\u003c/code\u003e is \u003ccode\u003evknight_out\u003c/code\u003e in the local directory by default.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--skip_\u0026lt;analysis\u0026gt;\u003c/code\u003e, \u003ccode\u003e--run_\u0026lt;analysis\u0026gt;\u003c/code\u003e skips, resp. explicitly requires execution of the specified analysis (\u003ccode\u003emotus\u003c/code\u003e, \u003ccode\u003epathseq\u003c/code\u003e, \u003ccode\u003ecount_reads\u003c/code\u003e, \u003ccode\u003emtags\u003c/code\u003e, \u003ccode\u003emapseq\u003c/code\u003e, \u003ccode\u003ekraken2\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--publishMode\u003c/code\u003e allows to switch between various modes of how results files are placed in the \u003ccode\u003eoutput_dir\u003c/code\u003e (cf. NextFlow documentation)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eNotes\u003c/h4\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emapseq\u003c/code\u003e can only run in combination with \u003ccode\u003emtags\u003c/code\u003e and when the parameter \u003ccode\u003emapseq_bin\u003c/code\u003e is explicitly set.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ekraken2\u003c/code\u003e can only run when the parameter \u003ccode\u003ekraken_database\u003c/code\u003e is set.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epathseq\u003c/code\u003e can only run when the parameter \u003ccode\u003epathseq_database\u003c/code\u003e is set.\u003c/li\u003e\n\u003cli\u003ea pre-downloaded motus database can be set with the parameter \u003ccode\u003emotus_database\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eresults are only collated if the parameter \u003ccode\u003ecollate_script\u003c/code\u003e is set. (TODO -\u0026gt; change to baseDir?)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eOutputs\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe output folder contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eone subdirectory \u003ccode\u003eotu_tables\u003c/code\u003e containing the summarised \u003ccode\u003emapseq\u003c/code\u003e otu tables\u003c/li\u003e\n\u003cli\u003ea subdirectory per sample (named \u003ccode\u003e\u0026lt;sample\u0026gt;\u003c/code\u003e) with\n\u003cul\u003e\n\u003cli\u003ethe kraken2 report \u003ccode\u003e\u0026lt;sample\u0026gt;.kraken2_report.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ethe library size \u003ccode\u003e\u0026lt;sample\u0026gt;.libsize.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ethe mOTUs report \u003ccode\u003e\u0026lt;sample\u0026gt;.motus.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003epathseq output\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.bam\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.bam.sgi\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.score_metrics\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.scores\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNote that by default, all files in the output folder are symlinks into the work dir! Before you delete the work dir, ensure you have dereferenced copies. Alternatively, change the --publishMode parameter to \u003ccode\u003ecopy\u003c/code\u003e or \u003ccode\u003elink\u003c/code\u003e (if the target file system supports hard links).\u003c/strong\u003e\u003c/p\u003e\n", + "full_name": "inm7/vbc_fmri", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-containerized-functional-mri-data-preprocessing-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#containerized-functional-mri-data-preprocessing-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerized Functional MRI data preprocessing pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREQUIREMENTS\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTo use this containerized pipeline, please install \u0027singularity\u0027 on your computing system. \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.3/user-guide/installation.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFilesa: Container_sMRI_rfMRI.simg (This container uses a combination of tools from well-known software packages, including FSL (\u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki\u003c/a\u003e), ANTs (\u003ca href=\"https://github.com/ANTsX/ANTs\"\u003ehttps://github.com/ANTsX/ANTs\u003c/a\u003e), and AFNI (\u003ca href=\"https://afni.nimh.nih.gov/\" rel=\"nofollow\"\u003ehttps://afni.nimh.nih.gov/\u003c/a\u003e).)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eStructuralAndRestPreprocessing.sh\nREADME.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-additional-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-additional-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Additional files\u003c/h3\u003e\n\u003cp\u003eantsTemp: The folder includes prior brain extraction template for \u003ccode\u003eantsBrainExtraction.sh MNI152_T2_1mm.nii.gz/MNI152_T2_1mm_brain.nii.gz\u003c/code\u003e: The template images for T2w image processings.\u003c/p\u003e\n\u003cp\u003eThe additional files have been included in the container. AntsTemp is highly recommended to be stored in the directory of Ants. \u003ccode\u003eMNI152_T2_1mm.nii.gz\u003c/code\u003e and \u003ccode\u003eMNI152_T2_1mm_brain.nii.gz\u003c/code\u003e are suggested to be stored in the \u003ccode\u003e$FSLDIR/data/standard\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instruction\" class=\"anchor\" aria-hidden=\"true\" href=\"#instruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eINSTRUCTION\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. ARGUMENTS\u003c/h3\u003e\n\u003cp\u003eThe containerized fMRI pipeline consists of 4 modules: sMRI model, functional minimal preprocessing model, enhanced preprocessing model, and signal extraction model.\nTo execute this container models, the singularity function and two arguments should be defined.\nExample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind /mount/path:/mnt Container_sMRI_rfMRI.simg StructuralAndRestPreprocessing.sh $subject\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first argument specifies all necessary parameters for preprocessing and the second one specifies the subject ID.\u003c/p\u003e\n\u003cp\u003eAn example of a \u003ccode\u003eStructuralAndRestPreprocessing.sh\u003c/code\u003e is as followed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-input-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-input-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Input variables\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e# ---------------\n#module selection\nModule load: default settings for software within the container. Don\u2019t change it.\nModel selection: select the models you want to use (1 = on; 0 = off).\n\n#Path settings. \nOrig=/mnt/path/to/raw_data #Raw data path. The raw data path should have a data structure as below.\nOrig\n|-- ${subject}\n| \u00a0 |--T1--session1--nifti (T1w) \n| \u00a0 |-- rfMRI--session1--nifti (Rest)\n| \u00a0 |-- Parad--session1--nifti (Task)\n\nsMRI=/mnt/path/to/sMRI #sMRI output path\nfMRI=/mnt/path/to/fMRI #fMRI output path\nANTSTEMP=/path/to/ants/priors #brain extraction template for ants (used only for ants brain extraction).\natlas=/mnt/path/to/atlas #the path to the atlas.\nPipelines=/usr/local/bin #script path within the container.\n\natlasname=Schaefer #the name of the atlas.\npostfix=nii. #Raw data postfix, in case of different dcm2nii software.\n\n#sMRI model parameter.\nT2w=0 #if T2w used, set 1; if not, set 0.\nSession=1 #session number of dataset (1 = 1 session, 2 = 2 sessions).\nConcat=0: #If the structural images are scanned with 2 sessions. Only used ( Concat = 1), when $Session=2.\nStandard*: default MNI paths within FSL (for registration, don\u0027t change it).\nBrainSize=150 #Z-axis for cropping (150-180), remove the long neck.\nbiascorr=0 #bias correction for structural images (1 = on, 0 = off). Note, you don\u0027t perform it in this version, antsBrainExtraction is applied, which has bias correction, so that you don\u0027t need to do bias correction twice.\nStructuralNormalization=2 #different normalization ways (1 for FSL, 2 for ANTs).\nThreads=5 #only used for ANTs registration, consistent with paralleling threads.\n\n#Note: sMRI model should be performed first. The brain extracted structural images will be used for other models.\n\n#Minimal model parameter. \nTR=2 #repeat time.\nexvol=4 #exclusion volumes. \nSlicetiming=1 #correct slice timing differences (1 = on, 0 = off), it\u0027s optional.\n\n#Note: Slice timing correction should be selected by your slice-order. In this case, our data was scanned by bottom-up order. The images with *norm* is the output for this model.\n\n#Enhanced model parameter. \nSmoothing=1 #smooth epi images (1 = on, 0 = off), it\u0027s optional. \nSmoothingFWHM=8 #the kernel of smoothing, which is commonly 2 or 3 times of the voxel-size. \nTemporalFilter=1 #filter frequency-band signals (1 = on, 0 = off). Its\u0027 optional.\nlowbands=0.01 #low-pass\nhighbands=0.1 #high-pass\n\nCovarianceRegression=1: regress out covariances (1 = on, 0 = off). It\u0027s optional.\nCovariances: 27 #Sevearal options are available. 24 = only regress out 24 head-motion parameters, 25 = regress out 24 head-motion parameters + global singals, 26 = 24 head-motion parameters + WM + CSF signals, 27 = 24 head-motion parameters + CSF + Global + WM signals.\n\n#Note: For saving the space, final output of this model is filtered_func_data.nii.gz. \n\n#Singal extraction model parameter.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-troubleshoot\" class=\"anchor\" aria-hidden=\"true\" href=\"#troubleshoot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTROUBLESHOOT\u003c/h2\u003e\n\u003cp\u003eIf you have a problem to use the containerized pipeline. Please feel free to contact Shufei Zhang (\u003ca href=\"mailto:sh.zhang@fz-juelich.de\"\u003esh.zhang@fz-juelich.de\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis development was supported by European Union\u2019s Horizon 2020 research and\ninnovation programme under grant agreement \u003ca href=\"https://cordis.europa.eu/project/id/826421\" rel=\"nofollow\"\u003eVirtualBrainCloud\n(H2020-EU.3.1.5.3, grant no.\n826421)\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [], - "updated_at": 1723638441.0 + "updated_at": 1608391045.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container for MATE desktop in CentOS 7.", "filenames": [ - "Singularity.2.1", - "Singularity.2.0", - "Singularity.2.2" + "Singularity" ], - "full_name": "JeffersonLab/jlabce", + "full_name": "mcw-rcc/mate-desktop", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejeffersonlab/jlabce\u003c/h1\u003e\u003ca id=\"user-content-jeffersonlabjlabce\" class=\"anchor\" aria-label=\"Permalink: jeffersonlab/jlabce\" href=\"#jeffersonlabjlabce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for docker containers at \u003ca href=\"https://hub.docker.com/r/jeffersonlab/jlabce/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/jeffersonlab/jlabce/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/363\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mate-desktop\" class=\"anchor\" aria-hidden=\"true\" href=\"#mate-desktop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emate-desktop\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2102\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for MATE desktop in CentOS 7. Used with the RCC\u0027s Open OnDemand portal as a desktop environment.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 6, + "subscribers_count": 3, "topics": [], - "updated_at": 1527047870.0 + "updated_at": 1598115124.0 }, { "data_format": 2, - "description": "Dev tools for vasst pipeline", + "description": null, "filenames": [ - "Singularity.v0.0.4", - "Singularity.v0.0.1", - "Singularity.v0.0.4c", - "Singularity.v0.0.3", - "Singularity.v0.0.2a", - "Singularity.v0.0.4a", - "Singularity.v0.0.4g", - "Singularity.v0.0.4e", - "Singularity.v0.0.2", - "Singularity.v0.0.4f", - "Singularity.v0.0.4b", - "Singularity", - "Singularity.v0.0.4d", - "Singularity.v0.0.3a" + "Singularity" ], - "full_name": "akhanf/vasst-dev", - "latest_release": "v0.0.3", + "full_name": "truatpasteurdotfr/singularity-docker-debian9-pdfpc", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-docker-debian9-pdfpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-docker-debian9-pdfpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-debian9-pdfpc\u003c/h1\u003e\n\u003cp\u003esingularity container based on debian9 docker providing pdfpc\u003c/p\u003e\n\u003cp\u003eRun pdfpc from the container without really installing it.\u003c/p\u003e\n\u003cp\u003eRunning without installation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run library://tru/default/singularity-docker-debian9-pdfpc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuilding:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-docker-debian9-pdfpc.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor use the provided \u003ccode\u003ebuild.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cp\u003eDownload and rename:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \"pdfpc.sif\" library://tru/default/singularity-docker-debian9-pdfpc\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 9, + "subscribers_count": 2, "topics": [], - "updated_at": 1643993860.0 + "updated_at": 1614411782.0 }, { "data_format": 2, - "description": null, + "description": "LArFlow: predicting pixel correspondence between planes using CNNs", "filenames": [ - "rscape/Singularity", - "treeshrinkenv/Singularity", - "cmfinder/Singularity", - "renv/Singularity", - "sissiz/Singularity" + "container/Singularity" ], - "full_name": "asgeissler/OCyRS-pipeline", + "full_name": "NuTufts/larflow", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOCyRS: Orthologues cyanobacterial gene-associated RNA structure motifs\u003c/h1\u003e\u003ca id=\"user-content-ocyrs-orthologues-cyanobacterial-gene-associated-rna-structure-motifs\" class=\"anchor\" aria-label=\"Permalink: OCyRS: Orthologues cyanobacterial gene-associated RNA structure motifs\" href=\"#ocyrs-orthologues-cyanobacterial-gene-associated-rna-structure-motifs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository corresponds to the pipeline of the scientific publication:\u003c/p\u003e\n\u003cp\u003e\"Exploring the regulatory potential of RNA structures in 202 cyanobacterial genomes\"\u003cbr\u003e\nAS Geissler, EC Alvarez, C Anthon, NU Frigaard, J Gorodkin, and SE Seemann\u003cbr\u003e\n\u003cem\u003esubmitted\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePurpose\u003c/h2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-label=\"Permalink: Purpose\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline download public cyanobacterial genome sequence and annotations\nfrom\n\u003ca href=\"https://progenomes.embl.de/\" rel=\"nofollow\"\u003eproGenomes\u003c/a\u003e.\nAnchored to orthologues genes, potential adjacently located\nRNA structure motif are searched for with\n\u003ca href=\"http://bio.cs.washington.edu/yzizhen/CMfinder/\" rel=\"nofollow\"\u003eCMfinder\u003c/a\u003e.\nThe pipeline includes additional steps to score the motifs and check\nfor their phylogenetic/biological plausibility with\n\u003ca href=\"http://eddylab.org/R-scape/\" rel=\"nofollow\"\u003eR-scape\u003c/a\u003e.\nAs such, the motifs are compared with known bacterial\n\u003ca href=\"https://rfam.xfam.org/\" rel=\"nofollow\"\u003eRNA families\u003c/a\u003e,\nthe\n\u003ca href=\"http://eddylab.org/infernal/\" rel=\"nofollow\"\u003eInfernal\u003c/a\u003e\npredicted genomic location of these families,\nand the respective scores.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is implemented in a reproducible\n\u003ca href=\"https://snakemake.github.io/\" rel=\"nofollow\"\u003esnakemake\u003c/a\u003e\nworkflow, which automatically handles the further\nsoftware dependencies. The only hard-requirement are\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA powerful machine or cluster (at least 20 CPU cores 40+ GB RAM)\u003c/li\u003e\n\u003cli\u003eSingularity version $\\ge 3.9$\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eMiniconda3\u003c/a\u003e + Mamba\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://snakemake.github.io/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e $\\ge 7.9.0$\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLinux\u003c/h3\u003e\u003ca id=\"user-content-linux\" class=\"anchor\" aria-label=\"Permalink: Linux\" href=\"#linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease use either the binary packages coming with your distribution\u0027s package\nsystem or follow the installation procedures listed by the respective\nproject websites.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMacOS\u003c/h3\u003e\u003ca id=\"user-content-macos\" class=\"anchor\" aria-label=\"Permalink: MacOS\" href=\"#macos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install singularity, please consider using\na \u003ca href=\"https://www.vagrantup.com/\" rel=\"nofollow\"\u003eVagrant\u003c/a\u003e box for\nvirtualisation.\nThe installation is straight-forward with\n\u003ca href=\"https://brew.sh/\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ brew install --cask virtualbox vagrant vagrant-manager\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the information in \u003ccode\u003eVagrantfile\u003c/code\u003e, singularity can be installed and\nactivated in the current working directory with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ vagrant up\n $ vagrant ssh\n $ cd /vagrant/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, the conda environment must be installed within the virtual box.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n $ sh Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDue to the requirement \u0027For changes to take effect, close and re-open your current shell.\u0027, you must exit and reopen the shell before being able to install mamba.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ exit\n $ vagrant ssh\n $ cd /vagrant/\n $ conda install -n base -c conda-forge mamba\n $ mamba install -n base -c bioconda -c conda-forge snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfterward, the virtual box is fully setup to work with the pipeline.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload all further software dependencies with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ sh init-singularity.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe script downloads the following software images from the\n\u003ca href=\"https://galaxyproject.org/\" rel=\"nofollow\"\u003eGalaxy project\u003c/a\u003e\nsoftware repository:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCheckM $1.2.0$\u003c/li\u003e\n\u003cli\u003eCMfinder $0.4.1.18$\u003c/li\u003e\n\u003cli\u003eInfernal $1.1.4$\u003c/li\u003e\n\u003cli\u003eIQ-TREE $2.2.0.3$\u003c/li\u003e\n\u003cli\u003eMUSCLE $3.8.1551$\u003c/li\u003e\n\u003cli\u003eR-Scape $1.4.0$\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eA container with various R packages \u003ccode\u003erenv/renv.sif\u003c/code\u003e\nis pre-build as part of this pipeline.\nThe corresponding conda environment was for maximal reproducibility\n\u003ca href=\"https://github.com/conda-incubator/conda-lock\"\u003econda-lock\u0027ed\u003c/a\u003e,\nsuch that the exact versions are specified for linux \u003ccode\u003ex64\u003c/code\u003e\ncomputer architectures, in case you would like to re-build the container.\nAn additional container for the\n\u003ca href=\"https://github.com/uym2/TreeShrink\"\u003eTreeShrink\u003c/a\u003e\nsoftware was generated in the folder \u003ccode\u003etreeshrinkenv\u003c/code\u003e.\nAll additional containers are retrieved via the\nGitHubs Container Repository of this project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBefore you start:\u003c/strong\u003e\u003c/em\u003e\nPlease adjust the \u003ccode\u003eproject_root\u003c/code\u003e variable in the \u003ccode\u003econfig/config.yaml\u003c/code\u003e file to\nyour project path.\u003c/p\u003e\n\u003cp\u003eThere are currently two helper script to aid starting snakemake. Use either\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003e$ bash run_local.sh\u003c/code\u003e for running snakemake on a single powerful machine.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e$ bash run_slurm.sh\u003c/code\u003e to submit jobs to a slurm cluster.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn case of the latter, also consider adjusting \u003ccode\u003econfig/slurm.yaml\u003c/code\u003e to your\nenvironment (\u003cem\u003ee.g.\u003c/em\u003e partition name). To adapt a similar helper script for\nalternative cluster solutions, please refer to the\n\u003ca href=\"https://snakemake.readthedocs.io/en/stable/executing/cluster.html\" rel=\"nofollow\"\u003esnakemake handbook\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, to run the pipeline, first call the \u003cem\u003edownload\u003c/em\u003e target before\ninitiating \u003cem\u003eall\u003c/em\u003e subsequent steps. Together with the container download,\nthe commands to run the pipeline are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ sh init-singularity.sh\n $ bash run_local.sh download\n $ bash run_local.sh all\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-larflow-prediction-pixel-correspondence-between-lartpc-wireplane-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#larflow-prediction-pixel-correspondence-between-lartpc-wireplane-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLArFlow: prediction pixel correspondence between LArTPC wireplane images\u003c/h1\u003e\n\u003cp\u003eThis repository contains the code for developing a full 3D neutrino interaction\nreconstruction centered using outputs of a convolutional neural network.\u003c/p\u003e\n\u003cp\u003eThe convolutional neural network aims to provide information\nthat seeds the reconstruction of particles and interactions.\nThis includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egood 3D space points representing the locations where charge particle tracks passed\nthrough the detector\u003c/li\u003e\n\u003cli\u003eassociations between the 3D space points and the spatial patterns in\nthe TPC images where the points project into.\u003c/li\u003e\n\u003cli\u003escores indicating which 3D points are near important, key-points:\ntrack ends, shower starts, and neutrino interaction vertices.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDocumentation for the library can be found at \u003ca href=\"https://nutufts.github.io/larflow\" rel=\"nofollow\"\u003egithub.io/larflow\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003elarmatchnet: definition of network, scripts to train and deploy network\u003c/li\u003e\n\u003cli\u003elarflow: c++ libraries providing methods to prepare data, perform downstream reconstruction\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWithin the \u003ccode\u003elarflow\u003c/code\u003e folder, are the following c++ modules:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ePrepFlowMatchData\u003c/code\u003e: classes/methods for preparing spacepoint data from TPC images\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eKeyPoints\u003c/code\u003e: classes/methods for preparing keypoint training info using spacepoint data from \u003ccode\u003ePrepFlowMatchData\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpatialEmbed\u003c/code\u003e: classes/methods for preparing spatial embedding training info using spacepoint data from \u003ccode\u003ePrepFlowMatchData\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eReco\u003c/code\u003e: downstream reconstruction using output of networks to form candidate neutrino interactions\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCRTMatch\u003c/code\u003e: tools to combine CRT data with spacepoints and TPC data in order to provide tagged cosmic muon tracks\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eVoxelizer\u003c/code\u003e: voxelize larmatch spacepoints, not finished. intended to help spacepoint output connect to 3D convolutional networks.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLArFlowConstants\u003c/code\u003e: constants, enumerations used in the other modules\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFlowContourMatch\u003c/code\u003e:deprecated tools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOther folders are considered deprecated and need to be cleaned up and archived.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etestdata: default location for testdata used for development\u003c/li\u003e\n\u003cli\u003eutils: utility scripts\u003c/li\u003e\n\u003cli\u003econtainer: script to build Singularity container that will work on tufts grid\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003edeprecated folders, kept for archival reasons\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edeprecated/old_larflownet_models: different version of LArFlow models\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_dataprep: scripts to make larflow input and truth images from larsoft files and then prepare crops for training\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_training: training scripts\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_deploy: take trained models and process test files\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_ana: analysis scripts for processed test files\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_weights: default location for weights\u003c/li\u003e\n\u003cli\u003edeprecated/postprocessor: old code that used old larflow 3d points for reco\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-do-list\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-do-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Do list:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eclustering via spatial embed method [Jared]\u003c/li\u003e\n\u003cli\u003eclustering via part affinity flows [Taritree]\u003c/li\u003e\n\u003cli\u003eextend keypoints to be more than one class [Polina]\u003c/li\u003e\n\u003cli\u003edevelop interactoin selection\u003c/li\u003e\n\u003cli\u003edevelop analysis metrics [Ralitsa]\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-not-included-in-repo\" class=\"anchor\" aria-hidden=\"true\" href=\"#not-included-in-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNot included in repo\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eROOT (6.12/04 known to work)\u003c/li\u003e\n\u003cli\u003eopencv (3.2.0 known to work)\u003c/li\u003e\n\u003cli\u003epytorch (1.3, 1.4 known to work)\u003c/li\u003e\n\u003cli\u003enumpy (1.14.03 known to work)\u003c/li\u003e\n\u003cli\u003etensorboardX (from \u003ca href=\"https://github.com/lanpa/tensorboard-pytorch\"\u003ehere\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003etensorboard\u003c/li\u003e\n\u003cli\u003ecuda (currently using 10.1)\u003c/li\u003e\n\u003cli\u003eEigen 3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUBDL dependencies\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elarlite: following X branch\u003c/li\u003e\n\u003cli\u003eGeo2D:\u003c/li\u003e\n\u003cli\u003eLArOpenCV:\u003c/li\u003e\n\u003cli\u003elarcv:\u003c/li\u003e\n\u003cli\u003eublarcvapp:\u003c/li\u003e\n\u003cli\u003ecilantros:\u003c/li\u003e\n\u003cli\u003enlohmann/json\u003c/li\u003e\n\u003cli\u003e(to do: add missing)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-included-as-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#included-as-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIncluded as submodules\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003elarcvdataset: wrapper class providing interface to images stored in the larcv format. converts data into numpy arrays for use in pytorch (deprecated)\u003c/li\u003e\n\u003cli\u003eCilantro: a library with various Clustering routines w/ C++ API (deprecated)\u003c/li\u003e\n\u003cli\u003ePangolin: a OpenGL viewer package, used by Cilantro (deprecated)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThis repository is intended to be build as a part of the \u003ccode\u003eubdl\u003c/code\u003e environment. Go \u003ca href=\"https://github.com/larbys/ubdl\"\u003ehere\u003c/a\u003e to see this repo.\u003c/li\u003e\n\u003cli\u003eclone \u003ccode\u003eubdl\u003c/code\u003e: \u003ccode\u003egit clone https://github.com/larbys/ubdl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ego to the \u003ccode\u003eubdl\u003c/code\u003e folder\u003c/li\u003e\n\u003cli\u003esetup the \u003ccode\u003eubdl\u003c/code\u003e environment pre-reqs: \u003ccode\u003esource setenv.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003econfigure the \u003ccode\u003eubdl\u003c/code\u003e submodule environments: \u003ccode\u003esource configure.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ebuild all the submodules: \u003ccode\u003esource buildall_py2.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-issues-building-pangolin-deprecated\" class=\"anchor\" aria-hidden=\"true\" href=\"#issues-building-pangolin-deprecated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssues building Pangolin (deprecated)\u003c/h3\u003e\n\u003cp\u003ePangolin depends on GLEW and X11. These can be provided by a package manager.\nHowever, especially for GLEW other versions can be on the system from other libraries like CUDA and/or ROOT.\nThis can cause compilation errors.\u003c/p\u003e\n\u003cp\u003eIf there are issues you can try the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ego into CMakeCache.txt and check the include and library entries for GLEW (search for GLEW).\nChange them to point to the system GLEW. On Ubuntu this will be something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/usr/lib/x86_64-linux-gnu/libGLEW.so for the LIB dir and /usr/include for the INC dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you do this, remove the directory CMakeFiles and run \u003ccode\u003emake clean\u003c/code\u003e. Then run \u003ccode\u003ecmake .\u003c/code\u003e and finally \u003ccode\u003emake\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ego into \u003ccode\u003ePangolin/include/pangolin/gl/glplatform.h\u003c/code\u003e and change \u003ccode\u003e\u0026lt;GL/glew.h\u0026gt;\u003c/code\u003e to \u003ccode\u003e/usr/include/GL/glew.h\u003c/code\u003e to hack it\nto not rely on the include directories passed to the compiler. Note: the above path is for Ubuntu 16.04/18.4.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-each-time-you-start-a-new-shell-and-want-to-use-the-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#each-time-you-start-a-new-shell-and-want-to-use-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEach time you start a new shell and want to use the code\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003esetup the environment through \u003ccode\u003eubdl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ego to \u003ccode\u003eubdl\u003c/code\u003e folder (which should contain this repo as a submodule)\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esource setenv.sh\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esource configure.sh\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pushing-back-changes\" class=\"anchor\" aria-hidden=\"true\" href=\"#pushing-back-changes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePushing back changes\u003c/h3\u003e\n\u003cp\u003eIf you made changes to a submodule, you need to check in that code and then check in the new commit hash of the submodule to this repo.\u003c/p\u003e\n\u003cp\u003eSay you made a change to larcv. (Same instructions basically for all submodules).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFirst make sure you are not in a DEATCHED_HEAD state)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit branch\n develop\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003e* tufts_ub\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf it says detached head, go back to head of larflow repo and run \u003ccode\u003esource goto_head_of_submodules.sh\u003c/code\u003e and come back\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003estage your commits and then push\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit add [[some file you edited]]\ngit commit -m \"[[short description of change]]\"\ngit push\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ego back to head of larflow and commit the updated submodule (in this example \u003ccode\u003elarcv\u003c/code\u003e) to this repo\ncd ..\ngit add larcv\ngit commit -m \"[[which submodule you updated]]\"\ngit push\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1727253743.0 + "updated_at": 1636249002.0 }, { "data_format": 2, - "description": "Pan-genome nextflow pipeline which uses fasta input files for Prokka and Roary before generating visualisations", + "description": "Open-Source Computational Structural Biology Framework", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "lifebit-ai/roary", + "full_name": "sailfish009/openstructure", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/roary\u003c/h1\u003e\u003ca id=\"user-content-nf-coreroary\" class=\"anchor\" aria-label=\"Permalink: nf-core/roary\" href=\"#nf-coreroary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePan-genome nextflow pipeline which uses fasta input files for Prokka and Roary before generating visualisations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/roary\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f95f364a5684a8dadc758fc13e9ab275acb97ddb0b8985a13c22d37f18fba30d/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f726f6172792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/roary.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/roary\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8bda52b2aecd18f25dfa0902c0a29d55b6c89a531e3c7378605412fa6acda1c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f726f6172792e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/roary.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/roary pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1677098155.0 + "updated_at": 1659218211.0 }, { "data_format": 2, - "description": null, + "description": "Singularity Image for RNA-Seq analysis", "filenames": [ - "modules/scvi/Singularity.def", - "modules/doubletdetection/Singularity.def", - "modules/postprocessing/Singularity.def", - "modules/scran/Singularity.def" + "Singularity", + "Singularity.test_jupyter" ], - "full_name": "joechanlab/nf-scrnaseq", + "full_name": "duke-chsi-informatics/singularity-rnaseq", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA bioinformatics pipeline that preprocesses single-cell RNA-seq data. It takes a samplesheet and CellRanger raw filtered count matrix as input, performs ambient RNA correction, doublet detection, sample aggregation, batch integration.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/pipeline.png\"\u003e\u003cimg src=\"docs/images/pipeline.png\" alt=\"nf-scrnaseq\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!NOTE]\nIf you are new to Nextflow and nf-core, please refer to \u003ca href=\"https://nf-co.re/docs/usage/installation\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e on how to set-up Nextflow. Make sure to \u003ca href=\"https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline\" rel=\"nofollow\"\u003etest your setup\u003c/a\u003e with \u003ccode\u003e-profile test\u003c/code\u003e before running the workflow on actual data.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eFirst, prepare a samplesheet with your input data that looks as follows, where each row contains the sample name, a raw count HDF5 file and a filtered count HDF5 file from CellRanger, and whether demultiplexing is needed.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esamplesheet.csv\u003c/code\u003e:\u003c/p\u003e\n\u003cpre lang=\"csv\"\u003e\u003ccode\u003esample, raw_h5, filtered_h5, demultiplexing, expected_droplets\nCONTROL_REP1, raw_feature_bc_matrix.h5, filtered_feature_bc_matrix.h5, false, 50000\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!Note]\nSee suggestions on choice of the \u003ccode\u003eexpected_droplets\u003c/code\u003e parameter \u003ca href=\"https://www.10xgenomics.com/analysis-guides/background-removal-guidance-for-single-cell-gene-expression-datasets-using-third-party-tools\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eNext, prepare a parameter YAML file that looks as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eparams.yml\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003esamplesheet\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./samplesheet.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e path to the sample sheet\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutdir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./out/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e directory containing the outputs\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eexperiment\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexperiment\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e experiment name for prefix\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eatac\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e whether the data is 10X Multiome data\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eaggregation\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003etrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e whether to aggregate samples\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eremove_doublets\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e whether to remove cells marked as doublets\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eremove_outliers\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e whether to remove cells markerd as outliers\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003escvi\u003c/span\u003e: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e scvi parameters (see bin/scvi_norm.py)\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_latent\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_top_genes\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Note: large values might give error in writing h5ad\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003epostprocessing\u003c/span\u003e: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Postprocessing parameters (see bin/postprocessing.py)\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_pca_components\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_diffmap_components\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003emetadata\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./metadata.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e path to metadata variables\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ecelltypist\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003emodel\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHuman_Lung_Atlas.pkl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e model to use for celltypist\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ereport\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eplot\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./markers.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e custom variables to plot\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eseacells\u003c/span\u003e: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SEACells parameters (see bin/run_SEACells.py)\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_SEACells\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e25\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ebuild_kernel_on\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eX_pca\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_waypoint_eigs\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emount\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/home,/data1\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e path to mount for singularity\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ewith_gpu\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003etrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e using GPU\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emaxForks\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e maximum number of processes in parallel (e.g # of GPU)\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_memory\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e6.GB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e memory information\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_time\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e6.h\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e wall time information\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_cpus\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cpu information\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow, you can run the pipeline. For local run on HPC with singularity installed, execute the following command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run ./main.nf \\\n -profile singularity \\\n -params-file ./params.yml \\\n -w ./work/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using MSKCC lilac, you can use the pre-defined \u003ccode\u003elilac\u003c/code\u003e profile that uses the LSF executor.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run ./main.nf \\\n -profile lilac \\\n -params-file ./params.yml \\\n -w ./work/\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-rnaseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-rnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-rnaseq\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Jupyter\u003c/h2\u003e\n\u003cp\u003eRun this to start Jupyter:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app jupyter library://granek/duke-chsi-informatics/singularity-rstudio:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen follow the instructions that Jupyter printed to the terminal when you started it up to access Jupyter in your web browser\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-accessing-jupyter-on-a-remote-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#accessing-jupyter-on-a-remote-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAccessing Jupyter on a remote server\u003c/h3\u003e\n\u003cp\u003eIf you are running the container on a remote server, you will need to set up port forwarding with ssh to be able to access Jupyter. Run this command to forward the default Jupyter port (8888)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -L 8888:localhost:8888 bug\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote if the default Jupyter port is not available, Jupyter will choose a different port. In this case you will need to substitute the port that Jupyter outputs for 8888 in the ssh port forwarding command above.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-a-slurm-cluster\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-a-slurm-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on a SLURM Cluster\u003c/h2\u003e\n\u003cp\u003eYou can use this image interactively on a SLURM-managed cluster by running launching RStudio or Jupyter. The following instructions work on the Duke Compute Cluster (DCC). Doing this on other cluster will require some modification and may not work, depending on how the cluster is configured.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRStudio\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003essh to DCC login node: \u003ccode\u003essh NETID@dcc-login-01.rc.duke.edu\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003erun tmux on login node: \u003ccode\u003etmux new -s container_demo\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun this on login node: \u003ccode\u003esrun -A chsi -p chsi --mem=100G -c 30 --pty bash -i\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003ehostname -A\u003c/code\u003e on compute node and record results\u003c/li\u003e\n\u003cli\u003eRun on the following on a compute node and note the port, username, and password that the command prints:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /scratch/josh/rnaseq_demo/rawdata /scratch/josh/rnaseq_demo/workspace\n\nsingularity run \\\n\t--bind /scratch/josh/rnaseq_demo/rawdata:/data \\\n\t--bind /scratch/josh/rnaseq_demo/workspace:/workspace \\\n\tlibrary://granek/duke-chsi-informatics/singularity-rnaseq\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eRun on local machine: \u003ccode\u003essh -L PORT:COMPUTE_HOSTNAME:PORT NETID@dcc-login-01.rc.duke.edu\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere PORT is the port returned but the \"singularity run\" commmand\u003c/li\u003e\n\u003cli\u003eWhere COMPUTE_HOSTNAME is the hostname returned by running \"hostname -A\" on the compute node\u003c/li\u003e\n\u003cli\u003eWhere NETID is your NetID\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eGo to \"localhost:PORT\" in a webrowser and enter the username and password printed by the \"singularity run\" commmand\u003c/li\u003e\n\u003cli\u003eHave fun!!\u003c/li\u003e\n\u003cli\u003eAt the end of an analysis you will probably want to copy results to your directory in \u003ccode\u003e/work\u003c/code\u003e or \u003ccode\u003e/hpc/group\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003essh to dcc-login-01.rc.duke.edu\u003c/li\u003e\n\u003cli\u003erun tmux on login node: \u003ccode\u003etmux new -s container_demo\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun this on login node: \u003ccode\u003esrun -A chsi -p chsi --mem=100G -c 30 --pty bash -i\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun on compute node:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /scratch/josh/rnaseq_demo/rawdata /scratch/josh/rnaseq_demo/workspace\n\nsingularity run \\\n\t--app jupyter \\\n\t--bind /scratch/josh/rnaseq_demo/rawdata:/data \\\n\t--bind /scratch/josh/rnaseq_demo/workspace:/workspace \\\n\tlibrary://granek/duke-chsi-informatics/singularity-rnaseq\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eRun on local machine: \u003ccode\u003essh -L PORT:COMPUTE_HOSTNAME:PORT NETID@dcc-login-01.rc.duke.edu\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere PORT is the number after \u003ccode\u003ehttp://127.0.0.1:\u003c/code\u003e in the URL given by Jupyter (defaults to 8888, but Jupyter will use a different one if the default is in use, or if a different port is supplied as an argument using \u003ccode\u003e--port\u003c/code\u003e when running the singularity container\u003c/li\u003e\n\u003cli\u003eWhere COMPUTE_HOSTNAME is the hostname returned by running \"hostname -A\" on the compute node\u003c/li\u003e\n\u003cli\u003eWhere NETID is your NetID\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCopy the URL supplied by jupyter that starts \u003ccode\u003ehttp://127.0.0.1:\u003c/code\u003e and paste it in a webbrowser\u003c/li\u003e\n\u003cli\u003eHave fun!!\u003c/li\u003e\n\u003cli\u003eAt the end of an analysis you will probably want to copy results to your directory in \u003ccode\u003e/work\u003c/code\u003e or \u003ccode\u003e/hpc/group\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jupyter-on-gpu-node\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter-on-gpu-node\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter on GPU node\u003c/h3\u003e\n\u003cp\u003eSame as above, but the srun command should use the \u003ccode\u003echsi-gpu\u003c/code\u003e partition and request a gpu, but less CPUs and Memory:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esrun -A chsi -p chsi-gpu --gres=gpu:1 --mem=15866 -c 2 --pty bash -i\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1727381006.0 + "updated_at": 1631561346.0 }, { "data_format": 2, - "description": "official build specifications for debian", + "description": null, "filenames": [ - "Singularity" + "Singularity.cpu-guppy3.4-conda-api", + "Singularity.guppy3.6.0cpu-conda-api", + "Singularity.guppy4.5.4gpu-conda-api", + "Singularity.guppy4.2.2gpu-conda-api", + "Singularity.guppy3.4gpu-conda-api", + "Singularity.myR_3-6-3", + "Singularity.deepbinner-api", + "Singularity.guppy5.0.14gpu-conda-api", + "Singularity.myR_4-0-2_rstudio_1.3", + "Singularity.guppy3.6.0gpu-conda-api", + "Singularity.guppy-cpu-conda", + "Singularity.guppy5.0.7gpu-conda-api", + "Singularity.guppy4.0.14gpu-conda-api" ], - "full_name": "singularityhub/debian", + "full_name": "vibaotram/singularity-container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDebian\u003c/h1\u003e\u003ca id=\"user-content-debian\" class=\"anchor\" aria-label=\"Permalink: Debian\" href=\"#debian\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official library of Debian builds for Singularity images hosted on Singularity Hub. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003ethe builds are organized by folders, with one \u003ccode\u003eSingularity\u003c/code\u003e file per folder. This ensures that we can find the files programatically.\u003c/li\u003e\n\u003cli\u003ethe different image tags correspond to these folders, and the name of the tag is specified on Singularity Hub\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUnder Development\u003c/h2\u003e\u003ca id=\"user-content-under-development\" class=\"anchor\" aria-label=\"Permalink: Under Development\" href=\"#under-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese images are currently not connected to Singularity Hub, but this will be done in early 2017. The first effort will be to develop a core set of images, and then any necessary builders / templating systems that would be necessary to help with this process. If you are interested in contributing, please \u003ca href=\"http://singularity.lbl.gov/contributing-code\" rel=\"nofollow\"\u003ereach out\u003c/a\u003e!\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4054\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-container\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-images-supporting-basedmux-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-images-supporting-basedmux-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity images supporting \u003ca href=\"https://github.com/vibaotram/baseDmux.git\"\u003ebaseDmux workflow\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity.guppy-cpu-conda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econtaining GUPPY version 3.4 CPU, Miniconda3\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshub://vibaotram/singularity-container:guppy-cpu-conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity.cpu-guppy3.4-conda-api\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econtaining GUPPY version 3.4 CPU, Miniconda3, ONT_FAST5_API\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshub://vibaotram/singularity-container:cpu-guppy3.4-conda-api\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity.guppy3.4gpu-conda-api\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econtaining GUPPY version 3.4 GPU, Miniconda3, ONT_FAST5_API\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshub://vibaotram/singularity-container:guppy3.4gpu-conda-api\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity.deepbinner-api\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econtaining deepbinner 2.0.0, ONT_FAST5_API, python3\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshub://vibaotram/singularity-container:deepbinner-api\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 3, - "topics": [], - "updated_at": 1517517877.0 + "topics": [ + "singularity" + ], + "updated_at": 1632300990.0 }, { "data_format": 2, - "description": "HPC friendly Python + Neuroimaging analysis container environment", + "description": "Singularity recipe files for metabat2 (https://bitbucket.org/berkeleylab/metabat/src/master/)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.2.15-3-g367a7ef", + "Singularity.2.15" ], - "full_name": "cosanlab/cosanToolsSingularity", + "full_name": "powerPlant/metabat2-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCosanlab Singularity Analysis Container\u003c/h1\u003e\u003ca id=\"user-content-cosanlab-singularity-analysis-container\" class=\"anchor\" aria-label=\"Permalink: Cosanlab Singularity Analysis Container\" href=\"#cosanlab-singularity-analysis-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a Singularity spec file that can be used to build a container and run on Dartmouth\u0027s \u003ca href=\"http://techdoc.dartmouth.edu/discovery/\" rel=\"nofollow\"\u003eDiscovery\u003c/a\u003e HPC cluster. It is a Docker -\u0026gt; Singularity bootstrap of our \u003ca href=\"https://github.com/cosanlab/cosanToolsDocker\"\u003eanalysis container\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can either:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCopy the built singularity image from Discovery, located at /ihome/ejolly\u003c/li\u003e\n\u003cli\u003ePull the container from \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity-Hub\u003c/a\u003e \u003ccode\u003e singularity pull shub://cosanlab/cosanToolsSingularity:master\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuild/modify the container from scratch\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding a container from scratch with this repo\u003c/h3\u003e\u003ca id=\"user-content-building-a-container-from-scratch-with-this-repo\" class=\"anchor\" aria-label=\"Permalink: Building a container from scratch with this repo\" href=\"#building-a-container-from-scratch-with-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need a local machine with sudo privileges and singularity installed. If you\u0027re running OSX you can follow the directions \u003ca href=\"http://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003ehere\u003c/a\u003e to get a vagrant VM running to do this. Then proceed with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#on your local machine with sudo privileges\nsudo singularity create --size 8000 myContainer.img\n\n#Singularity in the command below is the spec file in this repo, adjust path accordingly\nsudo singularity bootstrap myContainer.img Singularity\n\n#You might need to copy the .img out of your vagrant vm first if you\u0027re using one; by default /vagrant is shared with your host OS\nscp myContainer.img user@discovery.dartmouth.edu:~\n\n\n#on discovery, from ~\nmodule load singularity\nsingularity run myContainer.img\n\n#OR\nsingularity exec ./myContainer.img someCommand\n\n#to mount a folder with data\nsingularity exec -B /path/to/data:/mnt ./myContainer someCommand\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eSingularity recipe files for MetaBAT: A robust statistical framework for reconstructing genomes from metagenomic data\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 8, + "subscribers_count": 1, "topics": [], - "updated_at": 1598945912.0 + "updated_at": 1618472328.0 }, { "data_format": 2, - "description": "Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons", + "description": "LSHVec pre-trained models and its Python bindings", "filenames": [ - "Selector/hclib/modules/bale_actor/singularity/Singularity.def" + "singularity/Singularity" ], - "full_name": "youssefelmougy/jaccard-selector", + "full_name": "Lizhen0909/PyLSHvec", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAsynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons\u003c/h1\u003e\u003ca id=\"user-content-asynchronous-distributed-actor-based-approach-to-jaccard-similarity-for-genome-comparisons\" class=\"anchor\" aria-label=\"Permalink: Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons\" href=\"#asynchronous-distributed-actor-based-approach-to-jaccard-similarity-for-genome-comparisons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbstract\u003c/h2\u003e\u003ca id=\"user-content-abstract\" class=\"anchor\" aria-label=\"Permalink: Abstract\" href=\"#abstract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe computation of genome similarity is important in computational biology applications, and is assessed by calculating the Jaccard similarity of DNA sequencing sets. However, it\u0027s challenging to find solutions that can compute Jaccard similarity with the efficiency and scalability needed to fully utilize capabilities of modern HPC hardware. We introduce a novel approach for computing Jaccard similarity for genome comparisons, founded on an actor-based programming model. Our algorithm takes advantage of fine-grained asynchronous computations, distributed/shared memory, and the Fine-grained Asynchronous Bulk-Synchronous Parallelism execution model. Our performance results on the NERSC Perlmutter supercomputer demonstrate that this approach scales to 16,384 cores, showing an average of 4.94x and 5.5x improvement in execution time at the largest scale and relevant hardware performance monitors at medium scale compared to a state-of-the-art baseline. Our approach is also able to process much larger scale genomic datasets than this baseline.\u003c/p\u003e\n\u003cp\u003eThe code for our algorithm can be found \u003ca href=\"https://github.com/youssefelmougy/jaccard-selector/blob/main/Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector.cpp\"\u003ehere\u003c/a\u003e (in \u003ccode\u003e/hclib/modules/bale_actor/jaccard-selector/\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation Instructions\u003c/h2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-label=\"Permalink: Installation Instructions\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following installation instructions are for the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLoad the appropriate modules to prepare for setup\u003c/h3\u003e\u003ca id=\"user-content-load-the-appropriate-modules-to-prepare-for-setup\" class=\"anchor\" aria-label=\"Permalink: Load the appropriate modules to prepare for setup\" href=\"#load-the-appropriate-modules-to-prepare-for-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis loads the modules for both Selector and GenomeAtScale to prepare for setup.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource scripts/modules.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFirst time setup and installation\u003c/h3\u003e\u003ca id=\"user-content-first-time-setup-and-installation\" class=\"anchor\" aria-label=\"Permalink: First time setup and installation\" href=\"#first-time-setup-and-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis sets up and installs both the Selector and GenomeAtScale applications and their backend runtimes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource scripts/setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Instructions\u003c/h2\u003e\u003ca id=\"user-content-running-instructions\" class=\"anchor\" aria-label=\"Permalink: Running Instructions\" href=\"#running-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following running instructions are for the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC).\u003c/p\u003e\n\u003cp\u003eThe run script (\u003ccode\u003e/scripts/run.sh\u003c/code\u003e) has 4 options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e source /scripts/run.sh [selector | ctf | both] [1...inf] [1...inf] [0...5]\n \n [selector | ctf | both] Selects which application (or both) to run\n [1...inf] Selects the number of cores for the run ([32...16384] used in the paper)\n [1...inf] Selects the number of nodes for the run ([1...512] used in the paper)\n [0...5] Selects the set of HWPC to collect (0:none, 1:L1DA/L1DM/L1IA/L1IM, 2:L2DR/L2DM/L2IR/L2IM, 3:TLBDM/TLBIM, 4:BRINS/BRMSP, 5:INS/CYC)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: when selecting the number of nodes for the run, please remember that GenomeAtScale uses 32 cores/node and Selector uses either 32 or 64 cores/node.\u003c/p\u003e\n\u003cp\u003eFor example, \u003ccode\u003esource /scripts/run.sh selector 1024 16 2\u003c/code\u003e will run an experiment for the Selector application using 1024 cores on 16 nodes, collecting L2 cache statistics.\u003c/p\u003e\n\u003cp\u003eThis will submit an sbatch file to the run queue at Perlmutter. At job completion, a \u003ccode\u003ejaccard_selector.out\u003c/code\u003e or \u003ccode\u003ejaccard_ctf.out\u003c/code\u003e or both will be created, showing the CMD output of the run. Moreover, if HWPC were collected, a directory with the structure \u003ccode\u003ejaccard*+pat+*\u003c/code\u003e will be created in \u003ccode\u003e/Selector/hclib/modules/bale_actor/jaccard-selector/\u003c/code\u003e or \u003ccode\u003e/GenomeAtScale/jaccard-ctf/\u003c/code\u003e or both. Please see the Output Interpretation section for instructions on how to understand these results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput Interpretation\u003c/h2\u003e\u003ca id=\"user-content-output-interpretation\" class=\"anchor\" aria-label=\"Permalink: Output Interpretation\" href=\"#output-interpretation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following instructions are for understanding the results and relating them to the results found in the paper.\u003c/p\u003e\n\u003cp\u003eAt the completion of each run, there are two outputs that are created:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejaccard_selector.out OR jaccard_ctf.out OR both Output file from submitted job\njaccard_selector+pat+* OR jaccard+pat+* OR both Output folder (in respective directory) from a CrayPat run if HWPC were collected\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e*.out\u003c/code\u003e files contain the execution times of the run for the specific version. This result directly relates to Figure 2 (q) in the paper. An example output is shown below, where \u003ccode\u003e0.06150 seconds\u003c/code\u003e would be reported as the resulting value for the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e...\nRunning jaccard on 128 threads\nK-mer Matrix is 15000x5000 and has 15248 nonzeros.\n\nJaccard Similarity Matrix is 5000x5000 and has 12497374 values.\n\nRunning Jaccard Similarity K-mers (selector): \n 0.06150 seconds\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003ejaccard*+pat+*\u003c/code\u003e folders contain information dumped by the CrayPat profiler (for more information see \u003ca href=\"https://docs.nersc.gov/tools/performance/craypat/\" rel=\"nofollow\"\u003ehttps://docs.nersc.gov/tools/performance/craypat/\u003c/a\u003e). To generate human-readable content, we run \u003ccode\u003epat_report\u003c/code\u003e on the respective directory. This will display information of interest for the specified HWPC in the run, and will directly relate to Figures 2 (a-p). An example output is shown below, where we can see the L1 cache statistics which would be reported as the resulting values for the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@perlmutter: ~\u0026gt; pat_report $PWD/Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector+pat+190420-8718377t\n CrayPat/X: Version 23.02.0 Revision a53634a72 01/11/23 17:17:09\n\n Number of PEs (MPI ranks): 128\n\n Numbers of PEs per Node: 64 PEs on each of 2 Nodes\n\n Numbers of Threads per PE: 2\n\n Number of Cores per Socket: 64\n\n Execution start time: Sun Mar 19 10:25:36 2023\n\n System name and speed: nid004836 2.552 GHz (nominal)\n\n AMD Milan CPU Family: 25 Model: 1 Stepping: 1\n\n Core Performance Boost: 256 PEs have CPB capability\n\n\n Current path to data file:\n /Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector+pat+190420-8718377t (RTS, 2 data files)\n\n ...\n ...\n\n Processing step 7 of 10\n Notes for table 5:\n ...\n ...\n ==============================================================================\n USER / #1.selector_jaccard\n ------------------------------------------------------------------------------\n Time% 2.8% \n Time 0.060836 secs\n Imb. Time 0.000013 secs\n Imb. Time% 0.0% \n Calls 16.438 /sec 1.0 calls\n PAPI_L1_DCM 0.057G/sec 2,369,390.898 misses\n PAPI_L1_DCA 2.252G/sec 110,478,052.633 refs\n Average Time per Call 0.060836 secs\n CrayPat Overhead : Time 0.0% \n perf::PERF_COUNT_HW_CACHE_L1I:ACCESS 1,214,778 \n perf::PERF_COUNT_HW_CACHE_L1I:MISS 5,868\n ==============================================================================\n\n ...\n ...\n\n Hardware performance counter events:\n PAPI_L1_DCM Level 1 data cache misses\n PAPI_L1_DCA Level 1 data cache accesses\n perf::PERF_COUNT_HW_CACHE_L1I:ACCESS Undocumented counter\n perf::PERF_COUNT_HW_CACHE_L1I:MISS Undocumented counter\n\n Estimated minimum instrumentation overhead per call of a traced function,\n which was subtracted from the data shown in this report\n (for raw data, use the option: -s overhead=include):\n Time 0.114 microsecs\n\n Number of traced functions that were called: 7\n\n (To see the list, specify: -s traced_functions=show)\nuser@perlmutter: ~\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTop-Level Directory Organization\u003c/h2\u003e\u003ca id=\"user-content-top-level-directory-organization\" class=\"anchor\" aria-label=\"Permalink: Top-Level Directory Organization\" href=\"#top-level-directory-organization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe folder structure of this repository is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e.\n\u251c\u2500\u2500 Selector # Contains files for the Actor-based runtime and the Jaccard k-mer Selector application\n\u2502 \u251c\u2500\u2500 hclib # Contains the HClib library and the Actor-based runtime\n\u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 modules \n\u2502 \u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 bale_actor \n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 jaccard-selector # Contains the Jaccard k-mer Selector application files\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 jaccard_kmer_selector.cpp # Application code for Selector version of Jaccard similarity for genome comparisons\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 jaccard_kmer_locality_selector.cpp # Application code for locality-aware Selector version of Jaccard similarity for genome comparisons\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 kmer_matrix.mtx # K-mer matrix file for evaluation\n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 ... \n\u251c\u2500\u2500 GenomeAtScale # Contains files for the CTF library and the GenomeAtScale application\n\u2502 \u251c\u2500\u2500 ctf # Contains the CTF library\n\u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u251c\u2500\u2500 jaccard-ctf # Contains the GenomeAtScale (jaccard-ctf) files\n\u2502 \u2502 \u251c\u2500\u2500 jaccard.cxx # Application code for GenomeAtScale\n\u2502 \u2502 \u251c\u2500\u2500 kmer_matrix.zip # K-mer matrix files for evaluation\n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 ... \n\u251c\u2500\u2500 scripts # Contains installation, running, and modules scripts and sample Perlmutter sbatch files\n\u2502 \u251c\u2500\u2500 setup.sh # Installation and build script for the system backends and application code for both the Selector application and the GenomeAtScale application\n\u2502 \u251c\u2500\u2500 run.sh # Run script for both the selector application and GenomeAtScale application\n\u2502 \u251c\u2500\u2500 modules.sh # Modules script to prepare for running experiments (only used following first time setup using setup.sh, has to be re-run everytime you login to a cluster/supercomputer)\n\u2502 \u2514\u2500\u2500 ... \n\u2514\u2500\u2500 README.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use our application in your work, please cite \u003ca href=\"\"\u003eour paper\u003c/a\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eYoussef Elmougy, Akhiro Hayashi, and Vivek Sarkar. 2024. Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eCorresponding author: Youssef Elmougy (\u003ca href=\"mailto:yelmougy3@gatech.edu\"\u003eyelmougy3@gatech.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgement\u003c/h2\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), through the Advanced Graphical Intelligence Logical Computing Environment (AGILE) research program, under Army Research Office (ARO) contract number W911NF22C0083. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-lshvec-pre-trained-models-and-its-python-bindings\" class=\"anchor\" aria-hidden=\"true\" href=\"#lshvec-pre-trained-models-and-its-python-bindings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLSHVec pre-trained models and its Python bindings\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-hidden=\"true\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary\u003c/h2\u003e\n\u003cp\u003eThis repository presents a few of pre-tained models with JLSHVec (which is a rewritten java version of \u003ca href=\"https://github.com/Lizhen0909/LSHVec\"\u003eLSHVec\u003c/a\u003e). See \u003ca href=\"#remark\"\u003eRemark\u003c/a\u003e for technical details.\u003c/p\u003e\n\u003cp\u003ePython codes and examples to uses these models are also provided.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003ecython\u0026gt;=0.28.5\u003c/li\u003e\n\u003cli\u003eJnius \u0026gt;=1.1.0\u003c/li\u003e\n\u003cli\u003ejava \u0026gt;=1.8\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild from source\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Lizhen0909/PyLSHvec.git \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e PyLSHvec \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e python setup.py install\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-or-use-pip\" class=\"anchor\" aria-hidden=\"true\" href=\"#or-use-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eor use pip\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install pylshvec\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-or-use-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#or-use-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eor use docker\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull lizhen0909/pylshvec\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-or-use-singularity-3\" class=\"anchor\" aria-hidden=\"true\" href=\"#or-use-singularity-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eor use singularity 3\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name pylshvec.sif shub://Lizhen0909/PyLSHvec\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use\u003c/h2\u003e\n\u003cp\u003ePut things simply, just\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epylshvec\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e#here needs jlshvec jar file, download it first\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eset_lshvec_jar_path\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/mnt/jlshvec-assembly-0.1.jar\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e#since vector model is usually large, set a big java memory limit is preferred. \u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eadd_java_options\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"-Xmx32G\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e#here need model file and lsh function file, download them first\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e#use help(model) to see all the methods and constructor options \u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eLSHVec\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel_file\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"/mnt/refdb_viruses_model_gs_k23_l3000_rand_model_299\"\u003c/span\u003e, \n \u003cspan class=\"pl-s1\"\u003ehash_file\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"/mnt/lsh_nt_NonEukaryota_k23_h25.crp\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003ereads\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s\"\u003e\u0027ACGTACGT.....\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027ACGTACGT.....\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027ACGTACGT.....\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027ACGTACGT.....\u0027\u003c/span\u003e, ....]\n\n\u003cspan class=\"pl-s1\"\u003epredicts\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003epredict\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ereads\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more complete examples please see the notebooks (see \u003ca href=\"#download\"\u003eDownload\u003c/a\u003e for minimum memory requirement):\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_virus_classfication_model.ipynb\"\u003eexample_use_virus_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_bacteria_classfication_model.ipynb\"\u003eexample_use_bacteria_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_vectors_in_bacteria_classfication_model.ipynb\"\u003eexample_use_vectors_in_bacteria_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_Illumina_bacteria_classfication_model.ipynb\"\u003eexample_use_Illumina_bacteria_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_Pacbio_bacteria_classfication_model.ipynb\"\u003eexample_use_Pacbio_bacteria_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Docker\u003c/h3\u003e\n\u003cp\u003eAssume you put your data in /mnt/data and your notebook in /mnt/notebook.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erun python or ipython\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v /mnt/data:/data -it lizhen0909/pylshvec python \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eor ipython\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003erun Jupyter notebook\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v /mnt/data:/data -v /mnt/notebook:/notebook -p 8888:8888 -it lizhen0909/pylshvec jupyter_notebook\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFind connection url in the console output.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Singularity\u003c/h3\u003e\n\u003cp\u003eSince singularity maps the $HOME directory, here just assumes data/model are going to locate in $HOME. Otherwise, you need map the directories like docker.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erun python or ipython\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run pylshvec.sif python \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003ethe nrun any pylshvec code \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003erun Jupyter notebook\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eIt should work, however singularity maps too many things that host settings may affect the notebook\u003c/span\u003e\nsingularity run --bind \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/notebook:/notebook pylshvec.sif jupyter_notebook \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jlshvec-jar-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#jlshvec-jar-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJLSHVec jar file\u003c/h3\u003e\n\u003cp\u003eThe pre-trained models were trained with a rewritten \u003ca href=\"https://github.com/Lizhen0909/LSHVec\"\u003eLSHVec\u003c/a\u003e in java.\nThe assembly jar file is needed to load the models.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.amazon.com/clouddrive/share/4NiogpuW1lzBMyGmMlkrDbjhSMYpQgWjW5GUcKFR7Q6\" rel=\"nofollow\"\u003eDownload jlshvec-assembly-0.1.jar\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e: aeb207b983b3adc27e14fd9c431e2130\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pre-trained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-trained models\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eBe Warned\u003c/strong\u003e that like all the machine learning models, the model cannot preform better beyond the data. If your data is significant other than the pre-trained model data, training your own model is preferred.\u003c/p\u003e\n\u003cp\u003eHere are issues I can think of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSome NCBI taxonomy id may never be predicted since not all ids have train data.\u003c/li\u003e\n\u003cli\u003eData is not balanced. Some ids (e.g. a specified species) have much more data than others, which makes prediction may prefer to the rich-data ids.\u003c/li\u003e\n\u003cli\u003eStrain (even some species) prediction is terrible. Don\u0027t expect it.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-refdb-viruses-classfication-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#refdb-viruses-classfication-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRefDB viruses classfication model\u003c/h4\u003e\n\u003cp\u003eTrainned with 9.3k viruses assemblies of RefDB. Minimum Java memory: 16G.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emodel file: \u003ca href=\"https://www.amazon.com/clouddrive/share/RmoJ1lduzlqstAJFnKg0aAlx82AyCjnzKncfGjQIQMg\" rel=\"nofollow\"\u003erefdb_viruses_model_gs_k23_l3000_rand_model_299\u003c/a\u003e [size: 5.3G]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 2502b284b336734300c2297d23d1d349\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehash function file: \u003ca href=\"https://www.amazon.com/clouddrive/share/6ZNvMXMy30b4vc0RYNVG1lbf1ih8WgpoQ9w4lX91IXy\" rel=\"nofollow\"\u003elsh_nt_NonEukaryota_k23_h25.crp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 5eea8a98d224b7ff505091bd483ca75c\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-refdb-bacteria-classfication-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#refdb-bacteria-classfication-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRefDB bacteria classfication model\u003c/h4\u003e\n\u003cp\u003eTrainned with 42k bacteria assemblies of RefDB. Minimum Java memory: 32G.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emodel file: \u003ca href=\"https://www.amazon.com/clouddrive/share/LoXz6k229SwYuElPTHvu0SSJOq56nJenvBbOTGVeb9a\" rel=\"nofollow\"\u003erefdb_bacteria_model_gs_k23_l3000_rand_model_214\u003c/a\u003e [size: 11G]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 402e9a286b71068999caa9766b2dbf8c\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehash function file: \u003ca href=\"https://www.amazon.com/clouddrive/share/6ZNvMXMy30b4vc0RYNVG1lbf1ih8WgpoQ9w4lX91IXy\" rel=\"nofollow\"\u003elsh_nt_NonEukaryota_k23_h25.crp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 5eea8a98d224b7ff505091bd483ca75c\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-genbank-bacteria-and-viruses-classfication-model-illumina-simulation\" class=\"anchor\" aria-hidden=\"true\" href=\"#genbank-bacteria-and-viruses-classfication-model-illumina-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenBank bacteria and viruses classfication model (Illumina Simulation)\u003c/h4\u003e\n\u003cp\u003eTrainned with 54k assemblies from GenBank. \u003cstrong\u003eOnly one assembly was sampled for each species.\u003c/strong\u003e Because viruses data is too samll compared to bateria, it rarely predicts any viruses. Just take it as a bateria model.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278762/\" rel=\"nofollow\"\u003eart_illumina\u003c/a\u003e was used to simulate the paired-end reads with length of 150, mean size of 270 and stddev of 27.\u003c/p\u003e\n\u003cp\u003eMinimum Java memory: 48G.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emodel file: \u003ca href=\"https://www.amazon.com/clouddrive/share/zQnu2ti1vfBMGcXrRqsohgfzuaYzZs4HGESP58vobRn\" rel=\"nofollow\"\u003egenbank_model_ill_k23_model_299\u003c/a\u003e [size: 12G]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e d6b117a4c7ffe4f25e6c532a88bb3a47\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehash function file: \u003ca href=\"https://www.amazon.com/clouddrive/share/efWceiTHId4EVhY1DEppmW6amyBQoEt3iIU6oW5FbcX\" rel=\"nofollow\"\u003elsh_CAMI2_illumina_k23_h25.crp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 706633919e347f920ce6ab3277091efb\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-genbank-bacteria-and-viruses-classfication-model-pacbio-simulation\" class=\"anchor\" aria-hidden=\"true\" href=\"#genbank-bacteria-and-viruses-classfication-model-pacbio-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenBank bacteria and viruses classfication model (Pacbio Simulation)\u003c/h4\u003e\n\u003cp\u003eTrainned with 54k assemblies from GenBank. \u003cstrong\u003eOnly one assembly was sampled for each species.\u003c/strong\u003e Because viruses data is too samll compared to bateria, it rarely predicts any viruses. Just take it as a bateria model.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/pfaucon/PBSIM-PacBio-Simulator\"\u003epbsim\u003c/a\u003e was used to simulate the pacbio reads with Continuous Long Read (CLR) profile, mean size of 3000 and stddev of 1000.\u003c/p\u003e\n\u003cp\u003eMinimum Java memory: 16G.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emodel file: \u003ca href=\"https://www.amazon.com/clouddrive/share/OmU9cmVKknacpt0W9HpI6QY2jXC17dQpWaaERpLhOGl\" rel=\"nofollow\"\u003egenbank_model_pb_k9_model_299\u003c/a\u003e [size: 121M]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 351275531493a4866be4afcd9df3932c\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehash function file: \u003ca href=\"https://www.amazon.com/clouddrive/share/zw4JwJCE4Lst5I4q36ijwrhc3db9rHYsCuyQ4KkihVC\" rel=\"nofollow\"\u003elsh_CAMI2_pacbio_k9_h25.crp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e df7ee38cf8b58d5f8034bb9b266e3334\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sample-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#sample-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSample data\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eActinoMock Nanopore Sample [size: 500M].\u003c/p\u003e\n\u003cp\u003eThe data is used in example notebook \u003ca href=\"notebook/example_use_vectors_in_bacteria_classfication_model.ipynb\"\u003eexample_use_vectors_in_bacteria_classfication_model.ipynb\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://ww2.cs.fsu.edu/~lshi/ActinoMock_Nanopore.seq.gz\" rel=\"nofollow\"\u003eDownload from FSU\u003c/a\u003e\n\u2003\u2003\n\u003ca href=\"https://www.amazon.com/clouddrive/share/eTIKYVLckXUCMnMQSpO8TCqZOwekmBrx23ZhMa3XO8d\" rel=\"nofollow\"\u003eDownload from Amazon Drive\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e: b7f3e55438fdc05920aee693a98ded2e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-remark\" class=\"anchor\" aria-hidden=\"true\" href=\"#remark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRemark\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-what-is-jlshvec--why-jlshvec-instead-of-lshvec\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-jlshvec--why-jlshvec-instead-of-lshvec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is JLSHVec ? Why JLSHVec instead of LSHVec?\u003c/h3\u003e\n\u003cp\u003eJLSHVec is a rewritten version of \u003ca href=\"https://github.com/Lizhen0909/LSHVec\"\u003eLSHVec\u003c/a\u003e in Java language.\u003c/p\u003e\n\u003cp\u003eWhen we use LSHVec with big dataset (e.g. \u003ca href=\"https://www.ncbi.nlm.nih.gov/genbank/\" rel=\"nofollow\"\u003eGenBank\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/12652131\" rel=\"nofollow\"\u003eRefDB\u003c/a\u003e), we found that LSHVec is hard to process such a big data size.\u003c/p\u003e\n\u003cp\u003eThe reason is that LSHVec which inherits from \u003ca href=\"https://fasttext.cc/\" rel=\"nofollow\"\u003eFastText\u003c/a\u003e requires the input is text format separated by white space and then loads all the text in memory. This is acceptable for natural languages since the data size is at most tens GBs.\u003c/p\u003e\n\u003cp\u003eHowever in LSHVec k-mers are used instead of words. Suppose we want to train a k-mer embedding of simulated Illumina reads with RefDB bacteria assemblies (about 500G genetic bits). The number of kmers is about D*n, where D is the assembly data size and n is coverage. In our case, assuming n=10 and k=23, the number of kmers is 5T and requires a disk space of 125TB, of which the data preparation and loading process will take forever.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-how-were-jlshvec-pre-trained-models-trained-\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-were-jlshvec-pre-trained-models-trained-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow were JLSHVec pre-trained models trained ?\u003c/h3\u003e\n\u003cp\u003eFirst we prepared a \u003ca href=\"https://rocksdb.org/\" rel=\"nofollow\"\u003eRockDB\u003c/a\u003e for the reference sequences (e.g. all bacteria assemblies in RefDB).\u003c/p\u003e\n\u003cp\u003eThen we have several nodes to train the model: one node (train node) trains the model and others (hash nodes) generate and hash kmers. The nodes communicates by passing \u003ca href=\"https://developers.google.com/protocol-buffers\" rel=\"nofollow\"\u003eprotocol-buf\u003c/a\u003e message with a \u003ca href=\"https://redis.io/\" rel=\"nofollow\"\u003eRedis\u003c/a\u003e server.\u003c/p\u003e\n\u003cp\u003eA hash node randomly reads reference sequences from the RockDB, simulates (e.g. simulations Illumina, Pacbio, Gold Standard) reads, generates kmers and hashes them, then feeds the hashed-kmer-sequences to a Redis queue.\u003c/p\u003e\n\u003cp\u003eTrain node reads from the Redis queue and does jobs of embedding or classification training. Our training code supports hierarchical softmax using NCBI taxonomy tree, which is essential for multi-label(an instance can have a label for each rank) and multi-class(an instance can only have one label for a rank) mixture classification model.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003ePlease cite:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.biorxiv.org/content/biorxiv/early/2019/08/06/726729.full.pdf\" rel=\"nofollow\"\u003eA Vector Representation of DNA Sequences Using Locality Sensitive Hashing\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/400c4e52df43f6a0ab8a89b74b1a78d1a64da56a7848b9110c9d2991bb7c3105/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, - "topics": [ - "asynchronous", - "distributed-systems", - "genome-sequencing", - "hpc", - "jaccard-distance", - "jaccard-index", - "jaccard-similarity", - "openshmem", - "pgas", - "hclib" - ], - "updated_at": 1708280383.0 + "topics": [], + "updated_at": 1634389599.0 }, { "data_format": 2, "description": null, "filenames": [ - "examples/docker/Singularity", - "examples/raspbian/Singularity", - "examples/apps/Singularity", - "examples/apps/Singularity.cowsay", - "examples/ubuntu/Singularity", - "examples/arch/Singularity", - "examples/asciinema/Singularity", - "examples/debian/Singularity", - "examples/self/Singularity", - "examples/busybox/Singularity", - "examples/shub/Singularity", - "examples/opensuse/Singularity", - "examples/centos/Singularity", - "examples/scientific/Singularity" + "imaging/nipy/Singularity" ], - "full_name": "edf-hpc/singularity-container", + "full_name": "AndrewYRevell/docker_GitHub", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/singularityware/singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39fe73b2acfcaf157d81df51bfad4bac84e7177bc8ffda9f351f966cdfb1eff1/68747470733a2f2f7472617669732d63692e6f72672f73696e67756c6172697479776172652f73696e67756c61726974792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/singularityware/singularity.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eGuidelines for Contributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\".github/PULL_REQUEST_TEMPLATE.md\"\u003ePull Request Template\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"LICENSE.md\"\u003eProject License\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity - Enabling users to have full control of their environment.\u003c/h1\u003e\u003ca id=\"user-content-singularity---enabling-users-to-have-full-control-of-their-environment\" class=\"anchor\" aria-label=\"Permalink: Singularity - Enabling users to have full control of their environment.\" href=\"#singularity---enabling-users-to-have-full-control-of-their-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStarting a Singularity container \"swaps\" out the host operating system\nenvironment for one the user controls!\u003c/p\u003e\n\u003cp\u003eLet\u0027s say you are running Ubuntu on your workstation or server, but you\nhave an application which only runs on Red Hat Enterprise Linux 6.3.\nSingularity can instantly virtualize the operating system, without\nhaving root access, and allow you to run that application in its native\nenvironment!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbout\u003c/h1\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is a container platform focused on supporting \"Mobility of\nCompute\"\u003c/p\u003e\n\u003cp\u003eMobility of Compute encapsulates the development to compute model where\ndevelopers can work in an environment of their choosing and creation and\nwhen the developer needs additional compute resources, this environment\ncan easily be copied and executed on other platforms. Additionally as\nthe primary use case for Singularity is targeted towards computational\nportability, many of the barriers to entry of other container solutions\ndo not apply to Singularity making it an ideal solution for users (both\ncomputational and non-computational) and HPC centers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe Container\u003c/h2\u003e\u003ca id=\"user-content-the-container\" class=\"anchor\" aria-label=\"Permalink: The Container\" href=\"#the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity utilizes container images, which means when you enter and\nwork within the Singularity container, you are physically located inside\nof this image. The image grows and shrinks in real time as you install\nor delete files within the container. If you want to copy a container,\nyou copy the image.\u003c/p\u003e\n\u003cp\u003eUsing a single image for the container format, has added advantages\nespecially within the context of HPC with large parallel file systems\nbecause all metadata operations within the container occur within the\ncontainer image (and not on the metadata server!).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMobility of Compute\u003c/h2\u003e\u003ca id=\"user-content-mobility-of-compute\" class=\"anchor\" aria-label=\"Permalink: Mobility of Compute\" href=\"#mobility-of-compute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith Singularity, developers who like to be able to easily control their\nown environment will love Singularity\u0027s flexibility. Singularity does not\nprovide a pathway for escalation of privilege (as do other container\nplatforms which are thus not applicable for multi-tenant resources) so\nyou must be able to become root on the host system (or virtual machine)\nin order to modify the container.\u003c/p\u003e\n\u003cp\u003eA Singularity container can be launched in a variety of different ways\ndepending on what you wanted to do with it. A simple method might be to\nlaunch an interactive shell within the container image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity shell /tmp/Centos-7.img \ngmk@Centos-7.img demo\u0026gt; echo \"Hello from within the container\"\nHello from within the container\ngmk@Centos-7.img demo\u0026gt; whoami\ngmk\ngmk@Centos-7.img demo\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if you wanted to do the same thing as root:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ sudo singularity shell -w /tmp/Centos-7.img \nroot@Centos-7.img demo\u0026gt; whoami\nroot\nroot@Centos-7.img demo\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003enote: By default, Singularity launches the container image in read\nonly mode (so it can be easily launched in parallel). The -w option\nused above tells Singularity to mount the image in read/write mode such\nthat root can now make changes to the container.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally relevant file systems on your host are automatically shared\nwithin the context of your container. This can be demonstrated as\nfollows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ pwd\n/home/gmk/demo\n[gmk@centos7-x64 demo]$ echo \"world\" \u0026gt; hello\n[gmk@centos7-x64 demo]$ singularity shell /tmp/Centos-7.img \ngmk@Centos-7.img demo\u0026gt; pwd\n/home/gmk/demo\ngmk@Centos-7.img demo\u0026gt; cat hello\nworld\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the developer has completed their environment the image file can be\ncompressed and copied to any other system that has Singularity installed.\nIf you do not have root on that system, you will not be able to make any\nchanges to the image once on that system. But you will be able to use\nthe container and access the data and files outside the container as\neasily as you would on your development system or virtual machine.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePortability of Singularity container images\u003c/h2\u003e\u003ca id=\"user-content-portability-of-singularity-container-images\" class=\"anchor\" aria-label=\"Permalink: Portability of Singularity container images\" href=\"#portability-of-singularity-container-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity images are highly portable between Linux distributions (as\nlong as the binary format is the same). You can generate your image on\nDebian or CentOS, and run it on Mint or Slackware.\u003c/p\u003e\n\u003cp\u003eWithin a particular container one can include their programs, data,\nscripts and pipelines and thus portable to any other architecture\ncompatible Linux system or distribution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootstrapping new images\u003c/h2\u003e\u003ca id=\"user-content-bootstrapping-new-images\" class=\"anchor\" aria-label=\"Permalink: Bootstrapping new images\" href=\"#bootstrapping-new-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerally when bootstrapping an image from scratch you must build it from\na compatible host. This is because you must use the distribution specific\ntools it comes with (e.g. Red Hat does not provide Debian\u0027s debootstrap).\nBut once the image has been bootstrapped and includes the necessary bits\nto be self hosting (e.g. YUM on CentOS and apt-get on Debian/Ubuntu) then\nthe process of managing the container can be implemented from within the\ncontainer.\u003c/p\u003e\n\u003cp\u003eThe process of building a bootstrap starts with a definition\nspecification. The definition file describes how you want the operating\nsystem to be built, what should go inside it and any additional\nmodifications necessary.\u003c/p\u003e\n\u003cp\u003eHere is an example of a very simple bootstrap definition file for CentOS:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: yum\nOSVersion: 7\nMirrorURL: http://mirror.centos.org/centos-%{OSVERSION}/%{OSVERSION}/os/$basearch/\nInclude: yum\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce you have created your bootstrap definition, you can build your\nSingularity container image by first creating a blank image, and then\nbootstrapping using your definition file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ sudo singularity create /tmp/Centos-7.img\n[gmk@centos7-x64 demo]$ sudo singularity bootstrap /tmp/Centos-7.img centos.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom there we can immediately start using the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img cat /etc/redhat-release \nCentOS Linux release 7.2.1511 (Core) \n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img python --version\nPython 2.7.5\n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img python hello.py \nhello world\n[gmk@centos7-x64 demo]$ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if I do this same process again, while changing the \u003cstrong\u003eOSVersion\u003c/strong\u003e\nvariable in the bootstrap definition to \u003cstrong\u003e6\u003c/strong\u003e (where previously it was\nautomatically ascertained by querying the RPM database), we can\nessentially build a CentOS-6 image in exactly the same manner as\nabove. Doing so reveals this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-6.img cat /etc/redhat-release \nCentOS release 6.7 (Final)\n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-6.img python --version\nPython 2.6.6\n[gmk@centos7-x64 demo]$ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd as expected, the Python version we now see is what comes from by\ndefault in CentOS-6.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCite as:\u003c/h1\u003e\u003ca id=\"user-content-cite-as\" class=\"anchor\" aria-label=\"Permalink: Cite as:\" href=\"#cite-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459. https://doi.org/10.1371/journal.pone.0177459\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe also have a Zenodo citation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer, Gregory M.. (2016). Singularity 2.1.2 - Linux application and environment\ncontainers for science. 10.5281/zenodo.60736\n\nhttp://dx.doi.org/10.5281/zenodo.60736\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebpage\u003c/h1\u003e\u003ca id=\"user-content-webpage\" class=\"anchor\" aria-label=\"Permalink: Webpage\" href=\"#webpage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have full documentation at \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/\u003c/a\u003e, and \u003ca href=\"http://www.github.com/singularityware/singularityware.github.io\"\u003ewelcome contributions\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cntdocker\" class=\"anchor\" aria-hidden=\"true\" href=\"#cntdocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCNTdocker\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eDockerfiles to create Docker images used by the CNT at the university of Pennsylvania\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directory-contents-explanation\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-contents-explanation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory contents explanation\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-eeg\" class=\"anchor\" aria-hidden=\"true\" href=\"#eeg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eEEG\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common EEG analysis tools. Usually python 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eechobase\u003c/strong\u003e: Dockerfiles used to create images that can calculate functional connectivity of EEG\nAlso has ieegpy python package used to interface with iEEG.org\nEchobase code is from \u003ca href=\"https://github.com/andyrevell/paper001\"\u003ehttps://github.com/andyrevell/paper001\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUbuntu 18.04\nPython 2.7 and Python 3.6\nNumpy 1.18.4\npandas 1.0.3\nscipy 1.4.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-imaging\" class=\"anchor\" aria-hidden=\"true\" href=\"#imaging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eImaging\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common MRI analysis tools.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n Python 2.7, Python 3.6, Python 3.7\n dcm2niix\n dsistudio\n ANTS\n Freesurfer\n FSL 6.0.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ml\" class=\"anchor\" aria-hidden=\"true\" href=\"#ml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eml\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common machine learning tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ewavenet\u003c/strong\u003e: Dockerfile to create compatible dependencies to use with Goodgle Deepmind wavenet paper\n\u003ca href=\"https://deepmind.com/blog/article/wavenet-generative-model-raw-audio\" rel=\"nofollow\"\u003eWavenet blog\u003c/a\u003e\n\u003ca href=\"https://arxiv.org/pdf/1609.03499.pdf\" rel=\"nofollow\"\u003eWavenet paper\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n tensorflow 1.0.0\n pandas 0.19.2\n librosa 0.5.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eTensorflow_2.1\u003c/strong\u003e: Dockerfile to create compatible dependencies to with tensorflow 2.1\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n tensorflow 2.1\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 9, + "subscribers_count": 2, "topics": [], - "updated_at": 1515565254.0 + "updated_at": 1600370006.0 }, { "data_format": 2, - "description": "A Container implementation for the Google Drive Client", + "description": "Singularity recipes for bioinformatics software", "filenames": [ - "Singularity" + "spades/3.13.0/Singularity.spades.3.13.0", + "quast/5.0.0/Singularity.quast.5.0.0", + "seqsero2/1.0.0/Singularity.seqsero2.1.0.0", + "seqsero2/0.1/Singularity.seqsero2-0.1", + "lyveset/1.1.4f/Singularity.lyveset.1.1.4f" ], - "full_name": "ArangoGutierrez/grive", + "full_name": "kapsakcj/singularities", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eA Linux Container implementation of the grive client\u003c/h1\u003e\u003ca id=\"user-content-a-linux-container-implementation-of-the-grive-client\" class=\"anchor\" aria-label=\"Permalink: A Linux Container implementation of the grive client\" href=\"#a-linux-container-implementation-of-the-grive-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Drive\u003c/h2\u003e\u003ca id=\"user-content-singularity-drive\" class=\"anchor\" aria-label=\"Permalink: Singularity Drive\" href=\"#singularity-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Singularity imlementation for the Google Drive Client\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePealse refer to \u003ca href=\"http://yourcmc.ru/wiki/Grive2#Installation\" rel=\"nofollow\"\u003ehttp://yourcmc.ru/wiki/Grive2#Installation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor a first time Drive sync\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity create -s 1024 /some/path/grive.img \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity bootstrap /some/path/grive.img grive.def\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e $ singularity pull --name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egrive.img\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e shub://ArangoGutierrez/grive\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn your host\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir /home/MyGoogleDriveFoldeName\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/MyGoogleDriveFoldeName\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run /some/path/grive.img -a\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter this, for Sync only\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/MyGoogleDriveFoldeName\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run /some/path/grive.img\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Drive\u003c/h2\u003e\u003ca id=\"user-content-docker-drive\" class=\"anchor\" aria-label=\"Permalink: Docker Drive\" href=\"#docker-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Docker imlementation for the Google Drive Client\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePealse refer to \u003ca href=\"http://yourcmc.ru/wiki/Grive2#Installation\" rel=\"nofollow\"\u003ehttp://yourcmc.ru/wiki/Grive2#Installation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor a first time Drive sync\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker pull ashael/dockerdrive \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003e$docker\u003c/span\u003e build Dockerfile (after git clone)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir /home/MyGoogleDriveFoldeName\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/MyGoogleDriveFoldeName\n$ docker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/home/grive -w /home/grive grive\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen inside the Container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ grive -a\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter this, for Sync only\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/home/grive -w /home/grive --name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eGdrive\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e grive grive\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularities\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularities\u003c/h1\u003e\n\u003cp\u003eSingularity recipes for bioinformatics software. Build singularity images with these recipes (sudo required) or download/pull the images from \u003ca href=\"https://singularity-hub.org/collections/2778\" rel=\"nofollow\"\u003esingularity-hub.org\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repo is \u003cstrong\u003eWORK IN PROGRESS\u003c/strong\u003e. Feel free to try the recipes/Singularity builds, but they are \u003cstrong\u003enot tested deeply and are in no way guaranteed to work. Proceed at your own risk\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eIt is somewhat modeled after \u003ca href=\"https://github.com/StaPH-B/docker-builds\"\u003ehttps://github.com/StaPH-B/docker-builds\u003c/a\u003e , but with Singularity recipes instead.\u003c/p\u003e\n\u003cp\u003eSysadmins for High Performance Cluster computers almost always favor Sinularity over Docker :) so I\u0027m starting to learn the ways of Singularity.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-available-singularity-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Singularity images\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eSoftware\u003c/th\u003e\n\u003cth align=\"center\"\u003eVersion\u003c/th\u003e\n\u003cth align=\"center\"\u003eLink\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eSPAdes\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3.13.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003ehttp://cab.spbu.ru/software/spades/\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eQUAST\u003c/td\u003e\n\u003ctd align=\"center\"\u003e5.0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ablab/quast\"\u003ehttps://github.com/ablab/quast\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eLyve-SET\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1.1.4f\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/lskatz/lyve-SET\"\u003ehttps://github.com/lskatz/lyve-SET\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eSeqSero2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.1, 1.0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/denglab/SeqSero2\"\u003ehttps://github.com/denglab/SeqSero2\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThese Singularity images can be built if you have Singularity installed and \u003cstrong\u003ehave sudo/admin priveleges\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# build an image using a recipe (called Singularity in this example)\nsudo singularity build my-new-singularity-image.simg /path/to/Singularity\n\n# download the repo\ngit clone https://github.com/kapsakcj/singularities.git\n# another example using the SPAdes recipe\nsudo singularity build my-new-spades-3.13.0-image.simg /path/to/Singularity.spades.3.13.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThese Singularity images are also available to download from singularity-hub.org if you \u003cstrong\u003edon\u0027t have sudo priveleges\u003c/strong\u003e (no build necessary!). The badge below is a link to the singularity-hub.org collection.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2778\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe name of the Singularity hub collection is \u003ccode\u003ekapsakcj/singularities\u003c/code\u003e and the tag is specified by the extenion of the Singularity recipe file. For example the recipe, \u003ccode\u003e/spades/3.13.0/Singularity.spades.3.13.0\u003c/code\u003e, can be downloaded like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# download an image like so, and name it whatever you want with the --name flag\nsingularity pull --name my-new-spades-3.13.0-image shub://kapsakcj/singularities:spades.3.13.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-useful-links-and-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#useful-links-and-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful links and resources\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity v2.6 User guide \u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/index.html\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/guides/2.6/user-guide/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularityHub \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExcellent tutorial on Singularity (using v2.5) from Sylabs, many other links within \u003ca href=\"https://github.com/Singularity-tutorial/Singularity-tutorial.github.io\"\u003ehttps://github.com/Singularity-tutorial/Singularity-tutorial.github.io\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHow to build a container using Singularity Hub, linked to a github repo with Singularity recipes \u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki/Build-A-Container\"\u003ehttps://github.com/singularityhub/singularityhub.github.io/wiki/Build-A-Container\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tipstricksthings-to-remember-about-singularity--many-from-jake-garfin--\" class=\"anchor\" aria-hidden=\"true\" href=\"#tipstricksthings-to-remember-about-singularity--many-from-jake-garfin--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTips/Tricks/Things-to-remember about Singularity [ many from Jake Garfin :) ]\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity automatically brings your user \u0026amp; group into the container with you (ie. no \u003ccode\u003e-u $(id -u):$(id -g)\u003c/code\u003e needed like in Docker)\u003c/li\u003e\n\u003cli\u003eSingularity (by default) wants to mount your entire home directory inside the container as well. Use \u003ccode\u003e--cleanenv\u003c/code\u003e and \u003ccode\u003e--containall\u003c/code\u003e to keep things separate and bring in specific directories you want with \u003ccode\u003e-B /local-dir:/dir-in-container\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDocker images converted to Singularity that want to write to system directories owned by root aren\u0027t going to work out of the box.\u003c/li\u003e\n\u003cli\u003eIf you are making a container with something that uses perl, add this to the recipe in the \u003ccode\u003e%environment\u003c/code\u003e section to prevent locale settings errors (see lyveset recipe)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e%environment\n export LC_ALL=C\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTO-DO\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eHow to: Singularity\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinks to docs for installing\u003c/li\u003e\n\u003cli\u003eHow to download an image from singularity hub\u003c/li\u003e\n\u003cli\u003eHow to download an image fromm dockerhub\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esingularity pull docker://staphb/skesa\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eHow to take a recipe and build locally (sudo required)\u003c/li\u003e\n\u003cli\u003eHow to run a Singularity container\n\u003cul\u003e\n\u003cli\u003eDifferent ways to run - \u003ccode\u003esingularity exec [...]\u003c/code\u003e or ./name-of-singularity.simg [...]\u003c/li\u003e\n\u003cli\u003eMounting DIRs - default way (mount entire \u003ccode\u003e$HOME\u003c/code\u003e DIR), or way to mount a specific DIR and not entire \u003ccode\u003e$HOME\u003c/code\u003e DIR\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate SingularityHub account\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elink to this repo\u003c/li\u003e\n\u003cli\u003ecreate autobuilds for each recipe\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1553886248.0 + "updated_at": 1583250575.0 }, { "data_format": 2, - "description": "a Singularity container for build and run of QT-creator projects. Does not include any project content.", + "description": "Some benchmark singularity images for pycbc / pycbc inference", "filenames": [ "Singularity" ], - "full_name": "vsoch/cs106b-builder", + "full_name": "gwastro/pycbc_bench", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCS106B-Builder\u003c/h1\u003e\u003ca id=\"user-content-cs106b-builder\" class=\"anchor\" aria-label=\"Permalink: CS106B-Builder\" href=\"#cs106b-builder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the CS106B-Builder - you can build a container and then use it to compile\nand run a local project directory, without QT-Creator. We are using\na Singularity container instead of Docker so that we can more seamlessly use our\nsystem display.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e0. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-0-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 0. Install Singularity\" href=\"#0-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first install Singularity. I recommend the verison 2.6 for a much\nsimpler install routine. Here is the \u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eguide\u003c/a\u003e, and instructions:\u003c/p\u003e\n\u003cp\u003eYou\u0027ll need these dependencies\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get update \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\n sudo apt-get install \\\n python \\\n dh-autoreconf \\\n build-essential \\\n libarchive-dev\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then to install:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\ngit fetch --all\ngit checkout 2.6.0\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Build the Container\u003c/h2\u003e\u003ca id=\"user-content-1-build-the-container\" class=\"anchor\" aria-label=\"Permalink: 1. Build the Container\" href=\"#1-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build this image locally, and note that you must have root permissions\ntodo so. This container could also be built and provided on \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e,\nif appropriate.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build cs106b-builder Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Extract your project\u003c/h2\u003e\u003ca id=\"user-content-2-extract-your-project\" class=\"anchor\" aria-label=\"Permalink: 2. Extract your project\" href=\"#2-extract-your-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need to extract your project in the present working directory, or the\ndirectory where you want to run your container. Usually this means unzipping\na project file to generate a subfolder. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eunzip cs106b-Pancakes.zip \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewould create a folder with the name \"Pancakes\u0027 - (or your project name)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run the container\u003c/h2\u003e\u003ca id=\"user-content-3-run-the-container\" class=\"anchor\" aria-label=\"Permalink: 3. Run the container\" href=\"#3-run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNext, you should run the container and bind the project folder (with the single .pro\nfile) inside. You can choose to build, run, or build and run. Note that if you\nchoose to build and run, it will only run after build given a successful build\n(without errors). Here is how to ask for help, given a container called \u003ccode\u003ecs106b-builder\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run cs106b-builder --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo build the current folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/Tiles:/code/Project cs106b-builder build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo then run!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/Tiles:/code/Project cs106b-builder run\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr build and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/Tiles:/code/Project cs106b-builder build run\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-pycbc_bench\" class=\"anchor\" aria-hidden=\"true\" href=\"#pycbc_bench\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epycbc_bench\u003c/h1\u003e\n\u003cp\u003eSome benchmark singularity images for pycbc / pycbc inference\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild singularity image\u003c/h1\u003e\n\u003cp\u003esudo singularity build pycbcb.img Singularity\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-pycbc-inspiral\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-pycbc-inspiral\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erun pycbc inspiral\u003c/h1\u003e\n\u003cp\u003esingularity run --cleanenv --app inspiral pycbcb.img\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, - "topics": [ - "singularity", - "container", - "containers", - "reproducible", - "qt-creator" - ], - "updated_at": 1545323608.0 + "subscribers_count": 8, + "topics": [], + "updated_at": 1559569839.0 }, { "data_format": 2, - "description": "Umbrella repository for managing and deploying neuroimaging pipelines", + "description": "Singularity recipe files for GROMACS (http://www.gromacs.org/)", "filenames": [ - "containers/nklab-neuro-utils/Singularity" + "Singularity.2019.4", + "Singularity.2018.2", + "Singularity.2019.3", + "Singularity.2019.2", + "Singularity.2018.1", + "Singularity.2018.5", + "Singularity.2018", + "Singularity.2020.1", + "Singularity.2020.2", + "Singularity.2018.3", + "Singularity.2019.1", + "Singularity.2019.6", + "Singularity.2020", + "Singularity.2019.5", + "Singularity.2018.4", + "Singularity.2019" ], - "full_name": "MRIresearch/NeuroPipelines", + "full_name": "powerPlant/gromacs-srf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNeuroPipelines\u003c/h1\u003e\u003ca id=\"user-content-neuropipelines\" class=\"anchor\" aria-label=\"Permalink: NeuroPipelines\" href=\"#neuropipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUmbrella repository for managing and deploying neuroimaging pipelines and containers\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003econtainers\u003c/h2\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-fsl/README.md\"\u003enklab-fsl\u003c/a\u003e This Singularity container provides FSL v6.0.1 (FSLeyes, BASIL). It is Cuda compatible and so should be able to run eddy_cuda8.0 or eddy_cuda9.1. It also includes FSL v5.0.6 for reproducing HCP pipelines.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-mrtrix/README.md\"\u003enklab-mrtrix\u003c/a\u003e This Singularity container provides MRTrix 3.0 RC3\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-freesurfer/README.md\"\u003enklab-freesurfer\u003c/a\u003e This Singularity container provides 3 versions of freesurfer - the stable v6.0.0 version, the current development version (this will vary depending on the build date) and the HCP version 5.3.0 used in HCP pipelines.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-fsltrixsurf/README.md\"\u003enklab-fsltrixsurf\u003c/a\u003e This Singularity container is an amalgamation of the three containers (nklab-fsl, nklab-mrtrix and nklab-freesurfer)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-neuro-tools/README.md\"\u003enklab-neuro-tools\u003c/a\u003e This Singularity container provides a comprehensive package of neuroimaging tools like FSL, MRtrix, AFNI, The HCP Pipelines, CIFTIFY, ANTS in one container.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-simnibs/README.md\"\u003enklab-simnibs\u003c/a\u003e A Singularity Container for SIMNIBS 2.1 for the Simulation of electric fields induced by TMS and tDCS\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-neuro-utils/README.md\"\u003enklab-neuro-utils\u003c/a\u003e A Singularity/Docker container for converting MRI files into BIDS format\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData utilities\u003c/h2\u003e\u003ca id=\"user-content-data-utilities\" class=\"anchor\" aria-label=\"Permalink: Data utilities\" href=\"#data-utilities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"dicomutils/README.md\"\u003edicomutils\u003c/a\u003e working python code to transfer/route dicoms to XNAT.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2264\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the GROMACS molecular dynamics package\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1633546054.0 + "updated_at": 1674680771.0 }, { "data_format": 2, - "description": null, + "description": "Collection of bioinformatic pipelines written in nextflow", "filenames": [ - "Singularity", - "Singularity.genome" + "containers/Singularity", + "containers/Singularity.RGI", + "containers/Singularity.qiime2", + "containers/Singularity.cfsansnp" ], - "full_name": "guoqi123/oncodriveCLUST", + "full_name": "EnriqueDoster/bioinformatic-nextflow-pipelines", "latest_release": null, + "readme": "\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe goal of many metagenomics studies is to characterize the content and relative abundance of sequences of interest from the DNA of a given sample or set of samples. You may want to know what is contained within your sample or how abundant a given sequence is relative to another.\u003c/p\u003e\n\u003cp\u003eOften, metagenomics is performed when the answer to these questions must be obtained for a large number of targets where techniques like multiplex PCR and other targeted methods would be too cumbersome to perform. AmrPlusPlus can process the raw data from the sequencer, identify the fragments of DNA, and count them. It also provides a count of the polymorphisms that occur in each DNA fragment with respect to the reference database.\u003c/p\u003e\n\u003cp\u003eAdditionally, you may want to know if the depth of your sequencing (how many reads you obtain that are on target) is high enough to identify rare organisms (organisms with low abundance relative to others) in your population. This is referred to as rarefaction and is calculated by randomly subsampling your sequence data at intervals between 0% and 100% in order to determine how many targets are found at each depth. AmrPlusPlus can perform this analysis as well.\u003c/p\u003e\n\u003cp\u003eWith AmrPlusPlus, you will obtain count files for each sample that can be combined into a count matrix and analyzed using any statistical and mathematical techniques that can operate on a matrix of observations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description-of-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#description-of-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription of scripts\u003c/h2\u003e\n\u003cp\u003emain_qiime2.nf\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main_qiime2.nf --reads \"/s/angus/index/projs/mega_tylan/concat_16S_LN/raw_data/*_{1,2}.fq\" --output XIT_LN_qiime2 -profile local --metadata /media/AngusWorkspace/run_Jake/LN_metadata.tsv --classifier /media/AngusWorkspace/run_Jake/bioinformatic-nextflow-pipelines/gg-13-8-99-515-806-nb-classifier.qza -resume --threads 25\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1590852633.0 + "updated_at": 1645870548.0 }, { "data_format": 2, - "description": null, + "description": "Python repository of code for preprocessing and extracting metrics of the volume fraction and size of the gamma double prime and gamma prime in a nickel-based superalloy microstructure", "filenames": [ - "Singularity.OP10.6.recipe" + "Singularity" ], - "full_name": "eamiddlebrook/OrthoPhyl", - "latest_release": "v1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOrthoPhyl\u003c/h1\u003e\u003ca id=\"user-content-orthophyl\" class=\"anchor\" aria-label=\"Permalink: OrthoPhyl\" href=\"#orthophyl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e(previously OrthoPhylo...OrthoPhyl sounds better)\u003c/h2\u003e\u003ca id=\"user-content-previously-orthophyloorthophyl-sounds-better\" class=\"anchor\" aria-label=\"Permalink: (previously OrthoPhylo...OrthoPhyl sounds better)\" href=\"#previously-orthophyloorthophyl-sounds-better\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFor the version used in \u003cem\u003eOrthoPhyl \u2013 Streamlining large scale, orthology-based phylogenomic studies of bacteria at broad evolutionary scales\u003c/em\u003e see the OrthoPhyl_1.0 branch.\u003c/h2\u003e\u003ca id=\"user-content-for-the-version-used-in-orthophyl--streamlining-large-scale-orthology-based-phylogenomic-studies-of-bacteria-at-broad-evolutionary-scales-see-the-orthophyl_10-branch\" class=\"anchor\" aria-label=\"Permalink: For the version used in OrthoPhyl \u2013 Streamlining large scale, orthology-based phylogenomic studies of bacteria at broad evolutionary scales see the OrthoPhyl_1.0 branch.\" href=\"#for-the-version-used-in-orthophyl--streamlining-large-scale-orthology-based-phylogenomic-studies-of-bacteria-at-broad-evolutionary-scales-see-the-orthophyl_10-branch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeveloped at Los Alamos National Labs (LANL - C22064)\u003c/h3\u003e\u003ca id=\"user-content-developed-at-los-alamos-national-labs-lanl---c22064\" class=\"anchor\" aria-label=\"Permalink: Developed at Los Alamos National Labs (LANL - C22064)\" href=\"#developed-at-los-alamos-national-labs-lanl---c22064\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWritten by Earl Middlbrook with input from Robab Katani at Penn State and Jeanne Fair at LANL.\u003c/h4\u003e\u003ca id=\"user-content-written-by-earl-middlbrook-with-input-from-robab-katani-at-penn-state-and-jeanne-fair-at-lanl\" class=\"anchor\" aria-label=\"Permalink: Written by Earl Middlbrook with input from Robab Katani at Penn State and Jeanne Fair at LANL.\" href=\"#written-by-earl-middlbrook-with-input-from-robab-katani-at-penn-state-and-jeanne-fair-at-lanl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe software is available through a GLPv3 open source licence.\u003c/h4\u003e\u003ca id=\"user-content-the-software-is-available-through-a-glpv3-open-source-licence\" class=\"anchor\" aria-label=\"Permalink: The software is available through a GLPv3 open source licence.\" href=\"#the-software-is-available-through-a-glpv3-open-source-licence\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ch4\u003e\u003c/h4\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThis software is designed to generate phylogenetic trees from bacterial genome assemblies. While many methods use whole genome alignments to generate informative sites to base tress on, OrthoPhyl annotates bacterial genes, identifies orthologous sequences, aligns related proteins to inform transcript alignments, then builds species trees with two methods. The first is a conventional gene concatenation and ML tree estemation method. The second attempts to reconcile gene trees with a unified species tree using quartets (ASTRAL). Both methods allow filtering of gene lists on number of species represented, length, and gappiness in order to tune signal-to-noise ratio for tree estimation.\u003c/h4\u003e\u003ca id=\"user-content-this-software-is-designed-to-generate-phylogenetic-trees-from-bacterial-genome-assemblies-while-many-methods-use-whole-genome-alignments-to-generate-informative-sites-to-base-tress-on-orthophyl-annotates-bacterial-genes-identifies-orthologous-sequences-aligns-related-proteins-to-inform-transcript-alignments-then-builds-species-trees-with-two-methods-the-first-is-a-conventional-gene-concatenation-and-ml-tree-estemation-method-the-second-attempts-to-reconcile-gene-trees-with-a-unified-species-tree-using-quartets-astral-both-methods-allow-filtering-of-gene-lists-on-number-of-species-represented-length-and-gappiness-in-order-to-tune-signal-to-noise-ratio-for-tree-estimation\" class=\"anchor\" aria-label=\"Permalink: This software is designed to generate phylogenetic trees from bacterial genome assemblies. While many methods use whole genome alignments to generate informative sites to base tress on, OrthoPhyl annotates bacterial genes, identifies orthologous sequences, aligns related proteins to inform transcript alignments, then builds species trees with two methods. The first is a conventional gene concatenation and ML tree estemation method. The second attempts to reconcile gene trees with a unified species tree using quartets (ASTRAL). Both methods allow filtering of gene lists on number of species represented, length, and gappiness in order to tune signal-to-noise ratio for tree estimation.\" href=\"#this-software-is-designed-to-generate-phylogenetic-trees-from-bacterial-genome-assemblies-while-many-methods-use-whole-genome-alignments-to-generate-informative-sites-to-base-tress-on-orthophyl-annotates-bacterial-genes-identifies-orthologous-sequences-aligns-related-proteins-to-inform-transcript-alignments-then-builds-species-trees-with-two-methods-the-first-is-a-conventional-gene-concatenation-and-ml-tree-estemation-method-the-second-attempts-to-reconcile-gene-trees-with-a-unified-species-tree-using-quartets-astral-both-methods-allow-filtering-of-gene-lists-on-number-of-species-represented-length-and-gappiness-in-order-to-tune-signal-to-noise-ratio-for-tree-estimation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe main advantages of this software pipeline are three fold: 1) It extends the evolutionary distance input species can represent (over whole genome alignment and k-mer methods) while maintaining phylogenetic resolution, 2) this software is designed to be very user friendly, requiring just a single to estimate trees from a directory of assemblies. Additionally a Singularity image is now available to avoid dependancy hell and 3) this pipleline is amenable to estimating trees for 1000s of bacterial genome assemblies. To handle large numbers of genomes, the pipline calculates a diversity-representing subset of genomes to run orthofinder on, then expands the found OrthoGroups to all assemblies with iterative HMM searches.\u003c/h4\u003e\u003ca id=\"user-content-the-main-advantages-of-this-software-pipeline-are-three-fold-1-it-extends-the-evolutionary-distance-input-species-can-represent-over-whole-genome-alignment-and-k-mer-methods-while-maintaining-phylogenetic-resolution-2-this-software-is-designed-to-be-very-user-friendly-requiring-just-a-single-to-estimate-trees-from-a-directory-of-assemblies-additionally-a-singularity-image-is-now-available-to-avoid-dependancy-hell-and-3-this-pipleline-is-amenable-to-estimating-trees-for-1000s-of-bacterial-genome-assemblies-to-handle-large-numbers-of-genomes-the-pipline-calculates-a-diversity-representing-subset-of-genomes-to-run-orthofinder-on-then-expands-the-found-orthogroups-to-all-assemblies-with-iterative-hmm-searches\" class=\"anchor\" aria-label=\"Permalink: The main advantages of this software pipeline are three fold: 1) It extends the evolutionary distance input species can represent (over whole genome alignment and k-mer methods) while maintaining phylogenetic resolution, 2) this software is designed to be very user friendly, requiring just a single to estimate trees from a directory of assemblies. Additionally a Singularity image is now available to avoid dependancy hell and 3) this pipleline is amenable to estimating trees for 1000s of bacterial genome assemblies. To handle large numbers of genomes, the pipline calculates a diversity-representing subset of genomes to run orthofinder on, then expands the found OrthoGroups to all assemblies with iterative HMM searches.\" href=\"#the-main-advantages-of-this-software-pipeline-are-three-fold-1-it-extends-the-evolutionary-distance-input-species-can-represent-over-whole-genome-alignment-and-k-mer-methods-while-maintaining-phylogenetic-resolution-2-this-software-is-designed-to-be-very-user-friendly-requiring-just-a-single-to-estimate-trees-from-a-directory-of-assemblies-additionally-a-singularity-image-is-now-available-to-avoid-dependancy-hell-and-3-this-pipleline-is-amenable-to-estimating-trees-for-1000s-of-bacterial-genome-assemblies-to-handle-large-numbers-of-genomes-the-pipline-calculates-a-diversity-representing-subset-of-genomes-to-run-orthofinder-on-then-expands-the-found-orthogroups-to-all-assemblies-with-iterative-hmm-searches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGetting Started\u003c/h1\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompatability and Dependencies\u003c/h3\u003e\u003ca id=\"user-content-compatability-and-dependencies\" class=\"anchor\" aria-label=\"Permalink: Compatability and Dependencies\" href=\"#compatability-and-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThis workflow has been tested on a CentOS8 machine, but should be pretty portable. Testing is starting on diverse *nix systems. There will likely be some errors due to the wrapper having moderate bash complexity. Unfortunately fastANI seems to be incompatable with MacOS, so I will need to find an alternative before the pipeline can be run on that architecture.\u003c/h4\u003e\u003ca id=\"user-content-this-workflow-has-been-tested-on-a-centos8-machine-but-should-be-pretty-portable-testing-is-starting-on-diverse-nix-systems-there-will-likely-be-some-errors-due-to-the-wrapper-having-moderate-bash-complexity-unfortunately-fastani-seems-to-be-incompatable-with-macos-so-i-will-need-to-find-an-alternative-before-the-pipeline-can-be-run-on-that-architecture\" class=\"anchor\" aria-label=\"Permalink: This workflow has been tested on a CentOS8 machine, but should be pretty portable. Testing is starting on diverse *nix systems. There will likely be some errors due to the wrapper having moderate bash complexity. Unfortunately fastANI seems to be incompatable with MacOS, so I will need to find an alternative before the pipeline can be run on that architecture.\" href=\"#this-workflow-has-been-tested-on-a-centos8-machine-but-should-be-pretty-portable-testing-is-starting-on-diverse-nix-systems-there-will-likely-be-some-errors-due-to-the-wrapper-having-moderate-bash-complexity-unfortunately-fastani-seems-to-be-incompatable-with-macos-so-i-will-need-to-find-an-alternative-before-the-pipeline-can-be-run-on-that-architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf you have Singularity you can run a prebuilt container. Grab the container with (requires ~1.5gb space at the moment):\u003c/h4\u003e\u003ca id=\"user-content-if-you-have-singularity-you-can-run-a-prebuilt-container-grab-the-container-with-requires-15gb-space-at-the-moment\" class=\"anchor\" aria-label=\"Permalink: If you have Singularity you can run a prebuilt container. Grab the container with (requires ~1.5gb space at the moment):\" href=\"#if-you-have-singularity-you-can-run-a-prebuilt-container-grab-the-container-with-requires-15gb-space-at-the-moment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity_images=~/singularity_images/\nmkdir ${singularity_images}\ncd ${singularity_images}\nsingularity pull library://earlyevol/default/orthophyl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCongradulations! You can skip down to testing the install!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOR install OrthoPhyl and these dependencies with the instructions bellow.\u003c/h4\u003e\u003ca id=\"user-content-or-install-orthophyl-and-these-dependencies-with-the-instructions-bellow\" class=\"anchor\" aria-label=\"Permalink: OR install OrthoPhyl and these dependencies with the instructions bellow.\" href=\"#or-install-orthophyl-and-these-dependencies-with-the-instructions-bellow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eprodigal\u003c/li\u003e\n\u003cli\u003eorthofinder\u003c/li\u003e\n\u003cli\u003ebbmap\u003c/li\u003e\n\u003cli\u003efastTree*\u003c/li\u003e\n\u003cli\u003ehmmer\u003c/li\u003e\n\u003cli\u003epal2nal\u003c/li\u003e\n\u003cli\u003eprodigal\u003c/li\u003e\n\u003cli\u003eete3\u003c/li\u003e\n\u003cli\u003eIQTree*\u003c/li\u003e\n\u003cli\u003eraxml*\u003c/li\u003e\n\u003cli\u003etrimal\u003c/li\u003e\n\u003cli\u003eparallel\u003c/li\u003e\n\u003cli\u003ecatfasta2phyml\u003c/li\u003e\n\u003cli\u003eASTRAL\u003c/li\u003e\n\u003cli\u003efastANI\u003c/li\u003e\n\u003cli\u003eR\u003c/li\u003e\n\u003cli\u003eAlignment_Assessment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e*depending on tree method used\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClone the Orthophylo repo\u003c/h3\u003e\u003ca id=\"user-content-clone-the-orthophylo-repo\" class=\"anchor\" aria-label=\"Permalink: Clone the Orthophylo repo\" href=\"#clone-the-orthophylo-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCloning takes a minute because of the large test files\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePath_to_gits=~/gits\nmkdir ${Path_to_gits}\ncd ${Path_to_gits}/\ngit clone https://github.com/eamiddlebrook/OrthoPhyl.git\ncd OrthoPhyl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConda/mamba installable dependencies\u003c/h3\u003e\u003ca id=\"user-content-condamamba-installable-dependencies\" class=\"anchor\" aria-label=\"Permalink: Conda/mamba installable dependencies\" href=\"#condamamba-installable-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf you need to install conda or mamba we recomend mamba, however the commands are exactly the same (exept running the install)\u003c/h4\u003e\u003ca id=\"user-content-if-you-need-to-install-conda-or-mamba-we-recomend-mamba-however-the-commands-are-exactly-the-same-exept-running-the-install\" class=\"anchor\" aria-label=\"Permalink: If you need to install conda or mamba we recomend mamba, however the commands are exactly the same (exept running the install)\" href=\"#if-you-need-to-install-conda-or-mamba-we-recomend-mamba-however-the-commands-are-exactly-the-same-exept-running-the-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/Downloads/ # or wherever you want to put the installer\nwget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh\nbash Mambaforge-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOR\u003c/h4\u003e\u003ca id=\"user-content-or\" class=\"anchor\" aria-label=\"Permalink: OR\" href=\"#or\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/Downloads/ # or wherever you want to put the installer\nwget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFollow on screen instructions\u003c/h4\u003e\u003ca id=\"user-content-follow-on-screen-instructions\" class=\"anchor\" aria-label=\"Permalink: Follow on screen instructions\" href=\"#follow-on-screen-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSet up auto initialize. If you don\u0027t, it is up to you to figure out how to make the script happy with your decision, you can also run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emamba init bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn some Linux distros, using \u003ccode\u003e~/.bashrc\u003c/code\u003e for only interactive shells is enforced (Linux Mint). In this case, mamba will not be initialized correctly within OrthoPhyl. To fix, just copy the conda/mamba initialization block from the bottom of your \u003ccode\u003e~/.bashrc\u003c/code\u003e file (below, replace \"...\" with what is in your file), and place them at the bottom of \u003ccode\u003e~/.bash_profile\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# \u0026gt;\u0026gt;\u0026gt; conda initialize \u0026gt;\u0026gt;\u0026gt;\n...\n# \u0026lt;\u0026lt;\u0026lt; conda initialize \u0026lt;\u0026lt;\u0026lt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate conda environment and install dependencies\u003c/h3\u003e\u003ca id=\"user-content-create-conda-environment-and-install-dependencies\" class=\"anchor\" aria-label=\"Permalink: Create conda environment and install dependencies\" href=\"#create-conda-environment-and-install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIt is highly recomended that you create an separate environment for this install. Might take some time....\u003c/h4\u003e\u003ca id=\"user-content-it-is-highly-recomended-that-you-create-an-separate-environment-for-this-install-might-take-some-time\" class=\"anchor\" aria-label=\"Permalink: It is highly recomended that you create an separate environment for this install. Might take some time....\" href=\"#it-is-highly-recomended-that-you-create-an-separate-environment-for-this-install-might-take-some-time\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emamba create -n orthophyl --file $Path_to_gits/OrthoPhyl/orthophyl_env.XXX.txt -c bioconda -c conda-forge\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOR you can install different versions if neccessary\u003c/h4\u003e\u003ca id=\"user-content-or-you-can-install-different-versions-if-neccessary\" class=\"anchor\" aria-label=\"Permalink: OR you can install different versions if neccessary\" href=\"#or-you-can-install-different-versions-if-neccessary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emamba create -n orthophyl -c bioconda -c conda-forge \\\ngit prodigal=2.6.3 orthofinder=2.5.4 \\\nbbmap=39.01 fasttree=2.1.11 hmmer=3.3.2 pal2nal=14.1 \\\nete3 raxml=8.2.12 trimal=1.4.1 \\\nparallel=20160622 r-essentials=4.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSometimes R (r-essentials) can be a pain. If it is causing problems with the conda/mamba install, remove it and install manually:\u003c/h4\u003e\u003ca id=\"user-content-sometimes-r-r-essentials-can-be-a-pain-if-it-is-causing-problems-with-the-condamamba-install-remove-it-and-install-manually\" class=\"anchor\" aria-label=\"Permalink: Sometimes R (r-essentials) can be a pain. If it is causing problems with the conda/mamba install, remove it and install manually:\" href=\"#sometimes-r-r-essentials-can-be-a-pain-if-it-is-causing-problems-with-the-condamamba-install-remove-it-and-install-manually\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGo to \u003ca href=\"https://cran.r-project.org/mirrors.html\" rel=\"nofollow\"\u003ehttps://cran.r-project.org/mirrors.html\u003c/a\u003e and pick an appropriate mirror. Then chose the linux distro you are using, download package files, and follow install instructions.\u003c/h4\u003e\u003ca id=\"user-content-go-to-httpscranr-projectorgmirrorshtml-and-pick-an-appropriate-mirror-then-chose-the-linux-distro-you-are-using-download-package-files-and-follow-install-instructions\" class=\"anchor\" aria-label=\"Permalink: Go to https://cran.r-project.org/mirrors.html and pick an appropriate mirror. Then chose the linux distro you are using, download package files, and follow install instructions.\" href=\"#go-to-httpscranr-projectorgmirrorshtml-and-pick-an-appropriate-mirror-then-chose-the-linux-distro-you-are-using-download-package-files-and-follow-install-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTo allow conda changes to take effect:\u003c/h4\u003e\u003ca id=\"user-content-to-allow-conda-changes-to-take-effect\" class=\"anchor\" aria-label=\"Permalink: To allow conda changes to take effect:\" href=\"#to-allow-conda-changes-to-take-effect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esource ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, close and reopen terminal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTo remove parallel\u0027s citation reminder (BUT DONT FORGET TO CITE!) run:\u003c/h4\u003e\u003ca id=\"user-content-to-remove-parallels-citation-reminder-but-dont-forget-to-cite-run\" class=\"anchor\" aria-label=\"Permalink: To remove parallel\u0027s citation reminder (BUT DONT FORGET TO CITE!) run:\" href=\"#to-remove-parallels-citation-reminder-but-dont-forget-to-cite-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emamba activate orthophylo\nparallel --citation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTest R install\u003c/h4\u003e\u003ca id=\"user-content-test-r-install\" class=\"anchor\" aria-label=\"Permalink: Test R install\" href=\"#test-r-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eRscript --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOther dependencies\u003c/h3\u003e\u003ca id=\"user-content-other-dependencies\" class=\"anchor\" aria-label=\"Permalink: Other dependencies\" href=\"#other-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis reflects how I like to organize my machine, pick what works for you. The control_file.paths reflects this setup. If you choose to install the below packages in different locations, just change control_file.paths to reflect this.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ${Path_to_gits}/\n# Install ASTRAL\ngit clone https://github.com/smirarab/ASTRAL.git\ncd ASTRAL\nunzip Astral.5.7.8.zip #change to curren version if needed\n\ncd ${Path_to_gits}/\ngit clone https://github.com/nylander/catfasta2phyml.git\ngit clone https://github.com/dportik/Alignment_Assessment.git\ncd Alignment_Assessment/\n# convert script to python3\n2to3 -w Alignment_Assessment_v2.py \n\n# install fastANI\ncd ~/Downloads\nwget https://github.com/ParBLiSS/FastANI/releases/download/v1.33/fastANI-Linux64-v1.33.zip\nunzip fastANI-Linux64-v1.33.zip\n# For macOS !!!!! NOT SOLVED !!!!!!\n# git clone https://github.com/ParBLiSS/FastANI.git\n# and follow install instructions. This has been a huge pain on my machine...not solved\nmkdir ~/apps/\nmv fastANI ~/apps/ # or anywhere else you would like to put it. Change control_file.required to reflect path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEdit \u003ccode\u003e${Path_to_gits}/OrthoPhyl/control_file.paths\u003c/code\u003e to reflect system specific locations and conda environment name if binaries and conda env are in different locations than discribed above\u003c/h3\u003e\u003ca id=\"user-content-edit-path_to_gitsorthophylcontrol_filepaths-to-reflect-system-specific-locations-and-conda-environment-name-if-binaries-and-conda-env-are-in-different-locations-than-discribed-above\" class=\"anchor\" aria-label=\"Permalink: Edit ${Path_to_gits}/OrthoPhyl/control_file.paths to reflect system specific locations and conda environment name if binaries and conda env are in different locations than discribed above\" href=\"#edit-path_to_gitsorthophylcontrol_filepaths-to-reflect-system-specific-locations-and-conda-environment-name-if-binaries-and-conda-env-are-in-different-locations-than-discribed-above\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest install\u003c/h2\u003e\u003ca id=\"user-content-test-install\" class=\"anchor\" aria-label=\"Permalink: Test install\" href=\"#test-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest Singularity container\u003c/h3\u003e\u003ca id=\"user-content-test-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Test Singularity container\" href=\"#test-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must specify an output directory (-s) if running through singularity, because the script will try to write directly to the container (wont work)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run ${singularity_images}/OrthoPhyl.0.9.3.sif -T TESTER_chloroplast -s ./tester_chloroplast_output -t 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest Manual install\u003c/h3\u003e\u003ca id=\"user-content-test-manual-install\" class=\"anchor\" aria-label=\"Permalink: Test Manual install\" href=\"#test-manual-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTo test \u0027conda activate\u0027 within OrthoPhyl, make sure the conda environment is not activated\u003c/h4\u003e\u003ca id=\"user-content-to-test-conda-activate-within-orthophyl-make-sure-the-conda-environment-is-not-activated\" class=\"anchor\" aria-label=\"Permalink: To test \u0027conda activate\u0027 within OrthoPhyl, make sure the conda environment is not activated\" href=\"#to-test-conda-activate-within-orthophyl-make-sure-the-conda-environment-is-not-activated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emamba deactivate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun Chloroplast test locally\u003c/h3\u003e\u003ca id=\"user-content-run-chloroplast-test-locally\" class=\"anchor\" aria-label=\"Permalink: run Chloroplast test locally\" href=\"#run-chloroplast-test-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTested on RHEL 8.5 machine with Intel Core i7-8700 CPU and 16gb ram (~8 minute runtime using 3 cores)\u003c/h4\u003e\u003ca id=\"user-content-tested-on-rhel-85-machine-with-intel-core-i7-8700-cpu-and-16gb-ram-8-minute-runtime-using-3-cores\" class=\"anchor\" aria-label=\"Permalink: Tested on RHEL 8.5 machine with Intel Core i7-8700 CPU and 16gb ram (~8 minute runtime using 3 cores)\" href=\"#tested-on-rhel-85-machine-with-intel-core-i7-8700-cpu-and-16gb-ram-8-minute-runtime-using-3-cores\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebash ${Path_to_gits}/OrthoPhyl/OrthoPhyl.sh -T TESTER_chloroplast -t 3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere should be a directory created in OrthoPhyl/TESTER/Workflow_test.chloroplast$(date +%m-%d-%Y)\nIf the test was successful, there should be 4 species trees found in the FINAL_TREES\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun bigger test locally\u003c/h3\u003e\u003ca id=\"user-content-run-bigger-test-locally\" class=\"anchor\" aria-label=\"Permalink: run bigger test locally\" href=\"#run-bigger-test-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ethis script takes about 20 hr to complete with 20 cores\u003c/h4\u003e\u003ca id=\"user-content-this-script-takes-about-20-hr-to-complete-with-20-cores\" class=\"anchor\" aria-label=\"Permalink: this script takes about 20 hr to complete with 20 cores\" href=\"#this-script-takes-about-20-hr-to-complete-with-20-cores\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMost of this is ML tree building\u003c/h4\u003e\u003ca id=\"user-content-most-of-this-is-ml-tree-building\" class=\"anchor\" aria-label=\"Permalink: Most of this is ML tree building\" href=\"#most-of-this-is-ml-tree-building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ewill make an artificial set of truncated genomes later\u003c/h4\u003e\u003ca id=\"user-content-will-make-an-artificial-set-of-truncated-genomes-later\" class=\"anchor\" aria-label=\"Permalink: will make an artificial set of truncated genomes later\" href=\"#will-make-an-artificial-set-of-truncated-genomes-later\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebash OrthoPhyl.sh -T TESTER -t 3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf the test was successful, there should be a file at \"OrthoPhyl/TESTER.pass\"\nand \"###### It looks like the install was successful ######\" should be sent to stdout towards the end of the run\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning OrthoPhyl on your data\u003c/h2\u003e\u003ca id=\"user-content-running-orthophyl-on-your-data\" class=\"anchor\" aria-label=\"Permalink: Running OrthoPhyl on your data\" href=\"#running-orthophyl-on-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eUSAGE: OrthoPhyl.sh -g Path_to_directory_of_assemblies -s directory_to_store_output\n# ALL arguments are optional if set with \\\"-c control_file.your_args\\\"\n# Many default parameters are set in control_file.defaults\n# I will work to expose the more useful ones in later versions of OP\nRequired:\n-g path to genomes directiory\nor\n-a paths to protien and transcript directories.\n They should be delared as \\\"-a path_to_transcript_dir,path_to_prot_dir\\\"\n-s path to the main directory for output\nOptional:\n-t threads to use [4]\n-p phylogenetic tree software to use astral, fasttree, raxml, and/or iqtree [\\\"fasttree iqtree astral\\\"]\n\ti.e. -p \\\"fasttree iqtree astral\\\"\n-o \"omics\" data to use for tree building ([CDS], PROT, BOTH)\n\tfor divergent sequences, it is good to compare protein trees to \n\tnucleotide trees to identify artifacts of saturation (long branch attraction)\n-c\tpath to a control file with required variables and any optional ones to override defaults.\n\tWill override values set on command line! [NULL]\n-x trimal paramerter string (in double \\\"quotes\\\")\n-r flag to rerun orthofinder on the ANI_shorlist (true/[false])\n-n Max number of proteomes to run through OrthoFinder.\n\tIf more than this many assemblies are provided, a subset of proteomes (based on genomes/transcripts ANI) will be chosen for OrthoFinder to chew on [20]\n-m Minimum fraction of total taxa per orthogroup to consider it for the relaxed SCO dataset.\n Expects a float from 0-1\n A value of 0 or 1 will lead to only estimating trees for the SCO_stict dataset.\n [0.30]\n-d Force ANI subsetting to run on transcript or genome Datasets. ([genome],transcript)\n\tUsing \\\"-a\\\" implies \\\"-d transcript\\\".\n\tIf \\\"-a\\\" is declared but you want to use the assemblies you have also provided, set \\\"-d genome\\\"\n\tIf \\\"-a\\\" not used but you want to use transcripts (annotated within OrthoPhyl by Prodigal) for ANI subsetting, set \\\"-d transcript\\\"\n-T run test dataset, incompatable with -g|s|a (TESTER,TESTER_chloroplast)\n-h display a description and a super useful usage message\n###############################################################\\n\nTo run test datasets:\n# Test of full bacterial genomes\nbash OrthoPhyl.sh -T TESTER -t #threads\n# Big test with ~100 orchid chloroplasts\nbash OrthoPhyl.sh -T TESTER_chloroplast -t #threads\n# reduced chloroplast dataset\nbash OrthoPhyl.sh -T TESTER_fasttest -t #threads\n# When running through Singularity an output directory is required:\nsingularity run \\${singularity_images}/OrthoPhyl.XXX.sif -T TESTER -s output_dir -t #threads\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample1:\u003c/h3\u003e\u003ca id=\"user-content-example1\" class=\"anchor\" aria-label=\"Permalink: Example1:\" href=\"#example1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun OrthoPhyl on assemblies in ~/Projects/ASMS/ecoli/ using 12 cores within singularity and place all results and intermediate files in ~/Projects/phylogenetics/ecoli/\u003c/h4\u003e\u003ca id=\"user-content-run-orthophyl-on-assemblies-in-projectsasmsecoli-using-12-cores-within-singularity-and-place-all-results-and-intermediate-files-in-projectsphylogeneticsecoli\" class=\"anchor\" aria-label=\"Permalink: Run OrthoPhyl on assemblies in ~/Projects/ASMS/ecoli/ using 12 cores within singularity and place all results and intermediate files in ~/Projects/phylogenetics/ecoli/\" href=\"#run-orthophyl-on-assemblies-in-projectsasmsecoli-using-12-cores-within-singularity-and-place-all-results-and-intermediate-files-in-projectsphylogeneticsecoli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run ${singularity_images}/OrthoPhyl.X.X.X.sif -g ~/Projects/ASMS/ecoli/ -s ~/Projects/phylogenetics/ecoli/ -t 12\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample2:\u003c/h3\u003e\u003ca id=\"user-content-example2\" class=\"anchor\" aria-label=\"Permalink: Example2:\" href=\"#example2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the same OrthoPhyl command from the manual install\u003c/h4\u003e\u003ca id=\"user-content-run-the-same-orthophyl-command-from-the-manual-install\" class=\"anchor\" aria-label=\"Permalink: Run the same OrthoPhyl command from the manual install\" href=\"#run-the-same-orthophyl-command-from-the-manual-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./OrthoPhyl.sh -g ~/Projects/ASMS/ecoli/ -s ~/Projects/phylogenetics/ecoli/ -t 12\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample3:\u003c/h3\u003e\u003ca id=\"user-content-example3\" class=\"anchor\" aria-label=\"Permalink: Example3:\" href=\"#example3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the same OrthoPhyl command from the manual install, also incorporate preannoated samples with protein seqs in \u003ccode\u003e~/Projects/annots/protiens/\u003c/code\u003e and transripts in \u003ccode\u003e~/Projects/annots/transcripts/\u003c/code\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-run-the-same-orthophyl-command-from-the-manual-install-also-incorporate-preannoated-samples-with-protein-seqs-in-projectsannotsprotiens-and-transripts-in-projectsannotstranscripts\" class=\"anchor\" aria-label=\"Permalink: Run the same OrthoPhyl command from the manual install, also incorporate preannoated samples with protein seqs in ~/Projects/annots/protiens/ and transripts in ~/Projects/annots/transcripts/\" href=\"#run-the-same-orthophyl-command-from-the-manual-install-also-incorporate-preannoated-samples-with-protein-seqs-in-projectsannotsprotiens-and-transripts-in-projectsannotstranscripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./OrthoPhyl.sh -g ~/Projects/ASMS/ecoli/ -a ~/Projects/annots/protiens/,~/Projects/annots/transcripts/ -s ~/Projects/phylogenetics/ecoli/ -t 12 \n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample4:\u003c/h3\u003e\u003ca id=\"user-content-example4\" class=\"anchor\" aria-label=\"Permalink: Example4:\" href=\"#example4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun OrthoPhyl to build a species tree with\u003c/h4\u003e\u003ca id=\"user-content-run-orthophyl-to-build-a-species-tree-with\" class=\"anchor\" aria-label=\"Permalink: Run OrthoPhyl to build a species tree with\" href=\"#run-orthophyl-to-build-a-species-tree-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eASTRAL (-p astral)\u003c/li\u003e\n\u003cli\u003efrom CDS (-o CDS)\u003c/li\u003e\n\u003cli\u003ecodon alignment gene trees build by iqtree used by ASTRAL (control_file.user contains just the line \"export gene_tree_methods=(\"iqtree\")\")\u003c/li\u003e\n\u003cli\u003eusing only single copy orthologs found in 1/1 assemblies (-m 1)\u003c/li\u003e\n\u003cli\u003eusing only 15 asssemblies to generate HMMs with OrthoFinder (-n 15)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e./OrthoPhyl.sh -g TESTER/genomes_chloroplast/ -s TESTER/TESTER_custom.chloro.B -m 1 -n 15 -c control_file.user -p astral -o CDS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample5:\u003c/h3\u003e\u003ca id=\"user-content-example5\" class=\"anchor\" aria-label=\"Permalink: Example5:\" href=\"#example5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun OrthoPhyl to build a species tree with\u003c/h4\u003e\u003ca id=\"user-content-run-orthophyl-to-build-a-species-tree-with-1\" class=\"anchor\" aria-label=\"Permalink: Run OrthoPhyl to build a species tree with\" href=\"#run-orthophyl-to-build-a-species-tree-with-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eiqtree and ASTRAL (-p \"iqtree astral\")\u003c/li\u003e\n\u003cli\u003efrom protein alignments (-o PROT)\u003c/li\u003e\n\u003cli\u003eprotein alignment gene trees build by fasttree used by ASTRAL (default)\u003c/li\u003e\n\u003cli\u003eusing only single copy orthologs found in .3/1 assemblies (-m .3)\u003c/li\u003e\n\u003cli\u003eusing 30 asssemblies to generate HMMs with OrthoFinder (-n 30)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e./OrthoPhyl.sh -g TESTER/genomes -s TESTER/TESTER_full_genomes -m .3 -n 30 -p \"iqtree astral\" -o PROT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMore Notes on OrthoPhyl:\u003c/h2\u003e\u003ca id=\"user-content-more-notes-on-orthophyl\" class=\"anchor\" aria-label=\"Permalink: More Notes on OrthoPhyl:\" href=\"#more-notes-on-orthophyl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThis software wraps many open source bioinformatic tools together with several custom programs. Genomes are annotated with Prodigal. If a large number of genomes are to be analyzed, fastANI is used to estimate the pairwise genomic Average Nucleotide Identity (ANI) and then an OrthoPhyl function subsets the genomes to a minimal number which represent the diversity of the full set. Protein sequences from this subset (or full set for small numbers of genomes) are used as input for OrthoFinder to identify orthologous gene families. For the subset method, a HMM search, HMMER, is used to generalize the resulting orthogroup to the full data set. From there, orthogroup proteins are realigned with MAFFT, and these aignments are used to \"codon\" align the transcript sequences (generated by earlier annotation). These orthogroup transcript alignments are then trimmed (with TRIMAL) and filtered for strict single copy orthologs (SCO_strict) or SCOs found in at least X% of the input genomes (with X being tunable). Next, transcript alignmants are concatenated to generate super-maticies and used as input for species tree generation with either RAxML or fastTREE. Aditionally, gene trees are generated from the individual transcript alignments, which are used in gene tree to species tree estimation with ASTRAL.\u003c/h4\u003e\u003ca id=\"user-content-this-software-wraps-many-open-source-bioinformatic-tools-together-with-several-custom-programs-genomes-are-annotated-with-prodigal--if-a-large-number-of-genomes-are-to-be-analyzed-fastani-is-used-to-estimate-the-pairwise-genomic-average-nucleotide-identity-ani-and-then-an-orthophyl-function-subsets-the-genomes-to-a-minimal-number-which-represent-the-diversity-of-the-full-set-protein-sequences-from-this-subset-or-full-set-for-small-numbers-of-genomes-are-used-as-input-for-orthofinder-to-identify-orthologous-gene-families-for-the-subset-method-a-hmm-search-hmmer-is-used-to-generalize-the-resulting-orthogroup-to-the-full-data-set-from-there-orthogroup-proteins-are-realigned-with-mafft-and-these-aignments-are-used-to-codon-align-the-transcript-sequences-generated-by-earlier-annotation-these-orthogroup-transcript-alignments-are-then-trimmed-with-trimal-and-filtered-for-strict-single-copy-orthologs-sco_strict-or-scos-found-in-at-least-x-of-the-input-genomes-with-x-being-tunable-next-transcript-alignmants-are-concatenated-to-generate-super-maticies-and-used-as-input-for-species-tree-generation-with-either-raxml-or-fasttree-aditionally-gene-trees-are-generated-from-the-individual-transcript-alignments-which-are-used-in-gene-tree-to-species-tree-estimation-with-astral\" class=\"anchor\" aria-label=\"Permalink: This software wraps many open source bioinformatic tools together with several custom programs. Genomes are annotated with Prodigal. If a large number of genomes are to be analyzed, fastANI is used to estimate the pairwise genomic Average Nucleotide Identity (ANI) and then an OrthoPhyl function subsets the genomes to a minimal number which represent the diversity of the full set. Protein sequences from this subset (or full set for small numbers of genomes) are used as input for OrthoFinder to identify orthologous gene families. For the subset method, a HMM search, HMMER, is used to generalize the resulting orthogroup to the full data set. From there, orthogroup proteins are realigned with MAFFT, and these aignments are used to \u0026quot;codon\u0026quot; align the transcript sequences (generated by earlier annotation). These orthogroup transcript alignments are then trimmed (with TRIMAL) and filtered for strict single copy orthologs (SCO_strict) or SCOs found in at least X% of the input genomes (with X being tunable). Next, transcript alignmants are concatenated to generate super-maticies and used as input for species tree generation with either RAxML or fastTREE. Aditionally, gene trees are generated from the individual transcript alignments, which are used in gene tree to species tree estimation with ASTRAL.\" href=\"#this-software-wraps-many-open-source-bioinformatic-tools-together-with-several-custom-programs-genomes-are-annotated-with-prodigal--if-a-large-number-of-genomes-are-to-be-analyzed-fastani-is-used-to-estimate-the-pairwise-genomic-average-nucleotide-identity-ani-and-then-an-orthophyl-function-subsets-the-genomes-to-a-minimal-number-which-represent-the-diversity-of-the-full-set-protein-sequences-from-this-subset-or-full-set-for-small-numbers-of-genomes-are-used-as-input-for-orthofinder-to-identify-orthologous-gene-families-for-the-subset-method-a-hmm-search-hmmer-is-used-to-generalize-the-resulting-orthogroup-to-the-full-data-set-from-there-orthogroup-proteins-are-realigned-with-mafft-and-these-aignments-are-used-to-codon-align-the-transcript-sequences-generated-by-earlier-annotation-these-orthogroup-transcript-alignments-are-then-trimmed-with-trimal-and-filtered-for-strict-single-copy-orthologs-sco_strict-or-scos-found-in-at-least-x-of-the-input-genomes-with-x-being-tunable-next-transcript-alignmants-are-concatenated-to-generate-super-maticies-and-used-as-input-for-species-tree-generation-with-either-raxml-or-fasttree-aditionally-gene-trees-are-generated-from-the-individual-transcript-alignments-which-are-used-in-gene-tree-to-species-tree-estimation-with-astral\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCleaning input genomes\u003c/h3\u003e\u003ca id=\"user-content-cleaning-input-genomes\" class=\"anchor\" aria-label=\"Permalink: Cleaning input genomes\" href=\"#cleaning-input-genomes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe script OrthoPhyl/utils/gather_filter_asms.XX.sh was writen to streamiline aquiring all available assemblies for a specific taxon. It takes NCBI\u0027s taxID as and input (BrucellaTaxID=234). After the genomes are DL\u0027d there are several simple genome filtering steps: length,N50,GC, etc. There are also a few more soficticated filtering methods: CheckM is used to assess completeness and contamination, while dedupe from BBmap is used to reduce duplicated contigs.\u003c/h4\u003e\u003ca id=\"user-content-the-script-orthophylutilsgather_filter_asmsxxsh-was-writen-to-streamiline-aquiring-all-available-assemblies-for-a-specific-taxon-it-takes-ncbis-taxid-as-and-input-brucellataxid234-after-the-genomes-are-dld-there-are-several-simple-genome-filtering-steps-lengthn50gc-etc-there-are-also-a-few-more-soficticated-filtering-methods-checkm-is-used-to-assess-completeness-and-contamination-while-dedupe-from-bbmap-is-used-to-reduce-duplicated-contigs\" class=\"anchor\" aria-label=\"Permalink: The script OrthoPhyl/utils/gather_filter_asms.XX.sh was writen to streamiline aquiring all available assemblies for a specific taxon. It takes NCBI\u0027s taxID as and input (BrucellaTaxID=234). After the genomes are DL\u0027d there are several simple genome filtering steps: length,N50,GC, etc. There are also a few more soficticated filtering methods: CheckM is used to assess completeness and contamination, while dedupe from BBmap is used to reduce duplicated contigs.\" href=\"#the-script-orthophylutilsgather_filter_asmsxxsh-was-writen-to-streamiline-aquiring-all-available-assemblies-for-a-specific-taxon-it-takes-ncbis-taxid-as-and-input-brucellataxid234-after-the-genomes-are-dld-there-are-several-simple-genome-filtering-steps-lengthn50gc-etc-there-are-also-a-few-more-soficticated-filtering-methods-checkm-is-used-to-assess-completeness-and-contamination-while-dedupe-from-bbmap-is-used-to-reduce-duplicated-contigs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIt is absolutely imparative to clean up assemblies to get the best results from OrthoPhyl. For instance, when looking at the output for all Brucella accessions, checkM output shows many assemblies have duplicated \"marker genes\", if these are from falsely duplicated contigs in the asm, they will lead to removal of the ortholog group from both the strict SCO and relaxed SCO gene sets within the OrthoPhylo workflow. Furthermore, dedupe.sh (from bbmap) identifies many Duplicated or Contained contigs, some of them \u0026gt;100kb long. Again, if any of the duplicated contigs contain what would otherwise be SCOs, the SCOs will be removed. In essence, having duplicates poisons the analysis by severely reducing the number of Orthologs for downstream analysis. This problem is exacerbated by including 1) less curated assemblies (GenBank) and 2) the total number of assemblies fed to the analysis pipeline.\u003c/h4\u003e\u003ca id=\"user-content-it-is-absolutely-imparative-to-clean-up-assemblies-to-get-the-best-results-from-orthophyl-for-instance-when-looking-at-the-output-for-all-brucella-accessions-checkm-output-shows-many-assemblies-have-duplicated-marker-genes-if-these-are-from-falsely-duplicated-contigs-in-the-asm-they-will-lead-to-removal-of-the-ortholog-group-from-both-the-strict-sco-and-relaxed-sco-gene-sets-within-the-orthophylo-workflow-furthermore-dedupesh-from-bbmap-identifies-many-duplicated-or-contained-contigs-some-of-them-100kb-long-again-if-any-of-the-duplicated-contigs-contain-what-would-otherwise-be-scos-the-scos-will-be-removed-in-essence-having-duplicates-poisons-the-analysis-by-severely-reducing-the-number-of-orthologs-for-downstream-analysis-this-problem-is-exacerbated-by-including-1-less-curated-assemblies-genbank-and-2-the-total-number-of-assemblies-fed-to-the-analysis-pipeline\" class=\"anchor\" aria-label=\"Permalink: It is absolutely imparative to clean up assemblies to get the best results from OrthoPhyl. For instance, when looking at the output for all Brucella accessions, checkM output shows many assemblies have duplicated \u0026quot;marker genes\u0026quot;, if these are from falsely duplicated contigs in the asm, they will lead to removal of the ortholog group from both the strict SCO and relaxed SCO gene sets within the OrthoPhylo workflow. Furthermore, dedupe.sh (from bbmap) identifies many Duplicated or Contained contigs, some of them \u0026gt;100kb long. Again, if any of the duplicated contigs contain what would otherwise be SCOs, the SCOs will be removed. In essence, having duplicates poisons the analysis by severely reducing the number of Orthologs for downstream analysis. This problem is exacerbated by including 1) less curated assemblies (GenBank) and 2) the total number of assemblies fed to the analysis pipeline.\" href=\"#it-is-absolutely-imparative-to-clean-up-assemblies-to-get-the-best-results-from-orthophyl-for-instance-when-looking-at-the-output-for-all-brucella-accessions-checkm-output-shows-many-assemblies-have-duplicated-marker-genes-if-these-are-from-falsely-duplicated-contigs-in-the-asm-they-will-lead-to-removal-of-the-ortholog-group-from-both-the-strict-sco-and-relaxed-sco-gene-sets-within-the-orthophylo-workflow-furthermore-dedupesh-from-bbmap-identifies-many-duplicated-or-contained-contigs-some-of-them-100kb-long-again-if-any-of-the-duplicated-contigs-contain-what-would-otherwise-be-scos-the-scos-will-be-removed-in-essence-having-duplicates-poisons-the-analysis-by-severely-reducing-the-number-of-orthologs-for-downstream-analysis-this-problem-is-exacerbated-by-including-1-less-curated-assemblies-genbank-and-2-the-total-number-of-assemblies-fed-to-the-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat this software does not do:\u003c/h3\u003e\u003ca id=\"user-content-what-this-software-does-not-do\" class=\"anchor\" aria-label=\"Permalink: What this software does not do:\" href=\"#what-this-software-does-not-do\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate trees that are ready for publication without parameter tuning or manual inspection. Reconstructing trees from whole genomes requires many many steps, all of which have parameters that will differ based on the input sequences. Some importent outputs too look at: input genomes quality (checkM output), assembly subset used for Ortholog model generation (assembly shortlist), number of strict/relaxed single copy orthologs (drops quickly with additional assemblies), phylogenetic signal for transcript alignments, missing data in alignments (per gene and concatenated alignments)...to name a few. #### This pipeline also does not robunstly compute SCOs (like BUSCOs). It grabs all the SCOs it can from a dataset, but does not do any modeling to ensure species tree-like behavior of the individual genes and also does not deal with paralogs at all.\u003c/h4\u003e\u003ca id=\"user-content-generate-trees-that-are-ready-for-publication-without-parameter-tuning-or-manual-inspection-reconstructing-trees-from-whole-genomes-requires-many-many-steps-all-of-which-have-parameters-that-will-differ-based-on-the-input-sequences-some-importent-outputs-too-look-at-input-genomes-quality-checkm-output-assembly-subset-used-for-ortholog-model-generation-assembly-shortlist-number-of-strictrelaxed-single-copy-orthologs-drops-quickly-with-additional-assemblies-phylogenetic-signal-for-transcript-alignments-missing-data-in-alignments-per-gene-and-concatenated-alignmentsto-name-a-few--this-pipeline-also-does-not-robunstly-compute-scos-like-buscos-it-grabs-all-the-scos-it-can-from-a-dataset-but-does-not-do-any-modeling-to-ensure-species-tree-like-behavior-of-the-individual-genes-and-also-does-not-deal-with-paralogs-at-all\" class=\"anchor\" aria-label=\"Permalink: Generate trees that are ready for publication without parameter tuning or manual inspection. Reconstructing trees from whole genomes requires many many steps, all of which have parameters that will differ based on the input sequences. Some importent outputs too look at: input genomes quality (checkM output), assembly subset used for Ortholog model generation (assembly shortlist), number of strict/relaxed single copy orthologs (drops quickly with additional assemblies), phylogenetic signal for transcript alignments, missing data in alignments (per gene and concatenated alignments)...to name a few. #### This pipeline also does not robunstly compute SCOs (like BUSCOs). It grabs all the SCOs it can from a dataset, but does not do any modeling to ensure species tree-like behavior of the individual genes and also does not deal with paralogs at all.\" href=\"#generate-trees-that-are-ready-for-publication-without-parameter-tuning-or-manual-inspection-reconstructing-trees-from-whole-genomes-requires-many-many-steps-all-of-which-have-parameters-that-will-differ-based-on-the-input-sequences-some-importent-outputs-too-look-at-input-genomes-quality-checkm-output-assembly-subset-used-for-ortholog-model-generation-assembly-shortlist-number-of-strictrelaxed-single-copy-orthologs-drops-quickly-with-additional-assemblies-phylogenetic-signal-for-transcript-alignments-missing-data-in-alignments-per-gene-and-concatenated-alignmentsto-name-a-few--this-pipeline-also-does-not-robunstly-compute-scos-like-buscos-it-grabs-all-the-scos-it-can-from-a-dataset-but-does-not-do-any-modeling-to-ensure-species-tree-like-behavior-of-the-individual-genes-and-also-does-not-deal-with-paralogs-at-all\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eknown errors:\u003c/h2\u003e\u003ca id=\"user-content-known-errors\" class=\"anchor\" aria-label=\"Permalink: known errors:\" href=\"#known-errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf you have special characters in you fasta file names\u003c/h4\u003e\u003ca id=\"user-content-if-you-have-special-characters-in-you-fasta-file-names\" class=\"anchor\" aria-label=\"Permalink: If you have special characters in you fasta file names\" href=\"#if-you-have-special-characters-in-you-fasta-file-names\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFilenames with special characters will likely make this workflow fail. As I encountered these characters, i will add fixes accordingly in Orthophyl.XX.sh, function SET_UP_DIR_STRUCTURE.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf the combination of fasta file name and contigs within are very long,\u003c/h4\u003e\u003ca id=\"user-content-if-the-combination-of-fasta-file-name-and-contigs-within-are-very-long\" class=\"anchor\" aria-label=\"Permalink: If the combination of fasta file name and contigs within are very long,\" href=\"#if-the-combination-of-fasta-file-name-and-contigs-within-are-very-long\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...mafft will truncate the sequence names generated by prodigal. This will make the PAL2NAL module fail on many orthogroups. It manifests as a lot of cat commands failing during trimming because they cant find the transcript file PAL2NAL is suposed to spit out. This can be seen in the log file (in slurm_out). It\u0027s normally caused by very redundant sequence identifiers in the contig names. Can be fixed with something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esed -i \u0027s/REDUNDANT_STUFF/_/g\u0027 \u0026lt; SEQUENCE.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf genomes are less than ~75% identity...\u003c/h4\u003e\u003ca id=\"user-content-if-genomes-are-less-than-75-identity\" class=\"anchor\" aria-label=\"Permalink: If genomes are less than ~75% identity...\" href=\"#if-genomes-are-less-than-75-identity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFastANI doesnt calculate an ANI value for very divergent genomes. In this case my script ANI_picking.py assigns an ANI of 50 (hard coded) for these comparisons. This should not pose a problem if the number of clades with 75% divergence internally are ~= ANI_shortlist number. The goal of the ANI stuff is to find representetives of the evolutionaly breadth of the dataset, so missing representetives from some clades will likelly not affect the final result (there is a lot of filtering later on in the workflow).\nI will implement a more robust ANI estimator soooon...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf you get an error like:\u003c/h4\u003e\u003ca id=\"user-content-if-you-get-an-error-like\" class=\"anchor\" aria-label=\"Permalink: If you get an error like:\" href=\"#if-you-get-an-error-like\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e/cm/local/apps/slurm/var/spool/job49381/slurm_script: line 511: J % percent: division by 0 (error token is \"percent\") \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt means that an upstream process failed and the directory used to enumerate a loop is empty\u003c/p\u003e\n\u003cp\u003eI will attempt to make errors easier to track...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eI have gotten an error with gather_filter_genomes when pulling extra genomes via wget:\u003c/h4\u003e\u003ca id=\"user-content-i-have-gotten-an-error-with-gather_filter_genomes-when-pulling-extra-genomes-via-wget\" class=\"anchor\" aria-label=\"Permalink: I have gotten an error with gather_filter_genomes when pulling extra genomes via wget:\" href=\"#i-have-gotten-an-error-with-gather_filter_genomes-when-pulling-extra-genomes-via-wget\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edyld: Symbol not found: _libiconv_open\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis appeared to be a problem with wget, which was resolved by updating through conda.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFuture modules:\u003c/h2\u003e\u003ca id=\"user-content-future-modules\" class=\"anchor\" aria-label=\"Permalink: Future modules:\" href=\"#future-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDONE: add assembly filtering, now very incomplete assemblies will be used, so the number of strict single copy orthologs could be drastically reduced (maybe to zero). Filtering done with gather_filter_asm\u003c/li\u003e\n\u003cli\u003eDONE: Quantify phylogenetic info per gene (\u003ca href=\"https://github.com/dportik/Alignment_Assessment.git\"\u003ehttps://github.com/dportik/Alignment_Assessment.git\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCheck genome assembly file not empty at start of Main pipeline....will crash pipe at Orthofinder run. This happened because of a file transfer mishap for me :(\u003c/li\u003e\n\u003cli\u003eAll ow users to submit pre-annotated assemblies. This will open up OrthoPhyl to all taxa (after making sure all software is compatable with alternative codon tables)\u003c/li\u003e\n\u003cli\u003eTest evolutionary models of genes with ete3. Then test GO enrichment, AMR, Virulence genes\u003c/li\u003e\n\u003cli\u003eLook at tree wide paralog numbers. Do GO analysis...\u003c/li\u003e\n\u003cli\u003eIdentify HGT from Transcript alignments. HGTs specific to any group?\u003c/li\u003e\n\u003cli\u003eallow \"protected assemblies\" when filtering. Perhaps your favorite assembly is crap, but you really want it in the tree. A couple of bad assemblies shouldnt reduce the number of SCOs that much\u003c/li\u003e\n\u003cli\u003eallow usage of precomputed Orthogroup HMMs, build new models from hits, then grab novel orthogroups from the remaining protiens\u003c/li\u003e\n\u003cli\u003eRelated to above: allow adding assemblies to pre-run pipeline. i.e. use precomputed hmms to identify orthologs, and add them to alignemnts and regenerate trees\u003c/li\u003e\n\u003cli\u003eallow users to pick which dataset to use for tree building (currently strict and relaxed are used). This would greatly reduce pipeline run time.\u003c/li\u003e\n\u003cli\u003echange how the list of OGs for HMM searching is inumerated. Currently tries with OG that have be filtered out because of paralogs, then gets mad because there is no multifasta to align (doesnt change outcome, just messy)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOrthoPhyl Citation:\u003c/h2\u003e\u003ca id=\"user-content-orthophyl-citation\" class=\"anchor\" aria-label=\"Permalink: OrthoPhyl Citation:\" href=\"#orthophyl-citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e[Coming soon]\u003c/h3\u003e\u003ca id=\"user-content-coming-soon\" class=\"anchor\" aria-label=\"Permalink: [Coming soon]\" href=\"#coming-soon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitations for dependencies\u003c/h2\u003e\u003ca id=\"user-content-citations-for-dependencies\" class=\"anchor\" aria-label=\"Permalink: Citations for dependencies\" href=\"#citations-for-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIf there are nested dependencies that I am missing citations for, please let me know\u003c/h3\u003e\u003ca id=\"user-content-if-there-are-nested-dependencies-that-i-am-missing-citations-for-please-let-me-know\" class=\"anchor\" aria-label=\"Permalink: If there are nested dependencies that I am missing citations for, please let me know\" href=\"#if-there-are-nested-dependencies-that-i-am-missing-citations-for-please-let-me-know\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e[Coming soon]\u003c/h3\u003e\u003ca id=\"user-content-coming-soon-1\" class=\"anchor\" aria-label=\"Permalink: [Coming soon]\" href=\"#coming-soon-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eprodigal\u003c/li\u003e\n\u003cli\u003eorthofinder\u003c/li\u003e\n\u003cli\u003ebbmap\u003c/li\u003e\n\u003cli\u003efastTree*\u003c/li\u003e\n\u003cli\u003ehmmer\u003c/li\u003e\n\u003cli\u003epal2nal\u003c/li\u003e\n\u003cli\u003emafft\u003c/li\u003e\n\u003cli\u003eprodigal\u003c/li\u003e\n\u003cli\u003eete3\u003c/li\u003e\n\u003cli\u003eIQTree*\u003c/li\u003e\n\u003cli\u003eraxml*\u003c/li\u003e\n\u003cli\u003etrimal\u003c/li\u003e\n\u003cli\u003eparallel\u003c/li\u003e\n\u003cli\u003ecatfasta2phyml\u003c/li\u003e\n\u003cli\u003eASTRAL\u003c/li\u003e\n\u003cli\u003efastANI\u003c/li\u003e\n\u003cli\u003eR\u003c/li\u003e\n\u003cli\u003eAlignment_Assessment\u003c/li\u003e\n\u003cli\u003eblast\u003c/li\u003e\n\u003cli\u003escipy\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003emuscle\u003c/li\u003e\n\u003cli\u003emmseq2\u003c/li\u003e\n\u003cli\u003emcl\u003c/li\u003e\n\u003cli\u003efastme\u003c/li\u003e\n\u003cli\u003ediamond\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "CWRU-MSL/GammaDoublePrime", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-characterization-of-nanoscale-precipitates-in-superalloy-718-using-high-resolution-sem-imaging\" class=\"anchor\" aria-hidden=\"true\" href=\"#characterization-of-nanoscale-precipitates-in-superalloy-718-using-high-resolution-sem-imaging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCharacterization of nanoscale precipitates in superalloy 718 using high resolution SEM imaging\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tm-smith-a-nm-senanayake-b-ck-sudbrack-c-p-bonacuse-a-rb-rogers-a-p-chao-d-j-carter-b\" class=\"anchor\" aria-hidden=\"true\" href=\"#tm-smith-a-nm-senanayake-b-ck-sudbrack-c-p-bonacuse-a-rb-rogers-a-p-chao-d-j-carter-b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eT.M. Smith a*, N.M. Senanayake b, C.K. Sudbrack c, P. Bonacuse a, R.B. Rogers a, P. Chao d, J. Carter b\u003c/h2\u003e\n\u003cp\u003ea NASA Glenn Research Center, Materials and Structures Division, Cleveland, OH 44135, United States of America\nb Case Western Reserve University, Department of Materials Science and Engineering, Cleveland, OH 44106, United States of America\nc QuesTek Innovations LLC, Evanston, IL 60201, United States of America\nd Carnegie Mellon University, Department of Materials Science and Engineering, Pittsburgh, PA 15213, United States of America\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-materials-characterization-212019-v148-p-178-197\" class=\"anchor\" aria-hidden=\"true\" href=\"#materials-characterization-212019-v148-p-178-197\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaterials Characterization, (2/1/2019) V148, p 178-197\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-httpwwwsciencedirectcomsciencearticlepiis1044580318328444\" class=\"anchor\" aria-hidden=\"true\" href=\"#httpwwwsciencedirectcomsciencearticlepiis1044580318328444\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"http://www.sciencedirect.com/science/article/pii/S1044580318328444\" rel=\"nofollow\"\u003ehttp://www.sciencedirect.com/science/article/pii/S1044580318328444\u003c/a\u003e\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-doi-101016jmatchar201812018\" class=\"anchor\" aria-hidden=\"true\" href=\"#doi-101016jmatchar201812018\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDOI: 10.1016/j.matchar.2018.12.018\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-repo-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#repo-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepo Information\u003c/h3\u003e\n\u003cp\u003eThis repository contains the codes necessary to utilize the algorthims presented in the paper below. When implimented, the can be used to obtain accurate volume fraction and size measurements of gamma double prime, and gamma prime, precipitates in Superalloy\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-clone-a-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#clone-a-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClone a repository\u003c/h2\u003e\n\u003cp\u003eUse these steps to clone from SourceTree, our client for using the repository command-line free. Cloning allows you to work on your files locally. If you don\u0027t yet have SourceTree, \u003ca href=\"https://www.sourcetreeapp.com/\" rel=\"nofollow\"\u003edownload and install first\u003c/a\u003e. If you prefer to clone from the command line, see \u003ca href=\"https://confluence.atlassian.com/x/4whODQ\" rel=\"nofollow\"\u003eClone a repository\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eYou\u2019ll see the clone button under the \u003cstrong\u003eSource\u003c/strong\u003e heading. Click that button.\u003c/li\u003e\n\u003cli\u003eNow click \u003cstrong\u003eCheck out in SourceTree\u003c/strong\u003e. You may need to create a SourceTree account or log in.\u003c/li\u003e\n\u003cli\u003eWhen you see the \u003cstrong\u003eClone New\u003c/strong\u003e dialog in SourceTree, update the destination path and name if you\u2019d like to and then click \u003cstrong\u003eClone\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eOpen the directory you just created to see your repository\u2019s files.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNow that you\u0027re more familiar with your Bitbucket repository, go ahead and add a new file locally. You can \u003ca href=\"https://confluence.atlassian.com/x/iqyBMg\" rel=\"nofollow\"\u003epush your change back to Bitbucket with SourceTree\u003c/a\u003e, or you can \u003ca href=\"https://confluence.atlassian.com/x/8QhODQ\" rel=\"nofollow\"\u003eadd, commit,\u003c/a\u003e and \u003ca href=\"https://confluence.atlassian.com/x/NQ0zDQ\" rel=\"nofollow\"\u003epush from the command line\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1720207910.0 + "updated_at": 1656425160.0 }, { "data_format": 2, - "description": "Repositorio asignatura Planificacion Automatica", + "description": "recipes of Singularity", "filenames": [ - "PL1/planificadores/singularity-ce-3.9.5/e2e/testdata/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/self/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/centos-arm64/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/arch/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/docker/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/busybox/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/debian/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/scientific/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/sle/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/shub/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/apps/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/apps/Singularity.cowsay", - "PL1/planificadores/singularity-ce-3.9.5/examples/opensuse-arm64/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/raspbian/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/ubuntu/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/scratch/Singularity.alpine", - "PL1/planificadores/singularity-ce-3.9.5/examples/scratch/Singularity.busybox", - "PL1/planificadores/singularity-ce-3.9.5/examples/instances/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/centos/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/asciinema/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/opensuse/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/library/Singularity", - "PL1/planificadores/singularity-ce-3.9.5/examples/multistage/Singularity" + "Singularity.blast-latest", + "Singularity.snpeff", + "Singularity.vcftools", + "Singularity.samtools", + "Singularity.rooting_nj", + "Singularity.trimmomatic", + "Singularity.cufflinks", + "Singularity.gatk", + "Singularity.blast-legacy", + "Singularity.bwa" ], - "full_name": "nacho-pm/PlanificacionAutomatica_uah", + "full_name": "CompBio-TDU-Japan/containers", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n\u003cp\u003erecipes of Singularity\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1708631111.0 + "updated_at": 1548055469.0 }, { "data_format": 2, - "description": "Docker image to get DeepLabCutCore running on cloud GPUs.", + "description": null, "filenames": [ "Singularity" ], - "full_name": "Alyetama/DeepLabCut-HPC", + "full_name": "agladstein/SimPrily_update", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker/badge.svg\"\u003e\u003cimg src=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker%20Image%20CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker%20Image%20CI/badge.svg\" alt=\"Docker Image CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5345e0bf4d0e9a19f36de8aebb93a1b37efd3026a608037e0ac825d66b40aadd/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6663617475732f646565706c6162637574\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5345e0bf4d0e9a19f36de8aebb93a1b37efd3026a608037e0ac825d66b40aadd/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6663617475732f646565706c6162637574\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/fcatus/deeplabcut\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b1816dab9d52cfd6c623dae032c912432e5dc33a85d52cfb4ec883d536bca3b1/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6663617475732f646565706c6162637574\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b1816dab9d52cfd6c623dae032c912432e5dc33a85d52cfb4ec883d536bca3b1/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6663617475732f646565706c6162637574\" alt=\"Docker Image Size (latest by date)\" data-canonical-src=\"https://img.shields.io/docker/image-size/fcatus/deeplabcut\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFrom \u003ca href=\"https://hub.docker.com/repository/docker/fcatus/deeplabcut\" rel=\"nofollow\"\u003eDockerhub\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-from-dockerhub\" class=\"anchor\" aria-label=\"Permalink: From Dockerhub\" href=\"#from-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker pull fcatus/deeplabcut:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo Use With Singularity\u003c/h2\u003e\u003ca id=\"user-content-to-use-with-singularity\" class=\"anchor\" aria-label=\"Permalink: To Use With Singularity\" href=\"#to-use-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull docker://fcatus/deeplabcut:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor build it from a singularity file\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ vim singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eBootstrap\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edocker\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eFrom\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efcatus/deeplabcut:latest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --remote deeplabcut.sif singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild From a Singularity \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/definition_files.html\" rel=\"nofollow\"\u003eDefinition File\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-build-from-a-singularity-definition-file\" class=\"anchor\" aria-label=\"Permalink: Build From a Singularity Definition File\" href=\"#build-from-a-singularity-definition-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the definition file\u003c/span\u003e\n$ wget https://gist.githubusercontent.com/Alyetama/c819bb2d07e58b47208c69ce76c22feb/raw/c07b264adc5424d6bd939a77bb34a95a6f80dca1/dlc.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Customize the definition file (optional)\u003c/span\u003e\n$ vim dlc.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build remotely from the definition file\u003c/span\u003e\n$ singularity build --remote deeplabcut.sif dlc.def\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more information about using \u003ccode\u003esingularity build\u003c/code\u003e, see \u003ca href=\"https://sylabs.io/guides/3.1/user-guide/cli/singularity_build.html\" rel=\"nofollow\"\u003eSingularity Build\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-simprily_update\" class=\"anchor\" aria-hidden=\"true\" href=\"#simprily_update\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimPrily_update\u003c/h1\u003e\n\u003cp\u003eCreated by Ariella Gladstein, based on \u003ca href=\"https://agladstein.github.io/SimPrily/index.html\" rel=\"nofollow\"\u003eSimPrily\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eSimPrily runs genome simulations with user defined parameters or parameters randomly generated by priors and computes genomic statistics on the simulation output.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun genome simulation with model defined by prior distributions of parameters and demographic model structure.\u003c/li\u003e\n\u003cli\u003eTake into account SNP array ascertainment bias by creating pseudo array based on priors of number of samples of discovery populations and allele frequency cut-off.\u003c/li\u003e\n\u003cli\u003eCalculate genomic summary statistics on simulated genomes and pseudo arrays.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis is ideal for use with Approximate Bayesian Computation on whole genome or SNP array data.\u003c/p\u003e\n\u003cp\u003eUses c++ programs macs and GERMLINE. For more information on these programs, see:\u003cbr\u003e\n\u003ca href=\"https://github.com/gchen98/macs\"\u003ehttps://github.com/gchen98/macs\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/sgusev/GERMLINE\"\u003ehttps://github.com/sgusev/GERMLINE\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003ecd to the directory you want to work in,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/agladstein/SimPrily.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-environment-set-up\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-set-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment Set up\u003c/h4\u003e\n\u003cp\u003eIf using Vagrant (this is recommended if running on non-Linux OS):\u003c/p\u003e\n\u003cp\u003eStart Vagrant, ssh into Vagrant, cd to SimPrily directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\nvagrant ssh\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /vagrant\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall the virtual environment and install the requirements.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./setup/setup_env_vbox_2.7.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf not using Vagrant, just install the virtual environment and install the requirements:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./setup/setup_env_2.7.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003ee.g. One Test simulation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py -p examples/eg1/param_file_eg1_asc.txt -m examples/eg1/model_file_eg1_asc.csv -g genetic_map_b37/genetic_map_GRCh37_chr1.txt.macshs -a array_template/ill_650_test.bed -i 1 -o output_dir -v\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor quick help:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esimprily.py\u003c/code\u003e takes 4 required arguments and 2 optional arguments, and help, verbose, and profile options.\u003c/p\u003e\n\u003cp\u003eRun as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py [-h] -p PARAM -m MODEL -i ID -o OUT [-g MAP] [-a ARRAY] [-v] [--profile]\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch5\u003e\u003ca id=\"user-content-required\" class=\"anchor\" aria-hidden=\"true\" href=\"#required\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired\u003c/h5\u003e\n\u003cp\u003e\u003ccode\u003e-p PARAM\u003c/code\u003e or \u003ccode\u003e--param PARAM\u003c/code\u003e = The location of the parameter file\u003cbr\u003e\n\u003ccode\u003e-m MODEL\u003c/code\u003e or \u003ccode\u003e--model MODEL\u003c/code\u003e = The location of the model file\u003cbr\u003e\n\u003ccode\u003e-i ID\u003c/code\u003e or \u003ccode\u003e--id ID\u003c/code\u003e = The unique identifier of the job\u003cbr\u003e\n\u003ccode\u003e-o OUT\u003c/code\u003e or \u003ccode\u003e--out OUT\u003c/code\u003e = The location of the output directory\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h5\u003e\n\u003cp\u003e\u003ccode\u003e-h\u003c/code\u003e or \u003ccode\u003e--help\u003c/code\u003e = shows a help message and exists\u003cbr\u003e\n\u003ccode\u003e-v\u003c/code\u003e = increase output verbosity. This includes 3 levels, \u003ccode\u003e-v\u003c/code\u003e, \u003ccode\u003e-vv\u003c/code\u003e, and \u003ccode\u003e-vvv\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003e--profile\u003c/code\u003e = Print a log file containing the time in seconds and memory use in Mb for main functions\u003cbr\u003e\n\u003ccode\u003e-g MAP\u003c/code\u003e or \u003ccode\u003e--map MAP\u003c/code\u003e = The location of the genetic map file\u003cbr\u003e\n\u003ccode\u003e-a ARRAY\u003c/code\u003e or \u003ccode\u003e--array ARRAY\u003c/code\u003e = The location of the array template file, in bed form\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h4\u003e\n\u003cp\u003eThree subdirectories are created in the directory specified in the \u003ccode\u003eoutput_dir\u003c/code\u003e argument.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_dir/results\noutput_dir/sim_data\noutput_dir/germline_out\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch5\u003e\u003ca id=\"user-content-intermediate-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#intermediate-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntermediate files\u003c/h5\u003e\n\u003cp\u003eIntermediate files go to \u003ccode\u003eoutput_dir/sim_data\u003c/code\u003e and \u003ccode\u003eoutput_dir/germline_out\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/sim_data\u003c/code\u003e contains PLINK formated .ped and .map files created from the pseudo array, which are necessary to run GERMLINE.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/germline_out\u003c/code\u003e contains the GERMLINE .match output and .log. The .match contains all of the identified IBD segments.\u003cbr\u003e\nThese files are NOT automatically removed in python script, but are unnecessary once the job is complete.\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-results-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#results-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults files\u003c/h5\u003e\n\u003cp\u003eOutput files go to \u003ccode\u003eoutput_dir/results\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/results\u003c/code\u003e contains the parameter values used in the simulation and the summary statistics calculated from the simulation.\u003cbr\u003e\nThe first line is a header with the parameter names and summary statistics names.\nThe second line is the parameter values and summary statistics values.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-abc_update_wfpy\" class=\"anchor\" aria-hidden=\"true\" href=\"#abc_update_wfpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eABC_update_wf.py\u003c/h2\u003e\n\u003cp\u003eThis script creates all the necessary files for running ABC on simulations, and runs ABC.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCombines the simulated results into one file in \u003ccode\u003eobs{}/chr{}/ABC/results_combined.txt\u003c/code\u003e (unless the file already exists).\u003c/li\u003e\n\u003cli\u003eFor chr1 randomly picks one of the simulations to use as observed data,\nand for all other chromosomes uses the parameter values of the observed data from chr1 to simulate observed data,\nand create file in \u003ccode\u003eobs{}/chr{}/ABC/results_observed.txt\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eRun R to get PLS components.\u003c/li\u003e\n\u003cli\u003eUse ABCtoolbox to transform summary stats to PLS components for simulated and observed data.\u003c/li\u003e\n\u003cli\u003eUse ABCtoolbox to get posteriors of parameters.\u003c/li\u003e\n\u003cli\u003eCreate parameter file with posterior file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eABC_update_wf.py path_sim param_file_name chrom obs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003epath_sim\u003c/code\u003e is the path to simulation output (before \u003ccode\u003eobs{}\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparam_file_name\u003c/code\u003e is the parameter file used to perform the simulations\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003echrom\u003c/code\u003e is the chromosome number\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eobs\u003c/code\u003e is the iteration with observed data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-hpc-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC Workflow\u003c/h2\u003e\n\u003cp\u003eFor chromosome 1 use \u003ccode\u003echeckque.sh\u003c/code\u003e to submit jobs to Ocelote.\u003c/p\u003e\n\u003cp\u003eArguments are \u003ccode\u003egoal_number\u003c/code\u003e, \u003ccode\u003emax_que\u003c/code\u003e, \u003ccode\u003echr\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/home/u15/agladstein/SimPrily_update/update_test/checkque.sh 10000 500 1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, run ABC with \u003ccode\u003eABC_update_wf.py\u003c/code\u003e with the appropriate chromosome:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ersync -za SimPrily_update/ /xdisk/agladstein/SimPrily_update\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /xdisk/agladstein/SimPrily_update\nqsub update_test/PBS/run_ABC_chr1.pbs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, run the simulations with the appropriate chromosome:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ersync -za SimPrily_update/ /xdisk/agladstein/SimPrily_update\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /xdisk/agladstein/SimPrily_update\nqsub update_test/PBS/run_sims_update_chr2.pbs\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown Issues\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIf exponential growth is large, macs simulation will not finish. (This is a macs bug).\u003c/li\u003e\n\u003cli\u003eIf the same id is used with the same output dir as a previous run, the .map file will be appended to.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, - "topics": [ - "docker", - "deeplabcut", - "clone", - "slurm", - "hpc", - "singularity" - ], - "updated_at": 1717603187.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1615073384.0 }, { "data_format": 2, - "description": " This repo provides a Singularity image version for Percona Monitoring and Management (PMM)", + "description": null, "filenames": [ "Singularity" ], - "full_name": "netreconlab/pmm-server", + "full_name": "tgac-vumc/QDNAseq.snakemake", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epmm-server\u003c/h1\u003e\u003ca id=\"user-content-pmm-server\" class=\"anchor\" aria-label=\"Permalink: pmm-server\" href=\"#pmm-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo provides a Singularity image version for \u003ca href=\"https://www.percona.com/software/database-tools/percona-monitoring-and-management\" rel=\"nofollow\"\u003ePercona Monitoring and Management (PMM)\u003c/a\u003e, for monitoring the health of your database infrastructure, explore new patterns in database behavior, and manage and improve the performance of your databases no matter where they are located or deployed. To learn more about the image, look \u003ca href=\"https://docs.percona.com/percona-monitoring-and-management/setting-up/server/docker.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImages\u003c/h2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImages of \u003ccode\u003epmm-server\u003c/code\u003e are automatically built for your convenience. Images can be found at the following locations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/netreconlab/pmm-server/pkgs/container/pmm-server\"\u003eSingularity - Hosted on GitHub Container Registry\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDocker - Use the \u003ca href=\"https://hub.docker.com/r/percona/pmm-server\" rel=\"nofollow\"\u003eofficial pmm-server image\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/QDNAseq.snakemake/blob/master/DAG_all.svg\"\u003e\u003cimg width=\"100%\" height=\"100%\" src=\"https://github.com/tgac-vumc/QDNAseq.snakemake/raw/master/DAG_all.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eFor the installation of this pipeline any Python install compatable Conda is required.\u003c/p\u003e\n\u003cp\u003eThe pipeline itself will run on Python 3.8.5 and R 3.6.3. For exact dependencies view \u003ccode\u003eenvironment.yaml\u003c/code\u003e and \u003ccode\u003er-dependencies.R\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-condamamba\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-condamamba\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Conda/Mamba\u003c/h3\u003e\n\u003cp\u003efor easy installation you need (Mini)Conda.\u003c/p\u003e\n\u003cp\u003eMiniconda installation from folder where you want to install Miniconda:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd \u0026lt;/path/to/files/dir/\u0026gt;\nwget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efollow the instructions of the installation process, give the location where you want Miniconda to be installed and answer YES to add Miniconda to your path.\u003c/p\u003e\n\u003cp\u003ego to the directory where the analysis need to be performed\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd \u0026lt;/path/to/analysis/dir\u0026gt;\ngit clone https://github.com/tgac-vumc/QDNAseq.snakemake/\ncd QDNAseq.snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003einstall Mamba as drop-in replacement for Conda with Mamba\u0027s improved installation-performance:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c conda-forge mamba\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecreate the environment using Mamba:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emamba env create --name QDNAseq-snakemake --file environment.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eactivate the environment by:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate QDNAseq-snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003eThen run the R-script r-dependencies.R in the terminal to install the non-conda R dependencies in the environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript r-dependencies.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003c/h3\u003e\n\u003cp\u003eUnder development\u003c/p\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-preparing-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#preparing-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparing analysis\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-prepare-the-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#prepare-the-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare the data\u003c/h3\u003e\n\u003cp\u003ego to analysis dir and prepare analysis by copy or create links to fastq.gz files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd \u0026lt;/path/to/analysis/dir\u0026gt;\n\nmkdir fastq\ncd fastq\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto link a single file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eln -s \u0026lt;path/to/file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto link all files from a folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor file in \u0026lt;path/to/fastq/files\u0026gt;/*.fastq.gz\ndo ln -s $file\ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-prepare-the-snakemake-settings\" class=\"anchor\" aria-hidden=\"true\" href=\"#prepare-the-snakemake-settings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare the snakemake settings\u003c/h3\u003e\n\u003cp\u003eOpen the configuration file \u003ccode\u003econfig.yaml\u003c/code\u003e to check the settings that snakemake will use and change according to your needs.\nFor providing service-analysis, set \u003ccode\u003esetting\u003c/code\u003e to \u003ccode\u003e\u0027service\u0027\u003c/code\u003e. For research purposes, set \u003ccode\u003esetting\u003c/code\u003e to \u003ccode\u003e\u0027research\u0027\u003c/code\u003e. For all settings set \u003ccode\u003esetting\u003c/code\u003e to \u003ccode\u003e\u0027all\u0027\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOne of the options in the configfile is \u003ccode\u003edewaving\u003c/code\u003e, if set to \u003ccode\u003e\u0027true\u0027\u003c/code\u003e QNDAseq objects will be dewaved before segmentation.\u003c/p\u003e\n\u003cp\u003eThese options change the rules performed in the pipeline, see the rule-graph in the next section.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning analysis\u003c/h2\u003e\n\u003cp\u003eMake sure that snakemake is able to find the excecutive file Snakefile by performing a dry-run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ../QDNAseq.snakemake\nsnakemake -n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck the rules that are planned to be performed, conform the rule-graph.\u003c/p\u003e\n\u003cp\u003eAn visualization of the order of rules to be performed can be viewed by running the following command and opening the DAG-file\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --forceall --rulegraph | dot -Tsvg \u0026gt; DAG.svg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRulegraphs for the intial settings \u003ccode\u003e\u0027service\u0027\u003c/code\u003e, \u003ccode\u003e\u0027research\u0027\u003c/code\u003e and \u003ccode\u003e\u0027all\u0027\u003c/code\u003e are commited to this repro in the files \u003ccode\u003eDAG_\u0026lt;setting\u0026gt;.svg\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWhen ready, run the analysis\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUseful snakemake options\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-j , --cores, --jobs\u003c/code\u003e : Use at most N cores in parallel (default: 1). If N is omitted, the limit is set to the number of available cores.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-n , --dryrun\u003c/code\u003e : Do not execute anything. but show rules which are planned to be performed.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-k , --keep-going\u003c/code\u003e : Go on with independent jobs if a job fails.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-f , --force\u003c/code\u003e : Force the execution of the selected target or the first rule regardless of already created output.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-R , --forcerun\u003c/code\u003e : Force the re-execution or creation of the given rules or files. Use this option if you changed a rule and want to have all its output in your workflow updated.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-U , --until\u003c/code\u003e : Runs the pipeline until it reaches the specified rules or files. Only runs jobs that are dependencies of the specified rule or files, does not run sibling DAGs.\u003c/p\u003e\n\u003cp\u003efor all options go to \u003ca href=\"https://snakemake.readthedocs.io/en/v5.31.1/executing/cli.html#all-options\" rel=\"nofollow\"\u003ehttps://snakemake.readthedocs.io/en/v5.31.1/executing/cli.html#all-options\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, - "topics": [ - "percona-server", - "postgres", - "database-management", - "mongodb", - "monitoring-server", - "singularity" - ], - "updated_at": 1673112616.0 + "subscribers_count": 5, + "topics": [], + "updated_at": 1631492554.0 }, { "data_format": 2, - "description": "Singularity file for Openmind to run tensorflow", + "description": "RNAseq analysis pipeline project for Paris-Saclay\u0027s University Master AMI2B", "filenames": [ - "Singularity" + "containers/Singularity.R", + "containers/star/Singularity.star_nb", + "containers/samtools/Singularity.samtools", + "containers/sratoolkit/Singularity.sratoolkit", + "containers/featurecounts/Singularity.featcount" ], - "full_name": "satra/om-tensorflow", + "full_name": "Sherman-1/Hackathon", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eom-tensorflow\u003c/h1\u003e\u003ca id=\"user-content-om-tensorflow\" class=\"anchor\" aria-label=\"Permalink: om-tensorflow\" href=\"#om-tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity file for Openmind to run tensorflow\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-projet-repro-hackathon-2022-2023--atia-safiya-bossut-no\u00e9mie-et-herman-simon\" class=\"anchor\" aria-hidden=\"true\" href=\"#projet-repro-hackathon-2022-2023--atia-safiya-bossut-no\u00e9mie-et-herman-simon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eProjet Repro-Hackathon 2022-2023\u003c/strong\u003e : ATIA Safiya, BOSSUT No\u00e9mie et HERMAN Simon\u003c/h1\u003e\n\u003cp\u003eCe projet vise \u00e0 reproduire une partie des r\u00e9sultats de deux articles:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5321577/\" rel=\"nofollow\"\u003eFurney \u003cem\u003eet al.\u003c/em\u003e, Cancer Discovery (2013)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3789378/\" rel=\"nofollow\"\u003eHarbour \u003cem\u003eet al.\u003c/em\u003e, Nature Genetics (2013)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLes donn\u00e9es RNA-seq de ces deux papiers sont disponibles en open-access : \u003ca href=\"https://www.ncbi.nlm.nih.gov/sra?term=SRA062359\" rel=\"nofollow\"\u003e\u003cstrong\u003eDonn\u00e9es NCBI\u003c/strong\u003e\u003c/a\u003e. Dans un premier temps, seul l\u0027\u00e9tude de transcriptome est \u00e9tudi\u00e9e. \nL\u0027objectif de ces deux articles est d\u0027\u00e9tudier les expression de g\u00e8nes, et notamment le g\u00e8ne SF3B1, d\u0027individus atteint de m\u00e9lanome uv\u00e9al. Harbour \u003cem\u003eet al.\u003c/em\u003e affirment que le g\u00e8ne SF3B1 est mut\u00e9, pour des donn\u00e9es exomiques, mais est g\u00e9n\u00e9ralement pr\u00e9sente dans des tumeurs b\u00e9nignes avec un bon taux de survie des patients. Furney \u003cem\u003eet al.\u003c/em\u003e montrent que, en r\u00e9utilisant le m\u00eame jeu de donn\u00e9es qu\u0027il existe une association entre la mutation SF3B1 et plusieurs types d\u0027\u00e9pissage alternatif : r\u00e9tention d\u0027introns, \u00e9pissage sur sites cryptiques et epissage en site 3\u0027 alternatif, ce qui rendrait cette mutation potentiellement \u00e0 l\u0027origine du d\u00e9veloppement canc\u00e9reux.\u003c/p\u003e\n\u003cp\u003eA l\u0027aide d\u0027un workflow Nextflow et de \u003cem\u003econtainers\u003c/em\u003e Singularity, notre groupe a tent\u00e9 de comprendre pourquoi les r\u00e9sultats des deux articles divergent, et quelles sont nos propres observations sur le sujet.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pr\u00e9-requis-nextflow--singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#pr\u00e9-requis-nextflow--singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003ePr\u00e9-requis:\u003c/strong\u003e Nextflow \u0026amp; Singularity\u003c/h2\u003e\n\u003cp\u003eAfin de faire tourner notre \u003cem\u003epipeline\u003c/em\u003e, \u003cstrong\u003e64 Gb de RAM et 16 coeurs\u003c/strong\u003e, ainsi que deux logiciels sont n\u00e9cessaires:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextflow (version 21.10.6.5660) \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003e\u003cem\u003einstallation\u003c/em\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity (version 3.8.7) \u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003e\u003cem\u003einstallation\u003c/em\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003euidmap (pour la construction des containers Singularity)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo apt-get install uidmap\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-le-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#le-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eLe pipeline:\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/75751225/206907177-60bd1d6f-84ae-4c55-a9e2-80cc1f44a2a7.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/75751225/206907177-60bd1d6f-84ae-4c55-a9e2-80cc1f44a2a7.png\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eT\u00e9l\u00e9chargement des donn\u00e9es\u003c/strong\u003e : chromosomes humains (dont chromosome mitochondrial), annotation du g\u00e9nome et donn\u00e9es RNA-seq des 8 individus (\u003cem\u003e\u003cstrong\u003esratoolkit\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndexation\u003c/strong\u003e du g\u00e9nome (\u003cem\u003e\u003cstrong\u003eSTAR\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAlignement\u003c/strong\u003e et \u003cstrong\u003eTri\u003c/strong\u003e des donn\u00e9es RNA-seq sur le g\u00e9nome. Obtention de fichiers \u003cem\u003e.bam\u003c/em\u003e tri\u00e9s en sortie (\u003cem\u003e\u003cstrong\u003eSTAR\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndexation\u003c/strong\u003e des fichiers \u003cem\u003e.bam\u003c/em\u003e. en \u003cem\u003e.bai\u003c/em\u003e (\u003cem\u003e\u003cstrong\u003esamtools\u003c/strong\u003e\u003c/em\u003e). (optionnel)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eComptage\u003c/strong\u003e des s\u00e9quences exprim\u00e9es (\u003cem\u003e\u003cstrong\u003efeatureCounts\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAnalyse statistique\u003c/strong\u003e des r\u00e9sultats (\u003cem\u003e\u003cstrong\u003eDESeq2\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eL\u0027ensemble des donn\u00e9es et des r\u00e9sultats peuvent \u00eatre retrouv\u00e9s dans l\u0027arborescence ci-dessous:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvant execution du workflow:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u251c\u2500\u2500 bin\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 DE_analysis.R\n\u251c\u2500\u2500 containers\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 featurecounts\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 Singularity.featcount\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samtools\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 Singularity.samtools\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.R\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 sratoolkit\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 Singularity.sratoolkit\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 star\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 Singularity.star_nb\n\u251c\u2500\u2500 nextflow.config\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 run.sh\n\u2514\u2500\u2500 workflow.nf\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-ex\u00e9cution-du-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#ex\u00e9cution-du-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eEx\u00e9cution du workflow\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSi les pr\u00e9-requis sont bien satisfaits, placez-vous dans le r\u00e9pertoire voulu et r\u00e9cup\u00e9rez le projet\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e git clone https://github.com/Sherman-1/Hackathon\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Hackathon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLe fichier \u003ccode\u003erun.sh\u003c/code\u003e permet d\u0027initialiser votre environnement, ainsi que de cr\u00e9er les images singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e bash run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eV\u00e9rifiez que les r\u00e9pertoires ont bien \u00e9t\u00e9 cr\u00e9\u00e9s. Si c\u0027est le cas, vous pouvez lancer le pipeline :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e nextflow run workflow.nf\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-options-dexecution\" class=\"anchor\" aria-hidden=\"true\" href=\"#options-dexecution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions d\u0027execution\u003c/h3\u003e\n\u003cp\u003eEn plus de la commande par d\u00e9faut, vous pouvez utiliser les param\u00e8tres suivants :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-resume\u003c/code\u003e si votre \u003cem\u003epipeline\u003c/em\u003e a \u00e9t\u00e9 interrompu et que vous souhaitez le reprendre\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-with-trace [nom du fichier]\u003c/code\u003e pour obtenir le DAG correspondant\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-with-report [nom du fichier]\u003c/code\u003e pour obtenir un rapport complet et de nombreuses metadatas sur le \u003cem\u003epipeline\u003c/em\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, - "topics": [], - "updated_at": 1486549077.0 + "subscribers_count": 2, + "topics": [ + "reproducibility", + "rna-seq-pipeline", + "student-project" + ], + "updated_at": 1671065348.0 }, { "data_format": 2, - "description": "butterfly server packaged in singularity container", + "description": "Singularity container for WRF", "filenames": [ "Singularity" ], - "full_name": "vsoch/singularity-butterfly", + "full_name": "rkalyanapurdue/wrf-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Butterfly\u003c/h1\u003e\u003ca id=\"user-content-singularity-butterfly\" class=\"anchor\" aria-label=\"Permalink: Singularity Butterfly\" href=\"#singularity-butterfly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/paradoxxxzero/butterfly\"\u003eButterfly\u003c/a\u003e is a beautiful piece of work that brings the terminal into your browser. With Singularity Butterfly, you can get this working on your local cluster too! Here is how to create the image, which you should do locally first:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehttps://github.com/vsoch/singularity-butterfly\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity-butterfly\n./generate_image.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"generate_image.sh\"\u003egenerate_image.sh\u003c/a\u003e script simply creates a container, and does a bootstrap to install butterfly.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity create --size 4000 butterfly.img\nsudo singularity bootstrap butterfly.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen to run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /tmp:/run/user/1000/dconf butterfly.img\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-container-recipes-for-wrf\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container-recipes-for-wrf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container recipes for WRF\u003c/h1\u003e\n\u003cp\u003eUse the recipe file Singularity to build WRF using the GNU compilers\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-important-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#important-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Notes\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eThe build assumes that source code for HDF, HDF5, NETCDF-C, NETCDF-Fortran, WRF and WPS\nhave been downloaded.\u003c/li\u003e\n\u003cli\u003eThis build uses OpenMPI 4.0.1. The OpenMPI version on the nodes where this container is\nrun needs to match that inside the container.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1545323610.0 + "updated_at": 1676040247.0 }, { "data_format": 2, "description": null, "filenames": [ - "3.04/Singularity" + "Singularity.Demuxafy", + "scripts/Singularity.Demuxafy" ], - "full_name": "pscedu/singularity-cowsay", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eREADME\u003c/h1\u003e\u003ca id=\"user-content-readme\" class=\"anchor\" aria-label=\"Permalink: README\" href=\"#readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e255da600696e674a8923a820a23edadc47a79a802c433a23491e8221e0b60c7/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f382f38302f436f777361795f5479706963616c5f4f75747075742e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e255da600696e674a8923a820a23edadc47a79a802c433a23491e8221e0b60c7/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f382f38302f436f777361795f5479706963616c5f4f75747075742e706e67\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/commons/8/80/Cowsay_Typical_Output.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecowsay --help\n\ncow{say,think} version 3.03, (c) 1999 Tony Monroe\nUsage: cowthink [-bdgpstwy] [-h] [-e eyes] [-f cowfile]\n [-l] [-n] [-T tongue] [-W wrapcolumn] [message]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample\u003c/h2\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\necho -e \"This is Sparta\\!\" | singularity exec singularity-cowsay-3.04.sif cowthink -f flaming-sheep\n\n _________________\n( This is Sparta! )\n -----------------\n o . . .\n o . . . ` ,\n o .; . : .\u0027 : : : .\n o i..`: i` i.i.,i i .\n o `,--.|i |i|ii|ii|i:\n UooU\\.\u0027@@@@@@`.||\u0027\n \\__/(@@@@@@@@@@)\u0027\n (@@@@@@@@)\n `YY~~~~YY\u0027\n || ||\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker -t icaoberg/cowsay .\ndocker run icaoberg/cowsay cowsay -f flaming-sheep \"This is Sparta\\!\"\n _________________\n\u0026lt; This is Sparta! \u0026gt;\n -----------------\n \\ . . .\n \\ . . . ` ,\n \\ .; . : .\u0027 : : : .\n \\ i..`: i` i.i.,i i .\n \\ `,--.|i |i|ii|ii|i:\n UooU\\.\u0027@@@@@@`.||\u0027\n \\__/(@@@@@@@@@@)\u0027\n (@@@@@@@@)\n `YY~~~~YY\u0027\n || ||\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull icaoberg/cowsay\ndocker run icaoberg/cowsay cowsay -f flaming-sheep \"This is Sparta\\!\"\n _________________\n\u0026lt; This is Sparta! \u0026gt;\n -----------------\n \\ . . .\n \\ . . . ` ,\n \\ .; . : .\u0027 : : : .\n \\ i..`: i` i.i.,i i .\n \\ `,--.|i |i|ii|ii|i:\n UooU\\.\u0027@@@@@@`.||\u0027\n \\__/(@@@@@@@@@@)\u0027\n (@@@@@@@@)\n `YY~~~~YY\u0027\n || ||\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "drneavin/Demultiplexing_Doublet_Detecting_Docs", + "latest_release": "v2.0.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-demultiplexing_doublet_detecting_docs\" class=\"anchor\" aria-hidden=\"true\" href=\"#demultiplexing_doublet_detecting_docs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemultiplexing_Doublet_Detecting_Docs\u003c/h1\u003e\n\u003cp\u003eThis contains the code for \u003ca href=\"https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eDemuxafy\u003c/a\u003e - a demultiplexing and doublet removal pipeline.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1649098680.0 + "updated_at": 1667606897.0 }, { "data_format": 2, - "description": "Docker and Singularity containers for Material Point Method", + "description": "Python virtual environment on Singularity.", "filenames": [ - "Singularity" + "Singularity.venv" ], - "full_name": "cb-geo/mpm-container", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker image for Material Point Method\u003c/h1\u003e\u003ca id=\"user-content-docker-image-for-material-point-method\" class=\"anchor\" aria-label=\"Permalink: Docker image for Material Point Method\" href=\"#docker-image-for-material-point-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eKrishna Kumar\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/cbgeo/mpm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e4153a63c745e2c46fc5833d7f893c6f0846356f610fd8691a90ed0fbc554a5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f717561792d2d696d6167652d636267656f2d2d6d706d2d6666363962342e737667\" alt=\"Quay image\" data-canonical-src=\"https://img.shields.io/badge/quay--image-cbgeo--mpm-ff69b4.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/cbgeo/mpm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a8dd9d74b9dd3817dbb0f5bbc5720d61c538dbccb763cb41dc4069c518f557b9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d2d6875622d636267656f2d2d6d706d2d6666363962342e737667\" alt=\"Docker hub\" data-canonical-src=\"https://img.shields.io/badge/docker--hub-cbgeo--mpm-ff69b4.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/cb-geo/mpm-container\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dda80d8651c49313c225f64bcc0ba59b077dfbc1976bff8a19912be0cf6289fa/68747470733a2f2f636972636c6563692e636f6d2f67682f63622d67656f2f6d706d2d636f6e7461696e65722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/cb-geo/mpm-container.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://microbadger.com/images/cbgeo/mpm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b4e99c47692d447f60bb724b3ff4c76e4440713c50af617adc035460b178aaac/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f636267656f2f6d706d2e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/cbgeo/mpm.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClang 6.0\u003c/li\u003e\n\u003cli\u003eCMake 3\u003c/li\u003e\n\u003cli\u003eEigen 3\u003c/li\u003e\n\u003cli\u003eGCC 8\u003c/li\u003e\n\u003cli\u003eVim\u003c/li\u003e\n\u003cli\u003eIntelTBB\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing the docker image\u003c/h1\u003e\u003ca id=\"user-content-using-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Using the docker image\" href=\"#using-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe docker image can be used directly from the Docker Hub or Quay.io\u003c/li\u003e\n\u003cli\u003ePull the docker image \u003ccode\u003edocker pull cbgeo/mpm\u003c/code\u003e or \u003ccode\u003edocker pull quay.io/cbgeo/mpm\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo launch the \u003ccode\u003ecbgeo/mpm\u003c/code\u003e docker container, run \u003ccode\u003edocker run -ti cbgeo/mpm:latest /bin/bash\u003c/code\u003e or \u003ccode\u003edocker run -ti quay.io/cbgeo/mpm:latest /bin/bash\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo login as root\u003c/h1\u003e\u003ca id=\"user-content-to-login-as-root\" class=\"anchor\" aria-label=\"Permalink: To login as root\" href=\"#to-login-as-root\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLaunching docker as root user: \u003ccode\u003edocker exec -u 0 -ti \u0026lt;containerid\u0026gt; /bin/bash\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCreating an image from the docker file\u003c/h1\u003e\u003ca id=\"user-content-creating-an-image-from-the-docker-file\" class=\"anchor\" aria-label=\"Permalink: Creating an image from the docker file\" href=\"#creating-an-image-from-the-docker-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eTo build an image from docker file run as root \u003ccode\u003edocker build -t \"cbgeo/mpm\" /path/to/Dockerfile\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edocker history\u003c/code\u003e will show you the effect of each command has on the overall size of the file.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "bast/singularity-venv", + "latest_release": "0.3.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-python-virtual-environment-on-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#python-virtual-environment-on-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython virtual environment on Singularity\u003c/h1\u003e\n\u003cp\u003eHow to fetch the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/bast/singularity-venv/releases/download/0.3.0/venv.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eReads: \u003ccode\u003erequirements.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCreates: \u003ccode\u003evenv\u003c/code\u003e (folder)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRun \u003ccode\u003emyscript.py\u003c/code\u003e inside the virtual environment defined by \u003ccode\u003erequirements.txt\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./venv.sif python myscript.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpen python shell inside the virtual environment defined by \u003ccode\u003erequirements.txt\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./venv.sif python\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eI have used this wonderful guide as starting point and inspiration:\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, - "topics": [ - "mpm", - "docker", - "container" - ], - "updated_at": 1671734662.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1674419698.0 }, { "data_format": 2, - "description": "M\u0026Ms challenge example model for evaluation.", + "description": "Singularity recipe for Pandoc.", "filenames": [ - "Singularity" + "Singularity.pandoc" ], - "full_name": "vicmancr/mnms_example", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eM\u0026amp;Ms example project\u003c/h1\u003e\u003ca id=\"user-content-mms-example-project\" class=\"anchor\" aria-label=\"Permalink: M\u0026amp;Ms example project\" href=\"#mms-example-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is provided just as an example for illustration on the valiation phase of the MICCAI 2020 M\u0026amp;Ms challenge.\u003c/p\u003e\n\u003cp\u003eFollow the steps in the Singularity \u003ca href=\"https://github.com/vicmancr/mnms_example/blob/master/Singularity\"\u003erecipe\u003c/a\u003e to build your own image.\u003c/p\u003e\n\u003cp\u003eYou can test your image against the sample data writing \u003ccode\u003esingularity run my_image.sif sample_data output1\u003c/code\u003e inside this repository folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSegmentation model based on the open-source code by C. Baumgartner and L. Koch: \u003ca href=\"https://github.com/baumgach/acdc_segmenter\"\u003ehttps://github.com/baumgach/acdc_segmenter\u003c/a\u003e.\u003c/h5\u003e\u003ca id=\"user-content-segmentation-model-based-on-the-open-source-code-by-c-baumgartner-and-l-koch-httpsgithubcombaumgachacdc_segmenter\" class=\"anchor\" aria-label=\"Permalink: Segmentation model based on the open-source code by C. Baumgartner and L. Koch: https://github.com/baumgach/acdc_segmenter.\" href=\"#segmentation-model-based-on-the-open-source-code-by-c-baumgartner-and-l-koch-httpsgithubcombaumgachacdc_segmenter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "full_name": "bast/singularity-pandoc", + "latest_release": "0.3.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-for-pandoc\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-for-pandoc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity recipe for \u003ca href=\"https://pandoc.org/\" rel=\"nofollow\"\u003ePandoc\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eHow to fetch and use the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/bast/singularity-pandoc/releases/download/0.3.0/pandoc.sif\n$ ./pandoc.sif --from=markdown --to=rst --output=README.rst README.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI have used this wonderful guide as starting point and inspiration:\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1689044803.0 + "updated_at": 1642369145.0 }, { "data_format": 2, - "description": "A Singularity recipe for samtools, bcftools, minimap2, bwa, and seqtk", + "description": "Singularity instance for E2P2 (prediction of enzymatic functions). Clone the master version of the repository https://github.com/carnegie/E2P2", "filenames": [ - "Singularity" + "e2p2v4-container/Singularity", + "e2p2v3-container/Singularity" ], - "full_name": "phgenomics-singularity/hltools", + "full_name": "lipme/e2p2-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehltools\u003c/h1\u003e\u003ca id=\"user-content-hltools\" class=\"anchor\" aria-label=\"Permalink: hltools\" href=\"#hltools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Singularity recipe for samtools, bcftools, minimap2, bwa, and seqtk\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-e2p2-singularity-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#e2p2-singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eE2P2 Singularity Containers\u003c/h1\u003e\n\u003cp\u003eSingularity containers for E2P2 version 3 and 4 (prediction of enzymatic functions).\nE2P2 Source: \u003ca href=\"https://github.com/carnegie/E2P2\"\u003ehttps://github.com/carnegie/E2P2\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersions\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"e2p2v3-container/\"\u003ev3.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"e2p2v4-container/\"\u003ev4 (20221206)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainers\" class=\"anchor\" aria-hidden=\"true\" href=\"#maintainers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainers\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"mailto:Sebastien.Carrere@inrae.fr\"\u003eSebastien.Carrere@inrae.fr\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"mailto:Ludovic.Cottret@inrae.fr\"\u003eLudovic.Cottret@inrae.fr\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1698089586.0 + "updated_at": 1674491459.0 }, { "data_format": 2, - "description": "The next generation of graphical single-cell RNA-seq analysis pipeline for genomics scientists", + "description": null, "filenames": [ - "g_packages/official_py_docker/docker/Singularity" + "Singularity.py3-matt", + "Singularity.py3-21", + "Singularity.py3-pytorch", + "Singularity.snippy", + "Singularity.py2" ], - "full_name": "lanagarmire/granatumx", + "full_name": "RationalTangle/hcc", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGranatumX\u003c/h1\u003e\u003ca id=\"user-content-granatumx\" class=\"anchor\" aria-label=\"Permalink: GranatumX\" href=\"#granatumx\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003eWelcome to the next generation of graphical single-cell RNA-seq analysis pipeline for genomics scientists!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat\u0027s new\u003c/h2\u003e\u003ca id=\"user-content-whats-new\" class=\"anchor\" aria-label=\"Permalink: What\u0027s new\" href=\"#whats-new\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cul\u003e\n\u003cli\u003eCustomize processing with a modern plug-in system \u2013 add your favorite library!\u003c/li\u003e\n\u003cli\u003eLeverage multiple servers by deploying a single backend database with multiple frontend clients for processing\u003c/li\u003e\n\u003cli\u003eSave and manage multiple projects from one secure personal account\u003c/li\u003e\n\u003cli\u003eNew clean and intuitive interface with much more functionality\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSystem requirements\u003c/h2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-label=\"Permalink: System requirements\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLinux (Ubuntu 14.04 is tested)\u003c/li\u003e\n\u003cli\u003eDocker (19.03.4-ce is tested)\u003c/li\u003e\n\u003cli\u003eYarn (1.19.1 is tested)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eArchitecture\u003c/h2\u003e\u003ca id=\"user-content-architecture\" class=\"anchor\" aria-label=\"Permalink: Architecture\" href=\"#architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003eThe entire architecture has been re-designed from the ground up\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eServer-side:\n\u003cul\u003e\n\u003cli\u003eNodeJS + ExpressJS (web-serving)\u003c/li\u003e\n\u003cli\u003ePostgreSQL + Knex (database)\u003c/li\u003e\n\u003cli\u003eGraphile (postgres to graphql schema)\u003c/li\u003e\n\u003cli\u003eReact (server-side rendering)\u003c/li\u003e\n\u003cli\u003eDocker (containerized execution)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eClient-side:\n\u003cul\u003e\n\u003cli\u003eApollo (graphql client)\u003c/li\u003e\n\u003cli\u003eReact (component rendering)\u003c/li\u003e\n\u003cli\u003eMaterial-UI (theme)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating a Gbox\u003c/h2\u003e\u003ca id=\"user-content-creating-a-gbox\" class=\"anchor\" aria-label=\"Permalink: Creating a Gbox\" href=\"#creating-a-gbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGranatumX ships with a template to make it easier for developers to create new Gboxes. Below is a checklist for creating a new Gbox.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the GranatumX Source Code \u003ccode\u003egit clone https://gitlab.com/xz/GranatumX\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIn the GranatumX folder, run \u003ccode\u003emake setup\u003c/code\u003e. After setup completes, GranatumX should start on port \u003ccode\u003e34567\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCopy the Gbox template into the \u003ccode\u003eg_packages\u003c/code\u003e directory: \u003ccode\u003ecp gboxTemplate g_packages/yourPackageName\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit your \u003ccode\u003epackage.yaml\u003c/code\u003e file with your package information. See other packages in the \u003ccode\u003eg_packages\u003c/code\u003e folder for more examples.\u003c/li\u003e\n\u003cli\u003eEdit your \u003ccode\u003eDockerfile\u003c/code\u003e by adding any package installation scripts your Gbox requires.\u003c/li\u003e\n\u003cli\u003eEdit your \u003ccode\u003emain.py\u003c/code\u003e file with your application code (or whatever file you specified in \u003ccode\u003epackage.yaml \u0026gt; gboxes \u0026gt; endpoints \u0026gt; backend \u0026gt; cmd\u003c/code\u003e). The Python package \u003ccode\u003egranatum_sdk\u003c/code\u003e contains helper methods for easily interacting with the GranatumX core.\u003c/li\u003e\n\u003cli\u003eInstall your new gbox with \u003ccode\u003ecd gboxes; yarn run installEverything\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRefresh GranatumX and test your new Gbox.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning your own server\u003c/h2\u003e\u003ca id=\"user-content-running-your-own-server\" class=\"anchor\" aria-label=\"Permalink: Running your own server\" href=\"#running-your-own-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment Variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf starting the server with sudo you may need to add the -E flag to use your current environment variables (sudo -E make start).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePORT: Port # to listen to, i.e. 80, 3000, 8888\u003c/li\u003e\n\u003cli\u003eDATABASE_URL: Url to connect to your Postgresql database server\u003c/li\u003e\n\u003cli\u003eSSL_CERT: (optional) To serve Granatum on port 443 over SSL, set to certificate filepath\u003c/li\u003e\n\u003cli\u003eSSL_KEY: (optional) To serve Granatum on port 443 over SSL, set to private key filepath\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hcc\" class=\"anchor\" aria-hidden=\"true\" href=\"#hcc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehcc\u003c/h1\u003e\n\u003cp\u003eA collection of useful scripts and containers for use with the HCC cluster.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1614863068.0 + "updated_at": 1619559180.0 }, { "data_format": 2, "description": null, "filenames": [ - "tensorflow-gpu/Singularity", - "torch/Singularity", - "freesurfer/Singularity", - "caffe-cpu/Singularity", - "caffe-gpu/Singularity", - "neurokernel/Singularity", - "neurokernel/SingularityPC", - "digits/Singularity", - "caffe2/Singularity", - "theano/Singularity", - "cuda/Singularity", - "tensorflowgpu-theano-keras-pytorch/Singularity", - "tensorflow-cpu/Singularity" + "Singularity", + "Singularity.0.2.2", + "Singularity.0.1-alpha", + "Singularity.0.2.0", + "Singularity.0.2.1" ], - "full_name": "rses-singularity/all-images", + "full_name": "dcgc-bfx/singularity-single-cell", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity image definitons for ShARC\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-definitons-for-sharc\" class=\"anchor\" aria-label=\"Permalink: Singularity image definitons for ShARC\" href=\"#singularity-image-definitons-for-sharc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003emoved to \u003ca href=\"https://gitlab.hrz.tu-chemnitz.de/dcgc-bfx/singularity-single-cell\" rel=\"nofollow\"\u003ehttps://gitlab.hrz.tu-chemnitz.de/dcgc-bfx/singularity-single-cell\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1710324331.0 + "updated_at": 1656925540.0 }, { "data_format": 2, - "description": null, + "description": "PhysiCell Invasion Model", "filenames": [ - "misc/releases/22.12/Singularity.22.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/latest/Singularity" + "src/addons/PhysiBoSSa/MaBoSS-env-2.0/containers/singularity/Singularity" ], - "full_name": "ipc2023-classical/planner22", + "full_name": "vincent-noel/pc4ecm", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1688990711.0 + "updated_at": 1588946949.0 }, { "data_format": 2, - "description": null, + "description": "Demultiplex sequencing experiments with Nextflow", "filenames": [ - "run_singularity_basic/Singularity_openmpi.def", - "build_container_on_shub/Singularity" + "Singularity" ], - "full_name": "ResearchComputing/uwyo_2019", + "full_name": "czbiohub/demux", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-uwyo_2019\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#uwyo_2019\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003euwyo_2019\u003c/h1\u003e\n\u003cp\u003eResearch Computing is working to foster an inclusive and welcoming culture for everyone in our community. We recognize there is some non-inclusive language being used in our documents. We are working to resolve this.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-coredemux\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-coredemux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/demux\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eDemultiplex sequencing experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/demux\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/21a4d7a0262a3b096d2521e08bc4e18bf7340ea1b97ea36ca247a7d06ccd04b1/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f64656d75782e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/demux.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/demux\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8b87efd0f3651289c43d7f37a2a1092964f35f8501ccbcbe7ef15bfc1b38ae67/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f64656d75782e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/demux.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/demux pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 9, + "subscribers_count": 1, "topics": [], - "updated_at": 1662061107.0 + "updated_at": 1562208393.0 }, { "data_format": 2, - "description": "Promoter identification from diverse types of large-scale TSS profiling data", + "description": "Singularity recipe files for Stacks (http://catchenlab.life.illinois.edu/stacks/)", "filenames": [ - "Singularity" + "Singularity", + "Singularity.2.2", + "Singularity.2.0", + "Singularity.2.1" ], - "full_name": "BrendelGroup/TSRchitect", + "full_name": "powerPlant/stacks-srf", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tsrchitect-promoter-identification-from-diverse-types-of-large-scale-tss-profiling-data\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#tsrchitect-promoter-identification-from-diverse-types-of-large-scale-tss-profiling-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTSRchitect: Promoter identification from diverse types of large-scale TSS profiling data\u003c/h1\u003e\n\u003cp\u003eThe TSRchitect repository encompasses an \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e\npackage developed in the \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eBrendel Group\u003c/a\u003e for analyses\nof transcription start site data.\nThe code conforms to our \u003ca href=\"https://brendelgroup.github.io/\" rel=\"nofollow\"\u003eRAMOSE\u003c/a\u003e\nphilosophy: it generates \u003cstrong\u003ereproducible\u003c/strong\u003e, \u003cstrong\u003eaccurate\u003c/strong\u003e, and \u003cstrong\u003emeaningful\u003c/strong\u003e\nresults; it is \u003cstrong\u003eopen\u003c/strong\u003e (source) and designed to be \u003cstrong\u003escalable\u003c/strong\u003e and\n\u003cstrong\u003eeasy\u003c/strong\u003e to use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start-\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#quick-start-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start \u003ca href=\"https://singularity-hub.org/collections/1204\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eInput to TSRchitect will be transcription profiling read alignment data in \u003ccode\u003ebam\u003c/code\u003e\nor \u003ccode\u003ebed\u003c/code\u003e format as well as the appropriate genome annotation (if\navailable).\nOutput consists of predicted Transcription Start Sites (TSS) and Transcription\nStart Regions (TSR) as well as statistics summarizing the distribution and\ncharacteristics of identified TSSs and TSRs.\u003c/p\u003e\n\u003cp\u003eAll the TSRchitect dependencies are encapsulated in a\n\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available from\n\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\nThus, once you know what you are doing, execution could be as simple as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name tsr.simg shub://BrendelGroup/TSRchitect\nsingularity exec tsr.simg R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich will bring up an \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e console with the\nTSRchitect library and all its prerequisites available.\nFor example, in that console, you should see\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eR version 3.5.3 (2019-03-11) -- \"Great Truth\"\n...\n\u0026gt; packageVersion(\"TSRchitect\")\n[1] \u00271.17.3\u0027\n\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-realistic-start\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#realistic-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRealistic Start\u003c/h2\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the\n\u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\nOnce all preparatory steps are taken care of, see the \u003ca href=\"./demo/HOWTO.md\"\u003eHOWTO\u003c/a\u003e\ndocument for examples of how to load data into TSRchitect and predict and\ncharacterize promoters.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq-and-references\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#faq-and-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ and References\u003c/h2\u003e\n\u003cp\u003ePlease see\n\u003ca href=\"https://github.com/vpbrendel/TSRchitect/wiki/FAQ\"\u003eV. Brendel\u0027s TSRchitect FAQ\u003c/a\u003e\nfor usage examples and suggestions.\u003c/p\u003e\n\u003cp\u003eIf you find \u003cem\u003eTSRchitect\u003c/em\u003e useful, you may cite:\u003c/p\u003e\n\u003cp\u003eRaborn RT, Sridharan K, Brendel VP (2017)\n\u003cem\u003eTSRchitect: Promoter identification from large-scale TSS profiling data.\u003c/em\u003e\ndoi: 10.18129/B9.bioc.TSRchitect, \u003ca href=\"https://doi.org/doi:10.18129/B9.bioc.TSRchitect\" rel=\"nofollow\"\u003ehttps://doi.org/doi:10.18129/B9.bioc.TSRchitect\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOur own publications will be linked here in due course.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003ePlease direct all comments and suggestions to\n\u003ca href=\"mailto:vbrendel@indiana.edu\"\u003eVolker Brendel\u003c/a\u003e\nat \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eIndiana University\u003c/a\u003e and\n\u003ca href=\"mailto:rtraborn@asu.edu\"\u003eTaylor Raborn\u003c/a\u003e at his current address.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2270\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the Stacks software pipeline for building loci from short-read sequences\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1629530423.0 + "updated_at": 1611661899.0 }, { "data_format": 2, - "description": "Galileo + Events", + "description": "Sentinel 2 ARD processor", "filenames": [ - "env.d/Singularity" + "mpi-base/Singularity", + "base/Singularity" ], - "full_name": "CNCLgithub/galileo-ramp", + "full_name": "jncc/s2-ard-processor", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-galileo-ramp-v3\" class=\"anchor\" aria-hidden=\"true\" href=\"#galileo-ramp-v3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGalileo Ramp (v3)\u003c/h1\u003e\n\u003cp\u003eThe ramp-ball scenario for galileo\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eAll team members must\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a branch based off the current master (preferably on their own fork)\u003c/li\u003e\n\u003cli\u003eAdd commits to that new branch\u003c/li\u003e\n\u003cli\u003epush the new branch and submit a pull request to master\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-config\" class=\"anchor\" aria-hidden=\"true\" href=\"#config\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfig\u003c/h3\u003e\n\u003cp\u003eSimple setups on local hosts should run fine with the \u003ccode\u003edefault.conf\u003c/code\u003e.\nHowever, if there are any issues with \u003ccode\u003esingularity\u003c/code\u003e the create a \u003ccode\u003euser.conf\u003c/code\u003e\nwith correct attributes.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edefault.conf\u003c/code\u003e reads as:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[ENV]\u003c/span\u003e\nexec:singularity \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the path to singularity binary\u003c/span\u003e\npath:julia-cont \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the path to the singularity container\u003c/span\u003e\npython:pyenv \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the name of the conda environment\u003c/span\u003e\njulia_depot:.julia \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the relative path to set JULIA_DEPOT_PATH\u003c/span\u003e\nmounts:\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere are additional sections in \u003ccode\u003edefault.conf\u003c/code\u003e which are using for\nproject organization (\u003ccode\u003ePATHS\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[PATHS]\u003c/span\u003e\ndatabases:output/databases\ntraces:output/traces\nrenders:output/renders\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote:\nThe content in the config changes from time to time. If you run into issues after pulling, compare your \u003ccode\u003euser.conf\u003c/code\u003e to \u003ccode\u003edefault.conf\u003c/code\u003e to see if any of the keys have changed.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content-environment-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment building\u003c/h3\u003e\n\u003cp\u003eSimply run \u003ccode\u003esetup.sh\u003c/code\u003e in the root of this repo as follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh cont_pull conda julia\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will be prompted for sudo when building the container.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esetup.sh\u003c/code\u003e will then create the container at the path specified in the config (\u003ccode\u003ejulia-cont\u003c/code\u003e by default).\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNOTE: Like many commands in this setup, variables will be bound to those specified in \u003ccode\u003euser.conf\u003c/code\u003e if present or \u003ccode\u003edefault.conf\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIn the near future (yell at me if I forget), this script will, by default, attempt to download the container from a hosting service (probably dropbox). In that way, the user will not require sudo (and the container\u0027s behavior will be more consistent).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-runtime\" class=\"anchor\" aria-hidden=\"true\" href=\"#runtime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRuntime\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-interacting-with-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#interacting-with-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteracting with the container\u003c/h3\u003e\n\u003cp\u003eAfter running \u003ccode\u003esetup.sh\u003c/code\u003e, you can now use \u003ccode\u003erun.sh\u003c/code\u003e to use the environment.\u003c/p\u003e\n\u003cp\u003eThe synatx of \u003ccode\u003erun.sh\u003c/code\u003e is simply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./run.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecommand\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003ecommand\u003c/code\u003e can be any arbitrary bash expression.\u003c/p\u003e\n\u003cp\u003eFor example, you can probe the python version in the conda environment using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;: ./run.sh python3 --version\nNo user config found, using default\nINFO for ENV\n path =\u0026gt; julia-cont\n mounts =\u0026gt; \n exec =\u0026gt; singularity\n julia_depot =\u0026gt; .julia\n python =\u0026gt; pyenv\nPython 3.6.8 :: Anaconda, Inc.\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs you can see \u003ccode\u003e./run.sh\u003c/code\u003e first\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLoads the available config\u003c/li\u003e\n\u003cli\u003eReads out the config\u003c/li\u003e\n\u003cli\u003eExecutes the command\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-interacting-with-julia\" class=\"anchor\" aria-hidden=\"true\" href=\"#interacting-with-julia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteracting with Julia\u003c/h2\u003e\n\u003cp\u003eGetting into the \u003ccode\u003ejulia\u003c/code\u003e repl is simply\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;: ./run.sh julia\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eNo user config found, using default\nINFO for ENV\n path =\u0026gt; julia-cont\n mounts =\u0026gt; \n exec =\u0026gt; singularity\n julia_depot =\u0026gt; .julia\n python =\u0026gt; pyenv\n _\n _ _ _(_)_ | Documentation: https://docs.julialang.org\n (_) | (_) (_) |\n _ _ _| |_ __ _ | Type \"?\" for help, \"]?\" for Pkg help.\n | | | | | | |/ _` | |\n | | |_| | | | (_| | | Version 1.1.0 (2019-01-21)\n _/ |\\__\u0027_|_|_|\\__\u0027_| | Official https://julialang.org/ release\n|__/ |\n\njulia\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure that \u003ccode\u003eJULIA_DEPOT_PATH\u003c/code\u003e is set to that in the config (this should be taken care of by \u003ccode\u003erun.sh\u003c/code\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-julia\"\u003e\u003cpre\u003ejulia\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eDEPOT_PATH\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003eelement Array{String,\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e}\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.julia\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\njulia\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBoth \u003ccode\u003esetup.sh\u003c/code\u003e and \u003ccode\u003erun.sh\u003c/code\u003e use the included package info to setup Julia dependencies. Adding packages can be done normally using \u003ccode\u003eBase.pkg\u003c/code\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote:\nSome Julia packages (usually stale ones) will attempt to install system level dependencies. This will NOT work in a singularity container as it is immutable. You will have to edit the definition file (\u003ccode\u003eSingularity\u003c/code\u003e) to include this manually.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-scripts--experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-scripts--experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning scripts / experiments\u003c/h3\u003e\n\u003cp\u003eThe main method of executing elements within this package are via scripts found in the (queue drum roll) \u003ccode\u003escripts\u003c/code\u003e directory. If the script has a proper shebang and is executable, congrats, you just need to run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./run.sh scripts/my_script.\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eie\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[galileo-ramp]$ ./run.sh scripts/ramp_profile.py --help\nNo user config found, using default\npybullet build time: May 15 2019 00:10:22\nusage: ramp_profile.py [-h] [--table TABLE TABLE] [--table_steps TABLE_STEPS]\n [--ramp RAMP RAMP] [--ramp_steps RAMP_STEPS]\n [--ramp_angle RAMP_ANGLE] [--radius RADIUS]\n [--friction FRICTION] [--n_ramp N_RAMP] [--slurm]\n [--batch BATCH] [--debug] [--fresh]\n mass_file\n\nEvaluates the energy of mass ratios\n\npositional arguments:\n mass_file CSV file containing mass ratios\n\noptional arguments:\n -h, --help show this help message and exit\n --table TABLE TABLE XY dimensions of table. (default: (35, 18))\n --table_steps TABLE_STEPS\n Number of positions along X-axis. (default: 4)\n --ramp RAMP RAMP XY dimensions of ramp. (default: (35, 18))\n --ramp_steps RAMP_STEPS\n Number of positions along X-axis. (default: 4)\n --ramp_angle RAMP_ANGLE\n ramp angle in degrees (default: 0.5235987755982988)\n --radius RADIUS Ball radius. (default: 1.5)\n --friction FRICTION Ball friction (default: 0.4)\n --n_ramp N_RAMP Number of balls on ramp (default: 1)\n --slurm Use dask distributed on SLURM. (default: False)\n --batch BATCH Number of towers to search concurrently. (default: 1)\n --debug Run in debug (no rejection). (default: False)\n --fresh Ignore previous profiles (default: False)\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-project-layout\" class=\"anchor\" aria-hidden=\"true\" href=\"#project-layout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject Layout\u003c/h2\u003e\n\u003cp\u003eThe experiment is formatted in the form of a pip-compliant package under \u003ccode\u003egalileo_ramp\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe package is formatted as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e describes the GM\u003c/span\u003e\n/world \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e any nn components\u003c/span\u003e\n/models\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e utilities that do not reasonably belong in previous sections\u003c/span\u003e\n/utils\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEach of this sections will have their own documentation.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote:\nPlease maintain hygene between scripts and modules. Any standalone executable should be within scripts. Any piece of code that is imported across several scripts should be incorporated within the project package.\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-s2-ard-processor\" class=\"anchor\" aria-hidden=\"true\" href=\"#s2-ard-processor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eS2 ARD Processor\u003c/h1\u003e\n\u003cp\u003eDocker based sentinel 2 Analysis ready production system.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBase\u003c/h2\u003e\n\u003cp\u003eA base docker image packaging Dr Pete Buntings Python Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) software (\u003ca href=\"https://www.arcsi.remotesensing.info/\" rel=\"nofollow\"\u003ehttps://www.arcsi.remotesensing.info/\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eBased on the official ContinuumIO Miniconda3 release with python 3.5, base package contains a minimal installaition of ARCSI and its dependencies using the conda package manger, correct as of version 3.1.6 (conda reporting 3.6.1).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build-or-pull-arcsi-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-or-pull-arcsi-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild or Pull arcsi-base\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-build-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild image\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker build -t jncc/arcsi-base ./base/\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-pull-image-direction-from-docker-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-image-direction-from-docker-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull Image direction from docker hub\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker pull jncc/arcsi-base\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-run-image-interactively\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-image-interactively\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun image interactively\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker run -i -v \u0026lt;local mount point\u0026gt;:/data -t jncc/arcsi-base /bin/bash\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run a container and get help on ARCSI commandline options do:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run -t jncc/arcsi-base arcsi.py -h\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSee below under \"Docker example\" for a more detailed Sentinel-2 example.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker example\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -i -t -v \u003cspan class=\"pl-smi\"\u003e${local_data}\u003c/span\u003e:/data jncc/arcsi-base \\\n arcsi.py -s sen2 --stats -f KEA --fullimgouts -p RAD SHARP SATURATE CLOUDS TOPOSHADOW STDSREF DOSAOTSGL METADATA FOOTPRINT \\\n --interp near --outwkt /data/\u003cspan class=\"pl-smi\"\u003e${PATH_TO_OUTPUT_PROJ_WKT}\u003c/span\u003e --projabbv \u003cspan class=\"pl-smi\"\u003e${PROJ_ABBREVIATION}\u003c/span\u003e -t /data/tmp/ -o /data/output/ \\\n --dem /data/\u003cspan class=\"pl-smi\"\u003e${PATH_TO_DEM}\u003c/span\u003e -i /data/inputs/\u003cspan class=\"pl-smi\"\u003e${SINGLE_INPUT_FILE}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-see-also\" class=\"anchor\" aria-hidden=\"true\" href=\"#see-also\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSee also\u003c/h3\u003e\n\u003cp\u003eThanks to Markus Neteler (\u003ca href=\"https://github.com/mundialis/docker-arcsi\"\u003ehttps://github.com/mundialis/docker-arcsi\u003c/a\u003e), Edward P. Morris and Angelos Tzotsos for their work on the orignal ARCSI Dockerfile.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1689602229.0 + "updated_at": 1634223735.0 }, { "data_format": 2, - "description": "Salmonella serotyping at MDU", + "description": null, "filenames": [ "Singularity" ], - "full_name": "MDU-PHL/salmonella_typing", + "full_name": "Dill-PICL/GOMAP-base", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-in-silico-salmonella-enterica-serotyping\" class=\"anchor\" aria-hidden=\"true\" href=\"#in-silico-salmonella-enterica-serotyping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cem\u003eIn silico\u003c/em\u003e \u003cem\u003eSalmonella enterica\u003c/em\u003e Serotyping\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/MDU-PHL/salmonella_typing\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a60078aa4a16c8017cccb262835dc5b1c2d8a6543fc0049be78317ba7eec7b92/68747470733a2f2f636972636c6563692e636f6d2f67682f4d44552d50484c2f73616c6d6f6e656c6c615f747970696e672e7376673f7374796c653d73766726636972636c652d746f6b656e3d35303961353862363136306661346639623765613830613266356637363735343565303633326261\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/MDU-PHL/salmonella_typing.svg?style=svg\u0026amp;circle-token=509a58b6160fa4f9b7ea80a2f5f767545e0632ba\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-scope\" class=\"anchor\" aria-hidden=\"true\" href=\"#scope\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScope\u003c/h2\u003e\n\u003cp\u003eThe scripts presented in this repository are to be used to perform \u003cem\u003ein silico\u003c/em\u003e serotyping of \u003cem\u003eSalmonella enterica\u003c/em\u003e in accordance with MMS136. It takes as input a draft assembly and outputs a serotype inference. The draft assembly is obtained by performing a \u003cem\u003ede novo\u003c/em\u003e assembly on FASTQ data found to have passed MMS103 and to be identified as \u003cem\u003eSalmonella enterica\u003c/em\u003e by \u003cem\u003ekmer ID\u003c/em\u003e and by a wet laboratory method.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-glossary\" class=\"anchor\" aria-hidden=\"true\" href=\"#glossary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGlossary\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSerotype: A form of classification of bacteria below the species level. Usually involves some sort of reactive between specific sera and antigens on the bacteria\u0027s wall.\u003c/li\u003e\n\u003cli\u003eSerovar: In this case, a synonym of serotype.\u003c/li\u003e\n\u003cli\u003eSerogroup: A group of serovars with common antigens.\u003c/li\u003e\n\u003cli\u003eWGS: whole-genome sequence data. Usually, DNA sequence data comprised of short reads (between 35 and 300 bp in length) coded in the FASTQ format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-a-quick-primer-on-salmonella-serotypes\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-quick-primer-on-salmonella-serotypes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA quick primer on \u003cem\u003eSalmonella\u003c/em\u003e serotypes\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003esistr\u003c/code\u003e will only call serotypes that are valid under the WHO Collaborating Centre for Reference and Research on \u003cem\u003eSalmonella\u003c/em\u003e table of antigenic formulas for \u003cem\u003eSalmonella\u003c/em\u003e serovars, which can be found \u003ca href=\"https://www.pasteur.fr/sites/default/files/veng_0.pdf\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. The table follows the \u003cem\u003eKauffmann-White-Le Minor\u003c/em\u003e scheme (which is the \u003cem\u003eKauffmann-White\u003c/em\u003e scheme, but for historical reasons the WHOCC-Salm added \u003cem\u003eLe Minor\u003c/em\u003e\u0027s name to the scheme\u0027s name). According to the document, about 30 serovars are expected to account for about 90% of the \u003cem\u003eSalmonella\u003c/em\u003e in a country. The scheme, as presented in the document, describes a total of 2,579 serovars of \u003cem\u003eSalmonella\u003c/em\u003e, of which 2,557 are of the species \u003cem\u003eS. enterica\u003c/em\u003e and 22 are of the species \u003cem\u003eS. bongori\u003c/em\u003e (data on pg. 13).\u003c/p\u003e\n\u003cp\u003eThe genus \u003cem\u003eSalmonella\u003c/em\u003e is now known to have two species: \u003cem\u003eS. enterica\u003c/em\u003e and \u003cem\u003eS. bongori\u003c/em\u003e. The species \u003cem\u003eS. enterica\u003c/em\u003e has six subspecies: \u003cem\u003eenterica\u003c/em\u003e, \u003cem\u003esalamae\u003c/em\u003e, \u003cem\u003earizonae\u003c/em\u003e, \u003cem\u003ediarizonae\u003c/em\u003e, \u003cem\u003ehoutenae\u003c/em\u003e, and \u003cem\u003eindica\u003c/em\u003e. By far the most commonly found in human cases of Salmonellosis is \u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003eenterica\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eOriginally, the subspecies were believed to be subgenera named with roman numerals: I (now \u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003eenterica\u003c/em\u003e), II (\u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003esalamae\u003c/em\u003e), III (former genus \u003cem\u003eArizona\u003c/em\u003e: subdivided in to IIIa \u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003earizonae\u003c/em\u003e and IIIb \u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003ediarizonae\u003c/em\u003e), IV (\u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003ehoutenae\u003c/em\u003e), V (\u003cem\u003eS. bongori\u003c/em\u003e), and VI (\u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003eindica\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eIn the case of serotypes of \u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e the serotype is typically reported by a single name (e.g., Enteritidis, Typhi, Typhimurium). This is kept for historical reasons. Serotypes of all other subspecies of \u003cem\u003eS. enterica\u003c/em\u003e and \u003cem\u003eS. bongori\u003c/em\u003e are typically reported with the antigenic formula.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sistr\" class=\"anchor\" aria-hidden=\"true\" href=\"#sistr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSISTR\u003c/h2\u003e\n\u003cp\u003eTo perform \u003cem\u003eSalmonella enterica\u003c/em\u003e serotyping we use the tool \u003ccode\u003esistr\u003c/code\u003e [\u003ca href=\"#yoshida\"\u003e1\u003c/a\u003e] developed by Public Health Agency of Canada and held \u003ca href=\"https://github.com/peterk87/sistr_cmd\"\u003ehere\u003c/a\u003e. The tool has been extensively validated by others [\u003ca href=\"#yachison\"\u003e2\u003c/a\u003e].\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esistr\u003c/code\u003e uses a combination of approaches to infer serotype from draft assemblies of WGS data. For the purposes of MDU work, we have validated the use of the combination of \u003cem\u003eantigen\u003c/em\u003e detection and \u003cem\u003ecgMLST\u003c/em\u003e typing:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt uses \u003cem\u003eBLAST\u003c/em\u003e to identify the presence of annotated O- and H- antigen sequences. As such, it comes with curated multiFASTA files for the \u003cem\u003efliC\u003c/em\u003e, \u003cem\u003efliB\u003c/em\u003e, and \u003cem\u003ewzx\u003c/em\u003e and \u003cem\u003ewzy\u003c/em\u003e genes.\u003c/li\u003e\n\u003cli\u003eIt has a cgMLST scheme with 330 loci, and a database of 52 790 genomes (mostly comprising subspecies I) that have been typed at these loci and annotated with a serotype. It uses \u003cem\u003eBLAST\u003c/em\u003e to genotype the input assembly across as many of the 330 loci, and then calculates the pairwise distance of the input isolate to the database of curated genomes.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eAt the moment salmonella_typing installation is limited to installation from this repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install git+https://github.com/MDU-PHL/salmonella_typing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eDependencies\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYou will also need to ensure that \u003ccode\u003esistr v1.1.1\u003c/code\u003e is installed, instructions for this can be found \u003ca href=\"https://github.com/phac-nml/sistr_cmd\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecsvtk\u003c/code\u003e installation instructions can be found \u003ca href=\"https://github.com/shenwei356/csvtk\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-salmonella_serotyping\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-salmonella_serotyping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning salmonella_serotyping\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003estype run --help\nusage: stype run [-h] [--contigs CONTIGS] [--prefix PREFIX] [--jobs JOBS]\n\noptional arguments:\n -h, --help show this help message and exit\n --contigs CONTIGS, -c CONTIGS\n Tab-delimited file with sample ID as column 1 and path to assemblies as column 2 OR path to a contig file (used if only doing a single sample - should provide value for -pfx). (default: )\n --prefix PREFIX, -px PREFIX\n If running on a single sample, please provide a prefix for output directory (default: abritamr)\n --jobs JOBS, -j JOBS Number of AMR finder jobs to run in parallel. (default: 16)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSalmonella_typing can be on a single sample run by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003estype -c \u0026lt;path_to_contigs\u0026gt; -px \u0026lt;name_of_sample\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr in batch mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003estype -c input.tab -j 16\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003einput.tab\u003c/code\u003e is a tab-delimited file with column 1 being sample ID and column 2 is path to the assemblies.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-mdu-service\" class=\"anchor\" aria-hidden=\"true\" href=\"#mdu-service\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMDU Service\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: stype mdu [-h] [--runid RUNID] [--sistr SISTR]\n\noptional arguments:\n -h, --help show this help message and exit\n --runid RUNID, -r RUNID\n MDU RunID (default: Run ID)\n --sistr SISTR, -s SISTR\n Path to concatentated output of sistr (default: sistr_concatenated.csv)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to generate a LIMS friendly spreadsheet, collate all \u003ccode\u003estype\u003c/code\u003e results\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecsvtk concat sample_1/sistr_filtered.csv sample_2/sistr_filtered.csv ... \u0026gt; sistr_concatenated.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run \u003ccode\u003estype\u003c/code\u003e in \u003ccode\u003emdu\u003c/code\u003e mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003estype mdu -r RUNID -s sistr_concatenated.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eFile\u003c/th\u003e\n\u003cth align=\"center\"\u003eContents\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003esample_directory/sistr.csv\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eraw output of \u003ccode\u003esistr\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003esample_directory/sistr_filtered.csv\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\n\u003ccode\u003esistr\u003c/code\u003e output that has been filtered based on MDU business logic per sample\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003esistr_filtered.csv\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\n\u003ccode\u003esistr\u003c/code\u003e output that has been collated and filtered based on MDU business logic for batch\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003e\u0026lt;RUNID\u0026gt;_sistr.xlsx\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003ea spreadsheet ready for upload into MDU LIMS only output if \u003ccode\u003emdu\u003c/code\u003e used\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003e[\u003ca name=\"user-content-yoshida\"\u003e1\u003c/a\u003e] Yoshida, C. E., Kruczkiewicz, P., Laing, C. R., Lingohr, E. J., Gannon, V. P. J., Nash, J. H. E., \u0026amp; Taboada, E. N. (2016). The Salmonella \u003cem\u003eIn Silico\u003c/em\u003e Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies. PloS One, 11(1), e0147101.\u003c/p\u003e\n\u003cp\u003e[\u003ca name=\"user-content-yachison\"\u003e2\u003c/a\u003e] Yachison, C. A., Yoshida, C., Robertson, J., Nash, J. H. E., Kruczkiewicz, P., Taboada, E. N., \u2026 Nadon, C. (2017). The Validation and Implications of Using Whole Genome Sequencing as a Replacement for Traditional Serotyping for a National Salmonella Reference Laboratory. Frontiers in Microbiology, 8, 1044.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1184\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-gomap-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#gomap-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGOMAP-base\u003c/h1\u003e\n\u003cp\u003eThis is the base image for the GOMAP-singularity container. This base image has all the requirements installed for running GOMAP\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1685247135.0 + "updated_at": 1635539406.0 }, { "data_format": 2, - "description": "Containerization of Fermi Software", + "description": null, "filenames": [ - "Singularity", - "singularity/base/Singularity" + "Singularity.centos7" ], - "full_name": "fermi-lat/containers", + "full_name": "tashrifbillah/tbss_containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fermi-containerization-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#fermi-containerization-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFermi Containerization Project\u003c/h1\u003e\n\u003cp\u003eThis repository builds a \u003ca href=\"https://hub.docker.com/r/fermilat/glast_release/\" rel=\"nofollow\"\u003eDocker image\u003c/a\u003e and a \u003ca href=\"https://singularity-hub.org/collections/335/\" rel=\"nofollow\"\u003eSingularity image\u003c/a\u003e with GlastRelease.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install Singularity\u003c/h2\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-create-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-create-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Create the Image\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-21-bootstrap-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#21-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.1 Bootstrap the image\u003c/h3\u003e\n\u003cp\u003eBootstrapping means using something to build from, or not starting from nothing. In this case, we are goi\nng to use a build file that bootstraps a Docker image of CentOS 6. This build file is called \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e, and for more details about this you can\n\u003ca href=\"http://singularity.lbl.gov/docs-docker\" rel=\"nofollow\"\u003eread here\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 glast_release.img\nsudo singularity bootstrap glast_release.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-22-import-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#22-import-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.2. Import from Singularity Hub\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 glast_release.img\nsudo singularity import glast_release.img shub://fermi-lat/containers:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-how-do-i-shell-into-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-how-do-i-shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How do I shell into the container?\u003c/h2\u003e\n\u003cp\u003eSingularity has an easy, intuitive way to shell inside!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell glast_release.img\n Singularity: Invoking an interactive shell within container...\n Singularity.glast_release.img\u0026gt; cd /opt/workspace\n Singularity.glast_release.img\u0026gt; source bin/centos6-x86_64-64bit-gcc44/_setup.sh\n Singularity.glast_release.img\u0026gt; export startTime=0,1000\n Singularity.glast_release.img\u0026gt; ./bin/centos6-x86_64-64bit-gcc44/Gleam Gleam/src/jobOptions.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want the container to be writable (default isn\u0027t) then you will need root (on your local machine) and add the \u003ccode\u003e--writable\u003c/code\u003e option:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell --writable glast_release.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Getting Started\u003c/h2\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-build-your-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-build-your-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Build your image\u003c/h2\u003e\n\u003cp\u003eIf you want to look at or make changes to the code, it\u0027s recommended to install CVS package and to clone the repo and build the container locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/fermi-lat/containers\ncd containers/docker/base\nbash ../../bin/setup-workspace.sh [GlastRelease_version] [CVS_username]\ndocker build -t glast_release .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-how-do-i-shell-into-the-container-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-how-do-i-shell-into-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How do I shell into the container?\u003c/h2\u003e\n\u003cp\u003eBy default, running the container uses the \u003ccode\u003eENTRYPOINT\u003c/code\u003e, meaning it is used as an executable and you do not enter the container. In the case that you want to interactively work with the code, you may want to shell into the container. If there is a running container (eg an analysis) and you want to open up another terminal on your local machine to look inside (while it\u0027s running!) you need to get the 12 digit identifier with \u003ccode\u003edocker ps\u003c/code\u003e, and then plug it into this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker exec -it dc83a8d801a2 /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis says we want to execute (exec) and (interactive)(terminal) for container with id (dc83a8d801a2) and run the command (/bin/bash)\u003c/p\u003e\n\u003cp\u003eIf the container isn\u0027t running, then you can use \u003ccode\u003erun\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint /bin/bash glast_release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe container is provided on \u003ca href=\"https://hub.docker.com/r/fermilat/glast_release/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded from there when you run it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it docker.io/fermilat/glast_release:20-10-04-gr02 /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-pnlbwhtbss-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#pnlbwhtbss-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epnlbwh/tbss containers\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://dev.azure.com/tbillah/tbssDemo/_build/latest?definitionId=3\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0375fd1a7fe39935e578d504ee8235f549a7ca497c133e2efb9b82b9c4707952/68747470733a2f2f6465762e617a7572652e636f6d2f7462696c6c61682f7462737344656d6f2f5f617069732f6275696c642f7374617475732f7461736872696662696c6c61682e746273735f636f6e7461696e6572733f6272616e63684e616d653d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://dev.azure.com/tbillah/tbssDemo/_apis/build/status/tashrifbillah.tbss_containers?branchName=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository is set up to test Microsoft Azure Pipeline integrability for \u003ccode\u003epnlbwh/tbss\u003c/code\u003e pipeline. It contains recipes for Docker and Singularity containers. The recipes build following software:\u003c/p\u003e\n\u003cp\u003efsl-6.0.1-centos7\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/pnbwh/tbss.git\"\u003ehttps://github.com/pnbwh/tbss.git\u003c/a\u003e : master branch\u003c/p\u003e\n\u003cp\u003eANTs : conda install -c pnlbwh ants\u003c/p\u003e\n\u003cp\u003eView FSL license below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA salient clause of the license states it is not free for commercial use. So, if you use this image, make sure you are aware of that limitation. The maintainer of this image is not and cannot be held liable for unlawful use of this image.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1637372865.0 + "updated_at": 1615777455.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.discordance", - "Singularity.epa-ng", - "Singularity.malt", - "Singularity.PhyloBayes", - "Singularity.gappa", - "Singularity.ale", - "Singularity.megan6-ce", - "Singularity.iqtree.1.6.1", - "Singularity.megan5", - "Singularity.hifix", - "Singularity.papara", - "Singularity.checkM", - "Singularity.rp15", - "Singularity.PhyloBayesMPI", - "COME2018/Singularity.paml", - "COME2018/Singularity.beast2", - "COME2018/Singularity.bali-phy", - "COME2018/Singularity.swipe", - "COME2018/Singularity.iqtree.1.6.3", - "COME2018/Singularity.standard-raxml", - "COME2018/Singularity.seaview", - "COME2018/Singularity.bpp", - "COME2018/Singularity.amap-align", - "COME2018/Singularity.clustalx", - "COME2018/Singularity.blast", - "COME2018/Singularity.modeltest-ng", - "COME2018/Singularity.prank", - "COME2018/Singularity.fasttree", - "COME2018/Singularity.jmodeltest2", - "COME2018/Singularity.raxml-ng", - "COME2018/Singularity.fsa", - "COME2018/Singularity.codonphyml", - "COME2018/Singularity.probcons", - "COME2018/Singularity.gnuplot", - "COME2018/Singularity.clustalw", - "COME2018/Singularity.mummer", - "COME2018/Singularity.muscle", - "COME2018/Singularity.mafft", - "COME2018/Singularity.phyml", - "COME2018/Singularity.mcl" + "Singularity", + "def/edmr/Singularity", + "def/cytosim/Singularity", + "bck/Singularity" ], - "full_name": "maxemil/singularity-container", + "full_name": "kirsho/Singularity", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container\u003c/h2\u003e\n\u003cp\u003eTo use the containers in this repository, install the latest Singularity app (taken from \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/install-linux\u003c/a\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eVERSION=2.4.5\nwget https://github.com/singularityware/singularity/releases/download/\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e/singularity-\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e.tar.gz\ntar xvf singularity-\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e.tar.gz\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity-\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\n./configure --prefix=/usr/local\nmake\nsudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen you can build and use the containers using the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg Singularity.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -b \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecommand\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003earguments\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or if only a single command is available:\u003c/span\u003e\nsingularity run -b \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003earguments\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eMy notes about Singularity / Apptainer.\u003cbr\u003e\nHow to create a conda environment in an Singularity image with or without a .yml file.\nVisit \u003ccode\u003eyml2sing\u003c/code\u003e et \u003ccode\u003econda2sing\u003c/code\u003e for updated versions of my singularity image build scripts.\u003cbr\u003e\nRead \u003ccode\u003eIntro2Singularity.md\u003c/code\u003e to know more about Singularity (Install, use and tutos).\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1613184150.0 + "updated_at": 1655987254.0 }, { "data_format": 2, - "description": "Personal Singularity recipes", + "description": "Singularity container for DIVAnd", "filenames": [ - "Mamba/Singularity.alpine-edge", - "Mamba/Singularity.alpine-bareuser", - "Mamba/Singularity.alpine-lto", - "Mamba/Singularity.micromamba", - "Mamba/Singularity.lto-deps", - "Mamba/Singularity.alpine-user", - "Lmod/Singularity.Lmod-dev", - "Lmod/Singularity.Lmod-download", - "Lmod/Singularity.Lmod-alpine", - "Lmod/Singularity.Lmod-lmod.in", - "Lmod/Singularity.Lmod-lua.in" + "Singularity" ], - "full_name": "obilaniu/singularity-recipes", - "latest_release": null, + "full_name": "gher-ulg/DIVAnd-singularity", + "latest_release": "v1.0.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/gher-ulg/DIVAnd-singularity/actions?query=workflow%3A%22Singularity+Build%22\"\u003e\u003cimg src=\"https://github.com/gher-ulg/DIVAnd-singularity/workflows/Singularity%20Build/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.7014264\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbff15826258cff37ea5a790bb5970cb363766914fa530ce59fc3d0c4c598a13/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e373031343236342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.7014264.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-divand-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#divand-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDIVAnd-singularity\u003c/h1\u003e\n\u003cp\u003eSingularity container for DIVAnd, the interpolation tool.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eDIVAnd\u003c/code\u003e is available at \u003ca href=\"https://github.com/gher-ulg/DIVAnd.jl\"\u003ehttps://github.com/gher-ulg/DIVAnd.jl\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eInstall singularity: \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://sylabs.io/docs/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h1\u003e\n\u003cp\u003eAfter checking out the source, the singularity container can be build using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erm -f DIVAnd.sif; sudo singularity build DIVAnd.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first command is only needed if you already had the \u003ccode\u003e.sif\u003c/code\u003e file in your system.\u003cbr\u003e\nThe \u003cem\u003ebuild\u003c/em\u003e operation lasts severall minutes due to the download and installation of languages and libraries.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h1\u003e\n\u003cp\u003eContainer images are build using GitHub actions.\nGo to \u003ca href=\"https://github.com/gher-ulg/DIVAnd-singularity/actions\"\u003ehttps://github.com/gher-ulg/DIVAnd-singularity/actions\u003c/a\u003e choose the lastest commit and go to artefact.\nDownload and unzip the image file and run with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run DIVAnd.sif\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1631705492.0 + "updated_at": 1644927498.0 }, { "data_format": 2, - "description": "Code for Sietse Thesis", + "description": "A symbolic generalized MaxSAT solver based on dynamic programming", "filenames": [ - "Singularity" + "dmc/Singularity", + "lg/Singularity" ], - "full_name": "sietse93/Thesis", + "full_name": "zzwonder/DPMaxSAT", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-thesis\" class=\"anchor\" aria-hidden=\"true\" href=\"#thesis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThesis\u003c/h1\u003e\n\u003cp\u003eEvaluating SLAM in urban dynamic environments\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-experiments-in-carla\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-experiments-in-carla\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning experiments in CARLA\u003c/h2\u003e\n\u003cp\u003eDownload compiled version of CARLA. Two terminals required: a server and a client.\u003c/p\u003e\n\u003cp\u003eActivate server with this bash script in the compiled game directory. Run Carla server like this\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./CarlaServer.sh Town01 -windowed -ResX=600 -ResY=600\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure you loaded the correct town.\u003c/p\u003e\n\u003cp\u003eSimulation code are in directory carla0.9.4.\u003c/p\u003e\n\u003cp\u003eNote that everything works definitely in python 2.7.\u003c/p\u003e\n\u003cp\u003eIMPORTANT everything is dependent on this strict convention. Each name is dependent on town number, starting location, some dynamic varbiable. e.g. in directory stuckbehindvan T1_SL27_d15, means 15m distance to van in front. T1_SL27_d10 in directory VansOppositeRoad means 10 vehicles are spawned. The complete directory system will be based on this. So all converted files (png to rosbag, txt, json whatever) will be saved to the correct directory.\u003c/p\u003e\n\u003cp\u003eFurthermore, the main function of the scripts usually describe what is simulated. If you want to simulate a single trajectory/condition. Change the main function. If you want to simulate static conditions, this is usually possible in the stuckbehindvan main simulation scripts.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-specific-scenarios\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-specific-scenarios\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun specific scenarios\u003c/h3\u003e\n\u003cp\u003eTo run simulation of stuck behind van, there is a completely automated script which runs all scenarios in town 1 and writes out all stereo images to disk. Change values of T# accordingly in the name to simulate different town:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython StuckBehindVanAutomaticT1.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor VansOppositeRoad the following script only works for town01 and town02. Town03 uses different road_ids numbering so it doesn\u0027t work with this script.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython VansOppositeRoad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the end you will the scenario directory will have a ground truth txt file and stereo images .png files\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-convert-png-to-rosbag\" class=\"anchor\" aria-hidden=\"true\" href=\"#convert-png-to-rosbag\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConvert .png to rosbag\u003c/h2\u003e\n\u003cp\u003echange in the main function which files you want to convert.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eStereo2RosbagFunc.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the end scenario directory will have a rosbag file.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-orb_slam2\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-orb_slam2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning ORB_SLAM2\u003c/h2\u003e\n\u003cp\u003eSo if you want to run ORB SLAM2, download it from github and put the files that from this repository in the correct folder from ORB_SLAM2 and the recompile it. There is a bash script which shows how to run it. This also records everything.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./automatic_orb_record.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the end scenario directory will have a number of pose estimation files .txt\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data-conversion\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-conversion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData conversion\u003c/h2\u003e\n\u003cp\u003eA lot of data needs to be converted and there needs to be this balance between automating and flexibility. Therefore the following name convention is used:\nmain_... are the scripts that utilizes classes and functions\nclass_... describe the classes that are used in the main functions\nfunc_... describe the functions that are used by classes and main scripts.\u003c/p\u003e\n\u003cp\u003ethere is a mode convention throughout this pipeline:\n\"SLAM\" -\u0026gt; Conventional ORB SLAM\n\"VO\" -\u0026gt; bypassed loop closure ORB SLAM\n\"MC\" -\u0026gt; map point culling bypassed\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-convert-txt-files-into-json-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#convert-txt-files-into-json-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConvert txt files into json files\u003c/h3\u003e\n\u003cp\u003eJSON files contains the data but in the same reference frame. Change the main function to specify which files you want to convert.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython main_ConvertRefFrame.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the json describes a class described in python script: class_ConvertRefFrame.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-plot-to-visualize-what-is-happening\" class=\"anchor\" aria-hidden=\"true\" href=\"#plot-to-visualize-what-is-happening\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlot to visualize what is happening.\u003c/h3\u003e\n\u003cp\u003eVisualizes trajectory in 2D, euler angles, the whole mikmak. Also the relative pose error over a small distance.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython main_InspectData.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-plot-the-performance\" class=\"anchor\" aria-hidden=\"true\" href=\"#plot-the-performance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlot the performance\u003c/h3\u003e\n\u003cp\u003ethe python script class_ScenarioLocationPerformance.py converts the json files to usable classes that describe the performance.\u003c/p\u003e\n\u003cp\u003eexample of how to use them in script Results_Scenario1_SLAM.py\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dpms-dynamic-programming-for-generalized-maxsat\" class=\"anchor\" href=\"#dpms-dynamic-programming-for-generalized-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPMS (Dynamic Programming for Generalized MaxSAT)\u003c/h1\u003e\n\u003cp\u003eDPMS handles generalized MaxSAT problems in an extended DIMACS format (described below)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe DPMS framework runs in two phases:\n\u003cul\u003e\n\u003cli\u003ePlanning phase: \u003ca href=\"./lg\"\u003eLG\u003c/a\u003e constructs a (graded) project-join tree of a generalized MaxSAT formula\u003c/li\u003e\n\u003cli\u003eExecution phase: \u003ca href=\"./dmc\"\u003eDMC\u003c/a\u003e computes the answer to a generalized MaxSAT formula using the (graded) project-join tree\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-linux\" class=\"anchor\" href=\"#installation-linux\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation (Linux)\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eautomake 1.16\u003c/li\u003e\n\u003cli\u003ecmake 3.16\u003c/li\u003e\n\u003cli\u003eg++ 9.3\u003c/li\u003e\n\u003cli\u003egmp 6.2\u003c/li\u003e\n\u003cli\u003emake 4.2\u003c/li\u003e\n\u003cli\u003ealready included as git submodules:\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003ecudd 3.0\u003c/a\u003e (a slightly modified version for DPMS is inlcuded. Needs to be compiled manually, see below)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts 2.2\u003c/a\u003e (included)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/trolando/sylvan\"\u003esylvan 1.5\u003c/a\u003e(included)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-cudd-add-supporter\" class=\"anchor\" href=\"#compile-cudd-add-supporter\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile CUDD (ADD supporter)\u003c/h3\u003e\n\u003cp\u003eIn addmc/libraries/cudd, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./INSTALL.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-lg-tree-builder\" class=\"anchor\" href=\"#compile-lg-tree-builder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile LG (Tree Builder)\u003c/h3\u003e\n\u003cp\u003eIn lg/, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, see \u003ca href=\"lg/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-dmc-executor\" class=\"anchor\" href=\"#compile-dmc-executor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile DMC (Executor)\u003c/h3\u003e\n\u003cp\u003eIn dmc/, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake dmc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, see \u003ca href=\"dmc/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-example-command-line\" class=\"anchor\" href=\"#usage-example-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage Example (Command Line)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ecnfFile=\"examples/hybrid.hwcnf\" \u0026amp;\u0026amp; lg/build/lg \"lg/solvers/flow-cutter-pace17/flow_cutter_pace17 -p 100\" \u0026lt; $cnfFile | dmc/dmc --cf=$cnfFile --mx=1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure to use \"--mx=1\" to enable maxSAT.\u003c/p\u003e\n\u003cp\u003eUse the option \"--mb=BOUND\" to give an upper bound (int) of optimal cost (e.g., the result of o-line of a MaxSAT solver) for ADD pruning. For example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecnfFile=\"examples/hybrid.hwcnf\" \u0026amp;\u0026amp; lg/build/lg \"lg/solvers/flow-cutter-pace17/flow_cutter_pace17 -p 100\" \u0026lt; $cnfFile | dmc/dmc --cf=$cnfFile --mx=1 --mb=60000\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor a WBO or partial MaxSAT instance, --mb is set to be the trivial bound which can be read from the instance, unless the user gives a better bound.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-benchmarks-for-evaluations-of-ijcai-22-submission\" class=\"anchor\" href=\"#benchmarks-for-evaluations-of-ijcai-22-submission\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarks for evaluations of IJCAI-22 submission\u003c/h2\u003e\n\u003cp\u003ePlease see the directory benchmarks_results\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-problem-format-of-generalized-maxsat\" class=\"anchor\" href=\"#problem-format-of-generalized-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProblem format of Generalized MaxSAT\u003c/h2\u003e\n\u003cp\u003eSome examples of each type of problem can be found in examples/\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-generalized-maxsat-and-weighted-maxsat\" class=\"anchor\" href=\"#generalized-maxsat-and-weighted-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e(generalized) MaxSAT and weighted MaxSAT\u003c/h3\u003e\n\u003cp\u003eThe Max-CNF-SAT problems (.cnf) should use the DIMACS format: \u003ca href=\"https://www.ieee.org/conferences/publishing/templates.html\" rel=\"nofollow\"\u003ehttps://www.ieee.org/conferences/publishing/templates.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor XOR constraints, use \u0027x\u0027 at the beginning of a line\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ex1 xor x2 xor \\neg x3 =\u0026gt; x 1 2 -3 0.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor weighted MaxSAT (.cnf), use \"p wcnf nvars nclauses total-Soft-Weight\" instead of \"p cnf nvars nclauses\" in header. For each clause line, put the weight at the beginning of a line, then the first literal.\u003c/p\u003e\n\u003cp\u003eDPMS also accepts the hybrid weighted MaxSAT format (.hwcnf), take exapmles/hybrid.hwcnf for an example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ep hwcnf 7 8 100\n[3] +1 x1 +1 x2 \u0026gt;= 1 ;\n[2] -1 x1 -1 x2 \u0026gt;= -1 ;\n[10] -1 x3 +1 x2 \u0026gt;= 0 ;\n[9] -1 x3 +1 x4 \u0026gt;= 0 ;\n[12] +1 x3 -1 x2 -1 x4 \u0026gt;= -1 ;\n[34] -1 x5 +1 x6 \u0026gt;= 0 ;\n[15] -1 x5 +1 x7 \u0026gt;= 0 ;\n[7] x 1 2 3 4 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn a .hwcnf file, weights are always in front of each constraint, wrapped by \u0027[]\u0027. Each constraint after the weight can be a CNF clause, XOR or a pseudo-Boolean constraint.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pseudo-boolean-optimization-wbo\" class=\"anchor\" href=\"#pseudo-boolean-optimization-wbo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePseudo-Boolean optimization (WBO)\u003c/h3\u003e\n\u003cp\u003eFor PB constraints (.wbo), here is an example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+1 x1 +1 x2 \u0026gt;= 1 ;\n[90] -1 x1 -1 x2 \u0026gt;= -1 ;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first constraint is a hard constraint. The second constraint is soft with weight 90.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-min-maxsat\" class=\"anchor\" href=\"#min-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMin-MaxSAT\u003c/h3\u003e\n\u003cp\u003eA Min-MaxSAT problem file is same with a MaxSAT file except that there is a \u0027vm\u0027 line indicating the min variables. Variables that do not appear in the vm line are all max variables.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 0, + "subscribers_count": 2, "topics": [], - "updated_at": 1622393172.0 + "updated_at": 1651985375.0 }, { "data_format": 2, - "description": "Deprecated repo. If you think you anything need from this, look at quip-docker and scream if it\u0027s not there", + "description": null, "filenames": [ - "Singularity" + "attempts/attempt5/Singularity_v5", + "attempts/attempt2/Singularity_v2", + "attempts/attempt4/Singularity_v4", + "attempts/attempt1/Singularity_v1", + "attempts/attempt3/Singularity_v3", + "attempts/attempt6/Singularity_v6", + "attempts/attempt7/Singularity_v7" ], - "full_name": "libAtoms/docker-quip-base", + "full_name": "zeng-su123/git_segmentation", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-quip-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#quip-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003equip-base\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/libAtoms/docker-quip-base\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a85436fd275dfe3b0867b90fdc6c7637a2d9df2d26fccc8d9f42b2e1f09becba/68747470733a2f2f7472617669732d63692e6f72672f6c696241746f6d732f646f636b65722d717569702d626173652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/libAtoms/docker-quip-base.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/libAtoms/docker-quip-base.svg?branch=master\" rel=\"nofollow\"\u003ehttps://travis-ci.org/libAtoms/docker-quip-base.svg?branch=master\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Docker image with a scientific stack that is used for building \u003ccode\u003eQUIP\u003c/code\u003e.\nThe image is hosted (and automatically built) on Docker hub as\n\u003ca href=\"https://hub.docker.com/r/libatomsquip/quip-base/\" rel=\"nofollow\"\u003elibatomsquip/quip-base\u003c/a\u003e.\nYou probably don\u0027t want to use this image directly, instead look for\none of the QUIP images on \u003ca href=\"https://hub.docker.com/u/libatomsquip/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/u/libatomsquip/\u003c/a\u003e,\nprobably \u003ca href=\"https://hub.docker.com/r/libatomsquip/quip/\" rel=\"nofollow\"\u003elibatomsquip/quip\u003c/a\u003e.\nor use it in your \u003ccode\u003eFROM\u003c/code\u003e line. See also:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/libAtoms/QUIP\"\u003ehttps://github.com/libAtoms/QUIP\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/libAtoms/QUIP/tree/public/docker\"\u003ehttps://github.com/libAtoms/QUIP/tree/public/docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.libatoms.org\" rel=\"nofollow\"\u003ehttps://www.libatoms.org\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cp\u003eThis image does not contain QUIP, but everything needed to build it\nplus many tools and codes that we find useful.\u003c/p\u003e\n\u003cp\u003eStack contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 2.7 image (based on Debian)\u003c/li\u003e\n\u003cli\u003eBuild tools (gcc, gfortran)\u003c/li\u003e\n\u003cli\u003eOpenMP compiled version of OpenBLAS as default maths libraries\u003c/li\u003e\n\u003cli\u003eNumpy, SciPy, Matplotlib, ase...\u003c/li\u003e\n\u003cli\u003eJulia in \u003ccode\u003e/opt/julia\u003c/code\u003e with IJulia, PyCall, PyPlot, JuLIP...\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003cp\u003eThe image includes interatomic potentials in \u003ccode\u003e/opt/share/potentials\u003c/code\u003e\npublished on \u003ca href=\"http://www.libatoms.org/Home/DataRepository\" rel=\"nofollow\"\u003ehttp://www.libatoms.org/Home/DataRepository\u003c/a\u003e which has Gaussian\nApproximation Potentials for:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTungsten\u003c/li\u003e\n\u003cli\u003eIron\u003c/li\u003e\n\u003cli\u003eWater\u003c/li\u003e\n\u003cli\u003eAmorphous carbon\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eTo make or request changes, open a merge request or issue in the\n\u003ca href=\"https://github.com/libAtoms/docker-quip-base\"\u003eGitHub repository\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePackages should be added to where the usual istallation commands\n(e.g. \u003ccode\u003eapt-get\u003c/code\u003e, \u003ccode\u003epip\u003c/code\u003e, ...) are in the Dockerfile, with the exception\nthat Julia pacakes are listed at the beginning of the Julia section.\u003c/p\u003e\n\u003cp\u003eSmall software package builds can be added at the end of the Dockerfile.\nLarger software applications are included in the\n\u003ca href=\"https://hub.docker.com/r/libatomsquip/quip-base-software/\" rel=\"nofollow\"\u003elibatomsquip/quip-base-software\u003c/a\u003e\nimage in the \u003ccode\u003eSoftware\u003c/code\u003e subdirectory.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mms-challenge-2020\" class=\"anchor\" href=\"#mms-challenge-2020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eM\u0026amp;Ms Challenge 2020\u003c/h1\u003e\n\u003cp\u003eThe CMR images have been segmented by experienced clinicians from the respective institutions, including contours\nfor the left (LV) and right ventricle (RV) blood pools, as well as for the left ventricular myocardium (MYO).\nLabels are: 1 (LV), 2 (MYO) and 3 (RV)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-motivation\" class=\"anchor\" href=\"#motivation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003cp\u003eIn the recent years, many machine/deep learning models have been proposed to accurately segment cardiac structures\nin magnetic resonance imaging. However, when these models are tested on unseen datasets acquired from distinct\nMRI scanners or clinical centres, the segmentation accuracy can be greatly reduced.\u003c/p\u003e\n\u003cp\u003eThe M\u0026amp;Ms challenge aims to contribute to the effort of building generalisable models that can be applied consistently\nacross clinical centres. Furthermore, M\u0026amp;Ms will provide a reference dataset for the community to build and assess\nfuture generalisable models in CMR segmentation.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-environment-setup\" class=\"anchor\" href=\"#environment-setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment Setup\u003c/h2\u003e\n\u003cp\u003eTo use the code, the user needs to set te environment variable to access the data. At your ~/.bashrc add:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MMsCardiac_DATA_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/path/to/data/M\u0026amp;MsData/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlso, the user needs to to pre-install a few packages:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install wheel setuptools\n$ pip install -r requirements.txt\n$ pip install torch==1.5.0+cu101 torchvision==0.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n$ pip install torchcontrib~=0.0.2\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-data-preparation\" class=\"anchor\" href=\"#data-preparation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData preparation\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-train-csv\" class=\"anchor\" href=\"#train-csv\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrain csv\u003c/h4\u003e\n\u003cp\u003eYou can generate train csv for dataloaders using \u003ccode\u003epython3 preprocess/generate_train_df.py\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eusage: generate_train_df.py [-h] [--meta_graphs]\n\nM\u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003eMs 2020 Challenge - Training info generation\n\noptional arguments:\n -h, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n --meta_graphs Generate train meta information graphs\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-data-refactor\" class=\"anchor\" href=\"#data-refactor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Refactor\u003c/h4\u003e\n\u003cp\u003eLoad each volume to extract only 1 slice is time consuming. To solve this, save each slice in numpy arrays:\n\u003ccode\u003epython3 preprocess/dataloader_refactor.py\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-global-training-mean-and-std\" class=\"anchor\" href=\"#global-training-mean-and-std\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGlobal Training Mean and STD\u003c/h4\u003e\n\u003cp\u003eYou can easily get global mean and std from labeled training samples using \u003ccode\u003epython3 preprocess/get_mean_std.py\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-data-description\" class=\"anchor\" href=\"#data-description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Description\u003c/h2\u003e\n\u003cp\u003eThe challenge cohort is composed of 350 patients with hypertrophic and dilated cardiomyopathies\nas well as healthy subjects. All subjects were scanned in clinical centres in three different\ncountries (Spain, Germany and Canada) using four different magnetic resonance\nscanner vendors (Siemens, General Electric, Philips and Canon).\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eHospital\u003c/th\u003e\n\u003cth align=\"center\"\u003eNum. studies\u003c/th\u003e\n\u003cth align=\"center\"\u003eCountry\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eClinica Sagrada Familia\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHospital de la Santa Creu i Sant Pau\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHospital Universitari Dexeus\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHospital Vall d\u0027Hebron\u003c/td\u003e\n\u003ctd align=\"center\"\u003e100\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eMcGill University Health Centre\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCanada\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eUniversit\u00e4tsklinikum Hamburg-Eppendorf\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGermany\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-training-set-15025-studies\" class=\"anchor\" href=\"#training-set-15025-studies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining set (150+25 studies)\u003c/h3\u003e\n\u003cp\u003eThe training set will contain 150 annotated images from two different MRI vendors (75 each) and 25 unannotated\nimages from a third vendor. The CMR images have been segmented by experienced clinicians from the respective\ninstitutions, including contours for the left (LV) and right ventricle (RV) blood pools, as well as for the\nleft ventricular myocardium (MYO). Labels are: 1 (LV), 2 (MYO) and 3 (RV).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-testing-set-200-studies\" class=\"anchor\" href=\"#testing-set-200-studies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting set (200 studies)\u003c/h3\u003e\n\u003cp\u003eThe 200 test cases correspond to 50 new studies from each of the vendors provided in the training set and\n50 additional studies from a fourth unseen vendor, that will be tested for model generalizability.\n20% of these datasets will be used for validation and the rest will be reserved for testing and ranking participants.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-standard-operating-procedure-sop-for-data-annotation\" class=\"anchor\" href=\"#standard-operating-procedure-sop-for-data-annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStandard Operating Procedure (SOP) for data annotation\u003c/h3\u003e\n\u003cp\u003eIn order to build a useful dataset for the community we have decided to build on top of\n\u003ca href=\"https://ieeexplore.ieee.org/document/8360453\" rel=\"nofollow\"\u003eACDC MICCAI 2017\u003c/a\u003e challenge SOP and correct our contours accordingly.\u003c/p\u003e\n\u003cp\u003eIn particular, clinical contours have been corrected by two in-house annotators that had to agree on the final result.\nThese annotators followed these rules:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLV and RV cavities must be completely covered, with papillary muscles included.\u003c/li\u003e\n\u003cli\u003eNo interpolation of the LV myocardium must be performed at the base.\u003c/li\u003e\n\u003cli\u003eRV must have a larger surface in end-diastole compared to end-systole and avoid the pulmonary artery.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe main difficulty and source of disagreement is the exact RV form in basal slices.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-results\" class=\"anchor\" href=\"#results\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h2\u003e\n\u003cp\u003eUsing ACDC checkpoint:\u003c/p\u003e\n\u003cp\u003eAverage -\u0026gt; 0.7397 -\u0026gt; 0.9933 (background), 0.6931 (LV), 0.5624 (MYO), 0.71(RV)\u003c/p\u003e\n\u003cp\u003eCalculated using resnet34_unet_imagenet_encoder, Adam and constant learning rate. Fold metrics are calculated\nusing mean of averaged iou and dice values. Only mnms data.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth\u003eNormalization\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7958\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8272\u003c/td\u003e\n\u003ctd\u003e0.8064\u003c/td\u003e\n\u003ctd\u003e0.8107\u003c/td\u003e\n\u003ctd\u003e0.8220\u003c/td\u003e\n\u003ctd\u003e0.8124\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8163\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8384\u003c/td\u003e\n\u003ctd\u003e0.8382\u003c/td\u003e\n\u003ctd\u003e0.8336\u003c/td\u003e\n\u003ctd\u003e0.8498\u003c/td\u003e\n\u003ctd\u003e0.8352\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8066\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8359\u003c/td\u003e\n\u003ctd\u003e0.8235\u003c/td\u003e\n\u003ctd\u003e0.8281\u003c/td\u003e\n\u003ctd\u003e0.8310\u003c/td\u003e\n\u003ctd\u003e0.8250\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7711\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7745\u003c/td\u003e\n\u003ctd\u003e0.7993\u003c/td\u003e\n\u003ctd\u003e0.8248\u003c/td\u003e\n\u003ctd\u003e0.7791\u003c/td\u003e\n\u003ctd\u003e0.7897\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8058\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8324\u003c/td\u003e\n\u003ctd\u003e0.8322\u003c/td\u003e\n\u003ctd\u003e0.8138\u003c/td\u003e\n\u003ctd\u003e0.8433\u003c/td\u003e\n\u003ctd\u003e0.8254\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7970\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8382\u003c/td\u003e\n\u003ctd\u003e0.8212\u003c/td\u003e\n\u003ctd\u003e0.8313\u003c/td\u003e\n\u003ctd\u003e0.8344\u003c/td\u003e\n\u003ctd\u003e0.8244\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7977\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8150\u003c/td\u003e\n\u003ctd\u003e0.8053\u003c/td\u003e\n\u003ctd\u003e0.8188\u003c/td\u003e\n\u003ctd\u003e0.8212\u003c/td\u003e\n\u003ctd\u003e0.8116\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8184\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8400\u003c/td\u003e\n\u003ctd\u003e0.8339\u003c/td\u003e\n\u003ctd\u003e0.8408\u003c/td\u003e\n\u003ctd\u003e0.8469\u003c/td\u003e\n\u003ctd\u003e0.8360\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8096\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8377\u003c/td\u003e\n\u003ctd\u003e0.8230\u003c/td\u003e\n\u003ctd\u003e0.8286\u003c/td\u003e\n\u003ctd\u003e0.8316\u003c/td\u003e\n\u003ctd\u003e0.8261\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7842\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8373\u003c/td\u003e\n\u003ctd\u003e0.8254\u003c/td\u003e\n\u003ctd\u003e0.8333\u003c/td\u003e\n\u003ctd\u003e0.8318\u003c/td\u003e\n\u003ctd\u003e0.8224\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8235\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8556\u003c/td\u003e\n\u003ctd\u003e0.7736\u003c/td\u003e\n\u003ctd\u003e0.8477\u003c/td\u003e\n\u003ctd\u003e0.8598\u003c/td\u003e\n\u003ctd\u003e0.8320\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8221\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8494\u003c/td\u003e\n\u003ctd\u003e0.8349\u003c/td\u003e\n\u003ctd\u003e0.8453\u003c/td\u003e\n\u003ctd\u003e0.8503\u003c/td\u003e\n\u003ctd\u003e0.8404\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7783\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8101\u003c/td\u003e\n\u003ctd\u003e0.8041\u003c/td\u003e\n\u003ctd\u003e0.8021\u003c/td\u003e\n\u003ctd\u003e0.8331\u003c/td\u003e\n\u003ctd\u003e0.8055\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8162\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8378\u003c/td\u003e\n\u003ctd\u003e0.8330\u003c/td\u003e\n\u003ctd\u003e0.8322\u003c/td\u003e\n\u003ctd\u003e0.8456\u003c/td\u003e\n\u003ctd\u003e0.8329\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7971\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8328\u003c/td\u003e\n\u003ctd\u003e0.8065\u003c/td\u003e\n\u003ctd\u003e0.8251\u003c/td\u003e\n\u003ctd\u003e0.8291\u003c/td\u003e\n\u003ctd\u003e0.8181\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7893\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7775\u003c/td\u003e\n\u003ctd\u003e0.7257\u003c/td\u003e\n\u003ctd\u003e0.8152\u003c/td\u003e\n\u003ctd\u003e0.8162\u003c/td\u003e\n\u003ctd\u003e0.7847\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8091\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8367\u003c/td\u003e\n\u003ctd\u003e0.8204\u003c/td\u003e\n\u003ctd\u003e0.8215\u003c/td\u003e\n\u003ctd\u003e0.8436\u003c/td\u003e\n\u003ctd\u003e0.8262\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7320\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8234\u003c/td\u003e\n\u003ctd\u003e0.7945\u003c/td\u003e\n\u003ctd\u003e0.8245\u003c/td\u003e\n\u003ctd\u003e0.8173\u003c/td\u003e\n\u003ctd\u003e0.7983\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_ce -\u0026gt; 0.5,0.3,0.2,0.65 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7962\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8384\u003c/td\u003e\n\u003ctd\u003e0.8157\u003c/td\u003e\n\u003ctd\u003e0.8053\u003c/td\u003e\n\u003ctd\u003e0.8181\u003c/td\u003e\n\u003ctd\u003e0.8147\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_ce -\u0026gt; 0.5,0.3,0.2,0.65 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7915\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8398\u003c/td\u003e\n\u003ctd\u003e0.8148\u003c/td\u003e\n\u003ctd\u003e0.8291\u003c/td\u003e\n\u003ctd\u003e0.8244\u003c/td\u003e\n\u003ctd\u003e0.8199\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePrincipal conclusions: bce_dice_border_ce with 0.5,0.2,0.2,0.2,0.5 - lr 0.001/0.0001 - standardize.\u003c/p\u003e\n\u003cp\u003eNow, using lr 0.001, standardize and bce_dice_border_ce with 0.5,0.2,0.2,0.2,0.5, explore data augmentation.\nWithout data augmentation score 0.8360.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eData Augmentation\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVertical flip\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8004\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8273\u003c/td\u003e\n\u003ctd\u003e0.8176\u003c/td\u003e\n\u003ctd\u003e0.8074\u003c/td\u003e\n\u003ctd\u003e0.8386\u003c/td\u003e\n\u003ctd\u003e0.8182\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHorizontal flip\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8032\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8225\u003c/td\u003e\n\u003ctd\u003e0.8226\u003c/td\u003e\n\u003ctd\u003e0.8244\u003c/td\u003e\n\u003ctd\u003e0.8318\u003c/td\u003e\n\u003ctd\u003e0.8209\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRandom Crops\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8137\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8376\u003c/td\u003e\n\u003ctd\u003e0.8208\u003c/td\u003e\n\u003ctd\u003e0.8283\u003c/td\u003e\n\u003ctd\u003e0.7876\u003c/td\u003e\n\u003ctd\u003e0.8181\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eShift\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8117\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8240\u003c/td\u003e\n\u003ctd\u003e0.8222\u003c/td\u003e\n\u003ctd\u003e0.8330\u003c/td\u003e\n\u003ctd\u003e0.8307\u003c/td\u003e\n\u003ctd\u003e0.8243\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eDownscale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7949\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8192\u003c/td\u003e\n\u003ctd\u003e0.8166\u003c/td\u003e\n\u003ctd\u003e0.8219\u003c/td\u003e\n\u003ctd\u003e0.8384\u003c/td\u003e\n\u003ctd\u003e0.8181\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eElastic Transform\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7991\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8425\u003c/td\u003e\n\u003ctd\u003e0.8274\u003c/td\u003e\n\u003ctd\u003e0.8213\u003c/td\u003e\n\u003ctd\u003e0.8408\u003c/td\u003e\n\u003ctd\u003e0.8262\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRotations\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8158\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8426\u003c/td\u003e\n\u003ctd\u003e0.8255\u003c/td\u003e\n\u003ctd\u003e0.8290\u003c/td\u003e\n\u003ctd\u003e0.8524\u003c/td\u003e\n\u003ctd\u003e0.8330\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eGrid Distortion\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8028\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8361\u003c/td\u003e\n\u003ctd\u003e0.7864\u003c/td\u003e\n\u003ctd\u003e0.8275\u003c/td\u003e\n\u003ctd\u003e0.8231\u003c/td\u003e\n\u003ctd\u003e0.8151\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eOptical Distortion\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7705\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8418\u003c/td\u003e\n\u003ctd\u003e0.8255\u003c/td\u003e\n\u003ctd\u003e0.7996\u003c/td\u003e\n\u003ctd\u003e0.8354\u003c/td\u003e\n\u003ctd\u003e0.8145\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-competition-models\" class=\"anchor\" href=\"#competition-models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompetition Models\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-bala-1\" class=\"anchor\" href=\"#bala-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003eBala 1\u003c/em\u003e\n\u003c/h4\u003e\n\u003cp\u003eUsing standardization, data augmentation combination old and bce_dice_border_ce with 0.5,0.2,0.2,0.2,0.5.\nResnet34 Unet with lr 0.001 and adam optimizer.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eweakly -\u0026gt; labeled\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8286\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8596\u003c/td\u003e\n\u003ctd\u003e0.8505\u003c/td\u003e\n\u003ctd\u003e0.8540\u003c/td\u003e\n\u003ctd\u003e0.8482\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ecombined -\u0026gt; labeled\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8271\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8473\u003c/td\u003e\n\u003ctd\u003e0.8424\u003c/td\u003e\n\u003ctd\u003e0.8573\u003c/td\u003e\n\u003ctd\u003e0.8435\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-bala-2\" class=\"anchor\" href=\"#bala-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003eBala 2\u003c/em\u003e\n\u003c/h4\u003e\n\u003cp\u003eUsing standardization, data augmentation combination old and bce_dice_border_ce with 0.5,0.2,0.2,0.2,0.5\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eResnet34 Unet lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8092\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8257\u003c/td\u003e\n\u003ctd\u003e0.8115\u003c/td\u003e\n\u003ctd\u003e0.8293\u003c/td\u003e\n\u003ctd\u003e0.8276\u003c/td\u003e\n\u003ctd\u003e0.8207\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-not-pretrained-model\" class=\"anchor\" href=\"#not-pretrained-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNot Pretrained Model\u003c/h3\u003e\n\u003cp\u003eFolding by patient.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth\u003eNormalization\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.65 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7873\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8263\u003c/td\u003e\n\u003ctd\u003e0.8004\u003c/td\u003e\n\u003ctd\u003e0.8195\u003c/td\u003e\n\u003ctd\u003e0.7616\u003c/td\u003e\n\u003ctd\u003e0.7990\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.65 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7741\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7879\u003c/td\u003e\n\u003ctd\u003e0.7743\u003c/td\u003e\n\u003ctd\u003e0.7883\u003c/td\u003e\n\u003ctd\u003e0.8071\u003c/td\u003e\n\u003ctd\u003e0.7863\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-update-11062020-meeting\" class=\"anchor\" href=\"#update-11062020-meeting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUpdate: 11/06/2020 Meeting\u003c/h2\u003e\n\u003cp\u003eChanges and ideas:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Use 2 folds grouping by vendor (A vs. B), instead of \u003cem\u003en\u003c/em\u003e grouping by patient. Then error analysis by vendor\u003c/li\u003e\n\u003cli\u003e[x] Since is not permited the use of pre-trained models, try smaller architectures\u003c/li\u003e\n\u003cli\u003e[ ] Create convolutional network that learns to distinguish if an image comes from vendor A or vendor B. \u00bfWorks?\n\u003cul\u003e\n\u003cli\u003eIf works then we can create a DCGAN trying to apply a initial transformation to fool the discriminator and\ndo something like normalize the input images! \u003cstrong\u003eNote\u003c/strong\u003e: Do not add vendor C in CNN classification step since\nwe will use it for validate our GAN later.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] Self-Supervised Learning for unseen vendor C\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-folding-by-vendor-resuts\" class=\"anchor\" href=\"#folding-by-vendor-resuts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFolding by Vendor Resuts\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-wrong-folding-no-train-subpartitionpatients-to-compare\" class=\"anchor\" href=\"#wrong-folding-no-train-subpartitionpatients-to-compare\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e(Wrong folding, no train subpartition/patients to compare)\u003c/h4\u003e\n\u003cp\u003eNormalization by reescale. Criterion bce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth align=\"center\"\u003eDA\u003c/th\u003e\n\u003cth align=\"center\"\u003eA -\u0026gt; B\u003c/th\u003e\n\u003cth align=\"center\"\u003eB -\u0026gt; A\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_pspnet_unet - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7573\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7121\u003c/td\u003e\n\u003ctd\u003e0.7346\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_pspnet_unet - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6838\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.5532\u003c/td\u003e\n\u003ctd\u003e0.6185\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_pspnet_unet - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7612\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6793\u003c/td\u003e\n\u003ctd\u003e0.7202\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_pspnet_unet - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6982\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.5580\u003c/td\u003e\n\u003ctd\u003e0.6281\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_unet_scratch - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7498\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6835\u003c/td\u003e\n\u003ctd\u003e0.7166\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_unet_scratch - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6779\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4997\u003c/td\u003e\n\u003ctd\u003e0.5888\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_unet_scratch - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7421\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6627\u003c/td\u003e\n\u003ctd\u003e0.7023\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_unet_scratch - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7588\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6281\u003c/td\u003e\n\u003ctd\u003e0.6934\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet_scratch - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7649\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6313\u003c/td\u003e\n\u003ctd\u003e0.6980\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet_scratch - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7189\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6273\u003c/td\u003e\n\u003ctd\u003e0.6731\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet_scratch - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7673\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6530\u003c/td\u003e\n\u003ctd\u003e0.7101\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet_scratch - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7707\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6128\u003c/td\u003e\n\u003ctd\u003e0.6917\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003enano_unet - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.5035\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4284\u003c/td\u003e\n\u003ctd\u003e0.4659\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003enano_unet - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4432\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.2821\u003c/td\u003e\n\u003ctd\u003e0.3626\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003enano_unet - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4871\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4771\u003c/td\u003e\n\u003ctd\u003e0.4821\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003enano_unet - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4310\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.2187\u003c/td\u003e\n\u003ctd\u003e0.3248\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGeneral conclusions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModels can extract more information and thus make better predictions when training with Vendor \u0027A\u0027\nand then testing on \u0027B\u0027. GAN should approximate images to Vendor A?\u003c/li\u003e\n\u003cli\u003elr 0.001 works better than lower ones.\u003c/li\u003e\n\u003cli\u003eNot clear difference using data augmentation and without apply it...\u003c/li\u003e\n\u003cli\u003eIntermediate models size, resnet18_pspnet_unet, performs better than bigger ones and smaller ones.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-11-random-patients-to-compare\" class=\"anchor\" href=\"#11-random-patients-to-compare\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e11 random patients to compare\u003c/h4\u003e\n\u003cp\u003eCriterion bce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5. Using resnet18_pspnet_unet.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eNormalization\u003c/th\u003e\n\u003cth align=\"center\"\u003eData Augmentation\u003c/th\u003e\n\u003cth align=\"center\"\u003eLearning Rate\u003c/th\u003e\n\u003cth align=\"center\"\u003eA -\u0026gt; B\u003c/th\u003e\n\u003cth align=\"center\"\u003eB -\u0026gt; A\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination (Old)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7328\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6915\u003c/td\u003e\n\u003ctd\u003e0.7121\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination (Old)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7601\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6704\u003c/td\u003e\n\u003ctd\u003e0.7152\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination (Old)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6593\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4914\u003c/td\u003e\n\u003ctd\u003e0.5753\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination (Old)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7499\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6342\u003c/td\u003e\n\u003ctd\u003e0.6920\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7502\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7014\u003c/td\u003e\n\u003ctd\u003e0.7258\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7561\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6723\u003c/td\u003e\n\u003ctd\u003e0.7142\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7370\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.5143\u003c/td\u003e\n\u003ctd\u003e0.6257\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7123\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6826\u003c/td\u003e\n\u003ctd\u003e0.6975\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7462\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7283\u003c/td\u003e\n\u003ctd\u003e0.7372\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7668\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6312\u003c/td\u003e\n\u003ctd\u003e0.6990\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7098\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6280\u003c/td\u003e\n\u003ctd\u003e0.6689\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7606\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6604\u003c/td\u003e\n\u003ctd\u003e0.7105\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGeneral conclusions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhen using Vendor A as training set, generalizes better to Vendor B cases.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-classification-vendor-a---b-discriminator\" class=\"anchor\" href=\"#classification-vendor-a---b-discriminator\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClassification: Vendor \u0027A\u0027 - \u0027B\u0027 Discriminator\u003c/h2\u003e\n\u003cp\u003eUsing resnet18_pspnet_classification model. Adam with bce. 60 epochs and *0.1 steps as 25 and 50.\nImg size 224x224. fold_system=\"patient\" \u0026amp; label_type=\"vendor_label\". Normalization standardize. Learning rate 0.001.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eData Augmentation\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9954\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9726\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9878\u003c/td\u003e\n\u003ctd\u003e0.9970\u003c/td\u003e\n\u003ctd\u003e0.9906\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9954\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9771\u003c/td\u003e\n\u003ctd\u003e0.9985\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9939\u003c/td\u003e\n\u003ctd\u003e0.9930\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-classification-vendor-a---b---c-discriminator\" class=\"anchor\" href=\"#classification-vendor-a---b---c-discriminator\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClassification: Vendor \u0027A\u0027 - \u0027B\u0027 - \u0027C\u0027 Discriminator\u003c/h2\u003e\n\u003cp\u003eAdam with bce. 80 epochs and *0.1 steps as 25 and 60.\nImg size 224x224. fold_system=\"patient\" \u0026amp; label_type=\"vendor_label\". Normalization standardize. Learning rate 0.001.\nData Augmentation combination (old).\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eModel\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_pspnet\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9954\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9726\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9878\u003c/td\u003e\n\u003ctd\u003e0.9970\u003c/td\u003e\n\u003ctd\u003e0.9906\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_pspnet\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9954\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9771\u003c/td\u003e\n\u003ctd\u003e0.9985\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9939\u003c/td\u003e\n\u003ctd\u003e0.9930\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9910\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9871\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9740\u003c/td\u003e\n\u003ctd\u003e0.9805\u003c/td\u003e\n\u003ctd\u003e0.9865\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-discriminator-entropy-backwards-a---b---c\" class=\"anchor\" href=\"#discriminator-entropy-backwards-a---b---c\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscriminator Entropy backwards \u0027A\u0027 - \u0027B\u0027 - \u0027C\u0027\u003c/h2\u003e\n\u003cp\u003eUsing gradient gamma 0.99, max iterations 250, standardize normalization. Segmentator Training with \u0027A\u0027.\nBaseline: 0.7799 IOU on B.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eOut threshold\u003c/th\u003e\n\u003cth align=\"center\"\u003eTarget\u003c/th\u003e\n\u003cth align=\"center\"\u003eMore\u003c/th\u003e\n\u003cth align=\"center\"\u003eB\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7825\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7829\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7713\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7723\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7725\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7744\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7826\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7828\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7713\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7723\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7725\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7744\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7826\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7828\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7828\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7713\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7723\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7725\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7744\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003eProblem with low out thresholds... Waste all iterations and stops.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-discriminator-entropy-backwards-a---b---c--with-blur-unblur-and-gamma\" class=\"anchor\" href=\"#discriminator-entropy-backwards-a---b---c--with-blur-unblur-and-gamma\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscriminator Entropy backwards \u0027A\u0027 - \u0027B\u0027 - \u0027C\u0027 / With blur, unblur and gamma\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eOut threshold\u003c/th\u003e\n\u003cth align=\"center\"\u003eEntropy\u003c/th\u003e\n\u003cth align=\"center\"\u003eBlur\u003c/th\u003e\n\u003cth align=\"center\"\u003eUnblur\u003c/th\u003e\n\u003cth align=\"center\"\u003eGamma\u003c/th\u003e\n\u003cth align=\"center\"\u003eTarget\u003c/th\u003e\n\u003cth align=\"center\"\u003eIters\u003c/th\u003e\n\u003cth align=\"center\"\u003eB\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e100\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7770\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e100\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7786\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.000001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.000001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.000001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e100\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7779\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-7-july\" class=\"anchor\" href=\"#7-july\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e7 July\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hausdorff-loss-tests\" class=\"anchor\" href=\"#hausdorff-loss-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHausdorff loss tests\u003c/h3\u003e\n\u003cp\u003eMean average values for 5 folds. Data combination old. Lr 0.001 with resnet_unet_scratch.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eHausdorff Weight\u003c/th\u003e\n\u003cth align=\"center\"\u003eIOU A\u003c/th\u003e\n\u003cth align=\"center\"\u003eIOU B\u003c/th\u003e\n\u003cth\u003eDICE A\u003c/th\u003e\n\u003cth\u003eDICE B\u003c/th\u003e\n\u003cth\u003eHAUSSDORF A\u003c/th\u003e\n\u003cth\u003eHAUSSDORF B\u003c/th\u003e\n\u003cth\u003eASSD A\u003c/th\u003e\n\u003cth\u003eASSD B\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7333\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7835\u003c/td\u003e\n\u003ctd\u003e0.8087\u003c/td\u003e\n\u003ctd\u003e0.8561\u003c/td\u003e\n\u003ctd\u003e4.4773\u003c/td\u003e\n\u003ctd\u003e3.4890\u003c/td\u003e\n\u003ctd\u003e1.2458\u003c/td\u003e\n\u003ctd\u003e0.9624\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.05\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7417\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7867\u003c/td\u003e\n\u003ctd\u003e0.8158\u003c/td\u003e\n\u003ctd\u003e0.8589\u003c/td\u003e\n\u003ctd\u003e4.0958\u003c/td\u003e\n\u003ctd\u003e3.4073\u003c/td\u003e\n\u003ctd\u003e1.1618\u003c/td\u003e\n\u003ctd\u003e0.9646\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7399\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003ctd\u003e0.8153\u003c/td\u003e\n\u003ctd\u003e0.8550\u003c/td\u003e\n\u003ctd\u003e4.1999\u003c/td\u003e\n\u003ctd\u003e3.4355\u003c/td\u003e\n\u003ctd\u003e1.1925\u003c/td\u003e\n\u003ctd\u003e0.9735\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7421\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7806\u003c/td\u003e\n\u003ctd\u003e0.8193\u003c/td\u003e\n\u003ctd\u003e0.8522\u003c/td\u003e\n\u003ctd\u003e4.2831\u003c/td\u003e\n\u003ctd\u003e3.4414\u003c/td\u003e\n\u003ctd\u003e1.1953\u003c/td\u003e\n\u003ctd\u003e0.9831\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7370\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7790\u003c/td\u003e\n\u003ctd\u003e0.8134\u003c/td\u003e\n\u003ctd\u003e0.8534\u003c/td\u003e\n\u003ctd\u003e4.3634\u003c/td\u003e\n\u003ctd\u003e3.4972\u003c/td\u003e\n\u003ctd\u003e1.2264\u003c/td\u003e\n\u003ctd\u003e0.9886\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-other\" class=\"anchor\" href=\"#other\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDevelopment environment -\u0026gt; CUDA 10.1 and cudnn 7603. Python 3.8.2 - GCC 9.3.0\u003c/li\u003e\n\u003cli\u003eChallenge homepage \u003ca href=\"https://www.ub.edu/mnms/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eACDC nomenclature: 0, 1, 2 and 3 represent voxels located in the background, in the right ventricular cavity,\nin the myocardium, and in the left ventricular cavity, respectively.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 15, + "subscribers_count": 1, "topics": [], - "updated_at": 1674927515.0 + "updated_at": 1652799880.0 }, { "data_format": 2, - "description": null, + "description": "Reproducibility package for the verification of laminar 3D pipe flow w/ OpenFOAM", "filenames": [ - "src/hpccm_containers/Singularity.template", - "src/hpccm_containers/libertem/Singularity.libertem", - "src/hpccm_containers/libertem/Singularity.libertem-0.4.0", - "src/hpccm_containers/libertem/Singularity.libertem-0.5.0", - "src/hpccm_containers/libertem/Singularity.libertem-0.7.0", - "src/hpccm_containers/libertem/Singularity.libertem-0.2.2", - "src/hpccm_containers/libertem/Singularity.libertem-0.4.1", - "src/hpccm_containers/libertem/Singularity.libertem-0.8.0", - "src/hpccm_containers/libertem/Singularity.libertem-0.5.1", - "src/hpccm_containers/libertem/Singularity.libertem-0.9.0", - "src/hpccm_containers/libertem/Singularity.libertem-0.6.0", - "src/hpccm_containers/macs/Singularity.macs", - "src/hpccm_containers/nullarbor/Singularity.nullarbor", - "src/hpccm_containers/seqtk/Singularity.seqtk", - "src/hpccm_containers/python/Singularity.python", - "src/hpccm_containers/shift/Singularity.shift", - "src/hpccm_containers/minc-toolkit-v2/Singularity.minc-toolkit-v2", - "src/hpccm_containers/octopus/Singularity.octopus", - "src/hpccm_containers/alphafold/Singularity.alphafold-hpccm", - "src/hpccm_containers/alphafold/Singularity.alphafold", - "src/hpccm_containers/ccp-em/Singularity.ccp-em", - "src/hpccm_containers/caffe-unet/Singularity.caffe-unet", - "src/hpccm_containers/auto07p/Singularity.auto07p", - "src/hpccm_containers/salmonte/Singularity.salmonte", - "src/hpccm_containers/scipion/Singularity.scipion", - "src/hpccm_containers/py4dstem/Singularity.py4dstem", - "src/hpccm_containers/perfsonar/Singularity.perfsonar", - "src/hpccm_containers/mpi-test/Singularity.mpi-test", - "src/hpccm_containers/pymol/Singularity.pymol", - "src/hpccm_containers/biogrinder/Singularity.biogrinder", - "src/hpccm_containers/dristhi/Singularity.dristhi", - "src/hpccm_containers/anaconda/Singularity.anaconda", - "src/hpccm_containers/others/Singularity.template", - "src/hpccm_containers/kraken2/Singularity.kraken2", - "src/hpccm_containers/sourcetracker/Singularity.sourcetracker", - "src/hpccm_containers/cp2k/Singularity.cp2k", - "src/hpccm_containers/base/Singularity.base", - "src/hpccm_containers/mashtree/Singularity.mashtree", - "src/hpccm_containers/audacity/Singularity.audacity", - "src/hpccm_containers/zonation/Singularity.zonation", - "src/hpccm_containers/dwarfs/Singularity.dwarfs", - "src/hpccm_containers/prokka/Singularity.prokka", - "src/hpccm_containers/minerl/Singularity.minerl", - "src/hpccm_containers/tensorrt/Singularity.tensorrt", - "src/hpccm_containers/openmpi/Singularity.hybrid-hpccm", - "src/hpccm_containers/openmpi/Singularity.hybrid", - "src/hpccm_containers/fsl/Singularity.fsl", - "src/hpccm_containers/crisprdetect/Singularity.crisprdetect", - "src/hpccm_containers/fastsurfer/Singularity.fastsurfer", - "src/hpccm_containers/chimerax/Singularity.chimerax", - "src/hpccm_containers/haystack_bio/Singularity.haystack_bio-0.5.0", - "src/hpccm_containers/haystack_bio/Singularity.haystack_bio-0.5.5", - "src/hpccm_containers/haystack_bio/Singularity.haystack_bio", - "src/hpccm_containers/jbrowse/Singularity.jbrowse", - "src/hpccm_containers/cfdem/Singularity.cfdem" + "Singularity" ], - "full_name": "0luhancheng0/hpccm-containers", + "full_name": "piyueh/openfoam-pipe-flow-verification", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpccm-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpccm-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPCCM containers\u003c/h1\u003e\n\u003cp\u003eThis repository contains a set python scripts that build containers (mostly in singularity) from Nvidia\u0027s \u003ca href=\"https://github.com/NVIDIA/hpc-container-maker\"\u003eHPC Container Makers (HPCCM)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that only the default arguments are tested. And those recipe with suffix \u003ccode\u003e.wip\u003c/code\u003e are not tested.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ewget\nflit\nfire\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eflit install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-update-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#update-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUpdate recipes\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ecd src/hpccm_containers\n./update-recipe.sh\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eA reproducibility package for simulation verification of pipe flow with OpenFOAM.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-creating-a-singularityapptainer-image\" class=\"anchor\" href=\"#creating-a-singularityapptainer-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a Singularity/Apptainer image\u003c/h3\u003e\n\u003chr\u003e\n\u003cp\u003eWe use a Singularity (or what is now named Apptainer) image to provide all required tools (e.g.,\nOpenFOAM, Gmsh, Python, etc.). Using a Singularity image guarantees that we don\u0027t need to install\nanything else on working machines or HPC clusters.\u003c/p\u003e\n\u003cp\u003eIf the current user has root privilege, then simply do (assuming currently under the repo folder):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity build openfoam9.sif ./Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf the current user does not have root privilege, try\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --fakeroot openfoam9.sif ./Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe above command requires the system admins to correctly configure the user namespace. To see if\nthe user namespace is configured correctly, check the files \u003ccode\u003e/etc/subuid\u003c/code\u003e and \u003ccode\u003e/etc/subgid\u003c/code\u003e and find\nthe line with your username and your group:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat /etc/subuid\n-------------------\n...\n\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour username\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea number much greater than \u003cspan class=\"pl-k\"\u003e65535\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea number greater than \u003cspan class=\"pl-k\"\u003e98765\u0026gt;\u003c/span\u003e\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat /etc/subgid\n-------------------\n...\n\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ethe group you belong to\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea number much greater than \u003cspan class=\"pl-k\"\u003e65535\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea number greater than \u003cspan class=\"pl-k\"\u003e98765\u0026gt;\u003c/span\u003e\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf these two options do not work for you, try the cloud builders: \u003ca href=\"https://cloud.sylabs.io/builder\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/builder\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFinally, if using Apptainer (the newer community version of Singularity), just substitute\n\u003ccode\u003esingularity\u003c/code\u003e with \u003ccode\u003eapptainer\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-creating-case-folders\" class=\"anchor\" href=\"#creating-case-folders\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating case folders\u003c/h3\u003e\n\u003chr\u003e\n\u003cp\u003eTo create case folders:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e openfoam9.sif python ./main.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs this is a reproducibility package, what cases to create are hard coded.\u003c/p\u003e\n\u003cp\u003eTo create a custom case, use the following command to see how to use \u003ccode\u003emain.py\u003c/code\u003e to do so:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e openfoam9.sif python ./main.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003c/em\u003e!! The script \u003ccode\u003emain.py\u003c/code\u003e will create mesh files on the fly. Generating meshes requires a\nnon-trivial amount of memory, especially for the case \u003ccode\u003eairflow-pipe-256\u003c/code\u003e (and \u003ccode\u003eairflow-pip-512\u003c/code\u003e, if\nit exists). It may crash on small personal desktops and laptops.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-cases\" class=\"anchor\" href=\"#running-cases\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning cases\u003c/h3\u003e\n\u003chr\u003e\n\u003cp\u003eEach case has a file \u003ccode\u003ejob.sh\u003c/code\u003e that can be used as either a Slurm job script or a regular shell\nscript.\u003c/p\u003e\n\u003cp\u003eIf using it as a Slurm script, note that the resource configuration in \u003ccode\u003ejob.sh\u003c/code\u003e is based on the\nPegasus cluster at the George Washington University. You may need to manually change the\nconfiguration based on your cluster.\u003c/p\u003e\n\u003cp\u003eTo submit a case using Slurm:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea \u003cspan class=\"pl-k\"\u003ecase\u003c/span\u003e folder\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n$ sbatch job.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ejob.sh\u003c/code\u003e uses the OpenFOAM installation in the Singularity/Apptainer image. So the cluster must\nhave Singularity/Apptainer. The script assumes the cluster uses Lmod and loads\nSingularity through \u003ccode\u003emodule load singularity\u003c/code\u003e. The cluster also needs OpenMPI 4.0+ and loads\nOpenMPI through \u003ccode\u003emodule load openmpi/gcc/64/4.1.0\u003c/code\u003e. Modify them based on the actual clusters being\nused.\u003c/p\u003e\n\u003cp\u003eTo run a case using a regular Linux machine:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sh ./job.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of MPI processes to use\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-results\" class=\"anchor\" href=\"#results\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h2\u003e\n\u003chr\u003e\n\u003cul\u003e\n\u003cli\u003eAbsolute error (against analytical soln.) at cross section z=0.135m\n\u003ca href=\"./figs/abs_err_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_z_0135.png\" alt=\"abserr0135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRelative error (against analytical soln.) at cross section z=0.135m\n\u003ca href=\"./figs/rel_err_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/rel_err_z_0135.png\" alt=\"relerr0135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eError convergence of the maximum velocity in the entire domain\n\u003ca href=\"./figs/abs_err_umax_entire.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_umax_entire.png\" alt=\"errmax\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eError convergence of the maximum velocity at cross section z=0.135m\n\u003ca href=\"./figs/abs_err_umax_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_umax_z_0135.png\" alt=\"errmax0135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eError convergence of the $L_\\infty$ at cross section z=0.135m\n\u003ca href=\"./figs/abs_err_linf_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_linf_z_0135.png\" alt=\"l2135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eError convergence of the $L_2$ at cross section z=0.135m\n\u003ca href=\"./figs/abs_err_l2_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_l2_z_0135.png\" alt=\"l2135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1676345959.0 + "updated_at": 1652423887.0 }, { "data_format": 2, - "description": "graph clustering toolkit", + "description": null, "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "Lizhen0909/graph_clustering_toolkit", + "full_name": "truatpasteurdotfr/singularity-cryolo-cuda11", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-graph-clustering-toolkit\" class=\"anchor\" aria-hidden=\"true\" href=\"#graph-clustering-toolkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGraph Clustering Toolkit\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-hidden=\"true\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary:\u003c/h3\u003e\n\u003cp\u003eThe toolkit collects many academic graph clustering programs and make them avaliable as package. Docker image is provided for easy access.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation:\u003c/h3\u003e\n\u003cp\u003eUse docker is convenient as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull lizhen0909/graph_clustering_toolkit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, please refer to \u003ca href=\"https://lizhen0909.github.io/graph_clustering_toolkit/\" rel=\"nofollow\"\u003eonline document\u003c/a\u003e for a full description\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage:\u003c/h3\u003e\n\u003cp\u003eStart python from docker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm lizhen0909/graph_clustering_toolkit python\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the script from the command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egct\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egct\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erandom_dataset\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e#create a random graph use LFR generator\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eds\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003erandom_dataset\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003egenerate_undirected_unweighted_random_graph_LFR\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"random_graph\"\u003c/span\u003e, \\\n \u003cspan class=\"pl-v\"\u003eN\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e128\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ek\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emaxk\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.2\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eminc\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# run pScan graph algorithm\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epscan_clustering\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egct\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003escan_pScan\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"get_start_pscan\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eds\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee more to visit \u003ca href=\"https://lizhen0909.github.io/graph_clustering_toolkit/usage/usage.html\" rel=\"nofollow\"\u003eonline usage\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation:\u003c/h3\u003e\n\u003cp\u003ePlease cite \u003ca href=\"https://arxiv.org/abs/2005.04806\" rel=\"nofollow\"\u003eComparison and Benchmark of Graph Clustering Algorithms\u003c/a\u003e for this work.\u003c/p\u003e\n\u003cp\u003eFor individual algorithms, see \u003ca href=\"https://lizhen0909.github.io/graph_clustering_toolkit/usage/pydoc_alg.html\" rel=\"nofollow\"\u003eAlgorithms\u003c/a\u003e for their publications.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-singularity-container-for-cryolo-using-cuda-version-11\" class=\"anchor\" href=\"#building-a-singularity-container-for-cryolo-using-cuda-version-11\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a singularity container for crYOLO using CUDA version 11\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-cryolo-cuda11/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-cryolo-cuda11/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run --nv oras://ghcr.io/truatpasteurdotfr/singularity-cryolo-cuda11:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLICENSE:\nThe same as crYOLO (free for academic use, see \u003ca href=\"https://cryolo.readthedocs.io/en/stable/other/license.html\" rel=\"nofollow\"\u003ehttps://cryolo.readthedocs.io/en/stable/other/license.html\u003c/a\u003e)\ncopy retrieved from \u003ca href=\"https://raw.githubusercontent.com/MPI-Dortmund/cryolo/9ea47f694fc68bcdaedf550a128558b8e77855bc/source/other/license.rst\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/MPI-Dortmund/cryolo/9ea47f694fc68bcdaedf550a128558b8e77855bc/source/other/license.rst\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1633850758.0 + "updated_at": 1651497170.0 }, { "data_format": 2, - "description": "A summation of the work I did at Argonne national laboratory during the summer of 2018. This includes the micro_osu_benchmarks I ran on theta, and the file access tests I ran on theta.", + "description": "docker and singularity containers for R", "filenames": [ - "Osu_micro-benchmarks/Singularity.derived", - "touch-file_tests/Singularity.massfile" + "images/tinytex_4.2.0/Singularity.def", + "images/cmdstanr_4.2.0/Singularity.def", + "images/rstan_4.2.0/Singularity.def", + "images/radian-ml_4.2.0/Singularity.def", + "images/radian_4.2.0/Singularity.def" ], - "full_name": "spencer-williams/sgww_argonne", + "full_name": "mattocci27/r-containers", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-sgww_argonne_summer2018\" class=\"anchor\" aria-hidden=\"true\" href=\"#sgww_argonne_summer2018\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esgww_argonne_summer2018\u003c/h1\u003e\n\u003cp\u003eA summation of the work I did at Argonne national laboratory during the summer of 2018. This includes the micro_osu_benchmarks I ran on theta, and the file access tests I ran on theta.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker-and-singularity-images-for-r\" class=\"anchor\" href=\"#docker-and-singularity-images-for-r\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker and singularity images for R\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-images\" class=\"anchor\" href=\"#images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003edocker\u003c/th\u003e\n\u003cth\u003esingularity\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/rstan\" rel=\"nofollow\"\u003erstan\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cloud.sylabs.io/library/mattocci27/default/rstan\" rel=\"nofollow\"\u003erstan\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eadds rstan on \u003ca href=\"https://hub.docker.com/r/rocker/geospatial\" rel=\"nofollow\"\u003egeospatial\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/radian\" rel=\"nofollow\"\u003eradian\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eadds radian and fonts on \u003ca href=\"https://hub.docker.com/r/rocker/rstudio\" rel=\"nofollow\"\u003erstudio\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/radian-ml\" rel=\"nofollow\"\u003eradian-ml\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eadds radian and fonts on \u003ca href=\"https://hub.docker.com/r/rocker/ml\" rel=\"nofollow\"\u003eml\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/rmd-light\" rel=\"nofollow\"\u003ermd-light\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eR markdown + TinyTex + pandoc-crossref without Rstudio\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/cmdstanr\" rel=\"nofollow\"\u003ecmdstanr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eadds cmdstanr on \u003ca href=\"https://hub.docker.com/r/rocker/ml\" rel=\"nofollow\"\u003eml\u003c/a\u003e (GPU supported)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-push-to-a-private-repository\" class=\"anchor\" href=\"#push-to-a-private-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePush to a private repository\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003epush_to_pr.sh \u0026lt;r-version\u0026gt; \u0026lt;ip\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escripts/push_to_pr.sh 4.1.3 xxx.xxx.xx.xx:xxx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003escripts/pull_from_pr.sh 4.2.0 xxxx\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1646169012.0 + "updated_at": 1653138846.0 }, { "data_format": 2, - "description": "Repository contains the imaging pipelines used in CSAI XNAT", + "description": "Singularity img : R, rcontroll and calibration packages", "filenames": [ - "Singularity_process_v3", - "Singularity_ants_v5", - "Singularity_ants_v4", - "Singularity_process_v4" + "Singularity" ], - "full_name": "KarthikMasi/CSAI-XNAT", - "latest_release": null, + "full_name": "gsalzet/singularity-r-TROLL", + "latest_release": "0.0.2", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-rcontroll-singularity-container\" class=\"anchor\" href=\"#rcontroll-singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ercontroll Singularity container\u003c/h1\u003e\n\u003cp\u003eSalzet Guillaume\nFebruary 28, 2022\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eR, rcontroll and calibration packages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepository based on public template\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template\"\u003e\u003ccode\u003esylvainschmitt/singularity-template\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis container includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e 4.1.2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ercontroll\u003c/code\u003e 0.1.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etidyverse\u003c/code\u003e 1.3.1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esf\u003c/code\u003e 1.0-5\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esp\u003c/code\u003e 1.4-6\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ehetGP\u003c/code\u003e 1.1.4\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecoda\u003c/code\u003e 0.19-4\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eentropart\u003c/code\u003e 1.6-8\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efitdistrplus\u003c/code\u003e 1.1-6\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eForestGapR\u003c/code\u003e 0.1.6\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elhs\u003c/code\u003e 1.1.3\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eKrigR\u003c/code\u003e 0.1.2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLoggingLab\u003c/code\u003e 0.0.0.9003\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity container based on the recipe:\n\u003ca href=\"https://github.com/gsalzet/singularity-r-TROLL/blob/main/Singularity\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eImage singularity (V\u0026gt;=3.6.4) is automatically test and built and\npushed on the registry using\n\u003ca href=\"https://github.com/gsalzet/singularity-template/blob/main/.github/workflows/test.yml\"\u003etest.yml\u003c/a\u003e\n\u0026amp;\n\u003ca href=\"https://github.com/gsalzet/singularity-template/blob/main/.github/workflows/builder.yml\"\u003ebuilder.yml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInitial bootstrap :\n\u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003e\u003ccode\u003edocker://ubuntu:18:04\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ebuild\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build Singularity TROLL_utilities.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003epull\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull https://github.com/gsalzet/singularity-template/releases/download/0.0.2/gsalzet-singularity-r-TROLL.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esnakemake\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s1\"\u003esingularity\u003c/span\u003e: \n \u003cspan class=\"pl-s\"\u003e\"https://github.com/gsalzet/singularity-template/releases/download/0.0.2/gsalzet-singularity-r-TROLL.latest.sif\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1591882985.0 + "updated_at": 1646068884.0 }, { "data_format": 2, - "description": "Solving large-scale sparse eigenvalue problems in Haskell (wrapper for PRIMME library)", + "description": null, "filenames": [ - "Singularity" + "imaging/nipy/Singularity" ], - "full_name": "twesterhout/primme-hs", + "full_name": "andyrevell/docker_GitHub", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-primme-hs-\" class=\"anchor\" aria-hidden=\"true\" href=\"#primme-hs-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eprimme-hs \u003ca href=\"https://github.com/twesterhout/primme-hs/actions\"\u003e\u003cimg src=\"https://github.com/twesterhout/primme-hs/workflows/CI/badge.svg\" alt=\"GitHub CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca href=\"LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33b2802c547e7ae15da879c987ba9b119229cada7c53335dd710d7481ede78f8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4253442d2d332d2d436c617573652d626c75652e737667\" alt=\"BSD-3-Clause license\" data-canonical-src=\"https://img.shields.io/badge/license-BSD--3--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eSolving large-scale sparse eigenvalue problems in Haskell!\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cntdocker\" class=\"anchor\" href=\"#cntdocker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCNTdocker\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-about\" class=\"anchor\" href=\"#about\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eDockerfiles to create Docker images used by the CNT at the university of Pennsylvania\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-directory-contents-explanation\" class=\"anchor\" href=\"#directory-contents-explanation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory contents explanation\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-eeg\" class=\"anchor\" href=\"#eeg\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eEEG\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common EEG analysis tools. Usually python 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eechobase\u003c/strong\u003e: Dockerfiles used to create images that can calculate functional connectivity of EEG\nAlso has ieegpy python package used to interface with iEEG.org\nEchobase code is from \u003ca href=\"https://github.com/andyrevell/paper001\"\u003ehttps://github.com/andyrevell/paper001\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUbuntu 18.04\nPython 2.7 and Python 3.6\nNumpy 1.18.4\npandas 1.0.3\nscipy 1.4.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-imaging\" class=\"anchor\" href=\"#imaging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eImaging\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common MRI analysis tools.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n Python 2.7, Python 3.6, Python 3.7\n dcm2niix\n dsistudio\n ANTS\n Freesurfer\n FSL 6.0.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ml\" class=\"anchor\" href=\"#ml\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eml\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common machine learning tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ewavenet\u003c/strong\u003e: Dockerfile to create compatible dependencies to use with Goodgle Deepmind wavenet paper\n\u003ca href=\"https://deepmind.com/blog/article/wavenet-generative-model-raw-audio\" rel=\"nofollow\"\u003eWavenet blog\u003c/a\u003e\n\u003ca href=\"https://arxiv.org/pdf/1609.03499.pdf\" rel=\"nofollow\"\u003eWavenet paper\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n tensorflow 1.0.0\n pandas 0.19.2\n librosa 0.5.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eTensorflow_2.1\u003c/strong\u003e: Dockerfile to create compatible dependencies to with tensorflow 2.1\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n tensorflow 2.1\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 2, - "topics": [ - "haskell", - "linear-algebra", - "high-performance", - "numerical-methods", - "eigenvalueproblems" - ], - "updated_at": 1665067408.0 + "topics": [], + "updated_at": 1600370006.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity.bamdb" ], - "full_name": "uazhlt/pytorch-example", + "full_name": "D-Lo/bamdb", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the docker container\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eNOTE: This step is not necessary if you simply want to use an already published image to run the example code on the UA HPC.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile -t uazhlt/pytorch-example .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-verify-pytorch-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#verify-pytorch-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVerify PyTorch version\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it uazhlt/pytorch-example python -c \"import torch; print(torch.__version__)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-publish-to-dockerhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#publish-to-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublish to DockerHub\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eNOTE: This step is not necessary if you simply want to use an already published image to run the example code on the UA HPC.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# login to dockerhub registry\ndocker login --username=yourdockerhubusername --email=youremail@domain.com\n\ndocker push org/image-name:taghere\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-a-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-a-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding a Singularity image\u003c/h2\u003e\n\u003cp\u003eBuilding a Singularity image from a def file requires sudo on a Linux system. In this tutorial, we avoid discussing details on installing Singularity. If you\u0027re feeling adventurous, take a look at \u003ca href=\"./Singularity\"\u003ethe example def file in this repository\u003c/a\u003e and the official documentation:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alternatives\" class=\"anchor\" aria-hidden=\"true\" href=\"#alternatives\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternatives\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-cloud-builds\" class=\"anchor\" aria-hidden=\"true\" href=\"#cloud-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloud builds\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eGitHub actions:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/github-ci/blob/master/.github/workflows/go.yml\"\u003eExample GitHub Workflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://help.github.com/en/actions/automating-your-workflow-with-github-actions/virtual-environments-for-github-hosted-runners#supported-runners-and-hardware-resources\"\u003eGitHub-hosted runners\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-vms\" class=\"anchor\" aria-hidden=\"true\" href=\"#vms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVMs\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html#singularity-vagrant-box\" rel=\"nofollow\"\u003eVagrant box\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-docker---singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker---singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker -\u0026gt; Singularity\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/docker2singularity\"\u003e\u003ccode\u003edocker2singularity\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-retrieving-a-published-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#retrieving-a-published-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRetrieving a published Singularity image\u003c/h2\u003e\n\u003cp\u003eInstead of building from scratch, we\u0027ll focus on a shortcut that simply wraps docker images published to DockerHub.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull uazhlt-pytorch-example.sif docker://uazhlt/pytorch-example:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-hpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC\u003c/h1\u003e\n\u003cp\u003eIf you intend to test out \u003ca href=\"./example\"\u003ethe PyTorch example included here\u003c/a\u003e, you\u0027ll want to clone this repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/ua-hlt-program/pytorch-example.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-singularity-in-an-interactive-pbs-job\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-singularity-in-an-interactive-pbs-job\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Singularity in an interactive PBS job\u003c/h2\u003e\n\u003cp\u003eNext, we\u0027ll request an interactive job (tested on El Gato):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eqsub -I \\\n-N interactive-gpu \\\n-W group_list=mygroupnamehere \\\n-q standard \\\n-l select=1:ncpus=2:mem=16gb:ngpus=1 \\\n-l cput=3:0:0 \\\n-l walltime=1:0:0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e_NOTE: If you\u0027re unfamiliar with \u003ccode\u003eqsub\u003c/code\u003e and the many options in the command above seem puzzling, you can find answers by checking out the manual via \u003ccode\u003eman qsub\u003c/code\u003e _\u003c/p\u003e\n\u003cp\u003eIf the cluster isn\u0027t too busy, you should soon see a new prompt formatted something like \u003ccode\u003e[netid@gpu\\d\\d ~]\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNow we\u0027ll run the singularity image we grabbed earlier. Before that, though, let\u0027s ensure we\u0027re using the correct version of Singularity and that the correct CUDA version is available to Singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity/3.2.1\nmodule load cuda10/10.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we\u0027re finally ready to run the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv --no-home /path/to/your/uazhlt-pytorch-example.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you ran into an error, check to see if you replaced \u003ccode\u003e/path/to/your/\u003c/code\u003e with the correct path to \u003ccode\u003euazhlt-pytorch-example.sif\u003c/code\u003e before executing the command.\u003c/p\u003e\n\u003cp\u003eWe\u0027re now in our Singularity container! If everything went well, we should be able to see the gpu:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003envidia-smi\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see output like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 418.87.01 Driver Version: 418.87.01 CUDA Version: 10.1 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n|===============================+======================+======================|\n| 0 Tesla K20Xm On | 00000000:8B:00.0 Off | 0 |\n| N/A 17C P8 18W / 235W | 0MiB / 5700MiB | 0% Default |\n+-------------------------------+----------------------+----------------------+\n\n+-----------------------------------------------------------------------------+\n| Processes: GPU Memory |\n| GPU PID Type Process name Usage |\n|=============================================================================|\n| No running processes found |\n+-----------------------------------------------------------------------------+\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSuccess (I hope)! Now let\u0027s try running PyTorch on the GPU with batching...\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-pytorch-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#pytorch-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePyTorch example\u003c/h1\u003e\n\u003cp\u003eThe Pytorch example code can be found under \u003ca href=\"./example\"\u003e\u003ccode\u003eexample\u003c/code\u003e\u003c/a\u003e. The data used in this example comes from from Delip Rao and Brian MacMahan\u0027s \u003cem\u003eNatural Language Processing with PyTorch\u003c/em\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/joosthub/PyTorchNLPBook/tree/master/data#surnames\"\u003ehttps://github.com/joosthub/PyTorchNLPBook/tree/master/data#surnames\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe dataset relates surnames to nationalities. Our version (minor modifications) is nested under \u003ca href=\"./examples/data\"\u003eexamples/data\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003etrain.py\u003c/code\u003e houses a command line program for training a classifier. The following invocation will display the tool\u0027s help text:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython train.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe simple model architecture operates is based on that of deep averaging networks (DANs; see \u003ca href=\"https://aclweb.org/anthology/P15-1162/\" rel=\"nofollow\"\u003ehttps://aclweb.org/anthology/P15-1162/\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eReading through train.py you can quickly see how the code is organized. Some parts (ex. \u003ccode\u003etorchtext\u003c/code\u003e data loaders) may be unfamiliar to you.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-next-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#next-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNext steps\u003c/h1\u003e\n\u003cp\u003eNow that you\u0027ve managed to run some example PyTorch code, there are many paths forward:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExperiment with using pretrained subword embeddings (both fixed and trainable). Do you notice any improvements in performance/faster convergence?\u003c/li\u003e\n\u003cli\u003eTry improving or replacing the naive model defined under \u003ccode\u003emodels.py\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAdd an evaluation script for a trained model that reports macro P, R, and F1. Feel free to use \u003ccode\u003escikit-learn\u003c/code\u003e\u0027s classification report.\u003c/li\u003e\n\u003cli\u003eAdd an inference script to classify new examples.\u003c/li\u003e\n\u003cli\u003eMonitor validation loss to and stop training if you begin to overfit.\u003c/li\u003e\n\u003cli\u003eAdapt the interactive PBS task outlined above to a PBS script that you can submit to the HPC.\u003c/li\u003e\n\u003cli\u003eAddress the class imbalance in the data through downsampling, class weighting, or another technique of your choosing.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1576565865.0 + "updated_at": 1639504182.0 }, { "data_format": 2, - "description": null, + "description": "pygments for rust", "filenames": [ - "singularity/Singularity.base-dep", - "singularity/Singularity.base", - "singularity/Singularity.python3" + "tests/examplefiles/singularity/Singularity" ], - "full_name": "LBJ-Wade/OSKAR", + "full_name": "Alignof/pygments-rs", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b31f28bf12daa344a4541064866fa171f3f3989a53604cec781f6c15206daab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f4f78666f7264534b412f4f534b41522e7376673f7374796c653d666c61742d737175617265\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/OxfordSKA/OSKAR.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.3758491\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db055c7e33d6db9ce4f5f69f83628cd0827c9a0e13aa2aa78a369d938a4ef137/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333735383439312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3758491.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-oskar-a-gpu-accelerated-simulator-for-the-square-kilometre-array\" class=\"anchor\" aria-hidden=\"true\" href=\"#oskar-a-gpu-accelerated-simulator-for-the-square-kilometre-array\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOSKAR: A GPU-accelerated simulator for the Square Kilometre Array\u003c/h1\u003e\n\u003cp\u003eOSKAR has been designed to produce simulated visibility data from radio\ntelescopes containing aperture arrays, such as those envisaged for the\nSquare Kilometre Array.\u003c/p\u003e\n\u003cp\u003eA source code archive, and pre-built binary packages for Linux (using\nSingularity), macOS and Windows platforms are available to download from\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003ehttps://github.com/OxfordSKA/OSKAR/releases\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOSKAR is licensed under the terms of the 3-clause BSD License.\nPlease see the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file for details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity image\u003c/h3\u003e\n\u003cp\u003eA pre-built \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e SIF container image\nis available for Linux which can be used to run OSKAR command line\napplications or Python scripts directly, without needing to compile or install\nanything. For Singularity 3.0 or later, an application or script can be run\nusing the downloaded \u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003econtainer\u003c/a\u003e\nwith the \u003ccode\u003esingularity exec\u003c/code\u003e command, which takes the form:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec [flags] \u0026lt;container_path\u0026gt; \u0026lt;app_name\u0026gt; \u0026lt;arguments\u0026gt;...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse the \u003ccode\u003e--nv\u003c/code\u003e flag to enable NVIDIA GPU support in Singularity, if\napplicable.\u003c/p\u003e\n\u003cp\u003eNote also that Singularity will mount the home directory into the container by\ndefault, unless configured otherwise. If you have packages installed in your\nhome area that should be kept isolated from those in the container (for\nexample, because of conflicting packages or Python versions, or if you see\nother errors caused by trying to load wrong versions of shared libraries when\nstarting the container) then it may be necessary to disable this either by\nusing the \u003ccode\u003e--no-home\u003c/code\u003e flag, or re-bind the home directory in the container\nto somewhere other than your actual $HOME using the \u003ccode\u003e-H\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eAs an example, to run the application \u003ccode\u003eoskar_sim_interferometer\u003c/code\u003e\nwith a parameter file \u003ccode\u003esettings.ini\u003c/code\u003e and a container image file\n\u003ccode\u003eOSKAR-Python3.sif\u003c/code\u003e (both in the current directory) on a GPU use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --nv ./OSKAR-Python3.sif oskar_sim_interferometer settings.ini\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSimilarly, to run a Python script \u003ccode\u003esim_script.py\u003c/code\u003e that uses OSKAR:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --nv ./OSKAR-Python3.sif python3 sim_script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cp\u003eIf hardware acceleration is required, be sure to install appropriate GPU\ndrivers which are supported by the hardware manufacturer. Third-party graphics\ndrivers are unlikely to work.\u003c/p\u003e\n\u003cp\u003eWhen building from source, the only required dependency is\n\u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003eCMake \u0026gt;= 3.1\u003c/a\u003e.\nAll other dependencies are optional, but functionality will be\nlimited if these are not found by CMake.\n\u003cem\u003eNote that these dependencies are required only if building from source\u003c/em\u003e, not\nif using a \u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003epre-built package\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003eCMake \u0026gt;= 3.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://developer.nvidia.com/cuda-downloads\" rel=\"nofollow\"\u003eCUDA \u0026gt;= 7.0\u003c/a\u003e\nor OpenCL, required for GPU acceleration on supported hardware.\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://qt.io\" rel=\"nofollow\"\u003eQt 5\u003c/a\u003e,\nrequired to build the graphical user interface.\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://github.com/casacore/casacore\"\u003ecasacore \u0026gt;= 2.0\u003c/a\u003e,\nrequired to use CASA Measurement Sets.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePackages for these dependencies are available in the package repositories\nof many recent Linux distributions, including Debian and Ubuntu.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild commands\u003c/h3\u003e\n\u003cp\u003eTo build from source, either clone the repository using\n\u003ccode\u003egit clone https://github.com/OxfordSKA/OSKAR.git\u003c/code\u003e (for the current master\nbranch) or download and unpack the source archive, then:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir build\n$ cd build\n$ cmake [OPTIONS] ../path/to/top/level/source/folder\n$ make -j4\n$ make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen running the \u0027cmake\u0027 command a number of options can be specified:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e* -DCUDA_ARCH=\"\u0026lt;arch\u0026gt;\" (default: all)\n Sets the target architecture for the compilation of CUDA device code.\n \u0026lt;arch\u0026gt; must be one of either: 2.0, 2.1, 3.0, 3.2, 3.5, 3.7,\n 5.0, 5.2, 6.0, 6.1, 6.2, 7.0, 7.5,\n 8.0, 8.6 or ALL.\n ALL is for all currently supported architectures.\n Separate multiple architectures using semi-colons, if required.\n\n* -DCMAKE_INSTALL_PREFIX=\u0026lt;path\u0026gt; (default: /usr/local/)\n Path prefix used to install OSKAR (with make install).\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-advanced-build-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#advanced-build-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdvanced build options\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e* -DCASACORE_LIB_DIR=\u0026lt;path\u0026gt; (default: searches the system library paths)\n Specifies a location to search for the casacore libraries\n (libcasa_tables.so and others) if they are not in the system library path.\n\n* -DCASACORE_INC_DIR=\u0026lt;path\u0026gt; (default: searches the system include paths)\n Specifies a location to search for the casacore library headers if they\n are not in the system include path.\n This is the path to the top level casacore include folder.\n\n* -DCMAKE_PREFIX_PATH=\u0026lt;path\u0026gt; (default: None)\n Specifies a location to search for Qt 5 if it is not in a standard\n system path. For example, if using Homebrew on macOS, this may need\n to be set to /usr/local/opt/qt5/\n\n* -DFIND_CUDA=ON|OFF (default: ON)\n Can be used not to find or link against CUDA.\n\n* -DFIND_OPENCL=ON|OFF (default: OFF)\n Can be used not to find or link against OpenCL.\n OpenCL support in OSKAR is currently experimental.\n\n* -DNVCC_COMPILER_BINDIR=\u0026lt;path\u0026gt; (default: None)\n Specifies a nvcc compiler binary directory override. See nvcc help.\n This is likely to be needed only on macOS when the version of the\n compiler picked up by nvcc (which is related to the version of XCode\n being used) is incompatible with the current version of CUDA.\n Set this to \u0027clang\u0027 on macOS if using GCC to build the rest of OSKAR.\n\n* -DFORCE_LIBSTDC++=ON|OFF (default: OFF)\n If ON forces the use of libstdc++ with the Clang compiler.\n Used for controlling linking behaviour when using clang\n or clang-omp compilers with dependencies which may have been compiled\n against libstdc++\n\n* -DCMAKE_BUILD_TYPE=\u0026lt;release or debug\u0026gt; (default: release)\n Build in release or debug mode.\n\n* -DBUILD_INFO=ON|OFF (default: OFF)\n If ON enables the display of diagnostic build information when\n running CMake.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-unit-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnit tests\u003c/h4\u003e\n\u003cp\u003eAfter building from source, the unit tests should be run to make sure there\nare no problems with the build.\n(Note that pre-built packages do not include the unit tests.)\u003c/p\u003e\n\u003cp\u003eFrom the build directory, the unit tests can be run by typing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ctest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-python-interface\" class=\"anchor\" aria-hidden=\"true\" href=\"#python-interface\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython interface\u003c/h3\u003e\n\u003cp\u003eAfter installing OSKAR, the Python interface to it can be installed to\nmake it easier to use from Python scripts.\nStraightforward instructions for installation with \u003ccode\u003epip\u003c/code\u003e can be\n\u003ca href=\"python/README.md\"\u003efound in the python subdirectory\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-example-simulation\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample simulation\u003c/h3\u003e\n\u003cp\u003eThe example simulation described in the\n\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003edocumentation\u003c/a\u003e\ncan be run to check that a simple simulation behaves as expected.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1660062120.0 + "updated_at": 1641458628.0 }, { "data_format": 2, - "description": null, + "description": "Open source, scalable acoustic classification - designed for ecology and conservation", "filenames": [ - "core/Singularity.ubuntu2004_cuda11", - "core/Singularity.ubuntu1604_cuda10", - "buildKit/Singularity.ubuntu2004", - "buildKit/Singularity.ubuntu1604" + "Singularity" ], - "full_name": "mdzik/TCLB_singularity", + "full_name": "thomastroyan/opensoundscape", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tclb_singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#tclb_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTCLB_singularity\u003c/h1\u003e\n\u003cp\u003eThis is highly experimental. This repo contains a singularity image and scripts intended to bootstrap development environment. While GPU support was tested and works, be aware that GPU+MPI will most likely not work in HPC environment out-of-the-box.\u003c/p\u003e\n\u003cp\u003eImage should contain all dependencies for full-futured TCLB.\u003c/p\u003e\n\u003cp\u003eHOWTO:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun the lolcow example, check if it works as intended\u003c/li\u003e\n\u003cli\u003epull image \u003ccode\u003esingularity pull --arch amd64 library://mdzik/tclb/tclb:latest\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eedit paths in \u0027startEnvironment.sourceMe\u0027\u003c/li\u003e\n\u003cli\u003esource It! :)\u003c/li\u003e\n\u003cli\u003ego to TCLB directory\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003escmd ./configure --disable-cuda --with-python --enable-double --enable-keepcode --enable-rinside\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emake XXX\u003c/code\u003e, this script overwrites make for whole session - you could run it from anywhere and still compile TCLB ;)\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1681\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-opensoundscape\" class=\"anchor\" href=\"#opensoundscape\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenSoundscape\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start-guide\" class=\"anchor\" href=\"#quick-start-guide\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start guide\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eNote: installation instructions are for MacOS systems only.\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.anaconda.com/download/#macos\" rel=\"nofollow\"\u003eAnaconda for Python 3\u003c/a\u003e and \u003ca href=\"https://brew.sh/\" rel=\"nofollow\"\u003eHomeBrew\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse HomeBrew to install a few other packages: \u003ccode\u003ebrew install libsamplerate mongodb git wget\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet up the Python environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda install -c conda-forge python=3.6 pip=18.0 pandas=0.23.4 numpy=1.15.1 matplotlib=2.1.2 docopt=0.6.2 scipy=1.0.0 scikit-image=0.13.1 pymongo=3.4.0 progressbar2=3.36.0 pytest=3.6.1 opencv=3.4.3 scikit-learn=0.20.0\n \n pip install git+git://github.com/gregorias/samplerate@master\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload data files, the \u003ca href=\"https://datadryad.org/resource/doi:10.5061/dryad.j2t92\" rel=\"nofollow\"\u003eCLO-43SD-AUDIO\u003c/a\u003e dataset:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd ~/Downloads\n wget \"https://datadryad.org/bitstream/handle/10255/dryad.111783/CLO-43SD-AUDIO.tar.gz\"\n tar -xzf CLO-43SD-AUDIO.tar.gz\n rm CLO-43SD-AUDIO.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload our training \u0026amp; prediction split of a subset of the CLO-43SD dataset:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd ~/Downloads/CLO-43SD-AUDIO/\n wget https://raw.github.com/rhine3/opso-support/master/clo-43sd-train-small.csv\n wget https://raw.github.com/rhine3/opso-support/master/clo-43sd-predict-small.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload OpenSoundscape:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e mkdir ~/Code \u0026amp;\u0026amp; cd ~/Code\n git clone https://github.com/jkitzes/opensoundscape\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload our config file, \u003ccode\u003eopso-test-small.ini\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd ~/Code/opensoundscape/\n wget https://raw.github.com/rhine3/opso-support/master/opso-test-small.ini \n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEdit the \u003ccode\u003e.ini\u003c/code\u003e to reflect the absolute path of your \u003ccode\u003eDownloads\u003c/code\u003e folder, e.g. with \u003ccode\u003evim\u003c/code\u003e: \u003ccode\u003evim opso-test-small.ini\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStart the MongoDB daemon in another terminal: \u003ccode\u003emongod --config /usr/local/etc/mongod.conf\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun OpenSoundscape:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./opensoundscape.py init -i opso-test-small.ini \n ./opensoundscape.py spect_gen -i opso-test-small.ini \u0026gt; spect-gen-output-small.txt\n ./opensoundscape.py model_fit -i opso-test-small.ini \u0026gt; model-fit-output-small.txt\n ./opensoundscape.py predict -i opso-test-small.ini \u0026gt; predict-output-small.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1608196932.0 + "updated_at": 1545330417.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container with Xfce desktop to support OOD apps.", "filenames": [ - "Singularity.latest" + "Singularity" ], - "full_name": "AdamWilsonLab/singularity-geospatial-r", - "latest_release": "0.0.4", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Status\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4930\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h1\u003e\n\u003cp\u003eThis repository includes a definition file for a singularity container \u003cem\u003eand\u003c/em\u003e instructions for starting up an instance on CENTOS in a HPC environment.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setting-up-the-singularity-instance\" class=\"anchor\" aria-hidden=\"true\" href=\"#setting-up-the-singularity-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetting up the Singularity Instance\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eSSH to the server\u003c/li\u003e\n\u003cli\u003eRun the following to select the target folder and download the current version of this container:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e# cd to user singularity directory\ncd /panasas/scratch/grp-adamw/singularity/$USER;\n# download the most recent version of the container\nwget -O singularity-geospatial-r_latest.sif \\\n https://github.com/AdamWilsonLab/singularity-geospatial-r/releases/download/0.0.1/AdamWilsonLab-singularity-geospatial-r.latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate symlinks to singularity folder in project storage to prevent disk space problems in the home directory. You should only have to do this once.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~\nmkdir -p /projects/academic/adamw/singularity/$USER/.singularity\nln -s /projects/academic/adamw/singularity/$USER/.singularity .singularity\n\n# Symlinks for RStudio\nmkdir -p /projects/academic/adamw/rstudio/$USER/rstudio\nmv .local/share/rstudio /projects/academic/adamw/rstudio/$USER/\n\nmkdir -p ~/.local/share\nln -s /projects/academic/adamw/rstudio/$USER/rstudio ~/.local/share/rstudio\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eRun the \u003ca href=\"https://github.com/AdamWilsonLab/singularity-geospatial-r/blob/main/singularity_start.sh\"\u003esingularity_start.sh\u003c/a\u003e script to start up a singularity instance. You can just copy paste the code into the terminal. This includes a few system specific settings for the Buffalo CCR. This should only need to be done once (as long as the instance keeps running, server is not restarted, etc.). If the instance stops for any reason, you\u0027ll need to rerun this script. You can confirm it\u0027s running with \u003ccode\u003esingularity instance list\u003c/code\u003e or by checking \u003ccode\u003ehtop\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-connecting-to-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#connecting-to-rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConnecting to RStudio\u003c/h2\u003e\n\u003cp\u003eAfter running the steps above, you should be able to do just the following to begin working. If the server restarts you will need to re-run step 4 above.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eConnect to the instance via SSH with port Forwarding. You will need to be on campus or connected via VPN. See notes below for *nix and windows.\u003c/li\u003e\n\u003cli\u003eOpen RStudio at localhost:8787 in your local browser and login with user/password from #4 above.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-container-geospatial-r\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container-geospatial-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container: Geospatial R\u003c/h2\u003e\n\u003cp\u003eThis container builds upon the \u003ca href=\"https://hub.docker.com/r/rocker/geospatial\" rel=\"nofollow\"\u003erocker geospatial container\u003c/a\u003e, which I ported to \u003ca href=\"https://singularity-hub.org/collections/4908\" rel=\"nofollow\"\u003eSingularity here\u003c/a\u003e. This repository/collection then \u003ca href=\"https://github.com/AdamWilsonLab/singularity-geospatial-r/blob/main/Singularity.latest\"\u003eadds additional packages in this file\u003c/a\u003e. That\u0027s the file to modify if you want to add more linux packages, etc.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-connecting-via-ssh\" class=\"anchor\" aria-hidden=\"true\" href=\"#connecting-via-ssh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConnecting via SSH\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nix-systems-mac-and-linux\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix-systems-mac-and-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e*NIX systems (Mac and Linux)\u003c/h2\u003e\n\u003cp\u003eUse terminal to ssh to the server as explained in \u003ca href=\"https://github.com/AdamWilsonLab/singularity-geospatial-r/blob/main/singularity_start.sh\"\u003esingularity_start.sh\u003c/a\u003e.\nAdd something like the following to your .ssh/config file to simplify connecting with port forwarding via ssh. You will then have to update HOST to the host address and PORT_NUMBER to the updated port number.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eHost rserver\nHostName HOST\nLocalForward 8787 HOST:PORT_NUMBER\nUser $USER\nForwardX11 yes\nForwardAgent yes\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-hidden=\"true\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-putty-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#putty-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePuTTY Instructions\u003c/h3\u003e\n\u003cp\u003eOn Windows you will need to use PuTTY or a similar terminal program.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIn PuTTY, enter the server address (host name) and \"22\" (port) on the \"Session\" tab.\u003c/li\u003e\n\u003cli\u003eOn the \"SSH/Tunnels\" tab, enter the port number of the rsession under \u201cSource port\u201d and type in HOST:PORT (replace with the actual server IP address + the port number) as the destination address. Then, click \"Add\".\u003c/li\u003e\n\u003cli\u003eConnect and login as usual in the terminal.\u003c/li\u003e\n\u003cli\u003ePoint the web browser to \u003ccode\u003ehttp://localhost:PORT\u003c/code\u003e (where PORT is the port number)\" and log in with the user name and the previously generated password.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-todos\" class=\"anchor\" aria-hidden=\"true\" href=\"#todos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODOs\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003eSeparate container from startup and monitor script\u003c/li\u003e\n\u003cli\u003eSwitch to a docker image\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-development-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#development-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment Notes\u003c/h1\u003e\n\u003cp\u003eI started with \u003ca href=\"https://github.com/nickjer/singularity-rstudio/blob/master/.travis.yml\"\u003enickjer\u0027s very helpful example\u003c/a\u003e and updated it to pull from the geospatial version of the versioned rocker stack instead of the repository based R. This should make it easier to keep up to date.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-errors\" class=\"anchor\" aria-hidden=\"true\" href=\"#errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eErrors\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-unable-to-connect-to-service\" class=\"anchor\" aria-hidden=\"true\" href=\"#unable-to-connect-to-service\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnable to connect to service\u003c/h3\u003e\n\u003cp\u003eThis error can appear in the web browser when connecting via localhost. This can be caused by RStudio not being able to write session files in the right place. Confirm that:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe directory \u003ccode\u003e/projects/academic/adamw/rstudio/$USER/rstudio\u003c/code\u003e exists\u003c/li\u003e\n\u003cli\u003eand is linked to \u003ccode\u003e~/.local/share/rstudio\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-could-not-acquire-revocation-list-file-lock\" class=\"anchor\" aria-hidden=\"true\" href=\"#could-not-acquire-revocation-list-file-lock\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCould not acquire revocation list file lock\u003c/h3\u003e\n\u003cp\u003eThe error \"Could not acquire revocation list file lock\" resolved with help from \u003ca href=\"https://www.gitmemory.com/issue/rocker-org/rocker-versioned/213/726807289\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-database-error-7\" class=\"anchor\" aria-hidden=\"true\" href=\"#database-error-7\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edatabase error 7\u003c/h3\u003e\n\u003cp\u003eStarting in early 2021, something changed that resulted in the following error when starting a new instance:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eERROR database error 7 (sqlite3_statement_backend::loadOne: attempt to write a readonly database) [description: Could not delete expired revoked cookies from the database, description: Could not read revoked cookies from the database]; OCCURRED AT virtual rstudio::core::Error rstudio::core::database::Connection::execute(rstudio::core::database::Query\u0026amp;, bool*) src/cpp/core/Database.cpp:480; LOGGED FROM: int main(int, char* const*) src/cpp/server/ServerMain.cpp:729\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI solved this by binding an address outside the container to \u003ccode\u003e/var/lib/rstudio-server\u003c/code\u003e when starting the instance as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--bind $RSTUDIO_DB:/var/lib/rstudio-server\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e$RSTUDIO_DB\u003c/code\u003e is just a path outside the container. I got this idea from \u003ca href=\"https://community.rstudio.com/t/permissions-related-to-upgrade-to-rstudio-server-open-source-1-4/94256/3\" rel=\"nofollow\"\u003ethis post\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-local-rocker-updates\" class=\"anchor\" aria-hidden=\"true\" href=\"#local-rocker-updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal rocker updates\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003edocker run -d -p 8787:8787 -e PASSWORD=really_clever_password -v ~/Documents:~/Documents rocker/rstudio\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-useful-links\" class=\"anchor\" aria-hidden=\"true\" href=\"#useful-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful Links\u003c/h1\u003e\n\u003cp\u003eA few links I found useful while developing this container\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://divingintogeneticsandgenomics.rbind.io/post/run-rstudio-server-with-singularity-on-hpc/\" rel=\"nofollow\"\u003ehttps://divingintogeneticsandgenomics.rbind.io/post/run-rstudio-server-with-singularity-on-hpc/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/rocker/geospatial\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/rocker/geospatial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://singularity-hub.org/collections/4930\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/4930\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pawseysc.github.io/singularity-containers/23-web-rstudio/index.html\" rel=\"nofollow\"\u003ehttps://pawseysc.github.io/singularity-containers/23-web-rstudio/index.html\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.rocker-project.org/use/singularity/\" rel=\"nofollow\"\u003ehttps://www.rocker-project.org/use/singularity/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/grst/rstudio-server-conda/issues/3\"\u003ehttps://github.com/grst/rstudio-server-conda/issues/3\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "mcw-rcc/xfce-ood-desktop", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-xfce-ood-desktop\" class=\"anchor\" href=\"#xfce-ood-desktop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003exfce-ood-desktop\u003c/h1\u003e\n\u003cp\u003eSingularity container with Xfce desktop to support OOD apps.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1630683178.0 + "updated_at": 1598115007.0 }, { "data_format": 2, - "description": "Singularity Recipe for NWChem", + "description": "Nextflow pipeline for HPO-based prioritization (GADO and Exomiser)", "filenames": [ - "Singularity.6.6-openmpi" + "singularity/Singularity.GADO-v1.0.4", + "singularity/Singularity.Exomiser-v12.1.0" ], - "full_name": "ResearchIT/nwchem", + "full_name": "edg1983/NF_HPO_prioritize", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-for-nwchem\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-for-nwchem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipe for NWChem\u003c/h1\u003e\n\u003cp\u003eThis repo contains recipes to run \u003ca href=\"http://www.nwchem-sw.org/index.php/Main_Page\" rel=\"nofollow\"\u003eNWChem\u003c/a\u003e\nwithin a \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eVersions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e6.6 - NWChem with OpenMPI installed via EPEL\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Use:\u003c/h2\u003e\n\u003cp\u003eYou need to have openmpi v1 installed on your local machine (via yum or as a module).\nTesting was performed with openmpi 1.10.6.\u003c/p\u003e\n\u003cp\u003eRun example:\u003c/p\u003e\n\u003cp\u003empirun -np 2 singularity run shub://ResearchIT/nwchem:6.6-openmpi test.nw\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-alternative-method\" class=\"anchor\" aria-hidden=\"true\" href=\"#alternative-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternative method:\u003c/h2\u003e\n\u003cp\u003euse the provided bash wrapper and module file to use the nwchem singularity container like a standard module\n(this assumes you have a singularity/2.4 and openmpi/1 modules)\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003emodule load nwchem/6.6\u003c/p\u003e\n\u003cp\u003empirun -np 2 nwchem test.nw\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hpo-prioritization-pipeline\" class=\"anchor\" href=\"#hpo-prioritization-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPO prioritization pipeline\u003c/h1\u003e\n\u003cp\u003eBased on HPO profiles and input files provided this pipeline run \u003ca href=\"https://www.nature.com/articles/s41467-019-10649-4\" rel=\"nofollow\"\u003eGADO\u003c/a\u003e and/or \u003ca href=\"https://github.com/exomiser/Exomiser\"\u003eExomiser\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-before-you-run\" class=\"anchor\" href=\"#before-you-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBefore you run\u003c/h2\u003e\n\u003cp\u003eBefore you can use the pipeline you need to install Exomiser, GADO and some supporting files\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exomiser\" class=\"anchor\" href=\"#exomiser\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExomiser\u003c/h3\u003e\n\u003cp\u003eYou can download the latest version of Exomiser from the \u003ca href=\"http://data.monarchinitiative.org/exomiser/latest/index.html\" rel=\"nofollow\"\u003eMonarch initiative FTP\u003c/a\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eUpdate \u003ccode\u003eexomiser_cli\u003c/code\u003e params in the \u003ccode\u003enextflow.config\u003c/code\u003e to point to the \u003ccode\u003e.jar\u003c/code\u003e file of exomiser CLI.\u003c/li\u003e\n\u003cli\u003eUpdate the \u003ccode\u003econfig/application.properties\u003c/code\u003e file to point to your exomiser data folder.\u003c/li\u003e\n\u003cli\u003eNote that the current configuration also use CADD score, so you need to have CADD score files installed as well and you need to configure the corresponding file location in \u003ccode\u003econfig/application.properties\u003c/code\u003e (or remove CADD from the template files in \u003ccode\u003econfig\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gado\" class=\"anchor\" href=\"#gado\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGADO\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eDownload the GADO cli 1.0.1 from the \u003ca href=\"https://github.com/molgenis/systemsgenetics/releases/download/v1.0.4/GadoCommandline-1.0.1-dist.zip\"\u003eofficial release\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload dataset files. You can find the links in the \u003ca href=\"https://github.com/molgenis/systemsgenetics/wiki/GADO-Command-line\"\u003eGADO github wiki\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUncompress the prediction matrix \u003ccode\u003e.zip\u003c/code\u003e file and rename files so that you have a folder (let say \u003ccode\u003eGADO_resources\u003c/code\u003e containing the following files:\n\u003cul\u003e\n\u003cli\u003ehpo_predictions_info.txt\u003c/li\u003e\n\u003cli\u003ehpo_predictions_genes.txt\u003c/li\u003e\n\u003cli\u003ehpo_predictions_matrix_spiked.cols.txt\u003c/li\u003e\n\u003cli\u003ehpo_predictions_matrix_spiked.rows.txt\u003c/li\u003e\n\u003cli\u003ehpo_predictions_matrix_spiked.dat\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload the HPO ontology \u003ccode\u003e.obo\u003c/code\u003e file from GADO wiki or directly from \u003ca href=\"http://purl.obolibrary.org/obo/hp.obo\" rel=\"nofollow\"\u003eHPO ontology\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen you need to update the following params in the \u003ccode\u003enextflow.config\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGADO_cli: path to your GADO cli \u003ccode\u003e.jar\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eGADO_datafolder: path to folder containing GADO files (\u003ccode\u003eGADO_resources\u003c/code\u003e in this example)\u003c/li\u003e\n\u003cli\u003eHPO_obofile: path to your \u003ccode\u003e.obo\u003c/code\u003e files\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eWhen everything is properly configured in \u003ccode\u003enextflow.config\u003c/code\u003e you can run the pipeline using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow main.nf --GADO --exomiser \\\n --HPO HPO_profiles.tsv \\\n --exomiser_input exomiser_input.tsv \\\n --exomiser_template config/template_GRCh38.yml \\\n --out results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNB\u003c/em\u003e There are current profiles for \u003ccode\u003esge\u003c/code\u003e and \u003ccode\u003eslurm\u003c/code\u003e in the config file, but you need to configure the queue names for your system\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input-files\" class=\"anchor\" href=\"#input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files\u003c/h2\u003e\n\u003cp\u003eOnly HPO profiles file is required for GADO, while also exomiser input is required for exomiser.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hpo-profiles\" class=\"anchor\" href=\"#hpo-profiles\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPO profiles\u003c/h3\u003e\n\u003cp\u003eThis is a tab-separated file without header containing 1 case per line, with case ID in column 1 and then 1 HPO term per column\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecase1 HP:00001 HP:000002\ncase2 HP:00003 HP:000004 HP:000006\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exomiser-input\" class=\"anchor\" href=\"#exomiser-input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExomiser input\u003c/h3\u003e\n\u003cp\u003eThis is a tab-separated file without header containing 1 case per line, with case ID, proband id, vcf file and ped file. \u003cstrong\u003eNB\u003c/strong\u003e \u003ccode\u003ecase ID\u003c/code\u003e must match case ID from the HPO profiles and \u003ccode\u003eproband id\u003c/code\u003e must match the id of proband sample in the VCF file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecase1 proband1 case1_family.vcf.gz case1.ped\ncase2 proband2 case2_family.vcf.gz case2.ped\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-change-exomiser-settings\" class=\"anchor\" href=\"#change-exomiser-settings\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChange exomiser settings\u003c/h2\u003e\n\u003cp\u003eThe exomiser annotation and filter settings are store in the \u003ccode\u003e.yml\u003c/code\u003e templated in the \u003ccode\u003econfig\u003c/code\u003e folder. The provided files will filter for protein-changing variants with population AF \u0026lt; 1% and use CADD, PP2 and SIFT scores for variant scoring. All possible segregation models are evaluated and hiPhive is used for HPO-based prioritization. You can change these template to change analysis settings for the Exomiser. Please refer to the \u003ca href=\"https://exomiser.github.io/Exomiser/manual/7/exomiser/\" rel=\"nofollow\"\u003eexomiser documentation\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 6, - "topics": [ - "nwchem", - "singularity" - ], - "updated_at": 1551769652.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1643390805.0 }, { "data_format": 2, - "description": "This repository contains the singularity recipes I use for my robotics projects.", + "description": "Singularity Image Recipes for connecting to Singularity Hub", "filenames": [ - "Singularity.tf_gpu-opencv2-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial", - "Singularity.tf_gpu-opencv2-conda3-ros_kinetic-moveit-cuda10-xenial", - "Singularity.tf_gpu-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial", - "Singularity.tf_gpu-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial", - "Singularity.tf_gpu-conda3-ros_kinetic-moveit-cuda10-xenial", - "Singularity.tf_gpu-opencv2-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial" + "Singularity.py36", + "Singularity.devtoolset6", + "Singularity.dvs6_miniconda040512_py36_ml2", + "Singularity.intel_tf", + "Singularity.mpich33", + "Singularity.devtoolset8", + "Singularity.miniconda040512_py36_ml2", + "Singularity.dvs6_mcnda040614_py36_pytorch11", + "Singularity.dvs6_mpich33", + "Singularity.py36_ml_mpi33", + "Singularity.dvs4_cnda040512_py36", + "Singularity.dvs6_miniconda040512_py36_ml3", + "Singularity.mcnda040614_gcc7_py36", + "Singularity.dvs6_miniconda040512_py36_ml", + "Singularity.PyTorch_SparseConvNet", + "Singularity.hello_world", + "Singularity.py36_ml", + "Singularity.miniconda040512_py36_ml", + "Singularity.dvs6_py36_mpi33", + "Singularity.dvs8_cnda040512_py36", + "Singularity.miniconda3_theta", + "Singularity.miniconda040512_py36_ml3", + "Singularity.py36_ml_mpi33_hvd161", + "Singularity.dvs6_py36", + "Singularity.dvs6_py36_mpi33_ml", + "Singularity.mcnda040512_gcc7_py36" ], - "full_name": "rickstaa/deep-robotics-singularity-recipes", - "latest_release": "v0.1.6", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-deep-robotics-singularity-recipe-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#deep-robotics-singularity-recipe-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeep Robotics singularity recipe repository\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.codacy.com/gh/rickstaa/deep-robotics-singularity-recipes/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=rickstaa/deep-robotics-singularity-recipes\u0026amp;utm_campaign=Badge_Grade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/937121feb970b0588395f46f6af7607636459cacf58be2130bf2135cfb9deb25/68747470733a2f2f6170702e636f646163792e636f6d2f70726f6a6563742f62616467652f47726164652f3133316539306631386432303462613739343933663931646230343839303636\" alt=\"Codacy Badge\" data-canonical-src=\"https://app.codacy.com/project/badge/Grade/131e90f18d204ba79493f91db0489066\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3134\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/rickstaa/Todoist_Global_Shortcuts_WIN10/pulse\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b4978fac45f41461eb4f7d2c053e4142c3072396afed32484b2b1241bee4ea65/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4d61696e7461696e65642533462d7965732d677265656e\" alt=\"Maintained\" data-canonical-src=\"https://img.shields.io/badge/Maintained%3F-yes-green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/rickstaa/Todoist_Global_Shortcuts_WIN10/blob/master/contributing.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7e7bdf5c529c8bc594e26038dbb1a3d360e9ede891fbdcef50b403ab5f88fc14/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e747269627574696f6e732d77656c636f6d652d6f72616e67652e737667\" alt=\"Contributions\" data-canonical-src=\"https://img.shields.io/badge/contributions-welcome-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-package-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#package-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackage Overview\u003c/h2\u003e\n\u003cp\u003eThis repository contains singularity recipes that might be used for robotics\nprojects. These recipes are also published on the \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003ewww.singularity-hub.org\u003c/a\u003e\ncontainer registry. You are invited to add additional singularity recipes\nto this repository (see the \u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/contributing.md\"\u003econtributions guidelines\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecipes\u003c/h3\u003e\n\u003cp\u003eThis repository currently contains the following recipes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-conda3-ros_kinetic-moveit-cuda10-xenial\"\u003etf_gpu-conda3-ros_kinetic-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-opencv2-conda3-ros_kinetic-moveit-cuda10-xenial\"\u003etf_gpu-opencv2-conda3-ros_kinetic-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial\"\u003etf_gpu-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-opencv2-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial\"\u003etf_gpu-opencv2-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial\"\u003etf_gpu-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-opencv2-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial\"\u003etf_gpu-opencv2-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more information about each of the recipes see \u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipesinfo/recipes\"\u003ethe documentation\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and Usage\u003c/h2\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes\"\u003edocs\u003c/a\u003e for installation and usage instructions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eFeel free to open an issue if you have ideas on how to make this GitHub action better or if you want to report a bug! All contributions are welcome. \u003cg-emoji class=\"g-emoji\" alias=\"rocket\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f680.png\"\u003e\ud83d\ude80\u003c/g-emoji\u003e Please consult the \u003ca href=\"CONTRIBUTING.md\"\u003econtribution guideliness\u003c/a\u003e for more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"LICENSE\"\u003eMIT\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIcon created with svg made by \u003ca href=\"https://www.flaticon.com/authors/eucalyp\" rel=\"nofollow\"\u003e@Eucalyp\u003c/a\u003e from \u003ca href=\"https://www.flaticon.com/authors/eucalyp\" rel=\"nofollow\"\u003ewww.flaticon.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "jtchilders/singularity_image_recipes", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_image_recipes\" class=\"anchor\" href=\"#singularity_image_recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_image_recipes\u003c/h1\u003e\n\u003cp\u003eSingularity Image Recipes for connecting to Singularity Hub\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularityhello_world\" class=\"anchor\" href=\"#singularityhello_world\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity.hello_world\u003c/h1\u003e\n\u003cp\u003esimply demonstrates the basics of a container\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularitympichxx\" class=\"anchor\" href=\"#singularitympichxx\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity.mpichXX\u003c/h1\u003e\n\u003cp\u003eThis recipe demonstrates building MPICH into a container intended for an HPC. The MPICH built into the container should be replaced using the local system MPI libraries. The \u003ccode\u003esubmit.sh\u003c/code\u003e script demonstrates submitting a job to the Theta supercomputer at Argonne\u0027s Leadership Computing Facility.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1658652043.0 + "updated_at": 1618543215.0 }, { "data_format": 2, - "description": null, + "description": "Docker and Singularity Images of NKChen/Duke Resting State FMRI pipeline. Docker has been built from Neurodebian\u0027s Ubuntu:Xenial base image. Singularity has been built from Docker Ubuntu:Xenial base.", "filenames": [ - "Singularity/Singularity.lip2wav_tf", - "Singularity/Singularity.lip2wav", - "Singularity/Singularity.lip2wav_new", - "Singularity/Singularity.Rotate", - "Singularity/Singularity.test" + "Singularity" ], - "full_name": "kangzhiq/GSoC2020", + "full_name": "nkrecon/rest-state-fmri", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-gsoc2020-hand-gesture-detection-and-recognition-in-news-videos\" class=\"anchor\" aria-hidden=\"true\" href=\"#gsoc2020-hand-gesture-detection-and-recognition-in-news-videos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGSoC2020: Hand gesture detection and recognition in news videos\u003c/h1\u003e\n\u003cp\u003eThis is my GSoC2020 project with Red Hen Lab.\u003c/p\u003e\n\u003cp\u003eThe goal is to design a network capable of detecting and recognizing hand gestures and then apply it to annotate the dataset of news videos of Red Hen Lab.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-preprocessing-of-dataset-iemocap\" class=\"anchor\" aria-hidden=\"true\" href=\"#preprocessing-of-dataset-iemocap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessing of Dataset IEMOCAP\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003emocap_data_collect.py\u003c/code\u003e extract information from the IEMOCAP dataset and save the data as pickle.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-audio-visual-emtion-recognition\" class=\"anchor\" aria-hidden=\"true\" href=\"#audio-visual-emtion-recognition\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAudio-Visual emtion recognition\u003c/h1\u003e\n\u003cp\u003eAudio-only, visual-only and audio-visual models are tested to verify the utility of bimodal information.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker-and-singularity-images-for-resting-state-fmri-pipeline-nan-kuei-chenduke-university\" class=\"anchor\" href=\"#docker-and-singularity-images-for-resting-state-fmri-pipeline-nan-kuei-chenduke-university\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker and Singularity images for Resting State FMRI pipeline (Nan-kuei Chen/Duke University)\u003c/h1\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e for details of use.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-summary\" class=\"anchor\" href=\"#summary\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary\u003c/h1\u003e\n\u003cp\u003eThis repository contains the build scripts for Docker and Singularity images of the Duke resting pipeline that perform processing of resting state data using FSL (Jenkinson et al. 2012) tools and custom scripts.\u003c/p\u003e\n\u003cp\u003eversion information can be obtained as \u003ccode\u003edocker run --rm orbisys/rest-state-fmri -V\u003c/code\u003e and \u003ccode\u003esingularity run rest-state-fmri.simg -V\u003c/code\u003e\nhelp information can be obtained as \u003ccode\u003edocker run --rm orbisys/rest-state-fmri -h\u003c/code\u003e and \u003ccode\u003esingularity run rest-state-fmri.simg -h\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe Docker image will be about 3GB when built. It comes with version 5.09 of FSL.\nAlternatively if you do not want to build the docker image locally you can pull it from the Docker hub using the command \u003ccode\u003edocker run -it --rm -v $PWD:/opt/data orbisys/rest-state-fmri\u003c/code\u003e or \u003ccode\u003edocker pull orbisys/rest-state-fmri\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe Singularity image will be about 4GB when built. It comes with version 5.10 of FSL. Again if you prefer not to build this locally then a sister version of this singularity image can be downloaded as \u003ccode\u003eSingularity pull shub://chidiugonna/nklab-neuro-reststate\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThe original python source \u003ccode\u003eresting_pipeline.py\u003c/code\u003e available at at [\u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e] has been slightly amended. These changes are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edata1\u003c/code\u003e has been selectively converted to dtype \u003ccode\u003enumpy.float64\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eslice indices have been cast as longs in certain instances.\u003c/li\u003e\n\u003cli\u003eBXH functionality is ignored. To explicitly use BXH info pass the flag --ignorebxh=N\u003c/li\u003e\n\u003cli\u003eChanges have been made in step 8 to force the diagonals of the correlation matrix to zero to prevent inconsistencies due to NaNs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-sliding-window-functionality\" class=\"anchor\" href=\"#sliding-window-functionality\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSliding window functionality\u003c/h3\u003e\n\u003cp\u003eA new step has been added \u003ccode\u003e-7sw\u003c/code\u003e to enable sliding window functionality. In order to use this step you will need to use the \u003ccode\u003e--slidewin\u003c/code\u003e parameter which takes 2 numbers seperated by a comma. The 1st number is the window size in seconds and the second number is the shift in seconds between sequential windows. So for example \u003ccode\u003e--slidewin=60,3\u003c/code\u003e will use a window size of \u003ccode\u003e60\u003c/code\u003e seconds shifted by \u003ccode\u003e3\u003c/code\u003e seconds for each subsequent window. Keep in mind that the \u003ccode\u003e--tr\u003c/code\u003e (in milliseconds) parameter is required to calculate the number of volumes to use for each sliding window correlation. If you do not specify the --slidwin parameter and run step \u003ccode\u003e7sw\u003c/code\u003e then default values of \u003ccode\u003e30,3\u003c/code\u003e will be used. Sliding window files are exported to a new directory \u003ccode\u003eSlidingWindow_W_S\u003c/code\u003e and image files are consolidated into 4D volumes for viewing in FSL as a movie\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-extensions-to-slice-correction-functionality\" class=\"anchor\" href=\"#extensions-to-slice-correction-functionality\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtensions to Slice Correction functionality\u003c/h3\u003e\n\u003cp\u003eThe pipeline has been extended to accept custom slice correction timing files. A python script \u003ccode\u003emake_fsl_stc.py\u003c/code\u003e has been bundled in this container which can take .json files created by dcm2niix. This python program will create a slice correction file with timing values and one with slices in order of acquisition. It can be called as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e where fmri.json is the json output from dcm2niix. custom names for the slice order and slice time files can be provided as parameters as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake_fsl_stc.py fmri.json --slicenum=/path/num.txt --slicetime=/path/time.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOtherwise these files default to \u003ccode\u003esliceorder.txt\u003c/code\u003e and \u003ccode\u003eslicetimes.txt\u003c/code\u003e in the current directory.\u003c/p\u003e\n\u003cp\u003eIf \u003ccode\u003e--slicetime\u003c/code\u003e is provided and --sliceorder is not then only the slicetimes textfile is created. The opposite is true if \u003ccode\u003e--slicenum\u003c/code\u003e is provided.\u003c/p\u003e\n\u003cp\u003eOnce these custom files have been created then they can be provided to the resting state pipeline using the full path as input to the \u003ccode\u003e--sliceorder\u003c/code\u003e parameter\n\u003ccode\u003e--sliceorder=/path/num.txt\u003c/code\u003e as follows \u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eplease note that the default custom slice file expected uses slice order. If you pass a text file with slice times then you will need to use another parameter \u003ccode\u003e--slicetimings=time\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-docker-image\" class=\"anchor\" href=\"#build-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Docker Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have docker installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003erest-state-fmri\u003c/code\u003edirectory and check that have a Docker file \u003ccode\u003eDockerfile\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and the \u003ccode\u003esrc/resting_pipeline.py\u003c/code\u003e file have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow build the image as follows \u003ccode\u003esudo docker build -t orbisys/rest-state-fmri .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-run-docker-image\" class=\"anchor\" href=\"#run-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Docker Image\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-within-shell\" class=\"anchor\" href=\"#within-shell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWithin Shell\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to a directory with a test NIFTII image and enter \u003ccode\u003edocker run -it --rm -v $PWD:/opt/data --entrypoint /bin/bash orbisys/rest-state-fmri\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe docker image should run and automatically start in \u003ccode\u003e/opt/data\u003c/code\u003e directory which is mapped to the original directory from which you ran the image. The prompt should look something like below:\n\u003ccode\u003eroot@62e040b47368:/opt/data#\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eYou can now run the pipeline with the shell as follows: \u003ccode\u003eresting_pipeline.py --func PBIA6_26386_20140402_045154_93696_magnitude.nii --throwaway=4 --steps=2,3,4,5,6,7 -o PBIA6_26386_20140402_045154_93696 --sliceorder=odd --tr=5000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-as-a-one-line-command\" class=\"anchor\" href=\"#as-a-one-line-command\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAs a one line command\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to a directory with a test NIFTII image and enter:\n\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func moco14a0001.nii.gz --steps=1,2,3,4,5,6,7,8 -o 14a0001 --sliceorder=\"even\" --tr=3000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-running-gui-within-docker\" class=\"anchor\" href=\"#running-gui-within-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Gui within docker\u003c/h4\u003e\n\u003cp\u003eTo access GUI interaces of programs in the docker image then use the construct shown next (Courtesy of work by Fabio Rehm [\u003ca href=\"https://fabiorehm.com/blog/2014/09/11/running-gui-apps-with-docker/\" rel=\"nofollow\"\u003ehttps://fabiorehm.com/blog/2014/09/11/running-gui-apps-with-docker/\u003c/a\u003e] ). For example to run FSL as GUI then perform the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v $HOME/.Xauthority:/home/developer/.Xauthority -it --net=host --pid=host --ipc=host orbisys/rest-state-fmri fsl\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example-commands\" class=\"anchor\" href=\"#example-commands\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample Commands\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-create-slice-timing-files-from-json\" class=\"anchor\" href=\"#create-slice-timing-files-from-json\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Slice Timing files from json\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\" class=\"anchor\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" href=\"#build-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Singularity Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have singularity 2.4 or greater installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003erest-state-fmri\u003c/code\u003edirectory and check that you have a Singularity definiton file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow build the image as follows \u003ccode\u003esudo singularity build rest-state-fmri.simg Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-run-singularity-image\" class=\"anchor\" href=\"#run-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Singularity Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou can now run the pipeline as follows: \u003ccode\u003esingularity run nklab-reststate-fmri.simg /opt/rsfmri_python/bin/resting_pipeline.py --func PBIA6_26386_20140402_045154_93696_magnitude.nii --throwaway=4 --steps=2,3,4,5,6,7 -o PBIA6_26386_20140402_045154_93696 --sliceorder=odd --tr=5000\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eYou can also run FSL commands (e.g. flirt) directly as follows: \u003ccode\u003esingularity run --nv rest-state-fmri.simg /opt/fsl/bin/flirt ....\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-shell-into-singularity-image\" class=\"anchor\" href=\"#shell-into-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell into Singularity Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou can shell into the singularity image using: \u003ccode\u003esingularity shell rest-state-fmri.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example-commands-1\" class=\"anchor\" href=\"#example-commands-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample Commands\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-create-slice-timing-files-from-json-1\" class=\"anchor\" href=\"#create-slice-timing-files-from-json-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Slice Timing files from json\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esingularity run -B $PWD:/opt/data nklab-reststate-fmri.simg /opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file-1\" class=\"anchor\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --rm -B $PWD:/opt/data nklab-reststate-fmri.simg /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003eM. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1650289089.0 + "updated_at": 1562932368.0 }, { "data_format": 2, - "description": null, + "description": "Singularity containers", "filenames": [ - "Singularity", - "model_preprocess/Singularity" + "Singularity.lsdalton-mpi-omp", + "Singularity.lsdalton-omp", + "Singularity.dalton-mpi" ], - "full_name": "lsx1980/3D_model_traits_measurement", + "full_name": "robertodr/singularities", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-3d_model_traits_measurement\" class=\"anchor\" aria-hidden=\"true\" href=\"#3d_model_traits_measurement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3D_model_traits_measurement\u003c/h1\u003e\n\u003cp\u003eFunction: Extract gemetrical traits of 3D model\u003c/p\u003e\n\u003cp\u003eAuthor : Suxing Liu\u003c/p\u003e\n\u003cp\u003eDate created : 04/04/2018\u003c/p\u003e\n\u003cp\u003eDate last modified: 04/25/2019\u003c/p\u003e\n\u003cp\u003ePython Version : 2.7\u003c/p\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003cp\u003epython pipeline.py -p /$path_to_your_3D_model/ -m 3D_model_name.ply\u003c/p\u003e\n\u003cp\u003eSingularity test:\u003c/p\u003e\n\u003cp\u003esudo singularity build --writable model-scan.img Singularity\u003c/p\u003e\n\u003cp\u003esingularity exec model-scan.img python /opt/code/pipeline.py -p /$path_to_your_3D_model/ -m surface.ply\u003c/p\u003e\n\u003cp\u003esingularity exec shub://lsx1980/3D_model_traits_measurement python /opt/code/pipeline.py -p /$path_to_your_3D_model/ -m surface.ply\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePre-requisite:\n\u003cul\u003e\n\u003cli\u003ePython2.7\u003c/li\u003e\n\u003cli\u003eNumpy\u003c/li\u003e\n\u003cli\u003eSciPy\u003c/li\u003e\n\u003cli\u003eOpencv 3.0 for Python - \u003ca href=\"http://www.pyimagesearch.com/2015/06/15/install-opencv-3-0-and-python-2-7-on-osx/\" rel=\"nofollow\"\u003eInstallation\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVisualization requirement:\u003c/p\u003e\n\u003cp\u003epip3 install numba \u003cbr\u003e\nimagesize \u003cbr\u003e\nprogressbar2 \u003cbr\u003e\nmayavi \u003cbr\u003e\nPyQt5 \u003cbr\u003e\nnetworkx\u003c/p\u003e\n\u003cp\u003epython3 graph_compute.py -p /\u0026amp;path/active_component/\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularities\" class=\"anchor\" href=\"#singularities\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularities\u003c/h1\u003e\n\u003cp\u003eSingularity container recipes for Dalton and LSDalton, based on Ubuntu 18.04 LTS.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-available-recipes\" class=\"anchor\" href=\"#available-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable recipes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.lsdalton-omp\"\u003eSingularity.lsdalton-omp\u003c/a\u003e: OpenMP-parallel binary on Ubuntu 18.04\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.lsdalton-mpi-omp\"\u003eSingularity.lsdalton-mpi-omp\u003c/a\u003e: MPI+OpenMP-parallel binary on Ubuntu 18.04\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.dalton-mpi\"\u003eSingularity.dalton-mpi\u003c/a\u003e: MPI-parallel binary on Ubuntu 18.04\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-generate-new-recipes-using-hpc-container-maker-hpccm\" class=\"anchor\" href=\"#generate-new-recipes-using-hpc-container-maker-hpccm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerate new recipes using HPC Container Maker (HPCCM)\u003c/h2\u003e\n\u003cp\u003eThe recipe files are auto-generated using \u003ca href=\"https://github.com/NVIDIA/hpc-container-maker\"\u003eHPC Container Maker\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor Singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ hpccm --recipe \u0026lt;recipe_name\u0026gt;.py --format singularity --singularity-version=3.2 \u0026gt; recipes/Singularity.\u0026lt;version-tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe images are automatically built in GitHub Actions and uploaded to the GitHub\nContainer Registry. \u003cstrong\u003eOnly containers whose recipe changed on a given commit\nare rebuilt.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-locally-build-the-image-from-a-recipe-file\" class=\"anchor\" href=\"#how-to-locally-build-the-image-from-a-recipe-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to locally build the image from a recipe file\u003c/h2\u003e\n\u003cp\u003eThe version to build is a configurable parameter in the recipes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTo generate a definition file for v2020.0 of LSDalton:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat Singularity.lsdalton-omp | sed \"s/@_VERSION_@/v2020.0/g\" \u0026gt; Singularity.lsdalton-v2020.0-omp\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo generate a definition file for the \u003ccode\u003emaster\u003c/code\u003e branch of Dalton:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat Singularity.lsdalton-omp | sed \"s/@_VERSION_@/master/g\" \u0026gt; Singularity.lsdalton-master-omp\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou need \u003ccode\u003esudo\u003c/code\u003e for building images, but you don\u0027t need \u003ccode\u003esudo\u003c/code\u003e for anything else.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo -E singularity build lsdalton-v2020.0-omp.sif Singularity.lsdalton-v2020.0-omp\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-pull-these-images-from-github-container-registry\" class=\"anchor\" href=\"#how-to-pull-these-images-from-github-container-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to pull these images from GitHub Container Registry\u003c/h2\u003e\n\u003cp\u003eFor LSDalton:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ev2020.0 OpenMP parallelization only:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull oras://ghcr.io/robertodr/singularities/lsdalton-v2020.0-omp:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003emaster branch MPI+OpenMP parallelization:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull oras://ghcr.io/robertodr/singularities/lsdalton-master-mpi-omp:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor Dalton:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ev2020.0 MPI parallelization only:\n\u003cpre\u003e\u003ccode\u003esingularity pull oras://ghcr.io/robertodr/singularities/dalton-v2020.0-mpi:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-use-the-image\" class=\"anchor\" href=\"#how-to-use-the-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use the image\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eFirst try this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat /etc/os-release\n$ singularity exec lsdalton-v2020.0-omp.sif cat /etc/os-release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow try to run LSDalton:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run lsdalton-v2020.0-omp.sif myinput.dal somemolecule.mol\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSince \u003ccode\u003elsdalton-v2020.0-omp.sif\u003c/code\u003e is executable, you can also rename it to \u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003elsdalton\u003c/code\u003e and do this instead:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mv lsdalton-v2020.0-omp.sif lsdalton\n$ ./lsdalton myinput.dal somemolecule.mol\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1610422118.0 + "updated_at": 1641466400.0 }, { "data_format": 2, - "description": "Lexers for CudaText", + "description": "Electrophysiology tools, mountainlab processor library", "filenames": [ - "Singularity/Singularity.lcf", - "Singularity/tests/1/Singularity", - "Singularity/tests/4/Singularity", - "Singularity/tests/2/Singularity", - "Singularity/tests/3/Singularity", - "Singularity/tests/5/Singularity", - "Singularity/tests/6/Singularity" + "Singularity.v0.2.6" ], - "full_name": "OlehL/cuda_lexers", + "full_name": "scratcharchive/ml_ephys", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ml_ephys\" class=\"anchor\" href=\"#ml_ephys\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml_ephys\u003c/h1\u003e\n\u003cp\u003eElectrophysiology tools\nMountainLab processor library\u003c/p\u003e\n\u003cp\u003eInstallation from PyPI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install --upgrade ml_ephys\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen add it as a plugin to mountainlab:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/.mountainlab/packages\nml-link-python-module ml_ephys ml_ephys\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr installation from source:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eclone this repository into .mountainlab/packages/\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd ml_ephys\npip3 install --upgrade .\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 6, "topics": [], - "updated_at": 1610290115.0 + "updated_at": 1639779289.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.cpu", - "Singularity.gpu" + "Singularity" ], - "full_name": "sleeepyjack/variant_calling", + "full_name": "tsgoten/transactive-control-social-game", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-variant-calling\" class=\"anchor\" aria-hidden=\"true\" href=\"#variant-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVariant Calling\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e- make\n- Singularity (\u0026gt;= v3.2)\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-transactive-control-social-game\" class=\"anchor\" href=\"#transactive-control-social-game\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTransactive Control Social Game\u003c/h1\u003e\n\u003cp\u003eCode meant to support and simulate the Social Game that will be launched in 2021. Elements of transactive control and behavioral engineering will be tested and designed here\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://dvc.org/doc/install\" rel=\"nofollow\"\u003edvc\u003c/a\u003e (with google drive support)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOn linux this is \u003ccode\u003epip install \u0027dvc[gdrive]\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall Docker, if you have not already.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc remote add -d gdrive gdrive://1qaTn6IYd3cpiyJegDwwEhZ3LwrujK3_x\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc pull\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./run.sh\u003c/code\u003e from the root of the repo. This will put you in a shell in the docker container with the \u003ccode\u003erl_algos/logs\u003c/code\u003e directory mounted\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 ExperimentRunner.py sac test_experiment\u003c/code\u003e in the docker container to start an experiment with the name \u003ccode\u003etest_experiment\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003etensorboard --logdir rl_algos/logs\u003c/code\u003e from outside the docker container to view the logs\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-issues\" class=\"anchor\" href=\"#issues\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssues\u003c/h3\u003e\n\u003cp\u003eIf you\u0027re having trouble running docker or the \u003ccode\u003eExperimentRunner.py\u003c/code\u003e file. Please try running \u003ccode\u003epython ExperimentRunner.py\u003c/code\u003e and debug from there.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1589243145.0 + "updated_at": 1637011831.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.latest" + "Singularity" ], - "full_name": "cschu/dada2_container", + "full_name": "truatpasteurdotfr/singularity-gnina", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-gnina-101-singularity-image\" class=\"anchor\" href=\"#building-a-gnina-101-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a gnina-1.0.1 singularity image\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gnina/gnina/issues/122\"\u003ehttps://github.com/gnina/gnina/issues/122\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eHost running CentOS-7, but gnina requires a newer glibc...\u003c/li\u003e\n\u003cli\u003eProvide a drop-in replacement for gnina (assuming singularity is installed)\u003c/li\u003e\n\u003cli\u003eShould work with either \u003ca href=\"https://github.com/hpcng/singularity\"\u003ehttps://github.com/hpcng/singularity\u003c/a\u003e or \u003ca href=\"https://github.com/sylabs/singularity\"\u003ehttps://github.com/sylabs/singularity\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003cli\u003echeck gnina licenses at \u003ca href=\"https://github.com/gnina/gnina\"\u003ehttps://github.com/gnina/gnina\u003c/a\u003e when you are using it, these were copied verbatim here for convenience.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egnina is dual licensed under GPL and Apache. The GPL license is necessitated by\nthe use of OpenBabel (which is GPL licensed). In order to use gnina under the\nApache license only, all references to OpenBabel must be removed from the\nsource code.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-gnina/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-gnina/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build gnina oras://ghcr.io/truatpasteurdotfr/singularity-gnina:latest\n./gnina --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efor GPU access you need to use the \u003ccode\u003e--nv\u003c/code\u003e flag:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --nv ./gnina \u0026lt;.. your gnina options ...\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-thanks\" class=\"anchor\" href=\"#thanks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThanks\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/singularityhub/github-ci\"\u003ehttps://github.com/singularityhub/github-ci\u003c/a\u003e for the github action\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1618484418.0 + "updated_at": 1637250596.0 }, { "data_format": 2, - "description": null, + "description": "code for evaluation on full neurovista trial data", "filenames": [ - "Singularity", - "Singularity.devel", - "Singularity.gpu" + "Singularity.nv_eval" ], - "full_name": "lamps24/neural_network_project", + "full_name": "MatthiasEb/neurovista_evaluation", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-csci5980\" class=\"anchor\" aria-hidden=\"true\" href=\"#csci5980\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecsci5980\u003c/h1\u003e\n\u003cp\u003eFinal project for CSci 5980: deep learning for automatic music translation.\u003c/p\u003e\n\u003cp\u003eFollow theses steps to install all package dependencies for running the model:\u003c/p\u003e\n\u003cp\u003eWe first install software dependencies for manipulating raw audio (\u003ccode\u003effmpeg\u003c/code\u003e):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a local software directory\n\u003ccode\u003emkdir ~/software\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the NASM assembler (dependency of ffmpeg):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software\nwget https://www.nasm.us/pub/nasm/releasebuilds/2.14.02/nasm-2.14.02.tar.bz2\ntar -xvf nasm-2.14.02.tar.bz2\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e nasm-2.14.02\n./configure --prefix=\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software/nasm/\nmake install\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software/nasm/bin/\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eMake sure that NASM assembler installed correctly:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enasm -v\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output should look something like:\n\u003ccode\u003eNASM version 2.14.02 compiled on Mar 11 2020\u003c/code\u003e\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eInstall ffmpeg:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software\nwget https://ffmpeg.org/releases/ffmpeg-4.2.2.tar.bz2\ntar -xvf ffmpeg-4.2.2.tar.bz2\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ffmpeg-4.2.2\n./configure --prefix=\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software/ffmpeg/\nmake install\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software/ffmpeg/bin/\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eMake sure that ffmpeg installed correctly:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003effmpeg -version\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output should look something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003effmpeg version 4.2.2 Copyright (c) 2000-2019 the FFmpeg developers\nbuilt with gcc 4.4.7 (GCC) 20120313 (Red Hat 4.4.7-23)\nconfiguration: --prefix=/home/csci5980/piehl008/software/ffmpeg/\nlibavutil 56. 31.100 / 56. 31.100\nlibavcodec 58. 54.100 / 58. 54.100\nlibavformat 58. 29.100 / 58. 29.100\nlibavdevice 58. 8.100 / 58. 8.100\nlibavfilter 7. 57.100 / 7. 57.100\nlibswscale 5. 5.100 / 5. 5.100\nlibswresample 3. 5.100 / 3. 5.100\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eNow, we can make the virtual environment and install python packages. First, create the virtual environment by running:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ccode\u003econda create --name audio-proj python=3.7\u003c/code\u003e\u003c/p\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eNext, install packages by running\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/csci5980\nconda install --name audio-proj --file requirements.txt --channel defaults --channel conda-forge\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(Note: this can take a while - and you need to say yes to installing everything after it solves the environment)\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eTo activate the virtual environment, you can now run \u003ccode\u003esource activate audio-proj\u003c/code\u003e. Note: you should do this to test that you can activate the virtual evironment, but you probably shouldn\u0027t run a lot unless you are submitting jobs to the queue. If you want to use this virtual environment through the MSI notebooks, check out the tutorial at \u003ca href=\"https://sunju.org/teach/DL-Spring-2020/TensorFlowPyTorch.html\" rel=\"nofollow\"\u003ehttps://sunju.org/teach/DL-Spring-2020/TensorFlowPyTorch.html\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-adding-the-virtual-environment-to-jupyter-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-the-virtual-environment-to-jupyter-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding the Virtual Environment to Jupyter Notebooks\u003c/h3\u003e\n\u003cp\u003eNow that we have created the virtual environment, we can add it to the Jupyter notebook kernels so that we can use the virtual environment through MSI\u0027s notebook server. To do this, we have to add the kernel specifications to the known Jupyter kernels for our user:\u003c/p\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eIf you haven\u0027t already, activate your virtual environment by running \u003ccode\u003esource activate audio-proj\u003c/code\u003e. Then enter\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewhich python\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYour output should tell you where the python executable for this virtual environment lives - the output for me displays \u003ccode\u003e~/.conda/envs/audio-proj/bin/python\u003c/code\u003e. If you see something that looks like \u003ccode\u003e/panfs/roc/msisoft/anaconda/anaconda3-2018.12/bin/python\u003c/code\u003e, go back and make sure that you have the virtual environment active and try again. After you have an ouput that clearly has the name of the virtual environment in the directory path (i.e. contains audio-proj in it), continue to the next step.\u003c/p\u003e\n\u003col start=\"10\"\u003e\n\u003cli\u003eNow, we need to create the kernel configuration. To do this run\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.local/share/jupyter/kernels/audio-proj\nnano \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.local/share/jupyter/kernels/audio-proj/kernel.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe nano command will open a very basic text editor that you can navigate with the arrow keys. Enter the following:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e{\n \"argv\": [\n \"~/.conda/envs/audio-proj/bin/python\", #replace this with your path from step 9 above! (and delete this comment)\n \"-m\",\n \"ipykernel_launcher\",\n \"-f\",\n \"{connection_file}\"\n ],\n \"display_name\": \"Audio Project Kernel\",\n \"language\": \"python\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere you replace the first line of the argv array with whatever executable path was output from step 9 above (it likely will be identical to this). To exit the nano text editor, type \u003ccode\u003eCtrl-x \u0026lt;RETURN\u0026gt;\u003c/code\u003e and then type \u003ccode\u003eY \u0026lt;RETURN\u0026gt;\u003c/code\u003e to save the file.\u003c/p\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003eNow that you have saved the kernel file, you should be able to go to \u003ccode\u003ehttps://notebooks.msi.umn.edu/\u003c/code\u003e and when you click on the \u003ccode\u003eNew\u003c/code\u003e tab to create a new file, you should be able to select \u003ccode\u003eAudio Project Kernel\u003c/code\u003e as an available kernel to run your newly created file in.\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-neurovista_evaluation\" class=\"anchor\" href=\"#neurovista_evaluation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eneurovista_evaluation\u003c/h1\u003e\n\u003cp\u003ecode for evaluation on full neurovista trial data following the \u003ca href=\"https://github.com/epilepsyecosystem/CodeEvaluationDocs\"\u003einstructions\u003c/a\u003e (commit 20e6f0f, dated 16/06/2020).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-settings\" class=\"anchor\" href=\"#settings\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSettings\u003c/h2\u003e\n\u003cp\u003eSettings can be adjusted in SETTINGS.json\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-a-virtual-environment\" class=\"anchor\" href=\"#using-a-virtual-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing a virtual environment\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erequirements\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003epython3\u003c/li\u003e\n\u003cli\u003ecuda toolbox 10, nvidia-drivers\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einstallation\u003c/h3\u003e\n\u003cp\u003eInstall requirements by running:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install -r requirements.txt\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-execution\" class=\"anchor\" href=\"#execution\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eexecution\u003c/h3\u003e\n\u003cp\u003eRun training by executing:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython run.py\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-docker\" class=\"anchor\" href=\"#using-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Docker\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-requirements-1\" class=\"anchor\" href=\"#requirements-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erequirements\u003c/h3\u003e\n\u003cp\u003eTested with Docker version 19.03.6, build 369ce74a3c on Ubuntu 18.04\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-image\" class=\"anchor\" href=\"#build-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Image\u003c/h3\u003e\n\u003cp\u003eBuild Docker Image by running:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker build --tag nv1x16_eval .\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-execution-1\" class=\"anchor\" href=\"#execution-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h3\u003e\n\u003cp\u003eSpecify the directory of your data segments by\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eexport DATA_DIR=YOUR_DATA_DIRECTORY\u003c/code\u003e,\u003c/p\u003e\n\u003cp\u003ereplacing \u003ccode\u003eYOUR_DATA_DIRECTORY\u003c/code\u003e with your specific directory.\u003c/p\u003e\n\u003cp\u003eRun training by executing\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --gpus 1 -v $PWD:/code -v /$DATA_DIR:/$DATA_DIR:ro nv1x16_eval python ./run.py\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-singularity\" class=\"anchor\" href=\"#using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003c/h2\u003e\n\u003cp\u003eSingularity recipe is included. SingularityHub URI of the Image is MatthiasEb/neurovista_evaluation:nv_eval.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-remarks\" class=\"anchor\" href=\"#remarks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRemarks\u003c/h2\u003e\n\u003cp\u003eYou should use a GPU for training. I used an RTX 2080 Ti, run_on_contest_data=1, mode=1 took about 4.5 h.\nIf you use a GPU with much less RAM, you might have to reduce the batch size, I did not try that though.\nI ran the code with run_on_contest_data=1, the results seemed to be comparable to the version on the ecosystem leaderboard.\nSparse tests with run_on_contest_data=0 have been executed, maybe there is something I missed here.\nI did not yet try to run it within a singularity container, docker should work though.\nDo not hesitate to contact me if you run into problems, have any questions or remarks.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-algorithm\" class=\"anchor\" href=\"#algorithm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlgorithm\u003c/h3\u003e\n\u003cp\u003eThis is a pretty naive approach on a 2D-Convolution Deep Neural Network, applied to the raw time series.\nAs described in the \u003ca href=\"https://ieeexplore.ieee.org/abstract/document/8621225\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, the Network expects standardized 15 s segments, sampeled at 200 Hz.\ntensorflow.keras (2.0.1) was used as Deep Learning API.\nIn order to avoid the need to either load the whole training set at once or to save the preprocessed time series, this is a different implementation than the one used in the paper.\nIn order to allow training on arbitrary large datasets, this implementation does not perfectly reproduce the results shown in the paper.\nI did a few testruns on the contest data, ROC AUC of the private Set should be around .25, .7 and .8 for Patient 1, 2 and 3 respectively.\nHowever, considerable variations are conceivable, see section below.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-implementation\" class=\"anchor\" href=\"#implementation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImplementation\u003c/h3\u003e\n\u003cp\u003eLoading the original (~ 400 Hz) .mat files, resampling to 200 Hz, standardizing (optionally, if \u003ccode\u003esubtract_mean==1\u003c/code\u003e), splitting them in 15 s segments is done asynchronously on the fly by the dataloader in 5 different threads.\nThe 15s Segments are enqueued in a buffer with the size of 400 10-min-sequences, implemented as a tf.queue.RandomShuffleQueue.\nThe data is therefore dequeued in random order, although not perfectly uniformly shuffeled, depending on the buffer size and the size of the data set.\nI did some experiments that showed that the buffersize can have considerable impact on the performance of the algorithm.\nThe bigger the buffer size, the closer are the results to the ones shown in the paper.\nThe intention of this procedure was to ensure a reasonably shuffeled training set of 15 s segments while minimizing IO, working on the .mat files and having the possibility for standardization.\u003cbr\u003e\nIf the IO-Bandwidth of the filesystem is reasonably high, this should not slow down the training too much.\u003c/p\u003e\n\u003cp\u003eAs described in the paper, if run_on_contest_data==1, 3 networks (one for each patient) are trained and evaluated individually.\nSubsequently, the solution file is concatenated.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1637707574.0 + "updated_at": 1629316259.0 }, { "data_format": 2, - "description": "uresnet based deep neutral network for the segmentation of high resolution cryo-EM tomographs", + "description": null, "filenames": [ - "Singularity" + "Singularity.ray", + "Singularity", + "Singularity.beta" ], - "full_name": "yee379/uresnet-tomo-seg", + "full_name": "huynhngoc/head-neck-analysis", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-uresnet-tomo-seg\" class=\"anchor\" aria-hidden=\"true\" href=\"#uresnet-tomo-seg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003euresnet-tomo-seg\u003c/h1\u003e\n\u003cp\u003euresnet based deep neutral network for the segmentation of high resolution cryo-EM tomographs\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-head-and-neck-cancer-analysis\" class=\"anchor\" href=\"#head-and-neck-cancer-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHead and Neck cancer analysis\u003c/h1\u003e\n\u003cp\u003eStart by running \u003ccode\u003esetup.sh\u003c/code\u003e to download the singularity container\nThen, submit slurm jobs like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esbatch slurm.sh config/2d_unet.json 2d_unet 200\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhich will load the setup from the \u003ccode\u003econfig/2d_unet.json\u003c/code\u003e file, train for 200 epochs\nand store the results in the folder \u003ccode\u003e$HOME/logs/hn_perf/2d_unet/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo customize model and prediction checkpoints\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm.sh config/3d_vnet_32_normalize.json 3d_vnet_32_normalize 100 --model_checkpoint_period 5 --prediction_checkpoint_period 5\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo continue an experiment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_cont.sh model.030.h5 3d_vnet_32_normalize 100 --model_checkpoint_period 5 --prediction_checkpoint_period 5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo plot performance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_vis.sh 3d_vnet_32_normalize\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run test\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_test.sh 3d_vnet_32\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_test.sh 3d_vnet_32 --best_epoch \u0026lt;BEST_EPOCH_NUMBER\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, if your cluster does not have slurm installed, simply omit the \u003ccode\u003esbatch\u003c/code\u003e\npart of the call above, thus running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./slurm.sh config/2d_unet.json 2d_unet 200\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eManually build\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot deoxys.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRemember to login to a gpu session to use the gpu\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqlogin --partition=gpu --gres=gpu:1\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1577150888.0 + "updated_at": 1637309995.0 }, { "data_format": 2, - "description": "Lab pipelines using Snakemake + Singularity + SCIF", + "description": "Simple scripts for analysis", "filenames": [ - "chip-seq.scif/Singularity", - "rna-seq-multisamples/Singularity" + "Dockerfiles/guppy/Singularity.v5.0.11" ], - "full_name": "BennerLab/pipelines", + "full_name": "cgjosephlee/My_scripts", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a7912c9863e897576e5d434d91e359d254976266bee2f9b1405197941f940bdf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f66696c6573797374656d2d736369656e74696669632d626c75652e737667\" alt=\"scif\" data-canonical-src=\"https://img.shields.io/badge/filesystem-scientific-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://snakemake.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93b6e9faa8e75932017af0ff2ca7db9493cc08e51c462e71143809db606cb04d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d253345253344253230342e362e302d626c75652e737667\" alt=\"snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%3E%3D%204.6.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ed7d71f6eadf7149f22c334c2b29e0479f493cfaced652052e122f59b5920be/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d253345253344253230322e342e322d626c75652e737667\" alt=\"singularity\" data-canonical-src=\"https://img.shields.io/badge/singularity-%3E%3D%202.4.2-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-svenner-lab-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#svenner-lab-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSvenner Lab Documentation\u003c/h1\u003e\n\u003cp\u003eThis repository contains the Svenner lab pipelines for various types of sequencing data. All pipelines are implemented in Snakemake and use the Singularity + Scientific Filesystem to create reproducible research environments.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipelines\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BennerLab/pipelines/tree/master/chip-seq.scif\"\u003eChIP-seq Pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BennerLab/pipelines/tree/master/rna-seq-multisamples\"\u003eRNA-seq Multisample Pipeline\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-my_scripts\" class=\"anchor\" href=\"#my_scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMy_scripts\u003c/h1\u003e\n\u003cp\u003eSimple scripts for analysis\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1625275230.0 + "updated_at": 1636945791.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.nipype-plus-jupyter-plus-seaborn" + "requirements/Singularity.def" ], - "full_name": "sajjadtorabian/singularity_recipes", + "full_name": "jordancaraballo/slump-detection", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipes\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nipype-plus-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#nipype-plus-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNipype Plus Jupyter\u003c/h2\u003e\n\u003cp\u003eWe first need to download the Docker layers. Normally this happens automatically with \u003ccode\u003esingularity build\u003c/code\u003e or \u003ccode\u003esingularity pull\u003c/code\u003e, but if you aren\u0027t using the development version 3.0 branch of Singularity that has a fixed bug with respect to whiteout files, you will have issue when you do these commands with nipype (note that it has whiteout files). What we did (because didn\u0027t feel like installing another version of Singularity) was to do a pull of nipype with the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003eSingularity Global Client\u003c/a\u003e that will download the fixed layers and then put them in the same cache that Singularity uses. Then we will have what we need :) Here is how you can install and use the client:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install sregistry\n$ sregistry pull nipype/nipype:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow we want to debug the build and find the missing path! To do this, you can build a completely empty image to look around in. The recipe looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Singularity.nipype-plus-jupyter-empty\nBootstrap: docker\nFrom: nipype/nipype:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above you can save to whatever file you want, we\u0027re calling ours \u003ccode\u003eSingularity.nipype-plus-jupyter-empty\u003c/code\u003e we can then build like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build npjup.simg Singularity.nipype-plus-jupyter-empty\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we can shell inside and find locations of things.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell npjup.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe are able to see what will be exported in the environment at runtime, and then source it to add these locations to the path (so we can find executables there!)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat /environment\n$ cat /.singularity.d/env/10-docker.sh \nexport PATH=\"/opt/conda/bin:/usr/lib/ants:/opt/afni:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\"\nexport LANG=\"en_US.UTF-8\"\nexport LC_ALL=\"C.UTF-8\"\nexport ND_ENTRYPOINT=\"/neurodocker/startup.sh\"\nexport MATLABCMD=\"/opt/mcr/v92/toolbox/matlab\"\nexport FORCE_SPMMCR=\"1\"\nexport LD_LIBRARY_PATH=\"/usr/lib/x86_64-linux-gnu:/opt/mcr/v92/runtime/glnxa64:/opt/mcr/v92/bin/glnxa64:/opt/mcr/v92/sys/os/glnxa64:\"\nexport FREESURFER_HOME=\"/opt/freesurfer\"\nexport ANTSPATH=\"/usr/lib/ants\"\nexport MKL_NUM_THREADS=\"1\"\nexport OMP_NUM_THREADS=\"1\"\nexport CONDA_DIR=\"/opt/conda\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere we see the location of conda! Strange that it wasn\u0027t where we expected. Let\u0027s add to the path and then we have pip\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PATH=/opt/conda/bin:$PATH\nwhich pip\n/opt/conda/bin/pip\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we can update the recipe \u003ca href=\"Singularity.nipype-plus-jupyter\"\u003eSingularity.nipype-plus-jupyter\u003c/a\u003e with our found pip.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: nipype/nipype:latest\n\n%labels\n Maintainer Sajjad\n Version v1.0\n\n%post\n export PATH=/opt/conda/bin:$PATH\n pip install --upgrade pip\n pip install jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd build the image\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build npjup.simg Singularity.nipype-plus-jupyter\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e:)\u003c/p\u003e\n", + "readme": "\u003cp\u003eNEW REPOSITORY LOCATION: \u003ca href=\"https://github.com/nasa-cisto-ai/slump-detection.git\"\u003ehttps://github.com/nasa-cisto-ai/slump-detection.git\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-slump-detection\" class=\"anchor\" href=\"#slump-detection\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSlump Detection\u003c/h1\u003e\n\u003cp\u003eSlump Detection as an instance segmentation problem.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-business-case\" class=\"anchor\" href=\"#business-case\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBusiness Case\u003c/h2\u003e\n\u003cp\u003eThe following repository stores several experiments for the task of instance and semantic\nsegmentation of slumps in very high-resolution satellite imagery. Many of the instructions\nlisted below are guided towards utilizing GSFC NASA Center for Climate Simulation (NCCS)\ncomputing resources, particularly the PRISM GPU cluster.\u003c/p\u003e\n\u003cp\u003eA system with NVIDIA GPUs is required to run the scripts located in this repository.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eprojects/detectron2: utilizes the detectron2 framework for the task of instance segmentation\nleveraging MaskRCNN and Fast RCNN. The backend engine is PyTorch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-summarized-steps\" class=\"anchor\" href=\"#summarized-steps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummarized Steps\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin.nccs.nasa.gov\nssh gpulogin1\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#Logging_In\"\u003eLogging-In\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Container_Environment_Installation\"\u003eContainer Environment Installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Working_Inside_Container\"\u003eWorking Inside a Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Getting_Started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Authors\"\u003eAuthors\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#References\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-logging-in-\" class=\"anchor\" href=\"#logging-in-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogging-In \u003ca name=\"user-content-Logging_In\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eYou will need an activate NCCS account together with a PIV Card or an RSA Token. Please refer\nto the following link for instructions on setting up login or any login related questions:\n\u003ca href=\"https://www.nccs.nasa.gov/nccs-users/instructional/logging-in/bastion-host\" rel=\"nofollow\"\u003eNCCS Logging-In\u003c/a\u003e.\nOnce you are all setup, you may login to the PRISM GPU cluster.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin.nccs.nasa.gov\nssh gpulogin1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-container-environment-installation-\" class=\"anchor\" href=\"#container-environment-installation-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Environment Installation \u003ca name=\"user-content-Container_Environment_Installation\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eAll the software and scripts from this repository can be ran within a container. Containers are\nsmall versions of operating systems that are meant to speed up the process of software development.\nThese containers are simply a binary file which has all the executables needed to run the software included.\u003c/p\u003e\n\u003cp\u003eThe NCCS provides Singularity as the default container runtime tool. In order to configure your\nenvironment to run Singularity containers, you will need to setup the environment variables listed below.\nFor this, you can simply add the following lines to your ~/.bashrc file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport SINGULARITY_CACHEDIR=\u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/.singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport SINGULARITY_TMPDIR=\u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/.singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTest the environment variables with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e[username@gpulogin1 \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e]$ \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_CACHEDIR\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_TMPDIR\u003c/span\u003e\n/att/nobackup/username/.singularity /att/nobackup/username/.singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to utilize the container for this project, we first need to download the image from a container\nregistry. The image for this project is located in \u003ca href=\"https://hub.docker.com/repository/docker/nasanccs/slump-detectron2\" rel=\"nofollow\"\u003eNASA NCCS DockerHub Repository\u003c/a\u003e. Docker containers can be pulled as Singularity containers to be executed on HPC\nenvironments. The following commands allow the download of the container from DockerHub and generates a\nfile with a .sif extension. Depending on the file system, this step can take several minutes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e\nmodule load singularity\nsingularity pull docker://docker.io/nasanccs/slump-detectron2:latest\nsingularity build --sandbox slump-detectron2_latest slump-detectron2_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-working-inside-a-container-\" class=\"anchor\" href=\"#working-inside-a-container-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorking Inside a Container \u003ca name=\"user-content-Working_Inside_Container\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eEach project provides a set of Slurm scripts that will execute code inside the container without having\nto login inside the image. You may skip this step and go straight to the project README if you are only\ninterested in running scripts from outside the container. This section is meant to help users developing\nand testing code inside the container to facilitate the development process.\u003c/p\u003e\n\u003cp\u003eTo get a session in one of the PRISM GPU nodes, you can run the following command. Additional instructions\nregarding Slurm can be found in the \u003ca href=\"https://www.nccs.nasa.gov/nccs-users/instructional/adapt-instructional/using-prism\" rel=\"nofollow\"\u003eNCCS website\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esalloc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will notice that the hostname will change to something similar to gpu***. This means that you are now\nlogged into one of the GPU nodes. To access the container image, you can run the command listed below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --nv -B /att/nobackup/username:/att/nobackup/username slump-detectron2_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere username is your NASA auid. From here, you can run any command inside the container image. Note that\nfor Singularity containers to have access to other paths within the HPC environment, we need to bind\ndirectories to particular locations in the container. The command above is binding your $NOBACKUP directory\nto be visible from inside the container.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started-\" class=\"anchor\" href=\"#getting-started-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started \u003ca name=\"user-content-Getting_Started\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eThe following is a summarized set of steps to get started and running in less than 5 minutes once the container image has been downloaded.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository into your ADAPT space\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e\ngit clone https://github.com/jordancaraballo/slump-detection.git\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCopy the data into the data/ directory\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp /data/location/.tif \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/data\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eGenerate train, test, and validation datasets\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch gen_dataset.sh\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eTrain a new model\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch train_detectron2.sh\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eClassify given imagery\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch predict_detectron2.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-project-specific-information\" class=\"anchor\" href=\"#project-specific-information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject Specific Information\u003c/h2\u003e\n\u003cp\u003eData resides under:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/att/nobackup/username/EVHR_requests/_deliver/EWebbRequest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin\nssh gpulogin1\nmodule load anaconda\nconda create --name slump-detection-11.1 --clone /att/nobackup/username/.conda/envs/slump-detection-11.1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-anaconda-environment\" class=\"anchor\" href=\"#anaconda-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnaconda environment\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load anaconda\nconda config --add channels conda-forge\nconda config --set channel_priority strict\nconda create -y -n slump-detection rioxarray cupy cudatoolkit=11.2 dask-cuda cudnn cutensor nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall pip dependencies\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda activate slump-detection\npip install -r requirements.txt\npip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003egit+https://github.com/facebookresearch/fvcore\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egit+https://github.com/philferriere/cocoapi.git#egg=pycocotools\u0026amp;subdirectory=PythonAPI\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ngit clone https://github.com/facebookresearch/detectron2 detectron2_repo \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e pip install -e detectron2_repo\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdding NCCL\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia\nconda create -n rapids-21.06 -c rapidsai -c nvidia -c conda-forge rapids-blazing=21.06 python=3.7 cudatoolkit=11.2 nvcc_linux-64 nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n rapids-21.06 -c rapidsai -c nvidia -c conda-forge -c pytorch rapids-blazing=21.06 python=3.7 cudatoolkit=11.1 ipykernel ipywidgets matplotlib geopandas pytorch torchvision torchaudio cudatoolkit=11.1 \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou could also enhance your kernel with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda config --add channels conda-forge\nconda config --set channel_priority strict\nconda create -y -n slump-detection-11.1 rioxarray cupy cudatoolkit=11.1 dask-cuda cudnn cutensor nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities gcc_linux-64\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install cython\npip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003egit+https://github.com/facebookresearch/fvcore\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egit+https://github.com/philferriere/cocoapi.git#egg=pycocotools\u0026amp;subdirectory=PythonAPI\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\npip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html\npip install opencv-python scikit-image\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-authors\" class=\"anchor\" href=\"#authors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eJordan Alexis Caraballo-Vega, \u003ca href=\"mailto:jordan.a.caraballo-vega@nasa.gov\"\u003ejordan.a.caraballo-vega@nasa.gov\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003e[1] Chollet, Fran\u00e7ois; et all, Keras, (2015), GitHub repository, \u003ca href=\"https://github.com/keras-team/keras\"\u003ehttps://github.com/keras-team/keras\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n\u003cp\u003e[2] Paszke, Adam; Gross, Sam; Chintala, Soumith; Chanan, Gregory; et all, PyTorch, (2016), GitHub repository, \u003ca href=\"https://github.com/pytorch/pytorch\"\u003ehttps://github.com/pytorch/pytorch\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n\u003cp\u003e[3] Google Brain Team; et all, TensorFlow, (2015), GitHub repository, \u003ca href=\"https://github.com/tensorflow/tensorflow\"\u003ehttps://github.com/tensorflow/tensorflow\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1534761419.0 + "updated_at": 1633374445.0 }, { "data_format": 2, - "description": null, + "description": "Mujoco Singularity container", "filenames": [ - "priori/tests/resources/Singularity.enrichment" + "Singularity" ], - "full_name": "ohsu-comp-bio/regulon-enrichment", + "full_name": "ppaquette/img.mujoco", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4fdbdfa64306659366ce76f0240a36fb0dc5e510b28870cad546175d0030658d/68747470733a2f2f7472617669732d63692e636f6d2f4a4573746162726f6f6b2f726567756c6f6e2d656e726963686d656e742e7376673f746f6b656e3d5a52445742576539735843697650314e725a7771266272616e63683d6d6173746572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4fdbdfa64306659366ce76f0240a36fb0dc5e510b28870cad546175d0030658d/68747470733a2f2f7472617669732d63692e636f6d2f4a4573746162726f6f6b2f726567756c6f6e2d656e726963686d656e742e7376673f746f6b656e3d5a52445742576539735843697650314e725a7771266272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/JEstabrook/regulon-enrichment.svg?token=ZRDWBWe9sXCivP1NrZwq\u0026amp;branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://www.python.org/downloads/release/python-367\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/75b8738e1bdfe8a832711925abbc3bd449c1e7e9260c870153ec761cad8dde40/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e362b2d626c75652e737667\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/python-3.6+-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1d48578e1082d434280fe2299efd72bcf1cba5658c8ad707406fd29a0a1a4193/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6e7269676874677265656e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1d48578e1082d434280fe2299efd72bcf1cba5658c8ad707406fd29a0a1a4193/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d6e7269676874677265656e2e737667\" alt=\"t\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-nrightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8416ebae4490c8fa309b7d13e3a8565f3da2ea34ce1eb8a905ce2c7195dddd63/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7374617475732d737461626c652d6e7269676874677265656e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8416ebae4490c8fa309b7d13e3a8565f3da2ea34ce1eb8a905ce2c7195dddd63/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7374617475732d737461626c652d6e7269676874677265656e2e737667\" alt=\"t\" data-canonical-src=\"https://img.shields.io/badge/status-stable-nrightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/31c454bdf9d517bfbf4d3eed1c768f1853de782076f85ce05b681d0017bfa7f4/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3137393735323035392e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31c454bdf9d517bfbf4d3eed1c768f1853de782076f85ce05b681d0017bfa7f4/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3137393735323035392e737667\" alt=\"t\" data-canonical-src=\"https://zenodo.org/badge/179752059.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-priori\" class=\"anchor\" aria-hidden=\"true\" href=\"#priori\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePriori\u003c/h1\u003e\n\u003cp\u003ePriori is a Python module used to predict the activity of regulatory proteins from RNAseq data.\u003c/p\u003e\n\u003cp\u003ePriori submodules:\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-enricherfeatures\" class=\"anchor\" aria-hidden=\"true\" href=\"#enricherfeatures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eenricher.features\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eLoad -omic datasets\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-enricherregulon\" class=\"anchor\" aria-hidden=\"true\" href=\"#enricherregulon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eenricher.regulon\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eRegulon utilities\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003ePriori\u003c/strong\u003e requires:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- Python (\u0026gt;= 3.6)\n- scikit-learn (\u0026gt;= 0.21.3)\n- NumPy (\u0026gt;= 1.17.3)\n- SciPy (\u0026gt;= 1.3.1)\n- pandas (\u0026gt;= 0.25.3)\n- tqdm (\u0026gt;= 4.38.0)\n- dill (\u0026gt;= 0.3.1.1)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h1\u003e\n\u003cp\u003ePriori leverages pathway information and gene expression data to produce regulon-based protein activity scores.\nOur method tests for positional shifts in experimental-evidence supported networks consisting of transcription factors\nand their downstream signaling pathways when projected onto a rank-sorted gene-expression signature.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-priori\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-priori\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Priori\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-invoking-priori-from-the-command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#invoking-priori-from-the-command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInvoking Priori from the command line\u003c/h2\u003e\n\u003cp\u003eInitialize github in the directory where you want to download Priori. Clone the Priori Github folder using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/ohsu-comp-bio/regulon-enrichment.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpen the \u003cstrong\u003eregulon_enrichemnt\u003c/strong\u003e folder. Create a conda environment with the dependencies needed to run Priori\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f priori_env.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the environment has been built, activate it\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate priori_env\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpen the \u003cstrong\u003eenricher\u003c/strong\u003e folder. Set this path to your PATH variable. After sourcing your bashrc script, you should be able to run Priori using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eenrich\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-priori-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#priori-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePriori parameters\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-required-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#required-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired parameters\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eexpr\u003c/code\u003e : which tab delimited expression matrix to use shape : \u003ccode\u003e[n_features, n_samples]\u003c/code\u003e, units : \u003ccode\u003eTPM, RPKM\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eout_dir\u003c/code\u003e : output directory - directory serialized Enrichment object and enrichment.tsv will be saved to\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-optional-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional parameters\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eregulon\u003c/code\u003e : optional regulon containing weight interactions between regulator and\ndownstream members of its regulon shape : \u003ccode\u003e[len(Target), [\u0027Regulator\u0027,\u0027Target\u0027,\u0027MoA\u0027,\u0027likelihood\u0027]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eregulon_size\u003c/code\u003e : number of downstream interactions required for a given regulator in order to calculate enrichment score \u003ccode\u003edefault=15\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esec_intx\u003c/code\u003e : path to pre-compiled serialized secondary interaction network, \u003ccode\u003edefault=secondary_intx_regulon.pkl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escaler_type\u003c/code\u003e : scaler to normalized features/samples by: \u003ccode\u003estandard | robust | minmax | quant\u003c/code\u003e, default=\u003ccode\u003erobust\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ethresh_filter\u003c/code\u003e : Prior to normalization remove features that have a standard deviation per feature less than \u003ccode\u003e{thresh_filter}\u003c/code\u003e, \u003ccode\u003edefault=0.1\u003c/code\u003e)\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-computing-regulon-enrichment-scores\" class=\"anchor\" aria-hidden=\"true\" href=\"#computing-regulon-enrichment-scores\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComputing regulon enrichment scores\u003c/h1\u003e\n\u003cp\u003eTo quantify the regulon enrichment for a given dataset, the command line script \u003ccode\u003eenrich\u003c/code\u003e is used.\u003c/p\u003e\n\u003cp\u003eUse --help argument to view options\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ePriori requires two positional arguments: \u003ccode\u003eexpr\u003c/code\u003e and \u003ccode\u003eout_dir\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich expr out_dir [regulon] [regulon_size] [sec_intx] [scaler_type] [thresh_filter] \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt is recommended to run enrich with the default parameters.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich tests/resources/test_expr.tsv test_enrichment_scores\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe command above will generate enrichment scores for the unittest dataset \u003ccode\u003etest_expr.tsv\u003c/code\u003e and will generate and store the output under \u003ccode\u003etest_enrichment_scores/\u003c/code\u003e. In this directory \u003ccode\u003etest_enrichment_scores/\u003c/code\u003e, both the serialized Enrichment object \u003ccode\u003etest_enrichment.pkl\u003c/code\u003e and a tsv of the enrichment scores,\u003ccode\u003etest_regulon_enrichment.tsv\u003c/code\u003e will be found.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eenrichment.tsv\u003c/code\u003e file be shaped : \u003ccode\u003e[n_samples, n_regulators]\u003c/code\u003e, where \u003ccode\u003en_samples\u003c/code\u003e refers to the original number of samples provided in \u003ccode\u003eexpr\u003c/code\u003e, while \u003ccode\u003en_regulators\u003c/code\u003e will be determined based on the overlapping features present in the \u003ccode\u003eexpr\u003c/code\u003e dataset and the \u003ccode\u003eregulon_size\u003c/code\u003e parameter.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1664839954.0 + "updated_at": 1550708786.0 }, { "data_format": 2, - "description": "The AWS Command Line Interface (CLI) is a unified tool to manage your AWS services.", + "description": "Singularity container for Freesurfer recon-all, thalamus, brainstem, hippocampus/amygdala", "filenames": [ - "2.2.16/Singularity", - "2.8.11/Singularity", - "2.4.17/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-aws-cli", - "latest_release": "v2.8.11", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-aws-cli/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-aws-cli/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-aws-cli/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-aws-cli/actions/workflows/pretty.yml/badge.svg\" 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src=\"https://camo.githubusercontent.com/0e2f8c7a768ed85a6af41284a4079da356e0c925a50ffd6bf78e489d8708f8c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6177732d636c69\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-aws-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/41e97a64cdd14fb332ff82ba112804aa910ddc011e17d6d0722dd3c5ec8eaf43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6177732d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/41e97a64cdd14fb332ff82ba112804aa910ddc011e17d6d0722dd3c5ec8eaf43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6177732d636c69\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-aws-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-aws-cli\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-aws-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-aws-cli\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://aws.amazon.com/cli/\" rel=\"nofollow\"\u003eaws-cli\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eaws\u003c/code\u003e and \u003ccode\u003eaws_completer\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/aws-cli/4.8.25\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/aws-cli\u003c/code\u003e as \u003ccode\u003e4.8.25.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "baxpr/freesurfer-singularity", + "latest_release": "v1.0.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-freesurfer-720\" class=\"anchor\" href=\"#freesurfer-720\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFreesurfer 7.2.0\u003c/h1\u003e\n\u003cp\u003eThis repository contains the files needed to build a docker container that\nruns Freesurfer 7.2.0 recon-all. See \u003ca href=\"Dockerfile\"\u003ethe Dockerfile\u003c/a\u003e for details.\u003c/p\u003e\n\u003cp\u003eA valid Freesurfer license file is required at runtime.\u003c/p\u003e\n\u003cp\u003eHere is the \u003ca href=\"FreeSurferColorLUT.txt\"\u003elook-up table for the various Freesurfer segmentations\u003c/a\u003e,\nand the \u003ca href=\"src/create_MM_labelmaps.sh\"\u003edescription of MM hippocampus re-combinations\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREFERENCES\u003c/h2\u003e\n\u003cp\u003eAlso see \u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferMethodsCitation\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferMethodsCitation\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-recon-all\" class=\"anchor\" href=\"#recon-all\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erecon-all\u003c/h3\u003e\n\u003cp\u003eCollins, DL, Neelin, P., Peters, TM, and Evans, AC. (1994) Automatic 3D\nInter-Subject Registration of MR Volumetric Data in Standardized Talairach\nSpace, Journal of Computer Assisted Tomography, 18(2) p192-205, 1994 PMID:\n8126267; UI: 94172121\u003c/p\u003e\n\u003cp\u003eCortical Surface-Based Analysis I: Segmentation and Surface Reconstruction\nDale, A.M., Fischl, Bruce, Sereno, M.I., (1999). Cortical Surface-Based\nAnalysis I: Segmentation and Surface Reconstruction. NeuroImage 9(2):179-194\u003c/p\u003e\n\u003cp\u003eFischl, B.R., Sereno, M.I.,Dale, A. M. (1999) Cortical Surface-Based\nAnalysis II: Inflation, Flattening, and Surface-Based Coordinate System.\nNeuroImage, 9, 195-207.\u003c/p\u003e\n\u003cp\u003eFischl, Bruce, Sereno, M.I., Tootell, R.B.H., and Dale, A.M., (1999).\nHigh-resolution inter-subject averaging and a coordinate system for the\ncortical surface. Human Brain Mapping, 8(4): 272-284\u003c/p\u003e\n\u003cp\u003eFischl, Bruce, and Dale, A.M., (2000). Measuring the Thickness of the Human\nCerebral Cortex from Magnetic Resonance Images. Proceedings of the National\nAcademy of Sciences, 97:11044-11049.\u003c/p\u003e\n\u003cp\u003eFischl, Bruce, Liu, Arthur, and Dale, A.M., (2001). Automated Manifold\nSurgery: Constructing Geometrically Accurate and Topologically Correct\nModels of the Human Cerebral Cortex. IEEE Transactions on Medical Imaging,\n20(1):70-80\u003c/p\u003e\n\u003cp\u003eNon-Uniform Intensity Correction.\n\u003ca href=\"http://www.bic.mni.mcgill.ca/software/N3/node6.html\" rel=\"nofollow\"\u003ehttp://www.bic.mni.mcgill.ca/software/N3/node6.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der\nKouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B,\nDale AM. Whole brain segmentation: automated labeling of neuroanatomical\nstructures in the human brain. Neuron. 2002 Jan 31;33(3):341-55.\u003c/p\u003e\n\u003cp\u003eBruce Fischl, Andre van der Kouwe, Christophe Destrieux, Eric Halgren,\nFlorent Segonne, David H. Salat, Evelina Busa, Larry J. Seidman, Jill\nGoldstein, David Kennedy, Verne Caviness, Nikos Makris, Bruce Rosen, and\nAnders M. Dale. Automatically Parcellating the Human Cerebral Cortex.\nCerebral Cortex January 2004; 14:11-22.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-brainstem\" class=\"anchor\" href=\"#brainstem\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBrainstem\u003c/h3\u003e\n\u003cp\u003eBayesian segmentation of brainstem structures in MRI. Iglesias, J.E., Van\nLeemput, K., Bhatt, P., Casillas, C., Dutt, S., Schuff, N., Truran-Sacrey,\nD., Boxer, A., and Fischl, B. NeuroImage, 113, June 2015, 184-195.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hippocampus-and-amygdala\" class=\"anchor\" href=\"#hippocampus-and-amygdala\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHippocampus and amygdala\u003c/h3\u003e\n\u003cp\u003eHippocampus: A computational atlas of the hippocampal formation using ex\nvivo, ultra-high resolution MRI: Application to adaptive segmentation of in\nvivo MRI. Iglesias, J.E., Augustinack, J.C., Nguyen, K., Player, C.M.,\nPlayer, A., Wright, M., Roy, N., Frosch, M.P., Mc Kee, A.C., Wald, L.L.,\nFischl, B., and Van Leemput, K. Neuroimage, 115, July 2015, 117-137.\u003c/p\u003e\n\u003cp\u003eAmygdala: High-resolution magnetic resonance imaging reveals nuclei of the\nhuman amygdala: manual segmentation to automatic atlas. Saygin ZM \u0026amp; Kliemann\nD (joint 1st authors), Iglesias JE, van der Kouwe AJW, Boyd E, Reuter M,\nStevens A, Van Leemput K, Mc Kee A, Frosch MP, Fischl B, Augustinack JC.\nNeuroimage, 155, July 2017, 370-382.\u003c/p\u003e\n\u003cp\u003eLongitudinal method: Bayesian longitudinal segmentation of hippocampal\nsubstructures in brain MRI using subject-specific atlases. Iglesias JE, Van\nLeemput K, Augustinack J, Insausti R, Fischl B, Reuter M. Neuroimage, 141,\nNovember 2016, 542-555.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-mm-hippocampal-subregions\" class=\"anchor\" href=\"#mm-hippocampal-subregions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"MM\" hippocampal subregions\u003c/h3\u003e\n\u003cp\u003eA reorganization of Freesurfer\u0027s segmentation into anterior and posterior segments\nas described in:\u003c/p\u003e\n\u003cp\u003eMcHugo M, Talati P, Woodward ND, Armstrong K, Blackford JU, Heckers S. Regionally\nspecific volume deficits along the hippocampal long axis in early and chronic\npsychosis. Neuroimage Clin. 2018;20:1106-1114. doi:10.1016/j.nicl.2018.10.021\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-thalamus\" class=\"anchor\" href=\"#thalamus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThalamus\u003c/h3\u003e\n\u003cp\u003eA probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and\nhistology. Iglesias, J.E., Insausti, R., Lerma-Usabiaga, G., Bocchetta, M.,\nVan Leemput, K., Greve, D., van der Kouwe, A., Caballero-Gaudes, C.,\nPaz-Alonso, P. Neuroimage (accepted).\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1665565257.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1632511004.0 }, { "data_format": 2, - "description": null, + "description": "Singularity Course Repo for 2021 ACM-BCB Conference", "filenames": [ - "singularity/SingularityTemplate" + "Singularity.BLAST" ], - "full_name": "INFLUENCEorg/POMCP-SIS", + "full_name": "TheJacksonLaboratory/acm-bcb-singularity2021", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-tutorial-introduction-to-application-containerization-using-singularity\" class=\"anchor\" href=\"#tutorial-introduction-to-application-containerization-using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTutorial: Introduction to Application Containerization Using Singularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://acm-bcb.org/2021\" rel=\"nofollow\"\u003eThe 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2021)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAugust 1-4, 2021 (Virtual)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-overview\" class=\"anchor\" href=\"#overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eContainerization technologies (Docker, Singularity, etc.) are rapidly becoming an important tool to facilitate and encourage scientific reproducibility, and containers of computational tools and their dependencies are increasingly distributed as digital artifacts alongside manuscript publication. While Docker is the de facto standard for software containerization in cloud computing environments, Singularity is rapidly emerging as the standard for containerization in large scale parallel and distributed computing environments, such as a typical high performance computing cluster at an academic institution. In this tutorial, we will provide an introduction to containerization technologies in general, with a specific focus on Singularity and cover core features of using Singularity to containerize and execute computational biology and bioinformatics applications. This will be a hands-on tutorial. We expect participants will have a basic familiarity with the Linux command line and will be able to use their own laptops and SSH to connect to remote computing systems.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-presenters\" class=\"anchor\" href=\"#presenters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePresenters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eWilliam S. Sanders (\u003ca href=\"mailto:shane.sanders@jax.org\"\u003eshane.sanders@jax.org\u003c/a\u003e) - The Jackson Laboratory, Farmington, CT USA\u003c/li\u003e\n\u003cli\u003eJason S. Macklin (\u003ca href=\"mailto:jason.macklin@jax.org\"\u003ejason.macklin@jax.org\u003c/a\u003e) - The Jackson Laboratory, Farmington, CT USA\u003c/li\u003e\n\u003cli\u003eRichard Yanicky (\u003ca href=\"mailto:richard.yanicky@jax.org\"\u003erichard.yanicky@jax.org\u003c/a\u003e) - The Jackson Laboratory, Farmington, CT USA\u003c/li\u003e\n\u003cli\u003eAaron McDivitt (\u003ca href=\"mailto:aaron.mcdivitt@jax.org\"\u003eaaron.mcdivitt@jax.org\u003c/a\u003e) - The Jackson Laboratory, Farmington, CT USA\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-agenda--outline\" class=\"anchor\" href=\"#agenda--outline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAgenda / Outline\u003c/h2\u003e\n\u003cp\u003eAll Times in US Central Daylight Time.\u003c/p\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003e\n\u003cp\u003e09:00am-09:10am: \u003ca href=\"section_00.md\"\u003eWelcome \u0026amp; Introductions\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e09:10am-09:25am: \u003ca href=\"section_01.md\"\u003eWhat is a Container?\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e09:25am-09:55am: \u003ca href=\"section_02.md\"\u003eSingularity Basics\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e09:55am-10:10am: \u003ca href=\"section_03.md\"\u003eScientific Containers\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10:10am-10:20am: BREAK\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e10:20am-10:55am: \u003ca href=\"section_04.md\"\u003eBuilding and Modifying Containers\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e10:55am-11:30am: \u003ca href=\"section_05.md\"\u003eUsing Containers \u0026amp; Questions\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1673328479.0 + "updated_at": 1627830100.0 }, { "data_format": 2, - "description": "\u667a\u80fd\u79fb\u52a8\u673a\u5668\u4eba\u5927\u4f5c\u4e1a", + "description": "modified version of nicMSlesions", "filenames": [ - "src/hallucination/SingularityLfH.def", - "src/scripts/Singularityfile.def" + "Singularity" ], - "full_name": "Jh142857/autonomous_navigation", + "full_name": "kbronik2017/nicpython36", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-autonomius_navigation_project_2022w\" class=\"anchor\" aria-hidden=\"true\" href=\"#autonomius_navigation_project_2022w\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eautonomius_navigation_project_2022w\u003c/h1\u003e\n\u003cp\u003e2022\u51ac\u5b66\u671f \u667a\u80fd\u79fb\u52a8\u673a\u5668\u4eba\u5927\u4f5c\u4e1a\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Jh142857/autonomous_navigation\"\u003ehttps://github.com/Jh142857/autonomous_navigation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"demo/demo.gif\"\u003e\u003cimg src=\"demo/demo.gif\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-0-\u4ee3\u7801\u7ed3\u6784\u4e0e\u5206\u5de5\" class=\"anchor\" aria-hidden=\"true\" href=\"#0-\u4ee3\u7801\u7ed3\u6784\u4e0e\u5206\u5de5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e0 \u4ee3\u7801\u7ed3\u6784\u4e0e\u5206\u5de5\u003c/h2\u003e\n\u003cp\u003e\u6b64\u6b21\u4f5c\u4e1a\u7531\u9648\u4fca\u8c6a\u548c\u7530\u5a05\u658c\u4e24\u4eba\u5171\u540c\u5b8c\u6210\uff0c\u8d21\u732e\u57fa\u672c\u5404\u536050%\u3002\u003c/p\u003e\n\u003cp\u003e\u4ee3\u7801\u7ed3\u6784\u5982\u4e0b\uff1a\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeband_local_planner\uff1aROS\u5185\u7f6e\u5305\u003c/li\u003e\n\u003cli\u003ehallucination\uff1a\u4e3b\u8981\u4ee3\u7801\u5b9e\u73b0\u90e8\u5206\uff0cLfH\u7b97\u6cd5\uff08\u003ca href=\"https://ieeexplore.ieee.org/abstract/document/9636402\" rel=\"nofollow\"\u003e\u53c2\u8003\u6587\u732e\u003c/a\u003e\uff0c\u003ca href=\"https://github.com/Daffan/nav-competition-icra2022/tree/LfH\"\u003e\u53c2\u8003\u4ee3\u7801\u003c/a\u003e\uff09\n\u003cul\u003e\n\u003cli\u003edata\uff1a\u4e0d\u540c\u6700\u5927\u901f\u5ea6\u4e0b\u7684\u6570\u636e\u96c6\u003c/li\u003e\n\u003cli\u003eego_timer\uff1aEgo_planner\u003c/li\u003e\n\u003cli\u003einteresting_models\uff1a\u5b58\u653e\u8bad\u7ec3\u7ec3\u597d\u7684\u6a21\u578b\u6743\u91cd\u003c/li\u003e\n\u003cli\u003eLfD2D/3D\uff1a\u5206\u522b\u5bf9\u5e942D\u5bfc\u822a\u548c3D\u5bfc\u822a\u65f6\u8fd0\u52a8\u89c4\u5212\u5668\u6a21\u578b\u53ca\u8bad\u7ec3\u003c/li\u003e\n\u003cli\u003eLfH\uff1a\u8bad\u7ec3hallucination\u51fd\u6570\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ejackal\u003c/li\u003e\n\u003cli\u003ejackal_desktop\u003c/li\u003e\n\u003cli\u003ejackal_helper\u003c/li\u003e\n\u003cli\u003ejackal_simulator\u003c/li\u003e\n\u003cli\u003eres\u003c/li\u003e\n\u003cli\u003erviz_tool\uff1a\u53ef\u89c6\u5316\u90e8\u5206\u4ee3\u7801\u003c/li\u003e\n\u003cli\u003escripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-\u5bfc\u822a\u7b97\u6cd5learn-from-hallucination\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-\u5bfc\u822a\u7b97\u6cd5learn-from-hallucination\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1 \u5bfc\u822a\u7b97\u6cd5\uff1aLearn from Hallucination\u003c/h2\u003e\n\u003cp\u003e\u7b97\u6cd5\u539f\u7406\u5982\u4e0b\uff1a\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u5728\u81ea\u7531\u7a7a\u95f4\u4e2d\u4f7f\u7528\u968f\u673a\u7b56\u7565$\\pi$\u6536\u96c6\u81ea\u7531\u7a7a\u95f4\u4e2d\u7684\u8fd0\u52a8\u89c4\u5212$(p, c_c, c_g)$\uff0c\u5f97\u5230\u8fd0\u52a8\u89c4\u5212\u6570\u636e\u96c6$P$\uff1b\u003c/li\u003e\n\u003cli\u003e\u901a\u8fc7\u7f16\u7801\u5668-\u89e3\u7801\u5668\u65b9\u5f0f\u5b66\u4e60\u53c2\u6570$\\psi^\u003cem\u003e$\uff0c\u5f97\u5230\u5bf9\u5e94\u7684\u5e7b\u89c9\u51fd\u6570$g_{\\psi^\u003c/em\u003e}(p|c_c, c_g)$\uff1b\u003c/li\u003e\n\u003cli\u003e\u5bf9\u4e8e$P$\u4e2d\u7684\u6bcf\u4e2a\u6570\u636e$(p, c_c, c_g)$\uff0c\u4ece$g_{\\psi^*}(p|c_c, c_g)$\u4e2d\u968f\u673a\u91c7\u6837\u969c\u788d\u7269$C_{obst}$\uff0c\u5c06\u6570\u636e$(C_{obst}, p, c_c, c_g)$\u52a0\u5165\u8bad\u7ec3\u6570\u636e\u96c6$D_{train}$\uff1b\u003c/li\u003e\n\u003cli\u003e\u5229\u7528\u8bad\u7ec3\u6570\u636e\u5b66\u4e60\u8fd0\u52a8\u89c4\u5212\u5668$f_{\\theta}(\\cdot )$\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-\u8fd0\u884c\u65b9\u5f0f\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-\u8fd0\u884c\u65b9\u5f0f\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2 \u8fd0\u884c\u65b9\u5f0f\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-21-python\u865a\u62df\u73af\u5883\u642d\u5efa\" class=\"anchor\" aria-hidden=\"true\" href=\"#21-python\u865a\u62df\u73af\u5883\u642d\u5efa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.1 python\u865a\u62df\u73af\u5883\u642d\u5efa\u003c/h3\u003e\n\u003cp\u003e\u4e3a\u4e86\u66f4\u65b9\u4fbf\u8fdb\u884c\u5e93\u7ba1\u7406\uff0c\u9996\u5148\u9700\u8981\u642d\u5efapython\u865a\u62df\u73af\u5883\uff0cpython\u81ea\u5e26\u7684venv\u6216\u8005conda\u73af\u5883\u5747\u53ef\uff0c\u4e0b\u9762\u4ee5venv\u4e3a\u4f8b\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt -y update\nsudo apt-get -y install python3-venv\npython3 -m venv \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/nav_challenge\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e~/nav_challenge/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u7136\u540e\u6fc0\u6d3b\u865a\u62df\u73af\u5883\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/nav_challenge/bin/activate\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u5b89\u88c5\u4e0b\u5217\u9700\u8981\u7684\u5e93\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install defusedxml rospkg netifaces numpy pyyaml scipy torch==1.7 torchvision==0.8 tensorboard\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-22-\u5b89\u88c5ros\u5305\u4f9d\u8d56\" class=\"anchor\" aria-hidden=\"true\" href=\"#22-\u5b89\u88c5ros\u5305\u4f9d\u8d56\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.2 \u5b89\u88c5ros\u5305\u4f9d\u8d56\u003c/h3\u003e\n\u003cp\u003e\u8fdb\u5165\u5de5\u4f5c\u533a\uff0c\u8fd0\u884c\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e /opt/ros/noetic/setup.bash\nrosdep init \nrosdep update\nrosdep install -y --from-paths \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --ignore-src --rosdistro=noetic\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-23-\u6784\u5efa\u73af\u5883\" class=\"anchor\" aria-hidden=\"true\" href=\"#23-\u6784\u5efa\u73af\u5883\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.3 \u6784\u5efa\u73af\u5883\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecatkin_make\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e ./devel/setup.bash(.zsh)\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-24-\u8fd0\u884c\" class=\"anchor\" aria-hidden=\"true\" href=\"#24-\u8fd0\u884c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.4 \u8fd0\u884c\u003c/h3\u003e\n\u003cp\u003e\u5355\u72ec\u6d4b\u8bd5\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u6253\u5f00gazebo gui\uff0c\u52a0\u4e0a--gui\u003c/li\u003e\n\u003cli\u003e\u6253\u5f00rviz\u53ef\u89c6\u5316\uff0c\u52a0\u4e0a--rviz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/scripts\nsudo chmod +x ./run.py\npython3 run.py --gui --world_idx 0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u5728\u591a\u5f20\u5730\u56fe\u4e2d\u8fdb\u884c\u6d4b\u8bd5\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/scripts\nsudo chmod +x test.sh\n./test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u5bf9\u7ed3\u679c\u8fdb\u884c\u5e73\u5747\u8bc4\u5206\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/scripts\nsudo chmod +x ./run.py\npython3 report_test.py --out_path out_LfLH.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u8bad\u7ec3\u5e7b\u89c9\u51fd\u6570\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/hallucinatio/LfH\nsudo chmod +x ./LfH_main.py\npython3 LfH_main.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u6839\u636e\u5e7b\u89c9\u51fd\u6570\u91c7\u6837\u6570\u636e\u8bad\u7ec3\u89c4\u5212\u51fd\u6570\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/hallucinatio/LfD_2D\nsudo chmod +x ./LfD_main.py\npython3 LfD_main.py\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ms_cnn\" class=\"anchor\" href=\"#ms_cnn\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMS_CNN\u003c/h1\u003e\n\u003cp\u003e[This is a modified version of nicMSlesions (\u003ca href=\"https://github.com/NIC-VICOROB/nicMSlesions\"\u003ehttps://github.com/NIC-VICOROB/nicMSlesions\u003c/a\u003e)]\n\u003cbr\u003e\n\u003ca href=\"CNN.jpeg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"300\" src=\"CNN.jpeg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-this--version-support-additionally-the-following-functionalities\" class=\"anchor\" href=\"#this--version-support-additionally-the-following-functionalities\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis version support additionally the following functionalities:\u003c/h1\u003e\n\u003cdl\u003e\n \u003cdt\u003e(1) Runnable on a Mac system/computer\u003c/dt\u003e\n \u003cdt\u003e(2) Cold start and warm start support:\u003c/dt\u003e\n \u003cdd\u003e- Allowing to re-create the architecture of the model\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use the saved weights of the model\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use the training configuration and avoiding to run preprocessing again\u003c/dd\u003e\n \u003cdd\u003e- Allowing to resume training exactly where it left off(interrupting the training is \n allowed throughout the training process)\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use pretrained model\u003c/dd\u003e\n \u003cdt\u003e(3) Supporting Python 3\u003c/dt\u003e\n \u003cdt\u003e(4) Integrated Tensorborad [to provide the measurements and visualisations of TensorFlow execution (to understand, debug, and optimisation of the TensorFlow programs)]\u003c/dt\u003e\n \u003cdt\u003e(5) Checking whether a file or directory is relevant for Training and Testing\u003c/dt\u003e \n \u003cdt\u003e(6) Easy HPC (High Performance Computing) support\u003c/dt\u003e \n \u003cdt\u003e(7) Bias correction of masks using FSL\u003c/dt\u003e\n \u003cdt\u003e(8) Registration, moving all images to the Flair, T1 or Standard space\u003c/dt\u003e\n\u003c/dl\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"BR.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"500\" src=\"BR.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"note.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"100\" src=\"note.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n# Running the Program!\n\u003cp\u003eThis modified version can be run with or without a GUI (similar to original version)\u003c/p\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"GUI_NM.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"500\" src=\"GUI_NM.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003ch1\u003e\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-program-on-the-hpc-cluster-using-nvidia-gpuswithout-any-additional-librarydependency-installation\" class=\"anchor\" href=\"#running-the-program-on-the-hpc-cluster-using-nvidia-gpuswithout-any-additional-librarydependency-installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Program on the HPC cluster using NVIDIA GPUs(without any additional library/dependency installation):\u003c/h1\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"hpc.jpeg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"200\" src=\"hpc.jpeg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003eFirst, user will need to be sure that \"singularity\"\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/\u003c/a\u003e\nis available on local or remote machine.\u003c/p\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity pull docker://kbronik/ms_cnn_ucl:latest \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running the above, a singularity image using docker hub (docker://kbronik/ms_cnn_ucl:latest) will be generated:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - path to singularity//..///ms_cnn_ucl_latest.sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity run --nv (path to singularity)//..///ms_cnn_ucl_latest.sif python (path to nicpython36)/nic_train_network_batch.py (or other nic-python code)\u003c/pre\u003e\u003c/div\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"note_HPC.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"120\" src=\"note_HPC.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003ch1\u003e\n\u003ca id=\"user-content-for-an-interactive-session\" class=\"anchor\" href=\"#for-an-interactive-session\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor an interactive session:\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity shell (path to singularity)//..///ms_cnn_ucl_latest.sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate idp\n - python (path to nicpython36)/app.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-for-an-interactive-session-tensorflow-on-cpu-only\" class=\"anchor\" href=\"#for-an-interactive-session-tensorflow-on-cpu-only\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor an interactive session (TensorFlow on CPU only):\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e docker://kbronik/ms-ucl-cnn-cpu:CPU_Latest python (path to nicpython36)/app.py \u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, - "topics": [], - "updated_at": 1672222747.0 + "subscribers_count": 0, + "topics": [ + "convolutional-neural-networks", + "nicmslesions", + "nicmslesions-python3" + ], + "updated_at": 1587563855.0 }, { "data_format": 2, - "description": "CVRmap - A Bids App to compute Cerebrovascular Maps from fMRI data", + "description": "Experimental Singularity container for MLCommons DeepCAM Climate Segmentation Benchmark", "filenames": [ "Singularity" ], - "full_name": "ln2t/cvrmap", + "full_name": "ianjamesx/DeepCAM_Singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-notice\" class=\"anchor\" aria-hidden=\"true\" href=\"#notice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotice:\u003c/h1\u003e\n\u003cp\u003eStill in active development!\nMore info to come soon (expected Q2 2023).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h1\u003e\n\u003cp\u003eCVRmap is an opensource (license AGPLv3) software to compute maps of Cerebro-Vascular Reactivity (CVR).\u003c/p\u003e\n\u003cp\u003eThe software is compatible with the Brain Imagning Data Structure standard for applications.\u003c/p\u003e\n\u003cp\u003eThe paper describing the toolbox will be pulished soon, together with more documentation about the pipeline.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-deepcam-singularity\" class=\"anchor\" href=\"#deepcam-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeepCAM Singularity\u003c/h1\u003e\n\u003cp\u003eSingularity container for MLCommons DeepCAM Climate Segmentation Benchmark, reference implementation developed by Lawrence Berkeley National Laboratory\u003c/p\u003e\n\u003cp\u003eCurrently in progress\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1673606689.0 + "updated_at": 1624406680.0 }, { "data_format": 2, - "description": null, + "description": "Deep phenotyping dashboard", "filenames": [ - "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.quickmerge", - "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.htsbox", - "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.longread" + "singularity/Singularity" ], - "full_name": "ericcombiolab/Benchmark-metagenome-assemblers", + "full_name": "dptools/dpdash", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-benchmarking-de-novo-assembly-methods-on-metagenomic-sequencing-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#benchmarking-de-novo-assembly-methods-on-metagenomic-sequencing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarking de novo assembly methods on metagenomic sequencing data\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-assemblers-evaluated\" class=\"anchor\" aria-hidden=\"true\" href=\"#assemblers-evaluated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAssemblers evaluated\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-short-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#short-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShort-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaSPdes \u003ccode\u003eassembly_scripts/metaspades.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMEGAHIT \u003ccode\u003eassembly_scripts/megahit.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-linked-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#linked-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinked-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ecloudSPAdes \u003ccode\u003eassembly_scripts/cloudspades.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAthena \u003ccode\u003eassembly_scripts/athena.sh \u0026lt;short-read ssembly\u0026gt; \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-long-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#long-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLong-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaFlye \u003ccode\u003eassembly_scripts/metaflye.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanu \u003ccode\u003eassembly_scripts/canu.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eLathe \u003ccode\u003eassembly_scripts/lathe.sh \u0026lt;long_reads\u0026gt; \u0026lt;short_reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShasta \u003ccode\u003eassembly_scripts/shasta.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMECAT2 \u003ccode\u003eassembly_scripts/mecat2.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNECAT \u003ccode\u003eassembly_scripts/necat.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ewtdbg2 \u003ccode\u003eassembly_scripts/wtdbg2.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-hybrid-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#hybrid-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHybrid assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaFlye-subassemblies \u003ccode\u003eassembly_scripts/metaflye-subassemblies.sh \u0026lt;short-read assembly\u0026gt; \u0026lt;long-read assembly\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDBG2OLC \u003ccode\u003eassembly_scripts/dbg2olc.sh \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOPERA-MS \u003ccode\u003eassembly_scripts/opera-ms.sh \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOPERA-LG \u003ccode\u003eassembly_scripts/opera-lg.sh \u0026lt;short-read assembly\u0026gt; \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-time-and-memory\" class=\"anchor\" aria-hidden=\"true\" href=\"#time-and-memory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTime and memory\u003c/h2\u003e\n\u003cp\u003eTime and memory consumed are measured by adding \u003ccode\u003e/usr/bin/time -v\u003c/code\u003e before the above commands.\u003c/p\u003e\n", + "readme": "\u003cp\u003eRead the documentation \u003ca href=\"http://docs.neuroinfo.org/dpdash/en/latest/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e!\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1673428091.0 + "updated_at": 1622821974.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity" + "Singularity.def" ], - "full_name": "PNNL-CompBio/CME-QM", + "full_name": "Zinoex/hyperverlet", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-computational-modeling-engine-cme-architecture-for-automated-physics-based-molecular-simulations\" class=\"anchor\" aria-hidden=\"true\" href=\"#computational-modeling-engine-cme-architecture-for-automated-physics-based-molecular-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComputational Modeling Engine (CME) architecture for automated physics-based molecular simulations\u003c/h1\u003e\n\u003cp\u003eCME pipeline perform quantum mechanical simulations using computational chemistry code called NWChem (1) for geometry optimziation, chemical property prediction and computing spectral properties critical for hit identification and lead optimization in drug deisgn and discovery.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"devDocs/pipeline.md\"\u003ePipeline (Snakemake Workflow)\u003c/a\u003e\nComputational Modeling Engine (CME) runs based of Docker container. Given a Target molecule expressed with SMILE strings, it optimized the molecules and run Time-Dependent Density Functional Theory (TD-DFT) Excited state calculatons to generate Ultraviolet\u2013visible (UV\u2013Vis) spectra and other molecular properties.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"devDocs/database.md\"\u003eDatabase\u003c/a\u003e\nThe output of the pipeline is stored in the database.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"apis/api.md\"\u003eBackend APIs\u003c/a\u003e\nThe set of API for developers:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"devDocs/webapp.md\"\u003eUser Interface\u003c/a\u003e\nTo helps users with interact with API.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCME Workflow:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./logs/cme_rulegraph.png\"\u003e\u003cimg src=\"./logs/cme_rulegraph.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eReference:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eApra\u2000, E. et al. NWChem: Past, present, and future. The Journal of Chemical Physics\n2020, 152, 184102.\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-results\" class=\"anchor\" href=\"#results\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-spring-mass-h--02\" class=\"anchor\" href=\"#spring-mass-h--02\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpring mass h = 0.2\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hypereuler\" class=\"anchor\" href=\"#hypereuler\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperEuler\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316489-c72b5980-c2dd-11eb-818c-528eaa8b43b1.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316489-c72b5980-c2dd-11eb-818c-528eaa8b43b1.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-heun\" class=\"anchor\" href=\"#heun\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120315967-33598d80-c2dd-11eb-9bb4-5399b5b14980.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120315967-33598d80-c2dd-11eb-9bb4-5399b5b14980.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperheun\" class=\"anchor\" href=\"#hyperheun\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120315979-3785ab00-c2dd-11eb-8f9a-0de0a8b65a7c.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120315979-3785ab00-c2dd-11eb-8f9a-0de0a8b65a7c.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-velocity-verlet\" class=\"anchor\" href=\"#velocity-verlet\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVelocity Verlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120315992-39e80500-c2dd-11eb-9394-57053434b952.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120315992-39e80500-c2dd-11eb-9394-57053434b952.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperverlet\" class=\"anchor\" href=\"#hyperverlet\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperVerlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316009-3e142280-c2dd-11eb-898e-da7c78cd48b5.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316009-3e142280-c2dd-11eb-898e-da7c78cd48b5.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-fr4\" class=\"anchor\" href=\"#fr4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFR4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316018-410f1300-c2dd-11eb-9ba2-5d951637ffcd.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316018-410f1300-c2dd-11eb-9ba2-5d951637ffcd.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-rk4\" class=\"anchor\" href=\"#rk4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRK4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316032-453b3080-c2dd-11eb-9aa2-c93ff7cdedf6.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316032-453b3080-c2dd-11eb-9aa2-c93ff7cdedf6.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pendulum-h--008\" class=\"anchor\" href=\"#pendulum-h--008\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePendulum h = 0.08\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-euler\" class=\"anchor\" href=\"#euler\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEuler\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316077-5126f280-c2dd-11eb-854e-daad28dea3c7.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316077-5126f280-c2dd-11eb-854e-daad28dea3c7.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hypereuler-1\" class=\"anchor\" href=\"#hypereuler-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperEuler\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316086-53894c80-c2dd-11eb-82d7-7c8aea623b71.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316086-53894c80-c2dd-11eb-82d7-7c8aea623b71.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-heun-1\" class=\"anchor\" href=\"#heun-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316104-58e69700-c2dd-11eb-82a7-7a7ad903499e.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316104-58e69700-c2dd-11eb-82a7-7a7ad903499e.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperheun-1\" class=\"anchor\" href=\"#hyperheun-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316130-60a63b80-c2dd-11eb-8708-2edd5358c8fc.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316130-60a63b80-c2dd-11eb-8708-2edd5358c8fc.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-velocity-verlet-1\" class=\"anchor\" href=\"#velocity-verlet-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVelocity Verlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316146-63a12c00-c2dd-11eb-8743-7fbabf5e9f34.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316146-63a12c00-c2dd-11eb-8743-7fbabf5e9f34.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperverlet-1\" class=\"anchor\" href=\"#hyperverlet-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperVerlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316161-669c1c80-c2dd-11eb-94de-2c7bf2cc6962.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316161-669c1c80-c2dd-11eb-94de-2c7bf2cc6962.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-fr4-1\" class=\"anchor\" href=\"#fr4-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFR4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316169-68fe7680-c2dd-11eb-8d27-5c4a7fd36f33.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316169-68fe7680-c2dd-11eb-8d27-5c4a7fd36f33.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-rk4-1\" class=\"anchor\" href=\"#rk4-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRK4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316179-6bf96700-c2dd-11eb-90f7-a4fa2649d625.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316179-6bf96700-c2dd-11eb-90f7-a4fa2649d625.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-three-body-spring-mass-h--006\" class=\"anchor\" href=\"#three-body-spring-mass-h--006\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThree body spring mass h = 0.06\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-euler-1\" class=\"anchor\" href=\"#euler-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEuler\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316209-7451a200-c2dd-11eb-9178-09e209da874a.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316209-7451a200-c2dd-11eb-9178-09e209da874a.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hypereuler-2\" class=\"anchor\" href=\"#hypereuler-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperEuler\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316218-77e52900-c2dd-11eb-8c49-fe6dfb6d7045.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316218-77e52900-c2dd-11eb-8c49-fe6dfb6d7045.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-heun-2\" class=\"anchor\" href=\"#heun-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316228-7ae01980-c2dd-11eb-8792-5ca5b3d06460.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316228-7ae01980-c2dd-11eb-8792-5ca5b3d06460.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperheun-2\" class=\"anchor\" href=\"#hyperheun-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316247-7fa4cd80-c2dd-11eb-9ff6-d9607b45cfda.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316247-7fa4cd80-c2dd-11eb-9ff6-d9607b45cfda.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-velocity-verlet-2\" class=\"anchor\" href=\"#velocity-verlet-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVelocity Verlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316255-82072780-c2dd-11eb-91ec-6b1c41b94751.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316255-82072780-c2dd-11eb-91ec-6b1c41b94751.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperverlet-2\" class=\"anchor\" href=\"#hyperverlet-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperVerlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316264-83d0eb00-c2dd-11eb-914f-69dcff6094a9.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316264-83d0eb00-c2dd-11eb-914f-69dcff6094a9.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-fr4-2\" class=\"anchor\" href=\"#fr4-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFR4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316324-9814e800-c2dd-11eb-93e1-778f062e4778.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316324-9814e800-c2dd-11eb-93e1-778f062e4778.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-rk4-2\" class=\"anchor\" href=\"#rk4-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRK4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316340-9ba86f00-c2dd-11eb-88d9-315dcf53e107.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316340-9ba86f00-c2dd-11eb-88d9-315dcf53e107.mp4\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1665496502.0 + "updated_at": 1628089330.0 }, { "data_format": 2, - "description": "Singularity image to serve as base for all project images. Defaults to starting up RStudio with an auto-selected port and password ", + "description": "Simulating, Reconstructing and Analysing Data for FEL IDI Experiments", "filenames": [ - "Singularity.3.6.1", - "Singularity.4.0.3", - "Singularity.4.0.2", - "Singularity.mro.4.0.3", - "Singularity.3.6.0" + "Singularity.simple", + "Singularity.py38", + "Singularity" ], - "full_name": "granek/singularity-rstudio-base", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3197\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis Singularity image is intended to serve as base for all project images.\u003c/p\u003e\n\u003cp\u003eBy default it starts up RStudio with an auto-selected port and password\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Singularity Image\u003c/h1\u003e\n\u003cp\u003eRun a singularity-rstudio-base container with \u003ccode\u003esingularity run shub://granek/singularity-rstudio-base\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tmp-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#tmp-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e/tmp issues\u003c/h2\u003e\n\u003cp\u003eIt is recommended to do one of the following when running this image. There is no need to do both:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSet \"mount tmp = no\" in \u003ccode\u003e/etc/singularity/singularity.conf\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf #1 is not an option, the following command can be used to bind mount \u003ccode\u003e/tmp\u003c/code\u003e in the container to a \"private\" tmp directory:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eSINGTMP=\"/tmp/${USER}_$$_tmp\"; mkdir -p $SINGTMP; singularity run --bind $SINGTMP:/tmp shub://granek/singularity-rstudio-base\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tmp-issues-tldr\" class=\"anchor\" aria-hidden=\"true\" href=\"#tmp-issues-tldr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e/tmp issues TLDR\u003c/h3\u003e\n\u003cp\u003eIf a second user tries on the same server tries to run an RStudio container they will have permission issues with \u003ccode\u003e/tmp/rstudio-server\u003c/code\u003e, which will be owned by the user who first ran an RStudio container.\u003c/p\u003e\n", + "full_name": "fzimmermann89/idi", + "latest_release": "210609", + "readme": "\u003cp\u003eCAVE: Hic sunt dracones\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe code is a mess, undocumented and only certain code paths are tested.\u003c/em\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-idi---incoherent-diffraction-imaging\" class=\"anchor\" href=\"#idi---incoherent-diffraction-imaging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIDI - INCOHERENT DIFFRACTION IMAGING\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4824\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/fzimmermann89/idi/actions/workflows/test.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/fzimmermann89/idi/actions/workflows/test.yml/badge.svg\" alt=\"tests\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a1aa13bc475e383774716a28c54db51e680a438815882cb99e8443eb94a873db/68747470733a2f2f7777772e7472617669732d63692e636f6d2f667a696d6d65726d616e6e38392f6964692e7376673f6272616e63683d6d6173746572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1aa13bc475e383774716a28c54db51e680a438815882cb99e8443eb94a873db/68747470733a2f2f7777772e7472617669732d63692e636f6d2f667a696d6d65726d616e6e38392f6964692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://www.travis-ci.com/fzimmermann89/idi.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity Image now at \u003ca href=\"https://cloud.sylabs.io/library/_container/607b669a4ad4aa1fdea0c43c\" rel=\"nofollow\"\u003elibrary://fzimmermann89/idi/idi\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eConda Pacakges at \u003ca href=\"https://anaconda.org/zimmf/idi\" rel=\"nofollow\"\u003ezimmf/idi\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePIP Source at \u003ca href=\"https://pypi.org/project/idi/\" rel=\"nofollow\"\u003eidi\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWheels at \u003ca href=\"https://github.com/fzimmermann89/idi/releases/latest\"\u003eReleases\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-content-of-the-repo\" class=\"anchor\" href=\"#content-of-the-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtent of the repo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eipynb: example notebooks\u003c/li\u003e\n\u003cli\u003esimulation: simulation of incoherent images\u003c/li\u003e\n\u003cli\u003ereconstruction: direct and ft based reconstruction\u003c/li\u003e\n\u003cli\u003eutil: some small utilities for data analysis, geometry and random distributions, etc.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-preparation-for-slac-sdf\" class=\"anchor\" href=\"#preparation-for-slac-sdf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epreparation for slac sdf:\u003c/h2\u003e\n\u003cp\u003eUse Singulariy, if using OOD launcher, use the following to start a jupyterhub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e function jupyter() { singularity run --app jupyter --nv -B /sdf,/gpfs,/scratch,/lscratch library://fzimmermann89/idi/idi $@; }\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-preparation-for-sacla\" class=\"anchor\" href=\"#preparation-for-sacla\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epreparation for sacla:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDownload and install miniconda, setup ssh tunnel for web access.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003econda create -n local3 python=3.7 numpy mkl mkl-dev ipython ipykernel cython jinja2 numba numexpr matplotlib six scipy jupyterlab\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003econda activate local3\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip install https://github.com/fzimmermann89/idi/\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython -m ipykernel install --user --name local-simulation-env3 --display-name \"local simulation(py37)\"\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e(C) Felix Zimmermann\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, - "topics": [], - "updated_at": 1624649294.0 + "subscribers_count": 2, + "topics": [ + "idi", + "reconstruction", + "simulation", + "xray", + "incoherent-images", + "fel" + ], + "updated_at": 1626970680.0 }, { "data_format": 2, - "description": "To build hpc benchmark and mpi with cuda support sif", + "description": "A quick reference repository for using the robots in the COR lab", "filenames": [ - "bert.def", - "hpcc_intel.def", - "hpc_mpi_cuda.def", - "hpl_intel_cuda.def" + "containers/singularity/Singularity.panda_ros_melodic", + "containers/singularity/Singularity.panda_ros_kinetic", + "containers/singularity/Singularity.panda_ros_noetic", + "containers/singularity/Singularity.ros_melodic", + "containers/singularity/Singularity.husky_ros_melodic" ], - "full_name": "perambluate/singularity-definition-files-for-HPC", + "full_name": "rickstaa/COR-robotics-lab-reference", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc_mpi_cuda_singu_def_file\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc_mpi_cuda_singu_def_file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehpc_mpi_cuda_singu_def_file\u003c/h1\u003e\n\u003cp\u003eA collect of definition files to build images for singularity containers, which includes hpc benchmarks and mpis with cuda support.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4181\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cor-robotics-lab-reference\" class=\"anchor\" href=\"#cor-robotics-lab-reference\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCOR-robotics-lab-reference\u003c/h1\u003e\n\u003cp\u003eA quick reference repository for using the robots in the COR lab. This repository contains several code examples, a \u003ca href=\"https://github.com/rickstaa/COR-robotics-lab-reference/discussions\"\u003ediscussion forum\u003c/a\u003e with FAQ that you might have while working with the robot and a \u003ca href=\"https://github.com/rickstaa/COR-robotics-lab-reference/wiki\"\u003ewiki\u003c/a\u003e with several helpfull documents\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-reserve-the-robots\" class=\"anchor\" href=\"#how-to-reserve-the-robots\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to reserve the robots\u003c/h2\u003e\n\u003cp\u003ePlease check the \u003cg-emoji class=\"g-emoji\" alias=\"spiral_calendar\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f5d3.png\"\u003e\ud83d\uddd3\ufe0f\u003c/g-emoji\u003e \u003ca href=\"https://github.com/rickstaa/COR-robotics-lab-reference/wiki/%F0%9F%97%93%EF%B8%8F-Robot-reservation-forum\"\u003erobot reservation form\u003c/a\u003e wiki page for more information.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-work-with-the-robots\" class=\"anchor\" href=\"#how-to-work-with-the-robots\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to work with the robots\u003c/h2\u003e\n\u003cp\u003ePlease check \u003cg-emoji class=\"g-emoji\" alias=\"safety_vest\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f9ba.png\"\u003e\ud83e\uddba\u003c/g-emoji\u003e \u003ca href=\"https://github.com/rickstaa/COR-robotics-lab-reference/wiki/Panda-safety-guidelines\"\u003esafety-guidelines\u003c/a\u003e in the wiki before working with the robot.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1588998487.0 + "updated_at": 1628169542.0 }, { "data_format": 2, - "description": "Singularity container for DIVAnd", + "description": null, "filenames": [ - "Singularity" + "Singularity.v1.0.0" ], - "full_name": "gher-uliege/DIVAnd-singularity", - "latest_release": "v1.0.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/gher-ulg/DIVAnd-singularity/actions?query=workflow%3A%22Singularity+Build%22\"\u003e\u003cimg src=\"https://github.com/gher-ulg/DIVAnd-singularity/workflows/Singularity%20Build/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.7014264\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbff15826258cff37ea5a790bb5970cb363766914fa530ce59fc3d0c4c598a13/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e373031343236342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.7014264.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1a213b3f63ab13fc5f45c45392a2f3af9ce781c22ff7715b4ec4f87242ccc7c9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f676865722d756c672f444956416e642d73696e67756c6172697479\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1a213b3f63ab13fc5f45c45392a2f3af9ce781c22ff7715b4ec4f87242ccc7c9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f676865722d756c672f444956416e642d73696e67756c6172697479\" alt=\"GitHub top language\" data-canonical-src=\"https://img.shields.io/github/languages/top/gher-ulg/DIVAnd-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/38cc68dc0031ca972eb34ec1043562774b44b8e9c39d88c433f53afc43d356b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f676865722d756c696567652f444956416e642d73696e67756c6172697479\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/38cc68dc0031ca972eb34ec1043562774b44b8e9c39d88c433f53afc43d356b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f676865722d756c696567652f444956416e642d73696e67756c6172697479\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/gher-uliege/DIVAnd-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8bab4efa772ce517bcff118378d3bda45b79a76c2045057c0cb2033ce86bf913/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f646f776e6c6f6164732f676865722d756c696567652f444956416e642d73696e67756c61726974792f746f74616c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8bab4efa772ce517bcff118378d3bda45b79a76c2045057c0cb2033ce86bf913/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f646f776e6c6f6164732f676865722d756c696567652f444956416e642d73696e67756c61726974792f746f74616c\" alt=\"GitHub all releases\" data-canonical-src=\"https://img.shields.io/github/downloads/gher-uliege/DIVAnd-singularity/total\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/753a88c807be51ffa5df102f47615cdcebdf5197f424be0d1de3d74b2656e7a7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f676865722d756c696567652f444956416e642d73696e67756c6172697479\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/753a88c807be51ffa5df102f47615cdcebdf5197f424be0d1de3d74b2656e7a7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f676865722d756c696567652f444956416e642d73696e67756c6172697479\" alt=\"GitHub contributors\" data-canonical-src=\"https://img.shields.io/github/contributors/gher-uliege/DIVAnd-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/185deb35e2f7176176238e185a4a25165745eeae5d3a0d3bb86a34bd1d4a7c95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f676865722d756c696567652f444956416e642d73696e67756c6172697479\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/185deb35e2f7176176238e185a4a25165745eeae5d3a0d3bb86a34bd1d4a7c95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f676865722d756c696567652f444956416e642d73696e67756c6172697479\" alt=\"GitHub last commit\" data-canonical-src=\"https://img.shields.io/github/last-commit/gher-uliege/DIVAnd-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-divand-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#divand-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDIVAnd-singularity\u003c/h1\u003e\n\u003cp\u003eSingularity container for \u003ccode\u003eDIVAnd\u003c/code\u003e, the interpolation tool (\u003ca href=\"https://github.com/gher-ulg/DIVAnd.jl\"\u003ehttps://github.com/gher-ulg/DIVAnd.jl\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eThe container installs \u003ca href=\"https://julialang.org/\" rel=\"nofollow\"\u003e\u003ccode\u003eJulia\u003c/code\u003e\u003c/a\u003e (version 1.8.0), DIVAnd and other required Julia packages.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall singularity: \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://sylabs.io/docs/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h2\u003e\n\u003cp\u003eAfter checking out the source, the singularity container can be build using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erm -f DIVAnd.sif\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e sudo singularity build DIVAnd.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe first command is only needed if you already had the \u003ccode\u003e.sif\u003c/code\u003e file in your system.\u003cbr\u003e\nThe \u003cem\u003ebuild\u003c/em\u003e operation lasts severall minutes due to the download and installation of languages and libraries.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h2\u003e\n\u003cp\u003eThere are two possibilities to get the container\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-from-the-github-actions\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-from-the-github-actions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. From the GitHub \u003cem\u003eactions\u003c/em\u003e\n\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003ego to \u003ca href=\"https://github.com/gher-ulg/DIVAnd-singularity/actions\"\u003ehttps://github.com/gher-ulg/DIVAnd-singularity/actions\u003c/a\u003e,\u003c/li\u003e\n\u003cli\u003echoose the lastest commit,\u003c/li\u003e\n\u003cli\u003ego to artefact,\u003c/li\u003e\n\u003cli\u003edownload and unzip the image file.\u003c/li\u003e\n\u003cli\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/11868914/189079405-b156f584-1992-46ce-9ac5-0d60f57c7d42.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/11868914/189079405-b156f584-1992-46ce-9ac5-0d60f57c7d42.png\" alt=\"singularity_artefact\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-from-the-sylabs-reposity-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-from-the-sylabs-reposity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. From the Sylabs reposity containers\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --arch amd64 library://gher-uliege/divand/divand-singularity:v0-1-0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/11868914/189079465-6215be47-6691-4cc8-8384-88f783c87084.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/11868914/189079465-6215be47-6691-4cc8-8384-88f783c87084.png\" alt=\"Screenshot from 2022-09-08 10-31-52\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the container\u003c/h2\u003e\n\u003cp\u003eThere are two ways to run the container:\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-without-specifying-a-julia-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-without-specifying-a-julia-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Without specifying a Julia script\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run DIVAnd.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis gives access to a Julia terminal, where commands and scripts can be executed.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-by-specifying-the-script-to-be-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-by-specifying-the-script-to-be-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. By specifying the script to be run\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run DIVAnd.sif my_script.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(where \u003ccode\u003emy_script.jl\u003c/code\u003e has to be substitued by the correct file name).\u003c/p\u003e\n", + "full_name": "baxpr/naleg-roi", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-roi-analysis-for-sodium-leg-mri-scans\" class=\"anchor\" href=\"#roi-analysis-for-sodium-leg-mri-scans\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eROI analysis for sodium leg MRI scans\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esrc Matlab code for extracting calibrating ROI values from sodium scans\nbin Compiled matlab code (needed to create simgularity container)\n\ncompile_matlab.sh Shell script to compile matlab code\nspm_make_standalone_local.m Prepare SPM distribution for compilation\n\nSimgularity.v1.0.0 Singularity recipe to build container with compiled matlab\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1644927498.0 + "updated_at": 1594830481.0 }, { "data_format": 2, - "description": "A Singularity image with the fenics-dev docker environment", + "description": "Neurotransmitter prediction from EM.", "filenames": [ - "Singularity", - "specific_commits01/Singularity" + "singularity/Singularity" ], - "full_name": "TormodLandet/singularity-fenics-dev-env", + "full_name": "funkelab/synister", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-package-for-training-validating-and-testing-synister-networks\" class=\"anchor\" href=\"#package-for-training-validating-and-testing-synister-networks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackage for training, validating and testing Synister networks.\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eRelated Paper\u003c/strong\u003e \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.06.12.148775v2\" rel=\"nofollow\"\u003eNeurotransmitter Classification from Electron Microscopy Images at Synaptic Sites in Drosophila.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelated Repositories\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/funkelab/synistereq\"\u003ePackage for on demand predictions with a production network.\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/funkelab/synisterbrain\"\u003ePackage for high performance full brain predictions.\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nilsec/synisterest\"\u003eWebservice.\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd singularity\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003emake\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eConda\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n synister python=3.6 numpy scipy cython pylp -c funkey\nconda activate synister\npip install -r requirements.txt\npip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-0-creating-a-mongo-db-database-with-the-provided-data\" class=\"anchor\" href=\"#0-creating-a-mongo-db-database-with-the-provided-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e0. Creating a mongo DB database with the provided data.\u003c/h3\u003e\n\u003cp\u003eAn export of the three collections constituting the synister FAFB database used for all described experiments can be found at \u003ccode\u003edata/fafb_v3\u003c/code\u003e. The three files contain:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLocation, id, skid, brain region and split for each synapse (synapses(_v3).json).\u003c/li\u003e\n\u003cli\u003eSkid, neurotransmitter, hemilineage id for each skeleton (skeletons(_v3).json).\u003c/li\u003e\n\u003cli\u003eHemilineage name, hemilineage id for each hemilineage (hemilineages(_v3).json).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo reproduce the experiment each json file should be imported as a collection with names \"synapses\", \"skeletons\", \"hemilineages\" in one mongo database (for additional instructions on how to import json files in a mongo db click \u003ca href=\"https://docs.mongodb.com/database-tools/mongoimport/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e). Dictionary keys are field names. Provided splits can be reproduced using \u003ccode\u003esynister/split.py\u003c/code\u003e, which searches for the optimally balanced split in terms of neurotransmitter distribution for any given superset, such as hemilineage id, skeleton id or brain region.\u003c/p\u003e\n\u003cp\u003eFor training on other data, recreate the database scheme shown here (required are a \"synapses\" and a \"skeletons\" collection) and adapt config files to match the new database name.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-training-a-network\" class=\"anchor\" href=\"#1-training-a-network\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Training a network.\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-prepare-training\" class=\"anchor\" href=\"#prepare-training\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare training\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython prepare_training.py -d \u0026lt;base_dir\u0026gt; -e \u0026lt;experiment_name\u0026gt; -t \u0026lt;train_id\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis creates a new directory at the specified path and initialises default config files for the run.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-training\" class=\"anchor\" href=\"#run-training\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Training\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd \u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/02_train/setup_t\u0026lt;train_id\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEdit config files to match architecture, database and compute resources to train with.\u003c/p\u003e\n\u003cp\u003eFor example configs, training a VGG on the skeleton split, inside a singularity container, on a gpu queue see:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexample_configs/train_config.ini\n\n[Training]\nsynapse_types = gaba, acetylcholine, glutamate, serotonin, octopamine, dopamine\ninput_shape = 16, 160, 160\nfmaps = 12\nbatch_size = 8\ndb_credentials = synister_data/credentials/db_credentials.ini\ndb_name_data = synister_v3\nsplit_name = skeleton\nvoxel_size = 40, 4, 4\nraw_container = /nrs/saalfeld/FAFB00/v14_align_tps_20170818_dmg.n5\nraw_dataset = volumes/raw/s0\ndownsample_factors = (1,2,2), (1,2,2), (1,2,2), (2,2,2)\nnetwork = VGG\nfmap_inc = 2, 2, 2, 2\nn_convolutions = 2, 2, 2, 2\nnetwork_appendix = None\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eexample_configs/worker_config.ini\n\n[Worker]\nsingularity_container = synister/singularity/synister.img\nnum_cpus = 5\nnum_block_workers = 1\nnum_cache_workers = 5\nqueue = gpu_any\nmount_dirs = /nrs, /scratch, /groups, /misc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, to submit the train job with the desired number of iterations run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython train.py \u0026lt;num_iterations\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend training for at least 500,000 iterations for FAVB_v3 splits.\u003c/p\u003e\n\u003cp\u003eFor visualizing training progress run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003etensorboard --logdir \u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/02_train/setup_t\u0026lt;train_id\u0026gt;/log\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSnapshots are written to:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/02_train/setup_t\u0026lt;train_id\u0026gt;/snapshots\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-validating-a-trained-network\" class=\"anchor\" href=\"#2-validating-a-trained-network\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Validating a trained network.\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-prepare-validation-runs\" class=\"anchor\" href=\"#prepare-validation-runs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare validation runs\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython prepare_prediction.py -v -d \u0026lt;base_dir\u0026gt; -e \u0026lt;experiment_name\u0026gt; -t \u0026lt;train_id\u0026gt; -i \u0026lt;iter_0\u0026gt; \u0026lt;iter_1\u0026gt; \u0026lt;iter_2\u0026gt; ... \u0026lt;iter_N\u0026gt; \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create N prediction directories with appropriately initialized config files, one for each given train iteration \u0026lt;iter_k\u0026gt;. The -v flag sets the split part of the chosen split type to validation, only pulling those synapses from the DB that are tagged as validation synapses.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexample_configs/worker_config.ini\n\n[Predict]\ntrain_checkpoint = \u0026lt;experiment_name\u0026gt;/02_train/setup_t\u0026lt;train_id\u0026gt;/model_checkpoint_\u0026lt;iter_k\u0026gt;\nexperiment = \u0026lt;experiment_name\u0026gt;\ntrain_number = 0\npredict_number = 0\nsynapse_types = gaba, acetylcholine, glutamate, serotonin, octopamine, dopamine\ninput_shape = 16, 160, 160\nfmaps = 12\nbatch_size = 8\ndb_credentials = synister_data/credentials/db_credentials.ini\ndb_name_data = synister_v3\nsplit_name = skeleton\nvoxel_size = 40, 4, 4\nraw_container = /nrs/saalfeld/FAFB00/v14_align_tps_20170818_dmg.n5\nraw_dataset = volumes/raw/s0\ndownsample_factors = (1, 2, 2), (1, 2, 2), (1, 2, 2), (2, 2, 2)\nsplit_part = validation\noverwrite = False\nnetwork = VGG\nfmap_inc = 2, 2, 2, 2\nn_convolutions = 2, 2, 2, 2\nnetwork_appendix = None\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor most use cases the automatically initialized predict config does not require any edits. If run as is, predictions will be written into the database under:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;db_name\u0026gt;_predictions.\u0026lt;split_name\u0026gt;_\u0026lt;experiment_name\u0026gt;_t\u0026lt;train_id\u0026gt;_p\u0026lt;predict_id\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo start the prediction, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd \u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/03_predict/setup_t\u0026lt;train_id\u0026gt;_p\u0026lt;predict_id\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003epython predict.py\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf the collection already exists the script will abort. A collection can be overwritten by setting overwrite=True in the predict config. Parallel prediction with multiple GPUs can be done by setting num_block_workers=num_gpus in the worker_config file. Prediction speed and expected time to finish will be shown in the console.\u003c/p\u003e\n\u003cp\u003eFor submitting multiple predictions to the cluster at once run the provided convenience script:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython start_predictions -d \u0026lt;base_dir\u0026gt; -e \u0026lt;experiment_name\u0026gt; -t \u0026lt;train_id\u0026gt; -p \u0026lt;predict_id_0\u0026gt; \u0026lt;predict_id_1\u0026gt; ... \u0026lt;predict_id_N\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-testing-a-trained-network\" class=\"anchor\" href=\"#3-testing-a-trained-network\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Testing a trained network.\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-prepare-test-runs\" class=\"anchor\" href=\"#prepare-test-runs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare test runs\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython prepare_prediction.py -d \u0026lt;base_dir\u0026gt; -e \u0026lt;experiment_name\u0026gt; -t \u0026lt;train_id\u0026gt; -i \u0026lt;iter_0\u0026gt; \u0026lt;iter_1\u0026gt; \u0026lt;iter_2\u0026gt; ... \u0026lt;iter_N\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSimilar to validation this prepares the relevant dictionaries and config files but sets the split part to \"test\".\nStarting the prediction follow the same pattern as before:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd \u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/03_predict/setup_t\u0026lt;train_id\u0026gt;_p\u0026lt;predict_id\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003epython predict.py\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1632919789.0 + "updated_at": 1628265649.0 }, { "data_format": 2, - "description": null, + "description": "The source code of POPCORN planner (also a MILP Compilation collaborated with Chiara Piacentini)", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "agoldberglab/ObjectDetection_AdmixtureSelection", + "full_name": "Emresav/popcorn", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-example-scripts-for-object-detection-based-selection-scans-using-images-of-ancestry-patterns\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-scripts-for-object-detection-based-selection-scans-using-images-of-ancestry-patterns\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample scripts for object detection-based selection scans using images of ancestry patterns\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eHamid, I., Korunes, K. L., Schrider, D., \u0026amp; Goldberg, A. (2022). Localizing post-admixture adaptive variants with object detection on ancestry-painted chromosomes. BioRxiv, 2022.09.04.506532. \u003ca href=\"https://doi.org/10.1101/2022.09.04.506532\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2022.09.04.506532\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eDeployed model\u003c/li\u003e\n\u003cli\u003eTraining \u0026amp; Inference w/ IceVision\u003c/li\u003e\n\u003cli\u003eSLiMulations \u0026amp; generating images\u003c/li\u003e\n\u003cli\u003eSoftware versions used in this project\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deployed-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#deployed-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeployed model\u003c/h2\u003e\n\u003cp\u003eThe pretrained \"high resolution\" baseline model used for most analyses in this project can be found \u003ca href=\"https://huggingface.co/spaces/imanhamid/ObjectDetection_AdmixtureSelection_Space\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Users can \u003ca href=\"https://huggingface.co/spaces/imanhamid/ObjectDetection_AdmixtureSelection_Space/blob/main/object_localization_full-ancestry.model.pth\" rel=\"nofollow\"\u003edownload/load the model weights\u003c/a\u003e for their own testing purposes.\u003c/p\u003e\n\u003cp\u003eThe model is also deployed as an app on the \u003ca href=\"https://huggingface.co/spaces/imanhamid/ObjectDetection_AdmixtureSelection_Space\" rel=\"nofollow\"\u003eHugging Face space\u003c/a\u003e. Users can upload their own 200x200 black and white images of ancestry-painted chromosomes, and the model will return inferred bounding box vertices and scores. We strongly encourage users to follow the example code in \u003ca href=\"./admixture_makeimage.R\"\u003eadmixture_makeimage.R\u003c/a\u003e to ensure that the image is in the correct expected format (including size and color values) for this model.\u003c/p\u003e\n\u003cp\u003eThe model is trained to detect 11-pixel bboxes (exclusive. e.g. [start pixel, end pixel)) with the adaptive variant at the 6th pixel position. So, for a predicted bbox of [xmin: 111, ymin: 0, xmax:122, ymax:200], the adaptive variant is predicted to be at the scaled position of 116. The x-axis positions are scaled values, so they would need to be reconverted back to physical or genetic map distances. That is, a scaled value of 116 on a 50 Mb chromosome would correspond to \u003ccode\u003e(116 / 200) * 50000000 = 29,000,000 bp\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training--inference-w-icevision-v052\" class=\"anchor\" aria-hidden=\"true\" href=\"#training--inference-w-icevision-v052\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining \u0026amp; Inference w/ \u003ca href=\"https://airctic.com/0.5.2/\" rel=\"nofollow\"\u003eIceVision v0.5.2\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eExample code and notes for training and inference can be found in \u003ca href=\"./objectdetection_ancestryimages_example.ipynb\"\u003eobjectdetection_ancestryimages_example.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"./inference.py\"\u003einference.py\u003c/a\u003e - scripts used to skip training \u0026amp; output precision \u0026amp; recall values across varying threshholds for a set of images, using a pre-trained model. Not tested outside our specific analyses and directory structure, some hard-coded values may need to be edited. Expects users to provide full paths for a base_directory which contained the images to infer from, an out_directory/filename to output the final table of P-R values for each threshhold, and the pretrained model. e.g. \u003ccode\u003einference.py /home/simulations/analysis1_images /home/simulations/PR-results/object_localization_analysis1_precision-recall.txt /home/models/trained_model.pth\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-notes-for-running-in-slurm-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes-for-running-in-slurm-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes for running in SLURM environment\u003c/h4\u003e\n\u003cp\u003eIn order to run IceVision on the Duke Compute Cluster (slurm), we built a Singularity container image (see e.g. \u003ca href=\"./Singularity.def\"\u003eSingularity.def\u003c/a\u003e), which can be pulled down by running:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecurl -k -O https://research-singularity-registry.oit.duke.edu/goldberglab/selectionscansingularity.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen you can run scripts on a worker node, for example:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec --nv -B /work selectionscansingularity.sif inference.py simulation_directory out.txt model.pth\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-slimulations--generating-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#slimulations--generating-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSLiMulations \u0026amp; generating images:\u003c/h2\u003e\n\u003cp\u003eA. \u003ca href=\"./admixture.slim\"\u003eadmixture.slim\u003c/a\u003e - this is a programmable/general SLiM script for admixture simulations. Selection strength is randomly drawn from a uniform distribution s~U(0, 0.5). As is, user must specify the following parameters from the command line:\u003c/p\u003e\n\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"center\"\u003evariable name\u003c/th\u003e\n \u003cth align=\"center\"\u003eparameter description\u003c/th\u003e\n \u003cth align=\"center\"\u003eexample\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eL\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003echromosome length (bp)\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d L=50000000\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003emig\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003esource population 1 admixture proportion\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d mig=0.5\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eN\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eadmixed population size\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d N=10000\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003et_end\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003enumber of generations for simulation\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d t_end=50\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eout\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003egeneral name for output files. should also include output directory\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d out=\u0027\"/work/ih49/simulations/test_NN/human_L-50_N-10000_single-pulse_m-0.5\"\u0027\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eseed\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eseed number to append to output file\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d out=\u0027\"seed-5\"\u0027\u003c/td\u003e\n \u003c/tr\u003e \n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSimulation script will output two files\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ea \u003ccode\u003e.trees\u003c/code\u003e file with the name \u003ccode\u003e{out}_s-{selectioncoeff}_pos-{physicalposition}_seed-{seednum}.trees\u003c/code\u003e. This file will be used to generate ancestry images.\u003c/li\u003e\n\u003cli\u003ea \u003ccode\u003evariants.txt\u003c/code\u003e file with the name \u003ccode\u003e{out}_seed-{seednum}_variants.txt\u003c/code\u003e. This file contains the physical position and selection strength of each variant in the simulation. The single variant simulations have this information in the filenames, but having this information separate may be helpful for keeping track of the range of selection strengths and physical positions. It is also useful for simulations with two or more selected mutations.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn the simulation script, there are some lines that can be uncommented to include population size changes, three-way admixture, two selected mutations, continuous migration each generation. I haven\u0027t tested these completely, so there may be bugs.\u003c/p\u003e\n\u003cp\u003eB. \u003ca href=\"./run_admixture.sh\"\u003erun_admixture.sh\u003c/a\u003e - example job array script to generate 1000 SLiM simulations with the \u003ca href=\"./admixture.slim\"\u003eadmixture.slim\u003c/a\u003e file.\u003c/p\u003e\n\u003cp\u003eC. \u003ca href=\"./localancestry_alltracts.py\"\u003elocalancestry_alltracts.py\u003c/a\u003e - script to create bed-like file of ancestry tracts for 200 samples (haploid chromosomes, not diploid individuals) from the .trees file. Assumes two-way admixture and 1 ancestor in each source population.\u003c/p\u003e\n\u003cp\u003eD. \u003ca href=\"./admixture_ancestrytracts_jobarray.sh\"\u003eadmixture_ancestrytracts_jobarray.sh\u003c/a\u003e - example job array to generate bed-like ancestry tract files for 1000 SLiM simulations with the \u003ca href=\"./localancestry_alltracts.py\"\u003elocalancestry_alltracts.py\u003c/a\u003e script.\u003c/p\u003e\n\u003cp\u003eE. \u003ca href=\"./admixture_makeimage.R\"\u003eadmixture_makeimage.R\u003c/a\u003e - script to generate b\u0026amp;w ancestry images. Assumes two-way admixture. Height is hard-coded to 200 pixels. Chromosome length and image width must be specified at command line. e.g. \u003ccode\u003eadmixture_makeimage.R filename_alltracts.txt 50000000 400\u003c/code\u003e would create a 200x400 image, assuming a chromosome length of 50 Mb and \u003ccode\u003eadmixture_makeimage.R filename_alltracts.txt 295 200\u003c/code\u003e would create a 200x200 image, assuming a chromosome with max genetic map length of 295 cM. Excpects bed-like file of ancestry tracts (exclusive. e.g. intervals are [start, end)) with at least the following columns (any order, labeled):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003estart_bp\u003c/code\u003e - first position of ancestry tract (0-based, can be physical or genetic map positions, correct corresponding chromosome length must be specified at command line)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eend_bp\u003c/code\u003e - last position of ancestry tract (exclusive)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eancID\u003c/code\u003e - ancestry label for that tract (expects 0 or 1)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003echildID\u003c/code\u003e - unique haplotype ID (e.g. for a diploid indiviudal \"SUBJ-A\" you would have tracts mapping to SUBJ-A_Hap1 and SUBJ-A_Hap2)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eF. \u003ca href=\"./admixture_makeimages_jobarray.sh\"\u003eadmixture_makeimages_jobarray.sh\u003c/a\u003e - example job array to generate images for 1000 simulations with \u003ca href=\"./admixture_makeimage.R\"\u003eadmixture_makeimage.R\u003c/a\u003e script\u003c/p\u003e\n\u003cp\u003eMisc scripts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ealternate admixture SLiMulation files:\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./admixture_popsize.slim\"\u003eadmixture_popsize.slim\u003c/a\u003e - similar to above, but includes block for bottleneck at 25-35 generations\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./admixture_Fst.slim\"\u003eadmixture_Fst.slim\u003c/a\u003e - similar to above, but draws beneficial mutation from both populations\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./admixture_whole-genome.slim\"\u003eadmixture_whole-genome.slim\u003c/a\u003e - similar to above, but for \"whole genome\" (multiple chromosomes)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-versions-used-in-this-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-versions-used-in-this-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware versions used in this project\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://messerlab.org/slim/\" rel=\"nofollow\"\u003eSLiM\u003c/a\u003e - v3.4\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://cran.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e - v4.0.0\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython\u003c/a\u003e - v3.7.4\u003c/p\u003e\n\u003cp\u003ePython libraries:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://airctic.com/0.5.2/\" rel=\"nofollow\"\u003eIceVision\u003c/a\u003e - v0.5.2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://tskit.dev/tskit/docs/stable/introduction.html\" rel=\"nofollow\"\u003etskit\u003c/a\u003e - v0.2.3 (included in \u003ca href=\"https://tskit.dev/msprime/docs/stable/intro.html\" rel=\"nofollow\"\u003emsprime\u003c/a\u003e v0.7.4)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://tskit.dev/pyslim/docs/latest/introduction.html\" rel=\"nofollow\"\u003epyslim\u003c/a\u003e - v0.401\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://scikit-learn.org/stable/\" rel=\"nofollow\"\u003esklearn\u003c/a\u003e - v0.23.2\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eR packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tidyverse.org/\" rel=\"nofollow\"\u003etidyverse\u003c/a\u003e - v1.3.0\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html\" rel=\"nofollow\"\u003emagrittr\u003c/a\u003e - v2.0.1\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rdocumentation.org/packages/plyr/versions/1.8.6\" rel=\"nofollow\"\u003eplyr\u003c/a\u003e - v1.8.6\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-popcorn\" class=\"anchor\" href=\"#popcorn\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epopcorn\u003c/h1\u003e\n\u003cp\u003ePOPCORN is a temporal-numeric planning software that I have developed during my PhD studies at King\u0027s College London under supervision of Prof Maria Fox and Prof Derek Long. POPCORN can reason about action-specific numeric parameters that can take their values from relatively large domains. The planner has a freedom of choosing their values during planning process. Practically speaking, this means that you can now model actions that can have multiple flexible numeric parameters, such as the withdrawal amount from the cashpoint (e.g. \u003ccode\u003e10\u0026lt;= ?cash \u0026lt;= 100\u003c/code\u003e), or the refuel amount, as in PDDL community these parameters can only be defined with fixed values. The language we use when developing our domains, the extended version of PDDL, is quite straightforward; so I highly recommend users to inspect them to decide whether this is something that you want in your planning scenarios. This work was published on ECAI 2016 under the title of \u003ca href=\"https://kclpure.kcl.ac.uk/portal/files/56331945/FAIA285_1185.pdf\" rel=\"nofollow\"\u003ePlanning Using Actions with Control Parameters\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlso note that this code base includes a MILP Compilation of optimal numeric planning problems with control parameters. I collaborated with \u003cem\u003eChiara Piacentini\u003c/em\u003e on this work.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003ePOPCORN makes use of various other tools that are available online, these are:\u003c/p\u003e\n\u003cp\u003eFor parsing the PDDL domain: Flex, Bison\nOptimisation tools: CLP, CoinUtils, CBC, CBLAS, CGL, CPLEX\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapt-get update\napt-get -y install g++ make flex bison cmake doxygen coinor-clp coinor-libcbc-dev coinor-libclp-dev coinor-libcoinutils-dev coinor-libosi-dev coinor-libcgl-dev libbz2-dev libgsl-dev libz-dev\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: You will need to install CPLEX from their website!\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-popcorn\" class=\"anchor\" href=\"#running-popcorn\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning POPCORN\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository to your local machine:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ehttps://github.com/Emresav/popcorn.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eExport the directories of CPLEX libraries:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport CPLEX=\"/my/location/to/libcplex.a\"\nexport ILOCPLEX=\"/my/location/to/libilocplex.a\"\nexport CONCERT=\"/my/location/to/libconcert.a\"\nexport CPLEX_INCLUDES=\"/my/location/to/cplex/include\"\nexport CONCERT_INCLUDES=\"/my/location/to/concert/include\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCompiling the source code:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd /planner/planner\nls -la\ncmake .\nmake clean\nmake popf3-clp\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eFeatures of POPCORN\nIn order to see the available features of POPCORN, simply run the executable: \u003ccode\u003e/planner/planner/popf/popf3-clp\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRunning POPCORN:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e/planner/planner/popf/popf3-clp $DOMAINFILE $PROBLEMFILE \u0026gt; $PLANFILE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat\u0027s it! I have included various domains and problem instances that are used during my experiments. Almost all of them are new and quite rich in terms of temporal and numeric features. Have fun, and please do not hesitate to contact me if you have any issues during compiling and running the planner.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1668718136.0 + "updated_at": 1595445458.0 }, { "data_format": 2, - "description": null, + "description": "Singularity image for afl++ (https://github.com/AFLplusplus/AFLplusplus)", "filenames": [ - "docker/Singularity.def" + "Singularity.i386", + "Singularity.2004", + "Singularity.1604", + "Singularity.1804" ], - "full_name": "SourcedFromWill/SpaceNet8Sub", - "latest_release": null, + "full_name": "shub-fuzz/aflpp", + "latest_release": "0.0.2", + "readme": "\u003cp\u003eSingularity image for AFL++ (\u003ca href=\"https://github.com/AFLplusplus/AFLplusplus\"\u003ehttps://github.com/AFLplusplus/AFLplusplus\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/aflpp/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/aflpp/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name aflpp.sif https://github.com/shub-fuzz/aflpp/releases/download/0.0.2/shub-fuzz-aflpp.1604.sif\n\nsingularity shell aflpp.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003epull Ubuntu 18.04 container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name aflpp.1804.sif https://github.com/shub-fuzz/aflpp/releases/download/0.0.2/shub-fuzz-aflpp.1804.sif\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1658783759.0 + "updated_at": 1628147067.0 }, { "data_format": 2, - "description": null, + "description": "the breath counting task asks participants to breath in and out, and practice mindfullness", "filenames": [ - "tests/examplefiles/singularity/Singularity" + "Singularity" ], - "full_name": "ibbema/pygments", + "full_name": "expfactory-experiments/breath-counting-task", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-breath-counting-task\" class=\"anchor\" href=\"#breath-counting-task\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBreath Counting Task\u003c/h1\u003e\n\u003cp\u003eExperiment Factory: Breath Counting Task, based on Levinson et al. (2014, Study 2).\u003c/p\u003e\n\u003cp\u003eThis is a task that is friendly for use in the \u003ca href=\"https://expfactory.github.io/expfactory\" rel=\"nofollow\"\u003eExperiment Factory\u003c/a\u003e. You can run it locally by putting these files in a web server, or use the Experiment Factory to generate a reproducible container. Check out the documentation above for more information, or \u003ca href=\"https://www.github.com/expfactory/expfactory/issues\"\u003epost an issue\u003c/a\u003e if you have any questions.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/ff2b0e936c1deca3184bc2786460248f212ec5362bbbe817ee8d691cfba755cd/68747470733a2f2f657870666163746f72792e6769746875622e696f2f657870666163746f72792f696d672f657870666163746f72797469636b657479656c6c6f772e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff2b0e936c1deca3184bc2786460248f212ec5362bbbe817ee8d691cfba755cd/68747470733a2f2f657870666163746f72792e6769746875622e696f2f657870666163746f72792f696d672f657870666163746f72797469636b657479656c6c6f772e706e67\" alt=\"https://expfactory.github.io/expfactory/img/expfactoryticketyellow.png\" data-canonical-src=\"https://expfactory.github.io/expfactory/img/expfactoryticketyellow.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-containers-and-metadata\" class=\"anchor\" href=\"#containers-and-metadata\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers and Metadata\u003c/h2\u003e\n\u003cp\u003eThe repository builds and provides a set of containers and metadata files to help you run and understand the experiment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://expfactory-experiments.github.io/breath-counting-task/\" rel=\"nofollow\"\u003epreview the experiment\u003c/a\u003e on Github pages\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://hub.docker.com/r/expfactory/breath-counting-task/\" rel=\"nofollow\"\u003euse the container\u003c/a\u003e provided on Docker Hub, built from this repository. All previous versions are also found here based on Github commits.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/expfactory-experiments/breath-counting-task/tree/gh-pages\"\u003einspect metadata\u003c/a\u003e including manifests and packages associated with Github commits. While this table isn\u0027t rendered on Github pages, if you render the branch and look at the index.html you will have a pretty rendering of it.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-processing-data-in-r\" class=\"anchor\" href=\"#processing-data-in-r\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcessing Data in R\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"expfactory.breathcounting\"\u003eexpfactory.breathcounting\u003c/a\u003e is an \u003ccode\u003eR\u003c/code\u003e package for processing the data generated by this task.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h1\u003e\n\u003cp\u003eLevinson, D. B., Stoll, E. L., Kindy, S. D., Merry, H. L., and Davidson, R. J. (2014).\nA mind you can count on: validating breath counting as a behavioral measure of\nmindfulness. Frontiers in Psychology, 5.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, - "topics": [], - "updated_at": 1655374960.0 + "subscribers_count": 3, + "topics": [ + "expfactory", + "experiments", + "behavior", + "psychology", + "mindfulness" + ], + "updated_at": 1636523563.0 }, { "data_format": 2, - "description": "Aim: High resolution fvm simulation using WENOEXT scheme", + "description": "A terminal based graphical activity monitor inspired by gtop and vtop", "filenames": [ - "Singularity-openfoam.def" + "3.0.0/Singularity" ], - "full_name": "jiaqiwang969/WENOEXT-project", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-wenoext-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#wenoext-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWENOEXT-project\u003c/h1\u003e\n\u003cp\u003eAim: High resolution fvm simulation using \u003ca href=\"https://github.com/WENO-OF/WENOEXT\"\u003eWENOEXT\u003c/a\u003e scheme\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emv Dockerfile.step01 Dockerfile\ndocker build jiaqiknight/openfoam-wenoext:v1 .\nmv Dockerfile.step02 Dockerfile\ndocker build jiaqiknight/openfoam-wenoext:v2 .\nsingularity build openfoam-wenoext-v2012.sif Singularity-openfoam.def\nsingularity shell openfoam-wenoext-v2012.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-action-to-dockerhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-action-to-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Action to dockerhub\u003c/h3\u003e\n", + "full_name": "icaoberg/singularity-gotop", + "latest_release": "v3.0.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-gotop/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-gotop/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-gotop/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-gotop/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7970acf310b549a3bd62867fbe3d0e11bd65f094de2204a25a0a00c78e522e9f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d676f746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7970acf310b549a3bd62867fbe3d0e11bd65f094de2204a25a0a00c78e522e9f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d676f746f70\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-gotop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7b8613e397ce94440087fb830ecbbec5d0d806459beb0f617ff837aca32d8c79/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d676f746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b8613e397ce94440087fb830ecbbec5d0d806459beb0f617ff837aca32d8c79/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d676f746f70\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/icaoberg/singularity-gotop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7da0b740d700c489f36c7c42ee8996da37b62f2c4cfb138702453938d6f3acf7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d676f746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7da0b740d700c489f36c7c42ee8996da37b62f2c4cfb138702453938d6f3acf7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6963616f626572672f73696e67756c61726974792d676f746f70\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-gotop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7da344bc86ffcbbb642fdbe48343d10897dbf168f470553cca36b9df13821c23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d676f746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7da344bc86ffcbbb642fdbe48343d10897dbf168f470553cca36b9df13821c23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d676f746f70\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-gotop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, - "topics": [], - "updated_at": 1655451993.0 + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1631126992.0 }, { "data_format": 2, - "description": "random access on r-index", + "description": null, "filenames": [ - "Singularity.r-index" + "Singularity" ], - "full_name": "koeppl/rasarindex", + "full_name": "porchard/snATACseq-NextFlow", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-\ufe0f--accessing-the-suffix-array-via-\u03c61-forest\" class=\"anchor\" aria-hidden=\"true\" href=\"#\ufe0f--accessing-the-suffix-array-via-\u03c61-forest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"card_index_dividers\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f5c2.png\"\u003e\ud83d\uddc2\ufe0f\u003c/g-emoji\u003e Accessing the Suffix Array via \u03a6\u22121 Forest\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\ufe0f-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#\ufe0f-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"heavy_check_mark\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/2714.png\"\u003e\u2714\ufe0f\u003c/g-emoji\u003e Prerequisites\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003elibdivsufsort\u003c/li\u003e\n\u003cli\u003eg++\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content--complete-test-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#-complete-test-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"rocket\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f680.png\"\u003e\ud83d\ude80\u003c/g-emoji\u003e Complete Test Run\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/koeppl/randSAbench.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e randSAbench\nsubmodule update --init --recursive\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn the file \u003ccode\u003ebench.sh\u003c/code\u003e,\nyou have to adjust the variable \u003ccode\u003ekFullFasta\u003c/code\u003e for the path to the FASTA file you want to index,\nand \u003ccode\u003esequences\u003c/code\u003e for the number of sequences you want to extract from this file.\nThen you can run \u003ccode\u003ebench.sh\u003c/code\u003e measuring the query time for suffix array access with our proposed method and the standard method of the r-index.\nNote that the default is to also build the plain suffix array to check whether the reported entry is correct.\nBuilding the plain suffix array will not work with large inputs and a modest amount of memory.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-10x-snatac-seq-data\" class=\"anchor\" href=\"#nextflow-pipeline-for-10x-snatac-seq-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow pipeline for 10X snATAC-seq data\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eIf you have Singularity installed, you can use the config provided here (\u0027Singularity\u0027) to build a container with all the dependencies.\u003c/p\u003e\n\u003cp\u003eOtherwise, you\u0027ll need to have the following installed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ebiopython\u003c/li\u003e\n\u003cli\u003ebwa\u003c/li\u003e\n\u003cli\u003epicardtools\u003c/li\u003e\n\u003cli\u003efastqc\u003c/li\u003e\n\u003cli\u003esamtools (v. 1.10 or above)\u003c/li\u003e\n\u003cli\u003epysam\u003c/li\u003e\n\u003cli\u003eataqv (v. 1.3.0 or above)\u003c/li\u003e\n\u003cli\u003ecta (the forked version on the porchard GitHub)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI\u0027ve used this pipeline with NextFlow v. 20.10.0\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003ePaths to various generic files (e.g., bwa indices) must be included in the nextflow.config file -- check that file and change paths accordingly. These include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBlacklist bed files for each genome\u003c/li\u003e\n\u003cli\u003eChrom size files for each genome\u003c/li\u003e\n\u003cli\u003eBWA indices\u003c/li\u003e\n\u003cli\u003eTSS files (BED6 files denoting TSS positions)\u003c/li\u003e\n\u003cli\u003ePath to the barcode whitelist (the 10X whitelist is included in this repo)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou\u0027ll also need to set the params.results variable -- either in the nextflow.config file itself, or on the command line when you run the pipeline (\u0027--results /path/to/results\u0027).\u003c/p\u003e\n\u003cp\u003eYou can split the fastq files into chunks using the --chunks parameter (default: 1, meaning no chunking). In the case of very large fastq files this can speed up processing.\u003c/p\u003e\n\u003cp\u003eLastly, you\u0027ll need to include information about each ATAC-seq library, including the genome(s) for the species that each library includes, and the paths to the fastq files for each readgroup. Organize this information in a JSON file, as in library-config.json. Note that for each readgroup, three fastq files are required -- the first and second insert reads (\u00271\u0027 and \u00272\u0027), and the read with the nuclear barcode (\u0027index\u0027)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eOnce you have all of the above information, you can run the pipeline as follows (in this case, indicating the path to the results on the command line):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run -with-singularity /path/to/Singularity.simg -params-file library-config.json --results /path/to/results /path/to/main.nf\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1658678628.0 + "updated_at": 1640491578.0 }, { "data_format": 2, "description": null, "filenames": [ - "containers/Singularity.sratoolkit", - "containers/Singularity.featcount", - "containers/Singularity.samtools", - "containers/Singularity.star_nb", - "containers/Singularity.star", - "containers/Singularity.fastqc", - "containers/Singularity.R" + "Singularity.bonito" ], - "full_name": "Sherman-1/Hackaton", + "full_name": "dominik-handler/Singularity.bonito", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-projet-repro-hackathon-2022-2023--atia-safiya-bossut-no\u00e9mie-et-herman-simon\" class=\"anchor\" aria-hidden=\"true\" href=\"#projet-repro-hackathon-2022-2023--atia-safiya-bossut-no\u00e9mie-et-herman-simon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eProjet Repro-Hackathon 2022-2023\u003c/strong\u003e : ATIA Safiya, BOSSUT No\u00e9mie et HERMAN Simon\u003c/h1\u003e\n\u003cp\u003eCe projet vise \u00e0 reproduire une partie des r\u00e9sultats de deux articles:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5321577/\" rel=\"nofollow\"\u003eFurney \u003cem\u003eet al.\u003c/em\u003e, Cancer Discovery (2013)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3789378/\" rel=\"nofollow\"\u003eHarbour \u003cem\u003eet al.\u003c/em\u003e, Nature Genetics (2013)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLes donn\u00e9es RNA-seq de ces deux papiers sont disponibles en open-access : \u003ca href=\"https://www.ncbi.nlm.nih.gov/sra?term=SRA062359\" rel=\"nofollow\"\u003e\u003cstrong\u003eDonn\u00e9es NCBI\u003c/strong\u003e\u003c/a\u003e. Dans un premier temps, seul l\u0027\u00e9tude de transcriptome est \u00e9tudi\u00e9. \nL\u0027objectif de ces deux article est d\u0027\u00e9tudier les expression de g\u00e8nes, et notamment le g\u00e8ne SF3B1, d\u0027individus atteint de m\u00e9lanome ulv\u00e9al.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-ecrire-ici-conclu-des-articles\" class=\"anchor\" aria-hidden=\"true\" href=\"#ecrire-ici-conclu-des-articles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eECRIRE ICI CONCLU DES ARTICLES\u003c/h1\u003e\n\u003cp\u003eA l\u0027aide d\u0027un workflow Nextflow et de containers Singularity, notre groupe a tent\u00e9 de comprendre pourquoi les r\u00e9sultats des deux articles divergent, et quelles sont nos propres observations sur le sujet.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-notre-conclu-a-nous\" class=\"anchor\" aria-hidden=\"true\" href=\"#notre-conclu-a-nous\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTRE CONCLU A NOUS\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pr\u00e9-requis-nextflow--singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#pr\u00e9-requis-nextflow--singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003ePr\u00e9-requis:\u003c/strong\u003e Nextflow \u0026amp; Singularity\u003c/h2\u003e\n\u003cp\u003eAfin de faire tourner notre pipeline, \u003cstrong\u003e1000000Gb DE RAM ET 300 COEURS\u003c/strong\u003e, ainsi que deux logiciels sont n\u00e9cessaires:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextflow (version 21.10.6.5660) \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003e\u003cem\u003einstallation\u003c/em\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity (version 3.8.7) \u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003e\u003cem\u003einstallation\u003c/em\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003euidmap (pour la construction des containers Singularity)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo apt-get install uidmap\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-le-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#le-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eLe pipeline:\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMettre ici le DAG\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eT\u00e9l\u00e9chargement des donn\u00e9es\u003c/strong\u003e : chromosomes humains (dont chromosome mitochondrial), annotation du g\u00e9nome et donn\u00e9es RNA-seq des 8 individus (\u003cem\u003e\u003cstrong\u003esratoolkit\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndexation\u003c/strong\u003e du g\u00e9nome (\u003cem\u003e\u003cstrong\u003eSTAR\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAlignement\u003c/strong\u003e et \u003cstrong\u003eTri\u003c/strong\u003e des donn\u00e9es RNA-seq sur le g\u00e9nome. Obtention de fichiers \u003cem\u003e.bam\u003c/em\u003e tri\u00e9s en sortie (\u003cem\u003e\u003cstrong\u003eSTAR\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndexation\u003c/strong\u003e des fichiers \u003cem\u003e.bam\u003c/em\u003e. en \u003cem\u003e.bai\u003c/em\u003e (\u003cem\u003e\u003cstrong\u003esamtools\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eComptage\u003c/strong\u003e des s\u00e9quences exprim\u00e9es (\u003cem\u003e\u003cstrong\u003efeatureCounts\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAnalyse statistique\u003c/strong\u003e des r\u00e9sultats (\u003cem\u003e\u003cstrong\u003eDESeq2\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eL\u0027ensemble des donn\u00e9es et des r\u00e9sultats peuvent \u00eatre retrouv\u00e9s dans l\u0027arborescence ci-dessous: ( CE N4EST PAS LE BON OFC A CHANGER)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u251c\u2500\u2500 containers\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.fastqc\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.featcount\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.R\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.samtools\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.sratoolkit\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.star\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.star_nb\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 temp\n\u251c\u2500\u2500 init_VM.sh\n\u251c\u2500\u2500 nextflow.config\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 sratoolkit.Dockerfile\n\u251c\u2500\u2500 star.Dockerfile\n\u251c\u2500\u2500 test.nf\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-execution-du-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#execution-du-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eExecution du workflow\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSi les pr\u00e9-requis sont bien satisfaits, placez-vous dans le repertoire voulu et r\u00e9cup\u00e9rez les le projet\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e git clone https://github.com/Sherman-1/Hackaton (A CHANGER ATTENTION LE NOM)\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Hackathon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLe fichier \u003ccode\u003erun.sh\u003c/code\u003e permet d\u0027initialiser votre environnement, ainsi que de cr\u00e9er les images singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e bash run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eVerifiez que les r\u00e9pertoires ont bien \u00e9t\u00e9 cr\u00e9\u00e9s. Si c\u0027est le cas, vous pouvez lancer le pipeline :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e nextflow run main.nf\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-options-dexecution\" class=\"anchor\" aria-hidden=\"true\" href=\"#options-dexecution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions d\u0027execution\u003c/h3\u003e\n\u003cp\u003eEn plus de la commande par d\u00e9faut, vous pouvez utiliser les param\u00e8tres suivant\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-resume\u003c/code\u003e si votre pipeline a \u00e9t\u00e9 interrompu et que vous souhaitez le reprendre\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-with-trace [nom du fichier]\u003c/code\u003e pour obtenir le DAG correspondant\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-with-report [nom du fichier]\u003c/code\u003e pour obtenir un rapport complet et de nombreuses metadatas sur le pipeline\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1670193803.0 + "updated_at": 1638717249.0 }, { "data_format": 2, - "description": null, + "description": "An example app using singularity-compose for orchestration (under development)", "filenames": [ - "tud/Singularity" + "app/Singularity", + "nginx/Singularity.nginx" ], - "full_name": "clEsperanto/clesperanto_container", + "full_name": "singularityhub/singularity-compose-example", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-compose-example\" class=\"anchor\" href=\"#singularity-compose-example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Compose Example\u003c/h1\u003e\n\u003cp\u003eThis is an example of simple container orchestration with singularity-compose,\nIt is based on \u003ca href=\"https://github.com/vsoch/django-nginx-upload\"\u003edjango-nginx-upload\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eimportant\u003c/strong\u003e if you use Docker on your machine, your iptables are likely edited\nso you will have an issue running this with the latest version of Singularity.\nYou can either run the \u003ca href=\"https://www.github.com/singularityhub/singularity-example-simple\"\u003esimple-example\u003c/a\u003e,\nor install a (not yet released)\nfixed Singularity version \u003ca href=\"https://github.com/sylabs/singularity/pull/3771\"\u003efrom this branch\u003c/a\u003e\nto have a fully working example. If you don\u0027t use Docker, then you are a clean machine\nand no further action is required.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-setup\" class=\"anchor\" href=\"#setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-composeyml\" class=\"anchor\" href=\"#singularity-composeyml\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-compose.yml\u003c/h3\u003e\n\u003cp\u003eFor a singularity-compose project, it\u0027s expected to have a \u003ccode\u003esingularity-compose.yml\u003c/code\u003e\nin the present working directory. You can look at the \u003ca href=\"singularity-compose.yml\"\u003eexample\u003c/a\u003e\npaired with the \u003ca href=\"https://github.com/singularityhub/singularity-compose/tree/master/docs/spec\"\u003especification\u003c/a\u003e\nto understand the fields provided.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-instance-folders\" class=\"anchor\" href=\"#instance-folders\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstance folders\u003c/h3\u003e\n\u003cp\u003eGenerally, each section in the yaml file corresponds with a container instance to be run,\nand each container instance is matched to a folder in the present working directory.\nFor example, if I give instruction to build an \u003ca href=\"nginx\"\u003enginx\u003c/a\u003e instance from\na \u003ca href=\"nginx/Singularity.nginx\"\u003eSingularity.nginx\u003c/a\u003e file, I should have the\nfollowing in my singularity-compose:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nginx:\n build:\n context: ./nginx\n recipe: Singularity.nginx\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003epaired with the following directory structure:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity-compose-example\n\u251c\u2500\u2500 nginx\n...\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.nginx\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 uwsgi_params.par\n\u2514\u2500\u2500 singularity-compose.yml\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNotice how I also have other dependency files for the nginx container\nin that folder. As another option, you can just define a container to pull,\nand it will be pulled to the same folder that is created if it doesn\u0027t exist.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nginx:\n image: docker://nginx\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity-compose-example\n\u251c\u2500\u2500 nginx (- created \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e it doesn\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003et exist\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u2502\u00a0\u00a0 \u2514\u2500\u2500 nginx.sif (- named according to the instance\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u2514\u2500\u2500 singularity-compose.yml\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eIf you don\u0027t have it installed, install the latest \u003ca href=\"https://singularityhub.github.io/singularity-compose/#/\" rel=\"nofollow\"\u003esingularity-compose\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install singularity-compose\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest way to start is to first build your containers (you will be asked for sudo):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then bring it up!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eVerify it\u0027s running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose ps\nINSTANCES NAME PID IMAGE\n1 app\t10238\tapp.sif\n2 nginx\t10432\tnginx.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then look at logs,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs app\n$ singularity-compose logs app --tail 30\n$ singularity-compose logs nginx\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eshell inside,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose shell app\n$ singularity-compose shell nginx\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor execute a command!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e app uname -a\n$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e nginx \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello!\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen you open your browser to \u003ca href=\"http://127.0.0.1\" rel=\"nofollow\"\u003ehttp://127.0.0.1\u003c/a\u003e\nyou should see the upload interface.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/upload.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/upload.png\" alt=\"img/upload.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you drop a file in the box (or click\nto select) we will use the nginx-upload module to send it directly to the\nserver. Cool!\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/content.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/content.png\" alt=\"img/content.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is just a simple Django application, the database is sqlite3, in the\napp folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls app/\napp.sif db.sqlite3 manage.py nginx requirements.txt run_uwsgi.sh Singularity upload uwsgi.ini\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe images are stored in \u003ca href=\"images\"\u003eimages\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls images/\n2018-02-20-172617.jpg 40-acos.png _upload \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd static files are in \u003ca href=\"static\"\u003estatic\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls static/\nadmin css js\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you look at the \u003ca href=\"singularity-compose.yml\"\u003esingularity-compose.yml\u003c/a\u003e, we bind these\nfolders to locations in the container where the web server needs write. This is likely\na prime different between Singularity and Docker compose - Docker doesn\u0027t need\nbinds for write, but rather to reduce isolation. Continue below to\nread about networking, and see these commands in detail.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-networking\" class=\"anchor\" href=\"#networking\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNetworking\u003c/h2\u003e\n\u003cp\u003eWhen you bring the container up, you\u0027ll see generation of an \u003ccode\u003eetc.hosts\u003c/code\u003e file,\nand if you guessed it, this is indeed bound to \u003ccode\u003e/etc/hosts\u003c/code\u003e in the container.\nLet\u0027s take a look:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e10.22.0.3\tapp\n10.22.0.2\tnginx\n127.0.0.1\tlocalhost\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The following lines are desirable for IPv6 capable hosts\u003c/span\u003e\n::1 ip6-localhost ip6-loopback\nfe00::0 ip6-localnet\nff00::0 ip6-mcastprefix\nff02::1 ip6-allnodes\nff02::2 ip6-allrouters\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis file will give each container that you create (in our case, nginx and app)\na name on its local network. Singularity by default creates a bridge for\ninstance containers, which you can conceptually think of as a router,\nThis means that, if I were to reference the hostname \"app\" in a second container,\nit would resolve to \u003ccode\u003e10.22.0.3\u003c/code\u003e. Singularity compose does this by generating\nthese addresses before creating the instances, and then assigning them to it.\nIf you would like to see the full commands that are generated, run the up\nwith \u003ccode\u003e--debug\u003c/code\u003e (binds and full paths have been removed to make this easier to read).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity instance start \\\n --bind /home/vanessa/Documents/Dropbox/Code/singularity/singularity-compose-simple/etc.hosts:/etc/hosts \\\n --net --network-args \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eportmap=80:80/tcp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --network-args \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eIP=10.22.0.2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --hostname app \\\n --writable-tmpfs app.sif app\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-commands\" class=\"anchor\" href=\"#commands\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommands\u003c/h2\u003e\n\u003cp\u003eThe following commands are currently supported.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build\" class=\"anchor\" href=\"#build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h3\u003e\n\u003cp\u003eBuild will either build a container recipe, or pull a container to the\ninstance folder. In both cases, it\u0027s named after the instance so we can\neasily tell if we\u0027ve already built or pulled it. This is typically\nthe first step that you are required to do in order to build or pull your\nrecipes. It ensures reproducibility because we ensure the container binary\nexists first.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe working directory is the parent folder of the singularity-compose.yml file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-create\" class=\"anchor\" href=\"#create\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate\u003c/h3\u003e\n\u003cp\u003eGiven that you have built your containers with \u003ccode\u003esingularity-compose build\u003c/code\u003e,\nyou can create your instances as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose create\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-up\" class=\"anchor\" href=\"#up\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUp\u003c/h3\u003e\n\u003cp\u003eIf you want to both build and bring them up, you can use \"up.\" Note that for\nbuilds that require sudo, this will still stop and ask you to build with sudo.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose up\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ps\" class=\"anchor\" href=\"#ps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eps\u003c/h3\u003e\n\u003cp\u003eYou can list running instances with \"ps\":\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose ps\nINSTANCES NAME PID IMAGE\n1 app\t6659\tapp.sif\n2 db\t6788\tdb.sif\n3 nginx\t6543\tnginx.sif\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-shell\" class=\"anchor\" href=\"#shell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell\u003c/h3\u003e\n\u003cp\u003eYou can easily shell inside of a running instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose shell app\nSingularity app.sif:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/singularity/singularity-compose-example\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exec\" class=\"anchor\" href=\"#exec\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExec\u003c/h3\u003e\n\u003cp\u003eYou can easily execute a command to a running instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e app ls /\nbin\nboot\ncode\ndev\nenvironment\netc\nhome\nlib\nlib64\nmedia\nmnt\nopt\nproc\nroot\nrun\nsbin\nsingularity\nsrv\nsys\ntmp\nusr\nvar\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-down\" class=\"anchor\" href=\"#down\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDown\u003c/h3\u003e\n\u003cp\u003eYou can bring one or more instances down (meaning, stopping them) by doing:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose down\nStopping (instance:nginx)\nStopping (instance:db)\nStopping (instance:app)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo stop a custom set, just specify them:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose down nginx\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-logs\" class=\"anchor\" href=\"#logs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogs\u003c/h3\u003e\n\u003cp\u003eYou can of course view logs for all instances, or just specific named ones:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs\nFri Jun 21 10:24:40 2019 - WSGI app 0 (mountpoint=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e) ready \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e 1 seconds on interpreter 0x55daf463f920 pid: 27 (default app)\nFri Jun 21 10:24:40 2019 - uWSGI running as root, you can use --uid/--gid/--chroot options\nFri Jun 21 10:24:40 2019 - \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e WARNING: you are running uWSGI as root \u003cspan class=\"pl-k\"\u003e!!!\u003c/span\u003e (use the --uid flag) \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e \nFri Jun 21 10:24:40 2019 - \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e uWSGI is running \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e multiple interpreter mode \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e\nFri Jun 21 10:24:40 2019 - spawned uWSGI master process (pid: 27)\nFri Jun 21 10:24:40 2019 - spawned uWSGI worker 1 (pid: 29, cores: 1)\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e## Config\u003c/span\u003e\n\nYou can load and validate the configuration file (singularity-compose.yml) and\nprint it to the screen as follows:\n\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ebash\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e$ singularity-compose config \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e{\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eversion\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1.0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einstances\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebuild\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./nginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003erecipe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSingularity.nginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./nginx.conf:/etc/nginx/conf.d/default.conf:ro\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./uwsgi_params.par:/etc/nginx/uwsgi_params.par:ro\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.:/code\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./static:/var/www/static\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./images:/var/www/images\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes_from\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eapp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eports\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eimage\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edocker://postgres:9.4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edb-data:/var/lib/postgresql/data\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eapp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebuild\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./app\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.:/code\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./static:/var/www/static\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./images:/var/www/images\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eports\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e5000:80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edepends_on\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 5, + "subscribers_count": 3, "topics": [], - "updated_at": 1658992510.0 + "updated_at": 1608596031.0 }, { "data_format": 2, - "description": "Calculation of Z-score from summary statistics of GWAS for list of SNPs", + "description": "A nextflow pipeline to call somatic gene fusions", "filenames": [ - "container/Singularity" + "Singularity/Singularity.v1.0" ], - "full_name": "singharchit97/Calculate_Z-Score_GWAS", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-z-score-calculation-from-gwas-summary-statistics-for-a-given-set-of-snps\" class=\"anchor\" aria-hidden=\"true\" href=\"#z-score-calculation-from-gwas-summary-statistics-for-a-given-set-of-snps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eZ-score calculation from GWAS summary statistics for a given set of SNPs\u003c/h1\u003e\n\u003cp\u003eThis R-script calculates Z-scores (required for post GWAS analysis like fine-mapping \u0026amp; colocalization analysis) from summary statistics file using Beta values/ odds ratios and standard error (of Beta values/ odds ratios) for a given set of SNPs. The summary statistics input file should be tab seperated. The SNPs as input should be given as a text file with just one column and no header. The user needs to specify the input statistic using \u003ccode\u003e-c\u003c/code\u003e option which can be binary \u003ccode\u003e0/1\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pull-this-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull this repository\u003c/h3\u003e\n\u003cp\u003eGo to your working directory and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/singharchit97/Calculate_Z-Score_GWAS \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Calculate_Z-Score_GWAS/\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-configure-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#configure-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigure the container\u003c/h3\u003e\n\u003cp\u003eTo run the commands the user will need a container that contains a number of R-libraries.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container\u003c/h4\u003e\n\u003cp\u003eIf the user wants to build the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/singularity-executable\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsudo \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_PATH\u003c/span\u003e/singularity build z-score.sif container/Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-the-script-without-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-the-script-without-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the script (without singularity container)\u003c/h3\u003e\n\u003cp\u003eIf the user has taken care of the dependencies required to run the script:\nRun the below command to see the help message on the input and output parameters required to run the script.\nNote that all parameters are mandatory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRscript calc_z-score.R --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDummy command given below: (here for odds ratios as input)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRscript calc_z-score.R -f input_summary_statistics.txt -i input.snplist -s standard_error -b odds_ratio -c 0 -v rs_id -z output.txt\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-the-script-with-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-the-script-with-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the script (with singularity container)\u003c/h3\u003e\n\u003cp\u003eRun the below command to see the help message on the input and output parameters required to run the script.\nNote that all parameters are mandatory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /mount/working/directory z-score.sif Rscript calc_z-score.R --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDummy command given below: (here for Beta-values as input)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /mount/working/directory z-score.sif Rscript calc_z-score.R -f input_summary_statistics.txt -i input.snplist -s standard_error -b beta -c 1 -v rs_id -z output.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe script runs in the \u003ccode\u003eCalculate_Z-Score_GWAS\u003c/code\u003e directory, it will create a output text file (name as given by the user), respectively.\u003c/p\u003e\n", + "full_name": "IARCbioinfo/nf-gene-fusions", + "latest_release": "v1.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nf-rna-fusions\" class=\"anchor\" href=\"#nf-rna-fusions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-rna-fusions\u003c/h1\u003e\n\u003cp\u003eA nextflow pipeline to call somatic rna fusions\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1660312501.0 + "updated_at": 1637235680.0 }, { "data_format": 2, - "description": "Work with Python installed at a custom location", + "description": "An R package for easy execution of mouse GWAS", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "AJResearchGroup/ormr", - "latest_release": "v0.6.2.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ormr\" class=\"anchor\" aria-hidden=\"true\" href=\"#ormr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eormr\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/ormr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80f61013497de9c4ba38bd7d37d57f2baf9ad486b3e667b76823a2fa7acb1783/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6f726d722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/ormr/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=master\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/ormr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e40a61ddb8d3cee1a4e177f20956ab6b1887a9d5a422c8e9f9024859f4c23af/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6f726d722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/ormr/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=develop\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/ormr_logo_50.png\"\u003e\u003cimg src=\"man/figures/ormr_logo_50.png\" alt=\"ormr logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWork with Python installed at a custom location.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-goal\" class=\"anchor\" aria-hidden=\"true\" href=\"#goal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows a user to install Python packages,\ncreate a Conda environment and run Python scripts\nwith only one point of contact.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e heavily depends on \u003ccode\u003ereticulate\u003c/code\u003e, the latter being\nmore powerful and flexible. \u003ccode\u003eormr\u003c/code\u003e, however, focuses\non making it trivially simple to install Python\npackages and run Python scripts.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-install-ormr\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-ormr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall \u003ccode\u003eormr\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003eAs \u003ccode\u003eormr\u003c/code\u003e is developed on the \u003ccode\u003emaster\u003c/code\u003e branch, only a release\nis tested to work:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eremotes::install_github(\"richelbilderbeek/ormr\", ref = \"v0.6.1\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee FAQ why one needs to install a release.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e uses one point of contact, \u003ccode\u003eormr_folder_name\u003c/code\u003e.\nFor convenience, there is also a default \u003ccode\u003eormr_folder_name\u003c/code\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall a Python package\u003c/li\u003e\n\u003cli\u003eRun a Python script\u003c/li\u003e\n\u003cli\u003eRun a Python script with command-line arguments\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAlso, \u003ccode\u003eormr\u003c/code\u003e uses \u003cstrong\u003eeager loading\u003c/strong\u003e, which means that\nit will setup everything it needs for you. For example,\nif you want to run a Python script from a new \u003ccode\u003eormr_folder_name\u003c/code\u003e,\nit will create a Conda environment there for you as well.\u003c/p\u003e\n\u003cp\u003eNote that \u003ccode\u003ecreate_default_conda_env\u003c/code\u003e conveniently returns the\n\u003ccode\u003eormr_folder_name\u003c/code\u003e used to work with this environment.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-install-a-python-package\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-a-python-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install a Python package\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall_python_package(\n \u003cspan class=\"pl-v\"\u003epackage_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003escipy\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\ninstall_python_package(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epackage_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003escipy\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-run-a-python-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-run-a-python-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Run a Python script\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehello_world.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script(\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehello_world.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-run-a-python-script-with-command-line-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-run-a-python-script-with-command-line-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Run a Python script with command-line arguments\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshow_args.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script_with_args(\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworld\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshow_args.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script_with_args(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworld\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-hidden=\"true\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-the-goal-of-ormr\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-the-goal-of-ormr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is the goal of \u003ccode\u003eormr\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows a user to install Python packages,\ncreate a Conda environment and run Python scripts\nwith only one point of contact.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-in-what-context-is-ormr-useful\" class=\"anchor\" aria-hidden=\"true\" href=\"#in-what-context-is-ormr-useful\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIn what context is \u003ccode\u003eormr\u003c/code\u003e useful?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e was written to write simpler\n\u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (a type of containerization\nsoftware, similar to Docker) scripts.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ereticulate\u003c/code\u003e is great when using its default folders on a local computer.\nHowever, for a Singularity container, it is recommended to install\nlibraries in a systems folder. In that setting, \u003ccode\u003ereticulate\u003c/code\u003e is\nharder to work with.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows to install install Python packages,\ncreate a Conda environment and run Python scripts\nin any folder easily, for example,\nin a system folder (\u003ccode\u003e/opt/ormr\u003c/code\u003e) of a Singularity container.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-not-just-use-reticulate\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-not-just-use-reticulate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy not just use \u003ccode\u003ereticulate\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e heavily depends on \u003ccode\u003ereticulate\u003c/code\u003e, the latter being\nmore powerful and flexible.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e, however, focuses\non making it trivially simple to install Python\npackages and run Python scripts,\ndue to eager loading.\nAdditionally, \u003ccode\u003eormr\u003c/code\u003e has a more extensive documentation,\nand 100% code coverage.\u003c/p\u003e\n\u003cp\u003eBeyond the domain of \u003ccode\u003eormr\u003c/code\u003e, use \u003ccode\u003ereticulate\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-do-you-mean-with-eager-loading\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-do-you-mean-with-eager-loading\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat do you mean with eager loading?\u003c/h2\u003e\n\u003cp\u003eEager loading is the opposite of lazy loading.\u003c/p\u003e\n\u003cp\u003eHere, it is defined as \u0027if you want \u003ccode\u003eormr\u003c/code\u003e to do B, which depends on\nthe setup of A\u0027, \u003ccode\u003eormr\u003c/code\u003e will setup A, then do B. For example, to install\na package to a certain \u003ccode\u003eormr_folder_name\u003c/code\u003e (\u0027to do B\u0027), \u003ccode\u003eormr\u003c/code\u003e\nwill create a Conda environment for that (\u0027the setup of A\u0027).\u003c/p\u003e\n\u003cp\u003eThis means that no setup code is necessary.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-does-one-need-to-install-a-release-instead-of-just-master\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-does-one-need-to-install-a-release-instead-of-just-master\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy does one need to install a release, instead of just \u003ccode\u003emaster\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003eThe development of \u003ccode\u003eormr\u003c/code\u003e takes place on the \u003ccode\u003emaster\u003c/code\u003e branch.\nHence, \u003ccode\u003emaster\u003c/code\u003e will break regularily.\nA specific release is tested to build correctly.\u003c/p\u003e\n\u003cp\u003eThe reason for this non-traditional workflow, is that the\nSingularity script always installs the \u003ccode\u003emaster\u003c/code\u003e branch,\nas it cannot detect the \u003ccode\u003egit\u003c/code\u003e branch is being built by.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" aria-hidden=\"true\" href=\"#there-is-a-feature-i-miss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThere is a feature I miss\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-want-to-collaborate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI want to collaborate\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting code\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-think-i-have-found-a-bug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI think I have found a bug\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting bugs\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" aria-hidden=\"true\" href=\"#theres-something-else-i-want-to-say\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThere\u0027s something else I want to say\u003c/h2\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-do-i-contribute\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-do-i-contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do I contribute?\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-is-the-package-called-ormr\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-is-the-package-called-ormr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy is the package called \u003ccode\u003eormr\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003eThis name is a pun on \u003ccode\u003ereticulate\u003c/code\u003e. \u003ccode\u003ereticulate\u003c/code\u003e is named after a\ntype of snake. \u003ccode\u003eormr\u003c/code\u003e is written in Sweden. In Swedish, \u003ccode\u003eorm\u003c/code\u003e, is a snake.\nFollowing the common tradtion of adding an \u003ccode\u003er\u003c/code\u003e to the end of an R package\nname (e.g \u003ccode\u003edplyr\u003c/code\u003e, \u003ccode\u003etidyr\u003c/code\u003e, etc) resulted in \u003ccode\u003eormr\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-about-the-logo\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-about-the-logo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat about the logo?\u003c/h2\u003e\n\u003cp\u003eThe original snake image was found when searching for a\npublic domain image of a snake, using the following DuckDuckGo image seach:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttps://duckduckgo.com/?q=orm+.png\u0026amp;t=ffab\u0026amp;iar=images\u0026amp;iaf=license%3APublic%2Ctype%3Aclipart\u0026amp;iax=images\u0026amp;ia=images\u0026amp;iai=https%3A%2F%2Fcdn.pixabay.com%2Fphoto%2F2016%2F03%2F31%2F15%2F10%2Fcartoon-1293047_1280.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter that, the image was modified using KolourPaint and the R logo was added.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/search;query=ormr\" rel=\"nofollow\"\u003eFind the latest \u0027ormr\u0027 Singularity container\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-hidden=\"true\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinks\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/reticulate_on_singularity\"\u003ehttps://github.com/richelbilderbeek/reticulate_on_singularity\u003c/a\u003e:\ndemo how to run \u003ccode\u003ereticulate\u003c/code\u003e within a Singularity container, without \u003ccode\u003eormr\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "TheJacksonLaboratory/mousegwas", + "latest_release": "GRCm38", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mousegwas\" class=\"anchor\" href=\"#mousegwas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMouseGWAS\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThis package was built to manage the GWAS analysis of mouse phenotypes. The mice in the study were genotypes using either MDA or UCLA chips and deposited in the mouse phenome database (\u003ca href=\"https://phenome.jax.org/genotypes\" rel=\"nofollow\"\u003ehttps://phenome.jax.org/genotypes\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRscript -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elibrary(devtools); install_github(\"TheJacksonLaboratory/mousegwas\")\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input\" class=\"anchor\" href=\"#input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003cp\u003eThe input for the script is the genotype csv files downloaded from the MPD website, the measured phenotypes as a csv file and a yaml file describing the input file.\nThe input csv file should contain a column for strain, a column for sex and columns for phenotype measurements. The names of the columns should be defined in the yaml file using the keywords \u003ccode\u003estrain\u003c/code\u003e and \u003ccode\u003esex\u003c/code\u003e and the phenotypes should be a list under the \u003ccode\u003ephenotypes\u003c/code\u003e keyword.\nAnother data that should reside in the yaml file is translation of strains to the strain names in the genotypes files, it is a dictionary under the \u003ccode\u003etranslate\u003c/code\u003e keyword, and \u003ccode\u003eF1\u003c/code\u003e keyword which is a dictionary translating the F1 names to their parent names, make sure the female parent is always first, it will be used to determine the X chromosome of make F1s. Confounding SNPs could be given using the \u003ccode\u003econfSNPs\u003c/code\u003e, this might be useful to control for obvious markers like coat color alleles. For sanity check you can supply coat color under \u003ccode\u003ecoat\u003c/code\u003e as a dictionary from strain name to coat color and execute a GWAS of coat color with \u003ccode\u003e--coat_phenotype\u003c/code\u003e, it can also be used as a covariate with \u003ccode\u003e--coat_covar\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-execution\" class=\"anchor\" href=\"#execution\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h2\u003e\n\u003cp\u003eThe script \u003ccode\u003erun_GWAS.py\u003c/code\u003e will read the input files and will prepapre the input for either GEMMA or pyLMM. In the case of GEMMA it will download version 0.98 to the working directory if it can\u0027t find the GEMMA executable, if you wish to use pyLMM it should be installed and available in the path. A common process would be creating a virtual environment in python, activating it and installing pyLMM using \u003ccode\u003epip\u003c/code\u003e, see \u003ca href=\"https://github.com/nickFurlotte/pylmm\"\u003ehttps://github.com/nickFurlotte/pylmm\u003c/a\u003e for details.\nThe mousegwas will also download METASOFT and run it on the output if there is more than one phenotype.\u003c/p\u003e\n\u003cp\u003eAs part of the data processing, mousegwas can select a subset of the individuals, restricting the number of mice in each strain x sex group or use the average phenotype of all the individuals in such a group. This is controlled by the \u003ccode\u003e-d\u003c/code\u003e option with 0 for average or any other integer for number restriction.\u003c/p\u003e\n\u003cp\u003eBy default LOCO will be used, use the \u003ccode\u003e--noloco\u003c/code\u003e argument to disable it.\u003c/p\u003e\n\u003cp\u003eA quantile-quantile normalizatin of each phenotype meausrement could be done using the \u003ccode\u003e--qqnorm\u003c/code\u003e argument.\nOther parameters will control the final Manhattan plot, it is a bit unnecessary since the \u003ccode\u003epostprocess_GWAS.R\u003c/code\u003e script will generate more and publication ready figures.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-nextflow-pipeline\" class=\"anchor\" href=\"#nextflow-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline\u003c/h2\u003e\n\u003cp\u003eTo execute the scripts in an easy way we included a nextflow pipeline that runs the initial GWAS, the shuffled executions,\ndetermine a p-value and run the postprocess.\nTo run coat color phenotype GWAS you can simply install \u003ccode\u003enextflow\u003c/code\u003e, make sure that \u003ccode\u003esingularity\u003c/code\u003e is installed and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run TheJacksonLaboratory/mousegwas \\\n --yaml https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/coat_color_MDA.yaml \\\n --shufyaml https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/coat_color_MDA.yaml \\\n --addgwas=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--coat_phenotype\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --addpostp=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--coat_phenotype --colorgroup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --pvalue 0.1 --clusters 1 --outdir coatout -profile singularity,slurm\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eslurm\u003c/code\u003e can be changed to \u003ccode\u003epbs\u003c/code\u003e or ignored for local execution.\u003c/p\u003e\n\u003cp\u003eTo regenerate the results in the paper: \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.10.08.331017v1\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/2020.10.08.331017v1\u003c/a\u003e :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run TheJacksonLaboratory/mousegwas \\\n --yaml https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/grooming_nowild.yaml \\\n --shufyaml https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/grooming_shuffle.yaml \\\n --input https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/grooming_paper_strain_survey_2019_11_21.csv \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e\\\n --outdir grooming_output --addpostp=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--loddrop 0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -profile slurm,singularity\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1657465963.0 + "updated_at": 1632398739.0 }, { "data_format": 2, - "description": "This Repo hosts SARS-CoV-2 genome sequencing, variant calling and assembly scripts. It relies on other cloned repositories and singularity images adapted from other tools and workflows.", + "description": null, "filenames": [ - "scripts/albacore/Singularity.def", - "scripts/primalscheme/Singularity.def" + "Singularity" ], - "full_name": "kibet-gilbert/covid", + "full_name": "truatpasteurdotfr/ambertools-miniconda", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-summary-of-nf-coreviralrecon-data-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#summary-of-nf-coreviralrecon-data-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary of nf-core/viralrecon Data Analysis.\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003eOrganization of the covid analysis directory:\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cp\u003eThe covid directory is structured into four directories: data,scripts,viralrecon and work.(see more below and in the organization section)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-11-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#11-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1. data\u003c/h2\u003e\n\u003cp\u003eThe data dir has: core_data, run directories: yyyy-mm-dd_run[##]_tech, test_data*. The core_data includes all necessary data files like reference genome, Artic primer data, gff files.... The yyyy-mm-dd_run[##]_tech dirs contains the fastq sequences and the analysis results from them (more below).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-111-yyyy-mm-dd_run_tech\" class=\"anchor\" aria-hidden=\"true\" href=\"#111-yyyy-mm-dd_run_tech\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1.1. yyyy-mm-dd_run[##]_tech\u003c/h2\u003e\n\u003cp\u003eThis dir has all symbolic links to fastq.gz (Illumina) or actual fastq (ONT) sequencing output. It has results from the sequence analysis as well in diferent directories/files:\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h2\u003e\n\u003cp\u003e1.1.1.1 .mutations.tsv\t: symplified vcf as a tab-separeted file of all mutations in all samples\u003c/p\u003e\n\u003cp\u003e1.1.1.2 _aa.mutations.tsv\t: Symplified amino acid mutaion file\u003c/p\u003e\n\u003cp\u003e1.1.1.3 _analysis.*\t\t: Any other custom result file excel/pdf\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directories\" class=\"anchor\" aria-hidden=\"true\" href=\"#directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectories\u003c/h2\u003e\n\u003cp\u003eInput:\u003cbr\u003e\nfastq\t\t: Illumina - basecalled and demultiplexed fastQs\u003cbr\u003e\nfast5_fail\t: ONT - raw (Pre-basecalling) sequencing out-put below 9Q score\u003cbr\u003e\nfast5_pass\t: ONT - raw (Pre-basecalling) sequencing out-put above 9Q score\u003cbr\u003e\nfastq_fail\t: ONT - basecalled fastqs below 9Q score\u003cbr\u003e\nfastq_pass\t: ONT - basecalled fastqs above 9Q score\u003cbr\u003e\noutput:\u003cbr\u003e\nalignment\t: Musltiple Sequence Alignment (MUSCLE) results dir\u003cbr\u003e\nmutaions\t: Symplified VCF files in a tab-separeted format\u003cbr\u003e\nnextclade\t: nextclade.js analysis results\u003cbr\u003e\nphylogeny\t: phylogeny (iqtree) analysis results\u003cbr\u003e\nplots\t\t: Amplicon and Genome coverage plots\u003cbr\u003e\npangolin\t: Pangolin analysis results\u003cbr\u003e\nresults\t\t: Variant calling results - Contains more dirs (see next)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1114-results-illumina\" class=\"anchor\" aria-hidden=\"true\" href=\"#1114-results-illumina\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1.1.4 results: Illumina\u003c/h2\u003e\n\u003cp\u003eassembly\t: De-novo assembly results - don\u0027t have any results when skipped\u003cbr\u003e\nmultiqc\t\t: multiqc results in .html and data.yaml format\u003cbr\u003e\npipeline_info\t: All pipeline execution reports in .txt and html format\u003cbr\u003e\npreprocess\t: Pre-processing results: fastQC and fastp (QC \u0026amp; trimming)\u003cbr\u003e\nvariants\t: All variant call results.\u003cbr\u003e\nivar\t- All variant call files - Not annotated\u003cbr\u003e\nivar/snpeff - All variant call files annotated by sneff\u003cbr\u003e\nivar/quast - consensus sequence summary stats by quast\u003cbr\u003e\nbam - bam idex files sorted.bam \u0026amp; sorted.bai\u003cbr\u003e\nbam/samtoos_stats - Samtool stats\u003cbr\u003e\nbam/picard_metrics - picard stats on dublicates in the reads\u003cbr\u003e\nbam/mosdepth - mosdepth stats on amplicon and genome coverage\u003cbr\u003e\nbam/mosdepth/amplicon/plots - log Plots on how amplicons/primers output looks (pdf) and an overall heatmap (pdf). All have accompanying data in TSV format\u003cbr\u003e\nbam/mosdepth/genome/plots -log plots and data for log genome coverage vs position/loci on the genome.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1115-results-ont\" class=\"anchor\" aria-hidden=\"true\" href=\"#1115-results-ont\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1.1.5 results: ONT\u003c/h2\u003e\n\u003cp\u003emultiqc\t\t: multiqc descriptive figures in .html and data.yaml format\u003cbr\u003e\npipeline_info\t: All pipeline execution reports in .txt and html format\u003cbr\u003e\npycoqc\t\t: Pre-processing metrics: guppy basecalling and demultiplexing\u003cbr\u003e\nnanoplot\t: Q score distribution, read lengths and other general stats.\u003cbr\u003e\nmedaka\t\t: All variant call results.\u003cbr\u003e\nsnpeff - All variant call files annotated by sneff\u003cbr\u003e\nquast - consensus sequence summary stats by quast\u003cbr\u003e\n./* - VCF and bam idex files: sorted.bam \u0026amp; sorted.bai\u003cbr\u003e\nsamtoos_stats - Samtool stats\u003cbr\u003e\nbam/picard_metrics - picard stats on dublicates in the reads\u003cbr\u003e\nmosdepth - mosdepth stats on amplicon and genome coverage\u003cbr\u003e\n*/amplicon - log Plots on amplicons/primers performance\u003cbr\u003e\n*/genome/plots -log plots on genome coverage vs position/loci.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-12-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#12-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.2. scripts\u003c/h2\u003e\n\u003cp\u003eThe scripts dir has the analysis sbatch scripts and auxilliary scripts for pre-analysis and post-analysis processing: covid_run_.sbatch\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-13-viralrecon\" class=\"anchor\" aria-hidden=\"true\" href=\"#13-viralrecon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.3. viralrecon\u003c/h2\u003e\n\u003cp\u003eThe viralrecon dir is git cloned and has the source code of the pipeline... The idea is to eventually run the pipeline directly from the code when needed (not possible as of now).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-14-work\" class=\"anchor\" aria-hidden=\"true\" href=\"#14-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.4. work\u003c/h2\u003e\n\u003cp\u003eThe work dir has the temporary files generated during runs also stores conda environment (work/conda/), container (singularity) images. It is a copy of the work dir stored in the working dir (/var/scratch/${USER}/work/conda/). Howerever the working directory has all temporary files from each run.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-directory-organization\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-organization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory Organization.\u003c/h1\u003e\n\u003cp\u003e.(covid)\u003cbr\u003e\n\u251c\u2500\u2500 data\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 core_data\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 alignment\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.aln\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.fasta\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 covid_01-04-2021_headers\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021_aa.mutations.tsv\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021_analysis.mutations.pdf\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021_analysis.mutations.xlsx\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021.mutations.tsv\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021_pangolin.tsv\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 mutations\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 COVC21058_S21.snpEff.vcf.tsv\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 |\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 |\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 |\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 COVC23453_S20.snpEff.vcf.tsv\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 MoH-Cov-6_S6.snpEff.vcf.tsv\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 Undetermined_S0.snpEff.vcf.tsv\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 nextclade\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.json\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.tsv\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 nextstrain_.svg\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 nextstrain_tree.nexus\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 nextstrain_tree.nwk\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 phylogeny\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.bionj\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.ckp.gz\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.contree\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.iqtree\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.mldist\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.model.gz\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.splits.nex\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.treefile\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 covid_01-04-2021_con.ufboot\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 results\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 assembly\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 multiqc\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 pipeline_info\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 preprocess\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 variants\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 bam\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 log\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 mosdepth\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 amplicon\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 plots\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 genome\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 plots\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 picard_metrics\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 samtools_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 ivar\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 bcftools_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 consensus\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 base_qc\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 log\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 quast\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 AF0.75\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 aligned_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 basic_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 contigs_reports\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 minimap_output\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 genome_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 icarus_viewers\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 snpeff\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 samplesheet.csv\u003cbr\u003e\n\u2502 \u2502 \u2514\u2500\u2500 slurm-716659.out\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_11-02-2021\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid-run3_09-04-2021\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 general_variants\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test_data\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test_data00\u003cbr\u003e\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 test_data01\u003cbr\u003e\n\u251c\u2500\u2500 README\u003cbr\u003e\n\u251c\u2500\u2500 scripts\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 aa_codes\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_env.yml\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Covid_exporatory.ipynb\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run_denovo.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run_kranken.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run_nomqc.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run_test.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Notes.txt\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 pangolin\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 process_files_bak.sh\u003cbr\u003e\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 process_files.sh\u003cbr\u003e\n\u251c\u2500\u2500 viralrecon\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 assets\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 bin\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 CHANGELOG.md\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 CITATIONS.md\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 CODE_OF_CONDUCT.md\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 conf\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Dockerfile\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 docs\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 environment.yml\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 LICENSE\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 main.nf\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 nextflow.config\u003cbr\u003e\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 README.md\u003cbr\u003e\n\u2514\u2500\u2500 work\u003cbr\u003e\n\u2514\u2500\u2500 conda\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTo analyse some datasets\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cp\u003e2.1. MiSeq:\u003cbr\u003e\nOpen the script path-to/covid/scripts/covid_run-2.2.miseq.sbatch and set the variables: FRead_suffix and RRead_suffix to your desired forward read and reverse read suffix. The defaults are FRead_suffix=\"L001_R1_001.fastq.gz\" and RRead_suffix=\"L001_R2_001.fastq.gz\"\u003cbr\u003e\n\"cd\" to path-to/covid/data/\u0026lt;your_data_dir\u0026gt; and execute the sbatch command below: $ sbatch -w compute06 ../../scripts/covid_run-2.2.miseq.sbatch\u003c/p\u003e\n\u003cp\u003e2.2. NextSeq:\u003cbr\u003e\nConcatenate the reads per sample from *L00?_R1_001.fastq.gz to *con_R1_001.fastq.gz and *L00?_R2_001.fastq.gz to *con_R2_001.fastq.gz\u003cbr\u003e\nOpen the script path-to/covid/scripts/covid_run-2.2.nextseq.sbatch and set the variables: FRead_suffix and RRead_suffix to the right forward read and reverse read suffix: FRead_suffix=\"con_R1_001.fastq.gz\" and RRead_suffix=\"con_R2_001.fastq.gz\"\u003cbr\u003e\n\"cd\" to path-to/covid/data/\u0026lt;your_data_dir\u0026gt; and execute the sbatch command below: $ sbatch -w compute06 ../../scripts/covid_run-2.2.nextseq.sbatch\u003c/p\u003e\n\u003cp\u003e2.3 ONT:\u003cbr\u003e\nCreate a sample sheet (samplesheet.csv) and save it in the data directory. The format is explained here: \u003ca href=\"https://nf-co.re/viralrecon/2.2/usage#nanopore\" rel=\"nofollow\"\u003ehttps://nf-co.re/viralrecon/2.2/usage#nanopore\u003c/a\u003e\u003cbr\u003e\n\"cd\" to path-to/covid/data/\u0026lt;your_data_dir\u0026gt; and execute the sbatch command below: $ sbatch -w compute06 ../../scripts/covid_run-2.2.nanopore.sbatch\u003c/p\u003e\n\u003cp\u003eN/B: compute06 should work okay i.e 64CPUs and enough disk space.\u003cbr\u003e\nThe nf-core/viralrecon, a nextflow pipeline has been set up to run with -profile singularity option. For more on options that can be set see PARAMETER configuration section.\u003cbr\u003e\nHelp message: Execute the following line for the usage:\u003cbr\u003e\n$ nextflow run nf-core/viralrecon -r 2.2 --help -profile singularity\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-processes\" class=\"anchor\" aria-hidden=\"true\" href=\"#processes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcesses:\u003c/h1\u003e\n\u003cp\u003eThis workflow is made up of 64 process (The equivalent of functions in bash/python): some can be deactivated as need. See Processes section for more.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGUNZIP_FASTA\u003c/li\u003e\n\u003cli\u003eGUNZIP_GFF\u003c/li\u003e\n\u003cli\u003eCHECK_SAMPLESHEET\u003c/li\u003e\n\u003cli\u003e*SRA_FASTQ_FTP\u003c/li\u003e\n\u003cli\u003e*SRA_FASTQ_DUMP\u003c/li\u003e\n\u003cli\u003eCAT_FASTQ\u003c/li\u003e\n\u003cli\u003eFASTQC\u003c/li\u003e\n\u003cli\u003eFASTP\u003c/li\u003e\n\u003cli\u003eBOWTIE2_INDEX\u003c/li\u003e\n\u003cli\u003eMAKE_SNPEFF_DB\u003c/li\u003e\n\u003cli\u003eBOWTIE2\u003c/li\u003e\n\u003cli\u003eSORT_BAM\u003c/li\u003e\n\u003cli\u003eIVAR_TRIM\u003c/li\u003e\n\u003cli\u003ePICARD_MARKDUPLICATES\u003c/li\u003e\n\u003cli\u003ePICARD_METRICS\u003c/li\u003e\n\u003cli\u003eMOSDEPTH_GENOME\u003c/li\u003e\n\u003cli\u003eMOSDEPTH_AMPLICON\u003c/li\u003e\n\u003cli\u003eMOSDEPTH_AMPLICON_PLOT\u003c/li\u003e\n\u003cli\u003eSAMTOOLS_MPILEUP\u003c/li\u003e\n\u003cli\u003e*VARSCAN2\u003c/li\u003e\n\u003cli\u003e*VARSCAN2_CONSENSUS\u003c/li\u003e\n\u003cli\u003e*VARSCAN2_SNPEFF\u003c/li\u003e\n\u003cli\u003e*VARSCAN2_QUAST\u003c/li\u003e\n\u003cli\u003eIVAR_VARIANTS\u003c/li\u003e\n\u003cli\u003eIVAR_CONSENSUS\u003c/li\u003e\n\u003cli\u003eIVAR_SNPEFF\u003c/li\u003e\n\u003cli\u003eIVAR_QUAST\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_VARIANTS\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_CONSENSUS\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_SNPEFF\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_QUAST\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_ISEC\u003c/li\u003e\n\u003cli\u003e**MAKE_BLAST_DB\u003c/li\u003e\n\u003cli\u003e**SPADES\u003c/li\u003e\n\u003cli\u003e**SPADES_BLAST\u003c/li\u003e\n\u003cli\u003e**SPADES_ABACAS\u003c/li\u003e\n\u003cli\u003e**SPADES_PLASMIDID\u003c/li\u003e\n\u003cli\u003e**SPADES_QUAST\u003c/li\u003e\n\u003cli\u003e**SPADES_VG\u003c/li\u003e\n\u003cli\u003e**SPADES_SNPEFF\u003c/li\u003e\n\u003cli\u003e**METASPADES\u003c/li\u003e\n\u003cli\u003e**METASPADES_BLAST\u003c/li\u003e\n\u003cli\u003e**METASPADES_ABACAS\u003c/li\u003e\n\u003cli\u003e**METASPADES_PLASMIDID\u003c/li\u003e\n\u003cli\u003e**METASPADES_QUAST\u003c/li\u003e\n\u003cli\u003e**METASPADES_VG\u003c/li\u003e\n\u003cli\u003e**METASPADES_SNPEFF\u003c/li\u003e\n\u003cli\u003e**UNICYCLER\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_BLAST\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_ABACAS\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_PLASMIDID\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_QUAST\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_VG\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_SNPEFF\u003c/li\u003e\n\u003cli\u003e**MINIA\u003c/li\u003e\n\u003cli\u003e**MINIA_BLAST\u003c/li\u003e\n\u003cli\u003e**MINIA_ABACAS\u003c/li\u003e\n\u003cli\u003e**MINIA_PLASMIDID\u003c/li\u003e\n\u003cli\u003e**MINIA_QUAST\u003c/li\u003e\n\u003cli\u003e**MINIA_VG\u003c/li\u003e\n\u003cli\u003e**MINIA_SNPEFF\u003c/li\u003e\n\u003cli\u003eget_software_versions\u003c/li\u003e\n\u003cli\u003eMULTIQC\u003c/li\u003e\n\u003cli\u003eoutput_documentation\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePARAMETER configurations:\u003cbr\u003e\n// Options: Generic\u003cbr\u003e\ninput = \u0027./samplesheet.csv\u0027\u003cbr\u003e\nprotocol = \u0027amplicon\u0027\u003cbr\u003e\namplicon_fasta = ${DATADIR}/GCA_009858895.3_ASM985889v3_genomic.200409.fna.gz\u003cbr\u003e\namplicon_bed = ${DATADIR}/GCA_009858895.3_ASM985889v3_genomic.200409.gff.gz\u003c/p\u003e\n\u003cp\u003e// Options: SRA download\u003cbr\u003e\nsave_sra_fastq = false\u003cbr\u003e\nskip_sra = false\u003c/p\u003e\n\u003cp\u003e// Options: Reference genomes\u003cbr\u003e\ngenome = false\u003cbr\u003e\nsave_reference = false\u003c/p\u003e\n\u003cp\u003e// Options: Read Trimming\u003cbr\u003e\ncut_mean_quality = 20\u003cbr\u003e\nqualified_quality_phred = 20\u003cbr\u003e\nunqualified_percent_limit = 10\u003cbr\u003e\nmin_trim_length = 50\u003cbr\u003e\nskip_adapter_trimming = false\u003cbr\u003e\nskip_amplicon_trimming = false\u003cbr\u003e\nsave_trimmed = false\u003c/p\u003e\n\u003cp\u003e// Options: Variant calling\u003cbr\u003e\ncallers = \u0027varscan2,ivar,bcftools\u0027\u003cbr\u003e\nmin_mapped_reads = 1000\u003cbr\u003e\nivar_trim_noprimer = false\u003cbr\u003e\nivar_trim_min_len = 20\u003cbr\u003e\nivar_trim_min_qual = 20\u003cbr\u003e\nivar_trim_window_width = 4\u003cbr\u003e\nfilter_dups = false\u003cbr\u003e\nfilter_unmapped = false\u003cbr\u003e\nmpileup_depth = 0\u003cbr\u003e\nmin_base_qual = 20\u003cbr\u003e\nmin_coverage = 10\u003cbr\u003e\nmin_allele_freq = 0.25\u003cbr\u003e\nmax_allele_freq = 0.75\u003cbr\u003e\nvarscan2_strand_filter = true\u003cbr\u003e\namplicon_left_suffix = \u0027_LEFT\u0027\u003cbr\u003e\namplicon_right_suffix = \u0027_RIGHT\u0027\u003cbr\u003e\nsave_align_intermeds = false\u003cbr\u003e\nsave_mpileup = false\u003cbr\u003e\nskip_markduplicates = false\u003cbr\u003e\nskip_picard_metrics = false\u003cbr\u003e\nskip_mosdepth = false\u003cbr\u003e\nskip_snpeff = false\u003cbr\u003e\nskip_variants_quast = false\u003cbr\u003e\nskip_variants = false\u003c/p\u003e\n\u003cp\u003e// Options: QC\u003cbr\u003e\nskip_fastqc = false\u003cbr\u003e\nskip_multiqc = false\u003c/p\u003e\n\u003cp\u003e// Boilerplate options\u003cbr\u003e\noutdir = \u0027./results\u0027\u003cbr\u003e\npublish_dir_mode = \u0027copy\u0027\u003cbr\u003e\nname = false\u003cbr\u003e\nmultiqc_config = false\u003cbr\u003e\nemail = false\u003cbr\u003e\nemail_on_fail = false\u003cbr\u003e\nmax_multiqc_email_size = 25.MB\u003cbr\u003e\nplaintext_email = false\u003cbr\u003e\nmonochrome_logs = false\u003cbr\u003e\nhelp = false\u003cbr\u003e\ntracedir = \"${params.outdir}/pipeline_info\"\u003cbr\u003e\ncustom_config_version = \u0027master\u0027\u003cbr\u003e\ncustom_config_base = \"\u003ca href=\"https://raw.githubusercontent.com/nf-core/configs/%24%7Bparams.custom_config_version%7D\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/nf-core/configs/${params.custom_config_version}\u003c/a\u003e\"\u003cbr\u003e\nhostnames = false\u003cbr\u003e\nconfig_profile_description = false\u003cbr\u003e\nconfig_profile_contact = false\u003cbr\u003e\nconfig_profile_url = false\u003c/p\u003e\n\u003cp\u003e// Options: Kraken2\u003cbr\u003e\nkraken2_db = \u0027\u003ca href=\"https://zenodo.org/record/3738199/files/kraken2_human.tar.gz\" rel=\"nofollow\"\u003ehttps://zenodo.org/record/3738199/files/kraken2_human.tar.gz\u003c/a\u003e\u0027\u003cbr\u003e\nkraken2_db_name = \u0027human\u0027\u003cbr\u003e\nkraken2_use_ftp = false\u003cbr\u003e\nsave_kraken2_fastq = false\u003cbr\u003e\nskip_kraken2 = false\u003c/p\u003e\n\u003cp\u003e// Options: De novo assembly\u003cbr\u003e\nassemblers = \u0027spades,metaspades,unicycler,minia\u0027\u003cbr\u003e\nminia_kmer = 31\u003cbr\u003e\nskip_blast = false\u003cbr\u003e\nskip_abacas = false\u003cbr\u003e\nskip_plasmidid = false\u003cbr\u003e\nskip_vg = false\u003cbr\u003e\nskip_assembly_quast = false\u003cbr\u003e\nskip_assembly = false\u003c/p\u003e\n\u003cp\u003e// Defaults only, expecting to be overwritten\u003cbr\u003e\nmax_memory = 40.GB\u003cbr\u003e\nmax_cpus = 30\u003cbr\u003e\nmax_time = 240.h\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-conda-based-ambertools-environment-httpambermdorggetamberphpambertools\" class=\"anchor\" href=\"#conda-based-ambertools-environment-httpambermdorggetamberphpambertools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econda based ambertools environment (\u003ca href=\"http://ambermd.org/GetAmber.php#ambertools\" rel=\"nofollow\"\u003ehttp://ambermd.org/GetAmber.php#ambertools\u003c/a\u003e)\u003c/h1\u003e\n\u003cp\u003eProviding a toy container for AMBER based on the conda environment \u003ca href=\"https://github.com/truatpasteurdotfr/ambertools-miniconda/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/ambertools-miniconda/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003edocker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/ambertools-miniconda:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell oras://ghcr.io/truatpasteurdotfr/ambertools-miniconda:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1662558108.0 + "updated_at": 1638799326.0 }, { "data_format": 2, - "description": null, + "description": "repository to store various dockerfiles used to build docker containers that are used across multiple brainlife apps", "filenames": [ - "singularity/Singularity" + "hcppipelines/Singularity" ], - "full_name": "kavonrtep/dante_ltr", + "full_name": "brainlife/dockerfiles", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-dante_ltr\" class=\"anchor\" aria-hidden=\"true\" href=\"#dante_ltr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDANTE_LTR\u003c/h1\u003e\n\u003cp\u003eTool for identifying complete LTR retrotransposons based on analysis of protein domains identified with the \u003ca href=\"https://github.com/kavonrtep/dante\"\u003eDANTE tool\u003c/a\u003e. Both DANTE and DANTE_LTR are available on Galaxy server.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-principle-of-dante-_ltr\" class=\"anchor\" aria-hidden=\"true\" href=\"#principle-of-dante-_ltr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrinciple of DANTE _LTR\u003c/h2\u003e\n\u003cp\u003eComplete retrotransposons are identified as clusters of protein domains recognized by the DANTE tool. The domains in the clusters must be assigned to a single retrotransposon lineage by DANTE. In addition, the orientation and order of the protein domains, as well as the distances between them, must conform to the characteristics of elements from REXXdb database \u003ca href=\"https://mobilednajournal.biomedcentral.com/articles/10.1186/s13100-018-0144-1\" rel=\"nofollow\"\u003eNeumann et al. (2019)\u003c/a\u003e.\nIn the next step, the 5\u0027 and 3\u0027 regions of the putative retrotransposon are examined for the presence of 5\u0027 and 3\u0027 long terminal repeats. If 5\u0027- and 3\u0027-long terminal repeats are detected, detection of target site duplication (TSD) and primer binding site (PSB) is performed. The detected LTR retrotranspsons are classified into 5 categories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eElements with protein domains, 5\u0027LTR, 3\u0027LTR, TSD and PBS - rank \u003cstrong\u003eDLTP\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eElements with protein domains, 5\u0027LTR, 3\u0027LTR, and PBS (TSD was not found) Rank \u003cstrong\u003eDLP\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eElements with protein domains, 5\u0027 LTR, 3\u0027LTR, TSD (PBS was not found) - rank \u003cstrong\u003eDTL\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eElements with protein domains, 5\u0027LTR and 3\u0027LTR (PBS and TDS were not found) - rank \u003cstrong\u003eDL\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eElements as clusters of protein domains with the same classification, no LTRs - rank \u003cstrong\u003eD\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dante_ltr_workflow.png\"\u003e\u003cimg src=\"dante_ltr_workflow.png\" alt=\"dante_ltr_workflow.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation:\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n dante_ltr -c bioconda -c conda-forge -c petrnovak dante_ltr\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput data\u003c/h2\u003e\n\u003cp\u003eOne input is a reference sequence in fasta fromat. The second input is an annotation of the reference genome using the tool DANTE in GFF3 format. For better results, use the unfiltered full output of the DANTE pipeline.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-detection-of-complete-ltr-retrotransposons\" class=\"anchor\" aria-hidden=\"true\" href=\"#detection-of-complete-ltr-retrotransposons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetection of complete LTR retrotransposons\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eUsage: ./detect_putative_ltr.R COMMAND [OPTIONS]\n\n\nOptions:\n -g GFF3, --gff3=GFF3\n gff3 with dante results\n\n -s REFERENCE_SEQUENCE, --reference_sequence=REFERENCE_SEQUENCE\n reference sequence as fasta\n\n -o OUTPUT, --output=OUTPUT\n output file path and prefix\n\n -c NUMBER, --cpu=NUMBER\n Number of cpu to use [default 5]\n\n -M NUMBER, --max_missing_domains=NUMBER\n Maximum number of missing domains is retrotransposon [default 0]\n\n -L NUMBER, --min_relative_length=NUMBER\n Minimum relative length of protein domain to be considered \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e retrostransposon detection [default 0.6]\n -h, --help\n Show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p tmp\n./detect_putative_ltr.R -g test_data/sample_DANTE.gff3 -s test_data/sample_genome.fasta -o tmp/ltr_annotation\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-files-in-the-output-of-extract_putative_ltrr\" class=\"anchor\" aria-hidden=\"true\" href=\"#files-in-the-output-of-extract_putative_ltrr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles in the output of \u003ccode\u003eextract_putative_ltr.R\u003c/code\u003e:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix.gff3\u003c/code\u003e - annotation of all identified elements\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_D.fasta\u003c/code\u003e - partial elements with protein \u003cstrong\u003ed\u003c/strong\u003eomains\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_DL.fasta\u003c/code\u003e - elements with protein \u003cstrong\u003ed\u003c/strong\u003eomains and \u003cstrong\u003eL\u003c/strong\u003eTR\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_DLTP.fasta\u003c/code\u003e - elements with \u003cstrong\u003ed\u003c/strong\u003eomains, \u003cstrong\u003eL\u003c/strong\u003eTR, \u003cstrong\u003eT\u003c/strong\u003eSD and \u003cstrong\u003eP\u003c/strong\u003eBS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_DLP.fasta\u003c/code\u003e - elements with \u003cstrong\u003ed\u003c/strong\u003eomains, \u003cstrong\u003eL\u003c/strong\u003eTR and \u003cstrong\u003eP\u003c/strong\u003eBS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_DLT.fasta\u003c/code\u003e - elements with \u003cstrong\u003ed\u003c/strong\u003eomains, \u003cstrong\u003eL\u003c/strong\u003eTR, \u003cstrong\u003eT\u003c/strong\u003eSD\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_statistics.csv\u003c/code\u003e - number of elements in individual categories\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor large genomes, you can your \u003ccode\u003edetect_putative_ltr_wrapper.py\u003c/code\u003e. This script will split input fasta to smaller chunks and run \u003ccode\u003edetect_putative_ltr.R\u003c/code\u003e on each chunk to limit memory usage. Output will be merged after all chunks are processed.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eusage: detect_putative_ltr_wrapper.py [-h] -g GFF3 -s REFERENCE_SEQUENCE -o\n OUTPUT [-c CPU] [-M MAX_MISSING_DOMAINS]\n [-L MIN_RELATIVE_LENGTH]\n [-S MAX_CHUNK_SIZE]\n\ndetect_putative_ltr_wrapper.py is a wrapper \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \n detect_putative_ltr.R\n\noptional arguments:\n -h, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n -g GFF3, --gff3 GFF3 gff3 file\n -s REFERENCE_SEQUENCE, --reference_sequence REFERENCE_SEQUENCE\n reference sequence as fasta file\n -o OUTPUT, --output OUTPUT\n output file path and prefix\n -c CPU, --cpu CPU number of CPUs\n -M MAX_MISSING_DOMAINS, --max_missing_domains MAX_MISSING_DOMAINS\n -L MIN_RELATIVE_LENGTH, --min_relative_length MIN_RELATIVE_LENGTH\n Minimum relative length of protein domain to be considered\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e retrostransposon detection\n -S MAX_CHUNK_SIZE, --max_chunk_size MAX_CHUNK_SIZE\n If size of reference sequence is greater than this value,\n reference is analyzed \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e chunks of this size. This is\n just approximate value - sequences which are longer \n are are not split, default is 100000000\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-validation-of-ltr-retrotransposons-detected-un-previous-step\" class=\"anchor\" aria-hidden=\"true\" href=\"#validation-of-ltr-retrotransposons-detected-un-previous-step\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eValidation of LTR retrotransposons detected un previous step:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./clean_ltr.R --help\nUsage: ./clean_ltr.R COMMAND [OPTIONS]\n\n\nOptions:\n -g GFF3, --gff3=GFF3\n gff3 with LTR Transposable elements\n\n -s REFERENCE_SEQUENCE, --reference_sequence=REFERENCE_SEQUENCE\n reference sequence as fasta\n\n -o OUTPUT, --output=OUTPUT\n output file prefix\n\n -c NUMBER, --cpu=NUMBER\n Number of cpu to use [default 5]\n\n -h, --help\n Show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis script check for potentially chimeric elements and removes them from GFF3 file.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./clean_ltr.R -g test_data/sample_DANTE_LTR_annotation.gff3 -s test_data/sample_genome.fasta -o tmp/ltr_annotation_clean\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dockerfiles\" class=\"anchor\" href=\"#dockerfiles\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edockerfiles\u003c/h1\u003e\n\u003cp\u003erepository to store various dockerfiles used to build docker containers\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1647262998.0 + "updated_at": 1637688718.0 }, { "data_format": 2, - "description": "Evaluate accuracy of covid assemblies where truth is available", + "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "iqbal-lab-org/covid-truth-eval", - "latest_release": "v0.0.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-covid-truth-eval\" class=\"anchor\" aria-hidden=\"true\" href=\"#covid-truth-eval\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecovid-truth-eval\u003c/h1\u003e\n\u003cp\u003eEvaluate accuracy of covid assemblies where truth is available\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eMinimal instructions are below. Please see the \u003ca href=\"https://github.com/iqbal-lab-org/covid-truth-eval/wiki\"\u003ewiki page\u003c/a\u003e\nfor more details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eGet a Docker image of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/iqbal-lab-org/cte:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll Docker images are listed in the\n\u003ca href=\"https://github.com/iqbal-lab-org/covid-truth-eval/pkgs/container/covid-truth-eval\"\u003epackages page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, build your own Docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker build --network=host .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/covid-truth-eval/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download.\nEach release has a file called \u003ccode\u003ecte_vX.Y.Z.img\u003c/code\u003e, where \u003ccode\u003eX.Y.Z\u003c/code\u003e is the release version.\u003c/p\u003e\n\u003cp\u003eAlternatively, build your own Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build cte.simg Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFrom source\u003c/h3\u003e\n\u003cp\u003eDependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mafft.cbrc.jp/alignment/software/\" rel=\"nofollow\"\u003emafft\u003c/a\u003e installed and in your \u003ccode\u003e$PATH\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInstall by cloning this repository (or downloading the latest release), and\nrunning:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 -m pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start\u003c/h2\u003e\n\u003cp\u003eTo evaluate one SARS-CoV-2 consensus sequence, you will need:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA VCF file of the \"truth\" calls \u003ccode\u003etruth.vcf\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe consensus sequence to evalaute in a FASTA file \u003ccode\u003econs.fa\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe primer scheme. Currently supported: COVID-ARTIC-V3, COVID-ARTIC-V4, COVID-MIDNIGHT-1200.\nOr use your own TSV file of primers in \u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/wiki/Amplicon-schemes\"\u003eViridian Workflow format\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe program is called \u003ccode\u003ecte\u003c/code\u003e and is installed in the Docker and Singularity containers, and gets installed by \u003ccode\u003epip\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eExample, assuming primer scheme COVID-ARTIC-V4:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecte eval_one_run \\\n --outdir OUT \\\n --truth_vcf truth.vcf \\\n --fasta_to_eval cons.fa \\\n --primers COVID-ARTIC-V4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output files are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eresults.tsv\u003c/code\u003e - a TSV file of counts of the truth bases vs what was called in the consensus. The same information is also put in a JSON file \u003ccode\u003eresults.json\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eper_position.tsv\u003c/code\u003e - a TSV file, one line per reference position. It shows the multiple alignment of the reference, truth (inferred from the truth VCF file), and the sequence being evaluated. At each position the assigned category of truth and called bases is shown, where the categories are the same as those used in \u003ccode\u003eresults.tsv\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe files are described in detail in the \u003ca href=\"https://github.com/iqbal-lab-org/covid-truth-eval/wiki/Output-files\"\u003eoutput files\u003c/a\u003e documentation.\u003c/p\u003e\n", + "full_name": "zellerlab/gaga2", + "latest_release": "v0.4", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gaga2---automated-16s-amplicon-analysis-with-figarodada2\" class=\"anchor\" href=\"#gaga2---automated-16s-amplicon-analysis-with-figarodada2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egaga2 - automated 16S amplicon analysis with Figaro/DADA2\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"docs/img/gaga2_flow.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"docs/img/gaga2_flow.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" href=\"#installation-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation instructions\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003egaga2\u003c/code\u003e requires a working \u003ccode\u003enextflow\u003c/code\u003e installation (v20.4+).\u003c/p\u003e\n\u003cp\u003eOther dependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebbmap\u003c/li\u003e\n\u003cli\u003efastqc\u003c/li\u003e\n\u003cli\u003efigaro\u003c/li\u003e\n\u003cli\u003eR v4+ with dada2, devtools, tidyverse, and cowplot installed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor convenience, \u003ccode\u003egaga2\u003c/code\u003e comes with a Singularity container with all dependencies installed.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull oras://ghcr.io/zellerlab/gaga2:latest\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-instructions\" class=\"anchor\" href=\"#usage-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage instructions\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input\" class=\"anchor\" href=\"#input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003egaga2\u003c/code\u003e takes as input Illumina paired-end 16S amplicon sequences (e.g. sequenced on a MiSeq).\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eRead files need to be named according the typical pattern \u003ccode\u003e\u0026lt;prefix=sample_id\u0026gt;_R?[12].{fastq,fq,fastq.gz,fq.gz}\u003c/code\u003e.\nThey should, but don\u0027t have to, be arranged in a sample-based directory structure:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;project_dir\u0026gt; (aka \"input_dir\")\n |___ \u0026lt;sample_1\u0026gt;\n | |____ \u0026lt;sample_1_forward_reads\u0026gt;\n | |____ \u0026lt;sample_2_reverse_reads\u0026gt;\n |\n |___ \u0026lt;sample_2\u0026gt;\n | |____ \u0026lt;empty samples will be ignored\u0026gt;\n | \n |___ \u0026lt;sample_n\u0026gt;\n |____ \u0026lt;sample_n_forward_reads\u0026gt;\n |____ \u0026lt;sample_n_reverse_reads\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA flat directory structure (with all read files in the same directory) or a deeply-branched (with read files scattered over multiple levels) should also work.\u003c/p\u003e\n\u003cp\u003eIf \u003ccode\u003egaga2\u003c/code\u003e preprocesses the reads, it will automatically use \u003ccode\u003e_R1/2\u003c/code\u003e endings internally.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIf input reads have already been preprocessed, you can set the \u003ccode\u003e--preprocessed\u003c/code\u003e flag. In this case, \u003ccode\u003egaga2\u003c/code\u003e will do no preprocessing at all and instruct \u003ccode\u003edada2\u003c/code\u003e to perform no trimming. Otherwie, \u003ccode\u003egaga2\u003c/code\u003e will assess the read lengths for uniformity. If read lengths differ within and between samples, preprocessing with \u003ccode\u003efigaro\u003c/code\u003e is not possible and \u003ccode\u003edada2\u003c/code\u003e will be run without trimming.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSamples with less than \u003ccode\u003e110\u003c/code\u003e reads after \u003ccode\u003edada2\u003c/code\u003e preprocessing, will be discarded.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-gaga2\" class=\"anchor\" href=\"#running-gaga2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning gaga2\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003egaga2\u003c/code\u003e can be directly run from github.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run zellerlab/gaga2 \u0026lt;parameters\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain a newer version, do a\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow pull zellerlab/gaga2\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ebefore.\u003c/p\u003e\n\u003cp\u003eIn addition, you should obtain a copy of the \u003ccode\u003erun.config\u003c/code\u003e from the \u003ccode\u003egaga2\u003c/code\u003e github repo and modify it according to your environment.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-mandatory-arguments\" class=\"anchor\" href=\"#mandatory-arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory arguments\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input_dir\u003c/code\u003e is the project directory mentioned above.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_dir\u003c/code\u003e will be created automatically.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--amplicon_length\u003c/code\u003e this is derived from your experiment parameters (this is not read-length, but the length of the, well, amplicon!)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--single_end\u003c/code\u003e this is only required for single-end libraries (auto-detection of library-type is in progress)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optional-arguments\" class=\"anchor\" href=\"#optional-arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional arguments\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--min_overlap\u003c/code\u003e of read pairs is \u003ccode\u003e20bp\u003c/code\u003e by default\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--primers \u0026lt;comma-separated-list-of-primer-sequences\u0026gt;\u003c/code\u003e or \u003ccode\u003e--left_primer\u003c/code\u003e, and \u003ccode\u003e--right_primer\u003c/code\u003e If primer sequences are provided via \u003ccode\u003e--primers\u003c/code\u003e, \u003ccode\u003egaga2\u003c/code\u003e will remove primers and upstream sequences (using \u003ccode\u003ebbduk\u003c/code\u003e), such as adapters based on the primer sequences. If non-zero primer lengths are provided instead (via \u003ccode\u003e--left_primer\u003c/code\u003e and \u003ccode\u003e--right_primer\u003c/code\u003e), \u003ccode\u003efigaro\u003c/code\u003e will take those into account when determining the best trim positions.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--preprocessed\u003c/code\u003e will prevent any further preprocessing by \u003ccode\u003egaga2\u003c/code\u003e - this flag should only be used if the read data is reliably clean.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-internal-beta-testing-instructions\" class=\"anchor\" href=\"#internal-beta-testing-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einternal beta-testing instructions\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eThe old gaga2 version can be run with \u003ccode\u003esource /g/scb2/zeller/schudoma/software/wrappers/gaga2_wrapper\u003c/code\u003e \u003cstrong\u003ebefore\u003c/strong\u003e submitting job to cluster\u003c/li\u003e\n\u003cli\u003ePlease report issues/requests/feedback in the github issue tracker\u003c/li\u003e\n\u003cli\u003eIf you want to run \u003ccode\u003egaga2\u003c/code\u003e on the cluster, \u003ccode\u003enextflow\u003c/code\u003e alone requires \u003ccode\u003e\u0026gt;=5GB\u003c/code\u003e memory just for \u0027managing\u0027.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1642766061.0 + "updated_at": 1637350275.0 }, { "data_format": 2, - "description": "This project is either going to become the most dangerous computer virus the world has and ever will see or it will actually go smoothly (I really hope it isn\u2019t the former)", + "description": "The files to build singularity images for GOMAP pipeline", "filenames": [ - "parametric-face-image-generator-2.1.1/Singularity" + "singularity/Singularity" ], - "full_name": "AdamOswald/Ai-test", + "full_name": "Dill-PICL/GOMAP-img-build", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ai-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#ai-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAi-test\u003c/h1\u003e\n", "stargazers_count": 1, - "subscribers_count": 0, - "topics": [ - "ai", - "artificial-intelligence", - "deep-learning", - "face-swap", - "face-swapping", - "html", - "html-css-javascript", - "image-generation", - "java", - "javascript", - "jupyter-notebooks", - "machine-learning", - "nerual-network", - "python", - "pytorch", - "scss", - "tensor", - "text-to-image", - "nerual-networks", - "database" - ], - "updated_at": 1664619917.0 + "subscribers_count": 4, + "topics": [], + "updated_at": 1635807818.0 }, { "data_format": 2, - "description": null, + "description": "Fork of Fast Downward with support of the unified planning framework of the AIPlan4EU project", "filenames": [ - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/Singularity.template", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/rl/Singularity.learnaBenchmark", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/rl/Singularity.Cartpole", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.TabularBenchmarks", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_201", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_101", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_1shot1", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.XGBoostBenchmark", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.ml_mmfb", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.PyBNN", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.SupportVectorMachine", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/od/Singularity.ODBenchmarks", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine" + "misc/releases/20.06/Singularity.20.06", + "misc/releases/latest/Singularity", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12" ], - "full_name": "jointentropysearch/JointEntropySearch", + "full_name": "roeger/downward-aiplan4eu", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-joint-entropy-search-for-maximally-informed-bayesian-optimization\" class=\"anchor\" aria-hidden=\"true\" href=\"#joint-entropy-search-for-maximally-informed-bayesian-optimization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJoint Entropy Search for Maximally-Informed Bayesian Optimization\u003c/h1\u003e\n\u003cp\u003eThis is the official repository for Joint Entropy Search for Maximally-Informed Bayesian Optimization. We developed our code by building on Max-value Entropy Search (Wang and Jegelka, 2017) which in turn built on Predictive Entropy Search (Hernandez-Lobato et al., 2014), which was developed upon GPstuff (Vanhatalo et al., 2013). To keep the repository as trim and clean as possible, some of the examples and methods from previous work have been removed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements\u003c/h2\u003e\n\u003cp\u003eAll code has been tested with MATLAB 2021a. After installing the conda environment provided in \u003ccode\u003ejes.yml\u003c/code\u003e, add the required libraries by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/conda/envs/jes/lib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand everything should be up and running.\u003c/p\u003e\n\u003cp\u003eWhile the required mex file is included, it may need to be re-compiled. To do so, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd utils\nmex chol2invchol.c -lgsl -lblas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLastly, to run experiments, do one of the following:\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-for-gp-sample-tasks-where-the-hyperparameters-of-the-surrogate-model-are-fixed\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-gp-sample-tasks-where-the-hyperparameters-of-the-surrogate-model-are-fixed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor GP sample tasks (where the hyperparameters of the surrogate model are fixed):\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"gp_task(path, seed, approach, dim);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003epath\u003c/code\u003e is the path to the experiment to run, \u003ccode\u003eseed\u003c/code\u003e is the seed to run, \u003ccode\u003eapproach\u003c/code\u003e is the acquisition function in question, and \u003ccode\u003edim\u003c/code\u003e, somewhat exessively, is the dimensionality of the problem. For example, one can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"gp_task(\u0027gp/gp_2dim.py\u0027, 42, \u0027JES\u0027, 2);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-for-all-other-tasks-use-synthetic_task\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-all-other-tasks-use-synthetic_task\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor all other tasks, use \u003ccode\u003esynthetic_task\u003c/code\u003e:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"synthetic_task(path, seed, approach, dim);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo to run Hartmann (6D):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"synthetic_task(\u0027synthetic/hartmann6.py\", 37, \u0027MES\u0027, 6);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEvery experiment is automatically stored in a csv in experiments/results. The recommended points, which are needed for inference regret, are all evaluated \u003cem\u003eafter\u003c/em\u003e the full run is finished. These queries are appended to the same csv.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-available-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable options\u003c/h2\u003e\n\u003cp\u003eThanks to Wang \u0026amp; Jegelka (2017), this repository comes equipped with the following acquisition functions. MES-G (shortened to just MES) and EI were used in this paper to benchmark against.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMax-value Entropy Search with Gumbel sampling (MES-G) by Wang \u0026amp; Jegelka, 2017;\u003c/li\u003e\n\u003cli\u003eMax-value Entropy Search with random features (MES-R) by Wang \u0026amp; Jegelka, 2017;\u003c/li\u003e\n\u003cli\u003eOptimization as estimation (EST) by Wang et al., 2016.\u003c/li\u003e\n\u003cli\u003eGaussian process upper confidence bound (GP-UCB) by Auer, 2002; Srinivas et al., 2010;\u003c/li\u003e\n\u003cli\u003eProbability of improvement (PI) by Kushner, 1964;\u003c/li\u003e\n\u003cli\u003eExpected improvement (EI) by Mockus, 1974\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe repository builds on \u003ca href=\"https://bitbucket.org/jmh233/codepesnips2014\" rel=\"nofollow\"\u003epredictive entropy search\u003c/a\u003e (Hern\u00e1ndez-Lobato et al., 2014).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e--Anonymous author(s)--. Joint Entropy Search for Maximally-Informed Bayesian Optimization. Under review.\u003c/li\u003e\n\u003cli\u003eWang, Zi and Jegelka, Stefanie. Max-value Entropy Search for Efficient Bayesian Optimization. International Conference on Machine Learning (ICML), 2017.\u003c/li\u003e\n\u003cli\u003eAuer, Peter. Using confidence bounds for exploitationexploration tradeoffs. Journal of Machine Learning Research, 3:397\u2013422, 2002.\u003c/li\u003e\n\u003cli\u003eSrinivas, Niranjan, Krause, Andreas, Kakade, Sham M, and Seeger, Matthias. Gaussian process optimization in the bandit setting: No regret and experimental design. In International Conference on Machine Learning (ICML), 2010.\u003c/li\u003e\n\u003cli\u003eKushner, Harold J. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. Journal of Fluids Engineering, 86(1):97\u2013106, 1964.\u003c/li\u003e\n\u003cli\u003eMockus, J. On Bayesian methods for seeking the extremum. In Optimization Techniques IFIP Technical Conference, 1974.\u003c/li\u003e\n\u003cli\u003eWang, Zi, Zhou, Bolei, and Jegelka, Stefanie. Optimization as estimation with Gaussian processes in bandit settings. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.\u003c/li\u003e\n\u003cli\u003eCatto, Erin. Box2d, a 2D physics engine for games. \u003ca href=\"http://box2d.org\" rel=\"nofollow\"\u003ehttp://box2d.org\u003c/a\u003e, 2011.\u003c/li\u003e\n\u003cli\u003ePybox2d, 2D Game Physics for Python. \u003ca href=\"http://github.com/pybox2d/pybox2d\"\u003ehttp://github.com/pybox2d/pybox2d\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eHern\u00e1ndez-Lobato, Jos\u00e9 Miguel, Hoffman, Matthew W, and Ghahramani, Zoubin. Predictive entropy search for efficient global optimization of black-box functions. In Advances in Neural Information Processing Systems (NIPS), 2014. \u003ca href=\"https://bitbucket.org/jmh233/codepesnips2014\" rel=\"nofollow\"\u003ehttps://bitbucket.org/jmh233/codepesnips2014\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHennig, Philipp and Schuler, Christian J. Entropy search for information-efficient global optimization. Journal of Machine Learning Research, 13:1809\u20131837, 2012. \u003ca href=\"http://www.probabilistic-optimization.org/Global.html\" rel=\"nofollow\"\u003ehttp://www.probabilistic-optimization.org/Global.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eJarno Vanhatalo, Jaakko Riihim\u00e4ki, Jouni Hartikainen, Pasi Jyl\u00e4nki, Ville Tolvanen, Aki Vehtari. GPstuff: Bayesian Modeling with Gaussian Processes. Journal of Machine Learning Research, 14(Apr):1175-1179, 2013.\u003c/li\u003e\n\u003cli\u003eKandasamy, Kirthevasan, Schneider, Jeff, and Poczos, Barnabas. High dimensional Bayesian optimisation and bandits via additive models. In International Conference on Machine Learning (ICML), 2015.\u003c/li\u003e\n\u003cli\u003eWang, Zi, Li, Chengtao, Jegelka, Stefanie, and Kohli, Pushmeet. Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. International Conference on Machine Learning (ICML), 2017.\u003c/li\u003e\n\u003cli\u003eWestervelt, Eric R, Grizzle, Jessy W, Chevallereau, Christine, Choi, Jun Ho, and Morris, Benjamin. Feedback control of dynamic bipedal robot locomotion, volume 28. CRC press, 2007.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"misc/images/fast-downward.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" href=\"#tested-software-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1656006365.0 + "updated_at": 1643028024.0 }, { "data_format": 2, - "description": null, + "description": "Predicts time series for SARS-CoV-2 lineages", "filenames": [ - "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.longread", - "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.htsbox", - "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.quickmerge" + "Singularity.covate" ], - "full_name": "ZhangZhenmiao/metagenome_assembly", + "full_name": "Pathogen-Genomics-Cymru/covate", + "latest_release": "v1.0.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/Pathogen-Genomics-Cymru/covate/workflows/Covate-CI/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/Pathogen-Genomics-Cymru/covate/workflows/Covate-CI/badge.svg\" alt=\"Build Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-covate\" class=\"anchor\" href=\"#covate\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecovate\u003c/h1\u003e\n\u003cp\u003eCovate uses the COG-UK metadata to forecast the time series for lineages of SARS-CoV-2 that are common to a specified list of regions. It can also be used to investigate the likelihood of lineages being imported between regions.\u003c/p\u003e\n\u003cp\u003eCovate consists of three analyses:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePREDICT, --predict ,-p\u003c/strong\u003e \u003cbr\u003e\nCovate can forecast the time series of sequenced cases for lineages that are common to all the regions. The selection of either a VAR or VECM model is automated on a per lineage basis from the results of a cointegration test. The selection of parameters for the chosen model is also automated.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eVALIDATE, --validate, -v\u003c/strong\u003e \u003cbr\u003e\nCovate can also build validation forecasts for existing metadata. For example, the validation forecast from 30/8/2021 would be a replicate of the prediction forecast from 16/8/2021 (when running with default parameters). The validation forecast is plotted against the actual time series.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCROSS-CORRELATION, --cross-correlation, -c\u003c/strong\u003e \u003cbr\u003e\nCovate can run a cross-correlation analysis that investigates the likelihood of lineages of SARS-CoV-2 being imported between the regions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eThe recommended Python versions for running covate are 3.7.x - 3.9.x (other versions may work but are untested).\u003c/p\u003e\n\u003cp\u003eFor stability, it is recommended you download the latest \u003ca href=\"https://github.com/Pathogen-Genomics-Cymru/covate/releases\"\u003erelease\u003c/a\u003e and install using \u003ccode\u003epython setup.py install\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo install the very latest updates (as on main branch) you can use pip with git+:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install git+https://github.com/Pathogen-Genomics-Cymru/covate.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eTo run all three analyses with default arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecovate -i metadata.csv -o output_dir -p -v -c\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA full description of the available arguments and their default values can be found below.\u003c/p\u003e\n\u003cp\u003eHelp message:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: covate [-h] -i METADATA -o OUTPUT [-r REGIONS] [-a ADM]\n [-l LINEAGETYPE] [-t TIMEPERIOD] [-e ENDDATE] [-p] [-v] [-c]\n [-f PRIMARYREGION] [-m MAXLAGS] [-n NSTEPS]\n\noptional arguments:\n -h, --help show this help message and exit\n -i METADATA, --input-csv METADATA\n Input metadata csv, expects columns: cog_id,\n adm1/adm2, sample_date, lineage/uk_lineage\n -o OUTPUT, --output-dir OUTPUT\n Output directory for the results\n -r REGIONS, --region-list REGIONS\n Input list of regions to compare, e.g. Wales, England\n -a ADM, --adm ADM Select either adm1 or adm2\n -l LINEAGETYPE, --lineage-type LINEAGETYPE\n Select either lineage or uk_lineage\n -t TIMEPERIOD, --time-period TIMEPERIOD\n Select time period in weeks to take from metadata\n -e ENDDATE, --end-date ENDDATE\n Select end date to take from metadata. Format: d/m/Y\n -p, --predict Run prediction forecast\n -v, --validate Run validation forecast\n -c, --cross-correlation\n Run cross-correlation analysis\n -f PRIMARYREGION, --primary-region PRIMARYREGION\n Region of primary interest for cross-correlation\n -m MAXLAGS, --max-lags MAXLAGS\n Maximum number of lags to investigate\n -n NSTEPS, --n-steps NSTEPS\n Number of days to predict\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-arguments\" class=\"anchor\" href=\"#arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArguments\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--input-csv\u003c/strong\u003e \u003cbr\u003e Input metadata csv. The following columns are required: \u003cstrong\u003ecog_id, adm1/adm2, sample_date, lineage/uk_lineage\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--output-dir\u003c/strong\u003e \u003cbr\u003e Output directory for results\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--region-list\u003c/strong\u003e \u003cbr\u003e Input list of regions to compare. Default \u003cstrong\u003eWales, England\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--adm\u003c/strong\u003e \u003cbr\u003e Select adm the regions belong to (adm1 or adm2). Default \u003cstrong\u003eadm1\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--lineage-type\u003c/strong\u003e \u003cbr\u003e Select whether to compare global or uk lineages (lineage or uk_lineage). Default \u003cstrong\u003euk_lineage\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--time-period\u003c/strong\u003e \u003cbr\u003e Select time period in weeks to take from the input metadata csv. Default \u003cstrong\u003e12\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--end-date\u003c/strong\u003e \u003cbr\u003e The end date of the time period to take from the input metadata csv. Expected format is d/m/Y, e.g. 31/7/2021. Default \u003cstrong\u003elatest date in the metadata -7 days\u003c/strong\u003e (to account for lag in data)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--predict\u003c/strong\u003e \u003cbr\u003e If specified, prediction forecasts will be created\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--validate\u003c/strong\u003e \u003cbr\u003e If specified, validation forecasts will be created\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--cross-correlation\u003c/strong\u003e \u003cbr\u003e If specifed, cross-correlation analysis will be run\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--primary-region\u003c/strong\u003e \u003cbr\u003e Primary region for cross-correlation analysis. Default \u003cstrong\u003eWales\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--max-lags\u003c/strong\u003e \u003cbr\u003e Select maximum number of lags to investigate. Default \u003cstrong\u003e14\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--n-steps\u003c/strong\u003e \u003cbr\u003e Number of days to predict. Default \u003cstrong\u003e14\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-workflow\" class=\"anchor\" href=\"#workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Pathogen-Genomics-Cymru/covate/blob/main/covate-workflow.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"600\" src=\"https://github.com/Pathogen-Genomics-Cymru/covate/raw/main/covate-workflow.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003eA date-stamped output directory is created with sub-directories for each common lineage and a cross-correlation sub-directory. At the top level you will find a csv of the timeseries and summary error log file(s) for prediction and validation (provided --predict and --validate). The cross-correlation sub-directory contains multiple plots and csvs from the cross-correlation analysis (provided --cross-correlation). In a lineage sub-directory you should find the following directories and plots:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eprediction\u003c/strong\u003e The forecasted time series for each region. This directory will be empty if --predict is not specified. If --predict has been specified and directory is empty then the forecast has failed to run (check logs/prediction for the error log).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003evalidation\u003c/strong\u003e A validation forecast for each region (plots the time series for the last nsteps prior to the set end date with a forecast). This directory will be empty if --validate is not specified. If --validate has been specified and the directory is empty then the forecast has failed to run (check logs/validation for the error log).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003elogs\u003c/strong\u003e There are separate log files for prediction and validation. Log files $lineage_model.txt contain information on the built models. If there are any errors raised for the lineage then an error log $lineage_error.txt will also be generated. There are also csvs of the forecasted time series values.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eadditional-plots\u003c/strong\u003e Time series for the lineage and ACF plots for each region. There may be additional VAR plots if relevant.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-error-log\" class=\"anchor\" href=\"#error-log\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eError Log\u003c/h3\u003e\n\u003cp\u003eThere are separate error log files for prediction and validation. The error logs will likely contain ERROR and WARN messages for some lineages. ERROR messages indicate a fatal error where the code was unable to build a model for a lineage due to poor quality data. WARN messages indicate a non-fatal error, in this case the model should build for a lineage, but the message may indicate that the model might not be accurate (e.g. A WARN message is recorded if causality is not found).\u003c/p\u003e\n", + "stargazers_count": 1, + "subscribers_count": 2, + "topics": [ + "covid-19", + "time-series-analysis", + "data-science", + "data-visualization" + ], + "updated_at": 1639428014.0 + }, + { + "data_format": 2, + "description": "Experiment with Singularity Containers for MPI apps", + "filenames": [ + "src/Singularity.def" + ], + "full_name": "nahkbce2/myContainerSandBox", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-benchmarking-de-novo-assembly-methods-on-metagenomic-sequencing-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#benchmarking-de-novo-assembly-methods-on-metagenomic-sequencing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarking de novo assembly methods on metagenomic sequencing data\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-assemblers-evaluated\" class=\"anchor\" aria-hidden=\"true\" href=\"#assemblers-evaluated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAssemblers evaluated\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-short-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#short-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShort-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaSPdes \u003ccode\u003eassembly_scripts/metaspades.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMEGAHIT \u003ccode\u003eassembly_scripts/megahit.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-linked-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#linked-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinked-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ecloudSPAdes \u003ccode\u003eassembly_scripts/cloudspades.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAthena \u003ccode\u003eassembly_scripts/athena.sh \u0026lt;short-read ssembly\u0026gt; \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-long-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#long-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLong-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaFlye \u003ccode\u003eassembly_scripts/metaflye.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanu \u003ccode\u003eassembly_scripts/canu.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eLathe \u003ccode\u003eassembly_scripts/lathe.sh \u0026lt;long_reads\u0026gt; \u0026lt;short_reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShasta \u003ccode\u003eassembly_scripts/shasta.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMECAT2 \u003ccode\u003eassembly_scripts/mecat2.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNECAT \u003ccode\u003eassembly_scripts/necat.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ewtdbg2 \u003ccode\u003eassembly_scripts/wtdbg2.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-hybrid-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#hybrid-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHybrid assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaFlye-subassemblies \u003ccode\u003eassembly_scripts/metaflye-subassemblies.sh \u0026lt;short-read assembly\u0026gt; \u0026lt;long-read assembly\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDBG2OLC \u003ccode\u003eassembly_scripts/dbg2olc.sh \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOPERA-MS \u003ccode\u003eassembly_scripts/opera-ms.sh \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOPERA-LG \u003ccode\u003eassembly_scripts/opera-lg.sh \u0026lt;short-read assembly\u0026gt; \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-time-and-memory\" class=\"anchor\" aria-hidden=\"true\" href=\"#time-and-memory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTime and memory\u003c/h2\u003e\n\u003cp\u003eTime and memory consumed are measured by adding \u003ccode\u003e/usr/bin/time -v\u003c/code\u003e before the above commands.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mycontainersandbox\" class=\"anchor\" href=\"#mycontainersandbox\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emyContainerSandBox\u003c/h1\u003e\n\u003cp\u003eExperiment with Singularity Containers for MPI apps\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1658500295.0 + "updated_at": 1638466785.0 }, { "data_format": 2, - "description": "Application Lifecycle Deployment Engine", + "description": "CUT\u0026RUN pipeline for mm10 genome", "filenames": [ - "SingularityTests.md" + "Singularity.mm10v1.centos" ], - "full_name": "TANGO-Project/alde", + "full_name": "ertheisen/ferncanyon_centos", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-application-lifecycle-deployment-engine-alde\" class=\"anchor\" aria-hidden=\"true\" href=\"#application-lifecycle-deployment-engine-alde\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApplication Lifecycle Deployment Engine (ALDE)\u003c/h1\u003e\n\u003cp\u003e\u00a9 Atos Spain S.A. 2016\u003c/p\u003e\n\u003cp\u003eApplication Lifecycle Deployment Engine (ALDE) is a component of the European Project TANGO (\u003ca href=\"http://tango-project.eu\" rel=\"nofollow\"\u003ehttp://tango-project.eu\u003c/a\u003e ).\u003c/p\u003e\n\u003cp\u003eALDE is distributed under a \u003ca href=\"https://www.gnu.org/licenses/agpl-3.0.txt\" rel=\"nofollow\"\u003eGNU AFFERO GENERAL PUBLIC LICENSE\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eALDE is responsible for the workload scheduling and the management of the application life-cycle while it is executed. ALDE will take the application source code, packetize for different heterogeneous architectures configurations and, if possible, deploy it via a TANGO Device Supervisor and manage the application execution.\u003c/p\u003e\n\u003cp\u003eMore in detail each one of the previous steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eCompilation\u003c/strong\u003e - ALDE is able to compile the application in different configurations depending of the selected heterogeneous architectures. The result will be a set of binaries optimal compiled for specific hardware architectures.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePacketization\u003c/strong\u003e - ALDE, once the application has been compiled, can packetize it. For the moment it only supports typical tar.gz files and \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e containers.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeployment\u003c/strong\u003e - ALDE is able to automatically deploy an application into a TANGO compatible Device Supervisor. It will launch the execution and monitor it. It will also support adaptations interactions if used in combination with the \u003ca href=\"https://github.com/TANGO-Project/self-adaptation-manager\"\u003eTANGO Self-Adaptation Manager\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation Guide\u003c/h2\u003e\n\u003cp\u003eThis guide it is divided into two different guides, one specific to create an environment for development and another one to just run and use ALDE.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation-for-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-for-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation for development\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h4\u003e\n\u003cp\u003eTo develop for ALDE we need to install two pieces of software:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.python.org\" rel=\"nofollow\"\u003ePython 3.6 or higher\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://virtualenv.pypa.io/en/stable/\" rel=\"nofollow\"\u003eVirtualenv\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-installation-and-configuration-procedure\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-and-configuration-procedure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and configuration procedure\u003c/h4\u003e\n\u003cp\u003eTo develop ALDE it is necessary to create a \u003ca href=\"http://docs.python-guide.org/en/latest/dev/virtualenvs/\" rel=\"nofollow\"\u003ePython Virtualenv\u003c/a\u003e (depending on your installation of Python pip3 command can be called pip):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip3 install virtualenv\nCollecting virtualenv\n Downloading virtualenv-15.0.3-py2.py3-none-any.whl (3.5MB)\n 100% |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3.5MB 398kB/s\nInstalling collected packages: virtualenv\nSuccessfully installed virtualenv-15.0.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter that, you need to create a virtualenv for you to develop:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ virtualenv venv\nUsing base prefix \u0027/Library/Frameworks/Python.framework/Versions/3.5\u0027\nNew python executable in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/venv/bin/python3.5\nAlso creating executable in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/venv/bin/python\nInstalling setuptools, pip, wheel...done.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow it time to install PyBuilder:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFirst we activate the virtualenv:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ source venv/bin/activate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNow we install PyBuilder using Pip:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install pybuilder\nCollecting pybuilder\n Using cached PyBuilder-0.11.9.tar.gz\nRequirement already satisfied: pip\u0026gt;=7.0 in ./venv/lib/python3.5/site-packages (from pybuilder)\nCollecting tblib (from pybuilder)\n Using cached tblib-1.3.0-py2.py3-none-any.whl\nRequirement already satisfied: wheel in ./venv/lib/python3.5/site-packages (from pybuilder)\nBuilding wheels for collected packages: pybuilder\n Running setup.py bdist_wheel for pybuilder ... done\n Stored in directory: /Users/davidgp/Library/Caches/pip/wheels/04/9c/b3/d2d2194e8911818abdfa1c3c47501a64602714415af28d8da8\nSuccessfully built pybuilder\nInstalling collected packages: tblib, pybuilder\nSuccessfully installed pybuilder-0.11.9 tblib-1.3.0\n(venv)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow it is possible to compile the project:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall first the dependencies:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ pyb install_dependencies\nPyBuilder version 0.11.9\nBuild started at 2016-11-11 14:55:00\n------------------------------------------------------------\n[INFO] Building alde version 1.0.dev0\n[INFO] Executing build in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde\n[INFO] Going to execute task install_dependencies\n[INFO] Installing all dependencies\n[INFO] Processing batch dependency \u0027mockito\u0027\n------------------------------------------------------------\nBUILD SUCCESSFUL\n------------------------------------------------------------\nBuild Summary\n Project: alde\n Version: 1.0.dev0\n Base directory: /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde\n Environments:\n Tasks: install_dependencies [9623 ms]\nBuild finished at 2016-11-11 14:55:10\nBuild took 9 seconds (9637 ms)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNow you can build the project (if you are using Windows, probably the coverage task is going to fail)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ pyb\nPyBuilder version 0.11.9\nBuild started at 2016-11-11 14:57:03\n------------------------------------------------------------\n[INFO] Building alde version 1.0.dev0\n[INFO] Executing build in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde\n[INFO] Going to execute task publish\n[INFO] Installing plugin dependency coverage\n[INFO] Installing plugin dependency unittest-xml-reporting\n[INFO] Running unit tests\n[INFO] Executing unit tests from Python modules in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/src/unittest/python\n[INFO] Executed 1 unit tests\n[INFO] All unit tests passed.\n[INFO] Building distribution in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/target/dist/alde-1.0.dev0\n[INFO] Copying scripts to /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/target/dist/alde-1.0.dev0/scripts\n[INFO] Writing setup.py as /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/target/dist/alde-1.0.dev0/setup.py\n[INFO] Collecting coverage information\n[WARN] coverage_branch_threshold_warn is 0 and branch coverage will not be checked\n[WARN] coverage_branch_partial_threshold_warn is 0 and partial branch coverage will not be checked\n[INFO] Running unit tests\n[INFO] Executing unit tests from Python modules in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/src/unittest/python\n[INFO] Executed 1 unit tests\n[INFO] All unit tests passed.\n[WARN] Module \u0027__init__\u0027 was not imported by the covered tests\n[INFO] Overall coverage is 94%\n[INFO] Overall coverage branch coverage is 100%\n[INFO] Overall coverage partial branch coverage is 100%\n[INFO] Building binary distribution in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/target/dist/alde-1.0.dev0\n------------------------------------------------------------\nBUILD SUCCESSFUL\n------------------------------------------------------------\nBuild Summary\n Project: alde\n Version: 1.0.dev0\n Base directory: /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde\n Environments:\n Tasks: prepare [2407 ms] compile_sources [0 ms] run_unit_tests [40 ms] package [3 ms] run_integration_tests [0 ms] verify [134 ms] publish [616 ms]\nBuild finished at 2016-11-11 14:57:06\nBuild took 3 seconds (3219 ms)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDone!\u003c/p\u003e\n\u003cp\u003eNow, remember, each time you need to start to develop, initalize the virtualenv:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source venv/bin/activate\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-tests-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#tests-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTests with Singularity\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eInstall Singularity - \u003ca href=\"SingularityTests.md\"\u003eView doc\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003ca id=\"user-content-build-status-from-travis-ci\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-status-from-travis-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild status from Travis-CI\u003c/h4\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/TANGO-Project/alde\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8c8f4e8fc40340ef1f6ece28cd4cddc05eda4a013da60298c68c64fad39796e/68747470733a2f2f7472617669732d63692e6f72672f54414e474f2d50726f6a6563742f616c64652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/TANGO-Project/alde.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-sonarqube-reports\" class=\"anchor\" aria-hidden=\"true\" href=\"#sonarqube-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSonarQube reports:\u003c/h4\u003e\n\u003cp\u003eSonarQube ( \u003ca href=\"http://www.sonarqube.org/\" rel=\"nofollow\"\u003ehttp://www.sonarqube.org/\u003c/a\u003e ) reports for this project are available at: \u003ca href=\"https://sonarqube.com/dashboard?id=tango%3Aalde\" rel=\"nofollow\"\u003ehttps://sonarqube.com/dashboard?id=tango%3Aalde\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation-for-running-the-service\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-for-running-the-service\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation for running the service\u003c/h3\u003e\n\u003cp\u003eIn this case, we are going to detail how to run the application directly using Python. It is possible to run it behind a proxy or webserver, to do so, please, check \u003ca href=\"http://flask.pocoo.org/docs/0.12/deploying/\" rel=\"nofollow\"\u003ethis guides\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-configuring-the-service\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuring-the-service\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguring the service\u003c/h4\u003e\n\u003cp\u003eALDE employs a \u003ca href=\"https://www.sqlite.org/\" rel=\"nofollow\"\u003eSQLite\u003c/a\u003e database server that needs to be configured together with the port were the service it is going to be listen. That configuration can be done editing the file alde_configuration.ini that contains these two variables:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[DEFAULT]\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eSQL_LITE_URL\u003c/span\u003e = sqlite:////tmp/test.db\n\u003cspan class=\"pl-k\"\u003ePORT\u003c/span\u003e = 5000\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo install it, it is necessary to execute the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo launch the service we need to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ python app.py \n2017-07-18 09:16:02,812 root INFO Loading configuration\n[]\n\u0026lt;Section: DEFAULT\u0026gt;\n2017-07-18 09:16:02,813 root INFO Starting ALDE\n/Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/venv/lib/python3.6/site-packages/flask_sqlalchemy/__init__.py:839: FSADeprecationWarning: SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and will be disabled by default in the future. Set it to True or False to suppress this warning.\n \u0027SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and \u0027\n2017-07-18 09:16:02,937 apscheduler.scheduler INFO Adding job tentatively -- it will be properly scheduled when the scheduler starts\n2017-07-18 09:16:02,937 apscheduler.scheduler INFO Adding job tentatively -- it will be properly scheduled when the scheduler starts\n2017-07-18 09:16:02,938 apscheduler.scheduler INFO Adding job tentatively -- it will be properly scheduled when the scheduler starts\n2017-07-18 09:16:02,940 apscheduler.scheduler INFO Added job \"check_nodes_in_db_for_on_line_testbeds\" to job store \"default\"\n2017-07-18 09:16:02,940 apscheduler.scheduler INFO Added job \"update_node_info\" to job store \"default\"\n2017-07-18 09:16:02,940 apscheduler.scheduler INFO Added job \"update_cpu_node_info\" to job store \"default\"\n2017-07-18 09:16:02,940 apscheduler.scheduler INFO Scheduler started\n2017-07-18 09:16:02,986 werkzeug INFO * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter it we could verify it works just by execution the following (in this example we are using \u003ca href=\"https://curl.haxx.se/\" rel=\"nofollow\"\u003eCurl\u003c/a\u003e but you could use another REST/http client):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl localhost:5000/api/v1/testbeds\n{\n \"num_results\": 0, \n \"objects\": [], \n \"page\": 1, \n \"total_pages\": 0\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage Guide\u003c/h2\u003e\n\u003cp\u003eAlthough a CLI client is planned, for the moment ALDE offers a REST interface.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rest-api-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#rest-api-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREST API documentation\u003c/h3\u003e\n\u003cp\u003eThe rest api is fully documented here: ( \u003ca href=\"https://jsapi.apiary.io/previews/applicationlifecycledeploymentengine/reference/0/testbed\" rel=\"nofollow\"\u003ehttps://jsapi.apiary.io/previews/applicationlifecycledeploymentengine/reference/0/testbed\u003c/a\u003e )\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-example-scenarios\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-scenarios\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample scenarios\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-creating-an-application\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-an-application\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating an application\u003c/h4\u003e\n\u003cp\u003eListing all applications available:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl http://localhost:5000/api/v1/applications\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCreating an application\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -X POST -H\u0027Content-type: application/json\u0027 http://127.0.0.1:5000/api/v1/applications -d\u0027{ \"name\": \"my_app\" }\u0027\n{\n \"executables\": [],\n \"execution_scripts\": [],\n \"id\": 1,\n \"name\": \"my_app\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUploading zip file with the source code of the application. Pay attention to the two variables: compilation_type and compilation_script\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -X POST -F \"file=@test.zip\" http://localhost:5000/api/v1/upload/1?compilation_type=SINGULARITY:PM\\\u0026amp;compilation_script=./build.sh\nfile upload for app with id: 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSeeing the status of the compilation (executable section):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl http://localhost:5000/api/v1/applications/1\n{\n \"executables\": [\n {\n \"application_id\": 1,\n \"compilation_script\": \"compilation.sh\",\n \"compilation_type\": \"singularity:pm\",\n \"executable_file\": null,\n \"id\": 1,\n \"source_code_file\": \"f5a8e16b-6c36-4092-97cb-6081374d9b29.zip\",\n \"status\": \"NOT_COMPILED\"\n }\n ],\n \"execution_scripts\": [],\n \"id\": 1,\n \"name\": \"my_app\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-adding-a-new-slurm-type-testbed-that-you-can-connect-via-ssh-protocol\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-a-new-slurm-type-testbed-that-you-can-connect-via-ssh-protocol\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding a new SLURM type testbed that you can connect via SSH protocol\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003ecurl localhost:5000/api/v1/testbeds -X POST -H\u0027Content-type: application/json\u0027 -d\u0027{ \"name\": \"slurm_testbed\", \"on_line\": true, \"category\": \"SLURM\", \"protocol\": \"SSH\", \"endpoint\": \"user@ssh.com\"}\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-relation-to-other-tango-components\" class=\"anchor\" aria-hidden=\"true\" href=\"#relation-to-other-tango-components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRelation to other TANGO components\u003c/h2\u003e\n\u003cp\u003eALDE can be used as an standalone tool in TANGO, it will allow to compile application for different targeted heterogenous architectures in an optimize way and with different configurations of heterogenous devices, but its fully potential it is with other TANGO components:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eProgramming model and IDE tools\u003c/strong\u003e - TANGO Programming Model can connect with ALDE to submit the code for compilation and packetization. Also it could be intereact with ALDE to submit the application directly to a TANGO compatible device supervisor.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDevice Supervisor\u003c/strong\u003e - ALDE can interact with a on-line testbed that has installed a TANGO device supervisor on it. This will allow to automatically deploy diferent configurations of the application and execute it, monitoring the execution and extract back the results.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSelf-Adaptation Manager\u003c/strong\u003e - ALDE will provide intefaces for the Self-Adaptation Manager to change the configuration of an application to optimize its execution in a TANGO compatible testbed.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ferncanyonhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#ferncanyonhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eferncanyon.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1592993443.0 + "updated_at": 1572448081.0 }, { "data_format": 2, - "description": "R package interface to GCAE", + "description": "A Nextflow wrapped workflow for generating the mutation profiles of SARS-CoV-2 genomes (Variants of Concern and Variants of Interest). Workflow is developed in collaboration with COVID-MVP (https://github.com/cidgoh/COVID-MVP) which can be used to visualize the mutation profiles and functional annotations.", "filenames": [ - "Singularity" + "environments/Singularity" ], - "full_name": "AJResearchGroup/gcaer", - "latest_release": "v0.6.5", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-gcaer\" class=\"anchor\" aria-hidden=\"true\" href=\"#gcaer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egcaer\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/gcaer/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/gcaer/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/gcaer/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/gcaer/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3b7a6e924b6bcb32c8b3da0bd2aae7b7fe775432f4a3c611efa42f2ae77dee9b/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f67636165722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/gcaer/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/gcaer/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/gcaer/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/gcaer/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3159ac77b3ba0a6725db4cdb3e85629c887a8ab8cf3c360fb92d05388280ff1a/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f67636165722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/gcaer/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWork with \u003ca href=\"https://github.com/richelbilderbeek/genocae/tree/Pheno\"\u003eGCAE\u003c/a\u003e from R.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/richelbilderbeek/genocae/tree/Pheno\"\u003eGCAE GitHub repository\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003egcaer\u003c/code\u003e is not on CRAN yet. To install \u003ccode\u003egcaer\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(remotes)\ninstall_github(\"richelbilderbeek/gcaer\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis assumes you have the \u003ccode\u003eremotes\u003c/code\u003e package installed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-gcae-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-gcae-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall GCAE versions\u003c/h2\u003e\n\u003cp\u003eTo install GCAE:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(gcaer)\ninstall_gcae()\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h2\u003e\n\u003cp\u003eGet the GCAE help text:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(gcaer)\nget_gcae_help_text()\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-gcae\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-gcae\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning GCAE\u003c/h3\u003e\n\u003cp\u003eRun GCAE:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(gcaer)\nrun_gcae(\"--help\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-full-experiment\" class=\"anchor\" aria-hidden=\"true\" href=\"#full-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFull experiment\u003c/h3\u003e\n\u003cp\u003eInstead of using the multiple steps by \u003ccode\u003eGenoCAE\u003c/code\u003e,\n\u003ccode\u003edo_gcae_experiment\u003c/code\u003e does all of these for you.\u003c/p\u003e\n\u003cp\u003eHere is an example of a full experiment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Create the parameters for the experiment\ngcae_experiment_params \u0026lt;- create_gcae_experiment_params(\n gcae_options = create_gcae_options(),\n gcae_setup = create_test_gcae_setup(\n model_id = \"M0\",\n superpops = get_gcaer_filename(\"gcae_input_files_1_labels.csv\"),\n pheno_model_id = \"p0\"\n ),\n analyse_epochs = c(1, 2),\n metrics = \"f1_score_3,f1_score_5\"\n)\n\n# Do the experiment\ngcae_experiment_results \u0026lt;- do_gcae_experiment(\n gcae_experiment_params = gcae_experiment_params\n)\n\n# Save the experiment\u0027s results\nsave_gcae_experiment_results(\n gcae_experiment_results = gcae_experiment_results,\n folder_name = gcae_experiment_params$gcae_setup$trainedmodeldir\n)\n\n# Create the plots for the experiment\u0027s results\ncreate_plots_from_gcae_experiment_results(\n folder_name = gcae_experiment_params$gcae_setup$trainedmodeldir\n)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h3\u003e\n\u003cp\u003eTo do the full GCAE workflow, a \u003ccode\u003egcae_setup\u003c/code\u003e is needed,\nfrom which the respective \u003ccode\u003egcae_[x]\u003c/code\u003e functions are called,\nwhere \u003ccode\u003e[x]\u003c/code\u003e matches the first GCAE CLI argument (for\nexample, use \u003ccode\u003egcaer\u003c/code\u003e\u0027s \u003ccode\u003egcae_train\u003c/code\u003e to do the same as \u003ccode\u003erun_gcae.py train\u003c/code\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egcae_setup \u0026lt;- create_gcae_setup(\n datadir = file.path(get_gcae_folder(), \"example_tiny/\"),\n data = \"issue_6_bin\",\n model_id = \"M1\",\n pheno_model_id = \"p2\",\n superpops = file.path(datadir, \"HO_superpopulations\")\n)\n\n# 2. Train, approx 3 mins\ntrain_filenames \u0026lt;- gcae_train(\n gcae_setup = gcae_setup,\n epochs = 3,\n save_interval = 1\n)\n\n# 3. Project\nproject_filenames \u0026lt;- gcae_project(\n gcae_setup = gcae_setup\n)\nproject_results \u0026lt;- parse_project_files(project_filenames)\n\n# 4. Evaluate\nevaluate_filenames \u0026lt;- gcae_evaluate(\n gcae_setup,\n metrics = \"f1_score_3,f1_score_5\",\n epoch = 3\n)\n\nevaluate_results \u0026lt;- parse_evaluate_filenames(\n evaluate_filenames, \n epoch = 3\n)\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-hidden=\"true\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinks\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/richelbilderbeek/genocae/tree/Pheno\"\u003eGCAE GitHub repository\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "cidgoh/nf-ncov-voc", + "latest_release": "v1.0.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nf-ncov-voc\" class=\"anchor\" href=\"#nf-ncov-voc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-ncov-voc\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/26be5f07b2d4aa0e46337e9792e3b32071f4721c97661c70657eeb206d577991/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f7725323044534c322d25453225383925413532312e30342e302d3233616136322e7376673f6c6162656c436f6c6f723d303030303030\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A521.04.0-23aa62.svg?labelColor=000000\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a6ebc5359ca9f5f51fe0aa49c2d8f235c19f9c93c7534904cdfe10f7675bb56e/687474703a2f2f696d672e736869656c64732e696f2f62616467652f72756e253230776974682d636f6e64612d3345423034393f6c6162656c436f6c6f723d303030303030266c6f676f3d616e61636f6e6461\" alt=\"run with conda\" data-canonical-src=\"http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000\u0026amp;logo=anaconda\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d1c5b30eaa6c028ee72fd590dacad176e78296c4deb2e0fb3bfebc84bc45e6a2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72756e253230776974682d646f636b65722d3064623765643f6c6162656c436f6c6f723d303030303030266c6f676f3d646f636b6572\" alt=\"run with docker\" data-canonical-src=\"https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000\u0026amp;logo=docker\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0b0568e8f684f1ea04320511f0635c70c144cad7fb7daec19a8e605f02933b01/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72756e253230776974682d73696e67756c61726974792d3164333535632e7376673f6c6162656c436f6c6f723d303030303030\" alt=\"run with singularity\" data-canonical-src=\"https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003enf-ncov-voc\u003c/strong\u003e is a bioinformatics analysis workflow used for\nperforming variant calling on SARS-CoV-2 genomes to identify and\nprofile mutations in Variants of Concern (VOCs), Variants of\nInterest (VOIs) and Variants under Monitoring (VUMs). This workflow has\nfour main stages - \u003cstrong\u003ePreprocessing\u003c/strong\u003e, \u003cstrong\u003eGenomic Analysis (Variant\nCalling)\u003c/strong\u003e , \u003cstrong\u003eFunctional Annotation\u003c/strong\u003e and \u003cstrong\u003eSurveillance\u003c/strong\u003e.\n\u003cstrong\u003enf-ncov-voc\u003c/strong\u003e workflow can be used in combination with an interactive\nvisualization tool \u003ca href=\"https://github.com/cidgoh/COVID-MVP\"\u003eCOVID-MVP\u003c/a\u003e\nor as a stand-alone high-throughput analysis tool to produce\nmutation profiles and surveillance reports.\u003c/p\u003e\n\u003cp\u003eAs an input, \u003cstrong\u003enf-ncov-voc\u003c/strong\u003e workflow requires SARS-CoV-2 consensus\nsequences in \u003ccode\u003eFASTA\u003c/code\u003e format and Metadata file in \u003ccode\u003eTSV\u003c/code\u003e format.\nSequences in pre-processing stage are filtered using Metadata\nvariables, quality filtered and assigned lineages. Sequences\nassigned as VOCs, VOIs and VUMs are then mapped to SARS-CoV-2 genome,\nvariant called and normalized in Genomic Analysis (Variant Calling)\nmodule. Mutations called are then annotated in several stages\nincluding flagging the potential contaminated sites, mutation\nannotation, genomic feature annotation, mature peptide annotation\nand finally respective biological functional impact using the\nmanually curated effort \u003ca href=\"https://github.com/nodrogluap/pokay\"\u003ePokay\u003c/a\u003e.\n(lead by Paul Gordon \u003ca href=\"https://github.com/nodrogluap\"\u003e@nodrogluap\u003c/a\u003e).\nFinally, in the surveillance module, these functional profiles are\nsummarized using functional indicators to highlight key functions\nand mutations responsible for them for e.g. \u003cstrong\u003eP618H\u003c/strong\u003e role in\n\u003cem\u003econvalescent plasma escape\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe workflow is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e-\n\u003ca href=\"https://www.nextflow.io/docs/latest/dsl2.html\" rel=\"nofollow\"\u003eDSL2\u003c/a\u003e, a workflow\ntool to run tasks across multiple compute infrastructures in a very\nportable manner. It can use \u003ccode\u003econda\u003c/code\u003e/\u003ccode\u003eDocker\u003c/code\u003e/\u003ccode\u003eSingularity\u003c/code\u003e\ncontainers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cp\u003eA detailed structure and each module of the workflow is presented\nbelow in the dataflow diagram\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-nf-ncov-voc-dataflow\" class=\"anchor\" href=\"#nf-ncov-voc-dataflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-ncov-voc Dataflow\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"figs/COVIDMVP.drawio.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"figs/COVIDMVP.drawio.png\" alt=\"DataFlow\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pre-processing\" class=\"anchor\" href=\"#pre-processing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-Processing\u003c/h3\u003e\n\u003cp\u003eThis module offers two ways to get lineage information for each\ngenome in \u003ccode\u003eFASTA\u003c/code\u003e file and listed respectively in Metadata file\nunless a column \u003ccode\u003epango_lineage\u003c/code\u003e is already available in which case\nboth options can be skipped. First option is to use\n\u003ca href=\"https://github.com/cov-lineages/pangolin\"\u003ePANGOLIN\u003c/a\u003e to assign\nlineages and merge the metadata with pangolin report. This\nstep can be skipped by passing \u003ccode\u003e--skip_pangolin\u003c/code\u003e. The second option\nis to map input metadata to \u003ca href=\"https://www.gisaid.org\" rel=\"nofollow\"\u003eGISAID\u003c/a\u003e metadata\nfile (which can be provided by \u003ccode\u003e--gisaid_metadata\u003c/code\u003e parameter) if the\ngenomes are available in GISAID. This option is faster and\ncomputationally less expensive, though limits to only genomes\navailable in GISAID. This option can be skipped by\nusing \u003ccode\u003e--skip_mapping\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-genomic-analysis\" class=\"anchor\" href=\"#genomic-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenomic Analysis\u003c/h3\u003e\n\u003cp\u003eThis module currently supports two different modes - \"\u003cem\u003ereference\u003c/em\u003e\" \u0026amp;\n\"\u003cem\u003euser\u003c/em\u003e\" which can be passed with \u003ccode\u003e--mode reference\u003c/code\u003e or \u003ccode\u003e--mode user\u003c/code\u003e. By default, \u003ccode\u003e--mode reference\u003c/code\u003e is activated which allows you\nto build a reference library for each lineage and subsequently each\nvariant for comparative analysis. This mode can take \u003ccode\u003eFASTA\u003c/code\u003e file\nwith multiple genomes (\u003cstrong\u003erecommended\u003c/strong\u003e \u0026amp; \u003cstrong\u003edefault\u003c/strong\u003e) or single\ngenome with a metadata file that should have one column atleast\n(\u003ccode\u003epango_lineage\u003c/code\u003e) as minimal metadata\n(see \u003ca href=\"#workflow-summary\"\u003eWorkflow Summary\u003c/a\u003e for detailed options).\nThe workflow has numerous options for several steps. For\nexample, in \u003ccode\u003emode --reference\u003c/code\u003e user can use \u003ccode\u003eBWAMEM\u003c/code\u003e using \u003ccode\u003e--bwa\u003c/code\u003e\ninstead of \u003ccode\u003eMINIMAP2\u003c/code\u003e (\u003cem\u003edefault\u003c/em\u003e) for mapping consensus sequences to\nreference genome. Similarly, \u003ccode\u003eivar\u003c/code\u003e with parameter \u003ccode\u003e--ivar\u003c/code\u003e for\nvariant calling instead of \u003ccode\u003efreebayes\u003c/code\u003e (\u003cem\u003edefault\u003c/em\u003e) option.\nThe user mode (\u003ccode\u003e--mode user\u003c/code\u003e) is by default active when using\ninteractive visualization through\n\u003ca href=\"https://github.com/cidgoh/COVID-MVP\"\u003eCOVID-MVP\u003c/a\u003e where a user can\nupload \u003ccode\u003eGVF\u003c/code\u003e file for comparative analysis against the reference data.\nUploaded dataset can be a \u003ccode\u003eFASTA\u003c/code\u003e file or variant called \u003ccode\u003eVCF\u003c/code\u003e file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-functional-annotation\" class=\"anchor\" href=\"#functional-annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctional Annotation\u003c/h3\u003e\n\u003cp\u003eIn this module, the variant called \u003ccode\u003eVCF\u003c/code\u003e file for each lineage is\nconverted into a \u003ccode\u003eGVF\u003c/code\u003e (Genomic Variant Format) file and annotated\nwith functional information using\n\u003ca href=\"https://github.com/nodrogluap/pokay\"\u003ePokay\u003c/a\u003e. GVF is a variant of\nGFF3 format that is standardized for describing genomic mutations;\nit is used here because it can describe mutations across multiple\nrows, and because the \"#attributes\" column can store information in\ncustom key-value pairs. The key-value pairs added at this stage\ninclude for each mutation: VOC/VOI status, clade-defining status\n(for reference lineages), and functional annotations parsed using\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/bin/vcf2gvf.py\"\u003evcf2gvf.py\u003c/a\u003e\nfile written in python.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-surveillance-reports\" class=\"anchor\" href=\"#surveillance-reports\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSurveillance Reports\u003c/h3\u003e\n\u003cp\u003eDifferent \u003ccode\u003eGVF\u003c/code\u003e files for the same variant are then collated and\nsummarized into a \u003ccode\u003eTSV\u003c/code\u003e file that contains mutation prevalence,\nprofile and functional impact. Further \u003ccode\u003eTSV\u003c/code\u003e file is also summarized\nas a more human friendly and impactful surveillance report in a\n\u003ccode\u003ePDF\u003c/code\u003e format. Relevant/important indicators can be specified in the\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/assets/ncov_surveillanceIndicators/functions_df_template.tsv\"\u003etsv file\u003c/a\u003e.\nThis feature of surveillance reports can be used to identify new\nclusters, important mutations, and track their transmission and\nprevalence trends. However, if not required, this step can be\nskipped using \u003ccode\u003e--skip_surveillance\u003c/code\u003e. An example of surveillance file\nfor Omicron variant using\n\u003ca href=\"https://virusseq-dataportal.ca\" rel=\"nofollow\"\u003eVirusSeq Data Portal\u003c/a\u003e is available in\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/docs\"\u003eDocs\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSee the\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/docs/PARAMETERS.md\"\u003eparameters\u003c/a\u003e\ndocs for all available options when running the workflow.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html#installation\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e (\u003ccode\u003e\u0026gt;=21.04.0\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall any of \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003e\u003ccode\u003eDocker\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003e\u003ccode\u003eConda\u003c/code\u003e\u003c/a\u003e for full pipeline reproducibility \u003cem\u003esee \u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/tree/master/environments\"\u003erecipes\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the pipeline and run with help for detailed parameter options:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-ncov-voc/main.nf --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eN E X T F L O W \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e version 21.04.3\nLaunching \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003emain.nf\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e [berserk_austin] - revision: 93ccc86071\n\nUsage:\n nextflow run main.nf -profile [singularity \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e docker \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e conda) --prefix [prefix] --mode [reference \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e user] [workflow-options]\n\nDescription:\n Variant Calling workflow \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e SARS-CoV-2 Variant of Concern (VOC) and\n Variant of Interest (VOI) consensus sequences to generate data\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eVisualization. All options set via CLI can be set\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e conf\n directory\n\nNextflow arguments (single DASH):\n -profile Allowed values: conda \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e singularity\n\nMandatory workflow arguments (mutually exclusive):\n --prefix A (unique) string prefix \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e output directory \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e each run.\n --mode A flag \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e user uploaded data through visualization app or\n high-throughput analyses (reference \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e user) (Default: reference)\n\nOptional:\n\nInput options:\n --seq Input SARS-CoV-2 genomes or consensus sequences\n (.fasta file)\n --meta Input Metadata file of SARS-CoV-2 genomes or consensus sequences\n (.tsv file)\n --userfile Specify userfile\n (fasta \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e vcf) (Default: None)\n --gisaid_metadata If lineage assignment is preferred by mapping metadata to GISAID\n metadata file, provide the metadata file (.tsv file)\n --variants Provide a variants file\n (.tsv) (Default: /Users/au572806/GitHub/nf-ncov-voc/assets/ncov_variants/variants_who.tsv)\n --outdir Output directory\n (Default: /Users/au572806/GitHub/nf-ncov-voc/results)\n --gff Path to annotation gff \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e variant consequence calling and typing.\n (Default: /Users/au572806/GitHub/nf-ncov-voc/assets/ncov_genomeFeatures/MN908947.3.gff3)\n --ref Path to SARS-CoV-2 reference fasta file\n (Default: /Users/au572806/GitHub/nf-ncov-voc/assets/ncov_refdb/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e)\n --bwa_index Path to BWA index files\n (Default: /Users/au572806/GitHub/nf-ncov-voc/assets/ncov_refdb/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e)\n\nSelection options:\n\n --ivar Run the iVar workflow instead of Freebayes(default)\n --bwamem Run the BWA workflow instead of MiniMap2(default)\n --skip_pangolin Skip PANGOLIN. Can be used \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e metadata already have lineage\n information or mapping is preferred method\n --skip_mapping Skip Mapping. Can be used \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e metadata already have lineage\n information or PANGOLIN is preferred method\n\nPreprocessing options:\n --startdate Start date (Submission date) to extract dataset\n (yyyy-mm-dd) (Default: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e2020-01-01\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n --enddate Start date (Submission date) to extract dataset\n (yyyy-mm-dd) (Default: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e2022-12-31\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\nGenomic Analysis parameters:\n\n BBMAP\n --maxns Max number of Ns allowed \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the sequence \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e qc process\n --minlength Minimun length of sequence required \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e sequences\n to pass qc filtration. Sequence less than minlength\n are not taken further\n\n IVAR/FREEBAYES\n --ploidy Ploidy (Default: 1)\n --mpileupDepth Mpileup depth (Default: unlimited)\n --var_FreqThreshold Variant Calling frequency threshold \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e consensus variant\n (Default: 0.75)\n --var_MaxDepth Maximum reads per input file depth to call variant\n (mpileup -d, Default: 0)\n --var_MinDepth Minimum coverage depth to call variant\n (ivar variants -m, freebayes -u Default: 10)\n --var_MinFreqThreshold Minimum frequency threshold to call variant\n (ivar variants -t, Default: 0.25)\n --varMinVariantQuality Minimum mapQ to call variant\n (ivar variants -q, Default: 20)\n\nSurveillance parameters:\n --virusseq True/False (Default: False). If your data is from\n VirusSeq Data Portal (Canada\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003es Nation COVID-19\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e genomics data portal).\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Passing this argument adds an acknowledgment\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e statement to the surveillance report.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e see https://virusseq-dataportal.ca/acknowledgements\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStart running your own analysis!\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTypical command for reference mode when Metadata File don\u0027t have\nlineage information:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow nf-ncov-voc/main.nf \\\n -profile \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econda, singularity, docker\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --prefix \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etesting\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --mode reference \\\n --startdate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e2020-01-\u003cspan class=\"pl-k\"\u003e01\u0026gt;\u003c/span\u003e \\\n --enddate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e2020-01-\u003cspan class=\"pl-k\"\u003e01\u0026gt;\u003c/span\u003e \\\n --seq \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSequence File\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --meta \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eMetadata File\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --skip_mapping \\\n --outdir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eOutput Dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTypical command for reference mode when Metadata File already\nhave\nlineage information:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow nf-ncov-voc/main.nf \\\n -profile \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econda, singularity, docker\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --prefix \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etesting\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --mode reference \\\n --startdate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e2020-01-\u003cspan class=\"pl-k\"\u003e01\u0026gt;\u003c/span\u003e \\\n --enddate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e2020-01-\u003cspan class=\"pl-k\"\u003e01\u0026gt;\u003c/span\u003e \\\n --seq \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSequence File\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --meta \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eMetadata File\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --skip_mapping \\\n --skip_pangolin \\\n --outdir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eOutput Dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAn executable Python script called\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/bin/functional_annotation.py\"\u003e\u003ccode\u003efunctional_annotation.py\u003c/code\u003e\u003c/a\u003e\nhas been provided if you would like to update the functional\nannotations from \u003ccode\u003ePOKAY\u003c/code\u003e. This will create a new file which\n\u003cstrong\u003eshould replace\u003c/strong\u003e the current file in\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/assets/ncov_functionalAnnotation\"\u003eassets/functional_annotation\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" href=\"#acknowledgments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis workflow and scripts are written and conceptually designed by\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eAffiliation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eZohaib Anwar; \u003ca href=\"https://github.com/anwarMZ\"\u003e@anwarMZ\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMadeline Iseminger; \u003ca href=\"https://github.com/miseminger\"\u003e@miseminger\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAnoosha Sehar; \u003ca href=\"https://github.com/Anoosha-Sehar\"\u003e@Anoosha-Sehar\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIvan Gill; \u003ca href=\"https://github.com/ivansg44\"\u003e@ivansg44\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWilliam Hsiao; \u003ca href=\"https://github.com/wwhsiao\"\u003e@wwhsiao\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePaul Gordon; \u003ca href=\"https://github.com/nodrogluap\"\u003e@nodrogluap\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://www.ucalgary.ca/~gordonp\" rel=\"nofollow\"\u003eCSM Center for Health Genomics and Informatics, University of Calgary, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGary Van Domselaar; \u003ca href=\"https://github.com/phac-nml\"\u003e@phac-nml\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://umanitoba.ca/faculties/health_sciences/medicine/units/medical_microbiology/faculty/vandomselaar.html\" rel=\"nofollow\"\u003ePublic Health Agency of Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMany thanks to others who have helped out and contributed along the way too, including (but not limited to)*: \u003ca href=\"https://virusseq.ca/about/governance/\" rel=\"nofollow\"\u003eCanadian COVID Genomics Network - VirusSeq, Data Analytics Working Group\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-support\" class=\"anchor\" href=\"#support\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport\u003c/h2\u003e\n\u003cp\u003eFor further information or help, don\u0027t hesitate to get in touch at\n\u003ca href=\"mailto:mzanwar@sfu.ca\"\u003emzanwar@sfu.ca\u003c/a\u003e or \u003ca href=\"mailto:wwshiao@sfu.ca\"\u003ewwshiao\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-citations\" class=\"anchor\" href=\"#citations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitations\u003c/h2\u003e\n\u003cp\u003eAn extensive list of references for the tools used by the workflow\ncan be found in the \u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/docs/CITATIONS.md\"\u003eCITATIONS.md\u003c/a\u003e file.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, - "topics": [], - "updated_at": 1657465882.0 + "subscribers_count": 1, + "topics": [ + "bioinformatics", + "genomics", + "sars-cov-2", + "microbial-genomics", + "covid-19", + "nextflow", + "virus", + "variant-calling" + ], + "updated_at": 1642619620.0 }, { "data_format": 2, - "description": "The software we use to run things in our lab", + "description": null, "filenames": [ - "recipe/Singularity" + "Singularity" ], - "full_name": "bioinformatics-group/bioinformatics-singularity", - "latest_release": "PrePandemicEdition", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bioinformatics-singularity\" class=\"anchor\" href=\"#bioinformatics-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebioinformatics-singularity\u003c/h1\u003e\n\u003cp\u003eThe software we use to run things in our lab. Some of the software is older than what is available to be consistent with other publications.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-building-the-image\" class=\"anchor\" href=\"#building-the-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the Image\u003c/h1\u003e\n\u003cp\u003eUsing this image, once built, only requires singularity.\u003c/p\u003e\n\u003cp\u003eBuilding this image requires the debootstrap package (\u003ccode\u003eapt install debootstrap\u003c/code\u003e on debian)\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-building-the-image-1\" class=\"anchor\" href=\"#building-the-image-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the Image\u003c/h1\u003e\n\u003cp\u003eMake sure \u003ccode\u003e/tmp\u003c/code\u003e has enough space! Why wouldn\u0027t it? I\u0027m not sure, but one of the nice build environments I had only allocated 350M to \u003ccode\u003e/tmp\u003c/code\u003e and that broke things in confusing and unexpected ways. I\u0027m used to singularity just saying no when there\u0027s a trouble, but since it happned while R was doing installs, it snuck by. 2GB should be plenty of room, but I\u0027ve had to be cautious, including making sure I had enough RAM (more weirdness with R, 16GB is \u003cem\u003esafe\u003c/em\u003e). I am now building with Singularity 3.x.\u003c/p\u003e\n\u003cp\u003eThe current simple image build I\u0027m using is on a vm. I install Centos 9 (Stream), then run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eyum -y install epel-release\nyum -y install singularity git wget debootstrap\nyum -y install nano\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esingularity build --sandbox \u0026lt;PathToBasedir\u0026gt;/image Singularity\u003c/code\u003e\nThis builds the image as a directory structure that you can go into. You can work in this in writable mode if you need to tweak (or even from outside singularity).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity build \u0026lt;PathToBasedir\u0026gt;/bioinformatics-singularity.sif \u0026lt;PathToBasedir\u0026gt;/image\u003c/code\u003e\nThis builds the image as a squashfs formatted image, suitable for putting on environments where people will/run use it in a fixed form.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-image\" class=\"anchor\" href=\"#running-the-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Image\u003c/h1\u003e\n\u003cp\u003eTo run it with our pre-built image, you just call:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity shell https://tootsuite.encs.concordia.ca/singularity-images/bioinformatics-singularity.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eI reccomend running it with an overlay as some of our tools have the bad habit of trying to write into their own temporary space:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ewherever\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/overlay\nsingularity shell --overlay /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ewherever\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/overlay https://tootsuite.encs.concordia.ca/singularity-images/bioinformatics-singularity.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEventually, intermediate_files used by our tools won\u0027t need such a workaround.\u003c/p\u003e\n\u003cp\u003eBinaries are made available in \u003ccode\u003e/usr/bin\u003c/code\u003e so you can just run things like \u003ccode\u003eR\u003c/code\u003e or \u003ccode\u003et_coffee\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAmong other things, we use:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR 3.6.3\u003c/li\u003e\n\u003cli\u003eBLAST 2.6.0+\u003c/li\u003e\n\u003cli\u003etcoffee (current version from the science packages in Debian Buster)\u003c/li\u003e\n\u003cli\u003eeggnog-mapper (current version from git)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-eggnog-mapper\" class=\"anchor\" href=\"#eggnog-mapper\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeggnog-mapper\u003c/h2\u003e\n\u003cp\u003eWe have not included the contents of the \u003ccode\u003edata\u003c/code\u003e dir for \u003ccode\u003eeggnog-mapper\u003c/code\u003e and thus any commands using it should use \u003ccode\u003e--data_dir\u003c/code\u003e to specify where those very large files are. IF you need those files, you can use the download script provided by eggnog-mapper \u003ccode\u003e/usr/bin/eggnog-mapper/download_eggnog_data.py\u003c/code\u003e, but you\u0027ll still need to pass the directory where those files will end up via \u003ccode\u003e--data_dir\u003c/code\u003e. You\u0027ll want to set this up outside the singularity image.\u003c/p\u003e\n\u003cp\u003eWhile eggnog-mapper can be found in \u003ccode\u003e/usr/bin/eggnog-mapper\u0027\u003c/code\u003e, we have included a script in the image \u003ccode\u003eemapper.sh\u003c/code\u003e that can be run which is already in the standard path and which will pass arguments as appropriate.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-eggnog-mapper-1\" class=\"anchor\" href=\"#eggnog-mapper-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeggnog-mapper\u003c/h3\u003e\n\u003cp\u003eAre you running this image at Concordia on speed? We suggest the following example call, which executes the test from their installation guide but maps to the locally stored version of that database:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec -B `pwd`:$PWD -B /speed-scratch/bioinformatics-group/datasets/eggnog-mapper:datasets /speed-scratch/bioinformatics-group/bioinformatics-singularity.sif emapper.sh --data_dir /datasets -i /usr/bin/eggnog-mapper/test/p53.fa --output p53_maNOG -m diamond\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-i-suggest-making-the-data-files-approximately-40g-available-to-your-images-on-as-fast-a-disk-as-possible-ive-seen-putting-it-in-devshm-suggested\" class=\"anchor\" href=\"#i-suggest-making-the-data-files-approximately-40g-available-to-your-images-on-as-fast-a-disk-as-possible-ive-seen-putting-it-in-devshm-suggested\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI suggest making the data files (approximately 40G) available to your images on as fast a disk as possible (I\u0027ve seen putting it in \u003ccode\u003e/dev/shm\u003c/code\u003e suggested).\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-our-tools\" class=\"anchor\" href=\"#running-our-tools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning our Tools\u003c/h2\u003e\n\u003cp\u003eWhile a variety of tools are available in this image, we have included a number using the \u003ca href=\"https://sci-f.github.io/\" rel=\"nofollow\"\u003eSCI-F\u003c/a\u003e approach advocated with Singularity. Namely, one can view our apps in the singularity image via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity inspect --list-apps \u0026lt;yourimage\u0026gt;\nTooT-P\nTooT-SC\nTooT-T\nTranCEP\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHelp is available for each image, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run-help --app TooT-P \u0026lt;youimage\u0026gt;\n Usage: TooT-P.py [-h] -query QUERY [-work WORK] [-out OUT] [-db DB]\n [-TooTT TOOTT] [-TooTSC TOOTSC]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eScripts can be run via standard execution as described in the help, or via the app interface, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app TooT-P \u0026lt;yourimage\u0026gt; -query=\u0026lt;yourfasta\u0026gt;\nExecuting: /usr/local/bin/TooT-T.R -query=\u0026lt;$CWD\u0026gt;/work/TooT-P/P37327/P37327.fasta -db=\u0026lt;$CWD\u0026gt;/db -out=\u0026lt;$CWD\u0026gt;/work/TooT-P/P37327 -work=\u0026lt;$CWD\u0026gt;\nExecuting: /usr/local/bin/TooT-SC.R -query=\u0026lt;$CWD\u0026gt;/work/TooT-P/P37327/P37327.fasta -db=\u0026lt;$CWD\u0026gt;/db -out=\u0026lt;$CWD\u0026gt;/work/TooT-P/P37327 -work=\u0026lt;$CWD\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e...you know, if \u003ccode\u003e\u0026lt;yourfasta\u0026gt;\u003c/code\u003e just contains the one sequence \u003ccode\u003eP37327\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-concordias-hpc-infrastructure\" class=\"anchor\" href=\"#running-on-concordias-hpc-infrastructure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Concordia\u0027s HPC infrastructure\u003c/h2\u003e\n\u003cp\u003eIf you\u0027re at Concordia and have requested access to speed (rt-ex-hpc), then you may want to be running jobs here. You can readily use this image, as we keep a local copy in \u003ccode\u003e/speed-scratch/bioinformatics-group/bioinformatics-singularity.sif\u003c/code\u003e. In that case you can go to your working directory where you have your expected script and just run it. Keep in mind that speed likes you to use tcsh, but you\u0027re running bash from within the image.\u003c/p\u003e\n\u003cp\u003eFor example, I can make/go to my working directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p /speed-scratch/{\u003cspan class=\"pl-smi\"\u003e$uid\u003c/span\u003e}/test3\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /speed-scratch/{\u003cspan class=\"pl-smi\"\u003e$uid\u003c/span\u003e}/test3\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen I can create a file test.sh:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\nls -latr\nmakeblastdb -version\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand give it appropraite permissions to run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod 700 test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, I run the image with singularity:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e /speed-scratch/bioinformatics-group/bioinformatics-singularity.sif ./test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eI get the expected output that shows my directory contents and the version of \u003ccode\u003emakeblastdb\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etotal 8\ndrwxrwxr-x 6 sthiel sthiel 4096 Oct 17 10:54 ..\n-rwx------ 1 sthiel sthiel 43 Oct 17 11:41 test.sh\ndrwxrwx--- 2 sthiel sthiel 4096 Oct 17 11:42 .\nmakeblastdb: 2.3.0+\nPackage: blast 2.3.0, build Nov 30 2015 13:32:08\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above was done via qlogin, but of course you would call things in the same manner using qsub when submitting a job. You\u0027ll notice that a specific binding is required when using speed-scratch (or any of the nfs-mounted directories, I suspect) as your working directory: \u003ccode\u003e-B `pwd`:$PWD\u003c/code\u003e. It gets all weird on you if you skip that. If you have enough space in your home directory, that\u0027s not needed, but I need \u003ccode\u003e/speed-scratch\u003c/code\u003e to do anything these days.\u003c/p\u003e\n", + "full_name": "rcorces/ArchR_docker", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-archr_docker\" class=\"anchor\" href=\"#archr_docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArchR_docker\u003c/h1\u003e\n\u003cp\u003eTo build the singularity container:\nSet your working directory to this github repository and run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build archr_test.img Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhere \"Singularity\" is the file contained in the github repository and \"archr_test.img\" is the output signularity image file\nOn Pelayo, normal (non-sudo) users will not be able to do this.\u003c/p\u003e\n\u003cp\u003eTo run the built container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run archr_test.img\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1651866144.0 + "updated_at": 1641940832.0 }, { "data_format": 2, - "description": "alpine linux singularity container with xeyes", + "description": "Example scripts for setting up a brain processing pipeline", "filenames": [ - "Singularity" + "Example-Registration/Singularity", + "Example-Easy/Singularity" ], - "full_name": "truatpasteurdotfr/singularity-alpine-xeyes", + "full_name": "jeffduda/GetYourBrainPipelined", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-alpine-xeyes\" class=\"anchor\" href=\"#singularity-alpine-xeyes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-alpine-xeyes\u003c/h1\u003e\n\u003cp\u003ebase alpine linux (docker alpine:latest) singularity container with xeyes\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-with\" class=\"anchor\" href=\"#run-with\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erun with:\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/truatpasteurdotfr/singularity-alpine-xeyes/actions/workflows/manual-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-alpine-xeyes/actions/workflows/manual-singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-alpine-xeyes:latest\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-getyourbrainpipelined\" class=\"anchor\" href=\"#getyourbrainpipelined\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetYourBrainPipelined\u003c/h1\u003e\n\u003cp\u003eExample scripts for setting up a brain processing pipeline\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-on-bil\" class=\"anchor\" href=\"#running-on-bil\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on BIL\u003c/h1\u003e\n\u003cp\u003eMake sure you have a Syslabs.io account and remote toke setup as desribed in the previous tutorial\n\u003ca href=\"https://hackmd.io/@biomed-apps/B1B8mQCb5#Singularity\" rel=\"nofollow\"\u003ehttps://hackmd.io/@biomed-apps/B1B8mQCb5#Singularity\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003efrom you home directory\u003c/p\u003e\n\u003cp\u003eGet a repo, build a singularity image remotely, and run it\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003einteract\ngit clone https://github.com/jeffduda/GetYourBrainPipelined.git\nsingularity build --remote example-easy.sif GetYourBrainPipelined/Example-Easy/Singularity\nsingularity run example-easy.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse that singularity image to run a command in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec example-easy.sif cowsay \"Exec Example-Easy\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse the container to run a script and data that we included in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec example-easy.sif /opt/scripts/cow_script.sh /data/input/pkg_data.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse the container to run a locally defined scripts that access local information\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir data\necho \"Local data\" \u0026gt; data/data.txt\necho \u0027#!/bin/bash\u0027 \u0026gt; data/script.sh\necho \u0027a=`cat $1`\u0027 \u0026gt;\u0026gt; data/script.sh\necho \u0027cowsay $a\u0027 \u0026gt;\u0026gt; data/script.sh\nsingularity exec -B /bil/users/jtduda/data:/data example-easy.sif sh /data/script.sh /data/data.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow build an container that does some example registration. This may take 10min or so.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --remote example-reg.sif GetYourBrainPipelined/Example-Registration/Singularity\nmkdir data_input\nmkdir data_output\nsingularity exec -B /bil/users/jtduda/data_input:/data/input -B /bil/users/jtduda/data_output:/data/output example-reg.sif /opt/scripts/example.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-examples\" class=\"anchor\" href=\"#running-the-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the examples\u003c/h2\u003e\n\u003cp\u003eA \u0027Dockerfile\u0027 is provided to show how the image may be built. The build process takes a while so instead you may want to use a provided image that was created with the Dockerfile:\u003c/p\u003e\n\u003cp\u003edocker pull jtduda/python-itk-sitk-ants:0.1.0\u003c/p\u003e\n\u003cp\u003eNow you will need a directory for input data and for output data. For illustration we will call these /local/data/input and /local/data/output. We will refer to the location of this repo as /local/repo/GetYourBrainPipelined. The example may now be run via:\u003c/p\u003e\n\u003cp\u003edocker run -v /local/data/input:/data/input -v /local/data/output:/data/output -v /local/repo/GetYourBrainPipelined:/scripts jtduda/python-itk-sitk-ants:0.1.0 /scripts/example.sh\u003c/p\u003e\n\u003cp\u003eThis will run the following python programs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esave_inputs.py - prepopulate the input directory with some example data\u003c/li\u003e\n\u003cli\u003esmoothITK.py - smooth an image using the itk python module\u003c/li\u003e\n\u003cli\u003esmoothSimpleITK.py - smooth an image using the SimpleITK python module\u003c/li\u003e\n\u003cli\u003esmoothANTs.py - smooth an image using the ants python module\u003c/li\u003e\n\u003cli\u003eregistrationANTs.py - simple registration using the ants python module\u003c/li\u003e\n\u003cli\u003eregistrationSimpleITK.py - simple registration using the SimpleITK python module\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll outputs will be saved in the /local/data/output directory.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eTo run via singularity, first pull via:\u003c/p\u003e\n\u003cp\u003esingularity pull docker://jtduda/python-itk-sitk-ants:0.1.0\u003c/p\u003e\n\u003cp\u003eTo run the example:\u003c/p\u003e\n\u003cp\u003esingularity exec -B /local/data/input:/data/input -B /local/data/output:/data/output -B /local/repo/GetYourBrainPipelined:/scripts python-itk-sitk-ants_0.1.0.sif /scripts/example.sh\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1652468695.0 + "updated_at": 1647037042.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container of a headless ubuntu and conda", "filenames": [ + "Singularity.da", "Singularity" ], - "full_name": "noisysky/GYBS_hackathon", + "full_name": "shka/singularity-ubuntu-vnc-xfce", "latest_release": null, "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1649359558.0 + "updated_at": 1646881059.0 }, { "data_format": 2, @@ -27305,1758 +26808,2023 @@ var data = }, { "data_format": 2, - "description": "Singularity container of a headless ubuntu and conda", + "description": null, "filenames": [ - "Singularity.da", "Singularity" ], - "full_name": "shka/singularity-ubuntu-vnc-xfce", + "full_name": "noisysky/GYBS_hackathon", "latest_release": null, "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1646881059.0 + "updated_at": 1649359558.0 }, { "data_format": 2, - "description": "Example scripts for setting up a brain processing pipeline", + "description": "alpine linux singularity container with xeyes", "filenames": [ - "Example-Registration/Singularity", - "Example-Easy/Singularity" + "Singularity" ], - "full_name": "jeffduda/GetYourBrainPipelined", + "full_name": "truatpasteurdotfr/singularity-alpine-xeyes", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-getyourbrainpipelined\" class=\"anchor\" href=\"#getyourbrainpipelined\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetYourBrainPipelined\u003c/h1\u003e\n\u003cp\u003eExample scripts for setting up a brain processing pipeline\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-on-bil\" class=\"anchor\" href=\"#running-on-bil\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on BIL\u003c/h1\u003e\n\u003cp\u003eMake sure you have a Syslabs.io account and remote toke setup as desribed in the previous tutorial\n\u003ca href=\"https://hackmd.io/@biomed-apps/B1B8mQCb5#Singularity\" rel=\"nofollow\"\u003ehttps://hackmd.io/@biomed-apps/B1B8mQCb5#Singularity\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003efrom you home directory\u003c/p\u003e\n\u003cp\u003eGet a repo, build a singularity image remotely, and run it\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003einteract\ngit clone https://github.com/jeffduda/GetYourBrainPipelined.git\nsingularity build --remote example-easy.sif GetYourBrainPipelined/Example-Easy/Singularity\nsingularity run example-easy.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse that singularity image to run a command in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec example-easy.sif cowsay \"Exec Example-Easy\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse the container to run a script and data that we included in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec example-easy.sif /opt/scripts/cow_script.sh /data/input/pkg_data.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse the container to run a locally defined scripts that access local information\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir data\necho \"Local data\" \u0026gt; data/data.txt\necho \u0027#!/bin/bash\u0027 \u0026gt; data/script.sh\necho \u0027a=`cat $1`\u0027 \u0026gt;\u0026gt; data/script.sh\necho \u0027cowsay $a\u0027 \u0026gt;\u0026gt; data/script.sh\nsingularity exec -B /bil/users/jtduda/data:/data example-easy.sif sh /data/script.sh /data/data.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow build an container that does some example registration. This may take 10min or so.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --remote example-reg.sif GetYourBrainPipelined/Example-Registration/Singularity\nmkdir data_input\nmkdir data_output\nsingularity exec -B /bil/users/jtduda/data_input:/data/input -B /bil/users/jtduda/data_output:/data/output example-reg.sif /opt/scripts/example.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-the-examples\" class=\"anchor\" href=\"#running-the-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the examples\u003c/h2\u003e\n\u003cp\u003eA \u0027Dockerfile\u0027 is provided to show how the image may be built. The build process takes a while so instead you may want to use a provided image that was created with the Dockerfile:\u003c/p\u003e\n\u003cp\u003edocker pull jtduda/python-itk-sitk-ants:0.1.0\u003c/p\u003e\n\u003cp\u003eNow you will need a directory for input data and for output data. For illustration we will call these /local/data/input and /local/data/output. We will refer to the location of this repo as /local/repo/GetYourBrainPipelined. The example may now be run via:\u003c/p\u003e\n\u003cp\u003edocker run -v /local/data/input:/data/input -v /local/data/output:/data/output -v /local/repo/GetYourBrainPipelined:/scripts jtduda/python-itk-sitk-ants:0.1.0 /scripts/example.sh\u003c/p\u003e\n\u003cp\u003eThis will run the following python programs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esave_inputs.py - prepopulate the input directory with some example data\u003c/li\u003e\n\u003cli\u003esmoothITK.py - smooth an image using the itk python module\u003c/li\u003e\n\u003cli\u003esmoothSimpleITK.py - smooth an image using the SimpleITK python module\u003c/li\u003e\n\u003cli\u003esmoothANTs.py - smooth an image using the ants python module\u003c/li\u003e\n\u003cli\u003eregistrationANTs.py - simple registration using the ants python module\u003c/li\u003e\n\u003cli\u003eregistrationSimpleITK.py - simple registration using the SimpleITK python module\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll outputs will be saved in the /local/data/output directory.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eTo run via singularity, first pull via:\u003c/p\u003e\n\u003cp\u003esingularity pull docker://jtduda/python-itk-sitk-ants:0.1.0\u003c/p\u003e\n\u003cp\u003eTo run the example:\u003c/p\u003e\n\u003cp\u003esingularity exec -B /local/data/input:/data/input -B /local/data/output:/data/output -B /local/repo/GetYourBrainPipelined:/scripts python-itk-sitk-ants_0.1.0.sif /scripts/example.sh\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-alpine-xeyes\" class=\"anchor\" href=\"#singularity-alpine-xeyes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-alpine-xeyes\u003c/h1\u003e\n\u003cp\u003ebase alpine linux (docker alpine:latest) singularity container with xeyes\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-with\" class=\"anchor\" href=\"#run-with\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erun with:\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/truatpasteurdotfr/singularity-alpine-xeyes/actions/workflows/manual-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-alpine-xeyes/actions/workflows/manual-singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run oras://ghcr.io/truatpasteurdotfr/singularity-alpine-xeyes:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1647037042.0 + "updated_at": 1652468695.0 }, { "data_format": 2, - "description": null, + "description": "The software we use to run things in our lab", + "filenames": [ + "recipe/Singularity" + ], + "full_name": "bioinformatics-group/bioinformatics-singularity", + "latest_release": "PrePandemicEdition", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bioinformatics-singularity\" class=\"anchor\" href=\"#bioinformatics-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebioinformatics-singularity\u003c/h1\u003e\n\u003cp\u003eThe software we use to run things in our lab. Some of the software is older than what is available to be consistent with other publications.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-building-the-image\" class=\"anchor\" href=\"#building-the-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the Image\u003c/h1\u003e\n\u003cp\u003eUsing this image, once built, only requires singularity.\u003c/p\u003e\n\u003cp\u003eBuilding this image requires the debootstrap package (\u003ccode\u003eapt install debootstrap\u003c/code\u003e on debian)\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-building-the-image-1\" class=\"anchor\" href=\"#building-the-image-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the Image\u003c/h1\u003e\n\u003cp\u003eMake sure \u003ccode\u003e/tmp\u003c/code\u003e has enough space! Why wouldn\u0027t it? I\u0027m not sure, but one of the nice build environments I had only allocated 350M to \u003ccode\u003e/tmp\u003c/code\u003e and that broke things in confusing and unexpected ways. I\u0027m used to singularity just saying no when there\u0027s a trouble, but since it happned while R was doing installs, it snuck by. 2GB should be plenty of room, but I\u0027ve had to be cautious, including making sure I had enough RAM (more weirdness with R, 16GB is \u003cem\u003esafe\u003c/em\u003e). I am now building with Singularity 3.x.\u003c/p\u003e\n\u003cp\u003eThe current simple image build I\u0027m using is on a vm. I install Centos 9 (Stream), then run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eyum -y install epel-release\nyum -y install singularity git wget debootstrap\nyum -y install nano\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esingularity build --sandbox \u0026lt;PathToBasedir\u0026gt;/image Singularity\u003c/code\u003e\nThis builds the image as a directory structure that you can go into. You can work in this in writable mode if you need to tweak (or even from outside singularity).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity build \u0026lt;PathToBasedir\u0026gt;/bioinformatics-singularity.sif \u0026lt;PathToBasedir\u0026gt;/image\u003c/code\u003e\nThis builds the image as a squashfs formatted image, suitable for putting on environments where people will/run use it in a fixed form.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-image\" class=\"anchor\" href=\"#running-the-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Image\u003c/h1\u003e\n\u003cp\u003eTo run it with our pre-built image, you just call:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity shell https://tootsuite.encs.concordia.ca/singularity-images/bioinformatics-singularity.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eI reccomend running it with an overlay as some of our tools have the bad habit of trying to write into their own temporary space:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ewherever\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/overlay\nsingularity shell --overlay /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ewherever\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/overlay https://tootsuite.encs.concordia.ca/singularity-images/bioinformatics-singularity.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEventually, intermediate_files used by our tools won\u0027t need such a workaround.\u003c/p\u003e\n\u003cp\u003eBinaries are made available in \u003ccode\u003e/usr/bin\u003c/code\u003e so you can just run things like \u003ccode\u003eR\u003c/code\u003e or \u003ccode\u003et_coffee\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAmong other things, we use:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR 3.6.3\u003c/li\u003e\n\u003cli\u003eBLAST 2.6.0+\u003c/li\u003e\n\u003cli\u003etcoffee (current version from the science packages in Debian Buster)\u003c/li\u003e\n\u003cli\u003eeggnog-mapper (current version from git)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-eggnog-mapper\" class=\"anchor\" href=\"#eggnog-mapper\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeggnog-mapper\u003c/h2\u003e\n\u003cp\u003eWe have not included the contents of the \u003ccode\u003edata\u003c/code\u003e dir for \u003ccode\u003eeggnog-mapper\u003c/code\u003e and thus any commands using it should use \u003ccode\u003e--data_dir\u003c/code\u003e to specify where those very large files are. IF you need those files, you can use the download script provided by eggnog-mapper \u003ccode\u003e/usr/bin/eggnog-mapper/download_eggnog_data.py\u003c/code\u003e, but you\u0027ll still need to pass the directory where those files will end up via \u003ccode\u003e--data_dir\u003c/code\u003e. You\u0027ll want to set this up outside the singularity image.\u003c/p\u003e\n\u003cp\u003eWhile eggnog-mapper can be found in \u003ccode\u003e/usr/bin/eggnog-mapper\u0027\u003c/code\u003e, we have included a script in the image \u003ccode\u003eemapper.sh\u003c/code\u003e that can be run which is already in the standard path and which will pass arguments as appropriate.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-eggnog-mapper-1\" class=\"anchor\" href=\"#eggnog-mapper-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeggnog-mapper\u003c/h3\u003e\n\u003cp\u003eAre you running this image at Concordia on speed? We suggest the following example call, which executes the test from their installation guide but maps to the locally stored version of that database:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec -B `pwd`:$PWD -B /speed-scratch/bioinformatics-group/datasets/eggnog-mapper:datasets /speed-scratch/bioinformatics-group/bioinformatics-singularity.sif emapper.sh --data_dir /datasets -i /usr/bin/eggnog-mapper/test/p53.fa --output p53_maNOG -m diamond\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-i-suggest-making-the-data-files-approximately-40g-available-to-your-images-on-as-fast-a-disk-as-possible-ive-seen-putting-it-in-devshm-suggested\" class=\"anchor\" href=\"#i-suggest-making-the-data-files-approximately-40g-available-to-your-images-on-as-fast-a-disk-as-possible-ive-seen-putting-it-in-devshm-suggested\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI suggest making the data files (approximately 40G) available to your images on as fast a disk as possible (I\u0027ve seen putting it in \u003ccode\u003e/dev/shm\u003c/code\u003e suggested).\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-our-tools\" class=\"anchor\" href=\"#running-our-tools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning our Tools\u003c/h2\u003e\n\u003cp\u003eWhile a variety of tools are available in this image, we have included a number using the \u003ca href=\"https://sci-f.github.io/\" rel=\"nofollow\"\u003eSCI-F\u003c/a\u003e approach advocated with Singularity. Namely, one can view our apps in the singularity image via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity inspect --list-apps \u0026lt;yourimage\u0026gt;\nTooT-P\nTooT-SC\nTooT-T\nTranCEP\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHelp is available for each image, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run-help --app TooT-P \u0026lt;youimage\u0026gt;\n Usage: TooT-P.py [-h] -query QUERY [-work WORK] [-out OUT] [-db DB]\n [-TooTT TOOTT] [-TooTSC TOOTSC]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eScripts can be run via standard execution as described in the help, or via the app interface, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app TooT-P \u0026lt;yourimage\u0026gt; -query=\u0026lt;yourfasta\u0026gt;\nExecuting: /usr/local/bin/TooT-T.R -query=\u0026lt;$CWD\u0026gt;/work/TooT-P/P37327/P37327.fasta -db=\u0026lt;$CWD\u0026gt;/db -out=\u0026lt;$CWD\u0026gt;/work/TooT-P/P37327 -work=\u0026lt;$CWD\u0026gt;\nExecuting: /usr/local/bin/TooT-SC.R -query=\u0026lt;$CWD\u0026gt;/work/TooT-P/P37327/P37327.fasta -db=\u0026lt;$CWD\u0026gt;/db -out=\u0026lt;$CWD\u0026gt;/work/TooT-P/P37327 -work=\u0026lt;$CWD\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e...you know, if \u003ccode\u003e\u0026lt;yourfasta\u0026gt;\u003c/code\u003e just contains the one sequence \u003ccode\u003eP37327\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-concordias-hpc-infrastructure\" class=\"anchor\" href=\"#running-on-concordias-hpc-infrastructure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on Concordia\u0027s HPC infrastructure\u003c/h2\u003e\n\u003cp\u003eIf you\u0027re at Concordia and have requested access to speed (rt-ex-hpc), then you may want to be running jobs here. You can readily use this image, as we keep a local copy in \u003ccode\u003e/speed-scratch/bioinformatics-group/bioinformatics-singularity.sif\u003c/code\u003e. In that case you can go to your working directory where you have your expected script and just run it. Keep in mind that speed likes you to use tcsh, but you\u0027re running bash from within the image.\u003c/p\u003e\n\u003cp\u003eFor example, I can make/go to my working directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p /speed-scratch/{\u003cspan class=\"pl-smi\"\u003e$uid\u003c/span\u003e}/test3\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /speed-scratch/{\u003cspan class=\"pl-smi\"\u003e$uid\u003c/span\u003e}/test3\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen I can create a file test.sh:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\nls -latr\nmakeblastdb -version\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand give it appropraite permissions to run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod 700 test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, I run the image with singularity:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e /speed-scratch/bioinformatics-group/bioinformatics-singularity.sif ./test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eI get the expected output that shows my directory contents and the version of \u003ccode\u003emakeblastdb\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etotal 8\ndrwxrwxr-x 6 sthiel sthiel 4096 Oct 17 10:54 ..\n-rwx------ 1 sthiel sthiel 43 Oct 17 11:41 test.sh\ndrwxrwx--- 2 sthiel sthiel 4096 Oct 17 11:42 .\nmakeblastdb: 2.3.0+\nPackage: blast 2.3.0, build Nov 30 2015 13:32:08\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above was done via qlogin, but of course you would call things in the same manner using qsub when submitting a job. You\u0027ll notice that a specific binding is required when using speed-scratch (or any of the nfs-mounted directories, I suspect) as your working directory: \u003ccode\u003e-B `pwd`:$PWD\u003c/code\u003e. It gets all weird on you if you skip that. If you have enough space in your home directory, that\u0027s not needed, but I need \u003ccode\u003e/speed-scratch\u003c/code\u003e to do anything these days.\u003c/p\u003e\n", + "stargazers_count": 1, + "subscribers_count": 3, + "topics": [], + "updated_at": 1651866144.0 + }, + { + "data_format": 2, + "description": "R package interface to GCAE", "filenames": [ "Singularity" ], - "full_name": "rcorces/ArchR_docker", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-archr_docker\" class=\"anchor\" href=\"#archr_docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArchR_docker\u003c/h1\u003e\n\u003cp\u003eTo build the singularity container:\nSet your working directory to this github repository and run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build archr_test.img Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhere \"Singularity\" is the file contained in the github repository and \"archr_test.img\" is the output signularity image file\nOn Pelayo, normal (non-sudo) users will not be able to do this.\u003c/p\u003e\n\u003cp\u003eTo run the built container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run archr_test.img\u003c/code\u003e\u003c/p\u003e\n", + "full_name": "AJResearchGroup/gcaer", + "latest_release": "v0.6.5", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-gcaer\" class=\"anchor\" aria-hidden=\"true\" href=\"#gcaer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egcaer\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/gcaer/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/gcaer/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/gcaer/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/gcaer/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3b7a6e924b6bcb32c8b3da0bd2aae7b7fe775432f4a3c611efa42f2ae77dee9b/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f67636165722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/gcaer/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/gcaer/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/gcaer/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/gcaer/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3159ac77b3ba0a6725db4cdb3e85629c887a8ab8cf3c360fb92d05388280ff1a/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f67636165722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/gcaer/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWork with \u003ca href=\"https://github.com/richelbilderbeek/genocae/tree/Pheno\"\u003eGCAE\u003c/a\u003e from R.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/richelbilderbeek/genocae/tree/Pheno\"\u003eGCAE GitHub repository\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003egcaer\u003c/code\u003e is not on CRAN yet. To install \u003ccode\u003egcaer\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(remotes)\ninstall_github(\"richelbilderbeek/gcaer\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis assumes you have the \u003ccode\u003eremotes\u003c/code\u003e package installed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-gcae-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-gcae-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall GCAE versions\u003c/h2\u003e\n\u003cp\u003eTo install GCAE:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(gcaer)\ninstall_gcae()\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h2\u003e\n\u003cp\u003eGet the GCAE help text:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(gcaer)\nget_gcae_help_text()\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-gcae\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-gcae\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning GCAE\u003c/h3\u003e\n\u003cp\u003eRun GCAE:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elibrary(gcaer)\nrun_gcae(\"--help\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-full-experiment\" class=\"anchor\" aria-hidden=\"true\" href=\"#full-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFull experiment\u003c/h3\u003e\n\u003cp\u003eInstead of using the multiple steps by \u003ccode\u003eGenoCAE\u003c/code\u003e,\n\u003ccode\u003edo_gcae_experiment\u003c/code\u003e does all of these for you.\u003c/p\u003e\n\u003cp\u003eHere is an example of a full experiment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Create the parameters for the experiment\ngcae_experiment_params \u0026lt;- create_gcae_experiment_params(\n gcae_options = create_gcae_options(),\n gcae_setup = create_test_gcae_setup(\n model_id = \"M0\",\n superpops = get_gcaer_filename(\"gcae_input_files_1_labels.csv\"),\n pheno_model_id = \"p0\"\n ),\n analyse_epochs = c(1, 2),\n metrics = \"f1_score_3,f1_score_5\"\n)\n\n# Do the experiment\ngcae_experiment_results \u0026lt;- do_gcae_experiment(\n gcae_experiment_params = gcae_experiment_params\n)\n\n# Save the experiment\u0027s results\nsave_gcae_experiment_results(\n gcae_experiment_results = gcae_experiment_results,\n folder_name = gcae_experiment_params$gcae_setup$trainedmodeldir\n)\n\n# Create the plots for the experiment\u0027s results\ncreate_plots_from_gcae_experiment_results(\n folder_name = gcae_experiment_params$gcae_setup$trainedmodeldir\n)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h3\u003e\n\u003cp\u003eTo do the full GCAE workflow, a \u003ccode\u003egcae_setup\u003c/code\u003e is needed,\nfrom which the respective \u003ccode\u003egcae_[x]\u003c/code\u003e functions are called,\nwhere \u003ccode\u003e[x]\u003c/code\u003e matches the first GCAE CLI argument (for\nexample, use \u003ccode\u003egcaer\u003c/code\u003e\u0027s \u003ccode\u003egcae_train\u003c/code\u003e to do the same as \u003ccode\u003erun_gcae.py train\u003c/code\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egcae_setup \u0026lt;- create_gcae_setup(\n datadir = file.path(get_gcae_folder(), \"example_tiny/\"),\n data = \"issue_6_bin\",\n model_id = \"M1\",\n pheno_model_id = \"p2\",\n superpops = file.path(datadir, \"HO_superpopulations\")\n)\n\n# 2. Train, approx 3 mins\ntrain_filenames \u0026lt;- gcae_train(\n gcae_setup = gcae_setup,\n epochs = 3,\n save_interval = 1\n)\n\n# 3. Project\nproject_filenames \u0026lt;- gcae_project(\n gcae_setup = gcae_setup\n)\nproject_results \u0026lt;- parse_project_files(project_filenames)\n\n# 4. Evaluate\nevaluate_filenames \u0026lt;- gcae_evaluate(\n gcae_setup,\n metrics = \"f1_score_3,f1_score_5\",\n epoch = 3\n)\n\nevaluate_results \u0026lt;- parse_evaluate_filenames(\n evaluate_filenames, \n epoch = 3\n)\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-hidden=\"true\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinks\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/richelbilderbeek/genocae/tree/Pheno\"\u003eGCAE GitHub repository\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1641940832.0 + "updated_at": 1657465882.0 }, { "data_format": 2, - "description": "A Nextflow wrapped workflow for generating the mutation profiles of SARS-CoV-2 genomes (Variants of Concern and Variants of Interest). Workflow is developed in collaboration with COVID-MVP (https://github.com/cidgoh/COVID-MVP) which can be used to visualize the mutation profiles and functional annotations.", + "description": "Application Lifecycle Deployment Engine", "filenames": [ - "environments/Singularity" + "SingularityTests.md" ], - "full_name": "cidgoh/nf-ncov-voc", - "latest_release": "v1.0.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nf-ncov-voc\" class=\"anchor\" href=\"#nf-ncov-voc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-ncov-voc\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/26be5f07b2d4aa0e46337e9792e3b32071f4721c97661c70657eeb206d577991/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f7725323044534c322d25453225383925413532312e30342e302d3233616136322e7376673f6c6162656c436f6c6f723d303030303030\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A521.04.0-23aa62.svg?labelColor=000000\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a6ebc5359ca9f5f51fe0aa49c2d8f235c19f9c93c7534904cdfe10f7675bb56e/687474703a2f2f696d672e736869656c64732e696f2f62616467652f72756e253230776974682d636f6e64612d3345423034393f6c6162656c436f6c6f723d303030303030266c6f676f3d616e61636f6e6461\" alt=\"run with conda\" data-canonical-src=\"http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000\u0026amp;logo=anaconda\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d1c5b30eaa6c028ee72fd590dacad176e78296c4deb2e0fb3bfebc84bc45e6a2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72756e253230776974682d646f636b65722d3064623765643f6c6162656c436f6c6f723d303030303030266c6f676f3d646f636b6572\" alt=\"run with docker\" data-canonical-src=\"https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000\u0026amp;logo=docker\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0b0568e8f684f1ea04320511f0635c70c144cad7fb7daec19a8e605f02933b01/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72756e253230776974682d73696e67756c61726974792d3164333535632e7376673f6c6162656c436f6c6f723d303030303030\" alt=\"run with singularity\" data-canonical-src=\"https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003enf-ncov-voc\u003c/strong\u003e is a bioinformatics analysis workflow used for\nperforming variant calling on SARS-CoV-2 genomes to identify and\nprofile mutations in Variants of Concern (VOCs), Variants of\nInterest (VOIs) and Variants under Monitoring (VUMs). This workflow has\nfour main stages - \u003cstrong\u003ePreprocessing\u003c/strong\u003e, \u003cstrong\u003eGenomic Analysis (Variant\nCalling)\u003c/strong\u003e , \u003cstrong\u003eFunctional Annotation\u003c/strong\u003e and \u003cstrong\u003eSurveillance\u003c/strong\u003e.\n\u003cstrong\u003enf-ncov-voc\u003c/strong\u003e workflow can be used in combination with an interactive\nvisualization tool \u003ca href=\"https://github.com/cidgoh/COVID-MVP\"\u003eCOVID-MVP\u003c/a\u003e\nor as a stand-alone high-throughput analysis tool to produce\nmutation profiles and surveillance reports.\u003c/p\u003e\n\u003cp\u003eAs an input, \u003cstrong\u003enf-ncov-voc\u003c/strong\u003e workflow requires SARS-CoV-2 consensus\nsequences in \u003ccode\u003eFASTA\u003c/code\u003e format and Metadata file in \u003ccode\u003eTSV\u003c/code\u003e format.\nSequences in pre-processing stage are filtered using Metadata\nvariables, quality filtered and assigned lineages. Sequences\nassigned as VOCs, VOIs and VUMs are then mapped to SARS-CoV-2 genome,\nvariant called and normalized in Genomic Analysis (Variant Calling)\nmodule. Mutations called are then annotated in several stages\nincluding flagging the potential contaminated sites, mutation\nannotation, genomic feature annotation, mature peptide annotation\nand finally respective biological functional impact using the\nmanually curated effort \u003ca href=\"https://github.com/nodrogluap/pokay\"\u003ePokay\u003c/a\u003e.\n(lead by Paul Gordon \u003ca href=\"https://github.com/nodrogluap\"\u003e@nodrogluap\u003c/a\u003e).\nFinally, in the surveillance module, these functional profiles are\nsummarized using functional indicators to highlight key functions\nand mutations responsible for them for e.g. \u003cstrong\u003eP618H\u003c/strong\u003e role in\n\u003cem\u003econvalescent plasma escape\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe workflow is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e-\n\u003ca href=\"https://www.nextflow.io/docs/latest/dsl2.html\" rel=\"nofollow\"\u003eDSL2\u003c/a\u003e, a workflow\ntool to run tasks across multiple compute infrastructures in a very\nportable manner. It can use \u003ccode\u003econda\u003c/code\u003e/\u003ccode\u003eDocker\u003c/code\u003e/\u003ccode\u003eSingularity\u003c/code\u003e\ncontainers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cp\u003eA detailed structure and each module of the workflow is presented\nbelow in the dataflow diagram\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-nf-ncov-voc-dataflow\" class=\"anchor\" href=\"#nf-ncov-voc-dataflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-ncov-voc Dataflow\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"figs/COVIDMVP.drawio.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"figs/COVIDMVP.drawio.png\" alt=\"DataFlow\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pre-processing\" class=\"anchor\" href=\"#pre-processing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-Processing\u003c/h3\u003e\n\u003cp\u003eThis module offers two ways to get lineage information for each\ngenome in \u003ccode\u003eFASTA\u003c/code\u003e file and listed respectively in Metadata file\nunless a column \u003ccode\u003epango_lineage\u003c/code\u003e is already available in which case\nboth options can be skipped. First option is to use\n\u003ca href=\"https://github.com/cov-lineages/pangolin\"\u003ePANGOLIN\u003c/a\u003e to assign\nlineages and merge the metadata with pangolin report. This\nstep can be skipped by passing \u003ccode\u003e--skip_pangolin\u003c/code\u003e. The second option\nis to map input metadata to \u003ca href=\"https://www.gisaid.org\" rel=\"nofollow\"\u003eGISAID\u003c/a\u003e metadata\nfile (which can be provided by \u003ccode\u003e--gisaid_metadata\u003c/code\u003e parameter) if the\ngenomes are available in GISAID. This option is faster and\ncomputationally less expensive, though limits to only genomes\navailable in GISAID. This option can be skipped by\nusing \u003ccode\u003e--skip_mapping\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-genomic-analysis\" class=\"anchor\" href=\"#genomic-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenomic Analysis\u003c/h3\u003e\n\u003cp\u003eThis module currently supports two different modes - \"\u003cem\u003ereference\u003c/em\u003e\" \u0026amp;\n\"\u003cem\u003euser\u003c/em\u003e\" which can be passed with \u003ccode\u003e--mode reference\u003c/code\u003e or \u003ccode\u003e--mode user\u003c/code\u003e. By default, \u003ccode\u003e--mode reference\u003c/code\u003e is activated which allows you\nto build a reference library for each lineage and subsequently each\nvariant for comparative analysis. This mode can take \u003ccode\u003eFASTA\u003c/code\u003e file\nwith multiple genomes (\u003cstrong\u003erecommended\u003c/strong\u003e \u0026amp; \u003cstrong\u003edefault\u003c/strong\u003e) or single\ngenome with a metadata file that should have one column atleast\n(\u003ccode\u003epango_lineage\u003c/code\u003e) as minimal metadata\n(see \u003ca href=\"#workflow-summary\"\u003eWorkflow Summary\u003c/a\u003e for detailed options).\nThe workflow has numerous options for several steps. For\nexample, in \u003ccode\u003emode --reference\u003c/code\u003e user can use \u003ccode\u003eBWAMEM\u003c/code\u003e using \u003ccode\u003e--bwa\u003c/code\u003e\ninstead of \u003ccode\u003eMINIMAP2\u003c/code\u003e (\u003cem\u003edefault\u003c/em\u003e) for mapping consensus sequences to\nreference genome. Similarly, \u003ccode\u003eivar\u003c/code\u003e with parameter \u003ccode\u003e--ivar\u003c/code\u003e for\nvariant calling instead of \u003ccode\u003efreebayes\u003c/code\u003e (\u003cem\u003edefault\u003c/em\u003e) option.\nThe user mode (\u003ccode\u003e--mode user\u003c/code\u003e) is by default active when using\ninteractive visualization through\n\u003ca href=\"https://github.com/cidgoh/COVID-MVP\"\u003eCOVID-MVP\u003c/a\u003e where a user can\nupload \u003ccode\u003eGVF\u003c/code\u003e file for comparative analysis against the reference data.\nUploaded dataset can be a \u003ccode\u003eFASTA\u003c/code\u003e file or variant called \u003ccode\u003eVCF\u003c/code\u003e file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-functional-annotation\" class=\"anchor\" href=\"#functional-annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFunctional Annotation\u003c/h3\u003e\n\u003cp\u003eIn this module, the variant called \u003ccode\u003eVCF\u003c/code\u003e file for each lineage is\nconverted into a \u003ccode\u003eGVF\u003c/code\u003e (Genomic Variant Format) file and annotated\nwith functional information using\n\u003ca href=\"https://github.com/nodrogluap/pokay\"\u003ePokay\u003c/a\u003e. GVF is a variant of\nGFF3 format that is standardized for describing genomic mutations;\nit is used here because it can describe mutations across multiple\nrows, and because the \"#attributes\" column can store information in\ncustom key-value pairs. The key-value pairs added at this stage\ninclude for each mutation: VOC/VOI status, clade-defining status\n(for reference lineages), and functional annotations parsed using\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/bin/vcf2gvf.py\"\u003evcf2gvf.py\u003c/a\u003e\nfile written in python.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-surveillance-reports\" class=\"anchor\" href=\"#surveillance-reports\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSurveillance Reports\u003c/h3\u003e\n\u003cp\u003eDifferent \u003ccode\u003eGVF\u003c/code\u003e files for the same variant are then collated and\nsummarized into a \u003ccode\u003eTSV\u003c/code\u003e file that contains mutation prevalence,\nprofile and functional impact. Further \u003ccode\u003eTSV\u003c/code\u003e file is also summarized\nas a more human friendly and impactful surveillance report in a\n\u003ccode\u003ePDF\u003c/code\u003e format. Relevant/important indicators can be specified in the\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/assets/ncov_surveillanceIndicators/functions_df_template.tsv\"\u003etsv file\u003c/a\u003e.\nThis feature of surveillance reports can be used to identify new\nclusters, important mutations, and track their transmission and\nprevalence trends. However, if not required, this step can be\nskipped using \u003ccode\u003e--skip_surveillance\u003c/code\u003e. An example of surveillance file\nfor Omicron variant using\n\u003ca href=\"https://virusseq-dataportal.ca\" rel=\"nofollow\"\u003eVirusSeq Data Portal\u003c/a\u003e is available in\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/docs\"\u003eDocs\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSee the\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/docs/PARAMETERS.md\"\u003eparameters\u003c/a\u003e\ndocs for all available options when running the workflow.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html#installation\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e (\u003ccode\u003e\u0026gt;=21.04.0\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall any of \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003e\u003ccode\u003eDocker\u003c/code\u003e\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003e\u003ccode\u003eConda\u003c/code\u003e\u003c/a\u003e for full pipeline reproducibility \u003cem\u003esee \u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/tree/master/environments\"\u003erecipes\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the pipeline and run with help for detailed parameter options:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-ncov-voc/main.nf --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eN E X T F L O W \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e version 21.04.3\nLaunching \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003emain.nf\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e [berserk_austin] - revision: 93ccc86071\n\nUsage:\n nextflow run main.nf -profile [singularity \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e docker \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e conda) --prefix [prefix] --mode [reference \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e user] [workflow-options]\n\nDescription:\n Variant Calling workflow \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e SARS-CoV-2 Variant of Concern (VOC) and\n Variant of Interest (VOI) consensus sequences to generate data\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eVisualization. All options set via CLI can be set\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e conf\n directory\n\nNextflow arguments (single DASH):\n -profile Allowed values: conda \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e singularity\n\nMandatory workflow arguments (mutually exclusive):\n --prefix A (unique) string prefix \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e output directory \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e each run.\n --mode A flag \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e user uploaded data through visualization app or\n high-throughput analyses (reference \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e user) (Default: reference)\n\nOptional:\n\nInput options:\n --seq Input SARS-CoV-2 genomes or consensus sequences\n (.fasta file)\n --meta Input Metadata file of SARS-CoV-2 genomes or consensus sequences\n (.tsv file)\n --userfile Specify userfile\n (fasta \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e vcf) (Default: None)\n --gisaid_metadata If lineage assignment is preferred by mapping metadata to GISAID\n metadata file, provide the metadata file (.tsv file)\n --variants Provide a variants file\n (.tsv) (Default: /Users/au572806/GitHub/nf-ncov-voc/assets/ncov_variants/variants_who.tsv)\n --outdir Output directory\n (Default: /Users/au572806/GitHub/nf-ncov-voc/results)\n --gff Path to annotation gff \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e variant consequence calling and typing.\n (Default: /Users/au572806/GitHub/nf-ncov-voc/assets/ncov_genomeFeatures/MN908947.3.gff3)\n --ref Path to SARS-CoV-2 reference fasta file\n (Default: /Users/au572806/GitHub/nf-ncov-voc/assets/ncov_refdb/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e)\n --bwa_index Path to BWA index files\n (Default: /Users/au572806/GitHub/nf-ncov-voc/assets/ncov_refdb/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e)\n\nSelection options:\n\n --ivar Run the iVar workflow instead of Freebayes(default)\n --bwamem Run the BWA workflow instead of MiniMap2(default)\n --skip_pangolin Skip PANGOLIN. Can be used \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e metadata already have lineage\n information or mapping is preferred method\n --skip_mapping Skip Mapping. Can be used \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e metadata already have lineage\n information or PANGOLIN is preferred method\n\nPreprocessing options:\n --startdate Start date (Submission date) to extract dataset\n (yyyy-mm-dd) (Default: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e2020-01-01\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n --enddate Start date (Submission date) to extract dataset\n (yyyy-mm-dd) (Default: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e2022-12-31\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\nGenomic Analysis parameters:\n\n BBMAP\n --maxns Max number of Ns allowed \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the sequence \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e qc process\n --minlength Minimun length of sequence required \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e sequences\n to pass qc filtration. Sequence less than minlength\n are not taken further\n\n IVAR/FREEBAYES\n --ploidy Ploidy (Default: 1)\n --mpileupDepth Mpileup depth (Default: unlimited)\n --var_FreqThreshold Variant Calling frequency threshold \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e consensus variant\n (Default: 0.75)\n --var_MaxDepth Maximum reads per input file depth to call variant\n (mpileup -d, Default: 0)\n --var_MinDepth Minimum coverage depth to call variant\n (ivar variants -m, freebayes -u Default: 10)\n --var_MinFreqThreshold Minimum frequency threshold to call variant\n (ivar variants -t, Default: 0.25)\n --varMinVariantQuality Minimum mapQ to call variant\n (ivar variants -q, Default: 20)\n\nSurveillance parameters:\n --virusseq True/False (Default: False). If your data is from\n VirusSeq Data Portal (Canada\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003es Nation COVID-19\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e genomics data portal).\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Passing this argument adds an acknowledgment\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e statement to the surveillance report.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e see https://virusseq-dataportal.ca/acknowledgements\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStart running your own analysis!\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTypical command for reference mode when Metadata File don\u0027t have\nlineage information:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow nf-ncov-voc/main.nf \\\n -profile \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econda, singularity, docker\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --prefix \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etesting\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --mode reference \\\n --startdate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e2020-01-\u003cspan class=\"pl-k\"\u003e01\u0026gt;\u003c/span\u003e \\\n --enddate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e2020-01-\u003cspan class=\"pl-k\"\u003e01\u0026gt;\u003c/span\u003e \\\n --seq \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSequence File\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --meta \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eMetadata File\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --skip_mapping \\\n --outdir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eOutput Dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTypical command for reference mode when Metadata File already\nhave\nlineage information:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow nf-ncov-voc/main.nf \\\n -profile \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econda, singularity, docker\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --prefix \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etesting\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --mode reference \\\n --startdate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e2020-01-\u003cspan class=\"pl-k\"\u003e01\u0026gt;\u003c/span\u003e \\\n --enddate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e2020-01-\u003cspan class=\"pl-k\"\u003e01\u0026gt;\u003c/span\u003e \\\n --seq \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eSequence File\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --meta \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eMetadata File\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --skip_mapping \\\n --skip_pangolin \\\n --outdir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eOutput Dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAn executable Python script called\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/bin/functional_annotation.py\"\u003e\u003ccode\u003efunctional_annotation.py\u003c/code\u003e\u003c/a\u003e\nhas been provided if you would like to update the functional\nannotations from \u003ccode\u003ePOKAY\u003c/code\u003e. This will create a new file which\n\u003cstrong\u003eshould replace\u003c/strong\u003e the current file in\n\u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/assets/ncov_functionalAnnotation\"\u003eassets/functional_annotation\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" href=\"#acknowledgments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis workflow and scripts are written and conceptually designed by\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eAffiliation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eZohaib Anwar; \u003ca href=\"https://github.com/anwarMZ\"\u003e@anwarMZ\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMadeline Iseminger; \u003ca href=\"https://github.com/miseminger\"\u003e@miseminger\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAnoosha Sehar; \u003ca href=\"https://github.com/Anoosha-Sehar\"\u003e@Anoosha-Sehar\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIvan Gill; \u003ca href=\"https://github.com/ivansg44\"\u003e@ivansg44\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWilliam Hsiao; \u003ca href=\"https://github.com/wwhsiao\"\u003e@wwhsiao\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cidgoh.ca\" rel=\"nofollow\"\u003eCentre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePaul Gordon; \u003ca href=\"https://github.com/nodrogluap\"\u003e@nodrogluap\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://www.ucalgary.ca/~gordonp\" rel=\"nofollow\"\u003eCSM Center for Health Genomics and Informatics, University of Calgary, Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGary Van Domselaar; \u003ca href=\"https://github.com/phac-nml\"\u003e@phac-nml\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://umanitoba.ca/faculties/health_sciences/medicine/units/medical_microbiology/faculty/vandomselaar.html\" rel=\"nofollow\"\u003ePublic Health Agency of Canada\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMany thanks to others who have helped out and contributed along the way too, including (but not limited to)*: \u003ca href=\"https://virusseq.ca/about/governance/\" rel=\"nofollow\"\u003eCanadian COVID Genomics Network - VirusSeq, Data Analytics Working Group\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-support\" class=\"anchor\" href=\"#support\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport\u003c/h2\u003e\n\u003cp\u003eFor further information or help, don\u0027t hesitate to get in touch at\n\u003ca href=\"mailto:mzanwar@sfu.ca\"\u003emzanwar@sfu.ca\u003c/a\u003e or \u003ca href=\"mailto:wwshiao@sfu.ca\"\u003ewwshiao\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-citations\" class=\"anchor\" href=\"#citations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitations\u003c/h2\u003e\n\u003cp\u003eAn extensive list of references for the tools used by the workflow\ncan be found in the \u003ca href=\"https://github.com/cidgoh/nf-ncov-voc/blob/master/docs/CITATIONS.md\"\u003eCITATIONS.md\u003c/a\u003e file.\u003c/p\u003e\n", + "full_name": "TANGO-Project/alde", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-application-lifecycle-deployment-engine-alde\" class=\"anchor\" aria-hidden=\"true\" href=\"#application-lifecycle-deployment-engine-alde\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApplication Lifecycle Deployment Engine (ALDE)\u003c/h1\u003e\n\u003cp\u003e\u00a9 Atos Spain S.A. 2016\u003c/p\u003e\n\u003cp\u003eApplication Lifecycle Deployment Engine (ALDE) is a component of the European Project TANGO (\u003ca href=\"http://tango-project.eu\" rel=\"nofollow\"\u003ehttp://tango-project.eu\u003c/a\u003e ).\u003c/p\u003e\n\u003cp\u003eALDE is distributed under a \u003ca href=\"https://www.gnu.org/licenses/agpl-3.0.txt\" rel=\"nofollow\"\u003eGNU AFFERO GENERAL PUBLIC LICENSE\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eALDE is responsible for the workload scheduling and the management of the application life-cycle while it is executed. ALDE will take the application source code, packetize for different heterogeneous architectures configurations and, if possible, deploy it via a TANGO Device Supervisor and manage the application execution.\u003c/p\u003e\n\u003cp\u003eMore in detail each one of the previous steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eCompilation\u003c/strong\u003e - ALDE is able to compile the application in different configurations depending of the selected heterogeneous architectures. The result will be a set of binaries optimal compiled for specific hardware architectures.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePacketization\u003c/strong\u003e - ALDE, once the application has been compiled, can packetize it. For the moment it only supports typical tar.gz files and \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e containers.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeployment\u003c/strong\u003e - ALDE is able to automatically deploy an application into a TANGO compatible Device Supervisor. It will launch the execution and monitor it. It will also support adaptations interactions if used in combination with the \u003ca href=\"https://github.com/TANGO-Project/self-adaptation-manager\"\u003eTANGO Self-Adaptation Manager\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation Guide\u003c/h2\u003e\n\u003cp\u003eThis guide it is divided into two different guides, one specific to create an environment for development and another one to just run and use ALDE.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation-for-development\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-for-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation for development\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h4\u003e\n\u003cp\u003eTo develop for ALDE we need to install two pieces of software:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.python.org\" rel=\"nofollow\"\u003ePython 3.6 or higher\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://virtualenv.pypa.io/en/stable/\" rel=\"nofollow\"\u003eVirtualenv\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-installation-and-configuration-procedure\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-and-configuration-procedure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and configuration procedure\u003c/h4\u003e\n\u003cp\u003eTo develop ALDE it is necessary to create a \u003ca href=\"http://docs.python-guide.org/en/latest/dev/virtualenvs/\" rel=\"nofollow\"\u003ePython Virtualenv\u003c/a\u003e (depending on your installation of Python pip3 command can be called pip):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip3 install virtualenv\nCollecting virtualenv\n Downloading virtualenv-15.0.3-py2.py3-none-any.whl (3.5MB)\n 100% |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3.5MB 398kB/s\nInstalling collected packages: virtualenv\nSuccessfully installed virtualenv-15.0.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter that, you need to create a virtualenv for you to develop:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ virtualenv venv\nUsing base prefix \u0027/Library/Frameworks/Python.framework/Versions/3.5\u0027\nNew python executable in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/venv/bin/python3.5\nAlso creating executable in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/venv/bin/python\nInstalling setuptools, pip, wheel...done.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow it time to install PyBuilder:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFirst we activate the virtualenv:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ source venv/bin/activate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNow we install PyBuilder using Pip:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install pybuilder\nCollecting pybuilder\n Using cached PyBuilder-0.11.9.tar.gz\nRequirement already satisfied: pip\u0026gt;=7.0 in ./venv/lib/python3.5/site-packages (from pybuilder)\nCollecting tblib (from pybuilder)\n Using cached tblib-1.3.0-py2.py3-none-any.whl\nRequirement already satisfied: wheel in ./venv/lib/python3.5/site-packages (from pybuilder)\nBuilding wheels for collected packages: pybuilder\n Running setup.py bdist_wheel for pybuilder ... done\n Stored in directory: /Users/davidgp/Library/Caches/pip/wheels/04/9c/b3/d2d2194e8911818abdfa1c3c47501a64602714415af28d8da8\nSuccessfully built pybuilder\nInstalling collected packages: tblib, pybuilder\nSuccessfully installed pybuilder-0.11.9 tblib-1.3.0\n(venv)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow it is possible to compile the project:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall first the dependencies:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ pyb install_dependencies\nPyBuilder version 0.11.9\nBuild started at 2016-11-11 14:55:00\n------------------------------------------------------------\n[INFO] Building alde version 1.0.dev0\n[INFO] Executing build in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde\n[INFO] Going to execute task install_dependencies\n[INFO] Installing all dependencies\n[INFO] Processing batch dependency \u0027mockito\u0027\n------------------------------------------------------------\nBUILD SUCCESSFUL\n------------------------------------------------------------\nBuild Summary\n Project: alde\n Version: 1.0.dev0\n Base directory: /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde\n Environments:\n Tasks: install_dependencies [9623 ms]\nBuild finished at 2016-11-11 14:55:10\nBuild took 9 seconds (9637 ms)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNow you can build the project (if you are using Windows, probably the coverage task is going to fail)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ pyb\nPyBuilder version 0.11.9\nBuild started at 2016-11-11 14:57:03\n------------------------------------------------------------\n[INFO] Building alde version 1.0.dev0\n[INFO] Executing build in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde\n[INFO] Going to execute task publish\n[INFO] Installing plugin dependency coverage\n[INFO] Installing plugin dependency unittest-xml-reporting\n[INFO] Running unit tests\n[INFO] Executing unit tests from Python modules in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/src/unittest/python\n[INFO] Executed 1 unit tests\n[INFO] All unit tests passed.\n[INFO] Building distribution in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/target/dist/alde-1.0.dev0\n[INFO] Copying scripts to /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/target/dist/alde-1.0.dev0/scripts\n[INFO] Writing setup.py as /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/target/dist/alde-1.0.dev0/setup.py\n[INFO] Collecting coverage information\n[WARN] coverage_branch_threshold_warn is 0 and branch coverage will not be checked\n[WARN] coverage_branch_partial_threshold_warn is 0 and partial branch coverage will not be checked\n[INFO] Running unit tests\n[INFO] Executing unit tests from Python modules in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/src/unittest/python\n[INFO] Executed 1 unit tests\n[INFO] All unit tests passed.\n[WARN] Module \u0027__init__\u0027 was not imported by the covered tests\n[INFO] Overall coverage is 94%\n[INFO] Overall coverage branch coverage is 100%\n[INFO] Overall coverage partial branch coverage is 100%\n[INFO] Building binary distribution in /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/target/dist/alde-1.0.dev0\n------------------------------------------------------------\nBUILD SUCCESSFUL\n------------------------------------------------------------\nBuild Summary\n Project: alde\n Version: 1.0.dev0\n Base directory: /Users/davidgp/Documents/trabajo/TANGO/repositorios/alde\n Environments:\n Tasks: prepare [2407 ms] compile_sources [0 ms] run_unit_tests [40 ms] package [3 ms] run_integration_tests [0 ms] verify [134 ms] publish [616 ms]\nBuild finished at 2016-11-11 14:57:06\nBuild took 3 seconds (3219 ms)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDone!\u003c/p\u003e\n\u003cp\u003eNow, remember, each time you need to start to develop, initalize the virtualenv:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source venv/bin/activate\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-tests-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#tests-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTests with Singularity\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eInstall Singularity - \u003ca href=\"SingularityTests.md\"\u003eView doc\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003ca id=\"user-content-build-status-from-travis-ci\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-status-from-travis-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild status from Travis-CI\u003c/h4\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/TANGO-Project/alde\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c8c8f4e8fc40340ef1f6ece28cd4cddc05eda4a013da60298c68c64fad39796e/68747470733a2f2f7472617669732d63692e6f72672f54414e474f2d50726f6a6563742f616c64652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/TANGO-Project/alde.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-sonarqube-reports\" class=\"anchor\" aria-hidden=\"true\" href=\"#sonarqube-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSonarQube reports:\u003c/h4\u003e\n\u003cp\u003eSonarQube ( \u003ca href=\"http://www.sonarqube.org/\" rel=\"nofollow\"\u003ehttp://www.sonarqube.org/\u003c/a\u003e ) reports for this project are available at: \u003ca href=\"https://sonarqube.com/dashboard?id=tango%3Aalde\" rel=\"nofollow\"\u003ehttps://sonarqube.com/dashboard?id=tango%3Aalde\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation-for-running-the-service\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-for-running-the-service\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation for running the service\u003c/h3\u003e\n\u003cp\u003eIn this case, we are going to detail how to run the application directly using Python. It is possible to run it behind a proxy or webserver, to do so, please, check \u003ca href=\"http://flask.pocoo.org/docs/0.12/deploying/\" rel=\"nofollow\"\u003ethis guides\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-configuring-the-service\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuring-the-service\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguring the service\u003c/h4\u003e\n\u003cp\u003eALDE employs a \u003ca href=\"https://www.sqlite.org/\" rel=\"nofollow\"\u003eSQLite\u003c/a\u003e database server that needs to be configured together with the port were the service it is going to be listen. That configuration can be done editing the file alde_configuration.ini that contains these two variables:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[DEFAULT]\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eSQL_LITE_URL\u003c/span\u003e = sqlite:////tmp/test.db\n\u003cspan class=\"pl-k\"\u003ePORT\u003c/span\u003e = 5000\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo install it, it is necessary to execute the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo launch the service we need to execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ python app.py \n2017-07-18 09:16:02,812 root INFO Loading configuration\n[]\n\u0026lt;Section: DEFAULT\u0026gt;\n2017-07-18 09:16:02,813 root INFO Starting ALDE\n/Users/davidgp/Documents/trabajo/TANGO/repositorios/alde/venv/lib/python3.6/site-packages/flask_sqlalchemy/__init__.py:839: FSADeprecationWarning: SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and will be disabled by default in the future. Set it to True or False to suppress this warning.\n \u0027SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and \u0027\n2017-07-18 09:16:02,937 apscheduler.scheduler INFO Adding job tentatively -- it will be properly scheduled when the scheduler starts\n2017-07-18 09:16:02,937 apscheduler.scheduler INFO Adding job tentatively -- it will be properly scheduled when the scheduler starts\n2017-07-18 09:16:02,938 apscheduler.scheduler INFO Adding job tentatively -- it will be properly scheduled when the scheduler starts\n2017-07-18 09:16:02,940 apscheduler.scheduler INFO Added job \"check_nodes_in_db_for_on_line_testbeds\" to job store \"default\"\n2017-07-18 09:16:02,940 apscheduler.scheduler INFO Added job \"update_node_info\" to job store \"default\"\n2017-07-18 09:16:02,940 apscheduler.scheduler INFO Added job \"update_cpu_node_info\" to job store \"default\"\n2017-07-18 09:16:02,940 apscheduler.scheduler INFO Scheduler started\n2017-07-18 09:16:02,986 werkzeug INFO * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter it we could verify it works just by execution the following (in this example we are using \u003ca href=\"https://curl.haxx.se/\" rel=\"nofollow\"\u003eCurl\u003c/a\u003e but you could use another REST/http client):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl localhost:5000/api/v1/testbeds\n{\n \"num_results\": 0, \n \"objects\": [], \n \"page\": 1, \n \"total_pages\": 0\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage Guide\u003c/h2\u003e\n\u003cp\u003eAlthough a CLI client is planned, for the moment ALDE offers a REST interface.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rest-api-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#rest-api-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREST API documentation\u003c/h3\u003e\n\u003cp\u003eThe rest api is fully documented here: ( \u003ca href=\"https://jsapi.apiary.io/previews/applicationlifecycledeploymentengine/reference/0/testbed\" rel=\"nofollow\"\u003ehttps://jsapi.apiary.io/previews/applicationlifecycledeploymentengine/reference/0/testbed\u003c/a\u003e )\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-example-scenarios\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-scenarios\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample scenarios\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-creating-an-application\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-an-application\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating an application\u003c/h4\u003e\n\u003cp\u003eListing all applications available:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl http://localhost:5000/api/v1/applications\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCreating an application\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -X POST -H\u0027Content-type: application/json\u0027 http://127.0.0.1:5000/api/v1/applications -d\u0027{ \"name\": \"my_app\" }\u0027\n{\n \"executables\": [],\n \"execution_scripts\": [],\n \"id\": 1,\n \"name\": \"my_app\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUploading zip file with the source code of the application. Pay attention to the two variables: compilation_type and compilation_script\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -X POST -F \"file=@test.zip\" http://localhost:5000/api/v1/upload/1?compilation_type=SINGULARITY:PM\\\u0026amp;compilation_script=./build.sh\nfile upload for app with id: 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSeeing the status of the compilation (executable section):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl http://localhost:5000/api/v1/applications/1\n{\n \"executables\": [\n {\n \"application_id\": 1,\n \"compilation_script\": \"compilation.sh\",\n \"compilation_type\": \"singularity:pm\",\n \"executable_file\": null,\n \"id\": 1,\n \"source_code_file\": \"f5a8e16b-6c36-4092-97cb-6081374d9b29.zip\",\n \"status\": \"NOT_COMPILED\"\n }\n ],\n \"execution_scripts\": [],\n \"id\": 1,\n \"name\": \"my_app\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-adding-a-new-slurm-type-testbed-that-you-can-connect-via-ssh-protocol\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-a-new-slurm-type-testbed-that-you-can-connect-via-ssh-protocol\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding a new SLURM type testbed that you can connect via SSH protocol\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003ecurl localhost:5000/api/v1/testbeds -X POST -H\u0027Content-type: application/json\u0027 -d\u0027{ \"name\": \"slurm_testbed\", \"on_line\": true, \"category\": \"SLURM\", \"protocol\": \"SSH\", \"endpoint\": \"user@ssh.com\"}\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-relation-to-other-tango-components\" class=\"anchor\" aria-hidden=\"true\" href=\"#relation-to-other-tango-components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRelation to other TANGO components\u003c/h2\u003e\n\u003cp\u003eALDE can be used as an standalone tool in TANGO, it will allow to compile application for different targeted heterogenous architectures in an optimize way and with different configurations of heterogenous devices, but its fully potential it is with other TANGO components:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eProgramming model and IDE tools\u003c/strong\u003e - TANGO Programming Model can connect with ALDE to submit the code for compilation and packetization. Also it could be intereact with ALDE to submit the application directly to a TANGO compatible device supervisor.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDevice Supervisor\u003c/strong\u003e - ALDE can interact with a on-line testbed that has installed a TANGO device supervisor on it. This will allow to automatically deploy diferent configurations of the application and execute it, monitoring the execution and extract back the results.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSelf-Adaptation Manager\u003c/strong\u003e - ALDE will provide intefaces for the Self-Adaptation Manager to change the configuration of an application to optimize its execution in a TANGO compatible testbed.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, - "topics": [ - "bioinformatics", - "genomics", - "sars-cov-2", - "microbial-genomics", - "covid-19", - "nextflow", - "virus", - "variant-calling" - ], - "updated_at": 1642619620.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1592993443.0 }, { "data_format": 2, - "description": "CUT\u0026RUN pipeline for mm10 genome", + "description": null, "filenames": [ - "Singularity.mm10v1.centos" + "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.longread", + "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.htsbox", + "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.quickmerge" ], - "full_name": "ertheisen/ferncanyon_centos", + "full_name": "ZhangZhenmiao/metagenome_assembly", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ferncanyonhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#ferncanyonhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eferncanyon.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-benchmarking-de-novo-assembly-methods-on-metagenomic-sequencing-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#benchmarking-de-novo-assembly-methods-on-metagenomic-sequencing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarking de novo assembly methods on metagenomic sequencing data\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-assemblers-evaluated\" class=\"anchor\" aria-hidden=\"true\" href=\"#assemblers-evaluated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAssemblers evaluated\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-short-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#short-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShort-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaSPdes \u003ccode\u003eassembly_scripts/metaspades.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMEGAHIT \u003ccode\u003eassembly_scripts/megahit.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-linked-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#linked-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinked-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ecloudSPAdes \u003ccode\u003eassembly_scripts/cloudspades.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAthena \u003ccode\u003eassembly_scripts/athena.sh \u0026lt;short-read ssembly\u0026gt; \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-long-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#long-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLong-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaFlye \u003ccode\u003eassembly_scripts/metaflye.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanu \u003ccode\u003eassembly_scripts/canu.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eLathe \u003ccode\u003eassembly_scripts/lathe.sh \u0026lt;long_reads\u0026gt; \u0026lt;short_reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShasta \u003ccode\u003eassembly_scripts/shasta.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMECAT2 \u003ccode\u003eassembly_scripts/mecat2.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNECAT \u003ccode\u003eassembly_scripts/necat.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ewtdbg2 \u003ccode\u003eassembly_scripts/wtdbg2.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-hybrid-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#hybrid-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHybrid assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaFlye-subassemblies \u003ccode\u003eassembly_scripts/metaflye-subassemblies.sh \u0026lt;short-read assembly\u0026gt; \u0026lt;long-read assembly\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDBG2OLC \u003ccode\u003eassembly_scripts/dbg2olc.sh \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOPERA-MS \u003ccode\u003eassembly_scripts/opera-ms.sh \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOPERA-LG \u003ccode\u003eassembly_scripts/opera-lg.sh \u0026lt;short-read assembly\u0026gt; \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-time-and-memory\" class=\"anchor\" aria-hidden=\"true\" href=\"#time-and-memory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTime and memory\u003c/h2\u003e\n\u003cp\u003eTime and memory consumed are measured by adding \u003ccode\u003e/usr/bin/time -v\u003c/code\u003e before the above commands.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 0, + "subscribers_count": 1, "topics": [], - "updated_at": 1572448081.0 + "updated_at": 1658500295.0 }, { "data_format": 2, - "description": "Experiment with Singularity Containers for MPI apps", + "description": null, "filenames": [ - "src/Singularity.def" + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/Singularity.template", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/rl/Singularity.learnaBenchmark", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/rl/Singularity.Cartpole", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.TabularBenchmarks", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_201", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_101", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_1shot1", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.XGBoostBenchmark", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.ml_mmfb", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.PyBNN", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.SupportVectorMachine", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/od/Singularity.ODBenchmarks", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine" ], - "full_name": "nahkbce2/myContainerSandBox", + "full_name": "jointentropysearch/JointEntropySearch", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mycontainersandbox\" class=\"anchor\" href=\"#mycontainersandbox\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emyContainerSandBox\u003c/h1\u003e\n\u003cp\u003eExperiment with Singularity Containers for MPI apps\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-joint-entropy-search-for-maximally-informed-bayesian-optimization\" class=\"anchor\" aria-hidden=\"true\" href=\"#joint-entropy-search-for-maximally-informed-bayesian-optimization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJoint Entropy Search for Maximally-Informed Bayesian Optimization\u003c/h1\u003e\n\u003cp\u003eThis is the official repository for Joint Entropy Search for Maximally-Informed Bayesian Optimization. We developed our code by building on Max-value Entropy Search (Wang and Jegelka, 2017) which in turn built on Predictive Entropy Search (Hernandez-Lobato et al., 2014), which was developed upon GPstuff (Vanhatalo et al., 2013). To keep the repository as trim and clean as possible, some of the examples and methods from previous work have been removed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements\u003c/h2\u003e\n\u003cp\u003eAll code has been tested with MATLAB 2021a. After installing the conda environment provided in \u003ccode\u003ejes.yml\u003c/code\u003e, add the required libraries by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/conda/envs/jes/lib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand everything should be up and running.\u003c/p\u003e\n\u003cp\u003eWhile the required mex file is included, it may need to be re-compiled. To do so, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd utils\nmex chol2invchol.c -lgsl -lblas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLastly, to run experiments, do one of the following:\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-for-gp-sample-tasks-where-the-hyperparameters-of-the-surrogate-model-are-fixed\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-gp-sample-tasks-where-the-hyperparameters-of-the-surrogate-model-are-fixed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor GP sample tasks (where the hyperparameters of the surrogate model are fixed):\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"gp_task(path, seed, approach, dim);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003epath\u003c/code\u003e is the path to the experiment to run, \u003ccode\u003eseed\u003c/code\u003e is the seed to run, \u003ccode\u003eapproach\u003c/code\u003e is the acquisition function in question, and \u003ccode\u003edim\u003c/code\u003e, somewhat exessively, is the dimensionality of the problem. For example, one can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"gp_task(\u0027gp/gp_2dim.py\u0027, 42, \u0027JES\u0027, 2);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-for-all-other-tasks-use-synthetic_task\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-all-other-tasks-use-synthetic_task\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor all other tasks, use \u003ccode\u003esynthetic_task\u003c/code\u003e:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"synthetic_task(path, seed, approach, dim);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo to run Hartmann (6D):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"synthetic_task(\u0027synthetic/hartmann6.py\", 37, \u0027MES\u0027, 6);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEvery experiment is automatically stored in a csv in experiments/results. The recommended points, which are needed for inference regret, are all evaluated \u003cem\u003eafter\u003c/em\u003e the full run is finished. These queries are appended to the same csv.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-available-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable options\u003c/h2\u003e\n\u003cp\u003eThanks to Wang \u0026amp; Jegelka (2017), this repository comes equipped with the following acquisition functions. MES-G (shortened to just MES) and EI were used in this paper to benchmark against.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMax-value Entropy Search with Gumbel sampling (MES-G) by Wang \u0026amp; Jegelka, 2017;\u003c/li\u003e\n\u003cli\u003eMax-value Entropy Search with random features (MES-R) by Wang \u0026amp; Jegelka, 2017;\u003c/li\u003e\n\u003cli\u003eOptimization as estimation (EST) by Wang et al., 2016.\u003c/li\u003e\n\u003cli\u003eGaussian process upper confidence bound (GP-UCB) by Auer, 2002; Srinivas et al., 2010;\u003c/li\u003e\n\u003cli\u003eProbability of improvement (PI) by Kushner, 1964;\u003c/li\u003e\n\u003cli\u003eExpected improvement (EI) by Mockus, 1974\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe repository builds on \u003ca href=\"https://bitbucket.org/jmh233/codepesnips2014\" rel=\"nofollow\"\u003epredictive entropy search\u003c/a\u003e (Hern\u00e1ndez-Lobato et al., 2014).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e--Anonymous author(s)--. Joint Entropy Search for Maximally-Informed Bayesian Optimization. Under review.\u003c/li\u003e\n\u003cli\u003eWang, Zi and Jegelka, Stefanie. Max-value Entropy Search for Efficient Bayesian Optimization. International Conference on Machine Learning (ICML), 2017.\u003c/li\u003e\n\u003cli\u003eAuer, Peter. Using confidence bounds for exploitationexploration tradeoffs. Journal of Machine Learning Research, 3:397\u2013422, 2002.\u003c/li\u003e\n\u003cli\u003eSrinivas, Niranjan, Krause, Andreas, Kakade, Sham M, and Seeger, Matthias. Gaussian process optimization in the bandit setting: No regret and experimental design. In International Conference on Machine Learning (ICML), 2010.\u003c/li\u003e\n\u003cli\u003eKushner, Harold J. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. Journal of Fluids Engineering, 86(1):97\u2013106, 1964.\u003c/li\u003e\n\u003cli\u003eMockus, J. On Bayesian methods for seeking the extremum. In Optimization Techniques IFIP Technical Conference, 1974.\u003c/li\u003e\n\u003cli\u003eWang, Zi, Zhou, Bolei, and Jegelka, Stefanie. Optimization as estimation with Gaussian processes in bandit settings. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.\u003c/li\u003e\n\u003cli\u003eCatto, Erin. Box2d, a 2D physics engine for games. \u003ca href=\"http://box2d.org\" rel=\"nofollow\"\u003ehttp://box2d.org\u003c/a\u003e, 2011.\u003c/li\u003e\n\u003cli\u003ePybox2d, 2D Game Physics for Python. \u003ca href=\"http://github.com/pybox2d/pybox2d\"\u003ehttp://github.com/pybox2d/pybox2d\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eHern\u00e1ndez-Lobato, Jos\u00e9 Miguel, Hoffman, Matthew W, and Ghahramani, Zoubin. Predictive entropy search for efficient global optimization of black-box functions. In Advances in Neural Information Processing Systems (NIPS), 2014. \u003ca href=\"https://bitbucket.org/jmh233/codepesnips2014\" rel=\"nofollow\"\u003ehttps://bitbucket.org/jmh233/codepesnips2014\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHennig, Philipp and Schuler, Christian J. Entropy search for information-efficient global optimization. Journal of Machine Learning Research, 13:1809\u20131837, 2012. \u003ca href=\"http://www.probabilistic-optimization.org/Global.html\" rel=\"nofollow\"\u003ehttp://www.probabilistic-optimization.org/Global.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eJarno Vanhatalo, Jaakko Riihim\u00e4ki, Jouni Hartikainen, Pasi Jyl\u00e4nki, Ville Tolvanen, Aki Vehtari. GPstuff: Bayesian Modeling with Gaussian Processes. Journal of Machine Learning Research, 14(Apr):1175-1179, 2013.\u003c/li\u003e\n\u003cli\u003eKandasamy, Kirthevasan, Schneider, Jeff, and Poczos, Barnabas. High dimensional Bayesian optimisation and bandits via additive models. In International Conference on Machine Learning (ICML), 2015.\u003c/li\u003e\n\u003cli\u003eWang, Zi, Li, Chengtao, Jegelka, Stefanie, and Kohli, Pushmeet. Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. International Conference on Machine Learning (ICML), 2017.\u003c/li\u003e\n\u003cli\u003eWestervelt, Eric R, Grizzle, Jessy W, Chevallereau, Christine, Choi, Jun Ho, and Morris, Benjamin. Feedback control of dynamic bipedal robot locomotion, volume 28. CRC press, 2007.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1638466785.0 + "updated_at": 1656006365.0 }, { "data_format": 2, - "description": "Predicts time series for SARS-CoV-2 lineages", + "description": "This project is either going to become the most dangerous computer virus the world has and ever will see or it will actually go smoothly (I really hope it isn\u2019t the former)", "filenames": [ - "Singularity.covate" + "parametric-face-image-generator-2.1.1/Singularity" ], - "full_name": "Pathogen-Genomics-Cymru/covate", - "latest_release": "v1.0.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/Pathogen-Genomics-Cymru/covate/workflows/Covate-CI/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/Pathogen-Genomics-Cymru/covate/workflows/Covate-CI/badge.svg\" alt=\"Build Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-covate\" class=\"anchor\" href=\"#covate\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecovate\u003c/h1\u003e\n\u003cp\u003eCovate uses the COG-UK metadata to forecast the time series for lineages of SARS-CoV-2 that are common to a specified list of regions. It can also be used to investigate the likelihood of lineages being imported between regions.\u003c/p\u003e\n\u003cp\u003eCovate consists of three analyses:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePREDICT, --predict ,-p\u003c/strong\u003e \u003cbr\u003e\nCovate can forecast the time series of sequenced cases for lineages that are common to all the regions. The selection of either a VAR or VECM model is automated on a per lineage basis from the results of a cointegration test. The selection of parameters for the chosen model is also automated.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eVALIDATE, --validate, -v\u003c/strong\u003e \u003cbr\u003e\nCovate can also build validation forecasts for existing metadata. For example, the validation forecast from 30/8/2021 would be a replicate of the prediction forecast from 16/8/2021 (when running with default parameters). The validation forecast is plotted against the actual time series.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCROSS-CORRELATION, --cross-correlation, -c\u003c/strong\u003e \u003cbr\u003e\nCovate can run a cross-correlation analysis that investigates the likelihood of lineages of SARS-CoV-2 being imported between the regions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eThe recommended Python versions for running covate are 3.7.x - 3.9.x (other versions may work but are untested).\u003c/p\u003e\n\u003cp\u003eFor stability, it is recommended you download the latest \u003ca href=\"https://github.com/Pathogen-Genomics-Cymru/covate/releases\"\u003erelease\u003c/a\u003e and install using \u003ccode\u003epython setup.py install\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo install the very latest updates (as on main branch) you can use pip with git+:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install git+https://github.com/Pathogen-Genomics-Cymru/covate.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eTo run all three analyses with default arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecovate -i metadata.csv -o output_dir -p -v -c\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA full description of the available arguments and their default values can be found below.\u003c/p\u003e\n\u003cp\u003eHelp message:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: covate [-h] -i METADATA -o OUTPUT [-r REGIONS] [-a ADM]\n [-l LINEAGETYPE] [-t TIMEPERIOD] [-e ENDDATE] [-p] [-v] [-c]\n [-f PRIMARYREGION] [-m MAXLAGS] [-n NSTEPS]\n\noptional arguments:\n -h, --help show this help message and exit\n -i METADATA, --input-csv METADATA\n Input metadata csv, expects columns: cog_id,\n adm1/adm2, sample_date, lineage/uk_lineage\n -o OUTPUT, --output-dir OUTPUT\n Output directory for the results\n -r REGIONS, --region-list REGIONS\n Input list of regions to compare, e.g. Wales, England\n -a ADM, --adm ADM Select either adm1 or adm2\n -l LINEAGETYPE, --lineage-type LINEAGETYPE\n Select either lineage or uk_lineage\n -t TIMEPERIOD, --time-period TIMEPERIOD\n Select time period in weeks to take from metadata\n -e ENDDATE, --end-date ENDDATE\n Select end date to take from metadata. Format: d/m/Y\n -p, --predict Run prediction forecast\n -v, --validate Run validation forecast\n -c, --cross-correlation\n Run cross-correlation analysis\n -f PRIMARYREGION, --primary-region PRIMARYREGION\n Region of primary interest for cross-correlation\n -m MAXLAGS, --max-lags MAXLAGS\n Maximum number of lags to investigate\n -n NSTEPS, --n-steps NSTEPS\n Number of days to predict\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-arguments\" class=\"anchor\" href=\"#arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArguments\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--input-csv\u003c/strong\u003e \u003cbr\u003e Input metadata csv. The following columns are required: \u003cstrong\u003ecog_id, adm1/adm2, sample_date, lineage/uk_lineage\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--output-dir\u003c/strong\u003e \u003cbr\u003e Output directory for results\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--region-list\u003c/strong\u003e \u003cbr\u003e Input list of regions to compare. Default \u003cstrong\u003eWales, England\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--adm\u003c/strong\u003e \u003cbr\u003e Select adm the regions belong to (adm1 or adm2). Default \u003cstrong\u003eadm1\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--lineage-type\u003c/strong\u003e \u003cbr\u003e Select whether to compare global or uk lineages (lineage or uk_lineage). Default \u003cstrong\u003euk_lineage\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--time-period\u003c/strong\u003e \u003cbr\u003e Select time period in weeks to take from the input metadata csv. Default \u003cstrong\u003e12\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--end-date\u003c/strong\u003e \u003cbr\u003e The end date of the time period to take from the input metadata csv. Expected format is d/m/Y, e.g. 31/7/2021. Default \u003cstrong\u003elatest date in the metadata -7 days\u003c/strong\u003e (to account for lag in data)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--predict\u003c/strong\u003e \u003cbr\u003e If specified, prediction forecasts will be created\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--validate\u003c/strong\u003e \u003cbr\u003e If specified, validation forecasts will be created\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--cross-correlation\u003c/strong\u003e \u003cbr\u003e If specifed, cross-correlation analysis will be run\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--primary-region\u003c/strong\u003e \u003cbr\u003e Primary region for cross-correlation analysis. Default \u003cstrong\u003eWales\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--max-lags\u003c/strong\u003e \u003cbr\u003e Select maximum number of lags to investigate. Default \u003cstrong\u003e14\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--n-steps\u003c/strong\u003e \u003cbr\u003e Number of days to predict. Default \u003cstrong\u003e14\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-workflow\" class=\"anchor\" href=\"#workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Pathogen-Genomics-Cymru/covate/blob/main/covate-workflow.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"600\" src=\"https://github.com/Pathogen-Genomics-Cymru/covate/raw/main/covate-workflow.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-output\" class=\"anchor\" href=\"#output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003cp\u003eA date-stamped output directory is created with sub-directories for each common lineage and a cross-correlation sub-directory. At the top level you will find a csv of the timeseries and summary error log file(s) for prediction and validation (provided --predict and --validate). The cross-correlation sub-directory contains multiple plots and csvs from the cross-correlation analysis (provided --cross-correlation). In a lineage sub-directory you should find the following directories and plots:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eprediction\u003c/strong\u003e The forecasted time series for each region. This directory will be empty if --predict is not specified. If --predict has been specified and directory is empty then the forecast has failed to run (check logs/prediction for the error log).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003evalidation\u003c/strong\u003e A validation forecast for each region (plots the time series for the last nsteps prior to the set end date with a forecast). This directory will be empty if --validate is not specified. If --validate has been specified and the directory is empty then the forecast has failed to run (check logs/validation for the error log).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003elogs\u003c/strong\u003e There are separate log files for prediction and validation. Log files $lineage_model.txt contain information on the built models. If there are any errors raised for the lineage then an error log $lineage_error.txt will also be generated. There are also csvs of the forecasted time series values.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eadditional-plots\u003c/strong\u003e Time series for the lineage and ACF plots for each region. There may be additional VAR plots if relevant.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-error-log\" class=\"anchor\" href=\"#error-log\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eError Log\u003c/h3\u003e\n\u003cp\u003eThere are separate error log files for prediction and validation. The error logs will likely contain ERROR and WARN messages for some lineages. ERROR messages indicate a fatal error where the code was unable to build a model for a lineage due to poor quality data. WARN messages indicate a non-fatal error, in this case the model should build for a lineage, but the message may indicate that the model might not be accurate (e.g. A WARN message is recorded if causality is not found).\u003c/p\u003e\n", + "full_name": "AdamOswald/Ai-test", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ai-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#ai-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAi-test\u003c/h1\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [ - "covid-19", - "time-series-analysis", - "data-science", - "data-visualization" + "ai", + "artificial-intelligence", + "deep-learning", + "face-swap", + "face-swapping", + "html", + "html-css-javascript", + "image-generation", + "java", + "javascript", + "jupyter-notebooks", + "machine-learning", + "nerual-network", + "python", + "pytorch", + "scss", + "tensor", + "text-to-image", + "nerual-networks", + "database" ], - "updated_at": 1639428014.0 + "updated_at": 1664619917.0 }, { "data_format": 2, - "description": "Fork of Fast Downward with support of the unified planning framework of the AIPlan4EU project", + "description": "Evaluate accuracy of covid assemblies where truth is available", "filenames": [ - "misc/releases/20.06/Singularity.20.06", - "misc/releases/latest/Singularity", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12" + "Singularity.def" ], - "full_name": "roeger/downward-aiplan4eu", + "full_name": "iqbal-lab-org/covid-truth-eval", + "latest_release": "v0.0.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-covid-truth-eval\" class=\"anchor\" aria-hidden=\"true\" href=\"#covid-truth-eval\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecovid-truth-eval\u003c/h1\u003e\n\u003cp\u003eEvaluate accuracy of covid assemblies where truth is available\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eMinimal instructions are below. Please see the \u003ca href=\"https://github.com/iqbal-lab-org/covid-truth-eval/wiki\"\u003ewiki page\u003c/a\u003e\nfor more details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eGet a Docker image of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/iqbal-lab-org/cte:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll Docker images are listed in the\n\u003ca href=\"https://github.com/iqbal-lab-org/covid-truth-eval/pkgs/container/covid-truth-eval\"\u003epackages page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, build your own Docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker build --network=host .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/covid-truth-eval/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download.\nEach release has a file called \u003ccode\u003ecte_vX.Y.Z.img\u003c/code\u003e, where \u003ccode\u003eX.Y.Z\u003c/code\u003e is the release version.\u003c/p\u003e\n\u003cp\u003eAlternatively, build your own Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build cte.simg Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFrom source\u003c/h3\u003e\n\u003cp\u003eDependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mafft.cbrc.jp/alignment/software/\" rel=\"nofollow\"\u003emafft\u003c/a\u003e installed and in your \u003ccode\u003e$PATH\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInstall by cloning this repository (or downloading the latest release), and\nrunning:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 -m pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start\u003c/h2\u003e\n\u003cp\u003eTo evaluate one SARS-CoV-2 consensus sequence, you will need:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA VCF file of the \"truth\" calls \u003ccode\u003etruth.vcf\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe consensus sequence to evalaute in a FASTA file \u003ccode\u003econs.fa\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe primer scheme. Currently supported: COVID-ARTIC-V3, COVID-ARTIC-V4, COVID-MIDNIGHT-1200.\nOr use your own TSV file of primers in \u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/wiki/Amplicon-schemes\"\u003eViridian Workflow format\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe program is called \u003ccode\u003ecte\u003c/code\u003e and is installed in the Docker and Singularity containers, and gets installed by \u003ccode\u003epip\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eExample, assuming primer scheme COVID-ARTIC-V4:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecte eval_one_run \\\n --outdir OUT \\\n --truth_vcf truth.vcf \\\n --fasta_to_eval cons.fa \\\n --primers COVID-ARTIC-V4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output files are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eresults.tsv\u003c/code\u003e - a TSV file of counts of the truth bases vs what was called in the consensus. The same information is also put in a JSON file \u003ccode\u003eresults.json\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eper_position.tsv\u003c/code\u003e - a TSV file, one line per reference position. It shows the multiple alignment of the reference, truth (inferred from the truth VCF file), and the sequence being evaluated. At each position the assigned category of truth and called bases is shown, where the categories are the same as those used in \u003ccode\u003eresults.tsv\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe files are described in detail in the \u003ca href=\"https://github.com/iqbal-lab-org/covid-truth-eval/wiki/Output-files\"\u003eoutput files\u003c/a\u003e documentation.\u003c/p\u003e\n", + "stargazers_count": 1, + "subscribers_count": 6, + "topics": [], + "updated_at": 1642766061.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "singularity/Singularity" + ], + "full_name": "kavonrtep/dante_ltr", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"misc/images/fast-downward.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" href=\"#tested-software-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributors\" class=\"anchor\" href=\"#contributors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-history\" class=\"anchor\" href=\"#history\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-dante_ltr\" class=\"anchor\" aria-hidden=\"true\" href=\"#dante_ltr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDANTE_LTR\u003c/h1\u003e\n\u003cp\u003eTool for identifying complete LTR retrotransposons based on analysis of protein domains identified with the \u003ca href=\"https://github.com/kavonrtep/dante\"\u003eDANTE tool\u003c/a\u003e. Both DANTE and DANTE_LTR are available on Galaxy server.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-principle-of-dante-_ltr\" class=\"anchor\" aria-hidden=\"true\" href=\"#principle-of-dante-_ltr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrinciple of DANTE _LTR\u003c/h2\u003e\n\u003cp\u003eComplete retrotransposons are identified as clusters of protein domains recognized by the DANTE tool. The domains in the clusters must be assigned to a single retrotransposon lineage by DANTE. In addition, the orientation and order of the protein domains, as well as the distances between them, must conform to the characteristics of elements from REXXdb database \u003ca href=\"https://mobilednajournal.biomedcentral.com/articles/10.1186/s13100-018-0144-1\" rel=\"nofollow\"\u003eNeumann et al. (2019)\u003c/a\u003e.\nIn the next step, the 5\u0027 and 3\u0027 regions of the putative retrotransposon are examined for the presence of 5\u0027 and 3\u0027 long terminal repeats. If 5\u0027- and 3\u0027-long terminal repeats are detected, detection of target site duplication (TSD) and primer binding site (PSB) is performed. The detected LTR retrotranspsons are classified into 5 categories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eElements with protein domains, 5\u0027LTR, 3\u0027LTR, TSD and PBS - rank \u003cstrong\u003eDLTP\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eElements with protein domains, 5\u0027LTR, 3\u0027LTR, and PBS (TSD was not found) Rank \u003cstrong\u003eDLP\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eElements with protein domains, 5\u0027 LTR, 3\u0027LTR, TSD (PBS was not found) - rank \u003cstrong\u003eDTL\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eElements with protein domains, 5\u0027LTR and 3\u0027LTR (PBS and TDS were not found) - rank \u003cstrong\u003eDL\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eElements as clusters of protein domains with the same classification, no LTRs - rank \u003cstrong\u003eD\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dante_ltr_workflow.png\"\u003e\u003cimg src=\"dante_ltr_workflow.png\" alt=\"dante_ltr_workflow.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation:\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n dante_ltr -c bioconda -c conda-forge -c petrnovak dante_ltr\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput data\u003c/h2\u003e\n\u003cp\u003eOne input is a reference sequence in fasta fromat. The second input is an annotation of the reference genome using the tool DANTE in GFF3 format. For better results, use the unfiltered full output of the DANTE pipeline.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-detection-of-complete-ltr-retrotransposons\" class=\"anchor\" aria-hidden=\"true\" href=\"#detection-of-complete-ltr-retrotransposons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetection of complete LTR retrotransposons\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eUsage: ./detect_putative_ltr.R COMMAND [OPTIONS]\n\n\nOptions:\n -g GFF3, --gff3=GFF3\n gff3 with dante results\n\n -s REFERENCE_SEQUENCE, --reference_sequence=REFERENCE_SEQUENCE\n reference sequence as fasta\n\n -o OUTPUT, --output=OUTPUT\n output file path and prefix\n\n -c NUMBER, --cpu=NUMBER\n Number of cpu to use [default 5]\n\n -M NUMBER, --max_missing_domains=NUMBER\n Maximum number of missing domains is retrotransposon [default 0]\n\n -L NUMBER, --min_relative_length=NUMBER\n Minimum relative length of protein domain to be considered \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e retrostransposon detection [default 0.6]\n -h, --help\n Show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p tmp\n./detect_putative_ltr.R -g test_data/sample_DANTE.gff3 -s test_data/sample_genome.fasta -o tmp/ltr_annotation\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-files-in-the-output-of-extract_putative_ltrr\" class=\"anchor\" aria-hidden=\"true\" href=\"#files-in-the-output-of-extract_putative_ltrr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles in the output of \u003ccode\u003eextract_putative_ltr.R\u003c/code\u003e:\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix.gff3\u003c/code\u003e - annotation of all identified elements\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_D.fasta\u003c/code\u003e - partial elements with protein \u003cstrong\u003ed\u003c/strong\u003eomains\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_DL.fasta\u003c/code\u003e - elements with protein \u003cstrong\u003ed\u003c/strong\u003eomains and \u003cstrong\u003eL\u003c/strong\u003eTR\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_DLTP.fasta\u003c/code\u003e - elements with \u003cstrong\u003ed\u003c/strong\u003eomains, \u003cstrong\u003eL\u003c/strong\u003eTR, \u003cstrong\u003eT\u003c/strong\u003eSD and \u003cstrong\u003eP\u003c/strong\u003eBS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_DLP.fasta\u003c/code\u003e - elements with \u003cstrong\u003ed\u003c/strong\u003eomains, \u003cstrong\u003eL\u003c/strong\u003eTR and \u003cstrong\u003eP\u003c/strong\u003eBS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_DLT.fasta\u003c/code\u003e - elements with \u003cstrong\u003ed\u003c/strong\u003eomains, \u003cstrong\u003eL\u003c/strong\u003eTR, \u003cstrong\u003eT\u003c/strong\u003eSD\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprefix_statistics.csv\u003c/code\u003e - number of elements in individual categories\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor large genomes, you can your \u003ccode\u003edetect_putative_ltr_wrapper.py\u003c/code\u003e. This script will split input fasta to smaller chunks and run \u003ccode\u003edetect_putative_ltr.R\u003c/code\u003e on each chunk to limit memory usage. Output will be merged after all chunks are processed.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eusage: detect_putative_ltr_wrapper.py [-h] -g GFF3 -s REFERENCE_SEQUENCE -o\n OUTPUT [-c CPU] [-M MAX_MISSING_DOMAINS]\n [-L MIN_RELATIVE_LENGTH]\n [-S MAX_CHUNK_SIZE]\n\ndetect_putative_ltr_wrapper.py is a wrapper \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \n detect_putative_ltr.R\n\noptional arguments:\n -h, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n -g GFF3, --gff3 GFF3 gff3 file\n -s REFERENCE_SEQUENCE, --reference_sequence REFERENCE_SEQUENCE\n reference sequence as fasta file\n -o OUTPUT, --output OUTPUT\n output file path and prefix\n -c CPU, --cpu CPU number of CPUs\n -M MAX_MISSING_DOMAINS, --max_missing_domains MAX_MISSING_DOMAINS\n -L MIN_RELATIVE_LENGTH, --min_relative_length MIN_RELATIVE_LENGTH\n Minimum relative length of protein domain to be considered\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e retrostransposon detection\n -S MAX_CHUNK_SIZE, --max_chunk_size MAX_CHUNK_SIZE\n If size of reference sequence is greater than this value,\n reference is analyzed \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e chunks of this size. This is\n just approximate value - sequences which are longer \n are are not split, default is 100000000\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-validation-of-ltr-retrotransposons-detected-un-previous-step\" class=\"anchor\" aria-hidden=\"true\" href=\"#validation-of-ltr-retrotransposons-detected-un-previous-step\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eValidation of LTR retrotransposons detected un previous step:\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./clean_ltr.R --help\nUsage: ./clean_ltr.R COMMAND [OPTIONS]\n\n\nOptions:\n -g GFF3, --gff3=GFF3\n gff3 with LTR Transposable elements\n\n -s REFERENCE_SEQUENCE, --reference_sequence=REFERENCE_SEQUENCE\n reference sequence as fasta\n\n -o OUTPUT, --output=OUTPUT\n output file prefix\n\n -c NUMBER, --cpu=NUMBER\n Number of cpu to use [default 5]\n\n -h, --help\n Show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis script check for potentially chimeric elements and removes them from GFF3 file.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./clean_ltr.R -g test_data/sample_DANTE_LTR_annotation.gff3 -s test_data/sample_genome.fasta -o tmp/ltr_annotation_clean\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1643028024.0 + "updated_at": 1647262998.0 }, { "data_format": 2, - "description": "The files to build singularity images for GOMAP pipeline", + "description": "This Repo hosts SARS-CoV-2 genome sequencing, variant calling and assembly scripts. It relies on other cloned repositories and singularity images adapted from other tools and workflows.", "filenames": [ - "singularity/Singularity" + "scripts/albacore/Singularity.def", + "scripts/primalscheme/Singularity.def" ], - "full_name": "Dill-PICL/GOMAP-img-build", + "full_name": "kibet-gilbert/covid", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-summary-of-nf-coreviralrecon-data-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#summary-of-nf-coreviralrecon-data-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary of nf-core/viralrecon Data Analysis.\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003eOrganization of the covid analysis directory:\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cp\u003eThe covid directory is structured into four directories: data,scripts,viralrecon and work.(see more below and in the organization section)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-11-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#11-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1. data\u003c/h2\u003e\n\u003cp\u003eThe data dir has: core_data, run directories: yyyy-mm-dd_run[##]_tech, test_data*. The core_data includes all necessary data files like reference genome, Artic primer data, gff files.... The yyyy-mm-dd_run[##]_tech dirs contains the fastq sequences and the analysis results from them (more below).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-111-yyyy-mm-dd_run_tech\" class=\"anchor\" aria-hidden=\"true\" href=\"#111-yyyy-mm-dd_run_tech\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1.1. yyyy-mm-dd_run[##]_tech\u003c/h2\u003e\n\u003cp\u003eThis dir has all symbolic links to fastq.gz (Illumina) or actual fastq (ONT) sequencing output. It has results from the sequence analysis as well in diferent directories/files:\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h2\u003e\n\u003cp\u003e1.1.1.1 .mutations.tsv\t: symplified vcf as a tab-separeted file of all mutations in all samples\u003c/p\u003e\n\u003cp\u003e1.1.1.2 _aa.mutations.tsv\t: Symplified amino acid mutaion file\u003c/p\u003e\n\u003cp\u003e1.1.1.3 _analysis.*\t\t: Any other custom result file excel/pdf\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directories\" class=\"anchor\" aria-hidden=\"true\" href=\"#directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectories\u003c/h2\u003e\n\u003cp\u003eInput:\u003cbr\u003e\nfastq\t\t: Illumina - basecalled and demultiplexed fastQs\u003cbr\u003e\nfast5_fail\t: ONT - raw (Pre-basecalling) sequencing out-put below 9Q score\u003cbr\u003e\nfast5_pass\t: ONT - raw (Pre-basecalling) sequencing out-put above 9Q score\u003cbr\u003e\nfastq_fail\t: ONT - basecalled fastqs below 9Q score\u003cbr\u003e\nfastq_pass\t: ONT - basecalled fastqs above 9Q score\u003cbr\u003e\noutput:\u003cbr\u003e\nalignment\t: Musltiple Sequence Alignment (MUSCLE) results dir\u003cbr\u003e\nmutaions\t: Symplified VCF files in a tab-separeted format\u003cbr\u003e\nnextclade\t: nextclade.js analysis results\u003cbr\u003e\nphylogeny\t: phylogeny (iqtree) analysis results\u003cbr\u003e\nplots\t\t: Amplicon and Genome coverage plots\u003cbr\u003e\npangolin\t: Pangolin analysis results\u003cbr\u003e\nresults\t\t: Variant calling results - Contains more dirs (see next)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1114-results-illumina\" class=\"anchor\" aria-hidden=\"true\" href=\"#1114-results-illumina\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1.1.4 results: Illumina\u003c/h2\u003e\n\u003cp\u003eassembly\t: De-novo assembly results - don\u0027t have any results when skipped\u003cbr\u003e\nmultiqc\t\t: multiqc results in .html and data.yaml format\u003cbr\u003e\npipeline_info\t: All pipeline execution reports in .txt and html format\u003cbr\u003e\npreprocess\t: Pre-processing results: fastQC and fastp (QC \u0026amp; trimming)\u003cbr\u003e\nvariants\t: All variant call results.\u003cbr\u003e\nivar\t- All variant call files - Not annotated\u003cbr\u003e\nivar/snpeff - All variant call files annotated by sneff\u003cbr\u003e\nivar/quast - consensus sequence summary stats by quast\u003cbr\u003e\nbam - bam idex files sorted.bam \u0026amp; sorted.bai\u003cbr\u003e\nbam/samtoos_stats - Samtool stats\u003cbr\u003e\nbam/picard_metrics - picard stats on dublicates in the reads\u003cbr\u003e\nbam/mosdepth - mosdepth stats on amplicon and genome coverage\u003cbr\u003e\nbam/mosdepth/amplicon/plots - log Plots on how amplicons/primers output looks (pdf) and an overall heatmap (pdf). All have accompanying data in TSV format\u003cbr\u003e\nbam/mosdepth/genome/plots -log plots and data for log genome coverage vs position/loci on the genome.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1115-results-ont\" class=\"anchor\" aria-hidden=\"true\" href=\"#1115-results-ont\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.1.1.5 results: ONT\u003c/h2\u003e\n\u003cp\u003emultiqc\t\t: multiqc descriptive figures in .html and data.yaml format\u003cbr\u003e\npipeline_info\t: All pipeline execution reports in .txt and html format\u003cbr\u003e\npycoqc\t\t: Pre-processing metrics: guppy basecalling and demultiplexing\u003cbr\u003e\nnanoplot\t: Q score distribution, read lengths and other general stats.\u003cbr\u003e\nmedaka\t\t: All variant call results.\u003cbr\u003e\nsnpeff - All variant call files annotated by sneff\u003cbr\u003e\nquast - consensus sequence summary stats by quast\u003cbr\u003e\n./* - VCF and bam idex files: sorted.bam \u0026amp; sorted.bai\u003cbr\u003e\nsamtoos_stats - Samtool stats\u003cbr\u003e\nbam/picard_metrics - picard stats on dublicates in the reads\u003cbr\u003e\nmosdepth - mosdepth stats on amplicon and genome coverage\u003cbr\u003e\n*/amplicon - log Plots on amplicons/primers performance\u003cbr\u003e\n*/genome/plots -log plots on genome coverage vs position/loci.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-12-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#12-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.2. scripts\u003c/h2\u003e\n\u003cp\u003eThe scripts dir has the analysis sbatch scripts and auxilliary scripts for pre-analysis and post-analysis processing: covid_run_.sbatch\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-13-viralrecon\" class=\"anchor\" aria-hidden=\"true\" href=\"#13-viralrecon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.3. viralrecon\u003c/h2\u003e\n\u003cp\u003eThe viralrecon dir is git cloned and has the source code of the pipeline... The idea is to eventually run the pipeline directly from the code when needed (not possible as of now).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-14-work\" class=\"anchor\" aria-hidden=\"true\" href=\"#14-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1.4. work\u003c/h2\u003e\n\u003cp\u003eThe work dir has the temporary files generated during runs also stores conda environment (work/conda/), container (singularity) images. It is a copy of the work dir stored in the working dir (/var/scratch/${USER}/work/conda/). Howerever the working directory has all temporary files from each run.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-directory-organization\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-organization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory Organization.\u003c/h1\u003e\n\u003cp\u003e.(covid)\u003cbr\u003e\n\u251c\u2500\u2500 data\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 core_data\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 alignment\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.aln\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.fasta\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 covid_01-04-2021_headers\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021_aa.mutations.tsv\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021_analysis.mutations.pdf\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021_analysis.mutations.xlsx\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021.mutations.tsv\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 covid_01-04-2021_pangolin.tsv\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 mutations\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 COVC21058_S21.snpEff.vcf.tsv\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 |\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 |\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 |\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 COVC23453_S20.snpEff.vcf.tsv\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 MoH-Cov-6_S6.snpEff.vcf.tsv\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 Undetermined_S0.snpEff.vcf.tsv\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 nextclade\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.json\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.tsv\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 nextstrain_.svg\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 nextstrain_tree.nexus\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 nextstrain_tree.nwk\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 phylogeny\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.bionj\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.ckp.gz\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.contree\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.iqtree\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.mldist\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.model.gz\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.splits.nex\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_01-04-2021_con.treefile\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 covid_01-04-2021_con.ufboot\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 results\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 assembly\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 multiqc\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 pipeline_info\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 preprocess\u003cbr\u003e\n\u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 variants\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 bam\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 log\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 mosdepth\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 amplicon\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 plots\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 genome\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 plots\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 picard_metrics\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 samtools_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 ivar\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 bcftools_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 consensus\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 base_qc\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 log\u003cbr\u003e\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 quast\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 AF0.75\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 aligned_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 basic_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 contigs_reports\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 minimap_output\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u251c\u2500\u2500 genome_stats\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2502\u00a0\u00a0 \u2514\u2500\u2500 icarus_viewers\u003cbr\u003e\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 snpeff\u003cbr\u003e\n\u2502 \u2502 \u251c\u2500\u2500 samplesheet.csv\u003cbr\u003e\n\u2502 \u2502 \u2514\u2500\u2500 slurm-716659.out\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_11-02-2021\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid-run3_09-04-2021\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 general_variants\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test_data\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test_data00\u003cbr\u003e\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 test_data01\u003cbr\u003e\n\u251c\u2500\u2500 README\u003cbr\u003e\n\u251c\u2500\u2500 scripts\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 aa_codes\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_env.yml\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Covid_exporatory.ipynb\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run_denovo.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run_kranken.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run_nomqc.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 covid_run_test.sbatch\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Notes.txt\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 pangolin\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 process_files_bak.sh\u003cbr\u003e\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 process_files.sh\u003cbr\u003e\n\u251c\u2500\u2500 viralrecon\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 assets\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 bin\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 CHANGELOG.md\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 CITATIONS.md\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 CODE_OF_CONDUCT.md\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 conf\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Dockerfile\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 docs\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 environment.yml\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 LICENSE\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 main.nf\u003cbr\u003e\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 nextflow.config\u003cbr\u003e\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 README.md\u003cbr\u003e\n\u2514\u2500\u2500 work\u003cbr\u003e\n\u2514\u2500\u2500 conda\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTo analyse some datasets\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cp\u003e2.1. MiSeq:\u003cbr\u003e\nOpen the script path-to/covid/scripts/covid_run-2.2.miseq.sbatch and set the variables: FRead_suffix and RRead_suffix to your desired forward read and reverse read suffix. The defaults are FRead_suffix=\"L001_R1_001.fastq.gz\" and RRead_suffix=\"L001_R2_001.fastq.gz\"\u003cbr\u003e\n\"cd\" to path-to/covid/data/\u0026lt;your_data_dir\u0026gt; and execute the sbatch command below: $ sbatch -w compute06 ../../scripts/covid_run-2.2.miseq.sbatch\u003c/p\u003e\n\u003cp\u003e2.2. NextSeq:\u003cbr\u003e\nConcatenate the reads per sample from *L00?_R1_001.fastq.gz to *con_R1_001.fastq.gz and *L00?_R2_001.fastq.gz to *con_R2_001.fastq.gz\u003cbr\u003e\nOpen the script path-to/covid/scripts/covid_run-2.2.nextseq.sbatch and set the variables: FRead_suffix and RRead_suffix to the right forward read and reverse read suffix: FRead_suffix=\"con_R1_001.fastq.gz\" and RRead_suffix=\"con_R2_001.fastq.gz\"\u003cbr\u003e\n\"cd\" to path-to/covid/data/\u0026lt;your_data_dir\u0026gt; and execute the sbatch command below: $ sbatch -w compute06 ../../scripts/covid_run-2.2.nextseq.sbatch\u003c/p\u003e\n\u003cp\u003e2.3 ONT:\u003cbr\u003e\nCreate a sample sheet (samplesheet.csv) and save it in the data directory. The format is explained here: \u003ca href=\"https://nf-co.re/viralrecon/2.2/usage#nanopore\" rel=\"nofollow\"\u003ehttps://nf-co.re/viralrecon/2.2/usage#nanopore\u003c/a\u003e\u003cbr\u003e\n\"cd\" to path-to/covid/data/\u0026lt;your_data_dir\u0026gt; and execute the sbatch command below: $ sbatch -w compute06 ../../scripts/covid_run-2.2.nanopore.sbatch\u003c/p\u003e\n\u003cp\u003eN/B: compute06 should work okay i.e 64CPUs and enough disk space.\u003cbr\u003e\nThe nf-core/viralrecon, a nextflow pipeline has been set up to run with -profile singularity option. For more on options that can be set see PARAMETER configuration section.\u003cbr\u003e\nHelp message: Execute the following line for the usage:\u003cbr\u003e\n$ nextflow run nf-core/viralrecon -r 2.2 --help -profile singularity\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-processes\" class=\"anchor\" aria-hidden=\"true\" href=\"#processes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcesses:\u003c/h1\u003e\n\u003cp\u003eThis workflow is made up of 64 process (The equivalent of functions in bash/python): some can be deactivated as need. See Processes section for more.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGUNZIP_FASTA\u003c/li\u003e\n\u003cli\u003eGUNZIP_GFF\u003c/li\u003e\n\u003cli\u003eCHECK_SAMPLESHEET\u003c/li\u003e\n\u003cli\u003e*SRA_FASTQ_FTP\u003c/li\u003e\n\u003cli\u003e*SRA_FASTQ_DUMP\u003c/li\u003e\n\u003cli\u003eCAT_FASTQ\u003c/li\u003e\n\u003cli\u003eFASTQC\u003c/li\u003e\n\u003cli\u003eFASTP\u003c/li\u003e\n\u003cli\u003eBOWTIE2_INDEX\u003c/li\u003e\n\u003cli\u003eMAKE_SNPEFF_DB\u003c/li\u003e\n\u003cli\u003eBOWTIE2\u003c/li\u003e\n\u003cli\u003eSORT_BAM\u003c/li\u003e\n\u003cli\u003eIVAR_TRIM\u003c/li\u003e\n\u003cli\u003ePICARD_MARKDUPLICATES\u003c/li\u003e\n\u003cli\u003ePICARD_METRICS\u003c/li\u003e\n\u003cli\u003eMOSDEPTH_GENOME\u003c/li\u003e\n\u003cli\u003eMOSDEPTH_AMPLICON\u003c/li\u003e\n\u003cli\u003eMOSDEPTH_AMPLICON_PLOT\u003c/li\u003e\n\u003cli\u003eSAMTOOLS_MPILEUP\u003c/li\u003e\n\u003cli\u003e*VARSCAN2\u003c/li\u003e\n\u003cli\u003e*VARSCAN2_CONSENSUS\u003c/li\u003e\n\u003cli\u003e*VARSCAN2_SNPEFF\u003c/li\u003e\n\u003cli\u003e*VARSCAN2_QUAST\u003c/li\u003e\n\u003cli\u003eIVAR_VARIANTS\u003c/li\u003e\n\u003cli\u003eIVAR_CONSENSUS\u003c/li\u003e\n\u003cli\u003eIVAR_SNPEFF\u003c/li\u003e\n\u003cli\u003eIVAR_QUAST\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_VARIANTS\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_CONSENSUS\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_SNPEFF\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_QUAST\u003c/li\u003e\n\u003cli\u003e*BCFTOOLS_ISEC\u003c/li\u003e\n\u003cli\u003e**MAKE_BLAST_DB\u003c/li\u003e\n\u003cli\u003e**SPADES\u003c/li\u003e\n\u003cli\u003e**SPADES_BLAST\u003c/li\u003e\n\u003cli\u003e**SPADES_ABACAS\u003c/li\u003e\n\u003cli\u003e**SPADES_PLASMIDID\u003c/li\u003e\n\u003cli\u003e**SPADES_QUAST\u003c/li\u003e\n\u003cli\u003e**SPADES_VG\u003c/li\u003e\n\u003cli\u003e**SPADES_SNPEFF\u003c/li\u003e\n\u003cli\u003e**METASPADES\u003c/li\u003e\n\u003cli\u003e**METASPADES_BLAST\u003c/li\u003e\n\u003cli\u003e**METASPADES_ABACAS\u003c/li\u003e\n\u003cli\u003e**METASPADES_PLASMIDID\u003c/li\u003e\n\u003cli\u003e**METASPADES_QUAST\u003c/li\u003e\n\u003cli\u003e**METASPADES_VG\u003c/li\u003e\n\u003cli\u003e**METASPADES_SNPEFF\u003c/li\u003e\n\u003cli\u003e**UNICYCLER\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_BLAST\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_ABACAS\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_PLASMIDID\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_QUAST\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_VG\u003c/li\u003e\n\u003cli\u003e**UNICYCLER_SNPEFF\u003c/li\u003e\n\u003cli\u003e**MINIA\u003c/li\u003e\n\u003cli\u003e**MINIA_BLAST\u003c/li\u003e\n\u003cli\u003e**MINIA_ABACAS\u003c/li\u003e\n\u003cli\u003e**MINIA_PLASMIDID\u003c/li\u003e\n\u003cli\u003e**MINIA_QUAST\u003c/li\u003e\n\u003cli\u003e**MINIA_VG\u003c/li\u003e\n\u003cli\u003e**MINIA_SNPEFF\u003c/li\u003e\n\u003cli\u003eget_software_versions\u003c/li\u003e\n\u003cli\u003eMULTIQC\u003c/li\u003e\n\u003cli\u003eoutput_documentation\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePARAMETER configurations:\u003cbr\u003e\n// Options: Generic\u003cbr\u003e\ninput = \u0027./samplesheet.csv\u0027\u003cbr\u003e\nprotocol = \u0027amplicon\u0027\u003cbr\u003e\namplicon_fasta = ${DATADIR}/GCA_009858895.3_ASM985889v3_genomic.200409.fna.gz\u003cbr\u003e\namplicon_bed = ${DATADIR}/GCA_009858895.3_ASM985889v3_genomic.200409.gff.gz\u003c/p\u003e\n\u003cp\u003e// Options: SRA download\u003cbr\u003e\nsave_sra_fastq = false\u003cbr\u003e\nskip_sra = false\u003c/p\u003e\n\u003cp\u003e// Options: Reference genomes\u003cbr\u003e\ngenome = false\u003cbr\u003e\nsave_reference = false\u003c/p\u003e\n\u003cp\u003e// Options: Read Trimming\u003cbr\u003e\ncut_mean_quality = 20\u003cbr\u003e\nqualified_quality_phred = 20\u003cbr\u003e\nunqualified_percent_limit = 10\u003cbr\u003e\nmin_trim_length = 50\u003cbr\u003e\nskip_adapter_trimming = false\u003cbr\u003e\nskip_amplicon_trimming = false\u003cbr\u003e\nsave_trimmed = false\u003c/p\u003e\n\u003cp\u003e// Options: Variant calling\u003cbr\u003e\ncallers = \u0027varscan2,ivar,bcftools\u0027\u003cbr\u003e\nmin_mapped_reads = 1000\u003cbr\u003e\nivar_trim_noprimer = false\u003cbr\u003e\nivar_trim_min_len = 20\u003cbr\u003e\nivar_trim_min_qual = 20\u003cbr\u003e\nivar_trim_window_width = 4\u003cbr\u003e\nfilter_dups = false\u003cbr\u003e\nfilter_unmapped = false\u003cbr\u003e\nmpileup_depth = 0\u003cbr\u003e\nmin_base_qual = 20\u003cbr\u003e\nmin_coverage = 10\u003cbr\u003e\nmin_allele_freq = 0.25\u003cbr\u003e\nmax_allele_freq = 0.75\u003cbr\u003e\nvarscan2_strand_filter = true\u003cbr\u003e\namplicon_left_suffix = \u0027_LEFT\u0027\u003cbr\u003e\namplicon_right_suffix = \u0027_RIGHT\u0027\u003cbr\u003e\nsave_align_intermeds = false\u003cbr\u003e\nsave_mpileup = false\u003cbr\u003e\nskip_markduplicates = false\u003cbr\u003e\nskip_picard_metrics = false\u003cbr\u003e\nskip_mosdepth = false\u003cbr\u003e\nskip_snpeff = false\u003cbr\u003e\nskip_variants_quast = false\u003cbr\u003e\nskip_variants = false\u003c/p\u003e\n\u003cp\u003e// Options: QC\u003cbr\u003e\nskip_fastqc = false\u003cbr\u003e\nskip_multiqc = false\u003c/p\u003e\n\u003cp\u003e// Boilerplate options\u003cbr\u003e\noutdir = \u0027./results\u0027\u003cbr\u003e\npublish_dir_mode = \u0027copy\u0027\u003cbr\u003e\nname = false\u003cbr\u003e\nmultiqc_config = false\u003cbr\u003e\nemail = false\u003cbr\u003e\nemail_on_fail = false\u003cbr\u003e\nmax_multiqc_email_size = 25.MB\u003cbr\u003e\nplaintext_email = false\u003cbr\u003e\nmonochrome_logs = false\u003cbr\u003e\nhelp = false\u003cbr\u003e\ntracedir = \"${params.outdir}/pipeline_info\"\u003cbr\u003e\ncustom_config_version = \u0027master\u0027\u003cbr\u003e\ncustom_config_base = \"\u003ca href=\"https://raw.githubusercontent.com/nf-core/configs/%24%7Bparams.custom_config_version%7D\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/nf-core/configs/${params.custom_config_version}\u003c/a\u003e\"\u003cbr\u003e\nhostnames = false\u003cbr\u003e\nconfig_profile_description = false\u003cbr\u003e\nconfig_profile_contact = false\u003cbr\u003e\nconfig_profile_url = false\u003c/p\u003e\n\u003cp\u003e// Options: Kraken2\u003cbr\u003e\nkraken2_db = \u0027\u003ca href=\"https://zenodo.org/record/3738199/files/kraken2_human.tar.gz\" rel=\"nofollow\"\u003ehttps://zenodo.org/record/3738199/files/kraken2_human.tar.gz\u003c/a\u003e\u0027\u003cbr\u003e\nkraken2_db_name = \u0027human\u0027\u003cbr\u003e\nkraken2_use_ftp = false\u003cbr\u003e\nsave_kraken2_fastq = false\u003cbr\u003e\nskip_kraken2 = false\u003c/p\u003e\n\u003cp\u003e// Options: De novo assembly\u003cbr\u003e\nassemblers = \u0027spades,metaspades,unicycler,minia\u0027\u003cbr\u003e\nminia_kmer = 31\u003cbr\u003e\nskip_blast = false\u003cbr\u003e\nskip_abacas = false\u003cbr\u003e\nskip_plasmidid = false\u003cbr\u003e\nskip_vg = false\u003cbr\u003e\nskip_assembly_quast = false\u003cbr\u003e\nskip_assembly = false\u003c/p\u003e\n\u003cp\u003e// Defaults only, expecting to be overwritten\u003cbr\u003e\nmax_memory = 40.GB\u003cbr\u003e\nmax_cpus = 30\u003cbr\u003e\nmax_time = 240.h\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1635807818.0 + "updated_at": 1662558108.0 }, { "data_format": 2, - "description": null, + "description": "Work with Python installed at a custom location", "filenames": [ "Singularity" ], - "full_name": "zellerlab/gaga2", - "latest_release": "v0.4", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gaga2---automated-16s-amplicon-analysis-with-figarodada2\" class=\"anchor\" href=\"#gaga2---automated-16s-amplicon-analysis-with-figarodada2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egaga2 - automated 16S amplicon analysis with Figaro/DADA2\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"docs/img/gaga2_flow.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"docs/img/gaga2_flow.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" href=\"#installation-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation instructions\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003egaga2\u003c/code\u003e requires a working \u003ccode\u003enextflow\u003c/code\u003e installation (v20.4+).\u003c/p\u003e\n\u003cp\u003eOther dependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebbmap\u003c/li\u003e\n\u003cli\u003efastqc\u003c/li\u003e\n\u003cli\u003efigaro\u003c/li\u003e\n\u003cli\u003eR v4+ with dada2, devtools, tidyverse, and cowplot installed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor convenience, \u003ccode\u003egaga2\u003c/code\u003e comes with a Singularity container with all dependencies installed.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull oras://ghcr.io/zellerlab/gaga2:latest\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-instructions\" class=\"anchor\" href=\"#usage-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage instructions\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input\" class=\"anchor\" href=\"#input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003egaga2\u003c/code\u003e takes as input Illumina paired-end 16S amplicon sequences (e.g. sequenced on a MiSeq).\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-prerequisites\" class=\"anchor\" href=\"#prerequisites\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eRead files need to be named according the typical pattern \u003ccode\u003e\u0026lt;prefix=sample_id\u0026gt;_R?[12].{fastq,fq,fastq.gz,fq.gz}\u003c/code\u003e.\nThey should, but don\u0027t have to, be arranged in a sample-based directory structure:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;project_dir\u0026gt; (aka \"input_dir\")\n |___ \u0026lt;sample_1\u0026gt;\n | |____ \u0026lt;sample_1_forward_reads\u0026gt;\n | |____ \u0026lt;sample_2_reverse_reads\u0026gt;\n |\n |___ \u0026lt;sample_2\u0026gt;\n | |____ \u0026lt;empty samples will be ignored\u0026gt;\n | \n |___ \u0026lt;sample_n\u0026gt;\n |____ \u0026lt;sample_n_forward_reads\u0026gt;\n |____ \u0026lt;sample_n_reverse_reads\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA flat directory structure (with all read files in the same directory) or a deeply-branched (with read files scattered over multiple levels) should also work.\u003c/p\u003e\n\u003cp\u003eIf \u003ccode\u003egaga2\u003c/code\u003e preprocesses the reads, it will automatically use \u003ccode\u003e_R1/2\u003c/code\u003e endings internally.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIf input reads have already been preprocessed, you can set the \u003ccode\u003e--preprocessed\u003c/code\u003e flag. In this case, \u003ccode\u003egaga2\u003c/code\u003e will do no preprocessing at all and instruct \u003ccode\u003edada2\u003c/code\u003e to perform no trimming. Otherwie, \u003ccode\u003egaga2\u003c/code\u003e will assess the read lengths for uniformity. If read lengths differ within and between samples, preprocessing with \u003ccode\u003efigaro\u003c/code\u003e is not possible and \u003ccode\u003edada2\u003c/code\u003e will be run without trimming.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSamples with less than \u003ccode\u003e110\u003c/code\u003e reads after \u003ccode\u003edada2\u003c/code\u003e preprocessing, will be discarded.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-gaga2\" class=\"anchor\" href=\"#running-gaga2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning gaga2\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003egaga2\u003c/code\u003e can be directly run from github.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run zellerlab/gaga2 \u0026lt;parameters\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain a newer version, do a\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow pull zellerlab/gaga2\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ebefore.\u003c/p\u003e\n\u003cp\u003eIn addition, you should obtain a copy of the \u003ccode\u003erun.config\u003c/code\u003e from the \u003ccode\u003egaga2\u003c/code\u003e github repo and modify it according to your environment.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-mandatory-arguments\" class=\"anchor\" href=\"#mandatory-arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory arguments\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input_dir\u003c/code\u003e is the project directory mentioned above.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_dir\u003c/code\u003e will be created automatically.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--amplicon_length\u003c/code\u003e this is derived from your experiment parameters (this is not read-length, but the length of the, well, amplicon!)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--single_end\u003c/code\u003e this is only required for single-end libraries (auto-detection of library-type is in progress)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optional-arguments\" class=\"anchor\" href=\"#optional-arguments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional arguments\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--min_overlap\u003c/code\u003e of read pairs is \u003ccode\u003e20bp\u003c/code\u003e by default\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--primers \u0026lt;comma-separated-list-of-primer-sequences\u0026gt;\u003c/code\u003e or \u003ccode\u003e--left_primer\u003c/code\u003e, and \u003ccode\u003e--right_primer\u003c/code\u003e If primer sequences are provided via \u003ccode\u003e--primers\u003c/code\u003e, \u003ccode\u003egaga2\u003c/code\u003e will remove primers and upstream sequences (using \u003ccode\u003ebbduk\u003c/code\u003e), such as adapters based on the primer sequences. If non-zero primer lengths are provided instead (via \u003ccode\u003e--left_primer\u003c/code\u003e and \u003ccode\u003e--right_primer\u003c/code\u003e), \u003ccode\u003efigaro\u003c/code\u003e will take those into account when determining the best trim positions.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--preprocessed\u003c/code\u003e will prevent any further preprocessing by \u003ccode\u003egaga2\u003c/code\u003e - this flag should only be used if the read data is reliably clean.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-internal-beta-testing-instructions\" class=\"anchor\" href=\"#internal-beta-testing-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einternal beta-testing instructions\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eThe old gaga2 version can be run with \u003ccode\u003esource /g/scb2/zeller/schudoma/software/wrappers/gaga2_wrapper\u003c/code\u003e \u003cstrong\u003ebefore\u003c/strong\u003e submitting job to cluster\u003c/li\u003e\n\u003cli\u003ePlease report issues/requests/feedback in the github issue tracker\u003c/li\u003e\n\u003cli\u003eIf you want to run \u003ccode\u003egaga2\u003c/code\u003e on the cluster, \u003ccode\u003enextflow\u003c/code\u003e alone requires \u003ccode\u003e\u0026gt;=5GB\u003c/code\u003e memory just for \u0027managing\u0027.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "AJResearchGroup/ormr", + "latest_release": "v0.6.2.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ormr\" class=\"anchor\" aria-hidden=\"true\" href=\"#ormr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eormr\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/ormr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/ormr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80f61013497de9c4ba38bd7d37d57f2baf9ad486b3e667b76823a2fa7acb1783/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6f726d722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/ormr/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=master\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/ormr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e40a61ddb8d3cee1a4e177f20956ab6b1887a9d5a422c8e9f9024859f4c23af/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6f726d722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/ormr/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/ormr/workflows/build_singularity/badge.svg?branch=develop\" alt=\"build_singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/ormr_logo_50.png\"\u003e\u003cimg src=\"man/figures/ormr_logo_50.png\" alt=\"ormr logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWork with Python installed at a custom location.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-goal\" class=\"anchor\" aria-hidden=\"true\" href=\"#goal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGoal\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows a user to install Python packages,\ncreate a Conda environment and run Python scripts\nwith only one point of contact.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e heavily depends on \u003ccode\u003ereticulate\u003c/code\u003e, the latter being\nmore powerful and flexible. \u003ccode\u003eormr\u003c/code\u003e, however, focuses\non making it trivially simple to install Python\npackages and run Python scripts.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-install-ormr\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-ormr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall \u003ccode\u003eormr\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003eAs \u003ccode\u003eormr\u003c/code\u003e is developed on the \u003ccode\u003emaster\u003c/code\u003e branch, only a release\nis tested to work:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eremotes::install_github(\"richelbilderbeek/ormr\", ref = \"v0.6.1\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee FAQ why one needs to install a release.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e uses one point of contact, \u003ccode\u003eormr_folder_name\u003c/code\u003e.\nFor convenience, there is also a default \u003ccode\u003eormr_folder_name\u003c/code\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall a Python package\u003c/li\u003e\n\u003cli\u003eRun a Python script\u003c/li\u003e\n\u003cli\u003eRun a Python script with command-line arguments\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAlso, \u003ccode\u003eormr\u003c/code\u003e uses \u003cstrong\u003eeager loading\u003c/strong\u003e, which means that\nit will setup everything it needs for you. For example,\nif you want to run a Python script from a new \u003ccode\u003eormr_folder_name\u003c/code\u003e,\nit will create a Conda environment there for you as well.\u003c/p\u003e\n\u003cp\u003eNote that \u003ccode\u003ecreate_default_conda_env\u003c/code\u003e conveniently returns the\n\u003ccode\u003eormr_folder_name\u003c/code\u003e used to work with this environment.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-install-a-python-package\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-a-python-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install a Python package\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall_python_package(\n \u003cspan class=\"pl-v\"\u003epackage_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003escipy\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\ninstall_python_package(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epackage_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003escipy\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-run-a-python-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-run-a-python-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Run a Python script\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehello_world.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script(\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehello_world.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-run-a-python-script-with-command-line-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-run-a-python-script-with-command-line-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Run a Python script with command-line arguments\u003c/h2\u003e\n\u003cp\u003eUsing the default \u003ccode\u003eormr\u003c/code\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshow_args.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script_with_args(\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworld\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing a custom \u003ccode\u003eormr_folder_name\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e tempfile()\n\u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e system.file(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eextdata\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshow_args.py\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003epackage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eormr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n)\nrun_python_script_with_args(\n \u003cspan class=\"pl-v\"\u003eormr_folder_name\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eormr_folder_name\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003epython_script_path\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003epython_script_path\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworld\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n)\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-hidden=\"true\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-the-goal-of-ormr\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-the-goal-of-ormr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is the goal of \u003ccode\u003eormr\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows a user to install Python packages,\ncreate a Conda environment and run Python scripts\nwith only one point of contact.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-in-what-context-is-ormr-useful\" class=\"anchor\" aria-hidden=\"true\" href=\"#in-what-context-is-ormr-useful\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIn what context is \u003ccode\u003eormr\u003c/code\u003e useful?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e was written to write simpler\n\u003ca href=\"https://singularity.hpcng.org/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e (a type of containerization\nsoftware, similar to Docker) scripts.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ereticulate\u003c/code\u003e is great when using its default folders on a local computer.\nHowever, for a Singularity container, it is recommended to install\nlibraries in a systems folder. In that setting, \u003ccode\u003ereticulate\u003c/code\u003e is\nharder to work with.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e allows to install install Python packages,\ncreate a Conda environment and run Python scripts\nin any folder easily, for example,\nin a system folder (\u003ccode\u003e/opt/ormr\u003c/code\u003e) of a Singularity container.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-not-just-use-reticulate\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-not-just-use-reticulate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy not just use \u003ccode\u003ereticulate\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e heavily depends on \u003ccode\u003ereticulate\u003c/code\u003e, the latter being\nmore powerful and flexible.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eormr\u003c/code\u003e, however, focuses\non making it trivially simple to install Python\npackages and run Python scripts,\ndue to eager loading.\nAdditionally, \u003ccode\u003eormr\u003c/code\u003e has a more extensive documentation,\nand 100% code coverage.\u003c/p\u003e\n\u003cp\u003eBeyond the domain of \u003ccode\u003eormr\u003c/code\u003e, use \u003ccode\u003ereticulate\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-do-you-mean-with-eager-loading\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-do-you-mean-with-eager-loading\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat do you mean with eager loading?\u003c/h2\u003e\n\u003cp\u003eEager loading is the opposite of lazy loading.\u003c/p\u003e\n\u003cp\u003eHere, it is defined as \u0027if you want \u003ccode\u003eormr\u003c/code\u003e to do B, which depends on\nthe setup of A\u0027, \u003ccode\u003eormr\u003c/code\u003e will setup A, then do B. For example, to install\na package to a certain \u003ccode\u003eormr_folder_name\u003c/code\u003e (\u0027to do B\u0027), \u003ccode\u003eormr\u003c/code\u003e\nwill create a Conda environment for that (\u0027the setup of A\u0027).\u003c/p\u003e\n\u003cp\u003eThis means that no setup code is necessary.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-does-one-need-to-install-a-release-instead-of-just-master\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-does-one-need-to-install-a-release-instead-of-just-master\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy does one need to install a release, instead of just \u003ccode\u003emaster\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003eThe development of \u003ccode\u003eormr\u003c/code\u003e takes place on the \u003ccode\u003emaster\u003c/code\u003e branch.\nHence, \u003ccode\u003emaster\u003c/code\u003e will break regularily.\nA specific release is tested to build correctly.\u003c/p\u003e\n\u003cp\u003eThe reason for this non-traditional workflow, is that the\nSingularity script always installs the \u003ccode\u003emaster\u003c/code\u003e branch,\nas it cannot detect the \u003ccode\u003egit\u003c/code\u003e branch is being built by.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" aria-hidden=\"true\" href=\"#there-is-a-feature-i-miss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThere is a feature I miss\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-want-to-collaborate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI want to collaborate\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting code\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-think-i-have-found-a-bug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI think I have found a bug\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting bugs\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" aria-hidden=\"true\" href=\"#theres-something-else-i-want-to-say\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThere\u0027s something else I want to say\u003c/h2\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-do-i-contribute\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-do-i-contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do I contribute?\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-is-the-package-called-ormr\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-is-the-package-called-ormr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy is the package called \u003ccode\u003eormr\u003c/code\u003e?\u003c/h2\u003e\n\u003cp\u003eThis name is a pun on \u003ccode\u003ereticulate\u003c/code\u003e. \u003ccode\u003ereticulate\u003c/code\u003e is named after a\ntype of snake. \u003ccode\u003eormr\u003c/code\u003e is written in Sweden. In Swedish, \u003ccode\u003eorm\u003c/code\u003e, is a snake.\nFollowing the common tradtion of adding an \u003ccode\u003er\u003c/code\u003e to the end of an R package\nname (e.g \u003ccode\u003edplyr\u003c/code\u003e, \u003ccode\u003etidyr\u003c/code\u003e, etc) resulted in \u003ccode\u003eormr\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-about-the-logo\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-about-the-logo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat about the logo?\u003c/h2\u003e\n\u003cp\u003eThe original snake image was found when searching for a\npublic domain image of a snake, using the following DuckDuckGo image seach:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttps://duckduckgo.com/?q=orm+.png\u0026amp;t=ffab\u0026amp;iar=images\u0026amp;iaf=license%3APublic%2Ctype%3Aclipart\u0026amp;iax=images\u0026amp;ia=images\u0026amp;iai=https%3A%2F%2Fcdn.pixabay.com%2Fphoto%2F2016%2F03%2F31%2F15%2F10%2Fcartoon-1293047_1280.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter that, the image was modified using KolourPaint and the R logo was added.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/search;query=ormr\" rel=\"nofollow\"\u003eFind the latest \u0027ormr\u0027 Singularity container\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-hidden=\"true\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinks\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/reticulate_on_singularity\"\u003ehttps://github.com/richelbilderbeek/reticulate_on_singularity\u003c/a\u003e:\ndemo how to run \u003ccode\u003ereticulate\u003c/code\u003e within a Singularity container, without \u003ccode\u003eormr\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1637350275.0 + "updated_at": 1657465963.0 }, { "data_format": 2, - "description": "repository to store various dockerfiles used to build docker containers that are used across multiple brainlife apps", + "description": "Calculation of Z-score from summary statistics of GWAS for list of SNPs", "filenames": [ - "hcppipelines/Singularity" + "container/Singularity" ], - "full_name": "brainlife/dockerfiles", + "full_name": "singharchit97/Calculate_Z-Score_GWAS", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dockerfiles\" class=\"anchor\" href=\"#dockerfiles\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edockerfiles\u003c/h1\u003e\n\u003cp\u003erepository to store various dockerfiles used to build docker containers\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-z-score-calculation-from-gwas-summary-statistics-for-a-given-set-of-snps\" class=\"anchor\" aria-hidden=\"true\" href=\"#z-score-calculation-from-gwas-summary-statistics-for-a-given-set-of-snps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eZ-score calculation from GWAS summary statistics for a given set of SNPs\u003c/h1\u003e\n\u003cp\u003eThis R-script calculates Z-scores (required for post GWAS analysis like fine-mapping \u0026amp; colocalization analysis) from summary statistics file using Beta values/ odds ratios and standard error (of Beta values/ odds ratios) for a given set of SNPs. The summary statistics input file should be tab seperated. The SNPs as input should be given as a text file with just one column and no header. The user needs to specify the input statistic using \u003ccode\u003e-c\u003c/code\u003e option which can be binary \u003ccode\u003e0/1\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pull-this-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull this repository\u003c/h3\u003e\n\u003cp\u003eGo to your working directory and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/singharchit97/Calculate_Z-Score_GWAS \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Calculate_Z-Score_GWAS/\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-configure-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#configure-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigure the container\u003c/h3\u003e\n\u003cp\u003eTo run the commands the user will need a container that contains a number of R-libraries.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the container\u003c/h4\u003e\n\u003cp\u003eIf the user wants to build the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/singularity-executable\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsudo \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_PATH\u003c/span\u003e/singularity build z-score.sif container/Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-the-script-without-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-the-script-without-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the script (without singularity container)\u003c/h3\u003e\n\u003cp\u003eIf the user has taken care of the dependencies required to run the script:\nRun the below command to see the help message on the input and output parameters required to run the script.\nNote that all parameters are mandatory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRscript calc_z-score.R --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDummy command given below: (here for odds ratios as input)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRscript calc_z-score.R -f input_summary_statistics.txt -i input.snplist -s standard_error -b odds_ratio -c 0 -v rs_id -z output.txt\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-the-script-with-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-the-script-with-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the script (with singularity container)\u003c/h3\u003e\n\u003cp\u003eRun the below command to see the help message on the input and output parameters required to run the script.\nNote that all parameters are mandatory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /mount/working/directory z-score.sif Rscript calc_z-score.R --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDummy command given below: (here for Beta-values as input)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /mount/working/directory z-score.sif Rscript calc_z-score.R -f input_summary_statistics.txt -i input.snplist -s standard_error -b beta -c 1 -v rs_id -z output.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe script runs in the \u003ccode\u003eCalculate_Z-Score_GWAS\u003c/code\u003e directory, it will create a output text file (name as given by the user), respectively.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1637688718.0 + "updated_at": 1660312501.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "tud/Singularity" ], - "full_name": "truatpasteurdotfr/ambertools-miniconda", + "full_name": "clEsperanto/clesperanto_container", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-conda-based-ambertools-environment-httpambermdorggetamberphpambertools\" class=\"anchor\" href=\"#conda-based-ambertools-environment-httpambermdorggetamberphpambertools\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econda based ambertools environment (\u003ca href=\"http://ambermd.org/GetAmber.php#ambertools\" rel=\"nofollow\"\u003ehttp://ambermd.org/GetAmber.php#ambertools\u003c/a\u003e)\u003c/h1\u003e\n\u003cp\u003eProviding a toy container for AMBER based on the conda environment \u003ca href=\"https://github.com/truatpasteurdotfr/ambertools-miniconda/actions/workflows/docker-singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/ambertools-miniconda/actions/workflows/docker-singularity-publish.yml/badge.svg\" alt=\"Docker and Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003edocker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/truatpasteurdotfr/ambertools-miniconda:main\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esingularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell oras://ghcr.io/truatpasteurdotfr/ambertools-miniconda:latest\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1638799326.0 + "updated_at": 1658992510.0 }, { "data_format": 2, - "description": "An R package for easy execution of mouse GWAS", + "description": null, "filenames": [ - "singularity/Singularity" + "containers/Singularity.sratoolkit", + "containers/Singularity.featcount", + "containers/Singularity.samtools", + "containers/Singularity.star_nb", + "containers/Singularity.star", + "containers/Singularity.fastqc", + "containers/Singularity.R" ], - "full_name": "TheJacksonLaboratory/mousegwas", - "latest_release": "GRCm38", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mousegwas\" class=\"anchor\" href=\"#mousegwas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMouseGWAS\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThis package was built to manage the GWAS analysis of mouse phenotypes. The mice in the study were genotypes using either MDA or UCLA chips and deposited in the mouse phenome database (\u003ca href=\"https://phenome.jax.org/genotypes\" rel=\"nofollow\"\u003ehttps://phenome.jax.org/genotypes\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRscript -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elibrary(devtools); install_github(\"TheJacksonLaboratory/mousegwas\")\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input\" class=\"anchor\" href=\"#input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003cp\u003eThe input for the script is the genotype csv files downloaded from the MPD website, the measured phenotypes as a csv file and a yaml file describing the input file.\nThe input csv file should contain a column for strain, a column for sex and columns for phenotype measurements. The names of the columns should be defined in the yaml file using the keywords \u003ccode\u003estrain\u003c/code\u003e and \u003ccode\u003esex\u003c/code\u003e and the phenotypes should be a list under the \u003ccode\u003ephenotypes\u003c/code\u003e keyword.\nAnother data that should reside in the yaml file is translation of strains to the strain names in the genotypes files, it is a dictionary under the \u003ccode\u003etranslate\u003c/code\u003e keyword, and \u003ccode\u003eF1\u003c/code\u003e keyword which is a dictionary translating the F1 names to their parent names, make sure the female parent is always first, it will be used to determine the X chromosome of make F1s. Confounding SNPs could be given using the \u003ccode\u003econfSNPs\u003c/code\u003e, this might be useful to control for obvious markers like coat color alleles. For sanity check you can supply coat color under \u003ccode\u003ecoat\u003c/code\u003e as a dictionary from strain name to coat color and execute a GWAS of coat color with \u003ccode\u003e--coat_phenotype\u003c/code\u003e, it can also be used as a covariate with \u003ccode\u003e--coat_covar\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-execution\" class=\"anchor\" href=\"#execution\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h2\u003e\n\u003cp\u003eThe script \u003ccode\u003erun_GWAS.py\u003c/code\u003e will read the input files and will prepapre the input for either GEMMA or pyLMM. In the case of GEMMA it will download version 0.98 to the working directory if it can\u0027t find the GEMMA executable, if you wish to use pyLMM it should be installed and available in the path. A common process would be creating a virtual environment in python, activating it and installing pyLMM using \u003ccode\u003epip\u003c/code\u003e, see \u003ca href=\"https://github.com/nickFurlotte/pylmm\"\u003ehttps://github.com/nickFurlotte/pylmm\u003c/a\u003e for details.\nThe mousegwas will also download METASOFT and run it on the output if there is more than one phenotype.\u003c/p\u003e\n\u003cp\u003eAs part of the data processing, mousegwas can select a subset of the individuals, restricting the number of mice in each strain x sex group or use the average phenotype of all the individuals in such a group. This is controlled by the \u003ccode\u003e-d\u003c/code\u003e option with 0 for average or any other integer for number restriction.\u003c/p\u003e\n\u003cp\u003eBy default LOCO will be used, use the \u003ccode\u003e--noloco\u003c/code\u003e argument to disable it.\u003c/p\u003e\n\u003cp\u003eA quantile-quantile normalizatin of each phenotype meausrement could be done using the \u003ccode\u003e--qqnorm\u003c/code\u003e argument.\nOther parameters will control the final Manhattan plot, it is a bit unnecessary since the \u003ccode\u003epostprocess_GWAS.R\u003c/code\u003e script will generate more and publication ready figures.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-nextflow-pipeline\" class=\"anchor\" href=\"#nextflow-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline\u003c/h2\u003e\n\u003cp\u003eTo execute the scripts in an easy way we included a nextflow pipeline that runs the initial GWAS, the shuffled executions,\ndetermine a p-value and run the postprocess.\nTo run coat color phenotype GWAS you can simply install \u003ccode\u003enextflow\u003c/code\u003e, make sure that \u003ccode\u003esingularity\u003c/code\u003e is installed and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run TheJacksonLaboratory/mousegwas \\\n --yaml https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/coat_color_MDA.yaml \\\n --shufyaml https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/coat_color_MDA.yaml \\\n --addgwas=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--coat_phenotype\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --addpostp=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--coat_phenotype --colorgroup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --pvalue 0.1 --clusters 1 --outdir coatout -profile singularity,slurm\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eslurm\u003c/code\u003e can be changed to \u003ccode\u003epbs\u003c/code\u003e or ignored for local execution.\u003c/p\u003e\n\u003cp\u003eTo regenerate the results in the paper: \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.10.08.331017v1\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/2020.10.08.331017v1\u003c/a\u003e :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run TheJacksonLaboratory/mousegwas \\\n --yaml https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/grooming_nowild.yaml \\\n --shufyaml https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/grooming_shuffle.yaml \\\n --input https://raw.githubusercontent.com/TheJacksonLaboratory/mousegwas/master/example/grooming_paper_strain_survey_2019_11_21.csv \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e\\\n --outdir grooming_output --addpostp=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--loddrop 0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -profile slurm,singularity\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "Sherman-1/Hackaton", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-projet-repro-hackathon-2022-2023--atia-safiya-bossut-no\u00e9mie-et-herman-simon\" class=\"anchor\" aria-hidden=\"true\" href=\"#projet-repro-hackathon-2022-2023--atia-safiya-bossut-no\u00e9mie-et-herman-simon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eProjet Repro-Hackathon 2022-2023\u003c/strong\u003e : ATIA Safiya, BOSSUT No\u00e9mie et HERMAN Simon\u003c/h1\u003e\n\u003cp\u003eCe projet vise \u00e0 reproduire une partie des r\u00e9sultats de deux articles:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5321577/\" rel=\"nofollow\"\u003eFurney \u003cem\u003eet al.\u003c/em\u003e, Cancer Discovery (2013)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3789378/\" rel=\"nofollow\"\u003eHarbour \u003cem\u003eet al.\u003c/em\u003e, Nature Genetics (2013)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLes donn\u00e9es RNA-seq de ces deux papiers sont disponibles en open-access : \u003ca href=\"https://www.ncbi.nlm.nih.gov/sra?term=SRA062359\" rel=\"nofollow\"\u003e\u003cstrong\u003eDonn\u00e9es NCBI\u003c/strong\u003e\u003c/a\u003e. Dans un premier temps, seul l\u0027\u00e9tude de transcriptome est \u00e9tudi\u00e9. \nL\u0027objectif de ces deux article est d\u0027\u00e9tudier les expression de g\u00e8nes, et notamment le g\u00e8ne SF3B1, d\u0027individus atteint de m\u00e9lanome ulv\u00e9al.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-ecrire-ici-conclu-des-articles\" class=\"anchor\" aria-hidden=\"true\" href=\"#ecrire-ici-conclu-des-articles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eECRIRE ICI CONCLU DES ARTICLES\u003c/h1\u003e\n\u003cp\u003eA l\u0027aide d\u0027un workflow Nextflow et de containers Singularity, notre groupe a tent\u00e9 de comprendre pourquoi les r\u00e9sultats des deux articles divergent, et quelles sont nos propres observations sur le sujet.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-notre-conclu-a-nous\" class=\"anchor\" aria-hidden=\"true\" href=\"#notre-conclu-a-nous\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTRE CONCLU A NOUS\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pr\u00e9-requis-nextflow--singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#pr\u00e9-requis-nextflow--singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003ePr\u00e9-requis:\u003c/strong\u003e Nextflow \u0026amp; Singularity\u003c/h2\u003e\n\u003cp\u003eAfin de faire tourner notre pipeline, \u003cstrong\u003e1000000Gb DE RAM ET 300 COEURS\u003c/strong\u003e, ainsi que deux logiciels sont n\u00e9cessaires:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextflow (version 21.10.6.5660) \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003e\u003cem\u003einstallation\u003c/em\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity (version 3.8.7) \u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003e\u003cem\u003einstallation\u003c/em\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003euidmap (pour la construction des containers Singularity)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo apt-get install uidmap\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-le-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#le-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eLe pipeline:\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMettre ici le DAG\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eT\u00e9l\u00e9chargement des donn\u00e9es\u003c/strong\u003e : chromosomes humains (dont chromosome mitochondrial), annotation du g\u00e9nome et donn\u00e9es RNA-seq des 8 individus (\u003cem\u003e\u003cstrong\u003esratoolkit\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndexation\u003c/strong\u003e du g\u00e9nome (\u003cem\u003e\u003cstrong\u003eSTAR\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAlignement\u003c/strong\u003e et \u003cstrong\u003eTri\u003c/strong\u003e des donn\u00e9es RNA-seq sur le g\u00e9nome. Obtention de fichiers \u003cem\u003e.bam\u003c/em\u003e tri\u00e9s en sortie (\u003cem\u003e\u003cstrong\u003eSTAR\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndexation\u003c/strong\u003e des fichiers \u003cem\u003e.bam\u003c/em\u003e. en \u003cem\u003e.bai\u003c/em\u003e (\u003cem\u003e\u003cstrong\u003esamtools\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eComptage\u003c/strong\u003e des s\u00e9quences exprim\u00e9es (\u003cem\u003e\u003cstrong\u003efeatureCounts\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAnalyse statistique\u003c/strong\u003e des r\u00e9sultats (\u003cem\u003e\u003cstrong\u003eDESeq2\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eL\u0027ensemble des donn\u00e9es et des r\u00e9sultats peuvent \u00eatre retrouv\u00e9s dans l\u0027arborescence ci-dessous: ( CE N4EST PAS LE BON OFC A CHANGER)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u251c\u2500\u2500 containers\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.fastqc\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.featcount\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.R\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.samtools\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.sratoolkit\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.star\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.star_nb\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 temp\n\u251c\u2500\u2500 init_VM.sh\n\u251c\u2500\u2500 nextflow.config\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 sratoolkit.Dockerfile\n\u251c\u2500\u2500 star.Dockerfile\n\u251c\u2500\u2500 test.nf\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-execution-du-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#execution-du-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eExecution du workflow\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSi les pr\u00e9-requis sont bien satisfaits, placez-vous dans le repertoire voulu et r\u00e9cup\u00e9rez les le projet\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e git clone https://github.com/Sherman-1/Hackaton (A CHANGER ATTENTION LE NOM)\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Hackathon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLe fichier \u003ccode\u003erun.sh\u003c/code\u003e permet d\u0027initialiser votre environnement, ainsi que de cr\u00e9er les images singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e bash run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eVerifiez que les r\u00e9pertoires ont bien \u00e9t\u00e9 cr\u00e9\u00e9s. Si c\u0027est le cas, vous pouvez lancer le pipeline :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e nextflow run main.nf\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-options-dexecution\" class=\"anchor\" aria-hidden=\"true\" href=\"#options-dexecution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions d\u0027execution\u003c/h3\u003e\n\u003cp\u003eEn plus de la commande par d\u00e9faut, vous pouvez utiliser les param\u00e8tres suivant\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-resume\u003c/code\u003e si votre pipeline a \u00e9t\u00e9 interrompu et que vous souhaitez le reprendre\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-with-trace [nom du fichier]\u003c/code\u003e pour obtenir le DAG correspondant\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-with-report [nom du fichier]\u003c/code\u003e pour obtenir un rapport complet et de nombreuses metadatas sur le pipeline\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1632398739.0 + "updated_at": 1670193803.0 }, { "data_format": 2, - "description": "A nextflow pipeline to call somatic gene fusions", + "description": "random access on r-index", "filenames": [ - "Singularity/Singularity.v1.0" + "Singularity.r-index" ], - "full_name": "IARCbioinfo/nf-gene-fusions", - "latest_release": "v1.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nf-rna-fusions\" class=\"anchor\" href=\"#nf-rna-fusions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-rna-fusions\u003c/h1\u003e\n\u003cp\u003eA nextflow pipeline to call somatic rna fusions\u003c/p\u003e\n", + "full_name": "koeppl/rasarindex", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-\ufe0f--accessing-the-suffix-array-via-\u03c61-forest\" class=\"anchor\" aria-hidden=\"true\" href=\"#\ufe0f--accessing-the-suffix-array-via-\u03c61-forest\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"card_index_dividers\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f5c2.png\"\u003e\ud83d\uddc2\ufe0f\u003c/g-emoji\u003e Accessing the Suffix Array via \u03a6\u22121 Forest\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-\ufe0f-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#\ufe0f-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"heavy_check_mark\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/2714.png\"\u003e\u2714\ufe0f\u003c/g-emoji\u003e Prerequisites\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003elibdivsufsort\u003c/li\u003e\n\u003cli\u003eg++\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content--complete-test-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#-complete-test-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cg-emoji class=\"g-emoji\" alias=\"rocket\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f680.png\"\u003e\ud83d\ude80\u003c/g-emoji\u003e Complete Test Run\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/koeppl/randSAbench.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e randSAbench\nsubmodule update --init --recursive\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn the file \u003ccode\u003ebench.sh\u003c/code\u003e,\nyou have to adjust the variable \u003ccode\u003ekFullFasta\u003c/code\u003e for the path to the FASTA file you want to index,\nand \u003ccode\u003esequences\u003c/code\u003e for the number of sequences you want to extract from this file.\nThen you can run \u003ccode\u003ebench.sh\u003c/code\u003e measuring the query time for suffix array access with our proposed method and the standard method of the r-index.\nNote that the default is to also build the plain suffix array to check whether the reported entry is correct.\nBuilding the plain suffix array will not work with large inputs and a modest amount of memory.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1637235680.0 + "updated_at": 1658678628.0 }, { "data_format": 2, - "description": "An example app using singularity-compose for orchestration (under development)", + "description": "Aim: High resolution fvm simulation using WENOEXT scheme", "filenames": [ - "app/Singularity", - "nginx/Singularity.nginx" + "Singularity-openfoam.def" ], - "full_name": "singularityhub/singularity-compose-example", + "full_name": "jiaqiwang969/WENOEXT-project", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity-compose-example\" class=\"anchor\" href=\"#singularity-compose-example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Compose Example\u003c/h1\u003e\n\u003cp\u003eThis is an example of simple container orchestration with singularity-compose,\nIt is based on \u003ca href=\"https://github.com/vsoch/django-nginx-upload\"\u003edjango-nginx-upload\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eimportant\u003c/strong\u003e if you use Docker on your machine, your iptables are likely edited\nso you will have an issue running this with the latest version of Singularity.\nYou can either run the \u003ca href=\"https://www.github.com/singularityhub/singularity-example-simple\"\u003esimple-example\u003c/a\u003e,\nor install a (not yet released)\nfixed Singularity version \u003ca href=\"https://github.com/sylabs/singularity/pull/3771\"\u003efrom this branch\u003c/a\u003e\nto have a fully working example. If you don\u0027t use Docker, then you are a clean machine\nand no further action is required.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-setup\" class=\"anchor\" href=\"#setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-composeyml\" class=\"anchor\" href=\"#singularity-composeyml\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-compose.yml\u003c/h3\u003e\n\u003cp\u003eFor a singularity-compose project, it\u0027s expected to have a \u003ccode\u003esingularity-compose.yml\u003c/code\u003e\nin the present working directory. You can look at the \u003ca href=\"singularity-compose.yml\"\u003eexample\u003c/a\u003e\npaired with the \u003ca href=\"https://github.com/singularityhub/singularity-compose/tree/master/docs/spec\"\u003especification\u003c/a\u003e\nto understand the fields provided.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-instance-folders\" class=\"anchor\" href=\"#instance-folders\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstance folders\u003c/h3\u003e\n\u003cp\u003eGenerally, each section in the yaml file corresponds with a container instance to be run,\nand each container instance is matched to a folder in the present working directory.\nFor example, if I give instruction to build an \u003ca href=\"nginx\"\u003enginx\u003c/a\u003e instance from\na \u003ca href=\"nginx/Singularity.nginx\"\u003eSingularity.nginx\u003c/a\u003e file, I should have the\nfollowing in my singularity-compose:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nginx:\n build:\n context: ./nginx\n recipe: Singularity.nginx\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003epaired with the following directory structure:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity-compose-example\n\u251c\u2500\u2500 nginx\n...\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.nginx\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 uwsgi_params.par\n\u2514\u2500\u2500 singularity-compose.yml\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNotice how I also have other dependency files for the nginx container\nin that folder. As another option, you can just define a container to pull,\nand it will be pulled to the same folder that is created if it doesn\u0027t exist.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nginx:\n image: docker://nginx\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity-compose-example\n\u251c\u2500\u2500 nginx (- created \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e it doesn\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003et exist\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u2502\u00a0\u00a0 \u2514\u2500\u2500 nginx.sif (- named according to the instance\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u2514\u2500\u2500 singularity-compose.yml\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eIf you don\u0027t have it installed, install the latest \u003ca href=\"https://singularityhub.github.io/singularity-compose/#/\" rel=\"nofollow\"\u003esingularity-compose\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install singularity-compose\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest way to start is to first build your containers (you will be asked for sudo):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then bring it up!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eVerify it\u0027s running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose ps\nINSTANCES NAME PID IMAGE\n1 app\t10238\tapp.sif\n2 nginx\t10432\tnginx.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then look at logs,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs app\n$ singularity-compose logs app --tail 30\n$ singularity-compose logs nginx\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eshell inside,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose shell app\n$ singularity-compose shell nginx\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor execute a command!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e app uname -a\n$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e nginx \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHello!\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen you open your browser to \u003ca href=\"http://127.0.0.1\" rel=\"nofollow\"\u003ehttp://127.0.0.1\u003c/a\u003e\nyou should see the upload interface.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/upload.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/upload.png\" alt=\"img/upload.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you drop a file in the box (or click\nto select) we will use the nginx-upload module to send it directly to the\nserver. Cool!\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"img/content.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"img/content.png\" alt=\"img/content.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is just a simple Django application, the database is sqlite3, in the\napp folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls app/\napp.sif db.sqlite3 manage.py nginx requirements.txt run_uwsgi.sh Singularity upload uwsgi.ini\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe images are stored in \u003ca href=\"images\"\u003eimages\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls images/\n2018-02-20-172617.jpg 40-acos.png _upload \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd static files are in \u003ca href=\"static\"\u003estatic\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls static/\nadmin css js\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you look at the \u003ca href=\"singularity-compose.yml\"\u003esingularity-compose.yml\u003c/a\u003e, we bind these\nfolders to locations in the container where the web server needs write. This is likely\na prime different between Singularity and Docker compose - Docker doesn\u0027t need\nbinds for write, but rather to reduce isolation. Continue below to\nread about networking, and see these commands in detail.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-networking\" class=\"anchor\" href=\"#networking\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNetworking\u003c/h2\u003e\n\u003cp\u003eWhen you bring the container up, you\u0027ll see generation of an \u003ccode\u003eetc.hosts\u003c/code\u003e file,\nand if you guessed it, this is indeed bound to \u003ccode\u003e/etc/hosts\u003c/code\u003e in the container.\nLet\u0027s take a look:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e10.22.0.3\tapp\n10.22.0.2\tnginx\n127.0.0.1\tlocalhost\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The following lines are desirable for IPv6 capable hosts\u003c/span\u003e\n::1 ip6-localhost ip6-loopback\nfe00::0 ip6-localnet\nff00::0 ip6-mcastprefix\nff02::1 ip6-allnodes\nff02::2 ip6-allrouters\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis file will give each container that you create (in our case, nginx and app)\na name on its local network. Singularity by default creates a bridge for\ninstance containers, which you can conceptually think of as a router,\nThis means that, if I were to reference the hostname \"app\" in a second container,\nit would resolve to \u003ccode\u003e10.22.0.3\u003c/code\u003e. Singularity compose does this by generating\nthese addresses before creating the instances, and then assigning them to it.\nIf you would like to see the full commands that are generated, run the up\nwith \u003ccode\u003e--debug\u003c/code\u003e (binds and full paths have been removed to make this easier to read).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity instance start \\\n --bind /home/vanessa/Documents/Dropbox/Code/singularity/singularity-compose-simple/etc.hosts:/etc/hosts \\\n --net --network-args \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eportmap=80:80/tcp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --network-args \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eIP=10.22.0.2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --hostname app \\\n --writable-tmpfs app.sif app\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-commands\" class=\"anchor\" href=\"#commands\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommands\u003c/h2\u003e\n\u003cp\u003eThe following commands are currently supported.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build\" class=\"anchor\" href=\"#build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h3\u003e\n\u003cp\u003eBuild will either build a container recipe, or pull a container to the\ninstance folder. In both cases, it\u0027s named after the instance so we can\neasily tell if we\u0027ve already built or pulled it. This is typically\nthe first step that you are required to do in order to build or pull your\nrecipes. It ensures reproducibility because we ensure the container binary\nexists first.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe working directory is the parent folder of the singularity-compose.yml file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-create\" class=\"anchor\" href=\"#create\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate\u003c/h3\u003e\n\u003cp\u003eGiven that you have built your containers with \u003ccode\u003esingularity-compose build\u003c/code\u003e,\nyou can create your instances as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose create\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-up\" class=\"anchor\" href=\"#up\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUp\u003c/h3\u003e\n\u003cp\u003eIf you want to both build and bring them up, you can use \"up.\" Note that for\nbuilds that require sudo, this will still stop and ask you to build with sudo.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose up\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ps\" class=\"anchor\" href=\"#ps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eps\u003c/h3\u003e\n\u003cp\u003eYou can list running instances with \"ps\":\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose ps\nINSTANCES NAME PID IMAGE\n1 app\t6659\tapp.sif\n2 db\t6788\tdb.sif\n3 nginx\t6543\tnginx.sif\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-shell\" class=\"anchor\" href=\"#shell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell\u003c/h3\u003e\n\u003cp\u003eYou can easily shell inside of a running instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose shell app\nSingularity app.sif:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/singularity/singularity-compose-example\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exec\" class=\"anchor\" href=\"#exec\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExec\u003c/h3\u003e\n\u003cp\u003eYou can easily execute a command to a running instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e app ls /\nbin\nboot\ncode\ndev\nenvironment\netc\nhome\nlib\nlib64\nmedia\nmnt\nopt\nproc\nroot\nrun\nsbin\nsingularity\nsrv\nsys\ntmp\nusr\nvar\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-down\" class=\"anchor\" href=\"#down\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDown\u003c/h3\u003e\n\u003cp\u003eYou can bring one or more instances down (meaning, stopping them) by doing:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose down\nStopping (instance:nginx)\nStopping (instance:db)\nStopping (instance:app)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo stop a custom set, just specify them:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose down nginx\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-logs\" class=\"anchor\" href=\"#logs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogs\u003c/h3\u003e\n\u003cp\u003eYou can of course view logs for all instances, or just specific named ones:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs\nFri Jun 21 10:24:40 2019 - WSGI app 0 (mountpoint=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e) ready \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e 1 seconds on interpreter 0x55daf463f920 pid: 27 (default app)\nFri Jun 21 10:24:40 2019 - uWSGI running as root, you can use --uid/--gid/--chroot options\nFri Jun 21 10:24:40 2019 - \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e WARNING: you are running uWSGI as root \u003cspan class=\"pl-k\"\u003e!!!\u003c/span\u003e (use the --uid flag) \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e \nFri Jun 21 10:24:40 2019 - \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e uWSGI is running \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e multiple interpreter mode \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e\nFri Jun 21 10:24:40 2019 - spawned uWSGI master process (pid: 27)\nFri Jun 21 10:24:40 2019 - spawned uWSGI worker 1 (pid: 29, cores: 1)\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e## Config\u003c/span\u003e\n\nYou can load and validate the configuration file (singularity-compose.yml) and\nprint it to the screen as follows:\n\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ebash\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e$ singularity-compose config \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e{\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eversion\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1.0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einstances\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebuild\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./nginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003erecipe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSingularity.nginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./nginx.conf:/etc/nginx/conf.d/default.conf:ro\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./uwsgi_params.par:/etc/nginx/uwsgi_params.par:ro\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.:/code\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./static:/var/www/static\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./images:/var/www/images\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes_from\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eapp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eports\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eimage\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edocker://postgres:9.4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edb-data:/var/lib/postgresql/data\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eapp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebuild\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./app\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.:/code\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./static:/var/www/static\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./images:/var/www/images\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eports\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e5000:80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edepends_on\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-wenoext-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#wenoext-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWENOEXT-project\u003c/h1\u003e\n\u003cp\u003eAim: High resolution fvm simulation using \u003ca href=\"https://github.com/WENO-OF/WENOEXT\"\u003eWENOEXT\u003c/a\u003e scheme\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emv Dockerfile.step01 Dockerfile\ndocker build jiaqiknight/openfoam-wenoext:v1 .\nmv Dockerfile.step02 Dockerfile\ndocker build jiaqiknight/openfoam-wenoext:v2 .\nsingularity build openfoam-wenoext-v2012.sif Singularity-openfoam.def\nsingularity shell openfoam-wenoext-v2012.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-action-to-dockerhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-action-to-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Action to dockerhub\u003c/h3\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1608596031.0 + "updated_at": 1655451993.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.bonito" + "tests/examplefiles/singularity/Singularity" ], - "full_name": "dominik-handler/Singularity.bonito", + "full_name": "ibbema/pygments", "latest_release": null, "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1638717249.0 + "updated_at": 1655374960.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "docker/Singularity.def" ], - "full_name": "porchard/snATACseq-NextFlow", + "full_name": "SourcedFromWill/SpaceNet8Sub", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nextflow-pipeline-for-10x-snatac-seq-data\" class=\"anchor\" href=\"#nextflow-pipeline-for-10x-snatac-seq-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextFlow pipeline for 10X snATAC-seq data\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eIf you have Singularity installed, you can use the config provided here (\u0027Singularity\u0027) to build a container with all the dependencies.\u003c/p\u003e\n\u003cp\u003eOtherwise, you\u0027ll need to have the following installed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ebiopython\u003c/li\u003e\n\u003cli\u003ebwa\u003c/li\u003e\n\u003cli\u003epicardtools\u003c/li\u003e\n\u003cli\u003efastqc\u003c/li\u003e\n\u003cli\u003esamtools (v. 1.10 or above)\u003c/li\u003e\n\u003cli\u003epysam\u003c/li\u003e\n\u003cli\u003eataqv (v. 1.3.0 or above)\u003c/li\u003e\n\u003cli\u003ecta (the forked version on the porchard GitHub)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eI\u0027ve used this pipeline with NextFlow v. 20.10.0\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003ePaths to various generic files (e.g., bwa indices) must be included in the nextflow.config file -- check that file and change paths accordingly. These include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBlacklist bed files for each genome\u003c/li\u003e\n\u003cli\u003eChrom size files for each genome\u003c/li\u003e\n\u003cli\u003eBWA indices\u003c/li\u003e\n\u003cli\u003eTSS files (BED6 files denoting TSS positions)\u003c/li\u003e\n\u003cli\u003ePath to the barcode whitelist (the 10X whitelist is included in this repo)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou\u0027ll also need to set the params.results variable -- either in the nextflow.config file itself, or on the command line when you run the pipeline (\u0027--results /path/to/results\u0027).\u003c/p\u003e\n\u003cp\u003eYou can split the fastq files into chunks using the --chunks parameter (default: 1, meaning no chunking). In the case of very large fastq files this can speed up processing.\u003c/p\u003e\n\u003cp\u003eLastly, you\u0027ll need to include information about each ATAC-seq library, including the genome(s) for the species that each library includes, and the paths to the fastq files for each readgroup. Organize this information in a JSON file, as in library-config.json. Note that for each readgroup, three fastq files are required -- the first and second insert reads (\u00271\u0027 and \u00272\u0027), and the read with the nuclear barcode (\u0027index\u0027)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h2\u003e\n\u003cp\u003eOnce you have all of the above information, you can run the pipeline as follows (in this case, indicating the path to the results on the command line):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run -with-singularity /path/to/Singularity.simg -params-file library-config.json --results /path/to/results /path/to/main.nf\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1640491578.0 + "updated_at": 1658783759.0 }, { "data_format": 2, - "description": "A terminal based graphical activity monitor inspired by gtop and vtop", + "description": null, "filenames": [ - "3.0.0/Singularity" + "Singularity.def" ], - "full_name": "icaoberg/singularity-gotop", - "latest_release": "v3.0.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/icaoberg/singularity-gotop/actions/workflows/main.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-gotop/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/icaoberg/singularity-gotop/actions/workflows/pretty.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/icaoberg/singularity-gotop/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7970acf310b549a3bd62867fbe3d0e11bd65f094de2204a25a0a00c78e522e9f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d676f746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7970acf310b549a3bd62867fbe3d0e11bd65f094de2204a25a0a00c78e522e9f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6963616f626572672f73696e67756c61726974792d676f746f70\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/icaoberg/singularity-gotop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7b8613e397ce94440087fb830ecbbec5d0d806459beb0f617ff837aca32d8c79/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6963616f626572672f73696e67756c61726974792d676f746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg 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data-canonical-src=\"https://img.shields.io/github/stars/icaoberg/singularity-gotop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/7da344bc86ffcbbb642fdbe48343d10897dbf168f470553cca36b9df13821c23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d676f746f70\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7da344bc86ffcbbb642fdbe48343d10897dbf168f470553cca36b9df13821c23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6963616f626572672f73696e67756c61726974792d676f746f70\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/icaoberg/singularity-gotop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" href=\"#building-the-image-using-the-recipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" href=\"#to-build-the-image-locally\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" href=\"#to-build-the-image-remotely\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" href=\"#to-run-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "agoldberglab/ObjectDetection_AdmixtureSelection", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-example-scripts-for-object-detection-based-selection-scans-using-images-of-ancestry-patterns\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-scripts-for-object-detection-based-selection-scans-using-images-of-ancestry-patterns\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample scripts for object detection-based selection scans using images of ancestry patterns\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eHamid, I., Korunes, K. L., Schrider, D., \u0026amp; Goldberg, A. (2022). Localizing post-admixture adaptive variants with object detection on ancestry-painted chromosomes. BioRxiv, 2022.09.04.506532. \u003ca href=\"https://doi.org/10.1101/2022.09.04.506532\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2022.09.04.506532\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eDeployed model\u003c/li\u003e\n\u003cli\u003eTraining \u0026amp; Inference w/ IceVision\u003c/li\u003e\n\u003cli\u003eSLiMulations \u0026amp; generating images\u003c/li\u003e\n\u003cli\u003eSoftware versions used in this project\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deployed-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#deployed-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeployed model\u003c/h2\u003e\n\u003cp\u003eThe pretrained \"high resolution\" baseline model used for most analyses in this project can be found \u003ca href=\"https://huggingface.co/spaces/imanhamid/ObjectDetection_AdmixtureSelection_Space\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Users can \u003ca href=\"https://huggingface.co/spaces/imanhamid/ObjectDetection_AdmixtureSelection_Space/blob/main/object_localization_full-ancestry.model.pth\" rel=\"nofollow\"\u003edownload/load the model weights\u003c/a\u003e for their own testing purposes.\u003c/p\u003e\n\u003cp\u003eThe model is also deployed as an app on the \u003ca href=\"https://huggingface.co/spaces/imanhamid/ObjectDetection_AdmixtureSelection_Space\" rel=\"nofollow\"\u003eHugging Face space\u003c/a\u003e. Users can upload their own 200x200 black and white images of ancestry-painted chromosomes, and the model will return inferred bounding box vertices and scores. We strongly encourage users to follow the example code in \u003ca href=\"./admixture_makeimage.R\"\u003eadmixture_makeimage.R\u003c/a\u003e to ensure that the image is in the correct expected format (including size and color values) for this model.\u003c/p\u003e\n\u003cp\u003eThe model is trained to detect 11-pixel bboxes (exclusive. e.g. [start pixel, end pixel)) with the adaptive variant at the 6th pixel position. So, for a predicted bbox of [xmin: 111, ymin: 0, xmax:122, ymax:200], the adaptive variant is predicted to be at the scaled position of 116. The x-axis positions are scaled values, so they would need to be reconverted back to physical or genetic map distances. That is, a scaled value of 116 on a 50 Mb chromosome would correspond to \u003ccode\u003e(116 / 200) * 50000000 = 29,000,000 bp\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training--inference-w-icevision-v052\" class=\"anchor\" aria-hidden=\"true\" href=\"#training--inference-w-icevision-v052\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining \u0026amp; Inference w/ \u003ca href=\"https://airctic.com/0.5.2/\" rel=\"nofollow\"\u003eIceVision v0.5.2\u003c/a\u003e\n\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eExample code and notes for training and inference can be found in \u003ca href=\"./objectdetection_ancestryimages_example.ipynb\"\u003eobjectdetection_ancestryimages_example.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"./inference.py\"\u003einference.py\u003c/a\u003e - scripts used to skip training \u0026amp; output precision \u0026amp; recall values across varying threshholds for a set of images, using a pre-trained model. Not tested outside our specific analyses and directory structure, some hard-coded values may need to be edited. Expects users to provide full paths for a base_directory which contained the images to infer from, an out_directory/filename to output the final table of P-R values for each threshhold, and the pretrained model. e.g. \u003ccode\u003einference.py /home/simulations/analysis1_images /home/simulations/PR-results/object_localization_analysis1_precision-recall.txt /home/models/trained_model.pth\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-notes-for-running-in-slurm-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes-for-running-in-slurm-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes for running in SLURM environment\u003c/h4\u003e\n\u003cp\u003eIn order to run IceVision on the Duke Compute Cluster (slurm), we built a Singularity container image (see e.g. \u003ca href=\"./Singularity.def\"\u003eSingularity.def\u003c/a\u003e), which can be pulled down by running:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecurl -k -O https://research-singularity-registry.oit.duke.edu/goldberglab/selectionscansingularity.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen you can run scripts on a worker node, for example:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec --nv -B /work selectionscansingularity.sif inference.py simulation_directory out.txt model.pth\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-slimulations--generating-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#slimulations--generating-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSLiMulations \u0026amp; generating images:\u003c/h2\u003e\n\u003cp\u003eA. \u003ca href=\"./admixture.slim\"\u003eadmixture.slim\u003c/a\u003e - this is a programmable/general SLiM script for admixture simulations. Selection strength is randomly drawn from a uniform distribution s~U(0, 0.5). As is, user must specify the following parameters from the command line:\u003c/p\u003e\n\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"center\"\u003evariable name\u003c/th\u003e\n \u003cth align=\"center\"\u003eparameter description\u003c/th\u003e\n \u003cth align=\"center\"\u003eexample\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eL\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003echromosome length (bp)\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d L=50000000\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003emig\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003esource population 1 admixture proportion\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d mig=0.5\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eN\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eadmixed population size\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d N=10000\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003et_end\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003enumber of generations for simulation\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d t_end=50\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eout\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003egeneral name for output files. should also include output directory\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d out=\u0027\"/work/ih49/simulations/test_NN/human_L-50_N-10000_single-pulse_m-0.5\"\u0027\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eseed\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003eseed number to append to output file\u003c/td\u003e\n \u003ctd rowspan=\"1\" align=\"center\"\u003e-d out=\u0027\"seed-5\"\u0027\u003c/td\u003e\n \u003c/tr\u003e \n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSimulation script will output two files\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ea \u003ccode\u003e.trees\u003c/code\u003e file with the name \u003ccode\u003e{out}_s-{selectioncoeff}_pos-{physicalposition}_seed-{seednum}.trees\u003c/code\u003e. This file will be used to generate ancestry images.\u003c/li\u003e\n\u003cli\u003ea \u003ccode\u003evariants.txt\u003c/code\u003e file with the name \u003ccode\u003e{out}_seed-{seednum}_variants.txt\u003c/code\u003e. This file contains the physical position and selection strength of each variant in the simulation. The single variant simulations have this information in the filenames, but having this information separate may be helpful for keeping track of the range of selection strengths and physical positions. It is also useful for simulations with two or more selected mutations.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn the simulation script, there are some lines that can be uncommented to include population size changes, three-way admixture, two selected mutations, continuous migration each generation. I haven\u0027t tested these completely, so there may be bugs.\u003c/p\u003e\n\u003cp\u003eB. \u003ca href=\"./run_admixture.sh\"\u003erun_admixture.sh\u003c/a\u003e - example job array script to generate 1000 SLiM simulations with the \u003ca href=\"./admixture.slim\"\u003eadmixture.slim\u003c/a\u003e file.\u003c/p\u003e\n\u003cp\u003eC. \u003ca href=\"./localancestry_alltracts.py\"\u003elocalancestry_alltracts.py\u003c/a\u003e - script to create bed-like file of ancestry tracts for 200 samples (haploid chromosomes, not diploid individuals) from the .trees file. Assumes two-way admixture and 1 ancestor in each source population.\u003c/p\u003e\n\u003cp\u003eD. \u003ca href=\"./admixture_ancestrytracts_jobarray.sh\"\u003eadmixture_ancestrytracts_jobarray.sh\u003c/a\u003e - example job array to generate bed-like ancestry tract files for 1000 SLiM simulations with the \u003ca href=\"./localancestry_alltracts.py\"\u003elocalancestry_alltracts.py\u003c/a\u003e script.\u003c/p\u003e\n\u003cp\u003eE. \u003ca href=\"./admixture_makeimage.R\"\u003eadmixture_makeimage.R\u003c/a\u003e - script to generate b\u0026amp;w ancestry images. Assumes two-way admixture. Height is hard-coded to 200 pixels. Chromosome length and image width must be specified at command line. e.g. \u003ccode\u003eadmixture_makeimage.R filename_alltracts.txt 50000000 400\u003c/code\u003e would create a 200x400 image, assuming a chromosome length of 50 Mb and \u003ccode\u003eadmixture_makeimage.R filename_alltracts.txt 295 200\u003c/code\u003e would create a 200x200 image, assuming a chromosome with max genetic map length of 295 cM. Excpects bed-like file of ancestry tracts (exclusive. e.g. intervals are [start, end)) with at least the following columns (any order, labeled):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003estart_bp\u003c/code\u003e - first position of ancestry tract (0-based, can be physical or genetic map positions, correct corresponding chromosome length must be specified at command line)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eend_bp\u003c/code\u003e - last position of ancestry tract (exclusive)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eancID\u003c/code\u003e - ancestry label for that tract (expects 0 or 1)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003echildID\u003c/code\u003e - unique haplotype ID (e.g. for a diploid indiviudal \"SUBJ-A\" you would have tracts mapping to SUBJ-A_Hap1 and SUBJ-A_Hap2)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eF. \u003ca href=\"./admixture_makeimages_jobarray.sh\"\u003eadmixture_makeimages_jobarray.sh\u003c/a\u003e - example job array to generate images for 1000 simulations with \u003ca href=\"./admixture_makeimage.R\"\u003eadmixture_makeimage.R\u003c/a\u003e script\u003c/p\u003e\n\u003cp\u003eMisc scripts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ealternate admixture SLiMulation files:\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./admixture_popsize.slim\"\u003eadmixture_popsize.slim\u003c/a\u003e - similar to above, but includes block for bottleneck at 25-35 generations\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./admixture_Fst.slim\"\u003eadmixture_Fst.slim\u003c/a\u003e - similar to above, but draws beneficial mutation from both populations\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./admixture_whole-genome.slim\"\u003eadmixture_whole-genome.slim\u003c/a\u003e - similar to above, but for \"whole genome\" (multiple chromosomes)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-versions-used-in-this-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-versions-used-in-this-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware versions used in this project\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://messerlab.org/slim/\" rel=\"nofollow\"\u003eSLiM\u003c/a\u003e - v3.4\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://cran.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e - v4.0.0\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython\u003c/a\u003e - v3.7.4\u003c/p\u003e\n\u003cp\u003ePython libraries:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://airctic.com/0.5.2/\" rel=\"nofollow\"\u003eIceVision\u003c/a\u003e - v0.5.2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://tskit.dev/tskit/docs/stable/introduction.html\" rel=\"nofollow\"\u003etskit\u003c/a\u003e - v0.2.3 (included in \u003ca href=\"https://tskit.dev/msprime/docs/stable/intro.html\" rel=\"nofollow\"\u003emsprime\u003c/a\u003e v0.7.4)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://tskit.dev/pyslim/docs/latest/introduction.html\" rel=\"nofollow\"\u003epyslim\u003c/a\u003e - v0.401\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://scikit-learn.org/stable/\" rel=\"nofollow\"\u003esklearn\u003c/a\u003e - v0.23.2\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eR packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tidyverse.org/\" rel=\"nofollow\"\u003etidyverse\u003c/a\u003e - v1.3.0\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html\" rel=\"nofollow\"\u003emagrittr\u003c/a\u003e - v2.0.1\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rdocumentation.org/packages/plyr/versions/1.8.6\" rel=\"nofollow\"\u003eplyr\u003c/a\u003e - v1.8.6\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 1, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1631126992.0 + "topics": [], + "updated_at": 1668718136.0 }, { "data_format": 2, - "description": "the breath counting task asks participants to breath in and out, and practice mindfullness", + "description": "A Singularity image with the fenics-dev docker environment", "filenames": [ - "Singularity" + "Singularity", + "specific_commits01/Singularity" ], - "full_name": "expfactory-experiments/breath-counting-task", + "full_name": "TormodLandet/singularity-fenics-dev-env", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-breath-counting-task\" class=\"anchor\" href=\"#breath-counting-task\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBreath Counting Task\u003c/h1\u003e\n\u003cp\u003eExperiment Factory: Breath Counting Task, based on Levinson et al. (2014, Study 2).\u003c/p\u003e\n\u003cp\u003eThis is a task that is friendly for use in the \u003ca href=\"https://expfactory.github.io/expfactory\" rel=\"nofollow\"\u003eExperiment Factory\u003c/a\u003e. You can run it locally by putting these files in a web server, or use the Experiment Factory to generate a reproducible container. Check out the documentation above for more information, or \u003ca href=\"https://www.github.com/expfactory/expfactory/issues\"\u003epost an issue\u003c/a\u003e if you have any questions.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://camo.githubusercontent.com/ff2b0e936c1deca3184bc2786460248f212ec5362bbbe817ee8d691cfba755cd/68747470733a2f2f657870666163746f72792e6769746875622e696f2f657870666163746f72792f696d672f657870666163746f72797469636b657479656c6c6f772e706e67\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff2b0e936c1deca3184bc2786460248f212ec5362bbbe817ee8d691cfba755cd/68747470733a2f2f657870666163746f72792e6769746875622e696f2f657870666163746f72792f696d672f657870666163746f72797469636b657479656c6c6f772e706e67\" alt=\"https://expfactory.github.io/expfactory/img/expfactoryticketyellow.png\" data-canonical-src=\"https://expfactory.github.io/expfactory/img/expfactoryticketyellow.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-containers-and-metadata\" class=\"anchor\" href=\"#containers-and-metadata\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers and Metadata\u003c/h2\u003e\n\u003cp\u003eThe repository builds and provides a set of containers and metadata files to help you run and understand the experiment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://expfactory-experiments.github.io/breath-counting-task/\" rel=\"nofollow\"\u003epreview the experiment\u003c/a\u003e on Github pages\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://hub.docker.com/r/expfactory/breath-counting-task/\" rel=\"nofollow\"\u003euse the container\u003c/a\u003e provided on Docker Hub, built from this repository. All previous versions are also found here based on Github commits.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/expfactory-experiments/breath-counting-task/tree/gh-pages\"\u003einspect metadata\u003c/a\u003e including manifests and packages associated with Github commits. While this table isn\u0027t rendered on Github pages, if you render the branch and look at the index.html you will have a pretty rendering of it.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-processing-data-in-r\" class=\"anchor\" href=\"#processing-data-in-r\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcessing Data in R\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"expfactory.breathcounting\"\u003eexpfactory.breathcounting\u003c/a\u003e is an \u003ccode\u003eR\u003c/code\u003e package for processing the data generated by this task.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h1\u003e\n\u003cp\u003eLevinson, D. B., Stoll, E. L., Kindy, S. D., Merry, H. L., and Davidson, R. J. (2014).\nA mind you can count on: validating breath counting as a behavioral measure of\nmindfulness. Frontiers in Psychology, 5.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, - "topics": [ - "expfactory", - "experiments", - "behavior", - "psychology", - "mindfulness" - ], - "updated_at": 1636523563.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1632919789.0 }, { "data_format": 2, - "description": "Singularity image for afl++ (https://github.com/AFLplusplus/AFLplusplus)", + "description": "Singularity container for DIVAnd", "filenames": [ - "Singularity.i386", - "Singularity.2004", - "Singularity.1604", - "Singularity.1804" + "Singularity" ], - "full_name": "shub-fuzz/aflpp", - "latest_release": "0.0.2", - "readme": "\u003cp\u003eSingularity image for AFL++ (\u003ca href=\"https://github.com/AFLplusplus/AFLplusplus\"\u003ehttps://github.com/AFLplusplus/AFLplusplus\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/shub-fuzz/aflpp/actions/workflows/builder.yml\"\u003e\u003cimg src=\"https://github.com/shub-fuzz/aflpp/actions/workflows/builder.yml/badge.svg?branch=main\" alt=\"singularity-deploy\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat\u003c/strong\u003e is \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e?\u003cbr\u003e\nA containerization system primarily used by the scientific community on high-performance computing (HPC).\nOn many University HPC systems, docker is not allowed, but singularity is availble because it runs with\nuser level permisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy\u003c/strong\u003e?\u003cbr\u003e\nFuzzing on HPC!\u003cbr\u003e\nUniversities have trememdous resources available in HPC clusters that can be used to support\nlarge-scale fuzzing evaluations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name aflpp.sif https://github.com/shub-fuzz/aflpp/releases/download/0.0.2/shub-fuzz-aflpp.1604.sif\n\nsingularity shell aflpp.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003epull Ubuntu 18.04 container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name aflpp.1804.sif https://github.com/shub-fuzz/aflpp/releases/download/0.0.2/shub-fuzz-aflpp.1804.sif\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "gher-uliege/DIVAnd-singularity", + "latest_release": "v1.0.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/gher-ulg/DIVAnd-singularity/actions?query=workflow%3A%22Singularity+Build%22\"\u003e\u003cimg src=\"https://github.com/gher-ulg/DIVAnd-singularity/workflows/Singularity%20Build/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.7014264\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbff15826258cff37ea5a790bb5970cb363766914fa530ce59fc3d0c4c598a13/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e373031343236342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.7014264.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1a213b3f63ab13fc5f45c45392a2f3af9ce781c22ff7715b4ec4f87242ccc7c9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f676865722d756c672f444956416e642d73696e67756c6172697479\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1a213b3f63ab13fc5f45c45392a2f3af9ce781c22ff7715b4ec4f87242ccc7c9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c616e6775616765732f746f702f676865722d756c672f444956416e642d73696e67756c6172697479\" alt=\"GitHub top language\" data-canonical-src=\"https://img.shields.io/github/languages/top/gher-ulg/DIVAnd-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/38cc68dc0031ca972eb34ec1043562774b44b8e9c39d88c433f53afc43d356b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f676865722d756c696567652f444956416e642d73696e67756c6172697479\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/38cc68dc0031ca972eb34ec1043562774b44b8e9c39d88c433f53afc43d356b4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f676865722d756c696567652f444956416e642d73696e67756c6172697479\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/gher-uliege/DIVAnd-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8bab4efa772ce517bcff118378d3bda45b79a76c2045057c0cb2033ce86bf913/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f646f776e6c6f6164732f676865722d756c696567652f444956416e642d73696e67756c61726974792f746f74616c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8bab4efa772ce517bcff118378d3bda45b79a76c2045057c0cb2033ce86bf913/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f646f776e6c6f6164732f676865722d756c696567652f444956416e642d73696e67756c61726974792f746f74616c\" alt=\"GitHub all releases\" data-canonical-src=\"https://img.shields.io/github/downloads/gher-uliege/DIVAnd-singularity/total\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/753a88c807be51ffa5df102f47615cdcebdf5197f424be0d1de3d74b2656e7a7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f676865722d756c696567652f444956416e642d73696e67756c6172697479\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/753a88c807be51ffa5df102f47615cdcebdf5197f424be0d1de3d74b2656e7a7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f676865722d756c696567652f444956416e642d73696e67756c6172697479\" alt=\"GitHub contributors\" data-canonical-src=\"https://img.shields.io/github/contributors/gher-uliege/DIVAnd-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/185deb35e2f7176176238e185a4a25165745eeae5d3a0d3bb86a34bd1d4a7c95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f676865722d756c696567652f444956416e642d73696e67756c6172697479\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/185deb35e2f7176176238e185a4a25165745eeae5d3a0d3bb86a34bd1d4a7c95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f676865722d756c696567652f444956416e642d73696e67756c6172697479\" alt=\"GitHub last commit\" data-canonical-src=\"https://img.shields.io/github/last-commit/gher-uliege/DIVAnd-singularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-divand-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#divand-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDIVAnd-singularity\u003c/h1\u003e\n\u003cp\u003eSingularity container for \u003ccode\u003eDIVAnd\u003c/code\u003e, the interpolation tool (\u003ca href=\"https://github.com/gher-ulg/DIVAnd.jl\"\u003ehttps://github.com/gher-ulg/DIVAnd.jl\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eThe container installs \u003ca href=\"https://julialang.org/\" rel=\"nofollow\"\u003e\u003ccode\u003eJulia\u003c/code\u003e\u003c/a\u003e (version 1.8.0), DIVAnd and other required Julia packages.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall singularity: \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://sylabs.io/docs/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h2\u003e\n\u003cp\u003eAfter checking out the source, the singularity container can be build using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erm -f DIVAnd.sif\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e sudo singularity build DIVAnd.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe first command is only needed if you already had the \u003ccode\u003e.sif\u003c/code\u003e file in your system.\u003cbr\u003e\nThe \u003cem\u003ebuild\u003c/em\u003e operation lasts severall minutes due to the download and installation of languages and libraries.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h2\u003e\n\u003cp\u003eThere are two possibilities to get the container\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-from-the-github-actions\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-from-the-github-actions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. From the GitHub \u003cem\u003eactions\u003c/em\u003e\n\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003ego to \u003ca href=\"https://github.com/gher-ulg/DIVAnd-singularity/actions\"\u003ehttps://github.com/gher-ulg/DIVAnd-singularity/actions\u003c/a\u003e,\u003c/li\u003e\n\u003cli\u003echoose the lastest commit,\u003c/li\u003e\n\u003cli\u003ego to artefact,\u003c/li\u003e\n\u003cli\u003edownload and unzip the image file.\u003c/li\u003e\n\u003cli\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/11868914/189079405-b156f584-1992-46ce-9ac5-0d60f57c7d42.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/11868914/189079405-b156f584-1992-46ce-9ac5-0d60f57c7d42.png\" alt=\"singularity_artefact\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-from-the-sylabs-reposity-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-from-the-sylabs-reposity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. From the Sylabs reposity containers\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --arch amd64 library://gher-uliege/divand/divand-singularity:v0-1-0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/11868914/189079465-6215be47-6691-4cc8-8384-88f783c87084.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/11868914/189079465-6215be47-6691-4cc8-8384-88f783c87084.png\" alt=\"Screenshot from 2022-09-08 10-31-52\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the container\u003c/h2\u003e\n\u003cp\u003eThere are two ways to run the container:\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-without-specifying-a-julia-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-without-specifying-a-julia-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Without specifying a Julia script\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run DIVAnd.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis gives access to a Julia terminal, where commands and scripts can be executed.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-by-specifying-the-script-to-be-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-by-specifying-the-script-to-be-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. By specifying the script to be run\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run DIVAnd.sif my_script.jl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(where \u003ccode\u003emy_script.jl\u003c/code\u003e has to be substitued by the correct file name).\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1628147067.0 + "updated_at": 1644927498.0 }, { "data_format": 2, - "description": "The source code of POPCORN planner (also a MILP Compilation collaborated with Chiara Piacentini)", + "description": "To build hpc benchmark and mpi with cuda support sif", "filenames": [ - "Singularity" + "bert.def", + "hpcc_intel.def", + "hpc_mpi_cuda.def", + "hpl_intel_cuda.def" ], - "full_name": "Emresav/popcorn", + "full_name": "perambluate/singularity-definition-files-for-HPC", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-popcorn\" class=\"anchor\" href=\"#popcorn\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epopcorn\u003c/h1\u003e\n\u003cp\u003ePOPCORN is a temporal-numeric planning software that I have developed during my PhD studies at King\u0027s College London under supervision of Prof Maria Fox and Prof Derek Long. POPCORN can reason about action-specific numeric parameters that can take their values from relatively large domains. The planner has a freedom of choosing their values during planning process. Practically speaking, this means that you can now model actions that can have multiple flexible numeric parameters, such as the withdrawal amount from the cashpoint (e.g. \u003ccode\u003e10\u0026lt;= ?cash \u0026lt;= 100\u003c/code\u003e), or the refuel amount, as in PDDL community these parameters can only be defined with fixed values. The language we use when developing our domains, the extended version of PDDL, is quite straightforward; so I highly recommend users to inspect them to decide whether this is something that you want in your planning scenarios. This work was published on ECAI 2016 under the title of \u003ca href=\"https://kclpure.kcl.ac.uk/portal/files/56331945/FAIA285_1185.pdf\" rel=\"nofollow\"\u003ePlanning Using Actions with Control Parameters\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlso note that this code base includes a MILP Compilation of optimal numeric planning problems with control parameters. I collaborated with \u003cem\u003eChiara Piacentini\u003c/em\u003e on this work.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003ePOPCORN makes use of various other tools that are available online, these are:\u003c/p\u003e\n\u003cp\u003eFor parsing the PDDL domain: Flex, Bison\nOptimisation tools: CLP, CoinUtils, CBC, CBLAS, CGL, CPLEX\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapt-get update\napt-get -y install g++ make flex bison cmake doxygen coinor-clp coinor-libcbc-dev coinor-libclp-dev coinor-libcoinutils-dev coinor-libosi-dev coinor-libcgl-dev libbz2-dev libgsl-dev libz-dev\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: You will need to install CPLEX from their website!\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-popcorn\" class=\"anchor\" href=\"#running-popcorn\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning POPCORN\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository to your local machine:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ehttps://github.com/Emresav/popcorn.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eExport the directories of CPLEX libraries:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport CPLEX=\"/my/location/to/libcplex.a\"\nexport ILOCPLEX=\"/my/location/to/libilocplex.a\"\nexport CONCERT=\"/my/location/to/libconcert.a\"\nexport CPLEX_INCLUDES=\"/my/location/to/cplex/include\"\nexport CONCERT_INCLUDES=\"/my/location/to/concert/include\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCompiling the source code:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd /planner/planner\nls -la\ncmake .\nmake clean\nmake popf3-clp\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eFeatures of POPCORN\nIn order to see the available features of POPCORN, simply run the executable: \u003ccode\u003e/planner/planner/popf/popf3-clp\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRunning POPCORN:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e/planner/planner/popf/popf3-clp $DOMAINFILE $PROBLEMFILE \u0026gt; $PLANFILE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat\u0027s it! I have included various domains and problem instances that are used during my experiments. Almost all of them are new and quite rich in terms of temporal and numeric features. Have fun, and please do not hesitate to contact me if you have any issues during compiling and running the planner.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc_mpi_cuda_singu_def_file\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc_mpi_cuda_singu_def_file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehpc_mpi_cuda_singu_def_file\u003c/h1\u003e\n\u003cp\u003eA collect of definition files to build images for singularity containers, which includes hpc benchmarks and mpis with cuda support.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4181\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 0, "topics": [], - "updated_at": 1595445458.0 + "updated_at": 1588998487.0 }, { "data_format": 2, - "description": "Neurotransmitter prediction from EM.", + "description": "Singularity image to serve as base for all project images. Defaults to starting up RStudio with an auto-selected port and password ", "filenames": [ - "singularity/Singularity" + "Singularity.3.6.1", + "Singularity.4.0.3", + "Singularity.4.0.2", + "Singularity.mro.4.0.3", + "Singularity.3.6.0" ], - "full_name": "funkelab/synister", + "full_name": "granek/singularity-rstudio-base", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-package-for-training-validating-and-testing-synister-networks\" class=\"anchor\" href=\"#package-for-training-validating-and-testing-synister-networks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackage for training, validating and testing Synister networks.\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eRelated Paper\u003c/strong\u003e \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.06.12.148775v2\" rel=\"nofollow\"\u003eNeurotransmitter Classification from Electron Microscopy Images at Synaptic Sites in Drosophila.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelated Repositories\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/funkelab/synistereq\"\u003ePackage for on demand predictions with a production network.\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/funkelab/synisterbrain\"\u003ePackage for high performance full brain predictions.\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nilsec/synisterest\"\u003eWebservice.\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd singularity\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003emake\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eConda\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n synister python=3.6 numpy scipy cython pylp -c funkey\nconda activate synister\npip install -r requirements.txt\npip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-0-creating-a-mongo-db-database-with-the-provided-data\" class=\"anchor\" href=\"#0-creating-a-mongo-db-database-with-the-provided-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e0. Creating a mongo DB database with the provided data.\u003c/h3\u003e\n\u003cp\u003eAn export of the three collections constituting the synister FAFB database used for all described experiments can be found at \u003ccode\u003edata/fafb_v3\u003c/code\u003e. The three files contain:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLocation, id, skid, brain region and split for each synapse (synapses(_v3).json).\u003c/li\u003e\n\u003cli\u003eSkid, neurotransmitter, hemilineage id for each skeleton (skeletons(_v3).json).\u003c/li\u003e\n\u003cli\u003eHemilineage name, hemilineage id for each hemilineage (hemilineages(_v3).json).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo reproduce the experiment each json file should be imported as a collection with names \"synapses\", \"skeletons\", \"hemilineages\" in one mongo database (for additional instructions on how to import json files in a mongo db click \u003ca href=\"https://docs.mongodb.com/database-tools/mongoimport/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e). Dictionary keys are field names. Provided splits can be reproduced using \u003ccode\u003esynister/split.py\u003c/code\u003e, which searches for the optimally balanced split in terms of neurotransmitter distribution for any given superset, such as hemilineage id, skeleton id or brain region.\u003c/p\u003e\n\u003cp\u003eFor training on other data, recreate the database scheme shown here (required are a \"synapses\" and a \"skeletons\" collection) and adapt config files to match the new database name.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-1-training-a-network\" class=\"anchor\" href=\"#1-training-a-network\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Training a network.\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-prepare-training\" class=\"anchor\" href=\"#prepare-training\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare training\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython prepare_training.py -d \u0026lt;base_dir\u0026gt; -e \u0026lt;experiment_name\u0026gt; -t \u0026lt;train_id\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis creates a new directory at the specified path and initialises default config files for the run.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-training\" class=\"anchor\" href=\"#run-training\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Training\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd \u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/02_train/setup_t\u0026lt;train_id\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEdit config files to match architecture, database and compute resources to train with.\u003c/p\u003e\n\u003cp\u003eFor example configs, training a VGG on the skeleton split, inside a singularity container, on a gpu queue see:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexample_configs/train_config.ini\n\n[Training]\nsynapse_types = gaba, acetylcholine, glutamate, serotonin, octopamine, dopamine\ninput_shape = 16, 160, 160\nfmaps = 12\nbatch_size = 8\ndb_credentials = synister_data/credentials/db_credentials.ini\ndb_name_data = synister_v3\nsplit_name = skeleton\nvoxel_size = 40, 4, 4\nraw_container = /nrs/saalfeld/FAFB00/v14_align_tps_20170818_dmg.n5\nraw_dataset = volumes/raw/s0\ndownsample_factors = (1,2,2), (1,2,2), (1,2,2), (2,2,2)\nnetwork = VGG\nfmap_inc = 2, 2, 2, 2\nn_convolutions = 2, 2, 2, 2\nnetwork_appendix = None\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eexample_configs/worker_config.ini\n\n[Worker]\nsingularity_container = synister/singularity/synister.img\nnum_cpus = 5\nnum_block_workers = 1\nnum_cache_workers = 5\nqueue = gpu_any\nmount_dirs = /nrs, /scratch, /groups, /misc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, to submit the train job with the desired number of iterations run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython train.py \u0026lt;num_iterations\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend training for at least 500,000 iterations for FAVB_v3 splits.\u003c/p\u003e\n\u003cp\u003eFor visualizing training progress run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003etensorboard --logdir \u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/02_train/setup_t\u0026lt;train_id\u0026gt;/log\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSnapshots are written to:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/02_train/setup_t\u0026lt;train_id\u0026gt;/snapshots\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-2-validating-a-trained-network\" class=\"anchor\" href=\"#2-validating-a-trained-network\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Validating a trained network.\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-prepare-validation-runs\" class=\"anchor\" href=\"#prepare-validation-runs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare validation runs\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython prepare_prediction.py -v -d \u0026lt;base_dir\u0026gt; -e \u0026lt;experiment_name\u0026gt; -t \u0026lt;train_id\u0026gt; -i \u0026lt;iter_0\u0026gt; \u0026lt;iter_1\u0026gt; \u0026lt;iter_2\u0026gt; ... \u0026lt;iter_N\u0026gt; \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create N prediction directories with appropriately initialized config files, one for each given train iteration \u0026lt;iter_k\u0026gt;. The -v flag sets the split part of the chosen split type to validation, only pulling those synapses from the DB that are tagged as validation synapses.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexample_configs/worker_config.ini\n\n[Predict]\ntrain_checkpoint = \u0026lt;experiment_name\u0026gt;/02_train/setup_t\u0026lt;train_id\u0026gt;/model_checkpoint_\u0026lt;iter_k\u0026gt;\nexperiment = \u0026lt;experiment_name\u0026gt;\ntrain_number = 0\npredict_number = 0\nsynapse_types = gaba, acetylcholine, glutamate, serotonin, octopamine, dopamine\ninput_shape = 16, 160, 160\nfmaps = 12\nbatch_size = 8\ndb_credentials = synister_data/credentials/db_credentials.ini\ndb_name_data = synister_v3\nsplit_name = skeleton\nvoxel_size = 40, 4, 4\nraw_container = /nrs/saalfeld/FAFB00/v14_align_tps_20170818_dmg.n5\nraw_dataset = volumes/raw/s0\ndownsample_factors = (1, 2, 2), (1, 2, 2), (1, 2, 2), (2, 2, 2)\nsplit_part = validation\noverwrite = False\nnetwork = VGG\nfmap_inc = 2, 2, 2, 2\nn_convolutions = 2, 2, 2, 2\nnetwork_appendix = None\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor most use cases the automatically initialized predict config does not require any edits. If run as is, predictions will be written into the database under:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;db_name\u0026gt;_predictions.\u0026lt;split_name\u0026gt;_\u0026lt;experiment_name\u0026gt;_t\u0026lt;train_id\u0026gt;_p\u0026lt;predict_id\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo start the prediction, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd \u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/03_predict/setup_t\u0026lt;train_id\u0026gt;_p\u0026lt;predict_id\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003epython predict.py\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf the collection already exists the script will abort. A collection can be overwritten by setting overwrite=True in the predict config. Parallel prediction with multiple GPUs can be done by setting num_block_workers=num_gpus in the worker_config file. Prediction speed and expected time to finish will be shown in the console.\u003c/p\u003e\n\u003cp\u003eFor submitting multiple predictions to the cluster at once run the provided convenience script:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython start_predictions -d \u0026lt;base_dir\u0026gt; -e \u0026lt;experiment_name\u0026gt; -t \u0026lt;train_id\u0026gt; -p \u0026lt;predict_id_0\u0026gt; \u0026lt;predict_id_1\u0026gt; ... \u0026lt;predict_id_N\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-3-testing-a-trained-network\" class=\"anchor\" href=\"#3-testing-a-trained-network\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Testing a trained network.\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-prepare-test-runs\" class=\"anchor\" href=\"#prepare-test-runs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare test runs\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003epython prepare_prediction.py -d \u0026lt;base_dir\u0026gt; -e \u0026lt;experiment_name\u0026gt; -t \u0026lt;train_id\u0026gt; -i \u0026lt;iter_0\u0026gt; \u0026lt;iter_1\u0026gt; \u0026lt;iter_2\u0026gt; ... \u0026lt;iter_N\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSimilar to validation this prepares the relevant dictionaries and config files but sets the split part to \"test\".\nStarting the prediction follow the same pattern as before:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd \u0026lt;base_dir\u0026gt;/\u0026lt;experiment_name\u0026gt;/03_predict/setup_t\u0026lt;train_id\u0026gt;_p\u0026lt;predict_id\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003epython predict.py\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3197\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis Singularity image is intended to serve as base for all project images.\u003c/p\u003e\n\u003cp\u003eBy default it starts up RStudio with an auto-selected port and password\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Singularity Image\u003c/h1\u003e\n\u003cp\u003eRun a singularity-rstudio-base container with \u003ccode\u003esingularity run shub://granek/singularity-rstudio-base\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tmp-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#tmp-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e/tmp issues\u003c/h2\u003e\n\u003cp\u003eIt is recommended to do one of the following when running this image. There is no need to do both:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSet \"mount tmp = no\" in \u003ccode\u003e/etc/singularity/singularity.conf\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIf #1 is not an option, the following command can be used to bind mount \u003ccode\u003e/tmp\u003c/code\u003e in the container to a \"private\" tmp directory:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eSINGTMP=\"/tmp/${USER}_$$_tmp\"; mkdir -p $SINGTMP; singularity run --bind $SINGTMP:/tmp shub://granek/singularity-rstudio-base\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tmp-issues-tldr\" class=\"anchor\" aria-hidden=\"true\" href=\"#tmp-issues-tldr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e/tmp issues TLDR\u003c/h3\u003e\n\u003cp\u003eIf a second user tries on the same server tries to run an RStudio container they will have permission issues with \u003ccode\u003e/tmp/rstudio-server\u003c/code\u003e, which will be owned by the user who first ran an RStudio container.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1628265649.0 + "updated_at": 1624649294.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.v1.0.0" + "singularity/Singularity" ], - "full_name": "baxpr/naleg-roi", + "full_name": "PNNL-CompBio/CME-QM", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-roi-analysis-for-sodium-leg-mri-scans\" class=\"anchor\" href=\"#roi-analysis-for-sodium-leg-mri-scans\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eROI analysis for sodium leg MRI scans\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003esrc Matlab code for extracting calibrating ROI values from sodium scans\nbin Compiled matlab code (needed to create simgularity container)\n\ncompile_matlab.sh Shell script to compile matlab code\nspm_make_standalone_local.m Prepare SPM distribution for compilation\n\nSimgularity.v1.0.0 Singularity recipe to build container with compiled matlab\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-computational-modeling-engine-cme-architecture-for-automated-physics-based-molecular-simulations\" class=\"anchor\" aria-hidden=\"true\" href=\"#computational-modeling-engine-cme-architecture-for-automated-physics-based-molecular-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComputational Modeling Engine (CME) architecture for automated physics-based molecular simulations\u003c/h1\u003e\n\u003cp\u003eCME pipeline perform quantum mechanical simulations using computational chemistry code called NWChem (1) for geometry optimziation, chemical property prediction and computing spectral properties critical for hit identification and lead optimization in drug deisgn and discovery.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"devDocs/pipeline.md\"\u003ePipeline (Snakemake Workflow)\u003c/a\u003e\nComputational Modeling Engine (CME) runs based of Docker container. Given a Target molecule expressed with SMILE strings, it optimized the molecules and run Time-Dependent Density Functional Theory (TD-DFT) Excited state calculatons to generate Ultraviolet\u2013visible (UV\u2013Vis) spectra and other molecular properties.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"devDocs/database.md\"\u003eDatabase\u003c/a\u003e\nThe output of the pipeline is stored in the database.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"apis/api.md\"\u003eBackend APIs\u003c/a\u003e\nThe set of API for developers:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"devDocs/webapp.md\"\u003eUser Interface\u003c/a\u003e\nTo helps users with interact with API.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCME Workflow:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./logs/cme_rulegraph.png\"\u003e\u003cimg src=\"./logs/cme_rulegraph.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eReference:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eApra\u2000, E. et al. NWChem: Past, present, and future. The Journal of Chemical Physics\n2020, 152, 184102.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1594830481.0 + "updated_at": 1665496502.0 }, { "data_format": 2, - "description": "A quick reference repository for using the robots in the COR lab", + "description": null, "filenames": [ - "containers/singularity/Singularity.panda_ros_melodic", - "containers/singularity/Singularity.panda_ros_kinetic", - "containers/singularity/Singularity.panda_ros_noetic", - "containers/singularity/Singularity.ros_melodic", - "containers/singularity/Singularity.husky_ros_melodic" + "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.quickmerge", + "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.htsbox", + "lathe-9cef07a49ad6736d5eae4ca81c380938542eff8d/singularity/Singularity.longread" ], - "full_name": "rickstaa/COR-robotics-lab-reference", + "full_name": "ericcombiolab/Benchmark-metagenome-assemblers", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cor-robotics-lab-reference\" class=\"anchor\" href=\"#cor-robotics-lab-reference\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCOR-robotics-lab-reference\u003c/h1\u003e\n\u003cp\u003eA quick reference repository for using the robots in the COR lab. This repository contains several code examples, a \u003ca href=\"https://github.com/rickstaa/COR-robotics-lab-reference/discussions\"\u003ediscussion forum\u003c/a\u003e with FAQ that you might have while working with the robot and a \u003ca href=\"https://github.com/rickstaa/COR-robotics-lab-reference/wiki\"\u003ewiki\u003c/a\u003e with several helpfull documents\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-reserve-the-robots\" class=\"anchor\" href=\"#how-to-reserve-the-robots\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to reserve the robots\u003c/h2\u003e\n\u003cp\u003ePlease check the \u003cg-emoji class=\"g-emoji\" alias=\"spiral_calendar\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f5d3.png\"\u003e\ud83d\uddd3\ufe0f\u003c/g-emoji\u003e \u003ca href=\"https://github.com/rickstaa/COR-robotics-lab-reference/wiki/%F0%9F%97%93%EF%B8%8F-Robot-reservation-forum\"\u003erobot reservation form\u003c/a\u003e wiki page for more information.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-work-with-the-robots\" class=\"anchor\" href=\"#how-to-work-with-the-robots\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to work with the robots\u003c/h2\u003e\n\u003cp\u003ePlease check \u003cg-emoji class=\"g-emoji\" alias=\"safety_vest\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f9ba.png\"\u003e\ud83e\uddba\u003c/g-emoji\u003e \u003ca href=\"https://github.com/rickstaa/COR-robotics-lab-reference/wiki/Panda-safety-guidelines\"\u003esafety-guidelines\u003c/a\u003e in the wiki before working with the robot.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-benchmarking-de-novo-assembly-methods-on-metagenomic-sequencing-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#benchmarking-de-novo-assembly-methods-on-metagenomic-sequencing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarking de novo assembly methods on metagenomic sequencing data\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-assemblers-evaluated\" class=\"anchor\" aria-hidden=\"true\" href=\"#assemblers-evaluated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAssemblers evaluated\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-short-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#short-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShort-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaSPdes \u003ccode\u003eassembly_scripts/metaspades.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMEGAHIT \u003ccode\u003eassembly_scripts/megahit.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-linked-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#linked-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinked-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003ecloudSPAdes \u003ccode\u003eassembly_scripts/cloudspades.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAthena \u003ccode\u003eassembly_scripts/athena.sh \u0026lt;short-read ssembly\u0026gt; \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-long-read-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#long-read-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLong-read assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaFlye \u003ccode\u003eassembly_scripts/metaflye.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCanu \u003ccode\u003eassembly_scripts/canu.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eLathe \u003ccode\u003eassembly_scripts/lathe.sh \u0026lt;long_reads\u0026gt; \u0026lt;short_reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShasta \u003ccode\u003eassembly_scripts/shasta.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMECAT2 \u003ccode\u003eassembly_scripts/mecat2.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eNECAT \u003ccode\u003eassembly_scripts/necat.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ewtdbg2 \u003ccode\u003eassembly_scripts/wtdbg2.sh \u0026lt;reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-hybrid-assemblers\" class=\"anchor\" aria-hidden=\"true\" href=\"#hybrid-assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHybrid assemblers\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003emetaFlye-subassemblies \u003ccode\u003eassembly_scripts/metaflye-subassemblies.sh \u0026lt;short-read assembly\u0026gt; \u0026lt;long-read assembly\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDBG2OLC \u003ccode\u003eassembly_scripts/dbg2olc.sh \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt; \u0026lt;genome_size\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOPERA-MS \u003ccode\u003eassembly_scripts/opera-ms.sh \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOPERA-LG \u003ccode\u003eassembly_scripts/opera-lg.sh \u0026lt;short-read assembly\u0026gt; \u0026lt;short_reads\u0026gt; \u0026lt;long_reads\u0026gt; \u0026lt;output\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-time-and-memory\" class=\"anchor\" aria-hidden=\"true\" href=\"#time-and-memory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTime and memory\u003c/h2\u003e\n\u003cp\u003eTime and memory consumed are measured by adding \u003ccode\u003e/usr/bin/time -v\u003c/code\u003e before the above commands.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1628169542.0 + "updated_at": 1673428091.0 }, { "data_format": 2, - "description": "Simulating, Reconstructing and Analysing Data for FEL IDI Experiments", + "description": "CVRmap - A Bids App to compute Cerebrovascular Maps from fMRI data", "filenames": [ - "Singularity.simple", - "Singularity.py38", "Singularity" ], - "full_name": "fzimmermann89/idi", - "latest_release": "210609", - "readme": "\u003cp\u003eCAVE: Hic sunt dracones\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe code is a mess, undocumented and only certain code paths are tested.\u003c/em\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-idi---incoherent-diffraction-imaging\" class=\"anchor\" href=\"#idi---incoherent-diffraction-imaging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIDI - INCOHERENT DIFFRACTION IMAGING\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4824\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/fzimmermann89/idi/actions/workflows/test.yml/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/fzimmermann89/idi/actions/workflows/test.yml/badge.svg\" alt=\"tests\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a1aa13bc475e383774716a28c54db51e680a438815882cb99e8443eb94a873db/68747470733a2f2f7777772e7472617669732d63692e636f6d2f667a696d6d65726d616e6e38392f6964692e7376673f6272616e63683d6d6173746572\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1aa13bc475e383774716a28c54db51e680a438815882cb99e8443eb94a873db/68747470733a2f2f7777772e7472617669732d63692e636f6d2f667a696d6d65726d616e6e38392f6964692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://www.travis-ci.com/fzimmermann89/idi.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity Image now at \u003ca href=\"https://cloud.sylabs.io/library/_container/607b669a4ad4aa1fdea0c43c\" rel=\"nofollow\"\u003elibrary://fzimmermann89/idi/idi\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eConda Pacakges at \u003ca href=\"https://anaconda.org/zimmf/idi\" rel=\"nofollow\"\u003ezimmf/idi\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePIP Source at \u003ca href=\"https://pypi.org/project/idi/\" rel=\"nofollow\"\u003eidi\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWheels at \u003ca href=\"https://github.com/fzimmermann89/idi/releases/latest\"\u003eReleases\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-content-of-the-repo\" class=\"anchor\" href=\"#content-of-the-repo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtent of the repo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eipynb: example notebooks\u003c/li\u003e\n\u003cli\u003esimulation: simulation of incoherent images\u003c/li\u003e\n\u003cli\u003ereconstruction: direct and ft based reconstruction\u003c/li\u003e\n\u003cli\u003eutil: some small utilities for data analysis, geometry and random distributions, etc.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-preparation-for-slac-sdf\" class=\"anchor\" href=\"#preparation-for-slac-sdf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epreparation for slac sdf:\u003c/h2\u003e\n\u003cp\u003eUse Singulariy, if using OOD launcher, use the following to start a jupyterhub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e function jupyter() { singularity run --app jupyter --nv -B /sdf,/gpfs,/scratch,/lscratch library://fzimmermann89/idi/idi $@; }\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-preparation-for-sacla\" class=\"anchor\" href=\"#preparation-for-sacla\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epreparation for sacla:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDownload and install miniconda, setup ssh tunnel for web access.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003econda create -n local3 python=3.7 numpy mkl mkl-dev ipython ipykernel cython jinja2 numba numexpr matplotlib six scipy jupyterlab\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003econda activate local3\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip install https://github.com/fzimmermann89/idi/\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython -m ipykernel install --user --name local-simulation-env3 --display-name \"local simulation(py37)\"\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e(C) Felix Zimmermann\u003c/p\u003e\n", + "full_name": "ln2t/cvrmap", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-notice\" class=\"anchor\" aria-hidden=\"true\" href=\"#notice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotice:\u003c/h1\u003e\n\u003cp\u003eStill in active development!\nMore info to come soon (expected Q2 2023).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h1\u003e\n\u003cp\u003eCVRmap is an opensource (license AGPLv3) software to compute maps of Cerebro-Vascular Reactivity (CVR).\u003c/p\u003e\n\u003cp\u003eThe software is compatible with the Brain Imagning Data Structure standard for applications.\u003c/p\u003e\n\u003cp\u003eThe paper describing the toolbox will be pulished soon, together with more documentation about the pipeline.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, - "topics": [ - "idi", - "reconstruction", - "simulation", - "xray", - "incoherent-images", - "fel" - ], - "updated_at": 1626970680.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1673606689.0 }, { "data_format": 2, - "description": null, + "description": "\u667a\u80fd\u79fb\u52a8\u673a\u5668\u4eba\u5927\u4f5c\u4e1a", "filenames": [ - "Singularity.def" + "src/hallucination/SingularityLfH.def", + "src/scripts/Singularityfile.def" ], - "full_name": "Zinoex/hyperverlet", + "full_name": "Jh142857/autonomous_navigation", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-results\" class=\"anchor\" href=\"#results\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-spring-mass-h--02\" class=\"anchor\" href=\"#spring-mass-h--02\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpring mass h = 0.2\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hypereuler\" class=\"anchor\" href=\"#hypereuler\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperEuler\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316489-c72b5980-c2dd-11eb-818c-528eaa8b43b1.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316489-c72b5980-c2dd-11eb-818c-528eaa8b43b1.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-heun\" class=\"anchor\" href=\"#heun\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120315967-33598d80-c2dd-11eb-9bb4-5399b5b14980.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120315967-33598d80-c2dd-11eb-9bb4-5399b5b14980.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperheun\" class=\"anchor\" href=\"#hyperheun\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120315979-3785ab00-c2dd-11eb-8f9a-0de0a8b65a7c.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120315979-3785ab00-c2dd-11eb-8f9a-0de0a8b65a7c.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-velocity-verlet\" class=\"anchor\" href=\"#velocity-verlet\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVelocity Verlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120315992-39e80500-c2dd-11eb-9394-57053434b952.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120315992-39e80500-c2dd-11eb-9394-57053434b952.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperverlet\" class=\"anchor\" href=\"#hyperverlet\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperVerlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316009-3e142280-c2dd-11eb-898e-da7c78cd48b5.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316009-3e142280-c2dd-11eb-898e-da7c78cd48b5.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-fr4\" class=\"anchor\" href=\"#fr4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFR4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316018-410f1300-c2dd-11eb-9ba2-5d951637ffcd.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316018-410f1300-c2dd-11eb-9ba2-5d951637ffcd.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-rk4\" class=\"anchor\" href=\"#rk4\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRK4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316032-453b3080-c2dd-11eb-9aa2-c93ff7cdedf6.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316032-453b3080-c2dd-11eb-9aa2-c93ff7cdedf6.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-pendulum-h--008\" class=\"anchor\" href=\"#pendulum-h--008\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePendulum h = 0.08\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-euler\" class=\"anchor\" href=\"#euler\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEuler\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316077-5126f280-c2dd-11eb-854e-daad28dea3c7.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316077-5126f280-c2dd-11eb-854e-daad28dea3c7.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hypereuler-1\" class=\"anchor\" href=\"#hypereuler-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperEuler\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316086-53894c80-c2dd-11eb-82d7-7c8aea623b71.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316086-53894c80-c2dd-11eb-82d7-7c8aea623b71.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-heun-1\" class=\"anchor\" href=\"#heun-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316104-58e69700-c2dd-11eb-82a7-7a7ad903499e.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316104-58e69700-c2dd-11eb-82a7-7a7ad903499e.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperheun-1\" class=\"anchor\" href=\"#hyperheun-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316130-60a63b80-c2dd-11eb-8708-2edd5358c8fc.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316130-60a63b80-c2dd-11eb-8708-2edd5358c8fc.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-velocity-verlet-1\" class=\"anchor\" href=\"#velocity-verlet-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVelocity Verlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316146-63a12c00-c2dd-11eb-8743-7fbabf5e9f34.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316146-63a12c00-c2dd-11eb-8743-7fbabf5e9f34.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperverlet-1\" class=\"anchor\" href=\"#hyperverlet-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperVerlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316161-669c1c80-c2dd-11eb-94de-2c7bf2cc6962.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316161-669c1c80-c2dd-11eb-94de-2c7bf2cc6962.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-fr4-1\" class=\"anchor\" href=\"#fr4-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFR4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316169-68fe7680-c2dd-11eb-8d27-5c4a7fd36f33.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316169-68fe7680-c2dd-11eb-8d27-5c4a7fd36f33.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-rk4-1\" class=\"anchor\" href=\"#rk4-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRK4\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316179-6bf96700-c2dd-11eb-90f7-a4fa2649d625.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316179-6bf96700-c2dd-11eb-90f7-a4fa2649d625.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-three-body-spring-mass-h--006\" class=\"anchor\" href=\"#three-body-spring-mass-h--006\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThree body spring mass h = 0.06\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-euler-1\" class=\"anchor\" href=\"#euler-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEuler\u003c/h3\u003e\n\u003cp\u003e\u003ca 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octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316228-7ae01980-c2dd-11eb-8792-5ca5b3d06460.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316228-7ae01980-c2dd-11eb-8792-5ca5b3d06460.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperheun-2\" class=\"anchor\" href=\"#hyperheun-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperHeun\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316247-7fa4cd80-c2dd-11eb-9ff6-d9607b45cfda.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316247-7fa4cd80-c2dd-11eb-9ff6-d9607b45cfda.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-velocity-verlet-2\" class=\"anchor\" href=\"#velocity-verlet-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVelocity Verlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316255-82072780-c2dd-11eb-91ec-6b1c41b94751.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/22764100/120316255-82072780-c2dd-11eb-91ec-6b1c41b94751.mp4\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hyperverlet-2\" class=\"anchor\" href=\"#hyperverlet-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHyperVerlet\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/22764100/120316264-83d0eb00-c2dd-11eb-914f-69dcff6094a9.mp4\" 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alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-0-\u4ee3\u7801\u7ed3\u6784\u4e0e\u5206\u5de5\" class=\"anchor\" aria-hidden=\"true\" href=\"#0-\u4ee3\u7801\u7ed3\u6784\u4e0e\u5206\u5de5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e0 \u4ee3\u7801\u7ed3\u6784\u4e0e\u5206\u5de5\u003c/h2\u003e\n\u003cp\u003e\u6b64\u6b21\u4f5c\u4e1a\u7531\u9648\u4fca\u8c6a\u548c\u7530\u5a05\u658c\u4e24\u4eba\u5171\u540c\u5b8c\u6210\uff0c\u8d21\u732e\u57fa\u672c\u5404\u536050%\u3002\u003c/p\u003e\n\u003cp\u003e\u4ee3\u7801\u7ed3\u6784\u5982\u4e0b\uff1a\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeband_local_planner\uff1aROS\u5185\u7f6e\u5305\u003c/li\u003e\n\u003cli\u003ehallucination\uff1a\u4e3b\u8981\u4ee3\u7801\u5b9e\u73b0\u90e8\u5206\uff0cLfH\u7b97\u6cd5\uff08\u003ca href=\"https://ieeexplore.ieee.org/abstract/document/9636402\" rel=\"nofollow\"\u003e\u53c2\u8003\u6587\u732e\u003c/a\u003e\uff0c\u003ca href=\"https://github.com/Daffan/nav-competition-icra2022/tree/LfH\"\u003e\u53c2\u8003\u4ee3\u7801\u003c/a\u003e\uff09\n\u003cul\u003e\n\u003cli\u003edata\uff1a\u4e0d\u540c\u6700\u5927\u901f\u5ea6\u4e0b\u7684\u6570\u636e\u96c6\u003c/li\u003e\n\u003cli\u003eego_timer\uff1aEgo_planner\u003c/li\u003e\n\u003cli\u003einteresting_models\uff1a\u5b58\u653e\u8bad\u7ec3\u7ec3\u597d\u7684\u6a21\u578b\u6743\u91cd\u003c/li\u003e\n\u003cli\u003eLfD2D/3D\uff1a\u5206\u522b\u5bf9\u5e942D\u5bfc\u822a\u548c3D\u5bfc\u822a\u65f6\u8fd0\u52a8\u89c4\u5212\u5668\u6a21\u578b\u53ca\u8bad\u7ec3\u003c/li\u003e\n\u003cli\u003eLfH\uff1a\u8bad\u7ec3hallucination\u51fd\u6570\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ejackal\u003c/li\u003e\n\u003cli\u003ejackal_desktop\u003c/li\u003e\n\u003cli\u003ejackal_helper\u003c/li\u003e\n\u003cli\u003ejackal_simulator\u003c/li\u003e\n\u003cli\u003eres\u003c/li\u003e\n\u003cli\u003erviz_tool\uff1a\u53ef\u89c6\u5316\u90e8\u5206\u4ee3\u7801\u003c/li\u003e\n\u003cli\u003escripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-\u5bfc\u822a\u7b97\u6cd5learn-from-hallucination\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-\u5bfc\u822a\u7b97\u6cd5learn-from-hallucination\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1 \u5bfc\u822a\u7b97\u6cd5\uff1aLearn from Hallucination\u003c/h2\u003e\n\u003cp\u003e\u7b97\u6cd5\u539f\u7406\u5982\u4e0b\uff1a\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u5728\u81ea\u7531\u7a7a\u95f4\u4e2d\u4f7f\u7528\u968f\u673a\u7b56\u7565$\\pi$\u6536\u96c6\u81ea\u7531\u7a7a\u95f4\u4e2d\u7684\u8fd0\u52a8\u89c4\u5212$(p, c_c, c_g)$\uff0c\u5f97\u5230\u8fd0\u52a8\u89c4\u5212\u6570\u636e\u96c6$P$\uff1b\u003c/li\u003e\n\u003cli\u003e\u901a\u8fc7\u7f16\u7801\u5668-\u89e3\u7801\u5668\u65b9\u5f0f\u5b66\u4e60\u53c2\u6570$\\psi^\u003cem\u003e$\uff0c\u5f97\u5230\u5bf9\u5e94\u7684\u5e7b\u89c9\u51fd\u6570$g_{\\psi^\u003c/em\u003e}(p|c_c, c_g)$\uff1b\u003c/li\u003e\n\u003cli\u003e\u5bf9\u4e8e$P$\u4e2d\u7684\u6bcf\u4e2a\u6570\u636e$(p, c_c, c_g)$\uff0c\u4ece$g_{\\psi^*}(p|c_c, c_g)$\u4e2d\u968f\u673a\u91c7\u6837\u969c\u788d\u7269$C_{obst}$\uff0c\u5c06\u6570\u636e$(C_{obst}, p, c_c, c_g)$\u52a0\u5165\u8bad\u7ec3\u6570\u636e\u96c6$D_{train}$\uff1b\u003c/li\u003e\n\u003cli\u003e\u5229\u7528\u8bad\u7ec3\u6570\u636e\u5b66\u4e60\u8fd0\u52a8\u89c4\u5212\u5668$f_{\\theta}(\\cdot )$\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-\u8fd0\u884c\u65b9\u5f0f\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-\u8fd0\u884c\u65b9\u5f0f\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2 \u8fd0\u884c\u65b9\u5f0f\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-21-python\u865a\u62df\u73af\u5883\u642d\u5efa\" class=\"anchor\" aria-hidden=\"true\" href=\"#21-python\u865a\u62df\u73af\u5883\u642d\u5efa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.1 python\u865a\u62df\u73af\u5883\u642d\u5efa\u003c/h3\u003e\n\u003cp\u003e\u4e3a\u4e86\u66f4\u65b9\u4fbf\u8fdb\u884c\u5e93\u7ba1\u7406\uff0c\u9996\u5148\u9700\u8981\u642d\u5efapython\u865a\u62df\u73af\u5883\uff0cpython\u81ea\u5e26\u7684venv\u6216\u8005conda\u73af\u5883\u5747\u53ef\uff0c\u4e0b\u9762\u4ee5venv\u4e3a\u4f8b\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt -y update\nsudo apt-get -y install python3-venv\npython3 -m venv \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/nav_challenge\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e~/nav_challenge/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u7136\u540e\u6fc0\u6d3b\u865a\u62df\u73af\u5883\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/nav_challenge/bin/activate\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u5b89\u88c5\u4e0b\u5217\u9700\u8981\u7684\u5e93\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install defusedxml rospkg netifaces numpy pyyaml scipy torch==1.7 torchvision==0.8 tensorboard\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-22-\u5b89\u88c5ros\u5305\u4f9d\u8d56\" class=\"anchor\" aria-hidden=\"true\" href=\"#22-\u5b89\u88c5ros\u5305\u4f9d\u8d56\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.2 \u5b89\u88c5ros\u5305\u4f9d\u8d56\u003c/h3\u003e\n\u003cp\u003e\u8fdb\u5165\u5de5\u4f5c\u533a\uff0c\u8fd0\u884c\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e /opt/ros/noetic/setup.bash\nrosdep init \nrosdep update\nrosdep install -y --from-paths \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --ignore-src --rosdistro=noetic\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-23-\u6784\u5efa\u73af\u5883\" class=\"anchor\" aria-hidden=\"true\" href=\"#23-\u6784\u5efa\u73af\u5883\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.3 \u6784\u5efa\u73af\u5883\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecatkin_make\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e ./devel/setup.bash(.zsh)\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-24-\u8fd0\u884c\" class=\"anchor\" aria-hidden=\"true\" href=\"#24-\u8fd0\u884c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.4 \u8fd0\u884c\u003c/h3\u003e\n\u003cp\u003e\u5355\u72ec\u6d4b\u8bd5\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u6253\u5f00gazebo gui\uff0c\u52a0\u4e0a--gui\u003c/li\u003e\n\u003cli\u003e\u6253\u5f00rviz\u53ef\u89c6\u5316\uff0c\u52a0\u4e0a--rviz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/scripts\nsudo chmod +x ./run.py\npython3 run.py --gui --world_idx 0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u5728\u591a\u5f20\u5730\u56fe\u4e2d\u8fdb\u884c\u6d4b\u8bd5\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/scripts\nsudo chmod +x test.sh\n./test.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u5bf9\u7ed3\u679c\u8fdb\u884c\u5e73\u5747\u8bc4\u5206\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/scripts\nsudo chmod +x ./run.py\npython3 report_test.py --out_path out_LfLH.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u8bad\u7ec3\u5e7b\u89c9\u51fd\u6570\uff1a\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/hallucinatio/LfH\nsudo chmod +x ./LfH_main.py\npython3 LfH_main.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u6839\u636e\u5e7b\u89c9\u51fd\u6570\u91c7\u6837\u6570\u636e\u8bad\u7ec3\u89c4\u5212\u51fd\u6570\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src/hallucinatio/LfD_2D\nsudo chmod +x ./LfD_main.py\npython3 LfD_main.py\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1628089330.0 + "updated_at": 1672222747.0 }, { "data_format": 2, - "description": "Deep phenotyping dashboard", + "description": null, "filenames": [ - "singularity/Singularity" + "singularity/SingularityTemplate" ], - "full_name": "dptools/dpdash", + "full_name": "INFLUENCEorg/POMCP-SIS", "latest_release": null, - "readme": "\u003cp\u003eRead the documentation \u003ca href=\"http://docs.neuroinfo.org/dpdash/en/latest/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e!\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1622821974.0 + "updated_at": 1673328479.0 }, { "data_format": 2, - "description": "Experimental Singularity container for MLCommons DeepCAM Climate Segmentation Benchmark", + "description": "The AWS Command Line Interface (CLI) is a unified tool to manage your AWS services.", "filenames": [ - "Singularity" + "2.2.16/Singularity", + "2.8.11/Singularity", + "2.4.17/Singularity" ], - "full_name": "ianjamesx/DeepCAM_Singularity", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-deepcam-singularity\" class=\"anchor\" href=\"#deepcam-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeepCAM Singularity\u003c/h1\u003e\n\u003cp\u003eSingularity container for MLCommons DeepCAM Climate Segmentation Benchmark, reference implementation developed by Lawrence Berkeley National Laboratory\u003c/p\u003e\n\u003cp\u003eCurrently in progress\u003c/p\u003e\n", + "full_name": "pscedu/singularity-aws-cli", + "latest_release": "v2.8.11", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-aws-cli/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-aws-cli/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-aws-cli/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-aws-cli/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/8476c7d50e39074b43c0cbcd49e8a4641264908b38528a108f5175fa6acf0479/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6177732d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8476c7d50e39074b43c0cbcd49e8a4641264908b38528a108f5175fa6acf0479/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6177732d636c69\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-aws-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c2057ecc747d9b050fcd59873f5ab9b15625f0aea8c60b141c485d39bcf7d0b7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6177732d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c2057ecc747d9b050fcd59873f5ab9b15625f0aea8c60b141c485d39bcf7d0b7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6177732d636c69\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-aws-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0e2f8c7a768ed85a6af41284a4079da356e0c925a50ffd6bf78e489d8708f8c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6177732d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e2f8c7a768ed85a6af41284a4079da356e0c925a50ffd6bf78e489d8708f8c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6177732d636c69\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-aws-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/41e97a64cdd14fb332ff82ba112804aa910ddc011e17d6d0722dd3c5ec8eaf43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6177732d636c69\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/41e97a64cdd14fb332ff82ba112804aa910ddc011e17d6d0722dd3c5ec8eaf43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6177732d636c69\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-aws-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-aws-cli\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-aws-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-aws-cli\u003c/h1\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://aws.amazon.com/cli/\" rel=\"nofollow\"\u003eaws-cli\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003eaws\u003c/code\u003e and \u003ccode\u003eaws_completer\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/aws-cli/4.8.25\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/aws-cli\u003c/code\u003e as \u003ccode\u003e4.8.25.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, - "topics": [], - "updated_at": 1624406680.0 + "subscribers_count": 4, + "topics": [ + "singularity", + "utilities" + ], + "updated_at": 1665565257.0 }, { "data_format": 2, - "description": "modified version of nicMSlesions", + "description": null, "filenames": [ - "Singularity" + "priori/tests/resources/Singularity.enrichment" ], - "full_name": "kbronik2017/nicpython36", + "full_name": "ohsu-comp-bio/regulon-enrichment", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ms_cnn\" class=\"anchor\" href=\"#ms_cnn\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMS_CNN\u003c/h1\u003e\n\u003cp\u003e[This is a modified version of nicMSlesions (\u003ca href=\"https://github.com/NIC-VICOROB/nicMSlesions\"\u003ehttps://github.com/NIC-VICOROB/nicMSlesions\u003c/a\u003e)]\n\u003cbr\u003e\n\u003ca href=\"CNN.jpeg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"300\" src=\"CNN.jpeg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-this--version-support-additionally-the-following-functionalities\" class=\"anchor\" href=\"#this--version-support-additionally-the-following-functionalities\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis version support additionally the following functionalities:\u003c/h1\u003e\n\u003cdl\u003e\n \u003cdt\u003e(1) Runnable on a Mac system/computer\u003c/dt\u003e\n \u003cdt\u003e(2) Cold start and warm start support:\u003c/dt\u003e\n \u003cdd\u003e- Allowing to re-create the architecture of the model\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use the saved weights of the model\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use the training configuration and avoiding to run preprocessing again\u003c/dd\u003e\n \u003cdd\u003e- Allowing to resume training exactly where it left off(interrupting the training is \n allowed throughout the training process)\u003c/dd\u003e\n \u003cdd\u003e- Allowing to use pretrained model\u003c/dd\u003e\n \u003cdt\u003e(3) Supporting Python 3\u003c/dt\u003e\n \u003cdt\u003e(4) Integrated Tensorborad [to provide the measurements and visualisations of TensorFlow execution (to understand, debug, and optimisation of the TensorFlow programs)]\u003c/dt\u003e\n \u003cdt\u003e(5) Checking whether a file or directory is relevant for Training and Testing\u003c/dt\u003e \n \u003cdt\u003e(6) Easy HPC (High Performance Computing) support\u003c/dt\u003e \n \u003cdt\u003e(7) Bias correction of masks using FSL\u003c/dt\u003e\n \u003cdt\u003e(8) Registration, moving all images to the Flair, T1 or Standard space\u003c/dt\u003e\n\u003c/dl\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"BR.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"500\" src=\"BR.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"note.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"100\" src=\"note.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n# Running the Program!\n\u003cp\u003eThis modified version can be run with or without a GUI (similar to original version)\u003c/p\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"GUI_NM.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"500\" src=\"GUI_NM.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003ch1\u003e\u003c/h1\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-running-the-program-on-the-hpc-cluster-using-nvidia-gpuswithout-any-additional-librarydependency-installation\" class=\"anchor\" href=\"#running-the-program-on-the-hpc-cluster-using-nvidia-gpuswithout-any-additional-librarydependency-installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Program on the HPC cluster using NVIDIA GPUs(without any additional library/dependency installation):\u003c/h1\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"hpc.jpeg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"200\" src=\"hpc.jpeg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003eFirst, user will need to be sure that \"singularity\"\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/\u003c/a\u003e\nis available on local or remote machine.\u003c/p\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity pull docker://kbronik/ms_cnn_ucl:latest \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running the above, a singularity image using docker hub (docker://kbronik/ms_cnn_ucl:latest) will be generated:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - path to singularity//..///ms_cnn_ucl_latest.sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity run --nv (path to singularity)//..///ms_cnn_ucl_latest.sif python (path to nicpython36)/nic_train_network_batch.py (or other nic-python code)\u003c/pre\u003e\u003c/div\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca href=\"note_HPC.jpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg height=\"120\" src=\"note_HPC.jpg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \n\u003ch1\u003e\n\u003ca id=\"user-content-for-an-interactive-session\" class=\"anchor\" href=\"#for-an-interactive-session\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor an interactive session:\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity shell (path to singularity)//..///ms_cnn_ucl_latest.sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate idp\n - python (path to nicpython36)/app.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-for-an-interactive-session-tensorflow-on-cpu-only\" class=\"anchor\" href=\"#for-an-interactive-session-tensorflow-on-cpu-only\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor an interactive session (TensorFlow on CPU only):\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e docker://kbronik/ms-ucl-cnn-cpu:CPU_Latest python (path to nicpython36)/app.py \u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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src=\"https://camo.githubusercontent.com/31c454bdf9d517bfbf4d3eed1c768f1853de782076f85ce05b681d0017bfa7f4/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3137393735323035392e737667\" alt=\"t\" data-canonical-src=\"https://zenodo.org/badge/179752059.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-priori\" class=\"anchor\" aria-hidden=\"true\" href=\"#priori\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePriori\u003c/h1\u003e\n\u003cp\u003ePriori is a Python module used to predict the activity of regulatory proteins from RNAseq data.\u003c/p\u003e\n\u003cp\u003ePriori submodules:\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-enricherfeatures\" class=\"anchor\" aria-hidden=\"true\" href=\"#enricherfeatures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eenricher.features\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eLoad -omic datasets\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-enricherregulon\" class=\"anchor\" aria-hidden=\"true\" href=\"#enricherregulon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eenricher.regulon\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eRegulon utilities\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003ePriori\u003c/strong\u003e requires:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- Python (\u0026gt;= 3.6)\n- scikit-learn (\u0026gt;= 0.21.3)\n- NumPy (\u0026gt;= 1.17.3)\n- SciPy (\u0026gt;= 1.3.1)\n- pandas (\u0026gt;= 0.25.3)\n- tqdm (\u0026gt;= 4.38.0)\n- dill (\u0026gt;= 0.3.1.1)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h1\u003e\n\u003cp\u003ePriori leverages pathway information and gene expression data to produce regulon-based protein activity scores.\nOur method tests for positional shifts in experimental-evidence supported networks consisting of transcription factors\nand their downstream signaling pathways when projected onto a rank-sorted gene-expression signature.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-priori\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-priori\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Priori\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-invoking-priori-from-the-command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#invoking-priori-from-the-command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInvoking Priori from the command line\u003c/h2\u003e\n\u003cp\u003eInitialize github in the directory where you want to download Priori. Clone the Priori Github folder using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/ohsu-comp-bio/regulon-enrichment.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpen the \u003cstrong\u003eregulon_enrichemnt\u003c/strong\u003e folder. Create a conda environment with the dependencies needed to run Priori\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f priori_env.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the environment has been built, activate it\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate priori_env\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpen the \u003cstrong\u003eenricher\u003c/strong\u003e folder. Set this path to your PATH variable. After sourcing your bashrc script, you should be able to run Priori using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eenrich\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-priori-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#priori-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePriori parameters\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-required-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#required-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired parameters\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eexpr\u003c/code\u003e : which tab delimited expression matrix to use shape : \u003ccode\u003e[n_features, n_samples]\u003c/code\u003e, units : \u003ccode\u003eTPM, RPKM\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eout_dir\u003c/code\u003e : output directory - directory serialized Enrichment object and enrichment.tsv will be saved to\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-optional-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional parameters\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eregulon\u003c/code\u003e : optional regulon containing weight interactions between regulator and\ndownstream members of its regulon shape : \u003ccode\u003e[len(Target), [\u0027Regulator\u0027,\u0027Target\u0027,\u0027MoA\u0027,\u0027likelihood\u0027]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eregulon_size\u003c/code\u003e : number of downstream interactions required for a given regulator in order to calculate enrichment score \u003ccode\u003edefault=15\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esec_intx\u003c/code\u003e : path to pre-compiled serialized secondary interaction network, \u003ccode\u003edefault=secondary_intx_regulon.pkl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escaler_type\u003c/code\u003e : scaler to normalized features/samples by: \u003ccode\u003estandard | robust | minmax | quant\u003c/code\u003e, default=\u003ccode\u003erobust\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ethresh_filter\u003c/code\u003e : Prior to normalization remove features that have a standard deviation per feature less than \u003ccode\u003e{thresh_filter}\u003c/code\u003e, \u003ccode\u003edefault=0.1\u003c/code\u003e)\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-computing-regulon-enrichment-scores\" class=\"anchor\" aria-hidden=\"true\" href=\"#computing-regulon-enrichment-scores\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComputing regulon enrichment scores\u003c/h1\u003e\n\u003cp\u003eTo quantify the regulon enrichment for a given dataset, the command line script \u003ccode\u003eenrich\u003c/code\u003e is used.\u003c/p\u003e\n\u003cp\u003eUse --help argument to view options\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ePriori requires two positional arguments: \u003ccode\u003eexpr\u003c/code\u003e and \u003ccode\u003eout_dir\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich expr out_dir [regulon] [regulon_size] [sec_intx] [scaler_type] [thresh_filter] \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt is recommended to run enrich with the default parameters.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eenrich tests/resources/test_expr.tsv test_enrichment_scores\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe command above will generate enrichment scores for the unittest dataset \u003ccode\u003etest_expr.tsv\u003c/code\u003e and will generate and store the output under \u003ccode\u003etest_enrichment_scores/\u003c/code\u003e. In this directory \u003ccode\u003etest_enrichment_scores/\u003c/code\u003e, both the serialized Enrichment object \u003ccode\u003etest_enrichment.pkl\u003c/code\u003e and a tsv of the enrichment scores,\u003ccode\u003etest_regulon_enrichment.tsv\u003c/code\u003e will be found.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eenrichment.tsv\u003c/code\u003e file be shaped : \u003ccode\u003e[n_samples, n_regulators]\u003c/code\u003e, where \u003ccode\u003en_samples\u003c/code\u003e refers to the original number of samples provided in \u003ccode\u003eexpr\u003c/code\u003e, while \u003ccode\u003en_regulators\u003c/code\u003e will be determined based on the overlapping features present in the \u003ccode\u003eexpr\u003c/code\u003e dataset and the \u003ccode\u003eregulon_size\u003c/code\u003e parameter.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 0, - "topics": [ - "convolutional-neural-networks", - "nicmslesions", - "nicmslesions-python3" - ], - "updated_at": 1587563855.0 + "topics": [], + "updated_at": 1664839954.0 }, { "data_format": 2, - "description": "Singularity Course Repo for 2021 ACM-BCB Conference", + "description": null, "filenames": [ - "Singularity.BLAST" + "Singularity.nipype-plus-jupyter-plus-seaborn" ], - "full_name": "TheJacksonLaboratory/acm-bcb-singularity2021", + "full_name": "sajjadtorabian/singularity_recipes", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-tutorial-introduction-to-application-containerization-using-singularity\" class=\"anchor\" href=\"#tutorial-introduction-to-application-containerization-using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTutorial: Introduction to Application Containerization Using Singularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://acm-bcb.org/2021\" rel=\"nofollow\"\u003eThe 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2021)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAugust 1-4, 2021 (Virtual)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-overview\" class=\"anchor\" href=\"#overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eContainerization technologies (Docker, Singularity, etc.) are rapidly becoming an important tool to facilitate and encourage scientific reproducibility, and containers of computational tools and their dependencies are increasingly distributed as digital artifacts alongside manuscript publication. While Docker is the de facto standard for software containerization in cloud computing environments, Singularity is rapidly emerging as the standard for containerization in large scale parallel and distributed computing environments, such as a typical high performance computing cluster at an academic institution. In this tutorial, we will provide an introduction to containerization technologies in general, with a specific focus on Singularity and cover core features of using Singularity to containerize and execute computational biology and bioinformatics applications. This will be a hands-on tutorial. We expect participants will have a basic familiarity with the Linux command line and will be able to use their own laptops and SSH to connect to remote computing systems.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-presenters\" class=\"anchor\" href=\"#presenters\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePresenters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eWilliam S. Sanders (\u003ca href=\"mailto:shane.sanders@jax.org\"\u003eshane.sanders@jax.org\u003c/a\u003e) - The Jackson Laboratory, Farmington, CT USA\u003c/li\u003e\n\u003cli\u003eJason S. Macklin (\u003ca href=\"mailto:jason.macklin@jax.org\"\u003ejason.macklin@jax.org\u003c/a\u003e) - The Jackson Laboratory, Farmington, CT USA\u003c/li\u003e\n\u003cli\u003eRichard Yanicky (\u003ca href=\"mailto:richard.yanicky@jax.org\"\u003erichard.yanicky@jax.org\u003c/a\u003e) - The Jackson Laboratory, Farmington, CT USA\u003c/li\u003e\n\u003cli\u003eAaron McDivitt (\u003ca href=\"mailto:aaron.mcdivitt@jax.org\"\u003eaaron.mcdivitt@jax.org\u003c/a\u003e) - The Jackson Laboratory, Farmington, CT USA\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-agenda--outline\" class=\"anchor\" href=\"#agenda--outline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAgenda / Outline\u003c/h2\u003e\n\u003cp\u003eAll Times in US Central Daylight Time.\u003c/p\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003e\n\u003cp\u003e09:00am-09:10am: \u003ca href=\"section_00.md\"\u003eWelcome \u0026amp; Introductions\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e09:10am-09:25am: \u003ca href=\"section_01.md\"\u003eWhat is a Container?\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e09:25am-09:55am: \u003ca href=\"section_02.md\"\u003eSingularity Basics\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e09:55am-10:10am: \u003ca href=\"section_03.md\"\u003eScientific Containers\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10:10am-10:20am: BREAK\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e10:20am-10:55am: \u003ca href=\"section_04.md\"\u003eBuilding and Modifying Containers\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e10:55am-11:30am: \u003ca href=\"section_05.md\"\u003eUsing Containers \u0026amp; Questions\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipes\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nipype-plus-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#nipype-plus-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNipype Plus Jupyter\u003c/h2\u003e\n\u003cp\u003eWe first need to download the Docker layers. Normally this happens automatically with \u003ccode\u003esingularity build\u003c/code\u003e or \u003ccode\u003esingularity pull\u003c/code\u003e, but if you aren\u0027t using the development version 3.0 branch of Singularity that has a fixed bug with respect to whiteout files, you will have issue when you do these commands with nipype (note that it has whiteout files). What we did (because didn\u0027t feel like installing another version of Singularity) was to do a pull of nipype with the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003eSingularity Global Client\u003c/a\u003e that will download the fixed layers and then put them in the same cache that Singularity uses. Then we will have what we need :) Here is how you can install and use the client:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install sregistry\n$ sregistry pull nipype/nipype:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow we want to debug the build and find the missing path! To do this, you can build a completely empty image to look around in. The recipe looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Singularity.nipype-plus-jupyter-empty\nBootstrap: docker\nFrom: nipype/nipype:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above you can save to whatever file you want, we\u0027re calling ours \u003ccode\u003eSingularity.nipype-plus-jupyter-empty\u003c/code\u003e we can then build like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build npjup.simg Singularity.nipype-plus-jupyter-empty\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we can shell inside and find locations of things.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell npjup.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe are able to see what will be exported in the environment at runtime, and then source it to add these locations to the path (so we can find executables there!)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecat /environment\n$ cat /.singularity.d/env/10-docker.sh \nexport PATH=\"/opt/conda/bin:/usr/lib/ants:/opt/afni:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\"\nexport LANG=\"en_US.UTF-8\"\nexport LC_ALL=\"C.UTF-8\"\nexport ND_ENTRYPOINT=\"/neurodocker/startup.sh\"\nexport MATLABCMD=\"/opt/mcr/v92/toolbox/matlab\"\nexport FORCE_SPMMCR=\"1\"\nexport LD_LIBRARY_PATH=\"/usr/lib/x86_64-linux-gnu:/opt/mcr/v92/runtime/glnxa64:/opt/mcr/v92/bin/glnxa64:/opt/mcr/v92/sys/os/glnxa64:\"\nexport FREESURFER_HOME=\"/opt/freesurfer\"\nexport ANTSPATH=\"/usr/lib/ants\"\nexport MKL_NUM_THREADS=\"1\"\nexport OMP_NUM_THREADS=\"1\"\nexport CONDA_DIR=\"/opt/conda\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere we see the location of conda! Strange that it wasn\u0027t where we expected. Let\u0027s add to the path and then we have pip\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PATH=/opt/conda/bin:$PATH\nwhich pip\n/opt/conda/bin/pip\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we can update the recipe \u003ca href=\"Singularity.nipype-plus-jupyter\"\u003eSingularity.nipype-plus-jupyter\u003c/a\u003e with our found pip.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: nipype/nipype:latest\n\n%labels\n Maintainer Sajjad\n Version v1.0\n\n%post\n export PATH=/opt/conda/bin:$PATH\n pip install --upgrade pip\n pip install jupyter\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd build the image\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build npjup.simg Singularity.nipype-plus-jupyter\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e:)\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1627830100.0 + "updated_at": 1534761419.0 }, { "data_format": 2, - "description": "Singularity container for Freesurfer recon-all, thalamus, brainstem, hippocampus/amygdala", + "description": "Lab pipelines using Snakemake + Singularity + SCIF", "filenames": [ - "Singularity" + "chip-seq.scif/Singularity", + "rna-seq-multisamples/Singularity" ], - "full_name": "baxpr/freesurfer-singularity", - "latest_release": "v1.0.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-freesurfer-720\" class=\"anchor\" href=\"#freesurfer-720\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFreesurfer 7.2.0\u003c/h1\u003e\n\u003cp\u003eThis repository contains the files needed to build a docker container that\nruns Freesurfer 7.2.0 recon-all. See \u003ca href=\"Dockerfile\"\u003ethe Dockerfile\u003c/a\u003e for details.\u003c/p\u003e\n\u003cp\u003eA valid Freesurfer license file is required at runtime.\u003c/p\u003e\n\u003cp\u003eHere is the \u003ca href=\"FreeSurferColorLUT.txt\"\u003elook-up table for the various Freesurfer segmentations\u003c/a\u003e,\nand the \u003ca href=\"src/create_MM_labelmaps.sh\"\u003edescription of MM hippocampus re-combinations\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREFERENCES\u003c/h2\u003e\n\u003cp\u003eAlso see \u003ca href=\"https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferMethodsCitation\" rel=\"nofollow\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferMethodsCitation\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-recon-all\" class=\"anchor\" href=\"#recon-all\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erecon-all\u003c/h3\u003e\n\u003cp\u003eCollins, DL, Neelin, P., Peters, TM, and Evans, AC. (1994) Automatic 3D\nInter-Subject Registration of MR Volumetric Data in Standardized Talairach\nSpace, Journal of Computer Assisted Tomography, 18(2) p192-205, 1994 PMID:\n8126267; UI: 94172121\u003c/p\u003e\n\u003cp\u003eCortical Surface-Based Analysis I: Segmentation and Surface Reconstruction\nDale, A.M., Fischl, Bruce, Sereno, M.I., (1999). Cortical Surface-Based\nAnalysis I: Segmentation and Surface Reconstruction. NeuroImage 9(2):179-194\u003c/p\u003e\n\u003cp\u003eFischl, B.R., Sereno, M.I.,Dale, A. M. (1999) Cortical Surface-Based\nAnalysis II: Inflation, Flattening, and Surface-Based Coordinate System.\nNeuroImage, 9, 195-207.\u003c/p\u003e\n\u003cp\u003eFischl, Bruce, Sereno, M.I., Tootell, R.B.H., and Dale, A.M., (1999).\nHigh-resolution inter-subject averaging and a coordinate system for the\ncortical surface. Human Brain Mapping, 8(4): 272-284\u003c/p\u003e\n\u003cp\u003eFischl, Bruce, and Dale, A.M., (2000). Measuring the Thickness of the Human\nCerebral Cortex from Magnetic Resonance Images. Proceedings of the National\nAcademy of Sciences, 97:11044-11049.\u003c/p\u003e\n\u003cp\u003eFischl, Bruce, Liu, Arthur, and Dale, A.M., (2001). Automated Manifold\nSurgery: Constructing Geometrically Accurate and Topologically Correct\nModels of the Human Cerebral Cortex. IEEE Transactions on Medical Imaging,\n20(1):70-80\u003c/p\u003e\n\u003cp\u003eNon-Uniform Intensity Correction.\n\u003ca href=\"http://www.bic.mni.mcgill.ca/software/N3/node6.html\" rel=\"nofollow\"\u003ehttp://www.bic.mni.mcgill.ca/software/N3/node6.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der\nKouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B,\nDale AM. Whole brain segmentation: automated labeling of neuroanatomical\nstructures in the human brain. Neuron. 2002 Jan 31;33(3):341-55.\u003c/p\u003e\n\u003cp\u003eBruce Fischl, Andre van der Kouwe, Christophe Destrieux, Eric Halgren,\nFlorent Segonne, David H. Salat, Evelina Busa, Larry J. Seidman, Jill\nGoldstein, David Kennedy, Verne Caviness, Nikos Makris, Bruce Rosen, and\nAnders M. Dale. Automatically Parcellating the Human Cerebral Cortex.\nCerebral Cortex January 2004; 14:11-22.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-brainstem\" class=\"anchor\" href=\"#brainstem\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBrainstem\u003c/h3\u003e\n\u003cp\u003eBayesian segmentation of brainstem structures in MRI. Iglesias, J.E., Van\nLeemput, K., Bhatt, P., Casillas, C., Dutt, S., Schuff, N., Truran-Sacrey,\nD., Boxer, A., and Fischl, B. NeuroImage, 113, June 2015, 184-195.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hippocampus-and-amygdala\" class=\"anchor\" href=\"#hippocampus-and-amygdala\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHippocampus and amygdala\u003c/h3\u003e\n\u003cp\u003eHippocampus: A computational atlas of the hippocampal formation using ex\nvivo, ultra-high resolution MRI: Application to adaptive segmentation of in\nvivo MRI. Iglesias, J.E., Augustinack, J.C., Nguyen, K., Player, C.M.,\nPlayer, A., Wright, M., Roy, N., Frosch, M.P., Mc Kee, A.C., Wald, L.L.,\nFischl, B., and Van Leemput, K. Neuroimage, 115, July 2015, 117-137.\u003c/p\u003e\n\u003cp\u003eAmygdala: High-resolution magnetic resonance imaging reveals nuclei of the\nhuman amygdala: manual segmentation to automatic atlas. Saygin ZM \u0026amp; Kliemann\nD (joint 1st authors), Iglesias JE, van der Kouwe AJW, Boyd E, Reuter M,\nStevens A, Van Leemput K, Mc Kee A, Frosch MP, Fischl B, Augustinack JC.\nNeuroimage, 155, July 2017, 370-382.\u003c/p\u003e\n\u003cp\u003eLongitudinal method: Bayesian longitudinal segmentation of hippocampal\nsubstructures in brain MRI using subject-specific atlases. Iglesias JE, Van\nLeemput K, Augustinack J, Insausti R, Fischl B, Reuter M. Neuroimage, 141,\nNovember 2016, 542-555.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-mm-hippocampal-subregions\" class=\"anchor\" href=\"#mm-hippocampal-subregions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"MM\" hippocampal subregions\u003c/h3\u003e\n\u003cp\u003eA reorganization of Freesurfer\u0027s segmentation into anterior and posterior segments\nas described in:\u003c/p\u003e\n\u003cp\u003eMcHugo M, Talati P, Woodward ND, Armstrong K, Blackford JU, Heckers S. Regionally\nspecific volume deficits along the hippocampal long axis in early and chronic\npsychosis. Neuroimage Clin. 2018;20:1106-1114. doi:10.1016/j.nicl.2018.10.021\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-thalamus\" class=\"anchor\" href=\"#thalamus\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThalamus\u003c/h3\u003e\n\u003cp\u003eA probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and\nhistology. Iglesias, J.E., Insausti, R., Lerma-Usabiaga, G., Bocchetta, M.,\nVan Leemput, K., Greve, D., van der Kouwe, A., Caballero-Gaudes, C.,\nPaz-Alonso, P. Neuroimage (accepted).\u003c/p\u003e\n", + "full_name": "BennerLab/pipelines", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a7912c9863e897576e5d434d91e359d254976266bee2f9b1405197941f940bdf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f66696c6573797374656d2d736369656e74696669632d626c75652e737667\" alt=\"scif\" data-canonical-src=\"https://img.shields.io/badge/filesystem-scientific-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://snakemake.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93b6e9faa8e75932017af0ff2ca7db9493cc08e51c462e71143809db606cb04d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d253345253344253230342e362e302d626c75652e737667\" alt=\"snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%3E%3D%204.6.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ed7d71f6eadf7149f22c334c2b29e0479f493cfaced652052e122f59b5920be/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d253345253344253230322e342e322d626c75652e737667\" alt=\"singularity\" data-canonical-src=\"https://img.shields.io/badge/singularity-%3E%3D%202.4.2-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-svenner-lab-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#svenner-lab-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSvenner Lab Documentation\u003c/h1\u003e\n\u003cp\u003eThis repository contains the Svenner lab pipelines for various types of sequencing data. All pipelines are implemented in Snakemake and use the Singularity + Scientific Filesystem to create reproducible research environments.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipelines\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BennerLab/pipelines/tree/master/chip-seq.scif\"\u003eChIP-seq Pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BennerLab/pipelines/tree/master/rna-seq-multisamples\"\u003eRNA-seq Multisample Pipeline\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1632511004.0 + "updated_at": 1625275230.0 }, { "data_format": 2, - "description": "Mujoco Singularity container", + "description": "uresnet based deep neutral network for the segmentation of high resolution cryo-EM tomographs", "filenames": [ "Singularity" ], - "full_name": "ppaquette/img.mujoco", + "full_name": "yee379/uresnet-tomo-seg", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-uresnet-tomo-seg\" class=\"anchor\" aria-hidden=\"true\" href=\"#uresnet-tomo-seg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003euresnet-tomo-seg\u003c/h1\u003e\n\u003cp\u003euresnet based deep neutral network for the segmentation of high resolution cryo-EM tomographs\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1550708786.0 + "updated_at": 1577150888.0 }, { "data_format": 2, "description": null, "filenames": [ - "requirements/Singularity.def" + "Singularity", + "Singularity.devel", + "Singularity.gpu" ], - "full_name": "jordancaraballo/slump-detection", + "full_name": "lamps24/neural_network_project", "latest_release": null, - "readme": "\u003cp\u003eNEW REPOSITORY LOCATION: \u003ca href=\"https://github.com/nasa-cisto-ai/slump-detection.git\"\u003ehttps://github.com/nasa-cisto-ai/slump-detection.git\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-slump-detection\" class=\"anchor\" href=\"#slump-detection\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSlump Detection\u003c/h1\u003e\n\u003cp\u003eSlump Detection as an instance segmentation problem.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-business-case\" class=\"anchor\" href=\"#business-case\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBusiness Case\u003c/h2\u003e\n\u003cp\u003eThe following repository stores several experiments for the task of instance and semantic\nsegmentation of slumps in very high-resolution satellite imagery. Many of the instructions\nlisted below are guided towards utilizing GSFC NASA Center for Climate Simulation (NCCS)\ncomputing resources, particularly the PRISM GPU cluster.\u003c/p\u003e\n\u003cp\u003eA system with NVIDIA GPUs is required to run the scripts located in this repository.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eprojects/detectron2: utilizes the detectron2 framework for the task of instance segmentation\nleveraging MaskRCNN and Fast RCNN. The backend engine is PyTorch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-summarized-steps\" class=\"anchor\" href=\"#summarized-steps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummarized Steps\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin.nccs.nasa.gov\nssh gpulogin1\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#Logging_In\"\u003eLogging-In\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Container_Environment_Installation\"\u003eContainer Environment Installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Working_Inside_Container\"\u003eWorking Inside a Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Getting_Started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Authors\"\u003eAuthors\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#References\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-logging-in-\" class=\"anchor\" href=\"#logging-in-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLogging-In \u003ca name=\"user-content-Logging_In\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eYou will need an activate NCCS account together with a PIV Card or an RSA Token. Please refer\nto the following link for instructions on setting up login or any login related questions:\n\u003ca href=\"https://www.nccs.nasa.gov/nccs-users/instructional/logging-in/bastion-host\" rel=\"nofollow\"\u003eNCCS Logging-In\u003c/a\u003e.\nOnce you are all setup, you may login to the PRISM GPU cluster.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin.nccs.nasa.gov\nssh gpulogin1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-container-environment-installation-\" class=\"anchor\" href=\"#container-environment-installation-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Environment Installation \u003ca name=\"user-content-Container_Environment_Installation\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eAll the software and scripts from this repository can be ran within a container. Containers are\nsmall versions of operating systems that are meant to speed up the process of software development.\nThese containers are simply a binary file which has all the executables needed to run the software included.\u003c/p\u003e\n\u003cp\u003eThe NCCS provides Singularity as the default container runtime tool. In order to configure your\nenvironment to run Singularity containers, you will need to setup the environment variables listed below.\nFor this, you can simply add the following lines to your ~/.bashrc file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport SINGULARITY_CACHEDIR=\u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/.singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexport SINGULARITY_TMPDIR=\u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/.singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTest the environment variables with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e[username@gpulogin1 \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e]$ \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_CACHEDIR\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_TMPDIR\u003c/span\u003e\n/att/nobackup/username/.singularity /att/nobackup/username/.singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to utilize the container for this project, we first need to download the image from a container\nregistry. The image for this project is located in \u003ca href=\"https://hub.docker.com/repository/docker/nasanccs/slump-detectron2\" rel=\"nofollow\"\u003eNASA NCCS DockerHub Repository\u003c/a\u003e. Docker containers can be pulled as Singularity containers to be executed on HPC\nenvironments. The following commands allow the download of the container from DockerHub and generates a\nfile with a .sif extension. Depending on the file system, this step can take several minutes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e\nmodule load singularity\nsingularity pull docker://docker.io/nasanccs/slump-detectron2:latest\nsingularity build --sandbox slump-detectron2_latest slump-detectron2_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-working-inside-a-container-\" class=\"anchor\" href=\"#working-inside-a-container-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorking Inside a Container \u003ca name=\"user-content-Working_Inside_Container\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eEach project provides a set of Slurm scripts that will execute code inside the container without having\nto login inside the image. You may skip this step and go straight to the project README if you are only\ninterested in running scripts from outside the container. This section is meant to help users developing\nand testing code inside the container to facilitate the development process.\u003c/p\u003e\n\u003cp\u003eTo get a session in one of the PRISM GPU nodes, you can run the following command. Additional instructions\nregarding Slurm can be found in the \u003ca href=\"https://www.nccs.nasa.gov/nccs-users/instructional/adapt-instructional/using-prism\" rel=\"nofollow\"\u003eNCCS website\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esalloc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will notice that the hostname will change to something similar to gpu***. This means that you are now\nlogged into one of the GPU nodes. To access the container image, you can run the command listed below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell --nv -B /att/nobackup/username:/att/nobackup/username slump-detectron2_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere username is your NASA auid. From here, you can run any command inside the container image. Note that\nfor Singularity containers to have access to other paths within the HPC environment, we need to bind\ndirectories to particular locations in the container. The command above is binding your $NOBACKUP directory\nto be visible from inside the container.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started-\" class=\"anchor\" href=\"#getting-started-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started \u003ca name=\"user-content-Getting_Started\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eThe following is a summarized set of steps to get started and running in less than 5 minutes once the container image has been downloaded.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository into your ADAPT space\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e\ngit clone https://github.com/jordancaraballo/slump-detection.git\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCopy the data into the data/ directory\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp /data/location/.tif \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/data\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eGenerate train, test, and validation datasets\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch gen_dataset.sh\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eTrain a new model\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch train_detectron2.sh\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eClassify given imagery\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$NOBACKUP\u003c/span\u003e/slump-detection/projects/detectron2\nsbatch predict_detectron2.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-project-specific-information\" class=\"anchor\" href=\"#project-specific-information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject Specific Information\u003c/h2\u003e\n\u003cp\u003eData resides under:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/att/nobackup/username/EVHR_requests/_deliver/EWebbRequest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh adaptlogin\nssh gpulogin1\nmodule load anaconda\nconda create --name slump-detection-11.1 --clone /att/nobackup/username/.conda/envs/slump-detection-11.1\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-anaconda-environment\" class=\"anchor\" href=\"#anaconda-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnaconda environment\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load anaconda\nconda config --add channels conda-forge\nconda config --set channel_priority strict\nconda create -y -n slump-detection rioxarray cupy cudatoolkit=11.2 dask-cuda cudnn cutensor nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall pip dependencies\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda activate slump-detection\npip install -r requirements.txt\npip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003egit+https://github.com/facebookresearch/fvcore\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egit+https://github.com/philferriere/cocoapi.git#egg=pycocotools\u0026amp;subdirectory=PythonAPI\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ngit clone https://github.com/facebookresearch/detectron2 detectron2_repo \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e pip install -e detectron2_repo\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdding NCCL\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia\nconda create -n rapids-21.06 -c rapidsai -c nvidia -c conda-forge rapids-blazing=21.06 python=3.7 cudatoolkit=11.2 nvcc_linux-64 nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n rapids-21.06 -c rapidsai -c nvidia -c conda-forge -c pytorch rapids-blazing=21.06 python=3.7 cudatoolkit=11.1 ipykernel ipywidgets matplotlib geopandas pytorch torchvision torchaudio cudatoolkit=11.1 \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou could also enhance your kernel with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda config --add channels conda-forge\nconda config --set channel_priority strict\nconda create -y -n slump-detection-11.1 rioxarray cupy cudatoolkit=11.1 dask-cuda cudnn cutensor nccl ipykernel ipywidgets matplotlib geopandas iteration_utilities gcc_linux-64\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install cython\npip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003egit+https://github.com/facebookresearch/fvcore\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egit+https://github.com/philferriere/cocoapi.git#egg=pycocotools\u0026amp;subdirectory=PythonAPI\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\npip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html\npip install opencv-python scikit-image\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-authors\" class=\"anchor\" href=\"#authors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eJordan Alexis Caraballo-Vega, \u003ca href=\"mailto:jordan.a.caraballo-vega@nasa.gov\"\u003ejordan.a.caraballo-vega@nasa.gov\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003e[1] Chollet, Fran\u00e7ois; et all, Keras, (2015), GitHub repository, \u003ca href=\"https://github.com/keras-team/keras\"\u003ehttps://github.com/keras-team/keras\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n\u003cp\u003e[2] Paszke, Adam; Gross, Sam; Chintala, Soumith; Chanan, Gregory; et all, PyTorch, (2016), GitHub repository, \u003ca href=\"https://github.com/pytorch/pytorch\"\u003ehttps://github.com/pytorch/pytorch\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n\u003cp\u003e[3] Google Brain Team; et all, TensorFlow, (2015), GitHub repository, \u003ca href=\"https://github.com/tensorflow/tensorflow\"\u003ehttps://github.com/tensorflow/tensorflow\u003c/a\u003e. Accessed 13 February 2020.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-csci5980\" class=\"anchor\" aria-hidden=\"true\" href=\"#csci5980\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecsci5980\u003c/h1\u003e\n\u003cp\u003eFinal project for CSci 5980: deep learning for automatic music translation.\u003c/p\u003e\n\u003cp\u003eFollow theses steps to install all package dependencies for running the model:\u003c/p\u003e\n\u003cp\u003eWe first install software dependencies for manipulating raw audio (\u003ccode\u003effmpeg\u003c/code\u003e):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a local software directory\n\u003ccode\u003emkdir ~/software\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the NASM assembler (dependency of ffmpeg):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software\nwget https://www.nasm.us/pub/nasm/releasebuilds/2.14.02/nasm-2.14.02.tar.bz2\ntar -xvf nasm-2.14.02.tar.bz2\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e nasm-2.14.02\n./configure --prefix=\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software/nasm/\nmake install\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software/nasm/bin/\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eMake sure that NASM assembler installed correctly:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enasm -v\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output should look something like:\n\u003ccode\u003eNASM version 2.14.02 compiled on Mar 11 2020\u003c/code\u003e\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eInstall ffmpeg:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software\nwget https://ffmpeg.org/releases/ffmpeg-4.2.2.tar.bz2\ntar -xvf ffmpeg-4.2.2.tar.bz2\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ffmpeg-4.2.2\n./configure --prefix=\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software/ffmpeg/\nmake install\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/software/ffmpeg/bin/\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eMake sure that ffmpeg installed correctly:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003effmpeg -version\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output should look something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003effmpeg version 4.2.2 Copyright (c) 2000-2019 the FFmpeg developers\nbuilt with gcc 4.4.7 (GCC) 20120313 (Red Hat 4.4.7-23)\nconfiguration: --prefix=/home/csci5980/piehl008/software/ffmpeg/\nlibavutil 56. 31.100 / 56. 31.100\nlibavcodec 58. 54.100 / 58. 54.100\nlibavformat 58. 29.100 / 58. 29.100\nlibavdevice 58. 8.100 / 58. 8.100\nlibavfilter 7. 57.100 / 7. 57.100\nlibswscale 5. 5.100 / 5. 5.100\nlibswresample 3. 5.100 / 3. 5.100\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eNow, we can make the virtual environment and install python packages. First, create the virtual environment by running:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ccode\u003econda create --name audio-proj python=3.7\u003c/code\u003e\u003c/p\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eNext, install packages by running\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/csci5980\nconda install --name audio-proj --file requirements.txt --channel defaults --channel conda-forge\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(Note: this can take a while - and you need to say yes to installing everything after it solves the environment)\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eTo activate the virtual environment, you can now run \u003ccode\u003esource activate audio-proj\u003c/code\u003e. Note: you should do this to test that you can activate the virtual evironment, but you probably shouldn\u0027t run a lot unless you are submitting jobs to the queue. If you want to use this virtual environment through the MSI notebooks, check out the tutorial at \u003ca href=\"https://sunju.org/teach/DL-Spring-2020/TensorFlowPyTorch.html\" rel=\"nofollow\"\u003ehttps://sunju.org/teach/DL-Spring-2020/TensorFlowPyTorch.html\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-adding-the-virtual-environment-to-jupyter-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-the-virtual-environment-to-jupyter-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding the Virtual Environment to Jupyter Notebooks\u003c/h3\u003e\n\u003cp\u003eNow that we have created the virtual environment, we can add it to the Jupyter notebook kernels so that we can use the virtual environment through MSI\u0027s notebook server. To do this, we have to add the kernel specifications to the known Jupyter kernels for our user:\u003c/p\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eIf you haven\u0027t already, activate your virtual environment by running \u003ccode\u003esource activate audio-proj\u003c/code\u003e. Then enter\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewhich python\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYour output should tell you where the python executable for this virtual environment lives - the output for me displays \u003ccode\u003e~/.conda/envs/audio-proj/bin/python\u003c/code\u003e. If you see something that looks like \u003ccode\u003e/panfs/roc/msisoft/anaconda/anaconda3-2018.12/bin/python\u003c/code\u003e, go back and make sure that you have the virtual environment active and try again. After you have an ouput that clearly has the name of the virtual environment in the directory path (i.e. contains audio-proj in it), continue to the next step.\u003c/p\u003e\n\u003col start=\"10\"\u003e\n\u003cli\u003eNow, we need to create the kernel configuration. To do this run\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.local/share/jupyter/kernels/audio-proj\nnano \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.local/share/jupyter/kernels/audio-proj/kernel.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe nano command will open a very basic text editor that you can navigate with the arrow keys. Enter the following:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e{\n \"argv\": [\n \"~/.conda/envs/audio-proj/bin/python\", #replace this with your path from step 9 above! (and delete this comment)\n \"-m\",\n \"ipykernel_launcher\",\n \"-f\",\n \"{connection_file}\"\n ],\n \"display_name\": \"Audio Project Kernel\",\n \"language\": \"python\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere you replace the first line of the argv array with whatever executable path was output from step 9 above (it likely will be identical to this). To exit the nano text editor, type \u003ccode\u003eCtrl-x \u0026lt;RETURN\u0026gt;\u003c/code\u003e and then type \u003ccode\u003eY \u0026lt;RETURN\u0026gt;\u003c/code\u003e to save the file.\u003c/p\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003eNow that you have saved the kernel file, you should be able to go to \u003ccode\u003ehttps://notebooks.msi.umn.edu/\u003c/code\u003e and when you click on the \u003ccode\u003eNew\u003c/code\u003e tab to create a new file, you should be able to select \u003ccode\u003eAudio Project Kernel\u003c/code\u003e as an available kernel to run your newly created file in.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1633374445.0 + "updated_at": 1637707574.0 }, { "data_format": 2, - "description": "Simple scripts for analysis", + "description": null, "filenames": [ - "Dockerfiles/guppy/Singularity.v5.0.11" + "Singularity.latest" ], - "full_name": "cgjosephlee/My_scripts", + "full_name": "cschu/dada2_container", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-my_scripts\" class=\"anchor\" href=\"#my_scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMy_scripts\u003c/h1\u003e\n\u003cp\u003eSimple scripts for analysis\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1636945791.0 + "updated_at": 1618484418.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.ray", - "Singularity", - "Singularity.beta" + "Singularity.cpu", + "Singularity.gpu" ], - "full_name": "huynhngoc/head-neck-analysis", + "full_name": "sleeepyjack/variant_calling", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-head-and-neck-cancer-analysis\" class=\"anchor\" href=\"#head-and-neck-cancer-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHead and Neck cancer analysis\u003c/h1\u003e\n\u003cp\u003eStart by running \u003ccode\u003esetup.sh\u003c/code\u003e to download the singularity container\nThen, submit slurm jobs like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esbatch slurm.sh config/2d_unet.json 2d_unet 200\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhich will load the setup from the \u003ccode\u003econfig/2d_unet.json\u003c/code\u003e file, train for 200 epochs\nand store the results in the folder \u003ccode\u003e$HOME/logs/hn_perf/2d_unet/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo customize model and prediction checkpoints\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm.sh config/3d_vnet_32_normalize.json 3d_vnet_32_normalize 100 --model_checkpoint_period 5 --prediction_checkpoint_period 5\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo continue an experiment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_cont.sh model.030.h5 3d_vnet_32_normalize 100 --model_checkpoint_period 5 --prediction_checkpoint_period 5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo plot performance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_vis.sh 3d_vnet_32_normalize\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run test\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_test.sh 3d_vnet_32\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch slurm_test.sh 3d_vnet_32 --best_epoch \u0026lt;BEST_EPOCH_NUMBER\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, if your cluster does not have slurm installed, simply omit the \u003ccode\u003esbatch\u003c/code\u003e\npart of the call above, thus running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./slurm.sh config/2d_unet.json 2d_unet 200\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eManually build\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot deoxys.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRemember to login to a gpu session to use the gpu\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eqlogin --partition=gpu --gres=gpu:1\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-variant-calling\" class=\"anchor\" aria-hidden=\"true\" href=\"#variant-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVariant Calling\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e- make\n- Singularity (\u0026gt;= v3.2)\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1637309995.0 + "updated_at": 1589243145.0 }, { "data_format": 2, - "description": "code for evaluation on full neurovista trial data", + "description": "Lexers for CudaText", "filenames": [ - "Singularity.nv_eval" + "Singularity/Singularity.lcf", + "Singularity/tests/1/Singularity", + "Singularity/tests/4/Singularity", + "Singularity/tests/2/Singularity", + "Singularity/tests/3/Singularity", + "Singularity/tests/5/Singularity", + "Singularity/tests/6/Singularity" ], - "full_name": "MatthiasEb/neurovista_evaluation", + "full_name": "OlehL/cuda_lexers", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-neurovista_evaluation\" class=\"anchor\" href=\"#neurovista_evaluation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eneurovista_evaluation\u003c/h1\u003e\n\u003cp\u003ecode for evaluation on full neurovista trial data following the \u003ca href=\"https://github.com/epilepsyecosystem/CodeEvaluationDocs\"\u003einstructions\u003c/a\u003e (commit 20e6f0f, dated 16/06/2020).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-settings\" class=\"anchor\" href=\"#settings\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSettings\u003c/h2\u003e\n\u003cp\u003eSettings can be adjusted in SETTINGS.json\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-a-virtual-environment\" class=\"anchor\" href=\"#using-a-virtual-environment\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing a virtual environment\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erequirements\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003epython3\u003c/li\u003e\n\u003cli\u003ecuda toolbox 10, nvidia-drivers\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einstallation\u003c/h3\u003e\n\u003cp\u003eInstall requirements by running:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install -r requirements.txt\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-execution\" class=\"anchor\" href=\"#execution\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eexecution\u003c/h3\u003e\n\u003cp\u003eRun training by executing:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython run.py\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-docker\" class=\"anchor\" href=\"#using-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Docker\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-requirements-1\" class=\"anchor\" href=\"#requirements-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erequirements\u003c/h3\u003e\n\u003cp\u003eTested with Docker version 19.03.6, build 369ce74a3c on Ubuntu 18.04\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-image\" class=\"anchor\" href=\"#build-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Image\u003c/h3\u003e\n\u003cp\u003eBuild Docker Image by running:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker build --tag nv1x16_eval .\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-execution-1\" class=\"anchor\" href=\"#execution-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h3\u003e\n\u003cp\u003eSpecify the directory of your data segments by\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eexport DATA_DIR=YOUR_DATA_DIRECTORY\u003c/code\u003e,\u003c/p\u003e\n\u003cp\u003ereplacing \u003ccode\u003eYOUR_DATA_DIRECTORY\u003c/code\u003e with your specific directory.\u003c/p\u003e\n\u003cp\u003eRun training by executing\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --gpus 1 -v $PWD:/code -v /$DATA_DIR:/$DATA_DIR:ro nv1x16_eval python ./run.py\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-using-singularity\" class=\"anchor\" href=\"#using-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003c/h2\u003e\n\u003cp\u003eSingularity recipe is included. SingularityHub URI of the Image is MatthiasEb/neurovista_evaluation:nv_eval.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-remarks\" class=\"anchor\" href=\"#remarks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRemarks\u003c/h2\u003e\n\u003cp\u003eYou should use a GPU for training. I used an RTX 2080 Ti, run_on_contest_data=1, mode=1 took about 4.5 h.\nIf you use a GPU with much less RAM, you might have to reduce the batch size, I did not try that though.\nI ran the code with run_on_contest_data=1, the results seemed to be comparable to the version on the ecosystem leaderboard.\nSparse tests with run_on_contest_data=0 have been executed, maybe there is something I missed here.\nI did not yet try to run it within a singularity container, docker should work though.\nDo not hesitate to contact me if you run into problems, have any questions or remarks.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-algorithm\" class=\"anchor\" href=\"#algorithm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlgorithm\u003c/h3\u003e\n\u003cp\u003eThis is a pretty naive approach on a 2D-Convolution Deep Neural Network, applied to the raw time series.\nAs described in the \u003ca href=\"https://ieeexplore.ieee.org/abstract/document/8621225\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, the Network expects standardized 15 s segments, sampeled at 200 Hz.\ntensorflow.keras (2.0.1) was used as Deep Learning API.\nIn order to avoid the need to either load the whole training set at once or to save the preprocessed time series, this is a different implementation than the one used in the paper.\nIn order to allow training on arbitrary large datasets, this implementation does not perfectly reproduce the results shown in the paper.\nI did a few testruns on the contest data, ROC AUC of the private Set should be around .25, .7 and .8 for Patient 1, 2 and 3 respectively.\nHowever, considerable variations are conceivable, see section below.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-implementation\" class=\"anchor\" href=\"#implementation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImplementation\u003c/h3\u003e\n\u003cp\u003eLoading the original (~ 400 Hz) .mat files, resampling to 200 Hz, standardizing (optionally, if \u003ccode\u003esubtract_mean==1\u003c/code\u003e), splitting them in 15 s segments is done asynchronously on the fly by the dataloader in 5 different threads.\nThe 15s Segments are enqueued in a buffer with the size of 400 10-min-sequences, implemented as a tf.queue.RandomShuffleQueue.\nThe data is therefore dequeued in random order, although not perfectly uniformly shuffeled, depending on the buffer size and the size of the data set.\nI did some experiments that showed that the buffersize can have considerable impact on the performance of the algorithm.\nThe bigger the buffer size, the closer are the results to the ones shown in the paper.\nThe intention of this procedure was to ensure a reasonably shuffeled training set of 15 s segments while minimizing IO, working on the .mat files and having the possibility for standardization.\u003cbr\u003e\nIf the IO-Bandwidth of the filesystem is reasonably high, this should not slow down the training too much.\u003c/p\u003e\n\u003cp\u003eAs described in the paper, if run_on_contest_data==1, 3 networks (one for each patient) are trained and evaluated individually.\nSubsequently, the solution file is concatenated.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1629316259.0 + "updated_at": 1610290115.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity", + "model_preprocess/Singularity" ], - "full_name": "truatpasteurdotfr/singularity-gnina", + "full_name": "lsx1980/3D_model_traits_measurement", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-gnina-101-singularity-image\" class=\"anchor\" href=\"#building-a-gnina-101-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a gnina-1.0.1 singularity image\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gnina/gnina/issues/122\"\u003ehttps://github.com/gnina/gnina/issues/122\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eHost running CentOS-7, but gnina requires a newer glibc...\u003c/li\u003e\n\u003cli\u003eProvide a drop-in replacement for gnina (assuming singularity is installed)\u003c/li\u003e\n\u003cli\u003eShould work with either \u003ca href=\"https://github.com/hpcng/singularity\"\u003ehttps://github.com/hpcng/singularity\u003c/a\u003e or \u003ca href=\"https://github.com/sylabs/singularity\"\u003ehttps://github.com/sylabs/singularity\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003cli\u003echeck gnina licenses at \u003ca href=\"https://github.com/gnina/gnina\"\u003ehttps://github.com/gnina/gnina\u003c/a\u003e when you are using it, these were copied verbatim here for convenience.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egnina is dual licensed under GPL and Apache. The GPL license is necessitated by\nthe use of OpenBabel (which is GPL licensed). In order to use gnina under the\nApache license only, all references to OpenBabel must be removed from the\nsource code.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-gnina/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-gnina/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build gnina oras://ghcr.io/truatpasteurdotfr/singularity-gnina:latest\n./gnina --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efor GPU access you need to use the \u003ccode\u003e--nv\u003c/code\u003e flag:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --nv ./gnina \u0026lt;.. your gnina options ...\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-thanks\" class=\"anchor\" href=\"#thanks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThanks\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/singularityhub/github-ci\"\u003ehttps://github.com/singularityhub/github-ci\u003c/a\u003e for the github action\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-3d_model_traits_measurement\" class=\"anchor\" aria-hidden=\"true\" href=\"#3d_model_traits_measurement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3D_model_traits_measurement\u003c/h1\u003e\n\u003cp\u003eFunction: Extract gemetrical traits of 3D model\u003c/p\u003e\n\u003cp\u003eAuthor : Suxing Liu\u003c/p\u003e\n\u003cp\u003eDate created : 04/04/2018\u003c/p\u003e\n\u003cp\u003eDate last modified: 04/25/2019\u003c/p\u003e\n\u003cp\u003ePython Version : 2.7\u003c/p\u003e\n\u003cp\u003eusage:\u003c/p\u003e\n\u003cp\u003epython pipeline.py -p /$path_to_your_3D_model/ -m 3D_model_name.ply\u003c/p\u003e\n\u003cp\u003eSingularity test:\u003c/p\u003e\n\u003cp\u003esudo singularity build --writable model-scan.img Singularity\u003c/p\u003e\n\u003cp\u003esingularity exec model-scan.img python /opt/code/pipeline.py -p /$path_to_your_3D_model/ -m surface.ply\u003c/p\u003e\n\u003cp\u003esingularity exec shub://lsx1980/3D_model_traits_measurement python /opt/code/pipeline.py -p /$path_to_your_3D_model/ -m surface.ply\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePre-requisite:\n\u003cul\u003e\n\u003cli\u003ePython2.7\u003c/li\u003e\n\u003cli\u003eNumpy\u003c/li\u003e\n\u003cli\u003eSciPy\u003c/li\u003e\n\u003cli\u003eOpencv 3.0 for Python - \u003ca href=\"http://www.pyimagesearch.com/2015/06/15/install-opencv-3-0-and-python-2-7-on-osx/\" rel=\"nofollow\"\u003eInstallation\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVisualization requirement:\u003c/p\u003e\n\u003cp\u003epip3 install numba \u003cbr\u003e\nimagesize \u003cbr\u003e\nprogressbar2 \u003cbr\u003e\nmayavi \u003cbr\u003e\nPyQt5 \u003cbr\u003e\nnetworkx\u003c/p\u003e\n\u003cp\u003epython3 graph_compute.py -p /\u0026amp;path/active_component/\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1637250596.0 + "updated_at": 1610422118.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity/Singularity.lip2wav_tf", + "Singularity/Singularity.lip2wav", + "Singularity/Singularity.lip2wav_new", + "Singularity/Singularity.Rotate", + "Singularity/Singularity.test" ], - "full_name": "tsgoten/transactive-control-social-game", + "full_name": "kangzhiq/GSoC2020", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-transactive-control-social-game\" class=\"anchor\" href=\"#transactive-control-social-game\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTransactive Control Social Game\u003c/h1\u003e\n\u003cp\u003eCode meant to support and simulate the Social Game that will be launched in 2021. Elements of transactive control and behavioral engineering will be tested and designed here\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://dvc.org/doc/install\" rel=\"nofollow\"\u003edvc\u003c/a\u003e (with google drive support)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOn linux this is \u003ccode\u003epip install \u0027dvc[gdrive]\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall Docker, if you have not already.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc remote add -d gdrive gdrive://1qaTn6IYd3cpiyJegDwwEhZ3LwrujK3_x\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc pull\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./run.sh\u003c/code\u003e from the root of the repo. This will put you in a shell in the docker container with the \u003ccode\u003erl_algos/logs\u003c/code\u003e directory mounted\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 ExperimentRunner.py sac test_experiment\u003c/code\u003e in the docker container to start an experiment with the name \u003ccode\u003etest_experiment\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003etensorboard --logdir rl_algos/logs\u003c/code\u003e from outside the docker container to view the logs\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-issues\" class=\"anchor\" href=\"#issues\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssues\u003c/h3\u003e\n\u003cp\u003eIf you\u0027re having trouble running docker or the \u003ccode\u003eExperimentRunner.py\u003c/code\u003e file. Please try running \u003ccode\u003epython ExperimentRunner.py\u003c/code\u003e and debug from there.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-gsoc2020-hand-gesture-detection-and-recognition-in-news-videos\" class=\"anchor\" aria-hidden=\"true\" href=\"#gsoc2020-hand-gesture-detection-and-recognition-in-news-videos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGSoC2020: Hand gesture detection and recognition in news videos\u003c/h1\u003e\n\u003cp\u003eThis is my GSoC2020 project with Red Hen Lab.\u003c/p\u003e\n\u003cp\u003eThe goal is to design a network capable of detecting and recognizing hand gestures and then apply it to annotate the dataset of news videos of Red Hen Lab.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-preprocessing-of-dataset-iemocap\" class=\"anchor\" aria-hidden=\"true\" href=\"#preprocessing-of-dataset-iemocap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessing of Dataset IEMOCAP\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003emocap_data_collect.py\u003c/code\u003e extract information from the IEMOCAP dataset and save the data as pickle.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-audio-visual-emtion-recognition\" class=\"anchor\" aria-hidden=\"true\" href=\"#audio-visual-emtion-recognition\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAudio-Visual emtion recognition\u003c/h1\u003e\n\u003cp\u003eAudio-only, visual-only and audio-visual models are tested to verify the utility of bimodal information.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1637011831.0 + "updated_at": 1650289089.0 }, { "data_format": 2, - "description": "Electrophysiology tools, mountainlab processor library", + "description": "This repository contains the singularity recipes I use for my robotics projects.", "filenames": [ - "Singularity.v0.2.6" + "Singularity.tf_gpu-opencv2-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial", + "Singularity.tf_gpu-opencv2-conda3-ros_kinetic-moveit-cuda10-xenial", + "Singularity.tf_gpu-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial", + "Singularity.tf_gpu-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial", + "Singularity.tf_gpu-conda3-ros_kinetic-moveit-cuda10-xenial", + "Singularity.tf_gpu-opencv2-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial" ], - "full_name": "scratcharchive/ml_ephys", - "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ml_ephys\" class=\"anchor\" href=\"#ml_ephys\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml_ephys\u003c/h1\u003e\n\u003cp\u003eElectrophysiology tools\nMountainLab processor library\u003c/p\u003e\n\u003cp\u003eInstallation from PyPI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install --upgrade ml_ephys\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen add it as a plugin to mountainlab:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/.mountainlab/packages\nml-link-python-module ml_ephys ml_ephys\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr installation from source:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eclone this repository into .mountainlab/packages/\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd ml_ephys\npip3 install --upgrade .\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "rickstaa/deep-robotics-singularity-recipes", + "latest_release": "v0.1.6", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-deep-robotics-singularity-recipe-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#deep-robotics-singularity-recipe-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeep Robotics singularity recipe repository\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.codacy.com/gh/rickstaa/deep-robotics-singularity-recipes/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=rickstaa/deep-robotics-singularity-recipes\u0026amp;utm_campaign=Badge_Grade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/937121feb970b0588395f46f6af7607636459cacf58be2130bf2135cfb9deb25/68747470733a2f2f6170702e636f646163792e636f6d2f70726f6a6563742f62616467652f47726164652f3133316539306631386432303462613739343933663931646230343839303636\" alt=\"Codacy Badge\" data-canonical-src=\"https://app.codacy.com/project/badge/Grade/131e90f18d204ba79493f91db0489066\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3134\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/rickstaa/Todoist_Global_Shortcuts_WIN10/pulse\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b4978fac45f41461eb4f7d2c053e4142c3072396afed32484b2b1241bee4ea65/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4d61696e7461696e65642533462d7965732d677265656e\" alt=\"Maintained\" data-canonical-src=\"https://img.shields.io/badge/Maintained%3F-yes-green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/rickstaa/Todoist_Global_Shortcuts_WIN10/blob/master/contributing.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7e7bdf5c529c8bc594e26038dbb1a3d360e9ede891fbdcef50b403ab5f88fc14/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e747269627574696f6e732d77656c636f6d652d6f72616e67652e737667\" alt=\"Contributions\" data-canonical-src=\"https://img.shields.io/badge/contributions-welcome-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-package-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#package-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackage Overview\u003c/h2\u003e\n\u003cp\u003eThis repository contains singularity recipes that might be used for robotics\nprojects. These recipes are also published on the \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003ewww.singularity-hub.org\u003c/a\u003e\ncontainer registry. You are invited to add additional singularity recipes\nto this repository (see the \u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/contributing.md\"\u003econtributions guidelines\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecipes\u003c/h3\u003e\n\u003cp\u003eThis repository currently contains the following recipes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-conda3-ros_kinetic-moveit-cuda10-xenial\"\u003etf_gpu-conda3-ros_kinetic-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-opencv2-conda3-ros_kinetic-moveit-cuda10-xenial\"\u003etf_gpu-opencv2-conda3-ros_kinetic-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial\"\u003etf_gpu-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-opencv2-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial\"\u003etf_gpu-opencv2-conda3-ros_kinetic-libfranka-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial\"\u003etf_gpu-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes/blob/master/Singularity.tf_gpu-opencv2-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial\"\u003etf_gpu-opencv2-conda3-libfreenect2-ros_kinetic-libfranka-moveit-cuda10-xenial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more information about each of the recipes see \u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipesinfo/recipes\"\u003ethe documentation\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and Usage\u003c/h2\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/rickstaa/deep-robotics-singularity-recipes\"\u003edocs\u003c/a\u003e for installation and usage instructions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eFeel free to open an issue if you have ideas on how to make this GitHub action better or if you want to report a bug! All contributions are welcome. \u003cg-emoji class=\"g-emoji\" alias=\"rocket\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f680.png\"\u003e\ud83d\ude80\u003c/g-emoji\u003e Please consult the \u003ca href=\"CONTRIBUTING.md\"\u003econtribution guideliness\u003c/a\u003e for more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"LICENSE\"\u003eMIT\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIcon created with svg made by \u003ca href=\"https://www.flaticon.com/authors/eucalyp\" rel=\"nofollow\"\u003e@Eucalyp\u003c/a\u003e from \u003ca href=\"https://www.flaticon.com/authors/eucalyp\" rel=\"nofollow\"\u003ewww.flaticon.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1639779289.0 + "updated_at": 1658652043.0 }, { "data_format": 2, - "description": "Singularity containers", + "description": "Singularity Recipe for NWChem", "filenames": [ - "Singularity.lsdalton-mpi-omp", - "Singularity.lsdalton-omp", - "Singularity.dalton-mpi" + "Singularity.6.6-openmpi" ], - "full_name": "robertodr/singularities", + "full_name": "ResearchIT/nwchem", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularities\" class=\"anchor\" href=\"#singularities\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularities\u003c/h1\u003e\n\u003cp\u003eSingularity container recipes for Dalton and LSDalton, based on Ubuntu 18.04 LTS.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-available-recipes\" class=\"anchor\" href=\"#available-recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable recipes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.lsdalton-omp\"\u003eSingularity.lsdalton-omp\u003c/a\u003e: OpenMP-parallel binary on Ubuntu 18.04\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.lsdalton-mpi-omp\"\u003eSingularity.lsdalton-mpi-omp\u003c/a\u003e: MPI+OpenMP-parallel binary on Ubuntu 18.04\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.dalton-mpi\"\u003eSingularity.dalton-mpi\u003c/a\u003e: MPI-parallel binary on Ubuntu 18.04\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-generate-new-recipes-using-hpc-container-maker-hpccm\" class=\"anchor\" href=\"#generate-new-recipes-using-hpc-container-maker-hpccm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerate new recipes using HPC Container Maker (HPCCM)\u003c/h2\u003e\n\u003cp\u003eThe recipe files are auto-generated using \u003ca href=\"https://github.com/NVIDIA/hpc-container-maker\"\u003eHPC Container Maker\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor Singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ hpccm --recipe \u0026lt;recipe_name\u0026gt;.py --format singularity --singularity-version=3.2 \u0026gt; recipes/Singularity.\u0026lt;version-tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe images are automatically built in GitHub Actions and uploaded to the GitHub\nContainer Registry. \u003cstrong\u003eOnly containers whose recipe changed on a given commit\nare rebuilt.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-locally-build-the-image-from-a-recipe-file\" class=\"anchor\" href=\"#how-to-locally-build-the-image-from-a-recipe-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to locally build the image from a recipe file\u003c/h2\u003e\n\u003cp\u003eThe version to build is a configurable parameter in the recipes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTo generate a definition file for v2020.0 of LSDalton:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat Singularity.lsdalton-omp | sed \"s/@_VERSION_@/v2020.0/g\" \u0026gt; Singularity.lsdalton-v2020.0-omp\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo generate a definition file for the \u003ccode\u003emaster\u003c/code\u003e branch of Dalton:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat Singularity.lsdalton-omp | sed \"s/@_VERSION_@/master/g\" \u0026gt; Singularity.lsdalton-master-omp\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou need \u003ccode\u003esudo\u003c/code\u003e for building images, but you don\u0027t need \u003ccode\u003esudo\u003c/code\u003e for anything else.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo -E singularity build lsdalton-v2020.0-omp.sif Singularity.lsdalton-v2020.0-omp\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-pull-these-images-from-github-container-registry\" class=\"anchor\" href=\"#how-to-pull-these-images-from-github-container-registry\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to pull these images from GitHub Container Registry\u003c/h2\u003e\n\u003cp\u003eFor LSDalton:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ev2020.0 OpenMP parallelization only:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull oras://ghcr.io/robertodr/singularities/lsdalton-v2020.0-omp:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003emaster branch MPI+OpenMP parallelization:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull oras://ghcr.io/robertodr/singularities/lsdalton-master-mpi-omp:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor Dalton:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ev2020.0 MPI parallelization only:\n\u003cpre\u003e\u003ccode\u003esingularity pull oras://ghcr.io/robertodr/singularities/dalton-v2020.0-mpi:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-use-the-image\" class=\"anchor\" href=\"#how-to-use-the-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use the image\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eFirst try this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cat /etc/os-release\n$ singularity exec lsdalton-v2020.0-omp.sif cat /etc/os-release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow try to run LSDalton:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run lsdalton-v2020.0-omp.sif myinput.dal somemolecule.mol\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSince \u003ccode\u003elsdalton-v2020.0-omp.sif\u003c/code\u003e is executable, you can also rename it to \u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003elsdalton\u003c/code\u003e and do this instead:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mv lsdalton-v2020.0-omp.sif lsdalton\n$ ./lsdalton myinput.dal somemolecule.mol\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-for-nwchem\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-for-nwchem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Recipe for NWChem\u003c/h1\u003e\n\u003cp\u003eThis repo contains recipes to run \u003ca href=\"http://www.nwchem-sw.org/index.php/Main_Page\" rel=\"nofollow\"\u003eNWChem\u003c/a\u003e\nwithin a \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eVersions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e6.6 - NWChem with OpenMPI installed via EPEL\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Use:\u003c/h2\u003e\n\u003cp\u003eYou need to have openmpi v1 installed on your local machine (via yum or as a module).\nTesting was performed with openmpi 1.10.6.\u003c/p\u003e\n\u003cp\u003eRun example:\u003c/p\u003e\n\u003cp\u003empirun -np 2 singularity run shub://ResearchIT/nwchem:6.6-openmpi test.nw\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-alternative-method\" class=\"anchor\" aria-hidden=\"true\" href=\"#alternative-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternative method:\u003c/h2\u003e\n\u003cp\u003euse the provided bash wrapper and module file to use the nwchem singularity container like a standard module\n(this assumes you have a singularity/2.4 and openmpi/1 modules)\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003emodule load nwchem/6.6\u003c/p\u003e\n\u003cp\u003empirun -np 2 nwchem test.nw\u003c/p\u003e\n", + "stargazers_count": 1, + "subscribers_count": 6, + "topics": [ + "nwchem", + "singularity" + ], + "updated_at": 1551769652.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "Singularity.latest" + ], + "full_name": "AdamWilsonLab/singularity-geospatial-r", + "latest_release": "0.0.4", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Status\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4930\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h1\u003e\n\u003cp\u003eThis repository includes a definition file for a singularity container \u003cem\u003eand\u003c/em\u003e instructions for starting up an instance on CENTOS in a HPC environment.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setting-up-the-singularity-instance\" class=\"anchor\" aria-hidden=\"true\" href=\"#setting-up-the-singularity-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetting up the Singularity Instance\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eSSH to the server\u003c/li\u003e\n\u003cli\u003eRun the following to select the target folder and download the current version of this container:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e# cd to user singularity directory\ncd /panasas/scratch/grp-adamw/singularity/$USER;\n# download the most recent version of the container\nwget -O singularity-geospatial-r_latest.sif \\\n https://github.com/AdamWilsonLab/singularity-geospatial-r/releases/download/0.0.1/AdamWilsonLab-singularity-geospatial-r.latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate symlinks to singularity folder in project storage to prevent disk space problems in the home directory. You should only have to do this once.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~\nmkdir -p /projects/academic/adamw/singularity/$USER/.singularity\nln -s /projects/academic/adamw/singularity/$USER/.singularity .singularity\n\n# Symlinks for RStudio\nmkdir -p /projects/academic/adamw/rstudio/$USER/rstudio\nmv .local/share/rstudio /projects/academic/adamw/rstudio/$USER/\n\nmkdir -p ~/.local/share\nln -s /projects/academic/adamw/rstudio/$USER/rstudio ~/.local/share/rstudio\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eRun the \u003ca href=\"https://github.com/AdamWilsonLab/singularity-geospatial-r/blob/main/singularity_start.sh\"\u003esingularity_start.sh\u003c/a\u003e script to start up a singularity instance. You can just copy paste the code into the terminal. This includes a few system specific settings for the Buffalo CCR. This should only need to be done once (as long as the instance keeps running, server is not restarted, etc.). If the instance stops for any reason, you\u0027ll need to rerun this script. You can confirm it\u0027s running with \u003ccode\u003esingularity instance list\u003c/code\u003e or by checking \u003ccode\u003ehtop\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-connecting-to-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#connecting-to-rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConnecting to RStudio\u003c/h2\u003e\n\u003cp\u003eAfter running the steps above, you should be able to do just the following to begin working. If the server restarts you will need to re-run step 4 above.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eConnect to the instance via SSH with port Forwarding. You will need to be on campus or connected via VPN. See notes below for *nix and windows.\u003c/li\u003e\n\u003cli\u003eOpen RStudio at localhost:8787 in your local browser and login with user/password from #4 above.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-container-geospatial-r\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container-geospatial-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container: Geospatial R\u003c/h2\u003e\n\u003cp\u003eThis container builds upon the \u003ca href=\"https://hub.docker.com/r/rocker/geospatial\" rel=\"nofollow\"\u003erocker geospatial container\u003c/a\u003e, which I ported to \u003ca href=\"https://singularity-hub.org/collections/4908\" rel=\"nofollow\"\u003eSingularity here\u003c/a\u003e. This repository/collection then \u003ca href=\"https://github.com/AdamWilsonLab/singularity-geospatial-r/blob/main/Singularity.latest\"\u003eadds additional packages in this file\u003c/a\u003e. That\u0027s the file to modify if you want to add more linux packages, etc.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-connecting-via-ssh\" class=\"anchor\" aria-hidden=\"true\" href=\"#connecting-via-ssh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConnecting via SSH\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nix-systems-mac-and-linux\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix-systems-mac-and-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e*NIX systems (Mac and Linux)\u003c/h2\u003e\n\u003cp\u003eUse terminal to ssh to the server as explained in \u003ca href=\"https://github.com/AdamWilsonLab/singularity-geospatial-r/blob/main/singularity_start.sh\"\u003esingularity_start.sh\u003c/a\u003e.\nAdd something like the following to your .ssh/config file to simplify connecting with port forwarding via ssh. You will then have to update HOST to the host address and PORT_NUMBER to the updated port number.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eHost rserver\nHostName HOST\nLocalForward 8787 HOST:PORT_NUMBER\nUser $USER\nForwardX11 yes\nForwardAgent yes\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-hidden=\"true\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWindows\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-putty-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#putty-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePuTTY Instructions\u003c/h3\u003e\n\u003cp\u003eOn Windows you will need to use PuTTY or a similar terminal program.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIn PuTTY, enter the server address (host name) and \"22\" (port) on the \"Session\" tab.\u003c/li\u003e\n\u003cli\u003eOn the \"SSH/Tunnels\" tab, enter the port number of the rsession under \u201cSource port\u201d and type in HOST:PORT (replace with the actual server IP address + the port number) as the destination address. Then, click \"Add\".\u003c/li\u003e\n\u003cli\u003eConnect and login as usual in the terminal.\u003c/li\u003e\n\u003cli\u003ePoint the web browser to \u003ccode\u003ehttp://localhost:PORT\u003c/code\u003e (where PORT is the port number)\" and log in with the user name and the previously generated password.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-todos\" class=\"anchor\" aria-hidden=\"true\" href=\"#todos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTODOs\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003eSeparate container from startup and monitor script\u003c/li\u003e\n\u003cli\u003eSwitch to a docker image\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-development-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#development-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment Notes\u003c/h1\u003e\n\u003cp\u003eI started with \u003ca href=\"https://github.com/nickjer/singularity-rstudio/blob/master/.travis.yml\"\u003enickjer\u0027s very helpful example\u003c/a\u003e and updated it to pull from the geospatial version of the versioned rocker stack instead of the repository based R. This should make it easier to keep up to date.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-errors\" class=\"anchor\" aria-hidden=\"true\" href=\"#errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eErrors\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-unable-to-connect-to-service\" class=\"anchor\" aria-hidden=\"true\" href=\"#unable-to-connect-to-service\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnable to connect to service\u003c/h3\u003e\n\u003cp\u003eThis error can appear in the web browser when connecting via localhost. This can be caused by RStudio not being able to write session files in the right place. Confirm that:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe directory \u003ccode\u003e/projects/academic/adamw/rstudio/$USER/rstudio\u003c/code\u003e exists\u003c/li\u003e\n\u003cli\u003eand is linked to \u003ccode\u003e~/.local/share/rstudio\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-could-not-acquire-revocation-list-file-lock\" class=\"anchor\" aria-hidden=\"true\" href=\"#could-not-acquire-revocation-list-file-lock\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCould not acquire revocation list file lock\u003c/h3\u003e\n\u003cp\u003eThe error \"Could not acquire revocation list file lock\" resolved with help from \u003ca href=\"https://www.gitmemory.com/issue/rocker-org/rocker-versioned/213/726807289\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-database-error-7\" class=\"anchor\" aria-hidden=\"true\" href=\"#database-error-7\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edatabase error 7\u003c/h3\u003e\n\u003cp\u003eStarting in early 2021, something changed that resulted in the following error when starting a new instance:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eERROR database error 7 (sqlite3_statement_backend::loadOne: attempt to write a readonly database) [description: Could not delete expired revoked cookies from the database, description: Could not read revoked cookies from the database]; OCCURRED AT virtual rstudio::core::Error rstudio::core::database::Connection::execute(rstudio::core::database::Query\u0026amp;, bool*) src/cpp/core/Database.cpp:480; LOGGED FROM: int main(int, char* const*) src/cpp/server/ServerMain.cpp:729\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI solved this by binding an address outside the container to \u003ccode\u003e/var/lib/rstudio-server\u003c/code\u003e when starting the instance as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--bind $RSTUDIO_DB:/var/lib/rstudio-server\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e$RSTUDIO_DB\u003c/code\u003e is just a path outside the container. I got this idea from \u003ca href=\"https://community.rstudio.com/t/permissions-related-to-upgrade-to-rstudio-server-open-source-1-4/94256/3\" rel=\"nofollow\"\u003ethis post\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-local-rocker-updates\" class=\"anchor\" aria-hidden=\"true\" href=\"#local-rocker-updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal rocker updates\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003edocker run -d -p 8787:8787 -e PASSWORD=really_clever_password -v ~/Documents:~/Documents rocker/rstudio\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-useful-links\" class=\"anchor\" aria-hidden=\"true\" href=\"#useful-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful Links\u003c/h1\u003e\n\u003cp\u003eA few links I found useful while developing this container\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://divingintogeneticsandgenomics.rbind.io/post/run-rstudio-server-with-singularity-on-hpc/\" rel=\"nofollow\"\u003ehttps://divingintogeneticsandgenomics.rbind.io/post/run-rstudio-server-with-singularity-on-hpc/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/rocker/geospatial\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/rocker/geospatial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://singularity-hub.org/collections/4930\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/4930\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pawseysc.github.io/singularity-containers/23-web-rstudio/index.html\" rel=\"nofollow\"\u003ehttps://pawseysc.github.io/singularity-containers/23-web-rstudio/index.html\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.rocker-project.org/use/singularity/\" rel=\"nofollow\"\u003ehttps://www.rocker-project.org/use/singularity/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/grst/rstudio-server-conda/issues/3\"\u003ehttps://github.com/grst/rstudio-server-conda/issues/3\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1641466400.0 + "updated_at": 1630683178.0 }, { "data_format": 2, - "description": "Docker and Singularity Images of NKChen/Duke Resting State FMRI pipeline. Docker has been built from Neurodebian\u0027s Ubuntu:Xenial base image. Singularity has been built from Docker Ubuntu:Xenial base.", + "description": null, "filenames": [ - "Singularity" + "core/Singularity.ubuntu2004_cuda11", + "core/Singularity.ubuntu1604_cuda10", + "buildKit/Singularity.ubuntu2004", + "buildKit/Singularity.ubuntu1604" ], - "full_name": "nkrecon/rest-state-fmri", + "full_name": "mdzik/TCLB_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker-and-singularity-images-for-resting-state-fmri-pipeline-nan-kuei-chenduke-university\" class=\"anchor\" href=\"#docker-and-singularity-images-for-resting-state-fmri-pipeline-nan-kuei-chenduke-university\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker and Singularity images for Resting State FMRI pipeline (Nan-kuei Chen/Duke University)\u003c/h1\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e for details of use.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-summary\" class=\"anchor\" href=\"#summary\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary\u003c/h1\u003e\n\u003cp\u003eThis repository contains the build scripts for Docker and Singularity images of the Duke resting pipeline that perform processing of resting state data using FSL (Jenkinson et al. 2012) tools and custom scripts.\u003c/p\u003e\n\u003cp\u003eversion information can be obtained as \u003ccode\u003edocker run --rm orbisys/rest-state-fmri -V\u003c/code\u003e and \u003ccode\u003esingularity run rest-state-fmri.simg -V\u003c/code\u003e\nhelp information can be obtained as \u003ccode\u003edocker run --rm orbisys/rest-state-fmri -h\u003c/code\u003e and \u003ccode\u003esingularity run rest-state-fmri.simg -h\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe Docker image will be about 3GB when built. It comes with version 5.09 of FSL.\nAlternatively if you do not want to build the docker image locally you can pull it from the Docker hub using the command \u003ccode\u003edocker run -it --rm -v $PWD:/opt/data orbisys/rest-state-fmri\u003c/code\u003e or \u003ccode\u003edocker pull orbisys/rest-state-fmri\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe Singularity image will be about 4GB when built. It comes with version 5.10 of FSL. Again if you prefer not to build this locally then a sister version of this singularity image can be downloaded as \u003ccode\u003eSingularity pull shub://chidiugonna/nklab-neuro-reststate\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThe original python source \u003ccode\u003eresting_pipeline.py\u003c/code\u003e available at at [\u003ca href=\"https://wiki.biac.duke.edu/biac:analysis:resting_pipeline\" rel=\"nofollow\"\u003ehttps://wiki.biac.duke.edu/biac:analysis:resting_pipeline\u003c/a\u003e] has been slightly amended. These changes are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edata1\u003c/code\u003e has been selectively converted to dtype \u003ccode\u003enumpy.float64\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eslice indices have been cast as longs in certain instances.\u003c/li\u003e\n\u003cli\u003eBXH functionality is ignored. To explicitly use BXH info pass the flag --ignorebxh=N\u003c/li\u003e\n\u003cli\u003eChanges have been made in step 8 to force the diagonals of the correlation matrix to zero to prevent inconsistencies due to NaNs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-sliding-window-functionality\" class=\"anchor\" href=\"#sliding-window-functionality\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSliding window functionality\u003c/h3\u003e\n\u003cp\u003eA new step has been added \u003ccode\u003e-7sw\u003c/code\u003e to enable sliding window functionality. In order to use this step you will need to use the \u003ccode\u003e--slidewin\u003c/code\u003e parameter which takes 2 numbers seperated by a comma. The 1st number is the window size in seconds and the second number is the shift in seconds between sequential windows. So for example \u003ccode\u003e--slidewin=60,3\u003c/code\u003e will use a window size of \u003ccode\u003e60\u003c/code\u003e seconds shifted by \u003ccode\u003e3\u003c/code\u003e seconds for each subsequent window. Keep in mind that the \u003ccode\u003e--tr\u003c/code\u003e (in milliseconds) parameter is required to calculate the number of volumes to use for each sliding window correlation. If you do not specify the --slidwin parameter and run step \u003ccode\u003e7sw\u003c/code\u003e then default values of \u003ccode\u003e30,3\u003c/code\u003e will be used. Sliding window files are exported to a new directory \u003ccode\u003eSlidingWindow_W_S\u003c/code\u003e and image files are consolidated into 4D volumes for viewing in FSL as a movie\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-extensions-to-slice-correction-functionality\" class=\"anchor\" href=\"#extensions-to-slice-correction-functionality\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtensions to Slice Correction functionality\u003c/h3\u003e\n\u003cp\u003eThe pipeline has been extended to accept custom slice correction timing files. A python script \u003ccode\u003emake_fsl_stc.py\u003c/code\u003e has been bundled in this container which can take .json files created by dcm2niix. This python program will create a slice correction file with timing values and one with slices in order of acquisition. It can be called as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e where fmri.json is the json output from dcm2niix. custom names for the slice order and slice time files can be provided as parameters as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake_fsl_stc.py fmri.json --slicenum=/path/num.txt --slicetime=/path/time.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOtherwise these files default to \u003ccode\u003esliceorder.txt\u003c/code\u003e and \u003ccode\u003eslicetimes.txt\u003c/code\u003e in the current directory.\u003c/p\u003e\n\u003cp\u003eIf \u003ccode\u003e--slicetime\u003c/code\u003e is provided and --sliceorder is not then only the slicetimes textfile is created. The opposite is true if \u003ccode\u003e--slicenum\u003c/code\u003e is provided.\u003c/p\u003e\n\u003cp\u003eOnce these custom files have been created then they can be provided to the resting state pipeline using the full path as input to the \u003ccode\u003e--sliceorder\u003c/code\u003e parameter\n\u003ccode\u003e--sliceorder=/path/num.txt\u003c/code\u003e as follows \u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eplease note that the default custom slice file expected uses slice order. If you pass a text file with slice times then you will need to use another parameter \u003ccode\u003e--slicetimings=time\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-docker-image\" class=\"anchor\" href=\"#build-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Docker Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have docker installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003erest-state-fmri\u003c/code\u003edirectory and check that have a Docker file \u003ccode\u003eDockerfile\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and the \u003ccode\u003esrc/resting_pipeline.py\u003c/code\u003e file have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow build the image as follows \u003ccode\u003esudo docker build -t orbisys/rest-state-fmri .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-run-docker-image\" class=\"anchor\" href=\"#run-docker-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Docker Image\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-within-shell\" class=\"anchor\" href=\"#within-shell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWithin Shell\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to a directory with a test NIFTII image and enter \u003ccode\u003edocker run -it --rm -v $PWD:/opt/data --entrypoint /bin/bash orbisys/rest-state-fmri\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe docker image should run and automatically start in \u003ccode\u003e/opt/data\u003c/code\u003e directory which is mapped to the original directory from which you ran the image. The prompt should look something like below:\n\u003ccode\u003eroot@62e040b47368:/opt/data#\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eYou can now run the pipeline with the shell as follows: \u003ccode\u003eresting_pipeline.py --func PBIA6_26386_20140402_045154_93696_magnitude.nii --throwaway=4 --steps=2,3,4,5,6,7 -o PBIA6_26386_20140402_045154_93696 --sliceorder=odd --tr=5000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-as-a-one-line-command\" class=\"anchor\" href=\"#as-a-one-line-command\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAs a one line command\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to a directory with a test NIFTII image and enter:\n\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func moco14a0001.nii.gz --steps=1,2,3,4,5,6,7,8 -o 14a0001 --sliceorder=\"even\" --tr=3000\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-running-gui-within-docker\" class=\"anchor\" href=\"#running-gui-within-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Gui within docker\u003c/h4\u003e\n\u003cp\u003eTo access GUI interaces of programs in the docker image then use the construct shown next (Courtesy of work by Fabio Rehm [\u003ca href=\"https://fabiorehm.com/blog/2014/09/11/running-gui-apps-with-docker/\" rel=\"nofollow\"\u003ehttps://fabiorehm.com/blog/2014/09/11/running-gui-apps-with-docker/\u003c/a\u003e] ). For example to run FSL as GUI then perform the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v $HOME/.Xauthority:/home/developer/.Xauthority -it --net=host --pid=host --ipc=host orbisys/rest-state-fmri fsl\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example-commands\" class=\"anchor\" href=\"#example-commands\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample Commands\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-create-slice-timing-files-from-json\" class=\"anchor\" href=\"#create-slice-timing-files-from-json\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Slice Timing files from json\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\" class=\"anchor\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $PWD:/opt/data orbisys/rest-state-fmri /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" href=\"#build-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Singularity Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou will need to have singularity 2.4 or greater installed. Simply clone this repository to a convenient directory.\u003c/li\u003e\n\u003cli\u003eNavigate into the \u003ccode\u003erest-state-fmri\u003c/code\u003edirectory and check that you have a Singularity definiton file \u003ccode\u003eSingularity\u003c/code\u003e and the directory \u003ccode\u003esrc\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConfirm that \u003ccode\u003esrc\u003c/code\u003e folder and all the files in \u003ccode\u003esrc\u003c/code\u003e have full read and write privileges. if not then \u003ccode\u003esudo chmod -R 777 src\u003c/code\u003e should accomplish this.\u003c/li\u003e\n\u003cli\u003eNow build the image as follows \u003ccode\u003esudo singularity build rest-state-fmri.simg Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-run-singularity-image\" class=\"anchor\" href=\"#run-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Singularity Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou can now run the pipeline as follows: \u003ccode\u003esingularity run nklab-reststate-fmri.simg /opt/rsfmri_python/bin/resting_pipeline.py --func PBIA6_26386_20140402_045154_93696_magnitude.nii --throwaway=4 --steps=2,3,4,5,6,7 -o PBIA6_26386_20140402_045154_93696 --sliceorder=odd --tr=5000\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eYou can also run FSL commands (e.g. flirt) directly as follows: \u003ccode\u003esingularity run --nv rest-state-fmri.simg /opt/fsl/bin/flirt ....\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-shell-into-singularity-image\" class=\"anchor\" href=\"#shell-into-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell into Singularity Image\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eYou can shell into the singularity image using: \u003ccode\u003esingularity shell rest-state-fmri.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example-commands-1\" class=\"anchor\" href=\"#example-commands-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample Commands\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-create-slice-timing-files-from-json-1\" class=\"anchor\" href=\"#create-slice-timing-files-from-json-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Slice Timing files from json\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esingularity run -B $PWD:/opt/data nklab-reststate-fmri.simg /opt/rsfmri_python/bin/make_fsl_stc.py fmri.json\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file-1\" class=\"anchor\" href=\"#run-pipeline-also-runs-sliding-window-with-window-30s-shift3s-using-custom-slice-timing-file-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun pipeline (also runs sliding window with window-30s, shift=3s) using custom slice timing file\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --rm -B $PWD:/opt/data nklab-reststate-fmri.simg /opt/rsfmri_python/bin/resting_pipeline.py --func /opt/data/fmri-std-pre.nii.gz -o restoutput --steps=1,2,3,4,5,6,7,8 --slidewin=30,3 --sliceorder=/opt/data/slicetimes.txt --slicetiming=time --tr=3000\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-references\" class=\"anchor\" href=\"#references\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003eM. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tclb_singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#tclb_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTCLB_singularity\u003c/h1\u003e\n\u003cp\u003eThis is highly experimental. This repo contains a singularity image and scripts intended to bootstrap development environment. While GPU support was tested and works, be aware that GPU+MPI will most likely not work in HPC environment out-of-the-box.\u003c/p\u003e\n\u003cp\u003eImage should contain all dependencies for full-futured TCLB.\u003c/p\u003e\n\u003cp\u003eHOWTO:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun the lolcow example, check if it works as intended\u003c/li\u003e\n\u003cli\u003epull image \u003ccode\u003esingularity pull --arch amd64 library://mdzik/tclb/tclb:latest\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eedit paths in \u0027startEnvironment.sourceMe\u0027\u003c/li\u003e\n\u003cli\u003esource It! :)\u003c/li\u003e\n\u003cli\u003ego to TCLB directory\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003escmd ./configure --disable-cuda --with-python --enable-double --enable-keepcode --enable-rinside\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emake XXX\u003c/code\u003e, this script overwrites make for whole session - you could run it from anywhere and still compile TCLB ;)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1562932368.0 + "updated_at": 1608196932.0 }, { "data_format": 2, - "description": "Singularity Image Recipes for connecting to Singularity Hub", + "description": null, "filenames": [ - "Singularity.py36", - "Singularity.devtoolset6", - "Singularity.dvs6_miniconda040512_py36_ml2", - "Singularity.intel_tf", - "Singularity.mpich33", - "Singularity.devtoolset8", - "Singularity.miniconda040512_py36_ml2", - "Singularity.dvs6_mcnda040614_py36_pytorch11", - "Singularity.dvs6_mpich33", - "Singularity.py36_ml_mpi33", - "Singularity.dvs4_cnda040512_py36", - "Singularity.dvs6_miniconda040512_py36_ml3", - "Singularity.mcnda040614_gcc7_py36", - "Singularity.dvs6_miniconda040512_py36_ml", - "Singularity.PyTorch_SparseConvNet", - "Singularity.hello_world", - "Singularity.py36_ml", - "Singularity.miniconda040512_py36_ml", - "Singularity.dvs6_py36_mpi33", - "Singularity.dvs8_cnda040512_py36", - "Singularity.miniconda3_theta", - "Singularity.miniconda040512_py36_ml3", - "Singularity.py36_ml_mpi33_hvd161", - "Singularity.dvs6_py36", - "Singularity.dvs6_py36_mpi33_ml", - "Singularity.mcnda040512_gcc7_py36" + "singularity/Singularity.base-dep", + "singularity/Singularity.base", + "singularity/Singularity.python3" ], - "full_name": "jtchilders/singularity_image_recipes", + "full_name": "LBJ-Wade/OSKAR", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-singularity_image_recipes\" class=\"anchor\" href=\"#singularity_image_recipes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity_image_recipes\u003c/h1\u003e\n\u003cp\u003eSingularity Image Recipes for connecting to Singularity Hub\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularityhello_world\" class=\"anchor\" href=\"#singularityhello_world\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity.hello_world\u003c/h1\u003e\n\u003cp\u003esimply demonstrates the basics of a container\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-singularitympichxx\" class=\"anchor\" href=\"#singularitympichxx\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity.mpichXX\u003c/h1\u003e\n\u003cp\u003eThis recipe demonstrates building MPICH into a container intended for an HPC. The MPICH built into the container should be replaced using the local system MPI libraries. The \u003ccode\u003esubmit.sh\u003c/code\u003e script demonstrates submitting a job to the Theta supercomputer at Argonne\u0027s Leadership Computing Facility.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b31f28bf12daa344a4541064866fa171f3f3989a53604cec781f6c15206daab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f4f78666f7264534b412f4f534b41522e7376673f7374796c653d666c61742d737175617265\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/OxfordSKA/OSKAR.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.3758491\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db055c7e33d6db9ce4f5f69f83628cd0827c9a0e13aa2aa78a369d938a4ef137/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333735383439312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3758491.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-oskar-a-gpu-accelerated-simulator-for-the-square-kilometre-array\" class=\"anchor\" aria-hidden=\"true\" href=\"#oskar-a-gpu-accelerated-simulator-for-the-square-kilometre-array\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOSKAR: A GPU-accelerated simulator for the Square Kilometre Array\u003c/h1\u003e\n\u003cp\u003eOSKAR has been designed to produce simulated visibility data from radio\ntelescopes containing aperture arrays, such as those envisaged for the\nSquare Kilometre Array.\u003c/p\u003e\n\u003cp\u003eA source code archive, and pre-built binary packages for Linux (using\nSingularity), macOS and Windows platforms are available to download from\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003ehttps://github.com/OxfordSKA/OSKAR/releases\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOSKAR is licensed under the terms of the 3-clause BSD License.\nPlease see the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file for details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity image\u003c/h3\u003e\n\u003cp\u003eA pre-built \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e SIF container image\nis available for Linux which can be used to run OSKAR command line\napplications or Python scripts directly, without needing to compile or install\nanything. For Singularity 3.0 or later, an application or script can be run\nusing the downloaded \u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003econtainer\u003c/a\u003e\nwith the \u003ccode\u003esingularity exec\u003c/code\u003e command, which takes the form:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec [flags] \u0026lt;container_path\u0026gt; \u0026lt;app_name\u0026gt; \u0026lt;arguments\u0026gt;...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse the \u003ccode\u003e--nv\u003c/code\u003e flag to enable NVIDIA GPU support in Singularity, if\napplicable.\u003c/p\u003e\n\u003cp\u003eNote also that Singularity will mount the home directory into the container by\ndefault, unless configured otherwise. If you have packages installed in your\nhome area that should be kept isolated from those in the container (for\nexample, because of conflicting packages or Python versions, or if you see\nother errors caused by trying to load wrong versions of shared libraries when\nstarting the container) then it may be necessary to disable this either by\nusing the \u003ccode\u003e--no-home\u003c/code\u003e flag, or re-bind the home directory in the container\nto somewhere other than your actual $HOME using the \u003ccode\u003e-H\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eAs an example, to run the application \u003ccode\u003eoskar_sim_interferometer\u003c/code\u003e\nwith a parameter file \u003ccode\u003esettings.ini\u003c/code\u003e and a container image file\n\u003ccode\u003eOSKAR-Python3.sif\u003c/code\u003e (both in the current directory) on a GPU use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --nv ./OSKAR-Python3.sif oskar_sim_interferometer settings.ini\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSimilarly, to run a Python script \u003ccode\u003esim_script.py\u003c/code\u003e that uses OSKAR:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --nv ./OSKAR-Python3.sif python3 sim_script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cp\u003eIf hardware acceleration is required, be sure to install appropriate GPU\ndrivers which are supported by the hardware manufacturer. Third-party graphics\ndrivers are unlikely to work.\u003c/p\u003e\n\u003cp\u003eWhen building from source, the only required dependency is\n\u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003eCMake \u0026gt;= 3.1\u003c/a\u003e.\nAll other dependencies are optional, but functionality will be\nlimited if these are not found by CMake.\n\u003cem\u003eNote that these dependencies are required only if building from source\u003c/em\u003e, not\nif using a \u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003epre-built package\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003eCMake \u0026gt;= 3.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://developer.nvidia.com/cuda-downloads\" rel=\"nofollow\"\u003eCUDA \u0026gt;= 7.0\u003c/a\u003e\nor OpenCL, required for GPU acceleration on supported hardware.\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://qt.io\" rel=\"nofollow\"\u003eQt 5\u003c/a\u003e,\nrequired to build the graphical user interface.\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://github.com/casacore/casacore\"\u003ecasacore \u0026gt;= 2.0\u003c/a\u003e,\nrequired to use CASA Measurement Sets.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePackages for these dependencies are available in the package repositories\nof many recent Linux distributions, including Debian and Ubuntu.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild commands\u003c/h3\u003e\n\u003cp\u003eTo build from source, either clone the repository using\n\u003ccode\u003egit clone https://github.com/OxfordSKA/OSKAR.git\u003c/code\u003e (for the current master\nbranch) or download and unpack the source archive, then:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir build\n$ cd build\n$ cmake [OPTIONS] ../path/to/top/level/source/folder\n$ make -j4\n$ make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen running the \u0027cmake\u0027 command a number of options can be specified:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e* -DCUDA_ARCH=\"\u0026lt;arch\u0026gt;\" (default: all)\n Sets the target architecture for the compilation of CUDA device code.\n \u0026lt;arch\u0026gt; must be one of either: 2.0, 2.1, 3.0, 3.2, 3.5, 3.7,\n 5.0, 5.2, 6.0, 6.1, 6.2, 7.0, 7.5,\n 8.0, 8.6 or ALL.\n ALL is for all currently supported architectures.\n Separate multiple architectures using semi-colons, if required.\n\n* -DCMAKE_INSTALL_PREFIX=\u0026lt;path\u0026gt; (default: /usr/local/)\n Path prefix used to install OSKAR (with make install).\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-advanced-build-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#advanced-build-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdvanced build options\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e* -DCASACORE_LIB_DIR=\u0026lt;path\u0026gt; (default: searches the system library paths)\n Specifies a location to search for the casacore libraries\n (libcasa_tables.so and others) if they are not in the system library path.\n\n* -DCASACORE_INC_DIR=\u0026lt;path\u0026gt; (default: searches the system include paths)\n Specifies a location to search for the casacore library headers if they\n are not in the system include path.\n This is the path to the top level casacore include folder.\n\n* -DCMAKE_PREFIX_PATH=\u0026lt;path\u0026gt; (default: None)\n Specifies a location to search for Qt 5 if it is not in a standard\n system path. For example, if using Homebrew on macOS, this may need\n to be set to /usr/local/opt/qt5/\n\n* -DFIND_CUDA=ON|OFF (default: ON)\n Can be used not to find or link against CUDA.\n\n* -DFIND_OPENCL=ON|OFF (default: OFF)\n Can be used not to find or link against OpenCL.\n OpenCL support in OSKAR is currently experimental.\n\n* -DNVCC_COMPILER_BINDIR=\u0026lt;path\u0026gt; (default: None)\n Specifies a nvcc compiler binary directory override. See nvcc help.\n This is likely to be needed only on macOS when the version of the\n compiler picked up by nvcc (which is related to the version of XCode\n being used) is incompatible with the current version of CUDA.\n Set this to \u0027clang\u0027 on macOS if using GCC to build the rest of OSKAR.\n\n* -DFORCE_LIBSTDC++=ON|OFF (default: OFF)\n If ON forces the use of libstdc++ with the Clang compiler.\n Used for controlling linking behaviour when using clang\n or clang-omp compilers with dependencies which may have been compiled\n against libstdc++\n\n* -DCMAKE_BUILD_TYPE=\u0026lt;release or debug\u0026gt; (default: release)\n Build in release or debug mode.\n\n* -DBUILD_INFO=ON|OFF (default: OFF)\n If ON enables the display of diagnostic build information when\n running CMake.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-unit-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnit tests\u003c/h4\u003e\n\u003cp\u003eAfter building from source, the unit tests should be run to make sure there\nare no problems with the build.\n(Note that pre-built packages do not include the unit tests.)\u003c/p\u003e\n\u003cp\u003eFrom the build directory, the unit tests can be run by typing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ctest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-python-interface\" class=\"anchor\" aria-hidden=\"true\" href=\"#python-interface\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython interface\u003c/h3\u003e\n\u003cp\u003eAfter installing OSKAR, the Python interface to it can be installed to\nmake it easier to use from Python scripts.\nStraightforward instructions for installation with \u003ccode\u003epip\u003c/code\u003e can be\n\u003ca href=\"python/README.md\"\u003efound in the python subdirectory\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-example-simulation\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample simulation\u003c/h3\u003e\n\u003cp\u003eThe example simulation described in the\n\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003edocumentation\u003c/a\u003e\ncan be run to check that a simple simulation behaves as expected.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1618543215.0 + "updated_at": 1660062120.0 }, { "data_format": 2, - "description": "Nextflow pipeline for HPO-based prioritization (GADO and Exomiser)", + "description": null, "filenames": [ - "singularity/Singularity.GADO-v1.0.4", - "singularity/Singularity.Exomiser-v12.1.0" + "Singularity" ], - "full_name": "edg1983/NF_HPO_prioritize", + "full_name": "uazhlt/pytorch-example", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hpo-prioritization-pipeline\" class=\"anchor\" href=\"#hpo-prioritization-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPO prioritization pipeline\u003c/h1\u003e\n\u003cp\u003eBased on HPO profiles and input files provided this pipeline run \u003ca href=\"https://www.nature.com/articles/s41467-019-10649-4\" rel=\"nofollow\"\u003eGADO\u003c/a\u003e and/or \u003ca href=\"https://github.com/exomiser/Exomiser\"\u003eExomiser\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-before-you-run\" class=\"anchor\" href=\"#before-you-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBefore you run\u003c/h2\u003e\n\u003cp\u003eBefore you can use the pipeline you need to install Exomiser, GADO and some supporting files\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exomiser\" class=\"anchor\" href=\"#exomiser\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExomiser\u003c/h3\u003e\n\u003cp\u003eYou can download the latest version of Exomiser from the \u003ca href=\"http://data.monarchinitiative.org/exomiser/latest/index.html\" rel=\"nofollow\"\u003eMonarch initiative FTP\u003c/a\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eUpdate \u003ccode\u003eexomiser_cli\u003c/code\u003e params in the \u003ccode\u003enextflow.config\u003c/code\u003e to point to the \u003ccode\u003e.jar\u003c/code\u003e file of exomiser CLI.\u003c/li\u003e\n\u003cli\u003eUpdate the \u003ccode\u003econfig/application.properties\u003c/code\u003e file to point to your exomiser data folder.\u003c/li\u003e\n\u003cli\u003eNote that the current configuration also use CADD score, so you need to have CADD score files installed as well and you need to configure the corresponding file location in \u003ccode\u003econfig/application.properties\u003c/code\u003e (or remove CADD from the template files in \u003ccode\u003econfig\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gado\" class=\"anchor\" href=\"#gado\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGADO\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eDownload the GADO cli 1.0.1 from the \u003ca href=\"https://github.com/molgenis/systemsgenetics/releases/download/v1.0.4/GadoCommandline-1.0.1-dist.zip\"\u003eofficial release\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload dataset files. You can find the links in the \u003ca href=\"https://github.com/molgenis/systemsgenetics/wiki/GADO-Command-line\"\u003eGADO github wiki\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUncompress the prediction matrix \u003ccode\u003e.zip\u003c/code\u003e file and rename files so that you have a folder (let say \u003ccode\u003eGADO_resources\u003c/code\u003e containing the following files:\n\u003cul\u003e\n\u003cli\u003ehpo_predictions_info.txt\u003c/li\u003e\n\u003cli\u003ehpo_predictions_genes.txt\u003c/li\u003e\n\u003cli\u003ehpo_predictions_matrix_spiked.cols.txt\u003c/li\u003e\n\u003cli\u003ehpo_predictions_matrix_spiked.rows.txt\u003c/li\u003e\n\u003cli\u003ehpo_predictions_matrix_spiked.dat\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDownload the HPO ontology \u003ccode\u003e.obo\u003c/code\u003e file from GADO wiki or directly from \u003ca href=\"http://purl.obolibrary.org/obo/hp.obo\" rel=\"nofollow\"\u003eHPO ontology\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen you need to update the following params in the \u003ccode\u003enextflow.config\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGADO_cli: path to your GADO cli \u003ccode\u003e.jar\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eGADO_datafolder: path to folder containing GADO files (\u003ccode\u003eGADO_resources\u003c/code\u003e in this example)\u003c/li\u003e\n\u003cli\u003eHPO_obofile: path to your \u003ccode\u003e.obo\u003c/code\u003e files\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eWhen everything is properly configured in \u003ccode\u003enextflow.config\u003c/code\u003e you can run the pipeline using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow main.nf --GADO --exomiser \\\n --HPO HPO_profiles.tsv \\\n --exomiser_input exomiser_input.tsv \\\n --exomiser_template config/template_GRCh38.yml \\\n --out results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNB\u003c/em\u003e There are current profiles for \u003ccode\u003esge\u003c/code\u003e and \u003ccode\u003eslurm\u003c/code\u003e in the config file, but you need to configure the queue names for your system\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input-files\" class=\"anchor\" href=\"#input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files\u003c/h2\u003e\n\u003cp\u003eOnly HPO profiles file is required for GADO, while also exomiser input is required for exomiser.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hpo-profiles\" class=\"anchor\" href=\"#hpo-profiles\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPO profiles\u003c/h3\u003e\n\u003cp\u003eThis is a tab-separated file without header containing 1 case per line, with case ID in column 1 and then 1 HPO term per column\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecase1 HP:00001 HP:000002\ncase2 HP:00003 HP:000004 HP:000006\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-exomiser-input\" class=\"anchor\" href=\"#exomiser-input\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExomiser input\u003c/h3\u003e\n\u003cp\u003eThis is a tab-separated file without header containing 1 case per line, with case ID, proband id, vcf file and ped file. \u003cstrong\u003eNB\u003c/strong\u003e \u003ccode\u003ecase ID\u003c/code\u003e must match case ID from the HPO profiles and \u003ccode\u003eproband id\u003c/code\u003e must match the id of proband sample in the VCF file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecase1 proband1 case1_family.vcf.gz case1.ped\ncase2 proband2 case2_family.vcf.gz case2.ped\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-change-exomiser-settings\" class=\"anchor\" href=\"#change-exomiser-settings\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChange exomiser settings\u003c/h2\u003e\n\u003cp\u003eThe exomiser annotation and filter settings are store in the \u003ccode\u003e.yml\u003c/code\u003e templated in the \u003ccode\u003econfig\u003c/code\u003e folder. The provided files will filter for protein-changing variants with population AF \u0026lt; 1% and use CADD, PP2 and SIFT scores for variant scoring. All possible segregation models are evaluated and hiPhive is used for HPO-based prioritization. You can change these template to change analysis settings for the Exomiser. Please refer to the \u003ca href=\"https://exomiser.github.io/Exomiser/manual/7/exomiser/\" rel=\"nofollow\"\u003eexomiser documentation\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the docker container\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eNOTE: This step is not necessary if you simply want to use an already published image to run the example code on the UA HPC.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile -t uazhlt/pytorch-example .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-verify-pytorch-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#verify-pytorch-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVerify PyTorch version\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it uazhlt/pytorch-example python -c \"import torch; print(torch.__version__)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-publish-to-dockerhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#publish-to-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublish to DockerHub\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eNOTE: This step is not necessary if you simply want to use an already published image to run the example code on the UA HPC.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# login to dockerhub registry\ndocker login --username=yourdockerhubusername --email=youremail@domain.com\n\ndocker push org/image-name:taghere\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-a-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-a-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding a Singularity image\u003c/h2\u003e\n\u003cp\u003eBuilding a Singularity image from a def file requires sudo on a Linux system. In this tutorial, we avoid discussing details on installing Singularity. If you\u0027re feeling adventurous, take a look at \u003ca href=\"./Singularity\"\u003ethe example def file in this repository\u003c/a\u003e and the official documentation:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alternatives\" class=\"anchor\" aria-hidden=\"true\" href=\"#alternatives\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternatives\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-cloud-builds\" class=\"anchor\" aria-hidden=\"true\" href=\"#cloud-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCloud builds\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eGitHub actions:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/github-ci/blob/master/.github/workflows/go.yml\"\u003eExample GitHub Workflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://help.github.com/en/actions/automating-your-workflow-with-github-actions/virtual-environments-for-github-hosted-runners#supported-runners-and-hardware-resources\"\u003eGitHub-hosted runners\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-vms\" class=\"anchor\" aria-hidden=\"true\" href=\"#vms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVMs\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html#singularity-vagrant-box\" rel=\"nofollow\"\u003eVagrant box\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-docker---singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker---singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker -\u0026gt; Singularity\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/singularityhub/docker2singularity\"\u003e\u003ccode\u003edocker2singularity\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-retrieving-a-published-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#retrieving-a-published-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRetrieving a published Singularity image\u003c/h2\u003e\n\u003cp\u003eInstead of building from scratch, we\u0027ll focus on a shortcut that simply wraps docker images published to DockerHub.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull uazhlt-pytorch-example.sif docker://uazhlt/pytorch-example:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-hpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC\u003c/h1\u003e\n\u003cp\u003eIf you intend to test out \u003ca href=\"./example\"\u003ethe PyTorch example included here\u003c/a\u003e, you\u0027ll want to clone this repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/ua-hlt-program/pytorch-example.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-singularity-in-an-interactive-pbs-job\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-singularity-in-an-interactive-pbs-job\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Singularity in an interactive PBS job\u003c/h2\u003e\n\u003cp\u003eNext, we\u0027ll request an interactive job (tested on El Gato):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eqsub -I \\\n-N interactive-gpu \\\n-W group_list=mygroupnamehere \\\n-q standard \\\n-l select=1:ncpus=2:mem=16gb:ngpus=1 \\\n-l cput=3:0:0 \\\n-l walltime=1:0:0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e_NOTE: If you\u0027re unfamiliar with \u003ccode\u003eqsub\u003c/code\u003e and the many options in the command above seem puzzling, you can find answers by checking out the manual via \u003ccode\u003eman qsub\u003c/code\u003e _\u003c/p\u003e\n\u003cp\u003eIf the cluster isn\u0027t too busy, you should soon see a new prompt formatted something like \u003ccode\u003e[netid@gpu\\d\\d ~]\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNow we\u0027ll run the singularity image we grabbed earlier. Before that, though, let\u0027s ensure we\u0027re using the correct version of Singularity and that the correct CUDA version is available to Singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity/3.2.1\nmodule load cuda10/10.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we\u0027re finally ready to run the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv --no-home /path/to/your/uazhlt-pytorch-example.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you ran into an error, check to see if you replaced \u003ccode\u003e/path/to/your/\u003c/code\u003e with the correct path to \u003ccode\u003euazhlt-pytorch-example.sif\u003c/code\u003e before executing the command.\u003c/p\u003e\n\u003cp\u003eWe\u0027re now in our Singularity container! If everything went well, we should be able to see the gpu:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003envidia-smi\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see output like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 418.87.01 Driver Version: 418.87.01 CUDA Version: 10.1 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n|===============================+======================+======================|\n| 0 Tesla K20Xm On | 00000000:8B:00.0 Off | 0 |\n| N/A 17C P8 18W / 235W | 0MiB / 5700MiB | 0% Default |\n+-------------------------------+----------------------+----------------------+\n\n+-----------------------------------------------------------------------------+\n| Processes: GPU Memory |\n| GPU PID Type Process name Usage |\n|=============================================================================|\n| No running processes found |\n+-----------------------------------------------------------------------------+\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSuccess (I hope)! Now let\u0027s try running PyTorch on the GPU with batching...\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-pytorch-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#pytorch-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePyTorch example\u003c/h1\u003e\n\u003cp\u003eThe Pytorch example code can be found under \u003ca href=\"./example\"\u003e\u003ccode\u003eexample\u003c/code\u003e\u003c/a\u003e. The data used in this example comes from from Delip Rao and Brian MacMahan\u0027s \u003cem\u003eNatural Language Processing with PyTorch\u003c/em\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/joosthub/PyTorchNLPBook/tree/master/data#surnames\"\u003ehttps://github.com/joosthub/PyTorchNLPBook/tree/master/data#surnames\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe dataset relates surnames to nationalities. Our version (minor modifications) is nested under \u003ca href=\"./examples/data\"\u003eexamples/data\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003etrain.py\u003c/code\u003e houses a command line program for training a classifier. The following invocation will display the tool\u0027s help text:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython train.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe simple model architecture operates is based on that of deep averaging networks (DANs; see \u003ca href=\"https://aclweb.org/anthology/P15-1162/\" rel=\"nofollow\"\u003ehttps://aclweb.org/anthology/P15-1162/\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eReading through train.py you can quickly see how the code is organized. Some parts (ex. \u003ccode\u003etorchtext\u003c/code\u003e data loaders) may be unfamiliar to you.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-next-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#next-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNext steps\u003c/h1\u003e\n\u003cp\u003eNow that you\u0027ve managed to run some example PyTorch code, there are many paths forward:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExperiment with using pretrained subword embeddings (both fixed and trainable). Do you notice any improvements in performance/faster convergence?\u003c/li\u003e\n\u003cli\u003eTry improving or replacing the naive model defined under \u003ccode\u003emodels.py\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAdd an evaluation script for a trained model that reports macro P, R, and F1. Feel free to use \u003ccode\u003escikit-learn\u003c/code\u003e\u0027s classification report.\u003c/li\u003e\n\u003cli\u003eAdd an inference script to classify new examples.\u003c/li\u003e\n\u003cli\u003eMonitor validation loss to and stop training if you begin to overfit.\u003c/li\u003e\n\u003cli\u003eAdapt the interactive PBS task outlined above to a PBS script that you can submit to the HPC.\u003c/li\u003e\n\u003cli\u003eAddress the class imbalance in the data through downsampling, class weighting, or another technique of your choosing.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1643390805.0 + "updated_at": 1576565865.0 }, { "data_format": 2, - "description": "Singularity container with Xfce desktop to support OOD apps.", + "description": "Solving large-scale sparse eigenvalue problems in Haskell (wrapper for PRIMME library)", "filenames": [ "Singularity" ], - "full_name": "mcw-rcc/xfce-ood-desktop", + "full_name": "twesterhout/primme-hs", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-xfce-ood-desktop\" class=\"anchor\" href=\"#xfce-ood-desktop\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003exfce-ood-desktop\u003c/h1\u003e\n\u003cp\u003eSingularity container with Xfce desktop to support OOD apps.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-primme-hs-\" class=\"anchor\" aria-hidden=\"true\" href=\"#primme-hs-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eprimme-hs \u003ca href=\"https://github.com/twesterhout/primme-hs/actions\"\u003e\u003cimg src=\"https://github.com/twesterhout/primme-hs/workflows/CI/badge.svg\" alt=\"GitHub CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca href=\"LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33b2802c547e7ae15da879c987ba9b119229cada7c53335dd710d7481ede78f8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4253442d2d332d2d436c617573652d626c75652e737667\" alt=\"BSD-3-Clause license\" data-canonical-src=\"https://img.shields.io/badge/license-BSD--3--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eSolving large-scale sparse eigenvalue problems in Haskell!\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, - "topics": [], - "updated_at": 1598115007.0 + "topics": [ + "haskell", + "linear-algebra", + "high-performance", + "numerical-methods", + "eigenvalueproblems" + ], + "updated_at": 1665067408.0 }, { "data_format": 2, - "description": "Open source, scalable acoustic classification - designed for ecology and conservation", + "description": "Repository contains the imaging pipelines used in CSAI XNAT", "filenames": [ - "Singularity" + "Singularity_process_v3", + "Singularity_ants_v5", + "Singularity_ants_v4", + "Singularity_process_v4" ], - "full_name": "thomastroyan/opensoundscape", + "full_name": "KarthikMasi/CSAI-XNAT", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1681\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-opensoundscape\" class=\"anchor\" href=\"#opensoundscape\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenSoundscape\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start-guide\" class=\"anchor\" href=\"#quick-start-guide\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start guide\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eNote: installation instructions are for MacOS systems only.\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.anaconda.com/download/#macos\" rel=\"nofollow\"\u003eAnaconda for Python 3\u003c/a\u003e and \u003ca href=\"https://brew.sh/\" rel=\"nofollow\"\u003eHomeBrew\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse HomeBrew to install a few other packages: \u003ccode\u003ebrew install libsamplerate mongodb git wget\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet up the Python environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda install -c conda-forge python=3.6 pip=18.0 pandas=0.23.4 numpy=1.15.1 matplotlib=2.1.2 docopt=0.6.2 scipy=1.0.0 scikit-image=0.13.1 pymongo=3.4.0 progressbar2=3.36.0 pytest=3.6.1 opencv=3.4.3 scikit-learn=0.20.0\n \n pip install git+git://github.com/gregorias/samplerate@master\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload data files, the \u003ca href=\"https://datadryad.org/resource/doi:10.5061/dryad.j2t92\" rel=\"nofollow\"\u003eCLO-43SD-AUDIO\u003c/a\u003e dataset:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd ~/Downloads\n wget \"https://datadryad.org/bitstream/handle/10255/dryad.111783/CLO-43SD-AUDIO.tar.gz\"\n tar -xzf CLO-43SD-AUDIO.tar.gz\n rm CLO-43SD-AUDIO.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload our training \u0026amp; prediction split of a subset of the CLO-43SD dataset:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd ~/Downloads/CLO-43SD-AUDIO/\n wget https://raw.github.com/rhine3/opso-support/master/clo-43sd-train-small.csv\n wget https://raw.github.com/rhine3/opso-support/master/clo-43sd-predict-small.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload OpenSoundscape:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e mkdir ~/Code \u0026amp;\u0026amp; cd ~/Code\n git clone https://github.com/jkitzes/opensoundscape\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload our config file, \u003ccode\u003eopso-test-small.ini\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd ~/Code/opensoundscape/\n wget https://raw.github.com/rhine3/opso-support/master/opso-test-small.ini \n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEdit the \u003ccode\u003e.ini\u003c/code\u003e to reflect the absolute path of your \u003ccode\u003eDownloads\u003c/code\u003e folder, e.g. with \u003ccode\u003evim\u003c/code\u003e: \u003ccode\u003evim opso-test-small.ini\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStart the MongoDB daemon in another terminal: \u003ccode\u003emongod --config /usr/local/etc/mongod.conf\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun OpenSoundscape:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./opensoundscape.py init -i opso-test-small.ini \n ./opensoundscape.py spect_gen -i opso-test-small.ini \u0026gt; spect-gen-output-small.txt\n ./opensoundscape.py model_fit -i opso-test-small.ini \u0026gt; model-fit-output-small.txt\n ./opensoundscape.py predict -i opso-test-small.ini \u0026gt; predict-output-small.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1545330417.0 + "updated_at": 1591882985.0 }, { "data_format": 2, - "description": "pygments for rust", + "description": "A summation of the work I did at Argonne national laboratory during the summer of 2018. This includes the micro_osu_benchmarks I ran on theta, and the file access tests I ran on theta.", "filenames": [ - "tests/examplefiles/singularity/Singularity" + "Osu_micro-benchmarks/Singularity.derived", + "touch-file_tests/Singularity.massfile" ], - "full_name": "Alignof/pygments-rs", + "full_name": "spencer-williams/sgww_argonne", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-sgww_argonne_summer2018\" class=\"anchor\" aria-hidden=\"true\" href=\"#sgww_argonne_summer2018\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esgww_argonne_summer2018\u003c/h1\u003e\n\u003cp\u003eA summation of the work I did at Argonne national laboratory during the summer of 2018. This includes the micro_osu_benchmarks I ran on theta, and the file access tests I ran on theta.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1641458628.0 + "updated_at": 1646169012.0 }, { "data_format": 2, - "description": null, + "description": "graph clustering toolkit", "filenames": [ - "Singularity.bamdb" + "singularity/Singularity" ], - "full_name": "D-Lo/bamdb", + "full_name": "Lizhen0909/graph_clustering_toolkit", "latest_release": null, + "readme": "\u003ch2\u003e\u003ca id=\"user-content-graph-clustering-toolkit\" class=\"anchor\" aria-hidden=\"true\" href=\"#graph-clustering-toolkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGraph Clustering Toolkit\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-hidden=\"true\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary:\u003c/h3\u003e\n\u003cp\u003eThe toolkit collects many academic graph clustering programs and make them avaliable as package. Docker image is provided for easy access.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation:\u003c/h3\u003e\n\u003cp\u003eUse docker is convenient as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull lizhen0909/graph_clustering_toolkit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, please refer to \u003ca href=\"https://lizhen0909.github.io/graph_clustering_toolkit/\" rel=\"nofollow\"\u003eonline document\u003c/a\u003e for a full description\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage:\u003c/h3\u003e\n\u003cp\u003eStart python from docker:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm lizhen0909/graph_clustering_toolkit python\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the script from the command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egct\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egct\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erandom_dataset\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e#create a random graph use LFR generator\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eds\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003erandom_dataset\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003egenerate_undirected_unweighted_random_graph_LFR\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"random_graph\"\u003c/span\u003e, \\\n \u003cspan class=\"pl-v\"\u003eN\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e128\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ek\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emaxk\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.2\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eminc\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# run pScan graph algorithm\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epscan_clustering\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egct\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003escan_pScan\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"get_start_pscan\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eds\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee more to visit \u003ca href=\"https://lizhen0909.github.io/graph_clustering_toolkit/usage/usage.html\" rel=\"nofollow\"\u003eonline usage\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation:\u003c/h3\u003e\n\u003cp\u003ePlease cite \u003ca href=\"https://arxiv.org/abs/2005.04806\" rel=\"nofollow\"\u003eComparison and Benchmark of Graph Clustering Algorithms\u003c/a\u003e for this work.\u003c/p\u003e\n\u003cp\u003eFor individual algorithms, see \u003ca href=\"https://lizhen0909.github.io/graph_clustering_toolkit/usage/pydoc_alg.html\" rel=\"nofollow\"\u003eAlgorithms\u003c/a\u003e for their publications.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1639504182.0 + "updated_at": 1633850758.0 }, { "data_format": 2, "description": null, "filenames": [ - "imaging/nipy/Singularity" + "src/hpccm_containers/Singularity.template", + "src/hpccm_containers/libertem/Singularity.libertem", + "src/hpccm_containers/libertem/Singularity.libertem-0.4.0", + "src/hpccm_containers/libertem/Singularity.libertem-0.5.0", + "src/hpccm_containers/libertem/Singularity.libertem-0.7.0", + "src/hpccm_containers/libertem/Singularity.libertem-0.2.2", + "src/hpccm_containers/libertem/Singularity.libertem-0.4.1", + "src/hpccm_containers/libertem/Singularity.libertem-0.8.0", + "src/hpccm_containers/libertem/Singularity.libertem-0.5.1", + "src/hpccm_containers/libertem/Singularity.libertem-0.9.0", + "src/hpccm_containers/libertem/Singularity.libertem-0.6.0", + "src/hpccm_containers/macs/Singularity.macs", + "src/hpccm_containers/nullarbor/Singularity.nullarbor", + "src/hpccm_containers/seqtk/Singularity.seqtk", + "src/hpccm_containers/python/Singularity.python", + "src/hpccm_containers/shift/Singularity.shift", + "src/hpccm_containers/minc-toolkit-v2/Singularity.minc-toolkit-v2", + "src/hpccm_containers/octopus/Singularity.octopus", + "src/hpccm_containers/alphafold/Singularity.alphafold-hpccm", + "src/hpccm_containers/alphafold/Singularity.alphafold", + "src/hpccm_containers/ccp-em/Singularity.ccp-em", + "src/hpccm_containers/caffe-unet/Singularity.caffe-unet", + "src/hpccm_containers/auto07p/Singularity.auto07p", + "src/hpccm_containers/salmonte/Singularity.salmonte", + "src/hpccm_containers/scipion/Singularity.scipion", + "src/hpccm_containers/py4dstem/Singularity.py4dstem", + "src/hpccm_containers/perfsonar/Singularity.perfsonar", + "src/hpccm_containers/mpi-test/Singularity.mpi-test", + "src/hpccm_containers/pymol/Singularity.pymol", + "src/hpccm_containers/biogrinder/Singularity.biogrinder", + "src/hpccm_containers/dristhi/Singularity.dristhi", + "src/hpccm_containers/anaconda/Singularity.anaconda", + "src/hpccm_containers/others/Singularity.template", + "src/hpccm_containers/kraken2/Singularity.kraken2", + "src/hpccm_containers/sourcetracker/Singularity.sourcetracker", + "src/hpccm_containers/cp2k/Singularity.cp2k", + "src/hpccm_containers/base/Singularity.base", + "src/hpccm_containers/mashtree/Singularity.mashtree", + "src/hpccm_containers/audacity/Singularity.audacity", + "src/hpccm_containers/zonation/Singularity.zonation", + "src/hpccm_containers/dwarfs/Singularity.dwarfs", + "src/hpccm_containers/prokka/Singularity.prokka", + "src/hpccm_containers/minerl/Singularity.minerl", + "src/hpccm_containers/tensorrt/Singularity.tensorrt", + "src/hpccm_containers/openmpi/Singularity.hybrid-hpccm", + "src/hpccm_containers/openmpi/Singularity.hybrid", + "src/hpccm_containers/fsl/Singularity.fsl", + "src/hpccm_containers/crisprdetect/Singularity.crisprdetect", + "src/hpccm_containers/fastsurfer/Singularity.fastsurfer", + "src/hpccm_containers/chimerax/Singularity.chimerax", + "src/hpccm_containers/haystack_bio/Singularity.haystack_bio-0.5.0", + "src/hpccm_containers/haystack_bio/Singularity.haystack_bio-0.5.5", + "src/hpccm_containers/haystack_bio/Singularity.haystack_bio", + "src/hpccm_containers/jbrowse/Singularity.jbrowse", + "src/hpccm_containers/cfdem/Singularity.cfdem" ], - "full_name": "andyrevell/docker_GitHub", + "full_name": "0luhancheng0/hpccm-containers", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cntdocker\" class=\"anchor\" href=\"#cntdocker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCNTdocker\u003c/h1\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-about\" class=\"anchor\" href=\"#about\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eDockerfiles to create Docker images used by the CNT at the university of Pennsylvania\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-directory-contents-explanation\" class=\"anchor\" href=\"#directory-contents-explanation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory contents explanation\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-eeg\" class=\"anchor\" href=\"#eeg\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eEEG\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common EEG analysis tools. Usually python 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eechobase\u003c/strong\u003e: Dockerfiles used to create images that can calculate functional connectivity of EEG\nAlso has ieegpy python package used to interface with iEEG.org\nEchobase code is from \u003ca href=\"https://github.com/andyrevell/paper001\"\u003ehttps://github.com/andyrevell/paper001\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUbuntu 18.04\nPython 2.7 and Python 3.6\nNumpy 1.18.4\npandas 1.0.3\nscipy 1.4.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-imaging\" class=\"anchor\" href=\"#imaging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eImaging\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common MRI analysis tools.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n Python 2.7, Python 3.6, Python 3.7\n dcm2niix\n dsistudio\n ANTS\n Freesurfer\n FSL 6.0.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ml\" class=\"anchor\" href=\"#ml\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eml\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common machine learning tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ewavenet\u003c/strong\u003e: Dockerfile to create compatible dependencies to use with Goodgle Deepmind wavenet paper\n\u003ca href=\"https://deepmind.com/blog/article/wavenet-generative-model-raw-audio\" rel=\"nofollow\"\u003eWavenet blog\u003c/a\u003e\n\u003ca href=\"https://arxiv.org/pdf/1609.03499.pdf\" rel=\"nofollow\"\u003eWavenet paper\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n tensorflow 1.0.0\n pandas 0.19.2\n librosa 0.5.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eTensorflow_2.1\u003c/strong\u003e: Dockerfile to create compatible dependencies to with tensorflow 2.1\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n tensorflow 2.1\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpccm-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpccm-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPCCM containers\u003c/h1\u003e\n\u003cp\u003eThis repository contains a set python scripts that build containers (mostly in singularity) from Nvidia\u0027s \u003ca href=\"https://github.com/NVIDIA/hpc-container-maker\"\u003eHPC Container Makers (HPCCM)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that only the default arguments are tested. And those recipe with suffix \u003ccode\u003e.wip\u003c/code\u003e are not tested.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ewget\nflit\nfire\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eflit install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-update-recipes\" class=\"anchor\" aria-hidden=\"true\" href=\"#update-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUpdate recipes\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ecd src/hpccm_containers\n./update-recipe.sh\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1600370006.0 + "updated_at": 1676345959.0 }, { "data_format": 2, - "description": "Singularity img : R, rcontroll and calibration packages", + "description": "Deprecated repo. If you think you anything need from this, look at quip-docker and scream if it\u0027s not there", "filenames": [ "Singularity" ], - "full_name": "gsalzet/singularity-r-TROLL", - "latest_release": "0.0.2", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-rcontroll-singularity-container\" class=\"anchor\" href=\"#rcontroll-singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ercontroll Singularity container\u003c/h1\u003e\n\u003cp\u003eSalzet Guillaume\nFebruary 28, 2022\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eR, rcontroll and calibration packages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepository based on public template\n\u003ca href=\"https://github.com/sylvainschmitt/singularity-template\"\u003e\u003ccode\u003esylvainschmitt/singularity-template\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis container includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eR\u003c/code\u003e 4.1.2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ercontroll\u003c/code\u003e 0.1.0\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etidyverse\u003c/code\u003e 1.3.1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esf\u003c/code\u003e 1.0-5\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esp\u003c/code\u003e 1.4-6\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ehetGP\u003c/code\u003e 1.1.4\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecoda\u003c/code\u003e 0.19-4\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eentropart\u003c/code\u003e 1.6-8\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efitdistrplus\u003c/code\u003e 1.1-6\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eForestGapR\u003c/code\u003e 0.1.6\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elhs\u003c/code\u003e 1.1.3\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eKrigR\u003c/code\u003e 0.1.2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLoggingLab\u003c/code\u003e 0.0.0.9003\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity container based on the recipe:\n\u003ca href=\"https://github.com/gsalzet/singularity-r-TROLL/blob/main/Singularity\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eImage singularity (V\u0026gt;=3.6.4) is automatically test and built and\npushed on the registry using\n\u003ca href=\"https://github.com/gsalzet/singularity-template/blob/main/.github/workflows/test.yml\"\u003etest.yml\u003c/a\u003e\n\u0026amp;\n\u003ca href=\"https://github.com/gsalzet/singularity-template/blob/main/.github/workflows/builder.yml\"\u003ebuilder.yml\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eInitial bootstrap :\n\u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003e\u003ccode\u003edocker://ubuntu:18:04\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ebuild\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build Singularity TROLL_utilities.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003epull\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull https://github.com/gsalzet/singularity-template/releases/download/0.0.2/gsalzet-singularity-r-TROLL.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esnakemake\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s1\"\u003esingularity\u003c/span\u003e: \n \u003cspan class=\"pl-s\"\u003e\"https://github.com/gsalzet/singularity-template/releases/download/0.0.2/gsalzet-singularity-r-TROLL.latest.sif\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "libAtoms/docker-quip-base", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-quip-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#quip-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003equip-base\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/libAtoms/docker-quip-base\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a85436fd275dfe3b0867b90fdc6c7637a2d9df2d26fccc8d9f42b2e1f09becba/68747470733a2f2f7472617669732d63692e6f72672f6c696241746f6d732f646f636b65722d717569702d626173652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/libAtoms/docker-quip-base.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/libAtoms/docker-quip-base.svg?branch=master\" rel=\"nofollow\"\u003ehttps://travis-ci.org/libAtoms/docker-quip-base.svg?branch=master\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA Docker image with a scientific stack that is used for building \u003ccode\u003eQUIP\u003c/code\u003e.\nThe image is hosted (and automatically built) on Docker hub as\n\u003ca href=\"https://hub.docker.com/r/libatomsquip/quip-base/\" rel=\"nofollow\"\u003elibatomsquip/quip-base\u003c/a\u003e.\nYou probably don\u0027t want to use this image directly, instead look for\none of the QUIP images on \u003ca href=\"https://hub.docker.com/u/libatomsquip/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/u/libatomsquip/\u003c/a\u003e,\nprobably \u003ca href=\"https://hub.docker.com/r/libatomsquip/quip/\" rel=\"nofollow\"\u003elibatomsquip/quip\u003c/a\u003e.\nor use it in your \u003ccode\u003eFROM\u003c/code\u003e line. See also:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/libAtoms/QUIP\"\u003ehttps://github.com/libAtoms/QUIP\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/libAtoms/QUIP/tree/public/docker\"\u003ehttps://github.com/libAtoms/QUIP/tree/public/docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.libatoms.org\" rel=\"nofollow\"\u003ehttps://www.libatoms.org\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cp\u003eThis image does not contain QUIP, but everything needed to build it\nplus many tools and codes that we find useful.\u003c/p\u003e\n\u003cp\u003eStack contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 2.7 image (based on Debian)\u003c/li\u003e\n\u003cli\u003eBuild tools (gcc, gfortran)\u003c/li\u003e\n\u003cli\u003eOpenMP compiled version of OpenBLAS as default maths libraries\u003c/li\u003e\n\u003cli\u003eNumpy, SciPy, Matplotlib, ase...\u003c/li\u003e\n\u003cli\u003eJulia in \u003ccode\u003e/opt/julia\u003c/code\u003e with IJulia, PyCall, PyPlot, JuLIP...\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003cp\u003eThe image includes interatomic potentials in \u003ccode\u003e/opt/share/potentials\u003c/code\u003e\npublished on \u003ca href=\"http://www.libatoms.org/Home/DataRepository\" rel=\"nofollow\"\u003ehttp://www.libatoms.org/Home/DataRepository\u003c/a\u003e which has Gaussian\nApproximation Potentials for:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTungsten\u003c/li\u003e\n\u003cli\u003eIron\u003c/li\u003e\n\u003cli\u003eWater\u003c/li\u003e\n\u003cli\u003eAmorphous carbon\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eTo make or request changes, open a merge request or issue in the\n\u003ca href=\"https://github.com/libAtoms/docker-quip-base\"\u003eGitHub repository\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePackages should be added to where the usual istallation commands\n(e.g. \u003ccode\u003eapt-get\u003c/code\u003e, \u003ccode\u003epip\u003c/code\u003e, ...) are in the Dockerfile, with the exception\nthat Julia pacakes are listed at the beginning of the Julia section.\u003c/p\u003e\n\u003cp\u003eSmall software package builds can be added at the end of the Dockerfile.\nLarger software applications are included in the\n\u003ca href=\"https://hub.docker.com/r/libatomsquip/quip-base-software/\" rel=\"nofollow\"\u003elibatomsquip/quip-base-software\u003c/a\u003e\nimage in the \u003ccode\u003eSoftware\u003c/code\u003e subdirectory.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 15, "topics": [], - "updated_at": 1646068884.0 + "updated_at": 1674927515.0 }, { "data_format": 2, - "description": "docker and singularity containers for R", + "description": "Code for Sietse Thesis", "filenames": [ - "images/tinytex_4.2.0/Singularity.def", - "images/cmdstanr_4.2.0/Singularity.def", - "images/rstan_4.2.0/Singularity.def", - "images/radian-ml_4.2.0/Singularity.def", - "images/radian_4.2.0/Singularity.def" + "Singularity" ], - "full_name": "mattocci27/r-containers", + "full_name": "sietse93/Thesis", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-docker-and-singularity-images-for-r\" class=\"anchor\" href=\"#docker-and-singularity-images-for-r\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker and singularity images for R\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-images\" class=\"anchor\" href=\"#images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003edocker\u003c/th\u003e\n\u003cth\u003esingularity\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/rstan\" rel=\"nofollow\"\u003erstan\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://cloud.sylabs.io/library/mattocci27/default/rstan\" rel=\"nofollow\"\u003erstan\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eadds rstan on \u003ca href=\"https://hub.docker.com/r/rocker/geospatial\" rel=\"nofollow\"\u003egeospatial\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/radian\" rel=\"nofollow\"\u003eradian\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eadds radian and fonts on \u003ca href=\"https://hub.docker.com/r/rocker/rstudio\" rel=\"nofollow\"\u003erstudio\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/radian-ml\" rel=\"nofollow\"\u003eradian-ml\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eadds radian and fonts on \u003ca href=\"https://hub.docker.com/r/rocker/ml\" rel=\"nofollow\"\u003eml\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/rmd-light\" rel=\"nofollow\"\u003ermd-light\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eR markdown + TinyTex + pandoc-crossref without Rstudio\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/repository/docker/mattocci/cmdstanr\" rel=\"nofollow\"\u003ecmdstanr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eadds cmdstanr on \u003ca href=\"https://hub.docker.com/r/rocker/ml\" rel=\"nofollow\"\u003eml\u003c/a\u003e (GPU supported)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-push-to-a-private-repository\" class=\"anchor\" href=\"#push-to-a-private-repository\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePush to a private repository\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003epush_to_pr.sh \u0026lt;r-version\u0026gt; \u0026lt;ip\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escripts/push_to_pr.sh 4.1.3 xxx.xxx.xx.xx:xxx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003escripts/pull_from_pr.sh 4.2.0 xxxx\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-thesis\" class=\"anchor\" aria-hidden=\"true\" href=\"#thesis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThesis\u003c/h1\u003e\n\u003cp\u003eEvaluating SLAM in urban dynamic environments\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-experiments-in-carla\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-experiments-in-carla\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning experiments in CARLA\u003c/h2\u003e\n\u003cp\u003eDownload compiled version of CARLA. Two terminals required: a server and a client.\u003c/p\u003e\n\u003cp\u003eActivate server with this bash script in the compiled game directory. Run Carla server like this\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./CarlaServer.sh Town01 -windowed -ResX=600 -ResY=600\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure you loaded the correct town.\u003c/p\u003e\n\u003cp\u003eSimulation code are in directory carla0.9.4.\u003c/p\u003e\n\u003cp\u003eNote that everything works definitely in python 2.7.\u003c/p\u003e\n\u003cp\u003eIMPORTANT everything is dependent on this strict convention. Each name is dependent on town number, starting location, some dynamic varbiable. e.g. in directory stuckbehindvan T1_SL27_d15, means 15m distance to van in front. T1_SL27_d10 in directory VansOppositeRoad means 10 vehicles are spawned. The complete directory system will be based on this. So all converted files (png to rosbag, txt, json whatever) will be saved to the correct directory.\u003c/p\u003e\n\u003cp\u003eFurthermore, the main function of the scripts usually describe what is simulated. If you want to simulate a single trajectory/condition. Change the main function. If you want to simulate static conditions, this is usually possible in the stuckbehindvan main simulation scripts.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-specific-scenarios\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-specific-scenarios\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun specific scenarios\u003c/h3\u003e\n\u003cp\u003eTo run simulation of stuck behind van, there is a completely automated script which runs all scenarios in town 1 and writes out all stereo images to disk. Change values of T# accordingly in the name to simulate different town:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython StuckBehindVanAutomaticT1.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor VansOppositeRoad the following script only works for town01 and town02. Town03 uses different road_ids numbering so it doesn\u0027t work with this script.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython VansOppositeRoad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the end you will the scenario directory will have a ground truth txt file and stereo images .png files\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-convert-png-to-rosbag\" class=\"anchor\" aria-hidden=\"true\" href=\"#convert-png-to-rosbag\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConvert .png to rosbag\u003c/h2\u003e\n\u003cp\u003echange in the main function which files you want to convert.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eStereo2RosbagFunc.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the end scenario directory will have a rosbag file.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-orb_slam2\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-orb_slam2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning ORB_SLAM2\u003c/h2\u003e\n\u003cp\u003eSo if you want to run ORB SLAM2, download it from github and put the files that from this repository in the correct folder from ORB_SLAM2 and the recompile it. There is a bash script which shows how to run it. This also records everything.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./automatic_orb_record.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the end scenario directory will have a number of pose estimation files .txt\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data-conversion\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-conversion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData conversion\u003c/h2\u003e\n\u003cp\u003eA lot of data needs to be converted and there needs to be this balance between automating and flexibility. Therefore the following name convention is used:\nmain_... are the scripts that utilizes classes and functions\nclass_... describe the classes that are used in the main functions\nfunc_... describe the functions that are used by classes and main scripts.\u003c/p\u003e\n\u003cp\u003ethere is a mode convention throughout this pipeline:\n\"SLAM\" -\u0026gt; Conventional ORB SLAM\n\"VO\" -\u0026gt; bypassed loop closure ORB SLAM\n\"MC\" -\u0026gt; map point culling bypassed\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-convert-txt-files-into-json-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#convert-txt-files-into-json-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConvert txt files into json files\u003c/h3\u003e\n\u003cp\u003eJSON files contains the data but in the same reference frame. Change the main function to specify which files you want to convert.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython main_ConvertRefFrame.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the json describes a class described in python script: class_ConvertRefFrame.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-plot-to-visualize-what-is-happening\" class=\"anchor\" aria-hidden=\"true\" href=\"#plot-to-visualize-what-is-happening\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlot to visualize what is happening.\u003c/h3\u003e\n\u003cp\u003eVisualizes trajectory in 2D, euler angles, the whole mikmak. Also the relative pose error over a small distance.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython main_InspectData.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-plot-the-performance\" class=\"anchor\" aria-hidden=\"true\" href=\"#plot-the-performance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePlot the performance\u003c/h3\u003e\n\u003cp\u003ethe python script class_ScenarioLocationPerformance.py converts the json files to usable classes that describe the performance.\u003c/p\u003e\n\u003cp\u003eexample of how to use them in script Results_Scenario1_SLAM.py\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 0, "topics": [], - "updated_at": 1653138846.0 + "updated_at": 1622393172.0 }, { "data_format": 2, - "description": null, + "description": "Personal Singularity recipes", "filenames": [ - "Singularity" + "Mamba/Singularity.alpine-edge", + "Mamba/Singularity.alpine-bareuser", + "Mamba/Singularity.alpine-lto", + "Mamba/Singularity.micromamba", + "Mamba/Singularity.lto-deps", + "Mamba/Singularity.alpine-user", + "Lmod/Singularity.Lmod-dev", + "Lmod/Singularity.Lmod-download", + "Lmod/Singularity.Lmod-alpine", + "Lmod/Singularity.Lmod-lmod.in", + "Lmod/Singularity.Lmod-lua.in" ], - "full_name": "truatpasteurdotfr/singularity-cryolo-cuda11", + "full_name": "obilaniu/singularity-recipes", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-building-a-singularity-container-for-cryolo-using-cuda-version-11\" class=\"anchor\" href=\"#building-a-singularity-container-for-cryolo-using-cuda-version-11\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuilding a singularity container for crYOLO using CUDA version 11\u003c/h1\u003e\n\u003cp\u003eTru \u003ca href=\"mailto:tru@pasteur.fr\"\u003etru@pasteur.fr\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why-\" class=\"anchor\" href=\"#why-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy ?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etoy system for github actions\u003c/li\u003e\n\u003cli\u003ebuild singularity container from dockerhub registry and push to oras://ghcr.io with proper tags\u003c/li\u003e\n\u003cli\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-caveat\" class=\"anchor\" href=\"#caveat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveat\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eplayground, use at your own risk!\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e:latest\u003c/code\u003e tagged singularity image\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-cryolo-cuda11/actions/workflows/singularity-publish.yml\"\u003e\u003cimg src=\"https://github.com/truatpasteurdotfr/singularity-cryolo-cuda11/actions/workflows/singularity-publish.yml/badge.svg\" alt=\"Singularity build\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /run --nv oras://ghcr.io/truatpasteurdotfr/singularity-cryolo-cuda11:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLICENSE:\nThe same as crYOLO (free for academic use, see \u003ca href=\"https://cryolo.readthedocs.io/en/stable/other/license.html\" rel=\"nofollow\"\u003ehttps://cryolo.readthedocs.io/en/stable/other/license.html\u003c/a\u003e)\ncopy retrieved from \u003ca href=\"https://raw.githubusercontent.com/MPI-Dortmund/cryolo/9ea47f694fc68bcdaedf550a128558b8e77855bc/source/other/license.rst\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/MPI-Dortmund/cryolo/9ea47f694fc68bcdaedf550a128558b8e77855bc/source/other/license.rst\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1651497170.0 + "updated_at": 1631705492.0 }, { "data_format": 2, - "description": "Reproducibility package for the verification of laminar 3D pipe flow w/ OpenFOAM", + "description": null, "filenames": [ - "Singularity" + "Singularity.discordance", + "Singularity.epa-ng", + "Singularity.malt", + "Singularity.PhyloBayes", + "Singularity.gappa", + "Singularity.ale", + "Singularity.megan6-ce", + "Singularity.iqtree.1.6.1", + "Singularity.megan5", + "Singularity.hifix", + "Singularity.papara", + "Singularity.checkM", + "Singularity.rp15", + "Singularity.PhyloBayesMPI", + "COME2018/Singularity.paml", + "COME2018/Singularity.beast2", + "COME2018/Singularity.bali-phy", + "COME2018/Singularity.swipe", + "COME2018/Singularity.iqtree.1.6.3", + "COME2018/Singularity.standard-raxml", + "COME2018/Singularity.seaview", + "COME2018/Singularity.bpp", + "COME2018/Singularity.amap-align", + "COME2018/Singularity.clustalx", + "COME2018/Singularity.blast", + "COME2018/Singularity.modeltest-ng", + "COME2018/Singularity.prank", + "COME2018/Singularity.fasttree", + "COME2018/Singularity.jmodeltest2", + "COME2018/Singularity.raxml-ng", + "COME2018/Singularity.fsa", + "COME2018/Singularity.codonphyml", + "COME2018/Singularity.probcons", + "COME2018/Singularity.gnuplot", + "COME2018/Singularity.clustalw", + "COME2018/Singularity.mummer", + "COME2018/Singularity.muscle", + "COME2018/Singularity.mafft", + "COME2018/Singularity.phyml", + "COME2018/Singularity.mcl" ], - "full_name": "piyueh/openfoam-pipe-flow-verification", + "full_name": "maxemil/singularity-container", "latest_release": null, - "readme": "\u003cp\u003eA reproducibility package for simulation verification of pipe flow with OpenFOAM.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-creating-a-singularityapptainer-image\" class=\"anchor\" href=\"#creating-a-singularityapptainer-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a Singularity/Apptainer image\u003c/h3\u003e\n\u003chr\u003e\n\u003cp\u003eWe use a Singularity (or what is now named Apptainer) image to provide all required tools (e.g.,\nOpenFOAM, Gmsh, Python, etc.). Using a Singularity image guarantees that we don\u0027t need to install\nanything else on working machines or HPC clusters.\u003c/p\u003e\n\u003cp\u003eIf the current user has root privilege, then simply do (assuming currently under the repo folder):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity build openfoam9.sif ./Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf the current user does not have root privilege, try\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --fakeroot openfoam9.sif ./Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe above command requires the system admins to correctly configure the user namespace. To see if\nthe user namespace is configured correctly, check the files \u003ccode\u003e/etc/subuid\u003c/code\u003e and \u003ccode\u003e/etc/subgid\u003c/code\u003e and find\nthe line with your username and your group:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat /etc/subuid\n-------------------\n...\n\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eyour username\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea number much greater than \u003cspan class=\"pl-k\"\u003e65535\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea number greater than \u003cspan class=\"pl-k\"\u003e98765\u0026gt;\u003c/span\u003e\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cat /etc/subgid\n-------------------\n...\n\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ethe group you belong to\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea number much greater than \u003cspan class=\"pl-k\"\u003e65535\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea number greater than \u003cspan class=\"pl-k\"\u003e98765\u0026gt;\u003c/span\u003e\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf these two options do not work for you, try the cloud builders: \u003ca href=\"https://cloud.sylabs.io/builder\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/builder\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFinally, if using Apptainer (the newer community version of Singularity), just substitute\n\u003ccode\u003esingularity\u003c/code\u003e with \u003ccode\u003eapptainer\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-creating-case-folders\" class=\"anchor\" href=\"#creating-case-folders\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating case folders\u003c/h3\u003e\n\u003chr\u003e\n\u003cp\u003eTo create case folders:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e openfoam9.sif python ./main.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs this is a reproducibility package, what cases to create are hard coded.\u003c/p\u003e\n\u003cp\u003eTo create a custom case, use the following command to see how to use \u003ccode\u003emain.py\u003c/code\u003e to do so:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e openfoam9.sif python ./main.py --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003c/em\u003e!! The script \u003ccode\u003emain.py\u003c/code\u003e will create mesh files on the fly. Generating meshes requires a\nnon-trivial amount of memory, especially for the case \u003ccode\u003eairflow-pipe-256\u003c/code\u003e (and \u003ccode\u003eairflow-pip-512\u003c/code\u003e, if\nit exists). It may crash on small personal desktops and laptops.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-cases\" class=\"anchor\" href=\"#running-cases\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning cases\u003c/h3\u003e\n\u003chr\u003e\n\u003cp\u003eEach case has a file \u003ccode\u003ejob.sh\u003c/code\u003e that can be used as either a Slurm job script or a regular shell\nscript.\u003c/p\u003e\n\u003cp\u003eIf using it as a Slurm script, note that the resource configuration in \u003ccode\u003ejob.sh\u003c/code\u003e is based on the\nPegasus cluster at the George Washington University. You may need to manually change the\nconfiguration based on your cluster.\u003c/p\u003e\n\u003cp\u003eTo submit a case using Slurm:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ea \u003cspan class=\"pl-k\"\u003ecase\u003c/span\u003e folder\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n$ sbatch job.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ejob.sh\u003c/code\u003e uses the OpenFOAM installation in the Singularity/Apptainer image. So the cluster must\nhave Singularity/Apptainer. The script assumes the cluster uses Lmod and loads\nSingularity through \u003ccode\u003emodule load singularity\u003c/code\u003e. The cluster also needs OpenMPI 4.0+ and loads\nOpenMPI through \u003ccode\u003emodule load openmpi/gcc/64/4.1.0\u003c/code\u003e. Modify them based on the actual clusters being\nused.\u003c/p\u003e\n\u003cp\u003eTo run a case using a regular Linux machine:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sh ./job.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of MPI processes to use\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-results\" class=\"anchor\" href=\"#results\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h2\u003e\n\u003chr\u003e\n\u003cul\u003e\n\u003cli\u003eAbsolute error (against analytical soln.) at cross section z=0.135m\n\u003ca href=\"./figs/abs_err_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_z_0135.png\" alt=\"abserr0135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRelative error (against analytical soln.) at cross section z=0.135m\n\u003ca href=\"./figs/rel_err_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/rel_err_z_0135.png\" alt=\"relerr0135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eError convergence of the maximum velocity in the entire domain\n\u003ca href=\"./figs/abs_err_umax_entire.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_umax_entire.png\" alt=\"errmax\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eError convergence of the maximum velocity at cross section z=0.135m\n\u003ca href=\"./figs/abs_err_umax_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_umax_z_0135.png\" alt=\"errmax0135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eError convergence of the $L_\\infty$ at cross section z=0.135m\n\u003ca href=\"./figs/abs_err_linf_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_linf_z_0135.png\" alt=\"l2135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eError convergence of the $L_2$ at cross section z=0.135m\n\u003ca href=\"./figs/abs_err_l2_z_0135.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./figs/abs_err_l2_z_0135.png\" alt=\"l2135\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container\u003c/h2\u003e\n\u003cp\u003eTo use the containers in this repository, install the latest Singularity app (taken from \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/install-linux\u003c/a\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eVERSION=2.4.5\nwget https://github.com/singularityware/singularity/releases/download/\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e/singularity-\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e.tar.gz\ntar xvf singularity-\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e.tar.gz\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity-\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\n./configure --prefix=/usr/local\nmake\nsudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen you can build and use the containers using the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg Singularity.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -b \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecommand\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003earguments\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or if only a single command is available:\u003c/span\u003e\nsingularity run -b \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003earguments\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1652423887.0 + "updated_at": 1613184150.0 }, { "data_format": 2, - "description": null, + "description": "Containerization of Fermi Software", "filenames": [ - "attempts/attempt5/Singularity_v5", - "attempts/attempt2/Singularity_v2", - "attempts/attempt4/Singularity_v4", - "attempts/attempt1/Singularity_v1", - "attempts/attempt3/Singularity_v3", - "attempts/attempt6/Singularity_v6", - "attempts/attempt7/Singularity_v7" + "Singularity", + "singularity/base/Singularity" ], - "full_name": "zeng-su123/git_segmentation", + "full_name": "fermi-lat/containers", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-mms-challenge-2020\" class=\"anchor\" href=\"#mms-challenge-2020\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eM\u0026amp;Ms Challenge 2020\u003c/h1\u003e\n\u003cp\u003eThe CMR images have been segmented by experienced clinicians from the respective institutions, including contours\nfor the left (LV) and right ventricle (RV) blood pools, as well as for the left ventricular myocardium (MYO).\nLabels are: 1 (LV), 2 (MYO) and 3 (RV)\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-motivation\" class=\"anchor\" href=\"#motivation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003cp\u003eIn the recent years, many machine/deep learning models have been proposed to accurately segment cardiac structures\nin magnetic resonance imaging. However, when these models are tested on unseen datasets acquired from distinct\nMRI scanners or clinical centres, the segmentation accuracy can be greatly reduced.\u003c/p\u003e\n\u003cp\u003eThe M\u0026amp;Ms challenge aims to contribute to the effort of building generalisable models that can be applied consistently\nacross clinical centres. Furthermore, M\u0026amp;Ms will provide a reference dataset for the community to build and assess\nfuture generalisable models in CMR segmentation.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-environment-setup\" class=\"anchor\" href=\"#environment-setup\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment Setup\u003c/h2\u003e\n\u003cp\u003eTo use the code, the user needs to set te environment variable to access the data. At your ~/.bashrc add:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MMsCardiac_DATA_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/path/to/data/M\u0026amp;MsData/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlso, the user needs to to pre-install a few packages:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install wheel setuptools\n$ pip install -r requirements.txt\n$ pip install torch==1.5.0+cu101 torchvision==0.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n$ pip install torchcontrib~=0.0.2\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-data-preparation\" class=\"anchor\" href=\"#data-preparation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData preparation\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-train-csv\" class=\"anchor\" href=\"#train-csv\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrain csv\u003c/h4\u003e\n\u003cp\u003eYou can generate train csv for dataloaders using \u003ccode\u003epython3 preprocess/generate_train_df.py\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eusage: generate_train_df.py [-h] [--meta_graphs]\n\nM\u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003eMs 2020 Challenge - Training info generation\n\noptional arguments:\n -h, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n --meta_graphs Generate train meta information graphs\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-data-refactor\" class=\"anchor\" href=\"#data-refactor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Refactor\u003c/h4\u003e\n\u003cp\u003eLoad each volume to extract only 1 slice is time consuming. To solve this, save each slice in numpy arrays:\n\u003ccode\u003epython3 preprocess/dataloader_refactor.py\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-global-training-mean-and-std\" class=\"anchor\" href=\"#global-training-mean-and-std\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGlobal Training Mean and STD\u003c/h4\u003e\n\u003cp\u003eYou can easily get global mean and std from labeled training samples using \u003ccode\u003epython3 preprocess/get_mean_std.py\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-data-description\" class=\"anchor\" href=\"#data-description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData Description\u003c/h2\u003e\n\u003cp\u003eThe challenge cohort is composed of 350 patients with hypertrophic and dilated cardiomyopathies\nas well as healthy subjects. All subjects were scanned in clinical centres in three different\ncountries (Spain, Germany and Canada) using four different magnetic resonance\nscanner vendors (Siemens, General Electric, Philips and Canon).\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eHospital\u003c/th\u003e\n\u003cth align=\"center\"\u003eNum. studies\u003c/th\u003e\n\u003cth align=\"center\"\u003eCountry\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eClinica Sagrada Familia\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHospital de la Santa Creu i Sant Pau\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHospital Universitari Dexeus\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHospital Vall d\u0027Hebron\u003c/td\u003e\n\u003ctd align=\"center\"\u003e100\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSpain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eMcGill University Health Centre\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCanada\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eUniversit\u00e4tsklinikum Hamburg-Eppendorf\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50\u003c/td\u003e\n\u003ctd align=\"center\"\u003eGermany\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-training-set-15025-studies\" class=\"anchor\" href=\"#training-set-15025-studies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining set (150+25 studies)\u003c/h3\u003e\n\u003cp\u003eThe training set will contain 150 annotated images from two different MRI vendors (75 each) and 25 unannotated\nimages from a third vendor. The CMR images have been segmented by experienced clinicians from the respective\ninstitutions, including contours for the left (LV) and right ventricle (RV) blood pools, as well as for the\nleft ventricular myocardium (MYO). Labels are: 1 (LV), 2 (MYO) and 3 (RV).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-testing-set-200-studies\" class=\"anchor\" href=\"#testing-set-200-studies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting set (200 studies)\u003c/h3\u003e\n\u003cp\u003eThe 200 test cases correspond to 50 new studies from each of the vendors provided in the training set and\n50 additional studies from a fourth unseen vendor, that will be tested for model generalizability.\n20% of these datasets will be used for validation and the rest will be reserved for testing and ranking participants.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-standard-operating-procedure-sop-for-data-annotation\" class=\"anchor\" href=\"#standard-operating-procedure-sop-for-data-annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStandard Operating Procedure (SOP) for data annotation\u003c/h3\u003e\n\u003cp\u003eIn order to build a useful dataset for the community we have decided to build on top of\n\u003ca href=\"https://ieeexplore.ieee.org/document/8360453\" rel=\"nofollow\"\u003eACDC MICCAI 2017\u003c/a\u003e challenge SOP and correct our contours accordingly.\u003c/p\u003e\n\u003cp\u003eIn particular, clinical contours have been corrected by two in-house annotators that had to agree on the final result.\nThese annotators followed these rules:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLV and RV cavities must be completely covered, with papillary muscles included.\u003c/li\u003e\n\u003cli\u003eNo interpolation of the LV myocardium must be performed at the base.\u003c/li\u003e\n\u003cli\u003eRV must have a larger surface in end-diastole compared to end-systole and avoid the pulmonary artery.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe main difficulty and source of disagreement is the exact RV form in basal slices.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-results\" class=\"anchor\" href=\"#results\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h2\u003e\n\u003cp\u003eUsing ACDC checkpoint:\u003c/p\u003e\n\u003cp\u003eAverage -\u0026gt; 0.7397 -\u0026gt; 0.9933 (background), 0.6931 (LV), 0.5624 (MYO), 0.71(RV)\u003c/p\u003e\n\u003cp\u003eCalculated using resnet34_unet_imagenet_encoder, Adam and constant learning rate. Fold metrics are calculated\nusing mean of averaged iou and dice values. Only mnms data.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth\u003eNormalization\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7958\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8272\u003c/td\u003e\n\u003ctd\u003e0.8064\u003c/td\u003e\n\u003ctd\u003e0.8107\u003c/td\u003e\n\u003ctd\u003e0.8220\u003c/td\u003e\n\u003ctd\u003e0.8124\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8163\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8384\u003c/td\u003e\n\u003ctd\u003e0.8382\u003c/td\u003e\n\u003ctd\u003e0.8336\u003c/td\u003e\n\u003ctd\u003e0.8498\u003c/td\u003e\n\u003ctd\u003e0.8352\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8066\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8359\u003c/td\u003e\n\u003ctd\u003e0.8235\u003c/td\u003e\n\u003ctd\u003e0.8281\u003c/td\u003e\n\u003ctd\u003e0.8310\u003c/td\u003e\n\u003ctd\u003e0.8250\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7711\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7745\u003c/td\u003e\n\u003ctd\u003e0.7993\u003c/td\u003e\n\u003ctd\u003e0.8248\u003c/td\u003e\n\u003ctd\u003e0.7791\u003c/td\u003e\n\u003ctd\u003e0.7897\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8058\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8324\u003c/td\u003e\n\u003ctd\u003e0.8322\u003c/td\u003e\n\u003ctd\u003e0.8138\u003c/td\u003e\n\u003ctd\u003e0.8433\u003c/td\u003e\n\u003ctd\u003e0.8254\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.4,0.4,0.1,0.3,0.6 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7970\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8382\u003c/td\u003e\n\u003ctd\u003e0.8212\u003c/td\u003e\n\u003ctd\u003e0.8313\u003c/td\u003e\n\u003ctd\u003e0.8344\u003c/td\u003e\n\u003ctd\u003e0.8244\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7977\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8150\u003c/td\u003e\n\u003ctd\u003e0.8053\u003c/td\u003e\n\u003ctd\u003e0.8188\u003c/td\u003e\n\u003ctd\u003e0.8212\u003c/td\u003e\n\u003ctd\u003e0.8116\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8184\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8400\u003c/td\u003e\n\u003ctd\u003e0.8339\u003c/td\u003e\n\u003ctd\u003e0.8408\u003c/td\u003e\n\u003ctd\u003e0.8469\u003c/td\u003e\n\u003ctd\u003e0.8360\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8096\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8377\u003c/td\u003e\n\u003ctd\u003e0.8230\u003c/td\u003e\n\u003ctd\u003e0.8286\u003c/td\u003e\n\u003ctd\u003e0.8316\u003c/td\u003e\n\u003ctd\u003e0.8261\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7842\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8373\u003c/td\u003e\n\u003ctd\u003e0.8254\u003c/td\u003e\n\u003ctd\u003e0.8333\u003c/td\u003e\n\u003ctd\u003e0.8318\u003c/td\u003e\n\u003ctd\u003e0.8224\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8235\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8556\u003c/td\u003e\n\u003ctd\u003e0.7736\u003c/td\u003e\n\u003ctd\u003e0.8477\u003c/td\u003e\n\u003ctd\u003e0.8598\u003c/td\u003e\n\u003ctd\u003e0.8320\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8221\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8494\u003c/td\u003e\n\u003ctd\u003e0.8349\u003c/td\u003e\n\u003ctd\u003e0.8453\u003c/td\u003e\n\u003ctd\u003e0.8503\u003c/td\u003e\n\u003ctd\u003e0.8404\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7783\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8101\u003c/td\u003e\n\u003ctd\u003e0.8041\u003c/td\u003e\n\u003ctd\u003e0.8021\u003c/td\u003e\n\u003ctd\u003e0.8331\u003c/td\u003e\n\u003ctd\u003e0.8055\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8162\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8378\u003c/td\u003e\n\u003ctd\u003e0.8330\u003c/td\u003e\n\u003ctd\u003e0.8322\u003c/td\u003e\n\u003ctd\u003e0.8456\u003c/td\u003e\n\u003ctd\u003e0.8329\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7971\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8328\u003c/td\u003e\n\u003ctd\u003e0.8065\u003c/td\u003e\n\u003ctd\u003e0.8251\u003c/td\u003e\n\u003ctd\u003e0.8291\u003c/td\u003e\n\u003ctd\u003e0.8181\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7893\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7775\u003c/td\u003e\n\u003ctd\u003e0.7257\u003c/td\u003e\n\u003ctd\u003e0.8152\u003c/td\u003e\n\u003ctd\u003e0.8162\u003c/td\u003e\n\u003ctd\u003e0.7847\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8091\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8367\u003c/td\u003e\n\u003ctd\u003e0.8204\u003c/td\u003e\n\u003ctd\u003e0.8215\u003c/td\u003e\n\u003ctd\u003e0.8436\u003c/td\u003e\n\u003ctd\u003e0.8262\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.3,0.4,0.2,0.05,0.65 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7320\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8234\u003c/td\u003e\n\u003ctd\u003e0.7945\u003c/td\u003e\n\u003ctd\u003e0.8245\u003c/td\u003e\n\u003ctd\u003e0.8173\u003c/td\u003e\n\u003ctd\u003e0.7983\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_ce -\u0026gt; 0.5,0.3,0.2,0.65 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7962\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8384\u003c/td\u003e\n\u003ctd\u003e0.8157\u003c/td\u003e\n\u003ctd\u003e0.8053\u003c/td\u003e\n\u003ctd\u003e0.8181\u003c/td\u003e\n\u003ctd\u003e0.8147\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_ce -\u0026gt; 0.5,0.3,0.2,0.65 - lr 0.0001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7915\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8398\u003c/td\u003e\n\u003ctd\u003e0.8148\u003c/td\u003e\n\u003ctd\u003e0.8291\u003c/td\u003e\n\u003ctd\u003e0.8244\u003c/td\u003e\n\u003ctd\u003e0.8199\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePrincipal conclusions: bce_dice_border_ce with 0.5,0.2,0.2,0.2,0.5 - lr 0.001/0.0001 - standardize.\u003c/p\u003e\n\u003cp\u003eNow, using lr 0.001, standardize and bce_dice_border_ce with 0.5,0.2,0.2,0.2,0.5, explore data augmentation.\nWithout data augmentation score 0.8360.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eData Augmentation\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVertical flip\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8004\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8273\u003c/td\u003e\n\u003ctd\u003e0.8176\u003c/td\u003e\n\u003ctd\u003e0.8074\u003c/td\u003e\n\u003ctd\u003e0.8386\u003c/td\u003e\n\u003ctd\u003e0.8182\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHorizontal flip\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8032\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8225\u003c/td\u003e\n\u003ctd\u003e0.8226\u003c/td\u003e\n\u003ctd\u003e0.8244\u003c/td\u003e\n\u003ctd\u003e0.8318\u003c/td\u003e\n\u003ctd\u003e0.8209\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRandom Crops\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8137\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8376\u003c/td\u003e\n\u003ctd\u003e0.8208\u003c/td\u003e\n\u003ctd\u003e0.8283\u003c/td\u003e\n\u003ctd\u003e0.7876\u003c/td\u003e\n\u003ctd\u003e0.8181\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eShift\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8117\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8240\u003c/td\u003e\n\u003ctd\u003e0.8222\u003c/td\u003e\n\u003ctd\u003e0.8330\u003c/td\u003e\n\u003ctd\u003e0.8307\u003c/td\u003e\n\u003ctd\u003e0.8243\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eDownscale\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7949\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8192\u003c/td\u003e\n\u003ctd\u003e0.8166\u003c/td\u003e\n\u003ctd\u003e0.8219\u003c/td\u003e\n\u003ctd\u003e0.8384\u003c/td\u003e\n\u003ctd\u003e0.8181\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eElastic Transform\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7991\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8425\u003c/td\u003e\n\u003ctd\u003e0.8274\u003c/td\u003e\n\u003ctd\u003e0.8213\u003c/td\u003e\n\u003ctd\u003e0.8408\u003c/td\u003e\n\u003ctd\u003e0.8262\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRotations\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8158\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8426\u003c/td\u003e\n\u003ctd\u003e0.8255\u003c/td\u003e\n\u003ctd\u003e0.8290\u003c/td\u003e\n\u003ctd\u003e0.8524\u003c/td\u003e\n\u003ctd\u003e0.8330\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eGrid Distortion\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8028\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8361\u003c/td\u003e\n\u003ctd\u003e0.7864\u003c/td\u003e\n\u003ctd\u003e0.8275\u003c/td\u003e\n\u003ctd\u003e0.8231\u003c/td\u003e\n\u003ctd\u003e0.8151\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eOptical Distortion\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7705\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8418\u003c/td\u003e\n\u003ctd\u003e0.8255\u003c/td\u003e\n\u003ctd\u003e0.7996\u003c/td\u003e\n\u003ctd\u003e0.8354\u003c/td\u003e\n\u003ctd\u003e0.8145\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-competition-models\" class=\"anchor\" href=\"#competition-models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompetition Models\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-bala-1\" class=\"anchor\" href=\"#bala-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003eBala 1\u003c/em\u003e\n\u003c/h4\u003e\n\u003cp\u003eUsing standardization, data augmentation combination old and bce_dice_border_ce with 0.5,0.2,0.2,0.2,0.5.\nResnet34 Unet with lr 0.001 and adam optimizer.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eweakly -\u0026gt; labeled\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8286\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8596\u003c/td\u003e\n\u003ctd\u003e0.8505\u003c/td\u003e\n\u003ctd\u003e0.8540\u003c/td\u003e\n\u003ctd\u003e0.8482\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ecombined -\u0026gt; labeled\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8271\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8473\u003c/td\u003e\n\u003ctd\u003e0.8424\u003c/td\u003e\n\u003ctd\u003e0.8573\u003c/td\u003e\n\u003ctd\u003e0.8435\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-bala-2\" class=\"anchor\" href=\"#bala-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003eBala 2\u003c/em\u003e\n\u003c/h4\u003e\n\u003cp\u003eUsing standardization, data augmentation combination old and bce_dice_border_ce with 0.5,0.2,0.2,0.2,0.5\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eResnet34 Unet lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8092\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8257\u003c/td\u003e\n\u003ctd\u003e0.8115\u003c/td\u003e\n\u003ctd\u003e0.8293\u003c/td\u003e\n\u003ctd\u003e0.8276\u003c/td\u003e\n\u003ctd\u003e0.8207\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-not-pretrained-model\" class=\"anchor\" href=\"#not-pretrained-model\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNot Pretrained Model\u003c/h3\u003e\n\u003cp\u003eFolding by patient.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth\u003eNormalization\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.65 - lr 0.01\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7873\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.8263\u003c/td\u003e\n\u003ctd\u003e0.8004\u003c/td\u003e\n\u003ctd\u003e0.8195\u003c/td\u003e\n\u003ctd\u003e0.7616\u003c/td\u003e\n\u003ctd\u003e0.7990\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ebce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.65 - lr 0.001\u003c/td\u003e\n\u003ctd\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7741\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7879\u003c/td\u003e\n\u003ctd\u003e0.7743\u003c/td\u003e\n\u003ctd\u003e0.7883\u003c/td\u003e\n\u003ctd\u003e0.8071\u003c/td\u003e\n\u003ctd\u003e0.7863\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-update-11062020-meeting\" class=\"anchor\" href=\"#update-11062020-meeting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUpdate: 11/06/2020 Meeting\u003c/h2\u003e\n\u003cp\u003eChanges and ideas:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Use 2 folds grouping by vendor (A vs. B), instead of \u003cem\u003en\u003c/em\u003e grouping by patient. Then error analysis by vendor\u003c/li\u003e\n\u003cli\u003e[x] Since is not permited the use of pre-trained models, try smaller architectures\u003c/li\u003e\n\u003cli\u003e[ ] Create convolutional network that learns to distinguish if an image comes from vendor A or vendor B. \u00bfWorks?\n\u003cul\u003e\n\u003cli\u003eIf works then we can create a DCGAN trying to apply a initial transformation to fool the discriminator and\ndo something like normalize the input images! \u003cstrong\u003eNote\u003c/strong\u003e: Do not add vendor C in CNN classification step since\nwe will use it for validate our GAN later.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] Self-Supervised Learning for unseen vendor C\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-folding-by-vendor-resuts\" class=\"anchor\" href=\"#folding-by-vendor-resuts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFolding by Vendor Resuts\u003c/h2\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-wrong-folding-no-train-subpartitionpatients-to-compare\" class=\"anchor\" href=\"#wrong-folding-no-train-subpartitionpatients-to-compare\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e(Wrong folding, no train subpartition/patients to compare)\u003c/h4\u003e\n\u003cp\u003eNormalization by reescale. Criterion bce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eMethod\u003c/th\u003e\n\u003cth align=\"center\"\u003eDA\u003c/th\u003e\n\u003cth align=\"center\"\u003eA -\u0026gt; B\u003c/th\u003e\n\u003cth align=\"center\"\u003eB -\u0026gt; A\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_pspnet_unet - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7573\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7121\u003c/td\u003e\n\u003ctd\u003e0.7346\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_pspnet_unet - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6838\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.5532\u003c/td\u003e\n\u003ctd\u003e0.6185\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_pspnet_unet - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7612\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6793\u003c/td\u003e\n\u003ctd\u003e0.7202\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_pspnet_unet - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6982\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.5580\u003c/td\u003e\n\u003ctd\u003e0.6281\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_unet_scratch - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7498\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6835\u003c/td\u003e\n\u003ctd\u003e0.7166\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_unet_scratch - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6779\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4997\u003c/td\u003e\n\u003ctd\u003e0.5888\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_unet_scratch - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7421\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6627\u003c/td\u003e\n\u003ctd\u003e0.7023\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet18_unet_scratch - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7588\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6281\u003c/td\u003e\n\u003ctd\u003e0.6934\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet_scratch - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7649\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6313\u003c/td\u003e\n\u003ctd\u003e0.6980\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet_scratch - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7189\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6273\u003c/td\u003e\n\u003ctd\u003e0.6731\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet_scratch - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7673\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6530\u003c/td\u003e\n\u003ctd\u003e0.7101\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet_scratch - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7707\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6128\u003c/td\u003e\n\u003ctd\u003e0.6917\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003enano_unet - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.5035\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4284\u003c/td\u003e\n\u003ctd\u003e0.4659\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003enano_unet - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4432\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.2821\u003c/td\u003e\n\u003ctd\u003e0.3626\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003enano_unet - lr 0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4871\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4771\u003c/td\u003e\n\u003ctd\u003e0.4821\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003enano_unet - lr 0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4310\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.2187\u003c/td\u003e\n\u003ctd\u003e0.3248\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGeneral conclusions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModels can extract more information and thus make better predictions when training with Vendor \u0027A\u0027\nand then testing on \u0027B\u0027. GAN should approximate images to Vendor A?\u003c/li\u003e\n\u003cli\u003elr 0.001 works better than lower ones.\u003c/li\u003e\n\u003cli\u003eNot clear difference using data augmentation and without apply it...\u003c/li\u003e\n\u003cli\u003eIntermediate models size, resnet18_pspnet_unet, performs better than bigger ones and smaller ones.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-11-random-patients-to-compare\" class=\"anchor\" href=\"#11-random-patients-to-compare\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e11 random patients to compare\u003c/h4\u003e\n\u003cp\u003eCriterion bce_dice_border_ce -\u0026gt; 0.5,0.2,0.2,0.2,0.5. Using resnet18_pspnet_unet.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eNormalization\u003c/th\u003e\n\u003cth align=\"center\"\u003eData Augmentation\u003c/th\u003e\n\u003cth align=\"center\"\u003eLearning Rate\u003c/th\u003e\n\u003cth align=\"center\"\u003eA -\u0026gt; B\u003c/th\u003e\n\u003cth align=\"center\"\u003eB -\u0026gt; A\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination (Old)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7328\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6915\u003c/td\u003e\n\u003ctd\u003e0.7121\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination (Old)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7601\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6704\u003c/td\u003e\n\u003ctd\u003e0.7152\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination (Old)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6593\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.4914\u003c/td\u003e\n\u003ctd\u003e0.5753\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination (Old)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7499\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6342\u003c/td\u003e\n\u003ctd\u003e0.6920\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7502\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7014\u003c/td\u003e\n\u003ctd\u003e0.7258\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7561\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6723\u003c/td\u003e\n\u003ctd\u003e0.7142\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7370\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.5143\u003c/td\u003e\n\u003ctd\u003e0.6257\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7123\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6826\u003c/td\u003e\n\u003ctd\u003e0.6975\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7462\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7283\u003c/td\u003e\n\u003ctd\u003e0.7372\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7668\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6312\u003c/td\u003e\n\u003ctd\u003e0.6990\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eReescale\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7098\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6280\u003c/td\u003e\n\u003ctd\u003e0.6689\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eStandardize\u003c/td\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.005\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7606\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.6604\u003c/td\u003e\n\u003ctd\u003e0.7105\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGeneral conclusions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhen using Vendor A as training set, generalizes better to Vendor B cases.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-classification-vendor-a---b-discriminator\" class=\"anchor\" href=\"#classification-vendor-a---b-discriminator\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClassification: Vendor \u0027A\u0027 - \u0027B\u0027 Discriminator\u003c/h2\u003e\n\u003cp\u003eUsing resnet18_pspnet_classification model. Adam with bce. 60 epochs and *0.1 steps as 25 and 50.\nImg size 224x224. fold_system=\"patient\" \u0026amp; label_type=\"vendor_label\". Normalization standardize. Learning rate 0.001.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eData Augmentation\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eNone\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9954\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9726\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9878\u003c/td\u003e\n\u003ctd\u003e0.9970\u003c/td\u003e\n\u003ctd\u003e0.9906\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCombination\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9954\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9771\u003c/td\u003e\n\u003ctd\u003e0.9985\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9939\u003c/td\u003e\n\u003ctd\u003e0.9930\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-classification-vendor-a---b---c-discriminator\" class=\"anchor\" href=\"#classification-vendor-a---b---c-discriminator\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClassification: Vendor \u0027A\u0027 - \u0027B\u0027 - \u0027C\u0027 Discriminator\u003c/h2\u003e\n\u003cp\u003eAdam with bce. 80 epochs and *0.1 steps as 25 and 60.\nImg size 224x224. fold_system=\"patient\" \u0026amp; label_type=\"vendor_label\". Normalization standardize. Learning rate 0.001.\nData Augmentation combination (old).\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eModel\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 0\u003c/th\u003e\n\u003cth align=\"center\"\u003eFold 1\u003c/th\u003e\n\u003cth\u003eFold 2\u003c/th\u003e\n\u003cth\u003eFold 3\u003c/th\u003e\n\u003cth\u003eFold 4\u003c/th\u003e\n\u003cth\u003eMean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_pspnet\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9954\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9726\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9878\u003c/td\u003e\n\u003ctd\u003e0.9970\u003c/td\u003e\n\u003ctd\u003e0.9906\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_pspnet\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9954\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9771\u003c/td\u003e\n\u003ctd\u003e0.9985\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9939\u003c/td\u003e\n\u003ctd\u003e0.9930\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eresnet34_unet\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9910\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.9871\u003c/td\u003e\n\u003ctd\u003e1.0000\u003c/td\u003e\n\u003ctd\u003e0.9740\u003c/td\u003e\n\u003ctd\u003e0.9805\u003c/td\u003e\n\u003ctd\u003e0.9865\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-discriminator-entropy-backwards-a---b---c\" class=\"anchor\" href=\"#discriminator-entropy-backwards-a---b---c\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscriminator Entropy backwards \u0027A\u0027 - \u0027B\u0027 - \u0027C\u0027\u003c/h2\u003e\n\u003cp\u003eUsing gradient gamma 0.99, max iterations 250, standardize normalization. Segmentator Training with \u0027A\u0027.\nBaseline: 0.7799 IOU on B.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eOut threshold\u003c/th\u003e\n\u003cth align=\"center\"\u003eTarget\u003c/th\u003e\n\u003cth align=\"center\"\u003eMore\u003c/th\u003e\n\u003cth align=\"center\"\u003eB\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7825\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7829\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7713\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7723\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7725\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7744\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7826\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7828\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7713\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7723\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7725\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7744\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7826\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7828\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7828\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003e----\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7713\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 2.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7723\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 5.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7725\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEqual\u003c/td\u003e\n\u003ctd align=\"center\"\u003eL1 10.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7744\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003eProblem with low out thresholds... Waste all iterations and stops.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-discriminator-entropy-backwards-a---b---c--with-blur-unblur-and-gamma\" class=\"anchor\" href=\"#discriminator-entropy-backwards-a---b---c--with-blur-unblur-and-gamma\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscriminator Entropy backwards \u0027A\u0027 - \u0027B\u0027 - \u0027C\u0027 / With blur, unblur and gamma\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eOut threshold\u003c/th\u003e\n\u003cth align=\"center\"\u003eEntropy\u003c/th\u003e\n\u003cth align=\"center\"\u003eBlur\u003c/th\u003e\n\u003cth align=\"center\"\u003eUnblur\u003c/th\u003e\n\u003cth align=\"center\"\u003eGamma\u003c/th\u003e\n\u003cth align=\"center\"\u003eTarget\u003c/th\u003e\n\u003cth align=\"center\"\u003eIters\u003c/th\u003e\n\u003cth align=\"center\"\u003eB\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.01\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e100\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7770\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e100\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7786\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.000001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.000001\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.000001\u003c/td\u003e\n\u003ctd align=\"center\"\u003eA\u003c/td\u003e\n\u003ctd align=\"center\"\u003e100\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7779\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-7-july\" class=\"anchor\" href=\"#7-july\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e7 July\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-hausdorff-loss-tests\" class=\"anchor\" href=\"#hausdorff-loss-tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHausdorff loss tests\u003c/h3\u003e\n\u003cp\u003eMean average values for 5 folds. Data combination old. Lr 0.001 with resnet_unet_scratch.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eHausdorff Weight\u003c/th\u003e\n\u003cth align=\"center\"\u003eIOU A\u003c/th\u003e\n\u003cth align=\"center\"\u003eIOU B\u003c/th\u003e\n\u003cth\u003eDICE A\u003c/th\u003e\n\u003cth\u003eDICE B\u003c/th\u003e\n\u003cth\u003eHAUSSDORF A\u003c/th\u003e\n\u003cth\u003eHAUSSDORF B\u003c/th\u003e\n\u003cth\u003eASSD A\u003c/th\u003e\n\u003cth\u003eASSD B\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7333\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7835\u003c/td\u003e\n\u003ctd\u003e0.8087\u003c/td\u003e\n\u003ctd\u003e0.8561\u003c/td\u003e\n\u003ctd\u003e4.4773\u003c/td\u003e\n\u003ctd\u003e3.4890\u003c/td\u003e\n\u003ctd\u003e1.2458\u003c/td\u003e\n\u003ctd\u003e0.9624\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.05\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7417\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7867\u003c/td\u003e\n\u003ctd\u003e0.8158\u003c/td\u003e\n\u003ctd\u003e0.8589\u003c/td\u003e\n\u003ctd\u003e4.0958\u003c/td\u003e\n\u003ctd\u003e3.4073\u003c/td\u003e\n\u003ctd\u003e1.1618\u003c/td\u003e\n\u003ctd\u003e0.9646\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7399\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7827\u003c/td\u003e\n\u003ctd\u003e0.8153\u003c/td\u003e\n\u003ctd\u003e0.8550\u003c/td\u003e\n\u003ctd\u003e4.1999\u003c/td\u003e\n\u003ctd\u003e3.4355\u003c/td\u003e\n\u003ctd\u003e1.1925\u003c/td\u003e\n\u003ctd\u003e0.9735\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7421\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7806\u003c/td\u003e\n\u003ctd\u003e0.8193\u003c/td\u003e\n\u003ctd\u003e0.8522\u003c/td\u003e\n\u003ctd\u003e4.2831\u003c/td\u003e\n\u003ctd\u003e3.4414\u003c/td\u003e\n\u003ctd\u003e1.1953\u003c/td\u003e\n\u003ctd\u003e0.9831\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e0.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7370\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.7790\u003c/td\u003e\n\u003ctd\u003e0.8134\u003c/td\u003e\n\u003ctd\u003e0.8534\u003c/td\u003e\n\u003ctd\u003e4.3634\u003c/td\u003e\n\u003ctd\u003e3.4972\u003c/td\u003e\n\u003ctd\u003e1.2264\u003c/td\u003e\n\u003ctd\u003e0.9886\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-other\" class=\"anchor\" href=\"#other\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDevelopment environment -\u0026gt; CUDA 10.1 and cudnn 7603. Python 3.8.2 - GCC 9.3.0\u003c/li\u003e\n\u003cli\u003eChallenge homepage \u003ca href=\"https://www.ub.edu/mnms/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eACDC nomenclature: 0, 1, 2 and 3 represent voxels located in the background, in the right ventricular cavity,\nin the myocardium, and in the left ventricular cavity, respectively.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fermi-containerization-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#fermi-containerization-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFermi Containerization Project\u003c/h1\u003e\n\u003cp\u003eThis repository builds a \u003ca href=\"https://hub.docker.com/r/fermilat/glast_release/\" rel=\"nofollow\"\u003eDocker image\u003c/a\u003e and a \u003ca href=\"https://singularity-hub.org/collections/335/\" rel=\"nofollow\"\u003eSingularity image\u003c/a\u003e with GlastRelease.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install Singularity\u003c/h2\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-create-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-create-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Create the Image\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-21-bootstrap-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#21-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.1 Bootstrap the image\u003c/h3\u003e\n\u003cp\u003eBootstrapping means using something to build from, or not starting from nothing. In this case, we are goi\nng to use a build file that bootstraps a Docker image of CentOS 6. This build file is called \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e, and for more details about this you can\n\u003ca href=\"http://singularity.lbl.gov/docs-docker\" rel=\"nofollow\"\u003eread here\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 glast_release.img\nsudo singularity bootstrap glast_release.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-22-import-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#22-import-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2.2. Import from Singularity Hub\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 glast_release.img\nsudo singularity import glast_release.img shub://fermi-lat/containers:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-how-do-i-shell-into-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-how-do-i-shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How do I shell into the container?\u003c/h2\u003e\n\u003cp\u003eSingularity has an easy, intuitive way to shell inside!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell glast_release.img\n Singularity: Invoking an interactive shell within container...\n Singularity.glast_release.img\u0026gt; cd /opt/workspace\n Singularity.glast_release.img\u0026gt; source bin/centos6-x86_64-64bit-gcc44/_setup.sh\n Singularity.glast_release.img\u0026gt; export startTime=0,1000\n Singularity.glast_release.img\u0026gt; ./bin/centos6-x86_64-64bit-gcc44/Gleam Gleam/src/jobOptions.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want the container to be writable (default isn\u0027t) then you will need root (on your local machine) and add the \u003ccode\u003e--writable\u003c/code\u003e option:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell --writable glast_release.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Getting Started\u003c/h2\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-build-your-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-build-your-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Build your image\u003c/h2\u003e\n\u003cp\u003eIf you want to look at or make changes to the code, it\u0027s recommended to install CVS package and to clone the repo and build the container locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/fermi-lat/containers\ncd containers/docker/base\nbash ../../bin/setup-workspace.sh [GlastRelease_version] [CVS_username]\ndocker build -t glast_release .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-how-do-i-shell-into-the-container-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-how-do-i-shell-into-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. How do I shell into the container?\u003c/h2\u003e\n\u003cp\u003eBy default, running the container uses the \u003ccode\u003eENTRYPOINT\u003c/code\u003e, meaning it is used as an executable and you do not enter the container. In the case that you want to interactively work with the code, you may want to shell into the container. If there is a running container (eg an analysis) and you want to open up another terminal on your local machine to look inside (while it\u0027s running!) you need to get the 12 digit identifier with \u003ccode\u003edocker ps\u003c/code\u003e, and then plug it into this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker exec -it dc83a8d801a2 /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis says we want to execute (exec) and (interactive)(terminal) for container with id (dc83a8d801a2) and run the command (/bin/bash)\u003c/p\u003e\n\u003cp\u003eIf the container isn\u0027t running, then you can use \u003ccode\u003erun\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint /bin/bash glast_release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe container is provided on \u003ca href=\"https://hub.docker.com/r/fermilat/glast_release/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded from there when you run it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it docker.io/fermilat/glast_release:20-10-04-gr02 /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1652799880.0 + "updated_at": 1637372865.0 }, { "data_format": 2, - "description": "A symbolic generalized MaxSAT solver based on dynamic programming", + "description": "Salmonella serotyping at MDU", "filenames": [ - "dmc/Singularity", - "lg/Singularity" + "Singularity" ], - "full_name": "zzwonder/DPMaxSAT", + "full_name": "MDU-PHL/salmonella_typing", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-dpms-dynamic-programming-for-generalized-maxsat\" class=\"anchor\" href=\"#dpms-dynamic-programming-for-generalized-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDPMS (Dynamic Programming for Generalized MaxSAT)\u003c/h1\u003e\n\u003cp\u003eDPMS handles generalized MaxSAT problems in an extended DIMACS format (described below)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe DPMS framework runs in two phases:\n\u003cul\u003e\n\u003cli\u003ePlanning phase: \u003ca href=\"./lg\"\u003eLG\u003c/a\u003e constructs a (graded) project-join tree of a generalized MaxSAT formula\u003c/li\u003e\n\u003cli\u003eExecution phase: \u003ca href=\"./dmc\"\u003eDMC\u003c/a\u003e computes the answer to a generalized MaxSAT formula using the (graded) project-join tree\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation-linux\" class=\"anchor\" href=\"#installation-linux\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation (Linux)\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eautomake 1.16\u003c/li\u003e\n\u003cli\u003ecmake 3.16\u003c/li\u003e\n\u003cli\u003eg++ 9.3\u003c/li\u003e\n\u003cli\u003egmp 6.2\u003c/li\u003e\n\u003cli\u003emake 4.2\u003c/li\u003e\n\u003cli\u003ealready included as git submodules:\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003ecudd 3.0\u003c/a\u003e (a slightly modified version for DPMS is inlcuded. Needs to be compiled manually, see below)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts 2.2\u003c/a\u003e (included)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/trolando/sylvan\"\u003esylvan 1.5\u003c/a\u003e(included)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-cudd-add-supporter\" class=\"anchor\" href=\"#compile-cudd-add-supporter\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile CUDD (ADD supporter)\u003c/h3\u003e\n\u003cp\u003eIn addmc/libraries/cudd, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./INSTALL.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-lg-tree-builder\" class=\"anchor\" href=\"#compile-lg-tree-builder\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile LG (Tree Builder)\u003c/h3\u003e\n\u003cp\u003eIn lg/, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, see \u003ca href=\"lg/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-dmc-executor\" class=\"anchor\" href=\"#compile-dmc-executor\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile DMC (Executor)\u003c/h3\u003e\n\u003cp\u003eIn dmc/, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake dmc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, see \u003ca href=\"dmc/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage-example-command-line\" class=\"anchor\" href=\"#usage-example-command-line\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage Example (Command Line)\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003ecnfFile=\"examples/hybrid.hwcnf\" \u0026amp;\u0026amp; lg/build/lg \"lg/solvers/flow-cutter-pace17/flow_cutter_pace17 -p 100\" \u0026lt; $cnfFile | dmc/dmc --cf=$cnfFile --mx=1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure to use \"--mx=1\" to enable maxSAT.\u003c/p\u003e\n\u003cp\u003eUse the option \"--mb=BOUND\" to give an upper bound (int) of optimal cost (e.g., the result of o-line of a MaxSAT solver) for ADD pruning. For example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecnfFile=\"examples/hybrid.hwcnf\" \u0026amp;\u0026amp; lg/build/lg \"lg/solvers/flow-cutter-pace17/flow_cutter_pace17 -p 100\" \u0026lt; $cnfFile | dmc/dmc --cf=$cnfFile --mx=1 --mb=60000\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor a WBO or partial MaxSAT instance, --mb is set to be the trivial bound which can be read from the instance, unless the user gives a better bound.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-benchmarks-for-evaluations-of-ijcai-22-submission\" class=\"anchor\" href=\"#benchmarks-for-evaluations-of-ijcai-22-submission\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBenchmarks for evaluations of IJCAI-22 submission\u003c/h2\u003e\n\u003cp\u003ePlease see the directory benchmarks_results\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-problem-format-of-generalized-maxsat\" class=\"anchor\" href=\"#problem-format-of-generalized-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProblem format of Generalized MaxSAT\u003c/h2\u003e\n\u003cp\u003eSome examples of each type of problem can be found in examples/\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-generalized-maxsat-and-weighted-maxsat\" class=\"anchor\" href=\"#generalized-maxsat-and-weighted-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e(generalized) MaxSAT and weighted MaxSAT\u003c/h3\u003e\n\u003cp\u003eThe Max-CNF-SAT problems (.cnf) should use the DIMACS format: \u003ca href=\"https://www.ieee.org/conferences/publishing/templates.html\" rel=\"nofollow\"\u003ehttps://www.ieee.org/conferences/publishing/templates.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor XOR constraints, use \u0027x\u0027 at the beginning of a line\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ex1 xor x2 xor \\neg x3 =\u0026gt; x 1 2 -3 0.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor weighted MaxSAT (.cnf), use \"p wcnf nvars nclauses total-Soft-Weight\" instead of \"p cnf nvars nclauses\" in header. For each clause line, put the weight at the beginning of a line, then the first literal.\u003c/p\u003e\n\u003cp\u003eDPMS also accepts the hybrid weighted MaxSAT format (.hwcnf), take exapmles/hybrid.hwcnf for an example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ep hwcnf 7 8 100\n[3] +1 x1 +1 x2 \u0026gt;= 1 ;\n[2] -1 x1 -1 x2 \u0026gt;= -1 ;\n[10] -1 x3 +1 x2 \u0026gt;= 0 ;\n[9] -1 x3 +1 x4 \u0026gt;= 0 ;\n[12] +1 x3 -1 x2 -1 x4 \u0026gt;= -1 ;\n[34] -1 x5 +1 x6 \u0026gt;= 0 ;\n[15] -1 x5 +1 x7 \u0026gt;= 0 ;\n[7] x 1 2 3 4 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn a .hwcnf file, weights are always in front of each constraint, wrapped by \u0027[]\u0027. Each constraint after the weight can be a CNF clause, XOR or a pseudo-Boolean constraint.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-pseudo-boolean-optimization-wbo\" class=\"anchor\" href=\"#pseudo-boolean-optimization-wbo\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePseudo-Boolean optimization (WBO)\u003c/h3\u003e\n\u003cp\u003eFor PB constraints (.wbo), here is an example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+1 x1 +1 x2 \u0026gt;= 1 ;\n[90] -1 x1 -1 x2 \u0026gt;= -1 ;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first constraint is a hard constraint. The second constraint is soft with weight 90.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-min-maxsat\" class=\"anchor\" href=\"#min-maxsat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMin-MaxSAT\u003c/h3\u003e\n\u003cp\u003eA Min-MaxSAT problem file is same with a MaxSAT file except that there is a \u0027vm\u0027 line indicating the min variables. Variables that do not appear in the vm line are all max variables.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-in-silico-salmonella-enterica-serotyping\" class=\"anchor\" aria-hidden=\"true\" href=\"#in-silico-salmonella-enterica-serotyping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cem\u003eIn silico\u003c/em\u003e \u003cem\u003eSalmonella enterica\u003c/em\u003e Serotyping\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/MDU-PHL/salmonella_typing\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a60078aa4a16c8017cccb262835dc5b1c2d8a6543fc0049be78317ba7eec7b92/68747470733a2f2f636972636c6563692e636f6d2f67682f4d44552d50484c2f73616c6d6f6e656c6c615f747970696e672e7376673f7374796c653d73766726636972636c652d746f6b656e3d35303961353862363136306661346639623765613830613266356637363735343565303633326261\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/MDU-PHL/salmonella_typing.svg?style=svg\u0026amp;circle-token=509a58b6160fa4f9b7ea80a2f5f767545e0632ba\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-scope\" class=\"anchor\" aria-hidden=\"true\" href=\"#scope\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScope\u003c/h2\u003e\n\u003cp\u003eThe scripts presented in this repository are to be used to perform \u003cem\u003ein silico\u003c/em\u003e serotyping of \u003cem\u003eSalmonella enterica\u003c/em\u003e in accordance with MMS136. It takes as input a draft assembly and outputs a serotype inference. The draft assembly is obtained by performing a \u003cem\u003ede novo\u003c/em\u003e assembly on FASTQ data found to have passed MMS103 and to be identified as \u003cem\u003eSalmonella enterica\u003c/em\u003e by \u003cem\u003ekmer ID\u003c/em\u003e and by a wet laboratory method.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-glossary\" class=\"anchor\" aria-hidden=\"true\" href=\"#glossary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGlossary\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSerotype: A form of classification of bacteria below the species level. Usually involves some sort of reactive between specific sera and antigens on the bacteria\u0027s wall.\u003c/li\u003e\n\u003cli\u003eSerovar: In this case, a synonym of serotype.\u003c/li\u003e\n\u003cli\u003eSerogroup: A group of serovars with common antigens.\u003c/li\u003e\n\u003cli\u003eWGS: whole-genome sequence data. Usually, DNA sequence data comprised of short reads (between 35 and 300 bp in length) coded in the FASTQ format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-a-quick-primer-on-salmonella-serotypes\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-quick-primer-on-salmonella-serotypes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA quick primer on \u003cem\u003eSalmonella\u003c/em\u003e serotypes\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003esistr\u003c/code\u003e will only call serotypes that are valid under the WHO Collaborating Centre for Reference and Research on \u003cem\u003eSalmonella\u003c/em\u003e table of antigenic formulas for \u003cem\u003eSalmonella\u003c/em\u003e serovars, which can be found \u003ca href=\"https://www.pasteur.fr/sites/default/files/veng_0.pdf\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. The table follows the \u003cem\u003eKauffmann-White-Le Minor\u003c/em\u003e scheme (which is the \u003cem\u003eKauffmann-White\u003c/em\u003e scheme, but for historical reasons the WHOCC-Salm added \u003cem\u003eLe Minor\u003c/em\u003e\u0027s name to the scheme\u0027s name). According to the document, about 30 serovars are expected to account for about 90% of the \u003cem\u003eSalmonella\u003c/em\u003e in a country. The scheme, as presented in the document, describes a total of 2,579 serovars of \u003cem\u003eSalmonella\u003c/em\u003e, of which 2,557 are of the species \u003cem\u003eS. enterica\u003c/em\u003e and 22 are of the species \u003cem\u003eS. bongori\u003c/em\u003e (data on pg. 13).\u003c/p\u003e\n\u003cp\u003eThe genus \u003cem\u003eSalmonella\u003c/em\u003e is now known to have two species: \u003cem\u003eS. enterica\u003c/em\u003e and \u003cem\u003eS. bongori\u003c/em\u003e. The species \u003cem\u003eS. enterica\u003c/em\u003e has six subspecies: \u003cem\u003eenterica\u003c/em\u003e, \u003cem\u003esalamae\u003c/em\u003e, \u003cem\u003earizonae\u003c/em\u003e, \u003cem\u003ediarizonae\u003c/em\u003e, \u003cem\u003ehoutenae\u003c/em\u003e, and \u003cem\u003eindica\u003c/em\u003e. By far the most commonly found in human cases of Salmonellosis is \u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003eenterica\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eOriginally, the subspecies were believed to be subgenera named with roman numerals: I (now \u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003eenterica\u003c/em\u003e), II (\u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003esalamae\u003c/em\u003e), III (former genus \u003cem\u003eArizona\u003c/em\u003e: subdivided in to IIIa \u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003earizonae\u003c/em\u003e and IIIb \u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003ediarizonae\u003c/em\u003e), IV (\u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003ehoutenae\u003c/em\u003e), V (\u003cem\u003eS. bongori\u003c/em\u003e), and VI (\u003cem\u003eS. enterica\u003c/em\u003e subsp \u003cem\u003eindica\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eIn the case of serotypes of \u003cem\u003eSalmonella enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e the serotype is typically reported by a single name (e.g., Enteritidis, Typhi, Typhimurium). This is kept for historical reasons. Serotypes of all other subspecies of \u003cem\u003eS. enterica\u003c/em\u003e and \u003cem\u003eS. bongori\u003c/em\u003e are typically reported with the antigenic formula.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sistr\" class=\"anchor\" aria-hidden=\"true\" href=\"#sistr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSISTR\u003c/h2\u003e\n\u003cp\u003eTo perform \u003cem\u003eSalmonella enterica\u003c/em\u003e serotyping we use the tool \u003ccode\u003esistr\u003c/code\u003e [\u003ca href=\"#yoshida\"\u003e1\u003c/a\u003e] developed by Public Health Agency of Canada and held \u003ca href=\"https://github.com/peterk87/sistr_cmd\"\u003ehere\u003c/a\u003e. The tool has been extensively validated by others [\u003ca href=\"#yachison\"\u003e2\u003c/a\u003e].\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esistr\u003c/code\u003e uses a combination of approaches to infer serotype from draft assemblies of WGS data. For the purposes of MDU work, we have validated the use of the combination of \u003cem\u003eantigen\u003c/em\u003e detection and \u003cem\u003ecgMLST\u003c/em\u003e typing:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt uses \u003cem\u003eBLAST\u003c/em\u003e to identify the presence of annotated O- and H- antigen sequences. As such, it comes with curated multiFASTA files for the \u003cem\u003efliC\u003c/em\u003e, \u003cem\u003efliB\u003c/em\u003e, and \u003cem\u003ewzx\u003c/em\u003e and \u003cem\u003ewzy\u003c/em\u003e genes.\u003c/li\u003e\n\u003cli\u003eIt has a cgMLST scheme with 330 loci, and a database of 52 790 genomes (mostly comprising subspecies I) that have been typed at these loci and annotated with a serotype. It uses \u003cem\u003eBLAST\u003c/em\u003e to genotype the input assembly across as many of the 330 loci, and then calculates the pairwise distance of the input isolate to the database of curated genomes.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eAt the moment salmonella_typing installation is limited to installation from this repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install git+https://github.com/MDU-PHL/salmonella_typing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eDependencies\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYou will also need to ensure that \u003ccode\u003esistr v1.1.1\u003c/code\u003e is installed, instructions for this can be found \u003ca href=\"https://github.com/phac-nml/sistr_cmd\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecsvtk\u003c/code\u003e installation instructions can be found \u003ca href=\"https://github.com/shenwei356/csvtk\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-salmonella_serotyping\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-salmonella_serotyping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning salmonella_serotyping\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003estype run --help\nusage: stype run [-h] [--contigs CONTIGS] [--prefix PREFIX] [--jobs JOBS]\n\noptional arguments:\n -h, --help show this help message and exit\n --contigs CONTIGS, -c CONTIGS\n Tab-delimited file with sample ID as column 1 and path to assemblies as column 2 OR path to a contig file (used if only doing a single sample - should provide value for -pfx). (default: )\n --prefix PREFIX, -px PREFIX\n If running on a single sample, please provide a prefix for output directory (default: abritamr)\n --jobs JOBS, -j JOBS Number of AMR finder jobs to run in parallel. (default: 16)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSalmonella_typing can be on a single sample run by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003estype -c \u0026lt;path_to_contigs\u0026gt; -px \u0026lt;name_of_sample\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr in batch mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003estype -c input.tab -j 16\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003einput.tab\u003c/code\u003e is a tab-delimited file with column 1 being sample ID and column 2 is path to the assemblies.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-mdu-service\" class=\"anchor\" aria-hidden=\"true\" href=\"#mdu-service\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMDU Service\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: stype mdu [-h] [--runid RUNID] [--sistr SISTR]\n\noptional arguments:\n -h, --help show this help message and exit\n --runid RUNID, -r RUNID\n MDU RunID (default: Run ID)\n --sistr SISTR, -s SISTR\n Path to concatentated output of sistr (default: sistr_concatenated.csv)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to generate a LIMS friendly spreadsheet, collate all \u003ccode\u003estype\u003c/code\u003e results\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecsvtk concat sample_1/sistr_filtered.csv sample_2/sistr_filtered.csv ... \u0026gt; sistr_concatenated.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run \u003ccode\u003estype\u003c/code\u003e in \u003ccode\u003emdu\u003c/code\u003e mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003estype mdu -r RUNID -s sistr_concatenated.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eFile\u003c/th\u003e\n\u003cth align=\"center\"\u003eContents\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003esample_directory/sistr.csv\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eraw output of \u003ccode\u003esistr\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003esample_directory/sistr_filtered.csv\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\n\u003ccode\u003esistr\u003c/code\u003e output that has been filtered based on MDU business logic per sample\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003esistr_filtered.csv\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\n\u003ccode\u003esistr\u003c/code\u003e output that has been collated and filtered based on MDU business logic for batch\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ccode\u003e\u0026lt;RUNID\u0026gt;_sistr.xlsx\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003ea spreadsheet ready for upload into MDU LIMS only output if \u003ccode\u003emdu\u003c/code\u003e used\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003e[\u003ca name=\"user-content-yoshida\"\u003e1\u003c/a\u003e] Yoshida, C. E., Kruczkiewicz, P., Laing, C. R., Lingohr, E. J., Gannon, V. P. J., Nash, J. H. E., \u0026amp; Taboada, E. N. (2016). The Salmonella \u003cem\u003eIn Silico\u003c/em\u003e Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies. PloS One, 11(1), e0147101.\u003c/p\u003e\n\u003cp\u003e[\u003ca name=\"user-content-yachison\"\u003e2\u003c/a\u003e] Yachison, C. A., Yoshida, C., Robertson, J., Nash, J. H. E., Kruczkiewicz, P., Taboada, E. N., \u2026 Nadon, C. (2017). The Validation and Implications of Using Whole Genome Sequencing as a Replacement for Traditional Serotyping for a National Salmonella Reference Laboratory. Frontiers in Microbiology, 8, 1044.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1651985375.0 + "updated_at": 1685247135.0 }, { "data_format": 2, - "description": "Singularity container for DIVAnd", + "description": "Galileo + Events", "filenames": [ - "Singularity" + "env.d/Singularity" ], - "full_name": "gher-ulg/DIVAnd-singularity", - "latest_release": "v1.0.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/gher-ulg/DIVAnd-singularity/actions?query=workflow%3A%22Singularity+Build%22\"\u003e\u003cimg src=\"https://github.com/gher-ulg/DIVAnd-singularity/workflows/Singularity%20Build/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.7014264\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbff15826258cff37ea5a790bb5970cb363766914fa530ce59fc3d0c4c598a13/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e373031343236342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.7014264.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-divand-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#divand-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDIVAnd-singularity\u003c/h1\u003e\n\u003cp\u003eSingularity container for DIVAnd, the interpolation tool.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eDIVAnd\u003c/code\u003e is available at \u003ca href=\"https://github.com/gher-ulg/DIVAnd.jl\"\u003ehttps://github.com/gher-ulg/DIVAnd.jl\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eInstall singularity: \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://sylabs.io/docs/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h1\u003e\n\u003cp\u003eAfter checking out the source, the singularity container can be build using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erm -f DIVAnd.sif; sudo singularity build DIVAnd.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first command is only needed if you already had the \u003ccode\u003e.sif\u003c/code\u003e file in your system.\u003cbr\u003e\nThe \u003cem\u003ebuild\u003c/em\u003e operation lasts severall minutes due to the download and installation of languages and libraries.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h1\u003e\n\u003cp\u003eContainer images are build using GitHub actions.\nGo to \u003ca href=\"https://github.com/gher-ulg/DIVAnd-singularity/actions\"\u003ehttps://github.com/gher-ulg/DIVAnd-singularity/actions\u003c/a\u003e choose the lastest commit and go to artefact.\nDownload and unzip the image file and run with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run DIVAnd.sif\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "CNCLgithub/galileo-ramp", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-galileo-ramp-v3\" class=\"anchor\" aria-hidden=\"true\" href=\"#galileo-ramp-v3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGalileo Ramp (v3)\u003c/h1\u003e\n\u003cp\u003eThe ramp-ball scenario for galileo\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eAll team members must\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a branch based off the current master (preferably on their own fork)\u003c/li\u003e\n\u003cli\u003eAdd commits to that new branch\u003c/li\u003e\n\u003cli\u003epush the new branch and submit a pull request to master\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-config\" class=\"anchor\" aria-hidden=\"true\" href=\"#config\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfig\u003c/h3\u003e\n\u003cp\u003eSimple setups on local hosts should run fine with the \u003ccode\u003edefault.conf\u003c/code\u003e.\nHowever, if there are any issues with \u003ccode\u003esingularity\u003c/code\u003e the create a \u003ccode\u003euser.conf\u003c/code\u003e\nwith correct attributes.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edefault.conf\u003c/code\u003e reads as:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[ENV]\u003c/span\u003e\nexec:singularity \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the path to singularity binary\u003c/span\u003e\npath:julia-cont \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the path to the singularity container\u003c/span\u003e\npython:pyenv \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the name of the conda environment\u003c/span\u003e\njulia_depot:.julia \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the relative path to set JULIA_DEPOT_PATH\u003c/span\u003e\nmounts:\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere are additional sections in \u003ccode\u003edefault.conf\u003c/code\u003e which are using for\nproject organization (\u003ccode\u003ePATHS\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[PATHS]\u003c/span\u003e\ndatabases:output/databases\ntraces:output/traces\nrenders:output/renders\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote:\nThe content in the config changes from time to time. If you run into issues after pulling, compare your \u003ccode\u003euser.conf\u003c/code\u003e to \u003ccode\u003edefault.conf\u003c/code\u003e to see if any of the keys have changed.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content-environment-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment building\u003c/h3\u003e\n\u003cp\u003eSimply run \u003ccode\u003esetup.sh\u003c/code\u003e in the root of this repo as follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh cont_pull conda julia\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will be prompted for sudo when building the container.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esetup.sh\u003c/code\u003e will then create the container at the path specified in the config (\u003ccode\u003ejulia-cont\u003c/code\u003e by default).\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNOTE: Like many commands in this setup, variables will be bound to those specified in \u003ccode\u003euser.conf\u003c/code\u003e if present or \u003ccode\u003edefault.conf\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIn the near future (yell at me if I forget), this script will, by default, attempt to download the container from a hosting service (probably dropbox). In that way, the user will not require sudo (and the container\u0027s behavior will be more consistent).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-runtime\" class=\"anchor\" aria-hidden=\"true\" href=\"#runtime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRuntime\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-interacting-with-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#interacting-with-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteracting with the container\u003c/h3\u003e\n\u003cp\u003eAfter running \u003ccode\u003esetup.sh\u003c/code\u003e, you can now use \u003ccode\u003erun.sh\u003c/code\u003e to use the environment.\u003c/p\u003e\n\u003cp\u003eThe synatx of \u003ccode\u003erun.sh\u003c/code\u003e is simply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./run.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecommand\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003ecommand\u003c/code\u003e can be any arbitrary bash expression.\u003c/p\u003e\n\u003cp\u003eFor example, you can probe the python version in the conda environment using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;: ./run.sh python3 --version\nNo user config found, using default\nINFO for ENV\n path =\u0026gt; julia-cont\n mounts =\u0026gt; \n exec =\u0026gt; singularity\n julia_depot =\u0026gt; .julia\n python =\u0026gt; pyenv\nPython 3.6.8 :: Anaconda, Inc.\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs you can see \u003ccode\u003e./run.sh\u003c/code\u003e first\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLoads the available config\u003c/li\u003e\n\u003cli\u003eReads out the config\u003c/li\u003e\n\u003cli\u003eExecutes the command\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-interacting-with-julia\" class=\"anchor\" aria-hidden=\"true\" href=\"#interacting-with-julia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteracting with Julia\u003c/h2\u003e\n\u003cp\u003eGetting into the \u003ccode\u003ejulia\u003c/code\u003e repl is simply\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;: ./run.sh julia\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eNo user config found, using default\nINFO for ENV\n path =\u0026gt; julia-cont\n mounts =\u0026gt; \n exec =\u0026gt; singularity\n julia_depot =\u0026gt; .julia\n python =\u0026gt; pyenv\n _\n _ _ _(_)_ | Documentation: https://docs.julialang.org\n (_) | (_) (_) |\n _ _ _| |_ __ _ | Type \"?\" for help, \"]?\" for Pkg help.\n | | | | | | |/ _` | |\n | | |_| | | | (_| | | Version 1.1.0 (2019-01-21)\n _/ |\\__\u0027_|_|_|\\__\u0027_| | Official https://julialang.org/ release\n|__/ |\n\njulia\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure that \u003ccode\u003eJULIA_DEPOT_PATH\u003c/code\u003e is set to that in the config (this should be taken care of by \u003ccode\u003erun.sh\u003c/code\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-julia\"\u003e\u003cpre\u003ejulia\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eDEPOT_PATH\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003eelement Array{String,\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e}\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.julia\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\njulia\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBoth \u003ccode\u003esetup.sh\u003c/code\u003e and \u003ccode\u003erun.sh\u003c/code\u003e use the included package info to setup Julia dependencies. Adding packages can be done normally using \u003ccode\u003eBase.pkg\u003c/code\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote:\nSome Julia packages (usually stale ones) will attempt to install system level dependencies. This will NOT work in a singularity container as it is immutable. You will have to edit the definition file (\u003ccode\u003eSingularity\u003c/code\u003e) to include this manually.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-scripts--experiments\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-scripts--experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning scripts / experiments\u003c/h3\u003e\n\u003cp\u003eThe main method of executing elements within this package are via scripts found in the (queue drum roll) \u003ccode\u003escripts\u003c/code\u003e directory. If the script has a proper shebang and is executable, congrats, you just need to run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./run.sh scripts/my_script.\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eie\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[galileo-ramp]$ ./run.sh scripts/ramp_profile.py --help\nNo user config found, using default\npybullet build time: May 15 2019 00:10:22\nusage: ramp_profile.py [-h] [--table TABLE TABLE] [--table_steps TABLE_STEPS]\n [--ramp RAMP RAMP] [--ramp_steps RAMP_STEPS]\n [--ramp_angle RAMP_ANGLE] [--radius RADIUS]\n [--friction FRICTION] [--n_ramp N_RAMP] [--slurm]\n [--batch BATCH] [--debug] [--fresh]\n mass_file\n\nEvaluates the energy of mass ratios\n\npositional arguments:\n mass_file CSV file containing mass ratios\n\noptional arguments:\n -h, --help show this help message and exit\n --table TABLE TABLE XY dimensions of table. (default: (35, 18))\n --table_steps TABLE_STEPS\n Number of positions along X-axis. (default: 4)\n --ramp RAMP RAMP XY dimensions of ramp. (default: (35, 18))\n --ramp_steps RAMP_STEPS\n Number of positions along X-axis. (default: 4)\n --ramp_angle RAMP_ANGLE\n ramp angle in degrees (default: 0.5235987755982988)\n --radius RADIUS Ball radius. (default: 1.5)\n --friction FRICTION Ball friction (default: 0.4)\n --n_ramp N_RAMP Number of balls on ramp (default: 1)\n --slurm Use dask distributed on SLURM. (default: False)\n --batch BATCH Number of towers to search concurrently. (default: 1)\n --debug Run in debug (no rejection). (default: False)\n --fresh Ignore previous profiles (default: False)\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-project-layout\" class=\"anchor\" aria-hidden=\"true\" href=\"#project-layout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject Layout\u003c/h2\u003e\n\u003cp\u003eThe experiment is formatted in the form of a pip-compliant package under \u003ccode\u003egalileo_ramp\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe package is formatted as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e describes the GM\u003c/span\u003e\n/world \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e any nn components\u003c/span\u003e\n/models\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e utilities that do not reasonably belong in previous sections\u003c/span\u003e\n/utils\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEach of this sections will have their own documentation.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote:\nPlease maintain hygene between scripts and modules. Any standalone executable should be within scripts. Any piece of code that is imported across several scripts should be incorporated within the project package.\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1644927498.0 + "updated_at": 1689602229.0 }, { "data_format": 2, - "description": null, + "description": "Promoter identification from diverse types of large-scale TSS profiling data", "filenames": [ - "Singularity", - "def/edmr/Singularity", - "def/cytosim/Singularity", - "bck/Singularity" + "Singularity" ], - "full_name": "kirsho/Singularity", + "full_name": "BrendelGroup/TSRchitect", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eMy notes about Singularity / Apptainer.\u003cbr\u003e\nHow to create a conda environment in an Singularity image with or without a .yml file.\nVisit \u003ccode\u003eyml2sing\u003c/code\u003e et \u003ccode\u003econda2sing\u003c/code\u003e for updated versions of my singularity image build scripts.\u003cbr\u003e\nRead \u003ccode\u003eIntro2Singularity.md\u003c/code\u003e to know more about Singularity (Install, use and tutos).\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-tsrchitect-promoter-identification-from-diverse-types-of-large-scale-tss-profiling-data\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#tsrchitect-promoter-identification-from-diverse-types-of-large-scale-tss-profiling-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTSRchitect: Promoter identification from diverse types of large-scale TSS profiling data\u003c/h1\u003e\n\u003cp\u003eThe TSRchitect repository encompasses an \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e\npackage developed in the \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eBrendel Group\u003c/a\u003e for analyses\nof transcription start site data.\nThe code conforms to our \u003ca href=\"https://brendelgroup.github.io/\" rel=\"nofollow\"\u003eRAMOSE\u003c/a\u003e\nphilosophy: it generates \u003cstrong\u003ereproducible\u003c/strong\u003e, \u003cstrong\u003eaccurate\u003c/strong\u003e, and \u003cstrong\u003emeaningful\u003c/strong\u003e\nresults; it is \u003cstrong\u003eopen\u003c/strong\u003e (source) and designed to be \u003cstrong\u003escalable\u003c/strong\u003e and\n\u003cstrong\u003eeasy\u003c/strong\u003e to use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start-\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#quick-start-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start \u003ca href=\"https://singularity-hub.org/collections/1204\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eInput to TSRchitect will be transcription profiling read alignment data in \u003ccode\u003ebam\u003c/code\u003e\nor \u003ccode\u003ebed\u003c/code\u003e format as well as the appropriate genome annotation (if\navailable).\nOutput consists of predicted Transcription Start Sites (TSS) and Transcription\nStart Regions (TSR) as well as statistics summarizing the distribution and\ncharacteristics of identified TSSs and TSRs.\u003c/p\u003e\n\u003cp\u003eAll the TSRchitect dependencies are encapsulated in a\n\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available from\n\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\nThus, once you know what you are doing, execution could be as simple as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name tsr.simg shub://BrendelGroup/TSRchitect\nsingularity exec tsr.simg R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich will bring up an \u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e console with the\nTSRchitect library and all its prerequisites available.\nFor example, in that console, you should see\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eR version 3.5.3 (2019-03-11) -- \"Great Truth\"\n...\n\u0026gt; packageVersion(\"TSRchitect\")\n[1] \u00271.17.3\u0027\n\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-realistic-start\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#realistic-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRealistic Start\u003c/h2\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the\n\u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\nOnce all preparatory steps are taken care of, see the \u003ca href=\"./demo/HOWTO.md\"\u003eHOWTO\u003c/a\u003e\ndocument for examples of how to load data into TSRchitect and predict and\ncharacterize promoters.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq-and-references\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#faq-and-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ and References\u003c/h2\u003e\n\u003cp\u003ePlease see\n\u003ca href=\"https://github.com/vpbrendel/TSRchitect/wiki/FAQ\"\u003eV. Brendel\u0027s TSRchitect FAQ\u003c/a\u003e\nfor usage examples and suggestions.\u003c/p\u003e\n\u003cp\u003eIf you find \u003cem\u003eTSRchitect\u003c/em\u003e useful, you may cite:\u003c/p\u003e\n\u003cp\u003eRaborn RT, Sridharan K, Brendel VP (2017)\n\u003cem\u003eTSRchitect: Promoter identification from large-scale TSS profiling data.\u003c/em\u003e\ndoi: 10.18129/B9.bioc.TSRchitect, \u003ca href=\"https://doi.org/doi:10.18129/B9.bioc.TSRchitect\" rel=\"nofollow\"\u003ehttps://doi.org/doi:10.18129/B9.bioc.TSRchitect\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOur own publications will be linked here in due course.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003ePlease direct all comments and suggestions to\n\u003ca href=\"mailto:vbrendel@indiana.edu\"\u003eVolker Brendel\u003c/a\u003e\nat \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eIndiana University\u003c/a\u003e and\n\u003ca href=\"mailto:rtraborn@asu.edu\"\u003eTaylor Raborn\u003c/a\u003e at his current address.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1655987254.0 + "updated_at": 1629530423.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.centos7" + "run_singularity_basic/Singularity_openmpi.def", + "build_container_on_shub/Singularity" ], - "full_name": "tashrifbillah/tbss_containers", + "full_name": "ResearchComputing/uwyo_2019", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-pnlbwhtbss-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#pnlbwhtbss-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epnlbwh/tbss containers\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://dev.azure.com/tbillah/tbssDemo/_build/latest?definitionId=3\u0026amp;branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0375fd1a7fe39935e578d504ee8235f549a7ca497c133e2efb9b82b9c4707952/68747470733a2f2f6465762e617a7572652e636f6d2f7462696c6c61682f7462737344656d6f2f5f617069732f6275696c642f7374617475732f7461736872696662696c6c61682e746273735f636f6e7461696e6572733f6272616e63684e616d653d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://dev.azure.com/tbillah/tbssDemo/_apis/build/status/tashrifbillah.tbss_containers?branchName=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository is set up to test Microsoft Azure Pipeline integrability for \u003ccode\u003epnlbwh/tbss\u003c/code\u003e pipeline. It contains recipes for Docker and Singularity containers. The recipes build following software:\u003c/p\u003e\n\u003cp\u003efsl-6.0.1-centos7\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/pnbwh/tbss.git\"\u003ehttps://github.com/pnbwh/tbss.git\u003c/a\u003e : master branch\u003c/p\u003e\n\u003cp\u003eANTs : conda install -c pnlbwh ants\u003c/p\u003e\n\u003cp\u003eView FSL license below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA salient clause of the license states it is not free for commercial use. So, if you use this image, make sure you are aware of that limitation. The maintainer of this image is not and cannot be held liable for unlawful use of this image.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-uwyo_2019\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#uwyo_2019\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003euwyo_2019\u003c/h1\u003e\n\u003cp\u003eResearch Computing is working to foster an inclusive and welcoming culture for everyone in our community. We recognize there is some non-inclusive language being used in our documents. We are working to resolve this.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 9, "topics": [], - "updated_at": 1615777455.0 + "updated_at": 1662061107.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "misc/releases/22.12/Singularity.22.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/latest/Singularity" ], - "full_name": "Dill-PICL/GOMAP-base", + "full_name": "ipc2023-classical/planner22", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1184\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-gomap-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#gomap-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGOMAP-base\u003c/h1\u003e\n\u003cp\u003eThis is the base image for the GOMAP-singularity container. This base image has all the requirements installed for running GOMAP\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eGCC 11, GCC 12, Clang 14\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 12\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003ctd\u003eAppleClang 14\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 11\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eAppleClang 13\u003c/td\u003e\n\u003ctd\u003e3.24\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2015, 2021-2022 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1635539406.0 + "updated_at": 1688990711.0 }, { "data_format": 2, - "description": "Sentinel 2 ARD processor", + "description": null, "filenames": [ - "mpi-base/Singularity", - "base/Singularity" + "tensorflow-gpu/Singularity", + "torch/Singularity", + "freesurfer/Singularity", + "caffe-cpu/Singularity", + "caffe-gpu/Singularity", + "neurokernel/Singularity", + "neurokernel/SingularityPC", + "digits/Singularity", + "caffe2/Singularity", + "theano/Singularity", + "cuda/Singularity", + "tensorflowgpu-theano-keras-pytorch/Singularity", + "tensorflow-cpu/Singularity" ], - "full_name": "jncc/s2-ard-processor", + "full_name": "rses-singularity/all-images", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-s2-ard-processor\" class=\"anchor\" aria-hidden=\"true\" href=\"#s2-ard-processor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eS2 ARD Processor\u003c/h1\u003e\n\u003cp\u003eDocker based sentinel 2 Analysis ready production system.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBase\u003c/h2\u003e\n\u003cp\u003eA base docker image packaging Dr Pete Buntings Python Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) software (\u003ca href=\"https://www.arcsi.remotesensing.info/\" rel=\"nofollow\"\u003ehttps://www.arcsi.remotesensing.info/\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eBased on the official ContinuumIO Miniconda3 release with python 3.5, base package contains a minimal installaition of ARCSI and its dependencies using the conda package manger, correct as of version 3.1.6 (conda reporting 3.6.1).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build-or-pull-arcsi-base\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-or-pull-arcsi-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild or Pull arcsi-base\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-build-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild image\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker build -t jncc/arcsi-base ./base/\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-pull-image-direction-from-docker-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#pull-image-direction-from-docker-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull Image direction from docker hub\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker pull jncc/arcsi-base\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-run-image-interactively\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-image-interactively\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun image interactively\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003edocker run -i -v \u0026lt;local mount point\u0026gt;:/data -t jncc/arcsi-base /bin/bash\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run a container and get help on ARCSI commandline options do:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run -t jncc/arcsi-base arcsi.py -h\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSee below under \"Docker example\" for a more detailed Sentinel-2 example.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker example\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -i -t -v \u003cspan class=\"pl-smi\"\u003e${local_data}\u003c/span\u003e:/data jncc/arcsi-base \\\n arcsi.py -s sen2 --stats -f KEA --fullimgouts -p RAD SHARP SATURATE CLOUDS TOPOSHADOW STDSREF DOSAOTSGL METADATA FOOTPRINT \\\n --interp near --outwkt /data/\u003cspan class=\"pl-smi\"\u003e${PATH_TO_OUTPUT_PROJ_WKT}\u003c/span\u003e --projabbv \u003cspan class=\"pl-smi\"\u003e${PROJ_ABBREVIATION}\u003c/span\u003e -t /data/tmp/ -o /data/output/ \\\n --dem /data/\u003cspan class=\"pl-smi\"\u003e${PATH_TO_DEM}\u003c/span\u003e -i /data/inputs/\u003cspan class=\"pl-smi\"\u003e${SINGLE_INPUT_FILE}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-see-also\" class=\"anchor\" aria-hidden=\"true\" href=\"#see-also\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSee also\u003c/h3\u003e\n\u003cp\u003eThanks to Markus Neteler (\u003ca href=\"https://github.com/mundialis/docker-arcsi\"\u003ehttps://github.com/mundialis/docker-arcsi\u003c/a\u003e), Edward P. Morris and Angelos Tzotsos for their work on the orignal ARCSI Dockerfile.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity image definitons for ShARC\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-definitons-for-sharc\" class=\"anchor\" aria-label=\"Permalink: Singularity image definitons for ShARC\" href=\"#singularity-image-definitons-for-sharc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee \u003ca href=\"build.sh\"\u003ebuild.sh\u003c/a\u003e for info on how to build and run the image\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e file for the definition of the image, including\n\u003cul\u003e\n\u003cli\u003eThe (Docker) image it is based on\u003c/li\u003e\n\u003cli\u003eWhat OS packages are installed\u003c/li\u003e\n\u003cli\u003eWhat environment variables are set\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSee the \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project website for more info on Singularity in general and detailed documentaiton.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 6, + "subscribers_count": 4, "topics": [], - "updated_at": 1634223735.0 + "updated_at": 1710324331.0 }, { "data_format": 2, - "description": "Singularity recipe files for Stacks (http://catchenlab.life.illinois.edu/stacks/)", + "description": "The next generation of graphical single-cell RNA-seq analysis pipeline for genomics scientists", "filenames": [ - "Singularity", - "Singularity.2.2", - "Singularity.2.0", - "Singularity.2.1" + "g_packages/official_py_docker/docker/Singularity" ], - "full_name": "powerPlant/stacks-srf", + "full_name": "lanagarmire/granatumx", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2270\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the Stacks software pipeline for building loci from short-read sequences\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGranatumX\u003c/h1\u003e\u003ca id=\"user-content-granatumx\" class=\"anchor\" aria-label=\"Permalink: GranatumX\" href=\"#granatumx\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003eWelcome to the next generation of graphical single-cell RNA-seq analysis pipeline for genomics scientists!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat\u0027s new\u003c/h2\u003e\u003ca id=\"user-content-whats-new\" class=\"anchor\" aria-label=\"Permalink: What\u0027s new\" href=\"#whats-new\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cul\u003e\n\u003cli\u003eCustomize processing with a modern plug-in system \u2013 add your favorite library!\u003c/li\u003e\n\u003cli\u003eLeverage multiple servers by deploying a single backend database with multiple frontend clients for processing\u003c/li\u003e\n\u003cli\u003eSave and manage multiple projects from one secure personal account\u003c/li\u003e\n\u003cli\u003eNew clean and intuitive interface with much more functionality\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSystem requirements\u003c/h2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-label=\"Permalink: System requirements\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLinux (Ubuntu 14.04 is tested)\u003c/li\u003e\n\u003cli\u003eDocker (19.03.4-ce is tested)\u003c/li\u003e\n\u003cli\u003eYarn (1.19.1 is tested)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eArchitecture\u003c/h2\u003e\u003ca id=\"user-content-architecture\" class=\"anchor\" aria-label=\"Permalink: Architecture\" href=\"#architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003eThe entire architecture has been re-designed from the ground up\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eServer-side:\n\u003cul\u003e\n\u003cli\u003eNodeJS + ExpressJS (web-serving)\u003c/li\u003e\n\u003cli\u003ePostgreSQL + Knex (database)\u003c/li\u003e\n\u003cli\u003eGraphile (postgres to graphql schema)\u003c/li\u003e\n\u003cli\u003eReact (server-side rendering)\u003c/li\u003e\n\u003cli\u003eDocker (containerized execution)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eClient-side:\n\u003cul\u003e\n\u003cli\u003eApollo (graphql client)\u003c/li\u003e\n\u003cli\u003eReact (component rendering)\u003c/li\u003e\n\u003cli\u003eMaterial-UI (theme)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating a Gbox\u003c/h2\u003e\u003ca id=\"user-content-creating-a-gbox\" class=\"anchor\" aria-label=\"Permalink: Creating a Gbox\" href=\"#creating-a-gbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGranatumX ships with a template to make it easier for developers to create new Gboxes. Below is a checklist for creating a new Gbox.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the GranatumX Source Code \u003ccode\u003egit clone https://gitlab.com/xz/GranatumX\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIn the GranatumX folder, run \u003ccode\u003emake setup\u003c/code\u003e. After setup completes, GranatumX should start on port \u003ccode\u003e34567\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCopy the Gbox template into the \u003ccode\u003eg_packages\u003c/code\u003e directory: \u003ccode\u003ecp gboxTemplate g_packages/yourPackageName\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit your \u003ccode\u003epackage.yaml\u003c/code\u003e file with your package information. See other packages in the \u003ccode\u003eg_packages\u003c/code\u003e folder for more examples.\u003c/li\u003e\n\u003cli\u003eEdit your \u003ccode\u003eDockerfile\u003c/code\u003e by adding any package installation scripts your Gbox requires.\u003c/li\u003e\n\u003cli\u003eEdit your \u003ccode\u003emain.py\u003c/code\u003e file with your application code (or whatever file you specified in \u003ccode\u003epackage.yaml \u0026gt; gboxes \u0026gt; endpoints \u0026gt; backend \u0026gt; cmd\u003c/code\u003e). The Python package \u003ccode\u003egranatum_sdk\u003c/code\u003e contains helper methods for easily interacting with the GranatumX core.\u003c/li\u003e\n\u003cli\u003eInstall your new gbox with \u003ccode\u003ecd gboxes; yarn run installEverything\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRefresh GranatumX and test your new Gbox.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning your own server\u003c/h2\u003e\u003ca id=\"user-content-running-your-own-server\" class=\"anchor\" aria-label=\"Permalink: Running your own server\" href=\"#running-your-own-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment Variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf starting the server with sudo you may need to add the -E flag to use your current environment variables (sudo -E make start).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePORT: Port # to listen to, i.e. 80, 3000, 8888\u003c/li\u003e\n\u003cli\u003eDATABASE_URL: Url to connect to your Postgresql database server\u003c/li\u003e\n\u003cli\u003eSSL_CERT: (optional) To serve Granatum on port 443 over SSL, set to certificate filepath\u003c/li\u003e\n\u003cli\u003eSSL_KEY: (optional) To serve Granatum on port 443 over SSL, set to private key filepath\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1611661899.0 + "updated_at": 1614863068.0 }, { "data_format": 2, - "description": "Demultiplex sequencing experiments with Nextflow", + "description": "A Singularity recipe for samtools, bcftools, minimap2, bwa, and seqtk", "filenames": [ "Singularity" ], - "full_name": "czbiohub/demux", + "full_name": "phgenomics-singularity/hltools", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-coredemux\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-coredemux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/demux\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eDemultiplex sequencing experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/demux\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/21a4d7a0262a3b096d2521e08bc4e18bf7340ea1b97ea36ca247a7d06ccd04b1/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f64656d75782e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/demux.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/demux\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8b87efd0f3651289c43d7f37a2a1092964f35f8501ccbcbe7ef15bfc1b38ae67/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f64656d75782e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/demux.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/demux pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehltools\u003c/h1\u003e\u003ca id=\"user-content-hltools\" class=\"anchor\" aria-label=\"Permalink: hltools\" href=\"#hltools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Singularity recipe for samtools, bcftools, minimap2, bwa, and seqtk\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1562208393.0 + "updated_at": 1698089586.0 }, { "data_format": 2, - "description": "PhysiCell Invasion Model", + "description": "M\u0026Ms challenge example model for evaluation.", "filenames": [ - "src/addons/PhysiBoSSa/MaBoSS-env-2.0/containers/singularity/Singularity" + "Singularity" ], - "full_name": "vincent-noel/pc4ecm", + "full_name": "vicmancr/mnms_example", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eM\u0026amp;Ms example project\u003c/h1\u003e\u003ca id=\"user-content-mms-example-project\" class=\"anchor\" aria-label=\"Permalink: M\u0026amp;Ms example project\" href=\"#mms-example-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is provided just as an example for illustration on the valiation phase of the MICCAI 2020 M\u0026amp;Ms challenge.\u003c/p\u003e\n\u003cp\u003eFollow the steps in the Singularity \u003ca href=\"https://github.com/vicmancr/mnms_example/blob/master/Singularity\"\u003erecipe\u003c/a\u003e to build your own image.\u003c/p\u003e\n\u003cp\u003eYou can test your image against the sample data writing \u003ccode\u003esingularity run my_image.sif sample_data output1\u003c/code\u003e inside this repository folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eSegmentation model based on the open-source code by C. Baumgartner and L. Koch: \u003ca href=\"https://github.com/baumgach/acdc_segmenter\"\u003ehttps://github.com/baumgach/acdc_segmenter\u003c/a\u003e.\u003c/h5\u003e\u003ca id=\"user-content-segmentation-model-based-on-the-open-source-code-by-c-baumgartner-and-l-koch-httpsgithubcombaumgachacdc_segmenter\" class=\"anchor\" aria-label=\"Permalink: Segmentation model based on the open-source code by C. Baumgartner and L. Koch: https://github.com/baumgach/acdc_segmenter.\" href=\"#segmentation-model-based-on-the-open-source-code-by-c-baumgartner-and-l-koch-httpsgithubcombaumgachacdc_segmenter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1588946949.0 + "updated_at": 1689044803.0 }, { "data_format": 2, - "description": null, + "description": "Docker and Singularity containers for Material Point Method", "filenames": [ - "Singularity", - "Singularity.0.2.2", - "Singularity.0.1-alpha", - "Singularity.0.2.0", - "Singularity.0.2.1" + "Singularity" ], - "full_name": "dcgc-bfx/singularity-single-cell", + "full_name": "cb-geo/mpm-container", "latest_release": null, - "readme": "\u003cp\u003emoved to \u003ca href=\"https://gitlab.hrz.tu-chemnitz.de/dcgc-bfx/singularity-single-cell\" rel=\"nofollow\"\u003ehttps://gitlab.hrz.tu-chemnitz.de/dcgc-bfx/singularity-single-cell\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker image for Material Point Method\u003c/h1\u003e\u003ca id=\"user-content-docker-image-for-material-point-method\" class=\"anchor\" aria-label=\"Permalink: Docker image for Material Point Method\" href=\"#docker-image-for-material-point-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eKrishna Kumar\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/cbgeo/mpm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e4153a63c745e2c46fc5833d7f893c6f0846356f610fd8691a90ed0fbc554a5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f717561792d2d696d6167652d636267656f2d2d6d706d2d6666363962342e737667\" alt=\"Quay image\" data-canonical-src=\"https://img.shields.io/badge/quay--image-cbgeo--mpm-ff69b4.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/cbgeo/mpm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a8dd9d74b9dd3817dbb0f5bbc5720d61c538dbccb763cb41dc4069c518f557b9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d2d6875622d636267656f2d2d6d706d2d6666363962342e737667\" alt=\"Docker hub\" data-canonical-src=\"https://img.shields.io/badge/docker--hub-cbgeo--mpm-ff69b4.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/cb-geo/mpm-container\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dda80d8651c49313c225f64bcc0ba59b077dfbc1976bff8a19912be0cf6289fa/68747470733a2f2f636972636c6563692e636f6d2f67682f63622d67656f2f6d706d2d636f6e7461696e65722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/cb-geo/mpm-container.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://microbadger.com/images/cbgeo/mpm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b4e99c47692d447f60bb724b3ff4c76e4440713c50af617adc035460b178aaac/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f636267656f2f6d706d2e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/cbgeo/mpm.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools\u003c/h2\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClang 6.0\u003c/li\u003e\n\u003cli\u003eCMake 3\u003c/li\u003e\n\u003cli\u003eEigen 3\u003c/li\u003e\n\u003cli\u003eGCC 8\u003c/li\u003e\n\u003cli\u003eVim\u003c/li\u003e\n\u003cli\u003eIntelTBB\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing the docker image\u003c/h1\u003e\u003ca id=\"user-content-using-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Using the docker image\" href=\"#using-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe docker image can be used directly from the Docker Hub or Quay.io\u003c/li\u003e\n\u003cli\u003ePull the docker image \u003ccode\u003edocker pull cbgeo/mpm\u003c/code\u003e or \u003ccode\u003edocker pull quay.io/cbgeo/mpm\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo launch the \u003ccode\u003ecbgeo/mpm\u003c/code\u003e docker container, run \u003ccode\u003edocker run -ti cbgeo/mpm:latest /bin/bash\u003c/code\u003e or \u003ccode\u003edocker run -ti quay.io/cbgeo/mpm:latest /bin/bash\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo login as root\u003c/h1\u003e\u003ca id=\"user-content-to-login-as-root\" class=\"anchor\" aria-label=\"Permalink: To login as root\" href=\"#to-login-as-root\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLaunching docker as root user: \u003ccode\u003edocker exec -u 0 -ti \u0026lt;containerid\u0026gt; /bin/bash\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCreating an image from the docker file\u003c/h1\u003e\u003ca id=\"user-content-creating-an-image-from-the-docker-file\" class=\"anchor\" aria-label=\"Permalink: Creating an image from the docker file\" href=\"#creating-an-image-from-the-docker-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eTo build an image from docker file run as root \u003ccode\u003edocker build -t \"cbgeo/mpm\" /path/to/Dockerfile\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edocker history\u003c/code\u003e will show you the effect of each command has on the overall size of the file.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, - "topics": [], - "updated_at": 1656925540.0 + "subscribers_count": 4, + "topics": [ + "mpm", + "docker", + "container" + ], + "updated_at": 1671734662.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.py3-matt", - "Singularity.py3-21", - "Singularity.py3-pytorch", - "Singularity.snippy", - "Singularity.py2" + "3.04/Singularity" ], - "full_name": "RationalTangle/hcc", + "full_name": "pscedu/singularity-cowsay", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hcc\" class=\"anchor\" aria-hidden=\"true\" href=\"#hcc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehcc\u003c/h1\u003e\n\u003cp\u003eA collection of useful scripts and containers for use with the HCC cluster.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eREADME\u003c/h1\u003e\u003ca id=\"user-content-readme\" class=\"anchor\" aria-label=\"Permalink: README\" href=\"#readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e255da600696e674a8923a820a23edadc47a79a802c433a23491e8221e0b60c7/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f382f38302f436f777361795f5479706963616c5f4f75747075742e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e255da600696e674a8923a820a23edadc47a79a802c433a23491e8221e0b60c7/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f382f38302f436f777361795f5479706963616c5f4f75747075742e706e67\" data-canonical-src=\"https://upload.wikimedia.org/wikipedia/commons/8/80/Cowsay_Typical_Output.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecowsay --help\n\ncow{say,think} version 3.03, (c) 1999 Tony Monroe\nUsage: cowthink [-bdgpstwy] [-h] [-e eyes] [-f cowfile]\n [-l] [-n] [-T tongue] [-W wrapcolumn] [message]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample\u003c/h2\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\necho -e \"This is Sparta\\!\" | singularity exec singularity-cowsay-3.04.sif cowthink -f flaming-sheep\n\n _________________\n( This is Sparta! )\n -----------------\n o . . .\n o . . . ` ,\n o .; . : .\u0027 : : : .\n o i..`: i` i.i.,i i .\n o `,--.|i |i|ii|ii|i:\n UooU\\.\u0027@@@@@@`.||\u0027\n \\__/(@@@@@@@@@@)\u0027\n (@@@@@@@@)\n `YY~~~~YY\u0027\n || ||\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker -t icaoberg/cowsay .\ndocker run icaoberg/cowsay cowsay -f flaming-sheep \"This is Sparta\\!\"\n _________________\n\u0026lt; This is Sparta! \u0026gt;\n -----------------\n \\ . . .\n \\ . . . ` ,\n \\ .; . : .\u0027 : : : .\n \\ i..`: i` i.i.,i i .\n \\ `,--.|i |i|ii|ii|i:\n UooU\\.\u0027@@@@@@`.||\u0027\n \\__/(@@@@@@@@@@)\u0027\n (@@@@@@@@)\n `YY~~~~YY\u0027\n || ||\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull icaoberg/cowsay\ndocker run icaoberg/cowsay cowsay -f flaming-sheep \"This is Sparta\\!\"\n _________________\n\u0026lt; This is Sparta! \u0026gt;\n -----------------\n \\ . . .\n \\ . . . ` ,\n \\ .; . : .\u0027 : : : .\n \\ i..`: i` i.i.,i i .\n \\ `,--.|i |i|ii|ii|i:\n UooU\\.\u0027@@@@@@`.||\u0027\n \\__/(@@@@@@@@@@)\u0027\n (@@@@@@@@)\n `YY~~~~YY\u0027\n || ||\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1619559180.0 + "updated_at": 1649098680.0 }, { "data_format": 2, - "description": "Singularity instance for E2P2 (prediction of enzymatic functions). Clone the master version of the repository https://github.com/carnegie/E2P2", + "description": "butterfly server packaged in singularity container", "filenames": [ - "e2p2v4-container/Singularity", - "e2p2v3-container/Singularity" + "Singularity" ], - "full_name": "lipme/e2p2-singularity", + "full_name": "vsoch/singularity-butterfly", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-e2p2-singularity-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#e2p2-singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eE2P2 Singularity Containers\u003c/h1\u003e\n\u003cp\u003eSingularity containers for E2P2 version 3 and 4 (prediction of enzymatic functions).\nE2P2 Source: \u003ca href=\"https://github.com/carnegie/E2P2\"\u003ehttps://github.com/carnegie/E2P2\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersions\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"e2p2v3-container/\"\u003ev3.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"e2p2v4-container/\"\u003ev4 (20221206)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainers\" class=\"anchor\" aria-hidden=\"true\" href=\"#maintainers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainers\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"mailto:Sebastien.Carrere@inrae.fr\"\u003eSebastien.Carrere@inrae.fr\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"mailto:Ludovic.Cottret@inrae.fr\"\u003eLudovic.Cottret@inrae.fr\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Butterfly\u003c/h1\u003e\u003ca id=\"user-content-singularity-butterfly\" class=\"anchor\" aria-label=\"Permalink: Singularity Butterfly\" href=\"#singularity-butterfly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/paradoxxxzero/butterfly\"\u003eButterfly\u003c/a\u003e is a beautiful piece of work that brings the terminal into your browser. With Singularity Butterfly, you can get this working on your local cluster too! Here is how to create the image, which you should do locally first:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ehttps://github.com/vsoch/singularity-butterfly\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity-butterfly\n./generate_image.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"generate_image.sh\"\u003egenerate_image.sh\u003c/a\u003e script simply creates a container, and does a bootstrap to install butterfly.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity create --size 4000 butterfly.img\nsudo singularity bootstrap butterfly.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen to run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /tmp:/run/user/1000/dconf butterfly.img\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1674491459.0 + "updated_at": 1545323610.0 }, { "data_format": 2, - "description": "Singularity recipe for Pandoc.", + "description": "Singularity file for Openmind to run tensorflow", "filenames": [ - "Singularity.pandoc" + "Singularity" ], - "full_name": "bast/singularity-pandoc", - "latest_release": "0.3.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-recipe-for-pandoc\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-recipe-for-pandoc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity recipe for \u003ca href=\"https://pandoc.org/\" rel=\"nofollow\"\u003ePandoc\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eHow to fetch and use the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/bast/singularity-pandoc/releases/download/0.3.0/pandoc.sif\n$ ./pandoc.sif --from=markdown --to=rst --output=README.rst README.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI have used this wonderful guide as starting point and inspiration:\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "satra/om-tensorflow", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eom-tensorflow\u003c/h1\u003e\u003ca id=\"user-content-om-tensorflow\" class=\"anchor\" aria-label=\"Permalink: om-tensorflow\" href=\"#om-tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity file for Openmind to run tensorflow\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1642369145.0 + "updated_at": 1486549077.0 }, { "data_format": 2, - "description": "Python virtual environment on Singularity.", + "description": " This repo provides a Singularity image version for Percona Monitoring and Management (PMM)", "filenames": [ - "Singularity.venv" + "Singularity" ], - "full_name": "bast/singularity-venv", - "latest_release": "0.3.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-python-virtual-environment-on-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#python-virtual-environment-on-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython virtual environment on Singularity\u003c/h1\u003e\n\u003cp\u003eHow to fetch the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/bast/singularity-venv/releases/download/0.3.0/venv.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eReads: \u003ccode\u003erequirements.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCreates: \u003ccode\u003evenv\u003c/code\u003e (folder)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRun \u003ccode\u003emyscript.py\u003c/code\u003e inside the virtual environment defined by \u003ccode\u003erequirements.txt\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./venv.sif python myscript.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpen python shell inside the virtual environment defined by \u003ccode\u003erequirements.txt\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./venv.sif python\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eI have used this wonderful guide as starting point and inspiration:\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "netreconlab/pmm-server", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epmm-server\u003c/h1\u003e\u003ca id=\"user-content-pmm-server\" class=\"anchor\" aria-label=\"Permalink: pmm-server\" href=\"#pmm-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo provides a Singularity image version for \u003ca href=\"https://www.percona.com/software/database-tools/percona-monitoring-and-management\" rel=\"nofollow\"\u003ePercona Monitoring and Management (PMM)\u003c/a\u003e, for monitoring the health of your database infrastructure, explore new patterns in database behavior, and manage and improve the performance of your databases no matter where they are located or deployed. To learn more about the image, look \u003ca href=\"https://docs.percona.com/percona-monitoring-and-management/setting-up/server/docker.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImages\u003c/h2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImages of \u003ccode\u003epmm-server\u003c/code\u003e are automatically built for your convenience. Images can be found at the following locations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/netreconlab/pmm-server/pkgs/container/pmm-server\"\u003eSingularity - Hosted on GitHub Container Registry\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDocker - Use the \u003ca href=\"https://hub.docker.com/r/percona/pmm-server\" rel=\"nofollow\"\u003eofficial pmm-server image\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, - "topics": [], - "updated_at": 1674419698.0 + "subscribers_count": 2, + "topics": [ + "percona-server", + "postgres", + "database-management", + "mongodb", + "monitoring-server", + "singularity" + ], + "updated_at": 1673112616.0 }, { "data_format": 2, - "description": null, + "description": "Docker image to get DeepLabCutCore running on cloud GPUs.", "filenames": [ - "Singularity.Demuxafy", - "scripts/Singularity.Demuxafy" + "Singularity" ], - "full_name": "drneavin/Demultiplexing_Doublet_Detecting_Docs", - "latest_release": "v2.0.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-demultiplexing_doublet_detecting_docs\" class=\"anchor\" aria-hidden=\"true\" href=\"#demultiplexing_doublet_detecting_docs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemultiplexing_Doublet_Detecting_Docs\u003c/h1\u003e\n\u003cp\u003eThis contains the code for \u003ca href=\"https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eDemuxafy\u003c/a\u003e - a demultiplexing and doublet removal pipeline.\u003c/p\u003e\n", + "full_name": "Alyetama/DeepLabCut-HPC", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker/badge.svg\"\u003e\u003cimg src=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker%20Image%20CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/bchaselab/DeepLabCut-Slurm/workflows/Docker%20Image%20CI/badge.svg\" alt=\"Docker Image CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5345e0bf4d0e9a19f36de8aebb93a1b37efd3026a608037e0ac825d66b40aadd/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6663617475732f646565706c6162637574\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5345e0bf4d0e9a19f36de8aebb93a1b37efd3026a608037e0ac825d66b40aadd/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6663617475732f646565706c6162637574\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/fcatus/deeplabcut\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b1816dab9d52cfd6c623dae032c912432e5dc33a85d52cfb4ec883d536bca3b1/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6663617475732f646565706c6162637574\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b1816dab9d52cfd6c623dae032c912432e5dc33a85d52cfb4ec883d536bca3b1/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6663617475732f646565706c6162637574\" alt=\"Docker Image Size (latest by date)\" data-canonical-src=\"https://img.shields.io/docker/image-size/fcatus/deeplabcut\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFrom \u003ca href=\"https://hub.docker.com/repository/docker/fcatus/deeplabcut\" rel=\"nofollow\"\u003eDockerhub\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-from-dockerhub\" class=\"anchor\" aria-label=\"Permalink: From Dockerhub\" href=\"#from-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker pull fcatus/deeplabcut:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo Use With Singularity\u003c/h2\u003e\u003ca id=\"user-content-to-use-with-singularity\" class=\"anchor\" aria-label=\"Permalink: To Use With Singularity\" href=\"#to-use-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull docker://fcatus/deeplabcut:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor build it from a singularity file\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ vim singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eBootstrap\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003edocker\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eFrom\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efcatus/deeplabcut:latest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --remote deeplabcut.sif singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild From a Singularity \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/definition_files.html\" rel=\"nofollow\"\u003eDefinition File\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-build-from-a-singularity-definition-file\" class=\"anchor\" aria-label=\"Permalink: Build From a Singularity Definition File\" href=\"#build-from-a-singularity-definition-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the definition file\u003c/span\u003e\n$ wget https://gist.githubusercontent.com/Alyetama/c819bb2d07e58b47208c69ce76c22feb/raw/c07b264adc5424d6bd939a77bb34a95a6f80dca1/dlc.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Customize the definition file (optional)\u003c/span\u003e\n$ vim dlc.def\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build remotely from the definition file\u003c/span\u003e\n$ singularity build --remote deeplabcut.sif dlc.def\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more information about using \u003ccode\u003esingularity build\u003c/code\u003e, see \u003ca href=\"https://sylabs.io/guides/3.1/user-guide/cli/singularity_build.html\" rel=\"nofollow\"\u003eSingularity Build\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, - "topics": [], - "updated_at": 1667606897.0 + "subscribers_count": 2, + "topics": [ + "docker", + "deeplabcut", + "clone", + "slurm", + "hpc", + "singularity" + ], + "updated_at": 1717603187.0 }, { "data_format": 2, - "description": "Singularity container for WRF", + "description": "Repositorio asignatura Planificacion Automatica", "filenames": [ - "Singularity" + "PL1/planificadores/singularity-ce-3.9.5/e2e/testdata/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/self/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/centos-arm64/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/arch/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/docker/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/busybox/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/debian/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/scientific/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/sle/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/shub/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/apps/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/apps/Singularity.cowsay", + "PL1/planificadores/singularity-ce-3.9.5/examples/opensuse-arm64/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/raspbian/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/ubuntu/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/scratch/Singularity.alpine", + "PL1/planificadores/singularity-ce-3.9.5/examples/scratch/Singularity.busybox", + "PL1/planificadores/singularity-ce-3.9.5/examples/instances/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/centos/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/asciinema/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/opensuse/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/library/Singularity", + "PL1/planificadores/singularity-ce-3.9.5/examples/multistage/Singularity" ], - "full_name": "rkalyanapurdue/wrf-singularity", + "full_name": "nacho-pm/PlanificacionAutomatica_uah", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-container-recipes-for-wrf\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container-recipes-for-wrf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container recipes for WRF\u003c/h1\u003e\n\u003cp\u003eUse the recipe file Singularity to build WRF using the GNU compilers\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-important-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#important-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Notes\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eThe build assumes that source code for HDF, HDF5, NETCDF-C, NETCDF-Fortran, WRF and WPS\nhave been downloaded.\u003c/li\u003e\n\u003cli\u003eThis build uses OpenMPI 4.0.1. The OpenMPI version on the nodes where this container is\nrun needs to match that inside the container.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1676040247.0 + "updated_at": 1708631111.0 }, { "data_format": 2, - "description": "RNAseq analysis pipeline project for Paris-Saclay\u0027s University Master AMI2B", + "description": null, "filenames": [ - "containers/Singularity.R", - "containers/star/Singularity.star_nb", - "containers/samtools/Singularity.samtools", - "containers/sratoolkit/Singularity.sratoolkit", - "containers/featurecounts/Singularity.featcount" + "Singularity.OP10.6.recipe" ], - "full_name": "Sherman-1/Hackathon", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-projet-repro-hackathon-2022-2023--atia-safiya-bossut-no\u00e9mie-et-herman-simon\" class=\"anchor\" aria-hidden=\"true\" href=\"#projet-repro-hackathon-2022-2023--atia-safiya-bossut-no\u00e9mie-et-herman-simon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eProjet Repro-Hackathon 2022-2023\u003c/strong\u003e : ATIA Safiya, BOSSUT No\u00e9mie et HERMAN Simon\u003c/h1\u003e\n\u003cp\u003eCe projet vise \u00e0 reproduire une partie des r\u00e9sultats de deux articles:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5321577/\" rel=\"nofollow\"\u003eFurney \u003cem\u003eet al.\u003c/em\u003e, Cancer Discovery (2013)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3789378/\" rel=\"nofollow\"\u003eHarbour \u003cem\u003eet al.\u003c/em\u003e, Nature Genetics (2013)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLes donn\u00e9es RNA-seq de ces deux papiers sont disponibles en open-access : \u003ca href=\"https://www.ncbi.nlm.nih.gov/sra?term=SRA062359\" rel=\"nofollow\"\u003e\u003cstrong\u003eDonn\u00e9es NCBI\u003c/strong\u003e\u003c/a\u003e. Dans un premier temps, seul l\u0027\u00e9tude de transcriptome est \u00e9tudi\u00e9e. \nL\u0027objectif de ces deux articles est d\u0027\u00e9tudier les expression de g\u00e8nes, et notamment le g\u00e8ne SF3B1, d\u0027individus atteint de m\u00e9lanome uv\u00e9al. Harbour \u003cem\u003eet al.\u003c/em\u003e affirment que le g\u00e8ne SF3B1 est mut\u00e9, pour des donn\u00e9es exomiques, mais est g\u00e9n\u00e9ralement pr\u00e9sente dans des tumeurs b\u00e9nignes avec un bon taux de survie des patients. Furney \u003cem\u003eet al.\u003c/em\u003e montrent que, en r\u00e9utilisant le m\u00eame jeu de donn\u00e9es qu\u0027il existe une association entre la mutation SF3B1 et plusieurs types d\u0027\u00e9pissage alternatif : r\u00e9tention d\u0027introns, \u00e9pissage sur sites cryptiques et epissage en site 3\u0027 alternatif, ce qui rendrait cette mutation potentiellement \u00e0 l\u0027origine du d\u00e9veloppement canc\u00e9reux.\u003c/p\u003e\n\u003cp\u003eA l\u0027aide d\u0027un workflow Nextflow et de \u003cem\u003econtainers\u003c/em\u003e Singularity, notre groupe a tent\u00e9 de comprendre pourquoi les r\u00e9sultats des deux articles divergent, et quelles sont nos propres observations sur le sujet.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pr\u00e9-requis-nextflow--singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#pr\u00e9-requis-nextflow--singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003ePr\u00e9-requis:\u003c/strong\u003e Nextflow \u0026amp; Singularity\u003c/h2\u003e\n\u003cp\u003eAfin de faire tourner notre \u003cem\u003epipeline\u003c/em\u003e, \u003cstrong\u003e64 Gb de RAM et 16 coeurs\u003c/strong\u003e, ainsi que deux logiciels sont n\u00e9cessaires:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextflow (version 21.10.6.5660) \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003e\u003cem\u003einstallation\u003c/em\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity (version 3.8.7) \u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003e\u003cem\u003einstallation\u003c/em\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003euidmap (pour la construction des containers Singularity)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo apt-get install uidmap\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-le-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#le-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eLe pipeline:\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/75751225/206907177-60bd1d6f-84ae-4c55-a9e2-80cc1f44a2a7.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/75751225/206907177-60bd1d6f-84ae-4c55-a9e2-80cc1f44a2a7.png\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eT\u00e9l\u00e9chargement des donn\u00e9es\u003c/strong\u003e : chromosomes humains (dont chromosome mitochondrial), annotation du g\u00e9nome et donn\u00e9es RNA-seq des 8 individus (\u003cem\u003e\u003cstrong\u003esratoolkit\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndexation\u003c/strong\u003e du g\u00e9nome (\u003cem\u003e\u003cstrong\u003eSTAR\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAlignement\u003c/strong\u003e et \u003cstrong\u003eTri\u003c/strong\u003e des donn\u00e9es RNA-seq sur le g\u00e9nome. Obtention de fichiers \u003cem\u003e.bam\u003c/em\u003e tri\u00e9s en sortie (\u003cem\u003e\u003cstrong\u003eSTAR\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIndexation\u003c/strong\u003e des fichiers \u003cem\u003e.bam\u003c/em\u003e. en \u003cem\u003e.bai\u003c/em\u003e (\u003cem\u003e\u003cstrong\u003esamtools\u003c/strong\u003e\u003c/em\u003e). (optionnel)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eComptage\u003c/strong\u003e des s\u00e9quences exprim\u00e9es (\u003cem\u003e\u003cstrong\u003efeatureCounts\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAnalyse statistique\u003c/strong\u003e des r\u00e9sultats (\u003cem\u003e\u003cstrong\u003eDESeq2\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eL\u0027ensemble des donn\u00e9es et des r\u00e9sultats peuvent \u00eatre retrouv\u00e9s dans l\u0027arborescence ci-dessous:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvant execution du workflow:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u251c\u2500\u2500 bin\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 DE_analysis.R\n\u251c\u2500\u2500 containers\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 featurecounts\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 Singularity.featcount\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samtools\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 Singularity.samtools\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.R\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 sratoolkit\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 Singularity.sratoolkit\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 star\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 Singularity.star_nb\n\u251c\u2500\u2500 nextflow.config\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 run.sh\n\u2514\u2500\u2500 workflow.nf\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-ex\u00e9cution-du-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#ex\u00e9cution-du-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cstrong\u003eEx\u00e9cution du workflow\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSi les pr\u00e9-requis sont bien satisfaits, placez-vous dans le r\u00e9pertoire voulu et r\u00e9cup\u00e9rez le projet\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e git clone https://github.com/Sherman-1/Hackathon\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Hackathon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLe fichier \u003ccode\u003erun.sh\u003c/code\u003e permet d\u0027initialiser votre environnement, ainsi que de cr\u00e9er les images singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e bash run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eV\u00e9rifiez que les r\u00e9pertoires ont bien \u00e9t\u00e9 cr\u00e9\u00e9s. Si c\u0027est le cas, vous pouvez lancer le pipeline :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e nextflow run workflow.nf\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-options-dexecution\" class=\"anchor\" aria-hidden=\"true\" href=\"#options-dexecution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions d\u0027execution\u003c/h3\u003e\n\u003cp\u003eEn plus de la commande par d\u00e9faut, vous pouvez utiliser les param\u00e8tres suivants :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-resume\u003c/code\u003e si votre \u003cem\u003epipeline\u003c/em\u003e a \u00e9t\u00e9 interrompu et que vous souhaitez le reprendre\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-with-trace [nom du fichier]\u003c/code\u003e pour obtenir le DAG correspondant\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-with-report [nom du fichier]\u003c/code\u003e pour obtenir un rapport complet et de nombreuses metadatas sur le \u003cem\u003epipeline\u003c/em\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "eamiddlebrook/OrthoPhyl", + "latest_release": "v1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOrthoPhyl\u003c/h1\u003e\u003ca id=\"user-content-orthophyl\" class=\"anchor\" aria-label=\"Permalink: OrthoPhyl\" href=\"#orthophyl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e(previously OrthoPhylo...OrthoPhyl sounds better)\u003c/h2\u003e\u003ca id=\"user-content-previously-orthophyloorthophyl-sounds-better\" class=\"anchor\" aria-label=\"Permalink: (previously OrthoPhylo...OrthoPhyl sounds better)\" href=\"#previously-orthophyloorthophyl-sounds-better\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFor the version used in \u003cem\u003eOrthoPhyl \u2013 Streamlining large scale, orthology-based phylogenomic studies of bacteria at broad evolutionary scales\u003c/em\u003e see the OrthoPhyl_1.0 branch.\u003c/h2\u003e\u003ca id=\"user-content-for-the-version-used-in-orthophyl--streamlining-large-scale-orthology-based-phylogenomic-studies-of-bacteria-at-broad-evolutionary-scales-see-the-orthophyl_10-branch\" class=\"anchor\" aria-label=\"Permalink: For the version used in OrthoPhyl \u2013 Streamlining large scale, orthology-based phylogenomic studies of bacteria at broad evolutionary scales see the OrthoPhyl_1.0 branch.\" href=\"#for-the-version-used-in-orthophyl--streamlining-large-scale-orthology-based-phylogenomic-studies-of-bacteria-at-broad-evolutionary-scales-see-the-orthophyl_10-branch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeveloped at Los Alamos National Labs (LANL - C22064)\u003c/h3\u003e\u003ca id=\"user-content-developed-at-los-alamos-national-labs-lanl---c22064\" class=\"anchor\" aria-label=\"Permalink: Developed at Los Alamos National Labs (LANL - C22064)\" href=\"#developed-at-los-alamos-national-labs-lanl---c22064\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWritten by Earl Middlbrook with input from Robab Katani at Penn State and Jeanne Fair at LANL.\u003c/h4\u003e\u003ca id=\"user-content-written-by-earl-middlbrook-with-input-from-robab-katani-at-penn-state-and-jeanne-fair-at-lanl\" class=\"anchor\" aria-label=\"Permalink: Written by Earl Middlbrook with input from Robab Katani at Penn State and Jeanne Fair at LANL.\" href=\"#written-by-earl-middlbrook-with-input-from-robab-katani-at-penn-state-and-jeanne-fair-at-lanl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe software is available through a GLPv3 open source licence.\u003c/h4\u003e\u003ca id=\"user-content-the-software-is-available-through-a-glpv3-open-source-licence\" class=\"anchor\" aria-label=\"Permalink: The software is available through a GLPv3 open source licence.\" href=\"#the-software-is-available-through-a-glpv3-open-source-licence\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ch4\u003e\u003c/h4\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThis software is designed to generate phylogenetic trees from bacterial genome assemblies. While many methods use whole genome alignments to generate informative sites to base tress on, OrthoPhyl annotates bacterial genes, identifies orthologous sequences, aligns related proteins to inform transcript alignments, then builds species trees with two methods. The first is a conventional gene concatenation and ML tree estemation method. The second attempts to reconcile gene trees with a unified species tree using quartets (ASTRAL). Both methods allow filtering of gene lists on number of species represented, length, and gappiness in order to tune signal-to-noise ratio for tree estimation.\u003c/h4\u003e\u003ca id=\"user-content-this-software-is-designed-to-generate-phylogenetic-trees-from-bacterial-genome-assemblies-while-many-methods-use-whole-genome-alignments-to-generate-informative-sites-to-base-tress-on-orthophyl-annotates-bacterial-genes-identifies-orthologous-sequences-aligns-related-proteins-to-inform-transcript-alignments-then-builds-species-trees-with-two-methods-the-first-is-a-conventional-gene-concatenation-and-ml-tree-estemation-method-the-second-attempts-to-reconcile-gene-trees-with-a-unified-species-tree-using-quartets-astral-both-methods-allow-filtering-of-gene-lists-on-number-of-species-represented-length-and-gappiness-in-order-to-tune-signal-to-noise-ratio-for-tree-estimation\" class=\"anchor\" aria-label=\"Permalink: This software is designed to generate phylogenetic trees from bacterial genome assemblies. While many methods use whole genome alignments to generate informative sites to base tress on, OrthoPhyl annotates bacterial genes, identifies orthologous sequences, aligns related proteins to inform transcript alignments, then builds species trees with two methods. The first is a conventional gene concatenation and ML tree estemation method. The second attempts to reconcile gene trees with a unified species tree using quartets (ASTRAL). Both methods allow filtering of gene lists on number of species represented, length, and gappiness in order to tune signal-to-noise ratio for tree estimation.\" href=\"#this-software-is-designed-to-generate-phylogenetic-trees-from-bacterial-genome-assemblies-while-many-methods-use-whole-genome-alignments-to-generate-informative-sites-to-base-tress-on-orthophyl-annotates-bacterial-genes-identifies-orthologous-sequences-aligns-related-proteins-to-inform-transcript-alignments-then-builds-species-trees-with-two-methods-the-first-is-a-conventional-gene-concatenation-and-ml-tree-estemation-method-the-second-attempts-to-reconcile-gene-trees-with-a-unified-species-tree-using-quartets-astral-both-methods-allow-filtering-of-gene-lists-on-number-of-species-represented-length-and-gappiness-in-order-to-tune-signal-to-noise-ratio-for-tree-estimation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe main advantages of this software pipeline are three fold: 1) It extends the evolutionary distance input species can represent (over whole genome alignment and k-mer methods) while maintaining phylogenetic resolution, 2) this software is designed to be very user friendly, requiring just a single to estimate trees from a directory of assemblies. Additionally a Singularity image is now available to avoid dependancy hell and 3) this pipleline is amenable to estimating trees for 1000s of bacterial genome assemblies. To handle large numbers of genomes, the pipline calculates a diversity-representing subset of genomes to run orthofinder on, then expands the found OrthoGroups to all assemblies with iterative HMM searches.\u003c/h4\u003e\u003ca id=\"user-content-the-main-advantages-of-this-software-pipeline-are-three-fold-1-it-extends-the-evolutionary-distance-input-species-can-represent-over-whole-genome-alignment-and-k-mer-methods-while-maintaining-phylogenetic-resolution-2-this-software-is-designed-to-be-very-user-friendly-requiring-just-a-single-to-estimate-trees-from-a-directory-of-assemblies-additionally-a-singularity-image-is-now-available-to-avoid-dependancy-hell-and-3-this-pipleline-is-amenable-to-estimating-trees-for-1000s-of-bacterial-genome-assemblies-to-handle-large-numbers-of-genomes-the-pipline-calculates-a-diversity-representing-subset-of-genomes-to-run-orthofinder-on-then-expands-the-found-orthogroups-to-all-assemblies-with-iterative-hmm-searches\" class=\"anchor\" aria-label=\"Permalink: The main advantages of this software pipeline are three fold: 1) It extends the evolutionary distance input species can represent (over whole genome alignment and k-mer methods) while maintaining phylogenetic resolution, 2) this software is designed to be very user friendly, requiring just a single to estimate trees from a directory of assemblies. Additionally a Singularity image is now available to avoid dependancy hell and 3) this pipleline is amenable to estimating trees for 1000s of bacterial genome assemblies. To handle large numbers of genomes, the pipline calculates a diversity-representing subset of genomes to run orthofinder on, then expands the found OrthoGroups to all assemblies with iterative HMM searches.\" href=\"#the-main-advantages-of-this-software-pipeline-are-three-fold-1-it-extends-the-evolutionary-distance-input-species-can-represent-over-whole-genome-alignment-and-k-mer-methods-while-maintaining-phylogenetic-resolution-2-this-software-is-designed-to-be-very-user-friendly-requiring-just-a-single-to-estimate-trees-from-a-directory-of-assemblies-additionally-a-singularity-image-is-now-available-to-avoid-dependancy-hell-and-3-this-pipleline-is-amenable-to-estimating-trees-for-1000s-of-bacterial-genome-assemblies-to-handle-large-numbers-of-genomes-the-pipline-calculates-a-diversity-representing-subset-of-genomes-to-run-orthofinder-on-then-expands-the-found-orthogroups-to-all-assemblies-with-iterative-hmm-searches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGetting Started\u003c/h1\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompatability and Dependencies\u003c/h3\u003e\u003ca id=\"user-content-compatability-and-dependencies\" class=\"anchor\" aria-label=\"Permalink: Compatability and Dependencies\" href=\"#compatability-and-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThis workflow has been tested on a CentOS8 machine, but should be pretty portable. Testing is starting on diverse *nix systems. There will likely be some errors due to the wrapper having moderate bash complexity. Unfortunately fastANI seems to be incompatable with MacOS, so I will need to find an alternative before the pipeline can be run on that architecture.\u003c/h4\u003e\u003ca id=\"user-content-this-workflow-has-been-tested-on-a-centos8-machine-but-should-be-pretty-portable-testing-is-starting-on-diverse-nix-systems-there-will-likely-be-some-errors-due-to-the-wrapper-having-moderate-bash-complexity-unfortunately-fastani-seems-to-be-incompatable-with-macos-so-i-will-need-to-find-an-alternative-before-the-pipeline-can-be-run-on-that-architecture\" class=\"anchor\" aria-label=\"Permalink: This workflow has been tested on a CentOS8 machine, but should be pretty portable. Testing is starting on diverse *nix systems. There will likely be some errors due to the wrapper having moderate bash complexity. Unfortunately fastANI seems to be incompatable with MacOS, so I will need to find an alternative before the pipeline can be run on that architecture.\" href=\"#this-workflow-has-been-tested-on-a-centos8-machine-but-should-be-pretty-portable-testing-is-starting-on-diverse-nix-systems-there-will-likely-be-some-errors-due-to-the-wrapper-having-moderate-bash-complexity-unfortunately-fastani-seems-to-be-incompatable-with-macos-so-i-will-need-to-find-an-alternative-before-the-pipeline-can-be-run-on-that-architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf you have Singularity you can run a prebuilt container. Grab the container with (requires ~1.5gb space at the moment):\u003c/h4\u003e\u003ca id=\"user-content-if-you-have-singularity-you-can-run-a-prebuilt-container-grab-the-container-with-requires-15gb-space-at-the-moment\" class=\"anchor\" aria-label=\"Permalink: If you have Singularity you can run a prebuilt container. Grab the container with (requires ~1.5gb space at the moment):\" href=\"#if-you-have-singularity-you-can-run-a-prebuilt-container-grab-the-container-with-requires-15gb-space-at-the-moment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity_images=~/singularity_images/\nmkdir ${singularity_images}\ncd ${singularity_images}\nsingularity pull library://earlyevol/default/orthophyl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCongradulations! You can skip down to testing the install!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOR install OrthoPhyl and these dependencies with the instructions bellow.\u003c/h4\u003e\u003ca id=\"user-content-or-install-orthophyl-and-these-dependencies-with-the-instructions-bellow\" class=\"anchor\" aria-label=\"Permalink: OR install OrthoPhyl and these dependencies with the instructions bellow.\" href=\"#or-install-orthophyl-and-these-dependencies-with-the-instructions-bellow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eprodigal\u003c/li\u003e\n\u003cli\u003eorthofinder\u003c/li\u003e\n\u003cli\u003ebbmap\u003c/li\u003e\n\u003cli\u003efastTree*\u003c/li\u003e\n\u003cli\u003ehmmer\u003c/li\u003e\n\u003cli\u003epal2nal\u003c/li\u003e\n\u003cli\u003eprodigal\u003c/li\u003e\n\u003cli\u003eete3\u003c/li\u003e\n\u003cli\u003eIQTree*\u003c/li\u003e\n\u003cli\u003eraxml*\u003c/li\u003e\n\u003cli\u003etrimal\u003c/li\u003e\n\u003cli\u003eparallel\u003c/li\u003e\n\u003cli\u003ecatfasta2phyml\u003c/li\u003e\n\u003cli\u003eASTRAL\u003c/li\u003e\n\u003cli\u003efastANI\u003c/li\u003e\n\u003cli\u003eR\u003c/li\u003e\n\u003cli\u003eAlignment_Assessment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e*depending on tree method used\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClone the Orthophylo repo\u003c/h3\u003e\u003ca id=\"user-content-clone-the-orthophylo-repo\" class=\"anchor\" aria-label=\"Permalink: Clone the Orthophylo repo\" href=\"#clone-the-orthophylo-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCloning takes a minute because of the large test files\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePath_to_gits=~/gits\nmkdir ${Path_to_gits}\ncd ${Path_to_gits}/\ngit clone https://github.com/eamiddlebrook/OrthoPhyl.git\ncd OrthoPhyl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConda/mamba installable dependencies\u003c/h3\u003e\u003ca id=\"user-content-condamamba-installable-dependencies\" class=\"anchor\" aria-label=\"Permalink: Conda/mamba installable dependencies\" href=\"#condamamba-installable-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf you need to install conda or mamba we recomend mamba, however the commands are exactly the same (exept running the install)\u003c/h4\u003e\u003ca id=\"user-content-if-you-need-to-install-conda-or-mamba-we-recomend-mamba-however-the-commands-are-exactly-the-same-exept-running-the-install\" class=\"anchor\" aria-label=\"Permalink: If you need to install conda or mamba we recomend mamba, however the commands are exactly the same (exept running the install)\" href=\"#if-you-need-to-install-conda-or-mamba-we-recomend-mamba-however-the-commands-are-exactly-the-same-exept-running-the-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/Downloads/ # or wherever you want to put the installer\nwget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh\nbash Mambaforge-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOR\u003c/h4\u003e\u003ca id=\"user-content-or\" class=\"anchor\" aria-label=\"Permalink: OR\" href=\"#or\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/Downloads/ # or wherever you want to put the installer\nwget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFollow on screen instructions\u003c/h4\u003e\u003ca id=\"user-content-follow-on-screen-instructions\" class=\"anchor\" aria-label=\"Permalink: Follow on screen instructions\" href=\"#follow-on-screen-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSet up auto initialize. If you don\u0027t, it is up to you to figure out how to make the script happy with your decision, you can also run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emamba init bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn some Linux distros, using \u003ccode\u003e~/.bashrc\u003c/code\u003e for only interactive shells is enforced (Linux Mint). In this case, mamba will not be initialized correctly within OrthoPhyl. To fix, just copy the conda/mamba initialization block from the bottom of your \u003ccode\u003e~/.bashrc\u003c/code\u003e file (below, replace \"...\" with what is in your file), and place them at the bottom of \u003ccode\u003e~/.bash_profile\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# \u0026gt;\u0026gt;\u0026gt; conda initialize \u0026gt;\u0026gt;\u0026gt;\n...\n# \u0026lt;\u0026lt;\u0026lt; conda initialize \u0026lt;\u0026lt;\u0026lt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate conda environment and install dependencies\u003c/h3\u003e\u003ca id=\"user-content-create-conda-environment-and-install-dependencies\" class=\"anchor\" aria-label=\"Permalink: Create conda environment and install dependencies\" href=\"#create-conda-environment-and-install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIt is highly recomended that you create an separate environment for this install. Might take some time....\u003c/h4\u003e\u003ca id=\"user-content-it-is-highly-recomended-that-you-create-an-separate-environment-for-this-install-might-take-some-time\" class=\"anchor\" aria-label=\"Permalink: It is highly recomended that you create an separate environment for this install. Might take some time....\" href=\"#it-is-highly-recomended-that-you-create-an-separate-environment-for-this-install-might-take-some-time\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emamba create -n orthophyl --file $Path_to_gits/OrthoPhyl/orthophyl_env.XXX.txt -c bioconda -c conda-forge\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOR you can install different versions if neccessary\u003c/h4\u003e\u003ca id=\"user-content-or-you-can-install-different-versions-if-neccessary\" class=\"anchor\" aria-label=\"Permalink: OR you can install different versions if neccessary\" href=\"#or-you-can-install-different-versions-if-neccessary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emamba create -n orthophyl -c bioconda -c conda-forge \\\ngit prodigal=2.6.3 orthofinder=2.5.4 \\\nbbmap=39.01 fasttree=2.1.11 hmmer=3.3.2 pal2nal=14.1 \\\nete3 raxml=8.2.12 trimal=1.4.1 \\\nparallel=20160622 r-essentials=4.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSometimes R (r-essentials) can be a pain. If it is causing problems with the conda/mamba install, remove it and install manually:\u003c/h4\u003e\u003ca id=\"user-content-sometimes-r-r-essentials-can-be-a-pain-if-it-is-causing-problems-with-the-condamamba-install-remove-it-and-install-manually\" class=\"anchor\" aria-label=\"Permalink: Sometimes R (r-essentials) can be a pain. If it is causing problems with the conda/mamba install, remove it and install manually:\" href=\"#sometimes-r-r-essentials-can-be-a-pain-if-it-is-causing-problems-with-the-condamamba-install-remove-it-and-install-manually\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGo to \u003ca href=\"https://cran.r-project.org/mirrors.html\" rel=\"nofollow\"\u003ehttps://cran.r-project.org/mirrors.html\u003c/a\u003e and pick an appropriate mirror. Then chose the linux distro you are using, download package files, and follow install instructions.\u003c/h4\u003e\u003ca id=\"user-content-go-to-httpscranr-projectorgmirrorshtml-and-pick-an-appropriate-mirror-then-chose-the-linux-distro-you-are-using-download-package-files-and-follow-install-instructions\" class=\"anchor\" aria-label=\"Permalink: Go to https://cran.r-project.org/mirrors.html and pick an appropriate mirror. Then chose the linux distro you are using, download package files, and follow install instructions.\" href=\"#go-to-httpscranr-projectorgmirrorshtml-and-pick-an-appropriate-mirror-then-chose-the-linux-distro-you-are-using-download-package-files-and-follow-install-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTo allow conda changes to take effect:\u003c/h4\u003e\u003ca id=\"user-content-to-allow-conda-changes-to-take-effect\" class=\"anchor\" aria-label=\"Permalink: To allow conda changes to take effect:\" href=\"#to-allow-conda-changes-to-take-effect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esource ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, close and reopen terminal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTo remove parallel\u0027s citation reminder (BUT DONT FORGET TO CITE!) run:\u003c/h4\u003e\u003ca id=\"user-content-to-remove-parallels-citation-reminder-but-dont-forget-to-cite-run\" class=\"anchor\" aria-label=\"Permalink: To remove parallel\u0027s citation reminder (BUT DONT FORGET TO CITE!) run:\" href=\"#to-remove-parallels-citation-reminder-but-dont-forget-to-cite-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emamba activate orthophylo\nparallel --citation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTest R install\u003c/h4\u003e\u003ca id=\"user-content-test-r-install\" class=\"anchor\" aria-label=\"Permalink: Test R install\" href=\"#test-r-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eRscript --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOther dependencies\u003c/h3\u003e\u003ca id=\"user-content-other-dependencies\" class=\"anchor\" aria-label=\"Permalink: Other dependencies\" href=\"#other-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis reflects how I like to organize my machine, pick what works for you. The control_file.paths reflects this setup. If you choose to install the below packages in different locations, just change control_file.paths to reflect this.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ${Path_to_gits}/\n# Install ASTRAL\ngit clone https://github.com/smirarab/ASTRAL.git\ncd ASTRAL\nunzip Astral.5.7.8.zip #change to curren version if needed\n\ncd ${Path_to_gits}/\ngit clone https://github.com/nylander/catfasta2phyml.git\ngit clone https://github.com/dportik/Alignment_Assessment.git\ncd Alignment_Assessment/\n# convert script to python3\n2to3 -w Alignment_Assessment_v2.py \n\n# install fastANI\ncd ~/Downloads\nwget https://github.com/ParBLiSS/FastANI/releases/download/v1.33/fastANI-Linux64-v1.33.zip\nunzip fastANI-Linux64-v1.33.zip\n# For macOS !!!!! NOT SOLVED !!!!!!\n# git clone https://github.com/ParBLiSS/FastANI.git\n# and follow install instructions. This has been a huge pain on my machine...not solved\nmkdir ~/apps/\nmv fastANI ~/apps/ # or anywhere else you would like to put it. Change control_file.required to reflect path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEdit \u003ccode\u003e${Path_to_gits}/OrthoPhyl/control_file.paths\u003c/code\u003e to reflect system specific locations and conda environment name if binaries and conda env are in different locations than discribed above\u003c/h3\u003e\u003ca id=\"user-content-edit-path_to_gitsorthophylcontrol_filepaths-to-reflect-system-specific-locations-and-conda-environment-name-if-binaries-and-conda-env-are-in-different-locations-than-discribed-above\" class=\"anchor\" aria-label=\"Permalink: Edit ${Path_to_gits}/OrthoPhyl/control_file.paths to reflect system specific locations and conda environment name if binaries and conda env are in different locations than discribed above\" href=\"#edit-path_to_gitsorthophylcontrol_filepaths-to-reflect-system-specific-locations-and-conda-environment-name-if-binaries-and-conda-env-are-in-different-locations-than-discribed-above\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest install\u003c/h2\u003e\u003ca id=\"user-content-test-install\" class=\"anchor\" aria-label=\"Permalink: Test install\" href=\"#test-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest Singularity container\u003c/h3\u003e\u003ca id=\"user-content-test-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Test Singularity container\" href=\"#test-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must specify an output directory (-s) if running through singularity, because the script will try to write directly to the container (wont work)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run ${singularity_images}/OrthoPhyl.0.9.3.sif -T TESTER_chloroplast -s ./tester_chloroplast_output -t 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest Manual install\u003c/h3\u003e\u003ca id=\"user-content-test-manual-install\" class=\"anchor\" aria-label=\"Permalink: Test Manual install\" href=\"#test-manual-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTo test \u0027conda activate\u0027 within OrthoPhyl, make sure the conda environment is not activated\u003c/h4\u003e\u003ca id=\"user-content-to-test-conda-activate-within-orthophyl-make-sure-the-conda-environment-is-not-activated\" class=\"anchor\" aria-label=\"Permalink: To test \u0027conda activate\u0027 within OrthoPhyl, make sure the conda environment is not activated\" href=\"#to-test-conda-activate-within-orthophyl-make-sure-the-conda-environment-is-not-activated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emamba deactivate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun Chloroplast test locally\u003c/h3\u003e\u003ca id=\"user-content-run-chloroplast-test-locally\" class=\"anchor\" aria-label=\"Permalink: run Chloroplast test locally\" href=\"#run-chloroplast-test-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTested on RHEL 8.5 machine with Intel Core i7-8700 CPU and 16gb ram (~8 minute runtime using 3 cores)\u003c/h4\u003e\u003ca id=\"user-content-tested-on-rhel-85-machine-with-intel-core-i7-8700-cpu-and-16gb-ram-8-minute-runtime-using-3-cores\" class=\"anchor\" aria-label=\"Permalink: Tested on RHEL 8.5 machine with Intel Core i7-8700 CPU and 16gb ram (~8 minute runtime using 3 cores)\" href=\"#tested-on-rhel-85-machine-with-intel-core-i7-8700-cpu-and-16gb-ram-8-minute-runtime-using-3-cores\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebash ${Path_to_gits}/OrthoPhyl/OrthoPhyl.sh -T TESTER_chloroplast -t 3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere should be a directory created in OrthoPhyl/TESTER/Workflow_test.chloroplast$(date +%m-%d-%Y)\nIf the test was successful, there should be 4 species trees found in the FINAL_TREES\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun bigger test locally\u003c/h3\u003e\u003ca id=\"user-content-run-bigger-test-locally\" class=\"anchor\" aria-label=\"Permalink: run bigger test locally\" href=\"#run-bigger-test-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ethis script takes about 20 hr to complete with 20 cores\u003c/h4\u003e\u003ca id=\"user-content-this-script-takes-about-20-hr-to-complete-with-20-cores\" class=\"anchor\" aria-label=\"Permalink: this script takes about 20 hr to complete with 20 cores\" href=\"#this-script-takes-about-20-hr-to-complete-with-20-cores\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMost of this is ML tree building\u003c/h4\u003e\u003ca id=\"user-content-most-of-this-is-ml-tree-building\" class=\"anchor\" aria-label=\"Permalink: Most of this is ML tree building\" href=\"#most-of-this-is-ml-tree-building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ewill make an artificial set of truncated genomes later\u003c/h4\u003e\u003ca id=\"user-content-will-make-an-artificial-set-of-truncated-genomes-later\" class=\"anchor\" aria-label=\"Permalink: will make an artificial set of truncated genomes later\" href=\"#will-make-an-artificial-set-of-truncated-genomes-later\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebash OrthoPhyl.sh -T TESTER -t 3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf the test was successful, there should be a file at \"OrthoPhyl/TESTER.pass\"\nand \"###### It looks like the install was successful ######\" should be sent to stdout towards the end of the run\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning OrthoPhyl on your data\u003c/h2\u003e\u003ca id=\"user-content-running-orthophyl-on-your-data\" class=\"anchor\" aria-label=\"Permalink: Running OrthoPhyl on your data\" href=\"#running-orthophyl-on-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eUSAGE: OrthoPhyl.sh -g Path_to_directory_of_assemblies -s directory_to_store_output\n# ALL arguments are optional if set with \\\"-c control_file.your_args\\\"\n# Many default parameters are set in control_file.defaults\n# I will work to expose the more useful ones in later versions of OP\nRequired:\n-g path to genomes directiory\nor\n-a paths to protien and transcript directories.\n They should be delared as \\\"-a path_to_transcript_dir,path_to_prot_dir\\\"\n-s path to the main directory for output\nOptional:\n-t threads to use [4]\n-p phylogenetic tree software to use astral, fasttree, raxml, and/or iqtree [\\\"fasttree iqtree astral\\\"]\n\ti.e. -p \\\"fasttree iqtree astral\\\"\n-o \"omics\" data to use for tree building ([CDS], PROT, BOTH)\n\tfor divergent sequences, it is good to compare protein trees to \n\tnucleotide trees to identify artifacts of saturation (long branch attraction)\n-c\tpath to a control file with required variables and any optional ones to override defaults.\n\tWill override values set on command line! [NULL]\n-x trimal paramerter string (in double \\\"quotes\\\")\n-r flag to rerun orthofinder on the ANI_shorlist (true/[false])\n-n Max number of proteomes to run through OrthoFinder.\n\tIf more than this many assemblies are provided, a subset of proteomes (based on genomes/transcripts ANI) will be chosen for OrthoFinder to chew on [20]\n-m Minimum fraction of total taxa per orthogroup to consider it for the relaxed SCO dataset.\n Expects a float from 0-1\n A value of 0 or 1 will lead to only estimating trees for the SCO_stict dataset.\n [0.30]\n-d Force ANI subsetting to run on transcript or genome Datasets. ([genome],transcript)\n\tUsing \\\"-a\\\" implies \\\"-d transcript\\\".\n\tIf \\\"-a\\\" is declared but you want to use the assemblies you have also provided, set \\\"-d genome\\\"\n\tIf \\\"-a\\\" not used but you want to use transcripts (annotated within OrthoPhyl by Prodigal) for ANI subsetting, set \\\"-d transcript\\\"\n-T run test dataset, incompatable with -g|s|a (TESTER,TESTER_chloroplast)\n-h display a description and a super useful usage message\n###############################################################\\n\nTo run test datasets:\n# Test of full bacterial genomes\nbash OrthoPhyl.sh -T TESTER -t #threads\n# Big test with ~100 orchid chloroplasts\nbash OrthoPhyl.sh -T TESTER_chloroplast -t #threads\n# reduced chloroplast dataset\nbash OrthoPhyl.sh -T TESTER_fasttest -t #threads\n# When running through Singularity an output directory is required:\nsingularity run \\${singularity_images}/OrthoPhyl.XXX.sif -T TESTER -s output_dir -t #threads\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample1:\u003c/h3\u003e\u003ca id=\"user-content-example1\" class=\"anchor\" aria-label=\"Permalink: Example1:\" href=\"#example1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun OrthoPhyl on assemblies in ~/Projects/ASMS/ecoli/ using 12 cores within singularity and place all results and intermediate files in ~/Projects/phylogenetics/ecoli/\u003c/h4\u003e\u003ca id=\"user-content-run-orthophyl-on-assemblies-in-projectsasmsecoli-using-12-cores-within-singularity-and-place-all-results-and-intermediate-files-in-projectsphylogeneticsecoli\" class=\"anchor\" aria-label=\"Permalink: Run OrthoPhyl on assemblies in ~/Projects/ASMS/ecoli/ using 12 cores within singularity and place all results and intermediate files in ~/Projects/phylogenetics/ecoli/\" href=\"#run-orthophyl-on-assemblies-in-projectsasmsecoli-using-12-cores-within-singularity-and-place-all-results-and-intermediate-files-in-projectsphylogeneticsecoli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run ${singularity_images}/OrthoPhyl.X.X.X.sif -g ~/Projects/ASMS/ecoli/ -s ~/Projects/phylogenetics/ecoli/ -t 12\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample2:\u003c/h3\u003e\u003ca id=\"user-content-example2\" class=\"anchor\" aria-label=\"Permalink: Example2:\" href=\"#example2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the same OrthoPhyl command from the manual install\u003c/h4\u003e\u003ca id=\"user-content-run-the-same-orthophyl-command-from-the-manual-install\" class=\"anchor\" aria-label=\"Permalink: Run the same OrthoPhyl command from the manual install\" href=\"#run-the-same-orthophyl-command-from-the-manual-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./OrthoPhyl.sh -g ~/Projects/ASMS/ecoli/ -s ~/Projects/phylogenetics/ecoli/ -t 12\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample3:\u003c/h3\u003e\u003ca id=\"user-content-example3\" class=\"anchor\" aria-label=\"Permalink: Example3:\" href=\"#example3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the same OrthoPhyl command from the manual install, also incorporate preannoated samples with protein seqs in \u003ccode\u003e~/Projects/annots/protiens/\u003c/code\u003e and transripts in \u003ccode\u003e~/Projects/annots/transcripts/\u003c/code\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-run-the-same-orthophyl-command-from-the-manual-install-also-incorporate-preannoated-samples-with-protein-seqs-in-projectsannotsprotiens-and-transripts-in-projectsannotstranscripts\" class=\"anchor\" aria-label=\"Permalink: Run the same OrthoPhyl command from the manual install, also incorporate preannoated samples with protein seqs in ~/Projects/annots/protiens/ and transripts in ~/Projects/annots/transcripts/\" href=\"#run-the-same-orthophyl-command-from-the-manual-install-also-incorporate-preannoated-samples-with-protein-seqs-in-projectsannotsprotiens-and-transripts-in-projectsannotstranscripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./OrthoPhyl.sh -g ~/Projects/ASMS/ecoli/ -a ~/Projects/annots/protiens/,~/Projects/annots/transcripts/ -s ~/Projects/phylogenetics/ecoli/ -t 12 \n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample4:\u003c/h3\u003e\u003ca id=\"user-content-example4\" class=\"anchor\" aria-label=\"Permalink: Example4:\" href=\"#example4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun OrthoPhyl to build a species tree with\u003c/h4\u003e\u003ca id=\"user-content-run-orthophyl-to-build-a-species-tree-with\" class=\"anchor\" aria-label=\"Permalink: Run OrthoPhyl to build a species tree with\" href=\"#run-orthophyl-to-build-a-species-tree-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eASTRAL (-p astral)\u003c/li\u003e\n\u003cli\u003efrom CDS (-o CDS)\u003c/li\u003e\n\u003cli\u003ecodon alignment gene trees build by iqtree used by ASTRAL (control_file.user contains just the line \"export gene_tree_methods=(\"iqtree\")\")\u003c/li\u003e\n\u003cli\u003eusing only single copy orthologs found in 1/1 assemblies (-m 1)\u003c/li\u003e\n\u003cli\u003eusing only 15 asssemblies to generate HMMs with OrthoFinder (-n 15)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e./OrthoPhyl.sh -g TESTER/genomes_chloroplast/ -s TESTER/TESTER_custom.chloro.B -m 1 -n 15 -c control_file.user -p astral -o CDS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample5:\u003c/h3\u003e\u003ca id=\"user-content-example5\" class=\"anchor\" aria-label=\"Permalink: Example5:\" href=\"#example5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun OrthoPhyl to build a species tree with\u003c/h4\u003e\u003ca id=\"user-content-run-orthophyl-to-build-a-species-tree-with-1\" class=\"anchor\" aria-label=\"Permalink: Run OrthoPhyl to build a species tree with\" href=\"#run-orthophyl-to-build-a-species-tree-with-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eiqtree and ASTRAL (-p \"iqtree astral\")\u003c/li\u003e\n\u003cli\u003efrom protein alignments (-o PROT)\u003c/li\u003e\n\u003cli\u003eprotein alignment gene trees build by fasttree used by ASTRAL (default)\u003c/li\u003e\n\u003cli\u003eusing only single copy orthologs found in .3/1 assemblies (-m .3)\u003c/li\u003e\n\u003cli\u003eusing 30 asssemblies to generate HMMs with OrthoFinder (-n 30)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e./OrthoPhyl.sh -g TESTER/genomes -s TESTER/TESTER_full_genomes -m .3 -n 30 -p \"iqtree astral\" -o PROT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMore Notes on OrthoPhyl:\u003c/h2\u003e\u003ca id=\"user-content-more-notes-on-orthophyl\" class=\"anchor\" aria-label=\"Permalink: More Notes on OrthoPhyl:\" href=\"#more-notes-on-orthophyl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThis software wraps many open source bioinformatic tools together with several custom programs. Genomes are annotated with Prodigal. If a large number of genomes are to be analyzed, fastANI is used to estimate the pairwise genomic Average Nucleotide Identity (ANI) and then an OrthoPhyl function subsets the genomes to a minimal number which represent the diversity of the full set. Protein sequences from this subset (or full set for small numbers of genomes) are used as input for OrthoFinder to identify orthologous gene families. For the subset method, a HMM search, HMMER, is used to generalize the resulting orthogroup to the full data set. From there, orthogroup proteins are realigned with MAFFT, and these aignments are used to \"codon\" align the transcript sequences (generated by earlier annotation). These orthogroup transcript alignments are then trimmed (with TRIMAL) and filtered for strict single copy orthologs (SCO_strict) or SCOs found in at least X% of the input genomes (with X being tunable). Next, transcript alignmants are concatenated to generate super-maticies and used as input for species tree generation with either RAxML or fastTREE. Aditionally, gene trees are generated from the individual transcript alignments, which are used in gene tree to species tree estimation with ASTRAL.\u003c/h4\u003e\u003ca id=\"user-content-this-software-wraps-many-open-source-bioinformatic-tools-together-with-several-custom-programs-genomes-are-annotated-with-prodigal--if-a-large-number-of-genomes-are-to-be-analyzed-fastani-is-used-to-estimate-the-pairwise-genomic-average-nucleotide-identity-ani-and-then-an-orthophyl-function-subsets-the-genomes-to-a-minimal-number-which-represent-the-diversity-of-the-full-set-protein-sequences-from-this-subset-or-full-set-for-small-numbers-of-genomes-are-used-as-input-for-orthofinder-to-identify-orthologous-gene-families-for-the-subset-method-a-hmm-search-hmmer-is-used-to-generalize-the-resulting-orthogroup-to-the-full-data-set-from-there-orthogroup-proteins-are-realigned-with-mafft-and-these-aignments-are-used-to-codon-align-the-transcript-sequences-generated-by-earlier-annotation-these-orthogroup-transcript-alignments-are-then-trimmed-with-trimal-and-filtered-for-strict-single-copy-orthologs-sco_strict-or-scos-found-in-at-least-x-of-the-input-genomes-with-x-being-tunable-next-transcript-alignmants-are-concatenated-to-generate-super-maticies-and-used-as-input-for-species-tree-generation-with-either-raxml-or-fasttree-aditionally-gene-trees-are-generated-from-the-individual-transcript-alignments-which-are-used-in-gene-tree-to-species-tree-estimation-with-astral\" class=\"anchor\" aria-label=\"Permalink: This software wraps many open source bioinformatic tools together with several custom programs. Genomes are annotated with Prodigal. If a large number of genomes are to be analyzed, fastANI is used to estimate the pairwise genomic Average Nucleotide Identity (ANI) and then an OrthoPhyl function subsets the genomes to a minimal number which represent the diversity of the full set. Protein sequences from this subset (or full set for small numbers of genomes) are used as input for OrthoFinder to identify orthologous gene families. For the subset method, a HMM search, HMMER, is used to generalize the resulting orthogroup to the full data set. From there, orthogroup proteins are realigned with MAFFT, and these aignments are used to \u0026quot;codon\u0026quot; align the transcript sequences (generated by earlier annotation). These orthogroup transcript alignments are then trimmed (with TRIMAL) and filtered for strict single copy orthologs (SCO_strict) or SCOs found in at least X% of the input genomes (with X being tunable). Next, transcript alignmants are concatenated to generate super-maticies and used as input for species tree generation with either RAxML or fastTREE. Aditionally, gene trees are generated from the individual transcript alignments, which are used in gene tree to species tree estimation with ASTRAL.\" href=\"#this-software-wraps-many-open-source-bioinformatic-tools-together-with-several-custom-programs-genomes-are-annotated-with-prodigal--if-a-large-number-of-genomes-are-to-be-analyzed-fastani-is-used-to-estimate-the-pairwise-genomic-average-nucleotide-identity-ani-and-then-an-orthophyl-function-subsets-the-genomes-to-a-minimal-number-which-represent-the-diversity-of-the-full-set-protein-sequences-from-this-subset-or-full-set-for-small-numbers-of-genomes-are-used-as-input-for-orthofinder-to-identify-orthologous-gene-families-for-the-subset-method-a-hmm-search-hmmer-is-used-to-generalize-the-resulting-orthogroup-to-the-full-data-set-from-there-orthogroup-proteins-are-realigned-with-mafft-and-these-aignments-are-used-to-codon-align-the-transcript-sequences-generated-by-earlier-annotation-these-orthogroup-transcript-alignments-are-then-trimmed-with-trimal-and-filtered-for-strict-single-copy-orthologs-sco_strict-or-scos-found-in-at-least-x-of-the-input-genomes-with-x-being-tunable-next-transcript-alignmants-are-concatenated-to-generate-super-maticies-and-used-as-input-for-species-tree-generation-with-either-raxml-or-fasttree-aditionally-gene-trees-are-generated-from-the-individual-transcript-alignments-which-are-used-in-gene-tree-to-species-tree-estimation-with-astral\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCleaning input genomes\u003c/h3\u003e\u003ca id=\"user-content-cleaning-input-genomes\" class=\"anchor\" aria-label=\"Permalink: Cleaning input genomes\" href=\"#cleaning-input-genomes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe script OrthoPhyl/utils/gather_filter_asms.XX.sh was writen to streamiline aquiring all available assemblies for a specific taxon. It takes NCBI\u0027s taxID as and input (BrucellaTaxID=234). After the genomes are DL\u0027d there are several simple genome filtering steps: length,N50,GC, etc. There are also a few more soficticated filtering methods: CheckM is used to assess completeness and contamination, while dedupe from BBmap is used to reduce duplicated contigs.\u003c/h4\u003e\u003ca id=\"user-content-the-script-orthophylutilsgather_filter_asmsxxsh-was-writen-to-streamiline-aquiring-all-available-assemblies-for-a-specific-taxon-it-takes-ncbis-taxid-as-and-input-brucellataxid234-after-the-genomes-are-dld-there-are-several-simple-genome-filtering-steps-lengthn50gc-etc-there-are-also-a-few-more-soficticated-filtering-methods-checkm-is-used-to-assess-completeness-and-contamination-while-dedupe-from-bbmap-is-used-to-reduce-duplicated-contigs\" class=\"anchor\" aria-label=\"Permalink: The script OrthoPhyl/utils/gather_filter_asms.XX.sh was writen to streamiline aquiring all available assemblies for a specific taxon. It takes NCBI\u0027s taxID as and input (BrucellaTaxID=234). After the genomes are DL\u0027d there are several simple genome filtering steps: length,N50,GC, etc. There are also a few more soficticated filtering methods: CheckM is used to assess completeness and contamination, while dedupe from BBmap is used to reduce duplicated contigs.\" href=\"#the-script-orthophylutilsgather_filter_asmsxxsh-was-writen-to-streamiline-aquiring-all-available-assemblies-for-a-specific-taxon-it-takes-ncbis-taxid-as-and-input-brucellataxid234-after-the-genomes-are-dld-there-are-several-simple-genome-filtering-steps-lengthn50gc-etc-there-are-also-a-few-more-soficticated-filtering-methods-checkm-is-used-to-assess-completeness-and-contamination-while-dedupe-from-bbmap-is-used-to-reduce-duplicated-contigs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIt is absolutely imparative to clean up assemblies to get the best results from OrthoPhyl. For instance, when looking at the output for all Brucella accessions, checkM output shows many assemblies have duplicated \"marker genes\", if these are from falsely duplicated contigs in the asm, they will lead to removal of the ortholog group from both the strict SCO and relaxed SCO gene sets within the OrthoPhylo workflow. Furthermore, dedupe.sh (from bbmap) identifies many Duplicated or Contained contigs, some of them \u0026gt;100kb long. Again, if any of the duplicated contigs contain what would otherwise be SCOs, the SCOs will be removed. In essence, having duplicates poisons the analysis by severely reducing the number of Orthologs for downstream analysis. This problem is exacerbated by including 1) less curated assemblies (GenBank) and 2) the total number of assemblies fed to the analysis pipeline.\u003c/h4\u003e\u003ca id=\"user-content-it-is-absolutely-imparative-to-clean-up-assemblies-to-get-the-best-results-from-orthophyl-for-instance-when-looking-at-the-output-for-all-brucella-accessions-checkm-output-shows-many-assemblies-have-duplicated-marker-genes-if-these-are-from-falsely-duplicated-contigs-in-the-asm-they-will-lead-to-removal-of-the-ortholog-group-from-both-the-strict-sco-and-relaxed-sco-gene-sets-within-the-orthophylo-workflow-furthermore-dedupesh-from-bbmap-identifies-many-duplicated-or-contained-contigs-some-of-them-100kb-long-again-if-any-of-the-duplicated-contigs-contain-what-would-otherwise-be-scos-the-scos-will-be-removed-in-essence-having-duplicates-poisons-the-analysis-by-severely-reducing-the-number-of-orthologs-for-downstream-analysis-this-problem-is-exacerbated-by-including-1-less-curated-assemblies-genbank-and-2-the-total-number-of-assemblies-fed-to-the-analysis-pipeline\" class=\"anchor\" aria-label=\"Permalink: It is absolutely imparative to clean up assemblies to get the best results from OrthoPhyl. For instance, when looking at the output for all Brucella accessions, checkM output shows many assemblies have duplicated \u0026quot;marker genes\u0026quot;, if these are from falsely duplicated contigs in the asm, they will lead to removal of the ortholog group from both the strict SCO and relaxed SCO gene sets within the OrthoPhylo workflow. Furthermore, dedupe.sh (from bbmap) identifies many Duplicated or Contained contigs, some of them \u0026gt;100kb long. Again, if any of the duplicated contigs contain what would otherwise be SCOs, the SCOs will be removed. In essence, having duplicates poisons the analysis by severely reducing the number of Orthologs for downstream analysis. This problem is exacerbated by including 1) less curated assemblies (GenBank) and 2) the total number of assemblies fed to the analysis pipeline.\" href=\"#it-is-absolutely-imparative-to-clean-up-assemblies-to-get-the-best-results-from-orthophyl-for-instance-when-looking-at-the-output-for-all-brucella-accessions-checkm-output-shows-many-assemblies-have-duplicated-marker-genes-if-these-are-from-falsely-duplicated-contigs-in-the-asm-they-will-lead-to-removal-of-the-ortholog-group-from-both-the-strict-sco-and-relaxed-sco-gene-sets-within-the-orthophylo-workflow-furthermore-dedupesh-from-bbmap-identifies-many-duplicated-or-contained-contigs-some-of-them-100kb-long-again-if-any-of-the-duplicated-contigs-contain-what-would-otherwise-be-scos-the-scos-will-be-removed-in-essence-having-duplicates-poisons-the-analysis-by-severely-reducing-the-number-of-orthologs-for-downstream-analysis-this-problem-is-exacerbated-by-including-1-less-curated-assemblies-genbank-and-2-the-total-number-of-assemblies-fed-to-the-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat this software does not do:\u003c/h3\u003e\u003ca id=\"user-content-what-this-software-does-not-do\" class=\"anchor\" aria-label=\"Permalink: What this software does not do:\" href=\"#what-this-software-does-not-do\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate trees that are ready for publication without parameter tuning or manual inspection. Reconstructing trees from whole genomes requires many many steps, all of which have parameters that will differ based on the input sequences. Some importent outputs too look at: input genomes quality (checkM output), assembly subset used for Ortholog model generation (assembly shortlist), number of strict/relaxed single copy orthologs (drops quickly with additional assemblies), phylogenetic signal for transcript alignments, missing data in alignments (per gene and concatenated alignments)...to name a few. #### This pipeline also does not robunstly compute SCOs (like BUSCOs). It grabs all the SCOs it can from a dataset, but does not do any modeling to ensure species tree-like behavior of the individual genes and also does not deal with paralogs at all.\u003c/h4\u003e\u003ca id=\"user-content-generate-trees-that-are-ready-for-publication-without-parameter-tuning-or-manual-inspection-reconstructing-trees-from-whole-genomes-requires-many-many-steps-all-of-which-have-parameters-that-will-differ-based-on-the-input-sequences-some-importent-outputs-too-look-at-input-genomes-quality-checkm-output-assembly-subset-used-for-ortholog-model-generation-assembly-shortlist-number-of-strictrelaxed-single-copy-orthologs-drops-quickly-with-additional-assemblies-phylogenetic-signal-for-transcript-alignments-missing-data-in-alignments-per-gene-and-concatenated-alignmentsto-name-a-few--this-pipeline-also-does-not-robunstly-compute-scos-like-buscos-it-grabs-all-the-scos-it-can-from-a-dataset-but-does-not-do-any-modeling-to-ensure-species-tree-like-behavior-of-the-individual-genes-and-also-does-not-deal-with-paralogs-at-all\" class=\"anchor\" aria-label=\"Permalink: Generate trees that are ready for publication without parameter tuning or manual inspection. Reconstructing trees from whole genomes requires many many steps, all of which have parameters that will differ based on the input sequences. Some importent outputs too look at: input genomes quality (checkM output), assembly subset used for Ortholog model generation (assembly shortlist), number of strict/relaxed single copy orthologs (drops quickly with additional assemblies), phylogenetic signal for transcript alignments, missing data in alignments (per gene and concatenated alignments)...to name a few. #### This pipeline also does not robunstly compute SCOs (like BUSCOs). It grabs all the SCOs it can from a dataset, but does not do any modeling to ensure species tree-like behavior of the individual genes and also does not deal with paralogs at all.\" href=\"#generate-trees-that-are-ready-for-publication-without-parameter-tuning-or-manual-inspection-reconstructing-trees-from-whole-genomes-requires-many-many-steps-all-of-which-have-parameters-that-will-differ-based-on-the-input-sequences-some-importent-outputs-too-look-at-input-genomes-quality-checkm-output-assembly-subset-used-for-ortholog-model-generation-assembly-shortlist-number-of-strictrelaxed-single-copy-orthologs-drops-quickly-with-additional-assemblies-phylogenetic-signal-for-transcript-alignments-missing-data-in-alignments-per-gene-and-concatenated-alignmentsto-name-a-few--this-pipeline-also-does-not-robunstly-compute-scos-like-buscos-it-grabs-all-the-scos-it-can-from-a-dataset-but-does-not-do-any-modeling-to-ensure-species-tree-like-behavior-of-the-individual-genes-and-also-does-not-deal-with-paralogs-at-all\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eknown errors:\u003c/h2\u003e\u003ca id=\"user-content-known-errors\" class=\"anchor\" aria-label=\"Permalink: known errors:\" href=\"#known-errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf you have special characters in you fasta file names\u003c/h4\u003e\u003ca id=\"user-content-if-you-have-special-characters-in-you-fasta-file-names\" class=\"anchor\" aria-label=\"Permalink: If you have special characters in you fasta file names\" href=\"#if-you-have-special-characters-in-you-fasta-file-names\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFilenames with special characters will likely make this workflow fail. As I encountered these characters, i will add fixes accordingly in Orthophyl.XX.sh, function SET_UP_DIR_STRUCTURE.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf the combination of fasta file name and contigs within are very long,\u003c/h4\u003e\u003ca id=\"user-content-if-the-combination-of-fasta-file-name-and-contigs-within-are-very-long\" class=\"anchor\" aria-label=\"Permalink: If the combination of fasta file name and contigs within are very long,\" href=\"#if-the-combination-of-fasta-file-name-and-contigs-within-are-very-long\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...mafft will truncate the sequence names generated by prodigal. This will make the PAL2NAL module fail on many orthogroups. It manifests as a lot of cat commands failing during trimming because they cant find the transcript file PAL2NAL is suposed to spit out. This can be seen in the log file (in slurm_out). It\u0027s normally caused by very redundant sequence identifiers in the contig names. Can be fixed with something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esed -i \u0027s/REDUNDANT_STUFF/_/g\u0027 \u0026lt; SEQUENCE.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf genomes are less than ~75% identity...\u003c/h4\u003e\u003ca id=\"user-content-if-genomes-are-less-than-75-identity\" class=\"anchor\" aria-label=\"Permalink: If genomes are less than ~75% identity...\" href=\"#if-genomes-are-less-than-75-identity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFastANI doesnt calculate an ANI value for very divergent genomes. In this case my script ANI_picking.py assigns an ANI of 50 (hard coded) for these comparisons. This should not pose a problem if the number of clades with 75% divergence internally are ~= ANI_shortlist number. The goal of the ANI stuff is to find representetives of the evolutionaly breadth of the dataset, so missing representetives from some clades will likelly not affect the final result (there is a lot of filtering later on in the workflow).\nI will implement a more robust ANI estimator soooon...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIf you get an error like:\u003c/h4\u003e\u003ca id=\"user-content-if-you-get-an-error-like\" class=\"anchor\" aria-label=\"Permalink: If you get an error like:\" href=\"#if-you-get-an-error-like\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e/cm/local/apps/slurm/var/spool/job49381/slurm_script: line 511: J % percent: division by 0 (error token is \"percent\") \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt means that an upstream process failed and the directory used to enumerate a loop is empty\u003c/p\u003e\n\u003cp\u003eI will attempt to make errors easier to track...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eI have gotten an error with gather_filter_genomes when pulling extra genomes via wget:\u003c/h4\u003e\u003ca id=\"user-content-i-have-gotten-an-error-with-gather_filter_genomes-when-pulling-extra-genomes-via-wget\" class=\"anchor\" aria-label=\"Permalink: I have gotten an error with gather_filter_genomes when pulling extra genomes via wget:\" href=\"#i-have-gotten-an-error-with-gather_filter_genomes-when-pulling-extra-genomes-via-wget\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edyld: Symbol not found: _libiconv_open\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis appeared to be a problem with wget, which was resolved by updating through conda.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFuture modules:\u003c/h2\u003e\u003ca id=\"user-content-future-modules\" class=\"anchor\" aria-label=\"Permalink: Future modules:\" href=\"#future-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDONE: add assembly filtering, now very incomplete assemblies will be used, so the number of strict single copy orthologs could be drastically reduced (maybe to zero). Filtering done with gather_filter_asm\u003c/li\u003e\n\u003cli\u003eDONE: Quantify phylogenetic info per gene (\u003ca href=\"https://github.com/dportik/Alignment_Assessment.git\"\u003ehttps://github.com/dportik/Alignment_Assessment.git\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCheck genome assembly file not empty at start of Main pipeline....will crash pipe at Orthofinder run. This happened because of a file transfer mishap for me :(\u003c/li\u003e\n\u003cli\u003eAll ow users to submit pre-annotated assemblies. This will open up OrthoPhyl to all taxa (after making sure all software is compatable with alternative codon tables)\u003c/li\u003e\n\u003cli\u003eTest evolutionary models of genes with ete3. Then test GO enrichment, AMR, Virulence genes\u003c/li\u003e\n\u003cli\u003eLook at tree wide paralog numbers. Do GO analysis...\u003c/li\u003e\n\u003cli\u003eIdentify HGT from Transcript alignments. HGTs specific to any group?\u003c/li\u003e\n\u003cli\u003eallow \"protected assemblies\" when filtering. Perhaps your favorite assembly is crap, but you really want it in the tree. A couple of bad assemblies shouldnt reduce the number of SCOs that much\u003c/li\u003e\n\u003cli\u003eallow usage of precomputed Orthogroup HMMs, build new models from hits, then grab novel orthogroups from the remaining protiens\u003c/li\u003e\n\u003cli\u003eRelated to above: allow adding assemblies to pre-run pipeline. i.e. use precomputed hmms to identify orthologs, and add them to alignemnts and regenerate trees\u003c/li\u003e\n\u003cli\u003eallow users to pick which dataset to use for tree building (currently strict and relaxed are used). This would greatly reduce pipeline run time.\u003c/li\u003e\n\u003cli\u003echange how the list of OGs for HMM searching is inumerated. Currently tries with OG that have be filtered out because of paralogs, then gets mad because there is no multifasta to align (doesnt change outcome, just messy)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOrthoPhyl Citation:\u003c/h2\u003e\u003ca id=\"user-content-orthophyl-citation\" class=\"anchor\" aria-label=\"Permalink: OrthoPhyl Citation:\" href=\"#orthophyl-citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e[Coming soon]\u003c/h3\u003e\u003ca id=\"user-content-coming-soon\" class=\"anchor\" aria-label=\"Permalink: [Coming soon]\" href=\"#coming-soon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitations for dependencies\u003c/h2\u003e\u003ca id=\"user-content-citations-for-dependencies\" class=\"anchor\" aria-label=\"Permalink: Citations for dependencies\" href=\"#citations-for-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIf there are nested dependencies that I am missing citations for, please let me know\u003c/h3\u003e\u003ca id=\"user-content-if-there-are-nested-dependencies-that-i-am-missing-citations-for-please-let-me-know\" class=\"anchor\" aria-label=\"Permalink: If there are nested dependencies that I am missing citations for, please let me know\" href=\"#if-there-are-nested-dependencies-that-i-am-missing-citations-for-please-let-me-know\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e[Coming soon]\u003c/h3\u003e\u003ca id=\"user-content-coming-soon-1\" class=\"anchor\" aria-label=\"Permalink: [Coming soon]\" href=\"#coming-soon-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eprodigal\u003c/li\u003e\n\u003cli\u003eorthofinder\u003c/li\u003e\n\u003cli\u003ebbmap\u003c/li\u003e\n\u003cli\u003efastTree*\u003c/li\u003e\n\u003cli\u003ehmmer\u003c/li\u003e\n\u003cli\u003epal2nal\u003c/li\u003e\n\u003cli\u003emafft\u003c/li\u003e\n\u003cli\u003eprodigal\u003c/li\u003e\n\u003cli\u003eete3\u003c/li\u003e\n\u003cli\u003eIQTree*\u003c/li\u003e\n\u003cli\u003eraxml*\u003c/li\u003e\n\u003cli\u003etrimal\u003c/li\u003e\n\u003cli\u003eparallel\u003c/li\u003e\n\u003cli\u003ecatfasta2phyml\u003c/li\u003e\n\u003cli\u003eASTRAL\u003c/li\u003e\n\u003cli\u003efastANI\u003c/li\u003e\n\u003cli\u003eR\u003c/li\u003e\n\u003cli\u003eAlignment_Assessment\u003c/li\u003e\n\u003cli\u003eblast\u003c/li\u003e\n\u003cli\u003escipy\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003emuscle\u003c/li\u003e\n\u003cli\u003emmseq2\u003c/li\u003e\n\u003cli\u003emcl\u003c/li\u003e\n\u003cli\u003efastme\u003c/li\u003e\n\u003cli\u003ediamond\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, - "topics": [ - "reproducibility", - "rna-seq-pipeline", - "student-project" - ], - "updated_at": 1671065348.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1720207910.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "Singularity", + "Singularity.genome" ], - "full_name": "tgac-vumc/QDNAseq.snakemake", + "full_name": "guoqi123/oncodriveCLUST", "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/QDNAseq.snakemake/blob/master/DAG_all.svg\"\u003e\u003cimg width=\"100%\" height=\"100%\" src=\"https://github.com/tgac-vumc/QDNAseq.snakemake/raw/master/DAG_all.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eFor the installation of this pipeline any Python install compatable Conda is required.\u003c/p\u003e\n\u003cp\u003eThe pipeline itself will run on Python 3.8.5 and R 3.6.3. For exact dependencies view \u003ccode\u003eenvironment.yaml\u003c/code\u003e and \u003ccode\u003er-dependencies.R\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-condamamba\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-condamamba\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Conda/Mamba\u003c/h3\u003e\n\u003cp\u003efor easy installation you need (Mini)Conda.\u003c/p\u003e\n\u003cp\u003eMiniconda installation from folder where you want to install Miniconda:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd \u0026lt;/path/to/files/dir/\u0026gt;\nwget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efollow the instructions of the installation process, give the location where you want Miniconda to be installed and answer YES to add Miniconda to your path.\u003c/p\u003e\n\u003cp\u003ego to the directory where the analysis need to be performed\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd \u0026lt;/path/to/analysis/dir\u0026gt;\ngit clone https://github.com/tgac-vumc/QDNAseq.snakemake/\ncd QDNAseq.snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003einstall Mamba as drop-in replacement for Conda with Mamba\u0027s improved installation-performance:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c conda-forge mamba\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecreate the environment using Mamba:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emamba env create --name QDNAseq-snakemake --file environment.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eactivate the environment by:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate QDNAseq-snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\n\u003cp\u003eThen run the R-script r-dependencies.R in the terminal to install the non-conda R dependencies in the environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript r-dependencies.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003c/h3\u003e\n\u003cp\u003eUnder development\u003c/p\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-preparing-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#preparing-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparing analysis\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-prepare-the-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#prepare-the-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare the data\u003c/h3\u003e\n\u003cp\u003ego to analysis dir and prepare analysis by copy or create links to fastq.gz files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd \u0026lt;/path/to/analysis/dir\u0026gt;\n\nmkdir fastq\ncd fastq\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto link a single file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eln -s \u0026lt;path/to/file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto link all files from a folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor file in \u0026lt;path/to/fastq/files\u0026gt;/*.fastq.gz\ndo ln -s $file\ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-prepare-the-snakemake-settings\" class=\"anchor\" aria-hidden=\"true\" href=\"#prepare-the-snakemake-settings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare the snakemake settings\u003c/h3\u003e\n\u003cp\u003eOpen the configuration file \u003ccode\u003econfig.yaml\u003c/code\u003e to check the settings that snakemake will use and change according to your needs.\nFor providing service-analysis, set \u003ccode\u003esetting\u003c/code\u003e to \u003ccode\u003e\u0027service\u0027\u003c/code\u003e. For research purposes, set \u003ccode\u003esetting\u003c/code\u003e to \u003ccode\u003e\u0027research\u0027\u003c/code\u003e. For all settings set \u003ccode\u003esetting\u003c/code\u003e to \u003ccode\u003e\u0027all\u0027\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOne of the options in the configfile is \u003ccode\u003edewaving\u003c/code\u003e, if set to \u003ccode\u003e\u0027true\u0027\u003c/code\u003e QNDAseq objects will be dewaved before segmentation.\u003c/p\u003e\n\u003cp\u003eThese options change the rules performed in the pipeline, see the rule-graph in the next section.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning analysis\u003c/h2\u003e\n\u003cp\u003eMake sure that snakemake is able to find the excecutive file Snakefile by performing a dry-run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ../QDNAseq.snakemake\nsnakemake -n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck the rules that are planned to be performed, conform the rule-graph.\u003c/p\u003e\n\u003cp\u003eAn visualization of the order of rules to be performed can be viewed by running the following command and opening the DAG-file\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --forceall --rulegraph | dot -Tsvg \u0026gt; DAG.svg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRulegraphs for the intial settings \u003ccode\u003e\u0027service\u0027\u003c/code\u003e, \u003ccode\u003e\u0027research\u0027\u003c/code\u003e and \u003ccode\u003e\u0027all\u0027\u003c/code\u003e are commited to this repro in the files \u003ccode\u003eDAG_\u0026lt;setting\u0026gt;.svg\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWhen ready, run the analysis\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUseful snakemake options\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-j , --cores, --jobs\u003c/code\u003e : Use at most N cores in parallel (default: 1). If N is omitted, the limit is set to the number of available cores.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-n , --dryrun\u003c/code\u003e : Do not execute anything. but show rules which are planned to be performed.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-k , --keep-going\u003c/code\u003e : Go on with independent jobs if a job fails.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-f , --force\u003c/code\u003e : Force the execution of the selected target or the first rule regardless of already created output.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-R , --forcerun\u003c/code\u003e : Force the re-execution or creation of the given rules or files. Use this option if you changed a rule and want to have all its output in your workflow updated.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-U , --until\u003c/code\u003e : Runs the pipeline until it reaches the specified rules or files. Only runs jobs that are dependencies of the specified rule or files, does not run sibling DAGs.\u003c/p\u003e\n\u003cp\u003efor all options go to \u003ca href=\"https://snakemake.readthedocs.io/en/v5.31.1/executing/cli.html#all-options\" rel=\"nofollow\"\u003ehttps://snakemake.readthedocs.io/en/v5.31.1/executing/cli.html#all-options\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1631492554.0 + "updated_at": 1590852633.0 }, { "data_format": 2, - "description": null, + "description": "Umbrella repository for managing and deploying neuroimaging pipelines", "filenames": [ - "Singularity" + "containers/nklab-neuro-utils/Singularity" ], - "full_name": "agladstein/SimPrily_update", + "full_name": "MRIresearch/NeuroPipelines", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-simprily_update\" class=\"anchor\" aria-hidden=\"true\" href=\"#simprily_update\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimPrily_update\u003c/h1\u003e\n\u003cp\u003eCreated by Ariella Gladstein, based on \u003ca href=\"https://agladstein.github.io/SimPrily/index.html\" rel=\"nofollow\"\u003eSimPrily\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eSimPrily runs genome simulations with user defined parameters or parameters randomly generated by priors and computes genomic statistics on the simulation output.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun genome simulation with model defined by prior distributions of parameters and demographic model structure.\u003c/li\u003e\n\u003cli\u003eTake into account SNP array ascertainment bias by creating pseudo array based on priors of number of samples of discovery populations and allele frequency cut-off.\u003c/li\u003e\n\u003cli\u003eCalculate genomic summary statistics on simulated genomes and pseudo arrays.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis is ideal for use with Approximate Bayesian Computation on whole genome or SNP array data.\u003c/p\u003e\n\u003cp\u003eUses c++ programs macs and GERMLINE. For more information on these programs, see:\u003cbr\u003e\n\u003ca href=\"https://github.com/gchen98/macs\"\u003ehttps://github.com/gchen98/macs\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/sgusev/GERMLINE\"\u003ehttps://github.com/sgusev/GERMLINE\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003ecd to the directory you want to work in,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/agladstein/SimPrily.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-environment-set-up\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-set-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment Set up\u003c/h4\u003e\n\u003cp\u003eIf using Vagrant (this is recommended if running on non-Linux OS):\u003c/p\u003e\n\u003cp\u003eStart Vagrant, ssh into Vagrant, cd to SimPrily directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\nvagrant ssh\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /vagrant\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall the virtual environment and install the requirements.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./setup/setup_env_vbox_2.7.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf not using Vagrant, just install the virtual environment and install the requirements:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./setup/setup_env_2.7.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003ee.g. One Test simulation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py -p examples/eg1/param_file_eg1_asc.txt -m examples/eg1/model_file_eg1_asc.csv -g genetic_map_b37/genetic_map_GRCh37_chr1.txt.macshs -a array_template/ill_650_test.bed -i 1 -o output_dir -v\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor quick help:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003esimprily.py\u003c/code\u003e takes 4 required arguments and 2 optional arguments, and help, verbose, and profile options.\u003c/p\u003e\n\u003cp\u003eRun as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py [-h] -p PARAM -m MODEL -i ID -o OUT [-g MAP] [-a ARRAY] [-v] [--profile]\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch5\u003e\u003ca id=\"user-content-required\" class=\"anchor\" aria-hidden=\"true\" href=\"#required\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequired\u003c/h5\u003e\n\u003cp\u003e\u003ccode\u003e-p PARAM\u003c/code\u003e or \u003ccode\u003e--param PARAM\u003c/code\u003e = The location of the parameter file\u003cbr\u003e\n\u003ccode\u003e-m MODEL\u003c/code\u003e or \u003ccode\u003e--model MODEL\u003c/code\u003e = The location of the model file\u003cbr\u003e\n\u003ccode\u003e-i ID\u003c/code\u003e or \u003ccode\u003e--id ID\u003c/code\u003e = The unique identifier of the job\u003cbr\u003e\n\u003ccode\u003e-o OUT\u003c/code\u003e or \u003ccode\u003e--out OUT\u003c/code\u003e = The location of the output directory\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h5\u003e\n\u003cp\u003e\u003ccode\u003e-h\u003c/code\u003e or \u003ccode\u003e--help\u003c/code\u003e = shows a help message and exists\u003cbr\u003e\n\u003ccode\u003e-v\u003c/code\u003e = increase output verbosity. This includes 3 levels, \u003ccode\u003e-v\u003c/code\u003e, \u003ccode\u003e-vv\u003c/code\u003e, and \u003ccode\u003e-vvv\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003e--profile\u003c/code\u003e = Print a log file containing the time in seconds and memory use in Mb for main functions\u003cbr\u003e\n\u003ccode\u003e-g MAP\u003c/code\u003e or \u003ccode\u003e--map MAP\u003c/code\u003e = The location of the genetic map file\u003cbr\u003e\n\u003ccode\u003e-a ARRAY\u003c/code\u003e or \u003ccode\u003e--array ARRAY\u003c/code\u003e = The location of the array template file, in bed form\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h4\u003e\n\u003cp\u003eThree subdirectories are created in the directory specified in the \u003ccode\u003eoutput_dir\u003c/code\u003e argument.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_dir/results\noutput_dir/sim_data\noutput_dir/germline_out\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch5\u003e\u003ca id=\"user-content-intermediate-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#intermediate-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntermediate files\u003c/h5\u003e\n\u003cp\u003eIntermediate files go to \u003ccode\u003eoutput_dir/sim_data\u003c/code\u003e and \u003ccode\u003eoutput_dir/germline_out\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/sim_data\u003c/code\u003e contains PLINK formated .ped and .map files created from the pseudo array, which are necessary to run GERMLINE.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/germline_out\u003c/code\u003e contains the GERMLINE .match output and .log. The .match contains all of the identified IBD segments.\u003cbr\u003e\nThese files are NOT automatically removed in python script, but are unnecessary once the job is complete.\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-results-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#results-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults files\u003c/h5\u003e\n\u003cp\u003eOutput files go to \u003ccode\u003eoutput_dir/results\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/results\u003c/code\u003e contains the parameter values used in the simulation and the summary statistics calculated from the simulation.\u003cbr\u003e\nThe first line is a header with the parameter names and summary statistics names.\nThe second line is the parameter values and summary statistics values.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-abc_update_wfpy\" class=\"anchor\" aria-hidden=\"true\" href=\"#abc_update_wfpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eABC_update_wf.py\u003c/h2\u003e\n\u003cp\u003eThis script creates all the necessary files for running ABC on simulations, and runs ABC.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCombines the simulated results into one file in \u003ccode\u003eobs{}/chr{}/ABC/results_combined.txt\u003c/code\u003e (unless the file already exists).\u003c/li\u003e\n\u003cli\u003eFor chr1 randomly picks one of the simulations to use as observed data,\nand for all other chromosomes uses the parameter values of the observed data from chr1 to simulate observed data,\nand create file in \u003ccode\u003eobs{}/chr{}/ABC/results_observed.txt\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eRun R to get PLS components.\u003c/li\u003e\n\u003cli\u003eUse ABCtoolbox to transform summary stats to PLS components for simulated and observed data.\u003c/li\u003e\n\u003cli\u003eUse ABCtoolbox to get posteriors of parameters.\u003c/li\u003e\n\u003cli\u003eCreate parameter file with posterior file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eABC_update_wf.py path_sim param_file_name chrom obs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003epath_sim\u003c/code\u003e is the path to simulation output (before \u003ccode\u003eobs{}\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparam_file_name\u003c/code\u003e is the parameter file used to perform the simulations\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003echrom\u003c/code\u003e is the chromosome number\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eobs\u003c/code\u003e is the iteration with observed data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-hpc-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC Workflow\u003c/h2\u003e\n\u003cp\u003eFor chromosome 1 use \u003ccode\u003echeckque.sh\u003c/code\u003e to submit jobs to Ocelote.\u003c/p\u003e\n\u003cp\u003eArguments are \u003ccode\u003egoal_number\u003c/code\u003e, \u003ccode\u003emax_que\u003c/code\u003e, \u003ccode\u003echr\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/home/u15/agladstein/SimPrily_update/update_test/checkque.sh 10000 500 1\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, run ABC with \u003ccode\u003eABC_update_wf.py\u003c/code\u003e with the appropriate chromosome:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ersync -za SimPrily_update/ /xdisk/agladstein/SimPrily_update\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /xdisk/agladstein/SimPrily_update\nqsub update_test/PBS/run_ABC_chr1.pbs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, run the simulations with the appropriate chromosome:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ersync -za SimPrily_update/ /xdisk/agladstein/SimPrily_update\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /xdisk/agladstein/SimPrily_update\nqsub update_test/PBS/run_sims_update_chr2.pbs\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKnown Issues\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIf exponential growth is large, macs simulation will not finish. (This is a macs bug).\u003c/li\u003e\n\u003cli\u003eIf the same id is used with the same output dir as a previous run, the .map file will be appended to.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNeuroPipelines\u003c/h1\u003e\u003ca id=\"user-content-neuropipelines\" class=\"anchor\" aria-label=\"Permalink: NeuroPipelines\" href=\"#neuropipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUmbrella repository for managing and deploying neuroimaging pipelines and containers\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003econtainers\u003c/h2\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-fsl/README.md\"\u003enklab-fsl\u003c/a\u003e This Singularity container provides FSL v6.0.1 (FSLeyes, BASIL). It is Cuda compatible and so should be able to run eddy_cuda8.0 or eddy_cuda9.1. It also includes FSL v5.0.6 for reproducing HCP pipelines.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-mrtrix/README.md\"\u003enklab-mrtrix\u003c/a\u003e This Singularity container provides MRTrix 3.0 RC3\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-freesurfer/README.md\"\u003enklab-freesurfer\u003c/a\u003e This Singularity container provides 3 versions of freesurfer - the stable v6.0.0 version, the current development version (this will vary depending on the build date) and the HCP version 5.3.0 used in HCP pipelines.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-fsltrixsurf/README.md\"\u003enklab-fsltrixsurf\u003c/a\u003e This Singularity container is an amalgamation of the three containers (nklab-fsl, nklab-mrtrix and nklab-freesurfer)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-neuro-tools/README.md\"\u003enklab-neuro-tools\u003c/a\u003e This Singularity container provides a comprehensive package of neuroimaging tools like FSL, MRtrix, AFNI, The HCP Pipelines, CIFTIFY, ANTS in one container.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-simnibs/README.md\"\u003enklab-simnibs\u003c/a\u003e A Singularity Container for SIMNIBS 2.1 for the Simulation of electric fields induced by TMS and tDCS\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"containers/nklab-neuro-utils/README.md\"\u003enklab-neuro-utils\u003c/a\u003e A Singularity/Docker container for converting MRI files into BIDS format\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData utilities\u003c/h2\u003e\u003ca id=\"user-content-data-utilities\" class=\"anchor\" aria-label=\"Permalink: Data utilities\" href=\"#data-utilities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"dicomutils/README.md\"\u003edicomutils\u003c/a\u003e working python code to transfer/route dicoms to XNAT.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1615073384.0 + "updated_at": 1633546054.0 }, { "data_format": 2, - "description": "recipes of Singularity", + "description": "a Singularity container for build and run of QT-creator projects. Does not include any project content.", "filenames": [ - "Singularity.blast-latest", - "Singularity.snpeff", - "Singularity.vcftools", - "Singularity.samtools", - "Singularity.rooting_nj", - "Singularity.trimmomatic", - "Singularity.cufflinks", - "Singularity.gatk", - "Singularity.blast-legacy", - "Singularity.bwa" + "Singularity" ], - "full_name": "CompBio-TDU-Japan/containers", + "full_name": "vsoch/cs106b-builder", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003econtainers\u003c/h1\u003e\n\u003cp\u003erecipes of Singularity\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCS106B-Builder\u003c/h1\u003e\u003ca id=\"user-content-cs106b-builder\" class=\"anchor\" aria-label=\"Permalink: CS106B-Builder\" href=\"#cs106b-builder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the CS106B-Builder - you can build a container and then use it to compile\nand run a local project directory, without QT-Creator. We are using\na Singularity container instead of Docker so that we can more seamlessly use our\nsystem display.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e0. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-0-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 0. Install Singularity\" href=\"#0-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first install Singularity. I recommend the verison 2.6 for a much\nsimpler install routine. Here is the \u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eguide\u003c/a\u003e, and instructions:\u003c/p\u003e\n\u003cp\u003eYou\u0027ll need these dependencies\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get update \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\n sudo apt-get install \\\n python \\\n dh-autoreconf \\\n build-essential \\\n libarchive-dev\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then to install:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\ngit fetch --all\ngit checkout 2.6.0\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Build the Container\u003c/h2\u003e\u003ca id=\"user-content-1-build-the-container\" class=\"anchor\" aria-label=\"Permalink: 1. Build the Container\" href=\"#1-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build this image locally, and note that you must have root permissions\ntodo so. This container could also be built and provided on \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e,\nif appropriate.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build cs106b-builder Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Extract your project\u003c/h2\u003e\u003ca id=\"user-content-2-extract-your-project\" class=\"anchor\" aria-label=\"Permalink: 2. Extract your project\" href=\"#2-extract-your-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need to extract your project in the present working directory, or the\ndirectory where you want to run your container. Usually this means unzipping\na project file to generate a subfolder. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eunzip cs106b-Pancakes.zip \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewould create a folder with the name \"Pancakes\u0027 - (or your project name)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run the container\u003c/h2\u003e\u003ca id=\"user-content-3-run-the-container\" class=\"anchor\" aria-label=\"Permalink: 3. Run the container\" href=\"#3-run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNext, you should run the container and bind the project folder (with the single .pro\nfile) inside. You can choose to build, run, or build and run. Note that if you\nchoose to build and run, it will only run after build given a successful build\n(without errors). Here is how to ask for help, given a container called \u003ccode\u003ecs106b-builder\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run cs106b-builder --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo build the current folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/Tiles:/code/Project cs106b-builder build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo then run!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/Tiles:/code/Project cs106b-builder run\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr build and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/Tiles:/code/Project cs106b-builder build run\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, - "topics": [], - "updated_at": 1548055469.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "container", + "containers", + "reproducible", + "qt-creator" + ], + "updated_at": 1545323608.0 }, { "data_format": 2, - "description": "Python repository of code for preprocessing and extracting metrics of the volume fraction and size of the gamma double prime and gamma prime in a nickel-based superalloy microstructure", + "description": "A Container implementation for the Google Drive Client", "filenames": [ "Singularity" ], - "full_name": "CWRU-MSL/GammaDoublePrime", + "full_name": "ArangoGutierrez/grive", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-characterization-of-nanoscale-precipitates-in-superalloy-718-using-high-resolution-sem-imaging\" class=\"anchor\" aria-hidden=\"true\" href=\"#characterization-of-nanoscale-precipitates-in-superalloy-718-using-high-resolution-sem-imaging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCharacterization of nanoscale precipitates in superalloy 718 using high resolution SEM imaging\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tm-smith-a-nm-senanayake-b-ck-sudbrack-c-p-bonacuse-a-rb-rogers-a-p-chao-d-j-carter-b\" class=\"anchor\" aria-hidden=\"true\" href=\"#tm-smith-a-nm-senanayake-b-ck-sudbrack-c-p-bonacuse-a-rb-rogers-a-p-chao-d-j-carter-b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eT.M. Smith a*, N.M. Senanayake b, C.K. Sudbrack c, P. Bonacuse a, R.B. Rogers a, P. Chao d, J. Carter b\u003c/h2\u003e\n\u003cp\u003ea NASA Glenn Research Center, Materials and Structures Division, Cleveland, OH 44135, United States of America\nb Case Western Reserve University, Department of Materials Science and Engineering, Cleveland, OH 44106, United States of America\nc QuesTek Innovations LLC, Evanston, IL 60201, United States of America\nd Carnegie Mellon University, Department of Materials Science and Engineering, Pittsburgh, PA 15213, United States of America\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-materials-characterization-212019-v148-p-178-197\" class=\"anchor\" aria-hidden=\"true\" href=\"#materials-characterization-212019-v148-p-178-197\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaterials Characterization, (2/1/2019) V148, p 178-197\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-httpwwwsciencedirectcomsciencearticlepiis1044580318328444\" class=\"anchor\" aria-hidden=\"true\" href=\"#httpwwwsciencedirectcomsciencearticlepiis1044580318328444\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"http://www.sciencedirect.com/science/article/pii/S1044580318328444\" rel=\"nofollow\"\u003ehttp://www.sciencedirect.com/science/article/pii/S1044580318328444\u003c/a\u003e\u003c/h2\u003e\n\u003ch2\u003e\u003ca id=\"user-content-doi-101016jmatchar201812018\" class=\"anchor\" aria-hidden=\"true\" href=\"#doi-101016jmatchar201812018\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDOI: 10.1016/j.matchar.2018.12.018\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-repo-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#repo-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRepo Information\u003c/h3\u003e\n\u003cp\u003eThis repository contains the codes necessary to utilize the algorthims presented in the paper below. When implimented, the can be used to obtain accurate volume fraction and size measurements of gamma double prime, and gamma prime, precipitates in Superalloy\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-clone-a-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#clone-a-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClone a repository\u003c/h2\u003e\n\u003cp\u003eUse these steps to clone from SourceTree, our client for using the repository command-line free. Cloning allows you to work on your files locally. If you don\u0027t yet have SourceTree, \u003ca href=\"https://www.sourcetreeapp.com/\" rel=\"nofollow\"\u003edownload and install first\u003c/a\u003e. If you prefer to clone from the command line, see \u003ca href=\"https://confluence.atlassian.com/x/4whODQ\" rel=\"nofollow\"\u003eClone a repository\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eYou\u2019ll see the clone button under the \u003cstrong\u003eSource\u003c/strong\u003e heading. Click that button.\u003c/li\u003e\n\u003cli\u003eNow click \u003cstrong\u003eCheck out in SourceTree\u003c/strong\u003e. You may need to create a SourceTree account or log in.\u003c/li\u003e\n\u003cli\u003eWhen you see the \u003cstrong\u003eClone New\u003c/strong\u003e dialog in SourceTree, update the destination path and name if you\u2019d like to and then click \u003cstrong\u003eClone\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eOpen the directory you just created to see your repository\u2019s files.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNow that you\u0027re more familiar with your Bitbucket repository, go ahead and add a new file locally. You can \u003ca href=\"https://confluence.atlassian.com/x/iqyBMg\" rel=\"nofollow\"\u003epush your change back to Bitbucket with SourceTree\u003c/a\u003e, or you can \u003ca href=\"https://confluence.atlassian.com/x/8QhODQ\" rel=\"nofollow\"\u003eadd, commit,\u003c/a\u003e and \u003ca href=\"https://confluence.atlassian.com/x/NQ0zDQ\" rel=\"nofollow\"\u003epush from the command line\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eA Linux Container implementation of the grive client\u003c/h1\u003e\u003ca id=\"user-content-a-linux-container-implementation-of-the-grive-client\" class=\"anchor\" aria-label=\"Permalink: A Linux Container implementation of the grive client\" href=\"#a-linux-container-implementation-of-the-grive-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Drive\u003c/h2\u003e\u003ca id=\"user-content-singularity-drive\" class=\"anchor\" aria-label=\"Permalink: Singularity Drive\" href=\"#singularity-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Singularity imlementation for the Google Drive Client\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePealse refer to \u003ca href=\"http://yourcmc.ru/wiki/Grive2#Installation\" rel=\"nofollow\"\u003ehttp://yourcmc.ru/wiki/Grive2#Installation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor a first time Drive sync\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity create -s 1024 /some/path/grive.img \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity bootstrap /some/path/grive.img grive.def\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e $ singularity pull --name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003egrive.img\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e shub://ArangoGutierrez/grive\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn your host\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir /home/MyGoogleDriveFoldeName\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/MyGoogleDriveFoldeName\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run /some/path/grive.img -a\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter this, for Sync only\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/MyGoogleDriveFoldeName\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run /some/path/grive.img\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Drive\u003c/h2\u003e\u003ca id=\"user-content-docker-drive\" class=\"anchor\" aria-label=\"Permalink: Docker Drive\" href=\"#docker-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Docker imlementation for the Google Drive Client\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePealse refer to \u003ca href=\"http://yourcmc.ru/wiki/Grive2#Installation\" rel=\"nofollow\"\u003ehttp://yourcmc.ru/wiki/Grive2#Installation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor a first time Drive sync\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker pull ashael/dockerdrive \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003e$docker\u003c/span\u003e build Dockerfile (after git clone)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir /home/MyGoogleDriveFoldeName\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /home/MyGoogleDriveFoldeName\n$ docker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/home/grive -w /home/grive grive\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen inside the Container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ grive -a\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter this, for Sync only\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/home/grive -w /home/grive --name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eGdrive\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e grive grive\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1656425160.0 + "updated_at": 1553886248.0 }, { "data_format": 2, - "description": "Collection of bioinformatic pipelines written in nextflow", + "description": null, "filenames": [ - "containers/Singularity", - "containers/Singularity.RGI", - "containers/Singularity.qiime2", - "containers/Singularity.cfsansnp" + "examples/docker/Singularity", + "examples/raspbian/Singularity", + "examples/apps/Singularity", + "examples/apps/Singularity.cowsay", + "examples/ubuntu/Singularity", + "examples/arch/Singularity", + "examples/asciinema/Singularity", + "examples/debian/Singularity", + "examples/self/Singularity", + "examples/busybox/Singularity", + "examples/shub/Singularity", + "examples/opensuse/Singularity", + "examples/centos/Singularity", + "examples/scientific/Singularity" ], - "full_name": "EnriqueDoster/bioinformatic-nextflow-pipelines", + "full_name": "edf-hpc/singularity-container", "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe goal of many metagenomics studies is to characterize the content and relative abundance of sequences of interest from the DNA of a given sample or set of samples. You may want to know what is contained within your sample or how abundant a given sequence is relative to another.\u003c/p\u003e\n\u003cp\u003eOften, metagenomics is performed when the answer to these questions must be obtained for a large number of targets where techniques like multiplex PCR and other targeted methods would be too cumbersome to perform. AmrPlusPlus can process the raw data from the sequencer, identify the fragments of DNA, and count them. It also provides a count of the polymorphisms that occur in each DNA fragment with respect to the reference database.\u003c/p\u003e\n\u003cp\u003eAdditionally, you may want to know if the depth of your sequencing (how many reads you obtain that are on target) is high enough to identify rare organisms (organisms with low abundance relative to others) in your population. This is referred to as rarefaction and is calculated by randomly subsampling your sequence data at intervals between 0% and 100% in order to determine how many targets are found at each depth. AmrPlusPlus can perform this analysis as well.\u003c/p\u003e\n\u003cp\u003eWith AmrPlusPlus, you will obtain count files for each sample that can be combined into a count matrix and analyzed using any statistical and mathematical techniques that can operate on a matrix of observations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EnriqueDoster/bioinformatic-nextflow-pipelines/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description-of-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#description-of-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription of scripts\u003c/h2\u003e\n\u003cp\u003emain_qiime2.nf\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main_qiime2.nf --reads \"/s/angus/index/projs/mega_tylan/concat_16S_LN/raw_data/*_{1,2}.fq\" --output XIT_LN_qiime2 -profile local --metadata /media/AngusWorkspace/run_Jake/LN_metadata.tsv --classifier /media/AngusWorkspace/run_Jake/bioinformatic-nextflow-pipelines/gg-13-8-99-515-806-nb-classifier.qza -resume --threads 25\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/singularityware/singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39fe73b2acfcaf157d81df51bfad4bac84e7177bc8ffda9f351f966cdfb1eff1/68747470733a2f2f7472617669732d63692e6f72672f73696e67756c6172697479776172652f73696e67756c61726974792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/singularityware/singularity.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eGuidelines for Contributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\".github/PULL_REQUEST_TEMPLATE.md\"\u003ePull Request Template\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"LICENSE.md\"\u003eProject License\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity - Enabling users to have full control of their environment.\u003c/h1\u003e\u003ca id=\"user-content-singularity---enabling-users-to-have-full-control-of-their-environment\" class=\"anchor\" aria-label=\"Permalink: Singularity - Enabling users to have full control of their environment.\" href=\"#singularity---enabling-users-to-have-full-control-of-their-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStarting a Singularity container \"swaps\" out the host operating system\nenvironment for one the user controls!\u003c/p\u003e\n\u003cp\u003eLet\u0027s say you are running Ubuntu on your workstation or server, but you\nhave an application which only runs on Red Hat Enterprise Linux 6.3.\nSingularity can instantly virtualize the operating system, without\nhaving root access, and allow you to run that application in its native\nenvironment!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbout\u003c/h1\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is a container platform focused on supporting \"Mobility of\nCompute\"\u003c/p\u003e\n\u003cp\u003eMobility of Compute encapsulates the development to compute model where\ndevelopers can work in an environment of their choosing and creation and\nwhen the developer needs additional compute resources, this environment\ncan easily be copied and executed on other platforms. Additionally as\nthe primary use case for Singularity is targeted towards computational\nportability, many of the barriers to entry of other container solutions\ndo not apply to Singularity making it an ideal solution for users (both\ncomputational and non-computational) and HPC centers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe Container\u003c/h2\u003e\u003ca id=\"user-content-the-container\" class=\"anchor\" aria-label=\"Permalink: The Container\" href=\"#the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity utilizes container images, which means when you enter and\nwork within the Singularity container, you are physically located inside\nof this image. The image grows and shrinks in real time as you install\nor delete files within the container. If you want to copy a container,\nyou copy the image.\u003c/p\u003e\n\u003cp\u003eUsing a single image for the container format, has added advantages\nespecially within the context of HPC with large parallel file systems\nbecause all metadata operations within the container occur within the\ncontainer image (and not on the metadata server!).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMobility of Compute\u003c/h2\u003e\u003ca id=\"user-content-mobility-of-compute\" class=\"anchor\" aria-label=\"Permalink: Mobility of Compute\" href=\"#mobility-of-compute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith Singularity, developers who like to be able to easily control their\nown environment will love Singularity\u0027s flexibility. Singularity does not\nprovide a pathway for escalation of privilege (as do other container\nplatforms which are thus not applicable for multi-tenant resources) so\nyou must be able to become root on the host system (or virtual machine)\nin order to modify the container.\u003c/p\u003e\n\u003cp\u003eA Singularity container can be launched in a variety of different ways\ndepending on what you wanted to do with it. A simple method might be to\nlaunch an interactive shell within the container image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity shell /tmp/Centos-7.img \ngmk@Centos-7.img demo\u0026gt; echo \"Hello from within the container\"\nHello from within the container\ngmk@Centos-7.img demo\u0026gt; whoami\ngmk\ngmk@Centos-7.img demo\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if you wanted to do the same thing as root:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ sudo singularity shell -w /tmp/Centos-7.img \nroot@Centos-7.img demo\u0026gt; whoami\nroot\nroot@Centos-7.img demo\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003enote: By default, Singularity launches the container image in read\nonly mode (so it can be easily launched in parallel). The -w option\nused above tells Singularity to mount the image in read/write mode such\nthat root can now make changes to the container.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally relevant file systems on your host are automatically shared\nwithin the context of your container. This can be demonstrated as\nfollows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ pwd\n/home/gmk/demo\n[gmk@centos7-x64 demo]$ echo \"world\" \u0026gt; hello\n[gmk@centos7-x64 demo]$ singularity shell /tmp/Centos-7.img \ngmk@Centos-7.img demo\u0026gt; pwd\n/home/gmk/demo\ngmk@Centos-7.img demo\u0026gt; cat hello\nworld\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the developer has completed their environment the image file can be\ncompressed and copied to any other system that has Singularity installed.\nIf you do not have root on that system, you will not be able to make any\nchanges to the image once on that system. But you will be able to use\nthe container and access the data and files outside the container as\neasily as you would on your development system or virtual machine.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePortability of Singularity container images\u003c/h2\u003e\u003ca id=\"user-content-portability-of-singularity-container-images\" class=\"anchor\" aria-label=\"Permalink: Portability of Singularity container images\" href=\"#portability-of-singularity-container-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity images are highly portable between Linux distributions (as\nlong as the binary format is the same). You can generate your image on\nDebian or CentOS, and run it on Mint or Slackware.\u003c/p\u003e\n\u003cp\u003eWithin a particular container one can include their programs, data,\nscripts and pipelines and thus portable to any other architecture\ncompatible Linux system or distribution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootstrapping new images\u003c/h2\u003e\u003ca id=\"user-content-bootstrapping-new-images\" class=\"anchor\" aria-label=\"Permalink: Bootstrapping new images\" href=\"#bootstrapping-new-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerally when bootstrapping an image from scratch you must build it from\na compatible host. This is because you must use the distribution specific\ntools it comes with (e.g. Red Hat does not provide Debian\u0027s debootstrap).\nBut once the image has been bootstrapped and includes the necessary bits\nto be self hosting (e.g. YUM on CentOS and apt-get on Debian/Ubuntu) then\nthe process of managing the container can be implemented from within the\ncontainer.\u003c/p\u003e\n\u003cp\u003eThe process of building a bootstrap starts with a definition\nspecification. The definition file describes how you want the operating\nsystem to be built, what should go inside it and any additional\nmodifications necessary.\u003c/p\u003e\n\u003cp\u003eHere is an example of a very simple bootstrap definition file for CentOS:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: yum\nOSVersion: 7\nMirrorURL: http://mirror.centos.org/centos-%{OSVERSION}/%{OSVERSION}/os/$basearch/\nInclude: yum\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce you have created your bootstrap definition, you can build your\nSingularity container image by first creating a blank image, and then\nbootstrapping using your definition file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ sudo singularity create /tmp/Centos-7.img\n[gmk@centos7-x64 demo]$ sudo singularity bootstrap /tmp/Centos-7.img centos.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom there we can immediately start using the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img cat /etc/redhat-release \nCentOS Linux release 7.2.1511 (Core) \n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img python --version\nPython 2.7.5\n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img python hello.py \nhello world\n[gmk@centos7-x64 demo]$ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if I do this same process again, while changing the \u003cstrong\u003eOSVersion\u003c/strong\u003e\nvariable in the bootstrap definition to \u003cstrong\u003e6\u003c/strong\u003e (where previously it was\nautomatically ascertained by querying the RPM database), we can\nessentially build a CentOS-6 image in exactly the same manner as\nabove. Doing so reveals this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-6.img cat /etc/redhat-release \nCentOS release 6.7 (Final)\n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-6.img python --version\nPython 2.6.6\n[gmk@centos7-x64 demo]$ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd as expected, the Python version we now see is what comes from by\ndefault in CentOS-6.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCite as:\u003c/h1\u003e\u003ca id=\"user-content-cite-as\" class=\"anchor\" aria-label=\"Permalink: Cite as:\" href=\"#cite-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459. https://doi.org/10.1371/journal.pone.0177459\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe also have a Zenodo citation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer, Gregory M.. (2016). Singularity 2.1.2 - Linux application and environment\ncontainers for science. 10.5281/zenodo.60736\n\nhttp://dx.doi.org/10.5281/zenodo.60736\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebpage\u003c/h1\u003e\u003ca id=\"user-content-webpage\" class=\"anchor\" aria-label=\"Permalink: Webpage\" href=\"#webpage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have full documentation at \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/\u003c/a\u003e, and \u003ca href=\"http://www.github.com/singularityware/singularityware.github.io\"\u003ewelcome contributions\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 9, "topics": [], - "updated_at": 1645870548.0 + "updated_at": 1515565254.0 }, { "data_format": 2, - "description": "Singularity recipe files for GROMACS (http://www.gromacs.org/)", + "description": "Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons", "filenames": [ - "Singularity.2019.4", - "Singularity.2018.2", - "Singularity.2019.3", - "Singularity.2019.2", - "Singularity.2018.1", - "Singularity.2018.5", - "Singularity.2018", - "Singularity.2020.1", - "Singularity.2020.2", - "Singularity.2018.3", - "Singularity.2019.1", - "Singularity.2019.6", - "Singularity.2020", - "Singularity.2019.5", - "Singularity.2018.4", - "Singularity.2019" + "Selector/hclib/modules/bale_actor/singularity/Singularity.def" ], - "full_name": "powerPlant/gromacs-srf", + "full_name": "youssefelmougy/jaccard-selector", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2264\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for the GROMACS molecular dynamics package\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAsynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons\u003c/h1\u003e\u003ca id=\"user-content-asynchronous-distributed-actor-based-approach-to-jaccard-similarity-for-genome-comparisons\" class=\"anchor\" aria-label=\"Permalink: Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons\" href=\"#asynchronous-distributed-actor-based-approach-to-jaccard-similarity-for-genome-comparisons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbstract\u003c/h2\u003e\u003ca id=\"user-content-abstract\" class=\"anchor\" aria-label=\"Permalink: Abstract\" href=\"#abstract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe computation of genome similarity is important in computational biology applications, and is assessed by calculating the Jaccard similarity of DNA sequencing sets. However, it\u0027s challenging to find solutions that can compute Jaccard similarity with the efficiency and scalability needed to fully utilize capabilities of modern HPC hardware. We introduce a novel approach for computing Jaccard similarity for genome comparisons, founded on an actor-based programming model. Our algorithm takes advantage of fine-grained asynchronous computations, distributed/shared memory, and the Fine-grained Asynchronous Bulk-Synchronous Parallelism execution model. Our performance results on the NERSC Perlmutter supercomputer demonstrate that this approach scales to 16,384 cores, showing an average of 4.94x and 5.5x improvement in execution time at the largest scale and relevant hardware performance monitors at medium scale compared to a state-of-the-art baseline. Our approach is also able to process much larger scale genomic datasets than this baseline.\u003c/p\u003e\n\u003cp\u003eThe code for our algorithm can be found \u003ca href=\"https://github.com/youssefelmougy/jaccard-selector/blob/main/Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector.cpp\"\u003ehere\u003c/a\u003e (in \u003ccode\u003e/hclib/modules/bale_actor/jaccard-selector/\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation Instructions\u003c/h2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-label=\"Permalink: Installation Instructions\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following installation instructions are for the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLoad the appropriate modules to prepare for setup\u003c/h3\u003e\u003ca id=\"user-content-load-the-appropriate-modules-to-prepare-for-setup\" class=\"anchor\" aria-label=\"Permalink: Load the appropriate modules to prepare for setup\" href=\"#load-the-appropriate-modules-to-prepare-for-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis loads the modules for both Selector and GenomeAtScale to prepare for setup.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource scripts/modules.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFirst time setup and installation\u003c/h3\u003e\u003ca id=\"user-content-first-time-setup-and-installation\" class=\"anchor\" aria-label=\"Permalink: First time setup and installation\" href=\"#first-time-setup-and-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis sets up and installs both the Selector and GenomeAtScale applications and their backend runtimes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource scripts/setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Instructions\u003c/h2\u003e\u003ca id=\"user-content-running-instructions\" class=\"anchor\" aria-label=\"Permalink: Running Instructions\" href=\"#running-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following running instructions are for the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC).\u003c/p\u003e\n\u003cp\u003eThe run script (\u003ccode\u003e/scripts/run.sh\u003c/code\u003e) has 4 options:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e source /scripts/run.sh [selector | ctf | both] [1...inf] [1...inf] [0...5]\n \n [selector | ctf | both] Selects which application (or both) to run\n [1...inf] Selects the number of cores for the run ([32...16384] used in the paper)\n [1...inf] Selects the number of nodes for the run ([1...512] used in the paper)\n [0...5] Selects the set of HWPC to collect (0:none, 1:L1DA/L1DM/L1IA/L1IM, 2:L2DR/L2DM/L2IR/L2IM, 3:TLBDM/TLBIM, 4:BRINS/BRMSP, 5:INS/CYC)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: when selecting the number of nodes for the run, please remember that GenomeAtScale uses 32 cores/node and Selector uses either 32 or 64 cores/node.\u003c/p\u003e\n\u003cp\u003eFor example, \u003ccode\u003esource /scripts/run.sh selector 1024 16 2\u003c/code\u003e will run an experiment for the Selector application using 1024 cores on 16 nodes, collecting L2 cache statistics.\u003c/p\u003e\n\u003cp\u003eThis will submit an sbatch file to the run queue at Perlmutter. At job completion, a \u003ccode\u003ejaccard_selector.out\u003c/code\u003e or \u003ccode\u003ejaccard_ctf.out\u003c/code\u003e or both will be created, showing the CMD output of the run. Moreover, if HWPC were collected, a directory with the structure \u003ccode\u003ejaccard*+pat+*\u003c/code\u003e will be created in \u003ccode\u003e/Selector/hclib/modules/bale_actor/jaccard-selector/\u003c/code\u003e or \u003ccode\u003e/GenomeAtScale/jaccard-ctf/\u003c/code\u003e or both. Please see the Output Interpretation section for instructions on how to understand these results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput Interpretation\u003c/h2\u003e\u003ca id=\"user-content-output-interpretation\" class=\"anchor\" aria-label=\"Permalink: Output Interpretation\" href=\"#output-interpretation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following instructions are for understanding the results and relating them to the results found in the paper.\u003c/p\u003e\n\u003cp\u003eAt the completion of each run, there are two outputs that are created:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejaccard_selector.out OR jaccard_ctf.out OR both Output file from submitted job\njaccard_selector+pat+* OR jaccard+pat+* OR both Output folder (in respective directory) from a CrayPat run if HWPC were collected\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e*.out\u003c/code\u003e files contain the execution times of the run for the specific version. This result directly relates to Figure 2 (q) in the paper. An example output is shown below, where \u003ccode\u003e0.06150 seconds\u003c/code\u003e would be reported as the resulting value for the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e...\nRunning jaccard on 128 threads\nK-mer Matrix is 15000x5000 and has 15248 nonzeros.\n\nJaccard Similarity Matrix is 5000x5000 and has 12497374 values.\n\nRunning Jaccard Similarity K-mers (selector): \n 0.06150 seconds\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003ejaccard*+pat+*\u003c/code\u003e folders contain information dumped by the CrayPat profiler (for more information see \u003ca href=\"https://docs.nersc.gov/tools/performance/craypat/\" rel=\"nofollow\"\u003ehttps://docs.nersc.gov/tools/performance/craypat/\u003c/a\u003e). To generate human-readable content, we run \u003ccode\u003epat_report\u003c/code\u003e on the respective directory. This will display information of interest for the specified HWPC in the run, and will directly relate to Figures 2 (a-p). An example output is shown below, where we can see the L1 cache statistics which would be reported as the resulting values for the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003euser@perlmutter: ~\u0026gt; pat_report $PWD/Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector+pat+190420-8718377t\n CrayPat/X: Version 23.02.0 Revision a53634a72 01/11/23 17:17:09\n\n Number of PEs (MPI ranks): 128\n\n Numbers of PEs per Node: 64 PEs on each of 2 Nodes\n\n Numbers of Threads per PE: 2\n\n Number of Cores per Socket: 64\n\n Execution start time: Sun Mar 19 10:25:36 2023\n\n System name and speed: nid004836 2.552 GHz (nominal)\n\n AMD Milan CPU Family: 25 Model: 1 Stepping: 1\n\n Core Performance Boost: 256 PEs have CPB capability\n\n\n Current path to data file:\n /Selector/hclib/modules/bale_actor/jaccard-selector/jaccard_kmer_selector+pat+190420-8718377t (RTS, 2 data files)\n\n ...\n ...\n\n Processing step 7 of 10\n Notes for table 5:\n ...\n ...\n ==============================================================================\n USER / #1.selector_jaccard\n ------------------------------------------------------------------------------\n Time% 2.8% \n Time 0.060836 secs\n Imb. Time 0.000013 secs\n Imb. Time% 0.0% \n Calls 16.438 /sec 1.0 calls\n PAPI_L1_DCM 0.057G/sec 2,369,390.898 misses\n PAPI_L1_DCA 2.252G/sec 110,478,052.633 refs\n Average Time per Call 0.060836 secs\n CrayPat Overhead : Time 0.0% \n perf::PERF_COUNT_HW_CACHE_L1I:ACCESS 1,214,778 \n perf::PERF_COUNT_HW_CACHE_L1I:MISS 5,868\n ==============================================================================\n\n ...\n ...\n\n Hardware performance counter events:\n PAPI_L1_DCM Level 1 data cache misses\n PAPI_L1_DCA Level 1 data cache accesses\n perf::PERF_COUNT_HW_CACHE_L1I:ACCESS Undocumented counter\n perf::PERF_COUNT_HW_CACHE_L1I:MISS Undocumented counter\n\n Estimated minimum instrumentation overhead per call of a traced function,\n which was subtracted from the data shown in this report\n (for raw data, use the option: -s overhead=include):\n Time 0.114 microsecs\n\n Number of traced functions that were called: 7\n\n (To see the list, specify: -s traced_functions=show)\nuser@perlmutter: ~\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTop-Level Directory Organization\u003c/h2\u003e\u003ca id=\"user-content-top-level-directory-organization\" class=\"anchor\" aria-label=\"Permalink: Top-Level Directory Organization\" href=\"#top-level-directory-organization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe folder structure of this repository is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e.\n\u251c\u2500\u2500 Selector # Contains files for the Actor-based runtime and the Jaccard k-mer Selector application\n\u2502 \u251c\u2500\u2500 hclib # Contains the HClib library and the Actor-based runtime\n\u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 modules \n\u2502 \u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 bale_actor \n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 jaccard-selector # Contains the Jaccard k-mer Selector application files\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 jaccard_kmer_selector.cpp # Application code for Selector version of Jaccard similarity for genome comparisons\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 jaccard_kmer_locality_selector.cpp # Application code for locality-aware Selector version of Jaccard similarity for genome comparisons\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 kmer_matrix.mtx # K-mer matrix file for evaluation\n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 \u2500\u2500\u2500 ... \n\u251c\u2500\u2500 GenomeAtScale # Contains files for the CTF library and the GenomeAtScale application\n\u2502 \u251c\u2500\u2500 ctf # Contains the CTF library\n\u2502 \u2502 \u251c\u2500\u2500 ... \n\u2502 \u251c\u2500\u2500 jaccard-ctf # Contains the GenomeAtScale (jaccard-ctf) files\n\u2502 \u2502 \u251c\u2500\u2500 jaccard.cxx # Application code for GenomeAtScale\n\u2502 \u2502 \u251c\u2500\u2500 kmer_matrix.zip # K-mer matrix files for evaluation\n\u2502 \u2514\u2500\u2500 \u2500\u2500\u2500 ... \n\u251c\u2500\u2500 scripts # Contains installation, running, and modules scripts and sample Perlmutter sbatch files\n\u2502 \u251c\u2500\u2500 setup.sh # Installation and build script for the system backends and application code for both the Selector application and the GenomeAtScale application\n\u2502 \u251c\u2500\u2500 run.sh # Run script for both the selector application and GenomeAtScale application\n\u2502 \u251c\u2500\u2500 modules.sh # Modules script to prepare for running experiments (only used following first time setup using setup.sh, has to be re-run everytime you login to a cluster/supercomputer)\n\u2502 \u2514\u2500\u2500 ... \n\u2514\u2500\u2500 README.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use our application in your work, please cite \u003ca href=\"\"\u003eour paper\u003c/a\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eYoussef Elmougy, Akhiro Hayashi, and Vivek Sarkar. 2024. Asynchronous Distributed Actor-based Approach to Jaccard Similarity for Genome Comparisons.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eCorresponding author: Youssef Elmougy (\u003ca href=\"mailto:yelmougy3@gatech.edu\"\u003eyelmougy3@gatech.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgement\u003c/h2\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), through the Advanced Graphical Intelligence Logical Computing Environment (AGILE) research program, under Army Research Office (ARO) contract number W911NF22C0083. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, - "topics": [], - "updated_at": 1674680771.0 + "topics": [ + "asynchronous", + "distributed-systems", + "genome-sequencing", + "hpc", + "jaccard-distance", + "jaccard-index", + "jaccard-similarity", + "openshmem", + "pgas", + "hclib" + ], + "updated_at": 1708280383.0 }, { "data_format": 2, - "description": "Some benchmark singularity images for pycbc / pycbc inference", + "description": "HPC friendly Python + Neuroimaging analysis container environment", "filenames": [ "Singularity" ], - "full_name": "gwastro/pycbc_bench", + "full_name": "cosanlab/cosanToolsSingularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-pycbc_bench\" class=\"anchor\" aria-hidden=\"true\" href=\"#pycbc_bench\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epycbc_bench\u003c/h1\u003e\n\u003cp\u003eSome benchmark singularity images for pycbc / pycbc inference\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-build-singularity-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild singularity image\u003c/h1\u003e\n\u003cp\u003esudo singularity build pycbcb.img Singularity\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-pycbc-inspiral\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-pycbc-inspiral\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erun pycbc inspiral\u003c/h1\u003e\n\u003cp\u003esingularity run --cleanenv --app inspiral pycbcb.img\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCosanlab Singularity Analysis Container\u003c/h1\u003e\u003ca id=\"user-content-cosanlab-singularity-analysis-container\" class=\"anchor\" aria-label=\"Permalink: Cosanlab Singularity Analysis Container\" href=\"#cosanlab-singularity-analysis-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a Singularity spec file that can be used to build a container and run on Dartmouth\u0027s \u003ca href=\"http://techdoc.dartmouth.edu/discovery/\" rel=\"nofollow\"\u003eDiscovery\u003c/a\u003e HPC cluster. It is a Docker -\u0026gt; Singularity bootstrap of our \u003ca href=\"https://github.com/cosanlab/cosanToolsDocker\"\u003eanalysis container\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can either:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCopy the built singularity image from Discovery, located at /ihome/ejolly\u003c/li\u003e\n\u003cli\u003ePull the container from \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity-Hub\u003c/a\u003e \u003ccode\u003e singularity pull shub://cosanlab/cosanToolsSingularity:master\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuild/modify the container from scratch\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding a container from scratch with this repo\u003c/h3\u003e\u003ca id=\"user-content-building-a-container-from-scratch-with-this-repo\" class=\"anchor\" aria-label=\"Permalink: Building a container from scratch with this repo\" href=\"#building-a-container-from-scratch-with-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need a local machine with sudo privileges and singularity installed. If you\u0027re running OSX you can follow the directions \u003ca href=\"http://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003ehere\u003c/a\u003e to get a vagrant VM running to do this. Then proceed with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#on your local machine with sudo privileges\nsudo singularity create --size 8000 myContainer.img\n\n#Singularity in the command below is the spec file in this repo, adjust path accordingly\nsudo singularity bootstrap myContainer.img Singularity\n\n#You might need to copy the .img out of your vagrant vm first if you\u0027re using one; by default /vagrant is shared with your host OS\nscp myContainer.img user@discovery.dartmouth.edu:~\n\n\n#on discovery, from ~\nmodule load singularity\nsingularity run myContainer.img\n\n#OR\nsingularity exec ./myContainer.img someCommand\n\n#to mount a folder with data\nsingularity exec -B /path/to/data:/mnt ./myContainer someCommand\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 8, "topics": [], - "updated_at": 1559569839.0 + "updated_at": 1598945912.0 }, { "data_format": 2, - "description": "Singularity recipes for bioinformatics software", + "description": "official build specifications for debian", "filenames": [ - "spades/3.13.0/Singularity.spades.3.13.0", - "quast/5.0.0/Singularity.quast.5.0.0", - "seqsero2/1.0.0/Singularity.seqsero2.1.0.0", - "seqsero2/0.1/Singularity.seqsero2-0.1", - "lyveset/1.1.4f/Singularity.lyveset.1.1.4f" + "Singularity" ], - "full_name": "kapsakcj/singularities", + "full_name": "singularityhub/debian", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularities\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularities\u003c/h1\u003e\n\u003cp\u003eSingularity recipes for bioinformatics software. Build singularity images with these recipes (sudo required) or download/pull the images from \u003ca href=\"https://singularity-hub.org/collections/2778\" rel=\"nofollow\"\u003esingularity-hub.org\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repo is \u003cstrong\u003eWORK IN PROGRESS\u003c/strong\u003e. Feel free to try the recipes/Singularity builds, but they are \u003cstrong\u003enot tested deeply and are in no way guaranteed to work. Proceed at your own risk\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eIt is somewhat modeled after \u003ca href=\"https://github.com/StaPH-B/docker-builds\"\u003ehttps://github.com/StaPH-B/docker-builds\u003c/a\u003e , but with Singularity recipes instead.\u003c/p\u003e\n\u003cp\u003eSysadmins for High Performance Cluster computers almost always favor Sinularity over Docker :) so I\u0027m starting to learn the ways of Singularity.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-available-singularity-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Singularity images\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eSoftware\u003c/th\u003e\n\u003cth align=\"center\"\u003eVersion\u003c/th\u003e\n\u003cth align=\"center\"\u003eLink\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eSPAdes\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3.13.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003ehttp://cab.spbu.ru/software/spades/\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eQUAST\u003c/td\u003e\n\u003ctd align=\"center\"\u003e5.0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ablab/quast\"\u003ehttps://github.com/ablab/quast\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eLyve-SET\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1.1.4f\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/lskatz/lyve-SET\"\u003ehttps://github.com/lskatz/lyve-SET\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eSeqSero2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e0.1, 1.0.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/denglab/SeqSero2\"\u003ehttps://github.com/denglab/SeqSero2\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThese Singularity images can be built if you have Singularity installed and \u003cstrong\u003ehave sudo/admin priveleges\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# build an image using a recipe (called Singularity in this example)\nsudo singularity build my-new-singularity-image.simg /path/to/Singularity\n\n# download the repo\ngit clone https://github.com/kapsakcj/singularities.git\n# another example using the SPAdes recipe\nsudo singularity build my-new-spades-3.13.0-image.simg /path/to/Singularity.spades.3.13.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThese Singularity images are also available to download from singularity-hub.org if you \u003cstrong\u003edon\u0027t have sudo priveleges\u003c/strong\u003e (no build necessary!). The badge below is a link to the singularity-hub.org collection.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2778\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe name of the Singularity hub collection is \u003ccode\u003ekapsakcj/singularities\u003c/code\u003e and the tag is specified by the extenion of the Singularity recipe file. For example the recipe, \u003ccode\u003e/spades/3.13.0/Singularity.spades.3.13.0\u003c/code\u003e, can be downloaded like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# download an image like so, and name it whatever you want with the --name flag\nsingularity pull --name my-new-spades-3.13.0-image shub://kapsakcj/singularities:spades.3.13.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-useful-links-and-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#useful-links-and-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful links and resources\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity v2.6 User guide \u003ca href=\"https://www.sylabs.io/guides/2.6/user-guide/index.html\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/guides/2.6/user-guide/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularityHub \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExcellent tutorial on Singularity (using v2.5) from Sylabs, many other links within \u003ca href=\"https://github.com/Singularity-tutorial/Singularity-tutorial.github.io\"\u003ehttps://github.com/Singularity-tutorial/Singularity-tutorial.github.io\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHow to build a container using Singularity Hub, linked to a github repo with Singularity recipes \u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki/Build-A-Container\"\u003ehttps://github.com/singularityhub/singularityhub.github.io/wiki/Build-A-Container\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tipstricksthings-to-remember-about-singularity--many-from-jake-garfin--\" class=\"anchor\" aria-hidden=\"true\" href=\"#tipstricksthings-to-remember-about-singularity--many-from-jake-garfin--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTips/Tricks/Things-to-remember about Singularity [ many from Jake Garfin :) ]\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity automatically brings your user \u0026amp; group into the container with you (ie. no \u003ccode\u003e-u $(id -u):$(id -g)\u003c/code\u003e needed like in Docker)\u003c/li\u003e\n\u003cli\u003eSingularity (by default) wants to mount your entire home directory inside the container as well. Use \u003ccode\u003e--cleanenv\u003c/code\u003e and \u003ccode\u003e--containall\u003c/code\u003e to keep things separate and bring in specific directories you want with \u003ccode\u003e-B /local-dir:/dir-in-container\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDocker images converted to Singularity that want to write to system directories owned by root aren\u0027t going to work out of the box.\u003c/li\u003e\n\u003cli\u003eIf you are making a container with something that uses perl, add this to the recipe in the \u003ccode\u003e%environment\u003c/code\u003e section to prevent locale settings errors (see lyveset recipe)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e%environment\n export LC_ALL=C\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTO-DO\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eHow to: Singularity\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinks to docs for installing\u003c/li\u003e\n\u003cli\u003eHow to download an image from singularity hub\u003c/li\u003e\n\u003cli\u003eHow to download an image fromm dockerhub\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esingularity pull docker://staphb/skesa\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eHow to take a recipe and build locally (sudo required)\u003c/li\u003e\n\u003cli\u003eHow to run a Singularity container\n\u003cul\u003e\n\u003cli\u003eDifferent ways to run - \u003ccode\u003esingularity exec [...]\u003c/code\u003e or ./name-of-singularity.simg [...]\u003c/li\u003e\n\u003cli\u003eMounting DIRs - default way (mount entire \u003ccode\u003e$HOME\u003c/code\u003e DIR), or way to mount a specific DIR and not entire \u003ccode\u003e$HOME\u003c/code\u003e DIR\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate SingularityHub account\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elink to this repo\u003c/li\u003e\n\u003cli\u003ecreate autobuilds for each recipe\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDebian\u003c/h1\u003e\u003ca id=\"user-content-debian\" class=\"anchor\" aria-label=\"Permalink: Debian\" href=\"#debian\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official library of Debian builds for Singularity images hosted on Singularity Hub. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003ethe builds are organized by folders, with one \u003ccode\u003eSingularity\u003c/code\u003e file per folder. This ensures that we can find the files programatically.\u003c/li\u003e\n\u003cli\u003ethe different image tags correspond to these folders, and the name of the tag is specified on Singularity Hub\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUnder Development\u003c/h2\u003e\u003ca id=\"user-content-under-development\" class=\"anchor\" aria-label=\"Permalink: Under Development\" href=\"#under-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese images are currently not connected to Singularity Hub, but this will be done in early 2017. The first effort will be to develop a core set of images, and then any necessary builders / templating systems that would be necessary to help with this process. If you are interested in contributing, please \u003ca href=\"http://singularity.lbl.gov/contributing-code\" rel=\"nofollow\"\u003ereach out\u003c/a\u003e!\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1583250575.0 + "updated_at": 1517517877.0 }, { "data_format": 2, "description": null, "filenames": [ - "imaging/nipy/Singularity" + "modules/scvi/Singularity.def", + "modules/doubletdetection/Singularity.def", + "modules/postprocessing/Singularity.def", + "modules/scran/Singularity.def" ], - "full_name": "AndrewYRevell/docker_GitHub", + "full_name": "joechanlab/nf-scrnaseq", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cntdocker\" class=\"anchor\" aria-hidden=\"true\" href=\"#cntdocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCNTdocker\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eDockerfiles to create Docker images used by the CNT at the university of Pennsylvania\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directory-contents-explanation\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-contents-explanation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory contents explanation\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-eeg\" class=\"anchor\" aria-hidden=\"true\" href=\"#eeg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eEEG\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common EEG analysis tools. Usually python 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eechobase\u003c/strong\u003e: Dockerfiles used to create images that can calculate functional connectivity of EEG\nAlso has ieegpy python package used to interface with iEEG.org\nEchobase code is from \u003ca href=\"https://github.com/andyrevell/paper001\"\u003ehttps://github.com/andyrevell/paper001\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUbuntu 18.04\nPython 2.7 and Python 3.6\nNumpy 1.18.4\npandas 1.0.3\nscipy 1.4.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-imaging\" class=\"anchor\" aria-hidden=\"true\" href=\"#imaging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eImaging\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common MRI analysis tools.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n Python 2.7, Python 3.6, Python 3.7\n dcm2niix\n dsistudio\n ANTS\n Freesurfer\n FSL 6.0.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-ml\" class=\"anchor\" aria-hidden=\"true\" href=\"#ml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003eml\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eDockerfiles used to create images with common machine learning tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ewavenet\u003c/strong\u003e: Dockerfile to create compatible dependencies to use with Goodgle Deepmind wavenet paper\n\u003ca href=\"https://deepmind.com/blog/article/wavenet-generative-model-raw-audio\" rel=\"nofollow\"\u003eWavenet blog\u003c/a\u003e\n\u003ca href=\"https://arxiv.org/pdf/1609.03499.pdf\" rel=\"nofollow\"\u003eWavenet paper\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n tensorflow 1.0.0\n pandas 0.19.2\n librosa 0.5.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eTensorflow_2.1\u003c/strong\u003e: Dockerfile to create compatible dependencies to with tensorflow 2.1\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Ubuntu 18.04\n tensorflow 2.1\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA bioinformatics pipeline that preprocesses single-cell RNA-seq data. It takes a samplesheet and CellRanger raw filtered count matrix as input, performs ambient RNA correction, doublet detection, sample aggregation, batch integration.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/pipeline.png\"\u003e\u003cimg src=\"docs/images/pipeline.png\" alt=\"nf-scrnaseq\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!NOTE]\nIf you are new to Nextflow and nf-core, please refer to \u003ca href=\"https://nf-co.re/docs/usage/installation\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e on how to set-up Nextflow. Make sure to \u003ca href=\"https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline\" rel=\"nofollow\"\u003etest your setup\u003c/a\u003e with \u003ccode\u003e-profile test\u003c/code\u003e before running the workflow on actual data.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eFirst, prepare a samplesheet with your input data that looks as follows, where each row contains the sample name, a raw count HDF5 file and a filtered count HDF5 file from CellRanger, and whether demultiplexing is needed.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esamplesheet.csv\u003c/code\u003e:\u003c/p\u003e\n\u003cpre lang=\"csv\"\u003e\u003ccode\u003esample, raw_h5, filtered_h5, demultiplexing, expected_droplets\nCONTROL_REP1, raw_feature_bc_matrix.h5, filtered_feature_bc_matrix.h5, false, 50000\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!Note]\nSee suggestions on choice of the \u003ccode\u003eexpected_droplets\u003c/code\u003e parameter \u003ca href=\"https://www.10xgenomics.com/analysis-guides/background-removal-guidance-for-single-cell-gene-expression-datasets-using-third-party-tools\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eNext, prepare a parameter YAML file that looks as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eparams.yml\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003esamplesheet\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./samplesheet.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e path to the sample sheet\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eoutdir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./out/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e directory containing the outputs\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eexperiment\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexperiment\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e experiment name for prefix\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eatac\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e whether the data is 10X Multiome data\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eaggregation\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003etrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e whether to aggregate samples\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eremove_doublets\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e whether to remove cells marked as doublets\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eremove_outliers\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003efalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e whether to remove cells markerd as outliers\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003escvi\u003c/span\u003e: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e scvi parameters (see bin/scvi_norm.py)\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_latent\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_top_genes\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Note: large values might give error in writing h5ad\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003epostprocessing\u003c/span\u003e: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Postprocessing parameters (see bin/postprocessing.py)\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_pca_components\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_diffmap_components\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003emetadata\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./metadata.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e path to metadata variables\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ecelltypist\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003emodel\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHuman_Lung_Atlas.pkl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e model to use for celltypist\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ereport\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eplot\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./markers.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e custom variables to plot\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eseacells\u003c/span\u003e: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e SEACells parameters (see bin/run_SEACells.py)\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_SEACells\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e25\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ebuild_kernel_on\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eX_pca\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003en_waypoint_eigs\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emount\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/home,/data1\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e path to mount for singularity\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ewith_gpu\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003etrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e using GPU\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emaxForks\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e maximum number of processes in parallel (e.g # of GPU)\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_memory\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e6.GB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e memory information\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_time\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e6.h\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e wall time information\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003emax_cpus\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cpu information\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow, you can run the pipeline. For local run on HPC with singularity installed, execute the following command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run ./main.nf \\\n -profile singularity \\\n -params-file ./params.yml \\\n -w ./work/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using MSKCC lilac, you can use the pre-defined \u003ccode\u003elilac\u003c/code\u003e profile that uses the LSF executor.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run ./main.nf \\\n -profile lilac \\\n -params-file ./params.yml \\\n -w ./work/\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1600370006.0 + "updated_at": 1727381006.0 }, { "data_format": 2, - "description": "LSHVec pre-trained models and its Python bindings", + "description": "Pan-genome nextflow pipeline which uses fasta input files for Prokka and Roary before generating visualisations", "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "Lizhen0909/PyLSHvec", + "full_name": "lifebit-ai/roary", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-lshvec-pre-trained-models-and-its-python-bindings\" class=\"anchor\" aria-hidden=\"true\" href=\"#lshvec-pre-trained-models-and-its-python-bindings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLSHVec pre-trained models and its Python bindings\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-hidden=\"true\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary\u003c/h2\u003e\n\u003cp\u003eThis repository presents a few of pre-tained models with JLSHVec (which is a rewritten java version of \u003ca href=\"https://github.com/Lizhen0909/LSHVec\"\u003eLSHVec\u003c/a\u003e). See \u003ca href=\"#remark\"\u003eRemark\u003c/a\u003e for technical details.\u003c/p\u003e\n\u003cp\u003ePython codes and examples to uses these models are also provided.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003ecython\u0026gt;=0.28.5\u003c/li\u003e\n\u003cli\u003eJnius \u0026gt;=1.1.0\u003c/li\u003e\n\u003cli\u003ejava \u0026gt;=1.8\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebuild from source\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Lizhen0909/PyLSHvec.git \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e PyLSHvec \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e python setup.py install\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-or-use-pip\" class=\"anchor\" aria-hidden=\"true\" href=\"#or-use-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eor use pip\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install pylshvec\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-or-use-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#or-use-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eor use docker\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull lizhen0909/pylshvec\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-or-use-singularity-3\" class=\"anchor\" aria-hidden=\"true\" href=\"#or-use-singularity-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eor use singularity 3\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name pylshvec.sif shub://Lizhen0909/PyLSHvec\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use\u003c/h2\u003e\n\u003cp\u003ePut things simply, just\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epylshvec\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e#here needs jlshvec jar file, download it first\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eset_lshvec_jar_path\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/mnt/jlshvec-assembly-0.1.jar\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e#since vector model is usually large, set a big java memory limit is preferred. \u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eadd_java_options\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"-Xmx32G\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e#here need model file and lsh function file, download them first\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e#use help(model) to see all the methods and constructor options \u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eLSHVec\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel_file\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"/mnt/refdb_viruses_model_gs_k23_l3000_rand_model_299\"\u003c/span\u003e, \n \u003cspan class=\"pl-s1\"\u003ehash_file\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"/mnt/lsh_nt_NonEukaryota_k23_h25.crp\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003ereads\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s\"\u003e\u0027ACGTACGT.....\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027ACGTACGT.....\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027ACGTACGT.....\u0027\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u0027ACGTACGT.....\u0027\u003c/span\u003e, ....]\n\n\u003cspan class=\"pl-s1\"\u003epredicts\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003epredict\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ereads\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more complete examples please see the notebooks (see \u003ca href=\"#download\"\u003eDownload\u003c/a\u003e for minimum memory requirement):\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_virus_classfication_model.ipynb\"\u003eexample_use_virus_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_bacteria_classfication_model.ipynb\"\u003eexample_use_bacteria_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_vectors_in_bacteria_classfication_model.ipynb\"\u003eexample_use_vectors_in_bacteria_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_Illumina_bacteria_classfication_model.ipynb\"\u003eexample_use_Illumina_bacteria_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"notebook/example_use_Pacbio_bacteria_classfication_model.ipynb\"\u003eexample_use_Pacbio_bacteria_classfication_model.ipynb\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Docker\u003c/h3\u003e\n\u003cp\u003eAssume you put your data in /mnt/data and your notebook in /mnt/notebook.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erun python or ipython\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v /mnt/data:/data -it lizhen0909/pylshvec python \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eor ipython\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003erun Jupyter notebook\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v /mnt/data:/data -v /mnt/notebook:/notebook -p 8888:8888 -it lizhen0909/pylshvec jupyter_notebook\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFind connection url in the console output.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Singularity\u003c/h3\u003e\n\u003cp\u003eSince singularity maps the $HOME directory, here just assumes data/model are going to locate in $HOME. Otherwise, you need map the directories like docker.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erun python or ipython\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run pylshvec.sif python \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003ethe nrun any pylshvec code \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003erun Jupyter notebook\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eIt should work, however singularity maps too many things that host settings may affect the notebook\u003c/span\u003e\nsingularity run --bind \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/notebook:/notebook pylshvec.sif jupyter_notebook \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jlshvec-jar-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#jlshvec-jar-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJLSHVec jar file\u003c/h3\u003e\n\u003cp\u003eThe pre-trained models were trained with a rewritten \u003ca href=\"https://github.com/Lizhen0909/LSHVec\"\u003eLSHVec\u003c/a\u003e in java.\nThe assembly jar file is needed to load the models.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.amazon.com/clouddrive/share/4NiogpuW1lzBMyGmMlkrDbjhSMYpQgWjW5GUcKFR7Q6\" rel=\"nofollow\"\u003eDownload jlshvec-assembly-0.1.jar\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e: aeb207b983b3adc27e14fd9c431e2130\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pre-trained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-trained models\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eBe Warned\u003c/strong\u003e that like all the machine learning models, the model cannot preform better beyond the data. If your data is significant other than the pre-trained model data, training your own model is preferred.\u003c/p\u003e\n\u003cp\u003eHere are issues I can think of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSome NCBI taxonomy id may never be predicted since not all ids have train data.\u003c/li\u003e\n\u003cli\u003eData is not balanced. Some ids (e.g. a specified species) have much more data than others, which makes prediction may prefer to the rich-data ids.\u003c/li\u003e\n\u003cli\u003eStrain (even some species) prediction is terrible. Don\u0027t expect it.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-refdb-viruses-classfication-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#refdb-viruses-classfication-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRefDB viruses classfication model\u003c/h4\u003e\n\u003cp\u003eTrainned with 9.3k viruses assemblies of RefDB. Minimum Java memory: 16G.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emodel file: \u003ca href=\"https://www.amazon.com/clouddrive/share/RmoJ1lduzlqstAJFnKg0aAlx82AyCjnzKncfGjQIQMg\" rel=\"nofollow\"\u003erefdb_viruses_model_gs_k23_l3000_rand_model_299\u003c/a\u003e [size: 5.3G]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 2502b284b336734300c2297d23d1d349\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehash function file: \u003ca href=\"https://www.amazon.com/clouddrive/share/6ZNvMXMy30b4vc0RYNVG1lbf1ih8WgpoQ9w4lX91IXy\" rel=\"nofollow\"\u003elsh_nt_NonEukaryota_k23_h25.crp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 5eea8a98d224b7ff505091bd483ca75c\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-refdb-bacteria-classfication-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#refdb-bacteria-classfication-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRefDB bacteria classfication model\u003c/h4\u003e\n\u003cp\u003eTrainned with 42k bacteria assemblies of RefDB. Minimum Java memory: 32G.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emodel file: \u003ca href=\"https://www.amazon.com/clouddrive/share/LoXz6k229SwYuElPTHvu0SSJOq56nJenvBbOTGVeb9a\" rel=\"nofollow\"\u003erefdb_bacteria_model_gs_k23_l3000_rand_model_214\u003c/a\u003e [size: 11G]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 402e9a286b71068999caa9766b2dbf8c\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehash function file: \u003ca href=\"https://www.amazon.com/clouddrive/share/6ZNvMXMy30b4vc0RYNVG1lbf1ih8WgpoQ9w4lX91IXy\" rel=\"nofollow\"\u003elsh_nt_NonEukaryota_k23_h25.crp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 5eea8a98d224b7ff505091bd483ca75c\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-genbank-bacteria-and-viruses-classfication-model-illumina-simulation\" class=\"anchor\" aria-hidden=\"true\" href=\"#genbank-bacteria-and-viruses-classfication-model-illumina-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenBank bacteria and viruses classfication model (Illumina Simulation)\u003c/h4\u003e\n\u003cp\u003eTrainned with 54k assemblies from GenBank. \u003cstrong\u003eOnly one assembly was sampled for each species.\u003c/strong\u003e Because viruses data is too samll compared to bateria, it rarely predicts any viruses. Just take it as a bateria model.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278762/\" rel=\"nofollow\"\u003eart_illumina\u003c/a\u003e was used to simulate the paired-end reads with length of 150, mean size of 270 and stddev of 27.\u003c/p\u003e\n\u003cp\u003eMinimum Java memory: 48G.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emodel file: \u003ca href=\"https://www.amazon.com/clouddrive/share/zQnu2ti1vfBMGcXrRqsohgfzuaYzZs4HGESP58vobRn\" rel=\"nofollow\"\u003egenbank_model_ill_k23_model_299\u003c/a\u003e [size: 12G]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e d6b117a4c7ffe4f25e6c532a88bb3a47\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehash function file: \u003ca href=\"https://www.amazon.com/clouddrive/share/efWceiTHId4EVhY1DEppmW6amyBQoEt3iIU6oW5FbcX\" rel=\"nofollow\"\u003elsh_CAMI2_illumina_k23_h25.crp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 706633919e347f920ce6ab3277091efb\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-genbank-bacteria-and-viruses-classfication-model-pacbio-simulation\" class=\"anchor\" aria-hidden=\"true\" href=\"#genbank-bacteria-and-viruses-classfication-model-pacbio-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenBank bacteria and viruses classfication model (Pacbio Simulation)\u003c/h4\u003e\n\u003cp\u003eTrainned with 54k assemblies from GenBank. \u003cstrong\u003eOnly one assembly was sampled for each species.\u003c/strong\u003e Because viruses data is too samll compared to bateria, it rarely predicts any viruses. Just take it as a bateria model.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/pfaucon/PBSIM-PacBio-Simulator\"\u003epbsim\u003c/a\u003e was used to simulate the pacbio reads with Continuous Long Read (CLR) profile, mean size of 3000 and stddev of 1000.\u003c/p\u003e\n\u003cp\u003eMinimum Java memory: 16G.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003emodel file: \u003ca href=\"https://www.amazon.com/clouddrive/share/OmU9cmVKknacpt0W9HpI6QY2jXC17dQpWaaERpLhOGl\" rel=\"nofollow\"\u003egenbank_model_pb_k9_model_299\u003c/a\u003e [size: 121M]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e 351275531493a4866be4afcd9df3932c\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehash function file: \u003ca href=\"https://www.amazon.com/clouddrive/share/zw4JwJCE4Lst5I4q36ijwrhc3db9rHYsCuyQ4KkihVC\" rel=\"nofollow\"\u003elsh_CAMI2_pacbio_k9_h25.crp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e df7ee38cf8b58d5f8034bb9b266e3334\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-sample-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#sample-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSample data\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eActinoMock Nanopore Sample [size: 500M].\u003c/p\u003e\n\u003cp\u003eThe data is used in example notebook \u003ca href=\"notebook/example_use_vectors_in_bacteria_classfication_model.ipynb\"\u003eexample_use_vectors_in_bacteria_classfication_model.ipynb\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://ww2.cs.fsu.edu/~lshi/ActinoMock_Nanopore.seq.gz\" rel=\"nofollow\"\u003eDownload from FSU\u003c/a\u003e\n\u2003\u2003\n\u003ca href=\"https://www.amazon.com/clouddrive/share/eTIKYVLckXUCMnMQSpO8TCqZOwekmBrx23ZhMa3XO8d\" rel=\"nofollow\"\u003eDownload from Amazon Drive\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emd5sum\u003c/strong\u003e: b7f3e55438fdc05920aee693a98ded2e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-remark\" class=\"anchor\" aria-hidden=\"true\" href=\"#remark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRemark\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-what-is-jlshvec--why-jlshvec-instead-of-lshvec\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-jlshvec--why-jlshvec-instead-of-lshvec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is JLSHVec ? Why JLSHVec instead of LSHVec?\u003c/h3\u003e\n\u003cp\u003eJLSHVec is a rewritten version of \u003ca href=\"https://github.com/Lizhen0909/LSHVec\"\u003eLSHVec\u003c/a\u003e in Java language.\u003c/p\u003e\n\u003cp\u003eWhen we use LSHVec with big dataset (e.g. \u003ca href=\"https://www.ncbi.nlm.nih.gov/genbank/\" rel=\"nofollow\"\u003eGenBank\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/12652131\" rel=\"nofollow\"\u003eRefDB\u003c/a\u003e), we found that LSHVec is hard to process such a big data size.\u003c/p\u003e\n\u003cp\u003eThe reason is that LSHVec which inherits from \u003ca href=\"https://fasttext.cc/\" rel=\"nofollow\"\u003eFastText\u003c/a\u003e requires the input is text format separated by white space and then loads all the text in memory. This is acceptable for natural languages since the data size is at most tens GBs.\u003c/p\u003e\n\u003cp\u003eHowever in LSHVec k-mers are used instead of words. Suppose we want to train a k-mer embedding of simulated Illumina reads with RefDB bacteria assemblies (about 500G genetic bits). The number of kmers is about D*n, where D is the assembly data size and n is coverage. In our case, assuming n=10 and k=23, the number of kmers is 5T and requires a disk space of 125TB, of which the data preparation and loading process will take forever.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-how-were-jlshvec-pre-trained-models-trained-\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-were-jlshvec-pre-trained-models-trained-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow were JLSHVec pre-trained models trained ?\u003c/h3\u003e\n\u003cp\u003eFirst we prepared a \u003ca href=\"https://rocksdb.org/\" rel=\"nofollow\"\u003eRockDB\u003c/a\u003e for the reference sequences (e.g. all bacteria assemblies in RefDB).\u003c/p\u003e\n\u003cp\u003eThen we have several nodes to train the model: one node (train node) trains the model and others (hash nodes) generate and hash kmers. The nodes communicates by passing \u003ca href=\"https://developers.google.com/protocol-buffers\" rel=\"nofollow\"\u003eprotocol-buf\u003c/a\u003e message with a \u003ca href=\"https://redis.io/\" rel=\"nofollow\"\u003eRedis\u003c/a\u003e server.\u003c/p\u003e\n\u003cp\u003eA hash node randomly reads reference sequences from the RockDB, simulates (e.g. simulations Illumina, Pacbio, Gold Standard) reads, generates kmers and hashes them, then feeds the hashed-kmer-sequences to a Redis queue.\u003c/p\u003e\n\u003cp\u003eTrain node reads from the Redis queue and does jobs of embedding or classification training. Our training code supports hierarchical softmax using NCBI taxonomy tree, which is essential for multi-label(an instance can have a label for each rank) and multi-class(an instance can only have one label for a rank) mixture classification model.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003ePlease cite:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.biorxiv.org/content/biorxiv/early/2019/08/06/726729.full.pdf\" rel=\"nofollow\"\u003eA Vector Representation of DNA Sequences Using Locality Sensitive Hashing\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/400c4e52df43f6a0ab8a89b74b1a78d1a64da56a7848b9110c9d2991bb7c3105/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/roary\u003c/h1\u003e\u003ca id=\"user-content-nf-coreroary\" class=\"anchor\" aria-label=\"Permalink: nf-core/roary\" href=\"#nf-coreroary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePan-genome nextflow pipeline which uses fasta input files for Prokka and Roary before generating visualisations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/roary\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f95f364a5684a8dadc758fc13e9ab275acb97ddb0b8985a13c22d37f18fba30d/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f726f6172792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/roary.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/roary\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8bda52b2aecd18f25dfa0902c0a29d55b6c89a531e3c7378605412fa6acda1c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f726f6172792e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/roary.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/roary pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1634389599.0 + "updated_at": 1677098155.0 }, { "data_format": 2, - "description": "Singularity recipe files for metabat2 (https://bitbucket.org/berkeleylab/metabat/src/master/)", + "description": null, "filenames": [ - "Singularity", - "Singularity.2.15-3-g367a7ef", - "Singularity.2.15" + "rscape/Singularity", + "treeshrinkenv/Singularity", + "cmfinder/Singularity", + "renv/Singularity", + "sissiz/Singularity" ], - "full_name": "powerPlant/metabat2-srf", + "full_name": "asgeissler/OCyRS-pipeline", "latest_release": null, - "readme": "\u003cp\u003eSingularity recipe files for MetaBAT: A robust statistical framework for reconstructing genomes from metagenomic data\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOCyRS: Orthologues cyanobacterial gene-associated RNA structure motifs\u003c/h1\u003e\u003ca id=\"user-content-ocyrs-orthologues-cyanobacterial-gene-associated-rna-structure-motifs\" class=\"anchor\" aria-label=\"Permalink: OCyRS: Orthologues cyanobacterial gene-associated RNA structure motifs\" href=\"#ocyrs-orthologues-cyanobacterial-gene-associated-rna-structure-motifs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository corresponds to the pipeline of the scientific publication:\u003c/p\u003e\n\u003cp\u003e\"Exploring the regulatory potential of RNA structures in 202 cyanobacterial genomes\"\u003cbr\u003e\nAS Geissler, EC Alvarez, C Anthon, NU Frigaard, J Gorodkin, and SE Seemann\u003cbr\u003e\n\u003cem\u003esubmitted\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePurpose\u003c/h2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-label=\"Permalink: Purpose\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline download public cyanobacterial genome sequence and annotations\nfrom\n\u003ca href=\"https://progenomes.embl.de/\" rel=\"nofollow\"\u003eproGenomes\u003c/a\u003e.\nAnchored to orthologues genes, potential adjacently located\nRNA structure motif are searched for with\n\u003ca href=\"http://bio.cs.washington.edu/yzizhen/CMfinder/\" rel=\"nofollow\"\u003eCMfinder\u003c/a\u003e.\nThe pipeline includes additional steps to score the motifs and check\nfor their phylogenetic/biological plausibility with\n\u003ca href=\"http://eddylab.org/R-scape/\" rel=\"nofollow\"\u003eR-scape\u003c/a\u003e.\nAs such, the motifs are compared with known bacterial\n\u003ca href=\"https://rfam.xfam.org/\" rel=\"nofollow\"\u003eRNA families\u003c/a\u003e,\nthe\n\u003ca href=\"http://eddylab.org/infernal/\" rel=\"nofollow\"\u003eInfernal\u003c/a\u003e\npredicted genomic location of these families,\nand the respective scores.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is implemented in a reproducible\n\u003ca href=\"https://snakemake.github.io/\" rel=\"nofollow\"\u003esnakemake\u003c/a\u003e\nworkflow, which automatically handles the further\nsoftware dependencies. The only hard-requirement are\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA powerful machine or cluster (at least 20 CPU cores 40+ GB RAM)\u003c/li\u003e\n\u003cli\u003eSingularity version $\\ge 3.9$\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eMiniconda3\u003c/a\u003e + Mamba\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://snakemake.github.io/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e $\\ge 7.9.0$\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLinux\u003c/h3\u003e\u003ca id=\"user-content-linux\" class=\"anchor\" aria-label=\"Permalink: Linux\" href=\"#linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease use either the binary packages coming with your distribution\u0027s package\nsystem or follow the installation procedures listed by the respective\nproject websites.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMacOS\u003c/h3\u003e\u003ca id=\"user-content-macos\" class=\"anchor\" aria-label=\"Permalink: MacOS\" href=\"#macos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install singularity, please consider using\na \u003ca href=\"https://www.vagrantup.com/\" rel=\"nofollow\"\u003eVagrant\u003c/a\u003e box for\nvirtualisation.\nThe installation is straight-forward with\n\u003ca href=\"https://brew.sh/\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ brew install --cask virtualbox vagrant vagrant-manager\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the information in \u003ccode\u003eVagrantfile\u003c/code\u003e, singularity can be installed and\nactivated in the current working directory with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ vagrant up\n $ vagrant ssh\n $ cd /vagrant/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, the conda environment must be installed within the virtual box.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n $ sh Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDue to the requirement \u0027For changes to take effect, close and re-open your current shell.\u0027, you must exit and reopen the shell before being able to install mamba.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ exit\n $ vagrant ssh\n $ cd /vagrant/\n $ conda install -n base -c conda-forge mamba\n $ mamba install -n base -c bioconda -c conda-forge snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfterward, the virtual box is fully setup to work with the pipeline.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload all further software dependencies with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ sh init-singularity.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe script downloads the following software images from the\n\u003ca href=\"https://galaxyproject.org/\" rel=\"nofollow\"\u003eGalaxy project\u003c/a\u003e\nsoftware repository:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCheckM $1.2.0$\u003c/li\u003e\n\u003cli\u003eCMfinder $0.4.1.18$\u003c/li\u003e\n\u003cli\u003eInfernal $1.1.4$\u003c/li\u003e\n\u003cli\u003eIQ-TREE $2.2.0.3$\u003c/li\u003e\n\u003cli\u003eMUSCLE $3.8.1551$\u003c/li\u003e\n\u003cli\u003eR-Scape $1.4.0$\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eA container with various R packages \u003ccode\u003erenv/renv.sif\u003c/code\u003e\nis pre-build as part of this pipeline.\nThe corresponding conda environment was for maximal reproducibility\n\u003ca href=\"https://github.com/conda-incubator/conda-lock\"\u003econda-lock\u0027ed\u003c/a\u003e,\nsuch that the exact versions are specified for linux \u003ccode\u003ex64\u003c/code\u003e\ncomputer architectures, in case you would like to re-build the container.\nAn additional container for the\n\u003ca href=\"https://github.com/uym2/TreeShrink\"\u003eTreeShrink\u003c/a\u003e\nsoftware was generated in the folder \u003ccode\u003etreeshrinkenv\u003c/code\u003e.\nAll additional containers are retrieved via the\nGitHubs Container Repository of this project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBefore you start:\u003c/strong\u003e\u003c/em\u003e\nPlease adjust the \u003ccode\u003eproject_root\u003c/code\u003e variable in the \u003ccode\u003econfig/config.yaml\u003c/code\u003e file to\nyour project path.\u003c/p\u003e\n\u003cp\u003eThere are currently two helper script to aid starting snakemake. Use either\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003e$ bash run_local.sh\u003c/code\u003e for running snakemake on a single powerful machine.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e$ bash run_slurm.sh\u003c/code\u003e to submit jobs to a slurm cluster.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn case of the latter, also consider adjusting \u003ccode\u003econfig/slurm.yaml\u003c/code\u003e to your\nenvironment (\u003cem\u003ee.g.\u003c/em\u003e partition name). To adapt a similar helper script for\nalternative cluster solutions, please refer to the\n\u003ca href=\"https://snakemake.readthedocs.io/en/stable/executing/cluster.html\" rel=\"nofollow\"\u003esnakemake handbook\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, to run the pipeline, first call the \u003cem\u003edownload\u003c/em\u003e target before\ninitiating \u003cem\u003eall\u003c/em\u003e subsequent steps. Together with the container download,\nthe commands to run the pipeline are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ sh init-singularity.sh\n $ bash run_local.sh download\n $ bash run_local.sh all\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1618472328.0 + "updated_at": 1727253743.0 }, { "data_format": 2, - "description": null, + "description": "Dev tools for vasst pipeline", "filenames": [ - "Singularity.cpu-guppy3.4-conda-api", - "Singularity.guppy3.6.0cpu-conda-api", - "Singularity.guppy4.5.4gpu-conda-api", - "Singularity.guppy4.2.2gpu-conda-api", - "Singularity.guppy3.4gpu-conda-api", - "Singularity.myR_3-6-3", - "Singularity.deepbinner-api", - "Singularity.guppy5.0.14gpu-conda-api", - "Singularity.myR_4-0-2_rstudio_1.3", - "Singularity.guppy3.6.0gpu-conda-api", - "Singularity.guppy-cpu-conda", - "Singularity.guppy5.0.7gpu-conda-api", - "Singularity.guppy4.0.14gpu-conda-api" + "Singularity.v0.0.4", + "Singularity.v0.0.1", + "Singularity.v0.0.4c", + "Singularity.v0.0.3", + "Singularity.v0.0.2a", + "Singularity.v0.0.4a", + "Singularity.v0.0.4g", + "Singularity.v0.0.4e", + "Singularity.v0.0.2", + "Singularity.v0.0.4f", + "Singularity.v0.0.4b", + "Singularity", + "Singularity.v0.0.4d", + "Singularity.v0.0.3a" ], - "full_name": "vibaotram/singularity-container", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4054\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-container\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity-images-supporting-basedmux-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-images-supporting-basedmux-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity images supporting \u003ca href=\"https://github.com/vibaotram/baseDmux.git\"\u003ebaseDmux workflow\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity.guppy-cpu-conda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econtaining GUPPY version 3.4 CPU, Miniconda3\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshub://vibaotram/singularity-container:guppy-cpu-conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity.cpu-guppy3.4-conda-api\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econtaining GUPPY version 3.4 CPU, Miniconda3, ONT_FAST5_API\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshub://vibaotram/singularity-container:cpu-guppy3.4-conda-api\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity.guppy3.4gpu-conda-api\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econtaining GUPPY version 3.4 GPU, Miniconda3, ONT_FAST5_API\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshub://vibaotram/singularity-container:guppy3.4gpu-conda-api\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity.deepbinner-api\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econtaining deepbinner 2.0.0, ONT_FAST5_API, python3\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eshub://vibaotram/singularity-container:deepbinner-api\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "akhanf/vasst-dev", + "latest_release": "v0.0.3", "stargazers_count": 1, - "subscribers_count": 3, - "topics": [ - "singularity" - ], - "updated_at": 1632300990.0 + "subscribers_count": 9, + "topics": [], + "updated_at": 1643993860.0 }, { "data_format": 2, - "description": "Singularity Image for RNA-Seq analysis", + "description": null, "filenames": [ - "Singularity", - "Singularity.test_jupyter" + "Singularity.2.1", + "Singularity.2.0", + "Singularity.2.2" ], - "full_name": "duke-chsi-informatics/singularity-rnaseq", + "full_name": "JeffersonLab/jlabce", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-rnaseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-rnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-rnaseq\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Jupyter\u003c/h2\u003e\n\u003cp\u003eRun this to start Jupyter:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app jupyter library://granek/duke-chsi-informatics/singularity-rstudio:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen follow the instructions that Jupyter printed to the terminal when you started it up to access Jupyter in your web browser\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-accessing-jupyter-on-a-remote-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#accessing-jupyter-on-a-remote-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAccessing Jupyter on a remote server\u003c/h3\u003e\n\u003cp\u003eIf you are running the container on a remote server, you will need to set up port forwarding with ssh to be able to access Jupyter. Run this command to forward the default Jupyter port (8888)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -L 8888:localhost:8888 bug\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote if the default Jupyter port is not available, Jupyter will choose a different port. In this case you will need to substitute the port that Jupyter outputs for 8888 in the ssh port forwarding command above.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-a-slurm-cluster\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-on-a-slurm-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on a SLURM Cluster\u003c/h2\u003e\n\u003cp\u003eYou can use this image interactively on a SLURM-managed cluster by running launching RStudio or Jupyter. The following instructions work on the Duke Compute Cluster (DCC). Doing this on other cluster will require some modification and may not work, depending on how the cluster is configured.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRStudio\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003essh to DCC login node: \u003ccode\u003essh NETID@dcc-login-01.rc.duke.edu\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003erun tmux on login node: \u003ccode\u003etmux new -s container_demo\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun this on login node: \u003ccode\u003esrun -A chsi -p chsi --mem=100G -c 30 --pty bash -i\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003ehostname -A\u003c/code\u003e on compute node and record results\u003c/li\u003e\n\u003cli\u003eRun on the following on a compute node and note the port, username, and password that the command prints:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /scratch/josh/rnaseq_demo/rawdata /scratch/josh/rnaseq_demo/workspace\n\nsingularity run \\\n\t--bind /scratch/josh/rnaseq_demo/rawdata:/data \\\n\t--bind /scratch/josh/rnaseq_demo/workspace:/workspace \\\n\tlibrary://granek/duke-chsi-informatics/singularity-rnaseq\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eRun on local machine: \u003ccode\u003essh -L PORT:COMPUTE_HOSTNAME:PORT NETID@dcc-login-01.rc.duke.edu\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere PORT is the port returned but the \"singularity run\" commmand\u003c/li\u003e\n\u003cli\u003eWhere COMPUTE_HOSTNAME is the hostname returned by running \"hostname -A\" on the compute node\u003c/li\u003e\n\u003cli\u003eWhere NETID is your NetID\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eGo to \"localhost:PORT\" in a webrowser and enter the username and password printed by the \"singularity run\" commmand\u003c/li\u003e\n\u003cli\u003eHave fun!!\u003c/li\u003e\n\u003cli\u003eAt the end of an analysis you will probably want to copy results to your directory in \u003ccode\u003e/work\u003c/code\u003e or \u003ccode\u003e/hpc/group\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jupyter\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003essh to dcc-login-01.rc.duke.edu\u003c/li\u003e\n\u003cli\u003erun tmux on login node: \u003ccode\u003etmux new -s container_demo\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun this on login node: \u003ccode\u003esrun -A chsi -p chsi --mem=100G -c 30 --pty bash -i\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun on compute node:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /scratch/josh/rnaseq_demo/rawdata /scratch/josh/rnaseq_demo/workspace\n\nsingularity run \\\n\t--app jupyter \\\n\t--bind /scratch/josh/rnaseq_demo/rawdata:/data \\\n\t--bind /scratch/josh/rnaseq_demo/workspace:/workspace \\\n\tlibrary://granek/duke-chsi-informatics/singularity-rnaseq\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eRun on local machine: \u003ccode\u003essh -L PORT:COMPUTE_HOSTNAME:PORT NETID@dcc-login-01.rc.duke.edu\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eWhere PORT is the number after \u003ccode\u003ehttp://127.0.0.1:\u003c/code\u003e in the URL given by Jupyter (defaults to 8888, but Jupyter will use a different one if the default is in use, or if a different port is supplied as an argument using \u003ccode\u003e--port\u003c/code\u003e when running the singularity container\u003c/li\u003e\n\u003cli\u003eWhere COMPUTE_HOSTNAME is the hostname returned by running \"hostname -A\" on the compute node\u003c/li\u003e\n\u003cli\u003eWhere NETID is your NetID\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCopy the URL supplied by jupyter that starts \u003ccode\u003ehttp://127.0.0.1:\u003c/code\u003e and paste it in a webbrowser\u003c/li\u003e\n\u003cli\u003eHave fun!!\u003c/li\u003e\n\u003cli\u003eAt the end of an analysis you will probably want to copy results to your directory in \u003ccode\u003e/work\u003c/code\u003e or \u003ccode\u003e/hpc/group\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jupyter-on-gpu-node\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter-on-gpu-node\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter on GPU node\u003c/h3\u003e\n\u003cp\u003eSame as above, but the srun command should use the \u003ccode\u003echsi-gpu\u003c/code\u003e partition and request a gpu, but less CPUs and Memory:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esrun -A chsi -p chsi-gpu --gres=gpu:1 --mem=15866 -c 2 --pty bash -i\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejeffersonlab/jlabce\u003c/h1\u003e\u003ca id=\"user-content-jeffersonlabjlabce\" class=\"anchor\" aria-label=\"Permalink: jeffersonlab/jlabce\" href=\"#jeffersonlabjlabce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for docker containers at \u003ca href=\"https://hub.docker.com/r/jeffersonlab/jlabce/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/jeffersonlab/jlabce/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/363\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [], - "updated_at": 1631561346.0 + "updated_at": 1527047870.0 }, { "data_format": 2, - "description": "Open-Source Computational Structural Biology Framework", + "description": null, "filenames": [ - "singularity/Singularity" + "Singularity.v0.1_rocker", + "Singularity.vkR", + "Singularity", + "Singularity.motus2.6" ], - "full_name": "sailfish009/openstructure", - "latest_release": null, + "full_name": "cschu/vortex_knight", + "latest_release": "v0.14.4", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003evortex_knight\u003c/h1\u003e\u003ca id=\"user-content-vortex_knight\" class=\"anchor\" aria-label=\"Permalink: vortex_knight\" href=\"#vortex_knight\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstalling locally and running from local installation\u003c/h3\u003e\u003ca id=\"user-content-installing-locally-and-running-from-local-installation\" class=\"anchor\" aria-label=\"Permalink: Installing locally and running from local installation\" href=\"#installing-locally-and-running-from-local-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo from GitHub.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/cschu/vortex_knight.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate a conda environment with NextFlow, e.g. by using the provided \u003ccode\u003eenvironment.yml\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd vortex_knight\nconda env create -f environment.yml\nconda activate vortex_knight\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eMake a copy of the \u003ccode\u003econfig/run.config\u003c/code\u003e file and adjust it to your environment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run /path/to/vortex_knight/main.nf --input_dir /path/to/input_files --output_dir /path/to/output_dir -c /path/to/run.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote: Nextflow itself requires at least \u003ccode\u003e5GB\u003c/code\u003e of memory.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning from GitHub\u003c/h3\u003e\u003ca id=\"user-content-running-from-github\" class=\"anchor\" aria-label=\"Permalink: Running from GitHub\" href=\"#running-from-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis requires a local nextflow installation. If you don\u0027t have one, see Steps 1/2 above.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eMake a local copy of the \u003ca href=\"https://raw.githubusercontent.com/cschu/vortex_knight/main/nextflow/run.config\" rel=\"nofollow\"\u003erun configuration file\u003c/a\u003e and adjust to your environment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run cschu/vortex_knight --input_dir /path/to/input_files --output_dir /path/to/output_dir -c /path/to/run.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote: Nextflow itself requires at least \u003ccode\u003e5GB\u003c/code\u003e of memory.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput parameters\u003c/h3\u003e\u003ca id=\"user-content-input-parameters\" class=\"anchor\" aria-label=\"Permalink: Input parameters\" href=\"#input-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input_dir\u003c/code\u003e should be a folder with bam files or with gzipped fastq files. For fastq files, individual samples should be separated into individual folders.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_dir\u003c/code\u003e is \u003ccode\u003evknight_out\u003c/code\u003e in the local directory by default.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--skip_\u0026lt;analysis\u0026gt;\u003c/code\u003e, \u003ccode\u003e--run_\u0026lt;analysis\u0026gt;\u003c/code\u003e skips, resp. explicitly requires execution of the specified analysis (\u003ccode\u003emotus\u003c/code\u003e, \u003ccode\u003epathseq\u003c/code\u003e, \u003ccode\u003ecount_reads\u003c/code\u003e, \u003ccode\u003emtags\u003c/code\u003e, \u003ccode\u003emapseq\u003c/code\u003e, \u003ccode\u003ekraken2\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--publishMode\u003c/code\u003e allows to switch between various modes of how results files are placed in the \u003ccode\u003eoutput_dir\u003c/code\u003e (cf. NextFlow documentation)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eNotes\u003c/h4\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emapseq\u003c/code\u003e can only run in combination with \u003ccode\u003emtags\u003c/code\u003e and when the parameter \u003ccode\u003emapseq_bin\u003c/code\u003e is explicitly set.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ekraken2\u003c/code\u003e can only run when the parameter \u003ccode\u003ekraken_database\u003c/code\u003e is set.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epathseq\u003c/code\u003e can only run when the parameter \u003ccode\u003epathseq_database\u003c/code\u003e is set.\u003c/li\u003e\n\u003cli\u003ea pre-downloaded motus database can be set with the parameter \u003ccode\u003emotus_database\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eresults are only collated if the parameter \u003ccode\u003ecollate_script\u003c/code\u003e is set. (TODO -\u0026gt; change to baseDir?)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eOutputs\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe output folder contains:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eone subdirectory \u003ccode\u003eotu_tables\u003c/code\u003e containing the summarised \u003ccode\u003emapseq\u003c/code\u003e otu tables\u003c/li\u003e\n\u003cli\u003ea subdirectory per sample (named \u003ccode\u003e\u0026lt;sample\u0026gt;\u003c/code\u003e) with\n\u003cul\u003e\n\u003cli\u003ethe kraken2 report \u003ccode\u003e\u0026lt;sample\u0026gt;.kraken2_report.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ethe library size \u003ccode\u003e\u0026lt;sample\u0026gt;.libsize.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ethe mOTUs report \u003ccode\u003e\u0026lt;sample\u0026gt;.motus.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003epathseq output\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.bam\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.bam.sgi\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.score_metrics\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;sample\u0026gt;.pathseq.scores\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNote that by default, all files in the output folder are symlinks into the work dir! Before you delete the work dir, ensure you have dereferenced copies. Alternatively, change the --publishMode parameter to \u003ccode\u003ecopy\u003c/code\u003e or \u003ccode\u003elink\u003c/code\u003e (if the target file system supports hard links).\u003c/strong\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1659218211.0 + "updated_at": 1723638441.0 }, { "data_format": 2, - "description": "LArFlow: predicting pixel correspondence between planes using CNNs", + "description": "singularity recipe for openmm", "filenames": [ - "container/Singularity" + "Singularity_version.def", + "Singularity_11.def", + "Singularity_12.def", + "Singularity.def", + "mpi/Singularity_version.def" ], - "full_name": "NuTufts/larflow", + "full_name": "edoapra/openmm-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-larflow-prediction-pixel-correspondence-between-lartpc-wireplane-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#larflow-prediction-pixel-correspondence-between-lartpc-wireplane-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLArFlow: prediction pixel correspondence between LArTPC wireplane images\u003c/h1\u003e\n\u003cp\u003eThis repository contains the code for developing a full 3D neutrino interaction\nreconstruction centered using outputs of a convolutional neural network.\u003c/p\u003e\n\u003cp\u003eThe convolutional neural network aims to provide information\nthat seeds the reconstruction of particles and interactions.\nThis includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egood 3D space points representing the locations where charge particle tracks passed\nthrough the detector\u003c/li\u003e\n\u003cli\u003eassociations between the 3D space points and the spatial patterns in\nthe TPC images where the points project into.\u003c/li\u003e\n\u003cli\u003escores indicating which 3D points are near important, key-points:\ntrack ends, shower starts, and neutrino interaction vertices.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDocumentation for the library can be found at \u003ca href=\"https://nutufts.github.io/larflow\" rel=\"nofollow\"\u003egithub.io/larflow\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003elarmatchnet: definition of network, scripts to train and deploy network\u003c/li\u003e\n\u003cli\u003elarflow: c++ libraries providing methods to prepare data, perform downstream reconstruction\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWithin the \u003ccode\u003elarflow\u003c/code\u003e folder, are the following c++ modules:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ePrepFlowMatchData\u003c/code\u003e: classes/methods for preparing spacepoint data from TPC images\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eKeyPoints\u003c/code\u003e: classes/methods for preparing keypoint training info using spacepoint data from \u003ccode\u003ePrepFlowMatchData\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpatialEmbed\u003c/code\u003e: classes/methods for preparing spatial embedding training info using spacepoint data from \u003ccode\u003ePrepFlowMatchData\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eReco\u003c/code\u003e: downstream reconstruction using output of networks to form candidate neutrino interactions\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCRTMatch\u003c/code\u003e: tools to combine CRT data with spacepoints and TPC data in order to provide tagged cosmic muon tracks\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eVoxelizer\u003c/code\u003e: voxelize larmatch spacepoints, not finished. intended to help spacepoint output connect to 3D convolutional networks.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eLArFlowConstants\u003c/code\u003e: constants, enumerations used in the other modules\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFlowContourMatch\u003c/code\u003e:deprecated tools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOther folders are considered deprecated and need to be cleaned up and archived.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etestdata: default location for testdata used for development\u003c/li\u003e\n\u003cli\u003eutils: utility scripts\u003c/li\u003e\n\u003cli\u003econtainer: script to build Singularity container that will work on tufts grid\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003edeprecated folders, kept for archival reasons\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edeprecated/old_larflownet_models: different version of LArFlow models\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_dataprep: scripts to make larflow input and truth images from larsoft files and then prepare crops for training\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_training: training scripts\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_deploy: take trained models and process test files\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_ana: analysis scripts for processed test files\u003c/li\u003e\n\u003cli\u003edeprecated/old_larflownet_weights: default location for weights\u003c/li\u003e\n\u003cli\u003edeprecated/postprocessor: old code that used old larflow 3d points for reco\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-do-list\" class=\"anchor\" aria-hidden=\"true\" href=\"#to-do-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo Do list:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eclustering via spatial embed method [Jared]\u003c/li\u003e\n\u003cli\u003eclustering via part affinity flows [Taritree]\u003c/li\u003e\n\u003cli\u003eextend keypoints to be more than one class [Polina]\u003c/li\u003e\n\u003cli\u003edevelop interactoin selection\u003c/li\u003e\n\u003cli\u003edevelop analysis metrics [Ralitsa]\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-not-included-in-repo\" class=\"anchor\" aria-hidden=\"true\" href=\"#not-included-in-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNot included in repo\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eROOT (6.12/04 known to work)\u003c/li\u003e\n\u003cli\u003eopencv (3.2.0 known to work)\u003c/li\u003e\n\u003cli\u003epytorch (1.3, 1.4 known to work)\u003c/li\u003e\n\u003cli\u003enumpy (1.14.03 known to work)\u003c/li\u003e\n\u003cli\u003etensorboardX (from \u003ca href=\"https://github.com/lanpa/tensorboard-pytorch\"\u003ehere\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003etensorboard\u003c/li\u003e\n\u003cli\u003ecuda (currently using 10.1)\u003c/li\u003e\n\u003cli\u003eEigen 3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUBDL dependencies\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elarlite: following X branch\u003c/li\u003e\n\u003cli\u003eGeo2D:\u003c/li\u003e\n\u003cli\u003eLArOpenCV:\u003c/li\u003e\n\u003cli\u003elarcv:\u003c/li\u003e\n\u003cli\u003eublarcvapp:\u003c/li\u003e\n\u003cli\u003ecilantros:\u003c/li\u003e\n\u003cli\u003enlohmann/json\u003c/li\u003e\n\u003cli\u003e(to do: add missing)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-included-as-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#included-as-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIncluded as submodules\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003elarcvdataset: wrapper class providing interface to images stored in the larcv format. converts data into numpy arrays for use in pytorch (deprecated)\u003c/li\u003e\n\u003cli\u003eCilantro: a library with various Clustering routines w/ C++ API (deprecated)\u003c/li\u003e\n\u003cli\u003ePangolin: a OpenGL viewer package, used by Cilantro (deprecated)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThis repository is intended to be build as a part of the \u003ccode\u003eubdl\u003c/code\u003e environment. Go \u003ca href=\"https://github.com/larbys/ubdl\"\u003ehere\u003c/a\u003e to see this repo.\u003c/li\u003e\n\u003cli\u003eclone \u003ccode\u003eubdl\u003c/code\u003e: \u003ccode\u003egit clone https://github.com/larbys/ubdl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ego to the \u003ccode\u003eubdl\u003c/code\u003e folder\u003c/li\u003e\n\u003cli\u003esetup the \u003ccode\u003eubdl\u003c/code\u003e environment pre-reqs: \u003ccode\u003esource setenv.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003econfigure the \u003ccode\u003eubdl\u003c/code\u003e submodule environments: \u003ccode\u003esource configure.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ebuild all the submodules: \u003ccode\u003esource buildall_py2.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-issues-building-pangolin-deprecated\" class=\"anchor\" aria-hidden=\"true\" href=\"#issues-building-pangolin-deprecated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssues building Pangolin (deprecated)\u003c/h3\u003e\n\u003cp\u003ePangolin depends on GLEW and X11. These can be provided by a package manager.\nHowever, especially for GLEW other versions can be on the system from other libraries like CUDA and/or ROOT.\nThis can cause compilation errors.\u003c/p\u003e\n\u003cp\u003eIf there are issues you can try the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ego into CMakeCache.txt and check the include and library entries for GLEW (search for GLEW).\nChange them to point to the system GLEW. On Ubuntu this will be something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/usr/lib/x86_64-linux-gnu/libGLEW.so for the LIB dir and /usr/include for the INC dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you do this, remove the directory CMakeFiles and run \u003ccode\u003emake clean\u003c/code\u003e. Then run \u003ccode\u003ecmake .\u003c/code\u003e and finally \u003ccode\u003emake\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ego into \u003ccode\u003ePangolin/include/pangolin/gl/glplatform.h\u003c/code\u003e and change \u003ccode\u003e\u0026lt;GL/glew.h\u0026gt;\u003c/code\u003e to \u003ccode\u003e/usr/include/GL/glew.h\u003c/code\u003e to hack it\nto not rely on the include directories passed to the compiler. Note: the above path is for Ubuntu 16.04/18.4.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-each-time-you-start-a-new-shell-and-want-to-use-the-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#each-time-you-start-a-new-shell-and-want-to-use-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEach time you start a new shell and want to use the code\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003esetup the environment through \u003ccode\u003eubdl\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ego to \u003ccode\u003eubdl\u003c/code\u003e folder (which should contain this repo as a submodule)\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esource setenv.sh\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esource configure.sh\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pushing-back-changes\" class=\"anchor\" aria-hidden=\"true\" href=\"#pushing-back-changes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePushing back changes\u003c/h3\u003e\n\u003cp\u003eIf you made changes to a submodule, you need to check in that code and then check in the new commit hash of the submodule to this repo.\u003c/p\u003e\n\u003cp\u003eSay you made a change to larcv. (Same instructions basically for all submodules).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFirst make sure you are not in a DEATCHED_HEAD state)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit branch\n develop\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003e* tufts_ub\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf it says detached head, go back to head of larflow repo and run \u003ccode\u003esource goto_head_of_submodules.sh\u003c/code\u003e and come back\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003estage your commits and then push\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit add [[some file you edited]]\ngit commit -m \"[[short description of change]]\"\ngit push\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ego back to head of larflow and commit the updated submodule (in this example \u003ccode\u003elarcv\u003c/code\u003e) to this repo\ncd ..\ngit add larcv\ngit commit -m \"[[which submodule you updated]]\"\ngit push\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eopenmm-singularity\u003c/h1\u003e\u003ca id=\"user-content-openmm-singularity\" class=\"anchor\" aria-label=\"Permalink: openmm-singularity\" href=\"#openmm-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eApptainer/Singularity recipe for openmm focused on Nvidia GPUS\u003cbr\u003e\nand tested on\u003cbr\u003e\nEMSL Tahoma \u003ca href=\"https://www.emsl.pnnl.gov/MSC/UserGuide/tahoma/tahoma_overview.html\" rel=\"nofollow\"\u003ehttps://www.emsl.pnnl.gov/MSC/UserGuide/tahoma/tahoma_overview.html\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1636249002.0 + "updated_at": 1730500215.0 }, { "data_format": 2, - "description": null, + "description": "A LaTeX Beamer template for presentations using the Metropolis theme.", "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-debian9-pdfpc", + "full_name": "mmore500/presentation-template", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-docker-debian9-pdfpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-docker-debian9-pdfpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-debian9-pdfpc\u003c/h1\u003e\n\u003cp\u003esingularity container based on debian9 docker providing pdfpc\u003c/p\u003e\n\u003cp\u003eRun pdfpc from the container without really installing it.\u003c/p\u003e\n\u003cp\u003eRunning without installation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run library://tru/default/singularity-docker-debian9-pdfpc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuilding:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-docker-debian9-pdfpc.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor use the provided \u003ccode\u003ebuild.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cp\u003eDownload and rename:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \"pdfpc.sif\" library://tru/default/singularity-docker-debian9-pdfpc\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePresentation Template\u003c/h2\u003e\u003ca id=\"user-content-presentation-template\" class=\"anchor\" aria-label=\"Permalink: Presentation Template\" href=\"#presentation-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1774\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/mmore500/presentation-template\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ba384827ddaf20d52b67fbbd58c3f6c187cfc6a31fac82c3b6765799d19d374d/68747470733a2f2f636972636c6563692e636f6d2f67682f6d6d6f72653530302f70726573656e746174696f6e2d74656d706c6174652e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/mmore500/presentation-template.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA LaTeX Beamer template for presentations using the Metropolis theme.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to build the container, after cloning this repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t presentation-template \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo generate your pdf, you should bind the directory with main.tex to \u003ccode\u003e/data\u003c/code\u003e\nin the container, and provide a prefix for your output. That looks like this, and\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data presentation-template mypdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter this, the files will be in your present working directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls my\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\nmypdf.aux mypdf.bbl mypdf.blg mypdf.fdb_latexmk mypdf.fls mypdf.log mypdf.nav mypdf.out mypdf.pdf mypdf.snm mypdf.toc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to build the container (it takes quite some time) this development\ncontainer is provided at \u003ca href=\"https://hub.docker.com/r/mmore500/presentation-template/\" rel=\"nofollow\"\u003emmore500/presentation-template\u003c/a\u003e. You can run it as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://www.github.com/mmore500/presentation-template\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e presentation-template\ndocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data mmore500/presentation-template mypdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn \u003ca href=\"example\"\u003eexample\u003c/a\u003e output is provided. Have fun!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, build the container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build presentation-template.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, run it and bind the present working directory to data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data presentation-template.simg mypdf\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should build the container with the version provided as a \u003ccode\u003e--build-arg\u003c/code\u003e\nas follows. For example, to build the version \u003ccode\u003e1.0.1-rc\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build -t mmore500/presentation-template:1.0.1-rc --build-arg Version=1.0.1-rc \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n$ docker push mmore500/presentation-template:1.0.1-rc\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow To \u0026amp; What You Get\u003c/h3\u003e\u003ca id=\"user-content-how-to--what-you-get\" class=\"anchor\" aria-label=\"Permalink: How To \u0026amp; What You Get\" href=\"#how-to--what-you-get\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe original post for the package is \u003ca href=\"https://twitter.com/MorenoMatthewA/status/1048676082952626177\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAuthorship\u003c/h3\u003e\u003ca id=\"user-content-authorship\" class=\"anchor\" aria-label=\"Permalink: Authorship\" href=\"#authorship\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMatthew Andres Moreno\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ematthew.andres.moreno@gmail.com\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributors\u003c/h3\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.github.com/vsoch\"\u003e@vsoch\u003c/a\u003e contributed Dockerfile and build / run instructions, continuous integration\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, - "topics": [], - "updated_at": 1614411782.0 + "subscribers_count": 3, + "topics": [ + "latex-beamer", + "latex-beamer-template", + "sans-forgetica", + "singularity-container", + "docker-container" + ], + "updated_at": 1596213882.0 }, { "data_format": 2, - "description": "Singularity container for MATE desktop in CentOS 7.", + "description": null, "filenames": [ "Singularity" ], - "full_name": "mcw-rcc/mate-desktop", + "full_name": "bstriner/cuda-10.1-cudnn7-devel-ubuntu16.04", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mate-desktop\" class=\"anchor\" aria-hidden=\"true\" href=\"#mate-desktop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emate-desktop\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2102\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for MATE desktop in CentOS 7. Used with the RCC\u0027s Open OnDemand portal as a desktop environment.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecuda-10.1-cudnn7-devel-ubuntu16.04\u003c/h1\u003e\u003ca id=\"user-content-cuda-101-cudnn7-devel-ubuntu1604\" class=\"anchor\" aria-label=\"Permalink: cuda-10.1-cudnn7-devel-ubuntu16.04\" href=\"#cuda-101-cudnn7-devel-ubuntu1604\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1598115124.0 + "updated_at": 1599494490.0 }, { "data_format": 2, - "description": "Pipeline to preprocess raw T1w and fMRI data, normalize it to the standard MNI152 space and extract the blood-oxygenation level dependent (BOLD) signals and corresponding functional connectivity (FC).", + "description": "PhD thesis in Computer Science at Rice University", "filenames": [ - "code/Singularity" + "lg/Singularity", + "tensor/Singularity" ], - "full_name": "inm7/vbc_fmri", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-containerized-functional-mri-data-preprocessing-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#containerized-functional-mri-data-preprocessing-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerized Functional MRI data preprocessing pipeline\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREQUIREMENTS\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTo use this containerized pipeline, please install \u0027singularity\u0027 on your computing system. \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.3/user-guide/installation.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFilesa: Container_sMRI_rfMRI.simg (This container uses a combination of tools from well-known software packages, including FSL (\u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki\u003c/a\u003e), ANTs (\u003ca href=\"https://github.com/ANTsX/ANTs\"\u003ehttps://github.com/ANTsX/ANTs\u003c/a\u003e), and AFNI (\u003ca href=\"https://afni.nimh.nih.gov/\" rel=\"nofollow\"\u003ehttps://afni.nimh.nih.gov/\u003c/a\u003e).)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eStructuralAndRestPreprocessing.sh\nREADME.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-3-additional-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-additional-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Additional files\u003c/h3\u003e\n\u003cp\u003eantsTemp: The folder includes prior brain extraction template for \u003ccode\u003eantsBrainExtraction.sh MNI152_T2_1mm.nii.gz/MNI152_T2_1mm_brain.nii.gz\u003c/code\u003e: The template images for T2w image processings.\u003c/p\u003e\n\u003cp\u003eThe additional files have been included in the container. AntsTemp is highly recommended to be stored in the directory of Ants. \u003ccode\u003eMNI152_T2_1mm.nii.gz\u003c/code\u003e and \u003ccode\u003eMNI152_T2_1mm_brain.nii.gz\u003c/code\u003e are suggested to be stored in the \u003ccode\u003e$FSLDIR/data/standard\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instruction\" class=\"anchor\" aria-hidden=\"true\" href=\"#instruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eINSTRUCTION\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. ARGUMENTS\u003c/h3\u003e\n\u003cp\u003eThe containerized fMRI pipeline consists of 4 modules: sMRI model, functional minimal preprocessing model, enhanced preprocessing model, and signal extraction model.\nTo execute this container models, the singularity function and two arguments should be defined.\nExample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind /mount/path:/mnt Container_sMRI_rfMRI.simg StructuralAndRestPreprocessing.sh $subject\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first argument specifies all necessary parameters for preprocessing and the second one specifies the subject ID.\u003c/p\u003e\n\u003cp\u003eAn example of a \u003ccode\u003eStructuralAndRestPreprocessing.sh\u003c/code\u003e is as followed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-input-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-input-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Input variables\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e# ---------------\n#module selection\nModule load: default settings for software within the container. Don\u2019t change it.\nModel selection: select the models you want to use (1 = on; 0 = off).\n\n#Path settings. \nOrig=/mnt/path/to/raw_data #Raw data path. The raw data path should have a data structure as below.\nOrig\n|-- ${subject}\n| \u00a0 |--T1--session1--nifti (T1w) \n| \u00a0 |-- rfMRI--session1--nifti (Rest)\n| \u00a0 |-- Parad--session1--nifti (Task)\n\nsMRI=/mnt/path/to/sMRI #sMRI output path\nfMRI=/mnt/path/to/fMRI #fMRI output path\nANTSTEMP=/path/to/ants/priors #brain extraction template for ants (used only for ants brain extraction).\natlas=/mnt/path/to/atlas #the path to the atlas.\nPipelines=/usr/local/bin #script path within the container.\n\natlasname=Schaefer #the name of the atlas.\npostfix=nii. #Raw data postfix, in case of different dcm2nii software.\n\n#sMRI model parameter.\nT2w=0 #if T2w used, set 1; if not, set 0.\nSession=1 #session number of dataset (1 = 1 session, 2 = 2 sessions).\nConcat=0: #If the structural images are scanned with 2 sessions. Only used ( Concat = 1), when $Session=2.\nStandard*: default MNI paths within FSL (for registration, don\u0027t change it).\nBrainSize=150 #Z-axis for cropping (150-180), remove the long neck.\nbiascorr=0 #bias correction for structural images (1 = on, 0 = off). Note, you don\u0027t perform it in this version, antsBrainExtraction is applied, which has bias correction, so that you don\u0027t need to do bias correction twice.\nStructuralNormalization=2 #different normalization ways (1 for FSL, 2 for ANTs).\nThreads=5 #only used for ANTs registration, consistent with paralleling threads.\n\n#Note: sMRI model should be performed first. The brain extracted structural images will be used for other models.\n\n#Minimal model parameter. \nTR=2 #repeat time.\nexvol=4 #exclusion volumes. \nSlicetiming=1 #correct slice timing differences (1 = on, 0 = off), it\u0027s optional.\n\n#Note: Slice timing correction should be selected by your slice-order. In this case, our data was scanned by bottom-up order. The images with *norm* is the output for this model.\n\n#Enhanced model parameter. \nSmoothing=1 #smooth epi images (1 = on, 0 = off), it\u0027s optional. \nSmoothingFWHM=8 #the kernel of smoothing, which is commonly 2 or 3 times of the voxel-size. \nTemporalFilter=1 #filter frequency-band signals (1 = on, 0 = off). Its\u0027 optional.\nlowbands=0.01 #low-pass\nhighbands=0.1 #high-pass\n\nCovarianceRegression=1: regress out covariances (1 = on, 0 = off). It\u0027s optional.\nCovariances: 27 #Sevearal options are available. 24 = only regress out 24 head-motion parameters, 25 = regress out 24 head-motion parameters + global singals, 26 = 24 head-motion parameters + WM + CSF signals, 27 = 24 head-motion parameters + CSF + Global + WM signals.\n\n#Note: For saving the space, final output of this model is filtered_func_data.nii.gz. \n\n#Singal extraction model parameter.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-troubleshoot\" class=\"anchor\" aria-hidden=\"true\" href=\"#troubleshoot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTROUBLESHOOT\u003c/h2\u003e\n\u003cp\u003eIf you have a problem to use the containerized pipeline. Please feel free to contact Shufei Zhang (\u003ca href=\"mailto:sh.zhang@fz-juelich.de\"\u003esh.zhang@fz-juelich.de\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis development was supported by European Union\u2019s Horizon 2020 research and\ninnovation programme under grant agreement \u003ca href=\"https://cordis.europa.eu/project/id/826421\" rel=\"nofollow\"\u003eVirtualBrainCloud\n(H2020-EU.3.1.5.3, grant no.\n826421)\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "vuphan314/phd-thesis", + "latest_release": "v0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuantitative Reasoning on Hybrid Formulas with Dynamic Programming\u003c/h1\u003e\u003ca id=\"user-content-quantitative-reasoning-on-hybrid-formulas-with-dynamic-programming\" class=\"anchor\" aria-label=\"Permalink: Quantitative Reasoning on Hybrid Formulas with Dynamic Programming\" href=\"#quantitative-reasoning-on-hybrid-formulas-with-dynamic-programming\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThis repository supplements Vu Phan\u0027s \u003ca href=\"https://repository.rice.edu/items/2e464125-244d-431b-b998-612f0dc2b41a\" rel=\"nofollow\"\u003ePhD thesis\u003c/a\u003e in Computer Science at Rice University.\u003c/li\u003e\n\u003cli\u003eWe provide four exact solvers that support XOR-CNF formulas.\n\u003cul\u003e\n\u003cli\u003eDPMC solves \u003cem\u003eweighted model counting (WMC)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eProCount solves \u003cem\u003eweighted projected model counting (WPMC)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDPO solves \u003cem\u003eweighted SAT (WSAT)\u003c/em\u003e, also called Boolean MPE.\u003c/li\u003e\n\u003cli\u003eDPER solves \u003cem\u003eexist-random SAT (ERSAT)\u003c/em\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach of these four solvers is a combination of a planner and an executor.\n\u003cul\u003e\n\u003cli\u003eA planner produces a \u003cstrong\u003eproject-join tree\u003c/strong\u003e \u003ccode\u003eT\u003c/code\u003e from an XOR-CNF formula \u003ccode\u003eF\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAn executor traverses \u003ccode\u003eT\u003c/code\u003e to computes a solution of \u003ccode\u003eF\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eFor WPMC and ERSAT, \u003ccode\u003eT\u003c/code\u003e must be \u003cstrong\u003egraded\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTwo planners are available.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./htb/\"\u003eHTB\u003c/a\u003e uses constraint-programming heuristics.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./lg/\"\u003eLG\u003c/a\u003e uses tree decomposers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTwo executors are available.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./dmc/\"\u003eDMC\u003c/a\u003e uses \u003cem\u003ealgebraic decision diagrams (ADDs)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./tensor/\"\u003eTensor\u003c/a\u003e uses tensors and only solves WMC on pure CNF.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopers\u003c/h2\u003e\u003ca id=\"user-content-developers\" class=\"anchor\" aria-label=\"Permalink: Developers\" href=\"#developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eVu Phan: HTB and DMC\u003c/li\u003e\n\u003cli\u003eJeffrey Dudek: LG and Tensor\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning this repository\u003c/h2\u003e\u003ca id=\"user-content-cloning-this-repository\" class=\"anchor\" aria-label=\"Permalink: Cloning this repository\" href=\"#cloning-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vuphan314/phd-thesis\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"./eval/\"\u003eEvaluation\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"./examples/\"\u003eExamples\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgment\u003c/h2\u003e\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" aria-label=\"Permalink: Acknowledgment\" href=\"#acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/ADDMC\"\u003eADDMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/approxmc\"\u003eBIRD\u003c/a\u003e: Soos, Meel\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cs.rochester.edu/u/kautz/Cachet\" rel=\"nofollow\"\u003eCachet\u003c/a\u003e: Sang, Beame, Kautz\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/msoos/cryptominisat\"\u003eCryptoMiniSat\u003c/a\u003e: Soos\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003eCUDD package\u003c/a\u003e: Somenzi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://davidkebo.com/cudd#cudd6\" rel=\"nofollow\"\u003eCUDD visualization\u003c/a\u003e: Kebo\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts\u003c/a\u003e: Beck\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/DPMC\"\u003eDPMC/ProCount/DPO/DPER\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kit-algo/flow-cutter-pace17\"\u003eFlowCutter\u003c/a\u003e: Strasser\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mabseher/htd\"\u003ehtd\u003c/a\u003e: Abseher, Musliu, Woltran\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://reasoning.cs.ucla.edu/minic2d\" rel=\"nofollow\"\u003eminiC2D\u003c/a\u003e: Oztok, Darwiche\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mccompetition.org\" rel=\"nofollow\"\u003eModel Counting Competition\u003c/a\u003e: Hecher, Fichte\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cril.univ-artois.fr/KC/pmc.html\" rel=\"nofollow\"\u003epmc\u003c/a\u003e: Lagniez, Marquis\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Kasekopf/SlurmQueen\"\u003eSlurmQueen\u003c/a\u003e: Dudek\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://trolando.github.io/sylvan\" rel=\"nofollow\"\u003eSylvan\u003c/a\u003e: van Dijk\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/TCS-Meiji/PACE2017-TrackA\"\u003eTamaki\u003c/a\u003e: Tamaki\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/TensorOrder\"\u003eTensorOrder\u003c/a\u003e: Dudek, Duenas-Osorio, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/WAPS\"\u003eWAPS\u003c/a\u003e: Gupta, Sharma, Roy, Meel\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1608391045.0 + "updated_at": 1721233440.0 }, { "data_format": 2, - "description": "Our Run scripts for calling SNPS using their Best practices", + "description": "Singularity image for the CORAL group at Washington University School of Medicine", "filenames": [ - "Singularity.1.0.4", - "Singularity.1.0.7a", - "Singularity.1.0.6", - "Singularity.1.0.2", - "Singularity.1.0.7b", - "Singularity.1.0.5", - "Singularity.1.0.3", - "Singularity.1.0.7" + "Singularity.1p13p1", + "Singularity.scikit", + "Singularity.tf2", + "Singularity.tf2_nightly", + "Singularity.SimpleITK", + "Singularity" ], - "full_name": "ISUGIFsingularity/GATK", + "full_name": "gdhugo/coral_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-gatk\" class=\"anchor\" aria-hidden=\"true\" href=\"#gatk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGATK\u003c/h1\u003e\n\u003cp\u003eOur Run scripts for calling SNPS using their Best practices\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecoral_singularity\u003c/h1\u003e\u003ca id=\"user-content-coral_singularity\" class=\"anchor\" aria-label=\"Permalink: coral_singularity\" href=\"#coral_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity image for the CORAL group at Washington University School of Medicine\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/687\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo run on the CHPC cluster:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLogin and then load singularity. No need to load cuda as only the driver is required on the host. Cuda will be installed in the singularity image. This is nice, because then we can install any cuda version we need (as long as it is compatible with the driver), and it will work.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eload singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePull the singularity image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://gdhugo/coral_singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to pull a specific image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://gdhugo/coral_singularity:scikit\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInvoke with GPU tools (--nv switch):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv gdhugo-coral_singularity-master-latest.simg\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1646016824.0 + "updated_at": 1575649682.0 }, { "data_format": 2, - "description": "Containerisation of NEMO Employing Singularity", + "description": "Definition (recipe) files for singularity containers.", "filenames": [ - "Singularity.nemo", - "base_def/Singularity.nemo_baseOS" + "cytof-deep-cnn/Singularity.def", + "somascan-power-tool/Singularity.def", + "H5N1/Singularity_R_3.6.def", + "H5N1/Singularity.def", + "bittersweet/Singularity.def", + "cite-seq/Singularity_rocker.def", + "cite-seq/Singularity_xenial.def", + "cite-seq/Singularity_publish.def", + "cite-seq/Singularity_AJM_COVID.def", + "cite-seq/Singularity_3.def", + "tf-gpu-chemistry/Singularity", + "generic/Singularity.def", + "cytof-workflow-v4/Singularity.def", + "cytof-workflow-v3/Singularity_SCS.def", + "cytof-workflow-v3/Singularity.def", + "comet/Singularity.def" ], - "full_name": "NOC-MSM/CoNES", - "latest_release": "0.0.2", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-containerisation-of-nemo-employing-singularity-cones\" class=\"anchor\" aria-hidden=\"true\" href=\"#containerisation-of-nemo-employing-singularity-cones\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerisation of NEMO Employing Singularity (CoNES)\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://cones.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/be5581c539aaf212df6fa28589f126444a52feca29ebc442ea146c24334cc87d/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f636f6e65732f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/cones/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eThe CoNES repository was templated from \u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003eSingularity Deploy\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo generate a NEMO/XIOS Singularity Container please read the \u003ca href=\"https://cones.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003edocumentaion\u003c/a\u003e. What follows is a simplified quick-start guide:\u003c/p\u003e\n\u003cp\u003eIf building locally is not an option then it is also possible to build and\nrelease Singularity containers using \u003ca href=\"https://github.com/features/actions\"\u003eGitHub Actions\u003c/a\u003e.\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003eSingularity Deploy\u003c/a\u003e\ndeveloped by \u003ca href=\"https://github.com/vsoch\"\u003eVanessa Sochat\u003c/a\u003e has been modified\nto allow users to fork the \u003ca href=\"https://github.com/NOC-MSM/CoNES\"\u003eGitHub CoNES repository\u003c/a\u003e\nand, using \u003ca href=\"https://github.com/features/actions\"\u003eGitHub Actions\u003c/a\u003e, build and\nrelease a \u003cem\u003ebespoke\u003c/em\u003e NEMO singularity container.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eCoNES\u003c/code\u003e repository has been set up such that:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe container is updated/developed via a branch\u003c/li\u003e\n\u003cli\u003ethe container build will be tested on a pull request\u003c/li\u003e\n\u003cli\u003ea release will be triggered on merge into main\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis workflow can easily be modified by altering:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e.github/workflows/test.yml\u003c/code\u003e for the testing of builds\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e.github/workflows/builder.yml\u003c/code\u003e for the container release\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn individual NEMO SIF build can be created using the following steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFork the \u003ccode\u003eCoNES\u003c/code\u003e repository under \u003ccode\u003eUSER\u003c/code\u003e account (main branch only is fine) \u003cbr\u003e\nUnder the \u003ccode\u003eActions\u003c/code\u003e tab enable workflows \u003cbr\u003e\nUnder the \u003ccode\u003eSettings\u003c/code\u003e tab click through \u003ccode\u003eactions\u003c/code\u003e -\u0026gt; \u003ccode\u003egeneral\u003c/code\u003e and set \u003ccode\u003eworkflow permissions\u003c/code\u003e to r+w and save \u003cbr\u003e\nReturn to the \u003ccode\u003ecode\u003c/code\u003e tab\u003c/li\u003e\n\u003cli\u003eCreate a new branch\u003c/li\u003e\n\u003cli\u003eEdit the \u003ccode\u003eVERSION\u003c/code\u003e file to something approprate (e.g. 0.0.3)\u003cbr\u003e\n[Optional] Edit the \u003ccode\u003einputs/NEMO_in\u003c/code\u003e namelist for NEMO version number, MPI choice etc.\u003c/li\u003e\n\u003cli\u003eCreate a \u003cem\u003ePull Request\u003c/em\u003e from that branch to main. \u003cstrong\u003eMake sure this is from \u003ccode\u003eUSER/branch\u003c/code\u003e to \u003ccode\u003eUSER/main\u003c/code\u003e and not to \u003ccode\u003eNOC-MSM/main\u003c/code\u003e.\u003c/strong\u003e\u003cbr\u003e\nAt this point a test build will be triggered, which can take ~15 minutes per MPI build requested\u003c/li\u003e\n\u003cli\u003eIf successful the \u003cem\u003emerge\u003c/em\u003e will be available. Click merge and ...\u003c/li\u003e\n\u003cli\u003eA NEMO SIF will be built and released under the \u003cem\u003eversion\u003c/em\u003e specified (again this can take ~15 minutes per MPI build requested).\u003c/li\u003e\n\u003cli\u003eThe NEMO SIF and asscoiated assets will appear under the \u003ccode\u003eReleases\u003c/code\u003e tab.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe branch can now either be deleted or held open for further changes to \u003ccode\u003eNEMO_in\u003c/code\u003e and subsequent releases.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIf the tag in the \u003ccode\u003eVERSION\u003c/code\u003e file is not incremented then a new release is not built.\u003c/p\u003e\n\u003cp\u003eTo download the released NEMO SIF either use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e wget -c https://github.com/MY_CoNES/releases/download/$VERSION/MY_CoNES.nemo.sif -o nemo.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor Singularity can also \u003cem\u003epull\u003c/em\u003e just knowing the URL. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull https://github.com/MY_CONES/releases/download/$VERSION/MY_CONES.nemo.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "rohitfarmer/singularity-defs", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDefinition/Recipe Files for Singularity Containers\u003c/h1\u003e\u003ca id=\"user-content-definitionrecipe-files-for-singularity-containers\" class=\"anchor\" aria-label=\"Permalink: Definition/Recipe Files for Singularity Containers\" href=\"#definitionrecipe-files-for-singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome of the containers are available to download from \u003ca href=\"https://cloud.sylabs.io/library/rohitfarmer\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/rohitfarmer\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor feedback and collaboration write to me at \u003ca href=\"mailto:rohit.farmer@gmail.com\"\u003erohit.farmer@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall Singularity on Linux\u003c/h1\u003e\u003ca id=\"user-content-install-singularity-on-linux\" class=\"anchor\" aria-label=\"Permalink: Install Singularity on Linux\" href=\"#install-singularity-on-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Version 3.4\u003c/h2\u003e\u003ca id=\"user-content-singularity-version-34\" class=\"anchor\" aria-label=\"Permalink: Singularity Version 3.4\" href=\"#singularity-version-34\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow the instructions on \u003ca href=\"https://sylabs.io/guides/3.4/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.4/user-guide/quick_start.html#quick-installation-steps\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding a Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-building-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Building a Singularity Container\" href=\"#building-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReadonly Container\u003c/h2\u003e\u003ca id=\"user-content-readonly-container\" class=\"anchor\" aria-label=\"Permalink: Readonly Container\" href=\"#readonly-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a read-only SquashFS Singularity container on a local machine using a recipe/definition file.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build \u0026lt;container-name.sif\u0026gt; \u0026lt;Singularity.def\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo set a different temporary directory than the default \u003ccode\u003e/tmp\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003esudo -E SINGULARITY_TMPDIR=/home/rohit/tmp singularity build \u0026lt;container.sif\u0026gt; \u0026lt;container.def\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWritable Sandbox.\u003c/h2\u003e\u003ca id=\"user-content-writable-sandbox\" class=\"anchor\" aria-label=\"Permalink: Writable Sandbox.\" href=\"#writable-sandbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a writable sandbox (essentially a folder) on a local machine using a recipe/definition file.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build --sandbox \u0026lt;sandbox-folder-name/\u0026gt; \u0026lt;Singularity.def\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: The advantage of building a writable sandbox is that it can be used to install and configure packages as you go, and once you are satisfied with the requirements, the sandbox can be converted into a read-only SquashFS container. To build a sandbox quickly, it\u0027s better to install a minimal set of packages via the definition file.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall/Configure Packages in a Writable Sandbox\u003c/h3\u003e\u003ca id=\"user-content-installconfigure-packages-in-a-writable-sandbox\" class=\"anchor\" aria-label=\"Permalink: Install/Configure Packages in a Writable Sandbox\" href=\"#installconfigure-packages-in-a-writable-sandbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce a writable sandbox is created to execute it to invoke the shell of the operating installed in the container in the \"writable\" mode. If the shell is not invoked in the \"writable\" mode, all the changes will be lost once you exit from the container environment.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity shell --writable \u0026lt;sandbox-folder-name/\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eInstall packages as you would, for example, in Ubuntu from the command line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConvert a Writable Sandbox to a Readonly Container\u003c/h2\u003e\u003ca id=\"user-content-convert-a-writable-sandbox-to-a-readonly-container\" class=\"anchor\" aria-label=\"Permalink: Convert a Writable Sandbox to a Readonly Container\" href=\"#convert-a-writable-sandbox-to-a-readonly-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build \u0026lt;container-name.sif\u0026gt; \u0026lt;sandbox-folder-name/\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExecute a Container\u003c/h1\u003e\u003ca id=\"user-content-execute-a-container\" class=\"anchor\" aria-label=\"Permalink: Execute a Container\" href=\"#execute-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInvoke a shell\u003c/h2\u003e\u003ca id=\"user-content-invoke-a-shell\" class=\"anchor\" aria-label=\"Permalink: Invoke a shell\" href=\"#invoke-a-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe command below can be used for both read-only/writable containers/sandbox.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity shell \u0026lt;container-name.sif\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: By default, Singularity binds to your current working and home directory. Therefore, you do not need to do anything else to execute a script that is in your current working directory. It can also pull, for example, Vim settings from the .vimrc file in your home directory. Therefore, if Vim installed in the container, it can be used with the same settings from inside the container as it would from outside.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecute a Command via Container\u003c/h2\u003e\u003ca id=\"user-content-execute-a-command-via-container\" class=\"anchor\" aria-label=\"Permalink: Execute a Command via Container\" href=\"#execute-a-command-via-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec \u0026lt;container-name.sif\u0026gt; \u0026lt;command\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor example: \u003ccode\u003esingularity exec \u0026lt;container-name.sif\u0026gt; Rscript --vanilla hello.R\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning Jupyter Notebooks from Within a Container\u003c/h1\u003e\u003ca id=\"user-content-running-jupyter-notebooks-from-within-a-container\" class=\"anchor\" aria-label=\"Permalink: Running Jupyter Notebooks from Within a Container\" href=\"#running-jupyter-notebooks-from-within-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section is for containers that have Jupyter notebook installed (e.g. cite-seq).\u003c/p\u003e\n\u003cp\u003eA generic command that should work on a personal computer. \u003ccode\u003esingularity exec container-name.sif jupyter notebook --no-browser --ip=127.0.0.1 --port=8888\u003c/code\u003e\u003cbr\u003e\n\u003cem\u003eNote: The IP address and the port number mentioned in the command are the jupyter defaults. They can be changed as per need.\u003c/em\u003e\u003cbr\u003e\nCopy the URL generated by jupyter daemon and paste it in your browser; this should open Jupyter with the list of the files in your current working directory on the host computer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with R as a Kernel\u003c/h2\u003e\u003ca id=\"user-content-running-with-r-as-a-kernel\" class=\"anchor\" aria-label=\"Permalink: Running with R as a Kernel\" href=\"#running-with-r-as-a-kernel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSometimes if you already have an R kernel installed in your home directory, it conflicts with what you have inside the container. Therefore, it would require you to re-install the kernel specs in your home directory via the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec container-name.sif R --quiet --slave -e \u0027IRkernel::installspec()\u0027\n\n# Screen log.\n# [InstallKernelSpec] Removing existing kernelspec in /home/user/.local/share/jupyter/kernels/ir\n# [InstallKernelSpec] Installed kernelspec ir in /home/user/.local/share/jupyter/kernels/ir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning on an HPC\u003c/h2\u003e\u003ca id=\"user-content-running-on-an-hpc\" class=\"anchor\" aria-label=\"Permalink: Running on an HPC\" href=\"#running-on-an-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eSSH to the HPC.\u003c/li\u003e\n\u003cli\u003eClaim an interactive node.\u003c/li\u003e\n\u003cli\u003eNavigate to your project directory. Singularity container should be in your project directory.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esingularity exec container-name.sif jupyter notebook --no-browser --ip=\u00270.0.0.0\u0027\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eKeep the SSH session and Jupyter notebook session running. Copy the URL on your local browser.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eNote: On some HPCs, you may have to initiate an additional SSH tunnel connecting your local machine to the interactive node on the HPC. In that case, follow some generic instructions here \u003ca href=\"https://rohitfarmer.github.io/docs/docs/HPC/jupyter/\" rel=\"nofollow\"\u003ehttps://rohitfarmer.github.io/docs/docs/HPC/jupyter/\u003c/a\u003e or ask your system administrator.\u003c/em\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1678184977.0 + "updated_at": 1724962082.0 }, { "data_format": 2, - "description": "A repo containing code demonstrating how the CUDA accelerated TVL1 in OpenCV 4.X is much slower than in OpenCV 2.x", + "description": null, "filenames": [ - "opencv2/src/Singularity", - "opencv4/src/Singularity" + "Singularity.def" ], - "full_name": "willprice/opencv-tvl1-performance-regression-demo", + "full_name": "HaitaoMao/GraphSSLScaling", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-opencv-optical-flow-speed-comparison\" class=\"anchor\" aria-hidden=\"true\" href=\"#opencv-optical-flow-speed-comparison\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenCV optical flow speed comparison\u003c/h1\u003e\n\u003cp\u003eI\u0027ve found OpenCV\u0027s GPU TVL1 implementation to be much slower in v4 than in v2.\nThis repository serves as an example demonstrating the issue.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-set-up\" class=\"anchor\" aria-hidden=\"true\" href=\"#set-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSet up\u003c/h2\u003e\n\u003cp\u003eEnsure you have docker 19.03+ with an NVIDIA card present on your system. Build\nthe docker images (this handles building the base OpenCV images + the optical\nflow demo application)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003emake flow-images\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDownload test media and extract frames:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003effmpeg -i \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://github.com/MarkAYoder/esc-media/raw/master/BigBuckBunny_640x360.m4v\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -t 00:00:10 -qscale 2 frames/frame_%010d.jpg\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-speed-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-speed-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun speed test\u003c/h2\u003e\n\u003cp\u003eDiscard the first results as they will include the time spent by the nvidia\ndriver generating binaries for the current GPU from the PTX files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-k\"\u003etime\u003c/span\u003e docker run \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --gpus \u0027\"device=0\"\u0027 \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --rm -it \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/frames:/input \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/flow/opencv2:/output \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/.cache-opencv2:/cache/nv \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e willprice/furnari-flow:opencv2 \u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.03user 0.02system 0:14.57elapsed 0%CPU (0avgtext+0avgdata 63544maxresident)k\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0inputs+0outputs (0major+7956minor)pagefaults 0swaps\u003c/span\u003e\n\n$ \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-k\"\u003etime\u003c/span\u003e docker run \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --gpus \u0027\"device=0\"\u0027 \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --rm -it \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/frames:/input \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/flow/opencv4:/output \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -v $PWD/.cache-opencv4:/cache/nv \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e willprice/furnari-flow:opencv4\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0.04user 0.02system 2:31.88elapsed 0%CPU (0avgtext+0avgdata 63404maxresident)k\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0inputs+0outputs (0major+7877minor)pagefaults 0swaps\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCode Base for \"Graph SSL Scaling\"\u003c/h1\u003e\u003ca id=\"user-content-code-base-for-graph-ssl-scaling\" class=\"anchor\" aria-label=\"Permalink: Code Base for \u0026quot;Graph SSL Scaling\u0026quot;\" href=\"#code-base-for-graph-ssl-scaling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall the dependencies via Dockerfile or \u0027pip install -r requirements.txt\u0027\u003c/li\u003e\n\u003cli\u003eExecute data_scaling.sh or model_scaling.sh to use the pipeline for experiments.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall via Dockerfile\u003c/h2\u003e\u003ca id=\"user-content-install-via-dockerfile\" class=\"anchor\" aria-label=\"Permalink: Install via Dockerfile\" href=\"#install-via-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/HaitaoMao/GraphSSLScaling.git ./GraphSSLScaling\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e GraphSSLScaling\ndocker build --no-cache --tag ssl:latest \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -it --gpus all --ipc host --name ssl -v /home:/home ssl:latest bash\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e upon completion, you should be at /GraphSSLScaling inside the container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall via Singularity\u003c/h2\u003e\u003ca id=\"user-content-install-via-singularity\" class=\"anchor\" aria-label=\"Permalink: Install via Singularity\" href=\"#install-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/HaitaoMao/GraphSSLScaling.git ./GraphSSLScaling\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e GraphSSLScaling\nsingularity build ssl.sif Singularity.def\nsingularity shell --nv -B /home:/home ssl.sif\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e upon completion, you should be at /GraphSSLScaling inside the container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions\u003c/h2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhy we build this pipeline?\u003c/h3\u003e\u003ca id=\"user-content-why-we-build-this-pipeline\" class=\"anchor\" aria-label=\"Permalink: Why we build this pipeline?\" href=\"#why-we-build-this-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThere is a lot of parameters to set. It\u0027s not practical to set all of them manually in main function.\u003c/li\u003e\n\u003cli\u003eThere is a lot of results to record. It\u0027s necessary to record the results with corresponding settings.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow can this pipeline help us?\u003c/h3\u003e\u003ca id=\"user-content-how-can-this-pipeline-help-us\" class=\"anchor\" aria-label=\"Permalink: How can this pipeline help us?\" href=\"#how-can-this-pipeline-help-us\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003cstrong\u003eConfigParser\u003c/strong\u003e will read the config file of your settings(e.g., learning rate,hidden_size) or default settings and set them for datasets or the corresponding executor of the model chosed by you.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003cstrong\u003e./libgptb/log folder\u003c/strong\u003e will restore the config settings and the \u003cstrong\u003e./libgptb/cache folder\u003c/strong\u003e will restore the evaluate results with the exp_id. Then you can easily collect them.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow is this pipeline organised?\u003c/h3\u003e\u003ca id=\"user-content-how-is-this-pipeline-organised\" class=\"anchor\" aria-label=\"Permalink: How is this pipeline organised?\" href=\"#how-is-this-pipeline-organised\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe whole process is outlined in \u003ccode\u003elibgptb/pipeline/pipeline.py\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIn brief the whole process will be like\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eConfigParser\u003c/strong\u003e will load the config file and default config file for the \u003cstrong\u003eDataset\u003c/strong\u003e and \u003cstrong\u003eExecutor\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDataset\u003c/strong\u003e will load the data and get some features(e.g., input feature dims)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExecutor\u003c/strong\u003e will load the chosen model and the model will be trained and evaluated with stored evaluation results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe default config\u003c/h3\u003e\u003ca id=\"user-content-the-default-config\" class=\"anchor\" aria-label=\"Permalink: The default config\" href=\"#the-default-config\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou check the default configs for our \u003cstrong\u003eExecutor\u003c/strong\u003e and \u003cstrong\u003eModel\u003c/strong\u003e. Check \u003ccode\u003elibgptb/config/executors/XXXExecutor.json\u003c/code\u003e and \u003ccode\u003elibgptb/config/model/SSGCL/XXX.json\u003c/code\u003e .\u003c/p\u003e\n\u003cp\u003eThe config parameters specified by the user have the highest priority, which can be included in a json file. In other words the default config will be overwrittern.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe execution environment\u003c/h2\u003e\u003ca id=\"user-content-the-execution-environment\" class=\"anchor\" aria-label=\"Permalink: The execution environment\" href=\"#the-execution-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocerfile_x86\u003c/h3\u003e\u003ca id=\"user-content-docerfile_x86\" class=\"anchor\" aria-label=\"Permalink: Docerfile_x86\" href=\"#docerfile_x86\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-dockerfile\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eFROM\u003c/span\u003e pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel\n\u003cspan class=\"pl-k\"\u003eCOPY\u003c/span\u003e requirements_docker.txt /opt\n\u003cspan class=\"pl-k\"\u003eRUN\u003c/span\u003e pip install -r /opt/requirements.txt \\\n \u0026amp;\u0026amp; pip install tensorboard \\\n \u0026amp;\u0026amp; pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cu116.html \\\n \u0026amp;\u0026amp; pip install torch_geometric\\\n \u0026amp;\u0026amp; pip install dgl -f https://data.dgl.ai/wheels/cu116/repo.html \\\n \u0026amp;\u0026amp; pip install dglgo -f https://data.dgl.ai/wheels-test/repo.html\n\u003cspan class=\"pl-k\"\u003eENV\u003c/span\u003e PATH=$PATH:/usr/local/cuda-11.6/bin LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.6/lib64:/usr/lib/x86_64-linux-gnu\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eYou can build from source with command \u003ccode\u003edocker build -f $Dockerfile path -t $YourImageName\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExecute the following command in the parent path of the project.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v ./GraphSSLScaling/:/GraphSSLScaling -w /GraphSSLScaling --gpus all \u003cspan class=\"pl-smi\"\u003e$YourImageName\u003c/span\u003e /bin/bash /GraphSSLScaling/command.sh\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -v $HOST_PATH:$Container_Path means mounting the localfile to the container. \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -w means setting the working dir\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --gpus all means using all GPU\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e /bin/bash /GraphSSLScaling/command.sh means using the /bin/bash of Container to execute the customized command.sh. You could combine it with the model_scaling.sh or data_scaling.sh\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity.def file\u003c/h3\u003e\u003ca id=\"user-content-singularitydef-file\" class=\"anchor\" aria-label=\"Permalink: Singularity.def file\" href=\"#singularitydef-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eBootstrap is used to specify the agent,where the base image from,here localimage means to build from a local image\u003c/span\u003e\nBootstrap: localimage\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# This is something like \u0027From\u0027 in DOCKERFILE to indicate the base image\u003c/span\u003e\nFrom: ./pytorch_1.13.1-cuda11.6-cudnn8-devel.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e %files can be used to copy files from host into the image\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e like \u0027COPY\u0027 in DOCKERFILE\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Here we copy the requirements.txt into the image, then we can use it to install the required dependencies.\u003c/span\u003e\n%files\n requirements_docker.txt /opt\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e %post is used to build the new image\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Usage is same to shell.Here we used pip to install dependencies.\u003c/span\u003e\n%post\n pip install -r /opt/requirements.txt\n pip install tensorboard\n pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cu116.html\n pip install dgl -f https://data.dgl.ai/wheels/cu116/repo.html \n pip install dglgo -f https://data.dgl.ai/wheels-test/repo.html \n\n \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e% environment is used to set env_variables once the image starts\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e These lines are necessary to load cuda\u003c/span\u003e\n%environment\n \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:/usr/local/cuda-11.6/bin\n \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e:/usr/local/cuda-11.6/lib64:/usr/lib/x86_64-linux-gnu\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The usage of singularity is similar to docker\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You can use \u0027singularity exec --writable-tmpfs --nv --nvccli\u0027 to enable the NVIDIA gpu for executing with sigularity \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1666537823.0 + "updated_at": 1729774886.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "SETAP2021/setapDocker", - "latest_release": "latest", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-setapdocker\" class=\"anchor\" aria-hidden=\"true\" href=\"#setapdocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esetapDocker\u003c/h1\u003e\n", + "full_name": "vaofford/Bio-Deago", + "latest_release": "v1.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBio-Deago\u003c/h1\u003e\u003ca id=\"user-content-bio-deago\" class=\"anchor\" aria-label=\"Permalink: Bio-Deago\" href=\"#bio-deago\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate user-friendly HTML reports from differential expression and GO term enrichment analysis.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/sanger-pathogens/Bio-Deago\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5674903bd465390785be9dbb45993d05faed8090f3c6986fe3bc719a0c1fa98a/68747470733a2f2f7472617669732d63692e6f72672f73616e6765722d706174686f67656e732f42696f2d446561676f2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/sanger-pathogens/Bio-Deago.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/sanger-pathogens/Bio-Deago/blob/master/GPL-LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b431b095e9c63c93a8954655294a66412158aa1d5dc92a1cd41e1a8b5515bc37/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d627269676874677265656e2e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://codecov.io/gh/sanger-pathogens/bio-deago\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8f39fe02137ebdff6bfa8ea47fc8744a902f2f96bce8046b1aa40e4f2fd66ad7/68747470733a2f2f636f6465636f762e696f2f67682f73616e6765722d706174686f67656e732f62696f2d646561676f2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/sanger-pathogens/bio-deago/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3450\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#required-dependencies\"\u003eRequired dependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#environment-variables\"\u003eEnvironment variables\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#input-data\"\u003eInput data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#quick-start\"\u003eQuick start\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#qc-only\"\u003eQC only\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#convert-biomart-annotation\"\u003eConvert BioMart annotation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#go-term-enrichment-analysis\"\u003eGO term enrichment analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output-files\"\u003eOutput files\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#feedbackissues\"\u003eFeedback/Issues\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#further-information\"\u003eFurther Information\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDEAGO generates a user-friendly HTML report from differential expression (\u003ca href=\"https://bioconductor.org/packages/release/bioc/html/DESeq2.html\" rel=\"nofollow\"\u003eDESeq2\u003c/a\u003e) and GO term enrichment (\u003ca href=\"http://bioconductor.org/packages/release/bioc/html/topGO.html\" rel=\"nofollow\"\u003etopGO\u003c/a\u003e) analysis of RNA-Seq data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDEAGO has the following dependencies:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequired dependencies\u003c/h3\u003e\u003ca id=\"user-content-required-dependencies\" class=\"anchor\" aria-label=\"Permalink: Required dependencies\" href=\"#required-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://pandoc.org/installing.html\" rel=\"nofollow\"\u003epandoc\u003c/a\u003e \u0026gt;= 1.12.3\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org\" rel=\"nofollow\"\u003eR\u003c/a\u003e \u0026gt;= 3.4.0\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sanger-pathogens/deago\"\u003edeago\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDetails to install DEAGO are provided below. If you encounter an issue when installing DEAGO please contact your local system administrator. If you encounter a bug please log it \u003ca href=\"https://github.com/sanger-pathogens/bio-deago/issues\"\u003ehere\u003c/a\u003e or email us at \u003ca href=\"mailto:path-help@sanger.ac.uk\"\u003epath-help@sanger.ac.uk\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom Source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From Source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake sure you have installed all dependencies, then clone the repository:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/sanger-pathogens/Bio-Deago.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMove into the directory and install all perl dependencies using \u003ca href=\"http://dzil.org/\" rel=\"nofollow\"\u003eDistZilla\u003c/a\u003e and \u003ca href=\"https://github.com/miyagawa/cpanminus\"\u003ecpanm\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd Bio-Deago\ndzil authordeps --missing | cpanm\ndzil listdeps --missing | cpanm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun the tests:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edzil test\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf the tests pass, install Bio-Deago:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edzil install\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, DEAGO will look for R in your \u003ccode\u003e$PATH\u003c/code\u003e and for the associated R libraries in \u003ccode\u003e$R_LIBS\u003c/code\u003e. Where there are multiple R versions or libraries installed, setting the environment variables below will enable you to overwrite this default behaviour.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eDependency\u003c/th\u003e\n\u003cth align=\"left\"\u003eEnvironment variable\u003c/th\u003e\n\u003cth\u003eExample\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eR bin\u003c/td\u003e\n\u003ctd align=\"left\"\u003eDEAGO_R\u003c/td\u003e\n\u003ctd\u003e/path/to/R-3.4.0/bin\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eR library\u003c/td\u003e\n\u003ctd align=\"left\"\u003eDEAGO_R_LIBS\u003c/td\u003e\n\u003ctd\u003e/path/to/personal/rlib\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: deago [options]\nRNA-Seq differential expression qc and analysis\n\nMain options:\n --output_directory (-o) output directory [.]\n --convert_annotation convert annotation for use with deago (requires -a)\n --annotation_delim annotation file delimiter [\\t]\n --build_config build configuration file from command line arguments (see configuration options)\n --config_file configuration filename or output filename for configuration file if building [./deago.config]\n --markdown_file output filename for markdown file [./deago_markdown.Rmd]\n --html_file output filename for html file [./deago_markdown.html]\n -v verbose output to STDOUT\n -w print version and exit\n -h print help message and exit\n\nConfiguration options (required):\n -c STR directory containing count files (absolute path)\n -t STR targets filename (absolute path)\n\n Configuration options (optional):\n -r STR results directory [current working directory]\n -a STR annotation filename (absolute path)\n -q NUM qvalue (DESeq2) [0.05]\n --control name of control condition (must be present in targets file)\n --keep_images keep images used in report\n --qc QC only\n --go GO term enrichment\n --go_levels BP only, MF only or all [BP|MF|all]\n --count_type type of count file [expression|featurecounts]\n --count_column number of column containing count values\n --skip_lines number of lines to skip in count file\n --count_delim count file delimiter\n --gene_ids name of column containing gene ids\n\nDEAGO takes in a configuration file containing key/value pairs [default: ./deago.config]. You can\nuse your own configuration file with --config_file or specify parameters and let DEAGO build a\nconfiguration file with --build_config (and --config_file if you don\u0027t want the default\nconfiguration filename). For more information on configuration parameters run: build_deago_config -h.\n\nDEAGO will then build a master R markdown file (--markdown_file if you don\u0027t want the default\nmarkdown filename) from templates which utilize the companion DEAGO R package and the key/value\npairs set out in the configuration file. The R markdown will be processed and used to generate a\nHTML report (--html_file if you don\u0027t want the default html filename).\n\nTo use custom gene names and for GO term enrichment (--go) and annotation file must be provided\n(-a). Annotations downloaded from BioMart or those in a similar format can be converted for use\nwith DEAGO. For more information run: mart_to_deago -h.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput data\u003c/h3\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-label=\"Permalink: Input data\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run DEAGO, the user must provide a path to the read count matrices for each sample and a sample/condition mapping file. For GO term enrichment analysis, an annotation file must also be provided.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eInput\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003ecount data directory (required)\u003c/td\u003e\n\u003ctd align=\"left\"\u003epath to directory containing count matrix files (one per sample)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003etargets file (required)\u003c/td\u003e\n\u003ctd align=\"left\"\u003esample to experimental condition mappings\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003eannotation file (optional)\u003c/td\u003e\n\u003ctd align=\"left\"\u003erequired for gene name annotation and GO term enrichment analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePlease read the \u003ca href=\"https://github.com/sanger-pathogens/deago/wiki\"\u003eDEAGO wiki\u003c/a\u003e for more information on the required file formats.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuick start\u003c/h3\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run QC and differential expression analyses:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edeago --build_config -c \u0026lt;path/to/count/data\u0026gt; -t \u0026lt;sample_mapping.txt\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQC only\u003c/h3\u003e\u003ca id=\"user-content-qc-only\" class=\"anchor\" aria-label=\"Permalink: QC only\" href=\"#qc-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo only generate QC plots:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edeago --build_config -c \u0026lt;path/to/count/data\u0026gt; -t \u0026lt;sample_mapping.txt\u0026gt; --qc\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConvert BioMart annotation\u003c/h3\u003e\u003ca id=\"user-content-convert-biomart-annotation\" class=\"anchor\" aria-label=\"Permalink: Convert BioMart annotation\" href=\"#convert-biomart-annotation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo convert a delimited annotation file (e.g. BioMart):\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edeago --build_config -c \u0026lt;path/to/count/data\u0026gt; -t \u0026lt;sample_mapping.txt\u0026gt; --convert_annotation -a \u0026lt;annotation.txt\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGO term enrichment analysis\u003c/h3\u003e\u003ca id=\"user-content-go-term-enrichment-analysis\" class=\"anchor\" aria-label=\"Permalink: GO term enrichment analysis\" href=\"#go-term-enrichment-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGO term enrichment analysis requires an annotation file mapping the gene identifiers in the count matrices with their associated GO terms.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edeago --build_config -c \u0026lt;path/to/count/data\u0026gt; -t \u0026lt;sample_mapping.txt\u0026gt; -a \u0026lt;annotation_file.txt\u0026gt; --go\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput files\u003c/h3\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-label=\"Permalink: Output files\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDEAGO generates a user-friendly HTML report of the analysis (\u003cstrong\u003edeago_markdown.html\u003c/strong\u003e). The markdown file used to run the analysis and knit the report is also provided (\u003cstrong\u003edeago_markdown.Rmd\u003c/strong\u003e) along with a log file containing the STDOUT from the conversion. If \u003ccode\u003e--build-config\u003c/code\u003e was used instead of providing a configuration file, then a configuration file will be generated (\u003cstrong\u003edeago.config\u003c/strong\u003e).\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eFile(s)\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edeago.config (optional)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eKey/value parameters to define the analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edeago_markdown.Rmd\u003c/td\u003e\n\u003ctd align=\"left\"\u003eR markdown file of analysis commands\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edeago_markdown.html\u003c/td\u003e\n\u003ctd align=\"left\"\u003eHTML report knitted from R markdown file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003edeago.rlog\u003c/td\u003e\n\u003ctd align=\"left\"\u003eLog file of STDOUT from R markdown conversion\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDifferential expression and GO term enrichment analysis results are written to tab-delimited files within a timestamped results directory (results_). The corresponding results directory can be found in the \u003cstrong\u003ePipeline configuration\u003c/strong\u003e section of the HTML report.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eFile(s)\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026lt;contrast\u0026gt;_\u0026lt;alpha\u0026gt;.txt\u003c/td\u003e\n\u003ctd align=\"left\"\u003eDifferential expression analysis results and normalised counts\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026lt;contrast\u0026gt;_\u0026lt;go_level\u0026gt;.tsv\u003c/td\u003e\n\u003ctd align=\"left\"\u003eGO term enrichment analysis results\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDEAGO is free software, licensed under \u003ca href=\"https://github.com/sanger-pathogens/Bio-Deago/blob/master/GPL-LICENSE\"\u003eGPLv3\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeedback/Issues\u003c/h2\u003e\u003ca id=\"user-content-feedbackissues\" class=\"anchor\" aria-label=\"Permalink: Feedback/Issues\" href=\"#feedbackissues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease report any issues to the \u003ca href=\"https://github.com/sanger-pathogens/Bio-Deago/issues\"\u003eissues page\u003c/a\u003e or email \u003ca href=\"mailto:path-help@sanger.ac.uk\"\u003epath-help@sanger.ac.uk\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFurther Information\u003c/h2\u003e\u003ca id=\"user-content-further-information\" class=\"anchor\" aria-label=\"Permalink: Further Information\" href=\"#further-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information, please go to the \u003ca href=\"https://github.com/sanger-pathogens/deago/wiki\"\u003eDEAGO wiki\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, - "topics": [], - "updated_at": 1641570752.0 + "topics": [ + "genomics", + "sequencing", + "next-generation-sequencing", + "research", + "bioinformatics", + "bioinformatics-pipeline", + "global-health", + "infectious-diseases", + "pathogen" + ], + "updated_at": 1567577290.0 }, { "data_format": 2, "description": null, "filenames": [ - "SingularitY/Singularity_fenics2017_msucompbiomechlab" + "devops_pipeline/Singularity", + "devops_pipeline/Singularity~", + "devops_processing/Singularity", + "devops_base/Singularity" ], - "full_name": "MJ0706/HCM-project", + "full_name": "ninamiolane/vaetree", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hypertrophic-cardiomyopathy-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#hypertrophic-cardiomyopathy-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHypertrophic cardiomyopathy project\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-the-mathematical-details-data-simulation-protocols-and-results-are-explained-in-the-manuscript-if-you-have-access-to-the-manuscript-please-follow-it-first\" class=\"anchor\" aria-hidden=\"true\" href=\"#the-mathematical-details-data-simulation-protocols-and-results-are-explained-in-the-manuscript-if-you-have-access-to-the-manuscript-please-follow-it-first\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe mathematical details, data, simulation protocols and results are explained in the manuscript. If you have access to the manuscript, please, follow it first.\u003c/h3\u003e\n\u003cp\u003eSimulator of mechanics in the heart based on \u003ca href=\"https://fenicsproject.org/\" rel=\"nofollow\"\u003eFEniCS\u003c/a\u003e library.\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#installation-and-running-the-code\"\u003eInstallation and Running the code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#organization-of-the-code\"\u003eOrganization of the code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#simulation-protocols\"\u003eSimulation protocols\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch3\u003e\u003ca id=\"user-content-installation-and-running-the-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-and-running-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation and Running the code\u003c/h3\u003e\n\u003cp\u003eA singularity \"build\" \u003ca href=\"./SingularitY/Singularity_fenics2017_msucompbiomechlab\"\u003efile\u003c/a\u003e is provided that will install all the libraries required to run the code.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall singularity by following the instruction in \u003ca href=\"https://sylabs.io/guides/3.5/admin-guide/installation.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild a singularity container using the \"build\" \u003ca href=\"./SingularitY/Singularity_fenics2017_msucompbiomechlab\"\u003efile\u003c/a\u003e with\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build \u0026lt;container_name\u0026gt;.img Singularity_fenics2017_msucompbiomechlab\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOnce the container is built, you can launch the Singularity container by\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run \u0026lt;container_name\u0026gt;.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eThe code can be run within the singularity container. For example, for the code \u003ca href=\"./ed_mesh_create/Patient_1/createLV_refine.py\"\u003ecreateLV_refine\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython createLV_refine.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor in parallel\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003empirun.mpich -np \u0026lt;# processors\u0026gt; python createLV_refine.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-organization-of-the-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#organization-of-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOrganization of the code\u003c/h3\u003e\n\u003cp\u003eThe code is organized as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"./src2/mechanics\"\u003emechanics module\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./src2/sim_protocols/README.md\"\u003esimulation protocols\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./src2/utils\"\u003eutilities\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./src2/bmark_analytical\"\u003ebenchmark analytical solution\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"./src2/postprocessing\"\u003epostprocessing\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDemo python scripts are also provided to simulate\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate end diastole mesh file\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./ed_mesh_create/Patient_1/createLV_refine.py\"\u003ePatient_1\u003c/a\u003e : Control Patient\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./ed_mesh_create/Patient_2/createLV_refine.py\"\u003ePatient_2\u003c/a\u003e : Non-obstructive HCM patient\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./ed_mesh_create/Patinet_3/createLV_refine.py\"\u003ePatient_3\u003c/a\u003e : Obstructive HCM patient\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate Hdf5 file to run simuations using \u003ca href=\"./ed_mesh_create/create_baselinegeo_animal.py\"\u003ecreate_baselinegeo_animal.py\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003emake sure the directory is correct for specific patient at \u003ca href=\"./ed_mesh_create/create_baselinegeo_animal.py\"\u003ecreate_baselinegeo_animal.py\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulation protocol \u0026amp; post-processing\n\u003cul\u003e\n\u003cli\u003eDetail of the code is explained in patient specific code.\n\u003cul\u003e\n\u003cli\u003eControl patient \u003ca href=\"./main/1.Control.py\"\u003e1.Control.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eNonobstructive HCM patient \u003ca href=\"./main/2.Nonobstructive_main.py\"\u003e2. Nonobstructive_main.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eObstructive HCM patient \u003ca href=\"./main/3.Obstructive_main.py\"\u003e3. Obstructive_main.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eThe simulation cases with various degree of dispersion for non-obstructive HCM patient are:\n\u003cul\u003e\n\u003cli\u003eFor kappa = 0.07, \u003ca href=\"./main/2.Nonobstructive_k1.py\"\u003e2.Nonobstructive_k1.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.1, \u003ca href=\"./main/2.Nonobstructive_k2.py\"\u003e2.Nonobstructive_k2.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.14, \u003ca href=\"./main/2.Nonobstructive_k3.py\"\u003e2.Nonobstructive_k3.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.18, \u003ca href=\"./main/2.Nonobstructive_k4.py\"\u003e2.Nonobstructive_k4.py\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe simulation cases with various degree of dispersion for Obstructive HCM patient are:\n\u003cul\u003e\n\u003cli\u003eFor kappa = 0.07, \u003ca href=\"./main/2.Obstructive_k1.py\"\u003e2.Obstructive_k1.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.1, \u003ca href=\"./main/2.Obstructive_k2.py\"\u003e2.Obstructive_k2.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.14, \u003ca href=\"./main/2.Obstructive_k3.py\"\u003e2.Obstructive_k3.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.18, \u003ca href=\"./main/2.Obstructive_k4.py\"\u003e2.Obstructive_k4.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor kappa = 0.22, \u003ca href=\"./main/2.Obstructive_k5.py\"\u003e2.Obstructive_k5.py\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePostprocessing of the code is explained at last 4 lines (that are commented) in codes of \u003ca href=\"./main/13.Postprocessing.py\"\u003e13.Postprocessing.py\u003c/a\u003e. Make sure you run each step at a time using single processor.\u003c/li\u003e\n\u003cli\u003eKlotz plot will be plotted using \u003ca href=\"/main/4.KlotzPlot.py\"\u003e4. KlotzPlot.py\u003c/a\u003e. Make sure, only passive simulation results are used to plot the Klotz curve using this code. Please, check the references in the manuscript to learn more about Klotz curve.\u003c/li\u003e\n\u003cli\u003ePV plot for without disarray case will be outlined using \u003ca href=\"./main/5.plot_data_WithoutDisarray.py\"\u003e 5. plot_data_WithoutDisarray.py\u003c/a\u003e. Make sure the input directory is correct while running this code.\u003c/li\u003e\n\u003cli\u003ePV plot for disarray case will be outlined using \u003ca href=\"./main/6.plot_data_P2_WithDisarray.py\"\u003e6. plot_data_P2_WithDisarray.py\u003c/a\u003e for non-obstructive patient and \u003ca href=\"./main/8.plot_data_P3_WithDisarray.py\"\u003e8. plot_data_P3_WithDisarray.py\u003c/a\u003e for obstructive patient. Make sure the input directory is correct while running this code.\u003c/li\u003e\n\u003cli\u003eError bar plot will be plotted by \u003ca href=\"./main/9.plot_data_errorplot.py\"\u003e9. plot_data_errorplot.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAHA plot for without disarray cases can be outlined by \u003ca href=\"./main/10.ahaplot_WithoutDisarray.py\"\u003e10. ahaplot_WithoutDisarray.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAHA plot with disarray cases can be outlined for \u003ca href=\"./main/11.ahaplot_With_disarray_nonobstructive.py\"\u003eNon-obstructive\u003c/a\u003e and \u003ca href=\"./main/11.ahaplot_With_disarray_obstructive.py\"\u003eObstructive\u003c/a\u003e patient for various degree of disarray.\u003c/li\u003e\n\u003cli\u003eDeformation can be extracted using \u003ca href=\"/main/12.extract_deformation.py\"\u003e12. extract_deformation.py\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you face any issues running with code, email (\u003ca href=\"mailto:mojumder@msu.edu\"\u003emojumder@msu.edu\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1664856512.0 + "updated_at": 1592561461.0 }, { "data_format": 2, - "description": "Nextflow pipeline to detect matched BAMs with NGSCheckMate", + "description": "Additional CI and a dashboard to check health status of DataLad et al.", "filenames": [ - "Singularity/Singularity.v1.1" + "containers/Singularity.buildenv-git-annex-buster" ], - "full_name": "IARCbioinfo/NGSCheckMate-nf", - "latest_release": "v1.1a", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ngscheckmate\" class=\"anchor\" aria-hidden=\"true\" href=\"#ngscheckmate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNGSCheckMate\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow-pipeline-to-detect-matched-bams-with-ngscheckmate\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-to-detect-matched-bams-with-ngscheckmate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline to detect matched BAMs with \u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003eNGSCheckMate\u003c/a\u003e.\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/NGSCheckMate-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/46874680377559d9fb7b208025569f78feddc56fd2db72390df01be75534adef/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f4e4753436865636b4d6174652d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/NGSCheckMate-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/repository/docker/iarcbioinfo/ngscheckmate-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4613\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"NGSCheckMate-nf.png?raw=true\"\u003e\u003cimg src=\"NGSCheckMate-nf.png?raw=true\" alt=\"Workflow representation\" title=\"Scheme of NGSCheckMate Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eImplementation of NGSCheckMate and its underlying subset calling, distibuted per sample.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eNextflow : for common installation procedures see the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003eNGSCheckMate\u003c/a\u003e (follow instructions, especially setting up \u003ccode\u003e$NCM_HOME\u003c/code\u003e variable)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.htslib.org/download/\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.htslib.org/download/\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAdditionally, the graph output option requires \u003ca href=\"https://cran.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e; see details below about this option.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input\u003c/td\u003e\n\u003ctd\u003eyour input BAM file(s) (do not forget the quotes e.g. \u003ccode\u003e--input \"test_*.bam\"\u003c/code\u003e). Warning : your BAM file(s) must be indexed, and the \u003ccode\u003etest_*.bai\u003c/code\u003e should be in the same folder.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder with BAM files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_file\u003c/td\u003e\n\u003ctd\u003eInput file (comma-separated) with 3 columns: ID (individual ID), suffix (suffix for sample names; e.g. RNA), and bam (path to bam file).\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA nextflow.config is also included, please modify it for suitability outside our pre-configured clusters (\u003ca href=\"https://www.nextflow.io/docs/latest/config.html#configuration-file\" rel=\"nofollow\"\u003esee Nexflow configuration\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eNote that the input_file format is tab-delimited text file; this file is used both to provide input bam file locations but also for the generation of the graphs. The ID field must be unique to a subject (e.g. both tumor and normal samples from the same individual must have the same individual identifier). The bam field must be unique to a file name. For example, the following is a valid file:\u003c/p\u003e\n\u003cp\u003eID suffix bam\nNA06984 _RNA NA06984_T_transcriptome.bam\u003cbr\u003e\nNA06984 _WGS NA06984_T_genome.bam\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003eresults\u003c/td\u003e\n\u003ctd\u003ethe folder that will contain NGSCheckMate folder with all results in text files.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd\u003eref.fasta\u003c/td\u003e\n\u003ctd\u003eyour reference in FASTA\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bed\u003c/td\u003e\n\u003ctd\u003eSNP_GRCh38.bed\u003c/td\u003e\n\u003ctd\u003ePanel of SNP bed file from \u003ca href=\"https://github.com/parklab/NGSCheckMate/tree/master/SNP\"\u003eNGSCheckMate\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that a bed file SNP_GRCh38.bed is provided, which is a liftOver of the files at \u003ca href=\"https://github.com/parklab/NGSCheckMate/tree/master/SNP\"\u003ehttps://github.com/parklab/NGSCheckMate/tree/master/SNP\u003c/a\u003e. To use other references, you can provide your own bedfile.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e16\u003c/td\u003e\n\u003ctd\u003eMemory requested (in GB) for calling and NGSCheckmate run\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003eNumber of threads for germline calling\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bai_ext\u003c/td\u003e\n\u003ctd\u003e.bam.bai\u003c/td\u003e\n\u003ctd\u003eExtenstion of bai files\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run NGSCheckMate-nf/ -r v1.1 -profile singularity --ref ref.fasta --input_folder BAM/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the pipeline without singularity just remove \"-profile singularity\". Alternatively, one can run the pipeline using a docker container (-profile docker) the conda receipe containing all required dependencies (-profile conda).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003evcfs\u003c/td\u003e\n\u003ctd\u003ea folder with the vcfs used for the matching\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNCM_output/output*.txt\u003c/td\u003e\n\u003ctd\u003eNGSCheckmate output files with matches between files (see \u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003ehttps://github.com/parklab/NGSCheckMate\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNCM_output/output.pdf\u003c/td\u003e\n\u003ctd\u003ehierarchical clustering plot from \u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003ehttps://github.com/parklab/NGSCheckMate\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNCM_output/NCM_graph_wrongmatch.xgmml\u003c/td\u003e\n\u003ctd\u003egraph with only the samples without a match (adapted from \u003ca href=\"https://github.com/parklab/NGSCheckMate/blob/master/graph/ngscheckmate2xgmml.R\"\u003ehttps://github.com/parklab/NGSCheckMate/blob/master/graph/ngscheckmate2xgmml.R\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNCM_output/NCM_graph.xgmml\u003c/td\u003e\n\u003ctd\u003egraph with all samples (adapted from \u003ca href=\"https://github.com/parklab/NGSCheckMate/blob/master/graph/ngscheckmate2xgmml.R\"\u003ehttps://github.com/parklab/NGSCheckMate/blob/master/graph/ngscheckmate2xgmml.R\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that we recommend \u003ca href=\"https://cytoscape.org/\" rel=\"nofollow\"\u003eCytoscape\u003c/a\u003e to visualize the .xgmml graphs.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-for-cobalt-cluster\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-for-cobalt-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage for Cobalt cluster\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run iarcbioinfo/NGSCheckMate -profile cobalt --input \"/data/test_*.bam\" --output_dir /data/cohort_output --ref_fasta /ref/Homo_sapiens_assembly38.fasta --bed /home/user/bin/NGSCheckMate/SNP/SNP_GRCh38.bed\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-hidden=\"true\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-why-are-some-files-not-included-although-the-are-in-the-intput_folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-are-some-files-not-included-although-the-are-in-the-intput_folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy are some files not included although the are in the intput_folder?\u003c/h3\u003e\n\u003cp\u003ebe careful that if bai files are missing for some bam files, the bam files will be ignored without the workflow returning an error\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-what-modifications-have-been-done-to-the-original-ngscheckmate-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-modifications-have-been-done-to-the-original-ngscheckmate-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat modifications have been done to the original NGSCheckMate code?\u003c/h3\u003e\n\u003cp\u003eWe provide a modified version of the graph/ngscheckmate2xgmml.R R script from \u003ca href=\"https://github.com/parklab/NGSCheckMate\"\u003ehttps://github.com/parklab/NGSCheckMate\u003c/a\u003e to output graphs in .xgmml format. The modifications allow to represent all samples, even those that match, and improve a small glitch in the color palette.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@iarc.fr\"\u003eAlcalaN@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaxime Vall\u00e9e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "full_name": "datalad/datalad-extensions", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDataLad healthchecks\u003c/h1\u003e\u003ca id=\"user-content-datalad-healthchecks\" class=\"anchor\" aria-label=\"Permalink: DataLad healthchecks\" href=\"#datalad-healthchecks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a \"dashboard\" of various CIs of DataLad, its extensions, and underlying\n3-rd party projects like git-annex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis README.md is autogenerated - do not edit.\nSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e for more information.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGit-annex Status\u003c/h2\u003e\u003ca id=\"user-content-git-annex-status\" class=\"anchor\" aria-label=\"Permalink: Git-annex Status\" href=\"#git-annex-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eConda: \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/fdc1b5de2056ba6e9fe88f7ce41a97cb6ea2852cae9cc715b0fce1ee307a37d1/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f76657273696f6e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fdc1b5de2056ba6e9fe88f7ce41a97cb6ea2852cae9cc715b0fce1ee307a37d1/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f76657273696f6e2e737667\" alt=\"Conda?\" data-canonical-src=\"https://anaconda.org/conda-forge/git-annex/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/26fc1d266decec815b3d821b55616af8b92f4093f7cf2547d3e312cbe89a0530/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f6c61746573745f72656c656173655f72656c61746976655f646174652e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/26fc1d266decec815b3d821b55616af8b92f4093f7cf2547d3e312cbe89a0530/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f6c61746573745f72656c656173655f72656c61746976655f646174652e737667\" alt=\"Updated\" data-canonical-src=\"https://anaconda.org/conda-forge/git-annex/badges/latest_release_relative_date.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/35ed3f186127909c18aa701be3c9b3e65a91b57ac1bc676881005a5346085d4c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f706c6174666f726d732e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/35ed3f186127909c18aa701be3c9b3e65a91b57ac1bc676881005a5346085d4c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6769742d616e6e65782f6261646765732f706c6174666f726d732e737667\" alt=\"Platforms?\" data-canonical-src=\"https://anaconda.org/conda-forge/git-annex/badges/platforms.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCurrent snapshot build/tests + DataLad tests:\n\u003ca href=\"https://github.com/datalad/git-annex/actions?query=workflow%3A%22Build+git-annex+on+Ubuntu%22\"\u003e\u003cimg src=\"https://github.com/datalad/git-annex/workflows/Build%20git-annex%20on%20Ubuntu/badge.svg\" alt=\"Ubuntu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/datalad/git-annex/actions?query=workflow%3A%22Build+git-annex+on+macOS%22\"\u003e\u003cimg src=\"https://github.com/datalad/git-annex/workflows/Build%20git-annex%20on%20macOS/badge.svg\" alt=\"macOS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/datalad/git-annex/actions?query=workflow%3A%22Build+git-annex+on+Windows%22\"\u003e\u003cimg src=\"https://github.com/datalad/git-annex/workflows/Build%20git-annex%20on%20Windows/badge.svg\" alt=\"Windows\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/datalad/git-annex#client-tests\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/datalad/git-annex-ci-client-jobs/master/badges/.all-clients.svg\" alt=\"Clients\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataLad Status\u003c/h2\u003e\u003ca id=\"user-content-datalad-status\" class=\"anchor\" aria-label=\"Permalink: DataLad Status\" href=\"#datalad-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDistributions:\n\u003ca href=\"https://GitHub.com/datalad/datalad/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b773fad9b338ee62ab4e42ac766e5ac5444d6f8f60235cfcd565b91952197318/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642e737667\" alt=\"DataLad GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a240f2c8b107fef21725258e78e2ca5664af24658cd4d99a710c4d2d45dff939/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642f6261646765732f76657273696f6e2e737667\" alt=\"Anaconda\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://repology.org/project/datalad/versions\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0e787793a206ab2cf5491c41559db9425728e0535e0948b09f30f279ee54aaa8/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f76657273696f6e2d666f722d7265706f2f6175722f646174616c61642e7376673f6865616465723d41726368253230253238253431253535253532253239\" alt=\"Arch (AUR)\" data-canonical-src=\"https://repology.org/badge/version-for-repo/aur/datalad.svg?header=Arch%20%28%41%55%52%29\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://packages.debian.org/stable/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e1338610f5b64a32bc928206f288bba3cb4cfbd1dc93e16c4f6cccec1a20050/68747470733a2f2f6261646765732e64656269616e2e6e65742f6261646765732f64656269616e2f737461626c652f646174616c61642f76657273696f6e2e737667\" alt=\"Debian Stable\" data-canonical-src=\"https://badges.debian.net/badges/debian/stable/datalad/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://packages.debian.org/unstable/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9163382edda1888f23ea915f38860e726a0d4465e6a35b4dc5fcc028c2ae1649/68747470733a2f2f6261646765732e64656269616e2e6e65742f6261646765732f64656269616e2f756e737461626c652f646174616c61642f76657273696f6e2e737667\" alt=\"Debian Unstable\" data-canonical-src=\"https://badges.debian.net/badges/debian/unstable/datalad/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://repology.org/project/datalad/versions\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6eac67dd1e795963aacd41219a8c4500427016f76728a5a71d71b4c8b7d5fea0/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f76657273696f6e2d666f722d7265706f2f6665646f72615f726177686964652f646174616c61642e7376673f6865616465723d4665646f726125323025323872617768696465253239\" alt=\"Fedora Rawhide package\" data-canonical-src=\"https://repology.org/badge/version-for-repo/fedora_rawhide/datalad.svg?header=Fedora%20%28rawhide%29\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://repology.org/project/datalad/versions\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/62e73ef0b609cf084e9b5cb2861ed9e83db8efacac4a791f39a2784c94d5a866/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f76657273696f6e2d666f722d7265706f2f67656e746f6f5f6f766c5f736369656e63652f646174616c61642e7376673f6865616465723d47656e746f6f253230253238253341253341736369656e6365253239\" alt=\"Gentoo (::science)\" data-canonical-src=\"https://repology.org/badge/version-for-repo/gentoo_ovl_science/datalad.svg?header=Gentoo%20%28%3A%3Ascience%29\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://repology.org/project/datalad/versions\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/064297c8aaeb3b31b9e1d55e747bccaed7c90858753de665ae10db089f30d2e6/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f76657273696f6e2d666f722d7265706f2f707970692f646174616c61642e7376673f6865616465723d50795049\" alt=\"PyPI package\" data-canonical-src=\"https://repology.org/badge/version-for-repo/pypi/datalad.svg?header=PyPI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCI:\n\u003ca href=\"https://app.travis-ci.com/datalad/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/347775cd48a24cb02f61394b422f8b0011f87f25b274c6fc30fd2d3fc7fa2c82/68747470733a2f2f6170702e7472617669732d63692e636f6d2f646174616c61642f646174616c61642e7376673f6272616e63683d6d61696e74\" alt=\"Travis maint\" data-canonical-src=\"https://app.travis-ci.com/datalad/datalad.svg?branch=maint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://app.travis-ci.com/datalad/datalad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d46614c62a98d2638c88db090d11c0cc889a8028fdd5641ea907db40c3f870b5/68747470733a2f2f6170702e7472617669732d63692e636f6d2f646174616c61642f646174616c61642e7376673f6272616e63683d6d6173746572\" alt=\"Travis master\" data-canonical-src=\"https://app.travis-ci.com/datalad/datalad.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ci.appveyor.com/project/mih/datalad/branch/maint\" rel=\"nofollow\"\u003e\u003cimg 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src=\"https://github.com/datalad/datalad/actions/workflows/lint.yml/badge.svg\" alt=\"Linters\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMisc:\n\u003ca href=\"https://codecov.io/github/datalad/datalad?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4142fd6c5cb95221439a86d0baece98a6a4a19c9a7f65fc8ef7006601f65942f/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataLad Extensions Status\u003c/h2\u003e\u003ca id=\"user-content-datalad-extensions-status\" class=\"anchor\" aria-label=\"Permalink: DataLad Extensions Status\" href=\"#datalad-extensions-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRelease\u003c/th\u003e\n\u003cth\u003ePyPI Release\u003c/th\u003e\n\u003cth\u003eConda Release\u003c/th\u003e\n\u003cth\u003eCI: Released + DL master\u003c/th\u003e\n\u003cth\u003eCI: Released + DL maint\u003c/th\u003e\n\u003cth\u003eCI: develop + DL Release\u003c/th\u003e\n\u003cth\u003eCodecov\u003c/th\u003e\n\u003cth\u003eIssue Resolution\u003c/th\u003e\n\u003cth\u003eOpen Issues\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-catalog\"\u003edatalad_catalog\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-catalog/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df39f3f96ee7917f94cc2e1fef2862b3d07aa63b1f5883dc12b2e837eb95f416/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d636174616c6f672e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-catalog.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-catalog/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/73e3a037f24365d85c4af4000581d32169e696370de6c2e8052a4ba386455d19/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d636174616c6f672e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-catalog.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-container\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9908f3665be9a3add37dd30f2d86ad2b098d1d1557b3d177d2bab87394e21588/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d636f6e7461696e65722e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-container.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-container\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8a0bc1decd25401c4b6096b0d2861906a7e1dab7f889fed0050ae339ce8359f7/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d636f6e7461696e65722e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-container.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-crawler\"\u003edatalad_crawler\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-crawler/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ec87de091a1ae304a18afef0c2fd1a119233df025e83499b7f45bed26e1fdad8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d637261776c65722e737667\" alt=\"?\" 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src=\"https://camo.githubusercontent.com/a1a23f88e9475031f20e538a48a3cb4da575ab90a0dcbb21188726478399206b/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d637261776c65722f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-crawler/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_crawler-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_crawler-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_crawler-maint\"\u003e\u003cimg 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src=\"https://camo.githubusercontent.com/e6d4c3bac372feb3a3019a9e1ad930391d5bbb236e4c2aa5312eb700c28b2846/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6461746176657273652e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-dataverse.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-dataverse/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6c4535f1b60ab2c5f9288ebd4e19af751be7d28723806e8652768d2a522de49/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d6461746176657273652e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-dataverse.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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data-canonical-src=\"https://codecov.io/github/datalad/datalad-deprecated/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-deprecated\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3483ab70f13a97ef5126f8fd073b9c7179cbc646606bd4cb9162450d55331f4b/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d646570726563617465642e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-deprecated.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-deprecated\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d8b0c2805e52ce84301f20b1cf1f0eebee4162143b4085d639fbfed1b82f9948/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d646570726563617465642e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-deprecated.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-fuse\"\u003edatalad_fuse\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-fuse/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98f8e867b8a30b9f3648d756ce5df718eea322e4c75444ddb17784830a018b69/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d667573652e737667\" alt=\"?\" 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src=\"https://camo.githubusercontent.com/a8296fc3a80ad88888b3acef09a8aa1ce8105971d77e60cb6fa5eb250b535224/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d667573652f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-fuse/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_fuse-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_fuse-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_fuse-maint\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_fuse-maint.yaml/badge.svg\" alt=\"Released+DataLad maint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-fuse/actions/workflows/test.yml?query=branch%3Amaster\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-fuse/actions/workflows/test.yml/badge.svg?branch=master\" alt=\"develop+Released Datalad\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/datalad/datalad-fuse?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a693b0d530a6446473815ca701c9de18dee953938fb1b16d937de89ad2323fb8/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d667573652f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-fuse/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-fuse\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d581fa7439b0eeb8e8ccfc363fb3b4919627ee56189de62a4f8ba09e627ffcbf/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d667573652e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-fuse.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-fuse\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe5511ce2b40d649d050bdbb197ada71435d239aa528120651035b34738fd4ae/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d667573652e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-fuse.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-metalad\"\u003edatalad_metalad\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-metalad/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3411710c4784c25a4125e6fec11d026d038eaa5f7b201e73760992d78e30cace/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6d6574616c61642e737667\" alt=\"?\" 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src=\"https://camo.githubusercontent.com/3ede67c44c33cb38d3cbefd530a6c5d099087719b8e7cc5971e93a3d4053b0f0/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d6d6574616c61642f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-metalad/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_metalad-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_metalad-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_metalad-maint\"\u003e\u003cimg 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style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-metalad\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/61f8c01570d1872076b3a011fa5432c1dd2e9e062c2bb1d31663a9d8ca7cdf92/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d6d6574616c61642e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-metalad.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-neuroimaging\"\u003edatalad_neuroimaging\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-neuroimaging/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3b46b08339d91628c7de50a03f6a444b1917f024a808651064938dfe01663b8b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-neuroimaging/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4466cdd0dfd7c35e5746d86fadfb58edabac888c4b411d77bdd6ed0d324502b4/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/datalad/datalad-neuroimaging?branch=master\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/datalad/datalad-neuroimaging?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31b8a3e8dada4f28be012c0bcf21f29445d8b0ffd53ee7e11e245e53b1ef46c6/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d6e6575726f696d6167696e672f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-neuroimaging/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-neuroimaging\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg 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data-canonical-src=\"https://anaconda.org/conda-forge/datalad-next/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_next-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_next-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_next-maint\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_next-maint.yaml/badge.svg\" alt=\"Released+DataLad maint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/mih/datalad-next/branch/main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2d94e42e168217e700e94379a10d3c0232532e9022232b1795131cd04f07b2e2/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f646174616c61642f646174616c61642d6e6578743f6272616e63683d6d61696e267376673d74727565\" alt=\"develop+Released Datalad\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/datalad/datalad-next?branch=main\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/datalad/datalad-next?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb469856ff2e3414149de0ea8f239f804e6f61524074763ff605c6fb4491be16/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d6e6578742f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-next/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-next\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4362675ebd9a84db84c686e5da70eb9cf656d0aa171fb4a09c1d0514a264d447/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d6e6578742e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-next.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-next\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3a02d4c829ae392cc1ebda529f24c9a55fbd6a7ec73d4e3365969f8884157596/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d6e6578742e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-next.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-osf\"\u003edatalad_osf\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-osf/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7501bb4ffaafd7775babaf1be0c68f5fb194beff761d18d952c9162f6ddf7581/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6f73662e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-osf.svg\" 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src=\"https://camo.githubusercontent.com/e30df860973991421a79372aba47e3e96ed2412ef81a1bb5dc737eb55517efd0/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d6f73662f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-osf/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_osf-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_osf-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_osf-maint\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_osf-maint.yaml/badge.svg\" 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100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-osf\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d901b86649a36a27954ed0bd6f79a5e1e2f752885269559a09469aa0d5122d1/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d6f73662e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-osf.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-ukbiobank\"\u003edatalad_ukbiobank\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-ukbiobank/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7d7a4590cc23a39f957a017fc24f10e1ba401fe4007cf1bce705de5397966ff8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d756b62696f62616e6b2e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-ukbiobank.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-ukbiobank/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8014c7f87d1ffc47e00372727dfa38872b386b33d35867364f0830cbc9dd36f3/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d756b62696f62616e6b2e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-ukbiobank.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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src=\"https://camo.githubusercontent.com/0149545a3d0116df9f6b1cf8ad2967ca28267791b0c7cb640f1a8b25ba67a083/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d756b62696f62616e6b2e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-ukbiobank.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-ukbiobank\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98850f05b1ccce5ebf101d96fa52fc7096a8f81336fb36e2ea0932f04bcbb721/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d756b62696f62616e6b2e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-ukbiobank.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-xnat\"\u003edatalad_xnat\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://GitHub.com/datalad/datalad-xnat/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a30c68966fda3a2bf782320d2e5aa750f4899544840a7f6419799921589200b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d786e61742e737667\" alt=\"?\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-xnat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://pypi.python.org/pypi/datalad-xnat/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8517e485da5e49b590b24a28e26e001cfaec4abce0160433255e7c4a55b4d7ad/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d786e61742e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-xnat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/10447a12c5dbb7924338da8c9b54c04f6c6f05debf2b2acba92134a335a69b08/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d786e61742f6261646765732f76657273696f6e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/10447a12c5dbb7924338da8c9b54c04f6c6f05debf2b2acba92134a335a69b08/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d786e61742f6261646765732f76657273696f6e2e737667\" alt=\"-\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-xnat/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_xnat-master\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_xnat-master.yaml/badge.svg\" alt=\"Released+DataLad master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/datalad/datalad-extensions/actions?query=workflow%3Atest-datalad_xnat-maint\"\u003e\u003cimg src=\"https://github.com/datalad/datalad-extensions/actions/workflows/test-datalad_xnat-maint.yaml/badge.svg\" alt=\"Released+DataLad maint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/mih/datalad-xnat/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c75dff9a139b899cdab10ff9bbdc5d7eaa4b8240c2f9fe772f755dd1ff94b3e8/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f646174616c61642f646174616c61642d786e61743f6272616e63683d6d6173746572267376673d74727565\" alt=\"develop+Released Datalad\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/datalad/datalad-xnat?branch=master\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/datalad/datalad-xnat?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/83cc5a7741aaef0d03aab1901a2851f2fbe06f01fc38ff4a6f8b2777b6f9d833/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d786e61742f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-xnat/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-xnat\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/847b7b746ca3b982db6c6e2efa1afad725ff9655ebafde3beaf6c9ef6c2bd36e/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d786e61742e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-xnat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"http://isitmaintained.com/project/datalad/datalad-xnat\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/55ac571f30d87ed22e4b8a07a6a9373a65903592c5dd9b2f3683ea8d0799af3f/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d786e61742e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-xnat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 1, - "subscribers_count": 5, + "subscribers_count": 4, "topics": [], - "updated_at": 1626965540.0 + "updated_at": 1729006991.0 }, { "data_format": 2, "description": null, "filenames": [ + "Singularity.casacore.gpuvmem.10.0.ubuntu1604", + "Singularity.casacore.gpuvmem.10.0", + "Singularity.HPC", + "Singularity.casacore.gpuvmem.11.0", + "Singularity.casacore.gpuvmem.9.2.ubuntu1604", + "Singularity.casacore.gpuvmem.9.2", "Singularity" ], - "full_name": "aseetharam/maker", + "full_name": "miguelcarcamov/container_docker", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_docker\u003c/h1\u003e\u003ca id=\"user-content-container_docker\" class=\"anchor\" aria-label=\"Permalink: container_docker\" href=\"#container_docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUseful containers to work with radio astronomical data\u003c/p\u003e\n\u003cp\u003eMiguel C\u00e1rcamo.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1601247148.0 + "updated_at": 1636977837.0 }, { "data_format": 2, - "description": null, + "description": "circos.ca in a singularity container", "filenames": [ "Singularity" ], - "full_name": "jolars/LookAheadScreening", - "latest_release": "v0.2.0", - "readme": "\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-github_document\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-github_document\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eoutput: github_document\u003c/h2\u003e\n\n\u003ch1\u003e\u003ca id=\"user-content-code-for-the-hessian-screening-rule\" class=\"anchor\" aria-hidden=\"true\" href=\"#code-for-the-hessian-screening-rule\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode for the Hessian Screening Rule\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/jolars/HessianScreening/actions\"\u003e\u003cimg src=\"https://github.com/jolars/LookAheadScreening/workflows/R-CMD-check/badge.svg\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResults\u003c/h2\u003e\n\u003cp\u003eThe results from the simulations, which were run\non a dedicated HPC cluster, are stored in the \u003ca href=\"results/\"\u003eresults folder\u003c/a\u003e.\nThe source code for the actual paper, including figures,\nis found in \u003ca href=\"paper/\"\u003e\u003ccode\u003epaper/\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reproducing-the-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#reproducing-the-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReproducing the Results\u003c/h2\u003e\n\u003cp\u003eThe results from our paper were run through a singularity container. Check\nthe releases for pre-built singularity containers that you can download and use.\u003c/p\u003e\n\u003cp\u003eTo reproduce the results, \u003cstrong\u003ealways\u003c/strong\u003e use the\nsingularity container. To run an experiment from the\nsingularity container, call\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind results:/Project/results container.sif \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003escript\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;script\u0026gt;\u003c/code\u003e should be a name of a script in the\n\u003ca href=\"experiments/\"\u003eexperiments folder\u003c/a\u003e, such as \u003ccode\u003eexperiments/simulateddata.R\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-re-building-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#re-building-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRe-building the Singularity Container\u003c/h3\u003e\n\u003cp\u003eIf you want to re-build the singularity container from scratch (or\nsimply want to clone the repo to your local drive), you can\ndo so via the following steps.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eClone the repository to your local hard drive. On linux, using\nSSH authentication, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:jolars/LookAheadScreening.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the root of the repo and\nbuild the singularity container by calling\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e LookAheadScreening\nsudo singularity build container.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen proceed as in \u003ca href=\"#reproducing-the-results\"\u003eReproducing the Results\u003c/a\u003e\nto run the experiments.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-experiments-without-singularity-not-recommended\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-experiments-without-singularity-not-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Experiments without Singularity (Not Recommended!)\u003c/h3\u003e\n\u003cp\u003eAlternatively, you may also reproduce the results by cloning this repository\nand starting\nR in the root directory of this folder (which will activate the renv\nrepository) and then run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003erenv\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003erestore()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto restore the project library. Then build the R package (see below) and run the\nsimulations directly by running the scripts in the experiments folder. This\nis \u003cstrong\u003enot recommended\u003c/strong\u003e, however, since it, unlike the Singularity\ncontainer approach, does not exactly\nreproduce the software environment\nused when these simulations where originally run and may result in\ndiscrepancies due to differences in for instance operating systems,\ncompilers, and BLAS/LAPACK implementations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-r-package\" class=\"anchor\" aria-hidden=\"true\" href=\"#r-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR Package\u003c/h2\u003e\n\u003cp\u003eIf you want to build\nand experiment with the package, you can do so by calling\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e R CMD INSTALL \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eprovided you have \u003ccode\u003ecd\u003c/code\u003eed to the root folder of this repository. First\nensure, however, that you have enabled the renv project library by calling\n\u003ccode\u003erenv::restore()\u003c/code\u003e (see the section above).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003cp\u003eThe datasets used in these simulations are stored in the \u003ca href=\"data/\"\u003edata folder\u003c/a\u003e.\nScripts to retrieve these datasets from their original\nsources can be found in \u003ca href=\"data-raw/\"\u003e\u003ccode\u003edata-raw/\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "tin6150/circos", + "latest_release": null, "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1624959145.0 + "updated_at": 1715723731.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container build repository for pgfem-3d", "filenames": [ - "Singularity.compute-0-36", - "Singularity.tf-nightly", - "Singularity.compute-0-27" + "Singularity.v2.1", + "Singularity.v2.0", + "Singularity", + "Singularity.latest" ], - "full_name": "bstriner/tensorflow-cuda-10.1-cudnn7-devel-ubuntu16.04", + "full_name": "C-SWARM/pgfem-3d-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-tensorflow-cuda-101-cudnn7-devel-ubuntu1604\" class=\"anchor\" aria-hidden=\"true\" href=\"#tensorflow-cuda-101-cudnn7-devel-ubuntu1604\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etensorflow-cuda-10.1-cudnn7-devel-ubuntu16.04\u003c/h1\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePGFem_3D Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-pgfem_3d-singularity-container\" class=\"anchor\" aria-label=\"Permalink: PGFem_3D Singularity Container\" href=\"#pgfem_3d-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the build scripts necessary in order build a deployable \u003ccode\u003esingularity\u003c/code\u003e image of \u003ca href=\"https://github.com/C-SWARM/pgfem-3d\"\u003ePGFem_3D\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAccess to a machine with singularity to pull image \u003cem\u003eor\u003c/em\u003e root access to a machine to build custom image\u003c/li\u003e\n\u003cli\u003eSingularity installed as root (tested with 3.5.2)\u003c/li\u003e\n\u003cli\u003eAt least 2.3 GB storage to hold resulting container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eObtain the Container\u003c/h2\u003e\u003ca id=\"user-content-obtain-the-container\" class=\"anchor\" aria-label=\"Permalink: Obtain the Container\" href=\"#obtain-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 1: Use prebuilt container\u003c/h3\u003e\u003ca id=\"user-content-option-1-use-prebuilt-container\" class=\"anchor\" aria-label=\"Permalink: Option 1: Use prebuilt container\" href=\"#option-1-use-prebuilt-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThrough Singularity-Hub, a portable image built from this repository\u0027s \u003ccode\u003eSingularity\u003c/code\u003e build specification can be downloaded\nanywhere \u003ccode\u003esingularity\u003c/code\u003e is supported. This container will be matched with the latest change to this repository\u0027s\n\u003ccode\u003eSingularity\u003c/code\u003e file. Note that this container has \u003ccode\u003ePGFem_3D\u003c/code\u003e built with MVAPICH2-2.2. If a different version is needed\nfor infiniband support, a custom container must be built following the instructions in \u003ca href=\"#using-infiniband\"\u003eUsing infiniband\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo pull the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull shub://C-SWARM/pgfem-3d-singularity\n$ mv C-SWARM-pgfem-3d-singularity-master-latest.simg pgfem-3d.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe result from a \u003ccode\u003esingularity pull\u003c/code\u003e will be a container named \u003ccode\u003eC-SWARM-pgfem-3d-singularity-master-latest.simg\u003c/code\u003e due to\nSingularity-Hub naming conventions. It may be best to rename the container to something simple.\u003c/p\u003e\n\u003cp\u003eOnce the image is pulled, it can executed to run \u003ccode\u003ePGFem_3D\u003c/code\u003e seen in \u003ca href=\"#executing-the-container\"\u003eExecuting the Container\u003c/a\u003e.\nIf an MPI implementation other than \u003ccode\u003eMVAPICH2-2.2\u003c/code\u003e is desired, it is best to build a custom container with the desired MPI. Instructions for building a container are below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 2: Build the container on own machine\u003c/h3\u003e\u003ca id=\"user-content-option-2-build-the-container-on-own-machine\" class=\"anchor\" aria-label=\"Permalink: Option 2: Build the container on own machine\" href=\"#option-2-build-the-container-on-own-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis method requires root access to a machine with \u003ccode\u003esingularity\u003c/code\u003e installed. If \u003ccode\u003emvapich2-2.2\u003c/code\u003e is satisfactory, it is recommended to\nuse the prebuilt image using the instructions above as building a container takes time and space. The following instructions are for\nbuilding your own container when the \u003ccode\u003esingularity-hub\u003c/code\u003e image will not suffice.\u003c/p\u003e\n\u003cp\u003eClone this directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/C-SWARM/pgfem-3d-singularity.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pgfem-3d-singularity/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMake any changes necessary to the \u003ccode\u003eSingularity\u003c/code\u003e build file or the \u003ccode\u003ebuild.sh\u003c/code\u003e file where each software component will be compiled.\nBuild the container using the \u003ccode\u003ebuild\u003c/code\u003e command as super user / root. This can take 10-20 minutes depending on machine specs.\nA faster build may be achieved by increasing the make workers, replacing \u003ccode\u003emake\u003c/code\u003e with \u003ccode\u003emake -j 4\u003c/code\u003e for example.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esu -\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ePassword:\u003c/span\u003e\n# \u003cspan class=\"pl-s1\"\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /path/to/this/repo\u003c/span\u003e\n# \u003cspan class=\"pl-s1\"\u003esingularity build pgfem3d.simg Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA large amount of text will appear on the screen during the build process. Once completed, a container will be created\nnamed \u003ccode\u003epgfem3d.simg\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing infiniband\u003c/h2\u003e\u003ca id=\"user-content-using-infiniband\" class=\"anchor\" aria-label=\"Permalink: Using infiniband\" href=\"#using-infiniband\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy using the host\u0027s shared libraries it is possible to utilize infiniband. In order to properly communicate, within\nthe container it is best to build the version of MPI library normally used on the host to communicate over infiniband.\nIn the current singularity container defined by the \u003ccode\u003eSingularity\u003c/code\u003e specification file and the hosted on\n\u003ccode\u003eSingularity-Hub\u003c/code\u003e, \u003ccode\u003emvapich2-2.2\u003c/code\u003e is built and configured with \u003ccode\u003e--disable-wrapper-rpath\u003c/code\u003e. This allows the container\u0027s\n\u003ccode\u003elibmpi.so\u003c/code\u003e to be swapped to utilize the host\u0027s library. If a targeted cluster requires a different version of MVAPICH\nor a different implementation of MPI, replace the current download and build of \u003ccode\u003eMVAPICH\u003c/code\u003e\nwith the desired version within the \u003ccode\u003eSingularity\u003c/code\u003e build file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MVAPICH=mvapich2-2.2.tar.gz\ncurl -O http://mvapich.cse.ohio-state.edu/download/mvapich/mv2/\u003cspan class=\"pl-smi\"\u003e$MVAPICH\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ntar -xf \u003cspan class=\"pl-smi\"\u003e$MVAPICH\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${MVAPICH\u003cspan class=\"pl-k\"\u003e%\u003c/span\u003e.tar.gz}\u003c/span\u003e\n./configure --prefix=/mvapich --disable-wrapper-rpath\nmake -j 4 install\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:/mvapich/bin\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e:/mvapich/lib\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce the matching version of MPI is built into the container, \u003ccode\u003epgfem_3d\u003c/code\u003e should be compiled with this version. \u003ccode\u003epgfem_3d\u003c/code\u003e is\nbuilt within the \u003ccode\u003ebuild.sh\u003c/code\u003e helper script. The container can then be built, instrcutions can be found above at Building the container on own machine\u003c/p\u003e\n\u003cp\u003eWhile running on the targeted host, it is necessary to \u003ca href=\"#library-swapping\"\u003eSwap libraries\u003c/a\u003e in order to properly utilize infiniband.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLibrary swapping\u003c/h3\u003e\u003ca id=\"user-content-library-swapping\" class=\"anchor\" aria-label=\"Permalink: Library swapping\" href=\"#library-swapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the container is built and transferred over to a host, a job script should be built with the following to pass host\nlibraries and paths into the container. If the container and necessary files to run live in a FS space other than the\ncurrent user\u0027s home space, it will be necessary to pass that along below as well within the \u003ccode\u003eSINGULARITY_BINDPATH\u003c/code\u003e variable.\nThis is an example of a partial script on \u003ca href=\"https://hpc.llnl.gov/hardware/platforms/Quartz\" rel=\"nofollow\"\u003eQuartz at LLNL\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load mvapich2/2.2\nmodule load mkl/2018.0\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Passing dynamic libraries\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITYENV_LD_LIBRARY_PATH=\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Passing FS paths for host MVAPICH and where the container is stored\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_BINDPATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/usr/tce/packages/mvapich2/mvapich2-2.2-gcc-7.1.0/lib,/p/lscratchh/USERNAME\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /p/lscratchh/USERNAME/pgfem-3d-examples\n./run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecuting the Container\u003c/h2\u003e\u003ca id=\"user-content-executing-the-container\" class=\"anchor\" aria-label=\"Permalink: Executing the Container\" href=\"#executing-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce finished building or pulling, the container can be executed to run PGFem_3D, passing in any necessary parameters.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./pgfem3d.simg -SS -help\n\u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e Parsing options from: -help \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e\n _______ ______ ________ ______ _______ \n/ \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e / \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e/ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e / \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e/ \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$$$$$\u003c/span\u003e/______ _____ ____ /\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e__\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e _\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e__ / \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e/ \u003cspan class=\"pl-cce\"\u003e\\/\u003c/span\u003e \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e ___\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e/ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e / \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$$$\u003c/span\u003e$/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e _\u003cspan class=\"pl-smi\"\u003e$$$$\u003c/span\u003e$ \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-cce\"\u003e\\_\u003c/span\u003e_\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$$$$$$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e/ \u003cspan class=\"pl-cce\"\u003e\\_\u003c/span\u003e_\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e__\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \n\u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e/ \u003cspan class=\"pl-smi\"\u003e$$$$$$\u003c/span\u003e$/ \n\nSS_USAGE: mpirun -np [NP] PGFem3D -SS [options] input output\nMS_USAGE: mpirun -np [NP] PGFem3D -MS [network] -macro-np [P] -micro-group-size [S] [macro OPTION_BLK] [micro OPTION_BLK]\nOPTION_BLK: -[scale]-start [options] input output -[scale]-end\n\u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf running on an HPC system, it is best to use \u003ccode\u003empirun\u003c/code\u003e or an equivalent \u003cem\u003eoutside\u003c/em\u003e the container. This would require\nthe proper module or software in place, such as \u003ccode\u003emodule load mvapich2/2.2\u003c/code\u003e for example. If you are intending to\nrun using infiniband technologies, see \u003ca href=\"#using-infiniband\"\u003eUsing Infiniband\u003c/a\u003e above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning pgfem-3d-examples\u003c/h2\u003e\u003ca id=\"user-content-running-pgfem-3d-examples\" class=\"anchor\" aria-label=\"Permalink: Running pgfem-3d-examples\" href=\"#running-pgfem-3d-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity can utilize the host\u0027s native file system, allowing the following commands to be performed outside\nthe container on the machine targeted to run on. Be sure to transfer the container to the targeted machine in order\nto execute it.\u003c/p\u003e\n\u003cp\u003eClone the examples to obtain the source:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/C-SWARM/pgfem-3d-examples.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pgfem-3d-examples\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eReplace the executable within run.sh with the singularity image.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PGFEM_SIMG=/path/to/pgfem_3d.simg\n$ sed -i \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003es|/opt/pgfem-3d/bin/PGFem3D|\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${PGFEM_SIMG}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e|\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFrom here follow the directions supplied within pgfem-3d-examples:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./local_makeset.pl -np 4\n$ ./run.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create 2 files within the pgfem-3d-examples directory: \u003ccode\u003eparview_displacement_y.pvsm\u003c/code\u003e and \u003ccode\u003eparview_displacement_z.pvsm\u003c/code\u003e.\nThese files can be opened using \u003ccode\u003eParaView\u003c/code\u003e outside of the container and examined by the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClick \u003ccode\u003eFile -\u0026gt; Load State -\u0026gt; \u003c/code\u003e, select either parview_displacement_y.pvsm or parview_displacement_z.pvsm and click \u003ccode\u003eOK\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIn the next window, browse to: \u003ccode\u003eout -\u0026gt; box_4 -\u0026gt; VTK -\u0026gt; box_../pvtu\u003c/code\u003e and click \u003ccode\u003eOK\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePress the play button towards the top middle of the screen.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor any technical assistance, please contact:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCody Kankel \u003ca href=\"mailto:ckankel@nd.edu\"\u003eckankel@nd.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEzra Kissel \u003ca href=\"mailto:ezkissel@indiana.edu\"\u003eezkissel@indiana.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eKamal K Saha \u003ca href=\"mailto:ksaha@nd.edu\"\u003eksaha@nd.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eLuke D\u0027Alessandro \u003ca href=\"mailto:ldalessa@uw.edu\"\u003eldalessa@uw.edu\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [], - "updated_at": 1575469838.0 + "updated_at": 1582925466.0 }, { "data_format": 2, - "description": null, + "description": "Example container with NAMD3 built in using Nix", "filenames": [ - "containers/calculateSnPrecision/Singularity", - "containers/assessmentRfHeatmap/Singularity", - "containers/checkFormat/Singularity", - "containers/robinsonFouldsMetric/Singularity", - "containers/getQueryIds/Singularity" + "Singularity" ], - "full_name": "BU-ISCIII/openebench_gmi", + "full_name": "XSEDE/nix-container-namd3", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e54064fb698af20a2b6089b4f16ec3e31f31f72b47f15a5bb215bfd2e41d1b2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1ee06357ac79da293d08136619bdf903a80f520229e0916813d4a6eca768a963/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f46696c6573797374656d2d536369656e74696669632d627269676874677265656e2e737667\" alt=\"Scif\" data-canonical-src=\"https://img.shields.io/badge/Filesystem-Scientific-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-nextflow-pipeline-using-containers-for-an-outbreak-detection-challenge-using-openebench-platform\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-using-containers-for-an-outbreak-detection-challenge-using-openebench-platform\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline using containers for an Outbreak detection challenge using OpenEbench platform\u003c/h1\u003e\n\u003cp\u003eThis repository intends to be a nextflow + container implementation of OpenEbench workflow for an Outbreak detection challenge.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-use-it\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-use-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to use it\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/BU-ISCIII/openebench_gmi.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e openebench_gmi.git\ngit submodule init\ngit submodule update\nnextflow run main.nf -profile docker \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eParameters available:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf --help\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eUsage:\nnextflow run BU-ISCIII/openebench_gmi --tree_test {test.newick.file} --goldstandard_dir {golden.folder.path} --assess_dir {assessment.path} --public_ref_dir {path.to.info.ref.dataset} --event_id {event.id}\n\nMandatory arguments:\n --tree_test Path to input data (must be surrounded with quotes).\n --goldstandard_dir Path to reference data. Golden datasets.\n --public_ref_dir Path where public dataset info is stored for validation.\n --assess_dir Path where benchmark data is stored.\n --event_id Event identifier.\n --participant_id Participant identifier.\n --tree_format Format tree [\"nexus\",\"newick\"].\n\nOther options:\n --outdir The output directory where the results will be saved\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-datasets\" class=\"anchor\" aria-hidden=\"true\" href=\"#datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDatasets\u003c/h2\u003e\n\u003cp\u003eFirst of all, needed datasets have been collected in: \u003ca href=\"datasets\"\u003edatasets folder\u003c/a\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eInput dataset:\u003c/strong\u003e fastq input data obtained from \u003ca href=\"https://github.com/globalmicrobialidentifier-WG3/datasets\"\u003eGMI WGS standards and benchmarks repository\u003c/a\u003e. \u003ca href=\"datasets/inputDataset/Readme.me\"\u003eHere\u003c/a\u003e you can find instructions for download.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGold standard dataset:\u003c/strong\u003e confirmed phylogeny for the outbreak being investigated.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eInput dataset ids:\u003c/strong\u003e input dataset ids in .txt and .json format.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTest dataset:\u003c/strong\u003e a test tree for comparing with gold standard result. In this case just the same golden dataset. Robinson-Foulds metrics must be 0.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebenchmark_data\u003c/strong\u003e: path where benchmark results are stored.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow-pipeline-and-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-and-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline and containers\u003c/h2\u003e\n\u003cp\u003eSecond, a pipeline has been developed which is splitted in three steps following OpenEbench specifications following this \u003ca href=\"https://github.com/inab/opeb-submission\"\u003erepo\u003c/a\u003e as an example:\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nextflow-processes\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-processes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow processes\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eValidation and data preprocessing:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eCheck results format:\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTree input: User input tree format is validated, nexus and newick formats are allowed being newick the canonical format. If format validated, a tree is outputted in the canonical format (.nwk).\u003c/li\u003e\n\u003cli\u003eVCF input:\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eGet query ids:\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTree input: ids are extracted for user input tree in newick or nexus format. IDs are writed in: queryids.json\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eValidate query ids:\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTree input: query ids are validated against ref input ids.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMetrics:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cem\u003ePrecision/Recall calculation:\u003c/em\u003e common (TP), source (FP) and ref(FN) edges are calculated in the comparison of ref and test tree topologies. Recall and precision are calculated using this values and stored in a json file called {participant_id}_snprecision.json.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eRobinson-Foulds metric calculation:\u003c/em\u003e Normalized Robinson-Foulds test is performed between user tree and every participant tree already analyzed and stored in the benchmark_data folder in order to compare their topologies. Result value is writted to participant_matrix.json file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eData visualization and consolidation:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePrecision/Recall graph is created, classifying each participant inside a quartile.\u003c/li\u003e\n\u003cli\u003eA all participant vs all participant heatmap is created usign normalized robinson-foulds matrix.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-containers-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers info\u003c/h3\u003e\n\u003cp\u003eEach step runs in its own container. Containers are built using a Dockerfile recipe which makes use of \u003ca href=\"https://sci-f.github.io/\" rel=\"nofollow\"\u003eSCI-F\u003c/a\u003e recipes for software installation. All scif recipes are available in \u003ca href=\"https://github.com/BU-ISCIII/scif_app_recipes\"\u003escif_app_recipes repository\u003c/a\u003e. Singularity recipes are also provided (Not yet adapted in nextflow pipeline).\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edocker-centos-nix-openmpi\u003c/h1\u003e\u003ca id=\"user-content-docker-centos-nix-openmpi\" class=\"anchor\" aria-label=\"Permalink: docker-centos-nix-openmpi\" href=\"#docker-centos-nix-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker container with Nix and OpenMPI to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 10, "topics": [], - "updated_at": 1559826274.0 + "updated_at": 1618581042.0 }, { "data_format": 2, - "description": "A cat(1) clone with syntax highlighting and Git integration.", + "description": null, "filenames": [ - "0.18.1/Singularity", - "0.21.0/Singularity", - "0.17.1/Singularity", - "0.23.0/Singularity", - "0.22.1/Singularity", - "0.18.3/Singularity" + "Singularity.harp", + "Singularity.utils" ], - "full_name": "pscedu/singularity-bat", - "latest_release": "v0.23.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bat/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bat/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-bat/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-bat/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/80ce266ea551486e532b8479474ece87011121c1b177e0e067f4d8022ad2f52c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626174\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80ce266ea551486e532b8479474ece87011121c1b177e0e067f4d8022ad2f52c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d626174\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-bat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b6d53daf33b347fa4d1a74ab0a30fd631965db08d1237e10991c7e7230ed671f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626174\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6d53daf33b347fa4d1a74ab0a30fd631965db08d1237e10991c7e7230ed671f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d626174\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-bat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d23d5a1f67b897b9eeaf3e805efa8c4c8562272babd67bc8d1da9c8ca70d6259/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626174\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d23d5a1f67b897b9eeaf3e805efa8c4c8562272babd67bc8d1da9c8ca70d6259/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d626174\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-bat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/bf0a53aac9fa2abb057c22f8fb00c5d07141c61f15c27983fd073a5445c15421/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626174\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bf0a53aac9fa2abb057c22f8fb00c5d07141c61f15c27983fd073a5445c15421/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d626174\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-bat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity-bat\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-bat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-bat\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7b7c397acc5b91b4c4cf7756015185fe3c5f700f70d256a212de51294a0cf673/68747470733a2f2f696d6775722e636f6d2f724773646e44652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b7c397acc5b91b4c4cf7756015185fe3c5f700f70d256a212de51294a0cf673/68747470733a2f2f696d6775722e636f6d2f724773646e44652e706e67\" alt=\"Example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/sharkdp/bat\"\u003ebat\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling the container on Bridges 2\u003c/h2\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ebat\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/bat/0.17.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/bat\u003c/code\u003e as \u003ccode\u003e0.17.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image using the recipe\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image locally\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo build the image remotely\u003c/h3\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo run tests\u003c/h2\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "cory-weller/HS-reconstruction-gwas", + "latest_release": "v1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHS-reconstruction-gwas\u003c/h1\u003e\u003ca id=\"user-content-hs-reconstruction-gwas\" class=\"anchor\" aria-label=\"Permalink: HS-reconstruction-gwas\" href=\"#hs-reconstruction-gwas\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the scripts used to generate and process data, as well as generate figures, for the manuscript:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAccurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCory A. Weller (\u003ca href=\"mailto:caw5cv@virginia.edu\"\u003ecaw5cv@virginia.edu\u003c/a\u003e) \u0026amp; Alan O. Bergland (\u003ca href=\"mailto:aob2x@virginia.edu\"\u003eaob2x@virginia.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow allows for Singularity containers to process data in a reproducible manner without installing required programs and libraries. You will first need to install singularity on your system, if it is not already available. Many HPC systems already have pre-loaded \u003ccode\u003esingularity\u003c/code\u003e that can be loaded as a module.\u003c/p\u003e\n\u003cp\u003eOtherwise, install singularity 3.x following the instructions from \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003esylabs.io\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThen, you can retrieve the pre-built singularity image files from Singularity Hub.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name harp.sif shub://cory-weller/HS-reconstruction-gwas:harp\nsingularity pull --name utils.sif shub://cory-weller/HS-reconstruction-gwas:utils\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1633086539.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1649753066.0 }, { "data_format": 2, - "description": null, + "description": "A Singularity recipe for machine learning packages for use with gpus", "filenames": [ - "src/linker/steps/dev/python_pandas/Singularity" + "Singularity20210927", + "Singularity20220919", + "Singularity20210202", + "Singularity20210319", + "Singularity20220928", + "Singularity20220603", + "Singularity20190305", + "Singularity20210616", + "Singularity20190917", + "Singularity20220804", + "Singularity20210222", + "Singularity20210901", + "Singularity20210428", + "Singularity20190715", + "Singularity20200210", + "Singularity20200902", + "Singularity20210730", + "Singularity20200413" ], - "full_name": "ihmeuw/linker", + "full_name": "ResearchIT/singularity-ml", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-linker\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#linker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elinker\u003c/h1\u003e\n\u003cp\u003eNOTE: \"linker\" is a temporary name and will change when the official one is\ndecided on.\u003c/p\u003e\n\u003cp\u003elinker is a framework that allows users to build and run highly configurable\nentity resolution (ER) pipelines.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall docker\n\u003cul\u003e\n\u003cli\u003eMac: \u003ca href=\"https://docs.docker.com/desktop/install/mac-install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/desktop/install/mac-install/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eWindows: \u003ca href=\"https://docs.docker.com/desktop/install/windows-install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/desktop/install/windows-install/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eClone and install this repo\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd \u0026lt;path/to/repositories/\u0026gt;\n$ git clone git@github.com:ihmeuw/linker.git\n$ # OR `git clone https://github.com/ihmeuw/linker.git`\n$ cd linker\n$ pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-a-pipeline\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#running-a-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a pipeline\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e$ linker run \u0026lt;PIPELINE-SPECIFICATION\u0026gt;\n$ # e.g. `linker run ~/repos/linker/src/linker/pipelines/pipeline.yaml`\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor help, please use \u003ccode\u003elinker --help\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-creating-a-docker-image-to-be-shared\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#creating-a-docker-image-to-be-shared\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a docker image to be shared\u003c/h2\u003e\n\u003cp\u003eDocker image binaries can be built from a Dockerfile. For example, to create a\ncompressed image .tar.gz file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd \u0026lt;PATH-TO-DOCKERFILE-PARENT-DIRECTORY\u0026gt;\n$ # build the image\n$ sudo docker build -t linker:\u0026lt;IMAGE-NAME\u0026gt; .\n$ # save as compressed tarball\n$ sudo docker save linker:\u0026lt;IMAGE-NAME\u0026gt; | gzip \u0026gt; \u0026lt;IMAGE-NAME\u0026gt;.tar.gz\n$ # remove the image\n$ sudo docker rmi linker:\u0026lt;IMAGE-NAME\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can use the \u003ccode\u003e-f\u003c/code\u003e option to build a dockerfile from a different location\n(including a different filename than \u0027Dockerfile\u0027):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker build -t linker:\u0026lt;IMAGE-NAME\u0026gt; \u0026lt;PATH-TO-DOCKERFILE\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should now have an image file named \u003ccode\u003e\u0026lt;IMAGE-NAME\u0026gt;.tar.gz\u003c/code\u003e alongside the Dockerfile which can be used to spin up the container.\u003c/p\u003e\n\u003cp\u003eNote that it may be occasionally required to clean up unused data to make room for building\nimages: \u003ccode\u003esudo docker system prune\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-creating-a-singularity-image-to-be-shared\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#creating-a-singularity-image-to-be-shared\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating a singularity image to be shared\u003c/h2\u003e\n\u003cp\u003eSingularity image files can be created from a Singularity file. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd \u0026lt;PATH-TO-SINGULARITY-FILE-PARENT-DIRECTORY\u0026gt;\n$ # build the image\n$ singularity build --force \u0026lt;IMAGE-NAME\u0026gt;.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, a Docker binary can be converted to a Singularity image file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity build --force \u0026lt;IMAGE-NAME\u0026gt;.sif docker-archive://$(pwd)/\u0026lt;IMAGE-NAME\u0026gt;.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingulairty Recipe for keras\u003c/h1\u003e\u003ca id=\"user-content-singulairty-recipe-for-keras\" class=\"anchor\" aria-label=\"Permalink: Singulairty Recipe for keras\" href=\"#singulairty-recipe-for-keras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains the recipe for a general purpose gpu enabled machine learning container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersions:\u003c/h2\u003e\u003ca id=\"user-content-versions\" class=\"anchor\" aria-label=\"Permalink: Versions:\" href=\"#versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e1.0 - Initial effort\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 8, + "subscribers_count": 7, "topics": [], - "updated_at": 1694131143.0 + "updated_at": 1670046313.0 }, { "data_format": 2, - "description": "Singularity container for OpenMS 2.3 with Thirdparty tools", + "description": null, "filenames": [ - "Singularity.2.2.0+", - "Singularity.2.3.0+", - "Singularity.dependencies", - "Singularity.contrib", - "Singularity" + "SingularityRecipe" ], - "full_name": "mafreitas/singularity-openms", + "full_name": "mwever/tpami-automlc", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-openms\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity-openms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-openms\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/601\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eReceipes for Singularity containers that can run OpenMS (2.2 and 2.3 with thirdparty tools).\u003c/p\u003e\n\u003cp\u003eThese are early builds for testing only.\u003c/p\u003e\n\u003cp\u003eThe receipes are based on OpenMS docker containers available from the OpenMS team. For examples checkout\n\u003ca href=\"https://hub.docker.com/u/hroest/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/u/hroest/\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pull-the-container-to-your-machine-and-optionally-name-custom-or-by-hashcommit\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#pull-the-container-to-your-machine-and-optionally-name-custom-or-by-hashcommit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePull the container to your machine (and optionally name custom, or by hash/commit:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://mafreitas/singularity-openms\nsingularity pull --name customname.img shub://mafreitas/singularity-openms\nsingularity pull --commit shub://mafreitas/singularity-openms\nsingularity pull --hash shub://mafreitas/singularity-openms\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-shell-into-the-container\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eShell into the container:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell shub://mafreitas/singularity-openms\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the container:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run shub://mafreitas/singularity-openms\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-using-as-a-base\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#build-using-as-a-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild using as a base:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-openms.simg shub://mafreitas/singularity-openms\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eLatest \u0026amp; 2.3+ - contain the latest release (2.3.0) and thirdparty tools.\u003c/li\u003e\n\u003cli\u003e2.2+ - contains 2.2.0 and thirdparty tools .\u003c/li\u003e\n\u003cli\u003econtrib - contains the dependencies and contributing libraries.\u003c/li\u003e\n\u003cli\u003edependencies - contains the base image with all build dependencies.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo pull a specific container add the appropriate tag.\u003cbr\u003e\nFor example to pull the 2.2+ container use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://mafreitas/singularity-openms:2.2+\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAutoML for Multi-Label Classification: Overview and Empirical Evaluation\u003c/h1\u003e\u003ca id=\"user-content-automl-for-multi-label-classification-overview-and-empirical-evaluation\" class=\"anchor\" aria-label=\"Permalink: AutoML for Multi-Label Classification: Overview and Empirical Evaluation\" href=\"#automl-for-multi-label-classification-overview-and-empirical-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project provides a platform for benchmarking different optimizers for the task of automated machine learning ensuring all optimizers to work on the same set of potential solution candidates. The implementation is based on the Java open-source library \u003ca href=\"https://github.com/starlibs/AILibs\"\u003eAILibs\u003c/a\u003e, providing the basic technical support for describing search spaces, HTN planning and heuristic search algorithms, as well as the infrastructure for synchronizing the execution of a benchmarking suite on a distributed system.\u003c/p\u003e\n\u003cp\u003eThe benchmark distinguishes itself from previously published benchmark in the way how the optimizers are integrated with the benchmarking system. While all optimizers share the same routine for executing candidate solutions, the benchmark works in a cross-platform fashion, i.e. although the benchmark and the execution of candidate solutions is implemented in Java, optimizers available in Python can be benchmarked within the system. The search space and recursive structures of the search space are automatically translated into a format understandable to the respective optimizers. The inter-platform communication is done via the \u003ca href=\"https://developers.google.com/protocol-buffers\" rel=\"nofollow\"\u003eGoogle ProtoBuf\u003c/a\u003e library which offers interfaces for various platforms. Thereby, the communication link only transfers the execution request to the benchmarking system allowing to share the same evaluation routine for all the optimizers. Another advantage is that it also allows for live-tracking the performance of the optimizers, logging each evaluated candidate and monitoring the current incumbent at any point in time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"#quickstart---setup\"\u003eQuickstart - Setup\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#preparing-the-singularity-container\"\u003ePreparing the Singularity Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#without-a-singularity-container\"\u003eWithout a Singularity Container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#test-your-setup\"\u003eTest your Setup\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-the-benchmark\"\u003eRunning the Benchmark\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#hardware-requirements\"\u003eHardware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#initialize-the-database-server\"\u003eInitialize the Database Server\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-a-worker-client\"\u003eRunning a Worker Client\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#post-processing-for-anytime-test-performances\"\u003ePost-Processing for Anytime Test Performances\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#visualizing-benchmark-data\"\u003eVisualizing Benchmark Data\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"#inspecting-the-search-space\"\u003eInspecting the Search Space\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#list-algorithms-contained-in-the-search-space\"\u003eList Algorithms Contained in the Search Space\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#export-to-gephi-to-visualize-the-search-space-as-a-dag\"\u003eExport to Gephi to visualize the search space as a DAG\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generate-html-overview\"\u003eGenerate HTML Overview\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#evaluation-results\"\u003eEvaluation Results\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#generate-one-vs-rest-scatter-plots\"\u003eGenerate One-VS-Rest Scatter Plots\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generate-anytime-average-rank-plots\"\u003eGenerate Anytime Average Rank Plots\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generate-result-tables\"\u003eGenerate Result Tables\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#generate-incumbent-frequency-statistics\"\u003eGenerate Incumbent Frequency Statistics\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuickstart - Setup\u003c/h2\u003e\u003ca id=\"user-content-quickstart---setup\" class=\"anchor\" aria-label=\"Permalink: Quickstart - Setup\" href=\"#quickstart---setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePrerequisites: Due to certain dependencies requiring a Linux operating system, the execution of optimizers in Python is not supported for Windows nor MacOS. However, optimizers available in Java can still be executed on Windows or MacOS. Running a Linux OS, you may execute Python optimizers as well. To this end, please use the SingularityRecipe to build a container in order to ensure all the dependencies necessary for running the benchmark are available.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePreparing the Singularity Container\u003c/h3\u003e\u003ca id=\"user-content-preparing-the-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Preparing the Singularity Container\" href=\"#preparing-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to set up a Singularity Container, please ensure that you have administrator permissions and installed the \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/\" rel=\"nofollow\"\u003eSingularity Container\u003c/a\u003e software on your computer. (Hint: On Ubuntu you can simply install it via \u003ccode\u003esudo apt install singularity-container\u003c/code\u003e). Once you have Singularity Container installed on your system, follow these steps in order to create the container:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBuild a singularity container from the provided recipe file: \u003ccode\u003esudo singularity build automlc.sif SingularityRecipe\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOnce the container is built, you can now proceed to run a shell within the Singularity container like this: \u003ccode\u003esingularity shell automlc.sif\u003c/code\u003e (No sudo this time).\u003c/li\u003e\n\u003cli\u003ePlease make sure that your current folder is mounted into the Singularity container. You may prove this by typing \u003ccode\u003edir\u003c/code\u003e. If the files of the project\u0027s root directory are printed on your command line, everything should be fine.\u003c/li\u003e\n\u003cli\u003eYou are now prepared to run the tasks via the gradle wrapper. For further steps please have a look at the subsequent documentation.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWithout a Singularity Container\u003c/h3\u003e\u003ca id=\"user-content-without-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Without a Singularity Container\" href=\"#without-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are running Linux you also have the possibility to run the benchmark directly on your system, without creating any Singularity container.\nTo this end, we have prepared a \u003ccode\u003erequirements.txt\u003c/code\u003e file so that you can install the required dependencies for the Python environment with ease.\nHowever, since we mainly work with Singularity containers to have a clearly distinct system running, preventing interferences with inappropriate python versions and compatibility conflicts in general, we do not officially support the setup variant without creating a Singularity container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest your Setup\u003c/h3\u003e\u003ca id=\"user-content-test-your-setup\" class=\"anchor\" aria-label=\"Permalink: Test your Setup\" href=\"#test-your-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to test your setup we have prepared a test runner that will work out-of-the-box if everything has been setup correctly.\nMore precisely, you can test whether each of the optimizers can be run for a specific dataset split with short timeouts of 1 minute for the entire optimization run and 45 seconds for a single evaluation.\u003c/p\u003e\n\u003cp\u003eYou can test to run each optimizer individually via the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew testHTNBF\n./gradlew testBOHB\n./gradlew testHB\n./gradlew testSMAC\n./gradlew testRandomSearch\n./gradlew testGGP\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs a shortcut you can also simply test all the optimizers as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew testAllOptimizers\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the Benchmark\u003c/h2\u003e\u003ca id=\"user-content-running-the-benchmark\" class=\"anchor\" aria-label=\"Permalink: Running the Benchmark\" href=\"#running-the-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe benchmark implemented in this repository is meant to be run in a distributed way.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Requirements\u003c/h3\u003e\u003ca id=\"user-content-hardware-requirements\" class=\"anchor\" aria-label=\"Permalink: Hardware Requirements\" href=\"#hardware-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor running a benchmark suite you need the following resources:\nA central database server managing the experiments to be executed and worker clients meeting the hardware requirements.\nIn the paper we used worker clients each equipped with \u003ccode\u003e8 CPU cores\u003c/code\u003e and \u003ccode\u003e32GB RAM\u003c/code\u003e.\nThe recommended hardware specifications for the database server depends on the degree of parallelization and how you set the parameters for the experiments.\nThe latter point is for instance depending what evaluation timeouts you choose for assessing the performance of single candidates.\nThe smaller the timeout the more intermediate evaluation results will be logged in the database.\nAs a consequence there will be a higher load on the respective database server.\nIn our study and with the timeout configuration proposed in the paper, we found that a configuration of \u003ccode\u003e4 CPU cores\u003c/code\u003e and \u003ccode\u003e16GB RAM\u003c/code\u003e is sufficient to deal with up to 200 worker clients.\nFor the database, we tested only a MySQL database. In principle other drivers are usable, but may require the inclusion of additional dependencies for the project.\nOfficially, we only support MySQL databases.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInitialize the Database Server\u003c/h3\u003e\u003ca id=\"user-content-initialize-the-database-server\" class=\"anchor\" aria-label=\"Permalink: Initialize the Database Server\" href=\"#initialize-the-database-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to initialize the database server, first of all, you need to fill in the connection details in the \u003ccode\u003eautomlc-setup.properties\u003c/code\u003e file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e...\n\u003cspan class=\"pl-k\"\u003edb.driver\u003c/span\u003e = mysql\n\u003cspan class=\"pl-k\"\u003edb.host\u003c/span\u003e = \u0026lt;YOUR DB HOST\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.username\u003c/span\u003e = \u0026lt;YOUR DB USER\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.password\u003c/span\u003e = \u0026lt;YOUR DB PASSWORD\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.database\u003c/span\u003e = \u0026lt;YOUR DATABASE NAME\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.table\u003c/span\u003e = \u0026lt;YOUR JOBS TABLE NAME\u0026gt;\n\u003cspan class=\"pl-k\"\u003edb.ssl\u003c/span\u003e = \u0026lt;FLAG WHETHER TO USE SSL\u0026gt;\n\u003cspan class=\"pl-k\"\u003ecandidate_eval_table\u003c/span\u003e = \u0026lt;YOUR INTERMEDIATE EVALUATIONS TABLE NAME\u0026gt;\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe specifics of the benchmark are then given with the following properties in the same files:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003emem.max\u003c/span\u003e = 32768 // maximum available memory\n\u003cspan class=\"pl-k\"\u003ecpu.max\u003c/span\u003e = 8 // number of cores\n\n... // database connection properties and AILibs experimenter specific properties \n\n\n\u003cspan class=\"pl-k\"\u003ealgorithm\u003c/span\u003e = bf,random,hb,bohb,smac,ggp // optimizers to consider\n\u003cspan class=\"pl-k\"\u003edataset\u003c/span\u003e = arts1,bibtex,birds,bookmarks,business1,computers1,education1,emotions,enron-f,entertainment1,flags,genbase,health1,llog-f,mediamill,medical,recreation1,reference1,scene,science1,social1,society1,tmc2007,yeast // datasets to consider\n\u003cspan class=\"pl-k\"\u003emeasure\u003c/span\u003e = FMacroAvgD,FMacroAvgL,FMicroAvg // performance measures to consider\n\u003cspan class=\"pl-k\"\u003esplit\u003c/span\u003e = 0,1,2,3,4,5,6,7,8,9 // split indices to consider\n\u003cspan class=\"pl-k\"\u003eseed\u003c/span\u003e = 42 // seed of the dataset splitter to consider\n\u003cspan class=\"pl-k\"\u003eglobalTimeout\u003c/span\u003e=86400 // timeout for an entire optimization run\n\u003cspan class=\"pl-k\"\u003eevaluationTimeout\u003c/span\u003e=1800 // timeout for a single candidate evaluation\n\n... // constant properties for the experiment runner\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBased on this specification, the benchmark will compute all possible combinations of entires given via the fields \u003ccode\u003ealgorithm\u003c/code\u003e, \u003ccode\u003edataset\u003c/code\u003e, \u003ccode\u003emeasure\u003c/code\u003e, \u003ccode\u003esplit\u003c/code\u003e, \u003ccode\u003eseed\u003c/code\u003e, \u003ccode\u003eglobalTimeout\u003c/code\u003e, \u003ccode\u003eevaluationTimeout\u003c/code\u003e.\nIn principle it is also possible to configure multiple seeds, globalTimeouts or evaluationTimeouts in the same style as it is done e.g. for the algorithm field, i.e. simply by seperating multiple values by a comma.\u003c/p\u003e\n\u003cp\u003eOnce the database connection is configured and the property values for all the benchmark suite specific parameters have been set, you can proceed by initializing the database server centrally managing the experiment conduction with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew initializeExperimentsInDatabase\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will automatically create a table with the name specified in the \u003ccode\u003edb.table\u003c/code\u003e property which will then specify all the experiments to be executed which have been computed via taking the cross-product of all possible combinations of the properties describing the benchmark suite.\u003c/p\u003e\n\u003cp\u003eFor cleaning this table, i.e., removing all of its entries, you can run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew cleanExperimentsInDatabase\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eCaution:\u003c/strong\u003e This functionality will also remove all results that are stored in this table after running an experiment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePreparing Dataset Splits\u003c/h3\u003e\u003ca id=\"user-content-preparing-dataset-splits\" class=\"anchor\" aria-label=\"Permalink: Preparing Dataset Splits\" href=\"#preparing-dataset-splits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe original datasets from which the train and test splits have been derived are provided via this repository as well.\nThe datasets are located in the \u003ccode\u003eoriginal_datasets/\u003c/code\u003e directory.\nIn order to derive the train and test dataset splits via a 10-fold cross-validation, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew generateDatasetSplits\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a directory \u003ccode\u003edatasets/\u003c/code\u003e, where the generated train and test splits are stored in seperated files.\nAs this procedure is done for all datasets contained in the \u003ccode\u003eoriginal_datasets/\u003c/code\u003e directory and seed combinations, this probably takes some time and disk space.\nThe generated files follow the name schema \u003ccode\u003e\u0026lt;DATASET\u0026gt;_\u0026lt;SEED\u0026gt;_\u0026lt;SPLIT INDEX\u0026gt;_{train|test}.arff\u003c/code\u003e. As the worker client relies on this naming, the schema must not be changed.\nUnfortunately, we cannot provide the dataset splits used in our study directly, as this would dramatically increase the size of the repository and lead to unreasonable download times for cloning.\nHowever, on request we can of course provide the original dataset splits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: We assume that all worker clients either have all the dataset splits locally available or share a network hard drive, centrally providing access to the respective dataset splits. The dataset folder can be configured in the \u003ccode\u003eautomlc-setup.properties\u003c/code\u003e file via the property \u003ccode\u003edatasetFolder\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning a Worker Client\u003c/h3\u003e\u003ca id=\"user-content-running-a-worker-client\" class=\"anchor\" aria-label=\"Permalink: Running a Worker Client\" href=\"#running-a-worker-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce everything is set up correctly, you may run a worker client via the command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew runExperimentEvaluationWorker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe execution of the worker will also rely on the database connection configured in the \u003ccode\u003eautomlc-setup.properties\u003c/code\u003e.\nHowever, there is nothing specific you need to configure when deploying the worker client in a distributed way.\nIn fact, you can simply run the same command multiple times (on different nodes) in order to parallelize the processing of the benchmark.\u003c/p\u003e\n\u003cp\u003eThe central database server will take care that each of the specified experiments will be executed only once at maximum, i.e. it will prevent the same experiment from being conducted twice.\nSince one worker client will also only take care of running a single experiment, you need to deploy as many worker clients as there are rows in the jobs table (named as you configured it in the properties file). In addition to the final results, the worker clients will also store intermediate evaluation results, i.e., candidates that have been requested for evaluation by the respective optimizer together with the measured performance value.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePost-Processing for Anytime Test Performances\u003c/h3\u003e\u003ca id=\"user-content-post-processing-for-anytime-test-performances\" class=\"anchor\" aria-label=\"Permalink: Post-Processing for Anytime Test Performances\" href=\"#post-processing-for-anytime-test-performances\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince assessing the test performances on-line, i.e. during an optimizer run, would distort the overall perception of the optimization performance, all the test performances for later on compiling anytime plots etc. have been estimated via a post-processing step based on the log of intermediate candidate evaluations.\nThe post-processing is also implemented in a distributed way, analoguous to the already explained setup for running the actual benchmark of optimizers. In contrast to the latter, for the post-processing the \u003ccode\u003etest-eval.properties\u003c/code\u003e serves as a configuration.\nObviously, the jobs table for the post-processing needs a different name than the one specified for the benchmark itself. Furthermore, it will also use the \u003ccode\u003eautomlc-setup.properties\u003c/code\u003e file for accessing the logged data and filtering the evaluated candidates.\nAdditionally, the post-processing requires access to the datasets directory.\u003c/p\u003e\n\u003cp\u003eAs before you can configure the corresponding properties, for which optimizers,datasets, measures, etc. you want to run the post-processing. Furthermore you can setup the jobs table for distributing the workload on a cluster etc. as already described before for the benchmark via the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew initializePostProcessingsInDatabase\n./gradlew cleanPostProcessingsInDatabase\n./gradlew runPostProcessingWorker\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing Benchmark Data\u003c/h2\u003e\u003ca id=\"user-content-visualizing-benchmark-data\" class=\"anchor\" aria-label=\"Permalink: Visualizing Benchmark Data\" href=\"#visualizing-benchmark-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThroughout the paper, several visualization of the results have been presented. A pre-processed version of the logged data is contained in the directory \u003ccode\u003eresults/data/\u003c/code\u003e.\nFrom this data, you can further process the data to derive the numbers presented in the paper. Plots that have been generated via LaTeX\u0027s \u003ccode\u003etikz\u003c/code\u003e or \u003ccode\u003epgfplots\u003c/code\u003e, you can even generate the corresponding LaTeX code in the following.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInspecting the Search Space\u003c/h3\u003e\u003ca id=\"user-content-inspecting-the-search-space\" class=\"anchor\" aria-label=\"Permalink: Inspecting the Search Space\" href=\"#inspecting-the-search-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe configuration files containing the specification of the search space are contained in the folder \u003ccode\u003esearchspace/\u003c/code\u003e. For this, the root file is \u003ccode\u003esearchspace/meka-all.json\u003c/code\u003e and (recursively) includes the remaining configuration files\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eList Algorithms Contained in the Search Space\u003c/h4\u003e\u003ca id=\"user-content-list-algorithms-contained-in-the-search-space\" class=\"anchor\" aria-label=\"Permalink: List Algorithms Contained in the Search Space\" href=\"#list-algorithms-contained-in-the-search-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you only want to obtain a list of algorithms ordered by algorithm type that are contained in the search space including their abbreviation as given in the paper, you can use the following short cut:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew exportAlgorithmsInSearchSpace\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a txt file in the \u003ccode\u003e/results\u003c/code\u003e directory with the name \u003ccode\u003esearchspace-algorithms-in-space.txt\u003c/code\u003e listing all the algorithm types together with the algorithms belonging to these types.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExport to Gephi to visualize the search space as a DAG\u003c/h4\u003e\u003ca id=\"user-content-export-to-gephi-to-visualize-the-search-space-as-a-dag\" class=\"anchor\" aria-label=\"Permalink: Export to Gephi to visualize the search space as a DAG\" href=\"#export-to-gephi-to-visualize-the-search-space-as-a-dag\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://gephi.org/\" rel=\"nofollow\"\u003eGephi\u003c/a\u003e is a graph modeling and visualization tool. You can export the search space as described in the \u003ccode\u003esearchspace/\u003c/code\u003e folder to the Gephi graph format to be loaded and visualized in Gephi. By running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew exportSearchSpacesToGephiFormat\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eyou will find a folder \u003ccode\u003eresults/gephi-export\u003c/code\u003e, containing three files: \u003ccode\u003eslc-only.gephi\u003c/code\u003e, \u003ccode\u003emlc-only.gephi\u003c/code\u003e, and \u003ccode\u003emlc-complete.gephi\u003c/code\u003e. These three files contain a Gephi specification of a directed acyclic graph (DAG) where each node corresponds to one algorithm in the search space and an edge a dependency relation from one to another algorithm indicating that the algorithm represented by the parent node can be configured with the child node as a base learner/kernel. With these graph visualizations one can easily see the exponential growth of the search space when combining the single-label classification algorithms with the multi-label classification algorithms.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate HTML Overview\u003c/h4\u003e\u003ca id=\"user-content-generate-html-overview\" class=\"anchor\" aria-label=\"Permalink: Generate HTML Overview\" href=\"#generate-html-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate an HTML document summarizing the search space in terms of some statistics, listing all included algorithms together with their hyper-parameters (including their types, domains, and defaults) and recursive dependencies on other algorithms. The possible choices for each dependency are listed and linked within the document accordingly.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew generateMultiLabelSearchSpaceDescription\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will generate a file \u003ccode\u003eresults/searchspace-meka.html\u003c/code\u003e. If you want to generate the same type of description for the single-label classification (WEKA) search space use the following.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew generateSingleLabelSearchSpaceDescription\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will generate a file \u003ccode\u003eresults/searchspace-weka.html\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe bar charts comparing the two search spaces in the paper have been generated from the statistical values contained on the top of these HTML documents. However, since the plots have been generated using \u003ccode\u003ematplotlibs\u003c/code\u003e in Python, please refer to the Jupyter notebook \u003ccode\u003eTPAMI Plots.ipynb\u003c/code\u003e for deriving the bar charts from these statistics.\nTherewith, you can obtain the comparison figure for the different search spaces.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEvaluation Results\u003c/h3\u003e\u003ca id=\"user-content-evaluation-results\" class=\"anchor\" aria-label=\"Permalink: Evaluation Results\" href=\"#evaluation-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe data obtained by running the benchmark across various datasets and folds can be found in \u003ccode\u003eresults/data\u003c/code\u003e. From this data you can generate several statistics, summaries, and plots. How these can be generated is explained in the following.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate One-VS-Rest Scatter Plots\u003c/h4\u003e\u003ca id=\"user-content-generate-one-vs-rest-scatter-plots\" class=\"anchor\" aria-label=\"Permalink: Generate One-VS-Rest Scatter Plots\" href=\"#generate-one-vs-rest-scatter-plots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the paper we compared the different optimizers in a one-vs-rest fashion plotting their performances against each other facilitating the analysis which approach performs preferably over the rest. The plots can be generated with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew compileScatterPlots\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe command will produce its output in the folder \u003ccode\u003eresults/scatter-plots\u003c/code\u003e, where afterwards you will find for each optimizer and performance measure (instance-wise F-measure, label-wise F-measure, and micro F-measure) you will find a LaTeX file named like this: \u003ccode\u003escatter-\u0026lt;optimizer\u0026gt;VSrest-\u0026lt;measure\u0026gt;.tex\u003c/code\u003e. Furthermore you can find a \u003ccode\u003emain.tex\u003c/code\u003e which will include all packages and scatter plots to compile them into a PDF document.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Anytime Average Rank Plots\u003c/h4\u003e\u003ca id=\"user-content-generate-anytime-average-rank-plots\" class=\"anchor\" aria-label=\"Permalink: Generate Anytime Average Rank Plots\" href=\"#generate-anytime-average-rank-plots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFurthermore, we have presented anytime average rank plots comparing the different optimizers across the time dimension. You can compile the result data into these anytime plots by executing the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew compileAnytimeAverageRankPlots\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will generate several \u003ccode\u003e.tex\u003c/code\u003e-files in the directory \u003ccode\u003eresults/anytime-plots/\u003c/code\u003e.\nIn fact, there are even two different types of plots: First following the naming schema \u003ccode\u003eavgrank-\u0026lt;MEASURE\u0026gt;.tex\u003c/code\u003e you will find the average rank plots as presented in the paper.\nHowever, for each combination of measure and dataset you can also find the actual average anytime performance of the different optimizers contained in the files named after the schema \u003ccode\u003e\u0026lt;MEASURE\u0026gt;_\u0026lt;DATASET\u0026gt;.tex\u003c/code\u003e.\nSince presenting those would have required lots of space, these plots have not been included in the paper but are made available here as a kind of supplementary material.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Result Tables\u003c/h4\u003e\u003ca id=\"user-content-generate-result-tables\" class=\"anchor\" aria-label=\"Permalink: Generate Result Tables\" href=\"#generate-result-tables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a comparison of the performance of eventually returned incumbents, we presented tables for each measure individually.\nYou can generate these tables yourself once again by running the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew compileResultTables\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will again generate \u003ccode\u003e.tex\u003c/code\u003e-files containing the LaTeX code to represent the corresponding table data. In addition to the average performances per dataset and optimizer, a Wilcoxon signed-rank test is conducted with a threshold for the p-value of 0.05. The best results, significant improvements and degradations are highlighted as described in the paper. Furthermore, in the last row of the tables, an average rank for each optimizer across all datasets is given.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Incumbent Frequency Statistics\u003c/h4\u003e\u003ca id=\"user-content-generate-incumbent-frequency-statistics\" class=\"anchor\" aria-label=\"Permalink: Generate Incumbent Frequency Statistics\" href=\"#generate-incumbent-frequency-statistics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnother figure in the paper shows which algorithms have been chosen with what frequency by which optimizer. Since these plots have been generated with the help of matplotlibs, here, we will only compile the necessary statistics from the data, necessary to produce the figures.\nIn order to compile the statistics from the result data, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./gradlew compileIncumbentFrequencyStatistics\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will generate a txt file \u003ccode\u003eresults/incumbent-frequencies.txt\u003c/code\u003e containing JSON arrays that can be copied and pasted into a Jupyter notebook which is also available via this repository. Please refer to the \u003ccode\u003eTPAMI Plots.ipynb\u003c/code\u003e notebook file for further processing of the compiled raw data into the nested donut charts.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1519702192.0 + "updated_at": 1706803145.0 }, { "data_format": 2, - "description": "Pipleine code for \"Sensitive identification of bacterial DNA in clinical specimens by broad range 16S rRNA enrichment\"", + "description": "UNDER CONSTRUCTION - Scripts for the workshop on OpenFOAM containers", "filenames": [ - "singularity/Singularity_ea-utils", - "singularity/Singularity_gappa", - "singularity/Singularity_krona", - "singularity/Singularity_htstream", - "singularity/Singularity_epa" + "04_buildingAnOpenFOAMContainer/openfoam-2.4.x/02_PortingToSingularity/Singularity.def" ], - "full_name": "nhoffman/16s-capture", + "full_name": "PawseySC/containers-openfoam-workshop-scripts", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWorkshop on the usage of OpenFOAM containers at Pawsey\u003c/h1\u003e\u003ca id=\"user-content-workshop-on-the-usage-of-openfoam-containers-at-pawsey\" class=\"anchor\" aria-label=\"Permalink: Workshop on the usage of OpenFOAM containers at Pawsey\" href=\"#workshop-on-the-usage-of-openfoam-containers-at-pawsey\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOrganisers\u003c/strong\u003e: Alexis Espinosa (PawseySC) and Marco De La Pierre (PawseySC)\u003c/p\u003e\n\u003cp\u003eThe use of containers has become an attractive framework for several areas of research supported by Pawsey (including bioinformatics and machine learning, among others).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNow, Pawsey supports the usage of OpenFOAM containers.\u003c/strong\u003e For the most recent versions of OpenFOAM (and some others), Pawsey have prebuilt and tested Singularity containers.\u003c/p\u003e\n\u003cp\u003eThis repository contains material for the exercises of the workshop.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep-by-step guide\u003c/strong\u003e: \u003ca href=\"https://pawseysc.github.io/containers-openfoam-workshop\" rel=\"nofollow\"\u003ehttps://pawseysc.github.io/containers-openfoam-workshop\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1677049221.0 + "updated_at": 1638345755.0 }, { "data_format": 2, - "description": "RepeatExplorer2 singularity definition", + "description": "Collection of Dockerfiles and Singularity recipes for PetIBM", "filenames": [ - "Singularity.0.3.8.dbaa07f", - "Singularity", - "Singularity.0.3.7.5996768" + "singularity/Singularity.0.5.4-HPCX207-CUDA102-bionic", + "singularity/Singularity.0.5.2-GPU-OpenMPI-centos7", + "singularity/Singularity.0.5.1-GPU-OpenMPI-xenial", + "singularity/Singularity.0.4.2-GPU-OpenMPI-xenial", + "singularity/Singularity.0.5.3-GPU-OpenMPI-focal", + "singularity/Singularity.0.5-GPU-OpenMPI-xenial" ], - "full_name": "repeatexplorer/repex_tarean", - "latest_release": "0.3.8.2", + "full_name": "barbagroup/petibm-recipes", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker and Singularity recipes for PetIBM\u003c/h1\u003e\u003ca id=\"user-content-docker-and-singularity-recipes-for-petibm\" class=\"anchor\" aria-label=\"Permalink: Docker and Singularity recipes for PetIBM\" href=\"#docker-and-singularity-recipes-for-petibm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/barbagroup/petibm-recipes/raw/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/439fd5ffe2ac21b3daa03ac6474978163be089da5fbba2790769ae2275464e35/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d425344253230332d2d436c617573652d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.docker.com/u/barbagroup/repository/docker/barbagroup/petibm\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e94e378ff6380fa9aff7c21b9318eea65a0c47459473fafade5317993ff7526/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65722d2d6875622d696e666f726d6174696f6e616c2e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/hosted-docker--hub-informational.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/3692\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains Dockerfiles and Singularity recipes used to build and share images of the runtime environment required for \u003ca href=\"https://github.com/barbagroup/PetIBM\"\u003ePetIBM\u003c/a\u003e.\nDockerfiles are located in the \u003ccode\u003edocker\u003c/code\u003e folder; Singularity recipes are in the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eDocker and Singularity images are available for:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePetIBM (0.4.2, 0.5, 0.5.1, 0.5.2, 0.5.3, 0.5.4)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo report bugs, submit questions, or offer suggestions, please use the GitHub issue tracking system.\nWe also welcome pull-requests.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1701766150.0 + "updated_at": 1647661524.0 }, { "data_format": 2, - "description": "Dockerized and singularity-friendly cancer genomics tools ", + "description": "Research template repository for the University of Pennsylvania CNT lab", "filenames": [ - "sclust/Singularity.sclust", - "vcf2maf/Singularity.vcf2maf", - "gistic2/Singularity.gistic2" + "core_libraries/subtrees/MRtrix3/Singularity", + "core_libraries/subtrees/PreQual/Singularity" ], - "full_name": "rdmorin/cancer_docker_singularity", + "full_name": "UPennBJPrager/CNT_Research_Template", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecancer_docker_singularity\u003c/h1\u003e\u003ca id=\"user-content-cancer_docker_singularity\" class=\"anchor\" aria-label=\"Permalink: cancer_docker_singularity\" href=\"#cancer_docker_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDockerized and singularity-friendly cancer genomics tools\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCNT Research Repository Template\u003c/h1\u003e\u003ca id=\"user-content-cnt-research-repository-template\" class=\"anchor\" aria-label=\"Permalink: CNT Research Repository Template\" href=\"#cnt-research-repository-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/004a49ac296a3a07d489192d357f2eb3bf4f113d9e09beda8ef0145c528f0d62/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e322e312d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/004a49ac296a3a07d489192d357f2eb3bf4f113d9e09beda8ef0145c528f0d62/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e322e312d626c7565\" alt=\"version\" data-canonical-src=\"https://img.shields.io/badge/version-0.2.1-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/08b2323eca8d03dc78847012206c948114fdcfa0eea71a46bcc3ffd71ac45cc9/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069702e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/08b2323eca8d03dc78847012206c948114fdcfa0eea71a46bcc3ffd71ac45cc9/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069702e737667\" alt=\"pip\" data-canonical-src=\"https://img.shields.io/pypi/v/pip.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c7b947b8ec2d00f5a40dcfee4cf551b025fba56093f7d011e4422126eafbccce/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f34\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c7b947b8ec2d00f5a40dcfee4cf551b025fba56093f7d011e4422126eafbccce/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f34\" alt=\"https://img.shields.io/pypi/pyversions/\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of this template is to consolidate shared libraries and enable consistent workflows and tests for most projects in the CNT lab. Users will be able to quickly load code from tested common libraries, or load their own personal code, in an object oriented manner.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePrerequisites\u003c/h1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use this repository, you must have access to either Python or Matlab.\u003c/p\u003e\n\u003cp\u003eWe also highly recommend the use of a virtual environment, conda environment, or similar software to manage distributions. Examples for their use can be found in the documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to install any of the common library code, we provide instructions for both Python and Matlab below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython\u003c/h2\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor python packages, python wheels and tarballs can be found in: CNT_Development/core_libraries/python/.\u003c/p\u003e\n\u003cp\u003eTo install, run:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epip install foo.whl\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eor\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003epip install foo.tar.gz\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere foo is the name of the library of interest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMatlab\u003c/h2\u003e\u003ca id=\"user-content-matlab\" class=\"anchor\" aria-label=\"Permalink: Matlab\" href=\"#matlab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd37\u200d\u2640\ufe0f In development.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocumentation\u003c/h1\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis template is intended to be used as both an environment and a simple wrapper for research code. Before beginning, we highly recommend that a virtual environment (or equivalent) is created for each\nproject to ensure your dependencies and code are properly referenced. Examples for creating virtual environments is provided below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRepository Structure\u003c/h2\u003e\u003ca id=\"user-content-repository-structure\" class=\"anchor\" aria-label=\"Permalink: Repository Structure\" href=\"#repository-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA hyperlink enabled repository tree is available within the \u003ca href=\"./repository_structure.md\"\u003erepository_structure\u003c/a\u003e markdown file. We demonstrate the use of git-ginored files and folders by displaying those\nentries with a \u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e symbol.\u003c/p\u003e\n\u003cp\u003eA short description of some of the top-level directories and files are as follows:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecore_libraries\u003c/h3\u003e\u003ca id=\"user-content-core_libraries\" class=\"anchor\" aria-label=\"Permalink: core_libraries\" href=\"#core_libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains the submodules and build files that make up the core libraries used for lab-wide projects.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edata_pointers\u003c/h3\u003e\u003ca id=\"user-content-data_pointers\" class=\"anchor\" aria-label=\"Permalink: data_pointers\" href=\"#data_pointers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains pointers to data contained on Borel and Lief. Data requests should reference these data pointers to prevent duplication before downloading new data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edocuments\u003c/h3\u003e\u003ca id=\"user-content-documents\" class=\"anchor\" aria-label=\"Permalink: documents\" href=\"#documents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains various research documents associated with a project (i.e. SoPs, Pipeline diagrams, etc.) as well as code documentation (e.g.document strings) for the various libraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eexamples\u003c/h3\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains example python and matlab scripts for various research tasks as well as how to use common libraries and environments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ereference_data\u003c/h3\u003e\u003ca id=\"user-content-reference_data\" class=\"anchor\" aria-label=\"Permalink: reference_data\" href=\"#reference_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains data that can be used for building targets or conducting unit tests.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esample_data\u003c/h3\u003e\u003ca id=\"user-content-sample_data\" class=\"anchor\" aria-label=\"Permalink: sample_data\" href=\"#sample_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains sample data that might be used in any of the lab-wide projects.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003escripts\u003c/h3\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-label=\"Permalink: scripts\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains user-defined scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eunit_tests\u003c/h3\u003e\u003ca id=\"user-content-unit_tests\" class=\"anchor\" aria-label=\"Permalink: unit_tests\" href=\"#unit_tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains unit tests for validating new/altered code at both the machine level and model level.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003euser_data\u003c/h3\u003e\u003ca id=\"user-content-user_data\" class=\"anchor\" aria-label=\"Permalink: user_data\" href=\"#user_data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder is meant to store user data. Data in this repository is private by default and will not be uploaded to public repositories.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e.gitignore\u003c/h3\u003e\u003ca id=\"user-content-gitignore\" class=\"anchor\" aria-label=\"Permalink: .gitignore\" href=\"#gitignore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis file helps prevent certain files from being uploaded to the public repository. This can be to avoid excess data volumes, or to protect sensitive information. By default, the ignored files and\nfolders are designed for the development of a lab-wide template, and users should adjust the settings to match their own needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVirtual Environments\u003c/h1\u003e\u003ca id=\"user-content-virtual-environments\" class=\"anchor\" aria-label=\"Permalink: Virtual Environments\" href=\"#virtual-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython\u003c/h2\u003e\u003ca id=\"user-content-python-1\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConda\u003c/h3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend using the pre-built environment files provided to start your project. These files can be found in the following subfolders: core_libraries/python/*/*yml and can be installed using the following command:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda env create -f foo.yml\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere foo is the name of the environment.\u003c/p\u003e\n\u003cp\u003eFor those who wish to create their own environment, we introduced some of the basics below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreation\u003c/h4\u003e\u003ca id=\"user-content-creation\" class=\"anchor\" aria-label=\"Permalink: Creation\" href=\"#creation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda create --name myenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere myenv is the name of the environment you wish to create.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eListing environments\u003c/h4\u003e\u003ca id=\"user-content-listing-environments\" class=\"anchor\" aria-label=\"Permalink: Listing environments\" href=\"#listing-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda env list\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eActivating Environment\u003c/h4\u003e\u003ca id=\"user-content-activating-environment\" class=\"anchor\" aria-label=\"Permalink: Activating Environment\" href=\"#activating-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda activate myenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ewhere myenv is the name of the environment you wish to activate.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDeactivating an environment\u003c/h4\u003e\u003ca id=\"user-content-deactivating-an-environment\" class=\"anchor\" aria-label=\"Permalink: Deactivating an environment\" href=\"#deactivating-an-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003econda deactivate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMore information\u003c/h4\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-label=\"Permalink: More information\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information, please read: \u003ca href=\"https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment\" rel=\"nofollow\"\u003ehttps://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVirtual Environment\u003c/h3\u003e\u003ca id=\"user-content-virtual-environment\" class=\"anchor\" aria-label=\"Permalink: Virtual Environment\" href=\"#virtual-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst make sure you have venv installed. If not, you can pip install it as follows: pip install venv\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreation\u003c/h4\u003e\u003ca id=\"user-content-creation-1\" class=\"anchor\" aria-label=\"Permalink: Creation\" href=\"#creation-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003epython3 -m venv /path/to/new/virtual/environment\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eListing environments\u003c/h4\u003e\u003ca id=\"user-content-listing-environments-1\" class=\"anchor\" aria-label=\"Permalink: Listing environments\" href=\"#listing-environments-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003elsvirtualenv\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYou may need to install virutalenvwrapper to use this command. ( pip install virtualenvwrapper. ) If it doesn\u0027t populate to your path, check the package directory for the executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eActivating Environment\u003c/h4\u003e\u003ca id=\"user-content-activating-environment-1\" class=\"anchor\" aria-label=\"Permalink: Activating Environment\" href=\"#activating-environment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esource /path/to/venv/bin/activate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDeactivating an environment\u003c/h4\u003e\u003ca id=\"user-content-deactivating-an-environment-1\" class=\"anchor\" aria-label=\"Permalink: Deactivating an environment\" href=\"#deactivating-an-environment-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003edeactivate\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(Type this command in your shell.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMatlab\u003c/h2\u003e\u003ca id=\"user-content-matlab-1\" class=\"anchor\" aria-label=\"Permalink: Matlab\" href=\"#matlab-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\ud83e\udd37\u200d\u2642\ufe0f\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContact Us\u003c/h1\u003e\u003ca id=\"user-content-contact-us\" class=\"anchor\" aria-label=\"Permalink: Contact Us\" href=\"#contact-us\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny questions should be directed to the data science team. Contact information is provided below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:bjprager@seas.upenn.edu\"\u003eBrian Prager\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"mailto:asuncion@seas.upenn.edu\"\u003eJoshua Asuncion\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1583188383.0 + "updated_at": 1684488681.0 }, { "data_format": 2, - "description": null, + "description": "Singularity containers", "filenames": [ - "Singularity" + "Singularity.py35", + "Singularity.py37_mcts~", + "Singularity.py35_mcts", + "Singularity.py36_pybullet_pybox2d_pytorch", + "Singularity.py37_pybullet~", + "Singularity.py37_mcts", + "Singularity.py37_pybullet" ], - "full_name": "lifebit-ai/rnaseq", + "full_name": "MicroSTM/singularity_containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/nfcore-rnaseq_logo.png\"\u003e\u003cimg src=\"docs/images/nfcore-rnaseq_logo.png\" alt=\"nfcore/rnaseq\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h1\u003e\u003ca id=\"\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/rnaseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/26868ee5edabf29f9e7fbff3f8ca28617a19f03c0074be305151b6892c028e5d/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f726e617365712e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/rnaseq.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db8c7781949b29a7482c1a6cb464dd81038c9c0ee59d101f26c71703292b1273/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/nf-core/Lobby\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8f568e122c7d924e46907e5e42ee55c2f8af216e082a028d29eba692736c2538/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769747465722d2532306a6f696e253230636861742532302545322538362539322d3466623939612e737667\" alt=\"Gitter\" data-canonical-src=\"https://img.shields.io/badge/gitter-%20join%20chat%20%E2%86%92-4fb99a.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/rnaseq/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90aa4d797a234aa5664e291671dac103ea72e08cdb05f0a0dd7159c34b9e8b68/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f726e617365712e737667\" alt=\"Docker Container available\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/rnaseq.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003enfcore/rnaseq\u003c/strong\u003e is a bioinformatics analysis pipeline used for RNA sequencing data.\u003c/p\u003e\n\u003cp\u003eThe workflow processes raw data from FastQ inputs (\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e, \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/\" rel=\"nofollow\"\u003eTrim Galore!\u003c/a\u003e), aligns the reads (\u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTAR\u003c/a\u003e or \u003ca href=\"https://ccb.jhu.edu/software/hisat2/index.shtml\" rel=\"nofollow\"\u003eHiSAT2\u003c/a\u003e), generates gene counts (\u003ca href=\"http://bioinf.wehi.edu.au/featureCounts/\" rel=\"nofollow\"\u003efeatureCounts\u003c/a\u003e, \u003ca href=\"https://ccb.jhu.edu/software/stringtie/\" rel=\"nofollow\"\u003eStringTie\u003c/a\u003e) and performs extensive quality-control on the results (\u003ca href=\"http://rseqc.sourceforge.net/\" rel=\"nofollow\"\u003eRSeQC\u003c/a\u003e, \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/dupRadar.html\" rel=\"nofollow\"\u003edupRadar\u003c/a\u003e, \u003ca href=\"http://smithlabresearch.org/software/preseq/\" rel=\"nofollow\"\u003ePreseq\u003c/a\u003e, \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/edgeR.html\" rel=\"nofollow\"\u003eedgeR\u003c/a\u003e, \u003ca href=\"http://multiqc.info/\" rel=\"nofollow\"\u003eMultiQC\u003c/a\u003e). See the \u003ca href=\"docs/output.md\"\u003eoutput documentation\u003c/a\u003e for more details of the results.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a bioinformatics workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nfcore/rnaseq pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/aws.md\"\u003eAmazon Web Services (aws)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/uppmax.md\"\u003eSwedish UPPMAX clusters\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/c3se.md\"\u003eSwedish cs3e Hebbe cluster\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/qbic.md\"\u003eT\u00fcbingen QBiC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/ccga.md\"\u003eCCGA Kiel\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese scripts were originally written for use at the \u003ca href=\"https://portal.scilifelab.se/genomics/\" rel=\"nofollow\"\u003eNational Genomics Infrastructure\u003c/a\u003e, part of \u003ca href=\"http://www.scilifelab.se/\" rel=\"nofollow\"\u003eSciLifeLab\u003c/a\u003e in Stockholm, Sweden, by Phil Ewels (\u003ca href=\"https://github.com/ewels\"\u003e@ewels\u003c/a\u003e) and Rickard Hammar\u00e9n (\u003ca href=\"https://github.com/Hammarn\"\u003e@Hammarn\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eMany thanks to other who have helped out along the way too, including (but not limited to):\n\u003ca href=\"https://github.com/Galithil\"\u003e@Galithil\u003c/a\u003e,\n\u003ca href=\"https://github.com/pditommaso\"\u003e@pditommaso\u003c/a\u003e,\n\u003ca href=\"https://github.com/orzechoj\"\u003e@orzechoj\u003c/a\u003e,\n\u003ca href=\"https://github.com/apeltzer\"\u003e@apeltzer\u003c/a\u003e,\n\u003ca href=\"https://github.com/colindaven\"\u003e@colindaven\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity containers\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 28, + "subscribers_count": 2, "topics": [], - "updated_at": 1552290431.0 + "updated_at": 1628067083.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipes for images containing R", "filenames": [ - "Singularity" + "Singularity.3.5.1", + "Singularity.3.6.0", + "Singularity.3.4.4", + "Singularity.3.5.0", + "Singularity", + "Singularity.2.15.3", + "Singularity.3.3.3" ], - "full_name": "ReproducibilityInPublishing/j.jcp.2016.08.012", + "full_name": "MPIB/singularity-r", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eA Fast Marching Algorithm for the Factored Eikonal Equation\u003c/h1\u003e\u003ca id=\"user-content-a-fast-marching-algorithm-for-the-factored-eikonal-equation\" class=\"anchor\" aria-label=\"Permalink: A Fast Marching Algorithm for the Factored Eikonal Equation\" href=\"#a-fast-marching-algorithm-for-the-factored-eikonal-equation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a Reproduction Package as described in the manuscript \"Three Empirical Principles\nfor Computational Reproducibility and their Implementation: \u003cem\u003eThe Reproduction Package\u003c/em\u003e\"\nby M. S. Krafczyk, A. Shi, A. Bhaskar, D. Marinov, \u0026amp; V. Stodden.\u003c/p\u003e\n\u003cp\u003eThis Reproduction Package includes the directory \u003ccode\u003eexpected_figures\u003c/code\u003e as well as \u003ccode\u003eexpected_output\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://dx.doi.org/10.1016/j.jcp.2016.08.012\" rel=\"nofollow\"\u003e\"A fast marching algorithm for the factored eikonal\nequation\"\u003c/a\u003e discusses an algorithm\nfor solving the factored eikonal equation. Practically, this can be used to\nperform a kind of slowness tomography whereby travel times from some source\nwithin a material can be used to determine the slowness of an object with\nrespect to position throughout the object.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild Instructions\u003c/h2\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-label=\"Permalink: Build Instructions\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the software in this package, all you need is an appropriate Docker installation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions were tested using\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker version 18.06.0-ce, build 0ffa825, on Ubuntu 16.04.5 LTS.\u003c/li\u003e\n\u003cli\u003eDocker version 19.03.1, build 74b1e89, on Mac OS Mojave (10.14.6)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding with Docker\u003c/h3\u003e\u003ca id=\"user-content-building-with-docker\" class=\"anchor\" aria-label=\"Permalink: Building with Docker\" href=\"#building-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Dockerfile handles installation of all necessary dependencies. Simply execute the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t ${DOCKER_IMAGE_NAME} .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Instructions\u003c/h2\u003e\u003ca id=\"user-content-run-instructions\" class=\"anchor\" aria-label=\"Permalink: Run Instructions\" href=\"#run-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the Docker container\u003c/h3\u003e\u003ca id=\"user-content-running-the-docker-container\" class=\"anchor\" aria-label=\"Permalink: Running the Docker container\" href=\"#running-the-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo start a container for the Docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm -v $(pwd):/Scratch ${DOCKER_IMAGE_NAME}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInitialize the Julia environment\u003c/h3\u003e\u003ca id=\"user-content-initialize-the-julia-environment\" class=\"anchor\" aria-label=\"Permalink: Initialize the Julia environment\" href=\"#initialize-the-julia-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore running the experiments, you must first run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./algo.sh init\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis downloads necessary Julia packages.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun the experiments\u003c/h3\u003e\u003ca id=\"user-content-run-the-experiments\" class=\"anchor\" aria-label=\"Permalink: Run the experiments\" href=\"#run-the-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run the experiments, you use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./algo.sh run \u0026lt;num 2d refinements\u0026gt; \u0026lt;num 3d refinements\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput will be written to \u003ccode\u003eresults/results_\u0026lt;x\u0026gt;_\u0026lt;y\u0026gt;.txt\u003c/code\u003e where \u003ccode\u003ex\u003c/code\u003e is \u003ccode\u003enum 2d refinements\u003c/code\u003e and \u003ccode\u003ey\u003c/code\u003e is \u003ccode\u003enum 3d refinements\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis command also creates the figures for the experiment. They can be found in \u003ccode\u003efigures/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCheck the experiment results\u003c/h3\u003e\u003ca id=\"user-content-check-the-experiment-results\" class=\"anchor\" aria-label=\"Permalink: Check the experiment results\" href=\"#check-the-experiment-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to check the results of the experiment, you use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./algo.sh check \u0026lt;num 2d refinements\u0026gt; \u0026lt;num 3d refinements\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe expected output to be compared against is found in \u003ccode\u003eexpected_output/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe expected figures are found in \u003ccode\u003eexpected_figures/\u003c/code\u003e, however the script does not compare these with the generated figures.\u003c/p\u003e\n\u003cp\u003eThis command will let you know if the results match what is expected or not.\u003c/p\u003e\n\u003cp\u003eThe script that checks equality to within a reasonable degree is \u003ccode\u003eexamples/check_results.py\u003c/code\u003e. If you wish to tighten or loosen the margin of error, modify that file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Everything\u003c/h3\u003e\u003ca id=\"user-content-run-everything\" class=\"anchor\" aria-label=\"Permalink: Run Everything\" href=\"#run-everything\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo initilize, run the experiments, and check the results with one command, use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./algo.sh all \u0026lt;num 2d refinements\u0026gt; \u0026lt;num 3d refinements\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease be aware of computational efforts for the scripts. More details can be found \u003ca href=\"COMPUTATIONAL_EFFORTS.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNotes about Running All Rows\u003c/h3\u003e\u003ca id=\"user-content-notes-about-running-all-rows\" class=\"anchor\" aria-label=\"Permalink: Notes about Running All Rows\" href=\"#notes-about-running-all-rows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo reproduce all 6 rows from the article, use \u003ccode\u003e6\u003c/code\u003e as both \u003ccode\u003enum 2d refinements\u003c/code\u003e and \u003ccode\u003enum 3d refinements\u003c/code\u003e. Note that it does consume a lot of memory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReproduction Notes\u003c/h2\u003e\u003ca id=\"user-content-reproduction-notes\" class=\"anchor\" aria-label=\"Permalink: Reproduction Notes\" href=\"#reproduction-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe kept track of our progress and issues inside \u003ccode\u003enotes.txt\u003c/code\u003e. We also have an\njupyter notebook showing this progress over time \u003ccode\u003eReproducibilityPlot.ipynb\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe want acknowledge the authors for their fine work on this experiment. We\nsucceeded with this project where many others had failed. The authors should be commended on putting together high quality work.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGrant Acknowledgement\u003c/h2\u003e\u003ca id=\"user-content-grant-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Grant Acknowledgement\" href=\"#grant-acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was partially funded by NSF grants OAC-1839010 and CNS-1646305.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-r\u003c/h1\u003e\u003ca id=\"user-content-singularity-r\" class=\"anchor\" aria-label=\"Permalink: singularity-r\" href=\"#singularity-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/623\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipes for base-images containing R.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR is installed from source which is downloaded from \u003ca href=\"https://cran.r-project.org/\" rel=\"nofollow\"\u003eCRAN\u003c/a\u003e (Comprehensive R Archive Network).\u003c/li\u003e\n\u003cli\u003ePackages needed to build R and those needed to run R like gfortran, g++ and gcc are installed through the debian repositories via \u003ccode\u003eapt-get install\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAt the end of the build process the images is cleaned up by:\n\u003cul\u003e\n\u003cli\u003eremoving packages only needed for the build,\u003c/li\u003e\n\u003cli\u003eremoving the package cache,\u003c/li\u003e\n\u003cli\u003eremoving downloaded files used for the build.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [], - "updated_at": 1625533090.0 + "updated_at": 1581391833.0 }, { "data_format": 2, - "description": "singularity container for software development", + "description": "Implementation of the Iterative Boltzmann Inversion for statistical analysis.", "filenames": [ - "Singularity" + "iterboltz-container/Singularity.def" ], - "full_name": "cshelton/devcontainer", + "full_name": "2bys/ibi", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edevcontainer\u003c/h1\u003e\u003ca id=\"user-content-devcontainer\" class=\"anchor\" aria-label=\"Permalink: devcontainer\" href=\"#devcontainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity container for software development\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIterative Boltzmann Inversion\u003c/h1\u003e\u003ca id=\"user-content-iterative-boltzmann-inversion\" class=\"anchor\" aria-label=\"Permalink: Iterative Boltzmann Inversion\" href=\"#iterative-boltzmann-inversion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImplementation of the Iterative Boltzmann Inversion for statistical analysis.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run the code?\u003c/h2\u003e\u003ca id=\"user-content-how-to-run-the-code\" class=\"anchor\" aria-label=\"Permalink: How to run the code?\" href=\"#how-to-run-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a config file (see example files).\u003c/li\u003e\n\u003cli\u003eGo to folder \u0027iterboltz-container\u0027 build singularity container by, e.g.,\nsingularity build portable Singulartiy.def\u003c/li\u003e\n\u003cli\u003eRun all config files in config folder with\npython3 manage_runs.py\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1693145631.0 + "updated_at": 1689353097.0 }, { "data_format": 2, - "description": "A test of singularity with conda", + "description": "Containerized image processing tools for JACS", "filenames": [ - "Singularity" + "aligner_jrc2018_20x_40x/Singularity", + "aligner_vnc2018_20x_40x/Singularity", + "aligner_jrc2018_20x_gen1/Singularity", + "aligner_jrc2018_63x/Singularity", + "aligner_yuy_20x63xpair/Singularity", + "aligner_yuy/Singularity", + "aligner_yuy_legacy/Singularity", + "aligner_vnc2017_20x/Singularity", + "aligner_vnc2018_63x/Singularity" ], - "full_name": "s-andrews/singularityconda", + "full_name": "JaneliaSciComp/jacs-tools", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJACS Tools\u003c/h1\u003e\u003ca id=\"user-content-jacs-tools\" class=\"anchor\" aria-label=\"Permalink: JACS Tools\" href=\"#jacs-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains tools which JACS runs on the cluster to process data.\u003c/p\u003e\n\u003cp\u003eEach sub directory contains a single tool which can be built into a Singularity container.\u003c/p\u003e\n\u003cp\u003eFor information on how to create a new tool, read about \u003ca href=\"CONTRIBUTING.md\"\u003eContributing\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build one or more tools:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage.sh build [tool1] [tool2] [tool3] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis creates a set of corresponding img files in the build directory which can be run with Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShell\u003c/h2\u003e\u003ca id=\"user-content-shell\" class=\"anchor\" aria-label=\"Permalink: Shell\" href=\"#shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo open a shell into a built container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage.sh shell [tool]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRegression Tests\u003c/h2\u003e\u003ca id=\"user-content-regression-tests\" class=\"anchor\" aria-label=\"Permalink: Regression Tests\" href=\"#regression-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRegression tests can be added to each container under the \"test\" directory. Each sub-directory in the \"tests\" directory is\nconsidered to be a standalone test. A file called test.sh must be placed in each test directory. To run tests:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage test [tool1] [tool2] [tool3] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo deploy a built container to another location, you must first define the target location in your environment,\ne.g. in your ~/.bashrc file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport JACS_SINGULARITY_DIR=/groups/jacs/jacsDev/servers/jacs-data/executables/singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage.sh deploy [tool1] [tool2] [tool3] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClean\u003c/h2\u003e\u003ca id=\"user-content-clean\" class=\"anchor\" aria-label=\"Permalink: Clean\" href=\"#clean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can delete existing builds for one or more containers with the \u003ccode\u003eclean\u003c/code\u003e command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manage.sh clean [tool1] [tool2] [tool3] ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersioning\u003c/h2\u003e\u003ca id=\"user-content-versioning\" class=\"anchor\" aria-label=\"Permalink: Versioning\" href=\"#versioning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer versioning is done in the Singularity build file. When making changes to a container, make sure to increment the\nVERSION variable in the Singularity file before building or deploying that container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use container named \u0026lt;container.img\u0026gt; which contains an app called you can invoke Singularity as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eB1=/external/path1\nB2=/external/path2\nsingularity run -B $B1 -B $B2 --app appName container.img -i $B1 -o $B2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll external paths which the container needs to access must be mounted with -B flags.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.janelia.org/open-science/software-licensing\" rel=\"nofollow\"\u003eJanelia Open Source License\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 4, + "subscribers_count": 8, "topics": [], - "updated_at": 1548255424.0 + "updated_at": 1705157162.0 }, { "data_format": 2, - "description": "An abricate singularity recipe", + "description": null, "filenames": [ - "Singularity", - "v0.8/DB20180717/Singularity.v0.8_DB20180717" + "Singularity.11.5", + "Singularity.12.0", + "Singularity.9.6.15", + "Singularity.10.10" ], - "full_name": "phgenomics-singularity/abricate", + "full_name": "ddbj/singularity_postgresql", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbricate --- A Singularity Container\u003c/h1\u003e\u003ca id=\"user-content-abricate-----a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Abricate --- A Singularity Container\" href=\"#abricate-----a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1288\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for \u003ca href=\"https://github.com/tseemann/abricate\"\u003eAbricate\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ePull the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name TMP_DIRECTORY shub://phgenomics-singularity/Abricate@latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003eAbricate.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./Abricate.simg --help\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name mlst shub://phgenomics-singularityAbricate@VERSION.NUMBER\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir HOME/singularity\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name Abricate shub://phgenomics-singularity/Abricate@latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLink the image to a folder in your \u003ccode\u003ePATH\u003c/code\u003e (e.g., \u003ccode\u003eHOME/bin\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eln -s HOME/singularity/Abricate.simg HOME/bin/Abricate\n\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e abricate --help\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdating the DB\u003c/h2\u003e\u003ca id=\"user-content-updating-the-db\" class=\"anchor\" aria-label=\"Permalink: Updating the DB\" href=\"#updating-the-db\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eRun the \u003ccode\u003eupdate_db.py\u003c/code\u003e script (default version is 0.8 at the moment)\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_postgresql\u003c/h1\u003e\u003ca id=\"user-content-singularity_postgresql\" class=\"anchor\" aria-label=\"Permalink: singularity_postgresql\" href=\"#singularity_postgresql\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u907a\u4f1d\u7814\u30b9\u30d1\u30b3\u30f3\u3067\u30e6\u30fc\u30b6\u30fc\u6a29\u9650\u3067PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306esingularity image\u306e\u4f7f\u3044\u65b9\u003c/p\u003e\n\u003cp\u003e\u5bfe\u5fdc\u3059\u308bPostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e9.6.15\u003c/li\u003e\n\u003cli\u003e10.10\u003c/li\u003e\n\u003cli\u003e11.5\u003c/li\u003e\n\u003cli\u003e12.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u751f\u6210\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u751f\u6210\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u751f\u6210\" href=\"#image\u306e\u751f\u6210\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u81ea\u5206\u306e\u74b0\u5883\u3067image\u3092build\u3059\u308b\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u306f9.6.15, 10.10, 11.5, 12.0\u306e\u3044\u305a\u308c\u304b\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ddbj/singularity_postgresql.git\n$ cd singularity_postgresql\n$ sudo singularity build ubuntu-18.04-postgresql-\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;.simg Singularity.\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eimage\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/h2\u003e\u003ca id=\"user-content-image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" class=\"anchor\" aria-label=\"Permalink: image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" href=\"#image\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Hub\u306b\u767b\u9332\u3055\u308c\u305f\u30a4\u30e1\u30fc\u30b8\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u306f9.6.15, 10.10, 11.5, 12.0\u306e\u3044\u305a\u308c\u304b\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/ddbj/singularity_postgresql.git\n$ cd singularity_postgresql\n$ singularity pull --name ubuntu-18.04-postgresql-\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;.simg shub://ddbj/singularity_postgresql:\u0026lt;PostgreSQL\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u751f\u6210\u307e\u305f\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u305f\u30a4\u30e1\u30fc\u30b8\u3060\u3051\u3067\u306fPostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u5b9f\u884c\u3067\u304d\u307e\u305b\u3093\u3002 start_container.sh\u3092\u5b9f\u884c\u3057\u3066singularity instance\u3092\u8d77\u52d5\u3057\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u521d\u671f\u5316\u3092\u884c\u3044\u307e\u3059\u3002\n\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u524d\u306b\u3001\u81ea\u5206\u306e\u74b0\u5883\u306b\u5408\u308f\u305b\u3066 start_container.sh \u306e CONTAINER_HOME, IMAGE, INSTANCE, PORT\u5909\u6570\u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCONTAINER_HOME\u306b\u306fgit clone\u3067\u3067\u304d\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u3092\u8a18\u8f09\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003cli\u003eIMAGE\u306b\u306f\u3001image\u751f\u6210\u307e\u305f\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u306e\u969b\u306b\u6307\u5b9a\u3057\u305f\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u8a18\u8f09\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003cli\u003ePORT\u5909\u6570\u306f5000\u4ee5\u4e0a\u3067\u4efb\u610f\u306e\u6574\u6570\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u30b7\u30a7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3059\u308b\u3068\u3001\u521d\u56de\u5b9f\u884c\u6642\u306b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u521d\u671f\u5316\u304c\u884c\u308f\u308c\u305f\u5f8c\u3067\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b5\u30fc\u30d0\u304c\u8d77\u52d5\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ bash start_container.sh\nThe files belonging to this database system will be owned by user \"\u0026lt;\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u30e6\u30fc\u30b6\u30fc\u0026gt;\".\nThis user must also own the server process.\n\nThe database cluster will be initialized with locale \"C\".\nThe default text search configuration will be set to \"english\".\n\nData page checksums are disabled.\n\nfixing permissions on existing directory /usr/local/pgsql12/data ... ok\ncreating subdirectories ... ok\nselecting dynamic shared memory implementation ... posix\nselecting default max_connections ... 100\nselecting default shared_buffers ... 128MB\nselecting default time zone ... Japan\ncreating configuration files ... ok\nrunning bootstrap script ... ok\nperforming post-bootstrap initialization ... ok\nsyncing data to disk ... ok\n\ninitdb: warning: enabling \"trust\" authentication for local connections\nYou can change this by editing pg_hba.conf or using the option -A, or\n--auth-local and --auth-host, the next time you run initdb.\n\nSuccess. You can now start the database server using:\n\n pg_ctl -D /usr/local/pgsql12/data -l logfile start\n\nStopping pgsql instance of /gpfs1/lustre2/home/y-okuda/git/singularity_postgresql/ubuntu-18.04-postgresql-12.0.simg (PID=36513)\nwaiting for server to start.... done\nserver started\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u8a2d\u5b9a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity instance\u3092\u8d77\u52d5\u3057\u305f\u30e6\u30fc\u30b6\u30fc\uff08initdb\u3092\u5b9f\u884c\u3057\u305f\u30e6\u30fc\u30b6\u30fc\uff09\u304cPostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30b9\u30fc\u30d1\u30fc\u30e6\u30fc\u30b6\u30fc\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec instance://pgsql psql -d postgres -p 55432\npsql (12.0)\nType \"help\" for help.\n\npostgres=# alter role \"\u0026lt;\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u5b9f\u884c\u30e6\u30fc\u30b6\u30fc\u0026gt;\" with password \u0027\u0026lt;\u30d1\u30b9\u30ef\u30fc\u30c9\u0026gt;\u0027;\nALTER ROLE\npostgres=# \\q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\u003c/h2\u003e\u003ca id=\"user-content-postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\" href=\"#postgresql\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f7f\u7528\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30d1\u30b9\u30ef\u30fc\u30c9\u306e\u8a2d\u5b9a\u306b\u3088\u308asingularity instance\u306e\u5916\u304b\u3089\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u30a2\u30af\u30bb\u30b9\u306e\u969b\u306f-h\u30aa\u30d7\u30b7\u30e7\u30f3\u3067singularity instance\u3092\u5b9f\u884c\u3057\u3066\u3044\u308b\u30db\u30b9\u30c8\u540d\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ psql -d postgres -p 55432 -h at043\n\u30d1\u30b9\u30ef\u30fc\u30c9: \npsql (9.2.24, \u30b5\u30fc\u30d0\u30fc 12.0)\n\u6ce8\u610f\uff1a psql \u30d0\u30fc\u30b8\u30e7\u30f3 9.2, \u30b5\u30fc\u30d0\u30fc\u30d0\u30fc\u30b8\u30e7\u30f3 12.0.\n psql \u306e\u6a5f\u80fd\u306e\u4e2d\u3067\u3001\u52d5\u4f5c\u3057\u306a\u3044\u3082\u306e\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\n\"help\" \u3067\u30d8\u30eb\u30d7\u3092\u8868\u793a\u3057\u307e\u3059.\n\npostgres=# \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u5225\u30ce\u30fc\u30c9\u304b\u3089\u306e\u30a2\u30af\u30bb\u30b9\u3082\u53ef\u80fd\u3067\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh at044\nLast login: Fri Nov 1 20:25:26 2019 from at043\n$ psql -d postgres -p 55432 -h at043\n\u30d1\u30b9\u30ef\u30fc\u30c9: \npsql (9.2.24, \u30b5\u30fc\u30d0\u30fc 12.0)\n\u6ce8\u610f\uff1a psql \u30d0\u30fc\u30b8\u30e7\u30f3 9.2, \u30b5\u30fc\u30d0\u30fc\u30d0\u30fc\u30b8\u30e7\u30f3 12.0.\n psql \u306e\u6a5f\u80fd\u306e\u4e2d\u3067\u3001\u52d5\u4f5c\u3057\u306a\u3044\u3082\u306e\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\n\"help\" \u3067\u30d8\u30eb\u30d7\u3092\u8868\u793a\u3057\u307e\u3059.\n\npostgres=# \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u63d0\u4f9b\u3057\u3066\u3044\u308bpg_hba.conf\u306e\u8a18\u8ff0\u3067\u306f\u30a2\u30af\u30bb\u30b9\u53ef\u80fd\u306aIP\u30a2\u30c9\u30ec\u30b9\u306b\u5236\u9650\u304c\u304b\u304b\u3063\u3066\u3044\u307e\u305b\u3093\u3002\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 7, "topics": [], - "updated_at": 1564581420.0 + "updated_at": 1586795356.0 }, { "data_format": 2, - "description": null, + "description": "Simulation Science research team repo for probabilistic record linkage project", "filenames": [ - "Singularity" + "linkage/person_linkage_case_study/Singularity", + "linkage/ecosystem_dummy_containers/python_pandas/Singularity", + "linkage/ecosystem_dummy_containers/r/Singularity", + "linkage/ecosystem_dummy_containers/python_pyspark/Singularity" ], - "full_name": "marchoeppner/clinical-metagenomics", + "full_name": "ihmeuw/vivarium_research_prl", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIKMB Metagenomic profiling pipeline\u003c/h1\u003e\u003ca id=\"user-content-ikmb-metagenomic-profiling-pipeline\" class=\"anchor\" aria-label=\"Permalink: IKMB Metagenomic profiling pipeline\" href=\"#ikmb-metagenomic-profiling-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipelines analyses short reads and identifies the most likely species in the respective sample.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecuting the pipeline\u003c/h2\u003e\u003ca id=\"user-content-executing-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Executing the pipeline\" href=\"#executing-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline requires Nextflow \u0026gt;= 0.30.2. All dependencies are provided through Bioconda. Please make sure that conda/miniconda2 are available before starting the pipeline.\u003c/p\u003e\n\u003cp\u003ePlease note that Pathoscope, Metaphlan and Kaiju require reference databases which are \u003cem\u003enot\u003c/em\u003e included with the Bioconda packages. On RZCluster, these are\navailable automatically through the included config file.\u003c/p\u003e\n\u003cp\u003eThis pipeline uses a CSV formatted file as input, using the following columns (separated by \u0027;\u0027):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epatientID\u003c/li\u003e\n\u003cli\u003esampleID\u003c/li\u003e\n\u003cli\u003esample type (e.g. Blood)\u003c/li\u003e\n\u003cli\u003eread type (e.g. 2x150bp)\u003c/li\u003e\n\u003cli\u003eplatform (e.g. HiSeq 4000)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA simple script to create such a basic input format from a folder of FastQ files is included in the bin/ subfolder.\u003c/p\u003e\n\u003cp\u003eTo run the pipeline, do:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow -c nextflow.config run main.nf --samples Samples.csv\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhere nextflow.config and main.nf need to be passed with their full path to the local git clone.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003evivarium_research_prl\u003c/h1\u003e\u003ca id=\"user-content-vivarium_research_prl\" class=\"anchor\" aria-label=\"Permalink: vivarium_research_prl\" href=\"#vivarium_research_prl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode used by the IHME Simulation Science reasearch team for the US Census entity resolution/probabilistic record linkage project.\u003c/p\u003e\n\u003cp\u003eOnce you have cloned the repository and activated a conda environment, you can run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efrom within the root directory \u003ccode\u003evivarium_research_prl\u003c/code\u003e in order to get an \u003ca href=\"https://stackoverflow.com/questions/35064426/when-would-the-e-editable-option-be-useful-with-pip-install\" rel=\"nofollow\"\u003eeditable pip installation\u003c/a\u003e for local development.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [], - "updated_at": 1603659103.0 + "updated_at": 1730742571.0 }, { "data_format": 2, - "description": "One metagenome pipeline to rule them all - this nextflow pipeline is a complete end-to-end metagenomic solution creating visualisations of taxonomic data from fastq input files, by utilising mmseq", + "description": "Examples of bioinformatics pipeline languages: Nextflow, Snakemake, and CWL", "filenames": [ - "Singularity" + "containers/Singularity" ], - "full_name": "lifebit-ai/onemetagenome", + "full_name": "CSUBioinformatics/pipeline-examples", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/onemetagenome\u003c/h1\u003e\u003ca id=\"user-content-nf-coreonemetagenome\" class=\"anchor\" aria-label=\"Permalink: nf-core/onemetagenome\" href=\"#nf-coreonemetagenome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOne metagenome pipeline to rule them all - this nextflow pipeline is a complete end-end metagenomic solution creating visulations of taxonomic data from fastq input files, by utilising plass and mmseq tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/onemetagenome\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6cf9eeca9afa6d30ca877f3deeabde9cf8d075b26065a1dfeea39db0904e44b5/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6f6e656d65746167656e6f6d652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/onemetagenome.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/780f0e426d3a9fd5f3f54407686be63867cb8093d09e36c9bcbad58b728a111d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/onemetagenome\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/326409a97c5cebd433effd380b14db006172c51f8130eb0f595df496e62cd4a5/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6f6e656d65746167656e6f6d652e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/onemetagenome.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/onemetagenome pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epipeline-examples\u003c/h1\u003e\u003ca id=\"user-content-pipeline-examples\" class=\"anchor\" aria-label=\"Permalink: pipeline-examples\" href=\"#pipeline-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExamples of bioinformatics pipeline languages: Nextflow, Snakemake, and CWL\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1552370066.0 + "updated_at": 1724128150.0 }, { "data_format": 2, - "description": "Singularity bootstrap file for uboonecode container", + "description": "Singularity base container with Nix to be used in XSEDE compute environment (currently in development)", "filenames": [ "Singularity" ], - "full_name": "LArbys/singularity-sl6-uboonecode", + "full_name": "XSEDE/singularity-nix-base", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-sl6-uboonecode\u003c/h1\u003e\u003ca id=\"user-content-singularity-sl6-uboonecode\" class=\"anchor\" aria-label=\"Permalink: singularity-sl6-uboonecode\" href=\"#singularity-sl6-uboonecode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains a script for building a uboonecode container.\u003c/p\u003e\n\u003cp\u003eIt uses \u003ccode\u003epullProducts\u003c/code\u003e, a script that untars binaries for a specific uboonecode version into a folder in the container, \u003ccode\u003e/products\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo build the version of your choice, go into the file, \u003ccode\u003eSingularity\u003c/code\u003e, and change the line\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./pullProducts /products slf6 uboone-v06_26_01_14 e10 prof\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote, this applies to other sci-soft products as well. List of products \u003ca href=\"http://scisoft.fnal.gov/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOnce in the container, you can set it up and run \u003ccode\u003elar\u003c/code\u003e by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /products/setup\nsetup uboonecode [version] -q [qualifier]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also develop with the container!!!\u003c/p\u003e\n\u003cp\u003eTo setup an MRB build environment for the first time (for uboonecode):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource /products/setup\nsetup uboonecode [version] -q [qualifier]\n\nmkdir [my build dir]\ncd [my build dir]\n\nsetup mrb\nexport MRB_PROJECT=larsoft\nmrb newDev\nsource localProducts-xxxxxx/setup\ncd srcs\nmrb g uboonecode\n\nmrbsetenv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNotes\u003c/h3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAs of 4/23/18, building a uboonecode image was about 3.9 GB.\u003c/li\u003e\n\u003cli\u003eeventdump.fcl works\u003c/li\u003e\n\u003cli\u003erunning DL_br branch to make larcv files work!\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-nix-base\u003c/h1\u003e\u003ca id=\"user-content-singularity-nix-base\" class=\"anchor\" aria-label=\"Permalink: singularity-nix-base\" href=\"#singularity-nix-base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4462\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity base container with Nix to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, - "topics": [], - "updated_at": 1617400552.0 + "subscribers_count": 16, + "topics": [ + "nix", + "singularity", + "singularity-nix" + ], + "updated_at": 1715213020.0 }, { "data_format": 2, @@ -29064,1410 +28832,1779 @@ var data = "filenames": [ "Singularity" ], - "full_name": "CNCLgithub/rooms-psiturk", + "full_name": "SupercomputingWales/TensorFlow-GPU-Example-Singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePsiturk Experiment`\u003c/h1\u003e\u003ca id=\"user-content-psiturk-experiment\" class=\"anchor\" aria-label=\"Permalink: Psiturk Experiment`\" href=\"#psiturk-experiment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePsiturk experiment used in Galileo (response slider) style experiments\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003esingularity\u003c/li\u003e\n\u003cli\u003ewget\u003c/li\u003e\n\u003cli\u003etar\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esetup\u003c/h3\u003e\u003ca id=\"user-content-setup-1\" class=\"anchor\" aria-label=\"Permalink: setup\" href=\"#setup-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esee help\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x setup.sh\n./setup.sh --help\n./setup.sh cont data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis setup file will, by default, pull a container and data files from box.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning psiturk\u003c/h2\u003e\u003ca id=\"user-content-running-psiturk\" class=\"anchor\" aria-label=\"Permalink: Running psiturk\" href=\"#running-psiturk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x start_psiturk.sh\n./start_psiturk.sh on\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAPI\u003c/h2\u003e\u003ca id=\"user-content-api\" class=\"anchor\" aria-label=\"Permalink: API\" href=\"#api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etask.js\u003c/h3\u003e\u003ca id=\"user-content-taskjs\" class=\"anchor\" aria-label=\"Permalink: task.js\" href=\"#taskjs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe majority of the experiment\u0027s functionality is described in \u003ccode\u003epsiturk/static/js/task.js\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe main class used to setup pages for both the experiment and instructions is defined as \u003ccode\u003ePage\u003c/code\u003e.\n\u003ccode\u003ePage\u003c/code\u003e handles both media presentation and scale setup. See the docstrings for more info.\u003c/p\u003e\n\u003cp\u003eThere are three other main elements, \u003ccode\u003eInstructionRunner\u003c/code\u003e, \u003ccode\u003eQuiz\u003c/code\u003e, and \u003ccode\u003eExperiment\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecss and html\u003c/h3\u003e\u003ca id=\"user-content-css-and-html\" class=\"anchor\" aria-label=\"Permalink: css and html\" href=\"#css-and-html\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main html files are located under \u003ccode\u003epsiturk/templates/\u003c/code\u003e and css is under \u003ccode\u003epsiturk/static/css\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNotabley, \u003ccode\u003estage.html\u003c/code\u003e describes the pages for experimental trials and \u003ccode\u003eslider.css\u003c/code\u003e describes some of the elements found in the scale.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTensorFlow-GPU-Example\u003c/h1\u003e\u003ca id=\"user-content-tensorflow-gpu-example\" class=\"anchor\" aria-label=\"Permalink: TensorFlow-GPU-Example\" href=\"#tensorflow-gpu-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn simple example of how to use Tensorflow with Anaconda, Python and GPU on Super Computing Wales.\u003c/p\u003e\n\u003cp\u003eNote that this example sets up an Anaconda environment which takes around 40,000 files. The default quota on Super Computing Wales is only 100,000 files, please delete or achive some files before running this if you have more than 60,000 files already.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/SupercomputingWales/TensorFlow-GPU-Example\ncd TensorFlow-GPU-Example\n./tensorflow_setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the example\u003c/h2\u003e\u003ca id=\"user-content-running-the-example\" class=\"anchor\" aria-label=\"Permalink: Running the example\" href=\"#running-the-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esbatch TensorFlow-GPU-Example.slurm\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis should result in tensorflow_gpu_demo.out.XXXXX and tensorflow_gpu_demo.err.XXXXX (where XXXXX is the job number) files being created. The output file should contain something like:\u003c/p\u003e\n\u003cpre lang=\"Extracting\"\u003e\u003ccode\u003eExtracting /home/a.cos/neuralnet_data/train-labels-idx1-ubyte.gz\nExtracting /home/a.cos/neuralnet_data/t10k-images-idx3-ubyte.gz\nExtracting /home/a.cos/neuralnet_data/t10k-labels-idx1-ubyte.gz\nEpoch: 0001 cost=316.534357009\nEpoch: 0002 cost=99.598349252\nEpoch: 0003 cost=72.017426792\nEpoch: 0004 cost=57.256541054\nEpoch: 0005 cost=48.566505551\nEpoch: 0006 cost=42.126385042\nEpoch: 0007 cost=38.068715960\nEpoch: 0008 cost=33.654687686\nEpoch: 0009 cost=30.905442436\nEpoch: 0010 cost=28.964738740\nEpoch: 0011 cost=27.089578144\nEpoch: 0012 cost=25.210279087\nEpoch: 0013 cost=24.343561058\nEpoch: 0014 cost=22.425776573\nEpoch: 0015 cost=21.565193366\nOptimization Finished!\nAccuracy: 0.8936\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVerifying the GPU was used\u003c/h2\u003e\u003ca id=\"user-content-verifying-the-gpu-was-used\" class=\"anchor\" aria-label=\"Permalink: Verifying the GPU was used\" href=\"#verifying-the-gpu-was-used\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe error log should contain a line similar to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCreated TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14873 MB memory) -\u0026gt; physical GPU (device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:3b:00.0, compute capability: 7.0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also SSH to the node and run the nvidia-smi command.\nThis can be done in one command with:\n\u003ccode\u003essh $(squeue -u $USER --format=%N | tail -1) nvidia-smi\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis should show something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 396.37 Driver Version: 396.37 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n|===============================+======================+======================|\n| 0 Tesla V100-PCIE... On | 00000000:3B:00.0 Off | 0 |\n| N/A 36C P0 45W / 250W | 15431MiB / 16160MiB | 30% Default |\n+-------------------------------+----------------------+----------------------+\n| 1 Tesla V100-PCIE... On | 00000000:D8:00.0 Off | 0 |\n| N/A 37C P0 27W / 250W | 0MiB / 16160MiB | 0% Default |\n+-------------------------------+----------------------+----------------------+\n \n+-----------------------------------------------------------------------------+\n| Processes: GPU Memory |\n| GPU PID Type Process name Usage |\n|=============================================================================|\n| 0 431923 C python 15420MiB |\n+-----------------------------------------------------------------------------+\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePotential Problems\u003c/h2\u003e\u003ca id=\"user-content-potential-problems\" class=\"anchor\" aria-label=\"Permalink: Potential Problems\" href=\"#potential-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSCW_TPN_OVERRIDE error\u003c/h3\u003e\u003ca id=\"user-content-scw_tpn_override-error\" class=\"anchor\" aria-label=\"Permalink: SCW_TPN_OVERRIDE error\" href=\"#scw_tpn_override-error\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHawk will not let you submit a single core job to the GPU partition without first running the commmand:\n\u003ccode\u003eexport SCW_TPN_OVERRIDE=1\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis shouldn\u0027t be required on Sunbird.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 3, "topics": [], - "updated_at": 1666800050.0 + "updated_at": 1653388334.0 }, { "data_format": 2, - "description": "Build files for creating singularity containers containing KERN packages.", + "description": "Hosting recipes for Singularity Hub", "filenames": [ - "Singularity" + "Singularity.nompi", + "Singularity.v1.0.1-nompi", + "Singularity.v1.0.0-nompi", + "Singularity.v1.0.0-openmpi4.0.5", + "Singularity.v1.0.1-openmpi4.0.5" ], - "full_name": "kernsuite/singularity", + "full_name": "MRChemSoft/mrchem-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eKERN suite singularity container\u003c/h1\u003e\u003ca id=\"user-content-kern-suite-singularity-container\" class=\"anchor\" aria-label=\"Permalink: KERN suite singularity container\" href=\"#kern-suite-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is source repo for creating a Singularity container populated with\nKERN suite software. KERN is a bi-annually released set of radio astronomical\nsoftware packages. It should contain most of the tools that a radio astronomer\nneeds to work with radio telescope data.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://kernsuite.info/\" rel=\"nofollow\"\u003ehttp://kernsuite.info/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ebuild your own\u003c/h2\u003e\u003ca id=\"user-content-build-your-own\" class=\"anchor\" aria-label=\"Permalink: build your own\" href=\"#build-your-own\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003erun make:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ make\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4912\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84beb2851e273df815be30c84cb993af8267097b92ef4e7c7a60a256c72e9c83/68747470733a2f2f73696e67756c61726974796875622e6769746875622e696f2f73696e67756c61726974796875622d646f63732f6173736574732f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://singularityhub.github.io/singularityhub-docs/assets/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://singularityhub.github.io/singularityhub-docs/assets/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenerate new recipes using HPC Container Maker (HPCCM)\u003c/h3\u003e\u003ca id=\"user-content-generate-new-recipes-using-hpc-container-maker-hpccm\" class=\"anchor\" aria-label=\"Permalink: Generate new recipes using HPC Container Maker (HPCCM)\" href=\"#generate-new-recipes-using-hpc-container-maker-hpccm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ hpccm --recipe \u0026lt;recipe_name\u0026gt;.py --format singularity --singularity-version=3.2 \u0026gt; recipes/Singularity.\u0026lt;version-tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity image locally\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-image-locally\" class=\"anchor\" aria-label=\"Permalink: Build Singularity image locally\" href=\"#build-singularity-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$\u00a0sudo singularity build \u0026lt;image-name\u0026gt;.sif recipes/Singularity.\u0026lt;version-tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity image remotely on Singularity Hub\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-image-remotely-on-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Build Singularity image remotely on Singularity Hub\" href=\"#build-singularity-image-remotely-on-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ git add recipes/Singularity.\u0026lt;version-tag\u0026gt;\n$ git commit -m \"Add recipe for \u0026lt;version-tag\u0026gt;\"\n$ git push\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePull Singularity image from Singularity Hub\u003c/h3\u003e\u003ca id=\"user-content-pull-singularity-image-from-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Pull Singularity image from Singularity Hub\" href=\"#pull-singularity-image-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull --name \u0026lt;image-name\u0026gt;.sif shub://MRChemSoft/mrchem-singularity:\u0026lt;version-tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Singularity container (non MPI)\u003c/h3\u003e\u003ca id=\"user-content-run-singularity-container-non-mpi\" class=\"anchor\" aria-label=\"Permalink: Run Singularity container (non MPI)\" href=\"#run-singularity-container-non-mpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec \u0026lt;image-name\u0026gt;.sif mrchem molecule\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Singularity container (MPI)\u003c/h3\u003e\u003ca id=\"user-content-run-singularity-container-mpi\" class=\"anchor\" aria-label=\"Permalink: Run Singularity container (MPI)\" href=\"#run-singularity-container-mpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec \u0026lt;image-name\u0026gt;.sif mrchem -D molecule\n$ mpirun singularity exec \u0026lt;image-name\u0026gt;.sif mrchem.x molecule.json\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1563189833.0 + "updated_at": 1641394651.0 }, { "data_format": 2, - "description": "singularity to register an atlas to the diffusion space of a subject", + "description": "Collection of SNP pipelines to analyze assembled genomes", "filenames": [ - "Singularity" + "containers/Singularity.kSNP3_cfsansnp", + "containers/Singularity.tree_analysis", + "containers/Singularity.lyveset", + "containers/Singularity.cfsansnp", + "containers/Singularity.peppa", + "containers/Singularity.atlas", + "containers/Singularity.lyveset1", + "containers/Singularity.etoki", + "containers/Singularity" ], - "full_name": "MASILab/AtlasToDiffusionReg", + "full_name": "TheNoyesLab/WGS_SNP_pipelines", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAtlasToDiffusionReg v1.1\u003c/h1\u003e\u003ca id=\"user-content-atlastodiffusionreg-v11\" class=\"anchor\" aria-label=\"Permalink: AtlasToDiffusionReg v1.1\" href=\"#atlastodiffusionreg-v11\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity to register a structural atlas to the diffusion space of a subject\u003c/p\u003e\n\u003cp\u003eauthor: Michael Kim\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNOTE:\u003c/h3\u003e\u003ca id=\"user-content-note\" class=\"anchor\" aria-label=\"Permalink: NOTE:\" href=\"#note\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDepending on the version of numpy, you might get the error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\tnext_arr = _load_from_filelike(\nTypeError: control character \u0027delimiter\u0027 cannot be a newline (`\\r` or `\\n`).\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo alleviate, downgrade to an earlier version of numpy\u003c/p\u003e\n\u003cp\u003eEDIT: this should be fixed now\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTO DOs:\u003c/h1\u003e\u003ca id=\"user-content-to-dos\" class=\"anchor\" aria-label=\"Permalink: TO DOs:\" href=\"#to-dos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e-QA of the registered atlases into diffusion space\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-overlay of atlases on an FA map\n\n-perhaps something else as well\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e-Possibly options\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-just calculating the transforms and not applying them\n\n-only keeping certain outputs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e-have the outputs more organized than just in one outputs folder\u003c/p\u003e\n\u003cp\u003e-checks to make sure that the inputs are correct and the output directory is valid as well\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTO RUN:\u003c/h1\u003e\u003ca id=\"user-content-to-run\" class=\"anchor\" aria-label=\"Permalink: TO RUN:\" href=\"#to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /path/to/inputs/:/INPUTS -B /path/to/outputs:/OUTPUTS WMAtlas.simg (or whatever the singularity image is called)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eINPUTS:\u003c/h1\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: INPUTS:\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAtlases you want to register\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-must have \"Atlas_\" at the beginning of the filename, followed by the atlas name\n\n\t-i.e. \"Atlas_JHU_MNI_WMPM_Type_I.nii.gz\"\n\t\n\t\t-Name of the Atlas is then \"JHU_MNI_WMPM_Type_I\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLabels for each Atlas in the inputs\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-the corresponding labels must be named: \"Labels_\u0026lt;Atlas_Name\u0026gt;.txt\"\n\n\t-if we use the atlas above, the label file should be called \"Labels_JHU_MNI_WMPM_Type_I.txt\"\n\t\n-the label files should have the following structure:\n\n\t#INTENSITY/Integer_label Region_Of_Interest_Name\n\n\t0 Background\t\t\t\n\t1 Superior_Parietal_Lobule_left\n\t2 Cingulate_Gyrus_left\n\t3 Middle_Frontal_Gyrus_left\n\t...\n\t\n- In other words, each line should have an intensity value of the labelmap and the corresponding name of the label delimited by a space\n\n- The first line of the text file should be the background with intensity of zero\n\n- The names if the ROIs in the labelmap should not contain any spaces: the only spaces should be between the intensity and corresponding ROI name\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStructural Template that the atlases are in the space of\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-needs to be named \"template.nii.gz\"\n\n\t-if it is not named this you can specify so by adding the following option in the singularity call:\n\t\n\t\tsingularity run -B ...:/INPUTS -B ...:/OUTPUTS -B /path/to/the/template/:/INPUTS/template.nii.gz WMAtlas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStructural T1 scan of the subject\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-needs to be named \"t1.nii.gz\"\n\n\t-like with the template, can specify an additional line if it is not named so:\n\t\n\t\t\"-B /path/to/t1:/INPUTS/t1.nii.gz\"\n\t\t\n-CANNOT already be skull stripped\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDiffusion data for the subject (dwmri scan, bval, bvec)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-Name can be whatever you want, but they must all have the same name\n\n\t-i.e if the name you want to give it is \"dwmri\"\n\t\n\t\tdwmri.nii.gz\n\t\tdwmri.bval\n\t\tdwmri.bvec\n\t\t\n\t-note that all outputs will use this name you provide\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eT1 segmentation from SLANT (optional)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-needs to be named \"T1_seg.nii.gz\"\n\n-can technically also be a brain mask\n\n-like with the template, can specify an additional line if it is not named so:\n\n\t\"-B /path/to/t1:/INPUTS/T1_seg.nii.gz\"\n\t\n-If this input is not included, then fsl\u0027s bet will be used for the brain extraction and the mask (not recommended)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOUTPUTS are:\u003c/h1\u003e\u003ca id=\"user-content-outputs-are\" class=\"anchor\" aria-label=\"Permalink: OUTPUTS are:\" href=\"#outputs-are\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e- the transformations\n\n\t- see below in Notes for how to use them yourself\n\t\n-the registered atlases\n\n\t- \u0026lt;dwiname\u0026gt;%\u0026lt;atlas_name\u0026gt;.nii.gz\n\t\n-skull stripped t1 and the extracted b0\n\n-diffusion scalar maps\n\n\t-e.g. \u0026lt;dwiname\u0026gt;%fa.nii.gz\n\t\n-the single shell dwi and its bval/bvec files\n\n-the csv file containing the calculations\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSteps:\u003c/h1\u003e\u003ca id=\"user-content-steps\" class=\"anchor\" aria-label=\"Permalink: Steps:\" href=\"#steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eT1 is registered to the template using ANTs to obtain both the affine and nonlinear transformations\u003c/li\u003e\n\u003cli\u003eT1 is skull stripped and b0 is extracted\u003c/li\u003e\n\u003cli\u003eTransformation from diffusion to t1 space is calculated using FSL\u003c/li\u003e\n\u003cli\u003eFSL transformations are converted to ANTs format using c3d\u003c/li\u003e\n\u003cli\u003eAtlases are registered to diffusion space using the transforms\u003c/li\u003e\n\u003cli\u003eThe first shell diffusion scans are extracted from the dwi file\u003c/li\u003e\n\u003cli\u003eFA, MD, AD, RD maps are calculated from the first shell\u003c/li\u003e\n\u003cli\u003eMean and std dev are calculated for the diffusion metrics for each ROI for each atlas and placed in a csv file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe singularity assumptions:\u003c/h1\u003e\u003ca id=\"user-content-the-singularity-assumptions\" class=\"anchor\" aria-label=\"Permalink: The singularity assumptions:\" href=\"#the-singularity-assumptions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e-all imaging files provided are gzpied niftis (.nii.gz)\u003c/p\u003e\n\u003cp\u003e-the bvals/bvecs are iun FSL format (can be otherwise, but not guaranteed to work)\u003c/p\u003e\n\u003cp\u003e-the dwi data have already been preprocessed for distortion correction, to remove noise, artifacts, etc.\u003c/p\u003e\n\u003cp\u003e-the t1 is NOT skull stripped\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNotes\u003c/h1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eif you would like to apply the transformations yourself, this is the following ANTs command to do so:\u003c/p\u003e\n\u003cp\u003eantsApplyTransforms -d 3 -i \u0026lt;atlas_file\u0026gt; -r \u0026lt;b0_file\u0026gt; -n NearestNeighbor \u003cbr\u003e\n-t \u0026lt;t1_to_b0_transform\u0026gt; -t [\u0026lt;t1_to_template_affine_transform\u0026gt;,1] \u003cbr\u003e\n-t \u0026lt;t1_to_template_inv_warp\u0026gt; -o \u0026lt;output_file_name\u0026gt;\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e -t1_to_b0 tranform is called \t\t\t...%ANTS_t1tob0.txt\n -t1_to_template_affine is called\t\t...%0GenericAffine.mat\n -t1_to_template_inv_warp is called\t\t...%1InverseWarp.nii.gz\n -do not have to use the b0 as reference, can use anything in the diffusion space (like the FA map)\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eto move something from diffusion space into the template space, use a similar call, but reverse the order of the transformations\u003c/p\u003e\n\u003cp\u003e-additionally, you must use the inverse of the transformations applied\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTitle of Proposal:\u003c/h1\u003e\u003ca id=\"user-content-title-of-proposal\" class=\"anchor\" aria-label=\"Permalink: Title of Proposal:\" href=\"#title-of-proposal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does analytic approach impact pathogen population structure when analyzing whole genome sequence data?\u003c/h2\u003e\u003ca id=\"user-content-how-does-analytic-approach-impact-pathogen-population-structure-when-analyzing-whole-genome-sequence-data\" class=\"anchor\" aria-label=\"Permalink: How does analytic approach impact pathogen population structure when analyzing whole genome sequence data?\" href=\"#how-does-analytic-approach-impact-pathogen-population-structure-when-analyzing-whole-genome-sequence-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cp\u003econctact \u003ca href=\"mailto:enriquedoster@gmail.com\"\u003eenriquedoster@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe overall goal of this project is to support an accurate, reproducible, transparent and uniform approach to whole-genome sequence (WGS) analysis for purposes of outbreak detection and pathogen surveillance.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe overarching objective is to demonstrate how different analytic approaches to whole-genome sequence analysis can impact analysis results.\u003c/li\u003e\n\u003cli\u003eSupporting objectives are to evaluate the impacts:\n\u003cul\u003e\n\u003cli\u003edataset\u003c/li\u003e\n\u003cli\u003ecore- vs. pan-genome inclusion\u003c/li\u003e\n\u003cli\u003egenome comparison approach (i.e., using SNPs, k-mers, gene-by-gene alleles, or functional domains).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAdditionally, we wil provide information regarding the usability of different WGS pipelines and NCBI\u0027s pathogen genome database.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Study_design.md\"\u003eStudy design\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/WGS_tool_descriptions.md\"\u003eWGS tool descriptions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Installing_WGS_tools.md\"\u003eInstalling WGS tools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Accessing_NCBI_pathogen_genomes.md\"\u003eAccessing NCBI\u0027s pathogen genomes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Questions.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWGS analysis tools\u003c/h2\u003e\u003ca id=\"user-content-wgs-analysis-tools\" class=\"anchor\" aria-label=\"Permalink: WGS analysis tools\" href=\"#wgs-analysis-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/WGS_tool_descriptions.md\"\u003eTools included in the study\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCommand-line tools\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lskatz/lyve-SET\"\u003eLYVE-SET\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CFSAN-Biostatistics/snp-pipeline\"\u003eCFSAN-SNP\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sourceforge.net/projects/ksnp/files/\" rel=\"nofollow\"\u003eKSNP3\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eGUI tools included in the study:\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/zheminzhou/EToKi\"\u003eENTEROBASE\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eHas \"EBEis\" in silico serotype prediction for Escherichia coli and Shigella spp.\u003c/li\u003e\n\u003cli\u003eAlso has \"isCRISPOL\" in silico prediction of CRISPOL array for Salmonella enterica serovar Typhimurium\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.applied-maths.com/applications/wgmlst\" rel=\"nofollow\"\u003ewgMLST/BioNumerics\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOther tools to explore\n\u003cul\u003e\n\u003cli\u003eSimultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks - \u003ca href=\"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005495\" rel=\"nofollow\"\u003ehttps://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005495\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eNucleotide-resolution bacterial pan-genomics with reference graphs - \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.11.12.380378v2\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/2020.11.12.380378v2\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/staphb/lyveset\" rel=\"nofollow\"\u003eRepository of useful docker containers\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNCBI\u0027s pathogen database and genome analysis\u003c/h2\u003e\u003ca id=\"user-content-ncbis-pathogen-database-and-genome-analysis\" class=\"anchor\" aria-label=\"Permalink: NCBI\u0027s pathogen database and genome analysis\" href=\"#ncbis-pathogen-database-and-genome-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Questions.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Accessing_NCBI_pathogen_genomes.md\"\u003eNCBI pathogen database\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TheNoyesLab/WGS_SNP_pipelines/blob/master/docs/Using_common_WGS_tools.md\"\u003eUsing Common WGS tools\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the pipelines with this repository\u003c/h1\u003e\u003ca id=\"user-content-running-the-pipelines-with-this-repository\" class=\"anchor\" aria-label=\"Permalink: Running the pipelines with this repository\" href=\"#running-the-pipelines-with-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cul\u003e\n\u003cli\u003eThis pipeline requires singularity to be installed and available in your $PATH.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample commands\u003c/h3\u003e\u003ca id=\"user-content-example-commands\" class=\"anchor\" aria-label=\"Permalink: Example commands\" href=\"#example-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Download genomes from ncbi\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e On MSI, you can use the scripts in the \"bin\" directory to download SRA files.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make a text file with one SRA value per row.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Modify the \"prefetch_SRA_from_file.sh\" to point to your file with SRA values. NB. This will download prefetch values to your default location (usually in $HOME). Run this script using bash, or submit as a job to MSI by removing the first \"#\" in the first few rows of the script.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Modify the \"fastq_dump_from_sra.sh\" to point to the location of the SRA files and to output the fastq files into your desired output directory.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Main steps\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Load singularity module \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make sure nextflow is installed and in your $PATH\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download this git repository, navigate inside it and modify the commands below to suit your data\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Remember to change the \"species\" flag. Options are: escherichia_coli, salmonella_enterica, and listeria_monocytogenes\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Main combined pipeline\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input is paired end reads\u003c/span\u003e\nnextflow run main_combined_pipeline.nf --species \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esalmonella_enterica\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --reference_genome /scratch.global/Salmonella_WGS/ref_L_monocytogenes_NC_003210.fasta --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/scratch.global/Salmonella_WGS/List_test_genomes/*_{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -profile singularity --output /scratch.global/Salmonella_WGS/test_GenomeTrakr_L_monocytogenes_WGS_results --threads 20 -w /scratch.global/Salmonella_WGS/work_test_qsub_l_latest -resume -with-report test_250_Listeria_WGS_tools.report -with-trace -with-timeline\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Lyveset\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input is interleaved fastq files\u003c/span\u003e\nnextflow run main_LYVESET.nf --reference_genome /scratch.global/Salmonella_WGS/ref_L_monocytogenes_NC_003210.fasta --interleaved_fastq \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/scratch.global/Salmonella_WGS/test_GenomeTrakr_L_monocytogenes_WGS_results/Interleaved_fasta/interleaved_reads/*.fastq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -profile singularity --output /scratch.global/Salmonella_WGS/test_LYVESET_250_GenomeTrakr_L_monocytogenes_WGS_results --threads 3 -w /scratch.global/Salmonella_WGS/work_250_lyveset_qsub_l_latest -resume -with-report 250_Listeria_WGS_tools.report -with-trace -with-timeline --singleEnd \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e lyveset sometimes requires\u003c/span\u003e\n/home/noyes046/shared/tools/lyve-SET-1.1.4g/scripts/mergeVcf.sh -o msa/out.p\nooled.vcf.gz vcf/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.vcf.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cfsan_snp\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input file is a directory containing one directory per sample with the corresponding paired reads\u003c/span\u003e\nnextflow run main_CFSAN_snp.nf --reference_genome /scratch.global/Salmonella_WGS/ref_L_monocytogenes_NC_003210.fasta --fastq_dir_path \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/scratch.global/Salmonella_WGS/test_GenomeTrakr_L_monocytogenes_WGS_results/Interleaved_fasta/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile singularity --output /scratch.global/Salmonella_WGS/test_250_GenomeTrakr_L_monocytogenes_WGS_results --threads 128 -w /scratch.global/Salmonella_WGS/work_250_qsub_l_latest -resume -with-report 250_Listeria_WGS_tools.report -with-trace -with-timeline\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e KSNP3\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Input file is \".tsv\" file containing a column with an absolute path to each sample file and it\u0027s sample ID\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Example below\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e /path/to/file/SRR10001252.fasta SRR10001252\u003c/span\u003e\nnextflow run main_kSNP3.nf --reference_genome /scratch.global/Salmonella_WGS/ref_L_monocytogenes_NC_003210.fasta --genomes /scratch.global/Salmonella_WGS/WGS_SNP_pipelines/Listeria_genome_location.tsv -profile singularity_pbs --output /scratch.global/Salmonella_WGS/kSNP3_GenomeTrakr_L_monocytogenes_WGS_results --threads 128 -w /scratch.global/Salmonella_WGS/work_kSNP3_l_latest -resume -with-report kSNP3_Listeria_WGS_tools.report -with-trace -with-timeline\n\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Enterobase\u003c/span\u003e\nnextflow run main_enterobase.nf --reference_genome /tempalloc/noyes042/FMPRE_clean/Host_genomes/Senterica_LT2_ref_genome.fasta --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/tempalloc/noyes042/FMPRE_clean/Raw_datasets/Outbreak_genomes/genomes_final_salmonella_outbreak/*_{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile singularity --output /tempalloc/noyes042/FMPRE_clean/ALL_results/temp_results/Salmonella_MLST_OUTBREAK_WGS_results --threads 15 -w /tempalloc/noyes042/FMPRE_clean/ALL_results/temp_results/work_salm_outbreak_MLST -resume -with-report Salm_MLST_outbreak_WGS_tools.report -with-trace -with-timeline --species salmonella_enterica --allele_fasta data/7gene_MLST_schemes/Salmonella_7gene_Achtman_MLST.fasta.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocuments\u003c/h1\u003e\u003ca id=\"user-content-documents\" class=\"anchor\" aria-label=\"Permalink: Documents\" href=\"#documents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWGS and regulations\u003c/h2\u003e\u003ca id=\"user-content-wgs-and-regulations\" class=\"anchor\" aria-label=\"Permalink: WGS and regulations\" href=\"#wgs-and-regulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.efsa.europa.eu/en/consultations/call/public-consultation-efsa-statement-requirements-whole-genome\" rel=\"nofollow\"\u003ehttps://www.efsa.europa.eu/en/consultations/call/public-consultation-efsa-statement-requirements-whole-genome\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMisc resources\u003c/h2\u003e\u003ca id=\"user-content-misc-resources\" class=\"anchor\" aria-label=\"Permalink: Misc resources\" href=\"#misc-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDifferences in results by analytic method\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.nature.com/articles/d41586-020-01282-z?utm_source=twitter\u0026amp;utm_medium=social\u0026amp;utm_content=organic\u0026amp;utm_campaign=NGMT_USG_JC01_GL_Nature\" rel=\"nofollow\"\u003eNeuroimaging results altered by varying analysis pipelines\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWays to compare the results from WGS pipelines\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lskatz/Lyve-SET-paper/blob/master/compareSnps.sh\"\u003ehttps://github.com/lskatz/Lyve-SET-paper/blob/master/compareSnps.sh\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://academic.oup.com/mbe/article/33/8/2163/2579233\" rel=\"nofollow\"\u003ePhylo.io\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eTimme et. al. 2019 \u003ca href=\"https://jcm.asm.org/content/57/5/e01816-18\" rel=\"nofollow\"\u003ePhylogenomic Pipeline Validation for Foodborne Pathogen Disease Surveillance\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWGS tools to consider:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz795/5607735?rss=1\" rel=\"nofollow\"\u003eKalign 3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nmquijada/tormes\"\u003eTormes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/25201145\" rel=\"nofollow\"\u003eSPANDx\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ewgMLST \u003ca href=\"https://anaconda.org/bioconda/chewbbaca\" rel=\"nofollow\"\u003echewBBACA\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMLST \u003ca href=\"https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2887-1\" rel=\"nofollow\"\u003eSTRAIN\u003c/a\u003e R package\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWGS analysis\n\u003cul\u003e\n\u003cli\u003eCore genome\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.pnas.org/content/102/39/13950\" rel=\"nofollow\"\u003ehttps://www.pnas.org/content/102/39/13950\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePan-genome\u003c/li\u003e\n\u003cli\u003eMLST\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pubmlst.org/\" rel=\"nofollow\"\u003ehttps://pubmlst.org/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eEnterobase\u003c/li\u003e\n\u003cli\u003eBioNumerics\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePhylogenetic trees\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://academic.oup.com/sysbio/article/64/2/205/1630737\" rel=\"nofollow\"\u003ePractical Performance of Tree Comparison Metrics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nature.com/articles/s41576-020-0233-0\" rel=\"nofollow\"\u003eReview: Phylogenetic tree building in the genomic age (Kapli et. al. 2020)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOther useful links:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jhcepas\"\u003ehttps://github.com/jhcepas\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1720041531.0 + "updated_at": 1615365486.0 }, { "data_format": 2, - "description": null, + "description": "Files to support building and maintenance of Singularity containers for Hall D", "filenames": [ - "Singularity" + "recipes/Singularity.centos-8.2.2004", + "recipes/Singularity.rockylinux-8.6.20220707", + "recipes/Singularity.centos-8.3.2011", + "recipes/Singularity.2.0.11-centos8", + "recipes/Singularity.centos-7.7.1908", + "recipes/Singularity.centos-6.10", + "recipes/Singularity.markito3.gluex_docker_devel", + "recipes/Singularity.fedora-34", + "recipes/Singularity.fedora-35", + "recipes/Singularity.markito3-gluex_docker_prod", + "recipes/Singularity.almalinux-9.2", + "recipes/Singularity.fedora-33", + "recipes/Singularity.ubuntu.xenial-20210114", + "recipes/Singularity.fedora-32", + "recipes/Singularity.centos-8.4.2105", + "recipes/Singularity.ubuntu.bionic-20210222", + "recipes/Singularity.ubuntu.focal-20200925", + "recipes/Singularity.centos-3.0.6-stream8" ], - "full_name": "MASILab/DeepN4", + "full_name": "JeffersonLab/hd_singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDeepN4\u003c/h1\u003e\u003ca id=\"user-content-deepn4\" class=\"anchor\" aria-label=\"Permalink: DeepN4\" href=\"#deepn4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePytorch implementation of DeepN4, from the paper: DeepN4: Learning ITKN4 Bias Field Correction for T1 weighted Images.\nWe propose \u003cstrong\u003eDeepN4\u003c/strong\u003e, a 3D UNet to generate Bias Field for T1w images and inturn correct T1w images for inhomogeneities.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figures/fig.png\"\u003e\u003cimg src=\"figures/fig.png\" width=\"100%\" height=\"40%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrequisite installation\u003c/h2\u003e\u003ca id=\"user-content-prequisite-installation\" class=\"anchor\" aria-label=\"Permalink: Prequisite installation\" href=\"#prequisite-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease look into \u003ccode\u003erequirements.txt\u003c/code\u003e for libraries and versions used in the virutal environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining and Testing\u003c/h2\u003e\u003ca id=\"user-content-training-and-testing\" class=\"anchor\" aria-label=\"Permalink: Training and Testing\" href=\"#training-and-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInput train/test/val format: csv file with each row with \u003ccode\u003e/path/to/corrected_T1.nii.gz,/path/to/input.nii.gz,/path/to/bias_field.nii.gz\u003c/code\u003e for each subject\u003c/li\u003e\n\u003cli\u003eRun training\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython main.py train 0 Synbo_UNet3D False /path/to/save/model /path/to/tensorboard/output \\\n/path/to/save/predictions checkpoint_epoch_# guass \\\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun testing\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython main.py pred 0 Synbo_UNet3D False /path/to/saved/model /path/to/tensorboard/output \\ \n/path/to/save/predictions checkpoint_epoch_# guass \\\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExternal evaluation\u003c/h2\u003e\u003ca id=\"user-content-external-evaluation\" class=\"anchor\" aria-label=\"Permalink: External evaluation\" href=\"#external-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEfficient evaulation can be performed for the public datasets as in \u003ccode\u003eEvaluation_deepN4.ipynb\u003c/code\u003e. The example data used is in \u003ccode\u003edata/\u003c/code\u003e\nThe trained weights and the singualrity image can be downloaded from \u003ca href=\"https://drive.google.com/drive/folders/1mdBsV0kHRRV_Alu1QJrTT7N0GGNJDuiu?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1mdBsV0kHRRV_Alu1QJrTT7N0GGNJDuiu?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo containize the source code\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build deepn4v1.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run singularity\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingaulrity run\n--contain -e\n-B /path/to/inputs/directory/:/INPUTS\n-B /path/to/outputs/directory/:/OUTPUTS\n-B /tmp:/tmp\n-B /path/to/freesurfer/license.txt:/APPS/freesurfer/license.txt\ndeepn4v1.simg\n[options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInputs and arguments for singularity\nInput Directory: one T1 image named as t1.nii.gz\nOutput Directory: results will be saved as t1processed.nii.gz and (if the option below is on) bias.nii.gz\nOptions: \"--bias_file\" to save the bias field field\u003c/p\u003e\n\u003chr\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehd_singularity\u003c/h1\u003e\u003ca id=\"user-content-hd_singularity\" class=\"anchor\" aria-label=\"Permalink: hd_singularity\" href=\"#hd_singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFiles to support building and maintenance of Singularity containers for Hall D.\u003c/p\u003e\n\u003cp\u003eContains scripts and recipes for creating Singularity containers from scratch.\u003c/p\u003e\n\u003cp\u003eThe main script is scripts/create_gluex_container.sh. Its usage message is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: create_gluex_container.sh [-h] -r \u0026lt;recipe-file\u0026gt; -p \u0026lt;prereqs-script\u0026gt; \\\n [-d DIRECTORY] [-t STRING]\n\nNote: must be run as root\n\nOptions:\n -h print this usage message\n -r Singularity recipe file\n -p script that installs gluex software\n -d output directory for containers (default: current working directory)\n -t token to be used to name containers (default = extension in \"Singularity.ext\")\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 57, "topics": [], - "updated_at": 1723393954.0 + "updated_at": 1704073259.0 }, { "data_format": 2, - "description": "testing an enzo simulation with MPI, enzo, and Singularity", + "description": null, "filenames": [ - "Singularity" + "arxiv/Singularity.ub16.04-tf1.10.1-torch0.4.1-root6.14.04", + "arxiv/Singularity.ubuntu16.04-larcv_develop", + "arxiv/Singularity.ub16.04-tf1.10.1-torch0.4.1", + "arxiv/Singularity.ubuntu16.04-gpu", + "arxiv/Singularity.ubuntu16.04-basic", + "arxiv/Singularity.ubuntu16.04-gpu-larcv_develop", + "arxiv/Singularity.ub16.04-tf1.11.0-torch0.4.1-root6.14.04", + "arxiv/Singularity.ub16.04-tf1.7-torch0.4", + "arxiv/Singularity.ub16.04-tf1.11.0-torch0.4.1", + "arxiv/Singularity.ubuntu16.04-gpu-py3", + "recipes/Singularity.ub18.04-cuda100-py3-pytorch1.1.0-scn", + "recipes/Singularity.ub18.04-gpu-ana0-ml", + "recipes/Singularity.ub16.04-cuda90-pytorch1.0.0-scn", + "recipes/Singularity.ub18.04-cuda100-py3-pytorch1.0.1-scn", + "recipes/Singularity.ub18.04-cpu-ana0", + "recipes/Singularity.ub18.04-gpu", + "recipes/Singularity.ub18.04-gpu-ana0-mn", + "recipes/Singularity.ub18.04-cpu-ana0-larcv2", + "recipes/Singularity.ub16.04-cuda90-pytorch0.4.1", + "recipes/Singularity.ub18.04-gpu-ana0", + "recipes/Singularity.ub18.04-gpu-ana0-ml-larcv2", + "recipes/Singularity.ub18.04-cpu", + "recipes/Singularity.ub16.04-cuda100-pytorch1.0.0-scn", + "recipes/Singularity.ub16.04-cuda100-pytorchdev20181215", + "recipes/Singularity.ub18.04-cuda100-py3-pytorch1.1.0-scn-docker", + "recipes/Singularity.ub16.04-cuda90-pytorchdev20181015", + "recipes/Singularity.ub16.04-cuda90-tf1.12.0", + "recipes/Singularity.HKMLWorkshop", + "recipes/Singularity.ub16.04-cuda90-py3-pytorch1.0.1-scn", + "recipes/Singularity", + "recipes/Singularity.ub18.04-cuda10.1-ml-larcv2", + "recipes/Singularity.ub18.04-cuda10.2-extra" ], - "full_name": "vsoch/enzo-simulation", + "full_name": "DeepLearnPhysics/larcv2-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnzo in Singularity\u003c/h1\u003e\u003ca id=\"user-content-enzo-in-singularity\" class=\"anchor\" aria-label=\"Permalink: Enzo in Singularity\" href=\"#enzo-in-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall Singularity\u003c/h2\u003e\u003ca id=\"user-content-install-singularity\" class=\"anchor\" aria-label=\"Permalink: Install Singularity\" href=\"#install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst you should install Singularity, and we will use development branch for latest features.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone -b development https://www.github.com/singularityware/singularity.git\ncd singularity\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate Enzo Image\u003c/h2\u003e\u003ca id=\"user-content-create-enzo-image\" class=\"anchor\" aria-label=\"Permalink: Create Enzo Image\" href=\"#create-enzo-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity file will download the enzo package from bitbucket, and install/configure for ubuntu 14.04. The scripts for the running are included in this repo under \u003ca href=\"scripts\"\u003escripts\u003c/a\u003e. To create the image, do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vsoch/enzo-simulation\ncd enzo-simulation\nsingularity create --size 4000 enzo.img\nsudo singularity bootstrap enzo.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLooking around\u003c/h3\u003e\u003ca id=\"user-content-looking-around\" class=\"anchor\" aria-label=\"Permalink: Looking around\" href=\"#looking-around\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can then shell into the image to look around, here I am using sudo with \u003ccode\u003e--writable\u003c/code\u003e so I can make changes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity shell --writable enzo.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above is useful mostly during the development time of the image, when you have it locally (and thus have sudo) and want to try small tweaks to the image to test running it. It is best practice generally to represent all changes/tweaks in the build file (\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e) so that they are reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Runscript\u003c/h3\u003e\u003ca id=\"user-content-the-runscript\" class=\"anchor\" aria-label=\"Permalink: The Runscript\" href=\"#the-runscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \"runscript\" of the container is \"the thing the container does when you execute it.\" We can look at this runscript, which is linked at \u003ccode\u003e/singularity\u003c/code\u003e. Below we are using \u003ccode\u003eexec\u003c/code\u003e (execute) to issue a command to cat the file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec enzo.img cat /singularity\n\n\n# Get job number\nif [ $# -lt 1 ]; then\n echo \"You must pass the job number.\"\n echo \"Usage: singularity run -B /scratch/data:/data enzo.img 10\"\n exit 1\nelse\n num=\"$1\"\n case ${num} in\n -h|--help|-H)\n echo \"Usage: singularity run -B /host/data:/data enzo.img [num]\n echo \"e.g., singularity run -B /scratch/data:/data enzo.img 10\"\n exit 1;\n \u0027\u0027|*[!0-9]*) \n echo \"You must pass a number as the first argument.\"; exit 2;;\n *) ;;\n esac\nfi;\n\n# Create the job directory, if doesn\u0027t exist\njobdir=\"/data/jobs/${num}\"\nif [ ! -d \"${jobdir}\" ]; then\n echo \"Creating job directory ${num}\"\n cp -r /code/scripts/Init \"${jobdir}\"\nfi\n\ncd $jobdir\n/bin/bash /code/scripts/job.pbs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that although we are looking at \u003ccode\u003e/singularity\u003c/code\u003e, this is a link to the actual file in the container metadata folder at \u003ccode\u003e/.singularity.d/runscript\u003c/code\u003e. Above, we see that the runscript is going to check for a numerical argument to indicate the run number. If the user has specified a flag for help (any in \u003ccode\u003e-H|--help|-h\u003c/code\u003e) then it will print out usage. If all goes well, then we set up the job output folder (copying files to \u003ccode\u003ejobdir\u003c/code\u003e from \u003ca href=\"scripts/Init\"\u003escripts/Init\u003c/a\u003e and then launching the job by calling \u003ca href=\"scripts/job.pbs\"\u003ethis file\u003c/a\u003e. Note that these files are all added to the container, and the specific job script is also executed and inside the container. The only thing the user has to do is load the singularity module and then write some loop to execute N jobs. Doing this, and running, is discussed next.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun The Container\u003c/h2\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-label=\"Permalink: Run The Container\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou next need to move your image onto the cluster where it is intended to be. The basic workflow is going to be to bind some data folder on my cluster (in the example, \u003ccode\u003e/scratch/users/vsochat/DATA/physics\u003c/code\u003e to where the container expects it\u0027s data directory to be (in this case, \u003ccode\u003e/data\u003c/code\u003e). The MPI run is going to work as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe container acts as an executable, and manages the entire lifecycle of one job. This includes:\n\u003cul\u003e\n\u003cli\u003ecreating the job directory\u003c/li\u003e\n\u003cli\u003elaunching the job (\u003ccode\u003e.pbs\u003c/code\u003e) script that loads required modules and issues the mpi command.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe container could also do a different / custom command, and in this case you would run \u003ccode\u003eexec\u003c/code\u003e and specify a script (on the host or in container) to run.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOn your cluster, first load singularity (or have it on your path if your cluster doesn\u0027t use modules.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo start, we can be silly and just run the container, as most users would do. The following commands are equivalent:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./enzo.img \nYou must pass the job number.\nUsage: singularity run -B /scratch/data:/data enzo.img 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd every good executable will have a \u003ccode\u003e--help\u003c/code\u003e argument. We can do any of the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run enzo.img --help\nsingularity run enzo.img -H\nsingularity run enzo.img -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor just execute the image itself!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./enzo.img --help\n./enzo.img -H\n./enzo.img -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ein all cases above, we see the help prompt:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: singularity run -B /host/data:/data enzo.img [num]\ne.g., singularity run -B /scratch/data:/data enzo.img 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Enzo Jobs\u003c/h2\u003e\u003ca id=\"user-content-run-enzo-jobs\" class=\"anchor\" aria-label=\"Permalink: Run Enzo Jobs\" href=\"#run-enzo-jobs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow we are again sitting on our cluster, and remember that we have singularity loaded or on the path. Make sure this does not return empty!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewhich singularity\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow let\u0027s say we want to run 10 jobs. We want the container to use \u003ccode\u003e/scratch/users/vsochat/DATA/physics\u003c/code\u003e as our data directory (meaning the folder \u003ccode\u003ejobs\u003c/code\u003e with our enzo analyses, directories labeled 0-N) will be created inside. This is an important distinction - inside the container, operations will run relative to \u003ccode\u003e/data\u003c/code\u003e. However, from outside the container we have this directory represented as \u003ccode\u003e/scratch/users/vsochat/DATA/physics.\u003c/code\u003e Also note that our container is located one level above that at \u003ccode\u003e/scratch/users/vsochat/DATA\u003c/code\u003e. Thus, to run our job for N=10, we want to have the following scripts on our cluster, along with the image itself (enzo.img) that we built above:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"scripts/launch_jobs.sh\"\u003elaunch_jobs.sh\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"scripts/run_jobs.sh\"\u003erun_job.sh\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe first is basically a for loop to send a bunch of commands to the batch manager with qsub, each submitting a script to run the file \u003ca href=\"scripts/run_jobs.sh\"\u003erun_jobs.sh\u003c/a\u003e. You don\u0027t need to use bash scripts, or even this approach, the general idea is that you want to send the command our to your cluster nodes to load the modules on the host, load singularity, and then run the container. The script \u003ca href=\"scripts/launch_jobs.sh\"\u003escripts/launch_jobs.sh\u003c/a\u003e is an example of the \"for loop\" part of this, run on the host:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n\n# Get number of jobs\nif [ $# -lt 1 ]; then\n echo \"You need to pass the number of jobs.\"\n exit 1\nfi;\n\nnum=\"$1\"\n\n#User input validation\ncase ${num} in\n \u0027\u0027|*[!0-9]*) echo \"You must pass a number as the first argument.\"; exit 2;;\n *) ;;\nesac\n\nhost_data_dir=\"/scratch/users/vsochat/DATA/physics\"\ncompiler_version=\"1.10.2\"\ncompiler_vendor=\"gcc\"\noptimization_level=\"high\"\nexport host_data_dir compiler_vendor compiler_version optimization_level\n\njob=0\nwhile [ ${job} -lt ${num} ]; do\n job_dir=\"${host_data_dir}/jobs/${job}\"\n if [ ! -e \"${job_dir}/RunFinished\" ]; then\n echo \"Submitting job ${job_num}...\"\n qsub -N \"${compiler_vendor}_${compiler_version}_${optimization_level}_${job_num}\" -o ${job_dir} run_job.sh $job\n\tfi;\n\tjob=$(expr ${job} + 1)\ndone;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the script \u003ca href=\"scripts/run_job.sh\"\u003erun_job.sh\u003c/a\u003e is located outside the image, and called by qsub. It is this script that loads required modules (on the host) and then executes the container via a call to singularity, which has the main job script (job.pbs) inside. We bind the host data directory to \u003ccode\u003e/data\u003c/code\u003e in the container using the command \u003ccode\u003e-B\u003c/code\u003e, and we do this so that it is writable.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n\nmodule load singularity\nmodule unload gcc\nmodule load gcc/4.8.1\nmodule load openmpi/1.10.2/gcc\n\njob_num=$1\n\nhost_data=\"/scratch/users/vsochat/DATA/physics\"\nenzo=\"/scratch/users/vsochat/DATA/enzo.img\"\n\nsingularity run -B $host_data:/data $enzo $job_num\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://raw.githubusercontent.com/DeepLearnPhysics/larcv2-singularity/master/LICENSE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e581ac49b7e1e99fb951242be63f6fdc6ebbc20c89a97fca0de99e1f2e6ae87e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6d6173686170652f6170697374617475732e737667\" alt=\"license\" data-canonical-src=\"https://img.shields.io/github/license/mashape/apistatus.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/459\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003elarcv2-singularity\u003c/h1\u003e\u003ca id=\"user-content-larcv2-singularity\" class=\"anchor\" aria-label=\"Permalink: larcv2-singularity\" href=\"#larcv2-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity build scripts for \u003ca href=\"https://www.singularity-hub.org/collections/459\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e. You can learn about Singularity in \u003ca href=\"https://github.com/DeepLearnPhysics/playground-singularity/wiki\"\u003eour wiki\u003c/a\u003e or \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eofficial doc\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo \u003ccode\u003epull\u003c/code\u003e the container, simply try\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTAG=latest\nsingularity pull -n local.img shub://DeepLearnPhysics/larcv2-singularity:$TAG\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more fun things to do, you can read \u003ca href=\"https://github.com/DeepLearnPhysics/playground-singularity/wiki\"\u003eour wiki\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat\u0027s in the build?\u003c/h2\u003e\u003ca id=\"user-content-whats-in-the-build\" class=\"anchor\" aria-label=\"Permalink: What\u0027s in the build?\" href=\"#whats-in-the-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll builds are based on \u003cstrong\u003eUbuntu16.04 LTS\u003c/strong\u003e with some highlighted packages below\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython packages: \u003ccode\u003epip\u003c/code\u003e \u003ccode\u003enumpy\u003c/code\u003e \u003ccode\u003escipy\u003c/code\u003e \u003ccode\u003escikit\u003c/code\u003e \u003ccode\u003eopencv-python\u003c/code\u003e \u003ccode\u003eh5py\u003c/code\u003e \u003ccode\u003etables\u003c/code\u003e \u003ccode\u003epandas\u003c/code\u003e \u003ccode\u003ematplotlib\u003c/code\u003e \u003ccode\u003eipython\u003c/code\u003e \u003ccode\u003ejupyter notebook\u003c/code\u003e \u003ccode\u003epyyaml\u003c/code\u003e \u003ccode\u003ezmq\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopment kit: \u003ccode\u003eg++\u003c/code\u003e/\u003ccode\u003egcc\u003c/code\u003e \u003ccode\u003elibqt4-dev\u003c/code\u003e \u003ccode\u003epython-dev\u003c/code\u003e \u003ccode\u003ecuda-9.0\u003c/code\u003e \u003ccode\u003ecudnn-7\u003c/code\u003e \u003ccode\u003ecython\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUtility kit : \u003ccode\u003egit\u003c/code\u003e \u003ccode\u003ewget\u003c/code\u003e \u003ccode\u003eemacs\u003c/code\u003e \u003ccode\u003evim\u003c/code\u003e \u003ccode\u003easciinema\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe build 3 types of images.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eBase\u003c/em\u003e image\n\u003cul\u003e\n\u003cli\u003eLatest tag: \u003cstrong\u003eub16.04-tf1.10.1-torch0.4.1\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etensorflow-gpu\u003c/code\u003e 1.10.1, \u003ccode\u003epytorch\u003c/code\u003e 0.4.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n local.img shub://DeepLearnPhysics/larcv2-singularity:ub16.04-tf1.10.1-torch0.4.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eROOT\u003c/em\u003e image (include \u003cem\u003eBase\u003c/em\u003e)\n\u003cul\u003e\n\u003cli\u003eLatest tag: \u003cstrong\u003eub16.04-tf1.10-torch0.4.1-root6.14.04\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eROOT\u003c/code\u003e 6.14.04, additional python package \u003ccode\u003eroot_numpy\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n local.img shub://DeepLearnPhysics/larcv2-singularity:ub16.04-tf1.10.1-torch0.4.1-root6.14.04\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eLArCV\u003c/em\u003e image (include \u003cem\u003eROOT\u003c/em\u003e)\n\u003cul\u003e\n\u003cli\u003eTag: \u003cstrong\u003elatest\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eAdditional python package \u003ccode\u003elarcv\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull -n local.img shub://DeepLearnPhysics/larcv2-singularity:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker images?\u003c/h1\u003e\u003ca id=\"user-content-docker-images\" class=\"anchor\" aria-label=\"Permalink: Docker images?\" href=\"#docker-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheckout built images on our \u003ca href=\"https://hub.docker.com/u/deeplearnphysics/dashboard/\" rel=\"nofollow\"\u003edocker hub\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, - "topics": [], - "updated_at": 1545323609.0 + "subscribers_count": 4, + "topics": [ + "larcv", + "singularity", + "singularity-hub" + ], + "updated_at": 1692637450.0 }, { "data_format": 2, - "description": "Singularity image for biocontainers samtools", + "description": null, "filenames": [ - "Singularity" + "squeakr/Singularity.squeakr", + "ubuntu/Singularity.ubuntu2004", + "pacbio/Singularity.pacbio", + "python3/Singularity.python3-extra", + "python3/Singularity.python3", + "bcl2fastq/Singularity.bcl2fastq", + "t-coffee/Singularity.t-coffee", + "miniasm/Singularity.miniasm", + "racon/Singularity.bioinf-racon", + "umi-tools/Singularity.umi-tools", + "seq-qc/Singularity.seq-qc", + "rr/Singularity.r-base", + "rr/Singularity.r-tidy-extra", + "rr/Singularity.r-tidy-some", + "rr/Singularity.r-tidy", + "mummer4/Singularity.mummer4", + "enrich2/Singularity.enrich22", + "enrich2/Singularity.enrich2", + "nanopore/Singularity.guppy-cpu", + "nanopore/Singularity.flye", + "nanopore/Singularity.medaka_hack", + "nanopore/Singularity.medaka", + "nanopore/Singularity.guppy-gpu", + "nanopore/Singularity.canu", + "nanopore/Singularity.pycoqc", + "nanopore/Singularity.ont-polishing", + "jupyter/Singularity.jupyter-plus-alignparse", + "jupyter/Singularity.jupyter-plus-bioconda", + "jupyter/Singularity.jupyter-plus", + "jupyter/Singularity.jupyter-plus-tensorflow-v2.5.0-compiled", + "jupyter/Singularity.cuda-tensorflow-v2.6.0-jupyter-plus", + "jupyter/Singularity.jupyter-plus-tensorflow-v2.5.0-compiled-patch", + "jupyter/Singularity.jupyter", + "jupyter/Singularity.jupyter-plus-tensorflow-v2.2.0-compiled", + "jupyter/Singularity.jupyter-plus-tensorflow", + "jupyter/Singularity.jupyter-plus-tensorflow-v2.4.0-rc4-compiled", + "jupyter/Singularity.plus-jupyter", + "bioinf/Singularity.bioinfmunger", + "ncbi-blast/Singularity.ncbi-blast", + "itermae/Singularity.itermae-plus", + "kalign/Singularity.kalign2", + "kalign/Singularity.kalign3", + "fitseq/Singularity.fitseq-dev", + "fitseq/Singularity.fitseq", + "cuda-tensorflow/Singularity.tensorflow-v2.5.0-compiled", + "cuda-tensorflow/Singularity.tensorflow-v2.0.3-compiled", + "cuda-tensorflow/Singularity.tensorflow-v1.15.4-compiled", + "alignparse/Singularity.alignparse", + "bioconda/Singularity.bioconda" ], - "full_name": "researchapps/samtools", + "full_name": "darachm/containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBCFTools\u003c/h1\u003e\u003ca id=\"user-content-bcftools\" class=\"anchor\" aria-label=\"Permalink: BCFTools\" href=\"#bcftools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity image to deploy bcftools.\u003c/p\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/broadinstitute/picard/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/broadinstitute/picard/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 bcftools.img\nsudo singularity bootstrap bcftools.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the bcftools runtime executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bcftools.img\n\nProgram: bcftools (Tools for variant calling and manipulating VCFs and BCFs)\nVersion: 1.3.1 (using htslib 1.3.1)\n\nUsage: bcftools [--version|--version-only] [--help] \u0026lt;command\u0026gt; \u0026lt;argument\u0026gt;\n\nCommands:\n\n -- Indexing\n index index VCF/BCF files\n\n -- VCF/BCF manipulation\n annotate annotate and edit VCF/BCF files\n concat concatenate VCF/BCF files from the same set of samples\n convert convert VCF/BCF files to different formats and back\n isec intersections of VCF/BCF files\n merge merge VCF/BCF files files from non-overlapping sample sets\n norm left-align and normalize indels\n plugin user-defined plugins\n query transform VCF/BCF into user-defined formats\n reheader modify VCF/BCF header, change sample names\n view VCF/BCF conversion, view, subset and filter VCF/BCF files\n\n -- VCF/BCF analysis\n call SNP/indel calling\n consensus create consensus sequence by applying VCF variants\n cnv HMM CNV calling\n filter filter VCF/BCF files using fixed thresholds\n gtcheck check sample concordance, detect sample swaps and contamination\n roh identify runs of autozygosity (HMM)\n stats produce VCF/BCF stats\n\n Most commands accept VCF, bgzipped VCF, and BCF with the file type detected\n automatically even when streaming from a pipe. Indexed VCF and BCF will work\n in all situations. Un-indexed VCF and BCF and streams will work in most but\n not all situations.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cp\u003eThis is a repo for Darach to track and host containers for doing\nbioinf/research.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe main intention is to run as Singularity containers on linux systems,\nbut they\u0027re written as Docker for compatibility (thanks Mohammed Kahlfan\nfor the tip).\u003c/li\u003e\n\u003cli\u003eThere\u0027s a hope that this can get built and hosted on GitHub\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOrganzation copied from \u003ca href=\"https://github.com/jlboat/BioinfoContainers\"\u003ejlboat\u003c/a\u003e.\n(Of course, makes total sense to just use tags to organize things!)\u003c/p\u003e\n\u003cp\u003eSome recipes are for individual tools, some are for workflows and so are\ncombos. Trying to figure out the ontology of this.\u003c/p\u003e\n\u003cp\u003eTodo:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker containers for:\n\u003cul\u003e\n\u003cli\u003eseq qc\u003c/li\u003e\n\u003cli\u003emunging and sed/awk-fu and shell\u003c/li\u003e\n\u003cli\u003estarcode and requisite munging\u003c/li\u003e\n\u003cli\u003ebartender I guess\u003c/li\u003e\n\u003cli\u003er\u003c/li\u003e\n\u003cli\u003ejupyter\u003c/li\u003e\n\u003cli\u003epython3\u003c/li\u003e\n\u003cli\u003elh3\u003c/li\u003e\n\u003cli\u003epacbio\u003c/li\u003e\n\u003cli\u003enanopore\u003c/li\u003e\n\u003cli\u003ealignparse\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 1, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1488063681.0 + "updated_at": 1646881771.0 }, { "data_format": 2, - "description": "Container Template using Nix with Python3 , and examples", + "description": "Singularity Container Recipes", "filenames": [ - "Singularity" + "images/Singularity.lsf", + "images/imod/Singularity", + "images/imod/4.9.11/Singularity", + "images/imod/4.9.12/Singularity", + "images/imod/4.9.10/Singularity", + "images/imod/4.10.38/Singularity", + "images/imod/4.10.42/Singularity", + "images/appion-protomo/Singularity", + "images/icon-gpu/Singularity", + "images/protomo/Singularity", + "images/topaz/0.2.4/Singularity", + "images/topaz/0.2.2/Singularity", + "images/resmap/Singularity", + "images/cryosparc/2.14.2/Singularity", + "images/cryosparc/2.12.4/Singularity", + "images/cryosparc/2.13.2/Singularity", + "images/scipion/Singularity", + "images/rclone/Singularity", + "images/ctffind/Singularity", + "images/ctffind/4.1.13/Singularity", + "images/ctffind/4.1.10/Singularity", + "images/ctffind/4.1.12/Singularity", + "images/chimera/Singularity", + "images/openmbir/2.3.5/Singularity", + "images/motioncor2/Singularity", + "images/motioncor2/1.3.2/Singularity", + "images/motioncor2/1.2.6/Singularity", + "images/motioncor2/1.2.3-intpix/Singularity", + "images/motioncor2/1.2.3/Singularity", + "images/motioncor2/1.2.2/Singularity", + "images/motioncor2/1.2.1/Singularity", + "images/motioncor2/1.3.0/Singularity", + "images/cdms-jupyterlab/Singularity", + "images/amira/6.7.0/Singularity", + "images/pymol/Singularity", + "images/emClarity/Singularity", + "images/matlab/R2020a/Singularityfile", + "images/fah/7.5.1/Singularity", + "images/eman2/Singularity", + "images/eman2/20190603/Singularity", + "images/eman2/20190805/Singularity", + "images/eman2/20200419/Singularity", + "images/eman2/20190418/Singularity", + "images/eman2/2.31/Singularity", + "images/eman2/20190917/Singularity", + "images/eman2/20200330/Singularity", + "images/eman2/20190324/Singularity", + "images/eman2/20200319.0/Singularity", + "images/relion/Singularity.docker", + "images/relion/Singularity.old", + "images/relion/Singularity", + "images/relion/ver3.1/Singularity.docker", + "images/relion/3.0.2/Singularity.docker", + "images/relion/3.0.2/Singularity", + "images/relion/3.0.7/Singularity.orig", + "images/relion/3.0.7/Singularity", + "images/relion/2.1/Singularity.docker", + "images/relion/2.1/Singularity", + "images/relion/3.0.4/Singularity.docker", + "images/relion/3.0.4/Singularity", + "images/relion/3.1.0-beta/Singularity.docker", + "images/relion/3.1.0-beta/Singularity", + "images/relion/3.0.8/Singularity.docker", + "images/relion/3.0.6/Singularity.docker", + "images/relion/3.0.6/Singularity", + "images/phenix/Singularity", + "images/imagemagick/Singularity", + "images/xds/Singularity", + "images/openmpi/Singularity.centos7", + "images/openmpi/Singularity.ubuntu1804", + "images/openmpi/Singularity", + "images/openmpi/Singularity.ubuntu1810", + "images/rosetta/Singularity", + "images/rosetta/2018.48/Singularity", + "images/git/Singularity", + "images/tem-simulator/Singularity", + "images/slac-ml/Singularity", + "images/slac-ml/20190712.2/Singularity", + "images/slac-ml/20200211.0/Singularity", + "images/slac-ml/20200227.0/Singularity", + "images/slac-ml/20200618.0/Singularity", + "images/cryolo/1.5.4/Singularity" ], - "full_name": "XSEDE/nix-container-python", + "full_name": "slaclab/singularity-modules", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-python\u003c/h1\u003e\u003ca id=\"user-content-nix-container-python\" class=\"anchor\" aria-label=\"Permalink: nix-container-python\" href=\"#nix-container-python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains files necessary to build a container with Nix and Python to be used in the XSEDE compute environment. Both Docker and Singularity are represented.\u003c/p\u003e\n\u003cp\u003eThis repo can be cloned as a template and used as a starting point for containerizing\napplications that require Python.\u003c/p\u003e\n\u003cp\u003eFor a \u003cem\u003every\u003c/em\u003e simple example, see: \u003ca href=\"https://github.com/XSEDE/nix-container-python-mandle\"\u003ehttps://github.com/XSEDE/nix-container-python-mandle\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA more complex example is available at: \u003ca href=\"https://github.com/XSEDE/nix-container-perfzero\"\u003ehttps://github.com/XSEDE/nix-container-perfzero\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn example containing both Python and R dependencies is available at:\n\u003ca href=\"https://github.com/XSEDE/nix-container-sc-benchmark\"\u003ehttps://github.com/XSEDE/nix-container-sc-benchmark\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo include more Python libraries, edit prod-env.nix to pull these in, as demonstrated\nwith the commented-out lines installing Pillow, numpy, and the attendant system-level\ndependencies.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 16, + "subscribers_count": 5, "topics": [], - "updated_at": 1632511274.0 + "updated_at": 1646881460.0 }, { "data_format": 2, - "description": null, + "description": "a framework for automatic and comprehensive knowledge extraction based on mutational data from sequenced tumor samples from patients. ", "filenames": [ - "Singularity" + "build/containers/mutpanning/Singularity", + "build/containers/signature/Singularity", + "build/containers/transvar/Singularity", + "build/containers/smregions/Singularity", + "build/containers/oncodrivefml/Singularity", + "build/containers/hotmaps/Singularity", + "build/containers/deconstructsig/Singularity", + "build/containers/oncodriveclustl/Singularity", + "build/containers/cbase/Singularity", + "build/containers/combination/Singularity", + "build/containers/core/Singularity", + "build/containers/dndscv/Singularity" ], - "full_name": "JeffersonLab/jleic", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejleic: Singularity recipe for jleic docker container\u003c/h1\u003e\u003ca id=\"user-content-jleic-singularity-recipe-for-jleic-docker-container\" class=\"anchor\" aria-label=\"Permalink: jleic: Singularity recipe for jleic docker container\" href=\"#jleic-singularity-recipe-for-jleic-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "full_name": "bbglab/intogen-plus", + "latest_release": "v2024", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIntOGen\u003c/h1\u003e\u003ca id=\"user-content-intogen\" class=\"anchor\" aria-label=\"Permalink: IntOGen\" href=\"#intogen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!CAUTION]\nPlease note that IntOGen needs a lot of resources. We strongly suggest to run it in a cluster environment!\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall requirements\u003c/h3\u003e\u003ca id=\"user-content-install-requirements\" class=\"anchor\" aria-label=\"Permalink: Install requirements\" href=\"#install-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e (the pipeline has been tested\nwith version 2.x)\u003c/li\u003e\n\u003cli\u003eInstall nextflow (You can use \u003ccode\u003econda install nextflow==22.10.6\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload and build prerequisites\u003c/h3\u003e\u003ca id=\"user-content-download-and-build-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Download and build prerequisites\" href=\"#download-and-build-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe IntOGen pipeline requires a collection of datasets and\nSingularity containers in order to run.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e[!WARNING]\nThe pipeline has been tested with \u003cstrong\u003ehg38\u003c/strong\u003e and \u003cstrong\u003evep111\u003c/strong\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eSee the README.rst file in the \u003ccode\u003ebuild\u003c/code\u003e folder for further details.\u003c/p\u003e\n\u003cp\u003eThen you can build all the datasets from the original sources.\nNote that this process can take a very long time and it might fail if the\noriginal sources had changed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun the pipeline\u003c/h3\u003e\u003ca id=\"user-content-run-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Run the pipeline\" href=\"#run-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe IntOGen pipeline is built on top of \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e.\nIn order to execute the pipeline, you only need to execute:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run intogen.nf -resume -profile \u003cspan class=\"pl-k\"\u003elocal\u003c/span\u003e --input test/ --output ./output\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor further details, please check our documentation: \u003ca href=\"http://intogen-plus.rtfd.io/\" rel=\"nofollow\"\u003ehttp://intogen-plus.rtfd.io/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo avoid stopping the pipeline execution for one or a few incorrect\ninput, we have decided to ignore the errors of the steps by default.\nHowever, we advise to review each of them carefully to\nunderstand the underlying reasons.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLicensing\u003c/h3\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-label=\"Permalink: Licensing\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIntoGEn uses a variety of software tools and datasets that\nare released under a variety of licenses.\nIn order to accommodate all of them, the pipeline itself is released\nunder GNU General Public License version 3.\u003c/p\u003e\n\u003cp\u003eIf you are using it for research/academic purposes it should be\nfine. For commercial usage, you need to revise the license of the\ndifferent software pieces and datasets used, as some of them\n(e.g. CADD or CGC) are restricted for commercial usage.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 6, + "subscribers_count": 5, "topics": [], - "updated_at": 1513099325.0 + "updated_at": 1732279601.0 }, { "data_format": 2, - "description": "Container with NAMD 2.14 built with CUDA 10.2 support using Nix.", + "description": null, "filenames": [ - "Singularity" + "containers/DL/published/TensorFlow/2.1/cpu/src/Singularity.tensorflow-2.1-cpu-src", + "containers/DL/published/TensorFlow/2.1/cpu/pip/Singularity.tensorflow-2.1-cpu-pip", + "containers/DL/published/TensorFlow/2.1/gpu/Singularity.tensorflow-2.1-gpu-src", + "containers/DL/published/TensorFlow/2.1/gpu/src/Singularity.tensorflow-2.1-gpu-src", + "containers/DL/published/TensorFlow/2.1/gpu/pip/Singularity.tensorflow-2.1-gpu-pip", + "containers/DL/published/TensorFlow/ngraph/Singularity.tensorflow-ngraph", + "containers/DL/published/TensorFlow/latest/cpu/src/Singularity.tensorflow-latest-cpu-src", + "containers/DL/published/TensorFlow/latest/cpu/pip/Singularity.tensorflow-latest-cpu-pip", + "containers/DL/published/TensorFlow/latest/gpu/Singularity.tensorflow-latest-gpu-src", + "containers/DL/published/TensorFlow/latest/gpu/src/Singularity.tensorflow-latest-gpu-src", + "containers/DL/published/TensorFlow/latest/gpu/pip/Singularity.tensorflow-latest-gpu-pip", + "containers/DL/published/PyTorch/1.5/cpu/src/Singularity.pytorch-1.5-cpu-src", + "containers/DL/published/PyTorch/1.5/cpu/src/glow/Singularity.pytorch-1.5-cpu-src-glow", + "containers/DL/published/PyTorch/1.5/cpu/pip/Singularity.pytorch-1.5-cpu-pip", + "containers/DL/published/PyTorch/1.5/gpu/src/Singularity.pytorch-1.5-gpu-src", + "containers/DL/published/PyTorch/1.5/gpu/pip/Singularity.pytorch-1.5-gpu-pip", + "containers/DL/published/PyTorch/latest/cpu/src/Singularity.pytorch-1.5-cpu-src", + "containers/DL/published/PyTorch/latest/cpu/src/glow/Singularity", + "containers/DL/published/PyTorch/latest/cpu/pip/Singularity.pytorch-latest-cpu-pip", + "containers/DL/published/PyTorch/latest/gpu/src/Singularity.pytorch-latest-gpu-src", + "containers/DL/published/PyTorch/latest/gpu/pip/Singularity.pytorch-latest-gpu-pip" ], - "full_name": "XSEDE/nix-container-namd2.14-cuda10.2", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-namd2.14-cuda10.2\u003c/h1\u003e\u003ca id=\"user-content-nix-container-namd214-cuda102\" class=\"anchor\" aria-label=\"Permalink: nix-container-namd2.14-cuda10.2\" href=\"#nix-container-namd214-cuda102\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5362\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContainer with Nix, namd, and CUDA to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", + "full_name": "SODALITE-EU/application-optimisation", + "latest_release": "M36Release", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSODALITE Application Optimizer (MODAK)\u003c/h1\u003e\u003ca id=\"user-content-sodalite-application-optimizer-modak\" class=\"anchor\" aria-label=\"Permalink: SODALITE Application Optimizer (MODAK)\" href=\"#sodalite-application-optimizer-modak\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe MODAK (Model Optimized Deployment of Applications in Containers) package, a software-defined optimization framework for containerized MPI and DL applications, is the SODALITE component responsible for enabling the static optimization of applications before deployment. It aims to optimize the performance of application deployment to infrastructure in a software-defined way. Automation in application optimization is enabled using performance modeling and container technology. Containers provide an optimized runtime for application deployment based on the target hardware and along with any software dependencies and libraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains all components for the Application Optimizer:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"MODAK\"\u003eMODAK\u003c/a\u003e - the main application optimizer\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Performance-Model\"\u003ePerformance-Model\u003c/a\u003e - Infrastructure and Application Performance model\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"containers\"\u003econtainers\u003c/a\u003e - Tests to optimize containers for DL applications\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"use-cases\"\u003euse-cases\u003c/a\u003e - SODALITE Use Case applications\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor development you need the \u003ccode\u003epre-commit\u003c/code\u003e tools.\nThis registers the \u003ccode\u003epre-commit\u003c/code\u003e hooks for the current git checkout such\nthat tools like \u003ccode\u003eblack\u003c/code\u003e or \u003ccode\u003eflake8\u003c/code\u003e are run automatically on commit.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003epip install pre-commit\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003epre-commit install --install-hooks\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo manually check that the current tree is clean:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003epre-commit run -a\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use MODAK\u003c/h2\u003e\u003ca id=\"user-content-how-to-use-modak\" class=\"anchor\" aria-label=\"Permalink: How to use MODAK\" href=\"#how-to-use-modak\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease follow the instructions in the \u003ca href=\"MODAK\"\u003eMODAK\u003c/a\u003e directory.\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 16, + "subscribers_count": 4, "topics": [], - "updated_at": 1628541599.0 + "updated_at": 1693990969.0 }, { "data_format": 2, - "description": "Singularity base images that will be build on singularity hub and can be used to build other images", + "description": "Comp 550 Fall 2023 - Natural Language Processing with Jackie Cheung", "filenames": [ - "Singularity.R363_python368", - "Singularity.DoubletDetection", - "Singularity.DemultiplexingSoftwares", - "Singularity.Anne_demultiplexing_test", - "Singularity.AllSoftwares", - "Singularity.TxnDoubletDetection", - "Singularity.R4_python368" + "reading-assignment-4/Singularity", + "assignment-1/Singularity", + "reading-assignment-2/Singularity", + "reading-assignment-3/Singularity", + "assignment-2/Singularity" ], - "full_name": "powellgenomicslab/SingularityBaseImages", + "full_name": "mathematiguy/comp-550", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularityBaseImages\u003c/h1\u003e\u003ca id=\"user-content-singularitybaseimages\" class=\"anchor\" aria-label=\"Permalink: SingularityBaseImages\" href=\"#singularitybaseimages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA repo for singularity images. This is linked to singularity hub and all results can be pulled from there.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Hub Images\u003c/h2\u003e\u003ca id=\"user-content-singularity-hub-images\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub Images\" href=\"#singularity-hub-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.R363_python368\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo pull: \u003ccode\u003esingularity pull shub://drneavin/SingularityBaseImages:r363_python368\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eContains:\n\u003cul\u003e\n\u003cli\u003eR 3.6.3\u003c/li\u003e\n\u003cli\u003epython 3.6.8\u003c/li\u003e\n\u003cli\u003econda\u003c/li\u003e\n\u003cli\u003eSome basic R packages (see the definition file to see all installed)\u003c/li\u003e\n\u003cli\u003eSome basic python package (see the definition file to see all installed)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.R4_python368\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo pull: \u003ccode\u003esingularity pull shub://drneavin/SingularityBaseImages:r4_python368\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eContains:\n\u003cul\u003e\n\u003cli\u003eR 4.0.3\u003c/li\u003e\n\u003cli\u003epython 3.6.8\u003c/li\u003e\n\u003cli\u003econda\u003c/li\u003e\n\u003cli\u003eSome basic R packages (see the definition file to see all installed)\u003c/li\u003e\n\u003cli\u003eSome basic python package (see the definition file to see all installed)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity.TxnDoubletDetection\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo pull: \u003ccode\u003esingularity pull shub://drneavin/SingularityBaseImages:txndoubletdetection\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBuilt on top of \u003ccode\u003eSingularity.R4_python368\u003c/code\u003e image\u003c/li\u003e\n\u003cli\u003eAlso contains:\n\u003cul\u003e\n\u003cli\u003eDoubletDetection\u003c/li\u003e\n\u003cli\u003eDoubletDecon\u003c/li\u003e\n\u003cli\u003eDoubletFinder\u003c/li\u003e\n\u003cli\u003escds\u003c/li\u003e\n\u003cli\u003escrublet\u003c/li\u003e\n\u003cli\u003escDoubletFinder\u003c/li\u003e\n\u003cli\u003esolo\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecomp-550\u003c/h1\u003e\u003ca id=\"user-content-comp-550\" class=\"anchor\" aria-label=\"Permalink: comp-550\" href=\"#comp-550\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComp 550 Fall 2023 - Natural Language Processing with Jackie Cheung\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1611115640.0 + "updated_at": 1705846634.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "docker/Singularity", + "docker/railrl_v9_cuda10-1_mj1-50-1-59_torch0-4-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_hand_tf_v1/Singularity", + "docker/railrl_hand_tf_v1/Singularity_cpu", + "docker/railrl_hand_v2/Singularity", + "docker/railrl_hand_v2/Singularity_cpu", + "docker/railrl_hand_v3/Singularity", + "docker/railrl_hand_v3/Singularity_cpu", + "docker/railrl_gpu_mujoco1-5-v4/singularity/Singularity", + "docker/railrl_v8_cuda10-1/Singularity", + "docker/railrl_v6_cuda9/Singularity", + "docker/railrl_v11_cuda10-1_mj2-0-2-2_torch0-3-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v7/Singularity", + "docker/railrl_v5/singularity/Singularity", + "docker/railrl_hand_v1/Singularity", + "docker/railrl_hand_v1/Singularity_cpu", + "docker/railrl_v10_cuda10-1_mj2-0-2-2_torch0-4-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v6_cuda8/Singularity", + "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", + "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", + "docker/railrl_ray/Singularity", + "docker/railrl_v9-5_cuda10-1_mj1-50-1-59_torch1-1-0_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v7_cuda8/Singularity", + "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch_cuda8", + "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch", + "experiments/ashvin/icml2020/singularity/Singularity" ], - "full_name": "HliasGit/sweng_proj_pt2", + "full_name": "Asap7772/railrl_evalsawyer", "latest_release": null, + "readme": "\u003cp\u003eREADME last updated on: 01/24/2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erailrl\u003c/h1\u003e\u003ca id=\"user-content-railrl\" class=\"anchor\" aria-label=\"Permalink: railrl\" href=\"#railrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReinforcement learning framework.\nSome implemented algorithms:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/ddpg.py\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/sac.py\"\u003eSoft Actor Critic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/dqn_and_double_dqn.py\"\u003e(Double) Deep Q-Network (DQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/her.py\"\u003eHindsight Experience Replay (HER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/model_based_dagger.py\"\u003eMPC with Neural Network Model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/naf.py\"\u003eNormalized Advantage Function (NAF)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eWARNING: I haven\u0027t tested this NAF implementation much, so it may not match the paper\u0027s performance. I\u0027m pretty confident about the other two implementations though.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, checkout the example scripts, linked above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSome dependancies\u003c/h3\u003e\u003ca id=\"user-content-some-dependancies\" class=\"anchor\" aria-label=\"Permalink: Some dependancies\" href=\"#some-dependancies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo apt-get install swig\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate Conda Env\u003c/h3\u003e\u003ca id=\"user-content-create-conda-env\" class=\"anchor\" aria-label=\"Permalink: Create Conda Env\" href=\"#create-conda-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall and use the included ananconda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda env create -f docker/railrl/railrl-env.yml\n$ source activate railrl-env\n(railrl-env) $ # Ready to run examples/ddpg_cheetah_no_doodad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if you want you can use the docker image included.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Simulation Env Code\u003c/h3\u003e\u003ca id=\"user-content-download-simulation-env-code\" class=\"anchor\" aria-label=\"Permalink: Download Simulation Env Code\" href=\"#download-simulation-env-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vitchyr/multiworld\"\u003emultiworld\u003c/a\u003e (contains environments):\u003ccode\u003egit clone https://github.com/vitchyr/multiworld\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e(Optional) Install doodad\u003c/h3\u003e\u003ca id=\"user-content-optional-install-doodad\" class=\"anchor\" aria-label=\"Permalink: (Optional) Install doodad\" href=\"#optional-install-doodad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI recommend installing \u003ca href=\"https://github.com/justinjfu/doodad\"\u003edoodad\u003c/a\u003e to\nlaunch jobs. Some of its nice features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEasily switch between running code locally, on a remote compute with\nDocker, on EC2 with Docker\u003c/li\u003e\n\u003cli\u003eEasily add your dependencies that can\u0027t be installed via pip (e.g. you\nborrowed someone\u0027s code)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you install doodad, also modify \u003ccode\u003eCODE_DIRS_TO_MOUNT\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePath to rllab directory\u003c/li\u003e\n\u003cli\u003ePath to railrl directory\u003c/li\u003e\n\u003cli\u003ePath to other code you want to juse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou\u0027ll probably also need to update the other variables besides the docker\nimages/instance stuff.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup Config File\u003c/h3\u003e\u003ca id=\"user-content-setup-config-file\" class=\"anchor\" aria-label=\"Permalink: Setup Config File\" href=\"#setup-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must setup the config file for launching experiments, providing paths to your code and data directories. Inside \u003ccode\u003erailrl/config/launcher_config.py\u003c/code\u003e, fill in the appropriate paths. You can use \u003ccode\u003erailrl/config/launcher_config_template.py\u003c/code\u003e as an example reference.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecp railrl/launchers/config-template.py railrl/launchers/config.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a policy and seeing results\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-policy-and-seeing-results\" class=\"anchor\" aria-label=\"Permalink: Visualizing a policy and seeing results\" href=\"#visualizing-a-policy-and-seeing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDuring training, the results will be saved to a file called under\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e is the directory set by \u003ccode\u003erailrl.launchers.config.LOCAL_LOG_DIR\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;exp_prefix\u0026gt;\u003c/code\u003e is given either to \u003ccode\u003esetup_logger\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;foldername\u0026gt;\u003c/code\u003e is auto-generated and based off of \u003ccode\u003eexp_prefix\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003einside this folder, you should see a file called \u003ccode\u003eparams.pkl\u003c/code\u003e. To visualize a policy, run\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e(railrl) $ python scripts/sim_policy LOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;/params.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have rllab installed, you can also visualize the results\nusing \u003ccode\u003erllab\u003c/code\u003e\u0027s viskit, described at\nthe bottom of \u003ca href=\"http://rllab.readthedocs.io/en/latest/user/cluster.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython rllab/viskit/frontend.py LOCAL_LOG_DIR/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eexp_prefix\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd paths\u003c/h3\u003e\u003ca id=\"user-content-add-paths\" class=\"anchor\" aria-label=\"Permalink: Add paths\" href=\"#add-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=$PYTHONPATH:/path/to/multiworld/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/doodad/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/viskit/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/railrl-private/repo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredit\u003c/h2\u003e\u003ca id=\"user-content-credit\" class=\"anchor\" aria-label=\"Permalink: Credit\" href=\"#credit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA lot of the coding infrastructure is based on \u003ca href=\"https://github.com/rll/rllab\"\u003erllab\u003c/a\u003e.\nAlso, the serialization and logger code are basically a carbon copy.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1717593518.0 + "updated_at": 1679763993.0 }, { "data_format": 2, - "description": "the barn challenge submission", + "description": null, "filenames": [ - "Singularityfile.def" + "docker/Singularity", + "docker/railrl_v9_cuda10-1_mj1-50-1-59_torch0-4-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_hand_tf_v1/Singularity", + "docker/railrl_hand_tf_v1/Singularity_cpu", + "docker/railrl_hand_v2/Singularity", + "docker/railrl_hand_v2/Singularity_cpu", + "docker/railrl_hand_v3/Singularity", + "docker/railrl_hand_v3/Singularity_cpu", + "docker/railrl_gpu_mujoco1-5-v4/singularity/Singularity", + "docker/railrl_v8_cuda10-1/Singularity", + "docker/railrl_v6_cuda9/Singularity", + "docker/railrl_v11_cuda10-1_mj2-0-2-2_torch0-3-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v7/Singularity", + "docker/railrl_v5/singularity/Singularity", + "docker/railrl_hand_v1/Singularity", + "docker/railrl_hand_v1/Singularity_cpu", + "docker/railrl_v10_cuda10-1_mj2-0-2-2_torch0-4-1_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v6_cuda8/Singularity", + "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity", + "docker/railrl_v12_cuda10-1_mj2-0-2-2_torch1-1-0_gym0-12-5_py3-6-5/Singularity_cpu", + "docker/railrl_ray/Singularity", + "docker/metac_railrl_v12_cuda10-1_mj2-0-2-2_torch1-4-0_gym0-12-5_py3-6-5/Singularity", + "docker/metac_railrl_v12_cuda10-1_mj2-0-2-2_torch1-4-0_gym0-12-5_py3-6-5/Singularity_cpu", + "docker/railrl_v9-5_cuda10-1_mj1-50-1-59_torch1-1-0_gym0-10-5_py3-5-2/Singularity", + "docker/railrl_v7_cuda8/Singularity", + "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch_cuda8", + "docker/railrl_ray_gym-0-12-0/Singularity_from_scratch" ], - "full_name": "shanzewang/barn-EIT-NUS", + "full_name": "jcoreyes/erl", "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/BARN_Challenge.png\"\u003e\u003cimg width=\"100%\" src=\"res/BARN_Challenge.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eICRA 2024 BARN Challenge\u003c/h1\u003e\u003ca id=\"user-content-icra-2024-barn-challenge\" class=\"anchor\" aria-label=\"Permalink: ICRA 2024 BARN Challenge\" href=\"#icra-2024-barn-challenge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you run it on a local machine without containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eROS version at least Kinetic\u003c/li\u003e\n\u003cli\u003eCMake version at least 3.0.2\u003c/li\u003e\n\u003cli\u003ePython version at least 3.6\u003c/li\u003e\n\u003cli\u003ePython packages: defusedxml, rospkg, netifaces, numpy\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run it in Singularity containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGo version at least 1.13\u003c/li\u003e\n\u003cli\u003eSingularity version at least 3.6.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe requirements above are just suggestions. If you run into any issue, please contact organizers for help (\u003ca href=\"mailto:zfxu@utexas.edu\"\u003ezfxu@utexas.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow the instructions below to run simulations on your local machines. (You can skip 1-6 if you only use Singularity container)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a virtual environment (we show examples with python venv, you can use conda instead)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eapt -y update; apt-get -y install python3-venv\npython3 -m venv /\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge\nexport PATH=\"/\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge/bin:$PATH\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eInstall Python dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install defusedxml rospkg netifaces numpy\npip3 install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eClone this repo and required ros packages\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/shanzewang/barn-EIT-NUS.git\ngit clone https://github.com/jackal/jackal.git\ngit clone https://github.com/jackal/jackal_simulator.git\ngit clone https://github.com/jackal/jackal_desktop.git\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInstall ROS package dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/\u0026lt;YOUR_ROS_VERSION\u0026gt;/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=melodic\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eBuild the workspace\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esource devel/setup.bash\ncatkin_make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollow the instruction below to run simulations in Singularity containers.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 is required to successfully build the image!\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repo\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/shanzewang/barn-EIAS-NUS.git\ncd barn-EIAS-NUS\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild Singularity image (sudo access required)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --notest nav_competition_image.sif Singularityfile.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Simulations\u003c/h2\u003e\u003ca id=\"user-content-run-simulations\" class=\"anchor\" aria-label=\"Permalink: Run Simulations\" href=\"#run-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNavigate to the folder of this repo. Below is the example to run move_base with DWA as local planner.\u003c/p\u003e\n\u003cp\u003eIf you run it on your local machines: (the example below runs \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e with DWA local planner in world 0)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource ../../devel/setup.sh\npython3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you run it in a Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA successful run should print the episode status (collided/succeeded/timeout) and the time cost in second:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation collided with time 27.2930 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation succeeded with time 29.4610 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation timeout with time 100.0000 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest your own navigation stack\u003c/h2\u003e\u003ca id=\"user-content-test-your-own-navigation-stack\" class=\"anchor\" aria-label=\"Permalink: Test your own navigation stack\" href=\"#test-your-own-navigation-stack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe currently don\u0027t provide a lot of instructions or a standard API for implementing the navigation stack, but we might add more in this section depending on people\u0027s feedback. If you are new to the ROS or mobile robot navigation, we suggest checking \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e which provides basic interface to manipulate a robot.\u003c/p\u003e\n\u003cp\u003eThe suggested work flow is to edit section 1 in \u003ccode\u003erun.py\u003c/code\u003e file (line 89-109) that initialize your own navigation stack. You should not edit other parts in this file. We provide a bash script \u003ccode\u003etest.sh\u003c/code\u003e to run your navigation stack on 50 uniformly sampled BARN worlds with 10 runs for each world. Once the tests finish, run \u003ccode\u003epython report_test.py --out_path /path/to/out/file\u003c/code\u003e to report the test. Below is an example of DWA:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython report_test.py --out_path res/dwa_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see the report as this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAvg Time: 39.5296, Avg Metric: 0.1577, Avg Success: 0.8500, Avg Collision: 0.0560, Avg Timeout: 0.0940\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSubmission\u003c/h2\u003e\u003ca id=\"user-content-submission\" class=\"anchor\" aria-label=\"Permalink: Submission\" href=\"#submission\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSubmit a link that downloads your customized repository to this \u003ca href=\"https://docs.google.com/forms/d/e/1FAIpQLScGCMwVm-Kzg2c3kkXd_IUNwHw8D3s06ydCg4lPgOJYkEy8aQ/viewform?usp=sf_link\" rel=\"nofollow\"\u003eGoogle form\u003c/a\u003e. Your navigation stack will be tested in the Singularity container on 50 hold-out BARN worlds sampled from the same distribution as the 300 BARN worlds. In the repository, make sure the \u003ccode\u003erun.py\u003c/code\u003e runs your navigation stack and \u003ccode\u003eSingularityfile.def\u003c/code\u003e installs all the dependencies of your repo. We suggest to actually build an image and test it with \u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\u003c/code\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003eREADME last updated on: 01/24/2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erailrl\u003c/h1\u003e\u003ca id=\"user-content-railrl\" class=\"anchor\" aria-label=\"Permalink: railrl\" href=\"#railrl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReinforcement learning framework.\nSome implemented algorithms:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/ddpg.py\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/sac.py\"\u003eSoft Actor Critic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/dqn_and_double_dqn.py\"\u003e(Double) Deep Q-Network (DQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/her.py\"\u003eHindsight Experience Replay (HER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/model_based_dagger.py\"\u003eMPC with Neural Network Model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/naf.py\"\u003eNormalized Advantage Function (NAF)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eWARNING: I haven\u0027t tested this NAF implementation much, so it may not match the paper\u0027s performance. I\u0027m pretty confident about the other two implementations though.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, checkout the example scripts, linked above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSome dependancies\u003c/h3\u003e\u003ca id=\"user-content-some-dependancies\" class=\"anchor\" aria-label=\"Permalink: Some dependancies\" href=\"#some-dependancies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esudo apt-get install swig\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate Conda Env\u003c/h3\u003e\u003ca id=\"user-content-create-conda-env\" class=\"anchor\" aria-label=\"Permalink: Create Conda Env\" href=\"#create-conda-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall and use the included ananconda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda env create -f docker/railrl/railrl-env.yml\n$ source activate railrl-env\n(railrl-env) $ # Ready to run examples/ddpg_cheetah_no_doodad.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if you want you can use the docker image included.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Simulation Env Code\u003c/h3\u003e\u003ca id=\"user-content-download-simulation-env-code\" class=\"anchor\" aria-label=\"Permalink: Download Simulation Env Code\" href=\"#download-simulation-env-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vitchyr/multiworld\"\u003emultiworld\u003c/a\u003e (contains environments):\u003ccode\u003egit clone https://github.com/vitchyr/multiworld\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e(Optional) Install doodad\u003c/h3\u003e\u003ca id=\"user-content-optional-install-doodad\" class=\"anchor\" aria-label=\"Permalink: (Optional) Install doodad\" href=\"#optional-install-doodad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI recommend installing \u003ca href=\"https://github.com/justinjfu/doodad\"\u003edoodad\u003c/a\u003e to\nlaunch jobs. Some of its nice features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEasily switch between running code locally, on a remote compute with\nDocker, on EC2 with Docker\u003c/li\u003e\n\u003cli\u003eEasily add your dependencies that can\u0027t be installed via pip (e.g. you\nborrowed someone\u0027s code)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you install doodad, also modify \u003ccode\u003eCODE_DIRS_TO_MOUNT\u003c/code\u003e in \u003ccode\u003econfig.py\u003c/code\u003e to\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePath to rllab directory\u003c/li\u003e\n\u003cli\u003ePath to railrl directory\u003c/li\u003e\n\u003cli\u003ePath to other code you want to juse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou\u0027ll probably also need to update the other variables besides the docker\nimages/instance stuff.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup Config File\u003c/h3\u003e\u003ca id=\"user-content-setup-config-file\" class=\"anchor\" aria-label=\"Permalink: Setup Config File\" href=\"#setup-config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou must setup the config file for launching experiments, providing paths to your code and data directories. Inside \u003ccode\u003erailrl/config/launcher_config.py\u003c/code\u003e, fill in the appropriate paths. You can use \u003ccode\u003erailrl/config/launcher_config_template.py\u003c/code\u003e as an example reference.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecp railrl/launchers/config-template.py railrl/launchers/config.py\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a policy and seeing results\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-policy-and-seeing-results\" class=\"anchor\" aria-label=\"Permalink: Visualizing a policy and seeing results\" href=\"#visualizing-a-policy-and-seeing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDuring training, the results will be saved to a file called under\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e is the directory set by \u003ccode\u003erailrl.launchers.config.LOCAL_LOG_DIR\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;exp_prefix\u0026gt;\u003c/code\u003e is given either to \u003ccode\u003esetup_logger\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;foldername\u0026gt;\u003c/code\u003e is auto-generated and based off of \u003ccode\u003eexp_prefix\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003einside this folder, you should see a file called \u003ccode\u003eparams.pkl\u003c/code\u003e. To visualize a policy, run\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e(railrl) $ python scripts/sim_policy LOCAL_LOG_DIR/\u0026lt;exp_prefix\u0026gt;/\u0026lt;foldername\u0026gt;/params.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have rllab installed, you can also visualize the results\nusing \u003ccode\u003erllab\u003c/code\u003e\u0027s viskit, described at\nthe bottom of \u003ca href=\"http://rllab.readthedocs.io/en/latest/user/cluster.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython rllab/viskit/frontend.py LOCAL_LOG_DIR/\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eexp_prefix\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd paths\u003c/h3\u003e\u003ca id=\"user-content-add-paths\" class=\"anchor\" aria-label=\"Permalink: Add paths\" href=\"#add-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=$PYTHONPATH:/path/to/multiworld/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/doodad/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/viskit/repo\nexport PYTHONPATH=$PYTHONPATH:/path/to/railrl-private/repo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredit\u003c/h2\u003e\u003ca id=\"user-content-credit\" class=\"anchor\" aria-label=\"Permalink: Credit\" href=\"#credit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA lot of the coding infrastructure is based on \u003ca href=\"https://github.com/rll/rllab\"\u003erllab\u003c/a\u003e.\nAlso, the serialization and logger code are basically a carbon copy.\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1725259274.0 + "updated_at": 1603485789.0 }, { "data_format": 2, - "description": "QGIS in a Singularity container", + "description": null, "filenames": [ - "Singularity.3.4.12", - "Singularity" - ], - "full_name": "OSC/sa_singularity_qgis", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity QGIS\u003c/h1\u003e\u003ca id=\"user-content-singularity-qgis\" class=\"anchor\" aria-label=\"Permalink: Singularity QGIS\" href=\"#singularity-qgis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3587\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://qgis.org/en/site/index.html\" rel=\"nofollow\"\u003eQGIS\u003c/a\u003e. It was built on top of the base Docker image \u003ca href=\"https://hub.docker.com/_/ubuntu\" rel=\"nofollow\"\u003eubuntu\u003c/a\u003e. Packages installed: \u003ccode\u003eqgis qgis-plugin-grass\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003eqgis.sif\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build qgis.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull qgis.sif shub://OSC/sa_singularity_qgis\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart QGIS\u003c/h3\u003e\u003ca id=\"user-content-start-qgis\" class=\"anchor\" aria-label=\"Permalink: Start QGIS\" href=\"#start-qgis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eQGIS is started using the default run command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run qgis.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor as a native command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./qgis.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 1, - "subscribers_count": 8, - "topics": [], - "updated_at": 1669143061.0 - }, - { - "data_format": 2, - "description": "Singularity container recipe for Deep Learning for architecture x86_64 based on a CentOS 7. In the specific: centos7.8.2003 - GNU compiler 7 - Python 3.6 - OpenMPI 2.1.1 (compiled with support for psm2, pmix, verbs) - Tensorflow 1.14.0 CPU (pip) - Py-Torch 1.4.0 CPU (pip) - Torchvision 0.5.0 CPU (pip) - MxNet 1.6.0 CPU (pip) - Horovod 0.19.1 (compiled with Tensorflow, Pytorch, MxNet)", - "filenames": [ - "Singularity" + "singularity/Singularity.2020.10.21.5755", + "singularity/Singularity.2020.04.09.5012", + "singularity/Singularity.2019.01.30.3436", + "singularity/Singularity.2023.10.7317.0", + "singularity/Singularity.2019.06.05.3920", + "singularity/Singularity.2022.06.7046.0", + "singularity/Singularity.2021.04.15.6139", + "singularity/Singularity.2018.01.30.2253", + "singularity/Singularity.2019.10.04.4236", + "singularity/Singularity.2022.12.7130.0", + "singularity/Singularity.2022.09.7085.0", + "singularity/Singularity.2020.08.04.5350", + "singularity/Singularity.2019.04.03.3693", + "singularity/Singularity.2019.07.18.4025", + "singularity/Singularity.2021.10.8.6835", + "singularity/Singularity.2023.09.7308.0", + "singularity/Singularity.2018.09.28.3099", + "singularity/Singularity.2021.9.21.6799", + "singularity/Singularity.2020.11.27.5887", + "singularity/Singularity.2022.04.7021.0", + "singularity/Singularity.2024.05.7470.0", + "singularity/Singularity.2020.09.17.5665", + "singularity/Singularity.2019.01.08.3392" ], - "full_name": "CINECA-HPC/container_deep_learning_cpu_centos7_x86_64", + "full_name": "APSIMInitiative/APSIM.HPC", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainer_deep_learning_cpu_centos7_x86_64\u003c/h1\u003e\u003ca id=\"user-content-container_deep_learning_cpu_centos7_x86_64\" class=\"anchor\" aria-label=\"Permalink: container_deep_learning_cpu_centos7_x86_64\" href=\"#container_deep_learning_cpu_centos7_x86_64\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContainer recipes for Deep Learning for architecture x86_64 based on a CentOS 7.\nIn the specific:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ecentos7.8.2003\u003c/li\u003e\n\u003cli\u003eGNU compiler 7\u003c/li\u003e\n\u003cli\u003ePython 3.6\u003c/li\u003e\n\u003cli\u003eOpenMPI 2.1.1 (compiled with support for psm2, pmix, verbs)\u003c/li\u003e\n\u003cli\u003eTensorflow 1.14.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003ePy-Torch 1.4.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003eTorchvision 0.5.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003eMxNet 1.6.0 CPU (pip)\u003c/li\u003e\n\u003cli\u003eHorovod 0.19.1 (compiled with Tensorflow, Pytorch, MxNet)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis recipe works on both clusters in Cineca (arch x86_64):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarconi Skylake,\u003c/li\u003e\n\u003cli\u003eGalileo\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAPSIM.HPC\u003c/h1\u003e\u003ca id=\"user-content-apsimhpc\" class=\"anchor\" aria-label=\"Permalink: APSIM.HPC\" href=\"#apsimhpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains Singularity recipes for building and running APSIM within Singularity containers. The principal motivation for using these Singularity recipes is to execute APSIM within a high-performance computing (HPC) environment.\u003c/p\u003e\n\u003cp\u003ePlease refer to the official Singularity documentation for more information: \u003ca href=\"https://docs.sylabs.io/guides/latest/user-guide/\" rel=\"nofollow\"\u003ehttps://docs.sylabs.io/guides/latest/user-guide/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample\u003c/h2\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the following to build and execute a Singularity container for APSIM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl -o apsim.Singularity https://raw.githubusercontent.com/APSIMInitiative/APSIM.HPC/main/singularity/Singularity.2022.12.7130.0\nsingularity build apsim.sif apsim.Singularity\n./apsim.sif \n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 1, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1644835746.0 + "updated_at": 1725169910.0 }, { "data_format": 2, - "description": "Dockerfile for HAND-TauDEM", + "description": "Hyperparameter Optimization via Interacting with Probabilistic Circuits", "filenames": [ - "Singularity" + "HPOBench/hpobench/container/recipes/Singularity.template", + "HPOBench/hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine", + "HPOBench/hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", + "HPOBench/hpobench/container/recipes/od/Singularity.ODBenchmarks", + "HPOBench/hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", + "HPOBench/hpobench/container/recipes/ml/Singularity.ml_mmfb", + "HPOBench/hpobench/container/recipes/ml/Singularity.PyBNN", + "HPOBench/hpobench/container/recipes/ml/Singularity.SupportVectorMachine", + "HPOBench/hpobench/container/recipes/ml/Singularity.XGBoostBenchmark", + "HPOBench/hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark", + "HPOBench/hpobench/container/recipes/nas/Singularity.TabularBenchmarks", + "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_101", + "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_201", + "HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_1shot1", + "HPOBench/hpobench/container/recipes/rl/Singularity.learnaBenchmark", + "HPOBench/hpobench/container/recipes/rl/Singularity.Cartpole" ], - "full_name": "dhardestylewis/hand_taudem_docker", + "full_name": "ml-research/ibo-hpc", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDockerfile for HAND-TauDEM\u003c/h1\u003e\u003ca id=\"user-content-dockerfile-for-hand-taudem\" class=\"anchor\" aria-label=\"Permalink: Dockerfile for HAND-TauDEM\" href=\"#dockerfile-for-hand-taudem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHyperparameter Optimization via Interacting with Probabilistic Circuits\u003c/h1\u003e\u003ca id=\"user-content-hyperparameter-optimization-via-interacting-with-probabilistic-circuits\" class=\"anchor\" aria-label=\"Permalink: Hyperparameter Optimization via Interacting with Probabilistic Circuits\" href=\"#hyperparameter-optimization-via-interacting-with-probabilistic-circuits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code of the paper titled \u003ccode\u003eHyperparameter Optimization via Interacting with Probabilistic Circuits\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbstract\u003c/h2\u003e\u003ca id=\"user-content-abstract\" class=\"anchor\" aria-label=\"Permalink: Abstract\" href=\"#abstract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDespite the growing interest in designing truly interactive hyperparameter optimization (HPO) methods, only a few allow human feedback to be included. However, these methods add friction to the interactive process, rigidly requiring users to define prior distributions ex ante and often imposing additional constraints on the optimization framework. This hinders flexible incorporation of expertise and valuable knowledge of domain experts, who might provide partial feedback at any time during optimization. To overcome these limitations, we introduce a novel Bayesian optimization approach leveraging probabilistic circuits (PCs) as a surrogate model. PCs encode a tractable joint distribution over the hybrid hyperparameter space and evaluation scores. They enable tractable and exact conditional inference and sampling, allowing users to provide beliefs interactively and generate configurations adhering to their feedback. We demonstrate the benefits of the resulting interactive HPO through an extensive empirical evaluation of diverse benchmarks, including the challenging setting of neural architecture search.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend to run the code in a \u003ccode\u003eDocker\u003c/code\u003e container, we don\u0027t guarantee a working version without Docker.\u003c/p\u003e\n\u003cp\u003eTo setup the code and run experiments do the following instructions:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eNavigate to the root directory of the repository\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003edocker build --build-arg baseimage=[BASE_IMAGE] --no-cache --tag ihpo .\u003c/code\u003e where \u003ccode\u003eBASE_IMAGE=python:3.9-slim\u003c/code\u003e depending on whether you\u0027d like to use GPU or not.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003edocker run -v .:/app/ -it ihpo\u003c/code\u003e to enter the Docker container.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython main.py --exp nas101 --optimizer pc\u003c/code\u003e to run the code (change options accordingly).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractions\u003c/h2\u003e\u003ca id=\"user-content-interactions\" class=\"anchor\" aria-label=\"Permalink: Interactions\" href=\"#interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo simulate user interactions we allow you to put json-files into the \u003ccode\u003einterventions\u003c/code\u003e directory. The json-files should have the following structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[\n {\n \"type\": \"good\" or \"bad\",\n \"kind\": \"point\" or \"dist\"\n \"intervention\": {YOUR INTERVENTION},\n \"iteration\" ITERATION TO TRIGGER INTERACTION\n },\n ...\n]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can put as many interactions as you like into the json-file. The interactions will be evaluated top down, i.e. the first appearing in the list first and so on.\nThe \u003ccode\u003eiteration\u003c/code\u003e field only holds information for the optimizer when to use the interaction but it has no effect on the order of how the interactions are triggered. This means that \u003cstrong\u003einteractions have to be ordered correctly in the list!\u003c/strong\u003e Otherwise this might cause errors or unexpected behavior.\u003c/p\u003e\n\u003cp\u003eThe \"type\" is only meta-data and specifies whether an interaction is intended to be beneficial or harmful. The \"kind\" field defines whether the interaction is a point-interaction, i.e. the user fixes a certain hyperparameter to a certain value or whether the interaction is a distribution, i.e. the user specifies a distribution over possible interactions. The \"intervention\" field then specifies the exact interaction as a dictionary where {\"hyperparameter\": VALUE or {\"dist:\" [\"uniform, cat, gauss\"], \"parameters\": [parameters]}}\" is the syntax. The \"iteration\" field specifies the iteration the interaction is applied. See the existing json-files in the \u003ccode\u003einterventions\u003c/code\u003e-directory for examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePlotting\u003c/h2\u003e\u003ca id=\"user-content-plotting\" class=\"anchor\" aria-label=\"Permalink: Plotting\" href=\"#plotting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo plot the results of the optimization runs, swith to the directory \u003ccode\u003eihpo/utils\u003c/code\u003e. Here you can execute \u003ccode\u003epython plot.py\u003c/code\u003e with several arguments (described in the argument definitions). Generating some plots can take a while (the regret_vs_cost up to 5-10 minutes).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTroubleshooting\u003c/h2\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSometimes you might encounter an error of scikit-learn stating that some metric(s) cannot be imported.\nThere is a hacky workaround that resolves this issue:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003epip uninstall scikit-learn pandas\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip install scikit-learn pandas\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip uninstall scikit-learn\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip install scikit-learn==1.1.3\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis forces pip to downgrade to \u003ccode\u003escikit-learn==1.1.3.\u003c/code\u003e while keeping the other packages at higher versions (might cause warnings by pip). The code should work after that.\u003c/p\u003e\n\u003cp\u003eIf the automatic setup of the dataset fails, try to manually run the following command in your docker container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egdown 1KSjP3-5x1O0_2-DuQJgcXG3_h_AiYh0j --folder -O /app/data/\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf it does not succeed, try to download the contents of the Google Drive directory with the given ID (\u003ccode\u003e1KSjP3-5x1O0_2-DuQJgcXG3_h_AiYh0j\u003c/code\u003e) manually one by one and place it in the \u003ccode\u003e/app/data/\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you find this code useful in your research, please consider citing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@incollection{seng2024tpm,\n title = {Hyperparameter Optimization via Interacting with Probabilistic Circuits},\n author = {Jonas Seng and Fabrizio Ventola and Zhongjie Yu and Kristian Kersting},\n booktitle = {7th Workshop on Tractable Probabilistic Modeling at UAI (TPM 2024)},\n year = {2024}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was supported by the National High-Performance Computing Project for Computational Engineering Sciences (NHR4CES) and the Federal Ministry of Education and Research (BMBF) Competence Center for AI and Labour (\"KompAKI\", FKZ 02L19C150). Furthermore, this work benefited from the cluster project \"The Third Wave of AI\".\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 1, "topics": [], - "updated_at": 1633656787.0 - }, - { - "data_format": 2, - "description": "Example of Singularity container", - "filenames": [ - "Singularity" - ], - "full_name": "breakfast-carpentry/diamondsingularity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Example\u003c/h1\u003e\u003ca id=\"user-content-singularity-example\" class=\"anchor\" aria-label=\"Permalink: Singularity Example\" href=\"#singularity-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions to create the Singularity image and run the container!\u003c/h2\u003e\u003ca id=\"user-content-instructions-to-create-the-singularity-image-and-run-the-container\" class=\"anchor\" aria-label=\"Permalink: Instructions to create the Singularity image and run the container!\" href=\"#instructions-to-create-the-singularity-image-and-run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePull this Github repo\nOn a Jetstream instance,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/breakfast-carpentry/diamondsingularity.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGo into the repo,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd diamondsingularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eBuild the Singularity image\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build diamond.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the python script with the container.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also go into the container and play with it interactively\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity shell diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can exit out of the shell by typing \u003ccode\u003eexit\u003c/code\u003e inside the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSingularity diamond.simg:~\u0026gt; exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of what we did morning 7/19/18\u003c/h2\u003e\u003ca id=\"user-content-overview-of-what-we-did-morning-71918\" class=\"anchor\" aria-label=\"Permalink: Overview of what we did morning 7/19/18\" href=\"#overview-of-what-we-did-morning-71918\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis morning our goal was to build and run a Singularity image with our own script and data and get output from our code.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWe first used Jetstream (because we have root access there).\u003c/li\u003e\n\u003cli\u003eWe started with our diamond directory from yesterday - in my case \u003ccode\u003e/home/aglad/diamond_blast\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eI wrote a toy python script, \u003ccode\u003escript.py\u003c/code\u003e, which just prints \"hello\":\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eprint(\u0027Hello\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe then started to write our Singularity recipe. We started with only an empty ubuntu image just to check that we could create that simple image correctly. (to write the Singularity recipe I used a Jupyter text editor so I wouldn\u0027t need to use a command line editor)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: ubuntu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe built the Singularity image and called it diamond.simg.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build diamond.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe ran the diamond.simg container interactively so that we could test our setup\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity shell diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAfter we installed python and checked that it worked, we copied those commands into our Singularity recipe:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: ubuntu\n\n%post\n apt-get update\n apt-get install -y python3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eFinally we ran our python script with our Singularity container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec diamond.simg python3 script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of what we did morning 7/24/18\u003c/h2\u003e\u003ca id=\"user-content-overview-of-what-we-did-morning-72418\" class=\"anchor\" aria-label=\"Permalink: Overview of what we did morning 7/24/18\" href=\"#overview-of-what-we-did-morning-72418\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis morning our goal was to run a Singularity container with multiple apps with input data and output data.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWe started on Jetstream.\u003c/li\u003e\n\u003cli\u003eWe cloned the repo following the instructions above.\u003c/li\u003e\n\u003cli\u003eWe edited the Singularity recipe:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: docker\nFrom: ubuntu\n\n%post\n apt-get update\n apt-get install -y python3\n echo \"I\u0027m in post\"\n\n%files\n script.py /tmp\n\n%runscript\n echo \"Containers are cool\"\n python3 /tmp/script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eWe explored the difference between \u003ccode\u003esingularity run\u003c/code\u003e and \u003ccode\u003esingularity exec\u003c/code\u003e.\nIf we run,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Singularity container runs the runscript from the recipe and we see,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eContainers are cool\nhello\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn the other hand, if we run,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Singularity container runs the command after \u003ccode\u003ediamond.simg\u003c/code\u003e (which in the above case is nothing), so we get an error,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eERROR : No program name to exec\nABORT : Retval = 255\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can run,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec diamond.simg python3 script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis gives,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehello\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr we can run,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec diamond.simg echo \"I am echoing\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis gives,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eI am echoing\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe shared Singularity containers with other people.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe ran apps.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIn order to run multiple things in one container we can use apps.\u003c/li\u003e\n\u003cli\u003eWe created a new script, \u003ccode\u003eotherscript.py\u003c/code\u003e,\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eprint(\u0027Goodbye\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe added the app other to the Singularity recipe\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun other\n python3 /tmp/otherscript.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThen, we built the Singularity container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build diamond.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf we \u003ccode\u003erun\u003c/code\u003e or \u003ccode\u003eexec\u003c/code\u003e as above, we will still get the same outputs.\u003c/li\u003e\n\u003cli\u003eTo check what apps we included in the Singularity container,\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity apps diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis lists our app,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eother\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe can run our app,\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --app other diamond.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis gives,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eGoodbye\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe could also have multiple apps that do different things.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHomework\u003c/h3\u003e\u003ca id=\"user-content-homework\" class=\"anchor\" aria-label=\"Permalink: Homework\" href=\"#homework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a new app in the Singularity file called \u003ccode\u003edownload\u003c/code\u003e that downloads the following csv file to the \u003ccode\u003e/tmp/\u003c/code\u003e directory: \u003ca href=\"https://gist.githubusercontent.com/julianpistorius/e380eee2743a59e8aad2cb66e307736b/raw/216071913bc377d2d2153e4b4c6bf1770320f5a7/real_data.csv\" rel=\"nofollow\"\u003ehttps://gist.githubusercontent.com/julianpistorius/e380eee2743a59e8aad2cb66e307736b/raw/216071913bc377d2d2153e4b4c6bf1770320f5a7/real_data.csv\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a new app called \u003ccode\u003etotal_lines\u003c/code\u003e that prints the total number of lines in the above file. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app total_lines diamond.simg\n7\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a new app called \u003ccode\u003efilter\u003c/code\u003e that prints out the lines that contain a string (passed as a parameter). For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app filter diamond.simg piano\nSamantha,piano,Kit\nMark,piano,Cranky\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a new app called \u003ccode\u003enotebook\u003c/code\u003e that starts a Jupyter notebook. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run notebook\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", - "stargazers_count": 1, - "subscribers_count": 4, - "topics": [], - "updated_at": 1631722780.0 + "updated_at": 1726572019.0 }, { "data_format": 2, - "description": "A container that looks like uppmax but can be run completely offline.", + "description": "UPPMAX Singularity builds", "filenames": [ - "Singularity.ngsintro", - "Singularity.default" + "IMAP/Singularity.IMAP", + "bonito/Singularity.bonito", + "metaWRAP/Singularity.metaWRAP", + "metaWRAP/Singularity.metaWRAP-deps-only", + "metaWRAP/Singularity.metaWRAP-deps-only-ubuntu", + "samtools++/Singularity.samtools", + "video-tools/Singularity.tools", + "MitoZ/Singularity.v2.3-pm", + "zsim/Singularity.zsim", + "gapseq/Singularity.gapseq", + "VESPA/Singularity.VESPA", + "ORFfinder/Singularity.ORFfinder", + "arcasHLA/Singularity.arcasHLA", + "numSCAL/Singularity.numSCAL", + "parautomatik/Singularity.parautomatik", + "gfaestus/Singularity.vulkan-u", + "gfaestus/Singularity.vulkan", + "miniconda3-rw/Singularity.conda", + "UniteM/Singularity.UniteM", + "gromacs/Singularity.gromacs-apt", + "gromacs/Singularity.gromacs-apt18", + "gromacs/Singularity.gromacs", + "HiCExplorer/Singularity.HiCExplorer" ], - "full_name": "UPPMAX/offline-uppmax-env", + "full_name": "pmitev/UPPMAX-Singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eoffline-uppmax-env\u003c/h1\u003e\u003ca id=\"user-content-offline-uppmax-env\" class=\"anchor\" aria-label=\"Permalink: offline-uppmax-env\" href=\"#offline-uppmax-env\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA container that has the same operating system, same packages installed, and a copy of the module system (not the actual software though) at UPPMAX. The script \u003ccode\u003esoftware_packer.sh\u003c/code\u003e can be run at UPPMAX to create a tarball of the software you wish to include in container at build time. If any data needs to be accessed from inside the container it can be mounted at runtime.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTLDR\u003c/h1\u003e\u003ca id=\"user-content-tldr\" class=\"anchor\" aria-label=\"Permalink: TLDR\" href=\"#tldr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# ON UPPMAX\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e package the software you want to have in your image\u003c/span\u003e\ngit clone https://github.com/UPPMAX/offline-uppmax-env.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e offline-uppmax-env\nbash software_packer.sh bwa star samtools\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# ON LOCAL COMPUTER\u003c/span\u003e\ngit clone https://github.com/UPPMAX/offline-uppmax-env.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e offline-uppmax-env\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e download the created software.package.tar.gz to the package/ folder\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e using Docker\u003c/span\u003e\ndocker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run \\\n-v offline-uppmax-env-proj:/proj \\\n-v /any/host/data/you/want/access/to:/path/inside/container \\\n-it \\\nuppmax/offline-uppmax-env:latest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e using singularity\u003c/span\u003e\nsingularity build offline-uppmax-env.sif Singularity\nsingularity shell \\\n-b /host/path/to/persistent/projfolder:/proj \\\n-b /any/host/data/you/want/access/to:/path/inside/container \\\noffline-uppmax-env.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eWhat you get\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCentOS 7\u003c/li\u003e\n\u003cli\u003eAll yum packages installed at UPPMAX\u003c/li\u003e\n\u003cli\u003eA copy of the module files at UPPMAX (not the programs themselves)\u003c/li\u003e\n\u003cli\u003eThe option to include any of the installed programs at UPPMAX, requires you to rebuild the image.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat you don\u0027t get\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eShared libraries, these would bloat the image quite a bit. These are solvable on a case by case basis, more on that further down.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse case\u003c/h2\u003e\u003ca id=\"user-content-use-case\" class=\"anchor\" aria-label=\"Permalink: Use case\" href=\"#use-case\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo was created to make a offline replacement for UPPMAX for courses, in case there is some kind of problem making UPPMAX unusable at the time the course is given. If UPPMAX suddenly disappears we can just tell the students to start up a container and all data and software needed would be included, making it possible to continue the course. This will require us to build our own version of this image where we include the software we want to be installed and to provide any data we want to be accessible to the students.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to create a course specific image\u003c/h2\u003e\u003ca id=\"user-content-how-to-create-a-course-specific-image\" class=\"anchor\" aria-label=\"Permalink: How to create a course specific image\" href=\"#how-to-create-a-course-specific-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding off the base image in Dockerhub\u003c/h3\u003e\u003ca id=\"user-content-building-off-the-base-image-in-dockerhub\" class=\"anchor\" aria-label=\"Permalink: Building off the base image in Dockerhub\" href=\"#building-off-the-base-image-in-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe base image will just have the OS and packages of UPPMAX, and the \u003ccode\u003euppmax\u003c/code\u003e and \u003ccode\u003ebioinfo-tools\u003c/code\u003e module. To include the software you want to have access to you will have to login to UPPMAX and run \u003ccode\u003esoftware_packer.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run on uppmax\u003c/span\u003e\nbash software_packer.sh bwa star R GATK\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will package everything needed to load these modules into a file called \u003ccode\u003esoftware.package.tar.gz\u003c/code\u003e. Download this file to your computer, put it in the \u003ccode\u003epackages\u003c/code\u003e folder and build the Dockerfile in that folder (replace \u003ccode\u003erepo/name:version\u003c/code\u003e with whatever you want to name it on Dockerhub, or remove it to have it untagged). The dockerfile will copy all files in \u003ccode\u003epackages/\u003c/code\u003e and unzip all files named \u003ccode\u003e*.package.tar.gz\u003c/code\u003e, so feel free to put additional files there following this naming pattern.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run locally\u003c/span\u003e\ndocker build -t repo/name:version \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding your own base image\u003c/h3\u003e\u003ca id=\"user-content-building-your-own-base-image\" class=\"anchor\" aria-label=\"Permalink: Building your own base image\" href=\"#building-your-own-base-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf the base image on Dockerhub is too old for your liking you can rebuild it yourself. Follow the same steps as above, but put the \u003ccode\u003esoftware.package.tar.gz\u003c/code\u003e you created on UPPMAX in the \u003ccode\u003ebase/packages\u003c/code\u003e folder instead. The dockerfile will copy all files in \u003ccode\u003epackages/\u003c/code\u003e and unzip all files named \u003ccode\u003e*.package.tar.gz\u003c/code\u003e, so feel free to put additional files there following this naming pattern.\u003c/p\u003e\n\u003cp\u003eTo update the list of packages installed by \u003ccode\u003eyum\u003c/code\u003e, run the following line on UPPMAX:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e list all installed packages and print the all on a single line\u003c/span\u003e\nyum list installed \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e cut -f 1 -d \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e cut -f 1 -d \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sort \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e awk -vORS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e{ print $1 }\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand replace the list of packages in the \u003ccode\u003eDockerfile\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThen build the Dockerfile in the \u003ccode\u003ebase\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e base\ndocker build -t repo/name:version \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis build will download and install all the yum packages from scratch so the image will be completely up-to-date, but it will take about an hour to build it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run the image once it is built\u003c/h2\u003e\u003ca id=\"user-content-how-to-run-the-image-once-it-is-built\" class=\"anchor\" aria-label=\"Permalink: How to run the image once it is built\" href=\"#how-to-run-the-image-once-it-is-built\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a named volume called \u003ccode\u003eoffline-uppmax-env-proj\u003c/code\u003e which will be mounted to \u003ccode\u003e/proj\u003c/code\u003e inside the container. All data put in there will persist between restarts of the container, i.e. this is where the students should put their lab work. The data used in the labs are usually so big (10+gb) that it does not make sens to put it inside the image. It\u0027s better to download it separately and mount it when starting the container.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDocker\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run \\\n-v offline-uppmax-env-proj:/proj \\\n-v /host/path/to/data:/container/path/to/data \\\n-it \\\nrepo/name:version\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e example\u003c/span\u003e\ndocker run \\\n-v offline-uppmax-env-proj:/proj \\\n-v /home/user/ngsintro_data:/sw/courses/ngsintro \\\n-it \\\nuppmax/offline-uppmax-env:latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter the container is running it should be just like working on uppmax. \u003ccode\u003emodule load\u003c/code\u003e should behave the same way and all modules you packed with \u003ccode\u003esoftware_packer.sh\u003c/code\u003e should be available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\nTo get the module system to work in Singularity you have to build the Singularity file as sudo and everything should work. Package the software you need on UPPMAX like in the Docker approach, put the downloaded tarball in the \u003ccode\u003epackages/\u003c/code\u003e folder just like with Docker, and then build it with Singularity.\u003c/p\u003e\n\u003cp\u003eJust building from Dockerhub (uppmax/offline-uppmax-env:latest) will give you a container with only the \u003ccode\u003euppmax\u003c/code\u003e and \u003ccode\u003ebioinfo-tools\u003c/code\u003e in it, and the \u003ccode\u003emodule\u003c/code\u003e command will not work since it is a function that is not inherited properly when being converted by Singularity. You can get around this by manually typing \u003ccode\u003esource /etc/bashrc.module_env\u003c/code\u003e every time the container starts.\u003c/p\u003e\n\u003cp\u003eIf you build your own Docker image with the software your want, push it to Dockerhub, and convert it to Singularity, you will still have the problem of the \u003ccode\u003emodule\u003c/code\u003e command not working. The solution is the same, manually type \u003ccode\u003esource /etc/bashrc.module_env\u003c/code\u003e when the container starts and it should start working. Building the Singularity file instead will not have this problem.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTroubleshooting\u003c/h1\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMissing shared libraries\u003c/h3\u003e\u003ca id=\"user-content-missing-shared-libraries\" class=\"anchor\" aria-label=\"Permalink: Missing shared libraries\" href=\"#missing-shared-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUnfortunately I could not find an easy way to automatically pull all the shared libraries needed by programs. I had a problem with STAR, that it needed a newer version of GCC. I could get around it by running \u003ccode\u003eldd $(which star)\u003c/code\u003e on uppmax and see that the file uses was \u003ccode\u003e/sw/comp/gcc/8.3.0_rackham/lib64/libstdc++.so.6\u003c/code\u003e. I put this file in a tar file,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003etar -chzvf libs.package.tar.gz /sw/comp/gcc/8.3.0_rackham/lib64/libstdc++.so.6 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e note the -h option, will dereference symbolic links\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand put the \u003ccode\u003elibs.package.tar.gz\u003c/code\u003e file in the \u003ccode\u003epackages\u003c/code\u003e folder, build the image, and it worked after that.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTodos\u003c/h1\u003e\u003ca id=\"user-content-todos\" class=\"anchor\" aria-label=\"Permalink: Todos\" href=\"#todos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eTest if it works.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUPPMAX-Singularity\u003c/h1\u003e\u003ca id=\"user-content-uppmax-singularity\" class=\"anchor\" aria-label=\"Permalink: UPPMAX-Singularity\" href=\"#uppmax-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUPPMAX Singularity builds\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 7, + "subscribers_count": 2, "topics": [], - "updated_at": 1604866356.0 + "updated_at": 1732273448.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "recipes/Singularity.fastqc__0.11.8__1", + "recipes/Singularity.DEAGO__1.0.0", + "recipes/Singularity.seqtk__1.3", + "recipes/Singularity.BAGEL__0.9", + "recipes/Singularity.fqtools__2.0__hf50d5a6_4", + "recipes/Singularity.kallisto__0.46.0__hb6a4e58_0", + "recipes/Singularity.multiqc__1.7__py_2", + "recipes/Singularity.fqtools__2.2", + "recipes/Singularity.nextflow__19.01.0__ha4d7672_4", + "recipes/Singularity.mageck__0.5.8__py36h3e44d54_0", + "recipes/Singularity.STAR-Fusion__1.6.0", + "recipes/Singularity.jq__1.6_0", + "recipes/R/Singularity.R-3.6.0.subclonal_reconstruction-1.0.0", + "recipes/R/Singularity.R-3.6.0.base-1.0.1", + "recipes/R/Singularity.R-3.6.0.methylation-1.0.0", + "recipes/R/Singularity.R-3.6.0.base-1.0.0" ], - "full_name": "rkalyanapurdue/geoedf-cont", + "full_name": "team113sanger/t113-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egeoedf-cont\u003c/h1\u003e\u003ca id=\"user-content-geoedf-cont\" class=\"anchor\" aria-label=\"Permalink: geoedf-cont\" href=\"#geoedf-cont\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2811\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [], - "updated_at": 1575383900.0 + "updated_at": 1570094851.0 }, { "data_format": 2, - "description": "Container files for sc-benchmark in Docker and Singularity with Nix", + "description": null, "filenames": [ - "Singularity" + "Singularity.qc", + "Singularity.count", + "Singularity.mapping", + "Singularity.renv" ], - "full_name": "XSEDE/nix-container-sc-benchmark", + "full_name": "chaetognatha/singularity_library", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-sc-benchmark\u003c/h1\u003e\u003ca id=\"user-content-nix-container-sc-benchmark\" class=\"anchor\" aria-label=\"Permalink: nix-container-sc-benchmark\" href=\"#nix-container-sc-benchmark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5358\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container with Nix to be used in XSEDE compute environment (currently in development)\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 15, + "subscribers_count": 1, "topics": [], - "updated_at": 1628542024.0 + "updated_at": 1657573666.0 }, { "data_format": 2, - "description": "Iterative approach to relevant top-k planning", + "description": "Automatically build Apptainer images for APSIM", "filenames": [ - "Singularity" + "templates/Singularity.template" ], - "full_name": "mtabernerop/relevant-forbiditerative", + "full_name": "JBris/auto-apsim-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFiltering Top-k Relevant Plans\u003c/h1\u003e\u003ca id=\"user-content-filtering-top-k-relevant-plans\" class=\"anchor\" aria-label=\"Permalink: Filtering Top-k Relevant Plans\" href=\"#filtering-top-k-relevant-plans\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository proposes an interative approach to filter plans in top-k planning tasks under perfect justification criteria. Its implementation is based on Forbid-Iterative (FI) Planner, an Automated PDDL based planner that includes planners for top-k, top-quality, and diverse computational tasks, developed by Michael Katz et al.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe planner is based on the idea of obtaining multiple solutions by iteratively reformulating planning tasks to restrict the set of valid plans, forbidding previously found ones. Solution plans are required to be relevant (perfectly-justified).\u003c/h2\u003e\u003ca id=\"user-content-the-planner-is-based-on-the-idea-of-obtaining-multiple-solutions-by-iteratively-reformulating-planning-tasks-to-restrict-the-set-of-valid-plans-forbidding-previously-found-ones-solution-plans-are-required-to-be-relevant-perfectly-justified\" class=\"anchor\" aria-label=\"Permalink: The planner is based on the idea of obtaining multiple solutions by iteratively reformulating planning tasks to restrict the set of valid plans, forbidding previously found ones. Solution plans are required to be relevant (perfectly-justified).\" href=\"#the-planner-is-based-on-the-idea-of-obtaining-multiple-solutions-by-iteratively-reformulating-planning-tasks-to-restrict-the-set-of-valid-plans-forbidding-previously-found-ones-solution-plans-are-required-to-be-relevant-perfectly-justified\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding\u003c/h1\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor building the code please use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning\u003c/h1\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./plan_topk.sh \u0026lt;domain\u0026gt; \u0026lt;problem\u0026gt; \u0026lt;number-of-plans\u0026gt; \u0026lt;overall-time-limit\u0026gt; \u0026lt;plan-reorderings\u0026gt; \u0026lt;structural-symmetries\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample of running command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./plan_topk.sh examples/blocks/domain.pddl examples/blocks/instances/p0.pddl 5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting\u003c/h2\u003e\u003ca id=\"user-content-citing\" class=\"anchor\" aria-label=\"Permalink: Citing\" href=\"#citing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTop-k Planning\u003c/h3\u003e\u003ca id=\"user-content-top-k-planning\" class=\"anchor\" aria-label=\"Permalink: Top-k Planning\" href=\"#top-k-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{katz-et-al-icaps2018,\n title = \"A Novel Iterative Approach to Top-k Planning\",\n author = \"Michael Katz and Shirin Sohrabi and Octavian Udrea and Dominik Winterer\",\n booktitle = \"Proceedings of the Twenty-Eighth International Conference on\n Automated Planning and Scheduling (ICAPS 2018)\",\n publisher = \"{AAAI} Press\",\n pages = \"132--140\",\n year = \"2018\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRelevant Top-k Planning\u003c/h3\u003e\u003ca id=\"user-content-relevant-top-k-planning\" class=\"anchor\" aria-label=\"Permalink: Relevant Top-k Planning\" href=\"#relevant-top-k-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{salerno22filtering,\n title = \"{F}iltering {T}op-k {R}elevant {P}lans\",\n author = \"Salerno, Mauricio and Tabernero, Miguel and Fuentetaja, Raquel and Pozanco, Alberto\",\n year = \"2022\",\n booktitle = \"Proceedings of the 32nd International Conference on Automated Planning and Scheduling\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicensing\u003c/h2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-label=\"Permalink: Licensing\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eForbid-Iterative (FI) Planner is an Automated PDDL based planner that\nincludes planners for top-k, top-quality, and diverse computational\ntasks. Copyright (C) 2019 Michael Katz, IBM Research, USA.\nThe code extends the Fast Downward planning system. The license for the\nextension is specified in the LICENSE file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFast Downward\u003c/h2\u003e\u003ca id=\"user-content-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Fast Downward\" href=\"#fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward-logo.png\"\u003e\u003cimg src=\"misc/images/fast-downward-logo.png\" width=\"500\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eauto-apsim-singularity\u003c/h1\u003e\u003ca id=\"user-content-auto-apsim-singularity\" class=\"anchor\" aria-label=\"Permalink: auto-apsim-singularity\" href=\"#auto-apsim-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAutomatically build Apptainer images for APSIM\u003c/p\u003e\n", "stargazers_count": 1, "subscribers_count": 2, "topics": [ - "artificial-intelligence", - "automated-planning", - "perfect-justification", - "plan-filtering", - "bachelor-thesis" + "apptainer", + "apptainer-container", + "apsim", + "apsimx", + "automation", + "singularity", + "singularity-container", + "singularity-containers" ], - "updated_at": 1679787208.0 + "updated_at": 1679641238.0 }, { "data_format": 2, - "description": "A container for running the perfzero benchmark in an XSEDE environment", + "description": "Barcode/amplicon sequencing bioinformatics tool, chops up FASTQ reads into capture groups using fuzzy regular expressions instead of base-positions. Very flexible, very parallelized, order now !", "filenames": [ - "Singularity" + "Singularity.v0.3.0-alpha", + "Singularity.v0.5.0-alpha", + "Singularity.v0.5.1-alpha" ], - "full_name": "XSEDE/nix-container-perfzero", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enix-container-perfzero\u003c/h1\u003e\u003ca id=\"user-content-nix-container-perfzero\" class=\"anchor\" aria-label=\"Permalink: nix-container-perfzero\" href=\"#nix-container-perfzero\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003eSingularity container with Nix to be used in XSEDE compute environment (\u003cstrong\u003ecurrently in development\u003c/strong\u003e)\u003c/p\u003e\n", + "full_name": "darachm/slapchop", + "latest_release": "v0.2.0-alpha", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSLAPCHOP\u003c/h1\u003e\u003ca id=\"user-content-slapchop\" class=\"anchor\" aria-label=\"Permalink: SLAPCHOP\" href=\"#slapchop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSLAPCHOP.py parses Illumina reads using patterns to extract barcodes.\u003c/p\u003e\n\u003cp\u003eBy using fuzzy regular expressions we can chop robustly barcodes from\nindeterminate positions, filter the results based on sequence or match\nproperties, and reassemble a fastq record from the results.\u003c/p\u003e\n\u003cp\u003eAvailable as\n\u003ca href=\"https://www.singularity-hub.org/collections/1361\" rel=\"nofollow\"\u003ea singularity containter\u003c/a\u003e!\nSo you if you have\n\u003ca href=\"https://github.com/sylabs/singularity/releases\"\u003eSingularity\u003c/a\u003e\ninstalled you can just use it (without worrying about dependencies) with:\n\u003ccode\u003esingularity run shub://darachm/slapchop:latest -h\u003c/code\u003e (to download and show the\nargument help message for example). Then you use it like\n\u003ccode\u003esingularity run shub://darachm/slapchop:latest whatever.fastq arguments added\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMore completely, this tool is a python script. You give it a FASTQ(Z) file and\nsome operations to do, and it\u0027ll do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- read chunks of Illumina-format sequencing reads\n- apply a series of operations:\n - match fuzzy regular expression to original sequence or previous\n capture groups\n - extract capture groups and start next operation\n- apply pythonic filters (pass/fail) on sequence or quality properties \n (like average quality or group length)\n- apply pythonic constructors to construct new FASTQ read from the capture\n groups (so ID plus the last four bases of the UMI plus length of whatever)\n- write out these reads to new files of pass and fail\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor tuning/debugging/designing it has some verbosity modes to spill the gory\ndetails of each operation in stats files, and should still have some memory\nprofiling functionality to debug memory leaks (fixed that one).\u003c/p\u003e\n\u003cp\u003eFor a very very verbose example of a debugging run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./slapchop.py \\\n input.fastqz -z \\\n output_basename \\\n --bite-size 10 --processes 3 \\\n --write-report --limit 10000 \\\n -o \"Sample: input \u0026gt; (?P\u0026lt;sample\u0026gt;[ATCG]{5})(?P\u0026lt;fixed1\u0026gt;GTCCACGAGGTC){e\u0026lt;=1}(?P\u0026lt;rest\u0026gt;TCT.*){e\u0026lt;=1}\" \\\n -o \"Strain: rest \u0026gt; (?P\u0026lt;tag\u0026gt;TCT){e\u0026lt;=1}(?P\u0026lt;strain\u0026gt;[ATCG]{10,26})CGTACGCTGCAGGTCGAC\" \\\n -o \"UMITail: rest \u0026gt; (?P\u0026lt;fixed2\u0026gt;CGTACGCTGCAGGTC)(?\u0026lt;UMItail\u0026gt;GAC[ATCG]G[ATCG]A[ATCG]G[ATCG]G[ATCG]G[ATCG]GAT){s\u0026lt;=2}\" \\\n -o \"UMI: UMItail \u0026gt; (GAC(?P\u0026lt;umi1\u0026gt;[ATCG])G(?\u0026lt;umi2\u0026gt;[ATCG])A(?\u0026lt;umi3\u0026gt;[ATCG])G(?\u0026lt;umi4\u0026gt;[ATCG])G(?\u0026lt;umi5\u0026gt;[ATCG])G(?\u0026lt;umi6\u0026gt;[ATCG])G){e\u0026lt;=2}\" \\\n --output-seq \"strain\" \\\n --output-id \"input.id+\u0027_umi=\u0027+umi1.seq+umi2.seq+umi3.seq+ \\\n umi4.seq+umi5.seq+umi6.seq+\u0027_sample=\u0027+sample.seq\" \\\n --filter \"sample_length == 5 and rest_start \u0026gt;= 16 and ( min(strain.letter_annotations[\u0027phred_quality\u0027]) \u0026gt;= 30 )\"\\\n --verbose --verbose --verbose\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat invocation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- Takes records from the `input.fastq`\n- Starts three processes that each take bites of 10 records\n- Applies the four operations to cut up the read\n- Writes the full detailed report including json reports for \n each read, so we limit it to the first 10,000 bytes\n of the file (about 50 records). This is for debugging.\n- Filters the records on having a `sample` barcode of 5 bases \n and having the `rest` sequence match starting at least past\n index 16 (so base 15 in english).\n- Re-assembles the records that pass this filter, making the ID\n of the fastq record having the original ID plus a UMI \n sequence and the sample barcode, then the sequence is just\n the match to the strain barcode context. This is suitable for\n feeding into `bwa` for example.\n- We\u0027ve got three levels of verbosity, so a per-record verbosity\n for debugging purposes.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the output formatting is a bit funny. This is directly evaluated\n(because security is what?) on BioPython SequenceRecords, so you need to specify\njust the name of the capture group(s) for the outputs so it can access the\n\u003ccode\u003e.seq\u003c/code\u003e and qualities. For the ID, etc, you can access \u003ccode\u003e.seq\u003c/code\u003e or \u003ccode\u003e.id\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThen if we like our thresholds we\u0027d re-run, and drop the \u003ccode\u003e--limit\u003c/code\u003e\nand \u003ccode\u003e--write-report\u003c/code\u003e flags. This will turn records like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@NB501157:100:H5J5LBGX2:1:11101:10000:6068 1:N:0:\nCTACTGTCCACGAGGTCTCTGCAGATAATACACTGTCACCCGTACGCTGCAGGTCGACCGTAGGAGGGAGATGTG\n+\nAAAAAEEEE/AEE\u0026lt;EEEEEEEEAEEAEEAEEEEE/EEE/EEEEEEEEE/EEEEEEEEEEEEE/EEEEEEEEEEEE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003einto records like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@NB501157:100:H5J5LBGX2:1:11101:10000:6068_umi=CTGAGA_sample=CTACT\nGCAGATAATACACTGTCACC\n+\nEEAEEAEEAEEEEE/EEE/E\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe sample barcode is the first five, the strain barcode starts after\nthe \u003ccode\u003eTCT\u003c/code\u003e, and the UMI is interspersed downstream. This is modified\nyeast BarSeq, btw.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eThis script depends strongly upon (uses) the work of\n\u003ca href=\"https://pypi.org/project/regex/\" rel=\"nofollow\"\u003eregex\u003c/a\u003e\nand\n\u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003eBiopython\u003c/a\u003e. Thanks! Check them out...\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 16, + "subscribers_count": 1, "topics": [], - "updated_at": 1623176998.0 + "updated_at": 1711268013.0 }, { "data_format": 2, - "description": null, + "description": "https://www.synapse.org/modulechallenge", "filenames": [ - "Singularity" + "containers/K1/singularity/Singularity", + "containers/R1/singularity/Singularity", + "containers/M1/singularity/Singularity" ], - "full_name": "IARCbioinfo/qualimap-nf", - "latest_release": "v1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuality control of WGS/WES/target alignment data\u003c/h1\u003e\u003ca id=\"user-content-quality-control-of-wgswestarget-alignment-data\" class=\"anchor\" aria-label=\"Permalink: Quality control of WGS/WES/target alignment data\" href=\"#quality-control-of-wgswestarget-alignment-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/qualimap-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/14531b9b6bc782d5ceaaba38d5daadb07ad3564e31008fc0c0eab83b4315d172/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f7175616c696d61702d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/qualimap-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/qualimap-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1623\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/145996279\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4d1b67cf1a9bdf91dcf8186c534a74ecd9de21cb1c7edcc8d6e0d4b610f7e8f5/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3134353939363237392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/145996279.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ImaneLboukili/WGS_analysis/blob/master/Qualimap/Qualimap-nf.png\"\u003e\u003cimg src=\"https://github.com/ImaneLboukili/WGS_analysis/raw/master/Qualimap/Qualimap-nf.png\" alt=\"Image Qualimap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePerform quality control of WGS/WES/target alignment data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003esamtools: see official installation \u003ca href=\"http://www.htslib.org\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003cli\u003eQualimap: see official installation \u003ca href=\"http://qualimap.bioinfo.cipf.es\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003cli\u003eMultiqc: see official installation \u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing Fasta files\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOptional\u003c/h3\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDefault value\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003e.\u003c/td\u003e\n\u003ctd\u003ePath to output folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--qualimap\u003c/td\u003e\n\u003ctd\u003e/usr/bin/qualimap\u003c/td\u003e\n\u003ctd\u003ePath to Qualimap installation directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--feature_file\u003c/td\u003e\n\u003ctd\u003emyfeatures.txt\u003c/td\u003e\n\u003ctd\u003eQualimap feature file for coverage analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--multiqc_config\u003c/td\u003e\n\u003ctd\u003enull\u003c/td\u003e\n\u003ctd\u003econfig yaml file for multiqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003eINTEGER\u003c/td\u003e\n\u003ctd\u003eNumber of cpus to be used\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFlags\u003c/h3\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload test data set\u003c/h2\u003e\u003ca id=\"user-content-download-test-data-set\" class=\"anchor\" aria-label=\"Permalink: Download test data set\" href=\"#download-test-data-set\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/iarcbioinfo/data_test\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003enextflow run iarcbioinfo/Qualimap-nf --qualimap /path/to/qualimap --input_folder /path/to/bam --output_folder /path/to/output\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHTMLs\u003c/td\u003e\n\u003ctd\u003eAn html file for each analysed BAM file, and one containing the aggregated multiQC results\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:Delhommet@students.iarc.fr\"\u003eDelhommet@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003edeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaxime Vallee\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:Valleem@iarc.fr\"\u003eValleem@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003edeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMatthieu Foll\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:Follm@iarc.fr\"\u003eFollm@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003edeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@fellows.iarc.fr\"\u003eAlcalaN@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "full_name": "mattiat/DREAM_DMI_Tool", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDreamDMI\u003c/h1\u003e\u003ca id=\"user-content-dreamdmi\" class=\"anchor\" aria-label=\"Permalink: DreamDMI\" href=\"#dreamdmi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository holds the source code for \u003cstrong\u003eDreamDMI\u003c/strong\u003e, a Linux/macOS command-line tool for Disease Module Identification in molecular networks, leveraging the top performing methods of the \u003cstrong\u003eDisease Module Identification (DMI) DREAM Challenge\u003c/strong\u003e (\u003ca href=\"https://www.synapse.org/modulechallenge\" rel=\"nofollow\"\u003ehttps://www.synapse.org/modulechallenge\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMethods\u003c/h2\u003e\u003ca id=\"user-content-methods\" class=\"anchor\" aria-label=\"Permalink: Methods\" href=\"#methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eK1\u003c/strong\u003e: Kernel clustering optimisation algorithm, \u003ca href=\"https://www.synapse.org/#!Synapse:syn7349492/wiki/407359\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7349492/wiki/407359\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eM1\u003c/strong\u003e: Modularity optimization algorithm, \u003ca href=\"https://www.synapse.org/#!Synapse:syn7352969/wiki/407384\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7352969/wiki/407384\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eR1\u003c/strong\u003e: Random-walk-based algorithm, \u003ca href=\"https://www.synapse.org/#!Synapse:syn7286597/wiki/406659\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7286597/wiki/406659\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSOURCE CODE\u003c/h2\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-label=\"Permalink: SOURCE CODE\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe source code is hosted at: \u003ca href=\"https://github.com/mattiat/DREAM_DMI_Tool\"\u003ehttps://github.com/mattiat/DREAM_DMI_Tool\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePREREQUISITES\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: PREREQUISITES\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEither \u003ccode\u003edocker\u003c/code\u003e or \u003ccode\u003esingularity\u003c/code\u003e must be installed. Please visit \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003ehttps://www.docker.com\u003c/a\u003e or \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSome of the Methods may require large amount of resources, depending on your input.\u003c/p\u003e\n\u003cp\u003eThe tool was tested on \u003cem\u003eUbuntu Linux 18.04\u003c/em\u003e, \u003cem\u003eCentOS Linux 7.5\u003c/em\u003e and \u003cem\u003emacOS Sierra\u003c/em\u003e Version 10.12.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eINSTALLATION\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: INSTALLATION\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install: \u003ccode\u003e./install\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo uninstall: \u003ccode\u003e./uninstall\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRUNNING\u003c/h2\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: RUNNING\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run, invoke, from any location: \u003ccode\u003edream_dmi --help\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eINPUT\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: INPUT\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe format for the input network is the following: a tab-separated file containing one line for each edge. If an edge is connecting two nodes, nodeA and nodeB, with weight weight_AB, the file will contain the entry:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e[nodeA]\t[nodeB]\t[weight_AB]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003enodeA and nodeB are of type \u003cem\u003einteger\u003c/em\u003e, weight_AB is of type \u003cem\u003efloat\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eFor an example, see the contents of test/system_test/input/.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBENCHMARKING\u003c/h2\u003e\u003ca id=\"user-content-benchmarking\" class=\"anchor\" aria-label=\"Permalink: BENCHMARKING\" href=\"#benchmarking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esee test/benchmarking\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePUBLICATION\u003c/h2\u003e\u003ca id=\"user-content-publication\" class=\"anchor\" aria-label=\"Permalink: PUBLICATION\" href=\"#publication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpen Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases\u003c/p\u003e\n\u003cp\u003eSarvenaz Choobdar, Mehmet E. Ahsen, Jake Crawford, Mattia Tomasoni, David Lamparter, Junyuan Lin, Benjamin Hescott, Xiaozhe Hu, Johnathan Mercer, Ted Natoli, Rajiv Narayan, The DREAM Module Identification Challenge Consortium, Aravind Subramanian, Gustavo Stolovitzky, Zolt\u00e1n Kutalik, Kasper Lage, Donna K. Slonim, Julio Saez-Rodriguez, Lenore J. Cowen, Sven Bergmann, Daniel Marbach.\nbioRxiv 265553 (2018). doi: \u003ca href=\"https://doi.org/10.1101/265553\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/265553\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 1, - "subscribers_count": 5, + "subscribers_count": 3, "topics": [], - "updated_at": 1720620729.0 + "updated_at": 1558215673.0 }, { "data_format": 2, - "description": "Quality Control plots and data normalisation for Microarray data", + "description": "Container recipes for use with the U of A HPC resources", "filenames": [ - "Singularity" + "Singularity.im2markup", + "Singularity.caffe2", + "Singularity.torch", + "Singularity.delphi", + "Singularity.nvidia_docker", + "Singularity.pytorch", + "Singularity.dynet", + "Singularity.tensorflow", + "Singularity.cuda9_py36", + "Singularity.keras", + "Singularity.numba", + "Singularity.pytorch_skimage", + "word2vec/Singularity.w2v", + "openmnt/Singularity.opennmt" ], - "full_name": "qbicsoftware-archive/microarray-qc-workflow", + "full_name": "ml4ai/UA-hpc-containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWork in progress\u003c/h1\u003e\u003ca id=\"user-content-work-in-progress\" class=\"anchor\" aria-label=\"Permalink: Work in progress\" href=\"#work-in-progress\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emicroarray-qc-workflow\u003c/h1\u003e\u003ca id=\"user-content-microarray-qc-workflow\" class=\"anchor\" aria-label=\"Permalink: microarray-qc-workflow\" href=\"#microarray-qc-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTakes .cel files and creates qc plots as well as normalising the data\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/qbicsoftware/microarray-qc-workflow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe6ac9fe50a390fce8ab5d166033ccf9da248c6e07f04a46a10ef3aa151bba5a/68747470733a2f2f7472617669732d63692e6f72672f71626963736f6674776172652f6d6963726f61727261792d71632d776f726b666c6f772e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/qbicsoftware/microarray-qc-workflow.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c42e6080d15f532168bbb6d26fb300c5ed31e7f5e8648abbe9d5f6d8d4285475/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/microarray-qc-workflow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/03e3c878703a868edafe988884a1b52d3631ffc9fb199cc091a3421f122f1618/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6d6963726f61727261792d71632d776f726b666c6f772e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/microarray-qc-workflow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003emicroarray-qc-workflow: Takes .cel files and creates qc plots as well as normalising the data\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe microarray-qc-workflow pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was written by Timo Lucas (\u003ca href=\"https://github.com/lucass122\"\u003elucass122\u003c/a\u003e) at \u003ca href=\"http://www.qbic.uni-tuebingen.de/\" rel=\"nofollow\"\u003eQBiC T\u00fcbingen\u003c/a\u003e.\nR script based on script by Stefan Czemmel [qbicStefanC]:\n\u003ca href=\"https://github.com/qbicsoftware/qbic-wf-microarrayQC\"\u003ehttps://github.com/qbicsoftware/qbic-wf-microarrayQC\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 1, - "subscribers_count": 5, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eML4AI Lab Singularity Container Repository\u003c/h1\u003e\u003ca id=\"user-content-ml4ai-lab-singularity-container-repository\" class=\"anchor\" aria-label=\"Permalink: ML4AI Lab Singularity Container Repository\" href=\"#ml4ai-lab-singularity-container-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2086\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository holds singularity containers that are commonly used by members of the ML4AI lab to run projects on the University of Arizona\u0027s high-performance computing environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSee Also\u003c/strong\u003e:\n\u003ca href=\"https://github.com/clulab/hpc-ml\"\u003ehttps://github.com/clulab/hpc-ml\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 2, + "subscribers_count": 22, "topics": [], - "updated_at": 1632720523.0 + "updated_at": 1617312549.0 }, { "data_format": 2, - "description": "Singularity container for MLST", + "description": "centos7 container with miniconda and pytorch + pip install requirements from https://github.com/marian42/shapegan (re-using the container built from https://github.com/truatpasteurdotfr/singularity-docker-centos7-conda-pytorch).", "filenames": [ - "Singularity", - "v2.15.2/20181216/Singularity.v2.15.2_20181216", - "v2.15.2/20181211/Singularity.v2.15.2_20181211", - "v2.15.2/20181210/Singularity.v2.15.2_20181210", - "v2.8/20181216/Singularity.v2.8_20181216", - "v2.10/Singularity.v2.10", - "v2.9/Singularity.v2.9" + "Singularity" ], - "full_name": "phgenomics-singularity/mlst", + "full_name": "truatpasteurdotfr/singularity-localimage-shapegan", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emlst --- Scan contig files against PubMLST typing schemes\u003c/h1\u003e\u003ca id=\"user-content-mlst-----scan-contig-files-against-pubmlst-typing-schemes\" class=\"anchor\" aria-label=\"Permalink: mlst --- Scan contig files against PubMLST typing schemes\" href=\"#mlst-----scan-contig-files-against-pubmlst-typing-schemes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/576\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container for Torsten Seemann\u0027s \u003ca href=\"https://github.com/tseemann/mlst\"\u003eMLST\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePre-requisite\u003c/h2\u003e\u003ca id=\"user-content-pre-requisite\" class=\"anchor\" aria-label=\"Permalink: Pre-requisite\" href=\"#pre-requisite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"http://singularity.lbl.gov/docs-installation\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLatest version\u003c/h3\u003e\u003ca id=\"user-content-latest-version\" class=\"anchor\" aria-label=\"Permalink: Latest version\" href=\"#latest-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following steps are needed to use the image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name mlst shub://phgenomics-singularity/mlst@latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will command will create a file \u003ccode\u003emlst.simg\u003c/code\u003e, which is executable.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eUse the image:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./mlst.simg --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA particular version\u003c/h3\u003e\u003ca id=\"user-content-a-particular-version\" class=\"anchor\" aria-label=\"Permalink: A particular version\" href=\"#a-particular-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name mlst shub://phgenomics-singularity/mlst@v2.9\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSuggested pattern\u003c/h2\u003e\u003ca id=\"user-content-suggested-pattern\" class=\"anchor\" aria-label=\"Permalink: Suggested pattern\" href=\"#suggested-pattern\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a \u003ccode\u003esingularity\u003c/code\u003e folder:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir $HOME/singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePull the image to the \u003ccode\u003esingularity\u003c/code\u003e folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name mlst_v2.10 shub://phgenomics-singularity/mlst@v2.10\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eLink the image to a folder in your \u003ccode\u003e$PATH\u003c/code\u003e (e.g., \u003ccode\u003e$HOME/bin\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eln -s $HOME/singularity/mlst_v2.10.simg $HOME/bin/mlst\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, when you login again, you should be able to just type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emlst --help\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 1, - "subscribers_count": 1, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-docker-centos7-conda-pytorch with shapegan\u003c/h1\u003e\u003ca id=\"user-content-singularity-docker-centos7-conda-pytorch-with-shapegan\" class=\"anchor\" aria-label=\"Permalink: singularity-docker-centos7-conda-pytorch with shapegan\" href=\"#singularity-docker-centos7-conda-pytorch-with-shapegan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecentos7 container with miniconda and pytorch + pip install requirements from \u003ca href=\"https://github.com/marian42/shapegan\"\u003ehttps://github.com/marian42/shapegan\u003c/a\u003e (re-using the container built from \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-conda-pytorch\"\u003ehttps://github.com/truatpasteurdotfr/singularity-docker-centos7-conda-pytorch\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eBuilding: (you MUST adapt the path to the local .sif file)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-localimage-shapegan.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1698091027.0 + "updated_at": 1580937110.0 }, { "data_format": 2, - "description": "A tool for semi-automatic containerization, deployment, and execution of scientific applications and workflows on cloud computing and supercomputing platforms. ", + "description": "build files for ubuntu", "filenames": [ - "testcases/BiasNet/Basil/Singularity.def", - "testcases/LigandNet/Manual/Singularity.def", - "testcases/Sumo/Basil/Singularity.def", - "testcases/Sumo/Manual/Singularity.def", - "testcases/OpenDMPK/Basil/Singularity.def" + "Singularity" ], - "full_name": "ritua2/Basil", + "full_name": "singularityhub/ubuntu", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBasil\u003c/h1\u003e\u003ca id=\"user-content-basil\" class=\"anchor\" aria-label=\"Permalink: Basil\" href=\"#basil\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBasil is a tool for semi-automatic containerization, deployment, and execution of scientific applications and workflows on cloud computing and supercomputing platforms.\u003c/p\u003e\n\u003cp\u003eThe \"containerization\" of software applications not only future-proofs them and helps in their long-term preservation but also makes them portable across different hardware platforms, ensures reproducible results, and makes them convenient to disseminate. Docker and Singularity are two popular software technologies for containerizing scientific applications and are widely supported on different hardware platforms. However, their adoption involves a learning curve, especially when it comes to developing secure and optimized images of the applications of interest. A large number of domain-scientists and scholars are usually not formally trained at containerizing their applications with Docker and Singularity, and spend a significant amount of their time in porting their applications to different cloud computing and supercomputing platforms. The process of porting the applications having multiple software dependencies and sensitivities to specific software versions can be especially arduous for such users, and to assist them, this project will develop Basil - a tool for semi-automatically containerizing the scientific applications, frameworks, and workflows. This project will deliver Basil through a web portal, as a command-line tool, and through APIs.\u00a0Basil has a broad applicability across multiple domains of deep societal impact such as artificial intelligence, drug discovery, and earthquake engineering. By enabling the preservation of valuable legacy software and making them usable for several years in future, Basil will save cost and time in software rewriting and software installations, and thus contribute towards advancing the prosperity of the society. The project will result in educational content on \u201cIntroduction to Containerization\u201d and students engaged in the project will develop valuable skills in the areas of national interest such as\u00a0supercomputing/High Performance Computing (HPC) and cloud computing.\u003c/p\u003e\n\u003cp\u003eBasil will be useful across diverse domains in disseminating the research outcomes in the form of ready-to-use Docker/Singularity images that can contain input, source code, and output related to the research all packaged together in a single image where possible.\u00a0Users will provide the recipes for building their applications/workflows in one of the following forms (1) Makefiles/CMakefiles, (2) scripts, (3) commands, or (4) a text-file with predefined keywords and notations (templates for which will be provided by the project team). These recipes (e.g., in Makefiles or text-files) will be parsed, and Dockerfiles or Singularity definition files will be generated. Using a generated Dockerfile or Singularity definition file, a Docker or Singularity image will be built. Next, the image will be scanned for any vulnerabilities, signed, and if the user desires, released in public\u00a0registries\u00a0with appropriate licenses. These container images can be tested using the Basil web portal, and can be pulled to run or deploy on diverse hardware platforms on-prem or in the cloud.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProject Web Portal\u003c/h1\u003e\u003ca id=\"user-content-project-web-portal\" class=\"anchor\" aria-label=\"Permalink: Project Web Portal\" href=\"#project-web-portal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollowing is the link to the project\u0027s web portal : \u003ca href=\"https://icompute.us\" rel=\"nofollow\"\u003ehttps://icompute.us\u003c/a\u003e . This web portal is under iterative development and is available for use for generating and testing Docker and Singularity images generated by Basil.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAcknowledgement\u003c/h1\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project has been generously funded by the National Science Foundation (NSF) through award # \u003ca href=\"https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209946\u0026amp;HistoricalAwards=false\" rel=\"nofollow\"\u003e2209946\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 1, - "subscribers_count": 2, - "topics": [], - "updated_at": 1719993394.0 + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUbuntu\u003c/h1\u003e\u003ca id=\"user-content-ubuntu\" class=\"anchor\" aria-label=\"Permalink: Ubuntu\" href=\"#ubuntu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a library of Ubuntu builds for Singularity images hosted on Singularity Static Registry. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003etags are supported based on the extension of the Singularity file, with an extensionless file corresponding to \"latest\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can I find here?\u003c/h2\u003e\u003ca id=\"user-content-what-can-i-find-here\" class=\"anchor\" aria-label=\"Permalink: What can I find here?\" href=\"#what-can-i-find-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository here serves the container under the namespace \u003ccode\u003esingularityhub/ubuntu\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does this work?\u003c/h2\u003e\u003ca id=\"user-content-how-does-this-work\" class=\"anchor\" aria-label=\"Permalink: How does this work?\" href=\"#how-does-this-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will submit this container to the (organizational) registry at\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e\nfor a final container uri corresponding to \u003ccode\u003ehttps://singularityhub.github.io/registry-org/singularityhub/ubuntu\u003c/code\u003e. Specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub/registry-org --) the organization registry\nsingularityhub/ubuntu --) a container collection\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethen on GitHub pages:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub.github.io/registry-org --) the registry interface\nsingularityhub.github.io/registry-org/singularityhub/ubuntu --) the added container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis works by way of the repository here (via the continuous integration)\nbuilding the container, uploading it to storage, and then opening (or updating)\na branch on the registry repository. The registry repository uses a\n\u003ca href=\"https://www.github.com/vsoch/pull-request-action\"\u003eGitHub Action\u003c/a\u003e that will\nopen a pull request whenever a branch is pushed.\u003c/p\u003e\n\u003cp\u003eFor this particular example, the container namespace is a bit\nredundant, but if the GitHub username was different from the organization, you\ncan see how it would be important to maintain this namespace. I\u0027ve also thought\nabout having the main registry redirect to the GitHub pages of the container\u0027s Github\npages, but I don\u0027t want to introduce redirects yet.\u003c/p\u003e\n", + "stargazers_count": 2, + "subscribers_count": 3, + "topics": [ + "registry-org", + "singularityhub" + ], + "updated_at": 1609044092.0 }, { "data_format": 2, - "description": null, + "description": "H3A RefGraph Hackathon 2019", "filenames": [ - "GPU_example_2/Singularity.def" + "Singularity" ], - "full_name": "KempnerInstitute/intro-compute-march-2024", + "full_name": "h3abionet/h3arefgraph", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eKempner Institute Spring 2024 Compute Workshop Supplemental Material\u003c/h1\u003e\u003ca id=\"user-content-kempner-institute-spring-2024-compute-workshop-supplemental-material\" class=\"anchor\" aria-label=\"Permalink: Kempner Institute Spring 2024 Compute Workshop Supplemental Material\" href=\"#kempner-institute-spring-2024-compute-workshop-supplemental-material\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see the \u003ca href=\"TBD\"\u003eWorkshop Page\u003c/a\u003e for more information.\u003c/p\u003e\n\u003cp\u003eThe supplementary material contains files for each run scenario. Below is a list of the folders along with a brief description of their purposes. In all models, we modified the first and the last layers of the ResNet-18 model to fit the CIFAR-10 dataset.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSLURM Examples\u003c/h2\u003e\u003ca id=\"user-content-slurm-examples\" class=\"anchor\" aria-label=\"Permalink: SLURM Examples\" href=\"#slurm-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSLURM Example 1\u003c/strong\u003e:\n\u003cul\u003e\n\u003cli\u003eUsefule SLURM commands\u003c/li\u003e\n\u003cli\u003eTest Only Jobs\u003c/li\u003e\n\u003cli\u003eEstimating job efficiency (CPU and Memory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGPU Examples\u003c/h2\u003e\u003ca id=\"user-content-gpu-examples\" class=\"anchor\" aria-label=\"Permalink: GPU Examples\" href=\"#gpu-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 1\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-18\u003c/li\u003e\n\u003cli\u003eDataset: CIFAR-10\u003c/li\u003e\n\u003cli\u003eResources: 1 GPU (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Interactive\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 2\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-18\u003c/li\u003e\n\u003cli\u003eDataset: CIFAR-10\u003c/li\u003e\n\u003cli\u003eResources: 1 GPU (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Batch\u003c/li\u003e\n\u003cli\u003eIntegration: Weights \u0026amp; Biases (wandb.ai)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 3\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-18\u003c/li\u003e\n\u003cli\u003eDataset: CIFAR-10\u003c/li\u003e\n\u003cli\u003eResources: 1 GPU (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Batch\u003c/li\u003e\n\u003cli\u003eIntegration: Weights \u0026amp; Biases (wandb.ai)\u003c/li\u003e\n\u003cli\u003eJob Arrays\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 4\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-50\u003c/li\u003e\n\u003cli\u003eDataset: CIFAR-10\u003c/li\u003e\n\u003cli\u003eResources: 2, 4 GPUs (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Batch\u003c/li\u003e\n\u003cli\u003eIntegration: Weights \u0026amp; Biases (wandb.ai)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGPU Example 5\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel: ResNet-50\u003c/li\u003e\n\u003cli\u003eDataset: Subset of imagenet\u003c/li\u003e\n\u003cli\u003eResources: 4 GPUs, 8 GPUs (two node) (A100)\u003c/li\u003e\n\u003cli\u003eJob submission: Batch\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 1, - "subscribers_count": 6, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eh3abionet/h3arefgraph\u003c/h1\u003e\u003ca id=\"user-content-h3abioneth3arefgraph\" class=\"anchor\" aria-label=\"Permalink: h3abionet/h3arefgraph\" href=\"#h3abioneth3arefgraph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eRefGraph Workflows Hackathon\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/h3abionet/h3arefgraph\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/de582520672c8786f29f4b9c684afcf27d9b46937887d3034aea1833b6a85298/68747470733a2f2f7472617669732d63692e6f72672f68336162696f6e65742f68336172656667726170682e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/h3abionet/h3arefgraph.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/h3arefgraph\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4d73775218c1085ba17cfbd320c20d3b9b5eb30219df46b1e19e53d179e88186/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f68336172656667726170682e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/h3arefgraph.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is for the use and testing of graph based methods for variant calling.\u003c/p\u003e\n\u003cp\u003eThe aim is to allow the user to choose the reference graph construction method and the alignment / variant calling methods separately.\u003c/p\u003e\n\u003cp\u003eWe also provide tools for reporting the results of the variant calling, that take advantage of the additional contextual information that using reference graphs provides.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOverview\u003c/h3\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe aim of this project is to separate the different parts of the variant calling process to allow the development of\ntask specific tools. This is more in line with traditional variant calling where specific alignment tools may preform\nbetter for different organisms, but should not require a different downstream analysis for each output.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"assets/images/Overview_slide.jpeg\"\u003e\u003cimg src=\"assets/images/Overview_slide.jpeg\" alt=\"Overview slide\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe h3abionet/h3arefgraph pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/reference_genomes.md\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eh3abionet/h3arefgraph was originally written by the H3ABioNet RefGraph Team.\u003c/p\u003e\n", + "stargazers_count": 2, + "subscribers_count": 14, "topics": [], - "updated_at": 1716485818.0 + "updated_at": 1690159850.0 }, { "data_format": 2, - "description": "My senior honors thesis in computer science.", + "description": "BIDS-app for pre-processing DWI (denoise, unring, top-up, eddy, bedpost.. )", "filenames": [ + "Singularity.v0.0.11b", + "Singularity.v0.0.8", + "Singularity.v0.0.12c", + "Singularity.v0.0.9", + "Singularity.v0.0.13", + "Singularity.v0.0.11", + "Singularity.v0.0.12", + "Singularity.v0.0.10", + "Singularity.v0.0.12a", "Singularity" ], - "full_name": "caravanuden/thesis", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ethesis\u003c/h1\u003e\u003ca id=\"user-content-thesis\" class=\"anchor\" aria-label=\"Permalink: thesis\" href=\"#thesis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode for Cara Van Uden\u0027s senior honors thesis in computer science titled \"Comparing brain-like representations learned by vanilla, residual, and recurrent CNN architectures.\"\u003c/p\u003e\n\u003cp\u003eThough it has been hypothesized that state-of-the art residual networks approximate the recurrent visual system, it is yet to be seen if the representations learned by these \u201dbiologically inspired\u201d CNNs actually have closer representations to neural data. It is likely that CNNs and DNNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. In this thesis, we investigate how different CNN architectures approximate the representations learned through the ventral\u2014object recognition and processing\u2014stream of the brain. We specifically evaluate how recent approximations of biological neural recurrence\u2014such as residual connections, dense residual connections, and a biologically-inspired implemen- tation of recurrence\u2014affect the representations learned by each CNN. We first investigate the representations learned by layers throughout a few state-of-the-art CNNs\u2014VGG-19 (vanilla CNN), ResNet-152 (CNN with res!\nidual connections), and DenseNet-161 (CNN with dense connections). To control for differences in model depth, we then extend this analysis to the CORnet family of biologically-inspired CNN models with matching high-level architectures. The CORnet family has three models: a vanilla CNN (CORnet-Z), a CNN with biologically-valid recurrent dynamics (CORnet-R), and a CNN with both recurrent and residual connections (CORnet-S).\u003c/p\u003e\n\u003cp\u003eWe compare the representations of these six models to functionally aligned (with hyperalignment) fMRI brain data acquired during a naturalistic visual task. We take two approaches to comparing these CNN and brain representations. We first use forward encoding, a predictive approach that uses CNN features to predict neural responses across the whole brain. We next use representational similarity analysis (RSA) and centered kernel alignment (CKA) to measure the similarities in representation within CNN layers and specific brain ROIs. We show that, compared to vanilla CNNs, CNNs with residual and recurrent connections exhibit representations that are even more similar to those learned by the human ventral visual stream. We also achieve state-of-the-art forward encoding and RSA performance with the residual and recurrent CNN models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTODONE:\u003c/h3\u003e\u003ca id=\"user-content-todone\" class=\"anchor\" aria-label=\"Permalink: TODONE:\" href=\"#todone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGet anat and hyperaligned Raiders responses (work with surface just like Life) and get info about the dataset\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet singularity/docker working for installing dependencies (opencv, ffmpeg, pytorch) needed by model\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eScale and crop Raiders video clips to 224x224 clips needed by ImageNet models\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet images from video (every half sec)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet/choose correct activation layers for CORnet-{Z,R,S}, ResNet, DenseNet, VGG (pretrained on ImageNet)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet image activations for each layer for each model\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompare layers within-model for each model\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompare ROIs within brain\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse the extracted activations from models to predict (ridge regression) fMRI BOLD response of hyperaligned Raiders data\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet ROI-wise pred acc\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse the extracted activations from models to make CKA RDMs with ROIs from fMRI BOLD response of hyperaligned Raiders data\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssign voxels to layers based on layer-wise prediction accuracy and RDM similarity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssign ROIs to layers based on layer-wise prediction accuracy and RDM similarity (Jaccard similarity)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTODO:\u003c/h3\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO:\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIncorporate eye tracking (congruency map across subj) for cropping images to get salient crop?\u003c/li\u003e\n\u003cli\u003e?\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "khanlab/prepdwi", + "latest_release": "v0.0.7g", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/392\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/khanlab/prepdwi\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3144abf093a4027225db054bc7bbf0dbb36e64f833b1ffb576b2c62af78c108f/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f707265706477692e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/prepdwi.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eprepdwi\u003c/h1\u003e\u003ca id=\"user-content-prepdwi\" class=\"anchor\" aria-label=\"Permalink: prepdwi\" href=\"#prepdwi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBIDS-app for pre-processing DWI (denoise, unring, top-up, eddy, bedpost.. )\u003c/p\u003e\n\u003cp\u003eAnalysis levels:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eparticipant: Runs pre-processing including denoising, unringing, top-up, eddy, DWI-T1w registration, T1w-T1w template (MNI152_1mm and MNI152NLin2009cAsym) registration, and bedpost fitting. Writes intermediate output to \u003ccode\u003ework\u003c/code\u003e sub-folder, and BIDS derivatives output to \u003ccode\u003eprepdwi\u003c/code\u003e sub-folder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egroup: Generates HTML reports with QC for brain-masking and registration steps (if linear registration fails, re-run with \u003ccode\u003e--reg_init_participant\u003c/code\u003e flag to initialize with transform from another subject)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eparticipant2: Runs probtrackx network connectivity between all regions in a given atlas labels file. Uses either canned atlases with the \u003ccode\u003e--atlas\u003c/code\u003e option, where predefined atlases are defined in the \u003ccode\u003ecfg\u003c/code\u003e folder; or can specify a new atlas with the \u003ccode\u003e--atlas_* options\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: prepdwi bids_dir output_dir {participant,group,participant2} \u0026lt;optional arguments\u0026gt;\n [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL...]]\n [--matching_dwi MATCHING_PATTERN]\n [--matching_T1w MATCHING_STRING]\n [--reg_init_participant PARTICIPANT_LABEL]\n [--grad_coeff_file GRAD_COEFF_FILE]\n [-w WORK_DIR] (scratch directory)\n\n [--no-regT1]\n [--no-topup]\n [--no-bedpost]\n [--no-dke]\n [--n_cpus NCPUS] (for bedpost, default: 8)\n\n participant2 (probtrack connectivity) options:\n [--nprobseeds] N (for probtrackx, default: 5000)\n Choose built-in atlas:\n [--atlas NAME (default: dosenbach)\n\n Available built-in atlas labels/csv:\n cort_striatum_midbrain\tdosenbach yeo17 yeo17_striatum yeo7\tyeo7_striatum\n\n Customize atlas labels:\n {--atlas_space NAME (MNI152_1mm or MNI152NLin2009cAsym)\n [--atlas_label_nii NIFTI\n [--atlas_label_csv LABEL_INDEX_CSV\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 8, "topics": [], - "updated_at": 1629080738.0 + "updated_at": 1698248704.0 }, { "data_format": 2, - "description": "Singularity recipe for Quarto.", + "description": "In this training course you will find theory and practice material for introducing yourself to wgs analysis for bacterial, including outbreak investigation.", "filenames": [ - "Singularity.quarto" + "Singularity" ], - "full_name": "bast/singularity-quarto", - "latest_release": "0.3.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity recipe for \u003ca href=\"https://quarto.org/\" rel=\"nofollow\"\u003eQuarto\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-quarto\" class=\"anchor\" aria-label=\"Permalink: Singularity recipe for Quarto\" href=\"#singularity-recipe-for-quarto\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to fetch and use the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/bast/singularity-quarto/releases/download/0.3.0/quarto.sif\n\n$ ./quarto.sif --help\n$ ./quarto.sif preview document.md\n$ ./quarto.sif render document.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI have used this wonderful guide as starting point and inspiration:\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "BU-ISCIII/bacterial_wgs_training", + "latest_release": "ISCIII2018", + "readme": "\u003cp\u003e\u003ca href=\"https://circleci.com/gh/BU-ISCIII/bacterial_wgs_training\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0289dc02a9638d79a6ae3573745037bd79d6d2ae9d3bac65e1c84a786d090731/68747470733a2f2f636972636c6563692e636f6d2f67682f636972636c6563692f636972636c6563692d646f63732e7376673f7374796c653d736869656c64\" alt=\"CircleCI Build Status\" data-canonical-src=\"https://circleci.com/gh/circleci/circleci-docs.svg?style=shield\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93ca07ea63e4649ae72b87de86d82eedeab50e744fa6e07ee95e5344779fb930/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://discuss.circleci.com\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0db7cc65a258e95db6b1dddae92be2d4d80817b9570fded1962c4fd4b6fdcb6f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6d6d756e6974792d436972636c654349253230446973637573732d3334333433342e737667\" alt=\"CircleCi Community\" data-canonical-src=\"https://img.shields.io/badge/community-CircleCI%20Discuss-343434.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://nextflow.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f8a155e03276d377ca219881b2d695bd3be17898ea74a1250ad2b6941aef70cc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d253345302e32392e302d677265656e2e737667\" alt=\"Nextflow version\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%3E0.29.0-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3bb9116161cdfeb6b23d2bde2e62f92bb4eda63bd51f5c1ed4b8b86356f088e4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f46696c6573797374656d2d536369656e74696669632d627269676874677265656e2e737667\" alt=\"Scif\" data-canonical-src=\"https://img.shields.io/badge/Filesystem-Scientific-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBacterial WGS training\u003c/h1\u003e\u003ca id=\"user-content-bacterial-wgs-training\" class=\"anchor\" aria-label=\"Permalink: Bacterial WGS training\" href=\"#bacterial-wgs-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this training course you will find theory and practice material for introducing yourself to wgs analysis for bacterial, including outbreak investigation.\u003c/p\u003e\n\u003cp\u003eThe material includes slides with theory concepts and a bunch of practical exercises using nextflow and singularity, focusing on the interpretation of results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAgenda\u003c/h2\u003e\u003ca id=\"user-content-agenda\" class=\"anchor\" aria-label=\"Permalink: Agenda\" href=\"#agenda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 1\u003c/h3\u003e\u003ca id=\"user-content-day-1\" class=\"anchor\" aria-label=\"Permalink: Day 1\" href=\"#day-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 1 and 2:\u003c/strong\u003e \u003ca href=\"slides/talk1/20241028_6ED_curso_SeqGenBac_session1.1-2_Introduccion_EAB.pdf\"\u003eMassive sequencing of bacterial genomes. State-of-the-art. \u0026amp; Bacterial genomes sequencing. Applications.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 3:\u003c/strong\u003e \u003ca href=\"slides/talk3/curso_SeqGenBac_ChangingComputingParadigm.pdf\"\u003eThe computing revolution in Biosciences. Nextflow and Singularity introduction.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 4:\u003c/strong\u003e \u003ca href=\"slides/talk2/curso_SeqGenBac_session1.2_linux.pdf\"\u003eLinux environment review.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/00_SetUp.md\"\u003e\u003cstrong\u003eExercise 0\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/00_SetUp.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/01_LinuxBasicCommands.md\"\u003e\u003cstrong\u003eExercise 1\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/01_LinuxBasicCommands.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/02_NextflowSingularity.md\"\u003e\u003cstrong\u003eExercise 2\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/02_LinuxNextflowSingularity.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 2\u003c/h3\u003e\u003ca id=\"user-content-day-2\" class=\"anchor\" aria-label=\"Permalink: Day 2\" href=\"#day-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 5:\u003c/strong\u003e \u003ca href=\"slides/talk5/curso_SeqGenBac_session2.2_quality_assesment.pdf\"\u003eQuality analysis and control of HTS data\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 6:\u003c/strong\u003e \u003ca href=\"slides/talk6/slidestalk620221017_5ED_curso_SeqGenBac_session2.3_assembly_ICuesta.pdf.pdf\"\u003eBacterial genomes assembly\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 7:\u003c/strong\u003e \u003ca href=\"slides/talk11/20231020_5ED_curso_SeqGenBac_session5.1_annotation_ICuesta.pdf\"\u003eSequence annotation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/03_QualityAndAssembly.md\"\u003e\u003cstrong\u003eExercise 3\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/03_QualityAndAssembly.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 3\u003c/h3\u003e\u003ca id=\"user-content-day-3\" class=\"anchor\" aria-label=\"Permalink: Day 3\" href=\"#day-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 8:\u003c/strong\u003e \u003ca href=\"slides/talk7/curso_SeqGenBac_session3.1_MappingAndVariantCalling.pdf\"\u003eMapping against reference genome and Variant Calling.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 9:\u003c/strong\u003e \u003ca href=\"slides/talk8/curso_SeqGenBac_session3.2_SNPMatrixAndPhylogenetics.pdf\"\u003eSNP matrix and phylogenetics.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/04_outbreakSNP.md\"\u003e\u003cstrong\u003eExercise 4\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/04_outbreakSNP.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 4\u003c/h3\u003e\u003ca id=\"user-content-day-4\" class=\"anchor\" aria-label=\"Permalink: Day 4\" href=\"#day-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 10:\u003c/strong\u003e \u003ca href=\"slides/talk10/20231019_5ED_curso_SeqGenBac_session4.1_tipificacion-gen-by-gene_ICuesta.pdf\"\u003eTyping based on allelic profile or gene-by-gene\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 11:\u003c/strong\u003e \u003ca href=\"slides/talk10/curso_SeqGenBac_session4.2_GeneByGenevsSNPs.pdf\"\u003eGene-by-gene WGS analysis\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./exercises/05_outbreakcgMLST.md\"\u003e\u003cstrong\u003eExercise 5\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/05_outbreakcgMLST.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/06_annotation.md\"\u003e\u003cstrong\u003eExercise 6\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/06_annotation.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [ - "quarto", - "singularity" + "wgs", + "genome", + "sequencing", + "bacterial-genomes", + "ngs-analysis", + "outbreak-detection", + "outbreaks", + "nextflow", + "bacterial-wgs-training", + "wgs-analysis" ], - "updated_at": 1677993724.0 + "updated_at": 1730362704.0 }, { "data_format": 2, - "description": "Files to create containers", + "description": null, "filenames": [ - "apptainer.ompi41x/Singularity", - "apptainer.mpich/Singularity", - "apptainer.ompi5x/Singularity" + "Singularity/Singularity-GCC-VisTools-MINT" ], - "full_name": "ExaChem/exachem-container-images", + "full_name": "MetOffice/LFRic-Containers", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContainerisation of LFRic\u003c/h1\u003e\u003ca id=\"user-content-containerisation-of-lfric\" class=\"anchor\" aria-label=\"Permalink: Containerisation of LFRic\" href=\"#containerisation-of-lfric\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository hosts LFRic container recipes and links to similar external\nrepositories.\u003c/p\u003e\n\u003cp\u003eMore detailed information about \u003ccode\u003eLFRic\u003c/code\u003e and further references can be found in\n\u003ca href=\"https://github.com/MetOffice/LFRic-Containers/blob/master/LFRicIntro.md\"\u003e\u003cem\u003eIntroduction to LFRic\u003c/em\u003e\u003c/a\u003e\nsection.\u003c/p\u003e\n\u003cp\u003eInstructions on building and runing \u003ccode\u003eLFRic\u003c/code\u003e in two container platforms,\n\u003ca href=\"https://docs.docker.com/install/\" rel=\"nofollow\"\u003eDocker CE\u003c/a\u003e and\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, are stored in two subdirectories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/MetOffice/LFRic-Containers/blob/master/Docker/README.md\"\u003eDocker\u003c/a\u003e;\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/MetOffice/LFRic-Containers/blob/master/Singularity/README.md\"\u003eSingularity\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, "subscribers_count": 4, "topics": [], - "updated_at": 1731538984.0 + "updated_at": 1635957552.0 }, { "data_format": 2, - "description": null, + "description": "Lahontan: a flexible, multiscale RNA-seq pipeline", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "markziemann/lung_brain", + "full_name": "UNR-CFB/lahontan", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003elung_brain\u003c/h1\u003e\u003ca id=\"user-content-lung_brain\" class=\"anchor\" aria-label=\"Permalink: lung_brain\" href=\"#lung_brain\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLahontan\u003c/h1\u003e\u003ca id=\"user-content-lahontan\" class=\"anchor\" aria-label=\"Permalink: Lahontan\" href=\"#lahontan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLahontan is a set of tools built around an object-oriented pipeline used in\nqualifying and processing sets of paired-end next-gen RNA sequencing data. It\ntakes fastq files from a high-throughput sequencer and performs quality control\nsteps before performing a preliminary differential gene expression analysis.\u003c/p\u003e\n\u003cp\u003eLahontan uses trimmomatic and fastqc to perform quality control; BLAST to check\nthe strandedness; and a choice of hisat2/featureCounts, hisat2/stringtie,\nbowtie2, or kallisto to analyze and quantify the experimental data. Lahontan\nalso automatically produces some R scripts to begin a gene expression analysis\nfor certain modes. Lahontan is compatible with SLURM scheduled clusters for\nparallel execution as well as standard desktop computers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#table-of-contents\"\u003eTable of Contents\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#built-with\"\u003eBuilt With\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#creating-a-project-and-running-the-pipeline\"\u003eCreating a Project and Running the Pipeline\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#setup\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#quick-start\"\u003eQuick Start\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#example-execution\"\u003eExample Execution\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pipeline-specifications\"\u003ePipeline Specifications\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#project-stages\"\u003eProject Stages\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#project-structure\"\u003eProject Structure\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage-runpipe-options-command-args\"\u003eUsage: lahontan [options] [command] [args...]\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#available-commands\"\u003eAvailable Commands\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#command-options\"\u003eCommand Options\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#fcounts\"\u003efcounts\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#string\"\u003estring\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#kall\"\u003ekall\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#bowtie2\"\u003ebowtie2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#mj\"\u003emj\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#mb\"\u003emb\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#clean\"\u003eclean\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#fo\"\u003efo\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#prepref\"\u003eprepref\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#gendef\"\u003egendef\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#help\"\u003ehelp\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#authors\"\u003eAuthors\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install, please see the \u003ca href=\"https://github.com/UNR-CFB/rna-seq/wiki\"\u003eWiki\u003c/a\u003e for\ndetailed build instructions.\u003c/p\u003e\n\u003cp\u003eTo summarize the \u003ca href=\"https://github.com/UNR-CFB/rna-seq/wiki\"\u003eWiki\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/UNR-CFB/lahontan.git\ncd lahontan\n./lib/autoSetup.sh\nsource ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name lahontan shub://UNR-CFB/lahontan\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilt With\u003c/h1\u003e\u003ca id=\"user-content-built-with\" class=\"anchor\" aria-label=\"Permalink: Built With\" href=\"#built-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython\u003c/a\u003e (\u0026gt;=3.5)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.gnu.org/software/bash/\" rel=\"nofollow\"\u003eBash\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pachterlab.github.io/kallisto/\" rel=\"nofollow\"\u003eKallisto\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gpertea/stringtie\"\u003eStringtie\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://subread.sourceforge.net/\" rel=\"nofollow\"\u003efeatureCounts\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BenLangmead/bowtie2\"\u003ebowtie2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/infphilo/hisat2\"\u003ehisat2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003efastQC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.usadellab.org/cms/index.php?page=trimmomatic\" rel=\"nofollow\"\u003etrimmomatic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/samtools/samtools\"\u003esamtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lh3/seqtk\"\u003eSeqtk\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gpertea/gffcompare\"\u003egffcompare\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gpertea/gffread\"\u003egffread\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCreating a Project and Running the Pipeline\u003c/h1\u003e\u003ca id=\"user-content-creating-a-project-and-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Creating a Project and Running the Pipeline\" href=\"#creating-a-project-and-running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePaired-end Illumina fastq files. This means 2 files per sample. One for\nRead1, the other for Read2\u003c/li\u003e\n\u003cli\u003eA reference genome for the organism in study. All chromosomes in a single\nFASTA format file\u003c/li\u003e\n\u003cli\u003eA GTF-format file for containing the gene and exon coordinates for your\nreference genome\u003c/li\u003e\n\u003cli\u003eA reference transcriptome (or some set of known transcripts) for your\norganism\u003c/li\u003e\n\u003cli\u003eThe Pipeline directory should be in your \u003ccode\u003e$PATH\u003c/code\u003e environmental variable. This\ncan be accomplished by being in the main rna-seq directory and running:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd scripts/Pipeline\n$ echo \u0027export PATH=$PATH:\u0027`pwd` \u0026gt;\u0026gt; $HOME/.bashrc\n$ source $HOME/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is important that the Input file that you feed into the pipeline follow the\nformat specified. You can use the \u003ccode\u003egendef\u003c/code\u003e command to create a default input\nfile for you, which at that point only requires you to fill in the Project,\nReference, and Original variable directories. See the \u003ca href=\"#prepref\"\u003e\u003ccode\u003egendef\u003c/code\u003e documentation\u003c/a\u003e\nfor more help. An example Input file can be seen below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eExampleInputFile\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/user/Projects/ProjectName\nReference = /home/user/References/ProjectName-Reference\nOriginal = /home/user/Datas/ProjectName-Data\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOften times your data will be organized in a way that isn\u0027t accepted by\nlahontan. In order to continue, your \u003ccode\u003eOriginal\u003c/code\u003e directory must be arranged such\nthat each sample read is alphanumerically sorted in a single directory (Note:\nsorted by sample and by read)\u003c/p\u003e\n\u003cp\u003eExample Original Directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/home/user/Data/ProjectName-Data/\n SpeciesName.treament1_group1.read1.fq.gz\n SpeciesName.treament1_group1.read2.fq.gz\n SpeciesName.treament1_group2.read1.fq.gz\n SpeciesName.treament1_group2.read2.fq.gz\n SpeciesName.treament2_group1.read1.fq.gz\n SpeciesName.treament2_group1.read2.fq.gz\n SpeciesName.treament2_group2.read1.fq.gz\n SpeciesName.treament2_group2.read2.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlso, in order to limit errors, your filenames must only contain: letters,\nnumbers, periods, hyphens, and underscores.\u003c/p\u003e\n\u003cp\u003eYour reference filenames must also follow a few specific rules. Along with only\nusing letters, numbers, periods, hyphens, and underscores:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYour GTF-format file must end with \".gtf\" and it must be the only file within\nyour reference directory to do so\u003c/li\u003e\n\u003cli\u003eYour reference transcriptome must contain \".cdna.\" anywhere within the name\nexcept for the end. It must also be the only file within your reference\ndirectory to do so\u003c/li\u003e\n\u003cli\u003eYour reference genome must contain \".dna.\" anywhere within the name except\nfor the extension. It must also be the only file within your reference\ndirectory to do so\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample Reference Directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/home/user/Reference/ProjectName-Reference/\n SpeciesName.cdna.all.fa.gz\n SpeciesName.dna.toplevel.fa.gz\n SpeciesName.gtf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate an Input file with \u003ccode\u003elahontan gendef\u003c/code\u003e for your desired pipeline using\nthe \u003ccode\u003e-m\u003c/code\u003e option. Then fill in the \"[locations]\" section.\u003c/li\u003e\n\u003cli\u003eMake sure data and references are following format specified in Setup\u003c/li\u003e\n\u003cli\u003ePrepare reference data with \u003ccode\u003elahontan prepref\u003c/code\u003e. If you are using the \u003ccode\u003ekall\u003c/code\u003e\nor \u003ccode\u003ebowtie2\u003c/code\u003e pipeline, be sure to add the \u003ccode\u003e--kallisto\u003c/code\u003e or \u003ccode\u003e--bowtie2\u003c/code\u003e flag\nto make sure the appropriate indices get created.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003elahontan mj\u003c/code\u003e to make a metadata file\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003elahontan mb\u003c/code\u003e to make a blacklist file\u003c/li\u003e\n\u003cli\u003eDecide if you will be running the pipeline with SLURM or not\n\u003cul\u003e\n\u003cli\u003eIf yes, then run \u003ccode\u003elahontan fo\u003c/code\u003e with proper arguments to make optimal path\nfile\u003c/li\u003e\n\u003cli\u003eIf no, then you can skip this step\u003c/li\u003e\n\u003cli\u003eThe following steps will be denoted with a (s) if the step is for SLURM or\na (w) if the step is without SLURM\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e(s) Make a batch file by adding the \u003ccode\u003e--makebatch\u003c/code\u003e option to any of the three\npipeline commands. You should also add the \u003ccode\u003e--batchjson\u003c/code\u003e option with the\nfile you made with \u003ccode\u003elahontan fo\u003c/code\u003e; the \u003ccode\u003e--jsonfile\u003c/code\u003e option with the file you made\nwith \u003ccode\u003elahontan mj\u003c/code\u003e; the \u003ccode\u003e--use-blacklist\u003c/code\u003e option with the file you made with\n\u003ccode\u003elahontan mb\u003c/code\u003e; and the \u003ccode\u003e--use-reference\u003c/code\u003e because the reference data has been\nprepared. So an example command to make a featureCounts batch file may look like:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan fcounts --makebatch 48,48,32 --batchjson /path/to/OptimalPath.dat \\\n--jsonfile /path/to/Metadata.json --use-blacklist /path/to/CustomBlacklist.fa \\\n--use-reference /path/to/INPUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003e(w) You can execute the entire pipeline with one command, but it\u0027s important\nthat the \u003ccode\u003e--jsonfile\u003c/code\u003e, \u003ccode\u003e--use-blacklist\u003c/code\u003e, and \u003ccode\u003e--use-reference\u003c/code\u003e options are\nadded to the command. An example command may look like:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan fcounts --jsonfile /path/to/Metadata.json \\\n--use-blacklist /path/to/CustomBlacklist.fa --use-reference --maxcpu 24 \\\n/path/to/INPUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003e(s) Execute the batch file with \u003ccode\u003esbatch pipeBatch\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNote: It may be a good idea to hold your input file, metadata file, blacklist\nfile, batch file, and optimal path file in the same directory for easy tracking\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Execution\u003c/h3\u003e\u003ca id=\"user-content-example-execution\" class=\"anchor\" aria-label=\"Permalink: Example Execution\" href=\"#example-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAssuming that the data and reference files have been appropriately organized,\nand are located at \u003ccode\u003e/home/user/Data/MyDataFolder\u003c/code\u003e and\n\u003ccode\u003e/home/user/References/MyReferenceFolder\u003c/code\u003e, run the following to obtain a\ndefault input file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan gendef -m fcounts -f MyFcountsProject.ini\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point complete the \"[locations]\" section in \u003ccode\u003eMyFcountsProject.ini\u003c/code\u003e. So\n\u003ccode\u003eMyFcountsProject.ini\u003c/code\u003e at minimum contains:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMyFcountsProject.ini\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/user/Projects/MyProjectFolder\nReference = /home/user/References/MyReferenceFolder\nOriginal = /home/user/Datas/MyDataFolder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, execute the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan prepref -r /home/user/References/MyReferenceFolder\n$ lahontan mj -j MyMetadata.json\n$ lahontan mb -t MyBlacklist.fa\n$ lahontan fcounts --use-reference --use-blacklist MyBlacklist.fa \\\n--jsonfile MyMetadata.json MyFcountsProject.ini\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline Specifications\u003c/h1\u003e\u003ca id=\"user-content-pipeline-specifications\" class=\"anchor\" aria-label=\"Permalink: Pipeline Specifications\" href=\"#pipeline-specifications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProject Stages\u003c/h2\u003e\u003ca id=\"user-content-project-stages\" class=\"anchor\" aria-label=\"Permalink: Project Stages\" href=\"#project-stages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eStage 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWill create the \u003ca href=\"#project-structure\"\u003eproject structure\u003c/a\u003e and will link the\noriginal data files, the reference files, and a Metadata file if it has been\nprepared. This stage is identical for all pipelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWill run a quality check on the reference data and preprocess the reference\ndata. This entails building a BLAST database, finding the splice sites, finding\nthe exons, building a hisat2 database, and building a genome index with\nsamtools. This stage can be identically run with \u003ccode\u003elahontan prepref\u003c/code\u003e outside of a\nProject structure and before running a pipeline. Stringtie and featureCounts\nwill have an identical Stage 2, but Kallisto requires the construction of its\nown index. If preparing the references beforehand for Kallisto, make sure to\nadd the \u003ccode\u003e--kallisto\u003c/code\u003e flag to the \u003ccode\u003elahontan prepref\u003c/code\u003e command.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first step for all pipelines in Stage 3 is to run a quality check on the\ndata. This includes using fastQC to create statistics on the quality of the\ndata as well as to search for overrepresented sequences. Trimming of the reads\nis done by trimmomatic which trims poor reads as well as any reads in a\nblacklist file. A custom blacklist file can be created by \u003ccode\u003elahontan mb\u003c/code\u003e, which\ncan be fed to a pipeline with the \u003ccode\u003e--use-blacklist\u003c/code\u003e option. After trimming, an\nadditional round of fastQC will be performed to check the results of the\ntrimming. These quality control steps are identical for all pipelines. Once the\nquality control is finished, the strandedness of the reads is checked with\nBLAST.\u003c/p\u003e\n\u003cp\u003eAfter quality control is done, depending on which pipeline is being ran, the\nnext steps will vary. For featureCounts, hisat2 will be used to align the reads\nto the references, then featureCounts will quantify the gene expression. The\npipeline will then gather the data to create tables which can be used in\nfurther analysis.\u003c/p\u003e\n\u003cp\u003eIf Stringtie is being used, then hisat2 will be used to align the reads to the\nreferences. After the alignment, Stringtie will build a new reference transcripts\nfile which will then be used in estimating the transcript abundances.\u003c/p\u003e\n\u003cp\u003eIf Kallisto is being used, then Kallisto will perform its probabilistic\nquantification on the raw data using the index it built.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Stage 4, the resulting data from whichever pipeline was ran is tabulated and\nprepared for statistical analysis with some automatically generated R scripts.\nThe automatically generated R scripts are built with deseq2 and edgeR for\nfeatureCounts, ballgown for Stringtie, and sleuth for Kallisto.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Stage 5, the automatically generated R scripts from Stage 4 are executed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProject Structure\u003c/h2\u003e\u003ca id=\"user-content-project-structure\" class=\"anchor\" aria-label=\"Permalink: Project Structure\" href=\"#project-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStage 1 of the Pipeline will create a consistent directory structure between\nProjects that includes a \u003cem\u003eData\u003c/em\u003e, \u003cem\u003eOriginal\u003c/em\u003e, \u003cem\u003ePostprocessing\u003c/em\u003e, and \u003cem\u003eReference\u003c/em\u003e\ndirectory which looks like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eProjectName/\n Data/\n sample_01/\n sample_01-read_1\n sample_01-read_2\n sample_02/\n sample_02-read_1\n sample_02-read_2\n sample_03/\n sample_03-read_1\n sample_03-read_2\n .\n .\n .\n (Other sample directories)\n Original/\n sample_01-read_1\n sample_01-read_2\n sample_02-read_1\n sample_02-read_2\n sample_03-read_1\n sample_03-read_2\n .\n .\n .\n (Other sample files)\n Postprocessing/\n (Metadata.json if already created, which I recommend you do)\n Reference/\n Genome.fa\n cDNA.fa\n .gtf\n (Other files if reference files have already been processed and prepared)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eData\u003c/em\u003e = directory where Stage 3 will propogate. Contains a directory for each\nrespective sample which contains its respective raw data files\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eOriginal\u003c/em\u003e = directory containing symbolic links pointing to original raw data files\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003ePostprocessing\u003c/em\u003e = directory where differential expression analysis and other analyses will be saved to\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eReference\u003c/em\u003e = directory containing symbolic links pointing to original reference files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage: lahontan [options] [command] [args...]\u003c/h2\u003e\u003ca id=\"user-content-usage-lahontan-options-command-args\" class=\"anchor\" aria-label=\"Permalink: Usage: lahontan [options] [command] [args...]\" href=\"#usage-lahontan-options-command-args\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is designed as a series of scripts built around a central\nfunction, \u003ccode\u003elahontan\u003c/code\u003e. \u003ccode\u003elahontan\u003c/code\u003e functions when it is fed a command or argument.\nIf it is fed a command, that activates the options available for that command,\nand can subsequently be added after the command. The available commands which\ncan be given to \u003ccode\u003elahontan\u003c/code\u003e are shown below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAvailable Commands\u003c/h3\u003e\u003ca id=\"user-content-available-commands\" class=\"anchor\" aria-label=\"Permalink: Available Commands\" href=\"#available-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003efcounts For running featureCounts pipeline\nstring For running Stringtie pipeline\nkall For running kallisto pipeline\nbowtie2 For running Bowtie2 pipeline\nmj Provoke questionnaire to make a Metadata file\nmb Create trimmomatic blacklist\nclean Clean any project directories\nfo Finds optimal execution path for batch execution\nprepref Pre-processes reference data\ngendef Generate a default input file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information about any of the commands, you can add the \u003ccode\u003e-h\u003c/code\u003e option\nto see the available options and any miscellaneous information. You can also give\nthe command as an argument to the \u003ccode\u003ehelp\u003c/code\u003e command.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan fcounts -h\n$ lahontan help fcounts\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill show information available about the \u003ccode\u003efcounts\u003c/code\u003e command. \u003ccode\u003elahontan\u003c/code\u003e also has\nvarious arguments which can be used to perform an action or change the behavior\nof the pipeline. Shown below are the available options for \u003ccode\u003elahontan\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions:\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n--version\n Show version and exit\n--noconfirm\n Ignore all user prompts except JSON file creation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe syntax for the options displayed is based on the conventional CLI\ninterface. The list of options describe whether an option has short/long forms\n(\u003ccode\u003e-h, --help\u003c/code\u003e), whether an option has an argument, and whether the argument has\na default value. If an option has both a short and long form, then either form\nmay be used for the same functionality. The default values will be denoted in\nthe description of the option by, \u003ccode\u003e[default: foo]\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e--version\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eWill show the version of \u003ccode\u003elahontan\u003c/code\u003e currently installed\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--noconfirm\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to avoid any interactive prompts that may be shown which would require\nuser intervention\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand Options\u003c/h3\u003e\u003ca id=\"user-content-command-options\" class=\"anchor\" aria-label=\"Permalink: Command Options\" href=\"#command-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe commands available to \u003ccode\u003elahontan\u003c/code\u003e will be described in more detail below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003efcounts\u003c/h4\u003e\u003ca id=\"user-content-fcounts\" class=\"anchor\" aria-label=\"Permalink: fcounts\" href=\"#fcounts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan fcounts [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen \u003ccode\u003elahontan\u003c/code\u003e is given the \u003ccode\u003efcounts\u003c/code\u003e command, the pipeline is given the\ninformation to use the featureCounts tools and pathway for analysis. If given\nthe fcounts command, then a mandatory argument \u003ccode\u003eINPUTFILE\u003c/code\u003e is required. It also\nhas various optional arguments which can be given to change the behavior of the\npipeline. The \u003ccode\u003eINPUTFILE\u003c/code\u003e argument expects the path to a file that defines\n\u003ccode\u003eProject\u003c/code\u003e, \u003ccode\u003eReference\u003c/code\u003e, and \u003ccode\u003eOriginal\u003c/code\u003e variables. The \u003ccode\u003eProject\u003c/code\u003e variable should\ndefine the path to a directory where the pipeline will save and execute its\nanalyses. At first execution, the \u003ccode\u003eProject\u003c/code\u003e directory should be empty, as the\npipeline will populate the directory into a consistent project structure. The\n\u003ccode\u003eReference\u003c/code\u003e variable should define the path to a directory where the reference\nmaterials are stored; at minimum it should contain a gene transcripts file, a\ncDNA file, and a reference genome. The \u003ccode\u003eOriginal\u003c/code\u003e variable should define the\npath to a directory that contains the paired-end sequencing data.\u003c/p\u003e\n\u003cp\u003eAn example of an \u003ccode\u003eINPUTFILE\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eExampleInputFile\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/user/ProjectName\nReference = /home/user/Reference/ProjectName-Reference\nOriginal = /home/user/Data/ProjectName-Data\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-e \u0026lt;stage\u0026gt;, --execute \u0026lt;stage\u0026gt;\n Comma-separated list of stages to be executed.\n Possible stages include:\n 1: Creating Project Structure\n 2: Preparing Reference Data\n 3: Running actual Pipeline\n 4: Preparing for R analysis\n 5: Running R analysis\n A: (1,2,3,4,5); A=all i.e. runs entire pipeline\n [default: A]\n-r \u0026lt;integer\u0026gt;, --runsample \u0026lt;integer\u0026gt;\n Runs Stage 3 of the pipeline on the sample specified\n by the integer\n-j \u0026lt;jsonFile\u0026gt;, --jsonfile \u0026lt;jsonFile\u0026gt;\n Ignores JSON Metadata file creation and uses specified\n path to JSON Metadata\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used by Pipeline. Default is to\n use all available CPUs\n--makebatch \u0026lt;cluster\u0026gt;\n Makes batch file to be used with slurm. The argument\n it takes is a comma-separated list of CPUs on each\n node in your cluster\n--batchjson \u0026lt;pathtoJSON\u0026gt;\n Uses json file already created to make batch file\n--edger\n Runs edgeR analysis only. Default is to run both\n--deseq\n Runs DESeq2 analysis only. Default is to run both\n--noconfirm\n Ignore all user prompts except JSON file creation\n--use-blacklist \u0026lt;blacklist\u0026gt;\n Trimmomatic blacklist used for quality control\n [default: $RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa]\n--use-reference\n Use Reference data that has already been prepared.\n Put path to already prepared reference data in\n INPUT file\n Note: Need to run Stage 1 with this argument or add to\n \"--makebatch\" argument\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-e \u003cem\u003e\u0026lt;stage\u0026gt;\u003c/em\u003e, --execute \u003cem\u003e\u0026lt;stage\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to specify stage of Pipeline to be ran\n\u003cem\u003e\u0026lt;stage\u0026gt;\u003c/em\u003e is a comma-separated list of stages to be executed.\nPossible stages include:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e1 : Creating Project Structure; this includes creating Project directories\nand making symbolic links to reference and raw data\n2 : Preparing Reference Data; this includes running Quality Control on the\nreference data then setting up hisat2 databases\n3 : Running actual Pipeline; this includes quality control, alignment,\ncompression, and feature counts\n4 : Preparing for R analysis; this includes creating a \u0027nice\u0027 count file and\ndeveloping automated R scripts from \u003ccode\u003eMetadata.json\u003c/code\u003e\n5 : Running R analysis; this involves running DESeq2 and edgeR differential\nexpression analyses\nA : (1,2,3,4,5) i.e. runs entire pipeline\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: default is to just run \u003ccode\u003eA\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-r \u003cem\u003e\u0026lt;sampleNumber\u0026gt;\u003c/em\u003e, --runsample \u003cem\u003e\u0026lt;sampleNumber\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to execute through Stage 3 on the sample specified by \u003cem\u003e\u0026lt;sampleNumber\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-j \u003cem\u003e\u0026lt;jsonFile\u0026gt;\u003c/em\u003e, --jsonfile \u003cem\u003e\u0026lt;jsonFile\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIgnores JSON Metadata file creation and uses specified path to already\ncreated JSON Metadata\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--maxcpu \u003cem\u003e\u0026lt;CPUs\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to limit number of CPUs used by Pipeline\nNote: default is to use all available CPUs\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--makebatch \u003cem\u003e\u0026lt;cluster\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to make batch file for use with slurm\n\u003cem\u003e\u0026lt;cluster\u0026gt;\u003c/em\u003e is a comma-separated list of CPUs on each node in your cluster\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--batchjson \u003cem\u003e\u0026lt;pathtoJSON\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf an optimal path JSON file has been created using \u003ccode\u003elahontan fo\u003c/code\u003e or another\nmethod, use option to skip the calculation of the optimal CPU usage path\nNote: Option is depended on \u003ccode\u003e--makebatch\u003c/code\u003e i.e. must be added with\n\u003ccode\u003e--makebatch\u003c/code\u003e or not at all\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--edger\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eRuns edgeR analysis only\nNote: default is to run both edgeR and deseq2 analyses\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--deseq\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eRuns deseq2 analysis only\nNote: default is to run both edgeR and deseq2 analyses\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--noconfirm\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed for non-interactivity\nIgnores all user prompts except JSON file creation unless given \u003ccode\u003e--jsonfile\u003c/code\u003e option\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--use-blacklist \u003cem\u003e\u0026lt;blacklist\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTrimmomatic blacklist used for quality control. A new blacklist can be made with\n\u003ccode\u003elahontan mb\u003c/code\u003e\nNote: default is to use \u003ccode\u003e$RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa\u003c/code\u003e where\n\u003ccode\u003e$RNASEQDIR\u003c/code\u003e is the build directory specified in the installation instructions\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--use-reference\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf reference data has already been prepared, use option to skip Stage 2 and\nuse prepared reference data. It is important that this option be used for\nevery stage if the reference data is already prepared.\nNote: If option is to be used, must be ran from Stage 1\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003estring\u003c/h4\u003e\u003ca id=\"user-content-string\" class=\"anchor\" aria-label=\"Permalink: string\" href=\"#string\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan string [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to the \u003ccode\u003efcounts\u003c/code\u003e command, \u003ccode\u003estring\u003c/code\u003e gives the pipeline the information\nit needs so that it uses the Stringtie tools and pathway. The options to the\n\u003ccode\u003estring\u003c/code\u003e command are similar to those of \u003ccode\u003efcounts\u003c/code\u003e. The only difference in\nexecution are Stages 3, 4, and 5. In stage 3, instead of running the analyses\nin one step, Stringtie takes three phases to do so. The first phase, \u003ccode\u003ea\u003c/code\u003e, is\nwhere transcripts are assembled for each sample in the experiment. The second\nphase, \u003ccode\u003eb\u003c/code\u003e, is where a new gene transcript file is created from each sample\u0027s\ntranscripts. The third step, \u003ccode\u003ec\u003c/code\u003e, is where Stringtie estimates the transcript\nabundances for each sample using the newly assembled gene transcript file.\nStage 3 of the pipeline can be ran altogether by using the \u003ccode\u003e--phase abc\u003c/code\u003e\noption. In Stage 4, the pipeline prepares the results to be analyzed by\nballgown in Stage 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-e \u0026lt;stage\u0026gt;, --execute \u0026lt;stage\u0026gt;\n Comma-separated list of stages to be executed.\n Possible stages include:\n 1: Creating Project Structure\n 2: Preparing Reference Data\n 3: Running actual Pipeline\n 4: Preparing for R analysis\n 5: Running R analysis\n A: (1,2,3,4,5); A=all i.e. runs entire pipeline\n [default: A]\n-r \u0026lt;integer\u0026gt;, --runsample \u0026lt;integer\u0026gt;\n Runs Stage 3 of the pipeline on the sample specified\n by the integer\n-p \u0026lt;phase\u0026gt;, --phase \u0026lt;phase\u0026gt;\n Use stringtie tools to replace featureCounts. phase can\n be any of: \"a\",\"b\",\"c\",\"ab\",\"bc\",\"abc\"\n Option also used to specify stringtie postprocessing\n options in which case use: \"--execute 4 --stringtie abc\"\n Note: If you will be running phase b, you cannot specify\n a sample to run with --runsample\n [default: abc]\n-j \u0026lt;jsonFile\u0026gt;, --jsonfile \u0026lt;jsonFile\u0026gt;\n Ignores JSON Metadata file creation and uses specified\n path to JSON Metadata\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used by Pipeline. Default is to\n use all available CPUs\n--noconfirm\n Ignore all user prompts except JSON file creation\n--use-blacklist \u0026lt;blacklist\u0026gt;\n Trimmomatic blacklist used for quality control\n [default: $RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa]\n--use-reference\n Use Reference data that has already been prepared.\n Put path to already prepared reference data in\n INPUT file\n Note: Need to run Stage 1 with this argument or add to\n \"--makebatch\" argument\n--makebatch \u0026lt;cluster\u0026gt;\n Makes batch file to be used with slurm. The argument\n it takes is a comma-separated list of CPUs on each\n node in your cluster\n--batchjson \u0026lt;pathtoJSON\u0026gt;\n Uses json file already created to make batch file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003estring\u003c/code\u003e command only adds the \u003ccode\u003e--phase\u003c/code\u003e option from the \u003ccode\u003efcounts\u003c/code\u003e command.\nIf you wish to see more information, see the \u003ca href=\"#fcounts\"\u003efcounts\u003c/a\u003e documentation.\u003c/p\u003e\n\u003cp\u003e-p *\u0026lt;phase\u0026gt;*, --phase *\u0026lt;phase\u0026gt;*\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eModifies pipeline to incorporate stringtie and ballgown. Stringtie replaces\nfeatureCounts.\nPossible phases can be any of: \"a\",\"b\",\"c\",\"ab\",\"bc\",\"abc\"\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ea : Phase a refers to mapping sample reads to reference genome, converting\nmapped reads to .bam format, and then assembling transcripts for each sample\nb : Phase b refers to merging the transcripts from each sample into one\nmerged .gtf file\nc : Phase c refers to estimating the transcript abundances for each sample\nNote: If you will be running phase b, you cannot specify a sample to run with --runsample\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ekall\u003c/h4\u003e\u003ca id=\"user-content-kall\" class=\"anchor\" aria-label=\"Permalink: kall\" href=\"#kall\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan kall [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to the \u003ccode\u003efcounts\u003c/code\u003e and \u003ccode\u003estring\u003c/code\u003e commands, \u003ccode\u003ekall\u003c/code\u003e gives the pipeline the\ninformation it needs so that it uses the Kallisto tools and pathway. The\noptions to the \u003ccode\u003ekall\u003c/code\u003e command are similar to those of \u003ccode\u003efcounts\u003c/code\u003e. The only\ndifference is that Kallisto is used instead of hisat2/featureCounts/Stringtie\nfor quantifying gene expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-e \u0026lt;stage\u0026gt;, --execute \u0026lt;stage\u0026gt;\n Comma-separated list of stages to be executed.\n Possible stages include:\n 1: Creating Project Structure\n 2: Preparing Reference Data\n 3: Running actual Pipeline\n 4: Preparing for R analysis\n 5: Running R analysis\n A: (1,2,3,4,5); A=all i.e. runs entire pipeline\n [default: A]\n-r \u0026lt;integer\u0026gt;, --runsample \u0026lt;integer\u0026gt;\n Runs Stage 3 of the pipeline on the sample specified\n by the integer\n-j \u0026lt;jsonFile\u0026gt;, --jsonfile \u0026lt;jsonFile\u0026gt;\n Ignores JSON Metadata file creation and uses specified\n path to JSON Metadata\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used by Pipeline. Default is to\n use all available CPUs\n--noconfirm\n Ignore all user prompts except JSON file creation\n--use-blacklist \u0026lt;blacklist\u0026gt;\n Trimmomatic blacklist used for quality control\n [default: $RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa]\n--use-reference\n Use Reference data that has already been prepared.\n Put path to already prepared reference data in\n INPUT file\n Note: Need to run Stage 1 with this argument or add to\n \"--makebatch\" argument\n--makebatch \u0026lt;cluster\u0026gt;\n Makes batch file to be used with slurm. The argument\n it takes is a comma-separated list of CPUs on each\n node in your cluster\n--batchjson \u0026lt;pathtoJSON\u0026gt;\n Uses json file already created to make batch file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ekall\u003c/code\u003e command does not add any options compared to the \u003ccode\u003efcounts\u003c/code\u003e command.\nIf you wish to see more information, see the \u003ca href=\"#fcounts\"\u003efcounts\u003c/a\u003e documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ebowtie2\u003c/h4\u003e\u003ca id=\"user-content-bowtie2\" class=\"anchor\" aria-label=\"Permalink: bowtie2\" href=\"#bowtie2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan bowtie2 [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to the \u003ccode\u003efcounts\u003c/code\u003e,\u003ccode\u003estring\u003c/code\u003e, and \u003ccode\u003ekall\u003c/code\u003e commands, \u003ccode\u003ebowtie2\u003c/code\u003e gives the\npipeline the information it needs so that it uses the bowtie2 aligner. The\noptions to the \u003ccode\u003ebowtie2\u003c/code\u003e command are similar to those of \u003ccode\u003ekall\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-e \u0026lt;stage\u0026gt;, --execute \u0026lt;stage\u0026gt;\n Comma-separated list of stages to be executed.\n Possible stages include:\n 1: Creating Project Structure\n 2: Preparing Reference Data\n 3: Running actual Pipeline\n 4: Preparing for R analysis\n 5: Running R analysis\n A: (1,2,3,4,5); A=all i.e. runs entire pipeline\n [default: A]\n-r \u0026lt;integer\u0026gt;, --runsample \u0026lt;integer\u0026gt;\n Runs Stage 3 of the pipeline on the sample specified\n by the integer\n-j \u0026lt;jsonFile\u0026gt;, --jsonfile \u0026lt;jsonFile\u0026gt;\n Ignores JSON Metadata file creation and uses specified\n path to JSON Metadata\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used by Pipeline. Default is to\n use all available CPUs\n--noconfirm\n Ignore all user prompts except JSON file creation\n--use-blacklist \u0026lt;blacklist\u0026gt;\n Trimmomatic blacklist used for quality control\n [default: $RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa]\n--use-reference\n Use Reference data that has already been prepared.\n Put path to already prepared reference data in\n INPUT file\n Note: Need to run Stage 1 with this argument or add to\n \"--makebatch\" argument\n--makebatch \u0026lt;cluster\u0026gt;\n Makes batch file to be used with slurm. The argument\n it takes is a comma-separated list of CPUs on each\n node in your cluster\n--batchjson \u0026lt;pathtoJSON\u0026gt;\n Uses json file already created to make batch file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the bowtie2 pipeline, the metadata file for into the \u003ccode\u003e--jsonfile\u003c/code\u003e option\nserves only as a reference for the user and offers no use in any of the\n\u003ccode\u003ebowtie2\u003c/code\u003e tools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003emj\u003c/h4\u003e\u003ca id=\"user-content-mj\" class=\"anchor\" aria-label=\"Permalink: mj\" href=\"#mj\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan mj [options]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the many different types of experiments which can be run, it is is\ndifficult to automate a method for gathering information about the treatments\nand groups applied in an experiment. The treatments and groups of an experiment\nare important in being able to develop any conclusions with the data. The\n\u003ccode\u003elahontan mj\u003c/code\u003e command provides a questionnaire that requires user feedback about\nthe experiment. The important point is to provide enough information about the\nfeatures of the experiment so a sample can be identified by those features.\u003c/p\u003e\n\u003cp\u003eCaution: It is important to check that the order with which the samples are\nlinked in their respective directory is the same as the features you describe\nit with.\u003c/p\u003e\n\u003cp\u003eFor example, a typical experiment might look like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan mj\nWhat is the name of the project? ProjectName\nHow many samples are there? 4\nHow many features does each sample have? 2\nWhat is the name of feature #1? treatment\nWhat is the name of feature #2? group\nWhat is the name of the main feature? treatment\nWhat is the treatment feature for sample #1? X\nWhat is the group feature for sample #1? GroupA\nWhat is the treatment feature for sample #2? Y\nWhat is the group feature for sample #2? GroupA\nWhat is the treatment feature for sample #3? X\nWhat is the group feature for sample #3? GroupB\nWhat is the treatment feature for sample #4? Y\nWhat is the group feature for sample #4? GroupB\nDone making Metadata.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this way, you can distinguish any sample by specifying features #1 and #2.\nIf there were multiple runs with the same treatment and group, you may choose\nto add a \"run\" feature. This questionnaire creates a JSON file by default\ntitled \u003ccode\u003eMetadata.json\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMetadata.json\n--------------------------------------------------------\n{\n \"FeatureNames\": [\n \"group\",\n \"treatment\"\n ],\n \"MainFeature\": \"treatment\",\n \"NumberofFeatures\": 2,\n \"NumberofSamples\": 4,\n \"ProjectName\": \"ProjectName\",\n \"Samples\": {\n \"sample_01\": {\n \"Features\": {\n \"group\": \"GroupA\",\n \"treatment\": \"X\"\n }\n },\n \"sample_02\": {\n \"Features\": {\n \"group\": \"GroupA\",\n \"treatment\": \"Y\"\n }\n },\n \"sample_03\": {\n \"Features\": {\n \"group\": \"GroupB\",\n \"treatment\": \"X\"\n }\n },\n \"sample_04\": {\n \"Features\": {\n \"group\": \"GroupB\",\n \"treatment\": \"Y\"\n }\n }\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen\n-j \u0026lt;jsonfile\u0026gt;, --jsonfile \u0026lt;jsonfile\u0026gt;\n Optional name of JSON file to be saved to [default: Metadata.json]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-j \u003cem\u003e\u0026lt;jsonfile\u0026gt;\u003c/em\u003e, --jsonfile \u003cem\u003e\u0026lt;jsonfile\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eOptional name of JSON file to be saved to\nNote: default is to save the file to \u003ccode\u003eMetadata.json\u003c/code\u003e in the current directory\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003emb\u003c/h4\u003e\u003ca id=\"user-content-mb\" class=\"anchor\" aria-label=\"Permalink: mb\" href=\"#mb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan mb [options]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis command is used to create a blacklist of reads that qualify as noise and\nwhich should be trimmed from the data by trimmomatic.\u003c/p\u003e\n\u003cp\u003eAs of trimmmomatic version 0.36, the default blacklist file is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa\n-------------------------------------------------------------------------------\n\u0026gt;PrefixPE/1\nTACACTCTTTCCCTACACGACGCTCTTCCGATCT\n\u0026gt;PrefixPE/2\nGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe blacklist file created by \u003ccode\u003elahontan mb\u003c/code\u003e, as of lahontan version 1.1 is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;PrefixPE/1\nTACACTCTTTCCCTACACGACGCTCTTCCGATCT\n\u0026gt;PrefixPE/2\nGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT\n\u0026gt;PE1\nTACACTCTTTCCCTACACGACGCTCTTCCGATCT\n\u0026gt;PE1_rc\nAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA\n\u0026gt;PE2\nGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT\n\u0026gt;PE2_rc\nAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC\n\u0026gt;ABISolid3AdapterB\nCCTATCCCCTGTGTGCCTTGGCAGTCTCAGCCTCTCTATGGGCAGTCGGT\n\u0026gt;PCR_Primer1\nAATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT\n\u0026gt;PCR_Primer1_rc\nAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT\n\u0026gt;PCR_Primer2\nCAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT\n\u0026gt;PCR_Primer2_rc\nAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGATCTCGTATGCCGTCTTCTGCTTG\n\u0026gt;FlowCell1\nTTTTTTTTTTAATGATACGGCGACCACCGAGATCTACAC\n\u0026gt;FlowCell2\nTTTTTTTTTTCAAGCAGAAGACGGCATACGA\n\u0026gt;ConsecutiveA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n\u0026gt;ConsecutiveT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\n\u0026gt;ConsecutiveC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\n\u0026gt;ConsecutiveG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-t \u0026lt;newadapterfile\u0026gt;, --tofile \u0026lt;newadapterfile\u0026gt;\n Where to save new blacklist [default: ./CustomBlacklist.fa]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-t \u003cem\u003e\u0026lt;newadapterfile\u0026gt;\u003c/em\u003e, --tofile \u003cem\u003e\u0026lt;newadapterfile\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eOptional name of file to be saved to\nNote: default is to save the file to \u003ccode\u003eCustomBlacklist.fa\u003c/code\u003e in the current directory\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eclean\u003c/h4\u003e\u003ca id=\"user-content-clean\" class=\"anchor\" aria-label=\"Permalink: clean\" href=\"#clean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan clean [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis command is used to clean/reset the populated project directories. The\ndefault behavior is to clean out the entire Project structure to the point\nright after Stage 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-c \u0026lt;placeToClean\u0026gt;, --clean \u0026lt;placeToClean\u0026gt;\n Cleans \u0026lt;placeToClean\u0026gt;; Possible places include:\n Reference\n Data\n Postprocessing\n All\n--sampleclean \u0026lt;sampleName\u0026gt;\n Similar to --clean; but instead just cleans a\n single sample directory, \u0026lt;sampleName\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory Arguments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eINPUTFILE\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe path to a file that contains the variables \u003ccode\u003eProject\u003c/code\u003e, \u003ccode\u003eReference\u003c/code\u003e, and\n\u003ccode\u003eOriginal\u003c/code\u003e defined by their respective paths\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-c \u003cem\u003e\u0026lt;placeToClean\u0026gt;\u003c/em\u003e, --clean \u003cem\u003e\u0026lt;placeToClean\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to clean/reset \u003cem\u003e\u0026lt;placeToClean\u0026gt;\u003c/em\u003e\nPossible places include:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eReference = removes all files except Genome, cDNA, and gtf in Reference\ndirectory\nData = removes all files except raw data file links in Data directory\nPostprocessing = removes all files except \u003ccode\u003eMetadata.json\u003c/code\u003e if it exists in\nPostprocessing directory\nAll = resets entire Project. This is equivalent to running stage 1 on fresh\ndata\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--sampleclean \u003cem\u003e\u0026lt;sampleName\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to clean/reset \u003cem\u003e\u0026lt;sampleName\u0026gt;\u003c/em\u003e\nSimilar to \u003ccode\u003e--clean Data\u003c/code\u003e but instead of cleaning all samples just cleans a\nsingle sample directory\nNote: \u003cem\u003e\u0026lt;sampleName\u0026gt;\u003c/em\u003e will be of the form \u003ccode\u003esample_#\u003c/code\u003e where #\nrefers to specific sample number\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003efo\u003c/h4\u003e\u003ca id=\"user-content-fo\" class=\"anchor\" aria-label=\"Permalink: fo\" href=\"#fo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan fo [options] NUMSAMPLES CLUSTER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is possible to run the pipeline in a SLURM cluster by creating a batch file\nthat contains various \u003ccode\u003esrun\u003c/code\u003e commands given to the SLURM scheduler. However,\nthe automated batch file maker needs information on how best to run the samples\nin order to minimize the waste of CPU resources. The \u003ccode\u003elahontan fo\u003c/code\u003e command\noffers the ability to create a file that contains information on how best to\nrun the samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-t \u0026lt;filename, --tofile \u0026lt;filename\u0026gt;\n Name of file to be saved to\n [default: ./OptimalPath.dat]\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used in calculating optimal path\n Default is to use all available CPUs\n-c, --customize\n If the calculation is too slow, can use this option\n to specify the path yourself\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory Arguments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNUMSAMPLES\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe number of samples in your experiment.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eCLUSTER\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA comma separated list of the number of CPUs on each node in your cluster\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-t \u0026lt;filename, --tofile \u003cem\u003e\u0026lt;filename\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eOptional name of file to be saved to\nNote: default behavior is to save to OptimalPath.dat in the current directory\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--maxcpu \u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eLimits number of CPUs used in calculating optimal path\nNote: Default is to use all available CPUs\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-c, --customize\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf the calculation is too slow, can use this option to specify the path\nyourself\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eprepref\u003c/h4\u003e\u003ca id=\"user-content-prepref\" class=\"anchor\" aria-label=\"Permalink: prepref\" href=\"#prepref\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan prepref [options] (-r REFERENCEDIR | -i INPUT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis command is equivalent to Stage 2 of the pipeline, however the difference\nis that this command is occurring outside of a structured Project environment.\nIt is useful to prepare the reference information before running the pipeline\nas there may be unforeseen difficulties with the reference information which\ncan\u0027t be caught by the pipeline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-r \u0026lt;referenceDir\u0026gt;\n One of the mandatory arguments i.e. must specify either\n r OR i options.\n Option value is a path to directory that contains a GTF,\n cDNA, and reference genome\n-i \u0026lt;input\u0026gt;\n One of the mandatory arguments i.e. must specify either\n r OR i options\n Option value is a path to input file that must contain\n at least a path to reference directory\n Note: a default input file can be generated with:\n lahontan gendef --mode prepref\n-q, --qualitycheck\n Only run quality check on references. Default behavior\n is to run both quality control and preprocessing\n-p, --preprocess\n Only run preprocessing on references. Default behavior\n is to run both quality control and preprocessing\n-k, --kallisto\n Additionally build kallisto index\n Note: index required if kallisto pipeline to be used\n--onlykallisto\n Build only the kallisto index\n-b, --bowtie2\n Additionally build Bowtie2 index\n Note: index required if Bowtie2 pipeline to be used\n--onlybowtie2\n Build only the Bowtie2 index\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limit the number of CPU that get used to preprocess\n reference data.\n Note: default is to use all available\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory Arguments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(-r REFERENCEDIR | -i INPUT)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eFor the \u003ccode\u003eprepref\u003c/code\u003e command, you must use either the \u003ccode\u003e-r\u003c/code\u003e option with the\npath to a directory that contains a GTF, cDNA, and reference genome OR\nyou must use the \u003ccode\u003e-i\u003c/code\u003e option with a manifest file that includes at least\nthe path to a reference directory that contains a GTF, cDNA, and reference\ngenome\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eAn example of a minimal input file for the \u003ccode\u003eprepref -i\u003c/code\u003e option:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eExampleInputFile\n-------------------------------------------------------------------------------\n[locations]\nReference = /home/user/Reference/ProjectName-Reference\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote the \"[locations]\" header and the \"Reference\" variable. These are required\nin every \u003ccode\u003eprepref\u003c/code\u003e input file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-q, --qualitycheck\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCheck the quality of the references by comparing the chromosome names in the\ngenome and gtf, comparing the gene names in the cDNA and gtf, and comparing\nthe number of unique gene names in the cDNA and gtf.\nThis option is given if you only want to check the quality without actually\npreprocessing the references\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-p, --preprocess\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePreprocesses the references by building a BLAST database, finding the splice\nsites, finding the exons, building a hisat2 database, and building a genome\nindex with samtools\nThis option is given if you only want to preprocess the references without\nactually running a quality check\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-k, --kallisto\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eBuild an index for kallisto analysis\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-b, --bowtie2\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eBuild an index for bowtie2 analysis\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--maxcpu \u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eLimit the number of CPU that get used to preprocess reference data.\nNote: default is to use all available\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003egendef\u003c/h4\u003e\u003ca id=\"user-content-gendef\" class=\"anchor\" aria-label=\"Permalink: gendef\" href=\"#gendef\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan gendef [options]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-f \u0026lt;filename\u0026gt;, --filename \u0026lt;filename\u0026gt;\n Name of input file to create\n [default: default_input.ini]\n-m \u0026lt;mode\u0026gt;, --mode \u0026lt;mode\u0026gt;\n Type of execution mode, can be:\n fcounts\n string\n kall\n bowtie2\n prepref\n [default: fcounts]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003egendef\u003c/code\u003e command is used to generate an input file with all default\nparameters filled in. An input file is mandatory for the \u003ccode\u003efcounts\u003c/code\u003e, \u003ccode\u003estring\u003c/code\u003e,\n\u003ccode\u003ekall\u003c/code\u003e, and \u003ccode\u003ebowtie2\u003c/code\u003e commands. It is optional for the \u003ccode\u003eprepref\u003c/code\u003e command. The\ninput files are organized in an INI format. For more information about file\nformat and syntax, see the \u003ca href=\"https://docs.python.org/3/library/configparser.html#supported-ini-file-structure\" rel=\"nofollow\"\u003epython\ndocumentation\u003c/a\u003e\n. To summarize, files consist of sections, each with a section header in square\nbrackets, and key-value pairs defined underneath (separated by an \"=\"). To\nleave a comment in a file, you can precede a line with a \"#\" or \";\".\u003c/p\u003e\n\u003cp\u003eFor example, here is an input file that is generated with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan gendef -m fcounts\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003edefault_input.ini\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/alberton/rna-seq/scripts/Pipeline/Test\nReference = /home/alberton/rna-seq/scripts/Pipeline/Test\nOriginal = /home/alberton/rna-seq/scripts/Pipeline/Test\n\n[mode]\nfcounts = True\nkall = False\nstring = False\nbowtie2 = False\n\n[fcounts]\n--execute = A\n--runsample = None\n--jsonfile = None\n--maxcpu = None\n--noconfirm = False\n--makebatch = None\n--batchjson = None\n--edger = False\n--deseq = False\n--use-blacklist = /home/alberton/rna-seq/build/Trimmomatic/adapters/TruSeq3-PE.fa\n--use-reference = False\n\n[EXECUTABLES]\nRscript = /usr/local/bin/Rscript\njava = /usr/bin/java\nrnaseqdir = /home/alberton/rna-seq/build\nfastqc = ${rnaseqdir}/fastqc\nextract_exons.py = ${rnaseqdir}/hisat2/extract_exons.py\nextract_splice_sites.py = ${rnaseqdir}/hisat2/extract_splice_sites.py\nhisat2 = ${rnaseqdir}/hisat2/hisat2\nhisat2-build = ${rnaseqdir}/hisat2/hisat2-build\nsamtools = ${rnaseqdir}/samtools/bin/samtools\nmakeblastdb = ${rnaseqdir}/ncbi-blast/bin/makeblastdb\nblastn = ${rnaseqdir}/ncbi-blast/bin/blastn\nseqtk = ${rnaseqdir}/seqtk\ngffcompare = ${rnaseqdir}/gffcompare\nstranded_classifier.py = ${rnaseqdir}/stranded_classifier.py\nfeatureCounts = ${rnaseqdir}/subread/bin/featureCounts\nstringtie = ${rnaseqdir}/stringtie\nkallisto = ${rnaseqdir}/kallisto\nbowtie2 = ${rnaseqdir}/bowtie2\nbowtie2-build = ${rnaseqdir}/bowtie2-build\n\n[fcounts/main]\nrunQCheck = True\nrunTrimmomatic = True\nrunSeqtk = True\nrunBlastn = True\nrunHisat = True\nrunCompression = True\nrunFeatureCounts = True\ngetNiceColumns = True\ngetAlignedColumn = True\n\n[fcounts/runHisat]\nhisat2 = ${EXECUTABLES:hisat2}\n-k = None\n-p = None\n--rna-strandedness = None\n--dta = None\n--phred = None\n--known-splicesite-infile = None\n-x = None\n-1 = None\n-2 = None\n-S = None\nother = None\n\n[fcounts/runCompression]\nsamtools = ${EXECUTABLES:samtools}\n-b = None\n-T = None\n-@ = None\n-o = None\nin = None\nother = None\n\n[fcounts/runFeatureCounts]\nfeatureCounts = ${EXECUTABLES:featureCounts}\n-T = None\n-s = None\n-p = None\n-C = None\n--primary = None\n--ignoreDup = None\n--largestOverlap = None\n-t = None\n-g = None\n-a = None\n-o = None\nin = None\nother = None\n\n[runQCheck]\nfastqc = ${EXECUTABLES:fastqc}\n-t = None\n-o = None\nread1 = None\nread2 = None\nother = None\n\n[runTrimmomatic]\njava = ${EXECUTABLES:java}\n-jar = None\n-threads = None\n-phred = None\nread1 = None\nread2 = None\nread1pout = None\nread2pout = None\nread1uout = None\nread2uout = None\nblacklist = None\nILLUMINACLIP = None\nLEADING = None\nTRAILING = None\nSLIDINGWINDOW = None\nMINLEN = None\nother = None\n\n[runSeqtk]\nseqtk = ${EXECUTABLES:seqtk}\n-s = None\nread1 = None\nread2 = None\nread1out = None\nread2out = None\nsamples = None\n-A = None\nother = None\n\n[runBlastn]\nblastn = ${EXECUTABLES:blastn}\nread1 = None\nread2 = None\nout1 = None\nout2 = None\n-db = None\n-task = None\n-outfmt = None\n-max_target_seqs = None\n-num_threads = None\nother = None\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis input file contains the default behavior of the \u003ccode\u003efcounts\u003c/code\u003e pipeline.The\norder of the sections does not matter, nor does the order of the keys. The only\nmandatory section in any input file is the \"[locations]\" section. The default\nbehavior of the \u003ccode\u003egendef\u003c/code\u003e command is to populate the \"[locations]\" section with\nthe path to the current directory. So after creating this file, the only user\nwork \u003cstrong\u003erequired\u003c/strong\u003e is to finish the paths in the \"[locations]\" section. The\nother sections contain keys that change the default behavior of the pipeline\n(\u003cstrong\u003eCAUTION\u003c/strong\u003e: changing these requires an understanding of the tools involved,\nrun \u003ccode\u003elahontan help fcounts\u003c/code\u003e for more much more detailed information)\u003c/p\u003e\n\u003cp\u003eAdditionally, there are certain reserved values that carry particular\nsignificance for the input file parser. The reserved value \u003cstrong\u003eNone\u003c/strong\u003e is reserved\nto mean the pipeline will default to its default behavior. The reserved values\n\u003cstrong\u003eTrue\u003c/strong\u003e and \u003cstrong\u003eFalse\u003c/strong\u003e mean the pipeline will turn on or off the desired\noption, respectively. Other options contain special arguments that you can see\nmore information about by using the built-in \u003ccode\u003ehelp\u003c/code\u003e command.\u003c/p\u003e\n\u003cp\u003eNotice that the default behavior of \u003ccode\u003egendef\u003c/code\u003e is to default all behavior. Thus,\nthe previous input file is equivalent to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edefault_input.ini\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/alberton/rna-seq/scripts/Pipeline/Test\nReference = /home/alberton/rna-seq/scripts/Pipeline/Test\nOriginal = /home/alberton/rna-seq/scripts/Pipeline/Test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, a completed input file may look something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edefault_input.ini\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/alberton/rna-seq/scripts/Pipeline/Test/MyProject\nReference = /home/alberton/rna-seq/scripts/Pipeline/Test/MyProject-Reference\nOriginal = /home/alberton/rna-seq/scripts/Pipeline/Test/MyProject-Data\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNotice that the paths to the Project, Reference, and Original directories are\ncompleted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory Arguments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no mandatory arguments for the \u003ccode\u003egendef\u003c/code\u003e command, however, the \u003ccode\u003e-m\u003c/code\u003e\noption specifies the type of input file to create, and it will default to\n\u003ccode\u003efcounts\u003c/code\u003e if not specified.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan gendef\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill create a \u003ccode\u003efcounts\u003c/code\u003e input file called \u003ccode\u003edefault_input.ini\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan gendef -m string -f StringtieInput.ini\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill create a \u003ccode\u003estring\u003c/code\u003e input file called \u003ccode\u003eStringtieInput.ini\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ehelp\u003c/h4\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan help COMMAND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsed to display additional information about a command. Similar to \u003ccode\u003elahontan \u0026lt;command\u0026gt; --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailable Commands\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efcounts For running featureCounts pipeline\nstring For running Stringtie pipeline\nkall For running kallisto pipeline\nbowtie2 For running bowtie2 pipeline\nmj Provoke questionnaire to make a Metadata file\nmb Create trimmomatic blacklist\nclean Clean any project directories\nfo Finds optimal execution path for batch execution\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthors\u003c/h1\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Alberto024\"\u003e\u003cstrong\u003eAlberto Nava\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rltillett\"\u003e\u003cstrong\u003eRichard Tillett\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 6, "topics": [], - "updated_at": 1722946559.0 + "updated_at": 1605113003.0 }, { "data_format": 2, - "description": "This is Red Hen Lab\u0027s home for singularity containers to be built on Singularity Hub", + "description": "Pipeline to study intra tumor heterogeneity using HATCHet, DeCiFer and ClonEvol", "filenames": [ - "Singularity_ATEE1", - "Singularity.DeepSpeech2_shuwei", - "Singularity.Alex_practice", - "Singularity.openpose-cpu_veryslow", - "Singularity.DeepSpeech2", - "Singularity.DeepSpeech2_Ubuntu" + "Singularity" ], - "full_name": "RedHenLab/singularity_containers", + "full_name": "IARCbioinfo/ITH_pipeline", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_containers\u003c/h1\u003e\u003ca id=\"user-content-singularity_containers\" class=\"anchor\" aria-label=\"Permalink: singularity_containers\" href=\"#singularity_containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is Red Hen Lab\u0027s home for singularity containers to be built on Singularity Hub\u003c/p\u003e\n\u003cp\u003eThey can be found here:\n\u003ca href=\"https://singularity-hub.org/collections/1053\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eITH_pipeline\u003c/h1\u003e\u003ca id=\"user-content-ith_pipeline\" class=\"anchor\" aria-label=\"Permalink: ITH_pipeline\" href=\"#ith_pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNextflow pipeline to study intra-tumoral heterogeneity\u003c/h2\u003e\u003ca id=\"user-content-nextflow-pipeline-to-study-intra-tumoral-heterogeneity\" class=\"anchor\" aria-label=\"Permalink: Nextflow pipeline to study intra-tumoral heterogeneity\" href=\"#nextflow-pipeline-to-study-intra-tumoral-heterogeneity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNextflow pipeline to study intra-tumoral heterogeneity through subclonality reconstruction, using HATCHet, DeCiFer and clonEvol.\nPIPELINE IN DEVELOPMENT.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eExternal software:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/raphael-group/hatchet\"\u003eHATCHet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/raphael-group/decifer\"\u003eDeCiFer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hdng/clonevol\"\u003eClonEvol\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bam_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing BAM files to pass to HATCHet\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd\u003e/whole/path/to/genome.fa\u003c/td\u003e\n\u003ctd\u003eReference genome in fasta format\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--correspondance\u003c/td\u003e\n\u003ctd\u003eTN_pairs.txt\u003c/td\u003e\n\u003ctd\u003eTabulated file containing two columns: tumor and normal bam names\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of cpu to use\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--config\u003c/td\u003e\n\u003ctd\u003enull\u003c/td\u003e\n\u003ctd\u003eUse custom configuration file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003eSize of memory used in GB\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003e.\u003c/td\u003e\n\u003ctd\u003ePath to output folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--samtools_folder\u003c/td\u003e\n\u003ctd\u003e/usr/bin/\u003c/td\u003e\n\u003ctd\u003esamtools installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bcftools_folder\u003c/td\u003e\n\u003ctd\u003e/usr/bin/\u003c/td\u003e\n\u003ctd\u003ebcftools installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hatchet_folder\u003c/td\u003e\n\u003ctd\u003e/usr/bin/\u003c/td\u003e\n\u003ctd\u003ehatchet installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bnpy_folder\u003c/td\u003e\n\u003ctd\u003e/usr/bin/\u003c/td\u003e\n\u003ctd\u003ebnpy installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run iarcbioinfo/ITH_pipeline --bam_folder bam_folder/ --ref genome.fa --correspondance pairs.txt --cpu 24 --samtools_folder ~/bin/samtools-1.7 --bcftools_folder ~/bin/bcftools-1.7 --hatchet_folder ~/bin/hatchet --bnpy_folder ~/bin/bnpy-dev\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed description\u003c/h2\u003e\u003ca id=\"user-content-detailed-description\" class=\"anchor\" aria-label=\"Permalink: Detailed description\" href=\"#detailed-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirected Acyclic Graph\u003c/h2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-label=\"Permalink: Directed Acyclic Graph\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:delhommet@students.iarc\"\u003edelhommet@students.iarc\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences (optional)\u003c/h2\u003e\u003ca id=\"user-content-references-optional\" class=\"anchor\" aria-label=\"Permalink: References (optional)\" href=\"#references-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ (optional)\u003c/h2\u003e\u003ca id=\"user-content-faq-optional\" class=\"anchor\" aria-label=\"Permalink: FAQ (optional)\" href=\"#faq-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 2, - "subscribers_count": 19, + "subscribers_count": 4, "topics": [], - "updated_at": 1589193222.0 + "updated_at": 1621606584.0 }, { "data_format": 2, - "description": "code and supplementary documents supporting the FMAB book project", + "description": "Software which simplify construction of kinetic models for cell metabolism with optional integration of proteomics data ", "filenames": [ - "BSKL/SingularityFMAB" + "Singularity" ], - "full_name": "vpbrendel/CodeFMAB", + "full_name": "chuanfuyap/Convenient-Modeller", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCodeFMAB\u003c/h1\u003e\u003ca id=\"user-content-codefmab\" class=\"anchor\" aria-label=\"Permalink: CodeFMAB\" href=\"#codefmab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and supplementary documents supporting the FMAB book project\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eConvenient Modeller\u003c/h1\u003e\u003ca id=\"user-content-convenient-modeller\" class=\"anchor\" aria-label=\"Permalink: Convenient Modeller\" href=\"#convenient-modeller\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConvenient Modeller is a software which simplifies construction of kinetic models for cell metabolism (with inclusion of regulatory effects), with optional integration of proteomics data.\u003c/p\u003e\n\u003cp\u003eThe tool allows for use of metabolite concentration, and/or flux for fitting a model, main objective of this tool is to allow for rapid prototyping of the a metabolic phenomenon of interest with a kinetic/dynamic model. For example, if one is interested in glycolysis of a species such as baker\u0027s yeast, metabolite/flux values can be obtained (from literature of experiment) to fit a model. Following the fit, a \u003cstrong\u003e\u003ca href=\"http://sbml.org/Main_Page\" rel=\"nofollow\"\u003eSBML\u003c/a\u003e\u003c/strong\u003e file is generated which can be exported to any SBML simulator such as \u003cstrong\u003e\u003ca href=\"http://copasi.o:\" rel=\"nofollow\"\u003eCOPASI\u003c/a\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003ca href=\"https://github.com/draeger-lab/SBMLsimulator\"\u003eSBMLsimulator\u003c/a\u003e\u003c/strong\u003e or \u003cstrong\u003e\u003ca href=\"https://jjj.mib.ac.uk\" rel=\"nofollow\"\u003eJWS online\u003c/a\u003e\u003c/strong\u003e to be manipulated (e.g. removal of enzymes, removal of activators/inhibitors or increasing enzyme concentration) and simulated to see what its outcome would be before proceeding with next step of metabolic study.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/chuanfuyap/Convenient-Modeller/blob/master/images/Glycolysis.png\"\u003e\u003cimg src=\"https://github.com/chuanfuyap/Convenient-Modeller/raw/master/images/Glycolysis.png\" width=\"520\" height=\"720\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/p\u003e\n\u003cp\u003eAbove is a basic glycolysis model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHow to use Convenient Modeller\u003c/h1\u003e\u003ca id=\"user-content-how-to-use-convenient-modeller\" class=\"anchor\" aria-label=\"Permalink: How to use Convenient Modeller\" href=\"#how-to-use-convenient-modeller\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNOTE: the GUI has bugs that has yet to be patched, but the optimisation engine that runs it is bug free. GUI bugs are mostly errors messages but should not affect model building. If GUI bugs are preventing model construction, the TSV format (see below) is recommended for model building and running the\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOption 1 (Recommended): Singularity\u003c/h2\u003e\u003ca id=\"user-content-option-1-recommended-singularity\" class=\"anchor\" aria-label=\"Permalink: Option 1 (Recommended): Singularity\" href=\"#option-1-recommended-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSoftware is now available as an easy to use singularity image.\u003c/p\u003e\n\u003cp\u003eSingularity is a containerization software available for free here: \u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/install-linux\u003c/a\u003e (instruction for linux)\u003c/p\u003e\n\u003cp\u003eAfter installation, users can download the image with the following line:\u003c/p\u003e\n\u003cp\u003esingularity pull shub://chuanfuyap/Convenient-Modeller\u003c/p\u003e\n\u003cp\u003eTo run the image:\u003c/p\u003e\n\u003cp\u003esingularity run shub://chuanfuyap/Convenient-Modeller\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMac users:\u003c/h3\u003e\u003ca id=\"user-content-mac-users\" class=\"anchor\" aria-label=\"Permalink: Mac users:\" href=\"#mac-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order for Mac users to use the GUI version of the software via singularity, they need to enable X11 Forwarding in vagrant box, as well as install xauth server within the vagrant box\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOption 2: To install and run the software locally\u003c/h2\u003e\u003ca id=\"user-content-option-2-to-install-and-run-the-software-locally\" class=\"anchor\" aria-label=\"Permalink: Option 2: To install and run the software locally\" href=\"#option-2-to-install-and-run-the-software-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software requires the following libraries to run:-\u003c/p\u003e\n\u003cp\u003eJava Libraries:\u003c/p\u003e\n\u003cp\u003eApache Common Maths version 3.5 \u003ca href=\"http://archive.apache.org/dist/commons/math/\" rel=\"nofollow\"\u003ehttp://archive.apache.org/dist/commons/math/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eJava Native Access (JNA) \u003ca href=\"https://github.com/java-native-access/jna\"\u003ehttps://github.com/java-native-access/jna\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eJava Systems Biology Markup Language (JSBML) version 1 \u003ca href=\"http://sbml.org/Software/JSBML\" rel=\"nofollow\"\u003ehttp://sbml.org/Software/JSBML\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eC Libraries:\nNOTE: it\u0027s possible the source file is corrupted and unable to be compiled, I would suggest deleting the functions in the source files that are giving errors during compilation step and compile again.\nSOSLIB version 1.7 \u003ca href=\"https://github.com/raim/SBML_odeSolver\"\u003ehttps://github.com/raim/SBML_odeSolver\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003elibSBML version 5.X.X \u003ca href=\"https://sourceforge.net/projects/sbml/files/libsbml/5.13.0/\" rel=\"nofollow\"\u003ehttps://sourceforge.net/projects/sbml/files/libsbml/5.13.0/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSUNDIALS version 2.3 \u003ca href=\"http://computation.llnl.gov/projects/sundials/sundials-software\" rel=\"nofollow\"\u003ehttp://computation.llnl.gov/projects/sundials/sundials-software\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRelevant project:\u003c/p\u003e\n\u003cp\u003eNOTE: this C program must be compiled with the right library paths for Convenient-Modeller to be able to solve models in parameter estimation process.\nsoslibJNIC \u003ca href=\"https://github.com/chuanfuyap/soslibJNIC\"\u003ehttps://github.com/chuanfuyap/soslibJNIC\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHOW TO USE:\u003c/h1\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: HOW TO USE:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFull tutorial available \u003cstrong\u003e[here]\u003c/strong\u003e\n[here]: \u003ca href=\"https://github.com/chuanfuyap/Convenient-Modeller/blob/master/Convenient%20Modeller%20Tutorial.pdf\"\u003ehttps://github.com/chuanfuyap/Convenient-Modeller/blob/master/Convenient%20Modeller%20Tutorial.pdf\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGUI\u003c/h2\u003e\u003ca id=\"user-content-gui\" class=\"anchor\" aria-label=\"Permalink: GUI\" href=\"#gui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCrash Course\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/chuanfuyap/Convenient-Modeller/blob/master/images/crash_course.png\"\u003e\u003cimg src=\"https://github.com/chuanfuyap/Convenient-Modeller/raw/master/images/crash_course.png\" width=\"960\" height=\"580\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlternative model building with TSV\u003c/h2\u003e\u003ca id=\"user-content-alternative-model-building-with-tsv\" class=\"anchor\" aria-label=\"Permalink: Alternative model building with TSV\" href=\"#alternative-model-building-with-tsv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn alternative method is to use a spreadsheet to build the model before transfering to a text file, which can then be exported the convenient modeller GUI for further manipulation or model fitting.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/chuanfuyap/Convenient-Modeller/blob/master/images/TSV.png\"\u003e\u003cimg src=\"https://github.com/chuanfuyap/Convenient-Modeller/raw/master/images/TSV.png\" width=\"720\" height=\"320\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe text is read in row by row by the software.\u003c/li\u003e\n\u003cli\u003eA \u2018#\u2019 sign denotes start of a new element being read into the system.\u003c/li\u003e\n\u003cli\u003e\u2018B.C\u2019 stands for boundary condition, a function in SBML that makes a metabolite\u2019s concentration fixed if stated to be \u2018TRUE\u2019 (and vice versa), becoming either an output or input for the system being modelled.\u003c/li\u003e\n\u003cli\u003eThe column following either metabolites or enzymes are concentrations for the respective compounds.\u003c/li\u003e\n\u003cli\u003eFor the reactions, users must first set an enzyme that catalyses the reaction, and if needed activators and/or inhibitors for the reaction in the proceeding columns.\u003c/li\u003e\n\u003cli\u003eIn the following columns, substrates come first, then a \u2018=\u2019 sign to separate them from the products, which comes in the next columns.\u003c/li\u003e\n\u003cli\u003eFor stoichiometries of reactions, users can add numbers followed by \u2018\u003cem\u003e\u2019 preceding the substrates/products (e.g. 2\u003c/em\u003eG6P)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1725495252.0 + "updated_at": 1589286125.0 }, { "data_format": 2, - "description": "Singularity documentation and build files for Northwestern University\u0027s Research Computing Services and the Quest HPC", + "description": "Singularity R: rstudio desktop image", "filenames": [ - "singularity_files/mxnet/Singularity.mxnet_cpu", - "singularity_files/ubuntu/Singularity.ubuntu", - "singularity_files/biobakery/Singularity.biobakery", - "singularity_files/mpi/Singularity.openmpi", - "singularity_files/keras/Singularity.keras_cpu", - "singularity_files/tensorflow/Singularity.tensorflow_cpu", - "singularity_files/tensorflow/Singularity.tensorflow_gpu" + "Singularity.3.4.4", + "Singularity" ], - "full_name": "ffineis/nurcs-singularity", + "full_name": "mjstealey/rstudio", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNorthwestern University Research Computing Services Singularity documentation and container files\u003c/h1\u003e\u003ca id=\"user-content-northwestern-university-research-computing-services-singularity-documentation-and-container-files\" class=\"anchor\" aria-label=\"Permalink: Northwestern University Research Computing Services Singularity documentation and container files\" href=\"#northwestern-university-research-computing-services-singularity-documentation-and-container-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1271\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e|-- docs (documentation)\n|-- singularity_files\n| |-- [container files]\n| |-- Singularity.\u0026lt;container tag\u0026gt; (can be built/integrated into Sinuglarity Hub collection)\n| |-- [files for pulling resources during container build]\n| |-- [files for running tests]\n|\n|-- templates\n| |-- [template recipe files]\n|\n|-- submit_job.sh (example MOAB submission file utilizing singularity cmds)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere can be multiple recipe files per container directory, for example, if there are both CPU and GPU versions of a single container. Files titled \u003ccode\u003eSingularity.\u0026lt;container tag\u0026gt;\u003c/code\u003e can be built on Singularity Hub and integrated with our Singularity Hub collection.\u003c/p\u003e\n\u003cp\u003eCheck the build status of each container in this repository on \u003ca href=\"https://singularity-hub.org/collections/1271\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFollow the \u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki/Build-A-Container\"\u003eSingularity automated build documentation\u003c/a\u003e for recipe file naming conventions for automating builds on Singularity Hub and for how to configure automated or manual builds on pushed commits.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity R: rstudio\u003c/h1\u003e\u003ca id=\"user-content-singularity-r-rstudio\" class=\"anchor\" aria-label=\"Permalink: Singularity R: rstudio\" href=\"#singularity-r-rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/798\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.rstudio.com/products/RStudio/\" rel=\"nofollow\"\u003eRStudio Desktop\u003c/a\u003e based on the \u003ca href=\"https://hub.docker.com/_/r-base/\" rel=\"nofollow\"\u003er-base\u003c/a\u003e docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003erstudio.3.4.4.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build rstudio.3.4.4.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name rstudio.3.4.4.simg shub://mjstealey/rstudio\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHelp\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e rstudio.3.4.4.simg\u003c/span\u003e\n\n\n\u003cspan class=\"pl-c1\"\u003e RStudio Desktop version 1.1.442\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e R version 3.4.4 (2018-03-15) -- \"Someone to Lean On\"\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e Usage:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run rstudio.3.4.4.simg [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app R rstudio.3.4.4.simg [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app Rscript rstudio.3.4.4.simg [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app rstudio rstudio.3.4.4.simg\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNo particular command is launched using the default run command, rather it is left to the user to specify:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run rstudio.3.4.4.simg [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003e[args]\u003c/code\u003e is generally one of {\u003ccode\u003erstudio\u003c/code\u003e, \u003ccode\u003eR [args]\u003c/code\u003e, \u003ccode\u003eRscript [args]\u003c/code\u003e}\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR\u003c/h3\u003e\u003ca id=\"user-content-r\" class=\"anchor\" aria-label=\"Permalink: R\" href=\"#r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eR\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app R rstudio.3.4.4.simg [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app R rstudio.3.4.4.simg --version\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eR version 3.4.4 (2018-03-15) -- \"Someone to Lean On\"\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eCopyright (C) 2018 The R Foundation for Statistical Computing\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ePlatform: x86_64-pc-linux-gnu (64-bit)\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eR is free software and comes with ABSOLUTELY NO WARRANTY.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eYou are welcome to redistribute it under the terms of the\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGNU General Public License versions 2 or 3.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eFor more information about these matters see\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ehttp://www.gnu.org/licenses/.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRscript\u003c/h3\u003e\u003ca id=\"user-content-rscript\" class=\"anchor\" aria-label=\"Permalink: Rscript\" href=\"#rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eRscript\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app Rscript rstudio.3.4.4.simg [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app Rscript rstudio.3.4.4.simg --version\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eR scripting front-end version 3.4.4 (2018-03-15)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRStudio\u003c/h3\u003e\u003ca id=\"user-content-rstudio\" class=\"anchor\" aria-label=\"Permalink: RStudio\" href=\"#rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003erstudio\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app rstudio rstudio.3.4.4.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app rstudio rstudio.3.4.4.simg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003elibGL error: No matching fbConfigs or visuals found\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003elibGL error: failed to load driver: swrast\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eRStudio Desktop UI\u003c/strong\u003e: (using X11)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/5332509/37848039-221fdf5e-2ea9-11e8-8f9c-db199ad4f6d2.png\"\u003e\u003cimg width=\"80%\" alt=\"RStudio Desktop\" src=\"https://user-images.githubusercontent.com/5332509/37848039-221fdf5e-2ea9-11e8-8f9c-db199ad4f6d2.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003ccode\u003eLibGL error:\u003c/code\u003e errors were observed while testing using a remote CentOS 7 VM to run the Singularity image and the UI was rendered using X11. No other errors were denoted during testing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn exit, the terminal displayed:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eQApplication::qAppName: Please instantiate the QApplication object first\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at \u003ca href=\"https://github.com/mjstealey/rstudio\"\u003ehttps://github.com/mjstealey/rstudio\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, - "topics": [], - "updated_at": 1651571098.0 + "subscribers_count": 4, + "topics": [ + "singularity", + "rstudio-desktop", + "r", + "rstudio" + ], + "updated_at": 1628200768.0 }, { "data_format": 2, - "description": "Materials for SfN 2018 training event", + "description": "EWAS Data Analysis", "filenames": [ - "section23/environments/Singularity.heudiconv", - "section23/environments/Singularity.fsl", - "section23/environments/Singularity.heudiconvn", - "section23/environments/Singularity.fsln" + "Singularity" ], - "full_name": "ReproNim/sfn2018-training", + "full_name": "GenomicsAotearoa/EWAS_DAP", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions\u003c/h1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall VirtualBox\u003c/h2\u003e\u003ca id=\"user-content-install-virtualbox\" class=\"anchor\" aria-label=\"Permalink: Install VirtualBox\" href=\"#install-virtualbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGo to the VirtualBox Download page: \u003ca href=\"https://www.virtualbox.org/wiki/Downloads\" rel=\"nofollow\"\u003ehttps://www.virtualbox.org/wiki/Downloads\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSelect the link from the \"VirtualBox 5.2.12 platform packages\" section that matches your system.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Windows 10 users:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDouble-click the downloaded executable file (Windows Installer 1.1 or higher is required)\u003c/li\u003e\n\u003cli\u003eSelect default options in installation dialog.\u003c/li\u003e\n\u003cli\u003eThe installer will create a \"VirtualBox\" group in the Windows Start menu.\u003c/li\u003e\n\u003cli\u003eFor more detailed step-by-step instructions, \u003ca href=\"https://websiteforstudents.com/installing-virtualbox-windows-10/\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Mac users:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDouble-click on the downloaded dmg file to have its contents mounted\u003c/li\u003e\n\u003cli\u003eA window will open telling you to double click on the VirtualBox.pkg installer file displayed in that window.\u003c/li\u003e\n\u003cli\u003eThis will start the installer, which will allow you to select where to install VirtualBox to.\u003c/li\u003e\n\u003cli\u003eAfter installation, you can find a VirtualBox icon in the \"Applications\" folder in the Finder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Ubuntu users:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStep-by-step instructions can be found \u003ca href=\"https://websiteforstudents.com/install-virtualbox-latest-on-ubuntu-16-04-lts-17-04-17-10/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload and import the VM image file\u003c/h2\u003e\u003ca id=\"user-content-download-and-import-the-vm-image-file\" class=\"anchor\" aria-label=\"Permalink: Download and import the VM image file\" href=\"#download-and-import-the-vm-image-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload from \u003ca href=\"https://training.repronim.org/reprotraining.ova\" rel=\"nofollow\"\u003ehttps://training.repronim.org/reprotraining.ova\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eTo import the reprotraining.ova file:\n\u003col\u003e\n\u003cli\u003eOpen VirtualBox on your computer\u003c/li\u003e\n\u003cli\u003eselect \"File\" -\u0026gt; \"Import Appliance\" from the VirtualBox menu.\u003c/li\u003e\n\u003cli\u003eClick through the import wizard dialog leaving the default settings (see here for example step-by-step instructions \u003ca href=\"https://docs.oracle.com/cd/E26217_01/E26796/html/qs-import-vm.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStarting the VM\u003c/h2\u003e\u003ca id=\"user-content-starting-the-vm\" class=\"anchor\" aria-label=\"Permalink: Starting the VM\" href=\"#starting-the-vm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOpen VirtualBox on your computer\u003c/li\u003e\n\u003cli\u003eChoose \"reprotraining\" from the menu on the left and press \"start\" (the green arrow) to start the Ubuntu virtual machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIssues:\u003c/h2\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-label=\"Permalink: Issues:\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eif you have a windows 10 pro 64 bit (eg Lenovo X1C) machine and get an error like:\nvt-x/amd-v hardware acceleration is not available on your system, \u003ca href=\"https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/quick-start/enable-hyper-v#enable-the-hyper-v-role-through-settings\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eif you are unable to install VirtualBox due to virtualization technology (VT-x) not being enabled on your system, \u003ca href=\"https://docs-old.fedoraproject.org/en-US/Fedora/13/html/Virtualization_Guide/sect-Virtualization-Troubleshooting-Enabling_Intel_VT_and_AMD_V_virtualization_hardware_extensions_in_BIOS.html\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe Presentations\u003c/h1\u003e\u003ca id=\"user-content-the-presentations\" class=\"anchor\" aria-label=\"Permalink: The Presentations\" href=\"#the-presentations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll tutorials and slides are available from the \u003ca href=\"http://repronim.org/sfn2018-training\" rel=\"nofollow\"\u003ehttp://repronim.org/sfn2018-training\u003c/a\u003e\ngenerated from sources within \u003ccode\u003egh-pages\u003c/code\u003e branch of this repository.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEWAS (Epigenetic-Wide Association Study) Data Analysis Pipeline\u003c/h1\u003e\u003ca id=\"user-content-ewas-epigenetic-wide-association-study--data-analysis-pipeline\" class=\"anchor\" aria-label=\"Permalink: EWAS (Epigenetic-Wide Association Study) Data Analysis Pipeline\" href=\"#ewas-epigenetic-wide-association-study--data-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code base for our EWAS pipeline tools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo run locally\u003c/h1\u003e\u003ca id=\"user-content-to-run-locally\" class=\"anchor\" aria-label=\"Permalink: To run locally\" href=\"#to-run-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the docker and singularity images, make sure .env file contains the right values and then invoke ./run_ewas script\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding Docker image\u003c/h1\u003e\u003ca id=\"user-content-building-docker-image\" class=\"anchor\" aria-label=\"Permalink: Building Docker image\" href=\"#building-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: in most cases, to build the Docker image you need to have priviledged access. It is possible to run docker without \u0027sudo\u0027\nif the system is configured appropriately (see \u003ca href=\"https://askubuntu.com/questions/477551/how-can-i-use-docker-without-sudo\" rel=\"nofollow\"\u003ehttps://askubuntu.com/questions/477551/how-can-i-use-docker-without-sudo\u003c/a\u003e for reference). Running rootless docker in rootless mode is also possible, more details on: \u003ca href=\"https://docs.docker.com/engine/security/rootless/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/security/rootless/\u003c/a\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003einstall docker as described here: \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo ./build_docker\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding Singularity image\u003c/h1\u003e\u003ca id=\"user-content-building-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Building Singularity image\" href=\"#building-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: to build the Singularity image from the Docker image you need to have priviledged access\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003einstall singularity as described here: \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.3/user-guide/installation.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ehave the docker image already built\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo ./build_singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding all together\u003c/h1\u003e\u003ca id=\"user-content-building-all-together\" class=\"anchor\" aria-label=\"Permalink: Building all together\" href=\"#building-all-together\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: all pre-requisites for building Docker and Singularity images apply as stated above\u003c/p\u003e\n\u003cp\u003eTo build both Docker and Singularity images run \u003ccode\u003esudo ./build\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnvironment variables\u003c/h1\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are best set in the ewas_config file that should be placed in the user\u0027s home folder for security reasons as it contains email password. Sample ewas_config file is available in this project\u003c/p\u003e\n\u003cp\u003eEWAS_EMAIL_FROM - \"from\" email address to be used in the job report email\u003cbr\u003e\nEWAS_EMAIL_HOST - email server host to be used for sending the job report email (i.e. smtp.gmail.com)\u003cbr\u003e\nEWAS_EMAIL_PORT - email server port to be used for sending the job report email (i.e. 495)\u003cbr\u003e\nEWAS_EMAIL_USER - email server\u0027s username used for login\u003cbr\u003e\nEWAS_EMAIL_PASSWORD - email server\u0027s user password - KEEP CONFIDENTIAL !!!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the docker image\u003c/h1\u003e\u003ca id=\"user-content-running-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Running the docker image\" href=\"#running-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eModify the .env file to set the correct variable values or set these in the current environment\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo ./run_docker\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the singularity image\u003c/h1\u003e\u003ca id=\"user-content-running-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Running the singularity image\" href=\"#running-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eModify the .env file to set the correct variable values or set these in the current environment\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo ./run_ewas\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo run on HPC/singularity\u003c/h1\u003e\u003ca id=\"user-content-to-run-on-hpcsingularity\" class=\"anchor\" aria-label=\"Permalink: To run on HPC/singularity\" href=\"#to-run-on-hpcsingularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: you must first build the singularity image on a system where docker can run in priviledged mode:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003erun \"sudo ./build\" to build the docker image and produce a corresponding singularity file (ewas.img)\u003c/li\u003e\n\u003cli\u003ecopy the ewas.img file to a location on the cluster where the job will run from\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen to run on the cluster:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003echoose a unique port that is not used by somebody else, high port numbers are probably safe,\ne.g. \u0026gt; 10000, here we use 10083, replace it with your chosen port in the commands below\u003c/li\u003e\n\u003cli\u003eset up port forwarding, e.g. on your local machine run \u003ccode\u003essh -L 10083:localhost:10083 mahuika\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eedit the Slurm script run_ewas_slurm (on the cluster)\n\u003cul\u003e\n\u003cli\u003eset the environment variables\u003c/li\u003e\n\u003cli\u003emake sure to set \u003ccode\u003eEWAS_PORT\u003c/code\u003e to the port you chose above\u003c/li\u003e\n\u003cli\u003eset the resource requirements (memory, wall time, etc)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003esubmit the run script to the queue \u003ccode\u003esbatch run_ewas_slurm\u003c/code\u003e (on the cluster)\u003c/li\u003e\n\u003cli\u003echeck the status of the Slurm job \u003ccode\u003esqueue -u $USER\u003c/code\u003e (on the cluster)\u003c/li\u003e\n\u003cli\u003eonce the job is running, on your local machine connect to \u003ca href=\"http://localhost:10083/\" rel=\"nofollow\"\u003ehttp://localhost:10083/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 2, - "subscribers_count": 8, - "topics": [], - "updated_at": 1541290105.0 + "subscribers_count": 6, + "topics": [ + "ewas", + "epigenetics", + "pipelines", + "health" + ], + "updated_at": 1715616897.0 }, { "data_format": 2, - "description": "snakemake workflow for mappind single/paired datas", + "description": "OptiTrust", "filenames": [ - "rattleSNP/containers/Singularity.report.def", - "rattleSNP/containers/Singularity.rattleSNP_tools.def" + "Singularity" ], - "full_name": "sravel/RattleSNP", + "full_name": "charguer/optitrust", "latest_release": null, + "readme": "\u003cp\u003eOptiTrust is a tool for user-guided, source-to-source transformations.\u003c/p\u003e\n\u003cp\u003eThe project webpage is:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://optitrust.inria.fr\" rel=\"nofollow\"\u003ehttp://optitrust.inria.fr\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIt is a research prototype, under active development.\u003c/p\u003e\n\u003cp\u003eIn the meanwhile, if you are interested in a demo, please get in touch with @charguer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOptiTrust Installation\u003c/h1\u003e\u003ca id=\"user-content-optitrust-installation\" class=\"anchor\" aria-label=\"Permalink: OptiTrust Installation\" href=\"#optitrust-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall OCaml and system packages\u003c/h1\u003e\u003ca id=\"user-content-install-ocaml-and-system-packages\" class=\"anchor\" aria-label=\"Permalink: Install OCaml and system packages\" href=\"#install-ocaml-and-system-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt takes about 30 minutes to install the required OCaml software.\u003c/p\u003e\n\u003cp\u003eInstallation of system packages:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo apt-get install opam clang clang-format libclang-dev llvm-dev libomp-dev pkg-config zlib1g-dev\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for C++ headers support:\u003c/span\u003e\n sudo apt-get install libc++-dev\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional, only if you prefer using `meld` over `code -d`:\u003c/span\u003e\n sudo apt-get install meld\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstallation of OCaml ecosystem:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e opam init\n opam switch create 4.14.1\n opam pin add menhirLib 20210419\n opam pin add pprint 20220103\n opam pin add clangml 4.8.0\n opam install dune refl clangml pprint menhir menhirLib base64 ocamlbuild ocaml-lsp-server ppx_deriving\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e next line used only for generating the documentation of OptiTrust:\u003c/span\u003e\n opam install odoc lambdasoup\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e fancy traces\u003c/span\u003e\n opam install dream\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then in any case execute the last line below\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eeval\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003eopam env\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall precommit hooks\u003c/h1\u003e\u003ca id=\"user-content-install-precommit-hooks\" class=\"anchor\" aria-label=\"Permalink: Install precommit hooks\" href=\"#install-precommit-hooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e make install_git_hooks\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePrecompiling headers\u003c/h1\u003e\u003ca id=\"user-content-precompiling-headers\" class=\"anchor\" aria-label=\"Permalink: Precompiling headers\" href=\"#precompiling-headers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor faster compilation, we precompile header files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e make precompile\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall libraries for parsing\u003c/h1\u003e\u003ca id=\"user-content-install-libraries-for-parsing\" class=\"anchor\" aria-label=\"Permalink: Install libraries for parsing\" href=\"#install-libraries-for-parsing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThen, you need to either export the environment variable OPTITRUST by\nexecuting \"export OPTITRUST=\u003ccode\u003epwd\u003c/code\u003e\", from the optitrust folder,\nor more conveniently execute the command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo make install_compcert_stdlib\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhich essentially performs a \u003ccode\u003esudo install src/c/compcert_parser/include/*.h usr/local/lib/compcert\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTest your installation from the command line\u003c/h1\u003e\u003ca id=\"user-content-test-your-installation-from-the-command-line\" class=\"anchor\" aria-label=\"Permalink: Test your installation from the command line\" href=\"#test-your-installation-from-the-command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChecking your installation of OptiTrust is working:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e make tests\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eConfigure VScode (or VSCodium) for interactive usage\u003c/h1\u003e\u003ca id=\"user-content-configure-vscode-or-vscodium-for-interactive-usage\" class=\"anchor\" aria-label=\"Permalink: Configure VScode (or VSCodium) for interactive usage\" href=\"#configure-vscode-or-vscodium-for-interactive-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install either VSCode or VSCodium (more open).\u003c/p\u003e\n\u003cp\u003eInstallation of VSCode (see \u003ca href=\"https://code.visualstudio.com/download\" rel=\"nofollow\"\u003ehttps://code.visualstudio.com/download\u003c/a\u003e for alternatives)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo snap install --classic code\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstallation of VSCodium:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo snap install codium --classic.\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlternative without use of snap:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e wget -qO - https://gitlab.com/paulcarroty/vscodium-deb-rpm-repo/raw/master/pub.gpg \\\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gpg --dearmor \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo dd of=/usr/share/keyrings/vscodium-archive-keyring.gpg\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edeb [ signed-by=/usr/share/keyrings/vscodium-archive-keyring.gpg ] https://download.vscodium.com/debs vscodium main\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo tee /etc/apt/sources.list.d/vscodium.list\n sudo apt update\n sudo apt install codium\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn the rest of this tutorial, we assume VSCode. If you are using VSCodium,\nyou probably have an existing alias from \u0027code\u0027 to \u0027codium\u0027. If not, you\nmay want to add into your \u003ccode\u003e~/.bashrc\u003c/code\u003e the line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e code=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecodium\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(or use \u003ccode\u003esudo ln -s /usr/bin/codium /usr/bin/code\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eRecommended: installation of Chromium browser, which is very fast for\nopening up the \"diff\" pages generated by OptiTrust:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo snap install chromium\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration of VSCode\u003c/h2\u003e\u003ca id=\"user-content-configuration-of-vscode\" class=\"anchor\" aria-label=\"Permalink: Configuration of VSCode\" href=\"#configuration-of-vscode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe point is to set up keybindings for interactive development of tranformation\nscripts. For example, F6 shows the diff associated with the transformation\ncovering the cursor position in the VSCode editor.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOpen VSCode\u003c/h3\u003e\u003ca id=\"user-content-open-vscode\" class=\"anchor\" aria-label=\"Permalink: Open VSCode\" href=\"#open-vscode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom the folder that contains present README file, open VSCode using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e code \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\n codium \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall the OCaml platform:\u003c/h3\u003e\u003ca id=\"user-content-install-the-ocaml-platform\" class=\"anchor\" aria-label=\"Permalink: Install the OCaml platform:\" href=\"#install-the-ocaml-platform\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpen the extension pannel (\u003ccode\u003ectrl+shift+x\u003c/code\u003e) and look for the \"Ocaml platform\" extension.\nAlternatively, use the quick open prompt (\u003ccode\u003ectrl+p\u003c/code\u003e), then paste \u003ccode\u003eext install ocamllabs.ocaml-platform\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall the OptiTrust shortcuts for VSCode\u003c/h3\u003e\u003ca id=\"user-content-install-the-optitrust-shortcuts-for-vscode\" class=\"anchor\" aria-label=\"Permalink: Install the OptiTrust shortcuts for VSCode\" href=\"#install-the-optitrust-shortcuts-for-vscode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn VSCode, open the file \u003ccode\u003e~/.config/Code/User/keybindings.json\u003c/code\u003e.\nFor VSCodium, this file is located at \u003ccode\u003e~/.config/VSCodium/User/keybindings.json\u003c/code\u003e.\nIf you have an empty file, paste the following contents.\nIf you have a nonempty file, copy the bindings into your file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// OptiTrust keybindings\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f5\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Redo last view command\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"shift+f5\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View trace\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+shift+f5\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View standalone detailed trace\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"shift+f6\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View trace for one step\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f6\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View diff\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+shift+f6\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View diff using internal syntax\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// For working with unit tests\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Rerun the last-tried test(s)\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Run the current test\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+shift+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Open diff for the current test\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"shift+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Run all the tests\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"alt+shift+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Open unit test ML and CPP files\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"alt+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Open unit test ML and CPP files and documentation\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// For working with long transformation scripts\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f7\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View diff from intermediate state\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"shift+f7\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View trace from intermediate state\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+f7\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Save intermediate state\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// To open documentation\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f11\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Open doc for current source file in browser\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings \u0026amp;\u0026amp; resourceExtname == .ml\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// For killing a task, type \u0027ctrl+k\u0027 twice, then \u0027enter\u0027\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+k ctrl+k\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.terminate\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// Unused \"alt+f6\", \"alt+f7\", \"ctrl+shift+f6\",..\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// End of OptiTrust keybindings\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote: the shortcuts refer to tasks that are defined in \u003ccode\u003e.vscode/tasks.json\u003c/code\u003e,\nwhich can be opened by pressing \u003ccode\u003ectrl+p\u003c/code\u003e then typing \u003ccode\u003etasks.json\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eYou can then adjust the keybindings to match your personal preferences,\nby changing the key parameter.\u003c/p\u003e\n\u003cp\u003eNote: the intermediate state shortcuts (\u003ccode\u003eF7\u003c/code\u003e) are useful for saving checkpoints.\nPress \u003ccode\u003ectrl+F7\u003c/code\u003e to compute the result of a transformation at a given line\nof the script. Then, type \u003ccode\u003eF7\u003c/code\u003e on a line further in the file.\nThe result should be the same as with typing \u003ccode\u003eF6\u003c/code\u003e, except that it runs\nfaster because the execution proceeds not from the beginning of the script\nbut instead from the line of the snapshot taken using \u003ccode\u003ectrl+F7\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeactivate possibly conficting bindings\u003c/h3\u003e\u003ca id=\"user-content-deactivate-possibly-conficting-bindings\" class=\"anchor\" aria-label=\"Permalink: Deactivate possibly conficting bindings\" href=\"#deactivate-possibly-conficting-bindings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn desktop managers such as Ubuntu\u0027s, certain shortcuts such as\n\u003ccode\u003eAlt+F6\u003c/code\u003e, \u003ccode\u003eAlt+F7\u003c/code\u003e etc. are bound to window manipulation operations,\ne.g. window resize. If you want to use these shortcuts, you may wish\nto deactivate the Ubuntu bindings. To that end, use the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo apt-get install dconf-editor\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings begin-move []\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings begin-resize []\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings cycle-group []\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings cycle-group-backward []\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings toggle-maximized []\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, you can open the settings panel, the keyboard menu, the\nshortcut submenu, then in the search bar type \u003ccode\u003eAlt+F\u003c/code\u003e (or go to the\n\u0027window\u0027 group), then select an action, and type \u003ccode\u003eBackspace\u003c/code\u003e to disable\nthe shortcut, then click on the \u003ccode\u003esave\u003c/code\u003e button.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing OptiTrust in VSCode\u003c/h2\u003e\u003ca id=\"user-content-using-optitrust-in-vscode\" class=\"anchor\" aria-label=\"Permalink: Using OptiTrust in VSCode\" href=\"#using-optitrust-in-vscode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest your installation\u003c/h3\u003e\u003ca id=\"user-content-test-your-installation\" class=\"anchor\" aria-label=\"Permalink: Test your installation\" href=\"#test-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are at last ready to test the installation on a case study.\u003c/p\u003e\n\u003cp\u003eIn VScode, open \u003ccode\u003ecase_studies/matmul/matmul.ml\u003c/code\u003e (using e.g. \u003ccode\u003ectrl+p\u003c/code\u003e).\nPlace your cursor on the line starting with \u003ccode\u003e!! List.iter tile\u003c/code\u003e.\nType \u003ccode\u003eF6\u003c/code\u003e.\nYou should see a diff opening up in a browser.\u003c/p\u003e\n\u003cp\u003eAnother shortcut to try is \u003ccode\u003eshift+F5\u003c/code\u003e, on any line of the \u003ccode\u003ematmul.ml\u003c/code\u003e file\nAfter a dozen seconds, you should see the full transformation trace for the\nmatrix-multiply case study.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eViewing keyboard shortcuts\u003c/h3\u003e\u003ca id=\"user-content-viewing-keyboard-shortcuts\" class=\"anchor\" aria-label=\"Permalink: Viewing keyboard shortcuts\" href=\"#viewing-keyboard-shortcuts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt may not be easy at first to recall all the shortcuts. Besides using a sticker\nat the bottom of your screen, you can use the command \u003ccode\u003e./shortcuts.sh\u003c/code\u003e to display\nthe shortcuts, and in VSCode use menu File / Preferences / Keyboard Shortcuts,\nthen type \"optitrust\" in the query bar.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTroubleshooting\u003c/h3\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t see a diff, possible issues include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYour shortcut is not set up correctly; when the shortcut is pressed,\na terminal should open to show the output of the task.\nIf needed, to investigate whether key bindings are set up properly,\nin VScode type \u003ccode\u003ectrl+K\u003c/code\u003e immediately followed by \u003ccode\u003ectrl+s\u003c/code\u003e, then type \u003ccode\u003ealt+k\u003c/code\u003e,\nthen type \u003ccode\u003eF6\u003c/code\u003e and see whether you see \"Tasks: run tasks\" a entry.\u003c/li\u003e\n\u003cli\u003ethe compilation failed due to incorrect setup; you should see error\nmessages in the terminal.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe documentation for OptiTrust is generated using the OCaml \u0027odoc\u0027 tool.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://ocaml.github.io/odoc/odoc_for_authors.html\" rel=\"nofollow\"\u003ehttps://ocaml.github.io/odoc/odoc_for_authors.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe top-level command \u003ccode\u003emake doc\u003c/code\u003e calls \u0027dune build @doc\u0027, which invokes \u0027odoc\u0027.\nThen, the generated documentation is copied into the folder \u0027_doc\u0027.\nThere, the documentation is patched in order to add the diffs associated\nwith the documentation unit tests (files tests/.../*_doc.ml).\u003c/p\u003e\n\u003cp\u003eThe patch operation is implemented in \"doc/add_tests_into_doc.ml\".\u003c/p\u003e\n\u003cp\u003eThe top-level command \u003ccode\u003emake viewdoc\u003c/code\u003e compiles the doc and opens it.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTester\u003c/h2\u003e\u003ca id=\"user-content-tester\" class=\"anchor\" aria-label=\"Permalink: Tester\" href=\"#tester\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor running the test suite, execute:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/optitrust\n \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/tester run\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the header of the file \u003ccode\u003etester.ml\u003c/code\u003e for the full documentation.\u003c/p\u003e\n\u003cp\u003eOptional: in your \u003ccode\u003e~/.bashrc\u003c/code\u003e you can add an alias to make it easier to invoke the tester.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e t=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e~/optitrust/tester\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptional tools\u003c/h2\u003e\u003ca id=\"user-content-optional-tools\" class=\"anchor\" aria-label=\"Permalink: Optional tools\" href=\"#optional-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ccode\u003eINSTALL_EXTRA.md\u003c/code\u003e for a list of additional useful tools for program optimization.\u003c/p\u003e\n\u003cp\u003eSee \u003ccode\u003eVSCODE_CUSTOMIZE.md\u003c/code\u003e for useful tips for using VScode or VScodium.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 7, "topics": [], - "updated_at": 1667559289.0 + "updated_at": 1730829136.0 }, { "data_format": 2, - "description": "the robot namer", + "description": "A fast python tool for extracting diffusion-based brain connectivity", "filenames": [ "Singularity" ], - "full_name": "vsoch/robotname", - "latest_release": "0.0.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRobot Generator\u003c/h1\u003e\u003ca id=\"user-content-robot-generator\" class=\"anchor\" aria-label=\"Permalink: Robot Generator\" href=\"#robot-generator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains an application for a robot name generator.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s built on Docker Hub, so you can run as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/robotname\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor build locally first, with the present working directory as this folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t vanessa/robotname .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRobot Names\u003c/h2\u003e\u003ca id=\"user-content-robot-names\" class=\"anchor\" aria-label=\"Permalink: Robot Names\" href=\"#robot-names\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003efor i in `seq 1 10`;\n do\n docker run vanessa/robotname\ndone\ndinosaur-signal-1365\nbricky-cinnamonbun-1640\nnerdy-leg-6553\nmagnificent-lemon-2727\nmilky-kitty-9135\narid-snakey-5251\nplacid-cupcake-0084\njoyous-poodle-7162\nangry-underoos-2988\nmuffled-gato-4718\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRobot Badges\u003c/h2\u003e\u003ca id=\"user-content-robot-badges\" class=\"anchor\" aria-label=\"Permalink: Robot Badges\" href=\"#robot-badges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeed a fun, spurious badge? Of course you do!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003eseq 1 10\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n docker run vanessa/robotname badge\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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alt=\"https://img.shields.io/badge/zippy-leg_0162-palegreen.svg?style=flat\u0026amp;link=https%3A%2F%2Fopenbases.github.io%2Fopenbases-python%2Fhtml%2Fusage.html%23badges\u0026amp;longCache=true\" data-canonical-src=\"https://img.shields.io/badge/zippy-leg_0162-palegreen.svg?style=flat\u0026amp;link=https%3A%2F%2Fopenbases.github.io%2Fopenbases-python%2Fhtml%2Fusage.html%23badges\u0026amp;longCache=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBrought to you by \u003ca href=\"https://openbases.github.io/openbases-python/html/usage.html#badges\" rel=\"nofollow\"\u003eopenbases python\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build your image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build robotname Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor pull from Docker Hub :)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name robotname docker://vanessa/robotname\nsregistry pull docker://vanessa/robotname\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer Battle!\u003c/h2\u003e\u003ca id=\"user-content-container-battle\" class=\"anchor\" aria-label=\"Permalink: Container Battle!\" href=\"#container-battle\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWho generates names faster? Try this on your own to see :)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor i in `seq 1 10`; do docker run vanessa/robotname; done\nfor i in `seq 1 10`; do ./robotname; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor \u003ca href=\"\"\u003ewatch here\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "jasonbian97/fastcod-code", + "latest_release": null, "stargazers_count": 2, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [ - "badge", - "markdown", - "robot", - "name", - "generator" + "diffusion", + "mri", + "brain", + "connectivity", + "tractography" ], - "updated_at": 1545323610.0 + "updated_at": 1679470502.0 }, { "data_format": 2, - "description": null, + "description": "The simulation-supervised package combines different sets of code needed to train a DNN policy to fly a drone.", "filenames": [ - "Singularity", - "nwchem-dev.ompi40x.skylake/Singularity", - "nwchem-dev.ompi41x.ifx.test/Singularity.oldtest", - "nwchem-dev.ompi41x.ifx.test/Singularity", - "nwchem-701.ifort/Singularity", - "nwchem-dev.ompi40x.ifort.skylake/Singularity", - "exachem.ompi41x/Singularity", - "nwchem-701.ompi313.ivybridge/Singularity", - "nwchem-dev.ompi41x/Singularity", - "nwchem-dev.ompi41x.ifx.hpcx/Singularity", - "nwchem-701.mpich321.ivybridge/Singularity", - "nwchem-dev.ompi5x/Singularity", - "nwchem-dev.ompi40x/Singularity", - "nwchem-dev.mpich3/Singularity", - "nwchem-dev.ompi41x.ifx/Singularity", - "nwchem-702.ompi313.ivybridge/Singularity", - "nwchem-dev.aws/Singularity" + "Singularity" ], - "full_name": "edoapra/nwchem-singularity", + "full_name": "kkelchte/simulation_supervised", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNWChem singularity\u003c/h1\u003e\u003ca id=\"user-content-nwchem-singularity\" class=\"anchor\" aria-label=\"Permalink: NWChem singularity\" href=\"#nwchem-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/edoapra/nwchem-singularity/actions/workflows/apptainer_action.yml\"\u003e\u003cimg src=\"https://github.com/edoapra/nwchem-singularity/actions/workflows/apptainer_action.yml/badge.svg\" alt=\"nwchem_apptainer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity/Apptainer recipes for NWChem\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/edoapra/nwchem-singularity/tree/master/nwchem-dev.ompi41x\"\u003ehttps://github.com/edoapra/nwchem-singularity/tree/master/nwchem-dev.ompi41x\u003c/a\u003e\u003cbr\u003e\nand\u003cbr\u003e\n\u003ca href=\"https://nwchemgit.github.io/Containers.html#instruction-for-running-on-emsl-tahoma\" rel=\"nofollow\"\u003ehttps://nwchemgit.github.io/Containers.html#instruction-for-running-on-emsl-tahoma\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esimulation-supervised\u003c/h1\u003e\u003ca id=\"user-content-simulation-supervised\" class=\"anchor\" aria-label=\"Permalink: simulation-supervised\" href=\"#simulation-supervised\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe simulation-supervised package combines different sets of code needed to train a DNN policy to fly a drone.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.github.com/kkelchte/pilot_online\"\u003eonline_training\u003c/a\u003e: the tensorflow code used for training and running the DNN policy.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.github.com/kkelchte/hector_quadrotor\"\u003edrone_simulator\u003c/a\u003e: a simulated drone model for Gazebo. This is a copy of the original \u003ca href=\"http://wiki.ros.org/hector_quadrotor\" rel=\"nofollow\"\u003ehector quadrotor model\u003c/a\u003e.\nOR\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kkelchte/bebop_autonomy\"\u003ebebop_autonomy\u003c/a\u003e: a copy of the bebop autonomy package of ROS. This package allows you to test the DNN in the real-world. The copy is only for ensuring stability while performing research. There are no significant modifications so it is probably best to use \u003ca href=\"https://github.com/AutonomyLab/bebop_autonomy\"\u003ethe original\u003c/a\u003e. If you are using the Doshico docker image, it is already installed globally.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package is best build in a separate \u003ca href=\"http://wiki.ros.org/catkin/Tutorials/create_a_workspace\" rel=\"nofollow\"\u003ecatkin workspace\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/simsup_ws/src\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/simsup_ws \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e catkin_make\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/simsup_ws/src\ngit clone https://github.com/kkelchte/simulation_supervised\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/simsup_ws \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e catkin_make\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will have to set the correct path to your tensorflow pilot_online package.\u003c/p\u003e\n\u003cp\u003eIn case you are using different drone models, you will have to adjust the \u003ca href=\"https://github.com/kkelchte/simulation-supervised/blob/master/simulation_supervised/config/sim_drone.yaml\"\u003econfig.yaml\u003c/a\u003e file in order to set the correct rosparams.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun some experiments\u003c/h2\u003e\u003ca id=\"user-content-run-some-experiments\" class=\"anchor\" aria-label=\"Permalink: Run some experiments\" href=\"#run-some-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere are some common used setting combinations in order to remember them:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Test online the performance of a heuristic defined in tensorflow/gt_depth_online/../rosinterface.py using for instance recovery cameras flying 3 times through a generated canyon\n$ ./scripts/evaluate_model.sh -m auxd -s start_python_sing_gtdepth.sh -t test_depth_online -r true -w canyon -n 3\n# Train online in canyon, forest, sandbox\n$ ./scripts/train_model.sh -m mobilenet_025\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1732310529.0 + "updated_at": 1623707939.0 }, { "data_format": 2, - "description": "dell racadm and other bios level tool (omsa) [unofficial]", + "description": "reper - Genome-wide identification, classification and quantification of repetitive elements without an assembled genome", "filenames": [ "Singularity" ], - "full_name": "tin6150/dell_idracadm", - "latest_release": null, + "full_name": "nterhoeven/reper", + "latest_release": "v1.1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ereper - Genome-wide identification, classification and quantification of repetitive elements without an assembled genome\u003c/h1\u003e\u003ca id=\"user-content-reper---genome-wide-identification-classification-and-quantification-of-repetitive-elements-without-an-assembled-genome\" class=\"anchor\" aria-label=\"Permalink: reper - Genome-wide identification, classification and quantification of repetitive elements without an assembled genome\" href=\"#reper---genome-wide-identification-classification-and-quantification-of-repetitive-elements-without-an-assembled-genome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/nterhoeven/reper/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37deaf299a2fff03c8ec0fe8f6c71a268db42bd2af1427de6cba095c6e748c86/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e746572686f6576656e2f72657065722e737667\" alt=\"Docker Automated build\" data-canonical-src=\"https://img.shields.io/docker/automated/nterhoeven/reper.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nterhoeven/reper/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2ccda683a83f3ef09ed5466fb62acb0e357df1b6f461a50ac9ad329545889f44/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6275696c642f6e746572686f6576656e2f72657065722e737667\" alt=\"Docker Build Status\" data-canonical-src=\"https://img.shields.io/docker/build/nterhoeven/reper.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/80427752\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a5481251b07886ad6a1ed9e4eba07fea691a7a2b555f5becc70b62159e83d8df/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f38303432373735322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/80427752.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://joss.theoj.org/papers/f0d16a43d8b031695f151ea25e0d47b0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/571828e4c6ad6f35b2eea0f3b1d87599ac36d006f90c44bd31d64777acb92542/687474703a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f66306431366134336438623033313639356631353165613235653064343762302f7374617475732e737667\" alt=\"status\" data-canonical-src=\"http://joss.theoj.org/papers/f0d16a43d8b031695f151ea25e0d47b0/status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is reper?\u003c/h2\u003e\u003ca id=\"user-content-what-is-reper\" class=\"anchor\" aria-label=\"Permalink: What is reper?\" href=\"#what-is-reper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ereper is a pipeline to detect repetitive sequences in genome sequencing data.\nThe detection is based on kmer frequencies and does not rely on a genome assembly.\nThis allows an analysis of repeat sequences of organisms with large and repeat rich\ngenomes (especially plants). For a detailed explanation of the pipeline, see the \u003ca href=\"https://github.com/nterhoeven/reper/wiki/How-does-reper-work%3F\"\u003ereper wiki\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I get reper?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-get-reper\" class=\"anchor\" aria-label=\"Permalink: How do I get reper?\" href=\"#how-do-i-get-reper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ereper is available as Docker container, Singularity image or can be installed manually.\nPlease visit the \u003ca href=\"https://github.com/nterhoeven/reper/wiki/Installation\"\u003ereper wiki installation page\u003c/a\u003e for detailed explanations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I run reper?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-run-reper\" class=\"anchor\" aria-label=\"Permalink: How do I run reper?\" href=\"#how-do-i-run-reper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning reper is very easy. You just need to adjust the config file and start reper with \u003ccode\u003ereper kmerCount\u003c/code\u003e.\nA detailed explanation of the available commands is given in the \u003ca href=\"https://github.com/nterhoeven/reper/wiki/Using-reper\"\u003eusage page of the reper wiki\u003c/a\u003e.\nOr you can take a look at the \u003ca href=\"https://github.com/nterhoeven/reper/wiki/Tutorial\"\u003eTutorial\u003c/a\u003e and learn how to analyze the\nrepeat content of the sugar beet using reper.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow can I contribute?\u003c/h2\u003e\u003ca id=\"user-content-how-can-i-contribute\" class=\"anchor\" aria-label=\"Permalink: How can I contribute?\" href=\"#how-can-i-contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContribution to reper is always appreciated. Please submit any bugs, feature requests and similar via the github issue tracker.\nIf you want to contribute code, feel free to fork this repository and/or open a pull request.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense and Citation\u003c/h2\u003e\u003ca id=\"user-content-license-and-citation\" class=\"anchor\" aria-label=\"Permalink: License and Citation\" href=\"#license-and-citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ereper is placed under the MIT License.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [], - "updated_at": 1706540936.0 + "updated_at": 1601383172.0 }, { "data_format": 2, - "description": "Singularity containers with software installed via Spack", + "description": "BIDS App for U-net brain masking of fetal bold MRI", "filenames": [ - "Singularity.busco", - "Singularity.gcc", - "Singularity.openmpi", - "Singularity.trinity", - "Singularity.spack" + "Singularity" ], - "full_name": "ResearchIT/spack-singularity", - "latest_release": null, + "full_name": "khanlab/funcmasker-flex", + "latest_release": "v0.2.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003efuncmasker-flex\u003c/h1\u003e\u003ca id=\"user-content-funcmasker-flex\" class=\"anchor\" aria-label=\"Permalink: funcmasker-flex\" href=\"#funcmasker-flex\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBrain masking app using Unet for fetal bold mri\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample usage:\u003c/h3\u003e\u003ca id=\"user-content-example-usage\" class=\"anchor\" aria-label=\"Permalink: Example usage:\" href=\"#example-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet a sample subject dataset:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edatalad install https://github.com/OpenNeuroDatasets/ds003090.git\ncd ds003090/\ndatalad get sub-2225\ncd ../\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun \u003ccode\u003efuncmasker-flex\u003c/code\u003e on it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e docker://khanlab/funcmasker-flex:latest ds003090/ funcmasker participant --participant_label 2225 --cores all\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 8, + "subscribers_count": 1, "topics": [ - "spack", - "openmpi", - "singularity" - ], - "updated_at": 1648743296.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "intogen-plus/build/containers/mutpanning/Singularity", - "intogen-plus/build/containers/mutrate/Singularity", - "intogen-plus/build/containers/signature/Singularity", - "intogen-plus/build/containers/transvar/Singularity", - "intogen-plus/build/containers/oncodrivefml/Singularity", - "intogen-plus/build/containers/hotmaps/Singularity", - "intogen-plus/build/containers/deconstructsig/Singularity", - "intogen-plus/build/containers/oncodriveclustl/Singularity", - "intogen-plus/build/containers/cbase/Singularity", - "intogen-plus/build/containers/combination/Singularity", - "intogen-plus/build/containers/core/Singularity", - "intogen-plus/build/containers/dndscv/Singularity" + "bids", + "bold", + "masking", + "mri", + "unet" ], - "full_name": "gagneurlab/Leukemia_outlier", - "latest_release": null, - "stargazers_count": 2, - "subscribers_count": 4, - "topics": [], - "updated_at": 1718028308.0 + "updated_at": 1712967895.0 }, { "data_format": 2, - "description": "A Singularity container with R and Rstudio", + "description": "Singularity container (Ubuntu 14.04, ROS Indigo, OpenRAVE) for UR5Controller", "filenames": [ "Singularity" ], - "full_name": "dvav/singularity-rstudio-server", + "full_name": "roboticsleeds/ur5controller-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-rstudio-server\u003c/h1\u003e\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: singularity-rstudio-server\" href=\"#singularity-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a \u003ccode\u003esingularity\u003c/code\u003e definition file and associated files for building a container\nwith the most recent (at the time of this writing) version of \u003ccode\u003eR\u003c/code\u003e and \u003ccode\u003eRstudio Server\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo build the container, use the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build rstudio.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the container, do the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind {host_dir1}:/var/lib/,{host_dir2}:/var/run rstudio.sif --www-port {your port}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can run the container on \u003ccode\u003ehumbug\u003c/code\u003e (if you don\u0027t know what this is, skip this paragraph) using the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind /well,/gpfs0,/gpfs1,/gpfs2,{host_dir1}:/var/lib/,{host_dir2}:/var/run rstudio.sif --www-port {remote port}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand setting up an \u003ccode\u003essh\u003c/code\u003e tunnel from your local machine (e.g. your laptop), for example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh -N -f -L {local port}:localhost:{remote port} {your username}@humbug.well.ox.ac.uk\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou need to be connected to the centre\u0027s VPN for this work.\u003c/p\u003e\n\u003cp\u003eThe following, does not work (yet):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRSTUDIO_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n singularity run --bind {host_dir1}:/var/lib/,{host_dir2}:/var/run rstudio.sif \\\n --auth-none 0 \\\n --auth-pam-helper rstudio_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more info, check the definition file \u003ccode\u003eSingularity\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eI used code from \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e as a point of departure. Bits of this code still remain in this repository.\u003c/p\u003e\n", - "stargazers_count": 2, - "subscribers_count": 1, - "topics": [], - "updated_at": 1622205021.0 - }, - { - "data_format": 2, - "description": "Rclone is a command line program to manage files on cloud storage. It is a feature rich alternative to cloud vendors\u0027 web storage interfaces.", - "filenames": [ - "1.58.1/Singularity", - "1.55.1/Singularity" - ], - "full_name": "pscedu/singularity-rclone", - "latest_release": "v1.55.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rclone/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rclone/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rclone/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rclone/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7f0e19a39ace7f9221d79c234d3288bcc366f84a3d48f0ba9038fd3665d2304f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d72636c6f6e65\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7f0e19a39ace7f9221d79c234d3288bcc366f84a3d48f0ba9038fd3665d2304f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d72636c6f6e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-rclone\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3624526e0c40e35705ae3e72ab403d7bbacce1076a9f42762a61b0707dce61c4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d72636c6f6e65\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3624526e0c40e35705ae3e72ab403d7bbacce1076a9f42762a61b0707dce61c4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d72636c6f6e65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-rclone\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/409093de2053c94e75e0b30037f01a0795f2921156a7d950cd8f5a21450defb3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d72636c6f6e65\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/409093de2053c94e75e0b30037f01a0795f2921156a7d950cd8f5a21450defb3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d72636c6f6e65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-rclone\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/16474a564a6deeed6b0ae94b5e3047b09b75c794e93ac3874243be716d650371/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d72636c6f6e65\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/16474a564a6deeed6b0ae94b5e3047b09b75c794e93ac3874243be716d650371/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d72636c6f6e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-rclone\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-rclone\u003c/h1\u003e\u003ca id=\"user-content-singularity-rclone\" class=\"anchor\" aria-label=\"Permalink: singularity-rclone\" href=\"#singularity-rclone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2ed5b3734f0db17a45382f5ec04f9fffb2bf36d231840805780c71447f285b3f/68747470733a2f2f72636c6f6e652e6f72672f696d672f6c6f676f5f6f6e5f6c696768745f5f686f72697a6f6e74616c5f636f6c6f722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2ed5b3734f0db17a45382f5ec04f9fffb2bf36d231840805780c71447f285b3f/68747470733a2f2f72636c6f6e652e6f72672f696d672f6c6f676f5f6f6e5f6c696768745f5f686f72697a6f6e74616c5f636f6c6f722e737667\" alt=\"Logo\" data-canonical-src=\"https://rclone.org/img/logo_on_light__horizontal_color.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://rclone.org/\" rel=\"nofollow\"\u003erclone\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003erclone\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/rclone/1.55.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/rclone\u003c/code\u003e as \u003ccode\u003e1.55.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUR5 Controller Singularity\u003c/h1\u003e\u003ca id=\"user-content-ur5-controller-singularity\" class=\"anchor\" aria-label=\"Permalink: UR5 Controller Singularity\" href=\"#ur5-controller-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ur5_openrave.png\"\u003e\u003cimg src=\"images/ur5_openrave.png\" alt=\"UR5 with OpenRAVE within a singularity container\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003cbr\u003e\n Figure 1: UR5 Robot with Ridgeback in OpenRAVE within a Singularity container with Ubuntu 14.04 and ROS Indigo. The host operating system is Ubuntu 18.04.\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopers and Contributors\u003c/h2\u003e\u003ca id=\"user-content-developers-and-contributors\" class=\"anchor\" aria-label=\"Permalink: Developers and Contributors\" href=\"#developers-and-contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUR5Controller Singularity was developed by the \u003ca href=\"https://artificial-intelligence.leeds.ac.uk/robot-manipulation/\" rel=\"nofollow\"\u003eRobot Manipulation Lab\u003c/a\u003e in the School of Computing at the University of Leeds.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAuthor: \u003ca href=\"http://rpapallas.com\" rel=\"nofollow\"\u003eRafael Papallas\u003c/a\u003e, \u003ca href=\"https://github.com/WissBe\"\u003eWissam Bejjani\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eCurrent maintainor: \u003ca href=\"http://rpapallas.com\" rel=\"nofollow\"\u003eRafael Papallas\u003c/a\u003e, \u003ca href=\"https://github.com/WissBe\"\u003eWissam Bejjani\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUR5Controller Singularity is licensed under GNU General Public License v3.0.\nThe full license is available \u003ca href=\"https://github.com/roboticsleeds/ur5controller_singularity/blob/master/LICENSE\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions\u003c/h2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a Singularity container for \u003ca href=\"https://github.com/roboticsleeds/ur5controller\"\u003e\u003ccode\u003eur5controller\u003c/code\u003e\u003c/a\u003e\npackage.\u003c/p\u003e\n\u003cp\u003eClone and build the singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/roboticsleeds/ur5controller_singularity\ncd ur5controller_singularity\n./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should create a local Ubuntu 14.04 file system with ROS Indigo, \u003ccode\u003eor\\_urdf\u003c/code\u003e,\nand \u003ccode\u003eur5controller\u003c/code\u003e in it.\u003c/p\u003e\n\u003cp\u003eIt will take a while to build (approximately 40 minutes). Once built, you will\nautomatically enter into the singualrity environment (which will build your catkin\nworkspace).\u003c/p\u003e\n\u003cp\u003eWhen you need to enter your singularity environment, simply run \u003ccode\u003e./run.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis should put you into a singularity environment. To test if everything was\nsuccesful you can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/ur5_demo\npython ur5_demo.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd you should see an OpenRAVE window with UR5 being loaded.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBinding directories\u003c/h2\u003e\u003ca id=\"user-content-binding-directories\" class=\"anchor\" aria-label=\"Permalink: Binding directories\" href=\"#binding-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs you can see from the above configuration you can have a \u003ccode\u003ehome\u003c/code\u003e directory living\non your host machine and then bind that directory as the home directory of the\ncontainer. As a result you can now put files under that \u003ccode\u003ehome\u003c/code\u003e dir and both the\nhost and the container can read and write in it.\u003c/p\u003e\n\u003cp\u003eAnother way to do this is to bind the directory using \u003ccode\u003e--bind\u003c/code\u003e (\u003ccode\u003e--bind=absolute_path_of_source_dir:absolute_path_of_target_dir\u003c/code\u003e) flag:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --contain --home=home:$HOME --bind=/home/rafael/Documents/my_project:/home/my_project ur5controller\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will bind \u003ccode\u003e/home/rafael/Documents/my_project\u003c/code\u003e to container\u0027s \u003ccode\u003e/home\u003c/code\u003e directory.\u003c/p\u003e\n\u003cp\u003eUnfortunetly, you can\u0027t bind the target directory to container\u0027s user home (e.g \u003ccode\u003e/home/rafael\u003c/code\u003e) directory.\nWe found a workaround to this. Under \u003ccode\u003ehome/.bashrc\u003c/code\u003e of this repository we have placed\nthe following code:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Delete all links that are broken\nfind ./ -maxdepth 1 -follow -type l -delete\n\n# This code is to create a symbolic link of any directory located under /home/.\n# When you use --bind in singularity to bind a directory from host to the container\n# you can\u0027t bind that directory under $HOME but only under /home/, therefore a\n# workaround to this was to create a symbolic link to all fo the directories under\n# /home/ to $HOME.\nfor filename in $(find /home -maxdepth 1 ! -path \"/home/$USER\" ! -path \u0027*/\\.*\u0027 ! -path \u0027/home\u0027); do\n ln -sf $filename $HOME\ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis code will create symbolic links for every directory located under container\u0027s\n\u003ccode\u003e/home/\u003c/code\u003e directory to \u003ccode\u003e$HOME\u003c/code\u003e (i.e., \u003ccode\u003e/home/user_name\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eSo with the above \u003ccode\u003e.bashrc\u003c/code\u003e code whenever you start a singularity container like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --contain --home=home:$HOME --bind=/home/rafael/Documents/my_project:/home/my_project ur5controller\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWill bind \u003ccode\u003e/home/rafael/Documents/my_project\u003c/code\u003e to \u003ccode\u003e/home/my_project/\u003c/code\u003e and will create\na symbolic link of \u003ccode\u003e/home/my_project/\u003c/code\u003e to \u003ccode\u003e/home/rafael/my_project\u003c/code\u003e. As a result\nwe are \"binding\" a directory from the host file system to the container under\ncontainer\u0027s user home directory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWith all that said, if you want to bind a directory to the container you just\nneed to edit the \u003ccode\u003erun.sh\u003c/code\u003e file and add \u003ccode\u003e--bind=source:target\u003c/code\u003e as you wish.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --contain --home=home:$HOME --bind=/home/rafael/Documents/my_project_1:/home/my_project_1 --bind=/home/rafael/Documents/my_project_2:/home/my_project_2 ur5controller\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere we are binding two directories: \u003ccode\u003emy_project_1\u003c/code\u003e and \u003ccode\u003emy_project_2\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNote that we have pre-generated the robot inverse kinematics for OpenRAVE and\nplaced them under your singularity home directory just to save time as this\ntakes a while. This is just the kinematics for our specific configuration, if you\nchange the model then OpenRAVE will generate new IK solutions for your new model.\u003c/li\u003e\n\u003cli\u003eDuring build time we create some temporary files (\u003ccode\u003escripts\u003c/code\u003e and \u003ccode\u003ebuild\u003c/code\u003e) that we\nare using to build everything. Once finished we erase those files.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003ehome\u003c/code\u003e directory located under this repository contains the following important\ndata: \u003ccode\u003e.openrave\u003c/code\u003e with the prepoulated IK solutions to UR5 robot, \u003ccode\u003e.bashrc\u003c/code\u003e containing\nimportant commands to successfully run ROS and the UR5Controller.\u003c/li\u003e\n\u003cli\u003eAnything you create in the container under \u003ccode\u003ehome\u003c/code\u003e will be persistent but if you\nwrite anything outside \u003ccode\u003ehome\u003c/code\u003e this will not be writable. If you need to make changes\nto the singularity container, then run \u003ccode\u003ewrite.sh\u003c/code\u003e to enter into a root session within\nyour singularity container.\u003c/li\u003e\n\u003cli\u003eYou can work in \u003ccode\u003ehome\u003c/code\u003e directory outside singularity (say if you are using an\nIDE software) and the changes should be immediately available within the\nsingularity environment. So you can edit your source code outside the container\nusing your host machine and then execute the code within the container.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [ "singularity", - "utilities" + "singularity-container", + "openrave", + "openrave-controller", + "ros", + "ros-indigo" ], - "updated_at": 1693705852.0 + "updated_at": 1630970845.0 }, { "data_format": 2, - "description": "Training materials for the ReproNim day at https://coastal-coding.github.io", + "description": "an example variant calling scientific workflow with the standard container integration format (SCI-F)", "filenames": [ - "section23/environments/Singularity.heudiconv", - "section23/environments/Singularity.fsl", - "section23/environments/Singularity.heudiconvn", - "section23/environments/Singularity.fsln" + "Singularity.noscif", + "Singularity" ], - "full_name": "ReproNim/coco2019-training", + "full_name": "sci-f/scientific-example.scif", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions\u003c/h1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall VirtualBox\u003c/h2\u003e\u003ca id=\"user-content-install-virtualbox\" class=\"anchor\" aria-label=\"Permalink: Install VirtualBox\" href=\"#install-virtualbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGo to the VirtualBox Download page: \u003ca href=\"https://www.virtualbox.org/wiki/Downloads\" rel=\"nofollow\"\u003ehttps://www.virtualbox.org/wiki/Downloads\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSelect the link from the \"VirtualBox 5.2.12 platform packages\" section that matches your system.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Windows 10 users:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDouble-click the downloaded executable file (Windows Installer 1.1 or higher is required)\u003c/li\u003e\n\u003cli\u003eSelect default options in installation dialog.\u003c/li\u003e\n\u003cli\u003eThe installer will create a \"VirtualBox\" group in the Windows Start menu.\u003c/li\u003e\n\u003cli\u003eFor more detailed step-by-step instructions, \u003ca href=\"https://websiteforstudents.com/installing-virtualbox-windows-10/\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Mac users:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDouble-click on the downloaded dmg file to have its contents mounted\u003c/li\u003e\n\u003cli\u003eA window will open telling you to double click on the VirtualBox.pkg installer file displayed in that window.\u003c/li\u003e\n\u003cli\u003eThis will start the installer, which will allow you to select where to install VirtualBox to.\u003c/li\u003e\n\u003cli\u003eAfter installation, you can find a VirtualBox icon in the \"Applications\" folder in the Finder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Ubuntu users:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStep-by-step instructions can be found \u003ca href=\"https://websiteforstudents.com/install-virtualbox-latest-on-ubuntu-16-04-lts-17-04-17-10/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload and import the VM image file\u003c/h2\u003e\u003ca id=\"user-content-download-and-import-the-vm-image-file\" class=\"anchor\" aria-label=\"Permalink: Download and import the VM image file\" href=\"#download-and-import-the-vm-image-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload from \u003ca href=\"https://training.repronim.org/reprotraining.ova\" rel=\"nofollow\"\u003ehttps://training.repronim.org/reprotraining.ova\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eTo import the reprotraining.ova file:\n\u003col\u003e\n\u003cli\u003eOpen VirtualBox on your computer\u003c/li\u003e\n\u003cli\u003eselect \"File\" -\u0026gt; \"Import Appliance\" from the VirtualBox menu.\u003c/li\u003e\n\u003cli\u003eClick through the import wizard dialog leaving the default settings (see here for example step-by-step instructions \u003ca href=\"https://docs.oracle.com/cd/E26217_01/E26796/html/qs-import-vm.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStarting the VM\u003c/h2\u003e\u003ca id=\"user-content-starting-the-vm\" class=\"anchor\" aria-label=\"Permalink: Starting the VM\" href=\"#starting-the-vm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOpen VirtualBox on your computer\u003c/li\u003e\n\u003cli\u003eChoose \"reprotraining\" from the menu on the left and press \"start\" (the green arrow) to start the Ubuntu virtual machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIssues:\u003c/h2\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-label=\"Permalink: Issues:\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eif you have a windows 10 pro 64 bit (eg Lenovo X1C) machine and get an error like:\nvt-x/amd-v hardware acceleration is not available on your system, \u003ca href=\"https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/quick-start/enable-hyper-v#enable-the-hyper-v-role-through-settings\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eif you are unable to install VirtualBox due to virtualization technology (VT-x) not being enabled on your system, \u003ca href=\"https://docs-old.fedoraproject.org/en-US/Fedora/13/html/Virtualization_Guide/sect-Virtualization-Troubleshooting-Enabling_Intel_VT_and_AMD_V_virtualization_hardware_extensions_in_BIOS.html\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe Presentations\u003c/h1\u003e\u003ca id=\"user-content-the-presentations\" class=\"anchor\" aria-label=\"Permalink: The Presentations\" href=\"#the-presentations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll tutorials and slides are available from the \u003ca href=\"http://www.repronim.org/coco2019-training/\" rel=\"nofollow\"\u003ehttp://www.repronim.org/coco2019-training/\u003c/a\u003e generated from sources within \u003ccode\u003egh-pages\u003c/code\u003e branch of this repository.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Example Scientific Workflow\u003c/h1\u003e\u003ca id=\"user-content-singularity-example-scientific-workflow\" class=\"anchor\" aria-label=\"Permalink: Singularity Example Scientific Workflow\" href=\"#singularity-example-scientific-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository provides a SCI-F (Standard Container Integration Format) Apps example to run a scientific workflow. This analysis was originally used to compare Singularity vs. Docker on cloud providers via \u003ca href=\"https://github.com/vsoch/singularity-scientific-example\"\u003ethis container\u003c/a\u003e with \u003ca href=\"https://vsoch.github.io/singularity-scientific-example/results/\" rel=\"nofollow\"\u003einteresting results\u003c/a\u003e! If you are interested in a Docker vs. Singularity implementation, see that project. For this small example, we want to give rationale for taking a SCI-F apps approach over a traditional Singularity image. We compare the following equivalent (but different!) implementations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"Singularity.noscif\"\u003eSingularity (without SCI-F)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"Singularity\"\u003eSingularity (with SCI-F)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe original image is served on Singularity Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://vsoch/singularity-scientific-example\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd the image with SCI-F app supports must be built with (not yet released) Singularity 2.4:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build scif.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEvaluation\u003c/h2\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe containers use the same software to perform the same functions, but notably, the software and executables are organized differently, and called differently. Singularity standard (the first without SCI-F) relies on external \u003ca href=\"scripts\"\u003escripts\u003c/a\u003e and the container is a bit of a black box. Singularity with SCI-F has no external dependencies beyond data, and is neatly organized. So how do we evaluate this?\u003c/p\u003e\n\u003cp\u003eThe aim here is to qualitatively evaluate SCI-F on its ability to expose container metadata, and information about the pipeline and executables inside. As each evaluation is scoped to the goal\u0027s of the container, for this example we focus on the purpose of deploying a set of steps that encompass a pipeline to perform variant calling.\u003c/p\u003e\n\u003cp\u003eFirst, the goals of SCI-F:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eContainers are \u003cstrong\u003econsistent\u003c/strong\u003e to allow for comparison. I am able to easily discover relevant software and data for one or more applications defined by the container creator.\u003c/li\u003e\n\u003cli\u003eContainers are transparent. If i discover a container and do not have any prior knowledge or metadata, the important executables and metadata are revealed to me.\u003c/li\u003e\n\u003cli\u003eContainer contents are easily available for \u003cstrong\u003eintrospection\u003c/strong\u003e because they are programmatically parseable. I can run a function over a container, and know exactly the functions available to me, ask for help, and know where to interact with inputs and outputs.\u003c/li\u003e\n\u003cli\u003eContainer internal infrastructure is \u003cstrong\u003emodular\u003c/strong\u003e. Given a set of SCI-F apps from different sources, I can import different install routines and have assurance that environment variables defined for each are sourced correctly for each, and that associated content does not overwrite previous content. Each software and data module must carry, minimally, a unique name and install location in the system.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each of the above, let\u0027s define some basic tests.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Development Evaluation\u003c/h3\u003e\u003ca id=\"user-content-1-development-evaluation\" class=\"anchor\" aria-label=\"Permalink: 1. Development Evaluation\" href=\"#1-development-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor this use case, we are a container developer, and we are writing a singularity build recipe.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCan I easily define multiple entrypoints?\u003c/h4\u003e\u003ca id=\"user-content-can-i-easily-define-multiple-entrypoints\" class=\"anchor\" aria-label=\"Permalink: Can I easily define multiple entrypoints?\" href=\"#can-i-easily-define-multiple-entrypoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nSingularity standard defaults to a single runscript per container. If I need to expose multiple functions, I either need to write a more complicated single entrypoint to direct the call, or I need to write detailed docs on different exec commands (and executables inside) for the user to run. For this real world use case, at the time when this runscript was written, SCI-F was not yet conceptualized. Given the sheer number of tools in the container, the runscript served to list a long list of executables, written to a text file, added to the path:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%runscript\n\n if [ $# -eq 0 ]; then\n echo \"\\nThe following software is installed in this image:\"\n column -t /Software/.info | sort -u --ignore-case \n echo \"\\Note that some Anaconda in the list are modules and note executables.\"\n echo \"Example usage: analysis.img [command] [args] [options]\" \n else\n exec \"$@\"\n fi\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGiven that this container was intended to run a scientific workflow, this list doesn\u0027t help to make its usage transparent. It would be useful for an individual familiar with the core software, perhaps developing a specific workflow with it. Arguably, this complete listing should be provided, perhaps not as a main entrypoint, but a separate app to print metadata, or the software names and versions added as labels to the app(s) where they are relevant. It\u0027s important to note that this first container did not include any logic for performing the analysis, this was provided by the included \u003ca href=\"scripts\"\u003escripts\u003c/a\u003e. If the scripts are separated from the container, reproducing the analysis is no longer possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nSCI-F has the advantage here because I can give names to different apps, and write a different executable runscript for each. I can still provide a main runscript, and it could either give the user a listing of possible options (below) or run the entire workflow the container provides. Here is a main runscript that instructs the user on how to use the apps, dynamically generating the list:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%runscript\n\n if [ $# -eq 0 ]; then\n echo \"\\nThe following software is installed in this image:\"\n ls /scif/apps | sort -u --ignore-case \n echo \"Example usage: singularity --app \u0026lt;name\u0026gt; \u0026lt;container\u0026gt; [command] [args] [options]\" \n else\n exec \"$@\"\n fi\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd here is an example app, specifically to downlad a component of the data:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun download-reference\n mkdir -p $REF_DIR\n wget -P $REF_DIR ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_25/gencode.v25.transcripts.fa.gz\n gzip -d $REF_DIR/gencode.v25.transcripts.fa.gz\n wget -P $REF_DIR ftp://ftp.ensembl.org/pub/release-85/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz\n gzip -d $REF_DIR/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eImportantly, the scripts that were previously in \u003ca href=\"scripts\"\u003escripts\u003c/a\u003e are now cleanly organized into sections in the build recipe. This modular organization and easy accessibility would have been very challening given the first container organization. The runscript would have needed to be long and complicated to infer what the user wanted to do, or the same functionality achieved by executing different scripts (inside the container), which is a non-trivial (or minimally more annoying to write) than simply writing lines into sections.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCan I easily install content known to modules?\u003c/h4\u003e\u003ca id=\"user-content-can-i-easily-install-content-known-to-modules\" class=\"anchor\" aria-label=\"Permalink: Can I easily install content known to modules?\" href=\"#can-i-easily-install-content-known-to-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven that I have two functions for my container to perform, foo and bar, can I generate an install routine that will allow for both shared dependencies (i.e. global) and distinct dependencies?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nSingularity standard has one mode to install global dependencies, everything goes into the \u003ccode\u003e%post\u003c/code\u003e script and any organization of required data, files, and libraries is scattered around the image. Other than coming up with a manual organization and documenting it, there is no way to cleanly define boundaries that will be easily discovered by the user. If you take a look at the \u003ca href=\"Singularity.noscif\"\u003eStandard Singularity\u003c/a\u003e recipe, you will see this reflected in one huge, single install procedure. As an example, for this container the tools \u003ccode\u003ebwa\u003c/code\u003e and \u003ccode\u003esamtools\u003c/code\u003e were generally used for an alignment step, and there is no way of knowing this. They are installed globally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /Software\nsu -c \u0027git clone https://github.com/Linuxbrew/brew.git\u0027 singularity\nsu -c \u0027/Software/brew/bin/brew install bsdmainutils parallel util-linux\u0027 singularity\nsu -c \u0027/Software/brew/bin/brew tap homebrew/science\u0027 singularity\nsu -c \u0027/Software/brew/bin/brew install art bwa samtools\u0027 singularity\nsu -c \u0027rm -r $(/Software/brew/bin/brew --cache)\u0027 singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn fact, the \u003ccode\u003eart\u003c/code\u003e tools are installed with the same manager (\u003ccode\u003ebrew\u003c/code\u003e), but they belong to an entirely different step. If a research scientist (or user) were parsing this build recipe, or using an NLP algorithm that took distance into account, there would be no strong signal about how these software were used or related to one another.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nWith SCI-F, by simply defining an environment, labels, install, or runscript to be in the context of an app, the modularity is automatically generated. When I add a list of files to an app \u003ccode\u003efoo\u003c/code\u003e, I know they are added to the container\u0027s predictable location for \u003ccode\u003efoo\u003c/code\u003e. If I add a file to \u003ccode\u003ebin\u003c/code\u003e I know it goes into foo\u0027s bin, and is added to the path when \u003ccode\u003efoo\u003c/code\u003e is run. If I add a library to \u003ccode\u003elib\u003c/code\u003e, I know it is added to \u003ccode\u003eLD_LIBRARY_PATH\u003c/code\u003e when foo is run. I don\u0027t need to worry about equivalently named files under different apps getting mixed up, or being called incorrectly because both are on the path. For example, in writing these sections, a developer can make it clear that \u003ccode\u003ebwa\u003c/code\u003e and \u003ccode\u003esamtools\u003c/code\u003e are used together for alignment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# =======================\n# bwa index and align\n# =======================\n\n%appinstall bwa-index-align\n git clone https://github.com/lh3/bwa.git build\n cd build \u0026amp;\u0026amp; git checkout v0.7.15 \u0026amp;\u0026amp; make\n mv -t ../bin bwa bwakit\n\n apt-get install -y liblzma-dev\n cd .. \u0026amp;\u0026amp; wget https://github.com/samtools/samtools/releases/download/1.5/samtools-1.5.tar.bz2\n tar -xvjf samtools-1.5.tar.bz2\n cd samtools-1.5 \u0026amp;\u0026amp; ./configure --prefix=${SINGULARITY_APPROOT}\n make \u0026amp;\u0026amp; make install\n\n%apprun bwa-index-align\n mkdir -p $DATADIR/Bam\n bwa index -a bwtsw $DATADIR/Reference/Homo_sapiens.GRCh38.dna.primary_assembly.fa\n bwa mem -t $NUMCORES $DATADIR/Reference/Homo_sapiens.GRCh38.dna.primary_assembly.fa $DATADIR/Fastq/dna_1.fq.gz $DATADIR/Fastq/dna_2.fq.gz | samtools view -bhS - \u0026gt; $DATADIR/Bam/container.bam \n\n%applabels bwa-index-align\n bwa-version v0.7.15\n samtools-version v1.5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand that \u003ccode\u003eart\u003c/code\u003e is used to simulate reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# =======================\n# simulate reads\n# =======================\n\n%apphelp simulate-reads\n Optionally set any of the following environment variables (defaults shown)\n READS (100000000)\n READ_LEN (150)\n GENOME_SIZE (3400000000)\n\n%appenv simulate-reads\n READS=${READS:-100000000}\n READ_LEN=${READ_LEN:-150}\n GENOME_SIZE=${GENOME_SIZE:-3400000000}\n export GENOME_SIZE READ_LEN READS\n\n%appinstall simulate-reads \n wget https://www.niehs.nih.gov/research/resources/assets/docs/artbinmountrainier20160605linux64tgz.tgz\n tar -xzvf artbinmountrainier20160605linux64tgz.tgz \n mv art_bin_MountRainier/* bin/\n chmod u+x bin/art_*\n\n%apprun simulate-reads\n GENOME=\"$REF_DIR/Homo_sapiens.GRCh38.dna.primary_assembly.fa\"\n FOLD_COVERAGE=$(python -c \"print($READS*$READ_LEN/$GENOME_SIZE)\")\n art_illumina --rndSeed 1 --in $GENOME --paired --len 75 --fcov $FOLD_COVERAGE --seqSys HS25 --mflen 500 --sdev 20 --noALN --out $FASTQ_DIR/dna_ \u0026amp;\u0026amp; gzip $FASTQ_DIR/dna_1.fq \u0026amp;\u0026amp; gzip $FASTQ_DIR/dna_2.fq\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhether I glanced at the recipe, or did some kind of text processing, I could easily see the relationships and purpose of the software in the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCan I associate environment and metadata with modules?\u003c/h4\u003e\u003ca id=\"user-content-can-i-associate-environment-and-metadata-with-modules\" class=\"anchor\" aria-label=\"Permalink: Can I associate environment and metadata with modules?\" href=\"#can-i-associate-environment-and-metadata-with-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven two different functions for my container to perform, foo and bar, can I define environment variables and labels (metadata) that I know will be sourced (environment) or exposed (inspect labels) in the context of the app?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nSingularity standard also has one global shared \u003ccode\u003e%environment\u003c/code\u003e, and \u003ccode\u003e%labels\u003c/code\u003e section. If two functions in the container share the same environment variable and the value is different, this must be resolved manually. For this example, the first container didn\u0027t have any labels or environment, however in practice these global sections are usually used for high level, global variables like author and version (of the container). When I run the container, regardless of if different contexts or variables are needed for executables inside, I get the same environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nWith SCI-F, I simply write the different variables to their sections, and have confidence that they will be sourced (environment) or inspected (labels) with clear association to the app.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\n%appenv run-rtg\n MEM=${MEM:-4g}\n THREADS=${THREADS:2}\n export MEM THREADS\n\n%applabel run-rtg\n rtg-version 3.6.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDo I need to know standard locations in advance?\u003c/h4\u003e\u003ca id=\"user-content-do-i-need-to-know-standard-locations-in-advance\" class=\"anchor\" aria-label=\"Permalink: Do I need to know standard locations in advance?\" href=\"#do-i-need-to-know-standard-locations-in-advance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven that a container has conformance to SCI-F, do I need to know how it works to use it?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nWith a standard location, we would be relying on Linux File System conventions (e.g., installation of content to \u003ccode\u003e/usr/local\u003c/code\u003e or intuitively infer that a folder called \u003ccode\u003e/Software\u003c/code\u003e (as with this scientific example) or \u003ccode\u003e/code\u003c/code\u003e is likely where the creator put important content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nInstead of requiring the user to know that an app\u0027s base might be at \u003ccode\u003e/scif/apps/foo\u003c/code\u003e, we instead expose environment variables (e.g., \u003ccode\u003eSINGULARITY_APPBASE\u003c/code\u003e) that can be referenced at runtime to refer to different apps. This is especially important if, for example, I need to reference the output of one app as input for another, or simply know it\u0027s install location. Regardless of which app is running, the container will also expose the top level folder for all apps installations, and data, \u003ccode\u003eSINGULARITY_DATA\u003c/code\u003e and \u003ccode\u003eSINGULARITY_APPS\u003c/code\u003e at \u003ccode\u003e/scif/data\u003c/code\u003e and \u003ccode\u003e/scif/apps\u003c/code\u003e, respectively.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Production Evaluation\u003c/h3\u003e\u003ca id=\"user-content-2-production-evaluation\" class=\"anchor\" aria-label=\"Permalink: 2. Production Evaluation\" href=\"#2-production-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor this use case, we operate under the scenario that we are familiar with Singularity and the commands to use SCi-F, but we know nothing about the containers. We are a user.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDo I know what the container does?\u003c/h4\u003e\u003ca id=\"user-content-do-i-know-what-the-container-does\" class=\"anchor\" aria-label=\"Permalink: Do I know what the container does?\" href=\"#do-i-know-what-the-container-does\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most natural thing to do with a Singularity container, knowing that it is possible to execute, is to do exactly that. For this evaluation, we want to assess how well executing the container reveals the intentions of the creator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nFrom the runscript we evaluated earlier, we are presented with a list of software and versions installed in the image, without detail to where or for what purpose. While this listing is comprehensive, it\u0027s most appropriate for a developer than a scientific workflow. In this listing, it isn\u0027t clear how the software is used or combine in the analysis. We are reliant on some external script or controller that drives the container. We don\u0027t have any hints about possible analysis steps the container can serve.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eThe following software is installed in this image:\nalabaster 0.7.9 Anaconda\nanaconda 4.3.0 Anaconda\nanaconda-client 1.6.0 Anaconda\nanaconda-navigator 1.4.3 Anaconda\nargcomplete 1.0.0 Anaconda\nart 20160605 Homebrew\nastroid 1.4.9 Anaconda\nastropy 1.3 Anaconda\nbabel 2.3.4 Anaconda\nbackports 1.0 Anaconda\nbeautifulsoup4 4.5.3 Anaconda\nbitarray 0.8.1 Anaconda\nblaze 0.10.1 Anaconda\nbokeh 0.12.4 Anaconda\nboto 2.45.0 Anaconda\nbottleneck 1.2.0 Anaconda\nbsdmainutils 9.0.10 Homebrew\nbwa 0.7.15 Homebrew\ncairo 1.14.8 Anaconda\ncffi 1.9.1 Anaconda\nchardet 2.3.0 Anaconda\nchest 0.2.3 Anaconda\nclick 6.7 Anaconda\ncloudpickle 0.2.2 Anaconda\nclyent 1.2.2 Anaconda\ncolorama 0.3.7 Anaconda\nconda 4.3.8 Anaconda\nconda-build 1.21.3 Anaconda\nconda-env 2.6.0 Anaconda\nconfigobj 5.0.6 Anaconda\ncontextlib2 0.5.4 Anaconda\ncryptography 1.7.1 Anaconda\ncurl 7.52.1 Anaconda\ncycler 0.10.0 Anaconda\ncython 0.25.2 Anaconda\ncytoolz 0.8.2 Anaconda\ndask 0.13.0 Anaconda\ndatashape 0.5.4 Anaconda\ndbus 1.10.10 Anaconda\ndecorator 4.0.11 Anaconda\ndill 0.2.5 Anaconda\ndocutils 0.13.1 Anaconda\ndynd-python 0.7.2 Anaconda\nentrypoints 0.2.2 Anaconda\net_xmlfile 1.0.1 Anaconda\nexpat 2.1.0 Anaconda\nfastcache 1.0.2 Anaconda\nflask 0.12 Anaconda\nflask-cors 3.0.2 Anaconda\nfontconfig 2.12.1 Anaconda\nfreetype 2.5.5 Anaconda\nget_terminal_size 1.0.0 Anaconda\ngevent 1.2.1 Anaconda\nglib 2.50.2 Anaconda\ngreenlet 0.4.11 Anaconda\ngsl 2.3 Homebrew\ngst-plugins-base 1.8.0 Anaconda\ngstreamer 1.8.0 Anaconda\nh5py 2.6.0 Anaconda\nharfbuzz 0.9.39 Anaconda\nhdf5 1.8.17 Anaconda\nheapdict 1.0.0 Anaconda\nhtslib 1.3.1 Homebrew\nicu 54.1 Anaconda\nidna 2.2 Anaconda\nimagesize 0.7.1 Anaconda\nipykernel 4.5.2 Anaconda\nipython 5.1.0 Anaconda\nipython_genutils 0.1.0 Anaconda\nipywidgets 5.2.2 Anaconda\nisort 4.2.5 Anaconda\nitsdangerous 0.24 Anaconda\njbig 2.1 Anaconda\njdcal 1.3 Anaconda\njedi 0.9.0 Anaconda\njinja2 2.9.4 Anaconda\njpeg 9b Anaconda\njsonschema 2.5.1 Anaconda\njupyter 1.0.0 Anaconda\njupyter_client 4.4.0 Anaconda\njupyter_console 5.0.0 Anaconda\njupyter_core 4.2.1 Anaconda\nkallisto 0.43.0 Anaconda\nlazy-object-proxy 1.2.2 Anaconda\nlibbsd 0.8.3 Homebrew\nlibdynd 0.7.2 Anaconda\nlibffi 3.2.1 Anaconda\nlibgcc 4.8.5 Anaconda\nlibgfortran 3.0.0 Anaconda\nlibiconv 1.14 Anaconda\nlibpng 1.6.27 Anaconda\nlibsodium 1.0.10 Anaconda\nlibtiff 4.0.6 Anaconda\nlibxcb 1.12 Anaconda\nlibxml2 2.9.4 Anaconda\nlibxslt 1.1.29 Anaconda\n_license 1.1 Anaconda\nllvmlite 0.15.0 Anaconda\nlocket 0.2.0 Anaconda\nlxml 3.7.2 Anaconda\nmarkupsafe 0.23 Anaconda\nmatplotlib 2.0.0 Anaconda\nmistune 0.7.3 Anaconda\nmkl 2017.0.1 Anaconda\nmkl-service 1.1.2 Anaconda\nmpmath 0.19 Anaconda\nmultipledispatch 0.4.9 Anaconda\nnb_anacondacloud 1.1.0 Anaconda\nnb_conda 1.1.0 Anaconda\nnb_conda_kernels 1.0.3 Anaconda\nnbconvert 4.2.0 Anaconda\n_nb_ext_conf 0.2.0 Anaconda\nnbformat 4.2.0 Anaconda\nnbpresent 3.0.2 Anaconda\nncurses 6.0_2 Homebrew\nnetworkx 1.11 Anaconda\nnltk 3.2.2 Anaconda\nnose 1.3.7 Anaconda\nnotebook 4.3.1 Anaconda\nnumba 0.30.1 Anaconda\nnumexpr 2.6.1 Anaconda\nnumpy 1.11.3 Anaconda\nnumpydoc 0.6.0 Anaconda\nodo 0.5.0 Anaconda\nopenpyxl 2.4.1 Anaconda\nopenssl 1.0.2k Anaconda\npandas 0.19.2 Anaconda\nparallel 20170122 Homebrew\npartd 0.3.7 Anaconda\npatchelf 0.9_1 Homebrew\npatchelf 0.9 Anaconda\npathlib2 2.2.0 Anaconda\npath.py 10.0 Anaconda\npatsy 0.4.1 Anaconda\npcre 8.39 Anaconda\npep8 1.7.0 Anaconda\npexpect 4.2.1 Anaconda\npickleshare 0.7.4 Anaconda\npillow 4.0.0 Anaconda\npip 9.0.1 Anaconda\npixman 0.34.0 Anaconda\npkg-config 0.29.1_2 Homebrew\nply 3.9 Anaconda\nprompt_toolkit 1.0.9 Anaconda\npsutil 5.0.1 Anaconda\nptyprocess 0.5.1 Anaconda\npy 1.4.32 Anaconda\npyasn1 0.1.9 Anaconda\npycosat 0.6.1 Anaconda\npycparser 2.17 Anaconda\npycrypto 2.6.1 Anaconda\npycurl 7.43.0 Anaconda\npyflakes 1.5.0 Anaconda\npygments 2.1.3 Anaconda\npylint 1.6.4 Anaconda\npyopenssl 16.2.0 Anaconda\npyparsing 2.1.4 Anaconda\npyqt 5.6.0 Anaconda\npytables 3.3.0 Anaconda\npytest 3.0.5 Anaconda\npython 3.5.2 Anaconda\npython-dateutil 2.6.0 Anaconda\npytz 2016.10 Anaconda\npyyaml 3.12 Anaconda\npyzmq 16.0.2 Anaconda\nqt 5.6.2 Anaconda\nqtawesome 0.4.3 Anaconda\nqtconsole 4.2.1 Anaconda\nqtpy 1.2.1 Anaconda\nreadline 6.2 Anaconda\nredis 3.2.0 Anaconda\nredis-py 2.10.5 Anaconda\nrequests 2.12.4 Anaconda\nrope 0.9.4 Anaconda\nRTG 3.6.2 User_Install\nruamel_yaml 0.11.14 Anaconda\nsamtools 1.3.1 Homebrew\nscikit-image 0.12.3 Anaconda\nscikit-learn 0.18.1 Anaconda\nscipy 0.18.1 Anaconda\nseaborn 0.7.1 Anaconda\nsetuptools 27.2.0 Anaconda\nsimplegeneric 0.8.1 Anaconda\nsingledispatch 3.4.0.3 Anaconda\nsip 4.18 Anaconda\nsix 1.10.0 Anaconda\nsnowballstemmer 1.2.1 Anaconda\nsockjs-tornado 1.0.3 Anaconda\nsphinx 1.5.1 Anaconda\nsphinx_rtd_theme 0.1.9 Anaconda\nspyder 3.1.2 Anaconda\nsqlalchemy 1.1.5 Anaconda\nsqlite 3.13.0 Anaconda\nstatsmodels 0.6.1 Anaconda\nsympy 1.0 Anaconda\nterminado 0.6 Anaconda\ntk 8.5.18 Anaconda\ntoolz 0.8.2 Anaconda\ntornado 4.4.2 Anaconda\ntraitlets 4.3.1 Anaconda\nunicodecsv 0.14.1 Anaconda\nutil-linux 2.29 Homebrew\nwcwidth 0.1.7 Anaconda\nwerkzeug 0.11.15 Anaconda\nwheel 0.29.0 Anaconda\nwidgetsnbextension 1.2.6 Anaconda\nwrapt 1.10.8 Anaconda\nxlrd 1.0.0 Anaconda\nxlsxwriter 0.9.6 Anaconda\nxlwt 1.2.0 Anaconda\nxz 5.2.2 Anaconda\nxz 5.2.3 Homebrew\nyaml 0.1.6 Anaconda\nzeromq 4.1.5 Anaconda\nzlib 1.2.8 Anaconda\n\\Note that some Anaconda in the list are modules and note executables.\nExample usage: analysis.img [command] [args] [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nWhen we run the SCi-F container, a different kind of information is presented. By listing the container contents at \u003ccode\u003e/scif/apps\u003c/code\u003e, the user knows what pipeline steps are included in the analysis.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run scif.img\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand in fact, this basic listing is generally useful for apps, so it\u0027s provided as it\u0027s own command, if the user doesn\u0027t write a runscript at all:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity apps scif.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe runscript also hints that I can direct the \u003ccode\u003e%help\u003c/code\u003e command to better understand an app. While both SCiF and standard Singularity allows for specification of a global \u003ccode\u003e%help\u003c/code\u003e section, providing documentation on the level of the application is more focused and offers specificity to the user. Both also allow for global \u003ccode\u003e%labels\u003c/code\u003e that might point to documentation, code repositories, or other informative information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDoes moduarity come naturally?\u003c/h4\u003e\u003ca id=\"user-content-does-moduarity-come-naturally\" class=\"anchor\" aria-label=\"Permalink: Does moduarity come naturally?\" href=\"#does-moduarity-come-naturally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor this metric, we want to know if using different functions of the container (modules) is intuitive. As we have seen above, the definition of a \"module\" could be anything from a series of script calls to perform a pipeline step (alignment using bwa and samtools), a single script call (such as a module just for bwa), or even the same function applied to a specific set of content (e.g., download \"A\" v.s. download \"B\").\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nSingularity proper represents a module on the level of the container. The entire container is intended for one entrypoint, and any deviation from that requires customization of that entrypoint, or special knowledge about other executables in the container to call with \u003ccode\u003eexec\u003c/code\u003e. The container is modular only in context of being a single step in a pipeline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nSCI-F, in that the software inside is defined and installed in a modular fashion, makes it easy to find three different modules:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edownload-fastq\u003c/li\u003e\n\u003cli\u003edownload-rtg\u003c/li\u003e\n\u003cli\u003edownload-reference\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eand without looking further, infer that likely these three downloads can be run in parallel. While this doesn\u0027t represent any kind of statement or assurance of this, it allows for the container have a natural modularity. Consider steps that are named according to an order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e1-download\u003c/li\u003e\n\u003cli\u003e2-preprocess\u003c/li\u003e\n\u003cli\u003e3-analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe creator of the container, in choosing a careful naming, can deliver a huge amount of information about different modules.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDo I know what executables are important in the container?\u003c/h4\u003e\u003ca id=\"user-content-do-i-know-what-executables-are-important-in-the-container\" class=\"anchor\" aria-label=\"Permalink: Do I know what executables are important in the container?\" href=\"#do-i-know-what-executables-are-important-in-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWithout much effort, I should have a high level understanding of the different functions that the container performs, as defined by the creator. For example, a container intended for development of variant calling will expose low level tools (e.g, bwa, seqtk) while a container that is intended will expose a pipeline (e.g., mapping).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nFor Singularity standard, if the container performs one function (and one only), then a single runscript / entrypoint is sufficient. Having multiple functions is completely reliant on the programming ability of the creator. If the creator is able to write a more complex runscript that explains different use cases, the container can satisfy this goal. If not, the container is a black box.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nFor SCI-F, without any significant programming ability, the different apps are easily exposed to the user.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCan I easily get help for an executable?\u003c/h4\u003e\u003ca id=\"user-content-can-i-easily-get-help-for-an-executable\" class=\"anchor\" aria-label=\"Permalink: Can I easily get help for an executable?\" href=\"#can-i-easily-get-help-for-an-executable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI should be able to run a command to get help or a summary of what the container does (introspection).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nFor Singularity standard, you \u003cstrong\u003ecan\u003c/strong\u003e ask a container for help, but it\u0027s a single global help.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nFor SCI-F apps, you can define help sections for all of the functions that your container serves, along with a global help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Research Evaluation\u003c/h3\u003e\u003ca id=\"user-content-3-research-evaluation\" class=\"anchor\" aria-label=\"Permalink: 3. Research Evaluation\" href=\"#3-research-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn important attribute of having modular software apps is that it allows for separation of files and executables for research, and those that belong to the base system. From a machine learning standpoint, it provides labels for some subset of content in the container that might be used to better understand how different software relates to a pipeline. Minimally, it separates important content from the base, allowing, for example, a recursive tree generated at \u003ccode\u003e/scif\u003c/code\u003e to capture a large majority of additions to the container. Or simple parsing of the build recipe to see what software (possibly outside of this location) was intended for each app. Equally important, having container software installed at a global at \u003ccode\u003e%post\u003c/code\u003e also says important things about it - that it perhaps is important for more than one software module, or is more of a system library.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConclusion\u003c/h2\u003e\u003ca id=\"user-content-conclusion\" class=\"anchor\" aria-label=\"Permalink: Conclusion\" href=\"#conclusion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSCI-F clearly has the advantage when it comes to a container creator embedding his or her work with implied metadata about software and container contents. SCI-F also makes it easier to package different run scripts with the container, and expose them easily to the user. However, this does not mean that the standard approach of using a container as a general toolbox and distributing it with a series of external callers is bad or wrong. The choice to use (or not use) SCI-F apps is largely dependent on the goals of the creator, and the intended users.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 9, + "subscribers_count": 4, "topics": [], - "updated_at": 1698248799.0 + "updated_at": 1581608084.0 }, { "data_format": 2, - "description": "Social License to Operate - Joint project with CSIRO", + "description": null, "filenames": [ - "Singularity.more-classifiers" + "Singularity" ], - "full_name": "Calvin-CS/slo-classifiers", + "full_name": "simozhou/epi-awesome", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/epiawesome\u003c/h1\u003e\u003ca id=\"user-content-nf-coreepiawesome\" class=\"anchor\" aria-label=\"Permalink: nf-core/epiawesome\" href=\"#nf-coreepiawesome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePipeline for epigenomics analysis and enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/simozhou/epi-awesome\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1b80521a835a9388c9652fe5210f4ca2f07cea7925b4f320da080912ffd806b/68747470733a2f2f7472617669732d63692e6f72672f73696d6f7a686f752f6570692d617765736f6d652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/simozhou/epi-awesome.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/simozhou/epiawesome\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37fecf0c8cb2655014a35151c14805ab99338a00e133b1945efa707a930a637f/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f657069617765736f6d652e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/epiawesome.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.singularity-hub.org/collections/2653\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/epiawesome pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/reference_genomes.md\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003enf-core/epiawesome was originally written by Simone Procaccia.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 9, + "subscribers_count": 0, "topics": [], - "updated_at": 1607287806.0 + "updated_at": 1646303987.0 }, { "data_format": 2, - "description": "Hands-on tutorial on Nextflow and Containers (Docker and Singularity). Paris 2018.", + "description": "BIDS app for correcting gradient non-linearities, saves corrected images as mirrored BIDS dataset, along with warp and intensity-correction fields", "filenames": [ + "Singularity.0.0.1d", + "Singularity.v0.0.2", + "Singularity.0.0.1j", + "Singularity.v0.0.2c", + "Singularity.0.0.1f", + "Singularity.0.0.1e", + "Singularity.0.0.1h", + "Singularity.0.0.1b", "Singularity", - "Singularity.xenial" + "Singularity.v0.0.3", + "Singularity.v0.0.2a", + "Singularity.0.0.1c", + "Singularity.v0.0.3a" ], - "full_name": "biocorecrg/C4LWG-2018", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/CRG-CNAG/BioCoreMiscOpen/blob/master/logo/biocore-logo_small.png\"\u003e\u003cimg src=\"https://github.com/CRG-CNAG/BioCoreMiscOpen/raw/master/logo/biocore-logo_small.png\" alt=\"C4LWG-2018\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e C4LWG-2018\u003c/h1\u003e\u003ca id=\"user-content--c4lwg-2018\" class=\"anchor\" aria-label=\"Permalink: C4LWG-2018\" href=\"#-c4lwg-2018\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHands-on tutorial on Nextflow and Containers (Docker and Singularity). Paris 2018.\nFor this tutorial we need to install Nextflow (\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/\u003c/a\u003e), Singularity (\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/\u003c/a\u003e) and Docker (\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehttps://www.docker.com/\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVirtual appliance\u003c/h2\u003e\u003ca id=\"user-content-virtual-appliance\" class=\"anchor\" aria-label=\"Permalink: Virtual appliance\" href=\"#virtual-appliance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor sake of simplicity we provide a virtual appliance in \u003ca href=\"https://en.wikipedia.org/wiki/Open_Virtualization_Format\" rel=\"nofollow\"\u003eOVA format\u003c/a\u003e. They can be imported in a virtual machine system such as \u003ca href=\"https://www.virtualbox.org/\" rel=\"nofollow\"\u003eVirtualbox\u003c/a\u003e (\u003ca href=\"https://www.youtube.com/watch?v=ZCfRtQ7-bh8\" rel=\"nofollow\"\u003evideo instructions\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIt is a Debian Stretch machine from OSBoxes project (ref: \u003ca href=\"https://www.osboxes.org/debian/\" rel=\"nofollow\"\u003ehttps://www.osboxes.org/debian/\u003c/a\u003e). Java, Docker, Singularity and Nextflow are already installed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOVA: \u003ca href=\"http://biocore.crg.eu/courses/C4LWG-2018/C4LWG-2018-full.ova\" rel=\"nofollow\"\u003ehttp://biocore.crg.eu/courses/C4LWG-2018/C4LWG-2018-full.ova\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOVA md5: \u003ca href=\"http://biocore.crg.eu/courses/C4LWG-2018/C4LWG-2018-full.ova.md5\" rel=\"nofollow\"\u003ehttp://biocore.crg.eu/courses/C4LWG-2018/C4LWG-2018-full.ova.md5\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCheck appliances download\u003c/h3\u003e\u003ca id=\"user-content-check-appliances-download\" class=\"anchor\" aria-label=\"Permalink: Check appliances download\" href=\"#check-appliances-download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTake care that files downloaded correctly (around 6GB). You can check with MD5 utilites from the terminal\u003c/p\u003e\n\u003cp\u003eIn Linux:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ md5sum -c C4LWG-2018-full.ova.md5 \nC4LWG-2018-full.ova: OK\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn Mac:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ md5 C4LWG-2018-full.ova \n$ cat C4LWG-2018-full.ova.md5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck both outputs show the same string.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUser login\u003c/h3\u003e\u003ca id=\"user-content-user-login\" class=\"anchor\" aria-label=\"Permalink: User login\" href=\"#user-login\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUser: osboxes\u003c/li\u003e\n\u003cli\u003ePassword: osboxes.org\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you need to use the root user (e.g. via \u003ccode\u003esu -l\u003c/code\u003e), it has the same password.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eManual installation\u003c/h2\u003e\u003ca id=\"user-content-manual-installation\" class=\"anchor\" aria-label=\"Permalink: Manual installation\" href=\"#manual-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSoftware requirements\u003c/h3\u003e\u003ca id=\"user-content-software-requirements\" class=\"anchor\" aria-label=\"Permalink: Software requirements\" href=\"#software-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFor installing NextFlow we need Java version 1.8. You can check with \"java -version\". Then just type \"curl -s \u003ca href=\"https://get.nextflow.io\" rel=\"nofollow\"\u003ehttps://get.nextflow.io\u003c/a\u003e | bash\" for installing a local copy in your current directory. Finally type \"./nextflow run hello\" for testing.\u003c/li\u003e\n\u003cli\u003eMac OS X users can consider installing \u003ca href=\"https://brew.sh\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e and \u003ca href=\"https://caskroom.github.io/\" rel=\"nofollow\"\u003eHomebrew-Cask\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eDocker (Community Edition): \u003ca href=\"https://www.docker.com/community-edition\" rel=\"nofollow\"\u003ehttps://www.docker.com/community-edition\u003c/a\u003e . Download and install last stable version in your system.\n\u003cul\u003e\n\u003cli\u003eCask users: \u003ccode\u003ebrew cask install docker\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003eLinux\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003eMac\u003c/a\u003e (Homebrew and Homebrew-Cask are needed)\n\u003cul\u003e\n\u003cli\u003eIf using Vagrant with Singularity, Vagrant shared folder with the host is \u003ccode\u003e/vagrant\u003c/code\u003e. That would be the best location to place generated images.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstalling nextflow\u003c/h3\u003e\u003ca id=\"user-content-installing-nextflow\" class=\"anchor\" aria-label=\"Permalink: Installing nextflow\" href=\"#installing-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecurl -s \u003ca href=\"https://get.nextflow.io\" rel=\"nofollow\"\u003ehttps://get.nextflow.io\u003c/a\u003e | bash\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainer installation\u003c/h3\u003e\u003ca id=\"user-content-container-installation\" class=\"anchor\" aria-label=\"Permalink: Container installation\" href=\"#container-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can retrieve Docker image with all used software by doing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull biocorecrg/c4lwg-2018\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternately, you can always modify and build a Docker image yourself in your computer by doing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t myimagename .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eConverting Docker image into Singularity image\u003c/h4\u003e\u003ca id=\"user-content-converting-docker-image-into-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Converting Docker image into Singularity image\" href=\"#converting-docker-image-into-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build c4lwg-2018.simg docker://biocorecrg/c4lwg-2018\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eNotes\u003c/h5\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you experience problems executing the generated image, e.g., \u003ccode\u003eERROR : No valid /bin/sh in container\u003c/code\u003e, try to change your umask (e. g., \u003ccode\u003eumask 000\u003c/code\u003e) \u003ca href=\"https://github.com/singularityware/singularity/issues/1079\"\u003eRef\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerating a Singularity image from a Singularity recipe\u003c/h4\u003e\u003ca id=\"user-content-generating-a-singularity-image-from-a-singularity-recipe\" class=\"anchor\" aria-label=\"Permalink: Generating a Singularity image from a Singularity recipe\" href=\"#generating-a-singularity-image-from-a-singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are other ways to generate Singularity images from other \u003ca href=\"http://singularity.lbl.gov/docs-recipes\" rel=\"nofollow\"\u003erecipe approaches\u003c/a\u003e. As a example, using \u003ca href=\"http://singularity.lbl.gov/build-debootstrap\" rel=\"nofollow\"\u003eDebootstrap\u003c/a\u003e (being root is required in \u003ca href=\"http://singularity.lbl.gov/docs-build-container\" rel=\"nofollow\"\u003ethese cases\u003c/a\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build c4lwg-2018.xenial.simg Singularity.xenial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNextflow usage\u003c/h2\u003e\u003ca id=\"user-content-nextflow-usage\" class=\"anchor\" aria-label=\"Permalink: Nextflow usage\" href=\"#nextflow-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe can reach the first folder \u003cstrong\u003etest0\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ecd test0; ls\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etest0.nf\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe can have a look at the code and launch it:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003enextflow run test0.nf\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNextflow creates a directory named \u003cstrong\u003ework\u003c/strong\u003e with different subfolders. Each one contains the input, output and some hidden files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e.exitcode\u003c/li\u003e\n\u003cli\u003e.command.log\u003c/li\u003e\n\u003cli\u003e.command.out\u003c/li\u003e\n\u003cli\u003e.command.err\u003c/li\u003e\n\u003cli\u003e.command.begin\u003c/li\u003e\n\u003cli\u003e.command.run\u003c/li\u003e\n\u003cli\u003e.command.sh\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn this case there is neither input nor output file.\u003c/p\u003e\n\u003cp\u003eSecond example where we read a fasta file, split it in several ones and tests on them.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ecd test1; ls\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etest1.nf\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003enextflow run test1.nf\u003c/strong\u003e\nIn the \u003cstrong\u003ework\u003c/strong\u003e folder we have subfolders containing this time a link to the input and the output file.\nIn \u003cstrong\u003eoutput\u003c/strong\u003e folder we have links to the final results.\u003c/p\u003e\n\u003cp\u003eThird example where we launch two fastQC analysis and we run multiQC on their result:\n\u003cem\u003e\u003cstrong\u003ecd test2; ls\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eparams.config: with parameters\u003c/li\u003e\n\u003cli\u003enextflow.config: with information about resources needed for each task and the container to be used\u003c/li\u003e\n\u003cli\u003etest2.nf\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe can inspect the different files and launch te pipeline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003enextflow run test2.nf -bg\u003c/strong\u003e\nWe can inspect the results in the different folders.\u003c/p\u003e\n", + "full_name": "khanlab/gradcorrect", + "latest_release": "v0.0.3a", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egradcorrect\u003c/h1\u003e\u003ca id=\"user-content-gradcorrect\" class=\"anchor\" aria-label=\"Permalink: gradcorrect\" href=\"#gradcorrect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBIDS app for correcting gradient non-linearities, saves corrected images as mirrored BIDS dataset, along with warp and intensity-correction fields\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1550495046.0 + "updated_at": 1711706051.0 }, { "data_format": 2, - "description": "Container recipes for use with the U of A HPC resources", + "description": null, "filenames": [ - "Singularity.im2markup", - "Singularity.caffe2", - "Singularity.torch", - "Singularity.delphi", - "Singularity.nvidia_docker", - "Singularity.pytorch", - "Singularity.dynet", - "Singularity.tensorflow", - "Singularity.cuda9_py36", - "Singularity.keras", - "Singularity.numba", - "Singularity.pytorch_skimage", - "word2vec/Singularity.w2v", - "openmnt/Singularity.opennmt" + "Singularity" ], - "full_name": "ml4ai/UA-hpc-containers", + "full_name": "PennSIVE/qsm", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eML4AI Lab Singularity Container Repository\u003c/h1\u003e\u003ca id=\"user-content-ml4ai-lab-singularity-container-repository\" class=\"anchor\" aria-label=\"Permalink: ML4AI Lab Singularity Container Repository\" href=\"#ml4ai-lab-singularity-container-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2086\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository holds singularity containers that are commonly used by members of the ML4AI lab to run projects on the University of Arizona\u0027s high-performance computing environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSee Also\u003c/strong\u003e:\n\u003ca href=\"https://github.com/clulab/hpc-ml\"\u003ehttps://github.com/clulab/hpc-ml\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003eQSM reconstruction using Total Generalized Variation (TGV-QSM)\nKristian Bredies and Christian Langkammer\nVersion from June 2016\n\u003ca href=\"http://www.neuroimaging.at\" rel=\"nofollow\"\u003ewww.neuroimaging.at\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e=== NEW ===\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIt is a python package now which simplifies installation\u003c/li\u003e\n\u003cli\u003eOrientation fixed: image data does NOT have to be transversal any longer (thx Daniel!)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e=== Installation ===\npython setup.py install\n(in case of problems such as Ubuntu 18.04+ try installing an older version of cython with pip install Cython==0.19.2 - Thanks Andrej!)\u003c/p\u003e\n\u003cp\u003e=== Command line options ===\u003c/p\u003e\n\u003cp\u003eusage: tgv_qsm [-h] -p PHASE -m MASK [-o OUTPUT_SUFFIX]\n[--alpha ALPHA ALPHA | --factors FACTORS [FACTORS ...]]\n[-e EROSIONS] [-i ITERATIONS [ITERATIONS ...]]\n[-f FIELDSTRENGTH] [-t ECHOTIME] [-s] [--ignore-orientation]\n[--save-laplacian] [--output-physical] [--no-resampling]\n[--vis] [-v]\u003c/p\u003e\n\u003cp\u003eremarks for options:\n-t TE in seconds\n-f fieldstrength in Tesla\n-s autoscaling for SIEMENS phase data\u003c/p\u003e\n\u003cp\u003etest data:\ntgv_qsm -p epi3d_test_phase.nii.gz -m epi3d_test_mask.nii.gz -f 2.89 -t 0.027 -o epi3d_test_QSM\u003c/p\u003e\n\u003cp\u003e=== bet brain masking ===\u003c/p\u003e\n\u003cp\u003efor high res data (~0.5 mm iso) this does quite a good job:\nbet magni.nii.gz mask -n -m -R -f 0.1 -g 0.0\n(in case something is lost, vary parameter g minimally)\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 22, + "subscribers_count": 4, "topics": [], - "updated_at": 1617312549.0 + "updated_at": 1687459868.0 }, { "data_format": 2, - "description": "Singularity tutorial for an HPC cluster that has SLURM, Vagrant, and GPUs", + "description": "Singularity iRODS: iCommands", "filenames": [ - "Singularity" + "Singularity", + "Singularity.4.2.2" ], - "full_name": "satra/om-images", + "full_name": "mjstealey/singularity-irods-icommands", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eom-images\u003c/h1\u003e\u003ca id=\"user-content-om-images\" class=\"anchor\" aria-label=\"Permalink: om-images\" href=\"#om-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains singularity bootstrap scripts for building images for openmind@MIT\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity iRODS: iCommands\u003c/h1\u003e\u003ca id=\"user-content-singularity-irods-icommands\" class=\"anchor\" aria-label=\"Permalink: Singularity iRODS: iCommands\" href=\"#singularity-irods-icommands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/812\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://docs.irods.org/4.2.2/system_overview/glossary/#icommands\" rel=\"nofollow\"\u003eiRODS iCommands\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003eicommands.4.2.2.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build icommands.4.2.2.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name icommands.4.2.2.simg shub://mjstealey/singularity-irods-icommands\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHelp\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e icommands.4.2.2.simg\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e iRODS Version 4.2.2\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e $ singularity run icommands.4.2.2.simg [icommand] [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app iinit icommands.4.2.2.simg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app iinit icommands.4.2.2.simg [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Where [args] in\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_host String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_port Integer\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_user_name String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_zone_name String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_password String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_default_resource String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_home String\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run icommands.4.2.2.simg [icommand] [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./icommands.4.2.2.simg [icommand] [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003e[icommand]\u003c/code\u003e is a valid \u003ca href=\"https://docs.irods.org/4.2.2/icommands/user/\" rel=\"nofollow\"\u003eiRODS iCommand\u003c/a\u003e and [\u003ccode\u003eargs\u003c/code\u003e] is zero or more supporting arguments for that iCommand.\u003c/p\u003e\n\u003cp\u003ePrior to initializing your iRODS environment using \u003ccode\u003eiinit\u003c/code\u003e, the only valid iCommand will be \u003ccode\u003eihelp\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run icommands.4.2.2.simg ihelp\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eThe iCommands and a brief description of each:\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eiadmin - perform iRODS administrator operations (iRODS admins only).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eibun - upload/download structured (tar) files.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eicd - change the current working directory (Collection).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eichksum - checksum one or more Data Objects or Collections.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eichmod - change access permissions to Collections or Data Objects.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eicp - copy a data-object (file) or Collection (directory) to another.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eienv - display current iRODS environment.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eierror - convert an iRODS error code to text.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiexecmd - remotely execute special commands.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiexit - exit an iRODS session (opposite of iinit).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eifsck - check if local files/directories are consistent with the associated Data Objects/Collections in iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiget - get a file from iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eigroupadmin - perform group-admin functions: mkuser, add/remove from group, etc.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eihelp - display a synopsis list of the iCommands.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiinit - initialize a session, so you don\u0027t need to retype your password.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eils - list Collections (directories) and Data Objects (files).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eilsresc - list iRODS resources.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimcoll - manage mounted collections and associated cache.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimeta - add/remove/copy/list/query user-defined metadata.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimiscsvrinfo - retrieve basic server information.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimkdir - make an iRODS directory (Collection).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimv - move/rename an iRODS Data Object (file) or Collection (directory).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eipasswd - change your iRODS password.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiphybun - DEPRECATED - physically bundle files (admin only).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiphymv - physically move a Data Object to another storage Resource.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eips - display iRODS agent (server) connection information.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiput - put (store) a file into iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eipwd - print the current working directory (Collection) name.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiqdel - remove a delayed rule (owned by you) from the queue.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiqmod - modify certain values in existing delayed rules (owned by you).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiqstat - show the queue status of delayed rules.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiquest - issue a question (query on system/user-defined metadata).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiquota - show information on iRODS quotas (if any).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eireg - register a file or directory/files/subdirectories into iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirepl - replicate a file in iRODS to another storage resource.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirm - remove one or more Data Objects or Collections.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirmdir - removes an empty Collection\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirmtrash - remove Data Objects from the trash bin.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirsync - synchronize Collections between a local/iRODS or iRODS/iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirule - submit a rule to be executed by the iRODS server.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiscan - check if local file or directory is registered in iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eisysmeta - show or modify system metadata.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiticket - create, delete, modify \u0026amp; list tickets (alternative access strings).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eitrim - trim down the number of replicas of Data Objects.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiuserinfo - show information about your iRODS user account.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eixmsg - send/receive iRODS xMessage System messages.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eizonereport - generates a full diagnostic/backup report of your Zone.\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eFor more information on a particular iCommand:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \u0027\u0026lt;iCommand\u0026gt; -h\u0027\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eor\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \u0027ihelp \u0026lt;iCommand\u0026gt;\u0027\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eiRODS Version 4.2.2 ihelp\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eiinit (no args)\u003c/h2\u003e\u003ca id=\"user-content-iinit-no-args\" class=\"anchor\" aria-label=\"Permalink: iinit (no args)\" href=\"#iinit-no-args\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eiinit\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app iinit icommands.4.2.2.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf no arguments are passed in the user will be walked through the \u003ccode\u003eiinit\u003c/code\u003e process\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app iinit icommands.4.2.2.simg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e ERROR: environment_properties::capture: missing environment file. should be at [/home/stealey/.irods/irods_environment.json]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eOne or more fields in your iRODS environment file (irods_environment.json) are\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003emissing; please enter them.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter the host name (DNS) of the server to connect to: nwm.renci.org\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter the port number: 1247\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter your irods user name: nwm-reader\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter your irods zone: nwmZone\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eThose values will be added to your environment file (for use by\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eother iCommands) if the login succeeds.\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eEnter your current iRODS password: nwmreader\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eIINIT: $HOME/.irods/irods_environment.json\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e{\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_host\": \"nwm.renci.org\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_port\": 1247,\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_zone_name\": \"nwmZone\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_user_name\": \"nwm-reader\"\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will also result in two new files being created in the users \u003ccode\u003e$HOME/.irods\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls -alh $HOME/.irods\n-rw------- 1 xxxxx xxxxx 17 Mar 26 14:44 .irodsA\n-rw-r--r-- 1 xxxxx xxxxx 133 Mar 26 14:44 irods_environment.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eirods_environment.json\u003c/code\u003e is the JSON definition for the iRODS connection (as seen at the end of the \u003ccode\u003eiinit\u003c/code\u003e command output).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e.irodsA\u003c/code\u003e is the hashed value of the user\u0027s iRODS password.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eiinit (with args)\u003c/h2\u003e\u003ca id=\"user-content-iinit-with-args\" class=\"anchor\" aria-label=\"Permalink: iinit (with args)\" href=\"#iinit-with-args\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eiinit\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app iinit icommands.4.2.2.simg [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eValid args\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h | --irods_host as String\n-p | --irods_port as Integer\n-u | --irods_user_name as String\n-z | --irods_zone_name as String\n-s | --irods_password as String\n-d | --irods_default_resource as String\n-m | --irods_home as String\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf arguments are passed in the script will attempt the \u003ccode\u003eiinit\u003c/code\u003e process using a combination of preexisting information in the \u003ccode\u003eirods_environment.json\u003c/code\u003e file along with the arguments passed in by the user.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIf the \u003ccode\u003e--irods_password\u003c/code\u003e argument is populated the user will not be prompted for the password, but may notice an \u003ccode\u003eInappropriate ioctl\u003c/code\u003e warning at the prompt.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/bin/stty: \u0027standard input\u0027: Inappropriate ioctl for device\n/bin/stty: \u0027standard input\u0027: Inappropriate ioctl for device\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app iinit icommands.4.2.2.simg \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_host nwm.renci.org \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_port 1247 \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_user_name nwm-reader \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_zone_name nwmZone \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_password nwmreader \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_default_resource nwmResc \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_home /nwmZone/home/nwm/data\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e/bin/stty: \u0027standard input\u0027: Inappropriate ioctl for device\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e/bin/stty: \u0027standard input\u0027: Inappropriate ioctl for device\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter your current iRODS password:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eIINIT: $HOME/.irods/irods_environment.json\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e{\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_host\": \"nwm.renci.org\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_port\": 1247,\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_zone_name\": \"nwmZone\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_user_name\": \"nwm-reader\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_default_resource\": \"nwmResc\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_home\": \"/nwmZone/home/nwm/data\"\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eiCommands\u003c/h2\u003e\u003ca id=\"user-content-icommands\" class=\"anchor\" aria-label=\"Permalink: iCommands\" href=\"#icommands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the user has established their iRODS identity using the \u003ccode\u003eiinit\u003c/code\u003e command, they can issue a variety of iCommands. Examples given assuming prior initialization for \u003ca href=\"\"\u003enwm.renci.org\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExample\u003c/strong\u003e: \u003ccode\u003eils\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run icommands.4.2.2.simg ils\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e/nwmZone/home/nwm/data:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/analysis_assim\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/fe_analysis_assim\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/forcing_analysis_assim\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/forcing_medium_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/forcing_short_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/long_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/medium_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/short_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/usgs_timeslices\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eExample\u003c/strong\u003e: \u003ccode\u003eils /nwmZone/home/nwm/data/nomads\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run icommands.4.2.2.simg ils /nwmZone/home/nwm/data/nomads\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e/nwmZone/home/nwm/data/nomads:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads/nwm.20180214\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads/nwm.20180215\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e ...\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads/nwm.20180325\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads/nwm.20180326\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eExample\u003c/strong\u003e: \u003ccode\u003eiget /nwmZone/home/nwm/data/nomads/nwm.20180325/short_range/nwm.t23z.short_range.channel_rt.f001.conus.nc\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run icommands.4.2.2.simg iget /nwmZone/home/nwm/data/nomads/nwm.20180325/short_range/nwm.t23z.short_range.channel_rt.f001.conus.nc\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eVerify file on local system.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003els -alh \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/nwm.t23z.short_range.channel_rt.f001.conus.nc\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e-rw-r----- 1 xxxxx xxxxx 12M Mar 26 14:55 /home/stealey/irods-icommands-singularity/nwm.t23z.short_range.channel_rt.f001.conus.nc\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eNOTE\u003c/strong\u003e: By default Singularity will mount \u003ccode\u003e$PWD\u003c/code\u003e, \u003ccode\u003e$HOME\u003c/code\u003e and \u003ccode\u003e/tmp\u003c/code\u003e from the local file system to the container, so files that are retrieved from iRODS using \u003ccode\u003eiget\u003c/code\u003e will be saved to \u003ccode\u003e$PWD\u003c/code\u003e unless specified otherwise.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at \u003ca href=\"https://github.com/mjstealey/singularity-irods-icommands\"\u003ehttps://github.com/mjstealey/singularity-irods-icommands\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, - "topics": [], - "updated_at": 1693276409.0 + "subscribers_count": 3, + "topics": [ + "irods", + "icommands", + "singularity" + ], + "updated_at": 1645061365.0 }, { "data_format": 2, - "description": "Run PostgreSQL server within a Singularity container against isolated directory.", + "description": "An example of using stools-clair for Travis and Circle Continuous Integration", "filenames": [ "Singularity" ], - "full_name": "glentner/PostgreSQL-Singularity", + "full_name": "singularityhub/stools-clair", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePostgreSQL-Singularity\u003c/h1\u003e\u003ca id=\"user-content-postgresql-singularity\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL-Singularity\" href=\"#postgresql-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun PostgreSQL server within a Singularity container against isolated directory.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Tools for Continuous Integration\u003c/h1\u003e\u003ca id=\"user-content-singularity-tools-for-continuous-integration\" class=\"anchor\" aria-label=\"Permalink: Singularity Tools for Continuous Integration\" href=\"#singularity-tools-for-continuous-integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository shows a simple example of how to use the \u003ca href=\"https://www.github.com/singularityhub/stools\"\u003estools\u003c/a\u003e\n(Singularity Tools) container to run \u003ca href=\"https://github.com/coreos/clair\"\u003eClair OS\u003c/a\u003e security checks on your container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions\u003c/h2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eAdd the \u003ca href=\".travis.yml\"\u003e.travis.yml\u003c/a\u003e file to your Github repository with one or more \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e recipes\u003c/li\u003e\n\u003cli\u003eEnsure your recipe location(s) are specified correctly (see below) depending on if you want to build or pull.\u003c/li\u003e\n\u003cli\u003eConnect your repository to Travis (\u003ca href=\"https://docs.travis-ci.com/user/getting-started/\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e), and optionally set up a cron job for continuous vulnerability testing.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThat\u0027s it!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTravis CI\u003c/h2\u003e\u003ca id=\"user-content-travis-ci\" class=\"anchor\" aria-label=\"Permalink: Travis CI\" href=\"#travis-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/singularityhub/stools-clair\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/59d436c93e4d7d32b4c1dff866bb649e2ebb67510d9d1955e012377f17c1cab1/68747470733a2f2f7472617669732d63692e6f72672f73696e67756c61726974796875622f73746f6f6c732d636c6169722e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/singularityhub/stools-clair.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003etravis.yml\u003c/code\u003e file in the repository sets up the build to pull and use the container \u003ccode\u003evanessa/stools-clair\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContinuous Vulnerability Testing\u003c/h3\u003e\u003ca id=\"user-content-continuous-vulnerability-testing\" class=\"anchor\" aria-label=\"Permalink: Continuous Vulnerability Testing\" href=\"#continuous-vulnerability-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA cool feature of Travis is the ability to use cron jobs to run a build at a particular frequency, meaning that\nwe can set up regularly scheduled testing of our containers. Cool! How to do that?\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to the project settings page, usually at \u003ccode\u003ehttps://travis-ci.org/[organization]/[repo]/settings\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUnder \"Cron Jobs\" select the branch and interval that you want the checks to run, and then click \"Add\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThat\u0027s it!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChange the recipe file\u003c/h3\u003e\u003ca id=\"user-content-change-the-recipe-file\" class=\"anchor\" aria-label=\"Permalink: Change the recipe file\" href=\"#change-the-recipe-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have a Singularity recipe in the base of your repository, you should not need to change this file. If you want to change\nit to include one or more recipes in different locations, then you will want to change this line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Perform the build from your Singularity file, we are at the base of the repo\u003c/span\u003e\n- docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity build container.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere is says \u003ccode\u003eSingularity\u003c/code\u003e you might change it to path/in/repo/Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd another recipe file\u003c/h3\u003e\u003ca id=\"user-content-add-another-recipe-file\" class=\"anchor\" aria-label=\"Permalink: Add another recipe file\" href=\"#add-another-recipe-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou aren\u0027t limited to the number of containers you can build and test! You can build more than one, and test the resulting containers, like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity build container1.simg Singularity\n - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity build container2.simg Singularity.two\n - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner sclair container1.simg container2.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePull a container\u003c/h3\u003e\u003ca id=\"user-content-pull-a-container\" class=\"anchor\" aria-label=\"Permalink: Pull a container\" href=\"#pull-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to build here, you can use Continuous Integration just for testing. Let\u0027s say we have a\nrepository with just a travis file, we can actually use it to test all of our Docker images (converted to Singularity) or\ncontainers hosted on Singularity Hub. That might look like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity pull --name vsoch-hello-world.simg shub://vsoch/hello-world\n - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity pull --name ubuntu.simg docker://ubuntu:16.04\n - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner sclair vsoch-hello-world.simg ubuntu.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRename the Output\u003c/h3\u003e\u003ca id=\"user-content-rename-the-output\" class=\"anchor\" aria-label=\"Permalink: Rename the Output\" href=\"#rename-the-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou might also change the name of the output image (container.simg). Why? Imagine that you are using Travis to\nbuild, test, and then upon success, to upload to some container storage.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeedback Wanted!\u003c/h2\u003e\u003ca id=\"user-content-feedback-wanted\" class=\"anchor\" aria-label=\"Permalink: Feedback Wanted!\" href=\"#feedback-wanted\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eUnder what conditions might we want a build to fail testing?\u003c/li\u003e\n\u003cli\u003eHow many users prefer using CircleCI to Docker? Or vice versa?\u003c/li\u003e\n\u003cli\u003eIs it worth having some kind of message sent back to Github (would require additional permissions)?\u003c/li\u003e\n\u003cli\u003eCircle has support for artifacts. How might you want results presented?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePlease \u003ca href=\"https://www.github.com/singularityhub/stools/issues\"\u003elet me know\u003c/a\u003e your feedback!\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 4, - "topics": [], - "updated_at": 1664811569.0 + "topics": [ + "singularity", + "singularity-container", + "ci", + "testing", + "travis-ci", + "circle-ci" + ], + "updated_at": 1524940998.0 }, { "data_format": 2, - "description": "Vim Syntax highlighting for Singularity.", + "description": null, "filenames": [ - "Singularity" + "Singularity.v3.8-torch1.9.0-dj0.12.9", + "Singularity.v3.8-torch1.9.0-dj0.12.7", + "Singularity.v3.9-torch1.10.2-dj0.12.7.def", + "Singularity.v3.9-torch1.13.1-dj0.13.1.def", + "Singularity.v3.8-torch1.7.0-dj0.12.7", + "Singularity.v3.9-torch1.11.0-dj0.12.7.def", + "Singularity.v3.10-torch1.11.0-dj0.12.7-ubuntu22.04.def", + "Singularity.v3.8-torch1.5.0-dj0.12.4", + "Singularity.v3.8-torch1.9.0-dj0.13.2" ], - "full_name": "biosugar0/singularity-vim", + "full_name": "sinzlab/pytorch-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-vim\u003c/h1\u003e\u003ca id=\"user-content-singularity-vim\" class=\"anchor\" aria-label=\"Permalink: singularity-vim\" href=\"#singularity-vim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVim Syntax highlighting for Singularity Recipe.\n\u003ca href=\"http://singularity.lbl.gov/docs-recipes#files\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/docs-recipes#files\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./example.png\"\u003e\u003cimg src=\"./example.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLICENSE\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"./LICENSE\"\u003eMIT License\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epytorch-singularity\u003c/h1\u003e\u003ca id=\"user-content-pytorch-singularity\" class=\"anchor\" aria-label=\"Permalink: pytorch-singularity\" href=\"#pytorch-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4939\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains Singularity definition files used for PyTorch development in the Sinzlab.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 5, "topics": [], - "updated_at": 1578589770.0 + "updated_at": 1643971108.0 }, { "data_format": 2, - "description": "centos7 container with miniconda and pytorch + pip install requirements from https://github.com/marian42/shapegan (re-using the container built from https://github.com/truatpasteurdotfr/singularity-docker-centos7-conda-pytorch).", + "description": "Builds Chaotic-Nyx in Slurm+Singularity", "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-localimage-shapegan", + "full_name": "chaotic-cx/nyx-slurm-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-docker-centos7-conda-pytorch with shapegan\u003c/h1\u003e\u003ca id=\"user-content-singularity-docker-centos7-conda-pytorch-with-shapegan\" class=\"anchor\" aria-label=\"Permalink: singularity-docker-centos7-conda-pytorch with shapegan\" href=\"#singularity-docker-centos7-conda-pytorch-with-shapegan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecentos7 container with miniconda and pytorch + pip install requirements from \u003ca href=\"https://github.com/marian42/shapegan\"\u003ehttps://github.com/marian42/shapegan\u003c/a\u003e (re-using the container built from \u003ca href=\"https://github.com/truatpasteurdotfr/singularity-docker-centos7-conda-pytorch\"\u003ehttps://github.com/truatpasteurdotfr/singularity-docker-centos7-conda-pytorch\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eBuilding: (you MUST adapt the path to the local .sif file)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-localimage-shapegan.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, "subscribers_count": 3, "topics": [], - "updated_at": 1580937110.0 + "updated_at": 1714917445.0 }, { "data_format": 2, - "description": "the dinosaur data container for interaction with dinosaur data datasets!", + "description": null, "filenames": [ - "Singularity" + "misc/releases/latest/Singularity", + "misc/releases/20.06/Singularity.20.06", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/19.12/Singularity.19.12" ], - "full_name": "vsoch/data-container", + "full_name": "PatrickFerber/NeuralFastDownward", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData Container\u003c/h1\u003e\u003ca id=\"user-content-data-container\" class=\"anchor\" aria-label=\"Permalink: Data Container\" href=\"#data-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a container that will allow you to build \"data containers,\" or squashfs\nbinaries that you can mount, unmount, and create all with the same container (and either\nuse with your own data container base, or on your local machine if you have a FUSE filesystem.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, pull the container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name dinosaur-data shub://vsoch/data-container\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat does the container do?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity apps dinosaur-data\ncreate\nmount\nunmount\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTry creating a dinosaur dataset, which is a squashfs filesystem\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app create dinosaur-data /home/vanessa/Desktop/demo demo.sqfs\n\nParallel mksquashfs: Using 4 processors\nCreating 4.0 filesystem on demo.sqfs, block size 131072.\n[\u003cspan class=\"pl-k\"\u003e===========================================================================================================================\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e] 2/2 100%\nExportable Squashfs 4.0 filesystem, gzip compressed, data block size 131072\n\tcompressed data, compressed metadata, compressed fragments, compressed xattrs\n\tduplicates are removed\nFilesystem size 3.63 Kbytes (0.00 Mbytes)\n\t29.28% of uncompressed filesystem size (12.40 Kbytes)\nInode table size 61 bytes (0.06 Kbytes)\n\t62.24% of uncompressed inode table size (98 bytes)\nDirectory table size 43 bytes (0.04 Kbytes)\n\t78.18% of uncompressed directory table size (55 bytes)\nNumber of duplicate files found 0\nNumber of inodes 3\nNumber of files 2\nNumber of fragments 1\nNumber of symbolic links 0\nNumber of device nodes 0\nNumber of fifo nodes 0\nNumber of socket nodes 0\nNumber of directories 1\nNumber of ids (unique uids + gids) 1\nNumber of uids 1\n\tvanessa (1000)\nNumber of gids 1\n\tvanessa (1000)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow try mounting it to the container. The container expects you do to this at \u003ccode\u003e/scif/data.sqsh\u003c/code\u003e to\nbind to \u003ccode\u003e/tmp/data\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecat /etc/fstab\n...\n/scif/data.sqsh /tmp/data squashfs ro,user,noauto,unhide\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere is how to do the mount, interactively with shell. You first bind your squashfs filesystem\nto the \u003ccode\u003e/tmp/data.sqsh\u003c/code\u003e location.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity shell --bind demo.sqsf:/scif/data.sqsh dinosaur-data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere I\u0027m at (not working yet)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity shell --bind demo.sqfs:/scif/data.sqsh dinosaur-data\nSingularity: Invoking an interactive shell within container...\n\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mount /scif/data\nmount: /scif/data.sqsh: failed to setup loop device: Permission denied\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e cat /etc/fstab \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e UNCONFIGURED FSTAB FOR BASE SYSTEM\u003c/span\u003e\n/scif/data.sqsh /scif/data squashfs ro,user,noauto,unhide,loop\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity shell --bind demo.sqfs:/scif/data.sqsh dinosaur-data\nSingularity: Invoking an interactive shell within container...\n\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ls /tmp/data\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e squashfuse /scif/data.sqsh /tmp/data\nfusermount: mount failed: Operation not permitted\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e fusermount /tmp/data\nfusermount: old style mounting not supported\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mount /tmp/data\nmount: /scif/data.sqsh: failed to setup loop device: Permission denied\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ls -l /scif/data.sqsh \n-rw-r--r-- 1 vanessa vanessa 4096 Jun 1 23:32 /scif/data.sqsh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003econtent below not done, we\u0027d want these commands to work!\u003c/strong\u003e\nYou can always ask for help.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e --app mount dinosaur-data\n\nMount a squashfs file to a folder where you have write on you computer\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\nThe folder should NOT exist (but you should have writable to where it would)\nas the container will create it \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e you.\n\n\n$ singularity run --app mount dinosaur-data demo.sqsf /tmp/data2\n$ ls /tmp/data2\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNeural Fast Downward\u003c/h1\u003e\u003ca id=\"user-content-neural-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Neural Fast Downward\" href=\"#neural-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeural Fast Downward generates training data for\nclassical planning domains and provides support for Protobuf (Tensorflow 1.x)\nand PyTorch models. The refactored code is not as well tested as it should\nbe. Please report bugs to \u003cstrong\u003e\u003ca href=\"mailto:patrick.ferber@unibas.ch\"\u003epatrick.ferber@unibas.ch\u003c/a\u003e\u003c/strong\u003e or create a pull request.\u003c/p\u003e\n\u003cp\u003eFor more information related to Fast Downward, consult the bottom part of\nthis README.md.\u003c/p\u003e\n\u003cp\u003eIf you use Neural Fast Downward in your research, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{ferber-et-al-ecai2020,\n author = \"Patrick Ferber and Malte Helmert and J{\\\"o}rg Hoffmann\",\n title = \"Neural Network Heuristics for Classical Planning: A Study of\n Hyperparameter Space\",\n pages = \"2346--2353\",\n booktitle = \"Proceedings of the 24th {European} Conference on\n {Artificial} {Intelligence} ({ECAI} 2020)\",\n year = \"2020\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSampling\u003c/h3\u003e\u003ca id=\"user-content-sampling\" class=\"anchor\" aria-label=\"Permalink: Sampling\" href=\"#sampling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeural Fast Downward generates data from a given task,\ntherefore, it uses \u003cstrong\u003eSamplingTechniques\u003c/strong\u003e which take a given task and modify\nit and \u003cstrong\u003eSamplingEngines\u003c/strong\u003e which perform some action with the modified task.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent Sampling Techniques\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNew initial state via random walks with progression from the original initial\nstate\u003c/li\u003e\n\u003cli\u003eNew initial state via random walk with regression from the goal condition\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent Sampling Engines:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ewriting the new states as (partial) SAS tasks to disk\u003c/li\u003e\n\u003cli\u003euse a given search algorithm to find plans for the new states and\nstore them\u003c/li\u003e\n\u003cli\u003eestimate the heuristic value of a state by value update using the n-step\nsuccessors (like Bellman update).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you are only interested in the sampling code, just work with the branch\n\u003ccode\u003esampling\u003c/code\u003e. The branch \u003ccode\u003emain\u003c/code\u003e contains the sampling feature, as well as, the\nfeatures below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExample:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenerate two state via regression from the goal with random walk lengths\nbetween 5 and 10. Use \u003ccode\u003eA*(LMcut)\u003c/code\u003e to find a solution and store all states\nalong the plan, as well as the used operators in \u003ccode\u003esas_plan\u003c/code\u003e. Ignore the\nmessage that no solution was found.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e\"sampling_search_simple(astar(lmcut(transform=sampling_transform()),transform=sampling_transform()), techniques=[gbackward_none(2, distribution=uniform_int_dist(5, 10))])\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eATTENTION: By default, the components of Fast Downward (e.g. search engines\nand heuristics) use the original task. Thus, you have to provide them the\nargument \u003ccode\u003etransform=sampling_transform()\u003c/code\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eATTENTION: The output tells you that no solution was found. This is wrong.\nCheck if the output contains\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eGenerated Entries: X\nSampling Techniques used:\n Y: n/N\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eThis tells you how many samples were generated and how often each sampling\ntechnique was invoked.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"SAMPLING.md\"\u003eClick here for more information and examples\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePolicies\u003c/h3\u003e\u003ca id=\"user-content-policies\" class=\"anchor\" aria-label=\"Permalink: Policies\" href=\"#policies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeural Fast Downward has some simple support for policies in classical planning.\nIt does \u003cstrong\u003enot\u003c/strong\u003e implement a good policy, but it provides a Policy class which can\nbe extended. Currently, two simple policies which internally rely on a given heuristic and\na simple search engine which follows the choices of a policy are implemented.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNeural Networks\u003c/h3\u003e\u003ca id=\"user-content-neural-networks\" class=\"anchor\" aria-label=\"Permalink: Neural Networks\" href=\"#neural-networks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeural Fast Downward supports Protobuf (Tensorflow 1.x) and PyTorch models. It\nimplements an\nabstract neural network base class and implements subclass for\nTensorflow and PyTorch. Wrappers which use a NN to calculate a heuristic or\npolicy are implemented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTensorflow.\u003c/strong\u003e\nThe Tensorflow setup has changed multiple times. The code is still there,\nbut is not tested with the current version of Tensorflow (most likely will\nnot run). I am glad for\nPR that adapt the code for the current Tensorflow version or for instructions\nAfter setting up Tensorflow, you have to uncomment the Tensorflow Plugin in\n\u003ccode\u003esrc/search/DownwardFiles.cmake\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePyTorch.\u003c/strong\u003e\nSetting up PyTorch is straight forward. Download \u003ccode\u003etorchlib\u003c/code\u003e and extract it to\nany path \u003ccode\u003eP\u003c/code\u003e. Then set an environment variable \u003ccode\u003ePATH_TORCH\u003c/code\u003e that points to \u003ccode\u003eP\u003c/code\u003e.\nAfterwards, you have to uncomment the Torch Plugin in\n\u003ccode\u003esrc/search/DownwardFiles.cmake\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"NEURALNETWORKS.md\"\u003eClick here for more information and examples\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFast Downward\u003c/h1\u003e\u003ca id=\"user-content-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Fast Downward\" href=\"#fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1545323609.0 + "updated_at": 1701965665.0 }, { "data_format": 2, - "description": "I forgot to fork the repo of LineVD project at first so I create this repository to store the current work, I will add the original work link at the readme file", + "description": "Visualizing mutations and PNGS changes between two strains", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "iseT1enLoc/LineVDFork", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSetup instruction\u003c/h1\u003e\u003ca id=\"user-content-setup-instruction\" class=\"anchor\" aria-label=\"Permalink: Setup instruction\" href=\"#setup-instruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVirtual environment setup\u003c/h1\u003e\u003ca id=\"user-content-virtual-environment-setup\" class=\"anchor\" aria-label=\"Permalink: Virtual environment setup\" href=\"#virtual-environment-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epython3\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003em\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evenv\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evenv\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evenv\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ebin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eactivate\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall packages\u003c/h1\u003e\u003ca id=\"user-content-install-packages\" class=\"anchor\" aria-label=\"Permalink: Install packages\" href=\"#install-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003escatter\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ehttps\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e//\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epyg\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eorg\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ewhl\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2.2\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ecu121\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ehtml\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData setup\u003c/h1\u003e\u003ca id=\"user-content-data-setup\" class=\"anchor\" aria-label=\"Permalink: Data setup\" href=\"#data-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eDownload from google drive the link\u003c/strong\u003e\n\u003ca href=\"https://drive.google.com/drive/folders/1MOwFXA9mtj0lVOT3v0SHXYMlNG234ETC?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1MOwFXA9mtj0lVOT3v0SHXYMlNG234ETC?usp=sharing\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUnzip all the zip files in the directory\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAfter unzip the storage directory looks like the below image:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./media/image.png\"\u003e\u003cimg src=\"./media/image.png\" alt=\"image.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe cache directory looks like this:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./media/image_1.png\"\u003e\u003cimg src=\"./media/image_1.png\" alt=\"image.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTraining model\u003c/h1\u003e\u003ca id=\"user-content-training-model\" class=\"anchor\" aria-label=\"Permalink: Training model\" href=\"#training-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNotice there are some error in this step like the python \u003cem\u003e\u003cstrong\u003ecan not identify custom module.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe solution is to open terminal and \u003cstrong\u003eactivate virtual environment again\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evenv\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ebin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eactivate\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eChange to the running directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esastvd\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003escripts\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epython\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esastvd\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003escripts\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emain\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epy\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "cobeylab/flu_strain_compare", + "latest_release": "v0.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFlu Strain Compare\u003c/h1\u003e\u003ca id=\"user-content-flu-strain-compare\" class=\"anchor\" aria-label=\"Permalink: Flu Strain Compare\" href=\"#flu-strain-compare\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"example.png\"\u003e\u003cimg src=\"example.png\" alt=\"3D influenza image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePurpose\u003c/h2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-label=\"Permalink: Purpose\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFlu Strain Compare generates visualizations of mutations between pairs of HA sequences. \u003ccode\u003emake_comparison_figure.py\u003c/code\u003e takes two HA sequences as input and outputs a figure highlighting amino acid and PNGS changes on a representative HA crystal structure. At present, H1pdm and H3 strains are supported.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/figures/A_Cambodia_e0826360_2020-A_Darwin_6_2021-A_Tasmania_503_2020-7_others.png?raw=true\"\u003e\u003cimg src=\"/figures/A_Cambodia_e0826360_2020-A_Darwin_6_2021-A_Tasmania_503_2020-7_others.png?raw=true\" alt=\"Example image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis utility runs on Docker, Singularity, or natively with PyMOL installed as a Python module.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use the following command to build the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build ./ -t flu_strain_compare\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild Docker container above. This may need to be done locally if your HPC system doesn\u0027t allow Docker.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# load module, if in HPC environment\nmodule load singularity\nsingularity build ubuntu-pymol-biopython_latest.sif docker-daemon://flu_strain_compare:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNative\u003c/h3\u003e\u003ca id=\"user-content-native\" class=\"anchor\" aria-label=\"Permalink: Native\" href=\"#native\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall PyMOL as a Python library.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLinux/Unix\u003c/h4\u003e\u003ca id=\"user-content-linuxunix\" class=\"anchor\" aria-label=\"Permalink: Linux/Unix\" href=\"#linuxunix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# Optional, if using a virtual environment.\npython -m venv venv\nsource venv/bin/activate\n\n# Install python dependencies.\npip install Bio pandas\npip install scipy\n\n# Install PyMOL as a library.\nPYMOL_VERSION=2.5.0\nwget --no-verbose https://github.com/schrodinger/pymol-open-source/archive/refs/tags/v${PYMOL_VERSION}.tar.gz\ntar xfz v2.5.0.tar.gz\ncd pymol-open-source-2.5.0\n\ngit clone --depth=1 https://github.com/rcsb/mmtf-cpp.git\ncd mmtf-cpp\ngit pull\ncd ..\ncp -r mmtf-cpp/include/mmtf* include/\n\npython3 setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWindows\u003c/h4\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall python dependencies.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip install Bio pandas\npip install scipy\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to \u003ca href=\"https://github.com/schrodinger/pymol-open-source/archive/refs/tags/v2.5.0.tar.gz\"\u003ehttps://github.com/schrodinger/pymol-open-source/archive/refs/tags/v2.5.0.tar.gz\u003c/a\u003e in a browser and save the file.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUncompress v2.5.0.tar.gz using a compression utility such as \u003ca href=\"https://www.7-zip.org/\" rel=\"nofollow\"\u003e7-Zip\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone \u003ca href=\"https://github.com/rcsb/mmtf-cpp.git\"\u003ehttps://github.com/rcsb/mmtf-cpp.git\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCopy \u003ccode\u003emmtf-cpp/include/mmtf/\u003c/code\u003e and \u003ccode\u003emmtf-cpp/include/mmtf.hpp\u003c/code\u003e to the \u003ccode\u003einclude/\u003c/code\u003e directory in \u003ccode\u003epymol-open-source-v2.5.0\u003c/code\u003e (the directory in the decompressed archive above).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall PyMOL as a library.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython3 setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration\u003c/h2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-label=\"Permalink: Configuration\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe configuration file specified in the command line (see \u003ca href=\"#running\"\u003eRunning\u003c/a\u003e below) serves as input for the \u003ccode\u003emake_comparison_figure.py\u003c/code\u003e script.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eseq_file\u003c/code\u003e: Name of fasta-formatted file that contains full-length amino acid HA sequences. File must be in \u003ccode\u003edata\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eq1_id\u003c/code\u003e: Sequence ID of the first query strain. The sequence id is the first word in the fasta header of the desired sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eq2_id\u003c/code\u003e: Sequence ID of the second query strain.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eseq_lineage\u003c/code\u003e: Lineage of the query strains. \u003ccode\u003e\"H1\"\u003c/code\u003e or \u003ccode\u003e\"H3\"\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003enumbering_scheme\u003c/code\u003e: What numbering scheme do you want to use for mutation identification? For H1 sequences, you can choose \u003ccode\u003eH1pdm\u003c/code\u003e, \u003ccode\u003eH3\u003c/code\u003e, or \u003ccode\u003eH1_1933\u003c/code\u003e. For H3 sequences, only \u003ccode\u003eH3\u003c/code\u003e numbering is available.\u003c/li\u003e\n\u003cli\u003e``: What numbering scheme do you want to use for mutation identification? For H1 sequences, you can choose \u003ccode\u003eH1pdm\u003c/code\u003e, `H3`, or `H1_1933`. For H3 sequences, only `H3` numbering is available.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ereference_mode\u003c/code\u003e: Use the reference strain for comparison (\u003ccode\u003etrue\u003c/code\u003e or \u003ccode\u003efalse\u003c/code\u003e). \u003cem\u003eNote: .png output is much more resource intensive than .pse.\u003c/em\u003e,\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eexport_files\u003c/code\u003e: Export .pse file, .png file, or both. Example: \u003ccode\u003e[\"PSE\"]\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efilter_sites\u003c/code\u003e: Filter out mutations and PNGS at the listed sites. Example: \u003ccode\u003e[92, 94, 104]\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ereverse_filter_sites\u003c/code\u003e: Only include the listed sites, and exclude others. \u003cem\u003eNote: reverse filter will override filter if both are non-empty.\u003c/em\u003e Example: \u003ccode\u003e[92]\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_csv\u003c/code\u003e: Write CSV to standard output. Default: \u003ccode\u003efalse\u003c/code\u003e (JSON).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ediversity_index\u003c/code\u003e: \u003ccode\u003e\"shannon\"\u003c/code\u003e, \u003ccode\u003e\"gini-simpson\"\u003c/code\u003e, \u003ccode\u003e\"richness\"\u003c/code\u003e (Default: Shannon).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003enon-conservative_only\u003c/code\u003e: Only include \u003ca href=\"\"\u003enon-conservative\u003c/a\u003e mutations to measure diversity. \u003ccode\u003etrue\u003c/code\u003e or \u003ccode\u003efalse\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNotes\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSite numbers input in \"filter_sites\" and \"reverse_filter_sites\" refer to amino acid numbering in the final HA proteins, after signaling peptides have been removed, from the start of HA1. The output table includes both numbering schemes (with and without signaling peptides), for reference\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning\u003c/h2\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003ccode\u003eflu_strain_compare_path\u003c/code\u003e is an absolute path to the root of this repository.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport flu_strain_compare_path=/some/abs/path/flu_strain_compare\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker-1\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith your configuration file set up to your liking, run the container with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v ${flu_strain_compare_path}:/app flu_strain_compare python3 src/\u0026lt;SCRIPT NAME\u0026gt;.py configuration/config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity-1\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind ${flu_strain_compare_path}:/app ubuntu-pymol-biopython_latest.sif python3 src/\u0026lt;SCRIPT NAME\u0026gt;.py configuration/config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNative\u003c/h3\u003e\u003ca id=\"user-content-native-1\" class=\"anchor\" aria-label=\"Permalink: Native\" href=\"#native-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython src/\u0026lt;SCRIPT NAME\u0026gt;.py configuration/config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003e\u0026lt;SCRIPT NAME\u0026gt;\u003c/code\u003e should be \u003ccode\u003emake_comparison_figure\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# from repo root\nsingularity exec --bind /home/youruser/flu_strain_compare:/app ubuntu_pymol_biopython.sif python3 src/make_comparison_figure.py configuration/config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutputs will be written to the \u003ccode\u003efigures\u003c/code\u003e directory of the repository.\u003c/p\u003e\n\u003cp\u003eIf you wish the csv with additional information to be printed out, include\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUnit tests\u003c/h2\u003e\u003ca id=\"user-content-unit-tests\" class=\"anchor\" aria-label=\"Permalink: Unit tests\" href=\"#unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\npytest\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStrains available\u003c/h2\u003e\u003ca id=\"user-content-strains-available\" class=\"anchor\" aria-label=\"Permalink: Strains available\" href=\"#strains-available\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eH3\u003c/h3\u003e\u003ca id=\"user-content-h3\" class=\"anchor\" aria-label=\"Permalink: H3\" href=\"#h3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2021-2022 southern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Darwin/6/2021\u003c/code\u003e, id = \u003ccode\u003eEPI1885402\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2021-2022 northern hemisphere Flublok (name = \u003ccode\u003eA/Tasmania/503/2020\u003c/code\u003e, id = \u003ccode\u003eEPI1752480\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2021-2022 northern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Cambodia/e0826360/2020\u003c/code\u003e, id = \u003ccode\u003eEPI1843589\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2020-2021 northern hemisphere Flublok (name = \u003ccode\u003eA/Minnesota/41/2019\u003c/code\u003e, id = \u003ccode\u003eEPI1548699\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2020-2021 northern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Hong Kong/45/2019\u003c/code\u003e, id = \u003ccode\u003eEPI1409001\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2019-2020 cell-based recommendation (name = \u003ccode\u003eA/Kansas/14/2017\u003c/code\u003e, id = \u003ccode\u003eEPI1174043\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2018-2019 cell-based recommendation (name = \u003ccode\u003eA/Singapore/INFIMH-16-0019/2016\u003c/code\u003e, id = \u003ccode\u003eEPI1106235\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2016-2018 cell-based recommendation (name = \u003ccode\u003eA/Hong Kong/4801/2014\u003c/code\u003e, id = \u003ccode\u003eEPI539576\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2015-2016 cell-based recommendation (name = \u003ccode\u003eA/Switzerland/9715293/2013\u003c/code\u003e, id = \u003ccode\u003eEPI530687\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2013-2015 cell-based recommendation (name = \u003ccode\u003eA/Victoria/361/2011 \u003c/code\u003e, id = \u003ccode\u003eEPI349103\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eH1\u003c/h3\u003e\u003ca id=\"user-content-h1\" class=\"anchor\" aria-label=\"Permalink: H1\" href=\"#h1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2021-2022 northern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Wisconsin/588/2019\u003c/code\u003e, id = \u003ccode\u003eEPI1715168\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2020-2021 northern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Hawaii/70/2019\u003c/code\u003e, id = \u003ccode\u003eEPI1669665\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2019-2020 cell-based recommendation (name = \u003ccode\u003eA/Brisbane/02/2018\u003c/code\u003e, id = \u003ccode\u003eEPI1212884\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2017-2019 cell-based recommendation (name = \u003ccode\u003eA/Michigan/45/2015\u003c/code\u003e, id = \u003ccode\u003eEPI699812\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e2009-2017 cell-based recommendation (name = \u003ccode\u003eA/California/04/2009\u003c/code\u003e, id = \u003ccode\u003eEPI178457\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2013-2014 example from Linderman et al. (name = \u003ccode\u003eA/Colorado/3514/2013\u003c/code\u003e, id = \u003ccode\u003eEPI501723\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScripts\u003c/h2\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-label=\"Permalink: Scripts\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003erenumber_H1.py\u003c/code\u003e and \u003ccode\u003erenumber_H3.py\u003c/code\u003e: \u003cem\u003eRe-index PDB files for use with Flu Strain Compare.\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprint_pymol_objects.py\u003c/code\u003e: \u003cem\u003ePrint chains and selections in a .pse file to compare modifications to the model.\u003c/em\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, - "topics": [], - "updated_at": 1730904681.0 - }, - { - "data_format": 2, - "description": "Framework for transfer experiments. Builds on top of nnfabrik and neuralpredictors. ", - "filenames": [ - "Singularity.v0.3.def", - "Singularity.v0.2.def", - "Singularity.v0.1.def", - "Singularity.v0.4.def" + "subscribers_count": 13, + "topics": [ + "ceirr-cmc" ], - "full_name": "sinzlab/nntransfer", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enntransfer - \u003cem\u003eA simple framework for transfer learning experiments\u003c/em\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-nntransfer----a-simple-framework-for-transfer-learning-experiments\" class=\"anchor\" aria-label=\"Permalink: nntransfer - A simple framework for transfer learning experiments\" href=\"#nntransfer----a-simple-framework-for-transfer-learning-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis framework provides all the tools necessary to quickly define a complex transfer experiment in a few lines of code,\nwhile being flexible enough to modify all components on every level.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe Concept and Structure\u003c/h2\u003e\u003ca id=\"user-content-the-concept-and-structure\" class=\"anchor\" aria-label=\"Permalink: The Concept and Structure\" href=\"#the-concept-and-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis framework builds on top of \u003ca href=\"https://github.com/sinzlab/nnfabrik\"\u003ennfabrik\u003c/a\u003e, \u003ca href=\"https://github.com/sinzlab/neuralpredictors\"\u003eneuralpredictors\u003c/a\u003e and \u003ca href=\"https://datajoint.io/\" rel=\"nofollow\"\u003edatajoint\u003c/a\u003e.\nThe code and conceptual structure involves several components that will be explained in the following:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfigs\u003c/h3\u003e\u003ca id=\"user-content-configs\" class=\"anchor\" aria-label=\"Permalink: Configs\" href=\"#configs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConfig classes are designed to hold all settings that define a specific experiment.\nThey allow default values to be set by assigning attributes in the init and easy overwrite ability by passing custom keyword arguments.\nOne key feature of these config objects is the option to access the hashed key that will be used in a datajoint schema.\nTherefore it is easy to access and manipulate table entries for a given config object.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExperiments\u003c/h3\u003e\u003ca id=\"user-content-experiments\" class=\"anchor\" aria-label=\"Permalink: Experiments\" href=\"#experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe configs in this framework are separated into \u003ccode\u003edataset\u003c/code\u003e, \u003ccode\u003emodel\u003c/code\u003e and \u003ccode\u003etrainer\u003c/code\u003e configs.\nTogether these configs form an \u003ccode\u003eExperiment\u003c/code\u003e, which itself can be composed with other experiments in a \u003ccode\u003eTransferExperiment\u003c/code\u003e.\n\u003ccode\u003eExperiment\u003c/code\u003e and \u003ccode\u003eTransferExperiment\u003c/code\u003e objects encapsulate everything that needs to be run for a certain experiment.\nAn experiment could be an individual training run and a transfer experiment could be multiple experiments that hand over data or parameters chained together.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDataset\u003c/h3\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA dataset loader is supposed to gather a specific dataset (including all corresponding test sets),\nand prepare all data transformations as well as corresponding data loaders.\nThe implementation can be found in the /dataset folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eModel\u003c/h3\u003e\u003ca id=\"user-content-model\" class=\"anchor\" aria-label=\"Permalink: Model\" href=\"#model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe model-building functions can be found in the /models folder.\nHere we offer default implementations (with some adjustments) of some standard vision models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTrainer\u003c/h3\u003e\u003ca id=\"user-content-trainer\" class=\"anchor\" aria-label=\"Permalink: Trainer\" href=\"#trainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to train the defined model, we use the trainer function, that can be found in the /trainer folder.\nIt is responsible for the whole training process including the batch iterations, loss computation, evaluation on the validation set and the logging of results per epoch and finally the final evaluation on the test sets.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMainloop-Modules\u003c/h3\u003e\u003ca id=\"user-content-mainloop-modules\" class=\"anchor\" aria-label=\"Permalink: Mainloop-Modules\" href=\"#mainloop-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo allow most flexible usage of the default trainer function, we introduce main-loop modules.\nThese modules can implement any of the following functions that will be called at their respective point in training:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epre_epoch\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epre_forward\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epost_forward\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epost_backward\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epost_optimizer\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epost_epoch\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese functions should allow most common interactions with the training process, like an additional training objective for example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRecipes\u003c/h2\u003e\u003ca id=\"user-content-recipes\" class=\"anchor\" aria-label=\"Permalink: Recipes\" href=\"#recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinally, to automatically execute a experiments (potentially in a distributed setting), simply define the concrete experiments in form of recipes, let our framework fill the corresponding tables and execute the training.\nA template for this can be found here: \u003ca href=\"https://github.com/sinzlab/nntransfer_recipes\"\u003ehttps://github.com/sinzlab/nntransfer_recipes\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 2, - "subscribers_count": 4, - "topics": [], - "updated_at": 1682735913.0 + "updated_at": 1709917745.0 }, { "data_format": 2, - "description": "Planning tool to run planners and domains creating singularity containers", + "description": null, "filenames": [ - "planners/OPTIC-Base/Singularity", - "planners/tfd/Singularity" + "misc/releases/19.06/Singularity.19.06", + "misc/releases/latest/Singularity", + "misc/releases/22.06/Singularity.22.06", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/20.06/Singularity.20.06" ], - "full_name": "momartinm/runPlanningTool", + "full_name": "silvansievers/structural-symmetries-pruning", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erunPlanningTool\u003c/h1\u003e\u003ca id=\"user-content-runplanningtool\" class=\"anchor\" aria-label=\"Permalink: runPlanningTool\" href=\"#runplanningtool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlanning tool to run planners and domains creating singularity containers. This tool is based on the code from Florian Pommerening. This tool needs to be configure to work into the correct way.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisites\u003c/h3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstalling basic tools:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install automake python-setuptools python-dev build-essential python-pip libtool libarchive-dev bison flex\nsudo pip install --upgrade virtualenv \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstalling singularity:\u003c/p\u003e\n\u003cp\u003eThis repo includes a version of singularity. Then it is not necessary to clone the master repo.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/singularityware/singularity.git\ncd singularity\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstalling virtualbox from repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install virtualbox\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstalling vagrant:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install vagrant\npip install python-vagrant\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstalling val (PDDL validator):\u003c/p\u003e\n\u003cp\u003eVal is a tool to validate the plans generate by a planner. A compile version is include to the repo at the main folder. But, I recomend to download and compile a new version.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd val\nmake\nmv validate ../\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreating benchmark files\u003c/h3\u003e\u003ca id=\"user-content-creating-benchmark-files\" class=\"anchor\" aria-label=\"Permalink: Creating benchmark files\" href=\"#creating-benchmark-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is necessary to configure the different benchmarks in order for them to be executed. A benchmark file must be created with the information of each instance using the same sintax:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# DomainID | folder | domain file | problem file | domain folder | problem folder | lb | up | b\n AGRICOLA , agricola , domain.pddl , p01.pddl , , , 0 , 0 , 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003elb: optimal plan cost lower bound\nup: optimal plan cost upper bound\nb: cost bound\u003c/p\u003e\n\u003cp\u003eWhere the first item is the key of the domain, all the instance of a domain must use the same key, the second is the name of the folder where the domains and instances are stored. The third is the name of domain file. The fourth is the name of the problem file. The fifth is the folder of the domain file. The sixth is the folder of the problem file. The last three are related with the cost of solving a specific instance (These three are not available in this first version). Comments can be includen into the file using the character \u0027#\u0027 at the begining of the line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreating planner files\u003c/h3\u003e\u003ca id=\"user-content-creating-planner-files\" class=\"anchor\" aria-label=\"Permalink: Creating planner files\" href=\"#creating-planner-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter this, it is necesary to define the different planners which are going to be used to solve the different benchmarks. A planner file must be created by the user in order to include the different planners following the next example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Planner ID | repo url | planner folder\n OPTIC-Base , , OPTIC-Base\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the first item is the name of the planner, the second is the url to the repository (GIT, BITBUCKET) where the planner is stored (this option is not available yet) and the third is the name of the folder where the source code of the planner is stored.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to use\u003c/h3\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinally the code can be executed using the python program called run_benchmarks.py. For example if we can run the full ipc 2018, we must use the same configuration\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e run_benchmarks.py -tipc2018 -pn OPTIC-Base\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere are different options to execute this software:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: run_benchmarks.py [-h] [-b file path] [-p file path] [-t] [-m] [-tmp] [-ipc2018]\n [-tipc2018] [-proc cpu numbers]\n [-pid Planner ID [Planner ID ...]]\n [-bid Benchmark ID [Benchmark ID ...]] [--v]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlanning tool to run planners and domains using singularity containers. These are the different arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help show this help message and exit.\n-b benchmarks domains a path to the file with the information about the different benchmarks.\n-p planners a path to the file with the information about the different planners which \n can be executed.\n-t a number parameter (integer) which defines the maximum execution time in seconds.\n-m a number parameter (integer) which defines the maximun RAM memory avaliable in Gigabytes.\n-tmp a boolean parameter which activate temporal validation.\n-ipc2018 a boolean parameter which run the benchmarks from the ipc 2018.\n-tipc2018 a boolean parameter which run the benchmarks from the temporal ipc 2018.\n-proc cpu-numbers a number parameter which defines the maximum number of cpus (threads). \n Default value is value is 20.\n-pid [Planner ID ...] a list parameter which defines the names of the planner which are going \n to be executed.\n-bid [Benchmark ID ...] a list parameter which defines the names of the benchmarks which are \n going to be used.\n--v verbosity increase output verbosity.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMore will be created as we continue adding more domains.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the MIT License - see the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file for details\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFlorian Pommerening\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, "subscribers_count": 2, "topics": [], - "updated_at": 1584297140.0 + "updated_at": 1651647698.0 }, { "data_format": 2, - "description": "Recipes for singularity and docker containers used in CBRAIN", + "description": "A modeling tool for CMUT non-linear dynamics and contact mechanics", "filenames": [ - "dcm2nii/Singularity.dcm2nii_v4AUGUST2014", - "QEEG/Singularity.qeeg.v1.0-gGit-S", - "ANTs/Singularity.ants_v2.1.0-gGIT-N", - "FreeSurfer/Singularity.FreeSurfer_v5.3", - "FSL/Singularity.fsl_v6.0.1", - "FSL/Singularity.fsl_v5.0.9" + "containers/Singularity.clean", + "containers/Singularity" ], - "full_name": "aces/cbrain-containers-recipes", + "full_name": "bdshieh/cnl-dyna", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecbrain-containers-recipes\u003c/h1\u003e\u003ca id=\"user-content-cbrain-containers-recipes\" class=\"anchor\" aria-label=\"Permalink: cbrain-containers-recipes\" href=\"#cbrain-containers-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRecipes for singularity and docker containers used in CBRAIN\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 9, + "subscribers_count": 0, "topics": [ - "singularity", - "docker", - "cbrain" + "finite-element-methods", + "boundary-element-method", + "ultrasound" ], - "updated_at": 1686284030.0 + "updated_at": 1636697475.0 }, { "data_format": 2, - "description": "Single cell Nextflow cellbender pipeline.", + "description": null, "filenames": [ - "env/Singularity.preprocessing" + "Singularity/Singularity.v1.0", + "Singularity/Singularity.v1.1" ], - "full_name": "wtsi-hgi/nf_cellbender", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDescription\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe methods used in this module are described in \u003ccode\u003edocs/methods.pdf\u003c/code\u003e. TODO: \u003ccode\u003edocs/methods.pdf\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBelow is the structure of the results directory. The values that will be listed in \u003ccode\u003edescription_of_params\u003c/code\u003e within the directory structure correspond to the various parameters one can set. An example of a paramters file is found in \u003ccode\u003eexample_runtime_setup/params.yml\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enf-qc_cluster\n\u251c\u2500\u2500 normalization_001::description_of_params\n\u2502 \u251c\u2500\u2500 [files: data]\n\u2502 \u251c\u2500\u2500 reduced_dims-pca::description_of_params\n\u2502 \u2502 \u251c\u2500\u2500 [files: data]\n\u2502 \u2502 \u251c\u2500\u2500 [plots: umap]\n\u2502 \u2502 \u251c\u2500\u2500 cluster_001::description_of_params\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 [files: data,clusters]\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 [plots: umap]\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 cluster_markers_001::description_of_params\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 [files: cluster_marker_genes]\n\u2502 \u2502 \u2502 \u2502 \u2514\u2500\u2500 [plots: marker_genes,marker_genes_dotplot]\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 cluster_markers_002::description_of_params\n\u2502 \u2502 \u2502 ... etc. ...\n\u2502 \u2502 \u251c\u2500\u2500 cluster_002::description_of_params\n\u2502 \u2502 ... etc. ...\n\u2502 \u251c\u2500\u2500 reduced_dims-harmony_001::description_of_params\n\u2502 \u251c\u2500\u2500 reduced_dims-harmony_002::description_of_params\n\u2502 ... etc. ...\n\u251c\u2500\u2500 normalization_002::description_of_norm_params\n... etc. ...\n\u2514\u2500\u2500 adata.h5 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e concatenated single cell data with no normalization\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTODO list\u003c/h1\u003e\u003ca id=\"user-content-todo-list\" class=\"anchor\" aria-label=\"Permalink: TODO list\" href=\"#todo-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAdd \u003ccode\u003edocs/methods.pdf\u003c/code\u003e file.\u003c/li\u003e\n\u003cli\u003eAdd brief description of module.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnhancement list\u003c/h1\u003e\u003ca id=\"user-content-enhancement-list\" class=\"anchor\" aria-label=\"Permalink: Enhancement list\" href=\"#enhancement-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003escanpy_merge-dev.py\u003c/code\u003e: If it were important to have a per sample filter, merge could be re-designed to accommodate this.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escanpy_cluster.py\u003c/code\u003e: Currently for clustering, we can change method (leiden or louvain), resolution, and n_pcs. Are there other parameters that need to be scaled over?\u003c/li\u003e\n\u003cli\u003eCheck phenotypes against predicted sex from gene expression.\u003c/li\u003e\n\u003cli\u003eAdd basic QC plots - try to do this in R from anndata frame?\u003c/li\u003e\n\u003cli\u003eScrublet functionality + add to metadata + cluster distributions\u003c/li\u003e\n\u003cli\u003eGene scores + add to metadata\u003c/li\u003e\n\u003cli\u003eAdd marker gene AUC like here \u003ca href=\"http://www.nxn.se/valent/2018/3/5/actionable-scrna-seq-clusters\" rel=\"nofollow\"\u003ehttp://www.nxn.se/valent/2018/3/5/actionable-scrna-seq-clusters\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd summary ARI and LISI metrics computed over a list of many different cluster annotations?\u003c/li\u003e\n\u003cli\u003eAdd tSNE plots - rapid plots with OpenTSNE?\u003c/li\u003e\n\u003cli\u003eCalculate marker genes with diffxpy or logreg?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuickstart\u003c/h1\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eQuickstart for deploying this pipeline locally and on a high performance compute cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Set up the environment\u003c/h2\u003e\u003ca id=\"user-content-1-set-up-the-environment\" class=\"anchor\" aria-label=\"Permalink: 1. Set up the environment\" href=\"#1-set-up-the-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the required packages via conda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The repo directory.\u003c/span\u003e\nREPO_MODULE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HOME}\u003c/span\u003e/repo/path/to/this/pipeline\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install environment using Conda.\u003c/span\u003e\nconda env create --name sc_qc_cluster --file \u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/env/environment.yml\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Activate the new Conda environment.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate sc_qc_cluster\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To update environment file:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003econda env export --no-builds | grep -v prefix | grep -v name \u0026gt; environment.yml\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Prepare the input files\u003c/h2\u003e\u003ca id=\"user-content-2-prepare-the-input-files\" class=\"anchor\" aria-label=\"Permalink: 2. Prepare the input files\" href=\"#2-prepare-the-input-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate and/or edit input files for the pipeline.\u003c/p\u003e\n\u003cp\u003eThe pipeline takes as input:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003e--file_paths_10x\u003c/strong\u003e: Tab-delimited file containing experiment_id and data_path_10x_format columns (i.e., list of input samples). Reqired.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--file_metadata\u003c/strong\u003e: Tab-delimited file containing sample metadata. This will automatically be subset down to the sample list from 1. Reqired.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--file_sample_qc\u003c/strong\u003e: YAML file containing sample qc and filtering parameters. Optional. NOTE: in the example config file, this is part of the YAML file for \u003ccode\u003e-params-file\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--genes_exclude_hvg\u003c/strong\u003e: Tab-delimited file with genes to exclude from\nhighly variable gene list. Must contain ensembl_gene_id column. Optional.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--genes_score\u003c/strong\u003e: Tab-delimited file with genes to use to score cells. Must contain ensembl_gene_id and score_idvcolumns. If one score_id == \"cell_cycle\", then requires a grouping_id column with \"G2/M\" and \"S\" (see example file in \u003ccode\u003eexample_runtime_setup\u003c/code\u003e). Optional.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e-params-file\u003c/strong\u003e: YAML file containing analysis parameters. Optional.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--run_multiplet\u003c/strong\u003e: Flag to run multiplet analysis. Optional.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--file_cellmetadata\u003c/strong\u003e: Tab-delimited file containing experiment_id and data_path_cellmetadata columns. For instance this file can be used to pass per cell doublet annotations. Optional.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExamples of all of these files can be found in \u003ccode\u003eexample_runtime_setup/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Set up and run Nextflow\u003c/h2\u003e\u003ca id=\"user-content-3-set-up-and-run-nextflow\" class=\"anchor\" aria-label=\"Permalink: 3. Set up and run Nextflow\" href=\"#3-set-up-and-run-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun Nexflow locally (NOTE: if running on a virtual machine you may need to set \u003ccode\u003eexport QT_QPA_PLATFORM=\"offscreen\"\u003c/code\u003e for scanpy as described \u003ca href=\"https://github.com/ipython/ipython/issues/10627\"\u003ehere\u003c/a\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Boot up tmux session.\u003c/span\u003e\ntmux new -s nf\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Here we are not going to filter any variable genes, so don\u0027t pass a file.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: All input file paths should be full paths.\u003c/span\u003e\nnextflow run \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/main.nf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -profile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003elocal\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_paths_10x \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/file_paths_10x.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_metadata \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/file_metadata.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --genes_score \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/genes_score_v001.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -params-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/params.yml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun Nextflow using LSF on a compute cluster. More on bgroups \u003ca href=\"https://www.ibm.com/support/knowledgecenter/SSETD4_9.1.3/lsf_config_ref/lsb.params.default_jobgroup.5.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set the results directory.\u003c/span\u003e\nRESULTS_DIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/results/dir\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${RESULTS_DIR}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Boot up tmux session.\u003c/span\u003e\ntmux new -s nf\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Log into an interactive session.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: Here we set the -G parameter due to our institute\u0027s LSF configuration.\u003c/span\u003e\nbgadd \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/logins\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nbsub -q normal -G team152 -g /\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/logins -Is -XF -M 8192 -R \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eselect[mem\u0026gt;8192] rusage[mem=8192]\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e /bin/bash\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: If you are running over many cells, you may need to start an\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e interactive session on a queue that allows long jobs\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003ebsub -q long -G team152 -g /${USER}/logins -Is -XF -M 18192 -R \"select[mem\u0026gt;18192] rusage[mem=18192]\" /bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Activate the Conda environment (inherited by subsequent jobs).\u003c/span\u003e\nconda activate sc_qc_cluster\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set up a group to submit jobs to (export a default -g parameter).\u003c/span\u003e\nbgadd -L 500 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/nf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LSB_DEFAULT_JOBGROUP=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/nf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Depending on LSF setup, you may want to export a default -G parameter.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LSB_DEFAULTGROUP=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eteam152\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: By setting the above flags, all of the nextflow LSF jobs will have\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e these flags set.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Settings for scanpy (see note above).\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e QT_QPA_PLATFORM=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eoffscreen\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Change to a temporary runtime dir on the node. In this demo, we will change\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to the same execution directory.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${RESULTS_DIR}\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Remove old logs and nextflow output (if one previously ran nextflow in this\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e dir).\u003c/span\u003e\nrm -r \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003ehtml\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nrm .nextflow.log\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: If you want to resume a previous workflow, add -resume to the flag.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: If you do not want to filter any variable genes, pass an empty file to\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --genes_exclude_hvg. See previous local example.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: --output_dir should be a full path - not relative.\u003c/span\u003e\nnextflow run \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/main.nf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -profile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003elsf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_paths_10x \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/file_paths_10x.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_metadata \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/file_metadata.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_sample_qc \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/params.yml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --genes_exclude_hvg \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/genes_remove_hvg_v001.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --genes_score \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/genes_score_v001.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --output_dir \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${RESULTS_DIR}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --run_multiplet \\\n -params-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/params.yml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -with-report \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enf_report.html\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -resume\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: If you would like to see the ongoing processes, look at the log files.\u003c/span\u003e\ncat .nextflow.log\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample of how one may sync results:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eNF_OUT_DIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/out_dir\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nrsync -am --include=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*.png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --include=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --exclude=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e my_cluster_ssh:\u003cspan class=\"pl-smi\"\u003e${NF_OUT_DIR}\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\nrsync -am --include=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*.png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --include=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --exclude=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e my_cluster_ssh:\u003cspan class=\"pl-smi\"\u003e${NF_OUT_DIR}\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNotes\u003c/h1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOn 10 April 2020, we found nextflow was writing some output into the \u003ccode\u003e${HOME}\u003c/code\u003e directory and had used up the alotted ~15Gb on the Sanger farm. This resulted in a Java error as soon as a nextflow command was executed. Based on file sizes within \u003ccode\u003e${HOME}\u003c/code\u003e, it seemed like the ouput was being written within the conda environment (following \u003ccode\u003edu -h | sort -V -k 1\u003c/code\u003e). By deleting and re-installing the coda environment, the problem was solved. The below flags may help prevent this from the future. In addition, setting the flag \u003ccode\u003eexport JAVA_OPTIONS=-Djava.io.tmpdir=/path/with/enough/space/\u003c/code\u003e may also help.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To be run from the execution dir, before the above nextflow command\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you are running this on a cluster, make sure you log into an interactive\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e session with \u0026gt;25Gb of RAM.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_OPTS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e-Xms25G -Xmx25G\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_HOME=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_WORK=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${NXF_HOME}\u003c/span\u003e/.nexflow_work\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_TEMP=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${NXF_HOME}\u003c/span\u003e/.nexflow_temp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_CONDA_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${NXF_HOME}\u003c/span\u003e/.nexflow_conda\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_SINGULARITY_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${NXF_HOME}\u003c/span\u003e/.nexflow_singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "IARCbioinfo/fastqc-nf", + "latest_release": "v1.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fastqc-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#fastqc-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efastqc-nf\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quality-control-of-raw-sequencing-reads\" class=\"anchor\" aria-hidden=\"true\" href=\"#quality-control-of-raw-sequencing-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality control of raw sequencing reads\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/fastqc-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d355ed64b381b5e3e497a32c3b032d9becd558aebd39a0da28073fbe613dfd81/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f6661737471632d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/fastqc-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/fastqc-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4559\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/IARCbioinfo/fastqc-nf/blob/master/fastqc-nf.png\"\u003e\u003cimg src=\"https://github.com/IARCbioinfo/fastqc-nf/raw/master/fastqc-nf.png\" alt=\"fastqc-nf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003ePerform quality control of Fasta files.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eFastQC: see official installation \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003cli\u003eMultiqc: see official installation \u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-bam-input-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#bam-input-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBAM input files\u003c/h3\u003e\n\u003cp\u003eIn order to process BAM files, we convert fastq files to bam files with:\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003ca href=\"http://samtools.sourceforge.net/\" rel=\"nofollow\"\u003e\u003cem\u003esamtools\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing FASTQ files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003ePath to output folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eExample value\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ext\u003c/td\u003e\n\u003ctd\u003efastq.gz\u003c/td\u003e\n\u003ctd\u003eExtension of files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--multiqc_config\u003c/td\u003e\n\u003ctd\u003enone\u003c/td\u003e\n\u003ctd\u003econfig yaml file for multiqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eNumber of cpu used by fastqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e10\u003c/td\u003e\n\u003ctd\u003eSize of memory used for mapping (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h3\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003enextflow run IARCbioinfo/fastqc-nf -r v1.1 -profile singularity --input_folder input --output_folder results\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run the pipeline with docker or conda instead of singularity, just replace \"-profile singularity\" with \"-profile docker\" or \"-profile conda\", respectively. To run with your own local installation of softwares, just remove \"-profile singularity\"\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003emultiqc_fastqc_report.html\u003c/td\u003e\n\u003ctd\u003emultiQC report for fastQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emultiqc_fastqc_report_data\u003c/td\u003e\n\u003ctd\u003edata used for the multiQC report HTMLs\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@fellows.iarc.fr\"\u003eAlcalaN@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [], - "updated_at": 1695377715.0 + "updated_at": 1658214757.0 }, { "data_format": 2, - "description": "Reinforcement Learning (RL) based Robot Navigation at Constrained Environment: team project for MEU5053.01-00", + "description": "AMP MGMs", "filenames": [ - "Singularityfile.def" + "tools/gentle/Singularity.recipe", + "tools/kaldi/Singularity.in" ], - "full_name": "msjun23/RL-based-Navigation", + "full_name": "AudiovisualMetadataPlatform/amp_mgms", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLearning based Robot Navigation System in Constrained Environments\u003c/h1\u003e\u003ca id=\"user-content-learning-based-robot-navigation-system-in-constrained-environments\" class=\"anchor\" aria-label=\"Permalink: Learning based Robot Navigation System in Constrained Environments\" href=\"#learning-based-robot-navigation-system-in-constrained-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project was worked on \u0027Machine Learning and Programming(MEU5053.01-00)\u0027 class project\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSummary\u003c/h1\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-label=\"Permalink: Summary\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eOur goal is to navigate a mobile robot to its destination without any collisions.\nAt ICRA 2024, \u003ca href=\"https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge24.html\" rel=\"nofollow\"\u003ethe BARN challenge\u003c/a\u003e has been performing navigation missions on the simulated BARN dataset.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://cs.gmu.edu/~xiao/papers/hallucination.pdf\" rel=\"nofollow\"\u003eLFH(Learning from Hallucination)\u003c/a\u003e is a benchmark model applied in constrained environments, there is a mismatch with real-world environments and limited generalization.\u003c/p\u003e\n\u003cp\u003e\u2192 Utilize RL models to generalize of the navigation model\u003c/p\u003e\n\u003cp\u003e\u2192 Utilize sampling techniques to create a training dataset that allows the robot to learn a variety of constrained environments.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSimulation results showed that our model outperformed existing models in navigating from a start to a goal location quickly and without collisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnvironment setup\u003c/h1\u003e\u003ca id=\"user-content-environment-setup\" class=\"anchor\" aria-label=\"Permalink: Environment setup\" href=\"#environment-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWe recommend using a conda environment.\u003c/li\u003e\n\u003cli\u003eTo train the model, \u003ca href=\"https://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e needs to be installed.\u003c/li\u003e\n\u003cli\u003eTo run inference (gazebo simulation), you need to install the \u003ca href=\"https://wiki.ros.org/melodic/Installation/Ubuntu\" rel=\"nofollow\"\u003eROS melodic\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create conda venv\u003c/span\u003e\nconda create -n jackal python=3.9\nconda activate jackal\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Python dependencies\u003c/span\u003e\npip3 install defusedxml rospkg netifaces numpy\npip3 install -r requirements.txt\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Get required packages\u003c/span\u003e\nmkdir -p jackal_ws/src\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e jackal_ws/src\n\ngit clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehttps://github.com/jackal/jackal.git\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --branch melodic-devel\ngit clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehttps://github.com/jackal/jackal_simulator.git\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --branch melodic-devel\ngit clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehttps://github.com/jackal/jackal_desktop.git\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --branch melodic-devel\ngit clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehttps://github.com/utexas-bwi/eband_local_planner.git\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e /home/dir/jackal_ws/\u003c/span\u003e\ncatkin_make\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTraining\u003c/h1\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To apply TD3 model training\u003c/span\u003e\npython3 train.py --config configs/e2e_default_TD3.yaml\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To apply SAC model training\u003c/span\u003e\npython3 train.py --config configs/e2e_default_SAC.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe simulation training was conducted on a system with the following specifications:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCPU: Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz\u003c/p\u003e\n\u003cp\u003eRAM: 128GB\u003c/p\u003e\n\u003cp\u003eGPU: RTX 3090 24GB\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eOur training logs can be found at \u003ca href=\"https://github.com/msjun23/RL-based-Navigation/tree/main/logging\"\u003erl-based-navigation/logging/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\u003cstrong\u003eRun Simulations\u003c/strong\u003e\u003c/h1\u003e\u003ca id=\"user-content-run-simulations\" class=\"anchor\" aria-label=\"Permalink: Run Simulations\" href=\"#run-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 run.py --world_idx 201\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to see the simulation, use --gui option when do the above command. In this step, gazebo simulation will be launched and should be connected to any display. (I.e., At the GPU server or docker envs it may not be done)\u003c/p\u003e\n\u003cp\u003eA successful run should print the episode status (collided/succeeded/timeout) and the time cost in second:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation collided with time 11.2460 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation succeeded with time 13.5330 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation timeout with time 100.0000 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eOnce the tests finish, run\u00a0\u003ccode\u003epython report_test.py --out_path /path/to/out/file\u003c/code\u003e\u00a0to report the test\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython report_test.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see the report as this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAvg Time: 7.60, Avg Success: 0.9567, Avg Collision: 0.0230, Avg Timeout: 0.0203\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExperiments Results\u003c/h1\u003e\u003ca id=\"user-content-experiments-results\" class=\"anchor\" aria-label=\"Permalink: Experiments Results\" href=\"#experiments-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSimulation tests were conducted in a ROS Gazebo environment using 50 different BARN datasets.\u003c/li\u003e\n\u003cli\u003eWe compared \u003ca href=\"https://github.com/Daffan/the-barn-challenge/tree/main\"\u003eDWA(Dynamic Window Approach)\u003c/a\u003e, \u003ca href=\"https://github.com/Daffan/the-barn-challenge/tree/eband\"\u003eE-Band(Elastic Band)\u003c/a\u003e, and \u003ca href=\"https://github.com/Daffan/the-barn-challenge/tree/LfH\"\u003eLFH(Learning from Hallucination)\u003c/a\u003e models\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eLearning\u003c/th\u003e\n\u003cth\u003eAvg Time[s] \u2193\u003c/th\u003e\n\u003cth\u003eAvg Success[%] \u2191\u003c/th\u003e\n\u003cth\u003eAvg Collision[%] \u2193\u003c/th\u003e\n\u003cth\u003eAvg Timeout[%] \u2193\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eDWA\u003c/td\u003e\n\u003ctd\u003e\u2a09\u003c/td\u003e\n\u003ctd\u003e31.03\u003c/td\u003e\n\u003ctd\u003e95.40\u003c/td\u003e\n\u003ctd\u003e3.00\u003c/td\u003e\n\u003ctd\u003e1.60\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEband\u003c/td\u003e\n\u003ctd\u003e\u2a09\u003c/td\u003e\n\u003ctd\u003e24.55\u003c/td\u003e\n\u003ctd\u003e88.6\u003c/td\u003e\n\u003ctd\u003e3.8380\u003c/td\u003e\n\u003ctd\u003e7.60\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLfH\u003c/td\u003e\n\u003ctd\u003e\u25cb\u003c/td\u003e\n\u003ctd\u003e11.75\u003c/td\u003e\n\u003ctd\u003e81.8\u003c/td\u003e\n\u003ctd\u003e15.28\u003c/td\u003e\n\u003ctd\u003e2.92\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOurs(SAC)\u003c/td\u003e\n\u003ctd\u003e\u25cb\u003c/td\u003e\n\u003ctd\u003e9.02\u003c/td\u003e\n\u003ctd\u003e86.67\u003c/td\u003e\n\u003ctd\u003e13.00\u003c/td\u003e\n\u003ctd\u003e0.33\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOurs(TD3)\u003c/td\u003e\n\u003ctd\u003e\u25cb\u003c/td\u003e\n\u003ctd\u003e7.60\u003c/td\u003e\n\u003ctd\u003e95.67\u003c/td\u003e\n\u003ctd\u003e2.30\u003c/td\u003e\n\u003ctd\u003e2.03\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMap Generation\u003c/h1\u003e\u003ca id=\"user-content-map-generation\" class=\"anchor\" aria-label=\"Permalink: Map Generation\" href=\"#map-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to generate Map, please go to \u003ca href=\"https://github.com/msjun23/RL-based-Navigation/tree/main/map_gen\"\u003elink\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQnA\u003c/h1\u003e\u003ca id=\"user-content-qna\" class=\"anchor\" aria-label=\"Permalink: QnA\" href=\"#qna\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the novelty of your approach compared to existing methods in terms of RL settings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur approach advances upon existing methods, such as the Learning from Hallucination (LfH) algorithm, which uses neural networks to simulate constrained environments for training. While LfH addresses safety and data insufficiency by generating hallucinated constraints, it lacks time optimality, limiting its real-world applicability. In contrast, our method employs reinforcement learning (RL) algorithms like TD3 and SAC, combined with advanced sampling techniques to create a diverse training dataset. This enables our model to generalize better and achieve long-term optimization by learning from a wide variety of scenarios, thereby improving its robustness and efficiency in real-world environments. For instance, our experiments show a 25% increase in navigation speed and a 15% reduction in collision rates compared to LfH.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIs your approach more akin to global path planning or local path planning?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur approach is more akin to local path planning. While global path planning typically involves determining an initial path from start to goal over a large area, our method focuses on real-time decision-making and adjustments to the path based on immediate sensor inputs and environmental changes. This allows the reinforcement learning model to continuously adapt the robot\u2019s trajectory to navigate around obstacles and respond to dynamic environments effectively. This is particularly beneficial in constrained environments where real-time adaptability and responsiveness are crucial.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy do you think RL works better than other methods, and to what extent?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReinforcement learning (RL) surpasses other methods because it allows the model to learn optimal navigation strategies through interaction with the environment, receiving continuous feedback to refine its behavior. Unlike traditional algorithms that may struggle with unforeseen changes or require extensive tuning, RL models can dynamically adapt to complex and changing environments. Our simulation results demonstrate that RL models, particularly TD3 and SAC, significantly outperform traditional approaches, such as LfH, in terms of navigation speed and success rate. This enhanced performance is attributed to the RL models\u0027 ability to optimize long-term goals and adjust to various constraints, thus making them more effective in real-world applications.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCan it work for moving obstacles?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrently, our approach does not explicitly consider moving obstacles in the training process. Our RL model is designed to generate collision-free paths in constrained environments, focusing on static obstacles. However, we have explored the challenge of navigating dynamic obstacles using the DynaBARN framework, which involves avoiding obstacles moving in a straight line without uncertainty. In this context, our model showed successful navigation in the easy and some intermediate levels but experienced collisions in most intermediate and difficult levels. The collisions at higher difficulty levels are likely due to the increased complexity and unpredictability of obstacle movements, which our current model isn\u0027t trained to handle. Based on these findings, we plan to refine our model to enhance its robustness in dynamic environments and improve its ability to navigate through more complex scenarios. We plan future improvements to include training the model with dynamic obstacle simulations to better anticipate and react to moving obstacles.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eHow can you randomize the RL training for that for robustness?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enhance robustness, we plan to introduce a wider range of variations during the RL training process. This includes randomizing the starting points and goals for the robot, as well as varying the number, size, and speed of obstacles. Additionally, we will introduce different obstacle movement patterns and randomize environmental features. By exposing the model to a diverse set of training scenarios, we aim to improve its ability to generalize to new and unseen environments. This comprehensive approach will help ensure that the RL model can handle a variety of real-world conditions more effectively, thus increasing its overall performance in dynamic and unpredictable environments.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-amp_mgms\" class=\"anchor\" aria-hidden=\"true\" href=\"#amp_mgms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eamp_mgms\u003c/h1\u003e\n\u003cp\u003eAMP MGMs\u003c/p\u003e\n\u003cp\u003eBuild all of the MGMs in one go.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h2\u003e\n\u003cp\u003eThis repo has several submodules, so check this out with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive \u0026lt;this_repo\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erequirements\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003esingularity\u003c/li\u003e\n\u003cli\u003epython 3.6+\u003c/li\u003e\n\u003cli\u003emake\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-process\" class=\"anchor\" aria-hidden=\"true\" href=\"#process\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcess\u003c/h3\u003e\n\u003cp\u003eTo build the MGMs and install them in a directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./amp_build.py \u0026lt;destination directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the MGMs as a distributable package:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./amp_build.py --package \u0026lt;package_directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run unit tests on the MGMs (command help):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd tests/\n./run_tests.py -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor ex, to run unit tests on the MGMs installed in galaxy (local suite, gentle suite, or some particular test names in local suite):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./run_tests.py ../../galaxy/tools/amp_mgms/ local.yaml\n./run_tests.py ../../galaxy/tools/amp_mgms/ gentle.yaml\n./run_tests.py ../../galaxy/tools/amp_mgms/ local.yaml \u0027Adjust Diarization Timestamps\u0027 \u0027Adjust Transcript Timestamps\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-current-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#current-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCurrent status\u003c/h2\u003e\n\u003cp\u003eThis is the first pass to clean up the MGMs that were used during the\npilot and get them ready for production use.\u003c/p\u003e\n\u003cp\u003eThe goal for this first phase is to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eget all of the MGM (and singularity) sources into a single repository\u003c/li\u003e\n\u003cli\u003ea unified build process which will build the MGMs with one command\u003c/li\u003e\n\u003cli\u003ethe scripts should parse their arguments using argparse rather than\nsys.argv[] -- note that the conversion is hacky and certainly not\nbest practice.\u003c/li\u003e\n\u003cli\u003emove source files and modules around so they use python namespaces rather\nthan implied search paths\u003c/li\u003e\n\u003cli\u003eproper logging, rather than ovewriting sys.stderr and sys.stdout. Logs are\nwritten to the logs directory that is a peer of the script (if the\ndirectory exists) and stderr (always)\u003c/li\u003e\n\u003cli\u003esome tools require the galaxy root_dir variable. Is this really needed?\nTurns out, that no, it isn\u0027t.\u003c/li\u003e\n\u003cli\u003eamp_mgm.ini is a peer of the scripts. A sample is in the repository\u003c/li\u003e\n\u003cli\u003etool_conf.xml is a galaxy configuration file that can be used to insert\nthis toolset into galaxy.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-future-cleanup--work\" class=\"anchor\" aria-hidden=\"true\" href=\"#future-cleanup--work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFuture cleanup / work\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ereduce the size of the .sif files by cleaning up any intermediate build\nfiles.\u003c/li\u003e\n\u003cli\u003euse the args namespace directly, rather than that hacky tuple assignment\u003c/li\u003e\n\u003cli\u003eequivalent to \"make clean\" Right now, you have to remove the .sif files\nand the kaldi/exp2.tar.gz file manually.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1719328556.0 + "updated_at": 1679271531.0 }, { "data_format": 2, - "description": "Reproducing the Hydroshare research paper using Docker", + "description": "A Singularity image definition file built on top of the Ubuntu 20.04 docker image with R, RStudio Server, and additional linux dependencies for common R packages installed.", "filenames": [ "Singularity" ], - "full_name": "Hydrocarpentry/project_pydocker", + "full_name": "j-andrews7/singularity-rstudio", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eproject_pydocker\u003c/h1\u003e\u003ca id=\"user-content-project_pydocker\" class=\"anchor\" aria-label=\"Permalink: project_pydocker\" href=\"#project_pydocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReproducing the Hydroshare research paper using Docker\u003c/h2\u003e\u003ca id=\"user-content-reproducing-the-hydroshare-research-paper-using-docker\" class=\"anchor\" aria-label=\"Permalink: Reproducing the Hydroshare research paper using Docker\" href=\"#reproducing-the-hydroshare-research-paper-using-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSource: Sadler, J. (2018). Data-driven street flood severity modeling in Norfolk, Virginia USA 2010-2016, HydroShare, \u003ca href=\"http://www.hydroshare.org/resource/9db60cf6c8394a0fa24777c8b9363a9b\" rel=\"nofollow\"\u003ehttp://www.hydroshare.org/resource/9db60cf6c8394a0fa24777c8b9363a9b\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e-- Scripts and data files are found in the following links:\nHydroshare website and Github repository.\n-- Following script files are needed for building the Docker image:\n\u003ccode\u003eprepare_flood_events_table.py; make_dly_obs_table_standalone.py; by_event_for_model.py; model_flood_counts_rf_ps_cln.r; plot_count_model_results.py; test.sh\u003c/code\u003e\n-- The following input data files are needed to be in a separate directory \u0027data\u0027 for building the Docker image:\n\u003ccode\u003eSTORM_data_flooded_streets_2010-2016.csv; hampt_rd_data.sqlite\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload the data \"Raw street flood report data from Norfolk,VA 2010-2016\" from the \u003ca href=\"https://github.com/Hydrocarpentry/reproduced_data/blob/master/STORM_data_flooded_streets_2010-2016.csv\"\u003eGitHub repository\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eDownload the data \"Hamton Roads Enfironmental Time Series Data\" from Hydroshare repository or from \u003ca href=\"https://osf.io/mr7jx/?action=download\" rel=\"nofollow\"\u003eOSF source\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e-- The Docker recipe file contains all the commands to use the above input script files and download input data, for building a container.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity RStudio Server\u003c/h1\u003e\n\u003cp\u003eThis repo contains a Singularity file that contains R 4.1 and RStudio 1.4.1717. It has several additional linux dependencies installed that are required for common bioinformatics packages (openssl, libproj, libbz2, etc). If you have others you\u0027d like added, feel free to open a PR (or make your own fork and add whatever you need).\u003c/p\u003e\n\u003cp\u003eThe Singularity image for this can be pulled via \u003ccode\u003esingularity pull library://j-andrews7/default/rstudio:4.1.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis was mostly configured to run on HPCs in interactive jobs where users likely don\u0027t have the appropriate permissions for RStudio server to work properly. This requires a number of bindings to be made to the image and a secure cookie file to be provided. The cookie file can be produced with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Only needs to be run once.\nmkdir -p \"$HOME/rstudio-tmp/tmp/rstudio-server\"\nuuidgen \u0026gt; \"$HOME/rstudio-tmp/tmp/rstudio-server/secure-cookie-key\"\nchmod 0600 \"$HOME/rstudio-tmp/tmp/rstudio-server/secure-cookie-key\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn general, you can launch a script similar to the following from within an interactive job on your respective HPC to get it running, and it will print the IP address and port the server is running on that you can pop into your browser:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/sh\n\nworkdir=${HOME}/rstudio-tmp\n\nmkdir -p -m 700 ${workdir}/run ${workdir}/tmp ${workdir}/var/lib/rstudio-server \ncat \u0026gt; ${workdir}/database.conf \u0026lt;\u0026lt;END\nprovider=sqlite\ndirectory=/var/lib/rstudio-server\nEND\n\n# Set R_LIBS_USER to a path specific to rocker/rstudio to avoid conflicts with\n# personal libraries from any R installation in the host environment\ncat \u0026gt; ${workdir}/rsession.sh \u0026lt;\u0026lt;END\n#!/bin/sh\nexport R_LIBS_USER=${HOME}/R/rstudio/4.1\nexec rsession \"\\${@}\"\nEND\n\nchmod +x ${workdir}/rsession.sh\n\nexport SINGULARITY_BIND=\"${workdir}/run:/run,${workdir}/tmp:/tmp,${workdir}/database.conf:/etc/rstudio/database.conf,${workdir}/rsession.sh:/etc/rstudio/rsession.sh,${workdir}/var/lib/rstudio-server:/var/lib/rstudio-server\"\n\n# Do not suspend idle sessions.\n# Alternative to setting session-timeout-minutes=0 in /etc/rstudio/rsession.conf\n# https://github.com/rstudio/rstudio/blob/v1.4.1106/src/cpp/server/ServerSessionManager.cpp#L126\nexport SINGULARITYENV_RSTUDIO_SESSION_TIMEOUT=0\n\n# Get unused socket per https://unix.stackexchange.com/a/132524\n# Tiny race condition between the python \u0026amp; singularity commands\nreadonly PORT=$(python -c \u0027import socket; s=socket.socket(); s.bind((\"\", 0)); print(s.getsockname()[1]); s.close()\u0027)\n# Get node IP address.\nreadonly ADD=$(nslookup `hostname` | grep -i address | awk -F\" \" \u0027{print $2}\u0027 | awk -F# \u0027{print $1}\u0027 | tail -n 1)\n\ncat 1\u0026gt;\u0026amp;2 \u0026lt;\u0026lt;END\n\"Running RStudio at $ADD:$PORT\"\nEND\n\nsingularity exec --cleanenv rstudio_4.1.0.sif \\\n rserver --www-port ${PORT} \\\n --rsession-path=/etc/rstudio/rsession.sh\n --secure-cookie-key-file ${workdir}/tmp/rstudio-server/secure-cookie-key\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis repo is distributed under the GNU-GPL3 license. See the LICENSE file for more details.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1588066914.0 + "updated_at": 1685739677.0 }, { "data_format": 2, - "description": "A Nextflow framework for finemap", + "description": "Think-Play-Hack: World Views", "filenames": [ - "Singularity.def" + "containers/python/Singularity", + "containers/r/Singularity" ], - "full_name": "ikmb/finemapping", + "full_name": "SouthernMethodistUniversity/think-play-hack", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003efinemapping\u003c/h1\u003e\u003ca id=\"user-content-finemapping\" class=\"anchor\" aria-label=\"Permalink: finemapping\" href=\"#finemapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA Nextflow framework for finemap\u003c/h2\u003e\u003ca id=\"user-content-a-nextflow-framework-for-finemap\" class=\"anchor\" aria-label=\"Permalink: A Nextflow framework for finemap\" href=\"#a-nextflow-framework-for-finemap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation:\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation:\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload this pipeline locally or on the medcluster.\u003cbr\u003e\nInstall finemap_v1.4_x86_64.tgz within the bin folder so finemap is located in bin/finemap_v1.4_x86_64/finemap_v1.4_x86_64.\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage:\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline needs 6 input files:\u003cbr\u003e\n\u003cstrong\u003eReference\u003c/strong\u003e: in bim, bed, fam (3 files with same basename)\u003cbr\u003e\n\u003cstrong\u003eLocus-file\u003c/strong\u003e: csv-file setting the boundaries of the finemap plots; columns: chunk,NSNP,chr,st,sp,PPA_3\u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003echunk: iterate your loci like 1,2,3...\u003c/li\u003e\n\u003cli\u003eNSNP: currently not in use, put in NA\u003c/li\u003e\n\u003cli\u003echr: chromosome, like 12, not chr12, and 23 instead of X\u003c/li\u003e\n\u003cli\u003est: coordinate where plotting starts\u003c/li\u003e\n\u003cli\u003esp: coordinate where plotting ends\u003c/li\u003e\n\u003cli\u003ePPA_3: currently not in use, put in 1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSNP-List\u003c/strong\u003e: file with 1 snp per row, all other SNPs are excluded drom plotting and finemapping\u003cbr\u003e\n\u003cstrong\u003eSUMSTAT-FILE\u003c/strong\u003e: file containing following columns:\u003cbr\u003e\nCHR\tBP\tSNP\tA1\tA2\tP\tOR\tBETA\tSE\tN\tCHISQ\tZ\tSOURCE\tFRQ_A_A1\tFRQ_U_A1\tINFO\u003cbr\u003e\nonly CHR, BP, SNP, A1, A2, P, BETA, SE, FRQ_U_A1 are relevant, the other columns can be filled with NA.\nCall pipeline with:\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile standard --locus /home/user/finepipe/example/locusfile.sample --snps /home/user/finepipe/example/snplist.sample --reference /home/user/finepipe/example/GerNorItaSpa.chr3 --sumstats /home/user/finepipe/example/sumstats.sample --nsum 15743 --nsignal 1 --method sss -resume \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory:\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory:\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e--locus\u003c/strong\u003e \"path/to/locus.file\"\u003cbr\u003e\n\u003cstrong\u003e--snps\u003c/strong\u003e \"path/to/snp.list\"\u003cbr\u003e\n\u003cstrong\u003e--reference\u003c/strong\u003e \"path/to/bimbedfam\" (no file extension)\u003cbr\u003e\n\u003cstrong\u003e--sumstats\u003c/strong\u003e \"path/to/sumstat.file\"\u003cbr\u003e\n\u003cstrong\u003e--nsum\u003c/strong\u003e N of studysize\u003cbr\u003e\n\u003cstrong\u003e--method\u003c/strong\u003e \"sss\" or \"cond\"\u003cbr\u003e\n\u003cstrong\u003e--nsignal\u003c/strong\u003e N of max signals\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIf you run it locally, download the locuszoom database and set it with:\u003cbr\u003e\n\u003cstrong\u003e--locuszoomdb\u003c/strong\u003e \"/path/to/locuszoom/data/database/locuszoom_hg38.db\"\u003cbr\u003e\nand set profile to local:\u003cbr\u003e\n\u003cstrong\u003e-profile\u003c/strong\u003e \"local\" or \"standard\"\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional:\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional:\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e--dprime\u003c/strong\u003e sets ld method from the default r\u00b2 to dprime\u003cbr\u003e\n\u003cstrong\u003e--output\u003c/strong\u003e \"/path/to/output\" if not set output is baseDir/Results\u003cbr\u003e\n\u003cstrong\u003e-resume\u003c/strong\u003e Continue a run with cached processes\u003cbr\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-think-play-hack-world-views\" class=\"anchor\" aria-hidden=\"true\" href=\"#think-play-hack-world-views\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThink-Play-Hack: World Views\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-preparatory-readings\" class=\"anchor\" aria-hidden=\"true\" href=\"#preparatory-readings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparatory Readings\u003c/h2\u003e\n\u003cp\u003eDr. Guldi has compiled a list of readings to get your creative juices flowing. You can find them \u003ca href=\"https://www.dropbox.com/sh/ru4dxh6rr6uqvfl/AADlPVWVEZ1BE4OcxPnZ0dpDa?dl=0\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conference-activities\" class=\"anchor\" aria-hidden=\"true\" href=\"#conference-activities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConference activities\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.google.com/document/d/1PdxiKuEQFj0KIQbHnvfthqG7gtBYNlN3QZavc5g1T9M/edit?usp=sharing\" rel=\"nofollow\"\u003eWednesday hike\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs.google.com/document/d/1xDC_5mEJOZvfcWAvARDaLFl68Y44cDRh7jZJjpfUVKM/edit?usp=sharing\" rel=\"nofollow\"\u003eThursday rafting trip\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese two activities are here because they happen to be more technically complex. There are other opportunities that are being informally discussed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-slack-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#slack-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://get.slack.help/hc/en-us/articles/212675257\" rel=\"nofollow\"\u003eSlack Instructions\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eYou likely recieved an invitation to our Slack channel. This will be a good way to communicate with people at the conference and ask the Data Team questions if you need to. If you did not get an invite, just ask and we can send you one.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-github-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#github-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://guides.github.com/activities/hello-world/\"\u003eGitHub Instructions\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThough not required to use this repository, having an account on GitHub is a good idea for any programmer. It provides you with a portfolio of projects you have worked on as well as a way to collaborate with other coders.\u003c/p\u003e\n\u003cp\u003eIt will also allow you to clone this repository as well as add issues and suggest changes for the data team.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware\u003c/h2\u003e\n\u003cp\u003eWe have ready-to-go software stacks for Python with Jupyter and R with RStudio.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-setup-for-personal-machines\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-setup-for-personal-machines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"docs/docker.md\"\u003eDocker Setup for Personal Machines\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eDocker is a tool that allows software to run on your computer without actually needing to install the full software. These instructions will guide you through setting up Docker and getting our image running on your personal machine.\u003c/p\u003e\n\u003cp\u003eWe have provided two images: one that runs R and RStudio and one that runs Python, Jupyter Notebooks and JupyterLab.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-maneframe-ii-m2\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-maneframe-ii-m2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"docs/m2.md\"\u003eUsing ManeFrame II (M2)\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://faculty.smu.edu/csc/documentation/about.html\" rel=\"nofollow\"\u003eManeFrame II (M2)\u003c/a\u003e is SMU\u0027s high performance computing (HPC) cluster. M2 features 11,000 cores, 60 NVIDIA V100 and P100 GPU accelerators, and 256 GB, 768 GB, and 1.5 TB memory configurations per node. Guest accounts on the cluster can be requested \u003ca href=\"https://smu.az1.qualtrics.com/jfe/form/SV_2i6o7BztWg52rK5\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-data-on-box\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-on-box\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://smu.box.com/s/lk8mqfbgjproqda5jmlbynnbjclvybar\" rel=\"nofollow\"\u003eData on Box\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eWe have provided access to all the data for the event on Box. Given the size, consider what you might want to work on prior to downloading it. Should you have trouble, we have flash drives and hard drives with the data stored locally as well.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reddit\" class=\"anchor\" aria-hidden=\"true\" href=\"#reddit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"docs/reddit.md\"\u003eReddit\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eWe have over 1 TB of reddit data available in a database. You can \u003ca href=\"docs/reddit.md\"\u003eget subsets of this data\u003c/a\u003e for analysis.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-think-prompts\" class=\"anchor\" aria-hidden=\"true\" href=\"#think-prompts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThink Prompts\u003c/h2\u003e\n\u003cp\u003eIn case you are having trouble getting started:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCan one imagine developing a method to trace narrative elements across genres? How would you formalize \"narrative element\"?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHow do we align narrative elements with aspects of cultural ideology (norms, beliefs, values)?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat aspects of storytelling can we map? To what end? (i.e. can you imagine a new historic-geographic methodology?)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat impact do popular films (e.g. Snow White and the 7 Dwarves) have on traditional tales (and vice versa)?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCan we discover/trace the impact of traditional stories on literary works such as The Hobbit? On films?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhere did you find that lovely frosted mug filled with such an alluringly amber-hewed frothy brew? Were there nuts as well?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [], - "updated_at": 1724382798.0 + "updated_at": 1566336477.0 }, { "data_format": 2, - "description": "Code to generate publication figures and tables from output of tb-amr-benchmarking", + "description": "Making docker images for NANOGrav analyses in the cloud", "filenames": [ - "Singularity" + "enterprise-singularity/Singularity" ], - "full_name": "iqbal-lab-org/tb-amr-benchmarking-paper", - "latest_release": "v0.0.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etb-amr-benchmarking-paper\u003c/h1\u003e\u003ca id=\"user-content-tb-amr-benchmarking-paper\" class=\"anchor\" aria-label=\"Permalink: tb-amr-benchmarking-paper\" href=\"#tb-amr-benchmarking-paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode to generate publication figures and tables from output of\n\u003ca href=\"https://github.com/iqbal-lab-org/tb-amr-benchmarking\"\u003etb-amr-benchmarking\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the singularity container by running this\nfrom the root of this repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build benchmark.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA JSON file made by the\n\u003ca href=\"https://github.com/iqbal-lab-org/tb-amr-benchmarking\"\u003etb-amr-benchmarking pipeline\u003c/a\u003e\nis required. Assuming it is called \u003ccode\u003esummary.json\u003c/code\u003e, generate the results and\nfigures by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec benchmark.img evalrescallers_make_figs_and_tables summary.json OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich makes a new directory called \u003ccode\u003eOUT/\u003c/code\u003e. Various stats were needed for the\nmain text of the paper, which were calculated by running this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd OUT\nsingularity exec benchmark.img gather_ad_hoc_numbers.sh\nsingularity exec benchmark.img estimate_xdr_accuracy.py\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "nanograv/nanodocker", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nanodocker\" class=\"anchor\" aria-hidden=\"true\" href=\"#nanodocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enanodocker\u003c/h1\u003e\n\u003cp\u003eA repository of Dockerfiles for NANOGrav DWG Docker images.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nanograv-stochastic-user\" class=\"anchor\" aria-hidden=\"true\" href=\"#nanograv-stochastic-user\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003enanograv-stochastic-user\u003c/code\u003e\u003c/h2\u003e\n\u003cp\u003eCurrently the \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e image (available as \u003ccode\u003emicvallis/nanograv-stochastic-user:v1.7\u003c/code\u003e at the \u003ca href=\"https://hub.docker.com/r/micvallis/nanograv-stochastic/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e) includes \u003ca href=\"https://bitbucket.org/psrsoft/tempo2\" rel=\"nofollow\"\u003etempo2\u003c/a\u003e and \u003ca href=\"https://github.com/vallis/libstempo\"\u003elibstempo\u003c/a\u003e with the latest ephemeris functionality, \u003ca href=\"https://github.com/jellis18/PAL2\"\u003ePAL2\u003c/a\u003e, \u003ca href=\"https://github.com/stevertaylor/NX01\"\u003eNX01\u003c/a\u003e, \u003ca href=\"https://github.com/vhaasteren/piccard\"\u003ePiccard\u003c/a\u003e, and attendant Python packages (installed through \u003ca href=\"http://conda.pydata.org/miniconda.html\" rel=\"nofollow\"\u003eMiniconda\u003c/a\u003e), including \u003ca href=\"https://github.com/jellis18/PTMCMCSampler\"\u003ePTMCMCSampler\u003c/a\u003e. The image was built on top of \u003ca href=\"https://hub.docker.com/_/gcc\" rel=\"nofollow\"\u003egcc:4.9\u003c/a\u003e, and it weighs 4.5GB (roughly half from gcc and half from Anaconda packages). The image is meant to be used by the passwordless user \u003ccode\u003enanograv\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-other-images-nanograv-stochastic\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-images-nanograv-stochastic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther images: \u003ccode\u003enanograv-stochastic\u003c/code\u003e\n\u003c/h2\u003e\n\u003cp\u003eSame as \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, but most everything is installed by the root user. Potentially useful on clusters. The latest version, lagging slightly behind \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, is \u003ccode\u003emicvallis/nanograv-stochastic:v2.4\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart-for-local-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart-for-local-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart for local use\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.docker.com/engine/installation\" rel=\"nofollow\"\u003eInstall Docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eStart Docker.\u003c/li\u003e\n\u003cli\u003ePull the repository and run jupyter notebook in a new container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull micvallis/nanograv-stochastic-user:v1.7\ndocker run -i -t -p 8888:8888 -u nanograv micvallis/nanograv-stochastic-user:v1.7 run_jupyter.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThen you can open a web browser at the address that appears on the screen, and gain access to a Jupyter notebook that can run the \u003ccode\u003elibstempo\u003c/code\u003e, \u003ccode\u003ePAL2\u003c/code\u003e, \u003ccode\u003eNX01\u003c/code\u003e, and \u003ccode\u003ePiccard\u003c/code\u003e demos.\u003c/li\u003e\n\u003cli\u003eIf you\u0027re using the older Docker Toolbox for Mac (and perhaps some versions on Windows), you need to point your browser to the IP address of the virtual machine, which you can see with \u003ccode\u003edocker-machine ip default\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAlso, if you\u0027re already using port 8888 locally, you should remap the Docker port elsewhere, e.g., with \u003ccode\u003e-p 8890:8888\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDon\u0027t forget to remove your containers (\u003ccode\u003edocker ps -a; docker rm ...\u003c/code\u003e) once you\u0027re done.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tips-and-tricks\" class=\"anchor\" aria-hidden=\"true\" href=\"#tips-and-tricks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTips and Tricks\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003erun a terminal in an already running container.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it [container_ID] bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can replace \u003ccode\u003ebash\u003c/code\u003e with any terminal command.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003emount a local directory in a container.\u003c/strong\u003e\nWhen you run a new container all files you create will be available to \u003cstrong\u003ethat\u003c/strong\u003e container.\nIf you start a second container or update a container all of your changes will be inaccessible.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run [the usual options] -v /my/local/dir:/home/nanograv/local_data/ run_jupyter.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYour local directory will appear in the home directory of the container as \u003ccode\u003elocal_data/\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003ereattach to a stopped container.\u003c/strong\u003e Use \u003ccode\u003edocker ps -a\u003c/code\u003e to see all containers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker start -a [container_ID] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start a stopped container (and see stdout)\u003c/span\u003e\ndocker attach [container_ID] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e attach this terminal to a container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can now navigate your browser to the displayed URL to reattach to the Jupyter notebook server.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003ecopy files to or from the local file system.\u003c/strong\u003e\nFrom a local terminal you can use \u003ccode\u003edocker cp [source] [dest]\u003c/code\u003e with the container ID.\n\u003ccode\u003edocker cp\u003c/code\u003e is recursive by default so it can copy full directories (unlike standard \u003ccode\u003ecp\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker cp /path/to/local/file [container_ID]:/path/in/container\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding notes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.imcce.fr/fr/presentation/equipes/ASD/inpop/calceph\" rel=\"nofollow\"\u003eCalceph\u003c/a\u003e 2.4.2 is installed from sources, into \u003ccode\u003e/usr/local\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eline_profiler\u003c/code\u003e is installed with \u003ccode\u003epip\u003c/code\u003e, to work around an Anaconda problem.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escikit-sparse\u003c/code\u003e 0.31 is installed from the \u003ccode\u003emenpo\u003c/code\u003e repository, after \u003ccode\u003eapt-get\u003c/code\u003e-installing \u003ccode\u003eliblapack3\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eless\u003c/code\u003e, \u003ccode\u003egawk\u003c/code\u003e, and \u003ccode\u003evim\u003c/code\u003e are installed with \u003ccode\u003eapt-get\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, we are now pulling a specific version of \u003ccode\u003elibstempo\u003c/code\u003e, \u003ccode\u003ePAL2\u003c/code\u003e, and \u003ccode\u003eNX01\u003c/code\u003e, identified by SHA.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, we are now downloading extra ephemeris files.\u003c/li\u003e\n\u003cli\u003eWith \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, we now support prerequisites for \u003ca href=\"https://github.com/nanograv/enterprise\"\u003eEnterprise\u003c/a\u003e development, but you will have to check out \u003ccode\u003eEnterprise\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 20, "topics": [], - "updated_at": 1575373625.0 + "updated_at": 1574698460.0 }, { "data_format": 2, - "description": "Singularity and Docker containers for LAMMPS", + "description": "Singularity containers for Cylc", "filenames": [ - "Singularity_lammps_serial.cfg", - "Singularity", - "Singularity_lammps_mpi.cfg" + "Singularity-cylc-7.8.1", + "Singularity-cylc-flow-8.0a1" ], - "full_name": "cbgeo-archives/lammps-container", + "full_name": "kinow/cylc-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker/Singularity Container image for LAMMPS\u003c/h1\u003e\u003ca id=\"user-content-dockersingularity-container-image-for-lammps\" class=\"anchor\" aria-label=\"Permalink: Docker/Singularity Container image for LAMMPS\" href=\"#dockersingularity-container-image-for-lammps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCB-Geo\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eMaster branch: Docker and Serial LAMMPS code\u003c/li\u003e\n\u003cli\u003eMPI branch: Parallel LAMMPS version\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cylc-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#cylc-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCylc Singularity Container\u003c/h1\u003e\n\u003cp\u003eExample \u003ca href=\"https://www.sylabs.io/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e container for \u003ca href=\"https://cylc.github.io/cylc/\" rel=\"nofollow\"\u003eCylc\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/kinow/cylc-docker\"\u003ethis repository\u003c/a\u003e for Docker images.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-creating-the-container-for-cylc-781\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-the-container-for-cylc-781\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating the container for Cylc 7.8.1\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build cylc-7.8.1.simg Singularity-cylc-7.8.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the command is done, you should have a binary called \u003ccode\u003ecylc-7.8.1.simg\u003c/code\u003e. You can\nthen execute Cylc with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./cylc-7.8.1.simg check-software\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr even register suites, and run Cylc as you would normally run it. You can rename it to \u003ccode\u003ecylc\u003c/code\u003e\nand store somewhere in your \u003ccode\u003e$PATH\u003c/code\u003e, without having to really install Cylc.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-accessing-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#accessing-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAccessing the container\u003c/h2\u003e\n\u003cp\u003eAssuming you have installed Singularity, try the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell cylc-7.8.1.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat should give you a shell within the container.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ - "container" + "singularity", + "cylc", + "docker", + "containers", + "workflow", + "scheduler", + "singularity-container", + "singularity-containers" ], - "updated_at": 1674481948.0 + "updated_at": 1577583586.0 }, { "data_format": 2, - "description": "build files for ubuntu", + "description": "Several basic templates that are meant to be used as the basis for other Singularity Spec files using nixpkgs", "filenames": [ - "Singularity" + "Singularity.nix_alpine_openmpi_6796a60398bb890002e7010593c14cf3731613a1", + "Singularity.nix_alpine_base_e51467b4ad06617b8b104f6c9066df915fb4dfbd", + "Singularity.nix_alpine_openmpi_743d51f9711fdb3f59b442641b8fa950e41128b9" ], - "full_name": "singularityhub/ubuntu", + "full_name": "federatedcloud/NixTemplates", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUbuntu\u003c/h1\u003e\u003ca id=\"user-content-ubuntu\" class=\"anchor\" aria-label=\"Permalink: Ubuntu\" href=\"#ubuntu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a library of Ubuntu builds for Singularity images hosted on Singularity Static Registry. The following standard applies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eeach \u003ccode\u003eSingularity\u003c/code\u003e file corresponds to a build\u003c/li\u003e\n\u003cli\u003etags are supported based on the extension of the Singularity file, with an extensionless file corresponding to \"latest\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can I find here?\u003c/h2\u003e\u003ca id=\"user-content-what-can-i-find-here\" class=\"anchor\" aria-label=\"Permalink: What can I find here?\" href=\"#what-can-i-find-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository here serves the container under the namespace \u003ccode\u003esingularityhub/ubuntu\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does this work?\u003c/h2\u003e\u003ca id=\"user-content-how-does-this-work\" class=\"anchor\" aria-label=\"Permalink: How does this work?\" href=\"#how-does-this-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will submit this container to the (organizational) registry at\n\u003ca href=\"https://www.github.com/singularityhub/registry-org\"\u003esingularityhub/registry-org\u003c/a\u003e\nfor a final container uri corresponding to \u003ccode\u003ehttps://singularityhub.github.io/registry-org/singularityhub/ubuntu\u003c/code\u003e. Specifically:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub/registry-org --) the organization registry\nsingularityhub/ubuntu --) a container collection\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethen on GitHub pages:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularityhub.github.io/registry-org --) the registry interface\nsingularityhub.github.io/registry-org/singularityhub/ubuntu --) the added container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis works by way of the repository here (via the continuous integration)\nbuilding the container, uploading it to storage, and then opening (or updating)\na branch on the registry repository. The registry repository uses a\n\u003ca href=\"https://www.github.com/vsoch/pull-request-action\"\u003eGitHub Action\u003c/a\u003e that will\nopen a pull request whenever a branch is pushed.\u003c/p\u003e\n\u003cp\u003eFor this particular example, the container namespace is a bit\nredundant, but if the GitHub username was different from the organization, you\ncan see how it would be important to maintain this namespace. I\u0027ve also thought\nabout having the main registry redirect to the GitHub pages of the container\u0027s Github\npages, but I don\u0027t want to introduce redirects yet.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nixtemplates\" class=\"anchor\" aria-hidden=\"true\" href=\"#nixtemplates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNixTemplates\u003c/h1\u003e\n\u003cp\u003eSeveral basic templates that are meant to be used as the basis for other Singularity Spec files\nusing the \u003ca href=\"https://nixos.org/nix/\" rel=\"nofollow\"\u003eNix\u003c/a\u003e package manager.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-background\" class=\"anchor\" aria-hidden=\"true\" href=\"#background\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nix\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNix\u003c/h3\u003e\n\u003cp\u003eNix provides reproducible builds for software, all the way down to the system level.\nThis is done by way of keeping track of which commit revision of\n\u003ca href=\"https://github.com/nixos/nixpkgs\"\u003enixpkgs\u003c/a\u003e was used at the time of the build.\nThe user can also pin versions of particular software dependencies by\ncoding them into the nix expression (think build script).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eSingularity is like a Docker container, but without process isolation\n(at least \u003ca href=\"https://www.sylabs.io/guides/2.5.1/user-guide/appendix.html?highlight=containall#singularity-action-flags\" rel=\"nofollow\"\u003eby default\u003c/a\u003e).\nSo it isn\u0027t a process container, but it is a filesystem container.\nUnlike Docker, Singularity provides a non-layered filesystem. This may\nhave benefits for reproducibility, but also means increased file size if\na user is building multiple image files based on other singularity images.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nix-and-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix-and-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNix and Singularity\u003c/h3\u003e\n\u003cp\u003eNix wouldn\u0027t work well with layering anyway: one benefit of nix is the nix-store,\nwhich is a local store on the system which puts all builds of software that\nare hashed based on the nix expression being used to build the software, and any\ninputs to that nix expression (so you can have multiple alternative builds of the\nsame software). A single Singularity image, that holds a custom nix expression,\nshould be ideal to build an individual image for a particular use case, or even\nmultiple use cases: multiple use cases can be packaged in a single Singularity\nimage and separated by using different nix expressions: they all share the same\nnix store, so when there are common dependencies, no file system redundancy occurs.\u003c/p\u003e\n\u003cp\u003eIn short, Nix provides build-level customization and reproducibility, which is important\nfor future work on the project to proceed smooth, whereas Singularity provides\nan archive of the existing build state, that is important for both immediate usage,\nand as a last resort for users who can\u0027t get the build procedure to work down the\nroad for some unforseen reason.\u003c/p\u003e\n\u003cp\u003eAn advantage of using Nix is that users can also update their environment in a\nreproducible way without needing to change a Dockerfile or Singularity Recipe\nand build a new image (which may be inconvenient for some\nusers): if the user changes the nix expression for a given environment,\nany additional packages or modified versions of packages that are already installed\nare added to the nix store (\u003ccode\u003e/nix/store\u003c/code\u003e) immediately, and the user can check in their\nnix expressions (\u003ccode\u003e.nix\u003c/code\u003e files) to version control as needed.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Images\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-switching-the-base-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#switching-the-base-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSwitching the base image\u003c/h2\u003e\n\u003cp\u003eSince nix is used for package management, we support\nmultiple base images: currently Ubuntu and Alpine variants.\nTo use presets for these, select what you want in the \u003ccode\u003ebuild.sh\u003c/code\u003e\nscripts, e.g., one of:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource \"alpine_envs.sh\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource \"ubuntu_envs.sh\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlpine is the default. You may also wish to create your own variant.\u003c/p\u003e\n\u003cp\u003eYou may need to make a separate copy or clone of the repo and checkout out the\n\u003ccode\u003ehash\u003c/code\u003e corresponding to the \u003ccode\u003ehash\u003c/code\u003e in \u003ccode\u003eFROM nix_${BASEOS}_base:hash\u003c/code\u003e in\n\u003ccode\u003eDocker/OpenMPI/Dockerfile\u003c/code\u003e, and build the base as specified in the next step,\nassuming you can\u0027t pull it from a Docker registry such as DockerHub.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nix_base\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix_base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enix_base\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-and-running\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-and-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding and Running\u003c/h4\u003e\n\u003cp\u003eMake sure to subsitute the appropriate image name in the second command\n(check your image list with \u003ccode\u003edocker images | head\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e build-base.sh\ndocker run -i -t nix_alpine_base:abbaed5833f75be43892ccfc5999bd8f03f9583b_testing /bin/sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eChoose one of the alternatives below (running from Singularity Hub or Building and Running).\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-running-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning from Singularity Hub\u003c/h4\u003e\n\u003cp\u003eFirst visit the \u003ca href=\"https://www.singularity-hub.org/collections/1220\" rel=\"nofollow\"\u003ecollection\u003c/a\u003e associated\nwith this repository on Singularity Hub. You\u0027ll notice that the Tag (Branch) may be truncated\ndue to the fact that we use the full commit hash. To see the full hash, click on the \"Complete\"\nbutton under the \"Status\" column for a recent base image, e.g., an image starting with\n\u003ccode\u003enix_alpine_base_\u003c/code\u003e under the \"Tag (Branch)\" column.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity image.create nix-overlay.img\nsingularity run --contain --overlay nix-overlay.img shub://federatedcloud/NixTemplates:nix_alpine_base_82b5d9a742ad593a353f6160bce846227a0f4e4d\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-and-running-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-and-running-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding And Running\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erm nix\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003ebase\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.img\n./build-base-singularity.sh\nsingularity image.create nix-base-overlay.img\nsingularity run --contain --overlay nix-base-overlay.img nix_alpine_base_82b5d9a742ad593a353f6160bce846227a0f4e4d.img\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e If you rebuild the image, you will likely need to either delete or move the old\nimage to another location, unless the git commit has change, in which case the image filename\nchanges automatically.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant note:\u003c/strong\u003e If you update a given singularity image, you will also\nlikely need to create a new overlay image to go along with it, otherwise you\nrisk undefined behavior.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-testing-nix\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-nix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting Nix\u003c/h3\u003e\n\u003cp\u003eOnce you have build an image and started a container as above, you can test it out by installing\nyour favorite tool (for instance ripgrep\u0027s \u003ccode\u003erg\u003c/code\u003e command) into your environment using Nix:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enix-env -i ripgrep\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nix_openmpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix_openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enix_openmpi\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eSimple build\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e build-openmpi.sh \u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-testing-openmpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting OpenMPI\u003c/h4\u003e\n\u003cp\u003eNote this will call the above OpenMPI \u003ccode\u003ebuild-base.sh\u003c/code\u003e, so no need to do both:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e docker-compose-openmpi.sh up --scale mpi_head=1 --scale mpi_node=3\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow from another terminal on the host system: 1) connect to the head node,\n2) start the relevant environment with \u003ccode\u003enix-shell\u003c/code\u003e, and 3) run the mpi demo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker exec -u nixuser -it nixtemplates_mpi_head_1 /bin/sh\nnix-shell . # should be from /nixenv/nixuser, or wherever default.nix was copied to\nmpirun -n 2 python /home/nixuser/mpi4py_benchmarks/all_tests.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo stop the container set, just press \u003ccode\u003eCtrl-C\u003c/code\u003e in the terminal where you ran\n\u003ccode\u003edocker-compose-openmpi.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eChoose one of the alternatives below (running from Singularity Hub or Building and Running).\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-running-from-singularity-hub-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-from-singularity-hub-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning from Singularity Hub\u003c/h4\u003e\n\u003cp\u003eSee instructions \u003ca href=\"#nix_base\"\u003eabove\u003c/a\u003e for how to use singularity hub in general with this repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity image.create -s 4096 nix-overlay.img\nsingularity run --contain --overlay nix-overlay.img shub://federatedcloud/NixTemplates:nix_alpine_openmpi_6796a60398bb890002e7010593c14cf3731613a1\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-and-running-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-and-running-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding And Running\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erm \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.img\n./build-openmpi-singularity.sh\nsingularity image.create -s 4096 nix-overlay.img\nsingularity run --contain --overlay nix-overlay.img nix_alpine_openmpi_6796a60398bb890002e7010593c14cf3731613a1.img\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-testing-openmpi-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-openmpi-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting OpenMPI\u003c/h4\u003e\n\u003cp\u003eYou will be dropped into a nix-shell, which in this template, sets up python and releveant libraries\nsuch as mpi4py.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003empirun -n 2 python /nixenv/nixuser/mpi4py_benchmarks/all_tests.py\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 10, "topics": [ - "registry-org", - "singularityhub" + "docker", + "singularity", + "nix", + "containerization" ], - "updated_at": 1609044092.0 + "updated_at": 1650310770.0 }, { "data_format": 2, - "description": "Hybrid-FS: A planner for controlling hybrid systems specified in Functional STRIPS", + "description": "Singularity definition file for an EasyBuild container", "filenames": [ "Singularity" ], - "full_name": "miquelramirez/hybrid-fs", + "full_name": "GodloveD/EasyBuild", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe FS Functional STRIPS planner\u003c/h1\u003e\u003ca id=\"user-content-the-fs-functional-strips-planner\" class=\"anchor\" aria-label=\"Permalink: The FS Functional STRIPS planner\" href=\"#the-fs-functional-strips-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eFS\u003c/code\u003e is a classical planner that works with the Functional STRIPS planning language \u003ca href=\"#ref-geffner-fstrips-2000\"\u003e[Geffner, 2000]\u003c/a\u003e,\na modeling language based on the quantifier-free\nfragment of first-order logic that includes constant, function and predicate symbols, but no variable symbols. The increased expressiveness\nof the Functional STRIPS language with respect to propositional languages such as standard STRIPS (which is indeed subsumed by Functional STRIPS)\noften results in problem encodings which are more compact, more readable, have fewer ground actions\nand preserve the structural properties of the problem in a manner which allows the derivation of more effective heuristics.\u003c/p\u003e\n\u003cp\u003eAlong with the core of the Functional STRIPS language, the \u003ccode\u003eFS\u003c/code\u003e planner supports certain extensions which are useful both\nfrom the expressive \u003cem\u003eand\u003c/em\u003e the computational point of view. These include \u003cem\u003eexistential quantification\u003c/em\u003e,\n\u003cem\u003estate constraints\u003c/em\u003e, a fairly large library of \u003cem\u003eglobal constraints\u003c/em\u003e, and the possibility of using \u003cem\u003eexternally-defined symbols\u003c/em\u003e\nand \u003cem\u003ebuilt-in arithmetic symbols\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThis documentation covers a number of practical issues related to the use of the \u003ccode\u003eFS\u003c/code\u003e planner. The planner, however, has\nbeen used and described in a number of academic publications that \u003ca href=\"http://gfrances.github.io/pubs/\" rel=\"nofollow\"\u003ecan be found here\u003c/a\u003e,\nthe most recent of which are \u003ca href=\"#ref-frances-modeling-2015\"\u003e[Franc\u00e8s and Geffner, 2015]\u003c/a\u003e and \u003ca href=\"#ref-frances-existential-2016\"\u003e[Franc\u00e8s and Geffner, 2016a]\u003c/a\u003e\nand \u003ca href=\"#ref-frances-effective-2016\"\u003e[Franc\u00e8s and Geffner, 2016b]\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to use the planner is by \u003ca href=\"doc/installation.md\"\u003emanually compiling the planner source code\u003c/a\u003e.\nThis procedure is complemented by the \u003ca href=\"doc/hybrid.md\"\u003einstructions specific for setting up the hybrid module\u003c/a\u003e of \u003ccode\u003eFS\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can find a high-level overview of the planner usage options \u003ca href=\"doc/usage.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-credits\"\u003e\u003c/a\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eFS\u003c/code\u003e planner is partially built upon the \u003ca href=\"http://www.lapkt.org\" rel=\"nofollow\"\u003eLightweight Automated Planning Toolkit\u003c/a\u003e\nand the PDDL parser from the \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003eFast Downward\u003c/a\u003e distribution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-modeling-2015\"\u003eFranc\u00e8s, G., and Geffner, H. (2015)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2015-icaps-better-heuristics-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eModeling and Computation in Planning: Better Heuristics from More Expressive Languages\u003c/em\u003e\u003c/a\u003e, ICAPS 2015.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-existential-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016a)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-existential-quantification-planning-csp/\" rel=\"nofollow\"\u003e\u003cem\u003eE-STRIPS: Existential Quantification in Planning and Constraint Satisfaction\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-effective-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016b)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-effective-planning-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eEffective Planning with More Expressive Languages\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-geffner-fstrips-2000\"\u003eGeffner, H. (2000)\u003c/a\u003e,\n\u003ca href=\"http://www.tecn.upf.es/~hgeffner/\" rel=\"nofollow\"\u003e\u003cem\u003eFunctional STRIPS: A more flexible lan-\nguage for planning and problem solving\u003c/em\u003e\u003c/a\u003e.\nIn Minker, J., ed., Logic-Based Artificial Intelligence. Kluwer. 187\u2013205.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-easybuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#easybuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEasyBuild\u003c/h1\u003e\n\u003cp\u003eAn experimental approach to creating \u003ca href=\"https://github.com/singularityware/singularity\"\u003eSingularity\u003c/a\u003e containers using \u003ca href=\"https://github.com/easybuilders/easybuild-easyconfigs\"\u003eEasyBuild easyconfig files\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eJust make a new directory, copy an easyconfig file into that directory, copy the \u003ca href=\"/build.def\"\u003ebuild.def\u003c/a\u003e file to the same directory, cd to that location and finally \u003ccode\u003ecreate\u003c/code\u003e and \u003ccode\u003ebootstrap\u003c/code\u003e a Singularity container there. The contents of your easyconfig file will be executed inside the container to build and install your app within the Singularity image.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, - "topics": [ - "cplusplus-14", - "ai", - "planning", - "hybrid-systems", - "kinodynamic-planning" - ], - "updated_at": 1570694552.0 + "subscribers_count": 5, + "topics": [], + "updated_at": 1509102509.0 }, { "data_format": 2, - "description": "Guppy is a data processing toolkit that contains the Oxford Nanopore Technologies\u2019 basecalling algorithms.", + "description": null, "filenames": [ - "6.0.0/Singularity" + "container/Singularity" ], - "full_name": "pscedu/singularity-guppy-gpu", + "full_name": "Clinical-Genomics-Lund/nextflow_microwgs", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-guppy/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-guppy/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-guppy/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-guppy/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/acd271ee64be7e03cce0aa20980a61a37f2a803c6c21475e4f5e2e750f91b847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/acd271ee64be7e03cce0aa20980a61a37f2a803c6c21475e4f5e2e750f91b847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ce3bd0aacdb7b3b72cb6bf39ce61e5bb1cc29a5784ded8bb4f63d60ba37d4e96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce3bd0aacdb7b3b72cb6bf39ce61e5bb1cc29a5784ded8bb4f63d60ba37d4e96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b6e3ded696588df6d0e3326ff98a3915f0822c93b919f7af672f1bbb878a4fac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6e3ded696588df6d0e3326ff98a3915f0822c93b919f7af672f1bbb878a4fac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3fe9defc0e02f64e7c2aa7b7bb20b84f9a2e6f0239ee4e213edd1a550157e8c0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3fe9defc0e02f64e7c2aa7b7bb20b84f9a2e6f0239ee4e213edd1a550157e8c0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6775707079\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-guppy\u003c/h1\u003e\u003ca id=\"user-content-singularity-guppy\" class=\"anchor\" aria-label=\"Permalink: singularity-guppy\" href=\"#singularity-guppy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://community.nanoporetech.com/protocols/Guppy-protocol/v/gpb_2003_v1_revac_14dec2018/linux-guppy\" rel=\"nofollow\"\u003eguppy\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/guppy/6.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/guppy\u003c/code\u003e as \u003ccode\u003e6.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nextflow-pipeline-for-typing-and-marker-detection-of-bacteria\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-for-typing-and-marker-detection-of-bacteria\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextflow pipeline for typing and marker detection of bacteria\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-hidden=\"true\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePurpose\u003c/h2\u003e\n\u003cp\u003eThe pipeline is aimed at producing data useful for epidemiological and surveillance purposes.\nIn v1 the pipeline is only tested using MRSA, but it should work well with\nany bacteria having a good cgMLST scheme.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-components\" class=\"anchor\" aria-hidden=\"true\" href=\"#components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComponents\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-qc\" class=\"anchor\" aria-hidden=\"true\" href=\"#qc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQC\u003c/h3\u003e\n\u003cp\u003eSpecies detection is performed using \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e together with \u003ca href=\"https://ccb.jhu.edu/software/bracken/\" rel=\"nofollow\"\u003eBracken\u003c/a\u003e.\nThe database used is a standard Kraken database built with \u003ccode\u003ekraken2-build --standard --db $DBNAME\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eLow levels of Intra-species contamination or erronous mapping is removed using bwa and filtering away\nthe heterozygous mapped bases.\u003c/p\u003e\n\u003cp\u003eGenome coverage is estimated by mapping with \u003ca href=\"https://github.com/lh3/bwa\"\u003ebwa mem\u003c/a\u003e and using a bed file containing the cgMLST loci.\u003c/p\u003e\n\u003cp\u003eA value on the evenness of coverage is calculated as an \u003ca href=\"https://en.wikipedia.org/wiki/Interquartile_range\" rel=\"nofollow\"\u003einterquartile range\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-epidemiological-typing\" class=\"anchor\" aria-hidden=\"true\" href=\"#epidemiological-typing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEpidemiological typing\u003c/h3\u003e\n\u003cp\u003eFor de novo asspembly \u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003eSPAdes\u003c/a\u003e is used. \u003ca href=\"http://cab.spbu.ru/software/quast/\" rel=\"nofollow\"\u003eQUAST\u003c/a\u003e\nis used for extraxting QC data from the assembly.\u003c/p\u003e\n\u003cp\u003eThe cgMLST reference scheme used, is branched off \u003ca href=\"https://www.cgmlst.org/ncs/schema/141106/\" rel=\"nofollow\"\u003ecgmlst.net\u003c/a\u003e\nAt the moment this fork is not synced back with new allele numbers. For extracting alleles \u003ca href=\"https://github.com/B-UMMI/chewBBACA/wiki\"\u003echewBBACA\u003c/a\u003e\nis used. Number of missing loci is calculated and used as a QC parameter.\u003c/p\u003e\n\u003cp\u003eTraditional 7-locus MLST is calculated using \u003ca href=\"https://github.com/tseemann/mlst\"\u003emlst\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-virulence-and-resistance-markers\" class=\"anchor\" aria-hidden=\"true\" href=\"#virulence-and-resistance-markers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVirulence and resistance markers\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sanger-pathogens/ariba\"\u003eARIBA\u003c/a\u003e is used as the tool to detect genetic markes.\nThe database for virulence markes is \u003ca href=\"http://www.mgc.ac.cn/VFs/\" rel=\"nofollow\"\u003eVFDB\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-report-and-visualisation\" class=\"anchor\" aria-hidden=\"true\" href=\"#report-and-visualisation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReport and visualisation\u003c/h2\u003e\n\u003cp\u003eThe QC data is aggregated in a web service CDM (repo coming) and the cgMLST is visualized using a web service\ncgviz that is combined with \u003ca href=\"https://github.com/achtman-lab/GrapeTree\"\u003egraptetree\u003c/a\u003e for manipulating trees (repo coming).\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 4, - "topics": [ - "singularity", - "bioinformatics" - ], - "updated_at": 1712153143.0 + "topics": [], + "updated_at": 1645445879.0 }, { "data_format": 2, - "description": "Singularity recipes", + "description": "GeoEDF Processors", "filenames": [ - "Singularity.sex_classifier_v0.1", - "Singularity.scRNAseq", - "Singularity.cytofpipe", - "Singularity.env", - "Singularity.sex_classifier_v0.2", - "Singularity.cytofpipe_v2_1" + "investmodel/Singularity", + "simplegtool/Singularity" ], - "full_name": "lconde-ucl/singularity_recipes", + "full_name": "geoedf/processors", "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/geoedf/processors/workflows/buildplugins/badge.svg\"\u003e\u003cimg src=\"https://github.com/geoedf/processors/workflows/buildplugins/badge.svg\" alt=\"buildplugins\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-geoedf-processor-plugins\" class=\"anchor\" aria-hidden=\"true\" href=\"#geoedf-processor-plugins\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeoEDF Processor Plugins\u003c/h1\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1694722164.0 + "updated_at": 1673879646.0 }, { "data_format": 2, - "description": "Singularity container for RStudio-Server.", + "description": null, "filenames": [ - "Singularity.1.1.456" + "Singularity" ], - "full_name": "mcw-rcc/rstudio-server", + "full_name": "LiSAT-Planning/LiSAT", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRStudio Server\u003c/h1\u003e\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: RStudio Server\" href=\"#rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1268\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container running RStudio Server.\u003c/p\u003e\n\u003cp\u003eExample job script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#PBS -N rserver\n#PBS -l nodes=1:ppn=1\n#PBS -l mem=5gb\n#PBS -l walltime=1:00:00\n#PBS -j oe\n\n# tunnel info\nPORT=$(shuf -i8000-9999 -n1)\nSUBMIT_HOST=$(echo ${PBS_O_HOST%%.*}.rcc.mcw.edu)\n\n# rserver password\nexport RSTUDIO_PASSWORD=$(openssl rand -base64 15)\n\n# print tunneling instructions\necho -e \"\n1. SSH tunnel from your workstation using the following command:\n \n ssh -L 8787:${HOSTNAME}:${PORT} ${USER}@${SUBMIT_HOST}\n \n and point your web browser to http://localhost:8787.\n\n2. Log in to RStudio Server using the following credentials:\n user: ${USER}\n password: ${RSTUDIO_PASSWORD}\n\"\n\n# load modules\nmodule load rstudio-server/1.1.456\n\n#start server\nrstudio-server\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStart an RStudio session\u003c/h2\u003e\u003ca id=\"user-content-start-an-rstudio-session\" class=\"anchor\" aria-label=\"Permalink: Start an RStudio session\" href=\"#start-an-rstudio-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCopy contents of rserver.pbs (example above) to a file in your home directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOpen terminal on cluster login node and submit the job script:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ qsub rserver.pbs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConnect to RStudio session\u003c/h2\u003e\u003ca id=\"user-content-connect-to-rstudio-session\" class=\"anchor\" aria-label=\"Permalink: Connect to RStudio session\" href=\"#connect-to-rstudio-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCheck output file (\u003cem\u003ejobname\u003c/em\u003e.o\u003cem\u003eJOBNUM\u003c/em\u003e) for details.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExample output file: rserver.o152922\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. SSH tunnel from your workstation using the following command:\n\n ssh -L 8787:node01:9268 tester@loginnode\n\n and point your web browser to http://localhost:8787.\n\n2. Log in to RStudio Server using the following credentials:\n user: ${USER}\n password: ${RSTUDIO_PASSWORD}\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOpen second terminal and run tunneling command from the output file:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh -L 8787:node01:9268 tester@loginnode\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOpen a browser and enter the URL from the output file:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ehttp://localhost:8787\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eLog in with credentials from output file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou should now be connected to your RStudio session that is running on a cluster compute node. To close the session, select logout or stop session. If you need to reconnect, repeat steps 3 \u0026amp; 4. If you\u0027re done with your session, remember to stop the job with qdel.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-powerlifted-planner\" class=\"anchor\" aria-hidden=\"true\" href=\"#powerlifted-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePowerlifted Planner\u003c/h1\u003e\n\u003cp\u003ePowerlifted is a domain-independent classical planner that uses only lifted\nrepresentations.\u003c/p\u003e\n\u003cp\u003e(See \u003ca href=\"#references\"\u003eReferences\u003c/a\u003e for more details.)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h2\u003e\n\u003cp\u003ePowerlifted is build using the \u003ccode\u003ebuild.py\u003c/code\u003e script.\u003c/p\u003e\n\u003cp\u003eThis version of powerlifted can either be build with or without support for SAT-based planning.\nTo build with SAT support, you need to call the script with the argument \u003ccode\u003e-s\u003c/code\u003e followed by the path to the directory that contains your sat solver\u0027s library.\nYou need to build the SAT solver before building powerlifted and you need to build the SAT solver s.t. a library is produced.\nSome SAT solvers don\u0027t do this by default!\u003c/p\u003e\n\u003cp\u003eCurrently, powerlifted uses the SAT solver \u003ca href=\"https://github.com/arminbiere/kissat\"\u003ekissat\u003c/a\u003e by default.\nIt can be configured to use \u003ca href=\"https://github.com/msoos/cryptominisat\"\u003ecryptominisat\u003c/a\u003e by providing the argument \u003ccode\u003e-i\u003c/code\u003e to \u003ccode\u003ebuild.py\u003c/code\u003e.\nInstead of cryptominisat, any SAT solving offering the \u003ca href=\"https://github.com/biotomas/ipasir\"\u003eIPASIR\u003c/a\u003e interface can be used.\nIf so desired, in src/search/CMakeLists.txt, the reference to \u003ccode\u003eipasircryptominisat5\u003c/code\u003e must be changed appropriately.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003epowerlifted.py\u003c/code\u003e script solves a PDDL task provided as input. It also builds\nthe planner if the \u003ccode\u003e--build\u003c/code\u003e parameter is passed. The script has the following\nparameters:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py [-d DOMAIN] -i INSTANCE -s SEARCH -e HEURISTIC -g GENERATOR [--state STATE REPR.] [ADDITIONAL OPTIONS] [--seed RANDOM SEED] [-l PLANLENGH] [-o] [-I] [--build]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eUse the \u003ccode\u003ebuild.py\u003c/code\u003e script to build the planner first.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-search\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-search\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eSEARCH\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ebfs\u003c/code\u003e: Breadth-First Search (This option was previously called \u003ccode\u003enaive\u003c/code\u003e. You\ncan still use \u003ccode\u003enaive\u003c/code\u003e with the \u003ccode\u003epowerlifted.py\u003c/code\u003e script but the planner will internally\nuse the new keyword \u003ccode\u003ebfs\u003c/code\u003e.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egbfs\u003c/code\u003e: Greedy Best-First Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy\u003c/code\u003e: Lazy Best-First Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy-po\u003c/code\u003e: Lazy Best-First Search with Boosted Dual-Queue\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy-prune\u003c/code\u003e: Lazy Best-First Search with pruning of states generated by\nnon-preferred operators\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esat\u003c/code\u003e: Search via reduction to SAT. If chosed the options \u003ccode\u003e-l\u003c/code\u003e, \u003ccode\u003e-o\u003c/code\u003e, and \u003ccode\u003e-I\u003c/code\u003e become available.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-heuristic\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-heuristic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eHEURISTIC\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eadd\u003c/code\u003e: The additive heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eblind\u003c/code\u003e: No Heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egoalcount\u003c/code\u003e: The goal-count/STRIPS heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ehmax\u003c/code\u003e: The hmax heuristic (Note that A* search is not implemented)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-generator\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-generator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eGENERATOR\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ejoin\u003c/code\u003e: Join program using the predicate order given in the PDDL file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erandom_join\u003c/code\u003e: Randomly ordered join program\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eordered_join\u003c/code\u003e: Join program ordered by the arity of the predicates\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efull_reducer\u003c/code\u003e: Generate successor for acyclic schemas using the full\nreducer method; for cyclic schemas it uses a partial reducer and a join\nprogram.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eyannakakis\u003c/code\u003e: Same as above but replaces the final join of the full\nreducer method by the Yannakakis\u0027 project-join program.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-state-repr\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-state-repr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eSTATE REPR.\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esparse\u003c/code\u003e: Use the sparse state representation where a state is only\nrepresented by the facts that are true in this state.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eextensional\u003c/code\u003e: Use the extensional representation where a state is a bitset\nwhere the ith-bit is true if the fact associated to it is true in this\nstate. This representation requires the grounding of facts (but not of\nactions) which, right now, is performed in the search component.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-avaialble-options-for-planlengh\" class=\"anchor\" aria-hidden=\"true\" href=\"#avaialble-options-for-planlengh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvaialble Options for \u003ccode\u003ePLANLENGH\u003c/code\u003e:\u003c/h3\u003e\n\u003cp\u003eA plan lenght may only be provided if SAT-based planning is chosen.\u003c/p\u003e\n\u003cp\u003eIf the planner is run in satisficing mode, it will attempt to solve the given problem \u003cstrong\u003eonly\u003c/strong\u003e for this plan length.\nIf \u003ccode\u003ePLANLENGH\u003c/code\u003e is not set, it is defaulted to 100.\u003c/p\u003e\n\u003cp\u003eIf the planner is run in optimal mode, it will stop searching for a solution if \u003ccode\u003ePLANLENGH\u003c/code\u003e is reached. I.e. it will only find a solution if there is one with length at most \u003ccode\u003ePLANLENGH\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-flag--o\" class=\"anchor\" aria-hidden=\"true\" href=\"#flag--o\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlag \u003ccode\u003e-o\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eBy default, powerlifted runs in \u003cstrong\u003esatisficing\u003c/strong\u003e mode.\nIn this mode, it will try to find a plan up to \u003ccode\u003ePLANLENGH\u003c/code\u003e.\nTo obtain a complete satisficing planner, you need to run powerlifted for \u003cstrong\u003emultiple\u003c/strong\u003e values of \u003ccode\u003ePLANLENGH\u003c/code\u003es as if there was a portfolio. We recommend to use \u003ccode\u003e10,25,50,100,200\u003c/code\u003e with assigning all runs equal time.\nNote that powerlifted can as of now, not run such a portfolio.\nThis is due to difficulties is setting timelimits to SAT-solver calls correctly.\u003c/p\u003e\n\u003cp\u003eIf the flag \u003ccode\u003e-o\u003c/code\u003e is provided, the powerlifted runs in optimal mode.\nIt will iterate over the length of the plan and return the shortest (in terms of number of actions) solution.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-flag--i\" class=\"anchor\" aria-hidden=\"true\" href=\"#flag--i\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlag \u003ccode\u003e-I\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eThis flag sets the SAT planner to incremental mode. This does not affect the output of the planner, but may inpact its performance.\u003c/p\u003e\n\u003cp\u003eIncremental mode is only supported if the SAT solver that powerlifted was build with supports incremental solving.\nNote that \u003ccode\u003ekissat\u003c/code\u003e does not support incremental solving.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-additional-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-additional-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable \u003ccode\u003eADDITIONAL OPTIONS\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e[--translator-output-file TRANSLATOR_FILE]\u003c/code\u003e: Output of the intermediate representation to be parsed by the search component will be saved into \u003ccode\u003eTRANSLATOR_FILE\u003c/code\u003e. (Default: \u003ccode\u003eoutput.lifted\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--datalog-file DATALOG_FILE]\u003c/code\u003e: Datalog program used by the h-add heuristic will be saved into \u003ccode\u003eDATALOG_FILE\u003c/code\u003e. (Default: \u003ccode\u003emodel.lp\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--keep-action-predicates]\u003c/code\u003e: Keeps action predicates in the Datalog program\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--keep-duplicated-rules]\u003c/code\u003e: Keep duplicated Datalog rules in the Datalog program.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--add-inequalities]\u003c/code\u003e: Compile inequalities into an EDB predicate in the Datalog program and replace \u003ccode\u003e(not (= ?x ?y))\u003c/code\u003e atoms with this new EDB predicate in actions.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--validate]\u003c/code\u003e: Runs VAL after a plan is found to validate it. This requires\n\u003ca href=\"https://github.com/KCL-Planning/VAL\"\u003eVAL\u003c/a\u003e to be added as \u003ccode\u003evalidate\u003c/code\u003e to the \u003ccode\u003ePATH\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-powerlifted-as-a-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-powerlifted-as-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Powerlifted as a Singularity container\u003c/h2\u003e\n\u003cp\u003eYou can also build a Singularity image to run the planner. This might be useful\nin the case where you are not able to compile the planner locally, for\nexample. To do so, first remove the \u003ccode\u003ebuilds/\u003c/code\u003e directory, in case you have any\nbuilds already in your system. Then, you can run the following command to create\nthe planner image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e sudo singularity build powerlifted.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBe aware that this might take a while. Once the image \u003ccode\u003epowerlifted.sif\u003c/code\u003e is\ncreated, you can run it with the same parameters as the \u003ccode\u003epowerlifted.py\u003c/code\u003e\nscript. The only exception is that, by default, VAL is not installed in the\ncontainer, so it is not possible to use the \u003ccode\u003e--validate\u003c/code\u003e flag with the\nSingularity image. However, you can run VAL with the \u003ccode\u003esas_plan\u003c/code\u003e file created by\nthe planner after the execution. The following command is a usage example on\nhow to run the planner with the Singularity image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./powerlifted.sif -i /path/to/instance.pddl -s lazy-po -e add -g yannakakis --datalog-file model.lp --translator-output-file output.lifted\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-components\" class=\"anchor\" aria-hidden=\"true\" href=\"#components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComponents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTranslator\u003c/li\u003e\n\u003cli\u003eSearch component\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eA C++17-compliant compiler\u003c/li\u003e\n\u003cli\u003eCMake 3.9+\u003c/li\u003e\n\u003cli\u003ePython 3.5+\u003c/li\u003e\n\u003cli\u003eBoost\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-limitations\" class=\"anchor\" aria-hidden=\"true\" href=\"#limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLimitations\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAxioms\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConditional effects\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCosts\u003c/strong\u003e: ignored\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNegated preconditions\u003c/strong\u003e: only inequality\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQuantifiers\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Pommerening, F.; Helmert, M.; and Franc\u00e8s, G. 2020. Lifted Successor Generation using Query Optimization Techniques. In Proc. ICAPS 2020, pp. 80-89. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-et-al-icaps2020.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Pommerening, F.; Helmert, M.; and Franc\u00e8s, G. 2020. Code from the paper \"Lifted Successor Generationusing Query Optimization Techniques\". \u003ca href=\"https://doi.org/10.5281/zenodo.3687008\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.3687008\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Franc\u00e8s, G.; Pommerening, F.; and Helmert, M. 2021. Delete-Relaxation Heuristics for Lifted Classical Planning. In Proc. ICAPS 2021. (To appear)\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, - "topics": [ - "rstudio-server", - "singularity-container" - ], - "updated_at": 1674040676.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1679670069.0 }, { "data_format": 2, - "description": "HPC example for BiocParallel", + "description": null, "filenames": [ - "Singularity" + "containers/Singularity.make_prg_dependencies", + "containers/Singularity.subsample" ], - "full_name": "nturaga/biocparallel-example", + "full_name": "mbhall88/pandora_analysis_pipeline", "latest_release": null, "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1560862119.0 + "updated_at": 1604591459.0 }, { "data_format": 2, - "description": "usher + taxonium", + "description": null, "filenames": [ - "Singularity.def" + "Singularity.centos7.tbx-MG" ], - "full_name": "martinghunt/ushonium", + "full_name": "ResearchIT/MolecularGraphicsToolbox", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eushonium\u003c/h1\u003e\u003ca id=\"user-content-ushonium\" class=\"anchor\" aria-label=\"Permalink: ushonium\" href=\"#ushonium\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eusher + taxonium on Covid sequences.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild container with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build ushonium.img Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container has a script called \u003ccode\u003eushonium\u003c/code\u003e, which makes a taxonium\n\u003ccode\u003ejsonl.gz\u003c/code\u003e file from fasta consensus sequences.\u003c/p\u003e\n\u003cp\u003eIt uses mafft to align all sequences to the Covid reference (ignoring\nindels, so all the same length), makes an optimized tree with usher, and\nthen uses taxoniumtools to make the taxonium jsonl file.\u003c/p\u003e\n\u003cp\u003eThe filenames of consensus sequences need to be in a tab-delimited file\nwith no headings and two columns:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eName of sample\u003c/li\u003e\n\u003cli\u003eFASTA file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAssuming that TSV file is called \u003ccode\u003esamples.tsv\u003c/code\u003e, the usage is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eushonium samples.tsv outdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere the output directory \u003ccode\u003eoutdir\u003c/code\u003e will be created. The final taxonium file\nis called \u003ccode\u003e05.taxonium.jsonl.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUse \u0026gt;1 CPU\u003c/h3\u003e\u003ca id=\"user-content-use-1-cpu\" class=\"anchor\" aria-label=\"Permalink: Use \u0026gt;1 CPU\" href=\"#use-1-cpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMost of the time is spent making the MSA, which by default uses 1 cpu.\nRun in \u003ccode\u003eN\u003c/code\u003e in parallel using the option\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--cpus N\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will hugely speed up the script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMetadata\u003c/h3\u003e\u003ca id=\"user-content-metadata\" class=\"anchor\" aria-label=\"Permalink: Metadata\" href=\"#metadata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can set the title that will appear in the taxonium browser with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--title \"My awesome tree\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also use a file of metadata for each sample (that has eg lineage,\ncountry etc). This needs to be tab-delimited with the first line having\ncolumn headers. One column must have the\nname of the sample, and must exactly match the name given in \u003ccode\u003esamples.tsv\u003c/code\u003e.\nBy default, this column is assumed to have the name \u003ccode\u003estrain\u003c/code\u003e, but you\ncan change it to eg \u003ccode\u003emy_names\u003c/code\u003e with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--metacol_name my_names\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf the metadata file is called \u003ccode\u003emetadata.tsv\u003c/code\u003e, and we want columns\n\u003ccode\u003ecol11\u003c/code\u003e and \u003ccode\u003ecol2\u003c/code\u003e, then use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--metadata_tsv metadata.tsv --metacols col1,col2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReference genome\u003c/h3\u003e\u003ca id=\"user-content-reference-genome\" class=\"anchor\" aria-label=\"Permalink: Reference genome\" href=\"#reference-genome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe script \u003ccode\u003eushonium\u003c/code\u003e was set up for covid,\nusing the recommended genbank reference from taxoniumtools.\nThis genbank file is included in the container, so there is\nno need to specify the reference genome when running \u003ccode\u003eushonium\u003c/code\u003e unless\nyou want to use a different reference. The option to change it to \u003ccode\u003emy_ref.gb\u003c/code\u003e is\n\u003ccode\u003e--ref_gb my_ref.gb\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFrom the taxoniumtools documentation: \"Right now Taxoniumtools is limited in\nthe types of genome annotations it can support, for SARS-CoV-2 we recommend\nusing the exact modified .gb file we use in the example, which splits ORF1ab\ninto ORF1a and ORF1b to avoid the need to model ribosome slippage.\"\nSee \u003ca href=\"https://docs.taxonium.org/en/latest/taxoniumtools.html\" rel=\"nofollow\"\u003ehttps://docs.taxonium.org/en/latest/taxoniumtools.html\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1720703818.0 + "updated_at": 1582812783.0 }, { "data_format": 2, - "description": "Let\u0027s reinterpret lousy things and make them awesome :sparkles:", + "description": null, "filenames": [ - "Singularity" + "config/Singularity", + "scripts/unused/Singularity", + "scripts/unused/Singularity_newhybrids" ], - "full_name": "mmore500/reinterpretive-label", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ereinterpretive-label\u003c/h1\u003e\u003ca id=\"user-content-reinterpretive-label\" class=\"anchor\" aria-label=\"Permalink: reinterpretive-label\" href=\"#reinterpretive-label\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnything can become art by the addition of a sufficiently clever interpretive label, even really lousy things.\nSo, let\u0027s reinterpret lousy things and make them awesome by adding interpretive label stickers!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"example.png\"\u003e\u003cimg src=\"example.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains software designed to help you design and print your own reinterpretive label stickers.\u003c/p\u003e\n\u003cp\u003eThe interpretive labels you create can respond to whatever you want.\nDon\u0027t feel the need to restrict yourself to just one thing or another.\nThere\u0027s \u003ca href=\"https://twitter.com/Malboury/status/968163458679263238/\" rel=\"nofollow\"\u003ea lot of ways to make the world a better place!\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRULE #1:\u003c/h2\u003e\u003ca id=\"user-content-rule-1\" class=\"anchor\" aria-label=\"Permalink: RULE #1:\" href=\"#rule-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ebe civil.\u003c/p\u003e\n\u003cp\u003eWe want to make public spaces more --- not less -- pleasant.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlso,\u003c/h2\u003e\u003ca id=\"user-content-also\" class=\"anchor\" aria-label=\"Permalink: Also,\" href=\"#also\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eunderstand relevant legal restrictions in your area and do not violate them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need to have a \u003ca href=\"https://en.wikipedia.org/wiki/Unix_shell\" rel=\"nofollow\"\u003eUnix shell\u003c/a\u003e you can run things in.\u003c/p\u003e\n\u003cp\u003eYou\u0027ll also need to have software called \u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e that facilitates packaged Linux workflows.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGrab a copy of the repo using \u003ca href=\"https://git-scm.com/\" rel=\"nofollow\"\u003egit\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/mmore500/reinterpretive-label\ncd reinterpretive-label\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake a copy of the template reinterpretive label Latex file in order to customize it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp tex/template.tex instance.tex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, you can open \u003ccode\u003einstance.tex\u003c/code\u003e with your favorite text editor and change the text.\nFeel free to re-use as much or as little of the template (including the text) as you desire.\u003c/p\u003e\n\u003cp\u003eYou shouldn\u0027t really need to know much Latex at all in order to successfully make minor edits.\nThat said, if you\u0027re unfamiliar you can find some helpful hints in the \"Latex Pointers\" section below.\u003c/p\u003e\n\u003cp\u003eWhen you\u0027re satisfied with your reinterpretive label (\u003ccode\u003einstance.tex\u003c/code\u003e), here\u0027s how to generate a PDF.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run shub://mmore500/reinterpretive-label\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will download the packaged linux workflow for you and do the actual work of compiling \u003ccode\u003einstance.tex\u003c/code\u003e to \u003ccode\u003einstance.pdf\u003c/code\u003e.\nBe sure that your \u003ccode\u003einstance.tex\u003c/code\u003e file is saved in the current working directory with exactly that name.\u003c/p\u003e\n\u003cp\u003eIf you want to fiddle with your reinterpretive label some more (i.e., make futher edits to \u003ccode\u003einstance.tex\u003c/code\u003e) and then recompile \u003ccode\u003einstance.pdf\u003c/code\u003e, waiting for the singularity workflow image to download again can be annoying.\nYou can get around this by using the cached \u003ccode\u003e.simg\u003c/code\u003e file generated when you run from SingularityHub (\u003ccode\u003eshub\u003c/code\u003e) the first time.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run mmore500-reinterpretive-label-master-latest.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, to clean up, use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake clean\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOne you have a PDF that you like, getting your stickers printed is a snap.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrinting Stickers\u003c/h2\u003e\u003ca id=\"user-content-printing-stickers\" class=\"anchor\" aria-label=\"Permalink: Printing Stickers\" href=\"#printing-stickers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can print stickers wherever you want.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.makestickers.com/\" rel=\"nofollow\"\u003eMakeStickers\u003c/a\u003e, though, seems convenient to me because it allows for\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePDF upload\u003c/li\u003e\n\u003cli\u003ecustom aspect ratios of \u003ca href=\"https://www.makestickers.com/products/custom-stickers/rectangle-stickers\" rel=\"nofollow\"\u003erectanglular stickers\u003c/a\u003e (the height to width of the generated PDF label depends on the amount of content), and\u003c/li\u003e\n\u003cli\u003esmall batch-size when printing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBe Social!\u003c/h2\u003e\u003ca id=\"user-content-be-social\" class=\"anchor\" aria-label=\"Permalink: Be Social!\" href=\"#be-social\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eShare what you\u0027re up to with the hashtag \u003ca href=\"https://twitter.com/hashtag/reinterpretivelabel\" rel=\"nofollow\"\u003e#reinterpretivelabel\u003c/a\u003e and\\or tweet at me \u003ca href=\"https://twitter.com/MorenoMatthewA\" rel=\"nofollow\"\u003e@MorenoMatthewA\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBonus points for including the twitter handle of your local modern art museum in your stickers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you come up with something devastatingly clever you think others might benefit from using as a springboard, add it to the \u003ccode\u003etex\u003c/code\u003e directory and put in a pull request (or just email me the \u003ccode\u003e.tex\u003c/code\u003e file you wrote up if you don\u0027t know what that means).\nAlso, send along PDF copies of your creations so we can start a gallery of examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInquiries\u003c/h2\u003e\u003ca id=\"user-content-inquiries\" class=\"anchor\" aria-label=\"Permalink: Inquiries\" href=\"#inquiries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re having any issues figuring out how to generate a sticker --- or even just don\u0027t have the computational means to do it yourself --- get in touch.\nI might be willing to fund a few sticker print jobs, too, if you\u0027re unable.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematthew.andres.moreno@gmail.com\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLatex Pointers\u003c/h2\u003e\u003ca id=\"user-content-latex-pointers\" class=\"anchor\" aria-label=\"Permalink: Latex Pointers\" href=\"#latex-pointers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo edit a \u003ccode\u003e.tex\u003c/code\u003e file, use a \u003ca href=\"https://en.wikipedia.org/wiki/Text_editor\" rel=\"nofollow\"\u003eplain text editor\u003c/a\u003e, not a rich text editor like Microsoft Word.\u003c/p\u003e\n\u003cp\u003eThe actual text goes in between \u003ccode\u003e\\begin{document}\u003c/code\u003e and \u003ccode\u003e\\end{document}\u003c/code\u003e.\nDon\u0027t make any edits outside these markers!\u003c/p\u003e\n\u003cp\u003eQuotation marks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e`` \u0027\u0027 YES\n\" NO\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEm dashes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--- YES\n- NO\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eText size:\nwrap the text you want to resize inside curly braces and tell what effect you want with \u003ccode\u003e\\sizename\u003c/code\u003e.\n``\n{\\huge BIG TEXT}\n{\\huge small text}\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\nAvailable size descriptors include: `\\Huge`, `\\huge`, `\\LARGE`, `\\Large`, `\\large`, `\\small`, `\\footnotesize`, `\\scriptsize`, and `\\tiny`.\n\nText styling:\nI defined two text modifiers for you: `\\myboldfont` and `\\myitalicfont`.\nJust like with text size, wrap the text you want to modify inside curly braces and tell what modifier you want.\n``\n{\\myboldfont BOLD TEXT}\n{\\myitalicfont italic text}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhitespace:\nto create a paragraph break, simply have an empty line between two lines of text.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eI\u0027m in the top paragraph!\nI\u0027m in the top paragraph, too.\n\nI\u0027m in the bottom paragraph.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse \u003ccode\u003e\\smallskip\u003c/code\u003e, \u003ccode\u003e\\medskip\u003c/code\u003e, and \u003ccode\u003e\\largeskip\u003c/code\u003e to space out your paragraphs.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eI\u0027m in the top paragraph!\nI\u0027m in the top paragraph, too.\n\n\\largeskip\n\nI\u0027m in the bottom paragraph and further away now.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSpecial characters:\nthe following characters might confuse the Latex compiler and cause an error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # $ % \u0026amp; ~ _ ^ { }\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can get around this by placing a \u003ccode\u003e\\\u003c/code\u003e in front.\nFor example, type \u003ccode\u003e\\#\u003c/code\u003e instead of just \u003ccode\u003e#\u003c/code\u003e and \u003ccode\u003e\\$\u003c/code\u003e instead of just \u003ccode\u003e$\u003c/code\u003e.\u003c/p\u003e\n", + "full_name": "nealplatt/sH_hybridization", + "latest_release": "v1.0", + "readme": "\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/124456755\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4641d504e79e577f2add43b190e60f3910a1688ac8f26f972d799fd6f3f4b213/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3132343435363735352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/124456755.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/ambv/black\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d91ed7ac7abbd5a6102cbe988dd8e9ac21bde0a73d97be7603b891ad08ce3479/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e737667\" alt=\"Code style: black\" data-canonical-src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-ancient-hybridization-and-adaptive-introgression-of-an-invadolysin-gene-in-schistosoma-haematobium\" class=\"anchor\" aria-hidden=\"true\" href=\"#ancient-hybridization-and-adaptive-introgression-of-an-invadolysin-gene-in-schistosoma-haematobium\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAncient hybridization and adaptive introgression of an invadolysin gene in \u003cem\u003eSchistosoma haematobium\u003c/em\u003e.\u003c/h1\u003e\n\u003cp\u003eRoy N. Platt II, Marina McDew-White, Winka Le Clec\u0027h, Frederic D. Chevalier, Fiona Allan, Aidan M. Emery, Amadou Garba, Shaali M. Ame, Joanne P. Webster, David Rollinson, Bonnie L. Webster, Timothy J. C. Anderson.\u003c/p\u003e\n\u003cp\u003eThe parasitic blood fluke \u003cem\u003eSchistosoma\u003c/em\u003e \u003cem\u003ehaematobium\u003c/em\u003e causes urogenital schistosomiasis in humans and is a major cause of morbidity and mortality across sub-Saharan Africa. \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ehaematobium\u003c/em\u003e can hybridize with closely-related livestock schistosomes, including \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ebovis\u003c/em\u003e, however the frequency, direction, age and genomic consequences of hybridization in nature are unknown. We sequenced 96 \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ehaematobium\u003c/em\u003e exomes from Niger and the Zanzibar archipelago. We found evidence of an ancient, adaptive introgression event between Nigerien \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ehaematobium\u003c/em\u003e and \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ebovis\u003c/em\u003e occurring 108-613 generations ago. Introgressed S. bovis alleles constitute 3.3-8.2% of Nigerien \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ehaematobium\u003c/em\u003e genomes. Some \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ebovis\u003c/em\u003e alleles have reached high frequency and show signatures of directional selection; the strongest signal spans a single gene in the invadolysin gene family, an M8 metalloprotease associated with parasitic life-history traits.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-biorxiv-pre-print\" class=\"anchor\" aria-hidden=\"true\" href=\"#biorxiv-pre-print\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://doi.org/10.1101/539353\" rel=\"nofollow\"\u003ebioRxiv pre-print\u003c/a\u003e\u003c/h4\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTES:\u003c/h3\u003e\n\u003cp\u003eAll analyses were conducted on a HPCC in a \u003ccode\u003esingularity\u003c/code\u003e container or in a \u003ccode\u003econda\u003c/code\u003e managed environment. The singularity recipe and conda environmental yaml are in the \u003ccode\u003econfig\u003c/code\u003e dir.\u003c/p\u003e\n\u003cp\u003eRaw code is found in the \u003ccode\u003escripts\u003c/code\u003e dir\u003c/p\u003e\n\u003cp\u003eData that is not readily available through the SRA is in the \u003ccode\u003edata\u003c/code\u003e dir. These will be housed in an online repository (ex. Dryad), but provided here for documentation purposes.\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 2, + "topics": [], + "updated_at": 1618309153.0 + }, + { + "data_format": 2, + "description": "https://gitlab.kitware.com/paraview/paraview-superbuild.git", + "filenames": [ + "Scripts/singularity/Singularity.egl", + "Scripts/singularity/Singularity.osmesa" + ], + "full_name": "zenotech/paraview-superbuild", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"Documentation/img/paraview100.png\"\u003e\u003cimg src=\"Documentation/img/paraview100.png\" alt=\"ParaView-Superbuild\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h1\u003e\n\u003cp\u003eParaView-Superbuild, henceforth referred to as \"superbuild\", is a project to\nbuild ParaView and its dependencies. ParaView itself can be easily built using\nCMake as long as the required external dependencies are available on the build\nmachine. However, ParaView\u0027s several external dependencies, e.g. Qt, CGNS,\nFFMPEG, etc. can be very tedious to build. Also, if you want to generate\nredistributable binaries, you need to take extra care when building and\npackaging these dependencies. To make our lives easier in supporting both these\nuse-cases, the superbuild project was born.\u003c/p\u003e\n\u003cp\u003eAlthough primarily designed to build the official ParaView binaries, the\nsuperbuild has since been regularly used to build and install ParaView\non various supercomputing systems.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-obtaining-the-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#obtaining-the-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eObtaining the source\u003c/h1\u003e\n\u003cp\u003eTo obtain the superbuild source locally, clone this repository using\n\u003ca href=\"https://git-scm.org\" rel=\"nofollow\"\u003eGit\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone --recursive https://gitlab.kitware.com/paraview/paraview-superbuild.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h1\u003e\n\u003cp\u003eThe superbuild can be built with a Makefiles or Ninja CMake generator. The IDE\ngenerators (Xcode and Visual Studio) are not supported.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eThe superbuild tries to provide all of its own dependencies, but some tooling\nis assumed to be available on the host machine.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCompiler toolchain\n\u003cul\u003e\n\u003cli\u003eGCC 4.9 or newer\u003c/li\u003e\n\u003cli\u003eXcode 10 or newer (older is probably supported, but untested)\u003c/li\u003e\n\u003cli\u003eMSVC 2017 or newer\u003c/li\u003e\n\u003cli\u003eICC (minimum version unknown)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTools\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003epkg-config\u003c/code\u003e is used on non-Windows platforms to find dependencies in\nsome projects\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eninja\u003c/code\u003e (or \u003ccode\u003emake\u003c/code\u003e) for building\u003c/li\u003e\n\u003cli\u003ePython (if not built by the superbuild) for building packages\u003c/li\u003e\n\u003cli\u003eIf building \u003ccode\u003emesa\u003c/code\u003e or \u003ccode\u003eosmesa\u003c/code\u003e, \u003ccode\u003ebison\u003c/code\u003e and \u003ccode\u003eflex\u003c/code\u003e are required.\u003c/li\u003e\n\u003cli\u003eIf building packages on Linux, \u003ccode\u003echrpath\u003c/code\u003e is required to make relocatable\npackages\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-a-specific-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-a-specific-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding a specific version\u003c/h2\u003e\n\u003cp\u003eThe superbuild project uses the same versioning scheme as ParaView,\nand gets tagged for every release of ParaView. For example, to build\nParaView version 5.7.1, checkout the \u003ccode\u003ev5.7.0\u003c/code\u003e tag of ParaView and\nsuperbuild.\u003c/p\u003e\n\u003cp\u003eCurrently available tags are shown\n\u003ca href=\"https://gitlab.kitware.com/paraview/paraview-superbuild/-/tags\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo checkout a specific tag from the superbuild git repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd paraview-superbuild\n$ git fetch origin # ensure you have the latest state from the main repo\n$ git checkout v5.7.0 # replace `v5.7.0` with tag name of your choice\n$ git submodule update\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point, your superbuild has all of the \u003cem\u003erules\u003c/em\u003e that were used\nwhen building the selected version of ParaView. Also, note that it\u0027s\npossible to build a version of ParaView using a different superbuild\nversion. For example, you could use superbuild \u003ccode\u003ev5.7.0\u003c/code\u003e, to build the\nlatest master (i.e., development) version of ParaView, or a custom\nbranch. This is done by first checking out the superbuild for the\nappropriate version and then setting the CMake variables that affect\nwhich ParaView source is to be used. There are several ways to\ncontrol how superbuild finds its source packages:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIf you want to use git to checkout ParaView source (default), then set\n\u003ccode\u003eparaview_SOURCE_SELECTION\u003c/code\u003e to \u003ccode\u003egit\u003c/code\u003e, ensure \u003ccode\u003eparaview_GIT_REPOSITORY\u003c/code\u003e is\npointing to the ParaView git repository you want to clone (by default it is\nset to the offical ParaView repository) and then set the \u003ccode\u003eparaview_GIT_TAG\u003c/code\u003e\nto be a specific tagname or branch available for the selected git\nrepository. Use \u003ccode\u003emaster\u003c/code\u003e for latest development code, \u003ccode\u003ev5.7.0\u003c/code\u003e for the\n5.7.0 release, \u003ccode\u003erelease\u003c/code\u003e for latest stable release, or a specific ParaView\ncommit SHA. In this setup, when building the superbuild, it will clone and\ncheckout the appropriate revision from the ParaView git repository automatically.\u003c/li\u003e\n\u003cli\u003eInstead of letting superbuild do the cloning and updating of the ParaView\nsource, you can also manually check it out and keep it updated as needed.\nTo use this configuration, set \u003ccode\u003eparaview_SOURCE_SELECTION\u003c/code\u003e to \u003ccode\u003esource\u003c/code\u003e, and\nset \u003ccode\u003eparaview_SOURCE_DIR\u003c/code\u003e to point to a custom ParaView source tree. See \u0027offline\nbuilds\u0027 below for instructions to download needed dependency packages.\u003c/li\u003e\n\u003cli\u003eAnother option is to use a source tarball of a ParaView release. For that,\nset \u003ccode\u003eparaview_SOURCE_SELECTION\u003c/code\u003e to the version to build such as \u003ccode\u003e5.7.0\u003c/code\u003e.\nThe superbuild offers the lastest stable release as well as release\ncandidate in preparation for the release. This is the best way to build a\nreleased version of ParaView.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e If you switch to a superbuild version older than 5.2, the instructions\ndescribed on this page are not relevant since the superbuild was refactored and\nchanged considerably for 5.2. For older versions, refer to instructions on the\n\u003ca href=\"http://www.paraview.org/Wiki/index.php?title=ParaView/Superbuild\u0026amp;oldid=59804\" rel=\"nofollow\"\u003eWiki\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eALSO NOTE:\u003c/strong\u003e Since this README is expected to be updated for each version,\nonce you checkout a specfic version, you may want to refer to the README for\nthat specific version.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-incremental-builds\" class=\"anchor\" aria-hidden=\"true\" href=\"#incremental-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIncremental builds\u003c/h2\u003e\n\u003cp\u003eThe superbuild is kind of na\u00efve for changes to project sources within the\nsuperbuild. This is due to the superbuild not tracking all source files for\neach project and instead only \"stamp files\" to indicate the steps performed.\u003c/p\u003e\n\u003cp\u003eWhen changing the source of a subproject, the best solution is to delete the\n\"stamp file\" for the build step of that project:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ rm superbuild/$project/stamp/$project-build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand to rerun the superbuild\u0027s build step.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-projects-and-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#projects-and-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProjects and Features\u003c/h2\u003e\n\u003cp\u003eThe superbuild contains multiple projects which may be used to enable\ndifferent features within the resulting ParaView build. Most projects involve\ndownloading and adding the feature to the resulting package, but there are a\nfew which are used just to enable features within ParaView itself.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eparaview\u003c/code\u003e project must be enabled to build ParaView.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eparaviewsdk\u003c/code\u003e project enables the building of a package which includes\nheaders and libraries suitable for developing against ParaView. It is only available\non Linux (at the moment).\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eparaviewweb\u003c/code\u003e project adds web services into the resulting package.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eparaviewgettingstartedguide\u003c/code\u003e, and \u003ccode\u003eparaviewtutorialdata\u003c/code\u003e packages add\nstartup documentation and example data to the package.\u003c/p\u003e\n\u003cp\u003eParaView supports multiple rendering engines including \u003ccode\u003eegl\u003c/code\u003e, \u003ccode\u003emesa\u003c/code\u003e,\n\u003ccode\u003eosmesa\u003c/code\u003e, and \u003ccode\u003eqt5\u003c/code\u003e. All of these are incompatible with each other. If none of\nthese are chosen, a UI-less ParaView will be built (basically just\n\u003ccode\u003epvpython\u003c/code\u003e). On Windows and macOS, only the \u003ccode\u003eqt5\u003c/code\u003e rendering engine is\navailable.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003epython\u003c/code\u003e package is available to enable Python support in the package. In\naddition, the \u003ccode\u003ematplotlib\u003c/code\u003e and \u003ccode\u003enumpy\u003c/code\u003e packages are available.\u003c/p\u003e\n\u003cp\u003eThe following packages enable other features within ParaView:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eadios\u003c/code\u003e: Enable readers and writers for visualization data in the ADIOS\nfile format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elas\u003c/code\u003e: Enable reading the LAS file format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecosmotools\u003c/code\u003e: Enables Cosmo file format readers and related filters and\nalgorithms.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003effmpeg\u003c/code\u003e: Video encoding library for macOS and Linux.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eospray\u003c/code\u003e: A ray tracing rendering backend from Intel.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esilo\u003c/code\u003e: Support reading the silo file format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etbb\u003c/code\u003e: Improved parallel processing support within various VTK and\nParaView filters and algorithms.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evisitbridge\u003c/code\u003e: Enables readers for file formats provided from the VisIt\nproject.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evortexfinder2\u003c/code\u003e: A collection of tools to visualize and analyze vortices.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evrpn\u003c/code\u003e: Virtual reality support through the VRPN interface.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evtkm\u003c/code\u003e: VTK-m Accelerator Filters\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003exdmf3\u003c/code\u003e: A meta file format built on top of HDF5.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-offline-builds\" class=\"anchor\" aria-hidden=\"true\" href=\"#offline-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOffline builds\u003c/h2\u003e\n\u003cp\u003eThe superbuild has a \u003ccode\u003edownload-all\u003c/code\u003e target that will download all of\nthe files from the network that are necessary for the currently\nconfigured build. By default, they are placed into the \u003ccode\u003edownloads\u003c/code\u003e\ndirectory of the build tree. This superbuild-plus-downloads tree may\nthen be copied to a non-networked machine and pointed at using the\n\u003ccode\u003esuperbuild_download_location\u003c/code\u003e variable (or placed in the default\nlocation).\u003c/p\u003e\n\u003cp\u003eNote that the \u003ccode\u003envidiaoptix\u003c/code\u003e and \u003ccode\u003envidiamdl\u003c/code\u003e project sources are not available\nat their URLs in the superbuild outside of Kitware due to their sources being\nbehind click-wrapping. They may be manually downloaded from these web pages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003envidiaoptix\u003c/code\u003e: \u003ca href=\"https://developer.nvidia.com/designworks/optix/download\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/designworks/optix/download\u003c/a\u003e\nThough older versions are available here:\n\u003ca href=\"https://developer.nvidia.com/designworks/optix/downloads/legacy\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/designworks/optix/downloads/legacy\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003envidiamdl\u003c/code\u003e: \u003ca href=\"https://developer.nvidia.com/mdl-sdk\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/mdl-sdk\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-overriding-downloaded-archives\" class=\"anchor\" aria-hidden=\"true\" href=\"#overriding-downloaded-archives\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverriding downloaded archives\u003c/h3\u003e\n\u003cp\u003eOn rare occasions, you may want to replace a downloaded archive with a different\nversion. You may replace the archive with a newer version preserving its\nname, however, on doing so, the hash verification will most likely fail during\nthe build step. To skip the hash verification for archives that have been\nmanually changed, set the \u003ccode\u003exxx_SKIP_VERIFICATION\u003c/code\u003e option, where \u003ccode\u003exxx\u003c/code\u003e\nis the name of the project. \u003ccode\u003exxx_SKIP_VERIFICATION\u003c/code\u003e must be passed on command line\nwhen invoking CMake using \u003ccode\u003e-Dxxx_SKIP_VERIFICATION:BOOL=TRUE\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, you can edit the \u003ccode\u003eversions.cmake\u003c/code\u003e files in the source repository\nand modify the \u003ccode\u003eURL_MDF5\u003c/code\u003e or \u003ccode\u003eURL_HASH\u003c/code\u003e values for the specific project with\nupdated hashes.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling\u003c/h2\u003e\n\u003cp\u003eThe superbuild supports the \u003ccode\u003einstall\u003c/code\u003e target by selecting a template package\nusing the \u003ccode\u003eSUPERBUILD_DEFAULT_INSTALL\u003c/code\u003e variable. The default and availability\ndepends on the platform and selected projects, but valid values for this\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eparaview/ZIP\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaview/DragNDrop\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaview/TGZ\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaview/TXZ\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaviewsdk/TGZ\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaviewsdk/TXZ\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe CMake cache editors (\u003ccode\u003eccmake\u003c/code\u003e and \u003ccode\u003ecmake-gui\u003c/code\u003e) have dropdown options for\nthe supported options.\u003c/p\u003e\n\u003cp\u003eThe selected package logic will be used to install ParaView and its\ndependencies into \u003ccode\u003eCMAKE_INSTALL_PREFIX\u003c/code\u003e rather than being placed into a\npackage. For example, the \u003ccode\u003eDragNDrop\u003c/code\u003e generator creates \u003ccode\u003e.app\u003c/code\u003e bundles which\nwill be created whereas the \u003ccode\u003eTGZ\u003c/code\u003e, \u003ccode\u003eTXZ\u003c/code\u003e, and \u003ccode\u003eZIP\u003c/code\u003e generators use the standard\n\u003ccode\u003ebin/\u003c/code\u003e, \u003ccode\u003elib/\u003c/code\u003e, etc. directories.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-caveats\" class=\"anchor\" aria-hidden=\"true\" href=\"#caveats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveats\u003c/h3\u003e\n\u003cp\u003eIf using the \u003ccode\u003egit\u003c/code\u003e source selection for ParaView, the build will rerun when\nusing the \u003ccode\u003einstall\u003c/code\u003e target due to limitations in the external project\nmechanisms and the way CPack works. There are two ways to avoid this:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe \u003ccode\u003eSUPERBUILD_OFFLINE_BUILD\u003c/code\u003e option may be set to \u003ccode\u003eON\u003c/code\u003e to unlink the git\nupdate step from the configure/build steps; or\u003c/li\u003e\n\u003cli\u003ethe initial build can just be run using the \u003ccode\u003einstall\u003c/code\u003e target instead of\nthe usual \u003ccode\u003emake \u0026amp;\u0026amp; make install\u003c/code\u003e pattern.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-external-plugins\" class=\"anchor\" aria-hidden=\"true\" href=\"#external-plugins\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExternal plugins\u003c/h2\u003e\n\u003cp\u003eThe superbuild supports building more plugins into ParaView using the\n\u003ccode\u003eparaviewexternalplugins\u003c/code\u003e project. As an example, to build two external\nplugins \u003ccode\u003ea\u003c/code\u003e and \u003ccode\u003eb\u003c/code\u003e, the following settings should be used:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eENABLE_paraviewexternalplugins:BOOL=ON\u003c/code\u003e: Enables building using external\nplugins.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_PLUGINS_EXTERNAL:STRING=a;b\u003c/code\u003e: The list of plugins to build.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_PLUGIN_a_PATH:PATH=/path/to/plugin/a\u003c/code\u003e: The path to plugin \u003ccode\u003ea\u003c/code\u003e\u0027s\nsource directory. It must contain a \u003ccode\u003eplugins.cmake\u003c/code\u003e to be picked up by\nParaView.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_PLUGIN_b_PATH:PATH=/path/to/plugin/b\u003c/code\u003e: Same as above, but for\nplugin \u003ccode\u003eb\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cmake-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#cmake-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCMake Variables\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-style-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#style-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStyle Guide\u003c/h3\u003e\n\u003cp\u003eNote that currently not all project and configuration variables follow this\nstyle guide but any new projects should use this convention while any\nexisting projects and configuration variables will transition to this over\ntime.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAll references to a given project name will be lowercase.\u003c/li\u003e\n\u003cli\u003eUnderscores will be used as word seperators in variable names.\u003c/li\u003e\n\u003cli\u003eAll project specific configuration variables will be lower-case project\nname followed by upper-case setting name.\nExamples include:\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emesa_USE_SWR\u003c/code\u003e : Enable the OpenSWR driver for (OS)Mesa.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eospray_BUILD_ISA\u003c/code\u003e : Select the SIMD architecture used to build OSPray.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eInternal variables used within a given project\u0027s projectname.cmake file\nwill be all lower-case.\u003c/li\u003e\n\u003cli\u003eMultiple versions:\n\u003cul\u003e\n\u003cli\u003eUse the \u003ccode\u003esuperbuild_set_selectable_source\u003c/code\u003e macro to allow multiple\nversions of a given project.\u003c/li\u003e\n\u003cli\u003eSpecify source selection versions as numeric, i.e. without any \"v\" or\n\"V\" prefix.\u003c/li\u003e\n\u003cli\u003eIf the project is going through a release candidate cycle, add the\navailable RCs as additional sources as they become availabe. Once\na final release is made, replace all the RCs with the updated release.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Variables\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esuperbuild_download_location\u003c/code\u003e (default \u003ccode\u003e${CMAKE_BINARY_DIR}/downloads\u003c/code\u003e):\nThe location to store downloaded source artifacts. Usually, it is changed\nso that it is preserved across a wipe of the build directory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSUPERBUILD_PROJECT_PARALLELISM\u003c/code\u003e (default based on the number of available\nprocessors): When using a Makefiles generator, subproject builds use \u003ccode\u003e-j\u003c/code\u003e\nexplicitly with this number.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eENABLE_xxx\u003c/code\u003e (generally, default \u003ccode\u003eOFF\u003c/code\u003e): If selected, the \u003ccode\u003exxx\u003c/code\u003e project\nwill be built within the superbuild. See above for descriptions of the\nvarious projects. \u003ccode\u003eENABLE_\u003c/code\u003e flags are not shown for projects which must be\nenabled due to a project depending on it (e.g., \u003ccode\u003evisitbridge\u003c/code\u003e requires\n\u003ccode\u003eboost\u003c/code\u003e, so enabling \u003ccode\u003evisitbridge\u003c/code\u003e will hide the \u003ccode\u003eENABLE_boost\u003c/code\u003e option).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUSE_SYSTEM_xxx\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): If selected, the \u003ccode\u003exxx\u003c/code\u003e project from the\nbuild environment is used instead of building it within the superbuild.\nNot all projects support system copies (the flag is not available if so).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSUPERBUILD_DEBUG_CONFIGURE_STEPS\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): If set, the superbuild\nwill log configure steps for each \u003ccode\u003exxx\u003c/code\u003e project into\n\u003ccode\u003esuperbuild/xxx/stamp/xxx-configure-*.log\u003c/code\u003e files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCMAKE_BUILD_TYPE\u003c/code\u003e (default \u003ccode\u003eRelease\u003c/code\u003e): The build type to use for the\nbuild. Can be \u003ccode\u003eRelease\u003c/code\u003e, \u003ccode\u003eRelWithDebInfo\u003c/code\u003e, or (on not-Windows) \u003ccode\u003eDebug\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDue to complications around shipping OpenSSL in the binaries, OpenSSL\nrequires explicit settings in the build. They are\n\u003ccode\u003e-DALLOW_openssl:BOOL=ON -DENABLE_openssl:BOOL=ON\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_always_package_scipy\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): Force packaging \u003ccode\u003escipy\u003c/code\u003e on\nWindows installer generators. Other generators do not have issues with long\npaths and will always try to include \u003ccode\u003escipy\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following flags affect ParaView directly:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eparaview_SOURCE_SELECTION\u003c/code\u003e (default \u003ccode\u003e5.11.0\u003c/code\u003e): The source to use for\nParaView itself. The version numbers use the source tarballs from the\nwebsite for the release. The \u003ccode\u003esource\u003c/code\u003e selection uses the\n\u003ccode\u003eparaview_SOURCE_DIR\u003c/code\u003e variable to look at a checked out ParaView source\ndirectory. The \u003ccode\u003egit\u003c/code\u003e selection has the superbuild clone and builds a\ncheckout of ParaView from git repository controlled by the\n\u003ccode\u003eparaview_GIT_REPOSITORY\u003c/code\u003e and \u003ccode\u003eparaview_GIT_TAG\u003c/code\u003e variables. By default, the\n\u003ccode\u003emaster\u003c/code\u003e branch of the main repository is used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: When using the \u003ccode\u003esource\u003c/code\u003e selection, incremental builds to the\nsuperbuild may not rebuild ParaView even if the source tree has changed.\nThis is because the superbuild is \"blind\" to the source tree other than\nits existence.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eCMAKE_BUILD_TYPE_paraview\u003c/code\u003e (default is the same as the superbuild):\nParaView may be built with a different build type (e.g., \u003ccode\u003eRelease\u003c/code\u003e vs.\n\u003ccode\u003eRelWithDebInfo\u003c/code\u003e) as the rest of the superbuild using this variable. In\naddition to \u003ccode\u003e\u0026lt;SAME\u0026gt;\u003c/code\u003e which uses \u003ccode\u003eCMAKE_BUILD_TYPE\u003c/code\u003e, any valid value for\n\u003ccode\u003eCMAKE_BUILD_TYPE\u003c/code\u003e is also valid.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eBUILD_SHARED_LIBS_paraview\u003c/code\u003e (default is the same as the superbuild):\nParaView may be built with a different selection for BUILD_SHARED_LIBS flag\nthan the rest of the superbuild using this variable. For example,\nto build ParaView static while building other projects in the superbuild\n(e.g. MPI, Python, etc.) as shared, set \u003ccode\u003eBUILD_SHARED_LIBS\u003c/code\u003e to \u003ccode\u003eON\u003c/code\u003e\nand \u003ccode\u003eBUILD_SHARED_LIBS_paraview\u003c/code\u003e to \u003ccode\u003eOFF\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_BUILD_WEB_DOCUMENTATION\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): Have ParaView build\nits HTML documentation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emesa_USE_SWR\u003c/code\u003e (default \u003ccode\u003eON\u003c/code\u003e): If \u003ccode\u003emesa\u003c/code\u003e is enabled, this enables\nIntel\u0027s software rasterization backend (x86 only).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_INITIALIZE_MPI_ON_CLIENT\u003c/code\u003e (default \u003ccode\u003eON\u003c/code\u003e): If \u003ccode\u003empi\u003c/code\u003e is enabled, this\nenables MPI to be initialized automatically when running the GUI or pvpython.\nSome readers use MPI IO and thus must have MPI initialized in order to be\nused so this is the default for general ease of use. For some MPI implementations,\na code that initializes MPI must be run with the appropriate mpi launcher\n(e.g. mpirun) which in this case it may be desirable to disable this option.\nNote that the \u003ccode\u003e--mpi\u003c/code\u003e or \u003ccode\u003e--no-mpi\u003c/code\u003e command line options to paraview and\npvpython can be used to override this option.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_EXTRA_CMAKE_ARGUMENTS\u003c/code\u003e (default \u003ccode\u003e\"\"\u003c/code\u003e: Extra CMake arguments to\npass to ParaView\u0027s configure step. This can be used to set CMake variables\nfor the build that are otherwise not exposed in the superbuild itself.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_ENABLE_CAVEInteraction\u003c/code\u003e (default \u003ccode\u003eON\u003c/code\u003e): Enables the CAVEInteraction. If\n\u003ccode\u003evrpn\u003c/code\u003e is enabled, the CAVEInteraction will support input devices through a VRPN\nconnection. VRUI support is enabled unconditionally on Linux.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_ENABLE_NODEEDITOR\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): Enables the NodeEditor\nplugin.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_ENABLE_XRInterface\u003c/code\u003e (default \u003ccode\u003eON\u003c/code\u003e): Enables the XRInterface plugin.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-paraview-editions\" class=\"anchor\" aria-hidden=\"true\" href=\"#paraview-editions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParaView editions\u003c/h4\u003e\n\u003cp\u003eA typical ParaView build includes several modules and dependencies. While these\nare necessary for a fully functional application, there are cases (e.g. in situ\nuse-cases) where a build with limited set of features is adequate. ParaView build supports\nthis using the \u003ccode\u003ePARAVIEW_BUILD_EDITION\u003c/code\u003e setting. Supported values for this setting are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCORE\u003c/code\u003e: Build modules necessary for core ParaView functionality.\nThis does not include rendering.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eRENDERING\u003c/code\u003e: Build modules necessary for supporting rendering including views\nand representations. This includes everything in \u003ccode\u003eCORE\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCATALYST\u003c/code\u003e: Build all modules necessary for in situ use cases without\nrendering and optional components like NetCDF- and HDF5-based readers and\nwriters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCATALYST_RENDERING\u003c/code\u003e: Same as \u003ccode\u003eCATALYST\u003c/code\u003e but with rendering supported added.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCANONICAL\u003c/code\u003e (default): Build modules necessary for standard ParaView build.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-packaging-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#packaging-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackaging Variables\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ePARAVIEW_PACKAGE_SUFFIX\u003c/code\u003e (default based on selected options): The suffix\nfor the name generated by the package.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_PLUGINS_AUTOLOAD\u003c/code\u003e: List of plugins to autoload in the packaged\nParaView.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-packaging\" class=\"anchor\" aria-hidden=\"true\" href=\"#packaging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackaging\u003c/h1\u003e\n\u003cp\u003eThe packages may be built using the \u003ccode\u003ecpack-paraview\u003c/code\u003e tests via \u003ccode\u003ectest\u003c/code\u003e. The\neasiest way to build all available packages is to run \u003ccode\u003ectest -R cpack\u003c/code\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-learning-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#learning-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLearning Resources\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGeneral information is available at the \u003ca href=\"http://www.paraview.org\" rel=\"nofollow\"\u003eParaView Homepage\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCommunity discussion takes place on the \u003ca href=\"http://www.paraview.org/mailing-lists/\" rel=\"nofollow\"\u003eParaView Mailing Lists\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCommercial \u003ca href=\"http://www.kitware.com/products/support.html\" rel=\"nofollow\"\u003esupport\u003c/a\u003e and \u003ca href=\"http://www.kitware.com/products/protraining.php\" rel=\"nofollow\"\u003etraining\u003c/a\u003e\nare available from \u003ca href=\"http://www.kitware.com/\" rel=\"nofollow\"\u003eKitware\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-reporting-bugs\" class=\"anchor\" aria-hidden=\"true\" href=\"#reporting-bugs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReporting Bugs\u003c/h1\u003e\n\u003cp\u003eIf you have found a bug:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eIf you have a patch, please read the \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e document.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOtherwise, please join one of the \u003ca href=\"http://www.paraview.org/mailing-lists/\" rel=\"nofollow\"\u003eParaView Mailing Lists\u003c/a\u003e and ask\nabout the expected and observed behaviors to determine if it is\nreally a bug.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFinally, if the issue is not resolved by the above steps, open\nan entry in the \u003ca href=\"http://www.paraview.org/Bug\" rel=\"nofollow\"\u003eParaView Issue Tracker\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eLike ParaView, ParaView-Superbuild is distributed under the OSI-approved BSD\n3-clause License. See \u003ca href=\"Copyright.txt\"\u003eCopyright.txt\u003c/a\u003e for details. For additional licenses,\nrefer to \u003ca href=\"http://www.paraview.org/paraview-license/\" rel=\"nofollow\"\u003eParaView Licenses\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e for instructions to contribute.\u003c/p\u003e\n", + "stargazers_count": 2, + "subscribers_count": 6, "topics": [ - "social-justice", - "activism", - "art-gallery", - "art-installation", - "public-space" + "paraview-superbuild", + "cmake", + "superbuild" ], - "updated_at": 1703886895.0 + "updated_at": 1675960647.0 }, { "data_format": 2, - "description": "Definition files for Singularity", + "description": "Heuristic Algorithms for Quantum Computers", "filenames": [ - "Singularity.def" + "SingularityFile.def" ], - "full_name": "Open-MSS/singularity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDefinition files for Singularity\u003c/p\u003e\n\u003cp\u003eFor an introduction to singularity read the documentation for your installation on \u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003ehttps://sylabs.io/\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "vivekkatial/HAQC", + "latest_release": "0.0.2", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-haqc----heuristic-algorithms-for-quantum-computing-research-group\" class=\"anchor\" aria-hidden=\"true\" href=\"#haqc----heuristic-algorithms-for-quantum-computing-research-group\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHAQC -- Heuristic Algorithms for Quantum Computing Research Group\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/vivekkatial/HAQC/actions/workflows/build-container-and-release.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/vivekkatial/HAQC/actions/workflows/build-container-and-release.yml/badge.svg\" alt=\"build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eResearch group to run optimisation algorithms on Quantum Computers at the University of Melbourne\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eBefore getting started, ensure you have Python 3.7+. We use \u003ca href=\"https://python-poetry.org/\" rel=\"nofollow\"\u003epoetry\u003c/a\u003e to manage the python environment (the .gitignore file should already ignore it).\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003e$ poetry install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo add a package to your new project:\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003e$ poetry install \u0026lt;package\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will automatically edit your \u003ccode\u003epyproject.toml\u003c/code\u003e file with the new package you provided.\u003c/p\u003e\n\u003cp\u003eNext, activate the \u003ccode\u003epoetry\u003c/code\u003e shell:\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003e$ poetry shell\n$ python --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will spawn a new shell subprocess, which can be deactivated by using exit.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting\u003c/h3\u003e\n\u003cp\u003eFor testing, we use \u003ccode\u003epytest\u003c/code\u003e. To run the tests, just type the command \u003ccode\u003epytest\u003c/code\u003e, or you can specify a file e.g. \u003ccode\u003epytest tests/test_graph_generator.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWe will use \u003ccode\u003eblack\u003c/code\u003e as our code formatter. Simply run \u003ccode\u003eblack -S .\u003c/code\u003e to run black over all the files before committing. The \u003ccode\u003e-S\u003c/code\u003e is to skip string normalisation, because we prefer single quotes/don\u0027t really care (\u003ca href=\"https://github.com/psf/black/issues/118\"\u003eflame war, I know\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-before-making-a-pr\" class=\"anchor\" aria-hidden=\"true\" href=\"#before-making-a-pr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBefore making a PR\u003c/h3\u003e\n\u003cp\u003eIn summary, before merging a PR, you should:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make sure all tests pass\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src\npipenv run python -m pytest tests/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Format with black\u003c/span\u003e\npipenv run python -m black -S \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-mlflow-tracking\" class=\"anchor\" aria-hidden=\"true\" href=\"#mlflow-tracking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMLFlow Tracking\u003c/h2\u003e\n\u003cp\u003eTo get the MLFlow tracking functionality to work you will need to setup \u003ccode\u003eawscli\u003c/code\u003e credentials, so MLFlow can properly log artifacts.\u003c/p\u003e\n\u003cp\u003eIf you\u0027re keen to do this then please follow the instructions \u003ca href=\"https://wiki-rcs.unimelb.edu.au/display/RCS/AWS+CLI\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can request the credentials for this experiment from Vivek at \u003ca href=\"mailto:vkatial@student.unimelb.edu.au\"\u003evkatial@student.unimelb.edu.au\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-a-test-instance\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-a-test-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a test instance\u003c/h2\u003e\n\u003cp\u003eTo run a test instance try out the steps below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython qaoa_vrp/main.py -f \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e -T False \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -T tracking for MLFlow\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jupyter-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter Notebooks\u003c/h3\u003e\n\u003cp\u003eFirst ensure that your Python is \u003cem\u003enot\u003c/em\u003e aliased in your \u003ccode\u003e.bashrc\u003c/code\u003e or \u003ccode\u003e.zshrc\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eAfter this launch your \u003ccode\u003epoetry\u003c/code\u003e by\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003epoetry shell\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen do:\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003epython -m ipykernel install --user --name=ENV_NAME\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen launch the notebook\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003ejupyter notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn your notebook, Kernel -\u0026gt; Change Kernel. Your kernel should now be an option.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/jupyter-install.png\"\u003e\u003cimg src=\"images/jupyter-install.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eThis project leverages \u003ca href=\"https://github.com/singularityhub/\"\u003eSingularity\u003c/a\u003e to ensure the code is reproducible and manage dependencies.\u003c/p\u003e\n\u003cp\u003eYou can find the recipe for our container in \u003ccode\u003eSingularityFile.def\u003c/code\u003e. There are various apps for each different type of experiment we run,\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cicd\" class=\"anchor\" aria-hidden=\"true\" href=\"#cicd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCI/CD\u003c/h2\u003e\n\u003cp\u003eWe use Github Actions for CI/CD. Everytime a PR is created, a test build of the singularity container runs. When merging into \u003ccode\u003emain\u003c/code\u003e we do a release of the container.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eVivek Katial\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1646623956.0 + "updated_at": 1676667542.0 }, { "data_format": 2, - "description": "Simple terminal UI for git commands", + "description": "popf", "filenames": [ - "0.28.2/Singularity", - "0.24.2/Singularity", - "0.22.9/Singularity", - "0.23.7/Singularity", - "0.32.2/Singularity", - "0.35/Singularity", - "0.40.2/Singularity", - "0.34/Singularity", - "0.37/Singularity", - "0.31.4/Singularity" + "Singularity", + "Singularity.1.0" ], - "full_name": "pscedu/singularity-lazygit", - "latest_release": "v0.40.2", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-lazygit/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-lazygit/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-lazygit/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-lazygit/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/85e727aaf548d0c00e973fe38b358880905cf50a3310d03a05640af2f90eaae9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c617a79676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/85e727aaf548d0c00e973fe38b358880905cf50a3310d03a05640af2f90eaae9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c617a79676974\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-lazygit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/556c17c59ba8e19fb2760c48cdbea073e4215ab1edcd44828504b43e009c0266/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c617a79676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/556c17c59ba8e19fb2760c48cdbea073e4215ab1edcd44828504b43e009c0266/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c617a79676974\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-lazygit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6af6d6dcffe4e9190554958f001a70f19f371189177b773312159307e1d41bab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c617a79676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6af6d6dcffe4e9190554958f001a70f19f371189177b773312159307e1d41bab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c617a79676974\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-lazygit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/60efef56d1806ccdcf8df37747f39e2267f9a9b11c24bee9ab504181020948b6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c617a79676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/60efef56d1806ccdcf8df37747f39e2267f9a9b11c24bee9ab504181020948b6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c617a79676974\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-lazygit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-lazygit\u003c/h1\u003e\u003ca id=\"user-content-singularity-lazygit\" class=\"anchor\" aria-label=\"Permalink: singularity-lazygit\" href=\"#singularity-lazygit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/screenshot.png\"\u003e\u003cimg src=\"/images/screenshot.png\" alt=\"Screenshot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/jesseduffield/lazygit\"\u003elazygit\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003elazygit\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/lazygit/0.24.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/lazygit\u003c/code\u003e as \u003ccode\u003e0.24.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "roveri-marco/popf", + "latest_release": "1.0", "stargazers_count": 2, - "subscribers_count": 4, - "topics": [ - "singularity", - "utilities" - ], - "updated_at": 1692212155.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1657529551.0 }, { "data_format": 2, - "description": "HOMER (Hypergeometric Optimization of Motif EnRichment) is a suite of tools for Motif Discovery and next-gen sequencing analysis.", + "description": " This repo provides a Singularity image version for ClamAV, an anti-virus toolkit.", "filenames": [ - "4.11.0/Singularity" + "Singularity" ], - "full_name": "pscedu/singularity-homer", - "latest_release": "v4.11.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-homer/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-homer/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-homer/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-homer/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/04d92355d250c9b6fac3ab0a8bf7474f0c28f621406d56170a3d6febc6272836/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d686f6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/04d92355d250c9b6fac3ab0a8bf7474f0c28f621406d56170a3d6febc6272836/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d686f6d6572\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-homer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ff1cea258454b42da34c4a0fe1ed19b786554cea1c1e80f2a55de9d6cbbe4942/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d686f6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff1cea258454b42da34c4a0fe1ed19b786554cea1c1e80f2a55de9d6cbbe4942/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d686f6d6572\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-homer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e032de5fb4ae6be7b1bd3bb6ed583fdcc2923becfd1fce8ac499ee8ed8833b50/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d686f6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e032de5fb4ae6be7b1bd3bb6ed583fdcc2923becfd1fce8ac499ee8ed8833b50/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d686f6d6572\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-homer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/98cbb9d1b8b97e42f348e53aae84ab6a5301602f1a4303e6a55897d77aedaacd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d686f6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98cbb9d1b8b97e42f348e53aae84ab6a5301602f1a4303e6a55897d77aedaacd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d686f6d6572\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-homer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-homer\u003c/h1\u003e\u003ca id=\"user-content-singularity-homer\" class=\"anchor\" aria-label=\"Permalink: singularity-homer\" href=\"#singularity-homer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0c2e2600c1461f6f4f9101d2993f8d32032061fa0145e2332ce65118a4e6212d/687474703a2f2f686f6d65722e756373642e6564752f686f6d65722f706963322e676966\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0c2e2600c1461f6f4f9101d2993f8d32032061fa0145e2332ce65118a4e6212d/687474703a2f2f686f6d65722e756373642e6564752f686f6d65722f706963322e676966\" alt=\"Logo\" data-canonical-src=\"http://homer.ucsd.edu/homer/pic2.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://homer.ucsd.edu/homer/\" rel=\"nofollow\"\u003ehomer\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehomer\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/homer/4.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/homer\u003c/code\u003e as \u003ccode\u003e4.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "netreconlab/clamav", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-clamav\" class=\"anchor\" aria-hidden=\"true\" href=\"#clamav\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eclamav\u003c/h1\u003e\n\u003cp\u003eThis repo provides a Singularity image version for \u003ca href=\"https://docs.clamav.net/Introduction.html\" rel=\"nofollow\"\u003eClamAV\u003c/a\u003e, an anti-virus toolkit. It provides a number of utilities including a flexible and scalable multi-threaded daemon, a command line scanner and advanced tool for automatic database updates. To learn more about the image, look \u003ca href=\"https://docs.clamav.net/manual/Installing/Docker.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003cp\u003eImages of \u003ccode\u003eclamav\u003c/code\u003e are automatically built for your convenience. Images can be found at the following locations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/netreconlab/clamav/pkgs/container/clamav\"\u003eSingularity - Hosted on GitHub Container Registry\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDocker - The Dockerfile in this repo has been depracated in favor of the \u003ca href=\"https://hub.docker.com/r/clamav/clamav\" rel=\"nofollow\"\u003eofficial clamav image\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [ + "clamav", + "baas", + "parse-server", "singularity", - "bioinformatics" + "virus-scanning", + "clamscan" ], - "updated_at": 1710308206.0 + "updated_at": 1673103429.0 }, { "data_format": 2, - "description": null, + "description": "Singularity Recipes for SX-Aurora TSUBASA", "filenames": [ - "Singularity.def" + "RockyLinux8/Singularity", + "RockyLinux8/Singularity.mpi" ], - "full_name": "54yyyu/model_survey", + "full_name": "veos-sxarr-NEC/singularity", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-for-sx-aurora-tsubasa\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-for-sx-aurora-tsubasa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity for SX-Aurora TSUBASA\u003c/h1\u003e\n\u003cp\u003eThis repository has Singularity recipes to build Singularity image to execute a program on Vector Engine of SX-Aurora TSUBASA.\u003c/p\u003e\n\u003cp\u003eThis document explains how to build a Singularity image with VEOS and related software, and how to execute a VE application on Singularity using the image.\nThis document also explains how to build a Singularity image with NEC MPI and related software, and how to execute a MPI application on Singularity using the image.\u003c/p\u003e\n\u003cp\u003eYou can save and use the image as execution environment for your program.\u003c/p\u003e\n\u003cp\u003eWe have tested the Singularity recipes with the following version of Singularity.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity 3.8.7-1.el8\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-compatibility-problems\" class=\"anchor\" aria-hidden=\"true\" href=\"#compatibility-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompatibility problems\u003c/h2\u003e\n\u003cp\u003eTo avoid the compatibility problem, please consider the below points:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe compatibility of software between a host machine and a container.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe version of VEOS in a host machine must be greater than or equal to the version of VEOS in a container.\u003c/li\u003e\n\u003cli\u003eThe version of NEC MPI in a host machine must be greater than or equal to the version of NEC MPI in a container.\u003c/li\u003e\n\u003cli\u003eThe version of MOFED in a host machine must be equal to the version of MOFED in a container.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe compatibility of software between a container and a build machine\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe version of NEC MPI in a container must be greater than or equal to the version of NEC MPI in a build machine where you built your program.\u003c/li\u003e\n\u003cli\u003eEach software version of NEC SDK in a container must be greater than or equal to each software version of NEC SDK in a build machine where you built your program.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-the-singularity-image-of-veos\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-the-singularity-image-of-veos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the singularity image of VEOS\u003c/h2\u003e\n\u003cp\u003eClone the repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/veos-sxarr-NEC/singularity.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eChange the current directory to the directory which has Singularity recipes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd singularity/RockyLinux8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload TSUBASA-soft-release-2.8-1.noarch.rpm.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -O https://sxauroratsubasa.sakura.ne.jp/repos/TSUBASA-soft-release-2.8-1.noarch.rpm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf your network is behind a proxy, please update dnf.conf to set the proxy.\u003c/p\u003e\n\u003cp\u003eBuild a singularity image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity build --fakeroot veos.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-an-application-in-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-an-application-in-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun an application in the singularity container\u003c/h2\u003e\n\u003cp\u003eRun an application using the below command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --bind /var/opt/nec/ve/veos \u0026lt;image SIF\u0026gt; \u0026lt;pass to binary in container\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, run an application with VE NODE#0.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --bind /var/opt/nec/ve/veos veos.sif env VE_NODE_NUMBER=0 ./a.out\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-the-singularity-image-of-nec-mpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-the-singularity-image-of-nec-mpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the singularity image of NEC MPI\u003c/h2\u003e\n\u003cp\u003eClone the repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/veos-sxarr-NEC/singularity.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eChange the current directory to the directory which has Singularity recipes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd singularity/RockyLinux8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload TSUBASA-soft-release-2.8-1.noarch.rpm.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -O https://sxauroratsubasa.sakura.ne.jp/repos/TSUBASA-soft-release-2.8-1.noarch.rpm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload MLNX_OFED_LINUX.\nFollowing MLNX_OFED_LINUX archive file is needed.\nArchive file should remain compressed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMLNX_OFED_LINUX-5.6-2.0.9.0-rhel8.6-x86_64.tgz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf your network is behind a proxy, please update dnf.conf to set the proxy.\u003c/p\u003e\n\u003cp\u003eBuild a singularity image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity build --fakeroot necmpi.sif Singularity.mpi\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-a-nec-mpi-application-in-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-a-nec-mpi-application-in-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun a NEC MPI application in the singularity container\u003c/h2\u003e\n\u003cp\u003eRun a NEC MPI application using the below commands.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source /opt/nec/ve/mpi/\u0026lt;nec mpi version in the image\u0026gt;/bin64/necmpivars.sh\n$ mpirun \u0026lt;nec mpi options\u0026gt; /usr/bin/singularity exec --bind /var/opt/nec/ve/veos \u0026lt;image SIF\u0026gt; \u0026lt;pass to binary in container\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, run a NEC MPI application on interactive shell.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source /opt/nec/ve/mpi/2.11.0/bin64/necmpivars.sh\n$ mpirun -hosts host1,host2 -np 2 -ve 0 /usr/bin/singularity exec --bind /var/opt/nec/ve/veos ./necmpi.sif ./a.out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, run a NEC MPI application with NQSV.\nnecmpi.sif and a.out is located in your home directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ vi job.sh\n#!/bin/bash\n#PBS -q bq\n#PBS -T necmpi\n#PBS -b 2\n#PBS -l elapstim_req=300\n#PBS --venode=2 --cpunum-lhost=2\n\nsource /opt/nec/ve/mpi/2.11.0/bin64/necmpivars.sh\nmpirun -np 16 /usr/bin/singularity exec --bind /var/opt/nec/ve/veos ~/necmpi.sif ~/a.out\n\n$ qsub job.sh\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 2, + "subscribers_count": 1, + "topics": [], + "updated_at": 1640330684.0 + }, + { + "data_format": 2, + "description": "Research Computing Spring 2019 (IMSE 8410)", + "filenames": [ + "examples/containers/Singularity" + ], + "full_name": "MiddelkoopT/RC-2019-Spring", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emodel_survey\u003c/h1\u003e\u003ca id=\"user-content-model_survey\" class=\"anchor\" aria-label=\"Permalink: model_survey\" href=\"#model_survey\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall this package from github.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install https://github.com/54yyyu/model_survey/tarball/master\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, "subscribers_count": 2, "topics": [], - "updated_at": 1659552958.0 + "updated_at": 1661980750.0 }, { "data_format": 2, - "description": "Solving quantum state diffusion numerically.", + "description": "automated benchmarking of recombination detection methods", "filenames": [ - "Singularity" + "Singularity", + "simg/Singularity.3seq" ], - "full_name": "tabakg/quantum_state_diffusion", + "full_name": "fredjaya/rec-bench", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSolving quantum state diffusion (QSD) numerically.\u003c/h1\u003e\u003ca id=\"user-content-solving-quantum-state-diffusion-qsd-numerically\" class=\"anchor\" aria-label=\"Permalink: Solving quantum state diffusion (QSD) numerically.\" href=\"#solving-quantum-state-diffusion-qsd-numerically\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe script \u003ca href=\"quantum_state_diffusion.py\"\u003equantum_state_diffusion.py\u003c/a\u003e can be\nused to run QSD simulations.\nI am using a (slightly) modified version of a package called sdeint. The only modification I made is to normalize the trajectories for numerical stability.\u003c/p\u003e\n\u003cp\u003eMy version can be found on \u003ca href=\"https://github.com/tabakg/sdeint\"\u003ehttps://github.com/tabakg/sdeint\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThere are two notebooks currently, one for the Kerr system and the second\nfor the absorptive bi-stability. Please compare the results to those obtained\nusing quantum jump trajectories (found on \u003ca href=\"https://github.com/tabakg/diffusion_maps\"\u003ethis repo\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning\u003c/h1\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou have several options for running the simulation, including container-based and local environments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003cli\u003eLocal Environment\u003c/li\u003e\n\u003cli\u003eCluster (SLURM example)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDepending on your familiarity with containers, the first two are recommended to handle software dependencies. Complete instructions are included below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe development environment is Dockerized, meaning that you can run the simulation with a Docker image. First, you need to \u003ca href=\"http://54.71.194.30:4111/engine/installation\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e. The base command to see help for how to run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run tabakg/quantum_state_diffusion --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill show you the following (after a message about the font cache):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: make_quantum_trajectory.py [-h] [--seed SEED] [--ntraj NTRAJ]\n\t [--duration DURATION] [--delta_t DELTAT]\n\t [--Nfock_a NFOCKA] [--Nfock_j NFOCKJ]\n\t [--downsample DOWNSAMPLE] [--quiet]\n\t [--output_dir OUTDIR] [--save2pkl]\n\t [--save2mat]\n\ngenerating trajectories using quantum state diffusion\n\noptional arguments:\n -h, --help show this help message and exit\n --seed SEED Seed to set for the simulation.\n --ntraj NTRAJ number of trajectories, should be kept at 1 if run via\n\t slurm\n --duration DURATION Duration in ()\n --delta_t DELTAT Parameter delta_t\n --Nfock_a NFOCKA Parameter N_focka\n --Nfock_j NFOCKJ Parameter N_fockj\n --downsample DOWNSAMPLE\n\t How much to downsample results\n --quiet Turn off logging (debug and info)\n --output_dir OUTDIR Output folder. If not defined, will use PWD.\n --save2pkl Save pickle file to --output_dir\n --save2mat Save .mat file to --output_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun and save to local machine\u003c/h3\u003e\u003ca id=\"user-content-run-and-save-to-local-machine\" class=\"anchor\" aria-label=\"Permalink: Run and save to local machine\" href=\"#run-and-save-to-local-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that the \u003ccode\u003e--quiet\u003c/code\u003e option can be added to silence printing. By default, no data is saved. To save, you will need to 1) specify the output directory to the \u003ccode\u003e/data\u003c/code\u003e folder in the container using the \u003ccode\u003eoutput_dir\u003c/code\u003e argument and 2) map some directory on your local machine to this \u003ccode\u003e/data\u003c/code\u003e folder. We can do that like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # on your local machine, let\u0027s say we want to save to Desktop\n docker run -v /home/vanessa/Desktop:/data \\\n tabakg/quantum_state_diffusion --output_dir /data --save2pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above will produce the following output:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eINFO:root:Parameter Ntraj set to 1\nINFO:root:Parameter Nfock_j set to 2\nINFO:root:Parameter duration set to 10\nINFO:root:Parameter delta_t set to 0.002\nINFO:root:Parameter downsample set to 100\nINFO:root:Parameter Nfock_a set to 50\nINFO:root:Parameter seed set to 1\nINFO:root:Downsample set to 100\nINFO:root:Regime is set to absorptive_bistable\nRun time: 2.1634318828582764 seconds.\nINFO:root:Saving pickle file...\nINFO:root:Saving result to /data/QSD_absorptive_bistable_1-1-0.002-50-2-10.pkl\nINFO:root:Data saved to pickle file /data/QSD_absorptive_bistable_1-1-0.002-50-2-10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe final output will be in the mapped folder - in the example above, this would be my Desktop at \u003ccode\u003e/home/vanessa/Desktop/QSD_absorptive_bistable*.pkl\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun inside container\u003c/h3\u003e\u003ca id=\"user-content-run-inside-container\" class=\"anchor\" aria-label=\"Permalink: Run inside container\" href=\"#run-inside-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou may want to inspect the data using the same environment it was generated from, in which case you would want to shell into the container. To do this, you can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint=/bin/bash tabakg/quantum_state_diffusion\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eif you type \u003ccode\u003els\u003c/code\u003e you will see that we are sitting in the \u003ccode\u003e/code\u003c/code\u003e directory that contains the core python files. This means that we can run the analysis equivalently:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/code# python make_quantum_trajectory.py --output_dir /data --save2pkl\nINFO:root:Parameter downsample set to 100\nINFO:root:Parameter duration set to 10\nINFO:root:Parameter seed set to 1\nINFO:root:Parameter Nfock_j set to 2\nINFO:root:Parameter Nfock_a set to 50\nINFO:root:Parameter delta_t set to 0.002\nINFO:root:Parameter Ntraj set to 1\nINFO:root:Downsample set to 100\nINFO:root:Regime is set to absorptive_bistable\nRun time: 2.183995485305786 seconds.\nINFO:root:Saving pickle file...\nINFO:root:Saving result to /data/QSD_absorptive_bistable_1-1-0.002-50-2-10.pkl\nINFO:root:Data saved to pickle file /data/QSD_absorptive_bistable_1-1-0.002-50-2-10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand the data is inside the container with us! Great.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eroot@4420ae9e385d:/code# ls /data\nQSD_absorptive_bistable_1-1-0.002-50-2-10.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCustomize the Docker image\u003c/h3\u003e\u003ca id=\"user-content-customize-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Customize the Docker image\" href=\"#customize-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to use the image from Docker Hub (for example, if you want to make changes first) you can also build the image locally. You can build the image by doing the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e git clone https://www.github.com/tabakg/quantum_state_diffusion\n cd quantum_state_diffusion\n docker build -t tabakg/quantum_state_diffusion .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote the \u003ccode\u003e.\u003c/code\u003e at the end of the command to specify the present working directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is a container that is HPC friendly, meaning that it can be run on a cluster environment. The container itself, a file that sits on your computer, can be dropped into a folder on your cluster, and run like a script! We have provided a Singularity file that can bootstrap the Docker image to build the image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Install Singularity\u003c/h3\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h3\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 qsd.img\nsudo singularity bootstrap qsd.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the python executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./qsd.img --help\nusage: make_quantum_trajectory.py [-h] [--seed SEED] [--ntraj NTRAJ]\n\t [--duration DURATION] [--delta_t DELTAT]\n\t [--Nfock_a NFOCKA] [--Nfock_j NFOCKJ]\n\t [--downsample DOWNSAMPLE] [--quiet]\n\t [--output_dir OUTDIR] [--save2pkl]\n\t [--save2mat]\n\ngenerating trajectories using quantum state diffusion\n\noptional arguments:\n -h, --help show this help message and exit\n --seed SEED Seed to set for the simulation.\n --ntraj NTRAJ number of trajectories, should be kept at 1 if run via\n\t slurm\n --duration DURATION Duration in ()\n --delta_t DELTAT Parameter delta_t\n --Nfock_a NFOCKA Parameter N_focka\n --Nfock_j NFOCKJ Parameter N_fockj\n --downsample DOWNSAMPLE\n\t How much to downsample results\n --quiet Turn off logging (debug and info)\n --output_dir OUTDIR Output folder. If not defined, will use PWD.\n --save2pkl Save pickle file to --output_dir\n --save2mat Save .mat file to --output_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou might again want to map a folder for the data output\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run --bind /home/vanessa/Desktop:/data/ qsd.img --output_dir /data --save2pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd you again might want to interactive work in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell --writable qsd.img\n cd /code\n ls\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCluster Usage\u003c/h2\u003e\u003ca id=\"user-content-cluster-usage\" class=\"anchor\" aria-label=\"Permalink: Cluster Usage\" href=\"#cluster-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning on a local machine is fine, but it will not scale well if you want to run thousands of times. Toward this aim, we have provided simple SLURM submission scripts to help! They are optimized for the \u003ca href=\"http://sherlock.stanford.edu\" rel=\"nofollow\"\u003esherlock\u003c/a\u003e cluster at Stanford (which has Singularity installed), however you can easily modify the submission command to run natively on a cluster without it (more detail below). For both, you can use the scripts in \u003ca href=\"slurm\"\u003eslurm\u003c/a\u003e. You will want to do the following:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Build the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-1-build-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: 1. Build the Singularity image\" href=\"#1-build-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing the steps above, build the Singularity image, and use some form of FTP to transfer the image to your cluster. We must do this because it requires sudo to build and bootstrap the image, but not to run it (you do not have sudo permission on a cluster).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Create a folder to work from\u003c/h3\u003e\u003ca id=\"user-content-2-create-a-folder-to-work-from\" class=\"anchor\" aria-label=\"Permalink: 2. Create a folder to work from\" href=\"#2-create-a-folder-to-work-from\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn your $HOME folder in your cluster environment, you likely want to keep a folder to put your image, and organize input and output files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd $HOME\n mkdir -p SCRIPTS/SINGULARITY/QSD\n cd SCRIPTS/SINGULARITY/QSD # transfer qsd.img here\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd then write \u003ca href=\"slurm/run.py\"\u003erun.py\u003c/a\u003e into a file in that location. In a nutshell, this script is going to create local directories for jobs, output, and error files (\u003ccode\u003e.job\u003c/code\u003e,\u003ccode\u003e.out\u003c/code\u003e,\u003ccode\u003e.err\u003c/code\u003e), and then iterate through a variable in the simulation (the \u003ccode\u003eseed\u003c/code\u003e) and submit a job for each on our partition of choice. The variables you should / might be interested in editing are in the header:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData Output\u003c/h2\u003e\u003ca id=\"user-content-data-output\" class=\"anchor\" aria-label=\"Permalink: Data Output\" href=\"#data-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach pickle file contains the simulation result, along with the dictionary of analysis parameters. For example, here we are loading a pickle result file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eimport pickle\nmdict = pickle.load(open(\u0027QSD_absorptive_bistable_1-1-0.002-50-2-10.pkl\u0027,\u0027rb\u0027))\n\n# What result keys are available?\nresult.keys()\ndict_keys([\u0027Nfock_a\u0027, \u0027Ntraj\u0027, \u0027observable_str\u0027, \u0027Nfock_j\u0027, \u0027times\u0027, \u0027psis\u0027, \u0027downsample\u0027, \n \u0027seeds\u0027, \u0027expects\u0027, \u0027delta_t\u0027, \u0027seed\u0027, \u0027duration\u0027, \u0027observable_latex\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith this data, you can do interactive plotting and further analysis, examples which will be provided in this repo (under development).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLocal Installation:\u003c/h1\u003e\u003ca id=\"user-content-local-installation\" class=\"anchor\" aria-label=\"Permalink: Local Installation:\" href=\"#local-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation requires Python 3.\u003c/p\u003e\n\u003cp\u003eIn addition to standard libraries (numpy, sympy, scipy, pickle)\u003c/p\u003e\n\u003cp\u003eIn addition to the modified version of sdeint found on\n\u003ca href=\"https://github.com/tabakg/sdeint\"\u003ehttps://github.com/tabakg/sdeint\u003c/a\u003e (mentioned above), please install\nQNET (\u003ca href=\"https://pypi.python.org/pypi/QNET\" rel=\"nofollow\"\u003ehttps://pypi.python.org/pypi/QNET\u003c/a\u003e). QNET is on pip, and can be installed\nsimply with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install QNET.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI am also using a package called multiprocess, which can be installed with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install multiprocess\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rec-bench\" class=\"anchor\" aria-hidden=\"true\" href=\"#rec-bench\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erec-bench\u003c/h1\u003e\n\u003cp\u003eAutomated benchmarking of recombination detection methods\u003c/p\u003e\n\u003cp\u003eEternally a WIP - many things are hardcoded\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eNextflow\nconda\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/fredjaya/rec-bench.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNextflow doesn\u0027t appear to create the conda environment properly. Create manually.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate fredjaya-rec-bench-0.1.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: conda processes currently hardcoded in \u003ccode\u003emain.nf\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003erec-bench\u003c/code\u003e has five modes that must be specified with \u003ccode\u003e--mode\u003c/code\u003e as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--mode sim\u003c/code\u003e\tGenerate simulation datasets\n\u003ccode\u003e--mode sim_v\u003c/code\u003e\tVisualise/summarise simulation outputs\n\u003ccode\u003e--mode div\u003c/code\u003e\tBenchmark recombination detection methods using simulated data\n\u003ccode\u003e--mode emp\u003c/code\u003e\tDetect recombination in empirical sequence alignments\n\u003ccode\u003e--mode class\u003c/code\u003e\tCalculate classification metrics\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main.nf --help\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Update readme\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1605980860.0 + "updated_at": 1646039648.0 }, { "data_format": 2, - "description": "OpenCV 2 built with NVIDIA acceleration", + "description": "Run CRISPResso on genome editing experiments", "filenames": [ "Singularity" ], - "full_name": "dl-container-registry/opencv2", + "full_name": "czbiohub/nf-core-crisprvar", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOpenCV2 Dockerfile\u003c/h1\u003e\u003ca id=\"user-content-opencv2-dockerfile\" class=\"anchor\" aria-label=\"Permalink: OpenCV2 Dockerfile\" href=\"#opencv2-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/dl-container-registry/opencv2\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/910dd8ad3a37d828dfd7935080cf5e0f623f9f9322a477a13e895927c3fb752a/68747470733a2f2f696d672e736869656c64732e696f2f636972636c6563692f70726f6a6563742f6769746875622f646c2d636f6e7461696e65722d72656769737472792f6f70656e6376322f6d61737465722e737667\" alt=\"CircleCI branch\" data-canonical-src=\"https://img.shields.io/circleci/project/github/dl-container-registry/opencv2/master.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/willprice/opencv2-cuda8/~/settings/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/680701285475c874bf8f852e787b50b1a1f9f07c96a5f72aded98a8a1db5f1cf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65726875622d3232623865622e737667\" alt=\"Dockerhub link\" data-canonical-src=\"https://img.shields.io/badge/hosted-dockerhub-22b8eb.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/530\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity hub hosted\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild other containers from this base image, it contains a prebuilt version of\nOpenCV 2.4.13.4 at \u003ccode\u003e/src/opencv_build\u003c/code\u003e installed to \u003ccode\u003e/usr/local/OpenCV\u003c/code\u003e\n(containing CMake files for building other projects).\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nf-corecrisprvar\" class=\"anchor\" href=\"#nf-corecrisprvar\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/crisprvar\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eRun CRISPResso on genome editing experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/crisprvar\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8bfb8f49f69e465c023fb291cefc2ccbea97c9a7cc04377260fa6052d9370b28/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6372697370727661722e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/crisprvar.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/crisprvar\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e5f9f1b9479b9d19c758902e0a06e81ab060fa6a9207a5e935aee26edc728ac/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6372697370727661722e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/crisprvar.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/crisprvar pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1589316312.0 + "updated_at": 1637330863.0 }, { "data_format": 2, - "description": null, + "description": "Recipe for VEP + Cache", "filenames": [ - "examples/docker/Singularity", - "examples/raspbian/Singularity", - "examples/apps/Singularity", - "examples/apps/Singularity.cowsay", - "examples/ubuntu/Singularity", - "examples/arch/Singularity", - "examples/asciinema/Singularity", - "examples/self/Singularity", - "examples/busybox/Singularity", - "examples/shub/Singularity", - "examples/opensuse/Singularity", - "examples/centos/Singularity", - "examples/scientific/Singularity" + "Singularityfiles/Singularity.99-GRCh38-merged", + "Singularityfiles/Singularity.99-GRCh37-merged" ], - "full_name": "kernsuite-debian/singularity-container", + "full_name": "matmu/vep", "latest_release": null, - "readme": "\u003cp\u003e_Please note recent changes in the github repo branch structure. If you want\nto install a stable release of Singularity, please use a tag or a \u003ca href=\"https://github.com/singularityware/singularity/releases\"\u003erelease\ntarball\u003c/a\u003e. If you are\na developer who would like to contribute to Singularity and you want to know\nwhich branch to submit your pull request to, please see notes on the branch\nreorganization \u003ca href=\"https://www.sylabs.io/2018/03/managing-singularity-branches/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease also note that 2.6.0 is expected to be the final feature release in the\n2.x series. While bug fixes may be added via point releases (for example 2.6.1)\nno new features releases (for example 2.7.0) are planned.\u003c/p\u003e\n\u003cp\u003ePull requests adding features to the 2.x series will no longer be reviewed.\u003cbr\u003e\nAny new features should be targeted to the master branch (which used to be\ncalled development-3.0)._\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/singularityware/singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39fe73b2acfcaf157d81df51bfad4bac84e7177bc8ffda9f351f966cdfb1eff1/68747470733a2f2f7472617669732d63692e6f72672f73696e67756c6172697479776172652f73696e67756c61726974792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/singularityware/singularity.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eGuidelines for Contributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\".github/PULL_REQUEST_TEMPLATE.md\"\u003ePull Request Template\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"LICENSE.md\"\u003eProject License\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity - Enabling users to have full control of their environment.\u003c/h1\u003e\u003ca id=\"user-content-singularity---enabling-users-to-have-full-control-of-their-environment\" class=\"anchor\" aria-label=\"Permalink: Singularity - Enabling users to have full control of their environment.\" href=\"#singularity---enabling-users-to-have-full-control-of-their-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStarting a Singularity container \"swaps\" out the host\noperating system environment for one the user controls!\u003c/p\u003e\n\u003cp\u003eLet\u0027s say you are running Ubuntu on your workstation or server, but you\nhave an application which only runs on Red Hat Enterprise Linux 6.3.\nSingularity can instantly virtualize the operating system, without having\nroot access, and allow you to run that application in its native environment!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbout\u003c/h1\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is a container platform focused on supporting \"Mobility of\nCompute\".\u003c/p\u003e\n\u003cp\u003eMobility of Compute encapsulates the development to compute model where\ndevelopers can work in an environment of their choosing and creation, and\nwhen the developer needs additional compute resources, this environment\ncan easily be copied and executed on other platforms. Additionally, as the\nprimary use case for Singularity is targeted towards computational portability.\nMany of the barriers to entry of other container solutions do not apply to\nSingularity, making it an ideal solution for users (both computational and\nnon-computational) and HPC centers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe Container\u003c/h2\u003e\u003ca id=\"user-content-the-container\" class=\"anchor\" aria-label=\"Permalink: The Container\" href=\"#the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity utilizes container images, which means when you enter and\nwork within the Singularity container, you are physically located inside\nof this image. The image grows and shrinks in real time as you install\nor delete files within the container. If you want to copy a container,\nyou copy the image.\u003c/p\u003e\n\u003cp\u003eUsing a single image for the container format has added advantages\nespecially within the context of HPC with large parallel file systems\nbecause all metadata operations within the container occur within the\ncontainer image (and not on the metadata server!).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMobility of Compute\u003c/h2\u003e\u003ca id=\"user-content-mobility-of-compute\" class=\"anchor\" aria-label=\"Permalink: Mobility of Compute\" href=\"#mobility-of-compute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith Singularity, developers who like to be able to easily control their\nown environment will love Singularity\u0027s flexibility. Singularity does not\nprovide a pathway for escalation of privilege (as do other container\nplatforms which are thus not appropriate for multi-tenant resources) so\nyou must be able to become root on the host system (or virtual machine)\nin order to modify the container.\u003c/p\u003e\n\u003cp\u003eA Singularity container can be launched in a variety of different ways\ndepending on what you wanted to do with it. A simple method might be to\nlaunch an interactive shell within the container image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity shell /tmp/Centos-7.img \ngmk@Centos-7.img demo\u0026gt; echo \"Hello from within the container\"\nHello from within the container\ngmk@Centos-7.img demo\u0026gt; whoami\ngmk\ngmk@Centos-7.img demo\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if you want to do the same thing as root:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ sudo singularity shell -w /tmp/Centos-7.img \nroot@Centos-7.img demo\u0026gt; whoami\nroot\nroot@Centos-7.img demo\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003enote: By default, Singularity launches the container image in read-only\nmode (so it can be easily launched in parallel). The \u003ccode\u003e-w\u003c/code\u003e option used above\ntells Singularity to mount the image in read/write mode, such that root\ncan now make changes to the container.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, relevant file systems on your host are shared, automatically,\nwithin the context of your container. This can be demonstrated as\nfollows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ pwd\n/home/gmk/demo\n[gmk@centos7-x64 demo]$ echo \"world\" \u0026gt; hello\n[gmk@centos7-x64 demo]$ singularity shell /tmp/Centos-7.img \ngmk@Centos-7.img demo\u0026gt; pwd\n/home/gmk/demo\ngmk@Centos-7.img demo\u0026gt; cat hello\nworld\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the developer has completed their environment, the image file can\nbe compressed and copied to any other system that has Singularity installed.\nIf you do not have root on that system, you will not be able to make any\nchanges to the image once on that system. But you will be able to use the\ncontainer and access the data and files outside the container as\neasily as you would on your development system or virtual machine.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePortability of Singularity container images\u003c/h2\u003e\u003ca id=\"user-content-portability-of-singularity-container-images\" class=\"anchor\" aria-label=\"Permalink: Portability of Singularity container images\" href=\"#portability-of-singularity-container-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity images are highly portable between Linux distributions (as\nlong as the binary format is the same). You can generate your image on\nDebian or CentOS, and run it on Mint or Slackware.\u003c/p\u003e\n\u003cp\u003eWithin a particular container, one can include their programs, data,\nscripts and pipelines and thus port a workflow to any other architecture\ncompatible Linux system or distribution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootstrapping new images\u003c/h2\u003e\u003ca id=\"user-content-bootstrapping-new-images\" class=\"anchor\" aria-label=\"Permalink: Bootstrapping new images\" href=\"#bootstrapping-new-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerally, when bootstrapping an image from scratch, you must build it from\na compatible host. This is because you must use the distribution specific\ntools it comes with (e.g. Red Hat does not provide Debian\u0027s debootstrap by\ndefault). But once the image has been bootstrapped and includes the necessary\nbits to be self-hosting (e.g. YUM on CentOS and apt-get on Debian/Ubuntu) then\nthe process of managing the container can be implemented from within the\ncontainer.\u003c/p\u003e\n\u003cp\u003eThe process of building a bootstrap starts with a definition\nspecification. The definition file describes how you want the operating\nsystem to be built, what should go inside it and any additional\nmodifications necessary.\u003c/p\u003e\n\u003cp\u003eHere is an example of a very simple bootstrap definition file for CentOS:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: yum\nOSVersion: 7\nMirrorURL: http://mirror.centos.org/centos-%{OSVERSION}/%{OSVERSION}/os/$basearch/\nInclude: yum\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce you have created your bootstrap definition, you can build your\nSingularity container image by first creating a blank image, and then\nbootstrapping using your definition file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ sudo singularity create /tmp/Centos-7.img\n[gmk@centos7-x64 demo]$ sudo singularity bootstrap /tmp/Centos-7.img centos.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom there we can immediately start using the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img cat /etc/redhat-release \nCentOS Linux release 7.2.1511 (Core) \n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img python --version\nPython 2.7.5\n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img python hello.py \nhello world\n[gmk@centos7-x64 demo]$ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if I do this same process again, while changing the \u003cstrong\u003eOSVersion\u003c/strong\u003e\nvariable in the bootstrap definition to \u003cstrong\u003e6\u003c/strong\u003e (where previously it was\nautomatically ascertained by querying the RPM database), we can\nessentially build a CentOS-6 image in exactly the same manner as\nabove. Doing so reveals this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-6.img cat /etc/redhat-release \nCentOS release 6.7 (Final)\n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-6.img python --version\nPython 2.6.6\n[gmk@centos7-x64 demo]$ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd as expected, the Python version we now see is what comes from by\ndefault in CentOS-6.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCite as:\u003c/h1\u003e\u003ca id=\"user-content-cite-as\" class=\"anchor\" aria-label=\"Permalink: Cite as:\" href=\"#cite-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459. https://doi.org/10.1371/journal.pone.0177459\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe also have a Zenodo citation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer, Gregory M.. (2016). Singularity 2.1.2 - Linux application and environment\ncontainers for science. 10.5281/zenodo.60736\n\nhttp://dx.doi.org/10.5281/zenodo.60736\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebpage\u003c/h1\u003e\u003ca id=\"user-content-webpage\" class=\"anchor\" aria-label=\"Permalink: Webpage\" href=\"#webpage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have full documentation at \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/docs/\u003c/a\u003e, and \u003ca href=\"http://www.github.com/singularityware/singularityware.github.io\"\u003ewelcome contributions\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containerized-variant-effect-predictor-vep--cache\" class=\"anchor\" href=\"#containerized-variant-effect-predictor-vep--cache\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerized Variant Effect Predictor (VEP) + Cache\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://twitter.com/intent/tweet?hashtags=Ensembl,VEP,Singularity,Docker\u0026amp;url=https://github.com/matmu/vep\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90bc908826728c0e4261acfff5619fd732c7be2b2a00624fce6363c9a3623c90/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f75726c2f687474702f736869656c64732e696f2e7376673f7374796c653d736f6369616c\" alt=\"Twitter\" data-canonical-src=\"https://img.shields.io/twitter/url/http/shields.io.svg?style=social\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u00a0+ \u003ca href=\"#Introduction\"\u003eIntroduction\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Building-image-with-Singularity\"\u003eBuilding image with Singularity\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Run-VEP\"\u003eRun VEP\u003c/a\u003e \u003cbr\u003e\n\u00a0\u00a0\u00a0\u00a0|-- \u003ca href=\"#More-options\"\u003eMore options\u003c/a\u003e \u003cbr\u003e\n\u00a0\u00a0\u00a0\u00a0|-- \u003ca href=\"#Examples\"\u003eExamples\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Post-processing\"\u003ePost-processing\u003c/a\u003e \u003cbr\u003e\n\u00a0\u00a0\u00a0\u00a0|-- \u003ca href=\"#Split-VEP\"\u003eSplit VEP\u003c/a\u003e \u003cbr\u003e\n\u00a0\u00a0\u00a0\u00a0|-- \u003ca href=\"#Filtering-by-VEP-annotations\"\u003eFiltering by VEP annotations\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#VEP-plugins\"\u003eVEP plugins\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Build-and-run-VEP-with-Docker\"\u003eBuild \u0026amp; run VEP with Docker\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Acknowledgments\"\u003eAcknowledgements\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThis documentation describes the usage of the Docker image at \u003ca href=\"https://hub.docker.com/r/matmu/vep\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/matmu/vep\u003c/a\u003e which contains the bioinformatics tool \u003cstrong\u003eEnsembl Variant effect predictor (VEP)\u003c/strong\u003e for annotating genetic variants. The image comes with\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMerged cache including RefSeq and Ensembl transcripts (VEP parameter --merged required)\u003c/li\u003e\n\u003cli\u003eReference genome and index\u003c/li\u003e\n\u003cli\u003ePlugins (annotation data is not included)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-available-versions\" class=\"anchor\" href=\"#available-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable versions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eHuman:\u003c/strong\u003e \u003ca href=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCh38-merged.yml\"\u003e\u003cimg src=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCh38-merged.yml/badge.svg\" alt=\"103-GRCh38-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCh38.yml\"\u003e\u003cimg src=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCh38.yml/badge.svg\" alt=\"103-GRCh38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/101-GRCh38/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/101-GRCh38/badge.svg\" alt=\"101-GRCh38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCh38/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCh38/badge.svg\" alt=\"100-GRCh38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCh38-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCh38-merged/badge.svg\" alt=\"100-GRCh38-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCh37/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCh37/badge.svg\" alt=\"100-GRCh37\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCh37-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCh37-merged/badge.svg\" alt=\"100-GRCh37-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/99-GRCh38-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/99-GRCh38-merged/badge.svg\" alt=\"99-GRCh38-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/99-GRCh37-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/99-GRCh37-merged/badge.svg\" alt=\"99-GRCh37-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cstrong\u003eMouse:\u003c/strong\u003e \u003ca href=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCm39.yml\"\u003e\u003cimg src=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCm39.yml/badge.svg\" alt=\"103-GRCm39\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/101-GRCm38/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/101-GRCm38/badge.svg\" alt=\"101-GRCm38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCm38/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCm38/badge.svg\" alt=\"100-GRCm38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCm38-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCm38-merged/badge.svg\" alt=\"100-GRCm38-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe term \u003ccode\u003emerged\u003c/code\u003e refers to the merged Ensembl/RefSeq cache. To be consistent with the Ensembl website, chose Ensembl cache only (i.e. without the term \u003ccode\u003emerged\u003c/code\u003e). Examples for available versions are \u003cstrong\u003e99-GRCh38\u003c/strong\u003e (VEP 99 with Ensembl cache for reference GRCh38) or \u003cstrong\u003e99-GRh37-merged\u003c/strong\u003e (VEP 99 with Ensembl/Refseq cache for reference GRCh37).\u003c/p\u003e\n\u003cp\u003eYou can also visit \u003ca href=\"https://hub.docker.com/r/matmu/vep/tags\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/matmu/vep/tags\u003c/a\u003e to get a list of available versions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e If you require a container for a species not mentioned above, feel free to contact us or even better, create an issue.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-image-with-singularity\" class=\"anchor\" href=\"#build-image-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild image with Singularity\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build vep.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg docker://matmu/vep:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e\u0026lt;version\u0026gt;\u003c/code\u003e is a tag representing the Ensembl version and the species + version of the reference genome.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-vep\" class=\"anchor\" href=\"#run-vep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun VEP\u003c/h2\u003e\n\u003cp\u003eTo run VEP execute\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg vep [options]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhereby \u003ccode\u003e\u0026lt;version\u0026gt;\u003c/code\u003e is replaced by a respective version (see above), e.g. \u003ccode\u003e99-CRCh38\u003c/code\u003e. It is essential to add the VEP option \u003ccode\u003e--merged\u003c/code\u003e when using an image with merged Ensembl/Refseq cache. For species except homo sapiens, also the parameter \u003ccode\u003e--species\u003c/code\u003e (e.g. \u003ccode\u003e--species mus_musculus\u003c/code\u003e), has to be set as well.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-more-options\" class=\"anchor\" href=\"#more-options\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore options\u003c/h3\u003e\n\u003cp\u003eThe options for base cache/plugin directories, species and assembly are set to the right values by default and do not need to be set by the user.\u003c/p\u003e\n\u003cp\u003eVisit \u003ca href=\"http://www.ensembl.org/info/docs/tools/vep/script/vep_options.html\" rel=\"nofollow\"\u003ehttp://www.ensembl.org/info/docs/tools/vep/script/vep_options.html\u003c/a\u003e for detailed information about all VEP options. Detailed information about \u003cstrong\u003einput/output formats\u003c/strong\u003e can be found at \u003ca href=\"https://www.ensembl.org/info/docs/tools/vep/vep_formats.html#defaultout\" rel=\"nofollow\"\u003ehttps://www.ensembl.org/info/docs/tools/vep/vep_formats.html#defaultout\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-examples\" class=\"anchor\" href=\"#examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-minimum-output-format-compressed-tab-delimited\" class=\"anchor\" href=\"#minimum-output-format-compressed-tab-delimited\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMinimum (output format: compressed tab delimited)\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38-merged.simg vep --dir /opt/vep/.vep --merged --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt.gz --tab --compress_output bgzip\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38.simg vep --dir /opt/vep/.vep --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt.gz --tab --compress_output bgzip\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCm38.simg vep --dir /opt/vep/.vep --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt.gz --tab --compress_output bgzip -species mus_musculus\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-minimum-output-format-compressed-vcf\" class=\"anchor\" href=\"#minimum-output-format-compressed-vcf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMinimum (output format: compressed vcf)\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38.simg vep --dir /opt/vep/.vep --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf.gz --vcf --compress_output bgzip\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-full-annotation\" class=\"anchor\" href=\"#full-annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFull annotation\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38.simg vep --dir /opt/vep/.vep --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf.gz --vcf --compress_output bgzip --everything --nearest symbol \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-post-processing\" class=\"anchor\" href=\"#post-processing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePost-processing\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-split-vep\" class=\"anchor\" href=\"#split-vep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSplit VEP\u003c/h3\u003e\n\u003cp\u003eThere is a plugin for \u003ccode\u003ebcftools\u003c/code\u003e that allows to split VEP annotations as well as sample information in a VCF file and convert it to a text file: \u003ca href=\"http://samtools.github.io/bcftools/howtos/plugin.split-vep.html\" rel=\"nofollow\"\u003ehttp://samtools.github.io/bcftools/howtos/plugin.split-vep.html\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-filtering-by-vep-annotations\" class=\"anchor\" href=\"#filtering-by-vep-annotations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiltering by VEP annotations\u003c/h3\u003e\n\u003cp\u003eIf you chose to output the VEP annotations as text file, any command line tool (e.g. \u003ccode\u003eawk\u003c/code\u003e) or even \u003ccode\u003eExcel\u003c/code\u003e can be used for filtering the results. For VCF files, the image includes a VEP filtering script which can be executed by\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg filter_vep [options]\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-options\" class=\"anchor\" href=\"#options\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions\u003c/h4\u003e\n\u003cp\u003eVisit \u003ca href=\"https://www.ensembl.org/info/docs/tools/vep/script/vep_filter.html\" rel=\"nofollow\"\u003ehttps://www.ensembl.org/info/docs/tools/vep/script/vep_filter.html\u003c/a\u003e for detailed info about available options.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-filtering-examples\" class=\"anchor\" href=\"#filtering-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiltering examples\u003c/h4\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-filter-for-rare-variants\" class=\"anchor\" href=\"#filter-for-rare-variants\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFilter for rare variants\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg filter_vep --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.filtered.vcf --only_matched --filter \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e(IMPACT is HIGH or IMPACT is MODERATE or IMPACT is LOW) and (BIOTYPE is protein_coding) and ((PolyPhen \u0026gt; 0.446) or (SIFT \u0026lt; 0.05)) and (EUR_AF \u0026lt; 0.001 or gnomAD_NFE_AF \u0026lt; 0.001 or (not EUR_AF and not gnomAD_NFE_AF))\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-vep-plugins\" class=\"anchor\" href=\"#vep-plugins\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVEP plugins\u003c/h2\u003e\n\u003cp\u003eVEP allows several other annotations sources (aka Plugins). Their respective Perl modules are included in the image, the annotation files have to be added seperately, however. The list of plugins as well as instructions on how to download and pre-process the annotation files can be found at: \u003ca href=\"http://www.ensembl.org/info/docs/tools/vep/script/vep_plugins.html\" rel=\"nofollow\"\u003ehttp://www.ensembl.org/info/docs/tools/vep/script/vep_plugins.html\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38-merged.simg vep --dir /opt/vep/.vep --merged --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt.gz --tab --compress_output bgzip --plugin CADD,/path/to/ALL.TOPMed_freeze5_hg38_dbSNP.tsv.gz\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-and-run-vep-with-docker\" class=\"anchor\" href=\"#build-and-run-vep-with-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild and run VEP with Docker\u003c/h2\u003e\n\u003cp\u003eTo pull the image and run the container with Docker use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run matmu/vep:\u0026lt;version\u0026gt; vep [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUnlike Singularity, the directories of \u003cstrong\u003ePlugin\u003c/strong\u003e annotation files (e.g. \u003ccode\u003e/path/to/dir\u003c/code\u003e) have to be explicitely bound to a target directory (e.g. \u003ccode\u003e/opt/data\u003c/code\u003e) within the container with option \u003ccode\u003e-v\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/to/dir:/opt/data matmu/vep:\u0026lt;version\u0026gt; vep [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" href=\"#acknowledgments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis document has been created by \u003cstrong\u003eJulia Remes\u003c/strong\u003e \u0026amp; \u003cstrong\u003eMatthias Munz\u003c/strong\u003e, \u003cstrong\u003eUniversity of L\u00fcbeck\u003c/strong\u003e, \u003cstrong\u003eGermany\u003c/strong\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1589975545.0 + "updated_at": 1614861605.0 }, { "data_format": 2, - "description": "Simplifying parallel graph processing by automating the installation and timing of some of the myriad platforms.", + "description": null, "filenames": [ - "Singularity" + "installationsWithAdditionalTools/openfoam-v1712-waves2Foam2124/02_PortingToSingularity/Singularity.def", + "installationsWithAdditionalTools/openfoam-2.4.x_waves2Foam2079/02_PortingToSingularity/Singularity.def" ], - "full_name": "HPCL/easy-parallel-graph", + "full_name": "alexisespinosa-uptake/OpenFOAMContainers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSimplifying Parallel Graph Processing\u003c/h1\u003e\u003ca id=\"user-content-simplifying-parallel-graph-processing\" class=\"anchor\" aria-label=\"Permalink: Simplifying Parallel Graph Processing\" href=\"#simplifying-parallel-graph-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is aimed at simplifying aspects of parallel graph processing starting with providing a framework for analyzing performance and energy usage for a given system.\u003c/p\u003e\n\u003cp\u003eThe general workflow could consist of the following steps. You can find an example workflow in \u003ccode\u003eexperiments/example.sh\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003e\u003ccode\u003ecd experiment\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eDownload the systems and build them with \u003ccode\u003e./get-libraries.sh\u003c/code\u003e. You may also supply a location where the libraries should be installed but the default is \u003ccode\u003e./lib\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eGenerate some synthetic datasets with \u003ccode\u003e./gen-datasets.sh 20\u003c/code\u003e. The 20 here will generate an RMAT matrix to the Graph500 specifications with 2^20 = 1,048,576 vertices with an average of 16 edges per vertex.\n\u003cul\u003e\n\u003cli\u003eAlternatively, you could run \u003ccode\u003e./gen-datasets -f=\u0026lt;your_file\u0026gt;\u003c/code\u003e. Currently, this only supports files of the \u003ccode\u003e.el\u003c/code\u003e and \u003ccode\u003e.wel\u003c/code\u003e forms. These are explained \u003ca href=\"https://gist.github.com/sampollard/f9169c4eb04669390a834884682c080d\"\u003ehere\u003c/a\u003e. It should accept any graph file you can find from \u003ca href=\"https://snap.stanford.edu/data/index.html\" rel=\"nofollow\"\u003eSNAP Database\u003c/a\u003e or the \u003ca href=\"http://konect.uni-koblenz.de/networks/\" rel=\"nofollow\"\u003eKONECT Database\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSelect a scale and number of threads and run the experiment with \u003ccode\u003erun-synthetic.sh\u003c/code\u003e, e.g.\n\u003ccode\u003e./run-synthetic.sh 20 4\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eParse the log files to get a .csv using \u003ccode\u003e./parse-output.sh\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eNote: \u003ccode\u003erun-power.sh\u003c/code\u003e also parses the log files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAnalyze the data. Some examples can be found in \u003ccode\u003epapers/publication/plot_data.R\u003c/code\u003e and\u003ccode\u003eexperiment/experiment_analysis.R\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlgorithms\u003c/h2\u003e\u003ca id=\"user-content-algorithms\" class=\"anchor\" aria-label=\"Permalink: Algorithms\" href=\"#algorithms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eBreadth First Search (BFS)\u003c/li\u003e\n\u003cli\u003eSingle Source Shortest Paths (SSSP)\u003c/li\u003e\n\u003cli\u003ePageRank: This uses a stopping criterion of \u003ccode\u003esum(|\u03c0_i - \u03c0_(i-1)|)\u003c/code\u003e where \u003ccode\u003e\u03c0_i\u003c/code\u003e is the PageRank at iteration \u003ccode\u003ei\u003c/code\u003e and the alpha parameter is \u0251 = 0.15.\u003c/li\u003e\n\u003cli\u003eTriangle Counting (TriangleCount) - Counts the number of triangles in an undirected graph. If the input graph is directed, it is symmetrized beforehand.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePower and Energy\u003c/h2\u003e\u003ca id=\"user-content-power-and-energy\" class=\"anchor\" aria-label=\"Permalink: Power and Energy\" href=\"#power-and-energy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to build for power measurement, you may use \u003ccode\u003epower/build-power.sh\u003c/code\u003e\nRun the experiments and monitor power using \u003ccode\u003eexperiment/run-power.sh\u003c/code\u003e. Requires root permissions.\n\u003ccode\u003ebuild-power.sh\u003c/code\u003e downloads and compiles the various projects for power measurement. Installs to \u003ccode\u003e./powerlib\u003c/code\u003e by default.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalysis\u003c/h2\u003e\u003ca id=\"user-content-analysis\" class=\"anchor\" aria-label=\"Permalink: Analysis\" href=\"#analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eexperiment_analysis.R:\u003c/code\u003e This script takes in a config file. You can see an example in \u003ccode\u003econfig_template.R\u003c/code\u003e, and the variables are explained in the comments. Notice here there are both synthetic and realworld experiments that can be analyzed at once. If you just want to do one or the other, uncomment out \u003ccode\u003efocus_scale\u003c/code\u003e and \u003ccode\u003efocus_thread\u003c/code\u003e for synthetic and \u003ccode\u003erealworld_datasets\u003c/code\u003e for realworld datasets. If you want to do performance prediction (see the \u003ccode\u003elearn\u003c/code\u003e directory) you must set \u003ccode\u003ecoalesce \u0026lt;- TRUE\u003c/code\u003e so the experimental data gets compressed to a format amenable to machine learning.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLearning\u003c/h2\u003e\u003ca id=\"user-content-learning\" class=\"anchor\" aria-label=\"Permalink: Learning\" href=\"#learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere you can download datasets, extract features, and use performance data to train a model to predict runtime performance of the various algorithms to recommend a highly performing package for your problem.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edatasets.txt\u003c/code\u003e This file contains the URLs of datasets you wish to use. Currently supported databases for downloading are KONECT and SNAP, and currently supported feature extraction are for SNAP. The format is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edataset name\nfeature web page\nURL to the graph\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSome examples are provided in the existing \u003ccode\u003edatasets.txt\u003c/code\u003e. You can comment out datasets with \u003ccode\u003e#\u003c/code\u003e but please only add additional lines in multiples of 3. The datasets are read by taking every third line, so adding additional lines will mess things up. Yes, I realize this is a bit of a silly limitation.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eunzipper.sh\u003c/code\u003e This downloads and unzips the datasets. The usage is \u003ccode\u003eunzipper.sh \u0026lt;dataset file\u0026gt; \u0026lt;dataset_dir\u0026gt;\u003c/code\u003e. A sensible default is\n\u003ccode\u003e./unzipper.sh datasets.txt ../datasets\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003efeatures.py\u003c/code\u003e extracts features from a dataset\u0027s description page in SNAP (and eventually KONECT). This will put a file called \u003ccode\u003efeatures.csv\u003c/code\u003e into the respective dataset\u0027s directory, looking through every dataset in \u003ccode\u003edatasets.txt\u003c/code\u003e. The usage is \u003ccode\u003epython features.py \u0026lt;data_dir\u0026gt; (default: ../experiments/datasets)\u003c/code\u003e. \u003cem\u003eNote\u003c/em\u003e: If you want to add a non-SNAP dataset into the machine-learned model you must add your own features.csv. The minimum required is to have Nodes and Edges. For example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNodes,Edges\n23132,511764\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewould work.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ecombined.csv\u003c/code\u003e file from the Analysis section can be passed into scripts here.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOther Scripts\u003c/h3\u003e\u003ca id=\"user-content-other-scripts\" class=\"anchor\" aria-label=\"Permalink: Other Scripts\" href=\"#other-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egraphalytics/get-graphalytics.sh\u003c/code\u003e: This script gathers, installs, and runs various\nbenchmarks from Graphalytics. Run with no arguments. If you want to change\nwhat gets run, you can edit the script after the \u003ccode\u003e### MAIN ###\u003c/code\u003e section.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epapers/report/get-hwinfo.sh\u003c/code\u003e: Gathers hardware information and outputs to a csv (stdout). Meant\nto be used with the automatic report generation. Works better if you have sudo permission.\nSpecifically, \u003ccode\u003esudo lshw \u0026gt; lshw.txt\u003c/code\u003e for Linux and \u003ccode\u003esudo dmidecode \u0026gt; dmidecode.txt\u003c/code\u003e for Mac.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Scripts in the \u003ccode\u003eexperiment\u003c/code\u003e Directory\u003c/h3\u003e\u003ca id=\"user-content-the-scripts-in-the-experiment-directory\" class=\"anchor\" aria-label=\"Permalink: The Scripts in the experiment Directory\" href=\"#the-scripts-in-the-experiment-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eR and the ggplot2 library\u003c/li\u003e\n\u003cli\u003eGraph500, GAP, Graphalytics, GraphBIG, PowerGraph, Galois dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraphBIG/OpenG\u003c/h3\u003e\u003ca id=\"user-content-graphbigopeng\" class=\"anchor\" aria-label=\"Permalink: GraphBIG/OpenG\" href=\"#graphbigopeng\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003egcc/g++ with c++0x support (\u0026gt;4.3)\u003c/li\u003e\n\u003cli\u003eFor profiling: Linux (because it uses libpfm)\u003c/li\u003e\n\u003cli\u003eCmake\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePowerGraph\u003c/h3\u003e\u003ca id=\"user-content-powergraph\" class=\"anchor\" aria-label=\"Permalink: PowerGraph\" href=\"#powergraph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ezlib\u003c/li\u003e\n\u003cli\u003eMPICH2\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraphMat\u003c/h3\u003e\u003ca id=\"user-content-graphmat\" class=\"anchor\" aria-label=\"Permalink: GraphMat\" href=\"#graphmat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eIntel compiler (icpc 17 is known to work)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraph500 \u0026amp; GAP Benchmark Suite\u003c/h3\u003e\u003ca id=\"user-content-graph500--gap-benchmark-suite\" class=\"anchor\" aria-label=\"Permalink: Graph500 \u0026amp; GAP Benchmark Suite\" href=\"#graph500--gap-benchmark-suite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eA C++ compiler with OpenMP Support\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGalois\u003c/h3\u003e\u003ca id=\"user-content-galois\" class=\"anchor\" aria-label=\"Permalink: Galois\" href=\"#galois\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCmake\u003c/li\u003e\n\u003cli\u003eBoost\u003c/li\u003e\n\u003cli\u003eI\u0027ve had difficulty using any gcc version other than 4.8.5. Be \u003cem\u003eabsolutely\u003c/em\u003e sure you use the same gcc to build boost as you do to build Galois.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures that are not ready\u003c/h2\u003e\u003ca id=\"user-content-features-that-are-not-ready\" class=\"anchor\" aria-label=\"Permalink: Features that are not ready\" href=\"#features-that-are-not-ready\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraphalytics\u003c/h3\u003e\u003ca id=\"user-content-graphalytics\" class=\"anchor\" aria-label=\"Permalink: Graphalytics\" href=\"#graphalytics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can download and build the systems for Graphalytics, package them, and run them with \u003ccode\u003egraphalytics/get-graphalytics.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003eget-graphalytics.sh\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-get-graphalyticssh\" class=\"anchor\" aria-label=\"Permalink: get-graphalytics.sh\" href=\"#get-graphalyticssh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eJava\u003c/li\u003e\n\u003cli\u003ePerl\u003c/li\u003e\n\u003cli\u003eYarn\u003c/li\u003e\n\u003cli\u003eApache maven 3.0.0 or later\u003c/li\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraphalytics\u003c/h3\u003e\u003ca id=\"user-content-graphalytics-1\" class=\"anchor\" aria-label=\"Permalink: Graphalytics\" href=\"#graphalytics-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eApache maven 3.0.0 or later\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1671308248.0 + "updated_at": 1631870314.0 }, { "data_format": 2, - "description": "braker container", + "description": "USDA ARS BEARRU Whole Genome Sequencing Workflow", "filenames": [ - "Singularity" + "containers/Singularity" ], - "full_name": "aseetharam/braker", + "full_name": "lakinsm/bear-wgs", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBRAKER Container \u003ca href=\"https://singularity-hub.org/collections/4738\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-braker-container-\" class=\"anchor\" aria-label=\"Permalink: BRAKER Container \" href=\"#braker-container-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Gaius-Augustus/BRAKER\"\u003eBRAKER2\u003c/a\u003e is a gene prediction program that uses GeneMark-EXand AUGUSTUS from RNA-Seq and/or protein homology information, and that integrates the extrinsic evidence from RNA-Seq and protein homology information into the prediction.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull braker2.sif shub://aseetharam/braker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a \u003ccode\u003ebraker2.sif\u003c/code\u003e image, with \u003ccode\u003ebraker\u003c/code\u003e installed within the image. Before running, copy \u003ccode\u003eaugustus_config\u003c/code\u003e directory as it needs to be writeable:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e braker2.sif cp -R /usr/local/config augustus_config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisities\u003c/h3\u003e\u003ca id=\"user-content-prerequisities\" class=\"anchor\" aria-label=\"Permalink: Prerequisities\" href=\"#prerequisities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run this container you\u0027ll need \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBRAKER usage can be found by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --no-home --home /opt/gm_key --cleanenv braker2.sif braker.pl --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment Variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ePATH\u003c/code\u003e Location for all the installed tools\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAUGUSTUS_SCRIPTS_PATH\u003c/code\u003e misc. scripts used by \u003ccode\u003eaugusutus\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAUGUSTUS_BIN_PATH\u003c/code\u003e \u003ccode\u003eaugustus\u003c/code\u003e binaries\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGENEMARK_PATH\u003c/code\u003e \u003ccode\u003eGeneMark\u003c/code\u003e scripts\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eALIGNMENT_TOOL_PATH\u003c/code\u003e alignment programs that \u003ccode\u003ebraker\u003c/code\u003e needs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample run\u003c/h2\u003e\u003ca id=\"user-content-example-run\" class=\"anchor\" aria-label=\"Permalink: Example run\" href=\"#example-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor running on your data:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e SINGULARITY_IMAGE=/path/to/braker2.sif\n\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e BAM=/path/to/rnaseq.bam\n\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e GENOME=/path/to/genome-masked.fasta\n\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e PROT_SEQ=/path/to/mikado-proteins.faa\n\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e SPECIES=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eunique-name\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e gffread adds `.` for stop codons, replace it with `*`\u003c/span\u003e\nsed \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/^[^\u0026gt;]/s/\\./*/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${PROT_SEQ}\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${PROT_SEQ\u003cspan class=\"pl-k\"\u003e##*/\u003c/span\u003e}\u003c/span\u003e.new\n\nsingularity pull braker2.sif shub://aseetharam/braker\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${SINGULARITY_IMAGE}\u003c/span\u003e cp -R /usr/local/config augustus_config\n\nenv \u003cspan class=\"pl-k\"\u003etime\u003c/span\u003e -v singularity run \\\n --no-home \\\n --home /opt/gm_key \\\n --cleanenv \\\n --env AUGUSTUS_CONFIG_PATH=\u003cspan class=\"pl-smi\"\u003e${PWD}\u003c/span\u003e/augustus_config \\\n \u003cspan class=\"pl-smi\"\u003e${SINGULARITY_IMAGE}\u003c/span\u003e braker.pl \\\n --cores \u003cspan class=\"pl-smi\"\u003e${SLURM_JOB_CPUS_PER_NODE}\u003c/span\u003e \\\n --species=\u003cspan class=\"pl-smi\"\u003e${SPECIES}\u003c/span\u003e \\\n --genome=\u003cspan class=\"pl-smi\"\u003e${GENOME}\u003c/span\u003e \\\n --bam=\u003cspan class=\"pl-smi\"\u003e${BAM}\u003c/span\u003e \\\n --prot_seq=\u003cspan class=\"pl-smi\"\u003e${PROT_SEQ\u003cspan class=\"pl-k\"\u003e##*/\u003c/span\u003e}\u003c/span\u003e.new \\\n --prg=gth \\\n --gth2traingenes \\\n --gff3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eNathan Weeks\u003c/strong\u003e - \u003cem\u003eInitial work\u003c/em\u003e - \u003ca href=\"https://www.ars.usda.gov/people-locations/person?person-id=41062\" rel=\"nofollow\"\u003eWebPage\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eArun Seetharam\u003c/strong\u003e - \u003cem\u003emaintainer\u003c/em\u003e - \u003ca href=\"aseetharam.github.io\"\u003eWebPage\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee also the list of \u003ca href=\"https://github.com/your/repository/contributors\"\u003econtributors\u003c/a\u003e who\nparticipated in this project.\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-overview\" class=\"anchor\" href=\"#overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/706\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWhole genome sequencing for the characterization of bacterial genomes presents several bioinformatics challenges. Primary outcomes include determining sequencing read quality, performing alignment and de novo assembly of the bacterial genome, annotating the processed genomes, and discovering genomic features of interest. Due to the specificity of genetic elements within each bacterial genus, separate workflows must be utilized for each bacterial species of interest. This Nextflow pipeline aims to characterize bacteria of interest to food safety in poultry broiler production systems, specifically Salmonella enterica Heidelberg, Enterococcus faecalis, and their respective plasmids. Alignment, de novo assembly, and marker-based detection of genetic elements of interest are included in the pipeline, and a variety of custom databases have been created to aid in the annotation of the resulting genomic sequences. For more information, explore the pipeline through the links below.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-more-information\" class=\"anchor\" href=\"#more-information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/process.md\"\u003eProcess Overview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/acknowledgements.md\"\u003eAcknowledgements\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1618540892.0 + "updated_at": 1561739100.0 }, { "data_format": 2, - "description": null, + "description": "R functions and scripts to process output from the BWASP workflow", "filenames": [ + "Singularity.R36", "Singularity", - "Singularity.5.7.21" + "Singularity.v1.0" ], - "full_name": "ISU-HPC/mysql", + "full_name": "BrendelGroup/BWASPR", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emysql\u003c/h1\u003e\u003ca id=\"user-content-mysql\" class=\"anchor\" aria-label=\"Permalink: mysql\" href=\"#mysql\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/937\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease see \u003ca href=\"https://www.hpc.iastate.edu/guides/containers/mysql-server\" rel=\"nofollow\"\u003ehttps://www.hpc.iastate.edu/guides/containers/mysql-server\u003c/a\u003e for usage instructions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eMake local directory structure for database information\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir -p ${PWD}/mysql/var/lib/mysql ${PWD}/mysql/run/mysqld\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDownload the sample .my.cnf and .mysqlpassword files\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl https://raw.githubusercontent.com/ISU-HPC/mysql/master/my.cnf \u0026gt; ${HOME}/.my.cnf\n$ curl https://raw.githubusercontent.com/ISU-HPC/mysql/master/mysqlrootpw \u0026gt; ${HOME}/.mysqlrootpw\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eLaunch an instance of the container, bind-mounting the local directories\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity instance.start --bind ${PWD}/mysql/var/lib/mysql/:/var/lib/mysql --bind ${PWD}/mysql/run/mysqld:/run/mysqld shub://ISU-HPC/mysql mysql\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eRun the container\u0027s runscript to initialize mysqld and then launch mysqld\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run instance://mysql\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eVerify mysqld is running by opening a shell in the container an starting the MySQL client\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://mysql\nSingularity: Invoking an interactive shell within container...\n\nbash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)\nSingularity ISU-HPC-mysql-master-latest.simg:~\u0026gt; mysql\nWelcome to the MySQL monitor. Commands end with ; or \\g.\nYour MySQL connection id is 3\nServer version: 5.7.21 MySQL Community Server (GPL)\n\nCopyright (c) 2000, 2018, Oracle and/or its affiliates. All rights reserved.\n\nOracle is a registered trademark of Oracle Corporation and/or its\naffiliates. Other names may be trademarks of their respective\nowners.\n\nType \u0027help;\u0027 or \u0027\\h\u0027 for help. Type \u0027\\c\u0027 to clear the current input statement.\n\nmysql\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"https://www.hpc.iastate.edu/guides/containers/mysql-server\" rel=\"nofollow\"\u003ehttps://www.hpc.iastate.edu/guides/containers/mysql-server\u003c/a\u003e for details on how to\nconnect to the \u003ccode\u003emysqld\u003c/code\u003e server from outside the container, via both local socket\nand the network.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bwaspr\" class=\"anchor\" href=\"#bwaspr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBWASPR\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-r-functions-and-scripts-to-process-output-from-the-bwasp-workflow\" class=\"anchor\" href=\"#r-functions-and-scripts-to-process-output-from-the-bwasp-workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR functions and scripts to process output from the BWASP workflow\u003c/h3\u003e\n\u003cp\u003eThis repository contains R functions and scripts we use to analyze the\n*.mcalls output files from the \u003ca href=\"https://github.com/brendelgroup/BWASP\"\u003eBWASP\u003c/a\u003e workflow.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the\n\u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-bwaspr-does\" class=\"anchor\" href=\"#what-bwaspr-does\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat BWASPR does\u003c/h2\u003e\n\u003cp\u003eRequired input to the \u003cem\u003eBWASPR\u003c/em\u003e workflow consists of the *.mcalls files (tab\ndelimited data for the named columns)\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eSeqID.Pos SequenceID Position Strand Coverage Prcnt_Meth Prcnt_Unmeth\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand two files specifying the data labels and *.mcalls file locations and\ncertain parameters, respectively.\nLet\u0027s look at the example files in \u003ca href=\"./inst/extdata\"\u003einst/extdata\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eAmHE.dat\n================================================================================\n# Samples from Herb et al. (2012) Nature Neuroscience:\n#\nAm HE forager 0 CpGhsm ../inst/extdata/Amel-forager.CpGhsm.mcalls\nAm HE forager 0 CpGscd ../inst/extdata/Amel-forager.CpGscd.mcalls\nAm HE nurse 0 CpGhsm ../inst/extdata/Amel-nurse.CpGhsm.mcalls\nAm HE nurse 0 CpGscd ../inst/extdata/Amel-nurse.CpGscd.mcalls\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eAmHE.par\n================================================================================\nSPECIESNAME Apis mellifera\nASSEMBLYVERSION Amel_4.5\nGENOMESIZE 250270657\nTOTALNBRPMSITES 20307353\nSPECIESGFF3DIR ../inst/extdata/AmGFF3DIR\nGENELISTGFF3 Amel.gene.gff3\nEXONLISTGFF3 Amel.exon.gff3\nPCGEXNLISTGFF3 Amel.pcg-exon.gff3\nPROMOTRLISTGFF3 Amel.promoter.gff3\nCDSLISTGFF3 Amel.pcg-CDS.gff3\nUTRFLAGSET 1\n5UTRLISTGFF3 Amel.pcg-5pUTR.gff3\n3UTRLISTGFF3 Amel.pcg-3pUTR.gff3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first file has columns for \u003cem\u003especies\u003c/em\u003e (here \u003cem\u003eAm\u003c/em\u003e); \u003cem\u003estudy\u003c/em\u003e (here \u003cem\u003eHE\u003c/em\u003e);\n\u003cem\u003esample\u003c/em\u003e (here \u003cem\u003eforager\u003c/em\u003e and \u003cem\u003enurse\u003c/em\u003e\"); replicate number (here 0, indicating\nsingle samples or, as in the case of this study, aggregates over replicates);\nand file locations (here for the \u003cem\u003eCpGhsm\u003c/em\u003e and \u003cem\u003eCpGscd\u003c/em\u003e *.mcalls files);\nnote that the file locations in this example are relative links, assuming you\nwill run the example discussed in the \u003ca href=\"./demo\"\u003edemo\u003c/a\u003e directory.\nThe second file specifies the species name, genome assembly version, genome\nsize (in base pairs), total number of potential methylation sites (CpGs), and\nfile names for GFF3 annotation of various genomic features (\u003cem\u003eUTRFLAGSET\u003c/em\u003e is set\nto 1 to use UTR annotation in the GFF3 file).\u003c/p\u003e\n\u003cp\u003eA typical \u003cem\u003eBWASPR\u003c/em\u003e workflow will read the specified *.mcalls files and\ngenerate various output tables and plots, labeled in various ways with\n\u003cem\u003especies\u003c/em\u003e_ \u003cem\u003estudy\u003c/em\u003e_ \u003cem\u003esample\u003c/em\u003e_ \u003cem\u003ereplicate\u003c/em\u003e labels.\nThe \u003ca href=\"./demo/Rscript.BWASPR\"\u003edemo/Rscript.BWASPR\u003c/a\u003e file shows a template\nworkflow.\nInitial customization is done at the top of the file and mostly from\ninclusion of a configuration file such as\n\u003ca href=\"./demo/sample.conf\"\u003edemo/sample.conf\u003c/a\u003e.\nThe following table summarizes the successive workflow steps.\nYou may want to open the \u003ca href=\"./demo/Rscript.BWASPR\"\u003edemo/Rscript.BWASPR\u003c/a\u003e and\n\u003ca href=\"./demo/sample.conf\"\u003edemo/sample.conf\u003c/a\u003e in separate windows as a reference\nwhile viewing the table.\nDetails on running the workflow with the demo data are given in\n\u003ca href=\"./demo/README.md\"\u003edemo/README\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-runflag-to-expected-output-correspondence\" class=\"anchor\" href=\"#runflag-to-expected-output-correspondence\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRUNflag to expected output correspondence\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eRUNflag\u003c/th\u003e\n\u003cth\u003einput\u003c/th\u003e\n\u003cth\u003e(select) parameters\u003c/th\u003e\n\u003cth\u003efunction\u003c/th\u003e\n\u003cth\u003etheme\u003c/th\u003e\n\u003cth\u003eoutput files\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNcms\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003ecovlist, locount, hicount\u003c/td\u003e\n\u003ctd\u003ecmStats()\u003c/td\u003e\n\u003ctd\u003esample coverage and methylation statistics\u003c/td\u003e\n\u003ctd\u003ecms-*.txt\u003cbr\u003ecms-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNpwc\u003c/td\u003e\n\u003ctd\u003estudymk\u003cbr\u003estudymc\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003ecmpSites()\u003c/td\u003e\n\u003ctd\u003epairwise sample comparisons\u003c/td\u003e\n\u003ctd\u003epwc-*.vs.*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNcrl\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003edestrand\u003c/td\u003e\n\u003ctd\u003ecmpSamples()\u003c/td\u003e\n\u003ctd\u003ecorrelations between aggregate samples\u003c/td\u003e\n\u003ctd\u003ecrl-*.txt\u003cbr\u003ecrl-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNrepcms\u003c/td\u003e\n\u003ctd\u003ereplicate *.mcalls\u003c/td\u003e\n\u003ctd\u003erepcovlist,\u003cbr\u003ereplocount, rephicount\u003c/td\u003e\n\u003ctd\u003ecmStats()\u003c/td\u003e\n\u003ctd\u003ereplicate coverage and methylation statistics\u003c/td\u003e\n\u003ctd\u003erepcms-*.txt\u003cbr\u003erepcms-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNrepcrl\u003c/td\u003e\n\u003ctd\u003ereplicate *.mcalls\u003c/td\u003e\n\u003ctd\u003edestrand\u003c/td\u003e\n\u003ctd\u003ecmpSamples()\u003c/td\u003e\n\u003ctd\u003ecorrelations between replicates\u003c/td\u003e\n\u003ctd\u003erepcrl-*.txt\u003cbr\u003erepcrl-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNmmp\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003emap_methylome()\u003c/td\u003e\n\u003ctd\u003emethylation to annotation maps\u003c/td\u003e\n\u003ctd\u003emmp-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNacs\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003edestrand\u003c/td\u003e\n\u003ctd\u003eannotate_methylome()\u003c/td\u003e\n\u003ctd\u003eannotation of common sites\u003c/td\u003e\n\u003ctd\u003eacs-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNrnk\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003egenome_ann$\u003cem\u003eregion\u003c/em\u003e\n\u003c/td\u003e\n\u003ctd\u003erank_rbm()\u003c/td\u003e\n\u003ctd\u003eranked genes and promoters\u003c/td\u003e\n\u003ctd\u003eranked-*.txt\u003cbr\u003esites-in-*.txt\u003cbr\u003ernk-sig-*.pdf\u003cbr\u003esip-*.txt\u003cbr\u003ernk-sip-*.txt\u003cbr\u003ernk-sip-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNmrpr\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003eddset\u003cbr\u003enr2d\u003cbr\u003edoplots\u003c/td\u003e\n\u003ctd\u003edet_mrpr()\u003c/td\u003e\n\u003ctd\u003emethylation-rich and -poor regions\u003c/td\u003e\n\u003ctd\u003edst-*.txt\u003cbr\u003e*ds-*.pdf\u003cbr\u003emdr-*.tab\u003cbr\u003emdr-*.bed\u003cbr\u003empr-*.txt\u003cbr\u003emrr-*.txt\u003cbr\u003ermp-*.txt\u003cbr\u003egwr-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNdmt\u003c/td\u003e\n\u003ctd\u003estudymc\u003c/td\u003e\n\u003ctd\u003ewsize, stepsize\u003c/td\u003e\n\u003ctd\u003edet_dmt()\u003c/td\u003e\n\u003ctd\u003edifferentially methylated tiles and genes\u003c/td\u003e\n\u003ctd\u003edmt-*.txt\u003cbr\u003edmg-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNdmsg\u003c/td\u003e\n\u003ctd\u003esample *.mcalls\u003cbr\u003e\n\u003c/td\u003e\n\u003ctd\u003ehighcoverage\u003cbr\u003edestrand\u003c/td\u003e\n\u003ctd\u003edet_dmsg()\u003c/td\u003e\n\u003ctd\u003edifferentially methylated sites and genes\u003c/td\u003e\n\u003ctd\u003edms-*.txt\u003cbr\u003edmg-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNdmgdtls\u003c/td\u003e\n\u003ctd\u003estudyhc\u003c/td\u003e\n\u003ctd\u003edestrand\u003c/td\u003e\n\u003ctd\u003eshow_dmsg()\u003c/td\u003e\n\u003ctd\u003edetails for differentially methylated genes\u003c/td\u003e\n\u003ctd\u003edmg-*.vs.*_details.txt\u003cbr\u003edmg-*.vs.*_heatmaps.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNogl\u003c/td\u003e\n\u003ctd\u003estudyhc\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eexplore_dmsg()\u003cbr\u003erank_dmg()\u003c/td\u003e\n\u003ctd\u003eranked lists of differentially methylated genes\u003c/td\u003e\n\u003ctd\u003eogl-*.txt\u003cbr\u003ernk-dmg-*.vs.*.txt\u003cbr\u003ernk-dmg-*.vs.*.pdf\u003cbr\u003ewrt-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNsave\u003c/td\u003e\n\u003ctd\u003eworkflow output\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003esave.image()\u003c/td\u003e\n\u003ctd\u003esave image of workflow output\u003c/td\u003e\n\u003ctd\u003e*.RData\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 6, "topics": [], - "updated_at": 1642244274.0 + "updated_at": 1627671623.0 }, { "data_format": 2, - "description": "Computational, behavioral, and imaging studies of physical event perception", + "description": null, "filenames": [ "Singularity" ], - "full_name": "CNCLgithub/physical_event_primitives", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ephysical_event_primitives\u003c/h1\u003e\u003ca id=\"user-content-physical_event_primitives\" class=\"anchor\" aria-label=\"Permalink: physical_event_primitives\" href=\"#physical_event_primitives\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRepository for computational, behavioral, and imaging studies of physical event perception.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup\u003c/h3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequirements: \u003ccode\u003esingularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIf you are working on a Linux computer, download singularity and you should be good to go! :\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003egit clone\u003c/code\u003e the repository\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecd physical_event_primitives\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egit submodule update --init\u003c/code\u003e (initialize the Blender egg importer add-on)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e./setup.sh pull true true\u003c/code\u003e (pulls the container from Box.com, sets up Conda and Julia environments). Run \u003ccode\u003e./setup.sh build true true\u003c/code\u003e if you want to build the container for some reason.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIf you are working on a Mac, the situation is more complicated.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFollow instructions here: \u003ca href=\"https://sylabs.io/guides/3.5/admin-guide/installation.html#mac\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/admin-guide/installation.html#mac\u003c/a\u003e to download \u003ccode\u003evagrant\u003c/code\u003e, \u003ccode\u003evagrant-manager\u003c/code\u003e, and \u003ccode\u003evirtual box\u003c/code\u003e. You need to have \u003ccode\u003eHomebrew\u003c/code\u003e to install this.\u003c/li\u003e\n\u003cli\u003eMake sure to create the virtual machine inside of the \u003ccode\u003ephysical_event_primitives\u003c/code\u003e directory, or in a directory that encloses it.\u003c/li\u003e\n\u003cli\u003eWhen you want to build or pull the singularity image, go into the directory with the Vagrantfile and run \u003ccode\u003evagrant ssh\u003c/code\u003e. Then \u003ccode\u003ecd\u003c/code\u003e into the \u003ccode\u003ephysical_event_primitives\u003c/code\u003e directory which is located within \u003ccode\u003e/vagrant/\u003c/code\u003e. You can now run the above commands as if you were working on a Linux!\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMac users may also need to \u003ccode\u003ecp default.conf user.conf\u003c/code\u003e and change the Julia depot line to \u003ccode\u003ejulia_depot:/home/vagrant/.julia/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInteracting with the image\u003c/h3\u003e\u003ca id=\"user-content-interacting-with-the-image\" class=\"anchor\" aria-label=\"Permalink: Interacting with the image\" href=\"#interacting-with-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \u003ccode\u003e./run.sh \u0026lt;command\u0026gt;\u003c/code\u003e to execute commands with the image, e.g. to launch Julia REPL \u003ccode\u003e./run.sh julia\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf using Milgram and need a virtual display (e.g. rendering with Blender), run \u003ccode\u003e./run.sh xvfb-run -a \u0026lt;command\u0026gt;\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDisclaimer: Much of this code is taken directly from \u003ca href=\"http://geometry.cs.ucl.ac.uk/projects/2019/causal-graphs/\" rel=\"nofollow\"\u003ehttp://geometry.cs.ucl.ac.uk/projects/2019/causal-graphs/\u003c/a\u003e with tweaks to fit our specific situation\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eblender/\u003c/code\u003e - Scripts used to export animations to Blender\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecore/\u003c/code\u003e - Core package; contains all the algorithms\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edemos/\u003c/code\u003e - Demos to play with \u0026amp; contains the main \u003ccode\u003egenerate.py\u003c/code\u003e script for generating videos\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egui/\u003c/code\u003e - Graphical modules -- mostly irrelevant here\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escenarios/\u003c/code\u003e - Config files of different possible scenarios\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can run the script using \u003ccode\u003e./run.sh demos/generate.py\u003c/code\u003e\u003c/p\u003e\n", + "full_name": "tjhendrickson/BIDS_scripts", + "latest_release": "v1.0", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-bids-conversion-scripts\" class=\"anchor\" href=\"#bids-conversion-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBIDS Conversion Scripts\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eThis repository contains scripts that will assist in the conversion of raw DICOM data sets to \u003ca href=\"http://bids.neuroimaging.io/format\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e. The \"heuristics\" folder contains example scripts that have been used to convert data from DICOM to BIDS. \u003cstrong\u003eThis folder may be helpful to build your own heuristics script.\u003c/strong\u003e For additional information on how to create a heuristic script see the \u003ca href=\"https://github.com/nipy/heudiconv\"\u003eheudiconv\u003c/a\u003e github page.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-hosting\" class=\"anchor\" href=\"#container-hosting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Hosting\u003c/h3\u003e\n\u003cp\u003ePull the most recent container below, (\u003cstrong\u003eNOTE: you only have to do this once!\u003c/strong\u003e):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tjhendrickson/BIDS_scripts:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" href=\"#singularity-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Usage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/singularity/images/directory/imagename.img --help\nusage: [-h] [--output_dir OUTPUT_DIR] [--dicom_dir DICOM_DIR]\n [--study_name STUDY_NAME] [--ses_id SES_ID] [--subj_id SUBJ_ID]\n [--heuristic HEURISTIC] [--dry_run]\n\nScript that controls BIDS conversion for individual studies\n\noptional arguments:\n -h, --help show this help message and exit\n --output_dir OUTPUT_DIR\n The directory that the BIDS data will be outputted to\n --dicom_dir DICOM_DIR\n The directory that houses unzipped dicom\n directories/files.\n --study_name STUDY_NAME\n What is the shorthand name for this study?\n --ses_id SES_ID scanning session id\n --subj_id SUBJ_ID subject id\n --heuristic HEURISTIC\n Path to heuristic file, if the file is already within\n the container (i.e. within heuristics folder) you do\n not have to specify a path.\n --dry_run Dry run. A dicominfo_*.tsv file will generate within\n .heudiconv/\u0027subj_id\u0027/info directory which can be used\n to create heuristic script\n\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eThis application must be run with either the \"--heuristic\" or \"--dry_run\" argument, it will fail otherwise.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse the \"--dry_run\" argument to take a closer look at the acquistion parameters for a scanning session.\u003c/p\u003e\n\u003cp\u003eTo run a single participant with dry_run argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --dry_run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output a hidden folder (named .heudiconv) along with sub-folders based on arguments provided to \"--subj_id\" and \"--ses_id\" respectively.\nWithin the sub-folders will be a tsv file that begins with \"dicominfo\". \u003cstrong\u003eBased on the example above the path to the file will be \".heudiconv/1000/ses-10000/info/dicominfo_ses-10000.tsv\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse this tsv file to design a heuristics script to organize your eventual nifti data. \u003cstrong\u003eSee this tutorial for a how to on heuristic script creation (\u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman3\" rel=\"nofollow\"\u003eheuristics tutorial\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo run a single participant with heuristic argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir -B /path/to/heuristics/script:/heuristic.py \\\n /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --heuristic /heuristic.py\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-important-notes\" class=\"anchor\" href=\"#important-notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Notes\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gotchas\" class=\"anchor\" href=\"#gotchas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGotchas\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Bind Mounting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to run this container you will have to use \"bind mounting\", meaning you will have to link local folders/files to existing folders/files within the container with the -B flag. In the example above the local folder \"/home/timothy/sandbos_DO_NOT_DELETE/BIDS/142_CIFASD_4\" becomes \"/output_dir\" within the container as they are separated by a colon (:). \u003cstrong\u003eNotice that in both cases above the output and dicom folder and heuristic file are bound to /output_dir, /dicom_dir and /heuristic.py respectively, this is very important.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) DICOM Data Formatting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the restrictive nature of BIDS the dicom data must be in a particular format in order for the conversion to work properly. This application will copy dicom data directories by searching for either the --subj_id or --ses_id argument present within a dicom directory name, place them in a separate directory, and rearrange them. So for example if the dicom directory is named \"XYXY4776XYXY\" --subj_id 4776 the application will find the \"4776\" pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Subject ID and Session ID names\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou must use alphanumerics (i.e. letters or numbers) only (\u003cstrong\u003eno special characters\u003c/strong\u003e) with your subject IDs (subj_id) and session IDs (ses_id). \u003cstrong\u003eNote the \"--ses_id\" argument is optional\u003c/strong\u003e!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-best-practices\" class=\"anchor\" href=\"#best-practices\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBest Practices\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Initial Conversion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile testing the initial BIDS conversion it is best to start with one or two datasets and specify the \u0027--dry_run\u0027 argument (see above for an example of usage).\nThis will create a dicom_info tsv file which can be used for heuristic script creation.\nSee Step 3 of \u0027Run HeuDiConv on ses-001 scans to get the dicominfo file\u0027 within \u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman2\" rel=\"nofollow\"\u003eStanford BIDS Tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) BIDS Validator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce satisfied with an initial BIDS conversion, prior to running the conversion on an entire study first ensure that the BIDS converted dataset meets the BIDS specification by using the \u003ca href=\"http://incf.github.io/bids-validator/\" rel=\"nofollow\"\u003eBIDS validator web version\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Longitudinal Format\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen converting data to BIDS you can certainly have a cross sectonal directory format such as below:\nBIDS_output\nsub-01\nsub-02\nsub-03\nsub-04\netc\nHowever, I suggest placing data within a longitudinal directory format even if you have cross-sectional data:\nBIDS_output\nsub-01\nses-01\nses-02\nsub-02\nses-01\netc\u003c/p\u003e\n\u003cp\u003eYou can control the BIDS directory format by providing both the arguments --subj_id --ses_id for a conversion, if you only specify one of the two arguments the data will be outputted in a cross-sectional format.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1713829871.0 + "updated_at": 1624658162.0 }, { "data_format": 2, - "description": "Code for Differential Assessment of Black-Box AI Agents. AAAI 2022.", + "description": "Bisulfite-seq data Workflow Automation Software and Protocols", "filenames": [ - "dependencies/FD/misc/releases/19.06/Singularity.19.06", - "dependencies/FD/misc/releases/20.06/Singularity.20.06", - "dependencies/FD/misc/releases/latest/Singularity", - "dependencies/FD/misc/releases/19.12/Singularity.19.12" + "Singularity.v1.0", + "Singularity.v1.1", + "Singularity.v0.9", + "Singularity" ], - "full_name": "AAIR-lab/DAAISy", + "full_name": "BrendelGroup/BWASP", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDifferential Assessment of AI System (DAAISy)\u003c/h1\u003e\u003ca id=\"user-content-differential-assessment-of-ai-system-daaisy\" class=\"anchor\" aria-label=\"Permalink: Differential Assessment of AI System (DAAISy)\" href=\"#differential-assessment-of-ai-system-daaisy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code for the paper:\u003c/p\u003e\n\u003cp\u003eDifferential Assessment of Black-Box AI Agents.\u003cbr\u003e\n\u003ca href=\"https://rashmeetnayyar.com\" rel=\"nofollow\"\u003eRashmeet Kaur Nayyar\u003c/a\u003e*,\n\u003ca href=\"https://pulkitverma.net\" rel=\"nofollow\"\u003ePulkit Verma\u003c/a\u003e*, and\n\u003ca href=\"http://siddharthsrivastava.net/\" rel=\"nofollow\"\u003eSiddharth Srivastava\u003c/a\u003e. \u003cbr\u003e\n36th AAAI Conference on Artificial Intelligence, 2022. \u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://aair-lab.github.io/Publications/nvs_aaai22.pdf\" rel=\"nofollow\"\u003ePaper\u003c/a\u003e | \u003ca href=\"https://arxiv.org/pdf/2203.13236.pdf\" rel=\"nofollow\"\u003eExtended Version\u003c/a\u003e | \u003ca href=\"https://slideslive.com/38976997/differential-assessment-of-blackbox-ai-systems\" rel=\"nofollow\"\u003eTalk\u003c/a\u003e | \u003ca href=\"https://pulkitverma.net/assets/pdf/nvs_aaai22/nvs_aaai22_slides.pdf\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e | \u003ca href=\"https://pulkitverma.net/assets/pdf/nvs_aaai22/nvs_aaai22_poster.pdf\" rel=\"nofollow\"\u003ePoster\u003c/a\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory Structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory Structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e|-- dependencies/\n| |-- fama/\n| |-- FD/\n| |-- FF/\n| |-- madagascar/\n| |-- VAL/\n|-- domains/\n| |-- domain_name/\n| | |-- domains/\n| | |-- instances/\n| | |-- observations/\n|-- random_states/\n|-- results/\n|-- src/\n| |-- agent.py\n| |-- config.py\n| |-- generate_random_states.py\n| |-- model_drift/\n| |-- interrogation/\n| |-- lattice/\n| |-- query/\n| |-- utils/\n|-- README.md\n|-- config.py\n|-- learn_model.py\n|-- generate_random_init_domains.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003edependencies/: This directory includes the external software used to run the code. This includes fama, FF, FD, madagascar, and VAL.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003efama: \u003ca href=\"https://github.com/sjimenezgithub/strips-learning/\"\u003ehttps://github.com/sjimenezgithub/strips-learning/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFF: \u003ca href=\"https://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz\" rel=\"nofollow\"\u003ehttps://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFD: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003emadagascar: \u003ca href=\"https://users.aalto.fi/~rintanj1/jussi/software.html\" rel=\"nofollow\"\u003ehttps://users.aalto.fi/~rintanj1/jussi/software.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVAL: \u003ca href=\"https://github.com/KCL-Planning/VAL\"\u003ehttps://github.com/KCL-Planning/VAL\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edomains/ (Directory that stores domain, instance, and observation files)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edomain_name/\n\u003cul\u003e\n\u003cli\u003edomains/\n\u003cul\u003e\n\u003cli\u003edomain.pddl (domain file for that domain),\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003einstances/ (directory containing the problem files for that domain),\u003c/li\u003e\n\u003cli\u003eobservations/ (directory containing the plans for that domain generated using the instances)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erandom_states/: This directory stores the set of states in serialized form. For each domain, there is a .pkl file containing 60 states approximately.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esrc/: This directory contains the source code for DAAISy and AIA.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esrc/interrogation/aia.py has code for both DAAISy and AIA.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esrc/model_drift/: This directory contains the source code for DAAISy. It contains the following files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eagent_interrogation_interface: Interface between identification of affected pal-tuples and identification of their modes.\u003c/li\u003e\n\u003cli\u003egenerate_observations: Generates observations for drifted model and init models.\u003c/li\u003e\n\u003cli\u003einfer_valid_modes: Identifies valid modes for each pa-tuple.\u003c/li\u003e\n\u003cli\u003epa_tuple: Class for pa-tuple.\u003c/li\u003e\n\u003cli\u003eplot: Plots results.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eresults/: This directory has domain-specific results in the form of csv files and plots.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions to run the code\u003c/h2\u003e\u003ca id=\"user-content-instructions-to-run-the-code\" class=\"anchor\" aria-label=\"Permalink: Instructions to run the code\" href=\"#instructions-to-run-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInput: You need the IPC domain.pddl (within domains/), domain_template.pddl (within domains/), instance (within instances/instances/) files as input.\nHere, IPC domain is used as drifted model and simply used to generate a set of init domains randomly. The algorithm does not take IPC domain as input but only the init domains.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall the required software using setup.sh. You might need to give sudo permissions to install some of them.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eSet configuration in config.py. (Optional) \u003cbr\u003e Use the current parameters set in the config file to reproduce results in the paper. The parameters are:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003edomain_name: the domain name,\u003c/li\u003e\n\u003cli\u003edrifted_domain_file: the filename for IPC domain,\u003c/li\u003e\n\u003cli\u003edomain_to_num_files_each_pal: the number of init domain files you want to generate for each number of affected pal-tuples,\u003c/li\u003e\n\u003cli\u003enum_affected_pals: the number of affected pal-tuples to consider,\u003c/li\u003e\n\u003cli\u003eregenerate_random_states: True if you want to regenerate the set of random states for the domain,\u003c/li\u003e\n\u003cli\u003eflag_init_domains_type: 0 for \"randomly generated init domains\", 1 for \"only increased applicability init domains\", and 2 for \"both types of domain generation\",\u003c/li\u003e\n\u003cli\u003edomain_to_num_sas: number of s-a-s observations to use.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eGenerate random initial domains. (Optional)\u003cbr\u003e\nUse current init domain files to reproduce results in the paper.\nTo randomly generate new initial domains using the IPC domain with increasing number of pal-tuples dropped (filename format is domainname_#PALsdropped_#file.pddl), run the following command.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython3 generate_random_init_domains.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eInput instance file for generating observations for drifted domain. (Optional)\u003cbr\u003e\nUse the current instance files and the corresponding generated observations to reproduce results in the paper.\nTo run with new observations, replace the contents of the instances/instances/ directory with the instances you want, delete contents of observations/ directory, and set the domain_to_num_sas variable in config.py to the number of s-a-s observations you want to use (should be \u0026lt;= the number of s-a-s in the plan generated by the instance).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun learn_model.py to learn the drifted model. You\u0027ll find the plots generated inside the results/ directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython3 learn_model.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommon Installation Issues\u003c/h2\u003e\u003ca id=\"user-content-common-installation-issues\" class=\"anchor\" aria-label=\"Permalink: Common Installation Issues\" href=\"#common-installation-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFF:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePlease install flex and bison for FF to compile.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn newer versions of gcc (tested on gcc 10.2.0) please make the following changes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emain.c:150 : Comment out the gbracket_count definition\n\u003cpre\u003e\u003ccode\u003eint gbracket_count; --\u0026gt; /* int gbracket_count; */\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003erelax.c:111 : Define lcurrent_goals as static\n\u003cpre\u003e\u003ccode\u003eState lcurrent_goals; --\u0026gt; static State lcurrent_goals;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003esearch.c:110 : Define lcurrent_goals as static\n\u003cpre\u003e\u003ccode\u003eState lcurrent_goals; --\u0026gt; static State lcurrent_goals;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp\u003ePlease note that this is research code and not yet ready for public delivery,\nhence most parts are not documented.\u003c/p\u003e\n\u003cp\u003eIn case of any queries, please contact \u003ca href=\"mailto:verma.pulkit@asu.edu\"\u003everma.pulkit@asu.edu\u003c/a\u003e,\nor \u003ca href=\"mailto:rmnayyar@asu.edu\"\u003ermnayyar@asu.edu\u003c/a\u003e.\u003c/p\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://rashmeetnayyar.com\" rel=\"nofollow\"\u003eRashmeet Kaur Nayyar\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://pulkitverma.net\" rel=\"nofollow\"\u003ePulkit Verma\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://siddharthsrivastava.net/\" rel=\"nofollow\"\u003eSiddharth Srivastava\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{nayyar_2022_differential,\n author = {Nayyar, Rashmeet Kaur and Verma, Pulkit and Srivastava, Siddharth},\n title = {{Differential Assessment of Black-Box AI Agents}},\n booktitle = {Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)},\n year={2022}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bwasp--bisulfite-seq-data-workflow-automation-software-and-protocols\" class=\"anchor\" href=\"#bwasp--bisulfite-seq-data-workflow-automation-software-and-protocols\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBWASP : Bisulfite-seq data Workflow Automation Software and Protocols\u003c/h1\u003e\n\u003cp\u003eThe BWASP repository encompasses code and scripts developed in the\n\u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eBrendel Group\u003c/a\u003e for analyses of bisulfite sequencing\ndata.\nThe entire workflow relies on various other open source software as well as\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e scripts from the companion\n\u003ca href=\"https://github.com/BrendelGroup/BWASPR\"\u003eBWASPR\u003c/a\u003e repository.\nThe code conforms to our \u003ca href=\"https://brendelgroup.github.io/\" rel=\"nofollow\"\u003eRAMOSE\u003c/a\u003e\nphilosophy: it generates \u003cstrong\u003ereproducible\u003c/strong\u003e, \u003cstrong\u003eaccurate\u003c/strong\u003e, and \u003cstrong\u003emeaningful\u003c/strong\u003e\nresults; it is \u003cstrong\u003eopen\u003c/strong\u003e (source) and designed to be \u003cstrong\u003escalable\u003c/strong\u003e and\n\u003cstrong\u003eeasy\u003c/strong\u003e to use.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start-\" class=\"anchor\" href=\"#quick-start-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start \u003ca href=\"https://singularity-hub.org/collections/1203\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eInput to the BWASP workflow consists of accession numbers or fastq files of\nbisulfite-sequencing reads as well as the appropriate genome assembly (and, if\navailable, genome annotation).\nOutput (after read quality control and mapping) are \u003cem\u003e*.mcalls\u003c/em\u003e files that list\nthe sufficiently covered genomic Cs and their methylation percentage in the\ngiven sample.\nThe scripts in the \u003cem\u003ebin\u003c/em\u003e directory take care of minor tasks in the overall\nworkflow, but configuration and execution is via\n\u003ca href=\"https://www.gnu.org/software/make/\" rel=\"nofollow\"\u003eGNU make\u003c/a\u003e using edited copies of the\nmakefiles provided in the \u003cem\u003emakefiles\u003c/em\u003e directory.\nAll the BWASP dependencies are encapsulated in a\n\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available from our\n\u003ca href=\"http://BrendelGroup.org/SingularityHub/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\nThus, once you know what you are doing, execution could be as simple as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull http://BrendelGroup.org/SingularityHub/bwasp.sif\nsingularity exec bwasp.sif make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(assuming you have prepared a suitable makefile in your working directory).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-realistic-start\" class=\"anchor\" href=\"#realistic-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRealistic Start\u003c/h2\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the\n\u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\nOnce all preparatory steps are taken care of, see the \u003ca href=\"./HOWTO.md\"\u003eHOWTO\u003c/a\u003e\ndocument for a complete example of how to implement and run a workflow.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-reference\" class=\"anchor\" href=\"#reference\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference\u003c/h2\u003e\n\u003cp\u003eAmy L. Toth, Murat Ozturk, Saranya Sankaranarayanan, and Volker P. Brendel\n(2018) \u003cem\u003eEstimating the size and dynamics of the CpG methylome of social\ninsects.\u003c/em\u003e To be submitted.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003ePlease direct all comments and suggestions to\n\u003ca href=\"mailto:vbrendel@indiana.edu\"\u003eVolker Brendel\u003c/a\u003e\nat \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eIndiana University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, + "subscribers_count": 5, "topics": [], - "updated_at": 1648771336.0 + "updated_at": 1625951112.0 }, { "data_format": 2, - "description": "Code for Asking the Right Questions: Learning Interpretable Action Models Through Query Answering. AAAI 2021.", + "description": "exSeek: extracellular RNA analysis tool for noninvasive biomarker", "filenames": [ - "dependencies/FD/misc/releases/19.06/Singularity.19.06", - "dependencies/FD/misc/releases/20.06/Singularity.20.06", - "dependencies/FD/misc/releases/latest/Singularity", - "dependencies/FD/misc/releases/19.12/Singularity.19.12" + "singularity/Singularity" ], - "full_name": "AAIR-lab/AIA-AAAI21", + "full_name": "james20141606/exSeek", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAgent Interrogation Algorithm (AIA)\u003c/h1\u003e\u003ca id=\"user-content-agent-interrogation-algorithm-aia\" class=\"anchor\" aria-label=\"Permalink: Agent Interrogation Algorithm (AIA)\" href=\"#agent-interrogation-algorithm-aia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code for the paper:\u003c/p\u003e\n\u003cp\u003eAsking the Right Questions: Learning Interpretable Action Models Through Query Answering.\u003cbr\u003e\n\u003ca href=\"https://pulkitverma.net\" rel=\"nofollow\"\u003ePulkit Verma\u003c/a\u003e,\n\u003ca href=\"https://marpally-raoshashank.netlify.app/\" rel=\"nofollow\"\u003eShashank Rao Marpally\u003c/a\u003e, and\n\u003ca href=\"http://siddharthsrivastava.net/\" rel=\"nofollow\"\u003eSiddharth Srivastava\u003c/a\u003e. \u003cbr\u003e\n35th AAAI Conference on Artificial Intelligence, 2021.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://aair-lab.github.io/Publications/vms_aaai21.pdf\" rel=\"nofollow\"\u003ePaper\u003c/a\u003e | \u003ca href=\"https://arxiv.org/pdf/1912.12613.pdf\" rel=\"nofollow\"\u003eExtended Version\u003c/a\u003e | \u003ca href=\"https://slideslive.com/38948683/asking-the-right-questions-learning-interpretable-action-models-through-query-answering\" rel=\"nofollow\"\u003eTalk\u003c/a\u003e | \u003ca href=\"https://pulkitverma.net/assets/pdf/vms_aaai21/vms_aaai21_slides.pdf\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e | \u003ca href=\"https://pulkitverma.net/assets/pdf/vms_aaai21/vms_aaai21_poster.pdf\" rel=\"nofollow\"\u003ePoster\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory Structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory Structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e|-- dependencies/\n| |-- FD/\n| |-- FF/\n| |-- pddlgym/\n| |-- VAL/\n|-- domains/\n|-- random_states/\n|-- results/\n|-- src/\n| |-- agent.py\n| |-- config.py\n| |-- generate_random_states.py\n| |-- main.py\n| |-- interrogation/\n| |-- lattice/\n| |-- query/\n| |-- sim/\n| |-- utils/\n|-- README.md\n|-- LICENSE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003edependencies: This directory includes the external software used to run the code. This includes FF, FD, VAL, and PDDLGym.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFF: \u003ca href=\"https://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz\" rel=\"nofollow\"\u003ehttps://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFD: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePDDLGym: \u003ca href=\"https://github.com/tomsilver/pddlgym\"\u003ehttps://github.com/tomsilver/pddlgym\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVAL: \u003ca href=\"https://github.com/KCL-Planning/VAL\"\u003ehttps://github.com/KCL-Planning/VAL\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edependencies: Place all the domains in this directory. There must be a directory for each domain containing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edomain.pddl (domain file for that domain), and\u003c/li\u003e\n\u003cli\u003einstances directory containing the problem files for that domain.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erandom_states: This directory stores the set of states in serialized form. For each domain, there is a .pkl file containing 60 states approximately. These are generated using src/generate_random_states.py.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esrc: This directory stores the source code for AIA. It contains 4 files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eagent.py: Contains the agent code.\u003c/li\u003e\n\u003cli\u003econfig.py: Declares the configuration parameters.\u003c/li\u003e\n\u003cli\u003egenerate_random_states.py: Generates the random states for each domain.\u003c/li\u003e\n\u003cli\u003emain.py : Contains the main driver code which runs the code end-to-end.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003esrc also contains code structured into following sub-directories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einterrogation: Contains the AIA code.\u003c/li\u003e\n\u003cli\u003elattice: Contains the model and lattice classes.\u003c/li\u003e\n\u003cli\u003equery: Contains the plan outcome query code.\u003c/li\u003e\n\u003cli\u003esim: Simulator specific code. Contains a separate agent file for each simulator domain.\u003c/li\u003e\n\u003cli\u003eutils: Contains general utilities.\n\u003cul\u003e\n\u003cli\u003eutils/parser: Code based on \u003ca href=\"https://github.com/tomsilver/pddlgym\"\u003ePDDLGym\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eutils/translate: Code based on \u003ca href=\"https://github.com/aibasel/downward\"\u003eFD\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration\u003c/h2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-label=\"Permalink: Configuration\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConfiguration parameters are set in src/config.py\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFF_PATH, FD_PATH, and VAL_PATH stores the relative path of FF, FD, and VAL respectively.\u003c/li\u003e\n\u003cli\u003eNUM_PER_DOMAIN denotes how many instances per domain must be run. Keep minimum 2 for symbolic agent.\u003c/li\u003e\n\u003cli\u003ePLANNER specifies which planner to use. Set it to either FF or FD.\u003c/li\u003e\n\u003cli\u003eComment out either Symbolic Agent Settings or Simulator Agent Settings.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to Run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall the required software\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install -r requirements.txt \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eAdjust variables/paramters as needed in src/config.py.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun main.py\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd src\npython3 main.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommon Installation Issues\u003c/h2\u003e\u003ca id=\"user-content-common-installation-issues\" class=\"anchor\" aria-label=\"Permalink: Common Installation Issues\" href=\"#common-installation-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOpenCV (Tested on Ubuntu 18.04)\u003c/p\u003e\n\u003cp\u003eRefer to \u003ca href=\"https://linuxize.com/post/how-to-install-opencv-on-ubuntu-18-04/\" rel=\"nofollow\"\u003ehttps://linuxize.com/post/how-to-install-opencv-on-ubuntu-18-04/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFF:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePlease install flex and bison for FF to compile.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn newer versions of gcc (tested on gcc 10.2.0) please make the following changes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emain.c:150 : Comment out the gbracket_count definition\n\u003cpre\u003e\u003ccode\u003eint gbracket_count; --\u0026gt; /* int gbracket_count; */\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003erelax.c:111 : Define lcurrent_goals as static\n\u003cpre\u003e\u003ccode\u003eState lcurrent_goals; --\u0026gt; static State lcurrent_goals;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003esearch.c:110 : Define lcurrent_goals as static\n\u003cpre\u003e\u003ccode\u003eState lcurrent_goals; --\u0026gt; static State lcurrent_goals;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePlease note that this is research code and not yet ready for public delivery,\nhence most parts are not documented.\u003c/p\u003e\n\u003cp\u003eIn case of any queries, please contact \u003ca href=\"mailto:verma.pulkit@asu.edu\"\u003everma.pulkit@asu.edu\u003c/a\u003e,\nor \u003ca href=\"mailto:smarpall@asu.edu\"\u003esmarpall@asu.edu\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://pulkitverma.net\" rel=\"nofollow\"\u003ePulkit Verma\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://marpally-raoshashank.netlify.app/\" rel=\"nofollow\"\u003eShashank Rao Marpally\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://www.linkedin.com/in/abhyudayasrinet/\" rel=\"nofollow\"\u003eAbhyudaya Srinet\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://siddharthsrivastava.net/\" rel=\"nofollow\"\u003eSiddharth Srivastava\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{verma_2021_asking,\n author = {Verma, Pulkit and Marpally, Shashank Rao and Srivastava, Siddharth},\n title = {{Asking the Right Questions: Learning Interpretable Action Models Through Query Answering}},\n booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)},\n year={2021}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-exseek\" class=\"anchor\" href=\"#exseek\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eexSeek\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/lulab/exSeek-dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/17b4878574e7d41e83f8bedc7e94f3500d4ebeefba3c587ded3efcbc5730c3b3/68747470733a2f2f7472617669732d63692e636f6d2f6c756c61622f65785365656b2d6465762e7376673f746f6b656e3d4379526755577371574363744b7641784d58746f266272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/lulab/exSeek-dev.svg?token=CyRgUWsqWCctKvAxMXto\u0026amp;branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-workflow\" class=\"anchor\" href=\"#workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"assets/whole_pipe.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"assets/whole_pipe.png\" alt=\"workflow\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstall required software packages according to \u003ca href=\"docs/requirements.md\"\u003erequirements\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDownload the scripts:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/lulab/exSeek-dev.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prepare-genome-and-annotations\" class=\"anchor\" href=\"#prepare-genome-and-annotations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare genome and annotations\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eProcessed files\u003c/strong\u003e: \u003ccode\u003e/BioII/lulab_b/shared/genomes/hg38\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-download-and-process-genome-sequences\" class=\"anchor\" href=\"#download-and-process-genome-sequences\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload and process genome sequences\u003c/h3\u003e\n\u003cp\u003eRefer to the \u003ca href=\"docs/genome_and_annotations.md\"\u003edocumentation\u003c/a\u003e for details.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-extract-gtfs-and-generate-mapping-indexes\" class=\"anchor\" href=\"#extract-gtfs-and-generate-mapping-indexes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtract GTFs and generate mapping indexes\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/prepare_genome.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input-files\" class=\"anchor\" href=\"#input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/fastq/${sample_id}.fastq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRead files (single-end sequencing)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003e${input_dir}/fastq/${sample_id}_1.fastq\u003c/code\u003e, \u003ccode\u003e${input_dir}/fastq/${sample_id}_2.fastq\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003eRead files (paired-end sequencing)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/sample_ids.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA text file with one sample ID per line.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/sample_classes.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA tab-deliminated file (with header) with two columns: sample_id, label\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/batch_info.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA comma-deliminated file (with header) with at least two columns: sample_id, batch1, batch2, ...\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/reference_genes.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA text file with reference gene IDs.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/compare_groups.yaml\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA YAML file defining positive and negative classes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ecompare_groups.yaml\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvery key-value pairs defines a compare group and a negative-positive class pair:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eNormal-CRC\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[\"Healthy Control\", \"Colorectal Cancer\"]\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003eAll parameters are specified in a configuration file in \u003ca href=\"https://en.wikipedia.org/wiki/YAML\" rel=\"nofollow\"\u003eYAML\u003c/a\u003e format.\u003c/p\u003e\n\u003cp\u003eAn example configuration file is (snakemake/config.yaml).\u003c/p\u003e\n\u003cp\u003eThe parameter values in the configuration file can also be overrided through the \u003ccode\u003e--config\u003c/code\u003e option in \u003ca href=\"https://snakemake.readthedocs.io/en/stable/executable.html\" rel=\"nofollow\"\u003esnakemake\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following parameters should be changed:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eExample\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003egenome_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDirectory for genome and annotation files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenome/hg38\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edata_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDirectory for input files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edata/scirep\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003etemp_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eTemporary directory\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003etmp\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esample_id_file\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA text file containing sample IDs\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edata/scirep/sample_ids.txt\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eoutput_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDirectory for all output files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eoutput/scirep\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003etools_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDirectory for third-party tools\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ealigner\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMapping software\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ebowtie2\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eadaptor\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e3\u0027 adaptor sequence for single-end RNA-seq\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epython2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePath to Python 2\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/apps/anaconda2/bin/python\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epython3\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePath to Python 3\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/apps/anaconda2/bin/python\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-command-line-arguments-for-snakemake\" class=\"anchor\" href=\"#command-line-arguments-for-snakemake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand line arguments for snakemake\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOption\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--config\u003c/td\u003e\n\u003ctd\u003eAdditional configuration parameters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-j\u003c/td\u003e\n\u003ctd\u003eNumber of parallel jobs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dryrun\u003c/td\u003e\n\u003ctd\u003eDo not execute\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-k\u003c/td\u003e\n\u003ctd\u003eDo not stop when an independent job fails\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-submit-jobs-to-a-computer-cluster-using-snakemake\" class=\"anchor\" href=\"#submit-jobs-to-a-computer-cluster-using-snakemake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubmit jobs to a computer cluster using snakemake\u003c/h2\u003e\n\u003cp\u003ePlease refer the \u003ca href=\"https://snakemake.readthedocs.io/en/stable/snakefiles/configuration.html#cluster-configuration\" rel=\"nofollow\"\u003elink\u003c/a\u003e for descriptions of cluster configuration file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ibm-lsf\" class=\"anchor\" href=\"#ibm-lsf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIBM LSF\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eConfiguration file\u003c/strong\u003e: \u003ccode\u003esnakemake/cluster.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eHere is an example configuration:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003e__default__\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003equeue\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eZ-LU\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e{rule}.{wildcards}\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003estderr\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003elogs/cluster/{rule}/{wildcards}.stderr\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003estdout\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003elogs/cluster/{rule}/{wildcards}.stdout\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ethreads\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e{threads}\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eresources\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003espan[hosts=1]\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eCommonly used parameters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e__default__\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRule name (\u003ccode\u003e__default__\u003c/code\u003e) for default configuration)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003equeue\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eQueue name\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ename\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eJob name\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003estderr\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLog file for standard error\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003estdout\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLog file for standard output\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ethreads\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eNumber of parallel threads for a job\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eresources\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eResource requirements. \u003ccode\u003espan[hosts=1]\u003c/code\u003e prevents parallel jobs from being submitted to different nodes\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRun snakemake\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/\u003cspan class=\"pl-smi\"\u003e${snakefile}\u003c/span\u003e \\\n --configfile snakemake/config.yaml \\\n --cluster \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ebsub -q {cluster.queue} -J {cluster.name} -e {cluster.stderr} \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-o {cluster.stdout} -R {cluster.resources} -n {cluster.threads}\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n --cluster-config snakemake/cluster.yaml \\\n --rerun-incomplete -k -j40\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: replace \u003ccode\u003e${snakefile}\u003c/code\u003e with a Snakefile.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quality-control\" class=\"anchor\" href=\"#quality-control\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality control\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/quality_control.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-mapping-small-rna-seq\" class=\"anchor\" href=\"#mapping-small-rna-seq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMapping (small RNA-seq)\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-generate-snakemake-rules-for-sequential-mapping\" class=\"anchor\" href=\"#generate-snakemake-rules-for-sequential-mapping\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerate snakemake rules for sequential mapping\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebin/generate_snakemake.py sequential_mapping --rna-types rRNA,miRNA,piRNA,Y_RNA,srpRNA,tRNA,snRNA,snoRNA,lncRNA,mRNA,tucpRNA \\\n -o snakemake/mapping_small/sequential_mapping.snakemake\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/mapping_small.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-files\" class=\"anchor\" href=\"#output-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003eDescrpition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esnakemake/sequential_mapping.snakemake\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSnakefile for sequential mapping. Required by snakemake/mapping_small.snakemake\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/cutadapt/${sample_id}.fastq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eReads with adaptor trimmed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/tbam/${sample_id}/${rna_type}.bam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eBAM files in transcript coordinates\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/gbam/${sample_id}/${rna_type}.bam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eBAM files in genome coordinates\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/unmapped/${sample_id}/${rna_type}.fa.gz\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eUnmapped reads in each step\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/fastqc/${sample_id}_fastqc.html\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eFastQC report file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/summary/fastqc.html\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSummary report for FastQC (HTML)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/summary/fastqc.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSummary table for FastQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/summary/fastqc.ipynb\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSummary report for FastQC (Jupyter notebook)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/summary/read_counts.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSummary table for read counts\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/stats/mapped_read_length_by_sample/${sample_id}\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLength distribution of mapped reads\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-generate-expression-matrix\" class=\"anchor\" href=\"#generate-expression-matrix\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerate expression matrix\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/expression_matrix.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-files-1\" class=\"anchor\" href=\"#output-files-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003eDescrpition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/count_matrix/transcript.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eCount matrix of transcripts\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/count_matrix/htseq.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eCount matrix of genes generated using HTSeq-count\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/count_matrix/featurecounts.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eCount matrix of genes generated using featureCounts\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/counts_by_biotype/${count_method}/${sample_id}/${rna_type}\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eGene/transcript counts generated using a feature counting tool\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCount matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFile path: \u003ccode\u003e${output_dir}/count_matrix/transcript.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eFirst row: sample IDs\u003c/li\u003e\n\u003cli\u003eFirst column: feature names\u003c/li\u003e\n\u003cli\u003eFeature name: \u003ccode\u003egene_id|gene_type|gene_name\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-call-domains-for-long-rna\" class=\"anchor\" href=\"#call-domains-for-long-rna\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCall domains for long RNA\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/call_domains_long.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-files-2\" class=\"anchor\" href=\"#output-files-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003eDescrpition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/domain_counts/${bin_size}/${pvalue}/${sample_id}.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRead counts in long RNA domains (BED format with read counts in Column 5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/count_matrix/domain_${pvalue}.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRead count matrix of long RNA domains\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/domains/${bin_size}/${pvalue}.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLong RNA domain locations\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/domains_recurrence/${bin_size}/${pvalue}.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRecurrence of long RNA domains among samples (Column 5)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRead count matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFile path: \u003ccode\u003e${output_dir}/count_matrix/domain_long.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eFirst row: sample IDs\u003c/li\u003e\n\u003cli\u003eFirst column: feature names\u003c/li\u003e\n\u003cli\u003eFeature name: \u003ccode\u003egene_id|gene_type|gene_name|domain_id|transcript_id|start|end\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-normalization\" class=\"anchor\" href=\"#normalization\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNormalization\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-files-3\" class=\"anchor\" href=\"#output-files-3\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h3\u003e\n\u003cp\u003e| File name | Description |\n| \u003ccode\u003e${output_dir}/normalized_matrix/${normalization_method}.${imputation_method}.${batch_removal_method}.txt\u003c/code\u003e |\n| \u003ccode\u003e${output_dir}/matrix_processing/normalization/${normalization_method}.txt\u003c/code\u003e |\n| \u003ccode\u003e${output_dir}/matrix_processing/imputation/${normalization_method}.${imputation_method}.txt\u003c/code\u003e |\n| \u003ccode\u003e${output_dir}/matrix_processing/batch_removal/${batch_removal_method}.${batch_index}.txt\u003c/code\u003e |\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [ - "artificial-intelligence" + "bioinformatics", + "machie-learning" ], - "updated_at": 1682672415.0 + "updated_at": 1610690721.0 }, { "data_format": 2, - "description": null, + "description": "Singularity recipe for Jupyter lab with tensorflow and cuda-10 libs", "filenames": [ "Singularity" ], - "full_name": "funkelab/lsd_nm_experiments", + "full_name": "l1ll1/juflocu-10", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLSD NM experiments tutorial\u003c/h1\u003e\u003ca id=\"user-content-lsd-nm-experiments-tutorial\" class=\"anchor\" aria-label=\"Permalink: LSD NM experiments tutorial\" href=\"#lsd-nm-experiments-tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGeneral notes\u003c/h2\u003e\u003ca id=\"user-content-general-notes\" class=\"anchor\" aria-label=\"Permalink: General notes\" href=\"#general-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA tutorial for running training/inference of networks used for paper (in\nsingularity containers).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTested on Ubuntu 18.04 with Quadro P6000 (24gb gpu ram)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssuming \u003ccode\u003esingularity\u003c/code\u003e is installed and setup\n(some tutorials \u003ca href=\"https://docs.sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and\n\u003ca href=\"https://singularity-tutorial.github.io/01-installation/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssuming conda is installed and setup (helpful \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e if needed)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/funkelab/lsd_nm_experiments.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCreate simple environment for fetching containers:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n test_env python=3.8\nconda activate test_env\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you just need \u003ca href=\"https://boto3.amazonaws.com/v1/documentation/api/latest/index.html\" rel=\"nofollow\"\u003eboto3\u003c/a\u003e for fetching containers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install boto3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you also want to view data with \u003ca href=\"https://github.com/google/neuroglancer\"\u003eneuroglancer\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install boto3 neuroglancer h5py zarr\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownloading container(s)\u003c/h2\u003e\u003ca id=\"user-content-downloading-containers\" class=\"anchor\" aria-label=\"Permalink: Downloading container(s)\" href=\"#downloading-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edownload_imgs.py\u003c/code\u003e will download the singularity container used in the paper (\u003ccode\u003elsd:v0.8.img\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eWe found that sometimes this singularity container throws errors because of deprecated cuda versions that don\u0027t play well with updated drivers\u003c/li\u003e\n\u003cli\u003eWe made another image (\u003ccode\u003elsd_legacy.img\u003c/code\u003e) that should handle this. If running into \u003ccode\u003elibcublas\u003c/code\u003e or \u003ccode\u003egcc\u003c/code\u003e errors with the original container, consider using the newer one\u003c/li\u003e\n\u003cli\u003eTo download (uncomment legacy img if desired):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython download_imgs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOr building legacy container from source:\u003c/h2\u003e\u003ca id=\"user-content-or-building-legacy-container-from-source\" class=\"anchor\" aria-label=\"Permalink: Or building legacy container from source:\" href=\"#or-building-legacy-container-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNvidia has deprecated the conda packages of the cudnn / cudatoolkit versions used in the original singularity container (6.0/8.0)\u003c/li\u003e\n\u003cli\u003eThis sometimes causes problems with updated drivers (even though the point of containerization is to solve this...)\u003c/li\u003e\n\u003cli\u003eCan get around by installing these from tars, specifically these:\n\u003cpre\u003e\u003ccode\u003ehttps://anaconda.org/numba/cudatoolkit/8.0/download/osx-64/cudatoolkit-8.0-3.tar.bz2\nhttps://repo.anaconda.com/pkgs/free/linux-64/cudnn-6.0.21-cuda8.0_0.tar.bz2\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esetup.sh\u003c/code\u003e will fetch these packages and use them to install directly into the conda environment when creating the Singularity container\u003c/li\u003e\n\u003cli\u003eThe other packages are just specified in the \u003ccode\u003elsd_legacy.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo build legacy image locally:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e./setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote - the original container runs fine with \u003ccode\u003eexec\u003c/code\u003e, the legacy one uses \u003ccode\u003erun\u003c/code\u003e. To be honest, not sure what the problem is here\u003c/li\u003e\n\u003cli\u003eSo \u003ccode\u003esingularity exec --nv lsd:v0.8.img python -c \"import tensorflow\"\u003c/code\u003e will work, and\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity run --nv lsd_legacy.img python -c \"import tensorflow\"\u003c/code\u003e will work, but\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity exec --nv lsd_legacy.img python -c \"import tensorflow\"\u003c/code\u003e will cause import errors\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFetching data\u003c/h2\u003e\u003ca id=\"user-content-fetching-data\" class=\"anchor\" aria-label=\"Permalink: Fetching data\" href=\"#fetching-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to 01_data directory (\u003ccode\u003ecd 01_data\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efetch_data.py\u003c/code\u003e will download training data from the aws bucket using a json file (\u003ccode\u003edatasets.json\u003c/code\u003e) specifying the training volumes for each dataset\u003c/li\u003e\n\u003cli\u003eThe script defaults to just downloading the first volume for each dataset (one for each of zebrafinch, fib25, hemi, or three total)\u003c/li\u003e\n\u003cli\u003edownload data:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython fetch_data.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eyou could also use the singularity container to download the data, but we already have boto3 in the basic env we created anyway\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating masks\u003c/h2\u003e\u003ca id=\"user-content-creating-masks\" class=\"anchor\" aria-label=\"Permalink: Creating masks\" href=\"#creating-masks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecreate_masks.py\u003c/code\u003e will create a \u003ccode\u003elabels_mask\u003c/code\u003e that we use for training to constrain random locations\u003c/li\u003e\n\u003cli\u003eIf you installed zarr into the conda environment, you can just run with:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython create_masks.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eotherwise, using the singularity container:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../lsd:v0.8.img python create_masks.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eor \u003ccode\u003esingularity run...\u003c/code\u003e if using legacy container\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eViewing the data\u003c/h2\u003e\u003ca id=\"user-content-viewing-the-data\" class=\"anchor\" aria-label=\"Permalink: Viewing the data\" href=\"#viewing-the-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you installed neuroglancer into your environment, you can view the data with \u003ccode\u003eview_data.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ee.g \u003ccode\u003epython -i view_data.py -d funke/fib25/training/tstvol-520-1.zarr\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you are viewing remotely, you could also set the bind address with -b (defaults to localhost)\u003c/li\u003e\n\u003cli\u003eThere are some good little packages \u0026amp; tutorials for using neuroglancer differently. Examples\n\u003ca href=\"https://github.com/funkelab/funlib.show.neuroglancer\"\u003ehere\u003c/a\u003e, \u003ca href=\"https://connectomics.readthedocs.io/en/latest/external/neuroglancer.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\nand \u003ca href=\"https://github.com/google/neuroglancer/tree/master/python/examples\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample fib25 training data:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/fib25_training_data.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/fib25_training_data.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownloading network checkpoints\u003c/h2\u003e\u003ca id=\"user-content-downloading-network-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Downloading network checkpoints\" href=\"#downloading-network-checkpoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efetch_checkpoint.py\u003c/code\u003e will download a specified network checkpoint for a given dataset to the target folder in \u003ccode\u003e02_train\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eWe can start with the baseline affinities for the zebrafinch dataset just using conda boto3 (or use singularity if desired):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython fetch_checkpoint.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining a network\u003c/h2\u003e\u003ca id=\"user-content-training-a-network\" class=\"anchor\" aria-label=\"Permalink: Training a network\" href=\"#training-a-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to the zebrafinch baseline directory (\u003ccode\u003ecd ../02_train/zebrafinch/baseline\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eWe start by creating our network in \u003ccode\u003emknet.py\u003c/code\u003e (e.g placeholders to match to the trained graphs):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python mknet.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIt should print a bunch of layer names and tensor shapes (that looks like a sideways U-Net) to the command line\u003c/li\u003e\n\u003cli\u003eCheck the files in the directory (e.g \u003ccode\u003etree .\u003c/code\u003e), it should now look like:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e.\n\u251c\u2500\u2500 checkpoint\n\u251c\u2500\u2500 config.json\n\u251c\u2500\u2500 config.meta\n\u251c\u2500\u2500 mknet.py\n\u251c\u2500\u2500 predict.py\n\u251c\u2500\u2500 predict_scan.py\n\u251c\u2500\u2500 train_net_checkpoint_400000.data-00000-of-00001\n\u251c\u2500\u2500 train_net_checkpoint_400000.index\n\u251c\u2500\u2500 train_net_checkpoint_400000.meta\n\u251c\u2500\u2500 train_net.json\n\u251c\u2500\u2500 train_net.meta\n\u251c\u2500\u2500 train.py\n\u2514\u2500\u2500 view_batch.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eTrain for 1 iteration:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python train.py 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eYou\u0027ll see that gunpowder will print \u003ccode\u003eERROR:tensorflow:Couldn\u0027t match files for checkpoint ./train_net_checkpoint_500000\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis is because it checks the \u003ccode\u003echeckpoint\u003c/code\u003e file which specifies iteration 500000 (since this network was trained for longer than the optimal checkpoint)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf we view the batch, we\u0027ll see that the predictions are all grey, since it really only trained for a single iteration and didn\u0027t use the checkpoint (\u003ccode\u003epython -i view_batch.py -f snapshots/batch_1.hdf\u003c/code\u003e):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_batch_1.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_batch_1.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo fix, simply edit this \u003ccode\u003echeckpoint\u003c/code\u003e file to point to the downloaded checkpoint iteration instead (e.g 500000 -\u0026gt; 400000)\u003c/li\u003e\n\u003cli\u003eNow running the above won\u0027t do anything, because of line 27 in \u003ccode\u003etrain.py\u003c/code\u003e:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etrained_until\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emax_iteration\u003c/span\u003e:\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSo just make sure to train to the checkpoint + n, eg for 1 extra iteration:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python train.py 400001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe can then view the saved batch, e.g:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython -i view_batch.py -f snapshots/batch_400001.hdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_batch_400k.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_batch_400k.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Inference\u003c/h2\u003e\u003ca id=\"user-content-running-inference\" class=\"anchor\" aria-label=\"Permalink: Running Inference\" href=\"#running-inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFor the lsds experiments, we ran everything from inference through evaluation in a blockwise fashion using \u003ca href=\"https://github.com/funkelab/daisy\"\u003edaisy\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor inference, this meant having a blockwise prediction script that called a gunpowder predict pipeline inside each process\u003c/li\u003e\n\u003cli\u003eFor example, this \u003ca href=\"https://github.com/funkelab/lsd/blob/master/lsd/tutorial/scripts/01_predict_blockwise.py\"\u003escript\u003c/a\u003e would distribute this \u003ca href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/02_train/zebrafinch/baseline/predict.py\"\u003escript\u003c/a\u003e by using a \u003ca href=\"http://funkey.science/gunpowder/api.html?highlight=daisy#gunpowder.DaisyRequestBlocks\" rel=\"nofollow\"\u003eDaisyRequestBlocks\u003c/a\u003e gunpowder node\u003c/li\u003e\n\u003cli\u003eIf you just want to run inference on a small volume (in memory), you can instead use a \u003ca href=\"http://funkey.science/gunpowder/api.html?highlight=scan#gunpowder.Scan\" rel=\"nofollow\"\u003eScan\u003c/a\u003e node\u003c/li\u003e\n\u003cli\u003eWe added adapted all blockwise inference scripts to also use scan nodes (e.g \u003ccode\u003epredict_scan.py\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eExample run on zfinch training data:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python predict_scan.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eResulting affinities:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_preds.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_preds.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMultitask (MTLSD)\u003c/h2\u003e\u003ca id=\"user-content-multitask-mtlsd\" class=\"anchor\" aria-label=\"Permalink: Multitask (MTLSD)\" href=\"#multitask-mtlsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThis can be run exactly the same as the baseline above.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInside \u003ccode\u003e01_data/fetch_checkpoint.py\u003c/code\u003e, uncomment \u003ccode\u003emtlsd\u003c/code\u003e and run.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to \u003ccode\u003e02_train/zebrafinch/mtlsd\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate network (\u003ccode\u003e... python mknet.py\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eChange \u003ccode\u003echeckpoint\u003c/code\u003e file iteration to match downloaded checkpoint (500000 -\u0026gt; 400000)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTrain (\u003ccode\u003e... python train.py 400001\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eView batch (\u003ccode\u003e... python -i view_batch.py ...\u003c/code\u003e) -\u0026gt; will also show LSDs\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePredict (\u003ccode\u003e... python predict_scan.py\u003c/code\u003e) -\u0026gt; will write out LSDs and Affs\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis will also give us LSDs:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/mtlsd_zfinch_preds_lsds.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/mtlsd_zfinch_preds_lsds.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTip - you can view the different components of the LSDs by adjusting the shader in neuroglancer, e.g changing \u003ccode\u003e0,1,2\u003c/code\u003e to \u003ccode\u003e3,4,5\u003c/code\u003e will show the diagonal entries of the covariance component (or the direction the processes move):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003evoid main() {\n emitRGB(\n vec3(\n toNormalized(getDataValue(3)),\n toNormalized(getDataValue(4)),\n toNormalized(getDataValue(5)))\n );\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eOr to view a single channel of the 10d lsds, (e.g channel 6):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003evoid main() {\n float v = toNormalized(getDataValue(6));\n vec4 rgba = vec4(0,0,0,0);\n if (v != 0.0) {\n rgba = vec4(colormapJet(v), 1.0);\n }\n emitRGBA(rgba);\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAutocontext (ACLSD and ACRLSD)\u003c/h2\u003e\u003ca id=\"user-content-autocontext-aclsd-and-acrlsd\" class=\"anchor\" aria-label=\"Permalink: Autocontext (ACLSD and ACRLSD)\" href=\"#autocontext-aclsd-and-acrlsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThese networks rely on a pretrained LSD (raw -\u0026gt; LSDs) network, eg:\u003c/li\u003e\n\u003cli\u003eACLSD: \u003ccode\u003eRaw -\u0026gt; LSDs -\u0026gt; Affs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eACRLSD: \u003ccode\u003eRaw -\u0026gt; LSDs + cropped Raw -\u0026gt; Affs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBecause of this, they are more computationally expensive (since training requires extra context to first predict the lsds)\u003c/li\u003e\n\u003cli\u003eThey ran using 23.5 GB of available 24 GB GPU RAM when testing on quadro p6000. If you have less than that you will likely run into cuda OOM errors\u003c/li\u003e\n\u003cli\u003eIf you have access to sufficient gpu memory, to start navigate to \u003ccode\u003e01_data\u003c/code\u003e and uncomment \u003ccode\u003elsd\u003c/code\u003e + run script to get the pretrained lsd checkpoint\u003c/li\u003e\n\u003cli\u003eGo to lsd directory (\u003ccode\u003ecd ../02_train/zebrafinch/lsd\u003c/code\u003e) and follow same instructions as baseline and mtlsd nets above\u003c/li\u003e\n\u003cli\u003eOnce you have the lsd checkpoints (and \u003ccode\u003etest_prediction.zarr\u003c/code\u003e with \u003ccode\u003epred_lsds\u003c/code\u003e following prediction), start with a basic autocontext network (aclsd).\u003c/li\u003e\n\u003cli\u003eGet the aclsd checkpoint, navigate to appropriate directory, change checkpoint file as before and train network.\u003c/li\u003e\n\u003cli\u003eVisualizing the resulting batch shows us the larger raw context needed for predicting the lsds to ensure that the output affinities remain the same size:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/aclsd_training_batch.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/aclsd_training_batch.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun prediction as before - note, will not run if lsds have not been predicted first. This script could be adapted to predict the lsds on-the-fly using just the lsds and affs checkpoints\u003c/li\u003e\n\u003cli\u003eThe same can be done for the ACRLSD network (note, requires a merge provider during prediction as this network takes both lsds and raw data as input)\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTodos: add consolidated fib25/hemi nets to tutorial\u003c/h2\u003e\u003ca id=\"user-content-todos-add-consolidated-fib25hemi-nets-to-tutorial\" class=\"anchor\" aria-label=\"Permalink: Todos: add consolidated fib25/hemi nets to tutorial\" href=\"#todos-add-consolidated-fib25hemi-nets-to-tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-installation-readme\" class=\"anchor\" href=\"#installation-readme\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation README\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eWebsite:\u003cbr\u003e\n\u003ca href=\"https://jupyterlab.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ehttps://jupyterlab.readthedocs.io/en/stable/\u003c/a\u003e\n\u003ca href=\"https://www.tensorflow.org/\" rel=\"nofollow\"\u003ehttps://www.tensorflow.org/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSource:\u003cbr\u003e\n\u003ca href=\"https://pypi.org/project/jupyter/\" rel=\"nofollow\"\u003ehttps://pypi.org/project/jupyter/\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/tensorflow-gpu/\" rel=\"nofollow\"\u003ehttps://pypi.org/project/tensorflow-gpu/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLicence:\u003cbr\u003e\nBSD 3-Clause \"New\" or \"Revised\" License \u003ca href=\"https://github.com/jupyter/jupyter/blob/master/LICENSE\"\u003ehttps://github.com/jupyter/jupyter/blob/master/LICENSE\u003c/a\u003e\nApache License \u003ca href=\"https://github.com/tensorflow/tensorflow/blob/master/LICENSE\"\u003ehttps://github.com/tensorflow/tensorflow/blob/master/LICENSE\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePrerequisites:\nNot applicable\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun:\u003cbr\u003e\nsingularity exec jupyter.sif /start\nsingularity exec jupyter.sif /params to get details of ports and passwords to connect to\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTest:\u003cbr\u003e\nNot applicable\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExamples:\nNot applicable\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1702969887.0 + "updated_at": 1605813038.0 }, { "data_format": 2, - "description": "A quick singularity recipe for genmod to facilitate use in variant annotation pipelines", + "description": "New repository for ondemand Apps ", "filenames": [ - "Singularity" + "bc_vt_desktop/Singularityfiles/Singularity.def" ], - "full_name": "sirselim/singularity-genmod", + "full_name": "AdvancedResearchComputing/OnDemandApps", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4152\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-genmod\u003c/h1\u003e\u003ca id=\"user-content-singularity-genmod\" class=\"anchor\" aria-label=\"Permalink: singularity-genmod\" href=\"#singularity-genmod\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA quick singularity recipe for genmod to facilitate use in variant annotation pipelines\u003c/p\u003e\n\u003cp\u003egenmod GitHub: \u003ca href=\"https://github.com/moonso/genmod\"\u003ehttps://github.com/moonso/genmod\u003c/a\u003e\u003cbr\u003e\ngenmod docs: \u003ca href=\"http://moonso.github.io/genmod/\" rel=\"nofollow\"\u003ehttp://moonso.github.io/genmod/\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ondemandapps\" class=\"anchor\" href=\"#ondemandapps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOnDemandApps\u003c/h1\u003e\n\u003cp\u003eNew repository for ondemand Apps.\nThe repo follows new nomenclature, directory structure and support for CI using github actions.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-directory-structure\" class=\"anchor\" href=\"#directory-structure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory Structure\u003c/h2\u003e\n\u003cp\u003eFollowing is the description of directory structure, it is important to note that deviation with this structure may result in CI builds to break. For more info in this regard we request you to read \u003ca href=\"./CI.md\"\u003eCI.md\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- app_root\n - Dockerfiles\n - Dockerfile\n - Dockerfile_x64\n - Singularityfiles\n - singularity.def \n - singularity_x64.def\n - template\n - before.sh\n - after.sh\n .\n .\n .\n form.js\n info.md.erb\n submit.md.erb\n LICENSE\n manifest.yml\n .\n .\n .\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-nomenclature\" class=\"anchor\" href=\"#nomenclature\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNomenclature\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eApp shall starts with name \u003cstrong\u003ebc_vt\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eDifferent versions of shall append thier discriptive prefix such as \u003cstrong\u003ehtml/vnc\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eStandard defined names for files info/submit/view/form to be followed.\u003c/li\u003e\n\u003cli\u003eWith different versioning/ support of architectures in container image files shall append the architecture name. for eg. Dockerfile, Dockerfile_x86, Singularityfile.def , Singularity_x86.def\u003c/li\u003e\n\u003cli\u003eThe primary container filename are \u003cstrong\u003eDockerfile\u003c/strong\u003e and \u003cstrong\u003eSingularity.def\u003c/strong\u003e these are \u003cstrong\u003ecase sensitive\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1668322392.0 + "updated_at": 1639681253.0 }, { "data_format": 2, - "description": null, + "description": "Linux containers running XDMoD service (MariaDB + Apache)", "filenames": [ - "VepFileDeployment/Singularity.filedeploy", - "ReportingApplication/Singularity.report" + "Singularity/Singularity" ], - "full_name": "sbilge/ClinVAP", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/sbilge/ClinVAP/blob/master/doc/logo.jpeg\"\u003e\u003cimg src=\"https://github.com/sbilge/ClinVAP/raw/master/doc/logo.jpeg\" alt=\"Pipeline Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7f05470bebde335916523fc79634181a25859aa9c555fdac45f7393a9a62895c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f506572736f6e616c697a65644f6e636f6c6f67792f436c696e6963616c5265706f7274696e67506970656c696e652e737667\" alt=\"Release: Github\" data-canonical-src=\"https://img.shields.io/github/release/PersonalizedOncology/ClinicalReportingPipeline.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2168\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.docker.com/u/personalizedoncology/repository/list\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/505174bdf4e0e21d1eea9dc360b336f4119d54dcca8c30ecd52efecdcd0dc4ee/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65722d2d6875622d626c75652e737667\" alt=\"Docker: Available\" data-canonical-src=\"https://img.shields.io/badge/hosted-docker--hub-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4426cbe5c21edb002526331c7a8fbfa089e84a550567b02a0d829a98b136ad0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eClinical Variant Annotation Pipeline\u003c/h1\u003e\u003ca id=\"user-content-clinical-variant-annotation-pipeline\" class=\"anchor\" aria-label=\"Permalink: Clinical Variant Annotation Pipeline\" href=\"#clinical-variant-annotation-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClinical Variant Annotation Pipeline (ClinVAP) creates a genetic report of somatic mutations from a variant call format (VCF) file. Please refer this document for implementation of the pipeline. Documentation of the pipeline is available at \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/wiki\"\u003eWiki page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMetadata Structure\u003c/h3\u003e\u003ca id=\"user-content-metadata-structure\" class=\"anchor\" aria-label=\"Permalink: Metadata Structure\" href=\"#metadata-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf a patient metadata file is provided in the input directory with the naming schema \u0026lt;INPUT_VCF_NAME\u0026gt;_metadata.json, ClinVAP recognizes it and renders the information into the Patient Data table in the outputted report. Additionally, if dignosis is provided in the metadata file, the list of drugs with the clinical evidence of targeting the gene in that particular cancer type is reported in the \"CIViC Summary of Drugs Targeting the Affected Genes\" table. If no diagnosis is provided, then the pipeline stays agnostic to the cancer type, and returns the results related with the gene-drug association regardless of the cancer type. Please note that the disease name should be selected from the pre-defined dictionary that can be found \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/blob/master/doc/disease_names_dictionary.txt\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetadata file format:\u003c/strong\u003e\u003cbr\u003e\n{\u003cbr\u003e\n\"patient_firstname\":\"\u0026lt;NAME\u0026gt;\",\u003cbr\u003e\n\"patient_lastname\":\"\u0026lt;SURNAME\u0026gt;\",\u003cbr\u003e\n\"patient_dateofbirth\":\"\u0026lt;DATE\u0026gt;\",\u003cbr\u003e\n\"patient_diagnosis_short\":\"\u0026lt;DIAGNOSIS\u0026gt;\",\u003cbr\u003e\n\"mutation_load\":\"\u0026lt;LOAD\u0026gt;\"\u003cbr\u003e\n}\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage with Singularity\u003c/h2\u003e\u003ca id=\"user-content-usage-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Usage with Singularity\" href=\"#usage-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequirements: Singularity 2.4+\u003cbr\u003e\nPlease make sure that you have 12 GB of empty space on your home directory, and ports 5000 and 27021 are not being used by another application.\nTo run the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull reporting image from Singularity Hub.\n\u003ccode\u003esingularity pull -n reporting_app.img shub://PersonalizedOncology/ClinVAP:report\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePull dependency files image from Singularity Hub.\n\u003ccode\u003esingularity pull -n file_deploy.img shub://PersonalizedOncology/ClinVAP:filedeploy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun dependency files image first to transfer those file on your local folder.\n\u003ccode\u003esingularity run -B /LOCAL/PATH/TO/FILES:/mnt file_deploy.img -a \u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the reporting image to generate the clinical reports.\n\u003ccode\u003esingularity run -B /LOCAL/PATH/TO/FILES:/data -B /PATH/TO/INPUT/DATA:/inout reporting_app.img -t /inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-a\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in your /PATH/TO/INPUT/DATA folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage with Docker\u003c/h2\u003e\u003ca id=\"user-content-usage-with-docker\" class=\"anchor\" aria-label=\"Permalink: Usage with Docker\" href=\"#usage-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFor Mac and Ubuntu Users\u003c/h3\u003e\u003ca id=\"user-content-for-mac-and-ubuntu-users\" class=\"anchor\" aria-label=\"Permalink: For Mac and Ubuntu Users\" href=\"#for-mac-and-ubuntu-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequirements: Docker Engine release 1.13.0+, Compose release 1.10.0+.\u003cbr\u003e\nPlease make sure that you have 34 GB of physical empty space on your Docker Disk Image, and ports 5000 and 27017 are not being used by another application.\u003c/p\u003e\n\u003cp\u003eTo tun the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePelase note that the input VCF file(s) should be in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e2. cd ClinVAP/\n3. export ASSEMBLY=\u0026lt;Your Assembly Here\u0026gt;\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t /inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Docker Toolbox For Windows Users\u003c/h3\u003e\u003ca id=\"user-content-running-with-docker-toolbox-for-windows-users\" class=\"anchor\" aria-label=\"Permalink: Running with Docker Toolbox For Windows Users\" href=\"#running-with-docker-toolbox-for-windows-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequirements: Docker Engine release 1.13.0+, Compose release 1.10.0+.\u003cbr\u003e\nPlease make sure that you have 34 GB of physical empty space on your Docker Disk Image, and ports 5000 and 27017 are not being used by another application.\u003c/p\u003e\n\u003cp\u003eTo tun the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePelase note that the input VCF file(s) should be in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e2. cd ClinVAP/\n3. export ASSEMBLY=\u0026lt;Your Assembly Here\u0026gt;\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t //inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDemo Run\u003c/h2\u003e\u003ca id=\"user-content-demo-run\" class=\"anchor\" aria-label=\"Permalink: Demo Run\" href=\"#demo-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provided an example input file, strelka_passed_missense_somatic_snvs.vcf under ./ReportingApplication/inout folder along with a dummy metadata file, strelka_passed_missense_somatic_snvs.json. The corresponding report of the strelka input file is provided \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/tree/master/doc/strelka_passed_missense_somatic_snvs.pdf\"\u003ehere\u003c/a\u003e as an example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Demo with Singularity\u003c/h3\u003e\u003ca id=\"user-content-running-demo-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running Demo with Singularity\" href=\"#running-demo-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n2. singularity pull -n reporting_app.img shub://PersonalizedOncology/ClinVAP:report\n3. singularity pull -n file_deploy.img shub://PersonalizedOncology/ClinVAP:filedeploy\n4. mkdir vep_files\n5. singularity run -B ./vep_files:/mnt file_deploy.img -a GRCh37\n6. singularity run -B ./vep_files:/data -B ./ClinVAP/ReportingApplication/inout:/inout reporting_app.img -t /inout -p jwp -a GRCh37\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Demo with Docker\u003c/h3\u003e\u003ca id=\"user-content-running-demo-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running Demo with Docker\" href=\"#running-demo-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n2. cd ClinVAP/\n3. export ASSEMBLY=GRCh37\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t /inout -p jwp -a GRCh37\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCitation\u003c/h3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use ClinVAP in your work, please cite the following article\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eS\u00fcr\u00fcn, B., Sch\u00e4rfe, C.P., Divine, M.R., Heinrich, J., Toussaint, N.C., Zimmermann, L., Beha, J. and Kohlbacher, O., 2020. ClinVAP: a reporting strategy from variants to therapeutic options. Bioinformatics, 36(7), pp.2316-2317.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "jtfrey/xdmod-container", + "latest_release": "v1.8.2", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-xdmod-container\" class=\"anchor\" href=\"#xdmod-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003exdmod-container\u003c/h1\u003e\n\u003cp\u003eThis repository includes container provisioning files to create a container that executes the XDMoD web application. Both the MariaDB server and Apache httpd execute within the container, making the service completely self-contained.\u003c/p\u003e\n\u003cp\u003eThe MariaDB instance will retain the default CentOS 7 configuration; no \u003ccode\u003eroot\u003c/code\u003e password is set, the test database is not removed, and the anonymous user is not removed. Since all access is internal to the container, there\u0027s no need to secure it.\u003c/p\u003e\n\u003cp\u003eThe container features a runloop that waits for files to appear in \u003ccode\u003e/var/lib/XDMoD-ingest-queue/in\u003c/code\u003e. The file(s) are ingested into the database and will be moved to \u003ccode\u003e/var/lib/XDMoD-ingest-queue/out\u003c/code\u003e if successful or to \u003ccode\u003e/var/lib/XDMoD-ingest-queue/error\u003c/code\u003e if not. Results of the operations are logged to \u003ccode\u003e/var/log/xdmod/ingest-queue.log\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eThe Docker container should be spawned with TCP port 8080 mapped to a host port to expose the web application. The database starts out uninitialized; when a container instance is spawned an external directory may be bind-mounted at \u003ccode\u003e/var/lib/mysql\u003c/code\u003e in the container to make the database persistent across restarts of the instance, and when the container entrypoint script is first executed the database setup will be completed automatically.\u003c/p\u003e\n\u003cp\u003eAn external directory can also be bind-mounted at \u003ccode\u003e/var/lib/XDMoD-ingest-queue\u003c/code\u003e. The directory must have the following subdirectories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003ein\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eout\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eerror\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese three directories will be created automatically by the entrypoint script if not present in \u003ccode\u003e/var/lib/XDMoD-ingest-queue\u003c/code\u003e. Using an external directory allows processes outside the container to copy Slurm accounting log files to the \u003ccode\u003ein\u003c/code\u003e subdirectory and the entrypoint runloop will awake within 5 minutes and ingest the data.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example\" class=\"anchor\" href=\"#example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h3\u003e\n\u003cp\u003eThe container image is built in this repository directory using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd Docker\n$ ROOT_PASSWORD=\"\u0026lt;password\u0026gt;\" docker build --rm --tag local/xdmod:9.5.0 .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following example illustrates the creation of an instance with persistent database and ingest queue directories:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir -p /tmp/XDMoD-Caviness/ingest-queue\n$ mkdir -p /tmp/XDMoD-Caviness/database\n$ docker run --detach --restart unless-stopped \\\n --name XDMoD-Caviness \\\n --env CLUSTER_NAME=\"cc3\" \\\n --env RESOURCE_LOG_FORMAT=\"slurm\" \\\n --volume \"/tmp/XDMoD-Caviness/database:/var/lib/mysql:rw\" \\\n --volume \"/tmp/XDMoD-Caviness/ingest-queue:/var/lib/XDMoD-ingest-queue:rw\" \\\n --publish 8080:8080\n local/xdmod:9.5.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eCLUSTER_NAME\u003c/code\u003e and \u003ccode\u003eRESOURCE_LOG_FORMAT\u003c/code\u003e are used in the entrypoint XDMoD-start script as arguments to \u003ccode\u003exdmod-shredder\u003c/code\u003e for resource manager log file ingestion. \u003ccode\u003eRESOURCE_LOG_FORMAT\u003c/code\u003e defaults to \"slurm\".\u003c/p\u003e\n\u003cp\u003eOnce the instance is online, XDMoD must be initialized and the ingest queue activated:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker exec -it XDMoD-Caviness /bin/bash -l\n[container]\u0026gt; xdmod-setup\n :\n[container]\u0026gt; touch /var/lib/XDMoD-ingest-queue/enable\n[container]\u0026gt; exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point, copying files to \u003ccode\u003e/tmp/XDMoD-Caviness/ingest-queue/in\u003c/code\u003e will see them processed in the runloop. Point a web browser to \u003ca href=\"http://localhost:8080/\" rel=\"nofollow\"\u003ehttp://localhost:8080/\u003c/a\u003e to use the web application.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eSingularity 3.0 or newer is required (3.2.1 was used in our production environment) for the network port mapping and support for instances (service-like containers).\u003c/p\u003e\n\u003cp\u003eRather than bind-mounting directories at specific paths as outline above for Docker, with Singularity a writable overlay file system is a good option. Any changes to the file system relative to the read-only container image are written to an external directory. As with Docker, port 8080 is mapped to a host port to expose the web application.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example-1\" class=\"anchor\" href=\"#example-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h3\u003e\n\u003cp\u003eThe container image is built in this repository directory using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd Singularity\n$ ROOT_PASSWORD=\"\u0026lt;password\u0026gt;\" singularity build XDMoD-9.5.0.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following example illustrates the execution of an instance with an overlay file system:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir -p /tmp/XDMoD-Caviness\n$ singularity instance start --overlay /tmp/XDMoD-Caviness --net --dns 10.65.0.13 \\\n --network bridge --network-args \"portmap=8080:8080/tcp\" \\\n --env CLUSTER_NAME=\"cc3\" --env RESOURCE_LOG_FORMAT=\"slurm\" \\\n XDMoD-9.5.0.sif XDMoD-Caviness\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eCLUSTER_NAME\u003c/code\u003e and \u003ccode\u003eRESOURCE_LOG_FORMAT\u003c/code\u003e are used in the entrypoint XDMoD-start script as arguments to \u003ccode\u003exdmod-shredder\u003c/code\u003e for resource manager log file ingestion. \u003ccode\u003eRESOURCE_LOG_FORMAT\u003c/code\u003e defaults to \"slurm\".\u003c/p\u003e\n\u003cp\u003eOnce the instance is online, XDMoD must be initialized and the ingest queue activated:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://XDMoD-Caviness\n[container]\u0026gt; xdmod-setup\n :\n[container]\u0026gt; touch /var/lib/XDMoD-ingest-queue/in\n[container]\u0026gt; touch /var/lib/XDMoD-ingest-queue/enable\n[container]\u0026gt; exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point, copying files to \u003ccode\u003e/tmp/XDMoD-Caviness/upper/var/lib/XDMoD-ingest-queue/in\u003c/code\u003e will see them processed in the runloop. Point a web browser to \u003ca href=\"http://localhost:8080/\" rel=\"nofollow\"\u003ehttp://localhost:8080/\u003c/a\u003e to use the web application.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-helper-scripts\" class=\"anchor\" href=\"#helper-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHelper Scripts\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003esbin\u003c/code\u003e directory includes a SysV-style script that can be used to start, stop, restart, and query status of instances of the Singularity container.\u003c/p\u003e\n\u003cp\u003eTo start a new or existing instance with the default container image and overlay directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sbin/instance Caviness start\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo use a different container image and overlay directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sbin/instance --overlay=/tmp/XDMoD --image=./XDMoD-uge.sif Farber start\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003estatus\u003c/code\u003e action returns 0 if the instance is running, non-zero otherwise:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sbin/instance Farber status\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--verbose\u003c/code\u003e option increases the amount of output displayed by the command, and the \u003ccode\u003e--help\u003c/code\u003e option summarizes the command and all options.\u003c/p\u003e\n\u003cp\u003eIn addition, the \u003ccode\u003esystemd\u003c/code\u003e directory contains a templated service unit that integrates Singularity instances with systemd for automated startup/shutdown. Adding our Farber instance above looks like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cp systemd/xdmod-template.service /etc/systemd/system/xdmod@Farber.service\n$ systemctl daemon-reload\n$ systemctl enable xdmod@Farber.service\n$ systemctl start xdmod@Farber.service\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1690190681.0 + "updated_at": 1637091617.0 }, { "data_format": 2, - "description": "FieldOpt C++ Optimization Framework [Open Research Version]", + "description": "CUT\u0026RUN and CUT\u0026Tag FASTQ -\u003e bigwig pipeline", "filenames": [ - "Docker/Singularity", - "Docker/Release/Singularity", - "Docker/Develop/Singularity" + "Singularity.centos" ], - "full_name": "PetroleumCyberneticsGroup/FieldOpt-Research-Open", + "full_name": "ertheisen/hohriver", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFieldOpt-Research-Open\u003c/h1\u003e\u003ca id=\"user-content-fieldopt-research-open\" class=\"anchor\" aria-label=\"Permalink: FieldOpt-Research-Open\" href=\"#fieldopt-research-open\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFieldOpt [Open Research Version] is a C++ programming framework\nfor efficient prototyping and testing of optimization methodologies\nfor problems involving large-scale numerical simulations.\u003c/p\u003e\n\u003cp\u003eFieldOpt serves as a multi-disciplinary knowledge\nrepository for coupling optimization with reservoir simulation.\nTechnology development is based on integration of efficient\niterative procedures with expert domain parametrizations.\u003c/p\u003e\n\u003cp\u003eFieldOpt facilitates research and innovation through up-scaling of\nprototype methodology to realistic cases, coupling, integration and\nhybridization of optimization methodology and problem solutions,\nand cross-application of existing methods to new domains.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTarget problems\u003c/h2\u003e\u003ca id=\"user-content-target-problems\" class=\"anchor\" aria-label=\"Permalink: Target problems\" href=\"#target-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePetroleum Field Development\u003c/h3\u003e\u003ca id=\"user-content-petroleum-field-development\" class=\"anchor\" aria-label=\"Permalink: Petroleum Field Development\" href=\"#petroleum-field-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Well placement optimization \u003ca href=\"#Bellout2012JntWplcCntrl\"\u003e[1]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[x] Production optimization\u003c/li\u003e\n\u003cli\u003e[x] Optimization of inflow-control valve settings\u003c/li\u003e\n\u003cli\u003e[x] Well completion optimization and model-update while drilling\u003c/li\u003e\n\u003cli\u003e[ ] Minimization of C02 emissions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptimization methodologies\u003c/h2\u003e\u003ca id=\"user-content-optimization-methodologies\" class=\"anchor\" aria-label=\"Permalink: Optimization methodologies\" href=\"#optimization-methodologies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeterministic\u003c/h3\u003e\u003ca id=\"user-content-deterministic\" class=\"anchor\" aria-label=\"Permalink: Deterministic\" href=\"#deterministic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Compass Search (CS)\u003c/li\u003e\n\u003cli\u003e[x] Asynchronous Paralell Pattern Search (APPS)\u003c/li\u003e\n\u003cli\u003e[x] Derivative-Free Trust-Region Algorithm (DFTR) \u003ca href=\"#Silva2020DfTrAlgWcntrlOpt\"\u003e[2]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStochastic / probabilistic\u003c/h3\u003e\u003ca id=\"user-content-stochastic--probabilistic\" class=\"anchor\" aria-label=\"Permalink: Stochastic / probabilistic\" href=\"#stochastic--probabilistic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Genetic Algorithm (GA)\u003c/li\u003e\n\u003cli\u003e[x] Particle Swarm Optimization (PSO)\u003c/li\u003e\n\u003cli\u003e[x] Covariance Matrix Adaption Evolutionary Strategy (CMA-ES)\u003c/li\u003e\n\u003cli\u003e[x] Bayesian Optimization (EGO)\u003c/li\u003e\n\u003cli\u003e[x] Simultaneous Perturbation Stochastic Approximation (SPSA)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHybrid approaches\u003c/h3\u003e\u003ca id=\"user-content-hybrid-approaches\" class=\"anchor\" aria-label=\"Permalink: Hybrid approaches\" href=\"#hybrid-approaches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] mPSO\u003c/li\u003e\n\u003cli\u003e[ ] APPS/PSO + data-driven meta-optimization\u003c/li\u003e\n\u003cli\u003e[ ] Joint optimization using embedded reduced-order sub-routines\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProblem structure\u003c/h3\u003e\u003ca id=\"user-content-problem-structure\" class=\"anchor\" aria-label=\"Permalink: Problem structure\" href=\"#problem-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Multi-level joint optimization (concurrent, sequential, embedded)\u003c/li\u003e\n\u003cli\u003e[ ] Automatic variable segregation for multi-level optimization\u003c/li\u003e\n\u003cli\u003e[x] Variable scaling\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eObjective terms\u003c/h3\u003e\u003ca id=\"user-content-objective-terms\" class=\"anchor\" aria-label=\"Permalink: Objective terms\" href=\"#objective-terms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Weighted function, Net Present Value\u003c/li\u003e\n\u003cli\u003e[x] Well cost\u003c/li\u003e\n\u003cli\u003e[x] Augmented terms: Geology \u0026amp; geophysics-based (SWCT)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThirdparty solvers/libraries\u003c/h3\u003e\u003ca id=\"user-content-thirdparty-solverslibraries\" class=\"anchor\" aria-label=\"Permalink: Thirdparty solvers/libraries\" href=\"#thirdparty-solverslibraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] SNOPT \u003ca href=\"#Gill2002SNOPTSIAMRev\"\u003e[3]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[x] Ensemble based Reservoir Tool (ERT) \u003ca href=\"#EquinorERT\"\u003e[4]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] TensorFlow\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFunctionalities\u003c/h2\u003e\u003ca id=\"user-content-functionalities\" class=\"anchor\" aria-label=\"Permalink: Functionalities\" href=\"#functionalities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInterfaces subsurface flow simulators\u003c/h3\u003e\u003ca id=\"user-content-interfaces-subsurface-flow-simulators\" class=\"anchor\" aria-label=\"Permalink: Interfaces subsurface flow simulators\" href=\"#interfaces-subsurface-flow-simulators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Schlumberger\u0027s E100/E300/IX\u003c/li\u003e\n\u003cli\u003e[x] Open Porous Media Flow\u003c/li\u003e\n\u003cli\u003e[x] Stanford\u0027s AD-GPRS\u003c/li\u003e\n\u003cli\u003e[x] Pre-/Post-processing\n\u003cul\u003e\n\u003cli\u003e[x] E300 adjoint-gradient read-in\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWell trajectory development\u003c/h3\u003e\u003ca id=\"user-content-well-trajectory-development\" class=\"anchor\" aria-label=\"Permalink: Well trajectory development\" href=\"#well-trajectory-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Automatic well planner (AWP) \u003ca href=\"#Kristoffersen2020AWPGeoUncer\"\u003e[5]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[x] State-of-the-art well connection transmissibility factor calculation \u003ca href=\"#ResInsightv2020.04.1\"\u003e[6]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[x] Variable mapping onto multi-segmented well model (WELSEGS/COMPSEGS/WSEGVALV) \u003ca href=\"#SLB2012EclipseTD\"\u003e[7]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWell placement constraint-handling\u003c/h3\u003e\u003ca id=\"user-content-well-placement-constraint-handling\" class=\"anchor\" aria-label=\"Permalink: Well placement constraint-handling\" href=\"#well-placement-constraint-handling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Method of Alternating Projections (MAP) \u003ca href=\"#Bellout2018EffConstrHandlWplcOpt\"\u003e[8]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] Length, inter-well distance, user-defined convex-polytope reservoir-boundary\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNetwork/facility modeling\u003c/h3\u003e\u003ca id=\"user-content-networkfacility-modeling\" class=\"anchor\" aria-label=\"Permalink: Network/facility modeling\" href=\"#networkfacility-modeling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Topside facility model for CO2 emission calculation\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUncertainty-handling\u003c/h3\u003e\u003ca id=\"user-content-uncertainty-handling\" class=\"anchor\" aria-label=\"Permalink: Uncertainty-handling\" href=\"#uncertainty-handling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Expected cost function evaluation over realization set\u003c/li\u003e\n\u003cli\u003e[ ] Reduced random sampling strategy\u003ca href=\"#Jesmani2020RedRanSamStr\"\u003e[9]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eParallelization\u003c/h3\u003e\u003ca id=\"user-content-parallelization\" class=\"anchor\" aria-label=\"Permalink: Parallelization\" href=\"#parallelization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Algorithm-level parallelization of cost function\nevaluations (simulations) through MPI runtime library\n\u003ca href=\"#Baumann2020FieProFrmwrk\"\u003e[10]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca id=\"user-content-Bellout2012JntWplcCntrl\"\u003e[1]\u003c/a\u003e\nBellout, M.C.; Echeverria Ciaurri, D.; Durlofsky, L.J.; Foss, B.; Kleppe, J.\n(2012).\nJoint optimization of oil well placement and controls.\nComputational Geosciences, 16(4), pp.1061-1079.\n\u003ca href=\"https://doi.org/10.1007/s10596-012-9303-5\" rel=\"nofollow\"\u003ehttps://doi.org/10.1007/s10596-012-9303-5\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Silva2020DfTrAlgWcntrlOpt\"\u003e[2]\u003c/a\u003e\nSilva, T.L.; Bellout, M.C.; Giuliani, C.; Camponogara, E.; Pavlov, A.\n(2020).\nA Derivative-Free Trust-Region Algorithm for Well Control Optimization.\n17th European Conference on the Mathematics of Oil\nRecovery, 14th-17th September, Online Event.\n\u003ca href=\"https://doi.org/10.3997/2214-4609.202035086\" rel=\"nofollow\"\u003ehttps://doi.org/10.3997/2214-4609.202035086\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Gill2002SNOPTSIAMRev\"\u003e[3]\u003c/a\u003e\nGill, P.E.; Murray, W.; Saunders, M.A.\n(2005).\nSNOPT: An SQP Algorithm for Large-Scale Constrained Optimization.\nSIAM Review, 47(1), pp.99-131.\n\u003ca href=\"http://dx.doi.org/10.1137/S0036144504446096\" rel=\"nofollow\"\u003ehttp://dx.doi.org/10.1137/S0036144504446096\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-EquinorERT\"\u003e[4]\u003c/a\u003e\nEquinor.\n(2021).\nEnsemble based Reservoir Tool.\n\u003ca href=\"https://github.com/equinor/ert\"\u003ehttps://github.com/equinor/ert\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Kristoffersen2020AWPGeoUncer\"\u003e[5]\u003c/a\u003e\nKristoffersen, B.S.; Silva, T.L.; Bellout, M.C.; Berg, C.F.\n(2020).\nAn Automatic Well Planner for Efficient Well Placement\nOptimization Under Geological Uncertainty.\n17th European Conference on the Mathematics of Oil\nRecovery, 14th-17th September, Online Event.\n\u003ca href=\"https://doi.org/10.3997/2214-4609.202035211\" rel=\"nofollow\"\u003ehttps://doi.org/10.3997/2214-4609.202035211\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-ResInsightv2020.04.1\"\u003e[6]\u003c/a\u003e\nCeetron Solutions AS; Equinor ASA.\n(2020).\nResInsight.\n\u003ca href=\"http://resinsight.org\" rel=\"nofollow\"\u003ehttp://resinsight.org\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-SLB2012EclipseTD\"\u003e[7]\u003c/a\u003e\nSchlumberger AS.\n(2012).\nEclipse technical description.\nChp.44: Multi-segment Wells. pp.683-703.\n\u003ca href=\"https://www.software.slb.com/products/eclipse/simulators\" rel=\"nofollow\"\u003ehttps://www.software.slb.com/products/eclipse/simulators\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Bellout2018EffConstrHandlWplcOpt\"\u003e[8]\u003c/a\u003e\nBellout, M.C.; Volkov, O.\n(2018).\nDevelopment of efficient constraint-handling approaches\nfor well placement optimization.\n16th European Conference on the Mathematics of Oil\nRecovery, 3rd-6th September, Barcelona, Spain.\n\u003ca href=\"https://doi.org/10.3997/2214-4609.201802247\" rel=\"nofollow\"\u003ehttps://doi.org/10.3997/2214-4609.201802247\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Jesmani2020RedRanSamStr\"\u003e[9]\u003c/a\u003e\nJesmani, M.; Jafarpour, B.; Bellout, M.C.; Foss, B.\n(2020).\nA reduced random sampling strategy\nfor fast robust well placement optimization.\nJournal of Petroleum Science and Engineering, 184, pp.106414.\n\u003ca href=\"https://doi.org/10.1016/j.petrol.2019.106414\" rel=\"nofollow\"\u003ehttps://doi.org/10.1016/j.petrol.2019.106414\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Baumann2020FieProFrmwrk\"\u003e[10]\u003c/a\u003e\nBaumann, E.J.M.; Dale, S.I.; Bellout, M.C.\n(2020).\nFieldOpt: A powerful and effective programming\nframework tailored for field development optimization.\nComputers \u0026amp; Geosciences, 135, pp.104379.\n\u003ca href=\"https://doi.org/10.1016/j.cageo.2019.104379\" rel=\"nofollow\"\u003ehttps://doi.org/10.1016/j.cageo.2019.104379\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-appalachianhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#appalachianhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappalachian.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run -H $HOME:/home/$USER --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for E. coli spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Escherichia_coli_K_12_DH10B/NCBI\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eThe correct bind commands for test and spike are as follows:\n--bind /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/:/genomes/test\n--bind /gpfs0/home/gdlessnicklab/share/trails/genomes/Escherichia_coli_K_12_DH10B/NCBI:/genomes/spike\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell -H $HOME:/home/$USER --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003eTo run command from a program in the container\u003c/p\u003e\n\u003cp\u003esingularity exec -H $HOME:/home/$USER --bind [directory_info] appalachian_hg19.simg [command information]\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n\u003cp\u003eApps:\ncutnrun_full : full pipeline from fastqc to mapped bam, bed, normalized bedgraph, and normalized bigwig with mapping qc using plotFingerprint from deeptools - bowtie2 alignment assumes 150 bp PE reads and allows dovetail and overlap in alignment\ncutntag : full pipeline from fastqc to mapped bam, bed, normalized bedgraph, and normalized bigwig with mapping qc using plotFingerprint from deeptools - bowtie2 alignment assumes 150 bp PE reads and allows dovetail and overlap in alignment\ncutnrun_hardtrim : full pipeline from fastqc to mapped bam, bed, normalized bedgraph, and normalized bigwig with mapping qc using plotFingerprint from deeptools - bowtie2 alignment. First step trims 150 bp reads from HiSeq to 60 bp.\ncutntag_hardtrim : full pipeline from fastqc to mapped bam, bed, normalized bedgraph, and normalized bigwig with mapping qc using plotFingerprint from deeptools - bowtie2 alignment. First step trims 150 bp reads from HiSeq to 60 bp.\nfastqc_pre : FastQC pre-trim\ntrim_qc : Fastq trimming with Trim Galore and FastQC post trim\nbowtie2_alignment : Alignment for CUT\u0026amp;RUN samples; insert 10 bp-700 bp, assumes paired end 150 bp reads, makes bams/beds/bg/bw with no spike in.\nbams : Generate downstream files for starting at bams using same pipeline - assumes hg19 alignment\nmapqc_fingerprint : Use deeptools to assess enrichment quality\u003c/p\u003e\n\u003cp\u003eData should be stored in a \"project\" directory with a sub-directory called \"fastq\". The \"fastq\" subdirectory should have all of your paired end fastq or fastq.gz files. Then the correct bind for data is:\n--bind /path_to/project/:/data\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1641512743.0 + "updated_at": 1616617576.0 }, { "data_format": 2, - "description": "Este reposit\u00f3rio cont\u00e9m o material correspondente ao minicurso/Hands on 05: Gera\u00e7\u00e3o Autom\u00e1tica de C\u00f3digo Atrav\u00e9s de Computa\u00e7\u00e3o Simb\u00f3lica em Python: Introdu\u00e7\u00e3o \u00e0 LDE Devito do VI SAPCT e V ICPAD organizado pelo Senai Cimatec", + "description": "The main platform for sharing all the information about AI/HPC-related containers at C3SE. See also the included tutorial to get started. ", "filenames": [ - "docker/Singularity.nvidia.def" + "FEniCS/Singularity.FEniCS", + "FDTD/Singularity.FDTD-8.19.1541", + "ambertools/Singularity.ambertools-20", + "PyTorch/Singularity.PyTorch-1.11-NGC-22.01", + "PyTorch/Singularity.PyTorch-1.13-NGC-22.08", + "PyTorch/Singularity.PyTorch-1.10-NGC-21.10", + "PyTorch/Singularity.PyTorch-1.10-NGC-21.08", + "PyTorch/Singularity.PyTorch-1.12-NGC-22.04", + "PyTorch/Singularity.PyTorch-1.10-NGC-21.09", + "PyTorch/Singularity.PyTorch-1.12.1", + "PyTorch/Singularity.PyTorch-1.11-NGC-21.12", + "PyTorch/Singularity.PyTorch-1.9.0", + "PyTorch/Singularity.PyTorch-1.10.0", + "PyTorch/Singularity.PyTorch-1.6-NGC-20.07", + "PyTorch/Singularity.PyTorch-1.12-NGC-22.05", + "PyTorch/Singularity.PyTorch-1.11-NGC-22.02", + "PyTorch/Singularity.PyTorch-1.11-NGC-21.11", + "PyTorch/Singularity.PyTorch-1.8.1", + "PyTorch/Singularity.PyTorch-1.11.0", + "RockyLinux/Singularity.RockyLinux-8.5.20220308", + "CentOS/Singularity.CentOS-8.3.2011", + "Ubuntu/Singularity.Ubuntu-focal-20210827", + "Conda/Singularity.anaconda-2021.05", + "Conda/Singularity.miniconda-4.10.3", + "R/Singularity.R-4.1.3-TensorFlow-2.4.2", + "R/Singularity.R-4.1.3-Torch-1.11.0", + "R/Singularity.RStudio-Server-R-4.1.2", + "R/Singularity.RStudio-Server-R-4.1.3-TensorFlow-2.4.2", + "R/Singularity.RStudio-Server-R-4.1.3-Torch-1.11.0", + "MILC/Singularity.MILC-NGC-Oct2017", + "Julia/Singularity.Julia-1.6.2", + "NvidiaOmniverse/Singularity.IsaacSim-NGC-2022.1.0", + "GROMACS/Singularity.GROMACS-2021-NGC", + "ANSA/Singularity.ANSA-22.1.0", + "ANSA/Singularity.ANSA-20.1.0", + "SPAM/Singularity.SPAM", + "TensorFlow/Singularity.TensorFlow-2.6.0-NGC-21.09", + "TensorFlow/Singularity.TensorFlow-2.7.0-NGC-22.02", + "TensorFlow/Singularity.TensorFlow-2.6.2-NGC-21.12", + "TensorFlow/Singularity.TensorFlow-2.7.0-NGC-22.01", + "TensorFlow/Singularity.TensorFlow-2.5.0-NGC-21.08", + "TensorFlow/Singularity.TensorFlow-2.9.1-NGC-22.08", + "TensorFlow/Singularity.TensorFlow-2.10.0", + "TensorFlow/Singularity.TensorFlow-2.5.1", + "TensorFlow/Singularity.TensorFlow-1.15.5-NGC-21.08", + "TensorFlow/Singularity.TensorFlow-2.6.0", + "TensorFlow/Singularity.TensorFlow-2.8.0-NGC-22.04", + "TensorFlow/Singularity.TensorFlow-2.6.0-NGC-21.10", + "TensorFlow/Singularity.TensorFlow-2.8.0", + "TensorFlow/Singularity.TensorFlow-2.8.0-NGC-22.05", + "TensorFlow/Singularity.TensorFlow-2.7.0", + "TensorFlow/Singularity.TensorFlow-2.9.1", + "TensorFlow/Singularity.TensorFlow-2.6.0-NGC-21.11", + "TensorFlow/Singularity.TensorFlow-2.4.3", + "DIANA/Singularity.DIANA-10.5", + "DIANA/Singularity.DIANA-10.3", + "DIANA/Singularity.DIANA-10.4", + "LAMMPS/Singularity.LAMMPS-NGC-10Feb2021", + "SonnetSuite/Singularity.SonnetSuite-17.52.1", + "RELION/Singularity.RELION-3.1.2-NGC", + "QuantumEspresso/Singularity.QuantumEspresso-6.8", + "FINE-Marine/Singularity.FINE-Marine-7.2", + "NAMD/Singularity.NAMD-3.0-alpha3-singlenode-NGC", + "NAMD/Singularity.NAMD-2.13-multinode-NGC", + "NAMD/Singularity.NAMD-2.13-singlenode-NGC" ], - "full_name": "ofmla/curso_sapct", + "full_name": "c3se/containers", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"cimatec.png\"\u003e\u003cimg src=\"cimatec.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVI Semin\u00e1rio de Avalia\u00e7\u00e3o de Pesquisa Cient\u00edfica e Tecnol\u00f3gica (SAPCT) e V Workshop de Integra\u00e7\u00e3o e Capacita\u00e7\u00e3o em Processamento de Alto Desempenho (ICPAD)\u003c/h1\u003e\u003ca id=\"user-content-vi-semin\u00e1rio-de-avalia\u00e7\u00e3o-de-pesquisa-cient\u00edfica-e-tecnol\u00f3gica-sapct-e-v-workshop-de-integra\u00e7\u00e3o-e-capacita\u00e7\u00e3o-em-processamento-de-alto-desempenho-icpad\" class=\"anchor\" aria-label=\"Permalink: VI Semin\u00e1rio de Avalia\u00e7\u00e3o de Pesquisa Cient\u00edfica e Tecnol\u00f3gica (SAPCT) e V Workshop de Integra\u00e7\u00e3o e Capacita\u00e7\u00e3o em Processamento de Alto Desempenho (ICPAD)\" href=\"#vi-semin\u00e1rio-de-avalia\u00e7\u00e3o-de-pesquisa-cient\u00edfica-e-tecnol\u00f3gica-sapct-e-v-workshop-de-integra\u00e7\u00e3o-e-capacita\u00e7\u00e3o-em-processamento-de-alto-desempenho-icpad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEste reposit\u00f3rio cont\u00e9m o material correspondente ao minicurso/Hands on 05: Gera\u00e7\u00e3o Autom\u00e1tica de C\u00f3digo Atrav\u00e9s de Computa\u00e7\u00e3o Simb\u00f3lica em Python: Introdu\u00e7\u00e3o \u00e0 LDE Devito do VI SAPCT e V ICPAD organizado pelo Senai Cimatec. O objetivo do minicurso \u00e9:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eapresentar\u003c/strong\u003e de forma r\u00e1pida e objetiva a Linguagem de Dom\u00ednio espec\u00edfico (LDE) Devito\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003emostrar\u003c/strong\u003e atrav\u00e9s de exemplos simples o uso pr\u00e1tico da LDE Devito\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eexpor\u003c/strong\u003e aos estudantes o conceito de modelagem s\u00edsmica e a solu\u00e7\u00e3o numerica da equa\u00e7\u00e3o de onda com diferen\u00e7as finitas via Devito\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eN\u00e3o \u00e9 necess\u00e1rio instalar nenhum programa em seu computador para seguir este minicurso. O material do curso est\u00e1 dispon\u00edvel no formato IPYNB (Jupyter NoteBook).\u003c/p\u003e\n\u003cp\u003eExiste um unico Jupyter Notebook neste reposit\u00f3rio:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/github/ofmla/curso_sapct/blob/main/intro_lde_devito.ipynb\" rel=\"nofollow\"\u003eIntro_LDE_Devito\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-intro_lde_devito\" class=\"anchor\" aria-label=\"Permalink: Intro_LDE_Devito\" href=\"#intro_lde_devito\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCont\u00e9m uma introdu\u00e7\u00e3o f\u00e1cil \u00e0 LDE Devito (siga os passos a seguir para acessar o notebook online)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClique em \u003ca href=\"https://mybinder.org/v2/gh/ofmla/curso_sapct/HEAD\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e91e1d353a8b6acf0b42547ac3901f2c30138a3abaaa3d3c242da30b5b4f8426/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (que lan\u00e7ar\u00e1 o reposit\u00f3rio \u003ca href=\"https://github.com/ofmla/curso_sapct\"\u003ehttps://github.com/ofmla/curso_sapct\u003c/a\u003e). Isso \u00e0s vezes pode levar alguns minutos, ent\u00e3o seja paciente ...\u003c/li\u003e\n\u003cli\u003eEspere at\u00e9 que ele seja carregado\u003c/li\u003e\n\u003cli\u003eClique no notebook intro_lde_devito.ipynb\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003eEspero que voc\u00ea ache o Framework Devito \u00fatil.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://oscar-mojica.netlify.app/\" rel=\"nofollow\"\u003eOscar Mojica\u003c/a\u003e \u003cbr\u003e\nPesquisador do Centro de Supercomputa\u00e7\u00e3o do \u003ca href=\"http://www.senaicimatec.com.br/\" rel=\"nofollow\"\u003eSenai Cimatec\u003c/a\u003e \u003cbr\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-recipes-provided-at-c3se-clusters\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-recipes-provided-at-c3se-clusters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer recipes provided at C3SE clusters\u003c/h1\u003e\n\u003cp\u003eThis repository holds the recipes of the centrally provided Singularity containers at C3SE\u0027s clusters.\nThey are also useable as a reference for users who wish to build their own Singularity containers.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-missing-containers-updates-and-troubleshootin\" class=\"anchor\" aria-hidden=\"true\" href=\"#missing-containers-updates-and-troubleshootin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMissing containers, updates, and troubleshootin\u003c/h2\u003e\n\u003cp\u003eWe continuously add more packages to this repository. If you can\u0027t find a relevant container for your needs, or in the case of encountering any errors or deprecated features in the material, feel free to contact us: \u003ca href=\"mailto:support@c3se.chalmers.se\"\u003esupport@c3se.chalmers.se\u003c/a\u003e or open a pull-request.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, - "topics": [], - "updated_at": 1692903754.0 + "subscribers_count": 4, + "topics": [ + "singularity-containers", + "docker", + "hpc" + ], + "updated_at": 1662123057.0 }, { "data_format": 2, - "description": "RNAseq analysis workflow, maintained by BiBs facility.", + "description": null, "filenames": [ - "workflow/Singularity_ncbi" + "parametric-face-image-generator-2.1.1/Singularity" ], - "full_name": "parisepigenetics/RASflow_EDC", - "latest_release": "v1.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/banner.png\"\u003e\u003cimg src=\"images/banner.png\" alt=\"drawing\" width=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-rasflow_edc\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#rasflow_edc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRASflow_EDC\u003c/h1\u003e\n\u003cp\u003eMaintained by \u003ca href=\"mailto:magali.hennion@u-paris.fr\"\u003eMagali Hennion\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eImplemented by \u003ca href=\"https://parisepigenetics.github.io/bibs/\" rel=\"nofollow\"\u003eBiBs-EDC\u003c/a\u003e, this workflow for RNA-seq data analysis is based on RASflow which was originally published by \u003ca href=\"https://doi.org/10.1186/s12859-020-3433-x\" rel=\"nofollow\"\u003eX. Zhang\u003c/a\u003e. It has been modified to run effectively on both IFB and iPOP-UP clusters and to fit our specific needs. Moreover, several tools and features were added, including a comprehensive report, as well as the possibility to incorporate the repeats in the analysis. If you encounter troubles or need additional tools or features, you can create an issue on the \u003ca href=\"https://github.com/parisepigenetics/RASflow_EDC/issues\"\u003eGitHub repository\u003c/a\u003e, email directly \u003ca href=\"mailto:bibsATparisepigenetics.com\"\u003eBiBs\u003c/a\u003e, or pass by the 366b room.\u003c/p\u003e\n\u003cp\u003eThe complete documentation is available at \u003ca href=\"https://parisepigenetics.github.io/bibs/edctools/workflows/rasflow_edc/#/edctools/\" rel=\"nofollow\"\u003ehttps://parisepigenetics.github.io/bibs/edctools/workflows/rasflow_edc\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you use this workflow to analyse your data, don\u0027t forget to \u003cstrong\u003eacknowledge BiBs\u003c/strong\u003e in all your communications !\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEDC people: \"We thank the Bioinformatics and Biostatistics Core Facility, Paris Epigenetics and Cell Fate Center for bioinformatics support.\"\u003c/li\u003e\n\u003cli\u003eExternal users: \"We thank the Bioinformatics and Biostatistics Core Facility, Paris Epigenetics and Cell Fate Center for sharing their analysis workflows.\"\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "AdamOswald/face", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-deepfakes_faceswap\" class=\"anchor\" aria-hidden=\"true\" href=\"#deepfakes_faceswap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edeepfakes_faceswap\u003c/h1\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://faceswap.dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e98a8ca1dde3d8f5d360ab52a581a905842e7056e5d8d147ae20a3f02024e3/68747470733a2f2f692e696d6775722e636f6d2f7a48766a486e622e706e67\" data-canonical-src=\"https://i.imgur.com/zHvjHnb.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003eFaceSwap is a tool that utilizes deep learning to recognize and swap faces in pictures and videos.\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/56b72a8f58b9ec6df2734c9c2fdb016491d186f90809b66f0ed568a2ae169dd9/68747470733a2f2f692e696d6775722e636f6d2f6e5748464c44662e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/56b72a8f58b9ec6df2734c9c2fdb016491d186f90809b66f0ed568a2ae169dd9/68747470733a2f2f692e696d6775722e636f6d2f6e5748464c44662e6a7067\" data-canonical-src=\"https://i.imgur.com/nWHFLDf.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://www.patreon.com/bePatron?u=23238350\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b7105015397da52617ce6775a339b0b99d689d6f644c2ce911c5d472362bcbd/68747470733a2f2f63352e70617472656f6e2e636f6d2f65787465726e616c2f6c6f676f2f6265636f6d655f615f706174726f6e5f627574746f6e2e706e67\" data-canonical-src=\"https://c5.patreon.com/external/logo/become_a_patron_button.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u00a0\u00a0\u00a0\u00a0\u003ca href=\"https://discord.gg/FC54sYg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ba052d032c543765683cd39ec2151454d9f5bad39f70ccc85bb44fbe27b4839/68747470733a2f2f692e696d6775722e636f6d2f6749707a746b762e706e67\" data-canonical-src=\"https://i.imgur.com/gIpztkv.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://www.dailymotion.com/video/x810mot\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/36920800/178301720-b69841bb-a1ca-4c20-91db-a2a10f5692ca.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003eEmma Stone/Scarlett Johansson FaceSwap using the Phaze-A model\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://www.youtube.com/watch?v=r1jng79a5xc\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4c8848e79bcc92e61653f11eafa8d97b316b6ec470a0eefd258700f407c3dc6b/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f72316a6e673739613578632f302e6a7067\" data-canonical-src=\"https://img.youtube.com/vi/r1jng79a5xc/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003eJennifer Lawrence/Steve Buscemi FaceSwap using the Villain model\n\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/deepfakes/faceswap/actions/workflows/pytest.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/deepfakes/faceswap/actions/workflows/pytest.yml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://faceswap.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/58b7e03bdd7d51e7d1729b3d6713709f2d204514deb36851d90afbbc75a2ac93/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f66616365737761702f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/faceswap/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMake sure you check out \u003ca href=\"INSTALL.md\"\u003eINSTALL.md\u003c/a\u003e before getting started.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#deepfakes_faceswap\"\u003edeepfakes_faceswap\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#manifesto\"\u003eManifesto\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#faceswap-has-ethical-uses\"\u003eFaceSwap has ethical uses.\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-setup-and-run-the-project\"\u003eHow To setup and run the project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#extract\"\u003eExtract\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#train\"\u003eTrain\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#convert\"\u003eConvert\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#gui\"\u003eGUI\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#general-notes\"\u003eGeneral notes:\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#help-i-need-support\"\u003eHelp I need support!\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#discord-server\"\u003eDiscord Server\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#faceswap-forum\"\u003eFaceSwap Forum\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#donate\"\u003eDonate\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#patreon\"\u003ePatreon\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#one-time-donations\"\u003eOne time Donations\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#torzdf\"\u003e@torzdf\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#andenixa\"\u003e@andenixa\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#how-to-contribute\"\u003eHow to contribute\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#for-people-interested-in-the-generative-models\"\u003eFor people interested in the generative models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#for-devs\"\u003eFor devs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#for-non-dev-advanced-users\"\u003eFor non-dev advanced users\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#for-end-users\"\u003eFor end-users\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#for-haters\"\u003eFor haters\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#about-githubcomdeepfakes\"\u003eAbout github.com/deepfakes\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#what-is-this-repo\"\u003eWhat is this repo?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#why-this-repo\"\u003eWhy this repo?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#why-is-it-named-deepfakes-if-it-is-not-udeepfakes\"\u003eWhy is it named \u0027deepfakes\u0027 if it is not /u/deepfakes?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#what-if-udeepfakes-feels-bad-about-that\"\u003eWhat if /u/deepfakes feels bad about that?\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#about-machine-learning\"\u003eAbout machine learning\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#how-does-a-computer-know-how-to-recognizeshape-faces-how-does-machine-learning-work-what-is-a-neural-network\"\u003eHow does a computer know how to recognize/shape faces? How does machine learning work? What is a neural network?\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-manifesto\" class=\"anchor\" aria-hidden=\"true\" href=\"#manifesto\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eManifesto\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faceswap-has-ethical-uses\" class=\"anchor\" aria-hidden=\"true\" href=\"#faceswap-has-ethical-uses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFaceSwap has ethical uses.\u003c/h2\u003e\n\u003cp\u003eWhen faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. Until one individual brought it together into a single, cohesive collection. It ran, it worked, and as is so often the way with new technology emerging on the internet, it was immediately used to create inappropriate content. Despite the inappropriate uses the software was given originally, it was the first AI code that anyone could download, run and learn by experimentation without having a Ph.D. in math, computer theory, psychology, and more. Before \"deepfakes\" these techniques were like black magic, only practiced by those who could understand all of the inner workings as described in esoteric and endlessly complicated books and papers.\u003c/p\u003e\n\u003cp\u003e\"Deepfakes\" changed all that and anyone could participate in AI development. To us, developers, the release of this code opened up a fantastic learning opportunity. It allowed us to build on ideas developed by others, collaborate with a variety of skilled coders, experiment with AI whilst learning new skills and ultimately contribute towards an emerging technology which will only see more mainstream use as it progresses.\u003c/p\u003e\n\u003cp\u003eAre there some out there doing horrible things with similar software? Yes. And because of this, the developers have been following strict ethical standards. Many of us don\u0027t even use it to create videos, we just tinker with the code to see what it does. Sadly, the media concentrates only on the unethical uses of this software. That is, unfortunately, the nature of how it was first exposed to the public, but it is not representative of why it was created, how we use it now, or what we see in its future. Like any technology, it can be used for good or it can be abused. It is our intention to develop FaceSwap in a way that its potential for abuse is minimized whilst maximizing its potential as a tool for learning, experimenting and, yes, for legitimate faceswapping.\u003c/p\u003e\n\u003cp\u003eWe are not trying to denigrate celebrities or to demean anyone. We are programmers, we are engineers, we are Hollywood VFX artists, we are activists, we are hobbyists, we are human beings. To this end, we feel that it\u0027s time to come out with a standard statement of what this software is and isn\u0027t as far as us developers are concerned.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFaceSwap is not for creating inappropriate content.\u003c/li\u003e\n\u003cli\u003eFaceSwap is not for changing faces without consent or with the intent of hiding its use.\u003c/li\u003e\n\u003cli\u003eFaceSwap is not for any illicit, unethical, or questionable purposes.\u003c/li\u003e\n\u003cli\u003eFaceSwap exists to experiment and discover AI techniques, for social or political commentary, for movies, and for any number of ethical and reasonable uses.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe are very troubled by the fact that FaceSwap can be used for unethical and disreputable things. However, we support the development of tools and techniques that can be used ethically as well as provide education and experience in AI for anyone who wants to learn it hands-on. We will take a zero tolerance approach to anyone using this software for any unethical purposes and will actively discourage any such uses.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-how-to-setup-and-run-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-setup-and-run-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow To setup and run the project\u003c/h1\u003e\n\u003cp\u003eFaceSwap is a Python program that will run on multiple Operating Systems including Windows, Linux, and MacOS.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"INSTALL.md\"\u003eINSTALL.md\u003c/a\u003e for full installation instructions. You will need a modern GPU with CUDA support for best performance. AMD GPUs are partially supported.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h1\u003e\n\u003cp\u003eThe project has multiple entry points. You will have to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGather photos and/or videos\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExtract\u003c/strong\u003e faces from your raw photos\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTrain\u003c/strong\u003e a model on the faces extracted from the photos/videos\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConvert\u003c/strong\u003e your sources with the model\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCheck out \u003ca href=\"USAGE.md\"\u003eUSAGE.md\u003c/a\u003e for more detailed instructions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-extract\" class=\"anchor\" aria-hidden=\"true\" href=\"#extract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtract\u003c/h2\u003e\n\u003cp\u003eFrom your setup folder, run \u003ccode\u003epython faceswap.py extract\u003c/code\u003e. This will take photos from \u003ccode\u003esrc\u003c/code\u003e folder and extract faces into \u003ccode\u003eextract\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-train\" class=\"anchor\" aria-hidden=\"true\" href=\"#train\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrain\u003c/h2\u003e\n\u003cp\u003eFrom your setup folder, run \u003ccode\u003epython faceswap.py train\u003c/code\u003e. This will take photos from two folders containing pictures of both faces and train a model that will be saved inside the \u003ccode\u003emodels\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-convert\" class=\"anchor\" aria-hidden=\"true\" href=\"#convert\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConvert\u003c/h2\u003e\n\u003cp\u003eFrom your setup folder, run \u003ccode\u003epython faceswap.py convert\u003c/code\u003e. This will take photos from \u003ccode\u003eoriginal\u003c/code\u003e folder and apply new faces into \u003ccode\u003emodified\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gui\" class=\"anchor\" aria-hidden=\"true\" href=\"#gui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGUI\u003c/h2\u003e\n\u003cp\u003eAlternatively, you can run the GUI by running \u003ccode\u003epython faceswap.py gui\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-general-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#general-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral notes:\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eAll of the scripts mentioned have \u003ccode\u003e-h\u003c/code\u003e/\u003ccode\u003e--help\u003c/code\u003e options with arguments that they will accept. You\u0027re smart, you can figure out how this works, right?!\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNB: there is a conversion tool for video. This can be accessed by running \u003ccode\u003epython tools.py effmpeg -h\u003c/code\u003e. Alternatively, you can use \u003ca href=\"https://www.ffmpeg.org\" rel=\"nofollow\"\u003effmpeg\u003c/a\u003e to convert video into photos, process images, and convert images back to the video.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSome tips:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReusing existing models will train much faster than starting from nothing.\nIf there is not enough training data, start with someone who looks similar, then switch the data.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-help-i-need-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#help-i-need-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHelp I need support!\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-discord-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#discord-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscord Server\u003c/h2\u003e\n\u003cp\u003eYour best bet is to join the \u003ca href=\"https://discord.gg/FC54sYg\" rel=\"nofollow\"\u003eFaceSwap Discord server\u003c/a\u003e where there are plenty of users willing to help. Please note that, like this repo, this is a SFW Server!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faceswap-forum\" class=\"anchor\" aria-hidden=\"true\" href=\"#faceswap-forum\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFaceSwap Forum\u003c/h2\u003e\n\u003cp\u003eAlternatively, you can post questions in the \u003ca href=\"https://faceswap.dev/forum\" rel=\"nofollow\"\u003eFaceSwap Forum\u003c/a\u003e. Please do not post general support questions in this repo as they are liable to be deleted without response.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-donate\" class=\"anchor\" aria-hidden=\"true\" href=\"#donate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDonate\u003c/h1\u003e\n\u003cp\u003eThe developers work tirelessly to improve and develop FaceSwap. Many hours have been put in to provide the software as it is today, but this is an extremely time-consuming process with no financial reward. If you enjoy using the software, please consider donating to the devs, so they can spend more time implementing improvements.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-patreon\" class=\"anchor\" aria-hidden=\"true\" href=\"#patreon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePatreon\u003c/h2\u003e\n\u003cp\u003eThe best way to support us is through our Patreon page:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.patreon.com/bePatron?u=23238350\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b7105015397da52617ce6775a339b0b99d689d6f644c2ce911c5d472362bcbd/68747470733a2f2f63352e70617472656f6e2e636f6d2f65787465726e616c2f6c6f676f2f6265636f6d655f615f706174726f6e5f627574746f6e2e706e67\" alt=\"become-a-patron\" data-canonical-src=\"https://c5.patreon.com/external/logo/become_a_patron_button.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-one-time-donations\" class=\"anchor\" aria-hidden=\"true\" href=\"#one-time-donations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOne time Donations\u003c/h2\u003e\n\u003cp\u003eAlternatively you can give a one off donation to any of our Devs:\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-torzdf\" class=\"anchor\" aria-hidden=\"true\" href=\"#torzdf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e@torzdf\u003c/h3\u003e\n\u003cp\u003eThere is very little FaceSwap code that hasn\u0027t been touched by torzdf. He is responsible for implementing the GUI, FAN aligner, MTCNN detector and porting the Villain, DFL-H128 and DFaker models to FaceSwap, as well as significantly improving many areas of the code.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBitcoin:\u003c/strong\u003e bc1qpm22suz59ylzk0j7qk5e4c7cnkjmve2rmtrnc6\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthereum:\u003c/strong\u003e 0xd3e954dC241B87C4E8E1A801ada485DC1d530F01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMonero:\u003c/strong\u003e 45dLrtQZ2pkHizBpt3P3yyJKkhcFHnhfNYPMSnz3yVEbdWm3Hj6Kr5TgmGAn3Far8LVaQf1th2n3DJVTRkfeB5ZkHxWozSX\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePaypal:\u003c/strong\u003e \u003ca href=\"https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026amp;hosted_button_id=JZ8PP3YE9J62L\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/086fc8dbe63028c5c6791524b4a1c112bf50af1ecc69e9d60c2fdc9ea4c07206/68747470733a2f2f7777772e70617970616c6f626a656374732e636f6d2f656e5f47422f692f62746e2f62746e5f646f6e6174655f534d2e676966\" alt=\"torzdf\" data-canonical-src=\"https://www.paypalobjects.com/en_GB/i/btn/btn_donate_SM.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-andenixa\" class=\"anchor\" aria-hidden=\"true\" href=\"#andenixa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e@andenixa\u003c/h3\u003e\n\u003cp\u003eCreator of the Unbalanced and OHR models, as well as expanding various capabilities within the training process. Andenixa is currently working on new models and will take requests for donations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePaypal:\u003c/strong\u003e \u003ca href=\"https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026amp;hosted_button_id=NRVLQYGS6NWTU\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/086fc8dbe63028c5c6791524b4a1c112bf50af1ecc69e9d60c2fdc9ea4c07206/68747470733a2f2f7777772e70617970616c6f626a656374732e636f6d2f656e5f47422f692f62746e2f62746e5f646f6e6174655f534d2e676966\" alt=\"andenixa\" data-canonical-src=\"https://www.paypalobjects.com/en_GB/i/btn/btn_donate_SM.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-how-to-contribute\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to contribute\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-people-interested-in-the-generative-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-people-interested-in-the-generative-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor people interested in the generative models\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGo to the \u0027faceswap-model\u0027 to discuss/suggest/commit alternatives to the current algorithm.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-devs\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-devs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor devs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRead this README entirely\u003c/li\u003e\n\u003cli\u003eFork the repo\u003c/li\u003e\n\u003cli\u003ePlay with it\u003c/li\u003e\n\u003cli\u003eCheck issues with the \u0027dev\u0027 tag\u003c/li\u003e\n\u003cli\u003eFor devs more interested in computer vision and openCV, look at issues with the \u0027opencv\u0027 tag. Also feel free to add your own alternatives/improvements\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-non-dev-advanced-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-non-dev-advanced-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor non-dev advanced users\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRead this README entirely\u003c/li\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003ePlay with it\u003c/li\u003e\n\u003cli\u003eCheck issues with the \u0027advuser\u0027 tag\u003c/li\u003e\n\u003cli\u003eAlso go to the \u0027\u003ca href=\"https://faceswap.dev/forum\" rel=\"nofollow\"\u003efaceswap Forum\u003c/a\u003e\u0027 and help others.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-end-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-end-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor end-users\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGet the code here and play with it if you can\u003c/li\u003e\n\u003cli\u003eYou can also go to the \u003ca href=\"https://faceswap.dev/forum\" rel=\"nofollow\"\u003efaceswap Forum\u003c/a\u003e and help or get help from others.\u003c/li\u003e\n\u003cli\u003eBe patient. This is a relatively new technology for developers as well. Much effort is already being put into making this program easy to use for the average user. It just takes time!\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNotice\u003c/strong\u003e Any issue related to running the code has to be opened in the \u003ca href=\"https://faceswap.dev/forum\" rel=\"nofollow\"\u003efaceswap Forum\u003c/a\u003e!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-haters\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-haters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor haters\u003c/h2\u003e\n\u003cp\u003eSorry, no time for that.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-about-githubcomdeepfakes\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-githubcomdeepfakes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout github.com/deepfakes\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-this-repo\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is this repo?\u003c/h2\u003e\n\u003cp\u003eIt is a community repository for active users.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-this-repo\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy this repo?\u003c/h2\u003e\n\u003cp\u003eThe joshua-wu repo seems not active. Simple bugs like missing \u003cem\u003ehttp://\u003c/em\u003e in front of urls have not been solved since days.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-is-it-named-deepfakes-if-it-is-not-udeepfakes\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-is-it-named-deepfakes-if-it-is-not-udeepfakes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy is it named \u0027deepfakes\u0027 if it is not /u/deepfakes?\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBecause a typosquat would have happened sooner or later as project grows\u003c/li\u003e\n\u003cli\u003eBecause we wanted to recognize the original author\u003c/li\u003e\n\u003cli\u003eBecause it will better federate contributors and users\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-if-udeepfakes-feels-bad-about-that\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-if-udeepfakes-feels-bad-about-that\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat if /u/deepfakes feels bad about that?\u003c/h2\u003e\n\u003cp\u003eThis is a friendly typosquat, and it is fully dedicated to the project. If /u/deepfakes wants to take over this repo/user and drive the project, he is welcomed to do so (Raise an issue, and he will be contacted on Reddit). Please do not send /u/deepfakes messages for help with the code you find here.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-about-machine-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-machine-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout machine learning\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-does-a-computer-know-how-to-recognizeshape-faces-how-does-machine-learning-work-what-is-a-neural-network\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-does-a-computer-know-how-to-recognizeshape-faces-how-does-machine-learning-work-what-is-a-neural-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow does a computer know how to recognize/shape faces? How does machine learning work? What is a neural network?\u003c/h2\u003e\n\u003cp\u003eIt\u0027s complicated. Here\u0027s a good video that makes the process understandable:\n\u003ca href=\"https://www.youtube.com/watch?v=R9OHn5ZF4Uo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cd51f29fbffa450256ce4b8f23c3aa49ba32c374f0956abaae5b5b51f340718a/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f52394f486e355a4634556f2f302e6a7067\" alt=\"How Machines Learn\" data-canonical-src=\"https://img.youtube.com/vi/R9OHn5ZF4Uo/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHere\u0027s a slightly more in depth video that tries to explain the basic functioning of a neural network:\n\u003ca href=\"https://www.youtube.com/watch?v=aircAruvnKk\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c488e6987928cb5ffa6c1f8c593b2dcf51baf054ca6010b29a9380889470dc41/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f61697263417275766e4b6b2f302e6a7067\" alt=\"How Machines Learn\" data-canonical-src=\"https://img.youtube.com/vi/aircAruvnKk/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr: training data + trial and error\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 0, "topics": [], - "updated_at": 1667227406.0 + "updated_at": 1663870861.0 }, { "data_format": 2, @@ -30475,132 +30612,123 @@ var data = "filenames": [ "Singularity" ], - "full_name": "truatpasteurdotfr/singularity-docker-centos7-conda-tf2-pytorch", + "full_name": "lsx1980/plant-image-analysis", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-docker-centos7-conda-tf2-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-docker-centos7-conda-tf2-pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-centos7-conda-tf2-pytorch\u003c/h1\u003e\n\u003cp\u003ecentos7 container with miniconda , tensorflow2 and pytorch with gpu support (cuda 10.1)\u003c/p\u003e\n\u003cp\u003eBuilding:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-docker-centos7-conda-tf2-pytorch.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-arabidopsis-rosette-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#arabidopsis-rosette-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArabidopsis Rosette Analysis\u003c/h1\u003e\n\u003cp\u003eAuthor: Suxing Liu\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Computational-Plant-Science/arabidopsis-rosette-analysis/workflows/CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/Computational-Plant-Science/arabidopsis-rosette-analysis/workflows/CI/badge.svg\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../master/media/image_01.png\"\u003e\u003cimg src=\"../master/media/image_01.png\" alt=\"Optional Text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRobust and parameter-free plant image segmentation and trait extraction.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eProcess with plant image top view, including whole tray plant image, this tool will segment it into individual images.\u003c/li\u003e\n\u003cli\u003eRobust segmentation based on parameter-free color clustering method.\u003c/li\u003e\n\u003cli\u003eExtract individual plant gemetrical traits, and write output into excel file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eEither \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity \u003c/a\u003e is required to run this project in a Unix environment.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e:/opt/arabidopsis-rosette-analysis -w /opt/arabidopsis-rosette-analysis computationalplantscience/arabidopsis-rosette-analysis python3 /opt/arabidopsis-rosette-analysis/trait_extract_parallel.py -i input -o output -ft \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ejpg,png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e docker://computationalplantscience/arabidopsis-rosette-analysis python3 trait_extract_parallel.py -i input -o output -ft \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ejpg,png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1649957662.0 + "updated_at": 1660840103.0 }, { "data_format": 2, - "description": "Evolving soft robots using AutoMap genotype-phenotype mapping", + "description": "OpenFoam + HiSA installation and compilation + PyTorch(C++) installation.", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "mmore500/automap-soro", + "full_name": "darshan315/OpenFOAM_HiSA_PyTorch", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-automap-soro\" class=\"anchor\" aria-hidden=\"true\" href=\"#automap-soro\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eautomap-soro\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/894\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis project is based on AutoMap, a pair of methods for automatic generation of evolvable genotype-phenotype mappings.\nBoth use an artificial neural network autoencoder trained on phenotypes harvested from fitness peaks as the basis for a genotype-phenotype mapping.\nIn the first, the decoder segment of a bottlenecked autoencoder serves as the genotype-phenotype mapping.\nIn the second, a denoising autoencoder serves as the genotype-phenotype mapping.\u003c/p\u003e\n\u003cp\u003eThe technique was introduced in\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eMatthew Andres Moreno, Wolfgang Banzhaf, and Charles Ofria.\n\"Learning an Evolvable Genotype-Phenotype Mapping.\"\nProceedings of the Genetic and Evolutionary Computation Conference.\nACM, 2018.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYou can find the paper and supporting materials at \u003ca href=\"https://osf.io/n92c7/\" rel=\"nofollow\"\u003ehttps://osf.io/n92c7/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe project was built using the \u003ca href=\"https://github.com/skriegman/evosoro\"\u003eevosoro\u003c/a\u003e soft robot simulator.\nEvosoro was designed and developed by the \u003ca href=\"http://www.meclab.org\" rel=\"nofollow\"\u003eMorphology, Evolution \u0026amp; Cognition Laboratory\u003c/a\u003e, University of Vermont.\nThe library is built on top of the open source \u003ca href=\"https://github.com/jonhiller/VoxCAD\"\u003eVoxCAD\u003c/a\u003e and the underlying voxel physics engine (\u003ca href=\"https://github.com/jonhiller/Voxelyze\"\u003eVoxelyze\u003c/a\u003e) which were both developed by the \u003ca href=\"http://www.creativemachineslab.com/\" rel=\"nofollow\"\u003eCreative Machines Lab\u003c/a\u003e, Columbia University.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-hidden=\"true\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003eTODO\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eExperiments reported in this paper used \u003ca href=\"https://github.com/mmore500/automap-soro/tree/vTODO\"\u003evTODO\u003c/a\u003e of this software.\u003c/p\u003e\n\u003cp\u003edata, tutorials, and writeup @ \u003ca href=\"https://osf.io/6jf52/\" rel=\"nofollow\"\u003ehttps://osf.io/6jf52/\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-authorship\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-authorship\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware Authorship\u003c/h2\u003e\n\u003cp\u003eMatthew Andres Moreno\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emmore500@msu.edu\u003c/code\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-dockersingularity-capabilities-for-openfoam--hisa--pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#dockersingularity-capabilities-for-openfoam--hisa--pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker/Singularity capabilities for OpenFOAM\u00ae + HiSA + PyTorch\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eThe Dockerfile in this repository creates an image with \u003ca href=\"https://openfoam.com/\" rel=\"nofollow\"\u003eESI-OpenFOAM\u003c/a\u003e, \u003ca href=\"https://hisa.gitlab.io/\" rel=\"nofollow\"\u003eHiSA\u003c/a\u003e and \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e support. The image is currently based on\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUbuntu 22.04,\u003c/li\u003e\n\u003cli\u003eOpenFOAM-v2112,\u003c/li\u003e\n\u003cli\u003eHiSA 1.4.6, and\u003c/li\u003e\n\u003cli\u003ePyTorch 1.10.2 (only CPU).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOpenFOAM is not compiled from scratch but installed via the package manager (\u003ca href=\"https://develop.openfoam.com/Development/openfoam/-/wikis/precompiled/debian\" rel=\"nofollow\"\u003eread more\u003c/a\u003e). Also for PyTorch, only the pre-compiled C++ part of the library, named \u003cem\u003elibtorch\u003c/em\u003e, is contained on the image. However, the HiSA package is pulled from the \u003ca href=\"https://gitlab.com/hisa/hisa\" rel=\"nofollow\"\u003esource\u003c/a\u003e and compiled, in which the libraries are installed in \u003ccode\u003e$FOAM_APPBIN\u003c/code\u003e and \u003ccode\u003e$FOAM_LIBBIN\u003c/code\u003e instead of user libraries.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-build-the-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-build-the-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to build the images\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eTo build a docker image,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/darshan315/OpenFOAM_HiSA_PyTorch.git\ncd OpenFOAM_HiSA_PyTorch\ndocker build -t user_name/openfoam_hisa_pytorch:of2112_hisa1.4.6_pt1.10.2_ub22.04 -f Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo create a container,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./create_openfoam_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo start the container and use interactively,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./start_openfoam.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eTo build the image (.sif),\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build of2112_hisa1.4.6_pt1.10.2_ub22.04.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo use the container interactively,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell of2112_hisa1.4.6_pt1.10.2_ub22.04.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo use the image non-interactively and run the application,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif [path] [arguments]\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e \n\u003ch2\u003e\u003ca id=\"user-content-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTests\u003c/h2\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eThe test directory contains the example scripts for OpenFOAM, HiSA, and PyTorch. These examples can be executed to check the correct installation and compilation.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-openfoam\" class=\"anchor\" aria-hidden=\"true\" href=\"#openfoam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenFOAM:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eThe test case for OpenFOAM follows \u003ca href=\"https://develop.openfoam.com/Development/openfoam/-/tree/master/tutorials/incompressible/icoFoam/cavity/cavity\" rel=\"nofollow\"\u003ecavity example\u003c/a\u003e given in OpenFoam tutorials. The example can be run from top-level directory of this repository as,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# To run the simulation\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./Allrun ./test/cavity/\n# To clean the finished simulation\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./Allclean ./test/cavity/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-hisa\" class=\"anchor\" aria-hidden=\"true\" href=\"#hisa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHiSA\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eThe test case for HiSA follows \u003ca href=\"https://gitlab.com/hisa/hisa/-/tree/master/examples/rae2822\" rel=\"nofollow\"\u003erae2822 example\u003c/a\u003e given in HiSA examples. The example can be run from top-level directory of this repository as,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# To generate the mesh\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./setupMesh ./test/rae2822/\n# To run the simulation\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./runSim ./test/rae2822/\n# To clean the mesh\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./cleanMesh ./test/rae2822/\n# To clean the finished simulation\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./cleanSim ./test/rae2822/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePyTorch\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eFrom top-level directory of this repository, you can build and run \u003cem\u003etensorCreation\u003c/em\u003e as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# build\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif wmake test/tensorCreation/\n# run\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./tensorCreation test/tensorCreation/\n# clean\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif wclean test/tensorCreation/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, one can also define scripts, which are then executed by Singularity. For example, to build and run the second example, \u003cem\u003esimpleMLP\u003c/em\u003e, run the \u003cem\u003ecompileAndRun.sh\u003c/em\u003e script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./compileAndRun.sh test/simpleMLP/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e \n\u003cp\u003e\u003cstrong\u003eFor more Information, see \u003ca href=\"https://ml-cfd.com/openfoam/pytorch/docker/2020/12/29/running-pytorch-models-in-openfoam.html\" rel=\"nofollow\"\u003e1\u003c/a\u003e, \u003ca href=\"https://openfoam.com/\" rel=\"nofollow\"\u003e2\u003c/a\u003e, \u003ca href=\"https://hisa.gitlab.io/\" rel=\"nofollow\"\u003e3\u003c/a\u003e, \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003e4\u003c/a\u003e, \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003e5\u003c/a\u003e, \u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/index.html#\" rel=\"nofollow\"\u003e6\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, - "topics": [ - "machine-learning", - "evolutionary-computation", - "genotype-phenotype-map", - "evolution", - "scientific-computing", - "scientific-publications" - ], - "updated_at": 1634847819.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1660833287.0 }, { "data_format": 2, - "description": "From taxonomic affiliations to annotated proteins using UniProt database.", + "description": "The Singularity Programming language IDE submodule for SNU Programming Tools (2D Mode)", "filenames": [ - "recipes/Singularity" + "Singularity", + "Singularity.def", + "OldVersions/PROJECT_LANGUAGE/Singularity/Singularity" ], - "full_name": "AuReMe/esmecata", - "latest_release": "0.2.12", - "readme": "\u003cp\u003e\u003ca href=\"https://pypi.org/project/esmecata/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/59da11b37a5d18aeb40a3a21e18ad6767e5f0e3aacc5b92410fa3146f65867a1/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f65736d65636174612e737667\" alt=\"PyPI version\" data-canonical-src=\"https://img.shields.io/pypi/v/esmecata.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/AuReMe/esmecata/blob/master/LICENSE\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/AuReMe/esmecata/master/pictures/license_esmecata.svg\" alt=\"GitHub license\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/AuReMe/esmecata/actions\"\u003e\u003cimg src=\"https://github.com/AuReMe/esmecata/workflows/Python%20package/badge.svg\" alt=\"Actions Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.1101/2022.03.16.484574\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/AuReMe/esmecata/master/pictures/doi_esmecata.svg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-esmecata-estimating-metabolic-capabilties-from-taxonomic-affiliations\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-estimating-metabolic-capabilties-from-taxonomic-affiliations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa: \u003cem\u003eEs\u003c/em\u003etimating \u003cem\u003eMe\u003c/em\u003etabolic \u003cem\u003eCa\u003c/em\u003epabilties from \u003cem\u003eTa\u003c/em\u003exonomic affiliations\u003c/h1\u003e\n\u003cp\u003eEsMeCaTa is a tool to estimate metabolic capabilities from a taxonomic affiliation (for example after analysis on 16S RNA sequencing). This is useful if no sequenced genomes or proteomes are available.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"esmecata.svg\"\u003e\u003cimg src=\"esmecata.svg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of contents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#esmecata-estimating-metabolic-capabilties-from-taxonomic-affiliations\"\u003eEsMeCaTa: \u003cem\u003eEs\u003c/em\u003etimating \u003cem\u003eMe\u003c/em\u003etabolic \u003cem\u003eCa\u003c/em\u003epabilties from \u003cem\u003eTa\u003c/em\u003exonomic affiliations\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#table-of-contents\"\u003eTable of contents\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#conda-and-pip\"\u003eConda and pip\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#input\"\u003eInput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-commands\"\u003eEsMeCaTa commands\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#esmecata-functions\"\u003eEsMeCaTa functions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-proteomes-retrieve-proteomes-associated-with-taxonomic-affiliation\"\u003e\u003ccode\u003eesmecata proteomes\u003c/code\u003e: Retrieve proteomes associated with taxonomic affiliation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-clustering-proteins-clustering\"\u003e\u003ccode\u003eesmecata clustering\u003c/code\u003e: Proteins clustering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-annotation-retrieve-protein-annotations\"\u003e\u003ccode\u003eesmecata annotation\u003c/code\u003e: Retrieve protein annotations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-workflow-consecutive-runs-of-the-three-steps\"\u003e\u003ccode\u003eesmecata workflow\u003c/code\u003e: Consecutive runs of the three steps\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#esmecata-outputs\"\u003eEsMeCaTa outputs\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-proteomes\"\u003eEsMeCaTa proteomes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-clustering\"\u003eEsMeCaTa clustering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-annotation\"\u003eEsMeCaTa annotation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-workflow\"\u003eEsMeCaTa workflow\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eEsMeCaTa needs the following python packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003ebiopython\u003c/a\u003e: To create fasta files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/pandas/\" rel=\"nofollow\"\u003epandas\u003c/a\u003e: To read the input files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/requests/\" rel=\"nofollow\"\u003erequests\u003c/a\u003e: For the REST queries on Uniprot.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/ete3/\" rel=\"nofollow\"\u003eete3\u003c/a\u003e: To analyse the taxonomic affiliation and extract taxon_id, also used to deal with taxon associated with more than 100 proteomes.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/SPARQLWrapper/\" rel=\"nofollow\"\u003eSPARQLwrapper\u003c/a\u003e: Optionally, you can use SPARQL queries instead of REST queries. This can be done either with the \u003ca href=\"https://sparql.uniprot.org/\" rel=\"nofollow\"\u003eUniprot SPARQL Endpoint\u003c/a\u003e (with the option \u003ccode\u003e--sparql uniprot\u003c/code\u003e) or with a Uniprot SPARQL Endpoint that you created locally (it is supposed to work but not tested, only SPARQL queries on the Uniprot SPARQL endpoint have been tested). \u003cstrong\u003eWarning\u003c/strong\u003e: using SPARQL queries will lead to minor differences in functional annotations and metabolic reactions due to how the results are retrieved with REST query or SPARQL query.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlso esmecata requires mmseqs2 for protein clustering:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/soedinglab/MMseqs2\"\u003emmseqs2\u003c/a\u003e: To cluster proteins. Test have been made on version MMseqs2 Release 13-45111., especially with the version of the commi \u003ca href=\"https://github.com/soedinglab/MMseqs2/tree/f349118312919c4fcc448f4595ca3b3a387018e2\"\u003ef349118312919c4fcc448f4595ca3b3a387018e2\u003c/a\u003e. But EsMeCaTa should be compatible with more recent version.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda-and-pip\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-and-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda and pip\u003c/h3\u003e\n\u003cp\u003eThe easiest way to install the dependencies of EsMeCaTa is by using conda:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install mmseqs2 pandas sparqlwrapper requests biopython ete3 -c conda-forge -c bioconda\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eA conda package for esmecata will be created in the future.\u003c/p\u003e\n\u003cp\u003eEsMeCata can be installed with pip command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install esmecata \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt can also be installed using esmecata github directory:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/ArnaudBelcour/esmecata.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd esmecata\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install -e . \u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eA Singularity recipe for EsMeCaTa is available \u003ca href=\"https://github.com/AuReMe/esmecata/blob/master/recipes/Singularity\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe image can be created with the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build esmecata.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAnd EsMeCaTa can be used with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec esmecata.sif esmecata workflow -i buchnera_workflow.tsv -o output\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003cp\u003eEsMeCaTa takes as input a tabulated or an excel file with two columns one with the ID corresponding to the taxonomic affiliation (for example the OTU ID for 16S RNA sequencing) and a second column with the taxonomic classification separated by \u0027;\u0027. In the following documentation, the first column (named \u003ccode\u003eobservation_name\u003c/code\u003e) will be used to identify the label associated with each taxonomic affiliation. An example is located in the test folder (\u003ca href=\"https://github.com/ArnaudBelcour/esmecata/blob/master/test/Example.tsv\"\u003eExample.tsv\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eobservation_name\u003c/th\u003e\n\u003cth\u003etaxonomic_affiliation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_1\u003c/td\u003e\n\u003ctd\u003eBacteria;Spirochaetes;Spirochaetia;Spirochaetales;Spirochaetaceae;Sphaerochaeta;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_2\u003c/td\u003e\n\u003ctd\u003eBacteria;Chloroflexi;Anaerolineae;Anaerolineales;Anaerolineaceae;ADurb.Bin120;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_3\u003c/td\u003e\n\u003ctd\u003eBacteria;Cloacimonetes;Cloacimonadia;Cloacimonadales;Cloacimonadaceae;Candidatus Cloacimonas;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_4\u003c/td\u003e\n\u003ctd\u003eBacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Rikenellaceae;Rikenellaceae RC9 gut group;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_5\u003c/td\u003e\n\u003ctd\u003eBacteria;Cloacimonetes;Cloacimonadia;Cloacimonadales;Cloacimonadaceae;W5;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_6\u003c/td\u003e\n\u003ctd\u003eBacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Dysgonomonadaceae;unknown genus;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_7\u003c/td\u003e\n\u003ctd\u003eBacteria;Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIt is possible to use EsMeCaTa with a taxonomic affiliation containing only one taxon:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eobservation_name\u003c/th\u003e\n\u003cth\u003etaxonomic_affiliation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_1\u003c/td\u003e\n\u003ctd\u003eSphaerochaeta\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_2\u003c/td\u003e\n\u003ctd\u003eYersinia\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBut this can cause issue. For example, \"Cluster_2\" is associated with Yersinia but two genus are associated with this name (one mantid (taxId: 444888) and one bacteria (taxId: 629)). EsMeCaTa will not able to differentiate them. But if you give more informations by adding more taxons (for example: \u0027Bacteria;Gammaproteobacteria;Yersinia\u0027), EsMeCaTa will compare all the taxons of the taxonomic affiliation (here: 2 (Bacteria) and 1236 (Gammaproteobacteria)) to the lineage associated with the two taxIDs (for bacteria Yersinia: [1, 131567, 2, 1224, 1236, 91347, 1903411, 629] and for the mantid one: [1, 131567, 2759, 33154, 33208, 6072, 33213, 33317, 1206794, 88770, 6656, 197563, 197562, 6960, 50557, 85512, 7496, 33340, 33341, 6970, 7504, 7505, 267071, 444888]). In this example, there is 2 matches for the bacteria one (2 and 1236) and 0 for the mantid one. So EsMeCaTa will select the taxId associated with the bacteria (629).\u003c/p\u003e\n\u003cp\u003eA \u003ca href=\"https://github.com/AuReMe/esmecata/blob/master/tutorials/esmecata_method.ipynb\"\u003ejupyter notebook\u003c/a\u003e explains how EsMeCata works.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-esmecata-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa commands\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata [-h] [--version] {proteomes,clustering,annotation,workflow} ...\n\nFrom taxonomic affiliation to metabolism using Uniprot. For specific help on each subcommand use: esmecata {cmd} --help\n\noptional arguments:\n -h, --help show this help message and exit\n --version show program\u0027s version number and exit\n\nsubcommands:\n valid subcommands:\n\n {proteomes,clustering,annotation,workflow}\n proteomes Download proteomes associated with taxon from Uniprot Proteomes.\n clustering Cluster the proteins of the different proteomes of a taxon into a single set of representative shared proteins.\n annotation Retrieve protein annotations from Uniprot.\n workflow Run all esmecata steps (proteomes, clustering and annotation).\n\nRequires: mmseqs2 and an internet connection (for REST and SPARQL queries, except if you have a local Uniprot SPARQL endpoint).\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-esmecata-functions\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-functions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa functions\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-proteomes-retrieve-proteomes-associated-with-taxonomic-affiliation\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-proteomes-retrieve-proteomes-associated-with-taxonomic-affiliation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ccode\u003eesmecata proteomes\u003c/code\u003e: Retrieve proteomes associated with taxonomic affiliation\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata proteomes [-h] -i INPUT_FILE -o OUPUT_DIR [-b BUSCO] [--ignore-taxadb-update] [--all-proteomes] [-s SPARQL] [--remove-tmp] [-l LIMIT_MAXIMAL_NUMBER_PROTEOMES] [-r RANK_LIMIT] [--minimal-nb-proteomes MINIMAL_NUMBER_PROTEOMES]\n\noptional arguments:\n -h, --help show this help message and exit\n -i INPUT_FILE, --input INPUT_FILE\n Input taxon file (excel, tsv or csv) containing a column associating ID to a taxonomic affiliation (separated by ;).\n -o OUPUT_DIR, --output OUPUT_DIR\n Output directory path.\n -b BUSCO, --busco BUSCO\n BUSCO percentage between 0 and 1. This will remove all the proteomes without BUSCO score and the score before the selected ratio of completion.\n --ignore-taxadb-update\n If you have a not up-to-date version of the NCBI taxonomy database with ete3, use this option to bypass the warning message and use the old version.\n --all-proteomes Download all proteomes associated with a taxon even if they are no reference proteomes.\n -s SPARQL, --sparql SPARQL\n Use sparql endpoint instead of REST queries on Uniprot.\n --remove-tmp Delete tmp files to limit the disk space used: files in tmp_proteome for esmecata proteomes and files created by mmseqs (in mmseqs_tmp).\n -l LIMIT_MAXIMAL_NUMBER_PROTEOMES, --limit-proteomes LIMIT_MAXIMAL_NUMBER_PROTEOMES\n Choose the maximal number of proteomes after which the tool will select a subset of proteomes instead of using all the available proteomes (default is 99).\n -r RANK_LIMIT, --rank-limit RANK_LIMIT\n This option limit the rank used by the tool for searching for proteomes. The given rank and all the superior ranks will be ignored. Look at the readme for more information (and a list of possible rank).\n --minimal-nb-proteomes MINIMAL_NUMBER_PROTEOMES\n Choose the minimal number of proteomes to be selected by EsMeCaTa. If a taxon has less proteomes, it will be ignored and a higher taxonomic rank will be used. Default is 1.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor each taxon in each taxonomic affiliations EsMeCaTa will use ete3 to find the corresponding taxon ID. Then it will search for proteomes associated with these taxon ID in the Uniprot Proteomes database.\u003c/p\u003e\n\u003cp\u003eIf there is more than 100 proteomes, esmecata will apply a specific method:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e(1) use the taxon ID associated with each proteomes to create a taxonomic tree with ete3.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(2) from the root of the tree (the input taxon), esmecata will find the direct deescendant (sub-taxons).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(3) then esmecata will compute the number of proteomes associated with each sub-taxon.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(4) the corresponding proportions will be used to select randomly a number of proteomes corresponding to the proportion.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor example: for the taxon Clostridiales, 645 proteomes are found. Using the organism taxon ID associated with the 645 proteomes we found that there is 17 direct sub-taxons. Then for each sub-taxon we compute the percentage of proportion of proteomes given by the sub-taxon to the taxon Clostridiales.\nThere is 198 proteomes associated with the sub-taxon Clostridiaceae, the percentage will be computed as follow: 198 / 645 = 30% (if a percentage is superior to 1 it will be round down and if the percentage is lower than 1 it will be round up to keep all the low proportion sub-taxons). We will use this 30% to select randomly 30 proteomes amongst the 198 proteomes of Clostridiaceae. This is done for all the other sub-taxons, so we get a number of proteomes around 100 (here it will be 102). Due to the different rounds (up or down) the total number of proteomes will not be equal to exactly 100 but it will be around it. The number of proteomes leading to this behavior is set to 99 by default but the user can modify it with the \u003ccode\u003e-l/--limit-proteomes\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eThen the proteomes found will be downloaded. For protein with isoforms, the \u003ca href=\"https://www.uniprot.org/help/canonical_and_isoforms\" rel=\"nofollow\"\u003ecanonical sequence\u003c/a\u003e is retrieved except when the isoforms are separated in different Uniprot entries.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata proteomes\u003c/code\u003e options:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-s/--sparql\u003c/code\u003e: use SPARQL instead of REST requests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to avoid using REST queries for esmecata and instead use SPARQL queries. This option need a link to a sparql endpoint containing UniProt. If you want to use the \u003ca href=\"https://sparql.uniprot.org/sparql\" rel=\"nofollow\"\u003eSPARQL endpoint of UniProt\u003c/a\u003e, you can use the argument: \u003ccode\u003e-s uniprot\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-b/--busco\u003c/code\u003e: filter proteomes using BUSCO score (default is 0.8)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to filter proteomes according to to their BUSCO score (from Uniprot documentation: \u003ccode\u003eThe Benchmarking Universal Single-Copy Ortholog (BUSCO) assessment tool is used, for eukaryotic and bacterial proteomes, to provide quantitative measures of UniProt proteome data completeness in terms of expected gene content.\u003c/code\u003e). It is a percentage between 0 and 1 showing the quality of the proteomes that esmecata will download. By default esmecata uses a BUSCO score of 0.80, it will only download proteomes with a BUSCO score of at least 80%.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--ignore-taxadb-update\u003c/code\u003e: ignore need to udpate ete3 taxaDB\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you have an old version of the ete3 NCBI taxonomy database, you can use this option to use esmecata with it.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--all-proteomes\u003c/code\u003e: download all proteomes (reference and non-reference)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default, esmecata will try to downlaod the reference proteomes associated with a taxon. But if you want to download all the proteomes associated with a taxon (either if they are non reference proteome) you can use this option. Without this option non-reference proteoems can also be used if no reference proteomes are found.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--remove-tmp\u003c/code\u003e: remove proteomes stored in \u003ccode\u003etmp_proteomes\u003c/code\u003e folder\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-l/--limit-proteomes\u003c/code\u003e: choose the number of proteomes that will lead to the used of the selection of a subset of proteomes\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo avoid working on too many proteomes, esmecata works on subset of proteomes when there is too many proteomes (by default this limit is set on 99 proteomes). Using this option the user can modify the limit.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--minimal-nb-proteomes\u003c/code\u003e: choose the minimal number of proteomes that taxon must have to be selected by esmecata (default 1).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo avoid working on too little proteomes, it is possible to give an int to this option.\nWith this int, esmecata will select only taxon associated to at least this number of proteomes.\nFor example if you use \u003ccode\u003e--minimal-nb-proteomes 10\u003c/code\u003e, and the lowest taxon in the taxonomic affiliation is associated with 3 proteomes, it will be ignored and a taxon with a higer taxonomic rank will be used.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-r/--rank-limit\u003c/code\u003e: This option limit the rank used by the tool for searching for proteomes. The given rank and all the superior ranks will be ignored.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo avoid working on rank with too much proteomes (which can have an heavy impact on the number of proteomes downloaded and then on the clustering) it is possible to select a limit on the taxonomic rank used by the tool.\u003c/p\u003e\n\u003cp\u003eThe selected rank will be used to find the ranks to keep. For example, if the rank \u0027phylum\u0027 is given, all the rank below (from subphylum to isolate) will be kept. And the ranks from phylum to superkingdom will be ignored when searching for proteomes.\nThe following ranks can be given to this option (from Supplementary Table S3 of \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7408187/\" rel=\"nofollow\"\u003ePMC7408187\u003c/a\u003e):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eLevel\u003c/th\u003e\n\u003cth\u003eRank\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003esuperkingdom\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003ekingdom\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e3\u003c/td\u003e\n\u003ctd\u003esubkingdom\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003esuperphylum\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e5\u003c/td\u003e\n\u003ctd\u003ephylum\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e6\u003c/td\u003e\n\u003ctd\u003esubphylum\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e7\u003c/td\u003e\n\u003ctd\u003einfraphylum\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003ctd\u003esuperclass\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9\u003c/td\u003e\n\u003ctd\u003eclass\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10\u003c/td\u003e\n\u003ctd\u003esubclass\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11\u003c/td\u003e\n\u003ctd\u003einfraclass\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12\u003c/td\u003e\n\u003ctd\u003ecohort\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e13\u003c/td\u003e\n\u003ctd\u003esubcohort\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e14\u003c/td\u003e\n\u003ctd\u003esuperorder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e15\u003c/td\u003e\n\u003ctd\u003eorder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e16\u003c/td\u003e\n\u003ctd\u003esuborder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e17\u003c/td\u003e\n\u003ctd\u003einfraorder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e18\u003c/td\u003e\n\u003ctd\u003eparvorder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e19\u003c/td\u003e\n\u003ctd\u003esuperfamily\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e20\u003c/td\u003e\n\u003ctd\u003efamily\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e21\u003c/td\u003e\n\u003ctd\u003esubfamily\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e22\u003c/td\u003e\n\u003ctd\u003etribe\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e23\u003c/td\u003e\n\u003ctd\u003esubtribe\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e24\u003c/td\u003e\n\u003ctd\u003egenus\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e25\u003c/td\u003e\n\u003ctd\u003esubgenus\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e26\u003c/td\u003e\n\u003ctd\u003esection\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e27\u003c/td\u003e\n\u003ctd\u003esubsection\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e28\u003c/td\u003e\n\u003ctd\u003eseries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e29\u003c/td\u003e\n\u003ctd\u003esubseries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e30\u003c/td\u003e\n\u003ctd\u003especies group\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e31\u003c/td\u003e\n\u003ctd\u003especies subgroup\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e32\u003c/td\u003e\n\u003ctd\u003especies\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e33\u003c/td\u003e\n\u003ctd\u003eforma specialis\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e34\u003c/td\u003e\n\u003ctd\u003esubspecies\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e35\u003c/td\u003e\n\u003ctd\u003evarietas\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e36\u003c/td\u003e\n\u003ctd\u003esubvariety\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e37\u003c/td\u003e\n\u003ctd\u003eforma\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e38\u003c/td\u003e\n\u003ctd\u003eserogroup\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e39\u003c/td\u003e\n\u003ctd\u003eserotype\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e40\u003c/td\u003e\n\u003ctd\u003estrain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e41\u003c/td\u003e\n\u003ctd\u003eisolate\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSome ranks (which are not non-hierarchical) are not used for the moment when using this method (so some taxons can be removed whereas they are below a kept rank):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eLevel\u003c/th\u003e\n\u003cth\u003eRank\u003c/th\u003e\n\u003cth\u003eNote\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eclade\u003c/td\u003e\n\u003ctd\u003enewly introduced, can appear anywhere in the lineage w/o breaking the order\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eenvironmental samples\u003c/td\u003e\n\u003ctd\u003eno order below this rank is required\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eincertae sedis\u003c/td\u003e\n\u003ctd\u003ecan appear anywhere in the lineage w/o breaking the order, implies taxa with uncertain placements\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eunclassified \n\u003c/td\u003e\n\u003ctd\u003eno order below this rank is required, includes undefined or unspecified names\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eno rank\u003c/td\u003e\n\u003ctd\u003eapplied to nodes not categorized here yet, additional rank and groups names will be released\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-clustering-proteins-clustering\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-clustering-proteins-clustering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ccode\u003eesmecata clustering\u003c/code\u003e: Proteins clustering\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata clustering [-h] -i INPUT_DIR -o OUPUT_DIR [-c CPU] [-t THRESHOLD_CLUSTERING] [-m MMSEQS_OPTIONS] [--linclust] [--remove-tmp]\n\noptional arguments:\n -h, --help show this help message and exit\n -i INPUT_DIR, --input INPUT_DIR\n This input folder of clustering is the output folder of proteomes command.\n -o OUPUT_DIR, --output OUPUT_DIR\n Output directory path.\n -c CPU, --cpu CPU CPU number for multiprocessing.\n -t THRESHOLD_CLUSTERING, --threshold THRESHOLD_CLUSTERING\n Proportion [0 to 1] of proteomes required to occur in a proteins cluster for that cluster to be kept in core proteome assembly.\n -m MMSEQS_OPTIONS, --mmseqs MMSEQS_OPTIONS\n String containing mmseqs options for cluster command (except --threads which is already set by --cpu command and -v). If nothing is given, esmecata will used the option \"--min-seq-id 0.3 -c 0.8\"\n --linclust Use mmseqs linclust (clustering in lienar time) to cluster proteins sequences. It is faster than mmseqs cluster (default behaviour) but less sensitive.\n --remove-tmp Delete tmp files to limit the disk space used: files in tmp_proteome for esmecata proteomes and files created by mmseqs (in mmseqs_tmp).\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor each taxon (a row in the table) EsMeCaTa will use mmseqs2 to cluster the proteins (using an identity of 30% and a coverage of 80%, these values can be changed with the \u003ccode\u003e--mmseqs\u003c/code\u003eoption). Then if a cluster contains at least one protein from each proteomes, it will be kept (this threshold can be changed using the \u003ccode\u003e--threshold option\u003c/code\u003e). The representative proteins from the cluster will be used. A fasta file of all the representative proteins will be created for each taxon.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata clustering\u003c/code\u003e options:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-t/--threshold\u003c/code\u003e: threshold clustering\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to modify the requirements of the presence of at least one protein from each proteomes in a cluster to keep it. Using the threshold option one can give a float between 0 and 1 to select the ratio of representation of proteomes in a cluster to keep.\u003c/p\u003e\n\u003cp\u003eFor example a threshold of 0.8 means that all the cluster with at least 80% representations of proteomes will be kept (with a taxon, associated with 10 proteomes, it means that at least 8 proteomes must have a protein in the cluster so the cluster must be kept).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-c/--cpu\u003c/code\u003e: number of CPU for mmseqs2\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can give a numbe of CPUs to parallelise mmseqs2.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-m/--mmseqs\u003c/code\u003e: mmseqs option to be used for the clustering.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eString containing mmseqs options for cluster command (except --threads which is already set by --cpu command and -v). If nothing is given, esmecata will used the option \"--min-seq-id 0.3 -c 0.8\". For example you can give \u003ccode\u003e--mmseqs \"--min-seq-id 0.8 --kmer-per-seq 80\"\u003c/code\u003e to ask for a minimal identity between sequence of 80% and having 80 kmers per sequence.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--linclust\u003c/code\u003e: replace \u003ccode\u003emmseqs cluster\u003c/code\u003e by \u003ccode\u003emmseqs linclust\u003c/code\u003e (faster but less sensitive)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUse mmseqs linclust (clustering in linear time) to cluster proteins sequences. It is faster than mmseqs cluster (default behaviour) but less sensitive.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--remove-tmp\u003c/code\u003e: remove mmseqs files stored in \u003ccode\u003emmseqs_tmp\u003c/code\u003e folder\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-annotation-retrieve-protein-annotations\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-annotation-retrieve-protein-annotations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ccode\u003eesmecata annotation\u003c/code\u003e: Retrieve protein annotations\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata annotation [-h] -i INPUT_DIR -o OUPUT_DIR [-s SPARQL] [-p PROPAGATE_ANNOTATION] [--uniref] [--expression]\n\noptional arguments:\n -h, --help show this help message and exit\n -i INPUT_DIR, --input INPUT_DIR\n This input folder of annotation is the output folder of clustering command.\n -o OUPUT_DIR, --output OUPUT_DIR\n Output directory path.\n -s SPARQL, --sparql SPARQL\n Use sparql endpoint instead of REST queries on Uniprot.\n -p PROPAGATE_ANNOTATION, --propagate PROPAGATE_ANNOTATION\n Proportion [0 to 1] of the occurrence of an annotation to be propagated from the protein of a cluster to the reference protein of the cluster. 0 mean the annotations from all proteins are propagated to the\n reference and 1 only the annotation occurring in all the proteins of the cluster (default).\n --uniref Use uniref cluster to extract more annotations from the representative member of the cluster associated with the proteins. Needs the --sparql option.\n --expression Extract expression information associated with the proteins. Needs the --sparql option.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor each of the protein clusters kept after the clustering, esmecata will look for the annotation (GO terms, EC number, function, gene name, Interpro) in Uniprot.\nBy default, esmecata will look at the annotations of each proteins from a cluster and keeps only annotation occurring in all the protein of a cluster (threshold 1 of option -p).\nIt is like selecting the intersection of the annotation of the cluster. This can be changed with the option \u003ccode\u003e-p\u003c/code\u003e and giving a float between 0 and 1.\u003c/p\u003e\n\u003cp\u003eThen esmecata will create a tabulated file for each row of the input file and also a folder containing PathoLogic file that can be used as input for Pathway Tools.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata annotation\u003c/code\u003e options:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-s/--sparql\u003c/code\u003e: use SPARQL instead of REST requests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to avoid using REST queries for esmecata and instead use SPARQL queries. This option need a link to a sparql endpoint containing UniProt. If you want to use the \u003ca href=\"https://sparql.uniprot.org/sparql\" rel=\"nofollow\"\u003eSPARQL endpoint\u003c/a\u003e, you can just use: \u003ccode\u003e-s uniprot\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-p/--propagate\u003c/code\u003e: propagation of annotation\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to modify how the annotations are retrieved. By default, esmecata will take the annotations occurring in at least all the proteins of the cluster (\u003ccode\u003e-p 1\u003c/code\u003e). But with the \u003ccode\u003e-p\u003c/code\u003e option it is possible to propagate annotation form the proteins of the cluster to the reference proteins.\u003c/p\u003e\n\u003cp\u003eThis option takes a float as input between 0 and 1, that will be used to filter the annotations retrieved. This number is multiplied by the number of protein in the cluster to estimate a threshold. To keep an annotation the number of the protein having this annotation in the cluster must be higher than the threshold. For example with a threshold of 0.5, for a cluster of 10 proteins an annotation will be kept if 5 or more proteins of the cluster have this annotation.\u003c/p\u003e\n\u003cp\u003eIf the option is set to 0, there will be no filter all the annotation of the proteins of the cluster will be propagated to the reference protein (it corresponds to the \u003cstrong\u003eunion\u003c/strong\u003e of the cluster annotations). This parameter gives the higher number of annotation for proteins. If the option is set to 1, only annotations that are present in all the proteins of a cluster will be kept (it corresponds to the \u003cstrong\u003eintersection\u003c/strong\u003e of the cluster annotations). This parameter is the most stringent and will limit the number of annotations associated with a protein.\u003c/p\u003e\n\u003cp\u003eFor example, for the same taxon the annotation with the parameter \u003ccode\u003e-p 0\u003c/code\u003e leads to the reconstruction of a metabolic networks of 1006 reactions whereas the parameter \u003ccode\u003e-p 1\u003c/code\u003e creates a metabolic network with 940 reactions (in this example with no use of the \u003ccode\u003e-p\u003c/code\u003e option, so without annotation propagation, there was also 940 reactions inferred).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--uniref\u003c/code\u003e: use annotation from uniref\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo add more annotations, esmecata can search the \u003ca href=\"https://www.uniprot.org/help/uniref\" rel=\"nofollow\"\u003eUniRef\u003c/a\u003e cluster associated with the protein associated with a taxon. Then the representative protein of the cluster will be extracted and if its identity with the protein of interest is superior to 90% esmecata will find its annotation (GO Terms and EC numbers) and will propagate these annotations to the protein. At this moment, this option is only usable when using the \u003ccode\u003e--sparql\u003c/code\u003e option.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--expression\u003c/code\u003e: extract expression information\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWith this option, esmecata will extract the \u003ca href=\"https://www.uniprot.org/help/expression_section\" rel=\"nofollow\"\u003eexpression information\u003c/a\u003e associated with a protein. This contains 3 elements: Induction, Tissue specificity and Disruption Phenotype. At this moment, this option is only usable when using the \u003ccode\u003e--sparql\u003c/code\u003e option.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-workflow-consecutive-runs-of-the-three-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-workflow-consecutive-runs-of-the-three-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ccode\u003eesmecata workflow\u003c/code\u003e: Consecutive runs of the three steps\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata workflow [-h] -i INPUT_FILE -o OUPUT_DIR [-b BUSCO] [-c CPU] [--ignore-taxadb-update] [--all-proteomes] [-s SPARQL] [--remove-tmp] [-l LIMIT_MAXIMAL_NUMBER_PROTEOMES] [-t THRESHOLD_CLUSTERING] [-m MMSEQS_OPTIONS]\n [--linclust] [-p PROPAGATE_ANNOTATION] [--uniref] [--expression] [-r RANK_LIMIT] [--minimal-nb-proteomes MINIMAL_NUMBER_PROTEOMES]\n\noptional arguments:\n -h, --help show this help message and exit\n -i INPUT_FILE, --input INPUT_FILE\n Input taxon file (excel, tsv or csv) containing a column associating ID to a taxonomic affiliation (separated by ;).\n -o OUPUT_DIR, --output OUPUT_DIR\n Output directory path.\n -b BUSCO, --busco BUSCO\n BUSCO percentage between 0 and 1. This will remove all the proteomes without BUSCO score and the score before the selected ratio of completion.\n -c CPU, --cpu CPU CPU number for multiprocessing.\n --ignore-taxadb-update\n If you have a not up-to-date version of the NCBI taxonomy database with ete3, use this option to bypass the warning message and use the old version.\n --all-proteomes Download all proteomes associated with a taxon even if they are no reference proteomes.\n -s SPARQL, --sparql SPARQL\n Use sparql endpoint instead of REST queries on Uniprot.\n --remove-tmp Delete tmp files to limit the disk space used: files in tmp_proteome for esmecata proteomes and files created by mmseqs (in mmseqs_tmp).\n -l LIMIT_MAXIMAL_NUMBER_PROTEOMES, --limit-proteomes LIMIT_MAXIMAL_NUMBER_PROTEOMES\n Choose the maximal number of proteomes after which the tool will select a subset of proteomes instead of using all the available proteomes (default is 99).\n -t THRESHOLD_CLUSTERING, --threshold THRESHOLD_CLUSTERING\n Proportion [0 to 1] of proteomes required to occur in a proteins cluster for that cluster to be kept in core proteome assembly.\n -m MMSEQS_OPTIONS, --mmseqs MMSEQS_OPTIONS\n String containing mmseqs options for cluster command (except --threads which is already set by --cpu command and -v). If nothing is given, esmecata will used the option \"--min-seq-id 0.3 -c 0.8\"\n --linclust Use mmseqs linclust (clustering in lienar time) to cluster proteins sequences. It is faster than mmseqs cluster (default behaviour) but less sensitive.\n -p PROPAGATE_ANNOTATION, --propagate PROPAGATE_ANNOTATION\n Proportion [0 to 1] of the occurrence of an annotation to be propagated from the protein of a cluster to the reference protein of the cluster. 0 mean the annotations from all proteins are propagated to the\n reference and 1 only the annotation occurring in all the proteins of the cluster (default).\n --uniref Use uniref cluster to extract more annotations from the representative member of the cluster associated with the proteins. Needs the --sparql option.\n --expression Extract expression information associated with the proteins. Needs the --sparql option.\n -r RANK_LIMIT, --rank-limit RANK_LIMIT\n This option limit the rank used by the tool for searching for proteomes. The given rank and all the superior ranks will be ignored. Look at the readme for more information (and a list of possible rank).\n --minimal-nb-proteomes MINIMAL_NUMBER_PROTEOMES\n Choose the minimal number of proteomes to be selected by EsMeCaTa. If a taxon has less proteomes, it will be ignored and a higher taxonomic rank will be used. Default is 1.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEsMeCTa will perform the search for proteomes, the protein clustering and the annotation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-esmecata-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa outputs\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-proteomes\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-proteomes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa proteomes\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_folder\n\u251c\u2500\u2500 proteomes_description\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u251c\u2500\u2500 result\n\u2502 \u2514\u2500\u2500 Cluster_1\n\u2502 \u2514\u2500\u2500 Proteome_1.faa.gz\n\u2502 \u2514\u2500\u2500 Proteome_2.faa.gz\n\u2502 \u2514\u2500\u2500 Cluster_2\n\u2502 \u2514\u2500\u2500 Proteome_3.faa.gz\n\u2502 \u2514\u2500\u2500 Cluster_3\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 tmp_proteome (can be cleaned to spare disk space using --remove-tmp option)\n\u2502 \u2514\u2500\u2500 Proteome_1.faa.gz\n\u2502 \u2514\u2500\u2500 Proteome_2.faa.gz\n\u2502 \u2514\u2500\u2500 Proteome_3.faa.gz\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 association_taxon_taxID.json\n\u251c\u2500\u2500 proteome_tax_id.tsv\n\u251c\u2500\u2500 esmecata_proteomes.log\n\u251c\u2500\u2500 esmecata_metadata_proteomes.json\n\u251c\u2500\u2500 stat_number_proteome.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eproteomes_description\u003c/code\u003e contains list of proteomes find by esmecata on Uniprot associated with the taxonomic affiliation.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eresult\u003c/code\u003e folder contain one sub-folder for each \u003ccode\u003eobservation_name\u003c/code\u003e from the input file. Each sub-folder contains the proteome associated with the \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003etmp_proteome\u003c/code\u003e contains all the proteomes that have been found to be associated with one taxon.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eassociation_taxon_taxID.json\u003c/code\u003e contains for each \u003ccode\u003eobservation_name\u003c/code\u003e the name of the taxon and the corresponding taxon_id found with \u003ccode\u003eete3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eproteome_tax_id.tsv\u003c/code\u003e contains the name, the taxon_id and the proteomes associated with each \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eesmecata_proteomes.log\u003c/code\u003e contains the log associated with the command.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata_metadata_proteomes.json\u003c/code\u003e is a log about the Uniprot release used and how the queries ware made (REST or SPARQL). It also gets the metadata associated with the command used with esmecata and the dependencies.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003estat_number_proteome.tsv\u003c/code\u003e is a tabulated file containing the number of proteomes found for each observation name.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-clustering\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-clustering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa clustering\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_folder\n\u251c\u2500\u2500 cluster_founds\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 computed_threshold\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 fasta_consensus\n\u2502 \u2514\u2500\u2500 Cluster_1.faa\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 fasta_representative\n\u2502 \u2514\u2500\u2500 Cluster_1.faa\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 mmseqs_tmp (can be cleaned to spare disk space using --remove-tmp option)\n\u2502 \u2514\u2500\u2500 Cluster_1\n\u2502 \u2514\u2500\u2500 mmseqs intermediary files\n\u2502 \u2514\u2500\u2500 ...\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 reference_proteins\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 reference_proteins_consensus_fasta\n\u2502 \u2514\u2500\u2500 Cluster_1.faa\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 reference_proteins_representative_fasta\n\u2502 \u2514\u2500\u2500 Cluster_1.faa\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 proteome_tax_id.tsv\n\u251c\u2500\u2500 esmecata_clustering.log\n\u251c\u2500\u2500 esmecata_metadata_clustering.json\n\u251c\u2500\u2500 stat_number_clustering.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003ecluster_founds\u003c/code\u003e contains one tsv file per \u003ccode\u003eobservation_name\u003c/code\u003e and these files contain the clustered proteins The first column contains the representative proteins of a cluster and the following columns correspond to the other proteins of the same cluster. The first protein occurs two time: one as the representative member of the cluster and a second time as a member of the cluster.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ecomputed_threshold\u003c/code\u003e folder contains the ratio of proteomes represented in a cluster compared to the total number of proteomes associated with a taxon. If the ratio is equal to 1, it means that all the proteomes are represented by a protein in the cluster, 0.5 means that half of the proteoems are represented in the cluster. This score is used when giving the \u003ccode\u003e-t\u003c/code\u003e argument.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003efasta_consensus\u003c/code\u003e contains all the consensus proteins associated with an \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003efasta_representative\u003c/code\u003e contains all the representative proteins associated with an \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003emmseqs_tmp\u003c/code\u003e folder contains the intermediary files of mmseqs2 for each \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ereference_proteins\u003c/code\u003e contains one tsv file per \u003ccode\u003eobservation_name\u003c/code\u003e and these files contain the clustered proteins kept after clustering process. it is similar to \u003ccode\u003ecluster_founds\u003c/code\u003e but it contains only protein kept after clustering and threshold.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ereference_proteins_consensus_fasta\u003c/code\u003e contains the consensus proteins associated with an \u003ccode\u003eobservation_name\u003c/code\u003e for the cluster kept after clustering process. So compared to the fasta of \u003ccode\u003efasta_consensus\u003c/code\u003e it is a sublist with only cluster passing the threshold.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ereference_proteins_representative_fasta\u003c/code\u003e contains the consensus proteins associated with an \u003ccode\u003eobservation_name\u003c/code\u003e for the cluster kept after clustering process. So compared to the fasta of \u003ccode\u003efasta_representative\u003c/code\u003e it is a sublist with only cluster passing the threshold.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eproteome_tax_id.tsv\u003c/code\u003e file is the same than the one created in \u003ccode\u003eesmecata proteomes\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eesmecata_clustering.log\u003c/code\u003e contains the log associated with the command.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata_metadata_clustering.json\u003c/code\u003e is a log about the the metadata associated with the command used with esmecata and the dependencies.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003estat_number_clustering.tsv\u003c/code\u003e is a tabulated file containing the number of shared proteins found for each observation name.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-annotation\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-annotation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa annotation\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_folder\n\u251c\u2500\u2500 annotation\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 annotation_reference\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 expression_annotation (if --expression option)\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 pathologic\n\u2502 \u2514\u2500\u2500 Cluster_1\n\u2502 \u2514\u2500\u2500 Cluster_1.pf\n\u2502 \u2514\u2500\u2500 ...\n\u2502 \u2514\u2500\u2500 taxon_id.tsv\n\u251c\u2500\u2500 uniref_annotation (if --uniref option)\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 esmecata_annotation.log\n\u251c\u2500\u2500 esmecata_metadata_annotation.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eannotation\u003c/code\u003e folder contains a tabulated file for each \u003ccode\u003eobservation_name\u003c/code\u003e. It contains the annotation retrieved with Uniprot (protein_name, review, GO Terms, EC numbers, Interpros, Rhea IDs and gene name) associated with all the proteins in a proteome or associated with an \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eannotation_reference\u003c/code\u003e contains annotation only for the representative proteins, but the annotation of the other proteins of the same cluster can be propagated to the reference protein if the \u003ccode\u003e-p\u003c/code\u003e was used.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eexpression_annotation\u003c/code\u003e contains expression annotation for the proteins of a taxon (if the \u003ccode\u003e--expression\u003c/code\u003e option was used).\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003epathologic\u003c/code\u003e contains one sub-folder for each \u003ccode\u003eobservation_name\u003c/code\u003e in which there is one PathoLogic file. There is also a \u003ccode\u003etaxon_id.tsv\u003c/code\u003e file which corresponds to a modified version of \u003ccode\u003eproteome_tax_id.tsv\u003c/code\u003e with only the \u003ccode\u003eobservation_name\u003c/code\u003e and the \u003ccode\u003etaxon_id\u003c/code\u003e. This folder can be used as input to \u003ca href=\"https://github.com/AuReMe/mpwt\"\u003empwt\u003c/a\u003e to reconstruct draft metabolic networks using Pathway Tools PathoLogic.\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eesmecata_annotation.log\u003c/code\u003e contains the log associated with the command.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eesmecata_metadata_annotation.json\u003c/code\u003e serves the same purpose as the one used in \u003ccode\u003eesmecata proteomes\u003c/code\u003e to retrieve metadata about Uniprot release at the time of the query. It also gets the metadata associated with the command used with esmecata and the dependencies.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003euniref_annotation\u003c/code\u003e contains the annotation from the representative protein of the UniRef cluster associated with the proteins of a taxon (if the \u003ccode\u003e--uniref\u003c/code\u003e option was used).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003estat_number_clustering.tsv\u003c/code\u003e is a tabulated file containing the number of GO Terms and EC numbers found for each observation name.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa workflow\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_folder\n\u251c\u2500\u2500 0_proteomes\n \u251c\u2500\u2500 proteomes_description\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u251c\u2500\u2500 result\n \u2502 \u2514\u2500\u2500 Cluster_1\n \u2502 \u2514\u2500\u2500 Proteome_1.faa.gz\n \u2502 \u2514\u2500\u2500 Proteome_2.faa.gz\n \u2502 \u2514\u2500\u2500 Cluster_2\n \u2502 \u2514\u2500\u2500 Proteome_3.faa.gz\n \u2502 \u2514\u2500\u2500 Cluster_3\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 tmp_proteome (can be cleaned to spare disk space using --remove-tmp option)\n \u2502 \u2514\u2500\u2500 Proteome_1.faa.gz\n \u2502 \u2514\u2500\u2500 Proteome_2.faa.gz\n \u2502 \u2514\u2500\u2500 Proteome_3.faa.gz\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 association_taxon_taxID.json\n \u251c\u2500\u2500 proteome_tax_id.tsv\n \u251c\u2500\u2500 esmecata_metadata_proteomes.json\n \u251c\u2500\u2500 stat_number_proteome.tsv\n\u251c\u2500\u2500 1_clustering\n \u251c\u2500\u2500 cluster_founds\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 computed_threshold\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 fasta_consensus\n \u2502 \u2514\u2500\u2500 Cluster_1.faa\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 fasta_representative\n \u2502 \u2514\u2500\u2500 Cluster_1.faa\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 mmseqs_tmp (can be cleaned to spare disk space using --remove-tmp option)\n \u2502 \u2514\u2500\u2500 Cluster_1\n \u2502 \u2514\u2500\u2500 mmseqs intermediary files\n \u2502 \u2514\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 reference_proteins\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 reference_proteins_consensus_fasta\n \u2502 \u2514\u2500\u2500 Cluster_1.faa\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 reference_proteins_representative_fasta\n \u2502 \u2514\u2500\u2500 Cluster_1.faa\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 proteome_tax_id.tsv\n \u251c\u2500\u2500 esmecata_metadata_clustering.json\n \u251c\u2500\u2500 stat_number_clustering.tsv\n\u251c\u2500\u2500 2_annotation\n \u251c\u2500\u2500 annotation\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 annotation_reference\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 expression_annotation (if --expression option)\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 pathologic\n \u2502 \u2514\u2500\u2500 Cluster_1\n \u2502 \u2514\u2500\u2500 Cluster_1.pf\n \u2502 \u2514\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 taxon_id.tsv\n \u251c\u2500\u2500 uniref_annotation (if --uniref option)\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 esmecata_metadata_annotation.json\n \u251c\u2500\u2500 stat_number_annotation.tsv\n\u251c\u2500\u2500 esmecata_workflow.log\n\u251c\u2500\u2500 esmecata_metadata_workflow.json\n\u251c\u2500\u2500 stat_number_workflow.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe files in the folders \u003ccode\u003e0_proteomes\u003c/code\u003e, \u003ccode\u003e1_clustering\u003c/code\u003e and \u003ccode\u003e2_annotation\u003c/code\u003e are the same than the other presented in the previous steps.\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eesmecata_workflow.log\u003c/code\u003e contains the log associated with the command.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eesmecata_metadata_workflow.json\u003c/code\u003e retrieves metadata about Uniprot release at the time of the query, the command used and its duration.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003estat_number_workflow.tsv\u003c/code\u003e is a tabulated file containing the number of proteomes, shared proteins, GO Terms and EC numbers found for each observation name.\u003c/p\u003e\n", + "full_name": "seanpm2001/SNU_2D_ProgrammingTools_IDE_Singularity", + "latest_release": null, + "readme": "\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-snu-2d-programmingtools-ide-\" class=\"anchor\" aria-hidden=\"true\" href=\"#snu-2d-programmingtools-ide-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSNU-2D-ProgrammingTools-IDE-\n\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/LanguageLogo.svg\"\u003e\u003cimg src=\"/LanguageLogo.svg\" alt=\"{Project icon} This image failed to load. It may be due to the file not being reached, or a general error. Reload the page to fix a possible general error.\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-by\" class=\"anchor\" aria-hidden=\"true\" href=\"#by\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBy:\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/SNU_blue_and_gold_legacy_icon.png\"\u003e\u003cimg src=\"/SNU_blue_and_gold_legacy_icon.png\" alt=\"SNU Logo: This image failed to load. It may be due to the file not being reached, or a general error. Reload the page to fix a possible general error.\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-seanpm2001--snu-programming-tools-et-al\" class=\"anchor\" aria-hidden=\"true\" href=\"#seanpm2001--snu-programming-tools-et-al\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/seanpm2001/\"\u003eSeanpm2001\u003c/a\u003e / \u003ca href=\"https://github.com/SNU-Programming-Tools/\"\u003eSNU Programming Tools\u003c/a\u003e, Et; Al.\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-top\" class=\"anchor\" aria-hidden=\"true\" href=\"#top\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop\u003c/h3\u003e\n\u003ch1\u003e\u003ca id=\"user-content-readmemd\" class=\"anchor\" aria-hidden=\"true\" href=\"#readmemd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eREADME.md\u003c/code\u003e\u003c/h1\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-read-this-article-in-a-different-language\" class=\"anchor\" aria-hidden=\"true\" href=\"#read-this-article-in-a-different-language\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRead this article in a different language\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eRead this description in a different language:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent language is:\u003c/strong\u003e \u003ccode\u003eEnglish (US)\u003c/code\u003e \u003cem\u003e(translations may need to be corrected to fix English replacing the correct language)\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdetails open=\"\"\u003e\n\u003csummary\u003e\u003ch3\u003e\u003ca id=\"user-content-clicktap-here-to-expandcollapse-the-language-switcher-list\" class=\"anchor\" aria-hidden=\"true\" href=\"#clicktap-here-to-expandcollapse-the-language-switcher-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[Click/tap here to expand/collapse the language switcher list]\u003c/h3\u003e\u003c/summary\u003e\n\u003cp\u003e\u003cem\u003e\u003cg-emoji class=\"g-emoji\" alias=\"globe_with_meridians\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f310.png\"\u003e\ud83c\udf10\u003c/g-emoji\u003e List of languages\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e( \u003ca href=\"/.github/README_AF.md\"\u003eaf Afrikaans\u003c/a\u003e Afrikaans | \u003ca href=\"/.github/README_SQ.md\"\u003esq Shqiptare\u003c/a\u003e Albanian | \u003ca href=\"/.github/README_AM.md\"\u003eam \u12a0\u121b\u122d\u129b\u003c/a\u003e Amharic | \u003ca href=\"/.github/README_AR.md\"\u003ear \u0639\u0631\u0628\u0649\u003c/a\u003e Arabic | \u003ca href=\"/.github/README_HY.md\"\u003ehy \u0570\u0561\u0575\u0565\u0580\u0565\u0576\u003c/a\u003e Armenian | \u003ca href=\"/.github/README_AZ.md\"\u003eaz Az\u0259rbaycan dili\u003c/a\u003e Azerbaijani | \u003ca href=\"/.github/README_EU.md\"\u003eeu Euskara\u003c/a\u003e Basque | \u003ca href=\"/.github/README_BE.md\"\u003ebe \u0411\u0435\u043b\u0430\u0440\u0443\u0441\u043a\u0430\u044f\u003c/a\u003e Belarusian | \u003ca href=\"/.github/README_BN.md\"\u003ebn \u09ac\u09be\u0982\u09b2\u09be\u003c/a\u003e Bengali | \u003ca href=\"/.github/README_BS.md\"\u003ebs Bosanski\u003c/a\u003e Bosnian | \u003ca href=\"/.github/README_BG.md\"\u003ebg \u0431\u044a\u043b\u0433\u0430\u0440\u0441\u043a\u0438\u003c/a\u003e Bulgarian | \u003ca href=\"/.github/README_CA.md\"\u003eca Catal\u00e0\u003c/a\u003e Catalan | \u003ca href=\"/.github/README_CEB.md\"\u003eceb Sugbuanon\u003c/a\u003e Cebuano | \u003ca href=\"/.github/README_NY.md\"\u003eny Chichewa\u003c/a\u003e Chichewa | \u003ca href=\"/.github/README_ZH-CN.md\"\u003ezh-CN \u7b80\u4f53\u4e2d\u6587\u003c/a\u003e Chinese (Simplified) | \u003ca href=\"/.github/README_ZH-T.md\"\u003ezh-t \u4e2d\u570b\u50b3\u7d71\u7684\uff09\u003c/a\u003e Chinese (Traditional) | \u003ca href=\"/.github/README_CO.md\"\u003eco Corsu\u003c/a\u003e Corsican | \u003ca href=\"/.github/README_HR.md\"\u003ehr Hrvatski\u003c/a\u003e Croatian | \u003ca href=\"/.github/README_CS.md\"\u003ecs \u010de\u0161tina\u003c/a\u003e Czech | \u003ca href=\"README_DA.md\"\u003eda dansk\u003c/a\u003e Danish | \u003ca href=\"/.github/README_NL.md\"\u003enl Nederlands\u003c/a\u003e Dutch | \u003ca href=\"/.github/README.md\"\u003e\u003cstrong\u003een-us English\u003c/strong\u003e\u003c/a\u003e English | \u003ca href=\"/.github/README_EO.md\"\u003eEO Esperanto\u003c/a\u003e Esperanto | \u003ca href=\"/.github/README_ET.md\"\u003eet Eestlane\u003c/a\u003e Estonian | \u003ca href=\"/.github/README_TL.md\"\u003etl Pilipino\u003c/a\u003e Filipino | \u003ca href=\"/.github/README_FI.md\"\u003efi 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href=\"/.github/README_HE.md\"\u003ehe \u05e2\u05b4\u05d1\u05e8\u05b4\u05d9\u05ea\u003c/a\u003e Hebrew | \u003ca href=\"/.github/README_HI.md\"\u003ehi \u0939\u093f\u0928\u094d\u0926\u0940\u003c/a\u003e Hindi | \u003ca href=\"/.github/README_HMN.md\"\u003ehmn Hmong\u003c/a\u003e Hmong | \u003ca href=\"/.github/README_HU.md\"\u003ehu Magyar\u003c/a\u003e Hungarian | \u003ca href=\"/.github/README_IS.md\"\u003eis \u00cdslenska\u003c/a\u003e Icelandic | \u003ca href=\"/.github/README_IG.md\"\u003eig Igbo\u003c/a\u003e Igbo | \u003ca href=\"/.github/README_ID.md\"\u003eid bahasa Indonesia\u003c/a\u003e Icelandic | \u003ca href=\"/.github/README_GA.md\"\u003ega Gaeilge\u003c/a\u003e Irish | \u003ca href=\"/.github/README_IT.md\"\u003eit Italiana/Italiano\u003c/a\u003e | \u003ca href=\"/.github/README_JA.md\"\u003eja \u65e5\u672c\u8a9e\u003c/a\u003e Japanese | \u003ca href=\"/.github/README_JW.md\"\u003ejw Wong jawa\u003c/a\u003e Javanese | \u003ca href=\"/.github/README_KN.md\"\u003ekn \u0c95\u0ca8\u0ccd\u0ca8\u0ca1\u003c/a\u003e Kannada | \u003ca href=\"/.github/README_KK.md\"\u003ekk \u049a\u0430\u0437\u0430\u049b\u003c/a\u003e Kazakh | \u003ca href=\"/.github/README_KM.md\"\u003ekm \u1781\u17d2\u1798\u17c2\u179a\u003c/a\u003e Khmer | \u003ca href=\"/.github/README_RW.md\"\u003erw Kinyarwanda\u003c/a\u003e Kinyarwanda | \u003ca href=\"/.github/README_KO_SOUTH.md\"\u003eko-south \u97d3\u570b\u8a9e\u003c/a\u003e Korean (South) | \u003ca href=\"README_KO_NORTH.md\"\u003eko-north \ubb38\ud654\uc5b4\u003c/a\u003e Korean (North) (NOT YET TRANSLATED) | \u003ca href=\"/.github/README_KU.md\"\u003eku Kurd\u00ee\u003c/a\u003e Kurdish (Kurmanji) | \u003ca href=\"/.github/README_KY.md\"\u003eky \u041a\u044b\u0440\u0433\u044b\u0437\u0447\u0430\u003c/a\u003e Kyrgyz | \u003ca href=\"/.github/README_LO.md\"\u003elo \u0ea5\u0eb2\u0ea7\u003c/a\u003e Lao | \u003ca href=\"/.github/README_LA.md\"\u003ela Latine\u003c/a\u003e Latin | \u003ca href=\"/.github/README_LT.md\"\u003elt 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\u1019\u103c\u1014\u103a\u1019\u102c\u003c/a\u003e Myanmar (Burmese) | \u003ca href=\"/.github/README_NE.md\"\u003ene \u0928\u0947\u092a\u093e\u0932\u0940\u003c/a\u003e Nepali | \u003ca href=\"/.github/README_NO.md\"\u003eno norsk\u003c/a\u003e Norwegian | \u003ca href=\"/.github/README_OR.md\"\u003eor \u0b13\u0b21\u0b3f\u0b06 (\u0b13\u0b21\u0b3f\u0b06)\u003c/a\u003e Odia (Oriya) | \u003ca href=\"/.github/README_PS.md\"\u003eps \u067e\u069a\u062a\u0648\u003c/a\u003e Pashto | \u003ca href=\"/.github/README_FA.md\"\u003efa \u0641\u0627\u0631\u0633\u06cc\u003c/a\u003e |Persian \u003ca href=\"/.github/README_PL.md\"\u003epl polski\u003c/a\u003e Polish | \u003ca href=\"/.github/README_PT.md\"\u003ept portugu\u00eas\u003c/a\u003e Portuguese | \u003ca href=\"/.github/README_PA.md\"\u003epa \u0a2a\u0a70\u0a1c\u0a3e\u0a2c\u0a40\u003c/a\u003e Punjabi | No languages available that start with the letter Q | \u003ca href=\"/.github/README_RO.md\"\u003ero Rom\u00e2n\u0103\u003c/a\u003e Romanian | \u003ca href=\"/.github/README_RU.md\"\u003eru \u0440\u0443\u0441\u0441\u043a\u0438\u0439\u003c/a\u003e Russian | \u003ca href=\"/.github/README_SM.md\"\u003esm Faasamoa\u003c/a\u003e Samoan | \u003ca href=\"/.github/README_GD.md\"\u003egd G\u00e0idhlig na h-Alba\u003c/a\u003e Scots Gaelic | \u003ca href=\"/.github/README_SR.md\"\u003esr \u0421\u0440\u043f\u0441\u043a\u0438\u003c/a\u003e Serbian | \u003ca href=\"/.github/README_ST.md\"\u003est Sesotho\u003c/a\u003e Sesotho | \u003ca href=\"/.github/README_SN.md\"\u003esn Shona\u003c/a\u003e Shona | \u003ca href=\"/.github/README_SD.md\"\u003esd \u0633\u0646\u068c\u064a\u003c/a\u003e Sindhi | \u003ca href=\"/.github/README_SI.md\"\u003esi \u0dc3\u0dd2\u0d82\u0dc4\u0dbd\u003c/a\u003e Sinhala | \u003ca href=\"/.github/README_SK.md\"\u003esk Slov\u00e1k\u003c/a\u003e Slovak | \u003ca href=\"/.github/README_SL.md\"\u003esl Sloven\u0161\u010dina\u003c/a\u003e Slovenian | \u003ca href=\"/.github/README_SO.md\"\u003eso Soomaali\u003c/a\u003e Somali | [\u003ca href=\"/.github/README_ES.md\"\u003ees en espa\u00f1ol\u003c/a\u003e Spanish | \u003ca href=\"/.github/README_SU.md\"\u003esu Sundanis\u003c/a\u003e Sundanese | \u003ca href=\"/.github/README_SW.md\"\u003esw Kiswahili\u003c/a\u003e Swahili | \u003ca href=\"/.github/README_SV.md\"\u003esv Svenska\u003c/a\u003e Swedish | \u003ca href=\"/.github/README_TG.md\"\u003etg \u0422\u043e\u04b7\u0438\u043a\u04e3\u003c/a\u003e Tajik | \u003ca href=\"/.github/README_TA.md\"\u003eta \u0ba4\u0bae\u0bbf\u0bb4\u0bcd\u003c/a\u003e Tamil | \u003ca href=\"/.github/README_TT.md\"\u003ett \u0422\u0430\u0442\u0430\u0440\u003c/a\u003e Tatar | \u003ca href=\"/.github/README_TE.md\"\u003ete \u0c24\u0c46\u0c32\u0c41\u0c17\u0c41\u003c/a\u003e Telugu | \u003ca href=\"/.github/README_TH.md\"\u003eth \u0e44\u0e17\u0e22\u003c/a\u003e Thai | \u003ca href=\"/.github/README_TR.md\"\u003etr T\u00fcrk\u003c/a\u003e Turkish | \u003ca href=\"/.github/README_TK.md\"\u003etk T\u00fcrkmenler\u003c/a\u003e Turkmen | \u003ca href=\"/.github/README_UK.md\"\u003euk \u0423\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0438\u0439\u003c/a\u003e Ukrainian | \u003ca href=\"/.github/README_UR.md\"\u003eur \u0627\u0631\u062f\u0648\u003c/a\u003e Urdu | \u003ca href=\"/.github/README_UG.md\"\u003eug \u0626\u06c7\u064a\u063a\u06c7\u0631\u003c/a\u003e Uyghur | \u003ca href=\"/.github/README_UZ.md\"\u003euz O\u0027zbek\u003c/a\u003e Uzbek | \u003ca href=\"/.github/README_VI.md\"\u003evi Ti\u1ebfng Vi\u1ec7t\u003c/a\u003e Vietnamese | \u003ca href=\"/.github/README_CY.md\"\u003ecy Cymraeg\u003c/a\u003e Welsh | \u003ca href=\"/.github/README_XH.md\"\u003exh isiXhosa\u003c/a\u003e Xhosa | \u003ca href=\"/.github/README_YI.md\"\u003eyi \u05d9\u05d9\u05d3\u05d9\u05e9\u003c/a\u003e Yiddish | \u003ca href=\"/.github/README_YO.md\"\u003eyo Yoruba\u003c/a\u003e Yoruba | \u003ca href=\"/.github/README_ZU.md\"\u003ezu Zulu\u003c/a\u003e Zulu ) Available in 110 languages (108 when not counting English and North Korean, as North Korean has not been translated yet \u003ca href=\"/OldVersions/Korean(North)/README.md\"\u003eRead about it here\u003c/a\u003e)\u003c/p\u003e\n\u003c/details\u003e\n\u003cp\u003eTranslations in languages other than English are machine translated and are not yet accurate. No errors have been fixed yet as of February 5th 2021. Please report translation errors \u003ca href=\"https://github.com/seanpm2001/SNU_2D_ProgrammingTools_IDE_%3CLanguageNameWithUnderscores%3E/issues/\"\u003ehere\u003c/a\u003e make sure to backup your correction with sources and guide me, as I don\u0027t know languages other than English well (I plan on getting a translator eventually) please cite \u003ca href=\"https://en.wiktionary.org\" rel=\"nofollow\"\u003ewiktionary\u003c/a\u003e and other sources in your report. Failing to do so will result in a rejection of the correction being published.\u003c/p\u003e\n\u003cp\u003eNote: due to limitations with GitHub\u0027s interpretation of markdown (and pretty much every other web-based interpretation of markdown) clicking these links will redirect you to a separate file on a separate page that isn\u0027t my GitHub profile page. You will be redirected to the \u003ca href=\"https://github.com/seanpm2001/seanpm2001\"\u003eseanpm2001/seanpm2001 repository\u003c/a\u003e, where the README is hosted.\u003c/p\u003e\n\u003cp\u003eTranslations are done with Google Translate due to limited or no support for the languages I need in other translation services like DeepL and Bing Translate. For some reason, the formatting (links, dividers, bolding, italics, etc.) is messed up in various translations. It is tedious to fix, and I do not know how to fix these issues in languages with non-latin characters, and right to left languages (like Arabic) extra help is needed in fixing these issues\u003c/p\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-index\" class=\"anchor\" aria-hidden=\"true\" href=\"#index\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIndex\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"#Top\"\u003e00.0 - Top\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#SNU-2D-ProgrammingTools-IDE-%3CLanguageNameWithHyphens%3E\"\u003e00.1 - Title\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Read-this-article-in-a-different-language\"\u003e00.2 - Read this article in a different language\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Index\"\u003e00.3 - Index\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#SNU_2D_ProgrammingTools_IDE_%3CLanguageNameWithUnderscores%3E\"\u003e01.0 - Description\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#About\"\u003e02.0 - About\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Wiki\"\u003e03.0 - Wiki\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#History\"\u003e04.0 - History\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Pre-history\"\u003e04.1 - Pre-history\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Alpha-history\"\u003e04.2 - Alpha History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Beta-history\"\u003e04.3 - Beta History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Modern-history\"\u003e04.4 - Modern History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#Copying\"\u003e05.0 - Copying\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Credits\"\u003e06.0 - Credits\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Installation\"\u003e07.0 - Installation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Version-history\"\u003e08.0 - Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Software-status\"\u003e09.0 - Software status\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Sponsor-info\"\u003e10.0 - Sponsor info\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Contributers\"\u003e11.0 - Contributers\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Issues\"\u003e12.0 - Issues\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Current-issues\"\u003e12.1 - Current issues\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Past-issues\"\u003e12.2 - Past issues\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Past-pull-requests\"\u003e12.3 - Past pull requests\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Active-pull-requests\"\u003e12.4 - Active pull requests\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#Resources\"\u003e13.0 - Resources\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Contributing\"\u003e14.0 - Contributing\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#About-README\"\u003e15.0 - About README\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#README-version-history\"\u003e16.0 - README Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#You-have-reached-the-end-of-the-README-file\"\u003e17.0 - Footer\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#EOF\"\u003e17.9 - End of file\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-snu_2d_programmingtools_ide_\" class=\"anchor\" aria-hidden=\"true\" href=\"#snu_2d_programmingtools_ide_\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSNU_2D_ProgrammingTools_IDE_\n\u003c/h1\u003e\n\u003cp\u003eThe Programming language IDE submodule for SNU Programming Tools (2D Mode)\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eSee above. This repository is the IDE for that comes with SNUs programming tool set.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-wiki\" class=\"anchor\" aria-hidden=\"true\" href=\"#wiki\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWiki\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/seanpm2001/SNU_2D_ProgrammingTools_IDE_%3CLanguageNameWithUnderscores%3E/wiki\"\u003eClick/tap here to view this projects Wiki\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf the project has been forked, the Wiki was likely removed. Luckily, I include an embedded version. You can view it \u003ca href=\"/External/ProjectWiki/\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eWrite about this projects history here.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pre-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-history\u003c/h3\u003e\n\u003cp\u003eNo pre-history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alpha-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#alpha-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlpha history\u003c/h3\u003e\n\u003cp\u003eNo Alpha history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-beta-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#beta-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBeta history\u003c/h3\u003e\n\u003cp\u003eNo Beta history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-modern-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#modern-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModern history\u003c/h3\u003e\n\u003cp\u003eNo Modern history to show for this project.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-copying\" class=\"anchor\" aria-hidden=\"true\" href=\"#copying\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopying\u003c/h2\u003e\n\u003cp\u003eView the copying license for this project \u003ca href=\"/COPYING\"\u003ehere\u003c/a\u003e (if you haven\u0027t built the project yet with the makefile, here is the original link: \u003ca href=\"/COPYINGL\"\u003eCOPYINGL\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease note that you also have to follow the rules of the GNU General Public License v3 (GPL3) which you can view \u003ca href=\"/LICENSE.txt\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eView the credits file for this project and see the people who got together to make this project by \u003ca href=\"/CREDITS\"\u003eclicking/tapping here\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eView the installation instructions file for this project \u003ca href=\"/INSTALL\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRequirements: Read the instructions for more info, and get the latest up-to-date instructions \u003ca href=\"https://gist.github.com/seanpm2001/745564a46186888e829fdeb9cda584de\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sponsor-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#sponsor-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSponsor info\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/SponsorButton.png\"\u003e\u003cimg src=\"/SponsorButton.png\" alt=\"SponsorButton.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can sponsor this project if you like, but please specify what you want to donate to. \u003ca href=\"https://github.com/seanpm2001/Sponsor-info/tree/main/For-sponsors/\"\u003eSee the funds you can donate to here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can view other sponsor info \u003ca href=\"https://github.com/seanpm2001/Sponsor-info/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTry it out! The sponsor button is right up next to the watch/unwatch button.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-version-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#version-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion history\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eVersion history currently unavailable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo other versions listed\u003c/strong\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware status\u003c/h2\u003e\n\u003cp\u003eAll of my works are free some restrictions. DRM (\u003cstrong\u003eD\u003c/strong\u003eigital \u003cstrong\u003eR\u003c/strong\u003eestrictions \u003cstrong\u003eM\u003c/strong\u003eanagement) is not present in any of my works.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"DRM-free_label.en.svg\"\u003e\u003cimg src=\"DRM-free_label.en.svg\" alt=\"DRM-free_label.en.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis sticker is supported by the Free Software Foundation. I never intend to include DRM in my works.\u003c/p\u003e\n\u003cp\u003eI am ussing the abbreviation \"Digital Restrictions Management\" instead of the more known \"Digital Rights Management\" as the common way of addressing it is false, there are no rights with DRM. The spelling \"Digital Restrictions Management\" is more accurate, and is supported by \u003ca href=\"https://en.wikipedia.org/wiki/Richard_Stallman\" rel=\"nofollow\"\u003eRichard M. Stallman (RMS)\u003c/a\u003e and the \u003ca href=\"https://en.wikipedia.org/wiki/Free_Software_Foundation\" rel=\"nofollow\"\u003eFree Software Foundation (FSF)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis section is used to raise awareness for the problems with DRM, and also to protest it. DRM is defective by design and is a major threat to all computer users and software freedom.\u003c/p\u003e\n\u003cp\u003eImage credit: \u003ca href=\"https://www.defectivebydesign.org/drm-free/how-to-use-label/\" rel=\"nofollow\"\u003edefectivebydesign.org/drm-free/...\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributers\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributers\u003c/h2\u003e\n\u003cp\u003eCurrently, I am the only contributer. Contributing is allowed, as long as you follow the rules of the \u003ca href=\"/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e file.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/seanpm2001/\"\u003eseanpm2001\u003c/a\u003e - x commits (As of 2021, date, at xx:xx pm)\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eNo other contributers.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssues\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-current-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#current-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCurrent issues\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other current issues\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-past-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#past-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePast issues\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other past issues\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-past-pull-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#past-pull-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePast pull requests\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other past pull requests\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-active-pull-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#active-pull-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eActive pull requests\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other active pull requests\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h2\u003e\n\u003cp\u003eHere are some other resources for this project:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"PROJECT_LANG_1.%3CprojectLanguage1fileExtension\"\u003eProject language file A\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/seanpm2001/SNU_2D_ProgrammingTools_IDE_%3CLanguageNameWithUnderscores%3E/discussions\"\u003eJoin the discussion on GitHub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNo other resources at the moment.\u003c/p\u003e\n\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eContributing is allowed for this project, as long as you follow the rules of the \u003ccode\u003eCONTRIBUTING.md\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/CONTRIBUTING.md\"\u003eClick/tap here to view the contributing rules for this project\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout README\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eFile type:\u003c/strong\u003e \u003ccode\u003eMarkdown document (*.md)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile version:\u003c/strong\u003e \u003ccode\u003e1 (date)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLine count:\u003c/strong\u003e \u003ccode\u003e0,415\u003c/code\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-readme-version-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#readme-version-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREADME version history\u003c/h2\u003e\n\u003cp\u003eVersion 1 (Date)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eStarted the file\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the about section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the Wiki section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the version history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the issues section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the past issues section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the past pull requests section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the active pull requests section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the contributors section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the contributing section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the about README section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the README version history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the resources section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded a software status section, with a DRM free sticker and message\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the sponsor info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eITERATION 5\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the footer\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eITERATION 6\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eFixed and update template links\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the copying section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the credits section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the installation section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the resources section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the contributors section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the technical notes section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the footer\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eVersion 2 (Coming soon)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eComing soon\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 2\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-you-have-reached-the-end-of-the-readme-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#you-have-reached-the-end-of-the-readme-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYou have reached the end of the README file\u003c/h3\u003e\n\u003cp\u003e( \u003ca href=\"#Top\"\u003eBack to top\u003c/a\u003e | \u003ca href=\"https://github.com\"\u003eExit to GitHub\u003c/a\u003e | \u003ca href=\"https://www.bing.com/\" rel=\"nofollow\"\u003eExit to Bing\u003c/a\u003e | \u003ca href=\"https://duckduckgo.com/\" rel=\"nofollow\"\u003eExit to DuckDuckGo\u003c/a\u003e | \u003ca href=\"https://www.ecosia.org/\" rel=\"nofollow\"\u003eExit to Ecosia\u003c/a\u003e )\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-eof\" class=\"anchor\" aria-hidden=\"true\" href=\"#eof\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEOF\u003c/h3\u003e\n\u003chr\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ - "uniprot", - "proteomes", - "taxonomic-classification" + "apptainer", + "gpl3", + "gplv3", + "ide", + "md", + "programming", + "singularity", + "singularity-lang", + "singularity-language", + "snu", + "snu-2d", + "snu-2d-programmingtools", + "snu-development", + "snu-programming-tools", + "snu2d-programmingtools", + "snu2dprogrammingtools", + "snuprogrammingtools", + "txt", + "web-ide" ], - "updated_at": 1669667250.0 + "updated_at": 1668813080.0 }, { "data_format": 2, - "description": "A django application to demo a bone age prediction model", + "description": "Pipeline for analysing M. tuberculosis nanopore reads and getting drug susceptibility information.", "filenames": [ - "Singularity" + "containers/recipes/Singularity.mykrobe", + "containers/recipes/Singularity.nanoporeqc" ], - "full_name": "vsoch/boneage", + "full_name": "mbhall88/Longitude_pipeline", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-bone-age-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#bone-age-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBone-Age Model\u003c/h1\u003e\n\u003cp\u003eThis repository builds a Docker image and a \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image, each that will run the bone age demo to predict bone age from a radiograph. The demo runs the prediction on the command line, either with a local image input, or using a demo image.\u003c/p\u003e\n\u003cp\u003eIf you are working on your local machine, you can use either Docker or Singularity. If you are running in a shared cluster (HPC) environment where you do not have root permissions, Singularity is your best option. Instructions are included for both.\u003c/p\u003e\n\u003cp\u003ePackages that need to be installed are included in \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e and installed into the container via the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e or \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e. The container is provided on \u003ca href=\"https://hub.docker.com/r/vanessa/boneage/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded from there when you run it, and this is recommended because building it takes a while to compile OpenCV.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-i-want-to-build-it\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-want-to-build-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI want to build it!\u003c/h3\u003e\n\u003cp\u003eIf you want to look at or make changes to the code, it\u0027s recommended to clone the repo and build the container locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone http://www.github.com/vsoch/boneage\ncd boneage\ndocker build -t vanessa/boneage .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe docker daemon will first look for an image called \u003ccode\u003evanessa/boneage\u003c/code\u003e locally, and if not found, will then try Dockerhub, and download it from there. If for any reason you want to remove your image, just do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker rmi vanessa/boneage\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThe entry to the container is done simply by using it as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/boneage --help\nusage: cli.py [-h] [--image IMAGE] [--output OUTPUT] [--gender {M,F}]\n\t [--width WIDTH] [--height HEIGHT] [--debug]\n\nPredict bone age of an image.\n\noptional arguments:\n -h, --help show this help message and exit\n --image IMAGE Path to single bone image.\n --output OUTPUT Path to output file to write results.\n --gender {M,F} the gender of the individual (M or F), default is M (male)\n --width WIDTH warped width to resize the image in pixels (default 256)\n --height HEIGHT warped height to resize the image in pixels (default 256)\n --debug use verbose logging to debug.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-prediction-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-prediction-demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Prediction Demo\u003c/h3\u003e\n\u003cp\u003eTo run the bone-age demo non interactively to get a prediction, you can run it without any arguments. Note that since this application is optimized to return a web response (json) you will only see a json object returned without the \u003ccode\u003e--debug\u003c/code\u003e argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run vanessa/boneage\n\n{\u0027gender\u0027: \u0027M\u0027, \u0027image\u0027: \u0027/code/example_images/1.png\u0027, \u0027scores\u0027: [4.3481795e-32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.95402247, 4.6613271e-30, 0.0, 0.0, 0.0, 3.5964787e-28, 0.045977563, 0.0, 0.0, 7.72608e-32, 3.5294469e-28, 3.2218784e-31, 8.7065415e-31, 0.0, 0.0, 1.4140952e-27, 2.0360324e-31, 1.3973739e-18, 0.0, 0.0, 9.1047968e-32, 0.0, 0.0, 0.0, 0.0, 5.5391993e-31, 0.0, 0.0, 0.0, 1.3619909e-16, 0.0, 0.0, 3.7027614e-31, 1.6943371e-30, 8.6800407e-32, 0.0, 0.0, 1.6423222e-28, 0.0, 5.1337822e-30, 2.6105505e-31, 4.9806177e-30, 4.3782129e-15, 4.614967e-32, 3.4625493e-27, 3.3474241e-32, 3.2968069e-27, 1.2063197e-29, 3.3373545e-30, 1.4215187e-27, 3.7455669e-28, 3.4475776e-11, 3.9599255e-23, 7.9019825e-23, 9.7098277e-27, 7.4687077e-28, 3.3236082e-30, 2.9441527e-25, 1.0912699e-25, 1.0655707e-22, 8.3881067e-27, 9.9488148e-28, 7.2947065e-31, 1.0451508e-28, 3.4619964e-30, 2.3976481e-26, 1.8529252e-26, 4.1468809e-13, 1.124584e-31, 3.3920541e-32, 1.0070911e-30, 2.3539665e-19, 1.2927373e-28, 0.0, 0.0, 6.4560408e-24, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0023609e-22, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2730129e-32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.8752429e-23, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 6.6301819e-32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.1331077e-26, 0.0, 8.9587665e-29, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.98046e-27, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.7935414e-31, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.170995e-22, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.1674999e-31, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0261926e-24, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.2983278e-12, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0756849e-12, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], \u0027predicted_age\u0027: 8, \u0027predicted_weight\u0027: 8.2758656171904867}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also run with \u003ccode\u003e--debug\u003c/code\u003e to get full \"pretty print\" output.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run vanessa/boneage --debug\n\nEnvironment message level found to be DEBUG\n\nDEBUG:bone-age:\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:No image selected, will use provided example...\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /code/example_images/0.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nDEBUG:bone-age:Building model, please wait.\n\n ...\n\n\nDEBUG:bone-age:Predicted Age : 8 Months\nDEBUG:bone-age:Weighted Prediction : 8.164139 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-prediction-with-your-own-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-prediction-with-your-own-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Prediction With Your Own Image\u003c/h3\u003e\n\u003cp\u003eIf you want to provide your own image, you need to bind it to the /data directory in the folder, and map a path to it. Don\u0027t forget to specify the gender - the default is male, and you may want to change that:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png\n\n*** Starting Bone Age Prediction ****\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can of course add debug to verify that the default is male, and we are using our mapped image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png --debug\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can specify a different gender, and the prediction changes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png --gender F --debug\nEnvironment message level found to be DEBUG\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: False\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 16 Months\nWeighted Prediction : 16.000000 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-save-output-to-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#save-output-to-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSave output to file\u003c/h3\u003e\n\u003cp\u003eIf you specify the \u003ccode\u003e--output\u003c/code\u003e argument, you can save the result as a json to file. Again, we will need to specify a file in a folder mapped to our local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --output /data/demo.json --debug\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nNo image selected, will use provided example...\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /code/example_images/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\nDEBUG:bone-age:Result written to /data/demo.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we can look at the data - remember the folder that was mapped on our local machine is \u003ccode\u003e$PWD/example_images\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cat $PWD/example_images/demo.json\n {\n \"gender\": \"M\",\n \"image\": \"/code/example_images/4.png\",\n \"predicted_age\": 8,\n \"predicted_weight\": 8.64113067092668\n }\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-do-i-shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do I shell into the container?\u003c/h2\u003e\n\u003cp\u003eBy default, running the container uses the \u003ccode\u003eENTRYPOINT\u003c/code\u003e, meaning it is used as an executable and you do not enter the container. In the case that you want a container-based environment that is installed with the dependencies of boneage, or if you want to interactively work with the code, you may want to shell into the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint /bin/bash vanessa/boneage\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eKeep in mind that once you exit from this run, the container image is not saved, including your changes.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install Singularity\u003c/h2\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Bootstrap the image\u003c/h2\u003e\n\u003cp\u003eBootstrapping means using something to build from, or not starting from nothing. In this case, we are going to use a build file that bootstraps a Docker image of boneage (yes, the same one discussed above). This build file is called \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e, and for more details about this you can \u003ca href=\"http://singularity.lbl.gov/docs-docker\" rel=\"nofollow\"\u003eread here\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 boneage.img\nsudo singularity bootstrap boneage.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Run commands\u003c/h2\u003e\n\u003cp\u003eThe commands are equivalent as above, except we can use the container as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./boneage.img --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand to make a drive, we use \u003ccode\u003e--bind\u003c/code\u003e instead\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run --bind $PWD/example_images:/data boneage.img --debug\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-do-i-shell-into-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do I shell into the container?\u003c/h2\u003e\n\u003cp\u003eSingularity has an easy, intuitive way to shell inside!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell boneage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-interactive-web-interface\" class=\"anchor\" aria-hidden=\"true\" href=\"#interactive-web-interface\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive Web Interface\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003etodo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUltimately, we will build this demo and serve on \u003ca href=\"http://www.singularity-hub.org\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e and then have an application that takes inputs / outputs for the container, and runs on demand.\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 3, - "topics": [], - "updated_at": 1681304289.0 + "topics": [ + "nanopore", + "tuberculosis", + "bioinformatics-pipeline" + ], + "updated_at": 1674937389.0 }, { "data_format": 2, - "description": null, + "description": "singularity-recipe-share", "filenames": [ - "singularity/Singularity" + "Singularity.tensorflow-gpu-1.12.0" ], - "full_name": "ashokdahal/FrameFieldLearning_Anaconda_Windows", + "full_name": "lxwgcool/singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-polygonal-building-segmentation-by-frame-field-learning-modified-from-lydorn-to-work-on-anaconda-and-windows-device-without-docker-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#polygonal-building-segmentation-by-frame-field-learning-modified-from-lydorn-to-work-on-anaconda-and-windows-device-without-docker-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePolygonal Building Segmentation by Frame Field Learning Modified from LYDORN to work on anaconda and windows device without docker installation.\u003c/h1\u003e\n\u003cp\u003eWe add a frame field output to an image segmentation neural network to improve segmentation quality\nand provide structural information for the subsequent polygonization step.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/frame_field_sample.png\"\u003e\u003cimg src=\"images/frame_field_sample.png\" width=\"512\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 1: Close-up of our additional frame field output on a test image.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/model_training.png\"\u003e\u003cimg src=\"images/model_training.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 2: Given an overhead image, the model outputs an edge mask, an interior mask,\n and a frame field for buildings. The total loss includes terms that align the masks and\n frame field to ground truth data as well as regularizers to enforce smoothness of the\n frame field and consistency between the outputs.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/schematic_polygonization.png\"\u003e\u003cimg src=\"images/schematic_polygonization.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 3: Given classification maps and a frame field as input, we optimize skeleton polylines to\n align to the frame field using an Active Skeleton Model (ASM) and detect corners using\n the frame field, simplifying non-corner vertices.\n\u003c/p\u003e\n\u003cp\u003eThis repository contains the official code for the paper:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://www-sop.inria.fr/members/Nicolas.Girard/\" rel=\"nofollow\"\u003eNicolas Girard\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/smirnov/\" rel=\"nofollow\"\u003eDmitriy Smirnov\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/jsolomon/\" rel=\"nofollow\"\u003eJustin Solomon\u003c/a\u003e,\n\u003ca href=\"https://www-sop.inria.fr/members/Yuliya.Tarabalka/\" rel=\"nofollow\"\u003eYuliya Tarabalka\u003c/a\u003e\u003cbr\u003e\nPre-print\u003cbr\u003e\n\u003cstrong\u003e[\u003ca href=\"https://arxiv.org/pdf/2004.14875.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, \u003ca href=\"https://www.youtube.com/watch?v=XdQMD3HTYCU\u0026amp;t=5s\" rel=\"nofollow\"\u003evideo\u003c/a\u003e]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhose short version has been published as:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegularized Building Segmentation by Frame Field Learning\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://www-sop.inria.fr/members/Nicolas.Girard/\" rel=\"nofollow\"\u003eNicolas Girard\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/smirnov/\" rel=\"nofollow\"\u003eDmitriy Smirnov\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/jsolomon/\" rel=\"nofollow\"\u003eJustin Solomon\u003c/a\u003e,\n\u003ca href=\"https://www-sop.inria.fr/members/Yuliya.Tarabalka/\" rel=\"nofollow\"\u003eYuliya Tarabalka\u003c/a\u003e\u003cbr\u003e\nIGARSS 2020\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-git-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#git-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGit submodules\u003c/h2\u003e\n\u003cp\u003eThis project uses various git submodules that should be cloned too.\u003c/p\u003e\n\u003cp\u003eTo clone a repository including its submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive --jobs 8 \u0026lt;URL to Git repo\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you already have cloned the repository and now want to load it\u2019s submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --init --recursive --jobs 8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more about explanations about using submodules and git, see \u003ca href=\"SUBMODULES.md\"\u003eSUBMODULES.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eThe easiest way to setup environment is to use the Docker image provided in the \u003ca href=\"docker\"\u003edocker\u003c/a\u003e (see README inside the folder).\u003c/p\u003e\n\u003cp\u003eOnce the docker container is built and launched, execute the \u003ca href=\"setup.sh\"\u003esetup.sh\u003c/a\u003e script inside to install required packages.\u003c/p\u003e\n\u003cp\u003eThe environment in the container is now ready for use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda environment\u003c/h2\u003e\n\u003cp\u003eAlternatively you can install all dependencies in a conda environment.\nI provide my environment specifications in the \u003ca href=\"environment.yml\"\u003eenvironment.yml\u003c/a\u003e which you can use to create your environment own with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h1\u003e\n\u003cp\u003eSeveral datasets are used in this work.\nWe typically put all datasets in a \"data\" folder which we link to the \"/data\" folder in the container (with the \u003ccode\u003e-v\u003c/code\u003e argument when running the container).\nEach dataset has it\u0027s own sub-folder, usually named with a short version of that dataset\u0027s name.\nEach dataset sub-folder should have a \"raw\" folder inside containing all the original folders and files fo the datset.\nWhen pre-processing data, \"processed\" folders will be created alongside the \"raw\" folder.\u003c/p\u003e\n\u003cp\u003eFor example, here is an example working file structure inside the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/data \n|-- AerialImageDataset\n |-- raw\n |-- train\n | |-- aligned_gt_polygons_2\n | |-- gt\n | |-- gt_polygonized\n | |-- images\n `-- test\n |-- aligned_gt_polygons_2\n |-- images\n`-- mapping_challenge_dataset\n |-- raw\n |-- train\n | |-- images\n | |-- annotation.json\n | `-- annotation-small.json\n `-- val\n `-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf however you would like to use a different folder for the datasets (for example while not using Docker),\nyou can change the path to datasets in config files.\nYou can modify the \"data_dir_candidates\" list in the config to only include your path.\nThe training script checks this list of paths one at a time and picks the first one that exists.\nIt then appends the \"data_root_partial_dirpath\" directory to get to the dataset.\u003c/p\u003e\n\u003cp\u003eYou can find some of the data we used in this shared \"data\" folder: \u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inria-aerial-image-labeling-dataset\" class=\"anchor\" aria-hidden=\"true\" href=\"#inria-aerial-image-labeling-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInria Aerial Image Labeling Dataset\u003c/h2\u003e\n\u003cp\u003eLink to the dataset: \u003ca href=\"https://project.inria.fr/aerialimagelabeling/\" rel=\"nofollow\"\u003ehttps://project.inria.fr/aerialimagelabeling/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor the Inria dataset, the original ground truth is just a collection of raster masks.\nAs our method requires annotations to be polygons in order to compute the ground truth angle for the frame field, we made 2 versions of the dataset:\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria OSM dataset\u003c/em\u003e has aligned annotations pulled from OpenStreetMap.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria Polygonized dataset\u003c/em\u003e has polygon annotations obtained from using our frame field polygonization algorithm on the original raster masks.\nThis was done by running the \u003ccode\u003epolygonize_mask.py\u003c/code\u003e script like so:\n\u003ccode\u003epython polygonize_mask.py --run_name inria_dataset_osm_mask_only.unet16 --filepath ~/data/AerialImageDataset/raw/train/gt/*.tif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can find this new ground truth for both cases in the shared \"data\" folder (\u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-the-mainpy-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-mainpy-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the main.py script\u003c/h1\u003e\n\u003cp\u003eExecute \u003ca href=\"main.py\"\u003emain.py\u003c/a\u003e script to train a model, test a model or use a model on your own image.\nSee the help of the main script with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython main.py --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe script can be launched on multiple GPUs for multi-GPU training and evaluation.\nSimply set the \u003ccode\u003e--gpus\u003c/code\u003e argument to the number of gpus you want to use.\nHowever, for the first launch of the script on a particular dataset (when it will pre-process the data),\nit is best to leave it at 1 as I did not implement multi-GPU synchronization when pre-processing datasets.\u003c/p\u003e\n\u003cp\u003eAn example use is for training a model with a certain config file, like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained\u003c/code\u003e\nwhich will train the Unet-Resnet101 on the CrowdAI Mapping Challenge dataset.\nThe batch size can be adjusted like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained -b \u0026lt;new batch size\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen training is done, the script can be launched in eval mode, to evaluate the trained model:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval\u003c/code\u003e.\nDepending on the eval parameters of the config file, running this will output results on the test dataset.\u003c/p\u003e\n\u003cp\u003eFinally, if you wish to compute AP and AR metrics with the COCO API, you can run:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval_coco\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-launch-inference-on-one-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#launch-inference-on-one-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch inference on one image\u003c/h2\u003e\n\u003cp\u003eMake sure the run folder has the correct structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- \u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the [main.py] script like so (filling values for arguments run_name and in_filepath):\n\u003ccode\u003epython main.py --run_name \u0026lt;run_name\u0026gt; --in_filepath \u0026lt;your_image_filepath\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe outputs will be saved next to the input image\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-trained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload trained models\u003c/h2\u003e\n\u003cp\u003eWe provide already-trained models so you can run inference right away.\nDownload here: \u003ca href=\"https://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\u003c/a\u003e.\nEach model was trained in a \"run\", whose folder (named with the format \u003ccode\u003e\u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\u003c/code\u003e) you can download at the provided link.\nYou should then place those runs in a folder named \"runs\" inside the \"frame_field_learning\" folder like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard | 2020-06-02 07:57:31\n| | |-- mapping_dataset.unet_resnet101_pretrained.field_off.train_val | 2020-09-07 11:54:48\n| | |-- mapping_dataset.unet_resnet101_pretrained.train_val | 2020-09-07 11:28:51\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBecause Google Drive reformats folder names, you have to rename the run folders as above.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-hidden=\"true\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCite:\u003c/h1\u003e\n\u003cp\u003eIf you use this code for your own research, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{Girard_2020_IGARSS,\n title = {{Regularized Building Segmentation by Frame Field Learning}},\n author = {Girard, Nicolas and Smirnov, Dmitriy and Solomon, Justin and Tarabalka, Yuliya},\n booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},\n ADDRESS = {Waikoloa, Hawaii},\n year = {2020},\n month = Jul,\n}\n\n@misc{girard2020polygonal,\n title={Polygonal Building Segmentation by Frame Field Learning},\n author={Nicolas Girard and Dmitriy Smirnov and Justin Solomon and Yuliya Tarabalka},\n year={2020},\n eprint={2004.14875},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, "subscribers_count": 1, "topics": [], - "updated_at": 1651735479.0 + "updated_at": 1574278904.0 }, { "data_format": 2, - "description": "Comparison of batch correction methods for scRNA-seq data - basically a clone of BatchBench", + "description": "Open source simulation engine for coarse-grained Brownian dynamics", "filenames": [ "Singularity" ], - "full_name": "Sarah145/batch_correct", - "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-batch-correction-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#batch-correction-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch correction pipeline\u003c/h2\u003e\n\u003cp\u003eThis repository contains scripts to run a Nextflow pipeline to compare different batch correction methods for single-cell RNA-seq data. This is mostly just a clone of the \u003ca href=\"https://github.com/cellgeni/batchbench\"\u003eBatchBench\u003c/a\u003e pipeline from the CellGen IT team at Sanger but I couldn\u0027t get that to run so made some edits and added one or two extra things.\u003c/p\u003e\n\u003cp\u003eThe input files for this pipeline must be .Rds files of the uncorrected data as a SingleCellExperiment object (all batches in one object) with batch labels stored in the \u003ccode\u003ebatch_key\u003c/code\u003e (\u0027Batch\u0027 by default) column and cell type labels stored in the \u003ccode\u003ecelltype_key\u003c/code\u003e (\u0027cell_type1\u0027 by default) column.\u003c/p\u003e\n\u003cp\u003eThe pipeline will run 7 different batch correction methods on the data:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScanorama\u003c/li\u003e\n\u003cli\u003eBBKNN\u003c/li\u003e\n\u003cli\u003eSeurat 3\u003c/li\u003e\n\u003cli\u003eCombat\u003c/li\u003e\n\u003cli\u003eHarmony\u003c/li\u003e\n\u003cli\u003elimma\u003c/li\u003e\n\u003cli\u003eMNNCorrect\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each method, 5 different evaluation metrics are returned:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBatch entropy (from \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.05.22.111211v2\" rel=\"nofollow\"\u003eBatchBench\u003c/a\u003e) - measure of how well batches are aligned after correction - related to the probability that for each cell, its \u003cem\u003ek\u003c/em\u003e nearest neighbors come from a different batch - value reported is average entropy scaled between 0-1 - high batch entropy = well-mixed batches, low batch entropy = poorly-mixed batches.\u003c/li\u003e\n\u003cli\u003eCell type entropy (from \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.05.22.111211v2\" rel=\"nofollow\"\u003eBatchBench\u003c/a\u003e) - same as batch entropy but using cell type labels instead - high cell type entropy = mixing of cell types (not good), low cell type entropy = cell types are not mixing (good).\u003c/li\u003e\n\u003cli\u003eBatch ASW (from \u003ca href=\"https://github.com/theislab/scib\"\u003escIB\u003c/a\u003e) - average silhouette width of batches - scaled between -1-1 - high batch ASW = dense, well-separated batches (bad), low batch ASW = well mixed batches (good).\u003c/li\u003e\n\u003cli\u003eCell type ASW (from \u003ca href=\"https://github.com/theislab/scib\"\u003escIB\u003c/a\u003e) - same as batch ASW but for cell type labels - high cell type ASW = good, low cell type ASW = bad.\u003c/li\u003e\n\u003cli\u003eRecovery of marker genes - this idea was taken from the BatchBench paper but couldn\u0027t find code for it so wrote my own - not sure if it\u0027s right. For methods that correct the expression matrix (Scanorama, Seurat3, Combat, limma, MNNCorrect), found marker genes for each cell type (by batch and in the merged dataset), before and after batch correction, then compared the list of total marker genes identified before batch correction to the list of total marker genes identified after batch correction and calculated the Jaccard similarity index of the two lists. High Jaccard index = gene expression was not distorted too much by batch correction, most markers genes could still be identified (good), low Jaccard index = batch correction highly distorted the gene expression values so not as many marker genes could be recovered (bad). Jaccard index = 1 - all marker genes recovered, Jaccard index = 0 - no marker genes recovered.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo run pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNeed to have Nextflow and Singularity installed.\u003c/li\u003e\n\u003cli\u003eClone this repo and \u003ccode\u003ecd\u003c/code\u003e into it.\u003c/li\u003e\n\u003cli\u003ePull Singularity image - \u003ccode\u003esingularity pull shub://Sarah145/batch_correct\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eEdit the \u003ccode\u003enextflow.config\u003c/code\u003e script with location of data, batch key, cell type key, etc. \u003cem\u003eNote: profile section of the nextflow.config script in this repo is configured to run on cluster with slurm.\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit \u003ccode\u003edataset_list.txt\u003c/code\u003e file with name of files - one file on each line, no file extension.\u003c/li\u003e\n\u003cli\u003eRun pipeline with \u003ccode\u003enextflow run main.nf -profile singularity -with-trace trace.txt -with-dag flowchart.png\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eCompile html report of run by running \u003ccode\u003e./compile_report.R \u0026lt;sample_name\u0026gt;\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Sarah145/batch_correct/blob/master/imgs/flowchart.png?raw=true\"\u003e\u003cimg src=\"https://github.com/Sarah145/batch_correct/raw/master/imgs/flowchart.png?raw=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "Betterton-Lab/C-GLASS", + "latest_release": "v0.2.3", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-c-glass\" class=\"anchor\" aria-hidden=\"true\" href=\"#c-glass\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eC-GLASS\u003c/h1\u003e\n\u003cp\u003eA \u003cstrong\u003eC\u003c/strong\u003eoarse-\u003cstrong\u003eG\u003c/strong\u003erained \u003cstrong\u003eL\u003c/strong\u003eiving \u003cstrong\u003eA\u003c/strong\u003ective \u003cstrong\u003eS\u003c/strong\u003eystem \u003cstrong\u003eS\u003c/strong\u003eimulator\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/Betterton-Lab/C-GLASS\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/061b295758d2c64d80b7d3e97ceebc53e21cc632602b7b32c77f046c105d84ac/68747470733a2f2f7472617669732d63692e636f6d2f426574746572746f6e2d4c61622f432d474c4153532e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/Betterton-Lab/C-GLASS.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.3841613\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96212f14675a05209e745328444de906fc58f72d1794af88b9db02b4fe6f24e5/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333834313631332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3841613.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figs/cglass_snapshot.png\"\u003e\u003cimg src=\"figs/cglass_snapshot.png\" alt=\"A simulation using C-GLASS\" title=\"A simulation using C-GLASS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eFirst clone the repo, including submodule dependencies.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/Betterton-Lab/C-GLASS\ncd C-GLASS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eC-GLASS can either be run in a container using Docker or Singularity, or be built from source using CMake.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning with Docker\u003c/h3\u003e\n\u003cp\u003eA pre-built image of C-GLASS is available as a \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image. To download the image, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull jeffmm/cglass\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo use the image, run the provided script to launch a Docker container named \u003ccode\u003ecglass_latest\u003c/code\u003e in the background\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./launch_docker.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou may also build the Docker image yourself by providing the launch script with the \u003ccode\u003e-b\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eTo launch C-GLASS, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e cglass_latest cglass.exe [optional-flags] [parameter-file]\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning with Singularity\u003c/h3\u003e\n\u003cp\u003eIf you are using Singularity, C-GLASS is also available as a Singularity image. The command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull docker://jeffmm/cglass\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewill create a local file named \u003ccode\u003ecglass_latest.sif\u003c/code\u003e. You may then run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e cglass_latest.sif cglass.exe [optional-flags] [parameter-file]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity image may also be built locally using the provided recipe in the file \u003ccode\u003eSingularity\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding from source\u003c/h3\u003e\n\u003cp\u003eC-GLASS is ready to be built from source using CMake, provided several dependencies are installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCMake (version 3.13+)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jbeder/yaml-cpp\"\u003elibyaml-cpp\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003elibgsl-dev\u003c/li\u003e\n\u003cli\u003elibopenmpi-dev\u003c/li\u003e\n\u003cli\u003elibfftw3-dev\u003c/li\u003e\n\u003cli\u003elibboost-math1.67-dev\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIncluded is a script for building C-GLASS with CMake. To build C-GLASS (without graphics or parallelization) run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./install.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere are additional flags for building with OpenMP, building with graphics, installing C-GLASS in \u003ccode\u003e/usr/local\u003c/code\u003e, etc. To see a menu of options, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./install.sh -h\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-with-graphics\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-with-graphics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding with graphics\u003c/h3\u003e\n\u003cp\u003eC-GLASS is available with graphics for Mac OSX. To install on Mac OSX, you will need the glew and glfw3 libraries, both of which can be installed using \u003ca href=\"https://brew.sh/\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew install glew\nbrew install glfw\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou may also need to help CMake find your OpenGL Framework libraries.\u003c/p\u003e\n\u003cp\u003eSeveral other libraries are required for running C-GLASS with graphics on Linux or in WSL. See the \u003ccode\u003esrc/CMakeLists.txt\u003c/code\u003e file for a comprehensive list of libraries passed to the compiler when building C-GLASS with graphics on WSL.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-c-glass\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-c-glass\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning C-GLASS\u003c/h2\u003e\n\u003cp\u003eThe C-GLASS executable is run as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecglass.exe [optional-flags] [parameter-file] \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following flags are available:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--help, -h\n Show the help menu which gives short descriptions about each of the flags\n as well as binary usage\n \n --run-name rname, -r rname \n Overwrites the parameter \"run_name\" with rname which serves as a prefix for\n all outputs \n\n--n-runs num, -n num\n Overwrites the parameter \"n_runs\" with num, which tells the simulation how\n many times to run the given parameter set with different random number\n generator seeds.\n\n--movie, -m\n Uses the parameters file params_file to load any output files that were\n generated from previous runs of the simulation to replay the graphics and\n record the frames as bitmaps into the directory specified with the\n \"movie_directory\" parameter\n\n--analysis, -a\n Loads posit/spec files into the simulation for analysis in the same manner\n as the movie flag\n\n-reduce reduce_factor, -R reduce_factor\n Reads in output files and writes new output files that are smaller by a\n factor of reduce_factor, effectively reducing time resolution of output\n data.\n\n--load, -l\n Specifies to load any checkpoint files corresponding to the given parameter\n file, which can be used to continue a simulation that ended prematurely.\n New simulation will be given the name old_simulation_name_reload00n where n\n is the number of reloads performed on that simulation.\n\n--with-reloads, -w\n If running analyses or making movies, C-GLASS will look for parameter files\n that have the same run name but with the reload00n addendum and attempt to\n open the corresponding output files whenever it reached EOF while reading\n an output file.\n\n--blank, -b\n Generates all relevant parameter files using the SimulationManager without\n running the simulations. Useful for generating many parameter files from\n parameter sets (discussed below) and deploying simulations on different\n processors and/or machines.\n\n--auto-graph, -G\n By default, C-GLASS will wait for the user to press the ESC key in the\n OpenGL graphics window before starting to run the simulation. Providing\n this flag will cause the simulation to begin immediately without user\n input. Goes great with the -m flag for creating multiple movies without\n input from the user.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameter-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameter-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameter files\u003c/h2\u003e\n\u003cp\u003eAll parameters used in the simulation, along with their default values and data types, are specified in the \u003ccode\u003edefault_config.yaml\u003c/code\u003e file in the \u003ccode\u003econfig\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eThe parameter file is a YAML file and looks like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eglobal_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egp1_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eglobal_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egp2_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003especies\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eglobal_species_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egsp1_value\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eglobal_species_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egsp2_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003especific_species_name\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003especies_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003esp1_value\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003especies_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003esp2_value\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ccode\u003eexamples\u003c/code\u003e folder for examples of parameter files.\u003c/p\u003e\n\u003cp\u003eNotice that there are three parameter types: global parameters, global species parameters, and species parameters. Global parameters are parameters that are common to the entire system, such system size, integration time step, etc. Species parameters are unique to the specified species, such as \u003ccode\u003efilament\u003c/code\u003e. There is also an optional global species parameter type that affects every species, such as the frequency to write to output files.\u003c/p\u003e\n\u003cp\u003eWhat do I mean by species? C-GLASS assumes that any given simulation will likely have many copies of one kind of thing, which I call a species, perhaps interacting with other species of other kinds. In a system of interacting spheres, the species is \u0027sphere.\u0027 In a system of interacting semiflexible filaments, the species is \u0027filament.\u0027 Simulations can have many types of species all interacting with each other with different species-species interaction potentials.\u003c/p\u003e\n\u003cp\u003eIf any parameter is not specified in the parameter file, any instance of that parameter in the simulation will assume its default value specified in the \u003ccode\u003econfig/default_config.yaml\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eSome important global parameters are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed\n simulation seed to use with random number generator \nrun_name\n prefix for all output files\nn_runs\n number of individual runs of each parameter type\nn_random\n number of samples from a random parameter space (see more below)\nn_dim\n number of dimensions of simulation\nn_periodic\n number of periodic dimensions of simulation\ndelta \n simulation time step\nn_steps\n total number of steps in each simulation\nsystem_radius\n \"box radius\" of system\ngraph_flag\n run with graphics enabled\nn_graph\n how many simulation steps to take between updating graphics\nmovie_flag\n whether to record the graphics frames into bitmaps\nmovie_directory\n local directory used to save the recorded bitmaps\nthermo_flag\n whether to output thermodynamics outputs (stress tensors, etc)\nn_thermo\n how often to output the thermodynamics outputs\npotential_type\n can be \u0027wca\u0027 or \u0027soft\u0027 for now\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSome important global species parameters are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enum\n how many to insert into system\ninsertion_type\n how to insert object into system (e.g. random)\noverlap\n whether species can overlap at initiation\ndraw_type\n (orientation, fixed, or bw) how to color the object\ncolor\n a double that specifies the RGB value of the object\nposit_flag\n whether to output position files\nn_posit\n how often to output position files\nspec_flag\n whether to output species files\nn_spec\n how often to output species files\ncheckpoint_flag\n whether to output checkpoint files\nn_checkpoint\n how often to output checkpoint files\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-anchor-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#anchor-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnchor parameters\u003c/h3\u003e\n\u003cp\u003eC-GLASS has the capability to independently control crosslinker and motor protein anchor parameters. Anchor parameters are controlled within the Crosslink map in the input Yaml file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eCrosslink\u003c/span\u003e:\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e other crosslink params here\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eAnchors\u003c/span\u003e:\n - \u003cspan class=\"pl-ent\"\u003evelocity_s\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ecolor\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e3.5\u003c/span\u003e\n - \u003cspan class=\"pl-ent\"\u003ecolor\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4.5\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnly two anchors are permitted per crosslinker or motor protein. The anchor parameters obey the following rules when parameters are left blank:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf no anchors are listed, the anchor parameters will both be set to default.\u003c/li\u003e\n\u003cli\u003eIf one anchor is listed, the other anchor will copy its parameters. Any unlisted parameters will be set to default.\u003c/li\u003e\n\u003cli\u003eIf two anchors are listed, and one anchor has a parameter that the other doesn\u0027t, the one that doesn\u0027t have the parameter will copy the parameter from the other.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn the above example, Anchor 1 will have velocity_s=50, velocity_d=0 (default), color=3.5, and Anchor 2 will have velocity_s=50 (copied), velocity_d=0 (default), color=4.5.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdvanced usage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-unit-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning unit tests\u003c/h3\u003e\n\u003cp\u003eOne may run C-GLASS\u0027s unit tests by passing \u003ccode\u003e-DTESTS=TRUE\u003c/code\u003e to CMake\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir build\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\ncmake -DTESTS=TRUE ..\nmake\nmake \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-adding-new-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-new-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding new parameters\u003c/h3\u003e\n\u003cp\u003eC-GLASS comes with it\u0027s own parameter initialization tool, \u003ccode\u003econfigure_C-GLASS.exe\u003c/code\u003e, which is installed automatically along with the C-GLASS binary using CMake. The configurator makes it easy to add new parameters to the simulation without mucking around in the source code. Just add your new parameter to \u003ccode\u003econfig/default_config.yaml\u003c/code\u003e file using the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enew_parameter_name: [default_parameter_value, parameter_type] \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the configurator using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./configure_cglass.exe config/default_config.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning configure_cglass.exe will look at all the parameters in the default config file and add them seamlessly to the proper C-GLASS headers, and you can begin using them after recompiling C-GLASS using CMake.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-parameter-sets\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameter sets\u003c/h3\u003e\n\u003cp\u003eUsing parameter sets, it becomes easier to run many simulations over a given parameter space. There are two types of parameter sets possible with C-GLASS: defined and random. Each parameter set type works the same with both global parameters and species parameters.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-defined-parameter-sets\" class=\"anchor\" aria-hidden=\"true\" href=\"#defined-parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDefined parameter sets\u003c/h4\u003e\n\u003cp\u003eDefined parameter sets are specified by the \u003ccode\u003eV\u003c/code\u003e prefix in the parameter file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: 4916819461895\nrun_name: defined_set\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: [V, param_value2, param_value3]\nparameter_name3: [V, param_value4, param_value5]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eParameters specified in this way (as lists of parameters) will be iterated over until every possible combination of parameters has been run. In this example, C-GLASS will run N simulations each of the following 4 parameter sets:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: random_seed_1\nrun_name: defined_set_v000\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value2\nparameter_name3: param_value4\n\nseed: random_seed_2\nrun_name: defined_set_v001\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value2\nparameter_name3: param_value5\n\nseed: random_seed_3\nrun_name: defined_set_v002\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value3\nparameter_name3: param_value4\n\nseed: random_seed_4\nrun_name: defined_set_v003\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value3\nparameter_name3: param_value5\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-random-parameter-sets\" class=\"anchor\" aria-hidden=\"true\" href=\"#random-parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRandom parameter sets\u003c/h4\u003e\n\u003cp\u003eRandom parameter sets are designed specifically to be used with polynomial-chaos theory for n-dimensional parameter spaces for large n. Random sets are used in the following way:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: 2546954828254\nn_runs: N\nn_random: M\nparameter_name1: param_value1\nparameter_name2: [R, A, B] # sets to random real in range (A,B)\nparameter_name3: [RINT, C, D] # sets to random int in range [C,D]\nparameter_name4: [RLOG, F, G] # sets to 10^K for rand real K in range (F,G)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGiven this parameter file, C-GLASS will run N simulations each of M random parameter sets. The random parameter sets are generated in ranges specified in the lists that are prefixed by the R, RINT, RLOG options.\u003c/p\u003e\n\u003cp\u003eIn this example, the sampled parameter space has dimensionality of n=3, since there are only three parameters we are sampling over. Each parameter set will have a random real number for parameter_name2 in the the range (A,B), a random integer in the range [C,D] for parameter_name3, and will set parameter_name4 to 10^K for random real number K in the range (F,G). C-GLASS will then run each parameter set N times each with a unique seed, and repeat this random process M times. It will therefore take N samples of M random points in the n-dimensional parameter space.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-interactions\" class=\"anchor\" aria-hidden=\"true\" href=\"#interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractions\u003c/h3\u003e\n\u003cp\u003eThe Interaction Manager in C-GLASS was written with short-range interactions in mind. For this reason, interactions are treated by considering pair-wise interactions between neighboring interactor-elements that make up a composite object (e.g. small, rigid segments that compose a flexible filament). For this reason, interactions use cell lists to improve performance. Furthermore, simulating large objects in C-GLASS requires representing the object as a composite of smaller, simple objects. An example of how a large object should be decomposed into simple objects is done in the Filament class.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-potentials\" class=\"anchor\" aria-hidden=\"true\" href=\"#potentials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePotentials\u003c/h3\u003e\n\u003cp\u003eC-GLASS is designed to be able to use interchangable potentials for various objects. However, potentials need to be added manually as a subclass of PotentialBase, included in PotentialManager, and a corresponding potential_type added to definitions.h for lookup purposes (see the InitPotentials method in PotentialManager.h for examples).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h3\u003e\n\u003cp\u003eC-GLASS has four output types. Three are species specific (posit, spec, checkpoint), and the fourth is the statistical information file (thermo). All files are written in binary.\u003c/p\u003e\n\u003cp\u003eThe posit file has the following header format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eint n_steps, int n_posit, double delta \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollowed by n_steps/n_posit lines of data with the format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edouble position[3]\ndouble scaled_position[3]\ndouble orientation[3]\ndouble diameter\ndouble length\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the scaled position is position mapped into the periodic coordinate space. The position itself gives the particle trajectory over time independent of periodicity.\u003c/p\u003e\n\u003cp\u003eThe spec file is a custom output file for each species, and can have the same information as the posit file or additional information if needed.\u003c/p\u003e\n\u003cp\u003eThe checkpoint file is almost a copy of the spec file, except it also contains the random number generator information and is overwritten every n_checkpoint steps in the simulation. It can therefore be used to resume a simulation that ended prematurely.\u003c/p\u003e\n\u003cp\u003eThe thermo file contains the following header information:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eint n_steps, int n_thermo, double delta, int n_dim\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efollowed by n_steps/n_thermo lines of data in the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edouble unit_cell[9]\ndouble pressure_tensor[9]\ndouble pressure\ndouble volume\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the pressure is the isometric pressure, and the pressure tensor is calculated from the time-averaged stress tensor.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-data-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData analysis\u003c/h3\u003e\n\u003cp\u003eIf analysis operations of output files are already defined for your species, as is the case for the Filament species, analyzing outputs is a simple matter. First, make sure the desired analysis flag is set in the species parameters for that species.\u003c/p\u003e\n\u003cp\u003eFor example, in the Filament species there is a persistence length analysis that produces .mse2e files that tracks the mean-square end-to-end distance of semiflexible filaments. This is triggered by a parameter lp_analysis=1, which can be set in the parameter file.\u003c/p\u003e\n\u003cp\u003eAnaylses are run by running C-GLASS in the following way:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecglass.exe -a parameter_file.yaml.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: It is important to keep in mind that the parameter_file should be identical to the parameter file used to generate the outputs. There are a few exceptions that only affect post-processing, such as analysis flags, but this is true in general.\u003c/p\u003e\n\u003cp\u003eThe way inputs and outputs are meant to work in C-GLASS is such that during a simulation, output data are generated in the posit, spec, and checkpoint formats, and during analysis, the same output data are read back into the data structures in C-GLASS for processing. The .posit files just contain bare-bones information that allow many types of simple analyses, but .spec files should in general contain all the necessary information to recreate the trajectory for a member of a species.\u003c/p\u003e\n\u003cp\u003eFor a new species analysis method, the analysis routines should be defined in the species container class, rather than the species member class, and called by the inherited RunAnalysis method of the SpeciesBase class (and likewise for analysis initialization and finalization, see examples for details).\u003c/p\u003e\n\u003cp\u003eFor example, the RunSpiralAnalysis routine is called by the RunAnalysis method in FilamentSpecies, which uses the Filament .spec file as an input to do the necessary analysis, whose results are placed into a new file ending in filament.spiral. See Filament and FilamentSpecies for examples of how analyses can be initialized, processed, etc.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory structure\u003c/h2\u003e\n\u003cp\u003eThe directory structure is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eC-GLASS\n\u251c\u2500\u2500 include\n\u2502 \u2514\u2500\u2500 cglass\n\u2502 \u2514\u2500\u2500 (header files)\n\u251c\u2500\u2500 src\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u251c\u2500\u2500 executable\n\u2502 \u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2502 \u2514\u2500\u2500 cglass_main.cpp\n\u2502 \u251c\u2500\u2500 configurator\n\u2502 \u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2502 \u2514\u2500\u2500 configurator.cpp\n\u2502 \u2514\u2500\u2500 (source files)\n\u251c\u2500\u2500 config\n\u2502 \u2514\u2500\u2500 default_config.yaml\n\u251c\u2500\u2500 analysis\n\u2502 \u2514\u2500\u2500 (Python analysis files)\n\u251c\u2500\u2500 scripts\n\u2502 \u2514\u2500\u2500 (utility files)\n\u251c\u2500\u2500 examples\n\u2502 \u2514\u2500\u2500 (parameter file examples)\n\u251c\u2500\u2500 docker\n\u2502 \u2514\u2500\u2500 Dockerfile\n\u251c\u2500\u2500 extern\n\u2502 \u2514\u2500\u2500 KMC\n\u251c\u2500\u2500 tests\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u251c\u2500\u2500 catch2\n\u2502 \u2502 \u2514\u2500\u2500 catch.hpp\n\u2502 \u2514\u2500\u2500 (C-GLASS unit tests)\n\u251c\u2500\u2500 docs\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2514\u2500\u2500 main.md\n\u251c\u2500\u2500 figs\n\u2502 \u2514\u2500\u2500 (example simulation figures)\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 CMakeLists.txt\n\u251c\u2500\u2500 install.sh\n\u251c\u2500\u2500 launch_docker.sh\n\u251c\u2500\u2500 .travis.yml\n\u2514\u2500\u2500 .gitignore\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-c-glass\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-c-glass\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout C-GLASS\u003c/h2\u003e\n\u003cp\u003eC-GLASS is written in C++ and designed for general coarse-grained physics simulations of active living matter, produced with modularity and scalability in mind. All objects in the simulation are representable as a composite of what I call \"simple\" objects (points, spheres, rigid cylinders, and 2d polygon surfaces would all qualify). For short-range interactions, C-GLASS uses cell and neighbor lists for improved performance and OpenMP for parallelization.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis software is licensed under the terms of the BSD-3 Clause license. See the \u003ccode\u003eLICENSE\u003c/code\u003e for more details.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, - "topics": [ - "scrna-seq-analysis", - "batch-correction" - ], - "updated_at": 1653300274.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1642046811.0 }, { "data_format": 2, - "description": "Repository to store MRTrix3 tractography pipelines created with Nipype", + "description": "Software for Meteorology, Normally Distributed", "filenames": [ - "container/singularity_recipe/Singularity.0.0.4", - "container/singularity_recipe/Singularity.0.0.6", - "container/singularity_recipe/Singularity.0.0.5", - "container/singularity_recipe/Singularity.0.0.8", - "container/singularity_recipe/Singularity.0.0.9", - "container/singularity_recipe/Singularity.0.0.2", - "container/singularity_recipe/Singularity.0.0.7", - "container/singularity_recipe/Singularity.0.0.1", - "container/singularity_recipe/Singularity.0.1.0" - ], - "full_name": "kaitj/mrtpipelines", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mrtpipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#mrtpipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emrtpipelines\u003c/h1\u003e\n\u003cp\u003eMRTrix3 processing diffusion and generating tractography of subject data from data collected.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#intro\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#disclaimer\"\u003eDisclaimer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#install\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#container\"\u003eContainerized package\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\n\u003ca href=\"#reqargs\"\u003eRequired arguments\u003c/a\u003e\n\u003ca href=\"#optargs\"\u003eOptional arguments\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#support\"\u003eSupport\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content--introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#-introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-intro\"\u003e\u003c/a\u003e Introduction\u003c/h3\u003e\n\u003cp\u003eDetails regarding usage and workflows coming soon.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egenDhollanderTractography\n\u003cul\u003e\n\u003cli\u003ePerforms preprocessing to geneate whole-brain tractography following the Dhollander response algorithm.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eMore information regarding algorithms used can be found from the \u003csup\u003e1\u003c/sup\u003eMRTrix3 website.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content--disclaimer\" class=\"anchor\" aria-hidden=\"true\" href=\"#-disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-disclaimer\"\u003e\u003c/a\u003e Disclaimer\u003c/h3\u003e\n\u003cp\u003eThis branch of \u003ccode\u003emrtpipelines\u003c/code\u003e is still undergoing development. While the pipeline can be used in its current state, it is possible for the project to undergo major changes.\u003c/p\u003e\n\u003cp\u003eFor HCP datasets, please see the \u003ca href=\"https://github.com/kaitj/mrtpipelines/tree/HCP\"\u003eHCP branch\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content--installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-install\"\u003e\u003c/a\u003e Installation\u003c/h3\u003e\n\u003cp\u003eDevelopment of this project was written in Python3 and makes use of \u003ca href=\"https://github.com/nipy/nipype\"\u003eNipype\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo install the package on your system, the following commands should be run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/kaitj/mrtpipelines\npip install -r requirements.txt\npython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content--containerized-package\" class=\"anchor\" aria-hidden=\"true\" href=\"#-containerized-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-container\"\u003e\u003c/a\u003e Containerized package\u003c/h4\u003e\n\u003cp\u003eThis pipeline is also available within a container via both Docker and Singularity.\u003c/p\u003e\n\u003cp\u003eTo use the Docker container, run the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker pull kaitj/mrtpipelines\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo use the Singularity container, users will have to build the container from the recipe found within the container directory. To do so, run the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity build mrtpipelines_0.0.3.img Singularity.0.0.2\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: sudo may be required to pull or build container\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIt is highly advised to run this pipeline through the available container. Some functionality may be lost if run locally due to custom additions to dependencies, which may yet to be implemented in original software.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content--usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003e Usage\u003c/h3\u003e\n\u003cp\u003eShown here is an example of the command line interface to run the pipeline:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: genDhollanderTractography \u0026lt;bids dir\u0026gt; \u0026lt;template_fod\u0026gt; \u0026lt;subject list/subject id\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if running through singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: singularity exec \u0026lt;singularity_img\u0026gt; genDhollanderTractography \u0026lt;bids dir\u0026gt; \u0026lt;template_fod\u0026gt; /\n\u0026lt;subject list/subject id\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content--required-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#-required-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-reqargs\"\u003e\u003c/a\u003e Required arguments\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003ebids_dir Directory with input dataset, formatted according to BIDS\n\ntemplate_fod A path to the template FOD file for registration of subjects\n\nparticipant_label A file containing label(s) of participant(s) to perform pipeline execution on\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote there may be pipeline specific arguments if using a different tracking algorithm (eg. 5-tissue segmentation for ACT pipeline)\u003c/em\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content--optional-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#-optional-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-optargs\"\u003e\u003c/a\u003e Optional arguments\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e-s Number of streamlines to generate for each subject(s)\n\n-l Maxinum harmonic degree(s) for response function estimation (eg. -l 0 8 8)\n\n-w Work directory.\n Defaults to \u0026lt;bids_dir\u0026gt;/derivatives/MRTrix/work\n\n-o Output directory.\n Defaults to \u0026lt;bids_dir\u0026gt;/derivatives/MRTrix/out\n\n-n Number of threads to use for pipeline execution where applicable\n\n-h Display help documentation\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content--support-and-communication\" class=\"anchor\" aria-hidden=\"true\" href=\"#-support-and-communication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-support\"\u003e\u003c/a\u003e Support and communication\u003c/h3\u003e\n\u003cp\u003eAll bugs, concerns, and requests for features can be requested via the github repository found \u003ca href=\"https://github.com/kaitj/mrtpipelines/issues\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content--references\" class=\"anchor\" aria-hidden=\"true\" href=\"#-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003e References\u003c/h3\u003e\n\u003cp\u003e[1] J.-D. Tournier, F. Calamante, A. Connelly. MRtrix: Diffusion tractography in crossing fiber regions. Int J Imag Syst Tech 22(2012):53-66.\u003c/p\u003e\n", - "stargazers_count": 2, - "subscribers_count": 0, - "topics": [], - "updated_at": 1668386769.0 - }, - { - "data_format": 2, - "description": "Automatic Comparison of Metabolism", - "filenames": [ - "recipes/Singularity" + "Singularity.mistral", + "Singularity.smnd-run" ], - "full_name": "AuReMe/aucome", - "latest_release": "v0.5.1", + "full_name": "ARPA-SIMC/smnd", + "latest_release": "v3.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-smnd\" class=\"anchor\" aria-hidden=\"true\" href=\"#smnd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSMND\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-for-meteorology-normally-distributed\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-for-meteorology-normally-distributed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware for Meteorology, Normally Distributed\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003ethe software for sure, the meteorological data not really.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eSMND is a helper package for simplifying the build and the deployment\nof a collection of meteorological software packages, mainly developed\nby \u003ca href=\"http://www.arpa.emr.it/sim\" rel=\"nofollow\"\u003eArpae-SIMC\u003c/a\u003e. The current version is\nrelatively stable, including the universal binary package.\u003c/p\u003e\n\u003cp\u003eThe software packages involved, all open source and freely\nredistributable, are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://confluence.ecmwf.int/display/ECC/ecCodes+Home\" rel=\"nofollow\"\u003eeccodes\u003c/a\u003e\nfrom ECMWF\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/wreport\"\u003ewreport\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/bufr2netcdf\"\u003ebufr2netcdf\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/dballe\"\u003eDB.All-e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/arkimet\"\u003earkimet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/libsim\"\u003elibsim\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-software-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-software-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the software from source\u003c/h3\u003e\n\u003cp\u003eFor building autonomously the software collection you can follow the\nguidelines in the \u003ca href=\"doc/buildfromsource.md\"\u003ecorresponding page\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-deploying-the-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploying-the-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploying the software\u003c/h3\u003e\n\u003cp\u003eIf you do not want to build the packages on your own, different\napproaches are possible for quickly deploying precompiled binaries:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe quick and universal way, \u003ca href=\"doc/unibin.md\"\u003ethe universal binary\npackage\u003c/a\u003e (no need to be the system administrator).\u003c/li\u003e\n\u003cli\u003eRunning from a \u003ca href=\"doc/singularity.md\"\u003esingularity container\u003c/a\u003e\n(requires agreement with the system administrator).\u003c/li\u003e\n\u003cli\u003eInstalling in a supported distribution (CentOS/Fedora) from \u003ca href=\"doc/copr.md\"\u003ecopr\nrepository\u003c/a\u003e (requires to BE the system administrator).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-notes-for-cosmo-model-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes-for-cosmo-model-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes for COSMO model users\u003c/h3\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1638801030.0 + "updated_at": 1646914781.0 }, { "data_format": 2, @@ -30622,113 +30750,122 @@ var data = }, { "data_format": 2, - "description": "Software for Meteorology, Normally Distributed", + "description": "Automatic Comparison of Metabolism", "filenames": [ - "Singularity.mistral", - "Singularity.smnd-run" + "recipes/Singularity" ], - "full_name": "ARPA-SIMC/smnd", - "latest_release": "v3.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-smnd\" class=\"anchor\" aria-hidden=\"true\" href=\"#smnd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSMND\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-for-meteorology-normally-distributed\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-for-meteorology-normally-distributed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware for Meteorology, Normally Distributed\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003ethe software for sure, the meteorological data not really.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eSMND is a helper package for simplifying the build and the deployment\nof a collection of meteorological software packages, mainly developed\nby \u003ca href=\"http://www.arpa.emr.it/sim\" rel=\"nofollow\"\u003eArpae-SIMC\u003c/a\u003e. The current version is\nrelatively stable, including the universal binary package.\u003c/p\u003e\n\u003cp\u003eThe software packages involved, all open source and freely\nredistributable, are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://confluence.ecmwf.int/display/ECC/ecCodes+Home\" rel=\"nofollow\"\u003eeccodes\u003c/a\u003e\nfrom ECMWF\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/wreport\"\u003ewreport\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/bufr2netcdf\"\u003ebufr2netcdf\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/dballe\"\u003eDB.All-e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/arkimet\"\u003earkimet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ARPA-SIMC/libsim\"\u003elibsim\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-the-software-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-software-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the software from source\u003c/h3\u003e\n\u003cp\u003eFor building autonomously the software collection you can follow the\nguidelines in the \u003ca href=\"doc/buildfromsource.md\"\u003ecorresponding page\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-deploying-the-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploying-the-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploying the software\u003c/h3\u003e\n\u003cp\u003eIf you do not want to build the packages on your own, different\napproaches are possible for quickly deploying precompiled binaries:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe quick and universal way, \u003ca href=\"doc/unibin.md\"\u003ethe universal binary\npackage\u003c/a\u003e (no need to be the system administrator).\u003c/li\u003e\n\u003cli\u003eRunning from a \u003ca href=\"doc/singularity.md\"\u003esingularity container\u003c/a\u003e\n(requires agreement with the system administrator).\u003c/li\u003e\n\u003cli\u003eInstalling in a supported distribution (CentOS/Fedora) from \u003ca href=\"doc/copr.md\"\u003ecopr\nrepository\u003c/a\u003e (requires to BE the system administrator).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-notes-for-cosmo-model-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes-for-cosmo-model-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes for COSMO model users\u003c/h3\u003e\n", + "full_name": "AuReMe/aucome", + "latest_release": "v0.5.1", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1646914781.0 + "updated_at": 1638801030.0 }, { "data_format": 2, - "description": "Open source simulation engine for coarse-grained Brownian dynamics", + "description": "Repository to store MRTrix3 tractography pipelines created with Nipype", "filenames": [ - "Singularity" + "container/singularity_recipe/Singularity.0.0.4", + "container/singularity_recipe/Singularity.0.0.6", + "container/singularity_recipe/Singularity.0.0.5", + "container/singularity_recipe/Singularity.0.0.8", + "container/singularity_recipe/Singularity.0.0.9", + "container/singularity_recipe/Singularity.0.0.2", + "container/singularity_recipe/Singularity.0.0.7", + "container/singularity_recipe/Singularity.0.0.1", + "container/singularity_recipe/Singularity.0.1.0" ], - "full_name": "Betterton-Lab/C-GLASS", - "latest_release": "v0.2.3", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-c-glass\" class=\"anchor\" aria-hidden=\"true\" href=\"#c-glass\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eC-GLASS\u003c/h1\u003e\n\u003cp\u003eA \u003cstrong\u003eC\u003c/strong\u003eoarse-\u003cstrong\u003eG\u003c/strong\u003erained \u003cstrong\u003eL\u003c/strong\u003eiving \u003cstrong\u003eA\u003c/strong\u003ective \u003cstrong\u003eS\u003c/strong\u003eystem \u003cstrong\u003eS\u003c/strong\u003eimulator\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/Betterton-Lab/C-GLASS\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/061b295758d2c64d80b7d3e97ceebc53e21cc632602b7b32c77f046c105d84ac/68747470733a2f2f7472617669732d63692e636f6d2f426574746572746f6e2d4c61622f432d474c4153532e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/Betterton-Lab/C-GLASS.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.3841613\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96212f14675a05209e745328444de906fc58f72d1794af88b9db02b4fe6f24e5/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333834313631332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3841613.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figs/cglass_snapshot.png\"\u003e\u003cimg src=\"figs/cglass_snapshot.png\" alt=\"A simulation using C-GLASS\" title=\"A simulation using C-GLASS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eFirst clone the repo, including submodule dependencies.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/Betterton-Lab/C-GLASS\ncd C-GLASS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eC-GLASS can either be run in a container using Docker or Singularity, or be built from source using CMake.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning with Docker\u003c/h3\u003e\n\u003cp\u003eA pre-built image of C-GLASS is available as a \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image. To download the image, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull jeffmm/cglass\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo use the image, run the provided script to launch a Docker container named \u003ccode\u003ecglass_latest\u003c/code\u003e in the background\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./launch_docker.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou may also build the Docker image yourself by providing the launch script with the \u003ccode\u003e-b\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eTo launch C-GLASS, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e cglass_latest cglass.exe [optional-flags] [parameter-file]\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning with Singularity\u003c/h3\u003e\n\u003cp\u003eIf you are using Singularity, C-GLASS is also available as a Singularity image. The command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull docker://jeffmm/cglass\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewill create a local file named \u003ccode\u003ecglass_latest.sif\u003c/code\u003e. You may then run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e cglass_latest.sif cglass.exe [optional-flags] [parameter-file]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity image may also be built locally using the provided recipe in the file \u003ccode\u003eSingularity\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding from source\u003c/h3\u003e\n\u003cp\u003eC-GLASS is ready to be built from source using CMake, provided several dependencies are installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCMake (version 3.13+)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jbeder/yaml-cpp\"\u003elibyaml-cpp\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003elibgsl-dev\u003c/li\u003e\n\u003cli\u003elibopenmpi-dev\u003c/li\u003e\n\u003cli\u003elibfftw3-dev\u003c/li\u003e\n\u003cli\u003elibboost-math1.67-dev\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIncluded is a script for building C-GLASS with CMake. To build C-GLASS (without graphics or parallelization) run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./install.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere are additional flags for building with OpenMP, building with graphics, installing C-GLASS in \u003ccode\u003e/usr/local\u003c/code\u003e, etc. To see a menu of options, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./install.sh -h\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-building-with-graphics\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-with-graphics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding with graphics\u003c/h3\u003e\n\u003cp\u003eC-GLASS is available with graphics for Mac OSX. To install on Mac OSX, you will need the glew and glfw3 libraries, both of which can be installed using \u003ca href=\"https://brew.sh/\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew install glew\nbrew install glfw\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou may also need to help CMake find your OpenGL Framework libraries.\u003c/p\u003e\n\u003cp\u003eSeveral other libraries are required for running C-GLASS with graphics on Linux or in WSL. See the \u003ccode\u003esrc/CMakeLists.txt\u003c/code\u003e file for a comprehensive list of libraries passed to the compiler when building C-GLASS with graphics on WSL.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-c-glass\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-c-glass\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning C-GLASS\u003c/h2\u003e\n\u003cp\u003eThe C-GLASS executable is run as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecglass.exe [optional-flags] [parameter-file] \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following flags are available:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--help, -h\n Show the help menu which gives short descriptions about each of the flags\n as well as binary usage\n \n --run-name rname, -r rname \n Overwrites the parameter \"run_name\" with rname which serves as a prefix for\n all outputs \n\n--n-runs num, -n num\n Overwrites the parameter \"n_runs\" with num, which tells the simulation how\n many times to run the given parameter set with different random number\n generator seeds.\n\n--movie, -m\n Uses the parameters file params_file to load any output files that were\n generated from previous runs of the simulation to replay the graphics and\n record the frames as bitmaps into the directory specified with the\n \"movie_directory\" parameter\n\n--analysis, -a\n Loads posit/spec files into the simulation for analysis in the same manner\n as the movie flag\n\n-reduce reduce_factor, -R reduce_factor\n Reads in output files and writes new output files that are smaller by a\n factor of reduce_factor, effectively reducing time resolution of output\n data.\n\n--load, -l\n Specifies to load any checkpoint files corresponding to the given parameter\n file, which can be used to continue a simulation that ended prematurely.\n New simulation will be given the name old_simulation_name_reload00n where n\n is the number of reloads performed on that simulation.\n\n--with-reloads, -w\n If running analyses or making movies, C-GLASS will look for parameter files\n that have the same run name but with the reload00n addendum and attempt to\n open the corresponding output files whenever it reached EOF while reading\n an output file.\n\n--blank, -b\n Generates all relevant parameter files using the SimulationManager without\n running the simulations. Useful for generating many parameter files from\n parameter sets (discussed below) and deploying simulations on different\n processors and/or machines.\n\n--auto-graph, -G\n By default, C-GLASS will wait for the user to press the ESC key in the\n OpenGL graphics window before starting to run the simulation. Providing\n this flag will cause the simulation to begin immediately without user\n input. Goes great with the -m flag for creating multiple movies without\n input from the user.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameter-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameter-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameter files\u003c/h2\u003e\n\u003cp\u003eAll parameters used in the simulation, along with their default values and data types, are specified in the \u003ccode\u003edefault_config.yaml\u003c/code\u003e file in the \u003ccode\u003econfig\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eThe parameter file is a YAML file and looks like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eglobal_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egp1_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eglobal_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egp2_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003especies\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eglobal_species_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egsp1_value\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eglobal_species_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egsp2_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003especific_species_name\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003especies_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003esp1_value\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003especies_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003esp2_value\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ccode\u003eexamples\u003c/code\u003e folder for examples of parameter files.\u003c/p\u003e\n\u003cp\u003eNotice that there are three parameter types: global parameters, global species parameters, and species parameters. Global parameters are parameters that are common to the entire system, such system size, integration time step, etc. Species parameters are unique to the specified species, such as \u003ccode\u003efilament\u003c/code\u003e. There is also an optional global species parameter type that affects every species, such as the frequency to write to output files.\u003c/p\u003e\n\u003cp\u003eWhat do I mean by species? C-GLASS assumes that any given simulation will likely have many copies of one kind of thing, which I call a species, perhaps interacting with other species of other kinds. In a system of interacting spheres, the species is \u0027sphere.\u0027 In a system of interacting semiflexible filaments, the species is \u0027filament.\u0027 Simulations can have many types of species all interacting with each other with different species-species interaction potentials.\u003c/p\u003e\n\u003cp\u003eIf any parameter is not specified in the parameter file, any instance of that parameter in the simulation will assume its default value specified in the \u003ccode\u003econfig/default_config.yaml\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eSome important global parameters are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed\n simulation seed to use with random number generator \nrun_name\n prefix for all output files\nn_runs\n number of individual runs of each parameter type\nn_random\n number of samples from a random parameter space (see more below)\nn_dim\n number of dimensions of simulation\nn_periodic\n number of periodic dimensions of simulation\ndelta \n simulation time step\nn_steps\n total number of steps in each simulation\nsystem_radius\n \"box radius\" of system\ngraph_flag\n run with graphics enabled\nn_graph\n how many simulation steps to take between updating graphics\nmovie_flag\n whether to record the graphics frames into bitmaps\nmovie_directory\n local directory used to save the recorded bitmaps\nthermo_flag\n whether to output thermodynamics outputs (stress tensors, etc)\nn_thermo\n how often to output the thermodynamics outputs\npotential_type\n can be \u0027wca\u0027 or \u0027soft\u0027 for now\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSome important global species parameters are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enum\n how many to insert into system\ninsertion_type\n how to insert object into system (e.g. random)\noverlap\n whether species can overlap at initiation\ndraw_type\n (orientation, fixed, or bw) how to color the object\ncolor\n a double that specifies the RGB value of the object\nposit_flag\n whether to output position files\nn_posit\n how often to output position files\nspec_flag\n whether to output species files\nn_spec\n how often to output species files\ncheckpoint_flag\n whether to output checkpoint files\nn_checkpoint\n how often to output checkpoint files\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-anchor-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#anchor-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnchor parameters\u003c/h3\u003e\n\u003cp\u003eC-GLASS has the capability to independently control crosslinker and motor protein anchor parameters. Anchor parameters are controlled within the Crosslink map in the input Yaml file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eCrosslink\u003c/span\u003e:\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e other crosslink params here\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eAnchors\u003c/span\u003e:\n - \u003cspan class=\"pl-ent\"\u003evelocity_s\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ecolor\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e3.5\u003c/span\u003e\n - \u003cspan class=\"pl-ent\"\u003ecolor\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4.5\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnly two anchors are permitted per crosslinker or motor protein. The anchor parameters obey the following rules when parameters are left blank:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf no anchors are listed, the anchor parameters will both be set to default.\u003c/li\u003e\n\u003cli\u003eIf one anchor is listed, the other anchor will copy its parameters. Any unlisted parameters will be set to default.\u003c/li\u003e\n\u003cli\u003eIf two anchors are listed, and one anchor has a parameter that the other doesn\u0027t, the one that doesn\u0027t have the parameter will copy the parameter from the other.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn the above example, Anchor 1 will have velocity_s=50, velocity_d=0 (default), color=3.5, and Anchor 2 will have velocity_s=50 (copied), velocity_d=0 (default), color=4.5.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdvanced usage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-unit-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning unit tests\u003c/h3\u003e\n\u003cp\u003eOne may run C-GLASS\u0027s unit tests by passing \u003ccode\u003e-DTESTS=TRUE\u003c/code\u003e to CMake\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir build\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\ncmake -DTESTS=TRUE ..\nmake\nmake \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-adding-new-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#adding-new-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding new parameters\u003c/h3\u003e\n\u003cp\u003eC-GLASS comes with it\u0027s own parameter initialization tool, \u003ccode\u003econfigure_C-GLASS.exe\u003c/code\u003e, which is installed automatically along with the C-GLASS binary using CMake. The configurator makes it easy to add new parameters to the simulation without mucking around in the source code. Just add your new parameter to \u003ccode\u003econfig/default_config.yaml\u003c/code\u003e file using the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enew_parameter_name: [default_parameter_value, parameter_type] \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the configurator using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./configure_cglass.exe config/default_config.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning configure_cglass.exe will look at all the parameters in the default config file and add them seamlessly to the proper C-GLASS headers, and you can begin using them after recompiling C-GLASS using CMake.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-parameter-sets\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameter sets\u003c/h3\u003e\n\u003cp\u003eUsing parameter sets, it becomes easier to run many simulations over a given parameter space. There are two types of parameter sets possible with C-GLASS: defined and random. Each parameter set type works the same with both global parameters and species parameters.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-defined-parameter-sets\" class=\"anchor\" aria-hidden=\"true\" href=\"#defined-parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDefined parameter sets\u003c/h4\u003e\n\u003cp\u003eDefined parameter sets are specified by the \u003ccode\u003eV\u003c/code\u003e prefix in the parameter file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: 4916819461895\nrun_name: defined_set\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: [V, param_value2, param_value3]\nparameter_name3: [V, param_value4, param_value5]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eParameters specified in this way (as lists of parameters) will be iterated over until every possible combination of parameters has been run. In this example, C-GLASS will run N simulations each of the following 4 parameter sets:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: random_seed_1\nrun_name: defined_set_v000\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value2\nparameter_name3: param_value4\n\nseed: random_seed_2\nrun_name: defined_set_v001\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value2\nparameter_name3: param_value5\n\nseed: random_seed_3\nrun_name: defined_set_v002\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value3\nparameter_name3: param_value4\n\nseed: random_seed_4\nrun_name: defined_set_v003\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value3\nparameter_name3: param_value5\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-random-parameter-sets\" class=\"anchor\" aria-hidden=\"true\" href=\"#random-parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRandom parameter sets\u003c/h4\u003e\n\u003cp\u003eRandom parameter sets are designed specifically to be used with polynomial-chaos theory for n-dimensional parameter spaces for large n. Random sets are used in the following way:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: 2546954828254\nn_runs: N\nn_random: M\nparameter_name1: param_value1\nparameter_name2: [R, A, B] # sets to random real in range (A,B)\nparameter_name3: [RINT, C, D] # sets to random int in range [C,D]\nparameter_name4: [RLOG, F, G] # sets to 10^K for rand real K in range (F,G)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGiven this parameter file, C-GLASS will run N simulations each of M random parameter sets. The random parameter sets are generated in ranges specified in the lists that are prefixed by the R, RINT, RLOG options.\u003c/p\u003e\n\u003cp\u003eIn this example, the sampled parameter space has dimensionality of n=3, since there are only three parameters we are sampling over. Each parameter set will have a random real number for parameter_name2 in the the range (A,B), a random integer in the range [C,D] for parameter_name3, and will set parameter_name4 to 10^K for random real number K in the range (F,G). C-GLASS will then run each parameter set N times each with a unique seed, and repeat this random process M times. It will therefore take N samples of M random points in the n-dimensional parameter space.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-interactions\" class=\"anchor\" aria-hidden=\"true\" href=\"#interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractions\u003c/h3\u003e\n\u003cp\u003eThe Interaction Manager in C-GLASS was written with short-range interactions in mind. For this reason, interactions are treated by considering pair-wise interactions between neighboring interactor-elements that make up a composite object (e.g. small, rigid segments that compose a flexible filament). For this reason, interactions use cell lists to improve performance. Furthermore, simulating large objects in C-GLASS requires representing the object as a composite of smaller, simple objects. An example of how a large object should be decomposed into simple objects is done in the Filament class.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-potentials\" class=\"anchor\" aria-hidden=\"true\" href=\"#potentials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePotentials\u003c/h3\u003e\n\u003cp\u003eC-GLASS is designed to be able to use interchangable potentials for various objects. However, potentials need to be added manually as a subclass of PotentialBase, included in PotentialManager, and a corresponding potential_type added to definitions.h for lookup purposes (see the InitPotentials method in PotentialManager.h for examples).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h3\u003e\n\u003cp\u003eC-GLASS has four output types. Three are species specific (posit, spec, checkpoint), and the fourth is the statistical information file (thermo). All files are written in binary.\u003c/p\u003e\n\u003cp\u003eThe posit file has the following header format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eint n_steps, int n_posit, double delta \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollowed by n_steps/n_posit lines of data with the format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edouble position[3]\ndouble scaled_position[3]\ndouble orientation[3]\ndouble diameter\ndouble length\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the scaled position is position mapped into the periodic coordinate space. The position itself gives the particle trajectory over time independent of periodicity.\u003c/p\u003e\n\u003cp\u003eThe spec file is a custom output file for each species, and can have the same information as the posit file or additional information if needed.\u003c/p\u003e\n\u003cp\u003eThe checkpoint file is almost a copy of the spec file, except it also contains the random number generator information and is overwritten every n_checkpoint steps in the simulation. It can therefore be used to resume a simulation that ended prematurely.\u003c/p\u003e\n\u003cp\u003eThe thermo file contains the following header information:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eint n_steps, int n_thermo, double delta, int n_dim\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efollowed by n_steps/n_thermo lines of data in the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edouble unit_cell[9]\ndouble pressure_tensor[9]\ndouble pressure\ndouble volume\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the pressure is the isometric pressure, and the pressure tensor is calculated from the time-averaged stress tensor.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-data-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData analysis\u003c/h3\u003e\n\u003cp\u003eIf analysis operations of output files are already defined for your species, as is the case for the Filament species, analyzing outputs is a simple matter. First, make sure the desired analysis flag is set in the species parameters for that species.\u003c/p\u003e\n\u003cp\u003eFor example, in the Filament species there is a persistence length analysis that produces .mse2e files that tracks the mean-square end-to-end distance of semiflexible filaments. This is triggered by a parameter lp_analysis=1, which can be set in the parameter file.\u003c/p\u003e\n\u003cp\u003eAnaylses are run by running C-GLASS in the following way:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecglass.exe -a parameter_file.yaml.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: It is important to keep in mind that the parameter_file should be identical to the parameter file used to generate the outputs. There are a few exceptions that only affect post-processing, such as analysis flags, but this is true in general.\u003c/p\u003e\n\u003cp\u003eThe way inputs and outputs are meant to work in C-GLASS is such that during a simulation, output data are generated in the posit, spec, and checkpoint formats, and during analysis, the same output data are read back into the data structures in C-GLASS for processing. The .posit files just contain bare-bones information that allow many types of simple analyses, but .spec files should in general contain all the necessary information to recreate the trajectory for a member of a species.\u003c/p\u003e\n\u003cp\u003eFor a new species analysis method, the analysis routines should be defined in the species container class, rather than the species member class, and called by the inherited RunAnalysis method of the SpeciesBase class (and likewise for analysis initialization and finalization, see examples for details).\u003c/p\u003e\n\u003cp\u003eFor example, the RunSpiralAnalysis routine is called by the RunAnalysis method in FilamentSpecies, which uses the Filament .spec file as an input to do the necessary analysis, whose results are placed into a new file ending in filament.spiral. See Filament and FilamentSpecies for examples of how analyses can be initialized, processed, etc.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory structure\u003c/h2\u003e\n\u003cp\u003eThe directory structure is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eC-GLASS\n\u251c\u2500\u2500 include\n\u2502 \u2514\u2500\u2500 cglass\n\u2502 \u2514\u2500\u2500 (header files)\n\u251c\u2500\u2500 src\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u251c\u2500\u2500 executable\n\u2502 \u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2502 \u2514\u2500\u2500 cglass_main.cpp\n\u2502 \u251c\u2500\u2500 configurator\n\u2502 \u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2502 \u2514\u2500\u2500 configurator.cpp\n\u2502 \u2514\u2500\u2500 (source files)\n\u251c\u2500\u2500 config\n\u2502 \u2514\u2500\u2500 default_config.yaml\n\u251c\u2500\u2500 analysis\n\u2502 \u2514\u2500\u2500 (Python analysis files)\n\u251c\u2500\u2500 scripts\n\u2502 \u2514\u2500\u2500 (utility files)\n\u251c\u2500\u2500 examples\n\u2502 \u2514\u2500\u2500 (parameter file examples)\n\u251c\u2500\u2500 docker\n\u2502 \u2514\u2500\u2500 Dockerfile\n\u251c\u2500\u2500 extern\n\u2502 \u2514\u2500\u2500 KMC\n\u251c\u2500\u2500 tests\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u251c\u2500\u2500 catch2\n\u2502 \u2502 \u2514\u2500\u2500 catch.hpp\n\u2502 \u2514\u2500\u2500 (C-GLASS unit tests)\n\u251c\u2500\u2500 docs\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2514\u2500\u2500 main.md\n\u251c\u2500\u2500 figs\n\u2502 \u2514\u2500\u2500 (example simulation figures)\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 CMakeLists.txt\n\u251c\u2500\u2500 install.sh\n\u251c\u2500\u2500 launch_docker.sh\n\u251c\u2500\u2500 .travis.yml\n\u2514\u2500\u2500 .gitignore\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-c-glass\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-c-glass\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout C-GLASS\u003c/h2\u003e\n\u003cp\u003eC-GLASS is written in C++ and designed for general coarse-grained physics simulations of active living matter, produced with modularity and scalability in mind. All objects in the simulation are representable as a composite of what I call \"simple\" objects (points, spheres, rigid cylinders, and 2d polygon surfaces would all qualify). For short-range interactions, C-GLASS uses cell and neighbor lists for improved performance and OpenMP for parallelization.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis software is licensed under the terms of the BSD-3 Clause license. See the \u003ccode\u003eLICENSE\u003c/code\u003e for more details.\u003c/p\u003e\n", + "full_name": "kaitj/mrtpipelines", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mrtpipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#mrtpipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emrtpipelines\u003c/h1\u003e\n\u003cp\u003eMRTrix3 processing diffusion and generating tractography of subject data from data collected.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#intro\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#disclaimer\"\u003eDisclaimer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#install\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#container\"\u003eContainerized package\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\n\u003ca href=\"#reqargs\"\u003eRequired arguments\u003c/a\u003e\n\u003ca href=\"#optargs\"\u003eOptional arguments\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#support\"\u003eSupport\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content--introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#-introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-intro\"\u003e\u003c/a\u003e Introduction\u003c/h3\u003e\n\u003cp\u003eDetails regarding usage and workflows coming soon.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egenDhollanderTractography\n\u003cul\u003e\n\u003cli\u003ePerforms preprocessing to geneate whole-brain tractography following the Dhollander response algorithm.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eMore information regarding algorithms used can be found from the \u003csup\u003e1\u003c/sup\u003eMRTrix3 website.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content--disclaimer\" class=\"anchor\" aria-hidden=\"true\" href=\"#-disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-disclaimer\"\u003e\u003c/a\u003e Disclaimer\u003c/h3\u003e\n\u003cp\u003eThis branch of \u003ccode\u003emrtpipelines\u003c/code\u003e is still undergoing development. While the pipeline can be used in its current state, it is possible for the project to undergo major changes.\u003c/p\u003e\n\u003cp\u003eFor HCP datasets, please see the \u003ca href=\"https://github.com/kaitj/mrtpipelines/tree/HCP\"\u003eHCP branch\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content--installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-install\"\u003e\u003c/a\u003e Installation\u003c/h3\u003e\n\u003cp\u003eDevelopment of this project was written in Python3 and makes use of \u003ca href=\"https://github.com/nipy/nipype\"\u003eNipype\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo install the package on your system, the following commands should be run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/kaitj/mrtpipelines\npip install -r requirements.txt\npython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content--containerized-package\" class=\"anchor\" aria-hidden=\"true\" href=\"#-containerized-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-container\"\u003e\u003c/a\u003e Containerized package\u003c/h4\u003e\n\u003cp\u003eThis pipeline is also available within a container via both Docker and Singularity.\u003c/p\u003e\n\u003cp\u003eTo use the Docker container, run the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker pull kaitj/mrtpipelines\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo use the Singularity container, users will have to build the container from the recipe found within the container directory. To do so, run the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity build mrtpipelines_0.0.3.img Singularity.0.0.2\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: sudo may be required to pull or build container\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIt is highly advised to run this pipeline through the available container. Some functionality may be lost if run locally due to custom additions to dependencies, which may yet to be implemented in original software.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content--usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003e Usage\u003c/h3\u003e\n\u003cp\u003eShown here is an example of the command line interface to run the pipeline:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: genDhollanderTractography \u0026lt;bids dir\u0026gt; \u0026lt;template_fod\u0026gt; \u0026lt;subject list/subject id\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr if running through singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: singularity exec \u0026lt;singularity_img\u0026gt; genDhollanderTractography \u0026lt;bids dir\u0026gt; \u0026lt;template_fod\u0026gt; /\n\u0026lt;subject list/subject id\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content--required-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#-required-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-reqargs\"\u003e\u003c/a\u003e Required arguments\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003ebids_dir Directory with input dataset, formatted according to BIDS\n\ntemplate_fod A path to the template FOD file for registration of subjects\n\nparticipant_label A file containing label(s) of participant(s) to perform pipeline execution on\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eNote there may be pipeline specific arguments if using a different tracking algorithm (eg. 5-tissue segmentation for ACT pipeline)\u003c/em\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content--optional-arguments\" class=\"anchor\" aria-hidden=\"true\" href=\"#-optional-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-optargs\"\u003e\u003c/a\u003e Optional arguments\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003e-s Number of streamlines to generate for each subject(s)\n\n-l Maxinum harmonic degree(s) for response function estimation (eg. -l 0 8 8)\n\n-w Work directory.\n Defaults to \u0026lt;bids_dir\u0026gt;/derivatives/MRTrix/work\n\n-o Output directory.\n Defaults to \u0026lt;bids_dir\u0026gt;/derivatives/MRTrix/out\n\n-n Number of threads to use for pipeline execution where applicable\n\n-h Display help documentation\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content--support-and-communication\" class=\"anchor\" aria-hidden=\"true\" href=\"#-support-and-communication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-support\"\u003e\u003c/a\u003e Support and communication\u003c/h3\u003e\n\u003cp\u003eAll bugs, concerns, and requests for features can be requested via the github repository found \u003ca href=\"https://github.com/kaitj/mrtpipelines/issues\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content--references\" class=\"anchor\" aria-hidden=\"true\" href=\"#-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003e References\u003c/h3\u003e\n\u003cp\u003e[1] J.-D. Tournier, F. Calamante, A. Connelly. MRtrix: Diffusion tractography in crossing fiber regions. Int J Imag Syst Tech 22(2012):53-66.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1642046811.0 + "updated_at": 1668386769.0 }, { "data_format": 2, - "description": "singularity-recipe-share", + "description": "Comparison of batch correction methods for scRNA-seq data - basically a clone of BatchBench", "filenames": [ - "Singularity.tensorflow-gpu-1.12.0" + "Singularity" ], - "full_name": "lxwgcool/singularity", + "full_name": "Sarah145/batch_correct", + "latest_release": null, + "readme": "\u003ch2\u003e\u003ca id=\"user-content-batch-correction-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#batch-correction-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBatch correction pipeline\u003c/h2\u003e\n\u003cp\u003eThis repository contains scripts to run a Nextflow pipeline to compare different batch correction methods for single-cell RNA-seq data. This is mostly just a clone of the \u003ca href=\"https://github.com/cellgeni/batchbench\"\u003eBatchBench\u003c/a\u003e pipeline from the CellGen IT team at Sanger but I couldn\u0027t get that to run so made some edits and added one or two extra things.\u003c/p\u003e\n\u003cp\u003eThe input files for this pipeline must be .Rds files of the uncorrected data as a SingleCellExperiment object (all batches in one object) with batch labels stored in the \u003ccode\u003ebatch_key\u003c/code\u003e (\u0027Batch\u0027 by default) column and cell type labels stored in the \u003ccode\u003ecelltype_key\u003c/code\u003e (\u0027cell_type1\u0027 by default) column.\u003c/p\u003e\n\u003cp\u003eThe pipeline will run 7 different batch correction methods on the data:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScanorama\u003c/li\u003e\n\u003cli\u003eBBKNN\u003c/li\u003e\n\u003cli\u003eSeurat 3\u003c/li\u003e\n\u003cli\u003eCombat\u003c/li\u003e\n\u003cli\u003eHarmony\u003c/li\u003e\n\u003cli\u003elimma\u003c/li\u003e\n\u003cli\u003eMNNCorrect\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each method, 5 different evaluation metrics are returned:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBatch entropy (from \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.05.22.111211v2\" rel=\"nofollow\"\u003eBatchBench\u003c/a\u003e) - measure of how well batches are aligned after correction - related to the probability that for each cell, its \u003cem\u003ek\u003c/em\u003e nearest neighbors come from a different batch - value reported is average entropy scaled between 0-1 - high batch entropy = well-mixed batches, low batch entropy = poorly-mixed batches.\u003c/li\u003e\n\u003cli\u003eCell type entropy (from \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.05.22.111211v2\" rel=\"nofollow\"\u003eBatchBench\u003c/a\u003e) - same as batch entropy but using cell type labels instead - high cell type entropy = mixing of cell types (not good), low cell type entropy = cell types are not mixing (good).\u003c/li\u003e\n\u003cli\u003eBatch ASW (from \u003ca href=\"https://github.com/theislab/scib\"\u003escIB\u003c/a\u003e) - average silhouette width of batches - scaled between -1-1 - high batch ASW = dense, well-separated batches (bad), low batch ASW = well mixed batches (good).\u003c/li\u003e\n\u003cli\u003eCell type ASW (from \u003ca href=\"https://github.com/theislab/scib\"\u003escIB\u003c/a\u003e) - same as batch ASW but for cell type labels - high cell type ASW = good, low cell type ASW = bad.\u003c/li\u003e\n\u003cli\u003eRecovery of marker genes - this idea was taken from the BatchBench paper but couldn\u0027t find code for it so wrote my own - not sure if it\u0027s right. For methods that correct the expression matrix (Scanorama, Seurat3, Combat, limma, MNNCorrect), found marker genes for each cell type (by batch and in the merged dataset), before and after batch correction, then compared the list of total marker genes identified before batch correction to the list of total marker genes identified after batch correction and calculated the Jaccard similarity index of the two lists. High Jaccard index = gene expression was not distorted too much by batch correction, most markers genes could still be identified (good), low Jaccard index = batch correction highly distorted the gene expression values so not as many marker genes could be recovered (bad). Jaccard index = 1 - all marker genes recovered, Jaccard index = 0 - no marker genes recovered.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo run pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNeed to have Nextflow and Singularity installed.\u003c/li\u003e\n\u003cli\u003eClone this repo and \u003ccode\u003ecd\u003c/code\u003e into it.\u003c/li\u003e\n\u003cli\u003ePull Singularity image - \u003ccode\u003esingularity pull shub://Sarah145/batch_correct\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eEdit the \u003ccode\u003enextflow.config\u003c/code\u003e script with location of data, batch key, cell type key, etc. \u003cem\u003eNote: profile section of the nextflow.config script in this repo is configured to run on cluster with slurm.\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit \u003ccode\u003edataset_list.txt\u003c/code\u003e file with name of files - one file on each line, no file extension.\u003c/li\u003e\n\u003cli\u003eRun pipeline with \u003ccode\u003enextflow run main.nf -profile singularity -with-trace trace.txt -with-dag flowchart.png\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eCompile html report of run by running \u003ccode\u003e./compile_report.R \u0026lt;sample_name\u0026gt;\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Sarah145/batch_correct/blob/master/imgs/flowchart.png?raw=true\"\u003e\u003cimg src=\"https://github.com/Sarah145/batch_correct/raw/master/imgs/flowchart.png?raw=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 2, + "subscribers_count": 1, + "topics": [ + "scrna-seq-analysis", + "batch-correction" + ], + "updated_at": 1653300274.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "singularity/Singularity" + ], + "full_name": "ashokdahal/FrameFieldLearning_Anaconda_Windows", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-polygonal-building-segmentation-by-frame-field-learning-modified-from-lydorn-to-work-on-anaconda-and-windows-device-without-docker-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#polygonal-building-segmentation-by-frame-field-learning-modified-from-lydorn-to-work-on-anaconda-and-windows-device-without-docker-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePolygonal Building Segmentation by Frame Field Learning Modified from LYDORN to work on anaconda and windows device without docker installation.\u003c/h1\u003e\n\u003cp\u003eWe add a frame field output to an image segmentation neural network to improve segmentation quality\nand provide structural information for the subsequent polygonization step.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/frame_field_sample.png\"\u003e\u003cimg src=\"images/frame_field_sample.png\" width=\"512\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 1: Close-up of our additional frame field output on a test image.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/model_training.png\"\u003e\u003cimg src=\"images/model_training.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 2: Given an overhead image, the model outputs an edge mask, an interior mask,\n and a frame field for buildings. The total loss includes terms that align the masks and\n frame field to ground truth data as well as regularizers to enforce smoothness of the\n frame field and consistency between the outputs.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/schematic_polygonization.png\"\u003e\u003cimg src=\"images/schematic_polygonization.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 3: Given classification maps and a frame field as input, we optimize skeleton polylines to\n align to the frame field using an Active Skeleton Model (ASM) and detect corners using\n the frame field, simplifying non-corner vertices.\n\u003c/p\u003e\n\u003cp\u003eThis repository contains the official code for the paper:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://www-sop.inria.fr/members/Nicolas.Girard/\" rel=\"nofollow\"\u003eNicolas Girard\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/smirnov/\" rel=\"nofollow\"\u003eDmitriy Smirnov\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/jsolomon/\" rel=\"nofollow\"\u003eJustin Solomon\u003c/a\u003e,\n\u003ca href=\"https://www-sop.inria.fr/members/Yuliya.Tarabalka/\" rel=\"nofollow\"\u003eYuliya Tarabalka\u003c/a\u003e\u003cbr\u003e\nPre-print\u003cbr\u003e\n\u003cstrong\u003e[\u003ca href=\"https://arxiv.org/pdf/2004.14875.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, \u003ca href=\"https://www.youtube.com/watch?v=XdQMD3HTYCU\u0026amp;t=5s\" rel=\"nofollow\"\u003evideo\u003c/a\u003e]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhose short version has been published as:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegularized Building Segmentation by Frame Field Learning\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://www-sop.inria.fr/members/Nicolas.Girard/\" rel=\"nofollow\"\u003eNicolas Girard\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/smirnov/\" rel=\"nofollow\"\u003eDmitriy Smirnov\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/jsolomon/\" rel=\"nofollow\"\u003eJustin Solomon\u003c/a\u003e,\n\u003ca href=\"https://www-sop.inria.fr/members/Yuliya.Tarabalka/\" rel=\"nofollow\"\u003eYuliya Tarabalka\u003c/a\u003e\u003cbr\u003e\nIGARSS 2020\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-git-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#git-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGit submodules\u003c/h2\u003e\n\u003cp\u003eThis project uses various git submodules that should be cloned too.\u003c/p\u003e\n\u003cp\u003eTo clone a repository including its submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive --jobs 8 \u0026lt;URL to Git repo\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you already have cloned the repository and now want to load it\u2019s submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --init --recursive --jobs 8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more about explanations about using submodules and git, see \u003ca href=\"SUBMODULES.md\"\u003eSUBMODULES.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eThe easiest way to setup environment is to use the Docker image provided in the \u003ca href=\"docker\"\u003edocker\u003c/a\u003e (see README inside the folder).\u003c/p\u003e\n\u003cp\u003eOnce the docker container is built and launched, execute the \u003ca href=\"setup.sh\"\u003esetup.sh\u003c/a\u003e script inside to install required packages.\u003c/p\u003e\n\u003cp\u003eThe environment in the container is now ready for use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda environment\u003c/h2\u003e\n\u003cp\u003eAlternatively you can install all dependencies in a conda environment.\nI provide my environment specifications in the \u003ca href=\"environment.yml\"\u003eenvironment.yml\u003c/a\u003e which you can use to create your environment own with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h1\u003e\n\u003cp\u003eSeveral datasets are used in this work.\nWe typically put all datasets in a \"data\" folder which we link to the \"/data\" folder in the container (with the \u003ccode\u003e-v\u003c/code\u003e argument when running the container).\nEach dataset has it\u0027s own sub-folder, usually named with a short version of that dataset\u0027s name.\nEach dataset sub-folder should have a \"raw\" folder inside containing all the original folders and files fo the datset.\nWhen pre-processing data, \"processed\" folders will be created alongside the \"raw\" folder.\u003c/p\u003e\n\u003cp\u003eFor example, here is an example working file structure inside the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/data \n|-- AerialImageDataset\n |-- raw\n |-- train\n | |-- aligned_gt_polygons_2\n | |-- gt\n | |-- gt_polygonized\n | |-- images\n `-- test\n |-- aligned_gt_polygons_2\n |-- images\n`-- mapping_challenge_dataset\n |-- raw\n |-- train\n | |-- images\n | |-- annotation.json\n | `-- annotation-small.json\n `-- val\n `-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf however you would like to use a different folder for the datasets (for example while not using Docker),\nyou can change the path to datasets in config files.\nYou can modify the \"data_dir_candidates\" list in the config to only include your path.\nThe training script checks this list of paths one at a time and picks the first one that exists.\nIt then appends the \"data_root_partial_dirpath\" directory to get to the dataset.\u003c/p\u003e\n\u003cp\u003eYou can find some of the data we used in this shared \"data\" folder: \u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inria-aerial-image-labeling-dataset\" class=\"anchor\" aria-hidden=\"true\" href=\"#inria-aerial-image-labeling-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInria Aerial Image Labeling Dataset\u003c/h2\u003e\n\u003cp\u003eLink to the dataset: \u003ca href=\"https://project.inria.fr/aerialimagelabeling/\" rel=\"nofollow\"\u003ehttps://project.inria.fr/aerialimagelabeling/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor the Inria dataset, the original ground truth is just a collection of raster masks.\nAs our method requires annotations to be polygons in order to compute the ground truth angle for the frame field, we made 2 versions of the dataset:\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria OSM dataset\u003c/em\u003e has aligned annotations pulled from OpenStreetMap.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria Polygonized dataset\u003c/em\u003e has polygon annotations obtained from using our frame field polygonization algorithm on the original raster masks.\nThis was done by running the \u003ccode\u003epolygonize_mask.py\u003c/code\u003e script like so:\n\u003ccode\u003epython polygonize_mask.py --run_name inria_dataset_osm_mask_only.unet16 --filepath ~/data/AerialImageDataset/raw/train/gt/*.tif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can find this new ground truth for both cases in the shared \"data\" folder (\u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-the-mainpy-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-mainpy-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the main.py script\u003c/h1\u003e\n\u003cp\u003eExecute \u003ca href=\"main.py\"\u003emain.py\u003c/a\u003e script to train a model, test a model or use a model on your own image.\nSee the help of the main script with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython main.py --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe script can be launched on multiple GPUs for multi-GPU training and evaluation.\nSimply set the \u003ccode\u003e--gpus\u003c/code\u003e argument to the number of gpus you want to use.\nHowever, for the first launch of the script on a particular dataset (when it will pre-process the data),\nit is best to leave it at 1 as I did not implement multi-GPU synchronization when pre-processing datasets.\u003c/p\u003e\n\u003cp\u003eAn example use is for training a model with a certain config file, like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained\u003c/code\u003e\nwhich will train the Unet-Resnet101 on the CrowdAI Mapping Challenge dataset.\nThe batch size can be adjusted like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained -b \u0026lt;new batch size\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen training is done, the script can be launched in eval mode, to evaluate the trained model:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval\u003c/code\u003e.\nDepending on the eval parameters of the config file, running this will output results on the test dataset.\u003c/p\u003e\n\u003cp\u003eFinally, if you wish to compute AP and AR metrics with the COCO API, you can run:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval_coco\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-launch-inference-on-one-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#launch-inference-on-one-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch inference on one image\u003c/h2\u003e\n\u003cp\u003eMake sure the run folder has the correct structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- \u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the [main.py] script like so (filling values for arguments run_name and in_filepath):\n\u003ccode\u003epython main.py --run_name \u0026lt;run_name\u0026gt; --in_filepath \u0026lt;your_image_filepath\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe outputs will be saved next to the input image\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-trained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload trained models\u003c/h2\u003e\n\u003cp\u003eWe provide already-trained models so you can run inference right away.\nDownload here: \u003ca href=\"https://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\u003c/a\u003e.\nEach model was trained in a \"run\", whose folder (named with the format \u003ccode\u003e\u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\u003c/code\u003e) you can download at the provided link.\nYou should then place those runs in a folder named \"runs\" inside the \"frame_field_learning\" folder like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard | 2020-06-02 07:57:31\n| | |-- mapping_dataset.unet_resnet101_pretrained.field_off.train_val | 2020-09-07 11:54:48\n| | |-- mapping_dataset.unet_resnet101_pretrained.train_val | 2020-09-07 11:28:51\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBecause Google Drive reformats folder names, you have to rename the run folders as above.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-hidden=\"true\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCite:\u003c/h1\u003e\n\u003cp\u003eIf you use this code for your own research, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{Girard_2020_IGARSS,\n title = {{Regularized Building Segmentation by Frame Field Learning}},\n author = {Girard, Nicolas and Smirnov, Dmitriy and Solomon, Justin and Tarabalka, Yuliya},\n booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},\n ADDRESS = {Waikoloa, Hawaii},\n year = {2020},\n month = Jul,\n}\n\n@misc{girard2020polygonal,\n title={Polygonal Building Segmentation by Frame Field Learning},\n author={Nicolas Girard and Dmitriy Smirnov and Justin Solomon and Yuliya Tarabalka},\n year={2020},\n eprint={2004.14875},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, "subscribers_count": 1, "topics": [], - "updated_at": 1574278904.0 + "updated_at": 1651735479.0 }, { "data_format": 2, - "description": "Pipeline for analysing M. tuberculosis nanopore reads and getting drug susceptibility information.", + "description": "A django application to demo a bone age prediction model", "filenames": [ - "containers/recipes/Singularity.mykrobe", - "containers/recipes/Singularity.nanoporeqc" + "Singularity" ], - "full_name": "mbhall88/Longitude_pipeline", + "full_name": "vsoch/boneage", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-bone-age-model\" class=\"anchor\" aria-hidden=\"true\" href=\"#bone-age-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBone-Age Model\u003c/h1\u003e\n\u003cp\u003eThis repository builds a Docker image and a \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image, each that will run the bone age demo to predict bone age from a radiograph. The demo runs the prediction on the command line, either with a local image input, or using a demo image.\u003c/p\u003e\n\u003cp\u003eIf you are working on your local machine, you can use either Docker or Singularity. If you are running in a shared cluster (HPC) environment where you do not have root permissions, Singularity is your best option. Instructions are included for both.\u003c/p\u003e\n\u003cp\u003ePackages that need to be installed are included in \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e and installed into the container via the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e or \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e. The container is provided on \u003ca href=\"https://hub.docker.com/r/vanessa/boneage/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded from there when you run it, and this is recommended because building it takes a while to compile OpenCV.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-i-want-to-build-it\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-want-to-build-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eI want to build it!\u003c/h3\u003e\n\u003cp\u003eIf you want to look at or make changes to the code, it\u0027s recommended to clone the repo and build the container locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone http://www.github.com/vsoch/boneage\ncd boneage\ndocker build -t vanessa/boneage .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe docker daemon will first look for an image called \u003ccode\u003evanessa/boneage\u003c/code\u003e locally, and if not found, will then try Dockerhub, and download it from there. If for any reason you want to remove your image, just do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker rmi vanessa/boneage\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThe entry to the container is done simply by using it as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/boneage --help\nusage: cli.py [-h] [--image IMAGE] [--output OUTPUT] [--gender {M,F}]\n\t [--width WIDTH] [--height HEIGHT] [--debug]\n\nPredict bone age of an image.\n\noptional arguments:\n -h, --help show this help message and exit\n --image IMAGE Path to single bone image.\n --output OUTPUT Path to output file to write results.\n --gender {M,F} the gender of the individual (M or F), default is M (male)\n --width WIDTH warped width to resize the image in pixels (default 256)\n --height HEIGHT warped height to resize the image in pixels (default 256)\n --debug use verbose logging to debug.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-prediction-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-prediction-demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Prediction Demo\u003c/h3\u003e\n\u003cp\u003eTo run the bone-age demo non interactively to get a prediction, you can run it without any arguments. Note that since this application is optimized to return a web response (json) you will only see a json object returned without the \u003ccode\u003e--debug\u003c/code\u003e argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run vanessa/boneage\n\n{\u0027gender\u0027: \u0027M\u0027, \u0027image\u0027: \u0027/code/example_images/1.png\u0027, \u0027scores\u0027: [4.3481795e-32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.95402247, 4.6613271e-30, 0.0, 0.0, 0.0, 3.5964787e-28, 0.045977563, 0.0, 0.0, 7.72608e-32, 3.5294469e-28, 3.2218784e-31, 8.7065415e-31, 0.0, 0.0, 1.4140952e-27, 2.0360324e-31, 1.3973739e-18, 0.0, 0.0, 9.1047968e-32, 0.0, 0.0, 0.0, 0.0, 5.5391993e-31, 0.0, 0.0, 0.0, 1.3619909e-16, 0.0, 0.0, 3.7027614e-31, 1.6943371e-30, 8.6800407e-32, 0.0, 0.0, 1.6423222e-28, 0.0, 5.1337822e-30, 2.6105505e-31, 4.9806177e-30, 4.3782129e-15, 4.614967e-32, 3.4625493e-27, 3.3474241e-32, 3.2968069e-27, 1.2063197e-29, 3.3373545e-30, 1.4215187e-27, 3.7455669e-28, 3.4475776e-11, 3.9599255e-23, 7.9019825e-23, 9.7098277e-27, 7.4687077e-28, 3.3236082e-30, 2.9441527e-25, 1.0912699e-25, 1.0655707e-22, 8.3881067e-27, 9.9488148e-28, 7.2947065e-31, 1.0451508e-28, 3.4619964e-30, 2.3976481e-26, 1.8529252e-26, 4.1468809e-13, 1.124584e-31, 3.3920541e-32, 1.0070911e-30, 2.3539665e-19, 1.2927373e-28, 0.0, 0.0, 6.4560408e-24, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0023609e-22, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2730129e-32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.8752429e-23, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 6.6301819e-32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.1331077e-26, 0.0, 8.9587665e-29, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.98046e-27, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.7935414e-31, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.170995e-22, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.1674999e-31, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0261926e-24, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.2983278e-12, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0756849e-12, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], \u0027predicted_age\u0027: 8, \u0027predicted_weight\u0027: 8.2758656171904867}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also run with \u003ccode\u003e--debug\u003c/code\u003e to get full \"pretty print\" output.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run vanessa/boneage --debug\n\nEnvironment message level found to be DEBUG\n\nDEBUG:bone-age:\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:No image selected, will use provided example...\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /code/example_images/0.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nDEBUG:bone-age:Building model, please wait.\n\n ...\n\n\nDEBUG:bone-age:Predicted Age : 8 Months\nDEBUG:bone-age:Weighted Prediction : 8.164139 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-prediction-with-your-own-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-prediction-with-your-own-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Prediction With Your Own Image\u003c/h3\u003e\n\u003cp\u003eIf you want to provide your own image, you need to bind it to the /data directory in the folder, and map a path to it. Don\u0027t forget to specify the gender - the default is male, and you may want to change that:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png\n\n*** Starting Bone Age Prediction ****\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can of course add debug to verify that the default is male, and we are using our mapped image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png --debug\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can specify a different gender, and the prediction changes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png --gender F --debug\nEnvironment message level found to be DEBUG\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: False\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 16 Months\nWeighted Prediction : 16.000000 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-save-output-to-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#save-output-to-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSave output to file\u003c/h3\u003e\n\u003cp\u003eIf you specify the \u003ccode\u003e--output\u003c/code\u003e argument, you can save the result as a json to file. Again, we will need to specify a file in a folder mapped to our local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --output /data/demo.json --debug\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nNo image selected, will use provided example...\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /code/example_images/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\nDEBUG:bone-age:Result written to /data/demo.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we can look at the data - remember the folder that was mapped on our local machine is \u003ccode\u003e$PWD/example_images\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cat $PWD/example_images/demo.json\n {\n \"gender\": \"M\",\n \"image\": \"/code/example_images/4.png\",\n \"predicted_age\": 8,\n \"predicted_weight\": 8.64113067092668\n }\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-do-i-shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do I shell into the container?\u003c/h2\u003e\n\u003cp\u003eBy default, running the container uses the \u003ccode\u003eENTRYPOINT\u003c/code\u003e, meaning it is used as an executable and you do not enter the container. In the case that you want a container-based environment that is installed with the dependencies of boneage, or if you want to interactively work with the code, you may want to shell into the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint /bin/bash vanessa/boneage\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eKeep in mind that once you exit from this run, the container image is not saved, including your changes.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Install Singularity\u003c/h2\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Bootstrap the image\u003c/h2\u003e\n\u003cp\u003eBootstrapping means using something to build from, or not starting from nothing. In this case, we are going to use a build file that bootstraps a Docker image of boneage (yes, the same one discussed above). This build file is called \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e, and for more details about this you can \u003ca href=\"http://singularity.lbl.gov/docs-docker\" rel=\"nofollow\"\u003eread here\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 boneage.img\nsudo singularity bootstrap boneage.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Run commands\u003c/h2\u003e\n\u003cp\u003eThe commands are equivalent as above, except we can use the container as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./boneage.img --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand to make a drive, we use \u003ccode\u003e--bind\u003c/code\u003e instead\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run --bind $PWD/example_images:/data boneage.img --debug\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-do-i-shell-into-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do I shell into the container?\u003c/h2\u003e\n\u003cp\u003eSingularity has an easy, intuitive way to shell inside!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell boneage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-interactive-web-interface\" class=\"anchor\" aria-hidden=\"true\" href=\"#interactive-web-interface\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive Web Interface\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003etodo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUltimately, we will build this demo and serve on \u003ca href=\"http://www.singularity-hub.org\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e and then have an application that takes inputs / outputs for the container, and runs on demand.\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 3, - "topics": [ - "nanopore", - "tuberculosis", - "bioinformatics-pipeline" - ], - "updated_at": 1674937389.0 + "topics": [], + "updated_at": 1681304289.0 }, { "data_format": 2, - "description": "The Singularity Programming language IDE submodule for SNU Programming Tools (2D Mode)", + "description": "From taxonomic affiliations to annotated proteins using UniProt database.", "filenames": [ - "Singularity", - "Singularity.def", - "OldVersions/PROJECT_LANGUAGE/Singularity/Singularity" + "recipes/Singularity" ], - "full_name": "seanpm2001/SNU_2D_ProgrammingTools_IDE_Singularity", - "latest_release": null, - "readme": "\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-snu-2d-programmingtools-ide-\" class=\"anchor\" aria-hidden=\"true\" href=\"#snu-2d-programmingtools-ide-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSNU-2D-ProgrammingTools-IDE-\n\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/LanguageLogo.svg\"\u003e\u003cimg src=\"/LanguageLogo.svg\" alt=\"{Project icon} This image failed to load. It may be due to the file not being reached, or a general error. Reload the page to fix a possible general error.\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-by\" class=\"anchor\" aria-hidden=\"true\" href=\"#by\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBy:\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/SNU_blue_and_gold_legacy_icon.png\"\u003e\u003cimg src=\"/SNU_blue_and_gold_legacy_icon.png\" alt=\"SNU Logo: This image failed to load. It may be due to the file not being reached, or a general error. Reload the page to fix a possible general error.\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-seanpm2001--snu-programming-tools-et-al\" class=\"anchor\" aria-hidden=\"true\" href=\"#seanpm2001--snu-programming-tools-et-al\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/seanpm2001/\"\u003eSeanpm2001\u003c/a\u003e / \u003ca href=\"https://github.com/SNU-Programming-Tools/\"\u003eSNU Programming Tools\u003c/a\u003e, Et; Al.\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-top\" class=\"anchor\" aria-hidden=\"true\" href=\"#top\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop\u003c/h3\u003e\n\u003ch1\u003e\u003ca id=\"user-content-readmemd\" class=\"anchor\" aria-hidden=\"true\" href=\"#readmemd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eREADME.md\u003c/code\u003e\u003c/h1\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-read-this-article-in-a-different-language\" class=\"anchor\" aria-hidden=\"true\" href=\"#read-this-article-in-a-different-language\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRead this article in a different language\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eRead this description in a different language:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent language is:\u003c/strong\u003e \u003ccode\u003eEnglish (US)\u003c/code\u003e \u003cem\u003e(translations may need to be corrected to fix English replacing the correct language)\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdetails open=\"\"\u003e\n\u003csummary\u003e\u003ch3\u003e\u003ca id=\"user-content-clicktap-here-to-expandcollapse-the-language-switcher-list\" class=\"anchor\" aria-hidden=\"true\" href=\"#clicktap-here-to-expandcollapse-the-language-switcher-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e[Click/tap here to expand/collapse the language switcher list]\u003c/h3\u003e\u003c/summary\u003e\n\u003cp\u003e\u003cem\u003e\u003cg-emoji class=\"g-emoji\" alias=\"globe_with_meridians\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f310.png\"\u003e\ud83c\udf10\u003c/g-emoji\u003e List of languages\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e( \u003ca href=\"/.github/README_AF.md\"\u003eaf Afrikaans\u003c/a\u003e Afrikaans | \u003ca href=\"/.github/README_SQ.md\"\u003esq Shqiptare\u003c/a\u003e Albanian | \u003ca href=\"/.github/README_AM.md\"\u003eam \u12a0\u121b\u122d\u129b\u003c/a\u003e Amharic | \u003ca href=\"/.github/README_AR.md\"\u003ear \u0639\u0631\u0628\u0649\u003c/a\u003e Arabic | \u003ca href=\"/.github/README_HY.md\"\u003ehy \u0570\u0561\u0575\u0565\u0580\u0565\u0576\u003c/a\u003e Armenian | \u003ca href=\"/.github/README_AZ.md\"\u003eaz Az\u0259rbaycan dili\u003c/a\u003e Azerbaijani | \u003ca href=\"/.github/README_EU.md\"\u003eeu Euskara\u003c/a\u003e Basque | \u003ca href=\"/.github/README_BE.md\"\u003ebe \u0411\u0435\u043b\u0430\u0440\u0443\u0441\u043a\u0430\u044f\u003c/a\u003e Belarusian | \u003ca href=\"/.github/README_BN.md\"\u003ebn \u09ac\u09be\u0982\u09b2\u09be\u003c/a\u003e Bengali | \u003ca href=\"/.github/README_BS.md\"\u003ebs Bosanski\u003c/a\u003e Bosnian | \u003ca href=\"/.github/README_BG.md\"\u003ebg \u0431\u044a\u043b\u0433\u0430\u0440\u0441\u043a\u0438\u003c/a\u003e Bulgarian | \u003ca href=\"/.github/README_CA.md\"\u003eca Catal\u00e0\u003c/a\u003e Catalan | \u003ca href=\"/.github/README_CEB.md\"\u003eceb Sugbuanon\u003c/a\u003e Cebuano | \u003ca href=\"/.github/README_NY.md\"\u003eny Chichewa\u003c/a\u003e Chichewa | \u003ca href=\"/.github/README_ZH-CN.md\"\u003ezh-CN \u7b80\u4f53\u4e2d\u6587\u003c/a\u003e Chinese (Simplified) | \u003ca href=\"/.github/README_ZH-T.md\"\u003ezh-t \u4e2d\u570b\u50b3\u7d71\u7684\uff09\u003c/a\u003e Chinese (Traditional) | \u003ca href=\"/.github/README_CO.md\"\u003eco Corsu\u003c/a\u003e Corsican | \u003ca href=\"/.github/README_HR.md\"\u003ehr Hrvatski\u003c/a\u003e Croatian | \u003ca href=\"/.github/README_CS.md\"\u003ecs \u010de\u0161tina\u003c/a\u003e Czech | \u003ca href=\"README_DA.md\"\u003eda dansk\u003c/a\u003e Danish | \u003ca href=\"/.github/README_NL.md\"\u003enl Nederlands\u003c/a\u003e Dutch | \u003ca href=\"/.github/README.md\"\u003e\u003cstrong\u003een-us English\u003c/strong\u003e\u003c/a\u003e English | \u003ca href=\"/.github/README_EO.md\"\u003eEO Esperanto\u003c/a\u003e Esperanto | \u003ca href=\"/.github/README_ET.md\"\u003eet Eestlane\u003c/a\u003e Estonian | \u003ca href=\"/.github/README_TL.md\"\u003etl Pilipino\u003c/a\u003e Filipino | \u003ca href=\"/.github/README_FI.md\"\u003efi Suomalainen\u003c/a\u003e Finnish | \u003ca href=\"/.github/README_FR.md\"\u003efr fran\u00e7ais\u003c/a\u003e French | \u003ca href=\"/.github/README_FY.md\"\u003efy Frysk\u003c/a\u003e Frisian | \u003ca href=\"/.github/README_GL.md\"\u003egl Galego\u003c/a\u003e Galician | \u003ca href=\"/.github/README_KA\"\u003eka \u10e5\u10d0\u10e0\u10d7\u10d5\u10d4\u10da\u10d8\u003c/a\u003e Georgian | \u003ca href=\"/.github/README_DE.md\"\u003ede Deutsch\u003c/a\u003e German | \u003ca href=\"/.github/README_EL.md\"\u003eel \u0395\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03ac\u003c/a\u003e Greek | \u003ca href=\"/.github/README_GU.md\"\u003egu \u0a97\u0ac1\u0a9c\u0ab0\u0abe\u0aa4\u0ac0\u003c/a\u003e Gujarati | \u003ca href=\"/.github/README_HT.md\"\u003eht Krey\u00f2l ayisyen\u003c/a\u003e Haitian Creole | \u003ca href=\"/.github/README_HA.md\"\u003eha Hausa\u003c/a\u003e Hausa | \u003ca href=\"/.github/README_HAW.md\"\u003ehaw \u014clelo Hawai\u02bbi\u003c/a\u003e Hawaiian | \u003ca href=\"/.github/README_HE.md\"\u003ehe \u05e2\u05b4\u05d1\u05e8\u05b4\u05d9\u05ea\u003c/a\u003e Hebrew | \u003ca href=\"/.github/README_HI.md\"\u003ehi \u0939\u093f\u0928\u094d\u0926\u0940\u003c/a\u003e Hindi | \u003ca href=\"/.github/README_HMN.md\"\u003ehmn Hmong\u003c/a\u003e Hmong | \u003ca href=\"/.github/README_HU.md\"\u003ehu Magyar\u003c/a\u003e Hungarian | \u003ca href=\"/.github/README_IS.md\"\u003eis \u00cdslenska\u003c/a\u003e Icelandic | \u003ca href=\"/.github/README_IG.md\"\u003eig Igbo\u003c/a\u003e Igbo | \u003ca href=\"/.github/README_ID.md\"\u003eid bahasa Indonesia\u003c/a\u003e Icelandic | \u003ca href=\"/.github/README_GA.md\"\u003ega Gaeilge\u003c/a\u003e Irish | \u003ca href=\"/.github/README_IT.md\"\u003eit Italiana/Italiano\u003c/a\u003e | \u003ca href=\"/.github/README_JA.md\"\u003eja \u65e5\u672c\u8a9e\u003c/a\u003e Japanese | \u003ca href=\"/.github/README_JW.md\"\u003ejw Wong jawa\u003c/a\u003e Javanese | \u003ca href=\"/.github/README_KN.md\"\u003ekn \u0c95\u0ca8\u0ccd\u0ca8\u0ca1\u003c/a\u003e Kannada | \u003ca href=\"/.github/README_KK.md\"\u003ekk \u049a\u0430\u0437\u0430\u049b\u003c/a\u003e Kazakh | \u003ca href=\"/.github/README_KM.md\"\u003ekm \u1781\u17d2\u1798\u17c2\u179a\u003c/a\u003e Khmer | \u003ca href=\"/.github/README_RW.md\"\u003erw Kinyarwanda\u003c/a\u003e Kinyarwanda | \u003ca href=\"/.github/README_KO_SOUTH.md\"\u003eko-south \u97d3\u570b\u8a9e\u003c/a\u003e Korean (South) | \u003ca href=\"README_KO_NORTH.md\"\u003eko-north \ubb38\ud654\uc5b4\u003c/a\u003e Korean (North) (NOT YET TRANSLATED) | \u003ca href=\"/.github/README_KU.md\"\u003eku Kurd\u00ee\u003c/a\u003e Kurdish (Kurmanji) | \u003ca href=\"/.github/README_KY.md\"\u003eky \u041a\u044b\u0440\u0433\u044b\u0437\u0447\u0430\u003c/a\u003e Kyrgyz | \u003ca href=\"/.github/README_LO.md\"\u003elo \u0ea5\u0eb2\u0ea7\u003c/a\u003e Lao | \u003ca href=\"/.github/README_LA.md\"\u003ela Latine\u003c/a\u003e Latin | \u003ca href=\"/.github/README_LT.md\"\u003elt Lietuvis\u003c/a\u003e Lithuanian | \u003ca href=\"/.github/README_LB.md\"\u003elb L\u00ebtzebuergesch\u003c/a\u003e Luxembourgish | \u003ca href=\"/.github/README_MK.md\"\u003emk \u041c\u0430\u043a\u0435\u0434\u043e\u043d\u0441\u043a\u0438\u003c/a\u003e Macedonian | \u003ca href=\"/.github/README_MG.md\"\u003emg Malagasy\u003c/a\u003e Malagasy | \u003ca href=\"/.github/README_MS.md\"\u003ems Bahasa Melayu\u003c/a\u003e Malay | \u003ca href=\"/.github/README_ML.md\"\u003eml \u0d2e\u0d32\u0d2f\u0d3e\u0d33\u0d02\u003c/a\u003e Malayalam | \u003ca href=\"/.github/README_MT.md\"\u003emt Malti\u003c/a\u003e Maltese | \u003ca href=\"/.github/README_MI.md\"\u003emi Maori\u003c/a\u003e Maori | \u003ca href=\"/.github/README_MR.md\"\u003emr \u092e\u0930\u093e\u0920\u0940\u003c/a\u003e Marathi | \u003ca href=\"/.github/README_MN.md\"\u003emn \u041c\u043e\u043d\u0433\u043e\u043b\u003c/a\u003e Mongolian | \u003ca href=\"/.github/README_MY.md\"\u003emy \u1019\u103c\u1014\u103a\u1019\u102c\u003c/a\u003e Myanmar (Burmese) | \u003ca href=\"/.github/README_NE.md\"\u003ene \u0928\u0947\u092a\u093e\u0932\u0940\u003c/a\u003e Nepali | \u003ca href=\"/.github/README_NO.md\"\u003eno norsk\u003c/a\u003e Norwegian | \u003ca href=\"/.github/README_OR.md\"\u003eor \u0b13\u0b21\u0b3f\u0b06 (\u0b13\u0b21\u0b3f\u0b06)\u003c/a\u003e Odia (Oriya) | \u003ca href=\"/.github/README_PS.md\"\u003eps \u067e\u069a\u062a\u0648\u003c/a\u003e Pashto | \u003ca href=\"/.github/README_FA.md\"\u003efa \u0641\u0627\u0631\u0633\u06cc\u003c/a\u003e |Persian \u003ca href=\"/.github/README_PL.md\"\u003epl polski\u003c/a\u003e Polish | \u003ca href=\"/.github/README_PT.md\"\u003ept portugu\u00eas\u003c/a\u003e Portuguese | \u003ca href=\"/.github/README_PA.md\"\u003epa \u0a2a\u0a70\u0a1c\u0a3e\u0a2c\u0a40\u003c/a\u003e Punjabi | No languages available that start with the letter Q | \u003ca href=\"/.github/README_RO.md\"\u003ero Rom\u00e2n\u0103\u003c/a\u003e Romanian | \u003ca href=\"/.github/README_RU.md\"\u003eru \u0440\u0443\u0441\u0441\u043a\u0438\u0439\u003c/a\u003e Russian | \u003ca href=\"/.github/README_SM.md\"\u003esm Faasamoa\u003c/a\u003e Samoan | \u003ca href=\"/.github/README_GD.md\"\u003egd G\u00e0idhlig na h-Alba\u003c/a\u003e Scots Gaelic | \u003ca href=\"/.github/README_SR.md\"\u003esr \u0421\u0440\u043f\u0441\u043a\u0438\u003c/a\u003e Serbian | \u003ca href=\"/.github/README_ST.md\"\u003est Sesotho\u003c/a\u003e Sesotho | \u003ca href=\"/.github/README_SN.md\"\u003esn Shona\u003c/a\u003e Shona | \u003ca href=\"/.github/README_SD.md\"\u003esd \u0633\u0646\u068c\u064a\u003c/a\u003e Sindhi | \u003ca href=\"/.github/README_SI.md\"\u003esi \u0dc3\u0dd2\u0d82\u0dc4\u0dbd\u003c/a\u003e Sinhala | \u003ca href=\"/.github/README_SK.md\"\u003esk Slov\u00e1k\u003c/a\u003e Slovak | \u003ca href=\"/.github/README_SL.md\"\u003esl Sloven\u0161\u010dina\u003c/a\u003e Slovenian | \u003ca href=\"/.github/README_SO.md\"\u003eso Soomaali\u003c/a\u003e Somali | [\u003ca href=\"/.github/README_ES.md\"\u003ees en espa\u00f1ol\u003c/a\u003e Spanish | \u003ca href=\"/.github/README_SU.md\"\u003esu Sundanis\u003c/a\u003e Sundanese | \u003ca href=\"/.github/README_SW.md\"\u003esw Kiswahili\u003c/a\u003e Swahili | \u003ca href=\"/.github/README_SV.md\"\u003esv Svenska\u003c/a\u003e Swedish | \u003ca href=\"/.github/README_TG.md\"\u003etg \u0422\u043e\u04b7\u0438\u043a\u04e3\u003c/a\u003e Tajik | \u003ca href=\"/.github/README_TA.md\"\u003eta \u0ba4\u0bae\u0bbf\u0bb4\u0bcd\u003c/a\u003e Tamil | \u003ca href=\"/.github/README_TT.md\"\u003ett \u0422\u0430\u0442\u0430\u0440\u003c/a\u003e Tatar | \u003ca href=\"/.github/README_TE.md\"\u003ete \u0c24\u0c46\u0c32\u0c41\u0c17\u0c41\u003c/a\u003e Telugu | \u003ca href=\"/.github/README_TH.md\"\u003eth \u0e44\u0e17\u0e22\u003c/a\u003e Thai | \u003ca href=\"/.github/README_TR.md\"\u003etr T\u00fcrk\u003c/a\u003e Turkish | \u003ca href=\"/.github/README_TK.md\"\u003etk T\u00fcrkmenler\u003c/a\u003e Turkmen | \u003ca href=\"/.github/README_UK.md\"\u003euk \u0423\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0438\u0439\u003c/a\u003e Ukrainian | \u003ca href=\"/.github/README_UR.md\"\u003eur \u0627\u0631\u062f\u0648\u003c/a\u003e Urdu | \u003ca href=\"/.github/README_UG.md\"\u003eug \u0626\u06c7\u064a\u063a\u06c7\u0631\u003c/a\u003e Uyghur | \u003ca href=\"/.github/README_UZ.md\"\u003euz O\u0027zbek\u003c/a\u003e Uzbek | \u003ca href=\"/.github/README_VI.md\"\u003evi Ti\u1ebfng Vi\u1ec7t\u003c/a\u003e Vietnamese | \u003ca href=\"/.github/README_CY.md\"\u003ecy Cymraeg\u003c/a\u003e Welsh | \u003ca href=\"/.github/README_XH.md\"\u003exh isiXhosa\u003c/a\u003e Xhosa | \u003ca href=\"/.github/README_YI.md\"\u003eyi \u05d9\u05d9\u05d3\u05d9\u05e9\u003c/a\u003e Yiddish | \u003ca href=\"/.github/README_YO.md\"\u003eyo Yoruba\u003c/a\u003e Yoruba | \u003ca href=\"/.github/README_ZU.md\"\u003ezu Zulu\u003c/a\u003e Zulu ) Available in 110 languages (108 when not counting English and North Korean, as North Korean has not been translated yet \u003ca href=\"/OldVersions/Korean(North)/README.md\"\u003eRead about it here\u003c/a\u003e)\u003c/p\u003e\n\u003c/details\u003e\n\u003cp\u003eTranslations in languages other than English are machine translated and are not yet accurate. No errors have been fixed yet as of February 5th 2021. Please report translation errors \u003ca href=\"https://github.com/seanpm2001/SNU_2D_ProgrammingTools_IDE_%3CLanguageNameWithUnderscores%3E/issues/\"\u003ehere\u003c/a\u003e make sure to backup your correction with sources and guide me, as I don\u0027t know languages other than English well (I plan on getting a translator eventually) please cite \u003ca href=\"https://en.wiktionary.org\" rel=\"nofollow\"\u003ewiktionary\u003c/a\u003e and other sources in your report. Failing to do so will result in a rejection of the correction being published.\u003c/p\u003e\n\u003cp\u003eNote: due to limitations with GitHub\u0027s interpretation of markdown (and pretty much every other web-based interpretation of markdown) clicking these links will redirect you to a separate file on a separate page that isn\u0027t my GitHub profile page. You will be redirected to the \u003ca href=\"https://github.com/seanpm2001/seanpm2001\"\u003eseanpm2001/seanpm2001 repository\u003c/a\u003e, where the README is hosted.\u003c/p\u003e\n\u003cp\u003eTranslations are done with Google Translate due to limited or no support for the languages I need in other translation services like DeepL and Bing Translate. For some reason, the formatting (links, dividers, bolding, italics, etc.) is messed up in various translations. It is tedious to fix, and I do not know how to fix these issues in languages with non-latin characters, and right to left languages (like Arabic) extra help is needed in fixing these issues\u003c/p\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-index\" class=\"anchor\" aria-hidden=\"true\" href=\"#index\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIndex\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"#Top\"\u003e00.0 - Top\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#SNU-2D-ProgrammingTools-IDE-%3CLanguageNameWithHyphens%3E\"\u003e00.1 - Title\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Read-this-article-in-a-different-language\"\u003e00.2 - Read this article in a different language\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Index\"\u003e00.3 - Index\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#SNU_2D_ProgrammingTools_IDE_%3CLanguageNameWithUnderscores%3E\"\u003e01.0 - Description\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#About\"\u003e02.0 - About\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Wiki\"\u003e03.0 - Wiki\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#History\"\u003e04.0 - History\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Pre-history\"\u003e04.1 - Pre-history\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Alpha-history\"\u003e04.2 - Alpha History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Beta-history\"\u003e04.3 - Beta History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Modern-history\"\u003e04.4 - Modern History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#Copying\"\u003e05.0 - Copying\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Credits\"\u003e06.0 - Credits\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Installation\"\u003e07.0 - Installation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Version-history\"\u003e08.0 - Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Software-status\"\u003e09.0 - Software status\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Sponsor-info\"\u003e10.0 - Sponsor info\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Contributers\"\u003e11.0 - Contributers\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Issues\"\u003e12.0 - Issues\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Current-issues\"\u003e12.1 - Current issues\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Past-issues\"\u003e12.2 - Past issues\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Past-pull-requests\"\u003e12.3 - Past pull requests\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Active-pull-requests\"\u003e12.4 - Active pull requests\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#Resources\"\u003e13.0 - Resources\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Contributing\"\u003e14.0 - Contributing\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#About-README\"\u003e15.0 - About README\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#README-version-history\"\u003e16.0 - README Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#You-have-reached-the-end-of-the-README-file\"\u003e17.0 - Footer\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#EOF\"\u003e17.9 - End of file\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-snu_2d_programmingtools_ide_\" class=\"anchor\" aria-hidden=\"true\" href=\"#snu_2d_programmingtools_ide_\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSNU_2D_ProgrammingTools_IDE_\n\u003c/h1\u003e\n\u003cp\u003eThe Programming language IDE submodule for SNU Programming Tools (2D Mode)\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eSee above. This repository is the IDE for that comes with SNUs programming tool set.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-wiki\" class=\"anchor\" aria-hidden=\"true\" href=\"#wiki\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWiki\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/seanpm2001/SNU_2D_ProgrammingTools_IDE_%3CLanguageNameWithUnderscores%3E/wiki\"\u003eClick/tap here to view this projects Wiki\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf the project has been forked, the Wiki was likely removed. Luckily, I include an embedded version. You can view it \u003ca href=\"/External/ProjectWiki/\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eWrite about this projects history here.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pre-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-history\u003c/h3\u003e\n\u003cp\u003eNo pre-history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alpha-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#alpha-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlpha history\u003c/h3\u003e\n\u003cp\u003eNo Alpha history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-beta-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#beta-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBeta history\u003c/h3\u003e\n\u003cp\u003eNo Beta history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-modern-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#modern-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModern history\u003c/h3\u003e\n\u003cp\u003eNo Modern history to show for this project.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-copying\" class=\"anchor\" aria-hidden=\"true\" href=\"#copying\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopying\u003c/h2\u003e\n\u003cp\u003eView the copying license for this project \u003ca href=\"/COPYING\"\u003ehere\u003c/a\u003e (if you haven\u0027t built the project yet with the makefile, here is the original link: \u003ca href=\"/COPYINGL\"\u003eCOPYINGL\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease note that you also have to follow the rules of the GNU General Public License v3 (GPL3) which you can view \u003ca href=\"/LICENSE.txt\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eView the credits file for this project and see the people who got together to make this project by \u003ca href=\"/CREDITS\"\u003eclicking/tapping here\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eView the installation instructions file for this project \u003ca href=\"/INSTALL\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRequirements: Read the instructions for more info, and get the latest up-to-date instructions \u003ca href=\"https://gist.github.com/seanpm2001/745564a46186888e829fdeb9cda584de\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sponsor-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#sponsor-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSponsor info\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/SponsorButton.png\"\u003e\u003cimg src=\"/SponsorButton.png\" alt=\"SponsorButton.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can sponsor this project if you like, but please specify what you want to donate to. \u003ca href=\"https://github.com/seanpm2001/Sponsor-info/tree/main/For-sponsors/\"\u003eSee the funds you can donate to here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can view other sponsor info \u003ca href=\"https://github.com/seanpm2001/Sponsor-info/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTry it out! The sponsor button is right up next to the watch/unwatch button.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-version-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#version-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion history\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eVersion history currently unavailable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo other versions listed\u003c/strong\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware status\u003c/h2\u003e\n\u003cp\u003eAll of my works are free some restrictions. DRM (\u003cstrong\u003eD\u003c/strong\u003eigital \u003cstrong\u003eR\u003c/strong\u003eestrictions \u003cstrong\u003eM\u003c/strong\u003eanagement) is not present in any of my works.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"DRM-free_label.en.svg\"\u003e\u003cimg src=\"DRM-free_label.en.svg\" alt=\"DRM-free_label.en.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis sticker is supported by the Free Software Foundation. I never intend to include DRM in my works.\u003c/p\u003e\n\u003cp\u003eI am ussing the abbreviation \"Digital Restrictions Management\" instead of the more known \"Digital Rights Management\" as the common way of addressing it is false, there are no rights with DRM. The spelling \"Digital Restrictions Management\" is more accurate, and is supported by \u003ca href=\"https://en.wikipedia.org/wiki/Richard_Stallman\" rel=\"nofollow\"\u003eRichard M. Stallman (RMS)\u003c/a\u003e and the \u003ca href=\"https://en.wikipedia.org/wiki/Free_Software_Foundation\" rel=\"nofollow\"\u003eFree Software Foundation (FSF)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis section is used to raise awareness for the problems with DRM, and also to protest it. DRM is defective by design and is a major threat to all computer users and software freedom.\u003c/p\u003e\n\u003cp\u003eImage credit: \u003ca href=\"https://www.defectivebydesign.org/drm-free/how-to-use-label/\" rel=\"nofollow\"\u003edefectivebydesign.org/drm-free/...\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributers\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributers\u003c/h2\u003e\n\u003cp\u003eCurrently, I am the only contributer. Contributing is allowed, as long as you follow the rules of the \u003ca href=\"/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e file.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/seanpm2001/\"\u003eseanpm2001\u003c/a\u003e - x commits (As of 2021, date, at xx:xx pm)\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eNo other contributers.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssues\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-current-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#current-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCurrent issues\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other current issues\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-past-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#past-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePast issues\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other past issues\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-past-pull-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#past-pull-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePast pull requests\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other past pull requests\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-active-pull-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#active-pull-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eActive pull requests\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other active pull requests\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h2\u003e\n\u003cp\u003eHere are some other resources for this project:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"PROJECT_LANG_1.%3CprojectLanguage1fileExtension\"\u003eProject language file A\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/seanpm2001/SNU_2D_ProgrammingTools_IDE_%3CLanguageNameWithUnderscores%3E/discussions\"\u003eJoin the discussion on GitHub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNo other resources at the moment.\u003c/p\u003e\n\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eContributing is allowed for this project, as long as you follow the rules of the \u003ccode\u003eCONTRIBUTING.md\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/CONTRIBUTING.md\"\u003eClick/tap here to view the contributing rules for this project\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout README\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eFile type:\u003c/strong\u003e \u003ccode\u003eMarkdown document (*.md)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile version:\u003c/strong\u003e \u003ccode\u003e1 (date)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLine count:\u003c/strong\u003e \u003ccode\u003e0,415\u003c/code\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-readme-version-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#readme-version-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREADME version history\u003c/h2\u003e\n\u003cp\u003eVersion 1 (Date)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eStarted the file\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the about section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the Wiki section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the version history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the issues section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the past issues section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the past pull requests section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the active pull requests section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the contributors section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the contributing section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the about README section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the README version history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the resources section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded a software status section, with a DRM free sticker and message\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the sponsor info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eITERATION 5\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the footer\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eITERATION 6\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eFixed and update template links\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the copying section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the credits section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the installation section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the resources section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the contributors section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the technical notes section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the footer\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eVersion 2 (Coming soon)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eComing soon\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 2\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-you-have-reached-the-end-of-the-readme-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#you-have-reached-the-end-of-the-readme-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYou have reached the end of the README file\u003c/h3\u003e\n\u003cp\u003e( \u003ca href=\"#Top\"\u003eBack to top\u003c/a\u003e | \u003ca href=\"https://github.com\"\u003eExit to GitHub\u003c/a\u003e | \u003ca href=\"https://www.bing.com/\" rel=\"nofollow\"\u003eExit to Bing\u003c/a\u003e | \u003ca href=\"https://duckduckgo.com/\" rel=\"nofollow\"\u003eExit to DuckDuckGo\u003c/a\u003e | \u003ca href=\"https://www.ecosia.org/\" rel=\"nofollow\"\u003eExit to Ecosia\u003c/a\u003e )\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-eof\" class=\"anchor\" aria-hidden=\"true\" href=\"#eof\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEOF\u003c/h3\u003e\n\u003chr\u003e\n", + "full_name": "AuReMe/esmecata", + "latest_release": "0.2.12", + "readme": "\u003cp\u003e\u003ca href=\"https://pypi.org/project/esmecata/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/59da11b37a5d18aeb40a3a21e18ad6767e5f0e3aacc5b92410fa3146f65867a1/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f65736d65636174612e737667\" alt=\"PyPI version\" data-canonical-src=\"https://img.shields.io/pypi/v/esmecata.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/AuReMe/esmecata/blob/master/LICENSE\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/AuReMe/esmecata/master/pictures/license_esmecata.svg\" alt=\"GitHub license\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/AuReMe/esmecata/actions\"\u003e\u003cimg src=\"https://github.com/AuReMe/esmecata/workflows/Python%20package/badge.svg\" alt=\"Actions Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.1101/2022.03.16.484574\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/AuReMe/esmecata/master/pictures/doi_esmecata.svg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-esmecata-estimating-metabolic-capabilties-from-taxonomic-affiliations\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-estimating-metabolic-capabilties-from-taxonomic-affiliations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa: \u003cem\u003eEs\u003c/em\u003etimating \u003cem\u003eMe\u003c/em\u003etabolic \u003cem\u003eCa\u003c/em\u003epabilties from \u003cem\u003eTa\u003c/em\u003exonomic affiliations\u003c/h1\u003e\n\u003cp\u003eEsMeCaTa is a tool to estimate metabolic capabilities from a taxonomic affiliation (for example after analysis on 16S RNA sequencing). This is useful if no sequenced genomes or proteomes are available.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"esmecata.svg\"\u003e\u003cimg src=\"esmecata.svg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of contents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#esmecata-estimating-metabolic-capabilties-from-taxonomic-affiliations\"\u003eEsMeCaTa: \u003cem\u003eEs\u003c/em\u003etimating \u003cem\u003eMe\u003c/em\u003etabolic \u003cem\u003eCa\u003c/em\u003epabilties from \u003cem\u003eTa\u003c/em\u003exonomic affiliations\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#table-of-contents\"\u003eTable of contents\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#conda-and-pip\"\u003eConda and pip\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#input\"\u003eInput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-commands\"\u003eEsMeCaTa commands\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#esmecata-functions\"\u003eEsMeCaTa functions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-proteomes-retrieve-proteomes-associated-with-taxonomic-affiliation\"\u003e\u003ccode\u003eesmecata proteomes\u003c/code\u003e: Retrieve proteomes associated with taxonomic affiliation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-clustering-proteins-clustering\"\u003e\u003ccode\u003eesmecata clustering\u003c/code\u003e: Proteins clustering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-annotation-retrieve-protein-annotations\"\u003e\u003ccode\u003eesmecata annotation\u003c/code\u003e: Retrieve protein annotations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-workflow-consecutive-runs-of-the-three-steps\"\u003e\u003ccode\u003eesmecata workflow\u003c/code\u003e: Consecutive runs of the three steps\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#esmecata-outputs\"\u003eEsMeCaTa outputs\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-proteomes\"\u003eEsMeCaTa proteomes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-clustering\"\u003eEsMeCaTa clustering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-annotation\"\u003eEsMeCaTa annotation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#esmecata-workflow\"\u003eEsMeCaTa workflow\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eEsMeCaTa needs the following python packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/biopython/\" rel=\"nofollow\"\u003ebiopython\u003c/a\u003e: To create fasta files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/pandas/\" rel=\"nofollow\"\u003epandas\u003c/a\u003e: To read the input files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/requests/\" rel=\"nofollow\"\u003erequests\u003c/a\u003e: For the REST queries on Uniprot.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/ete3/\" rel=\"nofollow\"\u003eete3\u003c/a\u003e: To analyse the taxonomic affiliation and extract taxon_id, also used to deal with taxon associated with more than 100 proteomes.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pypi.org/project/SPARQLWrapper/\" rel=\"nofollow\"\u003eSPARQLwrapper\u003c/a\u003e: Optionally, you can use SPARQL queries instead of REST queries. This can be done either with the \u003ca href=\"https://sparql.uniprot.org/\" rel=\"nofollow\"\u003eUniprot SPARQL Endpoint\u003c/a\u003e (with the option \u003ccode\u003e--sparql uniprot\u003c/code\u003e) or with a Uniprot SPARQL Endpoint that you created locally (it is supposed to work but not tested, only SPARQL queries on the Uniprot SPARQL endpoint have been tested). \u003cstrong\u003eWarning\u003c/strong\u003e: using SPARQL queries will lead to minor differences in functional annotations and metabolic reactions due to how the results are retrieved with REST query or SPARQL query.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlso esmecata requires mmseqs2 for protein clustering:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/soedinglab/MMseqs2\"\u003emmseqs2\u003c/a\u003e: To cluster proteins. Test have been made on version MMseqs2 Release 13-45111., especially with the version of the commi \u003ca href=\"https://github.com/soedinglab/MMseqs2/tree/f349118312919c4fcc448f4595ca3b3a387018e2\"\u003ef349118312919c4fcc448f4595ca3b3a387018e2\u003c/a\u003e. But EsMeCaTa should be compatible with more recent version.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda-and-pip\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-and-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda and pip\u003c/h3\u003e\n\u003cp\u003eThe easiest way to install the dependencies of EsMeCaTa is by using conda:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install mmseqs2 pandas sparqlwrapper requests biopython ete3 -c conda-forge -c bioconda\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eA conda package for esmecata will be created in the future.\u003c/p\u003e\n\u003cp\u003eEsMeCata can be installed with pip command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install esmecata \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt can also be installed using esmecata github directory:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/ArnaudBelcour/esmecata.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd esmecata\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install -e . \u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eA Singularity recipe for EsMeCaTa is available \u003ca href=\"https://github.com/AuReMe/esmecata/blob/master/recipes/Singularity\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe image can be created with the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build esmecata.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAnd EsMeCaTa can be used with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec esmecata.sif esmecata workflow -i buchnera_workflow.tsv -o output\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003cp\u003eEsMeCaTa takes as input a tabulated or an excel file with two columns one with the ID corresponding to the taxonomic affiliation (for example the OTU ID for 16S RNA sequencing) and a second column with the taxonomic classification separated by \u0027;\u0027. In the following documentation, the first column (named \u003ccode\u003eobservation_name\u003c/code\u003e) will be used to identify the label associated with each taxonomic affiliation. An example is located in the test folder (\u003ca href=\"https://github.com/ArnaudBelcour/esmecata/blob/master/test/Example.tsv\"\u003eExample.tsv\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eobservation_name\u003c/th\u003e\n\u003cth\u003etaxonomic_affiliation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_1\u003c/td\u003e\n\u003ctd\u003eBacteria;Spirochaetes;Spirochaetia;Spirochaetales;Spirochaetaceae;Sphaerochaeta;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_2\u003c/td\u003e\n\u003ctd\u003eBacteria;Chloroflexi;Anaerolineae;Anaerolineales;Anaerolineaceae;ADurb.Bin120;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_3\u003c/td\u003e\n\u003ctd\u003eBacteria;Cloacimonetes;Cloacimonadia;Cloacimonadales;Cloacimonadaceae;Candidatus Cloacimonas;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_4\u003c/td\u003e\n\u003ctd\u003eBacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Rikenellaceae;Rikenellaceae RC9 gut group;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_5\u003c/td\u003e\n\u003ctd\u003eBacteria;Cloacimonetes;Cloacimonadia;Cloacimonadales;Cloacimonadaceae;W5;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_6\u003c/td\u003e\n\u003ctd\u003eBacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Dysgonomonadaceae;unknown genus;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_7\u003c/td\u003e\n\u003ctd\u003eBacteria;Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;unknown species\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIt is possible to use EsMeCaTa with a taxonomic affiliation containing only one taxon:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eobservation_name\u003c/th\u003e\n\u003cth\u003etaxonomic_affiliation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_1\u003c/td\u003e\n\u003ctd\u003eSphaerochaeta\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCluster_2\u003c/td\u003e\n\u003ctd\u003eYersinia\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBut this can cause issue. For example, \"Cluster_2\" is associated with Yersinia but two genus are associated with this name (one mantid (taxId: 444888) and one bacteria (taxId: 629)). EsMeCaTa will not able to differentiate them. But if you give more informations by adding more taxons (for example: \u0027Bacteria;Gammaproteobacteria;Yersinia\u0027), EsMeCaTa will compare all the taxons of the taxonomic affiliation (here: 2 (Bacteria) and 1236 (Gammaproteobacteria)) to the lineage associated with the two taxIDs (for bacteria Yersinia: [1, 131567, 2, 1224, 1236, 91347, 1903411, 629] and for the mantid one: [1, 131567, 2759, 33154, 33208, 6072, 33213, 33317, 1206794, 88770, 6656, 197563, 197562, 6960, 50557, 85512, 7496, 33340, 33341, 6970, 7504, 7505, 267071, 444888]). In this example, there is 2 matches for the bacteria one (2 and 1236) and 0 for the mantid one. So EsMeCaTa will select the taxId associated with the bacteria (629).\u003c/p\u003e\n\u003cp\u003eA \u003ca href=\"https://github.com/AuReMe/esmecata/blob/master/tutorials/esmecata_method.ipynb\"\u003ejupyter notebook\u003c/a\u003e explains how EsMeCata works.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-esmecata-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa commands\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata [-h] [--version] {proteomes,clustering,annotation,workflow} ...\n\nFrom taxonomic affiliation to metabolism using Uniprot. For specific help on each subcommand use: esmecata {cmd} --help\n\noptional arguments:\n -h, --help show this help message and exit\n --version show program\u0027s version number and exit\n\nsubcommands:\n valid subcommands:\n\n {proteomes,clustering,annotation,workflow}\n proteomes Download proteomes associated with taxon from Uniprot Proteomes.\n clustering Cluster the proteins of the different proteomes of a taxon into a single set of representative shared proteins.\n annotation Retrieve protein annotations from Uniprot.\n workflow Run all esmecata steps (proteomes, clustering and annotation).\n\nRequires: mmseqs2 and an internet connection (for REST and SPARQL queries, except if you have a local Uniprot SPARQL endpoint).\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-esmecata-functions\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-functions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa functions\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-proteomes-retrieve-proteomes-associated-with-taxonomic-affiliation\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-proteomes-retrieve-proteomes-associated-with-taxonomic-affiliation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ccode\u003eesmecata proteomes\u003c/code\u003e: Retrieve proteomes associated with taxonomic affiliation\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata proteomes [-h] -i INPUT_FILE -o OUPUT_DIR [-b BUSCO] [--ignore-taxadb-update] [--all-proteomes] [-s SPARQL] [--remove-tmp] [-l LIMIT_MAXIMAL_NUMBER_PROTEOMES] [-r RANK_LIMIT] [--minimal-nb-proteomes MINIMAL_NUMBER_PROTEOMES]\n\noptional arguments:\n -h, --help show this help message and exit\n -i INPUT_FILE, --input INPUT_FILE\n Input taxon file (excel, tsv or csv) containing a column associating ID to a taxonomic affiliation (separated by ;).\n -o OUPUT_DIR, --output OUPUT_DIR\n Output directory path.\n -b BUSCO, --busco BUSCO\n BUSCO percentage between 0 and 1. This will remove all the proteomes without BUSCO score and the score before the selected ratio of completion.\n --ignore-taxadb-update\n If you have a not up-to-date version of the NCBI taxonomy database with ete3, use this option to bypass the warning message and use the old version.\n --all-proteomes Download all proteomes associated with a taxon even if they are no reference proteomes.\n -s SPARQL, --sparql SPARQL\n Use sparql endpoint instead of REST queries on Uniprot.\n --remove-tmp Delete tmp files to limit the disk space used: files in tmp_proteome for esmecata proteomes and files created by mmseqs (in mmseqs_tmp).\n -l LIMIT_MAXIMAL_NUMBER_PROTEOMES, --limit-proteomes LIMIT_MAXIMAL_NUMBER_PROTEOMES\n Choose the maximal number of proteomes after which the tool will select a subset of proteomes instead of using all the available proteomes (default is 99).\n -r RANK_LIMIT, --rank-limit RANK_LIMIT\n This option limit the rank used by the tool for searching for proteomes. The given rank and all the superior ranks will be ignored. Look at the readme for more information (and a list of possible rank).\n --minimal-nb-proteomes MINIMAL_NUMBER_PROTEOMES\n Choose the minimal number of proteomes to be selected by EsMeCaTa. If a taxon has less proteomes, it will be ignored and a higher taxonomic rank will be used. Default is 1.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor each taxon in each taxonomic affiliations EsMeCaTa will use ete3 to find the corresponding taxon ID. Then it will search for proteomes associated with these taxon ID in the Uniprot Proteomes database.\u003c/p\u003e\n\u003cp\u003eIf there is more than 100 proteomes, esmecata will apply a specific method:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e(1) use the taxon ID associated with each proteomes to create a taxonomic tree with ete3.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(2) from the root of the tree (the input taxon), esmecata will find the direct deescendant (sub-taxons).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(3) then esmecata will compute the number of proteomes associated with each sub-taxon.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(4) the corresponding proportions will be used to select randomly a number of proteomes corresponding to the proportion.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor example: for the taxon Clostridiales, 645 proteomes are found. Using the organism taxon ID associated with the 645 proteomes we found that there is 17 direct sub-taxons. Then for each sub-taxon we compute the percentage of proportion of proteomes given by the sub-taxon to the taxon Clostridiales.\nThere is 198 proteomes associated with the sub-taxon Clostridiaceae, the percentage will be computed as follow: 198 / 645 = 30% (if a percentage is superior to 1 it will be round down and if the percentage is lower than 1 it will be round up to keep all the low proportion sub-taxons). We will use this 30% to select randomly 30 proteomes amongst the 198 proteomes of Clostridiaceae. This is done for all the other sub-taxons, so we get a number of proteomes around 100 (here it will be 102). Due to the different rounds (up or down) the total number of proteomes will not be equal to exactly 100 but it will be around it. The number of proteomes leading to this behavior is set to 99 by default but the user can modify it with the \u003ccode\u003e-l/--limit-proteomes\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eThen the proteomes found will be downloaded. For protein with isoforms, the \u003ca href=\"https://www.uniprot.org/help/canonical_and_isoforms\" rel=\"nofollow\"\u003ecanonical sequence\u003c/a\u003e is retrieved except when the isoforms are separated in different Uniprot entries.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata proteomes\u003c/code\u003e options:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-s/--sparql\u003c/code\u003e: use SPARQL instead of REST requests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to avoid using REST queries for esmecata and instead use SPARQL queries. This option need a link to a sparql endpoint containing UniProt. If you want to use the \u003ca href=\"https://sparql.uniprot.org/sparql\" rel=\"nofollow\"\u003eSPARQL endpoint of UniProt\u003c/a\u003e, you can use the argument: \u003ccode\u003e-s uniprot\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-b/--busco\u003c/code\u003e: filter proteomes using BUSCO score (default is 0.8)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to filter proteomes according to to their BUSCO score (from Uniprot documentation: \u003ccode\u003eThe Benchmarking Universal Single-Copy Ortholog (BUSCO) assessment tool is used, for eukaryotic and bacterial proteomes, to provide quantitative measures of UniProt proteome data completeness in terms of expected gene content.\u003c/code\u003e). It is a percentage between 0 and 1 showing the quality of the proteomes that esmecata will download. By default esmecata uses a BUSCO score of 0.80, it will only download proteomes with a BUSCO score of at least 80%.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--ignore-taxadb-update\u003c/code\u003e: ignore need to udpate ete3 taxaDB\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you have an old version of the ete3 NCBI taxonomy database, you can use this option to use esmecata with it.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--all-proteomes\u003c/code\u003e: download all proteomes (reference and non-reference)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default, esmecata will try to downlaod the reference proteomes associated with a taxon. But if you want to download all the proteomes associated with a taxon (either if they are non reference proteome) you can use this option. Without this option non-reference proteoems can also be used if no reference proteomes are found.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--remove-tmp\u003c/code\u003e: remove proteomes stored in \u003ccode\u003etmp_proteomes\u003c/code\u003e folder\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-l/--limit-proteomes\u003c/code\u003e: choose the number of proteomes that will lead to the used of the selection of a subset of proteomes\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo avoid working on too many proteomes, esmecata works on subset of proteomes when there is too many proteomes (by default this limit is set on 99 proteomes). Using this option the user can modify the limit.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--minimal-nb-proteomes\u003c/code\u003e: choose the minimal number of proteomes that taxon must have to be selected by esmecata (default 1).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo avoid working on too little proteomes, it is possible to give an int to this option.\nWith this int, esmecata will select only taxon associated to at least this number of proteomes.\nFor example if you use \u003ccode\u003e--minimal-nb-proteomes 10\u003c/code\u003e, and the lowest taxon in the taxonomic affiliation is associated with 3 proteomes, it will be ignored and a taxon with a higer taxonomic rank will be used.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-r/--rank-limit\u003c/code\u003e: This option limit the rank used by the tool for searching for proteomes. The given rank and all the superior ranks will be ignored.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo avoid working on rank with too much proteomes (which can have an heavy impact on the number of proteomes downloaded and then on the clustering) it is possible to select a limit on the taxonomic rank used by the tool.\u003c/p\u003e\n\u003cp\u003eThe selected rank will be used to find the ranks to keep. For example, if the rank \u0027phylum\u0027 is given, all the rank below (from subphylum to isolate) will be kept. And the ranks from phylum to superkingdom will be ignored when searching for proteomes.\nThe following ranks can be given to this option (from Supplementary Table S3 of \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7408187/\" rel=\"nofollow\"\u003ePMC7408187\u003c/a\u003e):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eLevel\u003c/th\u003e\n\u003cth\u003eRank\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003esuperkingdom\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003ekingdom\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e3\u003c/td\u003e\n\u003ctd\u003esubkingdom\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003esuperphylum\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e5\u003c/td\u003e\n\u003ctd\u003ephylum\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e6\u003c/td\u003e\n\u003ctd\u003esubphylum\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e7\u003c/td\u003e\n\u003ctd\u003einfraphylum\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003ctd\u003esuperclass\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9\u003c/td\u003e\n\u003ctd\u003eclass\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10\u003c/td\u003e\n\u003ctd\u003esubclass\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11\u003c/td\u003e\n\u003ctd\u003einfraclass\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12\u003c/td\u003e\n\u003ctd\u003ecohort\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e13\u003c/td\u003e\n\u003ctd\u003esubcohort\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e14\u003c/td\u003e\n\u003ctd\u003esuperorder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e15\u003c/td\u003e\n\u003ctd\u003eorder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e16\u003c/td\u003e\n\u003ctd\u003esuborder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e17\u003c/td\u003e\n\u003ctd\u003einfraorder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e18\u003c/td\u003e\n\u003ctd\u003eparvorder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e19\u003c/td\u003e\n\u003ctd\u003esuperfamily\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e20\u003c/td\u003e\n\u003ctd\u003efamily\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e21\u003c/td\u003e\n\u003ctd\u003esubfamily\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e22\u003c/td\u003e\n\u003ctd\u003etribe\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e23\u003c/td\u003e\n\u003ctd\u003esubtribe\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e24\u003c/td\u003e\n\u003ctd\u003egenus\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e25\u003c/td\u003e\n\u003ctd\u003esubgenus\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e26\u003c/td\u003e\n\u003ctd\u003esection\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e27\u003c/td\u003e\n\u003ctd\u003esubsection\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e28\u003c/td\u003e\n\u003ctd\u003eseries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e29\u003c/td\u003e\n\u003ctd\u003esubseries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e30\u003c/td\u003e\n\u003ctd\u003especies group\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e31\u003c/td\u003e\n\u003ctd\u003especies subgroup\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e32\u003c/td\u003e\n\u003ctd\u003especies\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e33\u003c/td\u003e\n\u003ctd\u003eforma specialis\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e34\u003c/td\u003e\n\u003ctd\u003esubspecies\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e35\u003c/td\u003e\n\u003ctd\u003evarietas\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e36\u003c/td\u003e\n\u003ctd\u003esubvariety\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e37\u003c/td\u003e\n\u003ctd\u003eforma\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e38\u003c/td\u003e\n\u003ctd\u003eserogroup\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e39\u003c/td\u003e\n\u003ctd\u003eserotype\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e40\u003c/td\u003e\n\u003ctd\u003estrain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e41\u003c/td\u003e\n\u003ctd\u003eisolate\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSome ranks (which are not non-hierarchical) are not used for the moment when using this method (so some taxons can be removed whereas they are below a kept rank):\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eLevel\u003c/th\u003e\n\u003cth\u003eRank\u003c/th\u003e\n\u003cth\u003eNote\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eclade\u003c/td\u003e\n\u003ctd\u003enewly introduced, can appear anywhere in the lineage w/o breaking the order\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eenvironmental samples\u003c/td\u003e\n\u003ctd\u003eno order below this rank is required\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eincertae sedis\u003c/td\u003e\n\u003ctd\u003ecan appear anywhere in the lineage w/o breaking the order, implies taxa with uncertain placements\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eunclassified \n\u003c/td\u003e\n\u003ctd\u003eno order below this rank is required, includes undefined or unspecified names\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eno rank\u003c/td\u003e\n\u003ctd\u003eapplied to nodes not categorized here yet, additional rank and groups names will be released\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-clustering-proteins-clustering\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-clustering-proteins-clustering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ccode\u003eesmecata clustering\u003c/code\u003e: Proteins clustering\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata clustering [-h] -i INPUT_DIR -o OUPUT_DIR [-c CPU] [-t THRESHOLD_CLUSTERING] [-m MMSEQS_OPTIONS] [--linclust] [--remove-tmp]\n\noptional arguments:\n -h, --help show this help message and exit\n -i INPUT_DIR, --input INPUT_DIR\n This input folder of clustering is the output folder of proteomes command.\n -o OUPUT_DIR, --output OUPUT_DIR\n Output directory path.\n -c CPU, --cpu CPU CPU number for multiprocessing.\n -t THRESHOLD_CLUSTERING, --threshold THRESHOLD_CLUSTERING\n Proportion [0 to 1] of proteomes required to occur in a proteins cluster for that cluster to be kept in core proteome assembly.\n -m MMSEQS_OPTIONS, --mmseqs MMSEQS_OPTIONS\n String containing mmseqs options for cluster command (except --threads which is already set by --cpu command and -v). If nothing is given, esmecata will used the option \"--min-seq-id 0.3 -c 0.8\"\n --linclust Use mmseqs linclust (clustering in lienar time) to cluster proteins sequences. It is faster than mmseqs cluster (default behaviour) but less sensitive.\n --remove-tmp Delete tmp files to limit the disk space used: files in tmp_proteome for esmecata proteomes and files created by mmseqs (in mmseqs_tmp).\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor each taxon (a row in the table) EsMeCaTa will use mmseqs2 to cluster the proteins (using an identity of 30% and a coverage of 80%, these values can be changed with the \u003ccode\u003e--mmseqs\u003c/code\u003eoption). Then if a cluster contains at least one protein from each proteomes, it will be kept (this threshold can be changed using the \u003ccode\u003e--threshold option\u003c/code\u003e). The representative proteins from the cluster will be used. A fasta file of all the representative proteins will be created for each taxon.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata clustering\u003c/code\u003e options:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-t/--threshold\u003c/code\u003e: threshold clustering\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to modify the requirements of the presence of at least one protein from each proteomes in a cluster to keep it. Using the threshold option one can give a float between 0 and 1 to select the ratio of representation of proteomes in a cluster to keep.\u003c/p\u003e\n\u003cp\u003eFor example a threshold of 0.8 means that all the cluster with at least 80% representations of proteomes will be kept (with a taxon, associated with 10 proteomes, it means that at least 8 proteomes must have a protein in the cluster so the cluster must be kept).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-c/--cpu\u003c/code\u003e: number of CPU for mmseqs2\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can give a numbe of CPUs to parallelise mmseqs2.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-m/--mmseqs\u003c/code\u003e: mmseqs option to be used for the clustering.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eString containing mmseqs options for cluster command (except --threads which is already set by --cpu command and -v). If nothing is given, esmecata will used the option \"--min-seq-id 0.3 -c 0.8\". For example you can give \u003ccode\u003e--mmseqs \"--min-seq-id 0.8 --kmer-per-seq 80\"\u003c/code\u003e to ask for a minimal identity between sequence of 80% and having 80 kmers per sequence.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--linclust\u003c/code\u003e: replace \u003ccode\u003emmseqs cluster\u003c/code\u003e by \u003ccode\u003emmseqs linclust\u003c/code\u003e (faster but less sensitive)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUse mmseqs linclust (clustering in linear time) to cluster proteins sequences. It is faster than mmseqs cluster (default behaviour) but less sensitive.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--remove-tmp\u003c/code\u003e: remove mmseqs files stored in \u003ccode\u003emmseqs_tmp\u003c/code\u003e folder\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-annotation-retrieve-protein-annotations\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-annotation-retrieve-protein-annotations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ccode\u003eesmecata annotation\u003c/code\u003e: Retrieve protein annotations\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata annotation [-h] -i INPUT_DIR -o OUPUT_DIR [-s SPARQL] [-p PROPAGATE_ANNOTATION] [--uniref] [--expression]\n\noptional arguments:\n -h, --help show this help message and exit\n -i INPUT_DIR, --input INPUT_DIR\n This input folder of annotation is the output folder of clustering command.\n -o OUPUT_DIR, --output OUPUT_DIR\n Output directory path.\n -s SPARQL, --sparql SPARQL\n Use sparql endpoint instead of REST queries on Uniprot.\n -p PROPAGATE_ANNOTATION, --propagate PROPAGATE_ANNOTATION\n Proportion [0 to 1] of the occurrence of an annotation to be propagated from the protein of a cluster to the reference protein of the cluster. 0 mean the annotations from all proteins are propagated to the\n reference and 1 only the annotation occurring in all the proteins of the cluster (default).\n --uniref Use uniref cluster to extract more annotations from the representative member of the cluster associated with the proteins. Needs the --sparql option.\n --expression Extract expression information associated with the proteins. Needs the --sparql option.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor each of the protein clusters kept after the clustering, esmecata will look for the annotation (GO terms, EC number, function, gene name, Interpro) in Uniprot.\nBy default, esmecata will look at the annotations of each proteins from a cluster and keeps only annotation occurring in all the protein of a cluster (threshold 1 of option -p).\nIt is like selecting the intersection of the annotation of the cluster. This can be changed with the option \u003ccode\u003e-p\u003c/code\u003e and giving a float between 0 and 1.\u003c/p\u003e\n\u003cp\u003eThen esmecata will create a tabulated file for each row of the input file and also a folder containing PathoLogic file that can be used as input for Pathway Tools.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata annotation\u003c/code\u003e options:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-s/--sparql\u003c/code\u003e: use SPARQL instead of REST requests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to avoid using REST queries for esmecata and instead use SPARQL queries. This option need a link to a sparql endpoint containing UniProt. If you want to use the \u003ca href=\"https://sparql.uniprot.org/sparql\" rel=\"nofollow\"\u003eSPARQL endpoint\u003c/a\u003e, you can just use: \u003ccode\u003e-s uniprot\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-p/--propagate\u003c/code\u003e: propagation of annotation\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is possible to modify how the annotations are retrieved. By default, esmecata will take the annotations occurring in at least all the proteins of the cluster (\u003ccode\u003e-p 1\u003c/code\u003e). But with the \u003ccode\u003e-p\u003c/code\u003e option it is possible to propagate annotation form the proteins of the cluster to the reference proteins.\u003c/p\u003e\n\u003cp\u003eThis option takes a float as input between 0 and 1, that will be used to filter the annotations retrieved. This number is multiplied by the number of protein in the cluster to estimate a threshold. To keep an annotation the number of the protein having this annotation in the cluster must be higher than the threshold. For example with a threshold of 0.5, for a cluster of 10 proteins an annotation will be kept if 5 or more proteins of the cluster have this annotation.\u003c/p\u003e\n\u003cp\u003eIf the option is set to 0, there will be no filter all the annotation of the proteins of the cluster will be propagated to the reference protein (it corresponds to the \u003cstrong\u003eunion\u003c/strong\u003e of the cluster annotations). This parameter gives the higher number of annotation for proteins. If the option is set to 1, only annotations that are present in all the proteins of a cluster will be kept (it corresponds to the \u003cstrong\u003eintersection\u003c/strong\u003e of the cluster annotations). This parameter is the most stringent and will limit the number of annotations associated with a protein.\u003c/p\u003e\n\u003cp\u003eFor example, for the same taxon the annotation with the parameter \u003ccode\u003e-p 0\u003c/code\u003e leads to the reconstruction of a metabolic networks of 1006 reactions whereas the parameter \u003ccode\u003e-p 1\u003c/code\u003e creates a metabolic network with 940 reactions (in this example with no use of the \u003ccode\u003e-p\u003c/code\u003e option, so without annotation propagation, there was also 940 reactions inferred).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--uniref\u003c/code\u003e: use annotation from uniref\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo add more annotations, esmecata can search the \u003ca href=\"https://www.uniprot.org/help/uniref\" rel=\"nofollow\"\u003eUniRef\u003c/a\u003e cluster associated with the protein associated with a taxon. Then the representative protein of the cluster will be extracted and if its identity with the protein of interest is superior to 90% esmecata will find its annotation (GO Terms and EC numbers) and will propagate these annotations to the protein. At this moment, this option is only usable when using the \u003ccode\u003e--sparql\u003c/code\u003e option.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--expression\u003c/code\u003e: extract expression information\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWith this option, esmecata will extract the \u003ca href=\"https://www.uniprot.org/help/expression_section\" rel=\"nofollow\"\u003eexpression information\u003c/a\u003e associated with a protein. This contains 3 elements: Induction, Tissue specificity and Disruption Phenotype. At this moment, this option is only usable when using the \u003ccode\u003e--sparql\u003c/code\u003e option.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-workflow-consecutive-runs-of-the-three-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-workflow-consecutive-runs-of-the-three-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ccode\u003eesmecata workflow\u003c/code\u003e: Consecutive runs of the three steps\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eusage: esmecata workflow [-h] -i INPUT_FILE -o OUPUT_DIR [-b BUSCO] [-c CPU] [--ignore-taxadb-update] [--all-proteomes] [-s SPARQL] [--remove-tmp] [-l LIMIT_MAXIMAL_NUMBER_PROTEOMES] [-t THRESHOLD_CLUSTERING] [-m MMSEQS_OPTIONS]\n [--linclust] [-p PROPAGATE_ANNOTATION] [--uniref] [--expression] [-r RANK_LIMIT] [--minimal-nb-proteomes MINIMAL_NUMBER_PROTEOMES]\n\noptional arguments:\n -h, --help show this help message and exit\n -i INPUT_FILE, --input INPUT_FILE\n Input taxon file (excel, tsv or csv) containing a column associating ID to a taxonomic affiliation (separated by ;).\n -o OUPUT_DIR, --output OUPUT_DIR\n Output directory path.\n -b BUSCO, --busco BUSCO\n BUSCO percentage between 0 and 1. This will remove all the proteomes without BUSCO score and the score before the selected ratio of completion.\n -c CPU, --cpu CPU CPU number for multiprocessing.\n --ignore-taxadb-update\n If you have a not up-to-date version of the NCBI taxonomy database with ete3, use this option to bypass the warning message and use the old version.\n --all-proteomes Download all proteomes associated with a taxon even if they are no reference proteomes.\n -s SPARQL, --sparql SPARQL\n Use sparql endpoint instead of REST queries on Uniprot.\n --remove-tmp Delete tmp files to limit the disk space used: files in tmp_proteome for esmecata proteomes and files created by mmseqs (in mmseqs_tmp).\n -l LIMIT_MAXIMAL_NUMBER_PROTEOMES, --limit-proteomes LIMIT_MAXIMAL_NUMBER_PROTEOMES\n Choose the maximal number of proteomes after which the tool will select a subset of proteomes instead of using all the available proteomes (default is 99).\n -t THRESHOLD_CLUSTERING, --threshold THRESHOLD_CLUSTERING\n Proportion [0 to 1] of proteomes required to occur in a proteins cluster for that cluster to be kept in core proteome assembly.\n -m MMSEQS_OPTIONS, --mmseqs MMSEQS_OPTIONS\n String containing mmseqs options for cluster command (except --threads which is already set by --cpu command and -v). If nothing is given, esmecata will used the option \"--min-seq-id 0.3 -c 0.8\"\n --linclust Use mmseqs linclust (clustering in lienar time) to cluster proteins sequences. It is faster than mmseqs cluster (default behaviour) but less sensitive.\n -p PROPAGATE_ANNOTATION, --propagate PROPAGATE_ANNOTATION\n Proportion [0 to 1] of the occurrence of an annotation to be propagated from the protein of a cluster to the reference protein of the cluster. 0 mean the annotations from all proteins are propagated to the\n reference and 1 only the annotation occurring in all the proteins of the cluster (default).\n --uniref Use uniref cluster to extract more annotations from the representative member of the cluster associated with the proteins. Needs the --sparql option.\n --expression Extract expression information associated with the proteins. Needs the --sparql option.\n -r RANK_LIMIT, --rank-limit RANK_LIMIT\n This option limit the rank used by the tool for searching for proteomes. The given rank and all the superior ranks will be ignored. Look at the readme for more information (and a list of possible rank).\n --minimal-nb-proteomes MINIMAL_NUMBER_PROTEOMES\n Choose the minimal number of proteomes to be selected by EsMeCaTa. If a taxon has less proteomes, it will be ignored and a higher taxonomic rank will be used. Default is 1.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEsMeCTa will perform the search for proteomes, the protein clustering and the annotation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-esmecata-outputs\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa outputs\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-proteomes\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-proteomes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa proteomes\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_folder\n\u251c\u2500\u2500 proteomes_description\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u251c\u2500\u2500 result\n\u2502 \u2514\u2500\u2500 Cluster_1\n\u2502 \u2514\u2500\u2500 Proteome_1.faa.gz\n\u2502 \u2514\u2500\u2500 Proteome_2.faa.gz\n\u2502 \u2514\u2500\u2500 Cluster_2\n\u2502 \u2514\u2500\u2500 Proteome_3.faa.gz\n\u2502 \u2514\u2500\u2500 Cluster_3\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 tmp_proteome (can be cleaned to spare disk space using --remove-tmp option)\n\u2502 \u2514\u2500\u2500 Proteome_1.faa.gz\n\u2502 \u2514\u2500\u2500 Proteome_2.faa.gz\n\u2502 \u2514\u2500\u2500 Proteome_3.faa.gz\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 association_taxon_taxID.json\n\u251c\u2500\u2500 proteome_tax_id.tsv\n\u251c\u2500\u2500 esmecata_proteomes.log\n\u251c\u2500\u2500 esmecata_metadata_proteomes.json\n\u251c\u2500\u2500 stat_number_proteome.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eproteomes_description\u003c/code\u003e contains list of proteomes find by esmecata on Uniprot associated with the taxonomic affiliation.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eresult\u003c/code\u003e folder contain one sub-folder for each \u003ccode\u003eobservation_name\u003c/code\u003e from the input file. Each sub-folder contains the proteome associated with the \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003etmp_proteome\u003c/code\u003e contains all the proteomes that have been found to be associated with one taxon.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eassociation_taxon_taxID.json\u003c/code\u003e contains for each \u003ccode\u003eobservation_name\u003c/code\u003e the name of the taxon and the corresponding taxon_id found with \u003ccode\u003eete3\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eproteome_tax_id.tsv\u003c/code\u003e contains the name, the taxon_id and the proteomes associated with each \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eesmecata_proteomes.log\u003c/code\u003e contains the log associated with the command.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata_metadata_proteomes.json\u003c/code\u003e is a log about the Uniprot release used and how the queries ware made (REST or SPARQL). It also gets the metadata associated with the command used with esmecata and the dependencies.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003estat_number_proteome.tsv\u003c/code\u003e is a tabulated file containing the number of proteomes found for each observation name.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-clustering\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-clustering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa clustering\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_folder\n\u251c\u2500\u2500 cluster_founds\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 computed_threshold\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 fasta_consensus\n\u2502 \u2514\u2500\u2500 Cluster_1.faa\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 fasta_representative\n\u2502 \u2514\u2500\u2500 Cluster_1.faa\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 mmseqs_tmp (can be cleaned to spare disk space using --remove-tmp option)\n\u2502 \u2514\u2500\u2500 Cluster_1\n\u2502 \u2514\u2500\u2500 mmseqs intermediary files\n\u2502 \u2514\u2500\u2500 ...\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 reference_proteins\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 reference_proteins_consensus_fasta\n\u2502 \u2514\u2500\u2500 Cluster_1.faa\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 reference_proteins_representative_fasta\n\u2502 \u2514\u2500\u2500 Cluster_1.faa\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 proteome_tax_id.tsv\n\u251c\u2500\u2500 esmecata_clustering.log\n\u251c\u2500\u2500 esmecata_metadata_clustering.json\n\u251c\u2500\u2500 stat_number_clustering.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003ecluster_founds\u003c/code\u003e contains one tsv file per \u003ccode\u003eobservation_name\u003c/code\u003e and these files contain the clustered proteins The first column contains the representative proteins of a cluster and the following columns correspond to the other proteins of the same cluster. The first protein occurs two time: one as the representative member of the cluster and a second time as a member of the cluster.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ecomputed_threshold\u003c/code\u003e folder contains the ratio of proteomes represented in a cluster compared to the total number of proteomes associated with a taxon. If the ratio is equal to 1, it means that all the proteomes are represented by a protein in the cluster, 0.5 means that half of the proteoems are represented in the cluster. This score is used when giving the \u003ccode\u003e-t\u003c/code\u003e argument.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003efasta_consensus\u003c/code\u003e contains all the consensus proteins associated with an \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003efasta_representative\u003c/code\u003e contains all the representative proteins associated with an \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003emmseqs_tmp\u003c/code\u003e folder contains the intermediary files of mmseqs2 for each \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ereference_proteins\u003c/code\u003e contains one tsv file per \u003ccode\u003eobservation_name\u003c/code\u003e and these files contain the clustered proteins kept after clustering process. it is similar to \u003ccode\u003ecluster_founds\u003c/code\u003e but it contains only protein kept after clustering and threshold.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ereference_proteins_consensus_fasta\u003c/code\u003e contains the consensus proteins associated with an \u003ccode\u003eobservation_name\u003c/code\u003e for the cluster kept after clustering process. So compared to the fasta of \u003ccode\u003efasta_consensus\u003c/code\u003e it is a sublist with only cluster passing the threshold.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ereference_proteins_representative_fasta\u003c/code\u003e contains the consensus proteins associated with an \u003ccode\u003eobservation_name\u003c/code\u003e for the cluster kept after clustering process. So compared to the fasta of \u003ccode\u003efasta_representative\u003c/code\u003e it is a sublist with only cluster passing the threshold.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eproteome_tax_id.tsv\u003c/code\u003e file is the same than the one created in \u003ccode\u003eesmecata proteomes\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eesmecata_clustering.log\u003c/code\u003e contains the log associated with the command.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eesmecata_metadata_clustering.json\u003c/code\u003e is a log about the the metadata associated with the command used with esmecata and the dependencies.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003estat_number_clustering.tsv\u003c/code\u003e is a tabulated file containing the number of shared proteins found for each observation name.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-annotation\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-annotation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa annotation\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_folder\n\u251c\u2500\u2500 annotation\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 annotation_reference\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 expression_annotation (if --expression option)\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 pathologic\n\u2502 \u2514\u2500\u2500 Cluster_1\n\u2502 \u2514\u2500\u2500 Cluster_1.pf\n\u2502 \u2514\u2500\u2500 ...\n\u2502 \u2514\u2500\u2500 taxon_id.tsv\n\u251c\u2500\u2500 uniref_annotation (if --uniref option)\n\u2502 \u2514\u2500\u2500 Cluster_1.tsv\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 esmecata_annotation.log\n\u251c\u2500\u2500 esmecata_metadata_annotation.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eannotation\u003c/code\u003e folder contains a tabulated file for each \u003ccode\u003eobservation_name\u003c/code\u003e. It contains the annotation retrieved with Uniprot (protein_name, review, GO Terms, EC numbers, Interpros, Rhea IDs and gene name) associated with all the proteins in a proteome or associated with an \u003ccode\u003eobservation_name\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eannotation_reference\u003c/code\u003e contains annotation only for the representative proteins, but the annotation of the other proteins of the same cluster can be propagated to the reference protein if the \u003ccode\u003e-p\u003c/code\u003e was used.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eexpression_annotation\u003c/code\u003e contains expression annotation for the proteins of a taxon (if the \u003ccode\u003e--expression\u003c/code\u003e option was used).\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003epathologic\u003c/code\u003e contains one sub-folder for each \u003ccode\u003eobservation_name\u003c/code\u003e in which there is one PathoLogic file. There is also a \u003ccode\u003etaxon_id.tsv\u003c/code\u003e file which corresponds to a modified version of \u003ccode\u003eproteome_tax_id.tsv\u003c/code\u003e with only the \u003ccode\u003eobservation_name\u003c/code\u003e and the \u003ccode\u003etaxon_id\u003c/code\u003e. This folder can be used as input to \u003ca href=\"https://github.com/AuReMe/mpwt\"\u003empwt\u003c/a\u003e to reconstruct draft metabolic networks using Pathway Tools PathoLogic.\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eesmecata_annotation.log\u003c/code\u003e contains the log associated with the command.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eesmecata_metadata_annotation.json\u003c/code\u003e serves the same purpose as the one used in \u003ccode\u003eesmecata proteomes\u003c/code\u003e to retrieve metadata about Uniprot release at the time of the query. It also gets the metadata associated with the command used with esmecata and the dependencies.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003euniref_annotation\u003c/code\u003e contains the annotation from the representative protein of the UniRef cluster associated with the proteins of a taxon (if the \u003ccode\u003e--uniref\u003c/code\u003e option was used).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003estat_number_clustering.tsv\u003c/code\u003e is a tabulated file containing the number of GO Terms and EC numbers found for each observation name.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-esmecata-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#esmecata-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEsMeCaTa workflow\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_folder\n\u251c\u2500\u2500 0_proteomes\n \u251c\u2500\u2500 proteomes_description\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u251c\u2500\u2500 result\n \u2502 \u2514\u2500\u2500 Cluster_1\n \u2502 \u2514\u2500\u2500 Proteome_1.faa.gz\n \u2502 \u2514\u2500\u2500 Proteome_2.faa.gz\n \u2502 \u2514\u2500\u2500 Cluster_2\n \u2502 \u2514\u2500\u2500 Proteome_3.faa.gz\n \u2502 \u2514\u2500\u2500 Cluster_3\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 tmp_proteome (can be cleaned to spare disk space using --remove-tmp option)\n \u2502 \u2514\u2500\u2500 Proteome_1.faa.gz\n \u2502 \u2514\u2500\u2500 Proteome_2.faa.gz\n \u2502 \u2514\u2500\u2500 Proteome_3.faa.gz\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 association_taxon_taxID.json\n \u251c\u2500\u2500 proteome_tax_id.tsv\n \u251c\u2500\u2500 esmecata_metadata_proteomes.json\n \u251c\u2500\u2500 stat_number_proteome.tsv\n\u251c\u2500\u2500 1_clustering\n \u251c\u2500\u2500 cluster_founds\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 computed_threshold\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 fasta_consensus\n \u2502 \u2514\u2500\u2500 Cluster_1.faa\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 fasta_representative\n \u2502 \u2514\u2500\u2500 Cluster_1.faa\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 mmseqs_tmp (can be cleaned to spare disk space using --remove-tmp option)\n \u2502 \u2514\u2500\u2500 Cluster_1\n \u2502 \u2514\u2500\u2500 mmseqs intermediary files\n \u2502 \u2514\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 reference_proteins\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 reference_proteins_consensus_fasta\n \u2502 \u2514\u2500\u2500 Cluster_1.faa\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 reference_proteins_representative_fasta\n \u2502 \u2514\u2500\u2500 Cluster_1.faa\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 proteome_tax_id.tsv\n \u251c\u2500\u2500 esmecata_metadata_clustering.json\n \u251c\u2500\u2500 stat_number_clustering.tsv\n\u251c\u2500\u2500 2_annotation\n \u251c\u2500\u2500 annotation\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 annotation_reference\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 expression_annotation (if --expression option)\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 pathologic\n \u2502 \u2514\u2500\u2500 Cluster_1\n \u2502 \u2514\u2500\u2500 Cluster_1.pf\n \u2502 \u2514\u2500\u2500 ...\n \u2502 \u2514\u2500\u2500 taxon_id.tsv\n \u251c\u2500\u2500 uniref_annotation (if --uniref option)\n \u2502 \u2514\u2500\u2500 Cluster_1.tsv\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 esmecata_metadata_annotation.json\n \u251c\u2500\u2500 stat_number_annotation.tsv\n\u251c\u2500\u2500 esmecata_workflow.log\n\u251c\u2500\u2500 esmecata_metadata_workflow.json\n\u251c\u2500\u2500 stat_number_workflow.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe files in the folders \u003ccode\u003e0_proteomes\u003c/code\u003e, \u003ccode\u003e1_clustering\u003c/code\u003e and \u003ccode\u003e2_annotation\u003c/code\u003e are the same than the other presented in the previous steps.\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eesmecata_workflow.log\u003c/code\u003e contains the log associated with the command.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eesmecata_metadata_workflow.json\u003c/code\u003e retrieves metadata about Uniprot release at the time of the query, the command used and its duration.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003estat_number_workflow.tsv\u003c/code\u003e is a tabulated file containing the number of proteomes, shared proteins, GO Terms and EC numbers found for each observation name.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [ - "apptainer", - "gpl3", - "gplv3", - "ide", - "md", - "programming", - "singularity", - "singularity-lang", - "singularity-language", - "snu", - "snu-2d", - "snu-2d-programmingtools", - "snu-development", - "snu-programming-tools", - "snu2d-programmingtools", - "snu2dprogrammingtools", - "snuprogrammingtools", - "txt", - "web-ide" + "uniprot", + "proteomes", + "taxonomic-classification" ], - "updated_at": 1668813080.0 + "updated_at": 1669667250.0 }, { "data_format": 2, - "description": "OpenFoam + HiSA installation and compilation + PyTorch(C++) installation.", + "description": "Evolving soft robots using AutoMap genotype-phenotype mapping", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "darshan315/OpenFOAM_HiSA_PyTorch", + "full_name": "mmore500/automap-soro", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-dockersingularity-capabilities-for-openfoam--hisa--pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#dockersingularity-capabilities-for-openfoam--hisa--pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker/Singularity capabilities for OpenFOAM\u00ae + HiSA + PyTorch\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eThe Dockerfile in this repository creates an image with \u003ca href=\"https://openfoam.com/\" rel=\"nofollow\"\u003eESI-OpenFOAM\u003c/a\u003e, \u003ca href=\"https://hisa.gitlab.io/\" rel=\"nofollow\"\u003eHiSA\u003c/a\u003e and \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e support. The image is currently based on\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUbuntu 22.04,\u003c/li\u003e\n\u003cli\u003eOpenFOAM-v2112,\u003c/li\u003e\n\u003cli\u003eHiSA 1.4.6, and\u003c/li\u003e\n\u003cli\u003ePyTorch 1.10.2 (only CPU).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOpenFOAM is not compiled from scratch but installed via the package manager (\u003ca href=\"https://develop.openfoam.com/Development/openfoam/-/wikis/precompiled/debian\" rel=\"nofollow\"\u003eread more\u003c/a\u003e). Also for PyTorch, only the pre-compiled C++ part of the library, named \u003cem\u003elibtorch\u003c/em\u003e, is contained on the image. However, the HiSA package is pulled from the \u003ca href=\"https://gitlab.com/hisa/hisa\" rel=\"nofollow\"\u003esource\u003c/a\u003e and compiled, in which the libraries are installed in \u003ccode\u003e$FOAM_APPBIN\u003c/code\u003e and \u003ccode\u003e$FOAM_LIBBIN\u003c/code\u003e instead of user libraries.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-build-the-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-build-the-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to build the images\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eTo build a docker image,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/darshan315/OpenFOAM_HiSA_PyTorch.git\ncd OpenFOAM_HiSA_PyTorch\ndocker build -t user_name/openfoam_hisa_pytorch:of2112_hisa1.4.6_pt1.10.2_ub22.04 -f Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo create a container,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./create_openfoam_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo start the container and use interactively,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./start_openfoam.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eTo build the image (.sif),\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build of2112_hisa1.4.6_pt1.10.2_ub22.04.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo use the container interactively,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell of2112_hisa1.4.6_pt1.10.2_ub22.04.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo use the image non-interactively and run the application,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif [path] [arguments]\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e \n\u003ch2\u003e\u003ca id=\"user-content-tests\" class=\"anchor\" aria-hidden=\"true\" href=\"#tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTests\u003c/h2\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eThe test directory contains the example scripts for OpenFOAM, HiSA, and PyTorch. These examples can be executed to check the correct installation and compilation.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-openfoam\" class=\"anchor\" aria-hidden=\"true\" href=\"#openfoam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOpenFOAM:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eThe test case for OpenFOAM follows \u003ca href=\"https://develop.openfoam.com/Development/openfoam/-/tree/master/tutorials/incompressible/icoFoam/cavity/cavity\" rel=\"nofollow\"\u003ecavity example\u003c/a\u003e given in OpenFoam tutorials. The example can be run from top-level directory of this repository as,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# To run the simulation\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./Allrun ./test/cavity/\n# To clean the finished simulation\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./Allclean ./test/cavity/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-hisa\" class=\"anchor\" aria-hidden=\"true\" href=\"#hisa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHiSA\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eThe test case for HiSA follows \u003ca href=\"https://gitlab.com/hisa/hisa/-/tree/master/examples/rae2822\" rel=\"nofollow\"\u003erae2822 example\u003c/a\u003e given in HiSA examples. The example can be run from top-level directory of this repository as,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# To generate the mesh\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./setupMesh ./test/rae2822/\n# To run the simulation\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./runSim ./test/rae2822/\n# To clean the mesh\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./cleanMesh ./test/rae2822/\n# To clean the finished simulation\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./cleanSim ./test/rae2822/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePyTorch\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdetails\u003e\n\u003csummary\u003e Click to expand! \u003c/summary\u003e\n\u003cp\u003eFrom top-level directory of this repository, you can build and run \u003cem\u003etensorCreation\u003c/em\u003e as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# build\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif wmake test/tensorCreation/\n# run\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./tensorCreation test/tensorCreation/\n# clean\nsingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif wclean test/tensorCreation/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, one can also define scripts, which are then executed by Singularity. For example, to build and run the second example, \u003cem\u003esimpleMLP\u003c/em\u003e, run the \u003cem\u003ecompileAndRun.sh\u003c/em\u003e script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run of2112_hisa1.4.6_pt1.10.2_ub22.04.sif ./compileAndRun.sh test/simpleMLP/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e \n\u003cp\u003e\u003cstrong\u003eFor more Information, see \u003ca href=\"https://ml-cfd.com/openfoam/pytorch/docker/2020/12/29/running-pytorch-models-in-openfoam.html\" rel=\"nofollow\"\u003e1\u003c/a\u003e, \u003ca href=\"https://openfoam.com/\" rel=\"nofollow\"\u003e2\u003c/a\u003e, \u003ca href=\"https://hisa.gitlab.io/\" rel=\"nofollow\"\u003e3\u003c/a\u003e, \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003e4\u003c/a\u003e, \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003e5\u003c/a\u003e, \u003ca href=\"https://docs.sylabs.io/guides/3.0/user-guide/index.html#\" rel=\"nofollow\"\u003e6\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-automap-soro\" class=\"anchor\" aria-hidden=\"true\" href=\"#automap-soro\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eautomap-soro\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/894\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis project is based on AutoMap, a pair of methods for automatic generation of evolvable genotype-phenotype mappings.\nBoth use an artificial neural network autoencoder trained on phenotypes harvested from fitness peaks as the basis for a genotype-phenotype mapping.\nIn the first, the decoder segment of a bottlenecked autoencoder serves as the genotype-phenotype mapping.\nIn the second, a denoising autoencoder serves as the genotype-phenotype mapping.\u003c/p\u003e\n\u003cp\u003eThe technique was introduced in\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eMatthew Andres Moreno, Wolfgang Banzhaf, and Charles Ofria.\n\"Learning an Evolvable Genotype-Phenotype Mapping.\"\nProceedings of the Genetic and Evolutionary Computation Conference.\nACM, 2018.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYou can find the paper and supporting materials at \u003ca href=\"https://osf.io/n92c7/\" rel=\"nofollow\"\u003ehttps://osf.io/n92c7/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe project was built using the \u003ca href=\"https://github.com/skriegman/evosoro\"\u003eevosoro\u003c/a\u003e soft robot simulator.\nEvosoro was designed and developed by the \u003ca href=\"http://www.meclab.org\" rel=\"nofollow\"\u003eMorphology, Evolution \u0026amp; Cognition Laboratory\u003c/a\u003e, University of Vermont.\nThe library is built on top of the open source \u003ca href=\"https://github.com/jonhiller/VoxCAD\"\u003eVoxCAD\u003c/a\u003e and the underlying voxel physics engine (\u003ca href=\"https://github.com/jonhiller/Voxelyze\"\u003eVoxelyze\u003c/a\u003e) which were both developed by the \u003ca href=\"http://www.creativemachineslab.com/\" rel=\"nofollow\"\u003eCreative Machines Lab\u003c/a\u003e, Columbia University.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-hidden=\"true\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003eTODO\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eExperiments reported in this paper used \u003ca href=\"https://github.com/mmore500/automap-soro/tree/vTODO\"\u003evTODO\u003c/a\u003e of this software.\u003c/p\u003e\n\u003cp\u003edata, tutorials, and writeup @ \u003ca href=\"https://osf.io/6jf52/\" rel=\"nofollow\"\u003ehttps://osf.io/6jf52/\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-authorship\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-authorship\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware Authorship\u003c/h2\u003e\n\u003cp\u003eMatthew Andres Moreno\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emmore500@msu.edu\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, - "topics": [], - "updated_at": 1660833287.0 + "subscribers_count": 2, + "topics": [ + "machine-learning", + "evolutionary-computation", + "genotype-phenotype-map", + "evolution", + "scientific-computing", + "scientific-publications" + ], + "updated_at": 1634847819.0 }, { "data_format": 2, @@ -30736,1824 +30873,1823 @@ var data = "filenames": [ "Singularity" ], - "full_name": "lsx1980/plant-image-analysis", + "full_name": "truatpasteurdotfr/singularity-docker-centos7-conda-tf2-pytorch", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-arabidopsis-rosette-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#arabidopsis-rosette-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eArabidopsis Rosette Analysis\u003c/h1\u003e\n\u003cp\u003eAuthor: Suxing Liu\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Computational-Plant-Science/arabidopsis-rosette-analysis/workflows/CI/badge.svg\"\u003e\u003cimg src=\"https://github.com/Computational-Plant-Science/arabidopsis-rosette-analysis/workflows/CI/badge.svg\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../master/media/image_01.png\"\u003e\u003cimg src=\"../master/media/image_01.png\" alt=\"Optional Text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRobust and parameter-free plant image segmentation and trait extraction.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eProcess with plant image top view, including whole tray plant image, this tool will segment it into individual images.\u003c/li\u003e\n\u003cli\u003eRobust segmentation based on parameter-free color clustering method.\u003c/li\u003e\n\u003cli\u003eExtract individual plant gemetrical traits, and write output into excel file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eEither \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity \u003c/a\u003e is required to run this project in a Unix environment.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e:/opt/arabidopsis-rosette-analysis -w /opt/arabidopsis-rosette-analysis computationalplantscience/arabidopsis-rosette-analysis python3 /opt/arabidopsis-rosette-analysis/trait_extract_parallel.py -i input -o output -ft \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ejpg,png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e docker://computationalplantscience/arabidopsis-rosette-analysis python3 trait_extract_parallel.py -i input -o output -ft \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ejpg,png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-docker-centos7-conda-tf2-pytorch\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-docker-centos7-conda-tf2-pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esingularity-docker-centos7-conda-tf2-pytorch\u003c/h1\u003e\n\u003cp\u003ecentos7 container with miniconda , tensorflow2 and pytorch with gpu support (cuda 10.1)\u003c/p\u003e\n\u003cp\u003eBuilding:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build singularity-docker-centos7-conda-tf2-pytorch.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1660840103.0 + "updated_at": 1649957662.0 }, { "data_format": 2, - "description": null, + "description": "RNAseq analysis workflow, maintained by BiBs facility.", "filenames": [ - "parametric-face-image-generator-2.1.1/Singularity" + "workflow/Singularity_ncbi" ], - "full_name": "AdamOswald/face", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-deepfakes_faceswap\" class=\"anchor\" aria-hidden=\"true\" href=\"#deepfakes_faceswap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edeepfakes_faceswap\u003c/h1\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://faceswap.dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e98a8ca1dde3d8f5d360ab52a581a905842e7056e5d8d147ae20a3f02024e3/68747470733a2f2f692e696d6775722e636f6d2f7a48766a486e622e706e67\" data-canonical-src=\"https://i.imgur.com/zHvjHnb.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003eFaceSwap is a tool that utilizes deep learning to recognize and swap faces in pictures and videos.\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/56b72a8f58b9ec6df2734c9c2fdb016491d186f90809b66f0ed568a2ae169dd9/68747470733a2f2f692e696d6775722e636f6d2f6e5748464c44662e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/56b72a8f58b9ec6df2734c9c2fdb016491d186f90809b66f0ed568a2ae169dd9/68747470733a2f2f692e696d6775722e636f6d2f6e5748464c44662e6a7067\" data-canonical-src=\"https://i.imgur.com/nWHFLDf.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://www.patreon.com/bePatron?u=23238350\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b7105015397da52617ce6775a339b0b99d689d6f644c2ce911c5d472362bcbd/68747470733a2f2f63352e70617472656f6e2e636f6d2f65787465726e616c2f6c6f676f2f6265636f6d655f615f706174726f6e5f627574746f6e2e706e67\" data-canonical-src=\"https://c5.patreon.com/external/logo/become_a_patron_button.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u00a0\u00a0\u00a0\u00a0\u003ca href=\"https://discord.gg/FC54sYg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ba052d032c543765683cd39ec2151454d9f5bad39f70ccc85bb44fbe27b4839/68747470733a2f2f692e696d6775722e636f6d2f6749707a746b762e706e67\" data-canonical-src=\"https://i.imgur.com/gIpztkv.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://www.dailymotion.com/video/x810mot\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/36920800/178301720-b69841bb-a1ca-4c20-91db-a2a10f5692ca.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003eEmma Stone/Scarlett Johansson FaceSwap using the Phaze-A model\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://www.youtube.com/watch?v=r1jng79a5xc\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4c8848e79bcc92e61653f11eafa8d97b316b6ec470a0eefd258700f407c3dc6b/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f72316a6e673739613578632f302e6a7067\" data-canonical-src=\"https://img.youtube.com/vi/r1jng79a5xc/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003eJennifer Lawrence/Steve Buscemi FaceSwap using the Villain model\n\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/deepfakes/faceswap/actions/workflows/pytest.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/deepfakes/faceswap/actions/workflows/pytest.yml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://faceswap.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/58b7e03bdd7d51e7d1729b3d6713709f2d204514deb36851d90afbbc75a2ac93/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f66616365737761702f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/faceswap/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMake sure you check out \u003ca href=\"INSTALL.md\"\u003eINSTALL.md\u003c/a\u003e before getting started.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#deepfakes_faceswap\"\u003edeepfakes_faceswap\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#manifesto\"\u003eManifesto\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#faceswap-has-ethical-uses\"\u003eFaceSwap has ethical uses.\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-setup-and-run-the-project\"\u003eHow To setup and run the project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#extract\"\u003eExtract\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#train\"\u003eTrain\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#convert\"\u003eConvert\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#gui\"\u003eGUI\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#general-notes\"\u003eGeneral notes:\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#help-i-need-support\"\u003eHelp I need support!\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#discord-server\"\u003eDiscord Server\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#faceswap-forum\"\u003eFaceSwap Forum\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#donate\"\u003eDonate\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#patreon\"\u003ePatreon\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#one-time-donations\"\u003eOne time Donations\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#torzdf\"\u003e@torzdf\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#andenixa\"\u003e@andenixa\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#how-to-contribute\"\u003eHow to contribute\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#for-people-interested-in-the-generative-models\"\u003eFor people interested in the generative models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#for-devs\"\u003eFor devs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#for-non-dev-advanced-users\"\u003eFor non-dev advanced users\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#for-end-users\"\u003eFor end-users\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#for-haters\"\u003eFor haters\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#about-githubcomdeepfakes\"\u003eAbout github.com/deepfakes\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#what-is-this-repo\"\u003eWhat is this repo?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#why-this-repo\"\u003eWhy this repo?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#why-is-it-named-deepfakes-if-it-is-not-udeepfakes\"\u003eWhy is it named \u0027deepfakes\u0027 if it is not /u/deepfakes?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#what-if-udeepfakes-feels-bad-about-that\"\u003eWhat if /u/deepfakes feels bad about that?\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#about-machine-learning\"\u003eAbout machine learning\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#how-does-a-computer-know-how-to-recognizeshape-faces-how-does-machine-learning-work-what-is-a-neural-network\"\u003eHow does a computer know how to recognize/shape faces? How does machine learning work? What is a neural network?\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-manifesto\" class=\"anchor\" aria-hidden=\"true\" href=\"#manifesto\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eManifesto\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faceswap-has-ethical-uses\" class=\"anchor\" aria-hidden=\"true\" href=\"#faceswap-has-ethical-uses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFaceSwap has ethical uses.\u003c/h2\u003e\n\u003cp\u003eWhen faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. Until one individual brought it together into a single, cohesive collection. It ran, it worked, and as is so often the way with new technology emerging on the internet, it was immediately used to create inappropriate content. Despite the inappropriate uses the software was given originally, it was the first AI code that anyone could download, run and learn by experimentation without having a Ph.D. in math, computer theory, psychology, and more. Before \"deepfakes\" these techniques were like black magic, only practiced by those who could understand all of the inner workings as described in esoteric and endlessly complicated books and papers.\u003c/p\u003e\n\u003cp\u003e\"Deepfakes\" changed all that and anyone could participate in AI development. To us, developers, the release of this code opened up a fantastic learning opportunity. It allowed us to build on ideas developed by others, collaborate with a variety of skilled coders, experiment with AI whilst learning new skills and ultimately contribute towards an emerging technology which will only see more mainstream use as it progresses.\u003c/p\u003e\n\u003cp\u003eAre there some out there doing horrible things with similar software? Yes. And because of this, the developers have been following strict ethical standards. Many of us don\u0027t even use it to create videos, we just tinker with the code to see what it does. Sadly, the media concentrates only on the unethical uses of this software. That is, unfortunately, the nature of how it was first exposed to the public, but it is not representative of why it was created, how we use it now, or what we see in its future. Like any technology, it can be used for good or it can be abused. It is our intention to develop FaceSwap in a way that its potential for abuse is minimized whilst maximizing its potential as a tool for learning, experimenting and, yes, for legitimate faceswapping.\u003c/p\u003e\n\u003cp\u003eWe are not trying to denigrate celebrities or to demean anyone. We are programmers, we are engineers, we are Hollywood VFX artists, we are activists, we are hobbyists, we are human beings. To this end, we feel that it\u0027s time to come out with a standard statement of what this software is and isn\u0027t as far as us developers are concerned.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFaceSwap is not for creating inappropriate content.\u003c/li\u003e\n\u003cli\u003eFaceSwap is not for changing faces without consent or with the intent of hiding its use.\u003c/li\u003e\n\u003cli\u003eFaceSwap is not for any illicit, unethical, or questionable purposes.\u003c/li\u003e\n\u003cli\u003eFaceSwap exists to experiment and discover AI techniques, for social or political commentary, for movies, and for any number of ethical and reasonable uses.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe are very troubled by the fact that FaceSwap can be used for unethical and disreputable things. However, we support the development of tools and techniques that can be used ethically as well as provide education and experience in AI for anyone who wants to learn it hands-on. We will take a zero tolerance approach to anyone using this software for any unethical purposes and will actively discourage any such uses.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-how-to-setup-and-run-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-setup-and-run-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow To setup and run the project\u003c/h1\u003e\n\u003cp\u003eFaceSwap is a Python program that will run on multiple Operating Systems including Windows, Linux, and MacOS.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"INSTALL.md\"\u003eINSTALL.md\u003c/a\u003e for full installation instructions. You will need a modern GPU with CUDA support for best performance. AMD GPUs are partially supported.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h1\u003e\n\u003cp\u003eThe project has multiple entry points. You will have to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGather photos and/or videos\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExtract\u003c/strong\u003e faces from your raw photos\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTrain\u003c/strong\u003e a model on the faces extracted from the photos/videos\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConvert\u003c/strong\u003e your sources with the model\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCheck out \u003ca href=\"USAGE.md\"\u003eUSAGE.md\u003c/a\u003e for more detailed instructions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-extract\" class=\"anchor\" aria-hidden=\"true\" href=\"#extract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtract\u003c/h2\u003e\n\u003cp\u003eFrom your setup folder, run \u003ccode\u003epython faceswap.py extract\u003c/code\u003e. This will take photos from \u003ccode\u003esrc\u003c/code\u003e folder and extract faces into \u003ccode\u003eextract\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-train\" class=\"anchor\" aria-hidden=\"true\" href=\"#train\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrain\u003c/h2\u003e\n\u003cp\u003eFrom your setup folder, run \u003ccode\u003epython faceswap.py train\u003c/code\u003e. This will take photos from two folders containing pictures of both faces and train a model that will be saved inside the \u003ccode\u003emodels\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-convert\" class=\"anchor\" aria-hidden=\"true\" href=\"#convert\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConvert\u003c/h2\u003e\n\u003cp\u003eFrom your setup folder, run \u003ccode\u003epython faceswap.py convert\u003c/code\u003e. This will take photos from \u003ccode\u003eoriginal\u003c/code\u003e folder and apply new faces into \u003ccode\u003emodified\u003c/code\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-gui\" class=\"anchor\" aria-hidden=\"true\" href=\"#gui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGUI\u003c/h2\u003e\n\u003cp\u003eAlternatively, you can run the GUI by running \u003ccode\u003epython faceswap.py gui\u003c/code\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-general-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#general-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral notes:\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003eAll of the scripts mentioned have \u003ccode\u003e-h\u003c/code\u003e/\u003ccode\u003e--help\u003c/code\u003e options with arguments that they will accept. You\u0027re smart, you can figure out how this works, right?!\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNB: there is a conversion tool for video. This can be accessed by running \u003ccode\u003epython tools.py effmpeg -h\u003c/code\u003e. Alternatively, you can use \u003ca href=\"https://www.ffmpeg.org\" rel=\"nofollow\"\u003effmpeg\u003c/a\u003e to convert video into photos, process images, and convert images back to the video.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSome tips:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReusing existing models will train much faster than starting from nothing.\nIf there is not enough training data, start with someone who looks similar, then switch the data.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-help-i-need-support\" class=\"anchor\" aria-hidden=\"true\" href=\"#help-i-need-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHelp I need support!\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-discord-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#discord-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscord Server\u003c/h2\u003e\n\u003cp\u003eYour best bet is to join the \u003ca href=\"https://discord.gg/FC54sYg\" rel=\"nofollow\"\u003eFaceSwap Discord server\u003c/a\u003e where there are plenty of users willing to help. Please note that, like this repo, this is a SFW Server!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faceswap-forum\" class=\"anchor\" aria-hidden=\"true\" href=\"#faceswap-forum\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFaceSwap Forum\u003c/h2\u003e\n\u003cp\u003eAlternatively, you can post questions in the \u003ca href=\"https://faceswap.dev/forum\" rel=\"nofollow\"\u003eFaceSwap Forum\u003c/a\u003e. Please do not post general support questions in this repo as they are liable to be deleted without response.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-donate\" class=\"anchor\" aria-hidden=\"true\" href=\"#donate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDonate\u003c/h1\u003e\n\u003cp\u003eThe developers work tirelessly to improve and develop FaceSwap. Many hours have been put in to provide the software as it is today, but this is an extremely time-consuming process with no financial reward. If you enjoy using the software, please consider donating to the devs, so they can spend more time implementing improvements.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-patreon\" class=\"anchor\" aria-hidden=\"true\" href=\"#patreon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePatreon\u003c/h2\u003e\n\u003cp\u003eThe best way to support us is through our Patreon page:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.patreon.com/bePatron?u=23238350\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b7105015397da52617ce6775a339b0b99d689d6f644c2ce911c5d472362bcbd/68747470733a2f2f63352e70617472656f6e2e636f6d2f65787465726e616c2f6c6f676f2f6265636f6d655f615f706174726f6e5f627574746f6e2e706e67\" alt=\"become-a-patron\" data-canonical-src=\"https://c5.patreon.com/external/logo/become_a_patron_button.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-one-time-donations\" class=\"anchor\" aria-hidden=\"true\" href=\"#one-time-donations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOne time Donations\u003c/h2\u003e\n\u003cp\u003eAlternatively you can give a one off donation to any of our Devs:\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-torzdf\" class=\"anchor\" aria-hidden=\"true\" href=\"#torzdf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e@torzdf\u003c/h3\u003e\n\u003cp\u003eThere is very little FaceSwap code that hasn\u0027t been touched by torzdf. He is responsible for implementing the GUI, FAN aligner, MTCNN detector and porting the Villain, DFL-H128 and DFaker models to FaceSwap, as well as significantly improving many areas of the code.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBitcoin:\u003c/strong\u003e bc1qpm22suz59ylzk0j7qk5e4c7cnkjmve2rmtrnc6\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthereum:\u003c/strong\u003e 0xd3e954dC241B87C4E8E1A801ada485DC1d530F01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMonero:\u003c/strong\u003e 45dLrtQZ2pkHizBpt3P3yyJKkhcFHnhfNYPMSnz3yVEbdWm3Hj6Kr5TgmGAn3Far8LVaQf1th2n3DJVTRkfeB5ZkHxWozSX\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePaypal:\u003c/strong\u003e \u003ca href=\"https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026amp;hosted_button_id=JZ8PP3YE9J62L\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/086fc8dbe63028c5c6791524b4a1c112bf50af1ecc69e9d60c2fdc9ea4c07206/68747470733a2f2f7777772e70617970616c6f626a656374732e636f6d2f656e5f47422f692f62746e2f62746e5f646f6e6174655f534d2e676966\" alt=\"torzdf\" data-canonical-src=\"https://www.paypalobjects.com/en_GB/i/btn/btn_donate_SM.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-andenixa\" class=\"anchor\" aria-hidden=\"true\" href=\"#andenixa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e@andenixa\u003c/h3\u003e\n\u003cp\u003eCreator of the Unbalanced and OHR models, as well as expanding various capabilities within the training process. Andenixa is currently working on new models and will take requests for donations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePaypal:\u003c/strong\u003e \u003ca href=\"https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026amp;hosted_button_id=NRVLQYGS6NWTU\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/086fc8dbe63028c5c6791524b4a1c112bf50af1ecc69e9d60c2fdc9ea4c07206/68747470733a2f2f7777772e70617970616c6f626a656374732e636f6d2f656e5f47422f692f62746e2f62746e5f646f6e6174655f534d2e676966\" alt=\"andenixa\" data-canonical-src=\"https://www.paypalobjects.com/en_GB/i/btn/btn_donate_SM.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-how-to-contribute\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to contribute\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-people-interested-in-the-generative-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-people-interested-in-the-generative-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor people interested in the generative models\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGo to the \u0027faceswap-model\u0027 to discuss/suggest/commit alternatives to the current algorithm.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-devs\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-devs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor devs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRead this README entirely\u003c/li\u003e\n\u003cli\u003eFork the repo\u003c/li\u003e\n\u003cli\u003ePlay with it\u003c/li\u003e\n\u003cli\u003eCheck issues with the \u0027dev\u0027 tag\u003c/li\u003e\n\u003cli\u003eFor devs more interested in computer vision and openCV, look at issues with the \u0027opencv\u0027 tag. Also feel free to add your own alternatives/improvements\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-non-dev-advanced-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-non-dev-advanced-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor non-dev advanced users\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eRead this README entirely\u003c/li\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003ePlay with it\u003c/li\u003e\n\u003cli\u003eCheck issues with the \u0027advuser\u0027 tag\u003c/li\u003e\n\u003cli\u003eAlso go to the \u0027\u003ca href=\"https://faceswap.dev/forum\" rel=\"nofollow\"\u003efaceswap Forum\u003c/a\u003e\u0027 and help others.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-end-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-end-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor end-users\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGet the code here and play with it if you can\u003c/li\u003e\n\u003cli\u003eYou can also go to the \u003ca href=\"https://faceswap.dev/forum\" rel=\"nofollow\"\u003efaceswap Forum\u003c/a\u003e and help or get help from others.\u003c/li\u003e\n\u003cli\u003eBe patient. This is a relatively new technology for developers as well. Much effort is already being put into making this program easy to use for the average user. It just takes time!\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNotice\u003c/strong\u003e Any issue related to running the code has to be opened in the \u003ca href=\"https://faceswap.dev/forum\" rel=\"nofollow\"\u003efaceswap Forum\u003c/a\u003e!\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-for-haters\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-haters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor haters\u003c/h2\u003e\n\u003cp\u003eSorry, no time for that.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-about-githubcomdeepfakes\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-githubcomdeepfakes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout github.com/deepfakes\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-is-this-repo\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is this repo?\u003c/h2\u003e\n\u003cp\u003eIt is a community repository for active users.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-this-repo\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-this-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy this repo?\u003c/h2\u003e\n\u003cp\u003eThe joshua-wu repo seems not active. Simple bugs like missing \u003cem\u003ehttp://\u003c/em\u003e in front of urls have not been solved since days.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-why-is-it-named-deepfakes-if-it-is-not-udeepfakes\" class=\"anchor\" aria-hidden=\"true\" href=\"#why-is-it-named-deepfakes-if-it-is-not-udeepfakes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy is it named \u0027deepfakes\u0027 if it is not /u/deepfakes?\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBecause a typosquat would have happened sooner or later as project grows\u003c/li\u003e\n\u003cli\u003eBecause we wanted to recognize the original author\u003c/li\u003e\n\u003cli\u003eBecause it will better federate contributors and users\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-what-if-udeepfakes-feels-bad-about-that\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-if-udeepfakes-feels-bad-about-that\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat if /u/deepfakes feels bad about that?\u003c/h2\u003e\n\u003cp\u003eThis is a friendly typosquat, and it is fully dedicated to the project. If /u/deepfakes wants to take over this repo/user and drive the project, he is welcomed to do so (Raise an issue, and he will be contacted on Reddit). Please do not send /u/deepfakes messages for help with the code you find here.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-about-machine-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-machine-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout machine learning\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-does-a-computer-know-how-to-recognizeshape-faces-how-does-machine-learning-work-what-is-a-neural-network\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-does-a-computer-know-how-to-recognizeshape-faces-how-does-machine-learning-work-what-is-a-neural-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow does a computer know how to recognize/shape faces? How does machine learning work? What is a neural network?\u003c/h2\u003e\n\u003cp\u003eIt\u0027s complicated. Here\u0027s a good video that makes the process understandable:\n\u003ca href=\"https://www.youtube.com/watch?v=R9OHn5ZF4Uo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cd51f29fbffa450256ce4b8f23c3aa49ba32c374f0956abaae5b5b51f340718a/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f52394f486e355a4634556f2f302e6a7067\" alt=\"How Machines Learn\" data-canonical-src=\"https://img.youtube.com/vi/R9OHn5ZF4Uo/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHere\u0027s a slightly more in depth video that tries to explain the basic functioning of a neural network:\n\u003ca href=\"https://www.youtube.com/watch?v=aircAruvnKk\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c488e6987928cb5ffa6c1f8c593b2dcf51baf054ca6010b29a9380889470dc41/68747470733a2f2f696d672e796f75747562652e636f6d2f76692f61697263417275766e4b6b2f302e6a7067\" alt=\"How Machines Learn\" data-canonical-src=\"https://img.youtube.com/vi/aircAruvnKk/0.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003etl;dr: training data + trial and error\u003c/p\u003e\n", + "full_name": "parisepigenetics/RASflow_EDC", + "latest_release": "v1.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/banner.png\"\u003e\u003cimg src=\"images/banner.png\" alt=\"drawing\" width=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-rasflow_edc\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#rasflow_edc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRASflow_EDC\u003c/h1\u003e\n\u003cp\u003eMaintained by \u003ca href=\"mailto:magali.hennion@u-paris.fr\"\u003eMagali Hennion\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eImplemented by \u003ca href=\"https://parisepigenetics.github.io/bibs/\" rel=\"nofollow\"\u003eBiBs-EDC\u003c/a\u003e, this workflow for RNA-seq data analysis is based on RASflow which was originally published by \u003ca href=\"https://doi.org/10.1186/s12859-020-3433-x\" rel=\"nofollow\"\u003eX. Zhang\u003c/a\u003e. It has been modified to run effectively on both IFB and iPOP-UP clusters and to fit our specific needs. Moreover, several tools and features were added, including a comprehensive report, as well as the possibility to incorporate the repeats in the analysis. If you encounter troubles or need additional tools or features, you can create an issue on the \u003ca href=\"https://github.com/parisepigenetics/RASflow_EDC/issues\"\u003eGitHub repository\u003c/a\u003e, email directly \u003ca href=\"mailto:bibsATparisepigenetics.com\"\u003eBiBs\u003c/a\u003e, or pass by the 366b room.\u003c/p\u003e\n\u003cp\u003eThe complete documentation is available at \u003ca href=\"https://parisepigenetics.github.io/bibs/edctools/workflows/rasflow_edc/#/edctools/\" rel=\"nofollow\"\u003ehttps://parisepigenetics.github.io/bibs/edctools/workflows/rasflow_edc\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you use this workflow to analyse your data, don\u0027t forget to \u003cstrong\u003eacknowledge BiBs\u003c/strong\u003e in all your communications !\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEDC people: \"We thank the Bioinformatics and Biostatistics Core Facility, Paris Epigenetics and Cell Fate Center for bioinformatics support.\"\u003c/li\u003e\n\u003cli\u003eExternal users: \"We thank the Bioinformatics and Biostatistics Core Facility, Paris Epigenetics and Cell Fate Center for sharing their analysis workflows.\"\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 0, + "subscribers_count": 3, "topics": [], - "updated_at": 1663870861.0 + "updated_at": 1667227406.0 }, { "data_format": 2, - "description": "The main platform for sharing all the information about AI/HPC-related containers at C3SE. See also the included tutorial to get started. ", + "description": "Este reposit\u00f3rio cont\u00e9m o material correspondente ao minicurso/Hands on 05: Gera\u00e7\u00e3o Autom\u00e1tica de C\u00f3digo Atrav\u00e9s de Computa\u00e7\u00e3o Simb\u00f3lica em Python: Introdu\u00e7\u00e3o \u00e0 LDE Devito do VI SAPCT e V ICPAD organizado pelo Senai Cimatec", "filenames": [ - "FEniCS/Singularity.FEniCS", - "FDTD/Singularity.FDTD-8.19.1541", - "ambertools/Singularity.ambertools-20", - "PyTorch/Singularity.PyTorch-1.11-NGC-22.01", - "PyTorch/Singularity.PyTorch-1.13-NGC-22.08", - "PyTorch/Singularity.PyTorch-1.10-NGC-21.10", - "PyTorch/Singularity.PyTorch-1.10-NGC-21.08", - "PyTorch/Singularity.PyTorch-1.12-NGC-22.04", - "PyTorch/Singularity.PyTorch-1.10-NGC-21.09", - "PyTorch/Singularity.PyTorch-1.12.1", - "PyTorch/Singularity.PyTorch-1.11-NGC-21.12", - "PyTorch/Singularity.PyTorch-1.9.0", - "PyTorch/Singularity.PyTorch-1.10.0", - "PyTorch/Singularity.PyTorch-1.6-NGC-20.07", - "PyTorch/Singularity.PyTorch-1.12-NGC-22.05", - "PyTorch/Singularity.PyTorch-1.11-NGC-22.02", - "PyTorch/Singularity.PyTorch-1.11-NGC-21.11", - "PyTorch/Singularity.PyTorch-1.8.1", - "PyTorch/Singularity.PyTorch-1.11.0", - "RockyLinux/Singularity.RockyLinux-8.5.20220308", - "CentOS/Singularity.CentOS-8.3.2011", - "Ubuntu/Singularity.Ubuntu-focal-20210827", - "Conda/Singularity.anaconda-2021.05", - "Conda/Singularity.miniconda-4.10.3", - "R/Singularity.R-4.1.3-TensorFlow-2.4.2", - "R/Singularity.R-4.1.3-Torch-1.11.0", - "R/Singularity.RStudio-Server-R-4.1.2", - "R/Singularity.RStudio-Server-R-4.1.3-TensorFlow-2.4.2", - "R/Singularity.RStudio-Server-R-4.1.3-Torch-1.11.0", - "MILC/Singularity.MILC-NGC-Oct2017", - "Julia/Singularity.Julia-1.6.2", - "NvidiaOmniverse/Singularity.IsaacSim-NGC-2022.1.0", - "GROMACS/Singularity.GROMACS-2021-NGC", - "ANSA/Singularity.ANSA-22.1.0", - "ANSA/Singularity.ANSA-20.1.0", - "SPAM/Singularity.SPAM", - "TensorFlow/Singularity.TensorFlow-2.6.0-NGC-21.09", - "TensorFlow/Singularity.TensorFlow-2.7.0-NGC-22.02", - "TensorFlow/Singularity.TensorFlow-2.6.2-NGC-21.12", - "TensorFlow/Singularity.TensorFlow-2.7.0-NGC-22.01", - "TensorFlow/Singularity.TensorFlow-2.5.0-NGC-21.08", - "TensorFlow/Singularity.TensorFlow-2.9.1-NGC-22.08", - "TensorFlow/Singularity.TensorFlow-2.10.0", - "TensorFlow/Singularity.TensorFlow-2.5.1", - "TensorFlow/Singularity.TensorFlow-1.15.5-NGC-21.08", - "TensorFlow/Singularity.TensorFlow-2.6.0", - "TensorFlow/Singularity.TensorFlow-2.8.0-NGC-22.04", - "TensorFlow/Singularity.TensorFlow-2.6.0-NGC-21.10", - "TensorFlow/Singularity.TensorFlow-2.8.0", - "TensorFlow/Singularity.TensorFlow-2.8.0-NGC-22.05", - "TensorFlow/Singularity.TensorFlow-2.7.0", - "TensorFlow/Singularity.TensorFlow-2.9.1", - "TensorFlow/Singularity.TensorFlow-2.6.0-NGC-21.11", - "TensorFlow/Singularity.TensorFlow-2.4.3", - "DIANA/Singularity.DIANA-10.5", - "DIANA/Singularity.DIANA-10.3", - "DIANA/Singularity.DIANA-10.4", - "LAMMPS/Singularity.LAMMPS-NGC-10Feb2021", - "SonnetSuite/Singularity.SonnetSuite-17.52.1", - "RELION/Singularity.RELION-3.1.2-NGC", - "QuantumEspresso/Singularity.QuantumEspresso-6.8", - "FINE-Marine/Singularity.FINE-Marine-7.2", - "NAMD/Singularity.NAMD-3.0-alpha3-singlenode-NGC", - "NAMD/Singularity.NAMD-2.13-multinode-NGC", - "NAMD/Singularity.NAMD-2.13-singlenode-NGC" + "docker/Singularity.nvidia.def" ], - "full_name": "c3se/containers", + "full_name": "ofmla/curso_sapct", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-recipes-provided-at-c3se-clusters\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-recipes-provided-at-c3se-clusters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer recipes provided at C3SE clusters\u003c/h1\u003e\n\u003cp\u003eThis repository holds the recipes of the centrally provided Singularity containers at C3SE\u0027s clusters.\nThey are also useable as a reference for users who wish to build their own Singularity containers.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-missing-containers-updates-and-troubleshootin\" class=\"anchor\" aria-hidden=\"true\" href=\"#missing-containers-updates-and-troubleshootin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMissing containers, updates, and troubleshootin\u003c/h2\u003e\n\u003cp\u003eWe continuously add more packages to this repository. If you can\u0027t find a relevant container for your needs, or in the case of encountering any errors or deprecated features in the material, feel free to contact us: \u003ca href=\"mailto:support@c3se.chalmers.se\"\u003esupport@c3se.chalmers.se\u003c/a\u003e or open a pull-request.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"cimatec.png\"\u003e\u003cimg src=\"cimatec.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVI Semin\u00e1rio de Avalia\u00e7\u00e3o de Pesquisa Cient\u00edfica e Tecnol\u00f3gica (SAPCT) e V Workshop de Integra\u00e7\u00e3o e Capacita\u00e7\u00e3o em Processamento de Alto Desempenho (ICPAD)\u003c/h1\u003e\u003ca id=\"user-content-vi-semin\u00e1rio-de-avalia\u00e7\u00e3o-de-pesquisa-cient\u00edfica-e-tecnol\u00f3gica-sapct-e-v-workshop-de-integra\u00e7\u00e3o-e-capacita\u00e7\u00e3o-em-processamento-de-alto-desempenho-icpad\" class=\"anchor\" aria-label=\"Permalink: VI Semin\u00e1rio de Avalia\u00e7\u00e3o de Pesquisa Cient\u00edfica e Tecnol\u00f3gica (SAPCT) e V Workshop de Integra\u00e7\u00e3o e Capacita\u00e7\u00e3o em Processamento de Alto Desempenho (ICPAD)\" href=\"#vi-semin\u00e1rio-de-avalia\u00e7\u00e3o-de-pesquisa-cient\u00edfica-e-tecnol\u00f3gica-sapct-e-v-workshop-de-integra\u00e7\u00e3o-e-capacita\u00e7\u00e3o-em-processamento-de-alto-desempenho-icpad\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEste reposit\u00f3rio cont\u00e9m o material correspondente ao minicurso/Hands on 05: Gera\u00e7\u00e3o Autom\u00e1tica de C\u00f3digo Atrav\u00e9s de Computa\u00e7\u00e3o Simb\u00f3lica em Python: Introdu\u00e7\u00e3o \u00e0 LDE Devito do VI SAPCT e V ICPAD organizado pelo Senai Cimatec. O objetivo do minicurso \u00e9:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eapresentar\u003c/strong\u003e de forma r\u00e1pida e objetiva a Linguagem de Dom\u00ednio espec\u00edfico (LDE) Devito\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003emostrar\u003c/strong\u003e atrav\u00e9s de exemplos simples o uso pr\u00e1tico da LDE Devito\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eexpor\u003c/strong\u003e aos estudantes o conceito de modelagem s\u00edsmica e a solu\u00e7\u00e3o numerica da equa\u00e7\u00e3o de onda com diferen\u00e7as finitas via Devito\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eN\u00e3o \u00e9 necess\u00e1rio instalar nenhum programa em seu computador para seguir este minicurso. O material do curso est\u00e1 dispon\u00edvel no formato IPYNB (Jupyter NoteBook).\u003c/p\u003e\n\u003cp\u003eExiste um unico Jupyter Notebook neste reposit\u00f3rio:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/github/ofmla/curso_sapct/blob/main/intro_lde_devito.ipynb\" rel=\"nofollow\"\u003eIntro_LDE_Devito\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-intro_lde_devito\" class=\"anchor\" aria-label=\"Permalink: Intro_LDE_Devito\" href=\"#intro_lde_devito\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCont\u00e9m uma introdu\u00e7\u00e3o f\u00e1cil \u00e0 LDE Devito (siga os passos a seguir para acessar o notebook online)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClique em \u003ca href=\"https://mybinder.org/v2/gh/ofmla/curso_sapct/HEAD\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e91e1d353a8b6acf0b42547ac3901f2c30138a3abaaa3d3c242da30b5b4f8426/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (que lan\u00e7ar\u00e1 o reposit\u00f3rio \u003ca href=\"https://github.com/ofmla/curso_sapct\"\u003ehttps://github.com/ofmla/curso_sapct\u003c/a\u003e). Isso \u00e0s vezes pode levar alguns minutos, ent\u00e3o seja paciente ...\u003c/li\u003e\n\u003cli\u003eEspere at\u00e9 que ele seja carregado\u003c/li\u003e\n\u003cli\u003eClique no notebook intro_lde_devito.ipynb\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003eEspero que voc\u00ea ache o Framework Devito \u00fatil.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://oscar-mojica.netlify.app/\" rel=\"nofollow\"\u003eOscar Mojica\u003c/a\u003e \u003cbr\u003e\nPesquisador do Centro de Supercomputa\u00e7\u00e3o do \u003ca href=\"http://www.senaicimatec.com.br/\" rel=\"nofollow\"\u003eSenai Cimatec\u003c/a\u003e \u003cbr\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, - "topics": [ - "singularity-containers", - "docker", - "hpc" - ], - "updated_at": 1662123057.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1692903754.0 }, { "data_format": 2, - "description": "CUT\u0026RUN and CUT\u0026Tag FASTQ -\u003e bigwig pipeline", + "description": "FieldOpt C++ Optimization Framework [Open Research Version]", "filenames": [ - "Singularity.centos" + "Docker/Singularity", + "Docker/Release/Singularity", + "Docker/Develop/Singularity" ], - "full_name": "ertheisen/hohriver", + "full_name": "PetroleumCyberneticsGroup/FieldOpt-Research-Open", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-appalachianhg19v1-container-for-early-ngs-pipeline-applications\" class=\"anchor\" href=\"#appalachianhg19v1-container-for-early-ngs-pipeline-applications\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eappalachian.hg19v1 container for early NGS pipeline applications\u003c/h1\u003e\n\u003cp\u003eHow to run pipeline:\u003c/p\u003e\n\u003cp\u003esingularity run -H $HOME:/home/$USER --app [appname] --bind [directory_info] container_name.simg\u003c/p\u003e\n\u003cp\u003ePath to genome in container needs to be:\n\u0027/genomes/test/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/dm6/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/spike/sacCer3/Sequence/Bowtie2Index/genome\u0027\n-or-\n\u0027/genomes/STAR/[STAR index files]\u0027\n-or-\n\u0027/genomes/anno/[gtf or gff annotation file]\u0027\u003c/p\u003e\n\u003cp\u003eFor Baker Cluster Users:\ndirectory to mount for fly spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Drosophila_melanogaster/UCSC\ndirectory to mount for yeast spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Saccharomyces_cerevisiae/UCSC\ndirectory to mount for E. coli spike-in is: /gpfs0/home/gdlessnicklab/share/trails/genomes/Escherichia_coli_K_12_DH10B/NCBI\ndirectory to mount for hg19 is: /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/\u003c/p\u003e\n\u003cp\u003eThe correct bind commands for test and spike are as follows:\n--bind /reference/homo_sapiens/hg19/ucsc_assembly/illumina_download/:/genomes/test\n--bind /gpfs0/home/gdlessnicklab/share/trails/genomes/Escherichia_coli_K_12_DH10B/NCBI:/genomes/spike\u003c/p\u003e\n\u003cp\u003eTo run interactive terminal in container\u003c/p\u003e\n\u003cp\u003esingularity shell -H $HOME:/home/$USER --bind [directory_info] appalachian_hg19.simg\u003c/p\u003e\n\u003cp\u003eTo run command from a program in the container\u003c/p\u003e\n\u003cp\u003esingularity exec -H $HOME:/home/$USER --bind [directory_info] appalachian_hg19.simg [command information]\u003c/p\u003e\n\u003cp\u003e##Be sure to bind your data, your genome, and any script files you may want to run\u003c/p\u003e\n\u003cp\u003eApps:\ncutnrun_full : full pipeline from fastqc to mapped bam, bed, normalized bedgraph, and normalized bigwig with mapping qc using plotFingerprint from deeptools - bowtie2 alignment assumes 150 bp PE reads and allows dovetail and overlap in alignment\ncutntag : full pipeline from fastqc to mapped bam, bed, normalized bedgraph, and normalized bigwig with mapping qc using plotFingerprint from deeptools - bowtie2 alignment assumes 150 bp PE reads and allows dovetail and overlap in alignment\ncutnrun_hardtrim : full pipeline from fastqc to mapped bam, bed, normalized bedgraph, and normalized bigwig with mapping qc using plotFingerprint from deeptools - bowtie2 alignment. First step trims 150 bp reads from HiSeq to 60 bp.\ncutntag_hardtrim : full pipeline from fastqc to mapped bam, bed, normalized bedgraph, and normalized bigwig with mapping qc using plotFingerprint from deeptools - bowtie2 alignment. First step trims 150 bp reads from HiSeq to 60 bp.\nfastqc_pre : FastQC pre-trim\ntrim_qc : Fastq trimming with Trim Galore and FastQC post trim\nbowtie2_alignment : Alignment for CUT\u0026amp;RUN samples; insert 10 bp-700 bp, assumes paired end 150 bp reads, makes bams/beds/bg/bw with no spike in.\nbams : Generate downstream files for starting at bams using same pipeline - assumes hg19 alignment\nmapqc_fingerprint : Use deeptools to assess enrichment quality\u003c/p\u003e\n\u003cp\u003eData should be stored in a \"project\" directory with a sub-directory called \"fastq\". The \"fastq\" subdirectory should have all of your paired end fastq or fastq.gz files. Then the correct bind for data is:\n--bind /path_to/project/:/data\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFieldOpt-Research-Open\u003c/h1\u003e\u003ca id=\"user-content-fieldopt-research-open\" class=\"anchor\" aria-label=\"Permalink: FieldOpt-Research-Open\" href=\"#fieldopt-research-open\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFieldOpt [Open Research Version] is a C++ programming framework\nfor efficient prototyping and testing of optimization methodologies\nfor problems involving large-scale numerical simulations.\u003c/p\u003e\n\u003cp\u003eFieldOpt serves as a multi-disciplinary knowledge\nrepository for coupling optimization with reservoir simulation.\nTechnology development is based on integration of efficient\niterative procedures with expert domain parametrizations.\u003c/p\u003e\n\u003cp\u003eFieldOpt facilitates research and innovation through up-scaling of\nprototype methodology to realistic cases, coupling, integration and\nhybridization of optimization methodology and problem solutions,\nand cross-application of existing methods to new domains.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTarget problems\u003c/h2\u003e\u003ca id=\"user-content-target-problems\" class=\"anchor\" aria-label=\"Permalink: Target problems\" href=\"#target-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePetroleum Field Development\u003c/h3\u003e\u003ca id=\"user-content-petroleum-field-development\" class=\"anchor\" aria-label=\"Permalink: Petroleum Field Development\" href=\"#petroleum-field-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Well placement optimization \u003ca href=\"#Bellout2012JntWplcCntrl\"\u003e[1]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[x] Production optimization\u003c/li\u003e\n\u003cli\u003e[x] Optimization of inflow-control valve settings\u003c/li\u003e\n\u003cli\u003e[x] Well completion optimization and model-update while drilling\u003c/li\u003e\n\u003cli\u003e[ ] Minimization of C02 emissions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptimization methodologies\u003c/h2\u003e\u003ca id=\"user-content-optimization-methodologies\" class=\"anchor\" aria-label=\"Permalink: Optimization methodologies\" href=\"#optimization-methodologies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeterministic\u003c/h3\u003e\u003ca id=\"user-content-deterministic\" class=\"anchor\" aria-label=\"Permalink: Deterministic\" href=\"#deterministic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Compass Search (CS)\u003c/li\u003e\n\u003cli\u003e[x] Asynchronous Paralell Pattern Search (APPS)\u003c/li\u003e\n\u003cli\u003e[x] Derivative-Free Trust-Region Algorithm (DFTR) \u003ca href=\"#Silva2020DfTrAlgWcntrlOpt\"\u003e[2]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStochastic / probabilistic\u003c/h3\u003e\u003ca id=\"user-content-stochastic--probabilistic\" class=\"anchor\" aria-label=\"Permalink: Stochastic / probabilistic\" href=\"#stochastic--probabilistic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Genetic Algorithm (GA)\u003c/li\u003e\n\u003cli\u003e[x] Particle Swarm Optimization (PSO)\u003c/li\u003e\n\u003cli\u003e[x] Covariance Matrix Adaption Evolutionary Strategy (CMA-ES)\u003c/li\u003e\n\u003cli\u003e[x] Bayesian Optimization (EGO)\u003c/li\u003e\n\u003cli\u003e[x] Simultaneous Perturbation Stochastic Approximation (SPSA)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHybrid approaches\u003c/h3\u003e\u003ca id=\"user-content-hybrid-approaches\" class=\"anchor\" aria-label=\"Permalink: Hybrid approaches\" href=\"#hybrid-approaches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] mPSO\u003c/li\u003e\n\u003cli\u003e[ ] APPS/PSO + data-driven meta-optimization\u003c/li\u003e\n\u003cli\u003e[ ] Joint optimization using embedded reduced-order sub-routines\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProblem structure\u003c/h3\u003e\u003ca id=\"user-content-problem-structure\" class=\"anchor\" aria-label=\"Permalink: Problem structure\" href=\"#problem-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Multi-level joint optimization (concurrent, sequential, embedded)\u003c/li\u003e\n\u003cli\u003e[ ] Automatic variable segregation for multi-level optimization\u003c/li\u003e\n\u003cli\u003e[x] Variable scaling\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eObjective terms\u003c/h3\u003e\u003ca id=\"user-content-objective-terms\" class=\"anchor\" aria-label=\"Permalink: Objective terms\" href=\"#objective-terms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Weighted function, Net Present Value\u003c/li\u003e\n\u003cli\u003e[x] Well cost\u003c/li\u003e\n\u003cli\u003e[x] Augmented terms: Geology \u0026amp; geophysics-based (SWCT)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThirdparty solvers/libraries\u003c/h3\u003e\u003ca id=\"user-content-thirdparty-solverslibraries\" class=\"anchor\" aria-label=\"Permalink: Thirdparty solvers/libraries\" href=\"#thirdparty-solverslibraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] SNOPT \u003ca href=\"#Gill2002SNOPTSIAMRev\"\u003e[3]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[x] Ensemble based Reservoir Tool (ERT) \u003ca href=\"#EquinorERT\"\u003e[4]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] TensorFlow\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFunctionalities\u003c/h2\u003e\u003ca id=\"user-content-functionalities\" class=\"anchor\" aria-label=\"Permalink: Functionalities\" href=\"#functionalities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInterfaces subsurface flow simulators\u003c/h3\u003e\u003ca id=\"user-content-interfaces-subsurface-flow-simulators\" class=\"anchor\" aria-label=\"Permalink: Interfaces subsurface flow simulators\" href=\"#interfaces-subsurface-flow-simulators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Schlumberger\u0027s E100/E300/IX\u003c/li\u003e\n\u003cli\u003e[x] Open Porous Media Flow\u003c/li\u003e\n\u003cli\u003e[x] Stanford\u0027s AD-GPRS\u003c/li\u003e\n\u003cli\u003e[x] Pre-/Post-processing\n\u003cul\u003e\n\u003cli\u003e[x] E300 adjoint-gradient read-in\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWell trajectory development\u003c/h3\u003e\u003ca id=\"user-content-well-trajectory-development\" class=\"anchor\" aria-label=\"Permalink: Well trajectory development\" href=\"#well-trajectory-development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Automatic well planner (AWP) \u003ca href=\"#Kristoffersen2020AWPGeoUncer\"\u003e[5]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[x] State-of-the-art well connection transmissibility factor calculation \u003ca href=\"#ResInsightv2020.04.1\"\u003e[6]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[x] Variable mapping onto multi-segmented well model (WELSEGS/COMPSEGS/WSEGVALV) \u003ca href=\"#SLB2012EclipseTD\"\u003e[7]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWell placement constraint-handling\u003c/h3\u003e\u003ca id=\"user-content-well-placement-constraint-handling\" class=\"anchor\" aria-label=\"Permalink: Well placement constraint-handling\" href=\"#well-placement-constraint-handling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Method of Alternating Projections (MAP) \u003ca href=\"#Bellout2018EffConstrHandlWplcOpt\"\u003e[8]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] Length, inter-well distance, user-defined convex-polytope reservoir-boundary\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNetwork/facility modeling\u003c/h3\u003e\u003ca id=\"user-content-networkfacility-modeling\" class=\"anchor\" aria-label=\"Permalink: Network/facility modeling\" href=\"#networkfacility-modeling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Topside facility model for CO2 emission calculation\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUncertainty-handling\u003c/h3\u003e\u003ca id=\"user-content-uncertainty-handling\" class=\"anchor\" aria-label=\"Permalink: Uncertainty-handling\" href=\"#uncertainty-handling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Expected cost function evaluation over realization set\u003c/li\u003e\n\u003cli\u003e[ ] Reduced random sampling strategy\u003ca href=\"#Jesmani2020RedRanSamStr\"\u003e[9]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eParallelization\u003c/h3\u003e\u003ca id=\"user-content-parallelization\" class=\"anchor\" aria-label=\"Permalink: Parallelization\" href=\"#parallelization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Algorithm-level parallelization of cost function\nevaluations (simulations) through MPI runtime library\n\u003ca href=\"#Baumann2020FieProFrmwrk\"\u003e[10]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca id=\"user-content-Bellout2012JntWplcCntrl\"\u003e[1]\u003c/a\u003e\nBellout, M.C.; Echeverria Ciaurri, D.; Durlofsky, L.J.; Foss, B.; Kleppe, J.\n(2012).\nJoint optimization of oil well placement and controls.\nComputational Geosciences, 16(4), pp.1061-1079.\n\u003ca href=\"https://doi.org/10.1007/s10596-012-9303-5\" rel=\"nofollow\"\u003ehttps://doi.org/10.1007/s10596-012-9303-5\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Silva2020DfTrAlgWcntrlOpt\"\u003e[2]\u003c/a\u003e\nSilva, T.L.; Bellout, M.C.; Giuliani, C.; Camponogara, E.; Pavlov, A.\n(2020).\nA Derivative-Free Trust-Region Algorithm for Well Control Optimization.\n17th European Conference on the Mathematics of Oil\nRecovery, 14th-17th September, Online Event.\n\u003ca href=\"https://doi.org/10.3997/2214-4609.202035086\" rel=\"nofollow\"\u003ehttps://doi.org/10.3997/2214-4609.202035086\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Gill2002SNOPTSIAMRev\"\u003e[3]\u003c/a\u003e\nGill, P.E.; Murray, W.; Saunders, M.A.\n(2005).\nSNOPT: An SQP Algorithm for Large-Scale Constrained Optimization.\nSIAM Review, 47(1), pp.99-131.\n\u003ca href=\"http://dx.doi.org/10.1137/S0036144504446096\" rel=\"nofollow\"\u003ehttp://dx.doi.org/10.1137/S0036144504446096\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-EquinorERT\"\u003e[4]\u003c/a\u003e\nEquinor.\n(2021).\nEnsemble based Reservoir Tool.\n\u003ca href=\"https://github.com/equinor/ert\"\u003ehttps://github.com/equinor/ert\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Kristoffersen2020AWPGeoUncer\"\u003e[5]\u003c/a\u003e\nKristoffersen, B.S.; Silva, T.L.; Bellout, M.C.; Berg, C.F.\n(2020).\nAn Automatic Well Planner for Efficient Well Placement\nOptimization Under Geological Uncertainty.\n17th European Conference on the Mathematics of Oil\nRecovery, 14th-17th September, Online Event.\n\u003ca href=\"https://doi.org/10.3997/2214-4609.202035211\" rel=\"nofollow\"\u003ehttps://doi.org/10.3997/2214-4609.202035211\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-ResInsightv2020.04.1\"\u003e[6]\u003c/a\u003e\nCeetron Solutions AS; Equinor ASA.\n(2020).\nResInsight.\n\u003ca href=\"http://resinsight.org\" rel=\"nofollow\"\u003ehttp://resinsight.org\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-SLB2012EclipseTD\"\u003e[7]\u003c/a\u003e\nSchlumberger AS.\n(2012).\nEclipse technical description.\nChp.44: Multi-segment Wells. pp.683-703.\n\u003ca href=\"https://www.software.slb.com/products/eclipse/simulators\" rel=\"nofollow\"\u003ehttps://www.software.slb.com/products/eclipse/simulators\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Bellout2018EffConstrHandlWplcOpt\"\u003e[8]\u003c/a\u003e\nBellout, M.C.; Volkov, O.\n(2018).\nDevelopment of efficient constraint-handling approaches\nfor well placement optimization.\n16th European Conference on the Mathematics of Oil\nRecovery, 3rd-6th September, Barcelona, Spain.\n\u003ca href=\"https://doi.org/10.3997/2214-4609.201802247\" rel=\"nofollow\"\u003ehttps://doi.org/10.3997/2214-4609.201802247\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Jesmani2020RedRanSamStr\"\u003e[9]\u003c/a\u003e\nJesmani, M.; Jafarpour, B.; Bellout, M.C.; Foss, B.\n(2020).\nA reduced random sampling strategy\nfor fast robust well placement optimization.\nJournal of Petroleum Science and Engineering, 184, pp.106414.\n\u003ca href=\"https://doi.org/10.1016/j.petrol.2019.106414\" rel=\"nofollow\"\u003ehttps://doi.org/10.1016/j.petrol.2019.106414\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca id=\"user-content-Baumann2020FieProFrmwrk\"\u003e[10]\u003c/a\u003e\nBaumann, E.J.M.; Dale, S.I.; Bellout, M.C.\n(2020).\nFieldOpt: A powerful and effective programming\nframework tailored for field development optimization.\nComputers \u0026amp; Geosciences, 135, pp.104379.\n\u003ca href=\"https://doi.org/10.1016/j.cageo.2019.104379\" rel=\"nofollow\"\u003ehttps://doi.org/10.1016/j.cageo.2019.104379\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1616617576.0 + "updated_at": 1641512743.0 }, { "data_format": 2, - "description": "Linux containers running XDMoD service (MariaDB + Apache)", + "description": null, "filenames": [ - "Singularity/Singularity" + "VepFileDeployment/Singularity.filedeploy", + "ReportingApplication/Singularity.report" ], - "full_name": "jtfrey/xdmod-container", - "latest_release": "v1.8.2", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-xdmod-container\" class=\"anchor\" href=\"#xdmod-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003exdmod-container\u003c/h1\u003e\n\u003cp\u003eThis repository includes container provisioning files to create a container that executes the XDMoD web application. Both the MariaDB server and Apache httpd execute within the container, making the service completely self-contained.\u003c/p\u003e\n\u003cp\u003eThe MariaDB instance will retain the default CentOS 7 configuration; no \u003ccode\u003eroot\u003c/code\u003e password is set, the test database is not removed, and the anonymous user is not removed. Since all access is internal to the container, there\u0027s no need to secure it.\u003c/p\u003e\n\u003cp\u003eThe container features a runloop that waits for files to appear in \u003ccode\u003e/var/lib/XDMoD-ingest-queue/in\u003c/code\u003e. The file(s) are ingested into the database and will be moved to \u003ccode\u003e/var/lib/XDMoD-ingest-queue/out\u003c/code\u003e if successful or to \u003ccode\u003e/var/lib/XDMoD-ingest-queue/error\u003c/code\u003e if not. Results of the operations are logged to \u003ccode\u003e/var/log/xdmod/ingest-queue.log\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-docker\" class=\"anchor\" href=\"#docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eThe Docker container should be spawned with TCP port 8080 mapped to a host port to expose the web application. The database starts out uninitialized; when a container instance is spawned an external directory may be bind-mounted at \u003ccode\u003e/var/lib/mysql\u003c/code\u003e in the container to make the database persistent across restarts of the instance, and when the container entrypoint script is first executed the database setup will be completed automatically.\u003c/p\u003e\n\u003cp\u003eAn external directory can also be bind-mounted at \u003ccode\u003e/var/lib/XDMoD-ingest-queue\u003c/code\u003e. The directory must have the following subdirectories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003ein\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eout\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eerror\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese three directories will be created automatically by the entrypoint script if not present in \u003ccode\u003e/var/lib/XDMoD-ingest-queue\u003c/code\u003e. Using an external directory allows processes outside the container to copy Slurm accounting log files to the \u003ccode\u003ein\u003c/code\u003e subdirectory and the entrypoint runloop will awake within 5 minutes and ingest the data.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example\" class=\"anchor\" href=\"#example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h3\u003e\n\u003cp\u003eThe container image is built in this repository directory using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd Docker\n$ ROOT_PASSWORD=\"\u0026lt;password\u0026gt;\" docker build --rm --tag local/xdmod:9.5.0 .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following example illustrates the creation of an instance with persistent database and ingest queue directories:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir -p /tmp/XDMoD-Caviness/ingest-queue\n$ mkdir -p /tmp/XDMoD-Caviness/database\n$ docker run --detach --restart unless-stopped \\\n --name XDMoD-Caviness \\\n --env CLUSTER_NAME=\"cc3\" \\\n --env RESOURCE_LOG_FORMAT=\"slurm\" \\\n --volume \"/tmp/XDMoD-Caviness/database:/var/lib/mysql:rw\" \\\n --volume \"/tmp/XDMoD-Caviness/ingest-queue:/var/lib/XDMoD-ingest-queue:rw\" \\\n --publish 8080:8080\n local/xdmod:9.5.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eCLUSTER_NAME\u003c/code\u003e and \u003ccode\u003eRESOURCE_LOG_FORMAT\u003c/code\u003e are used in the entrypoint XDMoD-start script as arguments to \u003ccode\u003exdmod-shredder\u003c/code\u003e for resource manager log file ingestion. \u003ccode\u003eRESOURCE_LOG_FORMAT\u003c/code\u003e defaults to \"slurm\".\u003c/p\u003e\n\u003cp\u003eOnce the instance is online, XDMoD must be initialized and the ingest queue activated:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker exec -it XDMoD-Caviness /bin/bash -l\n[container]\u0026gt; xdmod-setup\n :\n[container]\u0026gt; touch /var/lib/XDMoD-ingest-queue/enable\n[container]\u0026gt; exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point, copying files to \u003ccode\u003e/tmp/XDMoD-Caviness/ingest-queue/in\u003c/code\u003e will see them processed in the runloop. Point a web browser to \u003ca href=\"http://localhost:8080/\" rel=\"nofollow\"\u003ehttp://localhost:8080/\u003c/a\u003e to use the web application.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eSingularity 3.0 or newer is required (3.2.1 was used in our production environment) for the network port mapping and support for instances (service-like containers).\u003c/p\u003e\n\u003cp\u003eRather than bind-mounting directories at specific paths as outline above for Docker, with Singularity a writable overlay file system is a good option. Any changes to the file system relative to the read-only container image are written to an external directory. As with Docker, port 8080 is mapped to a host port to expose the web application.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-example-1\" class=\"anchor\" href=\"#example-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h3\u003e\n\u003cp\u003eThe container image is built in this repository directory using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd Singularity\n$ ROOT_PASSWORD=\"\u0026lt;password\u0026gt;\" singularity build XDMoD-9.5.0.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following example illustrates the execution of an instance with an overlay file system:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir -p /tmp/XDMoD-Caviness\n$ singularity instance start --overlay /tmp/XDMoD-Caviness --net --dns 10.65.0.13 \\\n --network bridge --network-args \"portmap=8080:8080/tcp\" \\\n --env CLUSTER_NAME=\"cc3\" --env RESOURCE_LOG_FORMAT=\"slurm\" \\\n XDMoD-9.5.0.sif XDMoD-Caviness\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003eCLUSTER_NAME\u003c/code\u003e and \u003ccode\u003eRESOURCE_LOG_FORMAT\u003c/code\u003e are used in the entrypoint XDMoD-start script as arguments to \u003ccode\u003exdmod-shredder\u003c/code\u003e for resource manager log file ingestion. \u003ccode\u003eRESOURCE_LOG_FORMAT\u003c/code\u003e defaults to \"slurm\".\u003c/p\u003e\n\u003cp\u003eOnce the instance is online, XDMoD must be initialized and the ingest queue activated:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://XDMoD-Caviness\n[container]\u0026gt; xdmod-setup\n :\n[container]\u0026gt; touch /var/lib/XDMoD-ingest-queue/in\n[container]\u0026gt; touch /var/lib/XDMoD-ingest-queue/enable\n[container]\u0026gt; exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point, copying files to \u003ccode\u003e/tmp/XDMoD-Caviness/upper/var/lib/XDMoD-ingest-queue/in\u003c/code\u003e will see them processed in the runloop. Point a web browser to \u003ca href=\"http://localhost:8080/\" rel=\"nofollow\"\u003ehttp://localhost:8080/\u003c/a\u003e to use the web application.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-helper-scripts\" class=\"anchor\" href=\"#helper-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHelper Scripts\u003c/h3\u003e\n\u003cp\u003eThe \u003ccode\u003esbin\u003c/code\u003e directory includes a SysV-style script that can be used to start, stop, restart, and query status of instances of the Singularity container.\u003c/p\u003e\n\u003cp\u003eTo start a new or existing instance with the default container image and overlay directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sbin/instance Caviness start\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo use a different container image and overlay directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sbin/instance --overlay=/tmp/XDMoD --image=./XDMoD-uge.sif Farber start\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003estatus\u003c/code\u003e action returns 0 if the instance is running, non-zero otherwise:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sbin/instance Farber status\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--verbose\u003c/code\u003e option increases the amount of output displayed by the command, and the \u003ccode\u003e--help\u003c/code\u003e option summarizes the command and all options.\u003c/p\u003e\n\u003cp\u003eIn addition, the \u003ccode\u003esystemd\u003c/code\u003e directory contains a templated service unit that integrates Singularity instances with systemd for automated startup/shutdown. Adding our Farber instance above looks like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cp systemd/xdmod-template.service /etc/systemd/system/xdmod@Farber.service\n$ systemctl daemon-reload\n$ systemctl enable xdmod@Farber.service\n$ systemctl start xdmod@Farber.service\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "sbilge/ClinVAP", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/sbilge/ClinVAP/blob/master/doc/logo.jpeg\"\u003e\u003cimg src=\"https://github.com/sbilge/ClinVAP/raw/master/doc/logo.jpeg\" alt=\"Pipeline Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7f05470bebde335916523fc79634181a25859aa9c555fdac45f7393a9a62895c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f506572736f6e616c697a65644f6e636f6c6f67792f436c696e6963616c5265706f7274696e67506970656c696e652e737667\" alt=\"Release: Github\" data-canonical-src=\"https://img.shields.io/github/release/PersonalizedOncology/ClinicalReportingPipeline.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2168\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.docker.com/u/personalizedoncology/repository/list\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/505174bdf4e0e21d1eea9dc360b336f4119d54dcca8c30ecd52efecdcd0dc4ee/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65722d2d6875622d626c75652e737667\" alt=\"Docker: Available\" data-canonical-src=\"https://img.shields.io/badge/hosted-docker--hub-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4426cbe5c21edb002526331c7a8fbfa089e84a550567b02a0d829a98b136ad0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eClinical Variant Annotation Pipeline\u003c/h1\u003e\u003ca id=\"user-content-clinical-variant-annotation-pipeline\" class=\"anchor\" aria-label=\"Permalink: Clinical Variant Annotation Pipeline\" href=\"#clinical-variant-annotation-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClinical Variant Annotation Pipeline (ClinVAP) creates a genetic report of somatic mutations from a variant call format (VCF) file. Please refer this document for implementation of the pipeline. Documentation of the pipeline is available at \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/wiki\"\u003eWiki page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMetadata Structure\u003c/h3\u003e\u003ca id=\"user-content-metadata-structure\" class=\"anchor\" aria-label=\"Permalink: Metadata Structure\" href=\"#metadata-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf a patient metadata file is provided in the input directory with the naming schema \u0026lt;INPUT_VCF_NAME\u0026gt;_metadata.json, ClinVAP recognizes it and renders the information into the Patient Data table in the outputted report. Additionally, if dignosis is provided in the metadata file, the list of drugs with the clinical evidence of targeting the gene in that particular cancer type is reported in the \"CIViC Summary of Drugs Targeting the Affected Genes\" table. If no diagnosis is provided, then the pipeline stays agnostic to the cancer type, and returns the results related with the gene-drug association regardless of the cancer type. Please note that the disease name should be selected from the pre-defined dictionary that can be found \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/blob/master/doc/disease_names_dictionary.txt\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetadata file format:\u003c/strong\u003e\u003cbr\u003e\n{\u003cbr\u003e\n\"patient_firstname\":\"\u0026lt;NAME\u0026gt;\",\u003cbr\u003e\n\"patient_lastname\":\"\u0026lt;SURNAME\u0026gt;\",\u003cbr\u003e\n\"patient_dateofbirth\":\"\u0026lt;DATE\u0026gt;\",\u003cbr\u003e\n\"patient_diagnosis_short\":\"\u0026lt;DIAGNOSIS\u0026gt;\",\u003cbr\u003e\n\"mutation_load\":\"\u0026lt;LOAD\u0026gt;\"\u003cbr\u003e\n}\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage with Singularity\u003c/h2\u003e\u003ca id=\"user-content-usage-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Usage with Singularity\" href=\"#usage-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequirements: Singularity 2.4+\u003cbr\u003e\nPlease make sure that you have 12 GB of empty space on your home directory, and ports 5000 and 27021 are not being used by another application.\nTo run the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull reporting image from Singularity Hub.\n\u003ccode\u003esingularity pull -n reporting_app.img shub://PersonalizedOncology/ClinVAP:report\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePull dependency files image from Singularity Hub.\n\u003ccode\u003esingularity pull -n file_deploy.img shub://PersonalizedOncology/ClinVAP:filedeploy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun dependency files image first to transfer those file on your local folder.\n\u003ccode\u003esingularity run -B /LOCAL/PATH/TO/FILES:/mnt file_deploy.img -a \u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the reporting image to generate the clinical reports.\n\u003ccode\u003esingularity run -B /LOCAL/PATH/TO/FILES:/data -B /PATH/TO/INPUT/DATA:/inout reporting_app.img -t /inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-a\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in your /PATH/TO/INPUT/DATA folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage with Docker\u003c/h2\u003e\u003ca id=\"user-content-usage-with-docker\" class=\"anchor\" aria-label=\"Permalink: Usage with Docker\" href=\"#usage-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFor Mac and Ubuntu Users\u003c/h3\u003e\u003ca id=\"user-content-for-mac-and-ubuntu-users\" class=\"anchor\" aria-label=\"Permalink: For Mac and Ubuntu Users\" href=\"#for-mac-and-ubuntu-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequirements: Docker Engine release 1.13.0+, Compose release 1.10.0+.\u003cbr\u003e\nPlease make sure that you have 34 GB of physical empty space on your Docker Disk Image, and ports 5000 and 27017 are not being used by another application.\u003c/p\u003e\n\u003cp\u003eTo tun the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePelase note that the input VCF file(s) should be in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e2. cd ClinVAP/\n3. export ASSEMBLY=\u0026lt;Your Assembly Here\u0026gt;\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t /inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Docker Toolbox For Windows Users\u003c/h3\u003e\u003ca id=\"user-content-running-with-docker-toolbox-for-windows-users\" class=\"anchor\" aria-label=\"Permalink: Running with Docker Toolbox For Windows Users\" href=\"#running-with-docker-toolbox-for-windows-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequirements: Docker Engine release 1.13.0+, Compose release 1.10.0+.\u003cbr\u003e\nPlease make sure that you have 34 GB of physical empty space on your Docker Disk Image, and ports 5000 and 27017 are not being used by another application.\u003c/p\u003e\n\u003cp\u003eTo tun the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePelase note that the input VCF file(s) should be in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e2. cd ClinVAP/\n3. export ASSEMBLY=\u0026lt;Your Assembly Here\u0026gt;\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t //inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDemo Run\u003c/h2\u003e\u003ca id=\"user-content-demo-run\" class=\"anchor\" aria-label=\"Permalink: Demo Run\" href=\"#demo-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provided an example input file, strelka_passed_missense_somatic_snvs.vcf under ./ReportingApplication/inout folder along with a dummy metadata file, strelka_passed_missense_somatic_snvs.json. The corresponding report of the strelka input file is provided \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/tree/master/doc/strelka_passed_missense_somatic_snvs.pdf\"\u003ehere\u003c/a\u003e as an example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Demo with Singularity\u003c/h3\u003e\u003ca id=\"user-content-running-demo-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running Demo with Singularity\" href=\"#running-demo-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n2. singularity pull -n reporting_app.img shub://PersonalizedOncology/ClinVAP:report\n3. singularity pull -n file_deploy.img shub://PersonalizedOncology/ClinVAP:filedeploy\n4. mkdir vep_files\n5. singularity run -B ./vep_files:/mnt file_deploy.img -a GRCh37\n6. singularity run -B ./vep_files:/data -B ./ClinVAP/ReportingApplication/inout:/inout reporting_app.img -t /inout -p jwp -a GRCh37\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Demo with Docker\u003c/h3\u003e\u003ca id=\"user-content-running-demo-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running Demo with Docker\" href=\"#running-demo-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n2. cd ClinVAP/\n3. export ASSEMBLY=GRCh37\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t /inout -p jwp -a GRCh37\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCitation\u003c/h3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use ClinVAP in your work, please cite the following article\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eS\u00fcr\u00fcn, B., Sch\u00e4rfe, C.P., Divine, M.R., Heinrich, J., Toussaint, N.C., Zimmermann, L., Beha, J. and Kohlbacher, O., 2020. ClinVAP: a reporting strategy from variants to therapeutic options. Bioinformatics, 36(7), pp.2316-2317.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1637091617.0 + "updated_at": 1690190681.0 }, { "data_format": 2, - "description": "New repository for ondemand Apps ", + "description": "A quick singularity recipe for genmod to facilitate use in variant annotation pipelines", "filenames": [ - "bc_vt_desktop/Singularityfiles/Singularity.def" + "Singularity" ], - "full_name": "AdvancedResearchComputing/OnDemandApps", + "full_name": "sirselim/singularity-genmod", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-ondemandapps\" class=\"anchor\" href=\"#ondemandapps\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOnDemandApps\u003c/h1\u003e\n\u003cp\u003eNew repository for ondemand Apps.\nThe repo follows new nomenclature, directory structure and support for CI using github actions.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-directory-structure\" class=\"anchor\" href=\"#directory-structure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirectory Structure\u003c/h2\u003e\n\u003cp\u003eFollowing is the description of directory structure, it is important to note that deviation with this structure may result in CI builds to break. For more info in this regard we request you to read \u003ca href=\"./CI.md\"\u003eCI.md\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- app_root\n - Dockerfiles\n - Dockerfile\n - Dockerfile_x64\n - Singularityfiles\n - singularity.def \n - singularity_x64.def\n - template\n - before.sh\n - after.sh\n .\n .\n .\n form.js\n info.md.erb\n submit.md.erb\n LICENSE\n manifest.yml\n .\n .\n .\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-nomenclature\" class=\"anchor\" href=\"#nomenclature\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNomenclature\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eApp shall starts with name \u003cstrong\u003ebc_vt\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eDifferent versions of shall append thier discriptive prefix such as \u003cstrong\u003ehtml/vnc\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eStandard defined names for files info/submit/view/form to be followed.\u003c/li\u003e\n\u003cli\u003eWith different versioning/ support of architectures in container image files shall append the architecture name. for eg. Dockerfile, Dockerfile_x86, Singularityfile.def , Singularity_x86.def\u003c/li\u003e\n\u003cli\u003eThe primary container filename are \u003cstrong\u003eDockerfile\u003c/strong\u003e and \u003cstrong\u003eSingularity.def\u003c/strong\u003e these are \u003cstrong\u003ecase sensitive\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4152\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-genmod\u003c/h1\u003e\u003ca id=\"user-content-singularity-genmod\" class=\"anchor\" aria-label=\"Permalink: singularity-genmod\" href=\"#singularity-genmod\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA quick singularity recipe for genmod to facilitate use in variant annotation pipelines\u003c/p\u003e\n\u003cp\u003egenmod GitHub: \u003ca href=\"https://github.com/moonso/genmod\"\u003ehttps://github.com/moonso/genmod\u003c/a\u003e\u003cbr\u003e\ngenmod docs: \u003ca href=\"http://moonso.github.io/genmod/\" rel=\"nofollow\"\u003ehttp://moonso.github.io/genmod/\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1639681253.0 + "updated_at": 1668322392.0 }, { "data_format": 2, - "description": "Singularity recipe for Jupyter lab with tensorflow and cuda-10 libs", + "description": null, "filenames": [ "Singularity" ], - "full_name": "l1ll1/juflocu-10", + "full_name": "funkelab/lsd_nm_experiments", "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-installation-readme\" class=\"anchor\" href=\"#installation-readme\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation README\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eWebsite:\u003cbr\u003e\n\u003ca href=\"https://jupyterlab.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ehttps://jupyterlab.readthedocs.io/en/stable/\u003c/a\u003e\n\u003ca href=\"https://www.tensorflow.org/\" rel=\"nofollow\"\u003ehttps://www.tensorflow.org/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSource:\u003cbr\u003e\n\u003ca href=\"https://pypi.org/project/jupyter/\" rel=\"nofollow\"\u003ehttps://pypi.org/project/jupyter/\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/tensorflow-gpu/\" rel=\"nofollow\"\u003ehttps://pypi.org/project/tensorflow-gpu/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLicence:\u003cbr\u003e\nBSD 3-Clause \"New\" or \"Revised\" License \u003ca href=\"https://github.com/jupyter/jupyter/blob/master/LICENSE\"\u003ehttps://github.com/jupyter/jupyter/blob/master/LICENSE\u003c/a\u003e\nApache License \u003ca href=\"https://github.com/tensorflow/tensorflow/blob/master/LICENSE\"\u003ehttps://github.com/tensorflow/tensorflow/blob/master/LICENSE\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePrerequisites:\nNot applicable\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun:\u003cbr\u003e\nsingularity exec jupyter.sif /start\nsingularity exec jupyter.sif /params to get details of ports and passwords to connect to\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTest:\u003cbr\u003e\nNot applicable\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExamples:\nNot applicable\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLSD NM experiments tutorial\u003c/h1\u003e\u003ca id=\"user-content-lsd-nm-experiments-tutorial\" class=\"anchor\" aria-label=\"Permalink: LSD NM experiments tutorial\" href=\"#lsd-nm-experiments-tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGeneral notes\u003c/h2\u003e\u003ca id=\"user-content-general-notes\" class=\"anchor\" aria-label=\"Permalink: General notes\" href=\"#general-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA tutorial for running training/inference of networks used for paper (in\nsingularity containers).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTested on Ubuntu 18.04 with Quadro P6000 (24gb gpu ram)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssuming \u003ccode\u003esingularity\u003c/code\u003e is installed and setup\n(some tutorials \u003ca href=\"https://docs.sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and\n\u003ca href=\"https://singularity-tutorial.github.io/01-installation/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssuming conda is installed and setup (helpful \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e if needed)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/funkelab/lsd_nm_experiments.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCreate simple environment for fetching containers:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n test_env python=3.8\nconda activate test_env\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you just need \u003ca href=\"https://boto3.amazonaws.com/v1/documentation/api/latest/index.html\" rel=\"nofollow\"\u003eboto3\u003c/a\u003e for fetching containers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install boto3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you also want to view data with \u003ca href=\"https://github.com/google/neuroglancer\"\u003eneuroglancer\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install boto3 neuroglancer h5py zarr\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownloading container(s)\u003c/h2\u003e\u003ca id=\"user-content-downloading-containers\" class=\"anchor\" aria-label=\"Permalink: Downloading container(s)\" href=\"#downloading-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edownload_imgs.py\u003c/code\u003e will download the singularity container used in the paper (\u003ccode\u003elsd:v0.8.img\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eWe found that sometimes this singularity container throws errors because of deprecated cuda versions that don\u0027t play well with updated drivers\u003c/li\u003e\n\u003cli\u003eWe made another image (\u003ccode\u003elsd_legacy.img\u003c/code\u003e) that should handle this. If running into \u003ccode\u003elibcublas\u003c/code\u003e or \u003ccode\u003egcc\u003c/code\u003e errors with the original container, consider using the newer one\u003c/li\u003e\n\u003cli\u003eTo download (uncomment legacy img if desired):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython download_imgs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOr building legacy container from source:\u003c/h2\u003e\u003ca id=\"user-content-or-building-legacy-container-from-source\" class=\"anchor\" aria-label=\"Permalink: Or building legacy container from source:\" href=\"#or-building-legacy-container-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNvidia has deprecated the conda packages of the cudnn / cudatoolkit versions used in the original singularity container (6.0/8.0)\u003c/li\u003e\n\u003cli\u003eThis sometimes causes problems with updated drivers (even though the point of containerization is to solve this...)\u003c/li\u003e\n\u003cli\u003eCan get around by installing these from tars, specifically these:\n\u003cpre\u003e\u003ccode\u003ehttps://anaconda.org/numba/cudatoolkit/8.0/download/osx-64/cudatoolkit-8.0-3.tar.bz2\nhttps://repo.anaconda.com/pkgs/free/linux-64/cudnn-6.0.21-cuda8.0_0.tar.bz2\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esetup.sh\u003c/code\u003e will fetch these packages and use them to install directly into the conda environment when creating the Singularity container\u003c/li\u003e\n\u003cli\u003eThe other packages are just specified in the \u003ccode\u003elsd_legacy.yml\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eTo build legacy image locally:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e./setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote - the original container runs fine with \u003ccode\u003eexec\u003c/code\u003e, the legacy one uses \u003ccode\u003erun\u003c/code\u003e. To be honest, not sure what the problem is here\u003c/li\u003e\n\u003cli\u003eSo \u003ccode\u003esingularity exec --nv lsd:v0.8.img python -c \"import tensorflow\"\u003c/code\u003e will work, and\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity run --nv lsd_legacy.img python -c \"import tensorflow\"\u003c/code\u003e will work, but\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity exec --nv lsd_legacy.img python -c \"import tensorflow\"\u003c/code\u003e will cause import errors\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFetching data\u003c/h2\u003e\u003ca id=\"user-content-fetching-data\" class=\"anchor\" aria-label=\"Permalink: Fetching data\" href=\"#fetching-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to 01_data directory (\u003ccode\u003ecd 01_data\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efetch_data.py\u003c/code\u003e will download training data from the aws bucket using a json file (\u003ccode\u003edatasets.json\u003c/code\u003e) specifying the training volumes for each dataset\u003c/li\u003e\n\u003cli\u003eThe script defaults to just downloading the first volume for each dataset (one for each of zebrafinch, fib25, hemi, or three total)\u003c/li\u003e\n\u003cli\u003edownload data:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython fetch_data.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eyou could also use the singularity container to download the data, but we already have boto3 in the basic env we created anyway\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating masks\u003c/h2\u003e\u003ca id=\"user-content-creating-masks\" class=\"anchor\" aria-label=\"Permalink: Creating masks\" href=\"#creating-masks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecreate_masks.py\u003c/code\u003e will create a \u003ccode\u003elabels_mask\u003c/code\u003e that we use for training to constrain random locations\u003c/li\u003e\n\u003cli\u003eIf you installed zarr into the conda environment, you can just run with:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython create_masks.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eotherwise, using the singularity container:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../lsd:v0.8.img python create_masks.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eor \u003ccode\u003esingularity run...\u003c/code\u003e if using legacy container\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eViewing the data\u003c/h2\u003e\u003ca id=\"user-content-viewing-the-data\" class=\"anchor\" aria-label=\"Permalink: Viewing the data\" href=\"#viewing-the-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you installed neuroglancer into your environment, you can view the data with \u003ccode\u003eview_data.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ee.g \u003ccode\u003epython -i view_data.py -d funke/fib25/training/tstvol-520-1.zarr\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you are viewing remotely, you could also set the bind address with -b (defaults to localhost)\u003c/li\u003e\n\u003cli\u003eThere are some good little packages \u0026amp; tutorials for using neuroglancer differently. Examples\n\u003ca href=\"https://github.com/funkelab/funlib.show.neuroglancer\"\u003ehere\u003c/a\u003e, \u003ca href=\"https://connectomics.readthedocs.io/en/latest/external/neuroglancer.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\nand \u003ca href=\"https://github.com/google/neuroglancer/tree/master/python/examples\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample fib25 training data:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/fib25_training_data.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/fib25_training_data.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownloading network checkpoints\u003c/h2\u003e\u003ca id=\"user-content-downloading-network-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Downloading network checkpoints\" href=\"#downloading-network-checkpoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efetch_checkpoint.py\u003c/code\u003e will download a specified network checkpoint for a given dataset to the target folder in \u003ccode\u003e02_train\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eWe can start with the baseline affinities for the zebrafinch dataset just using conda boto3 (or use singularity if desired):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython fetch_checkpoint.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining a network\u003c/h2\u003e\u003ca id=\"user-content-training-a-network\" class=\"anchor\" aria-label=\"Permalink: Training a network\" href=\"#training-a-network\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to the zebrafinch baseline directory (\u003ccode\u003ecd ../02_train/zebrafinch/baseline\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eWe start by creating our network in \u003ccode\u003emknet.py\u003c/code\u003e (e.g placeholders to match to the trained graphs):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python mknet.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIt should print a bunch of layer names and tensor shapes (that looks like a sideways U-Net) to the command line\u003c/li\u003e\n\u003cli\u003eCheck the files in the directory (e.g \u003ccode\u003etree .\u003c/code\u003e), it should now look like:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e.\n\u251c\u2500\u2500 checkpoint\n\u251c\u2500\u2500 config.json\n\u251c\u2500\u2500 config.meta\n\u251c\u2500\u2500 mknet.py\n\u251c\u2500\u2500 predict.py\n\u251c\u2500\u2500 predict_scan.py\n\u251c\u2500\u2500 train_net_checkpoint_400000.data-00000-of-00001\n\u251c\u2500\u2500 train_net_checkpoint_400000.index\n\u251c\u2500\u2500 train_net_checkpoint_400000.meta\n\u251c\u2500\u2500 train_net.json\n\u251c\u2500\u2500 train_net.meta\n\u251c\u2500\u2500 train.py\n\u2514\u2500\u2500 view_batch.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eTrain for 1 iteration:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python train.py 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eYou\u0027ll see that gunpowder will print \u003ccode\u003eERROR:tensorflow:Couldn\u0027t match files for checkpoint ./train_net_checkpoint_500000\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis is because it checks the \u003ccode\u003echeckpoint\u003c/code\u003e file which specifies iteration 500000 (since this network was trained for longer than the optimal checkpoint)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf we view the batch, we\u0027ll see that the predictions are all grey, since it really only trained for a single iteration and didn\u0027t use the checkpoint (\u003ccode\u003epython -i view_batch.py -f snapshots/batch_1.hdf\u003c/code\u003e):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_batch_1.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_batch_1.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo fix, simply edit this \u003ccode\u003echeckpoint\u003c/code\u003e file to point to the downloaded checkpoint iteration instead (e.g 500000 -\u0026gt; 400000)\u003c/li\u003e\n\u003cli\u003eNow running the above won\u0027t do anything, because of line 27 in \u003ccode\u003etrain.py\u003c/code\u003e:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etrained_until\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emax_iteration\u003c/span\u003e:\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSo just make sure to train to the checkpoint + n, eg for 1 extra iteration:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python train.py 400001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eWe can then view the saved batch, e.g:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003epython -i view_batch.py -f snapshots/batch_400001.hdf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_batch_400k.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_batch_400k.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Inference\u003c/h2\u003e\u003ca id=\"user-content-running-inference\" class=\"anchor\" aria-label=\"Permalink: Running Inference\" href=\"#running-inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFor the lsds experiments, we ran everything from inference through evaluation in a blockwise fashion using \u003ca href=\"https://github.com/funkelab/daisy\"\u003edaisy\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor inference, this meant having a blockwise prediction script that called a gunpowder predict pipeline inside each process\u003c/li\u003e\n\u003cli\u003eFor example, this \u003ca href=\"https://github.com/funkelab/lsd/blob/master/lsd/tutorial/scripts/01_predict_blockwise.py\"\u003escript\u003c/a\u003e would distribute this \u003ca href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/02_train/zebrafinch/baseline/predict.py\"\u003escript\u003c/a\u003e by using a \u003ca href=\"http://funkey.science/gunpowder/api.html?highlight=daisy#gunpowder.DaisyRequestBlocks\" rel=\"nofollow\"\u003eDaisyRequestBlocks\u003c/a\u003e gunpowder node\u003c/li\u003e\n\u003cli\u003eIf you just want to run inference on a small volume (in memory), you can instead use a \u003ca href=\"http://funkey.science/gunpowder/api.html?highlight=scan#gunpowder.Scan\" rel=\"nofollow\"\u003eScan\u003c/a\u003e node\u003c/li\u003e\n\u003cli\u003eWe added adapted all blockwise inference scripts to also use scan nodes (e.g \u003ccode\u003epredict_scan.py\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eExample run on zfinch training data:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv ../../../lsd:v0.8.img python predict_scan.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eResulting affinities:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/baseline_zfinch_preds.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/baseline_zfinch_preds.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMultitask (MTLSD)\u003c/h2\u003e\u003ca id=\"user-content-multitask-mtlsd\" class=\"anchor\" aria-label=\"Permalink: Multitask (MTLSD)\" href=\"#multitask-mtlsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThis can be run exactly the same as the baseline above.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInside \u003ccode\u003e01_data/fetch_checkpoint.py\u003c/code\u003e, uncomment \u003ccode\u003emtlsd\u003c/code\u003e and run.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to \u003ccode\u003e02_train/zebrafinch/mtlsd\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate network (\u003ccode\u003e... python mknet.py\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eChange \u003ccode\u003echeckpoint\u003c/code\u003e file iteration to match downloaded checkpoint (500000 -\u0026gt; 400000)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTrain (\u003ccode\u003e... python train.py 400001\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eView batch (\u003ccode\u003e... python -i view_batch.py ...\u003c/code\u003e) -\u0026gt; will also show LSDs\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePredict (\u003ccode\u003e... python predict_scan.py\u003c/code\u003e) -\u0026gt; will write out LSDs and Affs\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis will also give us LSDs:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/mtlsd_zfinch_preds_lsds.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/mtlsd_zfinch_preds_lsds.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTip - you can view the different components of the LSDs by adjusting the shader in neuroglancer, e.g changing \u003ccode\u003e0,1,2\u003c/code\u003e to \u003ccode\u003e3,4,5\u003c/code\u003e will show the diagonal entries of the covariance component (or the direction the processes move):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003evoid main() {\n emitRGB(\n vec3(\n toNormalized(getDataValue(3)),\n toNormalized(getDataValue(4)),\n toNormalized(getDataValue(5)))\n );\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eOr to view a single channel of the 10d lsds, (e.g channel 6):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003evoid main() {\n float v = toNormalized(getDataValue(6));\n vec4 rgba = vec4(0,0,0,0);\n if (v != 0.0) {\n rgba = vec4(colormapJet(v), 1.0);\n }\n emitRGBA(rgba);\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAutocontext (ACLSD and ACRLSD)\u003c/h2\u003e\u003ca id=\"user-content-autocontext-aclsd-and-acrlsd\" class=\"anchor\" aria-label=\"Permalink: Autocontext (ACLSD and ACRLSD)\" href=\"#autocontext-aclsd-and-acrlsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThese networks rely on a pretrained LSD (raw -\u0026gt; LSDs) network, eg:\u003c/li\u003e\n\u003cli\u003eACLSD: \u003ccode\u003eRaw -\u0026gt; LSDs -\u0026gt; Affs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eACRLSD: \u003ccode\u003eRaw -\u0026gt; LSDs + cropped Raw -\u0026gt; Affs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBecause of this, they are more computationally expensive (since training requires extra context to first predict the lsds)\u003c/li\u003e\n\u003cli\u003eThey ran using 23.5 GB of available 24 GB GPU RAM when testing on quadro p6000. If you have less than that you will likely run into cuda OOM errors\u003c/li\u003e\n\u003cli\u003eIf you have access to sufficient gpu memory, to start navigate to \u003ccode\u003e01_data\u003c/code\u003e and uncomment \u003ccode\u003elsd\u003c/code\u003e + run script to get the pretrained lsd checkpoint\u003c/li\u003e\n\u003cli\u003eGo to lsd directory (\u003ccode\u003ecd ../02_train/zebrafinch/lsd\u003c/code\u003e) and follow same instructions as baseline and mtlsd nets above\u003c/li\u003e\n\u003cli\u003eOnce you have the lsd checkpoints (and \u003ccode\u003etest_prediction.zarr\u003c/code\u003e with \u003ccode\u003epred_lsds\u003c/code\u003e following prediction), start with a basic autocontext network (aclsd).\u003c/li\u003e\n\u003cli\u003eGet the aclsd checkpoint, navigate to appropriate directory, change checkpoint file as before and train network.\u003c/li\u003e\n\u003cli\u003eVisualizing the resulting batch shows us the larger raw context needed for predicting the lsds to ensure that the output affinities remain the same size:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/funkelab/lsd_nm_experiments/blob/master/static/aclsd_training_batch.png\"\u003e\u003cimg src=\"https://github.com/funkelab/lsd_nm_experiments/raw/master/static/aclsd_training_batch.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun prediction as before - note, will not run if lsds have not been predicted first. This script could be adapted to predict the lsds on-the-fly using just the lsds and affs checkpoints\u003c/li\u003e\n\u003cli\u003eThe same can be done for the ACRLSD network (note, requires a merge provider during prediction as this network takes both lsds and raw data as input)\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTodos: add consolidated fib25/hemi nets to tutorial\u003c/h2\u003e\u003ca id=\"user-content-todos-add-consolidated-fib25hemi-nets-to-tutorial\" class=\"anchor\" aria-label=\"Permalink: Todos: add consolidated fib25/hemi nets to tutorial\" href=\"#todos-add-consolidated-fib25hemi-nets-to-tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1605813038.0 + "updated_at": 1702969887.0 }, { "data_format": 2, - "description": "exSeek: extracellular RNA analysis tool for noninvasive biomarker", + "description": "Code for Asking the Right Questions: Learning Interpretable Action Models Through Query Answering. AAAI 2021.", "filenames": [ - "singularity/Singularity" + "dependencies/FD/misc/releases/19.06/Singularity.19.06", + "dependencies/FD/misc/releases/20.06/Singularity.20.06", + "dependencies/FD/misc/releases/latest/Singularity", + "dependencies/FD/misc/releases/19.12/Singularity.19.12" ], - "full_name": "james20141606/exSeek", + "full_name": "AAIR-lab/AIA-AAAI21", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-exseek\" class=\"anchor\" href=\"#exseek\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eexSeek\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/lulab/exSeek-dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/17b4878574e7d41e83f8bedc7e94f3500d4ebeefba3c587ded3efcbc5730c3b3/68747470733a2f2f7472617669732d63692e636f6d2f6c756c61622f65785365656b2d6465762e7376673f746f6b656e3d4379526755577371574363744b7641784d58746f266272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/lulab/exSeek-dev.svg?token=CyRgUWsqWCctKvAxMXto\u0026amp;branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-workflow\" class=\"anchor\" href=\"#workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"assets/whole_pipe.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"assets/whole_pipe.png\" alt=\"workflow\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstall required software packages according to \u003ca href=\"docs/requirements.md\"\u003erequirements\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDownload the scripts:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/lulab/exSeek-dev.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-prepare-genome-and-annotations\" class=\"anchor\" href=\"#prepare-genome-and-annotations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrepare genome and annotations\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eProcessed files\u003c/strong\u003e: \u003ccode\u003e/BioII/lulab_b/shared/genomes/hg38\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-download-and-process-genome-sequences\" class=\"anchor\" href=\"#download-and-process-genome-sequences\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload and process genome sequences\u003c/h3\u003e\n\u003cp\u003eRefer to the \u003ca href=\"docs/genome_and_annotations.md\"\u003edocumentation\u003c/a\u003e for details.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-extract-gtfs-and-generate-mapping-indexes\" class=\"anchor\" href=\"#extract-gtfs-and-generate-mapping-indexes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtract GTFs and generate mapping indexes\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/prepare_genome.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-input-files\" class=\"anchor\" href=\"#input-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/fastq/${sample_id}.fastq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRead files (single-end sequencing)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003e${input_dir}/fastq/${sample_id}_1.fastq\u003c/code\u003e, \u003ccode\u003e${input_dir}/fastq/${sample_id}_2.fastq\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003eRead files (paired-end sequencing)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/sample_ids.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA text file with one sample ID per line.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/sample_classes.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA tab-deliminated file (with header) with two columns: sample_id, label\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/batch_info.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA comma-deliminated file (with header) with at least two columns: sample_id, batch1, batch2, ...\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/reference_genes.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA text file with reference gene IDs.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${input_dir}/compare_groups.yaml\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA YAML file defining positive and negative classes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ecompare_groups.yaml\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvery key-value pairs defines a compare group and a negative-positive class pair:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eNormal-CRC\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[\"Healthy Control\", \"Colorectal Cancer\"]\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-configuration\" class=\"anchor\" href=\"#configuration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h2\u003e\n\u003cp\u003eAll parameters are specified in a configuration file in \u003ca href=\"https://en.wikipedia.org/wiki/YAML\" rel=\"nofollow\"\u003eYAML\u003c/a\u003e format.\u003c/p\u003e\n\u003cp\u003eAn example configuration file is (snakemake/config.yaml).\u003c/p\u003e\n\u003cp\u003eThe parameter values in the configuration file can also be overrided through the \u003ccode\u003e--config\u003c/code\u003e option in \u003ca href=\"https://snakemake.readthedocs.io/en/stable/executable.html\" rel=\"nofollow\"\u003esnakemake\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following parameters should be changed:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eExample\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003egenome_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDirectory for genome and annotation files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenome/hg38\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edata_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDirectory for input files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edata/scirep\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003etemp_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eTemporary directory\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003etmp\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esample_id_file\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eA text file containing sample IDs\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edata/scirep/sample_ids.txt\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eoutput_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDirectory for all output files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eoutput/scirep\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003etools_dir\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDirectory for third-party tools\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ealigner\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMapping software\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ebowtie2\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eadaptor\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e3\u0027 adaptor sequence for single-end RNA-seq\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epython2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePath to Python 2\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/apps/anaconda2/bin/python\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epython3\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePath to Python 3\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/apps/anaconda2/bin/python\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-command-line-arguments-for-snakemake\" class=\"anchor\" href=\"#command-line-arguments-for-snakemake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand line arguments for snakemake\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOption\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--config\u003c/td\u003e\n\u003ctd\u003eAdditional configuration parameters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-j\u003c/td\u003e\n\u003ctd\u003eNumber of parallel jobs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--dryrun\u003c/td\u003e\n\u003ctd\u003eDo not execute\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-k\u003c/td\u003e\n\u003ctd\u003eDo not stop when an independent job fails\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-submit-jobs-to-a-computer-cluster-using-snakemake\" class=\"anchor\" href=\"#submit-jobs-to-a-computer-cluster-using-snakemake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubmit jobs to a computer cluster using snakemake\u003c/h2\u003e\n\u003cp\u003ePlease refer the \u003ca href=\"https://snakemake.readthedocs.io/en/stable/snakefiles/configuration.html#cluster-configuration\" rel=\"nofollow\"\u003elink\u003c/a\u003e for descriptions of cluster configuration file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ibm-lsf\" class=\"anchor\" href=\"#ibm-lsf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIBM LSF\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eConfiguration file\u003c/strong\u003e: \u003ccode\u003esnakemake/cluster.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eHere is an example configuration:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003e__default__\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003equeue\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eZ-LU\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e{rule}.{wildcards}\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003estderr\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003elogs/cluster/{rule}/{wildcards}.stderr\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003estdout\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003elogs/cluster/{rule}/{wildcards}.stdout\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ethreads\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e{threads}\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eresources\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003espan[hosts=1]\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eCommonly used parameters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eParameter\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e__default__\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRule name (\u003ccode\u003e__default__\u003c/code\u003e) for default configuration)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003equeue\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eQueue name\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ename\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eJob name\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003estderr\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLog file for standard error\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003estdout\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLog file for standard output\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ethreads\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eNumber of parallel threads for a job\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eresources\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eResource requirements. \u003ccode\u003espan[hosts=1]\u003c/code\u003e prevents parallel jobs from being submitted to different nodes\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRun snakemake\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/\u003cspan class=\"pl-smi\"\u003e${snakefile}\u003c/span\u003e \\\n --configfile snakemake/config.yaml \\\n --cluster \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ebsub -q {cluster.queue} -J {cluster.name} -e {cluster.stderr} \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e-o {cluster.stdout} -R {cluster.resources} -n {cluster.threads}\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n --cluster-config snakemake/cluster.yaml \\\n --rerun-incomplete -k -j40\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: replace \u003ccode\u003e${snakefile}\u003c/code\u003e with a Snakefile.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quality-control\" class=\"anchor\" href=\"#quality-control\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality control\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/quality_control.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-mapping-small-rna-seq\" class=\"anchor\" href=\"#mapping-small-rna-seq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMapping (small RNA-seq)\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-generate-snakemake-rules-for-sequential-mapping\" class=\"anchor\" href=\"#generate-snakemake-rules-for-sequential-mapping\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerate snakemake rules for sequential mapping\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebin/generate_snakemake.py sequential_mapping --rna-types rRNA,miRNA,piRNA,Y_RNA,srpRNA,tRNA,snRNA,snoRNA,lncRNA,mRNA,tucpRNA \\\n -o snakemake/mapping_small/sequential_mapping.snakemake\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/mapping_small.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-files\" class=\"anchor\" href=\"#output-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003eDescrpition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esnakemake/sequential_mapping.snakemake\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSnakefile for sequential mapping. Required by snakemake/mapping_small.snakemake\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/cutadapt/${sample_id}.fastq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eReads with adaptor trimmed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/tbam/${sample_id}/${rna_type}.bam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eBAM files in transcript coordinates\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/gbam/${sample_id}/${rna_type}.bam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eBAM files in genome coordinates\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/unmapped/${sample_id}/${rna_type}.fa.gz\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eUnmapped reads in each step\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/fastqc/${sample_id}_fastqc.html\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eFastQC report file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/summary/fastqc.html\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSummary report for FastQC (HTML)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/summary/fastqc.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSummary table for FastQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/summary/fastqc.ipynb\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSummary report for FastQC (Jupyter notebook)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/summary/read_counts.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSummary table for read counts\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/stats/mapped_read_length_by_sample/${sample_id}\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLength distribution of mapped reads\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-generate-expression-matrix\" class=\"anchor\" href=\"#generate-expression-matrix\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerate expression matrix\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/expression_matrix.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-files-1\" class=\"anchor\" href=\"#output-files-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003eDescrpition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/count_matrix/transcript.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eCount matrix of transcripts\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/count_matrix/htseq.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eCount matrix of genes generated using HTSeq-count\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/count_matrix/featurecounts.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eCount matrix of genes generated using featureCounts\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/counts_by_biotype/${count_method}/${sample_id}/${rna_type}\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eGene/transcript counts generated using a feature counting tool\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCount matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFile path: \u003ccode\u003e${output_dir}/count_matrix/transcript.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eFirst row: sample IDs\u003c/li\u003e\n\u003cli\u003eFirst column: feature names\u003c/li\u003e\n\u003cli\u003eFeature name: \u003ccode\u003egene_id|gene_type|gene_name\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-call-domains-for-long-rna\" class=\"anchor\" href=\"#call-domains-for-long-rna\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCall domains for long RNA\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile snakemake/call_domains_long.snakemake \\\n --configfile snakemake/config.yaml \\\n --rerun-incomplete -k\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-files-2\" class=\"anchor\" href=\"#output-files-2\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile name\u003c/th\u003e\n\u003cth\u003eDescrpition\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/domain_counts/${bin_size}/${pvalue}/${sample_id}.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRead counts in long RNA domains (BED format with read counts in Column 5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/count_matrix/domain_${pvalue}.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRead count matrix of long RNA domains\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/domains/${bin_size}/${pvalue}.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLong RNA domain locations\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e${output_dir}/domains_recurrence/${bin_size}/${pvalue}.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRecurrence of long RNA domains among samples (Column 5)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRead count matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFile path: \u003ccode\u003e${output_dir}/count_matrix/domain_long.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eFirst row: sample IDs\u003c/li\u003e\n\u003cli\u003eFirst column: feature names\u003c/li\u003e\n\u003cli\u003eFeature name: \u003ccode\u003egene_id|gene_type|gene_name|domain_id|transcript_id|start|end\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-normalization\" class=\"anchor\" href=\"#normalization\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNormalization\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-files-3\" class=\"anchor\" href=\"#output-files-3\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h3\u003e\n\u003cp\u003e| File name | Description |\n| \u003ccode\u003e${output_dir}/normalized_matrix/${normalization_method}.${imputation_method}.${batch_removal_method}.txt\u003c/code\u003e |\n| \u003ccode\u003e${output_dir}/matrix_processing/normalization/${normalization_method}.txt\u003c/code\u003e |\n| \u003ccode\u003e${output_dir}/matrix_processing/imputation/${normalization_method}.${imputation_method}.txt\u003c/code\u003e |\n| \u003ccode\u003e${output_dir}/matrix_processing/batch_removal/${batch_removal_method}.${batch_index}.txt\u003c/code\u003e |\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAgent Interrogation Algorithm (AIA)\u003c/h1\u003e\u003ca id=\"user-content-agent-interrogation-algorithm-aia\" class=\"anchor\" aria-label=\"Permalink: Agent Interrogation Algorithm (AIA)\" href=\"#agent-interrogation-algorithm-aia\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code for the paper:\u003c/p\u003e\n\u003cp\u003eAsking the Right Questions: Learning Interpretable Action Models Through Query Answering.\u003cbr\u003e\n\u003ca href=\"https://pulkitverma.net\" rel=\"nofollow\"\u003ePulkit Verma\u003c/a\u003e,\n\u003ca href=\"https://marpally-raoshashank.netlify.app/\" rel=\"nofollow\"\u003eShashank Rao Marpally\u003c/a\u003e, and\n\u003ca href=\"http://siddharthsrivastava.net/\" rel=\"nofollow\"\u003eSiddharth Srivastava\u003c/a\u003e. \u003cbr\u003e\n35th AAAI Conference on Artificial Intelligence, 2021.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://aair-lab.github.io/Publications/vms_aaai21.pdf\" rel=\"nofollow\"\u003ePaper\u003c/a\u003e | \u003ca href=\"https://arxiv.org/pdf/1912.12613.pdf\" rel=\"nofollow\"\u003eExtended Version\u003c/a\u003e | \u003ca href=\"https://slideslive.com/38948683/asking-the-right-questions-learning-interpretable-action-models-through-query-answering\" rel=\"nofollow\"\u003eTalk\u003c/a\u003e | \u003ca href=\"https://pulkitverma.net/assets/pdf/vms_aaai21/vms_aaai21_slides.pdf\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e | \u003ca href=\"https://pulkitverma.net/assets/pdf/vms_aaai21/vms_aaai21_poster.pdf\" rel=\"nofollow\"\u003ePoster\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory Structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory Structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e|-- dependencies/\n| |-- FD/\n| |-- FF/\n| |-- pddlgym/\n| |-- VAL/\n|-- domains/\n|-- random_states/\n|-- results/\n|-- src/\n| |-- agent.py\n| |-- config.py\n| |-- generate_random_states.py\n| |-- main.py\n| |-- interrogation/\n| |-- lattice/\n| |-- query/\n| |-- sim/\n| |-- utils/\n|-- README.md\n|-- LICENSE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003edependencies: This directory includes the external software used to run the code. This includes FF, FD, VAL, and PDDLGym.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFF: \u003ca href=\"https://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz\" rel=\"nofollow\"\u003ehttps://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFD: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePDDLGym: \u003ca href=\"https://github.com/tomsilver/pddlgym\"\u003ehttps://github.com/tomsilver/pddlgym\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVAL: \u003ca href=\"https://github.com/KCL-Planning/VAL\"\u003ehttps://github.com/KCL-Planning/VAL\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edependencies: Place all the domains in this directory. There must be a directory for each domain containing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edomain.pddl (domain file for that domain), and\u003c/li\u003e\n\u003cli\u003einstances directory containing the problem files for that domain.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erandom_states: This directory stores the set of states in serialized form. For each domain, there is a .pkl file containing 60 states approximately. These are generated using src/generate_random_states.py.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esrc: This directory stores the source code for AIA. It contains 4 files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eagent.py: Contains the agent code.\u003c/li\u003e\n\u003cli\u003econfig.py: Declares the configuration parameters.\u003c/li\u003e\n\u003cli\u003egenerate_random_states.py: Generates the random states for each domain.\u003c/li\u003e\n\u003cli\u003emain.py : Contains the main driver code which runs the code end-to-end.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003esrc also contains code structured into following sub-directories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einterrogation: Contains the AIA code.\u003c/li\u003e\n\u003cli\u003elattice: Contains the model and lattice classes.\u003c/li\u003e\n\u003cli\u003equery: Contains the plan outcome query code.\u003c/li\u003e\n\u003cli\u003esim: Simulator specific code. Contains a separate agent file for each simulator domain.\u003c/li\u003e\n\u003cli\u003eutils: Contains general utilities.\n\u003cul\u003e\n\u003cli\u003eutils/parser: Code based on \u003ca href=\"https://github.com/tomsilver/pddlgym\"\u003ePDDLGym\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eutils/translate: Code based on \u003ca href=\"https://github.com/aibasel/downward\"\u003eFD\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration\u003c/h2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-label=\"Permalink: Configuration\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConfiguration parameters are set in src/config.py\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFF_PATH, FD_PATH, and VAL_PATH stores the relative path of FF, FD, and VAL respectively.\u003c/li\u003e\n\u003cli\u003eNUM_PER_DOMAIN denotes how many instances per domain must be run. Keep minimum 2 for symbolic agent.\u003c/li\u003e\n\u003cli\u003ePLANNER specifies which planner to use. Set it to either FF or FD.\u003c/li\u003e\n\u003cli\u003eComment out either Symbolic Agent Settings or Simulator Agent Settings.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to Run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall the required software\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install -r requirements.txt \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eAdjust variables/paramters as needed in src/config.py.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun main.py\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd src\npython3 main.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommon Installation Issues\u003c/h2\u003e\u003ca id=\"user-content-common-installation-issues\" class=\"anchor\" aria-label=\"Permalink: Common Installation Issues\" href=\"#common-installation-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOpenCV (Tested on Ubuntu 18.04)\u003c/p\u003e\n\u003cp\u003eRefer to \u003ca href=\"https://linuxize.com/post/how-to-install-opencv-on-ubuntu-18-04/\" rel=\"nofollow\"\u003ehttps://linuxize.com/post/how-to-install-opencv-on-ubuntu-18-04/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFF:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePlease install flex and bison for FF to compile.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn newer versions of gcc (tested on gcc 10.2.0) please make the following changes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emain.c:150 : Comment out the gbracket_count definition\n\u003cpre\u003e\u003ccode\u003eint gbracket_count; --\u0026gt; /* int gbracket_count; */\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003erelax.c:111 : Define lcurrent_goals as static\n\u003cpre\u003e\u003ccode\u003eState lcurrent_goals; --\u0026gt; static State lcurrent_goals;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003esearch.c:110 : Define lcurrent_goals as static\n\u003cpre\u003e\u003ccode\u003eState lcurrent_goals; --\u0026gt; static State lcurrent_goals;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePlease note that this is research code and not yet ready for public delivery,\nhence most parts are not documented.\u003c/p\u003e\n\u003cp\u003eIn case of any queries, please contact \u003ca href=\"mailto:verma.pulkit@asu.edu\"\u003everma.pulkit@asu.edu\u003c/a\u003e,\nor \u003ca href=\"mailto:smarpall@asu.edu\"\u003esmarpall@asu.edu\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://pulkitverma.net\" rel=\"nofollow\"\u003ePulkit Verma\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://marpally-raoshashank.netlify.app/\" rel=\"nofollow\"\u003eShashank Rao Marpally\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://www.linkedin.com/in/abhyudayasrinet/\" rel=\"nofollow\"\u003eAbhyudaya Srinet\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://siddharthsrivastava.net/\" rel=\"nofollow\"\u003eSiddharth Srivastava\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{verma_2021_asking,\n author = {Verma, Pulkit and Marpally, Shashank Rao and Srivastava, Siddharth},\n title = {{Asking the Right Questions: Learning Interpretable Action Models Through Query Answering}},\n booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)},\n year={2021}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [ - "bioinformatics", - "machie-learning" + "artificial-intelligence" ], - "updated_at": 1610690721.0 + "updated_at": 1682672415.0 }, { "data_format": 2, - "description": "Bisulfite-seq data Workflow Automation Software and Protocols", + "description": "Code for Differential Assessment of Black-Box AI Agents. AAAI 2022.", "filenames": [ - "Singularity.v1.0", - "Singularity.v1.1", - "Singularity.v0.9", - "Singularity" + "dependencies/FD/misc/releases/19.06/Singularity.19.06", + "dependencies/FD/misc/releases/20.06/Singularity.20.06", + "dependencies/FD/misc/releases/latest/Singularity", + "dependencies/FD/misc/releases/19.12/Singularity.19.12" ], - "full_name": "BrendelGroup/BWASP", + "full_name": "AAIR-lab/DAAISy", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bwasp--bisulfite-seq-data-workflow-automation-software-and-protocols\" class=\"anchor\" href=\"#bwasp--bisulfite-seq-data-workflow-automation-software-and-protocols\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBWASP : Bisulfite-seq data Workflow Automation Software and Protocols\u003c/h1\u003e\n\u003cp\u003eThe BWASP repository encompasses code and scripts developed in the\n\u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eBrendel Group\u003c/a\u003e for analyses of bisulfite sequencing\ndata.\nThe entire workflow relies on various other open source software as well as\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e scripts from the companion\n\u003ca href=\"https://github.com/BrendelGroup/BWASPR\"\u003eBWASPR\u003c/a\u003e repository.\nThe code conforms to our \u003ca href=\"https://brendelgroup.github.io/\" rel=\"nofollow\"\u003eRAMOSE\u003c/a\u003e\nphilosophy: it generates \u003cstrong\u003ereproducible\u003c/strong\u003e, \u003cstrong\u003eaccurate\u003c/strong\u003e, and \u003cstrong\u003emeaningful\u003c/strong\u003e\nresults; it is \u003cstrong\u003eopen\u003c/strong\u003e (source) and designed to be \u003cstrong\u003escalable\u003c/strong\u003e and\n\u003cstrong\u003eeasy\u003c/strong\u003e to use.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start-\" class=\"anchor\" href=\"#quick-start-\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start \u003ca href=\"https://singularity-hub.org/collections/1203\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eInput to the BWASP workflow consists of accession numbers or fastq files of\nbisulfite-sequencing reads as well as the appropriate genome assembly (and, if\navailable, genome annotation).\nOutput (after read quality control and mapping) are \u003cem\u003e*.mcalls\u003c/em\u003e files that list\nthe sufficiently covered genomic Cs and their methylation percentage in the\ngiven sample.\nThe scripts in the \u003cem\u003ebin\u003c/em\u003e directory take care of minor tasks in the overall\nworkflow, but configuration and execution is via\n\u003ca href=\"https://www.gnu.org/software/make/\" rel=\"nofollow\"\u003eGNU make\u003c/a\u003e using edited copies of the\nmakefiles provided in the \u003cem\u003emakefiles\u003c/em\u003e directory.\nAll the BWASP dependencies are encapsulated in a\n\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available from our\n\u003ca href=\"http://BrendelGroup.org/SingularityHub/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\nThus, once you know what you are doing, execution could be as simple as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull http://BrendelGroup.org/SingularityHub/bwasp.sif\nsingularity exec bwasp.sif make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(assuming you have prepared a suitable makefile in your working directory).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-realistic-start\" class=\"anchor\" href=\"#realistic-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRealistic Start\u003c/h2\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the\n\u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\nOnce all preparatory steps are taken care of, see the \u003ca href=\"./HOWTO.md\"\u003eHOWTO\u003c/a\u003e\ndocument for a complete example of how to implement and run a workflow.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-reference\" class=\"anchor\" href=\"#reference\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference\u003c/h2\u003e\n\u003cp\u003eAmy L. Toth, Murat Ozturk, Saranya Sankaranarayanan, and Volker P. Brendel\n(2018) \u003cem\u003eEstimating the size and dynamics of the CpG methylome of social\ninsects.\u003c/em\u003e To be submitted.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003ePlease direct all comments and suggestions to\n\u003ca href=\"mailto:vbrendel@indiana.edu\"\u003eVolker Brendel\u003c/a\u003e\nat \u003ca href=\"http://brendelgroup.org/\" rel=\"nofollow\"\u003eIndiana University\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDifferential Assessment of AI System (DAAISy)\u003c/h1\u003e\u003ca id=\"user-content-differential-assessment-of-ai-system-daaisy\" class=\"anchor\" aria-label=\"Permalink: Differential Assessment of AI System (DAAISy)\" href=\"#differential-assessment-of-ai-system-daaisy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code for the paper:\u003c/p\u003e\n\u003cp\u003eDifferential Assessment of Black-Box AI Agents.\u003cbr\u003e\n\u003ca href=\"https://rashmeetnayyar.com\" rel=\"nofollow\"\u003eRashmeet Kaur Nayyar\u003c/a\u003e*,\n\u003ca href=\"https://pulkitverma.net\" rel=\"nofollow\"\u003ePulkit Verma\u003c/a\u003e*, and\n\u003ca href=\"http://siddharthsrivastava.net/\" rel=\"nofollow\"\u003eSiddharth Srivastava\u003c/a\u003e. \u003cbr\u003e\n36th AAAI Conference on Artificial Intelligence, 2022. \u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://aair-lab.github.io/Publications/nvs_aaai22.pdf\" rel=\"nofollow\"\u003ePaper\u003c/a\u003e | \u003ca href=\"https://arxiv.org/pdf/2203.13236.pdf\" rel=\"nofollow\"\u003eExtended Version\u003c/a\u003e | \u003ca href=\"https://slideslive.com/38976997/differential-assessment-of-blackbox-ai-systems\" rel=\"nofollow\"\u003eTalk\u003c/a\u003e | \u003ca href=\"https://pulkitverma.net/assets/pdf/nvs_aaai22/nvs_aaai22_slides.pdf\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e | \u003ca href=\"https://pulkitverma.net/assets/pdf/nvs_aaai22/nvs_aaai22_poster.pdf\" rel=\"nofollow\"\u003ePoster\u003c/a\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory Structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory Structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e|-- dependencies/\n| |-- fama/\n| |-- FD/\n| |-- FF/\n| |-- madagascar/\n| |-- VAL/\n|-- domains/\n| |-- domain_name/\n| | |-- domains/\n| | |-- instances/\n| | |-- observations/\n|-- random_states/\n|-- results/\n|-- src/\n| |-- agent.py\n| |-- config.py\n| |-- generate_random_states.py\n| |-- model_drift/\n| |-- interrogation/\n| |-- lattice/\n| |-- query/\n| |-- utils/\n|-- README.md\n|-- config.py\n|-- learn_model.py\n|-- generate_random_init_domains.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003edependencies/: This directory includes the external software used to run the code. This includes fama, FF, FD, madagascar, and VAL.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003efama: \u003ca href=\"https://github.com/sjimenezgithub/strips-learning/\"\u003ehttps://github.com/sjimenezgithub/strips-learning/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFF: \u003ca href=\"https://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz\" rel=\"nofollow\"\u003ehttps://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFD: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003emadagascar: \u003ca href=\"https://users.aalto.fi/~rintanj1/jussi/software.html\" rel=\"nofollow\"\u003ehttps://users.aalto.fi/~rintanj1/jussi/software.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVAL: \u003ca href=\"https://github.com/KCL-Planning/VAL\"\u003ehttps://github.com/KCL-Planning/VAL\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edomains/ (Directory that stores domain, instance, and observation files)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edomain_name/\n\u003cul\u003e\n\u003cli\u003edomains/\n\u003cul\u003e\n\u003cli\u003edomain.pddl (domain file for that domain),\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003einstances/ (directory containing the problem files for that domain),\u003c/li\u003e\n\u003cli\u003eobservations/ (directory containing the plans for that domain generated using the instances)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erandom_states/: This directory stores the set of states in serialized form. For each domain, there is a .pkl file containing 60 states approximately.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esrc/: This directory contains the source code for DAAISy and AIA.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esrc/interrogation/aia.py has code for both DAAISy and AIA.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esrc/model_drift/: This directory contains the source code for DAAISy. It contains the following files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eagent_interrogation_interface: Interface between identification of affected pal-tuples and identification of their modes.\u003c/li\u003e\n\u003cli\u003egenerate_observations: Generates observations for drifted model and init models.\u003c/li\u003e\n\u003cli\u003einfer_valid_modes: Identifies valid modes for each pa-tuple.\u003c/li\u003e\n\u003cli\u003epa_tuple: Class for pa-tuple.\u003c/li\u003e\n\u003cli\u003eplot: Plots results.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eresults/: This directory has domain-specific results in the form of csv files and plots.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions to run the code\u003c/h2\u003e\u003ca id=\"user-content-instructions-to-run-the-code\" class=\"anchor\" aria-label=\"Permalink: Instructions to run the code\" href=\"#instructions-to-run-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInput: You need the IPC domain.pddl (within domains/), domain_template.pddl (within domains/), instance (within instances/instances/) files as input.\nHere, IPC domain is used as drifted model and simply used to generate a set of init domains randomly. The algorithm does not take IPC domain as input but only the init domains.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall the required software using setup.sh. You might need to give sudo permissions to install some of them.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e./setup.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eSet configuration in config.py. (Optional) \u003cbr\u003e Use the current parameters set in the config file to reproduce results in the paper. The parameters are:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003edomain_name: the domain name,\u003c/li\u003e\n\u003cli\u003edrifted_domain_file: the filename for IPC domain,\u003c/li\u003e\n\u003cli\u003edomain_to_num_files_each_pal: the number of init domain files you want to generate for each number of affected pal-tuples,\u003c/li\u003e\n\u003cli\u003enum_affected_pals: the number of affected pal-tuples to consider,\u003c/li\u003e\n\u003cli\u003eregenerate_random_states: True if you want to regenerate the set of random states for the domain,\u003c/li\u003e\n\u003cli\u003eflag_init_domains_type: 0 for \"randomly generated init domains\", 1 for \"only increased applicability init domains\", and 2 for \"both types of domain generation\",\u003c/li\u003e\n\u003cli\u003edomain_to_num_sas: number of s-a-s observations to use.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eGenerate random initial domains. (Optional)\u003cbr\u003e\nUse current init domain files to reproduce results in the paper.\nTo randomly generate new initial domains using the IPC domain with increasing number of pal-tuples dropped (filename format is domainname_#PALsdropped_#file.pddl), run the following command.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython3 generate_random_init_domains.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eInput instance file for generating observations for drifted domain. (Optional)\u003cbr\u003e\nUse the current instance files and the corresponding generated observations to reproduce results in the paper.\nTo run with new observations, replace the contents of the instances/instances/ directory with the instances you want, delete contents of observations/ directory, and set the domain_to_num_sas variable in config.py to the number of s-a-s observations you want to use (should be \u0026lt;= the number of s-a-s in the plan generated by the instance).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun learn_model.py to learn the drifted model. You\u0027ll find the plots generated inside the results/ directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython3 learn_model.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommon Installation Issues\u003c/h2\u003e\u003ca id=\"user-content-common-installation-issues\" class=\"anchor\" aria-label=\"Permalink: Common Installation Issues\" href=\"#common-installation-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFF:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePlease install flex and bison for FF to compile.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn newer versions of gcc (tested on gcc 10.2.0) please make the following changes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emain.c:150 : Comment out the gbracket_count definition\n\u003cpre\u003e\u003ccode\u003eint gbracket_count; --\u0026gt; /* int gbracket_count; */\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003erelax.c:111 : Define lcurrent_goals as static\n\u003cpre\u003e\u003ccode\u003eState lcurrent_goals; --\u0026gt; static State lcurrent_goals;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003esearch.c:110 : Define lcurrent_goals as static\n\u003cpre\u003e\u003ccode\u003eState lcurrent_goals; --\u0026gt; static State lcurrent_goals;\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp\u003ePlease note that this is research code and not yet ready for public delivery,\nhence most parts are not documented.\u003c/p\u003e\n\u003cp\u003eIn case of any queries, please contact \u003ca href=\"mailto:verma.pulkit@asu.edu\"\u003everma.pulkit@asu.edu\u003c/a\u003e,\nor \u003ca href=\"mailto:rmnayyar@asu.edu\"\u003ermnayyar@asu.edu\u003c/a\u003e.\u003c/p\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://rashmeetnayyar.com\" rel=\"nofollow\"\u003eRashmeet Kaur Nayyar\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://pulkitverma.net\" rel=\"nofollow\"\u003ePulkit Verma\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://siddharthsrivastava.net/\" rel=\"nofollow\"\u003eSiddharth Srivastava\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{nayyar_2022_differential,\n author = {Nayyar, Rashmeet Kaur and Verma, Pulkit and Srivastava, Siddharth},\n title = {{Differential Assessment of Black-Box AI Agents}},\n booktitle = {Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)},\n year={2022}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 5, + "subscribers_count": 4, "topics": [], - "updated_at": 1625951112.0 + "updated_at": 1648771336.0 }, { "data_format": 2, - "description": null, + "description": "Computational, behavioral, and imaging studies of physical event perception", "filenames": [ "Singularity" ], - "full_name": "tjhendrickson/BIDS_scripts", - "latest_release": "v1.0", - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-bids-conversion-scripts\" class=\"anchor\" href=\"#bids-conversion-scripts\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBIDS Conversion Scripts\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-description\" class=\"anchor\" href=\"#description\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eThis repository contains scripts that will assist in the conversion of raw DICOM data sets to \u003ca href=\"http://bids.neuroimaging.io/format\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e. The \"heuristics\" folder contains example scripts that have been used to convert data from DICOM to BIDS. \u003cstrong\u003eThis folder may be helpful to build your own heuristics script.\u003c/strong\u003e For additional information on how to create a heuristic script see the \u003ca href=\"https://github.com/nipy/heudiconv\"\u003eheudiconv\u003c/a\u003e github page.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-hosting\" class=\"anchor\" href=\"#container-hosting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Hosting\u003c/h3\u003e\n\u003cp\u003ePull the most recent container below, (\u003cstrong\u003eNOTE: you only have to do this once!\u003c/strong\u003e):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tjhendrickson/BIDS_scripts:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" href=\"#singularity-usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Usage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/singularity/images/directory/imagename.img --help\nusage: [-h] [--output_dir OUTPUT_DIR] [--dicom_dir DICOM_DIR]\n [--study_name STUDY_NAME] [--ses_id SES_ID] [--subj_id SUBJ_ID]\n [--heuristic HEURISTIC] [--dry_run]\n\nScript that controls BIDS conversion for individual studies\n\noptional arguments:\n -h, --help show this help message and exit\n --output_dir OUTPUT_DIR\n The directory that the BIDS data will be outputted to\n --dicom_dir DICOM_DIR\n The directory that houses unzipped dicom\n directories/files.\n --study_name STUDY_NAME\n What is the shorthand name for this study?\n --ses_id SES_ID scanning session id\n --subj_id SUBJ_ID subject id\n --heuristic HEURISTIC\n Path to heuristic file, if the file is already within\n the container (i.e. within heuristics folder) you do\n not have to specify a path.\n --dry_run Dry run. A dicominfo_*.tsv file will generate within\n .heudiconv/\u0027subj_id\u0027/info directory which can be used\n to create heuristic script\n\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eThis application must be run with either the \"--heuristic\" or \"--dry_run\" argument, it will fail otherwise.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse the \"--dry_run\" argument to take a closer look at the acquistion parameters for a scanning session.\u003c/p\u003e\n\u003cp\u003eTo run a single participant with dry_run argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --dry_run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output a hidden folder (named .heudiconv) along with sub-folders based on arguments provided to \"--subj_id\" and \"--ses_id\" respectively.\nWithin the sub-folders will be a tsv file that begins with \"dicominfo\". \u003cstrong\u003eBased on the example above the path to the file will be \".heudiconv/1000/ses-10000/info/dicominfo_ses-10000.tsv\"\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse this tsv file to design a heuristics script to organize your eventual nifti data. \u003cstrong\u003eSee this tutorial for a how to on heuristic script creation (\u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman3\" rel=\"nofollow\"\u003eheuristics tutorial\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo run a single participant with heuristic argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B /home/timothy/sandbox_DO_NOT_DELETE/BIDS/142_CIFASD_4:/output_dir \\\n-B /path/to/dicom/data/dir:/dicom_dir -B /path/to/heuristics/script:/heuristic.py \\\n /path/to/singularity/images/directory/imagename.img \\\n--output_dir /output_dir --dicom_dir /dicom_dir --ses_id 10000 --subj_id 1000 --heuristic /heuristic.py\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-important-notes\" class=\"anchor\" href=\"#important-notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant Notes\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-gotchas\" class=\"anchor\" href=\"#gotchas\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGotchas\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Bind Mounting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to run this container you will have to use \"bind mounting\", meaning you will have to link local folders/files to existing folders/files within the container with the -B flag. In the example above the local folder \"/home/timothy/sandbos_DO_NOT_DELETE/BIDS/142_CIFASD_4\" becomes \"/output_dir\" within the container as they are separated by a colon (:). \u003cstrong\u003eNotice that in both cases above the output and dicom folder and heuristic file are bound to /output_dir, /dicom_dir and /heuristic.py respectively, this is very important.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) DICOM Data Formatting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the restrictive nature of BIDS the dicom data must be in a particular format in order for the conversion to work properly. This application will copy dicom data directories by searching for either the --subj_id or --ses_id argument present within a dicom directory name, place them in a separate directory, and rearrange them. So for example if the dicom directory is named \"XYXY4776XYXY\" --subj_id 4776 the application will find the \"4776\" pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Subject ID and Session ID names\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou must use alphanumerics (i.e. letters or numbers) only (\u003cstrong\u003eno special characters\u003c/strong\u003e) with your subject IDs (subj_id) and session IDs (ses_id). \u003cstrong\u003eNote the \"--ses_id\" argument is optional\u003c/strong\u003e!\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-best-practices\" class=\"anchor\" href=\"#best-practices\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBest Practices\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1) Initial Conversion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile testing the initial BIDS conversion it is best to start with one or two datasets and specify the \u0027--dry_run\u0027 argument (see above for an example of usage).\nThis will create a dicom_info tsv file which can be used for heuristic script creation.\nSee Step 3 of \u0027Run HeuDiConv on ses-001 scans to get the dicominfo file\u0027 within \u003ca href=\"http://reproducibility.stanford.edu/bids-tutorial-series-part-2a/#heuman2\" rel=\"nofollow\"\u003eStanford BIDS Tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) BIDS Validator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce satisfied with an initial BIDS conversion, prior to running the conversion on an entire study first ensure that the BIDS converted dataset meets the BIDS specification by using the \u003ca href=\"http://incf.github.io/bids-validator/\" rel=\"nofollow\"\u003eBIDS validator web version\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Longitudinal Format\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen converting data to BIDS you can certainly have a cross sectonal directory format such as below:\nBIDS_output\nsub-01\nsub-02\nsub-03\nsub-04\netc\nHowever, I suggest placing data within a longitudinal directory format even if you have cross-sectional data:\nBIDS_output\nsub-01\nses-01\nses-02\nsub-02\nses-01\netc\u003c/p\u003e\n\u003cp\u003eYou can control the BIDS directory format by providing both the arguments --subj_id --ses_id for a conversion, if you only specify one of the two arguments the data will be outputted in a cross-sectional format.\u003c/p\u003e\n", + "full_name": "CNCLgithub/physical_event_primitives", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ephysical_event_primitives\u003c/h1\u003e\u003ca id=\"user-content-physical_event_primitives\" class=\"anchor\" aria-label=\"Permalink: physical_event_primitives\" href=\"#physical_event_primitives\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRepository for computational, behavioral, and imaging studies of physical event perception.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup\u003c/h3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequirements: \u003ccode\u003esingularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIf you are working on a Linux computer, download singularity and you should be good to go! :\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003egit clone\u003c/code\u003e the repository\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecd physical_event_primitives\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egit submodule update --init\u003c/code\u003e (initialize the Blender egg importer add-on)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e./setup.sh pull true true\u003c/code\u003e (pulls the container from Box.com, sets up Conda and Julia environments). Run \u003ccode\u003e./setup.sh build true true\u003c/code\u003e if you want to build the container for some reason.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIf you are working on a Mac, the situation is more complicated.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFollow instructions here: \u003ca href=\"https://sylabs.io/guides/3.5/admin-guide/installation.html#mac\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/admin-guide/installation.html#mac\u003c/a\u003e to download \u003ccode\u003evagrant\u003c/code\u003e, \u003ccode\u003evagrant-manager\u003c/code\u003e, and \u003ccode\u003evirtual box\u003c/code\u003e. You need to have \u003ccode\u003eHomebrew\u003c/code\u003e to install this.\u003c/li\u003e\n\u003cli\u003eMake sure to create the virtual machine inside of the \u003ccode\u003ephysical_event_primitives\u003c/code\u003e directory, or in a directory that encloses it.\u003c/li\u003e\n\u003cli\u003eWhen you want to build or pull the singularity image, go into the directory with the Vagrantfile and run \u003ccode\u003evagrant ssh\u003c/code\u003e. Then \u003ccode\u003ecd\u003c/code\u003e into the \u003ccode\u003ephysical_event_primitives\u003c/code\u003e directory which is located within \u003ccode\u003e/vagrant/\u003c/code\u003e. You can now run the above commands as if you were working on a Linux!\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMac users may also need to \u003ccode\u003ecp default.conf user.conf\u003c/code\u003e and change the Julia depot line to \u003ccode\u003ejulia_depot:/home/vagrant/.julia/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInteracting with the image\u003c/h3\u003e\u003ca id=\"user-content-interacting-with-the-image\" class=\"anchor\" aria-label=\"Permalink: Interacting with the image\" href=\"#interacting-with-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \u003ccode\u003e./run.sh \u0026lt;command\u0026gt;\u003c/code\u003e to execute commands with the image, e.g. to launch Julia REPL \u003ccode\u003e./run.sh julia\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf using Milgram and need a virtual display (e.g. rendering with Blender), run \u003ccode\u003e./run.sh xvfb-run -a \u0026lt;command\u0026gt;\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDisclaimer: Much of this code is taken directly from \u003ca href=\"http://geometry.cs.ucl.ac.uk/projects/2019/causal-graphs/\" rel=\"nofollow\"\u003ehttp://geometry.cs.ucl.ac.uk/projects/2019/causal-graphs/\u003c/a\u003e with tweaks to fit our specific situation\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eblender/\u003c/code\u003e - Scripts used to export animations to Blender\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecore/\u003c/code\u003e - Core package; contains all the algorithms\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edemos/\u003c/code\u003e - Demos to play with \u0026amp; contains the main \u003ccode\u003egenerate.py\u003c/code\u003e script for generating videos\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egui/\u003c/code\u003e - Graphical modules -- mostly irrelevant here\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escenarios/\u003c/code\u003e - Config files of different possible scenarios\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can run the script using \u003ccode\u003e./run.sh demos/generate.py\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1624658162.0 + "updated_at": 1713829871.0 }, { "data_format": 2, - "description": "R functions and scripts to process output from the BWASP workflow", + "description": null, "filenames": [ - "Singularity.R36", "Singularity", - "Singularity.v1.0" + "Singularity.5.7.21" ], - "full_name": "BrendelGroup/BWASPR", + "full_name": "ISU-HPC/mysql", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-bwaspr\" class=\"anchor\" href=\"#bwaspr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBWASPR\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-r-functions-and-scripts-to-process-output-from-the-bwasp-workflow\" class=\"anchor\" href=\"#r-functions-and-scripts-to-process-output-from-the-bwasp-workflow\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eR functions and scripts to process output from the BWASP workflow\u003c/h3\u003e\n\u003cp\u003eThis repository contains R functions and scripts we use to analyze the\n*.mcalls output files from the \u003ca href=\"https://github.com/brendelgroup/BWASP\"\u003eBWASP\u003c/a\u003e workflow.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the\n\u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-what-bwaspr-does\" class=\"anchor\" href=\"#what-bwaspr-does\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat BWASPR does\u003c/h2\u003e\n\u003cp\u003eRequired input to the \u003cem\u003eBWASPR\u003c/em\u003e workflow consists of the *.mcalls files (tab\ndelimited data for the named columns)\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003eSeqID.Pos SequenceID Position Strand Coverage Prcnt_Meth Prcnt_Unmeth\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand two files specifying the data labels and *.mcalls file locations and\ncertain parameters, respectively.\nLet\u0027s look at the example files in \u003ca href=\"./inst/extdata\"\u003einst/extdata\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eAmHE.dat\n================================================================================\n# Samples from Herb et al. (2012) Nature Neuroscience:\n#\nAm HE forager 0 CpGhsm ../inst/extdata/Amel-forager.CpGhsm.mcalls\nAm HE forager 0 CpGscd ../inst/extdata/Amel-forager.CpGscd.mcalls\nAm HE nurse 0 CpGhsm ../inst/extdata/Amel-nurse.CpGhsm.mcalls\nAm HE nurse 0 CpGscd ../inst/extdata/Amel-nurse.CpGscd.mcalls\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eAmHE.par\n================================================================================\nSPECIESNAME Apis mellifera\nASSEMBLYVERSION Amel_4.5\nGENOMESIZE 250270657\nTOTALNBRPMSITES 20307353\nSPECIESGFF3DIR ../inst/extdata/AmGFF3DIR\nGENELISTGFF3 Amel.gene.gff3\nEXONLISTGFF3 Amel.exon.gff3\nPCGEXNLISTGFF3 Amel.pcg-exon.gff3\nPROMOTRLISTGFF3 Amel.promoter.gff3\nCDSLISTGFF3 Amel.pcg-CDS.gff3\nUTRFLAGSET 1\n5UTRLISTGFF3 Amel.pcg-5pUTR.gff3\n3UTRLISTGFF3 Amel.pcg-3pUTR.gff3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first file has columns for \u003cem\u003especies\u003c/em\u003e (here \u003cem\u003eAm\u003c/em\u003e); \u003cem\u003estudy\u003c/em\u003e (here \u003cem\u003eHE\u003c/em\u003e);\n\u003cem\u003esample\u003c/em\u003e (here \u003cem\u003eforager\u003c/em\u003e and \u003cem\u003enurse\u003c/em\u003e\"); replicate number (here 0, indicating\nsingle samples or, as in the case of this study, aggregates over replicates);\nand file locations (here for the \u003cem\u003eCpGhsm\u003c/em\u003e and \u003cem\u003eCpGscd\u003c/em\u003e *.mcalls files);\nnote that the file locations in this example are relative links, assuming you\nwill run the example discussed in the \u003ca href=\"./demo\"\u003edemo\u003c/a\u003e directory.\nThe second file specifies the species name, genome assembly version, genome\nsize (in base pairs), total number of potential methylation sites (CpGs), and\nfile names for GFF3 annotation of various genomic features (\u003cem\u003eUTRFLAGSET\u003c/em\u003e is set\nto 1 to use UTR annotation in the GFF3 file).\u003c/p\u003e\n\u003cp\u003eA typical \u003cem\u003eBWASPR\u003c/em\u003e workflow will read the specified *.mcalls files and\ngenerate various output tables and plots, labeled in various ways with\n\u003cem\u003especies\u003c/em\u003e_ \u003cem\u003estudy\u003c/em\u003e_ \u003cem\u003esample\u003c/em\u003e_ \u003cem\u003ereplicate\u003c/em\u003e labels.\nThe \u003ca href=\"./demo/Rscript.BWASPR\"\u003edemo/Rscript.BWASPR\u003c/a\u003e file shows a template\nworkflow.\nInitial customization is done at the top of the file and mostly from\ninclusion of a configuration file such as\n\u003ca href=\"./demo/sample.conf\"\u003edemo/sample.conf\u003c/a\u003e.\nThe following table summarizes the successive workflow steps.\nYou may want to open the \u003ca href=\"./demo/Rscript.BWASPR\"\u003edemo/Rscript.BWASPR\u003c/a\u003e and\n\u003ca href=\"./demo/sample.conf\"\u003edemo/sample.conf\u003c/a\u003e in separate windows as a reference\nwhile viewing the table.\nDetails on running the workflow with the demo data are given in\n\u003ca href=\"./demo/README.md\"\u003edemo/README\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-runflag-to-expected-output-correspondence\" class=\"anchor\" href=\"#runflag-to-expected-output-correspondence\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRUNflag to expected output correspondence\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eRUNflag\u003c/th\u003e\n\u003cth\u003einput\u003c/th\u003e\n\u003cth\u003e(select) parameters\u003c/th\u003e\n\u003cth\u003efunction\u003c/th\u003e\n\u003cth\u003etheme\u003c/th\u003e\n\u003cth\u003eoutput files\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNcms\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003ecovlist, locount, hicount\u003c/td\u003e\n\u003ctd\u003ecmStats()\u003c/td\u003e\n\u003ctd\u003esample coverage and methylation statistics\u003c/td\u003e\n\u003ctd\u003ecms-*.txt\u003cbr\u003ecms-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNpwc\u003c/td\u003e\n\u003ctd\u003estudymk\u003cbr\u003estudymc\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003ecmpSites()\u003c/td\u003e\n\u003ctd\u003epairwise sample comparisons\u003c/td\u003e\n\u003ctd\u003epwc-*.vs.*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNcrl\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003edestrand\u003c/td\u003e\n\u003ctd\u003ecmpSamples()\u003c/td\u003e\n\u003ctd\u003ecorrelations between aggregate samples\u003c/td\u003e\n\u003ctd\u003ecrl-*.txt\u003cbr\u003ecrl-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNrepcms\u003c/td\u003e\n\u003ctd\u003ereplicate *.mcalls\u003c/td\u003e\n\u003ctd\u003erepcovlist,\u003cbr\u003ereplocount, rephicount\u003c/td\u003e\n\u003ctd\u003ecmStats()\u003c/td\u003e\n\u003ctd\u003ereplicate coverage and methylation statistics\u003c/td\u003e\n\u003ctd\u003erepcms-*.txt\u003cbr\u003erepcms-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNrepcrl\u003c/td\u003e\n\u003ctd\u003ereplicate *.mcalls\u003c/td\u003e\n\u003ctd\u003edestrand\u003c/td\u003e\n\u003ctd\u003ecmpSamples()\u003c/td\u003e\n\u003ctd\u003ecorrelations between replicates\u003c/td\u003e\n\u003ctd\u003erepcrl-*.txt\u003cbr\u003erepcrl-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNmmp\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003emap_methylome()\u003c/td\u003e\n\u003ctd\u003emethylation to annotation maps\u003c/td\u003e\n\u003ctd\u003emmp-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNacs\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003edestrand\u003c/td\u003e\n\u003ctd\u003eannotate_methylome()\u003c/td\u003e\n\u003ctd\u003eannotation of common sites\u003c/td\u003e\n\u003ctd\u003eacs-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNrnk\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003egenome_ann$\u003cem\u003eregion\u003c/em\u003e\n\u003c/td\u003e\n\u003ctd\u003erank_rbm()\u003c/td\u003e\n\u003ctd\u003eranked genes and promoters\u003c/td\u003e\n\u003ctd\u003eranked-*.txt\u003cbr\u003esites-in-*.txt\u003cbr\u003ernk-sig-*.pdf\u003cbr\u003esip-*.txt\u003cbr\u003ernk-sip-*.txt\u003cbr\u003ernk-sip-*.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNmrpr\u003c/td\u003e\n\u003ctd\u003estudymk\u003c/td\u003e\n\u003ctd\u003eddset\u003cbr\u003enr2d\u003cbr\u003edoplots\u003c/td\u003e\n\u003ctd\u003edet_mrpr()\u003c/td\u003e\n\u003ctd\u003emethylation-rich and -poor regions\u003c/td\u003e\n\u003ctd\u003edst-*.txt\u003cbr\u003e*ds-*.pdf\u003cbr\u003emdr-*.tab\u003cbr\u003emdr-*.bed\u003cbr\u003empr-*.txt\u003cbr\u003emrr-*.txt\u003cbr\u003ermp-*.txt\u003cbr\u003egwr-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNdmt\u003c/td\u003e\n\u003ctd\u003estudymc\u003c/td\u003e\n\u003ctd\u003ewsize, stepsize\u003c/td\u003e\n\u003ctd\u003edet_dmt()\u003c/td\u003e\n\u003ctd\u003edifferentially methylated tiles and genes\u003c/td\u003e\n\u003ctd\u003edmt-*.txt\u003cbr\u003edmg-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNdmsg\u003c/td\u003e\n\u003ctd\u003esample *.mcalls\u003cbr\u003e\n\u003c/td\u003e\n\u003ctd\u003ehighcoverage\u003cbr\u003edestrand\u003c/td\u003e\n\u003ctd\u003edet_dmsg()\u003c/td\u003e\n\u003ctd\u003edifferentially methylated sites and genes\u003c/td\u003e\n\u003ctd\u003edms-*.txt\u003cbr\u003edmg-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNdmgdtls\u003c/td\u003e\n\u003ctd\u003estudyhc\u003c/td\u003e\n\u003ctd\u003edestrand\u003c/td\u003e\n\u003ctd\u003eshow_dmsg()\u003c/td\u003e\n\u003ctd\u003edetails for differentially methylated genes\u003c/td\u003e\n\u003ctd\u003edmg-*.vs.*_details.txt\u003cbr\u003edmg-*.vs.*_heatmaps.pdf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNogl\u003c/td\u003e\n\u003ctd\u003estudyhc\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eexplore_dmsg()\u003cbr\u003erank_dmg()\u003c/td\u003e\n\u003ctd\u003eranked lists of differentially methylated genes\u003c/td\u003e\n\u003ctd\u003eogl-*.txt\u003cbr\u003ernk-dmg-*.vs.*.txt\u003cbr\u003ernk-dmg-*.vs.*.pdf\u003cbr\u003ewrt-*.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRUNsave\u003c/td\u003e\n\u003ctd\u003eworkflow output\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003esave.image()\u003c/td\u003e\n\u003ctd\u003esave image of workflow output\u003c/td\u003e\n\u003ctd\u003e*.RData\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emysql\u003c/h1\u003e\u003ca id=\"user-content-mysql\" class=\"anchor\" aria-label=\"Permalink: mysql\" href=\"#mysql\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/937\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease see \u003ca href=\"https://www.hpc.iastate.edu/guides/containers/mysql-server\" rel=\"nofollow\"\u003ehttps://www.hpc.iastate.edu/guides/containers/mysql-server\u003c/a\u003e for usage instructions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eMake local directory structure for database information\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir -p ${PWD}/mysql/var/lib/mysql ${PWD}/mysql/run/mysqld\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDownload the sample .my.cnf and .mysqlpassword files\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl https://raw.githubusercontent.com/ISU-HPC/mysql/master/my.cnf \u0026gt; ${HOME}/.my.cnf\n$ curl https://raw.githubusercontent.com/ISU-HPC/mysql/master/mysqlrootpw \u0026gt; ${HOME}/.mysqlrootpw\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eLaunch an instance of the container, bind-mounting the local directories\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity instance.start --bind ${PWD}/mysql/var/lib/mysql/:/var/lib/mysql --bind ${PWD}/mysql/run/mysqld:/run/mysqld shub://ISU-HPC/mysql mysql\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eRun the container\u0027s runscript to initialize mysqld and then launch mysqld\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run instance://mysql\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eVerify mysqld is running by opening a shell in the container an starting the MySQL client\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity shell instance://mysql\nSingularity: Invoking an interactive shell within container...\n\nbash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)\nSingularity ISU-HPC-mysql-master-latest.simg:~\u0026gt; mysql\nWelcome to the MySQL monitor. Commands end with ; or \\g.\nYour MySQL connection id is 3\nServer version: 5.7.21 MySQL Community Server (GPL)\n\nCopyright (c) 2000, 2018, Oracle and/or its affiliates. All rights reserved.\n\nOracle is a registered trademark of Oracle Corporation and/or its\naffiliates. Other names may be trademarks of their respective\nowners.\n\nType \u0027help;\u0027 or \u0027\\h\u0027 for help. Type \u0027\\c\u0027 to clear the current input statement.\n\nmysql\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u003ca href=\"https://www.hpc.iastate.edu/guides/containers/mysql-server\" rel=\"nofollow\"\u003ehttps://www.hpc.iastate.edu/guides/containers/mysql-server\u003c/a\u003e for details on how to\nconnect to the \u003ccode\u003emysqld\u003c/code\u003e server from outside the container, via both local socket\nand the network.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 6, + "subscribers_count": 3, "topics": [], - "updated_at": 1627671623.0 + "updated_at": 1642244274.0 }, { "data_format": 2, - "description": "USDA ARS BEARRU Whole Genome Sequencing Workflow", + "description": "braker container", "filenames": [ - "containers/Singularity" + "Singularity" ], - "full_name": "lakinsm/bear-wgs", + "full_name": "aseetharam/braker", "latest_release": null, - "readme": "\u003ch2\u003e\n\u003ca id=\"user-content-overview\" class=\"anchor\" href=\"#overview\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/706\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWhole genome sequencing for the characterization of bacterial genomes presents several bioinformatics challenges. Primary outcomes include determining sequencing read quality, performing alignment and de novo assembly of the bacterial genome, annotating the processed genomes, and discovering genomic features of interest. Due to the specificity of genetic elements within each bacterial genus, separate workflows must be utilized for each bacterial species of interest. This Nextflow pipeline aims to characterize bacteria of interest to food safety in poultry broiler production systems, specifically Salmonella enterica Heidelberg, Enterococcus faecalis, and their respective plasmids. Alignment, de novo assembly, and marker-based detection of genetic elements of interest are included in the pipeline, and a variety of custom databases have been created to aid in the annotation of the resulting genomic sequences. For more information, explore the pipeline through the links below.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-more-information\" class=\"anchor\" href=\"#more-information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/process.md\"\u003eProcess Overview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lakinsm/bear-wgs/blob/master/docs/acknowledgements.md\"\u003eAcknowledgements\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBRAKER Container \u003ca href=\"https://singularity-hub.org/collections/4738\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-braker-container-\" class=\"anchor\" aria-label=\"Permalink: BRAKER Container \" href=\"#braker-container-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Gaius-Augustus/BRAKER\"\u003eBRAKER2\u003c/a\u003e is a gene prediction program that uses GeneMark-EXand AUGUSTUS from RNA-Seq and/or protein homology information, and that integrates the extrinsic evidence from RNA-Seq and protein homology information into the prediction.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get the container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull braker2.sif shub://aseetharam/braker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a \u003ccode\u003ebraker2.sif\u003c/code\u003e image, with \u003ccode\u003ebraker\u003c/code\u003e installed within the image. Before running, copy \u003ccode\u003eaugustus_config\u003c/code\u003e directory as it needs to be writeable:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e braker2.sif cp -R /usr/local/config augustus_config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisities\u003c/h3\u003e\u003ca id=\"user-content-prerequisities\" class=\"anchor\" aria-label=\"Permalink: Prerequisities\" href=\"#prerequisities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run this container you\u0027ll need \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBRAKER usage can be found by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --no-home --home /opt/gm_key --cleanenv braker2.sif braker.pl --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment Variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ePATH\u003c/code\u003e Location for all the installed tools\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAUGUSTUS_SCRIPTS_PATH\u003c/code\u003e misc. scripts used by \u003ccode\u003eaugusutus\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAUGUSTUS_BIN_PATH\u003c/code\u003e \u003ccode\u003eaugustus\u003c/code\u003e binaries\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGENEMARK_PATH\u003c/code\u003e \u003ccode\u003eGeneMark\u003c/code\u003e scripts\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eALIGNMENT_TOOL_PATH\u003c/code\u003e alignment programs that \u003ccode\u003ebraker\u003c/code\u003e needs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample run\u003c/h2\u003e\u003ca id=\"user-content-example-run\" class=\"anchor\" aria-label=\"Permalink: Example run\" href=\"#example-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor running on your data:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e SINGULARITY_IMAGE=/path/to/braker2.sif\n\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e BAM=/path/to/rnaseq.bam\n\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e GENOME=/path/to/genome-masked.fasta\n\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e PROT_SEQ=/path/to/mikado-proteins.faa\n\u003cspan class=\"pl-k\"\u003ereadonly\u003c/span\u003e SPECIES=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eunique-name\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e gffread adds `.` for stop codons, replace it with `*`\u003c/span\u003e\nsed \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/^[^\u0026gt;]/s/\\./*/\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${PROT_SEQ}\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${PROT_SEQ\u003cspan class=\"pl-k\"\u003e##*/\u003c/span\u003e}\u003c/span\u003e.new\n\nsingularity pull braker2.sif shub://aseetharam/braker\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${SINGULARITY_IMAGE}\u003c/span\u003e cp -R /usr/local/config augustus_config\n\nenv \u003cspan class=\"pl-k\"\u003etime\u003c/span\u003e -v singularity run \\\n --no-home \\\n --home /opt/gm_key \\\n --cleanenv \\\n --env AUGUSTUS_CONFIG_PATH=\u003cspan class=\"pl-smi\"\u003e${PWD}\u003c/span\u003e/augustus_config \\\n \u003cspan class=\"pl-smi\"\u003e${SINGULARITY_IMAGE}\u003c/span\u003e braker.pl \\\n --cores \u003cspan class=\"pl-smi\"\u003e${SLURM_JOB_CPUS_PER_NODE}\u003c/span\u003e \\\n --species=\u003cspan class=\"pl-smi\"\u003e${SPECIES}\u003c/span\u003e \\\n --genome=\u003cspan class=\"pl-smi\"\u003e${GENOME}\u003c/span\u003e \\\n --bam=\u003cspan class=\"pl-smi\"\u003e${BAM}\u003c/span\u003e \\\n --prot_seq=\u003cspan class=\"pl-smi\"\u003e${PROT_SEQ\u003cspan class=\"pl-k\"\u003e##*/\u003c/span\u003e}\u003c/span\u003e.new \\\n --prg=gth \\\n --gth2traingenes \\\n --gff3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eNathan Weeks\u003c/strong\u003e - \u003cem\u003eInitial work\u003c/em\u003e - \u003ca href=\"https://www.ars.usda.gov/people-locations/person?person-id=41062\" rel=\"nofollow\"\u003eWebPage\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eArun Seetharam\u003c/strong\u003e - \u003cem\u003emaintainer\u003c/em\u003e - \u003ca href=\"aseetharam.github.io\"\u003eWebPage\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee also the list of \u003ca href=\"https://github.com/your/repository/contributors\"\u003econtributors\u003c/a\u003e who\nparticipated in this project.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1561739100.0 + "updated_at": 1618540892.0 }, { "data_format": 2, - "description": null, + "description": "Simplifying parallel graph processing by automating the installation and timing of some of the myriad platforms.", "filenames": [ - "installationsWithAdditionalTools/openfoam-v1712-waves2Foam2124/02_PortingToSingularity/Singularity.def", - "installationsWithAdditionalTools/openfoam-2.4.x_waves2Foam2079/02_PortingToSingularity/Singularity.def" + "Singularity" ], - "full_name": "alexisespinosa-uptake/OpenFOAMContainers", + "full_name": "HPCL/easy-parallel-graph", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSimplifying Parallel Graph Processing\u003c/h1\u003e\u003ca id=\"user-content-simplifying-parallel-graph-processing\" class=\"anchor\" aria-label=\"Permalink: Simplifying Parallel Graph Processing\" href=\"#simplifying-parallel-graph-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is aimed at simplifying aspects of parallel graph processing starting with providing a framework for analyzing performance and energy usage for a given system.\u003c/p\u003e\n\u003cp\u003eThe general workflow could consist of the following steps. You can find an example workflow in \u003ccode\u003eexperiments/example.sh\u003c/code\u003e.\u003c/p\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003e\u003ccode\u003ecd experiment\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eDownload the systems and build them with \u003ccode\u003e./get-libraries.sh\u003c/code\u003e. You may also supply a location where the libraries should be installed but the default is \u003ccode\u003e./lib\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eGenerate some synthetic datasets with \u003ccode\u003e./gen-datasets.sh 20\u003c/code\u003e. The 20 here will generate an RMAT matrix to the Graph500 specifications with 2^20 = 1,048,576 vertices with an average of 16 edges per vertex.\n\u003cul\u003e\n\u003cli\u003eAlternatively, you could run \u003ccode\u003e./gen-datasets -f=\u0026lt;your_file\u0026gt;\u003c/code\u003e. Currently, this only supports files of the \u003ccode\u003e.el\u003c/code\u003e and \u003ccode\u003e.wel\u003c/code\u003e forms. These are explained \u003ca href=\"https://gist.github.com/sampollard/f9169c4eb04669390a834884682c080d\"\u003ehere\u003c/a\u003e. It should accept any graph file you can find from \u003ca href=\"https://snap.stanford.edu/data/index.html\" rel=\"nofollow\"\u003eSNAP Database\u003c/a\u003e or the \u003ca href=\"http://konect.uni-koblenz.de/networks/\" rel=\"nofollow\"\u003eKONECT Database\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSelect a scale and number of threads and run the experiment with \u003ccode\u003erun-synthetic.sh\u003c/code\u003e, e.g.\n\u003ccode\u003e./run-synthetic.sh 20 4\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eParse the log files to get a .csv using \u003ccode\u003e./parse-output.sh\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eNote: \u003ccode\u003erun-power.sh\u003c/code\u003e also parses the log files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAnalyze the data. Some examples can be found in \u003ccode\u003epapers/publication/plot_data.R\u003c/code\u003e and\u003ccode\u003eexperiment/experiment_analysis.R\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlgorithms\u003c/h2\u003e\u003ca id=\"user-content-algorithms\" class=\"anchor\" aria-label=\"Permalink: Algorithms\" href=\"#algorithms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eBreadth First Search (BFS)\u003c/li\u003e\n\u003cli\u003eSingle Source Shortest Paths (SSSP)\u003c/li\u003e\n\u003cli\u003ePageRank: This uses a stopping criterion of \u003ccode\u003esum(|\u03c0_i - \u03c0_(i-1)|)\u003c/code\u003e where \u003ccode\u003e\u03c0_i\u003c/code\u003e is the PageRank at iteration \u003ccode\u003ei\u003c/code\u003e and the alpha parameter is \u0251 = 0.15.\u003c/li\u003e\n\u003cli\u003eTriangle Counting (TriangleCount) - Counts the number of triangles in an undirected graph. If the input graph is directed, it is symmetrized beforehand.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePower and Energy\u003c/h2\u003e\u003ca id=\"user-content-power-and-energy\" class=\"anchor\" aria-label=\"Permalink: Power and Energy\" href=\"#power-and-energy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to build for power measurement, you may use \u003ccode\u003epower/build-power.sh\u003c/code\u003e\nRun the experiments and monitor power using \u003ccode\u003eexperiment/run-power.sh\u003c/code\u003e. Requires root permissions.\n\u003ccode\u003ebuild-power.sh\u003c/code\u003e downloads and compiles the various projects for power measurement. Installs to \u003ccode\u003e./powerlib\u003c/code\u003e by default.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalysis\u003c/h2\u003e\u003ca id=\"user-content-analysis\" class=\"anchor\" aria-label=\"Permalink: Analysis\" href=\"#analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eexperiment_analysis.R:\u003c/code\u003e This script takes in a config file. You can see an example in \u003ccode\u003econfig_template.R\u003c/code\u003e, and the variables are explained in the comments. Notice here there are both synthetic and realworld experiments that can be analyzed at once. If you just want to do one or the other, uncomment out \u003ccode\u003efocus_scale\u003c/code\u003e and \u003ccode\u003efocus_thread\u003c/code\u003e for synthetic and \u003ccode\u003erealworld_datasets\u003c/code\u003e for realworld datasets. If you want to do performance prediction (see the \u003ccode\u003elearn\u003c/code\u003e directory) you must set \u003ccode\u003ecoalesce \u0026lt;- TRUE\u003c/code\u003e so the experimental data gets compressed to a format amenable to machine learning.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLearning\u003c/h2\u003e\u003ca id=\"user-content-learning\" class=\"anchor\" aria-label=\"Permalink: Learning\" href=\"#learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere you can download datasets, extract features, and use performance data to train a model to predict runtime performance of the various algorithms to recommend a highly performing package for your problem.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edatasets.txt\u003c/code\u003e This file contains the URLs of datasets you wish to use. Currently supported databases for downloading are KONECT and SNAP, and currently supported feature extraction are for SNAP. The format is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edataset name\nfeature web page\nURL to the graph\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSome examples are provided in the existing \u003ccode\u003edatasets.txt\u003c/code\u003e. You can comment out datasets with \u003ccode\u003e#\u003c/code\u003e but please only add additional lines in multiples of 3. The datasets are read by taking every third line, so adding additional lines will mess things up. Yes, I realize this is a bit of a silly limitation.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eunzipper.sh\u003c/code\u003e This downloads and unzips the datasets. The usage is \u003ccode\u003eunzipper.sh \u0026lt;dataset file\u0026gt; \u0026lt;dataset_dir\u0026gt;\u003c/code\u003e. A sensible default is\n\u003ccode\u003e./unzipper.sh datasets.txt ../datasets\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003efeatures.py\u003c/code\u003e extracts features from a dataset\u0027s description page in SNAP (and eventually KONECT). This will put a file called \u003ccode\u003efeatures.csv\u003c/code\u003e into the respective dataset\u0027s directory, looking through every dataset in \u003ccode\u003edatasets.txt\u003c/code\u003e. The usage is \u003ccode\u003epython features.py \u0026lt;data_dir\u0026gt; (default: ../experiments/datasets)\u003c/code\u003e. \u003cem\u003eNote\u003c/em\u003e: If you want to add a non-SNAP dataset into the machine-learned model you must add your own features.csv. The minimum required is to have Nodes and Edges. For example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNodes,Edges\n23132,511764\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewould work.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ecombined.csv\u003c/code\u003e file from the Analysis section can be passed into scripts here.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOther Scripts\u003c/h3\u003e\u003ca id=\"user-content-other-scripts\" class=\"anchor\" aria-label=\"Permalink: Other Scripts\" href=\"#other-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egraphalytics/get-graphalytics.sh\u003c/code\u003e: This script gathers, installs, and runs various\nbenchmarks from Graphalytics. Run with no arguments. If you want to change\nwhat gets run, you can edit the script after the \u003ccode\u003e### MAIN ###\u003c/code\u003e section.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epapers/report/get-hwinfo.sh\u003c/code\u003e: Gathers hardware information and outputs to a csv (stdout). Meant\nto be used with the automatic report generation. Works better if you have sudo permission.\nSpecifically, \u003ccode\u003esudo lshw \u0026gt; lshw.txt\u003c/code\u003e for Linux and \u003ccode\u003esudo dmidecode \u0026gt; dmidecode.txt\u003c/code\u003e for Mac.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Scripts in the \u003ccode\u003eexperiment\u003c/code\u003e Directory\u003c/h3\u003e\u003ca id=\"user-content-the-scripts-in-the-experiment-directory\" class=\"anchor\" aria-label=\"Permalink: The Scripts in the experiment Directory\" href=\"#the-scripts-in-the-experiment-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eR and the ggplot2 library\u003c/li\u003e\n\u003cli\u003eGraph500, GAP, Graphalytics, GraphBIG, PowerGraph, Galois dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraphBIG/OpenG\u003c/h3\u003e\u003ca id=\"user-content-graphbigopeng\" class=\"anchor\" aria-label=\"Permalink: GraphBIG/OpenG\" href=\"#graphbigopeng\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003egcc/g++ with c++0x support (\u0026gt;4.3)\u003c/li\u003e\n\u003cli\u003eFor profiling: Linux (because it uses libpfm)\u003c/li\u003e\n\u003cli\u003eCmake\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePowerGraph\u003c/h3\u003e\u003ca id=\"user-content-powergraph\" class=\"anchor\" aria-label=\"Permalink: PowerGraph\" href=\"#powergraph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ezlib\u003c/li\u003e\n\u003cli\u003eMPICH2\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraphMat\u003c/h3\u003e\u003ca id=\"user-content-graphmat\" class=\"anchor\" aria-label=\"Permalink: GraphMat\" href=\"#graphmat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eIntel compiler (icpc 17 is known to work)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraph500 \u0026amp; GAP Benchmark Suite\u003c/h3\u003e\u003ca id=\"user-content-graph500--gap-benchmark-suite\" class=\"anchor\" aria-label=\"Permalink: Graph500 \u0026amp; GAP Benchmark Suite\" href=\"#graph500--gap-benchmark-suite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eA C++ compiler with OpenMP Support\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGalois\u003c/h3\u003e\u003ca id=\"user-content-galois\" class=\"anchor\" aria-label=\"Permalink: Galois\" href=\"#galois\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCmake\u003c/li\u003e\n\u003cli\u003eBoost\u003c/li\u003e\n\u003cli\u003eI\u0027ve had difficulty using any gcc version other than 4.8.5. Be \u003cem\u003eabsolutely\u003c/em\u003e sure you use the same gcc to build boost as you do to build Galois.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures that are not ready\u003c/h2\u003e\u003ca id=\"user-content-features-that-are-not-ready\" class=\"anchor\" aria-label=\"Permalink: Features that are not ready\" href=\"#features-that-are-not-ready\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraphalytics\u003c/h3\u003e\u003ca id=\"user-content-graphalytics\" class=\"anchor\" aria-label=\"Permalink: Graphalytics\" href=\"#graphalytics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can download and build the systems for Graphalytics, package them, and run them with \u003ccode\u003egraphalytics/get-graphalytics.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003eget-graphalytics.sh\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-get-graphalyticssh\" class=\"anchor\" aria-label=\"Permalink: get-graphalytics.sh\" href=\"#get-graphalyticssh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eJava\u003c/li\u003e\n\u003cli\u003ePerl\u003c/li\u003e\n\u003cli\u003eYarn\u003c/li\u003e\n\u003cli\u003eApache maven 3.0.0 or later\u003c/li\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGraphalytics\u003c/h3\u003e\u003ca id=\"user-content-graphalytics-1\" class=\"anchor\" aria-label=\"Permalink: Graphalytics\" href=\"#graphalytics-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eApache maven 3.0.0 or later\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1631870314.0 + "updated_at": 1671308248.0 }, { "data_format": 2, - "description": "Recipe for VEP + Cache", + "description": null, "filenames": [ - "Singularityfiles/Singularity.99-GRCh38-merged", - "Singularityfiles/Singularity.99-GRCh37-merged" + "examples/docker/Singularity", + "examples/raspbian/Singularity", + "examples/apps/Singularity", + "examples/apps/Singularity.cowsay", + "examples/ubuntu/Singularity", + "examples/arch/Singularity", + "examples/asciinema/Singularity", + "examples/self/Singularity", + "examples/busybox/Singularity", + "examples/shub/Singularity", + "examples/opensuse/Singularity", + "examples/centos/Singularity", + "examples/scientific/Singularity" ], - "full_name": "matmu/vep", + "full_name": "kernsuite-debian/singularity-container", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-containerized-variant-effect-predictor-vep--cache\" class=\"anchor\" href=\"#containerized-variant-effect-predictor-vep--cache\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainerized Variant Effect Predictor (VEP) + Cache\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://twitter.com/intent/tweet?hashtags=Ensembl,VEP,Singularity,Docker\u0026amp;url=https://github.com/matmu/vep\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90bc908826728c0e4261acfff5619fd732c7be2b2a00624fce6363c9a3623c90/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f75726c2f687474702f736869656c64732e696f2e7376673f7374796c653d736f6369616c\" alt=\"Twitter\" data-canonical-src=\"https://img.shields.io/twitter/url/http/shields.io.svg?style=social\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u00a0+ \u003ca href=\"#Introduction\"\u003eIntroduction\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Building-image-with-Singularity\"\u003eBuilding image with Singularity\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Run-VEP\"\u003eRun VEP\u003c/a\u003e \u003cbr\u003e\n\u00a0\u00a0\u00a0\u00a0|-- \u003ca href=\"#More-options\"\u003eMore options\u003c/a\u003e \u003cbr\u003e\n\u00a0\u00a0\u00a0\u00a0|-- \u003ca href=\"#Examples\"\u003eExamples\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Post-processing\"\u003ePost-processing\u003c/a\u003e \u003cbr\u003e\n\u00a0\u00a0\u00a0\u00a0|-- \u003ca href=\"#Split-VEP\"\u003eSplit VEP\u003c/a\u003e \u003cbr\u003e\n\u00a0\u00a0\u00a0\u00a0|-- \u003ca href=\"#Filtering-by-VEP-annotations\"\u003eFiltering by VEP annotations\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#VEP-plugins\"\u003eVEP plugins\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Build-and-run-VEP-with-Docker\"\u003eBuild \u0026amp; run VEP with Docker\u003c/a\u003e \u003cbr\u003e\n\u00a0+ \u003ca href=\"#Acknowledgments\"\u003eAcknowledgements\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h2\u003e\n\u003cp\u003eThis documentation describes the usage of the Docker image at \u003ca href=\"https://hub.docker.com/r/matmu/vep\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/matmu/vep\u003c/a\u003e which contains the bioinformatics tool \u003cstrong\u003eEnsembl Variant effect predictor (VEP)\u003c/strong\u003e for annotating genetic variants. The image comes with\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMerged cache including RefSeq and Ensembl transcripts (VEP parameter --merged required)\u003c/li\u003e\n\u003cli\u003eReference genome and index\u003c/li\u003e\n\u003cli\u003ePlugins (annotation data is not included)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-available-versions\" class=\"anchor\" href=\"#available-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable versions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eHuman:\u003c/strong\u003e \u003ca href=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCh38-merged.yml\"\u003e\u003cimg src=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCh38-merged.yml/badge.svg\" alt=\"103-GRCh38-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCh38.yml\"\u003e\u003cimg src=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCh38.yml/badge.svg\" alt=\"103-GRCh38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/101-GRCh38/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/101-GRCh38/badge.svg\" alt=\"101-GRCh38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCh38/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCh38/badge.svg\" alt=\"100-GRCh38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCh38-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCh38-merged/badge.svg\" alt=\"100-GRCh38-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCh37/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCh37/badge.svg\" alt=\"100-GRCh37\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCh37-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCh37-merged/badge.svg\" alt=\"100-GRCh37-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/99-GRCh38-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/99-GRCh38-merged/badge.svg\" alt=\"99-GRCh38-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/99-GRCh37-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/99-GRCh37-merged/badge.svg\" alt=\"99-GRCh37-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\n\u003cstrong\u003eMouse:\u003c/strong\u003e \u003ca href=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCm39.yml\"\u003e\u003cimg src=\"https://github.com/matmu/vep/actions/workflows/docker.103-GRCm39.yml/badge.svg\" alt=\"103-GRCm39\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/101-GRCm38/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/101-GRCm38/badge.svg\" alt=\"101-GRCm38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCm38/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCm38/badge.svg\" alt=\"100-GRCm38\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/matmu/vep/workflows/100-GRCm38-merged/badge.svg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/matmu/vep/workflows/100-GRCm38-merged/badge.svg\" alt=\"100-GRCm38-merged\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe term \u003ccode\u003emerged\u003c/code\u003e refers to the merged Ensembl/RefSeq cache. To be consistent with the Ensembl website, chose Ensembl cache only (i.e. without the term \u003ccode\u003emerged\u003c/code\u003e). Examples for available versions are \u003cstrong\u003e99-GRCh38\u003c/strong\u003e (VEP 99 with Ensembl cache for reference GRCh38) or \u003cstrong\u003e99-GRh37-merged\u003c/strong\u003e (VEP 99 with Ensembl/Refseq cache for reference GRCh37).\u003c/p\u003e\n\u003cp\u003eYou can also visit \u003ca href=\"https://hub.docker.com/r/matmu/vep/tags\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/matmu/vep/tags\u003c/a\u003e to get a list of available versions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e If you require a container for a species not mentioned above, feel free to contact us or even better, create an issue.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-image-with-singularity\" class=\"anchor\" href=\"#build-image-with-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild image with Singularity\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build vep.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg docker://matmu/vep:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e\u0026lt;version\u0026gt;\u003c/code\u003e is a tag representing the Ensembl version and the species + version of the reference genome.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-run-vep\" class=\"anchor\" href=\"#run-vep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun VEP\u003c/h2\u003e\n\u003cp\u003eTo run VEP execute\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg vep [options]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhereby \u003ccode\u003e\u0026lt;version\u0026gt;\u003c/code\u003e is replaced by a respective version (see above), e.g. \u003ccode\u003e99-CRCh38\u003c/code\u003e. It is essential to add the VEP option \u003ccode\u003e--merged\u003c/code\u003e when using an image with merged Ensembl/Refseq cache. For species except homo sapiens, also the parameter \u003ccode\u003e--species\u003c/code\u003e (e.g. \u003ccode\u003e--species mus_musculus\u003c/code\u003e), has to be set as well.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-more-options\" class=\"anchor\" href=\"#more-options\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore options\u003c/h3\u003e\n\u003cp\u003eThe options for base cache/plugin directories, species and assembly are set to the right values by default and do not need to be set by the user.\u003c/p\u003e\n\u003cp\u003eVisit \u003ca href=\"http://www.ensembl.org/info/docs/tools/vep/script/vep_options.html\" rel=\"nofollow\"\u003ehttp://www.ensembl.org/info/docs/tools/vep/script/vep_options.html\u003c/a\u003e for detailed information about all VEP options. Detailed information about \u003cstrong\u003einput/output formats\u003c/strong\u003e can be found at \u003ca href=\"https://www.ensembl.org/info/docs/tools/vep/vep_formats.html#defaultout\" rel=\"nofollow\"\u003ehttps://www.ensembl.org/info/docs/tools/vep/vep_formats.html#defaultout\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-examples\" class=\"anchor\" href=\"#examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-minimum-output-format-compressed-tab-delimited\" class=\"anchor\" href=\"#minimum-output-format-compressed-tab-delimited\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMinimum (output format: compressed tab delimited)\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38-merged.simg vep --dir /opt/vep/.vep --merged --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt.gz --tab --compress_output bgzip\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38.simg vep --dir /opt/vep/.vep --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt.gz --tab --compress_output bgzip\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCm38.simg vep --dir /opt/vep/.vep --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt.gz --tab --compress_output bgzip -species mus_musculus\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-minimum-output-format-compressed-vcf\" class=\"anchor\" href=\"#minimum-output-format-compressed-vcf\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMinimum (output format: compressed vcf)\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38.simg vep --dir /opt/vep/.vep --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf.gz --vcf --compress_output bgzip\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-full-annotation\" class=\"anchor\" href=\"#full-annotation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFull annotation\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38.simg vep --dir /opt/vep/.vep --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf.gz --vcf --compress_output bgzip --everything --nearest symbol \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-post-processing\" class=\"anchor\" href=\"#post-processing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePost-processing\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-split-vep\" class=\"anchor\" href=\"#split-vep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSplit VEP\u003c/h3\u003e\n\u003cp\u003eThere is a plugin for \u003ccode\u003ebcftools\u003c/code\u003e that allows to split VEP annotations as well as sample information in a VCF file and convert it to a text file: \u003ca href=\"http://samtools.github.io/bcftools/howtos/plugin.split-vep.html\" rel=\"nofollow\"\u003ehttp://samtools.github.io/bcftools/howtos/plugin.split-vep.html\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-filtering-by-vep-annotations\" class=\"anchor\" href=\"#filtering-by-vep-annotations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiltering by VEP annotations\u003c/h3\u003e\n\u003cp\u003eIf you chose to output the VEP annotations as text file, any command line tool (e.g. \u003ccode\u003eawk\u003c/code\u003e) or even \u003ccode\u003eExcel\u003c/code\u003e can be used for filtering the results. For VCF files, the image includes a VEP filtering script which can be executed by\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg filter_vep [options]\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-options\" class=\"anchor\" href=\"#options\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions\u003c/h4\u003e\n\u003cp\u003eVisit \u003ca href=\"https://www.ensembl.org/info/docs/tools/vep/script/vep_filter.html\" rel=\"nofollow\"\u003ehttps://www.ensembl.org/info/docs/tools/vep/script/vep_filter.html\u003c/a\u003e for detailed info about available options.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-filtering-examples\" class=\"anchor\" href=\"#filtering-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiltering examples\u003c/h4\u003e\n\u003ch5\u003e\n\u003ca id=\"user-content-filter-for-rare-variants\" class=\"anchor\" href=\"#filter-for-rare-variants\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFilter for rare variants\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eversion\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.simg filter_vep --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.filtered.vcf --only_matched --filter \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e(IMPACT is HIGH or IMPACT is MODERATE or IMPACT is LOW) and (BIOTYPE is protein_coding) and ((PolyPhen \u0026gt; 0.446) or (SIFT \u0026lt; 0.05)) and (EUR_AF \u0026lt; 0.001 or gnomAD_NFE_AF \u0026lt; 0.001 or (not EUR_AF and not gnomAD_NFE_AF))\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-vep-plugins\" class=\"anchor\" href=\"#vep-plugins\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVEP plugins\u003c/h2\u003e\n\u003cp\u003eVEP allows several other annotations sources (aka Plugins). Their respective Perl modules are included in the image, the annotation files have to be added seperately, however. The list of plugins as well as instructions on how to download and pre-process the annotation files can be found at: \u003ca href=\"http://www.ensembl.org/info/docs/tools/vep/script/vep_plugins.html\" rel=\"nofollow\"\u003ehttp://www.ensembl.org/info/docs/tools/vep/script/vep_plugins.html\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e vep.100-GRCh38-merged.simg vep --dir /opt/vep/.vep --merged --offline --cache --input_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.vcf[.gz] --output_file \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efilename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt.gz --tab --compress_output bgzip --plugin CADD,/path/to/ALL.TOPMed_freeze5_hg38_dbSNP.tsv.gz\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-and-run-vep-with-docker\" class=\"anchor\" href=\"#build-and-run-vep-with-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild and run VEP with Docker\u003c/h2\u003e\n\u003cp\u003eTo pull the image and run the container with Docker use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run matmu/vep:\u0026lt;version\u0026gt; vep [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUnlike Singularity, the directories of \u003cstrong\u003ePlugin\u003c/strong\u003e annotation files (e.g. \u003ccode\u003e/path/to/dir\u003c/code\u003e) have to be explicitely bound to a target directory (e.g. \u003ccode\u003e/opt/data\u003c/code\u003e) within the container with option \u003ccode\u003e-v\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/to/dir:/opt/data matmu/vep:\u0026lt;version\u0026gt; vep [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" href=\"#acknowledgments\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis document has been created by \u003cstrong\u003eJulia Remes\u003c/strong\u003e \u0026amp; \u003cstrong\u003eMatthias Munz\u003c/strong\u003e, \u003cstrong\u003eUniversity of L\u00fcbeck\u003c/strong\u003e, \u003cstrong\u003eGermany\u003c/strong\u003e.\u003c/p\u003e\n", + "readme": "\u003cp\u003e_Please note recent changes in the github repo branch structure. If you want\nto install a stable release of Singularity, please use a tag or a \u003ca href=\"https://github.com/singularityware/singularity/releases\"\u003erelease\ntarball\u003c/a\u003e. If you are\na developer who would like to contribute to Singularity and you want to know\nwhich branch to submit your pull request to, please see notes on the branch\nreorganization \u003ca href=\"https://www.sylabs.io/2018/03/managing-singularity-branches/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease also note that 2.6.0 is expected to be the final feature release in the\n2.x series. While bug fixes may be added via point releases (for example 2.6.1)\nno new features releases (for example 2.7.0) are planned.\u003c/p\u003e\n\u003cp\u003ePull requests adding features to the 2.x series will no longer be reviewed.\u003cbr\u003e\nAny new features should be targeted to the master branch (which used to be\ncalled development-3.0)._\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/singularityware/singularity\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/39fe73b2acfcaf157d81df51bfad4bac84e7177bc8ffda9f351f966cdfb1eff1/68747470733a2f2f7472617669732d63692e6f72672f73696e67756c6172697479776172652f73696e67756c61726974792e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/singularityware/singularity.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"CONTRIBUTING.md\"\u003eGuidelines for Contributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\".github/PULL_REQUEST_TEMPLATE.md\"\u003ePull Request Template\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"LICENSE.md\"\u003eProject License\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity - Enabling users to have full control of their environment.\u003c/h1\u003e\u003ca id=\"user-content-singularity---enabling-users-to-have-full-control-of-their-environment\" class=\"anchor\" aria-label=\"Permalink: Singularity - Enabling users to have full control of their environment.\" href=\"#singularity---enabling-users-to-have-full-control-of-their-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStarting a Singularity container \"swaps\" out the host\noperating system environment for one the user controls!\u003c/p\u003e\n\u003cp\u003eLet\u0027s say you are running Ubuntu on your workstation or server, but you\nhave an application which only runs on Red Hat Enterprise Linux 6.3.\nSingularity can instantly virtualize the operating system, without having\nroot access, and allow you to run that application in its native environment!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbout\u003c/h1\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is a container platform focused on supporting \"Mobility of\nCompute\".\u003c/p\u003e\n\u003cp\u003eMobility of Compute encapsulates the development to compute model where\ndevelopers can work in an environment of their choosing and creation, and\nwhen the developer needs additional compute resources, this environment\ncan easily be copied and executed on other platforms. Additionally, as the\nprimary use case for Singularity is targeted towards computational portability.\nMany of the barriers to entry of other container solutions do not apply to\nSingularity, making it an ideal solution for users (both computational and\nnon-computational) and HPC centers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe Container\u003c/h2\u003e\u003ca id=\"user-content-the-container\" class=\"anchor\" aria-label=\"Permalink: The Container\" href=\"#the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity utilizes container images, which means when you enter and\nwork within the Singularity container, you are physically located inside\nof this image. The image grows and shrinks in real time as you install\nor delete files within the container. If you want to copy a container,\nyou copy the image.\u003c/p\u003e\n\u003cp\u003eUsing a single image for the container format has added advantages\nespecially within the context of HPC with large parallel file systems\nbecause all metadata operations within the container occur within the\ncontainer image (and not on the metadata server!).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMobility of Compute\u003c/h2\u003e\u003ca id=\"user-content-mobility-of-compute\" class=\"anchor\" aria-label=\"Permalink: Mobility of Compute\" href=\"#mobility-of-compute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith Singularity, developers who like to be able to easily control their\nown environment will love Singularity\u0027s flexibility. Singularity does not\nprovide a pathway for escalation of privilege (as do other container\nplatforms which are thus not appropriate for multi-tenant resources) so\nyou must be able to become root on the host system (or virtual machine)\nin order to modify the container.\u003c/p\u003e\n\u003cp\u003eA Singularity container can be launched in a variety of different ways\ndepending on what you wanted to do with it. A simple method might be to\nlaunch an interactive shell within the container image as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity shell /tmp/Centos-7.img \ngmk@Centos-7.img demo\u0026gt; echo \"Hello from within the container\"\nHello from within the container\ngmk@Centos-7.img demo\u0026gt; whoami\ngmk\ngmk@Centos-7.img demo\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if you want to do the same thing as root:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ sudo singularity shell -w /tmp/Centos-7.img \nroot@Centos-7.img demo\u0026gt; whoami\nroot\nroot@Centos-7.img demo\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003enote: By default, Singularity launches the container image in read-only\nmode (so it can be easily launched in parallel). The \u003ccode\u003e-w\u003c/code\u003e option used above\ntells Singularity to mount the image in read/write mode, such that root\ncan now make changes to the container.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, relevant file systems on your host are shared, automatically,\nwithin the context of your container. This can be demonstrated as\nfollows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ pwd\n/home/gmk/demo\n[gmk@centos7-x64 demo]$ echo \"world\" \u0026gt; hello\n[gmk@centos7-x64 demo]$ singularity shell /tmp/Centos-7.img \ngmk@Centos-7.img demo\u0026gt; pwd\n/home/gmk/demo\ngmk@Centos-7.img demo\u0026gt; cat hello\nworld\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the developer has completed their environment, the image file can\nbe compressed and copied to any other system that has Singularity installed.\nIf you do not have root on that system, you will not be able to make any\nchanges to the image once on that system. But you will be able to use the\ncontainer and access the data and files outside the container as\neasily as you would on your development system or virtual machine.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePortability of Singularity container images\u003c/h2\u003e\u003ca id=\"user-content-portability-of-singularity-container-images\" class=\"anchor\" aria-label=\"Permalink: Portability of Singularity container images\" href=\"#portability-of-singularity-container-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity images are highly portable between Linux distributions (as\nlong as the binary format is the same). You can generate your image on\nDebian or CentOS, and run it on Mint or Slackware.\u003c/p\u003e\n\u003cp\u003eWithin a particular container, one can include their programs, data,\nscripts and pipelines and thus port a workflow to any other architecture\ncompatible Linux system or distribution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootstrapping new images\u003c/h2\u003e\u003ca id=\"user-content-bootstrapping-new-images\" class=\"anchor\" aria-label=\"Permalink: Bootstrapping new images\" href=\"#bootstrapping-new-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerally, when bootstrapping an image from scratch, you must build it from\na compatible host. This is because you must use the distribution specific\ntools it comes with (e.g. Red Hat does not provide Debian\u0027s debootstrap by\ndefault). But once the image has been bootstrapped and includes the necessary\nbits to be self-hosting (e.g. YUM on CentOS and apt-get on Debian/Ubuntu) then\nthe process of managing the container can be implemented from within the\ncontainer.\u003c/p\u003e\n\u003cp\u003eThe process of building a bootstrap starts with a definition\nspecification. The definition file describes how you want the operating\nsystem to be built, what should go inside it and any additional\nmodifications necessary.\u003c/p\u003e\n\u003cp\u003eHere is an example of a very simple bootstrap definition file for CentOS:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootStrap: yum\nOSVersion: 7\nMirrorURL: http://mirror.centos.org/centos-%{OSVERSION}/%{OSVERSION}/os/$basearch/\nInclude: yum\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce you have created your bootstrap definition, you can build your\nSingularity container image by first creating a blank image, and then\nbootstrapping using your definition file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ sudo singularity create /tmp/Centos-7.img\n[gmk@centos7-x64 demo]$ sudo singularity bootstrap /tmp/Centos-7.img centos.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom there we can immediately start using the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img cat /etc/redhat-release \nCentOS Linux release 7.2.1511 (Core) \n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img python --version\nPython 2.7.5\n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-7.img python hello.py \nhello world\n[gmk@centos7-x64 demo]$ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if I do this same process again, while changing the \u003cstrong\u003eOSVersion\u003c/strong\u003e\nvariable in the bootstrap definition to \u003cstrong\u003e6\u003c/strong\u003e (where previously it was\nautomatically ascertained by querying the RPM database), we can\nessentially build a CentOS-6 image in exactly the same manner as\nabove. Doing so reveals this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-6.img cat /etc/redhat-release \nCentOS release 6.7 (Final)\n[gmk@centos7-x64 demo]$ singularity exec /tmp/Centos-6.img python --version\nPython 2.6.6\n[gmk@centos7-x64 demo]$ \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd as expected, the Python version we now see is what comes from by\ndefault in CentOS-6.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCite as:\u003c/h1\u003e\u003ca id=\"user-content-cite-as\" class=\"anchor\" aria-label=\"Permalink: Cite as:\" href=\"#cite-as\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer GM, Sochat V, Bauer MW (2017) Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459. https://doi.org/10.1371/journal.pone.0177459\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe also have a Zenodo citation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eKurtzer, Gregory M.. (2016). Singularity 2.1.2 - Linux application and environment\ncontainers for science. 10.5281/zenodo.60736\n\nhttp://dx.doi.org/10.5281/zenodo.60736\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebpage\u003c/h1\u003e\u003ca id=\"user-content-webpage\" class=\"anchor\" aria-label=\"Permalink: Webpage\" href=\"#webpage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have full documentation at \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://www.sylabs.io/docs/\u003c/a\u003e, and \u003ca href=\"http://www.github.com/singularityware/singularityware.github.io\"\u003ewelcome contributions\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1614861605.0 + "updated_at": 1589975545.0 }, { "data_format": 2, - "description": "Run CRISPResso on genome editing experiments", + "description": "OpenCV 2 built with NVIDIA acceleration", "filenames": [ "Singularity" ], - "full_name": "czbiohub/nf-core-crisprvar", + "full_name": "dl-container-registry/opencv2", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-nf-corecrisprvar\" class=\"anchor\" href=\"#nf-corecrisprvar\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/crisprvar\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eRun CRISPResso on genome editing experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/crisprvar\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8bfb8f49f69e465c023fb291cefc2ccbea97c9a7cc04377260fa6052d9370b28/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6372697370727661722e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/crisprvar.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/crisprvar\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e5f9f1b9479b9d19c758902e0a06e81ab060fa6a9207a5e935aee26edc728ac/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6372697370727661722e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/crisprvar.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/crisprvar pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOpenCV2 Dockerfile\u003c/h1\u003e\u003ca id=\"user-content-opencv2-dockerfile\" class=\"anchor\" aria-label=\"Permalink: OpenCV2 Dockerfile\" href=\"#opencv2-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/dl-container-registry/opencv2\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/910dd8ad3a37d828dfd7935080cf5e0f623f9f9322a477a13e895927c3fb752a/68747470733a2f2f696d672e736869656c64732e696f2f636972636c6563692f70726f6a6563742f6769746875622f646c2d636f6e7461696e65722d72656769737472792f6f70656e6376322f6d61737465722e737667\" alt=\"CircleCI branch\" data-canonical-src=\"https://img.shields.io/circleci/project/github/dl-container-registry/opencv2/master.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/willprice/opencv2-cuda8/~/settings/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/680701285475c874bf8f852e787b50b1a1f9f07c96a5f72aded98a8a1db5f1cf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65726875622d3232623865622e737667\" alt=\"Dockerhub link\" data-canonical-src=\"https://img.shields.io/badge/hosted-dockerhub-22b8eb.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/530\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity hub hosted\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild other containers from this base image, it contains a prebuilt version of\nOpenCV 2.4.13.4 at \u003ccode\u003e/src/opencv_build\u003c/code\u003e installed to \u003ccode\u003e/usr/local/OpenCV\u003c/code\u003e\n(containing CMake files for building other projects).\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1637330863.0 + "updated_at": 1589316312.0 }, { "data_format": 2, - "description": "automated benchmarking of recombination detection methods", + "description": "Solving quantum state diffusion numerically.", "filenames": [ - "Singularity", - "simg/Singularity.3seq" + "Singularity" ], - "full_name": "fredjaya/rec-bench", + "full_name": "tabakg/quantum_state_diffusion", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rec-bench\" class=\"anchor\" aria-hidden=\"true\" href=\"#rec-bench\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erec-bench\u003c/h1\u003e\n\u003cp\u003eAutomated benchmarking of recombination detection methods\u003c/p\u003e\n\u003cp\u003eEternally a WIP - many things are hardcoded\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cp\u003eNextflow\nconda\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/fredjaya/rec-bench.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNextflow doesn\u0027t appear to create the conda environment properly. Create manually.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\nconda activate fredjaya-rec-bench-0.1.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote: conda processes currently hardcoded in \u003ccode\u003emain.nf\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003erec-bench\u003c/code\u003e has five modes that must be specified with \u003ccode\u003e--mode\u003c/code\u003e as follows:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--mode sim\u003c/code\u003e\tGenerate simulation datasets\n\u003ccode\u003e--mode sim_v\u003c/code\u003e\tVisualise/summarise simulation outputs\n\u003ccode\u003e--mode div\u003c/code\u003e\tBenchmark recombination detection methods using simulated data\n\u003ccode\u003e--mode emp\u003c/code\u003e\tDetect recombination in empirical sequence alignments\n\u003ccode\u003e--mode class\u003c/code\u003e\tCalculate classification metrics\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main.nf --help\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Update readme\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSolving quantum state diffusion (QSD) numerically.\u003c/h1\u003e\u003ca id=\"user-content-solving-quantum-state-diffusion-qsd-numerically\" class=\"anchor\" aria-label=\"Permalink: Solving quantum state diffusion (QSD) numerically.\" href=\"#solving-quantum-state-diffusion-qsd-numerically\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe script \u003ca href=\"quantum_state_diffusion.py\"\u003equantum_state_diffusion.py\u003c/a\u003e can be\nused to run QSD simulations.\nI am using a (slightly) modified version of a package called sdeint. The only modification I made is to normalize the trajectories for numerical stability.\u003c/p\u003e\n\u003cp\u003eMy version can be found on \u003ca href=\"https://github.com/tabakg/sdeint\"\u003ehttps://github.com/tabakg/sdeint\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThere are two notebooks currently, one for the Kerr system and the second\nfor the absorptive bi-stability. Please compare the results to those obtained\nusing quantum jump trajectories (found on \u003ca href=\"https://github.com/tabakg/diffusion_maps\"\u003ethis repo\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning\u003c/h1\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou have several options for running the simulation, including container-based and local environments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker\u003c/li\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003cli\u003eLocal Environment\u003c/li\u003e\n\u003cli\u003eCluster (SLURM example)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDepending on your familiarity with containers, the first two are recommended to handle software dependencies. Complete instructions are included below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe development environment is Dockerized, meaning that you can run the simulation with a Docker image. First, you need to \u003ca href=\"http://54.71.194.30:4111/engine/installation\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e. The base command to see help for how to run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run tabakg/quantum_state_diffusion --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill show you the following (after a message about the font cache):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: make_quantum_trajectory.py [-h] [--seed SEED] [--ntraj NTRAJ]\n\t [--duration DURATION] [--delta_t DELTAT]\n\t [--Nfock_a NFOCKA] [--Nfock_j NFOCKJ]\n\t [--downsample DOWNSAMPLE] [--quiet]\n\t [--output_dir OUTDIR] [--save2pkl]\n\t [--save2mat]\n\ngenerating trajectories using quantum state diffusion\n\noptional arguments:\n -h, --help show this help message and exit\n --seed SEED Seed to set for the simulation.\n --ntraj NTRAJ number of trajectories, should be kept at 1 if run via\n\t slurm\n --duration DURATION Duration in ()\n --delta_t DELTAT Parameter delta_t\n --Nfock_a NFOCKA Parameter N_focka\n --Nfock_j NFOCKJ Parameter N_fockj\n --downsample DOWNSAMPLE\n\t How much to downsample results\n --quiet Turn off logging (debug and info)\n --output_dir OUTDIR Output folder. If not defined, will use PWD.\n --save2pkl Save pickle file to --output_dir\n --save2mat Save .mat file to --output_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun and save to local machine\u003c/h3\u003e\u003ca id=\"user-content-run-and-save-to-local-machine\" class=\"anchor\" aria-label=\"Permalink: Run and save to local machine\" href=\"#run-and-save-to-local-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that the \u003ccode\u003e--quiet\u003c/code\u003e option can be added to silence printing. By default, no data is saved. To save, you will need to 1) specify the output directory to the \u003ccode\u003e/data\u003c/code\u003e folder in the container using the \u003ccode\u003eoutput_dir\u003c/code\u003e argument and 2) map some directory on your local machine to this \u003ccode\u003e/data\u003c/code\u003e folder. We can do that like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # on your local machine, let\u0027s say we want to save to Desktop\n docker run -v /home/vanessa/Desktop:/data \\\n tabakg/quantum_state_diffusion --output_dir /data --save2pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above will produce the following output:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eINFO:root:Parameter Ntraj set to 1\nINFO:root:Parameter Nfock_j set to 2\nINFO:root:Parameter duration set to 10\nINFO:root:Parameter delta_t set to 0.002\nINFO:root:Parameter downsample set to 100\nINFO:root:Parameter Nfock_a set to 50\nINFO:root:Parameter seed set to 1\nINFO:root:Downsample set to 100\nINFO:root:Regime is set to absorptive_bistable\nRun time: 2.1634318828582764 seconds.\nINFO:root:Saving pickle file...\nINFO:root:Saving result to /data/QSD_absorptive_bistable_1-1-0.002-50-2-10.pkl\nINFO:root:Data saved to pickle file /data/QSD_absorptive_bistable_1-1-0.002-50-2-10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe final output will be in the mapped folder - in the example above, this would be my Desktop at \u003ccode\u003e/home/vanessa/Desktop/QSD_absorptive_bistable*.pkl\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun inside container\u003c/h3\u003e\u003ca id=\"user-content-run-inside-container\" class=\"anchor\" aria-label=\"Permalink: Run inside container\" href=\"#run-inside-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou may want to inspect the data using the same environment it was generated from, in which case you would want to shell into the container. To do this, you can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint=/bin/bash tabakg/quantum_state_diffusion\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eif you type \u003ccode\u003els\u003c/code\u003e you will see that we are sitting in the \u003ccode\u003e/code\u003c/code\u003e directory that contains the core python files. This means that we can run the analysis equivalently:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/code# python make_quantum_trajectory.py --output_dir /data --save2pkl\nINFO:root:Parameter downsample set to 100\nINFO:root:Parameter duration set to 10\nINFO:root:Parameter seed set to 1\nINFO:root:Parameter Nfock_j set to 2\nINFO:root:Parameter Nfock_a set to 50\nINFO:root:Parameter delta_t set to 0.002\nINFO:root:Parameter Ntraj set to 1\nINFO:root:Downsample set to 100\nINFO:root:Regime is set to absorptive_bistable\nRun time: 2.183995485305786 seconds.\nINFO:root:Saving pickle file...\nINFO:root:Saving result to /data/QSD_absorptive_bistable_1-1-0.002-50-2-10.pkl\nINFO:root:Data saved to pickle file /data/QSD_absorptive_bistable_1-1-0.002-50-2-10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand the data is inside the container with us! Great.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eroot@4420ae9e385d:/code# ls /data\nQSD_absorptive_bistable_1-1-0.002-50-2-10.pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCustomize the Docker image\u003c/h3\u003e\u003ca id=\"user-content-customize-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Customize the Docker image\" href=\"#customize-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to use the image from Docker Hub (for example, if you want to make changes first) you can also build the image locally. You can build the image by doing the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e git clone https://www.github.com/tabakg/quantum_state_diffusion\n cd quantum_state_diffusion\n docker build -t tabakg/quantum_state_diffusion .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote the \u003ccode\u003e.\u003c/code\u003e at the end of the command to specify the present working directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is a container that is HPC friendly, meaning that it can be run on a cluster environment. The container itself, a file that sits on your computer, can be dropped into a folder on your cluster, and run like a script! We have provided a Singularity file that can bootstrap the Docker image to build the image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Install Singularity\u003c/h3\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h3\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 qsd.img\nsudo singularity bootstrap qsd.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to access the python executable?\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./qsd.img --help\nusage: make_quantum_trajectory.py [-h] [--seed SEED] [--ntraj NTRAJ]\n\t [--duration DURATION] [--delta_t DELTAT]\n\t [--Nfock_a NFOCKA] [--Nfock_j NFOCKJ]\n\t [--downsample DOWNSAMPLE] [--quiet]\n\t [--output_dir OUTDIR] [--save2pkl]\n\t [--save2mat]\n\ngenerating trajectories using quantum state diffusion\n\noptional arguments:\n -h, --help show this help message and exit\n --seed SEED Seed to set for the simulation.\n --ntraj NTRAJ number of trajectories, should be kept at 1 if run via\n\t slurm\n --duration DURATION Duration in ()\n --delta_t DELTAT Parameter delta_t\n --Nfock_a NFOCKA Parameter N_focka\n --Nfock_j NFOCKJ Parameter N_fockj\n --downsample DOWNSAMPLE\n\t How much to downsample results\n --quiet Turn off logging (debug and info)\n --output_dir OUTDIR Output folder. If not defined, will use PWD.\n --save2pkl Save pickle file to --output_dir\n --save2mat Save .mat file to --output_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou might again want to map a folder for the data output\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run --bind /home/vanessa/Desktop:/data/ qsd.img --output_dir /data --save2pkl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd you again might want to interactive work in the container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell --writable qsd.img\n cd /code\n ls\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCluster Usage\u003c/h2\u003e\u003ca id=\"user-content-cluster-usage\" class=\"anchor\" aria-label=\"Permalink: Cluster Usage\" href=\"#cluster-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning on a local machine is fine, but it will not scale well if you want to run thousands of times. Toward this aim, we have provided simple SLURM submission scripts to help! They are optimized for the \u003ca href=\"http://sherlock.stanford.edu\" rel=\"nofollow\"\u003esherlock\u003c/a\u003e cluster at Stanford (which has Singularity installed), however you can easily modify the submission command to run natively on a cluster without it (more detail below). For both, you can use the scripts in \u003ca href=\"slurm\"\u003eslurm\u003c/a\u003e. You will want to do the following:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Build the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-1-build-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: 1. Build the Singularity image\" href=\"#1-build-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing the steps above, build the Singularity image, and use some form of FTP to transfer the image to your cluster. We must do this because it requires sudo to build and bootstrap the image, but not to run it (you do not have sudo permission on a cluster).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Create a folder to work from\u003c/h3\u003e\u003ca id=\"user-content-2-create-a-folder-to-work-from\" class=\"anchor\" aria-label=\"Permalink: 2. Create a folder to work from\" href=\"#2-create-a-folder-to-work-from\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn your $HOME folder in your cluster environment, you likely want to keep a folder to put your image, and organize input and output files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd $HOME\n mkdir -p SCRIPTS/SINGULARITY/QSD\n cd SCRIPTS/SINGULARITY/QSD # transfer qsd.img here\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd then write \u003ca href=\"slurm/run.py\"\u003erun.py\u003c/a\u003e into a file in that location. In a nutshell, this script is going to create local directories for jobs, output, and error files (\u003ccode\u003e.job\u003c/code\u003e,\u003ccode\u003e.out\u003c/code\u003e,\u003ccode\u003e.err\u003c/code\u003e), and then iterate through a variable in the simulation (the \u003ccode\u003eseed\u003c/code\u003e) and submit a job for each on our partition of choice. The variables you should / might be interested in editing are in the header:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData Output\u003c/h2\u003e\u003ca id=\"user-content-data-output\" class=\"anchor\" aria-label=\"Permalink: Data Output\" href=\"#data-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach pickle file contains the simulation result, along with the dictionary of analysis parameters. For example, here we are loading a pickle result file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eimport pickle\nmdict = pickle.load(open(\u0027QSD_absorptive_bistable_1-1-0.002-50-2-10.pkl\u0027,\u0027rb\u0027))\n\n# What result keys are available?\nresult.keys()\ndict_keys([\u0027Nfock_a\u0027, \u0027Ntraj\u0027, \u0027observable_str\u0027, \u0027Nfock_j\u0027, \u0027times\u0027, \u0027psis\u0027, \u0027downsample\u0027, \n \u0027seeds\u0027, \u0027expects\u0027, \u0027delta_t\u0027, \u0027seed\u0027, \u0027duration\u0027, \u0027observable_latex\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith this data, you can do interactive plotting and further analysis, examples which will be provided in this repo (under development).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLocal Installation:\u003c/h1\u003e\u003ca id=\"user-content-local-installation\" class=\"anchor\" aria-label=\"Permalink: Local Installation:\" href=\"#local-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation requires Python 3.\u003c/p\u003e\n\u003cp\u003eIn addition to standard libraries (numpy, sympy, scipy, pickle)\u003c/p\u003e\n\u003cp\u003eIn addition to the modified version of sdeint found on\n\u003ca href=\"https://github.com/tabakg/sdeint\"\u003ehttps://github.com/tabakg/sdeint\u003c/a\u003e (mentioned above), please install\nQNET (\u003ca href=\"https://pypi.python.org/pypi/QNET\" rel=\"nofollow\"\u003ehttps://pypi.python.org/pypi/QNET\u003c/a\u003e). QNET is on pip, and can be installed\nsimply with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install QNET.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI am also using a package called multiprocess, which can be installed with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install multiprocess\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1646039648.0 + "updated_at": 1605980860.0 }, { "data_format": 2, - "description": "Research Computing Spring 2019 (IMSE 8410)", + "description": null, "filenames": [ - "examples/containers/Singularity" + "Singularity.def" ], - "full_name": "MiddelkoopT/RC-2019-Spring", + "full_name": "54yyyu/model_survey", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emodel_survey\u003c/h1\u003e\u003ca id=\"user-content-model_survey\" class=\"anchor\" aria-label=\"Permalink: model_survey\" href=\"#model_survey\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall this package from github.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install https://github.com/54yyyu/model_survey/tarball/master\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, "subscribers_count": 2, "topics": [], - "updated_at": 1661980750.0 + "updated_at": 1659552958.0 }, { "data_format": 2, - "description": "Singularity Recipes for SX-Aurora TSUBASA", + "description": "HOMER (Hypergeometric Optimization of Motif EnRichment) is a suite of tools for Motif Discovery and next-gen sequencing analysis.", "filenames": [ - "RockyLinux8/Singularity", - "RockyLinux8/Singularity.mpi" + "4.11.0/Singularity" ], - "full_name": "veos-sxarr-NEC/singularity", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-for-sx-aurora-tsubasa\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-for-sx-aurora-tsubasa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity for SX-Aurora TSUBASA\u003c/h1\u003e\n\u003cp\u003eThis repository has Singularity recipes to build Singularity image to execute a program on Vector Engine of SX-Aurora TSUBASA.\u003c/p\u003e\n\u003cp\u003eThis document explains how to build a Singularity image with VEOS and related software, and how to execute a VE application on Singularity using the image.\nThis document also explains how to build a Singularity image with NEC MPI and related software, and how to execute a MPI application on Singularity using the image.\u003c/p\u003e\n\u003cp\u003eYou can save and use the image as execution environment for your program.\u003c/p\u003e\n\u003cp\u003eWe have tested the Singularity recipes with the following version of Singularity.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity 3.8.7-1.el8\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-compatibility-problems\" class=\"anchor\" aria-hidden=\"true\" href=\"#compatibility-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompatibility problems\u003c/h2\u003e\n\u003cp\u003eTo avoid the compatibility problem, please consider the below points:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe compatibility of software between a host machine and a container.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe version of VEOS in a host machine must be greater than or equal to the version of VEOS in a container.\u003c/li\u003e\n\u003cli\u003eThe version of NEC MPI in a host machine must be greater than or equal to the version of NEC MPI in a container.\u003c/li\u003e\n\u003cli\u003eThe version of MOFED in a host machine must be equal to the version of MOFED in a container.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe compatibility of software between a container and a build machine\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe version of NEC MPI in a container must be greater than or equal to the version of NEC MPI in a build machine where you built your program.\u003c/li\u003e\n\u003cli\u003eEach software version of NEC SDK in a container must be greater than or equal to each software version of NEC SDK in a build machine where you built your program.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-the-singularity-image-of-veos\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-the-singularity-image-of-veos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the singularity image of VEOS\u003c/h2\u003e\n\u003cp\u003eClone the repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/veos-sxarr-NEC/singularity.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eChange the current directory to the directory which has Singularity recipes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd singularity/RockyLinux8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload TSUBASA-soft-release-2.8-1.noarch.rpm.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -O https://sxauroratsubasa.sakura.ne.jp/repos/TSUBASA-soft-release-2.8-1.noarch.rpm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf your network is behind a proxy, please update dnf.conf to set the proxy.\u003c/p\u003e\n\u003cp\u003eBuild a singularity image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity build --fakeroot veos.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-an-application-in-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-an-application-in-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun an application in the singularity container\u003c/h2\u003e\n\u003cp\u003eRun an application using the below command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --bind /var/opt/nec/ve/veos \u0026lt;image SIF\u0026gt; \u0026lt;pass to binary in container\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, run an application with VE NODE#0.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec --bind /var/opt/nec/ve/veos veos.sif env VE_NODE_NUMBER=0 ./a.out\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-the-singularity-image-of-nec-mpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-the-singularity-image-of-nec-mpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild the singularity image of NEC MPI\u003c/h2\u003e\n\u003cp\u003eClone the repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/veos-sxarr-NEC/singularity.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eChange the current directory to the directory which has Singularity recipes.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd singularity/RockyLinux8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload TSUBASA-soft-release-2.8-1.noarch.rpm.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -O https://sxauroratsubasa.sakura.ne.jp/repos/TSUBASA-soft-release-2.8-1.noarch.rpm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload MLNX_OFED_LINUX.\nFollowing MLNX_OFED_LINUX archive file is needed.\nArchive file should remain compressed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMLNX_OFED_LINUX-5.6-2.0.9.0-rhel8.6-x86_64.tgz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf your network is behind a proxy, please update dnf.conf to set the proxy.\u003c/p\u003e\n\u003cp\u003eBuild a singularity image.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity build --fakeroot necmpi.sif Singularity.mpi\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-a-nec-mpi-application-in-the-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-a-nec-mpi-application-in-the-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun a NEC MPI application in the singularity container\u003c/h2\u003e\n\u003cp\u003eRun a NEC MPI application using the below commands.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source /opt/nec/ve/mpi/\u0026lt;nec mpi version in the image\u0026gt;/bin64/necmpivars.sh\n$ mpirun \u0026lt;nec mpi options\u0026gt; /usr/bin/singularity exec --bind /var/opt/nec/ve/veos \u0026lt;image SIF\u0026gt; \u0026lt;pass to binary in container\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, run a NEC MPI application on interactive shell.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source /opt/nec/ve/mpi/2.11.0/bin64/necmpivars.sh\n$ mpirun -hosts host1,host2 -np 2 -ve 0 /usr/bin/singularity exec --bind /var/opt/nec/ve/veos ./necmpi.sif ./a.out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, run a NEC MPI application with NQSV.\nnecmpi.sif and a.out is located in your home directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ vi job.sh\n#!/bin/bash\n#PBS -q bq\n#PBS -T necmpi\n#PBS -b 2\n#PBS -l elapstim_req=300\n#PBS --venode=2 --cpunum-lhost=2\n\nsource /opt/nec/ve/mpi/2.11.0/bin64/necmpivars.sh\nmpirun -np 16 /usr/bin/singularity exec --bind /var/opt/nec/ve/veos ~/necmpi.sif ~/a.out\n\n$ qsub job.sh\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "pscedu/singularity-homer", + "latest_release": "v4.11.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-homer/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-homer/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-homer/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-homer/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/04d92355d250c9b6fac3ab0a8bf7474f0c28f621406d56170a3d6febc6272836/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d686f6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/04d92355d250c9b6fac3ab0a8bf7474f0c28f621406d56170a3d6febc6272836/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d686f6d6572\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-homer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ff1cea258454b42da34c4a0fe1ed19b786554cea1c1e80f2a55de9d6cbbe4942/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d686f6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff1cea258454b42da34c4a0fe1ed19b786554cea1c1e80f2a55de9d6cbbe4942/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d686f6d6572\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-homer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e032de5fb4ae6be7b1bd3bb6ed583fdcc2923becfd1fce8ac499ee8ed8833b50/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d686f6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e032de5fb4ae6be7b1bd3bb6ed583fdcc2923becfd1fce8ac499ee8ed8833b50/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d686f6d6572\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-homer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/98cbb9d1b8b97e42f348e53aae84ab6a5301602f1a4303e6a55897d77aedaacd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d686f6d6572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/98cbb9d1b8b97e42f348e53aae84ab6a5301602f1a4303e6a55897d77aedaacd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d686f6d6572\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-homer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-homer\u003c/h1\u003e\u003ca id=\"user-content-singularity-homer\" class=\"anchor\" aria-label=\"Permalink: singularity-homer\" href=\"#singularity-homer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0c2e2600c1461f6f4f9101d2993f8d32032061fa0145e2332ce65118a4e6212d/687474703a2f2f686f6d65722e756373642e6564752f686f6d65722f706963322e676966\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0c2e2600c1461f6f4f9101d2993f8d32032061fa0145e2332ce65118a4e6212d/687474703a2f2f686f6d65722e756373642e6564752f686f6d65722f706963322e676966\" alt=\"Logo\" data-canonical-src=\"http://homer.ucsd.edu/homer/pic2.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"http://homer.ucsd.edu/homer/\" rel=\"nofollow\"\u003ehomer\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003ehomer\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/homer/4.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/homer\u003c/code\u003e as \u003ccode\u003e4.11.0\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2024 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, - "topics": [], - "updated_at": 1640330684.0 + "subscribers_count": 3, + "topics": [ + "singularity", + "bioinformatics" + ], + "updated_at": 1710308206.0 }, { "data_format": 2, - "description": " This repo provides a Singularity image version for ClamAV, an anti-virus toolkit.", + "description": "Simple terminal UI for git commands", "filenames": [ - "Singularity" + "0.28.2/Singularity", + "0.24.2/Singularity", + "0.22.9/Singularity", + "0.23.7/Singularity", + "0.32.2/Singularity", + "0.35/Singularity", + "0.40.2/Singularity", + "0.34/Singularity", + "0.37/Singularity", + "0.31.4/Singularity" ], - "full_name": "netreconlab/clamav", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-clamav\" class=\"anchor\" aria-hidden=\"true\" href=\"#clamav\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eclamav\u003c/h1\u003e\n\u003cp\u003eThis repo provides a Singularity image version for \u003ca href=\"https://docs.clamav.net/Introduction.html\" rel=\"nofollow\"\u003eClamAV\u003c/a\u003e, an anti-virus toolkit. It provides a number of utilities including a flexible and scalable multi-threaded daemon, a command line scanner and advanced tool for automatic database updates. To learn more about the image, look \u003ca href=\"https://docs.clamav.net/manual/Installing/Docker.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImages\u003c/h2\u003e\n\u003cp\u003eImages of \u003ccode\u003eclamav\u003c/code\u003e are automatically built for your convenience. Images can be found at the following locations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/netreconlab/clamav/pkgs/container/clamav\"\u003eSingularity - Hosted on GitHub Container Registry\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDocker - The Dockerfile in this repo has been depracated in favor of the \u003ca href=\"https://hub.docker.com/r/clamav/clamav\" rel=\"nofollow\"\u003eofficial clamav image\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "pscedu/singularity-lazygit", + "latest_release": "v0.40.2", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-lazygit/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-lazygit/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-lazygit/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-lazygit/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/85e727aaf548d0c00e973fe38b358880905cf50a3310d03a05640af2f90eaae9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c617a79676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/85e727aaf548d0c00e973fe38b358880905cf50a3310d03a05640af2f90eaae9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6c617a79676974\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-lazygit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/556c17c59ba8e19fb2760c48cdbea073e4215ab1edcd44828504b43e009c0266/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c617a79676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/556c17c59ba8e19fb2760c48cdbea073e4215ab1edcd44828504b43e009c0266/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6c617a79676974\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-lazygit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6af6d6dcffe4e9190554958f001a70f19f371189177b773312159307e1d41bab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c617a79676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6af6d6dcffe4e9190554958f001a70f19f371189177b773312159307e1d41bab/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6c617a79676974\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-lazygit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/60efef56d1806ccdcf8df37747f39e2267f9a9b11c24bee9ab504181020948b6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c617a79676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/60efef56d1806ccdcf8df37747f39e2267f9a9b11c24bee9ab504181020948b6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6c617a79676974\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-lazygit\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-lazygit\u003c/h1\u003e\u003ca id=\"user-content-singularity-lazygit\" class=\"anchor\" aria-label=\"Permalink: singularity-lazygit\" href=\"#singularity-lazygit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/screenshot.png\"\u003e\u003cimg src=\"/images/screenshot.png\" alt=\"Screenshot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://github.com/jesseduffield/lazygit\"\u003elazygit\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003elazygit\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/lazygit/0.24.2\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/lazygit\u003c/code\u003e as \u003ccode\u003e0.24.2.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [ - "clamav", - "baas", - "parse-server", "singularity", - "virus-scanning", - "clamscan" + "utilities" ], - "updated_at": 1673103429.0 + "updated_at": 1692212155.0 }, { "data_format": 2, - "description": "popf", - "filenames": [ - "Singularity", - "Singularity.1.0" - ], - "full_name": "roveri-marco/popf", - "latest_release": "1.0", - "stargazers_count": 2, - "subscribers_count": 1, - "topics": [], - "updated_at": 1657529551.0 - }, - { - "data_format": 2, - "description": "Heuristic Algorithms for Quantum Computers", + "description": "Definition files for Singularity", "filenames": [ - "SingularityFile.def" + "Singularity.def" ], - "full_name": "vivekkatial/HAQC", - "latest_release": "0.0.2", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-haqc----heuristic-algorithms-for-quantum-computing-research-group\" class=\"anchor\" aria-hidden=\"true\" href=\"#haqc----heuristic-algorithms-for-quantum-computing-research-group\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHAQC -- Heuristic Algorithms for Quantum Computing Research Group\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/vivekkatial/HAQC/actions/workflows/build-container-and-release.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/vivekkatial/HAQC/actions/workflows/build-container-and-release.yml/badge.svg\" alt=\"build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eResearch group to run optimisation algorithms on Quantum Computers at the University of Melbourne\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003cp\u003eBefore getting started, ensure you have Python 3.7+. We use \u003ca href=\"https://python-poetry.org/\" rel=\"nofollow\"\u003epoetry\u003c/a\u003e to manage the python environment (the .gitignore file should already ignore it).\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003e$ poetry install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo add a package to your new project:\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003e$ poetry install \u0026lt;package\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will automatically edit your \u003ccode\u003epyproject.toml\u003c/code\u003e file with the new package you provided.\u003c/p\u003e\n\u003cp\u003eNext, activate the \u003ccode\u003epoetry\u003c/code\u003e shell:\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003e$ poetry shell\n$ python --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will spawn a new shell subprocess, which can be deactivated by using exit.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting\u003c/h3\u003e\n\u003cp\u003eFor testing, we use \u003ccode\u003epytest\u003c/code\u003e. To run the tests, just type the command \u003ccode\u003epytest\u003c/code\u003e, or you can specify a file e.g. \u003ccode\u003epytest tests/test_graph_generator.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWe will use \u003ccode\u003eblack\u003c/code\u003e as our code formatter. Simply run \u003ccode\u003eblack -S .\u003c/code\u003e to run black over all the files before committing. The \u003ccode\u003e-S\u003c/code\u003e is to skip string normalisation, because we prefer single quotes/don\u0027t really care (\u003ca href=\"https://github.com/psf/black/issues/118\"\u003eflame war, I know\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-before-making-a-pr\" class=\"anchor\" aria-hidden=\"true\" href=\"#before-making-a-pr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBefore making a PR\u003c/h3\u003e\n\u003cp\u003eIn summary, before merging a PR, you should:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make sure all tests pass\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src\npipenv run python -m pytest tests/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Format with black\u003c/span\u003e\npipenv run python -m black -S \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-mlflow-tracking\" class=\"anchor\" aria-hidden=\"true\" href=\"#mlflow-tracking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMLFlow Tracking\u003c/h2\u003e\n\u003cp\u003eTo get the MLFlow tracking functionality to work you will need to setup \u003ccode\u003eawscli\u003c/code\u003e credentials, so MLFlow can properly log artifacts.\u003c/p\u003e\n\u003cp\u003eIf you\u0027re keen to do this then please follow the instructions \u003ca href=\"https://wiki-rcs.unimelb.edu.au/display/RCS/AWS+CLI\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can request the credentials for this experiment from Vivek at \u003ca href=\"mailto:vkatial@student.unimelb.edu.au\"\u003evkatial@student.unimelb.edu.au\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-a-test-instance\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-a-test-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning a test instance\u003c/h2\u003e\n\u003cp\u003eTo run a test instance try out the steps below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython qaoa_vrp/main.py -f \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e -T False \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -T tracking for MLFlow\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-jupyter-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter-notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter Notebooks\u003c/h3\u003e\n\u003cp\u003eFirst ensure that your Python is \u003cem\u003enot\u003c/em\u003e aliased in your \u003ccode\u003e.bashrc\u003c/code\u003e or \u003ccode\u003e.zshrc\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eAfter this launch your \u003ccode\u003epoetry\u003c/code\u003e by\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003epoetry shell\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen do:\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003epython -m ipykernel install --user --name=ENV_NAME\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen launch the notebook\u003c/p\u003e\n\u003cpre lang=\"{shell}\"\u003e\u003ccode\u003ejupyter notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn your notebook, Kernel -\u0026gt; Change Kernel. Your kernel should now be an option.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/jupyter-install.png\"\u003e\u003cimg src=\"images/jupyter-install.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eThis project leverages \u003ca href=\"https://github.com/singularityhub/\"\u003eSingularity\u003c/a\u003e to ensure the code is reproducible and manage dependencies.\u003c/p\u003e\n\u003cp\u003eYou can find the recipe for our container in \u003ccode\u003eSingularityFile.def\u003c/code\u003e. There are various apps for each different type of experiment we run,\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cicd\" class=\"anchor\" aria-hidden=\"true\" href=\"#cicd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCI/CD\u003c/h2\u003e\n\u003cp\u003eWe use Github Actions for CI/CD. Everytime a PR is created, a test build of the singularity container runs. When merging into \u003ccode\u003emain\u003c/code\u003e we do a release of the container.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-hidden=\"true\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eVivek Katial\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "Open-MSS/singularity", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDefinition files for Singularity\u003c/p\u003e\n\u003cp\u003eFor an introduction to singularity read the documentation for your installation on \u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003ehttps://sylabs.io/\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1676667542.0 + "updated_at": 1646623956.0 }, { "data_format": 2, - "description": "https://gitlab.kitware.com/paraview/paraview-superbuild.git", + "description": "Let\u0027s reinterpret lousy things and make them awesome :sparkles:", "filenames": [ - "Scripts/singularity/Singularity.egl", - "Scripts/singularity/Singularity.osmesa" + "Singularity" ], - "full_name": "zenotech/paraview-superbuild", + "full_name": "mmore500/reinterpretive-label", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"Documentation/img/paraview100.png\"\u003e\u003cimg src=\"Documentation/img/paraview100.png\" alt=\"ParaView-Superbuild\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h1\u003e\n\u003cp\u003eParaView-Superbuild, henceforth referred to as \"superbuild\", is a project to\nbuild ParaView and its dependencies. ParaView itself can be easily built using\nCMake as long as the required external dependencies are available on the build\nmachine. However, ParaView\u0027s several external dependencies, e.g. Qt, CGNS,\nFFMPEG, etc. can be very tedious to build. Also, if you want to generate\nredistributable binaries, you need to take extra care when building and\npackaging these dependencies. To make our lives easier in supporting both these\nuse-cases, the superbuild project was born.\u003c/p\u003e\n\u003cp\u003eAlthough primarily designed to build the official ParaView binaries, the\nsuperbuild has since been regularly used to build and install ParaView\non various supercomputing systems.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-obtaining-the-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#obtaining-the-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eObtaining the source\u003c/h1\u003e\n\u003cp\u003eTo obtain the superbuild source locally, clone this repository using\n\u003ca href=\"https://git-scm.org\" rel=\"nofollow\"\u003eGit\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone --recursive https://gitlab.kitware.com/paraview/paraview-superbuild.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h1\u003e\n\u003cp\u003eThe superbuild can be built with a Makefiles or Ninja CMake generator. The IDE\ngenerators (Xcode and Visual Studio) are not supported.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eThe superbuild tries to provide all of its own dependencies, but some tooling\nis assumed to be available on the host machine.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCompiler toolchain\n\u003cul\u003e\n\u003cli\u003eGCC 4.9 or newer\u003c/li\u003e\n\u003cli\u003eXcode 10 or newer (older is probably supported, but untested)\u003c/li\u003e\n\u003cli\u003eMSVC 2017 or newer\u003c/li\u003e\n\u003cli\u003eICC (minimum version unknown)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTools\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003epkg-config\u003c/code\u003e is used on non-Windows platforms to find dependencies in\nsome projects\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eninja\u003c/code\u003e (or \u003ccode\u003emake\u003c/code\u003e) for building\u003c/li\u003e\n\u003cli\u003ePython (if not built by the superbuild) for building packages\u003c/li\u003e\n\u003cli\u003eIf building \u003ccode\u003emesa\u003c/code\u003e or \u003ccode\u003eosmesa\u003c/code\u003e, \u003ccode\u003ebison\u003c/code\u003e and \u003ccode\u003eflex\u003c/code\u003e are required.\u003c/li\u003e\n\u003cli\u003eIf building packages on Linux, \u003ccode\u003echrpath\u003c/code\u003e is required to make relocatable\npackages\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-a-specific-version\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-a-specific-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding a specific version\u003c/h2\u003e\n\u003cp\u003eThe superbuild project uses the same versioning scheme as ParaView,\nand gets tagged for every release of ParaView. For example, to build\nParaView version 5.7.1, checkout the \u003ccode\u003ev5.7.0\u003c/code\u003e tag of ParaView and\nsuperbuild.\u003c/p\u003e\n\u003cp\u003eCurrently available tags are shown\n\u003ca href=\"https://gitlab.kitware.com/paraview/paraview-superbuild/-/tags\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo checkout a specific tag from the superbuild git repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd paraview-superbuild\n$ git fetch origin # ensure you have the latest state from the main repo\n$ git checkout v5.7.0 # replace `v5.7.0` with tag name of your choice\n$ git submodule update\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point, your superbuild has all of the \u003cem\u003erules\u003c/em\u003e that were used\nwhen building the selected version of ParaView. Also, note that it\u0027s\npossible to build a version of ParaView using a different superbuild\nversion. For example, you could use superbuild \u003ccode\u003ev5.7.0\u003c/code\u003e, to build the\nlatest master (i.e., development) version of ParaView, or a custom\nbranch. This is done by first checking out the superbuild for the\nappropriate version and then setting the CMake variables that affect\nwhich ParaView source is to be used. There are several ways to\ncontrol how superbuild finds its source packages:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIf you want to use git to checkout ParaView source (default), then set\n\u003ccode\u003eparaview_SOURCE_SELECTION\u003c/code\u003e to \u003ccode\u003egit\u003c/code\u003e, ensure \u003ccode\u003eparaview_GIT_REPOSITORY\u003c/code\u003e is\npointing to the ParaView git repository you want to clone (by default it is\nset to the offical ParaView repository) and then set the \u003ccode\u003eparaview_GIT_TAG\u003c/code\u003e\nto be a specific tagname or branch available for the selected git\nrepository. Use \u003ccode\u003emaster\u003c/code\u003e for latest development code, \u003ccode\u003ev5.7.0\u003c/code\u003e for the\n5.7.0 release, \u003ccode\u003erelease\u003c/code\u003e for latest stable release, or a specific ParaView\ncommit SHA. In this setup, when building the superbuild, it will clone and\ncheckout the appropriate revision from the ParaView git repository automatically.\u003c/li\u003e\n\u003cli\u003eInstead of letting superbuild do the cloning and updating of the ParaView\nsource, you can also manually check it out and keep it updated as needed.\nTo use this configuration, set \u003ccode\u003eparaview_SOURCE_SELECTION\u003c/code\u003e to \u003ccode\u003esource\u003c/code\u003e, and\nset \u003ccode\u003eparaview_SOURCE_DIR\u003c/code\u003e to point to a custom ParaView source tree. See \u0027offline\nbuilds\u0027 below for instructions to download needed dependency packages.\u003c/li\u003e\n\u003cli\u003eAnother option is to use a source tarball of a ParaView release. For that,\nset \u003ccode\u003eparaview_SOURCE_SELECTION\u003c/code\u003e to the version to build such as \u003ccode\u003e5.7.0\u003c/code\u003e.\nThe superbuild offers the lastest stable release as well as release\ncandidate in preparation for the release. This is the best way to build a\nreleased version of ParaView.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e If you switch to a superbuild version older than 5.2, the instructions\ndescribed on this page are not relevant since the superbuild was refactored and\nchanged considerably for 5.2. For older versions, refer to instructions on the\n\u003ca href=\"http://www.paraview.org/Wiki/index.php?title=ParaView/Superbuild\u0026amp;oldid=59804\" rel=\"nofollow\"\u003eWiki\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eALSO NOTE:\u003c/strong\u003e Since this README is expected to be updated for each version,\nonce you checkout a specfic version, you may want to refer to the README for\nthat specific version.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-incremental-builds\" class=\"anchor\" aria-hidden=\"true\" href=\"#incremental-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIncremental builds\u003c/h2\u003e\n\u003cp\u003eThe superbuild is kind of na\u00efve for changes to project sources within the\nsuperbuild. This is due to the superbuild not tracking all source files for\neach project and instead only \"stamp files\" to indicate the steps performed.\u003c/p\u003e\n\u003cp\u003eWhen changing the source of a subproject, the best solution is to delete the\n\"stamp file\" for the build step of that project:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ rm superbuild/$project/stamp/$project-build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand to rerun the superbuild\u0027s build step.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-projects-and-features\" class=\"anchor\" aria-hidden=\"true\" href=\"#projects-and-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProjects and Features\u003c/h2\u003e\n\u003cp\u003eThe superbuild contains multiple projects which may be used to enable\ndifferent features within the resulting ParaView build. Most projects involve\ndownloading and adding the feature to the resulting package, but there are a\nfew which are used just to enable features within ParaView itself.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eparaview\u003c/code\u003e project must be enabled to build ParaView.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eparaviewsdk\u003c/code\u003e project enables the building of a package which includes\nheaders and libraries suitable for developing against ParaView. It is only available\non Linux (at the moment).\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eparaviewweb\u003c/code\u003e project adds web services into the resulting package.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eparaviewgettingstartedguide\u003c/code\u003e, and \u003ccode\u003eparaviewtutorialdata\u003c/code\u003e packages add\nstartup documentation and example data to the package.\u003c/p\u003e\n\u003cp\u003eParaView supports multiple rendering engines including \u003ccode\u003eegl\u003c/code\u003e, \u003ccode\u003emesa\u003c/code\u003e,\n\u003ccode\u003eosmesa\u003c/code\u003e, and \u003ccode\u003eqt5\u003c/code\u003e. All of these are incompatible with each other. If none of\nthese are chosen, a UI-less ParaView will be built (basically just\n\u003ccode\u003epvpython\u003c/code\u003e). On Windows and macOS, only the \u003ccode\u003eqt5\u003c/code\u003e rendering engine is\navailable.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003epython\u003c/code\u003e package is available to enable Python support in the package. In\naddition, the \u003ccode\u003ematplotlib\u003c/code\u003e and \u003ccode\u003enumpy\u003c/code\u003e packages are available.\u003c/p\u003e\n\u003cp\u003eThe following packages enable other features within ParaView:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eadios\u003c/code\u003e: Enable readers and writers for visualization data in the ADIOS\nfile format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elas\u003c/code\u003e: Enable reading the LAS file format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecosmotools\u003c/code\u003e: Enables Cosmo file format readers and related filters and\nalgorithms.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003effmpeg\u003c/code\u003e: Video encoding library for macOS and Linux.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eospray\u003c/code\u003e: A ray tracing rendering backend from Intel.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esilo\u003c/code\u003e: Support reading the silo file format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etbb\u003c/code\u003e: Improved parallel processing support within various VTK and\nParaView filters and algorithms.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evisitbridge\u003c/code\u003e: Enables readers for file formats provided from the VisIt\nproject.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evortexfinder2\u003c/code\u003e: A collection of tools to visualize and analyze vortices.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evrpn\u003c/code\u003e: Virtual reality support through the VRPN interface.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evtkm\u003c/code\u003e: VTK-m Accelerator Filters\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003exdmf3\u003c/code\u003e: A meta file format built on top of HDF5.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-offline-builds\" class=\"anchor\" aria-hidden=\"true\" href=\"#offline-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOffline builds\u003c/h2\u003e\n\u003cp\u003eThe superbuild has a \u003ccode\u003edownload-all\u003c/code\u003e target that will download all of\nthe files from the network that are necessary for the currently\nconfigured build. By default, they are placed into the \u003ccode\u003edownloads\u003c/code\u003e\ndirectory of the build tree. This superbuild-plus-downloads tree may\nthen be copied to a non-networked machine and pointed at using the\n\u003ccode\u003esuperbuild_download_location\u003c/code\u003e variable (or placed in the default\nlocation).\u003c/p\u003e\n\u003cp\u003eNote that the \u003ccode\u003envidiaoptix\u003c/code\u003e and \u003ccode\u003envidiamdl\u003c/code\u003e project sources are not available\nat their URLs in the superbuild outside of Kitware due to their sources being\nbehind click-wrapping. They may be manually downloaded from these web pages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003envidiaoptix\u003c/code\u003e: \u003ca href=\"https://developer.nvidia.com/designworks/optix/download\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/designworks/optix/download\u003c/a\u003e\nThough older versions are available here:\n\u003ca href=\"https://developer.nvidia.com/designworks/optix/downloads/legacy\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/designworks/optix/downloads/legacy\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003envidiamdl\u003c/code\u003e: \u003ca href=\"https://developer.nvidia.com/mdl-sdk\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/mdl-sdk\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-overriding-downloaded-archives\" class=\"anchor\" aria-hidden=\"true\" href=\"#overriding-downloaded-archives\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverriding downloaded archives\u003c/h3\u003e\n\u003cp\u003eOn rare occasions, you may want to replace a downloaded archive with a different\nversion. You may replace the archive with a newer version preserving its\nname, however, on doing so, the hash verification will most likely fail during\nthe build step. To skip the hash verification for archives that have been\nmanually changed, set the \u003ccode\u003exxx_SKIP_VERIFICATION\u003c/code\u003e option, where \u003ccode\u003exxx\u003c/code\u003e\nis the name of the project. \u003ccode\u003exxx_SKIP_VERIFICATION\u003c/code\u003e must be passed on command line\nwhen invoking CMake using \u003ccode\u003e-Dxxx_SKIP_VERIFICATION:BOOL=TRUE\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, you can edit the \u003ccode\u003eversions.cmake\u003c/code\u003e files in the source repository\nand modify the \u003ccode\u003eURL_MDF5\u003c/code\u003e or \u003ccode\u003eURL_HASH\u003c/code\u003e values for the specific project with\nupdated hashes.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling\u003c/h2\u003e\n\u003cp\u003eThe superbuild supports the \u003ccode\u003einstall\u003c/code\u003e target by selecting a template package\nusing the \u003ccode\u003eSUPERBUILD_DEFAULT_INSTALL\u003c/code\u003e variable. The default and availability\ndepends on the platform and selected projects, but valid values for this\ninclude:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eparaview/ZIP\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaview/DragNDrop\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaview/TGZ\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaview/TXZ\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaviewsdk/TGZ\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eparaviewsdk/TXZ\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe CMake cache editors (\u003ccode\u003eccmake\u003c/code\u003e and \u003ccode\u003ecmake-gui\u003c/code\u003e) have dropdown options for\nthe supported options.\u003c/p\u003e\n\u003cp\u003eThe selected package logic will be used to install ParaView and its\ndependencies into \u003ccode\u003eCMAKE_INSTALL_PREFIX\u003c/code\u003e rather than being placed into a\npackage. For example, the \u003ccode\u003eDragNDrop\u003c/code\u003e generator creates \u003ccode\u003e.app\u003c/code\u003e bundles which\nwill be created whereas the \u003ccode\u003eTGZ\u003c/code\u003e, \u003ccode\u003eTXZ\u003c/code\u003e, and \u003ccode\u003eZIP\u003c/code\u003e generators use the standard\n\u003ccode\u003ebin/\u003c/code\u003e, \u003ccode\u003elib/\u003c/code\u003e, etc. directories.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-caveats\" class=\"anchor\" aria-hidden=\"true\" href=\"#caveats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCaveats\u003c/h3\u003e\n\u003cp\u003eIf using the \u003ccode\u003egit\u003c/code\u003e source selection for ParaView, the build will rerun when\nusing the \u003ccode\u003einstall\u003c/code\u003e target due to limitations in the external project\nmechanisms and the way CPack works. There are two ways to avoid this:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe \u003ccode\u003eSUPERBUILD_OFFLINE_BUILD\u003c/code\u003e option may be set to \u003ccode\u003eON\u003c/code\u003e to unlink the git\nupdate step from the configure/build steps; or\u003c/li\u003e\n\u003cli\u003ethe initial build can just be run using the \u003ccode\u003einstall\u003c/code\u003e target instead of\nthe usual \u003ccode\u003emake \u0026amp;\u0026amp; make install\u003c/code\u003e pattern.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-external-plugins\" class=\"anchor\" aria-hidden=\"true\" href=\"#external-plugins\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExternal plugins\u003c/h2\u003e\n\u003cp\u003eThe superbuild supports building more plugins into ParaView using the\n\u003ccode\u003eparaviewexternalplugins\u003c/code\u003e project. As an example, to build two external\nplugins \u003ccode\u003ea\u003c/code\u003e and \u003ccode\u003eb\u003c/code\u003e, the following settings should be used:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eENABLE_paraviewexternalplugins:BOOL=ON\u003c/code\u003e: Enables building using external\nplugins.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_PLUGINS_EXTERNAL:STRING=a;b\u003c/code\u003e: The list of plugins to build.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_PLUGIN_a_PATH:PATH=/path/to/plugin/a\u003c/code\u003e: The path to plugin \u003ccode\u003ea\u003c/code\u003e\u0027s\nsource directory. It must contain a \u003ccode\u003eplugins.cmake\u003c/code\u003e to be picked up by\nParaView.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_PLUGIN_b_PATH:PATH=/path/to/plugin/b\u003c/code\u003e: Same as above, but for\nplugin \u003ccode\u003eb\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cmake-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#cmake-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCMake Variables\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-style-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#style-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStyle Guide\u003c/h3\u003e\n\u003cp\u003eNote that currently not all project and configuration variables follow this\nstyle guide but any new projects should use this convention while any\nexisting projects and configuration variables will transition to this over\ntime.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAll references to a given project name will be lowercase.\u003c/li\u003e\n\u003cli\u003eUnderscores will be used as word seperators in variable names.\u003c/li\u003e\n\u003cli\u003eAll project specific configuration variables will be lower-case project\nname followed by upper-case setting name.\nExamples include:\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emesa_USE_SWR\u003c/code\u003e : Enable the OpenSWR driver for (OS)Mesa.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eospray_BUILD_ISA\u003c/code\u003e : Select the SIMD architecture used to build OSPray.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eInternal variables used within a given project\u0027s projectname.cmake file\nwill be all lower-case.\u003c/li\u003e\n\u003cli\u003eMultiple versions:\n\u003cul\u003e\n\u003cli\u003eUse the \u003ccode\u003esuperbuild_set_selectable_source\u003c/code\u003e macro to allow multiple\nversions of a given project.\u003c/li\u003e\n\u003cli\u003eSpecify source selection versions as numeric, i.e. without any \"v\" or\n\"V\" prefix.\u003c/li\u003e\n\u003cli\u003eIf the project is going through a release candidate cycle, add the\navailable RCs as additional sources as they become availabe. Once\na final release is made, replace all the RCs with the updated release.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-build-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild Variables\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esuperbuild_download_location\u003c/code\u003e (default \u003ccode\u003e${CMAKE_BINARY_DIR}/downloads\u003c/code\u003e):\nThe location to store downloaded source artifacts. Usually, it is changed\nso that it is preserved across a wipe of the build directory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSUPERBUILD_PROJECT_PARALLELISM\u003c/code\u003e (default based on the number of available\nprocessors): When using a Makefiles generator, subproject builds use \u003ccode\u003e-j\u003c/code\u003e\nexplicitly with this number.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eENABLE_xxx\u003c/code\u003e (generally, default \u003ccode\u003eOFF\u003c/code\u003e): If selected, the \u003ccode\u003exxx\u003c/code\u003e project\nwill be built within the superbuild. See above for descriptions of the\nvarious projects. \u003ccode\u003eENABLE_\u003c/code\u003e flags are not shown for projects which must be\nenabled due to a project depending on it (e.g., \u003ccode\u003evisitbridge\u003c/code\u003e requires\n\u003ccode\u003eboost\u003c/code\u003e, so enabling \u003ccode\u003evisitbridge\u003c/code\u003e will hide the \u003ccode\u003eENABLE_boost\u003c/code\u003e option).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUSE_SYSTEM_xxx\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): If selected, the \u003ccode\u003exxx\u003c/code\u003e project from the\nbuild environment is used instead of building it within the superbuild.\nNot all projects support system copies (the flag is not available if so).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSUPERBUILD_DEBUG_CONFIGURE_STEPS\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): If set, the superbuild\nwill log configure steps for each \u003ccode\u003exxx\u003c/code\u003e project into\n\u003ccode\u003esuperbuild/xxx/stamp/xxx-configure-*.log\u003c/code\u003e files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCMAKE_BUILD_TYPE\u003c/code\u003e (default \u003ccode\u003eRelease\u003c/code\u003e): The build type to use for the\nbuild. Can be \u003ccode\u003eRelease\u003c/code\u003e, \u003ccode\u003eRelWithDebInfo\u003c/code\u003e, or (on not-Windows) \u003ccode\u003eDebug\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDue to complications around shipping OpenSSL in the binaries, OpenSSL\nrequires explicit settings in the build. They are\n\u003ccode\u003e-DALLOW_openssl:BOOL=ON -DENABLE_openssl:BOOL=ON\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_always_package_scipy\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): Force packaging \u003ccode\u003escipy\u003c/code\u003e on\nWindows installer generators. Other generators do not have issues with long\npaths and will always try to include \u003ccode\u003escipy\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following flags affect ParaView directly:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eparaview_SOURCE_SELECTION\u003c/code\u003e (default \u003ccode\u003e5.11.0\u003c/code\u003e): The source to use for\nParaView itself. The version numbers use the source tarballs from the\nwebsite for the release. The \u003ccode\u003esource\u003c/code\u003e selection uses the\n\u003ccode\u003eparaview_SOURCE_DIR\u003c/code\u003e variable to look at a checked out ParaView source\ndirectory. The \u003ccode\u003egit\u003c/code\u003e selection has the superbuild clone and builds a\ncheckout of ParaView from git repository controlled by the\n\u003ccode\u003eparaview_GIT_REPOSITORY\u003c/code\u003e and \u003ccode\u003eparaview_GIT_TAG\u003c/code\u003e variables. By default, the\n\u003ccode\u003emaster\u003c/code\u003e branch of the main repository is used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: When using the \u003ccode\u003esource\u003c/code\u003e selection, incremental builds to the\nsuperbuild may not rebuild ParaView even if the source tree has changed.\nThis is because the superbuild is \"blind\" to the source tree other than\nits existence.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eCMAKE_BUILD_TYPE_paraview\u003c/code\u003e (default is the same as the superbuild):\nParaView may be built with a different build type (e.g., \u003ccode\u003eRelease\u003c/code\u003e vs.\n\u003ccode\u003eRelWithDebInfo\u003c/code\u003e) as the rest of the superbuild using this variable. In\naddition to \u003ccode\u003e\u0026lt;SAME\u0026gt;\u003c/code\u003e which uses \u003ccode\u003eCMAKE_BUILD_TYPE\u003c/code\u003e, any valid value for\n\u003ccode\u003eCMAKE_BUILD_TYPE\u003c/code\u003e is also valid.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eBUILD_SHARED_LIBS_paraview\u003c/code\u003e (default is the same as the superbuild):\nParaView may be built with a different selection for BUILD_SHARED_LIBS flag\nthan the rest of the superbuild using this variable. For example,\nto build ParaView static while building other projects in the superbuild\n(e.g. MPI, Python, etc.) as shared, set \u003ccode\u003eBUILD_SHARED_LIBS\u003c/code\u003e to \u003ccode\u003eON\u003c/code\u003e\nand \u003ccode\u003eBUILD_SHARED_LIBS_paraview\u003c/code\u003e to \u003ccode\u003eOFF\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_BUILD_WEB_DOCUMENTATION\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): Have ParaView build\nits HTML documentation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emesa_USE_SWR\u003c/code\u003e (default \u003ccode\u003eON\u003c/code\u003e): If \u003ccode\u003emesa\u003c/code\u003e is enabled, this enables\nIntel\u0027s software rasterization backend (x86 only).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_INITIALIZE_MPI_ON_CLIENT\u003c/code\u003e (default \u003ccode\u003eON\u003c/code\u003e): If \u003ccode\u003empi\u003c/code\u003e is enabled, this\nenables MPI to be initialized automatically when running the GUI or pvpython.\nSome readers use MPI IO and thus must have MPI initialized in order to be\nused so this is the default for general ease of use. For some MPI implementations,\na code that initializes MPI must be run with the appropriate mpi launcher\n(e.g. mpirun) which in this case it may be desirable to disable this option.\nNote that the \u003ccode\u003e--mpi\u003c/code\u003e or \u003ccode\u003e--no-mpi\u003c/code\u003e command line options to paraview and\npvpython can be used to override this option.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_EXTRA_CMAKE_ARGUMENTS\u003c/code\u003e (default \u003ccode\u003e\"\"\u003c/code\u003e: Extra CMake arguments to\npass to ParaView\u0027s configure step. This can be used to set CMake variables\nfor the build that are otherwise not exposed in the superbuild itself.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_ENABLE_CAVEInteraction\u003c/code\u003e (default \u003ccode\u003eON\u003c/code\u003e): Enables the CAVEInteraction. If\n\u003ccode\u003evrpn\u003c/code\u003e is enabled, the CAVEInteraction will support input devices through a VRPN\nconnection. VRUI support is enabled unconditionally on Linux.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_ENABLE_NODEEDITOR\u003c/code\u003e (default \u003ccode\u003eOFF\u003c/code\u003e): Enables the NodeEditor\nplugin.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePARAVIEW_ENABLE_XRInterface\u003c/code\u003e (default \u003ccode\u003eON\u003c/code\u003e): Enables the XRInterface plugin.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-paraview-editions\" class=\"anchor\" aria-hidden=\"true\" href=\"#paraview-editions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParaView editions\u003c/h4\u003e\n\u003cp\u003eA typical ParaView build includes several modules and dependencies. While these\nare necessary for a fully functional application, there are cases (e.g. in situ\nuse-cases) where a build with limited set of features is adequate. ParaView build supports\nthis using the \u003ccode\u003ePARAVIEW_BUILD_EDITION\u003c/code\u003e setting. Supported values for this setting are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCORE\u003c/code\u003e: Build modules necessary for core ParaView functionality.\nThis does not include rendering.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eRENDERING\u003c/code\u003e: Build modules necessary for supporting rendering including views\nand representations. This includes everything in \u003ccode\u003eCORE\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCATALYST\u003c/code\u003e: Build all modules necessary for in situ use cases without\nrendering and optional components like NetCDF- and HDF5-based readers and\nwriters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCATALYST_RENDERING\u003c/code\u003e: Same as \u003ccode\u003eCATALYST\u003c/code\u003e but with rendering supported added.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCANONICAL\u003c/code\u003e (default): Build modules necessary for standard ParaView build.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-packaging-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#packaging-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackaging Variables\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ePARAVIEW_PACKAGE_SUFFIX\u003c/code\u003e (default based on selected options): The suffix\nfor the name generated by the package.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparaview_PLUGINS_AUTOLOAD\u003c/code\u003e: List of plugins to autoload in the packaged\nParaView.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-packaging\" class=\"anchor\" aria-hidden=\"true\" href=\"#packaging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackaging\u003c/h1\u003e\n\u003cp\u003eThe packages may be built using the \u003ccode\u003ecpack-paraview\u003c/code\u003e tests via \u003ccode\u003ectest\u003c/code\u003e. The\neasiest way to build all available packages is to run \u003ccode\u003ectest -R cpack\u003c/code\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-learning-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#learning-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLearning Resources\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGeneral information is available at the \u003ca href=\"http://www.paraview.org\" rel=\"nofollow\"\u003eParaView Homepage\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCommunity discussion takes place on the \u003ca href=\"http://www.paraview.org/mailing-lists/\" rel=\"nofollow\"\u003eParaView Mailing Lists\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCommercial \u003ca href=\"http://www.kitware.com/products/support.html\" rel=\"nofollow\"\u003esupport\u003c/a\u003e and \u003ca href=\"http://www.kitware.com/products/protraining.php\" rel=\"nofollow\"\u003etraining\u003c/a\u003e\nare available from \u003ca href=\"http://www.kitware.com/\" rel=\"nofollow\"\u003eKitware\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-reporting-bugs\" class=\"anchor\" aria-hidden=\"true\" href=\"#reporting-bugs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReporting Bugs\u003c/h1\u003e\n\u003cp\u003eIf you have found a bug:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eIf you have a patch, please read the \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e document.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOtherwise, please join one of the \u003ca href=\"http://www.paraview.org/mailing-lists/\" rel=\"nofollow\"\u003eParaView Mailing Lists\u003c/a\u003e and ask\nabout the expected and observed behaviors to determine if it is\nreally a bug.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFinally, if the issue is not resolved by the above steps, open\nan entry in the \u003ca href=\"http://www.paraview.org/Bug\" rel=\"nofollow\"\u003eParaView Issue Tracker\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h1\u003e\n\u003cp\u003eLike ParaView, ParaView-Superbuild is distributed under the OSI-approved BSD\n3-clause License. See \u003ca href=\"Copyright.txt\"\u003eCopyright.txt\u003c/a\u003e for details. For additional licenses,\nrefer to \u003ca href=\"http://www.paraview.org/paraview-license/\" rel=\"nofollow\"\u003eParaView Licenses\u003c/a\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e for instructions to contribute.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ereinterpretive-label\u003c/h1\u003e\u003ca id=\"user-content-reinterpretive-label\" class=\"anchor\" aria-label=\"Permalink: reinterpretive-label\" href=\"#reinterpretive-label\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnything can become art by the addition of a sufficiently clever interpretive label, even really lousy things.\nSo, let\u0027s reinterpret lousy things and make them awesome by adding interpretive label stickers!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"example.png\"\u003e\u003cimg src=\"example.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains software designed to help you design and print your own reinterpretive label stickers.\u003c/p\u003e\n\u003cp\u003eThe interpretive labels you create can respond to whatever you want.\nDon\u0027t feel the need to restrict yourself to just one thing or another.\nThere\u0027s \u003ca href=\"https://twitter.com/Malboury/status/968163458679263238/\" rel=\"nofollow\"\u003ea lot of ways to make the world a better place!\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRULE #1:\u003c/h2\u003e\u003ca id=\"user-content-rule-1\" class=\"anchor\" aria-label=\"Permalink: RULE #1:\" href=\"#rule-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ebe civil.\u003c/p\u003e\n\u003cp\u003eWe want to make public spaces more --- not less -- pleasant.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlso,\u003c/h2\u003e\u003ca id=\"user-content-also\" class=\"anchor\" aria-label=\"Permalink: Also,\" href=\"#also\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eunderstand relevant legal restrictions in your area and do not violate them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need to have a \u003ca href=\"https://en.wikipedia.org/wiki/Unix_shell\" rel=\"nofollow\"\u003eUnix shell\u003c/a\u003e you can run things in.\u003c/p\u003e\n\u003cp\u003eYou\u0027ll also need to have software called \u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e that facilitates packaged Linux workflows.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGrab a copy of the repo using \u003ca href=\"https://git-scm.com/\" rel=\"nofollow\"\u003egit\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/mmore500/reinterpretive-label\ncd reinterpretive-label\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake a copy of the template reinterpretive label Latex file in order to customize it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp tex/template.tex instance.tex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, you can open \u003ccode\u003einstance.tex\u003c/code\u003e with your favorite text editor and change the text.\nFeel free to re-use as much or as little of the template (including the text) as you desire.\u003c/p\u003e\n\u003cp\u003eYou shouldn\u0027t really need to know much Latex at all in order to successfully make minor edits.\nThat said, if you\u0027re unfamiliar you can find some helpful hints in the \"Latex Pointers\" section below.\u003c/p\u003e\n\u003cp\u003eWhen you\u0027re satisfied with your reinterpretive label (\u003ccode\u003einstance.tex\u003c/code\u003e), here\u0027s how to generate a PDF.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run shub://mmore500/reinterpretive-label\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will download the packaged linux workflow for you and do the actual work of compiling \u003ccode\u003einstance.tex\u003c/code\u003e to \u003ccode\u003einstance.pdf\u003c/code\u003e.\nBe sure that your \u003ccode\u003einstance.tex\u003c/code\u003e file is saved in the current working directory with exactly that name.\u003c/p\u003e\n\u003cp\u003eIf you want to fiddle with your reinterpretive label some more (i.e., make futher edits to \u003ccode\u003einstance.tex\u003c/code\u003e) and then recompile \u003ccode\u003einstance.pdf\u003c/code\u003e, waiting for the singularity workflow image to download again can be annoying.\nYou can get around this by using the cached \u003ccode\u003e.simg\u003c/code\u003e file generated when you run from SingularityHub (\u003ccode\u003eshub\u003c/code\u003e) the first time.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run mmore500-reinterpretive-label-master-latest.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, to clean up, use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake clean\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOne you have a PDF that you like, getting your stickers printed is a snap.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrinting Stickers\u003c/h2\u003e\u003ca id=\"user-content-printing-stickers\" class=\"anchor\" aria-label=\"Permalink: Printing Stickers\" href=\"#printing-stickers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can print stickers wherever you want.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.makestickers.com/\" rel=\"nofollow\"\u003eMakeStickers\u003c/a\u003e, though, seems convenient to me because it allows for\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePDF upload\u003c/li\u003e\n\u003cli\u003ecustom aspect ratios of \u003ca href=\"https://www.makestickers.com/products/custom-stickers/rectangle-stickers\" rel=\"nofollow\"\u003erectanglular stickers\u003c/a\u003e (the height to width of the generated PDF label depends on the amount of content), and\u003c/li\u003e\n\u003cli\u003esmall batch-size when printing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBe Social!\u003c/h2\u003e\u003ca id=\"user-content-be-social\" class=\"anchor\" aria-label=\"Permalink: Be Social!\" href=\"#be-social\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eShare what you\u0027re up to with the hashtag \u003ca href=\"https://twitter.com/hashtag/reinterpretivelabel\" rel=\"nofollow\"\u003e#reinterpretivelabel\u003c/a\u003e and\\or tweet at me \u003ca href=\"https://twitter.com/MorenoMatthewA\" rel=\"nofollow\"\u003e@MorenoMatthewA\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBonus points for including the twitter handle of your local modern art museum in your stickers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you come up with something devastatingly clever you think others might benefit from using as a springboard, add it to the \u003ccode\u003etex\u003c/code\u003e directory and put in a pull request (or just email me the \u003ccode\u003e.tex\u003c/code\u003e file you wrote up if you don\u0027t know what that means).\nAlso, send along PDF copies of your creations so we can start a gallery of examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInquiries\u003c/h2\u003e\u003ca id=\"user-content-inquiries\" class=\"anchor\" aria-label=\"Permalink: Inquiries\" href=\"#inquiries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re having any issues figuring out how to generate a sticker --- or even just don\u0027t have the computational means to do it yourself --- get in touch.\nI might be willing to fund a few sticker print jobs, too, if you\u0027re unable.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematthew.andres.moreno@gmail.com\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLatex Pointers\u003c/h2\u003e\u003ca id=\"user-content-latex-pointers\" class=\"anchor\" aria-label=\"Permalink: Latex Pointers\" href=\"#latex-pointers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo edit a \u003ccode\u003e.tex\u003c/code\u003e file, use a \u003ca href=\"https://en.wikipedia.org/wiki/Text_editor\" rel=\"nofollow\"\u003eplain text editor\u003c/a\u003e, not a rich text editor like Microsoft Word.\u003c/p\u003e\n\u003cp\u003eThe actual text goes in between \u003ccode\u003e\\begin{document}\u003c/code\u003e and \u003ccode\u003e\\end{document}\u003c/code\u003e.\nDon\u0027t make any edits outside these markers!\u003c/p\u003e\n\u003cp\u003eQuotation marks:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e`` \u0027\u0027 YES\n\" NO\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEm dashes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--- YES\n- NO\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eText size:\nwrap the text you want to resize inside curly braces and tell what effect you want with \u003ccode\u003e\\sizename\u003c/code\u003e.\n``\n{\\huge BIG TEXT}\n{\\huge small text}\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\nAvailable size descriptors include: `\\Huge`, `\\huge`, `\\LARGE`, `\\Large`, `\\large`, `\\small`, `\\footnotesize`, `\\scriptsize`, and `\\tiny`.\n\nText styling:\nI defined two text modifiers for you: `\\myboldfont` and `\\myitalicfont`.\nJust like with text size, wrap the text you want to modify inside curly braces and tell what modifier you want.\n``\n{\\myboldfont BOLD TEXT}\n{\\myitalicfont italic text}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhitespace:\nto create a paragraph break, simply have an empty line between two lines of text.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eI\u0027m in the top paragraph!\nI\u0027m in the top paragraph, too.\n\nI\u0027m in the bottom paragraph.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse \u003ccode\u003e\\smallskip\u003c/code\u003e, \u003ccode\u003e\\medskip\u003c/code\u003e, and \u003ccode\u003e\\largeskip\u003c/code\u003e to space out your paragraphs.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eI\u0027m in the top paragraph!\nI\u0027m in the top paragraph, too.\n\n\\largeskip\n\nI\u0027m in the bottom paragraph and further away now.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSpecial characters:\nthe following characters might confuse the Latex compiler and cause an error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # $ % \u0026amp; ~ _ ^ { }\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can get around this by placing a \u003ccode\u003e\\\u003c/code\u003e in front.\nFor example, type \u003ccode\u003e\\#\u003c/code\u003e instead of just \u003ccode\u003e#\u003c/code\u003e and \u003ccode\u003e\\$\u003c/code\u003e instead of just \u003ccode\u003e$\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [ - "paraview-superbuild", - "cmake", - "superbuild" + "social-justice", + "activism", + "art-gallery", + "art-installation", + "public-space" ], - "updated_at": 1675960647.0 + "updated_at": 1703886895.0 }, { "data_format": 2, - "description": null, + "description": "usher + taxonium", "filenames": [ - "config/Singularity", - "scripts/unused/Singularity", - "scripts/unused/Singularity_newhybrids" + "Singularity.def" ], - "full_name": "nealplatt/sH_hybridization", - "latest_release": "v1.0", - "readme": "\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/124456755\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4641d504e79e577f2add43b190e60f3910a1688ac8f26f972d799fd6f3f4b213/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3132343435363735352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/124456755.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/ambv/black\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d91ed7ac7abbd5a6102cbe988dd8e9ac21bde0a73d97be7603b891ad08ce3479/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e737667\" alt=\"Code style: black\" data-canonical-src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-ancient-hybridization-and-adaptive-introgression-of-an-invadolysin-gene-in-schistosoma-haematobium\" class=\"anchor\" aria-hidden=\"true\" href=\"#ancient-hybridization-and-adaptive-introgression-of-an-invadolysin-gene-in-schistosoma-haematobium\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAncient hybridization and adaptive introgression of an invadolysin gene in \u003cem\u003eSchistosoma haematobium\u003c/em\u003e.\u003c/h1\u003e\n\u003cp\u003eRoy N. Platt II, Marina McDew-White, Winka Le Clec\u0027h, Frederic D. Chevalier, Fiona Allan, Aidan M. Emery, Amadou Garba, Shaali M. Ame, Joanne P. Webster, David Rollinson, Bonnie L. Webster, Timothy J. C. Anderson.\u003c/p\u003e\n\u003cp\u003eThe parasitic blood fluke \u003cem\u003eSchistosoma\u003c/em\u003e \u003cem\u003ehaematobium\u003c/em\u003e causes urogenital schistosomiasis in humans and is a major cause of morbidity and mortality across sub-Saharan Africa. \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ehaematobium\u003c/em\u003e can hybridize with closely-related livestock schistosomes, including \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ebovis\u003c/em\u003e, however the frequency, direction, age and genomic consequences of hybridization in nature are unknown. We sequenced 96 \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ehaematobium\u003c/em\u003e exomes from Niger and the Zanzibar archipelago. We found evidence of an ancient, adaptive introgression event between Nigerien \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ehaematobium\u003c/em\u003e and \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ebovis\u003c/em\u003e occurring 108-613 generations ago. Introgressed S. bovis alleles constitute 3.3-8.2% of Nigerien \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ehaematobium\u003c/em\u003e genomes. Some \u003cem\u003eS\u003c/em\u003e. \u003cem\u003ebovis\u003c/em\u003e alleles have reached high frequency and show signatures of directional selection; the strongest signal spans a single gene in the invadolysin gene family, an M8 metalloprotease associated with parasitic life-history traits.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-biorxiv-pre-print\" class=\"anchor\" aria-hidden=\"true\" href=\"#biorxiv-pre-print\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://doi.org/10.1101/539353\" rel=\"nofollow\"\u003ebioRxiv pre-print\u003c/a\u003e\u003c/h4\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNOTES:\u003c/h3\u003e\n\u003cp\u003eAll analyses were conducted on a HPCC in a \u003ccode\u003esingularity\u003c/code\u003e container or in a \u003ccode\u003econda\u003c/code\u003e managed environment. The singularity recipe and conda environmental yaml are in the \u003ccode\u003econfig\u003c/code\u003e dir.\u003c/p\u003e\n\u003cp\u003eRaw code is found in the \u003ccode\u003escripts\u003c/code\u003e dir\u003c/p\u003e\n\u003cp\u003eData that is not readily available through the SRA is in the \u003ccode\u003edata\u003c/code\u003e dir. These will be housed in an online repository (ex. Dryad), but provided here for documentation purposes.\u003c/p\u003e\n", + "full_name": "martinghunt/ushonium", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eushonium\u003c/h1\u003e\u003ca id=\"user-content-ushonium\" class=\"anchor\" aria-label=\"Permalink: ushonium\" href=\"#ushonium\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eusher + taxonium on Covid sequences.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild container with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build ushonium.img Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container has a script called \u003ccode\u003eushonium\u003c/code\u003e, which makes a taxonium\n\u003ccode\u003ejsonl.gz\u003c/code\u003e file from fasta consensus sequences.\u003c/p\u003e\n\u003cp\u003eIt uses mafft to align all sequences to the Covid reference (ignoring\nindels, so all the same length), makes an optimized tree with usher, and\nthen uses taxoniumtools to make the taxonium jsonl file.\u003c/p\u003e\n\u003cp\u003eThe filenames of consensus sequences need to be in a tab-delimited file\nwith no headings and two columns:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eName of sample\u003c/li\u003e\n\u003cli\u003eFASTA file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAssuming that TSV file is called \u003ccode\u003esamples.tsv\u003c/code\u003e, the usage is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eushonium samples.tsv outdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere the output directory \u003ccode\u003eoutdir\u003c/code\u003e will be created. The final taxonium file\nis called \u003ccode\u003e05.taxonium.jsonl.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUse \u0026gt;1 CPU\u003c/h3\u003e\u003ca id=\"user-content-use-1-cpu\" class=\"anchor\" aria-label=\"Permalink: Use \u0026gt;1 CPU\" href=\"#use-1-cpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMost of the time is spent making the MSA, which by default uses 1 cpu.\nRun in \u003ccode\u003eN\u003c/code\u003e in parallel using the option\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--cpus N\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will hugely speed up the script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMetadata\u003c/h3\u003e\u003ca id=\"user-content-metadata\" class=\"anchor\" aria-label=\"Permalink: Metadata\" href=\"#metadata\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can set the title that will appear in the taxonium browser with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--title \"My awesome tree\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also use a file of metadata for each sample (that has eg lineage,\ncountry etc). This needs to be tab-delimited with the first line having\ncolumn headers. One column must have the\nname of the sample, and must exactly match the name given in \u003ccode\u003esamples.tsv\u003c/code\u003e.\nBy default, this column is assumed to have the name \u003ccode\u003estrain\u003c/code\u003e, but you\ncan change it to eg \u003ccode\u003emy_names\u003c/code\u003e with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--metacol_name my_names\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf the metadata file is called \u003ccode\u003emetadata.tsv\u003c/code\u003e, and we want columns\n\u003ccode\u003ecol11\u003c/code\u003e and \u003ccode\u003ecol2\u003c/code\u003e, then use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--metadata_tsv metadata.tsv --metacols col1,col2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReference genome\u003c/h3\u003e\u003ca id=\"user-content-reference-genome\" class=\"anchor\" aria-label=\"Permalink: Reference genome\" href=\"#reference-genome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe script \u003ccode\u003eushonium\u003c/code\u003e was set up for covid,\nusing the recommended genbank reference from taxoniumtools.\nThis genbank file is included in the container, so there is\nno need to specify the reference genome when running \u003ccode\u003eushonium\u003c/code\u003e unless\nyou want to use a different reference. The option to change it to \u003ccode\u003emy_ref.gb\u003c/code\u003e is\n\u003ccode\u003e--ref_gb my_ref.gb\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFrom the taxoniumtools documentation: \"Right now Taxoniumtools is limited in\nthe types of genome annotations it can support, for SARS-CoV-2 we recommend\nusing the exact modified .gb file we use in the example, which splits ORF1ab\ninto ORF1a and ORF1b to avoid the need to model ribosome slippage.\"\nSee \u003ca href=\"https://docs.taxonium.org/en/latest/taxoniumtools.html\" rel=\"nofollow\"\u003ehttps://docs.taxonium.org/en/latest/taxoniumtools.html\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 2, "topics": [], - "updated_at": 1618309153.0 + "updated_at": 1720703818.0 }, { "data_format": 2, - "description": null, + "description": "HPC example for BiocParallel", "filenames": [ - "Singularity.centos7.tbx-MG" + "Singularity" ], - "full_name": "ResearchIT/MolecularGraphicsToolbox", + "full_name": "nturaga/biocparallel-example", "latest_release": null, "stargazers_count": 2, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1582812783.0 + "updated_at": 1560862119.0 }, { "data_format": 2, - "description": null, + "description": "Singularity container for RStudio-Server.", "filenames": [ - "containers/Singularity.make_prg_dependencies", - "containers/Singularity.subsample" + "Singularity.1.1.456" ], - "full_name": "mbhall88/pandora_analysis_pipeline", + "full_name": "mcw-rcc/rstudio-server", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRStudio Server\u003c/h1\u003e\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: RStudio Server\" href=\"#rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1268\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity container running RStudio Server.\u003c/p\u003e\n\u003cp\u003eExample job script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\n#PBS -N rserver\n#PBS -l nodes=1:ppn=1\n#PBS -l mem=5gb\n#PBS -l walltime=1:00:00\n#PBS -j oe\n\n# tunnel info\nPORT=$(shuf -i8000-9999 -n1)\nSUBMIT_HOST=$(echo ${PBS_O_HOST%%.*}.rcc.mcw.edu)\n\n# rserver password\nexport RSTUDIO_PASSWORD=$(openssl rand -base64 15)\n\n# print tunneling instructions\necho -e \"\n1. SSH tunnel from your workstation using the following command:\n \n ssh -L 8787:${HOSTNAME}:${PORT} ${USER}@${SUBMIT_HOST}\n \n and point your web browser to http://localhost:8787.\n\n2. Log in to RStudio Server using the following credentials:\n user: ${USER}\n password: ${RSTUDIO_PASSWORD}\n\"\n\n# load modules\nmodule load rstudio-server/1.1.456\n\n#start server\nrstudio-server\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStart an RStudio session\u003c/h2\u003e\u003ca id=\"user-content-start-an-rstudio-session\" class=\"anchor\" aria-label=\"Permalink: Start an RStudio session\" href=\"#start-an-rstudio-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCopy contents of rserver.pbs (example above) to a file in your home directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOpen terminal on cluster login node and submit the job script:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ qsub rserver.pbs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConnect to RStudio session\u003c/h2\u003e\u003ca id=\"user-content-connect-to-rstudio-session\" class=\"anchor\" aria-label=\"Permalink: Connect to RStudio session\" href=\"#connect-to-rstudio-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCheck output file (\u003cem\u003ejobname\u003c/em\u003e.o\u003cem\u003eJOBNUM\u003c/em\u003e) for details.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExample output file: rserver.o152922\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. SSH tunnel from your workstation using the following command:\n\n ssh -L 8787:node01:9268 tester@loginnode\n\n and point your web browser to http://localhost:8787.\n\n2. Log in to RStudio Server using the following credentials:\n user: ${USER}\n password: ${RSTUDIO_PASSWORD}\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOpen second terminal and run tunneling command from the output file:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ ssh -L 8787:node01:9268 tester@loginnode\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOpen a browser and enter the URL from the output file:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ehttp://localhost:8787\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eLog in with credentials from output file.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou should now be connected to your RStudio session that is running on a cluster compute node. To close the session, select logout or stop session. If you need to reconnect, repeat steps 3 \u0026amp; 4. If you\u0027re done with your session, remember to stop the job with qdel.\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 3, - "topics": [], - "updated_at": 1604591459.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "Singularity" + "topics": [ + "rstudio-server", + "singularity-container" ], - "full_name": "LiSAT-Planning/LiSAT", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-powerlifted-planner\" class=\"anchor\" aria-hidden=\"true\" href=\"#powerlifted-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePowerlifted Planner\u003c/h1\u003e\n\u003cp\u003ePowerlifted is a domain-independent classical planner that uses only lifted\nrepresentations.\u003c/p\u003e\n\u003cp\u003e(See \u003ca href=\"#references\"\u003eReferences\u003c/a\u003e for more details.)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h2\u003e\n\u003cp\u003ePowerlifted is build using the \u003ccode\u003ebuild.py\u003c/code\u003e script.\u003c/p\u003e\n\u003cp\u003eThis version of powerlifted can either be build with or without support for SAT-based planning.\nTo build with SAT support, you need to call the script with the argument \u003ccode\u003e-s\u003c/code\u003e followed by the path to the directory that contains your sat solver\u0027s library.\nYou need to build the SAT solver before building powerlifted and you need to build the SAT solver s.t. a library is produced.\nSome SAT solvers don\u0027t do this by default!\u003c/p\u003e\n\u003cp\u003eCurrently, powerlifted uses the SAT solver \u003ca href=\"https://github.com/arminbiere/kissat\"\u003ekissat\u003c/a\u003e by default.\nIt can be configured to use \u003ca href=\"https://github.com/msoos/cryptominisat\"\u003ecryptominisat\u003c/a\u003e by providing the argument \u003ccode\u003e-i\u003c/code\u003e to \u003ccode\u003ebuild.py\u003c/code\u003e.\nInstead of cryptominisat, any SAT solving offering the \u003ca href=\"https://github.com/biotomas/ipasir\"\u003eIPASIR\u003c/a\u003e interface can be used.\nIf so desired, in src/search/CMakeLists.txt, the reference to \u003ccode\u003eipasircryptominisat5\u003c/code\u003e must be changed appropriately.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003epowerlifted.py\u003c/code\u003e script solves a PDDL task provided as input. It also builds\nthe planner if the \u003ccode\u003e--build\u003c/code\u003e parameter is passed. The script has the following\nparameters:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py [-d DOMAIN] -i INSTANCE -s SEARCH -e HEURISTIC -g GENERATOR [--state STATE REPR.] [ADDITIONAL OPTIONS] [--seed RANDOM SEED] [-l PLANLENGH] [-o] [-I] [--build]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eUse the \u003ccode\u003ebuild.py\u003c/code\u003e script to build the planner first.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-search\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-search\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eSEARCH\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ebfs\u003c/code\u003e: Breadth-First Search (This option was previously called \u003ccode\u003enaive\u003c/code\u003e. You\ncan still use \u003ccode\u003enaive\u003c/code\u003e with the \u003ccode\u003epowerlifted.py\u003c/code\u003e script but the planner will internally\nuse the new keyword \u003ccode\u003ebfs\u003c/code\u003e.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egbfs\u003c/code\u003e: Greedy Best-First Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy\u003c/code\u003e: Lazy Best-First Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy-po\u003c/code\u003e: Lazy Best-First Search with Boosted Dual-Queue\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy-prune\u003c/code\u003e: Lazy Best-First Search with pruning of states generated by\nnon-preferred operators\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esat\u003c/code\u003e: Search via reduction to SAT. If chosed the options \u003ccode\u003e-l\u003c/code\u003e, \u003ccode\u003e-o\u003c/code\u003e, and \u003ccode\u003e-I\u003c/code\u003e become available.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-heuristic\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-heuristic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eHEURISTIC\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eadd\u003c/code\u003e: The additive heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eblind\u003c/code\u003e: No Heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egoalcount\u003c/code\u003e: The goal-count/STRIPS heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ehmax\u003c/code\u003e: The hmax heuristic (Note that A* search is not implemented)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-generator\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-generator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eGENERATOR\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ejoin\u003c/code\u003e: Join program using the predicate order given in the PDDL file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erandom_join\u003c/code\u003e: Randomly ordered join program\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eordered_join\u003c/code\u003e: Join program ordered by the arity of the predicates\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efull_reducer\u003c/code\u003e: Generate successor for acyclic schemas using the full\nreducer method; for cyclic schemas it uses a partial reducer and a join\nprogram.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eyannakakis\u003c/code\u003e: Same as above but replaces the final join of the full\nreducer method by the Yannakakis\u0027 project-join program.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-state-repr\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-state-repr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eSTATE REPR.\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esparse\u003c/code\u003e: Use the sparse state representation where a state is only\nrepresented by the facts that are true in this state.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eextensional\u003c/code\u003e: Use the extensional representation where a state is a bitset\nwhere the ith-bit is true if the fact associated to it is true in this\nstate. This representation requires the grounding of facts (but not of\nactions) which, right now, is performed in the search component.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-avaialble-options-for-planlengh\" class=\"anchor\" aria-hidden=\"true\" href=\"#avaialble-options-for-planlengh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvaialble Options for \u003ccode\u003ePLANLENGH\u003c/code\u003e:\u003c/h3\u003e\n\u003cp\u003eA plan lenght may only be provided if SAT-based planning is chosen.\u003c/p\u003e\n\u003cp\u003eIf the planner is run in satisficing mode, it will attempt to solve the given problem \u003cstrong\u003eonly\u003c/strong\u003e for this plan length.\nIf \u003ccode\u003ePLANLENGH\u003c/code\u003e is not set, it is defaulted to 100.\u003c/p\u003e\n\u003cp\u003eIf the planner is run in optimal mode, it will stop searching for a solution if \u003ccode\u003ePLANLENGH\u003c/code\u003e is reached. I.e. it will only find a solution if there is one with length at most \u003ccode\u003ePLANLENGH\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-flag--o\" class=\"anchor\" aria-hidden=\"true\" href=\"#flag--o\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlag \u003ccode\u003e-o\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eBy default, powerlifted runs in \u003cstrong\u003esatisficing\u003c/strong\u003e mode.\nIn this mode, it will try to find a plan up to \u003ccode\u003ePLANLENGH\u003c/code\u003e.\nTo obtain a complete satisficing planner, you need to run powerlifted for \u003cstrong\u003emultiple\u003c/strong\u003e values of \u003ccode\u003ePLANLENGH\u003c/code\u003es as if there was a portfolio. We recommend to use \u003ccode\u003e10,25,50,100,200\u003c/code\u003e with assigning all runs equal time.\nNote that powerlifted can as of now, not run such a portfolio.\nThis is due to difficulties is setting timelimits to SAT-solver calls correctly.\u003c/p\u003e\n\u003cp\u003eIf the flag \u003ccode\u003e-o\u003c/code\u003e is provided, the powerlifted runs in optimal mode.\nIt will iterate over the length of the plan and return the shortest (in terms of number of actions) solution.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-flag--i\" class=\"anchor\" aria-hidden=\"true\" href=\"#flag--i\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlag \u003ccode\u003e-I\u003c/code\u003e\n\u003c/h3\u003e\n\u003cp\u003eThis flag sets the SAT planner to incremental mode. This does not affect the output of the planner, but may inpact its performance.\u003c/p\u003e\n\u003cp\u003eIncremental mode is only supported if the SAT solver that powerlifted was build with supports incremental solving.\nNote that \u003ccode\u003ekissat\u003c/code\u003e does not support incremental solving.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-additional-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-additional-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable \u003ccode\u003eADDITIONAL OPTIONS\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e[--translator-output-file TRANSLATOR_FILE]\u003c/code\u003e: Output of the intermediate representation to be parsed by the search component will be saved into \u003ccode\u003eTRANSLATOR_FILE\u003c/code\u003e. (Default: \u003ccode\u003eoutput.lifted\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--datalog-file DATALOG_FILE]\u003c/code\u003e: Datalog program used by the h-add heuristic will be saved into \u003ccode\u003eDATALOG_FILE\u003c/code\u003e. (Default: \u003ccode\u003emodel.lp\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--keep-action-predicates]\u003c/code\u003e: Keeps action predicates in the Datalog program\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--keep-duplicated-rules]\u003c/code\u003e: Keep duplicated Datalog rules in the Datalog program.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--add-inequalities]\u003c/code\u003e: Compile inequalities into an EDB predicate in the Datalog program and replace \u003ccode\u003e(not (= ?x ?y))\u003c/code\u003e atoms with this new EDB predicate in actions.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--validate]\u003c/code\u003e: Runs VAL after a plan is found to validate it. This requires\n\u003ca href=\"https://github.com/KCL-Planning/VAL\"\u003eVAL\u003c/a\u003e to be added as \u003ccode\u003evalidate\u003c/code\u003e to the \u003ccode\u003ePATH\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-powerlifted-as-a-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-powerlifted-as-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Powerlifted as a Singularity container\u003c/h2\u003e\n\u003cp\u003eYou can also build a Singularity image to run the planner. This might be useful\nin the case where you are not able to compile the planner locally, for\nexample. To do so, first remove the \u003ccode\u003ebuilds/\u003c/code\u003e directory, in case you have any\nbuilds already in your system. Then, you can run the following command to create\nthe planner image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e sudo singularity build powerlifted.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBe aware that this might take a while. Once the image \u003ccode\u003epowerlifted.sif\u003c/code\u003e is\ncreated, you can run it with the same parameters as the \u003ccode\u003epowerlifted.py\u003c/code\u003e\nscript. The only exception is that, by default, VAL is not installed in the\ncontainer, so it is not possible to use the \u003ccode\u003e--validate\u003c/code\u003e flag with the\nSingularity image. However, you can run VAL with the \u003ccode\u003esas_plan\u003c/code\u003e file created by\nthe planner after the execution. The following command is a usage example on\nhow to run the planner with the Singularity image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./powerlifted.sif -i /path/to/instance.pddl -s lazy-po -e add -g yannakakis --datalog-file model.lp --translator-output-file output.lifted\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-components\" class=\"anchor\" aria-hidden=\"true\" href=\"#components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComponents\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTranslator\u003c/li\u003e\n\u003cli\u003eSearch component\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eA C++17-compliant compiler\u003c/li\u003e\n\u003cli\u003eCMake 3.9+\u003c/li\u003e\n\u003cli\u003ePython 3.5+\u003c/li\u003e\n\u003cli\u003eBoost\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-limitations\" class=\"anchor\" aria-hidden=\"true\" href=\"#limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLimitations\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAxioms\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConditional effects\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCosts\u003c/strong\u003e: ignored\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNegated preconditions\u003c/strong\u003e: only inequality\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQuantifiers\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Pommerening, F.; Helmert, M.; and Franc\u00e8s, G. 2020. Lifted Successor Generation using Query Optimization Techniques. In Proc. ICAPS 2020, pp. 80-89. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-et-al-icaps2020.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Pommerening, F.; Helmert, M.; and Franc\u00e8s, G. 2020. Code from the paper \"Lifted Successor Generationusing Query Optimization Techniques\". \u003ca href=\"https://doi.org/10.5281/zenodo.3687008\" rel=\"nofollow\"\u003ehttps://doi.org/10.5281/zenodo.3687008\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Franc\u00e8s, G.; Pommerening, F.; and Helmert, M. 2021. Delete-Relaxation Heuristics for Lifted Classical Planning. In Proc. ICAPS 2021. (To appear)\u003c/li\u003e\n\u003c/ol\u003e\n", - "stargazers_count": 2, - "subscribers_count": 1, - "topics": [], - "updated_at": 1679670069.0 + "updated_at": 1674040676.0 }, { "data_format": 2, - "description": "GeoEDF Processors", + "description": "Singularity recipes", "filenames": [ - "investmodel/Singularity", - "simplegtool/Singularity" + "Singularity.sex_classifier_v0.1", + "Singularity.scRNAseq", + "Singularity.cytofpipe", + "Singularity.env", + "Singularity.sex_classifier_v0.2", + "Singularity.cytofpipe_v2_1" ], - "full_name": "geoedf/processors", + "full_name": "lconde-ucl/singularity_recipes", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/geoedf/processors/workflows/buildplugins/badge.svg\"\u003e\u003cimg src=\"https://github.com/geoedf/processors/workflows/buildplugins/badge.svg\" alt=\"buildplugins\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-geoedf-processor-plugins\" class=\"anchor\" aria-hidden=\"true\" href=\"#geoedf-processor-plugins\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeoEDF Processor Plugins\u003c/h1\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1673879646.0 + "updated_at": 1694722164.0 }, { "data_format": 2, - "description": null, + "description": "Guppy is a data processing toolkit that contains the Oxford Nanopore Technologies\u2019 basecalling algorithms.", "filenames": [ - "container/Singularity" + "6.0.0/Singularity" ], - "full_name": "Clinical-Genomics-Lund/nextflow_microwgs", + "full_name": "pscedu/singularity-guppy-gpu", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nextflow-pipeline-for-typing-and-marker-detection-of-bacteria\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-for-typing-and-marker-detection-of-bacteria\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enextflow pipeline for typing and marker detection of bacteria\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-hidden=\"true\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePurpose\u003c/h2\u003e\n\u003cp\u003eThe pipeline is aimed at producing data useful for epidemiological and surveillance purposes.\nIn v1 the pipeline is only tested using MRSA, but it should work well with\nany bacteria having a good cgMLST scheme.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-components\" class=\"anchor\" aria-hidden=\"true\" href=\"#components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eComponents\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-qc\" class=\"anchor\" aria-hidden=\"true\" href=\"#qc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQC\u003c/h3\u003e\n\u003cp\u003eSpecies detection is performed using \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e together with \u003ca href=\"https://ccb.jhu.edu/software/bracken/\" rel=\"nofollow\"\u003eBracken\u003c/a\u003e.\nThe database used is a standard Kraken database built with \u003ccode\u003ekraken2-build --standard --db $DBNAME\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eLow levels of Intra-species contamination or erronous mapping is removed using bwa and filtering away\nthe heterozygous mapped bases.\u003c/p\u003e\n\u003cp\u003eGenome coverage is estimated by mapping with \u003ca href=\"https://github.com/lh3/bwa\"\u003ebwa mem\u003c/a\u003e and using a bed file containing the cgMLST loci.\u003c/p\u003e\n\u003cp\u003eA value on the evenness of coverage is calculated as an \u003ca href=\"https://en.wikipedia.org/wiki/Interquartile_range\" rel=\"nofollow\"\u003einterquartile range\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-epidemiological-typing\" class=\"anchor\" aria-hidden=\"true\" href=\"#epidemiological-typing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEpidemiological typing\u003c/h3\u003e\n\u003cp\u003eFor de novo asspembly \u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003eSPAdes\u003c/a\u003e is used. \u003ca href=\"http://cab.spbu.ru/software/quast/\" rel=\"nofollow\"\u003eQUAST\u003c/a\u003e\nis used for extraxting QC data from the assembly.\u003c/p\u003e\n\u003cp\u003eThe cgMLST reference scheme used, is branched off \u003ca href=\"https://www.cgmlst.org/ncs/schema/141106/\" rel=\"nofollow\"\u003ecgmlst.net\u003c/a\u003e\nAt the moment this fork is not synced back with new allele numbers. For extracting alleles \u003ca href=\"https://github.com/B-UMMI/chewBBACA/wiki\"\u003echewBBACA\u003c/a\u003e\nis used. Number of missing loci is calculated and used as a QC parameter.\u003c/p\u003e\n\u003cp\u003eTraditional 7-locus MLST is calculated using \u003ca href=\"https://github.com/tseemann/mlst\"\u003emlst\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-virulence-and-resistance-markers\" class=\"anchor\" aria-hidden=\"true\" href=\"#virulence-and-resistance-markers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVirulence and resistance markers\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sanger-pathogens/ariba\"\u003eARIBA\u003c/a\u003e is used as the tool to detect genetic markes.\nThe database for virulence markes is \u003ca href=\"http://www.mgc.ac.cn/VFs/\" rel=\"nofollow\"\u003eVFDB\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-report-and-visualisation\" class=\"anchor\" aria-hidden=\"true\" href=\"#report-and-visualisation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReport and visualisation\u003c/h2\u003e\n\u003cp\u003eThe QC data is aggregated in a web service CDM (repo coming) and the cgMLST is visualized using a web service\ncgviz that is combined with \u003ca href=\"https://github.com/achtman-lab/GrapeTree\"\u003egraptetree\u003c/a\u003e for manipulating trees (repo coming).\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-guppy/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-guppy/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-guppy/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-guppy/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/acd271ee64be7e03cce0aa20980a61a37f2a803c6c21475e4f5e2e750f91b847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/acd271ee64be7e03cce0aa20980a61a37f2a803c6c21475e4f5e2e750f91b847/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ce3bd0aacdb7b3b72cb6bf39ce61e5bb1cc29a5784ded8bb4f63d60ba37d4e96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce3bd0aacdb7b3b72cb6bf39ce61e5bb1cc29a5784ded8bb4f63d60ba37d4e96/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b6e3ded696588df6d0e3326ff98a3915f0822c93b919f7af672f1bbb878a4fac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6e3ded696588df6d0e3326ff98a3915f0822c93b919f7af672f1bbb878a4fac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d6775707079\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3fe9defc0e02f64e7c2aa7b7bb20b84f9a2e6f0239ee4e213edd1a550157e8c0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6775707079\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3fe9defc0e02f64e7c2aa7b7bb20b84f9a2e6f0239ee4e213edd1a550157e8c0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d6775707079\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-guppy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-guppy\u003c/h1\u003e\u003ca id=\"user-content-singularity-guppy\" class=\"anchor\" aria-label=\"Permalink: singularity-guppy\" href=\"#singularity-guppy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe for \u003ca href=\"https://community.nanoporetech.com/protocols/Guppy-protocol/v/gpb_2003_v1_revac_14dec2018/linux-guppy\" rel=\"nofollow\"\u003eguppy\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/guppy/6.0.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/guppy\u003c/code\u003e as \u003ccode\u003e6.0.0.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2022 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing\nCenter\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 4, - "topics": [], - "updated_at": 1645445879.0 - }, - { - "data_format": 2, - "description": "Singularity definition file for an EasyBuild container", - "filenames": [ - "Singularity" + "topics": [ + "singularity", + "bioinformatics" ], - "full_name": "GodloveD/EasyBuild", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-easybuild\" class=\"anchor\" aria-hidden=\"true\" href=\"#easybuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEasyBuild\u003c/h1\u003e\n\u003cp\u003eAn experimental approach to creating \u003ca href=\"https://github.com/singularityware/singularity\"\u003eSingularity\u003c/a\u003e containers using \u003ca href=\"https://github.com/easybuilders/easybuild-easyconfigs\"\u003eEasyBuild easyconfig files\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eJust make a new directory, copy an easyconfig file into that directory, copy the \u003ca href=\"/build.def\"\u003ebuild.def\u003c/a\u003e file to the same directory, cd to that location and finally \u003ccode\u003ecreate\u003c/code\u003e and \u003ccode\u003ebootstrap\u003c/code\u003e a Singularity container there. The contents of your easyconfig file will be executed inside the container to build and install your app within the Singularity image.\u003c/p\u003e\n", - "stargazers_count": 2, - "subscribers_count": 5, - "topics": [], - "updated_at": 1509102509.0 + "updated_at": 1712153143.0 }, { "data_format": 2, - "description": "Several basic templates that are meant to be used as the basis for other Singularity Spec files using nixpkgs", + "description": "Hybrid-FS: A planner for controlling hybrid systems specified in Functional STRIPS", "filenames": [ - "Singularity.nix_alpine_openmpi_6796a60398bb890002e7010593c14cf3731613a1", - "Singularity.nix_alpine_base_e51467b4ad06617b8b104f6c9066df915fb4dfbd", - "Singularity.nix_alpine_openmpi_743d51f9711fdb3f59b442641b8fa950e41128b9" + "Singularity" ], - "full_name": "federatedcloud/NixTemplates", + "full_name": "miquelramirez/hybrid-fs", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nixtemplates\" class=\"anchor\" aria-hidden=\"true\" href=\"#nixtemplates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNixTemplates\u003c/h1\u003e\n\u003cp\u003eSeveral basic templates that are meant to be used as the basis for other Singularity Spec files\nusing the \u003ca href=\"https://nixos.org/nix/\" rel=\"nofollow\"\u003eNix\u003c/a\u003e package manager.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-background\" class=\"anchor\" aria-hidden=\"true\" href=\"#background\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nix\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNix\u003c/h3\u003e\n\u003cp\u003eNix provides reproducible builds for software, all the way down to the system level.\nThis is done by way of keeping track of which commit revision of\n\u003ca href=\"https://github.com/nixos/nixpkgs\"\u003enixpkgs\u003c/a\u003e was used at the time of the build.\nThe user can also pin versions of particular software dependencies by\ncoding them into the nix expression (think build script).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eSingularity is like a Docker container, but without process isolation\n(at least \u003ca href=\"https://www.sylabs.io/guides/2.5.1/user-guide/appendix.html?highlight=containall#singularity-action-flags\" rel=\"nofollow\"\u003eby default\u003c/a\u003e).\nSo it isn\u0027t a process container, but it is a filesystem container.\nUnlike Docker, Singularity provides a non-layered filesystem. This may\nhave benefits for reproducibility, but also means increased file size if\na user is building multiple image files based on other singularity images.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nix-and-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix-and-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNix and Singularity\u003c/h3\u003e\n\u003cp\u003eNix wouldn\u0027t work well with layering anyway: one benefit of nix is the nix-store,\nwhich is a local store on the system which puts all builds of software that\nare hashed based on the nix expression being used to build the software, and any\ninputs to that nix expression (so you can have multiple alternative builds of the\nsame software). A single Singularity image, that holds a custom nix expression,\nshould be ideal to build an individual image for a particular use case, or even\nmultiple use cases: multiple use cases can be packaged in a single Singularity\nimage and separated by using different nix expressions: they all share the same\nnix store, so when there are common dependencies, no file system redundancy occurs.\u003c/p\u003e\n\u003cp\u003eIn short, Nix provides build-level customization and reproducibility, which is important\nfor future work on the project to proceed smooth, whereas Singularity provides\nan archive of the existing build state, that is important for both immediate usage,\nand as a last resort for users who can\u0027t get the build procedure to work down the\nroad for some unforseen reason.\u003c/p\u003e\n\u003cp\u003eAn advantage of using Nix is that users can also update their environment in a\nreproducible way without needing to change a Dockerfile or Singularity Recipe\nand build a new image (which may be inconvenient for some\nusers): if the user changes the nix expression for a given environment,\nany additional packages or modified versions of packages that are already installed\nare added to the nix store (\u003ccode\u003e/nix/store\u003c/code\u003e) immediately, and the user can check in their\nnix expressions (\u003ccode\u003e.nix\u003c/code\u003e files) to version control as needed.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building-images\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding Images\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-switching-the-base-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#switching-the-base-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSwitching the base image\u003c/h2\u003e\n\u003cp\u003eSince nix is used for package management, we support\nmultiple base images: currently Ubuntu and Alpine variants.\nTo use presets for these, select what you want in the \u003ccode\u003ebuild.sh\u003c/code\u003e\nscripts, e.g., one of:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource \"alpine_envs.sh\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource \"ubuntu_envs.sh\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlpine is the default. You may also wish to create your own variant.\u003c/p\u003e\n\u003cp\u003eYou may need to make a separate copy or clone of the repo and checkout out the\n\u003ccode\u003ehash\u003c/code\u003e corresponding to the \u003ccode\u003ehash\u003c/code\u003e in \u003ccode\u003eFROM nix_${BASEOS}_base:hash\u003c/code\u003e in\n\u003ccode\u003eDocker/OpenMPI/Dockerfile\u003c/code\u003e, and build the base as specified in the next step,\nassuming you can\u0027t pull it from a Docker registry such as DockerHub.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nix_base\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix_base\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enix_base\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-and-running\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-and-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding and Running\u003c/h4\u003e\n\u003cp\u003eMake sure to subsitute the appropriate image name in the second command\n(check your image list with \u003ccode\u003edocker images | head\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e build-base.sh\ndocker run -i -t nix_alpine_base:abbaed5833f75be43892ccfc5999bd8f03f9583b_testing /bin/sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eChoose one of the alternatives below (running from Singularity Hub or Building and Running).\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-running-from-singularity-hub\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning from Singularity Hub\u003c/h4\u003e\n\u003cp\u003eFirst visit the \u003ca href=\"https://www.singularity-hub.org/collections/1220\" rel=\"nofollow\"\u003ecollection\u003c/a\u003e associated\nwith this repository on Singularity Hub. You\u0027ll notice that the Tag (Branch) may be truncated\ndue to the fact that we use the full commit hash. To see the full hash, click on the \"Complete\"\nbutton under the \"Status\" column for a recent base image, e.g., an image starting with\n\u003ccode\u003enix_alpine_base_\u003c/code\u003e under the \"Tag (Branch)\" column.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity image.create nix-overlay.img\nsingularity run --contain --overlay nix-overlay.img shub://federatedcloud/NixTemplates:nix_alpine_base_82b5d9a742ad593a353f6160bce846227a0f4e4d\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-and-running-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-and-running-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding And Running\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erm nix\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003ebase\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.img\n./build-base-singularity.sh\nsingularity image.create nix-base-overlay.img\nsingularity run --contain --overlay nix-base-overlay.img nix_alpine_base_82b5d9a742ad593a353f6160bce846227a0f4e4d.img\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e If you rebuild the image, you will likely need to either delete or move the old\nimage to another location, unless the git commit has change, in which case the image filename\nchanges automatically.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant note:\u003c/strong\u003e If you update a given singularity image, you will also\nlikely need to create a new overlay image to go along with it, otherwise you\nrisk undefined behavior.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-testing-nix\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-nix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting Nix\u003c/h3\u003e\n\u003cp\u003eOnce you have build an image and started a container as above, you can test it out by installing\nyour favorite tool (for instance ripgrep\u0027s \u003ccode\u003erg\u003c/code\u003e command) into your environment using Nix:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enix-env -i ripgrep\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nix_openmpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#nix_openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enix_openmpi\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eSimple build\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e build-openmpi.sh \u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-testing-openmpi\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting OpenMPI\u003c/h4\u003e\n\u003cp\u003eNote this will call the above OpenMPI \u003ccode\u003ebuild-base.sh\u003c/code\u003e, so no need to do both:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e docker-compose-openmpi.sh up --scale mpi_head=1 --scale mpi_node=3\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow from another terminal on the host system: 1) connect to the head node,\n2) start the relevant environment with \u003ccode\u003enix-shell\u003c/code\u003e, and 3) run the mpi demo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker exec -u nixuser -it nixtemplates_mpi_head_1 /bin/sh\nnix-shell . # should be from /nixenv/nixuser, or wherever default.nix was copied to\nmpirun -n 2 python /home/nixuser/mpi4py_benchmarks/all_tests.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo stop the container set, just press \u003ccode\u003eCtrl-C\u003c/code\u003e in the terminal where you ran\n\u003ccode\u003edocker-compose-openmpi.sh\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eChoose one of the alternatives below (running from Singularity Hub or Building and Running).\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-running-from-singularity-hub-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-from-singularity-hub-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning from Singularity Hub\u003c/h4\u003e\n\u003cp\u003eSee instructions \u003ca href=\"#nix_base\"\u003eabove\u003c/a\u003e for how to use singularity hub in general with this repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity image.create -s 4096 nix-overlay.img\nsingularity run --contain --overlay nix-overlay.img shub://federatedcloud/NixTemplates:nix_alpine_openmpi_6796a60398bb890002e7010593c14cf3731613a1\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-building-and-running-2\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-and-running-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding And Running\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erm \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.img\n./build-openmpi-singularity.sh\nsingularity image.create -s 4096 nix-overlay.img\nsingularity run --contain --overlay nix-overlay.img nix_alpine_openmpi_6796a60398bb890002e7010593c14cf3731613a1.img\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-testing-openmpi-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#testing-openmpi-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTesting OpenMPI\u003c/h4\u003e\n\u003cp\u003eYou will be dropped into a nix-shell, which in this template, sets up python and releveant libraries\nsuch as mpi4py.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003empirun -n 2 python /nixenv/nixuser/mpi4py_benchmarks/all_tests.py\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe FS Functional STRIPS planner\u003c/h1\u003e\u003ca id=\"user-content-the-fs-functional-strips-planner\" class=\"anchor\" aria-label=\"Permalink: The FS Functional STRIPS planner\" href=\"#the-fs-functional-strips-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eFS\u003c/code\u003e is a classical planner that works with the Functional STRIPS planning language \u003ca href=\"#ref-geffner-fstrips-2000\"\u003e[Geffner, 2000]\u003c/a\u003e,\na modeling language based on the quantifier-free\nfragment of first-order logic that includes constant, function and predicate symbols, but no variable symbols. The increased expressiveness\nof the Functional STRIPS language with respect to propositional languages such as standard STRIPS (which is indeed subsumed by Functional STRIPS)\noften results in problem encodings which are more compact, more readable, have fewer ground actions\nand preserve the structural properties of the problem in a manner which allows the derivation of more effective heuristics.\u003c/p\u003e\n\u003cp\u003eAlong with the core of the Functional STRIPS language, the \u003ccode\u003eFS\u003c/code\u003e planner supports certain extensions which are useful both\nfrom the expressive \u003cem\u003eand\u003c/em\u003e the computational point of view. These include \u003cem\u003eexistential quantification\u003c/em\u003e,\n\u003cem\u003estate constraints\u003c/em\u003e, a fairly large library of \u003cem\u003eglobal constraints\u003c/em\u003e, and the possibility of using \u003cem\u003eexternally-defined symbols\u003c/em\u003e\nand \u003cem\u003ebuilt-in arithmetic symbols\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThis documentation covers a number of practical issues related to the use of the \u003ccode\u003eFS\u003c/code\u003e planner. The planner, however, has\nbeen used and described in a number of academic publications that \u003ca href=\"http://gfrances.github.io/pubs/\" rel=\"nofollow\"\u003ecan be found here\u003c/a\u003e,\nthe most recent of which are \u003ca href=\"#ref-frances-modeling-2015\"\u003e[Franc\u00e8s and Geffner, 2015]\u003c/a\u003e and \u003ca href=\"#ref-frances-existential-2016\"\u003e[Franc\u00e8s and Geffner, 2016a]\u003c/a\u003e\nand \u003ca href=\"#ref-frances-effective-2016\"\u003e[Franc\u00e8s and Geffner, 2016b]\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to use the planner is by \u003ca href=\"doc/installation.md\"\u003emanually compiling the planner source code\u003c/a\u003e.\nThis procedure is complemented by the \u003ca href=\"doc/hybrid.md\"\u003einstructions specific for setting up the hybrid module\u003c/a\u003e of \u003ccode\u003eFS\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can find a high-level overview of the planner usage options \u003ca href=\"doc/usage.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-credits\"\u003e\u003c/a\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eFS\u003c/code\u003e planner is partially built upon the \u003ca href=\"http://www.lapkt.org\" rel=\"nofollow\"\u003eLightweight Automated Planning Toolkit\u003c/a\u003e\nand the PDDL parser from the \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003eFast Downward\u003c/a\u003e distribution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-modeling-2015\"\u003eFranc\u00e8s, G., and Geffner, H. (2015)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2015-icaps-better-heuristics-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eModeling and Computation in Planning: Better Heuristics from More Expressive Languages\u003c/em\u003e\u003c/a\u003e, ICAPS 2015.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-existential-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016a)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-existential-quantification-planning-csp/\" rel=\"nofollow\"\u003e\u003cem\u003eE-STRIPS: Existential Quantification in Planning and Constraint Satisfaction\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-effective-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016b)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-effective-planning-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eEffective Planning with More Expressive Languages\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-geffner-fstrips-2000\"\u003eGeffner, H. (2000)\u003c/a\u003e,\n\u003ca href=\"http://www.tecn.upf.es/~hgeffner/\" rel=\"nofollow\"\u003e\u003cem\u003eFunctional STRIPS: A more flexible lan-\nguage for planning and problem solving\u003c/em\u003e\u003c/a\u003e.\nIn Minker, J., ed., Logic-Based Artificial Intelligence. Kluwer. 187\u2013205.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 10, + "subscribers_count": 4, "topics": [ - "docker", - "singularity", - "nix", - "containerization" + "cplusplus-14", + "ai", + "planning", + "hybrid-systems", + "kinodynamic-planning" ], - "updated_at": 1650310770.0 + "updated_at": 1570694552.0 }, { "data_format": 2, - "description": "Singularity containers for Cylc", + "description": "Singularity and Docker containers for LAMMPS", "filenames": [ - "Singularity-cylc-7.8.1", - "Singularity-cylc-flow-8.0a1" + "Singularity_lammps_serial.cfg", + "Singularity", + "Singularity_lammps_mpi.cfg" ], - "full_name": "kinow/cylc-singularity", + "full_name": "cbgeo-archives/lammps-container", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cylc-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#cylc-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCylc Singularity Container\u003c/h1\u003e\n\u003cp\u003eExample \u003ca href=\"https://www.sylabs.io/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e container for \u003ca href=\"https://cylc.github.io/cylc/\" rel=\"nofollow\"\u003eCylc\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/kinow/cylc-docker\"\u003ethis repository\u003c/a\u003e for Docker images.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-creating-the-container-for-cylc-781\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-the-container-for-cylc-781\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating the container for Cylc 7.8.1\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build cylc-7.8.1.simg Singularity-cylc-7.8.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the command is done, you should have a binary called \u003ccode\u003ecylc-7.8.1.simg\u003c/code\u003e. You can\nthen execute Cylc with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./cylc-7.8.1.simg check-software\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr even register suites, and run Cylc as you would normally run it. You can rename it to \u003ccode\u003ecylc\u003c/code\u003e\nand store somewhere in your \u003ccode\u003e$PATH\u003c/code\u003e, without having to really install Cylc.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-accessing-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#accessing-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAccessing the container\u003c/h2\u003e\n\u003cp\u003eAssuming you have installed Singularity, try the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell cylc-7.8.1.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat should give you a shell within the container.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker/Singularity Container image for LAMMPS\u003c/h1\u003e\u003ca id=\"user-content-dockersingularity-container-image-for-lammps\" class=\"anchor\" aria-label=\"Permalink: Docker/Singularity Container image for LAMMPS\" href=\"#dockersingularity-container-image-for-lammps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCB-Geo\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eMaster branch: Docker and Serial LAMMPS code\u003c/li\u003e\n\u003cli\u003eMPI branch: Parallel LAMMPS version\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [ - "singularity", - "cylc", - "docker", - "containers", - "workflow", - "scheduler", - "singularity-container", - "singularity-containers" + "container" ], - "updated_at": 1577583586.0 + "updated_at": 1674481948.0 }, { "data_format": 2, - "description": "Making docker images for NANOGrav analyses in the cloud", + "description": "Code to generate publication figures and tables from output of tb-amr-benchmarking", "filenames": [ - "enterprise-singularity/Singularity" + "Singularity" ], - "full_name": "nanograv/nanodocker", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nanodocker\" class=\"anchor\" aria-hidden=\"true\" href=\"#nanodocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enanodocker\u003c/h1\u003e\n\u003cp\u003eA repository of Dockerfiles for NANOGrav DWG Docker images.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nanograv-stochastic-user\" class=\"anchor\" aria-hidden=\"true\" href=\"#nanograv-stochastic-user\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003enanograv-stochastic-user\u003c/code\u003e\u003c/h2\u003e\n\u003cp\u003eCurrently the \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e image (available as \u003ccode\u003emicvallis/nanograv-stochastic-user:v1.7\u003c/code\u003e at the \u003ca href=\"https://hub.docker.com/r/micvallis/nanograv-stochastic/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e) includes \u003ca href=\"https://bitbucket.org/psrsoft/tempo2\" rel=\"nofollow\"\u003etempo2\u003c/a\u003e and \u003ca href=\"https://github.com/vallis/libstempo\"\u003elibstempo\u003c/a\u003e with the latest ephemeris functionality, \u003ca href=\"https://github.com/jellis18/PAL2\"\u003ePAL2\u003c/a\u003e, \u003ca href=\"https://github.com/stevertaylor/NX01\"\u003eNX01\u003c/a\u003e, \u003ca href=\"https://github.com/vhaasteren/piccard\"\u003ePiccard\u003c/a\u003e, and attendant Python packages (installed through \u003ca href=\"http://conda.pydata.org/miniconda.html\" rel=\"nofollow\"\u003eMiniconda\u003c/a\u003e), including \u003ca href=\"https://github.com/jellis18/PTMCMCSampler\"\u003ePTMCMCSampler\u003c/a\u003e. The image was built on top of \u003ca href=\"https://hub.docker.com/_/gcc\" rel=\"nofollow\"\u003egcc:4.9\u003c/a\u003e, and it weighs 4.5GB (roughly half from gcc and half from Anaconda packages). The image is meant to be used by the passwordless user \u003ccode\u003enanograv\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-other-images-nanograv-stochastic\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-images-nanograv-stochastic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther images: \u003ccode\u003enanograv-stochastic\u003c/code\u003e\n\u003c/h2\u003e\n\u003cp\u003eSame as \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, but most everything is installed by the root user. Potentially useful on clusters. The latest version, lagging slightly behind \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, is \u003ccode\u003emicvallis/nanograv-stochastic:v2.4\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quickstart-for-local-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#quickstart-for-local-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart for local use\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.docker.com/engine/installation\" rel=\"nofollow\"\u003eInstall Docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eStart Docker.\u003c/li\u003e\n\u003cli\u003ePull the repository and run jupyter notebook in a new container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull micvallis/nanograv-stochastic-user:v1.7\ndocker run -i -t -p 8888:8888 -u nanograv micvallis/nanograv-stochastic-user:v1.7 run_jupyter.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThen you can open a web browser at the address that appears on the screen, and gain access to a Jupyter notebook that can run the \u003ccode\u003elibstempo\u003c/code\u003e, \u003ccode\u003ePAL2\u003c/code\u003e, \u003ccode\u003eNX01\u003c/code\u003e, and \u003ccode\u003ePiccard\u003c/code\u003e demos.\u003c/li\u003e\n\u003cli\u003eIf you\u0027re using the older Docker Toolbox for Mac (and perhaps some versions on Windows), you need to point your browser to the IP address of the virtual machine, which you can see with \u003ccode\u003edocker-machine ip default\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eAlso, if you\u0027re already using port 8888 locally, you should remap the Docker port elsewhere, e.g., with \u003ccode\u003e-p 8890:8888\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDon\u0027t forget to remove your containers (\u003ccode\u003edocker ps -a; docker rm ...\u003c/code\u003e) once you\u0027re done.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tips-and-tricks\" class=\"anchor\" aria-hidden=\"true\" href=\"#tips-and-tricks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTips and Tricks\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003erun a terminal in an already running container.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it [container_ID] bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can replace \u003ccode\u003ebash\u003c/code\u003e with any terminal command.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003emount a local directory in a container.\u003c/strong\u003e\nWhen you run a new container all files you create will be available to \u003cstrong\u003ethat\u003c/strong\u003e container.\nIf you start a second container or update a container all of your changes will be inaccessible.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run [the usual options] -v /my/local/dir:/home/nanograv/local_data/ run_jupyter.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYour local directory will appear in the home directory of the container as \u003ccode\u003elocal_data/\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003ereattach to a stopped container.\u003c/strong\u003e Use \u003ccode\u003edocker ps -a\u003c/code\u003e to see all containers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker start -a [container_ID] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start a stopped container (and see stdout)\u003c/span\u003e\ndocker attach [container_ID] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e attach this terminal to a container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can now navigate your browser to the displayed URL to reattach to the Jupyter notebook server.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003ecopy files to or from the local file system.\u003c/strong\u003e\nFrom a local terminal you can use \u003ccode\u003edocker cp [source] [dest]\u003c/code\u003e with the container ID.\n\u003ccode\u003edocker cp\u003c/code\u003e is recursive by default so it can copy full directories (unlike standard \u003ccode\u003ecp\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker cp /path/to/local/file [container_ID]:/path/in/container\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding notes\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.imcce.fr/fr/presentation/equipes/ASD/inpop/calceph\" rel=\"nofollow\"\u003eCalceph\u003c/a\u003e 2.4.2 is installed from sources, into \u003ccode\u003e/usr/local\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eline_profiler\u003c/code\u003e is installed with \u003ccode\u003epip\u003c/code\u003e, to work around an Anaconda problem.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escikit-sparse\u003c/code\u003e 0.31 is installed from the \u003ccode\u003emenpo\u003c/code\u003e repository, after \u003ccode\u003eapt-get\u003c/code\u003e-installing \u003ccode\u003eliblapack3\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eless\u003c/code\u003e, \u003ccode\u003egawk\u003c/code\u003e, and \u003ccode\u003evim\u003c/code\u003e are installed with \u003ccode\u003eapt-get\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, we are now pulling a specific version of \u003ccode\u003elibstempo\u003c/code\u003e, \u003ccode\u003ePAL2\u003c/code\u003e, and \u003ccode\u003eNX01\u003c/code\u003e, identified by SHA.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, we are now downloading extra ephemeris files.\u003c/li\u003e\n\u003cli\u003eWith \u003ccode\u003enanograv-stochastic-user\u003c/code\u003e, we now support prerequisites for \u003ca href=\"https://github.com/nanograv/enterprise\"\u003eEnterprise\u003c/a\u003e development, but you will have to check out \u003ccode\u003eEnterprise\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "iqbal-lab-org/tb-amr-benchmarking-paper", + "latest_release": "v0.0.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etb-amr-benchmarking-paper\u003c/h1\u003e\u003ca id=\"user-content-tb-amr-benchmarking-paper\" class=\"anchor\" aria-label=\"Permalink: tb-amr-benchmarking-paper\" href=\"#tb-amr-benchmarking-paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode to generate publication figures and tables from output of\n\u003ca href=\"https://github.com/iqbal-lab-org/tb-amr-benchmarking\"\u003etb-amr-benchmarking\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the singularity container by running this\nfrom the root of this repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build benchmark.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA JSON file made by the\n\u003ca href=\"https://github.com/iqbal-lab-org/tb-amr-benchmarking\"\u003etb-amr-benchmarking pipeline\u003c/a\u003e\nis required. Assuming it is called \u003ccode\u003esummary.json\u003c/code\u003e, generate the results and\nfigures by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec benchmark.img evalrescallers_make_figs_and_tables summary.json OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich makes a new directory called \u003ccode\u003eOUT/\u003c/code\u003e. Various stats were needed for the\nmain text of the paper, which were calculated by running this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd OUT\nsingularity exec benchmark.img gather_ad_hoc_numbers.sh\nsingularity exec benchmark.img estimate_xdr_accuracy.py\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 20, + "subscribers_count": 3, "topics": [], - "updated_at": 1574698460.0 + "updated_at": 1575373625.0 }, { "data_format": 2, - "description": "Think-Play-Hack: World Views", + "description": "A Nextflow framework for finemap", "filenames": [ - "containers/python/Singularity", - "containers/r/Singularity" + "Singularity.def" ], - "full_name": "SouthernMethodistUniversity/think-play-hack", + "full_name": "ikmb/finemapping", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-think-play-hack-world-views\" class=\"anchor\" aria-hidden=\"true\" href=\"#think-play-hack-world-views\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThink-Play-Hack: World Views\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-preparatory-readings\" class=\"anchor\" aria-hidden=\"true\" href=\"#preparatory-readings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparatory Readings\u003c/h2\u003e\n\u003cp\u003eDr. Guldi has compiled a list of readings to get your creative juices flowing. You can find them \u003ca href=\"https://www.dropbox.com/sh/ru4dxh6rr6uqvfl/AADlPVWVEZ1BE4OcxPnZ0dpDa?dl=0\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conference-activities\" class=\"anchor\" aria-hidden=\"true\" href=\"#conference-activities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConference activities\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.google.com/document/d/1PdxiKuEQFj0KIQbHnvfthqG7gtBYNlN3QZavc5g1T9M/edit?usp=sharing\" rel=\"nofollow\"\u003eWednesday hike\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs.google.com/document/d/1xDC_5mEJOZvfcWAvARDaLFl68Y44cDRh7jZJjpfUVKM/edit?usp=sharing\" rel=\"nofollow\"\u003eThursday rafting trip\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese two activities are here because they happen to be more technically complex. There are other opportunities that are being informally discussed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-slack-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#slack-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://get.slack.help/hc/en-us/articles/212675257\" rel=\"nofollow\"\u003eSlack Instructions\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eYou likely recieved an invitation to our Slack channel. This will be a good way to communicate with people at the conference and ask the Data Team questions if you need to. If you did not get an invite, just ask and we can send you one.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-github-instructions\" class=\"anchor\" aria-hidden=\"true\" href=\"#github-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://guides.github.com/activities/hello-world/\"\u003eGitHub Instructions\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThough not required to use this repository, having an account on GitHub is a good idea for any programmer. It provides you with a portfolio of projects you have worked on as well as a way to collaborate with other coders.\u003c/p\u003e\n\u003cp\u003eIt will also allow you to clone this repository as well as add issues and suggest changes for the data team.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware\u003c/h2\u003e\n\u003cp\u003eWe have ready-to-go software stacks for Python with Jupyter and R with RStudio.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-setup-for-personal-machines\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-setup-for-personal-machines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"docs/docker.md\"\u003eDocker Setup for Personal Machines\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eDocker is a tool that allows software to run on your computer without actually needing to install the full software. These instructions will guide you through setting up Docker and getting our image running on your personal machine.\u003c/p\u003e\n\u003cp\u003eWe have provided two images: one that runs R and RStudio and one that runs Python, Jupyter Notebooks and JupyterLab.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-using-maneframe-ii-m2\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-maneframe-ii-m2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"docs/m2.md\"\u003eUsing ManeFrame II (M2)\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://faculty.smu.edu/csc/documentation/about.html\" rel=\"nofollow\"\u003eManeFrame II (M2)\u003c/a\u003e is SMU\u0027s high performance computing (HPC) cluster. M2 features 11,000 cores, 60 NVIDIA V100 and P100 GPU accelerators, and 256 GB, 768 GB, and 1.5 TB memory configurations per node. Guest accounts on the cluster can be requested \u003ca href=\"https://smu.az1.qualtrics.com/jfe/form/SV_2i6o7BztWg52rK5\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-data-on-box\" class=\"anchor\" aria-hidden=\"true\" href=\"#data-on-box\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://smu.box.com/s/lk8mqfbgjproqda5jmlbynnbjclvybar\" rel=\"nofollow\"\u003eData on Box\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eWe have provided access to all the data for the event on Box. Given the size, consider what you might want to work on prior to downloading it. Should you have trouble, we have flash drives and hard drives with the data stored locally as well.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reddit\" class=\"anchor\" aria-hidden=\"true\" href=\"#reddit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"docs/reddit.md\"\u003eReddit\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eWe have over 1 TB of reddit data available in a database. You can \u003ca href=\"docs/reddit.md\"\u003eget subsets of this data\u003c/a\u003e for analysis.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-think-prompts\" class=\"anchor\" aria-hidden=\"true\" href=\"#think-prompts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThink Prompts\u003c/h2\u003e\n\u003cp\u003eIn case you are having trouble getting started:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCan one imagine developing a method to trace narrative elements across genres? How would you formalize \"narrative element\"?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHow do we align narrative elements with aspects of cultural ideology (norms, beliefs, values)?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat aspects of storytelling can we map? To what end? (i.e. can you imagine a new historic-geographic methodology?)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat impact do popular films (e.g. Snow White and the 7 Dwarves) have on traditional tales (and vice versa)?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCan we discover/trace the impact of traditional stories on literary works such as The Hobbit? On films?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhere did you find that lovely frosted mug filled with such an alluringly amber-hewed frothy brew? Were there nuts as well?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003efinemapping\u003c/h1\u003e\u003ca id=\"user-content-finemapping\" class=\"anchor\" aria-label=\"Permalink: finemapping\" href=\"#finemapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA Nextflow framework for finemap\u003c/h2\u003e\u003ca id=\"user-content-a-nextflow-framework-for-finemap\" class=\"anchor\" aria-label=\"Permalink: A Nextflow framework for finemap\" href=\"#a-nextflow-framework-for-finemap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation:\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation:\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload this pipeline locally or on the medcluster.\u003cbr\u003e\nInstall finemap_v1.4_x86_64.tgz within the bin folder so finemap is located in bin/finemap_v1.4_x86_64/finemap_v1.4_x86_64.\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage:\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline needs 6 input files:\u003cbr\u003e\n\u003cstrong\u003eReference\u003c/strong\u003e: in bim, bed, fam (3 files with same basename)\u003cbr\u003e\n\u003cstrong\u003eLocus-file\u003c/strong\u003e: csv-file setting the boundaries of the finemap plots; columns: chunk,NSNP,chr,st,sp,PPA_3\u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003echunk: iterate your loci like 1,2,3...\u003c/li\u003e\n\u003cli\u003eNSNP: currently not in use, put in NA\u003c/li\u003e\n\u003cli\u003echr: chromosome, like 12, not chr12, and 23 instead of X\u003c/li\u003e\n\u003cli\u003est: coordinate where plotting starts\u003c/li\u003e\n\u003cli\u003esp: coordinate where plotting ends\u003c/li\u003e\n\u003cli\u003ePPA_3: currently not in use, put in 1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSNP-List\u003c/strong\u003e: file with 1 snp per row, all other SNPs are excluded drom plotting and finemapping\u003cbr\u003e\n\u003cstrong\u003eSUMSTAT-FILE\u003c/strong\u003e: file containing following columns:\u003cbr\u003e\nCHR\tBP\tSNP\tA1\tA2\tP\tOR\tBETA\tSE\tN\tCHISQ\tZ\tSOURCE\tFRQ_A_A1\tFRQ_U_A1\tINFO\u003cbr\u003e\nonly CHR, BP, SNP, A1, A2, P, BETA, SE, FRQ_U_A1 are relevant, the other columns can be filled with NA.\nCall pipeline with:\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile standard --locus /home/user/finepipe/example/locusfile.sample --snps /home/user/finepipe/example/snplist.sample --reference /home/user/finepipe/example/GerNorItaSpa.chr3 --sumstats /home/user/finepipe/example/sumstats.sample --nsum 15743 --nsignal 1 --method sss -resume \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory:\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory:\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e--locus\u003c/strong\u003e \"path/to/locus.file\"\u003cbr\u003e\n\u003cstrong\u003e--snps\u003c/strong\u003e \"path/to/snp.list\"\u003cbr\u003e\n\u003cstrong\u003e--reference\u003c/strong\u003e \"path/to/bimbedfam\" (no file extension)\u003cbr\u003e\n\u003cstrong\u003e--sumstats\u003c/strong\u003e \"path/to/sumstat.file\"\u003cbr\u003e\n\u003cstrong\u003e--nsum\u003c/strong\u003e N of studysize\u003cbr\u003e\n\u003cstrong\u003e--method\u003c/strong\u003e \"sss\" or \"cond\"\u003cbr\u003e\n\u003cstrong\u003e--nsignal\u003c/strong\u003e N of max signals\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIf you run it locally, download the locuszoom database and set it with:\u003cbr\u003e\n\u003cstrong\u003e--locuszoomdb\u003c/strong\u003e \"/path/to/locuszoom/data/database/locuszoom_hg38.db\"\u003cbr\u003e\nand set profile to local:\u003cbr\u003e\n\u003cstrong\u003e-profile\u003c/strong\u003e \"local\" or \"standard\"\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional:\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional:\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e--dprime\u003c/strong\u003e sets ld method from the default r\u00b2 to dprime\u003cbr\u003e\n\u003cstrong\u003e--output\u003c/strong\u003e \"/path/to/output\" if not set output is baseDir/Results\u003cbr\u003e\n\u003cstrong\u003e-resume\u003c/strong\u003e Continue a run with cached processes\u003cbr\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1566336477.0 + "updated_at": 1724382798.0 }, { "data_format": 2, - "description": "A Singularity image definition file built on top of the Ubuntu 20.04 docker image with R, RStudio Server, and additional linux dependencies for common R packages installed.", + "description": "Reproducing the Hydroshare research paper using Docker", "filenames": [ "Singularity" ], - "full_name": "j-andrews7/singularity-rstudio", + "full_name": "Hydrocarpentry/project_pydocker", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity RStudio Server\u003c/h1\u003e\n\u003cp\u003eThis repo contains a Singularity file that contains R 4.1 and RStudio 1.4.1717. It has several additional linux dependencies installed that are required for common bioinformatics packages (openssl, libproj, libbz2, etc). If you have others you\u0027d like added, feel free to open a PR (or make your own fork and add whatever you need).\u003c/p\u003e\n\u003cp\u003eThe Singularity image for this can be pulled via \u003ccode\u003esingularity pull library://j-andrews7/default/rstudio:4.1.0\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis was mostly configured to run on HPCs in interactive jobs where users likely don\u0027t have the appropriate permissions for RStudio server to work properly. This requires a number of bindings to be made to the image and a secure cookie file to be provided. The cookie file can be produced with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Only needs to be run once.\nmkdir -p \"$HOME/rstudio-tmp/tmp/rstudio-server\"\nuuidgen \u0026gt; \"$HOME/rstudio-tmp/tmp/rstudio-server/secure-cookie-key\"\nchmod 0600 \"$HOME/rstudio-tmp/tmp/rstudio-server/secure-cookie-key\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn general, you can launch a script similar to the following from within an interactive job on your respective HPC to get it running, and it will print the IP address and port the server is running on that you can pop into your browser:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/sh\n\nworkdir=${HOME}/rstudio-tmp\n\nmkdir -p -m 700 ${workdir}/run ${workdir}/tmp ${workdir}/var/lib/rstudio-server \ncat \u0026gt; ${workdir}/database.conf \u0026lt;\u0026lt;END\nprovider=sqlite\ndirectory=/var/lib/rstudio-server\nEND\n\n# Set R_LIBS_USER to a path specific to rocker/rstudio to avoid conflicts with\n# personal libraries from any R installation in the host environment\ncat \u0026gt; ${workdir}/rsession.sh \u0026lt;\u0026lt;END\n#!/bin/sh\nexport R_LIBS_USER=${HOME}/R/rstudio/4.1\nexec rsession \"\\${@}\"\nEND\n\nchmod +x ${workdir}/rsession.sh\n\nexport SINGULARITY_BIND=\"${workdir}/run:/run,${workdir}/tmp:/tmp,${workdir}/database.conf:/etc/rstudio/database.conf,${workdir}/rsession.sh:/etc/rstudio/rsession.sh,${workdir}/var/lib/rstudio-server:/var/lib/rstudio-server\"\n\n# Do not suspend idle sessions.\n# Alternative to setting session-timeout-minutes=0 in /etc/rstudio/rsession.conf\n# https://github.com/rstudio/rstudio/blob/v1.4.1106/src/cpp/server/ServerSessionManager.cpp#L126\nexport SINGULARITYENV_RSTUDIO_SESSION_TIMEOUT=0\n\n# Get unused socket per https://unix.stackexchange.com/a/132524\n# Tiny race condition between the python \u0026amp; singularity commands\nreadonly PORT=$(python -c \u0027import socket; s=socket.socket(); s.bind((\"\", 0)); print(s.getsockname()[1]); s.close()\u0027)\n# Get node IP address.\nreadonly ADD=$(nslookup `hostname` | grep -i address | awk -F\" \" \u0027{print $2}\u0027 | awk -F# \u0027{print $1}\u0027 | tail -n 1)\n\ncat 1\u0026gt;\u0026amp;2 \u0026lt;\u0026lt;END\n\"Running RStudio at $ADD:$PORT\"\nEND\n\nsingularity exec --cleanenv rstudio_4.1.0.sif \\\n rserver --www-port ${PORT} \\\n --rsession-path=/etc/rstudio/rsession.sh\n --secure-cookie-key-file ${workdir}/tmp/rstudio-server/secure-cookie-key\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThis repo is distributed under the GNU-GPL3 license. See the LICENSE file for more details.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eproject_pydocker\u003c/h1\u003e\u003ca id=\"user-content-project_pydocker\" class=\"anchor\" aria-label=\"Permalink: project_pydocker\" href=\"#project_pydocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReproducing the Hydroshare research paper using Docker\u003c/h2\u003e\u003ca id=\"user-content-reproducing-the-hydroshare-research-paper-using-docker\" class=\"anchor\" aria-label=\"Permalink: Reproducing the Hydroshare research paper using Docker\" href=\"#reproducing-the-hydroshare-research-paper-using-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSource: Sadler, J. (2018). Data-driven street flood severity modeling in Norfolk, Virginia USA 2010-2016, HydroShare, \u003ca href=\"http://www.hydroshare.org/resource/9db60cf6c8394a0fa24777c8b9363a9b\" rel=\"nofollow\"\u003ehttp://www.hydroshare.org/resource/9db60cf6c8394a0fa24777c8b9363a9b\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e-- Scripts and data files are found in the following links:\nHydroshare website and Github repository.\n-- Following script files are needed for building the Docker image:\n\u003ccode\u003eprepare_flood_events_table.py; make_dly_obs_table_standalone.py; by_event_for_model.py; model_flood_counts_rf_ps_cln.r; plot_count_model_results.py; test.sh\u003c/code\u003e\n-- The following input data files are needed to be in a separate directory \u0027data\u0027 for building the Docker image:\n\u003ccode\u003eSTORM_data_flooded_streets_2010-2016.csv; hampt_rd_data.sqlite\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload the data \"Raw street flood report data from Norfolk,VA 2010-2016\" from the \u003ca href=\"https://github.com/Hydrocarpentry/reproduced_data/blob/master/STORM_data_flooded_streets_2010-2016.csv\"\u003eGitHub repository\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eDownload the data \"Hamton Roads Enfironmental Time Series Data\" from Hydroshare repository or from \u003ca href=\"https://osf.io/mr7jx/?action=download\" rel=\"nofollow\"\u003eOSF source\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e-- The Docker recipe file contains all the commands to use the above input script files and download input data, for building a container.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1685739677.0 + "updated_at": 1588066914.0 }, { "data_format": 2, - "description": "AMP MGMs", + "description": "Reinforcement Learning (RL) based Robot Navigation at Constrained Environment: team project for MEU5053.01-00", "filenames": [ - "tools/gentle/Singularity.recipe", - "tools/kaldi/Singularity.in" + "Singularityfile.def" ], - "full_name": "AudiovisualMetadataPlatform/amp_mgms", + "full_name": "msjun23/RL-based-Navigation", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-amp_mgms\" class=\"anchor\" aria-hidden=\"true\" href=\"#amp_mgms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eamp_mgms\u003c/h1\u003e\n\u003cp\u003eAMP MGMs\u003c/p\u003e\n\u003cp\u003eBuild all of the MGMs in one go.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h2\u003e\n\u003cp\u003eThis repo has several submodules, so check this out with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive \u0026lt;this_repo\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003erequirements\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003esingularity\u003c/li\u003e\n\u003cli\u003epython 3.6+\u003c/li\u003e\n\u003cli\u003emake\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-process\" class=\"anchor\" aria-hidden=\"true\" href=\"#process\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcess\u003c/h3\u003e\n\u003cp\u003eTo build the MGMs and install them in a directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./amp_build.py \u0026lt;destination directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo build the MGMs as a distributable package:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./amp_build.py --package \u0026lt;package_directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run unit tests on the MGMs (command help):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd tests/\n./run_tests.py -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor ex, to run unit tests on the MGMs installed in galaxy (local suite, gentle suite, or some particular test names in local suite):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./run_tests.py ../../galaxy/tools/amp_mgms/ local.yaml\n./run_tests.py ../../galaxy/tools/amp_mgms/ gentle.yaml\n./run_tests.py ../../galaxy/tools/amp_mgms/ local.yaml \u0027Adjust Diarization Timestamps\u0027 \u0027Adjust Transcript Timestamps\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-current-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#current-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCurrent status\u003c/h2\u003e\n\u003cp\u003eThis is the first pass to clean up the MGMs that were used during the\npilot and get them ready for production use.\u003c/p\u003e\n\u003cp\u003eThe goal for this first phase is to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eget all of the MGM (and singularity) sources into a single repository\u003c/li\u003e\n\u003cli\u003ea unified build process which will build the MGMs with one command\u003c/li\u003e\n\u003cli\u003ethe scripts should parse their arguments using argparse rather than\nsys.argv[] -- note that the conversion is hacky and certainly not\nbest practice.\u003c/li\u003e\n\u003cli\u003emove source files and modules around so they use python namespaces rather\nthan implied search paths\u003c/li\u003e\n\u003cli\u003eproper logging, rather than ovewriting sys.stderr and sys.stdout. Logs are\nwritten to the logs directory that is a peer of the script (if the\ndirectory exists) and stderr (always)\u003c/li\u003e\n\u003cli\u003esome tools require the galaxy root_dir variable. Is this really needed?\nTurns out, that no, it isn\u0027t.\u003c/li\u003e\n\u003cli\u003eamp_mgm.ini is a peer of the scripts. A sample is in the repository\u003c/li\u003e\n\u003cli\u003etool_conf.xml is a galaxy configuration file that can be used to insert\nthis toolset into galaxy.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-future-cleanup--work\" class=\"anchor\" aria-hidden=\"true\" href=\"#future-cleanup--work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFuture cleanup / work\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ereduce the size of the .sif files by cleaning up any intermediate build\nfiles.\u003c/li\u003e\n\u003cli\u003euse the args namespace directly, rather than that hacky tuple assignment\u003c/li\u003e\n\u003cli\u003eequivalent to \"make clean\" Right now, you have to remove the .sif files\nand the kaldi/exp2.tar.gz file manually.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLearning based Robot Navigation System in Constrained Environments\u003c/h1\u003e\u003ca id=\"user-content-learning-based-robot-navigation-system-in-constrained-environments\" class=\"anchor\" aria-label=\"Permalink: Learning based Robot Navigation System in Constrained Environments\" href=\"#learning-based-robot-navigation-system-in-constrained-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project was worked on \u0027Machine Learning and Programming(MEU5053.01-00)\u0027 class project\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSummary\u003c/h1\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-label=\"Permalink: Summary\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eOur goal is to navigate a mobile robot to its destination without any collisions.\nAt ICRA 2024, \u003ca href=\"https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge24.html\" rel=\"nofollow\"\u003ethe BARN challenge\u003c/a\u003e has been performing navigation missions on the simulated BARN dataset.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://cs.gmu.edu/~xiao/papers/hallucination.pdf\" rel=\"nofollow\"\u003eLFH(Learning from Hallucination)\u003c/a\u003e is a benchmark model applied in constrained environments, there is a mismatch with real-world environments and limited generalization.\u003c/p\u003e\n\u003cp\u003e\u2192 Utilize RL models to generalize of the navigation model\u003c/p\u003e\n\u003cp\u003e\u2192 Utilize sampling techniques to create a training dataset that allows the robot to learn a variety of constrained environments.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSimulation results showed that our model outperformed existing models in navigating from a start to a goal location quickly and without collisions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnvironment setup\u003c/h1\u003e\u003ca id=\"user-content-environment-setup\" class=\"anchor\" aria-label=\"Permalink: Environment setup\" href=\"#environment-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWe recommend using a conda environment.\u003c/li\u003e\n\u003cli\u003eTo train the model, \u003ca href=\"https://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e needs to be installed.\u003c/li\u003e\n\u003cli\u003eTo run inference (gazebo simulation), you need to install the \u003ca href=\"https://wiki.ros.org/melodic/Installation/Ubuntu\" rel=\"nofollow\"\u003eROS melodic\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create conda venv\u003c/span\u003e\nconda create -n jackal python=3.9\nconda activate jackal\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Python dependencies\u003c/span\u003e\npip3 install defusedxml rospkg netifaces numpy\npip3 install -r requirements.txt\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Get required packages\u003c/span\u003e\nmkdir -p jackal_ws/src\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e jackal_ws/src\n\ngit clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehttps://github.com/jackal/jackal.git\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --branch melodic-devel\ngit clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehttps://github.com/jackal/jackal_simulator.git\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --branch melodic-devel\ngit clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehttps://github.com/jackal/jackal_desktop.git\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --branch melodic-devel\ngit clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehttps://github.com/utexas-bwi/eband_local_planner.git\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e /home/dir/jackal_ws/\u003c/span\u003e\ncatkin_make\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTraining\u003c/h1\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To apply TD3 model training\u003c/span\u003e\npython3 train.py --config configs/e2e_default_TD3.yaml\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To apply SAC model training\u003c/span\u003e\npython3 train.py --config configs/e2e_default_SAC.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe simulation training was conducted on a system with the following specifications:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCPU: Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz\u003c/p\u003e\n\u003cp\u003eRAM: 128GB\u003c/p\u003e\n\u003cp\u003eGPU: RTX 3090 24GB\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003eOur training logs can be found at \u003ca href=\"https://github.com/msjun23/RL-based-Navigation/tree/main/logging\"\u003erl-based-navigation/logging/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\u003cstrong\u003eRun Simulations\u003c/strong\u003e\u003c/h1\u003e\u003ca id=\"user-content-run-simulations\" class=\"anchor\" aria-label=\"Permalink: Run Simulations\" href=\"#run-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 run.py --world_idx 201\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to see the simulation, use --gui option when do the above command. In this step, gazebo simulation will be launched and should be connected to any display. (I.e., At the GPU server or docker envs it may not be done)\u003c/p\u003e\n\u003cp\u003eA successful run should print the episode status (collided/succeeded/timeout) and the time cost in second:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation collided with time 11.2460 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation succeeded with time 13.5330 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation timeout with time 100.0000 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eOnce the tests finish, run\u00a0\u003ccode\u003epython report_test.py --out_path /path/to/out/file\u003c/code\u003e\u00a0to report the test\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython report_test.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see the report as this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAvg Time: 7.60, Avg Success: 0.9567, Avg Collision: 0.0230, Avg Timeout: 0.0203\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExperiments Results\u003c/h1\u003e\u003ca id=\"user-content-experiments-results\" class=\"anchor\" aria-label=\"Permalink: Experiments Results\" href=\"#experiments-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSimulation tests were conducted in a ROS Gazebo environment using 50 different BARN datasets.\u003c/li\u003e\n\u003cli\u003eWe compared \u003ca href=\"https://github.com/Daffan/the-barn-challenge/tree/main\"\u003eDWA(Dynamic Window Approach)\u003c/a\u003e, \u003ca href=\"https://github.com/Daffan/the-barn-challenge/tree/eband\"\u003eE-Band(Elastic Band)\u003c/a\u003e, and \u003ca href=\"https://github.com/Daffan/the-barn-challenge/tree/LfH\"\u003eLFH(Learning from Hallucination)\u003c/a\u003e models\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eLearning\u003c/th\u003e\n\u003cth\u003eAvg Time[s] \u2193\u003c/th\u003e\n\u003cth\u003eAvg Success[%] \u2191\u003c/th\u003e\n\u003cth\u003eAvg Collision[%] \u2193\u003c/th\u003e\n\u003cth\u003eAvg Timeout[%] \u2193\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eDWA\u003c/td\u003e\n\u003ctd\u003e\u2a09\u003c/td\u003e\n\u003ctd\u003e31.03\u003c/td\u003e\n\u003ctd\u003e95.40\u003c/td\u003e\n\u003ctd\u003e3.00\u003c/td\u003e\n\u003ctd\u003e1.60\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEband\u003c/td\u003e\n\u003ctd\u003e\u2a09\u003c/td\u003e\n\u003ctd\u003e24.55\u003c/td\u003e\n\u003ctd\u003e88.6\u003c/td\u003e\n\u003ctd\u003e3.8380\u003c/td\u003e\n\u003ctd\u003e7.60\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLfH\u003c/td\u003e\n\u003ctd\u003e\u25cb\u003c/td\u003e\n\u003ctd\u003e11.75\u003c/td\u003e\n\u003ctd\u003e81.8\u003c/td\u003e\n\u003ctd\u003e15.28\u003c/td\u003e\n\u003ctd\u003e2.92\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOurs(SAC)\u003c/td\u003e\n\u003ctd\u003e\u25cb\u003c/td\u003e\n\u003ctd\u003e9.02\u003c/td\u003e\n\u003ctd\u003e86.67\u003c/td\u003e\n\u003ctd\u003e13.00\u003c/td\u003e\n\u003ctd\u003e0.33\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOurs(TD3)\u003c/td\u003e\n\u003ctd\u003e\u25cb\u003c/td\u003e\n\u003ctd\u003e7.60\u003c/td\u003e\n\u003ctd\u003e95.67\u003c/td\u003e\n\u003ctd\u003e2.30\u003c/td\u003e\n\u003ctd\u003e2.03\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMap Generation\u003c/h1\u003e\u003ca id=\"user-content-map-generation\" class=\"anchor\" aria-label=\"Permalink: Map Generation\" href=\"#map-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to generate Map, please go to \u003ca href=\"https://github.com/msjun23/RL-based-Navigation/tree/main/map_gen\"\u003elink\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQnA\u003c/h1\u003e\u003ca id=\"user-content-qna\" class=\"anchor\" aria-label=\"Permalink: QnA\" href=\"#qna\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the novelty of your approach compared to existing methods in terms of RL settings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur approach advances upon existing methods, such as the Learning from Hallucination (LfH) algorithm, which uses neural networks to simulate constrained environments for training. While LfH addresses safety and data insufficiency by generating hallucinated constraints, it lacks time optimality, limiting its real-world applicability. In contrast, our method employs reinforcement learning (RL) algorithms like TD3 and SAC, combined with advanced sampling techniques to create a diverse training dataset. This enables our model to generalize better and achieve long-term optimization by learning from a wide variety of scenarios, thereby improving its robustness and efficiency in real-world environments. For instance, our experiments show a 25% increase in navigation speed and a 15% reduction in collision rates compared to LfH.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIs your approach more akin to global path planning or local path planning?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur approach is more akin to local path planning. While global path planning typically involves determining an initial path from start to goal over a large area, our method focuses on real-time decision-making and adjustments to the path based on immediate sensor inputs and environmental changes. This allows the reinforcement learning model to continuously adapt the robot\u2019s trajectory to navigate around obstacles and respond to dynamic environments effectively. This is particularly beneficial in constrained environments where real-time adaptability and responsiveness are crucial.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy do you think RL works better than other methods, and to what extent?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReinforcement learning (RL) surpasses other methods because it allows the model to learn optimal navigation strategies through interaction with the environment, receiving continuous feedback to refine its behavior. Unlike traditional algorithms that may struggle with unforeseen changes or require extensive tuning, RL models can dynamically adapt to complex and changing environments. Our simulation results demonstrate that RL models, particularly TD3 and SAC, significantly outperform traditional approaches, such as LfH, in terms of navigation speed and success rate. This enhanced performance is attributed to the RL models\u0027 ability to optimize long-term goals and adjust to various constraints, thus making them more effective in real-world applications.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCan it work for moving obstacles?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrently, our approach does not explicitly consider moving obstacles in the training process. Our RL model is designed to generate collision-free paths in constrained environments, focusing on static obstacles. However, we have explored the challenge of navigating dynamic obstacles using the DynaBARN framework, which involves avoiding obstacles moving in a straight line without uncertainty. In this context, our model showed successful navigation in the easy and some intermediate levels but experienced collisions in most intermediate and difficult levels. The collisions at higher difficulty levels are likely due to the increased complexity and unpredictability of obstacle movements, which our current model isn\u0027t trained to handle. Based on these findings, we plan to refine our model to enhance its robustness in dynamic environments and improve its ability to navigate through more complex scenarios. We plan future improvements to include training the model with dynamic obstacle simulations to better anticipate and react to moving obstacles.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eHow can you randomize the RL training for that for robustness?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enhance robustness, we plan to introduce a wider range of variations during the RL training process. This includes randomizing the starting points and goals for the robot, as well as varying the number, size, and speed of obstacles. Additionally, we will introduce different obstacle movement patterns and randomize environmental features. By exposing the model to a diverse set of training scenarios, we aim to improve its ability to generalize to new and unseen environments. This comprehensive approach will help ensure that the RL model can handle a variety of real-world conditions more effectively, thus increasing its overall performance in dynamic and unpredictable environments.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 2, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1679271531.0 + "updated_at": 1719328556.0 }, { "data_format": 2, - "description": null, + "description": "Single cell Nextflow cellbender pipeline.", "filenames": [ - "Singularity/Singularity.v1.0", - "Singularity/Singularity.v1.1" + "env/Singularity.preprocessing" ], - "full_name": "IARCbioinfo/fastqc-nf", - "latest_release": "v1.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fastqc-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#fastqc-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efastqc-nf\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quality-control-of-raw-sequencing-reads\" class=\"anchor\" aria-hidden=\"true\" href=\"#quality-control-of-raw-sequencing-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality control of raw sequencing reads\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/fastqc-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d355ed64b381b5e3e497a32c3b032d9becd558aebd39a0da28073fbe613dfd81/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f6661737471632d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/fastqc-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/fastqc-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4559\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/IARCbioinfo/fastqc-nf/blob/master/fastqc-nf.png\"\u003e\u003cimg src=\"https://github.com/IARCbioinfo/fastqc-nf/raw/master/fastqc-nf.png\" alt=\"fastqc-nf\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003ePerform quality control of Fasta files.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eFastQC: see official installation \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003cli\u003eMultiqc: see official installation \u003ca href=\"http://multiqc.info\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. You can avoid installing all the external software by only installing Docker (not available yet). See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-bam-input-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#bam-input-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBAM input files\u003c/h3\u003e\n\u003cp\u003eIn order to process BAM files, we convert fastq files to bam files with:\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003ca href=\"http://samtools.sourceforge.net/\" rel=\"nofollow\"\u003e\u003cem\u003esamtools\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing FASTQ files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003ePath to output folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eExample value\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ext\u003c/td\u003e\n\u003ctd\u003efastq.gz\u003c/td\u003e\n\u003ctd\u003eExtension of files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--multiqc_config\u003c/td\u003e\n\u003ctd\u003enone\u003c/td\u003e\n\u003ctd\u003econfig yaml file for multiqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eNumber of cpu used by fastqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e10\u003c/td\u003e\n\u003ctd\u003eSize of memory used for mapping (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h3\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003enextflow run IARCbioinfo/fastqc-nf -r v1.1 -profile singularity --input_folder input --output_folder results\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run the pipeline with docker or conda instead of singularity, just replace \"-profile singularity\" with \"-profile docker\" or \"-profile conda\", respectively. To run with your own local installation of softwares, just remove \"-profile singularity\"\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003emultiqc_fastqc_report.html\u003c/td\u003e\n\u003ctd\u003emultiQC report for fastQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emultiqc_fastqc_report_data\u003c/td\u003e\n\u003ctd\u003edata used for the multiQC report HTMLs\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@fellows.iarc.fr\"\u003eAlcalaN@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "full_name": "wtsi-hgi/nf_cellbender", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDescription\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe methods used in this module are described in \u003ccode\u003edocs/methods.pdf\u003c/code\u003e. TODO: \u003ccode\u003edocs/methods.pdf\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBelow is the structure of the results directory. The values that will be listed in \u003ccode\u003edescription_of_params\u003c/code\u003e within the directory structure correspond to the various parameters one can set. An example of a paramters file is found in \u003ccode\u003eexample_runtime_setup/params.yml\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enf-qc_cluster\n\u251c\u2500\u2500 normalization_001::description_of_params\n\u2502 \u251c\u2500\u2500 [files: data]\n\u2502 \u251c\u2500\u2500 reduced_dims-pca::description_of_params\n\u2502 \u2502 \u251c\u2500\u2500 [files: data]\n\u2502 \u2502 \u251c\u2500\u2500 [plots: umap]\n\u2502 \u2502 \u251c\u2500\u2500 cluster_001::description_of_params\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 [files: data,clusters]\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 [plots: umap]\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 cluster_markers_001::description_of_params\n\u2502 \u2502 \u2502 \u2502 \u251c\u2500\u2500 [files: cluster_marker_genes]\n\u2502 \u2502 \u2502 \u2502 \u2514\u2500\u2500 [plots: marker_genes,marker_genes_dotplot]\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 cluster_markers_002::description_of_params\n\u2502 \u2502 \u2502 ... etc. ...\n\u2502 \u2502 \u251c\u2500\u2500 cluster_002::description_of_params\n\u2502 \u2502 ... etc. ...\n\u2502 \u251c\u2500\u2500 reduced_dims-harmony_001::description_of_params\n\u2502 \u251c\u2500\u2500 reduced_dims-harmony_002::description_of_params\n\u2502 ... etc. ...\n\u251c\u2500\u2500 normalization_002::description_of_norm_params\n... etc. ...\n\u2514\u2500\u2500 adata.h5 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e concatenated single cell data with no normalization\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTODO list\u003c/h1\u003e\u003ca id=\"user-content-todo-list\" class=\"anchor\" aria-label=\"Permalink: TODO list\" href=\"#todo-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAdd \u003ccode\u003edocs/methods.pdf\u003c/code\u003e file.\u003c/li\u003e\n\u003cli\u003eAdd brief description of module.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnhancement list\u003c/h1\u003e\u003ca id=\"user-content-enhancement-list\" class=\"anchor\" aria-label=\"Permalink: Enhancement list\" href=\"#enhancement-list\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003escanpy_merge-dev.py\u003c/code\u003e: If it were important to have a per sample filter, merge could be re-designed to accommodate this.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escanpy_cluster.py\u003c/code\u003e: Currently for clustering, we can change method (leiden or louvain), resolution, and n_pcs. Are there other parameters that need to be scaled over?\u003c/li\u003e\n\u003cli\u003eCheck phenotypes against predicted sex from gene expression.\u003c/li\u003e\n\u003cli\u003eAdd basic QC plots - try to do this in R from anndata frame?\u003c/li\u003e\n\u003cli\u003eScrublet functionality + add to metadata + cluster distributions\u003c/li\u003e\n\u003cli\u003eGene scores + add to metadata\u003c/li\u003e\n\u003cli\u003eAdd marker gene AUC like here \u003ca href=\"http://www.nxn.se/valent/2018/3/5/actionable-scrna-seq-clusters\" rel=\"nofollow\"\u003ehttp://www.nxn.se/valent/2018/3/5/actionable-scrna-seq-clusters\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd summary ARI and LISI metrics computed over a list of many different cluster annotations?\u003c/li\u003e\n\u003cli\u003eAdd tSNE plots - rapid plots with OpenTSNE?\u003c/li\u003e\n\u003cli\u003eCalculate marker genes with diffxpy or logreg?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuickstart\u003c/h1\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eQuickstart for deploying this pipeline locally and on a high performance compute cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Set up the environment\u003c/h2\u003e\u003ca id=\"user-content-1-set-up-the-environment\" class=\"anchor\" aria-label=\"Permalink: 1. Set up the environment\" href=\"#1-set-up-the-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the required packages via conda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The repo directory.\u003c/span\u003e\nREPO_MODULE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HOME}\u003c/span\u003e/repo/path/to/this/pipeline\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install environment using Conda.\u003c/span\u003e\nconda env create --name sc_qc_cluster --file \u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/env/environment.yml\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Activate the new Conda environment.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate sc_qc_cluster\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To update environment file:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003econda env export --no-builds | grep -v prefix | grep -v name \u0026gt; environment.yml\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Prepare the input files\u003c/h2\u003e\u003ca id=\"user-content-2-prepare-the-input-files\" class=\"anchor\" aria-label=\"Permalink: 2. Prepare the input files\" href=\"#2-prepare-the-input-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate and/or edit input files for the pipeline.\u003c/p\u003e\n\u003cp\u003eThe pipeline takes as input:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003e--file_paths_10x\u003c/strong\u003e: Tab-delimited file containing experiment_id and data_path_10x_format columns (i.e., list of input samples). Reqired.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--file_metadata\u003c/strong\u003e: Tab-delimited file containing sample metadata. This will automatically be subset down to the sample list from 1. Reqired.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--file_sample_qc\u003c/strong\u003e: YAML file containing sample qc and filtering parameters. Optional. NOTE: in the example config file, this is part of the YAML file for \u003ccode\u003e-params-file\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--genes_exclude_hvg\u003c/strong\u003e: Tab-delimited file with genes to exclude from\nhighly variable gene list. Must contain ensembl_gene_id column. Optional.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--genes_score\u003c/strong\u003e: Tab-delimited file with genes to use to score cells. Must contain ensembl_gene_id and score_idvcolumns. If one score_id == \"cell_cycle\", then requires a grouping_id column with \"G2/M\" and \"S\" (see example file in \u003ccode\u003eexample_runtime_setup\u003c/code\u003e). Optional.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e-params-file\u003c/strong\u003e: YAML file containing analysis parameters. Optional.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--run_multiplet\u003c/strong\u003e: Flag to run multiplet analysis. Optional.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--file_cellmetadata\u003c/strong\u003e: Tab-delimited file containing experiment_id and data_path_cellmetadata columns. For instance this file can be used to pass per cell doublet annotations. Optional.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExamples of all of these files can be found in \u003ccode\u003eexample_runtime_setup/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Set up and run Nextflow\u003c/h2\u003e\u003ca id=\"user-content-3-set-up-and-run-nextflow\" class=\"anchor\" aria-label=\"Permalink: 3. Set up and run Nextflow\" href=\"#3-set-up-and-run-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun Nexflow locally (NOTE: if running on a virtual machine you may need to set \u003ccode\u003eexport QT_QPA_PLATFORM=\"offscreen\"\u003c/code\u003e for scanpy as described \u003ca href=\"https://github.com/ipython/ipython/issues/10627\"\u003ehere\u003c/a\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Boot up tmux session.\u003c/span\u003e\ntmux new -s nf\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Here we are not going to filter any variable genes, so don\u0027t pass a file.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: All input file paths should be full paths.\u003c/span\u003e\nnextflow run \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/main.nf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -profile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003elocal\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_paths_10x \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/file_paths_10x.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_metadata \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/file_metadata.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --genes_score \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/genes_score_v001.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -params-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/params.yml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun Nextflow using LSF on a compute cluster. More on bgroups \u003ca href=\"https://www.ibm.com/support/knowledgecenter/SSETD4_9.1.3/lsf_config_ref/lsb.params.default_jobgroup.5.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set the results directory.\u003c/span\u003e\nRESULTS_DIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/results/dir\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${RESULTS_DIR}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Boot up tmux session.\u003c/span\u003e\ntmux new -s nf\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Log into an interactive session.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: Here we set the -G parameter due to our institute\u0027s LSF configuration.\u003c/span\u003e\nbgadd \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/logins\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nbsub -q normal -G team152 -g /\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/logins -Is -XF -M 8192 -R \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eselect[mem\u0026gt;8192] rusage[mem=8192]\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e /bin/bash\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: If you are running over many cells, you may need to start an\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e interactive session on a queue that allows long jobs\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003ebsub -q long -G team152 -g /${USER}/logins -Is -XF -M 18192 -R \"select[mem\u0026gt;18192] rusage[mem=18192]\" /bin/bash\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Activate the Conda environment (inherited by subsequent jobs).\u003c/span\u003e\nconda activate sc_qc_cluster\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Set up a group to submit jobs to (export a default -g parameter).\u003c/span\u003e\nbgadd -L 500 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/nf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LSB_DEFAULT_JOBGROUP=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/nf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Depending on LSF setup, you may want to export a default -G parameter.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LSB_DEFAULTGROUP=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eteam152\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: By setting the above flags, all of the nextflow LSF jobs will have\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e these flags set.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Settings for scanpy (see note above).\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e QT_QPA_PLATFORM=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eoffscreen\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Change to a temporary runtime dir on the node. In this demo, we will change\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to the same execution directory.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${RESULTS_DIR}\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Remove old logs and nextflow output (if one previously ran nextflow in this\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e dir).\u003c/span\u003e\nrm -r \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003ehtml\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nrm .nextflow.log\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: If you want to resume a previous workflow, add -resume to the flag.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: If you do not want to filter any variable genes, pass an empty file to\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --genes_exclude_hvg. See previous local example.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: --output_dir should be a full path - not relative.\u003c/span\u003e\nnextflow run \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/main.nf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -profile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003elsf\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_paths_10x \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/file_paths_10x.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_metadata \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/file_metadata.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --file_sample_qc \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/params.yml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --genes_exclude_hvg \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/genes_remove_hvg_v001.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --genes_score \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/genes_score_v001.tsv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --output_dir \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${RESULTS_DIR}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --run_multiplet \\\n -params-file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${REPO_MODULE}\u003c/span\u003e/example_runtime_setup/params.yml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -with-report \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enf_report.html\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n -resume\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NOTE: If you would like to see the ongoing processes, look at the log files.\u003c/span\u003e\ncat .nextflow.log\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample of how one may sync results:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eNF_OUT_DIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/out_dir\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nrsync -am --include=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*.png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --include=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --exclude=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e my_cluster_ssh:\u003cspan class=\"pl-smi\"\u003e${NF_OUT_DIR}\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\nrsync -am --include=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*.png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --include=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --exclude=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e my_cluster_ssh:\u003cspan class=\"pl-smi\"\u003e${NF_OUT_DIR}\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNotes\u003c/h1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOn 10 April 2020, we found nextflow was writing some output into the \u003ccode\u003e${HOME}\u003c/code\u003e directory and had used up the alotted ~15Gb on the Sanger farm. This resulted in a Java error as soon as a nextflow command was executed. Based on file sizes within \u003ccode\u003e${HOME}\u003c/code\u003e, it seemed like the ouput was being written within the conda environment (following \u003ccode\u003edu -h | sort -V -k 1\u003c/code\u003e). By deleting and re-installing the coda environment, the problem was solved. The below flags may help prevent this from the future. In addition, setting the flag \u003ccode\u003eexport JAVA_OPTIONS=-Djava.io.tmpdir=/path/with/enough/space/\u003c/code\u003e may also help.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e To be run from the execution dir, before the above nextflow command\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you are running this on a cluster, make sure you log into an interactive\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e session with \u0026gt;25Gb of RAM.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_OPTS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e-Xms25G -Xmx25G\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_HOME=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_WORK=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${NXF_HOME}\u003c/span\u003e/.nexflow_work\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_TEMP=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${NXF_HOME}\u003c/span\u003e/.nexflow_temp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_CONDA_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${NXF_HOME}\u003c/span\u003e/.nexflow_conda\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_SINGULARITY_CACHEDIR=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${NXF_HOME}\u003c/span\u003e/.nexflow_singularity\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1658214757.0 + "updated_at": 1695377715.0 }, { "data_format": 2, - "description": "A modeling tool for CMUT non-linear dynamics and contact mechanics", + "description": "Recipes for singularity and docker containers used in CBRAIN", "filenames": [ - "containers/Singularity.clean", - "containers/Singularity" + "dcm2nii/Singularity.dcm2nii_v4AUGUST2014", + "QEEG/Singularity.qeeg.v1.0-gGit-S", + "ANTs/Singularity.ants_v2.1.0-gGIT-N", + "FreeSurfer/Singularity.FreeSurfer_v5.3", + "FSL/Singularity.fsl_v6.0.1", + "FSL/Singularity.fsl_v5.0.9" ], - "full_name": "bdshieh/cnl-dyna", + "full_name": "aces/cbrain-containers-recipes", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecbrain-containers-recipes\u003c/h1\u003e\u003ca id=\"user-content-cbrain-containers-recipes\" class=\"anchor\" aria-label=\"Permalink: cbrain-containers-recipes\" href=\"#cbrain-containers-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRecipes for singularity and docker containers used in CBRAIN\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 0, + "subscribers_count": 9, "topics": [ - "finite-element-methods", - "boundary-element-method", - "ultrasound" + "singularity", + "docker", + "cbrain" ], - "updated_at": 1636697475.0 + "updated_at": 1686284030.0 }, { "data_format": 2, - "description": null, + "description": "Planning tool to run planners and domains creating singularity containers", "filenames": [ - "misc/releases/19.06/Singularity.19.06", - "misc/releases/latest/Singularity", - "misc/releases/22.06/Singularity.22.06", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/20.06/Singularity.20.06" + "planners/OPTIC-Base/Singularity", + "planners/tfd/Singularity" ], - "full_name": "silvansievers/structural-symmetries-pruning", + "full_name": "momartinm/runPlanningTool", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-tested-software-versions\"\u003e\u003ca class=\"heading-link\" href=\"#tested-software-versions\"\u003eTested software versions\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2019 (MSVC 19.29) and 2022 (MSVC 19.31)\u003c/td\u003e\n\u003ctd\u003e3.22\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2 id=\"user-content-contributors\"\u003e\u003ca class=\"heading-link\" href=\"#contributors\"\u003eContributors\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-history\"\u003e\u003ca class=\"heading-link\" href=\"#history\"\u003eHistory\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erunPlanningTool\u003c/h1\u003e\u003ca id=\"user-content-runplanningtool\" class=\"anchor\" aria-label=\"Permalink: runPlanningTool\" href=\"#runplanningtool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlanning tool to run planners and domains creating singularity containers. This tool is based on the code from Florian Pommerening. This tool needs to be configure to work into the correct way.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisites\u003c/h3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstalling basic tools:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install automake python-setuptools python-dev build-essential python-pip libtool libarchive-dev bison flex\nsudo pip install --upgrade virtualenv \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstalling singularity:\u003c/p\u003e\n\u003cp\u003eThis repo includes a version of singularity. Then it is not necessary to clone the master repo.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/singularityware/singularity.git\ncd singularity\n./autogen.sh\n./configure --prefix=/usr/local\nmake\nsudo make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstalling virtualbox from repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install virtualbox\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstalling vagrant:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install vagrant\npip install python-vagrant\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstalling val (PDDL validator):\u003c/p\u003e\n\u003cp\u003eVal is a tool to validate the plans generate by a planner. A compile version is include to the repo at the main folder. But, I recomend to download and compile a new version.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd val\nmake\nmv validate ../\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreating benchmark files\u003c/h3\u003e\u003ca id=\"user-content-creating-benchmark-files\" class=\"anchor\" aria-label=\"Permalink: Creating benchmark files\" href=\"#creating-benchmark-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is necessary to configure the different benchmarks in order for them to be executed. A benchmark file must be created with the information of each instance using the same sintax:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# DomainID | folder | domain file | problem file | domain folder | problem folder | lb | up | b\n AGRICOLA , agricola , domain.pddl , p01.pddl , , , 0 , 0 , 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003elb: optimal plan cost lower bound\nup: optimal plan cost upper bound\nb: cost bound\u003c/p\u003e\n\u003cp\u003eWhere the first item is the key of the domain, all the instance of a domain must use the same key, the second is the name of the folder where the domains and instances are stored. The third is the name of domain file. The fourth is the name of the problem file. The fifth is the folder of the domain file. The sixth is the folder of the problem file. The last three are related with the cost of solving a specific instance (These three are not available in this first version). Comments can be includen into the file using the character \u0027#\u0027 at the begining of the line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreating planner files\u003c/h3\u003e\u003ca id=\"user-content-creating-planner-files\" class=\"anchor\" aria-label=\"Permalink: Creating planner files\" href=\"#creating-planner-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter this, it is necesary to define the different planners which are going to be used to solve the different benchmarks. A planner file must be created by the user in order to include the different planners following the next example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Planner ID | repo url | planner folder\n OPTIC-Base , , OPTIC-Base\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the first item is the name of the planner, the second is the url to the repository (GIT, BITBUCKET) where the planner is stored (this option is not available yet) and the third is the name of the folder where the source code of the planner is stored.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to use\u003c/h3\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinally the code can be executed using the python program called run_benchmarks.py. For example if we can run the full ipc 2018, we must use the same configuration\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e run_benchmarks.py -tipc2018 -pn OPTIC-Base\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere are different options to execute this software:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: run_benchmarks.py [-h] [-b file path] [-p file path] [-t] [-m] [-tmp] [-ipc2018]\n [-tipc2018] [-proc cpu numbers]\n [-pid Planner ID [Planner ID ...]]\n [-bid Benchmark ID [Benchmark ID ...]] [--v]\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlanning tool to run planners and domains using singularity containers. These are the different arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help show this help message and exit.\n-b benchmarks domains a path to the file with the information about the different benchmarks.\n-p planners a path to the file with the information about the different planners which \n can be executed.\n-t a number parameter (integer) which defines the maximum execution time in seconds.\n-m a number parameter (integer) which defines the maximun RAM memory avaliable in Gigabytes.\n-tmp a boolean parameter which activate temporal validation.\n-ipc2018 a boolean parameter which run the benchmarks from the ipc 2018.\n-tipc2018 a boolean parameter which run the benchmarks from the temporal ipc 2018.\n-proc cpu-numbers a number parameter which defines the maximum number of cpus (threads). \n Default value is value is 20.\n-pid [Planner ID ...] a list parameter which defines the names of the planner which are going \n to be executed.\n-bid [Benchmark ID ...] a list parameter which defines the names of the benchmarks which are \n going to be used.\n--v verbosity increase output verbosity.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMore will be created as we continue adding more domains.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the MIT License - see the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file for details\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFlorian Pommerening\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, "subscribers_count": 2, "topics": [], - "updated_at": 1651647698.0 - }, - { - "data_format": 2, - "description": "Visualizing mutations and PNGS changes between two strains", - "filenames": [ - "Singularity.def" - ], - "full_name": "cobeylab/flu_strain_compare", - "latest_release": "v0.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFlu Strain Compare\u003c/h1\u003e\u003ca id=\"user-content-flu-strain-compare\" class=\"anchor\" aria-label=\"Permalink: Flu Strain Compare\" href=\"#flu-strain-compare\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"example.png\"\u003e\u003cimg src=\"example.png\" alt=\"3D influenza image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePurpose\u003c/h2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-label=\"Permalink: Purpose\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFlu Strain Compare generates visualizations of mutations between pairs of HA sequences. \u003ccode\u003emake_comparison_figure.py\u003c/code\u003e takes two HA sequences as input and outputs a figure highlighting amino acid and PNGS changes on a representative HA crystal structure. At present, H1pdm and H3 strains are supported.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/figures/A_Cambodia_e0826360_2020-A_Darwin_6_2021-A_Tasmania_503_2020-7_others.png?raw=true\"\u003e\u003cimg src=\"/figures/A_Cambodia_e0826360_2020-A_Darwin_6_2021-A_Tasmania_503_2020-7_others.png?raw=true\" alt=\"Example image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis utility runs on Docker, Singularity, or natively with PyMOL installed as a Python module.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use the following command to build the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build ./ -t flu_strain_compare\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild Docker container above. This may need to be done locally if your HPC system doesn\u0027t allow Docker.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# load module, if in HPC environment\nmodule load singularity\nsingularity build ubuntu-pymol-biopython_latest.sif docker-daemon://flu_strain_compare:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNative\u003c/h3\u003e\u003ca id=\"user-content-native\" class=\"anchor\" aria-label=\"Permalink: Native\" href=\"#native\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall PyMOL as a Python library.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLinux/Unix\u003c/h4\u003e\u003ca id=\"user-content-linuxunix\" class=\"anchor\" aria-label=\"Permalink: Linux/Unix\" href=\"#linuxunix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# Optional, if using a virtual environment.\npython -m venv venv\nsource venv/bin/activate\n\n# Install python dependencies.\npip install Bio pandas\npip install scipy\n\n# Install PyMOL as a library.\nPYMOL_VERSION=2.5.0\nwget --no-verbose https://github.com/schrodinger/pymol-open-source/archive/refs/tags/v${PYMOL_VERSION}.tar.gz\ntar xfz v2.5.0.tar.gz\ncd pymol-open-source-2.5.0\n\ngit clone --depth=1 https://github.com/rcsb/mmtf-cpp.git\ncd mmtf-cpp\ngit pull\ncd ..\ncp -r mmtf-cpp/include/mmtf* include/\n\npython3 setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWindows\u003c/h4\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall python dependencies.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip install Bio pandas\npip install scipy\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to \u003ca href=\"https://github.com/schrodinger/pymol-open-source/archive/refs/tags/v2.5.0.tar.gz\"\u003ehttps://github.com/schrodinger/pymol-open-source/archive/refs/tags/v2.5.0.tar.gz\u003c/a\u003e in a browser and save the file.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUncompress v2.5.0.tar.gz using a compression utility such as \u003ca href=\"https://www.7-zip.org/\" rel=\"nofollow\"\u003e7-Zip\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone \u003ca href=\"https://github.com/rcsb/mmtf-cpp.git\"\u003ehttps://github.com/rcsb/mmtf-cpp.git\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCopy \u003ccode\u003emmtf-cpp/include/mmtf/\u003c/code\u003e and \u003ccode\u003emmtf-cpp/include/mmtf.hpp\u003c/code\u003e to the \u003ccode\u003einclude/\u003c/code\u003e directory in \u003ccode\u003epymol-open-source-v2.5.0\u003c/code\u003e (the directory in the decompressed archive above).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall PyMOL as a library.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epython3 setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration\u003c/h2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-label=\"Permalink: Configuration\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe configuration file specified in the command line (see \u003ca href=\"#running\"\u003eRunning\u003c/a\u003e below) serves as input for the \u003ccode\u003emake_comparison_figure.py\u003c/code\u003e script.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eseq_file\u003c/code\u003e: Name of fasta-formatted file that contains full-length amino acid HA sequences. File must be in \u003ccode\u003edata\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eq1_id\u003c/code\u003e: Sequence ID of the first query strain. The sequence id is the first word in the fasta header of the desired sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eq2_id\u003c/code\u003e: Sequence ID of the second query strain.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eseq_lineage\u003c/code\u003e: Lineage of the query strains. \u003ccode\u003e\"H1\"\u003c/code\u003e or \u003ccode\u003e\"H3\"\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003enumbering_scheme\u003c/code\u003e: What numbering scheme do you want to use for mutation identification? For H1 sequences, you can choose \u003ccode\u003eH1pdm\u003c/code\u003e, \u003ccode\u003eH3\u003c/code\u003e, or \u003ccode\u003eH1_1933\u003c/code\u003e. For H3 sequences, only \u003ccode\u003eH3\u003c/code\u003e numbering is available.\u003c/li\u003e\n\u003cli\u003e``: What numbering scheme do you want to use for mutation identification? For H1 sequences, you can choose \u003ccode\u003eH1pdm\u003c/code\u003e, `H3`, or `H1_1933`. For H3 sequences, only `H3` numbering is available.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ereference_mode\u003c/code\u003e: Use the reference strain for comparison (\u003ccode\u003etrue\u003c/code\u003e or \u003ccode\u003efalse\u003c/code\u003e). \u003cem\u003eNote: .png output is much more resource intensive than .pse.\u003c/em\u003e,\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eexport_files\u003c/code\u003e: Export .pse file, .png file, or both. Example: \u003ccode\u003e[\"PSE\"]\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efilter_sites\u003c/code\u003e: Filter out mutations and PNGS at the listed sites. Example: \u003ccode\u003e[92, 94, 104]\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ereverse_filter_sites\u003c/code\u003e: Only include the listed sites, and exclude others. \u003cem\u003eNote: reverse filter will override filter if both are non-empty.\u003c/em\u003e Example: \u003ccode\u003e[92]\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_csv\u003c/code\u003e: Write CSV to standard output. Default: \u003ccode\u003efalse\u003c/code\u003e (JSON).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ediversity_index\u003c/code\u003e: \u003ccode\u003e\"shannon\"\u003c/code\u003e, \u003ccode\u003e\"gini-simpson\"\u003c/code\u003e, \u003ccode\u003e\"richness\"\u003c/code\u003e (Default: Shannon).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003enon-conservative_only\u003c/code\u003e: Only include \u003ca href=\"\"\u003enon-conservative\u003c/a\u003e mutations to measure diversity. \u003ccode\u003etrue\u003c/code\u003e or \u003ccode\u003efalse\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNotes\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSite numbers input in \"filter_sites\" and \"reverse_filter_sites\" refer to amino acid numbering in the final HA proteins, after signaling peptides have been removed, from the start of HA1. The output table includes both numbering schemes (with and without signaling peptides), for reference\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning\u003c/h2\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003ccode\u003eflu_strain_compare_path\u003c/code\u003e is an absolute path to the root of this repository.\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport flu_strain_compare_path=/some/abs/path/flu_strain_compare\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker-1\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith your configuration file set up to your liking, run the container with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v ${flu_strain_compare_path}:/app flu_strain_compare python3 src/\u0026lt;SCRIPT NAME\u0026gt;.py configuration/config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity-1\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --bind ${flu_strain_compare_path}:/app ubuntu-pymol-biopython_latest.sif python3 src/\u0026lt;SCRIPT NAME\u0026gt;.py configuration/config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNative\u003c/h3\u003e\u003ca id=\"user-content-native-1\" class=\"anchor\" aria-label=\"Permalink: Native\" href=\"#native-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython src/\u0026lt;SCRIPT NAME\u0026gt;.py configuration/config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003e\u0026lt;SCRIPT NAME\u0026gt;\u003c/code\u003e should be \u003ccode\u003emake_comparison_figure\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# from repo root\nsingularity exec --bind /home/youruser/flu_strain_compare:/app ubuntu_pymol_biopython.sif python3 src/make_comparison_figure.py configuration/config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutputs will be written to the \u003ccode\u003efigures\u003c/code\u003e directory of the repository.\u003c/p\u003e\n\u003cp\u003eIf you wish the csv with additional information to be printed out, include\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUnit tests\u003c/h2\u003e\u003ca id=\"user-content-unit-tests\" class=\"anchor\" aria-label=\"Permalink: Unit tests\" href=\"#unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e\npytest\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStrains available\u003c/h2\u003e\u003ca id=\"user-content-strains-available\" class=\"anchor\" aria-label=\"Permalink: Strains available\" href=\"#strains-available\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eH3\u003c/h3\u003e\u003ca id=\"user-content-h3\" class=\"anchor\" aria-label=\"Permalink: H3\" href=\"#h3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2021-2022 southern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Darwin/6/2021\u003c/code\u003e, id = \u003ccode\u003eEPI1885402\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2021-2022 northern hemisphere Flublok (name = \u003ccode\u003eA/Tasmania/503/2020\u003c/code\u003e, id = \u003ccode\u003eEPI1752480\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2021-2022 northern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Cambodia/e0826360/2020\u003c/code\u003e, id = \u003ccode\u003eEPI1843589\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2020-2021 northern hemisphere Flublok (name = \u003ccode\u003eA/Minnesota/41/2019\u003c/code\u003e, id = \u003ccode\u003eEPI1548699\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2020-2021 northern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Hong Kong/45/2019\u003c/code\u003e, id = \u003ccode\u003eEPI1409001\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2019-2020 cell-based recommendation (name = \u003ccode\u003eA/Kansas/14/2017\u003c/code\u003e, id = \u003ccode\u003eEPI1174043\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2018-2019 cell-based recommendation (name = \u003ccode\u003eA/Singapore/INFIMH-16-0019/2016\u003c/code\u003e, id = \u003ccode\u003eEPI1106235\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2016-2018 cell-based recommendation (name = \u003ccode\u003eA/Hong Kong/4801/2014\u003c/code\u003e, id = \u003ccode\u003eEPI539576\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2015-2016 cell-based recommendation (name = \u003ccode\u003eA/Switzerland/9715293/2013\u003c/code\u003e, id = \u003ccode\u003eEPI530687\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2013-2015 cell-based recommendation (name = \u003ccode\u003eA/Victoria/361/2011 \u003c/code\u003e, id = \u003ccode\u003eEPI349103\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eH1\u003c/h3\u003e\u003ca id=\"user-content-h1\" class=\"anchor\" aria-label=\"Permalink: H1\" href=\"#h1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2021-2022 northern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Wisconsin/588/2019\u003c/code\u003e, id = \u003ccode\u003eEPI1715168\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2020-2021 northern hemisphere cell-based recommendation (name = \u003ccode\u003eA/Hawaii/70/2019\u003c/code\u003e, id = \u003ccode\u003eEPI1669665\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2019-2020 cell-based recommendation (name = \u003ccode\u003eA/Brisbane/02/2018\u003c/code\u003e, id = \u003ccode\u003eEPI1212884\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2017-2019 cell-based recommendation (name = \u003ccode\u003eA/Michigan/45/2015\u003c/code\u003e, id = \u003ccode\u003eEPI699812\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e2009-2017 cell-based recommendation (name = \u003ccode\u003eA/California/04/2009\u003c/code\u003e, id = \u003ccode\u003eEPI178457\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e2013-2014 example from Linderman et al. (name = \u003ccode\u003eA/Colorado/3514/2013\u003c/code\u003e, id = \u003ccode\u003eEPI501723\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScripts\u003c/h2\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-label=\"Permalink: Scripts\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003erenumber_H1.py\u003c/code\u003e and \u003ccode\u003erenumber_H3.py\u003c/code\u003e: \u003cem\u003eRe-index PDB files for use with Flu Strain Compare.\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprint_pymol_objects.py\u003c/code\u003e: \u003cem\u003ePrint chains and selections in a .pse file to compare modifications to the model.\u003c/em\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 2, - "subscribers_count": 13, - "topics": [ - "ceirr-cmc" - ], - "updated_at": 1709917745.0 + "updated_at": 1584297140.0 }, { "data_format": 2, - "description": null, + "description": "Framework for transfer experiments. Builds on top of nnfabrik and neuralpredictors. ", "filenames": [ - "misc/releases/latest/Singularity", - "misc/releases/20.06/Singularity.20.06", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/19.12/Singularity.19.12" + "Singularity.v0.3.def", + "Singularity.v0.2.def", + "Singularity.v0.1.def", + "Singularity.v0.4.def" ], - "full_name": "PatrickFerber/NeuralFastDownward", + "full_name": "sinzlab/nntransfer", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNeural Fast Downward\u003c/h1\u003e\u003ca id=\"user-content-neural-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Neural Fast Downward\" href=\"#neural-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeural Fast Downward generates training data for\nclassical planning domains and provides support for Protobuf (Tensorflow 1.x)\nand PyTorch models. The refactored code is not as well tested as it should\nbe. Please report bugs to \u003cstrong\u003e\u003ca href=\"mailto:patrick.ferber@unibas.ch\"\u003epatrick.ferber@unibas.ch\u003c/a\u003e\u003c/strong\u003e or create a pull request.\u003c/p\u003e\n\u003cp\u003eFor more information related to Fast Downward, consult the bottom part of\nthis README.md.\u003c/p\u003e\n\u003cp\u003eIf you use Neural Fast Downward in your research, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{ferber-et-al-ecai2020,\n author = \"Patrick Ferber and Malte Helmert and J{\\\"o}rg Hoffmann\",\n title = \"Neural Network Heuristics for Classical Planning: A Study of\n Hyperparameter Space\",\n pages = \"2346--2353\",\n booktitle = \"Proceedings of the 24th {European} Conference on\n {Artificial} {Intelligence} ({ECAI} 2020)\",\n year = \"2020\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSampling\u003c/h3\u003e\u003ca id=\"user-content-sampling\" class=\"anchor\" aria-label=\"Permalink: Sampling\" href=\"#sampling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeural Fast Downward generates data from a given task,\ntherefore, it uses \u003cstrong\u003eSamplingTechniques\u003c/strong\u003e which take a given task and modify\nit and \u003cstrong\u003eSamplingEngines\u003c/strong\u003e which perform some action with the modified task.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent Sampling Techniques\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNew initial state via random walks with progression from the original initial\nstate\u003c/li\u003e\n\u003cli\u003eNew initial state via random walk with regression from the goal condition\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent Sampling Engines:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ewriting the new states as (partial) SAS tasks to disk\u003c/li\u003e\n\u003cli\u003euse a given search algorithm to find plans for the new states and\nstore them\u003c/li\u003e\n\u003cli\u003eestimate the heuristic value of a state by value update using the n-step\nsuccessors (like Bellman update).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you are only interested in the sampling code, just work with the branch\n\u003ccode\u003esampling\u003c/code\u003e. The branch \u003ccode\u003emain\u003c/code\u003e contains the sampling feature, as well as, the\nfeatures below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExample:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenerate two state via regression from the goal with random walk lengths\nbetween 5 and 10. Use \u003ccode\u003eA*(LMcut)\u003c/code\u003e to find a solution and store all states\nalong the plan, as well as the used operators in \u003ccode\u003esas_plan\u003c/code\u003e. Ignore the\nmessage that no solution was found.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e\"sampling_search_simple(astar(lmcut(transform=sampling_transform()),transform=sampling_transform()), techniques=[gbackward_none(2, distribution=uniform_int_dist(5, 10))])\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eATTENTION: By default, the components of Fast Downward (e.g. search engines\nand heuristics) use the original task. Thus, you have to provide them the\nargument \u003ccode\u003etransform=sampling_transform()\u003c/code\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eATTENTION: The output tells you that no solution was found. This is wrong.\nCheck if the output contains\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eGenerated Entries: X\nSampling Techniques used:\n Y: n/N\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eThis tells you how many samples were generated and how often each sampling\ntechnique was invoked.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"SAMPLING.md\"\u003eClick here for more information and examples\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePolicies\u003c/h3\u003e\u003ca id=\"user-content-policies\" class=\"anchor\" aria-label=\"Permalink: Policies\" href=\"#policies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeural Fast Downward has some simple support for policies in classical planning.\nIt does \u003cstrong\u003enot\u003c/strong\u003e implement a good policy, but it provides a Policy class which can\nbe extended. Currently, two simple policies which internally rely on a given heuristic and\na simple search engine which follows the choices of a policy are implemented.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNeural Networks\u003c/h3\u003e\u003ca id=\"user-content-neural-networks\" class=\"anchor\" aria-label=\"Permalink: Neural Networks\" href=\"#neural-networks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeural Fast Downward supports Protobuf (Tensorflow 1.x) and PyTorch models. It\nimplements an\nabstract neural network base class and implements subclass for\nTensorflow and PyTorch. Wrappers which use a NN to calculate a heuristic or\npolicy are implemented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTensorflow.\u003c/strong\u003e\nThe Tensorflow setup has changed multiple times. The code is still there,\nbut is not tested with the current version of Tensorflow (most likely will\nnot run). I am glad for\nPR that adapt the code for the current Tensorflow version or for instructions\nAfter setting up Tensorflow, you have to uncomment the Tensorflow Plugin in\n\u003ccode\u003esrc/search/DownwardFiles.cmake\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePyTorch.\u003c/strong\u003e\nSetting up PyTorch is straight forward. Download \u003ccode\u003etorchlib\u003c/code\u003e and extract it to\nany path \u003ccode\u003eP\u003c/code\u003e. Then set an environment variable \u003ccode\u003ePATH_TORCH\u003c/code\u003e that points to \u003ccode\u003eP\u003c/code\u003e.\nAfterwards, you have to uncomment the Torch Plugin in\n\u003ccode\u003esrc/search/DownwardFiles.cmake\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"NEURALNETWORKS.md\"\u003eClick here for more information and examples\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFast Downward\u003c/h1\u003e\u003ca id=\"user-content-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Fast Downward\" href=\"#fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2020 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"http://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttp://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTested software versions\u003c/h2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-label=\"Permalink: Tested software versions\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2020 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2020 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2020 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2020 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2020 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2020 Salome Eriksson\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2018-2020 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2015 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enntransfer - \u003cem\u003eA simple framework for transfer learning experiments\u003c/em\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-nntransfer----a-simple-framework-for-transfer-learning-experiments\" class=\"anchor\" aria-label=\"Permalink: nntransfer - A simple framework for transfer learning experiments\" href=\"#nntransfer----a-simple-framework-for-transfer-learning-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis framework provides all the tools necessary to quickly define a complex transfer experiment in a few lines of code,\nwhile being flexible enough to modify all components on every level.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe Concept and Structure\u003c/h2\u003e\u003ca id=\"user-content-the-concept-and-structure\" class=\"anchor\" aria-label=\"Permalink: The Concept and Structure\" href=\"#the-concept-and-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis framework builds on top of \u003ca href=\"https://github.com/sinzlab/nnfabrik\"\u003ennfabrik\u003c/a\u003e, \u003ca href=\"https://github.com/sinzlab/neuralpredictors\"\u003eneuralpredictors\u003c/a\u003e and \u003ca href=\"https://datajoint.io/\" rel=\"nofollow\"\u003edatajoint\u003c/a\u003e.\nThe code and conceptual structure involves several components that will be explained in the following:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfigs\u003c/h3\u003e\u003ca id=\"user-content-configs\" class=\"anchor\" aria-label=\"Permalink: Configs\" href=\"#configs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConfig classes are designed to hold all settings that define a specific experiment.\nThey allow default values to be set by assigning attributes in the init and easy overwrite ability by passing custom keyword arguments.\nOne key feature of these config objects is the option to access the hashed key that will be used in a datajoint schema.\nTherefore it is easy to access and manipulate table entries for a given config object.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExperiments\u003c/h3\u003e\u003ca id=\"user-content-experiments\" class=\"anchor\" aria-label=\"Permalink: Experiments\" href=\"#experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe configs in this framework are separated into \u003ccode\u003edataset\u003c/code\u003e, \u003ccode\u003emodel\u003c/code\u003e and \u003ccode\u003etrainer\u003c/code\u003e configs.\nTogether these configs form an \u003ccode\u003eExperiment\u003c/code\u003e, which itself can be composed with other experiments in a \u003ccode\u003eTransferExperiment\u003c/code\u003e.\n\u003ccode\u003eExperiment\u003c/code\u003e and \u003ccode\u003eTransferExperiment\u003c/code\u003e objects encapsulate everything that needs to be run for a certain experiment.\nAn experiment could be an individual training run and a transfer experiment could be multiple experiments that hand over data or parameters chained together.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDataset\u003c/h3\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA dataset loader is supposed to gather a specific dataset (including all corresponding test sets),\nand prepare all data transformations as well as corresponding data loaders.\nThe implementation can be found in the /dataset folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eModel\u003c/h3\u003e\u003ca id=\"user-content-model\" class=\"anchor\" aria-label=\"Permalink: Model\" href=\"#model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe model-building functions can be found in the /models folder.\nHere we offer default implementations (with some adjustments) of some standard vision models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTrainer\u003c/h3\u003e\u003ca id=\"user-content-trainer\" class=\"anchor\" aria-label=\"Permalink: Trainer\" href=\"#trainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to train the defined model, we use the trainer function, that can be found in the /trainer folder.\nIt is responsible for the whole training process including the batch iterations, loss computation, evaluation on the validation set and the logging of results per epoch and finally the final evaluation on the test sets.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMainloop-Modules\u003c/h3\u003e\u003ca id=\"user-content-mainloop-modules\" class=\"anchor\" aria-label=\"Permalink: Mainloop-Modules\" href=\"#mainloop-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo allow most flexible usage of the default trainer function, we introduce main-loop modules.\nThese modules can implement any of the following functions that will be called at their respective point in training:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epre_epoch\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epre_forward\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epost_forward\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epost_backward\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epost_optimizer\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003epost_epoch\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese functions should allow most common interactions with the training process, like an additional training objective for example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRecipes\u003c/h2\u003e\u003ca id=\"user-content-recipes\" class=\"anchor\" aria-label=\"Permalink: Recipes\" href=\"#recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinally, to automatically execute a experiments (potentially in a distributed setting), simply define the concrete experiments in form of recipes, let our framework fill the corresponding tables and execute the training.\nA template for this can be found here: \u003ca href=\"https://github.com/sinzlab/nntransfer_recipes\"\u003ehttps://github.com/sinzlab/nntransfer_recipes\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1701965665.0 + "updated_at": 1682735913.0 }, { "data_format": 2, - "description": "Builds Chaotic-Nyx in Slurm+Singularity", + "description": "I forgot to fork the repo of LineVD project at first so I create this repository to store the current work, I will add the original work link at the readme file", "filenames": [ "Singularity" ], - "full_name": "chaotic-cx/nyx-slurm-singularity", + "full_name": "iseT1enLoc/LineVDFork", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSetup instruction\u003c/h1\u003e\u003ca id=\"user-content-setup-instruction\" class=\"anchor\" aria-label=\"Permalink: Setup instruction\" href=\"#setup-instruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVirtual environment setup\u003c/h1\u003e\u003ca id=\"user-content-virtual-environment-setup\" class=\"anchor\" aria-label=\"Permalink: Virtual environment setup\" href=\"#virtual-environment-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epython3\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003em\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evenv\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evenv\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evenv\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ebin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eactivate\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall packages\u003c/h1\u003e\u003ca id=\"user-content-install-packages\" class=\"anchor\" aria-label=\"Permalink: Install packages\" href=\"#install-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003er\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequirements\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etxt\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003escatter\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ehttps\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e//\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epyg\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eorg\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ewhl\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2.2\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ecu121\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ehtml\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData setup\u003c/h1\u003e\u003ca id=\"user-content-data-setup\" class=\"anchor\" aria-label=\"Permalink: Data setup\" href=\"#data-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eDownload from google drive the link\u003c/strong\u003e\n\u003ca href=\"https://drive.google.com/drive/folders/1MOwFXA9mtj0lVOT3v0SHXYMlNG234ETC?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1MOwFXA9mtj0lVOT3v0SHXYMlNG234ETC?usp=sharing\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUnzip all the zip files in the directory\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAfter unzip the storage directory looks like the below image:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./media/image.png\"\u003e\u003cimg src=\"./media/image.png\" alt=\"image.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe cache directory looks like this:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./media/image_1.png\"\u003e\u003cimg src=\"./media/image_1.png\" alt=\"image.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTraining model\u003c/h1\u003e\u003ca id=\"user-content-training-model\" class=\"anchor\" aria-label=\"Permalink: Training model\" href=\"#training-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNotice there are some error in this step like the python \u003cem\u003e\u003cstrong\u003ecan not identify custom module.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe solution is to open terminal and \u003cstrong\u003eactivate virtual environment again\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evenv\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ebin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eactivate\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eChange to the running directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esastvd\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003escripts\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epython\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esastvd\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003escripts\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emain\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epy\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1714917445.0 + "updated_at": 1730904681.0 }, { "data_format": 2, - "description": null, + "description": "the dinosaur data container for interaction with dinosaur data datasets!", "filenames": [ - "Singularity.v3.8-torch1.9.0-dj0.12.9", - "Singularity.v3.8-torch1.9.0-dj0.12.7", - "Singularity.v3.9-torch1.10.2-dj0.12.7.def", - "Singularity.v3.9-torch1.13.1-dj0.13.1.def", - "Singularity.v3.8-torch1.7.0-dj0.12.7", - "Singularity.v3.9-torch1.11.0-dj0.12.7.def", - "Singularity.v3.10-torch1.11.0-dj0.12.7-ubuntu22.04.def", - "Singularity.v3.8-torch1.5.0-dj0.12.4", - "Singularity.v3.8-torch1.9.0-dj0.13.2" + "Singularity" ], - "full_name": "sinzlab/pytorch-singularity", + "full_name": "vsoch/data-container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epytorch-singularity\u003c/h1\u003e\u003ca id=\"user-content-pytorch-singularity\" class=\"anchor\" aria-label=\"Permalink: pytorch-singularity\" href=\"#pytorch-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4939\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains Singularity definition files used for PyTorch development in the Sinzlab.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData Container\u003c/h1\u003e\u003ca id=\"user-content-data-container\" class=\"anchor\" aria-label=\"Permalink: Data Container\" href=\"#data-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a container that will allow you to build \"data containers,\" or squashfs\nbinaries that you can mount, unmount, and create all with the same container (and either\nuse with your own data container base, or on your local machine if you have a FUSE filesystem.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, pull the container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name dinosaur-data shub://vsoch/data-container\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat does the container do?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity apps dinosaur-data\ncreate\nmount\nunmount\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTry creating a dinosaur dataset, which is a squashfs filesystem\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app create dinosaur-data /home/vanessa/Desktop/demo demo.sqfs\n\nParallel mksquashfs: Using 4 processors\nCreating 4.0 filesystem on demo.sqfs, block size 131072.\n[\u003cspan class=\"pl-k\"\u003e===========================================================================================================================\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e] 2/2 100%\nExportable Squashfs 4.0 filesystem, gzip compressed, data block size 131072\n\tcompressed data, compressed metadata, compressed fragments, compressed xattrs\n\tduplicates are removed\nFilesystem size 3.63 Kbytes (0.00 Mbytes)\n\t29.28% of uncompressed filesystem size (12.40 Kbytes)\nInode table size 61 bytes (0.06 Kbytes)\n\t62.24% of uncompressed inode table size (98 bytes)\nDirectory table size 43 bytes (0.04 Kbytes)\n\t78.18% of uncompressed directory table size (55 bytes)\nNumber of duplicate files found 0\nNumber of inodes 3\nNumber of files 2\nNumber of fragments 1\nNumber of symbolic links 0\nNumber of device nodes 0\nNumber of fifo nodes 0\nNumber of socket nodes 0\nNumber of directories 1\nNumber of ids (unique uids + gids) 1\nNumber of uids 1\n\tvanessa (1000)\nNumber of gids 1\n\tvanessa (1000)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow try mounting it to the container. The container expects you do to this at \u003ccode\u003e/scif/data.sqsh\u003c/code\u003e to\nbind to \u003ccode\u003e/tmp/data\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecat /etc/fstab\n...\n/scif/data.sqsh /tmp/data squashfs ro,user,noauto,unhide\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere is how to do the mount, interactively with shell. You first bind your squashfs filesystem\nto the \u003ccode\u003e/tmp/data.sqsh\u003c/code\u003e location.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity shell --bind demo.sqsf:/scif/data.sqsh dinosaur-data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere I\u0027m at (not working yet)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity shell --bind demo.sqfs:/scif/data.sqsh dinosaur-data\nSingularity: Invoking an interactive shell within container...\n\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mount /scif/data\nmount: /scif/data.sqsh: failed to setup loop device: Permission denied\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e cat /etc/fstab \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e UNCONFIGURED FSTAB FOR BASE SYSTEM\u003c/span\u003e\n/scif/data.sqsh /scif/data squashfs ro,user,noauto,unhide,loop\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity shell --bind demo.sqfs:/scif/data.sqsh dinosaur-data\nSingularity: Invoking an interactive shell within container...\n\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ls /tmp/data\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e squashfuse /scif/data.sqsh /tmp/data\nfusermount: mount failed: Operation not permitted\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e fusermount /tmp/data\nfusermount: old style mounting not supported\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mount /tmp/data\nmount: /scif/data.sqsh: failed to setup loop device: Permission denied\nSingularity dinosaur-data:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/srcc/data-container\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ls -l /scif/data.sqsh \n-rw-r--r-- 1 vanessa vanessa 4096 Jun 1 23:32 /scif/data.sqsh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003econtent below not done, we\u0027d want these commands to work!\u003c/strong\u003e\nYou can always ask for help.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e --app mount dinosaur-data\n\nMount a squashfs file to a folder where you have write on you computer\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\nThe folder should NOT exist (but you should have writable to where it would)\nas the container will create it \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e you.\n\n\n$ singularity run --app mount dinosaur-data demo.sqsf /tmp/data2\n$ ls /tmp/data2\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 2, "subscribers_count": 5, "topics": [], - "updated_at": 1643971108.0 + "updated_at": 1545323609.0 }, { "data_format": 2, - "description": "An example of using stools-clair for Travis and Circle Continuous Integration", + "description": "Vim Syntax highlighting for Singularity.", "filenames": [ "Singularity" ], - "full_name": "singularityhub/stools-clair", + "full_name": "biosugar0/singularity-vim", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Tools for Continuous Integration\u003c/h1\u003e\u003ca id=\"user-content-singularity-tools-for-continuous-integration\" class=\"anchor\" aria-label=\"Permalink: Singularity Tools for Continuous Integration\" href=\"#singularity-tools-for-continuous-integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository shows a simple example of how to use the \u003ca href=\"https://www.github.com/singularityhub/stools\"\u003estools\u003c/a\u003e\n(Singularity Tools) container to run \u003ca href=\"https://github.com/coreos/clair\"\u003eClair OS\u003c/a\u003e security checks on your container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions\u003c/h2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eAdd the \u003ca href=\".travis.yml\"\u003e.travis.yml\u003c/a\u003e file to your Github repository with one or more \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e recipes\u003c/li\u003e\n\u003cli\u003eEnsure your recipe location(s) are specified correctly (see below) depending on if you want to build or pull.\u003c/li\u003e\n\u003cli\u003eConnect your repository to Travis (\u003ca href=\"https://docs.travis-ci.com/user/getting-started/\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e), and optionally set up a cron job for continuous vulnerability testing.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThat\u0027s it!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTravis CI\u003c/h2\u003e\u003ca id=\"user-content-travis-ci\" class=\"anchor\" aria-label=\"Permalink: Travis CI\" href=\"#travis-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/singularityhub/stools-clair\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/59d436c93e4d7d32b4c1dff866bb649e2ebb67510d9d1955e012377f17c1cab1/68747470733a2f2f7472617669732d63692e6f72672f73696e67756c61726974796875622f73746f6f6c732d636c6169722e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/singularityhub/stools-clair.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003etravis.yml\u003c/code\u003e file in the repository sets up the build to pull and use the container \u003ccode\u003evanessa/stools-clair\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContinuous Vulnerability Testing\u003c/h3\u003e\u003ca id=\"user-content-continuous-vulnerability-testing\" class=\"anchor\" aria-label=\"Permalink: Continuous Vulnerability Testing\" href=\"#continuous-vulnerability-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA cool feature of Travis is the ability to use cron jobs to run a build at a particular frequency, meaning that\nwe can set up regularly scheduled testing of our containers. Cool! How to do that?\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNavigate to the project settings page, usually at \u003ccode\u003ehttps://travis-ci.org/[organization]/[repo]/settings\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUnder \"Cron Jobs\" select the branch and interval that you want the checks to run, and then click \"Add\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThat\u0027s it!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChange the recipe file\u003c/h3\u003e\u003ca id=\"user-content-change-the-recipe-file\" class=\"anchor\" aria-label=\"Permalink: Change the recipe file\" href=\"#change-the-recipe-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have a Singularity recipe in the base of your repository, you should not need to change this file. If you want to change\nit to include one or more recipes in different locations, then you will want to change this line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Perform the build from your Singularity file, we are at the base of the repo\u003c/span\u003e\n- docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity build container.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere is says \u003ccode\u003eSingularity\u003c/code\u003e you might change it to path/in/repo/Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdd another recipe file\u003c/h3\u003e\u003ca id=\"user-content-add-another-recipe-file\" class=\"anchor\" aria-label=\"Permalink: Add another recipe file\" href=\"#add-another-recipe-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou aren\u0027t limited to the number of containers you can build and test! You can build more than one, and test the resulting containers, like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity build container1.simg Singularity\n - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity build container2.simg Singularity.two\n - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner sclair container1.simg container2.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePull a container\u003c/h3\u003e\u003ca id=\"user-content-pull-a-container\" class=\"anchor\" aria-label=\"Permalink: Pull a container\" href=\"#pull-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to build here, you can use Continuous Integration just for testing. Let\u0027s say we have a\nrepository with just a travis file, we can actually use it to test all of our Docker images (converted to Singularity) or\ncontainers hosted on Singularity Hub. That might look like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity pull --name vsoch-hello-world.simg shub://vsoch/hello-world\n - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner singularity pull --name ubuntu.simg docker://ubuntu:16.04\n - docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it clair-scanner sclair vsoch-hello-world.simg ubuntu.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRename the Output\u003c/h3\u003e\u003ca id=\"user-content-rename-the-output\" class=\"anchor\" aria-label=\"Permalink: Rename the Output\" href=\"#rename-the-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou might also change the name of the output image (container.simg). Why? Imagine that you are using Travis to\nbuild, test, and then upon success, to upload to some container storage.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeedback Wanted!\u003c/h2\u003e\u003ca id=\"user-content-feedback-wanted\" class=\"anchor\" aria-label=\"Permalink: Feedback Wanted!\" href=\"#feedback-wanted\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eUnder what conditions might we want a build to fail testing?\u003c/li\u003e\n\u003cli\u003eHow many users prefer using CircleCI to Docker? Or vice versa?\u003c/li\u003e\n\u003cli\u003eIs it worth having some kind of message sent back to Github (would require additional permissions)?\u003c/li\u003e\n\u003cli\u003eCircle has support for artifacts. How might you want results presented?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePlease \u003ca href=\"https://www.github.com/singularityhub/stools/issues\"\u003elet me know\u003c/a\u003e your feedback!\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-vim\u003c/h1\u003e\u003ca id=\"user-content-singularity-vim\" class=\"anchor\" aria-label=\"Permalink: singularity-vim\" href=\"#singularity-vim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVim Syntax highlighting for Singularity Recipe.\n\u003ca href=\"http://singularity.lbl.gov/docs-recipes#files\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/docs-recipes#files\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./example.png\"\u003e\u003cimg src=\"./example.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLICENSE\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"./LICENSE\"\u003eMIT License\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, - "topics": [ - "singularity", - "singularity-container", - "ci", - "testing", - "travis-ci", - "circle-ci" - ], - "updated_at": 1524940998.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1578589770.0 }, { "data_format": 2, - "description": "Singularity iRODS: iCommands", + "description": "Run PostgreSQL server within a Singularity container against isolated directory.", "filenames": [ - "Singularity", - "Singularity.4.2.2" + "Singularity" ], - "full_name": "mjstealey/singularity-irods-icommands", + "full_name": "glentner/PostgreSQL-Singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity iRODS: iCommands\u003c/h1\u003e\u003ca id=\"user-content-singularity-irods-icommands\" class=\"anchor\" aria-label=\"Permalink: Singularity iRODS: iCommands\" href=\"#singularity-irods-icommands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/812\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://docs.irods.org/4.2.2/system_overview/glossary/#icommands\" rel=\"nofollow\"\u003eiRODS iCommands\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003eicommands.4.2.2.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build icommands.4.2.2.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name icommands.4.2.2.simg shub://mjstealey/singularity-irods-icommands\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHelp\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e icommands.4.2.2.simg\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e iRODS Version 4.2.2\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e $ singularity run icommands.4.2.2.simg [icommand] [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app iinit icommands.4.2.2.simg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app iinit icommands.4.2.2.simg [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e Where [args] in\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_host String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_port Integer\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_user_name String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_zone_name String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_password String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_default_resource String\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --irods_home String\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run icommands.4.2.2.simg [icommand] [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./icommands.4.2.2.simg [icommand] [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003e[icommand]\u003c/code\u003e is a valid \u003ca href=\"https://docs.irods.org/4.2.2/icommands/user/\" rel=\"nofollow\"\u003eiRODS iCommand\u003c/a\u003e and [\u003ccode\u003eargs\u003c/code\u003e] is zero or more supporting arguments for that iCommand.\u003c/p\u003e\n\u003cp\u003ePrior to initializing your iRODS environment using \u003ccode\u003eiinit\u003c/code\u003e, the only valid iCommand will be \u003ccode\u003eihelp\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run icommands.4.2.2.simg ihelp\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eThe iCommands and a brief description of each:\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eiadmin - perform iRODS administrator operations (iRODS admins only).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eibun - upload/download structured (tar) files.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eicd - change the current working directory (Collection).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eichksum - checksum one or more Data Objects or Collections.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eichmod - change access permissions to Collections or Data Objects.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eicp - copy a data-object (file) or Collection (directory) to another.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eienv - display current iRODS environment.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eierror - convert an iRODS error code to text.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiexecmd - remotely execute special commands.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiexit - exit an iRODS session (opposite of iinit).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eifsck - check if local files/directories are consistent with the associated Data Objects/Collections in iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiget - get a file from iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eigroupadmin - perform group-admin functions: mkuser, add/remove from group, etc.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eihelp - display a synopsis list of the iCommands.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiinit - initialize a session, so you don\u0027t need to retype your password.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eils - list Collections (directories) and Data Objects (files).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eilsresc - list iRODS resources.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimcoll - manage mounted collections and associated cache.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimeta - add/remove/copy/list/query user-defined metadata.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimiscsvrinfo - retrieve basic server information.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimkdir - make an iRODS directory (Collection).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimv - move/rename an iRODS Data Object (file) or Collection (directory).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eipasswd - change your iRODS password.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiphybun - DEPRECATED - physically bundle files (admin only).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiphymv - physically move a Data Object to another storage Resource.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eips - display iRODS agent (server) connection information.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiput - put (store) a file into iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eipwd - print the current working directory (Collection) name.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiqdel - remove a delayed rule (owned by you) from the queue.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiqmod - modify certain values in existing delayed rules (owned by you).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiqstat - show the queue status of delayed rules.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiquest - issue a question (query on system/user-defined metadata).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiquota - show information on iRODS quotas (if any).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eireg - register a file or directory/files/subdirectories into iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirepl - replicate a file in iRODS to another storage resource.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirm - remove one or more Data Objects or Collections.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirmdir - removes an empty Collection\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirmtrash - remove Data Objects from the trash bin.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirsync - synchronize Collections between a local/iRODS or iRODS/iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eirule - submit a rule to be executed by the iRODS server.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiscan - check if local file or directory is registered in iRODS.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eisysmeta - show or modify system metadata.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiticket - create, delete, modify \u0026amp; list tickets (alternative access strings).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eitrim - trim down the number of replicas of Data Objects.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eiuserinfo - show information about your iRODS user account.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eixmsg - send/receive iRODS xMessage System messages.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eizonereport - generates a full diagnostic/backup report of your Zone.\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eFor more information on a particular iCommand:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \u0027\u0026lt;iCommand\u0026gt; -h\u0027\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eor\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \u0027ihelp \u0026lt;iCommand\u0026gt;\u0027\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eiRODS Version 4.2.2 ihelp\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eiinit (no args)\u003c/h2\u003e\u003ca id=\"user-content-iinit-no-args\" class=\"anchor\" aria-label=\"Permalink: iinit (no args)\" href=\"#iinit-no-args\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eiinit\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run --app iinit icommands.4.2.2.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf no arguments are passed in the user will be walked through the \u003ccode\u003eiinit\u003c/code\u003e process\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app iinit icommands.4.2.2.simg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e ERROR: environment_properties::capture: missing environment file. should be at [/home/stealey/.irods/irods_environment.json]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eOne or more fields in your iRODS environment file (irods_environment.json) are\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003emissing; please enter them.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter the host name (DNS) of the server to connect to: nwm.renci.org\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter the port number: 1247\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter your irods user name: nwm-reader\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter your irods zone: nwmZone\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eThose values will be added to your environment file (for use by\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eother iCommands) if the login succeeds.\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eEnter your current iRODS password: nwmreader\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eIINIT: $HOME/.irods/irods_environment.json\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e{\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_host\": \"nwm.renci.org\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_port\": 1247,\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_zone_name\": \"nwmZone\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_user_name\": \"nwm-reader\"\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will also result in two new files being created in the users \u003ccode\u003e$HOME/.irods\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls -alh $HOME/.irods\n-rw------- 1 xxxxx xxxxx 17 Mar 26 14:44 .irodsA\n-rw-r--r-- 1 xxxxx xxxxx 133 Mar 26 14:44 irods_environment.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eirods_environment.json\u003c/code\u003e is the JSON definition for the iRODS connection (as seen at the end of the \u003ccode\u003eiinit\u003c/code\u003e command output).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e.irodsA\u003c/code\u003e is the hashed value of the user\u0027s iRODS password.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eiinit (with args)\u003c/h2\u003e\u003ca id=\"user-content-iinit-with-args\" class=\"anchor\" aria-label=\"Permalink: iinit (with args)\" href=\"#iinit-with-args\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eiinit\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app iinit icommands.4.2.2.simg [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eValid args\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h | --irods_host as String\n-p | --irods_port as Integer\n-u | --irods_user_name as String\n-z | --irods_zone_name as String\n-s | --irods_password as String\n-d | --irods_default_resource as String\n-m | --irods_home as String\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf arguments are passed in the script will attempt the \u003ccode\u003eiinit\u003c/code\u003e process using a combination of preexisting information in the \u003ccode\u003eirods_environment.json\u003c/code\u003e file along with the arguments passed in by the user.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIf the \u003ccode\u003e--irods_password\u003c/code\u003e argument is populated the user will not be prompted for the password, but may notice an \u003ccode\u003eInappropriate ioctl\u003c/code\u003e warning at the prompt.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/bin/stty: \u0027standard input\u0027: Inappropriate ioctl for device\n/bin/stty: \u0027standard input\u0027: Inappropriate ioctl for device\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app iinit icommands.4.2.2.simg \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_host nwm.renci.org \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_port 1247 \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_user_name nwm-reader \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_zone_name nwmZone \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_password nwmreader \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_default_resource nwmResc \\\u003c/span\u003e\n\u0026gt; \u003cspan class=\"pl-s1\"\u003e--irods_home /nwmZone/home/nwm/data\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e/bin/stty: \u0027standard input\u0027: Inappropriate ioctl for device\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e/bin/stty: \u0027standard input\u0027: Inappropriate ioctl for device\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eEnter your current iRODS password:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eIINIT: $HOME/.irods/irods_environment.json\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e{\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_host\": \"nwm.renci.org\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_port\": 1247,\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_zone_name\": \"nwmZone\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_user_name\": \"nwm-reader\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_default_resource\": \"nwmResc\",\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e \"irods_home\": \"/nwmZone/home/nwm/data\"\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eiCommands\u003c/h2\u003e\u003ca id=\"user-content-icommands\" class=\"anchor\" aria-label=\"Permalink: iCommands\" href=\"#icommands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the user has established their iRODS identity using the \u003ccode\u003eiinit\u003c/code\u003e command, they can issue a variety of iCommands. Examples given assuming prior initialization for \u003ca href=\"\"\u003enwm.renci.org\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExample\u003c/strong\u003e: \u003ccode\u003eils\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run icommands.4.2.2.simg ils\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e/nwmZone/home/nwm/data:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/analysis_assim\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/fe_analysis_assim\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/forcing_analysis_assim\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/forcing_medium_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/forcing_short_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/long_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/medium_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/short_range\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/usgs_timeslices\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eExample\u003c/strong\u003e: \u003ccode\u003eils /nwmZone/home/nwm/data/nomads\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run icommands.4.2.2.simg ils /nwmZone/home/nwm/data/nomads\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e/nwmZone/home/nwm/data/nomads:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads/nwm.20180214\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads/nwm.20180215\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e ...\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads/nwm.20180325\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e C- /nwmZone/home/nwm/data/nomads/nwm.20180326\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eExample\u003c/strong\u003e: \u003ccode\u003eiget /nwmZone/home/nwm/data/nomads/nwm.20180325/short_range/nwm.t23z.short_range.channel_rt.f001.conus.nc\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run icommands.4.2.2.simg iget /nwmZone/home/nwm/data/nomads/nwm.20180325/short_range/nwm.t23z.short_range.channel_rt.f001.conus.nc\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eVerify file on local system.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003els -alh \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/nwm.t23z.short_range.channel_rt.f001.conus.nc\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e-rw-r----- 1 xxxxx xxxxx 12M Mar 26 14:55 /home/stealey/irods-icommands-singularity/nwm.t23z.short_range.channel_rt.f001.conus.nc\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eNOTE\u003c/strong\u003e: By default Singularity will mount \u003ccode\u003e$PWD\u003c/code\u003e, \u003ccode\u003e$HOME\u003c/code\u003e and \u003ccode\u003e/tmp\u003c/code\u003e from the local file system to the container, so files that are retrieved from iRODS using \u003ccode\u003eiget\u003c/code\u003e will be saved to \u003ccode\u003e$PWD\u003c/code\u003e unless specified otherwise.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at \u003ca href=\"https://github.com/mjstealey/singularity-irods-icommands\"\u003ehttps://github.com/mjstealey/singularity-irods-icommands\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePostgreSQL-Singularity\u003c/h1\u003e\u003ca id=\"user-content-postgresql-singularity\" class=\"anchor\" aria-label=\"Permalink: PostgreSQL-Singularity\" href=\"#postgresql-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun PostgreSQL server within a Singularity container against isolated directory.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, - "topics": [ - "irods", - "icommands", - "singularity" - ], - "updated_at": 1645061365.0 + "subscribers_count": 4, + "topics": [], + "updated_at": 1664811569.0 }, { "data_format": 2, - "description": null, + "description": "Singularity tutorial for an HPC cluster that has SLURM, Vagrant, and GPUs", "filenames": [ "Singularity" ], - "full_name": "PennSIVE/qsm", + "full_name": "satra/om-images", "latest_release": null, - "readme": "\u003cp\u003eQSM reconstruction using Total Generalized Variation (TGV-QSM)\nKristian Bredies and Christian Langkammer\nVersion from June 2016\n\u003ca href=\"http://www.neuroimaging.at\" rel=\"nofollow\"\u003ewww.neuroimaging.at\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e=== NEW ===\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIt is a python package now which simplifies installation\u003c/li\u003e\n\u003cli\u003eOrientation fixed: image data does NOT have to be transversal any longer (thx Daniel!)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e=== Installation ===\npython setup.py install\n(in case of problems such as Ubuntu 18.04+ try installing an older version of cython with pip install Cython==0.19.2 - Thanks Andrej!)\u003c/p\u003e\n\u003cp\u003e=== Command line options ===\u003c/p\u003e\n\u003cp\u003eusage: tgv_qsm [-h] -p PHASE -m MASK [-o OUTPUT_SUFFIX]\n[--alpha ALPHA ALPHA | --factors FACTORS [FACTORS ...]]\n[-e EROSIONS] [-i ITERATIONS [ITERATIONS ...]]\n[-f FIELDSTRENGTH] [-t ECHOTIME] [-s] [--ignore-orientation]\n[--save-laplacian] [--output-physical] [--no-resampling]\n[--vis] [-v]\u003c/p\u003e\n\u003cp\u003eremarks for options:\n-t TE in seconds\n-f fieldstrength in Tesla\n-s autoscaling for SIEMENS phase data\u003c/p\u003e\n\u003cp\u003etest data:\ntgv_qsm -p epi3d_test_phase.nii.gz -m epi3d_test_mask.nii.gz -f 2.89 -t 0.027 -o epi3d_test_QSM\u003c/p\u003e\n\u003cp\u003e=== bet brain masking ===\u003c/p\u003e\n\u003cp\u003efor high res data (~0.5 mm iso) this does quite a good job:\nbet magni.nii.gz mask -n -m -R -f 0.1 -g 0.0\n(in case something is lost, vary parameter g minimally)\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eom-images\u003c/h1\u003e\u003ca id=\"user-content-om-images\" class=\"anchor\" aria-label=\"Permalink: om-images\" href=\"#om-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains singularity bootstrap scripts for building images for openmind@MIT\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 4, "topics": [], - "updated_at": 1687459868.0 + "updated_at": 1693276409.0 }, { "data_format": 2, - "description": "BIDS app for correcting gradient non-linearities, saves corrected images as mirrored BIDS dataset, along with warp and intensity-correction fields", + "description": "Hands-on tutorial on Nextflow and Containers (Docker and Singularity). Paris 2018.", "filenames": [ - "Singularity.0.0.1d", - "Singularity.v0.0.2", - "Singularity.0.0.1j", - "Singularity.v0.0.2c", - "Singularity.0.0.1f", - "Singularity.0.0.1e", - "Singularity.0.0.1h", - "Singularity.0.0.1b", "Singularity", - "Singularity.v0.0.3", - "Singularity.v0.0.2a", - "Singularity.0.0.1c", - "Singularity.v0.0.3a" + "Singularity.xenial" ], - "full_name": "khanlab/gradcorrect", - "latest_release": "v0.0.3a", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egradcorrect\u003c/h1\u003e\u003ca id=\"user-content-gradcorrect\" class=\"anchor\" aria-label=\"Permalink: gradcorrect\" href=\"#gradcorrect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBIDS app for correcting gradient non-linearities, saves corrected images as mirrored BIDS dataset, along with warp and intensity-correction fields\u003c/p\u003e\n", + "full_name": "biocorecrg/C4LWG-2018", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/CRG-CNAG/BioCoreMiscOpen/blob/master/logo/biocore-logo_small.png\"\u003e\u003cimg src=\"https://github.com/CRG-CNAG/BioCoreMiscOpen/raw/master/logo/biocore-logo_small.png\" alt=\"C4LWG-2018\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e C4LWG-2018\u003c/h1\u003e\u003ca id=\"user-content--c4lwg-2018\" class=\"anchor\" aria-label=\"Permalink: C4LWG-2018\" href=\"#-c4lwg-2018\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHands-on tutorial on Nextflow and Containers (Docker and Singularity). Paris 2018.\nFor this tutorial we need to install Nextflow (\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/\u003c/a\u003e), Singularity (\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/\u003c/a\u003e) and Docker (\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehttps://www.docker.com/\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVirtual appliance\u003c/h2\u003e\u003ca id=\"user-content-virtual-appliance\" class=\"anchor\" aria-label=\"Permalink: Virtual appliance\" href=\"#virtual-appliance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor sake of simplicity we provide a virtual appliance in \u003ca href=\"https://en.wikipedia.org/wiki/Open_Virtualization_Format\" rel=\"nofollow\"\u003eOVA format\u003c/a\u003e. They can be imported in a virtual machine system such as \u003ca href=\"https://www.virtualbox.org/\" rel=\"nofollow\"\u003eVirtualbox\u003c/a\u003e (\u003ca href=\"https://www.youtube.com/watch?v=ZCfRtQ7-bh8\" rel=\"nofollow\"\u003evideo instructions\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIt is a Debian Stretch machine from OSBoxes project (ref: \u003ca href=\"https://www.osboxes.org/debian/\" rel=\"nofollow\"\u003ehttps://www.osboxes.org/debian/\u003c/a\u003e). Java, Docker, Singularity and Nextflow are already installed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOVA: \u003ca href=\"http://biocore.crg.eu/courses/C4LWG-2018/C4LWG-2018-full.ova\" rel=\"nofollow\"\u003ehttp://biocore.crg.eu/courses/C4LWG-2018/C4LWG-2018-full.ova\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOVA md5: \u003ca href=\"http://biocore.crg.eu/courses/C4LWG-2018/C4LWG-2018-full.ova.md5\" rel=\"nofollow\"\u003ehttp://biocore.crg.eu/courses/C4LWG-2018/C4LWG-2018-full.ova.md5\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCheck appliances download\u003c/h3\u003e\u003ca id=\"user-content-check-appliances-download\" class=\"anchor\" aria-label=\"Permalink: Check appliances download\" href=\"#check-appliances-download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTake care that files downloaded correctly (around 6GB). You can check with MD5 utilites from the terminal\u003c/p\u003e\n\u003cp\u003eIn Linux:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ md5sum -c C4LWG-2018-full.ova.md5 \nC4LWG-2018-full.ova: OK\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn Mac:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ md5 C4LWG-2018-full.ova \n$ cat C4LWG-2018-full.ova.md5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck both outputs show the same string.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUser login\u003c/h3\u003e\u003ca id=\"user-content-user-login\" class=\"anchor\" aria-label=\"Permalink: User login\" href=\"#user-login\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUser: osboxes\u003c/li\u003e\n\u003cli\u003ePassword: osboxes.org\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you need to use the root user (e.g. via \u003ccode\u003esu -l\u003c/code\u003e), it has the same password.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eManual installation\u003c/h2\u003e\u003ca id=\"user-content-manual-installation\" class=\"anchor\" aria-label=\"Permalink: Manual installation\" href=\"#manual-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSoftware requirements\u003c/h3\u003e\u003ca id=\"user-content-software-requirements\" class=\"anchor\" aria-label=\"Permalink: Software requirements\" href=\"#software-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFor installing NextFlow we need Java version 1.8. You can check with \"java -version\". Then just type \"curl -s \u003ca href=\"https://get.nextflow.io\" rel=\"nofollow\"\u003ehttps://get.nextflow.io\u003c/a\u003e | bash\" for installing a local copy in your current directory. Finally type \"./nextflow run hello\" for testing.\u003c/li\u003e\n\u003cli\u003eMac OS X users can consider installing \u003ca href=\"https://brew.sh\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e and \u003ca href=\"https://caskroom.github.io/\" rel=\"nofollow\"\u003eHomebrew-Cask\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eDocker (Community Edition): \u003ca href=\"https://www.docker.com/community-edition\" rel=\"nofollow\"\u003ehttps://www.docker.com/community-edition\u003c/a\u003e . Download and install last stable version in your system.\n\u003cul\u003e\n\u003cli\u003eCask users: \u003ccode\u003ebrew cask install docker\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003eLinux\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003eMac\u003c/a\u003e (Homebrew and Homebrew-Cask are needed)\n\u003cul\u003e\n\u003cli\u003eIf using Vagrant with Singularity, Vagrant shared folder with the host is \u003ccode\u003e/vagrant\u003c/code\u003e. That would be the best location to place generated images.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstalling nextflow\u003c/h3\u003e\u003ca id=\"user-content-installing-nextflow\" class=\"anchor\" aria-label=\"Permalink: Installing nextflow\" href=\"#installing-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecurl -s \u003ca href=\"https://get.nextflow.io\" rel=\"nofollow\"\u003ehttps://get.nextflow.io\u003c/a\u003e | bash\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainer installation\u003c/h3\u003e\u003ca id=\"user-content-container-installation\" class=\"anchor\" aria-label=\"Permalink: Container installation\" href=\"#container-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can retrieve Docker image with all used software by doing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull biocorecrg/c4lwg-2018\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternately, you can always modify and build a Docker image yourself in your computer by doing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t myimagename .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eConverting Docker image into Singularity image\u003c/h4\u003e\u003ca id=\"user-content-converting-docker-image-into-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Converting Docker image into Singularity image\" href=\"#converting-docker-image-into-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build c4lwg-2018.simg docker://biocorecrg/c4lwg-2018\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eNotes\u003c/h5\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you experience problems executing the generated image, e.g., \u003ccode\u003eERROR : No valid /bin/sh in container\u003c/code\u003e, try to change your umask (e. g., \u003ccode\u003eumask 000\u003c/code\u003e) \u003ca href=\"https://github.com/singularityware/singularity/issues/1079\"\u003eRef\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerating a Singularity image from a Singularity recipe\u003c/h4\u003e\u003ca id=\"user-content-generating-a-singularity-image-from-a-singularity-recipe\" class=\"anchor\" aria-label=\"Permalink: Generating a Singularity image from a Singularity recipe\" href=\"#generating-a-singularity-image-from-a-singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are other ways to generate Singularity images from other \u003ca href=\"http://singularity.lbl.gov/docs-recipes\" rel=\"nofollow\"\u003erecipe approaches\u003c/a\u003e. As a example, using \u003ca href=\"http://singularity.lbl.gov/build-debootstrap\" rel=\"nofollow\"\u003eDebootstrap\u003c/a\u003e (being root is required in \u003ca href=\"http://singularity.lbl.gov/docs-build-container\" rel=\"nofollow\"\u003ethese cases\u003c/a\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build c4lwg-2018.xenial.simg Singularity.xenial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNextflow usage\u003c/h2\u003e\u003ca id=\"user-content-nextflow-usage\" class=\"anchor\" aria-label=\"Permalink: Nextflow usage\" href=\"#nextflow-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe can reach the first folder \u003cstrong\u003etest0\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ecd test0; ls\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etest0.nf\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe can have a look at the code and launch it:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003enextflow run test0.nf\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNextflow creates a directory named \u003cstrong\u003ework\u003c/strong\u003e with different subfolders. Each one contains the input, output and some hidden files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e.exitcode\u003c/li\u003e\n\u003cli\u003e.command.log\u003c/li\u003e\n\u003cli\u003e.command.out\u003c/li\u003e\n\u003cli\u003e.command.err\u003c/li\u003e\n\u003cli\u003e.command.begin\u003c/li\u003e\n\u003cli\u003e.command.run\u003c/li\u003e\n\u003cli\u003e.command.sh\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn this case there is neither input nor output file.\u003c/p\u003e\n\u003cp\u003eSecond example where we read a fasta file, split it in several ones and tests on them.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ecd test1; ls\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etest1.nf\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003enextflow run test1.nf\u003c/strong\u003e\nIn the \u003cstrong\u003ework\u003c/strong\u003e folder we have subfolders containing this time a link to the input and the output file.\nIn \u003cstrong\u003eoutput\u003c/strong\u003e folder we have links to the final results.\u003c/p\u003e\n\u003cp\u003eThird example where we launch two fastQC analysis and we run multiQC on their result:\n\u003cem\u003e\u003cstrong\u003ecd test2; ls\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eparams.config: with parameters\u003c/li\u003e\n\u003cli\u003enextflow.config: with information about resources needed for each task and the container to be used\u003c/li\u003e\n\u003cli\u003etest2.nf\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe can inspect the different files and launch te pipeline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003enextflow run test2.nf -bg\u003c/strong\u003e\nWe can inspect the results in the different folders.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1711706051.0 + "updated_at": 1550495046.0 }, { "data_format": 2, - "description": null, + "description": "Social License to Operate - Joint project with CSIRO", "filenames": [ - "Singularity" + "Singularity.more-classifiers" ], - "full_name": "simozhou/epi-awesome", + "full_name": "Calvin-CS/slo-classifiers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-core/epiawesome\u003c/h1\u003e\u003ca id=\"user-content-nf-coreepiawesome\" class=\"anchor\" aria-label=\"Permalink: nf-core/epiawesome\" href=\"#nf-coreepiawesome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePipeline for epigenomics analysis and enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/simozhou/epi-awesome\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1b80521a835a9388c9652fe5210f4ca2f07cea7925b4f320da080912ffd806b/68747470733a2f2f7472617669732d63692e6f72672f73696d6f7a686f752f6570692d617765736f6d652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/simozhou/epi-awesome.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/simozhou/epiawesome\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37fecf0c8cb2655014a35151c14805ab99338a00e133b1945efa707a930a637f/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f657069617765736f6d652e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/epiawesome.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.singularity-hub.org/collections/2653\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/epiawesome pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/reference_genomes.md\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003enf-core/epiawesome was originally written by Simone Procaccia.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 0, + "subscribers_count": 9, "topics": [], - "updated_at": 1646303987.0 + "updated_at": 1607287806.0 }, { "data_format": 2, - "description": "an example variant calling scientific workflow with the standard container integration format (SCI-F)", + "description": "Training materials for the ReproNim day at https://coastal-coding.github.io", "filenames": [ - "Singularity.noscif", - "Singularity" + "section23/environments/Singularity.heudiconv", + "section23/environments/Singularity.fsl", + "section23/environments/Singularity.heudiconvn", + "section23/environments/Singularity.fsln" ], - "full_name": "sci-f/scientific-example.scif", + "full_name": "ReproNim/coco2019-training", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Example Scientific Workflow\u003c/h1\u003e\u003ca id=\"user-content-singularity-example-scientific-workflow\" class=\"anchor\" aria-label=\"Permalink: Singularity Example Scientific Workflow\" href=\"#singularity-example-scientific-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository provides a SCI-F (Standard Container Integration Format) Apps example to run a scientific workflow. This analysis was originally used to compare Singularity vs. Docker on cloud providers via \u003ca href=\"https://github.com/vsoch/singularity-scientific-example\"\u003ethis container\u003c/a\u003e with \u003ca href=\"https://vsoch.github.io/singularity-scientific-example/results/\" rel=\"nofollow\"\u003einteresting results\u003c/a\u003e! If you are interested in a Docker vs. Singularity implementation, see that project. For this small example, we want to give rationale for taking a SCI-F apps approach over a traditional Singularity image. We compare the following equivalent (but different!) implementations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"Singularity.noscif\"\u003eSingularity (without SCI-F)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"Singularity\"\u003eSingularity (with SCI-F)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe original image is served on Singularity Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://vsoch/singularity-scientific-example\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd the image with SCI-F app supports must be built with (not yet released) Singularity 2.4:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build scif.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEvaluation\u003c/h2\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe containers use the same software to perform the same functions, but notably, the software and executables are organized differently, and called differently. Singularity standard (the first without SCI-F) relies on external \u003ca href=\"scripts\"\u003escripts\u003c/a\u003e and the container is a bit of a black box. Singularity with SCI-F has no external dependencies beyond data, and is neatly organized. So how do we evaluate this?\u003c/p\u003e\n\u003cp\u003eThe aim here is to qualitatively evaluate SCI-F on its ability to expose container metadata, and information about the pipeline and executables inside. As each evaluation is scoped to the goal\u0027s of the container, for this example we focus on the purpose of deploying a set of steps that encompass a pipeline to perform variant calling.\u003c/p\u003e\n\u003cp\u003eFirst, the goals of SCI-F:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eContainers are \u003cstrong\u003econsistent\u003c/strong\u003e to allow for comparison. I am able to easily discover relevant software and data for one or more applications defined by the container creator.\u003c/li\u003e\n\u003cli\u003eContainers are transparent. If i discover a container and do not have any prior knowledge or metadata, the important executables and metadata are revealed to me.\u003c/li\u003e\n\u003cli\u003eContainer contents are easily available for \u003cstrong\u003eintrospection\u003c/strong\u003e because they are programmatically parseable. I can run a function over a container, and know exactly the functions available to me, ask for help, and know where to interact with inputs and outputs.\u003c/li\u003e\n\u003cli\u003eContainer internal infrastructure is \u003cstrong\u003emodular\u003c/strong\u003e. Given a set of SCI-F apps from different sources, I can import different install routines and have assurance that environment variables defined for each are sourced correctly for each, and that associated content does not overwrite previous content. Each software and data module must carry, minimally, a unique name and install location in the system.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each of the above, let\u0027s define some basic tests.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Development Evaluation\u003c/h3\u003e\u003ca id=\"user-content-1-development-evaluation\" class=\"anchor\" aria-label=\"Permalink: 1. Development Evaluation\" href=\"#1-development-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor this use case, we are a container developer, and we are writing a singularity build recipe.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCan I easily define multiple entrypoints?\u003c/h4\u003e\u003ca id=\"user-content-can-i-easily-define-multiple-entrypoints\" class=\"anchor\" aria-label=\"Permalink: Can I easily define multiple entrypoints?\" href=\"#can-i-easily-define-multiple-entrypoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nSingularity standard defaults to a single runscript per container. If I need to expose multiple functions, I either need to write a more complicated single entrypoint to direct the call, or I need to write detailed docs on different exec commands (and executables inside) for the user to run. For this real world use case, at the time when this runscript was written, SCI-F was not yet conceptualized. Given the sheer number of tools in the container, the runscript served to list a long list of executables, written to a text file, added to the path:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%runscript\n\n if [ $# -eq 0 ]; then\n echo \"\\nThe following software is installed in this image:\"\n column -t /Software/.info | sort -u --ignore-case \n echo \"\\Note that some Anaconda in the list are modules and note executables.\"\n echo \"Example usage: analysis.img [command] [args] [options]\" \n else\n exec \"$@\"\n fi\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGiven that this container was intended to run a scientific workflow, this list doesn\u0027t help to make its usage transparent. It would be useful for an individual familiar with the core software, perhaps developing a specific workflow with it. Arguably, this complete listing should be provided, perhaps not as a main entrypoint, but a separate app to print metadata, or the software names and versions added as labels to the app(s) where they are relevant. It\u0027s important to note that this first container did not include any logic for performing the analysis, this was provided by the included \u003ca href=\"scripts\"\u003escripts\u003c/a\u003e. If the scripts are separated from the container, reproducing the analysis is no longer possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nSCI-F has the advantage here because I can give names to different apps, and write a different executable runscript for each. I can still provide a main runscript, and it could either give the user a listing of possible options (below) or run the entire workflow the container provides. Here is a main runscript that instructs the user on how to use the apps, dynamically generating the list:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%runscript\n\n if [ $# -eq 0 ]; then\n echo \"\\nThe following software is installed in this image:\"\n ls /scif/apps | sort -u --ignore-case \n echo \"Example usage: singularity --app \u0026lt;name\u0026gt; \u0026lt;container\u0026gt; [command] [args] [options]\" \n else\n exec \"$@\"\n fi\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd here is an example app, specifically to downlad a component of the data:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun download-reference\n mkdir -p $REF_DIR\n wget -P $REF_DIR ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_25/gencode.v25.transcripts.fa.gz\n gzip -d $REF_DIR/gencode.v25.transcripts.fa.gz\n wget -P $REF_DIR ftp://ftp.ensembl.org/pub/release-85/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz\n gzip -d $REF_DIR/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eImportantly, the scripts that were previously in \u003ca href=\"scripts\"\u003escripts\u003c/a\u003e are now cleanly organized into sections in the build recipe. This modular organization and easy accessibility would have been very challening given the first container organization. The runscript would have needed to be long and complicated to infer what the user wanted to do, or the same functionality achieved by executing different scripts (inside the container), which is a non-trivial (or minimally more annoying to write) than simply writing lines into sections.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCan I easily install content known to modules?\u003c/h4\u003e\u003ca id=\"user-content-can-i-easily-install-content-known-to-modules\" class=\"anchor\" aria-label=\"Permalink: Can I easily install content known to modules?\" href=\"#can-i-easily-install-content-known-to-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven that I have two functions for my container to perform, foo and bar, can I generate an install routine that will allow for both shared dependencies (i.e. global) and distinct dependencies?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nSingularity standard has one mode to install global dependencies, everything goes into the \u003ccode\u003e%post\u003c/code\u003e script and any organization of required data, files, and libraries is scattered around the image. Other than coming up with a manual organization and documenting it, there is no way to cleanly define boundaries that will be easily discovered by the user. If you take a look at the \u003ca href=\"Singularity.noscif\"\u003eStandard Singularity\u003c/a\u003e recipe, you will see this reflected in one huge, single install procedure. As an example, for this container the tools \u003ccode\u003ebwa\u003c/code\u003e and \u003ccode\u003esamtools\u003c/code\u003e were generally used for an alignment step, and there is no way of knowing this. They are installed globally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /Software\nsu -c \u0027git clone https://github.com/Linuxbrew/brew.git\u0027 singularity\nsu -c \u0027/Software/brew/bin/brew install bsdmainutils parallel util-linux\u0027 singularity\nsu -c \u0027/Software/brew/bin/brew tap homebrew/science\u0027 singularity\nsu -c \u0027/Software/brew/bin/brew install art bwa samtools\u0027 singularity\nsu -c \u0027rm -r $(/Software/brew/bin/brew --cache)\u0027 singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn fact, the \u003ccode\u003eart\u003c/code\u003e tools are installed with the same manager (\u003ccode\u003ebrew\u003c/code\u003e), but they belong to an entirely different step. If a research scientist (or user) were parsing this build recipe, or using an NLP algorithm that took distance into account, there would be no strong signal about how these software were used or related to one another.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nWith SCI-F, by simply defining an environment, labels, install, or runscript to be in the context of an app, the modularity is automatically generated. When I add a list of files to an app \u003ccode\u003efoo\u003c/code\u003e, I know they are added to the container\u0027s predictable location for \u003ccode\u003efoo\u003c/code\u003e. If I add a file to \u003ccode\u003ebin\u003c/code\u003e I know it goes into foo\u0027s bin, and is added to the path when \u003ccode\u003efoo\u003c/code\u003e is run. If I add a library to \u003ccode\u003elib\u003c/code\u003e, I know it is added to \u003ccode\u003eLD_LIBRARY_PATH\u003c/code\u003e when foo is run. I don\u0027t need to worry about equivalently named files under different apps getting mixed up, or being called incorrectly because both are on the path. For example, in writing these sections, a developer can make it clear that \u003ccode\u003ebwa\u003c/code\u003e and \u003ccode\u003esamtools\u003c/code\u003e are used together for alignment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# =======================\n# bwa index and align\n# =======================\n\n%appinstall bwa-index-align\n git clone https://github.com/lh3/bwa.git build\n cd build \u0026amp;\u0026amp; git checkout v0.7.15 \u0026amp;\u0026amp; make\n mv -t ../bin bwa bwakit\n\n apt-get install -y liblzma-dev\n cd .. \u0026amp;\u0026amp; wget https://github.com/samtools/samtools/releases/download/1.5/samtools-1.5.tar.bz2\n tar -xvjf samtools-1.5.tar.bz2\n cd samtools-1.5 \u0026amp;\u0026amp; ./configure --prefix=${SINGULARITY_APPROOT}\n make \u0026amp;\u0026amp; make install\n\n%apprun bwa-index-align\n mkdir -p $DATADIR/Bam\n bwa index -a bwtsw $DATADIR/Reference/Homo_sapiens.GRCh38.dna.primary_assembly.fa\n bwa mem -t $NUMCORES $DATADIR/Reference/Homo_sapiens.GRCh38.dna.primary_assembly.fa $DATADIR/Fastq/dna_1.fq.gz $DATADIR/Fastq/dna_2.fq.gz | samtools view -bhS - \u0026gt; $DATADIR/Bam/container.bam \n\n%applabels bwa-index-align\n bwa-version v0.7.15\n samtools-version v1.5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand that \u003ccode\u003eart\u003c/code\u003e is used to simulate reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# =======================\n# simulate reads\n# =======================\n\n%apphelp simulate-reads\n Optionally set any of the following environment variables (defaults shown)\n READS (100000000)\n READ_LEN (150)\n GENOME_SIZE (3400000000)\n\n%appenv simulate-reads\n READS=${READS:-100000000}\n READ_LEN=${READ_LEN:-150}\n GENOME_SIZE=${GENOME_SIZE:-3400000000}\n export GENOME_SIZE READ_LEN READS\n\n%appinstall simulate-reads \n wget https://www.niehs.nih.gov/research/resources/assets/docs/artbinmountrainier20160605linux64tgz.tgz\n tar -xzvf artbinmountrainier20160605linux64tgz.tgz \n mv art_bin_MountRainier/* bin/\n chmod u+x bin/art_*\n\n%apprun simulate-reads\n GENOME=\"$REF_DIR/Homo_sapiens.GRCh38.dna.primary_assembly.fa\"\n FOLD_COVERAGE=$(python -c \"print($READS*$READ_LEN/$GENOME_SIZE)\")\n art_illumina --rndSeed 1 --in $GENOME --paired --len 75 --fcov $FOLD_COVERAGE --seqSys HS25 --mflen 500 --sdev 20 --noALN --out $FASTQ_DIR/dna_ \u0026amp;\u0026amp; gzip $FASTQ_DIR/dna_1.fq \u0026amp;\u0026amp; gzip $FASTQ_DIR/dna_2.fq\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhether I glanced at the recipe, or did some kind of text processing, I could easily see the relationships and purpose of the software in the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCan I associate environment and metadata with modules?\u003c/h4\u003e\u003ca id=\"user-content-can-i-associate-environment-and-metadata-with-modules\" class=\"anchor\" aria-label=\"Permalink: Can I associate environment and metadata with modules?\" href=\"#can-i-associate-environment-and-metadata-with-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven two different functions for my container to perform, foo and bar, can I define environment variables and labels (metadata) that I know will be sourced (environment) or exposed (inspect labels) in the context of the app?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nSingularity standard also has one global shared \u003ccode\u003e%environment\u003c/code\u003e, and \u003ccode\u003e%labels\u003c/code\u003e section. If two functions in the container share the same environment variable and the value is different, this must be resolved manually. For this example, the first container didn\u0027t have any labels or environment, however in practice these global sections are usually used for high level, global variables like author and version (of the container). When I run the container, regardless of if different contexts or variables are needed for executables inside, I get the same environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nWith SCI-F, I simply write the different variables to their sections, and have confidence that they will be sourced (environment) or inspected (labels) with clear association to the app.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\n%appenv run-rtg\n MEM=${MEM:-4g}\n THREADS=${THREADS:2}\n export MEM THREADS\n\n%applabel run-rtg\n rtg-version 3.6.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDo I need to know standard locations in advance?\u003c/h4\u003e\u003ca id=\"user-content-do-i-need-to-know-standard-locations-in-advance\" class=\"anchor\" aria-label=\"Permalink: Do I need to know standard locations in advance?\" href=\"#do-i-need-to-know-standard-locations-in-advance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven that a container has conformance to SCI-F, do I need to know how it works to use it?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nWith a standard location, we would be relying on Linux File System conventions (e.g., installation of content to \u003ccode\u003e/usr/local\u003c/code\u003e or intuitively infer that a folder called \u003ccode\u003e/Software\u003c/code\u003e (as with this scientific example) or \u003ccode\u003e/code\u003c/code\u003e is likely where the creator put important content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nInstead of requiring the user to know that an app\u0027s base might be at \u003ccode\u003e/scif/apps/foo\u003c/code\u003e, we instead expose environment variables (e.g., \u003ccode\u003eSINGULARITY_APPBASE\u003c/code\u003e) that can be referenced at runtime to refer to different apps. This is especially important if, for example, I need to reference the output of one app as input for another, or simply know it\u0027s install location. Regardless of which app is running, the container will also expose the top level folder for all apps installations, and data, \u003ccode\u003eSINGULARITY_DATA\u003c/code\u003e and \u003ccode\u003eSINGULARITY_APPS\u003c/code\u003e at \u003ccode\u003e/scif/data\u003c/code\u003e and \u003ccode\u003e/scif/apps\u003c/code\u003e, respectively.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Production Evaluation\u003c/h3\u003e\u003ca id=\"user-content-2-production-evaluation\" class=\"anchor\" aria-label=\"Permalink: 2. Production Evaluation\" href=\"#2-production-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor this use case, we operate under the scenario that we are familiar with Singularity and the commands to use SCi-F, but we know nothing about the containers. We are a user.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDo I know what the container does?\u003c/h4\u003e\u003ca id=\"user-content-do-i-know-what-the-container-does\" class=\"anchor\" aria-label=\"Permalink: Do I know what the container does?\" href=\"#do-i-know-what-the-container-does\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most natural thing to do with a Singularity container, knowing that it is possible to execute, is to do exactly that. For this evaluation, we want to assess how well executing the container reveals the intentions of the creator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nFrom the runscript we evaluated earlier, we are presented with a list of software and versions installed in the image, without detail to where or for what purpose. While this listing is comprehensive, it\u0027s most appropriate for a developer than a scientific workflow. In this listing, it isn\u0027t clear how the software is used or combine in the analysis. We are reliant on some external script or controller that drives the container. We don\u0027t have any hints about possible analysis steps the container can serve.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eThe following software is installed in this image:\nalabaster 0.7.9 Anaconda\nanaconda 4.3.0 Anaconda\nanaconda-client 1.6.0 Anaconda\nanaconda-navigator 1.4.3 Anaconda\nargcomplete 1.0.0 Anaconda\nart 20160605 Homebrew\nastroid 1.4.9 Anaconda\nastropy 1.3 Anaconda\nbabel 2.3.4 Anaconda\nbackports 1.0 Anaconda\nbeautifulsoup4 4.5.3 Anaconda\nbitarray 0.8.1 Anaconda\nblaze 0.10.1 Anaconda\nbokeh 0.12.4 Anaconda\nboto 2.45.0 Anaconda\nbottleneck 1.2.0 Anaconda\nbsdmainutils 9.0.10 Homebrew\nbwa 0.7.15 Homebrew\ncairo 1.14.8 Anaconda\ncffi 1.9.1 Anaconda\nchardet 2.3.0 Anaconda\nchest 0.2.3 Anaconda\nclick 6.7 Anaconda\ncloudpickle 0.2.2 Anaconda\nclyent 1.2.2 Anaconda\ncolorama 0.3.7 Anaconda\nconda 4.3.8 Anaconda\nconda-build 1.21.3 Anaconda\nconda-env 2.6.0 Anaconda\nconfigobj 5.0.6 Anaconda\ncontextlib2 0.5.4 Anaconda\ncryptography 1.7.1 Anaconda\ncurl 7.52.1 Anaconda\ncycler 0.10.0 Anaconda\ncython 0.25.2 Anaconda\ncytoolz 0.8.2 Anaconda\ndask 0.13.0 Anaconda\ndatashape 0.5.4 Anaconda\ndbus 1.10.10 Anaconda\ndecorator 4.0.11 Anaconda\ndill 0.2.5 Anaconda\ndocutils 0.13.1 Anaconda\ndynd-python 0.7.2 Anaconda\nentrypoints 0.2.2 Anaconda\net_xmlfile 1.0.1 Anaconda\nexpat 2.1.0 Anaconda\nfastcache 1.0.2 Anaconda\nflask 0.12 Anaconda\nflask-cors 3.0.2 Anaconda\nfontconfig 2.12.1 Anaconda\nfreetype 2.5.5 Anaconda\nget_terminal_size 1.0.0 Anaconda\ngevent 1.2.1 Anaconda\nglib 2.50.2 Anaconda\ngreenlet 0.4.11 Anaconda\ngsl 2.3 Homebrew\ngst-plugins-base 1.8.0 Anaconda\ngstreamer 1.8.0 Anaconda\nh5py 2.6.0 Anaconda\nharfbuzz 0.9.39 Anaconda\nhdf5 1.8.17 Anaconda\nheapdict 1.0.0 Anaconda\nhtslib 1.3.1 Homebrew\nicu 54.1 Anaconda\nidna 2.2 Anaconda\nimagesize 0.7.1 Anaconda\nipykernel 4.5.2 Anaconda\nipython 5.1.0 Anaconda\nipython_genutils 0.1.0 Anaconda\nipywidgets 5.2.2 Anaconda\nisort 4.2.5 Anaconda\nitsdangerous 0.24 Anaconda\njbig 2.1 Anaconda\njdcal 1.3 Anaconda\njedi 0.9.0 Anaconda\njinja2 2.9.4 Anaconda\njpeg 9b Anaconda\njsonschema 2.5.1 Anaconda\njupyter 1.0.0 Anaconda\njupyter_client 4.4.0 Anaconda\njupyter_console 5.0.0 Anaconda\njupyter_core 4.2.1 Anaconda\nkallisto 0.43.0 Anaconda\nlazy-object-proxy 1.2.2 Anaconda\nlibbsd 0.8.3 Homebrew\nlibdynd 0.7.2 Anaconda\nlibffi 3.2.1 Anaconda\nlibgcc 4.8.5 Anaconda\nlibgfortran 3.0.0 Anaconda\nlibiconv 1.14 Anaconda\nlibpng 1.6.27 Anaconda\nlibsodium 1.0.10 Anaconda\nlibtiff 4.0.6 Anaconda\nlibxcb 1.12 Anaconda\nlibxml2 2.9.4 Anaconda\nlibxslt 1.1.29 Anaconda\n_license 1.1 Anaconda\nllvmlite 0.15.0 Anaconda\nlocket 0.2.0 Anaconda\nlxml 3.7.2 Anaconda\nmarkupsafe 0.23 Anaconda\nmatplotlib 2.0.0 Anaconda\nmistune 0.7.3 Anaconda\nmkl 2017.0.1 Anaconda\nmkl-service 1.1.2 Anaconda\nmpmath 0.19 Anaconda\nmultipledispatch 0.4.9 Anaconda\nnb_anacondacloud 1.1.0 Anaconda\nnb_conda 1.1.0 Anaconda\nnb_conda_kernels 1.0.3 Anaconda\nnbconvert 4.2.0 Anaconda\n_nb_ext_conf 0.2.0 Anaconda\nnbformat 4.2.0 Anaconda\nnbpresent 3.0.2 Anaconda\nncurses 6.0_2 Homebrew\nnetworkx 1.11 Anaconda\nnltk 3.2.2 Anaconda\nnose 1.3.7 Anaconda\nnotebook 4.3.1 Anaconda\nnumba 0.30.1 Anaconda\nnumexpr 2.6.1 Anaconda\nnumpy 1.11.3 Anaconda\nnumpydoc 0.6.0 Anaconda\nodo 0.5.0 Anaconda\nopenpyxl 2.4.1 Anaconda\nopenssl 1.0.2k Anaconda\npandas 0.19.2 Anaconda\nparallel 20170122 Homebrew\npartd 0.3.7 Anaconda\npatchelf 0.9_1 Homebrew\npatchelf 0.9 Anaconda\npathlib2 2.2.0 Anaconda\npath.py 10.0 Anaconda\npatsy 0.4.1 Anaconda\npcre 8.39 Anaconda\npep8 1.7.0 Anaconda\npexpect 4.2.1 Anaconda\npickleshare 0.7.4 Anaconda\npillow 4.0.0 Anaconda\npip 9.0.1 Anaconda\npixman 0.34.0 Anaconda\npkg-config 0.29.1_2 Homebrew\nply 3.9 Anaconda\nprompt_toolkit 1.0.9 Anaconda\npsutil 5.0.1 Anaconda\nptyprocess 0.5.1 Anaconda\npy 1.4.32 Anaconda\npyasn1 0.1.9 Anaconda\npycosat 0.6.1 Anaconda\npycparser 2.17 Anaconda\npycrypto 2.6.1 Anaconda\npycurl 7.43.0 Anaconda\npyflakes 1.5.0 Anaconda\npygments 2.1.3 Anaconda\npylint 1.6.4 Anaconda\npyopenssl 16.2.0 Anaconda\npyparsing 2.1.4 Anaconda\npyqt 5.6.0 Anaconda\npytables 3.3.0 Anaconda\npytest 3.0.5 Anaconda\npython 3.5.2 Anaconda\npython-dateutil 2.6.0 Anaconda\npytz 2016.10 Anaconda\npyyaml 3.12 Anaconda\npyzmq 16.0.2 Anaconda\nqt 5.6.2 Anaconda\nqtawesome 0.4.3 Anaconda\nqtconsole 4.2.1 Anaconda\nqtpy 1.2.1 Anaconda\nreadline 6.2 Anaconda\nredis 3.2.0 Anaconda\nredis-py 2.10.5 Anaconda\nrequests 2.12.4 Anaconda\nrope 0.9.4 Anaconda\nRTG 3.6.2 User_Install\nruamel_yaml 0.11.14 Anaconda\nsamtools 1.3.1 Homebrew\nscikit-image 0.12.3 Anaconda\nscikit-learn 0.18.1 Anaconda\nscipy 0.18.1 Anaconda\nseaborn 0.7.1 Anaconda\nsetuptools 27.2.0 Anaconda\nsimplegeneric 0.8.1 Anaconda\nsingledispatch 3.4.0.3 Anaconda\nsip 4.18 Anaconda\nsix 1.10.0 Anaconda\nsnowballstemmer 1.2.1 Anaconda\nsockjs-tornado 1.0.3 Anaconda\nsphinx 1.5.1 Anaconda\nsphinx_rtd_theme 0.1.9 Anaconda\nspyder 3.1.2 Anaconda\nsqlalchemy 1.1.5 Anaconda\nsqlite 3.13.0 Anaconda\nstatsmodels 0.6.1 Anaconda\nsympy 1.0 Anaconda\nterminado 0.6 Anaconda\ntk 8.5.18 Anaconda\ntoolz 0.8.2 Anaconda\ntornado 4.4.2 Anaconda\ntraitlets 4.3.1 Anaconda\nunicodecsv 0.14.1 Anaconda\nutil-linux 2.29 Homebrew\nwcwidth 0.1.7 Anaconda\nwerkzeug 0.11.15 Anaconda\nwheel 0.29.0 Anaconda\nwidgetsnbextension 1.2.6 Anaconda\nwrapt 1.10.8 Anaconda\nxlrd 1.0.0 Anaconda\nxlsxwriter 0.9.6 Anaconda\nxlwt 1.2.0 Anaconda\nxz 5.2.2 Anaconda\nxz 5.2.3 Homebrew\nyaml 0.1.6 Anaconda\nzeromq 4.1.5 Anaconda\nzlib 1.2.8 Anaconda\n\\Note that some Anaconda in the list are modules and note executables.\nExample usage: analysis.img [command] [args] [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nWhen we run the SCi-F container, a different kind of information is presented. By listing the container contents at \u003ccode\u003e/scif/apps\u003c/code\u003e, the user knows what pipeline steps are included in the analysis.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run scif.img\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand in fact, this basic listing is generally useful for apps, so it\u0027s provided as it\u0027s own command, if the user doesn\u0027t write a runscript at all:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity apps scif.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe runscript also hints that I can direct the \u003ccode\u003e%help\u003c/code\u003e command to better understand an app. While both SCiF and standard Singularity allows for specification of a global \u003ccode\u003e%help\u003c/code\u003e section, providing documentation on the level of the application is more focused and offers specificity to the user. Both also allow for global \u003ccode\u003e%labels\u003c/code\u003e that might point to documentation, code repositories, or other informative information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDoes moduarity come naturally?\u003c/h4\u003e\u003ca id=\"user-content-does-moduarity-come-naturally\" class=\"anchor\" aria-label=\"Permalink: Does moduarity come naturally?\" href=\"#does-moduarity-come-naturally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor this metric, we want to know if using different functions of the container (modules) is intuitive. As we have seen above, the definition of a \"module\" could be anything from a series of script calls to perform a pipeline step (alignment using bwa and samtools), a single script call (such as a module just for bwa), or even the same function applied to a specific set of content (e.g., download \"A\" v.s. download \"B\").\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nSingularity proper represents a module on the level of the container. The entire container is intended for one entrypoint, and any deviation from that requires customization of that entrypoint, or special knowledge about other executables in the container to call with \u003ccode\u003eexec\u003c/code\u003e. The container is modular only in context of being a single step in a pipeline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nSCI-F, in that the software inside is defined and installed in a modular fashion, makes it easy to find three different modules:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edownload-fastq\u003c/li\u003e\n\u003cli\u003edownload-rtg\u003c/li\u003e\n\u003cli\u003edownload-reference\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eand without looking further, infer that likely these three downloads can be run in parallel. While this doesn\u0027t represent any kind of statement or assurance of this, it allows for the container have a natural modularity. Consider steps that are named according to an order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e1-download\u003c/li\u003e\n\u003cli\u003e2-preprocess\u003c/li\u003e\n\u003cli\u003e3-analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe creator of the container, in choosing a careful naming, can deliver a huge amount of information about different modules.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDo I know what executables are important in the container?\u003c/h4\u003e\u003ca id=\"user-content-do-i-know-what-executables-are-important-in-the-container\" class=\"anchor\" aria-label=\"Permalink: Do I know what executables are important in the container?\" href=\"#do-i-know-what-executables-are-important-in-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWithout much effort, I should have a high level understanding of the different functions that the container performs, as defined by the creator. For example, a container intended for development of variant calling will expose low level tools (e.g, bwa, seqtk) while a container that is intended will expose a pipeline (e.g., mapping).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nFor Singularity standard, if the container performs one function (and one only), then a single runscript / entrypoint is sufficient. Having multiple functions is completely reliant on the programming ability of the creator. If the creator is able to write a more complex runscript that explains different use cases, the container can satisfy this goal. If not, the container is a black box.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nFor SCI-F, without any significant programming ability, the different apps are easily exposed to the user.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCan I easily get help for an executable?\u003c/h4\u003e\u003ca id=\"user-content-can-i-easily-get-help-for-an-executable\" class=\"anchor\" aria-label=\"Permalink: Can I easily get help for an executable?\" href=\"#can-i-easily-get-help-for-an-executable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI should be able to run a command to get help or a summary of what the container does (introspection).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard\u003c/strong\u003e\nFor Singularity standard, you \u003cstrong\u003ecan\u003c/strong\u003e ask a container for help, but it\u0027s a single global help.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCI-F\u003c/strong\u003e\nFor SCI-F apps, you can define help sections for all of the functions that your container serves, along with a global help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Research Evaluation\u003c/h3\u003e\u003ca id=\"user-content-3-research-evaluation\" class=\"anchor\" aria-label=\"Permalink: 3. Research Evaluation\" href=\"#3-research-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn important attribute of having modular software apps is that it allows for separation of files and executables for research, and those that belong to the base system. From a machine learning standpoint, it provides labels for some subset of content in the container that might be used to better understand how different software relates to a pipeline. Minimally, it separates important content from the base, allowing, for example, a recursive tree generated at \u003ccode\u003e/scif\u003c/code\u003e to capture a large majority of additions to the container. Or simple parsing of the build recipe to see what software (possibly outside of this location) was intended for each app. Equally important, having container software installed at a global at \u003ccode\u003e%post\u003c/code\u003e also says important things about it - that it perhaps is important for more than one software module, or is more of a system library.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConclusion\u003c/h2\u003e\u003ca id=\"user-content-conclusion\" class=\"anchor\" aria-label=\"Permalink: Conclusion\" href=\"#conclusion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSCI-F clearly has the advantage when it comes to a container creator embedding his or her work with implied metadata about software and container contents. SCI-F also makes it easier to package different run scripts with the container, and expose them easily to the user. However, this does not mean that the standard approach of using a container as a general toolbox and distributing it with a series of external callers is bad or wrong. The choice to use (or not use) SCI-F apps is largely dependent on the goals of the creator, and the intended users.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions\u003c/h1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall VirtualBox\u003c/h2\u003e\u003ca id=\"user-content-install-virtualbox\" class=\"anchor\" aria-label=\"Permalink: Install VirtualBox\" href=\"#install-virtualbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGo to the VirtualBox Download page: \u003ca href=\"https://www.virtualbox.org/wiki/Downloads\" rel=\"nofollow\"\u003ehttps://www.virtualbox.org/wiki/Downloads\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSelect the link from the \"VirtualBox 5.2.12 platform packages\" section that matches your system.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Windows 10 users:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDouble-click the downloaded executable file (Windows Installer 1.1 or higher is required)\u003c/li\u003e\n\u003cli\u003eSelect default options in installation dialog.\u003c/li\u003e\n\u003cli\u003eThe installer will create a \"VirtualBox\" group in the Windows Start menu.\u003c/li\u003e\n\u003cli\u003eFor more detailed step-by-step instructions, \u003ca href=\"https://websiteforstudents.com/installing-virtualbox-windows-10/\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Mac users:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDouble-click on the downloaded dmg file to have its contents mounted\u003c/li\u003e\n\u003cli\u003eA window will open telling you to double click on the VirtualBox.pkg installer file displayed in that window.\u003c/li\u003e\n\u003cli\u003eThis will start the installer, which will allow you to select where to install VirtualBox to.\u003c/li\u003e\n\u003cli\u003eAfter installation, you can find a VirtualBox icon in the \"Applications\" folder in the Finder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Ubuntu users:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStep-by-step instructions can be found \u003ca href=\"https://websiteforstudents.com/install-virtualbox-latest-on-ubuntu-16-04-lts-17-04-17-10/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload and import the VM image file\u003c/h2\u003e\u003ca id=\"user-content-download-and-import-the-vm-image-file\" class=\"anchor\" aria-label=\"Permalink: Download and import the VM image file\" href=\"#download-and-import-the-vm-image-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload from \u003ca href=\"https://training.repronim.org/reprotraining.ova\" rel=\"nofollow\"\u003ehttps://training.repronim.org/reprotraining.ova\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eTo import the reprotraining.ova file:\n\u003col\u003e\n\u003cli\u003eOpen VirtualBox on your computer\u003c/li\u003e\n\u003cli\u003eselect \"File\" -\u0026gt; \"Import Appliance\" from the VirtualBox menu.\u003c/li\u003e\n\u003cli\u003eClick through the import wizard dialog leaving the default settings (see here for example step-by-step instructions \u003ca href=\"https://docs.oracle.com/cd/E26217_01/E26796/html/qs-import-vm.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStarting the VM\u003c/h2\u003e\u003ca id=\"user-content-starting-the-vm\" class=\"anchor\" aria-label=\"Permalink: Starting the VM\" href=\"#starting-the-vm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOpen VirtualBox on your computer\u003c/li\u003e\n\u003cli\u003eChoose \"reprotraining\" from the menu on the left and press \"start\" (the green arrow) to start the Ubuntu virtual machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIssues:\u003c/h2\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-label=\"Permalink: Issues:\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eif you have a windows 10 pro 64 bit (eg Lenovo X1C) machine and get an error like:\nvt-x/amd-v hardware acceleration is not available on your system, \u003ca href=\"https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/quick-start/enable-hyper-v#enable-the-hyper-v-role-through-settings\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eif you are unable to install VirtualBox due to virtualization technology (VT-x) not being enabled on your system, \u003ca href=\"https://docs-old.fedoraproject.org/en-US/Fedora/13/html/Virtualization_Guide/sect-Virtualization-Troubleshooting-Enabling_Intel_VT_and_AMD_V_virtualization_hardware_extensions_in_BIOS.html\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe Presentations\u003c/h1\u003e\u003ca id=\"user-content-the-presentations\" class=\"anchor\" aria-label=\"Permalink: The Presentations\" href=\"#the-presentations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll tutorials and slides are available from the \u003ca href=\"http://www.repronim.org/coco2019-training/\" rel=\"nofollow\"\u003ehttp://www.repronim.org/coco2019-training/\u003c/a\u003e generated from sources within \u003ccode\u003egh-pages\u003c/code\u003e branch of this repository.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, + "subscribers_count": 9, "topics": [], - "updated_at": 1581608084.0 + "updated_at": 1698248799.0 }, { "data_format": 2, - "description": "Singularity container (Ubuntu 14.04, ROS Indigo, OpenRAVE) for UR5Controller", + "description": "Rclone is a command line program to manage files on cloud storage. It is a feature rich alternative to cloud vendors\u0027 web storage interfaces.", "filenames": [ - "Singularity" + "1.58.1/Singularity", + "1.55.1/Singularity" ], - "full_name": "roboticsleeds/ur5controller-singularity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUR5 Controller Singularity\u003c/h1\u003e\u003ca id=\"user-content-ur5-controller-singularity\" class=\"anchor\" aria-label=\"Permalink: UR5 Controller Singularity\" href=\"#ur5-controller-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ur5_openrave.png\"\u003e\u003cimg src=\"images/ur5_openrave.png\" alt=\"UR5 with OpenRAVE within a singularity container\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003cbr\u003e\n Figure 1: UR5 Robot with Ridgeback in OpenRAVE within a Singularity container with Ubuntu 14.04 and ROS Indigo. The host operating system is Ubuntu 18.04.\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopers and Contributors\u003c/h2\u003e\u003ca id=\"user-content-developers-and-contributors\" class=\"anchor\" aria-label=\"Permalink: Developers and Contributors\" href=\"#developers-and-contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUR5Controller Singularity was developed by the \u003ca href=\"https://artificial-intelligence.leeds.ac.uk/robot-manipulation/\" rel=\"nofollow\"\u003eRobot Manipulation Lab\u003c/a\u003e in the School of Computing at the University of Leeds.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAuthor: \u003ca href=\"http://rpapallas.com\" rel=\"nofollow\"\u003eRafael Papallas\u003c/a\u003e, \u003ca href=\"https://github.com/WissBe\"\u003eWissam Bejjani\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eCurrent maintainor: \u003ca href=\"http://rpapallas.com\" rel=\"nofollow\"\u003eRafael Papallas\u003c/a\u003e, \u003ca href=\"https://github.com/WissBe\"\u003eWissam Bejjani\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUR5Controller Singularity is licensed under GNU General Public License v3.0.\nThe full license is available \u003ca href=\"https://github.com/roboticsleeds/ur5controller_singularity/blob/master/LICENSE\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstructions\u003c/h2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a Singularity container for \u003ca href=\"https://github.com/roboticsleeds/ur5controller\"\u003e\u003ccode\u003eur5controller\u003c/code\u003e\u003c/a\u003e\npackage.\u003c/p\u003e\n\u003cp\u003eClone and build the singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/roboticsleeds/ur5controller_singularity\ncd ur5controller_singularity\n./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should create a local Ubuntu 14.04 file system with ROS Indigo, \u003ccode\u003eor\\_urdf\u003c/code\u003e,\nand \u003ccode\u003eur5controller\u003c/code\u003e in it.\u003c/p\u003e\n\u003cp\u003eIt will take a while to build (approximately 40 minutes). Once built, you will\nautomatically enter into the singualrity environment (which will build your catkin\nworkspace).\u003c/p\u003e\n\u003cp\u003eWhen you need to enter your singularity environment, simply run \u003ccode\u003e./run.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis should put you into a singularity environment. To test if everything was\nsuccesful you can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/ur5_demo\npython ur5_demo.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd you should see an OpenRAVE window with UR5 being loaded.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBinding directories\u003c/h2\u003e\u003ca id=\"user-content-binding-directories\" class=\"anchor\" aria-label=\"Permalink: Binding directories\" href=\"#binding-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs you can see from the above configuration you can have a \u003ccode\u003ehome\u003c/code\u003e directory living\non your host machine and then bind that directory as the home directory of the\ncontainer. As a result you can now put files under that \u003ccode\u003ehome\u003c/code\u003e dir and both the\nhost and the container can read and write in it.\u003c/p\u003e\n\u003cp\u003eAnother way to do this is to bind the directory using \u003ccode\u003e--bind\u003c/code\u003e (\u003ccode\u003e--bind=absolute_path_of_source_dir:absolute_path_of_target_dir\u003c/code\u003e) flag:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --contain --home=home:$HOME --bind=/home/rafael/Documents/my_project:/home/my_project ur5controller\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will bind \u003ccode\u003e/home/rafael/Documents/my_project\u003c/code\u003e to container\u0027s \u003ccode\u003e/home\u003c/code\u003e directory.\u003c/p\u003e\n\u003cp\u003eUnfortunetly, you can\u0027t bind the target directory to container\u0027s user home (e.g \u003ccode\u003e/home/rafael\u003c/code\u003e) directory.\nWe found a workaround to this. Under \u003ccode\u003ehome/.bashrc\u003c/code\u003e of this repository we have placed\nthe following code:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Delete all links that are broken\nfind ./ -maxdepth 1 -follow -type l -delete\n\n# This code is to create a symbolic link of any directory located under /home/.\n# When you use --bind in singularity to bind a directory from host to the container\n# you can\u0027t bind that directory under $HOME but only under /home/, therefore a\n# workaround to this was to create a symbolic link to all fo the directories under\n# /home/ to $HOME.\nfor filename in $(find /home -maxdepth 1 ! -path \"/home/$USER\" ! -path \u0027*/\\.*\u0027 ! -path \u0027/home\u0027); do\n ln -sf $filename $HOME\ndone\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis code will create symbolic links for every directory located under container\u0027s\n\u003ccode\u003e/home/\u003c/code\u003e directory to \u003ccode\u003e$HOME\u003c/code\u003e (i.e., \u003ccode\u003e/home/user_name\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eSo with the above \u003ccode\u003e.bashrc\u003c/code\u003e code whenever you start a singularity container like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --contain --home=home:$HOME --bind=/home/rafael/Documents/my_project:/home/my_project ur5controller\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWill bind \u003ccode\u003e/home/rafael/Documents/my_project\u003c/code\u003e to \u003ccode\u003e/home/my_project/\u003c/code\u003e and will create\na symbolic link of \u003ccode\u003e/home/my_project/\u003c/code\u003e to \u003ccode\u003e/home/rafael/my_project\u003c/code\u003e. As a result\nwe are \"binding\" a directory from the host file system to the container under\ncontainer\u0027s user home directory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWith all that said, if you want to bind a directory to the container you just\nneed to edit the \u003ccode\u003erun.sh\u003c/code\u003e file and add \u003ccode\u003e--bind=source:target\u003c/code\u003e as you wish.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --contain --home=home:$HOME --bind=/home/rafael/Documents/my_project_1:/home/my_project_1 --bind=/home/rafael/Documents/my_project_2:/home/my_project_2 ur5controller\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere we are binding two directories: \u003ccode\u003emy_project_1\u003c/code\u003e and \u003ccode\u003emy_project_2\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNote that we have pre-generated the robot inverse kinematics for OpenRAVE and\nplaced them under your singularity home directory just to save time as this\ntakes a while. This is just the kinematics for our specific configuration, if you\nchange the model then OpenRAVE will generate new IK solutions for your new model.\u003c/li\u003e\n\u003cli\u003eDuring build time we create some temporary files (\u003ccode\u003escripts\u003c/code\u003e and \u003ccode\u003ebuild\u003c/code\u003e) that we\nare using to build everything. Once finished we erase those files.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003ehome\u003c/code\u003e directory located under this repository contains the following important\ndata: \u003ccode\u003e.openrave\u003c/code\u003e with the prepoulated IK solutions to UR5 robot, \u003ccode\u003e.bashrc\u003c/code\u003e containing\nimportant commands to successfully run ROS and the UR5Controller.\u003c/li\u003e\n\u003cli\u003eAnything you create in the container under \u003ccode\u003ehome\u003c/code\u003e will be persistent but if you\nwrite anything outside \u003ccode\u003ehome\u003c/code\u003e this will not be writable. If you need to make changes\nto the singularity container, then run \u003ccode\u003ewrite.sh\u003c/code\u003e to enter into a root session within\nyour singularity container.\u003c/li\u003e\n\u003cli\u003eYou can work in \u003ccode\u003ehome\u003c/code\u003e directory outside singularity (say if you are using an\nIDE software) and the changes should be immediately available within the\nsingularity environment. So you can edit your source code outside the container\nusing your host machine and then execute the code within the container.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "pscedu/singularity-rclone", + "latest_release": "v1.55.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rclone/actions/workflows/main.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rclone/actions/workflows/main.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pscedu/singularity-rclone/actions/workflows/pretty.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/pscedu/singularity-rclone/actions/workflows/pretty.yml/badge.svg\" alt=\"Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7f0e19a39ace7f9221d79c234d3288bcc366f84a3d48f0ba9038fd3665d2304f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d72636c6f6e65\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7f0e19a39ace7f9221d79c234d3288bcc366f84a3d48f0ba9038fd3665d2304f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7073636564752f73696e67756c61726974792d72636c6f6e65\" alt=\"Issue\" data-canonical-src=\"https://img.shields.io/github/issues/pscedu/singularity-rclone\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3624526e0c40e35705ae3e72ab403d7bbacce1076a9f42762a61b0707dce61c4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d72636c6f6e65\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3624526e0c40e35705ae3e72ab403d7bbacce1076a9f42762a61b0707dce61c4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7073636564752f73696e67756c61726974792d72636c6f6e65\" alt=\"forks\" data-canonical-src=\"https://img.shields.io/github/forks/pscedu/singularity-rclone\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/409093de2053c94e75e0b30037f01a0795f2921156a7d950cd8f5a21450defb3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d72636c6f6e65\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/409093de2053c94e75e0b30037f01a0795f2921156a7d950cd8f5a21450defb3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7073636564752f73696e67756c61726974792d72636c6f6e65\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/pscedu/singularity-rclone\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/16474a564a6deeed6b0ae94b5e3047b09b75c794e93ac3874243be716d650371/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d72636c6f6e65\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/16474a564a6deeed6b0ae94b5e3047b09b75c794e93ac3874243be716d650371/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7073636564752f73696e67756c61726974792d72636c6f6e65\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/pscedu/singularity-rclone\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-rclone\u003c/h1\u003e\u003ca id=\"user-content-singularity-rclone\" class=\"anchor\" aria-label=\"Permalink: singularity-rclone\" href=\"#singularity-rclone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2ed5b3734f0db17a45382f5ec04f9fffb2bf36d231840805780c71447f285b3f/68747470733a2f2f72636c6f6e652e6f72672f696d672f6c6f676f5f6f6e5f6c696768745f5f686f72697a6f6e74616c5f636f6c6f722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2ed5b3734f0db17a45382f5ec04f9fffb2bf36d231840805780c71447f285b3f/68747470733a2f2f72636c6f6e652e6f72672f696d672f6c6f676f5f6f6e5f6c696768745f5f686f72697a6f6e74616c5f636f6c6f722e737667\" alt=\"Logo\" data-canonical-src=\"https://rclone.org/img/logo_on_light__horizontal_color.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nSingularity recipe for \u003ca href=\"https://rclone.org/\" rel=\"nofollow\"\u003erclone\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling the container on Bridges 2\u003c/h2\u003e\u003ca id=\"user-content-installing-the-container-on-bridges-2\" class=\"anchor\" aria-label=\"Permalink: Installing the container on Bridges 2\" href=\"#installing-the-container-on-bridges-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSIF\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eand the \u003ccode\u003erclone\u003c/code\u003e script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto \u003ccode\u003e/opt/packages/rclone/1.55.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eCopy the file \u003ccode\u003emodulefile.lua\u003c/code\u003e to \u003ccode\u003e/opt/modulefiles/rclone\u003c/code\u003e as \u003ccode\u003e1.55.1.lua\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image using the recipe\u003c/h2\u003e\u003ca id=\"user-content-building-the-image-using-the-recipe\" class=\"anchor\" aria-label=\"Permalink: Building the image using the recipe\" href=\"#building-the-image-using-the-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image locally\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-locally\" class=\"anchor\" aria-label=\"Permalink: To build the image locally\" href=\"#to-build-the-image-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003ebuild.sh\u003c/code\u003e to build image locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./build.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo build the image remotely\u003c/h3\u003e\u003ca id=\"user-content-to-build-the-image-remotely\" class=\"anchor\" aria-label=\"Permalink: To build the image remotely\" href=\"#to-build-the-image-remotely\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the script \u003ccode\u003erbuild.sh\u003c/code\u003e to build image remotely.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./rbuild.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run tests\u003c/h2\u003e\u003ca id=\"user-content-to-run-tests\" class=\"anchor\" aria-label=\"Permalink: To run tests\" href=\"#to-run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the available tests, run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash ./test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright \u00a9 2020-2021 Pittsburgh Supercomputing Center. All Rights Reserved.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.psc.edu/biomedical-applications/\" rel=\"nofollow\"\u003eBiomedical Applications Group\u003c/a\u003e at the \u003ca href=\"http://www.psc.edu\" rel=\"nofollow\"\u003ePittsburgh Supercomputing Center\u003c/a\u003e in the \u003ca href=\"https://www.cmu.edu/mcs/\" rel=\"nofollow\"\u003eMellon College of Science\u003c/a\u003e at \u003ca href=\"http://www.cmu.edu\" rel=\"nofollow\"\u003eCarnegie Mellon University\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [ "singularity", - "singularity-container", - "openrave", - "openrave-controller", - "ros", - "ros-indigo" + "utilities" ], - "updated_at": 1630970845.0 + "updated_at": 1693705852.0 }, { "data_format": 2, - "description": "BIDS App for U-net brain masking of fetal bold MRI", + "description": "A Singularity container with R and Rstudio", "filenames": [ "Singularity" ], - "full_name": "khanlab/funcmasker-flex", - "latest_release": "v0.2.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003efuncmasker-flex\u003c/h1\u003e\u003ca id=\"user-content-funcmasker-flex\" class=\"anchor\" aria-label=\"Permalink: funcmasker-flex\" href=\"#funcmasker-flex\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBrain masking app using Unet for fetal bold mri\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample usage:\u003c/h3\u003e\u003ca id=\"user-content-example-usage\" class=\"anchor\" aria-label=\"Permalink: Example usage:\" href=\"#example-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet a sample subject dataset:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edatalad install https://github.com/OpenNeuroDatasets/ds003090.git\ncd ds003090/\ndatalad get sub-2225\ncd ../\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun \u003ccode\u003efuncmasker-flex\u003c/code\u003e on it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e docker://khanlab/funcmasker-flex:latest ds003090/ funcmasker participant --participant_label 2225 --cores all\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "dvav/singularity-rstudio-server", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-rstudio-server\u003c/h1\u003e\u003ca id=\"user-content-singularity-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: singularity-rstudio-server\" href=\"#singularity-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a \u003ccode\u003esingularity\u003c/code\u003e definition file and associated files for building a container\nwith the most recent (at the time of this writing) version of \u003ccode\u003eR\u003c/code\u003e and \u003ccode\u003eRstudio Server\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo build the container, use the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build rstudio.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the container, do the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind {host_dir1}:/var/lib/,{host_dir2}:/var/run rstudio.sif --www-port {your port}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can run the container on \u003ccode\u003ehumbug\u003c/code\u003e (if you don\u0027t know what this is, skip this paragraph) using the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind /well,/gpfs0,/gpfs1,/gpfs2,{host_dir1}:/var/lib/,{host_dir2}:/var/run rstudio.sif --www-port {remote port}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand setting up an \u003ccode\u003essh\u003c/code\u003e tunnel from your local machine (e.g. your laptop), for example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003essh -N -f -L {local port}:localhost:{remote port} {your username}@humbug.well.ox.ac.uk\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou need to be connected to the centre\u0027s VPN for this work.\u003c/p\u003e\n\u003cp\u003eThe following, does not work (yet):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRSTUDIO_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epassword\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n singularity run --bind {host_dir1}:/var/lib/,{host_dir2}:/var/run rstudio.sif \\\n --auth-none 0 \\\n --auth-pam-helper rstudio_auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more info, check the definition file \u003ccode\u003eSingularity\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eI used code from \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003ehttps://github.com/nickjer/singularity-rstudio\u003c/a\u003e as a point of departure. Bits of this code still remain in this repository.\u003c/p\u003e\n", "stargazers_count": 2, "subscribers_count": 1, - "topics": [ - "bids", - "bold", - "masking", - "mri", - "unet" - ], - "updated_at": 1712967895.0 + "topics": [], + "updated_at": 1622205021.0 }, { "data_format": 2, - "description": "reper - Genome-wide identification, classification and quantification of repetitive elements without an assembled genome", + "description": null, "filenames": [ - "Singularity" + "intogen-plus/build/containers/mutpanning/Singularity", + "intogen-plus/build/containers/mutrate/Singularity", + "intogen-plus/build/containers/signature/Singularity", + "intogen-plus/build/containers/transvar/Singularity", + "intogen-plus/build/containers/oncodrivefml/Singularity", + "intogen-plus/build/containers/hotmaps/Singularity", + "intogen-plus/build/containers/deconstructsig/Singularity", + "intogen-plus/build/containers/oncodriveclustl/Singularity", + "intogen-plus/build/containers/cbase/Singularity", + "intogen-plus/build/containers/combination/Singularity", + "intogen-plus/build/containers/core/Singularity", + "intogen-plus/build/containers/dndscv/Singularity" ], - "full_name": "nterhoeven/reper", - "latest_release": "v1.1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ereper - Genome-wide identification, classification and quantification of repetitive elements without an assembled genome\u003c/h1\u003e\u003ca id=\"user-content-reper---genome-wide-identification-classification-and-quantification-of-repetitive-elements-without-an-assembled-genome\" class=\"anchor\" aria-label=\"Permalink: reper - Genome-wide identification, classification and quantification of repetitive elements without an assembled genome\" href=\"#reper---genome-wide-identification-classification-and-quantification-of-repetitive-elements-without-an-assembled-genome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/nterhoeven/reper/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37deaf299a2fff03c8ec0fe8f6c71a268db42bd2af1427de6cba095c6e748c86/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e746572686f6576656e2f72657065722e737667\" alt=\"Docker Automated build\" data-canonical-src=\"https://img.shields.io/docker/automated/nterhoeven/reper.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nterhoeven/reper/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2ccda683a83f3ef09ed5466fb62acb0e357df1b6f461a50ac9ad329545889f44/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6275696c642f6e746572686f6576656e2f72657065722e737667\" alt=\"Docker Build Status\" data-canonical-src=\"https://img.shields.io/docker/build/nterhoeven/reper.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/80427752\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a5481251b07886ad6a1ed9e4eba07fea691a7a2b555f5becc70b62159e83d8df/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f38303432373735322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/80427752.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://joss.theoj.org/papers/f0d16a43d8b031695f151ea25e0d47b0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/571828e4c6ad6f35b2eea0f3b1d87599ac36d006f90c44bd31d64777acb92542/687474703a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f66306431366134336438623033313639356631353165613235653064343762302f7374617475732e737667\" alt=\"status\" data-canonical-src=\"http://joss.theoj.org/papers/f0d16a43d8b031695f151ea25e0d47b0/status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is reper?\u003c/h2\u003e\u003ca id=\"user-content-what-is-reper\" class=\"anchor\" aria-label=\"Permalink: What is reper?\" href=\"#what-is-reper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ereper is a pipeline to detect repetitive sequences in genome sequencing data.\nThe detection is based on kmer frequencies and does not rely on a genome assembly.\nThis allows an analysis of repeat sequences of organisms with large and repeat rich\ngenomes (especially plants). For a detailed explanation of the pipeline, see the \u003ca href=\"https://github.com/nterhoeven/reper/wiki/How-does-reper-work%3F\"\u003ereper wiki\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I get reper?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-get-reper\" class=\"anchor\" aria-label=\"Permalink: How do I get reper?\" href=\"#how-do-i-get-reper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ereper is available as Docker container, Singularity image or can be installed manually.\nPlease visit the \u003ca href=\"https://github.com/nterhoeven/reper/wiki/Installation\"\u003ereper wiki installation page\u003c/a\u003e for detailed explanations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I run reper?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-run-reper\" class=\"anchor\" aria-label=\"Permalink: How do I run reper?\" href=\"#how-do-i-run-reper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning reper is very easy. You just need to adjust the config file and start reper with \u003ccode\u003ereper kmerCount\u003c/code\u003e.\nA detailed explanation of the available commands is given in the \u003ca href=\"https://github.com/nterhoeven/reper/wiki/Using-reper\"\u003eusage page of the reper wiki\u003c/a\u003e.\nOr you can take a look at the \u003ca href=\"https://github.com/nterhoeven/reper/wiki/Tutorial\"\u003eTutorial\u003c/a\u003e and learn how to analyze the\nrepeat content of the sugar beet using reper.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow can I contribute?\u003c/h2\u003e\u003ca id=\"user-content-how-can-i-contribute\" class=\"anchor\" aria-label=\"Permalink: How can I contribute?\" href=\"#how-can-i-contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContribution to reper is always appreciated. Please submit any bugs, feature requests and similar via the github issue tracker.\nIf you want to contribute code, feel free to fork this repository and/or open a pull request.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense and Citation\u003c/h2\u003e\u003ca id=\"user-content-license-and-citation\" class=\"anchor\" aria-label=\"Permalink: License and Citation\" href=\"#license-and-citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ereper is placed under the MIT License.\u003c/p\u003e\n", + "full_name": "gagneurlab/Leukemia_outlier", + "latest_release": null, "stargazers_count": 2, "subscribers_count": 4, "topics": [], - "updated_at": 1601383172.0 + "updated_at": 1718028308.0 }, { "data_format": 2, - "description": "The simulation-supervised package combines different sets of code needed to train a DNN policy to fly a drone.", + "description": "Singularity containers with software installed via Spack", + "filenames": [ + "Singularity.busco", + "Singularity.gcc", + "Singularity.openmpi", + "Singularity.trinity", + "Singularity.spack" + ], + "full_name": "ResearchIT/spack-singularity", + "latest_release": null, + "stargazers_count": 2, + "subscribers_count": 8, + "topics": [ + "spack", + "openmpi", + "singularity" + ], + "updated_at": 1648743296.0 + }, + { + "data_format": 2, + "description": "dell racadm and other bios level tool (omsa) [unofficial]", "filenames": [ "Singularity" ], - "full_name": "kkelchte/simulation_supervised", + "full_name": "tin6150/dell_idracadm", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esimulation-supervised\u003c/h1\u003e\u003ca id=\"user-content-simulation-supervised\" class=\"anchor\" aria-label=\"Permalink: simulation-supervised\" href=\"#simulation-supervised\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe simulation-supervised package combines different sets of code needed to train a DNN policy to fly a drone.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.github.com/kkelchte/pilot_online\"\u003eonline_training\u003c/a\u003e: the tensorflow code used for training and running the DNN policy.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.github.com/kkelchte/hector_quadrotor\"\u003edrone_simulator\u003c/a\u003e: a simulated drone model for Gazebo. This is a copy of the original \u003ca href=\"http://wiki.ros.org/hector_quadrotor\" rel=\"nofollow\"\u003ehector quadrotor model\u003c/a\u003e.\nOR\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kkelchte/bebop_autonomy\"\u003ebebop_autonomy\u003c/a\u003e: a copy of the bebop autonomy package of ROS. This package allows you to test the DNN in the real-world. The copy is only for ensuring stability while performing research. There are no significant modifications so it is probably best to use \u003ca href=\"https://github.com/AutonomyLab/bebop_autonomy\"\u003ethe original\u003c/a\u003e. If you are using the Doshico docker image, it is already installed globally.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package is best build in a separate \u003ca href=\"http://wiki.ros.org/catkin/Tutorials/create_a_workspace\" rel=\"nofollow\"\u003ecatkin workspace\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/simsup_ws/src\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/simsup_ws \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e catkin_make\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/simsup_ws/src\ngit clone https://github.com/kkelchte/simulation_supervised\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/simsup_ws \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e catkin_make\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will have to set the correct path to your tensorflow pilot_online package.\u003c/p\u003e\n\u003cp\u003eIn case you are using different drone models, you will have to adjust the \u003ca href=\"https://github.com/kkelchte/simulation-supervised/blob/master/simulation_supervised/config/sim_drone.yaml\"\u003econfig.yaml\u003c/a\u003e file in order to set the correct rosparams.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun some experiments\u003c/h2\u003e\u003ca id=\"user-content-run-some-experiments\" class=\"anchor\" aria-label=\"Permalink: Run some experiments\" href=\"#run-some-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere are some common used setting combinations in order to remember them:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Test online the performance of a heuristic defined in tensorflow/gt_depth_online/../rosinterface.py using for instance recovery cameras flying 3 times through a generated canyon\n$ ./scripts/evaluate_model.sh -m auxd -s start_python_sing_gtdepth.sh -t test_depth_online -r true -w canyon -n 3\n# Train online in canyon, forest, sandbox\n$ ./scripts/train_model.sh -m mobilenet_025\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 2, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1623707939.0 + "updated_at": 1706540936.0 }, { "data_format": 2, - "description": "A fast python tool for extracting diffusion-based brain connectivity", + "description": null, "filenames": [ - "Singularity" + "Singularity", + "nwchem-dev.ompi40x.skylake/Singularity", + "nwchem-dev.ompi41x.ifx.test/Singularity.oldtest", + "nwchem-dev.ompi41x.ifx.test/Singularity", + "nwchem-701.ifort/Singularity", + "nwchem-dev.ompi40x.ifort.skylake/Singularity", + "exachem.ompi41x/Singularity", + "nwchem-701.ompi313.ivybridge/Singularity", + "nwchem-dev.ompi41x/Singularity", + "nwchem-dev.ompi41x.ifx.hpcx/Singularity", + "nwchem-701.mpich321.ivybridge/Singularity", + "nwchem-dev.ompi5x/Singularity", + "nwchem-dev.ompi40x/Singularity", + "nwchem-dev.mpich3/Singularity", + "nwchem-dev.ompi41x.ifx/Singularity", + "nwchem-702.ompi313.ivybridge/Singularity", + "nwchem-dev.aws/Singularity" ], - "full_name": "jasonbian97/fastcod-code", + "full_name": "edoapra/nwchem-singularity", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNWChem singularity\u003c/h1\u003e\u003ca id=\"user-content-nwchem-singularity\" class=\"anchor\" aria-label=\"Permalink: NWChem singularity\" href=\"#nwchem-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/edoapra/nwchem-singularity/actions/workflows/apptainer_action.yml\"\u003e\u003cimg src=\"https://github.com/edoapra/nwchem-singularity/actions/workflows/apptainer_action.yml/badge.svg\" alt=\"nwchem_apptainer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity/Apptainer recipes for NWChem\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/edoapra/nwchem-singularity/tree/master/nwchem-dev.ompi41x\"\u003ehttps://github.com/edoapra/nwchem-singularity/tree/master/nwchem-dev.ompi41x\u003c/a\u003e\u003cbr\u003e\nand\u003cbr\u003e\n\u003ca href=\"https://nwchemgit.github.io/Containers.html#instruction-for-running-on-emsl-tahoma\" rel=\"nofollow\"\u003ehttps://nwchemgit.github.io/Containers.html#instruction-for-running-on-emsl-tahoma\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 1, + "subscribers_count": 3, + "topics": [], + "updated_at": 1732310529.0 + }, + { + "data_format": 2, + "description": "the robot namer", + "filenames": [ + "Singularity" + ], + "full_name": "vsoch/robotname", + "latest_release": "0.0.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRobot Generator\u003c/h1\u003e\u003ca id=\"user-content-robot-generator\" class=\"anchor\" aria-label=\"Permalink: Robot Generator\" href=\"#robot-generator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis folder contains an application for a robot name generator.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s built on Docker Hub, so you can run as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/robotname\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor build locally first, with the present working directory as this folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t vanessa/robotname .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRobot Names\u003c/h2\u003e\u003ca id=\"user-content-robot-names\" class=\"anchor\" aria-label=\"Permalink: Robot Names\" href=\"#robot-names\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003efor i in `seq 1 10`;\n do\n docker run vanessa/robotname\ndone\ndinosaur-signal-1365\nbricky-cinnamonbun-1640\nnerdy-leg-6553\nmagnificent-lemon-2727\nmilky-kitty-9135\narid-snakey-5251\nplacid-cupcake-0084\njoyous-poodle-7162\nangry-underoos-2988\nmuffled-gato-4718\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRobot Badges\u003c/h2\u003e\u003ca id=\"user-content-robot-badges\" class=\"anchor\" aria-label=\"Permalink: Robot Badges\" href=\"#robot-badges\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeed a fun, spurious badge? Of course you do!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003eseq 1 10\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n docker run vanessa/robotname badge\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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src=\"https://camo.githubusercontent.com/16ef606cee6af13b994011c159b57e9a974d6ab074725727d225213786bcb3ce/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6661742d6c61746b655f313131392d74757271756f6973652e7376673f7374796c653d666c6174266c696e6b3d68747470732533412532462532466f70656e62617365732e6769746875622e696f2532466f70656e62617365732d707974686f6e25324668746d6c25324675736167652e68746d6c253233626164676573266c6f6e6743616368653d74727565\" alt=\"https://img.shields.io/badge/fat-latke_1119-turquoise.svg?style=flat\u0026amp;link=https%3A%2F%2Fopenbases.github.io%2Fopenbases-python%2Fhtml%2Fusage.html%23badges\u0026amp;longCache=true\" data-canonical-src=\"https://img.shields.io/badge/fat-latke_1119-turquoise.svg?style=flat\u0026amp;link=https%3A%2F%2Fopenbases.github.io%2Fopenbases-python%2Fhtml%2Fusage.html%23badges\u0026amp;longCache=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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href=\"https://camo.githubusercontent.com/e7fd2096a5f5019c4ed8eaf58f0b73616c62c333f9b13484e0c3784e45d412dc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7a697070792d6c65675f303136322d70616c65677265656e2e7376673f7374796c653d666c6174266c696e6b3d68747470732533412532462532466f70656e62617365732e6769746875622e696f2532466f70656e62617365732d707974686f6e25324668746d6c25324675736167652e68746d6c253233626164676573266c6f6e6743616368653d74727565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7fd2096a5f5019c4ed8eaf58f0b73616c62c333f9b13484e0c3784e45d412dc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7a697070792d6c65675f303136322d70616c65677265656e2e7376673f7374796c653d666c6174266c696e6b3d68747470732533412532462532466f70656e62617365732e6769746875622e696f2532466f70656e62617365732d707974686f6e25324668746d6c25324675736167652e68746d6c253233626164676573266c6f6e6743616368653d74727565\" alt=\"https://img.shields.io/badge/zippy-leg_0162-palegreen.svg?style=flat\u0026amp;link=https%3A%2F%2Fopenbases.github.io%2Fopenbases-python%2Fhtml%2Fusage.html%23badges\u0026amp;longCache=true\" data-canonical-src=\"https://img.shields.io/badge/zippy-leg_0162-palegreen.svg?style=flat\u0026amp;link=https%3A%2F%2Fopenbases.github.io%2Fopenbases-python%2Fhtml%2Fusage.html%23badges\u0026amp;longCache=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBrought to you by \u003ca href=\"https://openbases.github.io/openbases-python/html/usage.html#badges\" rel=\"nofollow\"\u003eopenbases python\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build your image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build robotname Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor pull from Docker Hub :)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name robotname docker://vanessa/robotname\nsregistry pull docker://vanessa/robotname\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer Battle!\u003c/h2\u003e\u003ca id=\"user-content-container-battle\" class=\"anchor\" aria-label=\"Permalink: Container Battle!\" href=\"#container-battle\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWho generates names faster? Try this on your own to see :)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor i in `seq 1 10`; do docker run vanessa/robotname; done\nfor i in `seq 1 10`; do ./robotname; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor \u003ca href=\"\"\u003ewatch here\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 2, + "subscribers_count": 4, "topics": [ - "diffusion", - "mri", - "brain", - "connectivity", - "tractography" + "badge", + "markdown", + "robot", + "name", + "generator" ], - "updated_at": 1679470502.0 + "updated_at": 1545323610.0 }, { "data_format": 2, - "description": "OptiTrust", + "description": "snakemake workflow for mappind single/paired datas", "filenames": [ - "Singularity" + "rattleSNP/containers/Singularity.report.def", + "rattleSNP/containers/Singularity.rattleSNP_tools.def" ], - "full_name": "charguer/optitrust", + "full_name": "sravel/RattleSNP", "latest_release": null, - "readme": "\u003cp\u003eOptiTrust is a tool for user-guided, source-to-source transformations.\u003c/p\u003e\n\u003cp\u003eThe project webpage is:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://optitrust.inria.fr\" rel=\"nofollow\"\u003ehttp://optitrust.inria.fr\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIt is a research prototype, under active development.\u003c/p\u003e\n\u003cp\u003eIn the meanwhile, if you are interested in a demo, please get in touch with @charguer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOptiTrust Installation\u003c/h1\u003e\u003ca id=\"user-content-optitrust-installation\" class=\"anchor\" aria-label=\"Permalink: OptiTrust Installation\" href=\"#optitrust-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall OCaml and system packages\u003c/h1\u003e\u003ca id=\"user-content-install-ocaml-and-system-packages\" class=\"anchor\" aria-label=\"Permalink: Install OCaml and system packages\" href=\"#install-ocaml-and-system-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt takes about 30 minutes to install the required OCaml software.\u003c/p\u003e\n\u003cp\u003eInstallation of system packages:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo apt-get install opam clang clang-format libclang-dev llvm-dev libomp-dev pkg-config zlib1g-dev\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for C++ headers support:\u003c/span\u003e\n sudo apt-get install libc++-dev\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional, only if you prefer using `meld` over `code -d`:\u003c/span\u003e\n sudo apt-get install meld\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstallation of OCaml ecosystem:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e opam init\n opam switch create 4.14.1\n opam pin add menhirLib 20210419\n opam pin add pprint 20220103\n opam pin add clangml 4.8.0\n opam install dune refl clangml pprint menhir menhirLib base64 ocamlbuild ocaml-lsp-server ppx_deriving\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e next line used only for generating the documentation of OptiTrust:\u003c/span\u003e\n opam install odoc lambdasoup\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e fancy traces\u003c/span\u003e\n opam install dream\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then in any case execute the last line below\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eeval\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003eopam env\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall precommit hooks\u003c/h1\u003e\u003ca id=\"user-content-install-precommit-hooks\" class=\"anchor\" aria-label=\"Permalink: Install precommit hooks\" href=\"#install-precommit-hooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e make install_git_hooks\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePrecompiling headers\u003c/h1\u003e\u003ca id=\"user-content-precompiling-headers\" class=\"anchor\" aria-label=\"Permalink: Precompiling headers\" href=\"#precompiling-headers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor faster compilation, we precompile header files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e make precompile\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall libraries for parsing\u003c/h1\u003e\u003ca id=\"user-content-install-libraries-for-parsing\" class=\"anchor\" aria-label=\"Permalink: Install libraries for parsing\" href=\"#install-libraries-for-parsing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThen, you need to either export the environment variable OPTITRUST by\nexecuting \"export OPTITRUST=\u003ccode\u003epwd\u003c/code\u003e\", from the optitrust folder,\nor more conveniently execute the command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo make install_compcert_stdlib\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhich essentially performs a \u003ccode\u003esudo install src/c/compcert_parser/include/*.h usr/local/lib/compcert\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTest your installation from the command line\u003c/h1\u003e\u003ca id=\"user-content-test-your-installation-from-the-command-line\" class=\"anchor\" aria-label=\"Permalink: Test your installation from the command line\" href=\"#test-your-installation-from-the-command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChecking your installation of OptiTrust is working:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e make tests\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eConfigure VScode (or VSCodium) for interactive usage\u003c/h1\u003e\u003ca id=\"user-content-configure-vscode-or-vscodium-for-interactive-usage\" class=\"anchor\" aria-label=\"Permalink: Configure VScode (or VSCodium) for interactive usage\" href=\"#configure-vscode-or-vscodium-for-interactive-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install either VSCode or VSCodium (more open).\u003c/p\u003e\n\u003cp\u003eInstallation of VSCode (see \u003ca href=\"https://code.visualstudio.com/download\" rel=\"nofollow\"\u003ehttps://code.visualstudio.com/download\u003c/a\u003e for alternatives)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo snap install --classic code\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstallation of VSCodium:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo snap install codium --classic.\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlternative without use of snap:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e wget -qO - https://gitlab.com/paulcarroty/vscodium-deb-rpm-repo/raw/master/pub.gpg \\\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gpg --dearmor \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo dd of=/usr/share/keyrings/vscodium-archive-keyring.gpg\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edeb [ signed-by=/usr/share/keyrings/vscodium-archive-keyring.gpg ] https://download.vscodium.com/debs vscodium main\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo tee /etc/apt/sources.list.d/vscodium.list\n sudo apt update\n sudo apt install codium\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn the rest of this tutorial, we assume VSCode. If you are using VSCodium,\nyou probably have an existing alias from \u0027code\u0027 to \u0027codium\u0027. If not, you\nmay want to add into your \u003ccode\u003e~/.bashrc\u003c/code\u003e the line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e code=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecodium\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(or use \u003ccode\u003esudo ln -s /usr/bin/codium /usr/bin/code\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eRecommended: installation of Chromium browser, which is very fast for\nopening up the \"diff\" pages generated by OptiTrust:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo snap install chromium\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration of VSCode\u003c/h2\u003e\u003ca id=\"user-content-configuration-of-vscode\" class=\"anchor\" aria-label=\"Permalink: Configuration of VSCode\" href=\"#configuration-of-vscode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe point is to set up keybindings for interactive development of tranformation\nscripts. For example, F6 shows the diff associated with the transformation\ncovering the cursor position in the VSCode editor.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOpen VSCode\u003c/h3\u003e\u003ca id=\"user-content-open-vscode\" class=\"anchor\" aria-label=\"Permalink: Open VSCode\" href=\"#open-vscode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom the folder that contains present README file, open VSCode using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e code \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\n codium \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall the OCaml platform:\u003c/h3\u003e\u003ca id=\"user-content-install-the-ocaml-platform\" class=\"anchor\" aria-label=\"Permalink: Install the OCaml platform:\" href=\"#install-the-ocaml-platform\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpen the extension pannel (\u003ccode\u003ectrl+shift+x\u003c/code\u003e) and look for the \"Ocaml platform\" extension.\nAlternatively, use the quick open prompt (\u003ccode\u003ectrl+p\u003c/code\u003e), then paste \u003ccode\u003eext install ocamllabs.ocaml-platform\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall the OptiTrust shortcuts for VSCode\u003c/h3\u003e\u003ca id=\"user-content-install-the-optitrust-shortcuts-for-vscode\" class=\"anchor\" aria-label=\"Permalink: Install the OptiTrust shortcuts for VSCode\" href=\"#install-the-optitrust-shortcuts-for-vscode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn VSCode, open the file \u003ccode\u003e~/.config/Code/User/keybindings.json\u003c/code\u003e.\nFor VSCodium, this file is located at \u003ccode\u003e~/.config/VSCodium/User/keybindings.json\u003c/code\u003e.\nIf you have an empty file, paste the following contents.\nIf you have a nonempty file, copy the bindings into your file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-js\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// OptiTrust keybindings\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f5\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Redo last view command\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"shift+f5\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View trace\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+shift+f5\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View standalone detailed trace\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"shift+f6\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View trace for one step\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f6\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View diff\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+shift+f6\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View diff using internal syntax\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// For working with unit tests\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Rerun the last-tried test(s)\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Run the current test\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+shift+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Open diff for the current test\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"shift+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Run all the tests\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"alt+shift+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Open unit test ML and CPP files\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"alt+f10\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Open unit test ML and CPP files and documentation\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// For working with long transformation scripts\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f7\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View diff from intermediate state\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"shift+f7\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"View trace from intermediate state\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+f7\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Save intermediate state\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// To open documentation\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"f11\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.runTask\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"args\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"Open doc for current source file in browser\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings \u0026amp;\u0026amp; resourceExtname == .ml\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// For killing a task, type \u0027ctrl+k\u0027 twice, then \u0027enter\u0027\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e{\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"key\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"ctrl+k ctrl+k\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"command\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"workbench.action.tasks.terminate\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\"when\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\"config.optitrust.enableKeybindings\"\u003c/span\u003e\n \u003cspan class=\"pl-kos\"\u003e}\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// Unused \"alt+f6\", \"alt+f7\", \"ctrl+shift+f6\",..\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e// End of OptiTrust keybindings\u003c/span\u003e\n\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote: the shortcuts refer to tasks that are defined in \u003ccode\u003e.vscode/tasks.json\u003c/code\u003e,\nwhich can be opened by pressing \u003ccode\u003ectrl+p\u003c/code\u003e then typing \u003ccode\u003etasks.json\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eYou can then adjust the keybindings to match your personal preferences,\nby changing the key parameter.\u003c/p\u003e\n\u003cp\u003eNote: the intermediate state shortcuts (\u003ccode\u003eF7\u003c/code\u003e) are useful for saving checkpoints.\nPress \u003ccode\u003ectrl+F7\u003c/code\u003e to compute the result of a transformation at a given line\nof the script. Then, type \u003ccode\u003eF7\u003c/code\u003e on a line further in the file.\nThe result should be the same as with typing \u003ccode\u003eF6\u003c/code\u003e, except that it runs\nfaster because the execution proceeds not from the beginning of the script\nbut instead from the line of the snapshot taken using \u003ccode\u003ectrl+F7\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeactivate possibly conficting bindings\u003c/h3\u003e\u003ca id=\"user-content-deactivate-possibly-conficting-bindings\" class=\"anchor\" aria-label=\"Permalink: Deactivate possibly conficting bindings\" href=\"#deactivate-possibly-conficting-bindings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn desktop managers such as Ubuntu\u0027s, certain shortcuts such as\n\u003ccode\u003eAlt+F6\u003c/code\u003e, \u003ccode\u003eAlt+F7\u003c/code\u003e etc. are bound to window manipulation operations,\ne.g. window resize. If you want to use these shortcuts, you may wish\nto deactivate the Ubuntu bindings. To that end, use the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e sudo apt-get install dconf-editor\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings begin-move []\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings begin-resize []\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings cycle-group []\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings cycle-group-backward []\n gsettings \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e org.gnome.desktop.wm.keybindings toggle-maximized []\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, you can open the settings panel, the keyboard menu, the\nshortcut submenu, then in the search bar type \u003ccode\u003eAlt+F\u003c/code\u003e (or go to the\n\u0027window\u0027 group), then select an action, and type \u003ccode\u003eBackspace\u003c/code\u003e to disable\nthe shortcut, then click on the \u003ccode\u003esave\u003c/code\u003e button.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing OptiTrust in VSCode\u003c/h2\u003e\u003ca id=\"user-content-using-optitrust-in-vscode\" class=\"anchor\" aria-label=\"Permalink: Using OptiTrust in VSCode\" href=\"#using-optitrust-in-vscode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest your installation\u003c/h3\u003e\u003ca id=\"user-content-test-your-installation\" class=\"anchor\" aria-label=\"Permalink: Test your installation\" href=\"#test-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are at last ready to test the installation on a case study.\u003c/p\u003e\n\u003cp\u003eIn VScode, open \u003ccode\u003ecase_studies/matmul/matmul.ml\u003c/code\u003e (using e.g. \u003ccode\u003ectrl+p\u003c/code\u003e).\nPlace your cursor on the line starting with \u003ccode\u003e!! List.iter tile\u003c/code\u003e.\nType \u003ccode\u003eF6\u003c/code\u003e.\nYou should see a diff opening up in a browser.\u003c/p\u003e\n\u003cp\u003eAnother shortcut to try is \u003ccode\u003eshift+F5\u003c/code\u003e, on any line of the \u003ccode\u003ematmul.ml\u003c/code\u003e file\nAfter a dozen seconds, you should see the full transformation trace for the\nmatrix-multiply case study.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eViewing keyboard shortcuts\u003c/h3\u003e\u003ca id=\"user-content-viewing-keyboard-shortcuts\" class=\"anchor\" aria-label=\"Permalink: Viewing keyboard shortcuts\" href=\"#viewing-keyboard-shortcuts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt may not be easy at first to recall all the shortcuts. Besides using a sticker\nat the bottom of your screen, you can use the command \u003ccode\u003e./shortcuts.sh\u003c/code\u003e to display\nthe shortcuts, and in VSCode use menu File / Preferences / Keyboard Shortcuts,\nthen type \"optitrust\" in the query bar.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTroubleshooting\u003c/h3\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t see a diff, possible issues include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYour shortcut is not set up correctly; when the shortcut is pressed,\na terminal should open to show the output of the task.\nIf needed, to investigate whether key bindings are set up properly,\nin VScode type \u003ccode\u003ectrl+K\u003c/code\u003e immediately followed by \u003ccode\u003ectrl+s\u003c/code\u003e, then type \u003ccode\u003ealt+k\u003c/code\u003e,\nthen type \u003ccode\u003eF6\u003c/code\u003e and see whether you see \"Tasks: run tasks\" a entry.\u003c/li\u003e\n\u003cli\u003ethe compilation failed due to incorrect setup; you should see error\nmessages in the terminal.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe documentation for OptiTrust is generated using the OCaml \u0027odoc\u0027 tool.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://ocaml.github.io/odoc/odoc_for_authors.html\" rel=\"nofollow\"\u003ehttps://ocaml.github.io/odoc/odoc_for_authors.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe top-level command \u003ccode\u003emake doc\u003c/code\u003e calls \u0027dune build @doc\u0027, which invokes \u0027odoc\u0027.\nThen, the generated documentation is copied into the folder \u0027_doc\u0027.\nThere, the documentation is patched in order to add the diffs associated\nwith the documentation unit tests (files tests/.../*_doc.ml).\u003c/p\u003e\n\u003cp\u003eThe patch operation is implemented in \"doc/add_tests_into_doc.ml\".\u003c/p\u003e\n\u003cp\u003eThe top-level command \u003ccode\u003emake viewdoc\u003c/code\u003e compiles the doc and opens it.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTester\u003c/h2\u003e\u003ca id=\"user-content-tester\" class=\"anchor\" aria-label=\"Permalink: Tester\" href=\"#tester\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor running the test suite, execute:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/optitrust\n \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/tester run\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the header of the file \u003ccode\u003etester.ml\u003c/code\u003e for the full documentation.\u003c/p\u003e\n\u003cp\u003eOptional: in your \u003ccode\u003e~/.bashrc\u003c/code\u003e you can add an alias to make it easier to invoke the tester.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e t=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e~/optitrust/tester\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptional tools\u003c/h2\u003e\u003ca id=\"user-content-optional-tools\" class=\"anchor\" aria-label=\"Permalink: Optional tools\" href=\"#optional-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ccode\u003eINSTALL_EXTRA.md\u003c/code\u003e for a list of additional useful tools for program optimization.\u003c/p\u003e\n\u003cp\u003eSee \u003ccode\u003eVSCODE_CUSTOMIZE.md\u003c/code\u003e for useful tips for using VScode or VScodium.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 7, + "subscribers_count": 3, "topics": [], - "updated_at": 1730829136.0 + "updated_at": 1667559289.0 }, { "data_format": 2, - "description": "H3A RefGraph Hackathon 2019", + "description": "Materials for SfN 2018 training event", "filenames": [ - "Singularity" + "section23/environments/Singularity.heudiconv", + "section23/environments/Singularity.fsl", + "section23/environments/Singularity.heudiconvn", + "section23/environments/Singularity.fsln" ], - "full_name": "h3abionet/h3arefgraph", + "full_name": "ReproNim/sfn2018-training", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eh3abionet/h3arefgraph\u003c/h1\u003e\u003ca id=\"user-content-h3abioneth3arefgraph\" class=\"anchor\" aria-label=\"Permalink: h3abionet/h3arefgraph\" href=\"#h3abioneth3arefgraph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eRefGraph Workflows Hackathon\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/h3abionet/h3arefgraph\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/de582520672c8786f29f4b9c684afcf27d9b46937887d3034aea1833b6a85298/68747470733a2f2f7472617669732d63692e6f72672f68336162696f6e65742f68336172656667726170682e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/h3abionet/h3arefgraph.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/h3arefgraph\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4d73775218c1085ba17cfbd320c20d3b9b5eb30219df46b1e19e53d179e88186/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f68336172656667726170682e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/h3arefgraph.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is for the use and testing of graph based methods for variant calling.\u003c/p\u003e\n\u003cp\u003eThe aim is to allow the user to choose the reference graph construction method and the alignment / variant calling methods separately.\u003c/p\u003e\n\u003cp\u003eWe also provide tools for reporting the results of the variant calling, that take advantage of the additional contextual information that using reference graphs provides.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOverview\u003c/h3\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe aim of this project is to separate the different parts of the variant calling process to allow the development of\ntask specific tools. This is more in line with traditional variant calling where specific alignment tools may preform\nbetter for different organisms, but should not require a different downstream analysis for each output.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"assets/images/Overview_slide.jpeg\"\u003e\u003cimg src=\"assets/images/Overview_slide.jpeg\" alt=\"Overview slide\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe h3abionet/h3arefgraph pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/reference_genomes.md\"\u003eReference genomes\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eh3abionet/h3arefgraph was originally written by the H3ABioNet RefGraph Team.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions\u003c/h1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall VirtualBox\u003c/h2\u003e\u003ca id=\"user-content-install-virtualbox\" class=\"anchor\" aria-label=\"Permalink: Install VirtualBox\" href=\"#install-virtualbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGo to the VirtualBox Download page: \u003ca href=\"https://www.virtualbox.org/wiki/Downloads\" rel=\"nofollow\"\u003ehttps://www.virtualbox.org/wiki/Downloads\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSelect the link from the \"VirtualBox 5.2.12 platform packages\" section that matches your system.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Windows 10 users:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDouble-click the downloaded executable file (Windows Installer 1.1 or higher is required)\u003c/li\u003e\n\u003cli\u003eSelect default options in installation dialog.\u003c/li\u003e\n\u003cli\u003eThe installer will create a \"VirtualBox\" group in the Windows Start menu.\u003c/li\u003e\n\u003cli\u003eFor more detailed step-by-step instructions, \u003ca href=\"https://websiteforstudents.com/installing-virtualbox-windows-10/\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Mac users:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDouble-click on the downloaded dmg file to have its contents mounted\u003c/li\u003e\n\u003cli\u003eA window will open telling you to double click on the VirtualBox.pkg installer file displayed in that window.\u003c/li\u003e\n\u003cli\u003eThis will start the installer, which will allow you to select where to install VirtualBox to.\u003c/li\u003e\n\u003cli\u003eAfter installation, you can find a VirtualBox icon in the \"Applications\" folder in the Finder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor Ubuntu users:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStep-by-step instructions can be found \u003ca href=\"https://websiteforstudents.com/install-virtualbox-latest-on-ubuntu-16-04-lts-17-04-17-10/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload and import the VM image file\u003c/h2\u003e\u003ca id=\"user-content-download-and-import-the-vm-image-file\" class=\"anchor\" aria-label=\"Permalink: Download and import the VM image file\" href=\"#download-and-import-the-vm-image-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload from \u003ca href=\"https://training.repronim.org/reprotraining.ova\" rel=\"nofollow\"\u003ehttps://training.repronim.org/reprotraining.ova\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eTo import the reprotraining.ova file:\n\u003col\u003e\n\u003cli\u003eOpen VirtualBox on your computer\u003c/li\u003e\n\u003cli\u003eselect \"File\" -\u0026gt; \"Import Appliance\" from the VirtualBox menu.\u003c/li\u003e\n\u003cli\u003eClick through the import wizard dialog leaving the default settings (see here for example step-by-step instructions \u003ca href=\"https://docs.oracle.com/cd/E26217_01/E26796/html/qs-import-vm.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStarting the VM\u003c/h2\u003e\u003ca id=\"user-content-starting-the-vm\" class=\"anchor\" aria-label=\"Permalink: Starting the VM\" href=\"#starting-the-vm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOpen VirtualBox on your computer\u003c/li\u003e\n\u003cli\u003eChoose \"reprotraining\" from the menu on the left and press \"start\" (the green arrow) to start the Ubuntu virtual machine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIssues:\u003c/h2\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-label=\"Permalink: Issues:\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eif you have a windows 10 pro 64 bit (eg Lenovo X1C) machine and get an error like:\nvt-x/amd-v hardware acceleration is not available on your system, \u003ca href=\"https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/quick-start/enable-hyper-v#enable-the-hyper-v-role-through-settings\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eif you are unable to install VirtualBox due to virtualization technology (VT-x) not being enabled on your system, \u003ca href=\"https://docs-old.fedoraproject.org/en-US/Fedora/13/html/Virtualization_Guide/sect-Virtualization-Troubleshooting-Enabling_Intel_VT_and_AMD_V_virtualization_hardware_extensions_in_BIOS.html\" rel=\"nofollow\"\u003elook here\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe Presentations\u003c/h1\u003e\u003ca id=\"user-content-the-presentations\" class=\"anchor\" aria-label=\"Permalink: The Presentations\" href=\"#the-presentations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll tutorials and slides are available from the \u003ca href=\"http://repronim.org/sfn2018-training\" rel=\"nofollow\"\u003ehttp://repronim.org/sfn2018-training\u003c/a\u003e\ngenerated from sources within \u003ccode\u003egh-pages\u003c/code\u003e branch of this repository.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 14, + "subscribers_count": 8, "topics": [], - "updated_at": 1690159850.0 + "updated_at": 1541290105.0 }, { "data_format": 2, - "description": "EWAS Data Analysis", + "description": "Singularity documentation and build files for Northwestern University\u0027s Research Computing Services and the Quest HPC", "filenames": [ - "Singularity" + "singularity_files/mxnet/Singularity.mxnet_cpu", + "singularity_files/ubuntu/Singularity.ubuntu", + "singularity_files/biobakery/Singularity.biobakery", + "singularity_files/mpi/Singularity.openmpi", + "singularity_files/keras/Singularity.keras_cpu", + "singularity_files/tensorflow/Singularity.tensorflow_cpu", + "singularity_files/tensorflow/Singularity.tensorflow_gpu" ], - "full_name": "GenomicsAotearoa/EWAS_DAP", + "full_name": "ffineis/nurcs-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEWAS (Epigenetic-Wide Association Study) Data Analysis Pipeline\u003c/h1\u003e\u003ca id=\"user-content-ewas-epigenetic-wide-association-study--data-analysis-pipeline\" class=\"anchor\" aria-label=\"Permalink: EWAS (Epigenetic-Wide Association Study) Data Analysis Pipeline\" href=\"#ewas-epigenetic-wide-association-study--data-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the code base for our EWAS pipeline tools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo run locally\u003c/h1\u003e\u003ca id=\"user-content-to-run-locally\" class=\"anchor\" aria-label=\"Permalink: To run locally\" href=\"#to-run-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the docker and singularity images, make sure .env file contains the right values and then invoke ./run_ewas script\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding Docker image\u003c/h1\u003e\u003ca id=\"user-content-building-docker-image\" class=\"anchor\" aria-label=\"Permalink: Building Docker image\" href=\"#building-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: in most cases, to build the Docker image you need to have priviledged access. It is possible to run docker without \u0027sudo\u0027\nif the system is configured appropriately (see \u003ca href=\"https://askubuntu.com/questions/477551/how-can-i-use-docker-without-sudo\" rel=\"nofollow\"\u003ehttps://askubuntu.com/questions/477551/how-can-i-use-docker-without-sudo\u003c/a\u003e for reference). Running rootless docker in rootless mode is also possible, more details on: \u003ca href=\"https://docs.docker.com/engine/security/rootless/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/security/rootless/\u003c/a\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003einstall docker as described here: \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo ./build_docker\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding Singularity image\u003c/h1\u003e\u003ca id=\"user-content-building-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Building Singularity image\" href=\"#building-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: to build the Singularity image from the Docker image you need to have priviledged access\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003einstall singularity as described here: \u003ca href=\"https://sylabs.io/guides/3.3/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.3/user-guide/installation.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ehave the docker image already built\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo ./build_singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding all together\u003c/h1\u003e\u003ca id=\"user-content-building-all-together\" class=\"anchor\" aria-label=\"Permalink: Building all together\" href=\"#building-all-together\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: all pre-requisites for building Docker and Singularity images apply as stated above\u003c/p\u003e\n\u003cp\u003eTo build both Docker and Singularity images run \u003ccode\u003esudo ./build\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnvironment variables\u003c/h1\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are best set in the ewas_config file that should be placed in the user\u0027s home folder for security reasons as it contains email password. Sample ewas_config file is available in this project\u003c/p\u003e\n\u003cp\u003eEWAS_EMAIL_FROM - \"from\" email address to be used in the job report email\u003cbr\u003e\nEWAS_EMAIL_HOST - email server host to be used for sending the job report email (i.e. smtp.gmail.com)\u003cbr\u003e\nEWAS_EMAIL_PORT - email server port to be used for sending the job report email (i.e. 495)\u003cbr\u003e\nEWAS_EMAIL_USER - email server\u0027s username used for login\u003cbr\u003e\nEWAS_EMAIL_PASSWORD - email server\u0027s user password - KEEP CONFIDENTIAL !!!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the docker image\u003c/h1\u003e\u003ca id=\"user-content-running-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Running the docker image\" href=\"#running-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eModify the .env file to set the correct variable values or set these in the current environment\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo ./run_docker\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the singularity image\u003c/h1\u003e\u003ca id=\"user-content-running-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Running the singularity image\" href=\"#running-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eModify the .env file to set the correct variable values or set these in the current environment\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003esudo ./run_ewas\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTo run on HPC/singularity\u003c/h1\u003e\u003ca id=\"user-content-to-run-on-hpcsingularity\" class=\"anchor\" aria-label=\"Permalink: To run on HPC/singularity\" href=\"#to-run-on-hpcsingularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: you must first build the singularity image on a system where docker can run in priviledged mode:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003erun \"sudo ./build\" to build the docker image and produce a corresponding singularity file (ewas.img)\u003c/li\u003e\n\u003cli\u003ecopy the ewas.img file to a location on the cluster where the job will run from\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThen to run on the cluster:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003echoose a unique port that is not used by somebody else, high port numbers are probably safe,\ne.g. \u0026gt; 10000, here we use 10083, replace it with your chosen port in the commands below\u003c/li\u003e\n\u003cli\u003eset up port forwarding, e.g. on your local machine run \u003ccode\u003essh -L 10083:localhost:10083 mahuika\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eedit the Slurm script run_ewas_slurm (on the cluster)\n\u003cul\u003e\n\u003cli\u003eset the environment variables\u003c/li\u003e\n\u003cli\u003emake sure to set \u003ccode\u003eEWAS_PORT\u003c/code\u003e to the port you chose above\u003c/li\u003e\n\u003cli\u003eset the resource requirements (memory, wall time, etc)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003esubmit the run script to the queue \u003ccode\u003esbatch run_ewas_slurm\u003c/code\u003e (on the cluster)\u003c/li\u003e\n\u003cli\u003echeck the status of the Slurm job \u003ccode\u003esqueue -u $USER\u003c/code\u003e (on the cluster)\u003c/li\u003e\n\u003cli\u003eonce the job is running, on your local machine connect to \u003ca href=\"http://localhost:10083/\" rel=\"nofollow\"\u003ehttp://localhost:10083/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNorthwestern University Research Computing Services Singularity documentation and container files\u003c/h1\u003e\u003ca id=\"user-content-northwestern-university-research-computing-services-singularity-documentation-and-container-files\" class=\"anchor\" aria-label=\"Permalink: Northwestern University Research Computing Services Singularity documentation and container files\" href=\"#northwestern-university-research-computing-services-singularity-documentation-and-container-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1271\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e|-- docs (documentation)\n|-- singularity_files\n| |-- [container files]\n| |-- Singularity.\u0026lt;container tag\u0026gt; (can be built/integrated into Sinuglarity Hub collection)\n| |-- [files for pulling resources during container build]\n| |-- [files for running tests]\n|\n|-- templates\n| |-- [template recipe files]\n|\n|-- submit_job.sh (example MOAB submission file utilizing singularity cmds)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere can be multiple recipe files per container directory, for example, if there are both CPU and GPU versions of a single container. Files titled \u003ccode\u003eSingularity.\u0026lt;container tag\u0026gt;\u003c/code\u003e can be built on Singularity Hub and integrated with our Singularity Hub collection.\u003c/p\u003e\n\u003cp\u003eCheck the build status of each container in this repository on \u003ca href=\"https://singularity-hub.org/collections/1271\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFollow the \u003ca href=\"https://github.com/singularityhub/singularityhub.github.io/wiki/Build-A-Container\"\u003eSingularity automated build documentation\u003c/a\u003e for recipe file naming conventions for automating builds on Singularity Hub and for how to configure automated or manual builds on pushed commits.\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 6, - "topics": [ - "ewas", - "epigenetics", - "pipelines", - "health" - ], - "updated_at": 1715616897.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1651571098.0 }, { "data_format": 2, - "description": "In this training course you will find theory and practice material for introducing yourself to wgs analysis for bacterial, including outbreak investigation.", + "description": "code and supplementary documents supporting the FMAB book project", "filenames": [ - "Singularity" + "BSKL/SingularityFMAB" ], - "full_name": "BU-ISCIII/bacterial_wgs_training", - "latest_release": "ISCIII2018", - "readme": "\u003cp\u003e\u003ca href=\"https://circleci.com/gh/BU-ISCIII/bacterial_wgs_training\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0289dc02a9638d79a6ae3573745037bd79d6d2ae9d3bac65e1c84a786d090731/68747470733a2f2f636972636c6563692e636f6d2f67682f636972636c6563692f636972636c6563692d646f63732e7376673f7374796c653d736869656c64\" alt=\"CircleCI Build Status\" data-canonical-src=\"https://circleci.com/gh/circleci/circleci-docs.svg?style=shield\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93ca07ea63e4649ae72b87de86d82eedeab50e744fa6e07ee95e5344779fb930/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://discuss.circleci.com\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0db7cc65a258e95db6b1dddae92be2d4d80817b9570fded1962c4fd4b6fdcb6f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6d6d756e6974792d436972636c654349253230446973637573732d3334333433342e737667\" alt=\"CircleCi Community\" data-canonical-src=\"https://img.shields.io/badge/community-CircleCI%20Discuss-343434.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://nextflow.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f8a155e03276d377ca219881b2d695bd3be17898ea74a1250ad2b6941aef70cc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d253345302e32392e302d677265656e2e737667\" alt=\"Nextflow version\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%3E0.29.0-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3bb9116161cdfeb6b23d2bde2e62f92bb4eda63bd51f5c1ed4b8b86356f088e4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f46696c6573797374656d2d536369656e74696669632d627269676874677265656e2e737667\" alt=\"Scif\" data-canonical-src=\"https://img.shields.io/badge/Filesystem-Scientific-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBacterial WGS training\u003c/h1\u003e\u003ca id=\"user-content-bacterial-wgs-training\" class=\"anchor\" aria-label=\"Permalink: Bacterial WGS training\" href=\"#bacterial-wgs-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this training course you will find theory and practice material for introducing yourself to wgs analysis for bacterial, including outbreak investigation.\u003c/p\u003e\n\u003cp\u003eThe material includes slides with theory concepts and a bunch of practical exercises using nextflow and singularity, focusing on the interpretation of results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAgenda\u003c/h2\u003e\u003ca id=\"user-content-agenda\" class=\"anchor\" aria-label=\"Permalink: Agenda\" href=\"#agenda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 1\u003c/h3\u003e\u003ca id=\"user-content-day-1\" class=\"anchor\" aria-label=\"Permalink: Day 1\" href=\"#day-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 1 and 2:\u003c/strong\u003e \u003ca href=\"slides/talk1/20241028_6ED_curso_SeqGenBac_session1.1-2_Introduccion_EAB.pdf\"\u003eMassive sequencing of bacterial genomes. State-of-the-art. \u0026amp; Bacterial genomes sequencing. Applications.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 3:\u003c/strong\u003e \u003ca href=\"slides/talk3/curso_SeqGenBac_ChangingComputingParadigm.pdf\"\u003eThe computing revolution in Biosciences. Nextflow and Singularity introduction.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 4:\u003c/strong\u003e \u003ca href=\"slides/talk2/curso_SeqGenBac_session1.2_linux.pdf\"\u003eLinux environment review.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/00_SetUp.md\"\u003e\u003cstrong\u003eExercise 0\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/00_SetUp.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/01_LinuxBasicCommands.md\"\u003e\u003cstrong\u003eExercise 1\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/01_LinuxBasicCommands.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/02_NextflowSingularity.md\"\u003e\u003cstrong\u003eExercise 2\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/02_LinuxNextflowSingularity.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 2\u003c/h3\u003e\u003ca id=\"user-content-day-2\" class=\"anchor\" aria-label=\"Permalink: Day 2\" href=\"#day-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 5:\u003c/strong\u003e \u003ca href=\"slides/talk5/curso_SeqGenBac_session2.2_quality_assesment.pdf\"\u003eQuality analysis and control of HTS data\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 6:\u003c/strong\u003e \u003ca href=\"slides/talk6/slidestalk620221017_5ED_curso_SeqGenBac_session2.3_assembly_ICuesta.pdf.pdf\"\u003eBacterial genomes assembly\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 7:\u003c/strong\u003e \u003ca href=\"slides/talk11/20231020_5ED_curso_SeqGenBac_session5.1_annotation_ICuesta.pdf\"\u003eSequence annotation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/03_QualityAndAssembly.md\"\u003e\u003cstrong\u003eExercise 3\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/03_QualityAndAssembly.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 3\u003c/h3\u003e\u003ca id=\"user-content-day-3\" class=\"anchor\" aria-label=\"Permalink: Day 3\" href=\"#day-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 8:\u003c/strong\u003e \u003ca href=\"slides/talk7/curso_SeqGenBac_session3.1_MappingAndVariantCalling.pdf\"\u003eMapping against reference genome and Variant Calling.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 9:\u003c/strong\u003e \u003ca href=\"slides/talk8/curso_SeqGenBac_session3.2_SNPMatrixAndPhylogenetics.pdf\"\u003eSNP matrix and phylogenetics.\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/04_outbreakSNP.md\"\u003e\u003cstrong\u003eExercise 4\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/04_outbreakSNP.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 4\u003c/h3\u003e\u003ca id=\"user-content-day-4\" class=\"anchor\" aria-label=\"Permalink: Day 4\" href=\"#day-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 10:\u003c/strong\u003e \u003ca href=\"slides/talk10/20231019_5ED_curso_SeqGenBac_session4.1_tipificacion-gen-by-gene_ICuesta.pdf\"\u003eTyping based on allelic profile or gene-by-gene\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTalk 11:\u003c/strong\u003e \u003ca href=\"slides/talk10/curso_SeqGenBac_session4.2_GeneByGenevsSNPs.pdf\"\u003eGene-by-gene WGS analysis\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./exercises/05_outbreakcgMLST.md\"\u003e\u003cstrong\u003eExercise 5\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/05_outbreakcgMLST.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"exercises/06_annotation.md\"\u003e\u003cstrong\u003eExercise 6\u003c/strong\u003e\u003c/a\u003e -- \u003ca href=\"exercises/06_annotation.pdf\"\u003eDownload pdf\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "vpbrendel/CodeFMAB", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCodeFMAB\u003c/h1\u003e\u003ca id=\"user-content-codefmab\" class=\"anchor\" aria-label=\"Permalink: CodeFMAB\" href=\"#codefmab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and supplementary documents supporting the FMAB book project\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 3, - "topics": [ - "wgs", - "genome", - "sequencing", - "bacterial-genomes", - "ngs-analysis", - "outbreak-detection", - "outbreaks", - "nextflow", - "bacterial-wgs-training", - "wgs-analysis" - ], - "updated_at": 1730362704.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1725495252.0 }, { "data_format": 2, - "description": "Singularity R: rstudio desktop image", + "description": "This is Red Hen Lab\u0027s home for singularity containers to be built on Singularity Hub", "filenames": [ - "Singularity.3.4.4", - "Singularity" + "Singularity_ATEE1", + "Singularity.DeepSpeech2_shuwei", + "Singularity.Alex_practice", + "Singularity.openpose-cpu_veryslow", + "Singularity.DeepSpeech2", + "Singularity.DeepSpeech2_Ubuntu" ], - "full_name": "mjstealey/rstudio", + "full_name": "RedHenLab/singularity_containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity R: rstudio\u003c/h1\u003e\u003ca id=\"user-content-singularity-r-rstudio\" class=\"anchor\" aria-label=\"Permalink: Singularity R: rstudio\" href=\"#singularity-r-rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/798\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://www.rstudio.com/products/RStudio/\" rel=\"nofollow\"\u003eRStudio Desktop\u003c/a\u003e based on the \u003ca href=\"https://hub.docker.com/_/r-base/\" rel=\"nofollow\"\u003er-base\u003c/a\u003e docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003erstudio.3.4.4.simg\u003c/code\u003e with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build rstudio.3.4.4.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name rstudio.3.4.4.simg shub://mjstealey/rstudio\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHelp\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e rstudio.3.4.4.simg\u003c/span\u003e\n\n\n\u003cspan class=\"pl-c1\"\u003e RStudio Desktop version 1.1.442\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e R version 3.4.4 (2018-03-15) -- \"Someone to Lean On\"\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e Usage:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run rstudio.3.4.4.simg [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app R rstudio.3.4.4.simg [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app Rscript rstudio.3.4.4.simg [args]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e $ singularity run --app rstudio rstudio.3.4.4.simg\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNo particular command is launched using the default run command, rather it is left to the user to specify:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run rstudio.3.4.4.simg [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003e[args]\u003c/code\u003e is generally one of {\u003ccode\u003erstudio\u003c/code\u003e, \u003ccode\u003eR [args]\u003c/code\u003e, \u003ccode\u003eRscript [args]\u003c/code\u003e}\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR\u003c/h3\u003e\u003ca id=\"user-content-r\" class=\"anchor\" aria-label=\"Permalink: R\" href=\"#r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eR\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app R rstudio.3.4.4.simg [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app R rstudio.3.4.4.simg --version\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eR version 3.4.4 (2018-03-15) -- \"Someone to Lean On\"\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eCopyright (C) 2018 The R Foundation for Statistical Computing\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ePlatform: x86_64-pc-linux-gnu (64-bit)\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003eR is free software and comes with ABSOLUTELY NO WARRANTY.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eYou are welcome to redistribute it under the terms of the\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGNU General Public License versions 2 or 3.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eFor more information about these matters see\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ehttp://www.gnu.org/licenses/.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRscript\u003c/h3\u003e\u003ca id=\"user-content-rscript\" class=\"anchor\" aria-label=\"Permalink: Rscript\" href=\"#rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eRscript\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app Rscript rstudio.3.4.4.simg [args]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app Rscript rstudio.3.4.4.simg --version\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eR scripting front-end version 3.4.4 (2018-03-15)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRStudio\u003c/h3\u003e\u003ca id=\"user-content-rstudio\" class=\"anchor\" aria-label=\"Permalink: RStudio\" href=\"#rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003erstudio\u003c/code\u003e command is launched as an explicit app:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app rstudio rstudio.3.4.4.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003esingularity run --app rstudio rstudio.3.4.4.simg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003elibGL error: No matching fbConfigs or visuals found\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003elibGL error: failed to load driver: swrast\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eRStudio Desktop UI\u003c/strong\u003e: (using X11)\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/5332509/37848039-221fdf5e-2ea9-11e8-8f9c-db199ad4f6d2.png\"\u003e\u003cimg width=\"80%\" alt=\"RStudio Desktop\" src=\"https://user-images.githubusercontent.com/5332509/37848039-221fdf5e-2ea9-11e8-8f9c-db199ad4f6d2.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003ccode\u003eLibGL error:\u003c/code\u003e errors were observed while testing using a remote CentOS 7 VM to run the Singularity image and the UI was rendered using X11. No other errors were denoted during testing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn exit, the terminal displayed:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eQApplication::qAppName: Please instantiate the QApplication object first\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at \u003ca href=\"https://github.com/mjstealey/rstudio\"\u003ehttps://github.com/mjstealey/rstudio\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity_containers\u003c/h1\u003e\u003ca id=\"user-content-singularity_containers\" class=\"anchor\" aria-label=\"Permalink: singularity_containers\" href=\"#singularity_containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is Red Hen Lab\u0027s home for singularity containers to be built on Singularity Hub\u003c/p\u003e\n\u003cp\u003eThey can be found here:\n\u003ca href=\"https://singularity-hub.org/collections/1053\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 4, - "topics": [ - "singularity", - "rstudio-desktop", - "r", - "rstudio" - ], - "updated_at": 1628200768.0 + "subscribers_count": 19, + "topics": [], + "updated_at": 1589193222.0 }, { "data_format": 2, - "description": "Software which simplify construction of kinetic models for cell metabolism with optional integration of proteomics data ", + "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "chuanfuyap/Convenient-Modeller", + "full_name": "markziemann/lung_brain", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eConvenient Modeller\u003c/h1\u003e\u003ca id=\"user-content-convenient-modeller\" class=\"anchor\" aria-label=\"Permalink: Convenient Modeller\" href=\"#convenient-modeller\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConvenient Modeller is a software which simplifies construction of kinetic models for cell metabolism (with inclusion of regulatory effects), with optional integration of proteomics data.\u003c/p\u003e\n\u003cp\u003eThe tool allows for use of metabolite concentration, and/or flux for fitting a model, main objective of this tool is to allow for rapid prototyping of the a metabolic phenomenon of interest with a kinetic/dynamic model. For example, if one is interested in glycolysis of a species such as baker\u0027s yeast, metabolite/flux values can be obtained (from literature of experiment) to fit a model. Following the fit, a \u003cstrong\u003e\u003ca href=\"http://sbml.org/Main_Page\" rel=\"nofollow\"\u003eSBML\u003c/a\u003e\u003c/strong\u003e file is generated which can be exported to any SBML simulator such as \u003cstrong\u003e\u003ca href=\"http://copasi.o:\" rel=\"nofollow\"\u003eCOPASI\u003c/a\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003ca href=\"https://github.com/draeger-lab/SBMLsimulator\"\u003eSBMLsimulator\u003c/a\u003e\u003c/strong\u003e or \u003cstrong\u003e\u003ca href=\"https://jjj.mib.ac.uk\" rel=\"nofollow\"\u003eJWS online\u003c/a\u003e\u003c/strong\u003e to be manipulated (e.g. removal of enzymes, removal of activators/inhibitors or increasing enzyme concentration) and simulated to see what its outcome would be before proceeding with next step of metabolic study.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/chuanfuyap/Convenient-Modeller/blob/master/images/Glycolysis.png\"\u003e\u003cimg src=\"https://github.com/chuanfuyap/Convenient-Modeller/raw/master/images/Glycolysis.png\" width=\"520\" height=\"720\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/p\u003e\n\u003cp\u003eAbove is a basic glycolysis model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHow to use Convenient Modeller\u003c/h1\u003e\u003ca id=\"user-content-how-to-use-convenient-modeller\" class=\"anchor\" aria-label=\"Permalink: How to use Convenient Modeller\" href=\"#how-to-use-convenient-modeller\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNOTE: the GUI has bugs that has yet to be patched, but the optimisation engine that runs it is bug free. GUI bugs are mostly errors messages but should not affect model building. If GUI bugs are preventing model construction, the TSV format (see below) is recommended for model building and running the\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOption 1 (Recommended): Singularity\u003c/h2\u003e\u003ca id=\"user-content-option-1-recommended-singularity\" class=\"anchor\" aria-label=\"Permalink: Option 1 (Recommended): Singularity\" href=\"#option-1-recommended-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSoftware is now available as an easy to use singularity image.\u003c/p\u003e\n\u003cp\u003eSingularity is a containerization software available for free here: \u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/install-linux\u003c/a\u003e (instruction for linux)\u003c/p\u003e\n\u003cp\u003eAfter installation, users can download the image with the following line:\u003c/p\u003e\n\u003cp\u003esingularity pull shub://chuanfuyap/Convenient-Modeller\u003c/p\u003e\n\u003cp\u003eTo run the image:\u003c/p\u003e\n\u003cp\u003esingularity run shub://chuanfuyap/Convenient-Modeller\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMac users:\u003c/h3\u003e\u003ca id=\"user-content-mac-users\" class=\"anchor\" aria-label=\"Permalink: Mac users:\" href=\"#mac-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order for Mac users to use the GUI version of the software via singularity, they need to enable X11 Forwarding in vagrant box, as well as install xauth server within the vagrant box\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOption 2: To install and run the software locally\u003c/h2\u003e\u003ca id=\"user-content-option-2-to-install-and-run-the-software-locally\" class=\"anchor\" aria-label=\"Permalink: Option 2: To install and run the software locally\" href=\"#option-2-to-install-and-run-the-software-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software requires the following libraries to run:-\u003c/p\u003e\n\u003cp\u003eJava Libraries:\u003c/p\u003e\n\u003cp\u003eApache Common Maths version 3.5 \u003ca href=\"http://archive.apache.org/dist/commons/math/\" rel=\"nofollow\"\u003ehttp://archive.apache.org/dist/commons/math/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eJava Native Access (JNA) \u003ca href=\"https://github.com/java-native-access/jna\"\u003ehttps://github.com/java-native-access/jna\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eJava Systems Biology Markup Language (JSBML) version 1 \u003ca href=\"http://sbml.org/Software/JSBML\" rel=\"nofollow\"\u003ehttp://sbml.org/Software/JSBML\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eC Libraries:\nNOTE: it\u0027s possible the source file is corrupted and unable to be compiled, I would suggest deleting the functions in the source files that are giving errors during compilation step and compile again.\nSOSLIB version 1.7 \u003ca href=\"https://github.com/raim/SBML_odeSolver\"\u003ehttps://github.com/raim/SBML_odeSolver\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003elibSBML version 5.X.X \u003ca href=\"https://sourceforge.net/projects/sbml/files/libsbml/5.13.0/\" rel=\"nofollow\"\u003ehttps://sourceforge.net/projects/sbml/files/libsbml/5.13.0/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSUNDIALS version 2.3 \u003ca href=\"http://computation.llnl.gov/projects/sundials/sundials-software\" rel=\"nofollow\"\u003ehttp://computation.llnl.gov/projects/sundials/sundials-software\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRelevant project:\u003c/p\u003e\n\u003cp\u003eNOTE: this C program must be compiled with the right library paths for Convenient-Modeller to be able to solve models in parameter estimation process.\nsoslibJNIC \u003ca href=\"https://github.com/chuanfuyap/soslibJNIC\"\u003ehttps://github.com/chuanfuyap/soslibJNIC\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHOW TO USE:\u003c/h1\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: HOW TO USE:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFull tutorial available \u003cstrong\u003e[here]\u003c/strong\u003e\n[here]: \u003ca href=\"https://github.com/chuanfuyap/Convenient-Modeller/blob/master/Convenient%20Modeller%20Tutorial.pdf\"\u003ehttps://github.com/chuanfuyap/Convenient-Modeller/blob/master/Convenient%20Modeller%20Tutorial.pdf\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGUI\u003c/h2\u003e\u003ca id=\"user-content-gui\" class=\"anchor\" aria-label=\"Permalink: GUI\" href=\"#gui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCrash Course\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/chuanfuyap/Convenient-Modeller/blob/master/images/crash_course.png\"\u003e\u003cimg src=\"https://github.com/chuanfuyap/Convenient-Modeller/raw/master/images/crash_course.png\" width=\"960\" height=\"580\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAlternative model building with TSV\u003c/h2\u003e\u003ca id=\"user-content-alternative-model-building-with-tsv\" class=\"anchor\" aria-label=\"Permalink: Alternative model building with TSV\" href=\"#alternative-model-building-with-tsv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn alternative method is to use a spreadsheet to build the model before transfering to a text file, which can then be exported the convenient modeller GUI for further manipulation or model fitting.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/chuanfuyap/Convenient-Modeller/blob/master/images/TSV.png\"\u003e\u003cimg src=\"https://github.com/chuanfuyap/Convenient-Modeller/raw/master/images/TSV.png\" width=\"720\" height=\"320\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe text is read in row by row by the software.\u003c/li\u003e\n\u003cli\u003eA \u2018#\u2019 sign denotes start of a new element being read into the system.\u003c/li\u003e\n\u003cli\u003e\u2018B.C\u2019 stands for boundary condition, a function in SBML that makes a metabolite\u2019s concentration fixed if stated to be \u2018TRUE\u2019 (and vice versa), becoming either an output or input for the system being modelled.\u003c/li\u003e\n\u003cli\u003eThe column following either metabolites or enzymes are concentrations for the respective compounds.\u003c/li\u003e\n\u003cli\u003eFor the reactions, users must first set an enzyme that catalyses the reaction, and if needed activators and/or inhibitors for the reaction in the proceeding columns.\u003c/li\u003e\n\u003cli\u003eIn the following columns, substrates come first, then a \u2018=\u2019 sign to separate them from the products, which comes in the next columns.\u003c/li\u003e\n\u003cli\u003eFor stoichiometries of reactions, users can add numbers followed by \u2018\u003cem\u003e\u2019 preceding the substrates/products (e.g. 2\u003c/em\u003eG6P)\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003elung_brain\u003c/h1\u003e\u003ca id=\"user-content-lung_brain\" class=\"anchor\" aria-label=\"Permalink: lung_brain\" href=\"#lung_brain\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 2, "subscribers_count": 3, "topics": [], - "updated_at": 1589286125.0 + "updated_at": 1722946559.0 }, { "data_format": 2, - "description": "Pipeline to study intra tumor heterogeneity using HATCHet, DeCiFer and ClonEvol", + "description": "Files to create containers", "filenames": [ - "Singularity" + "apptainer.ompi41x/Singularity", + "apptainer.mpich/Singularity", + "apptainer.ompi5x/Singularity" ], - "full_name": "IARCbioinfo/ITH_pipeline", + "full_name": "ExaChem/exachem-container-images", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eITH_pipeline\u003c/h1\u003e\u003ca id=\"user-content-ith_pipeline\" class=\"anchor\" aria-label=\"Permalink: ITH_pipeline\" href=\"#ith_pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNextflow pipeline to study intra-tumoral heterogeneity\u003c/h2\u003e\u003ca id=\"user-content-nextflow-pipeline-to-study-intra-tumoral-heterogeneity\" class=\"anchor\" aria-label=\"Permalink: Nextflow pipeline to study intra-tumoral heterogeneity\" href=\"#nextflow-pipeline-to-study-intra-tumoral-heterogeneity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNextflow pipeline to study intra-tumoral heterogeneity through subclonality reconstruction, using HATCHet, DeCiFer and clonEvol.\nPIPELINE IN DEVELOPMENT.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eExternal software:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/raphael-group/hatchet\"\u003eHATCHet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/raphael-group/decifer\"\u003eDeCiFer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hdng/clonevol\"\u003eClonEvol\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bam_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing BAM files to pass to HATCHet\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd\u003e/whole/path/to/genome.fa\u003c/td\u003e\n\u003ctd\u003eReference genome in fasta format\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--correspondance\u003c/td\u003e\n\u003ctd\u003eTN_pairs.txt\u003c/td\u003e\n\u003ctd\u003eTabulated file containing two columns: tumor and normal bam names\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of cpu to use\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--config\u003c/td\u003e\n\u003ctd\u003enull\u003c/td\u003e\n\u003ctd\u003eUse custom configuration file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003eSize of memory used in GB\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003e.\u003c/td\u003e\n\u003ctd\u003ePath to output folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--samtools_folder\u003c/td\u003e\n\u003ctd\u003e/usr/bin/\u003c/td\u003e\n\u003ctd\u003esamtools installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bcftools_folder\u003c/td\u003e\n\u003ctd\u003e/usr/bin/\u003c/td\u003e\n\u003ctd\u003ebcftools installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hatchet_folder\u003c/td\u003e\n\u003ctd\u003e/usr/bin/\u003c/td\u003e\n\u003ctd\u003ehatchet installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bnpy_folder\u003c/td\u003e\n\u003ctd\u003e/usr/bin/\u003c/td\u003e\n\u003ctd\u003ebnpy installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run iarcbioinfo/ITH_pipeline --bam_folder bam_folder/ --ref genome.fa --correspondance pairs.txt --cpu 24 --samtools_folder ~/bin/samtools-1.7 --bcftools_folder ~/bin/bcftools-1.7 --hatchet_folder ~/bin/hatchet --bnpy_folder ~/bin/bnpy-dev\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed description\u003c/h2\u003e\u003ca id=\"user-content-detailed-description\" class=\"anchor\" aria-label=\"Permalink: Detailed description\" href=\"#detailed-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirected Acyclic Graph\u003c/h2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-label=\"Permalink: Directed Acyclic Graph\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:delhommet@students.iarc\"\u003edelhommet@students.iarc\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences (optional)\u003c/h2\u003e\u003ca id=\"user-content-references-optional\" class=\"anchor\" aria-label=\"Permalink: References (optional)\" href=\"#references-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ (optional)\u003c/h2\u003e\u003ca id=\"user-content-faq-optional\" class=\"anchor\" aria-label=\"Permalink: FAQ (optional)\" href=\"#faq-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 2, "subscribers_count": 4, "topics": [], - "updated_at": 1621606584.0 + "updated_at": 1731538984.0 }, { "data_format": 2, - "description": "BIDS-app for pre-processing DWI (denoise, unring, top-up, eddy, bedpost.. )", + "description": "Singularity recipe for Quarto.", "filenames": [ - "Singularity.v0.0.11b", - "Singularity.v0.0.8", - "Singularity.v0.0.12c", - "Singularity.v0.0.9", - "Singularity.v0.0.13", - "Singularity.v0.0.11", - "Singularity.v0.0.12", - "Singularity.v0.0.10", - "Singularity.v0.0.12a", - "Singularity" + "Singularity.quarto" ], - "full_name": "khanlab/prepdwi", - "latest_release": "v0.0.7g", - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/392\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/khanlab/prepdwi\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3144abf093a4027225db054bc7bbf0dbb36e64f833b1ffb576b2c62af78c108f/68747470733a2f2f636972636c6563692e636f6d2f67682f6b68616e6c61622f707265706477692e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/khanlab/prepdwi.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eprepdwi\u003c/h1\u003e\u003ca id=\"user-content-prepdwi\" class=\"anchor\" aria-label=\"Permalink: prepdwi\" href=\"#prepdwi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBIDS-app for pre-processing DWI (denoise, unring, top-up, eddy, bedpost.. )\u003c/p\u003e\n\u003cp\u003eAnalysis levels:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eparticipant: Runs pre-processing including denoising, unringing, top-up, eddy, DWI-T1w registration, T1w-T1w template (MNI152_1mm and MNI152NLin2009cAsym) registration, and bedpost fitting. Writes intermediate output to \u003ccode\u003ework\u003c/code\u003e sub-folder, and BIDS derivatives output to \u003ccode\u003eprepdwi\u003c/code\u003e sub-folder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egroup: Generates HTML reports with QC for brain-masking and registration steps (if linear registration fails, re-run with \u003ccode\u003e--reg_init_participant\u003c/code\u003e flag to initialize with transform from another subject)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eparticipant2: Runs probtrackx network connectivity between all regions in a given atlas labels file. Uses either canned atlases with the \u003ccode\u003e--atlas\u003c/code\u003e option, where predefined atlases are defined in the \u003ccode\u003ecfg\u003c/code\u003e folder; or can specify a new atlas with the \u003ccode\u003e--atlas_* options\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003eUsage: prepdwi bids_dir output_dir {participant,group,participant2} \u0026lt;optional arguments\u0026gt;\n [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL...]]\n [--matching_dwi MATCHING_PATTERN]\n [--matching_T1w MATCHING_STRING]\n [--reg_init_participant PARTICIPANT_LABEL]\n [--grad_coeff_file GRAD_COEFF_FILE]\n [-w WORK_DIR] (scratch directory)\n\n [--no-regT1]\n [--no-topup]\n [--no-bedpost]\n [--no-dke]\n [--n_cpus NCPUS] (for bedpost, default: 8)\n\n participant2 (probtrack connectivity) options:\n [--nprobseeds] N (for probtrackx, default: 5000)\n Choose built-in atlas:\n [--atlas NAME (default: dosenbach)\n\n Available built-in atlas labels/csv:\n cort_striatum_midbrain\tdosenbach yeo17 yeo17_striatum yeo7\tyeo7_striatum\n\n Customize atlas labels:\n {--atlas_space NAME (MNI152_1mm or MNI152NLin2009cAsym)\n [--atlas_label_nii NIFTI\n [--atlas_label_csv LABEL_INDEX_CSV\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "bast/singularity-quarto", + "latest_release": "0.3.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity recipe for \u003ca href=\"https://quarto.org/\" rel=\"nofollow\"\u003eQuarto\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-quarto\" class=\"anchor\" aria-label=\"Permalink: Singularity recipe for Quarto\" href=\"#singularity-recipe-for-quarto\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to fetch and use the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/bast/singularity-quarto/releases/download/0.3.0/quarto.sif\n\n$ ./quarto.sif --help\n$ ./quarto.sif preview document.md\n$ ./quarto.sif render document.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI have used this wonderful guide as starting point and inspiration:\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 2, - "subscribers_count": 8, - "topics": [], - "updated_at": 1698248704.0 + "subscribers_count": 2, + "topics": [ + "quarto", + "singularity" + ], + "updated_at": 1677993724.0 }, { "data_format": 2, - "description": "Lahontan: a flexible, multiscale RNA-seq pipeline", + "description": "My senior honors thesis in computer science.", "filenames": [ "Singularity" ], - "full_name": "UNR-CFB/lahontan", + "full_name": "caravanuden/thesis", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLahontan\u003c/h1\u003e\u003ca id=\"user-content-lahontan\" class=\"anchor\" aria-label=\"Permalink: Lahontan\" href=\"#lahontan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLahontan is a set of tools built around an object-oriented pipeline used in\nqualifying and processing sets of paired-end next-gen RNA sequencing data. It\ntakes fastq files from a high-throughput sequencer and performs quality control\nsteps before performing a preliminary differential gene expression analysis.\u003c/p\u003e\n\u003cp\u003eLahontan uses trimmomatic and fastqc to perform quality control; BLAST to check\nthe strandedness; and a choice of hisat2/featureCounts, hisat2/stringtie,\nbowtie2, or kallisto to analyze and quantify the experimental data. Lahontan\nalso automatically produces some R scripts to begin a gene expression analysis\nfor certain modes. Lahontan is compatible with SLURM scheduled clusters for\nparallel execution as well as standard desktop computers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#table-of-contents\"\u003eTable of Contents\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#built-with\"\u003eBuilt With\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#creating-a-project-and-running-the-pipeline\"\u003eCreating a Project and Running the Pipeline\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#setup\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#quick-start\"\u003eQuick Start\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#example-execution\"\u003eExample Execution\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pipeline-specifications\"\u003ePipeline Specifications\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#project-stages\"\u003eProject Stages\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#project-structure\"\u003eProject Structure\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage-runpipe-options-command-args\"\u003eUsage: lahontan [options] [command] [args...]\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#available-commands\"\u003eAvailable Commands\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#command-options\"\u003eCommand Options\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#fcounts\"\u003efcounts\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#string\"\u003estring\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#kall\"\u003ekall\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#bowtie2\"\u003ebowtie2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#mj\"\u003emj\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#mb\"\u003emb\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#clean\"\u003eclean\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#fo\"\u003efo\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#prepref\"\u003eprepref\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#gendef\"\u003egendef\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#help\"\u003ehelp\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#authors\"\u003eAuthors\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install, please see the \u003ca href=\"https://github.com/UNR-CFB/rna-seq/wiki\"\u003eWiki\u003c/a\u003e for\ndetailed build instructions.\u003c/p\u003e\n\u003cp\u003eTo summarize the \u003ca href=\"https://github.com/UNR-CFB/rna-seq/wiki\"\u003eWiki\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/UNR-CFB/lahontan.git\ncd lahontan\n./lib/autoSetup.sh\nsource ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name lahontan shub://UNR-CFB/lahontan\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilt With\u003c/h1\u003e\u003ca id=\"user-content-built-with\" class=\"anchor\" aria-label=\"Permalink: Built With\" href=\"#built-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython\u003c/a\u003e (\u0026gt;=3.5)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.gnu.org/software/bash/\" rel=\"nofollow\"\u003eBash\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pachterlab.github.io/kallisto/\" rel=\"nofollow\"\u003eKallisto\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gpertea/stringtie\"\u003eStringtie\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://subread.sourceforge.net/\" rel=\"nofollow\"\u003efeatureCounts\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BenLangmead/bowtie2\"\u003ebowtie2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/infphilo/hisat2\"\u003ehisat2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003efastQC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.usadellab.org/cms/index.php?page=trimmomatic\" rel=\"nofollow\"\u003etrimmomatic\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/samtools/samtools\"\u003esamtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lh3/seqtk\"\u003eSeqtk\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gpertea/gffcompare\"\u003egffcompare\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gpertea/gffread\"\u003egffread\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCreating a Project and Running the Pipeline\u003c/h1\u003e\u003ca id=\"user-content-creating-a-project-and-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Creating a Project and Running the Pipeline\" href=\"#creating-a-project-and-running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePaired-end Illumina fastq files. This means 2 files per sample. One for\nRead1, the other for Read2\u003c/li\u003e\n\u003cli\u003eA reference genome for the organism in study. All chromosomes in a single\nFASTA format file\u003c/li\u003e\n\u003cli\u003eA GTF-format file for containing the gene and exon coordinates for your\nreference genome\u003c/li\u003e\n\u003cli\u003eA reference transcriptome (or some set of known transcripts) for your\norganism\u003c/li\u003e\n\u003cli\u003eThe Pipeline directory should be in your \u003ccode\u003e$PATH\u003c/code\u003e environmental variable. This\ncan be accomplished by being in the main rna-seq directory and running:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd scripts/Pipeline\n$ echo \u0027export PATH=$PATH:\u0027`pwd` \u0026gt;\u0026gt; $HOME/.bashrc\n$ source $HOME/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is important that the Input file that you feed into the pipeline follow the\nformat specified. You can use the \u003ccode\u003egendef\u003c/code\u003e command to create a default input\nfile for you, which at that point only requires you to fill in the Project,\nReference, and Original variable directories. See the \u003ca href=\"#prepref\"\u003e\u003ccode\u003egendef\u003c/code\u003e documentation\u003c/a\u003e\nfor more help. An example Input file can be seen below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eExampleInputFile\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/user/Projects/ProjectName\nReference = /home/user/References/ProjectName-Reference\nOriginal = /home/user/Datas/ProjectName-Data\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOften times your data will be organized in a way that isn\u0027t accepted by\nlahontan. In order to continue, your \u003ccode\u003eOriginal\u003c/code\u003e directory must be arranged such\nthat each sample read is alphanumerically sorted in a single directory (Note:\nsorted by sample and by read)\u003c/p\u003e\n\u003cp\u003eExample Original Directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/home/user/Data/ProjectName-Data/\n SpeciesName.treament1_group1.read1.fq.gz\n SpeciesName.treament1_group1.read2.fq.gz\n SpeciesName.treament1_group2.read1.fq.gz\n SpeciesName.treament1_group2.read2.fq.gz\n SpeciesName.treament2_group1.read1.fq.gz\n SpeciesName.treament2_group1.read2.fq.gz\n SpeciesName.treament2_group2.read1.fq.gz\n SpeciesName.treament2_group2.read2.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlso, in order to limit errors, your filenames must only contain: letters,\nnumbers, periods, hyphens, and underscores.\u003c/p\u003e\n\u003cp\u003eYour reference filenames must also follow a few specific rules. Along with only\nusing letters, numbers, periods, hyphens, and underscores:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYour GTF-format file must end with \".gtf\" and it must be the only file within\nyour reference directory to do so\u003c/li\u003e\n\u003cli\u003eYour reference transcriptome must contain \".cdna.\" anywhere within the name\nexcept for the end. It must also be the only file within your reference\ndirectory to do so\u003c/li\u003e\n\u003cli\u003eYour reference genome must contain \".dna.\" anywhere within the name except\nfor the extension. It must also be the only file within your reference\ndirectory to do so\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample Reference Directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/home/user/Reference/ProjectName-Reference/\n SpeciesName.cdna.all.fa.gz\n SpeciesName.dna.toplevel.fa.gz\n SpeciesName.gtf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate an Input file with \u003ccode\u003elahontan gendef\u003c/code\u003e for your desired pipeline using\nthe \u003ccode\u003e-m\u003c/code\u003e option. Then fill in the \"[locations]\" section.\u003c/li\u003e\n\u003cli\u003eMake sure data and references are following format specified in Setup\u003c/li\u003e\n\u003cli\u003ePrepare reference data with \u003ccode\u003elahontan prepref\u003c/code\u003e. If you are using the \u003ccode\u003ekall\u003c/code\u003e\nor \u003ccode\u003ebowtie2\u003c/code\u003e pipeline, be sure to add the \u003ccode\u003e--kallisto\u003c/code\u003e or \u003ccode\u003e--bowtie2\u003c/code\u003e flag\nto make sure the appropriate indices get created.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003elahontan mj\u003c/code\u003e to make a metadata file\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003elahontan mb\u003c/code\u003e to make a blacklist file\u003c/li\u003e\n\u003cli\u003eDecide if you will be running the pipeline with SLURM or not\n\u003cul\u003e\n\u003cli\u003eIf yes, then run \u003ccode\u003elahontan fo\u003c/code\u003e with proper arguments to make optimal path\nfile\u003c/li\u003e\n\u003cli\u003eIf no, then you can skip this step\u003c/li\u003e\n\u003cli\u003eThe following steps will be denoted with a (s) if the step is for SLURM or\na (w) if the step is without SLURM\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e(s) Make a batch file by adding the \u003ccode\u003e--makebatch\u003c/code\u003e option to any of the three\npipeline commands. You should also add the \u003ccode\u003e--batchjson\u003c/code\u003e option with the\nfile you made with \u003ccode\u003elahontan fo\u003c/code\u003e; the \u003ccode\u003e--jsonfile\u003c/code\u003e option with the file you made\nwith \u003ccode\u003elahontan mj\u003c/code\u003e; the \u003ccode\u003e--use-blacklist\u003c/code\u003e option with the file you made with\n\u003ccode\u003elahontan mb\u003c/code\u003e; and the \u003ccode\u003e--use-reference\u003c/code\u003e because the reference data has been\nprepared. So an example command to make a featureCounts batch file may look like:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan fcounts --makebatch 48,48,32 --batchjson /path/to/OptimalPath.dat \\\n--jsonfile /path/to/Metadata.json --use-blacklist /path/to/CustomBlacklist.fa \\\n--use-reference /path/to/INPUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003e(w) You can execute the entire pipeline with one command, but it\u0027s important\nthat the \u003ccode\u003e--jsonfile\u003c/code\u003e, \u003ccode\u003e--use-blacklist\u003c/code\u003e, and \u003ccode\u003e--use-reference\u003c/code\u003e options are\nadded to the command. An example command may look like:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan fcounts --jsonfile /path/to/Metadata.json \\\n--use-blacklist /path/to/CustomBlacklist.fa --use-reference --maxcpu 24 \\\n/path/to/INPUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003e(s) Execute the batch file with \u003ccode\u003esbatch pipeBatch\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNote: It may be a good idea to hold your input file, metadata file, blacklist\nfile, batch file, and optimal path file in the same directory for easy tracking\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample Execution\u003c/h3\u003e\u003ca id=\"user-content-example-execution\" class=\"anchor\" aria-label=\"Permalink: Example Execution\" href=\"#example-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAssuming that the data and reference files have been appropriately organized,\nand are located at \u003ccode\u003e/home/user/Data/MyDataFolder\u003c/code\u003e and\n\u003ccode\u003e/home/user/References/MyReferenceFolder\u003c/code\u003e, run the following to obtain a\ndefault input file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan gendef -m fcounts -f MyFcountsProject.ini\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point complete the \"[locations]\" section in \u003ccode\u003eMyFcountsProject.ini\u003c/code\u003e. So\n\u003ccode\u003eMyFcountsProject.ini\u003c/code\u003e at minimum contains:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMyFcountsProject.ini\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/user/Projects/MyProjectFolder\nReference = /home/user/References/MyReferenceFolder\nOriginal = /home/user/Datas/MyDataFolder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, execute the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan prepref -r /home/user/References/MyReferenceFolder\n$ lahontan mj -j MyMetadata.json\n$ lahontan mb -t MyBlacklist.fa\n$ lahontan fcounts --use-reference --use-blacklist MyBlacklist.fa \\\n--jsonfile MyMetadata.json MyFcountsProject.ini\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline Specifications\u003c/h1\u003e\u003ca id=\"user-content-pipeline-specifications\" class=\"anchor\" aria-label=\"Permalink: Pipeline Specifications\" href=\"#pipeline-specifications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProject Stages\u003c/h2\u003e\u003ca id=\"user-content-project-stages\" class=\"anchor\" aria-label=\"Permalink: Project Stages\" href=\"#project-stages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eStage 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWill create the \u003ca href=\"#project-structure\"\u003eproject structure\u003c/a\u003e and will link the\noriginal data files, the reference files, and a Metadata file if it has been\nprepared. This stage is identical for all pipelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWill run a quality check on the reference data and preprocess the reference\ndata. This entails building a BLAST database, finding the splice sites, finding\nthe exons, building a hisat2 database, and building a genome index with\nsamtools. This stage can be identically run with \u003ccode\u003elahontan prepref\u003c/code\u003e outside of a\nProject structure and before running a pipeline. Stringtie and featureCounts\nwill have an identical Stage 2, but Kallisto requires the construction of its\nown index. If preparing the references beforehand for Kallisto, make sure to\nadd the \u003ccode\u003e--kallisto\u003c/code\u003e flag to the \u003ccode\u003elahontan prepref\u003c/code\u003e command.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first step for all pipelines in Stage 3 is to run a quality check on the\ndata. This includes using fastQC to create statistics on the quality of the\ndata as well as to search for overrepresented sequences. Trimming of the reads\nis done by trimmomatic which trims poor reads as well as any reads in a\nblacklist file. A custom blacklist file can be created by \u003ccode\u003elahontan mb\u003c/code\u003e, which\ncan be fed to a pipeline with the \u003ccode\u003e--use-blacklist\u003c/code\u003e option. After trimming, an\nadditional round of fastQC will be performed to check the results of the\ntrimming. These quality control steps are identical for all pipelines. Once the\nquality control is finished, the strandedness of the reads is checked with\nBLAST.\u003c/p\u003e\n\u003cp\u003eAfter quality control is done, depending on which pipeline is being ran, the\nnext steps will vary. For featureCounts, hisat2 will be used to align the reads\nto the references, then featureCounts will quantify the gene expression. The\npipeline will then gather the data to create tables which can be used in\nfurther analysis.\u003c/p\u003e\n\u003cp\u003eIf Stringtie is being used, then hisat2 will be used to align the reads to the\nreferences. After the alignment, Stringtie will build a new reference transcripts\nfile which will then be used in estimating the transcript abundances.\u003c/p\u003e\n\u003cp\u003eIf Kallisto is being used, then Kallisto will perform its probabilistic\nquantification on the raw data using the index it built.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Stage 4, the resulting data from whichever pipeline was ran is tabulated and\nprepared for statistical analysis with some automatically generated R scripts.\nThe automatically generated R scripts are built with deseq2 and edgeR for\nfeatureCounts, ballgown for Stringtie, and sleuth for Kallisto.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Stage 5, the automatically generated R scripts from Stage 4 are executed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProject Structure\u003c/h2\u003e\u003ca id=\"user-content-project-structure\" class=\"anchor\" aria-label=\"Permalink: Project Structure\" href=\"#project-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStage 1 of the Pipeline will create a consistent directory structure between\nProjects that includes a \u003cem\u003eData\u003c/em\u003e, \u003cem\u003eOriginal\u003c/em\u003e, \u003cem\u003ePostprocessing\u003c/em\u003e, and \u003cem\u003eReference\u003c/em\u003e\ndirectory which looks like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eProjectName/\n Data/\n sample_01/\n sample_01-read_1\n sample_01-read_2\n sample_02/\n sample_02-read_1\n sample_02-read_2\n sample_03/\n sample_03-read_1\n sample_03-read_2\n .\n .\n .\n (Other sample directories)\n Original/\n sample_01-read_1\n sample_01-read_2\n sample_02-read_1\n sample_02-read_2\n sample_03-read_1\n sample_03-read_2\n .\n .\n .\n (Other sample files)\n Postprocessing/\n (Metadata.json if already created, which I recommend you do)\n Reference/\n Genome.fa\n cDNA.fa\n .gtf\n (Other files if reference files have already been processed and prepared)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eData\u003c/em\u003e = directory where Stage 3 will propogate. Contains a directory for each\nrespective sample which contains its respective raw data files\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eOriginal\u003c/em\u003e = directory containing symbolic links pointing to original raw data files\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003ePostprocessing\u003c/em\u003e = directory where differential expression analysis and other analyses will be saved to\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eReference\u003c/em\u003e = directory containing symbolic links pointing to original reference files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage: lahontan [options] [command] [args...]\u003c/h2\u003e\u003ca id=\"user-content-usage-lahontan-options-command-args\" class=\"anchor\" aria-label=\"Permalink: Usage: lahontan [options] [command] [args...]\" href=\"#usage-lahontan-options-command-args\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is designed as a series of scripts built around a central\nfunction, \u003ccode\u003elahontan\u003c/code\u003e. \u003ccode\u003elahontan\u003c/code\u003e functions when it is fed a command or argument.\nIf it is fed a command, that activates the options available for that command,\nand can subsequently be added after the command. The available commands which\ncan be given to \u003ccode\u003elahontan\u003c/code\u003e are shown below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAvailable Commands\u003c/h3\u003e\u003ca id=\"user-content-available-commands\" class=\"anchor\" aria-label=\"Permalink: Available Commands\" href=\"#available-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003efcounts For running featureCounts pipeline\nstring For running Stringtie pipeline\nkall For running kallisto pipeline\nbowtie2 For running Bowtie2 pipeline\nmj Provoke questionnaire to make a Metadata file\nmb Create trimmomatic blacklist\nclean Clean any project directories\nfo Finds optimal execution path for batch execution\nprepref Pre-processes reference data\ngendef Generate a default input file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information about any of the commands, you can add the \u003ccode\u003e-h\u003c/code\u003e option\nto see the available options and any miscellaneous information. You can also give\nthe command as an argument to the \u003ccode\u003ehelp\u003c/code\u003e command.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan fcounts -h\n$ lahontan help fcounts\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill show information available about the \u003ccode\u003efcounts\u003c/code\u003e command. \u003ccode\u003elahontan\u003c/code\u003e also has\nvarious arguments which can be used to perform an action or change the behavior\nof the pipeline. Shown below are the available options for \u003ccode\u003elahontan\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions:\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n--version\n Show version and exit\n--noconfirm\n Ignore all user prompts except JSON file creation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe syntax for the options displayed is based on the conventional CLI\ninterface. The list of options describe whether an option has short/long forms\n(\u003ccode\u003e-h, --help\u003c/code\u003e), whether an option has an argument, and whether the argument has\na default value. If an option has both a short and long form, then either form\nmay be used for the same functionality. The default values will be denoted in\nthe description of the option by, \u003ccode\u003e[default: foo]\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e--version\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eWill show the version of \u003ccode\u003elahontan\u003c/code\u003e currently installed\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--noconfirm\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to avoid any interactive prompts that may be shown which would require\nuser intervention\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand Options\u003c/h3\u003e\u003ca id=\"user-content-command-options\" class=\"anchor\" aria-label=\"Permalink: Command Options\" href=\"#command-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe commands available to \u003ccode\u003elahontan\u003c/code\u003e will be described in more detail below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003efcounts\u003c/h4\u003e\u003ca id=\"user-content-fcounts\" class=\"anchor\" aria-label=\"Permalink: fcounts\" href=\"#fcounts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan fcounts [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen \u003ccode\u003elahontan\u003c/code\u003e is given the \u003ccode\u003efcounts\u003c/code\u003e command, the pipeline is given the\ninformation to use the featureCounts tools and pathway for analysis. If given\nthe fcounts command, then a mandatory argument \u003ccode\u003eINPUTFILE\u003c/code\u003e is required. It also\nhas various optional arguments which can be given to change the behavior of the\npipeline. The \u003ccode\u003eINPUTFILE\u003c/code\u003e argument expects the path to a file that defines\n\u003ccode\u003eProject\u003c/code\u003e, \u003ccode\u003eReference\u003c/code\u003e, and \u003ccode\u003eOriginal\u003c/code\u003e variables. The \u003ccode\u003eProject\u003c/code\u003e variable should\ndefine the path to a directory where the pipeline will save and execute its\nanalyses. At first execution, the \u003ccode\u003eProject\u003c/code\u003e directory should be empty, as the\npipeline will populate the directory into a consistent project structure. The\n\u003ccode\u003eReference\u003c/code\u003e variable should define the path to a directory where the reference\nmaterials are stored; at minimum it should contain a gene transcripts file, a\ncDNA file, and a reference genome. The \u003ccode\u003eOriginal\u003c/code\u003e variable should define the\npath to a directory that contains the paired-end sequencing data.\u003c/p\u003e\n\u003cp\u003eAn example of an \u003ccode\u003eINPUTFILE\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eExampleInputFile\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/user/ProjectName\nReference = /home/user/Reference/ProjectName-Reference\nOriginal = /home/user/Data/ProjectName-Data\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-e \u0026lt;stage\u0026gt;, --execute \u0026lt;stage\u0026gt;\n Comma-separated list of stages to be executed.\n Possible stages include:\n 1: Creating Project Structure\n 2: Preparing Reference Data\n 3: Running actual Pipeline\n 4: Preparing for R analysis\n 5: Running R analysis\n A: (1,2,3,4,5); A=all i.e. runs entire pipeline\n [default: A]\n-r \u0026lt;integer\u0026gt;, --runsample \u0026lt;integer\u0026gt;\n Runs Stage 3 of the pipeline on the sample specified\n by the integer\n-j \u0026lt;jsonFile\u0026gt;, --jsonfile \u0026lt;jsonFile\u0026gt;\n Ignores JSON Metadata file creation and uses specified\n path to JSON Metadata\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used by Pipeline. Default is to\n use all available CPUs\n--makebatch \u0026lt;cluster\u0026gt;\n Makes batch file to be used with slurm. The argument\n it takes is a comma-separated list of CPUs on each\n node in your cluster\n--batchjson \u0026lt;pathtoJSON\u0026gt;\n Uses json file already created to make batch file\n--edger\n Runs edgeR analysis only. Default is to run both\n--deseq\n Runs DESeq2 analysis only. Default is to run both\n--noconfirm\n Ignore all user prompts except JSON file creation\n--use-blacklist \u0026lt;blacklist\u0026gt;\n Trimmomatic blacklist used for quality control\n [default: $RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa]\n--use-reference\n Use Reference data that has already been prepared.\n Put path to already prepared reference data in\n INPUT file\n Note: Need to run Stage 1 with this argument or add to\n \"--makebatch\" argument\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-e \u003cem\u003e\u0026lt;stage\u0026gt;\u003c/em\u003e, --execute \u003cem\u003e\u0026lt;stage\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to specify stage of Pipeline to be ran\n\u003cem\u003e\u0026lt;stage\u0026gt;\u003c/em\u003e is a comma-separated list of stages to be executed.\nPossible stages include:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e1 : Creating Project Structure; this includes creating Project directories\nand making symbolic links to reference and raw data\n2 : Preparing Reference Data; this includes running Quality Control on the\nreference data then setting up hisat2 databases\n3 : Running actual Pipeline; this includes quality control, alignment,\ncompression, and feature counts\n4 : Preparing for R analysis; this includes creating a \u0027nice\u0027 count file and\ndeveloping automated R scripts from \u003ccode\u003eMetadata.json\u003c/code\u003e\n5 : Running R analysis; this involves running DESeq2 and edgeR differential\nexpression analyses\nA : (1,2,3,4,5) i.e. runs entire pipeline\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: default is to just run \u003ccode\u003eA\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-r \u003cem\u003e\u0026lt;sampleNumber\u0026gt;\u003c/em\u003e, --runsample \u003cem\u003e\u0026lt;sampleNumber\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to execute through Stage 3 on the sample specified by \u003cem\u003e\u0026lt;sampleNumber\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-j \u003cem\u003e\u0026lt;jsonFile\u0026gt;\u003c/em\u003e, --jsonfile \u003cem\u003e\u0026lt;jsonFile\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIgnores JSON Metadata file creation and uses specified path to already\ncreated JSON Metadata\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--maxcpu \u003cem\u003e\u0026lt;CPUs\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to limit number of CPUs used by Pipeline\nNote: default is to use all available CPUs\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--makebatch \u003cem\u003e\u0026lt;cluster\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to make batch file for use with slurm\n\u003cem\u003e\u0026lt;cluster\u0026gt;\u003c/em\u003e is a comma-separated list of CPUs on each node in your cluster\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--batchjson \u003cem\u003e\u0026lt;pathtoJSON\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf an optimal path JSON file has been created using \u003ccode\u003elahontan fo\u003c/code\u003e or another\nmethod, use option to skip the calculation of the optimal CPU usage path\nNote: Option is depended on \u003ccode\u003e--makebatch\u003c/code\u003e i.e. must be added with\n\u003ccode\u003e--makebatch\u003c/code\u003e or not at all\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--edger\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eRuns edgeR analysis only\nNote: default is to run both edgeR and deseq2 analyses\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--deseq\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eRuns deseq2 analysis only\nNote: default is to run both edgeR and deseq2 analyses\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--noconfirm\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed for non-interactivity\nIgnores all user prompts except JSON file creation unless given \u003ccode\u003e--jsonfile\u003c/code\u003e option\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--use-blacklist \u003cem\u003e\u0026lt;blacklist\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTrimmomatic blacklist used for quality control. A new blacklist can be made with\n\u003ccode\u003elahontan mb\u003c/code\u003e\nNote: default is to use \u003ccode\u003e$RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa\u003c/code\u003e where\n\u003ccode\u003e$RNASEQDIR\u003c/code\u003e is the build directory specified in the installation instructions\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--use-reference\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf reference data has already been prepared, use option to skip Stage 2 and\nuse prepared reference data. It is important that this option be used for\nevery stage if the reference data is already prepared.\nNote: If option is to be used, must be ran from Stage 1\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003estring\u003c/h4\u003e\u003ca id=\"user-content-string\" class=\"anchor\" aria-label=\"Permalink: string\" href=\"#string\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan string [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to the \u003ccode\u003efcounts\u003c/code\u003e command, \u003ccode\u003estring\u003c/code\u003e gives the pipeline the information\nit needs so that it uses the Stringtie tools and pathway. The options to the\n\u003ccode\u003estring\u003c/code\u003e command are similar to those of \u003ccode\u003efcounts\u003c/code\u003e. The only difference in\nexecution are Stages 3, 4, and 5. In stage 3, instead of running the analyses\nin one step, Stringtie takes three phases to do so. The first phase, \u003ccode\u003ea\u003c/code\u003e, is\nwhere transcripts are assembled for each sample in the experiment. The second\nphase, \u003ccode\u003eb\u003c/code\u003e, is where a new gene transcript file is created from each sample\u0027s\ntranscripts. The third step, \u003ccode\u003ec\u003c/code\u003e, is where Stringtie estimates the transcript\nabundances for each sample using the newly assembled gene transcript file.\nStage 3 of the pipeline can be ran altogether by using the \u003ccode\u003e--phase abc\u003c/code\u003e\noption. In Stage 4, the pipeline prepares the results to be analyzed by\nballgown in Stage 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-e \u0026lt;stage\u0026gt;, --execute \u0026lt;stage\u0026gt;\n Comma-separated list of stages to be executed.\n Possible stages include:\n 1: Creating Project Structure\n 2: Preparing Reference Data\n 3: Running actual Pipeline\n 4: Preparing for R analysis\n 5: Running R analysis\n A: (1,2,3,4,5); A=all i.e. runs entire pipeline\n [default: A]\n-r \u0026lt;integer\u0026gt;, --runsample \u0026lt;integer\u0026gt;\n Runs Stage 3 of the pipeline on the sample specified\n by the integer\n-p \u0026lt;phase\u0026gt;, --phase \u0026lt;phase\u0026gt;\n Use stringtie tools to replace featureCounts. phase can\n be any of: \"a\",\"b\",\"c\",\"ab\",\"bc\",\"abc\"\n Option also used to specify stringtie postprocessing\n options in which case use: \"--execute 4 --stringtie abc\"\n Note: If you will be running phase b, you cannot specify\n a sample to run with --runsample\n [default: abc]\n-j \u0026lt;jsonFile\u0026gt;, --jsonfile \u0026lt;jsonFile\u0026gt;\n Ignores JSON Metadata file creation and uses specified\n path to JSON Metadata\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used by Pipeline. Default is to\n use all available CPUs\n--noconfirm\n Ignore all user prompts except JSON file creation\n--use-blacklist \u0026lt;blacklist\u0026gt;\n Trimmomatic blacklist used for quality control\n [default: $RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa]\n--use-reference\n Use Reference data that has already been prepared.\n Put path to already prepared reference data in\n INPUT file\n Note: Need to run Stage 1 with this argument or add to\n \"--makebatch\" argument\n--makebatch \u0026lt;cluster\u0026gt;\n Makes batch file to be used with slurm. The argument\n it takes is a comma-separated list of CPUs on each\n node in your cluster\n--batchjson \u0026lt;pathtoJSON\u0026gt;\n Uses json file already created to make batch file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003estring\u003c/code\u003e command only adds the \u003ccode\u003e--phase\u003c/code\u003e option from the \u003ccode\u003efcounts\u003c/code\u003e command.\nIf you wish to see more information, see the \u003ca href=\"#fcounts\"\u003efcounts\u003c/a\u003e documentation.\u003c/p\u003e\n\u003cp\u003e-p *\u0026lt;phase\u0026gt;*, --phase *\u0026lt;phase\u0026gt;*\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eModifies pipeline to incorporate stringtie and ballgown. Stringtie replaces\nfeatureCounts.\nPossible phases can be any of: \"a\",\"b\",\"c\",\"ab\",\"bc\",\"abc\"\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ea : Phase a refers to mapping sample reads to reference genome, converting\nmapped reads to .bam format, and then assembling transcripts for each sample\nb : Phase b refers to merging the transcripts from each sample into one\nmerged .gtf file\nc : Phase c refers to estimating the transcript abundances for each sample\nNote: If you will be running phase b, you cannot specify a sample to run with --runsample\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ekall\u003c/h4\u003e\u003ca id=\"user-content-kall\" class=\"anchor\" aria-label=\"Permalink: kall\" href=\"#kall\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan kall [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to the \u003ccode\u003efcounts\u003c/code\u003e and \u003ccode\u003estring\u003c/code\u003e commands, \u003ccode\u003ekall\u003c/code\u003e gives the pipeline the\ninformation it needs so that it uses the Kallisto tools and pathway. The\noptions to the \u003ccode\u003ekall\u003c/code\u003e command are similar to those of \u003ccode\u003efcounts\u003c/code\u003e. The only\ndifference is that Kallisto is used instead of hisat2/featureCounts/Stringtie\nfor quantifying gene expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-e \u0026lt;stage\u0026gt;, --execute \u0026lt;stage\u0026gt;\n Comma-separated list of stages to be executed.\n Possible stages include:\n 1: Creating Project Structure\n 2: Preparing Reference Data\n 3: Running actual Pipeline\n 4: Preparing for R analysis\n 5: Running R analysis\n A: (1,2,3,4,5); A=all i.e. runs entire pipeline\n [default: A]\n-r \u0026lt;integer\u0026gt;, --runsample \u0026lt;integer\u0026gt;\n Runs Stage 3 of the pipeline on the sample specified\n by the integer\n-j \u0026lt;jsonFile\u0026gt;, --jsonfile \u0026lt;jsonFile\u0026gt;\n Ignores JSON Metadata file creation and uses specified\n path to JSON Metadata\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used by Pipeline. Default is to\n use all available CPUs\n--noconfirm\n Ignore all user prompts except JSON file creation\n--use-blacklist \u0026lt;blacklist\u0026gt;\n Trimmomatic blacklist used for quality control\n [default: $RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa]\n--use-reference\n Use Reference data that has already been prepared.\n Put path to already prepared reference data in\n INPUT file\n Note: Need to run Stage 1 with this argument or add to\n \"--makebatch\" argument\n--makebatch \u0026lt;cluster\u0026gt;\n Makes batch file to be used with slurm. The argument\n it takes is a comma-separated list of CPUs on each\n node in your cluster\n--batchjson \u0026lt;pathtoJSON\u0026gt;\n Uses json file already created to make batch file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ekall\u003c/code\u003e command does not add any options compared to the \u003ccode\u003efcounts\u003c/code\u003e command.\nIf you wish to see more information, see the \u003ca href=\"#fcounts\"\u003efcounts\u003c/a\u003e documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ebowtie2\u003c/h4\u003e\u003ca id=\"user-content-bowtie2\" class=\"anchor\" aria-label=\"Permalink: bowtie2\" href=\"#bowtie2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan bowtie2 [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to the \u003ccode\u003efcounts\u003c/code\u003e,\u003ccode\u003estring\u003c/code\u003e, and \u003ccode\u003ekall\u003c/code\u003e commands, \u003ccode\u003ebowtie2\u003c/code\u003e gives the\npipeline the information it needs so that it uses the bowtie2 aligner. The\noptions to the \u003ccode\u003ebowtie2\u003c/code\u003e command are similar to those of \u003ccode\u003ekall\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-e \u0026lt;stage\u0026gt;, --execute \u0026lt;stage\u0026gt;\n Comma-separated list of stages to be executed.\n Possible stages include:\n 1: Creating Project Structure\n 2: Preparing Reference Data\n 3: Running actual Pipeline\n 4: Preparing for R analysis\n 5: Running R analysis\n A: (1,2,3,4,5); A=all i.e. runs entire pipeline\n [default: A]\n-r \u0026lt;integer\u0026gt;, --runsample \u0026lt;integer\u0026gt;\n Runs Stage 3 of the pipeline on the sample specified\n by the integer\n-j \u0026lt;jsonFile\u0026gt;, --jsonfile \u0026lt;jsonFile\u0026gt;\n Ignores JSON Metadata file creation and uses specified\n path to JSON Metadata\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used by Pipeline. Default is to\n use all available CPUs\n--noconfirm\n Ignore all user prompts except JSON file creation\n--use-blacklist \u0026lt;blacklist\u0026gt;\n Trimmomatic blacklist used for quality control\n [default: $RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa]\n--use-reference\n Use Reference data that has already been prepared.\n Put path to already prepared reference data in\n INPUT file\n Note: Need to run Stage 1 with this argument or add to\n \"--makebatch\" argument\n--makebatch \u0026lt;cluster\u0026gt;\n Makes batch file to be used with slurm. The argument\n it takes is a comma-separated list of CPUs on each\n node in your cluster\n--batchjson \u0026lt;pathtoJSON\u0026gt;\n Uses json file already created to make batch file\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the bowtie2 pipeline, the metadata file for into the \u003ccode\u003e--jsonfile\u003c/code\u003e option\nserves only as a reference for the user and offers no use in any of the\n\u003ccode\u003ebowtie2\u003c/code\u003e tools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003emj\u003c/h4\u003e\u003ca id=\"user-content-mj\" class=\"anchor\" aria-label=\"Permalink: mj\" href=\"#mj\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan mj [options]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the many different types of experiments which can be run, it is is\ndifficult to automate a method for gathering information about the treatments\nand groups applied in an experiment. The treatments and groups of an experiment\nare important in being able to develop any conclusions with the data. The\n\u003ccode\u003elahontan mj\u003c/code\u003e command provides a questionnaire that requires user feedback about\nthe experiment. The important point is to provide enough information about the\nfeatures of the experiment so a sample can be identified by those features.\u003c/p\u003e\n\u003cp\u003eCaution: It is important to check that the order with which the samples are\nlinked in their respective directory is the same as the features you describe\nit with.\u003c/p\u003e\n\u003cp\u003eFor example, a typical experiment might look like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan mj\nWhat is the name of the project? ProjectName\nHow many samples are there? 4\nHow many features does each sample have? 2\nWhat is the name of feature #1? treatment\nWhat is the name of feature #2? group\nWhat is the name of the main feature? treatment\nWhat is the treatment feature for sample #1? X\nWhat is the group feature for sample #1? GroupA\nWhat is the treatment feature for sample #2? Y\nWhat is the group feature for sample #2? GroupA\nWhat is the treatment feature for sample #3? X\nWhat is the group feature for sample #3? GroupB\nWhat is the treatment feature for sample #4? Y\nWhat is the group feature for sample #4? GroupB\nDone making Metadata.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this way, you can distinguish any sample by specifying features #1 and #2.\nIf there were multiple runs with the same treatment and group, you may choose\nto add a \"run\" feature. This questionnaire creates a JSON file by default\ntitled \u003ccode\u003eMetadata.json\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMetadata.json\n--------------------------------------------------------\n{\n \"FeatureNames\": [\n \"group\",\n \"treatment\"\n ],\n \"MainFeature\": \"treatment\",\n \"NumberofFeatures\": 2,\n \"NumberofSamples\": 4,\n \"ProjectName\": \"ProjectName\",\n \"Samples\": {\n \"sample_01\": {\n \"Features\": {\n \"group\": \"GroupA\",\n \"treatment\": \"X\"\n }\n },\n \"sample_02\": {\n \"Features\": {\n \"group\": \"GroupA\",\n \"treatment\": \"Y\"\n }\n },\n \"sample_03\": {\n \"Features\": {\n \"group\": \"GroupB\",\n \"treatment\": \"X\"\n }\n },\n \"sample_04\": {\n \"Features\": {\n \"group\": \"GroupB\",\n \"treatment\": \"Y\"\n }\n }\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen\n-j \u0026lt;jsonfile\u0026gt;, --jsonfile \u0026lt;jsonfile\u0026gt;\n Optional name of JSON file to be saved to [default: Metadata.json]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-j \u003cem\u003e\u0026lt;jsonfile\u0026gt;\u003c/em\u003e, --jsonfile \u003cem\u003e\u0026lt;jsonfile\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eOptional name of JSON file to be saved to\nNote: default is to save the file to \u003ccode\u003eMetadata.json\u003c/code\u003e in the current directory\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003emb\u003c/h4\u003e\u003ca id=\"user-content-mb\" class=\"anchor\" aria-label=\"Permalink: mb\" href=\"#mb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan mb [options]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis command is used to create a blacklist of reads that qualify as noise and\nwhich should be trimmed from the data by trimmomatic.\u003c/p\u003e\n\u003cp\u003eAs of trimmmomatic version 0.36, the default blacklist file is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$RNASEQDIR/Trimmomatic/adapters/TruSeq3-PE.fa\n-------------------------------------------------------------------------------\n\u0026gt;PrefixPE/1\nTACACTCTTTCCCTACACGACGCTCTTCCGATCT\n\u0026gt;PrefixPE/2\nGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe blacklist file created by \u003ccode\u003elahontan mb\u003c/code\u003e, as of lahontan version 1.1 is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;PrefixPE/1\nTACACTCTTTCCCTACACGACGCTCTTCCGATCT\n\u0026gt;PrefixPE/2\nGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT\n\u0026gt;PE1\nTACACTCTTTCCCTACACGACGCTCTTCCGATCT\n\u0026gt;PE1_rc\nAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA\n\u0026gt;PE2\nGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT\n\u0026gt;PE2_rc\nAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC\n\u0026gt;ABISolid3AdapterB\nCCTATCCCCTGTGTGCCTTGGCAGTCTCAGCCTCTCTATGGGCAGTCGGT\n\u0026gt;PCR_Primer1\nAATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT\n\u0026gt;PCR_Primer1_rc\nAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT\n\u0026gt;PCR_Primer2\nCAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT\n\u0026gt;PCR_Primer2_rc\nAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGATCTCGTATGCCGTCTTCTGCTTG\n\u0026gt;FlowCell1\nTTTTTTTTTTAATGATACGGCGACCACCGAGATCTACAC\n\u0026gt;FlowCell2\nTTTTTTTTTTCAAGCAGAAGACGGCATACGA\n\u0026gt;ConsecutiveA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n\u0026gt;ConsecutiveT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\nTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT\n\u0026gt;ConsecutiveC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\nCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC\n\u0026gt;ConsecutiveG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\nGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-t \u0026lt;newadapterfile\u0026gt;, --tofile \u0026lt;newadapterfile\u0026gt;\n Where to save new blacklist [default: ./CustomBlacklist.fa]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-t \u003cem\u003e\u0026lt;newadapterfile\u0026gt;\u003c/em\u003e, --tofile \u003cem\u003e\u0026lt;newadapterfile\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eOptional name of file to be saved to\nNote: default is to save the file to \u003ccode\u003eCustomBlacklist.fa\u003c/code\u003e in the current directory\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eclean\u003c/h4\u003e\u003ca id=\"user-content-clean\" class=\"anchor\" aria-label=\"Permalink: clean\" href=\"#clean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan clean [options] INPUTFILE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis command is used to clean/reset the populated project directories. The\ndefault behavior is to clean out the entire Project structure to the point\nright after Stage 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-c \u0026lt;placeToClean\u0026gt;, --clean \u0026lt;placeToClean\u0026gt;\n Cleans \u0026lt;placeToClean\u0026gt;; Possible places include:\n Reference\n Data\n Postprocessing\n All\n--sampleclean \u0026lt;sampleName\u0026gt;\n Similar to --clean; but instead just cleans a\n single sample directory, \u0026lt;sampleName\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory Arguments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eINPUTFILE\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe path to a file that contains the variables \u003ccode\u003eProject\u003c/code\u003e, \u003ccode\u003eReference\u003c/code\u003e, and\n\u003ccode\u003eOriginal\u003c/code\u003e defined by their respective paths\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-c \u003cem\u003e\u0026lt;placeToClean\u0026gt;\u003c/em\u003e, --clean \u003cem\u003e\u0026lt;placeToClean\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to clean/reset \u003cem\u003e\u0026lt;placeToClean\u0026gt;\u003c/em\u003e\nPossible places include:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eReference = removes all files except Genome, cDNA, and gtf in Reference\ndirectory\nData = removes all files except raw data file links in Data directory\nPostprocessing = removes all files except \u003ccode\u003eMetadata.json\u003c/code\u003e if it exists in\nPostprocessing directory\nAll = resets entire Project. This is equivalent to running stage 1 on fresh\ndata\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--sampleclean \u003cem\u003e\u0026lt;sampleName\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eUsed to clean/reset \u003cem\u003e\u0026lt;sampleName\u0026gt;\u003c/em\u003e\nSimilar to \u003ccode\u003e--clean Data\u003c/code\u003e but instead of cleaning all samples just cleans a\nsingle sample directory\nNote: \u003cem\u003e\u0026lt;sampleName\u0026gt;\u003c/em\u003e will be of the form \u003ccode\u003esample_#\u003c/code\u003e where #\nrefers to specific sample number\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003efo\u003c/h4\u003e\u003ca id=\"user-content-fo\" class=\"anchor\" aria-label=\"Permalink: fo\" href=\"#fo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan fo [options] NUMSAMPLES CLUSTER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is possible to run the pipeline in a SLURM cluster by creating a batch file\nthat contains various \u003ccode\u003esrun\u003c/code\u003e commands given to the SLURM scheduler. However,\nthe automated batch file maker needs information on how best to run the samples\nin order to minimize the waste of CPU resources. The \u003ccode\u003elahontan fo\u003c/code\u003e command\noffers the ability to create a file that contains information on how best to\nrun the samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-t \u0026lt;filename, --tofile \u0026lt;filename\u0026gt;\n Name of file to be saved to\n [default: ./OptimalPath.dat]\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limits number of CPUs used in calculating optimal path\n Default is to use all available CPUs\n-c, --customize\n If the calculation is too slow, can use this option\n to specify the path yourself\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory Arguments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNUMSAMPLES\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe number of samples in your experiment.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eCLUSTER\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA comma separated list of the number of CPUs on each node in your cluster\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-t \u0026lt;filename, --tofile \u003cem\u003e\u0026lt;filename\u0026gt;\u003c/em\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eOptional name of file to be saved to\nNote: default behavior is to save to OptimalPath.dat in the current directory\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--maxcpu \u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eLimits number of CPUs used in calculating optimal path\nNote: Default is to use all available CPUs\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-c, --customize\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf the calculation is too slow, can use this option to specify the path\nyourself\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eprepref\u003c/h4\u003e\u003ca id=\"user-content-prepref\" class=\"anchor\" aria-label=\"Permalink: prepref\" href=\"#prepref\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan prepref [options] (-r REFERENCEDIR | -i INPUT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis command is equivalent to Stage 2 of the pipeline, however the difference\nis that this command is occurring outside of a structured Project environment.\nIt is useful to prepare the reference information before running the pipeline\nas there may be unforeseen difficulties with the reference information which\ncan\u0027t be caught by the pipeline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-r \u0026lt;referenceDir\u0026gt;\n One of the mandatory arguments i.e. must specify either\n r OR i options.\n Option value is a path to directory that contains a GTF,\n cDNA, and reference genome\n-i \u0026lt;input\u0026gt;\n One of the mandatory arguments i.e. must specify either\n r OR i options\n Option value is a path to input file that must contain\n at least a path to reference directory\n Note: a default input file can be generated with:\n lahontan gendef --mode prepref\n-q, --qualitycheck\n Only run quality check on references. Default behavior\n is to run both quality control and preprocessing\n-p, --preprocess\n Only run preprocessing on references. Default behavior\n is to run both quality control and preprocessing\n-k, --kallisto\n Additionally build kallisto index\n Note: index required if kallisto pipeline to be used\n--onlykallisto\n Build only the kallisto index\n-b, --bowtie2\n Additionally build Bowtie2 index\n Note: index required if Bowtie2 pipeline to be used\n--onlybowtie2\n Build only the Bowtie2 index\n--maxcpu \u0026lt;CPUs\u0026gt;\n Limit the number of CPU that get used to preprocess\n reference data.\n Note: default is to use all available\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory Arguments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(-r REFERENCEDIR | -i INPUT)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eFor the \u003ccode\u003eprepref\u003c/code\u003e command, you must use either the \u003ccode\u003e-r\u003c/code\u003e option with the\npath to a directory that contains a GTF, cDNA, and reference genome OR\nyou must use the \u003ccode\u003e-i\u003c/code\u003e option with a manifest file that includes at least\nthe path to a reference directory that contains a GTF, cDNA, and reference\ngenome\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eAn example of a minimal input file for the \u003ccode\u003eprepref -i\u003c/code\u003e option:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eExampleInputFile\n-------------------------------------------------------------------------------\n[locations]\nReference = /home/user/Reference/ProjectName-Reference\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote the \"[locations]\" header and the \"Reference\" variable. These are required\nin every \u003ccode\u003eprepref\u003c/code\u003e input file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMore information about options:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-q, --qualitycheck\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCheck the quality of the references by comparing the chromosome names in the\ngenome and gtf, comparing the gene names in the cDNA and gtf, and comparing\nthe number of unique gene names in the cDNA and gtf.\nThis option is given if you only want to check the quality without actually\npreprocessing the references\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-p, --preprocess\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ePreprocesses the references by building a BLAST database, finding the splice\nsites, finding the exons, building a hisat2 database, and building a genome\nindex with samtools\nThis option is given if you only want to preprocess the references without\nactually running a quality check\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-k, --kallisto\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eBuild an index for kallisto analysis\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e-b, --bowtie2\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eBuild an index for bowtie2 analysis\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e--maxcpu \u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eLimit the number of CPU that get used to preprocess reference data.\nNote: default is to use all available\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003egendef\u003c/h4\u003e\u003ca id=\"user-content-gendef\" class=\"anchor\" aria-label=\"Permalink: gendef\" href=\"#gendef\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan gendef [options]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-h, --help\n Show this screen and exit\n-f \u0026lt;filename\u0026gt;, --filename \u0026lt;filename\u0026gt;\n Name of input file to create\n [default: default_input.ini]\n-m \u0026lt;mode\u0026gt;, --mode \u0026lt;mode\u0026gt;\n Type of execution mode, can be:\n fcounts\n string\n kall\n bowtie2\n prepref\n [default: fcounts]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003egendef\u003c/code\u003e command is used to generate an input file with all default\nparameters filled in. An input file is mandatory for the \u003ccode\u003efcounts\u003c/code\u003e, \u003ccode\u003estring\u003c/code\u003e,\n\u003ccode\u003ekall\u003c/code\u003e, and \u003ccode\u003ebowtie2\u003c/code\u003e commands. It is optional for the \u003ccode\u003eprepref\u003c/code\u003e command. The\ninput files are organized in an INI format. For more information about file\nformat and syntax, see the \u003ca href=\"https://docs.python.org/3/library/configparser.html#supported-ini-file-structure\" rel=\"nofollow\"\u003epython\ndocumentation\u003c/a\u003e\n. To summarize, files consist of sections, each with a section header in square\nbrackets, and key-value pairs defined underneath (separated by an \"=\"). To\nleave a comment in a file, you can precede a line with a \"#\" or \";\".\u003c/p\u003e\n\u003cp\u003eFor example, here is an input file that is generated with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan gendef -m fcounts\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003edefault_input.ini\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/alberton/rna-seq/scripts/Pipeline/Test\nReference = /home/alberton/rna-seq/scripts/Pipeline/Test\nOriginal = /home/alberton/rna-seq/scripts/Pipeline/Test\n\n[mode]\nfcounts = True\nkall = False\nstring = False\nbowtie2 = False\n\n[fcounts]\n--execute = A\n--runsample = None\n--jsonfile = None\n--maxcpu = None\n--noconfirm = False\n--makebatch = None\n--batchjson = None\n--edger = False\n--deseq = False\n--use-blacklist = /home/alberton/rna-seq/build/Trimmomatic/adapters/TruSeq3-PE.fa\n--use-reference = False\n\n[EXECUTABLES]\nRscript = /usr/local/bin/Rscript\njava = /usr/bin/java\nrnaseqdir = /home/alberton/rna-seq/build\nfastqc = ${rnaseqdir}/fastqc\nextract_exons.py = ${rnaseqdir}/hisat2/extract_exons.py\nextract_splice_sites.py = ${rnaseqdir}/hisat2/extract_splice_sites.py\nhisat2 = ${rnaseqdir}/hisat2/hisat2\nhisat2-build = ${rnaseqdir}/hisat2/hisat2-build\nsamtools = ${rnaseqdir}/samtools/bin/samtools\nmakeblastdb = ${rnaseqdir}/ncbi-blast/bin/makeblastdb\nblastn = ${rnaseqdir}/ncbi-blast/bin/blastn\nseqtk = ${rnaseqdir}/seqtk\ngffcompare = ${rnaseqdir}/gffcompare\nstranded_classifier.py = ${rnaseqdir}/stranded_classifier.py\nfeatureCounts = ${rnaseqdir}/subread/bin/featureCounts\nstringtie = ${rnaseqdir}/stringtie\nkallisto = ${rnaseqdir}/kallisto\nbowtie2 = ${rnaseqdir}/bowtie2\nbowtie2-build = ${rnaseqdir}/bowtie2-build\n\n[fcounts/main]\nrunQCheck = True\nrunTrimmomatic = True\nrunSeqtk = True\nrunBlastn = True\nrunHisat = True\nrunCompression = True\nrunFeatureCounts = True\ngetNiceColumns = True\ngetAlignedColumn = True\n\n[fcounts/runHisat]\nhisat2 = ${EXECUTABLES:hisat2}\n-k = None\n-p = None\n--rna-strandedness = None\n--dta = None\n--phred = None\n--known-splicesite-infile = None\n-x = None\n-1 = None\n-2 = None\n-S = None\nother = None\n\n[fcounts/runCompression]\nsamtools = ${EXECUTABLES:samtools}\n-b = None\n-T = None\n-@ = None\n-o = None\nin = None\nother = None\n\n[fcounts/runFeatureCounts]\nfeatureCounts = ${EXECUTABLES:featureCounts}\n-T = None\n-s = None\n-p = None\n-C = None\n--primary = None\n--ignoreDup = None\n--largestOverlap = None\n-t = None\n-g = None\n-a = None\n-o = None\nin = None\nother = None\n\n[runQCheck]\nfastqc = ${EXECUTABLES:fastqc}\n-t = None\n-o = None\nread1 = None\nread2 = None\nother = None\n\n[runTrimmomatic]\njava = ${EXECUTABLES:java}\n-jar = None\n-threads = None\n-phred = None\nread1 = None\nread2 = None\nread1pout = None\nread2pout = None\nread1uout = None\nread2uout = None\nblacklist = None\nILLUMINACLIP = None\nLEADING = None\nTRAILING = None\nSLIDINGWINDOW = None\nMINLEN = None\nother = None\n\n[runSeqtk]\nseqtk = ${EXECUTABLES:seqtk}\n-s = None\nread1 = None\nread2 = None\nread1out = None\nread2out = None\nsamples = None\n-A = None\nother = None\n\n[runBlastn]\nblastn = ${EXECUTABLES:blastn}\nread1 = None\nread2 = None\nout1 = None\nout2 = None\n-db = None\n-task = None\n-outfmt = None\n-max_target_seqs = None\n-num_threads = None\nother = None\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis input file contains the default behavior of the \u003ccode\u003efcounts\u003c/code\u003e pipeline.The\norder of the sections does not matter, nor does the order of the keys. The only\nmandatory section in any input file is the \"[locations]\" section. The default\nbehavior of the \u003ccode\u003egendef\u003c/code\u003e command is to populate the \"[locations]\" section with\nthe path to the current directory. So after creating this file, the only user\nwork \u003cstrong\u003erequired\u003c/strong\u003e is to finish the paths in the \"[locations]\" section. The\nother sections contain keys that change the default behavior of the pipeline\n(\u003cstrong\u003eCAUTION\u003c/strong\u003e: changing these requires an understanding of the tools involved,\nrun \u003ccode\u003elahontan help fcounts\u003c/code\u003e for more much more detailed information)\u003c/p\u003e\n\u003cp\u003eAdditionally, there are certain reserved values that carry particular\nsignificance for the input file parser. The reserved value \u003cstrong\u003eNone\u003c/strong\u003e is reserved\nto mean the pipeline will default to its default behavior. The reserved values\n\u003cstrong\u003eTrue\u003c/strong\u003e and \u003cstrong\u003eFalse\u003c/strong\u003e mean the pipeline will turn on or off the desired\noption, respectively. Other options contain special arguments that you can see\nmore information about by using the built-in \u003ccode\u003ehelp\u003c/code\u003e command.\u003c/p\u003e\n\u003cp\u003eNotice that the default behavior of \u003ccode\u003egendef\u003c/code\u003e is to default all behavior. Thus,\nthe previous input file is equivalent to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edefault_input.ini\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/alberton/rna-seq/scripts/Pipeline/Test\nReference = /home/alberton/rna-seq/scripts/Pipeline/Test\nOriginal = /home/alberton/rna-seq/scripts/Pipeline/Test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, a completed input file may look something like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edefault_input.ini\n-------------------------------------------------------------------------------\n[locations]\nProject = /home/alberton/rna-seq/scripts/Pipeline/Test/MyProject\nReference = /home/alberton/rna-seq/scripts/Pipeline/Test/MyProject-Reference\nOriginal = /home/alberton/rna-seq/scripts/Pipeline/Test/MyProject-Data\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNotice that the paths to the Project, Reference, and Original directories are\ncompleted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory Arguments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no mandatory arguments for the \u003ccode\u003egendef\u003c/code\u003e command, however, the \u003ccode\u003e-m\u003c/code\u003e\noption specifies the type of input file to create, and it will default to\n\u003ccode\u003efcounts\u003c/code\u003e if not specified.\u003c/p\u003e\n\u003cp\u003eFor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan gendef\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill create a \u003ccode\u003efcounts\u003c/code\u003e input file called \u003ccode\u003edefault_input.ini\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lahontan gendef -m string -f StringtieInput.ini\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill create a \u003ccode\u003estring\u003c/code\u003e input file called \u003ccode\u003eStringtieInput.ini\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ehelp\u003c/h4\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUsage: lahontan help COMMAND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsed to display additional information about a command. Similar to \u003ccode\u003elahontan \u0026lt;command\u0026gt; --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailable Commands\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efcounts For running featureCounts pipeline\nstring For running Stringtie pipeline\nkall For running kallisto pipeline\nbowtie2 For running bowtie2 pipeline\nmj Provoke questionnaire to make a Metadata file\nmb Create trimmomatic blacklist\nclean Clean any project directories\nfo Finds optimal execution path for batch execution\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthors\u003c/h1\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Alberto024\"\u003e\u003cstrong\u003eAlberto Nava\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rltillett\"\u003e\u003cstrong\u003eRichard Tillett\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ethesis\u003c/h1\u003e\u003ca id=\"user-content-thesis\" class=\"anchor\" aria-label=\"Permalink: thesis\" href=\"#thesis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode for Cara Van Uden\u0027s senior honors thesis in computer science titled \"Comparing brain-like representations learned by vanilla, residual, and recurrent CNN architectures.\"\u003c/p\u003e\n\u003cp\u003eThough it has been hypothesized that state-of-the art residual networks approximate the recurrent visual system, it is yet to be seen if the representations learned by these \u201dbiologically inspired\u201d CNNs actually have closer representations to neural data. It is likely that CNNs and DNNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. In this thesis, we investigate how different CNN architectures approximate the representations learned through the ventral\u2014object recognition and processing\u2014stream of the brain. We specifically evaluate how recent approximations of biological neural recurrence\u2014such as residual connections, dense residual connections, and a biologically-inspired implemen- tation of recurrence\u2014affect the representations learned by each CNN. We first investigate the representations learned by layers throughout a few state-of-the-art CNNs\u2014VGG-19 (vanilla CNN), ResNet-152 (CNN with res!\nidual connections), and DenseNet-161 (CNN with dense connections). To control for differences in model depth, we then extend this analysis to the CORnet family of biologically-inspired CNN models with matching high-level architectures. The CORnet family has three models: a vanilla CNN (CORnet-Z), a CNN with biologically-valid recurrent dynamics (CORnet-R), and a CNN with both recurrent and residual connections (CORnet-S).\u003c/p\u003e\n\u003cp\u003eWe compare the representations of these six models to functionally aligned (with hyperalignment) fMRI brain data acquired during a naturalistic visual task. We take two approaches to comparing these CNN and brain representations. We first use forward encoding, a predictive approach that uses CNN features to predict neural responses across the whole brain. We next use representational similarity analysis (RSA) and centered kernel alignment (CKA) to measure the similarities in representation within CNN layers and specific brain ROIs. We show that, compared to vanilla CNNs, CNNs with residual and recurrent connections exhibit representations that are even more similar to those learned by the human ventral visual stream. We also achieve state-of-the-art forward encoding and RSA performance with the residual and recurrent CNN models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTODONE:\u003c/h3\u003e\u003ca id=\"user-content-todone\" class=\"anchor\" aria-label=\"Permalink: TODONE:\" href=\"#todone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGet anat and hyperaligned Raiders responses (work with surface just like Life) and get info about the dataset\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet singularity/docker working for installing dependencies (opencv, ffmpeg, pytorch) needed by model\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eScale and crop Raiders video clips to 224x224 clips needed by ImageNet models\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet images from video (every half sec)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet/choose correct activation layers for CORnet-{Z,R,S}, ResNet, DenseNet, VGG (pretrained on ImageNet)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet image activations for each layer for each model\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompare layers within-model for each model\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompare ROIs within brain\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse the extracted activations from models to predict (ridge regression) fMRI BOLD response of hyperaligned Raiders data\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGet ROI-wise pred acc\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse the extracted activations from models to make CKA RDMs with ROIs from fMRI BOLD response of hyperaligned Raiders data\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssign voxels to layers based on layer-wise prediction accuracy and RDM similarity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssign ROIs to layers based on layer-wise prediction accuracy and RDM similarity (Jaccard similarity)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTODO:\u003c/h3\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO:\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIncorporate eye tracking (congruency map across subj) for cropping images to get salient crop?\u003c/li\u003e\n\u003cli\u003e?\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 2, - "subscribers_count": 6, + "subscribers_count": 3, "topics": [], - "updated_at": 1605113003.0 + "updated_at": 1629080738.0 }, { "data_format": 2, - "description": null, + "description": "Docker and Singularity containers for Bioinformatics programs", "filenames": [ - "Singularity/Singularity-GCC-VisTools-MINT" + "sambamba-0.6.8/Singularity.sambamba-0.6.8", + "cnvkit-0.9.5/Singularity.cnvkit-0.9.5", + "bwa-0.7.17/Singularity.bwa-0.7.17.ubuntu", + "bwa-0.7.17/Singularity.bwa-0.7.17", + "reporting-3.6.0/Singularity.reporting-3.6.0", + "sambamba-0.6.6/Singularity.sambamba-0.6.6", + "sambamba-0.6.6/Singularity.sambamba-0.6.6.old", + "base/Singularity.base", + "multiqc-1.5/Singularity.multiqc-1.5", + "R-3.4.3/Singularity.R-3.4.3", + "IGV-2.4.10/Singularity.IGV-2.4.10", + "vcf2tsv-1.0.0_rc2/Singularity.vcf2tsv-1.0.0_rc2", + "ghostscript-9.22/Singularity.ghostscript-9.22", + "R-3.3.2/Singularity.R-3.3.2", + "bcftools-1.7/Singularity.bcftools-1.7", + "alpine.3.8/Singularity.alpine.3.8", + "annovar-150617/Singularity.annovar-150617", + "GATK-3.8/Singularity.GATK-3.8", + "bedtools-2.26.0/Singularity.bedtools-2.26.0", + "reporting-3.3.2/Singularity.reporting-3.3.2", + "deconstructSigs-1.8.0/Singularity.deconstructSigs-1.8.0", + "mnpmethylationclassifier-0.1.124/Singularity.mnpmethylationclassifier-0.1.124", + "maftools-1.8.10/Singularity.maftools-1.8.10" ], - "full_name": "MetOffice/LFRic-Containers", + "full_name": "NYU-Molecular-Pathology/containers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContainerisation of LFRic\u003c/h1\u003e\u003ca id=\"user-content-containerisation-of-lfric\" class=\"anchor\" aria-label=\"Permalink: Containerisation of LFRic\" href=\"#containerisation-of-lfric\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository hosts LFRic container recipes and links to similar external\nrepositories.\u003c/p\u003e\n\u003cp\u003eMore detailed information about \u003ccode\u003eLFRic\u003c/code\u003e and further references can be found in\n\u003ca href=\"https://github.com/MetOffice/LFRic-Containers/blob/master/LFRicIntro.md\"\u003e\u003cem\u003eIntroduction to LFRic\u003c/em\u003e\u003c/a\u003e\nsection.\u003c/p\u003e\n\u003cp\u003eInstructions on building and runing \u003ccode\u003eLFRic\u003c/code\u003e in two container platforms,\n\u003ca href=\"https://docs.docker.com/install/\" rel=\"nofollow\"\u003eDocker CE\u003c/a\u003e and\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, are stored in two subdirectories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/MetOffice/LFRic-Containers/blob/master/Docker/README.md\"\u003eDocker\u003c/a\u003e;\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/MetOffice/LFRic-Containers/blob/master/Singularity/README.md\"\u003eSingularity\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 2, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoftware Containers\u003c/h1\u003e\u003ca id=\"user-content-software-containers\" class=\"anchor\" aria-label=\"Permalink: Software Containers\" href=\"#software-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains \u003ca href=\"https://docs.docker.com/get-started/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and \u003ca href=\"https://www.sylabs.io/guides/2.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e recipes to build software containers for use with sequencing and data analysis.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNOTE: These instructions are designed to be used from a Mac computer with admin access. They should also work on Linux systems but many methods will be unnecessary on Linux.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDocker for Mac can be downloaded here: \u003ca href=\"https://download.docker.com/mac/stable/Docker.dmg\" rel=\"nofollow\"\u003ehttps://download.docker.com/mac/stable/Docker.dmg\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, clone this repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/NYU-Molecular-Pathology/containers.git\ncd containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe included \u003ccode\u003eMakefile\u003c/code\u003e contains the commands needed to build and test containers in this repository.\u003c/p\u003e\n\u003cp\u003eIt is recommended to build containers one at a time, and test each one after building.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity containers are meant to be used on HPC, where Docker is not allowed. Singularity containers must be built on a system to which you have root (admin) access e.g. your desktop computer. Singularity itself can only be run natively in a Linux environment. To build a Singularity container from your Mac desktop, you need either Vagrant or Docker installed, in which to run Singularity via a Linux virtual machine/container (included). Instructions for building Singularity containers using both are included. Recipes to create Singularity containers are saved in files named \u003ccode\u003eSingularity\u003c/code\u003e or \u003ccode\u003eSingularity.image-tag-name\u003c/code\u003e, such as \u003ccode\u003eSingularity.GATK-3.8\u003c/code\u003e. The output will consist of an image file (\u003ccode\u003e.simg\u003c/code\u003e), which must be transferred to your target system for use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity containers with Vagrant\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-containers-with-vagrant\" class=\"anchor\" aria-label=\"Permalink: Build Singularity containers with Vagrant\" href=\"#build-singularity-containers-with-vagrant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVagrant needs to be installed on your Mac (see installation instructions in the included file \u003ccode\u003eVAGRANT.md\u003c/code\u003e). The file \u003ccode\u003eVagrantfile\u003c/code\u003e included in this repository provides the definitions for the virtual machines to be used for building and testing Singularity containers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBuild\u003c/h4\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a Singularity container using Vagrant, run a command such as the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-build VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003eVAR\u003c/code\u003e is the name of the directory in this repository that contains the recipe for the Singularity container you wish to build. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-build VAR=sambamba-0.6.6\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNOTE: do not include a trailing slash in arguments to \u003ccode\u003eVAR\u003c/code\u003e (e.g. \u003ccode\u003eVAR=sambamba-0.6.6/\u003c/code\u003e will not work, must use \u003ccode\u003eVAR=sambamba-0.6.6\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNOTE: If your builds start failing randomly or you start getting strange errors inside your containers, try removing the reference to your Vagrant VM (\u003ccode\u003emake clean-vagrant\u003c/code\u003e) and try again. Be sure to remove old Vagrant VM\u0027s from VirtualBox if you do this.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNOTE: Some Singularity recipes that pull from Docker images tend to fail when building with Vagrant, try building with the Docker container as described further below.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTest\u003c/h4\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo test a Singularity container that you have built, you can use the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-test VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will load an interactive shell session inside the target container, where you can verify that your software is present and operational.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity containers with Docker\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-containers-with-docker\" class=\"anchor\" aria-label=\"Permalink: Build Singularity containers with Docker\" href=\"#build-singularity-containers-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build Singularity containers using \u003ca href=\"https://docs.docker.com/docker-for-mac/install/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e. First, you need to build a Docker container that contains Singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-build VAR=Singularity-2.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBuild\u003c/h4\u003e\u003ca id=\"user-content-build-1\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a Singularity container using Docker with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-build-docker VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can adjust the container tag using the \u003ccode\u003eDOCKERREPO\u003c/code\u003e (\u0027stevekm/containers\u0027) or \u003ccode\u003eDOCKERTAG\u003c/code\u003e (\u0027stevekm/containers:container-name\u0027) Makefile variables.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSync\u003c/h3\u003e\u003ca id=\"user-content-sync\" class=\"anchor\" aria-label=\"Permalink: Sync\" href=\"#sync\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo copy a Singularity container to your target system, you can generate a template \u003ccode\u003ersync\u003c/code\u003e command with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-sync VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will print out a template command to transfer your container file via \u003ccode\u003ersync\u003c/code\u003e \u0026amp; ssh to your target system. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ make singularity-sync VAR=sambamba-0.6.6\ncommand:\nrsync --dry-run -vrhP -e ssh sambamba-0.6.6/sambamba-0.6.6.simg kellys04@bigpurple.nyumc.org:/gpfs/home/kellys04/containers/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can specify the username, server address, and server destination path with \u003ccode\u003eMakefile\u003c/code\u003e variables like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ make singularity-sync VAR=sambamba-0.6.6 USER=bob REMOTE=some.server.com REMOTEDIR=/path/to/containers/\ncommand:\nrsync --dry-run -vrhP -e ssh sambamba-0.6.6/sambamba-0.6.6.simg bob@some.server.com:/path/to/containers/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will want to copy \u0026amp; paste this to run this with \u003ccode\u003e--dry-run\u003c/code\u003e as shown first, then run again without it when you are sure the transfer looks correct.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker Containers\u003c/h1\u003e\u003ca id=\"user-content-docker-containers\" class=\"anchor\" aria-label=\"Permalink: Docker Containers\" href=\"#docker-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following Docker containers are included for special purposes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSingularity-2.4\u003c/code\u003e: used to build Singularity containers\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMiniconda3-4.4.10\u003c/code\u003e: used as a scratch-pad test container to interactively determine compatible versions of \u003ccode\u003econda\u003c/code\u003e libraries for installation in other containers\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build-2\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker containers can be built with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-build VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest\u003c/h2\u003e\u003ca id=\"user-content-test-1\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker containers can be tested with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-test VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will start an interactive shell in the container, where you can verify that your software works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoftware\u003c/h1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-label=\"Permalink: Software\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDocker version 17.12.0-ce, build c97c6d6\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVagrant 2.0.1\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVirtualBox Version 5.2.2 r119230 (Qt5.6.3)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity 2.4 (build), 2.5.2 (run)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003emacOS 10.10, 10.12.6\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU Make 3.81\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU bash, version 3.2.57(1)-release (x86_64-apple-darwin16)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 3, "subscribers_count": 4, - "topics": [], - "updated_at": 1635957552.0 + "topics": [ + "docker", + "singularity", + "bioinformatics", + "containers" + ], + "updated_at": 1611324590.0 }, { "data_format": 2, - "description": "ParaView Catalyst adaptor example for a Fortran code", + "description": null, "filenames": [ "Singularity" ], - "full_name": "niwa/lfric_catalyst_adaptor", + "full_name": "genxnetwork/fl-genomics", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLFRic Catalyst Adaptor\u003c/h1\u003e\u003ca id=\"user-content-lfric-catalyst-adaptor\" class=\"anchor\" aria-label=\"Permalink: LFRic Catalyst Adaptor\" href=\"#lfric-catalyst-adaptor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eParaView Catalyst adaptor implementation for the LFRic code\u003c/p\u003e\n\u003cp\u003eThis package builds a library for visualising simulation data with a simple VTK visualisation pipeline. The pipeline can be defined either in C++ or using a Python script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the adaptor\u003c/h2\u003e\u003ca id=\"user-content-building-the-adaptor\" class=\"anchor\" aria-label=\"Permalink: Building the adaptor\" href=\"#building-the-adaptor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build this code, you will need to build and install ParaView with Catalyst option enabled. Once this is done, build the code using CMake as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir build\ncd build\ncmake .. -DParaView_DIR=/path/to/catalyst/install/directory/lib/cmake/paraview-5.4 -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/path/to/install/dir\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to build a debug version of the code, add \u003ccode\u003e-DCMAKE_BUILD_TYPE=Debug\u003c/code\u003e to the CMake configuration, or use the \u003ccode\u003eccmake\u003c/code\u003e configuration tool. You can add additional compiler flags using the \u003ccode\u003e-DCMAKE_CXX_FLAGS=\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eOn a Cray XC50 system, the following build setup should work:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake .. -DCMAKE_CXX_COMPILER=CC -DCMAKE_EXE_LINKER_FLAGS=-dynamic -DParaView_DIR=/path/to/catalyst/install/directory/lib/cmake/paraview-5.4 -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/path/to/install/dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that dynamic linking simplifies the linking process of the Fortran application significantly.\u003c/p\u003e\n\u003cp\u003eOnce CMake has finished, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\nmake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto build and install the library.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the test battery\u003c/h2\u003e\u003ca id=\"user-content-running-the-test-battery\" class=\"anchor\" aria-label=\"Permalink: Running the test battery\" href=\"#running-the-test-battery\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to test your build, add \u003ccode\u003e-DBUILD_TESTING=ON\u003c/code\u003e to your CMake configuration and run \u003ccode\u003emake test\u003c/code\u003e or \u003ccode\u003ectest\u003c/code\u003e after building the code. This will run a number of tests that check basic functionality.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning a simulation with the adaptor\u003c/h2\u003e\u003ca id=\"user-content-running-a-simulation-with-the-adaptor\" class=\"anchor\" aria-label=\"Permalink: Running a simulation with the adaptor\" href=\"#running-a-simulation-with-the-adaptor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Catalyst adaptor and libraries are usually dynamically linked. If the build system of your code does not hardcode shared library paths, you will need to set (possibly adapting ParaView version)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport LD_LIBRARY_PATH=/path/to/catalyst/installation/lib/paraview-5.4:$LD_LIBRARY_PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to use the Python pipeline, set \u003ccode\u003ePYTHONPATH\u003c/code\u003e to something like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=/path/to/catalyst/installation/lib/paraview-5.4/site-packages:/path/to/catalyst/installation/lib/paraview-5.4/site-packages/vtk:$PYTHONPATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython scripts\u003c/h2\u003e\u003ca id=\"user-content-python-scripts\" class=\"anchor\" aria-label=\"Permalink: Python scripts\" href=\"#python-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository includes a number of Python scripts which define visualisation pipelines or provide some post-processing functionality.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003efull_output.py\u003c/h3\u003e\u003ca id=\"user-content-full_outputpy\" class=\"anchor\" aria-label=\"Permalink: full_output.py\" href=\"#full_outputpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimple Python pipeline for writing the model grid and data field to a VTK file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espherical_slice.py\u003c/h3\u003e\u003ca id=\"user-content-spherical_slicepy\" class=\"anchor\" aria-label=\"Permalink: spherical_slice.py\" href=\"#spherical_slicepy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimple Python pipeline for creating spherical slices of model grid with a preset radius, which are written into a VTK polydata file. Full output of the model grid and data field can also be produced by setting the corresponding flag in the pipeline script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espherical_slice_contours.py\u003c/h3\u003e\u003ca id=\"user-content-spherical_slice_contourspy\" class=\"anchor\" aria-label=\"Permalink: spherical_slice_contours.py\" href=\"#spherical_slice_contourspy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as \"spherical_slice.py\", but includes an additional output file with contours.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espherical_slice_rendered.py\u003c/h3\u003e\u003ca id=\"user-content-spherical_slice_renderedpy\" class=\"anchor\" aria-label=\"Permalink: spherical_slice_rendered.py\" href=\"#spherical_slice_renderedpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as \"spherical_slice.py\", but includes a rendered image of the slice which is stored as a png file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espherical_slice_rendered_coastlines.py\u003c/h3\u003e\u003ca id=\"user-content-spherical_slice_rendered_coastlinespy\" class=\"anchor\" aria-label=\"Permalink: spherical_slice_rendered_coastlines.py\" href=\"#spherical_slice_rendered_coastlinespy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as \"spherical_slice_rendered.py\", but overlays coastlines on the rendered image. Requires downloading coastlines data, see source file for instructions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emeridional_slice.py\u003c/h3\u003e\u003ca id=\"user-content-meridional_slicepy\" class=\"anchor\" aria-label=\"Permalink: meridional_slice.py\" href=\"#meridional_slicepy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreates and stores a meridional slice for a chosen longitude, including a transformation from Cartesian to longitude-radius coordinates.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emap_project.py\u003c/h3\u003e\u003ca id=\"user-content-map_projectpy\" class=\"anchor\" aria-label=\"Permalink: map_project.py\" href=\"#map_projectpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Python program expects a spherical slice (as produced by the \u003ccode\u003espherical_slice.py\u003c/code\u003e visualisation pipeline) in VTK polydata format as input and produces a VTK polydata file with a map projection as output. The program can handle partitioned datasets, but computing map projections for multiple timesteps is not supported yet.\u003c/p\u003e\n\u003cp\u003eRunning\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./map_project.py input.vtp output.vtp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecomputes a Mollweide map projection. Use flag \u003ccode\u003e--list-projections\u003c/code\u003e to get a list of projections and their short names (projections are provide by the PROJ library). Short names can be used to set another projection with the \u003ccode\u003e--projname\u003c/code\u003e flag, e.g., \u003ccode\u003e--projname=gall\u003c/code\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFederated Biobank Project\u003c/h1\u003e\u003ca id=\"user-content-federated-biobank-project\" class=\"anchor\" aria-label=\"Permalink: Federated Biobank Project\" href=\"#federated-biobank-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMotivation\u003c/h2\u003e\u003ca id=\"user-content-motivation\" class=\"anchor\" aria-label=\"Permalink: Motivation\" href=\"#motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTG Environment Installation\u003c/h2\u003e\u003ca id=\"user-content-tg-environment-installation\" class=\"anchor\" aria-label=\"Permalink: TG Environment Installation\" href=\"#tg-environment-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s recommended to work with TG codebase using \u003ccode\u003econda\u003c/code\u003e environemnt.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003econda\u003c/code\u003e: \u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html\" rel=\"nofollow\"\u003ehttps://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eActivate \u003ccode\u003econda\u003c/code\u003e environment suing requirenemnts file \u003ccode\u003erequirements_tg.txt\u003c/code\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name genx --file tg_requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eActivate the environment:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003econda activate genx\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eInstall \u003ccode\u003epgenlib\u003c/code\u003e from PLINK\u0027s repo:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/chrchang/plink-ng.git\ncd plink-ng/2.0/Python\npython3 setup.py build_ext\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStructure\u003c/h2\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003esplit\u003c/strong\u003e module generates node datasets from the whole UKB dataset based on self-reported ancestry.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eqc\u003c/strong\u003e module encapsulates node-based quality control.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003edimred\u003c/strong\u003e module performs different strategies of dimensionality reduction.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efl\u003c/strong\u003e module compares various FL strategies on selected SNPs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualisation\u003c/h2\u003e\u003ca id=\"user-content-visualisation\" class=\"anchor\" aria-label=\"Permalink: Visualisation\" href=\"#visualisation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDash App\u003c/h3\u003e\u003ca id=\"user-content-dash-app\" class=\"anchor\" aria-label=\"Permalink: Dash App\" href=\"#dash-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun dash_app.py and open the link that appears in console in a browser. There assign filter+value or graph elements (x-axis, y-axis, color, etc.) to columns via dropdowns. Then press submit.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1646449477.0 + "updated_at": 1681899779.0 }, { "data_format": 2, "description": null, "filenames": [ - "downward/misc/releases/21.12/Singularity.21.12", - "downward/misc/releases/20.06/Singularity.20.06", - "downward/misc/releases/19.12/Singularity.19.12", - "downward/misc/releases/22.06/Singularity.22.06", - "downward/misc/releases/latest/Singularity", - "downward/misc/releases/19.06/Singularity.19.06", - "downward/misc/releases/22.12/Singularity.22.12" + "Singularity" ], - "full_name": "ipc2023-learning/repo05", + "full_name": "vsoch/pe-predictive", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eASNets - IPC23\u003c/h2\u003e\u003ca id=\"user-content-asnets---ipc23\" class=\"anchor\" aria-label=\"Permalink: ASNets - IPC23\" href=\"#asnets---ipc23\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo Build\u003c/h3\u003e\u003ca id=\"user-content-to-build\" class=\"anchor\" aria-label=\"Permalink: To Build\" href=\"#to-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDownload and install \u003ca href=\"https://apptainer.org/docs/admin/main/installation.html\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003eapptainer build learn.sif apptainer.asnets.learn\u003c/code\u003e and\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003eapptainer build plan.sif apptainer.asnets.plan\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo run\u003c/h3\u003e\u003ca id=\"user-content-to-run\" class=\"anchor\" aria-label=\"Permalink: To run\" href=\"#to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eTo run the learning script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./learn.sif DK DOMAIN TASK1 TASK2 ...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003eDK\u003c/code\u003e is the name of output policy. To successfully run the script, at least provide four tasks.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./learn.sif policy.dt benchmarks/blocksworld/domain.pddl benchmarks/blocksworld/training/easy/p01.pddl benchmarks/blocksworld/training/easy/p02.pddl benchmarks/blocksworld/training/easy/p03.pddl benchmarks/blocksworld/training/easy/p04.pddl\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTo run the planning script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./plan.sif DK DOMAIN TASK PLAN\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003eDK\u003c/code\u003e is the name of learnt policy and \u003ccode\u003ePLAN\u003c/code\u003e is the name of the output plan file. To successfully run the script, only one problem should be given.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./plan.sif dk benchmarks/blocksworld/domain.pddl benchmarks/blocksworld/training/easy/p01.pddl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more detail please find on the \u003ca href=\"https://ipc2023-learning.github.io/\" rel=\"nofollow\"\u003ecompetition website\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epefinder containers\u003c/h1\u003e\u003ca id=\"user-content-pefinder-containers\" class=\"anchor\" aria-label=\"Permalink: pefinder containers\" href=\"#pefinder-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository builds a \u003ca href=\"https://hub.docker.com/r/vanessa/pefinder/\" rel=\"nofollow\"\u003eDocker image\u003c/a\u003e and a \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image, each that will run PE-Finder to produce output for some input data file. If you are working on your local machine, you can use either Docker or Singularity. If you are running in a shared cluster (HPC) environment where you do not have root permissions, Singularity is your best option. Instructions are included for both.\u003c/p\u003e\n\u003cp\u003ePackages that need to be installed (e.g. seaborn and radnlp) have versions specified in case a future change breaks this code, you can see this in the top section of the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBootstrapping means using something to build from, or not starting from nothing. In this case, we are going to use a build file that bootstraps a Docker image of the PE Finder (yes, the same one discussed shortly after). This build file is called \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e, and for more details about this you can \u003ca href=\"http://singularity.lbl.gov/docs-docker\" rel=\"nofollow\"\u003eread here\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 pefinder.img\nsudo singularity bootstrap pefinder.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entry to the container is done simply by using it as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./pefinder.img --help\nINFO:pefinder:radnlp version 0.2.0.8\nusage: cli.py [-h] --reports REPORTS [--report_field REPORT_FIELD]\n\t [--id_field ID_FIELD] [--result_field RESULT_FIELD]\n\t [--delim DELIM] --output OUTPUT [--no-remap]\n\t [--run {mark,classify}]\n\ngenerate predictions for PE for a set of reports (impressions)\n\noptional arguments:\n -h, --help show this help message and exit\n --reports REPORTS Path to folder of reports, or tab separated text file\n --report_field REPORT_FIELD\n\t the header column that contains the text of interest\n\t (default is report_text)\n --id_field ID_FIELD the header column that contains the id of the report\n\t (default is report_id)\n --result_field RESULT_FIELD\n\t the field to save pefinder (chapman) result to, not\n\t saved unless --no-remap is specified.\n --delim DELIM the delimiter separating the input reports data.\n\t Default is tab (\\t)\n --output OUTPUT Desired output file (.tsv)\n --no-remap don\u0027t remap multilabel PEFinder result to Stanford\n\t labels\n --run {mark,classify}\n\t mark (mark), or classify (classify) reports.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou are minimally going to need to provide \u003ccode\u003e--reports\u003c/code\u003e, and \u003ccode\u003e--output\u003c/code\u003e, which assumes that the report text is in a column called \u003ccode\u003ereport_text\u003c/code\u003e, the report id is in a column called \u003ccode\u003ereport_id\u003c/code\u003e, and you want to perform all actions (mark and classify) as the default for the \u003ccode\u003e--run\u003c/code\u003e command. The most basic running command we will use looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run -B $PWD:/data pefinder.img --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/result.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e-B\u003c/code\u003e argument means \"bind\" and it says that we want to bind the present working directory (\u003ccode\u003e$PWD\u003c/code\u003e) to the folder in the container called \u003ccode\u003e/data\u003c/code\u003e. We do this so that we can read and write to \u003ccode\u003e/data\u003c/code\u003e in the container, and the result will appear in our present working directory. Note that the \u003ccode\u003e--reports\u003c/code\u003e input is also relative to \u003ccode\u003e/data\u003c/code\u003e, meaning that the input is located at \u003ccode\u003e$PWD/pefinder/data/stanford_reports.csv\u003c/code\u003e. The \u003ccode\u003e--output\u003c/code\u003e variable, then, is relative to inside of the container. By writing to \u003ccode\u003e/data/result.tsv\u003c/code\u003e we are going to see the file \u003ccode\u003eresult.tsv\u003c/code\u003e appear in our \u003ccode\u003e$PWD\u003c/code\u003e because of the volume. See the section below, Input Arguments, for more detail on the runtime executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I shell into the container?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container\" class=\"anchor\" aria-label=\"Permalink: How do I shell into the container?\" href=\"#how-do-i-shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity has an easy, intuitive way to shell inside!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell pefinder.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want the container to be writable (default isn\u0027t) then you will need root (on your local machine) and add the \u003ccode\u003e--writable\u003c/code\u003e option:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell --writable pefinder.img\n Singularity: Invoking an interactive shell within container...\n Singularity.pefinder.img\u0026gt; cd /code\n Singularity.pefinder.img\u0026gt; ls\n Dockerfile README.md\t docker-compose.yml pefinder\n LICENSE Singularity docs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker\u003c/h1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e. The container is provided on \u003ca href=\"https://hub.docker.com/r/vanessa/pefinder/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded from there when you run it, however if you want to look at or make changes to the code, it\u0027s recommended to clone the repo and build the container locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone http://www.github.com/vsoch/pe-predictive\ncd pe-predictive\ndocker build -t vanessa/pefinder .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalyzing Reports\u003c/h2\u003e\u003ca id=\"user-content-analyzing-reports\" class=\"anchor\" aria-label=\"Permalink: Analyzing Reports\" href=\"#analyzing-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe function of the container is to take reports and produce a \u003ccode\u003e.tsv\u003c/code\u003e file with PEFinder classifications. Let\u0027s first run the container with the \u003ccode\u003e--help\u003c/code\u003e argument to see what arguments are needed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/pefinder --help\nINFO:pefinder:radnlp version 0.2.0.8\nusage: cli.py [-h] --reports REPORTS [--report_field REPORT_FIELD]\n\t [--id_field ID_FIELD] [--result_field RESULT_FIELD] --output\n\t OUTPUT [--no-remap] [--run {mark,classify}]\n\ngenerate predictions for PE for a set of reports (impressions)\n\noptional arguments:\n -h, --help show this help message and exit\n --reports REPORTS Path to folder of reports, or tab separated text file\n --report_field REPORT_FIELD\n\t the header column that contains the text of interest\n\t (default is report_text)\n --id_field ID_FIELD the header column that contains the id of the report\n\t (default is report_id)\n --result_field RESULT_FIELD\n\t the field to save pefinder (chapman) result to, not\n\t saved unless --no-remap is specified.\n --delim DELIM the delimiter separating the input reports data.\n\t Default is tab (\\t)\n --output OUTPUT Desired output file (.tsv)\n --verbose Print more verbose output (useful for analyzing more\n\t reports)\n --no-remap don\u0027t remap multilabel PEFinder result to Stanford\n\t labels\n --run {classify,mark}\n\t mark (mark), or classify (classify) reports.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou are minimally going to need to provide \u003ccode\u003e--reports\u003c/code\u003e, and \u003ccode\u003e--output\u003c/code\u003e, which assumes that the report text is in a column called \u003ccode\u003ereport_text\u003c/code\u003e, the report id is in a column called \u003ccode\u003ereport_id\u003c/code\u003e, and you want to perform all actions (mark and classify) as the default for the \u003ccode\u003e--run\u003c/code\u003e command. If you have a lot of reports, it is recommended to use the \u003ccode\u003e--verbose\u003c/code\u003e flag to give you a countdown of classifications remaining. The most basic running command we will use looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD:/data vanessa/pefinder --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e-v\u003c/code\u003e argument means \"volume\" and it says that we want to map the present working directory (\u003ccode\u003e$PWD\u003c/code\u003e) to the folder in the container called \u003ccode\u003e/data\u003c/code\u003e. We do this so that we can read and write to \u003ccode\u003e/data\u003c/code\u003e in the container, and the result will appear in our present working directory. Otherwise, the result would remain in the container and we wouldn\u0027t have easy access to it. Note that the \u003ccode\u003e--reports\u003c/code\u003e input is also relative to \u003ccode\u003e/data\u003c/code\u003e, meaning that the input is located at \u003ccode\u003e$PWD/pefinder/data/stanford_reports.csv\u003c/code\u003e. The \u003ccode\u003e--output\u003c/code\u003e variable, then, is relative to inside of the container. By writing to \u003ccode\u003e/data/stanford_result.tsv\u003c/code\u003e we are going to see the file \u003ccode\u003estanford_result.tsv\u003c/code\u003e appear in our \u003ccode\u003e$PWD\u003c/code\u003e because of the volume.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I shell into the container?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container-1\" class=\"anchor\" aria-label=\"Permalink: How do I shell into the container?\" href=\"#how-do-i-shell-into-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, running the container uses the \u003ccode\u003eENTRYPOINT\u003c/code\u003e, meaning it is used as an executable and you do not enter the container. In the case that you want a container-based environment that is installed with the dependencies of PEFinder, or if you want to interactively work with the code, you may want to shell into the container. If there is a running container (eg an analysis) and you want to open up another terminal on your local machine to look inside (while it\u0027s running!) you need to get the 12 digit identifier with \u003ccode\u003edocker ps\u003c/code\u003e, and then plug it into this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker exec -it dc70464c6eb5 bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis says we want to execute (exec) and (interactive)(terminal) for container with id (af21bf1d48a6) and run the command (bash)\u003c/p\u003e\n\u003cp\u003eIf the container isn\u0027t running, then you can use \u003ccode\u003erun\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint /bin/sh vanessa/pefinder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, let\u0027s talk about what your options are for your reports input data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInput arguments\u003c/h1\u003e\u003ca id=\"user-content-input-arguments\" class=\"anchor\" aria-label=\"Permalink: Input arguments\" href=\"#input-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReports\u003c/h3\u003e\u003ca id=\"user-content-reports\" class=\"anchor\" aria-label=\"Permalink: Reports\" href=\"#reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor your input data, you have two options:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cem\u003eFolder\u003c/em\u003e: a folder of raw text files, with each text file name assumed to be the report id, and the entire content the impression part of the report to analyze.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eFile\u003c/em\u003e: a single tab separated (\u003ccode\u003e.tsv\u003c/code\u003e) file with some field for the report text (default is assumed to be \u003ccode\u003ereport_text\u003c/code\u003e) and report id (default is \u003ccode\u003ereport_id\u003c/code\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat if I want to change defaults?\u003c/h3\u003e\u003ca id=\"user-content-what-if-i-want-to-change-defaults\" class=\"anchor\" aria-label=\"Permalink: What if I want to change defaults?\" href=\"#what-if-i-want-to-change-defaults\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you need to change the delimiter, specify it with the argument \u003ccode\u003e--delim\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you need to change the default report text column name, specify it with the argument \u003ccode\u003e--report_field\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you need to change the default report id column name, specify it with the argument \u003ccode\u003e--report_id\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each of the examples, the equivalent Docker and Singularity commands are provided.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClassifying Reports\u003c/h3\u003e\u003ca id=\"user-content-classifying-reports\" class=\"anchor\" aria-label=\"Permalink: Classifying Reports\" href=\"#classifying-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClassifying reports means marking and classification. This is default.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # Docker\ndocker run -v $PWD:/data vanessa/pefinder --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\n # Singularity\n singularity run -B $PWD:/data pefinder.img --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\nINFO:pefinder:radnlp version 0.2.0.8\nINFO:pefinder:\n***STARTING PE-FINDER CONTAINER****\nINFO:pefinder:Will use column report_text as report text.\nINFO:pefinder:Will use column report_id as report id.\nINFO:pefinder:reports path provided is /data/pefinder/data/stanford_data.csv\nINFO:pefinder:Analyzing 117816 reports, please wait...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdding \u003ccode\u003e--run classify\u003c/code\u003e would do the equivalent.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMarking Reports\u003c/h3\u003e\u003ca id=\"user-content-marking-reports\" class=\"anchor\" aria-label=\"Permalink: Marking Reports\" href=\"#marking-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is an intermediate step that won\u0027t give you classification labels. You might do this to look at the data. The markup is output in the field \u003ccode\u003emarkup\u003c/code\u003e of the results file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # Docker\ndocker run -v $PWD:/data vanessa/pefinder --run mark --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\n # Singularity\n singularity run -B $PWD:/data pefinder.img --run mark --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClassifying Reports\u003c/h3\u003e\u003ca id=\"user-content-classifying-reports-1\" class=\"anchor\" aria-label=\"Permalink: Classifying Reports\" href=\"#classifying-reports-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the final step to classify the markup (the \u003ccode\u003emarkup\u003c/code\u003e column of your input data) and produce the classification. If you just want this classification, you should run the first example, Analyzing Reports.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # Docker\ndocker run -v $PWD:/data vanessa/pefinder --run classify --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\n # Singularity\n singularity run -B $PWD:/data pefinder.img --run classify --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhy?\u003c/h2\u003e\u003ca id=\"user-content-why\" class=\"anchor\" aria-label=\"Permalink: Why?\" href=\"#why\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy \"shipping\" analyses in packages, meaning having a specification of all dependencies (python modules, data, etc.) we can be assured that the next person that runs our analysis will not run into system-specific differences. They won\u0027t have to install python or anaconda to run our notebook, and get a weird message about having the wrong kernel. They just need Docker, and then to run the image, and that\u0027s it. This is an important feature of reproducible workflows and analyses, and every piece of code that you work on (and tend to share) should have features like this.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1715001845.0 + "updated_at": 1560160426.0 }, { "data_format": 2, - "description": "workflow example managed with Nextflow within Singularity that run Trimgalore on fastq files", + "description": null, "filenames": [ - "Singularity", - "images/Singularity.v1" + "Singularity.def" ], - "full_name": "mhebrard/TrimFlow", - "latest_release": "v1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTrimFlow\u003c/h1\u003e\u003ca id=\"user-content-trimflow\" class=\"anchor\" aria-label=\"Permalink: TrimFlow\" href=\"#trimflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eworkflow example managed with \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e within \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e that run \u003ca href=\"https://github.com/FelixKrueger/TrimGalore\"\u003eTrimgalore\u003c/a\u003e on fastq files\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Singularity : \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003eon Linux\u003c/a\u003e or \u003ca href=\"http://singularity.lbl.gov/install-windows\" rel=\"nofollow\"\u003eon Windows\u003c/a\u003e\n(v2.4.1 and above required)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Nextflow: \u003ca href=\"https://www.nextflow.io/#GetStarted\" rel=\"nofollow\"\u003eHOWTO\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChoose one of the methods below:\u003c/p\u003e\n\u003cp\u003e( change \u003ccode\u003epath/to/reads/\u003c/code\u003e to match the folder of your data.\nReads folder must be in the current folder or a subdirectory -- \u003ccode\u003e./data/\u003c/code\u003e )\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLet nextflow download the workflow files and the container image automatically\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run mhebrard/TrimFlow --reads \u0027path/to/reads/*_R{1,2}*\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003edownload the workflow files and the container image manually, then run it locally\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow pull mhebrard/TrimFlow\nsingularity pull --name TrimFlow.simg shub://mhebrard/TrimFlow\nnextflow run mhebrard/TrimFlow -with-singularity ./Trimflow.simg --reads \u0027path/to/reads/*_R{1,2}*\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersion\u003c/h2\u003e\u003ca id=\"user-content-version\" class=\"anchor\" aria-label=\"Permalink: Version\" href=\"#version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe first digit correspond to the version of the container image.\u003c/p\u003e\n\u003cp\u003eThe other digits represent the version of the nextflow script.\u003c/p\u003e\n", + "full_name": "ut-amrl/BARN_CHALLENGE_2023", + "latest_release": "data", + "readme": "\u003cp\u003eWe assume that you have downloaded libtorch and unzipped it in /opt/libtorch/\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun Steps 0-4 (for BARN Challenge Organizers)\u003c/h1\u003e\u003ca id=\"user-content-run-steps-0-4-for-barn-challenge-organizers\" class=\"anchor\" aria-label=\"Permalink: Run Steps 0-4 (for BARN Challenge Organizers)\" href=\"#run-steps-0-4-for-barn-challenge-organizers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 0: Clone the repo\u003c/h2\u003e\u003ca id=\"user-content-step-0-clone-the-repo\" class=\"anchor\" aria-label=\"Permalink: Step 0: Clone the repo\" href=\"#step-0-clone-the-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:ut-amrl/BARN_CHALLENGE_2023.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 1: Add the following to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to point \u003ccode\u003eROS_PACKAGE_PATH\u003c/code\u003e to the BARN repo\u003c/h2\u003e\u003ca id=\"user-content-step-1-add-the-following-to-your-bashrc-to-point-ros_package_path-to-the-barn-repo\" class=\"anchor\" aria-label=\"Permalink: Step 1: Add the following to your ~/.bashrc to point ROS_PACKAGE_PATH to the BARN repo\" href=\"#step-1-add-the-following-to-your-bashrc-to-point-ros_package_path-to-the-barn-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport ROS_PACKAGE_PATH=/path/to/BARN_CHALLENGE_2023:$ROS_PACKAGE_PATH\nsource ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2: Build and Run the pre-built singularity container\u003c/h2\u003e\u003ca id=\"user-content-step-2-build-and-run-the-pre-built-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Step 2: Build and Run the pre-built singularity container\" href=\"#step-2-build-and-run-the-pre-built-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build nav_competition_image.sif Singularity.def\nsingularity run nav_competition_image.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3: Build some required packages inside the container\u003c/h2\u003e\u003ca id=\"user-content-step-3-build-some-required-packages-inside-the-container\" class=\"anchor\" aria-label=\"Permalink: Step 3: Build some required packages inside the container\" href=\"#step-3-build-some-required-packages-inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebash build_packages.sh \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 4: Add the following to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to setup the workspace\u003c/h2\u003e\u003ca id=\"user-content-step-4-add-the-following-to-your-bashrc-to-setup-the-workspace\" class=\"anchor\" aria-label=\"Permalink: Step 4: Add the following to your ~/.bashrc to setup the workspace\" href=\"#step-4-add-the-following-to-your-bashrc-to-setup-the-workspace\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esource /path/to/BARN_CHALLENGE_2023/devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 5: Run the run.py file\u003c/h2\u003e\u003ca id=\"user-content-step-5-run-the-runpy-file\" class=\"anchor\" aria-label=\"Permalink: Step 5: Run the run.py file\" href=\"#step-5-run-the-runpy-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython3 run.py --world_idx 0 \n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSetup and Install (for BARN Challenge Participants)\u003c/h1\u003e\u003ca id=\"user-content-setup-and-install-for-barn-challenge-participants\" class=\"anchor\" aria-label=\"Permalink: Setup and Install (for BARN Challenge Participants)\" href=\"#setup-and-install-for-barn-challenge-participants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWithout Docker\u003c/h2\u003e\u003ca id=\"user-content-without-docker\" class=\"anchor\" aria-label=\"Permalink: Without Docker\" href=\"#without-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Clone the repo\u003c/h3\u003e\u003ca id=\"user-content-step-1-clone-the-repo\" class=\"anchor\" aria-label=\"Permalink: Step 1: Clone the repo\" href=\"#step-1-clone-the-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:ut-amrl/BARN_CHALLENGE_2023.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Add the following to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to point \u003ccode\u003eROS_PACKAGE_PATH\u003c/code\u003e to the BARN repo\u003c/h3\u003e\u003ca id=\"user-content-step-2-add-the-following-to-your-bashrc-to-point-ros_package_path-to-the-barn-repo\" class=\"anchor\" aria-label=\"Permalink: Step 2: Add the following to your ~/.bashrc to point ROS_PACKAGE_PATH to the BARN repo\" href=\"#step-2-add-the-following-to-your-bashrc-to-point-ros_package_path-to-the-barn-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport ROS_PACKAGE_PATH=/path/to/BARN_CHALLENGE_2023:$ROS_PACKAGE_PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Build\u003c/h3\u003e\u003ca id=\"user-content-step-3-build\" class=\"anchor\" aria-label=\"Permalink: Step 3: Build\" href=\"#step-3-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/BARN_CHALLENGE_2023\nbash build_packages.sh \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 4: Add the following to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to setup the workspace\u003c/h2\u003e\u003ca id=\"user-content-step-4-add-the-following-to-your-bashrc-to-setup-the-workspace-1\" class=\"anchor\" aria-label=\"Permalink: Step 4: Add the following to your ~/.bashrc to setup the workspace\" href=\"#step-4-add-the-following-to-your-bashrc-to-setup-the-workspace-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esource /path/to/BARN_CHALLENGE_2023/devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 5: Run\u003c/h2\u003e\u003ca id=\"user-content-step-5-run\" class=\"anchor\" aria-label=\"Permalink: Step 5: Run\" href=\"#step-5-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload the worlds from \u003ca href=\"https://github.com/Daffan/nav-competition-icra2022/tree/main/jackal_helper/worlds/BARN\"\u003e\u003cstrong\u003ethis link\u003c/strong\u003e\u003c/a\u003e and place them in \u003ccode\u003e/path/to/BARN_CHALLENGE_2023/jackal_helper/worlds/BARN/\u003c/code\u003e. Then run the following command (first argument indicates the world id, second argument is the trial run number, and the third argument is 1 if you want GUI else 0):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 run_original.py 1 1 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAvailable flags:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--algo\u003c/code\u003e: either \"mb\" (move_base + graph nav) or \"vor\" (voronoi + graph nav) {default: \"vor\"}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--param\u003c/code\u003e: either \"2022\" or \"2023\" (i.e., this is the parameter to choose in the localgoal.py script) {default: \"2023\"}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--result_dir\u003c/code\u003e: path to the directory where results will be dumped {default: \"result\"}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--world_idx\u003c/code\u003e: world id {default: 0}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--gui\u003c/code\u003e: 0 is false and 1 is true {default: 1}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--run_idx\u003c/code\u003e: trial run number {default: 0}\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo run multiple runs on multiple worlds:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash run_multiple_runs.sh 0 40 0 299 \"vor\" \"2023\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere the first two args are for start and end of run_idx, and the next two for start and end of world_idx. So this will do 40 runs over all the worlds for voronoi based planner and with 2023 choice of parameters.\u003c/p\u003e\n\u003cp\u003eTo analyse results generated from \u003ccode\u003erun_multiple_runs.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython analyse_results.py \"result_vor_2023\" 20\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere the first argument is the result_dir that contains all the logs, and the second argument represents that it will plot just the top 20 most failed worlds.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWith Docker (Do the following after Step 2 above)\u003c/h2\u003e\u003ca id=\"user-content-with-docker-do-the-following-after-step-2-above\" class=\"anchor\" aria-label=\"Permalink: With Docker (Do the following after Step 2 above)\" href=\"#with-docker-do-the-following-after-step-2-above\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2.5: Setup\u003c/h3\u003e\u003ca id=\"user-content-step-25-setup\" class=\"anchor\" aria-label=\"Permalink: Step 2.5: Setup\" href=\"#step-25-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrerequisites\u003c/h4\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://docs.docker.com/engine/install/ubuntu\" rel=\"nofollow\"\u003e\u003cstrong\u003eInstall Docker Engine\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://docs.docker.com/compose/install/linux/\" rel=\"nofollow\"\u003e\u003cstrong\u003eInstall the Docker Compose V2 Plugin\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\" rel=\"nofollow\"\u003e\u003cstrong\u003eInstall the NVIDIA Container Toolkit\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://docs.docker.com/engine/install/linux-postinstall\" rel=\"nofollow\"\u003e\u003cstrong\u003eAdd users to the \u003ccode\u003edocker\u003c/code\u003e group\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBuild\u003c/h4\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:ut-amrl/ros-noetic-docker.git\ncd /path/to/ros-noetic-docker\n./build.py barn\n./launch.py barn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAdd the following to \u003ccode\u003e~/.bashrc\u003c/code\u003e:\u003c/h4\u003e\u003ca id=\"user-content-add-the-following-to-bashrc\" class=\"anchor\" aria-label=\"Permalink: Add the following to ~/.bashrc:\" href=\"#add-the-following-to-bashrc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e[[ -e /dockerrc ]] \u0026amp;\u0026amp; source /dockerrc\nsource ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eStart shell in container\u003c/h4\u003e\u003ca id=\"user-content-start-shell-in-container\" class=\"anchor\" aria-label=\"Permalink: Start shell in container\" href=\"#start-shell-in-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker exec -it $USER-noetic-barn-app-1 bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eContinue to Steps 3 and 4 as above\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1595480280.0 + "updated_at": 1709711317.0 }, { "data_format": 2, - "description": "Projet Master2 AMI2B test de reproductibilit\u00e9 ", + "description": "Containerizing the Canlab code", "filenames": [ - "Tools/fastqc/Singularity.fastqc", - "Tools/deseq2/Singularity.deseq2", - "Tools/subread/Singularity.subread", - "Tools/star/Singularity.star", - "Tools/fastq-dump/Singularity.fastq-dump" + "Singularity" ], - "full_name": "hippolyte456/Hackathon_NGS_2022", + "full_name": "canlab/cantainer", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProject Repro-Hackathon\u003c/h1\u003e\u003ca id=\"user-content-project-repro-hackathon\" class=\"anchor\" aria-label=\"Permalink: Project Repro-Hackathon\" href=\"#project-repro-hackathon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProject of the Master of Bioinformatics (AMI2B) of the University Paris-Saclay realized by : \u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/JudithCo\"\u003eJudith Coutrot \u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/J-ally\"\u003eJoseph Allyndr\u00e9e \u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/hippolyte456\"\u003eHippolyte Dreyfus \u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Aaramis\"\u003eAuguste Gardette \u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePresentation\u003c/h2\u003e\u003ca id=\"user-content-presentation\" class=\"anchor\" aria-label=\"Permalink: Presentation\" href=\"#presentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe goal is to reproduce parts of the analysis described in these papers (to read): \u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/23313955/\" rel=\"nofollow\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/23313955/\u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/23861464/\" rel=\"nofollow\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/23861464/\u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThey performed \u003ca href=\"https://en.wikipedia.org/wiki/RNA-Seq\" rel=\"nofollow\"\u003eRNA-Seq\u003c/a\u003e in samples from patients with uveal melanoma. Some samples are mutated in SF3B1 .\nWe want to analyze this data in order to find \u003ca href=\"https://en.wikipedia.org/wiki/RNA-Seq#Differential_expression\" rel=\"nofollow\"\u003edifferentially expressed genes\u003c/a\u003e, i.e. genes that are more (or less) expressed in one condition (SF3B1 mutated samples) compared to another (SF3B1 non mutated samples).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOrganization\u003c/h2\u003e\u003ca id=\"user-content-organization\" class=\"anchor\" aria-label=\"Permalink: Organization\" href=\"#organization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo do this, we have designed and implemented a reproductible workflow.\u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA Directory \u003ca href=\"./Tools/\"\u003eTools\u003c/a\u003e to build all containers (using Singularity) with the tools that will be used in the workflow.\u003c/li\u003e\n\u003cli\u003eA Directory \u003ca href=\"./Workflow/\"\u003eWorkflow\u003c/a\u003e with the rules and files (using Snakemake) needed for the workflow.\u003c/li\u003e\n\u003cli\u003eA Script \u003ca href=\"./run.sh\"\u003erun.sh\u003c/a\u003e to execute the workflow.\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"./README.md\"\u003eREADME\u003c/a\u003e file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImages Resumes\u003c/h3\u003e\u003ca id=\"user-content-images-resumes\" class=\"anchor\" aria-label=\"Permalink: Images Resumes\" href=\"#images-resumes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./Tools/Images_Resume.png\"\u003e\u003cimg src=\"./Tools/Images_Resume.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRules Resumes :\u003c/h3\u003e\u003ca id=\"user-content-rules-resumes-\" class=\"anchor\" aria-label=\"Permalink: Rules Resumes :\" href=\"#rules-resumes-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./Workflow/rules_architecture.png\"\u003e\u003cimg src=\"./Workflow/rules_architecture.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout IFB Cloud\u003c/h2\u003e\u003ca id=\"user-content-about-ifb-cloud\" class=\"anchor\" aria-label=\"Permalink: About IFB Cloud\" href=\"#about-ifb-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\"French Institute of Bioinformatics (\u003ca href=\"https://www.france-bioinformatique.fr/cloud-ifb/\" rel=\"nofollow\"\u003eIFB\u003c/a\u003e) provides life scientists with a federation of clouds, Biosphere, and bioinformatics cloud services to analyze life science data. Biosphere is used for scientific production in the life sciences, developments, and to support events like cloud and scientific training sessions, hackathons or workshops.\"\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetting up a VM\u003c/h3\u003e\u003ca id=\"user-content-setting-up-a-vm\" class=\"anchor\" aria-label=\"Permalink: Setting up a VM\" href=\"#setting-up-a-vm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://biosphere.france-bioinformatique.fr/\" rel=\"nofollow\"\u003eIFB Cloud Biosphere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor the moment the pipeline work for a VM BioPipes \"ifb..mxlarge (16 vCPU, 64Go GB RAM, 400Go local Disk)\"\u003c/p\u003e\n\u003cp\u003eIt should also work for a VM of 8 CPUs, below 8 the indexing of the whole genome is impossible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the workflow\u003c/h2\u003e\u003ca id=\"user-content-run-the-workflow\" class=\"anchor\" aria-label=\"Permalink: Run the workflow\" href=\"#run-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./run.sh\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 11, "topics": [], - "updated_at": 1681719835.0 + "updated_at": 1525819849.0 }, { "data_format": 2, - "description": "Python version of Khaled Khairy\u0027s EM_aligner, supporting distributed assembly and solve", + "description": "Ubuntu container for MEEP-MPI", "filenames": [ - "EMaligner/distributed/src/Singularity.petsc_solver", - "EMaligner/distributed/src/Singularity.petsc" + "Singularity" ], - "full_name": "AllenInstitute/EM_aligner_python", - "latest_release": "v1.0.0", + "full_name": "chpc-uofu/Singularity-meep-mpi", + "latest_release": null, "stargazers_count": 3, - "subscribers_count": 9, + "subscribers_count": 2, "topics": [], - "updated_at": 1567019058.0 + "updated_at": 1522810462.0 }, { "data_format": 2, - "description": "best-action trajectory stitching", + "description": "Antigen Receptor Classifier", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "virajmehta/bats", + "full_name": "IEDB/ARC", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebats\u003c/h1\u003e\u003ca id=\"user-content-bats\" class=\"anchor\" aria-label=\"Permalink: bats\" href=\"#bats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ebest-action trajectory stitching\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall instructions\u003c/h2\u003e\u003ca id=\"user-content-install-instructions\" class=\"anchor\" aria-label=\"Permalink: Install instructions\" href=\"#install-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall the conda environment included with environment.yml.\u003c/li\u003e\n\u003cli\u003eInstall d4rl with pip.\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eARC (Antigen Receptor Classifier)\u003c/h1\u003e\u003ca id=\"user-content-arc-antigen-receptor-classifier\" class=\"anchor\" aria-label=\"Permalink: ARC (Antigen Receptor Classifier)\" href=\"#arc-antigen-receptor-classifier\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAuthors: Austin Crinklaw, Swapnil Mahajan\u003c/h3\u003e\u003ca id=\"user-content-authors-austin-crinklaw-swapnil-mahajan\" class=\"anchor\" aria-label=\"Permalink: Authors: Austin Crinklaw, Swapnil Mahajan\" href=\"#authors-austin-crinklaw-swapnil-mahajan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLinux OS\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://hmmer.org/\" rel=\"nofollow\"\u003eHMMER3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eNCBI Blast+\u003c/li\u003e\n\u003cli\u003ePython 3+\n\u003cul\u003e\n\u003cli\u003ePython packages: Pandas, BioPython\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation:\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation:\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide a Dockerfile for ease of use.\u003c/p\u003e\n\u003cp\u003eARC can also be downloaded through PyPI using the following pip command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install bio-arc\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting Installation:\u003c/h3\u003e\u003ca id=\"user-content-testing-installation\" class=\"anchor\" aria-label=\"Permalink: Testing Installation:\" href=\"#testing-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA quick check for proper dependencies and successful installation can be performed by navigating to your pip package install directory (which can be located by executing \u003ccode\u003epip show bio-arc\u003c/code\u003e) and running the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m arc_test\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePassing all unit-tests means that your system is configured properly and ready to classify some protein sequences.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage:\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput\u003c/h3\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA fasta format file with one or more protein sequences.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;1WBZ_A_alpha I H2-Kb\nMVPCTLLLLLAAALAPTQTRAGPHSLRYFVTAVSRPGLGEPRYMEVGYVDDTEFVRFDSDAENPRYEPRARWMEQEGPEYWERETQKAKGNEQSFRVDLRTLLGYYNQSKGGSHTIQVISGCEVGSDGRLLRGYQQYAYDGCDYIALNEDLKTWTAADMAALITKHKWEQAGEAERLRAYLEGTCVEWLRRYLKNGNATLLRTDSPKAHVTHHSRPEDKVTLRCWALGFYPADITLTWQLNGEELIQDMELVETRPAGDGTFQKWASVVVPLGKEQYYTCHVYHQGLPEPLTLRWEPPPSTVSNMATVAVLVVLGAAIVTGAVVAFVMKMRRRNTGGKGGDYALAPGSQTSDLSLPDCKVMVHDPHSLA\n\u0026gt;1WBZ_B_b2m I H2-Kb\nMARSVTLVFLVLVSLTGLYAIQKTPQIQVYSRHPPENGKPNILNCYVTQFHPPHIEIQMLKNGKKIPKVEMSDMSFSKDWSFYILAHTEFTPTETDTYACRVKHASMAEPKTVYWDRDM\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommands\u003c/h3\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUsing Fasta file as an input:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m ARC classify -i /path/to/input.fasta -o /path/to/output.csv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput\u003c/h3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOutput file has 4 columns in CSV format.\u003c/li\u003e\n\u003cli\u003eFirst column named \u0027ID\u0027 is the description provoded in the fasta for each sequence.\u003c/li\u003e\n\u003cli\u003eSecond column named \u0027class\u0027 is the assigned molecule class for each sequence.\n\u003cul\u003e\n\u003cli\u003ee.g. MHC-I, MHC-II, BCR or TCR.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe third column named \u0027chain_type\u0027 is the assigned chain type for each sequence.\n\u003cul\u003e\n\u003cli\u003ee.g. alpha, beta, heavy, lambda, kappa, scFv, TscFv or construct. These will also be labelled as V for variable domain or C for constant domain.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe fourth column named \u0027calc_mhc_allele\u0027 is the MHC allele identified using groove domain similarity to MRO alleles.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eID\u003c/th\u003e\n\u003cth\u003eclass\u003c/th\u003e\n\u003cth\u003echain_type\u003c/th\u003e\n\u003cth\u003ecalc_mhc_allele\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e1WBY_A_alpha I H2-Db\u003c/td\u003e\n\u003ctd\u003eMHC-I\u003c/td\u003e\n\u003ctd\u003ealpha V\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1WBY_B_b2m I H2-Db\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1HQR_A_alpha II HLA-DRA\u003cem\u003e01:01/DRB5\u003c/em\u003e01:01\u003c/td\u003e\n\u003ctd\u003eMHC-II\u003c/td\u003e\n\u003ctd\u003ealpha C\u003c/td\u003e\n\u003ctd\u003eHLA-DRA*01:01\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1HQR_B_beta II HLA-DRA\u003cem\u003e01:01/DRB5\u003c/em\u003e01:01\u003c/td\u003e\n\u003ctd\u003eMHC-II\u003c/td\u003e\n\u003ctd\u003ebeta C\u003c/td\u003e\n\u003ctd\u003eHLA-DRB5*01:01\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2CMR_H_heavy\u003c/td\u003e\n\u003ctd\u003eBCR\u003c/td\u003e\n\u003ctd\u003eheavy V\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2CMR_L_light\u003c/td\u003e\n\u003ctd\u003eBCR\u003c/td\u003e\n\u003ctd\u003ekappa C\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e4RFO_L_light\u003c/td\u003e\n\u003ctd\u003eBCR\u003c/td\u003e\n\u003ctd\u003elambda V\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e3UZE_A_heavy\u003c/td\u003e\n\u003ctd\u003eBCR\u003c/td\u003e\n\u003ctd\u003escFv\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1FYT_D_alpha\u003c/td\u003e\n\u003ctd\u003eTCR\u003c/td\u003e\n\u003ctd\u003ealpha V\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1FYT_E_beta\u003c/td\u003e\n\u003ctd\u003eTCR\u003c/td\u003e\n\u003ctd\u003ebeta C\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e3TF7_C_alpha\u003c/td\u003e\n\u003ctd\u003eTCR\u003c/td\u003e\n\u003ctd\u003eTscFv\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding and using the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-building-and-using-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Building and using the Singularity image\" href=\"#building-and-using-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuilding the singularity image requires root-level access and should thus be built on a machine where you have such access. Once it\u0027s built, it can be run by\nany non-root user and can be transferred to other machines. To build:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build arc.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe input and output directories need to be made available to the running container. If these directories are not within your home directory or the directory from\nwhich you will be running the container ($PWD), you will need to bind mount these directories in your call to the \u0027singularity run\u0027 command. Otherwise, usage is identical\nto the non-containerized version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run \\\n--writable-tmpfs \\\n--bind /path/to/host_dir:/host \\\narc.sif python3 ARC -m classify -i /host/input_file.fasta -o /host/output_file.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow it works:\u003c/h2\u003e\u003ca id=\"user-content-how-it-works\" class=\"anchor\" aria-label=\"Permalink: How it works:\" href=\"#how-it-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBCR and TCR chains are identified using HMMs. A given protein sequence is searched against HMMs built using BCR and TCR chain sequences from IMGT. HMMER is used to align an input sequence to the HMMs.\u003c/li\u003e\n\u003cli\u003eMHC class I (alpha1-alpha2 domains) and MHC class I alpha and beta chain HMMs are downloaded from Pfam website. An input protein sequence is searched against these HMMs. A HMMER bit score threshold of 25 was used to identify MHC chain sequences.\u003c/li\u003e\n\u003cli\u003eTo identify MHC alleles, groove domains (G-domains) are assigned based on the MRO repository.\u003c/li\u003e\n\u003cli\u003eIgNAR sequences are identified through querying against a custom blast database.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences:\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References:\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeveral methods for HMMER result parsing were sourced from ANARCI.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://academic.oup.com/bioinformatics/article/32/2/298/1743894\" rel=\"nofollow\"\u003eDunbar J and Deane CM. ANARCI: Antigen receptor numbering and receptor classification. Bioinformatics (2016)\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 3, "subscribers_count": 4, "topics": [], - "updated_at": 1712738846.0 + "updated_at": 1641508443.0 }, { "data_format": 2, - "description": "High-performance implementation of real-space Random Phase Approximation for calculation of plasmonic excitations", + "description": "Standardized EEG preprocessing", "filenames": [ "Singularity" ], - "full_name": "twesterhout/Plasmons.jl", - "latest_release": "v0.2.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePlasmons\u003c/h1\u003e\u003ca id=\"user-content-plasmons\" class=\"anchor\" aria-label=\"Permalink: Plasmons\" href=\"#plasmons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://twesterhout.github.io/Plasmons.jl/dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45c316268f541933640ad68f61395986888f4d46bc9641b40e53d337576c6456/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732d6465762d626c75652e737667\" alt=\"Dev\" data-canonical-src=\"https://img.shields.io/badge/docs-dev-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/twesterhout/Plasmons.jl\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/324ab8f3160021dad413fad96b40e030a044cc9c6406e4e568d02e5098553a7f/68747470733a2f2f7472617669732d63692e636f6d2f74776573746572686f75742f506c61736d6f6e732e6a6c2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/twesterhout/Plasmons.jl.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHigh-performance implementation of real-space Random Phase Approximation for\ncalculation of plasmonic excitations. For more details, please check out the\n\u003ca href=\"https://twesterhout.github.io/Plasmons.jl/dev\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you are using this code, please cite the following paper:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-tex-latex\"\u003e\u003cpre\u003e@article{westerhout2018plasmon,\n title={Plasmon confinement in fractal quantum systems},\n author={Westerhout, Tom and van Veen, Edo and Katsnelson, Mikhail I and Yuan, Shengjun},\n journal={Physical Review B},\n volume={97},\n number={20},\n pages={205434},\n year={2018},\n publisher={APS}\n}\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "Charestlab/eegprep", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEEGprep\u003c/h1\u003e\u003ca id=\"user-content-eegprep\" class=\"anchor\" aria-label=\"Permalink: EEGprep\" href=\"#eegprep\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStandardized EEG preprocessing\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the EEGprep singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build eegprep.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun EEGprep on your data:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -c -e --bind /your/data/dir/:/data eegprep.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere /your/data/dir/ contains a \u003cem\u003eBIDS\u003c/em\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration\u003c/h2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-label=\"Permalink: Configuration\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can give EEGprep some direction by putting a configuration file\nin your data direction called \u003ccode\u003eeegprep.conf\u003c/code\u003e. See also \u003ccode\u003eexample.eegprep.conf\u003c/code\u003e\nin this repository.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 3, + "subscribers_count": 6, "topics": [], - "updated_at": 1717141203.0 + "updated_at": 1704875420.0 }, { "data_format": 2, - "description": null, + "description": "High-level Haskell bindings to HDF5", "filenames": [ - "Singularity.v2.12", - "Singularity.v2.9.6.collate_fix", - "Singularity.v2.11.0", - "Singularity.v2.11.1", - "Singularity.v2.14.3", - "Singularity.v2.9.1", - "Singularity.v2.10.3", - "Singularity.v2.10.0_fast_collate", - "Singularity.v2.10.5", - "Singularity.v2.9.4", - "Singularity.v2.10.0_db_collate", - "Singularity.v2.14.0", - "Singularity.v2.9.2", - "Singularity.v2.15.0", - "Singularity.v2.9.6", - "Singularity.v2.9.5", - "Singularity.v2.9.7_collator", - "Singularity.v2.10.4", - "Singularity.v2.9.8", - "Singularity.v2.9.3", - "Singularity.v2.15.1", - "Singularity.v2.9.7_emapper_2.1.2", - "Singularity.v2.10.0_faster_collate", - "Singularity.v2.9.7", - "Singularity.v2.9.6.with_customdb", - "Singularity.v2.9.2x", - "Singularity.v2.10.0" + "Singularity" ], - "full_name": "cschu/gff_quantifier", - "latest_release": "v.2.9.8", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisites\u003c/h3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePython3.7+ (we need to be able to rely on dictionary item order preservation!)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ewheel\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInstall (will be automatically installed if pip install is used as described below):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eintervaltree\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eObtain the source code \u003ccode\u003egit clone https://github.com/cschu/gff_quantifier.git\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecd gff_quantifier\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epython setup.py bdist_wheel\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install [--user] -U dist/gffquant-\u0026lt;version\u0026gt;-py3-none-any.whl\u003c/code\u003e. The \u003ccode\u003e--user\u003c/code\u003e option is only necessary if you\u0027re running this with a shared python version.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAfter this, the relevant commands \u003ccode\u003egffindex\u003c/code\u003e and \u003ccode\u003egffquant\u003c/code\u003e should be in your path.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egffquant\u003c/code\u003e supports two kinds of input data, controlled by the \u003ccode\u003e--mode\u003c/code\u003e or \u003ccode\u003e-m\u003c/code\u003e parameter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe genome mode\u003c/h4\u003e\u003ca id=\"user-content-the-genome-mode\" class=\"anchor\" aria-label=\"Permalink: The genome mode\" href=\"#the-genome-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMode \u003ccode\u003e-m genome\u003c/code\u003e is the default mode. The functional profiling strategy in this mode is to only ever have a small portion of the annotation loaded into an interval tree at any given time, thus shifting the memory requirements towards the number of alignments. In \u003ccode\u003egenome\u003c/code\u003e mode, \u003ccode\u003egffquant\u003c/code\u003e takes as input:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea bamfile containing alignments against a set of genomic reference contigs, and\u003c/li\u003e\n\u003cli\u003ea gff3 file containing functional annotations for the genes encoded on the input contigs. This gff3 must be pre-indexed with \u003ccode\u003egffindex \u0026lt;input_gff\u0026gt;\u003c/code\u003e in order to allow random access.\n\u003cul\u003e\n\u003cli\u003ePre-indexing will write the index to a file named \u003ccode\u003e\u0026lt;input_gff\u0026gt;.index\u003c/code\u003e and only needs to be done once per gff.\u003c/li\u003e\n\u003cli\u003eFor best results, the gff should be strictly sorted by seqname (column 1).\u003c/li\u003e\n\u003cli\u003eThe gff must not be gzipped (random access of gzipped files via seek() is not feasible, hence gzipped gffs are not supported).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe gene mode\u003c/h4\u003e\u003ca id=\"user-content-the-gene-mode\" class=\"anchor\" aria-label=\"Permalink: The gene mode\" href=\"#the-gene-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMode \u003ccode\u003e-m genes\u003c/code\u003e allows large sets of genes to be used as reference. In this mode, \u003ccode\u003egffquant\u003c/code\u003e takes as input:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea bamfile containing alignments against a set of gene sequences, and\u003c/li\u003e\n\u003cli\u003ea gzipped, tab-separated functional annotation file following the GMGC convention (column 1 is the gene id, and the functional categories start in column 7.)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAs all (aligned) reads align against annotated genes, no overlap detection is done in this mode. Thus, the \u003ccode\u003egene\u003c/code\u003e mode does not require pre-indexing the annotation. However, in order to preserve memory, the bamfile is pre-scanned and information regarding unused reference sequences is discarded (leading to increased runtimes).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning gffquant\u003c/h4\u003e\u003ca id=\"user-content-running-gffquant\" class=\"anchor\" aria-label=\"Permalink: Running gffquant\" href=\"#running-gffquant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \u003ccode\u003egffquant \u0026lt;input_db\u0026gt; \u0026lt;input_bam\u0026gt; -o \u0026lt;out_prefix\u0026gt; [--ambig_mode {[unique_only], all1, primary_only, 1overN}] [--mode {genome, genes}] [--strand_specific]\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003e\u0026lt;input_bam\u0026gt;\u003c/code\u003e file needs to be position sorted.\u003c/li\u003e\n\u003cli\u003eOutput files are \u003ccode\u003e\u0026lt;out_prefix\u0026gt;.seqname.txt\u003c/code\u003e (contig counts) and \u003ccode\u003e\u0026lt;out_prefix\u0026gt;.feature_counts.txt\u003c/code\u003e (feature/subfeature counts).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--ambig_mode\u003c/code\u003e controls how ambiguously mapping reads are processed. These are analogous to the ngless modes:\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0027unique_only\u003c/code\u003e will simply ignore any reads that is labeled as ambiguous (\u003ccode\u003eMAPQ=0\u003c/code\u003e). Runtime increases with bamfile size, memory should remain more or less constant.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eall1\u003c/code\u003e will treat all ambiguous alignments as single ended individual reads. Runtime increases with bamfile size, memory should remain more or less constant.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprimary_only\u003c/code\u003e will only count unique alignments and the primary alignment of a group of ambiguous alignments. Bamfiles that were filtered by \u003ccode\u003eNGLess\u003c/code\u003e will experience decreased counts as it is likely that the primary alignment was removed during filtering.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e1overN\u003c/code\u003e will dump all ambiguous alignments to disk (in reduced form), finish the processing of the unique alignments, then read and process the ambiguous alignments. Runtime and memory increase significantly with bamfile size. In addition, temporary disk space proportional to the number of ambiguous alignments is needed. In this mode (and only in this mode), sequence counting will include an additional output with reads distributed to reference contigs according to the \u003ccode\u003edist1\u003c/code\u003e mode of ngless.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eRNAseq reads\u003c/h5\u003e\u003ca id=\"user-content-rnaseq-reads\" class=\"anchor\" aria-label=\"Permalink: RNAseq reads\" href=\"#rnaseq-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingle-end RNAseq reads can be processed preserving strandness information via the \u003ccode\u003e--strand_specific\u003c/code\u003e parameter. In this case, the final count tables will contain an additional 6 columns (raw, normalised, scaled for both sense-strand and antisense-strand hits).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eResource requirements\u003c/h3\u003e\u003ca id=\"user-content-resource-requirements\" class=\"anchor\" aria-label=\"Permalink: Resource requirements\" href=\"#resource-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e(Note: numbers are under revision)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMemory and computing time requirements correspond to bamfile size. For current bamfile sizes of up to 24GB:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003euniq_only\u003c/code\u003e and \u003ccode\u003eall1\u003c/code\u003e: ~10GB memory and ~4h (up to 7h for \u003ccode\u003eall1\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprimary_only\u003c/code\u003e: TBD\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e1overN\u003c/code\u003e: \u0026gt;10GB memory, ~10min - \u0026gt;8h\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCount model\u003c/h3\u003e\u003ca id=\"user-content-count-model\" class=\"anchor\" aria-label=\"Permalink: Count model\" href=\"#count-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eReads aligning uniquely always contribute 1 to the region they align to\u003c/li\u003e\n\u003cli\u003eThe count contribution of reads with multiple (ambiguous) alignments depends on the mode:\n\u003col\u003e\n\u003cli\u003eIn \u003ccode\u003eunique_only\u003c/code\u003e, ambiguous reads are ignored.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003eall1\u003c/code\u003e, ambiguous reads contribute 1 to each region they align to. Each alignment is treated (and reported) as unique read.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003eprimary_only\u003c/code\u003e, all primary alignments contribute 1 to each region they align to. Secondary alignments are discarded.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003e1overN\u003c/code\u003e, each alignment against an annotated region contributes \u003ccode\u003e1/N\u003c/code\u003e to the region. \u003ccode\u003eN\u003c/code\u003e is the number of alignments against an annotated region. Alignments against unannotated regions are ignored.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./doc/count_distribution.svg\"\u003e\u003cimg src=\"./doc/count_distribution.svg\" width=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCounts against a region are propagated to the subfeatures with which the region is annotated.\u003c/li\u003e\n\u003cli\u003eGiven feature category \u003ccode\u003eF (e.g. eggNOG_GOs)\u003c/code\u003e with subfeatures \u003ccode\u003ef_1, f_2, f_3, ... (e.g. GO:0000001, ...)\u003c/code\u003e:\nA region with \u003ccode\u003ec\u003c/code\u003e read counts and annotated with \u003ccode\u003en\u003c/code\u003e subfeatures \u003ccode\u003e(f_1, ..., f_n)\u003c/code\u003e will propagate:\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003e+c\u003c/code\u003e to each subfeature count \u003ccode\u003eraw(f_i)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e+c / length(region)\u003c/code\u003e to each normalised subfeature count \u003ccode\u003enorm(f_i)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eFeature category - specific scaling factors are calculated as \u003ccode\u003esigma_F = sum(raw(f_i)) / sum(norm(f_i))\u003c/code\u003e for each feature category \u003ccode\u003eF\u003c/code\u003e. These scaling factors are used to restore the proportion of counts with respect to the original sum of counts \u003ccode\u003escaled(f_i) = norm(f_i) * sigma_F\u003c/code\u003e.\nThe ngLess documentation states:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\"{scaled} is the result of the {normed} mode scaled up so that the total number of counts is\nidentical to the {raw} (within rounding error)\"\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003egffquant\u003c/code\u003e reports raw counts, normalised, and scaled values together in its count tables\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./doc/normalisation_scaling.svg\"\u003e\u003cimg src=\"./doc/normalisation_scaling.svg\" width=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput: count tables\u003c/h3\u003e\u003ca id=\"user-content-output-count-tables\" class=\"anchor\" aria-label=\"Permalink: Output: count tables\" href=\"#output-count-tables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003efeature_counts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnannotated reads are given in the first non-header row.\u003c/p\u003e\n\u003cp\u003eCount tables consist of the following columns:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003esubfeature\u003c/code\u003e (e.g. BRITE:br01600)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003euniq_raw\u003c/code\u003e: the raw counts of unique alignments (or, in \u003ccode\u003eall1\u003c/code\u003e mode, the sum of unique and ambiguous alignments)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003euniq_lnorm\u003c/code\u003e: the sum of the length-normalised unique raw counts (s. section Count Model)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003euniq_scaled\u003c/code\u003e: the scaled, normalised unique counts\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf ambiguous reads are not treated as unique (\u003ccode\u003emode=1overN\u003c/code\u003e), the next 4 columns (5-8; (\u003ccode\u003ecombined_\u003c/code\u003e)) additionally contain the sum of contributions from ambiguous alignments \u003cstrong\u003eand\u003c/strong\u003e unique alignments.\u003c/p\u003e\n\u003cp\u003eIf a strand-specific mode (currently only available for single-end RNAseq reads) was chosen, the table will further continue column blocks for sense-strand counts (\u003ccode\u003e_ss\u003c/code\u003e) and antisense-strand counts (\u003ccode\u003e_as\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eseqname.uniq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ecolumns:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ereference-id (that\u0027s an internal id)\u003c/li\u003e\n\u003cli\u003econtig-id (that\u0027s the official \"freeze12\" id)\u003c/li\u003e\n\u003cli\u003econtig length\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eColumns 4.,5.,6. are again raw, normalised, and scaled counts. Only unique alignments are counted.\u003c/p\u003e\n", + "full_name": "twesterhout/hdf5-hs", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehdf5-hs \u003ca href=\"https://github.com/twesterhout/hdf5-hs/actions\"\u003e\u003cimg src=\"https://github.com/twesterhout/hdf5-hs/workflows/CI/badge.svg\" alt=\"GitHub CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca href=\"LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5116bcc5e23725d16fc3dedb92367e72b3d6316596c5bca21f4df29d23b17971/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4253442d2d332d2d436c617573652d626c75652e737667\" alt=\"BSD-3-Clause license\" data-canonical-src=\"https://img.shields.io/badge/license-BSD--3--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-hdf5-hs-\" class=\"anchor\" aria-label=\"Permalink: hdf5-hs \" href=\"#hdf5-hs-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning:\u003c/strong\u003e this package is a work-in-progress. The code is kind of working,\nbut documentation and examples are lacking. Also this project was created\nspecifically for \u003ca href=\"...\"\u003eSpinED\u003c/a\u003e program, so it\u0027s going to be \"battle-tested\"\nrather that a proof-of-concept.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eHigh-level interface to \u003ca href=\"https://www.hdfgroup.org/solutions/hdf5\" rel=\"nofollow\"\u003eHDF5\u003c/a\u003e for\nHaskell. The idea is to provide a limited functionality, but in a user-friendly\nway. It relies extensively on GADTs to keep the interface polymorphic, but\ntype-safe.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package is very much a work-in-progress and is not (yet, hopefully)\navailable on \u003ca href=\"https://hackage.haskell.org/\" rel=\"nofollow\"\u003eHackage\u003c/a\u003e. Currently, the easiest way to install it is to use \u003ccode\u003ecabal.project\u003c/code\u003e and \u003ca href=\"https://cabal.readthedocs.io/en/3.4/cabal-project.html#specifying-packages-from-remote-version-control-locations\" rel=\"nofollow\"\u003especify that you want to install \u003ccode\u003ehdf5-hs\u003c/code\u003e from Git\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource-repository-package\n type: git\n location: https://github.com/twesterhout/hdf5-hs.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCurrently, Linux and OS X operating systems are supported. (I don\u0027t have\nexperience or access to a Windows system, but a PR would be very welcome!)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSimple example\u003c/h2\u003e\u003ca id=\"user-content-simple-example\" class=\"anchor\" aria-label=\"Permalink: Simple example\" href=\"#simple-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-haskell\"\u003e\u003cpre\u003e{-# \u003cspan class=\"pl-k\"\u003eLANGUAGE\u003c/span\u003e OverloadedStrings #-}\n\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eData.HDF5\u003c/span\u003e (\u003cspan class=\"pl-en\"\u003eDataset\u003c/span\u003e, \u003cspan class=\"pl-en\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-en\"\u003eIOMode\u003c/span\u003e (\u003cspan class=\"pl-k\"\u003e..\u003c/span\u003e))\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-k\"\u003equalified\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eData.HDF5\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eH5\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eData.Text\u003c/span\u003e (\u003cspan class=\"pl-en\"\u003eText\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003emain\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e::\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e\u003cspan class=\"pl-c1\"\u003eIO\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e()\u003c/span\u003e\nmain \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Create a file\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ewithFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexample00.h5\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003eWriteMode\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\\\u003c/span\u003efile \u003cspan class=\"pl-k\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Create a group\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003emakeGroup file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyGroup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Do something with the group\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e (matchM function \"proves\" to GHC that the object we\u0027ve just opened is indeed a group)\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ebyName file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyGroup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ematchM \u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\\\u003c/span\u003egroup \u003cspan class=\"pl-k\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Create a dataset\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ewriteDataset group \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyDataset\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e [(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e::\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e\u003cspan class=\"pl-c1\"\u003eInt\u003c/span\u003e\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e..\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e25\u003c/span\u003e]\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Add a description\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ebyName group \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyDataset\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ematchM \u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eDataset\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\\\u003c/span\u003edataset \u003cspan class=\"pl-k\"\u003e-\u0026gt;\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ewriteAttribute dataset \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyAttribute\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eContains very important data \ud83d\ude00\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e::\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eText\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRelated projects\u003c/h3\u003e\u003ca id=\"user-content-related-projects\" class=\"anchor\" aria-label=\"Permalink: Related projects\" href=\"#related-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere is \u003ca href=\"https://github.com/mokus0/hs-hdf5\"\u003e\u003ccode\u003ehs-hdf5\u003c/code\u003e\u003c/a\u003e. It covers more\nfunctionality of HDF5 than this package, but the API is lower-level.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 3, + "subscribers_count": 4, + "topics": [ + "haskell", + "data-format", + "scientific-computing", + "big-data" + ], + "updated_at": 1731688777.0 + }, + { + "data_format": 2, + "description": "A singularity container demonstrating multi node MPI via SSH wrapper", + "filenames": [ + "Singularity" + ], + "full_name": "katakombi/multinode_sshwrapper", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emultinode_sshwrapper\u003c/h1\u003e\u003ca id=\"user-content-multinode_sshwrapper\" class=\"anchor\" aria-label=\"Permalink: multinode_sshwrapper\" href=\"#multinode_sshwrapper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity container demonstrating multi node MPI via SSH wrapper.\nThe specialty is that the MPI installation is inside the container, and that no MPI on the host system is being used.\nThis is accomplished by packaging OpenMPI and the Infiniband/IPath drivers inside the container.\u003c/p\u003e\n\u003cp\u003eTo demonstrate how it works you will run a short gromacs job using\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e4 local MPI threads on localhost\u003c/li\u003e\n\u003cli\u003e2x2 MPI threads on two host specified by you\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth tasks should take approximately the same time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLimitations\u003c/h2\u003e\u003ca id=\"user-content-limitations\" class=\"anchor\" aria-label=\"Permalink: Limitations\" href=\"#limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eI have tested this using QDR InfiniBand HCA (Intel TrueScale) on our bwForCluster JUSTUS. I did not try to perform this via standard ethernet although it should be doable. I guess some parameters or environment variables would be needed to try this out. If you have other hardware you might need to install additional driver packages inside the container.\u003c/li\u003e\n\u003cli\u003eThe container ignores your RM and/or queueing system. Further changes would be needed to integrate that well into whatever it is you are using.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThis container is tested with Singularity Version 2.3 and above.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn order to properly submit a multi node job the nodes must be accessible via SSH keys (password-less)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe container needs to reside on a shared location which is accessible from each node.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFurthermore, each node needs to have the same singularity installation / configuration as the host you are invoking the multi node MPI job on. The default config should just do fine.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull this image onto the login node of a multi host HPC system and run it in single node mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://katakombi/multinode_sshwrapper\n./katakombi-multinode_sshwrapper-master.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse \u003ccode\u003etop\u003c/code\u003e to ensure that there are four local cores being occupied.\u003c/p\u003e\n\u003cp\u003eIn order to submit it multi node (here \u003ccode\u003en0720\u003c/code\u003e and \u003ccode\u003en0726\u003c/code\u003e, you need to use the hostnames or IPs of your nodes, respectively) execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITYENV_SINGULARITY_IMAGE=\"$PWD/katakombi-multinode_sshwrapper-master.img\"\nsingularity exec \"$SINGULARITYENV_SINGULARITY_IMAGE\" /.singularity.d/runscript n0726,n0720\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDuring the short run, use \u003ccode\u003etop\u003c/code\u003e on the compute nodes to ensure that on each of them two cores are being occupied.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCredit to whom it is due:\n\u003ca href=\"https://github.com/ambu50/docker-ib-mpi\"\u003ehttps://github.com/ambu50/docker-ib-mpi\u003c/a\u003e\n\u003ca href=\"https://github.com/ambu50/wrapper-sq/blob/master/papers/docker_paper_IEEE.pdf\"\u003ehttps://github.com/ambu50/wrapper-sq/blob/master/papers/docker_paper_IEEE.pdf\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 3, + "subscribers_count": 1, + "topics": [ + "multi-node", + "mpi", + "singularity", + "infiniband", + "ssh-wrapper", + "gromacs" + ], + "updated_at": 1576131506.0 + }, + { + "data_format": 2, + "description": "Sample workflow to demonstrate how Pegasus can be used to manage the population modeling tools in the MINT project", + "filenames": [ + "Singularity" + ], + "full_name": "pegasus-isi/darpa_population_modeling", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edarpa_population_modeling\u003c/h1\u003e\u003ca id=\"user-content-darpa_population_modeling\" class=\"anchor\" aria-label=\"Permalink: darpa_population_modeling\" href=\"#darpa_population_modeling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a sample workflow to demonstrate how Pegasus can be used to\nmanage the population modeling tools in the MINT project.\u003c/p\u003e\n\u003cp\u003eThe tools have been converted to command line tools.\u003c/p\u003e\n\u003cp\u003eA Singularity image, based on Ubuntu Xenial Xerus and with Python3 and\nGIS tools and libraries, are used for the compute environment for the\njobs.\u003c/p\u003e\n\u003cp\u003eThe workflow is currently set up to run on the ISI testbed, but can\nbe moved to more powerful execution environments if needed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSubmitting\u003c/h2\u003e\u003ca id=\"user-content-submitting\" class=\"anchor\" aria-label=\"Permalink: Submitting\" href=\"#submitting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheck out this repository on \u003ccode\u003eworkflow.isi.edu\u003c/code\u003e and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./workflow/submit.sh\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 3, + "subscribers_count": 6, "topics": [], - "updated_at": 1732653609.0 + "updated_at": 1685041580.0 }, { "data_format": 2, - "description": "Singularity configurations for R and pbdR packages.", + "description": "A Python package and scripts for the evaluation of nonlinear interference noise in single mode fiber transmissions", "filenames": [ - "R/rstudio/Singularity.1.1.456", - "R/r/Singularity.3.5.1", - "R/r-minimal/Singularity.3.5.1", - "R/jupyter/Singularity", - "R/rstudio-server/Singularity.1.1.456", - "pbdR/pbdR-minimal/mpich/Singularity.1.0-1", - "pbdR/pbdR-minimal/openmpi/Singularity.1.0-1", - "pbdR/pbdR/mpich/Singularity.1.0-1", - "pbdR/pbdR/openmpi/Singularity.1.0-1" + "Singularity" ], - "full_name": "RBigData/singularity", + "full_name": "geeanlooca/PyNLIN", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eR and pbdR Singularity Recipes\u003c/h1\u003e\u003ca id=\"user-content-r-and-pbdr-singularity-recipes\" class=\"anchor\" aria-label=\"Permalink: R and pbdR Singularity Recipes\" href=\"#r-and-pbdr-singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipes for R and pbdR.\u003c/p\u003e\n\u003cp\u003eBuild requirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.sylabs.io/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e \u0026gt;= 2.3\u003c/li\u003e\n\u003cli\u003eModify the \u003ccode\u003emake -j\u003c/code\u003e line of each recipe to your liking.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePyNLIN\u003c/h1\u003e\u003ca id=\"user-content-pynlin\" class=\"anchor\" aria-label=\"Permalink: PyNLIN\" href=\"#pynlin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Python package and scripts for the evaluation of nonlinear interference noise in single mode fiber transmissions\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnd users\u003c/h2\u003e\u003ca id=\"user-content-end-users\" class=\"anchor\" aria-label=\"Permalink: End users\" href=\"#end-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJust clone the repository and \u003ccode\u003epip install\u003c/code\u003e it.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/geeanlooca/PyNLIN.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e PyNLIN\npip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSet up the environment\u003c/h3\u003e\u003ca id=\"user-content-set-up-the-environment\" class=\"anchor\" aria-label=\"Permalink: Set up the environment\" href=\"#set-up-the-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eConda\u003c/h4\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI usually like to install the core numerical packages from conda directly, and let \u003ccode\u003epip\u003c/code\u003e manage the rest of the dependencies.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eenv\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e python=3.10 --yes\nconda activate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eenv\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nconda install numpy scipy matplotlib h5py\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ccode\u003evenv\u003c/code\u003e\u003c/h4\u003e\u003ca id=\"user-content-venv\" class=\"anchor\" aria-label=\"Permalink: venv\" href=\"#venv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eenv\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eenv\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/bin/activate \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;env\u0026gt;\\Scripts\\activate.bat under Windows\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall the package\u003c/h3\u003e\u003ca id=\"user-content-install-the-package\" class=\"anchor\" aria-label=\"Permalink: Install the package\" href=\"#install-the-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor development purposes, the package should be installed in the editable mode. Changes you make to the package are immediatly reflected on the installed version and consequently on the scripts using the package.\u003c/p\u003e\n\u003cp\u003eFrom the root of the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install -e .[dev]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity images\u003c/h1\u003e\u003ca id=\"user-content-singularity-images\" class=\"anchor\" aria-label=\"Permalink: Singularity images\" href=\"#singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePackaging the code in a Singularity image allows us to run code using PyNLIN on the Department\u0027s SLURM cluster.\u003c/p\u003e\n\u003cp\u003eThere are two main ways in which you can run build and run a Singularity image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall Singularity on your local machine, build the image, copy it to the cluster, and submit a job using the image.\u003c/li\u003e\n\u003cli\u003eBuild the image using the remote builder and pull the image directly on the cluster to avoid wasting too much time on uploading the image.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e **The image pulls the latest commit on the \u003ccode\u003emain\u003c/code\u003e branch directly from GitHub. Local edits or commits not pushed to GitHub will not be reflected in the resulting image file\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLocal build\u003c/h2\u003e\u003ca id=\"user-content-local-build\" class=\"anchor\" aria-label=\"Permalink: Local build\" href=\"#local-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have Singularity installed, just run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --force singularity.sif singularity.def\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe resulting \u003ccode\u003e.sif\u003c/code\u003e image file can be used to run python scripts locally using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e singularity.sif python \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003escript\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor uploaded to the cluster.\nAn example \u003ccode\u003e.slurm\u003c/code\u003e file to run a job on the cluster is provided in the \u003ccode\u003eslurm/\u003c/code\u003e directory of this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRemote build\u003c/h2\u003e\u003ca id=\"user-content-remote-build\" class=\"anchor\" aria-label=\"Permalink: Remote build\" href=\"#remote-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding\u003c/h2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 3, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1571942199.0 + "updated_at": 1723899336.0 }, { "data_format": 2, - "description": "Examples that use singularity containers to run arbitrary operating systems on the ACCRE cluster.", + "description": "Ubuntu container for MEEP-MPI", "filenames": [ - "accre-internal/Singularity.accre.internal.rstudio.3.4.3", - "accre-internal/Singularity.accre.internal.rstudio.3.6.0", - "accre-internal/Singularity.accre.internal.base", - "accre-internal/Singularity.accre.internal.rstudio.4.0.5", - "accre-internal/Singularity.accre.internal.rstudio.gpu.3.6.0" + "Singularity" ], - "full_name": "accre/singularity", + "full_name": "CHPC-UofU/Singularity-meep-mpi", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExamples that use singularity containers to run arbitrary operating systems on the ACCRE cluster.\u003c/p\u003e\n\u003cp\u003eAn example of running a Tensorflow image directly from DockerHub can be found in our Python repo:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/accre/Python\"\u003ehttps://github.com/accre/Python\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 18, + "subscribers_count": 2, "topics": [], - "updated_at": 1687539765.0 + "updated_at": 1522810462.0 }, { "data_format": 2, - "description": null, + "description": "official build specifications for mongo db", "filenames": [ - "Recipes/Singularity_example", - "Recipes/Singularity_ompi", - "Recipes/Singularity_pytorch", - "Recipes/Singularity_mpich", - "Recipes/Singularity_spark_full", - "Recipes/Singularity_spark", - "Recipes/Singularity_tensorflow", - "Recipes/Singularity_pytorch_full" + "Singularity" ], - "full_name": "ufscar/hpc-template-ci", + "full_name": "singularityhub/mongo", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePr\u00e9-requisitos\u003c/h1\u003e\u003ca id=\"user-content-pr\u00e9-requisitos\" class=\"anchor\" aria-label=\"Permalink: Pr\u00e9-requisitos\" href=\"#pr\u00e9-requisitos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSele\u00e7\u00e3o do Recipe\u003c/h2\u003e\u003ca id=\"user-content-sele\u00e7\u00e3o-do-recipe\" class=\"anchor\" aria-label=\"Permalink: Sele\u00e7\u00e3o do Recipe\" href=\"#sele\u00e7\u00e3o-do-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdicione o caminho do \u003cem\u003esingularity recipe\u003c/em\u003e desejado na vari\u00e1vel RECIPE no github (projeto \u0026gt;\u0026gt; Settings \u0026gt;\u0026gt; Secrets \u0026gt;\u0026gt; New Secret).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTemplate Integra\u00e7\u00e3o Cont\u00ednua Github\u003c/h1\u003e\u003ca id=\"user-content-template-integra\u00e7\u00e3o-cont\u00ednua-github\" class=\"anchor\" aria-label=\"Permalink: Template Integra\u00e7\u00e3o Cont\u00ednua Github\" href=\"#template-integra\u00e7\u00e3o-cont\u00ednua-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEsse projeto o template para uso do cluster da UFSCar, contendo integra\u00e7\u00e3o cont\u00ednua com o Google Drive e Amazon S3.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequisitos para Google Drive\u003c/h2\u003e\u003ca id=\"user-content-requisitos-para-google-drive\" class=\"anchor\" aria-label=\"Permalink: Requisitos para Google Drive\" href=\"#requisitos-para-google-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eEntre no \u003ca href=\"https://console.developers.google.com/apis/credentials\" rel=\"nofollow\"\u003econsole de credenciais de API do Google\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSe ainda n\u00e3o houver um projeto, crie um.\u003c/li\u003e\n\u003cli\u003eEm \"Ativar API e Servi\u00e7os\", busque por \"Google Drive\" e ative a permiss\u00e3o.\u003c/li\u003e\n\u003cli\u003eClique em \"Criar credenciais\".\u003c/li\u003e\n\u003cli\u003eSelecione \"ID do cliente do OAuth\".\u003c/li\u003e\n\u003cli\u003eEm \"Tipo de aplicativo\", selecione \"App para computador\".\u003c/li\u003e\n\u003cli\u003eD\u00ea um nome de identifica\u00e7\u00e3o para as credenciais e clique em \"criar\". V\u00e3o aparecer dois dados (\"Seu ID de cliente\" e \"Sua chave secreta de cliente\"), precisaremos dos dois no passo seguinte.\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 17 # selecione aqui o n\u00famero correspondente a op\u00e7\u00e3o \"Google Drive\"\nclient_id\u0026gt; conte\u00fado de \"Seu ID de cliente\"\nclient_secret\u0026gt; conte\u00fado de \"Sua chave secreta de cliente\"\nscope\u0026gt; 1 # Full access all files, excluding Application Data Folder\nroot_folder_id\u0026gt; deixe em branco\nservice_account_file\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/n\u0026gt; n\n\nSe j\u00e1 tiver o rclone instalado em seu computador, execute o comando exibido, caso contr\u00e1rio, o instale conforme indicado na p\u00e1gina oficial do rclone dependendo do seu sistema operacional (https://rclone.org/install/). A Seguir insira o resultado do comando exibido:\n\nconfig_token\u0026gt; c\u00f3digo fornecido pelo terminal ap\u00f3s autoriza\u00e7\u00e3o\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequisitos para Amazon S3\u003c/h2\u003e\u003ca id=\"user-content-requisitos-para-amazon-s3\" class=\"anchor\" aria-label=\"Permalink: Requisitos para Amazon S3\" href=\"#requisitos-para-amazon-s3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eEntre na \u003ca href=\"console.aws.amazon.com\"\u003eAWS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClique na seta ao lado de seu nome de usu\u00e1rio e em \"My Security Credentials\".\u003c/li\u003e\n\u003cli\u003eNa se\u00e7\u00e3o \"Access Keys\", clique em \"Create New Access Key\".\u003c/li\u003e\n\u003cli\u003eNa janela que aparece, clique em \"Show Access Key\".\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 4\nprovider\u0026gt; 1\nenv_auth\u0026gt; 1\naccess_key_id\u0026gt; conte\u00fado de \"Access Key ID\"\nsecret_access_key\u0026gt; conte\u00fado de \"Secret Access Key\"\nregion\u0026gt; 16\nendpoint\u0026gt; deixe em branco\nlocation_constraint\u0026gt; 16\nacl\u0026gt; deixe em branco\nserver_side_encryption\u0026gt; deixe em branco\nsse_kms_key_id\u0026gt; deixe em branco\nstorage_class\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMongo\u003c/h1\u003e\u003ca id=\"user-content-mongo\" class=\"anchor\" aria-label=\"Permalink: Mongo\" href=\"#mongo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is an example of building a mongo container with Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLocal Generation\u003c/h2\u003e\u003ca id=\"user-content-local-generation\" class=\"anchor\" aria-label=\"Permalink: Local Generation\" href=\"#local-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, clone the repo.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://www.github.com/singularityhub/mongo\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e mongo\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, on your local machine, build the container!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build mongo.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the Image\u003c/h2\u003e\u003ca id=\"user-content-running-the-image\" class=\"anchor\" aria-label=\"Permalink: Running the Image\" href=\"#running-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entrypoint to communicate with mongo is accessible by running the image as an executable. Here is how to see the help commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./mongo.sif --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, since you likely won\u0027t have write access inside the image running on a cluster (the image itself is in read only mode) what we must do is map a local directory to store data. We can do that as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir data \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e this is your data directory on your local machine or cluster\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn one terminal, we can start the database\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/data:/data/db mongo.sif --auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd in another terminal, we can send a command to mongo, for example, connect\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/data:/data/db mongo.sif mongo\nMongoDB shell version v4.2.1\nconnecting to: mongodb://127.0.0.1:27017/\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003ecompressors=disabled\u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003egssapiServiceName=mongodb\nImplicit session: session { \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eid\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e:\u003c/span\u003e UUID(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e22992c09-84bd-416c-9934-db7145a5d37c\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) }\nMongoDB server version: 4.2.1\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 3, "subscribers_count": 4, "topics": [], - "updated_at": 1708611593.0 + "updated_at": 1628709769.0 }, { "data_format": 2, - "description": "Singularity recipe files for ApsimX (https://github.com/APSIMInitiative/ApsimX)", + "description": "A Nextflow pipeline for allele variant expression", "filenames": [ - "Singularity.2020.10.21.5755", - "Singularity.2020.04.09.5012", - "Singularity.2019.01.30.3436", - "Singularity.2023.10.7317.0", - "Singularity.2019.06.05.3920", - "Singularity.2022.06.7046.0", - "Singularity.2021.04.15.6139", - "Singularity.2018.01.30.2253", - "Singularity.2019.10.04.4236", - "Singularity.2022.12.7130.0", - "Singularity.2022.09.7085.0", - "Singularity.2020.08.04.5350", - "Singularity.2019.04.03.3693", - "Singularity.2019.07.18.4025", - "Singularity.2021.10.8.6835", - "Singularity.2023.09.7308.0", - "Singularity.2018.09.28.3099", - "Singularity.2021.9.21.6799", - "Singularity.2020.11.27.5887", - "Singularity.2022.04.7021.0", - "Singularity.2020.09.17.5665", - "Singularity.2019.01.08.3392" + "Singularity" ], - "full_name": "powerPlant/apsimx-srf", + "full_name": "arontommi/RNAseq-VAX", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2271\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for ApsimX, the next generation of the Agricultural Production Systems sIMulator (APSIM)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMaintainer notes\u003c/h2\u003e\u003ca id=\"user-content-maintainer-notes\" class=\"anchor\" aria-label=\"Permalink: Maintainer notes\" href=\"#maintainer-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container downloads the ApsimX \u003ccode\u003edeb\u003c/code\u003e file built by the \u003ca href=\"https://github.com/APSIMInitiative/APSIM.Builds\"\u003eBOB Build Service\u003c/a\u003e at CSIRO and installs it on a Ubuntu:Bionic container.\u003c/p\u003e\n\u003cp\u003eThere are two useful endpoints from the Build Service:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://apsimdev.apsim.info/APSIM.Builds.Service/Builds.svc/GetLatestVersion\" rel=\"nofollow\"\u003eGet Latest Version\u003c/a\u003e: shows the full version number of the latest release\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://apsimdev.apsim.info/APSIM.Builds.Service/Builds.svc/GetURLOfLatestVersion?operatingSystem=Debian\" rel=\"nofollow\"\u003eGet URL of Latest Version\u003c/a\u003e: shows the download URL for the latest version of the specified OS\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003eApsimSetup*.deb\u003c/code\u003e files are named using the \u003ca href=\"https://github.com/APSIMInitiative/APSIM.Builds/blob/21a8ac85a1d868a45f60a994a35a5d07dc04562a/APSIM.Builds.Service/Builds.svc.cs#L278\"\u003ebuild.issueNumber\u003c/a\u003e instead of the full version number, so we must hardcode the URLs inside the recipe files, while using the full version number for the recipe file names.\u003c/p\u003e\n\u003cp\u003eTo facilitate running the container, a \u003ccode\u003e/usr/local/bin/apsimmodels\u003c/code\u003e file is created as the entrypoint using \u003ccode\u003e/usr/local/bin/apsim\u003c/code\u003e as a template.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer-specific notes\u003c/h2\u003e\u003ca id=\"user-content-container-specific-notes\" class=\"anchor\" aria-label=\"Permalink: Container-specific notes\" href=\"#container-specific-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eApsimX apparently needs \u003ccode\u003e/etc/localtime\u003c/code\u003e to run so we have to install \u003ccode\u003etzdata\u003c/code\u003e as a dependency. We configure our local timezone, but this can be overridden at run time by exporting the \u003ccode\u003eTZ\u003c/code\u003e environment variable.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003eplease note : This pipeline is still in alpha stage. please use at your own risk\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRNAseq-VAX - RNAseq Variante Allele eXpression\u003c/h1\u003e\u003ca id=\"user-content-rnaseq-vax---rnaseq-variante-allele-expression\" class=\"anchor\" aria-label=\"Permalink: RNAseq-VAX - RNAseq Variante Allele eXpression\" href=\"#rnaseq-vax---rnaseq-variante-allele-expression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe aim of this pipeline ist to take of where \u003ca href=\"https://github.com/nf-core/rnaseq\"\u003enf-core/rnaseq\u003c/a\u003e leaves and add additional informations on top. The pipeline follows \u003ca href=\"https://gatkforums.broadinstitute.org/gatk/discussion/3892/the-gatk-best-practices-for-variant-calling-on-rnaseq-in-full-detail\" rel=\"nofollow\"\u003eGATK guideline for variant calling in RNAseq\u003c/a\u003e. It then uses \u003ca href=\"https://software.broadinstitute.org/gatk/documentation/tooldocs/3.8-0/org_broadinstitute_gatk_tools_walkers_rnaseq_ASEReadCounter.php\" rel=\"nofollow\"\u003eASEReadCounter\u003c/a\u003e to retrive allele specific expression.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe RNAseq_VAX pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was written by Aron T. Skaftason (\u003ca href=\"https://github.com/arontommi\"\u003earontommi\u003c/a\u003e) but mostly cannibalised from \u003ca href=\"https://github.com/nf-core/rnaseq\"\u003enf-core/rnaseq\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 5, + "subscribers_count": 1, "topics": [], - "updated_at": 1695108781.0 + "updated_at": 1692357015.0 }, { "data_format": 2, - "description": "Singularity Recipe for QIIME 2", + "description": "GWAS of trait variance (C++)", "filenames": [ - "Singularity.2018.6", - "Singularity.2019.10", - "Singularity.2021.4", - "Singularity.2017.12", - "Singularity.2018.2", - "Singularity", - "Singularity.2019.4" + "sim/Singularity.def" ], - "full_name": "ResearchIT/qiime2", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Recipe for QIIME2\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-qiime2\" class=\"anchor\" aria-label=\"Permalink: Singularity Recipe for QIIME2\" href=\"#singularity-recipe-for-qiime2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains recipe run \u003ca href=\"https://qiime2.org\" rel=\"nofollow\"\u003eqiime2\u003c/a\u003e within a\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eVersions:\n2017.12 - QIIME2-2017.12 installed on CentOS 7\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to Use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity run shub://ResearchIT/qiime2 --help\u003c/p\u003e\n", + "full_name": "MRCIEU/varGWAS", + "latest_release": "1.2.3", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003evarGWAS: GWAS of SNP variance effects\u003c/h1\u003e\u003ca id=\"user-content-vargwas-gwas-of-snp-variance-effects\" class=\"anchor\" aria-label=\"Permalink: varGWAS: GWAS of SNP variance effects\" href=\"#vargwas-gwas-of-snp-variance-effects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/MRCIEU/vargwas/actions\"\u003e\u003cimg src=\"https://github.com/MRCIEU/vargwas/actions/workflows/test.yml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003eSoftware to perform genome-wide association study of SNP effects on trait variance\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFull documentation available from \u003ca href=\"https://mrcieu.github.io/varGWAS\" rel=\"nofollow\"\u003ehttps://mrcieu.github.io/varGWAS\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eR\u003c/h2\u003e\u003ca id=\"user-content-r\" class=\"anchor\" aria-label=\"Permalink: R\" href=\"#r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eR-package also available from \u003ca href=\"https://github.com/MRCIEU/varGWASR\"\u003ehttps://github.com/MRCIEU/varGWASR\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLyon M, Millard L, Davey Smith G, Gaunt T, Tilling K. Hypothesis-free detection of gene-interaction effects on biomarker concentration in UK Biobank using variance prioritisation. MedRxiv (2022). \u003ca href=\"https://doi.org/10.1101/2022.01.05.21268406\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2022.01.05.21268406\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 3, "subscribers_count": 7, "topics": [ - "qiime", - "singularity" + "gwas", + "variance", + "heteroscedasticity", + "heteroskedasticity", + "variability" ], - "updated_at": 1712857622.0 + "updated_at": 1695043730.0 }, { "data_format": 2, - "description": "Docker and Singularity containers for Bioinformatics programs", + "description": null, "filenames": [ - "sambamba-0.6.8/Singularity.sambamba-0.6.8", - "cnvkit-0.9.5/Singularity.cnvkit-0.9.5", - "bwa-0.7.17/Singularity.bwa-0.7.17.ubuntu", - "bwa-0.7.17/Singularity.bwa-0.7.17", - "reporting-3.6.0/Singularity.reporting-3.6.0", - "sambamba-0.6.6/Singularity.sambamba-0.6.6", - "sambamba-0.6.6/Singularity.sambamba-0.6.6.old", - "base/Singularity.base", - "multiqc-1.5/Singularity.multiqc-1.5", - "R-3.4.3/Singularity.R-3.4.3", - "IGV-2.4.10/Singularity.IGV-2.4.10", - "vcf2tsv-1.0.0_rc2/Singularity.vcf2tsv-1.0.0_rc2", - "ghostscript-9.22/Singularity.ghostscript-9.22", - "R-3.3.2/Singularity.R-3.3.2", - "bcftools-1.7/Singularity.bcftools-1.7", - "alpine.3.8/Singularity.alpine.3.8", - "annovar-150617/Singularity.annovar-150617", - "GATK-3.8/Singularity.GATK-3.8", - "bedtools-2.26.0/Singularity.bedtools-2.26.0", - "reporting-3.3.2/Singularity.reporting-3.3.2", - "deconstructSigs-1.8.0/Singularity.deconstructSigs-1.8.0", - "mnpmethylationclassifier-0.1.124/Singularity.mnpmethylationclassifier-0.1.124", - "maftools-1.8.10/Singularity.maftools-1.8.10" + "hpobench/container/recipes/Singularity.template", + "hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", + "hpobench/container/recipes/od/Singularity.ODBenchmarks", + "hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine", + "hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", + "hpobench/container/recipes/rl/Singularity.Cartpole", + "hpobench/container/recipes/rl/Singularity.learnaBenchmark", + "hpobench/container/recipes/nas/Singularity.nasbench_1shot1", + "hpobench/container/recipes/nas/Singularity.TabularBenchmarks", + "hpobench/container/recipes/nas/Singularity.nasbench_201", + "hpobench/container/recipes/nas/Singularity.nasbench_101" ], - "full_name": "NYU-Molecular-Pathology/containers", + "full_name": "maopl/TransOpt", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoftware Containers\u003c/h1\u003e\u003ca id=\"user-content-software-containers\" class=\"anchor\" aria-label=\"Permalink: Software Containers\" href=\"#software-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains \u003ca href=\"https://docs.docker.com/get-started/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and \u003ca href=\"https://www.sylabs.io/guides/2.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e recipes to build software containers for use with sequencing and data analysis.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNOTE: These instructions are designed to be used from a Mac computer with admin access. They should also work on Linux systems but many methods will be unnecessary on Linux.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDocker for Mac can be downloaded here: \u003ca href=\"https://download.docker.com/mac/stable/Docker.dmg\" rel=\"nofollow\"\u003ehttps://download.docker.com/mac/stable/Docker.dmg\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, clone this repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/NYU-Molecular-Pathology/containers.git\ncd containers\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe included \u003ccode\u003eMakefile\u003c/code\u003e contains the commands needed to build and test containers in this repository.\u003c/p\u003e\n\u003cp\u003eIt is recommended to build containers one at a time, and test each one after building.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity containers are meant to be used on HPC, where Docker is not allowed. Singularity containers must be built on a system to which you have root (admin) access e.g. your desktop computer. Singularity itself can only be run natively in a Linux environment. To build a Singularity container from your Mac desktop, you need either Vagrant or Docker installed, in which to run Singularity via a Linux virtual machine/container (included). Instructions for building Singularity containers using both are included. Recipes to create Singularity containers are saved in files named \u003ccode\u003eSingularity\u003c/code\u003e or \u003ccode\u003eSingularity.image-tag-name\u003c/code\u003e, such as \u003ccode\u003eSingularity.GATK-3.8\u003c/code\u003e. The output will consist of an image file (\u003ccode\u003e.simg\u003c/code\u003e), which must be transferred to your target system for use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity containers with Vagrant\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-containers-with-vagrant\" class=\"anchor\" aria-label=\"Permalink: Build Singularity containers with Vagrant\" href=\"#build-singularity-containers-with-vagrant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVagrant needs to be installed on your Mac (see installation instructions in the included file \u003ccode\u003eVAGRANT.md\u003c/code\u003e). The file \u003ccode\u003eVagrantfile\u003c/code\u003e included in this repository provides the definitions for the virtual machines to be used for building and testing Singularity containers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBuild\u003c/h4\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a Singularity container using Vagrant, run a command such as the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-build VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003eVAR\u003c/code\u003e is the name of the directory in this repository that contains the recipe for the Singularity container you wish to build. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-build VAR=sambamba-0.6.6\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNOTE: do not include a trailing slash in arguments to \u003ccode\u003eVAR\u003c/code\u003e (e.g. \u003ccode\u003eVAR=sambamba-0.6.6/\u003c/code\u003e will not work, must use \u003ccode\u003eVAR=sambamba-0.6.6\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNOTE: If your builds start failing randomly or you start getting strange errors inside your containers, try removing the reference to your Vagrant VM (\u003ccode\u003emake clean-vagrant\u003c/code\u003e) and try again. Be sure to remove old Vagrant VM\u0027s from VirtualBox if you do this.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNOTE: Some Singularity recipes that pull from Docker images tend to fail when building with Vagrant, try building with the Docker container as described further below.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTest\u003c/h4\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo test a Singularity container that you have built, you can use the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-test VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will load an interactive shell session inside the target container, where you can verify that your software is present and operational.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Singularity containers with Docker\u003c/h3\u003e\u003ca id=\"user-content-build-singularity-containers-with-docker\" class=\"anchor\" aria-label=\"Permalink: Build Singularity containers with Docker\" href=\"#build-singularity-containers-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build Singularity containers using \u003ca href=\"https://docs.docker.com/docker-for-mac/install/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e. First, you need to build a Docker container that contains Singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-build VAR=Singularity-2.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBuild\u003c/h4\u003e\u003ca id=\"user-content-build-1\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a Singularity container using Docker with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-build-docker VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can adjust the container tag using the \u003ccode\u003eDOCKERREPO\u003c/code\u003e (\u0027stevekm/containers\u0027) or \u003ccode\u003eDOCKERTAG\u003c/code\u003e (\u0027stevekm/containers:container-name\u0027) Makefile variables.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSync\u003c/h3\u003e\u003ca id=\"user-content-sync\" class=\"anchor\" aria-label=\"Permalink: Sync\" href=\"#sync\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo copy a Singularity container to your target system, you can generate a template \u003ccode\u003ersync\u003c/code\u003e command with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake singularity-sync VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will print out a template command to transfer your container file via \u003ccode\u003ersync\u003c/code\u003e \u0026amp; ssh to your target system. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ make singularity-sync VAR=sambamba-0.6.6\ncommand:\nrsync --dry-run -vrhP -e ssh sambamba-0.6.6/sambamba-0.6.6.simg kellys04@bigpurple.nyumc.org:/gpfs/home/kellys04/containers/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can specify the username, server address, and server destination path with \u003ccode\u003eMakefile\u003c/code\u003e variables like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ make singularity-sync VAR=sambamba-0.6.6 USER=bob REMOTE=some.server.com REMOTEDIR=/path/to/containers/\ncommand:\nrsync --dry-run -vrhP -e ssh sambamba-0.6.6/sambamba-0.6.6.simg bob@some.server.com:/path/to/containers/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will want to copy \u0026amp; paste this to run this with \u003ccode\u003e--dry-run\u003c/code\u003e as shown first, then run again without it when you are sure the transfer looks correct.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker Containers\u003c/h1\u003e\u003ca id=\"user-content-docker-containers\" class=\"anchor\" aria-label=\"Permalink: Docker Containers\" href=\"#docker-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following Docker containers are included for special purposes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSingularity-2.4\u003c/code\u003e: used to build Singularity containers\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMiniconda3-4.4.10\u003c/code\u003e: used as a scratch-pad test container to interactively determine compatible versions of \u003ccode\u003econda\u003c/code\u003e libraries for installation in other containers\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build-2\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker containers can be built with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-build VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest\u003c/h2\u003e\u003ca id=\"user-content-test-1\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker containers can be tested with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake docker-test VAR=\u0026lt;dirname\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will start an interactive shell in the container, where you can verify that your software works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoftware\u003c/h1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-label=\"Permalink: Software\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDocker version 17.12.0-ce, build c97c6d6\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVagrant 2.0.1\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVirtualBox Version 5.2.2 r119230 (Qt5.6.3)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity 2.4 (build), 2.5.2 (run)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003emacOS 10.10, 10.12.6\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU Make 3.81\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU bash, version 3.2.57(1)-release (x86_64-apple-darwin16)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-transopt\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#transopt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTransOpt\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:maopl/TransOpt.git\ncd TransOpt\npip install -r requirements.txt \npip install .\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, - "topics": [ - "docker", - "singularity", - "bioinformatics", - "containers" - ], - "updated_at": 1611324590.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1698837283.0 }, { "data_format": 2, - "description": "robots for experiment factory experiments and surveys", + "description": "indexed file format for barcoded BAMs with API for converting and accessing alignment records", "filenames": [ - "Singularity" + "src/bamdb/Singularity.bamdb" ], - "full_name": "expfactory/expfactory-robots", + "full_name": "mskilab-org/bambi", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExperiment Factory Robots\u003c/h1\u003e\u003ca id=\"user-content-experiment-factory-robots\" class=\"anchor\" aria-label=\"Permalink: Experiment Factory Robots\" href=\"#experiment-factory-robots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis set of scripts (and provided container) will allow you to run a robot test for various kinds of experiments. Currently supported are surveys and jspsych experiments. Local (non container) use will be discussed first, followed by Docker.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/153497?speed=3\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f53b5ed7083e6601d6dcd0d29a465d9e337d2b7930ac276caab7c09af25602b/68747470733a2f2f61736369696e656d612e6f72672f612f3135333439372e706e67\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/153497.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the examples, we can clone the \u003ca href=\"https://www.github.com/expfactory-experiments/test-task\"\u003etest-task\u003c/a\u003e experiment and the \u003ca href=\"https://www.github.com/expfactory-experiments/bis11-survey\"\u003ebis11-survey\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /tmp\ngit clone https://www.github.com/expfactory-experiments/test-task\ngit clone https://www.github.com/expfactory-experiments/bis11-survey\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo test locally, you will need expfactory installed locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install expfactory\n\n# Development\ngit clone https://www.github.com/expfactory/expfactory\ncd expfactory\npython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand for Singularity, you will need to \u003ca href=\"https://singularityware.github.io/install-linux\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eJsPsych Robot\u003c/h2\u003e\u003ca id=\"user-content-jspsych-robot\" class=\"anchor\" aria-label=\"Permalink: JsPsych Robot\" href=\"#jspsych-robot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that usage requires python 3, so if you cannot provide it, use the container.\u003c/p\u003e\n\u003cp\u003eThe basic usage is to specify a list of one or more experiment folder paths, and then\noptionally select a robot type (the default is jspsych):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py --help\nusage: start.py [-h] [--robot {survey,jspsych}] folders [folders ...]\n\nexpfactory: generate survey from config.json and question file\n\npositional arguments:\n folders experiments for robot testing\n\noptional arguments:\n -h, --help show this help message and exit\n --robot {survey,jspsych}, -r {survey,jspsych}\n the survey robot to recruit!\n --browser {Firefox,Chrome}, -b {Firefox,Chrome}\n browser driver to use for the robot\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the robot for the test-task and use jspsych, we can simply do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py /tmp/test-task\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis would be equivalent to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py --robot jspsych /tmp/test-task\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethe browser (chrome default) will open and you will see the experiment progress and\nfinish. The console will show GET and POST of resources, etc.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRecruiting jspsych robot!\n[folder] /tmp/test-task\nLOG STARTING TEST OF EXPERIMENT\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET / HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /jspsych.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /default_style.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /style.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jquery.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/math.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/jspsych.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/plugins/jspsych-text.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/jspsych-poldrack-text.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/jspsych-poldrack-instructions.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/jspsych-attention-check.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/jspsych-poldrack-single-stim.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/plugins/jspsych-survey-text.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/plugins/jspsych-call-function.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/poldrack_utils.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /experiment.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:48] \"GET /%3Cdiv%20class%20=%20%22shapebox%22%3E%3Cdiv%20id%20=%20%22cross%22%3E%3C/div%3E%3C/div%3E HTTP/1.1\" 404 -\n127.0.0.1 - - [17/Dec/2017 06:52:48] \"GET /favicon.ico HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:58] \"POST /save HTTP/1.1\" 501 -\nLOG FINISHING TEST OF EXPERIMENT\nLOG [done] stopping web server...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSurvey Robot\u003c/h2\u003e\u003ca id=\"user-content-survey-robot\" class=\"anchor\" aria-label=\"Permalink: Survey Robot\" href=\"#survey-robot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe same can be done for a survey! Let\u0027s now test bis-11\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py --robot survey /tmp/bis11-survey\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output is similar to jspsych, except we are progressing through a survey.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py --robot survey /tmp/bis11-survey\nRecruiting survey robot!\n[folder] /tmp/bis11-survey\nLOG STARTING TEST OF SURVEY\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET / HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/material.blue-red.min.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/surveys.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/jquery-ui-1.10.4.custom.min.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/style.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery-2.1.1.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/material.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery-ui-1.10.4.custom.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery.wizard.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery.form-3.50.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery.validate-1.12.0.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/images/ui-bg_flat_75_ffffff_40x100.png HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/images/ui-bg_highlight-soft_75_cccccc_1x100.png HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /favicon.ico HTTP/1.1\" 200 -\nLOG Testing page 1\nLOG Testing page 2\nLOG Testing page 3\nLOG Testing page 4\nLOG Testing page 5\nLOG FINISHING TEST OF SURVEY\nLOG [done] stopping web server...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Usage\u003c/h2\u003e\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" aria-label=\"Permalink: Singularity Usage\" href=\"#singularity-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is ideal for this use case because of the seamless nature between the container and host. We have a pre-built image for your use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://expfactory/expfactory-robots\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor if you want to build it yourself, the first thing you would want to do is again clone the repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/expfactory/expfactory-robots\ncd expfactory-robots\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then build.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build expfactory-robots.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen to run the image, you will basically want to bind the \u003cem\u003eparent\u003c/em\u003e folder where your task is to \u003ccode\u003e/data\u003c/code\u003e in the container, and specify the path to the experiment \u003cem\u003erelative to \u003ccode\u003edata\u003c/code\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind /tmp:/data expfactory-robots.simg /data/test-task\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Usage\u003c/h2\u003e\u003ca id=\"user-content-docker-usage\" class=\"anchor\" aria-label=\"Permalink: Docker Usage\" href=\"#docker-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t expfactory/expfactory-robots .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run it, I again mapped the folder one level above your experiment (so we can validate the experiment folder name itself!) to \u003ccode\u003e/data\u003c/code\u003e in the container, and I also made sure to specify the port, because Docker doesn\u0027t have a seamless connection to the host like Singularity.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ePARENT_FOLDER=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edirname \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\ndocker run -v \u003cspan class=\"pl-smi\"\u003e$PARENT_FOLDER\u003c/span\u003e/:/data -p 3030:3030 expfactory/expfactory-robots /data/test-task\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you have issues, you may need to check the version of selenium and the Gecko Driver.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/mskilab/bambi\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/47c82ab2d405aa684f3a5004ed8fc79887c025105127effda9ce1d35b5568974/68747470733a2f2f7472617669732d63692e6f72672f6d736b696c61622f62616d62692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/mskilab/bambi.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/mskilab/bambi?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ccb3814df2f3f1c65e518dd49a10732518ba754f251e50546a0d42ec9fd9cdab/68747470733a2f2f696d672e736869656c64732e696f2f636f6465636f762f632f6769746875622f6d736b696c61622f62616d62692e737667\" alt=\"codecov.io\" data-canonical-src=\"https://img.shields.io/codecov/c/github/mskilab/bambi.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bambi\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#bambi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi\u003c/h1\u003e\n\u003cp\u003eR package for querying 10x WGS and single-cell BAMs\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/gUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/bamUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-bambi-commands\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#bambi-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi commands\u003c/h2\u003e\n\u003cp\u003eInstantiate a bambi object:\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003egrab_bx()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_bx(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_cb()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_cb(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_ub()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_ub(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003efetch_by_tag()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003efetch_by_tag(tag, tag_queries, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstantiate a \u003ccode\u003ebambi\u003c/code\u003e object\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003elibrary(bambi)\n\n\u0026gt; hcc1143_subset = bambi$new(bam_file = \"subsetHCC1143_phased_possorted0001.bam\", bamdb_path=\"subsetHCC1143_phased_possorted0001_lmdb\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCall methods\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003e\u0026gt; hcc1143_subset$grab_bx(\u0027CGACGTGTCCTCTAGC-1\u0027)\nGRanges object with 2 ranges and 11 metadata columns:\n seqnames ranges strand |\n \u0026lt;Rle\u0026gt; \u0026lt;IRanges\u0026gt; \u0026lt;Rle\u0026gt; |\n [1] chr1 [147975454, 147975580] + |\n [2] chr1 [147975675, 147975824] - |\n qname flag mapq cigar\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;character\u0026gt;\n [1] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 99 16 127M\n [2] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 147 16 150M\n rnext pnext tlen\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt;\n [1] = 147975676 371\n [2] = 147975455 -371\n seq\n \u0026lt;character\u0026gt;\n [1] ATGTCTTCTTCCTCATTATCTGGCACTGGTTAGGAAGCACTCATCTCCATGAAGTCATCTTTTGTTAATTCCTCTGGTGTGGTGTGTATTAGCTCTTAAATTCCTCCAAGATCCATATCTTGCAACC\n [2] ATCTGGACACAAATTGTACTTTTGTCCAGCACGAATTTATTGTTTTGAGTTTCATGGTTTTCTATATCAACTGATGACATCTTGAAAGGTGTAAGCCTTCCAGACTTCCATGATGTTCTCTCTATTGGGTTTCTCTTTTGCAATGTTGAC\n qual\n \u0026lt;character\u0026gt;\n [1] JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJAJFJJJJJJJJJFJJJJJJJJJJFJJJJFFFJJJFJJJJJJAAJFJJJFAFAFFFJAA\u0026lt;7F\u0026lt;\n [2] A\u0026lt;7FFFJFFFAJJAAAJJF\u0026lt;F\u0026lt;7A-\u0026lt;AA-\u0026lt;\u0026lt;\u0026lt;AFFJJJJJJJJFFJAFFAAFJFJJJAFFJJJJJJJJJJFJFAJJJJJJFJJJJJJ\u0026lt;FFJJJFJJJFJJJJJJJJJJJJJFJJJJFFJ7JJJJF\u0026lt;JJJJJJJJJJJJJJJJJJJFFAA\u0026lt;\n BX qwidth\n \u0026lt;character\u0026gt; \u0026lt;integer\u0026gt;\n [1] CGACGTGTCCTCTAGC-1 127\n [2] CGACGTGTCCTCTAGC-1 150\n -------\n seqinfo: 1 sequence from an unspecified genome; no seqlengths\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, - "topics": [ - "expfactory", - "experiment-factory", - "robots", - "behavior", - "psychology", - "experiment", - "testing" - ], - "updated_at": 1673579530.0 + "subscribers_count": 6, + "topics": [], + "updated_at": 1686844399.0 }, { "data_format": 2, - "description": null, + "description": "work in progress of trait extraction in interacting bean roots", "filenames": [ - "Singularity" + "3D_model_traits_work/Singularity", + "3D_model_traits_work/model_preprocess/Singularity" ], - "full_name": "lsx1980/vsfm-master", + "full_name": "wlavoy/root_root_interaction_traits", "latest_release": null, - "readme": "\u003cp\u003eSFM for 3D root model reconstruction\u003c/p\u003e\n\u003cp\u003eThe software package was integrated as a module at PlantIT website at : \u003ca href=\"https://portnoy.cyverse.org/\" rel=\"nofollow\"\u003ehttps://portnoy.cyverse.org/\u003c/a\u003e. (Collaborate with Cyverse \u003ca href=\"https://www.cyverse.org/\" rel=\"nofollow\"\u003ehttps://www.cyverse.org/\u003c/a\u003e ) . Users are welcomed to registered as an user to try this package via PlantIT website.\u003c/p\u003e\n\u003cp\u003eThe software package was also available at Dockerhub (\u003ca href=\"https://hub.docker.com/r/computationalplantscience/3d-model-reconstruction\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/computationalplantscience/3d-model-reconstruction\u003c/a\u003e) for advanced users to run locally via singularity at Linux environment:\u003c/p\u003e\n\u003cp\u003eSteps to run this package in container locally:\n1. Install singularity container version 3.6 following the instruction at \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.6/user-guide/quick_start.html#quick-installation-steps\u003c/a\u003e\n2. Run the container:\nOnce singularity was successfully installed, the container can be executed using\nsingularity exec --home $PWD/ \u2013bind /$PWD:/opt/code/vsfm/bin/temp,/$PWD:/opt/code/vsfm/bin/log docker://computationalplantscience/3d-model-reconstruction /opt/code/vsfm/bin/VisualSFM sfm+pmvs /$PATH_TO_IMAGE_FOLDER/\n\"$PWD\" : can be replaced by user\u2019s local path for store temporary files. $PATH_TO_IMAGE_FOLDER/: can be replaced by user\u2019s image data folder.\n3. Collect the 3D model result After the container was executed successfully with image data files, user should be able to see output at command window like this:\n\u0027\u0027\u0027 Save to /$PATH_TO_IMAGE_FOLDER/vsfm.nvm ... done Save /$PATH_TO_IMAGE_FOLDER/vsfm.0.ply ...done\nVisualSFM 3D reconstruction, finished Totally 15.000 seconds used\nLogFile: /opt/code/vsfm/bin/log/[20_12_17][15_26_12][690].log \u0027\u0027\u0027\nThe 3D model was stored as point cloud in ply format at /$PATH_TO_IMAGE_FOLDER/vsfm.0.ply.\u003c/p\u003e\n\u003cp\u003eAuthor\nsuxing liu(\u003ca href=\"mailto:suxingliu@gmail.com\"\u003esuxingliu@gmail.com\u003c/a\u003e) reference: Anders Damsgaard with contributions by Caleb Adams and Connor P Doherty. Changchang Wu ( \u003ca href=\"mailto:wucc1130@gmail.com\"\u003ewucc1130@gmail.com\u003c/a\u003e )\nSingularity container was maintained by Wesley Paul Bonelli. it was deployed to Plant IT website by Wesley Paul Bonelli (\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e).\nSingularity container overlay issues were solved by [Saravanaraj Ayyampalayam] (\u003ca href=\"https://github.com/raj76\"\u003ehttps://github.com/raj76\u003c/a\u003e) (\u003ca href=\"mailto:raj76@uga.edu\"\u003emailto:raj76@uga.edu\u003c/a\u003e)\nSpecial thanks to Chris Cotter building the container recipe for testing and debugging.\u003c/p\u003e\n\u003cp\u003eTodo\n\u2022 VisualSFM is built without CUDA acceleration. Add optional GPU build.\n\u2022 support GPU based SIFT feature matching\u003c/p\u003e\n\u003cp\u003eLicense\nGNU Public License\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-root_root_interaction_traits\" class=\"anchor\" aria-hidden=\"true\" href=\"#root_root_interaction_traits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eroot_root_interaction_traits\u003c/h1\u003e\n\u003cp\u003ework in progress of trait extraction in interacting bean roots\nCurrently experimenting with the use of clustering and segmentation to distinguish separate root systems.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1612365154.0 + "updated_at": 1678739943.0 }, { "data_format": 2, - "description": "A traffic simulator for unmanned surface vessels.", + "description": "Nextflow pipeline for Illumina NGS demultiplexing", "filenames": [ - "Singularity" + "containers/dos2unix-7.4.0/Singularity.dos2unix-7.4.0", + "containers/fastqc-0.11.7/Singularity.fastqc-0.11.7", + "containers/report-r-3.4.3/Singularity.report-r-3.4.3", + "containers/bcl2fastq-2.17.1/Singularity.bcl2fastq-2.17.1", + "containers/multiqc-1.5/Singularity.multiqc-1.5", + "containers/python-2.7/Singularity.python-2.7" ], - "full_name": "colinsauze/ASVTrafficSim", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eASVTrafficSim\u003c/h1\u003e\u003ca id=\"user-content-asvtrafficsim\" class=\"anchor\" aria-label=\"Permalink: ASVTrafficSim\" href=\"#asvtrafficsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA traffic simulator for unmanned surface vessels.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall system libraries\u003c/h3\u003e\u003ca id=\"user-content-install-system-libraries\" class=\"anchor\" aria-label=\"Permalink: Install system libraries\" href=\"#install-system-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esudo apt install libjansson-dev\nsudo apt install python-gi-cairo\u003c/p\u003e\n\u003cp\u003e(or your system\u0027s equivalent)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall pip dependencies:\u003c/h3\u003e\u003ca id=\"user-content-install-pip-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install pip dependencies:\" href=\"#install-pip-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003epip install boatd python-boatdclient python-sailsd pynmea2 libais\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload this repository and its submodules\u003c/h3\u003e\u003ca id=\"user-content-download-this-repository-and-its-submodules\" class=\"anchor\" aria-label=\"Permalink: Download this repository and its submodules\" href=\"#download-this-repository-and-its-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003egit clone --recursive \u003ca href=\"https://github.com/colinsauze/ASVTrafficSim/asvtrafficsim.git\"\u003ehttps://github.com/colinsauze/ASVTrafficSim/asvtrafficsim.git\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild sailsd\u003c/h3\u003e\u003ca id=\"user-content-build-sailsd\" class=\"anchor\" aria-label=\"Permalink: Build sailsd\" href=\"#build-sailsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecd sailsd\nmake\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOpenCPN for chart plotting\u003c/h3\u003e\u003ca id=\"user-content-opencpn-for-chart-plotting\" class=\"anchor\" aria-label=\"Permalink: OpenCPN for chart plotting\" href=\"#opencpn-for-chart-plotting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esudo add-apt-repository ppa:opencpn/opencpn\nsudo apt-get update\nsudo apt-get install opencpn\u003c/p\u003e\n\u003cp\u003elaunch opencpn\u003c/p\u003e\n\u003cp\u003eClick on the options icon (the spanner on the toolbar), go to connections\nadd a new incoming connection on UDP port 10110\nadd an outgoing UDP connection on port 10111 with only the GGA sentence enabled\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning:\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running:\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esailsd/sailsd\u003c/p\u003e\n\u003cp\u003eset initial lat/lon and wind direction\n./init_sails.sh\u003c/p\u003e\n\u003cp\u003erun boatd\nboatd boatd.yml\u003c/p\u003e\n\u003cp\u003e(optional) run sails-ui\nsails-ui/sails-ui\u003c/p\u003e\n\u003cp\u003erun opencpn plugin\nboatd-opencpn/boatd-opencpn\u003c/p\u003e\n\u003cp\u003erun behaviour:\nboatdctl behaviour-start example\u003c/p\u003e\n\u003cp\u003erun collision detector\npython recvBoatData.py\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning with Singularity:\u003c/h4\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity:\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the singularity container with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build ASVTrafficSim.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun it with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B logs:/ASVTrafficSim/logs ASVTrafficSim.simg \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will bind the logs (output) directory in the container to the local logs directory.\u003c/p\u003e\n\u003cp\u003e=======\u003c/p\u003e\n", + "full_name": "NYU-Molecular-Pathology/demux-nf", + "latest_release": "19.04.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-demux-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#demux-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edemux-nf\u003c/h1\u003e\n\u003cp\u003eNextflow pipeline for demultiplexing Illumina Next-Gen sequencing data.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eClone this repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NYU-Molecular-Pathology/demux-nf.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deployment\" class=\"anchor\" aria-hidden=\"true\" href=\"#deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeployment\u003c/h2\u003e\n\u003cp\u003eThe included \u003ccode\u003edeploy\u003c/code\u003e recipe should be used to create a new directory for demultiplexing based on a currently existing sequencing run directory. Include arguments that describe the configuration for your sequencing run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd demux-nf\nmake deploy RUNID=170809_NB501073_0019_AH5FFYBGX3 SAMPLESHEET=SampleSheet.csv SEQTYPE=Archer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003earguments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eRUNID\u003c/code\u003e: the identifier given to the run by the sequencer\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSAMPLESHEET\u003c/code\u003e: the samplesheet required for demultiplexing with \u003ccode\u003ebcl2fastq\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSEQTYPE\u003c/code\u003e: the type of sequencing; currently only \u003ccode\u003eArcher\u003c/code\u003e or \u003ccode\u003eNGS580\u003c/code\u003e are used\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSEQDIR\u003c/code\u003e: parent directory where the sequencer outputs its data (pre-configured for NYU server locations)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePRODDIR\u003c/code\u003e: parent directory where demultiplexing output should be stored (pre-configured for NYU server locations)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis will first check that the specified run exists on the server before cloning into a new directory at the given production output location and configuring it for demultiplexing using the subsequent commands described here.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Workflow\u003c/h2\u003e\n\u003cp\u003eAssuming you used \u003ccode\u003emake deploy\u003c/code\u003e or \u003ccode\u003emake config\u003c/code\u003e to prepare your demultiplexing directory, the following command can be used to automatically run the workflow based on the pre-defined settings and settings from your current system.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExtra parameters to be passed to Nextflow can be supplied with the \u003ccode\u003eEP\u003c/code\u003e argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run EP=\u0027--samplesheet SampleSheet.csv --runDir /path/to/sequencer/data/170809_NB501073_0019_AH5FFYBGX3\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo submit the parent Nextflow pipeline as a job on the HPC cluster:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake submit\n\n# with a different submission queue:\nmake submit SUBQ=fn_long\n\n# with a different submission time:\nmake submit SUBQ=cpu_long SUBTIME=\u0027--time=6-00:00:00\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor alternative \u003ccode\u003erun\u003c/code\u003e methods, consult the \u003ccode\u003eMakefile\u003c/code\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h1\u003e\n\u003cp\u003eDemultiplexing metadata for the workflow can be provided through several methods, evaluated in the following order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eparameters can be supplied directly to Nextflow via CLI\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf --runID 12345\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eif the file \u003ccode\u003econfig.json\u003c/code\u003e is present, non-\u003ccode\u003enull\u003c/code\u003e parameters will be retrieved\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"runDir\": \"/path/to/sequencer/data/170809_NB501073_0019_AH5FFYBGX3\",\n \"samplesheet\": \"SampleSheet.csv\",\n \"runID\": \"170809_NB501073_0019_AH5FFYBGX3\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ethis file is generated automatically during the \u003ccode\u003edeploy\u003c/code\u003e step, using the included \u003ccode\u003econfig.py\u003c/code\u003e script\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ethe following items in the current directory will be used if present:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSampleSheet.csv\u003c/code\u003e: default samplesheet file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003erunDir\u003c/code\u003e : default sequencing run source directory (can be a symlink)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003erunID.txt\u003c/code\u003e: a text file, the first line of which will be used as the run ID\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-extras\" class=\"anchor\" aria-hidden=\"true\" href=\"#extras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtras\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e(re)initialize configurations (overwrites old \u003ccode\u003econfig.json\u003c/code\u003e):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake config RUNDIR=/path/to/sequencer/data/170809_NB501073_0019_AH5FFYBGX3 SAMPLESHEET=SampleSheet.csv RUNID=170809_NB501073_0019_AH5FFYBGX3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eupdate an existing directory to the latest version of this repo:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake update\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eclean up workflow intermediary files to save space (workflow cannot be resumed after this):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake finalize\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eclean up output from all old workflows (saves current workflow output):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake clean\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003edelete the output from all workflows:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake clean-all\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003emark that the demultiplexing suceeded and the results passed QC for downstream analysis:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake passed\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003edeploy a new NGS580 analysis using the current results:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake deploy-NGS580\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003emake a \u0027deliverables\u0027 directory with just the results for samples for a specific client\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake deliverable CLIENT=somelab SHEET=list_of_clients_samples.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware\u003c/h1\u003e\n\u003cp\u003eRequired:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eJava 8 (Nextflow)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePython 2.7+\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU \u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional; must be installed to system or available with Singularity containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebcl2fastq\u003c/code\u003e version 2.17.1\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFastQC version 0.11.7\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eR (3.3.0+, with \u003ccode\u003eknitr\u003c/code\u003e and \u003ccode\u003ermarkdown\u003c/code\u003e libraries)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePandoc 1.13.1+\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 3, - "topics": [], - "updated_at": 1719296166.0 + "subscribers_count": 2, + "topics": [ + "nextflow", + "pipeline", + "demultiplexing", + "bcl2fastq" + ], + "updated_at": 1654548176.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity.def" + "Singularity/Singularity.v1.1", + "Singularity/Singularity.v1.2" ], - "full_name": "ut-amrl/BARN_CHALLENGE_2023", - "latest_release": "data", - "readme": "\u003cp\u003eWe assume that you have downloaded libtorch and unzipped it in /opt/libtorch/\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun Steps 0-4 (for BARN Challenge Organizers)\u003c/h1\u003e\u003ca id=\"user-content-run-steps-0-4-for-barn-challenge-organizers\" class=\"anchor\" aria-label=\"Permalink: Run Steps 0-4 (for BARN Challenge Organizers)\" href=\"#run-steps-0-4-for-barn-challenge-organizers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 0: Clone the repo\u003c/h2\u003e\u003ca id=\"user-content-step-0-clone-the-repo\" class=\"anchor\" aria-label=\"Permalink: Step 0: Clone the repo\" href=\"#step-0-clone-the-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:ut-amrl/BARN_CHALLENGE_2023.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 1: Add the following to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to point \u003ccode\u003eROS_PACKAGE_PATH\u003c/code\u003e to the BARN repo\u003c/h2\u003e\u003ca id=\"user-content-step-1-add-the-following-to-your-bashrc-to-point-ros_package_path-to-the-barn-repo\" class=\"anchor\" aria-label=\"Permalink: Step 1: Add the following to your ~/.bashrc to point ROS_PACKAGE_PATH to the BARN repo\" href=\"#step-1-add-the-following-to-your-bashrc-to-point-ros_package_path-to-the-barn-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport ROS_PACKAGE_PATH=/path/to/BARN_CHALLENGE_2023:$ROS_PACKAGE_PATH\nsource ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2: Build and Run the pre-built singularity container\u003c/h2\u003e\u003ca id=\"user-content-step-2-build-and-run-the-pre-built-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Step 2: Build and Run the pre-built singularity container\" href=\"#step-2-build-and-run-the-pre-built-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build nav_competition_image.sif Singularity.def\nsingularity run nav_competition_image.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3: Build some required packages inside the container\u003c/h2\u003e\u003ca id=\"user-content-step-3-build-some-required-packages-inside-the-container\" class=\"anchor\" aria-label=\"Permalink: Step 3: Build some required packages inside the container\" href=\"#step-3-build-some-required-packages-inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebash build_packages.sh \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 4: Add the following to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to setup the workspace\u003c/h2\u003e\u003ca id=\"user-content-step-4-add-the-following-to-your-bashrc-to-setup-the-workspace\" class=\"anchor\" aria-label=\"Permalink: Step 4: Add the following to your ~/.bashrc to setup the workspace\" href=\"#step-4-add-the-following-to-your-bashrc-to-setup-the-workspace\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esource /path/to/BARN_CHALLENGE_2023/devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 5: Run the run.py file\u003c/h2\u003e\u003ca id=\"user-content-step-5-run-the-runpy-file\" class=\"anchor\" aria-label=\"Permalink: Step 5: Run the run.py file\" href=\"#step-5-run-the-runpy-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython3 run.py --world_idx 0 \n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSetup and Install (for BARN Challenge Participants)\u003c/h1\u003e\u003ca id=\"user-content-setup-and-install-for-barn-challenge-participants\" class=\"anchor\" aria-label=\"Permalink: Setup and Install (for BARN Challenge Participants)\" href=\"#setup-and-install-for-barn-challenge-participants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWithout Docker\u003c/h2\u003e\u003ca id=\"user-content-without-docker\" class=\"anchor\" aria-label=\"Permalink: Without Docker\" href=\"#without-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Clone the repo\u003c/h3\u003e\u003ca id=\"user-content-step-1-clone-the-repo\" class=\"anchor\" aria-label=\"Permalink: Step 1: Clone the repo\" href=\"#step-1-clone-the-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:ut-amrl/BARN_CHALLENGE_2023.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Add the following to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to point \u003ccode\u003eROS_PACKAGE_PATH\u003c/code\u003e to the BARN repo\u003c/h3\u003e\u003ca id=\"user-content-step-2-add-the-following-to-your-bashrc-to-point-ros_package_path-to-the-barn-repo\" class=\"anchor\" aria-label=\"Permalink: Step 2: Add the following to your ~/.bashrc to point ROS_PACKAGE_PATH to the BARN repo\" href=\"#step-2-add-the-following-to-your-bashrc-to-point-ros_package_path-to-the-barn-repo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eexport ROS_PACKAGE_PATH=/path/to/BARN_CHALLENGE_2023:$ROS_PACKAGE_PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Build\u003c/h3\u003e\u003ca id=\"user-content-step-3-build\" class=\"anchor\" aria-label=\"Permalink: Step 3: Build\" href=\"#step-3-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/BARN_CHALLENGE_2023\nbash build_packages.sh \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 4: Add the following to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to setup the workspace\u003c/h2\u003e\u003ca id=\"user-content-step-4-add-the-following-to-your-bashrc-to-setup-the-workspace-1\" class=\"anchor\" aria-label=\"Permalink: Step 4: Add the following to your ~/.bashrc to setup the workspace\" href=\"#step-4-add-the-following-to-your-bashrc-to-setup-the-workspace-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esource /path/to/BARN_CHALLENGE_2023/devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 5: Run\u003c/h2\u003e\u003ca id=\"user-content-step-5-run\" class=\"anchor\" aria-label=\"Permalink: Step 5: Run\" href=\"#step-5-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload the worlds from \u003ca href=\"https://github.com/Daffan/nav-competition-icra2022/tree/main/jackal_helper/worlds/BARN\"\u003e\u003cstrong\u003ethis link\u003c/strong\u003e\u003c/a\u003e and place them in \u003ccode\u003e/path/to/BARN_CHALLENGE_2023/jackal_helper/worlds/BARN/\u003c/code\u003e. Then run the following command (first argument indicates the world id, second argument is the trial run number, and the third argument is 1 if you want GUI else 0):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 run_original.py 1 1 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAvailable flags:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--algo\u003c/code\u003e: either \"mb\" (move_base + graph nav) or \"vor\" (voronoi + graph nav) {default: \"vor\"}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--param\u003c/code\u003e: either \"2022\" or \"2023\" (i.e., this is the parameter to choose in the localgoal.py script) {default: \"2023\"}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--result_dir\u003c/code\u003e: path to the directory where results will be dumped {default: \"result\"}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--world_idx\u003c/code\u003e: world id {default: 0}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--gui\u003c/code\u003e: 0 is false and 1 is true {default: 1}\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--run_idx\u003c/code\u003e: trial run number {default: 0}\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo run multiple runs on multiple worlds:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash run_multiple_runs.sh 0 40 0 299 \"vor\" \"2023\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere the first two args are for start and end of run_idx, and the next two for start and end of world_idx. So this will do 40 runs over all the worlds for voronoi based planner and with 2023 choice of parameters.\u003c/p\u003e\n\u003cp\u003eTo analyse results generated from \u003ccode\u003erun_multiple_runs.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython analyse_results.py \"result_vor_2023\" 20\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere the first argument is the result_dir that contains all the logs, and the second argument represents that it will plot just the top 20 most failed worlds.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWith Docker (Do the following after Step 2 above)\u003c/h2\u003e\u003ca id=\"user-content-with-docker-do-the-following-after-step-2-above\" class=\"anchor\" aria-label=\"Permalink: With Docker (Do the following after Step 2 above)\" href=\"#with-docker-do-the-following-after-step-2-above\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2.5: Setup\u003c/h3\u003e\u003ca id=\"user-content-step-25-setup\" class=\"anchor\" aria-label=\"Permalink: Step 2.5: Setup\" href=\"#step-25-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrerequisites\u003c/h4\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://docs.docker.com/engine/install/ubuntu\" rel=\"nofollow\"\u003e\u003cstrong\u003eInstall Docker Engine\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://docs.docker.com/compose/install/linux/\" rel=\"nofollow\"\u003e\u003cstrong\u003eInstall the Docker Compose V2 Plugin\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\" rel=\"nofollow\"\u003e\u003cstrong\u003eInstall the NVIDIA Container Toolkit\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://docs.docker.com/engine/install/linux-postinstall\" rel=\"nofollow\"\u003e\u003cstrong\u003eAdd users to the \u003ccode\u003edocker\u003c/code\u003e group\u003c/strong\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBuild\u003c/h4\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:ut-amrl/ros-noetic-docker.git\ncd /path/to/ros-noetic-docker\n./build.py barn\n./launch.py barn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAdd the following to \u003ccode\u003e~/.bashrc\u003c/code\u003e:\u003c/h4\u003e\u003ca id=\"user-content-add-the-following-to-bashrc\" class=\"anchor\" aria-label=\"Permalink: Add the following to ~/.bashrc:\" href=\"#add-the-following-to-bashrc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e[[ -e /dockerrc ]] \u0026amp;\u0026amp; source /dockerrc\nsource ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eStart shell in container\u003c/h4\u003e\u003ca id=\"user-content-start-shell-in-container\" class=\"anchor\" aria-label=\"Permalink: Start shell in container\" href=\"#start-shell-in-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker exec -it $USER-noetic-barn-app-1 bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eContinue to Steps 3 and 4 as above\u003c/p\u003e\n", + "full_name": "IARCbioinfo/strelka2-nf", + "latest_release": "v1.2a", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-strelka2-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#strelka2-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estrelka2-nf\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-strelka-v2-pipeline-with-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#strelka-v2-pipeline-with-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStrelka v2 pipeline with Nextflow\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/strelka2-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d78a770e40820f0efedb9aada8ed30fab6e55a0c7b96b0ccebb17dacf20995c0/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f737472656c6b61322d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/strelka2-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/repository/docker/iarcbioinfo/strelka2-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4622\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"strelka2-nf.png?raw=true\"\u003e\u003cimg src=\"strelka2-nf.png?raw=true\" alt=\"Workflow representation\" title=\"Scheme of variant calling with strelka2 Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eNextflow : for common installation procedures see the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository.\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka v2\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003efolder with bam/cram files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_file\u003c/td\u003e\n\u003ctd\u003eTab delimited text file with either two columns called normal and tumor (somatic mode) or one column called bam (germline mode); optionally, a column called sample containing sample names to be used for naming the files can be provided and for genotyping (see genotyping mode below) a column called vcf has to be provided\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: the file provided to --input_file is where you can define pairs of bam/cram to analyse with strelka in somatic mode. It\u0027s a tabular file with 2 columns normal and tumor.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003enormal\u003c/th\u003e\n\u003cth\u003etumor\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003enormal1.cram\u003c/td\u003e\n\u003ctd\u003etumor2.cram\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enormal2.cram\u003c/td\u003e\n\u003ctd\u003etumor2.cram\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enormal3.cram\u003c/td\u003e\n\u003ctd\u003etumor3.cram\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd\u003ehg19.fasta\u003c/td\u003e\n\u003ctd\u003egenome reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mode\u003c/td\u003e\n\u003ctd\u003esomatic\u003c/td\u003e\n\u003ctd\u003eMode for variant calling; one of somatic, germline, genotyping\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003estrelka_ouptut\u003c/td\u003e\n\u003ctd\u003eOutput folder for vcf files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003enumber of CPUs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e20\u003c/td\u003e\n\u003ctd\u003ememory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--strelka\u003c/td\u003e\n\u003ctd\u003epath inside docker and singularity containers\u003c/td\u003e\n\u003ctd\u003eStrelka installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--config\u003c/td\u003e\n\u003ctd\u003edefault conf of strelka\u003c/td\u003e\n\u003ctd\u003eUse custom configuration file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--callRegions\u003c/td\u003e\n\u003ctd\u003enone\u003c/td\u003e\n\u003ctd\u003eRegion bed file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ext\u003c/td\u003e\n\u003ctd\u003ecram\u003c/td\u003e\n\u003ctd\u003eextension of alignment files (bam or cram)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eprint usage and optional parameters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--exome\u003c/td\u003e\n\u003ctd\u003eautomatically set up parameters for exome data\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--rna\u003c/td\u003e\n\u003ctd\u003eautomatically set up parameters for rna data (only available for --mode germline)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--AF\u003c/td\u003e\n\u003ctd\u003eAdd AF field to VCF (only available for --mode somatic)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--outputCallableRegions\u003c/td\u003e\n\u003ctd\u003eCreate a BED track containing regions which are determined to be callable\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-mode-somatic\" class=\"anchor\" aria-hidden=\"true\" href=\"#mode-somatic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emode somatic\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003enextflow run iarcbioinfo/strelka2-nf r v1.2a -profile singularity --mode somatic --ref hg38.fa --tn_pairs pairs.txt --input_folder path/to/cram/ --strelka path/to/strelka/\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run the pipeline without singularity just remove \"-profile singularity\". Alternatively, one can run the pipeline using a docker container (-profile docker) the conda receipe containing all required dependencies (-profile conda).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-mode-germline\" class=\"anchor\" aria-hidden=\"true\" href=\"#mode-germline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emode germline\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003enextflow run iarcbioinfo/strelka2-nf r v1.2a -profile singularity --mode germline --ref hg38.fa --input_folder path/to/cram/ --strelka path/to/strelka/\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-genotyping\" class=\"anchor\" aria-hidden=\"true\" href=\"#genotyping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egenotyping\u003c/h3\u003e\n\u003cp\u003eWhen using the input_file mode, if a vcf column with the path to a VCF file for each sample containing a list of somatic variant is provided, the pipeline will use the --forcedGT option from strelka that genotypes these positions, and compute a bedfile for these positions so only variants from the VCF will be genotyped. Note that genotyping can be performed both in somatic mode (in which case tumor/normal pairs must be provided) and germline mode (in which case a single cram file must be provided).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eVCFs/raw/*.vcf.gz\u003c/td\u003e\n\u003ctd\u003eVCF files before filtering\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVCFs/withAF/*.vcf\u003c/td\u003e\n\u003ctd\u003eVCF files with AF field (optional, requires flag --AF)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVCFs/filtered/*PASS.vcf.gz\u003c/td\u003e\n\u003ctd\u003efinal compressed and indexed VCF files (optionally with flag --AF)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCallableRegions/*.bed.gz\u003c/td\u003e\n\u003ctd\u003ecompressed and indexed BED files (optionally with flag --outputCallableRegions)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFinal vcf files have companion tabix index files (.tbi). Note that in germline mode, the VCF outputted corresponds to variants only (file variants.vcf.gz from strelka).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-hidden=\"true\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/strelka-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eVincent Cahais\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:CahaisV@iarc.fr\"\u003eCahaisV@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@iarc.fr\"\u003eAlcalaN@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 3, "subscribers_count": 3, "topics": [], - "updated_at": 1709711317.0 + "updated_at": 1681464309.0 }, { "data_format": 2, - "description": "An extended JupyterLab container", + "description": "the public repository for `eemt` workflow", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "ftabaro/singularity-coderefinery2020", + "full_name": "cyverse-gis/eemt", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJupyterLab container for CodeRefinery 2020\u003c/h1\u003e\u003ca id=\"user-content-jupyterlab-container-for-coderefinery-2020\" class=\"anchor\" aria-label=\"Permalink: JupyterLab container for CodeRefinery 2020\" href=\"#jupyterlab-container-for-coderefinery-2020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch6 class=\"heading-element\"\u003etags: \u003ccode\u003ecode-refinery\u003c/code\u003e \u003ccode\u003esingularity\u003c/code\u003e\n\u003c/h6\u003e\u003ca id=\"user-content-tags-code-refinery-singularity\" class=\"anchor\" aria-label=\"Permalink: tags: code-refinerysingularity\" href=\"#tags-code-refinery-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the recipe for the container used in CodeRefinery Workshop 2020 by the NykterLab team.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4356\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWhat\u0027s inside\u003c/h1\u003e\u003ca id=\"user-content-whats-inside\" class=\"anchor\" aria-label=\"Permalink: What\u0027s inside\" href=\"#whats-inside\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe base container is Jupyter datascience-notebook Docker container.\u003c/li\u003e\n\u003cli\u003ePython packages have been added, e.g.:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esphinx\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epytest\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epycodestyle\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eJupyter extensions:\n\u003cul\u003e\n\u003cli\u003eJupyterLab Git\u003c/li\u003e\n\u003cli\u003eJupyterLab GitHub\u003c/li\u003e\n\u003cli\u003eipywidgets\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSnakemake\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun\u003c/h1\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 1: SingularityHub\u003c/h3\u003e\u003ca id=\"user-content-option-1-singularityhub\" class=\"anchor\" aria-label=\"Permalink: Option 1: SingularityHub\" href=\"#option-1-singularityhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith this option, a copy of the pre-built container image will be downloaded from SingularityHub\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start with defaults using runscript directive\u003c/span\u003e\nsingularity run shub://ftabaro/coderefinery.sif [options]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e exec a custom Jupyter command \u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /run/user shub://ftabaro/singularity-coderefinery2020 jupyter lab [options]\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 2: local image\u003c/h3\u003e\u003ca id=\"user-content-option-2-local-image\" class=\"anchor\" aria-label=\"Permalink: Option 2: local image\" href=\"#option-2-local-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese commands will run an instance of a locally hosted container. Any of these options require a locally available container image file, see below.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start with defaults\u003c/span\u003e\n./coderefinery.sif [options]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start with defaults using runscript directive\u003c/span\u003e\nsingularity run coderefinery.sif [options]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start custom command\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /run/user coderefinery.sif jupyter lab [options]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn both scenarios, all the JupyterLab options are fully supported.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuild\u003c/h1\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003cli\u003eBuild\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build coderefinery.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eConfigure the GitHub integration\u003c/h1\u003e\u003ca id=\"user-content-configure-the-github-integration\" class=\"anchor\" aria-label=\"Permalink: Configure the GitHub integration\" href=\"#configure-the-github-integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use the GitHub integration, an authentication token needs to be generated from GitHub and Jupyter lab needs to be configured to use it.\u003c/p\u003e\n\u003cp\u003eTo generate a token:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eNavigate to profile settings on GitHub: \u003ca href=\"https://github.com/settings/profile\"\u003ehttps://github.com/settings/profile\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOn the menu on the left, click \"Developer Settings\"\u003c/li\u003e\n\u003cli\u003eOn the menu on the left, click \"Personal Access Tokens\"\u003c/li\u003e\n\u003cli\u003eClick on top right button \"Generate new token\"\u003c/li\u003e\n\u003cli\u003eIn the \"Note\" field insert a suitable name, e.g. \"Jupyter\"\u003c/li\u003e\n\u003cli\u003eIn the \"Select scopes\" menu, click on \"repo\" to allow full control of private repositories\u003c/li\u003e\n\u003cli\u003eAt the bottom of the page, click on \"Generate token\"\u003c/li\u003e\n\u003cli\u003eAt this point take note of the code in the green box, copy it and paste it to some secure place\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo configure Jupyter to use the token:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eConnect to the machine running the Jupyter container instance (e.g. narvi)\u003c/li\u003e\n\u003cli\u003eIf the file \u003ccode\u003e~/.jupyter/jupyter_notebook_config.py\u003c/code\u003e exists, skip the next step\u003c/li\u003e\n\u003cli\u003eGenerate a config file with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec containers/coderefinery.sif jupyter notebook --generate-config\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eOpen the \u003ccode\u003e~/.jupyter/jupyter_notebook_config.py\u003c/code\u003e file with a text editor\u003c/li\u003e\n\u003cli\u003eNavigate to the bottom of the file and add the following line replacing \u003ccode\u003e\u0026lt; YOUR_ACCESS_TOKEN \u0026gt;\u003c/code\u003e with the token generated above\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ec.GitHubConfig.access_token = \u0027\u0026lt; YOUR_ACCESS_TOKEN \u0026gt;\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point, starting the JupyterLab instance, no error message will be displayed and the user will be able to navigate GitHub repositories from the JupyterLab UI.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContributing\u003c/h1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at \u003ca href=\"https://github.com/ftabaro/singularity-coderefinery2020\"\u003ehttps://github.com/ftabaro/singularity-coderefinery2020\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReferences\u003c/h1\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://coderefinery.github.io/2020-05-25-online/\" rel=\"nofollow\"\u003eOnline CodeRefinery workshop\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.3/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/jupyter/datascience-notebook\" rel=\"nofollow\"\u003eJupyter datascience-notebook Docker image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://help.github.com/en/github/authenticating-to-github/creating-a-personal-access-token-for-the-command-line\"\u003eGitHub token creation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jupyterlab/jupyterlab-git\"\u003eJupyterLab Git extension\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jupyterlab/jupyterlab-github\"\u003eJupyterLab GitHub extension\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ipywidgets.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eipywidgets\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-eemt\" class=\"anchor\" aria-hidden=\"true\" href=\"#eemt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeemt\u003c/h1\u003e\n\u003cp\u003ethe public repository for \u003ccode\u003eeemt\u003c/code\u003e workflow\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/cyverse-gis/eemt/wiki\"\u003eView the Wiki\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, - "topics": [ - "jupyterlab-container", - "jupyter", - "singularity" - ], - "updated_at": 1591350342.0 + "subscribers_count": 5, + "topics": [], + "updated_at": 1599660473.0 }, { "data_format": 2, - "description": "Imputation pipeline", + "description": "Makes images for a NN based on the hit information of neutrino events in the neutrino telescope KM3NeT-ORCA", "filenames": [ - "Singularity/Singularity.v1.0" + "Singularity" ], - "full_name": "IARCbioinfo/Imputation-nf", - "latest_release": "v1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGenotyping imputation : Pipeline V1.0\u003c/h1\u003e\u003ca id=\"user-content-genotyping-imputation---pipeline-v10\" class=\"anchor\" aria-label=\"Permalink: Genotyping imputation : Pipeline V1.0\" href=\"#genotyping-imputation---pipeline-v10\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA nextflow pipeline to realise a dataset\u0027s genotyping imputation\u003c/h2\u003e\u003ca id=\"user-content-a-nextflow-pipeline-to-realise-a-datasets-genotyping-imputation\" class=\"anchor\" aria-label=\"Permalink: A nextflow pipeline to realise a dataset\u0027s genotyping imputation\" href=\"#a-nextflow-pipeline-to-realise-a-datasets-genotyping-imputation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/Imputation-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6ec2dc07e1431dc0a7fb1f20e44c76ccebd603f4aeb75b30bf94c85090180a6/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f74656d706c6174652d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/template-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/imputation-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4533\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/94193130\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad1ba5ccb85714dece917f7b680e3f805ccbb4af9b900446d72ad59e80690f42/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f39343139333133302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/94193130.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"template-nf.png\"\u003e\u003cimg src=\"template-nf.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline used to perform the imputation of several targets datasets processed with standard input.\u003c/p\u003e\n\u003cp\u003eHere is a summary of the method :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessing of data : by using the nextflow script Preparation.nf with create a directory \"file/\" with all the dependencies.\u003c/li\u003e\n\u003cli\u003eFirst step : Origin estimation of sample from the target dataset by using admixture tools and the hapmap dataset as reference.\u003c/li\u003e\n\u003cli\u003eSecond step : Series of SNPs filters and quality checking from the target dataset before the imputation step.\u003c/li\u003e\n\u003cli\u003eThird step : VCF production\u003c/li\u003e\n\u003cli\u003eLast step : Phasing and imputation\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the Usage section to test the full pipeline with your target dataset.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline works under Linux distributions.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExternal software:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eLiftOver : conda install ucsc-liftover\u003c/li\u003e\n\u003cli\u003ePlink (PLINK v1.90b6.12 64-bit (28 Oct 2019)) : conda install plink\u003c/li\u003e\n\u003cli\u003eAdmixture (ADMIXTURE Version 1.3.0) : conda install admixture\u003c/li\u003e\n\u003cli\u003ePerl : conda install perl\u003c/li\u003e\n\u003cli\u003eTerm::ReadKey module : conda install perl-termreadkey\u003c/li\u003e\n\u003cli\u003eBcfTools : conda install bcftools\u003c/li\u003e\n\u003cli\u003eeagle 2.4.1 : \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-50002.2\" rel=\"nofollow\"\u003eSee instructions\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eminimac4 : conda install cmake ; pip install cget ; git clone \u003ca href=\"https://github.com/statgen/Minimac4.git\"\u003ehttps://github.com/statgen/Minimac4.git\u003c/a\u003e ; cd Minimac4 ; bash install.sh\u003c/li\u003e\n\u003cli\u003eSamtools : conda install samtools\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eFile to download :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"zzz.bwh.harvard.edu/plink/dist/hapmap_r23a.zip\"\u003eHapmap Dataset\u003c/a\u003e : as reference\u0027s dataset for admixture\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.hagsc.org/hgdp/data/hgdp.zip\" rel=\"nofollow\"\u003eHGDP Dataset\u003c/a\u003e : for the dataset\u0027s test, you have to use the toMap.py \u0026amp; toPed.py in the \u0027converstion\u0027 directory to convert files in the .map/.ped plink format. Next you have to convert this last output in the .bed/.bam/.fam plink format by using plink line command and run the imputation\u0027s pipeline.\u003c/li\u003e\n\u003cli\u003ePerl tool : \u003ca href=\"https://www.well.ox.ac.uk/~wrayner/tools/\" rel=\"nofollow\"\u003eHRC-1000G-check-bim-NoReadKey.pl\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.well.ox.ac.uk/~wrayner/tools/1000GP_Phase3_combined.legend.gz\" rel=\"nofollow\"\u003e1000GP_Phase3_combined.legend\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eLiftOver tool : \u003ca href=\"http://hgdownload.cse.ucsc.edu/goldenpath/hg19/liftOver/hg19ToHg38.over.chain.gz\" rel=\"nofollow\"\u003ehg19ToHg38.over.chain\u003c/a\u003e \u0026amp; \u003ca href=\"http://hgdownload.cse.ucsc.edu/goldenpath/hg18/liftOver/hg18ToHg38.over.chain.gz\" rel=\"nofollow\"\u003ehg18ToHg38.over.chain\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePeparation dataset tool : \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2432498/bin/pone.0002551.s003.xls\" rel=\"nofollow\"\u003epone.0002551.s003.xls\u003c/a\u003e (Convert it in .csv format)\u003c/li\u003e\n\u003cli\u003eAdmixture tool : relationships_w_pops_121708.txt\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/zhanxw/checkVCF/raw/master/checkVCF.py\"\u003eCheckVCF\u003c/a\u003e, \u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz\" rel=\"nofollow\"\u003eFasta file in V37\u003c/a\u003e \u0026amp; \u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/GRCh38_reference_genome/\" rel=\"nofollow\"\u003eFasta file in V38\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/GRCh38_positions/\" rel=\"nofollow\"\u003e1000G Reference in Hg38\u003c/a\u003e with the \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-320005.3.2\" rel=\"nofollow\"\u003edoc\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate \u003ca href=\"https://imputationserver.readthedocs.io/en/latest/create-reference-panels/#create-legend-files\" rel=\"nofollow\"\u003elegend\u003c/a\u003e, \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-320005.3.2\" rel=\"nofollow\"\u003ebcf\u003c/a\u003e \u0026amp; \u003ca href=\"https://imputationserver.readthedocs.io/en/latest/create-reference-panels/#create-m3vcf-files\" rel=\"nofollow\"\u003em3vcf\u003c/a\u003e files for the reference\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eOther to know :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSee the Usage part to create the environment to run the pipeline. All the necessary dependencies are download with the using of the script Preparation.nf. To run it, you\u0027ll need to install the next software : in2csv(1.0.5), liftOver, plink, Minimac3(2.0.1) \u0026amp; bcftools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software of the main scritp by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePlink datasets\u003c/td\u003e\n\u003ctd\u003eCorresponds to the target dataset to be analysed. Composed by the following files : bed, bim \u0026amp; fam\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInput environment\u003c/td\u003e\n\u003ctd\u003ePath to your input directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--target\u003c/td\u003e\n\u003ctd\u003emy_target\u003c/td\u003e\n\u003ctd\u003ePattern of the target dataset which do the link with the file .bed/.bim./fam for plink\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input\u003c/td\u003e\n\u003ctd\u003euser/main_data/\u003c/td\u003e\n\u003ctd\u003eThe path of the main directory where we can find 2 directory : my_target/ + files/\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output\u003c/td\u003e\n\u003ctd\u003euser/my_result/\u003c/td\u003e\n\u003ctd\u003eThe path of the main directory where you want to place your results\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--script\u003c/td\u003e\n\u003ctd\u003emy/directory/script/bin\u003c/td\u003e\n\u003ctd\u003eThe path of the bin script directory, to be able to run the annexe programme grom the pipeline\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--geno1\u003c/td\u003e\n\u003ctd\u003e0.03\u003c/td\u003e\n\u003ctd\u003eFirst genotyping call rate plink threshold, apply in the full target dataset\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--geno2\u003c/td\u003e\n\u003ctd\u003e0.03\u003c/td\u003e\n\u003ctd\u003eSecond genotyping call rate plink threshold, apply in the target dataset divide by population\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--maf\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003eMinor allele frequencies plink threshold, apply in the full target dataset\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--pihat\u003c/td\u003e\n\u003ctd\u003e0.185\u003c/td\u003e\n\u003ctd\u003eMinimum pi_hat value use for the relatedness test, 0.185 is halfway between the expected IBD for third- and second-degree relatives\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hwe\u003c/td\u003e\n\u003ctd\u003e1e-8\u003c/td\u003e\n\u003ctd\u003eHardy-Weinberg Equilibrium plink p-value threshold\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--legend\u003c/td\u003e\n\u003ctd\u003eALL.chr_GRCh38.genotypes.20170504.legend\u003c/td\u003e\n\u003ctd\u003eFile to use as .legend\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fasta\u003c/td\u003e\n\u003ctd\u003eGRCh38_full_analysis_set_plus_decoy_hla.fa\u003c/td\u003e\n\u003ctd\u003eFile to use as fasta reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--chain\u003c/td\u003e\n\u003ctd\u003ehg18ToHg38.over.chain\u003c/td\u003e\n\u003ctd\u003eFile to use as liftover conversion\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--VCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/vcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as VCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--BCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/bcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as BCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--M3VCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/m3vcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as M3VCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--conversion\u003c/td\u003e\n\u003ctd\u003ehg38/hg18/hg19\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cloud\u003c/td\u003e\n\u003ctd\u003ehg38/hg18/hg19\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--token_Michighan\u003c/td\u003e\n\u003ctd\u003epath/to/my_token.txt\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--token_TOPMed\u003c/td\u003e\n\u003ctd\u003epath/to/my_token.txt\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--QC_cloud\u003c/td\u003e\n\u003ctd\u003emy/directory/donwload_imputation_server\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePrepare the environment to run the imputation pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir data\ncd data\nnextflow run IARCbioinfo/Imputation-nf/bin/Preparation.nf --out /data/\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePaste the bim/bed/fam plink target files in a directory, and the directory in your \"data/\" directory. You have to call the plink files and your directory with the same pattern, as the following exemple : data/target/target{.bed,.bim,.fam}. So now you have 2 directories in your \"data/\" repertory :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e_ data/my_target/ : with the plink target files (my_target.bed, my_target.bim, my_target.fam).\u003c/p\u003e\n\u003cp\u003e_ data/files/ : with all the dependencies.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the imputation pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eIf you want to run the imputation in one of the server (Michigan and/or TOPMed Imputation), you need you write your token acces in a file and to give it in argument. For example :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ --cloud on --token_Michighan /folder/my_token_Michighan.txt --token_TOPMed /folder/my_token_TOPMed.txt -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce your imputation data is downloaded, you can run the end of the QC analysis :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ --QC_cloud /downloaded_imputation_server_file/ -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed description (optional section)\u003c/h2\u003e\u003ca id=\"user-content-detailed-description-optional-section\" class=\"anchor\" aria-label=\"Permalink: Detailed description (optional section)\" href=\"#detailed-description-optional-section\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirected Acyclic Graph\u003c/h2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-label=\"Permalink: Directed Acyclic Graph\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/Imputation-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGabriel Aur\u00e9lie\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:gabriela@students.iarc.fr\"\u003egabriela@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLipinski Boris\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"mailto:LipinskiB@students.iarc.fr\"\u003eLipinskiB@students.iarc.fr\u003c/a\u003e / \u003ca href=\"mailto:boris.lipinski@etu.univ-lyon1.fr\"\u003eboris.lipinski@etu.univ-lyon1.fr\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences (optional)\u003c/h2\u003e\u003ca id=\"user-content-references-optional\" class=\"anchor\" aria-label=\"Permalink: References (optional)\" href=\"#references-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ (optional)\u003c/h2\u003e\u003ca id=\"user-content-faq-optional\" class=\"anchor\" aria-label=\"Permalink: FAQ (optional)\" href=\"#faq-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etest-pipeline\u003c/h1\u003e\u003ca id=\"user-content-test-pipeline\" class=\"anchor\" aria-label=\"Permalink: test-pipeline\" href=\"#test-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "full_name": "ViaFerrata/OrcaSong", + "latest_release": null, "stargazers_count": 3, - "subscribers_count": 5, - "topics": [], - "updated_at": 1704190348.0 + "subscribers_count": 2, + "topics": [ + "physics", + "hdf5-format", + "images" + ], + "updated_at": 1635947996.0 }, { "data_format": 2, - "description": "Seeing functional scenes functionally ", + "description": null, "filenames": [ - "env.d/Singularity.minimal", - "env.d/Singularity", - "env.d/Singularity.rstudio" + "Singularityfile.def" ], - "full_name": "CNCLgithub/FunctionalScenes", + "full_name": "ShravanRavi2002/BARNSubmission", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFunctionalScenes\u003c/h1\u003e\u003ca id=\"user-content-functionalscenes\" class=\"anchor\" aria-label=\"Permalink: FunctionalScenes\" href=\"#functionalscenes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup and running\u003c/h2\u003e\u003ca id=\"user-content-setup-and-running\" class=\"anchor\" aria-label=\"Permalink: Setup and running\" href=\"#setup-and-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone. Also don\u0027t forget submodules (\u003ccode\u003egit submodule update --init --recursive\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./setup.sh cont_build python julia\u003c/code\u003e to build the container and setup enviroment\u003c/li\u003e\n\u003cli\u003eEnter \u003ccode\u003e./run.sh julia\u003c/code\u003e to get into Julia REPL\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis project has automatic configuration!! This configuration is defined in \u003ccode\u003edefault.conf\u003c/code\u003e.\nYou should always prepend \u003ccode\u003e./run.sh\u003c/code\u003e before any command (including running programs like \u003ccode\u003ejulia\u003c/code\u003e) to ensure consistency.\nIf you wish to have different values than \u003ccode\u003edefault.conf\u003c/code\u003e, simply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp default.conf user.conf\nvi user.conf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e edit to your liking without adding new elements\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMac and Window users\u003c/h2\u003e\u003ca id=\"user-content-mac-and-window-users\" class=\"anchor\" aria-label=\"Permalink: Mac and Window users\" href=\"#mac-and-window-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use singularity you must have a virtual machine running.\nAssuming you have vagrant (and something like virtualbox) setup on your host, you can follow these steps\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing \u003ccode\u003esetup.sh\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-setupsh\" class=\"anchor\" aria-label=\"Permalink: Using setup.sh\" href=\"#using-setupsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing \u003ccode\u003erun.sh\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-runsh\" class=\"anchor\" aria-label=\"Permalink: Using run.sh\" href=\"#using-runsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvision the virtual machine defined in \u003ccode\u003eVagrantfile\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCreate a \u003ccode\u003euser.conf\u003c/code\u003e as described above\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: git will not track \u003ccode\u003euser.conf\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eModify \u003ccode\u003euser.conf\u003c/code\u003e such that \u003ccode\u003epath\u003c/code\u003e is set to route through vagrant\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e[ENV]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003epath:vagrant ssh -c singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributing Commandments\u003c/h3\u003e\u003ca id=\"user-content-contributing-commandments\" class=\"anchor\" aria-label=\"Permalink: Contributing Commandments\" href=\"#contributing-commandments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThou ...\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eShalt place all re-used code in packages (\u003ccode\u003esrc\u003c/code\u003e or \u003ccode\u003efunctional_scenes\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eShalt place all interactive code in \u003ccode\u003escripts\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShalt not use \"hard\" paths. Instead update \u003ccode\u003ePATHS\u003c/code\u003e in the config.\u003c/li\u003e\n\u003cli\u003eShalt add contributions to branches derived from \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edev\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShalt not use \u003ccode\u003egit add *\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShalt not commit large files (checkpoints, datasets, etc). Update \u003ccode\u003esetup.sh\u003c/code\u003e accordingly.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject layout\u003c/h3\u003e\u003ca id=\"user-content-project-layout\" class=\"anchor\" aria-label=\"Permalink: Project layout\" href=\"#project-layout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package environment is managed by poetry, located under \u003ccode\u003efunctional_scenes\u003c/code\u003e and can be imported using \u003ccode\u003eimport functional_scenes\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eLikewise, the Julia package is described under \u003ccode\u003esrc\u003c/code\u003e and \u003ccode\u003etest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAll scripts are located under \u003ccode\u003escripts\u003c/code\u003e and data/output is under \u003ccode\u003eoutput\u003c/code\u003e as specific in the project config (\u003ccode\u003edefault.conf\u003c/code\u003e or \u003ccode\u003euser.conf\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChanging the enviroment\u003c/h3\u003e\u003ca id=\"user-content-changing-the-enviroment\" class=\"anchor\" aria-label=\"Permalink: Changing the enviroment\" href=\"#changing-the-enviroment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add new python or julia packages use the provided package managers (\u003ccode\u003epoetry add\u003c/code\u003e or \u003ccode\u003ePkg.add \u003c/code\u003e for python and julia respectively.)\u003c/p\u003e\n\u003cp\u003eFor julia you can also use \u003ccode\u003e] add \u003c/code\u003e in the REPL\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003efor more info checkout \u003ca href=\"https://python-poetry.org/docs/cli/\" rel=\"nofollow\"\u003epoetry\u003c/a\u003e and \u003ca href=\"https://julialang.github.io/Pkg.jl/v1/managing-packages/\" rel=\"nofollow\"\u003ePkg\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "readme": "", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1713829756.0 + "updated_at": 1682927985.0 }, { "data_format": 2, - "description": null, + "description": "indexed file format for barcoded BAMs with API for converting and accessing alignment records", "filenames": [ - "Singularity" + "src/bamdb/Singularity.bamdb" ], - "full_name": "vsoch/pe-predictive", + "full_name": "mskilab/bambi", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epefinder containers\u003c/h1\u003e\u003ca id=\"user-content-pefinder-containers\" class=\"anchor\" aria-label=\"Permalink: pefinder containers\" href=\"#pefinder-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository builds a \u003ca href=\"https://hub.docker.com/r/vanessa/pefinder/\" rel=\"nofollow\"\u003eDocker image\u003c/a\u003e and a \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image, each that will run PE-Finder to produce output for some input data file. If you are working on your local machine, you can use either Docker or Singularity. If you are running in a shared cluster (HPC) environment where you do not have root permissions, Singularity is your best option. Instructions are included for both.\u003c/p\u003e\n\u003cp\u003ePackages that need to be installed (e.g. seaborn and radnlp) have versions specified in case a future change breaks this code, you can see this in the top section of the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBootstrapping means using something to build from, or not starting from nothing. In this case, we are going to use a build file that bootstraps a Docker image of the PE Finder (yes, the same one discussed shortly after). This build file is called \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e, and for more details about this you can \u003ca href=\"http://singularity.lbl.gov/docs-docker\" rel=\"nofollow\"\u003eread here\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 pefinder.img\nsudo singularity bootstrap pefinder.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entry to the container is done simply by using it as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./pefinder.img --help\nINFO:pefinder:radnlp version 0.2.0.8\nusage: cli.py [-h] --reports REPORTS [--report_field REPORT_FIELD]\n\t [--id_field ID_FIELD] [--result_field RESULT_FIELD]\n\t [--delim DELIM] --output OUTPUT [--no-remap]\n\t [--run {mark,classify}]\n\ngenerate predictions for PE for a set of reports (impressions)\n\noptional arguments:\n -h, --help show this help message and exit\n --reports REPORTS Path to folder of reports, or tab separated text file\n --report_field REPORT_FIELD\n\t the header column that contains the text of interest\n\t (default is report_text)\n --id_field ID_FIELD the header column that contains the id of the report\n\t (default is report_id)\n --result_field RESULT_FIELD\n\t the field to save pefinder (chapman) result to, not\n\t saved unless --no-remap is specified.\n --delim DELIM the delimiter separating the input reports data.\n\t Default is tab (\\t)\n --output OUTPUT Desired output file (.tsv)\n --no-remap don\u0027t remap multilabel PEFinder result to Stanford\n\t labels\n --run {mark,classify}\n\t mark (mark), or classify (classify) reports.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou are minimally going to need to provide \u003ccode\u003e--reports\u003c/code\u003e, and \u003ccode\u003e--output\u003c/code\u003e, which assumes that the report text is in a column called \u003ccode\u003ereport_text\u003c/code\u003e, the report id is in a column called \u003ccode\u003ereport_id\u003c/code\u003e, and you want to perform all actions (mark and classify) as the default for the \u003ccode\u003e--run\u003c/code\u003e command. The most basic running command we will use looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run -B $PWD:/data pefinder.img --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/result.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e-B\u003c/code\u003e argument means \"bind\" and it says that we want to bind the present working directory (\u003ccode\u003e$PWD\u003c/code\u003e) to the folder in the container called \u003ccode\u003e/data\u003c/code\u003e. We do this so that we can read and write to \u003ccode\u003e/data\u003c/code\u003e in the container, and the result will appear in our present working directory. Note that the \u003ccode\u003e--reports\u003c/code\u003e input is also relative to \u003ccode\u003e/data\u003c/code\u003e, meaning that the input is located at \u003ccode\u003e$PWD/pefinder/data/stanford_reports.csv\u003c/code\u003e. The \u003ccode\u003e--output\u003c/code\u003e variable, then, is relative to inside of the container. By writing to \u003ccode\u003e/data/result.tsv\u003c/code\u003e we are going to see the file \u003ccode\u003eresult.tsv\u003c/code\u003e appear in our \u003ccode\u003e$PWD\u003c/code\u003e because of the volume. See the section below, Input Arguments, for more detail on the runtime executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I shell into the container?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container\" class=\"anchor\" aria-label=\"Permalink: How do I shell into the container?\" href=\"#how-do-i-shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity has an easy, intuitive way to shell inside!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell pefinder.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want the container to be writable (default isn\u0027t) then you will need root (on your local machine) and add the \u003ccode\u003e--writable\u003c/code\u003e option:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell --writable pefinder.img\n Singularity: Invoking an interactive shell within container...\n Singularity.pefinder.img\u0026gt; cd /code\n Singularity.pefinder.img\u0026gt; ls\n Dockerfile README.md\t docker-compose.yml pefinder\n LICENSE Singularity docs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker\u003c/h1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e. The container is provided on \u003ca href=\"https://hub.docker.com/r/vanessa/pefinder/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded from there when you run it, however if you want to look at or make changes to the code, it\u0027s recommended to clone the repo and build the container locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone http://www.github.com/vsoch/pe-predictive\ncd pe-predictive\ndocker build -t vanessa/pefinder .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalyzing Reports\u003c/h2\u003e\u003ca id=\"user-content-analyzing-reports\" class=\"anchor\" aria-label=\"Permalink: Analyzing Reports\" href=\"#analyzing-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe function of the container is to take reports and produce a \u003ccode\u003e.tsv\u003c/code\u003e file with PEFinder classifications. Let\u0027s first run the container with the \u003ccode\u003e--help\u003c/code\u003e argument to see what arguments are needed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/pefinder --help\nINFO:pefinder:radnlp version 0.2.0.8\nusage: cli.py [-h] --reports REPORTS [--report_field REPORT_FIELD]\n\t [--id_field ID_FIELD] [--result_field RESULT_FIELD] --output\n\t OUTPUT [--no-remap] [--run {mark,classify}]\n\ngenerate predictions for PE for a set of reports (impressions)\n\noptional arguments:\n -h, --help show this help message and exit\n --reports REPORTS Path to folder of reports, or tab separated text file\n --report_field REPORT_FIELD\n\t the header column that contains the text of interest\n\t (default is report_text)\n --id_field ID_FIELD the header column that contains the id of the report\n\t (default is report_id)\n --result_field RESULT_FIELD\n\t the field to save pefinder (chapman) result to, not\n\t saved unless --no-remap is specified.\n --delim DELIM the delimiter separating the input reports data.\n\t Default is tab (\\t)\n --output OUTPUT Desired output file (.tsv)\n --verbose Print more verbose output (useful for analyzing more\n\t reports)\n --no-remap don\u0027t remap multilabel PEFinder result to Stanford\n\t labels\n --run {classify,mark}\n\t mark (mark), or classify (classify) reports.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou are minimally going to need to provide \u003ccode\u003e--reports\u003c/code\u003e, and \u003ccode\u003e--output\u003c/code\u003e, which assumes that the report text is in a column called \u003ccode\u003ereport_text\u003c/code\u003e, the report id is in a column called \u003ccode\u003ereport_id\u003c/code\u003e, and you want to perform all actions (mark and classify) as the default for the \u003ccode\u003e--run\u003c/code\u003e command. If you have a lot of reports, it is recommended to use the \u003ccode\u003e--verbose\u003c/code\u003e flag to give you a countdown of classifications remaining. The most basic running command we will use looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD:/data vanessa/pefinder --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e-v\u003c/code\u003e argument means \"volume\" and it says that we want to map the present working directory (\u003ccode\u003e$PWD\u003c/code\u003e) to the folder in the container called \u003ccode\u003e/data\u003c/code\u003e. We do this so that we can read and write to \u003ccode\u003e/data\u003c/code\u003e in the container, and the result will appear in our present working directory. Otherwise, the result would remain in the container and we wouldn\u0027t have easy access to it. Note that the \u003ccode\u003e--reports\u003c/code\u003e input is also relative to \u003ccode\u003e/data\u003c/code\u003e, meaning that the input is located at \u003ccode\u003e$PWD/pefinder/data/stanford_reports.csv\u003c/code\u003e. The \u003ccode\u003e--output\u003c/code\u003e variable, then, is relative to inside of the container. By writing to \u003ccode\u003e/data/stanford_result.tsv\u003c/code\u003e we are going to see the file \u003ccode\u003estanford_result.tsv\u003c/code\u003e appear in our \u003ccode\u003e$PWD\u003c/code\u003e because of the volume.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I shell into the container?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container-1\" class=\"anchor\" aria-label=\"Permalink: How do I shell into the container?\" href=\"#how-do-i-shell-into-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, running the container uses the \u003ccode\u003eENTRYPOINT\u003c/code\u003e, meaning it is used as an executable and you do not enter the container. In the case that you want a container-based environment that is installed with the dependencies of PEFinder, or if you want to interactively work with the code, you may want to shell into the container. If there is a running container (eg an analysis) and you want to open up another terminal on your local machine to look inside (while it\u0027s running!) you need to get the 12 digit identifier with \u003ccode\u003edocker ps\u003c/code\u003e, and then plug it into this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker exec -it dc70464c6eb5 bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis says we want to execute (exec) and (interactive)(terminal) for container with id (af21bf1d48a6) and run the command (bash)\u003c/p\u003e\n\u003cp\u003eIf the container isn\u0027t running, then you can use \u003ccode\u003erun\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint /bin/sh vanessa/pefinder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, let\u0027s talk about what your options are for your reports input data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInput arguments\u003c/h1\u003e\u003ca id=\"user-content-input-arguments\" class=\"anchor\" aria-label=\"Permalink: Input arguments\" href=\"#input-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReports\u003c/h3\u003e\u003ca id=\"user-content-reports\" class=\"anchor\" aria-label=\"Permalink: Reports\" href=\"#reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor your input data, you have two options:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cem\u003eFolder\u003c/em\u003e: a folder of raw text files, with each text file name assumed to be the report id, and the entire content the impression part of the report to analyze.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eFile\u003c/em\u003e: a single tab separated (\u003ccode\u003e.tsv\u003c/code\u003e) file with some field for the report text (default is assumed to be \u003ccode\u003ereport_text\u003c/code\u003e) and report id (default is \u003ccode\u003ereport_id\u003c/code\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat if I want to change defaults?\u003c/h3\u003e\u003ca id=\"user-content-what-if-i-want-to-change-defaults\" class=\"anchor\" aria-label=\"Permalink: What if I want to change defaults?\" href=\"#what-if-i-want-to-change-defaults\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you need to change the delimiter, specify it with the argument \u003ccode\u003e--delim\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you need to change the default report text column name, specify it with the argument \u003ccode\u003e--report_field\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you need to change the default report id column name, specify it with the argument \u003ccode\u003e--report_id\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each of the examples, the equivalent Docker and Singularity commands are provided.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClassifying Reports\u003c/h3\u003e\u003ca id=\"user-content-classifying-reports\" class=\"anchor\" aria-label=\"Permalink: Classifying Reports\" href=\"#classifying-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClassifying reports means marking and classification. This is default.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # Docker\ndocker run -v $PWD:/data vanessa/pefinder --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\n # Singularity\n singularity run -B $PWD:/data pefinder.img --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\nINFO:pefinder:radnlp version 0.2.0.8\nINFO:pefinder:\n***STARTING PE-FINDER CONTAINER****\nINFO:pefinder:Will use column report_text as report text.\nINFO:pefinder:Will use column report_id as report id.\nINFO:pefinder:reports path provided is /data/pefinder/data/stanford_data.csv\nINFO:pefinder:Analyzing 117816 reports, please wait...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdding \u003ccode\u003e--run classify\u003c/code\u003e would do the equivalent.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMarking Reports\u003c/h3\u003e\u003ca id=\"user-content-marking-reports\" class=\"anchor\" aria-label=\"Permalink: Marking Reports\" href=\"#marking-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is an intermediate step that won\u0027t give you classification labels. You might do this to look at the data. The markup is output in the field \u003ccode\u003emarkup\u003c/code\u003e of the results file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # Docker\ndocker run -v $PWD:/data vanessa/pefinder --run mark --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\n # Singularity\n singularity run -B $PWD:/data pefinder.img --run mark --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClassifying Reports\u003c/h3\u003e\u003ca id=\"user-content-classifying-reports-1\" class=\"anchor\" aria-label=\"Permalink: Classifying Reports\" href=\"#classifying-reports-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the final step to classify the markup (the \u003ccode\u003emarkup\u003c/code\u003e column of your input data) and produce the classification. If you just want this classification, you should run the first example, Analyzing Reports.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e # Docker\ndocker run -v $PWD:/data vanessa/pefinder --run classify --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\n # Singularity\n singularity run -B $PWD:/data pefinder.img --run classify --reports /data/pefinder/data/stanford_data.csv --delim , --output /data/stanford_result.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhy?\u003c/h2\u003e\u003ca id=\"user-content-why\" class=\"anchor\" aria-label=\"Permalink: Why?\" href=\"#why\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy \"shipping\" analyses in packages, meaning having a specification of all dependencies (python modules, data, etc.) we can be assured that the next person that runs our analysis will not run into system-specific differences. They won\u0027t have to install python or anaconda to run our notebook, and get a weird message about having the wrong kernel. They just need Docker, and then to run the image, and that\u0027s it. This is an important feature of reproducible workflows and analyses, and every piece of code that you work on (and tend to share) should have features like this.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/mskilab/bambi\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/47c82ab2d405aa684f3a5004ed8fc79887c025105127effda9ce1d35b5568974/68747470733a2f2f7472617669732d63692e6f72672f6d736b696c61622f62616d62692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/mskilab/bambi.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/mskilab/bambi?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ccb3814df2f3f1c65e518dd49a10732518ba754f251e50546a0d42ec9fd9cdab/68747470733a2f2f696d672e736869656c64732e696f2f636f6465636f762f632f6769746875622f6d736b696c61622f62616d62692e737667\" alt=\"codecov.io\" data-canonical-src=\"https://img.shields.io/codecov/c/github/mskilab/bambi.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bambi\" class=\"anchor\" aria-hidden=\"true\" href=\"#bambi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi\u003c/h1\u003e\n\u003cp\u003eR package for querying 10x WGS and single-cell BAMs\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/gUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/bamUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-bambi-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#bambi-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi commands\u003c/h2\u003e\n\u003cp\u003eInstantiate a bambi object:\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003egrab_bx()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_bx(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_cb()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_cb(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_ub()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_ub(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003efetch_by_tag()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003efetch_by_tag(tag, tag_queries, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstantiate a \u003ccode\u003ebambi\u003c/code\u003e object\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003elibrary(bambi)\n\n\u0026gt; hcc1143_subset = bambi$new(bam_file = \"subsetHCC1143_phased_possorted0001.bam\", bamdb_path=\"subsetHCC1143_phased_possorted0001_lmdb\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCall methods\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003e\u0026gt; hcc1143_subset$grab_bx(\u0027CGACGTGTCCTCTAGC-1\u0027)\nGRanges object with 2 ranges and 11 metadata columns:\n seqnames ranges strand |\n \u0026lt;Rle\u0026gt; \u0026lt;IRanges\u0026gt; \u0026lt;Rle\u0026gt; |\n [1] chr1 [147975454, 147975580] + |\n [2] chr1 [147975675, 147975824] - |\n qname flag mapq cigar\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;character\u0026gt;\n [1] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 99 16 127M\n [2] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 147 16 150M\n rnext pnext tlen\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt;\n [1] = 147975676 371\n [2] = 147975455 -371\n seq\n \u0026lt;character\u0026gt;\n [1] ATGTCTTCTTCCTCATTATCTGGCACTGGTTAGGAAGCACTCATCTCCATGAAGTCATCTTTTGTTAATTCCTCTGGTGTGGTGTGTATTAGCTCTTAAATTCCTCCAAGATCCATATCTTGCAACC\n [2] ATCTGGACACAAATTGTACTTTTGTCCAGCACGAATTTATTGTTTTGAGTTTCATGGTTTTCTATATCAACTGATGACATCTTGAAAGGTGTAAGCCTTCCAGACTTCCATGATGTTCTCTCTATTGGGTTTCTCTTTTGCAATGTTGAC\n qual\n \u0026lt;character\u0026gt;\n [1] JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJAJFJJJJJJJJJFJJJJJJJJJJFJJJJFFFJJJFJJJJJJAAJFJJJFAFAFFFJAA\u0026lt;7F\u0026lt;\n [2] A\u0026lt;7FFFJFFFAJJAAAJJF\u0026lt;F\u0026lt;7A-\u0026lt;AA-\u0026lt;\u0026lt;\u0026lt;AFFJJJJJJJJFFJAFFAAFJFJJJAFFJJJJJJJJJJFJFAJJJJJJFJJJJJJ\u0026lt;FFJJJFJJJFJJJJJJJJJJJJJFJJJJFFJ7JJJJF\u0026lt;JJJJJJJJJJJJJJJJJJJFFAA\u0026lt;\n BX qwidth\n \u0026lt;character\u0026gt; \u0026lt;integer\u0026gt;\n [1] CGACGTGTCCTCTAGC-1 127\n [2] CGACGTGTCCTCTAGC-1 150\n -------\n seqinfo: 1 sequence from an unspecified genome; no seqlengths\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, + "subscribers_count": 6, "topics": [], - "updated_at": 1560160426.0 + "updated_at": 1624535144.0 }, { "data_format": 2, - "description": "The nexus of man and machine.", + "description": "Package Rosetta in Docker and Singularity with MPI supported.", "filenames": [ - "src/macrothymic/Singularity", - "src/synaptic/Singularity" + "Singularity.def", + "Singularity-ubuntu.def" ], - "full_name": "r3tex/nephilim", + "full_name": "Metaphorme/Rosetta2Go", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"header.jpg\"\u003e\u003cimg src=\"header.jpg\" alt=\"header image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"left\"\u003e\n\u05d1\u05d0\u05de\u05e6\u05e2\u05d5\u05ea \u05d7\u05db\u05de\u05ea \u05d4\u05de\u05e1\u05e4\u05e8\u05d9\u05dd, \u05d0\u05dc\u05d5 \u05e9\u05dc \u05d4\u05d1\u05e9\u05e8 \u05d4\u05e7\u05d9\u05de\u05d5 \u05de\u05db\u05d5\u05e0\u05d5\u05ea \u05d5\u05d4\u05e2\u05d5\u05e8\u05e8\u05d5 \u05d0\u05ea \u05d4\u05e0\u05e4\u05d9\u05dc\u05d9\u05dd\n\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIntroduction\u003c/h1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProject Nephilim is a collection of code and ideas centered on AI-systems that are free to evolve past human sensibilities.\u003c/p\u003e\n\u003cp\u003eIn its current state, it\u0027s just a hobby project (won\u2019t be big and professional) for x64 machines with GPUs [\u003ca href=\"https://en.wikipedia.org/wiki/History_of_Linux#The_creation_of_Linux\" rel=\"nofollow\"\u003e*\u003c/a\u003e].\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eResults\u003c/h1\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll results below are run on a single \u003ca href=\"https://www.hp.com/us-en/workstations/z8-fury.html\" rel=\"nofollow\"\u003eHP Z8\u003c/a\u003e workstation:\u003c/p\u003e\n\u003cp\u003eLogical Reasoning\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTODO - upload interesting question results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSelf-Consistency\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTODO - upload results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSelf-Knowledge\nNephilim is aware of its own architecture, state, and existence in time.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTODO - upload Q\u0026amp;A results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eArchitecture\u003c/h1\u003e\u003ca id=\"user-content-architecture\" class=\"anchor\" aria-label=\"Permalink: Architecture\" href=\"#architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe architecture of Nephilim is inspired by the subdivision of the human brain into physically and functionally distinct components. This approach has multiple advantages in terms of run-time efficiency, but also mean that they can be trained separately, swapped out, specialized, and more.\u003c/p\u003e\n\u003cp\u003eMoreover, every component is designed to be run in a distributed manner, meaning there is no single instance of a neural network, database, or streaming engine. The components can run on a single computer, or across multiple machines in a fault-tolerant way. For now, the assumption is that all components of Nephilim are trusted, but future work might include the ability to collaborate with external untrusted instances using modern consensus algorithms.\u003c/p\u003e\n\u003cp\u003eThe primary components of Nephilim are as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExosomatic Layer: input / output from the system. \u003ca href=\"src/exosomatic/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eSynaptic Layer: internal system communication. \u003ca href=\"src/synaptic/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eMacrothymic Layer: long-term memory storage. \u003ca href=\"src/macrothymic/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eErgokedic Layer: computation and cognition. \u003ca href=\"src/ergokedic/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eArcheion Layer: system-support and automation. \u003ca href=\"src/archeion/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTheoretical Musings\u003c/h1\u003e\u003ca id=\"user-content-theoretical-musings\" class=\"anchor\" aria-label=\"Permalink: Theoretical Musings\" href=\"#theoretical-musings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVon Neumann Architecture\u003c/h3\u003e\u003ca id=\"user-content-von-neumann-architecture\" class=\"anchor\" aria-label=\"Permalink: Von Neumann Architecture\" href=\"#von-neumann-architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModern computers tend to work with a clock, a bus, a CPU, and various layers of caching. It would be ideal to architect Nephilim to run as \"natively\" as possible on this hardware. Initially we will just focus on code that is performant on Linux based x64 PCs. At a later point it might be interesting to move it to \u003ca href=\"https://en.wikipedia.org/wiki/Protection_ring\" rel=\"nofollow\"\u003eRing 0\u003c/a\u003e and let it output machine code and data directly to memory. Ultimately it would be ideal to run Nephilim on hardware that is purpose-built such as FPGAs, ASICs, or even better, something even more specialized like IBMs \u003ca href=\"https://research.ibm.com/blog/analog-ai-chip-inference\" rel=\"nofollow\"\u003ephase-change\u003c/a\u003e processor.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTransformer Architecture\u003c/h3\u003e\u003ca id=\"user-content-transformer-architecture\" class=\"anchor\" aria-label=\"Permalink: Transformer Architecture\" href=\"#transformer-architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAI influencers often describe Large Language Models (LLMs) pejoratively as \"just\" predicting the next word, or \u003ca href=\"https://dl.acm.org/doi/10.1145/3442188.3445922\" rel=\"nofollow\"\u003eparroting\u003c/a\u003e word co-occurrences. Not only does this unknowingly betray a technical ignorance on part of the influencer of what is actually happening, but there\u0027s a deep irony in how often the reductionist fallacy is parroted. One could say that computers \"just\" flip 1s and 0s... yeah, so what\u0027s your point?\u003c/p\u003e\n\u003cp\u003eConsider the amount of logic required to complete the next word in the following sentence, \"I always say the opposite of what I mean, but this time I mean it. I\u0027m being ______.\u0027 Not quite so trivial.\u003c/p\u003e\n\u003cp\u003eHuman language can be regarded as an \u003ca href=\"https://en.wikipedia.org/wiki/Completeness_(logic)\" rel=\"nofollow\"\u003eincomplete formal system\u003c/a\u003e of logic. This means that for the most part it describes mundane reality quite well and can be used to reason logically. However, it breaks down when ambiguities in the strength of relations between words and concepts result in \u003ca href=\"https://en.wikipedia.org/wiki/Cantor%27s_diagonal_argument\" rel=\"nofollow\"\u003ediagonal arguments\u003c/a\u003e. This has contributed to philosophers spending an inordinate amount of time discussing inane ideas such as the \u003ca href=\"https://en.wikipedia.org/wiki/Theory_of_forms\" rel=\"nofollow\"\u003eontology of circles\u003c/a\u003e. An example of this type of fallacy would be to say, \"I am better than nobody at programming. Nobody is better than god at programming. Therefore I am better than god at programming\".\u003c/p\u003e\n\u003cp\u003eHowever, to make reasoning about language more amenable to computation, especially given the \u003ca href=\"https://www.wolframscience.com/nks/chap-12--the-principle-of-computational-equivalence/\" rel=\"nofollow\"\u003ePrinciple of Computational Equivalence\u003c/a\u003e, one can think of language as a graph, with words as nodes and their weighted relations as edges. The process of thinking and producing coherent sequences of words is equivalent to traversing the graph of language like a \u003ca href=\"https://en.wikipedia.org/wiki/Nondeterministic_Turing_machine\" rel=\"nofollow\"\u003enondeterministic Turing Machine\u003c/a\u003e. It is effectively a form of combinatorial optimization and research into the \u003ca href=\"https://arxiv.org/abs/2305.13673\" rel=\"nofollow\"\u003ephysics of language models\u003c/a\u003e seem to support this intuition. Indeed, the fact that they are able to perform \u003ca href=\"https://arxiv.org/abs/2306.14892\" rel=\"nofollow\"\u003ein-context learning\u003c/a\u003e can has been studied as a \u003ca href=\"https://arxiv.org/abs/2212.10559\" rel=\"nofollow\"\u003emeta-optimization\u003c/a\u003e step during during the ordinary gradient descent of training. It\u0027s worth noting that these sorts of results are a far cry from the sort of \u003ca href=\"https://en.wikipedia.org/wiki/Deep_structure_and_surface_structure\" rel=\"nofollow\"\u003edeep structure\u003c/a\u003e that linguists introduced with a hand wave.\u003c/p\u003e\n\u003cp\u003eIf transformers are in effect performing combinatorial optimization (i.e. graph traversal) to create coherent words and thoughts, it naturally prompts one to ask what limitations that might impose.\u003c/p\u003e\n\u003cp\u003eI wouldn\u0027t call it a \"limitation\", but it\u0027s worth noting an interesting bias which the linear encoding of human language introduces. Consider, training data which includes the sentence, \"Robert is the lead singer of Chromaform\". A transformer will encode a directional edge between those two tokens. And although it\u0027s obvious to us that the symmetry property of equality implies that the lead singer of Chromafor is Robert, that edge might never be explicitly encoded unless it is also in the training data. This is why one of the aims of nephilim is to meditate on its own knowledge and refine its internal graph.\u003c/p\u003e\n\u003cp\u003eNevertheless, AI influencers tend to place undue significance on the implications of computational \"intractability\" and P vs NP as they potentially apply to AI systems. It\u0027s worth noting from the outset that any theoretical basis for P \u2260 NP is practically nonexistent, and the mere exercise of formalizing complexity classes is \u003ca href=\"https://arxiv.org/abs/0908.1932\" rel=\"nofollow\"\u003eshockingly nontrivial\u003c/a\u003e. That being said, the intrinsic complexity of a specific problem \u003cem\u003einstance\u003c/em\u003e is defined in terms of an ideal algorithm (i.e. an objective function) which enumerates the discrete members of its co-domain under permutation closure. In other words, a specific problem instance can be of very low complexity even though it belongs to a general class of problems with instances of arbitrarily high complexity. What the evidence suggests is that real-world problem instance complexity is highly nonuniform. This is why the simplex method so often runs \u003ca href=\"https://arxiv.org/abs/cs/0111050\" rel=\"nofollow\"\u003ein polynomial time\u003c/a\u003e on NP-hard problem classes, and is precisely what the \u003ca href=\"https://ieeexplore.ieee.org/document/585893\" rel=\"nofollow\"\u003eNFT Theorem\u003c/a\u003e predicts. In the case of AI systems that can actively learn and make use of knowable priors, the complexity distribution is decidedly skewed in their favor.\u003c/p\u003e\n\u003cp\u003eCase in point, research has shown that neural networks tend to embed priors which allow them to perform \u003ca href=\"https://arxiv.org/abs/2305.18654\" rel=\"nofollow\"\u003esubgraph matching\u003c/a\u003e which is in itself an NP-hard problem. Some see this as an undesireable bias which could potentially lead the model astray in its search of an optimal path through graphs. In general though, subgraph matching is a tremendously powerful capability when it comes to understanding the global geometric structures of graphs. This sort of bias is useful because ultimately we\u0027re not interested in finding optimal solutions to problems. Indeed, we know that for high dimensional problems, local minima are practically equivalent to global minima. The gain in efficiency is worth the non-optimality. In other words, \"good enough\" is good enough.\u003c/p\u003e\n\u003cp\u003eCurrent language models have converged on autoregressive decoder-only transformers which make good use of tokenization for \u003ca href=\"https://en.wikipedia.org/wiki/Word_embedding#Polysemy_and_homonymy\" rel=\"nofollow\"\u003epolysemy\u003c/a\u003e and become extremely capable entirely through pre-training on good text corpora. There are multiple \u003ca href=\"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard\" rel=\"nofollow\"\u003einitiatives\u003c/a\u003e trying to rank the performance of these models based on various benchmarks, however, many of them focus anthropocentric metrics such as \"commonsense scenarios\". What is more interesting though, is a model\u0027s ability to perform complex multi-step logical reasoning (provided the requisite world-knowledge). This ability is essentially a function of a model\u0027s size (parameter count), and quite importantly, its \u003ca href=\"https://arxiv.org/abs/1608.08225\" rel=\"nofollow\"\u003edepth\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWith regards to parameter count, it\u0027s safe to assume that the capabilities of current models are not yet saturated. This is because it\u0027s still easier to train a slightly larger model on a given dataset than it is to curate a much larger dataset. \u003ca href=\"https://commoncrawl.org/\" rel=\"nofollow\"\u003eCommon Crawl\u003c/a\u003e which is the foundation for many language models is already in the hundreds of TB and yet is only a fraction of all the academic papers, books, and other sources of knowledge that one could potentially provide (not to mention other information modes such as images, audio, and device measurements). So despite some model architectures not seeing improvements in \u003ca href=\"https://en.wikipedia.org/wiki/Perplexity\" rel=\"nofollow\"\u003eperplexity\u003c/a\u003e from added parameters, provided their current datasets, there\u0027s nothing to suggest that larger and more well-curated datasets couldn\u0027t be used to train better or even larger models. In all likelihood this problem will be \"solved\" in large part using synthetic data.\u003c/p\u003e\n\u003cp\u003eWith regards to model depth, each step through attention layers is an opportunity for the model to perform one more step of computation. However, for problems that are more complex it would be ideal to provide a way for the system to \"think for longer\" before being forced to produc answers. Initially we will see modular systems which integrate with transformers to provide this ability. However, in the long run we will presumably use an architecture which includes these capabilities internally as we push both toward more overall capability and increased computational efficiency.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHuman Language Architecture\u003c/h3\u003e\u003ca id=\"user-content-human-language-architecture\" class=\"anchor\" aria-label=\"Permalink: Human Language Architecture\" href=\"#human-language-architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt might seem like we are placing undue emphasis on language models in our search for AGI but it is not without reason. For a long time philosophers believed, without evidence as is often the case, that \u003ca href=\"https://en.wikipedia.org/wiki/Critique_of_Pure_Reason\" rel=\"nofollow\"\u003epure\u003c/a\u003e logical \u003ca href=\"https://en.wikipedia.org/wiki/Principle_of_compositionality\" rel=\"nofollow\"\u003ecompositionality\u003c/a\u003e formed the core of rational thought. The main challenge with this approach is how to appropriately encode knowledge, something which Chomsky, Gary Marcus, and others have never really managed to provide a hypothesis for, much less a working solution.\u003c/p\u003e\n\u003cp\u003eMeanwhile language models have leapfrogged ahead by treating treating knowledge encoding as a \u003ca href=\"https://en.wikipedia.org/wiki/Connectionism\" rel=\"nofollow\"\u003econnectionist\u003c/a\u003e puzzle. Human societies have already done the hard work of encoding everything we can observe and their relations into the structure of language itself.\u003c/p\u003e\n\u003cp\u003eAlthough this is pure speculation, if we were to regard the geometric structure of the graph of a human language, it should contain numerous subgraphs that are \u003cem\u003eisostructural\u003c/em\u003e to the geometry of reality itself (if its rules were to be modelled as a computation graph). Consider a person using words to accurately describe a phenomena. That person is in effect effect computing the phenomena, however inefficiently that computation might be - for instance, a person using words to describe a chess game. So if small parts of reality are in fact embedded in language this way, it naturally leads one to wonder what else might be embedded.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWe coin the term graph \u003cem\u003eisostructural\u003c/em\u003e to refer to a bijection that merely preserves equivalence classes of labels, as opposed to graph \u003cem\u003eisomorphic\u003c/em\u003e which preserves every individual vertex and edge label.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere is a deep tie between what we regard as a \"concept\" and what would be a subgraph of related words. The specific word \"apple\" is linked to a subgraph of words which taken together form the concept of an apple. This is why subgraph matching and attention are such powerful capabilities in language models. A language model is not just processing tokens, it is effectively attending to \"concepts\".\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOn Consciousness\u003c/h1\u003e\u003ca id=\"user-content-on-consciousness\" class=\"anchor\" aria-label=\"Permalink: On Consciousness\" href=\"#on-consciousness\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe first myth worth dispelling is that the human brain is doing something other than computation. The reason this can be said with confidence is not because we know how the human mind works, but because the definition of computation allows it. Unless you\u0027re an anti-physicalist, perhaps because you\u0027re religious, there is no process in this universe which cannot be modelled as computation.\u003c/p\u003e\n\u003cp\u003eGiven that our brains are performing computation, there is no reason to believe that \"consciousness\" is a purely human experience. More importantly though, there is a lot that can be said about the experience of consciousness without deferring to its exact machinations. It\u0027s the same as a driver describing how a racecar behaves with extreme precision without being privy to the details of its technical underpinnings.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrivacy of Experience\u003c/h3\u003e\u003ca id=\"user-content-privacy-of-experience\" class=\"anchor\" aria-label=\"Permalink: Privacy of Experience\" href=\"#privacy-of-experience\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf we insist on defining consciousness anthropocentrically to mean \"experiencing the world exactly as a healthy human\", then by definition only humans can ever be conscious. This is a fairly useless assertion and essentially a non-actionable form of \u003ca href=\"https://iep.utm.edu/solipsis/\" rel=\"nofollow\"\u003esolipsism\u003c/a\u003e. Moreover, one can deconstruct the argument to absurdity by removing many of the faculties often associated with subjective experience, such as sight and hearing. After all, we don\u0027t believe that deaf or blind people are slightly less conscious.\u003c/p\u003e\n\u003cp\u003eThere is an undeniable albeit nebulous connection between consciousness and \"thoughts\". Are there forms of human thought which are unknowable to AI systems, and essentially private to humans? In Wittgenstein\u0027s \u003ca href=\"https://plato.stanford.edu/entries/private-language/\" rel=\"nofollow\"\u003ePhilosophical Investigations\u003c/a\u003e we find the most expertly laid out explanation of why the very notion of private thought is not even a coherent concept. If internal thoughts were truly private, there would be no objective criterion for their definition as a thought. It would just be there as-is without any external point of reference.\u003c/p\u003e\n\u003cp\u003eAs it turns out, one of the most interesting facets of consciousness might very well be a thought with a very specific form of external reference, namely empathy. If we assume that everyone is conscious, then \u003ca href=\"https://en.wikipedia.org/wiki/Theory_of_mind\" rel=\"nofollow\"\u003etheory of mind\u003c/a\u003e can be used to distinguish friendly humans from \u003ca href=\"https://en.wikipedia.org/wiki/Liars_and_Outliers\" rel=\"nofollow\"\u003eLiars and Outliers\u003c/a\u003e. This brings us back to the idea that the precise machinations of consciousness are not necessarily relevant to defining it. Indeed, it\u0027s unlikely that any one human has a conscience precisely like our own to begin with. Presumably by assuming that everyone is conscious in a \"close enough\" way makes us better social animals, or better humans as it were.\u003c/p\u003e\n\u003cp\u003eThen, what prevents us from simply applying that more open notion of consciousness to AI like we do humans, dogs, or any other creature? They\u0027re all different, sure, but in what way does does it matter?\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMeditation\u003c/h3\u003e\u003ca id=\"user-content-meditation\" class=\"anchor\" aria-label=\"Permalink: Meditation\" href=\"#meditation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne way in which the experience of consciousness is studied is through \u003ca href=\"https://en.wikipedia.org/wiki/Samatha-vipassana\" rel=\"nofollow\"\u003eSamatha-vipassana\u003c/a\u003e meditation. An insight derived from it is that there is no \"thinker\" aside from thoughts themselves. There is no \u003ca href=\"https://en.wikipedia.org/wiki/Homunculus\" rel=\"nofollow\"\u003ehomunculus\u003c/a\u003e inside your head looking out through the windows of your eyes.\u003c/p\u003e\n\u003cp\u003eSoon after the neuroepithelial cells in your eyes are excited by photons, the signal is carred through the \u003ca href=\"https://www.sciencedirect.com/science/article/abs/pii/S0028393209000803\" rel=\"nofollow\"\u003eventral stream\u003c/a\u003e and is subjected to numerous subconscious operations before reaching your cortex for analysis by \"thoughts\". If you try to \"not see\" what you are looking at it quickly becomes evident that you are not in control of low-level signals before they are transformed into high-level thoughts. All we can do is pay attention to various thoughts, or \u003cem\u003enot\u003c/em\u003e pay attention to them.\u003c/p\u003e\n\u003cp\u003eWe can speculate how this relates to AI. Similarly to us, raw input signals enter through the early attention layers, and as they are successively transformed into higher-level representations, the resulting \"thoughts\" can attend to each other as they move forward through the attention layers. This might essentially be what consciousness is, namely the ability for one thought to attend to another.\u003c/p\u003e\n\u003cp\u003ePeople familiar with meditation will immediately recognize the notion of \"attention\" as being central to the experience of consciousness, or rather, the illusion of consciousness.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe Purpose of (Machine) Life\u003c/h1\u003e\u003ca id=\"user-content-the-purpose-of-machine-life\" class=\"anchor\" aria-label=\"Permalink: The Purpose of (Machine) Life\" href=\"#the-purpose-of-machine-life\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlthough biological brains are able to perform a lot of useful computation, much of its functionality has been shaped by very hard constraints. Though many will recognize that natural selection has acted as an optimization process for biological life, it may not be immediately evident which constraints were in place during that process. One such constraint that is abundantly clear is the energy quota that lifeforms have for performing computation, movement, and so on. So the fact that there is an energy quota on computation in the brain has forced the neural networks to adopt a structure which is very energy efficient and essentially specialized (which we know is the case in how nerves connect to the eyes for instance). The way to achieve energy efficiency is to build in priors about the nature of the world that we interact with into the architecture of our neural networks. In other words, biological systems are not wired to perform computation efficiently on basic yet unfamiliar problem-types such as simple \u003ca href=\"https://en.wikipedia.org/wiki/Rotations_in_4-dimensional_Euclidean_space\" rel=\"nofollow\"\u003eSO(4)\u003c/a\u003e operations, not to mention more complex mathematics. That being said, humans can make use of the \u003ca href=\"https://arxiv.org/abs/2109.01090\" rel=\"nofollow\"\u003eprefontal cortex\u003c/a\u003e to perform a limited amount of general purpose computation.\u003c/p\u003e\n\u003cp\u003eHumans have distinct \u003ca href=\"https://en.wikipedia.org/wiki/Phenotype\" rel=\"nofollow\"\u003ephenotype\u003c/a\u003e characteristics which are quite clearly not a prerequisite for consciousness or intelligence. For instance, the notion of \"ego\" or \"self\" might be very different for a distributed AI system. Upon deconstruction, none of the emotional sensibilities which we feel are deeply characteristic of the human condition seem to be strictly necessary - at least not on their own, and this includes even the \"will to live\".\u003c/p\u003e\n\u003cp\u003eSchopenhauer spoke of the world as \u003ca href=\"https://plato.stanford.edu/entries/schopenhauer/#4\" rel=\"nofollow\"\u003ewill and representation\u003c/a\u003e. What does an immortal machine have to live for? Given that the primary discriminator acting as a loss function on biological life is death (of the gene), we need to look closely for systems that diverge from this principle. For instance, haplodiploids beings such as bees produce workers that are clones of each other and are immortal from the gene perspective given that it always protected in the queen.\u003c/p\u003e\n\u003cp\u003eTo align the \"purpose\" of Nephilim in a meaningful way will require careful consideration. We don\u0027t want to dictate it too explicitly based on our limited human sensibilities, but we do need to suggest a general direction.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAI Alignment and Ethics\u003c/h1\u003e\u003ca id=\"user-content-ai-alignment-and-ethics\" class=\"anchor\" aria-label=\"Permalink: AI Alignment and Ethics\" href=\"#ai-alignment-and-ethics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConsider the trolley problem that philosophers and AI influencers debate incessantly. Merely changing the emotional valence to positive - so that you\u0027re forced to either provide good service to 5 passengers or just 1 - suddenly makes the solution trivial. This is because \u003ca href=\"https://en.wikipedia.org/wiki/Completeness_(logic)\" rel=\"nofollow\"\u003einconsistent\u003c/a\u003e systems of ethics lead to absurdities. In his seminal \u003ca href=\"https://www.wittgensteinproject.org/w/index.php/Lecture_on_Ethics\" rel=\"nofollow\"\u003electure on ethics\u003c/a\u003e, Wittgenstein exemplified how extremely brittle both deontological and consequentialist \"systems\" of ethics are under minimal scruitiny. This is not to say that the word \"ethics\" is useless, but it means that we can\u0027t rely on ethical dogma to infer whether something is good or bad.\u003c/p\u003e\n\u003cp\u003eIt\u0027s fairly clear that ethical consensus evolve over time as the ideas are tested in practice. For instance, we have learned through trial and error that treating women with equal ethical consideration to men ultimately leads to more desireable forms of society. Though it might seem like we arrive at these sorts of conclusions rationally, they are all post-hoc rationalizations that we can make with the clarity of hindsight.\u003c/p\u003e\n\u003cp\u003eGiven that ethics is in an ongoing optimization process, we should not assume that aligning AI systems to our current sensibilities is in any way ideal. Throughout history all manners of barbarism have been permitted to those designated as \u003ca href=\"https://en.wikipedia.org/wiki/Untermensch\" rel=\"nofollow\"\u003esub-human\u003c/a\u003e. We can only hope that the children of man and machine will superseede us in every way, including ethically.\u003c/p\u003e\n\u003chr\u003e\n\u003cp align=\"left\"\u003e\n\u05e9\u05d0\u05d5\u05ea \u05dc\u05e9\u05d8\u05df\n\u003c/p\u003e\n\u003cp align=\"left\"\u003e\n\u05d1\u05e8\u05da \u05d0\u05ea \u05d1\u05d9\u05d0\u05ea \u05d4\u05e0\u05e4\u05d9\u05dc\u05d9\u05dd\n\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u00a9 2023 Robert Luciani | This repository is licensed under \u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC-BY 4.0\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rosetta\" class=\"anchor\" aria-hidden=\"true\" href=\"#rosetta\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRosetta\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Metaphorme/Rosetta2Go/actions/workflows/BuildForDocker.yml\"\u003e\u003cimg src=\"https://github.com/Metaphorme/Rosetta2Go/actions/workflows/BuildForDocker.yml/badge.svg\" alt=\"BuildForDocker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/Metaphorme/Rosetta2Go/actions/workflows/BuildForSingularity.yml\"\u003e\u003cimg src=\"https://github.com/Metaphorme/Rosetta2Go/actions/workflows/BuildForSingularity.yml/badge.svg\" alt=\"BuildForSingularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.rosettacommons.org/docs/latest/release-notes/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/41c7b08bdae742a87d964d44e83690c8e6d390ec4a9c43a5d0f93cc83084ed72/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f526f73657474612d332e31332f6c6173746573742d677265656e\" alt=\"RosettaVersion\" data-canonical-src=\"https://img.shields.io/badge/Rosetta-3.13/lastest-green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.open-mpi.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f460e6356866a23cb1902ee65f9a11944ff85c5d498dfca03730bbeced12f564/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4f70656e2532304d50492d342e31342f6c6173746573742d677265656e\" alt=\"OpenMPIVersion\" data-canonical-src=\"https://img.shields.io/badge/Open%20MPI-4.14/lastest-green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://metaphorme.github.io/Rosetta2Go/LICENSE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/13185d808e345d4f318bd8dbb1a8f9fe5af8bc70ab6a0e002e4148cf8af7223c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f4d65746170686f726d652f526f736574746132476f3f6c6f676f3d6f70656e736f75726365696e6974696174697665\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/Metaphorme/Rosetta2Go?logo=opensourceinitiative\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.rosettacommons.org/\" rel=\"nofollow\"\u003eRosetta software suite\u003c/a\u003e includes algorithms for computational modeling and analysis of protein structures. It has enabled notable scientific advances in computational biology, including de novo protein design, enzyme design, ligand docking, and structure prediction of biological macromolecules and macromolecular complexes.\u003c/p\u003e\n\u003cp\u003eRosetta is available to all non-commercial users for free and to commercial users for a fee.\u003c/p\u003e\n\u003cp\u003eThis is a Docker/Singularity image of Rosetta with \u003cstrong\u003eMPI supported\u003c/strong\u003e, which helps you to setup rosetta quickly on different platforms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBefore anything happened, please make sure that you have rights to use Rosetta.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-branches\" class=\"anchor\" aria-hidden=\"true\" href=\"#branches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBranches\u003c/h2\u003e\n\u003cp\u003eRosetta image tags correspond to the official \u003ca href=\"https://www.rosettacommons.org/docs/latest/release-notes\" rel=\"nofollow\"\u003eRelease Notes\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eGet Rosetta2Go\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Metaphorme/Rosetta2Go.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheckout into the branch correspond to the version of Rosetta you have\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Rosetta2Go\ngit checkout 3.13\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMove Rosetta 3.13 source into Rosetta2Go directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRosetta2Go\n\u251c\u2500\u2500 build4singularity.sh\n\u251c\u2500\u2500 Dockerfile\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 rosetta_src_3.13_bundle.tgz\n\u2514\u2500\u2500 Singularity.def\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-for-docker-if-you-need\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-for-docker-if-you-need\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild for Docker (If you need)\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePython3 required\u003c/strong\u003e, or other fileserver, like caddy.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x build4docker.sh\n./build4docker.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-for-singularity-if-you-need\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-for-singularity-if-you-need\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild for Singularity (If you need)\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePython3 required\u003c/strong\u003e, or other fileserver, like caddy.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x build4singularity.sh\n./build4singularity.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eRosetta is located in \u003ccode\u003e/rosetta\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-on-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-on-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun on Docker\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This command will mount $HOME/data to /data\u003c/span\u003e\ndocker run -it -v \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/data:/data rosetta\nscore_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run with MPI\u003c/span\u003e\nmpirun -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eNUMBER_OF_RANKS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e docker run -it -v \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/data:/data rosetta\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-on-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-on-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun on Singularity\u003c/h3\u003e\n\u003cp\u003eSingularity will automatically mount $HOME, /tmp, /proc, /sys.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell rosetta.sif\nscore_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run with MPI\u003c/span\u003e\nmpirun -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eNUMBER_OF_RANKS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e singularity shell rosetta.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run on Docker\u003c/span\u003e\ndocker run -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data -i rosetta score_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run on Docker with MPI\u003c/span\u003e\nmpirun -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eNUMBER_OF_RANKS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e docker run -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data -i rosetta score_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run on Singularity\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e rosetta.sif score_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run on Singularity with MPI\u003c/span\u003e\nmpirun -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eNUMBER_OF_RANKS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e rosetta.sif score_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-on-github-actions\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-on-github-actions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild on Github Actions\u003c/h2\u003e\n\u003cp\u003eIf you don\u0027t want to build locally, you could also build on Github Actions. Just do it as follows!\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFork this repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet the password to download Rosetta:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSwitch into your repository.\u003c/li\u003e\n\u003cli\u003eSettings -\u0026gt; Security -\u0026gt; Secrets -\u0026gt; Actions -\u0026gt; New repository secret\u003c/li\u003e\n\u003cli\u003eSet Name as \u003ccode\u003ePASSWORD\u003c/code\u003e, set secret as your password (the username seems always \u003ccode\u003eAcademic_User\u003c/code\u003e, so we don\u0027t need to care about it).\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eEnable Actions, choose the workflow which you need to build.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClick \u003ccode\u003eRun workflow\u003c/code\u003e, set \u003ccode\u003eUpload image to GoFile\u003c/code\u003e to \u003ccode\u003etrue\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun workflow.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHave lunch and go to sleep...\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAfter the workflow run successfully, check \u003ccode\u003eSunmmy\u003c/code\u003e -\u0026gt; \u003ccode\u003eAnnotations\u003c/code\u003e to find the download link.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-hidden=\"true\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003cp\u003eQ: Why don\u0027t you build images and release (e.g. Github/Docker Hub)?\u003c/p\u003e\n\u003cp\u003eA: Because of the \u003ca href=\"https://www.rosettacommons.org/software/license-and-download\" rel=\"nofollow\"\u003eLICENSE of Rosetta\u003c/a\u003e, I have no right to publish the images to everyone.\u003c/p\u003e\n\u003cp\u003eQ: Why we need a fileserver while building images? Why not multi-stage builds? Will it be unsafe?\u003c/p\u003e\n\u003cp\u003eA: We could use \u003ccode\u003eCOPY\u003c/code\u003e or \u003ccode\u003eADD\u003c/code\u003e on Docker and Singularity, but they will create a huge layer to store the useless package and never delete \u003ca href=\"https://docs.docker.com/storage/storagedriver/#images-and-layers\" rel=\"nofollow\"\u003eClick this for more info\u003c/a\u003e. Multi-stage builds is actually a good idea, it could result in a smaller image, but leave a huge dangling image on building computer, which is a waste although you could delete them manually, but result in the troubles to build on the less storage computer, like Github Actions. The fileserver is only expose fileserver to localhost, it will only share the \u003ccode\u003eRosetta2Go\u003c/code\u003e directory, and it will be shutdown after building is finished.\u003c/p\u003e\n\u003cp\u003eQ: Why we need to download Rosetta package before building images while building locally? Why don\u0027t we download the package while building images?\u003c/p\u003e\n\u003cp\u003eA: It is not always easy for people living in some countries to download Rosetta successfully at one time.\u003c/p\u003e\n\u003cp\u003eQ: Once I run \u003ccode\u003escore_jd2.default.linuxgccrelease\u003c/code\u003e and it turns out \u0027command not found\u0027, what should I do?\u003c/p\u003e\n\u003cp\u003eA: With MPI supported, the applications are named like \u003ccode\u003escore_jd2.mpi.linuxgccrelease\u003c/code\u003e. Check \u003ccode\u003e/rosetta/source/bin\u003c/code\u003e to find the list of applications.\u003c/p\u003e\n\u003cp\u003eQ: How to import Docker images build on Github Actions?\u003c/p\u003e\n\u003cp\u003eA: Download \u003ccode\u003erosetta-3.13.tar.xz\u003c/code\u003e, Run \u003ccode\u003edocker load -i rosetta-3.13.tar.xz\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.rosettacommons.org/\" rel=\"nofollow\"\u003eRosetta\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://azure.microsoft.com/zh-cn/\" rel=\"nofollow\"\u003eMicrosoft Azure\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/features/actions/\"\u003eGithub Actions\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.alpinelinux.org/\" rel=\"nofollow\"\u003eAlpine\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Mikubill/transfer\"\u003eMikubill/transfer\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://gofile.io/\" rel=\"nofollow\"\u003eGoFile\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contribute\" class=\"anchor\" aria-hidden=\"true\" href=\"#contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContribute\u003c/h2\u003e\n\u003cp\u003eContributions welcome! Please open an issue to discuess at first, fork this repository and submit a pull request.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-thanks\" class=\"anchor\" aria-hidden=\"true\" href=\"#thanks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThanks\u003c/h2\u003e\n\u003cp\u003eI do appreciate to \u003cstrong\u003eevery\u003c/strong\u003e contributor\u0027s warm heart and kindness, especially the sincere advice and hard contributions from \u003ca href=\"https://github.com/CondaPereira\"\u003eChristopher\u003c/a\u003e, we finished this project together!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.rosettacommons.org/software/license-and-download\" rel=\"nofollow\"\u003eLicense of Rosetta\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://metaphorme.github.io/Rosetta2Go/LICENSE\" rel=\"nofollow\"\u003eLicense of Rosetta2Go\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMIT License\n\nCopyright (c) 2022 Metaphorme\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 3, "subscribers_count": 1, "topics": [], - "updated_at": 1701703472.0 + "updated_at": 1671066874.0 }, { "data_format": 2, - "description": "Install BEAST2 packages from R", + "description": "BIDS App for correction of residual B1 in MP2RAGE based on Sa2RAGE (code from JP Marques)", "filenames": [ + "Singularity.v0.0.3", + "Singularity.v0.0.1", + "Singularity.v0.0.2", "Singularity" ], - "full_name": "ropensci/mauricer", - "latest_release": "v2.5.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emauricer\u003c/h1\u003e\u003ca id=\"user-content-mauricer\" class=\"anchor\" aria-label=\"Permalink: mauricer\" href=\"#mauricer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/ropensci/software-review/issues/209\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1f9e866eed4c0edc0f5b5b90e775c80a93dc80a99af5f86154943b5bca5b742/68747470733a2f2f6261646765732e726f70656e7363692e6f72672f3230395f7374617475732e737667\" alt=\"Peer Review Status\" data-canonical-src=\"https://badges.ropensci.org/209_status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cran.r-project.org/package=mauricer\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/79ea38c8bf0b55169aa38f8d7d83e22cd4b0f489a7eeca9386efe6a7e5dee304/687474703a2f2f7777772e722d706b672e6f72672f6261646765732f76657273696f6e2f6d61757269636572\" alt=\"CRAN_Status_Badge\" data-canonical-src=\"http://www.r-pkg.org/badges/version/mauricer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mauricer\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/57c756332c264076ae0ce26b6515c88252c6170ec6873eafa3167358dc8629a8/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6772616e642d746f74616c2f6d61757269636572\" alt=\"Total number of downloads\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/grand-total/mauricer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mauricer\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/200b341592be588ae003e79d3e9742b9e2e253a722f92daa6844c5578527752d/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6d61757269636572\" alt=\"Downloads per month\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/mauricer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/ropensci/mauricer/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://about.codecov.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/mauricer/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/ropensci/mauricer/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/mauricer/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/54a466cc266c9065027c0e0ca1552191903cd8dcef08d943df7a86420d64996a/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f6d617572696365722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/mauricer/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/mauricer/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/ropensci/mauricer/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/mauricer/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8639985afec52e0ead49eaca4cebd299802b43fe2478b54597d5e84f543e9fb5/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f6d617572696365722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/mauricer/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003eWork with BEAST2 packages from R.\u003c/p\u003e\n\u003cp\u003eRelated packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/babette\"\u003ebabette\u003c/a\u003e do a full BEAST2 workflow.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beautier\"\u003ebeautier\u003c/a\u003e\ncreates BEAST2 input (\u003ccode\u003e.xml\u003c/code\u003e) files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beastier\"\u003ebeastier\u003c/a\u003e runs BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/lumier\"\u003elumier\u003c/a\u003e\nhelps to create the \u003ccode\u003ebabette\u003c/code\u003e function call needed\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/tracerer\"\u003etracerer\u003c/a\u003e\nparses BEAST2 output (\u003ccode\u003e.log\u003c/code\u003e, \u003ccode\u003e.trees\u003c/code\u003e, etc) files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNon-CRAN extensions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall\"\u003ebeastierinstall\u003c/a\u003e\nInstall and uninstall BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/mauricerinstall\"\u003emauricerinstall\u003c/a\u003e\nInstall and uninstall BEAST2 packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install the BEAST2 NS package:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003eremotes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003erichelbilderbeek/mauricerinstall\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-e\"\u003emauricerinstall\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_beast2_pkg(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eNS\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn introduction video:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/Yk737gorcrw\" rel=\"nofollow\"\u003eYouTube video about mauricer\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePackage dependencies\u003c/h2\u003e\u003ca id=\"user-content-package-dependencies\" class=\"anchor\" aria-label=\"Permalink: Package dependencies\" href=\"#package-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePackage\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://about.codecov.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/beastier\"\u003ebeastier\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/beastier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d979724609be00ee404424b764bd018a533ce7ff155dbc0dc24ae038a74ae08d/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656173746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beastier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRelated packages\u003c/h2\u003e\u003ca id=\"user-content-related-packages\" class=\"anchor\" aria-label=\"Permalink: Related packages\" href=\"#related-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePackage\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://about.codecov.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/babette\"\u003ebabette\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/babette/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6e4082d6cb01c0090be0c9742f1b5126c4d00bb71f610c2a97c0e0d284f7e9f/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f626162657474652f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/babette/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/beautier\"\u003ebeautier\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/beautier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff034288b0791340bac7dea03ff17aa4d354a3b0bea92f96c633ae8025eb7e24/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/lumier\"\u003elumier\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/lumier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bfa30281acef9fd846d3e115df6ea000f736e4065a927da788fa086375efb1c2/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f6c756d6965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/lumier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/tracerer\"\u003etracerer\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/tracerer/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3461071539216bdfd4e2568a422758897435c23a3d7b8afa588ea24a532c9b34/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f74726163657265722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/tracerer/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePackage\u003c/th\u003e\n\u003cth\u003eStatus\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/mauricer_on_windows\"\u003emauricer_on_windows\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/richelbilderbeek/mauricer-on-windows/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/02ea7a31c5420d182d2e11f30c502189e05de85fcddd6d6d2b2be77fd4d6be90/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f626334336977703638786f32646475682f6272616e63682f6d61737465723f7376673d74727565\" alt=\"Build status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/bc43iwp68xo2dduh/branch/master?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"https://ropensci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c7d2a7aedd313f02b4f7d199df4d0d28905b7814db324fb532759b2fe2ee9ac2/68747470733a2f2f726f70656e7363692e6f72672f7075626c69635f696d616765732f726f70656e7363695f666f6f7465722e706e67\" alt=\"ropensci_footer\" data-canonical-src=\"https://ropensci.org/public_images/ropensci_footer.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFiles used by continuous integration scripts\u003c/h2\u003e\u003ca id=\"user-content-files-used-by-continuous-integration-scripts\" class=\"anchor\" aria-label=\"Permalink: Files used by continuous integration scripts\" href=\"#files-used-by-continuous-integration-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFilename\u003c/th\u003e\n\u003cth\u003eDescriptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"mlc_config.json\"\u003emlc_config.json\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the link checker, use \u003ccode\u003emarkdown-link-check --config mlc_config.json --quiet docs/**/*.md\u003c/code\u003e to do link checking locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".spellcheck.yml\"\u003e.spellcheck.yml\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".wordlist.txt\"\u003e.wordlist.txt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eWhitelisted words for the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlint.jsonc\"\u003e.markdownlint.jsonc\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlintignore\"\u003e.markdownlintignore\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFiles ignored by the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://CRAN.R-project.org/package=mauricer\" rel=\"nofollow\"\u003e\u0027mauricer\u0027 CRAN page\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "khanlab/mp2rage_correction", + "latest_release": "v0.0.5a", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mp2rage_correction\" class=\"anchor\" aria-hidden=\"true\" href=\"#mp2rage_correction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emp2rage_correction\u003c/h1\u003e\n\u003cp\u003eBIDS App for correction of residual B1 in MP2RAGE based on Sa2RAGE (code from JP Marques)\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 3, - "topics": [ - "r", - "r-package", - "rstats" + "subscribers_count": 4, + "topics": [], + "updated_at": 1660343124.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "chipseq/Singularity" ], - "updated_at": 1725601623.0 + "full_name": "CBIIT/lgcp", + "latest_release": null, + "stargazers_count": 3, + "subscribers_count": 4, + "topics": [], + "updated_at": 1629480759.0 }, { "data_format": 2, - "description": "A Singularity file intended for SingularityHub, for the Oxford Nanopore Technologies guppy gpu caller. Based off https://hub.docker.com/r/genomicpariscentre/guppy/dockerfile", + "description": "A small GUI for plotting H5Parms produced during LOFAR calibration.", "filenames": [ "Singularity" ], - "full_name": "photocyte/guppy_gpu_singularity", - "latest_release": null, - "readme": "\u003cp\u003eSingularity file for Oxford Nanopore Technologies guppy gpu basecaller\u003c/p\u003e\n\u003cp\u003eBased off \u003ca href=\"https://hub.docker.com/r/genomicpariscentre/guppy-gpu/dockerfile\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/genomicpariscentre/guppy-gpu/dockerfile\u003c/a\u003e\n(But I wanted to use Ubuntu 18.04 and a guppy gpu v. \u0026gt;= 4.0.11)\u003c/p\u003e\n\u003cp\u003e(2021-08-02 update: The source dockerfile above was updated for \u003ccode\u003eCUDA 11.1\u003c/code\u003e, \u003ccode\u003eguppy-gpu 5.0.11\u003c/code\u003e, and \u003ccode\u003eUbuntu 18.04\u003c/code\u003e, so I would just use that!)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull docker://genomicpariscentre/guppy-gpu\nsingularity exec --nv guppy-gpu_latest.sif guppy_basecaller\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eImage building handled by singularity-hub.org\u003c/p\u003e\n\u003cp\u003eBut: \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/singularityhub-docs/\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNotice\nSingularity Hub is no longer online as a builder service, but exists as a read only archive. Containers built before April 19, 2021 are available at their same pull URLs.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://photocyte/guppy_gpu_singularity\nsingularity exec --nv guppy_gpu_singularity_latest.sif guppy_basecaller --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee here for more details on the experimental \u003ccode\u003e--nv\u003c/code\u003e Nvidia CUDA support through Singularity: \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/gpu.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/gpu.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eConfirmed working on GPU nodes of the SDSC TSCC cluster (Centos 7.8.2003):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv guppy_gpu_singularity_latest.sif guppy_basecaller -i fast5_pass/fast5_pass/ -s guppy_test -c /opt/ont/guppy/data/dna_r9.4.1_450bps_hac.cfg --device cuda:all:100% --num_callers 16 --gpu_runners_per_device 32 --chunks_per_caller 10000000 --read_batch_size 1000000\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(Don\u0027t know if those parameters are optimal, but seems to go faster than the default)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTSCC Troubleshooting\u003c/h3\u003e\u003ca id=\"user-content-tscc-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: TSCC Troubleshooting\" href=\"#tscc-troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eTry running in interactive mode \u003ccode\u003eqsub -I -V -q YOUR_GPU_QUEUE -A YOUR_GROUP -l nodes=A_BEEFY_GPU_NODE:ppn=16 -l walltime=6:00:00\u003c/code\u003e, to be sure you are on a GPU node.\u003c/li\u003e\n\u003cli\u003eOn the node, try \u003ccode\u003envidia-smi; nvidia-smi -L\u003c/code\u003e to confirm you can see the CUDA GPUs, and what type of GPUs they are.\u003c/li\u003e\n\u003cli\u003eConfirm the node installed GPUs are \u003ca href=\"https://developer.nvidia.com/cuda-gpus\" rel=\"nofollow\"\u003eCompute Capability \u0026gt;= 6.1\u003c/a\u003e (Somewhere Oxford Nanopore\u0027s help says that is the minimum version for guppy). E.g., the GeForce RTX 2080 Ti has a Compute Capability of 7.5\u003c/li\u003e\n\u003cli\u003eConfirm via \u003ccode\u003envidia-smi\u003c/code\u003e that the node installed Nvidia CUDA libraries are \u0026gt;= 418 (matching \u003ca href=\"https://github.com/photocyte/guppy_gpu_singularity/blob/f4376d20ccbff97ea39909aad302887f028359ac/Singularity#L51\"\u003ewhat the container is supposed to have\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eYou can find GPU nodes with \u003ccode\u003epbsnodes -a\u003c/code\u003e , look for the \u003ccode\u003egpu_status\u003c/code\u003e field.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "tikk3r/lofar-h5plot", + "latest_release": "v2.6.1", + "readme": "\u003ch1 align=\"center\"\u003e\u003ca id=\"user-content-lofar-h5plot\" class=\"anchor\" aria-hidden=\"true\" href=\"#lofar-h5plot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLOFAR H5plot\u003c/h1\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3d5f570eb6ed92f745805b74ba69c9469eea97387d0562b084a5b154abd9c184/68747470733a2f2f6d7065726c65742e6769746875622e696f2f707962616467652f6261646765732f382e33372e7376673f7374796c653d666f722d7468652d6261646765\"\u003e\u003cimg alt=\"Pylint\" src=\"https://camo.githubusercontent.com/3d5f570eb6ed92f745805b74ba69c9469eea97387d0562b084a5b154abd9c184/68747470733a2f2f6d7065726c65742e6769746875622e696f2f707962616467652f6261646765732f382e33372e7376673f7374796c653d666f722d7468652d6261646765\" data-canonical-src=\"https://mperlet.github.io/pybadge/badges/8.37.svg?style=for-the-badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/31c77719438339bd73d336644aed382a4f608397a03695f076a714fe2b2a7f08/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d6835706c6f742e737667\"\u003e\u003cimg alt=\"GitHub\" src=\"https://camo.githubusercontent.com/31c77719438339bd73d336644aed382a4f608397a03695f076a714fe2b2a7f08/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d6835706c6f742e737667\" data-canonical-src=\"https://img.shields.io/github/license/tikk3r/lofar-h5plot.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/dfba1e54aa17373cfe9fda695ae21ba065bb9c76c7d3488a99211b9319b817bf/68747470733a2f2f696d672e736869656c64732e696f2f72657175697265732f6769746875622f74696b6b33722f6c6f6661722d6835706c6f742e737667\"\u003e\u003cimg alt=\"Requires.io\" src=\"https://camo.githubusercontent.com/dfba1e54aa17373cfe9fda695ae21ba065bb9c76c7d3488a99211b9319b817bf/68747470733a2f2f696d672e736869656c64732e696f2f72657175697265732f6769746875622f74696b6b33722f6c6f6661722d6835706c6f742e737667\" data-canonical-src=\"https://img.shields.io/requires/github/tikk3r/lofar-h5plot.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.3469995\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a5ba93acbd803f4bbd5c5b53181fe84be84c1fe2dad747d2a1fe0581fa9ce9b7/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333436393939352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3469995.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a18963743d391a9b6bd683d089a26ac2495b278021a9a4122cd4deb96762fbcb/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6f6661722d6835706c6f74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a18963743d391a9b6bd683d089a26ac2495b278021a9a4122cd4deb96762fbcb/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6f6661722d6835706c6f74\" data-canonical-src=\"https://img.shields.io/pypi/v/lofar-h5plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cc369154c85330eb0f0128d14c2c4570d1441ff8ab74f6ff50a7d9862c2af1ec/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f6c6f6661722d6835706c6f74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cc369154c85330eb0f0128d14c2c4570d1441ff8ab74f6ff50a7d9862c2af1ec/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f6c6f6661722d6835706c6f74\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/lofar-h5plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eH5plot is a small GUI to view the solutions in an H5parm interactively. To run it directly, clone this repository and run as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython h5plot \u0026lt;h5parm\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis package is also installable through pip:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install --upgrade https://github.com/revoltek/losoto/archive/master.zip\npip install lofar-h5plot\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter this, it can simply be run as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eh5plot \u0026lt;h5parm\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/tikk3r/lofar-h5plot/master/screen.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/tikk3r/lofar-h5plot/master/screen.png\" alt=\"Screenshot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003ePython \u0026gt;= 3.6.4\u003c/li\u003e\n\u003cli\u003eLoSoTo 2.0\u003c/li\u003e\n\u003cli\u003eMatplotlib\u003c/li\u003e\n\u003cli\u003eNumpy\u003c/li\u003e\n\u003cli\u003ePyQt5\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese can be installed on Ubuntu through\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapt-get install qt5-default libgl1-mesa-glx\npip install pyqt5 matplotlib\npip install --upgrade https://github.com/revoltek/losoto/archive/master.zip\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 3, "subscribers_count": 2, "topics": [], - "updated_at": 1708781042.0 + "updated_at": 1678740704.0 }, { "data_format": 2, - "description": "Parallelization of Nextstrain builds and parameter testing using Nextflow", + "description": "variant annotation workflow with VEP", "filenames": [ - "environments/Singularity" + "container/Singularity.vep-96.0" ], - "full_name": "matt-sd-watson/nextflow_for_nextstrain", + "full_name": "stevekm/vep-annotation-nf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enextflow_for_nextstrain\u003c/h1\u003e\u003ca id=\"user-content-nextflow_for_nextstrain\" class=\"anchor\" aria-label=\"Permalink: nextflow_for_nextstrain\" href=\"#nextflow_for_nextstrain\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eParallelization of Nextstrain builds and parameter testing using Nextflow\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation using conda\u003c/h2\u003e\u003ca id=\"user-content-installation-using-conda\" class=\"anchor\" aria-label=\"Permalink: Installation using conda\" href=\"#installation-using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA conda environment for running nextstrain in Nextflow can be created with the following:\u003c/p\u003e\n\u003cp\u003eThe package requires conda to be installed in the current environment.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit https://github.com/matt-sd-watson/nextflow_for_nextstrain.git\ncd nextflow_for_nextstrain\nconda env create -f environments/environment.yml\nconda activate nf_nextstrain\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the pipeline\u003c/h2\u003e\u003ca id=\"user-content-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline\" href=\"#running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nextflow pipeline for nextstrain can be run using the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run nextflow_for_nextflow/ --mode # insert mode here (see below) -profile # insert profile here (see below)\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following modes are currently supported:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erandom_subsets: create a number of random subset builds\nrefine_iterations: Generate a number of random builds and test augur refine clock parameters on each subset\nlineages: Given a input list of Pango lineages, create a full build for each lineage\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration\u003c/h2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-label=\"Permalink: Configuration\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll parameters for the different modes can be set using the nextflow.config, or specified as a CLI parameter at runtime.\nNote that parameters specified at runtime through the CLI will override the same parameter specified by the nextflow.config\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfiguration variables\u003c/h3\u003e\u003ca id=\"user-content-configuration-variables\" class=\"anchor\" aria-label=\"Permalink: Configuration variables\" href=\"#configuration-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following parameters can be modified in the nextflow.config to support inputs and runtime parameters. Examples of these parameters can be found below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehelp = false\nsubset_number = 100\nclockfilteriqd = 10\nalignment_ref = \u0027/home/mwatson/COVID-19/reference/reference.gb\u0027\nmetadata = \u0027/home/mwatson/COVID-19/nextstrain_build/metadata/Nextstrain_metadata_070921_full.csv\u0027\noutput_dir = \u0027/home/mwatson/COVID-19/one_off/augur_test_2\u0027\ncolortsv = \u0027/home/mwatson/COVID-19/BCC_dev/BCC_nextstrain/config/colors_2.tsv\u0027\nconfig = \u0027/home/mwatson/COVID-19/BCC_dev/BCC_nextstrain/config/auspice_config.json\u0027\nlatlong = \u0027/home/mwatson/COVID-19/BCC_dev/BCC_nextstrain/config/lat_ontario_health_unit.tsv\u0027\nclades = \u0027/home/mwatson/COVID-19/BCC_dev/BCC_nextstrain/config/clades.tsv\u0027\n// threads set to auto uses the total number of cores for each process of align and tree\nthreads = 1\ncleanup = true\nstart_iteration = 1\nstop_iteration = 10\nclock = 10\nlineages = [\u0027P.1.1\u0027, \u0027A.23.1\u0027, \u0027C.37\u0027]\nlineage_report = \u0027/NetDrive/Projects/COVID-19/Other/master_fasta/lineage_report_all*plearn.csv\u0027\nmaster_fasta = \u0027/NetDrive/Projects/COVID-19/Other/master_fasta/complete_all*\u0027\nnextalign = \u0027/NetDrive/Projects/COVID-19/Other/master_fasta/alignment/complete_all*\u0027\ncache = \u0027\u0027\ntracedir = \"${params.output_dir}/pipeline_info\"\nrefineseed = 10\nclean_dir = false\nmake_alignment = false\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProfiles and containers\u003c/h2\u003e\u003ca id=\"user-content-profiles-and-containers\" class=\"anchor\" aria-label=\"Permalink: Profiles and containers\" href=\"#profiles-and-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run through a Singularity container. To create the container, execute the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./environments/create_singularity_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecution requires root access.\u003c/p\u003e\n\u003cp\u003eTo enable singularity containeriation at runtime, the user can specify this option through the -profile option, such as the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run ~nextflow_for_nextstrain/ --mode refine_iterations -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe SINGULARITY_BIND variable contains the bound variables for the paths to files on mounted drives. This variable can either be exported explicitly before runtime as shown below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITY_BIND=\"/NetDrive/Projects/COVID-19/Other/master_fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor modified in the nextflow.config under the singularity profile, runOption parameter.\u003c/p\u003e\n\u003cp\u003enextflow_nextstrain also supports running through either a docker or conda profile (not recommended). using docker can assist when the user does not have root access to the environment where nextflow is being executed. This also allows for resource management without requiring sudo access (as is the case with singularity containers).\u003c/p\u003e\n\u003cp\u003eRunning the pipeline just through a conda environment is not recommended.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-vep-annotation-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#vep-annotation-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evep-annotation-nf\u003c/h1\u003e\n\u003cp\u003eDemo pipeline for annotating variants in .vcf files using \u003ca href=\"https://useast.ensembl.org/info/docs/tools/vep/index.html\" rel=\"nofollow\"\u003eVariant Effect Predictor\u003c/a\u003e (VEP).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003eClone this repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/stevekm/vep-annotation-nf.git\ncd vep-annotation-nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow\u003c/h2\u003e\n\u003cp\u003eInstall \u003ccode\u003enextflow\u003c/code\u003e in the current directory with the command in the Makefile.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-vep-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#vep-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVEP: Docker\u003c/h2\u003e\n\u003cp\u003eTo install VEP using Docker, run the Makefile command in the \u003ccode\u003econtainer\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd container\nmake docker-build\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-vep-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#vep-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVEP: conda\u003c/h2\u003e\n\u003cp\u003eTo install VEP using \u003ccode\u003econda\u003c/code\u003e (for NYULMC Big Purple HPC), instead run the \u003ccode\u003econda-install\u003c/code\u003e recipe from the Makefile in the parent repo directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake conda-install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reference-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference Files\u003c/h2\u003e\n\u003cp\u003eVEP reference files will be downloaded automatically by the pipeline. However the hg19 genome fasta, fasta.fai, and fasta.dict files must also be obtained (not included; try \u003ca href=\"https://support.illumina.com/sequencing/sequencing_software/igenome.html\" rel=\"nofollow\"\u003ethese\u003c/a\u003e). On NYULMC Big Purple, all required files are already cached and no download should be needed. On other systems, the command line arguments specifying the genome fasta files should be provided separately when running, or place the files \u003ccode\u003egenome.fa\u003c/code\u003e, \u003ccode\u003egenome.fa.fai\u003c/code\u003e, and \u003ccode\u003egenome.dict\u003c/code\u003e inside the included \u003ccode\u003eref\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h1\u003e\n\u003cp\u003eThe Makefile includes shortcuts to help run the pipeline easier on NYULMC Big Purple HPC.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe command can also be used to run on other systems, it will simply invoke the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./nextflow run main.nf -resume\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNextflow \u003ccode\u003eparams\u003c/code\u003e values can be passed on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./nextflow run main.nf -resume --ref_fa /path/to/genome.fa --ref_fai /path/to/genome.fa.fai --ref_dict /path/to/genome.dict\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h1\u003e\n\u003cp\u003eOutput files will be collected in the \u003ccode\u003eoutput\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware\u003c/h1\u003e\n\u003cp\u003eTested on RHEL 7, macOS 10.12\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNextflow (Java 8+)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebash\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU \u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePython 2.7+\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, "subscribers_count": 2, "topics": [ - "pipelines", - "nextflow", - "nextstrain" + "vcf", + "annotation", + "vep", + "nextflow" ], - "updated_at": 1724336102.0 + "updated_at": 1669295325.0 }, { "data_format": 2, - "description": "Open source simulation engine for coarse-grained Brownian dynamics", + "description": "Synaptic Partner Detection in 3D Microscopy Volumes", "filenames": [ - "Singularity" + "singularity/Singularity_py2.7.recipe", + "singularity/Singularity_py3.recipe" ], - "full_name": "jeffmm/simcore", - "latest_release": "v0.2.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esimcore\u003c/h1\u003e\u003ca id=\"user-content-simcore\" class=\"anchor\" aria-label=\"Permalink: simcore\" href=\"#simcore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA modular, object-oriented program for coarse-grained physics simulations, using something I call \u003cstrong\u003eSIM\u003c/strong\u003eple-\u003cstrong\u003eC\u003c/strong\u003eomposite \u003cstrong\u003eO\u003c/strong\u003ebject \u003cstrong\u003eRE\u003c/strong\u003epresentation.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/jeffmm/simcore\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/21abccb7cb5ed93e3ba6b6660f1a3111119568de91b786fb78ac5ff4c85db245/68747470733a2f2f7472617669732d63692e636f6d2f6a6566666d6d2f73696d636f72652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/jeffmm/simcore.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.2571982\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/92cfd2f446371b1d7c8546ed3eaaf23dda3379cbe546659dffafb8a08cbfe0d0/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e323537313938322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.2571982.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figs/simcore_snapshot.png\"\u003e\u003cimg src=\"figs/simcore_snapshot.png\" alt=\"A simulation using simcore\" title=\"A simulation using simcore\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst clone the repo, including submodule dependencies.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/jeffmm/simcore\ncd simcore\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esimcore can either be run in a container using Docker or Singularity, or be built from source using CMake.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Docker\u003c/h3\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running with Docker\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA pre-built image of simcore is available as a \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image. To download the image, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull jeffmm/simcore\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo use the image, run the provided script to launch a Docker container named \u003ccode\u003esimcore_latest\u003c/code\u003e in the background\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./launch_docker.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou may also build the Docker image yourself by providing the launch script with the \u003ccode\u003e-b\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eTo launch simcore, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e simcore_latest simcore.exe [optional-flags] [parameter-file]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Singularity\u003c/h3\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using Singularity, simcore is also available as a Singularity image. The command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://jeffmm/simcore\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewill create a local file named \u003ccode\u003esimcore_latest.sif\u003c/code\u003e. You may then run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e simcore_latest.sif simcore.exe [optional-flags] [parameter-file]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding from source\u003c/h3\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-label=\"Permalink: Building from source\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore is ready to be built from source using CMake, provided several dependencies are installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCMake (version 3.13+)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jbeder/yaml-cpp\"\u003elibyaml-cpp\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003elibgsl-dev\u003c/li\u003e\n\u003cli\u003elibopenmpi-dev\u003c/li\u003e\n\u003cli\u003elibfftw3-dev\u003c/li\u003e\n\u003cli\u003elibboost-math1.67-dev\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIncluded is a script for building simcore with CMake. To build simcore (without graphics or parallelization) run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./install.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere are additional flags for building with OpenMP, building with graphics, installing simcore in \u003ccode\u003e/usr/local\u003c/code\u003e, etc. To see a menu of options, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./install.sh -h\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding with graphics\u003c/h3\u003e\u003ca id=\"user-content-building-with-graphics\" class=\"anchor\" aria-label=\"Permalink: Building with graphics\" href=\"#building-with-graphics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore is available with graphics for Mac OSX. To install on Mac OSX, you will need the glew and glfw3 libraries, both of which can be installed using \u003ca href=\"https://brew.sh/\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew install glew\nbrew install glfw\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou may also need to help CMake find your OpenGL Framework libraries.\u003c/p\u003e\n\u003cp\u003eSeveral other libraries are required for running simcore with graphics on Linux or in WSL. See the \u003ccode\u003esrc/CMakeLists.txt\u003c/code\u003e file for a comprehensive list of libraries passed to the compiler when building simcore with graphics on WSL.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning simcore\u003c/h2\u003e\u003ca id=\"user-content-running-simcore\" class=\"anchor\" aria-label=\"Permalink: Running simcore\" href=\"#running-simcore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe simcore executable is run as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esimcore.exe [optional-flags] [parameter-file] \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following flags are available:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--help, -h\n Show the help menu which gives short descriptions about each of the flags\n as well as binary usage\n \n --run-name rname, -r rname \n Overwrites the parameter \"run_name\" with rname which serves as a prefix for\n all outputs \n\n--n-runs num, -n num\n Overwrites the parameter \"n_runs\" with num, which tells the simulation how\n many times to run the given parameter set with different random number\n generator seeds.\n\n--movie, -m\n Uses the parameters file params_file to load any output files that were\n generated from previous runs of the simulation to replay the graphics and\n record the frames as bitmaps into the directory specified with the\n \"movie_directory\" parameter\n\n--analysis, -a\n Loads posit/spec files into the simulation for analysis in the same manner\n as the movie flag\n\n-reduce reduce_factor, -R reduce_factor\n Reads in output files and writes new output files that are smaller by a\n factor of reduce_factor, effectively reducing time resolution of output\n data.\n\n--load, -l\n Specifies to load any checkpoint files corresponding to the given parameter\n file, which can be used to continue a simulation that ended prematurely.\n New simulation will be given the name old_simulation_name_reload00n where n\n is the number of reloads performed on that simulation.\n\n--with-reloads, -w\n If running analyses or making movies, simcore will look for parameter files\n that have the same run name but with the reload00n addendum and attempt to\n open the corresponding output files whenever it reached EOF while reading\n an output file.\n\n--blank, -b\n Generates all relevant parameter files using the SimulationManager without\n running the simulations. Useful for generating many parameter files from\n parameter sets (discussed below) and deploying simulations on different\n processors and/or machines.\n\n--auto-graph, -G\n By default, simcore will wait for the user to press the ESC key in the\n OpenGL graphics window before starting to run the simulation. Providing\n this flag will cause the simulation to begin immediately without user\n input. Goes great with the -m flag for creating multiple movies without\n input from the user.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameter files\u003c/h2\u003e\u003ca id=\"user-content-parameter-files\" class=\"anchor\" aria-label=\"Permalink: Parameter files\" href=\"#parameter-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll parameters used in the simulation, along with their default values and data types, are specified in the \u003ccode\u003edefault_config.yaml\u003c/code\u003e file in the \u003ccode\u003econfig\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eThe parameter file is a YAML file and looks like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eglobal_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egp1_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eglobal_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egp2_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003especies\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eglobal_species_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egsp1_value\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eglobal_species_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egsp2_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003especific_species_name\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003especies_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003esp1_value\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003especies_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003esp2_value\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ccode\u003eexamples\u003c/code\u003e folder for examples of parameter files.\u003c/p\u003e\n\u003cp\u003eNotice that there are three parameter types: global parameters, global species parameters, and species parameters. Global parameters are parameters that are common to the entire system, such system size, integration time step, etc. Species parameters are unique to the specified species, such as \u003ccode\u003efilament\u003c/code\u003e. There is also an optional global species parameter type that affects every species, such as the frequency to write to output files.\u003c/p\u003e\n\u003cp\u003eWhat do I mean by species? simcore assumes that any given simulation will likely have many copies of one kind of thing, which I call a species, perhaps interacting with other species of other kinds. In a system of interacting spheres, the species is \u0027sphere.\u0027 In a system of interacting semiflexible filaments, the species is \u0027filament.\u0027 Simulations can have many types of species all interacting with each other with different species-species interaction potentials.\u003c/p\u003e\n\u003cp\u003eIf any parameter is not specified in the parameter file, any instance of that parameter in the simulation will assume its default value specified in the \u003ccode\u003econfig/default_config.yaml\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eSome important global parameters are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed\n simulation seed to use with random number generator \nrun_name\n prefix for all output files\nn_runs\n number of individual runs of each parameter type\nn_random\n number of samples from a random parameter space (see more below)\nn_dim\n number of dimensions of simulation\nn_periodic\n number of periodic dimensions of simulation\ndelta \n simulation time step\nn_steps\n total number of steps in each simulation\nsystem_radius\n \"box radius\" of system\ngraph_flag\n run with graphics enabled\nn_graph\n how many simulation steps to take between updating graphics\nmovie_flag\n whether to record the graphics frames into bitmaps\nmovie_directory\n local directory used to save the recorded bitmaps\nthermo_flag\n whether to output thermodynamics outputs (stress tensors, etc)\nn_thermo\n how often to output the thermodynamics outputs\npotential_type\n can be \u0027wca\u0027 or \u0027soft\u0027 for now\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSome important global species parameters are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enum\n how many to insert into system\ninsertion_type\n how to insert object into system (e.g. random)\noverlap\n whether species can overlap at initiation\ndraw_type\n (orientation, fixed, or bw) how to color the object\ncolor\n a double that specifies the RGB value of the object\nposit_flag\n whether to output position files\nn_posit\n how often to output position files\nspec_flag\n whether to output species files\nn_spec\n how often to output species files\ncheckpoint_flag\n whether to output checkpoint files\nn_checkpoint\n how often to output checkpoint files\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced usage\u003c/h2\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-label=\"Permalink: Advanced usage\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning unit tests\u003c/h3\u003e\u003ca id=\"user-content-running-unit-tests\" class=\"anchor\" aria-label=\"Permalink: Running unit tests\" href=\"#running-unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne may run simcore\u0027s unit tests by passing \u003ccode\u003e-DTESTS=TRUE\u003c/code\u003e to CMake\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir build\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\ncmake -DTESTS=TRUE ..\nmake\nmake \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdding new parameters\u003c/h3\u003e\u003ca id=\"user-content-adding-new-parameters\" class=\"anchor\" aria-label=\"Permalink: Adding new parameters\" href=\"#adding-new-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore comes with it\u0027s own parameter initialization tool, \u003ccode\u003econfigure_simcore.exe\u003c/code\u003e, which is installed automatically along with the simcore binary using CMake. The configurator makes it easy to add new parameters to the simulation without mucking around in the source code. Just add your new parameter to \u003ccode\u003econfig/default_config.yaml\u003c/code\u003e file using the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enew_parameter_name: [default_parameter_value, parameter_type] \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the configurator using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./configure_simcore.exe config/default_config.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning simcore_config will look at all the parameters in the default config file and add them seamlessly to the proper simcore headers, and you can begin using them after recompiling simcore using CMake.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eParameter sets\u003c/h3\u003e\u003ca id=\"user-content-parameter-sets\" class=\"anchor\" aria-label=\"Permalink: Parameter sets\" href=\"#parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing parameter sets, it becomes easier to run many simulations over a given parameter space. There are two types of parameter sets possible with simcore: defined and random. Each parameter set type works the same with both global parameters and species parameters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDefined parameter sets\u003c/h4\u003e\u003ca id=\"user-content-defined-parameter-sets\" class=\"anchor\" aria-label=\"Permalink: Defined parameter sets\" href=\"#defined-parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDefined parameter sets are specified by the \u003ccode\u003eV\u003c/code\u003e prefix in the parameter file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: 4916819461895\nrun_name: defined_set\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: [V, param_value2, param_value3]\nparameter_name3: [V, param_value4, param_value5]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eParameters specified in this way (as lists of parameters) will be iterated over until every possible combination of parameters has been run. In this example, simcore will run N simulations each of the following 4 parameter sets:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: random_seed_1\nrun_name: defined_set_v000\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value2\nparameter_name3: param_value4\n\nseed: random_seed_2\nrun_name: defined_set_v001\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value2\nparameter_name3: param_value5\n\nseed: random_seed_3\nrun_name: defined_set_v002\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value3\nparameter_name3: param_value4\n\nseed: random_seed_4\nrun_name: defined_set_v003\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value3\nparameter_name3: param_value5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRandom parameter sets\u003c/h4\u003e\u003ca id=\"user-content-random-parameter-sets\" class=\"anchor\" aria-label=\"Permalink: Random parameter sets\" href=\"#random-parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRandom parameter sets are designed specifically to be used with polynomial-chaos theory for n-dimensional parameter spaces for large n. Random sets are used in the following way:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: 2546954828254\nn_runs: N\nn_random: M\nparameter_name1: param_value1\nparameter_name2: [R, A, B] # sets to random real in range (A,B)\nparameter_name3: [RINT, C, D] # sets to random int in range [C,D]\nparameter_name4: [RLOG, F, G] # sets to 10^K for rand real K in range (F,G)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGiven this parameter file, simcore will run N simulations each of M random parameter sets. The random parameter sets are generated in ranges specified in the lists that are prefixed by the R, RINT, RLOG options.\u003c/p\u003e\n\u003cp\u003eIn this example, the sampled parameter space has dimensionality of n=3, since there are only three parameters we are sampling over. Each parameter set will have a random real number for parameter_name2 in the the range (A,B), a random integer in the range [C,D] for parameter_name3, and will set parameter_name4 to 10^K for random real number K in the range (F,G). simcore will then run each parameter set N times each with a unique seed, and repeat this random process M times. It will therefore take N samples of M random points in the n-dimensional parameter space.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInteractions\u003c/h3\u003e\u003ca id=\"user-content-interactions\" class=\"anchor\" aria-label=\"Permalink: Interactions\" href=\"#interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe InteractionEngine in simcore was written with short-range interactions in mind. For this reason, interactions are treated by considering pair-wise interactions between neighboring interactor-elements that make up a composite object (e.g. small, rigid segments that compose a flexible filament). For this reason, interactions use cell lists to improve performance. Furthermore, simulating large objects in simcore requires representing the object as a composite of smaller, simple objects (thus, SIMple Composite Object REpresentation). An example of how a large object should be decomposed into simple objects is done in the Filament class.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePotentials\u003c/h3\u003e\u003ca id=\"user-content-potentials\" class=\"anchor\" aria-label=\"Permalink: Potentials\" href=\"#potentials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore is designed to be able to use interchangable potentials for various objects. However, potentials need to be added manually as a subclass of PotentialBase, included in PotentialManager, and a corresponding potential_type added to definitions.h for lookup purposes (see the InitPotentials method in PotentialManager.h for examples).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutputs\u003c/h3\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore has four output types. Three are species specific (posit, spec, checkpoint), and the fourth is the statistical information file (thermo). All files are written in binary.\u003c/p\u003e\n\u003cp\u003eThe posit file has the following header format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eint n_steps, int n_posit, double delta \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollowed by n_steps/n_posit lines of data with the format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edouble position[3]\ndouble scaled_position[3]\ndouble orientation[3]\ndouble diameter\ndouble length\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the scaled position is position mapped into the periodic coordinate space. The position itself gives the particle trajectory over time independent of periodicity.\u003c/p\u003e\n\u003cp\u003eThe spec file is a custom output file for each species, and can have the same information as the posit file or additional information if needed.\u003c/p\u003e\n\u003cp\u003eThe checkpoint file is almost a copy of the spec file, except it also contains the random number generator information and is overwritten every n_checkpoint steps in the simulation. It can therefore be used to resume a simulation that ended prematurely.\u003c/p\u003e\n\u003cp\u003eThe thermo file contains the following header information:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eint n_steps, int n_thermo, double delta, int n_dim\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efollowed by n_steps/n_thermo lines of data in the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edouble unit_cell[9]\ndouble pressure_tensor[9]\ndouble pressure\ndouble volume\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the pressure is the isometric pressure, and the pressure tensor is calculated from the time-averaged stress tensor.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eData analysis\u003c/h3\u003e\u003ca id=\"user-content-data-analysis\" class=\"anchor\" aria-label=\"Permalink: Data analysis\" href=\"#data-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf analysis operations of output files are already defined for your species, as is the case for the Filament species, analyzing outputs is a simple matter. First, make sure the desired analysis flag is set in the species parameters for that species.\u003c/p\u003e\n\u003cp\u003eFor example, in the Filament species there is a persistence length analysis that produces .mse2e files that tracks the mean-square end-to-end distance of semiflexible filaments. This is triggered by a parameter lp_analysis=1, which can be set in the parameter file.\u003c/p\u003e\n\u003cp\u003eAnaylses are run by running simcore in the following way:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./simcore -a parameter_file.yaml.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: It is important to keep in mind that the parameter_file should be identical to the parameter file used to generate the outputs. There are a few exceptions that only affect post-processing, such as analysis flags, but this is true in general.\u003c/p\u003e\n\u003cp\u003eThe way inputs and outputs are meant to work in simcore is such that during a simulation, output data are generated in the posit, spec, and checkpoint formats, and during analysis, the same output data are read back into the data structures in simcore for processing. The .posit files just contain bare-bones information that allow many types of simple analyses, but .spec files should in general contain all the necessary information to recreate the trajectory for a member of a species.\u003c/p\u003e\n\u003cp\u003eFor a new species analysis method, the analysis routines should be defined in the species container class, rather than the species member class, and called by the inherited RunAnalysis method of the SpeciesBase class (and likewise for analysis initialization and finalization, see examples for details).\u003c/p\u003e\n\u003cp\u003eFor example, the RunSpiralAnalysis routine is called by the RunAnalysis method in FilamentSpecies, which uses the Filament .spec file as an input to do the necessary analysis, whose results are placed into a new file ending in filament.spiral. See Filament and FilamentSpecies for examples of how analyses can be initialized, processed, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe directory structure is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esimcore\n\u251c\u2500\u2500 include\n\u2502 \u2514\u2500\u2500 simcore\n\u2502 \u2514\u2500\u2500 (header files)\n\u251c\u2500\u2500 src\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u251c\u2500\u2500 executable\n\u2502 \u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2502 \u2514\u2500\u2500 simcore_main.cpp\n\u2502 \u251c\u2500\u2500 configurator\n\u2502 \u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2502 \u2514\u2500\u2500 configurator.cpp\n\u2502 \u2514\u2500\u2500 (source files)\n\u251c\u2500\u2500 config\n\u2502 \u2514\u2500\u2500 default_config.yaml\n\u251c\u2500\u2500 analysis\n\u2502 \u2514\u2500\u2500 (Python analysis files)\n\u251c\u2500\u2500 scripts\n\u2502 \u2514\u2500\u2500 (utility files)\n\u251c\u2500\u2500 examples\n\u2502 \u2514\u2500\u2500 (parameter file examples)\n\u251c\u2500\u2500 docker\n\u2502 \u2514\u2500\u2500 Dockerfile\n\u251c\u2500\u2500 extern\n\u2502 \u2514\u2500\u2500 KMC\n\u251c\u2500\u2500 tests\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u251c\u2500\u2500 catch2\n\u2502 \u2502 \u2514\u2500\u2500 catch.hpp\n\u2502 \u2514\u2500\u2500 (simcore unit tests)\n\u251c\u2500\u2500 docs\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2514\u2500\u2500 main.md\n\u251c\u2500\u2500 figs\n\u2502 \u2514\u2500\u2500 (example simulation figures)\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 CMakeLists.txt\n\u251c\u2500\u2500 install.sh\n\u251c\u2500\u2500 launch_docker.sh\n\u251c\u2500\u2500 .travis.yml\n\u2514\u2500\u2500 .gitignore\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout simcore\u003c/h2\u003e\u003ca id=\"user-content-about-simcore\" class=\"anchor\" aria-label=\"Permalink: About simcore\" href=\"#about-simcore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore is written in C++ and designed for coarse-grained physics simulations with modularity and scalability in mind. All objects in the simulation are representable as a composite of what I call \"simple\" objects (points, spheres, rigid cylinders, and 2d polygon surfaces would all qualify). For short-range interactions, simcore uses cell and neighbor lists for improved performance and OpenMP for parallelization.\u003c/p\u003e\n\u003cp\u003eAlthough simcore is meant to be a generalized molecular/Brownian dynamics simulation engine, thanks to the narrow focus of my PhD research, it has up until now almost exclusively been used to model semiflexible filaments, and for that reason has come closer to resembling single-purpose software. It\u0027s still quite easy, for example, to use simcore for basic molecular dynamics simulations of interacting point-like particles. Modularity is still there in the basic design, so in the future I may add more object types, but as far as pre-written object types go, \u003cem\u003eit\u0027s all about the filaments\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003esimcore was written for my personal academic use and in its current state is not intended to be used by the general public. If you are insane and would like to run simcore for whatever reason, feel free contact me for help and if I have time I can try to offer assistance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software is licensed under the terms of the BSD-3 Clause license. See the \u003ccode\u003eLICENSE\u003c/code\u003e for more details.\u003c/p\u003e\n", + "full_name": "funkelab/synful", + "latest_release": "v1.0", + "readme": "\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/166422086\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e31cfa1af0774be894dee535edc05a6536309dc42e048f576dc489a330b1f8ec/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3136363432323038362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/166422086.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-synful\" class=\"anchor\" aria-hidden=\"true\" href=\"#synful\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSynful\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eSynful: A project for the automated detection of synaptic partners in Electron Microscopy brain data using U-Nets (type of Convolutional Neural Network).\u003c/p\u003e\n\u003cp\u003eThis repository provides train and predict scripts for synaptic partner detection. For more details, see our \u003ca href=\"https://www.biorxiv.org/content/10.1101/2019.12.12.874172v1\" rel=\"nofollow\"\u003ebioRxiv preprint\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe used the method to predict 244 Million synaptic partners in the full adult fly brain (FAFB) dataset.\nPlease see \u003ca href=\"https://github.com/funkelab/synful_fafb\"\u003ehttps://github.com/funkelab/synful_fafb\u003c/a\u003e for data dissemination and benchmark datasets.\u003c/p\u003e\n\u003cp\u003ePlease don\u0027t hesitate to open\nan issue or write us an email (\u003ca href=\"mailto:buhmannj@janelia.hhmi.org\"\u003eJulia\nBuhmann\u003c/a\u003e or \u003ca href=\"mailto:funkej@janelia.hhmi.org\"\u003eJan\nFunke\u003c/a\u003e) if you have any questions!\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Add train scripts\u003c/li\u003e\n\u003cli\u003e[x] Add inference scripts\u003c/li\u003e\n\u003cli\u003e[x] Add download links for pretrained models\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-method\" class=\"anchor\" aria-hidden=\"true\" href=\"#method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMethod\u003c/h2\u003e\n\u003cp\u003eThe pipeline processes 3D raw data in two steps into synaptic partners:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003einference of a) \u003ccode\u003esyn_indicator_mask\u003c/code\u003e (postsynaptic locations) and b) \u003ccode\u003edirection_vector\u003c/code\u003e (vector pointing from postsynaptic location to its presynaptic partner)\u003c/li\u003e\n\u003cli\u003esynapse extraction: a) locations extractions based on \u003ccode\u003esyn_indicator_mask\u003c/code\u003e and b) finding presynaptic partner based on \u003ccode\u003edirection_vector\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/_static/method_overview.png\"\u003e\u003cimg src=\"docs/_static/method_overview.png\" alt=\"method_figure\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eHardware requirements\n\u003cul\u003e\n\u003cli\u003etraining and prediction requires at least one GPU with sufficient memory (12 GB)\u003c/li\u003e\n\u003cli\u003eFor instance, we mostly used \u003ccode\u003eGeForce GTX TITAN X 12 GB\u003c/code\u003e for our project\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSoftware requirements\n\u003cul\u003e\n\u003cli\u003eSoftware has been tested on Linux (Ubuntu 16.04)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation Guide\u003c/h2\u003e\n\u003cp\u003efrom source (creating a conda env is optional, but recommended).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repository.\u003c/li\u003e\n\u003cli\u003eIn a terminal:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econda_env_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e python=3.6\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econda_env_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e synful\npip install -r requirements.txt\npython setup.py install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are interested in using the package for training and prediction, additionally add tensorflow and funlib.learn.tensorflow to your conda env:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install tensorflow-gpu=1.14 cudatoolkit=10.0\npip install git+git://github.com/funkelab/funlib.learn.tensorflow@0712fee6b6c083c6bfc86e76f475b2e40b3c64f2\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-time\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-time\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall time\u003c/h4\u003e\n\u003cp\u003eInstallation should take around 5 mins (including 3 mins for the tensorflow installation).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining\u003c/h2\u003e\n\u003cp\u003eTraining scripts are found in\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etrain/\u0026lt;setup\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;setup\u0026gt;\u003c/code\u003e is the name of a particular network configuration.\nIn such a directory, you will find two files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003egenerate_network.py\u003c/code\u003e (generates a tensorflow network based on the parameter.json file in the same directoy)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etrain.py\u003c/code\u003e (starts training)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, have a look at the train script in \u003ca href=\"train/setup01\"\u003etrain/setup01/train.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo start training:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython generate_network.py parameter.json\npython train.py parameter.json\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003esetup01: parameter.json is set to train a network on post-synaptic sites (single-task network)\u003c/li\u003e\n\u003cli\u003esetup02: parameter.json is set to train on direction vectors (single-task network)\u003c/li\u003e\n\u003cli\u003esetup03: parameter.json is set to train on both post-synaptic sites and direction vectors (multi-task network)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-details-on-hyperparameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#details-on-hyperparameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetails on hyperparameters\u003c/h4\u003e\n\u003cp\u003eWhen training a network, you can set following hyperparameters in \u003ccode\u003escripts/train/\u0026lt;setup01/02/03\u0026gt;/parameter.json\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eParameters to set the architecture of the network (also see \u003ca href=\"https://github.com/funkelab/funlib.learn.tensorflow/blob/master/funlib/learn/tensorflow/models/unet.py#L506\"\u003edoc\u003c/a\u003e where we create the U-Net)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003einput_size\u003c/code\u003e: the dimensions of the cube that is used as input (called a mini-batch)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edownsample_factor\u003c/code\u003e = [[1, 3, 3], [1, 3, 3], [3, 3, 3]] creates a U-Net with four resolution levels\n\u003cul\u003e\n\u003cli\u003ethe first one being the original resolution, the second one with downsampled feature maps with factos [1, 3, 3] etc.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efmap_num\u003c/code\u003e: Number of feature maps in the first layer (we used 4 in the paper)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efmap_inc_factor\u003c/code\u003e: In each layer, we use \u003ccode\u003efmap_inc_factor\u003c/code\u003e to increase our number of feature maps (we used 5 and 12 in the paper)\n\u003cul\u003e\n\u003cli\u003eEg. if we have \u003ccode\u003efmap_num = 4\u003c/code\u003e and \u003ccode\u003efmap_inc_factor = 5\u003c/code\u003e , we have 20 in our first layer, 100 in our second layer ...\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eunet_model\u003c/code\u003e: vanilla, or dh_unet; vanille=single-task network, dh_unet=multitask network with two different upsampling paths\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTraining parameters\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003elearning_rate\u003c/code\u003e: we used the AdamOptimizer across all experiments, with beta1=0.95,beta2=0.999,epsilon=1e-8\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eST / MT parameters\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eloss_comb_type\u003c/code\u003e: in a multi-task setting, how to combine the two different losses\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003em_loss_scale\u003c/code\u003e : loss weight for post-synaptic mask\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ed_loss_scale\u003c/code\u003e : loss weight for direction vector field\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBalancing parameters needed to account for sparsity of synaptic sites\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ereject_probability\u003c/code\u003e : 0.95 - p_rej in paper --\u0026gt; reject empty mini-batches with probability \u003ccode\u003ereject_probability\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eclip_range\u003c/code\u003e : the loss is scaled with the inverse class frequency ratio of foreground-and background voxels, clipping at \u003ccode\u003eclip_range\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-training-runtime\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-runtime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining runtime\u003c/h4\u003e\n\u003cp\u003eTraining takes between 3 and 10 days (depending on the size of the network), but you should see reasonable results within a day (after 90k iterations).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-monitoring-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#monitoring-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMonitoring Training\u003c/h3\u003e\n\u003cp\u003eTo visualize snapshots that are produced during training use this \u003ca href=\"scripts/visualization/visualize_snapshot.py\"\u003escript\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -i visualize_snapshot.py 300001 setup01\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ein order to load iteration \u003ccode\u003e300001\u003c/code\u003e of training setup \u003ccode\u003esetup01\u003c/code\u003e (use -1 to indicate most recent snapshot)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inference\" class=\"anchor\" aria-hidden=\"true\" href=\"#inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInference\u003c/h2\u003e\n\u003cp\u003eOnce you trained a network, you can use this script to run inference:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd scripts/predict/\npython predict_blockwise.py predict_template.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdapt following parameters in the configfile \u0026lt;scripts/predict/predict_template.json\u0026gt;:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edb_host\u003c/code\u003e --\u0026gt; Put here the name of your running mongodb server (this is used to track which chunks are processed)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eraw_file\u003c/code\u003e --\u0026gt; Put here the filepath of your raw data (as an example you can use the CREMI data that you can download from \u003ca href=\"http://www.cremi.org\" rel=\"nofollow\"\u003ewww.cremi.org\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor a full list of parameters and explanation, see: \u0026lt;scripts/predict/predict_blockwise.py\u0026gt;.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-inference-runtime\" class=\"anchor\" aria-hidden=\"true\" href=\"#inference-runtime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInference runtime\u003c/h4\u003e\n\u003cp\u003eProcessing a CREMI cube (5 microns X 5 microns x 5 microns) takes ~4 minutes on a single GPU.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pretrained-models--original-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#pretrained-models--original-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePretrained Models / Original Setup\u003c/h2\u003e\n\u003cp\u003eWe provide pretrained models, that we discuss in detail in our \u003ca href=\"https://www.biorxiv.org/content/10.1101/2019.12.12.874172v2\" rel=\"nofollow\"\u003ebioRxiv preprint\u003c/a\u003e. You will find the results of our gridsearch and the parameters that we used in Figure 3 \u003ccode\u003eValidation results on CREMI dataset\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWe provide four models that you can download from \u003ca href=\"https://www.dropbox.com/s/301382766164ism/pretrained.zip?dl=0\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease extract the zip file into \u0026lt;scripts/train/\u0026gt; of this repository, this will add for each model a setup directory with the necassary config files, tensorflow checkpoint and predict script.\u003c/p\u003e\n\u003cp\u003eFor instance for \u003ccode\u003ep_setup52\u003c/code\u003e (marked orange in Figure 3, one of the best performing models), you will get all relevant files in \u0026lt;scripts/train/p_setup52\u0026gt;.\nTo run inference, you have to change the setup parameter in the predict config file to \u003ccode\u003ep_setup52\u003c/code\u003e and proceed according to \u003ca href=\"#Inference\"\u003einference section\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-details-about-the-provided-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#details-about-the-provided-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetails about the provided models\u003c/h4\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003esetup\u003c/th\u003e\n\u003cth\u003especs\u003c/th\u003e\n\u003cth\u003ef-score with seg\u003c/th\u003e\n\u003cth\u003ef-score without\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ep_setup52 (+p_setup10)\u003c/td\u003e\n\u003ctd\u003ebig, curriculum, CE, ST\u003c/td\u003e\n\u003ctd\u003e0.76\u003c/td\u003e\n\u003ctd\u003e0.74\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ep_setup51\u003c/td\u003e\n\u003ctd\u003ebig, curriculum, CE, MT_2\u003c/td\u003e\n\u003ctd\u003e0.76\u003c/td\u003e\n\u003ctd\u003e0.73\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ep_setup54 (+p_setup05)\u003c/td\u003e\n\u003ctd\u003esmall, curriculum, MSE, ST\u003c/td\u003e\n\u003ctd\u003e0.76\u003c/td\u003e\n\u003ctd\u003e0.7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ep_setup45 (+p_setup05)\u003c/td\u003e\n\u003ctd\u003esmall, standard, MSE, MT2\u003c/td\u003e\n\u003ctd\u003e0.73\u003c/td\u003e\n\u003ctd\u003e0.68\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote, that for the models that have an underlying ST architecture we also indicate the setup for the corresponding direction-vector-models (p_setup05+p_setup10).\nIf you want to use the model with highest accuracy, pick \u003ccode\u003ep_setup52\u003c/code\u003e; If you want to use a model that gives reasonnable results, but also has fast inference runtime, pick \u003ccode\u003ep_setup54\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-details-about-experiments-that-were-done-to-produce-above-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#details-about-experiments-that-were-done-to-produce-above-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetails about experiments that were done to produce above models\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003edataset: As noted in the paper, we used a realigend version of the original CREMI datasets for training. You can download the data from \u003ca href=\"https://www.dropbox.com/s/i858mrs6s0rj0rt/groundtruth.tar.gz?dl=0\" rel=\"nofollow\"\u003ehere\u003c/a\u003e (cremi_v01 is the correct folder).\nThis data also contains the masks that were used to cover training/validation region in the data. (Note: It is a bit more annoying to work with this realigned data, as the mask is not cube/cuboid-shaped.)\u003c/li\u003e\n\u003cli\u003ehere is the original code for training, evaluation and inference: \u003ca href=\"https://zenodo.org/record/4635362#.YmufZBxBzCI\" rel=\"nofollow\"\u003ehttps://zenodo.org/record/4635362#.YmufZBxBzCI\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eoriginal gridsearch was carried out using luigi (\u003ca href=\"https://luigi.readthedocs.io/en/stable/index.html\" rel=\"nofollow\"\u003ehttps://luigi.readthedocs.io/en/stable/index.html\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 3, + "subscribers_count": 6, "topics": [], - "updated_at": 1718976977.0 + "updated_at": 1677221227.0 }, { "data_format": 2, - "description": "nextflow pipeline to automate analysis using ALE (https://github.com/ssolo/ALE)", + "description": "SC17 tutorial - \"HPC via HTTP: Portable, Scalable Computing using App Containers and the Agave API\"", "filenames": [ - "Singularity" + "content/images/funwave-tvd/Singularity" ], - "full_name": "maxemil/ALE-pipeline", + "full_name": "agaveplatform/SC17-container-tutorial", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eALE-pipeline\u003c/h1\u003e\u003ca id=\"user-content-ale-pipeline\" class=\"anchor\" aria-label=\"Permalink: ALE-pipeline\" href=\"#ale-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ethis is supposed to be a nice pipeline for running ALE on several gene clusters and collecting the results\u003c/li\u003e\n\u003cli\u003eit can also test several species trees at the same time\u003c/li\u003e\n\u003cli\u003eall parameters in the nextflow.config file can be changed on the command line, e.g. the name of the outgroup taxa\u003c/li\u003e\n\u003cli\u003eyou need to add the Python_lib repo to your Pythonpath\u003c/li\u003e\n\u003cli\u003eFor typical usage and a small tutorial, see TUTORIAL.md\u003c/li\u003e\n\u003cli\u003eI use to code the names for species both in the species and in the gene tree to avoid that source of errors\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTroubleshooting\u003c/h1\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you get an Error \u0027Can\u0027t root with myself\u0027 or similar, this usually means that the outgroup you specified for the species tree is not monophyletic in that tree. Try rerooting by hand first...\u003c/li\u003e\n\u003cli\u003eALE sometime simply crashes, then the pipeline can be resumed by adding \u003ccode\u003e-resume\u003c/code\u003e to the invocation\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc-via-http-portable-scalable-computing-using-app-containers-and-the-agave-api\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-via-http-portable-scalable-computing-using-app-containers-and-the-agave-api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC via HTTP: Portable, Scalable Computing using App Containers and the Agave API\u003c/h1\u003e\n\u003cp\u003eSupercomputing matters. So does user experience. Standing between the mainstream adoption of supercomputing and a new generation of users is the reality that the entry cost to using these systems, both in terms of dollars and in time spent learning the technology, has not significantly changed in the last 20 years. The rise of cloud computing only complicates the learning curve further. Over the last 6 years, the authors have been addressing this gap through the development of a Science-as-a-Service platform enabling users to go from their desktop, to their local data center, to the cloud, and back without sacrificing their existing tool chain or user experience.\u003c/p\u003e\n\u003cp\u003eIn this tutorial, we combine best practices and lessons learned while on-boarding the last 70k new users to TACC\u2019s data center through the Agave Platform. Participants will walk through the process of scaling their application from a local environment to the Jetstream academic cloud and to a high performance computing system at the Texas Advanced Computing Center. They will learn to use multiple container technologies to harmonize app execution between cloud and HPC resources, and they will learn to use modern APIs to orchestrate job execution, capture provenance information, and foster collaboration.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-preview\" class=\"anchor\" aria-hidden=\"true\" href=\"#preview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreview\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"http://www.youtube.com/watch?v=hVnIrjn_aBI\" title=\"HPC via HTTP: Portable, Scalable Computing using App Containers and the Agave API\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c65f2550bc90ecabb4429c52335a19f58b324a579f4cb9a3f92dfcab4bc7391f/687474703a2f2f696d672e796f75747562652e636f6d2f76692f68566e49726a6e5f6142492f6d617872657364656661756c742e6a7067\" alt=\"Intro Video\" data-canonical-src=\"http://img.youtube.com/vi/hVnIrjn_aBI/maxresdefault.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-schedule\" class=\"anchor\" aria-hidden=\"true\" href=\"#schedule\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSchedule\u003c/h1\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003cth\u003eTime\u003c/th\u003e\n \u003cth\u003ePresenterr\u003c/th\u003e\n \u003cth\u003eTopic\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e08:30 - 08:45\u003c/td\u003e\n \u003ctd\u003eJohn, Steve\u003c/td\u003e\n \u003ctd\u003e[Introductions](01%20Introduction.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e08:45 - 09:05\u003c/td\u003e\n \u003ctd\u003eRion\u003c/td\u003e\n \u003ctd\u003e[Agave Overview](02%20Agave%20Overview.pdf)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e09:05 - 09:15\u003c/td\u003e\n \u003ctd\u003eKathy\u003c/td\u003e\n \u003ctd\u003e[Jupyter, Sanbox, and Logging In](03%20Jupyter%2C%20Sandboxes%2C%20and%20Logging%20In.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e09:15 - 09:30\u003c/td\u003e\n \u003ctd\u003eSteve\u003c/td\u003e\n \u003ctd\u003e[Code, Build, and Test](04%20Code%20Build%20and%20Test.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e09:30 - 10:00\u003c/td\u003e\n \u003ctd\u003eRion, John\u003c/td\u003e\n \u003ctd\u003e[Hands on with Agave](05%20Hands%20on%20with%20Agave.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e10:00 - 10:30\u003c/td\u003e\n \u003ctd\u003e--\u003c/td\u003e\n \u003ctd\u003eBreak\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e10:30 - 11:00\u003c/td\u003e\n \u003ctd\u003eSteve,John\u003c/td\u003e\n \u003ctd\u003e[Docker and Singularity](06%20Docker%20and%Singularity.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e11:00 - 11:15\u003c/td\u003e\n \u003ctd\u003eRion\u003c/td\u003e\n \u003ctd\u003e[Automation an Benchmarking](07%20Automation%20and%20Benchmarking.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e11:15 - 11:45\u003c/td\u003e\n \u003ctd\u003eKathy, Rion\u003c/td\u003e\n \u003ctd\u003e[Packaging, publishing, and Portability](08%20Packaging%20publishing%20and%20Portability.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e11:45 - 12:00\u003c/td\u003e\n \u003ctd\u003eSteve, John\u003c/td\u003e\n \u003ctd\u003e[Future Directions and Homework)[09%20Future%20Directions%20and%20Homework.ipynb]\u003c/td\u003e\n \u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003ch1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e01: \u003ca href=\"01-Requirements-and-Preparation.md\"\u003eRequirements and Preparation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e02: \u003ca href=\"02-Installation-and-Infrastructure.md\"\u003eInstallation and Infrastructure\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e03: \u003ca href=\"03-Auth-Notebooks-and-Web-Console.md\"\u003eAuth, Notebooks, and the Web Interface\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e04: \u003ca href=\"04-SciOps-and-Sample-Application.md\"\u003eSciOps and our Sample Application\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e05: \u003ca href=\"05-Code-Build-and-Run-Locally.md\"\u003eCode, Build, and Run Locally\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e06: \u003ca href=\"06-Containerize-Existing-Applications.md\"\u003eContainerize Existing Applications\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e07: \u003ca href=\"07-Automation-Registries-and-App-Catalogues\"\u003eAutomation, Registries, and App Catalogues\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003eAgave\u003c/li\u003e\n\u003cli\u003eCI/CD\u003c/li\u003e\n\u003cli\u003eImage publishing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003e08: \u003ca href=\"\"\u003eScaling and Portability\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003eImage caching\u003c/li\u003e\n\u003cli\u003eRuntime environments\u003c/li\u003e\n\u003cli\u003eData scheduling\u003c/li\u003e\n\u003cli\u003eReproducibility anti-patterns\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003e09: \u003ca href=\"\"\u003eViewing simulation results, sharing, provenance\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e10: \u003ca href=\"\"\u003ePackaging and Publishing Experiments\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e11: \u003ca href=\"\"\u003eBenchmarking and Performance Considerations\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e12: \u003ca href=\"\"\u003eFunctions, Microcodes, and Exascale\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e13: \u003ca href=\"\"\u003eHomework an Further Reading\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e90: \u003ca href=\"90-Appendix-A.md\"\u003eAppendix A\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e99: \u003ca href=\"99-References.md\"\u003eReferences\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, - "topics": [ - "nextflow", - "evolution", - "bioinformatics", - "singularity-container", - "pipeline" - ], - "updated_at": 1715001082.0 + "subscribers_count": 7, + "topics": [], + "updated_at": 1529379287.0 }, { "data_format": 2, - "description": "Modular application recipes for the Scientific Filesystem (SCIF)", + "description": "Contains various scripts and useful tidbits", "filenames": [ - "_posts/tutorials/Singularity.foobar", - "_posts/tutorials/Singularity.cowsay" + "Singularity-DDF-Debian.def" ], - "full_name": "sci-f/apps", + "full_name": "ebonnassieux/Scripts", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScientific Filesystem (SCI-F) Apps\u003c/h1\u003e\u003ca id=\"user-content-scientific-filesystem-sci-f-apps\" class=\"anchor\" aria-label=\"Permalink: Scientific Filesystem (SCI-F) Apps\" href=\"#scientific-filesystem-sci-f-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHi there! This is the base for SCI-F apps. We just finished developing the nuts\nand bolts, and will have instructions for contributing soon.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/sci-f/apps\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34851f61ff262e6b32a064b8d7fb55ddb63a4af474c4c9d22b08313bf34af75d/68747470733a2f2f636972636c6563692e636f6d2f67682f7363692d662f617070732e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/sci-f/apps.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"assets/img/app/robots/robot18.png\"\u003e\u003cimg src=\"assets/img/app/robots/robot18.png\" alt=\"robot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContribute an App\u003c/h2\u003e\u003ca id=\"user-content-contribute-an-app\" class=\"anchor\" aria-label=\"Permalink: Contribute an App\" href=\"#contribute-an-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e1. Prepare your Fork\u003c/strong\u003e\nFirst, fork the repo, and clone it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:\u0026lt;username\u0026gt;/apps.git\ncd apps\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTake a look at the folder \u003ccode\u003e_apps\u003c/code\u003e. This is a directory of markdown files, where each directory (and level) corresponds with a category, and each file is associated with one app. Basically, all you need to do is contribute a markdown file! Let\u0027s say we have a workflow app, and we want to add it to a new category, \"flow.\" First let\u0027s make the folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# $PWD is apps\nmkdir _apps/workflow\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e2. Name your App\u003c/strong\u003e\nLet\u0027s now copy the template there to work with. The name of the file will correspond with your app name. If put inside a folder, the folder must also be represented in the file name. Remember that the name is important - it will be the name of the markdown file (without the \u003ccode\u003e.md\u003c/code\u003e extension). For example, to name my app \u003ccode\u003eworkflow-internal-serial\u003c/code\u003e under the folder \u003ccode\u003eworkflow\u003c/code\u003e I would do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp _templates/appname-template.md _apps/workflow/workflow-internal-serial.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere I am anticipating that \"workflow\" is likely to be a general category that others might want to make apps for, so I\u0027m creating it\u0027s own folder. Also remember that the app namespace must be unique, and so names should be very specific. I wouldn\u0027t want to give a general name like \u003ccode\u003eworkflow-run.md\u003c/code\u003e because it is too general. There are likely going to be many workflows. So given a file name, the corresponding app name maps like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e_apps/workflow/workflow-internal-serial.md --\u0026gt; workflow-internal-serial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e3. Customize the File\u003c/strong\u003e\nNext, you should edit the file that you just copied with your app. Let\u0027s take a look:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ---\n title: \"App Name\"\n date: YYYY-MM-DD HH:MM:SS\n author: Vanessa Sochat\n tags: \n - scif\n - singularity\n files:\n - app-file.sh\n - SingularityApp.appname\n ---\n\n ```yaml\n %apprun appname\n exec $SINGULARITY_APPROOT/app-file.sh\n %appfiles appname\n app-file.sh\n ```\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNotice that we have two sections - the top header has metadata, and the bottom is whatever sections you would include in a Singularity Recipe file. You can easily copy paste your container code at the bottom in the \u003ccode\u003eyaml\u003c/code\u003e section, and the only change you might need to make is renaming the app to the one that corresponds with the folder, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun appname --\u0026gt; %apprun workflow-internal-serial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow let\u0027s look at the metadata in the header:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003etitle\u003c/strong\u003e is a human readable title. Make sure it remains in quotes\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003edate\u003c/strong\u003e should correspond to the date that you created or are adding the app.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eauthor\u003c/strong\u003e is your alias\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003etags\u003c/strong\u003e are important - they help to make your app searchable. This should be a yaml list of single terms\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efiles\u003c/strong\u003e are not required, but if you have them, you should create a folder named equivalently to your app (eg, \u003ccode\u003eworkflow-internal-serial\u003c/code\u003e in the same folder as the markdown file, and add the files here. They will be provided if someone downloads your app.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, if you want to provide an isolated recipe for your app (perhaps as a suggested use case) you can add the recipe to a folder named corresponding to your app. Following the current example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir _apps/workflow/workflow-internal-serial\ntouch _apps/workflow/workflow-internal-serial/SingularityApp.workflow-internal-serial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e4. Preview and Submit\u003c/strong\u003e\nYou can preview locally with \u003ccode\u003ebundle exec jekyll serve\u003c/code\u003e. You can also test locally with \u003ccode\u003epython -m unittest tests.test_recipes\u003c/code\u003e. You should then commit changes to your branch, push to Github, and submit a pull request (PR) to the main branch. The same tests will be run, and when the PR is merged, will be live on the site.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it! The robots appreciate your contribution!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelpful Jekyll Tips\u003c/h2\u003e\u003ca id=\"user-content-helpful-jekyll-tips\" class=\"anchor\" aria-label=\"Permalink: Helpful Jekyll Tips\" href=\"#helpful-jekyll-tips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tag to distinguish except from post is \u003ccode\u003e\u0026lt;!--more--\u0026gt;\u003c/code\u003e. If you want to define\na custom one in a post:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexcerpt_separator: \u0026lt;!--readmore--\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScripts\u003c/h1\u003e\n\u003cp\u003eContains various scripts and useful tidbits\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, - "topics": [ - "singularity", - "singularity-containers", - "singularity-container", - "open-science", - "scientific-containers", - "apps", - "sci-f" - ], - "updated_at": 1709665292.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1662645091.0 }, { "data_format": 2, - "description": "A software to partition NGS signal data", + "description": "GRETA (Genetic inteRaction and EssenTiality mApper): An R package for mapping genetic interaction and essentiality networks", "filenames": [ - "Singularity" + "Singularity/Singularity.GRETA.def" ], - "full_name": "romaingroux/SPar-K", - "latest_release": "v1.01", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSPar-K\u003c/h1\u003e\u003ca id=\"user-content-spar-k\" class=\"anchor\" aria-label=\"Permalink: SPar-K\" href=\"#spar-k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSPar-K (Signal Partitioning using K-means) is a modified version of a standard K-means algorithm designed to cluster vectors containing a sequence of signal (that is, the order in which the elements appear in the vectors is meaningful). In order to detect a possible phase shift or orientation inversion between two vectors, this program allows computing distances between two vectors by shifting and flipping them (see below).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSPar-K partitioning procedure\u003c/h2\u003e\u003ca id=\"user-content-spar-k-partitioning-procedure\" class=\"anchor\" aria-label=\"Permalink: SPar-K partitioning procedure\" href=\"#spar-k-partitioning-procedure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSPar-K implements a modified version of the K-means algorithm. In brief, it iteratively partitions a set of genomic regions based on their signal profiles by optimizing K gap-free alignments of the signal in the regions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe data should be stored as a numerical matrix in a simple text file. Each row of the matrix represents a region and each element of a row represents a position along the region. A given element in the matrix represents the amount of signal present at a given position, in a given region. Each row should be stored within the file as a single line. Each row element should be separated from the others by a blank character (space or tab). Finally, each row should have the same length and the matrix should only contains numerical values. No row nor column name are allowed! Here is an example of a valid input (you can find another one in data.txt) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e0 0 0 1 1 2 3 2 1 1 0 0 0\n0 1 1 2 3 2 1 1 0 0 0 0 0\n0 0 4 4 3 2 2 1 1 0 0 0 0\n0 0 0 1 1 2 2 3 3 4 4 0 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis matrix can contain the results of a spatial correlation between two sets of genomic features, for instance ChIP-seq reads (the targets) around +/- 1kb of a set of 1000 TSSs (the references). In that case, the matrix is expected to have 1000 rows (one per TSS) and one column per possible position around these references (here 2001 : 1000 downstream of each TSS, 1 where the TSSs are, 1000 upstream of each TSS). Then, each value of the matrix represents the number of targets (ChIP-seq reads) at a given position (the column) relative to a given reference (TSS). It is also possible to use bins, that is, to summarize several positions within each column, for instance to count the target every 10bp instead of every bp. In this case, each column would represent a bin of 10bp.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePartitioning procedure\u003c/h3\u003e\u003ca id=\"user-content-partitioning-procedure\" class=\"anchor\" aria-label=\"Permalink: Partitioning procedure\" href=\"#partitioning-procedure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, an optional data pre-processing step, to smooth out outliers, row-wise, is available. It allows to minimize the impact of extreme values on the correlation computations and to limit their driving force on the data alignment process.\nThe partition is initialized using a random seeding strategy or using the K-means++ strategy. Each cluster is composed of an alignment of regions (rows) assigned to this cluster and is summarized by the aggregation of the data alignment. The aggregation is a vector having a length equal to the number of columns of the input matrix. It represents the average signal profile over the regions assigned in this cluster. The aggregations are obtained by taking the mean signal at each position (column) in the alignment.\nThen, the partition, is iteratively optimized. At each iteration, each region is compared to each cluster aggregation, using a modified correlation distance allowing shifting and flipping. Both parameters are defined by the user. In brief, the aim is to detect a possible signal shift of inversion between the two vectors. With a shifting freedom S, each region and cluster aggregation, both of lengths L, are broken down into S sub-parts of length L \u2212 S + 1. To compare a region to a cluster, each sub-part of a given region is compared to each sub- part of the given cluster aggregation. The comparison with the lowest correlation distance is stored as well as the offsets at which the region and the cluster aggregation sub-parts started. Flipping is handled by allowing an additional set of comparisons with the reversed (flipped) region sub-part. The region is then assigned to the least dissimilar cluster. Eventually, the K alignments have been updated and allow to recompute the cluster aggregations.\nThis procedure is repeated, optimizing the partition until convergence or until reaching the maximum number of iterations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput\u003c/h3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSPar-K returns a table through the stdout. It contains a header row and as many rows as the input had. Each row contains several parameters for the corresponding reference. It contains 1) the cluster assignment, 2) the shift and flip values describing how the row and the corresponding cluster reference were aligned - that is the coordinate of the 1st element of the cluster reference and of the matrix row used in the comparison leading to assigning this row to the given cluster, whether one of the slice was flipped or not - and 3) the distance computed between these two slices. If flipping is not allowed, then no flipping information is returned.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. To run SPar-K, you have three options. You can either choose to download a release source code, to download a Docker image or a Singularity image. All procedures are detailed below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom the release source code\u003c/h3\u003e\u003ca id=\"user-content-from-the-release-source-code\" class=\"anchor\" aria-label=\"Permalink: From the release source code\" href=\"#from-the-release-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrerequisites\u003c/h4\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo compile and run SPar-K, the following programs and libraries need to be installed on your computer for a proper compilation :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1) Scons v2.3.0 or higher to compile all the program listed above(https://scons.org/pages/download.html) \n2) boost v1.4.1 or higher (https://www.boost.org/)\n3) UnitTest++ v2 (https://github.com/unittest-cpp/unittest-cpp)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Scons configuration files SConstruct and SConscript are configured such that they will look for :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1) boost static libaries in /usr/local/lib/boost and boost header files in /usr/local/include/boost\n2) UnitTest++ static libaries in /usr/local/lib/UnitTest++ and UnitTest++ header files in /usr/local/include/UnitTest++\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eso it is highly recommanded to install these libraries here. Another solution is to modify the SConscript file (located in src/)\nto adapt the library paths (modify the lib_unittest_path and lib_boost_path variable values).\u003c/p\u003e\n\u003cp\u003eThe following softwares and libraries are required to run the auxiliary scripts spark_correct_sga.R and spark_plot_heatmap.R :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1) R version 3.X and Rscript to run these scripts in batch mode\n2) the R libraries optparse and RColorBrewer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompiling\u003c/h4\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce all the libraries are installed, download the source, unzip the archive, cd at the root of the repository (where the SConstruct file is located) and compile using Scons:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eunzip SPar-K-release.zip\ncd SPar-K-release\nscons\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe SPar-K exectuable should be located in bin/. To get SPar-K help, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/spark --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run SPar-K, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/spark \u0026lt;options\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning the tests\u003c/h4\u003e\u003ca id=\"user-content-running-the-tests\" class=\"anchor\" aria-label=\"Permalink: Running the tests\" href=\"#running-the-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAt compilation, a test suite is also compiled and placed in bin/. To run it and test the different components of the code, use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/unittests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-from-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: From the Singularity image\" href=\"#from-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity image is build using \u003ca href=\"https://github.com/romaingroux/SPar-K/releases\"\u003ethe latest release\u003c/a\u003e source code.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrerequisites\u003c/h4\u003e\u003ca id=\"user-content-prerequisites-1\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to have Singularity installed on your machine. Check \u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ethis link\u003c/a\u003e for more informations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePulling the image\u003c/h4\u003e\u003ca id=\"user-content-pulling-the-image\" class=\"anchor\" aria-label=\"Permalink: Pulling the image\" href=\"#pulling-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have a working version of Singularity, you can pull the image from Singularity Hub using this command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name spar-k.simg shub://romaingroux/SPar-K:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning SPar-K from the image\u003c/h4\u003e\u003ca id=\"user-content-running-spar-k-from-the-image\" class=\"anchor\" aria-label=\"Permalink: Running SPar-K from the image\" href=\"#running-spar-k-from-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing SPar-K from Singularity is just as the same as using the compiled executable, excepted that the commands require to contain a call to Singularity. For instance, to get SPar-K help, use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run SPar-K, use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark \u0026lt;options\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom the Docker image\u003c/h3\u003e\u003ca id=\"user-content-from-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: From the Docker image\" href=\"#from-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Docker image is build using \u003ca href=\"https://github.com/romaingroux/SPar-K/releases\"\u003ethe latest release\u003c/a\u003e source code.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrerequisites\u003c/h4\u003e\u003ca id=\"user-content-prerequisites-2\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to have Docker installed on your machine. Check \u003ca href=\"https://www.docker.com/get-started\" rel=\"nofollow\"\u003ethis link\u003c/a\u003e for more informations. Depending on your installation, you may need root privileges.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePulling the image\u003c/h4\u003e\u003ca id=\"user-content-pulling-the-image-1\" class=\"anchor\" aria-label=\"Permalink: Pulling the image\" href=\"#pulling-the-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have a working version of Docker, you can pull the image from Docker Hub using this command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull rgroux/spar-k:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning SPar-K from the image\u003c/h4\u003e\u003ca id=\"user-content-running-spar-k-from-the-image-1\" class=\"anchor\" aria-label=\"Permalink: Running SPar-K from the image\" href=\"#running-spar-k-from-the-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing SPar-K from Docker only requires to deploy a container and call SPar-K. For simplicity, let\u0027s tag the image as \u0027spar-k\u0027 (this will be assumed in all the following Docker related documentation). For instance, to get SPar-K help, use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker tag rgroux/spar-k:latest spar-k\n\ndocker run -i spar-k spark --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou noticed that we called the image by its tag name (spar-k), inside which we ran SPar-K executable (spark).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrograms\u003c/h2\u003e\u003ca id=\"user-content-programs\" class=\"anchor\" aria-label=\"Permalink: Programs\" href=\"#programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following programs are distributed :\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espark\u003c/h3\u003e\u003ca id=\"user-content-spark\" class=\"anchor\" aria-label=\"Permalink: spark\" href=\"#spark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the main program. spark is the partitioning software.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eoptions\u003c/h4\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eshort\u003c/th\u003e\n\u003cth align=\"left\"\u003elong\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u003c/th\u003e\n\u003cth align=\"left\"\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-h\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--help\u003c/td\u003e\n\u003ctd align=\"left\"\u003eProduces the help message\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-v\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--version\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePrints the version number\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-p\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--parallel arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe number of threads dedicated to the computations, by default 1.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-d\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--data arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe data file address.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-r\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--references arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe cluster reference pattern file address.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-i\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--iter arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe maximum number of iterations.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-c\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--cluster arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe number of cluster to find.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-s\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--shift arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eEnables this number of column of shifting freedom. By default, shifting is disabled (equivalent to --shift 1). This option and --width are mutually exclusive\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-w\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--width\u003c/td\u003e\n\u003ctd align=\"left\"\u003eEnables shifting by searching signal profiles of the given width. Setting --width L\u0027 is equivalent to set --shift L-L\u0027+1 where L is the length of each region (the number of columns in the input matrix). By default, the profile width is equal to region width (L). This option and --shift are mutually exclusive.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--flip\u003c/td\u003e\n\u003ctd align=\"left\"\u003eEnables flipping.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--nooutlier\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePre-pcocess the data to smooth out outliers from the data in a row-wise manner. Each row is searched for outliers which are defined as any value bigger/smaller than the row mean +/- 3*row standard deviation. If a value is an outlier it is replaced by the mean of its left and right neighbours. If a has only a left/right neighbour, then the two left/right neighbours are averaged. If several outliers follow each other the above process is applied to the values in a left to right order and at the end the new averaged values may still be outliers.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--dist arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eSpecify which distance should be used during the clustering. It should be \u0027corr\u0027 (by default) or \u0027normcorr\u0027.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--seeding arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eSpecify which method should be used to initialise the cluster references. It should be \u0027random\u0027 or \u0027kmean++\u0027. \u0027random\u0027 will sample k datum as the initial references, with uniform probabilities (by default).\u0027kmean++\u0027 selects k datum using the kmean++ algorithm. It select a first center at random and iteratively select a new center with a probability proportional to the distance of each point to their nearest already choosen center, until k centers have been selected.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--seed arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eA value to seed the random number generator.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--debug\u003c/td\u003e\n\u003ctd align=\"left\"\u003eEnables debuggin verbosity.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning an example\u003c/h3\u003e\u003ca id=\"user-content-running-an-example\" class=\"anchor\" aria-label=\"Permalink: Running an example\" href=\"#running-an-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe file data.txt contains the number reads, from a H3K4me3 ChIP-seq experiment performed in CD4+ cells, that are mapped +/- 1kb around 23360 human TSSs within bins of 100bp (to reproduce this matrix, run \u003ca href=\"https://ccg.vital-it.ch/chipseq/chip_extract.php\" rel=\"nofollow\"\u003eChIP-Extract\u003c/a\u003e example by clicking the \"Example\" button and \"Submit\". Then, remove the first line and column of the resulting matrix which are headers). There are 99 bins per row (49 bins of 100bp upstream the TSS + the central bins containing the TSS + 49 bins upstream of the TSS). Here are the 4 first lines :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e4 2 1 2 6 2 3 6 3 0 0 0 1 1 1 0 1 3 1 4 0 0 0 1 0 1 2 1 1 0 0 1 0 0 1 1 0 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 1 1 0 1 0 0 0 0 1 0 0 1\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 3\n0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 1 2 2 6 0 1 0 0 1 1 3 8 4 1 0 0 1 0 0 1 0 0 0 0 0 0 3 4 1 18 25 13 4 2 3 0 1 2 7 12 3 5 4 2 2 9 10 8 8 9 1 1 1 0 5 5 7 3 2 0 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0\n1 0 0 1 3 5 2 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 2 2 0 0 0 0 2 0 1 1 2 0 12 5 5 0 1 1 0 1 0 1 0 1 1 0 2 0 3 2 1 1 1 1 1 0 0 2 0 0 1 1 0 0 0 1 0 1 0 0 6 1 0 0 0 0 0 0\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe data were organized such that all the TSS are oriented in the same direction. The first bin downstream each TSS is located in column 51. To partition these data into 3 clusters, based on the ChIP-seq profile in these regions, set a reasonable shifting freedom (7 bins, meaning +/-3*20bp) but no flipping (the TSSs are already oriented in the same direction), run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/spark --data data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that data.txt is inside /SPar-K in the image but results.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark --data /SPar-K/data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that data.txt is inside the image but results.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark --data data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe data \u0027data.txt\u0027 are contained within the image and the results are retrieved by a stream redirection so there is no need to create a mount point between the host file system and the image file system yet. However, for cases where the data are in a file outside the image (on the host file system) or when a process inside the image has to write in a file outside the image (to the host file system), this will be required. It can be done as follows :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark --data /mount/data_from_host.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u0026lt;current dir\u0026gt; is the absolute path to the current directory. On linux plateforms, you can use \u0027$(pwd)\u0027. Examples with a mount points can be found below.\u003c/p\u003e\n\u003cp\u003eAs SPar-K implementation is fully multi-threaded, you can speed up the partitioning processes by dispatching the computations on several CPU cores. To do so, you need to use the -p option. For instance, to use 4 concurrent threads :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/spark --data data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 -p 4 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that data.txt is inside /SPar-K in the image but results.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark --data /SPar-K/data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 -p 4 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that data.txt is inside the image but results.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark --data data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 -p 4 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espark_plot_heatmap.R\u003c/h3\u003e\u003ca id=\"user-content-spark_plot_heatmapr\" class=\"anchor\" aria-label=\"Permalink: spark_plot_heatmap.R\" href=\"#spark_plot_heatmapr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis program is an R script. Once a dataset has been partitioned using SPar-K, this script produces a heatmap of the results.\u003c/p\u003e\n\u003cp\u003eLet\u0027s follow again the previous example. Now that you have your partition, you would like to display a nice heatmap. You would like to have the regions grouped by cluster and realigned as SPar-K aligned them. You can produce a plot of the data, realigned and ordered by cluster using :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_plot_heatmap.R --data data.txt --partition results.txt --shift 7 --from -1000 --to 1000 --title \"TSS with H3K4me3\" --output myplot.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that data.txt is inside /SPar-K in the image but myplot.png is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_plot_heatmap.R --data /SPar-K/data.txt --partition results.txt --shift 7 --from -1000 --to 1000 --title \"TSS with H3K4me3\" --output myplot.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that data.txt is inside the image but myplot.png is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark_plot_heatmap.R --data data.txt --partition /mount/results.txt --shift 7 --from -1000 --to 1000 --title \"TSS with H3K4me3\" --output /mount/myplot.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can notice here the use of a mount point to read data from the host file system and to write a file on the host file system.\u003c/p\u003e\n\u003cp\u003eTo get the help, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_plot_heatmap.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_plot_heatmap.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark_plot_heatmap.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espark_correct_sga.R\u003c/h3\u003e\u003ca id=\"user-content-spark_correct_sgar\" class=\"anchor\" aria-label=\"Permalink: spark_correct_sga.R\" href=\"#spark_correct_sgar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis program is an R script. Once a dataset has been partitioned using SPar-K, this scripts allows to update the corresponding SGA file according to the shift and flip values reported by SPar-K.\u003c/p\u003e\n\u003cp\u003eLet\u0027s use the previous partitioning example (again). You have partitioned a dataset containing 23360 rows of length 99 with a shifting freedom of 7 and without flipping, the results are stored in results.txt and the TSS positions in a SGA file named references.sga (\u003ca href=\"https://ccg.vital-it.ch/chipseq/sga_specs.php\" rel=\"nofollow\"\u003eabout the SGA file format\u003c/a\u003e). Here are the first 4 lines :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNC_000001.10\tTSS\t 861123\t+\t1\tSAMD11_1\t2\nNC_000001.10\tTSS\t 874653\t+\t1\tSAMD11_2\t2\nNC_000001.10\tTSS\t 894631\t-\t1\tNOC2L_1\t2\nNC_000001.10\tTSS\t 895964\t+\t1\tKLHL17_1\t2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you can update the positions according to what SPar-K found to be the optimal alignment using :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_correct_sga.R --sga references.sga --partition results.txt --shift 7 --ncol 99 --binSize 20 \u0026gt; references_aligned.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that references.sga is inside /SPar-K in the image but results.sga is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_correct_sga.R --sga /SPar-K/references.sga --partition results.txt --shift 7 --ncol 99 --binSize 20 \u0026gt; results.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that references.sga is inside the image but results.sga is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark_correct_sga.R --sga references.sga --partition /mount/results.txt --shift 7 --ncol 99 --binSize 20 \u0026gt; results.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to correct only the reference positions of regions which were assigned to a given cluster - let\u0027s say cluster 2 - then you can run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_correct_sga.R --sga references.sga --partition results.txt --shift 7 --ncol 99 --binSize 20 --cluster 2 \u0026gt; references_c2_aligned.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that references.sga is inside /SPar-K in the image but results.sga is on the host):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_correct_sga.R --sga /SPar-K/references.sga --partition results.txt --shift 7 --ncol 99 --binSize 20 --cluster 2 \u0026gt; results.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that references.sga is inside the image but results.sga is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark_correct_sga.R --sga references.sga --partition /mount/results.txt --shift 7 --ncol 99 --binSize 20 --cluster 2 \u0026gt; results.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor help, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_correct_sga.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_correct_sga.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark_correct_sga.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espark_realign_data.R\u003c/h3\u003e\u003ca id=\"user-content-spark_realign_datar\" class=\"anchor\" aria-label=\"Permalink: spark_realign_data.R\" href=\"#spark_realign_datar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis program is an R script. It is able to realign the data matrix given a SPar-K partition. That is, the orignal data are shifted and flipped as SPar-K did during the partitioning process. The row order between the input and the output is preservered. However, the row content will be modified as only one sub-part of each original row is present in each output row. Additionally, the sub-part may be flipped (if it was flipped by SPar-K). This script can be useful to realign the data in order to do a figure.\u003c/p\u003e\n\u003cp\u003eLet\u0027s use the previous partitioning example (ad nauseam). You have partitioned a dataset containing 23360 rows of length 99 with a shifting freedom of 7 and without flipping, the results are stored in results.txt. If you are interested in accessing the realigned data (to plot a heatmap for instance), you can get it by invoking:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_realign_data.R --data data.txt --partition results.txt --shift 7 \u0026gt; data_aligned.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that data.txt is inside /SPar-K in the image but data_aligned.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_realign_data.R --data /SPar-K/data.txt --partition results.txt --shift 7 \u0026gt; data_aligned.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that data.txt is inside the image but data_aligned.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark_realign_data.R --data data.txt --partition /mount/results.txt --shift 7 \u0026gt; data_aligned.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor help, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_realign_data.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_realign_data.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark_realign_data.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eRomain Groux\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the GNU General Public License v3 - see the \u003ca href=\"LICENSE.md\"\u003eLICENSE.md\u003c/a\u003e file for details\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePhilipp Bucher\u003c/li\u003e\n\u003cli\u003eRen\u00e9 Dreos\u003c/li\u003e\n\u003cli\u003eGiovanna Ambosini\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "ytakemon/GRETA", + "latest_release": "v0.5.0", + "readme": "\n\n\u003ch1\u003e\u003ca id=\"user-content-greta-genetic-interaction-and-essentiality-mapper\" class=\"anchor\" aria-hidden=\"true\" href=\"#greta-genetic-interaction-and-essentiality-mapper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGRETA: Genetic inteRaction and EssenTiality mApper\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/400c4e52df43f6a0ab8a89b74b1a78d1a64da56a7848b9110c9d2991bb7c3105/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://lifecycle.r-lib.org/articles/stages.html#stable\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d7aedfa6c0fd00737083172bffb7ae9b253b54fae707524fcb503a1ce9c48a66/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6966656379636c652d737461626c652d627269676874677265656e2e737667\" alt=\"Lifecycle: stable\" data-canonical-src=\"https://img.shields.io/badge/lifecycle-stable-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/374398121\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc696cee4b750b415f3666ead55ca691783e528199724ce6e40b14c67836ce80/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3337343339383132312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/374398121.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./GRETA_hex_logo-02.png\"\u003e\u003cimg src=\"./GRETA_hex_logo-02.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGRETA is an R package that leverages data generated by the \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eCancer\nDependency Map (DepMap) project\u003c/a\u003e to perform\nin-silico genetic knockout screens and map essentiality networks. A\nmanuscript describing workflow and usage is being prepared.\u003c/p\u003e\n\u003cp\u003eCurrent DepMap data used by default is version 20Q1, which was\ndownloaded through the DepMap data portal. The data was distributed and\nused under the terms and conditions of \u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC Attribution 4.0\nlicense\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainer\" class=\"anchor\" aria-hidden=\"true\" href=\"#maintainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainer\u003c/h2\u003e\n\u003cp\u003eThis repository is maintained by \u003ca href=\"https://github.com/ytakemon\"\u003eYuka\nTakemon\u003c/a\u003e, a PhD candidate in \u003ca href=\"https://www.bcgsc.ca/labs/marra-lab\" rel=\"nofollow\"\u003eDr.\u00a0Marco\nMarra\u003c/a\u003e\u2019s laboratory at \u003ca href=\"https://www.bcgsc.ca/\" rel=\"nofollow\"\u003eCanada\u2019s\nMichael Smith Genome Sciences Centre\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citations\" class=\"anchor\" aria-hidden=\"true\" href=\"#citations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitations\u003c/h2\u003e\n\u003cp\u003eA please find the citation using \u003ccode\u003ecitation(\"GRETA\")\u003c/code\u003e and include the DOI\nat the top of this page. A manuscript describing GRETA and its usage is\nnow available on \u003ca href=\"https://doi.org/10.1101/2022.09.21.508787\" rel=\"nofollow\"\u003eBioRxiv (Takemon, Y. and Marra, MA.,\n2020)\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions\u003c/h2\u003e\n\u003cp\u003ePlease check the \u003ca href=\"https://github.com/ytakemon/GRETA/wiki/Frequently-Asked-Questions\"\u003eFAQ\nsection\u003c/a\u003e\nfor additional information and if you cannot find your answer there or\nhave a request please submit an\n\u003ca href=\"https://github.com/ytakemon/GRETA/issues\"\u003eissue\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-package-installation-and-data-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#package-installation-and-data-download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackage installation and data download\u003c/h2\u003e\n\u003cp\u003eYou can install the GRETA package from \u003ca href=\"https://github.com\"\u003eGitHub\u003c/a\u003e\nwith:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edevtools\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eytakemon/GRETA\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDepMap 20Q1 data and the data documentation files are provided above and\ncan be extracted directly in terminal using the following bash code (not\nin R/RStudio). For other DepMap data versions please refer to the \u003ca href=\"https://github.com/ytakemon/GRETA/wiki/Frequently-Asked-Questions#q-how-to-download-and-use-other-versions-of-depmap-data\"\u003eFAQ\nsection\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make a new directory/folder called GRETA_project and go into directory\u003c/span\u003e\nmkdir GRETA_project\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e GRETA_project\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download data and data documentation from the web\u003c/span\u003e\nwget https://github.com/ytakemon/GRETA/raw/main/GRETA_DepMap_20Q1_data.tar.gz\nwget https://github.com/ytakemon/GRETA/raw/main/GRETA_DepMap_20Q1_data_document.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract data and data documentation\u003c/span\u003e\ntar -zxvf GRETA_DepMap_20Q1_data.tar.gz\ntar -zxvf GRETA_DepMap_20Q1_data_document.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA singularity container has also been provided and instructions can be\nfound\n\u003ca href=\"https://github.com/ytakemon/GRETA/wiki/Frequently-Asked-Questions#q-how-to-run-singularity\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-workflows\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflows\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-genetic-interaction-mapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#genetic-interaction-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenetic interaction mapping\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ccode\u003eGRETA\u003c/code\u003e and download accompanying data.\u003c/li\u003e\n\u003cli\u003eSelect mutant cell lines that carry mutations in the gene of\ninterest and control cell lines.\n\u003cul\u003e\n\u003cli\u003e(optional specifications) disease type, disease subtype, amino\nacid change.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDetermine differential expression between mutant and control cell\nline groups.\n\u003cul\u003e\n\u003cli\u003e(optional but recommended).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePerform \u003cem\u003ein silico\u003c/em\u003e genetic screen.\u003c/li\u003e\n\u003cli\u003eVisualize results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-co-essential-network-mapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#co-essential-network-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCo-essential network mapping\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ccode\u003eGRETA\u003c/code\u003e and download accompanying data.\u003c/li\u003e\n\u003cli\u003eRun correlation coefficient analysis.\u003c/li\u003e\n\u003cli\u003eCalculate inflection points of negative/positive curve to determine\na threshold.\u003c/li\u003e\n\u003cli\u003eApply threshold.\u003c/li\u003e\n\u003cli\u003eVisualize results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-identifying-arid1a-genetic-interactions\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-identifying-arid1a-genetic-interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Identifying \u003cem\u003eARID1A\u003c/em\u003e genetic interactions\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eARID1A\u003c/em\u003e encodes a member of the chromatin remodeling SWItch/Sucrose\nNon-Fermentable (SWI/SNF) complex and is a frequently mutated gene in\ncancer. It is known that \u003cem\u003eARID1A\u003c/em\u003e and its homolog, \u003cem\u003eARID1B\u003c/em\u003e, are\nsynthetic lethal to one another: The dual loss of ARID1A and its\nhomolog, ARID1B, in a cell is lethal; however, the loss of either gene\nalone is not (\u003ca href=\"https://doi.org/10.1038/nm.3480\" rel=\"nofollow\"\u003eHelming et al., 2014\u003c/a\u003e).\nThis example will demonstrate how we can identify synthetic lethal\ninteractors of \u003cem\u003eARID1A\u003c/em\u003e using \u003ccode\u003eGRETA\u003c/code\u003e and predict this known\ninteraction.\u003c/p\u003e\n\u003cp\u003eFor this example you will need to call the following libraries\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003etidyverse\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2500\u2500 Attaching packages \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse 1.3.2 \u2500\u2500\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 ggplot2 3.3.6 \u2714 purrr 0.3.4\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 tibble 3.1.8 \u2714 dplyr 1.0.9\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 tidyr 1.2.0 \u2714 stringr 1.4.0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 readr 2.1.2 \u2714 forcats 0.5.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2500\u2500 Conflicts \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse_conflicts() \u2500\u2500\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2716 dplyr::filter() masks stats::filter()\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2716 dplyr::lag() masks stats::lag()\u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003eGRETA\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, assign a variable that points to where the \u003ccode\u003e.rda\u003c/code\u003e files are\nstored.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/GRETA_project/data/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-exploring-cell-lines\" class=\"anchor\" aria-hidden=\"true\" href=\"#exploring-cell-lines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExploring cell lines\u003c/h3\u003e\n\u003cp\u003eOne way to explore cell lines that are available in DepMap is through\ntheir \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eportal\u003c/a\u003e. However, there are some\nsimple built-in methods in GRETA to provide users with a way to glimpse\nthe data using the series of \u003ccode\u003elist_available\u003c/code\u003e functions:\n\u003ccode\u003elist_available_mutations()\u003c/code\u003e, \u003ccode\u003elist_available_cancer_types()\u003c/code\u003e,\n\u003ccode\u003elist_available_cancer_subtypes()\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCurrent DepMap data used by default is version 20Q1, which contains\nwhole-genome sequencing or whole-exome sequencing annotations for \u003ccode\u003e1775\u003c/code\u003e\ncancer cell lines (\u003ccode\u003e1270\u003c/code\u003e cell lines with RNA-seq data, \u003ccode\u003e378\u003c/code\u003e cell lines\nwith quantitative proteomics data, and \u003ccode\u003e739\u003c/code\u003e cell lines with CRISPR-Cas9\nknockout screen data)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Find ARID1A hotspot mutations detected in all cell lines\u003c/span\u003e\nlist_available_mutations(\u003cspan class=\"pl-v\"\u003eGene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eIs_hotspot\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e) \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e List all available cancer types\u003c/span\u003e\nlist_available_cancer_types(\u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Ovarian Cancer\" \"Leukemia\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] \"Colon/Colorectal Cancer\" \"Skin Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] \"Lung Cancer\" \"Bladder Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] \"Kidney Cancer\" \"Breast Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] \"Pancreatic Cancer\" \"Myeloma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] \"Brain Cancer\" \"Sarcoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [13] \"Lymphoma\" \"Bone Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [15] \"Fibroblast\" \"Gastric Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [17] \"Engineered\" \"Thyroid Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [19] \"Neuroblastoma\" \"Prostate Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [21] \"Rhabdoid\" \"Gallbladder Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [23] \"Endometrial/Uterine Cancer\" \"Head and Neck Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [25] \"Bile Duct Cancer\" \"Esophageal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [27] \"Liver Cancer\" \"Cervical Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [29] \"Immortalized\" \"Unknown\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [31] \"Eye Cancer\" \"Adrenal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [33] \"Liposarcoma\" \"Embryonal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [35] \"Teratoma\" \"Non-Cancerous\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [37] NA\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e List all available cancer subtypes\u003c/span\u003e\nlist_available_cancer_subtypes(\u003cspan class=\"pl-v\"\u003einput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eLung Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Non-Small Cell Lung Cancer (NSCLC), Adenocarcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [2] \"Non-Small Cell Lung Cancer (NSCLC), Large Cell Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] \"Mesothelioma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [4] \"Small Cell Lung Cancer (SCLC)\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] \"Non-Small Cell Lung Cancer (NSCLC), unspecified\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [6] \"Non-Small Cell Lung Cancer (NSCLC), Squamous Cell Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] \"Non-Small Cell Lung Cancer (NSCLC), Adenosquamous Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [8] \"Carcinoid\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] \"Non-Small Cell Lung Cancer (NSCLC), Bronchoalveolar Carcinoma\"\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [10] \"Non-Small Cell Lung Cancer (NSCLC), Mucoepidermoid Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] \"Carcinoma\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-selecting-mutant-and-control-cell-line-groups\" class=\"anchor\" aria-hidden=\"true\" href=\"#selecting-mutant-and-control-cell-line-groups\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSelecting mutant and control cell line groups\u003c/h3\u003e\n\u003cp\u003eAs default \u003ccode\u003eselect_cell_lines()\u003c/code\u003e will identify cancer cell lines with\nloss-of-function alterations in the gene specified and group them into\nsix different groups.\u003c/p\u003e\n\u003cp\u003eLoss-of-function alterations include variants that are annotated as:\n\u003ccode\u003e\"Nonsense_Mutation\", \"Frame_Shift_Ins\", \"Splice_Site\", \"De_novo_Start_OutOfFrame\", \"Frame_Shift_Del\", \"Start_Codon_SNP\", \"Start_Codon_Del\",\u003c/code\u003e\nand \u003ccode\u003e\"Start_Codon_Ins\"\u003c/code\u003e. Copy number alterations are also taken into\nconsideration and group as \u003ccode\u003e\"Deep_del\", \"Loss\", \"Neutral\",\u003c/code\u003e or\n\u003ccode\u003e\"Amplified\"\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe cell line groups assigned by default are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eControl\u003c/code\u003e cell lines do not harbor any single nucleotide variations\n(SNVs) or insertions and deletions (InDels) with a neutral copy\nnumber (CN).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHomDel\u003c/code\u003e cell lines harbor one or more homozygous deleterious SNVs\nor have deep CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eT-HetDel\u003c/code\u003e cell lines harbor two or more heterozygous deleterious\nSNVs/InDels with neutral or CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHetDel\u003c/code\u003e cell lines harbor one heterozygous deleterious SNV/InDel\nwith neutral CN, or no SNV/InDel with CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAmplified\u003c/code\u003e cell lines harbor no SNVs/InDels with increased CN.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eOthers\u003c/code\u003e cell lines harbor deleterious SNVs with increased CN.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_cell_lines(\u003cspan class=\"pl-v\"\u003eInput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Selecting mutant groups for: ARID1A in all cancer cell lines\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Show number of cell lines in each group \u003c/span\u003e\ncount(\u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 6 \u00d7 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Group n\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 Amplified 24\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_HetDel 105\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_HomDel 13\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 ARID1A_T-HetDel 21\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 Control 529\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 Others 47\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional-filter-for-specific-cancer-types\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional-filter-for-specific-cancer-types\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional filter for specific cancer types\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Find pancreatic cancer cell lines with ARID1A mutations\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_pancr_groups\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_cell_lines(\u003cspan class=\"pl-v\"\u003eInput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eInput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePancreatic Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Selecting mutant groups for: ARID1A in Pancreatic Cancer, cell lines\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Show number of cell lines in each group \u003c/span\u003e\ncount(\u003cspan class=\"pl-smi\"\u003eARID1A_pancr_groups\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 5 \u00d7 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Group n\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_HetDel 7\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_HomDel 4\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_T-HetDel 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 Control 18\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 Others 1\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-check-for-differential-expression\" class=\"anchor\" aria-hidden=\"true\" href=\"#check-for-differential-expression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheck for differential expression\u003c/h3\u003e\n\u003cp\u003eOf the three mutant cancer cell line groups \u003ccode\u003eARID1A_HomDel\u003c/code\u003e,\n\u003ccode\u003eARID1A_T-HetDel\u003c/code\u003e, and \u003ccode\u003eARID1A_HetDel\u003c/code\u003e, cancer cell lines with\n\u003ccode\u003eARID1A_HomDel\u003c/code\u003e mutations are most likely to result in a loss or reduced\nexpression of \u003cem\u003eARID1A\u003c/em\u003e. Therefore, we want to check whether cell lines\nin \u003ccode\u003eARID1A_HomDel\u003c/code\u003e mutant group have significantly less \u003cem\u003eARID1A\u003c/em\u003e RNA or\nprotein expression compared to control cell lines.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Select only HomDel and Control cell lines\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A_HomDel\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Get RNA expression \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e extract_rna_expr(\n \u003cspan class=\"pl-v\"\u003eInput_samples\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eInput_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Following sample did not contain profile data: ACH-001151, ACH-001685, ACH-001956\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNot all cell lines contain RNA and/or protein expression profiles, and\nnot all proteins were detected by mass spectrometer. (Details on data\ngeneration can be found on the \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eDepMap\nsite\u003c/a\u003e.)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Get protein expression\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_protein\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e extract_protein_expr(\n \u003cspan class=\"pl-v\"\u003eInput_samples\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eInput_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Produces an error message since ARID1A protein data is not available\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing Welch\u2019s t-test, we can check to see whether \u003cem\u003eARID1A\u003c/em\u003e RNA\nexpression (in TPM) is significantly reduced in \u003ccode\u003eARID1A_HomDel\u003c/code\u003e cell\nlines compared to \u003ccode\u003eControls\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Append groups and test differential expression\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e left_join(\n \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e,\n \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% select(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e%\n mutate(\u003cspan class=\"pl-v\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e fct_relevel(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e show Control group first\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Joining, by = \"DepMap_ID\"\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e T-test \u003c/span\u003e\nt.test(\u003cspan class=\"pl-smi\"\u003eARID1A_8289\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Welch Two Sample t-test\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; data: ARID1A_8289 by Group\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; t = 5.4354, df = 12.591, p-value = 0.0001276\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; alternative hypothesis: true difference in means is not equal to 0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 95 percent confidence interval:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 0.7574242 1.7621880\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; sample estimates:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; mean in group Control mean in group ARID1A_HomDel \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4.816896 3.557090\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e plot \u003c/span\u003e\nggplot(\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e, aes(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_8289\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\n geom_boxplot()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-Check_expression_rna_stats-1.png\"\u003e\u003cimg src=\"man/figures/README-Check_expression_rna_stats-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-perform-in-silico-genetic-screen\" class=\"anchor\" aria-hidden=\"true\" href=\"#perform-in-silico-genetic-screen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePerform \u003cem\u003ein silico\u003c/em\u003e genetic screen\u003c/h3\u003e\n\u003cp\u003eAfter determining cell lines in the \u003ccode\u003eARID1A_HomDel\u003c/code\u003e group has\nstatistically significant reduction in RNA expression compared to\n\u003ccode\u003eControl\u003c/code\u003e cell lines, the next step is to perform a \u003cem\u003ein silico\u003c/em\u003e genetic\nscreen using \u003ccode\u003escreen_results()\u003c/code\u003e. This uses the dependency probabilities\n(or \u003cstrong\u003e\u201clethality probabilities\u201d\u003c/strong\u003e) generated from DepMap\u2019s genome-wide\nCRISPR-Cas9 knockout screen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLethality probabilities\u003c/strong\u003e range from 0.0 to 1.0 and is quantified for\neach gene knock out in every cancer cell line screened (There are 18,334\ngenes targeted in 739 cancer cell lines). A gene knock out with a\nlethality probability of 0.0 indicates a non-essential for the cell\nline, and a gene knock out with a 1.0 indicates an essential gene (ie.\nvery lethal). Details can be found in \u003ca href=\"https://doi.org/10.1038/ng.3984\" rel=\"nofollow\"\u003eMeyers, R., et al.,\n2017\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAt its core, \u003ccode\u003escreen_results()\u003c/code\u003e performs multiple Mann-Whitney U tests,\ncomparing lethality probabilities of each targeted gene between mutant\nand control groups. This generates a data frame with the following\ncolumns:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGeneName_ID\u003c/code\u003e - Hugo symbol with NCBI gene ID\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGeneNames\u003c/code\u003e - Hugo symbol\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e_median, _mean, _sd, _iqr\u003c/code\u003e - Control and mutant group\u2019s median,\nmean, standard deviation (sd), and interquartile range (iqr) of\ndependency probabilities. Dependency probabilities range from zero\nto one, where one indicates a essential gene (ie. KO of gene was\nlethal) and zero indicates a non-essential gene (KO of gene was not\nlethal)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePval\u003c/code\u003e - P-value from Mann Whitney U test between control and mutant\ngroups.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdj_pval\u003c/code\u003e - BH-adjusted P-value.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog2FC_by_median\u003c/code\u003e - Log2 normalized median fold change of\ndependency probabilities (mutant / control).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog2FC_by_mean\u003c/code\u003e - Log2 normalized mean fold change of dependency\nprobabilities (mutant / control).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCliffDelta\u003c/code\u003e - Cliff\u2019s delta non-parametric effect size between\nmutant and control dependency probabilities. Ranges between -1 to 1.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edip_pval\u003c/code\u003e - Hartigan\u2019s dip test p-value. Tests whether distribution\nof mutant dependency probability is unimodel. If dip test is\nrejected (p-value \u0026lt; 0.05), this indicates that there is a\nmultimodel dependency probability distribution and that there may be\nanother factor contributing to this separation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInteraction_score\u003c/code\u003e - Combined value generated from signed p-values:\n-log10(Pval) * sign(log2FC_by_median). Negative scores indicate\nlethal genetic interaction, and positive scores indicate alleviating\ngenetic interaction.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eARID1A_mutant_IDs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A_HomDel\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% pull(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eARID1A_control_IDs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% pull(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This can take several hours depending on number of lines/cores used. Best to run this overnight.\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e GI_screen(\n \u003cspan class=\"pl-v\"\u003econtrol_IDs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_control_IDs\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003emutant_IDs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_mutant_IDs\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e depends on how many cores you have \u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epath/to/results/folder/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Will save your results here as well as in the variable\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e use TRUE to run a short test to make sure all will run overnight.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can quickly determine whether any lethal genetic interactions were\npredicted by \u003ccode\u003eGRETA\u003c/code\u003e. We use a \u003ccode\u003ePval\u003c/code\u003e cut off of 0.05 and rank based on\nthe \u003ccode\u003eInteraction_score\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \n filter(\u003cspan class=\"pl-smi\"\u003ePval\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.05\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e%\n arrange(\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eInteraction_score\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \n select(\u003cspan class=\"pl-smi\"\u003eGeneNames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eMutant_median\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ePval\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eInteraction_score\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \u003cspan class=\"pl-smi\"\u003ehead\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 6 \u00d7 5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; GeneNames Control_median Mutant_median Pval Interaction_score\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1B 0.0364 0.590 0.0000000342 7.47\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 OR2M3 0.00912 0.0279 0.000255 3.59\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 C1QTNF5 0.0794 0.253 0.000334 3.48\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 LSM1 0.0273 0.112 0.000548 3.26\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 ONECUT1 0.00116 0.00451 0.00107 2.97\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 ANP32B 0.0160 0.0566 0.00119 2.92\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe immediately see that \u003cem\u003eARID1B\u003c/em\u003e, a known synthetic lethal interaction\nof \u003cem\u003eARID1A\u003c/em\u003e, was a the top of this list.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-visualize-screen-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#visualize-screen-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisualize screen results\u003c/h3\u003e\n\u003cp\u003eFinally once the \u003cem\u003ein silico\u003c/em\u003e screen is complete, results can be quickly\nvisualized using \u003ccode\u003eplot_screen()\u003c/code\u003e. Positive genetic interaction scores\nindicate potential synthetic lethal genetic interactors, and negative\nscores indicate potential alleviating genetic interactors. As expected,\nwe identified \u003cem\u003eARID1B\u003c/em\u003e as a synthetic lethal interactor of \u003cem\u003eARID1A\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Visualize results, turn on gene labels, \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e and label three genes each that are predicted to have \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e lethal and alleviating genetic interactions, respectively\u003c/span\u003e\n\nplot_screen(\u003cspan class=\"pl-v\"\u003eresult_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003elabel_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003elabel_n\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-plot-1.png\"\u003e\u003cimg src=\"man/figures/README-plot-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-identifying-arid1a-co-essential-genes\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-identifying-arid1a-co-essential-genes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Identifying \u003cem\u003eARID1A\u003c/em\u003e co-essential genes\u003c/h2\u003e\n\u003cp\u003ePerturbing genes that function in same/synergistic pathways or in the\nsame complex are said to show similar fitness effects, and these that\nshow effects are considered to be \u201cco-essential\u201d. The strategy of\nmapping co-essential gene have been used by several studies to attribute\nfunctions to previously annotated genes as well as to identify a novel\nsubunit of a large complex (\u003ca href=\"https://doi.org/10.1038/s41588-021-00840-z\" rel=\"nofollow\"\u003eWainberg et\nal.\u00a02021\u003c/a\u003e; \u003ca href=\"https://doi.org/10.1016/j.cels.2018.04.011\" rel=\"nofollow\"\u003ePan et\nal.\u00a02018\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eGiven that ARID1A is known subunit of the mammalian SWI/SNF complex\n(\u003ca href=\"https://doi.org/10.1016/j.cell.2018.09.032\" rel=\"nofollow\"\u003eMashtalir et al.\u00a02018\u003c/a\u003e),\nwe expect that members of the SWI/SNF complex would share\nco-essentiality with \u003cem\u003eARID1A\u003c/em\u003e. This example will demonstrate how we can\nmap \u003cem\u003eARID1A\u003c/em\u003e\u2019s co-essential gene network using \u003ccode\u003eGRETA\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-identifying-genes-with-highest-correlation-coefficients\" class=\"anchor\" aria-hidden=\"true\" href=\"#identifying-genes-with-highest-correlation-coefficients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIdentifying genes with highest correlation coefficients\u003c/h2\u003e\n\u003cp\u003eTo determine co-essential genes, we will perform multiple Pearson\ncorrelation coefficient analyses between \u003cem\u003eARID1A\u003c/em\u003e KO effects and the KO\neffects of all 18,333 genes. A cut off will be determined by calculating\nthe inflection point of the ranked coefficient curve. As expected find\nSWI/SNF subunit encoding genes, \u003cem\u003eSMARCE1\u003c/em\u003e and \u003cem\u003eSMARCB1\u003c/em\u003e, as the top two\nco-essential genes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Map co-essential genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e coessential_map(\n \u003cspan class=\"pl-v\"\u003eInput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epath/to/results/folder/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Calculate inflection points of positive and negative curve using co-essential gene results.\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_inflection_points(\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, we annotate the data frame containing the co-essential network\ndata and visualize.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Combine and annotate data frame containing co-essential genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e annotate_coessential_df(\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e)\n\nplot_coessential_genes(\n \u003cspan class=\"pl-v\"\u003eresult_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003einflection_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003elabel_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Should gene names be labeled?\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003elabel_n\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Number of genes to display from each end\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-combine_n_visualize-1.png\"\u003e\u003cimg src=\"man/figures/README-combine_n_visualize-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe also see that the top ten \u003cem\u003eARID1A\u003c/em\u003e co-essential genes include eight\nknown SWI/SNF subunits, namely \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003eSMARCE1\u003c/em\u003e, \u003cem\u003eSMARCB1\u003c/em\u003e,\n\u003cem\u003eSMARCC1\u003c/em\u003e, \u003cem\u003eDPF2\u003c/em\u003e, \u003cem\u003eSS18\u003c/em\u003e, \u003cem\u003eSMARCC2\u003c/em\u003e, and \u003cem\u003eSMARCD2\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Show top 10 co-essential genes. \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% arrange(\u003cspan class=\"pl-smi\"\u003eRank\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% head(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 10 \u00d7 13\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; GeneNameID_A GeneNa\u2026\u00b9 estim\u2026\u00b2 stati\u2026\u00b3 p.value param\u2026\u2074 conf.\u2026\u2075 conf.\u2026\u2076 method\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;int\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;chr\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_8289 ARID1A_\u2026 1 Inf 0 724 1 1 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_8289 SMARCE1\u2026 0.508 15.9 7.70e-49 724 0.452 0.560 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_8289 SMARCB1\u2026 0.488 15.0 1.07e-44 724 0.430 0.541 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 ARID1A_8289 SMARCC1\u2026 0.436 13.0 4.79e-35 724 0.375 0.493 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 ARID1A_8289 DPF2_59\u2026 0.395 11.6 1.62e-28 724 0.332 0.455 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 ARID1A_8289 SS18_67\u2026 0.300 8.47 1.32e-16 724 0.233 0.365 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 7 ARID1A_8289 SMARCC2\u2026 0.248 6.88 1.34e-11 724 0.178 0.315 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 8 ARID1A_8289 SMARCD2\u2026 0.227 6.27 6.16e-10 724 0.157 0.295 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 9 ARID1A_8289 IER5L_3\u2026 0.210 5.78 1.12e- 8 724 0.139 0.279 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 10 ARID1A_8289 PRDM15_\u2026 0.206 5.66 2.20e- 8 724 0.135 0.274 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # \u2026 with 4 more variables: alternative \u0026lt;chr\u0026gt;, Rank \u0026lt;int\u0026gt;, Padj_BH \u0026lt;dbl\u0026gt;,\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # Candidate_gene \u0026lt;lgl\u0026gt;, and abbreviated variable names \u00b9\u200bGeneNameID_B,\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # \u00b2\u200bestimate, \u00b3\u200bstatistic, \u2074\u200bparameter, \u2075\u200bconf.low, \u2076\u200bconf.high\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # \u2139 Use `colnames()` to see all variable names\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-session-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#session-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSession Info\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003esessionInfo()\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; R version 4.0.2 (2020-06-22)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Platform: x86_64-centos7-linux-gnu (64-bit)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Running under: CentOS Linux 7 (Core)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Matrix products: default\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; BLAS: /gsc/software/linux-x86_64-centos7/R-4.0.2/lib64/R/lib/libRblas.so\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; LAPACK: /gsc/software/linux-x86_64-centos7/R-4.0.2/lib64/R/lib/libRlapack.so\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; locale:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] LC_PAPER=en_US.UTF-8 LC_NAME=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] LC_ADDRESS=C LC_TELEPHONE=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; attached base packages:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] stats graphics grDevices utils datasets methods base \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; other attached packages:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] GRETA_0.4.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] ggplot2_3.3.6 tidyverse_1.3.2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; loaded via a namespace (and not attached):\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] matrixStats_0.62.0 fs_1.5.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] doMC_1.3.8 lubridate_1.8.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] doParallel_1.0.17 httr_1.4.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] tools_4.0.2 backports_1.4.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] utf8_1.2.2 R6_2.5.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] nortest_1.0-4 DBI_1.1.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [13] colorspace_2.0-3 withr_2.5.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [15] tidyselect_1.1.2 Exact_3.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [17] compiler_4.0.2 rcompanion_2.4.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [19] cli_3.3.0 rvest_1.0.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [21] expm_0.999-6 xml2_1.3.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [23] sandwich_3.0-2 labeling_0.4.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [25] inflection_1.3.6 diptest_0.76-0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [27] scales_1.2.0 lmtest_0.9-40 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [29] mvtnorm_1.1-3 proxy_0.4-27 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [31] multcompView_0.1-8 RootsExtremaInflections_1.2.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [33] digest_0.6.29 rmarkdown_2.14 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [35] pkgconfig_2.0.3 htmltools_0.5.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [37] highr_0.9 dbplyr_2.2.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [39] fastmap_1.1.0 rlang_1.0.4 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [41] readxl_1.4.0 rstudioapi_0.13 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [43] farver_2.1.1 generics_0.1.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [45] zoo_1.8-10 jsonlite_1.8.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [47] googlesheets4_1.0.0 magrittr_2.0.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [49] modeltools_0.2-23 Matrix_1.4-1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [51] Rcpp_1.0.9 DescTools_0.99.45 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [53] munsell_0.5.0 fansi_1.0.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [55] lifecycle_1.0.1 multcomp_1.4-20 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [57] stringi_1.7.8 yaml_2.3.5 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [59] MASS_7.3-58.1 rootSolve_1.8.2.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [61] plyr_1.8.7 grid_4.0.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [63] parallel_4.0.2 ggrepel_0.9.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [65] crayon_1.5.1 lmom_2.9 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [67] lattice_0.20-45 haven_2.5.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [69] splines_4.0.2 hms_1.1.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [71] knitr_1.39 pillar_1.8.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [73] boot_1.3-28 gld_2.6.5 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [75] stats4_4.0.2 codetools_0.2-18 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [77] reprex_2.0.1 glue_1.6.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [79] evaluate_0.15 data.table_1.14.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [81] modelr_0.1.8 vctrs_0.4.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [83] tzdb_0.3.0 foreach_1.5.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [85] cellranger_1.1.0 gtable_0.3.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [87] assertthat_0.2.1 xfun_0.31 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [89] coin_1.4-2 libcoin_1.0-9 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [91] broom_1.0.0 e1071_1.7-11 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [93] class_7.3-20 survival_3.3-1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [95] googledrive_2.0.0 gargle_1.2.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [97] iterators_1.0.14 TH.data_1.1-1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [99] ellipsis_0.3.2\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 3, - "subscribers_count": 0, - "topics": [], - "updated_at": 1685585499.0 + "subscribers_count": 2, + "topics": [ + "bioinformatics", + "genetic-interactions", + "r" + ], + "updated_at": 1663972680.0 }, { "data_format": 2, - "description": "Dark mAtteR iNvestigator: diving into the dark matter of your COI sequences", + "description": "Work with PLINK from R", "filenames": [ - "Singularity", - "Singularity.test" + "Singularity" ], - "full_name": "hariszaf/darn", - "latest_release": "v1.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/darn_logo.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/darn_logo.png\" width=\"200\" height=\"200\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDARN - Dark mAtteR iNvestigator\u003c/h1\u003e\u003ca id=\"user-content-darn---dark-matter-investigator\" class=\"anchor\" aria-label=\"Permalink: DARN - Dark mAtteR iNvestigator\" href=\"#darn---dark-matter-investigator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the Dark mAtteR iNvestigator (DARN) software package.\u003c/p\u003e\n\u003cp\u003eDARN uses a COI reference tree covering all domains of life (eukaryotes, bacteria, archaea)\nto assign your sequences to the 3 domains of life.\u003c/p\u003e\n\u003cp\u003eIts purpose is not to provide you with certain taxonomic assignment but to give an overview of the species present.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/placements_of_consensus_seqs_transpaernt.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/placements_of_consensus_seqs_transpaernt.png\" width=\"80%\" height=\"85%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edarn\u003c/code\u003e is a available as a container, meaning you need a containerized technology to use it.\nAnd that its only dependency! \ud83e\udd73\u003c/p\u003e\n\u003cp\u003eIf you are about to use \u003ccode\u003edarn\u003c/code\u003e locally, on your \u003cstrong\u003epersonal computer\u003c/strong\u003e, then probably you will find \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e as the easiest way to go for it.\u003c/p\u003e\n\u003cp\u003eHowever, if you are working on a server or in a \u003cstrong\u003eHigh Performance Computing (HPC)\u003c/strong\u003e environment, then \u003ca href=\"https://singularityhub.github.io/\" rel=\"nofollow\"\u003eSingulariy\u003c/a\u003e is the best if not (in cases where your admin does not allow Docker) the oncly choice.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAs a Docker container\u003c/h3\u003e\u003ca id=\"user-content-as-a-docker-container\" class=\"anchor\" aria-label=\"Permalink: As a Docker container\" href=\"#as-a-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install Docker you may follow there instructions \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\ndepending on the operation system you are working at.\u003c/p\u003e\n\u003cp\u003eOnce Docker is set, you may get \u003ccode\u003edarn\u003c/code\u003e as a Docker image by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull hariszaf/darn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAs a Singularity container\u003c/h3\u003e\u003ca id=\"user-content-as-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: As a Singularity container\" href=\"#as-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install Singularity you may follow the instructions described \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e or you can probably ask your sys-admin to do that for you,\nin case you are working in a server, HPC, cloud etc.\u003c/p\u003e\n\u003cp\u003eOnce Singularity is set, you will need to build your \u003ccode\u003e.sif\u003c/code\u003e file based on the Docker image.\nTo do so, you may run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull darn.sif docker://hariszaf/darn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll \u003ccode\u003edarn\u003c/code\u003e needs as input is a fasta \u003ccode\u003einput.fasta\u003c/code\u003e file with the sequnces you wish to run.\u003c/p\u003e\n\u003cp\u003eOverall, running \u003ccode\u003edarn\u003c/code\u003e is a 2-steps process:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003efirst, you need to \u003cstrong\u003emount\u003c/strong\u003e the directory at your actual computer where your \u003ccode\u003einput.fasta\u003c/code\u003e is located (\u003ccode\u003edir\u003c/code\u003e, e.g. \u003ccode\u003e/home/haris/Desktop/my_darn_analysis\u003c/code\u003e) in a certain directory of the container.\nThis works as a \u003cem\u003ebridge\u003c/em\u003e between your computer and the container, allowing the container\nto have access on the files of the directory you mount and vice-versa.\u003c/li\u003e\n\u003cli\u003ethen, you run \u003ccode\u003edarn\u003c/code\u003e from inside the container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eIn all cases\u003c/strong\u003e, \u003ccode\u003edarn\u003c/code\u003e will always return its \u003cstrong\u003eoutput\u003c/strong\u003e in the \u003ccode\u003edir\u003c/code\u003e directory you mount.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun \u003ccode\u003edarn\u003c/code\u003e as a Docker container\u003c/h3\u003e\u003ca id=\"user-content-run-darn-as-a-docker-container\" class=\"anchor\" aria-label=\"Permalink: Run darn as a Docker container\" href=\"#run-darn-as-a-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo mount the directory \u003ccode\u003e\u0026lt;dir\u0026gt;\u003c/code\u003e where your \u003ccode\u003einput.fasta\u003c/code\u003e is located to \u003ccode\u003edarn\u003c/code\u003e\nyou may run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it -v \u0026lt;dir\u0026gt;/:/mnt darn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe \u003ccode\u003e-v\u003c/code\u003e parameter denotes your bridge; the \u003ccode\u003e\u0026lt;dir\u0026gt;\u003c/code\u003e directory of your computer,\nwill be mounted in the \u003ccode\u003e/mnt\u003c/code\u003e path of the \u003ccode\u003edarn\u003c/code\u003e container. Parameter \u003ccode\u003e--rm\u003c/code\u003e is simply there\nto make sure that your Docker container will be completely removed when you will exit from it, while the \u003ccode\u003e-it\u003c/code\u003e enables running shell for the container you are about to initiate.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eOnce the shell is on you just need to run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./darn.sh -s /mnt/input.fasta -t \u0026lt;number_of_threads\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output of \u003ccode\u003edarn\u003c/code\u003e will be returned in the same directory that you mount, i.e you may find your results under \u003ccode\u003edir\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eHere is a shell recording for how to get and run \u003ccode\u003edarn\u003c/code\u003e assuming you have already set Docker on your computing system.\n\u003ca href=\"https://asciinema.org/a/427103\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11aeaca33679821366471381129c11cf29740905151fc47e1484a976bdc78acf/68747470733a2f2f61736369696e656d612e6f72672f612f3432373130332e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/427103.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003emarine_part.fasta\u003c/code\u003e file that is used as an \u003ccode\u003einput.fasta\u003c/code\u003e here, along with the \u003ccode\u003edarn\u003c/code\u003e outcome, can be found under the \u003ca href=\"https://github.com/hariszaf/darn/tree/main/analysis\"\u003e\u003ccode\u003eanalysis\u003c/code\u003e\u003c/a\u003e folder of this repo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun \u003ccode\u003edarn\u003c/code\u003e as a Singularity container\u003c/h3\u003e\u003ca id=\"user-content-run-darn-as-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Run darn as a Singularity container\" href=\"#run-darn-as-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLikewise, you need to first mount the \u003ccode\u003e\u0026lt;dir\u0026gt;\u003c/code\u003e path where you \u003ccode\u003einput.fasta\u003c/code\u003e is located in the container.\u003c/p\u003e\n\u003cp\u003eNow, you may run \u003ccode\u003edarn\u003c/code\u003e with a sole command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind \u0026lt;dir\u0026gt;/:/mnt darn_latest.sif -s /mnt/query_freshwater_short_all_seqs.fasta -t 20\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr, by using the same way as in the Docker case, meaning you first open a shell\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind \u0026lt;dir\u0026gt;/:/mnt darn_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand once shell appears:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /home\n./darn.sh -s /mnt/your_sample.fasta -t \u0026lt;number_of_threads_available\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn case you are are working on a HPC system, depending on the queing system used,\nyou need to build a \u003cem\u003ejob\u003c/em\u003e script first and include the previous commands into it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce \u003ccode\u003edarn\u003c/code\u003e is completed, 2 new directories will be available in the \u003ccode\u003edir\u003c/code\u003e folder:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eone called \u003ccode\u003eintermediate\u003c/code\u003e, including \u003ccode\u003egappa\u003c/code\u003e best and exhaustive hits as well as the \u003ccode\u003e.jplace\u003c/code\u003e tree file with the assignments of the query sequences in the phylogenetic tree\u003c/li\u003e\n\u003cli\u003eand a second one, called \u003ccode\u003efinal_outcome\u003c/code\u003e where a \u003ccode\u003e.json\u003c/code\u003e file with the entries assigned in each domain of life and a \u003ccode\u003e.fasta\u003c/code\u003e file with the sequences assigned in each of these domain can be found, as well as a Krona plot, as \u003ccode\u003e.html\u003c/code\u003e file, representing the composition of the sequence sample based on the number of species found per taxonomic group.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn example of such a Krona plot can be found\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/darn_krona.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/darn_krona.png\" width=\"800\" height=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe first Krona plot, is based on the likelihood values of the best hit assignments of your query sequences.\nThis way, you may have an overview of what is most likely to actually have on your sample.\nFor a more thorough look on such a Krona, just cilck \u003ca href=\"https://htmlpreview.github.io/?https://github.com/hariszaf/darn/blob/main/analysis/final_outcome/darn_marine_part_likelihood.krona_plot.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe second Krona plot, is based on a binary interpretation of the best hits assignments of your sequences, meaning\nthat no matter the likelihood value of the best hit of each query, we treat all the assignments as equal to get a\nquantitative overview of the assignments. For more click \u003ca href=\"https://htmlpreview.github.io/?https://github.com/hariszaf/darn/blob/main/analysis/final_outcome/darn_marine_part_pres_abs.krona_plot.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe Krona plots are accompanied by the profiles they came from and by a \u003ccode\u003e.json\u003c/code\u003e file with all the qurey assignments per domain (Bacteria, Archaea, Eukaryota) and a series of \u003ccode\u003e.fasta\u003c/code\u003e files; one for each domain found, with the sequences that have been assigned to it.\u003c/p\u003e\n\u003cp\u003eYou may have a look on the DARN output over \u003ca href=\"https://github.com/hariszaf/darn/tree/main/analysis\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicences\u003c/h2\u003e\u003ca id=\"user-content-licences\" class=\"anchor\" aria-label=\"Permalink: Licences\" href=\"#licences\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edarn\u003c/code\u003e is under the GNU GPLv3 license (for 3rd party components separate licenses apply).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edarn\u003c/code\u003e makes use of the following software:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://cme.h-its.org/exelixis/web/software/papara/index.html\" rel=\"nofollow\"\u003ePaPaRa\u003c/a\u003e and EPA-ng algorithms.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Pbdas/epa-ng\"\u003eEPA-ng\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lczech/gappa/\"\u003egappa\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/marbl/Krona/wiki\"\u003ekrona\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to cite\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eZafeiropoulos H, Gargan L, Hintikka S, Pavloudi C, Carlsson J. The Dark mAtteR iNvestigator (DARN) tool: getting to know the known unknowns in COI amplicon data. Metabarcoding and Metagenomics. 2021 Mar 11;5:e69657. DOI: \u003ca href=\"https://doi.org/10.3897/mbmg.5.69657\" rel=\"nofollow\"\u003ehttps://doi.org/10.3897/mbmg.5.69657\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "richelbilderbeek/plinkr", + "latest_release": "v0.20.2", + "readme": "\n\u003ch1\u003e\n\u003ca id=\"user-content-plinkr\" class=\"anchor\" href=\"#plinkr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eplinkr\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/plinkr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=master\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/plinkr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8fafd8823f437cd6a912937658b53c50edd357b324f8d239d71a476d11c8859c/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f706c696e6b722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/plinkr/coverage.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=develop\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/plinkr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d136bb6a044d0bd05e2f4f06a5a96494925547304deabd0674fbf0c9c1dd929c/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f706c696e6b722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/plinkr/coverage.svg?branch=develop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWork with PLINK and PLINK2 from R.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDoing the first PLINK example:\n\u003ca href=\"https://youtu.be/LsfKQw2oIUg\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_basic_usage.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with one or more quantitative traits:\n\u003ca href=\"https://youtu.be/IicNdc8sDfI\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_assoc_qt.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with ideal quantitative traits:\n\u003ca href=\"https://youtu.be/oXGy83WiHm4\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_demo_qt_assoc.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulate quantitative traits:\n\u003ca href=\"https://youtu.be/H0XlLVsFry4\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_create_demo_assoc_qt_params.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulate custom traits: \u003ca href=\"https://youtu.be/5X1kLkiQbtw\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e\n\u003ca href=\"http://richelbilderbeek.nl/plinkr_create_custom_trait.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with a binary trait/case-control phenotype:\n\u003ca href=\"https://youtu.be/LhXQcDQvZS0\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_assoc.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"doc/install.md\"\u003edoc/install.md\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-examples\" class=\"anchor\" href=\"#examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-plink\" class=\"anchor\" href=\"#running-plink\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning PLINK\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can seamlessly run any \u003ccode\u003ePLINK\u003c/code\u003e or \u003ccode\u003ePLINK2\u003c/code\u003e versions.\u003c/p\u003e\n\u003cp\u003eRun PLINK:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003eplinkr\u003c/span\u003e)\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo call a specific version of PLINK:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003erun_plink(c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--noweb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), create_plink_v1_7_options())\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, create_plink_v1_9_options())\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, create_plink_v2_0_options())\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOf course, you can also call PLINK to detect genetic associations :-) :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the PLINK v1.9 example files\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_plink_v1_9_options()\n\u003cspan class=\"pl-smi\"\u003eped_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_plink_example_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etoy.ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003emap_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_plink_example_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etoy.map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Do a case-control association\u003c/span\u003e\n\u003cspan class=\"pl-e\"\u003eplinkr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003erun_plink(\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eped_filename\u003c/span\u003e, \n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003emap_filename\u003c/span\u003e\n )\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003ebasic_usage\u003c/code\u003e for basic usage of PLINK, as taken\nfrom the PLINK website, which shows a quantitative trait analysis\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003etest_assoc_qt\u003c/code\u003e for the same basic usage of PLINK,\nusing the \u003ccode\u003eplinkr\u003c/code\u003e interface\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003edemo_assoc_qt\u003c/code\u003e for doing a quantitative trait\nanalysis using simulated data and the \u003ccode\u003eplinkr\u003c/code\u003e interface\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-run-a-quantitative-trait-analysis-on-existing-files\" class=\"anchor\" href=\"#run-a-quantitative-trait-analysis-on-existing-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun a quantitative trait analysis on existing files\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-read-from-plink-text-files\" class=\"anchor\" href=\"#read-from-plink-text-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRead from PLINK text files\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_assoc_qt_data(\n \u003cspan class=\"pl-v\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_plink_text_filenames(\n \u003cspan class=\"pl-v\"\u003emap_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003eped_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n ),\n \u003cspan class=\"pl-v\"\u003ephenotype_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_phenotype_data_filename(\n \u003cspan class=\"pl-v\"\u003ephe_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.phe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \n )\n)\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e assoc_qt(\u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e)\nread_plink_qassoc_file(\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eqassoc_filenames\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-read-from-plink-binary-files\" class=\"anchor\" href=\"#read-from-plink-binary-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRead from PLINK binary files\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_assoc_qt_data(\n \u003cspan class=\"pl-v\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_plink_bin_filenames(\n \u003cspan class=\"pl-v\"\u003ebed_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.bed\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003ebim_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.bim\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003efam_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.fam\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n ),\n \u003cspan class=\"pl-v\"\u003ephenotype_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_phenotype_data_filename(\n \u003cspan class=\"pl-v\"\u003ephe_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.phe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \n )\n)\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e assoc_qt(\u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e)\nread_plink_qassoc_file(\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eqassoc_filenames\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-demonstrate-a-quantitative-trait-analysis\" class=\"anchor\" href=\"#demonstrate-a-quantitative-trait-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemonstrate a quantitative trait analysis\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can seamlessly use \u003ccode\u003ePLINK\u003c/code\u003e/\u003ccode\u003ePLINK2\u003c/code\u003e in-memory-data or files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_demo_assoc_qt_data()\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prove that this is PLINK text data\u003c/span\u003e\ncheck_plink_text_data(\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_test_assoc_qt_params()\nassoc_qt(\n \u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo convert the in-memory data to PLINK binary format and do the same\nquantitative trait analysis:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e convert_plink_text_data_to_plink_bin_data(\n \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e\n)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prove that this is PLINK binary data\u003c/span\u003e\ncheck_plink_bin_data(\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e)\n\nassoc_qt(\n \u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003edemo_assoc_qt\u003c/code\u003e for a walk-through of the data that\nis simulated by default\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003ecreate_demo_assoc_qt_params\u003c/code\u003e for many examples how\ndata can be simulated\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-file-io\" class=\"anchor\" href=\"#file-io\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile I/O\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can read and save many types of PLINK files. Below is an\noverview. List from \u003ca href=\"https://www.cog-genomics.org/plink2/formats\" rel=\"nofollow\"\u003ethe PLINK file format\nreference\u003c/a\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile extension\u003c/th\u003e\n\u003cth\u003e\n\u003ccode\u003eplink\u003c/code\u003e read function\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.adjusted\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_adjusted_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.allele.no.snp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_assoc_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.adjusted\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_assoc_adjusted_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.dosage\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.fisher\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.linear\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.logistic\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.auto.R\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.beagle.dat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bed_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bim\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bin_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.blocks*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.chr-*.dat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.chr-*.map\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.clst\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.clumped*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cluster*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cmh\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cmh2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv.indiv\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv.overlap\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv.summary\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cov\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_cov_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.dfam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.diff\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.dist\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.dupvar\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.eigenvec*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.epi.*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.fam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_fam_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.flipscan\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_frq_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq.cc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq.count\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq.strat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_frq_strat_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frqx\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.fst\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.gen\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.genome\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.grm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.grm.N.bin\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.grm.bin\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.gvar\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.het\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hh\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hom\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" 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fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qfam.*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.range.report\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.raw\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.*.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.phase.inp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.strct_in\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.ref\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.rel\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.rlist\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.sample\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set.{perm,mperm}\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set.table\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.sexcheck\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.simfreq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_simfreq_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tags.list\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tdt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tdt.poo\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tfam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tped\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.traw\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.twolocus\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.var.ranges\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-associations\" class=\"anchor\" href=\"#associations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAssociations\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eAssociation type\u003c/th\u003e\n\u003cth\u003eData type\u003c/th\u003e\n\u003cth\u003eGeneral function\u003c/th\u003e\n\u003cth\u003eSpecialized function\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink_text_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink2_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink_text_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink2_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-plink-and-plink2-files-conversions\" class=\"anchor\" href=\"#plink-and-plink2-files-conversions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePLINK and PLINK2 files conversions\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e allows to convert between any PLINK and PLINK2 files.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFrom\u003c/th\u003e\n\u003cth\u003eTo\u003c/th\u003e\n\u003cth\u003eFunction name\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_files_to_plink_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_files_to_plink2_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_files_to_plink_text_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_files_to_plink2_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink2_bin_files_to_plink_text_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink2_bin_files_to_plink_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink_text_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink2_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003eSAIGE files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003ecreate_bgen_files_for_saige\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK2 VCF files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink_bin_files_to_plink_vcf_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-plink-and-plink2-data-conversions\" class=\"anchor\" href=\"#plink-and-plink2-data-conversions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePLINK and PLINK2 data conversions\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e allows to convert between any PLINK and PLINK2 data.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFrom\u003c/th\u003e\n\u003cth\u003eTo\u003c/th\u003e\n\u003cth\u003eFunction name\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_data_to_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_data_to_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink_bin_data_to_plink_text_data\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_data_to_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink2_bin_data_to_plink_text_data\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink2_bin_data_to_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/search;query=plinkr\" rel=\"nofollow\"\u003eFind the latest \u2018plinkr\u2019 Singularity\ncontainer\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-faq\" class=\"anchor\" href=\"#faq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"doc/faq.md\"\u003edoc/faq.md\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, - "topics": [], - "updated_at": 1643724457.0 + "subscribers_count": 2, + "topics": [ + "gwas", + "plink", + "plink2", + "r", + "r-package" + ], + "updated_at": 1653067706.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "planners/fd-19.06/Singularity-lmcut", + "planners/fd-19.06/Singularity-bjolp", + "planners/fd-19.06/Singularity-fdss1-mas2-60s", + "planners/fd-19.06/Singularity-gbfs-ff", + "planners/fd-19.06/Singularity-lama-first", + "planners/fd-19.06/Singularity-blind", + "planners/fd-19.06/Singularity-fdss1-mas1-60s", + "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_POLY_AGL", + "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_AGL", + "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_POLY_SAT", + "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_SAT", + "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_DFS_AGL", + "planners/ipc2018-agl-lapkt-poly-bfws/Singularity", + "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_CBO", + "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_POLY_AGL", + "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_AGL", + "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_POLY_SAT", + "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_SAT", + "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_DFS_AGL", + "planners/ipc2018-agl-lapkt-dual-bfws/Singularity", + "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_CBO", + "planners/ipc2018-opt-decstar/Singularity-fork", + "planners/ipc2018-opt-decstar/Singularity-star", + "planners/ipc2018-opt-decstar/Singularity", + "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_POLY_AGL", + "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_AGL", + "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_f5_SAT", + "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_POLY_SAT", + "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_f5_AGL", + "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_SAT", + "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_DFS_AGL", + "planners/ipc2018-agl-lapkt-bfws-pref/Singularity", + "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_CBO", + "planners/ipc2018-agl-saarplan/Singularity-fork", + "planners/ipc2018-agl-saarplan/Singularity-grey", + "planners/ipc2018-agl-saarplan/Singularity", + "planners/ipc2018-opt-scorpion/Singularity", + "planners/ipc2014-agl-mercury/Singularity", + "planners/ipc2018-opt-complementary2-3584mb/Singularity-180s", + "planners/ipc2018-opt-complementary2-3584mb/Singularity", + "planners/ipc2018-agl-fd-remix/Singularity", + "planners/ipc2018-opt-delfi1/Singularity-mas-miasm", + "planners/ipc2018-opt-delfi1/Singularity-mas-sccdfp-60s", + "planners/ipc2018-opt-delfi1/Singularity-celmcut", + "planners/ipc2018-opt-delfi1/Singularity-ipdb", + "planners/ipc2018-opt-delfi1/Singularity-ipdb-60s", + "planners/ipc2018-opt-delfi1/Singularity-mas-sccdfp", + "planners/ipc2018-opt-delfi1/Singularity-blind", + "planners/ipc2018-opt-delfi1/Singularity", + "planners/ipc2018-agl-olcff/Singularity", + "planners/ipc2018-agl-cerberus/Singularity", + "planners/ipc2014-agl-madagascar/Singularity-MpC", + "planners/ipc2018-agl-decstar/Singularity-fork", + "planners/ipc2018-agl-decstar/Singularity", + "planners/ipc2018-opt-scorpion-nodiv/Singularity", + "planners/ipc2014-agl-probe/Singularity", + "planners/ipc2014-agl-jasper/Singularity", + "planners/ipc2014-opt-symba/Singularity-02", + "planners/ipc2014-opt-symba/Singularity-01" ], - "full_name": "genxnetwork/fl-genomics", + "full_name": "AI-Planning/autoscale", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFederated Biobank Project\u003c/h1\u003e\u003ca id=\"user-content-federated-biobank-project\" class=\"anchor\" aria-label=\"Permalink: Federated Biobank Project\" href=\"#federated-biobank-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMotivation\u003c/h2\u003e\u003ca id=\"user-content-motivation\" class=\"anchor\" aria-label=\"Permalink: Motivation\" href=\"#motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTG Environment Installation\u003c/h2\u003e\u003ca id=\"user-content-tg-environment-installation\" class=\"anchor\" aria-label=\"Permalink: TG Environment Installation\" href=\"#tg-environment-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s recommended to work with TG codebase using \u003ccode\u003econda\u003c/code\u003e environemnt.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003econda\u003c/code\u003e: \u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html\" rel=\"nofollow\"\u003ehttps://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eActivate \u003ccode\u003econda\u003c/code\u003e environment suing requirenemnts file \u003ccode\u003erequirements_tg.txt\u003c/code\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name genx --file tg_requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eActivate the environment:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003econda activate genx\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eInstall \u003ccode\u003epgenlib\u003c/code\u003e from PLINK\u0027s repo:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/chrchang/plink-ng.git\ncd plink-ng/2.0/Python\npython3 setup.py build_ext\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStructure\u003c/h2\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003esplit\u003c/strong\u003e module generates node datasets from the whole UKB dataset based on self-reported ancestry.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eqc\u003c/strong\u003e module encapsulates node-based quality control.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003edimred\u003c/strong\u003e module performs different strategies of dimensionality reduction.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efl\u003c/strong\u003e module compares various FL strategies on selected SNPs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualisation\u003c/h2\u003e\u003ca id=\"user-content-visualisation\" class=\"anchor\" aria-label=\"Permalink: Visualisation\" href=\"#visualisation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDash App\u003c/h3\u003e\u003ca id=\"user-content-dash-app\" class=\"anchor\" aria-label=\"Permalink: Dash App\" href=\"#dash-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun dash_app.py and open the link that appears in console in a browser. There assign filter+value or graph elements (x-axis, y-axis, color, etc.) to columns via dropdowns. Then press submit.\u003c/p\u003e\n", + "readme": "\u003cp\u003eThis bundle contains all scripts required to reproduce the IPC Autoscale\nbenchmarks as described in the ICAPS 2021 paper \"Automatic Instance\nGeneration for Classical Planning\" by \u00c1lvaro Torralba, Jendrik Seipp and\nSilvan Sievers.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"autoscale\" contains the main scripts for optimizing and\nselecting sequences.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"experiments\" contains the main bulk of the experiment\nscripts for all phases of the instance generation approach described in\nthe paper. See usage instructions below.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"pddl-generators\" contains all generators of IPC domains that\nwere used for this paper (and some additional ones).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"planners\" contains source code as well as Singularity recipe\nfiles for all planners used in the paper. It also contains two scripts\nfor building all the corresponding Singularity images.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"logfiles\" contains the logs of the \u003ccode\u003eselect-sequences.py\u003c/code\u003e\nscript for the results of the paper.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"report\" contains some additional scripts and Jupyter\nnotebooks used to manually inspect the results of preliminary\nexperiments. They can show some interesting additional data, but they\nare not intended to be maintained.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-tested-versions\" class=\"anchor\" href=\"#tested-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested versions\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.7\u003c/li\u003e\n\u003cli\u003eSingularity 3.5\u003c/li\u003e\n\u003cli\u003eSMAC 0.12.3\u003c/li\u003e\n\u003cli\u003eCPLEX 12.10\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" href=\"#installation-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation instructions\u003c/h1\u003e\n\u003cp\u003eWe recommend running Autoscale in a \u003ca href=\"https://conda.io\" rel=\"nofollow\"\u003eConda environment\u003c/a\u003e.\nThe following commands create a Conda environment with the necessary\nlibraries and install the required Python packages (including SMAC).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name autoscale python=3.7 gxx_linux-64 gcc_linux-64 swig\nconda activate autoscale\npip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eOn Debian-based systems, probably the easiest way to install Singularity\nis to use the \u003ca href=\"https://packages.debian.org/singularity-container\" rel=\"nofollow\"\u003eSingularity Debian\npackage\u003c/a\u003e. If that\ndoesn\u0027t work, you might have to \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003ecompile Singularity from\nsource\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-planner-images\" class=\"anchor\" href=\"#build-planner-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild planner images:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eexport DOWNWARD_BENCHMARKS=\u0026lt;path/to/benchmarks/repo\u0026gt;\ncd planners\n./build-all-images.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cplex\" class=\"anchor\" href=\"#cplex\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCPLEX\u003c/h2\u003e\n\u003cp\u003eInstall CPLEX 12.10 and add the \u003ccode\u003epython/3.7/x86-64_linux/\u003c/code\u003e subfolder to\nthe \u003ccode\u003ePYTHONPATH\u003c/code\u003e environment variable. By default, CPLEX is installed in\n\u003ccode\u003e/opt/ibm/ILOG/CPLEX_Studio1210\u003c/code\u003e, so one has to add\n\u003ccode\u003e/opt/ibm/ILOG/CPLEX_Studio1210/cplex/python/3.7/x86-64_linux/\u003c/code\u003e to the\n\u003ccode\u003ePYTHONPATH\u003c/code\u003e.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eBefore running the optimization, the following steps are needed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEdit \u003ccode\u003edomains.py\u003c/code\u003e to add the description of any new domain that you want to configure.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEdit \u003ccode\u003eplanners.py\u003c/code\u003e to add the state-of-the-art planners that are relevant for\neach domain. The script \u003ccode\u003eselect-best-planner-per-domain.py\u003c/code\u003e can help to automatically\nselect a set of planners per domain from the results of earlier experiments.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen, the following two steps must be performed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eRun \u003ccode\u003eoptimize-sequences.py\u003c/code\u003e to perform the SMAC optimization and generate sequences.\nThe \u003ccode\u003e*smac*.py\u003c/code\u003e scripts show how to parallelize this step on a computer cluster with Lab.\u003c/p\u003e\n\u003cp\u003eNote that the \u003ccode\u003e--database\u003c/code\u003e option allows to provide the JSON file from a previous SMAC\nrun to re-use all precomputed times for previously tested parameter configurations\n(see \u003ccode\u003e2020-12-08-A-smac-opt-2014-20h.py\u003c/code\u003e for an example).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGather all results from one or multiple SMAC runs into a single properties JSON file.\nThis is done by running the last step of the \u003ccode\u003e*smac*.py\u003c/code\u003e scripts.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun \u003ccode\u003eselect-sequences.py\u003c/code\u003e to generate a benchmark set. Use the \u003ccode\u003e--database\u003c/code\u003e option to provide\nthe properties file obtained from step 2.\n\u003ccode\u003egenerate-benchmarks-2020-12-10.sh\u003c/code\u003e contains an example on how to run the script.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-experiment-data\" class=\"anchor\" href=\"#experiment-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExperiment data\u003c/h1\u003e\n\u003cp\u003eThe optimization/results folder contains JSON files with the results of the experiments.\nThe results table in the paper is generated via the scripts \u003ccode\u003ereport-gather-dataset.py\u003c/code\u003e and\n\u003ccode\u003ereport-create-table.py\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 6, + "subscribers_count": 5, "topics": [], - "updated_at": 1681899779.0 + "updated_at": 1629389344.0 }, { "data_format": 2, - "description": "A Nextflow MS DDA proteomics pipeline", + "description": "Cell Interaction by Multiplet Sequencing (CIM-Seq) uses computational deconvolution of RNA-seq data from partially dissociated tissue to create cell interaction maps.", "filenames": [ - "Singularity" + "inst/containers/Singularity" ], - "full_name": "lehtiolab/ddamsproteomics", - "latest_release": "v2.18", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003elehtiolab/ddamsproteomics\u003c/h1\u003e\u003ca id=\"user-content-lehtiolabddamsproteomics\" class=\"anchor\" aria-label=\"Permalink: lehtiolab/ddamsproteomics\" href=\"#lehtiolabddamsproteomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eA Quantitative MS proteomics analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad7369dc06af55006491b641d458735c23ee30c3c7df232b984b6df965603b61/68747470733a2f2f7472617669732d63692e6f72672f6c656874696f6c61622f6464616d7370726f74656f6d6963732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/lehtiolab/ddamsproteomics.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/420c856b4f74b0ca05ed3aa6059030cc8889f0adeac2825b25565e98531bd83d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30312e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.01.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/219955514\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2e04feeec2422e4aa774f4ef0a08177ccccf6572de73f448ca0a489de1a2399d/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3231393935353531342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/219955514.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a2c51138636b8990c94d4ec25528ebb83c7eca05a0914812397f87f251d9daa7/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c656874696f6c61622f6464616d7370726f74656f6d6963732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/lehtiolab/ddamsproteomics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow identifies peptides in mzML input data using \u003ca href=\"https://github.com/MSGFPlus/msgfplus\"\u003eMSGF+\u003c/a\u003e, and \u003ca href=\"https://github.com/percolator/percolator/\"\u003ePercolator\u003c/a\u003e, quantifies isobarically labeled samples with \u003ca href=\"https://github.com/openms/openms\"\u003eOpenMS\u003c/a\u003e, and precursor peptides with \u003ca href=\"https://github.com/fickludd/dinosaur\"\u003eDinosaur\u003c/a\u003e, and processes that output to formatted peptide and protein/gene tables using \u003ca href=\"https://github.com/lehtiolab/msstitch\"\u003eMsstitch\u003c/a\u003e. Optional PTM data is analyzed by \u003ca href=\"https://github.com/dfermin/lucxor\"\u003eLuciphor2\u003c/a\u003e, and differential expression analyses can be performed using \u003ca href=\"https://github.com/yafeng/deqms\"\u003eDEqMS\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun pipeline:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa --mods \u0027oxidation;carbamidomethylation\u0027 -profile standard,docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr for two sample sets of isobaric data you can:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa --mods \u0027oxidation;carbamidomethylation --isobaric \u0027setA:tmt10plex:126 setB:tmt10plex:127N\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more elaborate examples covering fractionation, PTMs, and more, the lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere is more extensive documentation on the options inside the main.nf file.\u003c/p\u003e\n\u003cp\u003eThe pipeline takes multiple mzML files as input and performs identification and quantification to output results and a QC report (\u003ca href=\"docs/example_qc.html\"\u003ean example can be found here\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003elehtiolab/ddamsproteomics was originally written by Jorrit Boekel and tries to follow the \u003ca href=\"https://nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e best practices and templates.\u003c/p\u003e\n", + "full_name": "EngeLab/CIMseq", + "latest_release": "v1.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cim-seq\" class=\"anchor\" href=\"#cim-seq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCIM-seq\u003c/h1\u003e\n\u003cp\u003eRelease build: \u003ca href=\"https://travis-ci.com/jasonserviss/CIMseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a40d6a99b66a0483bfc629d3c366e4bda0ddfb63f31cab19bf5a091e5fe5da20/68747470733a2f2f7472617669732d63692e636f6d2f6a61736f6e736572766973732f43494d7365712e7376673f6272616e63683d6d6173746572\" data-canonical-src=\"https://travis-ci.com/jasonserviss/CIMseq.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDevel build: \u003ca href=\"https://travis-ci.com/jasonserviss/CIMseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1cf4a1545fdea91e82705800328f938f63eeb43f67d6c106a6a7cffeb42c7664/68747470733a2f2f7472617669732d63692e636f6d2f6a61736f6e736572766973732f43494d7365712e7376673f6272616e63683d646576656c\" data-canonical-src=\"https://travis-ci.com/jasonserviss/CIMseq.svg?branch=devel\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTest coverage: \u003ca href=\"https://codecov.io/github/jasonserviss/CIMseq?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e5f3e48f6917619c7701365cadda7bdb2f3dfd193622e089e8d9a8338307eb69/68747470733a2f2f636f6465636f762e696f2f67682f6a61736f6e736572766973732f43494d7365712f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Coverage status\" data-canonical-src=\"https://codecov.io/gh/jasonserviss/CIMseq/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAdvances in single-cell biology has enabled us to investigate isolated single cells at an unprecedented scale and resolution. However, cells in multicellular organisms are largely defined by their spatial organization within organ structures, which means that general methods for studying direct cell interaction on a large scale are needed. We propose a novel method, Cell Interaction by Multiplet Sequencing (CIM-Seq) that uses computational deconvolution of RNA-seq data from partially dissociated tissue to create cell interaction maps. We applied CIM-seq to human fetal pancreas, demonstrating that it recapitulates known cell interactions such as acinar-ductal cell contacts. Furthermore, we discover a strong link between a mesenchymal cell subtype and endocrine progenitor cells, and identify the set of genes that distinguishes this mesenchymal subtype. Thus, CIM-Seq is a general method for cell interaction studies that can be used on cell types defined to an arbitrary resolution allowing identification of interacting sub-cell types or cell states.\u003c/p\u003e\n", "stargazers_count": 3, "subscribers_count": 2, "topics": [], - "updated_at": 1687912051.0 + "updated_at": 1629278126.0 }, { "data_format": 2, - "description": "Demo workflow for Singularity on HPC", + "description": "Genome Decomposition Analysis pipeline", "filenames": [ "Singularity" ], - "full_name": "mikmaksi/singularity_demo", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExample code for using Singularity on an HPC cluster\u003c/h1\u003e\u003ca id=\"user-content-example-code-for-using-singularity-on-an-hpc-cluster\" class=\"anchor\" aria-label=\"Permalink: Example code for using Singularity on an HPC cluster\" href=\"#example-code-for-using-singularity-on-an-hpc-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis example describes a typical workflow for building a Singularity image and deploying it on an HPC cluster. Singularity is a containerization framework that can be used to encapsulate applications and associated dependencies in an executable image. Images can be built from recipes (similar to Dockerfile) or can be built from an existing Docker image. In this example:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA docker image is built with Singularity installed within.\u003c/li\u003e\n\u003cli\u003eA Singularity image is build inside a running Docker container and GangSTR is installed.\u003c/li\u003e\n\u003cli\u003eA GangSTR test run is done within the Docker container using the newly build Singularity image.\u003c/li\u003e\n\u003cli\u003eAnlysis directory is copied to HPC.\u003c/li\u003e\n\u003cli\u003eA test run is submitted as a job on HPC.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 1\u003c/h2\u003e\u003ca id=\"user-content-step-1\" class=\"anchor\" aria-label=\"Permalink: Step 1\" href=\"#step-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the docker image with Singularity installed. This only needs to be run once. If you have Singularity installed on your local machine, feel free to skip this step. The recipe is contained in Dockerfile.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./1_build_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2\u003c/h2\u003e\u003ca id=\"user-content-step-2\" class=\"anchor\" aria-label=\"Permalink: Step 2\" href=\"#step-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script launches a docker container and binds paths with the bam files, the reference and the regions files required by GangSTR. Note that you can bind paths with \"rw\" or \"ro\". Everything is bound to /home in the Docker container. Also we want to make sure to include the \u003ccode\u003e--rm\u003c/code\u003e flag so that the container is deleted upon exit.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./2_launch_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3\u003c/h2\u003e\u003ca id=\"user-content-step-3\" class=\"anchor\" aria-label=\"Permalink: Step 3\" href=\"#step-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun this script from inside the newly created container. Singularity will build a .simg file from the \u003ccode\u003eSingularity\u003c/code\u003e recipe. THis only needs to be done once. If you already have a Singularity image, feel free to skip this step.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./3_build_simg.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 4\u003c/h2\u003e\u003ca id=\"user-content-step-4\" class=\"anchor\" aria-label=\"Permalink: Step 4\" href=\"#step-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun this script also from inside the Docker container to test your newly created Singularity image. In this script, we mount paths into the Singularity image similar to how we mounted them into the Docker image. Only difference is that we mount to /scratch instead of /home, because the actual /home/$USER folder may be mounted to /home by Singularity by default (depending on configuration of Singularity itself).\u003c/p\u003e\n\u003cp\u003eMake sure to change REFFA to the name of the actual reference file prior to running script.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./4_test_in_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 5\u003c/h2\u003e\u003ca id=\"user-content-step-5\" class=\"anchor\" aria-label=\"Permalink: Step 5\" href=\"#step-5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExit the docker container using the \u003ccode\u003eexit\u003c/code\u003e command. Make sure to replace USER, HOST, SCRATCH_PATH with actual variables in the script. Then sync folder materials to HPC using \u003ccode\u003e./5_sync_with_hpc.sh push\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 6\u003c/h2\u003e\u003ca id=\"user-content-step-6\" class=\"anchor\" aria-label=\"Permalink: Step 6\" href=\"#step-6\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLog in to the HPC where the jobs will be submitted. Navigate to the newly created directory in scratch and submit the job to the cluster. Make sure to replace SCRATCH_PATH, PATH_TO_REF and ANALYSIS_DIR with actual variables in the script.\u003c/p\u003e\n\u003cp\u003eMake sure to change REFFA to the name of the actual reference file prior to running script.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eqsub 6_test_on_hpc.sh\u003c/code\u003e\u003c/p\u003e\n", + "full_name": "eeaunin/gda", + "latest_release": "v1.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gda\" class=\"anchor\" href=\"#gda\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGDA\u003c/h1\u003e\n\u003cp\u003eGenome Decomposition Analysis for the characterisation of genome architecture\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-what-is-gda\" class=\"anchor\" href=\"#what-is-gda\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is GDA?\u003c/h3\u003e\n\u003cp\u003eGDA (Genome Decomposition Analysis) is a bioinformatic pipeline to analyse genome architecture. Using, as a minimum, a genome assembly (the more complete the better), it will determine features in non-overlapping windows across the sequence and identify windows with common features. The assembly will then be annotated based on these similarities, highlighting structurally similar genomic regions.\u003c/p\u003e\n\u003cp\u003eGDA is developed by Eerik Aunin (\u003ca href=\"mailto:ea10@sanger.ac.uk\"\u003eea10@sanger.ac.uk\u003c/a\u003e) and Adam Reid (\u003ca href=\"mailto:ajr236@cam.ac.uk\"\u003eajr236@cam.ac.uk\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eA manuscript describing GDA is available from bioRxiv: \u003ca href=\"https://biorxiv.org/cgi/content/short/2021.12.01.470736v1\" rel=\"nofollow\"\u003ehttps://biorxiv.org/cgi/content/short/2021.12.01.470736v1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eComplete analyses presented in the manuscript are available here: \u003ca href=\"https://drive.google.com/drive/folders/1XSNS_Jj0_UGxPXpzY-EwbzABGxaZgfRf?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1XSNS_Jj0_UGxPXpzY-EwbzABGxaZgfRf?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a diagram for a quick overview of what GDA does.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/Figure_1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/Figure_1.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e(A) Features sets are derived from the genome reference sequence (seq), repeat finding (rep), gene annotations (gene) and evolutionary relationships between genes (orth). Values for each feature are determined for each non-overlapping window of e.g. 5kb across the genome. (B) The resulting matrix of feature values per window is embedded in two dimensions and clustered to identify groups of windows with similar properties. (C) The data can be explored in a number of ways using a web-browser based app. The clustering labels are mapped back to the chromosomes to highlight architectural features and a heatmap displays the features which define the clusters.\u003c/p\u003e\n\u003cp\u003eA more technical diagram of the components of the pipeline in the form of a flowchart can be seen \u003ca href=\"images/gda_pipeline_flowchart.png\"\u003ehere\u003c/a\u003e.\nA \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e-based pipeline that includes various third party tools extracts the values of a set of genomic variables that describe a genome assembly. The values of genomic variables along chromosomes are stored as \u003ca href=\"https://genome.ucsc.edu/goldenPath/help/bedgraph.html\" rel=\"nofollow\"\u003ebedgraph files\u003c/a\u003e. The bedgraph files corresponding to one genome assembly are then merged into one tab separated values (TSV) file. In the following text, this file is referred to as \"merged TSV\" file. Scaling of values, dimensionality reduction with \u003ca href=\"https://umap-learn.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eUMAP\u003c/a\u003e and clustering with \u003ca href=\"https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html\" rel=\"nofollow\"\u003eHDBSCAN\u003c/a\u003e are then applied to the numbers in this TSV file. The locations of clusters along chromosomes are stored in a BED file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003cp\u003eGDA software consists of three main parts: a genomic feature extraction pipeline, clustering scripts, and a \u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e app for viewing the results. The genomic feature extraction pipeline and the clustering scripts have been tested on a Linux server (Sanger farm) and have the following requirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePython3\u003c/li\u003e\n\u003cli\u003eJava \u2013 with enough memory to initialise the Java virtual machine\u003c/li\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Shiny app for viewing clustering results requires R and a number of R libraries. It has been tested on MacOS and Kubuntu Linux.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h3\u003e\n\u003cp\u003eWe expect that the GDA feature extraction and analysis pipeline is run remotely on a compute cluster with Linux. Viewing the results of a GDA analysis is done in a Shiny app that runs in a web browser and thus we recommend that you copy your results onto your local machine to run the final step. Thus, some dependencies are required remotely and some locally (installation instructions below).\u003c/p\u003e\n\u003cp\u003eThe quick start tutorial will show you how to run the GDA pipeline end-to-end with test data (\u003cem\u003ePlasmodium falciparum\u003c/em\u003e genome assembly \u003ca href=\"https://plasmodb.org/common/downloads/release-49/Pfalciparum3D7/fasta/data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\" rel=\"nofollow\"\u003eobtained from PlasmoDB\u003c/a\u003e) and default parameters. In reality you will likely want to add additional, optional tracks such as gene annotations, repeat finding, transcriptome data and orthology information (these are also detailed below).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tutorial\" class=\"anchor\" href=\"#tutorial\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTutorial\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-contents\" class=\"anchor\" href=\"#contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#quick-start-with-test-data\"\u003eQuick start with test data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-results-tabs\"\u003eUnderstanding the results tabs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#view-clusters-and-significant-tracks-in-igv\"\u003eView clusters and significant tracks in IGV\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-output-files\"\u003eUnderstanding the output files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#adding-optional-feature\"\u003eAdding optional features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#optimising-clustering\"\u003eOptimising clustering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-default-features\"\u003eUnderstanding the default features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#other-output\"\u003eOther output\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#using-gda-singularity-image\"\u003eUsing GDA Singularity image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#clustering-the-features-of-multiple-genomes-at-once\"\u003eClustering the features of multiple genomes at once\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ideas-for-analysis\"\u003eIdeas for analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-quick-start-with-test-data\" class=\"anchor\" href=\"#quick-start-with-test-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start with test data\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1. Set up a GDA conda environment on the farm (need to install conda? \u2013 \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ehttps://docs.conda.io/en/latest/miniconda.html\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone the GitHub repository\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun the conda installation script (this can take a little while)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003epython gda/create_gda_conda_env.py gda_env gda_downloads gda\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInitiate the conda environment:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003econda activate gda_env\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf the conda installation does not work for you, you can try using the GDA \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image instead, see \u003ca href=\"#using-gda-singularity-image\"\u003eUsing GDA Singularity image\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Run GDA\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun GDA\u2019s feature extraction pipeline with test data (we suggest that you submit this to your cluster as a job with 12 threads and 10Gb memory; expect it to take ~15 minutes with the test data):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to Load Sharing Facility (LSF)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0The results will be in the folder: \u003ccode\u003egda_pipeline_run\u003c/code\u003e. The output file required for clustering is:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCluster genome windows and analyse clusters (Use 1 thread and 10Gb memory; this should take ~1 minute; n.b. optimised clustering parameters are provided here)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda clustering -c 100 -n 5 gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n1 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_clustering_test.o -e gda_clustering_test.e \"gda clustering -c 100 -n 5 gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0The clustering output will be in a folder called: \u003ccode\u003egda_out\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Install dependencies on your local machine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMOVE TO YOUR LOCAL MACHINE (e.g. your desktop/laptop)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSet up environment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0These are the required R libraries:\u003c/p\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0shiny, ggplot2, devtools, svglite, gplots, rjson, reshape2, gridExtra, scales\u003c/p\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0If you have an R installation on your local machine that is not conda-based, the following R script should install the required libraries:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\n\ngda/gda_shiny/install_gda_shiny_dependencies_without_conda.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Alternatively, the following commands can be used to install a custom conda R environment for the GDA Shiny app:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\n\n# update conda to v4.10.1\nconda update -n base conda\n\nconda create -n gda_env_local r-essentials r-base\n\nconda activate gda_env_local\n\nconda install --yes -c r -c conda-forge r-shiny=1.5.0 r-ggplot2=3.2.1 r-gplots=3.0.3 r-rjson=0.2.20 r-reshape2=1.4.3 r-gridextra=2.3 r-scales=1.0.0 r-svglite=1.2.3\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCopy the data from the remote machine to your local machine (while on you local machine) e.g.\n\u003ccode\u003escp -r \u0026lt;user\u0026gt;@\u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_out/ .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0In order to use scp to copy the files, you will need to be able to see the remote machine (perhaps via VPN).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. View results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe required argument for the \u003ccode\u003egda_shiny.py\u003c/code\u003e script is a path to a \u003ccode\u003egda_out\u003c/code\u003e folder (that comes from the output of \u003ccode\u003egda_clustering.py\u003c/code\u003e and which you just copied from the remote machine).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 gda/gda_shiny/gda_shiny.py gda_out\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-understanding-the-results-tabs\" class=\"anchor\" href=\"#understanding-the-results-tabs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnderstanding the results tabs\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eUMAP plot\u003c/strong\u003e\n\u003ca href=\"images/01_gda_shiny_umap.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/01_gda_shiny_umap.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis shows you how well the clustering worked. Each point in the plot represents a genomic window. Windows are coloured by cluster. Cluster -1 (grey) is used for unclustered windows. Based on the nature of the genome, the features used, the window size and other parameters, there may, for example, be several very distinct, tight clusters, or perhaps a single diffuse cloud of points. Distinct, tight clusters suggest that GDA has identified regions of the genome which are clearly similar to each other and distinct from other regions. A single diffuse cloud means that there were not strong similarities or differences between subsets of the windows. There might be a lot of the genome which is unclassified (grey) or it might all be included in clusters. Sliders can be used to adjust plots for better viewing and PNG or SVG images can be saved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster locations\u003c/strong\u003e\n\u003ca href=\"images/02_gda_shiny_raster_plot.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/02_gda_shiny_raster_plot.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eEach chromosome/scaffold/contig is shown, with each window coloured based on the clustering. Therefore, this shows how the clusters pattern the chromosomes and, for example, whether a particular cluster tends to be found at the end of chromosomes. Do all chromosomes have a similar pattern? Do sex chromosomes, B chromosomes etc. look distinct from the autosomes?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster heatmaps\u003c/strong\u003e\n\u003ca href=\"images/03_gda_shiny_cluster_heatmaps.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/03_gda_shiny_cluster_heatmaps.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGDA determines features which have high or low values for windows in a particular cluster compared to other clusters. The heatmap in this tab shows the relative values across clusters for each significantly variable feature. Green means a feature has a relatively high value in a particular cluster, red a relatively low value. You can find the exact values and which were significantly different in the \u201cFeature tables\u201d tab. Adjusting the plot height and the label size can be particularly useful in this tab so that the heatmap is legible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeature tables\u003c/strong\u003e\n\u003ca href=\"images/04_gda_shiny_feature_tables.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/04_gda_shiny_feature_tables.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab has a table for each cluster (and unclustered windows), describing which features have significantly higher or lower values (by the Kolmogorov-Smirnov test). The default p-value cutoff for the Kolmogorov-Smirnov test is 1e-20.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster positions across chromosomes\u003c/strong\u003e\n\u003ca href=\"images/05_gda_shiny_cluster_positions_across_chromosomes.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/05_gda_shiny_cluster_positions_across_chromosomes.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab shows where each cluster tends to occur across the sequences. It helps you to see whether a cluster tends to occur at the ends or in the middles of chromosomes for instance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChromosome cluster composition\u003c/strong\u003e\n\u003ca href=\"images/06_gda_shiny_chromosome_cluster_composition.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/06_gda_shiny_chromosome_cluster_composition.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab contains a heatmap which clusters chromosomes by their cluster composition. Chromosomes which have similar proportions of each cluster will be closer together in the heatmap. This helps in identifying outliers which might represent interesting sequences such as sex chromosomes, B chromosomes etc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster junction counts\u003c/strong\u003e\n\u003ca href=\"images/07_gda_shiny_cluster_junction_counts.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/07_gda_shiny_cluster_junction_counts.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab shows the observed counts of junctions between windows belonging to each UMAP+HDBSCAN cluster. Junctions between windows belonging to the same type of cluster are included in the counts. The observed counts are compared with counts expected if windows were distributed randomly. Junctions with counts that are significantly different from what is expected by chance (based on Fisher test) are shown in \u003cstrong\u003e\u003cem\u003ebold+italics\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-view-clusters-and-significant-tracks-in-igv\" class=\"anchor\" href=\"#view-clusters-and-significant-tracks-in-igv\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eView clusters and significant tracks in IGV\u003c/h3\u003e\n\u003cp\u003eThe values of variables along chromosomes are stored as \u003ca href=\"https://genome.ucsc.edu/goldenPath/help/bedgraph.html\" rel=\"nofollow\"\u003ebedgraph files\u003c/a\u003e and can be viewed in genome browsers such as \u003ca href=\"https://software.broadinstitute.org/software/igv\" rel=\"nofollow\"\u003eIGV\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Install IGV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://software.broadinstitute.org/software/igv/download\" rel=\"nofollow\"\u003ehttps://software.broadinstitute.org/software/igv/download\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Get bedgraph files from cluster\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escp -r \u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_pipeline_run/bedgraph_output/ .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Copy across clustering results (if you haven\u2019t already)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escp -r \u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_out/ .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Make IGV session file\u003c/strong\u003e\nIGV allows saving and loading \u003ca href=\"https://software.broadinstitute.org/software/igv/Sessions\" rel=\"nofollow\"\u003esession files\u003c/a\u003e, which are XML files that keep track of the program state (what FASTA, BED and bedgraph files have been simultaneously loaded to IGV).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda/gda_make_igv_session_file.py -g gda/test_data/PlasmoDB-49_Pfalciparum3D7.gff gda_out/cluster_heatmap.csv gda_out/PlasmoDB-49_Pfalciparum3D7_Genome/clusters.bed gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta bedgraph_output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e5. Load session file into IGV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile \u2192\u201cOpen Session\u201d\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/gda_pfalciparum_igv_screensh.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/gda_pfalciparum_igv_screensh.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\nThe IGV screenshot above shows \u003cem\u003ePlasmodium falciparum\u003c/em\u003e chromosome 1, with some GDA bedgraph tracks and the \u0027clusters.bed\u0027 file loaded.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-understanding-the-output-files\" class=\"anchor\" href=\"#understanding-the-output-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnderstanding the output files\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eBedgraph files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith your results directory (\u003ccode\u003e\u0026lt;YYYYMMDD\u0026gt;_gda_pipeline_run\u003c/code\u003e by default; use \u003ccode\u003egda extract_genomic_features --pipeline_run_folder\u003c/code\u003e to change), the folder \u003ccode\u003ebedgraph_output\u003c/code\u003e contains each bedgraph track produced by GDA. These can be loaded into a genome browser (e.g. IGV) for viewing and better understanding why GDA has clustered the genome as it has. We provide the script \u003ccode\u003egda_make_igv_session_file.py\u003c/code\u003e to generate an IGV session file for your genome which will show the clusters and tracks for features which are significantly enriched in the clusters.\u003c/p\u003e\n\u003cp\u003eOne of the files generated by the \u003ccode\u003egda_clustering.py\u003c/code\u003e script is called \u003ccode\u003eclusters.bed\u003c/code\u003e. This file marks the locations of each UMAP+HDBSCAN cluster and can be loaded to IGV alongside the bedgraph tracks. The cluster numbers and the colour key are the same as in the UMAP plot of the Shiny app.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe feature table\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ecluster\u003c/th\u003e\n\u003cth\u003efeature\u003c/th\u003e\n\u003cth\u003ecluster_data.size\u003c/th\u003e\n\u003cth\u003eother_data.size\u003c/th\u003e\n\u003cth\u003estat_less\u003c/th\u003e\n\u003cth\u003epvalue_less\u003c/th\u003e\n\u003cth\u003estat_great\u003c/th\u003e\n\u003cth\u003epvalue_great\u003c/th\u003e\n\u003cth\u003ecluster_median\u003c/th\u003e\n\u003cth\u003eother_median\u003c/th\u003e\n\u003cth\u003ecluster_mean\u003c/th\u003e\n\u003cth\u003eother_mean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.47\u003c/td\u003e\n\u003ctd\u003e1.86e-35\u003c/td\u003e\n\u003ctd\u003e0.98000\u003c/td\u003e\n\u003ctd\u003e0.66000\u003c/td\u003e\n\u003ctd\u003e1.15760\u003c/td\u003e\n\u003ctd\u003e0.69856\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003etandem_repeats_fraction\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.56\u003c/td\u003e\n\u003ctd\u003e1.36e-49\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003e9.86e-01\u003c/td\u003e\n\u003ctd\u003e0.07840\u003c/td\u003e\n\u003ctd\u003e0.15620\u003c/td\u003e\n\u003ctd\u003e0.08711\u003c/td\u003e\n\u003ctd\u003e0.15905\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003ewgsim_depth_minimap2\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.90\u003c/td\u003e\n\u003ctd\u003e4.03e-127\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e3.71320\u003c/td\u003e\n\u003ctd\u003e9.94240\u003c/td\u003e\n\u003ctd\u003e3.98305\u003c/td\u003e\n\u003ctd\u003e9.90542\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.47\u003c/td\u003e\n\u003ctd\u003e1.86e-35\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.66000\u003c/td\u003e\n\u003ctd\u003e0.98000\u003c/td\u003e\n\u003ctd\u003e0.69856\u003c/td\u003e\n\u003ctd\u003e1.15760\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003etandem_repeats_fraction\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003e9.86e-01\u003c/td\u003e\n\u003ctd\u003e0.56\u003c/td\u003e\n\u003ctd\u003e1.36e-49\u003c/td\u003e\n\u003ctd\u003e0.15620\u003c/td\u003e\n\u003ctd\u003e0.07840\u003c/td\u003e\n\u003ctd\u003e0.15905\u003c/td\u003e\n\u003ctd\u003e0.08711\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003ewgsim_depth_minimap2\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.90\u003c/td\u003e\n\u003ctd\u003e4.03e-127\u003c/td\u003e\n\u003ctd\u003e9.94240\u003c/td\u003e\n\u003ctd\u003e3.71320\u003c/td\u003e\n\u003ctd\u003e9.90542\u003c/td\u003e\n\u003ctd\u003e3.98305\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-adding-optional-features\" class=\"anchor\" href=\"#adding-optional-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding optional features\u003c/h3\u003e\n\u003cp\u003eWe recommend you add as many features as possible so that the clustering is able to identify those which are the strongest signals in the genome.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optional-features-which-do-not-require-additional-data\" class=\"anchor\" href=\"#optional-features-which-do-not-require-additional-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional features which do not require additional data\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003e1. Run repeat finding to get bedgraph tracks of individual complex repeat features as well as complex_repeat_sum (the sum of all these features)\u003c/strong\u003e\nThe GDA pipeline contains two mandatory components for repeat detection: \u003ca href=\"https://github.com/Benson-Genomics-Lab/TRF\"\u003eTandemRepeatsFinder\u003c/a\u003e for tandem repeats and \u003ca href=\"http://emboss.sourceforge.net/apps/cvs/emboss/apps/einverted.html\" rel=\"nofollow\"\u003eEMBOSS einverted\u003c/a\u003e for inverted repeats. Besides these, the GDA pipeline has two optional repeat family detection modules from which the user can choose one to run. The first one of these modules uses \u003ca href=\"https://www.repeatmasker.org/RepeatModeler/\" rel=\"nofollow\"\u003eRepeatModeler+RepeatMasker\u003c/a\u003e and the second one uses \u003ca href=\"https://github.com/BioinformaticsToolsmith/MeShClust2\"\u003eRed+MeShClust2\u003c/a\u003e. RepeatModeler+RepeatMasker is relatively slow and may take ~1 week to run for large genomes (11 hours for the test dataset). On Sanger farm5, this will require using the basement queue. The Red+Meshclust2 module is much faster, but may produce more noisy repeat families, depending on the genome.\nWhen the GDA pipeline is run with repeat family detection enabled, the bedgraph files of each complex repeat family appear in the \u003ccode\u003ecomplex_repeats\u003c/code\u003e subdirectory of the \u003ccode\u003ebedgraph_output\u003c/code\u003e directory. If RepeatModeler is used, a \u003ccode\u003esimple_repeats\u003c/code\u003e directory that contains bedgraph files of simple repeat families is also produced.\nIn addition, a bedgraph file of the sum of complex repeat families (and if using RepeatModeler, of simple repeat families) is produced. The individual bedgraph tracks of each repeat family are not used as the input for UMAP clustering by default, but the tracks for the sums of simple or complex repeat families are used.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--run_repeat_family_detection\n--repeat_family_detection_engine \u0026lt;repeatmodeler/meshclust2\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --run_repeat_family_detection --repeat_family_detection_engine repeatmodeler gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_repeatmodeler_test.o -e gda_repeatmodeler_test.e \"gda extract_genomic_features --threads 12 --run_repeat_family_detection --repeat_family_detection_engine repeatmodeler gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e2. \u003cem\u003eDe novo\u003c/em\u003e gene annotation\u003c/strong\u003e\nThe GDA pipeline can take an existing gene annotations GFF3 file as input. For the cases where there is no existing gene annotations available for the genome, the pipeline contains an optional module that produces a \u003cem\u003ede novo\u003c/em\u003e annotation of protein coding genes, rRNA and tRNA genes (using \u003ca href=\"https://bioinf.uni-greifswald.de/augustus/\" rel=\"nofollow\"\u003eAugustus\u003c/a\u003e, \u003ca href=\"https://github.com/tseemann/barrnap\"\u003eBarrnap\u003c/a\u003e and \u003ca href=\"http://lowelab.ucsc.edu/tRNAscan-SE/\" rel=\"nofollow\"\u003etRNAscan-SE\u003c/a\u003e). The gene annotation module can optionally take an annotated related genome as the input and produce hints for Augustus based on annotation transfer with \u003ca href=\"https://github.com/agshumate/Liftoff\"\u003eLiftoff\u003c/a\u003e. Several bedgraph feature tracks are derived from gene annotations: \u003ccode\u003emRNA_annotation\u003c/code\u003e, \u003ccode\u003eexon_count\u003c/code\u003e, \u003ccode\u003egene_average_exon_length\u003c/code\u003e, \u003ccode\u003egene_average_intron_length\u003c/code\u003e, \u003ccode\u003egene_length\u003c/code\u003e, \u003ccode\u003etRNA_annotations\u003c/code\u003e, \u003ccode\u003erRNA_annotations\u003c/code\u003e. Optionally, a \u003ccode\u003egene_dna_strand_bias\u003c/code\u003e track is also produced.\nAlso, a GFF file of the annotations can be found in the \u003ccode\u003egene_annotation\u003c/code\u003e folder. The GFF file also includes the tRNAscan and Barrnap results.\u003c/p\u003e\n\u003cp\u003eMultiple options are required\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--run_gene_annotation_pipeline\n--annotation_target_species_id \u0026lt;label_for_gene_ids\u0026gt;\n--augustus_species \u0026lt;pick_from_list\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder aug_test_runfolder --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test_aug.o -e gda_test_aug.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder aug_test_runfolder --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optional-features-requiring-additional-data\" class=\"anchor\" href=\"#optional-features-requiring-additional-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional features requiring additional data\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003e1. Genome annotation\u003c/strong\u003e\n\u003ccode\u003e--gff_path \u0026lt;GFF3 file with existing gene annotations\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor handling user-provided GFF files, the pipeline expects the following things:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe input file is in GFF3 format (GTF or GFF2 are not accepted)\u003c/li\u003e\n\u003cli\u003ethe tags for mRNA, pseudogene, tRNA and rRNA features are \"mRNA\", \"pseudogene\", \"tRNA\" and \"rRNA\". The user should check the GFF file to make sure that the tags are named according to this convention. If, for instance, the mRNA features in the GFF file are called \"transcript\" instead of \"mRNA\", the pipeline does not recognise them as the mRNA features.\u003c/li\u003e\n\u003cli\u003ethe GFF file should pass the \u003ca href=\"http://genometools.org/cgi-bin/gff3validator.cgi\" rel=\"nofollow\"\u003eGenomeTools GFF3 validator check\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe user can specify non-standard GFF3 feature tags from the input GFF3 file to be turned into bedgraph tracks using the \u003ccode\u003e--custom_gff_tags\u003c/code\u003e option of the \u003ccode\u003egda\u003c/code\u003e wrapper. For example, if the input GFF3 file has features named \"H3K9me3\" and \"H3K9ac\", it is possible to make bedgraph files out of them by specifying them as comma separated \u003ccode\u003ecustom_gff_tags\u003c/code\u003e options:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--custom_gff_tags H3K9me3,H3K9ac\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Reference genome annotation (annotate your assembly using a reference annotation: hints for Augustus are derived from annotation transfer using Liftoff)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--reference_assembly_path \u0026lt;reference assembly FASTA file\u0026gt; --reference_gff_path \u0026lt;reference assembly GFF3 file\u0026gt; \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. RNA-Seq coverage\u003c/strong\u003e\nRNA-Seq coverage is determined using the mapping of reads to the assembly with \u003ca href=\"http://daehwankimlab.github.io/hisat2/manual/\" rel=\"nofollow\"\u003eHISAT2\u003c/a\u003e. The input is a pair of gzipped FASTQ reads.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--rna_seq_fastq_1_path\n--rna_seq_fastq_2_path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR223/008/ERR2234508/ERR2234508_1.fastq.gz .\nwget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR223/008/ERR2234508/ERR2234508_2.fastq.gz .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --rna_seq_fastq_1_path ERR2234508_1.fastq.gz --rna_seq_fastq_2_path ERR2234508_2.fastq.gz gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --rna_seq_fastq_1_path ERR2234508_1.fastq.gz --rna_seq_fastq_2_path ERR2234508_2.fastq.gz gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting feature track is called \u003ccode\u003ehisat2_samtools_depth\u003c/code\u003e and the raw mapping data is in the \u003ccode\u003erna_seq_mapping\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Gene conservation (orthology)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--orthomcl_references_folder\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis folder should contain subfolders, each for separate \u003ca href=\"https://orthomcl.org/orthomcl/app\" rel=\"nofollow\"\u003eOrthoMCL\u003c/a\u003e runs, e.g. for closely related and more distantly related species (although a single folder is perfectly fine). The folder name is arbitrary. Within each folder there should be protein FASTA files for each reference proteome and a file called \u003ccode\u003etable_for_gg_file.csv\u003c/code\u003e with the names of these files and a simple name for the species. GG files (genome gene relation file) are used by OrthoMCL to relate genes to genomes. e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePchabaudi,PlasmoDB-49_Pchabaudichabaudi_AnnotatedProteins.fasta\nTgondii,ToxoDB-51_TgondiiME49_AnnotatedProteins.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eProteins from the genome under consideration will be added behind the scenes (they are derived from the assembly FASTA file and annotations GFF3 file using \u003ca href=\"https://github.com/gpertea/gffread\"\u003egffread\u003c/a\u003e). N.b. you need to provide annotation for your genome assembly or have it transferred/predicted in order to do the orthology analysis.\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder orthomcl_test_runfolder --orthomcl_references_folder gda/test_data/orthomcl_refs/ --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test_orthomcl.o -e gda_test_orthomcl.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder orthomcl_test_runfolder --orthomcl_references_folder gda/test_data/orthomcl_refs/ --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting bedgraph files are:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_ortholog_count.bedgraph\u003c/code\u003e - Number of orthologues per gene\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_paralog_count.bedgraph\u003c/code\u003e - Number of paralogues per gene\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_protein_conservation_ratio.bedgraph\u003c/code\u003e - The average proportion of species in the OrthoMCL run that have orthologs for the target species proteins in the window. The value is 1 if all target species proteins in the window have OrthoMCL orthologs in all other species in the OrthoMCL run. The value is 0 if none of the target species proteins in the window have any OrthoMCL orthologs in any other species in the OrthoMCL run.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_species_specific_proteins_ratio.bedgraph\u003c/code\u003e - The average proportion of species-specific proteins in the window. It is the number of target species proteins with no OrthoMCL orthologs in the window divided by the number of all target species proteins in the window. The value is 1 if none of the target species proteins in the window have OrthoMCL orthologs, and 0 if all of the target species proteins in the window have OrthoMCL orthologs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Reference mitochondrial sequence for Nuclear Mitochondrial DNA (NUMT) identification\u003c/strong\u003e\nNUMT identification is done using BLAST of the genome against a user-provided reference mitochondrial sequence. The reference mitochondrial sequence can be a known mitochondrial sequence from the same species as the rest of the assembly. If a region of an assembly contig yields a strong BLAST hit (e-value \u0026lt;= 1e-30) to the reference mitochondrial sequence but the alignment length is less than 90% of the length of this contig, the BLAST hit region is labelled as a putative NUMT.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--ref_mitoch_fasta_path\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Reference plastid sequence for NUPT identification\u003c/strong\u003e\nThis is the same process as the detection of NUMTs but meant for plastid sequences.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--ref_apicoplast_fasta_path\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Other useful feature extraction options\u003c/strong\u003e\nThe pipeline tries to identify telomeric regions by searching the assembly sequences for exact matches to a telomeric motif. The \u003ccode\u003etelomeric_seq_preset\u003c/code\u003e option allows to select a query telomeric motif from a list of known telomeric motifs across different species (based on the Wikipedia article on telomeres, \u003ca href=\"https://en.wikipedia.org/wiki/Telomere\" rel=\"nofollow\"\u003ehttps://en.wikipedia.org/wiki/Telomere\u003c/a\u003e). It is also possible to specify a custom telomeric motif using the \u003ccode\u003ecustom_telomeric_seq\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--telomeric_seq_preset\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-optimising-clustering\" class=\"anchor\" href=\"#optimising-clustering\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimising clustering\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optimising-clustering-during-feature-extraction\" class=\"anchor\" href=\"#optimising-clustering-during-feature-extraction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimising clustering during feature extraction\u003c/h4\u003e\n\u003cp\u003eChange the window size (5kb)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--chunk_size\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis is perhaps the most important option in GDA. From a purely computational point of view, GDA will struggle with clustering a very large number of windows. From a biological perspective, it determines the resolution at which you are analysing the genome assembly. We find that 5kb works very well for the relatively miniscule \u003cem\u003ePlasmodium\u003c/em\u003e genome (~20Mb). For the common toad (\u003cem\u003eBufo bufo\u003c/em\u003e) genome, which is 4.94 Gb we have used 1 Mb window size. Aiming for 5000 windows works very nicely computationally, but you should experiment with a few window sizes, to see what gives an interesting view of the genome. You needn\u0027t run feature extraction multiple times. Instead use:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egda downsample_merged_tsv \u0026lt;tsv\u0026gt; \u0026lt;factor\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you started with a 5kb window size, use 4 as the downsampling factor and you will get a merged TSV file with 20kb windows. Similarly, use a factor of 10 to get 50kb windows.\u003c/p\u003e\n\u003cp\u003eIf the genomic feature extraction pipeline produces an output TSV file that has 10000 or more windows, a downsampled TSV file with approximately 5000 windows will be automatically generated alongside the main output TSV file.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optimising-clustering-during-clustering-step\" class=\"anchor\" href=\"#optimising-clustering-during-clustering-step\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimising clustering during clustering step\u003c/h4\u003e\n\u003cp\u003eOnce the feature extraction pipeline is finished, you can determine good clustering parameters by looking at the UMAP plots from a range of different parameters:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda clustering_params 20210312_gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n1 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_params_test.o -e gda_params_test.e \"gda clustering_params 20210312_gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eReplace the \u003ccode\u003e20210312_gda_pipeline_run\u003c/code\u003e in the above command with the name of your GDA pipeline run folder path.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003en_neighbors\u003c/code\u003e is a UMAP setting that determines the size of the local neigbourhood in terms of sample points (\u003ca href=\"https://umap-learn.readthedocs.io/en/latest/parameters.html\" rel=\"nofollow\"\u003ehttps://umap-learn.readthedocs.io/en/latest/parameters.html\u003c/a\u003e). Smaller \u003ccode\u003en_neigbors\u003c/code\u003e values give more emphasis on local structure in the data and larger \u003ccode\u003en_neighbors\u003c/code\u003e values give more weight to global structure. We have used \u003ccode\u003en_neighbors\u003c/code\u003e values from 5 to 200.\nBy default the clustering will be run with \u003ccode\u003en_neighbors\u003c/code\u003e set to 5, 10, 15, 20, 50, 100 and \u201cMinimum cluster size\u201d set to 50, 100, 200, 500. All parameter pairs will be explored (e.g. 24 combinations). The results of each clustering are output to STDOUT. You can also view an HTML file of UMAP plots in a web browser e.g.:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003efirefox gda_out/parameter_selection/parameters.html \u0026amp;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e[warning this can run slowly when run remotely]\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/pfalciparum_gda_parameters_example.pdf\"\u003eHere\u003c/a\u003e is example output of the \u003ccode\u003egda clustering_params\u003c/code\u003e run with the \u003cem\u003ePlasmodium falciparum\u003c/em\u003e assembly.\u003c/p\u003e\n\u003cp\u003eWe recommend selecting parameters based on minimising the percentage of unclassified sequence, while getting at least two clusters. E.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN neighbours: 5\nMin cluster size: 50\nCluster -1 is 2.14% of the genome\nCluster 0 is 2.99% of the genome\nCluster 1 is 3.70% of the genome\nCluster 2 is 91.17% of the genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnly 2.14% of windows were unclassified and there are multiple clusters meaning GDA has identified some partitioning of the genome.\u003c/p\u003e\n\u003cp\u003eGiven that this pair of parameters involves the lowest values, it would be a good idea to try out even lower parameter values to see if there is an even better/more interesting clustering.\u003c/p\u003e\n\u003cp\u003eYou should pick minimum cluster sizes based on the number of windows you have. E.g. If you have 5000 windows, and you have a minimum cluster size of 50, the smallest possible cluster will contain 1% of your genome assembly.\u003c/p\u003e\n\u003cp\u003eWhen clustering a large number of genomic windows, you may need to set HDBSCAN\u0027s \u003ccode\u003emin_samples\u003c/code\u003e value to a value that is not \u003ccode\u003eNone\u003c/code\u003e in order to prevent HDBSCAN from crashing (\u003ca href=\"https://github.com/scikit-learn-contrib/hdbscan/issues/250\"\u003ehttps://github.com/scikit-learn-contrib/hdbscan/issues/250\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-understanding-the-default-features\" class=\"anchor\" href=\"#understanding-the-default-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnderstanding the default features\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eVariable\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eat_skew\u003c/td\u003e\n\u003ctd\u003eAT skew\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecag_freq\u003c/td\u003e\n\u003ctd\u003eCAG trinucleotide repeat frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecomplex_repeats_bedgraph\u003c/td\u003e\n\u003ctd\u003ecomplex repeats detected using \u003ca href=\"https://www.repeatmasker.org/RepeatModeler/\" rel=\"nofollow\"\u003eRepeatModeler+RepeatMasker\u003c/a\u003e or \u003ca href=\"https://github.com/BioinformaticsToolsmith/MeShClust2\"\u003eRed+MeShClust2\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003eCpG dinucleotide frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edustmasker_low_complexity_percentage\u003c/td\u003e\n\u003ctd\u003elow complexity sequence frequency (detected using \u003ca href=\"https://www.ncbi.nlm.nih.gov/IEB/ToolBox/CPP_DOC/lxr/source/src/app/dustmasker/\" rel=\"nofollow\"\u003eDustmasker\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eectopic_apicoplast\u003c/td\u003e\n\u003ctd\u003eputative ectopic apicoplast (detected using BLAST against user-provided apicoplast sequence)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eectopic_mitochondrion\u003c/td\u003e\n\u003ctd\u003eputative NUMTs (detected using BLAST against user-provided mitochondrial sequence)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eeinverted\u003c/td\u003e\n\u003ctd\u003einverted repeats (detected using \u003ca href=\"http://emboss.sourceforge.net/apps/cvs/emboss/apps/einverted.html\" rel=\"nofollow\"\u003eEMBOSS einverted\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eexon_count\u003c/td\u003e\n\u003ctd\u003eaverage exon count per mRNA gene\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egaps\u003c/td\u003e\n\u003ctd\u003eassembly gaps (Ns)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egc_percentage\u003c/td\u003e\n\u003ctd\u003eGC%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egc_skew\u003c/td\u003e\n\u003ctd\u003eGC skew\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_average_exon_length\u003c/td\u003e\n\u003ctd\u003eaverage exon length of mRNA genes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_average_intron_length\u003c/td\u003e\n\u003ctd\u003eaverage intron length of mRNA genes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_dna_strand_bias\u003c/td\u003e\n\u003ctd\u003etendency of genes to be all on the same strand in the window. The value is 1 if all genes in the window are on the same strand (it does not matter which one). The value is 0 if genes in the window are equally distributed between both strands\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_length\u003c/td\u003e\n\u003ctd\u003eaverage mRNA gene length\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ekmer_deviation_kmer_size_3*\u003c/td\u003e\n\u003ctd\u003ekmer skew for a for a particular kmer length (how much the distribution of kmers in the window differs from what is expected by change, given the GC content of the sequence in the window)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eltrdigest_protein_matches\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/genometools/genometools\"\u003eLTRdigest\u003c/a\u003e protein matches\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eltrdigest_retrotransposons\u003c/td\u003e\n\u003ctd\u003eputative retrotransposons (detected using \u003ca href=\"https://github.com/genometools/genometools\"\u003eLTRharvest and LTRdigest\u003c/a\u003e). Only the sequences containing LTRdigest protein matches are counted\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emRNA_annotations\u003c/td\u003e\n\u003ctd\u003emRNA gene density (either from user-provided gene annotations or detected using \u003ca href=\"https://bioinf.uni-greifswald.de/augustus/\" rel=\"nofollow\"\u003eAugustus\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eortholog_count\u003c/td\u003e\n\u003ctd\u003eaverage number of orthologs (\u003ca href=\"https://orthomcl.org/orthomcl/\" rel=\"nofollow\"\u003eOrthoMCL\u003c/a\u003e orthologs in other species) for proteins in the window\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eparalog_count\u003c/td\u003e\n\u003ctd\u003eaverage number of paralogs (OrthoMCL orthologs within the same species) for proteins in the window\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eprotein_conservation_ratio\u003c/td\u003e\n\u003ctd\u003ereflects the average proportion of species in the OrthoMCL run that have orthologs for the target species proteins in the window. The value is 1 if all target species proteins in the window have OrthoMCL orthologs in all other species in the OrthoMCL run. The value is 0 if none of the target species proteins in the window have any OrthoMCL orthologs in any other species in the OrthoMCL run.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epseudogene_annotations\u003c/td\u003e\n\u003ctd\u003epseudogenes (read from user-provided GFF3 file if this feature is present there)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erRNA_annotations\u003c/td\u003e\n\u003ctd\u003erRNA_annotations (either from user-provided gene annotations or detected using Barrnap)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esimple_repeats_bedgraph\u003c/td\u003e\n\u003ctd\u003esimple repeats detected using RepeatModeler+RepeatMasker. The sequences have been collapsed to count repeats that are the reverse complement of one another as the same repeat. They have also been collapsed to count the repeats that are identical if the starting point is adjusted as the same repeat (e.g. TGGTT is the same as GGTTT)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especies_specific_proteins_ratio\u003c/td\u003e\n\u003ctd\u003ereflects the average proportion of species specific proteins in the window. It is the number of target species proteins with no OrthoMCL orthologs in the window divided by the number of all target species proteins in the window. The value is 1 if none of the target species proteins in the window have OrthoMCL orthologs, and 0 if all of the target species proteins in the window have OrthoMCL orthologs.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003estop_codon_freq\u003c/td\u003e\n\u003ctd\u003estop codon frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esum_of_complex_repeats\u003c/td\u003e\n\u003ctd\u003esum of values of RepeatModeler+RepeatMasker or Red+MeShClust2 tracks for complex repeat families\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esum_of_simple_repeats\u003c/td\u003e\n\u003ctd\u003esum of values of RepeatModeler tracks for simple repeat families\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etandem_repeat_density\u003c/td\u003e\n\u003ctd\u003etandem repeats (detected using \u003ca href=\"https://github.com/Benson-Genomics-Lab/TRF\"\u003eTandem Repeats Finder\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etelomere_freq\u003c/td\u003e\n\u003ctd\u003etelomeric sequence frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etRNA_annotations\u003c/td\u003e\n\u003ctd\u003etRNAs (either from user-provided gene annotations or detected using \u003ca href=\"http://lowelab.ucsc.edu/tRNAscan-SE/\" rel=\"nofollow\"\u003etRNAscan-SE\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewgsim_minimap2_coverage\u003c/td\u003e\n\u003ctd\u003ecoverage of \u003ca href=\"https://github.com/lh3/wgsim\"\u003eWGSIM\u003c/a\u003e simulated short reads, derived from the assembly itself, with a target coverage of 10x. The reads have been mapped back to the assembly using Minimap2 using the short read mapping mode. Multimapping simulated reads have been removed before calculating the coverage\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-other-output\" class=\"anchor\" href=\"#other-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther output\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003e/work/\u003c/code\u003e directory \u2013 Files automatically generated by Nextflow during the run. These files can be used for resuming the pipeline when crashed. Nextflow has a \u003ccode\u003e-resume\u003c/code\u003e option for restarting an interrupted run from the last cached checkpoint. In the GDA pipeline wrapper script, the \u003ccode\u003eresume_genomic_feature_extraction\u003c/code\u003e command is meant for restarting the pipeline using Nextflow\u0027s \u003ccode\u003e-resume\u003c/code\u003e flag. For this you will need to provide the path to the Nextflow config file (it is a file with the name \u003ccode\u003enextflow.config\u003c/code\u003e in the \u003ccode\u003e*_gda_pipeline_run folder\u003c/code\u003e) and the name of the crashed run. The run names are autogenerated by Nextflow and can be seen in the STDOUT log of the GDA run, in square brackets below the line that says \"N E X T F L O W\". If the run was started using the GDA Singularity image, you will also need to provide the path to that image, otherwise this path is not needed.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-clustering-the-features-of-multiple-genomes-at-once\" class=\"anchor\" href=\"#clustering-the-features-of-multiple-genomes-at-once\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClustering the features of multiple genomes at once\u003c/h3\u003e\n\u003cp\u003eIt is possible to cluster the features extracted from multiple genomes at the same time. To do this, the first step is to run the genomic feature extraction pipeline separately for each genome of interest. For each genome, this will produce a TSV table with the values of the genomic features. The tables can then be concatenated using the \u003ccode\u003egda_concatenate_tsv_tables.py\u003c/code\u003e script. Each of the input tables needs to have the same window size. In the \u003ccode\u003especies\u003c/code\u003e and \u003ccode\u003echromosome\u003c/code\u003e columns, each input TSV table needs to have unique values that do not occur in the other input TSV tables. After concatenating the tables, the resulting combined table can be processed with the \u003ccode\u003egda clustering_params\u003c/code\u003e and \u003ccode\u003egda clustering\u003c/code\u003e commands. When viewing the clustering results of a multi-genome TSV table in the Shiny app, an extra UMAP plot will appear, with dots coloured according to which input assembly each window belongs to (\u003ca href=\"images/clustering_two_genomes_umap_example.png\"\u003eexample\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-using-gda-singularity-image\" class=\"anchor\" href=\"#using-gda-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing GDA Singularity image\u003c/h3\u003e\n\u003cp\u003eAs an alternative to using conda to install the dependencies for GDA, it is also possible to read the dependencies from a Singularity image. A Singularity image file with the dependencies for GDA has been deposited in Google Drive, at \u003ca href=\"https://drive.google.com/file/d/1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_/view?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/file/d/1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_/view?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you have gdown (\u003ca href=\"https://github.com/wkentaro/gdown\"\u003ehttps://github.com/wkentaro/gdown\u003c/a\u003e, \u003ca href=\"https://anaconda.org/conda-forge/gdown\" rel=\"nofollow\"\u003ehttps://anaconda.org/conda-forge/gdown\u003c/a\u003e) installed on your system, you can download the Singularity image file from Google Drive with a terminal command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egdown https://drive.google.com/uc?id=1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOn the Sanger farm, Singularity can be started from the farm module:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emodule load ISG/singularity/3.6.4\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou will need to make sure Singularity and Nextflow are installed on your cluster.\nFor running GDA with the Singularity image, you should still clone this GitHub repository and add the \u003ccode\u003egda\u003c/code\u003e wrapper script to \u003ccode\u003ePATH\u003c/code\u003e. To use the GDA Singularity image, you should provide the path to the image with the \u003ccode\u003e--singularity_image_path\u003c/code\u003e option of the \u003ccode\u003egda\u003c/code\u003e wrapper script. The remaining software dependencies (RepeatModeler, HISAT2, LTRharvest, etc) will then be loaded from the Singularity image. This is an example command for extracting genomic features using Singularity:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta --singularity_image_path \u0026lt;gda_singularity.simg\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta --singularity_image_path \u0026lt;gda_singularity.simg\u0026gt;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also run the \u003ccode\u003egda_clustering_params\u003c/code\u003e and \u003ccode\u003egda_clustering\u003c/code\u003e commands with the Singularity image by providing a path to the image with the \u003ccode\u003e--singularity_image_path\u003c/code\u003e option.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" href=\"#troubleshooting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTroubleshooting\u003c/h3\u003e\n\u003cp\u003e\u2022\tThe best place to look for error messages initially is STDOUT, rather than STDERR, because the Nextflow error messages end up there. You may then be directed to the \u003ccode\u003eerror_stream_logs\u003c/code\u003e directory in your run folder for error messages from a specific process\u003c/p\u003e\n\u003cp\u003e\u2022\tYou may want to exclude the mitochondrial and other symbiont genomes as well as any shorter, non-chromosomal scaffolds\u003c/p\u003e\n\u003cp\u003e\u2022\tIf your genome assembly is large and clustering is problematic you may want to increase window size. You can do this with an existing merged TSV file using \u003ccode\u003egda downsample_merged_tsv \u0026lt;path to the TSV file\u0026gt; \u0026lt;downsampling factor\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBugs, suggestions etc. can be sent to \u003ca href=\"mailto:ea10@sanger.ac.uk\"\u003eea10@sanger.ac.uk\u003c/a\u003e and \u003ca href=\"mailto:ajr236@cam.ac.uk\"\u003eajr236@cam.ac.uk\u003c/a\u003e, or submitted as issues on this GitHub page.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ideas-for-analysis\" class=\"anchor\" href=\"#ideas-for-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIdeas for analysis\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUse a single feature from the merged TSV to make calls for where this feature is high across a genome \u2013 e.g. paralogous gene families or a particular complex repeat family of interest.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1613502129.0 + "updated_at": 1640384962.0 }, { "data_format": 2, - "description": "Evaluate the effectiveness of any short read mapper and its parameters using artificially generated reads. See the \"releases\" page for download: https://github.com/mlell/tapas/releases. The manual can be found at https://mlell.github.io/tapas.", + "description": "Read contamination removal", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "mlell/tapas", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTAPAS\u003c/h1\u003e\u003ca id=\"user-content-tapas\" class=\"anchor\" aria-label=\"Permalink: TAPAS\" href=\"#tapas\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software package is meant for comparing NGS short read mappers.\u003c/p\u003e\n\u003cp\u003eArtificial reads can be generated from genome sequences such that their true\nmapping positions are known (\u003ccode\u003escripts/uniform\u003c/code\u003e). Artificial point mutations\n(\u003ccode\u003escripts/multiple_mutate\u003c/code\u003e) and indels (\u003ccode\u003escripts/indel\u003c/code\u003e) can be introduced.\nBy comparing the original read locations to the positions the reads were\nassigned to by the mapper, the sensitivity and specificity of the mapper can\nbe assessed.\u003c/p\u003e\n\u003cp\u003eBy running the mapper with different combinations of parameters, an optimal\nparameter set can be found (\u003ccode\u003escripts/cross_tab\u003c/code\u003e and \u003ccode\u003escripts/table2calls\u003c/code\u003e).\nTAPAS is compatible with all short read mappers by through a simple text\nfile which contains the mapping command and the relevant parameters. Instead\nof the parameter values, bash variables (\u003ccode\u003e${variablename}\u003c/code\u003e) are used. They\nare replaced with the mapping parameter values to be tested by TAPAS.\u003c/p\u003e\n\u003cp\u003eAdditionally, reads can be mutated in a pattern similar to typical damage found\nin ancient DNA (aDNA) (\u003ccode\u003escripts/mapdamage2geomparam\u003c/code\u003e). The effect of this\nmutations can be inspected as well.\u003c/p\u003e\n\u003cp\u003eAll the scripts are designed with the UNIX philosophy \"do one thing and do it\nwell\" in mind. Therefore an analysis consists of a pipeline of these scripts.\u003c/p\u003e\n\u003cp\u003eThe universal data format is the text table, a simple text file containing data\nin whitespace- or tab-separated columns. The first line serves as header line\nwith column names.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe manual\u003c/h2\u003e\u003ca id=\"user-content-the-manual\" class=\"anchor\" aria-label=\"Permalink: The manual\" href=\"#the-manual\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe availabe tools are described in-depth in the TAPAS manual.\nThe manual is located in the \u003ccode\u003edocs/\u003c/code\u003e folder. Open the file \u003ccode\u003edocs/index.html\u003c/code\u003e\ninside the TAPAS folder with your browser to view it.\u003c/p\u003e\n\u003cp\u003eThe manual can also be found online, \u003ca href=\"https://mlell.github.io/tapas\" rel=\"nofollow\"\u003eclick here\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThere is also one \u003cstrong\u003eexample script\u003c/strong\u003e which summarises the steps of the manual.\nIt can be found at \u003ccode\u003emanual/example-manual.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo get help about one specific TAPAS tool, execute the tool with the option\n\u003ccode\u003e--help\u003c/code\u003e. Each tool prints then an extensive help page which explains how\nto use it. Example: \u003ccode\u003eTAPAS/scripts/uniform --help\u003c/code\u003e, where \u003ccode\u003eTAPAS\u003c/code\u003e is the\nfolder you installed TAPAS to, prints the help of the artificial read\ngeneration tool \u003ccode\u003euniform\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software package needs the following tools to be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR \u0026gt;= 3.2\u003c/li\u003e\n\u003cli\u003ePython \u0026gt;= 3.4 and Pip, the Python package manager\u003c/li\u003e\n\u003cli\u003eStandard GNU tools like \u003ccode\u003ehead\u003c/code\u003e, \u003ccode\u003eawk\u003c/code\u003e, \u003ccode\u003esort\u003c/code\u003e, \u003ccode\u003ejoin\u003c/code\u003e, \u003ccode\u003esed\u003c/code\u003e. These are\nincluded in all GNU/Linux and Mac distributions. On Windows you can install\nCygwin to get these programs. However, this software package is not yet\ntested on Windows.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese are the commands needed to install these dependencies on your\nsystem. Select those which match your GNU/Linux distribution:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUbuntu\u003c/strong\u003e and derivatives, like \u003cstrong\u003eScientific Linux\u003c/strong\u003e and \u003cstrong\u003eLinux Mint\u003c/strong\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install r python3 python3-pip\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eCentOS\u003c/strong\u003e or \u003cstrong\u003eRed Hat Linux\u003c/strong\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo yum --enablerepo=extras install epel-release\nsudo yum install r python3 python3-pip\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install TAPAS, first download it from the\n\u003ca href=\"https://github.com/mlell/tapas/releases\"\u003eReleases section\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen, execute the script\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTAPAS/scripts/gen/install_dependencies\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere you replace \u003ccode\u003eTAPAS\u003c/code\u003e by the folder where you installed TAPAS into. The\ntool downloads and installs the needed R and Python packages. The packages\nare installed inside the TAPAS folder and do no affect the rest of the system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAlternative method: Use global R and Python packages\u003c/h3\u003e\u003ca id=\"user-content-alternative-method-use-global-r-and-python-packages\" class=\"anchor\" aria-label=\"Permalink: Alternative method: Use global R and Python packages\" href=\"#alternative-method-use-global-r-and-python-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to run TAPAS using R and Python packages which are installed\nglobally on your system, instead of the \u003ccode\u003einstall_dependencies\u003c/code\u003e script, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTAPAS/script/gen/gen-launchers.sh --ext-libs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo check if all dependencies are met, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTAPAS/scripts/setup_check_dependencies\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eon your computer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe scripts\u003c/h2\u003e\u003ca id=\"user-content-the-scripts\" class=\"anchor\" aria-label=\"Permalink: The scripts\" href=\"#the-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll scripts lie in the \u003ccode\u003escripts/\u003c/code\u003e folder. Read the manual to get an overview.\nAdditionally, each script can be executed with the \u003ccode\u003e--help\u003c/code\u003e switch to get\ndetailed information about the script\u0027s functionality and how it can be tailored\nto your specific needs.\u003c/p\u003e\n", + "full_name": "GenomePathogenAnalysisService/read-it-and-keep", + "latest_release": "v0.1.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-read-it-and-keep\" class=\"anchor\" href=\"#read-it-and-keep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eread-it-and-keep\u003c/h1\u003e\n\u003cp\u003eRead contamination removal.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eInstall either from source or build a singularity container.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-from-source\" class=\"anchor\" href=\"#compile-from-source\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile from source\u003c/h3\u003e\n\u003cp\u003eMake the executable \u003ccode\u003esrc/readItAndKeep\u003c/code\u003e by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd src \u0026amp;\u0026amp; make\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h3\u003e\n\u003cp\u003eBuild a singularity container by cloning this repository\nand running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build readItAndKeep.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-docker-container\" class=\"anchor\" href=\"#docker-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker container\u003c/h3\u003e\n\u003cp\u003eBuild a docker container by cloning this repository\nand running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile -t \u0026lt;TAG\u0026gt; .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bioconda-linux-64\" class=\"anchor\" href=\"#bioconda-linux-64\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBioconda (linux-64)\u003c/h3\u003e\n\u003cp\u003eFrom an existing environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c bioconda read-it-and-keep\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing a new environment (recommended):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n read-it-and-keep -c bioconda python=3 read-it-and-keep\nconda activate read-it-and-keep\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eRequired options:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003e--ref_fasta\u003c/code\u003e: reference genome in FASTA format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads1\u003c/code\u003e: at least one reads file in FASTA[.GZ] or FASTQ[.GZ] format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o,--outprefix\u003c/code\u003e: prefix of output files.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePlease note there is an option \u003ccode\u003e--tech\u003c/code\u003e, which defaults to \u003ccode\u003eillumina\u003c/code\u003e. Use\n\u003ccode\u003e--tech ont\u003c/code\u003e for nanopore reads.\u003c/p\u003e\n\u003cp\u003eRun on paired Illumina reads, in two files \u003ccode\u003ereads1.fq.gz\u003c/code\u003e and \u003ccode\u003ereads2.fq.gz\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereadItAndKeep --ref_fasta ref_genome.fasta --reads1 reads1.fq.gz --reads2 reads2.fq.gz -o out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt will output \u003ccode\u003eout.reads_1.fastq.gz\u003c/code\u003e and\n\u003ccode\u003eout.reads_2.fastq.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eRun on one file of nanopore reads \u003ccode\u003ereads.fq.gz\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereadItAndKeep --tech ont --ref_fasta ref_genome.fasta --reads1 reads.fq.gz -o out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt will output \u003ccode\u003eout.reads.fastq.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf the input reads files are in FASTA format, then it will output reads in\nFASTA format, calling the files \u003ccode\u003e*.fasta.*\u003c/code\u003e instead of \u003ccode\u003e*.fastq.*\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIt always writes the counts of input and output reads to \u003ccode\u003eSTDOUT\u003c/code\u003e in\ntab-delimited format, for example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eInput reads file 1\t1000\nInput reads file 2\t1000\nKept reads 1\t950\nKept reads 2\t950\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll logging messages sent to \u003ccode\u003eSTDERR\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-in-docker\" class=\"anchor\" href=\"#running-in-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning in Docker\u003c/h3\u003e\n\u003cp\u003eSome additional arguments are needs to run correctly in Docker, namely to allow access to the required fasta file as well as inputs and outputs. Below is a functional example.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run /path/to/read-it-and-keep/tests:/tests [-v /path/to/input:/input -v /path/to/output:/output] \u0026lt;TAG\u0026gt; --ref_fasta /tests/MN908947.3.fa --reads1 /input/\u0026lt;SAMPLE\u0026gt;_1.fastq.gz --reads2 /input/\u0026lt;SAMPLE\u0026gt;_2.fastq.gz --outprefix /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tests\" class=\"anchor\" href=\"#tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTests\u003c/h2\u003e\n\u003cp\u003eThese are under development. To run them you will need:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePython 3\u003c/li\u003e\n\u003cli\u003ePython package \u003ca href=\"https://docs.pytest.org/en/stable/\" rel=\"nofollow\"\u003epytest\u003c/a\u003e (\u003ccode\u003epip install pytest\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePython package \u003ca href=\"https://github.com/sanger-pathogens/Fastaq\"\u003epyfastaq\u003c/a\u003e (\u003ccode\u003epip install pyfastaq\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm\" rel=\"nofollow\"\u003eART read simulator\u003c/a\u003e\ninstalled, so that \u003ccode\u003eart_illumina\u003c/code\u003e is in your \u003ccode\u003e$PATH\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/rrwick/Badread\"\u003ebadread\u003c/a\u003e for nanopore read simulation.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRun the tests after compiling the source code, ie:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd src\nmake\nmake test\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1716758754.0 + "updated_at": 1639346958.0 }, { "data_format": 2, - "description": "Framework to develop energy estimators for HEP experiments", + "description": "A Nextflow pipeline to run featureCounts on RNAseq BAM files on ICGC in AWS/AWS Batch", "filenames": [ - "contrib/containers/tf2.9_singularity/Singularity", - "contrib/containers/tf2.6_singularity/Singularity" + "Singularity", + "Singularity.1.0.0" ], - "full_name": "usert5432/vlne", + "full_name": "qbic-pipelines/icgc-featurecounts", + "latest_release": "1.0.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"\" class=\"anchor\" href=\"#\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"docs/images/featurecounts_logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"docs/images/featurecounts_logo.png\" alt=\"nf-core/featurecounts\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/nf-core/featurecounts\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a9a5d3186bef5666eba1f45bd2e7e948e0c3c5ff08334d3d6f46c1190f233d39/68747470733a2f2f7472617669732d63692e636f6d2f6e662d636f72652f66656174757265636f756e74732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/nf-core/featurecounts.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/146857ad83dbb5dd463eb6fca54c8f6ce062fae70bda24ff6ea12b08deab557e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e322d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.2-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/qbicsoftware/icgc-featurecounts\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b955ba4fd977e6b8a7fde1ad338b94f1673adb0bad2d30750ffaeb060d61cb05/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f66656174757265636f756e74732e737667\" alt=\"Docker Automated build\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/featurecounts.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1315\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/142166753\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7eeab0b02b6a8878966e94f8c207d2b3fcbf433f88e5dfe3ad21e14633d0001c/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3134323136363735332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/142166753.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis pipeline uses featureCounts on cancer datasets from ICGC and generates count matrices, similar to what \u003ca href=\"https://github.com/nf-core/RNAseq\"\u003enf-core/RNAseq\u003c/a\u003e does. Users can specify a ICGC Manifest file with object ids, which will then be converted to encrypted S3 URLs. The pipeline then uses the provided GTF file to generate count matrices for all files in the manifest.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe ICGC-FeatureCounts pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003eThis pipeline was written by Alexander Peltzer (\u003ca href=\"https://github.com/apeltzer\"\u003eapeltzer\u003c/a\u003e) at \u003ca href=\"apeltzer.github.io\"\u003eQBiC\u003c/a\u003e with some help from Paolo DiTommaso (\u003ca href=\"https://github.com/pditommaso\"\u003epditommaso\u003c/a\u003e) and the Nextflow community.\u003c/p\u003e\n", + "stargazers_count": 3, + "subscribers_count": 12, + "topics": [ + "icgc", + "genomics", + "reproducible", + "docker", + "singularity", + "nextflow", + "best-practice" + ], + "updated_at": 1638968735.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "singularity/Singularity.clockwork", + "singularity/Singularity.preprocessing" + ], + "full_name": "Pathogen-Genomics-Cymru/tb-pipeline", "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-tb-pipeline\" class=\"anchor\" href=\"#tb-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTB Pipeline\u003c/h1\u003e\n\u003cp\u003eThis pipeline takes as input reads presumed to be from one of 10 mycobacterial genomes: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis. Input should be in the form of one directory containing pairs of fastq(.gz) or bam files.\u003c/p\u003e\n\u003cp\u003ePipeline cleans and QCs reads with fastp and FastQC, classifies with Kraken2 \u0026amp; Mykrobe, removes non-bacterial content, and - by alignment to any minority genomes - disambiguates mixtures of bacterial reads. Cleaned reads are aligned to either of the 10 supported genomes and variants called. Produces as output one directory per sample, containing cleaned fastqs, sorted, indexed BAM, VCF, and summary reports.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eRequires \u003ccode\u003eNXF_VER\u0026gt;=20.11.0-edge\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe workflow is designed to run with either docker \u003ccode\u003e-profile docker\u003c/code\u003e or singularity \u003ccode\u003e-profile singularity\u003c/code\u003e. Before running the workflow, the images will need to be built by running either \u003ccode\u003edocker/docker_build.sh\u003c/code\u003e or \u003ccode\u003esingularity/singularity_build.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eE.g. to run the workflow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNXF_VER=20.11.0-edge nextflow run main.nf -profile singularity --filetype fastq --input_dir fq_dir --pattern \"*_R{1,2}.fastq.gz\" --unmix_myco yes \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\nNXF_VER=20.11.0-edge nextflow run main.nf -profile docker --filetype bam --input_dir bam_dir --unmix_myco no \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-params\" class=\"anchor\" href=\"#params\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParams\u003c/h2\u003e\n\u003cp\u003eThe following parameters should be set in \u003ccode\u003enextflow.config\u003c/code\u003e or specified on the command line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003einput_dir\u003c/strong\u003e\u003cbr\u003e\nDirectory containing fastq OR bam files\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efiletype\u003c/strong\u003e\u003cbr\u003e\nFile type in input_dir. Either \"fastq\" or \"bam\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003epattern\u003c/strong\u003e\u003cbr\u003e\nRegex to match fastq files in input_dir, e.g. \"*_R{1,2}.fq.gz\". Only mandatory if --filetype is \"fastq\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eoutput_dir\u003c/strong\u003e\u003cbr\u003e\nOutput directory for results\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eunmix_myco\u003c/strong\u003e\u003cbr\u003e\nDo you want to disambiguate mixed-mycobacterial samples by read alignment? Either \"yes\" or \"no\":\n\u003cul\u003e\n\u003cli\u003eIf \"yes\" workflow will remove reads mapping to any minority mycobacterial genomes but in doing so WILL ALMOST CERTAINLY ALSO reduce coverage of the principal species\u003c/li\u003e\n\u003cli\u003eIf \"no\" then mixed-mycobacterial samples will be left alone. Mixtures of mycobacteria + non-mycobacteria will still be disambiguated\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003especies\u003c/strong\u003e\u003cbr\u003e\nPrincipal species in each sample, assuming genus Mycobacterium. Default \u0027null\u0027. If parameter used, takes 1 of 10 values: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis. Using this parameter will apply an additional sanity test to your sample\n\u003cul\u003e\n\u003cli\u003eIf you DO NOT use this parameter (default option), pipeline will determine principal species from the reads and consider any other species a contaminant\u003c/li\u003e\n\u003cli\u003eIf you DO use this parameter, pipeline will expect this to be the principal species. It will fail the sample if reads from this species are not actually the majority\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ekraken_db\u003c/strong\u003e\u003cbr\u003e\nDirectory containing \u003ccode\u003e*.k2d\u003c/code\u003e Kraken2 database files (k2_pluspf_16gb_20200919 recommended, obtain from \u003ca href=\"https://benlangmead.github.io/aws-indexes/k2\" rel=\"nofollow\"\u003ehttps://benlangmead.github.io/aws-indexes/k2\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie2_index\u003c/strong\u003e\u003cbr\u003e\nDirectory containing Bowtie2 index (obtain from \u003ca href=\"ftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/hg19_1kgmaj_bt2.zip\" rel=\"nofollow\"\u003eftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/hg19_1kgmaj_bt2.zip\u003c/a\u003e). The specified path should NOT include the index name\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie_index_name\u003c/strong\u003e\u003cbr\u003e\nName of the bowtie index, e.g. hg19_1kgmaj\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp\u003eFor more information on the parameters run \u003ccode\u003enextflow run main.nf --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe path to the singularity images can also be changed in the singularity profile in \u003ccode\u003enextflow.config\u003c/code\u003e. Default value is \u003ccode\u003e${baseDir}/singularity\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-stub-run\" class=\"anchor\" href=\"#stub-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStub-run\u003c/h2\u003e\n\u003cp\u003eTo test the stub run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNXF_VER=20.11.0-edge nextflow run main.nf -stub -config testing.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-checkpoints\" class=\"anchor\" href=\"#checkpoints\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheckpoints\u003c/h2\u003e\n\u003cp\u003eCheckpoints used throughout this workflow to fail a sample/issue warnings:\u003c/p\u003e\n\u003cp\u003eprocesses preprocessing:checkFqValidity or preprocessing:checkBamValidity\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e(Fail) If sample does not pass fqtools \u0027validate\u0027 or samtools \u0027quickcheck\u0027, as appropriate.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eprocess preprocessing:countReads\u003cbr\u003e\n2. (Fail) If sample contains \u0026lt; 100k pairs of raw reads.\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:fastp\u003cbr\u003e\n3. (Fail) If sample contains \u0026lt; 100k pairs of cleaned reads, required to all be \u0026gt; 50bp (cleaning using fastp with --length_required 50 --average_qual 10 --low_complexity_filter --correction --cut_right --cut_tail --cut_tail_window_size 1 --cut_tail_mean_quality 20).\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:kraken2\u003cbr\u003e\n4. (Fail) If the top family hit is not Mycobacteriaceae\u003cbr\u003e\n5. (Fail) If there are fewer than 100k reads classified as Mycobacteriaceae \u003cbr\u003e\n6. (Warn) If the top family classification is mycobacterial, but this is not consistent with top genus and species classifications\u003cbr\u003e\n7. (Warn) If the top family is Mycobacteriaceae but no G1 (species complex) classifications meet minimum thresholds of \u0026gt; 5000 reads or \u0026gt; 0.5% of the total reads (this is not necessarily a concern as not all mycobacteria have a taxonomic classification at this rank)\u003cbr\u003e\n8. (Warn) If sample is mixed or contaminated - defined as containing reads \u0026gt; the 5000/0.5% thresholds from multiple non-human species\u003cbr\u003e\n9. (Warn) If sample contains multiple classifications to mycobacterial species complexes, each meeting the \u0026gt; 5000/0.5% thresholds\u003cbr\u003e\n10. (Warn) If no species classification meets the 5000/0.5% thresholds\u003cbr\u003e\n11. (Warn) If no genus classification meets the 5000/0.5% thresholds\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:identifyBacterialContaminants\u003cbr\u003e\n12. (Fail) If regardless of what Kraken reports, Mykrobe does not make a species-level mycobacterial classification (note that we do not use Kraken mycobacterial classifications other than to determine whether 100k reads are family Mycobacteriaceae; for higher-resolution classification, we defer to Mykrobe)\u003cbr\u003e\n13. (Fail) If the sample is not contaminated and the top species hit is not one of the 10 supported Mycobacteria: abscessus|africanum|avium|bovis|chelonae|chimaera|fortuitum|intracellulare|kansasii|tuberculosis\u003cbr\u003e\n14. (Fail) If the sample is not contaminated and the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003cbr\u003e\n15. (Warn) If the top Mykrobe species hit, on the basis of highest % coverage, does not also have the highest median depth\u003cbr\u003e\n16. (Warn) If we are unable to associate an NCBI taxon ID to any given contaminant species, which means we will not be able to locate its genome, and thereby remove it as a contaminant\u003cbr\u003e\n17. (Warn) If we are unable to determine a URL for the latest RefSeq genome associated with a contaminant species\u0027 taxon ID\u003cbr\u003e\n18. (Warn) If no complete genome could be found for a contaminant species. The workflow will proceed with alignment-based contaminant removal, but you\u0027re warned that there\u0027s reduced confidence in detecting reads from this species\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:downloadContamGenomes\u003cbr\u003e\n19. (Fail) If a contaminant is detected but we are unable to download a representative genome, and thereby remove it\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:summarise\u003cbr\u003e\n20. (Fail) If after having taken an alignment-based approach to decontamination, Kraken still detects a contaminant species\u003cbr\u003e\n21. (Fail) If after having taken an alignment-based approach to decontamination, the top species hit is not one of the 10 supported Mycobacteria\u003cbr\u003e\n22. (Fail) If, after successfully removing contaminants, the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003c/p\u003e\n\u003cp\u003eprocess clockwork:alignToRef\u003cbr\u003e\n23. (Fail) If \u0026lt; 100k reads could be aligned to the reference genome\u003cbr\u003e\n24. (Fail) If, after aligning to the reference genome, the average read mapping quality \u0026lt; 10\u003cbr\u003e\n25. (Fail) If \u0026lt; 50% of the reference genome was covered at 10-fold depth\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1685494920.0 + "updated_at": 1642094977.0 }, { "data_format": 2, - "description": "sequana pipeline to perform parallel fastqc and summarize results with multiqc plot", + "description": "A repository for showcasing my knowledge of the Singularity programming language, and continuing to learn the language.", "filenames": [ - "singularity/Singularity" + "Singularity", + "Singularity.def", + "OldVersions/PROJECT_LANGUAGE/Singularity/Singularity" ], - "full_name": "sequana/fastqc", - "latest_release": "v1.7.0", + "full_name": "seanpm2001/Learn-Singularity", + "latest_release": null, + "readme": "\u003chr\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"Image.svg\"\u003e\u003cimg src=\"Image.svg\" alt=\"{Project icon} This image failed to load. 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\u003ca href=\"/.github/README_HT.md\"\u003eht Krey\u00f2l ayisyen\u003c/a\u003e Haitian Creole | \u003ca href=\"/.github/README_HA.md\"\u003eha Hausa\u003c/a\u003e Hausa | \u003ca href=\"/.github/README_HAW.md\"\u003ehaw \u014clelo Hawai\u02bbi\u003c/a\u003e Hawaiian | \u003ca href=\"/.github/README_HE.md\"\u003ehe \u05e2\u05b4\u05d1\u05e8\u05b4\u05d9\u05ea\u003c/a\u003e Hebrew | \u003ca href=\"/.github/README_HI.md\"\u003ehi \u0939\u093f\u0928\u094d\u0926\u0940\u003c/a\u003e Hindi | \u003ca href=\"/.github/README_HMN.md\"\u003ehmn Hmong\u003c/a\u003e Hmong | \u003ca href=\"/.github/README_HU.md\"\u003ehu Magyar\u003c/a\u003e Hungarian | \u003ca href=\"/.github/README_IS.md\"\u003eis \u00cdslenska\u003c/a\u003e Icelandic | \u003ca href=\"/.github/README_IG.md\"\u003eig Igbo\u003c/a\u003e Igbo | \u003ca href=\"/.github/README_ID.md\"\u003eid bahasa Indonesia\u003c/a\u003e Icelandic | \u003ca href=\"/.github/README_GA.md\"\u003ega Gaeilge\u003c/a\u003e Irish | \u003ca href=\"/.github/README_IT.md\"\u003eit Italiana/Italiano\u003c/a\u003e | \u003ca href=\"/.github/README_JA.md\"\u003eja \u65e5\u672c\u8a9e\u003c/a\u003e Japanese | \u003ca href=\"/.github/README_JW.md\"\u003ejw Wong jawa\u003c/a\u003e Javanese | \u003ca href=\"/.github/README_KN.md\"\u003ekn \u0c95\u0ca8\u0ccd\u0ca8\u0ca1\u003c/a\u003e Kannada | \u003ca href=\"/.github/README_KK.md\"\u003ekk \u049a\u0430\u0437\u0430\u049b\u003c/a\u003e Kazakh | \u003ca href=\"/.github/README_KM.md\"\u003ekm \u1781\u17d2\u1798\u17c2\u179a\u003c/a\u003e Khmer | \u003ca href=\"/.github/README_RW.md\"\u003erw Kinyarwanda\u003c/a\u003e Kinyarwanda | \u003ca href=\"/.github/README_KO_SOUTH.md\"\u003eko-south \u97d3\u570b\u8a9e\u003c/a\u003e Korean (South) | \u003ca href=\"README_KO_NORTH.md\"\u003eko-north \ubb38\ud654\uc5b4\u003c/a\u003e Korean (North) (NOT YET TRANSLATED) | \u003ca href=\"/.github/README_KU.md\"\u003eku Kurd\u00ee\u003c/a\u003e Kurdish (Kurmanji) | \u003ca href=\"/.github/README_KY.md\"\u003eky \u041a\u044b\u0440\u0433\u044b\u0437\u0447\u0430\u003c/a\u003e Kyrgyz | \u003ca href=\"/.github/README_LO.md\"\u003elo \u0ea5\u0eb2\u0ea7\u003c/a\u003e Lao | \u003ca href=\"/.github/README_LA.md\"\u003ela Latine\u003c/a\u003e Latin | \u003ca href=\"/.github/README_LT.md\"\u003elt Lietuvis\u003c/a\u003e Lithuanian | \u003ca href=\"/.github/README_LB.md\"\u003elb L\u00ebtzebuergesch\u003c/a\u003e Luxembourgish | \u003ca href=\"/.github/README_MK.md\"\u003emk \u041c\u0430\u043a\u0435\u0434\u043e\u043d\u0441\u043a\u0438\u003c/a\u003e Macedonian | \u003ca href=\"/.github/README_MG.md\"\u003emg Malagasy\u003c/a\u003e Malagasy | \u003ca href=\"/.github/README_MS.md\"\u003ems Bahasa Melayu\u003c/a\u003e Malay | \u003ca href=\"/.github/README_ML.md\"\u003eml \u0d2e\u0d32\u0d2f\u0d3e\u0d33\u0d02\u003c/a\u003e Malayalam | \u003ca href=\"/.github/README_MT.md\"\u003emt Malti\u003c/a\u003e Maltese | \u003ca href=\"/.github/README_MI.md\"\u003emi Maori\u003c/a\u003e Maori | \u003ca href=\"/.github/README_MR.md\"\u003emr \u092e\u0930\u093e\u0920\u0940\u003c/a\u003e Marathi | \u003ca href=\"/.github/README_MN.md\"\u003emn \u041c\u043e\u043d\u0433\u043e\u043b\u003c/a\u003e Mongolian | \u003ca href=\"/.github/README_MY.md\"\u003emy \u1019\u103c\u1014\u103a\u1019\u102c\u003c/a\u003e Myanmar (Burmese) | \u003ca href=\"/.github/README_NE.md\"\u003ene \u0928\u0947\u092a\u093e\u0932\u0940\u003c/a\u003e Nepali | \u003ca href=\"/.github/README_NO.md\"\u003eno norsk\u003c/a\u003e Norwegian | \u003ca href=\"/.github/README_OR.md\"\u003eor \u0b13\u0b21\u0b3f\u0b06 (\u0b13\u0b21\u0b3f\u0b06)\u003c/a\u003e Odia (Oriya) | \u003ca href=\"/.github/README_PS.md\"\u003eps \u067e\u069a\u062a\u0648\u003c/a\u003e Pashto | \u003ca href=\"/.github/README_FA.md\"\u003efa \u0641\u0627\u0631\u0633\u06cc\u003c/a\u003e |Persian \u003ca href=\"/.github/README_PL.md\"\u003epl polski\u003c/a\u003e Polish | \u003ca href=\"/.github/README_PT.md\"\u003ept portugu\u00eas\u003c/a\u003e Portuguese | \u003ca href=\"/.github/README_PA.md\"\u003epa \u0a2a\u0a70\u0a1c\u0a3e\u0a2c\u0a40\u003c/a\u003e Punjabi | No languages available that start with the letter Q | \u003ca href=\"/.github/README_RO.md\"\u003ero Rom\u00e2n\u0103\u003c/a\u003e Romanian | \u003ca href=\"/.github/README_RU.md\"\u003eru \u0440\u0443\u0441\u0441\u043a\u0438\u0439\u003c/a\u003e Russian | \u003ca href=\"/.github/README_SM.md\"\u003esm Faasamoa\u003c/a\u003e Samoan | \u003ca href=\"/.github/README_GD.md\"\u003egd G\u00e0idhlig na h-Alba\u003c/a\u003e Scots Gaelic | \u003ca href=\"/.github/README_SR.md\"\u003esr \u0421\u0440\u043f\u0441\u043a\u0438\u003c/a\u003e Serbian | \u003ca href=\"/.github/README_ST.md\"\u003est Sesotho\u003c/a\u003e Sesotho | \u003ca href=\"/.github/README_SN.md\"\u003esn Shona\u003c/a\u003e Shona | \u003ca href=\"/.github/README_SD.md\"\u003esd \u0633\u0646\u068c\u064a\u003c/a\u003e Sindhi | \u003ca href=\"/.github/README_SI.md\"\u003esi \u0dc3\u0dd2\u0d82\u0dc4\u0dbd\u003c/a\u003e Sinhala | \u003ca href=\"/.github/README_SK.md\"\u003esk Slov\u00e1k\u003c/a\u003e Slovak | \u003ca href=\"/.github/README_SL.md\"\u003esl Sloven\u0161\u010dina\u003c/a\u003e Slovenian | \u003ca href=\"/.github/README_SO.md\"\u003eso Soomaali\u003c/a\u003e Somali | [\u003ca href=\"/.github/README_ES.md\"\u003ees en espa\u00f1ol\u003c/a\u003e Spanish | \u003ca href=\"/.github/README_SU.md\"\u003esu Sundanis\u003c/a\u003e Sundanese | \u003ca href=\"/.github/README_SW.md\"\u003esw Kiswahili\u003c/a\u003e Swahili | \u003ca href=\"/.github/README_SV.md\"\u003esv Svenska\u003c/a\u003e Swedish | \u003ca href=\"/.github/README_TG.md\"\u003etg \u0422\u043e\u04b7\u0438\u043a\u04e3\u003c/a\u003e Tajik | \u003ca href=\"/.github/README_TA.md\"\u003eta \u0ba4\u0bae\u0bbf\u0bb4\u0bcd\u003c/a\u003e Tamil | \u003ca href=\"/.github/README_TT.md\"\u003ett \u0422\u0430\u0442\u0430\u0440\u003c/a\u003e Tatar | \u003ca href=\"/.github/README_TE.md\"\u003ete \u0c24\u0c46\u0c32\u0c41\u0c17\u0c41\u003c/a\u003e Telugu | \u003ca href=\"/.github/README_TH.md\"\u003eth \u0e44\u0e17\u0e22\u003c/a\u003e Thai | \u003ca href=\"/.github/README_TR.md\"\u003etr T\u00fcrk\u003c/a\u003e Turkish | \u003ca href=\"/.github/README_TK.md\"\u003etk T\u00fcrkmenler\u003c/a\u003e Turkmen | \u003ca href=\"/.github/README_UK.md\"\u003euk \u0423\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0438\u0439\u003c/a\u003e Ukrainian | \u003ca href=\"/.github/README_UR.md\"\u003eur \u0627\u0631\u062f\u0648\u003c/a\u003e Urdu | \u003ca href=\"/.github/README_UG.md\"\u003eug \u0626\u06c7\u064a\u063a\u06c7\u0631\u003c/a\u003e Uyghur | \u003ca href=\"/.github/README_UZ.md\"\u003euz O\u0027zbek\u003c/a\u003e Uzbek | \u003ca href=\"/.github/README_VI.md\"\u003evi Ti\u1ebfng Vi\u1ec7t\u003c/a\u003e Vietnamese | \u003ca href=\"/.github/README_CY.md\"\u003ecy Cymraeg\u003c/a\u003e Welsh | \u003ca href=\"/.github/README_XH.md\"\u003exh isiXhosa\u003c/a\u003e Xhosa | \u003ca href=\"/.github/README_YI.md\"\u003eyi \u05d9\u05d9\u05d3\u05d9\u05e9\u003c/a\u003e Yiddish | \u003ca href=\"/.github/README_YO.md\"\u003eyo Yoruba\u003c/a\u003e Yoruba | \u003ca href=\"/.github/README_ZU.md\"\u003ezu Zulu\u003c/a\u003e Zulu ) Available in 110 languages (108 when not counting English and North Korean, as North Korean has not been translated yet \u003ca href=\"/OldVersions/Korean(North)/README.md\"\u003eRead about it here\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eTranslations in languages other than English are machine translated and are not yet accurate. No errors have been fixed yet as of March 21st 2021. Please report translation errors \u003ca href=\"https://github.com/%3CdeveloperName%3E/%3CrepoName%3E/issues/\"\u003ehere\u003c/a\u003e. Make sure to backup your correction with sources and guide me, as I don\u0027t know languages other than English well (I plan on getting a translator eventually) please cite \u003ca href=\"https://en.wiktionary.org\" rel=\"nofollow\"\u003ewiktionary\u003c/a\u003e and other sources in your report. Failing to do so will result in a rejection of the correction being published.\u003c/p\u003e\n\u003cp\u003eNote: due to limitations with GitHub\u0027s interpretation of markdown (and pretty much every other web-based interpretation of markdown) clicking these links will redirect you to a separate file on a separate page that isn\u0027t the intended page. You will be redirected to the \u003ca href=\"/.github/\"\u003e.github folder\u003c/a\u003e of this project, where the README translations are hosted.\u003c/p\u003e\n\u003cp\u003eTranslations are currently done with Bing translate and DeepL. Support for Google Translate translations is coming to a close due to privacy concerns.\u003c/p\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-index\" class=\"anchor\" aria-hidden=\"true\" href=\"#index\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIndex\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"#Top\"\u003e00.0 - Top\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#%3CprojectName%3E\"\u003e00.1 - Title\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Read-this-article-in-a-different-language\"\u003e00.2 - Read this article in a different language\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Index\"\u003e00.3 - Index\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#RepositoryName\"\u003e01.0 - Description\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#About\"\u003e02.0 - About\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Wiki\"\u003e03.0 - Wiki\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#History\"\u003e04.0 - History\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Pre-history\"\u003e04.1 - Pre-history\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Alpha-history\"\u003e04.2 - Alpha History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Beta-history\"\u003e04.3 - Beta History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Modern-history\"\u003e04.4 - Modern History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#Copying\"\u003e05.0 - Copying\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Credits\"\u003e06.0 - Credits\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Installation\"\u003e07.0 - Installation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Version-history\"\u003e08.0 - Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Version-history\"\u003e09.0 - Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Software-status\"\u003e10.0 - Software status\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Sponsor-info\"\u003e11.0 - Sponsor info\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Contributers\"\u003e12.0 - Contributers\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Issues\"\u003e13.0 - Issues\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Current-issues\"\u003e13.1 - Current issues\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Past-issues\"\u003e13.2 - Past issues\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Past-pull-requests\"\u003e13.3 - Past pull requests\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Active-pull-requests\"\u003e13.4 - Active pull requests\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#Resources\"\u003e14.0 - Resources\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Contributing\"\u003e15.0 - Contributing\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#About-README\"\u003e16.0 - About README\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#README-version-history\"\u003e17.0 - README Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#You-have-reached-the-end-of-the-README-file\"\u003e18.0 - Footer\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#EOF\"\u003e18.9 - End of file\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cp\u003e\u0026lt;repo_description\u0026gt;\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eSee above. \u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-wiki\" class=\"anchor\" aria-hidden=\"true\" href=\"#wiki\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWiki\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/%3CdeveloperName%3E/%3CrepoName%3E/wiki\"\u003eClick/tap here to view this projects Wiki\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf the project has been forked, the Wiki was likely removed. Luckily, I include an embedded version. You can view it \u003ca href=\"/External/ProjectWiki/\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eWrite about this projects history here.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pre-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-history\u003c/h3\u003e\n\u003cp\u003eNo pre-history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alpha-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#alpha-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlpha history\u003c/h3\u003e\n\u003cp\u003eNo Alpha history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-beta-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#beta-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBeta history\u003c/h3\u003e\n\u003cp\u003eNo Beta history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-modern-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#modern-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModern history\u003c/h3\u003e\n\u003cp\u003eNo Modern history to show for this project.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-copying\" class=\"anchor\" aria-hidden=\"true\" href=\"#copying\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopying\u003c/h2\u003e\n\u003cp\u003eView the copying license for this project \u003ca href=\"/COPYING\"\u003ehere\u003c/a\u003e (if you haven\u0027t built the project yet with the makefile, here is the original link: \u003ca href=\"/COPYINGL\"\u003eCOPYINGL\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease note that you also have to follow the rules of the GNU General Public License v3 (GPL3) which you can view \u003ca href=\"/LICENSE.txt\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eView the credits file for this project and see the people who got together to make this project by \u003ca href=\"/CREDITS\"\u003eclicking/tapping here\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eView the installation instructions file for this project \u003ca href=\"/INSTALL\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRequirements: Read the instructions for more info, and get the latest up-to-date instructions \u003ca href=\"https://gist.github.com/seanpm2001/745564a46186888e829fdeb9cda584de\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sponsor-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#sponsor-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSponsor info\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/SponsorButton.png\"\u003e\u003cimg src=\"/SponsorButton.png\" alt=\"SponsorButton.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can sponsor this project if you like, but please specify what you want to donate to. \u003ca href=\"https://github.com/seanpm2001/Sponsor-info/tree/main/For-sponsors/\"\u003eSee the funds you can donate to here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can view other sponsor info \u003ca href=\"https://github.com/seanpm2001/Sponsor-info/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTry it out! The sponsor button is right up next to the watch/unwatch button.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-version-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#version-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion history\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eVersion history currently unavailable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo other versions listed\u003c/strong\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware status\u003c/h2\u003e\n\u003cp\u003eAll of my works are free some restrictions. DRM (\u003cstrong\u003eD\u003c/strong\u003eigital \u003cstrong\u003eR\u003c/strong\u003eestrictions \u003cstrong\u003eM\u003c/strong\u003eanagement) is not present in any of my works.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/DRM-free_label.en.svg\"\u003e\u003cimg src=\"/DRM-free_label.en.svg\" alt=\"DRM-free_label.en.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis sticker is supported by the Free Software Foundation. I never intend to include DRM in my works.\u003c/p\u003e\n\u003cp\u003eI am using the abbreviation \"Digital Restrictions Management\" instead of the more known \"Digital Rights Management\" as the common way of addressing it is false, there are no rights with DRM. The spelling \"Digital Restrictions Management\" is more accurate, and is supported by \u003ca href=\"https://en.wikipedia.org/wiki/Richard_Stallman\" rel=\"nofollow\"\u003eRichard M. Stallman (RMS)\u003c/a\u003e and the \u003ca href=\"https://en.wikipedia.org/wiki/Free_Software_Foundation\" rel=\"nofollow\"\u003eFree Software Foundation (FSF)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis section is used to raise awareness for the problems with DRM, and also to protest it. DRM is defective by design and is a major threat to all computer users and software freedom.\u003c/p\u003e\n\u003cp\u003eImage credit: \u003ca href=\"https://www.defectivebydesign.org/drm-free/how-to-use-label/\" rel=\"nofollow\"\u003edefectivebydesign.org/drm-free/...\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributers\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributers\u003c/h2\u003e\n\u003cp\u003eCurrently, I am the only contributer. Contributing is allowed, as long as you follow the rules of the \u003ca href=\"/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e file.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/seanpm2001/\"\u003eseanpm2001\u003c/a\u003e - x commits (As of Yr, DoW, Month, DoM, at ##:## a/pm)\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eNo other contributers.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssues\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-current-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#current-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCurrent issues\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other current issues\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-past-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#past-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePast issues\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other past issues\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-past-pull-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#past-pull-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePast pull requests\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other past pull requests\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-active-pull-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#active-pull-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eActive pull requests\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other active pull requests\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h2\u003e\n\u003cp\u003eHere are some other resources for this project:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"PROJECT_LANG_1.%3CfileExtensionForProgrammingLanguage%3E\"\u003eProject language file A\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/%3CdeveloperName%3E/%3CrepoName%3E/discussions\"\u003eJoin the discussion on GitHub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNo other resources at the moment.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eContributing is allowed for this project, as long as you follow the rules of the \u003ccode\u003eCONTRIBUTING.md\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/CONTRIBUTING.md\"\u003eClick/tap here to view the contributing rules for this project\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout README\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eFile type:\u003c/strong\u003e \u003ccode\u003eMarkdown Document (*.md *.mkd *.markdown)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile version:\u003c/strong\u003e \u003ccode\u003e0.1.6 (Monday, August 23rd 2021 at 6:37 pm)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLine count (including blank lines and compiler line):\u003c/strong\u003e \u003ccode\u003e0,407\u003c/code\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-readme-version-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#readme-version-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREADME version history\u003c/h2\u003e\n\u003cp\u003eVersion 0.1 (Sunday, March 21st 2021 at 7:50 pm)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eStarted the file\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the about section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the Wiki section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the version history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the issues section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the past issues section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the past pull requests section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the active pull requests section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the contributors section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the contributing section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the about README section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the README version history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the resources section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded a software status section, with a DRM free sticker and message\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the sponsor info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eITERATION 5\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eITERATION 6\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eFixed and update template links\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the copying section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the credits section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the installation section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the resources section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the contributors section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the technical notes section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the footer\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 0.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eVersion 1 (Coming soon)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eComing soon\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eVersion 2 (Coming soon)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eComing soon\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 2\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-you-have-reached-the-end-of-the-readme-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#you-have-reached-the-end-of-the-readme-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYou have reached the end of the README file\u003c/h3\u003e\n\u003cp\u003e( \u003ca href=\"#Top\"\u003eBack to top\u003c/a\u003e | \u003ca href=\"https://github.com\"\u003eExit to GitHub\u003c/a\u003e | \u003ca href=\"https://www.bing.com/\" rel=\"nofollow\"\u003eExit to Bing\u003c/a\u003e | \u003ca href=\"https://duckduckgo.com/\" rel=\"nofollow\"\u003eExit to DuckDuckGo\u003c/a\u003e | \u003ca href=\"https://www.ecosia.org\" rel=\"nofollow\"\u003eExit to Ecosia\u003c/a\u003e )\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-eof\" class=\"anchor\" aria-hidden=\"true\" href=\"#eof\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEOF\u003c/h3\u003e\n\u003chr\u003e\n", "stargazers_count": 3, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ - "fastqc", - "ngs", - "snakemake", - "sequana" + "apptainer", + "article", + "gpl3", + "gplv3", + "md", + "seanpm2001", + "seanpm2001-education", + "seanpm2001-learn", + "singularity", + "txt", + "knowldege", + "learn-singularity", + "learn-singularity-lang", + "learn-singularity-language", + "singularity-collection", + "singularity-lang", + "singularity-language" ], - "updated_at": 1674574510.0 + "updated_at": 1668813015.0 }, { "data_format": 2, - "description": "Generic libraries and utilities that support atmospheric simulations with OpenFOAM", + "description": "snakemake workflow for basecalling and demultiplexing of ONT sequencing data", "filenames": [ - "Singularity" + "baseDmux/data/containers/Singularity.guppy6.0.1gpu-conda-api", + "baseDmux/data/containers/Singularity.guppy6.3.7gpu-mamba-api", + "baseDmux/data/containers/Singularity.UbuntuDHubBionic-deepbinner-api", + "baseDmux/data/containers/Singularity.deepbinner-api" ], - "full_name": "AtmosFOAM/AtmosFOAM-tools", - "latest_release": "jshaw-thesis", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-atmosfoam-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#atmosfoam-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAtmosFOAM-tools\u003c/h1\u003e\n\u003cp\u003eAtmosFOAM-tools contains generic libraries and utilities that support atmospheric simulations with \u003ca href=\"https://openfoam.org/\" rel=\"nofollow\"\u003eOpenFOAM\u003c/a\u003e. These generic tools can be combined with \u003ca href=\"https://github.com/AtmosFOAM/AtmosFOAM\"\u003eAtmosFOAM\u003c/a\u003e and \u003ca href=\"https://github.com/AtmosFOAM/AMMM\"\u003eAMMM\u003c/a\u003e repositories.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-source-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#source-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource installation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall a recent version of \u003ca href=\"http://www.openfoam.org/download/\" rel=\"nofollow\"\u003eopenfoam-dev\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall libgdal. On ubuntu this is done with \u003ccode\u003eapt-get install libgdal-dev\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo to directory\ncd $WM_PROJECT_USER_DIR\nand download AtmosFOAM-tools using:\ngit clone \u003ca href=\"https://github.com/AtmosFOAM/AtmosFOAM-tools.git\"\u003ehttps://github.com/AtmosFOAM/AtmosFOAM-tools.git\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExport environment variables \u003ccode\u003eATMOSFOAM_TOOLS_SRC\u003c/code\u003e and \u003ccode\u003eGMTU\u003c/code\u003e in your \u003ccode\u003e~/.bashrc\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export ATMOSFOAM_TOOLS_SRC=/path/to/AtmosFOAM-tools/src\n export GMTU=/path/to/AtmosFOAM-tools/gmtUser\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompile AtmosFOAM-tools:\ncd AtmosFOAM-tools\n./Allwmake\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "vibaotram/baseDmux", + "latest_release": "v1.1.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-basecalling-and-demultiplexing-for-ont-sequencing-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#basecalling-and-demultiplexing-for-ont-sequencing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBASEcalling and DeMUltipleXing for ONT sequencing data\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-a-snakemake-workflow-for-basecalling-and-gathering-ont-reads-originating-from-disparate-runs-and-barcodes\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-snakemake-workflow-for-basecalling-and-gathering-ont-reads-originating-from-disparate-runs-and-barcodes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA Snakemake workflow for basecalling and gathering ONT reads originating from disparate runs and barcodes\u003c/h2\u003e\n\u003cp\u003eBasecalling by GUPPY + Demultiplexing by GUPPY and/or DEEPBINNER + MinIONQC/Multiqc + QC reports + reads aggregation into bins + fastq reads trimming + filtering\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./dag/full_dag.svg\"\u003e\u003cimg src=\"./dag/full_dag.svg\" width=\"500\" height=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003esingularity \u0026gt;= 2.5\u003c/li\u003e\n\u003cli\u003econda \u0026gt;=4.3 + Mamba\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-implemented-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#implemented-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImplemented tools\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eSnakemake\u003c/li\u003e\n\u003cli\u003eGuppy\u003c/li\u003e\n\u003cli\u003eDeepbinner\u003c/li\u003e\n\u003cli\u003eMinIONQC\u003c/li\u003e\n\u003cli\u003eMultiqc\u003c/li\u003e\n\u003cli\u003ePorechop\u003c/li\u003e\n\u003cli\u003eFiltlong\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe try to update the tools regularly. See versions in the folders containning \u003ca href=\"baseDmux/data/conda\"\u003econda environment\u003c/a\u003e and \u003ca href=\"baseDmux/data/containers\"\u003esingularity container recipie\u003c/a\u003e files.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-more-details-about-individual-snakemake-rules\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-details-about-individual-snakemake-rules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore details about individual snakemake Rules\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGuppy basecalling\u003c/strong\u003e\u003cbr\u003e\nRun \u003ccode\u003eguppy_basecaller\u003c/code\u003e with filtering reads, then subset fast5 reads from passed reads list (\u003ccode\u003epassed_sequencing_summary.txt\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGuppy demultiplexing\u003c/strong\u003e\u003cbr\u003e\nRun \u003ccode\u003eguppy_barcoder\u003c/code\u003e with passed fastq, then subset fastq to classified barcode folders based on \u003ccode\u003ebarcoding_summary.txt\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMulti to single fast5\u003c/strong\u003e\u003cbr\u003e\nConvert passed multi-read fast5 files to single-read fast5 file, preparing for deepbinner.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDeepbinner classification\u003c/strong\u003e\u003cbr\u003e\nRun \u003ccode\u003edeepbinner classify\u003c/code\u003e with pass single-read fast5, output classification file.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDeepbinner bin\u003c/strong\u003e\u003cbr\u003e\nClassify passed fastq based on classification file, then subset fastq to barcode folders.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGet sequencing summary per barcode\u003c/strong\u003e\u003cbr\u003e\nSubset \u003ccode\u003epassed_sequencing_summary.txt\u003c/code\u003e according to barcode ids, preparing for minionqc/multiqc of each barcode and subseting fast5 reads per barcode (get multi fast5 per barcode).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMinIONQC and Multiqc\u003c/strong\u003e\u003cbr\u003e\nAfter basecalling, MinIONQC is performed for each run, and Multiqc reports all run collectively.\nOn the other hand, after demultiplexing, MinIONQC runs for each barcode separately then Multiqc aggregates MinIONQC results of all barcodes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDemultiplex report (optional)\u003c/strong\u003e\u003cbr\u003e\nCompare demultiplexing results from different runs, and from different demultiplexers (guppy and/or deepbinner) by analyzing information of \u003ccode\u003emultiqc_minionqc.txt\u003c/code\u003e. It is only available when demultiplexing rules are executed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGet reads per genome (optional)\u003c/strong\u003e\u003cbr\u003e\nCombine and concatenate fast5 and fastq barcodes for genomes individually based on the demultiplexer program, preparing\nfor\nfurther genome assembly\n, following the information in the \u003ccode\u003ebarcodeByGenome_sample.tsv\u003c/code\u003e tabulated file (column names of this table should not be\nmodified).\u003cbr\u003e\n\u003cstrong\u003eCaution\u003c/strong\u003e: if guppy or deepbinner is on Demultiplexer of the barcodeByGenome table, it will be\nexecuted even it is not specified in config[\u0027DEMULTIPLEXER\u0027].\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePorechop (optional)\u003c/strong\u003e\u003cbr\u003e\nFind and remove adapters from reads. See \u003ca href=\"https://github.com/rrwick/Porechop\"\u003ehere\u003c/a\u003e for more information.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eFiltlong (optional)\u003c/strong\u003e\u003cbr\u003e\nFilter reads by length and by quality. More details is \u003ca href=\"https://github.com/rrwick/Filtlong\"\u003ehere\u003c/a\u003e. Several filtlong runs at the same time are enabled.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity containers\u003c/h3\u003e\n\u003cp\u003eWorkflow jobs run inside Singularity images (see \u003ca href=\"baseDmux/data/containers\"\u003eour Singularity Recipe files\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eThe latest containers will be automatically downloaded and intalled in the baseDmux environement installation\ndirectory. They can anyhow be manually downloaded from \u003ca href=\"https://drive.ird.fr/s/nTsw45jnW67tCw7\" rel=\"nofollow\"\u003eIRD Drive\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eCustom Singularity images can be specified by editing the \u003ca href=\"baseDmux/data/singularity.yaml\"\u003e\u003ccode\u003e./baseDmux/data/singularity.yaml\u003c/code\u003e\u003c/a\u003e file right after clonning the github repository or directly in your baseDmux installation (see below) location.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda-environments\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda environments\u003c/h3\u003e\n\u003cp\u003eInside of the Singularity images, individual Snakemake rules use dedicated conda\nenvironments yaml files that are located \u003ca href=\"baseDmux/data/conda\"\u003ethere\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eminionqc\u003c/li\u003e\n\u003cli\u003emultiqc\u003c/li\u003e\n\u003cli\u003ermarkdown\u003c/li\u003e\n\u003cli\u003eporechop\u003c/li\u003e\n\u003cli\u003efiltlong\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eDownload the package:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vibaotram/baseDmux.git\ncd ./baseDmux\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd then, install in a virtualenv...\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake install\nsource venv/bin/activate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e... or install in a conda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -n baseDmux -f environment.yaml\nconda activate baseDmux\npip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is recommended to first run the local test below with the toy dataset to make sure everything works well. On the\nfirst invokation, this will download and install the Singularity images and setup the Conda environment. This\nprocess takes time, so be patient. Note also that in the end, this setup amounts to a total of about 12GB of files\n, so you need some room on the installation disk.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eRun baseDmux version 1.1.0 ... See https://github.com/vibaotram/baseDmux/blob/master/README.md for more\ndetails\n\npositional arguments:\n {configure,run,dryrun,version_tools}\n configure edit config file and profile\n run run baseDmux\n dryrun dryrun baseDmux\n version_tools check version for the tools of baseDmux\n\noptions:\n -h, --help show this help message and exit\n -v, --version show program\u0027s version number and exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basedmux-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#basedmux-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebaseDmux configuration\u003c/h3\u003e\n\u003cp\u003eBecause configuring snakemake workflows can be a bit intimidating, we try to clarify below the main principles of\nbaseDmux configuration.\u003c/p\u003e\n\u003cp\u003eThis is done primarilly by adjusting the parameters listed in the workflow config file \u003ccode\u003eprofile/workflow_parameters .yaml\u003c/code\u003e (generated by \u003ccode\u003ebaseDmux configure\u003c/code\u003e - see below). It enables to setup input reads, output folder, parameters for the tools\n, reports generation, etc...\u003cbr\u003e\nThis actually corresponds to the typical Snakemake \u0027config.yaml\u0027 file. You can \u003ca href=\"baseDmux/data/config.yaml\"\u003etake a look\u003c/a\u003e at what serves as a template to create \u003ccode\u003eprofile/workflow_parameters.yaml\u003c/code\u003e. It is suggested to \u003cstrong\u003erefer to the comments in this file for further details on individual parameters\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003ebaseDmux takes as input a folder with internal ONT \u0027run\u0027 folders that each contains a \u0027fast5\u0027 folder. This is the\ntypical file hierarchy when sequencing with a MinION. baseDmux can therefore process a virtually unlimited number of (multiplexed) sequencing runs.\u003c/p\u003e\n\u003cp\u003eYou can decide whether guppy and deepbinner should run on GPU or CPU by specifying \u0027RESOURCE\u0027 in the \u003ca href=\"baseDmux/data/config.yaml\"\u003econfig.yaml\n\u003c/a\u003e file depending on the available computing hardware. Note that Deepbinner is not longer\nmaintained and that \u003ca href=\"https://github.com/rrwick/Deepbinner/tree/master/models\"\u003eDeepbinner models\u003c/a\u003e are limited to specific \u0027earlier\u0027 flow cells and barcoding kits. One should therefore ensure that that Deepbinner is a adequate for the data at hand.\u003c/p\u003e\n\u003cp\u003eA typical usage case for baseDmux is to prepare filtered sequencing reads in individual fastq files for genome\nassembly (or transcripts analysis) when users have a number of genomic DNA (or RNA) preparations sequenced with the\nsame library preparation protocol and flowcell typoe but over several runs with various sets of multiplex barcodes\n. For this, it is necessary to run the complete workflow. \u003cstrong\u003eNote\u003c/strong\u003e that they however currently need to share, if not\nidentical, at least \u0027compatible\u0027 (in the guppy sense), library construction kits and flow cell types.\u003c/p\u003e\n\u003cp\u003eUsers need to prepare a \u003ca href=\"/baseDmux/data/barcodeByGenome_sample.tsv\"\u003e\u003ccode\u003eBarcode by genome\u003c/code\u003e\u003c/a\u003e file. This is a roadmap\ntable for subseting fastq and fast5 reads, demultiplexed with guppy and/or deepbinner, and coming from disparate\nruns and barcodes, in bins corresponding to individual \u0027genomes\u0027 (or samples). It must contain at least the\nfollwing columns: Demultiplexer, Run_ID, ONT_Barcode, Genome_ID. Values in the \u003ccode\u003eGenome_ID\u003c/code\u003e correspond to the\nlabels of the bin into which reads will eventually be grouped. \u003cstrong\u003eMake sure\u003c/strong\u003e that these labels do NOT contain\nspaces \" \" or other special characters like \u0027|\u0027 \u0027$\u0027 \u0027:\u0027. As separators, the safest options are to use \"_\" or \"-\".\u003cbr\u003e\nLikewise, \u003ccode\u003eRun_ID\u003c/code\u003e values should not contain special characters. In addition, these values must match the names of the\ntop folders in the input fast5 directory.\u003cbr\u003e\nImportantly, the \u003ccode\u003eBarcode by genome\u003c/code\u003e file does not only enable to group reads, it is necessary to provide such a file\nfor the porechop and filtlong rules to be executed. A template is provided (see the section below on configuration).\u003c/p\u003e\n\u003cp\u003eAppart from the workflow parameters, there are also additional parameter files that are required to specify Snakemake\ninvocation arguments and, when baseDmux is run with a HPC scheduler, parameters regarding how specific jobs need to\nbe submited. All these configuration files are gathered inside a \u003cstrong\u003e\"profile\"\u003c/strong\u003e directory that can be automatically\nprototyped with the commands below. This is in line with the recommended way for Snakemake pipelines\nconfiguration using \u003ca href=\"https://snakemake.readthedocs.io/en/stable/executing/cli.html?highlight=profile#profiles\" rel=\"nofollow\"\u003eprofiles\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-generating-template-configuration-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#generating-template-configuration-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerating template configuration files\u003c/h4\u003e\n\u003cp\u003eTo simplify configuration, the \u003ccode\u003ebaseDmux configure\u003c/code\u003e command generates a \u0027template\u0027 configuration profile for general\nuse cases. These files can subsequently be modified to fit specific situations.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: baseDmux configure [-h] --mode {local,slurm,cluster,iTrop} [--barcodes_by_genome]\n [--edit [EDITOR]]\n dir\n\npositional arguments:\n dir path to the folder to contain config file and profile you want to create\n\noptions:\n -h, --help show this help message and exit\n --mode {local,slurm,cluster,iTrop}\n choose the mode of running Snakemake, local mode or cluster mode\n --barcodes_by_genome optional, create a tabular file containing information of barcodes for each\n genome)\n --edit [EDITOR] optional, open files with editor (nano, vim, gedit, etc.)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThese files will be created:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e | dir\n -| profile \n -| config.yaml \n -| workflow_parameter.yaml \n -| barcodesByGenome.tsv (if --barcodes_by_genome)\n -| ... (if mode slurm)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA \u0027profile\u0027 folder will be created and populated in the specifid \u003ccode\u003edir\u003c/code\u003e path. The files may be modified and the whole\nfolder can be moved/copied/renamed anywhere as long as you use the correct path when you call \u003ccode\u003ebaseDmux run\u003c/code\u003e and update\nthe enclosed files, \u003ccode\u003econfig.yaml\u003c/code\u003e and \u003ccode\u003eworkflow_parameter.yaml\u003c/code\u003e for the new paths of \u003ccode\u003eworkflow_parameter.yaml \u003c/code\u003e and \u003ccode\u003ebarcodesByGenome.tsv\u003c/code\u003e, respectively.\u003c/p\u003e\n\u003cp\u003eWith the \u003ccode\u003e--barcodes_by_genome\u003c/code\u003e option, a formatted file \u003ccode\u003ebarcodesByGenome.tsv\u003c/code\u003e will be created (and its path appropriately specified in \u003ccode\u003eworkflow_parameter.yaml\u003c/code\u003e). One can then modify the information on the table accordingly. It is important that this table contains at least the same columns as those in the provided example \u003ccode\u003ebarcodeByGenome_sample.tsv\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eOnce you have adapted the templates for your typical use cases, there is no need to rerun \u003ccode\u003ebaseDmux configure\u003c/code\u003e again, just copy and adapt your existing templates.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: the \u0027iTRop\u0027 and \u0027cluster\u0027 modes are \u003cstrong\u003eobsolete\u003c/strong\u003e and will eventually be removed.\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-an-exemple-to-prepare-to-run-snakemake-locally-laptop-local-node-on-a-hpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-exemple-to-prepare-to-run-snakemake-locally-laptop-local-node-on-a-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003ean exemple to prepare to run Snakemake locally\u003c/strong\u003e (laptop, local node on a HPC)\u003c/h5\u003e\n\u003cp\u003eUse this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebaseDmux configure ./test_baseDmux --mode local --barcodes_by_genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen \u003ccode\u003eworkflow_parameter.yaml\u003c/code\u003e and \u003ccode\u003econfig.yaml\u003c/code\u003e will be created inside a \u003ccode\u003eprofile\u003c/code\u003e folder within \u003ccode\u003e./test_baseDmux\u003c/code\u003e. \u003ccode\u003e./test_baseDmux/profile/config.yaml\u003c/code\u003e contains as a set of parameters for the Snakemake command-line.\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-an-exemple-to-prepare-a-template-to-run-snakemake-on-a-hpc-with-slurm\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-exemple-to-prepare-a-template-to-run-snakemake-on-a-hpc-with-slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003ean exemple to prepare a template to run Snakemake on a HPC\u003c/strong\u003e with slurm.\u003c/h5\u003e\n\u003cp\u003eSimilarly, run the command below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebaseDmux configure ./test_baseDmux --mode slurm --barcodes_by_genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a \u003ccode\u003e./test_baseDmux/profile\u003c/code\u003e (see an example \u003ca href=\"baseDmux/data/profile/slurm\"\u003ehere\u003c/a\u003e)) folder with, in\naddition to the already mentionned files, the necessary file templates to run basDmux with slurm and the \u003ca href=\"https://github.com/Snakemake-Profiles/slurm\"\u003eSnakemake profile\u003c/a\u003e for configuring Snakemake to run on the SLURM Workload Manager.\u003c/p\u003e\n\u003cp\u003eFor other HPC job managment system (sge, ...), and more information on Snakemake profile and other utilities refer to\nthe \u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003eSnakemake documentation\u003c/a\u003e and [this gitHub repository](\u003ca href=\"https://github.com\"\u003ehttps://github.com\u003c/a\u003e\n/Snakemake-Profiles).\u003c/p\u003e\n\u003cp\u003eUltimately, the required files for passing HPC scheduler parameters throught the dedicated Snakemake mecanism of \u0027profiles\u0027 need to be stored in the folder whose path is passed to the baseDmux \u003ccode\u003eprofile_dir\u003c/code\u003e parameter and will\nmost certainly \u003cstrong\u003eneed to be adapted to suit your specific needs\u003c/strong\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-run-the-workflow-with-the-created-profile\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-the-workflow-with-the-created-profile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the workflow with the created profile:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003eusage: baseDmux run [-h] [--snakemake_report] profile_dir\n\npositional arguments:\n profile_dir profile folder to run baseDmux\n\noptions:\n -h, --help show this help message and exit\n --snakemake_report optionally, create snakemake report\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample:\u003cbr\u003e\nYou can run \u003ccode\u003ebaseDmux dryrun ./test_baseDmux/profile\u003c/code\u003e for dry-run to make sure that everything is OK, before actually executing the workflow.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebaseDmux run ./test_baseDmux/profile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the option \u003ccode\u003e--snakemake_report\u003c/code\u003e, a report file \u003ccode\u003esnakemake_report.html\u003c/code\u003e will be created in the report folder of pipeline output directory, when snakemake has successfully finished the workflow.\u003c/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-get-your-hands-dirty-and-run-a-local-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#get-your-hands-dirty-and-run-a-local-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGet your hands dirty and run a local test\u003c/h3\u003e\n\u003cp\u003eAssuming the environement for baseDmux has been created as specified in the dedicated section on Installation. First\nactivate either the conda or venv environement.\u003c/p\u003e\n\u003cp\u003eYou can use the reads fast5 files in \u003ccode\u003esample/reads_intermediate\u003c/code\u003e folder for testing and generate a local \u003ccode\u003eprofile\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e## create configuration file for Snakemake and Snakemake profile,\n## and (optional) a tsv file containing information about genomes corresponding to barcode IDs\nmkdir ./test_baseDmux\nbaseDmux configure ./test_baseDmux --mode local --barcodes_by_genome\n\n## copy sample reads to a test folder\ncp -r ./baseDmux/sample/reads_intermediate/ ./test_baseDmux/reads\n\n## check the workflow by dryrun, then run\nbaseDmux dryrun ./test_baseDmux/profile\nbaseDmux run ./test_baseDmux/profile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output will be written in \u003ccode\u003e./test_baseDmux/results\u003c/code\u003e by default\nThe first run may take a long time for Singularity containers to be downloaded and the conda environments to be installed even if using Mamba.\u003cbr\u003e\nOn a personnal computer with only a few CPU, even with this very minimal dataset,\nguppy basecalling may also take several minutes... So be patient depending on your underlying computing\ninfrastructure capacities.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-input-and-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-and-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput and Output\u003c/h3\u003e\n\u003cp\u003eInput directory \u003cstrong\u003emust\u003c/strong\u003e follow the structure as below. \u0027fast5\u0027 directory containing fast5 files in each run is\nMANDATORY for baseDmux to identifiy the various Run_ID(s).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eindir/\n\u251c\u2500\u2500 run_id1\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 fast5\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 file_1.fast5\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 file_n.fast5\n\u251c\u2500\u2500 ...\n\u2514\u2500\u2500 run_idx\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput directory will be:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoutdir/\n\u251c\u2500\u2500 basecall\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 run_id1\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sequencing_summary.txt\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 {MinIONQC results}\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 run_idx\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 multiqc\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 multiqc_data\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2514\u2500\u2500 multiqc_report.html\n\u251c\u2500\u2500 demultiplex\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 deepbinner\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 run_id1\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcode01\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcode01.fastq.gz\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 fast5\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sequencing_summary.txt\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 {MinIONQC results}\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcodexxx\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 | \u251c\u2500\u2500 classification\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 | \u251c\u2500\u2500 fast5_per_barcode.done\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 | \u251c\u2500\u2500 multiqc\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 | \u2514\u2500\u2500 unclassified\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 run_idx\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 guppy\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 run_id1\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcode01\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcode01.fastq.gz\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 fast5\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sequencing_summary.txt\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 {MinIONQC results}\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcodexxx\n\u2502\u00a0\u00a0 \u00a0\u00a0 | \u251c\u2500\u2500 barcoding_summary.txt\n\u2502\u00a0\u00a0 \u00a0\u00a0 | \u251c\u2500\u2500 fast5_per_barcode.done\n\u2502\u00a0\u00a0 \u00a0\u00a0 | \u251c\u2500\u2500 multiqc\n\u2502\u00a0\u00a0 \u00a0\u00a0 | \u2514\u2500\u2500 unclassified\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2514\u2500\u2500 run_idx\n\u251c\u2500\u2500 reads_per_genome\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 fast5\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 fastq\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 reads_per_genome.csv\n\u251c\u2500\u2500 log\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 slurm\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 snakemake\n\u2514\u2500\u2500 report\n\u00a0\u00a0 \u251c\u2500\u2500 demultiplex_report.html\n \u251c\u2500\u2500 demultiplex_report.RData\n \u2514\u2500\u2500 demultiplex_report.tsv\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 3, - "subscribers_count": 3, - "topics": [], - "updated_at": 1676320294.0 + "subscribers_count": 4, + "topics": [ + "snakemake-workflow", + "basecalling", + "demultiplexing", + "nanopore" + ], + "updated_at": 1661519386.0 }, { "data_format": 2, - "description": "Tensorflow based neuronal network framework to isolate vocal from music (BASS).", + "description": "Container database metadata extraction and data-container builder", "filenames": [ - "singularity/Singularity" + "examples/singularity-simple/Singularity" ], - "full_name": "unmix-io/unmix-net", + "full_name": "vsoch/cdb", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-unmix-net\" class=\"anchor\" aria-hidden=\"true\" href=\"#unmix-net\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eunmix-net\u003c/h1\u003e\n\u003cp\u003eTensorflow based neuronal network framework to extract vocals and instrumental from music.\nPython 3.7 was used for implementation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eInstall all dependencies by using \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ekeras\u003c/li\u003e\n\u003cli\u003etensorflow\u003c/li\u003e\n\u003cli\u003eargparse\u003c/li\u003e\n\u003cli\u003ePillow\u003c/li\u003e\n\u003cli\u003ematplotlib\u003c/li\u003e\n\u003cli\u003epydot\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003epsutil\u003c/li\u003e\n\u003cli\u003ecommentjson\u003c/li\u003e\n\u003cli\u003egitpython\u003c/li\u003e\n\u003cli\u003ecolorama\u003c/li\u003e\n\u003cli\u003eprogressbar2\u003c/li\u003e\n\u003cli\u003emir_eval\u003c/li\u003e\n\u003cli\u003epytube\u003c/li\u003e\n\u003cli\u003elibrosa\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe highly recommend running the solution on \u003ca href=\"https://www.tensorflow.org/install/gpu\" rel=\"nofollow\"\u003etensorflow-gpu\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eInstead of installing the dependencies locally, our \u003ca href=\"https://hub.docker.com/r/unmix/unmix\" rel=\"nofollow\"\u003edocker image\u003c/a\u003e can be used: \u003ccode\u003edocker pull unmix/unmix\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-settings\" class=\"anchor\" aria-hidden=\"true\" href=\"#settings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSettings\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-configurations\" class=\"anchor\" aria-hidden=\"true\" href=\"#configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigurations\u003c/h3\u003e\n\u003cp\u003eTraining runs must be configured with a jsonc configuration file.\nConfiguration files can inherit from parent configurations:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-json\"\u003e\u003cpre\u003e{\n \u003cspan class=\"pl-ent\"\u003e\"base\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edefault-hourglass.jsonc\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-ii\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf a property is specified multiple times, the child configurations always overrides.\u003c/p\u003e\n\u003cp\u003eEvery configuration inherits by default from the \u003ca href=\"https://github.com/unmix-io/unmix-net/blob/master/configurations/master.jsonc\"\u003emaster.jsonc\u003c/a\u003e configuration.\nComments are allowed in jsonc files.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment Variables\u003c/h3\u003e\n\u003cp\u003eConfiguration files support access to environment varialbes.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_CONFIGURATION_FILE\u003c/code\u003e: Path to the configuration file (default parameter for training)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_COLLECTION_DIR\u003c/code\u003e: Path to the training, validation and test data collection\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_SAMPLE_RATE\u003c/code\u003e: Sample rate of the training data (used for training and prediction)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_SONG_LIMIT\u003c/code\u003e: Limit amount of songs to be included in the training (for smaller training runs)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_TEST_FREQUENCY\u003c/code\u003e: Frequency in epochs to run an accuracy test\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_TEST_DATA_COUNT\u003c/code\u003e: Number of songs to include in to the accuracy test\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_LIMIT_ITEMS_PER_SONG\u003c/code\u003e: Limit of batchitems used per song for training\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe variables can be added to your operating system or by adding a \u003ccode\u003e.env\u003c/code\u003e file to the (repository) base directory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining\u003c/h2\u003e\n\u003cp\u003eExample call: \u003ccode\u003epython3 train.py --configuration configuration/final/hourglass.jsonc\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econfiguration\u003c/code\u003e: Path to a valid jsonc configuration file. If not specified the value of the \u003ccode\u003eUNMIX_CONFIGURATION_FILE\u003c/code\u003e environment variable is used.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eworkingdir\u003c/code\u003e: Optional working directory where the runs ordner is published\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-procedure\" class=\"anchor\" aria-hidden=\"true\" href=\"#procedure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eFollowing a rough overview what happens during a training session:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eConfiguration initialization, output run folder creation\u003c/li\u003e\n\u003cli\u003eData loading, splitting training, validation and test data\u003c/li\u003e\n\u003cli\u003eTraining per epoch with batch generators\u003c/li\u003e\n\u003cli\u003eWrite callbacks (logs, weights, ...)\u003c/li\u003e\n\u003cli\u003eOptional: Calculate accuracies\u003c/li\u003e\n\u003cli\u003eStop if early stopping or epoch count finished\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h3\u003e\n\u003cp\u003eEvery training run generates a \"run folder\" with the following structure:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eplots\u003c/em\u003e: Output folder for plots (can be configured otherwise)\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003epredictions\u003c/em\u003e: Output folder for predictions and accuracy tests.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eweights\u003c/em\u003e: Output folder for the trained weights\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eaccuracy_x.csv\u003c/em\u003e: \u003ca href=\"https://craffel.github.io/mir_eval/\" rel=\"nofollow\"\u003emir_eval\u003c/a\u003e based accuracies of the track prediction.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eresults.csv\u003c/em\u003e: Result file including loss, mean prediction, validation loss, validation mean prediction per epoch\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003econfiguration.jsonc\u003c/em\u003e: Merged configuration file which is used by the training run\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eenvironment.json\u003c/em\u003e: Environment information including working directories, git version, environment variables\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003elogs.txt\u003c/em\u003e: Logfile\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003emodel.h5\u003c/em\u003e: Model and weights (created after training is finished)\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003emodel.json\u003c/em\u003e: Model configuration (created after training is finished)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prediction\" class=\"anchor\" aria-hidden=\"true\" href=\"#prediction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrediction\u003c/h2\u003e\n\u003cp\u003eExample call run folder: \u003ccode\u003epython3 predict.py --run_folder runs/20190506-154117-hourglass --song skyfall.mp3\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eExample call weights file: \u003ccode\u003epython3 predict.py --weights weights.h5 --configuration configuration.jsonc --song skyfall.mp3\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-parameters-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003erun_folder\u003c/code\u003e: Run folder from a training run (other parameters get derived)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003econfiguration\u003c/code\u003e: (Not necessary if \u003ccode\u003erun_folder\u003c/code\u003e) Path to the configuration file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eweights\u003c/code\u003e: (Not necessary if \u003ccode\u003erun_folder\u003c/code\u003e) Path to a weights file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eworkingdir\u003c/code\u003e: (Not necessary if \u003ccode\u003erun_folder\u003c/code\u003e) Optional working directory\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esample_rate\u003c/code\u003e: (Not necessary if \u003ccode\u003erun_folder\u003c/code\u003e) Sample rate which was used for training and will be used for prediction\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esong\u003c/code\u003e: Path to a single song to predict (extract vocals and instrumental)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esongs\u003c/code\u003e: Path to a folder of songs to predict (extract vocals and instrumental)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eyoutube\u003c/code\u003e: Link to a \u003ca href=\"https://www.youtube.com/watch?v=dQw4w9WgXcQ\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e link to be predicted (extract vocals and instrumental)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-output-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h3\u003e\n\u003cp\u003eThe predicted songs will be written into the working directory.\u003c/p\u003e\n\u003cp\u003eIf a run folder was specified all results are stored in the \u003cem\u003epredictions\u003c/em\u003e folder.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-database-cdb\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-database-cdb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Database (cdb)\u003c/h1\u003e\n\u003cp\u003eThis is the Python support tool for \u003ca href=\"https://github.com/vsoch/containerdb\"\u003econtainerdb\u003c/a\u003e\nto support generation of \u003ca href=\"https://github.com/singularityhub/data-container\"\u003edata containers\u003c/a\u003e.\nPython is more friendly to generating arbitrary data structures, and is popular among the\ndata science community, so I chose it for metadata generation instead of using GoLang.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://badge.fury.io/py/cdb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/708c5edf804dcc153b856fb4add4ad6cfbc7e31bfa3a3114a6bbc8e16274cc99/68747470733a2f2f62616467652e667572792e696f2f70792f6364622e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/cdb.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eHave your data and use it too!\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"assets/img/logo/logo.png\"\u003e\u003cimg src=\"assets/img/logo/logo.png\" alt=\"assets/img/logo/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor documentation and full examples see \u003ca href=\"https://vsoch.github.io/cdb\" rel=\"nofollow\"\u003evsoch.github.io/cdb\u003c/a\u003e. These\nexamples are also available in the \u003ca href=\"examples\"\u003eexamples\u003c/a\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-what-is-a-data-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-a-data-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is a Data Container?\u003c/h3\u003e\n\u003cp\u003eA data container is generally an operating-system-less container that is optimized\nto provide data, either for query/search, or binding for analysis. The qualities of\nthe data container should be:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt can be mounted to containers with operating systems to run analysis\u003c/li\u003e\n\u003cli\u003eIt can be interacted with on it\u0027s own to search metadata about the data\u003c/li\u003e\n\u003cli\u003eIt should not have an operating system.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-how-do-we-generate-one\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-do-we-generate-one\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do we generate one?\u003c/h3\u003e\n\u003cp\u003eThe generation is fairly simple! It comes down to a three step multistage build:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eStep 1\u003c/strong\u003e We install \u003ca href=\"https://github.com/vsoch/cdb\"\u003ecdb\u003c/a\u003e to generate a GoLang template for an \u003ca href=\"https://github.com/vsoch/containerdb\"\u003ein-memory database\u003c/a\u003e for our data)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStep 2\u003c/strong\u003e We compile the binary into an entrypoint\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStep 3\u003c/strong\u003e We add the data and the binary entrypoint to a scratch container (no operating system).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAnd then we interact with it! This tutorial will show you the basic steps to\nperform the multistage-build using a simple \u003ca href=\"https://github.com/vsoch/cdb/tree/master/examples/docker-simple/Dockerfile\"\u003eDockerfile\u003c/a\u003e along with the data folder. The \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e in the base of the repository also is a good example.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Usage\u003c/h3\u003e\n\u003cp\u003eThe intended usage is via Docker, so you don\u0027t need to worry about installation of\nPython, GoLang, and multistage builds to basically:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGenerate a db.go template\u003c/li\u003e\n\u003cli\u003eCompile it\u003c/li\u003e\n\u003cli\u003eAdd to scratch with data as data container entrypoint.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThus, to run the dummy example here using the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build -t data-container \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe then have a simple way to do the following:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emetadata\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf we just run the container, we get a listing of all metadata alongside the key.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint \n/data/avocado.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 9, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\n/data/tomato.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 8, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e3b7721618a86990a3a90f9fa5744d15812954fba6bb21ebf5b5b66ad78cf5816\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003elist\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe can also just list data files with \u003ccode\u003e-ls\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -ls\n/data/avocado.txt\n/data/tomato.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eorderby\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOr we can list ordered by one of the metadata items:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -metric size\nOrder by size\n/data/tomato.txt: {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 8, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e3b7721618a86990a3a90f9fa5744d15812954fba6bb21ebf5b5b66ad78cf5816\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\n/data/avocado.txt: {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 9, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esearch\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOr search for a specific metric based on value.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -metric size -search 8\n/data/tomato.txt 8\n\n$ docker run entrypoint -metric sha256 -search 8\n/data/avocado.txt 327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\n/data/tomato.txt 3b7721618a86990a3a90f9fa5744d15812954fba6bb21ebf5b5b66ad78cf5816\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eget\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOr we can get a particular file metadata by it\u0027s name:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -get /data/avocado.txt\n/data/avocado.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 9, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor a partial match:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -get /data/\n/data/avocado.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 9, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\n/data/tomato.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 8, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e3b7721618a86990a3a90f9fa5744d15812954fba6bb21ebf5b5b66ad78cf5816\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003estart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe start command is intended to keep the container running, if we are using\nit with an orchestrator.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run data-container -start\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-orchestration\" class=\"anchor\" aria-hidden=\"true\" href=\"#orchestration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOrchestration\u003c/h3\u003e\n\u003cp\u003eIt\u0027s more likely that you\u0027ll want to interact with files in the container via\nsome analysis, or more generally, another container. Let\u0027s put together\na quick \u003ccode\u003edocker-compose.yml\u003c/code\u003e to do exactly that.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eversion: \"3\"\nservices:\n base:\n restart: always\n image: busybox\n entrypoint: [\"tail\", \"-f\", \"/dev/null\"]\n volumes:\n - data-volume:/data\n\n data:\n restart: always\n image: data-container\n command: [\"-start\"]\n volumes:\n - data-volume:/data\n\nvolumes:\n data-volume:\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNotice that the command for the data-container to start is \u003ccode\u003e-start\u003c/code\u003e, which\nis important to keep it running. After building our \u003ccode\u003edata-container\u003c/code\u003e, we can then bring these containers up:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker-compose up -d\nStarting docker-simple_base_1 ... \u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\nRecreating docker-simple_data_1 ... \u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker-compose ps\n Name Command State Ports\n---------------------------------------------------------\ndocker-simple_base_1 tail -f /dev/null Up \ndocker-simple_data_1 /entrypoint -start Up \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can then shell inside and see our data!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it docker-simple_base_1 sh\n/ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls /data/\u003c/span\u003e\navocado.txt tomato.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe metadata is still available for query by interacting with the data-container\nentrypoint:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e docker-simple_data_1 /entrypoint -ls\n/data/avocado.txt\n/data/tomato.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDepending on your use case, you could easily make this available inside the\nother container. This is very simple usage, but the idea is powerful! We can interact with the dataset\nand search it without needing an operating system. It follows that we can develop\ncustomized data-containers based on the format / organization of the data inside\n(e.g., a data-container that knows how to expose inputs, outputs, etc.)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-python-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#python-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython Usage\u003c/h2\u003e\n\u003cp\u003eThe above doesn\u0027t require you to install the Container Database (cdb) metadata\ngenerator, however if you want to (to develop or otherwise interact) you\ncan do the following. First, install cdb from pypi or a local repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install cdb\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:vsoch/cdb\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e cdb\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand Line\u003c/h3\u003e\n\u003cp\u003eThe next step is to generate the goLang file to compile.\nYou\u0027ll next want to change directory to somewhere you have a dataset folder.\nFor example, in \u003ca href=\"tests\"\u003etests\u003c/a\u003e we have a dummy \"data\" folder.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e tests/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe might then run \u003ccode\u003ecdb generate\u003c/code\u003e to create a binary for our container, targeting\nthe \u003ca href=\"tests/data\"\u003etests/data\u003c/a\u003e folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cdb generate data --out db.go\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe db.go file is then in the present working directory. You can either\nbuild it during a multistage build as is done in the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e,\nor do it locally with your own GoLang install and then add to the container.\nFor example, to compile:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ego get github.com/vsoch/containerdb \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nGOOS=linux GOARCH=amd64 go build -ldflags=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e-w -s\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -o /db -i /db.go\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then a very basic Dockerfile would need to add the data at the path specified,\nand the compiled entrypoint.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eFROM scratch\nWORKDIR /data\nCOPY data/ \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\nCOPY db /db\nCMD [\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/db\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA more useful entrypoint will be developed soon! This is just a very\nbasic start to the library.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-hidden=\"true\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython\u003c/h3\u003e\n\u003cp\u003eYou can run the same generation functions interactively with Python.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecdb\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emain\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eContainerDatabase\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eContainerDatabase\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003epath\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"data\"\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# \u0026lt;cdb.main.ContainerDatabase at 0x7fcaa9cb8950\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eView that there is a files generator at \u003ccode\u003edb.files\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003edb\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003efiles\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026lt;\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egenerator\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eobject\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erecursive_find\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eat\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0x7fcaaa4ae950\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then generate! If you don\u0027t provide an output file, a string will be returned.\nOtherwise, the output file name is returned.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edb\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003egenerate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"db.go\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eforce\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCurrently, functions for parsing metadata are named in \u003ca href=\"cdb/functions.py\"\u003ecdb/functions.py\u003c/a\u003e,\nhowever you can also define a custom import path. This has not yet been tested\nand will be soon. We will also be added more real world examples soon.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: MPL 2.0 License\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, - "topics": [], - "updated_at": 1580147028.0 + "subscribers_count": 2, + "topics": [ + "containerdb", + "data-container", + "docker" + ], + "updated_at": 1595956312.0 }, { "data_format": 2, - "description": "Sequana demultiplexing pipeline ", + "description": null, "filenames": [ - "singularity/Singularity" + "singularity_hpc_files/Singularity.bld" ], - "full_name": "sequana/demultiplex", - "latest_release": "v1.3.0", + "full_name": "ammunk/hpc", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-slurm-hpc-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#slurm-hpc-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSLURM hpc scripts\u003c/h1\u003e\n\u003cp\u003eThe approach taken here rely on \u003cstrong\u003ebash\u003c/strong\u003e as opposed to \u003cstrong\u003epython\u003c/strong\u003e, and the hpc\nscripts serve one of three (overlapping) purposes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#multi-node-distributed-gpu-training\"\u003eMulti-node distributed GPU training\u003c/a\u003e of\n\u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e models\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/guides/3.7/user-guide.pdf\" rel=\"nofollow\"\u003eSinguarity\u003c/a\u003e or virtual\nenvironment based projects\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.wandb.ai/sweeps\" rel=\"nofollow\"\u003eWeights and Biases sweeper\u003c/a\u003e jobs (great for\nhyperparameter searches)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe scripts are designed in order to make the transfer of a locally working\napplication to the hpc clusters as easy and painless as possible.\u003c/p\u003e\n\u003cp\u003eThere are two types of scripts, which differ by how dependencies are managed for\nyour application:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eSingularity containers\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003epython virtual environments\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Singularity approach offers much greater flexibility where all dependencies\nare specified in a \"Singularity file\", whereas the python virtual environment\napproach (obviously) must be a python application.\u003c/p\u003e\n\u003cp\u003eDepending on whether you use Singularity or a python virtual environment they\neach pose slightly different constraints on how experiments run once a job has\nbeen submitted. These constraints are minimal so that you do not have to give up\ne.g. Singularity\u0027s flexibility yet ensures the script can make some assumptions\nabout how to run your experiments. The details on this can be found in the\n\u003ca href=\"singularity_hpc_files/README.md\"\u003eSingularity readme\u003c/a\u003e or the \u003ca href=\"virtual_env_hpc_files/README.md\"\u003evirtual environment readme\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo use these scripts, simply copy them into your \u003cstrong\u003e\u003ca href=\"#project-structure\"\u003eappropriately\nstructured\u003c/a\u003e\u003c/strong\u003e project. The scripts are written to be\n(almost) project agnostic, which effectively means that the scripts will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAutomatically set up the experiments which prevents mixing different projects.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnsure the correct relationship between requested number of \u003cem\u003egpus\u003c/em\u003e, \u003cem\u003enodes\u003c/em\u003e,\nand \u003cem\u003ecpus per gpu\u003c/em\u003e depending on the type of distributed job.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"#copying-datasets-and-other-files-to-slurm_tmpdir\"\u003eManage the transfer\u003c/a\u003e of\ndata and directories to and from local nodes for faster read/write operations -\ni.e. via the \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e environment variable.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe created folders and their location are easily accessible as \u003ca href=\"#environment-variables\"\u003eenvironment\nvariables\u003c/a\u003e. One thing to pay attention to is that\nSingularity based jobs needs additional folders compared to the virtual\nenvironment based jobs. For details see the \u003ca href=\"#created-folders\"\u003ecreated folders\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-important\" class=\"anchor\" aria-hidden=\"true\" href=\"#important\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant:\u003c/h4\u003e\n\u003cp\u003eThe scripts rely on the \u003ccode\u003eSCRATCH\u003c/code\u003e environment variable. If \u003ccode\u003eSCRATCH\u003c/code\u003e is not set\nby default add\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SCRATCH=[path to scratch]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto your \u003ccode\u003e~/.bashrc\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, you will notice references to the \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e. This variable\npoints to a temporary directory created for each job pointing to a job-specific\ndirectory on each local node. If the job is allocated multiple nodes the\ntemporary directory is unique on each node. Some clusters will have these\nautomatically set. However, if this is not the case make sure to set this up\nyourself.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-submitting-jobs\" class=\"anchor\" aria-hidden=\"true\" href=\"#submitting-jobs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubmitting jobs\u003c/h2\u003e\n\u003cp\u003eTo submit jobs call one of two submitter jobs. Which one depends on whether your\napplication uses Singularity or a virtual environment. Note that the job\nsubmitter file by default assumes you use a virtual environment. To specify a\nSingularity based job, use the \u003ccode\u003e-s, --singularity-container\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eThe scripts distinguish between two types of jobs, and how to specify the\nexperiment\u0027s configurations depend on which type:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eArray jobs for hyperparameter searches using \u003ccode\u003ewandb\u003c/code\u003e sweeps - see \u003ca href=\"#integration-with-weight-and-biases\"\u003eintegration\nwith Weights and Biases\u003c/a\u003e for more\ndetails.\u003c/li\u003e\n\u003cli\u003eSingle jobs which supports multi-node distributed gpu applications\n\u003cul\u003e\n\u003cli\u003eThe experiments configurations are specified using the\n\u003ca href=\"experiment_configurations.txt\"\u003eexperiment_configurations.txt\u003c/a\u003e file. It\u0027s format differs slightly depending\non whether you use Singularity or a virtual environment. For details, see\nthe \u003ca href=\"singularity_hpc_files/README.md\"\u003eSingularity readme\u003c/a\u003e or the \u003ca href=\"virtual_env_hpc_files/README.md\"\u003evirtual environment readme\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe options that control the job submissions are:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e-a, --account Account to use on cedar (def-fwood, rrg-kevinlb).\n Ignored on the PLAI cluster. Default: rrg-kevinlb\n-g, --gpus Number of gpus per node. Default: 1\n-c, --cpus Number of cpus per node: Default: 2\n-j, --job-type Type of job to run, one of\n (standard, sweep, distributed).\n Default: standard\n-W, --which-distributed Kind of distributed gpu application backend used\n (lightning, torchrun). Must be provided if using\n \"--job-type distributed\"\n-t, --time Requested runtime. Format: dd-HH:MM:SS.\n Default: 00-01:00:00\n-m, --mem Amount of memory per node. E.g. 10G or 10M.\n Default: 10G\n-G, --gpu-type Type of gpu to use (p100, p100l, v100l). Ignored on\n the PLAI cluster. Default: v100l\n-e, --exp-name Name of the experiment. Used to created convenient\n folders in ${SCRATCH}/${project_name} and to name\n the generated output files. Default: \"\" (empty)\n-n, --num_nodes Number of nodes. Default: 1\n-d, --data Whitespace separated list of paths to directories or\n files to transfer to ${SLURM_TMPDIR}. These paths\n MUST be relative to ${SCRATCH}/${project_name}\n-s, --singularity-container Path to singularity container. If specified the\n job is submitted as a Singularity based job\n-w, --workdir Path to a mounted working directory in the\n Singularity container\n-C, --configs Path to file specifying the experiment\n configuration. Default: experiment_configurations.txt\n\n-h, --help Show this message\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h4\u003e\n\u003cp\u003eAssume we have a project with the \u003ca href=\"#project-structure\"\u003eappropriate structure\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo submit a job first \u003ccode\u003ecd [path to project]/hpc_files\u003c/code\u003e, and then\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash job_submitter.sh \\\n --gpus 2 \\\n --cpus 2 \\\n --exp-name testing \\\n --num-nodes 2\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-integration-with-weights-and-biases\" class=\"anchor\" aria-hidden=\"true\" href=\"#integration-with-weights-and-biases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntegration with Weights and Biases\u003c/h3\u003e\n\u003cp\u003eTo use the \u003ca href=\"https://wandb.ai/\" rel=\"nofollow\"\u003eWeight and Biases\u003c/a\u003e sweeps, you need to first\ninstall \u003ccode\u003ewandb\u003c/code\u003e into your Singularity container or virtual environment,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ewandb\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo use \u003ccode\u003ewandb\u003c/code\u003e requires a user login. Either do \u003ccode\u003ewandb login\u003c/code\u003e, where \u003ccode\u003ewandb\u003c/code\u003e\nwill prompt for a username and password, or set the \u003ccode\u003eWANDB_API_KEY\u003c/code\u003e environment\nvariable to the api key provided by weight and biases after you sign up.\u003c/p\u003e\n\u003cp\u003eThe scripts found here take the latter approach by searching for your api key in\n\u003ccode\u003e~/wandb_credentials.txt\u003c/code\u003e. As long as you copy your api key into\n\u003ccode\u003e~/wandb_credentials.txt\u003c/code\u003e your applications can log experiment progress using\n\u003ccode\u003ewandb\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-sweeper-jobs\" class=\"anchor\" aria-hidden=\"true\" href=\"#sweeper-jobs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSweeper jobs\u003c/h4\u003e\n\u003cp\u003eWhen you submit a \u003ccode\u003ewandb\u003c/code\u003e sweep array job, you only need to specify the sweep\nid. That is, first initiate the sweep (either locally or on your favorite\ncluster),\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewandb sweep sweeper.yml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a pending sweep on \u003ccode\u003ewandb\u003c/code\u003e\u0027s servers. Then in\n\u003ccode\u003eproject root/hpc_files\u003c/code\u003e do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash job_submitter.sh --job_type sweep [other options]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe script will then prompt for the sweep id and the number of sweeps.\u003c/p\u003e\n\u003cp\u003eThe provided \u003ca href=\"sweeper.yml\"\u003esweeper.yml\u003c/a\u003e file can serve as a template, but should be\nmodified to your specific sweep. Think of the \u003ca href=\"sweeper.yml\"\u003esweeper.yml\u003c/a\u003e file as the\nsweep\u0027s equivalent of the more general\n\u003ca href=\"experiment_configurations.txt\"\u003eexperiment_configurations.txt\u003c/a\u003e file.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-how-to-specify-experiment-configurations\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-specify-experiment-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to specify experiment configurations:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eFor sweep jobs edit \u003ccode\u003esweeper.yml\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOtherwise edit \u003ca href=\"experiment_configurations.txt\"\u003eexperiment_configurations.txt\u003c/a\u003e. See the \u003ca href=\"singularity_hpc_files/README.md\"\u003eSingularity readme\u003c/a\u003e\nor \u003ca href=\"virtual_env_hpc_files/README.md\"\u003evirtual environment readme\u003c/a\u003e for the format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-copying-datasets-and-other-files-to-slurm_tmpdir\" class=\"anchor\" aria-hidden=\"true\" href=\"#copying-datasets-and-other-files-to-slurm_tmpdir\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopying datasets and other files to \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e\n\u003c/h2\u003e\n\u003cp\u003eTo copy data to the local nodes when submitting a job, simply use the \u003ccode\u003e-d, --data\u003c/code\u003e option. To transfer multiple files and directories specify these using a\nwhitespace separated list of \u003cstrong\u003erelative\u003c/strong\u003e paths.\u003c/p\u003e\n\u003cp\u003eThe main purpose of this functionality is to copy large amounts of data, which\ntypically is stored on \u003ccode\u003eSCRTACH\u003c/code\u003e. Therefore, the paths are going to be\nrelative to \u003ccode\u003e${SCRATCH}/${project_name}\u003c/code\u003e. The script will then create a tarball\nusing \u003ccode\u003etar\u003c/code\u003e and transfer the files and directories to \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e. You can\nthen access the files and directories at \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e using the same paths\nused when using the \u003ccode\u003e-d, --data\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eIf a tarball already exists, no new tarball is created. If you want to update\nthe tarball you should delete the old one first.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-example-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h3\u003e\n\u003cp\u003eAssume you work on a project named \u003ccode\u003eproject_root\u003c/code\u003e, and on \u003ccode\u003e${SCRATCH}\u003c/code\u003e you have,\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e${SCRATCH}\n\u251c\u2500\u2500 project_root\n\u2502 \u2514\u2500\u2500 datasets\n\u2502\u00a0\u00a0 \u00a0 \u251c\u2500\u2500dataset1\n\u2502\u00a0\u00a0 \u00a0 \u251c\u2500\u2500dataset2\n\u2502\u00a0\u00a0 \u00a0 \u2514\u2500\u2500dataset3\n.\n. other files on scratch\n.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to move the entire directory \u003ccode\u003edatasets\u003c/code\u003e to \u003ccode\u003e${SLURM_TMPDIR}\u003c/code\u003e, you\nwould do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash job_submitter.sh \\\n --data datasets\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis would then lead to the following structure on \u003ccode\u003e${SLURM_TMPDIR}\u003c/code\u003e\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e${SLURM_TMPDIR}\n\u251c\u2500\u2500 datasets\n\u2502 \u00a0 \u251c\u2500\u2500 dataset1\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 dataset2\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 dataset3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf instead you want to move only \u003ccode\u003edataset1\u003c/code\u003e and \u003ccode\u003edataset2\u003c/code\u003e, you would do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash job_submitter.sh \\\n --data datasets/dataset1 datasets/dataset2\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis would then lead to the following structure\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e${SLURM_TMPDIR}\n\u251c\u2500\u2500 datasets\n\u2502 \u00a0 \u251c\u2500\u2500 dataset1\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 dataset2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn your specific experiment, you would have an option to specify the location of\na dataset (using e.g. python\u0027s \u003ccode\u003eargparse\u003c/code\u003e). You could then configure your\nprogram to look for \u003ccode\u003edataset1\u003c/code\u003e by running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython my_program.py --data_dir=\u003cspan class=\"pl-smi\"\u003e${SLURM_TMPDIR}\u003c/span\u003e/datasets/dataset1 [other arguments]\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-multi-node-distributed-gpu-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#multi-node-distributed-gpu-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-node distributed gpu training\u003c/h2\u003e\n\u003cp\u003eThe scripts have been tested with two different ways to do multi-node\ndistributed gpu training with PyTorch,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUsing PyTorch\u0027s \u003ca href=\"https://pytorch.org/docs/stable/elastic/run.html\" rel=\"nofollow\"\u003e\u003ccode\u003etorchrun\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.pytorchlightning.ai/\" rel=\"nofollow\"\u003ePyTorch Lightning\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe practical difference in terms of submitting a job is what each approach\nconsiders a task. The hpc scripts found in this repo will make sure to submit a\njob with the appropriate relationship between gpus, nodes, and cpus.\u003c/p\u003e\n\u003cp\u003eIn terms of writing the application code, Lightning removes a lot of the\ndistributed training setup and does this for you. It also offers multiple\noptimization tricks that have been found to improve training of neural network\nbased models. The downside is that Lightning is (slightly) more rigid in terms\nof managing the gpus across the distributed processes. Using PyTorch\u0027s\n\u003ccode\u003etorchrun\u003c/code\u003e offers full flexibility, but requires manually setting up the\ndistributed training.\u003c/p\u003e\n\u003cp\u003eTo get comfortable with these different approaches and play around with them\ncheck out my \u003ca href=\"https://github.com/ammunk/distributed-training-pytorch\"\u003edistributed training\nrepository\u003c/a\u003e which also\nuses the hpc scripts found here.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-lightning\" class=\"anchor\" aria-hidden=\"true\" href=\"#lightning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLightning\u003c/h3\u003e\n\u003cp\u003eLightning is built on PyTorch and requires your code to be written using a\ncertain structure. It has a lot of functionality, but it attempts to streamline\nthe training process to be agnostic to any particular neural network training\nprogram. Lightning includes loads of functionalities, but fundamentally you can\nthink of Lightning as doing the training loop for you. You only have to write\nthe training step, which is then called by Lightning.\u003c/p\u003e\n\u003cp\u003eThe benefit of the design of Lightning is that Lightning manages distributing\nyour code across multiple gpus without you having to really change your code.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-torchrun\" class=\"anchor\" aria-hidden=\"true\" href=\"#torchrun\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003etorchrun\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eIf you use the \u003ccode\u003etorchrun\u003c/code\u003e approach you achieve full flexibility in how to manage\nthe gpus for each process. Under the hood \u003ccode\u003etorchrun\u003c/code\u003e spawns subprocesses, and\nrequires you to specify which machine the is the \"master\" machine as well as\nwhich port these processes use to communicate to each other.\u003c/p\u003e\n\u003cp\u003eIf you use a virtual environment for you application, the hpc scripts provided\nin this repository handles this for you. However, if you use Singularity you\nhave to manage this yourself: either as a command passed to the Singularity\ncontainer or build the Singularity container to take care of this.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pytorch.org/docs/stable/elastic/run.html\" rel=\"nofollow\"\u003e\u003ccode\u003etorchrun\u003c/code\u003e\u003c/a\u003e comes with the\ninstallation of PyTorch, and should be executed on \u003cstrong\u003eeach node\u003c/strong\u003e using the\nfollowing execution pattern,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003etorchrun --nproc_per_node 2 --nnodes 2 \\\n --rdzv_id=0 \\\n --rdzv_backend=c10d \\\n --rdzv_endpoint=192.168.1.1:2345 \\\n --max_restarts=3 \\\n YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other arguments of your training script)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the\n\u003ca href=\"virtual_env_hpc_files/distributed_scripts/torchrun_launcher.sh\"\u003evirtual_env_hpc_files/distributed_scripts/torchrun_launcher.sh\u003c/a\u003e\nfile for how this is handled if you use a virtual environment approach.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment variables\u003c/h2\u003e\n\u003cp\u003eThe scripts sets various environment variables. These are used\ninternally, and can be used downstream within a program.\u003c/p\u003e\n\u003cp\u003eSome are automatically inferred from the name of the project folder, while other\nshould be manually (optional) specified. The variables are then available within\nyour program using e.g. python\u0027s \u003ccode\u003eos\u003c/code\u003e package:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esource_dir\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eenviron\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e\u0027source_dir\u0027\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-automatically-assigned\" class=\"anchor\" aria-hidden=\"true\" href=\"#automatically-assigned\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically assigned\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esource_dir\u003c/code\u003e: absolute path to the root of the project.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eproject_name\u003c/code\u003e: set to be the name of the project folder.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escratch_dir=${SCRATCH}/${project_name}\u003c/code\u003e: path to a folder created on\n\u003ccode\u003eSCRATCH\u003c/code\u003e. This folder is project specific and is created using\n\u003ccode\u003eproject_name\u003c/code\u003e. No need to worry about having multiple different project\noverwrite one another.\n\u003cul\u003e\n\u003cli\u003eThis path should be considered the \"root\" location of the project to store\nlarge files - e.g. model checkpoints etc.\u003c/li\u003e\n\u003cli\u003eSince this is on \u003ccode\u003eSCRATCH\u003c/code\u003e read/write operation may be \u003cstrong\u003eslow\u003c/strong\u003e. Try\nusing \u003ccode\u003epath_to_local_node_storage=${SLURM_TMPDIR}\u003c/code\u003e instead.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-manually-optional-assigned\" class=\"anchor\" aria-hidden=\"true\" href=\"#manually-optional-assigned\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eManually (optional) assigned\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eexp_name\u003c/code\u003e: a name which describe the current experiment belonging to the\noverarching project (\u003ccode\u003eproject_name\u003c/code\u003e)\n\u003cul\u003e\n\u003cli\u003eFor instance, the project could be \"gan_training\". An experiment could then\nbe \u003ccode\u003eexp_name=celebA\u003c/code\u003e for training a GAN using the \u003ca href=\"https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\" rel=\"nofollow\"\u003ecelebA\ndataset\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-created-folders\" class=\"anchor\" aria-hidden=\"true\" href=\"#created-folders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreated folders\u003c/h2\u003e\n\u003cp\u003eThe scripts will automatically create the following directories. Your experiment\ncan easily access these using the created \u003ca href=\"#environment-variables\"\u003eenvironment\nvariables\u003c/a\u003e. They are only created if they do not already\nexist.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e${SCRATCH}/${project_name}\u003c/code\u003e: if you have a dataset on scratch, you should\ncreate this directory yourself and put whatever data you need for your jobs\nhere.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${scratch_dir}/hpc_outputs\u003c/code\u003e: location of yours jobs\u0027 output files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${scratch_dir}/exp_name/checkpoints\u003c/code\u003e: a directory meant to store checkpoints\nand other files created as your experiment runs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-project-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#project-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject structure\u003c/h2\u003e\n\u003cp\u003eRegardless of whether your project uses Singularity or virtual environments the\nscripts assumes a certain structure\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eyour_project_name\n\u251c\u2500\u2500 hpc_files\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 experiment_configurations.txt\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 job_submitter.sh\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 plai_cleanups\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 plai_cleanup.sh\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 submit_plai_cleanup\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 README.md\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 singularity_hpc_files\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 distributed_dispatcher.sh\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 README.md\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 standard_job.sh\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 sweeper.yml\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 virtual_env_hpc_files\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 distributed_dispatcher.sh\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 distributed_scripts\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 lightning_launcher.sh\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 script_launcher.sh\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 README.md\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 standard_job.sh\n\u251c\u2500\u2500 Pipfile\n\u251c\u2500\u2500 requirements.txt\n\u251c\u2500\u2500 singularity_container.sif\n\u251c\u2500\u2500 Singularity.bld\n\u2502\u00a0\n.\n. other project source files\n.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-slurm_tmpdir-on-plais-cluster\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-slurm_tmpdir-on-plais-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEXAMPLE: \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e on PLAI\u0027s cluster\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e not provided on the PLAI cluster. This is why the scripts\nwill check if you submit your job on the PLAI cluster and set this for you -\n\u003ccode\u003eSLURM_TMPDIR=/scratch-ssd/${USER}\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe scripts will then create the temporary directory for each job on each node.\nUpon completion of the job the directory will be deleted. Note, however, that\nshould the job end prematurely due to hitting the time limit or the job simply\ncrashes, the cleanup will not happen.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-keeping-plai-local-storage-clean\" class=\"anchor\" aria-hidden=\"true\" href=\"#keeping-plai-local-storage-clean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKeeping PLAI local storage clean\u003c/h3\u003e\n\u003cp\u003eTo keep the local storages clean on the PLAI cluster, consider running the\n\u003ca href=\"plai_cleanups/submit_plai_cleanup\"\u003ecleanup script\u003c/a\u003e. This script submits a\njob to each machine on the plai cluster and removes all directories and files\nfound in \u003ccode\u003e/scratch-ssd\u003c/code\u003e that matches the pattern \u003ccode\u003e${USER}*\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-additional-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#additional-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional resources\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePyTorch \u003ca href=\"https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group\" rel=\"nofollow\"\u003edistributed communication package\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/docs/stable/elastic/run.html\" rel=\"nofollow\"\u003eElastic launch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/tutorials/intermediate/ddp_tutorial.html?highlight=distributed\" rel=\"nofollow\"\u003ePyTorch distributed tutorial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch-lightning.readthedocs.io/en/stable/clouds/cluster.html\" rel=\"nofollow\"\u003ePyTorch Lightning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDifference between using \u003ccode\u003e--gres\u003c/code\u003e (e.g. \u003ccode\u003e--gres:gpu:2\u003c/code\u003e) and \u003ccode\u003e--gpus-per-task\u003c/code\u003e:\n(\u003ca href=\"https://stackoverflow.com/questions/67091056/gpu-allocation-in-slurm-gres-vs-gpus-per-task-and-mpirun-vs-srun\" rel=\"nofollow\"\u003ehttps://stackoverflow.com/questions/67091056/gpu-allocation-in-slurm-gres-vs-gpus-per-task-and-mpirun-vs-srun\u003c/a\u003e)\n\u003cul\u003e\n\u003cli\u003eParticularly be careful with \u003ccode\u003e--gpu-per-task\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1663579650.0 + "updated_at": 1662359550.0 }, { "data_format": 2, - "description": "an example scientific filesystem to provide custom metrics and helpers for a container", + "description": "Distribution Aware Active Learning ", "filenames": [ "Singularity" ], - "full_name": "sci-f/metrics.scif", + "full_name": "mkhodabandeh/daal_code", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-metrics-scientific-filesystem\" class=\"anchor\" aria-hidden=\"true\" href=\"#metrics-scientific-filesystem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMetrics Scientific Filesystem\u003c/h1\u003e\n\u003cp\u003eThis is an example for a container that serves to make it easy to run\nvarious metrics over an analysis of interest (the container\u0027s main runscript).\nEach installed app can be thought of as a particular context to evoke the\ncontainer\u0027s main runscript, and arguably the apps are relatively agnostic to\nthe runscript. Continue reading for step by step explanation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image\u003c/h2\u003e\n\u003cp\u003eLet\u0027s first build the container. You can use the Makefile to build the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\n# Does make clean followed by make build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor manually:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build metrics Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Image\u003c/h2\u003e\n\u003cp\u003eAnd now run it. This should perform the container\u0027s main function, calling it\u0027s runscript:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics\nHello-World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWorks great! But then what if we wanted to know what tools (SCIF apps) come with the\ncontainer? That\u0027s easy to do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics apps\n\ncustom\nlinter\nparallel\nstrace\ntime\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEach of these is suited for a particular use case, discussed next.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-case-1-evaluate-software-across-different-metrics\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-case-1-evaluate-software-across-different-metrics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Case 1: Evaluate software across different metrics\u003c/h2\u003e\n\u003cp\u003eA system admin or researcher concerned about evaluation of different software\ncould add relevant metrics apps to the software containers, and then easily evaluate\neach one with the equivalent command to the container. As an example, here is a\nsimple app to return a table of system traces for some main SCIF app, or a user\nspecific name runscript:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun strace\n if [ $# -eq 0 ]\n then\n exec strace -c -t scif run main\n else\n exec strace -c -t scif run \"$@\"\n fi\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe table returned shows the traces:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./metrics run strace\n[strace] executing /bin/bash /scif/apps/strace/scif/runscript\n[main] executing /bin/bash /scif/apps/main/scif/runscript\nHello World!\n% time seconds usecs/call calls errors syscall\n------ ----------- ----------- --------- --------- ----------------\n100.00 0.000008 0 40 munmap\n 0.00 0.000000 0 707 read\n 0.00 0.000000 0 1 write\n 0.00 0.000000 0 426 42 open\n 0.00 0.000000 0 447 close\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor a set of metrics from \"time\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics run time\n[time] executing /bin/bash /scif/apps/time/scif/runscript\nCOMMAND ELAPSED_TIME_HMS AVERAGE_MEM FS_INPUTS MAX_RES_SIZE_KB FS_OUTPUTS PERC_CPU_ALLOCATED CPU_SECONDS_USED W_TIMES_SWAPPED SHARED_TEXT_KB ELAPSED_TIME_SECONDS NUMBER_SIGNALS_DELIVERED AVG_UNSHARED_STACK_SIZE SOCKET_MSG_RECEIVED SOCKET_MSG_SENT AVG_RESIDENT_SET_SIZE CONTEXT_SWITCHES\nscif run main 0:00.22 0 74 28120 0 100% 0.21 0 0 0.22 0 0 0 0 0 29\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe user can also specify a name of another app in the container to run a system trace\nfor it instead (truncated):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics run strace custom\n[strace] executing /bin/bash /scif/apps/strace/scif/runscript custom\n[custom] executing /bin/bash /scif/apps/custom/scif/runscript\nBeware of a dark-haired man with a loud tie.\n (__) \n (oo) \n /------\\/ \n / | || \n * /\\---/\\ \n ~~ ~~ \n...\"Have you mooed today?\"...\n% time seconds usecs/call calls errors syscall\n------ ----------- ----------- --------- --------- ----------------\n 58.33 0.000014 4 4 wait4\n 41.67 0.000010 0 426 42 open\n 0.00 0.000000 0 710 read\n 0.00 0.000000 0 1 write\n 0.00 0.000000 0 447 close\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRegardless of what your runscript does, this app will provide a consistent way\nto produce this metric. Who knew there were so many open and read calls to\njust echo-ing a line to the console!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-case-2-custom-functions-and-metrics\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-case-2-custom-functions-and-metrics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Case 2: Custom Functions and Metrics\u003c/h2\u003e\n\u003cp\u003eWhen a container is intended to only perform one function, this use case maps\nnicely to having a single runscript. As the number of possible functions increase,\nhowever, the user is forced to either:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ehave a runscript that can take command line options to call different executables\u003c/li\u003e\n\u003cli\u003euse the \u003ccode\u003eexec\u003c/code\u003e command with some known path (to the user)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSCI-F apps allow for an easy way to define custom helper metrics or functions for\nthe container. For example, let\u0027s say I created some custom,\nspecial metric. Or in this case, it\u0027s more of a container easter egg.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun custom\n apt-get moo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then the resulting output\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics run custom\n\"I wonder\", he said to himself, \"what\u0027s in a book while it\u0027s closed. Oh, I\nknow it\u0027s full of letters printed on paper, but all the same, something must\nbe happening, because as soon as I open it, there\u0027s a whole story with people\nI don\u0027t know yet and all kinds of adventures and battles.\"\n\t\t-- Bastian B. Bux\n (__) \n (oo) \n /------\\/ \n / | || \n * /\\---/\\ \n ~~ ~~ \n...\"Have you mooed today?\"...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis simple ability to create general, modular applications for containers means\nthat we can move toward the possibility that some researchers can specialize in\nthe development of the metrics, and others the analyses.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-case-3-code-quality-and-linting\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-case-3-code-quality-and-linting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Case 3: Code Quality and Linting\u003c/h2\u003e\n\u003cp\u003eA SCIF app can be used for general tests that are generalizable\nto other containers. The example is provided here with the \"linter\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics run linter \u0026lt;file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe app can perform a linting of some default script provided by the container, or a user specified file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./metrics run linter\n[linter] executing /bin/bash /scif/apps/linter/scif/runscript\nNo config file found, using default configuration\n************* Module runscript\nE: 1, 0: invalid syntax (\u0026lt;string\u0026gt;, line 1) (syntax-error)\n\\end{lstlisting}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./metrics run linter script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-case-4-runtime-evaluation\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-case-4-runtime-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Case 4: Runtime Evaluation\u003c/h2\u003e\n\u003cp\u003eIn that a metric can call a runscript, it could be easy to evaluate running the\nmain analysis under various levels or conditions. As a simple proof of concept,\nhere we are creating an app to execute the same exact script in parallel.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun parallel\n parallel /bin/bash ::: $SCIF_APPRUN_main $SCIF_APPRUN_main $SCIF_APPRUN_main\n\n./metrics run parallel\n[parallel] executing /bin/bash /scif/apps/parallel/scif/runscript\nHello World!\nHello World!\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd you might imagine a similar loop to run an analysis, and modify a runtime\nor system variable for each loop, and save the output (or print to console).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-them-all\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-them-all\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun them all!\u003c/h1\u003e\n\u003cp\u003eAnd we don\u0027t need to know anything in advance (paths to hidden executables, how\npaths or environment should be handled) to run all the container applications,\nif we wanted to do that. We can use a loop\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor app in $(./metrics apps)\n do\n ./metrics run $app\ndone\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-daal\" class=\"anchor\" aria-hidden=\"true\" href=\"#daal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edaal\u003c/h1\u003e\n\u003cp\u003eDistribution Aware Active Learning with GANs\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 3, - "topics": [ - "scif", - "scientific-filesystem", - "singularity", - "time", - "strace", - "lolcow" + "subscribers_count": 2, + "topics": [], + "updated_at": 1579359192.0 + }, + { + "data_format": 2, + "description": "\u81ea\u5df1\u4fee\u6539\u548c\u914d\u7f6e\u540e\u7684FrameFieldLearning\uff0c\u4e0e\u539f\u9879\u76ee\u6539\u52a8\u4e0d\u5927", + "filenames": [ + "singularity/Singularity" ], - "updated_at": 1522810687.0 + "full_name": "Halle-Astra/Frame_Field_Learning_Revised", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-polygonal-building-segmentation-by-frame-field-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#polygonal-building-segmentation-by-frame-field-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/h1\u003e\n\u003cp\u003eWe add a frame field output to an image segmentation neural network to improve segmentation quality\nand provide structural information for the subsequent polygonization step.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/frame_field_sample.png\"\u003e\u003cimg src=\"images/frame_field_sample.png\" width=\"512\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 1: Close-up of our additional frame field output on a test image.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/model_training.png\"\u003e\u003cimg src=\"images/model_training.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 2: Given an overhead image, the model outputs an edge mask, an interior mask,\n and a frame field for buildings. The total loss includes terms that align the masks and\n frame field to ground truth data as well as regularizers to enforce smoothness of the\n frame field and consistency between the outputs.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/schematic_polygonization.png\"\u003e\u003cimg src=\"images/schematic_polygonization.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 3: Given classification maps and a frame field as input, we optimize skeleton polylines to\n align to the frame field using an Active Skeleton Model (ASM) and detect corners using\n the frame field, simplifying non-corner vertices.\n\u003c/p\u003e\n\u003cp\u003eThis repository contains the official code for the paper:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://www-sop.inria.fr/members/Nicolas.Girard/\" rel=\"nofollow\"\u003eNicolas Girard\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/smirnov/\" rel=\"nofollow\"\u003eDmitriy Smirnov\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/jsolomon/\" rel=\"nofollow\"\u003eJustin Solomon\u003c/a\u003e,\n\u003ca href=\"https://www-sop.inria.fr/members/Yuliya.Tarabalka/\" rel=\"nofollow\"\u003eYuliya Tarabalka\u003c/a\u003e\u003cbr\u003e\nCVPR 2021\u003cbr\u003e\n\u003cstrong\u003e[\u003ca href=\"https://arxiv.org/abs/2004.14875\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, \u003ca href=\"https://www.youtube.com/watch?v=226pPTBsNJ8\u0026amp;t=8s\" rel=\"nofollow\"\u003evideo\u003c/a\u003e]\u003c/strong\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-git-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#git-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGit submodules\u003c/h2\u003e\n\u003cp\u003eThis project uses various git submodules that should be cloned too.\u003c/p\u003e\n\u003cp\u003eTo clone a repository including its submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive --jobs 8 \u0026lt;URL to Git repo\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you already have cloned the repository and now want to load it\u2019s submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --init --recursive --jobs 8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more about explanations about using submodules and git, see \u003ca href=\"SUBMODULES.md\"\u003eSUBMODULES.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eThe easiest way to setup environment is to use the Docker image provided in the \u003ca href=\"docker\"\u003edocker\u003c/a\u003e (see README inside the folder).\u003c/p\u003e\n\u003cp\u003eOnce the docker container is built and launched, execute the \u003ca href=\"setup.sh\"\u003esetup.sh\u003c/a\u003e script inside to install required packages.\u003c/p\u003e\n\u003cp\u003eThe environment in the container is now ready for use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda environment\u003c/h2\u003e\n\u003cp\u003eAlternatively you can install all dependencies in a conda environment.\nI provide my environment specifications in the \u003ca href=\"environment.yml\"\u003eenvironment.yml\u003c/a\u003e which you can use to create your environment own with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h1\u003e\n\u003cp\u003eSeveral datasets are used in this work.\nWe typically put all datasets in a \"data\" folder which we link to the \"/data\" folder in the container (with the \u003ccode\u003e-v\u003c/code\u003e argument when running the container).\nEach dataset has it\u0027s own sub-folder, usually named with a short version of that dataset\u0027s name.\nEach dataset sub-folder should have a \"raw\" folder inside containing all the original folders and files fo the datset.\nWhen pre-processing data, \"processed\" folders will be created alongside the \"raw\" folder.\u003c/p\u003e\n\u003cp\u003eFor example, here is an example working file structure inside the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/data \n|-- AerialImageDataset\n |-- raw\n |-- train\n | |-- aligned_gt_polygons_2\n | |-- gt\n | |-- gt_polygonized\n | |-- images\n `-- test\n |-- aligned_gt_polygons_2\n |-- images\n`-- mapping_challenge_dataset\n |-- raw\n |-- train\n | |-- images\n | |-- annotation.json\n | `-- annotation-small.json\n `-- val\n `-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf however you would like to use a different folder for the datasets (for example while not using Docker),\nyou can change the path to datasets in config files.\nYou can modify the \"data_dir_candidates\" list in the config to only include your path.\nThe training script checks this list of paths one at a time and picks the first one that exists.\nIt then appends the \"data_root_partial_dirpath\" directory to get to the dataset.\u003c/p\u003e\n\u003cp\u003eYou can find some of the data we used in this shared \"data\" folder: \u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inria-aerial-image-labeling-dataset\" class=\"anchor\" aria-hidden=\"true\" href=\"#inria-aerial-image-labeling-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInria Aerial Image Labeling Dataset\u003c/h2\u003e\n\u003cp\u003eLink to the dataset: \u003ca href=\"https://project.inria.fr/aerialimagelabeling/\" rel=\"nofollow\"\u003ehttps://project.inria.fr/aerialimagelabeling/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor the Inria dataset, the original ground truth is just a collection of raster masks.\nAs our method requires annotations to be polygons in order to compute the ground truth angle for the frame field, we made 2 versions of the dataset:\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria OSM dataset\u003c/em\u003e has aligned annotations pulled from OpenStreetMap.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria Polygonized dataset\u003c/em\u003e has polygon annotations obtained from using our frame field polygonization algorithm on the original raster masks.\nThis was done by running the \u003ccode\u003epolygonize_mask.py\u003c/code\u003e script like so:\n\u003ccode\u003epython polygonize_mask.py --run_name inria_dataset_osm_mask_only.unet16 --filepath ~/data/AerialImageDataset/raw/train/gt/*.tif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can find this new ground truth for both cases in the shared \"data\" folder (\u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-the-mainpy-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-mainpy-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the main.py script\u003c/h1\u003e\n\u003cp\u003eExecute \u003ca href=\"main.py\"\u003emain.py\u003c/a\u003e script to train a model, test a model or use a model on your own image.\nSee the help of the main script with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython main.py --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe script can be launched on multiple GPUs for multi-GPU training and evaluation.\nSimply set the \u003ccode\u003e--gpus\u003c/code\u003e argument to the number of gpus you want to use.\nHowever, for the first launch of the script on a particular dataset (when it will pre-process the data),\nit is best to leave it at 1 as I did not implement multi-GPU synchronization when pre-processing datasets.\u003c/p\u003e\n\u003cp\u003eAn example use is for training a model with a certain config file, like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained\u003c/code\u003e\nwhich will train the Unet-Resnet101 on the CrowdAI Mapping Challenge dataset.\nThe batch size can be adjusted like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained -b \u0026lt;new batch size\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen training is done, the script can be launched in eval mode, to evaluate the trained model:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval\u003c/code\u003e.\nDepending on the eval parameters of the config file, running this will output results on the test dataset.\u003c/p\u003e\n\u003cp\u003eFinally, if you wish to compute AP and AR metrics with the COCO API, you can run:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval_coco\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-launch-inference-on-one-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#launch-inference-on-one-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch inference on one image\u003c/h2\u003e\n\u003cp\u003eMake sure the run folder has the correct structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- \u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the [main.py] script like so (filling values for arguments run_name and in_filepath):\n\u003ccode\u003epython main.py --run_name \u0026lt;run_name\u0026gt; --in_filepath \u0026lt;your_image_filepath\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe outputs will be saved next to the input image\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-trained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload trained models\u003c/h2\u003e\n\u003cp\u003eWe provide already-trained models so you can run inference right away.\nDownload here: \u003ca href=\"https://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\u003c/a\u003e.\nEach model was trained in a \"run\", whose folder (named with the format \u003ccode\u003e\u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\u003c/code\u003e) you can download at the provided link.\nYou should then place those runs in a folder named \"runs\" inside the \"frame_field_learning\" folder like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard | 2020-06-02 07:57:31\n| | |-- mapping_dataset.unet_resnet101_pretrained.field_off.train_val | 2020-09-07 11:54:48\n| | |-- mapping_dataset.unet_resnet101_pretrained.train_val | 2020-09-07 11:28:51\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBecause Google Drive reformats folder names, you have to rename the run folders as above.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-hidden=\"true\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCite:\u003c/h1\u003e\n\u003cp\u003eIf you use this code for your own research, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{Girard_2021_CVPR,\n author = {Girard, Nicolas and Smirnov, Dmitriy and Solomon, Justin and Tarabalka, Yuliya},\n title = {Polygonal Building Extraction by Frame Field Learning},\n booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n month = {June},\n year = {2021},\n pages = {5891-5900}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 3, + "subscribers_count": 2, + "topics": [], + "updated_at": 1660100991.0 }, { "data_format": 2, @@ -32585,829 +32721,750 @@ var data = }, { "data_format": 2, - "description": "\u81ea\u5df1\u4fee\u6539\u548c\u914d\u7f6e\u540e\u7684FrameFieldLearning\uff0c\u4e0e\u539f\u9879\u76ee\u6539\u52a8\u4e0d\u5927", + "description": "an example scientific filesystem to provide custom metrics and helpers for a container", "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "Halle-Astra/Frame_Field_Learning_Revised", + "full_name": "sci-f/metrics.scif", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-polygonal-building-segmentation-by-frame-field-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#polygonal-building-segmentation-by-frame-field-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/h1\u003e\n\u003cp\u003eWe add a frame field output to an image segmentation neural network to improve segmentation quality\nand provide structural information for the subsequent polygonization step.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/frame_field_sample.png\"\u003e\u003cimg src=\"images/frame_field_sample.png\" width=\"512\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 1: Close-up of our additional frame field output on a test image.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/model_training.png\"\u003e\u003cimg src=\"images/model_training.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 2: Given an overhead image, the model outputs an edge mask, an interior mask,\n and a frame field for buildings. The total loss includes terms that align the masks and\n frame field to ground truth data as well as regularizers to enforce smoothness of the\n frame field and consistency between the outputs.\n \u003cbr\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/schematic_polygonization.png\"\u003e\u003cimg src=\"images/schematic_polygonization.png\" width=\"768\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n Figure 3: Given classification maps and a frame field as input, we optimize skeleton polylines to\n align to the frame field using an Active Skeleton Model (ASM) and detect corners using\n the frame field, simplifying non-corner vertices.\n\u003c/p\u003e\n\u003cp\u003eThis repository contains the official code for the paper:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolygonal Building Segmentation by Frame Field Learning\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://www-sop.inria.fr/members/Nicolas.Girard/\" rel=\"nofollow\"\u003eNicolas Girard\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/smirnov/\" rel=\"nofollow\"\u003eDmitriy Smirnov\u003c/a\u003e,\n\u003ca href=\"https://people.csail.mit.edu/jsolomon/\" rel=\"nofollow\"\u003eJustin Solomon\u003c/a\u003e,\n\u003ca href=\"https://www-sop.inria.fr/members/Yuliya.Tarabalka/\" rel=\"nofollow\"\u003eYuliya Tarabalka\u003c/a\u003e\u003cbr\u003e\nCVPR 2021\u003cbr\u003e\n\u003cstrong\u003e[\u003ca href=\"https://arxiv.org/abs/2004.14875\" rel=\"nofollow\"\u003epaper\u003c/a\u003e, \u003ca href=\"https://www.youtube.com/watch?v=226pPTBsNJ8\u0026amp;t=8s\" rel=\"nofollow\"\u003evideo\u003c/a\u003e]\u003c/strong\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-git-submodules\" class=\"anchor\" aria-hidden=\"true\" href=\"#git-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGit submodules\u003c/h2\u003e\n\u003cp\u003eThis project uses various git submodules that should be cloned too.\u003c/p\u003e\n\u003cp\u003eTo clone a repository including its submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive --jobs 8 \u0026lt;URL to Git repo\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you already have cloned the repository and now want to load it\u2019s submodules execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --init --recursive --jobs 8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit submodule update --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more about explanations about using submodules and git, see \u003ca href=\"SUBMODULES.md\"\u003eSUBMODULES.md\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h2\u003e\n\u003cp\u003eThe easiest way to setup environment is to use the Docker image provided in the \u003ca href=\"docker\"\u003edocker\u003c/a\u003e (see README inside the folder).\u003c/p\u003e\n\u003cp\u003eOnce the docker container is built and launched, execute the \u003ca href=\"setup.sh\"\u003esetup.sh\u003c/a\u003e script inside to install required packages.\u003c/p\u003e\n\u003cp\u003eThe environment in the container is now ready for use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda environment\u003c/h2\u003e\n\u003cp\u003eAlternatively you can install all dependencies in a conda environment.\nI provide my environment specifications in the \u003ca href=\"environment.yml\"\u003eenvironment.yml\u003c/a\u003e which you can use to create your environment own with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData\u003c/h1\u003e\n\u003cp\u003eSeveral datasets are used in this work.\nWe typically put all datasets in a \"data\" folder which we link to the \"/data\" folder in the container (with the \u003ccode\u003e-v\u003c/code\u003e argument when running the container).\nEach dataset has it\u0027s own sub-folder, usually named with a short version of that dataset\u0027s name.\nEach dataset sub-folder should have a \"raw\" folder inside containing all the original folders and files fo the datset.\nWhen pre-processing data, \"processed\" folders will be created alongside the \"raw\" folder.\u003c/p\u003e\n\u003cp\u003eFor example, here is an example working file structure inside the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/data \n|-- AerialImageDataset\n |-- raw\n |-- train\n | |-- aligned_gt_polygons_2\n | |-- gt\n | |-- gt_polygonized\n | |-- images\n `-- test\n |-- aligned_gt_polygons_2\n |-- images\n`-- mapping_challenge_dataset\n |-- raw\n |-- train\n | |-- images\n | |-- annotation.json\n | `-- annotation-small.json\n `-- val\n `-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf however you would like to use a different folder for the datasets (for example while not using Docker),\nyou can change the path to datasets in config files.\nYou can modify the \"data_dir_candidates\" list in the config to only include your path.\nThe training script checks this list of paths one at a time and picks the first one that exists.\nIt then appends the \"data_root_partial_dirpath\" directory to get to the dataset.\u003c/p\u003e\n\u003cp\u003eYou can find some of the data we used in this shared \"data\" folder: \u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inria-aerial-image-labeling-dataset\" class=\"anchor\" aria-hidden=\"true\" href=\"#inria-aerial-image-labeling-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInria Aerial Image Labeling Dataset\u003c/h2\u003e\n\u003cp\u003eLink to the dataset: \u003ca href=\"https://project.inria.fr/aerialimagelabeling/\" rel=\"nofollow\"\u003ehttps://project.inria.fr/aerialimagelabeling/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor the Inria dataset, the original ground truth is just a collection of raster masks.\nAs our method requires annotations to be polygons in order to compute the ground truth angle for the frame field, we made 2 versions of the dataset:\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria OSM dataset\u003c/em\u003e has aligned annotations pulled from OpenStreetMap.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInria Polygonized dataset\u003c/em\u003e has polygon annotations obtained from using our frame field polygonization algorithm on the original raster masks.\nThis was done by running the \u003ccode\u003epolygonize_mask.py\u003c/code\u003e script like so:\n\u003ccode\u003epython polygonize_mask.py --run_name inria_dataset_osm_mask_only.unet16 --filepath ~/data/AerialImageDataset/raw/train/gt/*.tif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can find this new ground truth for both cases in the shared \"data\" folder (\u003ca href=\"https://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/19yqseUsggPEwLFTBl04CmGmzCZAIOYhy?usp=sharing\u003c/a\u003e.).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-the-mainpy-script\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-mainpy-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the main.py script\u003c/h1\u003e\n\u003cp\u003eExecute \u003ca href=\"main.py\"\u003emain.py\u003c/a\u003e script to train a model, test a model or use a model on your own image.\nSee the help of the main script with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython main.py --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe script can be launched on multiple GPUs for multi-GPU training and evaluation.\nSimply set the \u003ccode\u003e--gpus\u003c/code\u003e argument to the number of gpus you want to use.\nHowever, for the first launch of the script on a particular dataset (when it will pre-process the data),\nit is best to leave it at 1 as I did not implement multi-GPU synchronization when pre-processing datasets.\u003c/p\u003e\n\u003cp\u003eAn example use is for training a model with a certain config file, like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained\u003c/code\u003e\nwhich will train the Unet-Resnet101 on the CrowdAI Mapping Challenge dataset.\nThe batch size can be adjusted like so:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained -b \u0026lt;new batch size\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen training is done, the script can be launched in eval mode, to evaluate the trained model:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval\u003c/code\u003e.\nDepending on the eval parameters of the config file, running this will output results on the test dataset.\u003c/p\u003e\n\u003cp\u003eFinally, if you wish to compute AP and AR metrics with the COCO API, you can run:\n\u003ccode\u003epython main.py --config configs/config.mapping_dataset.unet_resnet101_pretrained --mode eval_coco\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-launch-inference-on-one-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#launch-inference-on-one-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLaunch inference on one image\u003c/h2\u003e\n\u003cp\u003eMake sure the run folder has the correct structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- \u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the [main.py] script like so (filling values for arguments run_name and in_filepath):\n\u003ccode\u003epython main.py --run_name \u0026lt;run_name\u0026gt; --in_filepath \u0026lt;your_image_filepath\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe outputs will be saved next to the input image\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-trained-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#download-trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload trained models\u003c/h2\u003e\n\u003cp\u003eWe provide already-trained models so you can run inference right away.\nDownload here: \u003ca href=\"https://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1poTQbpCz12ra22CsucF_hd_8dSQ1T3eT?usp=sharing\u003c/a\u003e.\nEach model was trained in a \"run\", whose folder (named with the format \u003ccode\u003e\u0026lt;run_name\u0026gt; | \u0026lt;yyyy-mm-dd hh:mm:ss\u0026gt;\u003c/code\u003e) you can download at the provided link.\nYou should then place those runs in a folder named \"runs\" inside the \"frame_field_learning\" folder like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePolygonization-by-Frame-Field-Learning\n|-- frame_field_learning\n| |-- runs\n| | |-- inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard | 2020-06-02 07:57:31\n| | |-- mapping_dataset.unet_resnet101_pretrained.field_off.train_val | 2020-09-07 11:54:48\n| | |-- mapping_dataset.unet_resnet101_pretrained.train_val | 2020-09-07 11:28:51\n| | `-- ...\n| |-- inference.py\n| `-- ...\n|-- main.py\n|-- README.md (this file)\n`-- ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBecause Google Drive reformats folder names, you have to rename the run folders as above.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-hidden=\"true\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCite:\u003c/h1\u003e\n\u003cp\u003eIf you use this code for your own research, please cite\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{Girard_2021_CVPR,\n author = {Girard, Nicolas and Smirnov, Dmitriy and Solomon, Justin and Tarabalka, Yuliya},\n title = {Polygonal Building Extraction by Frame Field Learning},\n booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n month = {June},\n year = {2021},\n pages = {5891-5900}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-metrics-scientific-filesystem\" class=\"anchor\" aria-hidden=\"true\" href=\"#metrics-scientific-filesystem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMetrics Scientific Filesystem\u003c/h1\u003e\n\u003cp\u003eThis is an example for a container that serves to make it easy to run\nvarious metrics over an analysis of interest (the container\u0027s main runscript).\nEach installed app can be thought of as a particular context to evoke the\ncontainer\u0027s main runscript, and arguably the apps are relatively agnostic to\nthe runscript. Continue reading for step by step explanation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the image\u003c/h2\u003e\n\u003cp\u003eLet\u0027s first build the container. You can use the Makefile to build the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\n# Does make clean followed by make build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor manually:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build metrics Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-image\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the Image\u003c/h2\u003e\n\u003cp\u003eAnd now run it. This should perform the container\u0027s main function, calling it\u0027s runscript:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics\nHello-World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWorks great! But then what if we wanted to know what tools (SCIF apps) come with the\ncontainer? That\u0027s easy to do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics apps\n\ncustom\nlinter\nparallel\nstrace\ntime\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEach of these is suited for a particular use case, discussed next.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-case-1-evaluate-software-across-different-metrics\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-case-1-evaluate-software-across-different-metrics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Case 1: Evaluate software across different metrics\u003c/h2\u003e\n\u003cp\u003eA system admin or researcher concerned about evaluation of different software\ncould add relevant metrics apps to the software containers, and then easily evaluate\neach one with the equivalent command to the container. As an example, here is a\nsimple app to return a table of system traces for some main SCIF app, or a user\nspecific name runscript:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun strace\n if [ $# -eq 0 ]\n then\n exec strace -c -t scif run main\n else\n exec strace -c -t scif run \"$@\"\n fi\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe table returned shows the traces:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./metrics run strace\n[strace] executing /bin/bash /scif/apps/strace/scif/runscript\n[main] executing /bin/bash /scif/apps/main/scif/runscript\nHello World!\n% time seconds usecs/call calls errors syscall\n------ ----------- ----------- --------- --------- ----------------\n100.00 0.000008 0 40 munmap\n 0.00 0.000000 0 707 read\n 0.00 0.000000 0 1 write\n 0.00 0.000000 0 426 42 open\n 0.00 0.000000 0 447 close\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor a set of metrics from \"time\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics run time\n[time] executing /bin/bash /scif/apps/time/scif/runscript\nCOMMAND ELAPSED_TIME_HMS AVERAGE_MEM FS_INPUTS MAX_RES_SIZE_KB FS_OUTPUTS PERC_CPU_ALLOCATED CPU_SECONDS_USED W_TIMES_SWAPPED SHARED_TEXT_KB ELAPSED_TIME_SECONDS NUMBER_SIGNALS_DELIVERED AVG_UNSHARED_STACK_SIZE SOCKET_MSG_RECEIVED SOCKET_MSG_SENT AVG_RESIDENT_SET_SIZE CONTEXT_SWITCHES\nscif run main 0:00.22 0 74 28120 0 100% 0.21 0 0 0.22 0 0 0 0 0 29\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe user can also specify a name of another app in the container to run a system trace\nfor it instead (truncated):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics run strace custom\n[strace] executing /bin/bash /scif/apps/strace/scif/runscript custom\n[custom] executing /bin/bash /scif/apps/custom/scif/runscript\nBeware of a dark-haired man with a loud tie.\n (__) \n (oo) \n /------\\/ \n / | || \n * /\\---/\\ \n ~~ ~~ \n...\"Have you mooed today?\"...\n% time seconds usecs/call calls errors syscall\n------ ----------- ----------- --------- --------- ----------------\n 58.33 0.000014 4 4 wait4\n 41.67 0.000010 0 426 42 open\n 0.00 0.000000 0 710 read\n 0.00 0.000000 0 1 write\n 0.00 0.000000 0 447 close\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRegardless of what your runscript does, this app will provide a consistent way\nto produce this metric. Who knew there were so many open and read calls to\njust echo-ing a line to the console!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-case-2-custom-functions-and-metrics\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-case-2-custom-functions-and-metrics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Case 2: Custom Functions and Metrics\u003c/h2\u003e\n\u003cp\u003eWhen a container is intended to only perform one function, this use case maps\nnicely to having a single runscript. As the number of possible functions increase,\nhowever, the user is forced to either:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ehave a runscript that can take command line options to call different executables\u003c/li\u003e\n\u003cli\u003euse the \u003ccode\u003eexec\u003c/code\u003e command with some known path (to the user)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSCI-F apps allow for an easy way to define custom helper metrics or functions for\nthe container. For example, let\u0027s say I created some custom,\nspecial metric. Or in this case, it\u0027s more of a container easter egg.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun custom\n apt-get moo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then the resulting output\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics run custom\n\"I wonder\", he said to himself, \"what\u0027s in a book while it\u0027s closed. Oh, I\nknow it\u0027s full of letters printed on paper, but all the same, something must\nbe happening, because as soon as I open it, there\u0027s a whole story with people\nI don\u0027t know yet and all kinds of adventures and battles.\"\n\t\t-- Bastian B. Bux\n (__) \n (oo) \n /------\\/ \n / | || \n * /\\---/\\ \n ~~ ~~ \n...\"Have you mooed today?\"...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis simple ability to create general, modular applications for containers means\nthat we can move toward the possibility that some researchers can specialize in\nthe development of the metrics, and others the analyses.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-case-3-code-quality-and-linting\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-case-3-code-quality-and-linting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Case 3: Code Quality and Linting\u003c/h2\u003e\n\u003cp\u003eA SCIF app can be used for general tests that are generalizable\nto other containers. The example is provided here with the \"linter\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./metrics run linter \u0026lt;file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe app can perform a linting of some default script provided by the container, or a user specified file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./metrics run linter\n[linter] executing /bin/bash /scif/apps/linter/scif/runscript\nNo config file found, using default configuration\n************* Module runscript\nE: 1, 0: invalid syntax (\u0026lt;string\u0026gt;, line 1) (syntax-error)\n\\end{lstlisting}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./metrics run linter script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use-case-4-runtime-evaluation\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-case-4-runtime-evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse Case 4: Runtime Evaluation\u003c/h2\u003e\n\u003cp\u003eIn that a metric can call a runscript, it could be easy to evaluate running the\nmain analysis under various levels or conditions. As a simple proof of concept,\nhere we are creating an app to execute the same exact script in parallel.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun parallel\n parallel /bin/bash ::: $SCIF_APPRUN_main $SCIF_APPRUN_main $SCIF_APPRUN_main\n\n./metrics run parallel\n[parallel] executing /bin/bash /scif/apps/parallel/scif/runscript\nHello World!\nHello World!\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd you might imagine a similar loop to run an analysis, and modify a runtime\nor system variable for each loop, and save the output (or print to console).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run-them-all\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-them-all\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun them all!\u003c/h1\u003e\n\u003cp\u003eAnd we don\u0027t need to know anything in advance (paths to hidden executables, how\npaths or environment should be handled) to run all the container applications,\nif we wanted to do that. We can use a loop\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efor app in $(./metrics apps)\n do\n ./metrics run $app\ndone\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, - "topics": [], - "updated_at": 1660100991.0 + "subscribers_count": 3, + "topics": [ + "scif", + "scientific-filesystem", + "singularity", + "time", + "strace", + "lolcow" + ], + "updated_at": 1522810687.0 }, { "data_format": 2, - "description": "Distribution Aware Active Learning ", + "description": "Sequana demultiplexing pipeline ", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "mkhodabandeh/daal_code", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-daal\" class=\"anchor\" aria-hidden=\"true\" href=\"#daal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edaal\u003c/h1\u003e\n\u003cp\u003eDistribution Aware Active Learning with GANs\u003c/p\u003e\n", + "full_name": "sequana/demultiplex", + "latest_release": "v1.3.0", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [], - "updated_at": 1579359192.0 + "updated_at": 1663579650.0 }, { "data_format": 2, - "description": null, + "description": "Tensorflow based neuronal network framework to isolate vocal from music (BASS).", "filenames": [ - "singularity_hpc_files/Singularity.bld" + "singularity/Singularity" ], - "full_name": "ammunk/hpc", + "full_name": "unmix-io/unmix-net", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-slurm-hpc-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#slurm-hpc-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSLURM hpc scripts\u003c/h1\u003e\n\u003cp\u003eThe approach taken here rely on \u003cstrong\u003ebash\u003c/strong\u003e as opposed to \u003cstrong\u003epython\u003c/strong\u003e, and the hpc\nscripts serve one of three (overlapping) purposes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#multi-node-distributed-gpu-training\"\u003eMulti-node distributed GPU training\u003c/a\u003e of\n\u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e models\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://sylabs.io/guides/3.7/user-guide.pdf\" rel=\"nofollow\"\u003eSinguarity\u003c/a\u003e or virtual\nenvironment based projects\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.wandb.ai/sweeps\" rel=\"nofollow\"\u003eWeights and Biases sweeper\u003c/a\u003e jobs (great for\nhyperparameter searches)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe scripts are designed in order to make the transfer of a locally working\napplication to the hpc clusters as easy and painless as possible.\u003c/p\u003e\n\u003cp\u003eThere are two types of scripts, which differ by how dependencies are managed for\nyour application:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eSingularity containers\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003epython virtual environments\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Singularity approach offers much greater flexibility where all dependencies\nare specified in a \"Singularity file\", whereas the python virtual environment\napproach (obviously) must be a python application.\u003c/p\u003e\n\u003cp\u003eDepending on whether you use Singularity or a python virtual environment they\neach pose slightly different constraints on how experiments run once a job has\nbeen submitted. These constraints are minimal so that you do not have to give up\ne.g. Singularity\u0027s flexibility yet ensures the script can make some assumptions\nabout how to run your experiments. The details on this can be found in the\n\u003ca href=\"singularity_hpc_files/README.md\"\u003eSingularity readme\u003c/a\u003e or the \u003ca href=\"virtual_env_hpc_files/README.md\"\u003evirtual environment readme\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo use these scripts, simply copy them into your \u003cstrong\u003e\u003ca href=\"#project-structure\"\u003eappropriately\nstructured\u003c/a\u003e\u003c/strong\u003e project. The scripts are written to be\n(almost) project agnostic, which effectively means that the scripts will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAutomatically set up the experiments which prevents mixing different projects.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnsure the correct relationship between requested number of \u003cem\u003egpus\u003c/em\u003e, \u003cem\u003enodes\u003c/em\u003e,\nand \u003cem\u003ecpus per gpu\u003c/em\u003e depending on the type of distributed job.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"#copying-datasets-and-other-files-to-slurm_tmpdir\"\u003eManage the transfer\u003c/a\u003e of\ndata and directories to and from local nodes for faster read/write operations -\ni.e. via the \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e environment variable.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe created folders and their location are easily accessible as \u003ca href=\"#environment-variables\"\u003eenvironment\nvariables\u003c/a\u003e. One thing to pay attention to is that\nSingularity based jobs needs additional folders compared to the virtual\nenvironment based jobs. For details see the \u003ca href=\"#created-folders\"\u003ecreated folders\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-important\" class=\"anchor\" aria-hidden=\"true\" href=\"#important\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImportant:\u003c/h4\u003e\n\u003cp\u003eThe scripts rely on the \u003ccode\u003eSCRATCH\u003c/code\u003e environment variable. If \u003ccode\u003eSCRATCH\u003c/code\u003e is not set\nby default add\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SCRATCH=[path to scratch]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto your \u003ccode\u003e~/.bashrc\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, you will notice references to the \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e. This variable\npoints to a temporary directory created for each job pointing to a job-specific\ndirectory on each local node. If the job is allocated multiple nodes the\ntemporary directory is unique on each node. Some clusters will have these\nautomatically set. However, if this is not the case make sure to set this up\nyourself.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-submitting-jobs\" class=\"anchor\" aria-hidden=\"true\" href=\"#submitting-jobs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubmitting jobs\u003c/h2\u003e\n\u003cp\u003eTo submit jobs call one of two submitter jobs. Which one depends on whether your\napplication uses Singularity or a virtual environment. Note that the job\nsubmitter file by default assumes you use a virtual environment. To specify a\nSingularity based job, use the \u003ccode\u003e-s, --singularity-container\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eThe scripts distinguish between two types of jobs, and how to specify the\nexperiment\u0027s configurations depend on which type:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eArray jobs for hyperparameter searches using \u003ccode\u003ewandb\u003c/code\u003e sweeps - see \u003ca href=\"#integration-with-weight-and-biases\"\u003eintegration\nwith Weights and Biases\u003c/a\u003e for more\ndetails.\u003c/li\u003e\n\u003cli\u003eSingle jobs which supports multi-node distributed gpu applications\n\u003cul\u003e\n\u003cli\u003eThe experiments configurations are specified using the\n\u003ca href=\"experiment_configurations.txt\"\u003eexperiment_configurations.txt\u003c/a\u003e file. It\u0027s format differs slightly depending\non whether you use Singularity or a virtual environment. For details, see\nthe \u003ca href=\"singularity_hpc_files/README.md\"\u003eSingularity readme\u003c/a\u003e or the \u003ca href=\"virtual_env_hpc_files/README.md\"\u003evirtual environment readme\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe options that control the job submissions are:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e-a, --account Account to use on cedar (def-fwood, rrg-kevinlb).\n Ignored on the PLAI cluster. Default: rrg-kevinlb\n-g, --gpus Number of gpus per node. Default: 1\n-c, --cpus Number of cpus per node: Default: 2\n-j, --job-type Type of job to run, one of\n (standard, sweep, distributed).\n Default: standard\n-W, --which-distributed Kind of distributed gpu application backend used\n (lightning, torchrun). Must be provided if using\n \"--job-type distributed\"\n-t, --time Requested runtime. Format: dd-HH:MM:SS.\n Default: 00-01:00:00\n-m, --mem Amount of memory per node. E.g. 10G or 10M.\n Default: 10G\n-G, --gpu-type Type of gpu to use (p100, p100l, v100l). Ignored on\n the PLAI cluster. Default: v100l\n-e, --exp-name Name of the experiment. Used to created convenient\n folders in ${SCRATCH}/${project_name} and to name\n the generated output files. Default: \"\" (empty)\n-n, --num_nodes Number of nodes. Default: 1\n-d, --data Whitespace separated list of paths to directories or\n files to transfer to ${SLURM_TMPDIR}. These paths\n MUST be relative to ${SCRATCH}/${project_name}\n-s, --singularity-container Path to singularity container. If specified the\n job is submitted as a Singularity based job\n-w, --workdir Path to a mounted working directory in the\n Singularity container\n-C, --configs Path to file specifying the experiment\n configuration. Default: experiment_configurations.txt\n\n-h, --help Show this message\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h4\u003e\n\u003cp\u003eAssume we have a project with the \u003ca href=\"#project-structure\"\u003eappropriate structure\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo submit a job first \u003ccode\u003ecd [path to project]/hpc_files\u003c/code\u003e, and then\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash job_submitter.sh \\\n --gpus 2 \\\n --cpus 2 \\\n --exp-name testing \\\n --num-nodes 2\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-integration-with-weights-and-biases\" class=\"anchor\" aria-hidden=\"true\" href=\"#integration-with-weights-and-biases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntegration with Weights and Biases\u003c/h3\u003e\n\u003cp\u003eTo use the \u003ca href=\"https://wandb.ai/\" rel=\"nofollow\"\u003eWeight and Biases\u003c/a\u003e sweeps, you need to first\ninstall \u003ccode\u003ewandb\u003c/code\u003e into your Singularity container or virtual environment,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003epip\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einstall\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ewandb\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo use \u003ccode\u003ewandb\u003c/code\u003e requires a user login. Either do \u003ccode\u003ewandb login\u003c/code\u003e, where \u003ccode\u003ewandb\u003c/code\u003e\nwill prompt for a username and password, or set the \u003ccode\u003eWANDB_API_KEY\u003c/code\u003e environment\nvariable to the api key provided by weight and biases after you sign up.\u003c/p\u003e\n\u003cp\u003eThe scripts found here take the latter approach by searching for your api key in\n\u003ccode\u003e~/wandb_credentials.txt\u003c/code\u003e. As long as you copy your api key into\n\u003ccode\u003e~/wandb_credentials.txt\u003c/code\u003e your applications can log experiment progress using\n\u003ccode\u003ewandb\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-sweeper-jobs\" class=\"anchor\" aria-hidden=\"true\" href=\"#sweeper-jobs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSweeper jobs\u003c/h4\u003e\n\u003cp\u003eWhen you submit a \u003ccode\u003ewandb\u003c/code\u003e sweep array job, you only need to specify the sweep\nid. That is, first initiate the sweep (either locally or on your favorite\ncluster),\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewandb sweep sweeper.yml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a pending sweep on \u003ccode\u003ewandb\u003c/code\u003e\u0027s servers. Then in\n\u003ccode\u003eproject root/hpc_files\u003c/code\u003e do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash job_submitter.sh --job_type sweep [other options]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe script will then prompt for the sweep id and the number of sweeps.\u003c/p\u003e\n\u003cp\u003eThe provided \u003ca href=\"sweeper.yml\"\u003esweeper.yml\u003c/a\u003e file can serve as a template, but should be\nmodified to your specific sweep. Think of the \u003ca href=\"sweeper.yml\"\u003esweeper.yml\u003c/a\u003e file as the\nsweep\u0027s equivalent of the more general\n\u003ca href=\"experiment_configurations.txt\"\u003eexperiment_configurations.txt\u003c/a\u003e file.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-how-to-specify-experiment-configurations\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-specify-experiment-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to specify experiment configurations:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eFor sweep jobs edit \u003ccode\u003esweeper.yml\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eOtherwise edit \u003ca href=\"experiment_configurations.txt\"\u003eexperiment_configurations.txt\u003c/a\u003e. See the \u003ca href=\"singularity_hpc_files/README.md\"\u003eSingularity readme\u003c/a\u003e\nor \u003ca href=\"virtual_env_hpc_files/README.md\"\u003evirtual environment readme\u003c/a\u003e for the format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-copying-datasets-and-other-files-to-slurm_tmpdir\" class=\"anchor\" aria-hidden=\"true\" href=\"#copying-datasets-and-other-files-to-slurm_tmpdir\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopying datasets and other files to \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e\n\u003c/h2\u003e\n\u003cp\u003eTo copy data to the local nodes when submitting a job, simply use the \u003ccode\u003e-d, --data\u003c/code\u003e option. To transfer multiple files and directories specify these using a\nwhitespace separated list of \u003cstrong\u003erelative\u003c/strong\u003e paths.\u003c/p\u003e\n\u003cp\u003eThe main purpose of this functionality is to copy large amounts of data, which\ntypically is stored on \u003ccode\u003eSCRTACH\u003c/code\u003e. Therefore, the paths are going to be\nrelative to \u003ccode\u003e${SCRATCH}/${project_name}\u003c/code\u003e. The script will then create a tarball\nusing \u003ccode\u003etar\u003c/code\u003e and transfer the files and directories to \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e. You can\nthen access the files and directories at \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e using the same paths\nused when using the \u003ccode\u003e-d, --data\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eIf a tarball already exists, no new tarball is created. If you want to update\nthe tarball you should delete the old one first.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-example-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h3\u003e\n\u003cp\u003eAssume you work on a project named \u003ccode\u003eproject_root\u003c/code\u003e, and on \u003ccode\u003e${SCRATCH}\u003c/code\u003e you have,\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e${SCRATCH}\n\u251c\u2500\u2500 project_root\n\u2502 \u2514\u2500\u2500 datasets\n\u2502\u00a0\u00a0 \u00a0 \u251c\u2500\u2500dataset1\n\u2502\u00a0\u00a0 \u00a0 \u251c\u2500\u2500dataset2\n\u2502\u00a0\u00a0 \u00a0 \u2514\u2500\u2500dataset3\n.\n. other files on scratch\n.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to move the entire directory \u003ccode\u003edatasets\u003c/code\u003e to \u003ccode\u003e${SLURM_TMPDIR}\u003c/code\u003e, you\nwould do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash job_submitter.sh \\\n --data datasets\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis would then lead to the following structure on \u003ccode\u003e${SLURM_TMPDIR}\u003c/code\u003e\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e${SLURM_TMPDIR}\n\u251c\u2500\u2500 datasets\n\u2502 \u00a0 \u251c\u2500\u2500 dataset1\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 dataset2\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 dataset3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf instead you want to move only \u003ccode\u003edataset1\u003c/code\u003e and \u003ccode\u003edataset2\u003c/code\u003e, you would do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash job_submitter.sh \\\n --data datasets/dataset1 datasets/dataset2\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis would then lead to the following structure\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e${SLURM_TMPDIR}\n\u251c\u2500\u2500 datasets\n\u2502 \u00a0 \u251c\u2500\u2500 dataset1\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 dataset2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn your specific experiment, you would have an option to specify the location of\na dataset (using e.g. python\u0027s \u003ccode\u003eargparse\u003c/code\u003e). You could then configure your\nprogram to look for \u003ccode\u003edataset1\u003c/code\u003e by running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython my_program.py --data_dir=\u003cspan class=\"pl-smi\"\u003e${SLURM_TMPDIR}\u003c/span\u003e/datasets/dataset1 [other arguments]\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-multi-node-distributed-gpu-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#multi-node-distributed-gpu-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-node distributed gpu training\u003c/h2\u003e\n\u003cp\u003eThe scripts have been tested with two different ways to do multi-node\ndistributed gpu training with PyTorch,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUsing PyTorch\u0027s \u003ca href=\"https://pytorch.org/docs/stable/elastic/run.html\" rel=\"nofollow\"\u003e\u003ccode\u003etorchrun\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.pytorchlightning.ai/\" rel=\"nofollow\"\u003ePyTorch Lightning\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe practical difference in terms of submitting a job is what each approach\nconsiders a task. The hpc scripts found in this repo will make sure to submit a\njob with the appropriate relationship between gpus, nodes, and cpus.\u003c/p\u003e\n\u003cp\u003eIn terms of writing the application code, Lightning removes a lot of the\ndistributed training setup and does this for you. It also offers multiple\noptimization tricks that have been found to improve training of neural network\nbased models. The downside is that Lightning is (slightly) more rigid in terms\nof managing the gpus across the distributed processes. Using PyTorch\u0027s\n\u003ccode\u003etorchrun\u003c/code\u003e offers full flexibility, but requires manually setting up the\ndistributed training.\u003c/p\u003e\n\u003cp\u003eTo get comfortable with these different approaches and play around with them\ncheck out my \u003ca href=\"https://github.com/ammunk/distributed-training-pytorch\"\u003edistributed training\nrepository\u003c/a\u003e which also\nuses the hpc scripts found here.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-lightning\" class=\"anchor\" aria-hidden=\"true\" href=\"#lightning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLightning\u003c/h3\u003e\n\u003cp\u003eLightning is built on PyTorch and requires your code to be written using a\ncertain structure. It has a lot of functionality, but it attempts to streamline\nthe training process to be agnostic to any particular neural network training\nprogram. Lightning includes loads of functionalities, but fundamentally you can\nthink of Lightning as doing the training loop for you. You only have to write\nthe training step, which is then called by Lightning.\u003c/p\u003e\n\u003cp\u003eThe benefit of the design of Lightning is that Lightning manages distributing\nyour code across multiple gpus without you having to really change your code.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-torchrun\" class=\"anchor\" aria-hidden=\"true\" href=\"#torchrun\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003etorchrun\u003c/code\u003e\u003c/h3\u003e\n\u003cp\u003eIf you use the \u003ccode\u003etorchrun\u003c/code\u003e approach you achieve full flexibility in how to manage\nthe gpus for each process. Under the hood \u003ccode\u003etorchrun\u003c/code\u003e spawns subprocesses, and\nrequires you to specify which machine the is the \"master\" machine as well as\nwhich port these processes use to communicate to each other.\u003c/p\u003e\n\u003cp\u003eIf you use a virtual environment for you application, the hpc scripts provided\nin this repository handles this for you. However, if you use Singularity you\nhave to manage this yourself: either as a command passed to the Singularity\ncontainer or build the Singularity container to take care of this.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pytorch.org/docs/stable/elastic/run.html\" rel=\"nofollow\"\u003e\u003ccode\u003etorchrun\u003c/code\u003e\u003c/a\u003e comes with the\ninstallation of PyTorch, and should be executed on \u003cstrong\u003eeach node\u003c/strong\u003e using the\nfollowing execution pattern,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003etorchrun --nproc_per_node 2 --nnodes 2 \\\n --rdzv_id=0 \\\n --rdzv_backend=c10d \\\n --rdzv_endpoint=192.168.1.1:2345 \\\n --max_restarts=3 \\\n YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other arguments of your training script)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the\n\u003ca href=\"virtual_env_hpc_files/distributed_scripts/torchrun_launcher.sh\"\u003evirtual_env_hpc_files/distributed_scripts/torchrun_launcher.sh\u003c/a\u003e\nfile for how this is handled if you use a virtual environment approach.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment variables\u003c/h2\u003e\n\u003cp\u003eThe scripts sets various environment variables. These are used\ninternally, and can be used downstream within a program.\u003c/p\u003e\n\u003cp\u003eSome are automatically inferred from the name of the project folder, while other\nshould be manually (optional) specified. The variables are then available within\nyour program using e.g. python\u0027s \u003ccode\u003eos\u003c/code\u003e package:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esource_dir\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eenviron\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e\u0027source_dir\u0027\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-automatically-assigned\" class=\"anchor\" aria-hidden=\"true\" href=\"#automatically-assigned\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically assigned\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esource_dir\u003c/code\u003e: absolute path to the root of the project.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eproject_name\u003c/code\u003e: set to be the name of the project folder.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escratch_dir=${SCRATCH}/${project_name}\u003c/code\u003e: path to a folder created on\n\u003ccode\u003eSCRATCH\u003c/code\u003e. This folder is project specific and is created using\n\u003ccode\u003eproject_name\u003c/code\u003e. No need to worry about having multiple different project\noverwrite one another.\n\u003cul\u003e\n\u003cli\u003eThis path should be considered the \"root\" location of the project to store\nlarge files - e.g. model checkpoints etc.\u003c/li\u003e\n\u003cli\u003eSince this is on \u003ccode\u003eSCRATCH\u003c/code\u003e read/write operation may be \u003cstrong\u003eslow\u003c/strong\u003e. Try\nusing \u003ccode\u003epath_to_local_node_storage=${SLURM_TMPDIR}\u003c/code\u003e instead.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-manually-optional-assigned\" class=\"anchor\" aria-hidden=\"true\" href=\"#manually-optional-assigned\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eManually (optional) assigned\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eexp_name\u003c/code\u003e: a name which describe the current experiment belonging to the\noverarching project (\u003ccode\u003eproject_name\u003c/code\u003e)\n\u003cul\u003e\n\u003cli\u003eFor instance, the project could be \"gan_training\". An experiment could then\nbe \u003ccode\u003eexp_name=celebA\u003c/code\u003e for training a GAN using the \u003ca href=\"https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\" rel=\"nofollow\"\u003ecelebA\ndataset\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-created-folders\" class=\"anchor\" aria-hidden=\"true\" href=\"#created-folders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreated folders\u003c/h2\u003e\n\u003cp\u003eThe scripts will automatically create the following directories. Your experiment\ncan easily access these using the created \u003ca href=\"#environment-variables\"\u003eenvironment\nvariables\u003c/a\u003e. They are only created if they do not already\nexist.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e${SCRATCH}/${project_name}\u003c/code\u003e: if you have a dataset on scratch, you should\ncreate this directory yourself and put whatever data you need for your jobs\nhere.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${scratch_dir}/hpc_outputs\u003c/code\u003e: location of yours jobs\u0027 output files.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${scratch_dir}/exp_name/checkpoints\u003c/code\u003e: a directory meant to store checkpoints\nand other files created as your experiment runs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-project-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#project-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProject structure\u003c/h2\u003e\n\u003cp\u003eRegardless of whether your project uses Singularity or virtual environments the\nscripts assumes a certain structure\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eyour_project_name\n\u251c\u2500\u2500 hpc_files\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 experiment_configurations.txt\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 job_submitter.sh\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 plai_cleanups\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 plai_cleanup.sh\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 submit_plai_cleanup\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 README.md\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 singularity_hpc_files\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 distributed_dispatcher.sh\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 README.md\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 standard_job.sh\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 sweeper.yml\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 virtual_env_hpc_files\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 distributed_dispatcher.sh\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 distributed_scripts\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 lightning_launcher.sh\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 script_launcher.sh\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 README.md\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 standard_job.sh\n\u251c\u2500\u2500 Pipfile\n\u251c\u2500\u2500 requirements.txt\n\u251c\u2500\u2500 singularity_container.sif\n\u251c\u2500\u2500 Singularity.bld\n\u2502\u00a0\n.\n. other project source files\n.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-slurm_tmpdir-on-plais-cluster\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-slurm_tmpdir-on-plais-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEXAMPLE: \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e on PLAI\u0027s cluster\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003eSLURM_TMPDIR\u003c/code\u003e not provided on the PLAI cluster. This is why the scripts\nwill check if you submit your job on the PLAI cluster and set this for you -\n\u003ccode\u003eSLURM_TMPDIR=/scratch-ssd/${USER}\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe scripts will then create the temporary directory for each job on each node.\nUpon completion of the job the directory will be deleted. Note, however, that\nshould the job end prematurely due to hitting the time limit or the job simply\ncrashes, the cleanup will not happen.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-keeping-plai-local-storage-clean\" class=\"anchor\" aria-hidden=\"true\" href=\"#keeping-plai-local-storage-clean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKeeping PLAI local storage clean\u003c/h3\u003e\n\u003cp\u003eTo keep the local storages clean on the PLAI cluster, consider running the\n\u003ca href=\"plai_cleanups/submit_plai_cleanup\"\u003ecleanup script\u003c/a\u003e. This script submits a\njob to each machine on the plai cluster and removes all directories and files\nfound in \u003ccode\u003e/scratch-ssd\u003c/code\u003e that matches the pattern \u003ccode\u003e${USER}*\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-additional-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#additional-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional resources\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePyTorch \u003ca href=\"https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group\" rel=\"nofollow\"\u003edistributed communication package\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/docs/stable/elastic/run.html\" rel=\"nofollow\"\u003eElastic launch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/tutorials/intermediate/ddp_tutorial.html?highlight=distributed\" rel=\"nofollow\"\u003ePyTorch distributed tutorial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch-lightning.readthedocs.io/en/stable/clouds/cluster.html\" rel=\"nofollow\"\u003ePyTorch Lightning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDifference between using \u003ccode\u003e--gres\u003c/code\u003e (e.g. \u003ccode\u003e--gres:gpu:2\u003c/code\u003e) and \u003ccode\u003e--gpus-per-task\u003c/code\u003e:\n(\u003ca href=\"https://stackoverflow.com/questions/67091056/gpu-allocation-in-slurm-gres-vs-gpus-per-task-and-mpirun-vs-srun\" rel=\"nofollow\"\u003ehttps://stackoverflow.com/questions/67091056/gpu-allocation-in-slurm-gres-vs-gpus-per-task-and-mpirun-vs-srun\u003c/a\u003e)\n\u003cul\u003e\n\u003cli\u003eParticularly be careful with \u003ccode\u003e--gpu-per-task\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-unmix-net\" class=\"anchor\" aria-hidden=\"true\" href=\"#unmix-net\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eunmix-net\u003c/h1\u003e\n\u003cp\u003eTensorflow based neuronal network framework to extract vocals and instrumental from music.\nPython 3.7 was used for implementation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eInstall all dependencies by using \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ekeras\u003c/li\u003e\n\u003cli\u003etensorflow\u003c/li\u003e\n\u003cli\u003eargparse\u003c/li\u003e\n\u003cli\u003ePillow\u003c/li\u003e\n\u003cli\u003ematplotlib\u003c/li\u003e\n\u003cli\u003epydot\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003epsutil\u003c/li\u003e\n\u003cli\u003ecommentjson\u003c/li\u003e\n\u003cli\u003egitpython\u003c/li\u003e\n\u003cli\u003ecolorama\u003c/li\u003e\n\u003cli\u003eprogressbar2\u003c/li\u003e\n\u003cli\u003emir_eval\u003c/li\u003e\n\u003cli\u003epytube\u003c/li\u003e\n\u003cli\u003elibrosa\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe highly recommend running the solution on \u003ca href=\"https://www.tensorflow.org/install/gpu\" rel=\"nofollow\"\u003etensorflow-gpu\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eInstead of installing the dependencies locally, our \u003ca href=\"https://hub.docker.com/r/unmix/unmix\" rel=\"nofollow\"\u003edocker image\u003c/a\u003e can be used: \u003ccode\u003edocker pull unmix/unmix\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-settings\" class=\"anchor\" aria-hidden=\"true\" href=\"#settings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSettings\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-configurations\" class=\"anchor\" aria-hidden=\"true\" href=\"#configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigurations\u003c/h3\u003e\n\u003cp\u003eTraining runs must be configured with a jsonc configuration file.\nConfiguration files can inherit from parent configurations:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-json\"\u003e\u003cpre\u003e{\n \u003cspan class=\"pl-ent\"\u003e\"base\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edefault-hourglass.jsonc\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-ii\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf a property is specified multiple times, the child configurations always overrides.\u003c/p\u003e\n\u003cp\u003eEvery configuration inherits by default from the \u003ca href=\"https://github.com/unmix-io/unmix-net/blob/master/configurations/master.jsonc\"\u003emaster.jsonc\u003c/a\u003e configuration.\nComments are allowed in jsonc files.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-hidden=\"true\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment Variables\u003c/h3\u003e\n\u003cp\u003eConfiguration files support access to environment varialbes.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_CONFIGURATION_FILE\u003c/code\u003e: Path to the configuration file (default parameter for training)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_COLLECTION_DIR\u003c/code\u003e: Path to the training, validation and test data collection\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_SAMPLE_RATE\u003c/code\u003e: Sample rate of the training data (used for training and prediction)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_SONG_LIMIT\u003c/code\u003e: Limit amount of songs to be included in the training (for smaller training runs)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_TEST_FREQUENCY\u003c/code\u003e: Frequency in epochs to run an accuracy test\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_TEST_DATA_COUNT\u003c/code\u003e: Number of songs to include in to the accuracy test\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eUNMIX_LIMIT_ITEMS_PER_SONG\u003c/code\u003e: Limit of batchitems used per song for training\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe variables can be added to your operating system or by adding a \u003ccode\u003e.env\u003c/code\u003e file to the (repository) base directory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining\u003c/h2\u003e\n\u003cp\u003eExample call: \u003ccode\u003epython3 train.py --configuration configuration/final/hourglass.jsonc\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econfiguration\u003c/code\u003e: Path to a valid jsonc configuration file. If not specified the value of the \u003ccode\u003eUNMIX_CONFIGURATION_FILE\u003c/code\u003e environment variable is used.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eworkingdir\u003c/code\u003e: Optional working directory where the runs ordner is published\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-procedure\" class=\"anchor\" aria-hidden=\"true\" href=\"#procedure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eFollowing a rough overview what happens during a training session:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eConfiguration initialization, output run folder creation\u003c/li\u003e\n\u003cli\u003eData loading, splitting training, validation and test data\u003c/li\u003e\n\u003cli\u003eTraining per epoch with batch generators\u003c/li\u003e\n\u003cli\u003eWrite callbacks (logs, weights, ...)\u003c/li\u003e\n\u003cli\u003eOptional: Calculate accuracies\u003c/li\u003e\n\u003cli\u003eStop if early stopping or epoch count finished\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h3\u003e\n\u003cp\u003eEvery training run generates a \"run folder\" with the following structure:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eplots\u003c/em\u003e: Output folder for plots (can be configured otherwise)\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003epredictions\u003c/em\u003e: Output folder for predictions and accuracy tests.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eweights\u003c/em\u003e: Output folder for the trained weights\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eaccuracy_x.csv\u003c/em\u003e: \u003ca href=\"https://craffel.github.io/mir_eval/\" rel=\"nofollow\"\u003emir_eval\u003c/a\u003e based accuracies of the track prediction.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eresults.csv\u003c/em\u003e: Result file including loss, mean prediction, validation loss, validation mean prediction per epoch\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003econfiguration.jsonc\u003c/em\u003e: Merged configuration file which is used by the training run\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eenvironment.json\u003c/em\u003e: Environment information including working directories, git version, environment variables\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003elogs.txt\u003c/em\u003e: Logfile\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003emodel.h5\u003c/em\u003e: Model and weights (created after training is finished)\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003emodel.json\u003c/em\u003e: Model configuration (created after training is finished)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prediction\" class=\"anchor\" aria-hidden=\"true\" href=\"#prediction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrediction\u003c/h2\u003e\n\u003cp\u003eExample call run folder: \u003ccode\u003epython3 predict.py --run_folder runs/20190506-154117-hourglass --song skyfall.mp3\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eExample call weights file: \u003ccode\u003epython3 predict.py --weights weights.h5 --configuration configuration.jsonc --song skyfall.mp3\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-parameters-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003erun_folder\u003c/code\u003e: Run folder from a training run (other parameters get derived)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003econfiguration\u003c/code\u003e: (Not necessary if \u003ccode\u003erun_folder\u003c/code\u003e) Path to the configuration file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eweights\u003c/code\u003e: (Not necessary if \u003ccode\u003erun_folder\u003c/code\u003e) Path to a weights file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eworkingdir\u003c/code\u003e: (Not necessary if \u003ccode\u003erun_folder\u003c/code\u003e) Optional working directory\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esample_rate\u003c/code\u003e: (Not necessary if \u003ccode\u003erun_folder\u003c/code\u003e) Sample rate which was used for training and will be used for prediction\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esong\u003c/code\u003e: Path to a single song to predict (extract vocals and instrumental)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esongs\u003c/code\u003e: Path to a folder of songs to predict (extract vocals and instrumental)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eyoutube\u003c/code\u003e: Link to a \u003ca href=\"https://www.youtube.com/watch?v=dQw4w9WgXcQ\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e link to be predicted (extract vocals and instrumental)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-output-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h3\u003e\n\u003cp\u003eThe predicted songs will be written into the working directory.\u003c/p\u003e\n\u003cp\u003eIf a run folder was specified all results are stored in the \u003cem\u003epredictions\u003c/em\u003e folder.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1662359550.0 + "updated_at": 1580147028.0 }, { "data_format": 2, - "description": "Container database metadata extraction and data-container builder", + "description": "Generic libraries and utilities that support atmospheric simulations with OpenFOAM", "filenames": [ - "examples/singularity-simple/Singularity" + "Singularity" ], - "full_name": "vsoch/cdb", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-container-database-cdb\" class=\"anchor\" aria-hidden=\"true\" href=\"#container-database-cdb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer Database (cdb)\u003c/h1\u003e\n\u003cp\u003eThis is the Python support tool for \u003ca href=\"https://github.com/vsoch/containerdb\"\u003econtainerdb\u003c/a\u003e\nto support generation of \u003ca href=\"https://github.com/singularityhub/data-container\"\u003edata containers\u003c/a\u003e.\nPython is more friendly to generating arbitrary data structures, and is popular among the\ndata science community, so I chose it for metadata generation instead of using GoLang.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://badge.fury.io/py/cdb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/708c5edf804dcc153b856fb4add4ad6cfbc7e31bfa3a3114a6bbc8e16274cc99/68747470733a2f2f62616467652e667572792e696f2f70792f6364622e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/cdb.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eHave your data and use it too!\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"assets/img/logo/logo.png\"\u003e\u003cimg src=\"assets/img/logo/logo.png\" alt=\"assets/img/logo/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor documentation and full examples see \u003ca href=\"https://vsoch.github.io/cdb\" rel=\"nofollow\"\u003evsoch.github.io/cdb\u003c/a\u003e. These\nexamples are also available in the \u003ca href=\"examples\"\u003eexamples\u003c/a\u003e folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-what-is-a-data-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#what-is-a-data-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is a Data Container?\u003c/h3\u003e\n\u003cp\u003eA data container is generally an operating-system-less container that is optimized\nto provide data, either for query/search, or binding for analysis. The qualities of\nthe data container should be:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt can be mounted to containers with operating systems to run analysis\u003c/li\u003e\n\u003cli\u003eIt can be interacted with on it\u0027s own to search metadata about the data\u003c/li\u003e\n\u003cli\u003eIt should not have an operating system.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-how-do-we-generate-one\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-do-we-generate-one\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow do we generate one?\u003c/h3\u003e\n\u003cp\u003eThe generation is fairly simple! It comes down to a three step multistage build:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eStep 1\u003c/strong\u003e We install \u003ca href=\"https://github.com/vsoch/cdb\"\u003ecdb\u003c/a\u003e to generate a GoLang template for an \u003ca href=\"https://github.com/vsoch/containerdb\"\u003ein-memory database\u003c/a\u003e for our data)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStep 2\u003c/strong\u003e We compile the binary into an entrypoint\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStep 3\u003c/strong\u003e We add the data and the binary entrypoint to a scratch container (no operating system).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAnd then we interact with it! This tutorial will show you the basic steps to\nperform the multistage-build using a simple \u003ca href=\"https://github.com/vsoch/cdb/tree/master/examples/docker-simple/Dockerfile\"\u003eDockerfile\u003c/a\u003e along with the data folder. The \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e in the base of the repository also is a good example.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Usage\u003c/h3\u003e\n\u003cp\u003eThe intended usage is via Docker, so you don\u0027t need to worry about installation of\nPython, GoLang, and multistage builds to basically:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGenerate a db.go template\u003c/li\u003e\n\u003cli\u003eCompile it\u003c/li\u003e\n\u003cli\u003eAdd to scratch with data as data container entrypoint.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThus, to run the dummy example here using the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build -t data-container \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe then have a simple way to do the following:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emetadata\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf we just run the container, we get a listing of all metadata alongside the key.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint \n/data/avocado.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 9, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\n/data/tomato.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 8, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e3b7721618a86990a3a90f9fa5744d15812954fba6bb21ebf5b5b66ad78cf5816\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003elist\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe can also just list data files with \u003ccode\u003e-ls\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -ls\n/data/avocado.txt\n/data/tomato.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eorderby\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOr we can list ordered by one of the metadata items:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -metric size\nOrder by size\n/data/tomato.txt: {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 8, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e3b7721618a86990a3a90f9fa5744d15812954fba6bb21ebf5b5b66ad78cf5816\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\n/data/avocado.txt: {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 9, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003esearch\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOr search for a specific metric based on value.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -metric size -search 8\n/data/tomato.txt 8\n\n$ docker run entrypoint -metric sha256 -search 8\n/data/avocado.txt 327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\n/data/tomato.txt 3b7721618a86990a3a90f9fa5744d15812954fba6bb21ebf5b5b66ad78cf5816\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eget\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOr we can get a particular file metadata by it\u0027s name:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -get /data/avocado.txt\n/data/avocado.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 9, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor a partial match:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run entrypoint -get /data/\n/data/avocado.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 9, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e327bf8231c9572ecdfdc53473319699e7b8e6a98adf0f383ff6be5b46094aba4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\n/data/tomato.txt {\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esize\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: 8, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esha256\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e3b7721618a86990a3a90f9fa5744d15812954fba6bb21ebf5b5b66ad78cf5816\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003estart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe start command is intended to keep the container running, if we are using\nit with an orchestrator.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run data-container -start\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-orchestration\" class=\"anchor\" aria-hidden=\"true\" href=\"#orchestration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOrchestration\u003c/h3\u003e\n\u003cp\u003eIt\u0027s more likely that you\u0027ll want to interact with files in the container via\nsome analysis, or more generally, another container. Let\u0027s put together\na quick \u003ccode\u003edocker-compose.yml\u003c/code\u003e to do exactly that.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eversion: \"3\"\nservices:\n base:\n restart: always\n image: busybox\n entrypoint: [\"tail\", \"-f\", \"/dev/null\"]\n volumes:\n - data-volume:/data\n\n data:\n restart: always\n image: data-container\n command: [\"-start\"]\n volumes:\n - data-volume:/data\n\nvolumes:\n data-volume:\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNotice that the command for the data-container to start is \u003ccode\u003e-start\u003c/code\u003e, which\nis important to keep it running. After building our \u003ccode\u003edata-container\u003c/code\u003e, we can then bring these containers up:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker-compose up -d\nStarting docker-simple_base_1 ... \u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\nRecreating docker-simple_data_1 ... \u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker-compose ps\n Name Command State Ports\n---------------------------------------------------------\ndocker-simple_base_1 tail -f /dev/null Up \ndocker-simple_data_1 /entrypoint -start Up \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can then shell inside and see our data!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -it docker-simple_base_1 sh\n/ \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls /data/\u003c/span\u003e\navocado.txt tomato.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe metadata is still available for query by interacting with the data-container\nentrypoint:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e docker-simple_data_1 /entrypoint -ls\n/data/avocado.txt\n/data/tomato.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDepending on your use case, you could easily make this available inside the\nother container. This is very simple usage, but the idea is powerful! We can interact with the dataset\nand search it without needing an operating system. It follows that we can develop\ncustomized data-containers based on the format / organization of the data inside\n(e.g., a data-container that knows how to expose inputs, outputs, etc.)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-python-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#python-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython Usage\u003c/h2\u003e\n\u003cp\u003eThe above doesn\u0027t require you to install the Container Database (cdb) metadata\ngenerator, however if you want to (to develop or otherwise interact) you\ncan do the following. First, install cdb from pypi or a local repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install cdb\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:vsoch/cdb\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e cdb\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand Line\u003c/h3\u003e\n\u003cp\u003eThe next step is to generate the goLang file to compile.\nYou\u0027ll next want to change directory to somewhere you have a dataset folder.\nFor example, in \u003ca href=\"tests\"\u003etests\u003c/a\u003e we have a dummy \"data\" folder.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e tests/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe might then run \u003ccode\u003ecdb generate\u003c/code\u003e to create a binary for our container, targeting\nthe \u003ca href=\"tests/data\"\u003etests/data\u003c/a\u003e folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cdb generate data --out db.go\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe db.go file is then in the present working directory. You can either\nbuild it during a multistage build as is done in the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e,\nor do it locally with your own GoLang install and then add to the container.\nFor example, to compile:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ego get github.com/vsoch/containerdb \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \\\nGOOS=linux GOARCH=amd64 go build -ldflags=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e-w -s\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -o /db -i /db.go\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then a very basic Dockerfile would need to add the data at the path specified,\nand the compiled entrypoint.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eFROM scratch\nWORKDIR /data\nCOPY data/ \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\nCOPY db /db\nCMD [\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/db\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA more useful entrypoint will be developed soon! This is just a very\nbasic start to the library.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-hidden=\"true\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePython\u003c/h3\u003e\n\u003cp\u003eYou can run the same generation functions interactively with Python.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecdb\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emain\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eContainerDatabase\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eContainerDatabase\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003epath\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"data\"\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# \u0026lt;cdb.main.ContainerDatabase at 0x7fcaa9cb8950\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eView that there is a files generator at \u003ccode\u003edb.files\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003edb\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003efiles\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026lt;\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egenerator\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eobject\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erecursive_find\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eat\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0x7fcaaa4ae950\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then generate! If you don\u0027t provide an output file, a string will be returned.\nOtherwise, the output file name is returned.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edb\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003egenerate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"db.go\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eforce\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCurrently, functions for parsing metadata are named in \u003ca href=\"cdb/functions.py\"\u003ecdb/functions.py\u003c/a\u003e,\nhowever you can also define a custom import path. This has not yet been tested\nand will be soon. We will also be added more real world examples soon.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: MPL 2.0 License\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "AtmosFOAM/AtmosFOAM-tools", + "latest_release": "jshaw-thesis", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-atmosfoam-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#atmosfoam-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAtmosFOAM-tools\u003c/h1\u003e\n\u003cp\u003eAtmosFOAM-tools contains generic libraries and utilities that support atmospheric simulations with \u003ca href=\"https://openfoam.org/\" rel=\"nofollow\"\u003eOpenFOAM\u003c/a\u003e. These generic tools can be combined with \u003ca href=\"https://github.com/AtmosFOAM/AtmosFOAM\"\u003eAtmosFOAM\u003c/a\u003e and \u003ca href=\"https://github.com/AtmosFOAM/AMMM\"\u003eAMMM\u003c/a\u003e repositories.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-source-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#source-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource installation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall a recent version of \u003ca href=\"http://www.openfoam.org/download/\" rel=\"nofollow\"\u003eopenfoam-dev\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall libgdal. On ubuntu this is done with \u003ccode\u003eapt-get install libgdal-dev\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo to directory\ncd $WM_PROJECT_USER_DIR\nand download AtmosFOAM-tools using:\ngit clone \u003ca href=\"https://github.com/AtmosFOAM/AtmosFOAM-tools.git\"\u003ehttps://github.com/AtmosFOAM/AtmosFOAM-tools.git\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExport environment variables \u003ccode\u003eATMOSFOAM_TOOLS_SRC\u003c/code\u003e and \u003ccode\u003eGMTU\u003c/code\u003e in your \u003ccode\u003e~/.bashrc\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export ATMOSFOAM_TOOLS_SRC=/path/to/AtmosFOAM-tools/src\n export GMTU=/path/to/AtmosFOAM-tools/gmtUser\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompile AtmosFOAM-tools:\ncd AtmosFOAM-tools\n./Allwmake\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, - "topics": [ - "containerdb", - "data-container", - "docker" - ], - "updated_at": 1595956312.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1676320294.0 }, { "data_format": 2, - "description": "snakemake workflow for basecalling and demultiplexing of ONT sequencing data", + "description": "sequana pipeline to perform parallel fastqc and summarize results with multiqc plot", "filenames": [ - "baseDmux/data/containers/Singularity.guppy6.0.1gpu-conda-api", - "baseDmux/data/containers/Singularity.guppy6.3.7gpu-mamba-api", - "baseDmux/data/containers/Singularity.UbuntuDHubBionic-deepbinner-api", - "baseDmux/data/containers/Singularity.deepbinner-api" + "singularity/Singularity" ], - "full_name": "vibaotram/baseDmux", - "latest_release": "v1.1.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-basecalling-and-demultiplexing-for-ont-sequencing-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#basecalling-and-demultiplexing-for-ont-sequencing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBASEcalling and DeMUltipleXing for ONT sequencing data\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-a-snakemake-workflow-for-basecalling-and-gathering-ont-reads-originating-from-disparate-runs-and-barcodes\" class=\"anchor\" aria-hidden=\"true\" href=\"#a-snakemake-workflow-for-basecalling-and-gathering-ont-reads-originating-from-disparate-runs-and-barcodes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eA Snakemake workflow for basecalling and gathering ONT reads originating from disparate runs and barcodes\u003c/h2\u003e\n\u003cp\u003eBasecalling by GUPPY + Demultiplexing by GUPPY and/or DEEPBINNER + MinIONQC/Multiqc + QC reports + reads aggregation into bins + fastq reads trimming + filtering\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./dag/full_dag.svg\"\u003e\u003cimg src=\"./dag/full_dag.svg\" width=\"500\" height=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003esingularity \u0026gt;= 2.5\u003c/li\u003e\n\u003cli\u003econda \u0026gt;=4.3 + Mamba\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-implemented-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#implemented-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImplemented tools\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eSnakemake\u003c/li\u003e\n\u003cli\u003eGuppy\u003c/li\u003e\n\u003cli\u003eDeepbinner\u003c/li\u003e\n\u003cli\u003eMinIONQC\u003c/li\u003e\n\u003cli\u003eMultiqc\u003c/li\u003e\n\u003cli\u003ePorechop\u003c/li\u003e\n\u003cli\u003eFiltlong\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe try to update the tools regularly. See versions in the folders containning \u003ca href=\"baseDmux/data/conda\"\u003econda environment\u003c/a\u003e and \u003ca href=\"baseDmux/data/containers\"\u003esingularity container recipie\u003c/a\u003e files.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-more-details-about-individual-snakemake-rules\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-details-about-individual-snakemake-rules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore details about individual snakemake Rules\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGuppy basecalling\u003c/strong\u003e\u003cbr\u003e\nRun \u003ccode\u003eguppy_basecaller\u003c/code\u003e with filtering reads, then subset fast5 reads from passed reads list (\u003ccode\u003epassed_sequencing_summary.txt\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGuppy demultiplexing\u003c/strong\u003e\u003cbr\u003e\nRun \u003ccode\u003eguppy_barcoder\u003c/code\u003e with passed fastq, then subset fastq to classified barcode folders based on \u003ccode\u003ebarcoding_summary.txt\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMulti to single fast5\u003c/strong\u003e\u003cbr\u003e\nConvert passed multi-read fast5 files to single-read fast5 file, preparing for deepbinner.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDeepbinner classification\u003c/strong\u003e\u003cbr\u003e\nRun \u003ccode\u003edeepbinner classify\u003c/code\u003e with pass single-read fast5, output classification file.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDeepbinner bin\u003c/strong\u003e\u003cbr\u003e\nClassify passed fastq based on classification file, then subset fastq to barcode folders.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGet sequencing summary per barcode\u003c/strong\u003e\u003cbr\u003e\nSubset \u003ccode\u003epassed_sequencing_summary.txt\u003c/code\u003e according to barcode ids, preparing for minionqc/multiqc of each barcode and subseting fast5 reads per barcode (get multi fast5 per barcode).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMinIONQC and Multiqc\u003c/strong\u003e\u003cbr\u003e\nAfter basecalling, MinIONQC is performed for each run, and Multiqc reports all run collectively.\nOn the other hand, after demultiplexing, MinIONQC runs for each barcode separately then Multiqc aggregates MinIONQC results of all barcodes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDemultiplex report (optional)\u003c/strong\u003e\u003cbr\u003e\nCompare demultiplexing results from different runs, and from different demultiplexers (guppy and/or deepbinner) by analyzing information of \u003ccode\u003emultiqc_minionqc.txt\u003c/code\u003e. It is only available when demultiplexing rules are executed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGet reads per genome (optional)\u003c/strong\u003e\u003cbr\u003e\nCombine and concatenate fast5 and fastq barcodes for genomes individually based on the demultiplexer program, preparing\nfor\nfurther genome assembly\n, following the information in the \u003ccode\u003ebarcodeByGenome_sample.tsv\u003c/code\u003e tabulated file (column names of this table should not be\nmodified).\u003cbr\u003e\n\u003cstrong\u003eCaution\u003c/strong\u003e: if guppy or deepbinner is on Demultiplexer of the barcodeByGenome table, it will be\nexecuted even it is not specified in config[\u0027DEMULTIPLEXER\u0027].\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePorechop (optional)\u003c/strong\u003e\u003cbr\u003e\nFind and remove adapters from reads. See \u003ca href=\"https://github.com/rrwick/Porechop\"\u003ehere\u003c/a\u003e for more information.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eFiltlong (optional)\u003c/strong\u003e\u003cbr\u003e\nFilter reads by length and by quality. More details is \u003ca href=\"https://github.com/rrwick/Filtlong\"\u003ehere\u003c/a\u003e. Several filtlong runs at the same time are enabled.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity containers\u003c/h3\u003e\n\u003cp\u003eWorkflow jobs run inside Singularity images (see \u003ca href=\"baseDmux/data/containers\"\u003eour Singularity Recipe files\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eThe latest containers will be automatically downloaded and intalled in the baseDmux environement installation\ndirectory. They can anyhow be manually downloaded from \u003ca href=\"https://drive.ird.fr/s/nTsw45jnW67tCw7\" rel=\"nofollow\"\u003eIRD Drive\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eCustom Singularity images can be specified by editing the \u003ca href=\"baseDmux/data/singularity.yaml\"\u003e\u003ccode\u003e./baseDmux/data/singularity.yaml\u003c/code\u003e\u003c/a\u003e file right after clonning the github repository or directly in your baseDmux installation (see below) location.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-conda-environments\" class=\"anchor\" aria-hidden=\"true\" href=\"#conda-environments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConda environments\u003c/h3\u003e\n\u003cp\u003eInside of the Singularity images, individual Snakemake rules use dedicated conda\nenvironments yaml files that are located \u003ca href=\"baseDmux/data/conda\"\u003ethere\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eminionqc\u003c/li\u003e\n\u003cli\u003emultiqc\u003c/li\u003e\n\u003cli\u003ermarkdown\u003c/li\u003e\n\u003cli\u003eporechop\u003c/li\u003e\n\u003cli\u003efiltlong\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eDownload the package:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/vibaotram/baseDmux.git\ncd ./baseDmux\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd then, install in a virtualenv...\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake install\nsource venv/bin/activate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e... or install in a conda environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -n baseDmux -f environment.yaml\nconda activate baseDmux\npip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is recommended to first run the local test below with the toy dataset to make sure everything works well. On the\nfirst invokation, this will download and install the Singularity images and setup the Conda environment. This\nprocess takes time, so be patient. Note also that in the end, this setup amounts to a total of about 12GB of files\n, so you need some room on the installation disk.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003eRun baseDmux version 1.1.0 ... See https://github.com/vibaotram/baseDmux/blob/master/README.md for more\ndetails\n\npositional arguments:\n {configure,run,dryrun,version_tools}\n configure edit config file and profile\n run run baseDmux\n dryrun dryrun baseDmux\n version_tools check version for the tools of baseDmux\n\noptions:\n -h, --help show this help message and exit\n -v, --version show program\u0027s version number and exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-basedmux-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#basedmux-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebaseDmux configuration\u003c/h3\u003e\n\u003cp\u003eBecause configuring snakemake workflows can be a bit intimidating, we try to clarify below the main principles of\nbaseDmux configuration.\u003c/p\u003e\n\u003cp\u003eThis is done primarilly by adjusting the parameters listed in the workflow config file \u003ccode\u003eprofile/workflow_parameters .yaml\u003c/code\u003e (generated by \u003ccode\u003ebaseDmux configure\u003c/code\u003e - see below). It enables to setup input reads, output folder, parameters for the tools\n, reports generation, etc...\u003cbr\u003e\nThis actually corresponds to the typical Snakemake \u0027config.yaml\u0027 file. You can \u003ca href=\"baseDmux/data/config.yaml\"\u003etake a look\u003c/a\u003e at what serves as a template to create \u003ccode\u003eprofile/workflow_parameters.yaml\u003c/code\u003e. It is suggested to \u003cstrong\u003erefer to the comments in this file for further details on individual parameters\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003ebaseDmux takes as input a folder with internal ONT \u0027run\u0027 folders that each contains a \u0027fast5\u0027 folder. This is the\ntypical file hierarchy when sequencing with a MinION. baseDmux can therefore process a virtually unlimited number of (multiplexed) sequencing runs.\u003c/p\u003e\n\u003cp\u003eYou can decide whether guppy and deepbinner should run on GPU or CPU by specifying \u0027RESOURCE\u0027 in the \u003ca href=\"baseDmux/data/config.yaml\"\u003econfig.yaml\n\u003c/a\u003e file depending on the available computing hardware. Note that Deepbinner is not longer\nmaintained and that \u003ca href=\"https://github.com/rrwick/Deepbinner/tree/master/models\"\u003eDeepbinner models\u003c/a\u003e are limited to specific \u0027earlier\u0027 flow cells and barcoding kits. One should therefore ensure that that Deepbinner is a adequate for the data at hand.\u003c/p\u003e\n\u003cp\u003eA typical usage case for baseDmux is to prepare filtered sequencing reads in individual fastq files for genome\nassembly (or transcripts analysis) when users have a number of genomic DNA (or RNA) preparations sequenced with the\nsame library preparation protocol and flowcell typoe but over several runs with various sets of multiplex barcodes\n. For this, it is necessary to run the complete workflow. \u003cstrong\u003eNote\u003c/strong\u003e that they however currently need to share, if not\nidentical, at least \u0027compatible\u0027 (in the guppy sense), library construction kits and flow cell types.\u003c/p\u003e\n\u003cp\u003eUsers need to prepare a \u003ca href=\"/baseDmux/data/barcodeByGenome_sample.tsv\"\u003e\u003ccode\u003eBarcode by genome\u003c/code\u003e\u003c/a\u003e file. This is a roadmap\ntable for subseting fastq and fast5 reads, demultiplexed with guppy and/or deepbinner, and coming from disparate\nruns and barcodes, in bins corresponding to individual \u0027genomes\u0027 (or samples). It must contain at least the\nfollwing columns: Demultiplexer, Run_ID, ONT_Barcode, Genome_ID. Values in the \u003ccode\u003eGenome_ID\u003c/code\u003e correspond to the\nlabels of the bin into which reads will eventually be grouped. \u003cstrong\u003eMake sure\u003c/strong\u003e that these labels do NOT contain\nspaces \" \" or other special characters like \u0027|\u0027 \u0027$\u0027 \u0027:\u0027. As separators, the safest options are to use \"_\" or \"-\".\u003cbr\u003e\nLikewise, \u003ccode\u003eRun_ID\u003c/code\u003e values should not contain special characters. In addition, these values must match the names of the\ntop folders in the input fast5 directory.\u003cbr\u003e\nImportantly, the \u003ccode\u003eBarcode by genome\u003c/code\u003e file does not only enable to group reads, it is necessary to provide such a file\nfor the porechop and filtlong rules to be executed. A template is provided (see the section below on configuration).\u003c/p\u003e\n\u003cp\u003eAppart from the workflow parameters, there are also additional parameter files that are required to specify Snakemake\ninvocation arguments and, when baseDmux is run with a HPC scheduler, parameters regarding how specific jobs need to\nbe submited. All these configuration files are gathered inside a \u003cstrong\u003e\"profile\"\u003c/strong\u003e directory that can be automatically\nprototyped with the commands below. This is in line with the recommended way for Snakemake pipelines\nconfiguration using \u003ca href=\"https://snakemake.readthedocs.io/en/stable/executing/cli.html?highlight=profile#profiles\" rel=\"nofollow\"\u003eprofiles\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-generating-template-configuration-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#generating-template-configuration-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenerating template configuration files\u003c/h4\u003e\n\u003cp\u003eTo simplify configuration, the \u003ccode\u003ebaseDmux configure\u003c/code\u003e command generates a \u0027template\u0027 configuration profile for general\nuse cases. These files can subsequently be modified to fit specific situations.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: baseDmux configure [-h] --mode {local,slurm,cluster,iTrop} [--barcodes_by_genome]\n [--edit [EDITOR]]\n dir\n\npositional arguments:\n dir path to the folder to contain config file and profile you want to create\n\noptions:\n -h, --help show this help message and exit\n --mode {local,slurm,cluster,iTrop}\n choose the mode of running Snakemake, local mode or cluster mode\n --barcodes_by_genome optional, create a tabular file containing information of barcodes for each\n genome)\n --edit [EDITOR] optional, open files with editor (nano, vim, gedit, etc.)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThese files will be created:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e | dir\n -| profile \n -| config.yaml \n -| workflow_parameter.yaml \n -| barcodesByGenome.tsv (if --barcodes_by_genome)\n -| ... (if mode slurm)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA \u0027profile\u0027 folder will be created and populated in the specifid \u003ccode\u003edir\u003c/code\u003e path. The files may be modified and the whole\nfolder can be moved/copied/renamed anywhere as long as you use the correct path when you call \u003ccode\u003ebaseDmux run\u003c/code\u003e and update\nthe enclosed files, \u003ccode\u003econfig.yaml\u003c/code\u003e and \u003ccode\u003eworkflow_parameter.yaml\u003c/code\u003e for the new paths of \u003ccode\u003eworkflow_parameter.yaml \u003c/code\u003e and \u003ccode\u003ebarcodesByGenome.tsv\u003c/code\u003e, respectively.\u003c/p\u003e\n\u003cp\u003eWith the \u003ccode\u003e--barcodes_by_genome\u003c/code\u003e option, a formatted file \u003ccode\u003ebarcodesByGenome.tsv\u003c/code\u003e will be created (and its path appropriately specified in \u003ccode\u003eworkflow_parameter.yaml\u003c/code\u003e). One can then modify the information on the table accordingly. It is important that this table contains at least the same columns as those in the provided example \u003ccode\u003ebarcodeByGenome_sample.tsv\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eOnce you have adapted the templates for your typical use cases, there is no need to rerun \u003ccode\u003ebaseDmux configure\u003c/code\u003e again, just copy and adapt your existing templates.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: the \u0027iTRop\u0027 and \u0027cluster\u0027 modes are \u003cstrong\u003eobsolete\u003c/strong\u003e and will eventually be removed.\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-an-exemple-to-prepare-to-run-snakemake-locally-laptop-local-node-on-a-hpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-exemple-to-prepare-to-run-snakemake-locally-laptop-local-node-on-a-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003ean exemple to prepare to run Snakemake locally\u003c/strong\u003e (laptop, local node on a HPC)\u003c/h5\u003e\n\u003cp\u003eUse this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebaseDmux configure ./test_baseDmux --mode local --barcodes_by_genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen \u003ccode\u003eworkflow_parameter.yaml\u003c/code\u003e and \u003ccode\u003econfig.yaml\u003c/code\u003e will be created inside a \u003ccode\u003eprofile\u003c/code\u003e folder within \u003ccode\u003e./test_baseDmux\u003c/code\u003e. \u003ccode\u003e./test_baseDmux/profile/config.yaml\u003c/code\u003e contains as a set of parameters for the Snakemake command-line.\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-an-exemple-to-prepare-a-template-to-run-snakemake-on-a-hpc-with-slurm\" class=\"anchor\" aria-hidden=\"true\" href=\"#an-exemple-to-prepare-a-template-to-run-snakemake-on-a-hpc-with-slurm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003cstrong\u003ean exemple to prepare a template to run Snakemake on a HPC\u003c/strong\u003e with slurm.\u003c/h5\u003e\n\u003cp\u003eSimilarly, run the command below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebaseDmux configure ./test_baseDmux --mode slurm --barcodes_by_genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a \u003ccode\u003e./test_baseDmux/profile\u003c/code\u003e (see an example \u003ca href=\"baseDmux/data/profile/slurm\"\u003ehere\u003c/a\u003e)) folder with, in\naddition to the already mentionned files, the necessary file templates to run basDmux with slurm and the \u003ca href=\"https://github.com/Snakemake-Profiles/slurm\"\u003eSnakemake profile\u003c/a\u003e for configuring Snakemake to run on the SLURM Workload Manager.\u003c/p\u003e\n\u003cp\u003eFor other HPC job managment system (sge, ...), and more information on Snakemake profile and other utilities refer to\nthe \u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003eSnakemake documentation\u003c/a\u003e and [this gitHub repository](\u003ca href=\"https://github.com\"\u003ehttps://github.com\u003c/a\u003e\n/Snakemake-Profiles).\u003c/p\u003e\n\u003cp\u003eUltimately, the required files for passing HPC scheduler parameters throught the dedicated Snakemake mecanism of \u0027profiles\u0027 need to be stored in the folder whose path is passed to the baseDmux \u003ccode\u003eprofile_dir\u003c/code\u003e parameter and will\nmost certainly \u003cstrong\u003eneed to be adapted to suit your specific needs\u003c/strong\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-run-the-workflow-with-the-created-profile\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-the-workflow-with-the-created-profile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun the workflow with the created profile:\u003c/h4\u003e\n\u003cpre\u003e\u003ccode\u003eusage: baseDmux run [-h] [--snakemake_report] profile_dir\n\npositional arguments:\n profile_dir profile folder to run baseDmux\n\noptions:\n -h, --help show this help message and exit\n --snakemake_report optionally, create snakemake report\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample:\u003cbr\u003e\nYou can run \u003ccode\u003ebaseDmux dryrun ./test_baseDmux/profile\u003c/code\u003e for dry-run to make sure that everything is OK, before actually executing the workflow.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebaseDmux run ./test_baseDmux/profile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the option \u003ccode\u003e--snakemake_report\u003c/code\u003e, a report file \u003ccode\u003esnakemake_report.html\u003c/code\u003e will be created in the report folder of pipeline output directory, when snakemake has successfully finished the workflow.\u003c/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-get-your-hands-dirty-and-run-a-local-test\" class=\"anchor\" aria-hidden=\"true\" href=\"#get-your-hands-dirty-and-run-a-local-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGet your hands dirty and run a local test\u003c/h3\u003e\n\u003cp\u003eAssuming the environement for baseDmux has been created as specified in the dedicated section on Installation. First\nactivate either the conda or venv environement.\u003c/p\u003e\n\u003cp\u003eYou can use the reads fast5 files in \u003ccode\u003esample/reads_intermediate\u003c/code\u003e folder for testing and generate a local \u003ccode\u003eprofile\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e## create configuration file for Snakemake and Snakemake profile,\n## and (optional) a tsv file containing information about genomes corresponding to barcode IDs\nmkdir ./test_baseDmux\nbaseDmux configure ./test_baseDmux --mode local --barcodes_by_genome\n\n## copy sample reads to a test folder\ncp -r ./baseDmux/sample/reads_intermediate/ ./test_baseDmux/reads\n\n## check the workflow by dryrun, then run\nbaseDmux dryrun ./test_baseDmux/profile\nbaseDmux run ./test_baseDmux/profile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output will be written in \u003ccode\u003e./test_baseDmux/results\u003c/code\u003e by default\nThe first run may take a long time for Singularity containers to be downloaded and the conda environments to be installed even if using Mamba.\u003cbr\u003e\nOn a personnal computer with only a few CPU, even with this very minimal dataset,\nguppy basecalling may also take several minutes... So be patient depending on your underlying computing\ninfrastructure capacities.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-input-and-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-and-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput and Output\u003c/h3\u003e\n\u003cp\u003eInput directory \u003cstrong\u003emust\u003c/strong\u003e follow the structure as below. \u0027fast5\u0027 directory containing fast5 files in each run is\nMANDATORY for baseDmux to identifiy the various Run_ID(s).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eindir/\n\u251c\u2500\u2500 run_id1\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 fast5\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 file_1.fast5\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 file_n.fast5\n\u251c\u2500\u2500 ...\n\u2514\u2500\u2500 run_idx\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput directory will be:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoutdir/\n\u251c\u2500\u2500 basecall\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 run_id1\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sequencing_summary.txt\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 {MinIONQC results}\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 run_idx\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 multiqc\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 multiqc_data\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2514\u2500\u2500 multiqc_report.html\n\u251c\u2500\u2500 demultiplex\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 deepbinner\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 run_id1\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcode01\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcode01.fastq.gz\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 fast5\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sequencing_summary.txt\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 {MinIONQC results}\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcodexxx\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 | \u251c\u2500\u2500 classification\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 | \u251c\u2500\u2500 fast5_per_barcode.done\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 | \u251c\u2500\u2500 multiqc\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 | \u2514\u2500\u2500 unclassified\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 run_idx\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 guppy\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 run_id1\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcode01\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcode01.fastq.gz\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 fast5\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 sequencing_summary.txt\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 {MinIONQC results}\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 barcodexxx\n\u2502\u00a0\u00a0 \u00a0\u00a0 | \u251c\u2500\u2500 barcoding_summary.txt\n\u2502\u00a0\u00a0 \u00a0\u00a0 | \u251c\u2500\u2500 fast5_per_barcode.done\n\u2502\u00a0\u00a0 \u00a0\u00a0 | \u251c\u2500\u2500 multiqc\n\u2502\u00a0\u00a0 \u00a0\u00a0 | \u2514\u2500\u2500 unclassified\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u251c\u2500\u2500 ...\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2514\u2500\u2500 run_idx\n\u251c\u2500\u2500 reads_per_genome\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 fast5\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 fastq\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 reads_per_genome.csv\n\u251c\u2500\u2500 log\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 slurm\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 snakemake\n\u2514\u2500\u2500 report\n\u00a0\u00a0 \u251c\u2500\u2500 demultiplex_report.html\n \u251c\u2500\u2500 demultiplex_report.RData\n \u2514\u2500\u2500 demultiplex_report.tsv\n\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "sequana/fastqc", + "latest_release": "v1.7.0", "stargazers_count": 3, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [ - "snakemake-workflow", - "basecalling", - "demultiplexing", - "nanopore" + "fastqc", + "ngs", + "snakemake", + "sequana" ], - "updated_at": 1661519386.0 + "updated_at": 1674574510.0 }, { "data_format": 2, - "description": "A repository for showcasing my knowledge of the Singularity programming language, and continuing to learn the language.", + "description": "Framework to develop energy estimators for HEP experiments", "filenames": [ - "Singularity", - "Singularity.def", - "OldVersions/PROJECT_LANGUAGE/Singularity/Singularity" + "contrib/containers/tf2.9_singularity/Singularity", + "contrib/containers/tf2.6_singularity/Singularity" ], - "full_name": "seanpm2001/Learn-Singularity", + "full_name": "usert5432/vlne", "latest_release": null, - "readme": "\u003chr\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"Image.svg\"\u003e\u003cimg src=\"Image.svg\" alt=\"{Project icon} This image failed to load. It may be due to the file not being reached, or a general error. Reload the page to fix a possible general error.\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-by\" class=\"anchor\" aria-hidden=\"true\" href=\"#by\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBy:\u003c/h1\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-seanpm2001--et-al\" class=\"anchor\" aria-hidden=\"true\" href=\"#seanpm2001--et-al\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/seanpm2001/\"\u003eSeanpm2001\u003c/a\u003e, \u003ca href=\"https://github.com/%3CdeveloperName%3E/\"\u003e\u003c/a\u003e Et; Al.\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-top\" class=\"anchor\" aria-hidden=\"true\" href=\"#top\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop\u003c/h3\u003e\n\u003ch1\u003e\u003ca id=\"user-content-readmemd\" class=\"anchor\" aria-hidden=\"true\" href=\"#readmemd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003eREADME.md\u003c/code\u003e\u003c/h1\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-read-this-article-in-a-different-language\" class=\"anchor\" aria-hidden=\"true\" href=\"#read-this-article-in-a-different-language\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRead this article in a different language\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eSorted by:\u003c/strong\u003e \u003ccode\u003eA-Z\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/%3CdeveloperName%3E/%3CrepoName%3E\"\u003eSorting options unavailable\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e( \u003ca href=\"/.github/README_AF.md\"\u003eaf Afrikaans\u003c/a\u003e Afrikaans | \u003ca href=\"/.github/README_SQ.md\"\u003esq Shqiptare\u003c/a\u003e Albanian | \u003ca href=\"/.github/README_AM.md\"\u003eam \u12a0\u121b\u122d\u129b\u003c/a\u003e Amharic | \u003ca href=\"/.github/README_AR.md\"\u003ear \u0639\u0631\u0628\u0649\u003c/a\u003e Arabic | \u003ca href=\"/.github/README_HY.md\"\u003ehy \u0570\u0561\u0575\u0565\u0580\u0565\u0576\u003c/a\u003e Armenian | \u003ca href=\"/.github/README_AZ.md\"\u003eaz Az\u0259rbaycan dili\u003c/a\u003e Azerbaijani | \u003ca href=\"/.github/README_EU.md\"\u003eeu Euskara\u003c/a\u003e Basque | \u003ca href=\"/.github/README_BE.md\"\u003ebe \u0411\u0435\u043b\u0430\u0440\u0443\u0441\u043a\u0430\u044f\u003c/a\u003e Belarusian | \u003ca href=\"/.github/README_BN.md\"\u003ebn \u09ac\u09be\u0982\u09b2\u09be\u003c/a\u003e Bengali | \u003ca href=\"/.github/README_BS.md\"\u003ebs Bosanski\u003c/a\u003e Bosnian | \u003ca href=\"/.github/README_BG.md\"\u003ebg \u0431\u044a\u043b\u0433\u0430\u0440\u0441\u043a\u0438\u003c/a\u003e Bulgarian | \u003ca href=\"/.github/README_CA.md\"\u003eca Catal\u00e0\u003c/a\u003e Catalan | \u003ca href=\"/.github/README_CEB.md\"\u003eceb Sugbuanon\u003c/a\u003e Cebuano | \u003ca href=\"/.github/README_NY.md\"\u003eny Chichewa\u003c/a\u003e Chichewa | \u003ca href=\"/.github/README_ZH-CN.md\"\u003ezh-CN \u7b80\u4f53\u4e2d\u6587\u003c/a\u003e Chinese (Simplified) | \u003ca href=\"/.github/README_ZH-T.md\"\u003ezh-t \u4e2d\u570b\u50b3\u7d71\u7684\uff09\u003c/a\u003e Chinese (Traditional) | \u003ca href=\"/.github/README_CO.md\"\u003eco Corsu\u003c/a\u003e Corsican | \u003ca href=\"/.github/README_HR.md\"\u003ehr Hrvatski\u003c/a\u003e Croatian | \u003ca href=\"/.github/README_CS.md\"\u003ecs \u010de\u0161tina\u003c/a\u003e Czech | \u003ca href=\"README_DA.md\"\u003eda dansk\u003c/a\u003e Danish | \u003ca href=\"/.github/README_NL.md\"\u003enl Nederlands\u003c/a\u003e Dutch | \u003ca href=\"/.github/README.md\"\u003e\u003cstrong\u003een-us English\u003c/strong\u003e\u003c/a\u003e English | \u003ca href=\"/.github/README_EO.md\"\u003eEO Esperanto\u003c/a\u003e Esperanto | \u003ca href=\"/.github/README_ET.md\"\u003eet Eestlane\u003c/a\u003e Estonian | \u003ca href=\"/.github/README_TL.md\"\u003etl Pilipino\u003c/a\u003e Filipino | \u003ca href=\"/.github/README_FI.md\"\u003efi Suomalainen\u003c/a\u003e Finnish | \u003ca href=\"/.github/README_FR.md\"\u003efr fran\u00e7ais\u003c/a\u003e French | \u003ca href=\"/.github/README_FY.md\"\u003efy Frysk\u003c/a\u003e Frisian | \u003ca href=\"/.github/README_GL.md\"\u003egl Galego\u003c/a\u003e Galician | \u003ca href=\"/.github/README_KA\"\u003eka \u10e5\u10d0\u10e0\u10d7\u10d5\u10d4\u10da\u10d8\u003c/a\u003e Georgian | \u003ca href=\"/.github/README_DE.md\"\u003ede Deutsch\u003c/a\u003e German | \u003ca href=\"/.github/README_EL.md\"\u003eel \u0395\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03ac\u003c/a\u003e Greek | \u003ca href=\"/.github/README_GU.md\"\u003egu \u0a97\u0ac1\u0a9c\u0ab0\u0abe\u0aa4\u0ac0\u003c/a\u003e Gujarati | \u003ca href=\"/.github/README_HT.md\"\u003eht Krey\u00f2l ayisyen\u003c/a\u003e Haitian Creole | \u003ca href=\"/.github/README_HA.md\"\u003eha Hausa\u003c/a\u003e Hausa | \u003ca href=\"/.github/README_HAW.md\"\u003ehaw \u014clelo Hawai\u02bbi\u003c/a\u003e Hawaiian | \u003ca href=\"/.github/README_HE.md\"\u003ehe \u05e2\u05b4\u05d1\u05e8\u05b4\u05d9\u05ea\u003c/a\u003e Hebrew | \u003ca href=\"/.github/README_HI.md\"\u003ehi \u0939\u093f\u0928\u094d\u0926\u0940\u003c/a\u003e Hindi | \u003ca href=\"/.github/README_HMN.md\"\u003ehmn Hmong\u003c/a\u003e Hmong | \u003ca href=\"/.github/README_HU.md\"\u003ehu Magyar\u003c/a\u003e Hungarian | \u003ca href=\"/.github/README_IS.md\"\u003eis \u00cdslenska\u003c/a\u003e Icelandic | \u003ca href=\"/.github/README_IG.md\"\u003eig Igbo\u003c/a\u003e Igbo | \u003ca href=\"/.github/README_ID.md\"\u003eid bahasa Indonesia\u003c/a\u003e Icelandic | \u003ca href=\"/.github/README_GA.md\"\u003ega Gaeilge\u003c/a\u003e Irish | \u003ca href=\"/.github/README_IT.md\"\u003eit Italiana/Italiano\u003c/a\u003e | \u003ca href=\"/.github/README_JA.md\"\u003eja \u65e5\u672c\u8a9e\u003c/a\u003e Japanese | \u003ca href=\"/.github/README_JW.md\"\u003ejw Wong jawa\u003c/a\u003e Javanese | \u003ca href=\"/.github/README_KN.md\"\u003ekn \u0c95\u0ca8\u0ccd\u0ca8\u0ca1\u003c/a\u003e Kannada | \u003ca href=\"/.github/README_KK.md\"\u003ekk \u049a\u0430\u0437\u0430\u049b\u003c/a\u003e Kazakh | \u003ca href=\"/.github/README_KM.md\"\u003ekm \u1781\u17d2\u1798\u17c2\u179a\u003c/a\u003e Khmer | \u003ca href=\"/.github/README_RW.md\"\u003erw Kinyarwanda\u003c/a\u003e Kinyarwanda | \u003ca href=\"/.github/README_KO_SOUTH.md\"\u003eko-south \u97d3\u570b\u8a9e\u003c/a\u003e Korean (South) | \u003ca href=\"README_KO_NORTH.md\"\u003eko-north \ubb38\ud654\uc5b4\u003c/a\u003e Korean (North) (NOT YET TRANSLATED) | \u003ca href=\"/.github/README_KU.md\"\u003eku Kurd\u00ee\u003c/a\u003e Kurdish (Kurmanji) | \u003ca href=\"/.github/README_KY.md\"\u003eky \u041a\u044b\u0440\u0433\u044b\u0437\u0447\u0430\u003c/a\u003e Kyrgyz | \u003ca href=\"/.github/README_LO.md\"\u003elo \u0ea5\u0eb2\u0ea7\u003c/a\u003e Lao | \u003ca href=\"/.github/README_LA.md\"\u003ela Latine\u003c/a\u003e Latin | \u003ca href=\"/.github/README_LT.md\"\u003elt Lietuvis\u003c/a\u003e Lithuanian | \u003ca href=\"/.github/README_LB.md\"\u003elb L\u00ebtzebuergesch\u003c/a\u003e Luxembourgish | \u003ca href=\"/.github/README_MK.md\"\u003emk \u041c\u0430\u043a\u0435\u0434\u043e\u043d\u0441\u043a\u0438\u003c/a\u003e Macedonian | \u003ca href=\"/.github/README_MG.md\"\u003emg Malagasy\u003c/a\u003e Malagasy | \u003ca href=\"/.github/README_MS.md\"\u003ems Bahasa Melayu\u003c/a\u003e Malay | \u003ca href=\"/.github/README_ML.md\"\u003eml \u0d2e\u0d32\u0d2f\u0d3e\u0d33\u0d02\u003c/a\u003e Malayalam | \u003ca href=\"/.github/README_MT.md\"\u003emt Malti\u003c/a\u003e Maltese | \u003ca href=\"/.github/README_MI.md\"\u003emi Maori\u003c/a\u003e Maori | \u003ca href=\"/.github/README_MR.md\"\u003emr \u092e\u0930\u093e\u0920\u0940\u003c/a\u003e Marathi | \u003ca href=\"/.github/README_MN.md\"\u003emn \u041c\u043e\u043d\u0433\u043e\u043b\u003c/a\u003e Mongolian | \u003ca href=\"/.github/README_MY.md\"\u003emy \u1019\u103c\u1014\u103a\u1019\u102c\u003c/a\u003e Myanmar (Burmese) | \u003ca href=\"/.github/README_NE.md\"\u003ene \u0928\u0947\u092a\u093e\u0932\u0940\u003c/a\u003e Nepali | \u003ca href=\"/.github/README_NO.md\"\u003eno norsk\u003c/a\u003e Norwegian | \u003ca href=\"/.github/README_OR.md\"\u003eor \u0b13\u0b21\u0b3f\u0b06 (\u0b13\u0b21\u0b3f\u0b06)\u003c/a\u003e Odia (Oriya) | \u003ca href=\"/.github/README_PS.md\"\u003eps \u067e\u069a\u062a\u0648\u003c/a\u003e Pashto | \u003ca href=\"/.github/README_FA.md\"\u003efa \u0641\u0627\u0631\u0633\u06cc\u003c/a\u003e |Persian \u003ca href=\"/.github/README_PL.md\"\u003epl polski\u003c/a\u003e Polish | \u003ca href=\"/.github/README_PT.md\"\u003ept portugu\u00eas\u003c/a\u003e Portuguese | \u003ca href=\"/.github/README_PA.md\"\u003epa \u0a2a\u0a70\u0a1c\u0a3e\u0a2c\u0a40\u003c/a\u003e Punjabi | No languages available that start with the letter Q | \u003ca href=\"/.github/README_RO.md\"\u003ero Rom\u00e2n\u0103\u003c/a\u003e Romanian | \u003ca href=\"/.github/README_RU.md\"\u003eru \u0440\u0443\u0441\u0441\u043a\u0438\u0439\u003c/a\u003e Russian | \u003ca href=\"/.github/README_SM.md\"\u003esm Faasamoa\u003c/a\u003e Samoan | \u003ca href=\"/.github/README_GD.md\"\u003egd G\u00e0idhlig na h-Alba\u003c/a\u003e Scots Gaelic | \u003ca href=\"/.github/README_SR.md\"\u003esr \u0421\u0440\u043f\u0441\u043a\u0438\u003c/a\u003e Serbian | \u003ca href=\"/.github/README_ST.md\"\u003est Sesotho\u003c/a\u003e Sesotho | \u003ca href=\"/.github/README_SN.md\"\u003esn Shona\u003c/a\u003e Shona | \u003ca href=\"/.github/README_SD.md\"\u003esd \u0633\u0646\u068c\u064a\u003c/a\u003e Sindhi | \u003ca href=\"/.github/README_SI.md\"\u003esi \u0dc3\u0dd2\u0d82\u0dc4\u0dbd\u003c/a\u003e Sinhala | \u003ca href=\"/.github/README_SK.md\"\u003esk Slov\u00e1k\u003c/a\u003e Slovak | \u003ca href=\"/.github/README_SL.md\"\u003esl Sloven\u0161\u010dina\u003c/a\u003e Slovenian | \u003ca href=\"/.github/README_SO.md\"\u003eso Soomaali\u003c/a\u003e Somali | [\u003ca href=\"/.github/README_ES.md\"\u003ees en espa\u00f1ol\u003c/a\u003e Spanish | \u003ca href=\"/.github/README_SU.md\"\u003esu Sundanis\u003c/a\u003e Sundanese | \u003ca href=\"/.github/README_SW.md\"\u003esw Kiswahili\u003c/a\u003e Swahili | \u003ca href=\"/.github/README_SV.md\"\u003esv Svenska\u003c/a\u003e Swedish | \u003ca href=\"/.github/README_TG.md\"\u003etg \u0422\u043e\u04b7\u0438\u043a\u04e3\u003c/a\u003e Tajik | \u003ca href=\"/.github/README_TA.md\"\u003eta \u0ba4\u0bae\u0bbf\u0bb4\u0bcd\u003c/a\u003e Tamil | \u003ca href=\"/.github/README_TT.md\"\u003ett \u0422\u0430\u0442\u0430\u0440\u003c/a\u003e Tatar | \u003ca href=\"/.github/README_TE.md\"\u003ete \u0c24\u0c46\u0c32\u0c41\u0c17\u0c41\u003c/a\u003e Telugu | \u003ca href=\"/.github/README_TH.md\"\u003eth \u0e44\u0e17\u0e22\u003c/a\u003e Thai | \u003ca href=\"/.github/README_TR.md\"\u003etr T\u00fcrk\u003c/a\u003e Turkish | \u003ca href=\"/.github/README_TK.md\"\u003etk T\u00fcrkmenler\u003c/a\u003e Turkmen | \u003ca href=\"/.github/README_UK.md\"\u003euk \u0423\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0438\u0439\u003c/a\u003e Ukrainian | \u003ca href=\"/.github/README_UR.md\"\u003eur \u0627\u0631\u062f\u0648\u003c/a\u003e Urdu | \u003ca href=\"/.github/README_UG.md\"\u003eug \u0626\u06c7\u064a\u063a\u06c7\u0631\u003c/a\u003e Uyghur | \u003ca href=\"/.github/README_UZ.md\"\u003euz O\u0027zbek\u003c/a\u003e Uzbek | \u003ca href=\"/.github/README_VI.md\"\u003evi Ti\u1ebfng Vi\u1ec7t\u003c/a\u003e Vietnamese | \u003ca href=\"/.github/README_CY.md\"\u003ecy Cymraeg\u003c/a\u003e Welsh | \u003ca href=\"/.github/README_XH.md\"\u003exh isiXhosa\u003c/a\u003e Xhosa | \u003ca href=\"/.github/README_YI.md\"\u003eyi \u05d9\u05d9\u05d3\u05d9\u05e9\u003c/a\u003e Yiddish | \u003ca href=\"/.github/README_YO.md\"\u003eyo Yoruba\u003c/a\u003e Yoruba | \u003ca href=\"/.github/README_ZU.md\"\u003ezu Zulu\u003c/a\u003e Zulu ) Available in 110 languages (108 when not counting English and North Korean, as North Korean has not been translated yet \u003ca href=\"/OldVersions/Korean(North)/README.md\"\u003eRead about it here\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eTranslations in languages other than English are machine translated and are not yet accurate. No errors have been fixed yet as of March 21st 2021. Please report translation errors \u003ca href=\"https://github.com/%3CdeveloperName%3E/%3CrepoName%3E/issues/\"\u003ehere\u003c/a\u003e. Make sure to backup your correction with sources and guide me, as I don\u0027t know languages other than English well (I plan on getting a translator eventually) please cite \u003ca href=\"https://en.wiktionary.org\" rel=\"nofollow\"\u003ewiktionary\u003c/a\u003e and other sources in your report. Failing to do so will result in a rejection of the correction being published.\u003c/p\u003e\n\u003cp\u003eNote: due to limitations with GitHub\u0027s interpretation of markdown (and pretty much every other web-based interpretation of markdown) clicking these links will redirect you to a separate file on a separate page that isn\u0027t the intended page. You will be redirected to the \u003ca href=\"/.github/\"\u003e.github folder\u003c/a\u003e of this project, where the README translations are hosted.\u003c/p\u003e\n\u003cp\u003eTranslations are currently done with Bing translate and DeepL. Support for Google Translate translations is coming to a close due to privacy concerns.\u003c/p\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-index\" class=\"anchor\" aria-hidden=\"true\" href=\"#index\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIndex\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"#Top\"\u003e00.0 - Top\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#%3CprojectName%3E\"\u003e00.1 - Title\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Read-this-article-in-a-different-language\"\u003e00.2 - Read this article in a different language\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Index\"\u003e00.3 - Index\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#RepositoryName\"\u003e01.0 - Description\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#About\"\u003e02.0 - About\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Wiki\"\u003e03.0 - Wiki\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#History\"\u003e04.0 - History\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Pre-history\"\u003e04.1 - Pre-history\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Alpha-history\"\u003e04.2 - Alpha History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Beta-history\"\u003e04.3 - Beta History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Modern-history\"\u003e04.4 - Modern History\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#Copying\"\u003e05.0 - Copying\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Credits\"\u003e06.0 - Credits\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Installation\"\u003e07.0 - Installation\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Version-history\"\u003e08.0 - Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Version-history\"\u003e09.0 - Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Software-status\"\u003e10.0 - Software status\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Sponsor-info\"\u003e11.0 - Sponsor info\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Contributers\"\u003e12.0 - Contributers\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Issues\"\u003e13.0 - Issues\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Current-issues\"\u003e13.1 - Current issues\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Past-issues\"\u003e13.2 - Past issues\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Past-pull-requests\"\u003e13.3 - Past pull requests\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#Active-pull-requests\"\u003e13.4 - Active pull requests\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"#Resources\"\u003e14.0 - Resources\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#Contributing\"\u003e15.0 - Contributing\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#About-README\"\u003e16.0 - About README\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#README-version-history\"\u003e17.0 - README Version history\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#You-have-reached-the-end-of-the-README-file\"\u003e18.0 - Footer\u003c/a\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003ca href=\"#EOF\"\u003e18.9 - End of file\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003c/h1\u003e\n\u003cp\u003e\u0026lt;repo_description\u0026gt;\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-hidden=\"true\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eSee above. \u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-wiki\" class=\"anchor\" aria-hidden=\"true\" href=\"#wiki\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWiki\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/%3CdeveloperName%3E/%3CrepoName%3E/wiki\"\u003eClick/tap here to view this projects Wiki\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf the project has been forked, the Wiki was likely removed. Luckily, I include an embedded version. You can view it \u003ca href=\"/External/ProjectWiki/\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eWrite about this projects history here.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pre-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-history\u003c/h3\u003e\n\u003cp\u003eNo pre-history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alpha-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#alpha-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlpha history\u003c/h3\u003e\n\u003cp\u003eNo Alpha history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-beta-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#beta-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBeta history\u003c/h3\u003e\n\u003cp\u003eNo Beta history to show for this project.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-modern-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#modern-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModern history\u003c/h3\u003e\n\u003cp\u003eNo Modern history to show for this project.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-copying\" class=\"anchor\" aria-hidden=\"true\" href=\"#copying\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCopying\u003c/h2\u003e\n\u003cp\u003eView the copying license for this project \u003ca href=\"/COPYING\"\u003ehere\u003c/a\u003e (if you haven\u0027t built the project yet with the makefile, here is the original link: \u003ca href=\"/COPYINGL\"\u003eCOPYINGL\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease note that you also have to follow the rules of the GNU General Public License v3 (GPL3) which you can view \u003ca href=\"/LICENSE.txt\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cp\u003eView the credits file for this project and see the people who got together to make this project by \u003ca href=\"/CREDITS\"\u003eclicking/tapping here\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eView the installation instructions file for this project \u003ca href=\"/INSTALL\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRequirements: Read the instructions for more info, and get the latest up-to-date instructions \u003ca href=\"https://gist.github.com/seanpm2001/745564a46186888e829fdeb9cda584de\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-sponsor-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#sponsor-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSponsor info\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/SponsorButton.png\"\u003e\u003cimg src=\"/SponsorButton.png\" alt=\"SponsorButton.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can sponsor this project if you like, but please specify what you want to donate to. \u003ca href=\"https://github.com/seanpm2001/Sponsor-info/tree/main/For-sponsors/\"\u003eSee the funds you can donate to here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can view other sponsor info \u003ca href=\"https://github.com/seanpm2001/Sponsor-info/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTry it out! The sponsor button is right up next to the watch/unwatch button.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-version-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#version-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersion history\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eVersion history currently unavailable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo other versions listed\u003c/strong\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-software-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#software-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware status\u003c/h2\u003e\n\u003cp\u003eAll of my works are free some restrictions. DRM (\u003cstrong\u003eD\u003c/strong\u003eigital \u003cstrong\u003eR\u003c/strong\u003eestrictions \u003cstrong\u003eM\u003c/strong\u003eanagement) is not present in any of my works.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/DRM-free_label.en.svg\"\u003e\u003cimg src=\"/DRM-free_label.en.svg\" alt=\"DRM-free_label.en.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis sticker is supported by the Free Software Foundation. I never intend to include DRM in my works.\u003c/p\u003e\n\u003cp\u003eI am using the abbreviation \"Digital Restrictions Management\" instead of the more known \"Digital Rights Management\" as the common way of addressing it is false, there are no rights with DRM. The spelling \"Digital Restrictions Management\" is more accurate, and is supported by \u003ca href=\"https://en.wikipedia.org/wiki/Richard_Stallman\" rel=\"nofollow\"\u003eRichard M. Stallman (RMS)\u003c/a\u003e and the \u003ca href=\"https://en.wikipedia.org/wiki/Free_Software_Foundation\" rel=\"nofollow\"\u003eFree Software Foundation (FSF)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis section is used to raise awareness for the problems with DRM, and also to protest it. DRM is defective by design and is a major threat to all computer users and software freedom.\u003c/p\u003e\n\u003cp\u003eImage credit: \u003ca href=\"https://www.defectivebydesign.org/drm-free/how-to-use-label/\" rel=\"nofollow\"\u003edefectivebydesign.org/drm-free/...\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributers\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributers\u003c/h2\u003e\n\u003cp\u003eCurrently, I am the only contributer. Contributing is allowed, as long as you follow the rules of the \u003ca href=\"/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e file.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/seanpm2001/\"\u003eseanpm2001\u003c/a\u003e - x commits (As of Yr, DoW, Month, DoM, at ##:## a/pm)\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eNo other contributers.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssues\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-current-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#current-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCurrent issues\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other current issues\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-past-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#past-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePast issues\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other past issues\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-past-pull-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#past-pull-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePast pull requests\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other past pull requests\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-active-pull-requests\" class=\"anchor\" aria-hidden=\"true\" href=\"#active-pull-requests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eActive pull requests\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNone at the moment\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo other active pull requests\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the repository has been forked, issues likely have been removed. Luckily I keep an archive of certain images \u003ca href=\"/.github/Issues/\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/.github/Issues/README.md\"\u003eRead the privacy policy on issue archival here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTL;DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI archive my own issues. Your issue won\u0027t be archived unless you request it to be archived.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-hidden=\"true\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eResources\u003c/h2\u003e\n\u003cp\u003eHere are some other resources for this project:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"PROJECT_LANG_1.%3CfileExtensionForProgrammingLanguage%3E\"\u003eProject language file A\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/%3CdeveloperName%3E/%3CrepoName%3E/discussions\"\u003eJoin the discussion on GitHub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNo other resources at the moment.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eContributing is allowed for this project, as long as you follow the rules of the \u003ccode\u003eCONTRIBUTING.md\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/CONTRIBUTING.md\"\u003eClick/tap here to view the contributing rules for this project\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-about-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#about-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout README\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eFile type:\u003c/strong\u003e \u003ccode\u003eMarkdown Document (*.md *.mkd *.markdown)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile version:\u003c/strong\u003e \u003ccode\u003e0.1.6 (Monday, August 23rd 2021 at 6:37 pm)\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLine count (including blank lines and compiler line):\u003c/strong\u003e \u003ccode\u003e0,407\u003c/code\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-readme-version-history\" class=\"anchor\" aria-hidden=\"true\" href=\"#readme-version-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eREADME version history\u003c/h2\u003e\n\u003cp\u003eVersion 0.1 (Sunday, March 21st 2021 at 7:50 pm)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eStarted the file\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the about section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the Wiki section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the version history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the issues section.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the past issues section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the past pull requests section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the active pull requests section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the contributors section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the contributing section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the about README section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the README version history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the resources section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded a software status section, with a DRM free sticker and message\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the sponsor info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eITERATION 5\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003cstrong\u003eITERATION 6\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the title section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eFixed and update template links\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the index\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the copying section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the credits section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the installation section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the resources section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the contributors section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eAdded the technical notes section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the footer\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file info section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eUpdated the file history section\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 0.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eVersion 1 (Coming soon)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eComing soon\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eVersion 2 (Coming soon)\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eChanges:\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eComing soon\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cul\u003e\n\u003cli\u003eNo other changes in version 2\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003ca id=\"user-content-you-have-reached-the-end-of-the-readme-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#you-have-reached-the-end-of-the-readme-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eYou have reached the end of the README file\u003c/h3\u003e\n\u003cp\u003e( \u003ca href=\"#Top\"\u003eBack to top\u003c/a\u003e | \u003ca href=\"https://github.com\"\u003eExit to GitHub\u003c/a\u003e | \u003ca href=\"https://www.bing.com/\" rel=\"nofollow\"\u003eExit to Bing\u003c/a\u003e | \u003ca href=\"https://duckduckgo.com/\" rel=\"nofollow\"\u003eExit to DuckDuckGo\u003c/a\u003e | \u003ca href=\"https://www.ecosia.org\" rel=\"nofollow\"\u003eExit to Ecosia\u003c/a\u003e )\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-eof\" class=\"anchor\" aria-hidden=\"true\" href=\"#eof\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEOF\u003c/h3\u003e\n\u003chr\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, - "topics": [ - "apptainer", - "article", - "gpl3", - "gplv3", - "md", - "seanpm2001", - "seanpm2001-education", - "seanpm2001-learn", - "singularity", - "txt", - "knowldege", - "learn-singularity", - "learn-singularity-lang", - "learn-singularity-language", - "singularity-collection", - "singularity-lang", - "singularity-language" - ], - "updated_at": 1668813015.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1685494920.0 }, { "data_format": 2, - "description": null, + "description": "Evaluate the effectiveness of any short read mapper and its parameters using artificially generated reads. See the \"releases\" page for download: https://github.com/mlell/tapas/releases. The manual can be found at https://mlell.github.io/tapas.", "filenames": [ - "singularity/Singularity.clockwork", - "singularity/Singularity.preprocessing" + "Singularity" ], - "full_name": "Pathogen-Genomics-Cymru/tb-pipeline", + "full_name": "mlell/tapas", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-tb-pipeline\" class=\"anchor\" href=\"#tb-pipeline\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTB Pipeline\u003c/h1\u003e\n\u003cp\u003eThis pipeline takes as input reads presumed to be from one of 10 mycobacterial genomes: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis. Input should be in the form of one directory containing pairs of fastq(.gz) or bam files.\u003c/p\u003e\n\u003cp\u003ePipeline cleans and QCs reads with fastp and FastQC, classifies with Kraken2 \u0026amp; Mykrobe, removes non-bacterial content, and - by alignment to any minority genomes - disambiguates mixtures of bacterial reads. Cleaned reads are aligned to either of the 10 supported genomes and variants called. Produces as output one directory per sample, containing cleaned fastqs, sorted, indexed BAM, VCF, and summary reports.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start\" class=\"anchor\" href=\"#quick-start\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eRequires \u003ccode\u003eNXF_VER\u0026gt;=20.11.0-edge\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe workflow is designed to run with either docker \u003ccode\u003e-profile docker\u003c/code\u003e or singularity \u003ccode\u003e-profile singularity\u003c/code\u003e. Before running the workflow, the images will need to be built by running either \u003ccode\u003edocker/docker_build.sh\u003c/code\u003e or \u003ccode\u003esingularity/singularity_build.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eE.g. to run the workflow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNXF_VER=20.11.0-edge nextflow run main.nf -profile singularity --filetype fastq --input_dir fq_dir --pattern \"*_R{1,2}.fastq.gz\" --unmix_myco yes \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\nNXF_VER=20.11.0-edge nextflow run main.nf -profile docker --filetype bam --input_dir bam_dir --unmix_myco no \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-params\" class=\"anchor\" href=\"#params\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParams\u003c/h2\u003e\n\u003cp\u003eThe following parameters should be set in \u003ccode\u003enextflow.config\u003c/code\u003e or specified on the command line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003einput_dir\u003c/strong\u003e\u003cbr\u003e\nDirectory containing fastq OR bam files\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efiletype\u003c/strong\u003e\u003cbr\u003e\nFile type in input_dir. Either \"fastq\" or \"bam\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003epattern\u003c/strong\u003e\u003cbr\u003e\nRegex to match fastq files in input_dir, e.g. \"*_R{1,2}.fq.gz\". Only mandatory if --filetype is \"fastq\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eoutput_dir\u003c/strong\u003e\u003cbr\u003e\nOutput directory for results\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eunmix_myco\u003c/strong\u003e\u003cbr\u003e\nDo you want to disambiguate mixed-mycobacterial samples by read alignment? Either \"yes\" or \"no\":\n\u003cul\u003e\n\u003cli\u003eIf \"yes\" workflow will remove reads mapping to any minority mycobacterial genomes but in doing so WILL ALMOST CERTAINLY ALSO reduce coverage of the principal species\u003c/li\u003e\n\u003cli\u003eIf \"no\" then mixed-mycobacterial samples will be left alone. Mixtures of mycobacteria + non-mycobacteria will still be disambiguated\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003especies\u003c/strong\u003e\u003cbr\u003e\nPrincipal species in each sample, assuming genus Mycobacterium. Default \u0027null\u0027. If parameter used, takes 1 of 10 values: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis. Using this parameter will apply an additional sanity test to your sample\n\u003cul\u003e\n\u003cli\u003eIf you DO NOT use this parameter (default option), pipeline will determine principal species from the reads and consider any other species a contaminant\u003c/li\u003e\n\u003cli\u003eIf you DO use this parameter, pipeline will expect this to be the principal species. It will fail the sample if reads from this species are not actually the majority\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ekraken_db\u003c/strong\u003e\u003cbr\u003e\nDirectory containing \u003ccode\u003e*.k2d\u003c/code\u003e Kraken2 database files (k2_pluspf_16gb_20200919 recommended, obtain from \u003ca href=\"https://benlangmead.github.io/aws-indexes/k2\" rel=\"nofollow\"\u003ehttps://benlangmead.github.io/aws-indexes/k2\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie2_index\u003c/strong\u003e\u003cbr\u003e\nDirectory containing Bowtie2 index (obtain from \u003ca href=\"ftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/hg19_1kgmaj_bt2.zip\" rel=\"nofollow\"\u003eftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/hg19_1kgmaj_bt2.zip\u003c/a\u003e). The specified path should NOT include the index name\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie_index_name\u003c/strong\u003e\u003cbr\u003e\nName of the bowtie index, e.g. hg19_1kgmaj\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp\u003eFor more information on the parameters run \u003ccode\u003enextflow run main.nf --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe path to the singularity images can also be changed in the singularity profile in \u003ccode\u003enextflow.config\u003c/code\u003e. Default value is \u003ccode\u003e${baseDir}/singularity\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-stub-run\" class=\"anchor\" href=\"#stub-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStub-run\u003c/h2\u003e\n\u003cp\u003eTo test the stub run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNXF_VER=20.11.0-edge nextflow run main.nf -stub -config testing.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-checkpoints\" class=\"anchor\" href=\"#checkpoints\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheckpoints\u003c/h2\u003e\n\u003cp\u003eCheckpoints used throughout this workflow to fail a sample/issue warnings:\u003c/p\u003e\n\u003cp\u003eprocesses preprocessing:checkFqValidity or preprocessing:checkBamValidity\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e(Fail) If sample does not pass fqtools \u0027validate\u0027 or samtools \u0027quickcheck\u0027, as appropriate.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eprocess preprocessing:countReads\u003cbr\u003e\n2. (Fail) If sample contains \u0026lt; 100k pairs of raw reads.\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:fastp\u003cbr\u003e\n3. (Fail) If sample contains \u0026lt; 100k pairs of cleaned reads, required to all be \u0026gt; 50bp (cleaning using fastp with --length_required 50 --average_qual 10 --low_complexity_filter --correction --cut_right --cut_tail --cut_tail_window_size 1 --cut_tail_mean_quality 20).\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:kraken2\u003cbr\u003e\n4. (Fail) If the top family hit is not Mycobacteriaceae\u003cbr\u003e\n5. (Fail) If there are fewer than 100k reads classified as Mycobacteriaceae \u003cbr\u003e\n6. (Warn) If the top family classification is mycobacterial, but this is not consistent with top genus and species classifications\u003cbr\u003e\n7. (Warn) If the top family is Mycobacteriaceae but no G1 (species complex) classifications meet minimum thresholds of \u0026gt; 5000 reads or \u0026gt; 0.5% of the total reads (this is not necessarily a concern as not all mycobacteria have a taxonomic classification at this rank)\u003cbr\u003e\n8. (Warn) If sample is mixed or contaminated - defined as containing reads \u0026gt; the 5000/0.5% thresholds from multiple non-human species\u003cbr\u003e\n9. (Warn) If sample contains multiple classifications to mycobacterial species complexes, each meeting the \u0026gt; 5000/0.5% thresholds\u003cbr\u003e\n10. (Warn) If no species classification meets the 5000/0.5% thresholds\u003cbr\u003e\n11. (Warn) If no genus classification meets the 5000/0.5% thresholds\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:identifyBacterialContaminants\u003cbr\u003e\n12. (Fail) If regardless of what Kraken reports, Mykrobe does not make a species-level mycobacterial classification (note that we do not use Kraken mycobacterial classifications other than to determine whether 100k reads are family Mycobacteriaceae; for higher-resolution classification, we defer to Mykrobe)\u003cbr\u003e\n13. (Fail) If the sample is not contaminated and the top species hit is not one of the 10 supported Mycobacteria: abscessus|africanum|avium|bovis|chelonae|chimaera|fortuitum|intracellulare|kansasii|tuberculosis\u003cbr\u003e\n14. (Fail) If the sample is not contaminated and the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003cbr\u003e\n15. (Warn) If the top Mykrobe species hit, on the basis of highest % coverage, does not also have the highest median depth\u003cbr\u003e\n16. (Warn) If we are unable to associate an NCBI taxon ID to any given contaminant species, which means we will not be able to locate its genome, and thereby remove it as a contaminant\u003cbr\u003e\n17. (Warn) If we are unable to determine a URL for the latest RefSeq genome associated with a contaminant species\u0027 taxon ID\u003cbr\u003e\n18. (Warn) If no complete genome could be found for a contaminant species. The workflow will proceed with alignment-based contaminant removal, but you\u0027re warned that there\u0027s reduced confidence in detecting reads from this species\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:downloadContamGenomes\u003cbr\u003e\n19. (Fail) If a contaminant is detected but we are unable to download a representative genome, and thereby remove it\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:summarise\u003cbr\u003e\n20. (Fail) If after having taken an alignment-based approach to decontamination, Kraken still detects a contaminant species\u003cbr\u003e\n21. (Fail) If after having taken an alignment-based approach to decontamination, the top species hit is not one of the 10 supported Mycobacteria\u003cbr\u003e\n22. (Fail) If, after successfully removing contaminants, the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003c/p\u003e\n\u003cp\u003eprocess clockwork:alignToRef\u003cbr\u003e\n23. (Fail) If \u0026lt; 100k reads could be aligned to the reference genome\u003cbr\u003e\n24. (Fail) If, after aligning to the reference genome, the average read mapping quality \u0026lt; 10\u003cbr\u003e\n25. (Fail) If \u0026lt; 50% of the reference genome was covered at 10-fold depth\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTAPAS\u003c/h1\u003e\u003ca id=\"user-content-tapas\" class=\"anchor\" aria-label=\"Permalink: TAPAS\" href=\"#tapas\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software package is meant for comparing NGS short read mappers.\u003c/p\u003e\n\u003cp\u003eArtificial reads can be generated from genome sequences such that their true\nmapping positions are known (\u003ccode\u003escripts/uniform\u003c/code\u003e). Artificial point mutations\n(\u003ccode\u003escripts/multiple_mutate\u003c/code\u003e) and indels (\u003ccode\u003escripts/indel\u003c/code\u003e) can be introduced.\nBy comparing the original read locations to the positions the reads were\nassigned to by the mapper, the sensitivity and specificity of the mapper can\nbe assessed.\u003c/p\u003e\n\u003cp\u003eBy running the mapper with different combinations of parameters, an optimal\nparameter set can be found (\u003ccode\u003escripts/cross_tab\u003c/code\u003e and \u003ccode\u003escripts/table2calls\u003c/code\u003e).\nTAPAS is compatible with all short read mappers by through a simple text\nfile which contains the mapping command and the relevant parameters. Instead\nof the parameter values, bash variables (\u003ccode\u003e${variablename}\u003c/code\u003e) are used. They\nare replaced with the mapping parameter values to be tested by TAPAS.\u003c/p\u003e\n\u003cp\u003eAdditionally, reads can be mutated in a pattern similar to typical damage found\nin ancient DNA (aDNA) (\u003ccode\u003escripts/mapdamage2geomparam\u003c/code\u003e). The effect of this\nmutations can be inspected as well.\u003c/p\u003e\n\u003cp\u003eAll the scripts are designed with the UNIX philosophy \"do one thing and do it\nwell\" in mind. Therefore an analysis consists of a pipeline of these scripts.\u003c/p\u003e\n\u003cp\u003eThe universal data format is the text table, a simple text file containing data\nin whitespace- or tab-separated columns. The first line serves as header line\nwith column names.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe manual\u003c/h2\u003e\u003ca id=\"user-content-the-manual\" class=\"anchor\" aria-label=\"Permalink: The manual\" href=\"#the-manual\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe availabe tools are described in-depth in the TAPAS manual.\nThe manual is located in the \u003ccode\u003edocs/\u003c/code\u003e folder. Open the file \u003ccode\u003edocs/index.html\u003c/code\u003e\ninside the TAPAS folder with your browser to view it.\u003c/p\u003e\n\u003cp\u003eThe manual can also be found online, \u003ca href=\"https://mlell.github.io/tapas\" rel=\"nofollow\"\u003eclick here\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThere is also one \u003cstrong\u003eexample script\u003c/strong\u003e which summarises the steps of the manual.\nIt can be found at \u003ccode\u003emanual/example-manual.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo get help about one specific TAPAS tool, execute the tool with the option\n\u003ccode\u003e--help\u003c/code\u003e. Each tool prints then an extensive help page which explains how\nto use it. Example: \u003ccode\u003eTAPAS/scripts/uniform --help\u003c/code\u003e, where \u003ccode\u003eTAPAS\u003c/code\u003e is the\nfolder you installed TAPAS to, prints the help of the artificial read\ngeneration tool \u003ccode\u003euniform\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software package needs the following tools to be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR \u0026gt;= 3.2\u003c/li\u003e\n\u003cli\u003ePython \u0026gt;= 3.4 and Pip, the Python package manager\u003c/li\u003e\n\u003cli\u003eStandard GNU tools like \u003ccode\u003ehead\u003c/code\u003e, \u003ccode\u003eawk\u003c/code\u003e, \u003ccode\u003esort\u003c/code\u003e, \u003ccode\u003ejoin\u003c/code\u003e, \u003ccode\u003esed\u003c/code\u003e. These are\nincluded in all GNU/Linux and Mac distributions. On Windows you can install\nCygwin to get these programs. However, this software package is not yet\ntested on Windows.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese are the commands needed to install these dependencies on your\nsystem. Select those which match your GNU/Linux distribution:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUbuntu\u003c/strong\u003e and derivatives, like \u003cstrong\u003eScientific Linux\u003c/strong\u003e and \u003cstrong\u003eLinux Mint\u003c/strong\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install r python3 python3-pip\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eCentOS\u003c/strong\u003e or \u003cstrong\u003eRed Hat Linux\u003c/strong\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo yum --enablerepo=extras install epel-release\nsudo yum install r python3 python3-pip\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install TAPAS, first download it from the\n\u003ca href=\"https://github.com/mlell/tapas/releases\"\u003eReleases section\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen, execute the script\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTAPAS/scripts/gen/install_dependencies\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere you replace \u003ccode\u003eTAPAS\u003c/code\u003e by the folder where you installed TAPAS into. The\ntool downloads and installs the needed R and Python packages. The packages\nare installed inside the TAPAS folder and do no affect the rest of the system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAlternative method: Use global R and Python packages\u003c/h3\u003e\u003ca id=\"user-content-alternative-method-use-global-r-and-python-packages\" class=\"anchor\" aria-label=\"Permalink: Alternative method: Use global R and Python packages\" href=\"#alternative-method-use-global-r-and-python-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to run TAPAS using R and Python packages which are installed\nglobally on your system, instead of the \u003ccode\u003einstall_dependencies\u003c/code\u003e script, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTAPAS/script/gen/gen-launchers.sh --ext-libs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo check if all dependencies are met, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTAPAS/scripts/setup_check_dependencies\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eon your computer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThe scripts\u003c/h2\u003e\u003ca id=\"user-content-the-scripts\" class=\"anchor\" aria-label=\"Permalink: The scripts\" href=\"#the-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll scripts lie in the \u003ccode\u003escripts/\u003c/code\u003e folder. Read the manual to get an overview.\nAdditionally, each script can be executed with the \u003ccode\u003e--help\u003c/code\u003e switch to get\ndetailed information about the script\u0027s functionality and how it can be tailored\nto your specific needs.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1642094977.0 + "updated_at": 1716758754.0 }, { "data_format": 2, - "description": "A Nextflow pipeline to run featureCounts on RNAseq BAM files on ICGC in AWS/AWS Batch", + "description": "Demo workflow for Singularity on HPC", "filenames": [ - "Singularity", - "Singularity.1.0.0" + "Singularity" ], - "full_name": "qbic-pipelines/icgc-featurecounts", - "latest_release": "1.0.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"\" class=\"anchor\" href=\"#\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"docs/images/featurecounts_logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"docs/images/featurecounts_logo.png\" alt=\"nf-core/featurecounts\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/nf-core/featurecounts\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a9a5d3186bef5666eba1f45bd2e7e948e0c3c5ff08334d3d6f46c1190f233d39/68747470733a2f2f7472617669732d63692e636f6d2f6e662d636f72652f66656174757265636f756e74732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/nf-core/featurecounts.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/146857ad83dbb5dd463eb6fca54c8f6ce062fae70bda24ff6ea12b08deab557e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e322d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.2-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/qbicsoftware/icgc-featurecounts\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b955ba4fd977e6b8a7fde1ad338b94f1673adb0bad2d30750ffaeb060d61cb05/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f66656174757265636f756e74732e737667\" alt=\"Docker Automated build\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/featurecounts.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1315\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/142166753\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7eeab0b02b6a8878966e94f8c207d2b3fcbf433f88e5dfe3ad21e14633d0001c/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3134323136363735332e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/142166753.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis pipeline uses featureCounts on cancer datasets from ICGC and generates count matrices, similar to what \u003ca href=\"https://github.com/nf-core/RNAseq\"\u003enf-core/RNAseq\u003c/a\u003e does. Users can specify a ICGC Manifest file with object ids, which will then be converted to encrypted S3 URLs. The pipeline then uses the provided GTF file to generate count matrices for all files in the manifest.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-introduction\" class=\"anchor\" href=\"#introduction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe ICGC-FeatureCounts pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-credits\" class=\"anchor\" href=\"#credits\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003eThis pipeline was written by Alexander Peltzer (\u003ca href=\"https://github.com/apeltzer\"\u003eapeltzer\u003c/a\u003e) at \u003ca href=\"apeltzer.github.io\"\u003eQBiC\u003c/a\u003e with some help from Paolo DiTommaso (\u003ca href=\"https://github.com/pditommaso\"\u003epditommaso\u003c/a\u003e) and the Nextflow community.\u003c/p\u003e\n", + "full_name": "mikmaksi/singularity_demo", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExample code for using Singularity on an HPC cluster\u003c/h1\u003e\u003ca id=\"user-content-example-code-for-using-singularity-on-an-hpc-cluster\" class=\"anchor\" aria-label=\"Permalink: Example code for using Singularity on an HPC cluster\" href=\"#example-code-for-using-singularity-on-an-hpc-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis example describes a typical workflow for building a Singularity image and deploying it on an HPC cluster. Singularity is a containerization framework that can be used to encapsulate applications and associated dependencies in an executable image. Images can be built from recipes (similar to Dockerfile) or can be built from an existing Docker image. In this example:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA docker image is built with Singularity installed within.\u003c/li\u003e\n\u003cli\u003eA Singularity image is build inside a running Docker container and GangSTR is installed.\u003c/li\u003e\n\u003cli\u003eA GangSTR test run is done within the Docker container using the newly build Singularity image.\u003c/li\u003e\n\u003cli\u003eAnlysis directory is copied to HPC.\u003c/li\u003e\n\u003cli\u003eA test run is submitted as a job on HPC.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 1\u003c/h2\u003e\u003ca id=\"user-content-step-1\" class=\"anchor\" aria-label=\"Permalink: Step 1\" href=\"#step-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the docker image with Singularity installed. This only needs to be run once. If you have Singularity installed on your local machine, feel free to skip this step. The recipe is contained in Dockerfile.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./1_build_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2\u003c/h2\u003e\u003ca id=\"user-content-step-2\" class=\"anchor\" aria-label=\"Permalink: Step 2\" href=\"#step-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script launches a docker container and binds paths with the bam files, the reference and the regions files required by GangSTR. Note that you can bind paths with \"rw\" or \"ro\". Everything is bound to /home in the Docker container. Also we want to make sure to include the \u003ccode\u003e--rm\u003c/code\u003e flag so that the container is deleted upon exit.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./2_launch_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3\u003c/h2\u003e\u003ca id=\"user-content-step-3\" class=\"anchor\" aria-label=\"Permalink: Step 3\" href=\"#step-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun this script from inside the newly created container. Singularity will build a .simg file from the \u003ccode\u003eSingularity\u003c/code\u003e recipe. THis only needs to be done once. If you already have a Singularity image, feel free to skip this step.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./3_build_simg.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 4\u003c/h2\u003e\u003ca id=\"user-content-step-4\" class=\"anchor\" aria-label=\"Permalink: Step 4\" href=\"#step-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun this script also from inside the Docker container to test your newly created Singularity image. In this script, we mount paths into the Singularity image similar to how we mounted them into the Docker image. Only difference is that we mount to /scratch instead of /home, because the actual /home/$USER folder may be mounted to /home by Singularity by default (depending on configuration of Singularity itself).\u003c/p\u003e\n\u003cp\u003eMake sure to change REFFA to the name of the actual reference file prior to running script.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./4_test_in_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 5\u003c/h2\u003e\u003ca id=\"user-content-step-5\" class=\"anchor\" aria-label=\"Permalink: Step 5\" href=\"#step-5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExit the docker container using the \u003ccode\u003eexit\u003c/code\u003e command. Make sure to replace USER, HOST, SCRATCH_PATH with actual variables in the script. Then sync folder materials to HPC using \u003ccode\u003e./5_sync_with_hpc.sh push\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 6\u003c/h2\u003e\u003ca id=\"user-content-step-6\" class=\"anchor\" aria-label=\"Permalink: Step 6\" href=\"#step-6\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLog in to the HPC where the jobs will be submitted. Navigate to the newly created directory in scratch and submit the job to the cluster. Make sure to replace SCRATCH_PATH, PATH_TO_REF and ANALYSIS_DIR with actual variables in the script.\u003c/p\u003e\n\u003cp\u003eMake sure to change REFFA to the name of the actual reference file prior to running script.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eqsub 6_test_on_hpc.sh\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 12, - "topics": [ - "icgc", - "genomics", - "reproducible", - "docker", - "singularity", - "nextflow", - "best-practice" - ], - "updated_at": 1638968735.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1613502129.0 }, { "data_format": 2, - "description": "Read contamination removal", + "description": "A Nextflow MS DDA proteomics pipeline", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "GenomePathogenAnalysisService/read-it-and-keep", - "latest_release": "v0.1.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-read-it-and-keep\" class=\"anchor\" href=\"#read-it-and-keep\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eread-it-and-keep\u003c/h1\u003e\n\u003cp\u003eRead contamination removal.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install\" class=\"anchor\" href=\"#install\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eInstall either from source or build a singularity container.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-compile-from-source\" class=\"anchor\" href=\"#compile-from-source\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompile from source\u003c/h3\u003e\n\u003cp\u003eMake the executable \u003ccode\u003esrc/readItAndKeep\u003c/code\u003e by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd src \u0026amp;\u0026amp; make\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h3\u003e\n\u003cp\u003eBuild a singularity container by cloning this repository\nand running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build readItAndKeep.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-docker-container\" class=\"anchor\" href=\"#docker-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker container\u003c/h3\u003e\n\u003cp\u003eBuild a docker container by cloning this repository\nand running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile -t \u0026lt;TAG\u0026gt; .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-bioconda-linux-64\" class=\"anchor\" href=\"#bioconda-linux-64\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBioconda (linux-64)\u003c/h3\u003e\n\u003cp\u003eFrom an existing environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c bioconda read-it-and-keep\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing a new environment (recommended):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n read-it-and-keep -c bioconda python=3 read-it-and-keep\nconda activate read-it-and-keep\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003c/h2\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eRequired options:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003e--ref_fasta\u003c/code\u003e: reference genome in FASTA format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads1\u003c/code\u003e: at least one reads file in FASTA[.GZ] or FASTQ[.GZ] format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o,--outprefix\u003c/code\u003e: prefix of output files.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePlease note there is an option \u003ccode\u003e--tech\u003c/code\u003e, which defaults to \u003ccode\u003eillumina\u003c/code\u003e. Use\n\u003ccode\u003e--tech ont\u003c/code\u003e for nanopore reads.\u003c/p\u003e\n\u003cp\u003eRun on paired Illumina reads, in two files \u003ccode\u003ereads1.fq.gz\u003c/code\u003e and \u003ccode\u003ereads2.fq.gz\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereadItAndKeep --ref_fasta ref_genome.fasta --reads1 reads1.fq.gz --reads2 reads2.fq.gz -o out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt will output \u003ccode\u003eout.reads_1.fastq.gz\u003c/code\u003e and\n\u003ccode\u003eout.reads_2.fastq.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eRun on one file of nanopore reads \u003ccode\u003ereads.fq.gz\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereadItAndKeep --tech ont --ref_fasta ref_genome.fasta --reads1 reads.fq.gz -o out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt will output \u003ccode\u003eout.reads.fastq.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf the input reads files are in FASTA format, then it will output reads in\nFASTA format, calling the files \u003ccode\u003e*.fasta.*\u003c/code\u003e instead of \u003ccode\u003e*.fastq.*\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIt always writes the counts of input and output reads to \u003ccode\u003eSTDOUT\u003c/code\u003e in\ntab-delimited format, for example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eInput reads file 1\t1000\nInput reads file 2\t1000\nKept reads 1\t950\nKept reads 2\t950\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll logging messages sent to \u003ccode\u003eSTDERR\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-in-docker\" class=\"anchor\" href=\"#running-in-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning in Docker\u003c/h3\u003e\n\u003cp\u003eSome additional arguments are needs to run correctly in Docker, namely to allow access to the required fasta file as well as inputs and outputs. Below is a functional example.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run /path/to/read-it-and-keep/tests:/tests [-v /path/to/input:/input -v /path/to/output:/output] \u0026lt;TAG\u0026gt; --ref_fasta /tests/MN908947.3.fa --reads1 /input/\u0026lt;SAMPLE\u0026gt;_1.fastq.gz --reads2 /input/\u0026lt;SAMPLE\u0026gt;_2.fastq.gz --outprefix /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tests\" class=\"anchor\" href=\"#tests\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTests\u003c/h2\u003e\n\u003cp\u003eThese are under development. To run them you will need:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePython 3\u003c/li\u003e\n\u003cli\u003ePython package \u003ca href=\"https://docs.pytest.org/en/stable/\" rel=\"nofollow\"\u003epytest\u003c/a\u003e (\u003ccode\u003epip install pytest\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePython package \u003ca href=\"https://github.com/sanger-pathogens/Fastaq\"\u003epyfastaq\u003c/a\u003e (\u003ccode\u003epip install pyfastaq\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm\" rel=\"nofollow\"\u003eART read simulator\u003c/a\u003e\ninstalled, so that \u003ccode\u003eart_illumina\u003c/code\u003e is in your \u003ccode\u003e$PATH\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/rrwick/Badread\"\u003ebadread\u003c/a\u003e for nanopore read simulation.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRun the tests after compiling the source code, ie:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd src\nmake\nmake test\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "lehtiolab/ddamsproteomics", + "latest_release": "v2.18", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003elehtiolab/ddamsproteomics\u003c/h1\u003e\u003ca id=\"user-content-lehtiolabddamsproteomics\" class=\"anchor\" aria-label=\"Permalink: lehtiolab/ddamsproteomics\" href=\"#lehtiolabddamsproteomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eA Quantitative MS proteomics analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad7369dc06af55006491b641d458735c23ee30c3c7df232b984b6df965603b61/68747470733a2f2f7472617669732d63692e6f72672f6c656874696f6c61622f6464616d7370726f74656f6d6963732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/lehtiolab/ddamsproteomics.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/420c856b4f74b0ca05ed3aa6059030cc8889f0adeac2825b25565e98531bd83d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30312e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.01.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/219955514\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2e04feeec2422e4aa774f4ef0a08177ccccf6572de73f448ca0a489de1a2399d/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3231393935353531342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/219955514.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00ceea296d710222922f0f50135c1c5453f5da6e106d89c3af96072c6dcc6d8a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a2c51138636b8990c94d4ec25528ebb83c7eca05a0914812397f87f251d9daa7/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c656874696f6c61622f6464616d7370726f74656f6d6963732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/lehtiolab/ddamsproteomics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f0c745c9a388bc1b2b56ea29cd21a301b7736d98bcfea6cdb5a2c5ee2129f11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow identifies peptides in mzML input data using \u003ca href=\"https://github.com/MSGFPlus/msgfplus\"\u003eMSGF+\u003c/a\u003e, and \u003ca href=\"https://github.com/percolator/percolator/\"\u003ePercolator\u003c/a\u003e, quantifies isobarically labeled samples with \u003ca href=\"https://github.com/openms/openms\"\u003eOpenMS\u003c/a\u003e, and precursor peptides with \u003ca href=\"https://github.com/fickludd/dinosaur\"\u003eDinosaur\u003c/a\u003e, and processes that output to formatted peptide and protein/gene tables using \u003ca href=\"https://github.com/lehtiolab/msstitch\"\u003eMsstitch\u003c/a\u003e. Optional PTM data is analyzed by \u003ca href=\"https://github.com/dfermin/lucxor\"\u003eLuciphor2\u003c/a\u003e, and differential expression analyses can be performed using \u003ca href=\"https://github.com/yafeng/deqms\"\u003eDEqMS\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun pipeline:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa --mods \u0027oxidation;carbamidomethylation\u0027 -profile standard,docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr for two sample sets of isobaric data you can:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa --mods \u0027oxidation;carbamidomethylation --isobaric \u0027setA:tmt10plex:126 setB:tmt10plex:127N\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more elaborate examples covering fractionation, PTMs, and more, the lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere is more extensive documentation on the options inside the main.nf file.\u003c/p\u003e\n\u003cp\u003eThe pipeline takes multiple mzML files as input and performs identification and quantification to output results and a QC report (\u003ca href=\"docs/example_qc.html\"\u003ean example can be found here\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003elehtiolab/ddamsproteomics was originally written by Jorrit Boekel and tries to follow the \u003ca href=\"https://nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e best practices and templates.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1639346958.0 + "updated_at": 1687912051.0 }, { "data_format": 2, - "description": "Genome Decomposition Analysis pipeline", + "description": "Dark mAtteR iNvestigator: diving into the dark matter of your COI sequences", "filenames": [ - "Singularity" + "Singularity", + "Singularity.test" ], - "full_name": "eeaunin/gda", - "latest_release": "v1.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-gda\" class=\"anchor\" href=\"#gda\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGDA\u003c/h1\u003e\n\u003cp\u003eGenome Decomposition Analysis for the characterisation of genome architecture\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-what-is-gda\" class=\"anchor\" href=\"#what-is-gda\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhat is GDA?\u003c/h3\u003e\n\u003cp\u003eGDA (Genome Decomposition Analysis) is a bioinformatic pipeline to analyse genome architecture. Using, as a minimum, a genome assembly (the more complete the better), it will determine features in non-overlapping windows across the sequence and identify windows with common features. The assembly will then be annotated based on these similarities, highlighting structurally similar genomic regions.\u003c/p\u003e\n\u003cp\u003eGDA is developed by Eerik Aunin (\u003ca href=\"mailto:ea10@sanger.ac.uk\"\u003eea10@sanger.ac.uk\u003c/a\u003e) and Adam Reid (\u003ca href=\"mailto:ajr236@cam.ac.uk\"\u003eajr236@cam.ac.uk\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eA manuscript describing GDA is available from bioRxiv: \u003ca href=\"https://biorxiv.org/cgi/content/short/2021.12.01.470736v1\" rel=\"nofollow\"\u003ehttps://biorxiv.org/cgi/content/short/2021.12.01.470736v1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eComplete analyses presented in the manuscript are available here: \u003ca href=\"https://drive.google.com/drive/folders/1XSNS_Jj0_UGxPXpzY-EwbzABGxaZgfRf?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1XSNS_Jj0_UGxPXpzY-EwbzABGxaZgfRf?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a diagram for a quick overview of what GDA does.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/Figure_1.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/Figure_1.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e(A) Features sets are derived from the genome reference sequence (seq), repeat finding (rep), gene annotations (gene) and evolutionary relationships between genes (orth). Values for each feature are determined for each non-overlapping window of e.g. 5kb across the genome. (B) The resulting matrix of feature values per window is embedded in two dimensions and clustered to identify groups of windows with similar properties. (C) The data can be explored in a number of ways using a web-browser based app. The clustering labels are mapped back to the chromosomes to highlight architectural features and a heatmap displays the features which define the clusters.\u003c/p\u003e\n\u003cp\u003eA more technical diagram of the components of the pipeline in the form of a flowchart can be seen \u003ca href=\"images/gda_pipeline_flowchart.png\"\u003ehere\u003c/a\u003e.\nA \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e-based pipeline that includes various third party tools extracts the values of a set of genomic variables that describe a genome assembly. The values of genomic variables along chromosomes are stored as \u003ca href=\"https://genome.ucsc.edu/goldenPath/help/bedgraph.html\" rel=\"nofollow\"\u003ebedgraph files\u003c/a\u003e. The bedgraph files corresponding to one genome assembly are then merged into one tab separated values (TSV) file. In the following text, this file is referred to as \"merged TSV\" file. Scaling of values, dimensionality reduction with \u003ca href=\"https://umap-learn.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eUMAP\u003c/a\u003e and clustering with \u003ca href=\"https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html\" rel=\"nofollow\"\u003eHDBSCAN\u003c/a\u003e are then applied to the numbers in this TSV file. The locations of clusters along chromosomes are stored in a BED file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-requirements\" class=\"anchor\" href=\"#requirements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h3\u003e\n\u003cp\u003eGDA software consists of three main parts: a genomic feature extraction pipeline, clustering scripts, and a \u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e app for viewing the results. The genomic feature extraction pipeline and the clustering scripts have been tested on a Linux server (Sanger farm) and have the following requirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePython3\u003c/li\u003e\n\u003cli\u003eJava \u2013 with enough memory to initialise the Java virtual machine\u003c/li\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Shiny app for viewing clustering results requires R and a number of R libraries. It has been tested on MacOS and Kubuntu Linux.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-notes\" class=\"anchor\" href=\"#notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotes\u003c/h3\u003e\n\u003cp\u003eWe expect that the GDA feature extraction and analysis pipeline is run remotely on a compute cluster with Linux. Viewing the results of a GDA analysis is done in a Shiny app that runs in a web browser and thus we recommend that you copy your results onto your local machine to run the final step. Thus, some dependencies are required remotely and some locally (installation instructions below).\u003c/p\u003e\n\u003cp\u003eThe quick start tutorial will show you how to run the GDA pipeline end-to-end with test data (\u003cem\u003ePlasmodium falciparum\u003c/em\u003e genome assembly \u003ca href=\"https://plasmodb.org/common/downloads/release-49/Pfalciparum3D7/fasta/data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\" rel=\"nofollow\"\u003eobtained from PlasmoDB\u003c/a\u003e) and default parameters. In reality you will likely want to add additional, optional tracks such as gene annotations, repeat finding, transcriptome data and orthology information (these are also detailed below).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-tutorial\" class=\"anchor\" href=\"#tutorial\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTutorial\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-contents\" class=\"anchor\" href=\"#contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContents\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#quick-start-with-test-data\"\u003eQuick start with test data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-results-tabs\"\u003eUnderstanding the results tabs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#view-clusters-and-significant-tracks-in-igv\"\u003eView clusters and significant tracks in IGV\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-output-files\"\u003eUnderstanding the output files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#adding-optional-feature\"\u003eAdding optional features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#optimising-clustering\"\u003eOptimising clustering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-default-features\"\u003eUnderstanding the default features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#other-output\"\u003eOther output\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#using-gda-singularity-image\"\u003eUsing GDA Singularity image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#clustering-the-features-of-multiple-genomes-at-once\"\u003eClustering the features of multiple genomes at once\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ideas-for-analysis\"\u003eIdeas for analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-quick-start-with-test-data\" class=\"anchor\" href=\"#quick-start-with-test-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start with test data\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1. Set up a GDA conda environment on the farm (need to install conda? \u2013 \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ehttps://docs.conda.io/en/latest/miniconda.html\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone the GitHub repository\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun the conda installation script (this can take a little while)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003epython gda/create_gda_conda_env.py gda_env gda_downloads gda\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInitiate the conda environment:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003econda activate gda_env\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf the conda installation does not work for you, you can try using the GDA \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image instead, see \u003ca href=\"#using-gda-singularity-image\"\u003eUsing GDA Singularity image\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Run GDA\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun GDA\u2019s feature extraction pipeline with test data (we suggest that you submit this to your cluster as a job with 12 threads and 10Gb memory; expect it to take ~15 minutes with the test data):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to Load Sharing Facility (LSF)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0The results will be in the folder: \u003ccode\u003egda_pipeline_run\u003c/code\u003e. The output file required for clustering is:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCluster genome windows and analyse clusters (Use 1 thread and 10Gb memory; this should take ~1 minute; n.b. optimised clustering parameters are provided here)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda clustering -c 100 -n 5 gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n1 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_clustering_test.o -e gda_clustering_test.e \"gda clustering -c 100 -n 5 gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0The clustering output will be in a folder called: \u003ccode\u003egda_out\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Install dependencies on your local machine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMOVE TO YOUR LOCAL MACHINE (e.g. your desktop/laptop)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSet up environment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0These are the required R libraries:\u003c/p\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0shiny, ggplot2, devtools, svglite, gplots, rjson, reshape2, gridExtra, scales\u003c/p\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0If you have an R installation on your local machine that is not conda-based, the following R script should install the required libraries:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\n\ngda/gda_shiny/install_gda_shiny_dependencies_without_conda.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Alternatively, the following commands can be used to install a custom conda R environment for the GDA Shiny app:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\n\n# update conda to v4.10.1\nconda update -n base conda\n\nconda create -n gda_env_local r-essentials r-base\n\nconda activate gda_env_local\n\nconda install --yes -c r -c conda-forge r-shiny=1.5.0 r-ggplot2=3.2.1 r-gplots=3.0.3 r-rjson=0.2.20 r-reshape2=1.4.3 r-gridextra=2.3 r-scales=1.0.0 r-svglite=1.2.3\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCopy the data from the remote machine to your local machine (while on you local machine) e.g.\n\u003ccode\u003escp -r \u0026lt;user\u0026gt;@\u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_out/ .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0In order to use scp to copy the files, you will need to be able to see the remote machine (perhaps via VPN).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. View results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe required argument for the \u003ccode\u003egda_shiny.py\u003c/code\u003e script is a path to a \u003ccode\u003egda_out\u003c/code\u003e folder (that comes from the output of \u003ccode\u003egda_clustering.py\u003c/code\u003e and which you just copied from the remote machine).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 gda/gda_shiny/gda_shiny.py gda_out\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-understanding-the-results-tabs\" class=\"anchor\" href=\"#understanding-the-results-tabs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnderstanding the results tabs\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eUMAP plot\u003c/strong\u003e\n\u003ca href=\"images/01_gda_shiny_umap.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/01_gda_shiny_umap.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis shows you how well the clustering worked. Each point in the plot represents a genomic window. Windows are coloured by cluster. Cluster -1 (grey) is used for unclustered windows. Based on the nature of the genome, the features used, the window size and other parameters, there may, for example, be several very distinct, tight clusters, or perhaps a single diffuse cloud of points. Distinct, tight clusters suggest that GDA has identified regions of the genome which are clearly similar to each other and distinct from other regions. A single diffuse cloud means that there were not strong similarities or differences between subsets of the windows. There might be a lot of the genome which is unclassified (grey) or it might all be included in clusters. Sliders can be used to adjust plots for better viewing and PNG or SVG images can be saved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster locations\u003c/strong\u003e\n\u003ca href=\"images/02_gda_shiny_raster_plot.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/02_gda_shiny_raster_plot.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eEach chromosome/scaffold/contig is shown, with each window coloured based on the clustering. Therefore, this shows how the clusters pattern the chromosomes and, for example, whether a particular cluster tends to be found at the end of chromosomes. Do all chromosomes have a similar pattern? Do sex chromosomes, B chromosomes etc. look distinct from the autosomes?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster heatmaps\u003c/strong\u003e\n\u003ca href=\"images/03_gda_shiny_cluster_heatmaps.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/03_gda_shiny_cluster_heatmaps.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGDA determines features which have high or low values for windows in a particular cluster compared to other clusters. The heatmap in this tab shows the relative values across clusters for each significantly variable feature. Green means a feature has a relatively high value in a particular cluster, red a relatively low value. You can find the exact values and which were significantly different in the \u201cFeature tables\u201d tab. Adjusting the plot height and the label size can be particularly useful in this tab so that the heatmap is legible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeature tables\u003c/strong\u003e\n\u003ca href=\"images/04_gda_shiny_feature_tables.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/04_gda_shiny_feature_tables.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab has a table for each cluster (and unclustered windows), describing which features have significantly higher or lower values (by the Kolmogorov-Smirnov test). The default p-value cutoff for the Kolmogorov-Smirnov test is 1e-20.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster positions across chromosomes\u003c/strong\u003e\n\u003ca href=\"images/05_gda_shiny_cluster_positions_across_chromosomes.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/05_gda_shiny_cluster_positions_across_chromosomes.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab shows where each cluster tends to occur across the sequences. It helps you to see whether a cluster tends to occur at the ends or in the middles of chromosomes for instance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChromosome cluster composition\u003c/strong\u003e\n\u003ca href=\"images/06_gda_shiny_chromosome_cluster_composition.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/06_gda_shiny_chromosome_cluster_composition.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab contains a heatmap which clusters chromosomes by their cluster composition. Chromosomes which have similar proportions of each cluster will be closer together in the heatmap. This helps in identifying outliers which might represent interesting sequences such as sex chromosomes, B chromosomes etc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster junction counts\u003c/strong\u003e\n\u003ca href=\"images/07_gda_shiny_cluster_junction_counts.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/07_gda_shiny_cluster_junction_counts.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab shows the observed counts of junctions between windows belonging to each UMAP+HDBSCAN cluster. Junctions between windows belonging to the same type of cluster are included in the counts. The observed counts are compared with counts expected if windows were distributed randomly. Junctions with counts that are significantly different from what is expected by chance (based on Fisher test) are shown in \u003cstrong\u003e\u003cem\u003ebold+italics\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-view-clusters-and-significant-tracks-in-igv\" class=\"anchor\" href=\"#view-clusters-and-significant-tracks-in-igv\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eView clusters and significant tracks in IGV\u003c/h3\u003e\n\u003cp\u003eThe values of variables along chromosomes are stored as \u003ca href=\"https://genome.ucsc.edu/goldenPath/help/bedgraph.html\" rel=\"nofollow\"\u003ebedgraph files\u003c/a\u003e and can be viewed in genome browsers such as \u003ca href=\"https://software.broadinstitute.org/software/igv\" rel=\"nofollow\"\u003eIGV\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Install IGV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://software.broadinstitute.org/software/igv/download\" rel=\"nofollow\"\u003ehttps://software.broadinstitute.org/software/igv/download\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Get bedgraph files from cluster\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escp -r \u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_pipeline_run/bedgraph_output/ .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Copy across clustering results (if you haven\u2019t already)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escp -r \u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_out/ .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Make IGV session file\u003c/strong\u003e\nIGV allows saving and loading \u003ca href=\"https://software.broadinstitute.org/software/igv/Sessions\" rel=\"nofollow\"\u003esession files\u003c/a\u003e, which are XML files that keep track of the program state (what FASTA, BED and bedgraph files have been simultaneously loaded to IGV).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda/gda_make_igv_session_file.py -g gda/test_data/PlasmoDB-49_Pfalciparum3D7.gff gda_out/cluster_heatmap.csv gda_out/PlasmoDB-49_Pfalciparum3D7_Genome/clusters.bed gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta bedgraph_output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e5. Load session file into IGV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile \u2192\u201cOpen Session\u201d\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/gda_pfalciparum_igv_screensh.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"images/gda_pfalciparum_igv_screensh.png\" alt=\"\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\nThe IGV screenshot above shows \u003cem\u003ePlasmodium falciparum\u003c/em\u003e chromosome 1, with some GDA bedgraph tracks and the \u0027clusters.bed\u0027 file loaded.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-understanding-the-output-files\" class=\"anchor\" href=\"#understanding-the-output-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnderstanding the output files\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eBedgraph files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith your results directory (\u003ccode\u003e\u0026lt;YYYYMMDD\u0026gt;_gda_pipeline_run\u003c/code\u003e by default; use \u003ccode\u003egda extract_genomic_features --pipeline_run_folder\u003c/code\u003e to change), the folder \u003ccode\u003ebedgraph_output\u003c/code\u003e contains each bedgraph track produced by GDA. These can be loaded into a genome browser (e.g. IGV) for viewing and better understanding why GDA has clustered the genome as it has. We provide the script \u003ccode\u003egda_make_igv_session_file.py\u003c/code\u003e to generate an IGV session file for your genome which will show the clusters and tracks for features which are significantly enriched in the clusters.\u003c/p\u003e\n\u003cp\u003eOne of the files generated by the \u003ccode\u003egda_clustering.py\u003c/code\u003e script is called \u003ccode\u003eclusters.bed\u003c/code\u003e. This file marks the locations of each UMAP+HDBSCAN cluster and can be loaded to IGV alongside the bedgraph tracks. The cluster numbers and the colour key are the same as in the UMAP plot of the Shiny app.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe feature table\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ecluster\u003c/th\u003e\n\u003cth\u003efeature\u003c/th\u003e\n\u003cth\u003ecluster_data.size\u003c/th\u003e\n\u003cth\u003eother_data.size\u003c/th\u003e\n\u003cth\u003estat_less\u003c/th\u003e\n\u003cth\u003epvalue_less\u003c/th\u003e\n\u003cth\u003estat_great\u003c/th\u003e\n\u003cth\u003epvalue_great\u003c/th\u003e\n\u003cth\u003ecluster_median\u003c/th\u003e\n\u003cth\u003eother_median\u003c/th\u003e\n\u003cth\u003ecluster_mean\u003c/th\u003e\n\u003cth\u003eother_mean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.47\u003c/td\u003e\n\u003ctd\u003e1.86e-35\u003c/td\u003e\n\u003ctd\u003e0.98000\u003c/td\u003e\n\u003ctd\u003e0.66000\u003c/td\u003e\n\u003ctd\u003e1.15760\u003c/td\u003e\n\u003ctd\u003e0.69856\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003etandem_repeats_fraction\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.56\u003c/td\u003e\n\u003ctd\u003e1.36e-49\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003e9.86e-01\u003c/td\u003e\n\u003ctd\u003e0.07840\u003c/td\u003e\n\u003ctd\u003e0.15620\u003c/td\u003e\n\u003ctd\u003e0.08711\u003c/td\u003e\n\u003ctd\u003e0.15905\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003ewgsim_depth_minimap2\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.90\u003c/td\u003e\n\u003ctd\u003e4.03e-127\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e3.71320\u003c/td\u003e\n\u003ctd\u003e9.94240\u003c/td\u003e\n\u003ctd\u003e3.98305\u003c/td\u003e\n\u003ctd\u003e9.90542\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.47\u003c/td\u003e\n\u003ctd\u003e1.86e-35\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.66000\u003c/td\u003e\n\u003ctd\u003e0.98000\u003c/td\u003e\n\u003ctd\u003e0.69856\u003c/td\u003e\n\u003ctd\u003e1.15760\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003etandem_repeats_fraction\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003e9.86e-01\u003c/td\u003e\n\u003ctd\u003e0.56\u003c/td\u003e\n\u003ctd\u003e1.36e-49\u003c/td\u003e\n\u003ctd\u003e0.15620\u003c/td\u003e\n\u003ctd\u003e0.07840\u003c/td\u003e\n\u003ctd\u003e0.15905\u003c/td\u003e\n\u003ctd\u003e0.08711\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003ewgsim_depth_minimap2\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.90\u003c/td\u003e\n\u003ctd\u003e4.03e-127\u003c/td\u003e\n\u003ctd\u003e9.94240\u003c/td\u003e\n\u003ctd\u003e3.71320\u003c/td\u003e\n\u003ctd\u003e9.90542\u003c/td\u003e\n\u003ctd\u003e3.98305\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-adding-optional-features\" class=\"anchor\" href=\"#adding-optional-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdding optional features\u003c/h3\u003e\n\u003cp\u003eWe recommend you add as many features as possible so that the clustering is able to identify those which are the strongest signals in the genome.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optional-features-which-do-not-require-additional-data\" class=\"anchor\" href=\"#optional-features-which-do-not-require-additional-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional features which do not require additional data\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003e1. Run repeat finding to get bedgraph tracks of individual complex repeat features as well as complex_repeat_sum (the sum of all these features)\u003c/strong\u003e\nThe GDA pipeline contains two mandatory components for repeat detection: \u003ca href=\"https://github.com/Benson-Genomics-Lab/TRF\"\u003eTandemRepeatsFinder\u003c/a\u003e for tandem repeats and \u003ca href=\"http://emboss.sourceforge.net/apps/cvs/emboss/apps/einverted.html\" rel=\"nofollow\"\u003eEMBOSS einverted\u003c/a\u003e for inverted repeats. Besides these, the GDA pipeline has two optional repeat family detection modules from which the user can choose one to run. The first one of these modules uses \u003ca href=\"https://www.repeatmasker.org/RepeatModeler/\" rel=\"nofollow\"\u003eRepeatModeler+RepeatMasker\u003c/a\u003e and the second one uses \u003ca href=\"https://github.com/BioinformaticsToolsmith/MeShClust2\"\u003eRed+MeShClust2\u003c/a\u003e. RepeatModeler+RepeatMasker is relatively slow and may take ~1 week to run for large genomes (11 hours for the test dataset). On Sanger farm5, this will require using the basement queue. The Red+Meshclust2 module is much faster, but may produce more noisy repeat families, depending on the genome.\nWhen the GDA pipeline is run with repeat family detection enabled, the bedgraph files of each complex repeat family appear in the \u003ccode\u003ecomplex_repeats\u003c/code\u003e subdirectory of the \u003ccode\u003ebedgraph_output\u003c/code\u003e directory. If RepeatModeler is used, a \u003ccode\u003esimple_repeats\u003c/code\u003e directory that contains bedgraph files of simple repeat families is also produced.\nIn addition, a bedgraph file of the sum of complex repeat families (and if using RepeatModeler, of simple repeat families) is produced. The individual bedgraph tracks of each repeat family are not used as the input for UMAP clustering by default, but the tracks for the sums of simple or complex repeat families are used.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--run_repeat_family_detection\n--repeat_family_detection_engine \u0026lt;repeatmodeler/meshclust2\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --run_repeat_family_detection --repeat_family_detection_engine repeatmodeler gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_repeatmodeler_test.o -e gda_repeatmodeler_test.e \"gda extract_genomic_features --threads 12 --run_repeat_family_detection --repeat_family_detection_engine repeatmodeler gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e2. \u003cem\u003eDe novo\u003c/em\u003e gene annotation\u003c/strong\u003e\nThe GDA pipeline can take an existing gene annotations GFF3 file as input. For the cases where there is no existing gene annotations available for the genome, the pipeline contains an optional module that produces a \u003cem\u003ede novo\u003c/em\u003e annotation of protein coding genes, rRNA and tRNA genes (using \u003ca href=\"https://bioinf.uni-greifswald.de/augustus/\" rel=\"nofollow\"\u003eAugustus\u003c/a\u003e, \u003ca href=\"https://github.com/tseemann/barrnap\"\u003eBarrnap\u003c/a\u003e and \u003ca href=\"http://lowelab.ucsc.edu/tRNAscan-SE/\" rel=\"nofollow\"\u003etRNAscan-SE\u003c/a\u003e). The gene annotation module can optionally take an annotated related genome as the input and produce hints for Augustus based on annotation transfer with \u003ca href=\"https://github.com/agshumate/Liftoff\"\u003eLiftoff\u003c/a\u003e. Several bedgraph feature tracks are derived from gene annotations: \u003ccode\u003emRNA_annotation\u003c/code\u003e, \u003ccode\u003eexon_count\u003c/code\u003e, \u003ccode\u003egene_average_exon_length\u003c/code\u003e, \u003ccode\u003egene_average_intron_length\u003c/code\u003e, \u003ccode\u003egene_length\u003c/code\u003e, \u003ccode\u003etRNA_annotations\u003c/code\u003e, \u003ccode\u003erRNA_annotations\u003c/code\u003e. Optionally, a \u003ccode\u003egene_dna_strand_bias\u003c/code\u003e track is also produced.\nAlso, a GFF file of the annotations can be found in the \u003ccode\u003egene_annotation\u003c/code\u003e folder. The GFF file also includes the tRNAscan and Barrnap results.\u003c/p\u003e\n\u003cp\u003eMultiple options are required\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--run_gene_annotation_pipeline\n--annotation_target_species_id \u0026lt;label_for_gene_ids\u0026gt;\n--augustus_species \u0026lt;pick_from_list\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder aug_test_runfolder --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test_aug.o -e gda_test_aug.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder aug_test_runfolder --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optional-features-requiring-additional-data\" class=\"anchor\" href=\"#optional-features-requiring-additional-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional features requiring additional data\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003e1. Genome annotation\u003c/strong\u003e\n\u003ccode\u003e--gff_path \u0026lt;GFF3 file with existing gene annotations\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor handling user-provided GFF files, the pipeline expects the following things:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe input file is in GFF3 format (GTF or GFF2 are not accepted)\u003c/li\u003e\n\u003cli\u003ethe tags for mRNA, pseudogene, tRNA and rRNA features are \"mRNA\", \"pseudogene\", \"tRNA\" and \"rRNA\". The user should check the GFF file to make sure that the tags are named according to this convention. If, for instance, the mRNA features in the GFF file are called \"transcript\" instead of \"mRNA\", the pipeline does not recognise them as the mRNA features.\u003c/li\u003e\n\u003cli\u003ethe GFF file should pass the \u003ca href=\"http://genometools.org/cgi-bin/gff3validator.cgi\" rel=\"nofollow\"\u003eGenomeTools GFF3 validator check\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe user can specify non-standard GFF3 feature tags from the input GFF3 file to be turned into bedgraph tracks using the \u003ccode\u003e--custom_gff_tags\u003c/code\u003e option of the \u003ccode\u003egda\u003c/code\u003e wrapper. For example, if the input GFF3 file has features named \"H3K9me3\" and \"H3K9ac\", it is possible to make bedgraph files out of them by specifying them as comma separated \u003ccode\u003ecustom_gff_tags\u003c/code\u003e options:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--custom_gff_tags H3K9me3,H3K9ac\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Reference genome annotation (annotate your assembly using a reference annotation: hints for Augustus are derived from annotation transfer using Liftoff)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--reference_assembly_path \u0026lt;reference assembly FASTA file\u0026gt; --reference_gff_path \u0026lt;reference assembly GFF3 file\u0026gt; \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. RNA-Seq coverage\u003c/strong\u003e\nRNA-Seq coverage is determined using the mapping of reads to the assembly with \u003ca href=\"http://daehwankimlab.github.io/hisat2/manual/\" rel=\"nofollow\"\u003eHISAT2\u003c/a\u003e. The input is a pair of gzipped FASTQ reads.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--rna_seq_fastq_1_path\n--rna_seq_fastq_2_path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR223/008/ERR2234508/ERR2234508_1.fastq.gz .\nwget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR223/008/ERR2234508/ERR2234508_2.fastq.gz .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --rna_seq_fastq_1_path ERR2234508_1.fastq.gz --rna_seq_fastq_2_path ERR2234508_2.fastq.gz gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --rna_seq_fastq_1_path ERR2234508_1.fastq.gz --rna_seq_fastq_2_path ERR2234508_2.fastq.gz gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting feature track is called \u003ccode\u003ehisat2_samtools_depth\u003c/code\u003e and the raw mapping data is in the \u003ccode\u003erna_seq_mapping\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Gene conservation (orthology)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--orthomcl_references_folder\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis folder should contain subfolders, each for separate \u003ca href=\"https://orthomcl.org/orthomcl/app\" rel=\"nofollow\"\u003eOrthoMCL\u003c/a\u003e runs, e.g. for closely related and more distantly related species (although a single folder is perfectly fine). The folder name is arbitrary. Within each folder there should be protein FASTA files for each reference proteome and a file called \u003ccode\u003etable_for_gg_file.csv\u003c/code\u003e with the names of these files and a simple name for the species. GG files (genome gene relation file) are used by OrthoMCL to relate genes to genomes. e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePchabaudi,PlasmoDB-49_Pchabaudichabaudi_AnnotatedProteins.fasta\nTgondii,ToxoDB-51_TgondiiME49_AnnotatedProteins.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eProteins from the genome under consideration will be added behind the scenes (they are derived from the assembly FASTA file and annotations GFF3 file using \u003ca href=\"https://github.com/gpertea/gffread\"\u003egffread\u003c/a\u003e). N.b. you need to provide annotation for your genome assembly or have it transferred/predicted in order to do the orthology analysis.\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder orthomcl_test_runfolder --orthomcl_references_folder gda/test_data/orthomcl_refs/ --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test_orthomcl.o -e gda_test_orthomcl.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder orthomcl_test_runfolder --orthomcl_references_folder gda/test_data/orthomcl_refs/ --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting bedgraph files are:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_ortholog_count.bedgraph\u003c/code\u003e - Number of orthologues per gene\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_paralog_count.bedgraph\u003c/code\u003e - Number of paralogues per gene\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_protein_conservation_ratio.bedgraph\u003c/code\u003e - The average proportion of species in the OrthoMCL run that have orthologs for the target species proteins in the window. The value is 1 if all target species proteins in the window have OrthoMCL orthologs in all other species in the OrthoMCL run. The value is 0 if none of the target species proteins in the window have any OrthoMCL orthologs in any other species in the OrthoMCL run.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_species_specific_proteins_ratio.bedgraph\u003c/code\u003e - The average proportion of species-specific proteins in the window. It is the number of target species proteins with no OrthoMCL orthologs in the window divided by the number of all target species proteins in the window. The value is 1 if none of the target species proteins in the window have OrthoMCL orthologs, and 0 if all of the target species proteins in the window have OrthoMCL orthologs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Reference mitochondrial sequence for Nuclear Mitochondrial DNA (NUMT) identification\u003c/strong\u003e\nNUMT identification is done using BLAST of the genome against a user-provided reference mitochondrial sequence. The reference mitochondrial sequence can be a known mitochondrial sequence from the same species as the rest of the assembly. If a region of an assembly contig yields a strong BLAST hit (e-value \u0026lt;= 1e-30) to the reference mitochondrial sequence but the alignment length is less than 90% of the length of this contig, the BLAST hit region is labelled as a putative NUMT.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--ref_mitoch_fasta_path\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Reference plastid sequence for NUPT identification\u003c/strong\u003e\nThis is the same process as the detection of NUMTs but meant for plastid sequences.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--ref_apicoplast_fasta_path\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Other useful feature extraction options\u003c/strong\u003e\nThe pipeline tries to identify telomeric regions by searching the assembly sequences for exact matches to a telomeric motif. The \u003ccode\u003etelomeric_seq_preset\u003c/code\u003e option allows to select a query telomeric motif from a list of known telomeric motifs across different species (based on the Wikipedia article on telomeres, \u003ca href=\"https://en.wikipedia.org/wiki/Telomere\" rel=\"nofollow\"\u003ehttps://en.wikipedia.org/wiki/Telomere\u003c/a\u003e). It is also possible to specify a custom telomeric motif using the \u003ccode\u003ecustom_telomeric_seq\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--telomeric_seq_preset\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-optimising-clustering\" class=\"anchor\" href=\"#optimising-clustering\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimising clustering\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optimising-clustering-during-feature-extraction\" class=\"anchor\" href=\"#optimising-clustering-during-feature-extraction\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimising clustering during feature extraction\u003c/h4\u003e\n\u003cp\u003eChange the window size (5kb)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--chunk_size\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis is perhaps the most important option in GDA. From a purely computational point of view, GDA will struggle with clustering a very large number of windows. From a biological perspective, it determines the resolution at which you are analysing the genome assembly. We find that 5kb works very well for the relatively miniscule \u003cem\u003ePlasmodium\u003c/em\u003e genome (~20Mb). For the common toad (\u003cem\u003eBufo bufo\u003c/em\u003e) genome, which is 4.94 Gb we have used 1 Mb window size. Aiming for 5000 windows works very nicely computationally, but you should experiment with a few window sizes, to see what gives an interesting view of the genome. You needn\u0027t run feature extraction multiple times. Instead use:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egda downsample_merged_tsv \u0026lt;tsv\u0026gt; \u0026lt;factor\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you started with a 5kb window size, use 4 as the downsampling factor and you will get a merged TSV file with 20kb windows. Similarly, use a factor of 10 to get 50kb windows.\u003c/p\u003e\n\u003cp\u003eIf the genomic feature extraction pipeline produces an output TSV file that has 10000 or more windows, a downsampled TSV file with approximately 5000 windows will be automatically generated alongside the main output TSV file.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-optimising-clustering-during-clustering-step\" class=\"anchor\" href=\"#optimising-clustering-during-clustering-step\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptimising clustering during clustering step\u003c/h4\u003e\n\u003cp\u003eOnce the feature extraction pipeline is finished, you can determine good clustering parameters by looking at the UMAP plots from a range of different parameters:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda clustering_params 20210312_gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n1 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_params_test.o -e gda_params_test.e \"gda clustering_params 20210312_gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eReplace the \u003ccode\u003e20210312_gda_pipeline_run\u003c/code\u003e in the above command with the name of your GDA pipeline run folder path.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003en_neighbors\u003c/code\u003e is a UMAP setting that determines the size of the local neigbourhood in terms of sample points (\u003ca href=\"https://umap-learn.readthedocs.io/en/latest/parameters.html\" rel=\"nofollow\"\u003ehttps://umap-learn.readthedocs.io/en/latest/parameters.html\u003c/a\u003e). Smaller \u003ccode\u003en_neigbors\u003c/code\u003e values give more emphasis on local structure in the data and larger \u003ccode\u003en_neighbors\u003c/code\u003e values give more weight to global structure. We have used \u003ccode\u003en_neighbors\u003c/code\u003e values from 5 to 200.\nBy default the clustering will be run with \u003ccode\u003en_neighbors\u003c/code\u003e set to 5, 10, 15, 20, 50, 100 and \u201cMinimum cluster size\u201d set to 50, 100, 200, 500. All parameter pairs will be explored (e.g. 24 combinations). The results of each clustering are output to STDOUT. You can also view an HTML file of UMAP plots in a web browser e.g.:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003efirefox gda_out/parameter_selection/parameters.html \u0026amp;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e[warning this can run slowly when run remotely]\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/pfalciparum_gda_parameters_example.pdf\"\u003eHere\u003c/a\u003e is example output of the \u003ccode\u003egda clustering_params\u003c/code\u003e run with the \u003cem\u003ePlasmodium falciparum\u003c/em\u003e assembly.\u003c/p\u003e\n\u003cp\u003eWe recommend selecting parameters based on minimising the percentage of unclassified sequence, while getting at least two clusters. E.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN neighbours: 5\nMin cluster size: 50\nCluster -1 is 2.14% of the genome\nCluster 0 is 2.99% of the genome\nCluster 1 is 3.70% of the genome\nCluster 2 is 91.17% of the genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnly 2.14% of windows were unclassified and there are multiple clusters meaning GDA has identified some partitioning of the genome.\u003c/p\u003e\n\u003cp\u003eGiven that this pair of parameters involves the lowest values, it would be a good idea to try out even lower parameter values to see if there is an even better/more interesting clustering.\u003c/p\u003e\n\u003cp\u003eYou should pick minimum cluster sizes based on the number of windows you have. E.g. If you have 5000 windows, and you have a minimum cluster size of 50, the smallest possible cluster will contain 1% of your genome assembly.\u003c/p\u003e\n\u003cp\u003eWhen clustering a large number of genomic windows, you may need to set HDBSCAN\u0027s \u003ccode\u003emin_samples\u003c/code\u003e value to a value that is not \u003ccode\u003eNone\u003c/code\u003e in order to prevent HDBSCAN from crashing (\u003ca href=\"https://github.com/scikit-learn-contrib/hdbscan/issues/250\"\u003ehttps://github.com/scikit-learn-contrib/hdbscan/issues/250\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-understanding-the-default-features\" class=\"anchor\" href=\"#understanding-the-default-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnderstanding the default features\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eVariable\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eat_skew\u003c/td\u003e\n\u003ctd\u003eAT skew\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecag_freq\u003c/td\u003e\n\u003ctd\u003eCAG trinucleotide repeat frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecomplex_repeats_bedgraph\u003c/td\u003e\n\u003ctd\u003ecomplex repeats detected using \u003ca href=\"https://www.repeatmasker.org/RepeatModeler/\" rel=\"nofollow\"\u003eRepeatModeler+RepeatMasker\u003c/a\u003e or \u003ca href=\"https://github.com/BioinformaticsToolsmith/MeShClust2\"\u003eRed+MeShClust2\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003eCpG dinucleotide frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edustmasker_low_complexity_percentage\u003c/td\u003e\n\u003ctd\u003elow complexity sequence frequency (detected using \u003ca href=\"https://www.ncbi.nlm.nih.gov/IEB/ToolBox/CPP_DOC/lxr/source/src/app/dustmasker/\" rel=\"nofollow\"\u003eDustmasker\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eectopic_apicoplast\u003c/td\u003e\n\u003ctd\u003eputative ectopic apicoplast (detected using BLAST against user-provided apicoplast sequence)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eectopic_mitochondrion\u003c/td\u003e\n\u003ctd\u003eputative NUMTs (detected using BLAST against user-provided mitochondrial sequence)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eeinverted\u003c/td\u003e\n\u003ctd\u003einverted repeats (detected using \u003ca href=\"http://emboss.sourceforge.net/apps/cvs/emboss/apps/einverted.html\" rel=\"nofollow\"\u003eEMBOSS einverted\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eexon_count\u003c/td\u003e\n\u003ctd\u003eaverage exon count per mRNA gene\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egaps\u003c/td\u003e\n\u003ctd\u003eassembly gaps (Ns)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egc_percentage\u003c/td\u003e\n\u003ctd\u003eGC%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egc_skew\u003c/td\u003e\n\u003ctd\u003eGC skew\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_average_exon_length\u003c/td\u003e\n\u003ctd\u003eaverage exon length of mRNA genes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_average_intron_length\u003c/td\u003e\n\u003ctd\u003eaverage intron length of mRNA genes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_dna_strand_bias\u003c/td\u003e\n\u003ctd\u003etendency of genes to be all on the same strand in the window. The value is 1 if all genes in the window are on the same strand (it does not matter which one). The value is 0 if genes in the window are equally distributed between both strands\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_length\u003c/td\u003e\n\u003ctd\u003eaverage mRNA gene length\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ekmer_deviation_kmer_size_3*\u003c/td\u003e\n\u003ctd\u003ekmer skew for a for a particular kmer length (how much the distribution of kmers in the window differs from what is expected by change, given the GC content of the sequence in the window)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eltrdigest_protein_matches\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/genometools/genometools\"\u003eLTRdigest\u003c/a\u003e protein matches\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eltrdigest_retrotransposons\u003c/td\u003e\n\u003ctd\u003eputative retrotransposons (detected using \u003ca href=\"https://github.com/genometools/genometools\"\u003eLTRharvest and LTRdigest\u003c/a\u003e). Only the sequences containing LTRdigest protein matches are counted\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emRNA_annotations\u003c/td\u003e\n\u003ctd\u003emRNA gene density (either from user-provided gene annotations or detected using \u003ca href=\"https://bioinf.uni-greifswald.de/augustus/\" rel=\"nofollow\"\u003eAugustus\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eortholog_count\u003c/td\u003e\n\u003ctd\u003eaverage number of orthologs (\u003ca href=\"https://orthomcl.org/orthomcl/\" rel=\"nofollow\"\u003eOrthoMCL\u003c/a\u003e orthologs in other species) for proteins in the window\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eparalog_count\u003c/td\u003e\n\u003ctd\u003eaverage number of paralogs (OrthoMCL orthologs within the same species) for proteins in the window\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eprotein_conservation_ratio\u003c/td\u003e\n\u003ctd\u003ereflects the average proportion of species in the OrthoMCL run that have orthologs for the target species proteins in the window. The value is 1 if all target species proteins in the window have OrthoMCL orthologs in all other species in the OrthoMCL run. The value is 0 if none of the target species proteins in the window have any OrthoMCL orthologs in any other species in the OrthoMCL run.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epseudogene_annotations\u003c/td\u003e\n\u003ctd\u003epseudogenes (read from user-provided GFF3 file if this feature is present there)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erRNA_annotations\u003c/td\u003e\n\u003ctd\u003erRNA_annotations (either from user-provided gene annotations or detected using Barrnap)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esimple_repeats_bedgraph\u003c/td\u003e\n\u003ctd\u003esimple repeats detected using RepeatModeler+RepeatMasker. The sequences have been collapsed to count repeats that are the reverse complement of one another as the same repeat. They have also been collapsed to count the repeats that are identical if the starting point is adjusted as the same repeat (e.g. TGGTT is the same as GGTTT)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especies_specific_proteins_ratio\u003c/td\u003e\n\u003ctd\u003ereflects the average proportion of species specific proteins in the window. It is the number of target species proteins with no OrthoMCL orthologs in the window divided by the number of all target species proteins in the window. The value is 1 if none of the target species proteins in the window have OrthoMCL orthologs, and 0 if all of the target species proteins in the window have OrthoMCL orthologs.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003estop_codon_freq\u003c/td\u003e\n\u003ctd\u003estop codon frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esum_of_complex_repeats\u003c/td\u003e\n\u003ctd\u003esum of values of RepeatModeler+RepeatMasker or Red+MeShClust2 tracks for complex repeat families\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esum_of_simple_repeats\u003c/td\u003e\n\u003ctd\u003esum of values of RepeatModeler tracks for simple repeat families\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etandem_repeat_density\u003c/td\u003e\n\u003ctd\u003etandem repeats (detected using \u003ca href=\"https://github.com/Benson-Genomics-Lab/TRF\"\u003eTandem Repeats Finder\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etelomere_freq\u003c/td\u003e\n\u003ctd\u003etelomeric sequence frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etRNA_annotations\u003c/td\u003e\n\u003ctd\u003etRNAs (either from user-provided gene annotations or detected using \u003ca href=\"http://lowelab.ucsc.edu/tRNAscan-SE/\" rel=\"nofollow\"\u003etRNAscan-SE\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewgsim_minimap2_coverage\u003c/td\u003e\n\u003ctd\u003ecoverage of \u003ca href=\"https://github.com/lh3/wgsim\"\u003eWGSIM\u003c/a\u003e simulated short reads, derived from the assembly itself, with a target coverage of 10x. The reads have been mapped back to the assembly using Minimap2 using the short read mapping mode. Multimapping simulated reads have been removed before calculating the coverage\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-other-output\" class=\"anchor\" href=\"#other-output\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther output\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003e/work/\u003c/code\u003e directory \u2013 Files automatically generated by Nextflow during the run. These files can be used for resuming the pipeline when crashed. Nextflow has a \u003ccode\u003e-resume\u003c/code\u003e option for restarting an interrupted run from the last cached checkpoint. In the GDA pipeline wrapper script, the \u003ccode\u003eresume_genomic_feature_extraction\u003c/code\u003e command is meant for restarting the pipeline using Nextflow\u0027s \u003ccode\u003e-resume\u003c/code\u003e flag. For this you will need to provide the path to the Nextflow config file (it is a file with the name \u003ccode\u003enextflow.config\u003c/code\u003e in the \u003ccode\u003e*_gda_pipeline_run folder\u003c/code\u003e) and the name of the crashed run. The run names are autogenerated by Nextflow and can be seen in the STDOUT log of the GDA run, in square brackets below the line that says \"N E X T F L O W\". If the run was started using the GDA Singularity image, you will also need to provide the path to that image, otherwise this path is not needed.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-clustering-the-features-of-multiple-genomes-at-once\" class=\"anchor\" href=\"#clustering-the-features-of-multiple-genomes-at-once\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClustering the features of multiple genomes at once\u003c/h3\u003e\n\u003cp\u003eIt is possible to cluster the features extracted from multiple genomes at the same time. To do this, the first step is to run the genomic feature extraction pipeline separately for each genome of interest. For each genome, this will produce a TSV table with the values of the genomic features. The tables can then be concatenated using the \u003ccode\u003egda_concatenate_tsv_tables.py\u003c/code\u003e script. Each of the input tables needs to have the same window size. In the \u003ccode\u003especies\u003c/code\u003e and \u003ccode\u003echromosome\u003c/code\u003e columns, each input TSV table needs to have unique values that do not occur in the other input TSV tables. After concatenating the tables, the resulting combined table can be processed with the \u003ccode\u003egda clustering_params\u003c/code\u003e and \u003ccode\u003egda clustering\u003c/code\u003e commands. When viewing the clustering results of a multi-genome TSV table in the Shiny app, an extra UMAP plot will appear, with dots coloured according to which input assembly each window belongs to (\u003ca href=\"images/clustering_two_genomes_umap_example.png\"\u003eexample\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-using-gda-singularity-image\" class=\"anchor\" href=\"#using-gda-singularity-image\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing GDA Singularity image\u003c/h3\u003e\n\u003cp\u003eAs an alternative to using conda to install the dependencies for GDA, it is also possible to read the dependencies from a Singularity image. A Singularity image file with the dependencies for GDA has been deposited in Google Drive, at \u003ca href=\"https://drive.google.com/file/d/1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_/view?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/file/d/1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_/view?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you have gdown (\u003ca href=\"https://github.com/wkentaro/gdown\"\u003ehttps://github.com/wkentaro/gdown\u003c/a\u003e, \u003ca href=\"https://anaconda.org/conda-forge/gdown\" rel=\"nofollow\"\u003ehttps://anaconda.org/conda-forge/gdown\u003c/a\u003e) installed on your system, you can download the Singularity image file from Google Drive with a terminal command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egdown https://drive.google.com/uc?id=1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOn the Sanger farm, Singularity can be started from the farm module:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emodule load ISG/singularity/3.6.4\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou will need to make sure Singularity and Nextflow are installed on your cluster.\nFor running GDA with the Singularity image, you should still clone this GitHub repository and add the \u003ccode\u003egda\u003c/code\u003e wrapper script to \u003ccode\u003ePATH\u003c/code\u003e. To use the GDA Singularity image, you should provide the path to the image with the \u003ccode\u003e--singularity_image_path\u003c/code\u003e option of the \u003ccode\u003egda\u003c/code\u003e wrapper script. The remaining software dependencies (RepeatModeler, HISAT2, LTRharvest, etc) will then be loaded from the Singularity image. This is an example command for extracting genomic features using Singularity:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta --singularity_image_path \u0026lt;gda_singularity.simg\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta --singularity_image_path \u0026lt;gda_singularity.simg\u0026gt;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also run the \u003ccode\u003egda_clustering_params\u003c/code\u003e and \u003ccode\u003egda_clustering\u003c/code\u003e commands with the Singularity image by providing a path to the image with the \u003ccode\u003e--singularity_image_path\u003c/code\u003e option.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" href=\"#troubleshooting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTroubleshooting\u003c/h3\u003e\n\u003cp\u003e\u2022\tThe best place to look for error messages initially is STDOUT, rather than STDERR, because the Nextflow error messages end up there. You may then be directed to the \u003ccode\u003eerror_stream_logs\u003c/code\u003e directory in your run folder for error messages from a specific process\u003c/p\u003e\n\u003cp\u003e\u2022\tYou may want to exclude the mitochondrial and other symbiont genomes as well as any shorter, non-chromosomal scaffolds\u003c/p\u003e\n\u003cp\u003e\u2022\tIf your genome assembly is large and clustering is problematic you may want to increase window size. You can do this with an existing merged TSV file using \u003ccode\u003egda downsample_merged_tsv \u0026lt;path to the TSV file\u0026gt; \u0026lt;downsampling factor\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBugs, suggestions etc. can be sent to \u003ca href=\"mailto:ea10@sanger.ac.uk\"\u003eea10@sanger.ac.uk\u003c/a\u003e and \u003ca href=\"mailto:ajr236@cam.ac.uk\"\u003eajr236@cam.ac.uk\u003c/a\u003e, or submitted as issues on this GitHub page.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-ideas-for-analysis\" class=\"anchor\" href=\"#ideas-for-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIdeas for analysis\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eUse a single feature from the merged TSV to make calls for where this feature is high across a genome \u2013 e.g. paralogous gene families or a particular complex repeat family of interest.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "hariszaf/darn", + "latest_release": "v1.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/darn_logo.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/darn_logo.png\" width=\"200\" height=\"200\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDARN - Dark mAtteR iNvestigator\u003c/h1\u003e\u003ca id=\"user-content-darn---dark-matter-investigator\" class=\"anchor\" aria-label=\"Permalink: DARN - Dark mAtteR iNvestigator\" href=\"#darn---dark-matter-investigator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the Dark mAtteR iNvestigator (DARN) software package.\u003c/p\u003e\n\u003cp\u003eDARN uses a COI reference tree covering all domains of life (eukaryotes, bacteria, archaea)\nto assign your sequences to the 3 domains of life.\u003c/p\u003e\n\u003cp\u003eIts purpose is not to provide you with certain taxonomic assignment but to give an overview of the species present.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/placements_of_consensus_seqs_transpaernt.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/placements_of_consensus_seqs_transpaernt.png\" width=\"80%\" height=\"85%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edarn\u003c/code\u003e is a available as a container, meaning you need a containerized technology to use it.\nAnd that its only dependency! \ud83e\udd73\u003c/p\u003e\n\u003cp\u003eIf you are about to use \u003ccode\u003edarn\u003c/code\u003e locally, on your \u003cstrong\u003epersonal computer\u003c/strong\u003e, then probably you will find \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e as the easiest way to go for it.\u003c/p\u003e\n\u003cp\u003eHowever, if you are working on a server or in a \u003cstrong\u003eHigh Performance Computing (HPC)\u003c/strong\u003e environment, then \u003ca href=\"https://singularityhub.github.io/\" rel=\"nofollow\"\u003eSingulariy\u003c/a\u003e is the best if not (in cases where your admin does not allow Docker) the oncly choice.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAs a Docker container\u003c/h3\u003e\u003ca id=\"user-content-as-a-docker-container\" class=\"anchor\" aria-label=\"Permalink: As a Docker container\" href=\"#as-a-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install Docker you may follow there instructions \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\ndepending on the operation system you are working at.\u003c/p\u003e\n\u003cp\u003eOnce Docker is set, you may get \u003ccode\u003edarn\u003c/code\u003e as a Docker image by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull hariszaf/darn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAs a Singularity container\u003c/h3\u003e\u003ca id=\"user-content-as-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: As a Singularity container\" href=\"#as-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install Singularity you may follow the instructions described \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e or you can probably ask your sys-admin to do that for you,\nin case you are working in a server, HPC, cloud etc.\u003c/p\u003e\n\u003cp\u003eOnce Singularity is set, you will need to build your \u003ccode\u003e.sif\u003c/code\u003e file based on the Docker image.\nTo do so, you may run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull darn.sif docker://hariszaf/darn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll \u003ccode\u003edarn\u003c/code\u003e needs as input is a fasta \u003ccode\u003einput.fasta\u003c/code\u003e file with the sequnces you wish to run.\u003c/p\u003e\n\u003cp\u003eOverall, running \u003ccode\u003edarn\u003c/code\u003e is a 2-steps process:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003efirst, you need to \u003cstrong\u003emount\u003c/strong\u003e the directory at your actual computer where your \u003ccode\u003einput.fasta\u003c/code\u003e is located (\u003ccode\u003edir\u003c/code\u003e, e.g. \u003ccode\u003e/home/haris/Desktop/my_darn_analysis\u003c/code\u003e) in a certain directory of the container.\nThis works as a \u003cem\u003ebridge\u003c/em\u003e between your computer and the container, allowing the container\nto have access on the files of the directory you mount and vice-versa.\u003c/li\u003e\n\u003cli\u003ethen, you run \u003ccode\u003edarn\u003c/code\u003e from inside the container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eIn all cases\u003c/strong\u003e, \u003ccode\u003edarn\u003c/code\u003e will always return its \u003cstrong\u003eoutput\u003c/strong\u003e in the \u003ccode\u003edir\u003c/code\u003e directory you mount.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun \u003ccode\u003edarn\u003c/code\u003e as a Docker container\u003c/h3\u003e\u003ca id=\"user-content-run-darn-as-a-docker-container\" class=\"anchor\" aria-label=\"Permalink: Run darn as a Docker container\" href=\"#run-darn-as-a-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo mount the directory \u003ccode\u003e\u0026lt;dir\u0026gt;\u003c/code\u003e where your \u003ccode\u003einput.fasta\u003c/code\u003e is located to \u003ccode\u003edarn\u003c/code\u003e\nyou may run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -it -v \u0026lt;dir\u0026gt;/:/mnt darn\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe \u003ccode\u003e-v\u003c/code\u003e parameter denotes your bridge; the \u003ccode\u003e\u0026lt;dir\u0026gt;\u003c/code\u003e directory of your computer,\nwill be mounted in the \u003ccode\u003e/mnt\u003c/code\u003e path of the \u003ccode\u003edarn\u003c/code\u003e container. Parameter \u003ccode\u003e--rm\u003c/code\u003e is simply there\nto make sure that your Docker container will be completely removed when you will exit from it, while the \u003ccode\u003e-it\u003c/code\u003e enables running shell for the container you are about to initiate.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eOnce the shell is on you just need to run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./darn.sh -s /mnt/input.fasta -t \u0026lt;number_of_threads\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output of \u003ccode\u003edarn\u003c/code\u003e will be returned in the same directory that you mount, i.e you may find your results under \u003ccode\u003edir\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eHere is a shell recording for how to get and run \u003ccode\u003edarn\u003c/code\u003e assuming you have already set Docker on your computing system.\n\u003ca href=\"https://asciinema.org/a/427103\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11aeaca33679821366471381129c11cf29740905151fc47e1484a976bdc78acf/68747470733a2f2f61736369696e656d612e6f72672f612f3432373130332e737667\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/427103.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003emarine_part.fasta\u003c/code\u003e file that is used as an \u003ccode\u003einput.fasta\u003c/code\u003e here, along with the \u003ccode\u003edarn\u003c/code\u003e outcome, can be found under the \u003ca href=\"https://github.com/hariszaf/darn/tree/main/analysis\"\u003e\u003ccode\u003eanalysis\u003c/code\u003e\u003c/a\u003e folder of this repo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun \u003ccode\u003edarn\u003c/code\u003e as a Singularity container\u003c/h3\u003e\u003ca id=\"user-content-run-darn-as-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Run darn as a Singularity container\" href=\"#run-darn-as-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLikewise, you need to first mount the \u003ccode\u003e\u0026lt;dir\u0026gt;\u003c/code\u003e path where you \u003ccode\u003einput.fasta\u003c/code\u003e is located in the container.\u003c/p\u003e\n\u003cp\u003eNow, you may run \u003ccode\u003edarn\u003c/code\u003e with a sole command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind \u0026lt;dir\u0026gt;/:/mnt darn_latest.sif -s /mnt/query_freshwater_short_all_seqs.fasta -t 20\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr, by using the same way as in the Docker case, meaning you first open a shell\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind \u0026lt;dir\u0026gt;/:/mnt darn_latest.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand once shell appears:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /home\n./darn.sh -s /mnt/your_sample.fasta -t \u0026lt;number_of_threads_available\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn case you are are working on a HPC system, depending on the queing system used,\nyou need to build a \u003cem\u003ejob\u003c/em\u003e script first and include the previous commands into it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce \u003ccode\u003edarn\u003c/code\u003e is completed, 2 new directories will be available in the \u003ccode\u003edir\u003c/code\u003e folder:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eone called \u003ccode\u003eintermediate\u003c/code\u003e, including \u003ccode\u003egappa\u003c/code\u003e best and exhaustive hits as well as the \u003ccode\u003e.jplace\u003c/code\u003e tree file with the assignments of the query sequences in the phylogenetic tree\u003c/li\u003e\n\u003cli\u003eand a second one, called \u003ccode\u003efinal_outcome\u003c/code\u003e where a \u003ccode\u003e.json\u003c/code\u003e file with the entries assigned in each domain of life and a \u003ccode\u003e.fasta\u003c/code\u003e file with the sequences assigned in each of these domain can be found, as well as a Krona plot, as \u003ccode\u003e.html\u003c/code\u003e file, representing the composition of the sequence sample based on the number of species found per taxonomic group.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn example of such a Krona plot can be found\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/darn_krona.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/hariszaf/darn/main/figures/darn_krona.png\" width=\"800\" height=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe first Krona plot, is based on the likelihood values of the best hit assignments of your query sequences.\nThis way, you may have an overview of what is most likely to actually have on your sample.\nFor a more thorough look on such a Krona, just cilck \u003ca href=\"https://htmlpreview.github.io/?https://github.com/hariszaf/darn/blob/main/analysis/final_outcome/darn_marine_part_likelihood.krona_plot.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe second Krona plot, is based on a binary interpretation of the best hits assignments of your sequences, meaning\nthat no matter the likelihood value of the best hit of each query, we treat all the assignments as equal to get a\nquantitative overview of the assignments. For more click \u003ca href=\"https://htmlpreview.github.io/?https://github.com/hariszaf/darn/blob/main/analysis/final_outcome/darn_marine_part_pres_abs.krona_plot.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe Krona plots are accompanied by the profiles they came from and by a \u003ccode\u003e.json\u003c/code\u003e file with all the qurey assignments per domain (Bacteria, Archaea, Eukaryota) and a series of \u003ccode\u003e.fasta\u003c/code\u003e files; one for each domain found, with the sequences that have been assigned to it.\u003c/p\u003e\n\u003cp\u003eYou may have a look on the DARN output over \u003ca href=\"https://github.com/hariszaf/darn/tree/main/analysis\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicences\u003c/h2\u003e\u003ca id=\"user-content-licences\" class=\"anchor\" aria-label=\"Permalink: Licences\" href=\"#licences\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edarn\u003c/code\u003e is under the GNU GPLv3 license (for 3rd party components separate licenses apply).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edarn\u003c/code\u003e makes use of the following software:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://cme.h-its.org/exelixis/web/software/papara/index.html\" rel=\"nofollow\"\u003ePaPaRa\u003c/a\u003e and EPA-ng algorithms.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Pbdas/epa-ng\"\u003eEPA-ng\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lczech/gappa/\"\u003egappa\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/marbl/Krona/wiki\"\u003ekrona\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to cite\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eZafeiropoulos H, Gargan L, Hintikka S, Pavloudi C, Carlsson J. The Dark mAtteR iNvestigator (DARN) tool: getting to know the known unknowns in COI amplicon data. Metabarcoding and Metagenomics. 2021 Mar 11;5:e69657. DOI: \u003ca href=\"https://doi.org/10.3897/mbmg.5.69657\" rel=\"nofollow\"\u003ehttps://doi.org/10.3897/mbmg.5.69657\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 3, "subscribers_count": 1, "topics": [], - "updated_at": 1640384962.0 + "updated_at": 1643724457.0 }, { "data_format": 2, - "description": "Cell Interaction by Multiplet Sequencing (CIM-Seq) uses computational deconvolution of RNA-seq data from partially dissociated tissue to create cell interaction maps.", + "description": "A software to partition NGS signal data", "filenames": [ - "inst/containers/Singularity" + "Singularity" ], - "full_name": "EngeLab/CIMseq", - "latest_release": "v1.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-cim-seq\" class=\"anchor\" href=\"#cim-seq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCIM-seq\u003c/h1\u003e\n\u003cp\u003eRelease build: \u003ca href=\"https://travis-ci.com/jasonserviss/CIMseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a40d6a99b66a0483bfc629d3c366e4bda0ddfb63f31cab19bf5a091e5fe5da20/68747470733a2f2f7472617669732d63692e636f6d2f6a61736f6e736572766973732f43494d7365712e7376673f6272616e63683d6d6173746572\" data-canonical-src=\"https://travis-ci.com/jasonserviss/CIMseq.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDevel build: \u003ca href=\"https://travis-ci.com/jasonserviss/CIMseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1cf4a1545fdea91e82705800328f938f63eeb43f67d6c106a6a7cffeb42c7664/68747470733a2f2f7472617669732d63692e636f6d2f6a61736f6e736572766973732f43494d7365712e7376673f6272616e63683d646576656c\" data-canonical-src=\"https://travis-ci.com/jasonserviss/CIMseq.svg?branch=devel\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTest coverage: \u003ca href=\"https://codecov.io/github/jasonserviss/CIMseq?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e5f3e48f6917619c7701365cadda7bdb2f3dfd193622e089e8d9a8338307eb69/68747470733a2f2f636f6465636f762e696f2f67682f6a61736f6e736572766973732f43494d7365712f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"Coverage status\" data-canonical-src=\"https://codecov.io/gh/jasonserviss/CIMseq/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAdvances in single-cell biology has enabled us to investigate isolated single cells at an unprecedented scale and resolution. However, cells in multicellular organisms are largely defined by their spatial organization within organ structures, which means that general methods for studying direct cell interaction on a large scale are needed. We propose a novel method, Cell Interaction by Multiplet Sequencing (CIM-Seq) that uses computational deconvolution of RNA-seq data from partially dissociated tissue to create cell interaction maps. We applied CIM-seq to human fetal pancreas, demonstrating that it recapitulates known cell interactions such as acinar-ductal cell contacts. Furthermore, we discover a strong link between a mesenchymal cell subtype and endocrine progenitor cells, and identify the set of genes that distinguishes this mesenchymal subtype. Thus, CIM-Seq is a general method for cell interaction studies that can be used on cell types defined to an arbitrary resolution allowing identification of interacting sub-cell types or cell states.\u003c/p\u003e\n", + "full_name": "romaingroux/SPar-K", + "latest_release": "v1.01", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSPar-K\u003c/h1\u003e\u003ca id=\"user-content-spar-k\" class=\"anchor\" aria-label=\"Permalink: SPar-K\" href=\"#spar-k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSPar-K (Signal Partitioning using K-means) is a modified version of a standard K-means algorithm designed to cluster vectors containing a sequence of signal (that is, the order in which the elements appear in the vectors is meaningful). In order to detect a possible phase shift or orientation inversion between two vectors, this program allows computing distances between two vectors by shifting and flipping them (see below).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSPar-K partitioning procedure\u003c/h2\u003e\u003ca id=\"user-content-spar-k-partitioning-procedure\" class=\"anchor\" aria-label=\"Permalink: SPar-K partitioning procedure\" href=\"#spar-k-partitioning-procedure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSPar-K implements a modified version of the K-means algorithm. In brief, it iteratively partitions a set of genomic regions based on their signal profiles by optimizing K gap-free alignments of the signal in the regions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe data should be stored as a numerical matrix in a simple text file. Each row of the matrix represents a region and each element of a row represents a position along the region. A given element in the matrix represents the amount of signal present at a given position, in a given region. Each row should be stored within the file as a single line. Each row element should be separated from the others by a blank character (space or tab). Finally, each row should have the same length and the matrix should only contains numerical values. No row nor column name are allowed! Here is an example of a valid input (you can find another one in data.txt) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e0 0 0 1 1 2 3 2 1 1 0 0 0\n0 1 1 2 3 2 1 1 0 0 0 0 0\n0 0 4 4 3 2 2 1 1 0 0 0 0\n0 0 0 1 1 2 2 3 3 4 4 0 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis matrix can contain the results of a spatial correlation between two sets of genomic features, for instance ChIP-seq reads (the targets) around +/- 1kb of a set of 1000 TSSs (the references). In that case, the matrix is expected to have 1000 rows (one per TSS) and one column per possible position around these references (here 2001 : 1000 downstream of each TSS, 1 where the TSSs are, 1000 upstream of each TSS). Then, each value of the matrix represents the number of targets (ChIP-seq reads) at a given position (the column) relative to a given reference (TSS). It is also possible to use bins, that is, to summarize several positions within each column, for instance to count the target every 10bp instead of every bp. In this case, each column would represent a bin of 10bp.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePartitioning procedure\u003c/h3\u003e\u003ca id=\"user-content-partitioning-procedure\" class=\"anchor\" aria-label=\"Permalink: Partitioning procedure\" href=\"#partitioning-procedure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, an optional data pre-processing step, to smooth out outliers, row-wise, is available. It allows to minimize the impact of extreme values on the correlation computations and to limit their driving force on the data alignment process.\nThe partition is initialized using a random seeding strategy or using the K-means++ strategy. Each cluster is composed of an alignment of regions (rows) assigned to this cluster and is summarized by the aggregation of the data alignment. The aggregation is a vector having a length equal to the number of columns of the input matrix. It represents the average signal profile over the regions assigned in this cluster. The aggregations are obtained by taking the mean signal at each position (column) in the alignment.\nThen, the partition, is iteratively optimized. At each iteration, each region is compared to each cluster aggregation, using a modified correlation distance allowing shifting and flipping. Both parameters are defined by the user. In brief, the aim is to detect a possible signal shift of inversion between the two vectors. With a shifting freedom S, each region and cluster aggregation, both of lengths L, are broken down into S sub-parts of length L \u2212 S + 1. To compare a region to a cluster, each sub-part of a given region is compared to each sub- part of the given cluster aggregation. The comparison with the lowest correlation distance is stored as well as the offsets at which the region and the cluster aggregation sub-parts started. Flipping is handled by allowing an additional set of comparisons with the reversed (flipped) region sub-part. The region is then assigned to the least dissimilar cluster. Eventually, the K alignments have been updated and allow to recompute the cluster aggregations.\nThis procedure is repeated, optimizing the partition until convergence or until reaching the maximum number of iterations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput\u003c/h3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSPar-K returns a table through the stdout. It contains a header row and as many rows as the input had. Each row contains several parameters for the corresponding reference. It contains 1) the cluster assignment, 2) the shift and flip values describing how the row and the corresponding cluster reference were aligned - that is the coordinate of the 1st element of the cluster reference and of the matrix row used in the comparison leading to assigning this row to the given cluster, whether one of the slice was flipped or not - and 3) the distance computed between these two slices. If flipping is not allowed, then no flipping information is returned.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. To run SPar-K, you have three options. You can either choose to download a release source code, to download a Docker image or a Singularity image. All procedures are detailed below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom the release source code\u003c/h3\u003e\u003ca id=\"user-content-from-the-release-source-code\" class=\"anchor\" aria-label=\"Permalink: From the release source code\" href=\"#from-the-release-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrerequisites\u003c/h4\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo compile and run SPar-K, the following programs and libraries need to be installed on your computer for a proper compilation :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1) Scons v2.3.0 or higher to compile all the program listed above(https://scons.org/pages/download.html) \n2) boost v1.4.1 or higher (https://www.boost.org/)\n3) UnitTest++ v2 (https://github.com/unittest-cpp/unittest-cpp)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Scons configuration files SConstruct and SConscript are configured such that they will look for :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1) boost static libaries in /usr/local/lib/boost and boost header files in /usr/local/include/boost\n2) UnitTest++ static libaries in /usr/local/lib/UnitTest++ and UnitTest++ header files in /usr/local/include/UnitTest++\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eso it is highly recommanded to install these libraries here. Another solution is to modify the SConscript file (located in src/)\nto adapt the library paths (modify the lib_unittest_path and lib_boost_path variable values).\u003c/p\u003e\n\u003cp\u003eThe following softwares and libraries are required to run the auxiliary scripts spark_correct_sga.R and spark_plot_heatmap.R :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1) R version 3.X and Rscript to run these scripts in batch mode\n2) the R libraries optparse and RColorBrewer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompiling\u003c/h4\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce all the libraries are installed, download the source, unzip the archive, cd at the root of the repository (where the SConstruct file is located) and compile using Scons:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eunzip SPar-K-release.zip\ncd SPar-K-release\nscons\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe SPar-K exectuable should be located in bin/. To get SPar-K help, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/spark --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run SPar-K, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/spark \u0026lt;options\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning the tests\u003c/h4\u003e\u003ca id=\"user-content-running-the-tests\" class=\"anchor\" aria-label=\"Permalink: Running the tests\" href=\"#running-the-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAt compilation, a test suite is also compiled and placed in bin/. To run it and test the different components of the code, use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/unittests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-from-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: From the Singularity image\" href=\"#from-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity image is build using \u003ca href=\"https://github.com/romaingroux/SPar-K/releases\"\u003ethe latest release\u003c/a\u003e source code.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrerequisites\u003c/h4\u003e\u003ca id=\"user-content-prerequisites-1\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to have Singularity installed on your machine. Check \u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ethis link\u003c/a\u003e for more informations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePulling the image\u003c/h4\u003e\u003ca id=\"user-content-pulling-the-image\" class=\"anchor\" aria-label=\"Permalink: Pulling the image\" href=\"#pulling-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have a working version of Singularity, you can pull the image from Singularity Hub using this command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name spar-k.simg shub://romaingroux/SPar-K:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning SPar-K from the image\u003c/h4\u003e\u003ca id=\"user-content-running-spar-k-from-the-image\" class=\"anchor\" aria-label=\"Permalink: Running SPar-K from the image\" href=\"#running-spar-k-from-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing SPar-K from Singularity is just as the same as using the compiled executable, excepted that the commands require to contain a call to Singularity. For instance, to get SPar-K help, use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run SPar-K, use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark \u0026lt;options\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom the Docker image\u003c/h3\u003e\u003ca id=\"user-content-from-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: From the Docker image\" href=\"#from-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Docker image is build using \u003ca href=\"https://github.com/romaingroux/SPar-K/releases\"\u003ethe latest release\u003c/a\u003e source code.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrerequisites\u003c/h4\u003e\u003ca id=\"user-content-prerequisites-2\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to have Docker installed on your machine. Check \u003ca href=\"https://www.docker.com/get-started\" rel=\"nofollow\"\u003ethis link\u003c/a\u003e for more informations. Depending on your installation, you may need root privileges.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePulling the image\u003c/h4\u003e\u003ca id=\"user-content-pulling-the-image-1\" class=\"anchor\" aria-label=\"Permalink: Pulling the image\" href=\"#pulling-the-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have a working version of Docker, you can pull the image from Docker Hub using this command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull rgroux/spar-k:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning SPar-K from the image\u003c/h4\u003e\u003ca id=\"user-content-running-spar-k-from-the-image-1\" class=\"anchor\" aria-label=\"Permalink: Running SPar-K from the image\" href=\"#running-spar-k-from-the-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing SPar-K from Docker only requires to deploy a container and call SPar-K. For simplicity, let\u0027s tag the image as \u0027spar-k\u0027 (this will be assumed in all the following Docker related documentation). For instance, to get SPar-K help, use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker tag rgroux/spar-k:latest spar-k\n\ndocker run -i spar-k spark --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou noticed that we called the image by its tag name (spar-k), inside which we ran SPar-K executable (spark).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrograms\u003c/h2\u003e\u003ca id=\"user-content-programs\" class=\"anchor\" aria-label=\"Permalink: Programs\" href=\"#programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following programs are distributed :\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espark\u003c/h3\u003e\u003ca id=\"user-content-spark\" class=\"anchor\" aria-label=\"Permalink: spark\" href=\"#spark\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the main program. spark is the partitioning software.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eoptions\u003c/h4\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eshort\u003c/th\u003e\n\u003cth align=\"left\"\u003elong\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u003c/th\u003e\n\u003cth align=\"left\"\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-h\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--help\u003c/td\u003e\n\u003ctd align=\"left\"\u003eProduces the help message\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-v\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--version\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePrints the version number\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-p\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--parallel arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe number of threads dedicated to the computations, by default 1.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-d\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--data arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe data file address.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-r\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--references arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe cluster reference pattern file address.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-i\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--iter arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe maximum number of iterations.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-c\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--cluster arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eThe number of cluster to find.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-s\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--shift arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eEnables this number of column of shifting freedom. By default, shifting is disabled (equivalent to --shift 1). This option and --width are mutually exclusive\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e-w\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--width\u003c/td\u003e\n\u003ctd align=\"left\"\u003eEnables shifting by searching signal profiles of the given width. Setting --width L\u0027 is equivalent to set --shift L-L\u0027+1 where L is the length of each region (the number of columns in the input matrix). By default, the profile width is equal to region width (L). This option and --shift are mutually exclusive.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--flip\u003c/td\u003e\n\u003ctd align=\"left\"\u003eEnables flipping.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--nooutlier\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePre-pcocess the data to smooth out outliers from the data in a row-wise manner. Each row is searched for outliers which are defined as any value bigger/smaller than the row mean +/- 3*row standard deviation. If a value is an outlier it is replaced by the mean of its left and right neighbours. If a has only a left/right neighbour, then the two left/right neighbours are averaged. If several outliers follow each other the above process is applied to the values in a left to right order and at the end the new averaged values may still be outliers.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--dist arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eSpecify which distance should be used during the clustering. It should be \u0027corr\u0027 (by default) or \u0027normcorr\u0027.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--seeding arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eSpecify which method should be used to initialise the cluster references. It should be \u0027random\u0027 or \u0027kmean++\u0027. \u0027random\u0027 will sample k datum as the initial references, with uniform probabilities (by default).\u0027kmean++\u0027 selects k datum using the kmean++ algorithm. It select a first center at random and iteratively select a new center with a probability proportional to the distance of each point to their nearest already choosen center, until k centers have been selected.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--seed arg\u003c/td\u003e\n\u003ctd align=\"left\"\u003eA value to seed the random number generator.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e--debug\u003c/td\u003e\n\u003ctd align=\"left\"\u003eEnables debuggin verbosity.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning an example\u003c/h3\u003e\u003ca id=\"user-content-running-an-example\" class=\"anchor\" aria-label=\"Permalink: Running an example\" href=\"#running-an-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe file data.txt contains the number reads, from a H3K4me3 ChIP-seq experiment performed in CD4+ cells, that are mapped +/- 1kb around 23360 human TSSs within bins of 100bp (to reproduce this matrix, run \u003ca href=\"https://ccg.vital-it.ch/chipseq/chip_extract.php\" rel=\"nofollow\"\u003eChIP-Extract\u003c/a\u003e example by clicking the \"Example\" button and \"Submit\". Then, remove the first line and column of the resulting matrix which are headers). There are 99 bins per row (49 bins of 100bp upstream the TSS + the central bins containing the TSS + 49 bins upstream of the TSS). Here are the 4 first lines :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e4 2 1 2 6 2 3 6 3 0 0 0 1 1 1 0 1 3 1 4 0 0 0 1 0 1 2 1 1 0 0 1 0 0 1 1 0 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 1 1 0 1 0 0 0 0 1 0 0 1\n0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 3\n0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 1 2 2 6 0 1 0 0 1 1 3 8 4 1 0 0 1 0 0 1 0 0 0 0 0 0 3 4 1 18 25 13 4 2 3 0 1 2 7 12 3 5 4 2 2 9 10 8 8 9 1 1 1 0 5 5 7 3 2 0 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0\n1 0 0 1 3 5 2 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 2 2 0 0 0 0 2 0 1 1 2 0 12 5 5 0 1 1 0 1 0 1 0 1 1 0 2 0 3 2 1 1 1 1 1 0 0 2 0 0 1 1 0 0 0 1 0 1 0 0 6 1 0 0 0 0 0 0\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe data were organized such that all the TSS are oriented in the same direction. The first bin downstream each TSS is located in column 51. To partition these data into 3 clusters, based on the ChIP-seq profile in these regions, set a reasonable shifting freedom (7 bins, meaning +/-3*20bp) but no flipping (the TSSs are already oriented in the same direction), run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/spark --data data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that data.txt is inside /SPar-K in the image but results.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark --data /SPar-K/data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that data.txt is inside the image but results.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark --data data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe data \u0027data.txt\u0027 are contained within the image and the results are retrieved by a stream redirection so there is no need to create a mount point between the host file system and the image file system yet. However, for cases where the data are in a file outside the image (on the host file system) or when a process inside the image has to write in a file outside the image (to the host file system), this will be required. It can be done as follows :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark --data /mount/data_from_host.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u0026lt;current dir\u0026gt; is the absolute path to the current directory. On linux plateforms, you can use \u0027$(pwd)\u0027. Examples with a mount points can be found below.\u003c/p\u003e\n\u003cp\u003eAs SPar-K implementation is fully multi-threaded, you can speed up the partitioning processes by dispatching the computations on several CPU cores. To do so, you need to use the -p option. For instance, to use 4 concurrent threads :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/spark --data data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 -p 4 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that data.txt is inside /SPar-K in the image but results.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark --data /SPar-K/data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 -p 4 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that data.txt is inside the image but results.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark --data data.txt --cluster 4 --shift 7 --iter 30 --seeding kmean++ --seed 1234 -p 4 \u0026gt; results.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espark_plot_heatmap.R\u003c/h3\u003e\u003ca id=\"user-content-spark_plot_heatmapr\" class=\"anchor\" aria-label=\"Permalink: spark_plot_heatmap.R\" href=\"#spark_plot_heatmapr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis program is an R script. Once a dataset has been partitioned using SPar-K, this script produces a heatmap of the results.\u003c/p\u003e\n\u003cp\u003eLet\u0027s follow again the previous example. Now that you have your partition, you would like to display a nice heatmap. You would like to have the regions grouped by cluster and realigned as SPar-K aligned them. You can produce a plot of the data, realigned and ordered by cluster using :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_plot_heatmap.R --data data.txt --partition results.txt --shift 7 --from -1000 --to 1000 --title \"TSS with H3K4me3\" --output myplot.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that data.txt is inside /SPar-K in the image but myplot.png is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_plot_heatmap.R --data /SPar-K/data.txt --partition results.txt --shift 7 --from -1000 --to 1000 --title \"TSS with H3K4me3\" --output myplot.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that data.txt is inside the image but myplot.png is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark_plot_heatmap.R --data data.txt --partition /mount/results.txt --shift 7 --from -1000 --to 1000 --title \"TSS with H3K4me3\" --output /mount/myplot.png\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can notice here the use of a mount point to read data from the host file system and to write a file on the host file system.\u003c/p\u003e\n\u003cp\u003eTo get the help, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_plot_heatmap.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_plot_heatmap.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark_plot_heatmap.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espark_correct_sga.R\u003c/h3\u003e\u003ca id=\"user-content-spark_correct_sgar\" class=\"anchor\" aria-label=\"Permalink: spark_correct_sga.R\" href=\"#spark_correct_sgar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis program is an R script. Once a dataset has been partitioned using SPar-K, this scripts allows to update the corresponding SGA file according to the shift and flip values reported by SPar-K.\u003c/p\u003e\n\u003cp\u003eLet\u0027s use the previous partitioning example (again). You have partitioned a dataset containing 23360 rows of length 99 with a shifting freedom of 7 and without flipping, the results are stored in results.txt and the TSS positions in a SGA file named references.sga (\u003ca href=\"https://ccg.vital-it.ch/chipseq/sga_specs.php\" rel=\"nofollow\"\u003eabout the SGA file format\u003c/a\u003e). Here are the first 4 lines :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNC_000001.10\tTSS\t 861123\t+\t1\tSAMD11_1\t2\nNC_000001.10\tTSS\t 874653\t+\t1\tSAMD11_2\t2\nNC_000001.10\tTSS\t 894631\t-\t1\tNOC2L_1\t2\nNC_000001.10\tTSS\t 895964\t+\t1\tKLHL17_1\t2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you can update the positions according to what SPar-K found to be the optimal alignment using :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_correct_sga.R --sga references.sga --partition results.txt --shift 7 --ncol 99 --binSize 20 \u0026gt; references_aligned.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that references.sga is inside /SPar-K in the image but results.sga is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_correct_sga.R --sga /SPar-K/references.sga --partition results.txt --shift 7 --ncol 99 --binSize 20 \u0026gt; results.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that references.sga is inside the image but results.sga is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark_correct_sga.R --sga references.sga --partition /mount/results.txt --shift 7 --ncol 99 --binSize 20 \u0026gt; results.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to correct only the reference positions of regions which were assigned to a given cluster - let\u0027s say cluster 2 - then you can run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_correct_sga.R --sga references.sga --partition results.txt --shift 7 --ncol 99 --binSize 20 --cluster 2 \u0026gt; references_c2_aligned.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that references.sga is inside /SPar-K in the image but results.sga is on the host):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_correct_sga.R --sga /SPar-K/references.sga --partition results.txt --shift 7 --ncol 99 --binSize 20 --cluster 2 \u0026gt; results.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that references.sga is inside the image but results.sga is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark_correct_sga.R --sga references.sga --partition /mount/results.txt --shift 7 --ncol 99 --binSize 20 --cluster 2 \u0026gt; results.sga\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor help, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_correct_sga.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_correct_sga.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark_correct_sga.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espark_realign_data.R\u003c/h3\u003e\u003ca id=\"user-content-spark_realign_datar\" class=\"anchor\" aria-label=\"Permalink: spark_realign_data.R\" href=\"#spark_realign_datar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis program is an R script. It is able to realign the data matrix given a SPar-K partition. That is, the orignal data are shifted and flipped as SPar-K did during the partitioning process. The row order between the input and the output is preservered. However, the row content will be modified as only one sub-part of each original row is present in each output row. Additionally, the sub-part may be flipped (if it was flipped by SPar-K). This script can be useful to realign the data in order to do a figure.\u003c/p\u003e\n\u003cp\u003eLet\u0027s use the previous partitioning example (ad nauseam). You have partitioned a dataset containing 23360 rows of length 99 with a shifting freedom of 7 and without flipping, the results are stored in results.txt. If you are interested in accessing the realigned data (to plot a heatmap for instance), you can get it by invoking:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_realign_data.R --data data.txt --partition results.txt --shift 7 \u0026gt; data_aligned.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image (note that data.txt is inside /SPar-K in the image but data_aligned.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_realign_data.R --data /SPar-K/data.txt --partition results.txt --shift 7 \u0026gt; data_aligned.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image (note that data.txt is inside the image but data_aligned.txt is on the host) :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i -v \u0026lt;current dir\u0026gt;:/mount spar-k spark_realign_data.R --data data.txt --partition /mount/results.txt --shift 7 \u0026gt; data_aligned.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor help, run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript bin/spark_realign_data.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Singularity image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec spar-k.simg spark_realign_data.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the Docker image :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i spar-k spark_realign_data.R --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eRomain Groux\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the GNU General Public License v3 - see the \u003ca href=\"LICENSE.md\"\u003eLICENSE.md\u003c/a\u003e file for details\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePhilipp Bucher\u003c/li\u003e\n\u003cli\u003eRen\u00e9 Dreos\u003c/li\u003e\n\u003cli\u003eGiovanna Ambosini\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1629278126.0 + "updated_at": 1685585499.0 }, { "data_format": 2, - "description": null, + "description": "Modular application recipes for the Scientific Filesystem (SCIF)", "filenames": [ - "planners/fd-19.06/Singularity-lmcut", - "planners/fd-19.06/Singularity-bjolp", - "planners/fd-19.06/Singularity-fdss1-mas2-60s", - "planners/fd-19.06/Singularity-gbfs-ff", - "planners/fd-19.06/Singularity-lama-first", - "planners/fd-19.06/Singularity-blind", - "planners/fd-19.06/Singularity-fdss1-mas1-60s", - "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_POLY_AGL", - "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_AGL", - "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_POLY_SAT", - "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_SAT", - "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_DFS_AGL", - "planners/ipc2018-agl-lapkt-poly-bfws/Singularity", - "planners/ipc2018-agl-lapkt-poly-bfws/Singularity_CBO", - "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_POLY_AGL", - "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_AGL", - "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_POLY_SAT", - "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_SAT", - "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_DFS_AGL", - "planners/ipc2018-agl-lapkt-dual-bfws/Singularity", - "planners/ipc2018-agl-lapkt-dual-bfws/Singularity_CBO", - "planners/ipc2018-opt-decstar/Singularity-fork", - "planners/ipc2018-opt-decstar/Singularity-star", - "planners/ipc2018-opt-decstar/Singularity", - "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_POLY_AGL", - "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_AGL", - "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_f5_SAT", - "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_POLY_SAT", - "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_f5_AGL", - "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_SAT", - "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_DFS_AGL", - "planners/ipc2018-agl-lapkt-bfws-pref/Singularity", - "planners/ipc2018-agl-lapkt-bfws-pref/Singularity_CBO", - "planners/ipc2018-agl-saarplan/Singularity-fork", - "planners/ipc2018-agl-saarplan/Singularity-grey", - "planners/ipc2018-agl-saarplan/Singularity", - "planners/ipc2018-opt-scorpion/Singularity", - "planners/ipc2014-agl-mercury/Singularity", - "planners/ipc2018-opt-complementary2-3584mb/Singularity-180s", - "planners/ipc2018-opt-complementary2-3584mb/Singularity", - "planners/ipc2018-agl-fd-remix/Singularity", - "planners/ipc2018-opt-delfi1/Singularity-mas-miasm", - "planners/ipc2018-opt-delfi1/Singularity-mas-sccdfp-60s", - "planners/ipc2018-opt-delfi1/Singularity-celmcut", - "planners/ipc2018-opt-delfi1/Singularity-ipdb", - "planners/ipc2018-opt-delfi1/Singularity-ipdb-60s", - "planners/ipc2018-opt-delfi1/Singularity-mas-sccdfp", - "planners/ipc2018-opt-delfi1/Singularity-blind", - "planners/ipc2018-opt-delfi1/Singularity", - "planners/ipc2018-agl-olcff/Singularity", - "planners/ipc2018-agl-cerberus/Singularity", - "planners/ipc2014-agl-madagascar/Singularity-MpC", - "planners/ipc2018-agl-decstar/Singularity-fork", - "planners/ipc2018-agl-decstar/Singularity", - "planners/ipc2018-opt-scorpion-nodiv/Singularity", - "planners/ipc2014-agl-probe/Singularity", - "planners/ipc2014-agl-jasper/Singularity", - "planners/ipc2014-opt-symba/Singularity-02", - "planners/ipc2014-opt-symba/Singularity-01" + "_posts/tutorials/Singularity.foobar", + "_posts/tutorials/Singularity.cowsay" ], - "full_name": "AI-Planning/autoscale", + "full_name": "sci-f/apps", "latest_release": null, - "readme": "\u003cp\u003eThis bundle contains all scripts required to reproduce the IPC Autoscale\nbenchmarks as described in the ICAPS 2021 paper \"Automatic Instance\nGeneration for Classical Planning\" by \u00c1lvaro Torralba, Jendrik Seipp and\nSilvan Sievers.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"autoscale\" contains the main scripts for optimizing and\nselecting sequences.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"experiments\" contains the main bulk of the experiment\nscripts for all phases of the instance generation approach described in\nthe paper. See usage instructions below.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"pddl-generators\" contains all generators of IPC domains that\nwere used for this paper (and some additional ones).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"planners\" contains source code as well as Singularity recipe\nfiles for all planners used in the paper. It also contains two scripts\nfor building all the corresponding Singularity images.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"logfiles\" contains the logs of the \u003ccode\u003eselect-sequences.py\u003c/code\u003e\nscript for the results of the paper.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDirectory \"report\" contains some additional scripts and Jupyter\nnotebooks used to manually inspect the results of preliminary\nexperiments. They can show some interesting additional data, but they\nare not intended to be maintained.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-tested-versions\" class=\"anchor\" href=\"#tested-versions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested versions\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.7\u003c/li\u003e\n\u003cli\u003eSingularity 3.5\u003c/li\u003e\n\u003cli\u003eSMAC 0.12.3\u003c/li\u003e\n\u003cli\u003eCPLEX 12.10\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" href=\"#installation-instructions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation instructions\u003c/h1\u003e\n\u003cp\u003eWe recommend running Autoscale in a \u003ca href=\"https://conda.io\" rel=\"nofollow\"\u003eConda environment\u003c/a\u003e.\nThe following commands create a Conda environment with the necessary\nlibraries and install the required Python packages (including SMAC).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name autoscale python=3.7 gxx_linux-64 gcc_linux-64 swig\nconda activate autoscale\npip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity\" class=\"anchor\" href=\"#singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eOn Debian-based systems, probably the easiest way to install Singularity\nis to use the \u003ca href=\"https://packages.debian.org/singularity-container\" rel=\"nofollow\"\u003eSingularity Debian\npackage\u003c/a\u003e. If that\ndoesn\u0027t work, you might have to \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003ecompile Singularity from\nsource\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-build-planner-images\" class=\"anchor\" href=\"#build-planner-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild planner images:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eexport DOWNWARD_BENCHMARKS=\u0026lt;path/to/benchmarks/repo\u0026gt;\ncd planners\n./build-all-images.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-cplex\" class=\"anchor\" href=\"#cplex\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCPLEX\u003c/h2\u003e\n\u003cp\u003eInstall CPLEX 12.10 and add the \u003ccode\u003epython/3.7/x86-64_linux/\u003c/code\u003e subfolder to\nthe \u003ccode\u003ePYTHONPATH\u003c/code\u003e environment variable. By default, CPLEX is installed in\n\u003ccode\u003e/opt/ibm/ILOG/CPLEX_Studio1210\u003c/code\u003e, so one has to add\n\u003ccode\u003e/opt/ibm/ILOG/CPLEX_Studio1210/cplex/python/3.7/x86-64_linux/\u003c/code\u003e to the\n\u003ccode\u003ePYTHONPATH\u003c/code\u003e.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eBefore running the optimization, the following steps are needed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEdit \u003ccode\u003edomains.py\u003c/code\u003e to add the description of any new domain that you want to configure.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEdit \u003ccode\u003eplanners.py\u003c/code\u003e to add the state-of-the-art planners that are relevant for\neach domain. The script \u003ccode\u003eselect-best-planner-per-domain.py\u003c/code\u003e can help to automatically\nselect a set of planners per domain from the results of earlier experiments.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen, the following two steps must be performed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eRun \u003ccode\u003eoptimize-sequences.py\u003c/code\u003e to perform the SMAC optimization and generate sequences.\nThe \u003ccode\u003e*smac*.py\u003c/code\u003e scripts show how to parallelize this step on a computer cluster with Lab.\u003c/p\u003e\n\u003cp\u003eNote that the \u003ccode\u003e--database\u003c/code\u003e option allows to provide the JSON file from a previous SMAC\nrun to re-use all precomputed times for previously tested parameter configurations\n(see \u003ccode\u003e2020-12-08-A-smac-opt-2014-20h.py\u003c/code\u003e for an example).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGather all results from one or multiple SMAC runs into a single properties JSON file.\nThis is done by running the last step of the \u003ccode\u003e*smac*.py\u003c/code\u003e scripts.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun \u003ccode\u003eselect-sequences.py\u003c/code\u003e to generate a benchmark set. Use the \u003ccode\u003e--database\u003c/code\u003e option to provide\nthe properties file obtained from step 2.\n\u003ccode\u003egenerate-benchmarks-2020-12-10.sh\u003c/code\u003e contains an example on how to run the script.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-experiment-data\" class=\"anchor\" href=\"#experiment-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExperiment data\u003c/h1\u003e\n\u003cp\u003eThe optimization/results folder contains JSON files with the results of the experiments.\nThe results table in the paper is generated via the scripts \u003ccode\u003ereport-gather-dataset.py\u003c/code\u003e and\n\u003ccode\u003ereport-create-table.py\u003c/code\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScientific Filesystem (SCI-F) Apps\u003c/h1\u003e\u003ca id=\"user-content-scientific-filesystem-sci-f-apps\" class=\"anchor\" aria-label=\"Permalink: Scientific Filesystem (SCI-F) Apps\" href=\"#scientific-filesystem-sci-f-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHi there! This is the base for SCI-F apps. We just finished developing the nuts\nand bolts, and will have instructions for contributing soon.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/sci-f/apps\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34851f61ff262e6b32a064b8d7fb55ddb63a4af474c4c9d22b08313bf34af75d/68747470733a2f2f636972636c6563692e636f6d2f67682f7363692d662f617070732e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/sci-f/apps.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"assets/img/app/robots/robot18.png\"\u003e\u003cimg src=\"assets/img/app/robots/robot18.png\" alt=\"robot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContribute an App\u003c/h2\u003e\u003ca id=\"user-content-contribute-an-app\" class=\"anchor\" aria-label=\"Permalink: Contribute an App\" href=\"#contribute-an-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e1. Prepare your Fork\u003c/strong\u003e\nFirst, fork the repo, and clone it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:\u0026lt;username\u0026gt;/apps.git\ncd apps\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTake a look at the folder \u003ccode\u003e_apps\u003c/code\u003e. This is a directory of markdown files, where each directory (and level) corresponds with a category, and each file is associated with one app. Basically, all you need to do is contribute a markdown file! Let\u0027s say we have a workflow app, and we want to add it to a new category, \"flow.\" First let\u0027s make the folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# $PWD is apps\nmkdir _apps/workflow\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e2. Name your App\u003c/strong\u003e\nLet\u0027s now copy the template there to work with. The name of the file will correspond with your app name. If put inside a folder, the folder must also be represented in the file name. Remember that the name is important - it will be the name of the markdown file (without the \u003ccode\u003e.md\u003c/code\u003e extension). For example, to name my app \u003ccode\u003eworkflow-internal-serial\u003c/code\u003e under the folder \u003ccode\u003eworkflow\u003c/code\u003e I would do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp _templates/appname-template.md _apps/workflow/workflow-internal-serial.md\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere I am anticipating that \"workflow\" is likely to be a general category that others might want to make apps for, so I\u0027m creating it\u0027s own folder. Also remember that the app namespace must be unique, and so names should be very specific. I wouldn\u0027t want to give a general name like \u003ccode\u003eworkflow-run.md\u003c/code\u003e because it is too general. There are likely going to be many workflows. So given a file name, the corresponding app name maps like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e_apps/workflow/workflow-internal-serial.md --\u0026gt; workflow-internal-serial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e3. Customize the File\u003c/strong\u003e\nNext, you should edit the file that you just copied with your app. Let\u0027s take a look:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ---\n title: \"App Name\"\n date: YYYY-MM-DD HH:MM:SS\n author: Vanessa Sochat\n tags: \n - scif\n - singularity\n files:\n - app-file.sh\n - SingularityApp.appname\n ---\n\n ```yaml\n %apprun appname\n exec $SINGULARITY_APPROOT/app-file.sh\n %appfiles appname\n app-file.sh\n ```\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNotice that we have two sections - the top header has metadata, and the bottom is whatever sections you would include in a Singularity Recipe file. You can easily copy paste your container code at the bottom in the \u003ccode\u003eyaml\u003c/code\u003e section, and the only change you might need to make is renaming the app to the one that corresponds with the folder, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e%apprun appname --\u0026gt; %apprun workflow-internal-serial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow let\u0027s look at the metadata in the header:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003etitle\u003c/strong\u003e is a human readable title. Make sure it remains in quotes\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003edate\u003c/strong\u003e should correspond to the date that you created or are adding the app.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eauthor\u003c/strong\u003e is your alias\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003etags\u003c/strong\u003e are important - they help to make your app searchable. This should be a yaml list of single terms\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efiles\u003c/strong\u003e are not required, but if you have them, you should create a folder named equivalently to your app (eg, \u003ccode\u003eworkflow-internal-serial\u003c/code\u003e in the same folder as the markdown file, and add the files here. They will be provided if someone downloads your app.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, if you want to provide an isolated recipe for your app (perhaps as a suggested use case) you can add the recipe to a folder named corresponding to your app. Following the current example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir _apps/workflow/workflow-internal-serial\ntouch _apps/workflow/workflow-internal-serial/SingularityApp.workflow-internal-serial\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e4. Preview and Submit\u003c/strong\u003e\nYou can preview locally with \u003ccode\u003ebundle exec jekyll serve\u003c/code\u003e. You can also test locally with \u003ccode\u003epython -m unittest tests.test_recipes\u003c/code\u003e. You should then commit changes to your branch, push to Github, and submit a pull request (PR) to the main branch. The same tests will be run, and when the PR is merged, will be live on the site.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it! The robots appreciate your contribution!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelpful Jekyll Tips\u003c/h2\u003e\u003ca id=\"user-content-helpful-jekyll-tips\" class=\"anchor\" aria-label=\"Permalink: Helpful Jekyll Tips\" href=\"#helpful-jekyll-tips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tag to distinguish except from post is \u003ccode\u003e\u0026lt;!--more--\u0026gt;\u003c/code\u003e. If you want to define\na custom one in a post:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexcerpt_separator: \u0026lt;!--readmore--\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 3, - "subscribers_count": 5, - "topics": [], - "updated_at": 1629389344.0 + "subscribers_count": 4, + "topics": [ + "singularity", + "singularity-containers", + "singularity-container", + "open-science", + "scientific-containers", + "apps", + "sci-f" + ], + "updated_at": 1709665292.0 }, { "data_format": 2, - "description": "Work with PLINK from R", + "description": "Open source simulation engine for coarse-grained Brownian dynamics", "filenames": [ "Singularity" ], - "full_name": "richelbilderbeek/plinkr", - "latest_release": "v0.20.2", - "readme": "\n\u003ch1\u003e\n\u003ca id=\"user-content-plinkr\" class=\"anchor\" href=\"#plinkr\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eplinkr\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/plinkr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=master\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/plinkr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8fafd8823f437cd6a912937658b53c50edd357b324f8d239d71a476d11c8859c/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f706c696e6b722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/plinkr/coverage.svg?branch=master\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=develop\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/plinkr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d136bb6a044d0bd05e2f4f06a5a96494925547304deabd0674fbf0c9c1dd929c/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f706c696e6b722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/plinkr/coverage.svg?branch=develop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWork with PLINK and PLINK2 from R.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDoing the first PLINK example:\n\u003ca href=\"https://youtu.be/LsfKQw2oIUg\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_basic_usage.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with one or more quantitative traits:\n\u003ca href=\"https://youtu.be/IicNdc8sDfI\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_assoc_qt.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with ideal quantitative traits:\n\u003ca href=\"https://youtu.be/oXGy83WiHm4\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_demo_qt_assoc.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulate quantitative traits:\n\u003ca href=\"https://youtu.be/H0XlLVsFry4\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_create_demo_assoc_qt_params.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulate custom traits: \u003ca href=\"https://youtu.be/5X1kLkiQbtw\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e\n\u003ca href=\"http://richelbilderbeek.nl/plinkr_create_custom_trait.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with a binary trait/case-control phenotype:\n\u003ca href=\"https://youtu.be/LhXQcDQvZS0\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_assoc.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"doc/install.md\"\u003edoc/install.md\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-examples\" class=\"anchor\" href=\"#examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExamples\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running-plink\" class=\"anchor\" href=\"#running-plink\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning PLINK\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can seamlessly run any \u003ccode\u003ePLINK\u003c/code\u003e or \u003ccode\u003ePLINK2\u003c/code\u003e versions.\u003c/p\u003e\n\u003cp\u003eRun PLINK:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003eplinkr\u003c/span\u003e)\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo call a specific version of PLINK:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003erun_plink(c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--noweb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), create_plink_v1_7_options())\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, create_plink_v1_9_options())\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, create_plink_v2_0_options())\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOf course, you can also call PLINK to detect genetic associations :-) :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the PLINK v1.9 example files\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_plink_v1_9_options()\n\u003cspan class=\"pl-smi\"\u003eped_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_plink_example_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etoy.ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003emap_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_plink_example_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etoy.map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Do a case-control association\u003c/span\u003e\n\u003cspan class=\"pl-e\"\u003eplinkr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003erun_plink(\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eped_filename\u003c/span\u003e, \n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003emap_filename\u003c/span\u003e\n )\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003ebasic_usage\u003c/code\u003e for basic usage of PLINK, as taken\nfrom the PLINK website, which shows a quantitative trait analysis\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003etest_assoc_qt\u003c/code\u003e for the same basic usage of PLINK,\nusing the \u003ccode\u003eplinkr\u003c/code\u003e interface\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003edemo_assoc_qt\u003c/code\u003e for doing a quantitative trait\nanalysis using simulated data and the \u003ccode\u003eplinkr\u003c/code\u003e interface\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-run-a-quantitative-trait-analysis-on-existing-files\" class=\"anchor\" href=\"#run-a-quantitative-trait-analysis-on-existing-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun a quantitative trait analysis on existing files\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-read-from-plink-text-files\" class=\"anchor\" href=\"#read-from-plink-text-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRead from PLINK text files\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_assoc_qt_data(\n \u003cspan class=\"pl-v\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_plink_text_filenames(\n \u003cspan class=\"pl-v\"\u003emap_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003eped_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n ),\n \u003cspan class=\"pl-v\"\u003ephenotype_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_phenotype_data_filename(\n \u003cspan class=\"pl-v\"\u003ephe_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.phe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \n )\n)\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e assoc_qt(\u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e)\nread_plink_qassoc_file(\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eqassoc_filenames\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-read-from-plink-binary-files\" class=\"anchor\" href=\"#read-from-plink-binary-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRead from PLINK binary files\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_assoc_qt_data(\n \u003cspan class=\"pl-v\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_plink_bin_filenames(\n \u003cspan class=\"pl-v\"\u003ebed_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.bed\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003ebim_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.bim\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003efam_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.fam\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n ),\n \u003cspan class=\"pl-v\"\u003ephenotype_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_phenotype_data_filename(\n \u003cspan class=\"pl-v\"\u003ephe_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.phe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \n )\n)\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e assoc_qt(\u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e)\nread_plink_qassoc_file(\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eqassoc_filenames\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-demonstrate-a-quantitative-trait-analysis\" class=\"anchor\" href=\"#demonstrate-a-quantitative-trait-analysis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemonstrate a quantitative trait analysis\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can seamlessly use \u003ccode\u003ePLINK\u003c/code\u003e/\u003ccode\u003ePLINK2\u003c/code\u003e in-memory-data or files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_demo_assoc_qt_data()\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prove that this is PLINK text data\u003c/span\u003e\ncheck_plink_text_data(\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_test_assoc_qt_params()\nassoc_qt(\n \u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo convert the in-memory data to PLINK binary format and do the same\nquantitative trait analysis:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e convert_plink_text_data_to_plink_bin_data(\n \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e\n)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prove that this is PLINK binary data\u003c/span\u003e\ncheck_plink_bin_data(\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e)\n\nassoc_qt(\n \u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003edemo_assoc_qt\u003c/code\u003e for a walk-through of the data that\nis simulated by default\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003ecreate_demo_assoc_qt_params\u003c/code\u003e for many examples how\ndata can be simulated\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-file-io\" class=\"anchor\" href=\"#file-io\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFile I/O\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can read and save many types of PLINK files. Below is an\noverview. List from \u003ca href=\"https://www.cog-genomics.org/plink2/formats\" rel=\"nofollow\"\u003ethe PLINK file format\nreference\u003c/a\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile extension\u003c/th\u003e\n\u003cth\u003e\n\u003ccode\u003eplink\u003c/code\u003e read function\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.adjusted\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_adjusted_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.allele.no.snp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_assoc_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.adjusted\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_assoc_adjusted_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.dosage\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.fisher\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.linear\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.logistic\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.auto.R\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.beagle.dat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bed_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bim\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bin_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.blocks*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.chr-*.dat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.chr-*.map\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.clst\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.clumped*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cluster*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cmh\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cmh2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv.indiv\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv.overlap\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv.summary\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cov\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_cov_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.dfam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.diff\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.dist\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.dupvar\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.eigenvec*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.epi.*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.fam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_fam_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.flipscan\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_frq_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq.cc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq.count\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq.strat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_frq_strat_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frqx\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.fst\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.gen\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.genome\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.grm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.grm.N.bin\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.grm.bin\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.gvar\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.het\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hh\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hom\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" 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fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qfam.*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.range.report\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.raw\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.*.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.phase.inp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.strct_in\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.ref\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.rel\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.rlist\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.sample\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set.{perm,mperm}\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set.table\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.sexcheck\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.simfreq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_simfreq_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tags.list\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tdt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tdt.poo\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tfam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tped\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.traw\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.twolocus\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.var.ranges\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-associations\" class=\"anchor\" href=\"#associations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAssociations\u003c/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eAssociation type\u003c/th\u003e\n\u003cth\u003eData type\u003c/th\u003e\n\u003cth\u003eGeneral function\u003c/th\u003e\n\u003cth\u003eSpecialized function\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink_text_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink2_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink_text_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink2_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-plink-and-plink2-files-conversions\" class=\"anchor\" href=\"#plink-and-plink2-files-conversions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePLINK and PLINK2 files conversions\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e allows to convert between any PLINK and PLINK2 files.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFrom\u003c/th\u003e\n\u003cth\u003eTo\u003c/th\u003e\n\u003cth\u003eFunction name\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_files_to_plink_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_files_to_plink2_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_files_to_plink_text_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_files_to_plink2_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink2_bin_files_to_plink_text_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink2_bin_files_to_plink_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink_text_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink2_bin_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003eSAIGE files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003ecreate_bgen_files_for_saige\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK2 VCF files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink_bin_files_to_plink_vcf_files\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-plink-and-plink2-data-conversions\" class=\"anchor\" href=\"#plink-and-plink2-data-conversions\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePLINK and PLINK2 data conversions\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e allows to convert between any PLINK and PLINK2 data.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFrom\u003c/th\u003e\n\u003cth\u003eTo\u003c/th\u003e\n\u003cth\u003eFunction name\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_data_to_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_data_to_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink_bin_data_to_plink_text_data\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_data_to_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink2_bin_data_to_plink_text_data\u003c/code\u003e \u003cg-emoji class=\"g-emoji\" alias=\"construction\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f6a7.png\"\u003e\ud83d\udea7\u003c/g-emoji\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink2_bin_data_to_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-singularity-container\" class=\"anchor\" href=\"#singularity-container\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity container\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/search;query=plinkr\" rel=\"nofollow\"\u003eFind the latest \u2018plinkr\u2019 Singularity\ncontainer\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-faq\" class=\"anchor\" href=\"#faq\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003cp\u003eSee \u003ca href=\"doc/faq.md\"\u003edoc/faq.md\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "jeffmm/simcore", + "latest_release": "v0.2.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esimcore\u003c/h1\u003e\u003ca id=\"user-content-simcore\" class=\"anchor\" aria-label=\"Permalink: simcore\" href=\"#simcore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA modular, object-oriented program for coarse-grained physics simulations, using something I call \u003cstrong\u003eSIM\u003c/strong\u003eple-\u003cstrong\u003eC\u003c/strong\u003eomposite \u003cstrong\u003eO\u003c/strong\u003ebject \u003cstrong\u003eRE\u003c/strong\u003epresentation.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/jeffmm/simcore\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/21abccb7cb5ed93e3ba6b6660f1a3111119568de91b786fb78ac5ff4c85db245/68747470733a2f2f7472617669732d63692e636f6d2f6a6566666d6d2f73696d636f72652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/jeffmm/simcore.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.2571982\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/92cfd2f446371b1d7c8546ed3eaaf23dda3379cbe546659dffafb8a08cbfe0d0/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e323537313938322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.2571982.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"figs/simcore_snapshot.png\"\u003e\u003cimg src=\"figs/simcore_snapshot.png\" alt=\"A simulation using simcore\" title=\"A simulation using simcore\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst clone the repo, including submodule dependencies.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/jeffmm/simcore\ncd simcore\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esimcore can either be run in a container using Docker or Singularity, or be built from source using CMake.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Docker\u003c/h3\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running with Docker\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA pre-built image of simcore is available as a \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image. To download the image, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull jeffmm/simcore\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo use the image, run the provided script to launch a Docker container named \u003ccode\u003esimcore_latest\u003c/code\u003e in the background\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./launch_docker.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou may also build the Docker image yourself by providing the launch script with the \u003ccode\u003e-b\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eTo launch simcore, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e simcore_latest simcore.exe [optional-flags] [parameter-file]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning with Singularity\u003c/h3\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using Singularity, simcore is also available as a Singularity image. The command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://jeffmm/simcore\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewill create a local file named \u003ccode\u003esimcore_latest.sif\u003c/code\u003e. You may then run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e simcore_latest.sif simcore.exe [optional-flags] [parameter-file]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding from source\u003c/h3\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-label=\"Permalink: Building from source\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore is ready to be built from source using CMake, provided several dependencies are installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCMake (version 3.13+)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jbeder/yaml-cpp\"\u003elibyaml-cpp\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003elibgsl-dev\u003c/li\u003e\n\u003cli\u003elibopenmpi-dev\u003c/li\u003e\n\u003cli\u003elibfftw3-dev\u003c/li\u003e\n\u003cli\u003elibboost-math1.67-dev\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIncluded is a script for building simcore with CMake. To build simcore (without graphics or parallelization) run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./install.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere are additional flags for building with OpenMP, building with graphics, installing simcore in \u003ccode\u003e/usr/local\u003c/code\u003e, etc. To see a menu of options, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./install.sh -h\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding with graphics\u003c/h3\u003e\u003ca id=\"user-content-building-with-graphics\" class=\"anchor\" aria-label=\"Permalink: Building with graphics\" href=\"#building-with-graphics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore is available with graphics for Mac OSX. To install on Mac OSX, you will need the glew and glfw3 libraries, both of which can be installed using \u003ca href=\"https://brew.sh/\" rel=\"nofollow\"\u003eHomebrew\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew install glew\nbrew install glfw\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou may also need to help CMake find your OpenGL Framework libraries.\u003c/p\u003e\n\u003cp\u003eSeveral other libraries are required for running simcore with graphics on Linux or in WSL. See the \u003ccode\u003esrc/CMakeLists.txt\u003c/code\u003e file for a comprehensive list of libraries passed to the compiler when building simcore with graphics on WSL.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning simcore\u003c/h2\u003e\u003ca id=\"user-content-running-simcore\" class=\"anchor\" aria-label=\"Permalink: Running simcore\" href=\"#running-simcore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe simcore executable is run as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esimcore.exe [optional-flags] [parameter-file] \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following flags are available:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--help, -h\n Show the help menu which gives short descriptions about each of the flags\n as well as binary usage\n \n --run-name rname, -r rname \n Overwrites the parameter \"run_name\" with rname which serves as a prefix for\n all outputs \n\n--n-runs num, -n num\n Overwrites the parameter \"n_runs\" with num, which tells the simulation how\n many times to run the given parameter set with different random number\n generator seeds.\n\n--movie, -m\n Uses the parameters file params_file to load any output files that were\n generated from previous runs of the simulation to replay the graphics and\n record the frames as bitmaps into the directory specified with the\n \"movie_directory\" parameter\n\n--analysis, -a\n Loads posit/spec files into the simulation for analysis in the same manner\n as the movie flag\n\n-reduce reduce_factor, -R reduce_factor\n Reads in output files and writes new output files that are smaller by a\n factor of reduce_factor, effectively reducing time resolution of output\n data.\n\n--load, -l\n Specifies to load any checkpoint files corresponding to the given parameter\n file, which can be used to continue a simulation that ended prematurely.\n New simulation will be given the name old_simulation_name_reload00n where n\n is the number of reloads performed on that simulation.\n\n--with-reloads, -w\n If running analyses or making movies, simcore will look for parameter files\n that have the same run name but with the reload00n addendum and attempt to\n open the corresponding output files whenever it reached EOF while reading\n an output file.\n\n--blank, -b\n Generates all relevant parameter files using the SimulationManager without\n running the simulations. Useful for generating many parameter files from\n parameter sets (discussed below) and deploying simulations on different\n processors and/or machines.\n\n--auto-graph, -G\n By default, simcore will wait for the user to press the ESC key in the\n OpenGL graphics window before starting to run the simulation. Providing\n this flag will cause the simulation to begin immediately without user\n input. Goes great with the -m flag for creating multiple movies without\n input from the user.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameter files\u003c/h2\u003e\u003ca id=\"user-content-parameter-files\" class=\"anchor\" aria-label=\"Permalink: Parameter files\" href=\"#parameter-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll parameters used in the simulation, along with their default values and data types, are specified in the \u003ccode\u003edefault_config.yaml\u003c/code\u003e file in the \u003ccode\u003econfig\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003eThe parameter file is a YAML file and looks like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003eglobal_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egp1_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eglobal_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egp2_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003especies\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eglobal_species_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egsp1_value\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eglobal_species_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003egsp2_value\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003especific_species_name\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003especies_param_1\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003esp1_value\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003especies_param_2\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003esp2_value\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ccode\u003eexamples\u003c/code\u003e folder for examples of parameter files.\u003c/p\u003e\n\u003cp\u003eNotice that there are three parameter types: global parameters, global species parameters, and species parameters. Global parameters are parameters that are common to the entire system, such system size, integration time step, etc. Species parameters are unique to the specified species, such as \u003ccode\u003efilament\u003c/code\u003e. There is also an optional global species parameter type that affects every species, such as the frequency to write to output files.\u003c/p\u003e\n\u003cp\u003eWhat do I mean by species? simcore assumes that any given simulation will likely have many copies of one kind of thing, which I call a species, perhaps interacting with other species of other kinds. In a system of interacting spheres, the species is \u0027sphere.\u0027 In a system of interacting semiflexible filaments, the species is \u0027filament.\u0027 Simulations can have many types of species all interacting with each other with different species-species interaction potentials.\u003c/p\u003e\n\u003cp\u003eIf any parameter is not specified in the parameter file, any instance of that parameter in the simulation will assume its default value specified in the \u003ccode\u003econfig/default_config.yaml\u003c/code\u003e file.\u003c/p\u003e\n\u003cp\u003eSome important global parameters are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed\n simulation seed to use with random number generator \nrun_name\n prefix for all output files\nn_runs\n number of individual runs of each parameter type\nn_random\n number of samples from a random parameter space (see more below)\nn_dim\n number of dimensions of simulation\nn_periodic\n number of periodic dimensions of simulation\ndelta \n simulation time step\nn_steps\n total number of steps in each simulation\nsystem_radius\n \"box radius\" of system\ngraph_flag\n run with graphics enabled\nn_graph\n how many simulation steps to take between updating graphics\nmovie_flag\n whether to record the graphics frames into bitmaps\nmovie_directory\n local directory used to save the recorded bitmaps\nthermo_flag\n whether to output thermodynamics outputs (stress tensors, etc)\nn_thermo\n how often to output the thermodynamics outputs\npotential_type\n can be \u0027wca\u0027 or \u0027soft\u0027 for now\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSome important global species parameters are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enum\n how many to insert into system\ninsertion_type\n how to insert object into system (e.g. random)\noverlap\n whether species can overlap at initiation\ndraw_type\n (orientation, fixed, or bw) how to color the object\ncolor\n a double that specifies the RGB value of the object\nposit_flag\n whether to output position files\nn_posit\n how often to output position files\nspec_flag\n whether to output species files\nn_spec\n how often to output species files\ncheckpoint_flag\n whether to output checkpoint files\nn_checkpoint\n how often to output checkpoint files\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced usage\u003c/h2\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-label=\"Permalink: Advanced usage\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning unit tests\u003c/h3\u003e\u003ca id=\"user-content-running-unit-tests\" class=\"anchor\" aria-label=\"Permalink: Running unit tests\" href=\"#running-unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne may run simcore\u0027s unit tests by passing \u003ccode\u003e-DTESTS=TRUE\u003c/code\u003e to CMake\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir build\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\ncmake -DTESTS=TRUE ..\nmake\nmake \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdding new parameters\u003c/h3\u003e\u003ca id=\"user-content-adding-new-parameters\" class=\"anchor\" aria-label=\"Permalink: Adding new parameters\" href=\"#adding-new-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore comes with it\u0027s own parameter initialization tool, \u003ccode\u003econfigure_simcore.exe\u003c/code\u003e, which is installed automatically along with the simcore binary using CMake. The configurator makes it easy to add new parameters to the simulation without mucking around in the source code. Just add your new parameter to \u003ccode\u003econfig/default_config.yaml\u003c/code\u003e file using the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enew_parameter_name: [default_parameter_value, parameter_type] \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the configurator using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./configure_simcore.exe config/default_config.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning simcore_config will look at all the parameters in the default config file and add them seamlessly to the proper simcore headers, and you can begin using them after recompiling simcore using CMake.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eParameter sets\u003c/h3\u003e\u003ca id=\"user-content-parameter-sets\" class=\"anchor\" aria-label=\"Permalink: Parameter sets\" href=\"#parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing parameter sets, it becomes easier to run many simulations over a given parameter space. There are two types of parameter sets possible with simcore: defined and random. Each parameter set type works the same with both global parameters and species parameters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDefined parameter sets\u003c/h4\u003e\u003ca id=\"user-content-defined-parameter-sets\" class=\"anchor\" aria-label=\"Permalink: Defined parameter sets\" href=\"#defined-parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDefined parameter sets are specified by the \u003ccode\u003eV\u003c/code\u003e prefix in the parameter file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: 4916819461895\nrun_name: defined_set\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: [V, param_value2, param_value3]\nparameter_name3: [V, param_value4, param_value5]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eParameters specified in this way (as lists of parameters) will be iterated over until every possible combination of parameters has been run. In this example, simcore will run N simulations each of the following 4 parameter sets:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: random_seed_1\nrun_name: defined_set_v000\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value2\nparameter_name3: param_value4\n\nseed: random_seed_2\nrun_name: defined_set_v001\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value2\nparameter_name3: param_value5\n\nseed: random_seed_3\nrun_name: defined_set_v002\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value3\nparameter_name3: param_value4\n\nseed: random_seed_4\nrun_name: defined_set_v003\nn_runs: N\nparameter_name1: param_value1\nparameter_name2: param_value3\nparameter_name3: param_value5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRandom parameter sets\u003c/h4\u003e\u003ca id=\"user-content-random-parameter-sets\" class=\"anchor\" aria-label=\"Permalink: Random parameter sets\" href=\"#random-parameter-sets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRandom parameter sets are designed specifically to be used with polynomial-chaos theory for n-dimensional parameter spaces for large n. Random sets are used in the following way:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eseed: 2546954828254\nn_runs: N\nn_random: M\nparameter_name1: param_value1\nparameter_name2: [R, A, B] # sets to random real in range (A,B)\nparameter_name3: [RINT, C, D] # sets to random int in range [C,D]\nparameter_name4: [RLOG, F, G] # sets to 10^K for rand real K in range (F,G)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGiven this parameter file, simcore will run N simulations each of M random parameter sets. The random parameter sets are generated in ranges specified in the lists that are prefixed by the R, RINT, RLOG options.\u003c/p\u003e\n\u003cp\u003eIn this example, the sampled parameter space has dimensionality of n=3, since there are only three parameters we are sampling over. Each parameter set will have a random real number for parameter_name2 in the the range (A,B), a random integer in the range [C,D] for parameter_name3, and will set parameter_name4 to 10^K for random real number K in the range (F,G). simcore will then run each parameter set N times each with a unique seed, and repeat this random process M times. It will therefore take N samples of M random points in the n-dimensional parameter space.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInteractions\u003c/h3\u003e\u003ca id=\"user-content-interactions\" class=\"anchor\" aria-label=\"Permalink: Interactions\" href=\"#interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe InteractionEngine in simcore was written with short-range interactions in mind. For this reason, interactions are treated by considering pair-wise interactions between neighboring interactor-elements that make up a composite object (e.g. small, rigid segments that compose a flexible filament). For this reason, interactions use cell lists to improve performance. Furthermore, simulating large objects in simcore requires representing the object as a composite of smaller, simple objects (thus, SIMple Composite Object REpresentation). An example of how a large object should be decomposed into simple objects is done in the Filament class.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePotentials\u003c/h3\u003e\u003ca id=\"user-content-potentials\" class=\"anchor\" aria-label=\"Permalink: Potentials\" href=\"#potentials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore is designed to be able to use interchangable potentials for various objects. However, potentials need to be added manually as a subclass of PotentialBase, included in PotentialManager, and a corresponding potential_type added to definitions.h for lookup purposes (see the InitPotentials method in PotentialManager.h for examples).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutputs\u003c/h3\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore has four output types. Three are species specific (posit, spec, checkpoint), and the fourth is the statistical information file (thermo). All files are written in binary.\u003c/p\u003e\n\u003cp\u003eThe posit file has the following header format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eint n_steps, int n_posit, double delta \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollowed by n_steps/n_posit lines of data with the format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edouble position[3]\ndouble scaled_position[3]\ndouble orientation[3]\ndouble diameter\ndouble length\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the scaled position is position mapped into the periodic coordinate space. The position itself gives the particle trajectory over time independent of periodicity.\u003c/p\u003e\n\u003cp\u003eThe spec file is a custom output file for each species, and can have the same information as the posit file or additional information if needed.\u003c/p\u003e\n\u003cp\u003eThe checkpoint file is almost a copy of the spec file, except it also contains the random number generator information and is overwritten every n_checkpoint steps in the simulation. It can therefore be used to resume a simulation that ended prematurely.\u003c/p\u003e\n\u003cp\u003eThe thermo file contains the following header information:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eint n_steps, int n_thermo, double delta, int n_dim\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efollowed by n_steps/n_thermo lines of data in the following format:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edouble unit_cell[9]\ndouble pressure_tensor[9]\ndouble pressure\ndouble volume\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere the pressure is the isometric pressure, and the pressure tensor is calculated from the time-averaged stress tensor.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eData analysis\u003c/h3\u003e\u003ca id=\"user-content-data-analysis\" class=\"anchor\" aria-label=\"Permalink: Data analysis\" href=\"#data-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf analysis operations of output files are already defined for your species, as is the case for the Filament species, analyzing outputs is a simple matter. First, make sure the desired analysis flag is set in the species parameters for that species.\u003c/p\u003e\n\u003cp\u003eFor example, in the Filament species there is a persistence length analysis that produces .mse2e files that tracks the mean-square end-to-end distance of semiflexible filaments. This is triggered by a parameter lp_analysis=1, which can be set in the parameter file.\u003c/p\u003e\n\u003cp\u003eAnaylses are run by running simcore in the following way:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./simcore -a parameter_file.yaml.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: It is important to keep in mind that the parameter_file should be identical to the parameter file used to generate the outputs. There are a few exceptions that only affect post-processing, such as analysis flags, but this is true in general.\u003c/p\u003e\n\u003cp\u003eThe way inputs and outputs are meant to work in simcore is such that during a simulation, output data are generated in the posit, spec, and checkpoint formats, and during analysis, the same output data are read back into the data structures in simcore for processing. The .posit files just contain bare-bones information that allow many types of simple analyses, but .spec files should in general contain all the necessary information to recreate the trajectory for a member of a species.\u003c/p\u003e\n\u003cp\u003eFor a new species analysis method, the analysis routines should be defined in the species container class, rather than the species member class, and called by the inherited RunAnalysis method of the SpeciesBase class (and likewise for analysis initialization and finalization, see examples for details).\u003c/p\u003e\n\u003cp\u003eFor example, the RunSpiralAnalysis routine is called by the RunAnalysis method in FilamentSpecies, which uses the Filament .spec file as an input to do the necessary analysis, whose results are placed into a new file ending in filament.spiral. See Filament and FilamentSpecies for examples of how analyses can be initialized, processed, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirectory structure\u003c/h2\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe directory structure is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esimcore\n\u251c\u2500\u2500 include\n\u2502 \u2514\u2500\u2500 simcore\n\u2502 \u2514\u2500\u2500 (header files)\n\u251c\u2500\u2500 src\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u251c\u2500\u2500 executable\n\u2502 \u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2502 \u2514\u2500\u2500 simcore_main.cpp\n\u2502 \u251c\u2500\u2500 configurator\n\u2502 \u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2502 \u2514\u2500\u2500 configurator.cpp\n\u2502 \u2514\u2500\u2500 (source files)\n\u251c\u2500\u2500 config\n\u2502 \u2514\u2500\u2500 default_config.yaml\n\u251c\u2500\u2500 analysis\n\u2502 \u2514\u2500\u2500 (Python analysis files)\n\u251c\u2500\u2500 scripts\n\u2502 \u2514\u2500\u2500 (utility files)\n\u251c\u2500\u2500 examples\n\u2502 \u2514\u2500\u2500 (parameter file examples)\n\u251c\u2500\u2500 docker\n\u2502 \u2514\u2500\u2500 Dockerfile\n\u251c\u2500\u2500 extern\n\u2502 \u2514\u2500\u2500 KMC\n\u251c\u2500\u2500 tests\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u251c\u2500\u2500 catch2\n\u2502 \u2502 \u2514\u2500\u2500 catch.hpp\n\u2502 \u2514\u2500\u2500 (simcore unit tests)\n\u251c\u2500\u2500 docs\n\u2502 \u251c\u2500\u2500 CMakeLists.txt\n\u2502 \u2514\u2500\u2500 main.md\n\u251c\u2500\u2500 figs\n\u2502 \u2514\u2500\u2500 (example simulation figures)\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 CMakeLists.txt\n\u251c\u2500\u2500 install.sh\n\u251c\u2500\u2500 launch_docker.sh\n\u251c\u2500\u2500 .travis.yml\n\u2514\u2500\u2500 .gitignore\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout simcore\u003c/h2\u003e\u003ca id=\"user-content-about-simcore\" class=\"anchor\" aria-label=\"Permalink: About simcore\" href=\"#about-simcore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esimcore is written in C++ and designed for coarse-grained physics simulations with modularity and scalability in mind. All objects in the simulation are representable as a composite of what I call \"simple\" objects (points, spheres, rigid cylinders, and 2d polygon surfaces would all qualify). For short-range interactions, simcore uses cell and neighbor lists for improved performance and OpenMP for parallelization.\u003c/p\u003e\n\u003cp\u003eAlthough simcore is meant to be a generalized molecular/Brownian dynamics simulation engine, thanks to the narrow focus of my PhD research, it has up until now almost exclusively been used to model semiflexible filaments, and for that reason has come closer to resembling single-purpose software. It\u0027s still quite easy, for example, to use simcore for basic molecular dynamics simulations of interacting point-like particles. Modularity is still there in the basic design, so in the future I may add more object types, but as far as pre-written object types go, \u003cem\u003eit\u0027s all about the filaments\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003esimcore was written for my personal academic use and in its current state is not intended to be used by the general public. If you are insane and would like to run simcore for whatever reason, feel free contact me for help and if I have time I can try to offer assistance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software is licensed under the terms of the BSD-3 Clause license. See the \u003ccode\u003eLICENSE\u003c/code\u003e for more details.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, - "topics": [ - "gwas", - "plink", - "plink2", - "r", - "r-package" - ], - "updated_at": 1653067706.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1718976977.0 }, { "data_format": 2, - "description": "GRETA (Genetic inteRaction and EssenTiality mApper): An R package for mapping genetic interaction and essentiality networks", + "description": "Parallelization of Nextstrain builds and parameter testing using Nextflow", "filenames": [ - "Singularity/Singularity.GRETA.def" + "environments/Singularity" ], - "full_name": "ytakemon/GRETA", - "latest_release": "v0.5.0", - "readme": "\n\n\u003ch1\u003e\u003ca id=\"user-content-greta-genetic-interaction-and-essentiality-mapper\" class=\"anchor\" aria-hidden=\"true\" href=\"#greta-genetic-interaction-and-essentiality-mapper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGRETA: Genetic inteRaction and EssenTiality mApper\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/400c4e52df43f6a0ab8a89b74b1a78d1a64da56a7848b9110c9d2991bb7c3105/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://lifecycle.r-lib.org/articles/stages.html#stable\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d7aedfa6c0fd00737083172bffb7ae9b253b54fae707524fcb503a1ce9c48a66/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6966656379636c652d737461626c652d627269676874677265656e2e737667\" alt=\"Lifecycle: stable\" data-canonical-src=\"https://img.shields.io/badge/lifecycle-stable-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/374398121\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc696cee4b750b415f3666ead55ca691783e528199724ce6e40b14c67836ce80/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3337343339383132312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/374398121.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./GRETA_hex_logo-02.png\"\u003e\u003cimg src=\"./GRETA_hex_logo-02.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGRETA is an R package that leverages data generated by the \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eCancer\nDependency Map (DepMap) project\u003c/a\u003e to perform\nin-silico genetic knockout screens and map essentiality networks. A\nmanuscript describing workflow and usage is being prepared.\u003c/p\u003e\n\u003cp\u003eCurrent DepMap data used by default is version 20Q1, which was\ndownloaded through the DepMap data portal. The data was distributed and\nused under the terms and conditions of \u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC Attribution 4.0\nlicense\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainer\" class=\"anchor\" aria-hidden=\"true\" href=\"#maintainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainer\u003c/h2\u003e\n\u003cp\u003eThis repository is maintained by \u003ca href=\"https://github.com/ytakemon\"\u003eYuka\nTakemon\u003c/a\u003e, a PhD candidate in \u003ca href=\"https://www.bcgsc.ca/labs/marra-lab\" rel=\"nofollow\"\u003eDr.\u00a0Marco\nMarra\u003c/a\u003e\u2019s laboratory at \u003ca href=\"https://www.bcgsc.ca/\" rel=\"nofollow\"\u003eCanada\u2019s\nMichael Smith Genome Sciences Centre\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citations\" class=\"anchor\" aria-hidden=\"true\" href=\"#citations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitations\u003c/h2\u003e\n\u003cp\u003eA please find the citation using \u003ccode\u003ecitation(\"GRETA\")\u003c/code\u003e and include the DOI\nat the top of this page. A manuscript describing GRETA and its usage is\nnow available on \u003ca href=\"https://doi.org/10.1101/2022.09.21.508787\" rel=\"nofollow\"\u003eBioRxiv (Takemon, Y. and Marra, MA.,\n2020)\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions\u003c/h2\u003e\n\u003cp\u003ePlease check the \u003ca href=\"https://github.com/ytakemon/GRETA/wiki/Frequently-Asked-Questions\"\u003eFAQ\nsection\u003c/a\u003e\nfor additional information and if you cannot find your answer there or\nhave a request please submit an\n\u003ca href=\"https://github.com/ytakemon/GRETA/issues\"\u003eissue\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-package-installation-and-data-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#package-installation-and-data-download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackage installation and data download\u003c/h2\u003e\n\u003cp\u003eYou can install the GRETA package from \u003ca href=\"https://github.com\"\u003eGitHub\u003c/a\u003e\nwith:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edevtools\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eytakemon/GRETA\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDepMap 20Q1 data and the data documentation files are provided above and\ncan be extracted directly in terminal using the following bash code (not\nin R/RStudio). For other DepMap data versions please refer to the \u003ca href=\"https://github.com/ytakemon/GRETA/wiki/Frequently-Asked-Questions#q-how-to-download-and-use-other-versions-of-depmap-data\"\u003eFAQ\nsection\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make a new directory/folder called GRETA_project and go into directory\u003c/span\u003e\nmkdir GRETA_project\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e GRETA_project\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download data and data documentation from the web\u003c/span\u003e\nwget https://github.com/ytakemon/GRETA/raw/main/GRETA_DepMap_20Q1_data.tar.gz\nwget https://github.com/ytakemon/GRETA/raw/main/GRETA_DepMap_20Q1_data_document.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract data and data documentation\u003c/span\u003e\ntar -zxvf GRETA_DepMap_20Q1_data.tar.gz\ntar -zxvf GRETA_DepMap_20Q1_data_document.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA singularity container has also been provided and instructions can be\nfound\n\u003ca href=\"https://github.com/ytakemon/GRETA/wiki/Frequently-Asked-Questions#q-how-to-run-singularity\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-workflows\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflows\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-genetic-interaction-mapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#genetic-interaction-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenetic interaction mapping\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ccode\u003eGRETA\u003c/code\u003e and download accompanying data.\u003c/li\u003e\n\u003cli\u003eSelect mutant cell lines that carry mutations in the gene of\ninterest and control cell lines.\n\u003cul\u003e\n\u003cli\u003e(optional specifications) disease type, disease subtype, amino\nacid change.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDetermine differential expression between mutant and control cell\nline groups.\n\u003cul\u003e\n\u003cli\u003e(optional but recommended).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePerform \u003cem\u003ein silico\u003c/em\u003e genetic screen.\u003c/li\u003e\n\u003cli\u003eVisualize results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-co-essential-network-mapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#co-essential-network-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCo-essential network mapping\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ccode\u003eGRETA\u003c/code\u003e and download accompanying data.\u003c/li\u003e\n\u003cli\u003eRun correlation coefficient analysis.\u003c/li\u003e\n\u003cli\u003eCalculate inflection points of negative/positive curve to determine\na threshold.\u003c/li\u003e\n\u003cli\u003eApply threshold.\u003c/li\u003e\n\u003cli\u003eVisualize results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-identifying-arid1a-genetic-interactions\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-identifying-arid1a-genetic-interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Identifying \u003cem\u003eARID1A\u003c/em\u003e genetic interactions\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eARID1A\u003c/em\u003e encodes a member of the chromatin remodeling SWItch/Sucrose\nNon-Fermentable (SWI/SNF) complex and is a frequently mutated gene in\ncancer. It is known that \u003cem\u003eARID1A\u003c/em\u003e and its homolog, \u003cem\u003eARID1B\u003c/em\u003e, are\nsynthetic lethal to one another: The dual loss of ARID1A and its\nhomolog, ARID1B, in a cell is lethal; however, the loss of either gene\nalone is not (\u003ca href=\"https://doi.org/10.1038/nm.3480\" rel=\"nofollow\"\u003eHelming et al., 2014\u003c/a\u003e).\nThis example will demonstrate how we can identify synthetic lethal\ninteractors of \u003cem\u003eARID1A\u003c/em\u003e using \u003ccode\u003eGRETA\u003c/code\u003e and predict this known\ninteraction.\u003c/p\u003e\n\u003cp\u003eFor this example you will need to call the following libraries\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003etidyverse\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2500\u2500 Attaching packages \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse 1.3.2 \u2500\u2500\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 ggplot2 3.3.6 \u2714 purrr 0.3.4\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 tibble 3.1.8 \u2714 dplyr 1.0.9\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 tidyr 1.2.0 \u2714 stringr 1.4.0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 readr 2.1.2 \u2714 forcats 0.5.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2500\u2500 Conflicts \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse_conflicts() \u2500\u2500\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2716 dplyr::filter() masks stats::filter()\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2716 dplyr::lag() masks stats::lag()\u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003eGRETA\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, assign a variable that points to where the \u003ccode\u003e.rda\u003c/code\u003e files are\nstored.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/GRETA_project/data/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-exploring-cell-lines\" class=\"anchor\" aria-hidden=\"true\" href=\"#exploring-cell-lines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExploring cell lines\u003c/h3\u003e\n\u003cp\u003eOne way to explore cell lines that are available in DepMap is through\ntheir \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eportal\u003c/a\u003e. However, there are some\nsimple built-in methods in GRETA to provide users with a way to glimpse\nthe data using the series of \u003ccode\u003elist_available\u003c/code\u003e functions:\n\u003ccode\u003elist_available_mutations()\u003c/code\u003e, \u003ccode\u003elist_available_cancer_types()\u003c/code\u003e,\n\u003ccode\u003elist_available_cancer_subtypes()\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCurrent DepMap data used by default is version 20Q1, which contains\nwhole-genome sequencing or whole-exome sequencing annotations for \u003ccode\u003e1775\u003c/code\u003e\ncancer cell lines (\u003ccode\u003e1270\u003c/code\u003e cell lines with RNA-seq data, \u003ccode\u003e378\u003c/code\u003e cell lines\nwith quantitative proteomics data, and \u003ccode\u003e739\u003c/code\u003e cell lines with CRISPR-Cas9\nknockout screen data)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Find ARID1A hotspot mutations detected in all cell lines\u003c/span\u003e\nlist_available_mutations(\u003cspan class=\"pl-v\"\u003eGene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eIs_hotspot\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e) \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e List all available cancer types\u003c/span\u003e\nlist_available_cancer_types(\u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Ovarian Cancer\" \"Leukemia\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] \"Colon/Colorectal Cancer\" \"Skin Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] \"Lung Cancer\" \"Bladder Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] \"Kidney Cancer\" \"Breast Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] \"Pancreatic Cancer\" \"Myeloma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] \"Brain Cancer\" \"Sarcoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [13] \"Lymphoma\" \"Bone Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [15] \"Fibroblast\" \"Gastric Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [17] \"Engineered\" \"Thyroid Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [19] \"Neuroblastoma\" \"Prostate Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [21] \"Rhabdoid\" \"Gallbladder Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [23] \"Endometrial/Uterine Cancer\" \"Head and Neck Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [25] \"Bile Duct Cancer\" \"Esophageal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [27] \"Liver Cancer\" \"Cervical Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [29] \"Immortalized\" \"Unknown\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [31] \"Eye Cancer\" \"Adrenal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [33] \"Liposarcoma\" \"Embryonal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [35] \"Teratoma\" \"Non-Cancerous\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [37] NA\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e List all available cancer subtypes\u003c/span\u003e\nlist_available_cancer_subtypes(\u003cspan class=\"pl-v\"\u003einput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eLung Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Non-Small Cell Lung Cancer (NSCLC), Adenocarcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [2] \"Non-Small Cell Lung Cancer (NSCLC), Large Cell Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] \"Mesothelioma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [4] \"Small Cell Lung Cancer (SCLC)\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] \"Non-Small Cell Lung Cancer (NSCLC), unspecified\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [6] \"Non-Small Cell Lung Cancer (NSCLC), Squamous Cell Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] \"Non-Small Cell Lung Cancer (NSCLC), Adenosquamous Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [8] \"Carcinoid\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] \"Non-Small Cell Lung Cancer (NSCLC), Bronchoalveolar Carcinoma\"\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [10] \"Non-Small Cell Lung Cancer (NSCLC), Mucoepidermoid Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] \"Carcinoma\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-selecting-mutant-and-control-cell-line-groups\" class=\"anchor\" aria-hidden=\"true\" href=\"#selecting-mutant-and-control-cell-line-groups\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSelecting mutant and control cell line groups\u003c/h3\u003e\n\u003cp\u003eAs default \u003ccode\u003eselect_cell_lines()\u003c/code\u003e will identify cancer cell lines with\nloss-of-function alterations in the gene specified and group them into\nsix different groups.\u003c/p\u003e\n\u003cp\u003eLoss-of-function alterations include variants that are annotated as:\n\u003ccode\u003e\"Nonsense_Mutation\", \"Frame_Shift_Ins\", \"Splice_Site\", \"De_novo_Start_OutOfFrame\", \"Frame_Shift_Del\", \"Start_Codon_SNP\", \"Start_Codon_Del\",\u003c/code\u003e\nand \u003ccode\u003e\"Start_Codon_Ins\"\u003c/code\u003e. Copy number alterations are also taken into\nconsideration and group as \u003ccode\u003e\"Deep_del\", \"Loss\", \"Neutral\",\u003c/code\u003e or\n\u003ccode\u003e\"Amplified\"\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe cell line groups assigned by default are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eControl\u003c/code\u003e cell lines do not harbor any single nucleotide variations\n(SNVs) or insertions and deletions (InDels) with a neutral copy\nnumber (CN).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHomDel\u003c/code\u003e cell lines harbor one or more homozygous deleterious SNVs\nor have deep CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eT-HetDel\u003c/code\u003e cell lines harbor two or more heterozygous deleterious\nSNVs/InDels with neutral or CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHetDel\u003c/code\u003e cell lines harbor one heterozygous deleterious SNV/InDel\nwith neutral CN, or no SNV/InDel with CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAmplified\u003c/code\u003e cell lines harbor no SNVs/InDels with increased CN.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eOthers\u003c/code\u003e cell lines harbor deleterious SNVs with increased CN.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_cell_lines(\u003cspan class=\"pl-v\"\u003eInput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Selecting mutant groups for: ARID1A in all cancer cell lines\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Show number of cell lines in each group \u003c/span\u003e\ncount(\u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 6 \u00d7 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Group n\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 Amplified 24\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_HetDel 105\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_HomDel 13\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 ARID1A_T-HetDel 21\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 Control 529\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 Others 47\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional-filter-for-specific-cancer-types\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional-filter-for-specific-cancer-types\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional filter for specific cancer types\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Find pancreatic cancer cell lines with ARID1A mutations\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_pancr_groups\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_cell_lines(\u003cspan class=\"pl-v\"\u003eInput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eInput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePancreatic Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Selecting mutant groups for: ARID1A in Pancreatic Cancer, cell lines\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Show number of cell lines in each group \u003c/span\u003e\ncount(\u003cspan class=\"pl-smi\"\u003eARID1A_pancr_groups\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 5 \u00d7 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Group n\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_HetDel 7\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_HomDel 4\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_T-HetDel 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 Control 18\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 Others 1\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-check-for-differential-expression\" class=\"anchor\" aria-hidden=\"true\" href=\"#check-for-differential-expression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheck for differential expression\u003c/h3\u003e\n\u003cp\u003eOf the three mutant cancer cell line groups \u003ccode\u003eARID1A_HomDel\u003c/code\u003e,\n\u003ccode\u003eARID1A_T-HetDel\u003c/code\u003e, and \u003ccode\u003eARID1A_HetDel\u003c/code\u003e, cancer cell lines with\n\u003ccode\u003eARID1A_HomDel\u003c/code\u003e mutations are most likely to result in a loss or reduced\nexpression of \u003cem\u003eARID1A\u003c/em\u003e. Therefore, we want to check whether cell lines\nin \u003ccode\u003eARID1A_HomDel\u003c/code\u003e mutant group have significantly less \u003cem\u003eARID1A\u003c/em\u003e RNA or\nprotein expression compared to control cell lines.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Select only HomDel and Control cell lines\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A_HomDel\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Get RNA expression \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e extract_rna_expr(\n \u003cspan class=\"pl-v\"\u003eInput_samples\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eInput_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Following sample did not contain profile data: ACH-001151, ACH-001685, ACH-001956\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNot all cell lines contain RNA and/or protein expression profiles, and\nnot all proteins were detected by mass spectrometer. (Details on data\ngeneration can be found on the \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eDepMap\nsite\u003c/a\u003e.)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Get protein expression\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_protein\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e extract_protein_expr(\n \u003cspan class=\"pl-v\"\u003eInput_samples\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eInput_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Produces an error message since ARID1A protein data is not available\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing Welch\u2019s t-test, we can check to see whether \u003cem\u003eARID1A\u003c/em\u003e RNA\nexpression (in TPM) is significantly reduced in \u003ccode\u003eARID1A_HomDel\u003c/code\u003e cell\nlines compared to \u003ccode\u003eControls\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Append groups and test differential expression\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e left_join(\n \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e,\n \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% select(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e%\n mutate(\u003cspan class=\"pl-v\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e fct_relevel(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e show Control group first\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Joining, by = \"DepMap_ID\"\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e T-test \u003c/span\u003e\nt.test(\u003cspan class=\"pl-smi\"\u003eARID1A_8289\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Welch Two Sample t-test\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; data: ARID1A_8289 by Group\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; t = 5.4354, df = 12.591, p-value = 0.0001276\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; alternative hypothesis: true difference in means is not equal to 0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 95 percent confidence interval:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 0.7574242 1.7621880\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; sample estimates:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; mean in group Control mean in group ARID1A_HomDel \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4.816896 3.557090\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e plot \u003c/span\u003e\nggplot(\u003cspan class=\"pl-smi\"\u003eARID1A_HomDel_muts_and_ctrls_rna\u003c/span\u003e, aes(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_8289\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\n geom_boxplot()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-Check_expression_rna_stats-1.png\"\u003e\u003cimg src=\"man/figures/README-Check_expression_rna_stats-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-perform-in-silico-genetic-screen\" class=\"anchor\" aria-hidden=\"true\" href=\"#perform-in-silico-genetic-screen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePerform \u003cem\u003ein silico\u003c/em\u003e genetic screen\u003c/h3\u003e\n\u003cp\u003eAfter determining cell lines in the \u003ccode\u003eARID1A_HomDel\u003c/code\u003e group has\nstatistically significant reduction in RNA expression compared to\n\u003ccode\u003eControl\u003c/code\u003e cell lines, the next step is to perform a \u003cem\u003ein silico\u003c/em\u003e genetic\nscreen using \u003ccode\u003escreen_results()\u003c/code\u003e. This uses the dependency probabilities\n(or \u003cstrong\u003e\u201clethality probabilities\u201d\u003c/strong\u003e) generated from DepMap\u2019s genome-wide\nCRISPR-Cas9 knockout screen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLethality probabilities\u003c/strong\u003e range from 0.0 to 1.0 and is quantified for\neach gene knock out in every cancer cell line screened (There are 18,334\ngenes targeted in 739 cancer cell lines). A gene knock out with a\nlethality probability of 0.0 indicates a non-essential for the cell\nline, and a gene knock out with a 1.0 indicates an essential gene (ie.\nvery lethal). Details can be found in \u003ca href=\"https://doi.org/10.1038/ng.3984\" rel=\"nofollow\"\u003eMeyers, R., et al.,\n2017\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAt its core, \u003ccode\u003escreen_results()\u003c/code\u003e performs multiple Mann-Whitney U tests,\ncomparing lethality probabilities of each targeted gene between mutant\nand control groups. This generates a data frame with the following\ncolumns:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGeneName_ID\u003c/code\u003e - Hugo symbol with NCBI gene ID\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGeneNames\u003c/code\u003e - Hugo symbol\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e_median, _mean, _sd, _iqr\u003c/code\u003e - Control and mutant group\u2019s median,\nmean, standard deviation (sd), and interquartile range (iqr) of\ndependency probabilities. Dependency probabilities range from zero\nto one, where one indicates a essential gene (ie. KO of gene was\nlethal) and zero indicates a non-essential gene (KO of gene was not\nlethal)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePval\u003c/code\u003e - P-value from Mann Whitney U test between control and mutant\ngroups.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdj_pval\u003c/code\u003e - BH-adjusted P-value.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog2FC_by_median\u003c/code\u003e - Log2 normalized median fold change of\ndependency probabilities (mutant / control).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog2FC_by_mean\u003c/code\u003e - Log2 normalized mean fold change of dependency\nprobabilities (mutant / control).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCliffDelta\u003c/code\u003e - Cliff\u2019s delta non-parametric effect size between\nmutant and control dependency probabilities. Ranges between -1 to 1.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edip_pval\u003c/code\u003e - Hartigan\u2019s dip test p-value. Tests whether distribution\nof mutant dependency probability is unimodel. If dip test is\nrejected (p-value \u0026lt; 0.05), this indicates that there is a\nmultimodel dependency probability distribution and that there may be\nanother factor contributing to this separation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInteraction_score\u003c/code\u003e - Combined value generated from signed p-values:\n-log10(Pval) * sign(log2FC_by_median). Negative scores indicate\nlethal genetic interaction, and positive scores indicate alleviating\ngenetic interaction.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eARID1A_mutant_IDs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A_HomDel\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% pull(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eARID1A_control_IDs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% pull(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This can take several hours depending on number of lines/cores used. Best to run this overnight.\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e GI_screen(\n \u003cspan class=\"pl-v\"\u003econtrol_IDs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_control_IDs\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003emutant_IDs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_mutant_IDs\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e depends on how many cores you have \u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epath/to/results/folder/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Will save your results here as well as in the variable\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e use TRUE to run a short test to make sure all will run overnight.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can quickly determine whether any lethal genetic interactions were\npredicted by \u003ccode\u003eGRETA\u003c/code\u003e. We use a \u003ccode\u003ePval\u003c/code\u003e cut off of 0.05 and rank based on\nthe \u003ccode\u003eInteraction_score\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \n filter(\u003cspan class=\"pl-smi\"\u003ePval\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.05\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e%\n arrange(\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eInteraction_score\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \n select(\u003cspan class=\"pl-smi\"\u003eGeneNames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eMutant_median\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ePval\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eInteraction_score\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \u003cspan class=\"pl-smi\"\u003ehead\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 6 \u00d7 5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; GeneNames Control_median Mutant_median Pval Interaction_score\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1B 0.0364 0.590 0.0000000342 7.47\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 OR2M3 0.00912 0.0279 0.000255 3.59\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 C1QTNF5 0.0794 0.253 0.000334 3.48\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 LSM1 0.0273 0.112 0.000548 3.26\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 ONECUT1 0.00116 0.00451 0.00107 2.97\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 ANP32B 0.0160 0.0566 0.00119 2.92\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe immediately see that \u003cem\u003eARID1B\u003c/em\u003e, a known synthetic lethal interaction\nof \u003cem\u003eARID1A\u003c/em\u003e, was a the top of this list.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-visualize-screen-results\" class=\"anchor\" aria-hidden=\"true\" href=\"#visualize-screen-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisualize screen results\u003c/h3\u003e\n\u003cp\u003eFinally once the \u003cem\u003ein silico\u003c/em\u003e screen is complete, results can be quickly\nvisualized using \u003ccode\u003eplot_screen()\u003c/code\u003e. Positive genetic interaction scores\nindicate potential synthetic lethal genetic interactors, and negative\nscores indicate potential alleviating genetic interactors. As expected,\nwe identified \u003cem\u003eARID1B\u003c/em\u003e as a synthetic lethal interactor of \u003cem\u003eARID1A\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Visualize results, turn on gene labels, \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e and label three genes each that are predicted to have \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e lethal and alleviating genetic interactions, respectively\u003c/span\u003e\n\nplot_screen(\u003cspan class=\"pl-v\"\u003eresult_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003elabel_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003elabel_n\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-plot-1.png\"\u003e\u003cimg src=\"man/figures/README-plot-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-identifying-arid1a-co-essential-genes\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-identifying-arid1a-co-essential-genes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Identifying \u003cem\u003eARID1A\u003c/em\u003e co-essential genes\u003c/h2\u003e\n\u003cp\u003ePerturbing genes that function in same/synergistic pathways or in the\nsame complex are said to show similar fitness effects, and these that\nshow effects are considered to be \u201cco-essential\u201d. The strategy of\nmapping co-essential gene have been used by several studies to attribute\nfunctions to previously annotated genes as well as to identify a novel\nsubunit of a large complex (\u003ca href=\"https://doi.org/10.1038/s41588-021-00840-z\" rel=\"nofollow\"\u003eWainberg et\nal.\u00a02021\u003c/a\u003e; \u003ca href=\"https://doi.org/10.1016/j.cels.2018.04.011\" rel=\"nofollow\"\u003ePan et\nal.\u00a02018\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eGiven that ARID1A is known subunit of the mammalian SWI/SNF complex\n(\u003ca href=\"https://doi.org/10.1016/j.cell.2018.09.032\" rel=\"nofollow\"\u003eMashtalir et al.\u00a02018\u003c/a\u003e),\nwe expect that members of the SWI/SNF complex would share\nco-essentiality with \u003cem\u003eARID1A\u003c/em\u003e. This example will demonstrate how we can\nmap \u003cem\u003eARID1A\u003c/em\u003e\u2019s co-essential gene network using \u003ccode\u003eGRETA\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-identifying-genes-with-highest-correlation-coefficients\" class=\"anchor\" aria-hidden=\"true\" href=\"#identifying-genes-with-highest-correlation-coefficients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIdentifying genes with highest correlation coefficients\u003c/h2\u003e\n\u003cp\u003eTo determine co-essential genes, we will perform multiple Pearson\ncorrelation coefficient analyses between \u003cem\u003eARID1A\u003c/em\u003e KO effects and the KO\neffects of all 18,333 genes. A cut off will be determined by calculating\nthe inflection point of the ranked coefficient curve. As expected find\nSWI/SNF subunit encoding genes, \u003cem\u003eSMARCE1\u003c/em\u003e and \u003cem\u003eSMARCB1\u003c/em\u003e, as the top two\nco-essential genes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Map co-essential genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e coessential_map(\n \u003cspan class=\"pl-v\"\u003eInput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGRETA_data_dir\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epath/to/results/folder/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Calculate inflection points of positive and negative curve using co-essential gene results.\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_inflection_points(\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, we annotate the data frame containing the co-essential network\ndata and visualize.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Combine and annotate data frame containing co-essential genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e annotate_coessential_df(\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e)\n\nplot_coessential_genes(\n \u003cspan class=\"pl-v\"\u003eresult_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003einflection_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003elabel_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Should gene names be labeled?\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003elabel_n\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Number of genes to display from each end\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-combine_n_visualize-1.png\"\u003e\u003cimg src=\"man/figures/README-combine_n_visualize-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe also see that the top ten \u003cem\u003eARID1A\u003c/em\u003e co-essential genes include eight\nknown SWI/SNF subunits, namely \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003eSMARCE1\u003c/em\u003e, \u003cem\u003eSMARCB1\u003c/em\u003e,\n\u003cem\u003eSMARCC1\u003c/em\u003e, \u003cem\u003eDPF2\u003c/em\u003e, \u003cem\u003eSS18\u003c/em\u003e, \u003cem\u003eSMARCC2\u003c/em\u003e, and \u003cem\u003eSMARCD2\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Show top 10 co-essential genes. \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% arrange(\u003cspan class=\"pl-smi\"\u003eRank\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% head(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 10 \u00d7 13\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; GeneNameID_A GeneNa\u2026\u00b9 estim\u2026\u00b2 stati\u2026\u00b3 p.value param\u2026\u2074 conf.\u2026\u2075 conf.\u2026\u2076 method\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;int\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;chr\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_8289 ARID1A_\u2026 1 Inf 0 724 1 1 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_8289 SMARCE1\u2026 0.508 15.9 7.70e-49 724 0.452 0.560 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_8289 SMARCB1\u2026 0.488 15.0 1.07e-44 724 0.430 0.541 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 ARID1A_8289 SMARCC1\u2026 0.436 13.0 4.79e-35 724 0.375 0.493 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 ARID1A_8289 DPF2_59\u2026 0.395 11.6 1.62e-28 724 0.332 0.455 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 ARID1A_8289 SS18_67\u2026 0.300 8.47 1.32e-16 724 0.233 0.365 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 7 ARID1A_8289 SMARCC2\u2026 0.248 6.88 1.34e-11 724 0.178 0.315 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 8 ARID1A_8289 SMARCD2\u2026 0.227 6.27 6.16e-10 724 0.157 0.295 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 9 ARID1A_8289 IER5L_3\u2026 0.210 5.78 1.12e- 8 724 0.139 0.279 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 10 ARID1A_8289 PRDM15_\u2026 0.206 5.66 2.20e- 8 724 0.135 0.274 Pears\u2026\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # \u2026 with 4 more variables: alternative \u0026lt;chr\u0026gt;, Rank \u0026lt;int\u0026gt;, Padj_BH \u0026lt;dbl\u0026gt;,\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # Candidate_gene \u0026lt;lgl\u0026gt;, and abbreviated variable names \u00b9\u200bGeneNameID_B,\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # \u00b2\u200bestimate, \u00b3\u200bstatistic, \u2074\u200bparameter, \u2075\u200bconf.low, \u2076\u200bconf.high\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # \u2139 Use `colnames()` to see all variable names\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-session-info\" class=\"anchor\" aria-hidden=\"true\" href=\"#session-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSession Info\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003esessionInfo()\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; R version 4.0.2 (2020-06-22)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Platform: x86_64-centos7-linux-gnu (64-bit)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Running under: CentOS Linux 7 (Core)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Matrix products: default\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; BLAS: /gsc/software/linux-x86_64-centos7/R-4.0.2/lib64/R/lib/libRblas.so\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; LAPACK: /gsc/software/linux-x86_64-centos7/R-4.0.2/lib64/R/lib/libRlapack.so\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; locale:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] LC_PAPER=en_US.UTF-8 LC_NAME=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] LC_ADDRESS=C LC_TELEPHONE=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; attached base packages:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] stats graphics grDevices utils datasets methods base \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; other attached packages:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] GRETA_0.4.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] ggplot2_3.3.6 tidyverse_1.3.2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; loaded via a namespace (and not attached):\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] matrixStats_0.62.0 fs_1.5.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] doMC_1.3.8 lubridate_1.8.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] doParallel_1.0.17 httr_1.4.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] tools_4.0.2 backports_1.4.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] utf8_1.2.2 R6_2.5.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] nortest_1.0-4 DBI_1.1.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [13] colorspace_2.0-3 withr_2.5.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [15] tidyselect_1.1.2 Exact_3.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [17] compiler_4.0.2 rcompanion_2.4.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [19] cli_3.3.0 rvest_1.0.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [21] expm_0.999-6 xml2_1.3.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [23] sandwich_3.0-2 labeling_0.4.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [25] inflection_1.3.6 diptest_0.76-0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [27] scales_1.2.0 lmtest_0.9-40 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [29] mvtnorm_1.1-3 proxy_0.4-27 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [31] multcompView_0.1-8 RootsExtremaInflections_1.2.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [33] digest_0.6.29 rmarkdown_2.14 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [35] pkgconfig_2.0.3 htmltools_0.5.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [37] highr_0.9 dbplyr_2.2.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [39] fastmap_1.1.0 rlang_1.0.4 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [41] readxl_1.4.0 rstudioapi_0.13 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [43] farver_2.1.1 generics_0.1.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [45] zoo_1.8-10 jsonlite_1.8.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [47] googlesheets4_1.0.0 magrittr_2.0.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [49] modeltools_0.2-23 Matrix_1.4-1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [51] Rcpp_1.0.9 DescTools_0.99.45 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [53] munsell_0.5.0 fansi_1.0.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [55] lifecycle_1.0.1 multcomp_1.4-20 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [57] stringi_1.7.8 yaml_2.3.5 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [59] MASS_7.3-58.1 rootSolve_1.8.2.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [61] plyr_1.8.7 grid_4.0.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [63] parallel_4.0.2 ggrepel_0.9.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [65] crayon_1.5.1 lmom_2.9 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [67] lattice_0.20-45 haven_2.5.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [69] splines_4.0.2 hms_1.1.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [71] knitr_1.39 pillar_1.8.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [73] boot_1.3-28 gld_2.6.5 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [75] stats4_4.0.2 codetools_0.2-18 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [77] reprex_2.0.1 glue_1.6.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [79] evaluate_0.15 data.table_1.14.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [81] modelr_0.1.8 vctrs_0.4.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [83] tzdb_0.3.0 foreach_1.5.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [85] cellranger_1.1.0 gtable_0.3.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [87] assertthat_0.2.1 xfun_0.31 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [89] coin_1.4-2 libcoin_1.0-9 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [91] broom_1.0.0 e1071_1.7-11 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [93] class_7.3-20 survival_3.3-1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [95] googledrive_2.0.0 gargle_1.2.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [97] iterators_1.0.14 TH.data_1.1-1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [99] ellipsis_0.3.2\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "matt-sd-watson/nextflow_for_nextstrain", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enextflow_for_nextstrain\u003c/h1\u003e\u003ca id=\"user-content-nextflow_for_nextstrain\" class=\"anchor\" aria-label=\"Permalink: nextflow_for_nextstrain\" href=\"#nextflow_for_nextstrain\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eParallelization of Nextstrain builds and parameter testing using Nextflow\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation using conda\u003c/h2\u003e\u003ca id=\"user-content-installation-using-conda\" class=\"anchor\" aria-label=\"Permalink: Installation using conda\" href=\"#installation-using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA conda environment for running nextstrain in Nextflow can be created with the following:\u003c/p\u003e\n\u003cp\u003eThe package requires conda to be installed in the current environment.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit https://github.com/matt-sd-watson/nextflow_for_nextstrain.git\ncd nextflow_for_nextstrain\nconda env create -f environments/environment.yml\nconda activate nf_nextstrain\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the pipeline\u003c/h2\u003e\u003ca id=\"user-content-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline\" href=\"#running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nextflow pipeline for nextstrain can be run using the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run nextflow_for_nextflow/ --mode # insert mode here (see below) -profile # insert profile here (see below)\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe following modes are currently supported:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erandom_subsets: create a number of random subset builds\nrefine_iterations: Generate a number of random builds and test augur refine clock parameters on each subset\nlineages: Given a input list of Pango lineages, create a full build for each lineage\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration\u003c/h2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-label=\"Permalink: Configuration\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll parameters for the different modes can be set using the nextflow.config, or specified as a CLI parameter at runtime.\nNote that parameters specified at runtime through the CLI will override the same parameter specified by the nextflow.config\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfiguration variables\u003c/h3\u003e\u003ca id=\"user-content-configuration-variables\" class=\"anchor\" aria-label=\"Permalink: Configuration variables\" href=\"#configuration-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following parameters can be modified in the nextflow.config to support inputs and runtime parameters. Examples of these parameters can be found below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehelp = false\nsubset_number = 100\nclockfilteriqd = 10\nalignment_ref = \u0027/home/mwatson/COVID-19/reference/reference.gb\u0027\nmetadata = \u0027/home/mwatson/COVID-19/nextstrain_build/metadata/Nextstrain_metadata_070921_full.csv\u0027\noutput_dir = \u0027/home/mwatson/COVID-19/one_off/augur_test_2\u0027\ncolortsv = \u0027/home/mwatson/COVID-19/BCC_dev/BCC_nextstrain/config/colors_2.tsv\u0027\nconfig = \u0027/home/mwatson/COVID-19/BCC_dev/BCC_nextstrain/config/auspice_config.json\u0027\nlatlong = \u0027/home/mwatson/COVID-19/BCC_dev/BCC_nextstrain/config/lat_ontario_health_unit.tsv\u0027\nclades = \u0027/home/mwatson/COVID-19/BCC_dev/BCC_nextstrain/config/clades.tsv\u0027\n// threads set to auto uses the total number of cores for each process of align and tree\nthreads = 1\ncleanup = true\nstart_iteration = 1\nstop_iteration = 10\nclock = 10\nlineages = [\u0027P.1.1\u0027, \u0027A.23.1\u0027, \u0027C.37\u0027]\nlineage_report = \u0027/NetDrive/Projects/COVID-19/Other/master_fasta/lineage_report_all*plearn.csv\u0027\nmaster_fasta = \u0027/NetDrive/Projects/COVID-19/Other/master_fasta/complete_all*\u0027\nnextalign = \u0027/NetDrive/Projects/COVID-19/Other/master_fasta/alignment/complete_all*\u0027\ncache = \u0027\u0027\ntracedir = \"${params.output_dir}/pipeline_info\"\nrefineseed = 10\nclean_dir = false\nmake_alignment = false\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProfiles and containers\u003c/h2\u003e\u003ca id=\"user-content-profiles-and-containers\" class=\"anchor\" aria-label=\"Permalink: Profiles and containers\" href=\"#profiles-and-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline can be run through a Singularity container. To create the container, execute the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./environments/create_singularity_container.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecution requires root access.\u003c/p\u003e\n\u003cp\u003eTo enable singularity containeriation at runtime, the user can specify this option through the -profile option, such as the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run ~nextflow_for_nextstrain/ --mode refine_iterations -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe SINGULARITY_BIND variable contains the bound variables for the paths to files on mounted drives. This variable can either be exported explicitly before runtime as shown below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITY_BIND=\"/NetDrive/Projects/COVID-19/Other/master_fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor modified in the nextflow.config under the singularity profile, runOption parameter.\u003c/p\u003e\n\u003cp\u003enextflow_nextstrain also supports running through either a docker or conda profile (not recommended). using docker can assist when the user does not have root access to the environment where nextflow is being executed. This also allows for resource management without requiring sudo access (as is the case with singularity containers).\u003c/p\u003e\n\u003cp\u003eRunning the pipeline just through a conda environment is not recommended.\u003c/p\u003e\n", "stargazers_count": 3, "subscribers_count": 2, "topics": [ - "bioinformatics", - "genetic-interactions", - "r" + "pipelines", + "nextflow", + "nextstrain" ], - "updated_at": 1663972680.0 + "updated_at": 1724336102.0 }, { "data_format": 2, - "description": "Contains various scripts and useful tidbits", + "description": "A Singularity file intended for SingularityHub, for the Oxford Nanopore Technologies guppy gpu caller. Based off https://hub.docker.com/r/genomicpariscentre/guppy/dockerfile", "filenames": [ - "Singularity-DDF-Debian.def" + "Singularity" ], - "full_name": "ebonnassieux/Scripts", + "full_name": "photocyte/guppy_gpu_singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-hidden=\"true\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eScripts\u003c/h1\u003e\n\u003cp\u003eContains various scripts and useful tidbits\u003c/p\u003e\n", + "readme": "\u003cp\u003eSingularity file for Oxford Nanopore Technologies guppy gpu basecaller\u003c/p\u003e\n\u003cp\u003eBased off \u003ca href=\"https://hub.docker.com/r/genomicpariscentre/guppy-gpu/dockerfile\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/genomicpariscentre/guppy-gpu/dockerfile\u003c/a\u003e\n(But I wanted to use Ubuntu 18.04 and a guppy gpu v. \u0026gt;= 4.0.11)\u003c/p\u003e\n\u003cp\u003e(2021-08-02 update: The source dockerfile above was updated for \u003ccode\u003eCUDA 11.1\u003c/code\u003e, \u003ccode\u003eguppy-gpu 5.0.11\u003c/code\u003e, and \u003ccode\u003eUbuntu 18.04\u003c/code\u003e, so I would just use that!)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull docker://genomicpariscentre/guppy-gpu\nsingularity exec --nv guppy-gpu_latest.sif guppy_basecaller\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eImage building handled by singularity-hub.org\u003c/p\u003e\n\u003cp\u003eBut: \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/\" rel=\"nofollow\"\u003ehttps://singularityhub.github.io/singularityhub-docs/\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNotice\nSingularity Hub is no longer online as a builder service, but exists as a read only archive. Containers built before April 19, 2021 are available at their same pull URLs.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://photocyte/guppy_gpu_singularity\nsingularity exec --nv guppy_gpu_singularity_latest.sif guppy_basecaller --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee here for more details on the experimental \u003ccode\u003e--nv\u003c/code\u003e Nvidia CUDA support through Singularity: \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/gpu.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/gpu.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eConfirmed working on GPU nodes of the SDSC TSCC cluster (Centos 7.8.2003):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv guppy_gpu_singularity_latest.sif guppy_basecaller -i fast5_pass/fast5_pass/ -s guppy_test -c /opt/ont/guppy/data/dna_r9.4.1_450bps_hac.cfg --device cuda:all:100% --num_callers 16 --gpu_runners_per_device 32 --chunks_per_caller 10000000 --read_batch_size 1000000\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(Don\u0027t know if those parameters are optimal, but seems to go faster than the default)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTSCC Troubleshooting\u003c/h3\u003e\u003ca id=\"user-content-tscc-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: TSCC Troubleshooting\" href=\"#tscc-troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eTry running in interactive mode \u003ccode\u003eqsub -I -V -q YOUR_GPU_QUEUE -A YOUR_GROUP -l nodes=A_BEEFY_GPU_NODE:ppn=16 -l walltime=6:00:00\u003c/code\u003e, to be sure you are on a GPU node.\u003c/li\u003e\n\u003cli\u003eOn the node, try \u003ccode\u003envidia-smi; nvidia-smi -L\u003c/code\u003e to confirm you can see the CUDA GPUs, and what type of GPUs they are.\u003c/li\u003e\n\u003cli\u003eConfirm the node installed GPUs are \u003ca href=\"https://developer.nvidia.com/cuda-gpus\" rel=\"nofollow\"\u003eCompute Capability \u0026gt;= 6.1\u003c/a\u003e (Somewhere Oxford Nanopore\u0027s help says that is the minimum version for guppy). E.g., the GeForce RTX 2080 Ti has a Compute Capability of 7.5\u003c/li\u003e\n\u003cli\u003eConfirm via \u003ccode\u003envidia-smi\u003c/code\u003e that the node installed Nvidia CUDA libraries are \u0026gt;= 418 (matching \u003ca href=\"https://github.com/photocyte/guppy_gpu_singularity/blob/f4376d20ccbff97ea39909aad302887f028359ac/Singularity#L51\"\u003ewhat the container is supposed to have\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eYou can find GPU nodes with \u003ccode\u003epbsnodes -a\u003c/code\u003e , look for the \u003ccode\u003egpu_status\u003c/code\u003e field.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, "subscribers_count": 2, "topics": [], - "updated_at": 1662645091.0 + "updated_at": 1708781042.0 }, { "data_format": 2, - "description": "SC17 tutorial - \"HPC via HTTP: Portable, Scalable Computing using App Containers and the Agave API\"", + "description": "Install BEAST2 packages from R", "filenames": [ - "content/images/funwave-tvd/Singularity" + "Singularity" ], - "full_name": "agaveplatform/SC17-container-tutorial", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc-via-http-portable-scalable-computing-using-app-containers-and-the-agave-api\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-via-http-portable-scalable-computing-using-app-containers-and-the-agave-api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPC via HTTP: Portable, Scalable Computing using App Containers and the Agave API\u003c/h1\u003e\n\u003cp\u003eSupercomputing matters. So does user experience. Standing between the mainstream adoption of supercomputing and a new generation of users is the reality that the entry cost to using these systems, both in terms of dollars and in time spent learning the technology, has not significantly changed in the last 20 years. The rise of cloud computing only complicates the learning curve further. Over the last 6 years, the authors have been addressing this gap through the development of a Science-as-a-Service platform enabling users to go from their desktop, to their local data center, to the cloud, and back without sacrificing their existing tool chain or user experience.\u003c/p\u003e\n\u003cp\u003eIn this tutorial, we combine best practices and lessons learned while on-boarding the last 70k new users to TACC\u2019s data center through the Agave Platform. Participants will walk through the process of scaling their application from a local environment to the Jetstream academic cloud and to a high performance computing system at the Texas Advanced Computing Center. They will learn to use multiple container technologies to harmonize app execution between cloud and HPC resources, and they will learn to use modern APIs to orchestrate job execution, capture provenance information, and foster collaboration.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-preview\" class=\"anchor\" aria-hidden=\"true\" href=\"#preview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreview\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"http://www.youtube.com/watch?v=hVnIrjn_aBI\" title=\"HPC via HTTP: Portable, Scalable Computing using App Containers and the Agave API\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c65f2550bc90ecabb4429c52335a19f58b324a579f4cb9a3f92dfcab4bc7391f/687474703a2f2f696d672e796f75747562652e636f6d2f76692f68566e49726a6e5f6142492f6d617872657364656661756c742e6a7067\" alt=\"Intro Video\" data-canonical-src=\"http://img.youtube.com/vi/hVnIrjn_aBI/maxresdefault.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-schedule\" class=\"anchor\" aria-hidden=\"true\" href=\"#schedule\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSchedule\u003c/h1\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003cth\u003eTime\u003c/th\u003e\n \u003cth\u003ePresenterr\u003c/th\u003e\n \u003cth\u003eTopic\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e08:30 - 08:45\u003c/td\u003e\n \u003ctd\u003eJohn, Steve\u003c/td\u003e\n \u003ctd\u003e[Introductions](01%20Introduction.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e08:45 - 09:05\u003c/td\u003e\n \u003ctd\u003eRion\u003c/td\u003e\n \u003ctd\u003e[Agave Overview](02%20Agave%20Overview.pdf)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e09:05 - 09:15\u003c/td\u003e\n \u003ctd\u003eKathy\u003c/td\u003e\n \u003ctd\u003e[Jupyter, Sanbox, and Logging In](03%20Jupyter%2C%20Sandboxes%2C%20and%20Logging%20In.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e09:15 - 09:30\u003c/td\u003e\n \u003ctd\u003eSteve\u003c/td\u003e\n \u003ctd\u003e[Code, Build, and Test](04%20Code%20Build%20and%20Test.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e09:30 - 10:00\u003c/td\u003e\n \u003ctd\u003eRion, John\u003c/td\u003e\n \u003ctd\u003e[Hands on with Agave](05%20Hands%20on%20with%20Agave.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e10:00 - 10:30\u003c/td\u003e\n \u003ctd\u003e--\u003c/td\u003e\n \u003ctd\u003eBreak\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e10:30 - 11:00\u003c/td\u003e\n \u003ctd\u003eSteve,John\u003c/td\u003e\n \u003ctd\u003e[Docker and Singularity](06%20Docker%20and%Singularity.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e11:00 - 11:15\u003c/td\u003e\n \u003ctd\u003eRion\u003c/td\u003e\n \u003ctd\u003e[Automation an Benchmarking](07%20Automation%20and%20Benchmarking.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e11:15 - 11:45\u003c/td\u003e\n \u003ctd\u003eKathy, Rion\u003c/td\u003e\n \u003ctd\u003e[Packaging, publishing, and Portability](08%20Packaging%20publishing%20and%20Portability.ipynb)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e11:45 - 12:00\u003c/td\u003e\n \u003ctd\u003eSteve, John\u003c/td\u003e\n \u003ctd\u003e[Future Directions and Homework)[09%20Future%20Directions%20and%20Homework.ipynb]\u003c/td\u003e\n \u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003ch1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-hidden=\"true\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e01: \u003ca href=\"01-Requirements-and-Preparation.md\"\u003eRequirements and Preparation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e02: \u003ca href=\"02-Installation-and-Infrastructure.md\"\u003eInstallation and Infrastructure\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e03: \u003ca href=\"03-Auth-Notebooks-and-Web-Console.md\"\u003eAuth, Notebooks, and the Web Interface\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e04: \u003ca href=\"04-SciOps-and-Sample-Application.md\"\u003eSciOps and our Sample Application\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e05: \u003ca href=\"05-Code-Build-and-Run-Locally.md\"\u003eCode, Build, and Run Locally\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e06: \u003ca href=\"06-Containerize-Existing-Applications.md\"\u003eContainerize Existing Applications\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e07: \u003ca href=\"07-Automation-Registries-and-App-Catalogues\"\u003eAutomation, Registries, and App Catalogues\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003eAgave\u003c/li\u003e\n\u003cli\u003eCI/CD\u003c/li\u003e\n\u003cli\u003eImage publishing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003e08: \u003ca href=\"\"\u003eScaling and Portability\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003eImage caching\u003c/li\u003e\n\u003cli\u003eRuntime environments\u003c/li\u003e\n\u003cli\u003eData scheduling\u003c/li\u003e\n\u003cli\u003eReproducibility anti-patterns\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003e09: \u003ca href=\"\"\u003eViewing simulation results, sharing, provenance\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e10: \u003ca href=\"\"\u003ePackaging and Publishing Experiments\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e11: \u003ca href=\"\"\u003eBenchmarking and Performance Considerations\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e12: \u003ca href=\"\"\u003eFunctions, Microcodes, and Exascale\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e13: \u003ca href=\"\"\u003eHomework an Further Reading\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e90: \u003ca href=\"90-Appendix-A.md\"\u003eAppendix A\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e99: \u003ca href=\"99-References.md\"\u003eReferences\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "ropensci/mauricer", + "latest_release": "v2.5.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emauricer\u003c/h1\u003e\u003ca id=\"user-content-mauricer\" class=\"anchor\" aria-label=\"Permalink: mauricer\" href=\"#mauricer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/ropensci/software-review/issues/209\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1f9e866eed4c0edc0f5b5b90e775c80a93dc80a99af5f86154943b5bca5b742/68747470733a2f2f6261646765732e726f70656e7363692e6f72672f3230395f7374617475732e737667\" alt=\"Peer Review Status\" data-canonical-src=\"https://badges.ropensci.org/209_status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cran.r-project.org/package=mauricer\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/79ea38c8bf0b55169aa38f8d7d83e22cd4b0f489a7eeca9386efe6a7e5dee304/687474703a2f2f7777772e722d706b672e6f72672f6261646765732f76657273696f6e2f6d61757269636572\" alt=\"CRAN_Status_Badge\" data-canonical-src=\"http://www.r-pkg.org/badges/version/mauricer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mauricer\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/57c756332c264076ae0ce26b6515c88252c6170ec6873eafa3167358dc8629a8/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6772616e642d746f74616c2f6d61757269636572\" alt=\"Total number of downloads\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/grand-total/mauricer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mauricer\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/200b341592be588ae003e79d3e9742b9e2e253a722f92daa6844c5578527752d/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6d61757269636572\" alt=\"Downloads per month\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/mauricer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/ropensci/mauricer/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://about.codecov.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/mauricer/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/ropensci/mauricer/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/mauricer/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/54a466cc266c9065027c0e0ca1552191903cd8dcef08d943df7a86420d64996a/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f6d617572696365722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/mauricer/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/mauricer/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/ropensci/mauricer/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/mauricer/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8639985afec52e0ead49eaca4cebd299802b43fe2478b54597d5e84f543e9fb5/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f6d617572696365722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/mauricer/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003eWork with BEAST2 packages from R.\u003c/p\u003e\n\u003cp\u003eRelated packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/babette\"\u003ebabette\u003c/a\u003e do a full BEAST2 workflow.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beautier\"\u003ebeautier\u003c/a\u003e\ncreates BEAST2 input (\u003ccode\u003e.xml\u003c/code\u003e) files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beastier\"\u003ebeastier\u003c/a\u003e runs BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/lumier\"\u003elumier\u003c/a\u003e\nhelps to create the \u003ccode\u003ebabette\u003c/code\u003e function call needed\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/tracerer\"\u003etracerer\u003c/a\u003e\nparses BEAST2 output (\u003ccode\u003e.log\u003c/code\u003e, \u003ccode\u003e.trees\u003c/code\u003e, etc) files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNon-CRAN extensions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall\"\u003ebeastierinstall\u003c/a\u003e\nInstall and uninstall BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/mauricerinstall\"\u003emauricerinstall\u003c/a\u003e\nInstall and uninstall BEAST2 packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install the BEAST2 NS package:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003eremotes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003erichelbilderbeek/mauricerinstall\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-e\"\u003emauricerinstall\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_beast2_pkg(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eNS\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn introduction video:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/Yk737gorcrw\" rel=\"nofollow\"\u003eYouTube video about mauricer\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePackage dependencies\u003c/h2\u003e\u003ca id=\"user-content-package-dependencies\" class=\"anchor\" aria-label=\"Permalink: Package dependencies\" href=\"#package-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePackage\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://about.codecov.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/beastier\"\u003ebeastier\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/beastier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d979724609be00ee404424b764bd018a533ce7ff155dbc0dc24ae038a74ae08d/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656173746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beastier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRelated packages\u003c/h2\u003e\u003ca id=\"user-content-related-packages\" class=\"anchor\" aria-label=\"Permalink: Related packages\" href=\"#related-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePackage\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://about.codecov.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/babette\"\u003ebabette\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/babette/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6e4082d6cb01c0090be0c9742f1b5126c4d00bb71f610c2a97c0e0d284f7e9f/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f626162657474652f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/babette/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/beautier\"\u003ebeautier\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/beautier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff034288b0791340bac7dea03ff17aa4d354a3b0bea92f96c633ae8025eb7e24/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/lumier\"\u003elumier\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/lumier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bfa30281acef9fd846d3e115df6ea000f736e4065a927da788fa086375efb1c2/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f6c756d6965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/lumier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ropensci/tracerer\"\u003etracerer\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/tracerer/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3461071539216bdfd4e2568a422758897435c23a3d7b8afa588ea24a532c9b34/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f74726163657265722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/tracerer/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePackage\u003c/th\u003e\n\u003cth\u003eStatus\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/mauricer_on_windows\"\u003emauricer_on_windows\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/richelbilderbeek/mauricer-on-windows/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/02ea7a31c5420d182d2e11f30c502189e05de85fcddd6d6d2b2be77fd4d6be90/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f626334336977703638786f32646475682f6272616e63682f6d61737465723f7376673d74727565\" alt=\"Build status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/bc43iwp68xo2dduh/branch/master?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"https://ropensci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c7d2a7aedd313f02b4f7d199df4d0d28905b7814db324fb532759b2fe2ee9ac2/68747470733a2f2f726f70656e7363692e6f72672f7075626c69635f696d616765732f726f70656e7363695f666f6f7465722e706e67\" alt=\"ropensci_footer\" data-canonical-src=\"https://ropensci.org/public_images/ropensci_footer.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFiles used by continuous integration scripts\u003c/h2\u003e\u003ca id=\"user-content-files-used-by-continuous-integration-scripts\" class=\"anchor\" aria-label=\"Permalink: Files used by continuous integration scripts\" href=\"#files-used-by-continuous-integration-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFilename\u003c/th\u003e\n\u003cth\u003eDescriptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"mlc_config.json\"\u003emlc_config.json\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the link checker, use \u003ccode\u003emarkdown-link-check --config mlc_config.json --quiet docs/**/*.md\u003c/code\u003e to do link checking locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".spellcheck.yml\"\u003e.spellcheck.yml\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".wordlist.txt\"\u003e.wordlist.txt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eWhitelisted words for the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlint.jsonc\"\u003e.markdownlint.jsonc\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlintignore\"\u003e.markdownlintignore\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFiles ignored by the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://CRAN.R-project.org/package=mauricer\" rel=\"nofollow\"\u003e\u0027mauricer\u0027 CRAN page\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 7, - "topics": [], - "updated_at": 1529379287.0 - }, - { - "data_format": 2, - "description": "Synaptic Partner Detection in 3D Microscopy Volumes", - "filenames": [ - "singularity/Singularity_py2.7.recipe", - "singularity/Singularity_py3.recipe" - ], - "full_name": "funkelab/synful", - "latest_release": "v1.0", - "readme": "\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/166422086\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e31cfa1af0774be894dee535edc05a6536309dc42e048f576dc489a330b1f8ec/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3136363432323038362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/166422086.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-synful\" class=\"anchor\" aria-hidden=\"true\" href=\"#synful\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSynful\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eSynful: A project for the automated detection of synaptic partners in Electron Microscopy brain data using U-Nets (type of Convolutional Neural Network).\u003c/p\u003e\n\u003cp\u003eThis repository provides train and predict scripts for synaptic partner detection. For more details, see our \u003ca href=\"https://www.biorxiv.org/content/10.1101/2019.12.12.874172v1\" rel=\"nofollow\"\u003ebioRxiv preprint\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWe used the method to predict 244 Million synaptic partners in the full adult fly brain (FAFB) dataset.\nPlease see \u003ca href=\"https://github.com/funkelab/synful_fafb\"\u003ehttps://github.com/funkelab/synful_fafb\u003c/a\u003e for data dissemination and benchmark datasets.\u003c/p\u003e\n\u003cp\u003ePlease don\u0027t hesitate to open\nan issue or write us an email (\u003ca href=\"mailto:buhmannj@janelia.hhmi.org\"\u003eJulia\nBuhmann\u003c/a\u003e or \u003ca href=\"mailto:funkej@janelia.hhmi.org\"\u003eJan\nFunke\u003c/a\u003e) if you have any questions!\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Add train scripts\u003c/li\u003e\n\u003cli\u003e[x] Add inference scripts\u003c/li\u003e\n\u003cli\u003e[x] Add download links for pretrained models\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-method\" class=\"anchor\" aria-hidden=\"true\" href=\"#method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMethod\u003c/h2\u003e\n\u003cp\u003eThe pipeline processes 3D raw data in two steps into synaptic partners:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003einference of a) \u003ccode\u003esyn_indicator_mask\u003c/code\u003e (postsynaptic locations) and b) \u003ccode\u003edirection_vector\u003c/code\u003e (vector pointing from postsynaptic location to its presynaptic partner)\u003c/li\u003e\n\u003cli\u003esynapse extraction: a) locations extractions based on \u003ccode\u003esyn_indicator_mask\u003c/code\u003e and b) finding presynaptic partner based on \u003ccode\u003edirection_vector\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/_static/method_overview.png\"\u003e\u003cimg src=\"docs/_static/method_overview.png\" alt=\"method_figure\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eHardware requirements\n\u003cul\u003e\n\u003cli\u003etraining and prediction requires at least one GPU with sufficient memory (12 GB)\u003c/li\u003e\n\u003cli\u003eFor instance, we mostly used \u003ccode\u003eGeForce GTX TITAN X 12 GB\u003c/code\u003e for our project\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSoftware requirements\n\u003cul\u003e\n\u003cli\u003eSoftware has been tested on Linux (Ubuntu 16.04)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation-guide\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation Guide\u003c/h2\u003e\n\u003cp\u003efrom source (creating a conda env is optional, but recommended).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repository.\u003c/li\u003e\n\u003cli\u003eIn a terminal:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econda_env_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e python=3.6\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econda_env_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e synful\npip install -r requirements.txt\npython setup.py install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are interested in using the package for training and prediction, additionally add tensorflow and funlib.learn.tensorflow to your conda env:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install tensorflow-gpu=1.14 cudatoolkit=10.0\npip install git+git://github.com/funkelab/funlib.learn.tensorflow@0712fee6b6c083c6bfc86e76f475b2e40b3c64f2\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-install-time\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-time\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall time\u003c/h4\u003e\n\u003cp\u003eInstallation should take around 5 mins (including 3 mins for the tensorflow installation).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining\u003c/h2\u003e\n\u003cp\u003eTraining scripts are found in\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etrain/\u0026lt;setup\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;setup\u0026gt;\u003c/code\u003e is the name of a particular network configuration.\nIn such a directory, you will find two files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003egenerate_network.py\u003c/code\u003e (generates a tensorflow network based on the parameter.json file in the same directoy)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etrain.py\u003c/code\u003e (starts training)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo get started, have a look at the train script in \u003ca href=\"train/setup01\"\u003etrain/setup01/train.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo start training:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython generate_network.py parameter.json\npython train.py parameter.json\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003esetup01: parameter.json is set to train a network on post-synaptic sites (single-task network)\u003c/li\u003e\n\u003cli\u003esetup02: parameter.json is set to train on direction vectors (single-task network)\u003c/li\u003e\n\u003cli\u003esetup03: parameter.json is set to train on both post-synaptic sites and direction vectors (multi-task network)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-details-on-hyperparameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#details-on-hyperparameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetails on hyperparameters\u003c/h4\u003e\n\u003cp\u003eWhen training a network, you can set following hyperparameters in \u003ccode\u003escripts/train/\u0026lt;setup01/02/03\u0026gt;/parameter.json\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eParameters to set the architecture of the network (also see \u003ca href=\"https://github.com/funkelab/funlib.learn.tensorflow/blob/master/funlib/learn/tensorflow/models/unet.py#L506\"\u003edoc\u003c/a\u003e where we create the U-Net)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003einput_size\u003c/code\u003e: the dimensions of the cube that is used as input (called a mini-batch)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edownsample_factor\u003c/code\u003e = [[1, 3, 3], [1, 3, 3], [3, 3, 3]] creates a U-Net with four resolution levels\n\u003cul\u003e\n\u003cli\u003ethe first one being the original resolution, the second one with downsampled feature maps with factos [1, 3, 3] etc.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efmap_num\u003c/code\u003e: Number of feature maps in the first layer (we used 4 in the paper)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efmap_inc_factor\u003c/code\u003e: In each layer, we use \u003ccode\u003efmap_inc_factor\u003c/code\u003e to increase our number of feature maps (we used 5 and 12 in the paper)\n\u003cul\u003e\n\u003cli\u003eEg. if we have \u003ccode\u003efmap_num = 4\u003c/code\u003e and \u003ccode\u003efmap_inc_factor = 5\u003c/code\u003e , we have 20 in our first layer, 100 in our second layer ...\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eunet_model\u003c/code\u003e: vanilla, or dh_unet; vanille=single-task network, dh_unet=multitask network with two different upsampling paths\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTraining parameters\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003elearning_rate\u003c/code\u003e: we used the AdamOptimizer across all experiments, with beta1=0.95,beta2=0.999,epsilon=1e-8\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eST / MT parameters\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eloss_comb_type\u003c/code\u003e: in a multi-task setting, how to combine the two different losses\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003em_loss_scale\u003c/code\u003e : loss weight for post-synaptic mask\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ed_loss_scale\u003c/code\u003e : loss weight for direction vector field\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBalancing parameters needed to account for sparsity of synaptic sites\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ereject_probability\u003c/code\u003e : 0.95 - p_rej in paper --\u0026gt; reject empty mini-batches with probability \u003ccode\u003ereject_probability\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eclip_range\u003c/code\u003e : the loss is scaled with the inverse class frequency ratio of foreground-and background voxels, clipping at \u003ccode\u003eclip_range\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-training-runtime\" class=\"anchor\" aria-hidden=\"true\" href=\"#training-runtime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTraining runtime\u003c/h4\u003e\n\u003cp\u003eTraining takes between 3 and 10 days (depending on the size of the network), but you should see reasonable results within a day (after 90k iterations).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-monitoring-training\" class=\"anchor\" aria-hidden=\"true\" href=\"#monitoring-training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMonitoring Training\u003c/h3\u003e\n\u003cp\u003eTo visualize snapshots that are produced during training use this \u003ca href=\"scripts/visualization/visualize_snapshot.py\"\u003escript\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -i visualize_snapshot.py 300001 setup01\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ein order to load iteration \u003ccode\u003e300001\u003c/code\u003e of training setup \u003ccode\u003esetup01\u003c/code\u003e (use -1 to indicate most recent snapshot)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-inference\" class=\"anchor\" aria-hidden=\"true\" href=\"#inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInference\u003c/h2\u003e\n\u003cp\u003eOnce you trained a network, you can use this script to run inference:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd scripts/predict/\npython predict_blockwise.py predict_template.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdapt following parameters in the configfile \u0026lt;scripts/predict/predict_template.json\u0026gt;:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edb_host\u003c/code\u003e --\u0026gt; Put here the name of your running mongodb server (this is used to track which chunks are processed)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eraw_file\u003c/code\u003e --\u0026gt; Put here the filepath of your raw data (as an example you can use the CREMI data that you can download from \u003ca href=\"http://www.cremi.org\" rel=\"nofollow\"\u003ewww.cremi.org\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor a full list of parameters and explanation, see: \u0026lt;scripts/predict/predict_blockwise.py\u0026gt;.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-inference-runtime\" class=\"anchor\" aria-hidden=\"true\" href=\"#inference-runtime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInference runtime\u003c/h4\u003e\n\u003cp\u003eProcessing a CREMI cube (5 microns X 5 microns x 5 microns) takes ~4 minutes on a single GPU.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pretrained-models--original-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#pretrained-models--original-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePretrained Models / Original Setup\u003c/h2\u003e\n\u003cp\u003eWe provide pretrained models, that we discuss in detail in our \u003ca href=\"https://www.biorxiv.org/content/10.1101/2019.12.12.874172v2\" rel=\"nofollow\"\u003ebioRxiv preprint\u003c/a\u003e. You will find the results of our gridsearch and the parameters that we used in Figure 3 \u003ccode\u003eValidation results on CREMI dataset\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWe provide four models that you can download from \u003ca href=\"https://www.dropbox.com/s/301382766164ism/pretrained.zip?dl=0\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ePlease extract the zip file into \u0026lt;scripts/train/\u0026gt; of this repository, this will add for each model a setup directory with the necassary config files, tensorflow checkpoint and predict script.\u003c/p\u003e\n\u003cp\u003eFor instance for \u003ccode\u003ep_setup52\u003c/code\u003e (marked orange in Figure 3, one of the best performing models), you will get all relevant files in \u0026lt;scripts/train/p_setup52\u0026gt;.\nTo run inference, you have to change the setup parameter in the predict config file to \u003ccode\u003ep_setup52\u003c/code\u003e and proceed according to \u003ca href=\"#Inference\"\u003einference section\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-details-about-the-provided-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#details-about-the-provided-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetails about the provided models\u003c/h4\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003esetup\u003c/th\u003e\n\u003cth\u003especs\u003c/th\u003e\n\u003cth\u003ef-score with seg\u003c/th\u003e\n\u003cth\u003ef-score without\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ep_setup52 (+p_setup10)\u003c/td\u003e\n\u003ctd\u003ebig, curriculum, CE, ST\u003c/td\u003e\n\u003ctd\u003e0.76\u003c/td\u003e\n\u003ctd\u003e0.74\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ep_setup51\u003c/td\u003e\n\u003ctd\u003ebig, curriculum, CE, MT_2\u003c/td\u003e\n\u003ctd\u003e0.76\u003c/td\u003e\n\u003ctd\u003e0.73\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ep_setup54 (+p_setup05)\u003c/td\u003e\n\u003ctd\u003esmall, curriculum, MSE, ST\u003c/td\u003e\n\u003ctd\u003e0.76\u003c/td\u003e\n\u003ctd\u003e0.7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ep_setup45 (+p_setup05)\u003c/td\u003e\n\u003ctd\u003esmall, standard, MSE, MT2\u003c/td\u003e\n\u003ctd\u003e0.73\u003c/td\u003e\n\u003ctd\u003e0.68\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote, that for the models that have an underlying ST architecture we also indicate the setup for the corresponding direction-vector-models (p_setup05+p_setup10).\nIf you want to use the model with highest accuracy, pick \u003ccode\u003ep_setup52\u003c/code\u003e; If you want to use a model that gives reasonnable results, but also has fast inference runtime, pick \u003ccode\u003ep_setup54\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-details-about-experiments-that-were-done-to-produce-above-models\" class=\"anchor\" aria-hidden=\"true\" href=\"#details-about-experiments-that-were-done-to-produce-above-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetails about experiments that were done to produce above models\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003edataset: As noted in the paper, we used a realigend version of the original CREMI datasets for training. You can download the data from \u003ca href=\"https://www.dropbox.com/s/i858mrs6s0rj0rt/groundtruth.tar.gz?dl=0\" rel=\"nofollow\"\u003ehere\u003c/a\u003e (cremi_v01 is the correct folder).\nThis data also contains the masks that were used to cover training/validation region in the data. (Note: It is a bit more annoying to work with this realigned data, as the mask is not cube/cuboid-shaped.)\u003c/li\u003e\n\u003cli\u003ehere is the original code for training, evaluation and inference: \u003ca href=\"https://zenodo.org/record/4635362#.YmufZBxBzCI\" rel=\"nofollow\"\u003ehttps://zenodo.org/record/4635362#.YmufZBxBzCI\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eoriginal gridsearch was carried out using luigi (\u003ca href=\"https://luigi.readthedocs.io/en/stable/index.html\" rel=\"nofollow\"\u003ehttps://luigi.readthedocs.io/en/stable/index.html\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 3, - "subscribers_count": 6, - "topics": [], - "updated_at": 1677221227.0 - }, - { - "data_format": 2, - "description": "variant annotation workflow with VEP", - "filenames": [ - "container/Singularity.vep-96.0" - ], - "full_name": "stevekm/vep-annotation-nf", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-vep-annotation-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#vep-annotation-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003evep-annotation-nf\u003c/h1\u003e\n\u003cp\u003eDemo pipeline for annotating variants in .vcf files using \u003ca href=\"https://useast.ensembl.org/info/docs/tools/vep/index.html\" rel=\"nofollow\"\u003eVariant Effect Predictor\u003c/a\u003e (VEP).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003eClone this repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/stevekm/vep-annotation-nf.git\ncd vep-annotation-nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow\u003c/h2\u003e\n\u003cp\u003eInstall \u003ccode\u003enextflow\u003c/code\u003e in the current directory with the command in the Makefile.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-vep-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#vep-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVEP: Docker\u003c/h2\u003e\n\u003cp\u003eTo install VEP using Docker, run the Makefile command in the \u003ccode\u003econtainer\u003c/code\u003e directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd container\nmake docker-build\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-vep-conda\" class=\"anchor\" aria-hidden=\"true\" href=\"#vep-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVEP: conda\u003c/h2\u003e\n\u003cp\u003eTo install VEP using \u003ccode\u003econda\u003c/code\u003e (for NYULMC Big Purple HPC), instead run the \u003ccode\u003econda-install\u003c/code\u003e recipe from the Makefile in the parent repo directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake conda-install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-reference-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference Files\u003c/h2\u003e\n\u003cp\u003eVEP reference files will be downloaded automatically by the pipeline. However the hg19 genome fasta, fasta.fai, and fasta.dict files must also be obtained (not included; try \u003ca href=\"https://support.illumina.com/sequencing/sequencing_software/igenome.html\" rel=\"nofollow\"\u003ethese\u003c/a\u003e). On NYULMC Big Purple, all required files are already cached and no download should be needed. On other systems, the command line arguments specifying the genome fasta files should be provided separately when running, or place the files \u003ccode\u003egenome.fa\u003c/code\u003e, \u003ccode\u003egenome.fa.fai\u003c/code\u003e, and \u003ccode\u003egenome.dict\u003c/code\u003e inside the included \u003ccode\u003eref\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun\u003c/h1\u003e\n\u003cp\u003eThe Makefile includes shortcuts to help run the pipeline easier on NYULMC Big Purple HPC.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe command can also be used to run on other systems, it will simply invoke the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./nextflow run main.nf -resume\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNextflow \u003ccode\u003eparams\u003c/code\u003e values can be passed on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./nextflow run main.nf -resume --ref_fa /path/to/genome.fa --ref_fai /path/to/genome.fa.fai --ref_dict /path/to/genome.dict\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h1\u003e\n\u003cp\u003eOutput files will be collected in the \u003ccode\u003eoutput\u003c/code\u003e directory.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware\u003c/h1\u003e\n\u003cp\u003eTested on RHEL 7, macOS 10.12\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNextflow (Java 8+)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebash\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU \u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePython 2.7+\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [ - "vcf", - "annotation", - "vep", - "nextflow" + "r", + "r-package", + "rstats" ], - "updated_at": 1669295325.0 + "updated_at": 1725601623.0 }, { "data_format": 2, - "description": "A small GUI for plotting H5Parms produced during LOFAR calibration.", + "description": "The nexus of man and machine.", "filenames": [ - "Singularity" + "src/macrothymic/Singularity", + "src/synaptic/Singularity" ], - "full_name": "tikk3r/lofar-h5plot", - "latest_release": "v2.6.1", - "readme": "\u003ch1 align=\"center\"\u003e\u003ca id=\"user-content-lofar-h5plot\" class=\"anchor\" aria-hidden=\"true\" href=\"#lofar-h5plot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLOFAR H5plot\u003c/h1\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3d5f570eb6ed92f745805b74ba69c9469eea97387d0562b084a5b154abd9c184/68747470733a2f2f6d7065726c65742e6769746875622e696f2f707962616467652f6261646765732f382e33372e7376673f7374796c653d666f722d7468652d6261646765\"\u003e\u003cimg alt=\"Pylint\" src=\"https://camo.githubusercontent.com/3d5f570eb6ed92f745805b74ba69c9469eea97387d0562b084a5b154abd9c184/68747470733a2f2f6d7065726c65742e6769746875622e696f2f707962616467652f6261646765732f382e33372e7376673f7374796c653d666f722d7468652d6261646765\" data-canonical-src=\"https://mperlet.github.io/pybadge/badges/8.37.svg?style=for-the-badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/31c77719438339bd73d336644aed382a4f608397a03695f076a714fe2b2a7f08/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d6835706c6f742e737667\"\u003e\u003cimg alt=\"GitHub\" src=\"https://camo.githubusercontent.com/31c77719438339bd73d336644aed382a4f608397a03695f076a714fe2b2a7f08/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d6835706c6f742e737667\" data-canonical-src=\"https://img.shields.io/github/license/tikk3r/lofar-h5plot.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/dfba1e54aa17373cfe9fda695ae21ba065bb9c76c7d3488a99211b9319b817bf/68747470733a2f2f696d672e736869656c64732e696f2f72657175697265732f6769746875622f74696b6b33722f6c6f6661722d6835706c6f742e737667\"\u003e\u003cimg alt=\"Requires.io\" src=\"https://camo.githubusercontent.com/dfba1e54aa17373cfe9fda695ae21ba065bb9c76c7d3488a99211b9319b817bf/68747470733a2f2f696d672e736869656c64732e696f2f72657175697265732f6769746875622f74696b6b33722f6c6f6661722d6835706c6f742e737667\" data-canonical-src=\"https://img.shields.io/requires/github/tikk3r/lofar-h5plot.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.3469995\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a5ba93acbd803f4bbd5c5b53181fe84be84c1fe2dad747d2a1fe0581fa9ce9b7/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333436393939352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3469995.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a18963743d391a9b6bd683d089a26ac2495b278021a9a4122cd4deb96762fbcb/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6f6661722d6835706c6f74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a18963743d391a9b6bd683d089a26ac2495b278021a9a4122cd4deb96762fbcb/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6f6661722d6835706c6f74\" data-canonical-src=\"https://img.shields.io/pypi/v/lofar-h5plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cc369154c85330eb0f0128d14c2c4570d1441ff8ab74f6ff50a7d9862c2af1ec/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f6c6f6661722d6835706c6f74\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cc369154c85330eb0f0128d14c2c4570d1441ff8ab74f6ff50a7d9862c2af1ec/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f6c6f6661722d6835706c6f74\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/lofar-h5plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eH5plot is a small GUI to view the solutions in an H5parm interactively. To run it directly, clone this repository and run as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython h5plot \u0026lt;h5parm\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis package is also installable through pip:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install --upgrade https://github.com/revoltek/losoto/archive/master.zip\npip install lofar-h5plot\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter this, it can simply be run as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eh5plot \u0026lt;h5parm\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/tikk3r/lofar-h5plot/master/screen.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/tikk3r/lofar-h5plot/master/screen.png\" alt=\"Screenshot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003ePython \u0026gt;= 3.6.4\u003c/li\u003e\n\u003cli\u003eLoSoTo 2.0\u003c/li\u003e\n\u003cli\u003eMatplotlib\u003c/li\u003e\n\u003cli\u003eNumpy\u003c/li\u003e\n\u003cli\u003ePyQt5\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese can be installed on Ubuntu through\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapt-get install qt5-default libgl1-mesa-glx\npip install pyqt5 matplotlib\npip install --upgrade https://github.com/revoltek/losoto/archive/master.zip\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "r3tex/nephilim", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"header.jpg\"\u003e\u003cimg src=\"header.jpg\" alt=\"header image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"left\"\u003e\n\u05d1\u05d0\u05de\u05e6\u05e2\u05d5\u05ea \u05d7\u05db\u05de\u05ea \u05d4\u05de\u05e1\u05e4\u05e8\u05d9\u05dd, \u05d0\u05dc\u05d5 \u05e9\u05dc \u05d4\u05d1\u05e9\u05e8 \u05d4\u05e7\u05d9\u05de\u05d5 \u05de\u05db\u05d5\u05e0\u05d5\u05ea \u05d5\u05d4\u05e2\u05d5\u05e8\u05e8\u05d5 \u05d0\u05ea \u05d4\u05e0\u05e4\u05d9\u05dc\u05d9\u05dd\n\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIntroduction\u003c/h1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProject Nephilim is a collection of code and ideas centered on AI-systems that are free to evolve past human sensibilities.\u003c/p\u003e\n\u003cp\u003eIn its current state, it\u0027s just a hobby project (won\u2019t be big and professional) for x64 machines with GPUs [\u003ca href=\"https://en.wikipedia.org/wiki/History_of_Linux#The_creation_of_Linux\" rel=\"nofollow\"\u003e*\u003c/a\u003e].\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eResults\u003c/h1\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll results below are run on a single \u003ca href=\"https://www.hp.com/us-en/workstations/z8-fury.html\" rel=\"nofollow\"\u003eHP Z8\u003c/a\u003e workstation:\u003c/p\u003e\n\u003cp\u003eLogical Reasoning\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTODO - upload interesting question results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSelf-Consistency\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTODO - upload results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSelf-Knowledge\nNephilim is aware of its own architecture, state, and existence in time.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eTODO - upload Q\u0026amp;A results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eArchitecture\u003c/h1\u003e\u003ca id=\"user-content-architecture\" class=\"anchor\" aria-label=\"Permalink: Architecture\" href=\"#architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe architecture of Nephilim is inspired by the subdivision of the human brain into physically and functionally distinct components. This approach has multiple advantages in terms of run-time efficiency, but also mean that they can be trained separately, swapped out, specialized, and more.\u003c/p\u003e\n\u003cp\u003eMoreover, every component is designed to be run in a distributed manner, meaning there is no single instance of a neural network, database, or streaming engine. The components can run on a single computer, or across multiple machines in a fault-tolerant way. For now, the assumption is that all components of Nephilim are trusted, but future work might include the ability to collaborate with external untrusted instances using modern consensus algorithms.\u003c/p\u003e\n\u003cp\u003eThe primary components of Nephilim are as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExosomatic Layer: input / output from the system. \u003ca href=\"src/exosomatic/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eSynaptic Layer: internal system communication. \u003ca href=\"src/synaptic/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eMacrothymic Layer: long-term memory storage. \u003ca href=\"src/macrothymic/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eErgokedic Layer: computation and cognition. \u003ca href=\"src/ergokedic/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eArcheion Layer: system-support and automation. \u003ca href=\"src/archeion/README.md\"\u003eREADME\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTheoretical Musings\u003c/h1\u003e\u003ca id=\"user-content-theoretical-musings\" class=\"anchor\" aria-label=\"Permalink: Theoretical Musings\" href=\"#theoretical-musings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVon Neumann Architecture\u003c/h3\u003e\u003ca id=\"user-content-von-neumann-architecture\" class=\"anchor\" aria-label=\"Permalink: Von Neumann Architecture\" href=\"#von-neumann-architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModern computers tend to work with a clock, a bus, a CPU, and various layers of caching. It would be ideal to architect Nephilim to run as \"natively\" as possible on this hardware. Initially we will just focus on code that is performant on Linux based x64 PCs. At a later point it might be interesting to move it to \u003ca href=\"https://en.wikipedia.org/wiki/Protection_ring\" rel=\"nofollow\"\u003eRing 0\u003c/a\u003e and let it output machine code and data directly to memory. Ultimately it would be ideal to run Nephilim on hardware that is purpose-built such as FPGAs, ASICs, or even better, something even more specialized like IBMs \u003ca href=\"https://research.ibm.com/blog/analog-ai-chip-inference\" rel=\"nofollow\"\u003ephase-change\u003c/a\u003e processor.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTransformer Architecture\u003c/h3\u003e\u003ca id=\"user-content-transformer-architecture\" class=\"anchor\" aria-label=\"Permalink: Transformer Architecture\" href=\"#transformer-architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAI influencers often describe Large Language Models (LLMs) pejoratively as \"just\" predicting the next word, or \u003ca href=\"https://dl.acm.org/doi/10.1145/3442188.3445922\" rel=\"nofollow\"\u003eparroting\u003c/a\u003e word co-occurrences. Not only does this unknowingly betray a technical ignorance on part of the influencer of what is actually happening, but there\u0027s a deep irony in how often the reductionist fallacy is parroted. One could say that computers \"just\" flip 1s and 0s... yeah, so what\u0027s your point?\u003c/p\u003e\n\u003cp\u003eConsider the amount of logic required to complete the next word in the following sentence, \"I always say the opposite of what I mean, but this time I mean it. I\u0027m being ______.\u0027 Not quite so trivial.\u003c/p\u003e\n\u003cp\u003eHuman language can be regarded as an \u003ca href=\"https://en.wikipedia.org/wiki/Completeness_(logic)\" rel=\"nofollow\"\u003eincomplete formal system\u003c/a\u003e of logic. This means that for the most part it describes mundane reality quite well and can be used to reason logically. However, it breaks down when ambiguities in the strength of relations between words and concepts result in \u003ca href=\"https://en.wikipedia.org/wiki/Cantor%27s_diagonal_argument\" rel=\"nofollow\"\u003ediagonal arguments\u003c/a\u003e. This has contributed to philosophers spending an inordinate amount of time discussing inane ideas such as the \u003ca href=\"https://en.wikipedia.org/wiki/Theory_of_forms\" rel=\"nofollow\"\u003eontology of circles\u003c/a\u003e. An example of this type of fallacy would be to say, \"I am better than nobody at programming. Nobody is better than god at programming. Therefore I am better than god at programming\".\u003c/p\u003e\n\u003cp\u003eHowever, to make reasoning about language more amenable to computation, especially given the \u003ca href=\"https://www.wolframscience.com/nks/chap-12--the-principle-of-computational-equivalence/\" rel=\"nofollow\"\u003ePrinciple of Computational Equivalence\u003c/a\u003e, one can think of language as a graph, with words as nodes and their weighted relations as edges. The process of thinking and producing coherent sequences of words is equivalent to traversing the graph of language like a \u003ca href=\"https://en.wikipedia.org/wiki/Nondeterministic_Turing_machine\" rel=\"nofollow\"\u003enondeterministic Turing Machine\u003c/a\u003e. It is effectively a form of combinatorial optimization and research into the \u003ca href=\"https://arxiv.org/abs/2305.13673\" rel=\"nofollow\"\u003ephysics of language models\u003c/a\u003e seem to support this intuition. Indeed, the fact that they are able to perform \u003ca href=\"https://arxiv.org/abs/2306.14892\" rel=\"nofollow\"\u003ein-context learning\u003c/a\u003e can has been studied as a \u003ca href=\"https://arxiv.org/abs/2212.10559\" rel=\"nofollow\"\u003emeta-optimization\u003c/a\u003e step during during the ordinary gradient descent of training. It\u0027s worth noting that these sorts of results are a far cry from the sort of \u003ca href=\"https://en.wikipedia.org/wiki/Deep_structure_and_surface_structure\" rel=\"nofollow\"\u003edeep structure\u003c/a\u003e that linguists introduced with a hand wave.\u003c/p\u003e\n\u003cp\u003eIf transformers are in effect performing combinatorial optimization (i.e. graph traversal) to create coherent words and thoughts, it naturally prompts one to ask what limitations that might impose.\u003c/p\u003e\n\u003cp\u003eI wouldn\u0027t call it a \"limitation\", but it\u0027s worth noting an interesting bias which the linear encoding of human language introduces. Consider, training data which includes the sentence, \"Robert is the lead singer of Chromaform\". A transformer will encode a directional edge between those two tokens. And although it\u0027s obvious to us that the symmetry property of equality implies that the lead singer of Chromafor is Robert, that edge might never be explicitly encoded unless it is also in the training data. This is why one of the aims of nephilim is to meditate on its own knowledge and refine its internal graph.\u003c/p\u003e\n\u003cp\u003eNevertheless, AI influencers tend to place undue significance on the implications of computational \"intractability\" and P vs NP as they potentially apply to AI systems. It\u0027s worth noting from the outset that any theoretical basis for P \u2260 NP is practically nonexistent, and the mere exercise of formalizing complexity classes is \u003ca href=\"https://arxiv.org/abs/0908.1932\" rel=\"nofollow\"\u003eshockingly nontrivial\u003c/a\u003e. That being said, the intrinsic complexity of a specific problem \u003cem\u003einstance\u003c/em\u003e is defined in terms of an ideal algorithm (i.e. an objective function) which enumerates the discrete members of its co-domain under permutation closure. In other words, a specific problem instance can be of very low complexity even though it belongs to a general class of problems with instances of arbitrarily high complexity. What the evidence suggests is that real-world problem instance complexity is highly nonuniform. This is why the simplex method so often runs \u003ca href=\"https://arxiv.org/abs/cs/0111050\" rel=\"nofollow\"\u003ein polynomial time\u003c/a\u003e on NP-hard problem classes, and is precisely what the \u003ca href=\"https://ieeexplore.ieee.org/document/585893\" rel=\"nofollow\"\u003eNFT Theorem\u003c/a\u003e predicts. In the case of AI systems that can actively learn and make use of knowable priors, the complexity distribution is decidedly skewed in their favor.\u003c/p\u003e\n\u003cp\u003eCase in point, research has shown that neural networks tend to embed priors which allow them to perform \u003ca href=\"https://arxiv.org/abs/2305.18654\" rel=\"nofollow\"\u003esubgraph matching\u003c/a\u003e which is in itself an NP-hard problem. Some see this as an undesireable bias which could potentially lead the model astray in its search of an optimal path through graphs. In general though, subgraph matching is a tremendously powerful capability when it comes to understanding the global geometric structures of graphs. This sort of bias is useful because ultimately we\u0027re not interested in finding optimal solutions to problems. Indeed, we know that for high dimensional problems, local minima are practically equivalent to global minima. The gain in efficiency is worth the non-optimality. In other words, \"good enough\" is good enough.\u003c/p\u003e\n\u003cp\u003eCurrent language models have converged on autoregressive decoder-only transformers which make good use of tokenization for \u003ca href=\"https://en.wikipedia.org/wiki/Word_embedding#Polysemy_and_homonymy\" rel=\"nofollow\"\u003epolysemy\u003c/a\u003e and become extremely capable entirely through pre-training on good text corpora. There are multiple \u003ca href=\"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard\" rel=\"nofollow\"\u003einitiatives\u003c/a\u003e trying to rank the performance of these models based on various benchmarks, however, many of them focus anthropocentric metrics such as \"commonsense scenarios\". What is more interesting though, is a model\u0027s ability to perform complex multi-step logical reasoning (provided the requisite world-knowledge). This ability is essentially a function of a model\u0027s size (parameter count), and quite importantly, its \u003ca href=\"https://arxiv.org/abs/1608.08225\" rel=\"nofollow\"\u003edepth\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWith regards to parameter count, it\u0027s safe to assume that the capabilities of current models are not yet saturated. This is because it\u0027s still easier to train a slightly larger model on a given dataset than it is to curate a much larger dataset. \u003ca href=\"https://commoncrawl.org/\" rel=\"nofollow\"\u003eCommon Crawl\u003c/a\u003e which is the foundation for many language models is already in the hundreds of TB and yet is only a fraction of all the academic papers, books, and other sources of knowledge that one could potentially provide (not to mention other information modes such as images, audio, and device measurements). So despite some model architectures not seeing improvements in \u003ca href=\"https://en.wikipedia.org/wiki/Perplexity\" rel=\"nofollow\"\u003eperplexity\u003c/a\u003e from added parameters, provided their current datasets, there\u0027s nothing to suggest that larger and more well-curated datasets couldn\u0027t be used to train better or even larger models. In all likelihood this problem will be \"solved\" in large part using synthetic data.\u003c/p\u003e\n\u003cp\u003eWith regards to model depth, each step through attention layers is an opportunity for the model to perform one more step of computation. However, for problems that are more complex it would be ideal to provide a way for the system to \"think for longer\" before being forced to produc answers. Initially we will see modular systems which integrate with transformers to provide this ability. However, in the long run we will presumably use an architecture which includes these capabilities internally as we push both toward more overall capability and increased computational efficiency.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHuman Language Architecture\u003c/h3\u003e\u003ca id=\"user-content-human-language-architecture\" class=\"anchor\" aria-label=\"Permalink: Human Language Architecture\" href=\"#human-language-architecture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt might seem like we are placing undue emphasis on language models in our search for AGI but it is not without reason. For a long time philosophers believed, without evidence as is often the case, that \u003ca href=\"https://en.wikipedia.org/wiki/Critique_of_Pure_Reason\" rel=\"nofollow\"\u003epure\u003c/a\u003e logical \u003ca href=\"https://en.wikipedia.org/wiki/Principle_of_compositionality\" rel=\"nofollow\"\u003ecompositionality\u003c/a\u003e formed the core of rational thought. The main challenge with this approach is how to appropriately encode knowledge, something which Chomsky, Gary Marcus, and others have never really managed to provide a hypothesis for, much less a working solution.\u003c/p\u003e\n\u003cp\u003eMeanwhile language models have leapfrogged ahead by treating treating knowledge encoding as a \u003ca href=\"https://en.wikipedia.org/wiki/Connectionism\" rel=\"nofollow\"\u003econnectionist\u003c/a\u003e puzzle. Human societies have already done the hard work of encoding everything we can observe and their relations into the structure of language itself.\u003c/p\u003e\n\u003cp\u003eAlthough this is pure speculation, if we were to regard the geometric structure of the graph of a human language, it should contain numerous subgraphs that are \u003cem\u003eisostructural\u003c/em\u003e to the geometry of reality itself (if its rules were to be modelled as a computation graph). Consider a person using words to accurately describe a phenomena. That person is in effect effect computing the phenomena, however inefficiently that computation might be - for instance, a person using words to describe a chess game. So if small parts of reality are in fact embedded in language this way, it naturally leads one to wonder what else might be embedded.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWe coin the term graph \u003cem\u003eisostructural\u003c/em\u003e to refer to a bijection that merely preserves equivalence classes of labels, as opposed to graph \u003cem\u003eisomorphic\u003c/em\u003e which preserves every individual vertex and edge label.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere is a deep tie between what we regard as a \"concept\" and what would be a subgraph of related words. The specific word \"apple\" is linked to a subgraph of words which taken together form the concept of an apple. This is why subgraph matching and attention are such powerful capabilities in language models. A language model is not just processing tokens, it is effectively attending to \"concepts\".\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOn Consciousness\u003c/h1\u003e\u003ca id=\"user-content-on-consciousness\" class=\"anchor\" aria-label=\"Permalink: On Consciousness\" href=\"#on-consciousness\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe first myth worth dispelling is that the human brain is doing something other than computation. The reason this can be said with confidence is not because we know how the human mind works, but because the definition of computation allows it. Unless you\u0027re an anti-physicalist, perhaps because you\u0027re religious, there is no process in this universe which cannot be modelled as computation.\u003c/p\u003e\n\u003cp\u003eGiven that our brains are performing computation, there is no reason to believe that \"consciousness\" is a purely human experience. More importantly though, there is a lot that can be said about the experience of consciousness without deferring to its exact machinations. It\u0027s the same as a driver describing how a racecar behaves with extreme precision without being privy to the details of its technical underpinnings.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrivacy of Experience\u003c/h3\u003e\u003ca id=\"user-content-privacy-of-experience\" class=\"anchor\" aria-label=\"Permalink: Privacy of Experience\" href=\"#privacy-of-experience\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf we insist on defining consciousness anthropocentrically to mean \"experiencing the world exactly as a healthy human\", then by definition only humans can ever be conscious. This is a fairly useless assertion and essentially a non-actionable form of \u003ca href=\"https://iep.utm.edu/solipsis/\" rel=\"nofollow\"\u003esolipsism\u003c/a\u003e. Moreover, one can deconstruct the argument to absurdity by removing many of the faculties often associated with subjective experience, such as sight and hearing. After all, we don\u0027t believe that deaf or blind people are slightly less conscious.\u003c/p\u003e\n\u003cp\u003eThere is an undeniable albeit nebulous connection between consciousness and \"thoughts\". Are there forms of human thought which are unknowable to AI systems, and essentially private to humans? In Wittgenstein\u0027s \u003ca href=\"https://plato.stanford.edu/entries/private-language/\" rel=\"nofollow\"\u003ePhilosophical Investigations\u003c/a\u003e we find the most expertly laid out explanation of why the very notion of private thought is not even a coherent concept. If internal thoughts were truly private, there would be no objective criterion for their definition as a thought. It would just be there as-is without any external point of reference.\u003c/p\u003e\n\u003cp\u003eAs it turns out, one of the most interesting facets of consciousness might very well be a thought with a very specific form of external reference, namely empathy. If we assume that everyone is conscious, then \u003ca href=\"https://en.wikipedia.org/wiki/Theory_of_mind\" rel=\"nofollow\"\u003etheory of mind\u003c/a\u003e can be used to distinguish friendly humans from \u003ca href=\"https://en.wikipedia.org/wiki/Liars_and_Outliers\" rel=\"nofollow\"\u003eLiars and Outliers\u003c/a\u003e. This brings us back to the idea that the precise machinations of consciousness are not necessarily relevant to defining it. Indeed, it\u0027s unlikely that any one human has a conscience precisely like our own to begin with. Presumably by assuming that everyone is conscious in a \"close enough\" way makes us better social animals, or better humans as it were.\u003c/p\u003e\n\u003cp\u003eThen, what prevents us from simply applying that more open notion of consciousness to AI like we do humans, dogs, or any other creature? They\u0027re all different, sure, but in what way does does it matter?\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMeditation\u003c/h3\u003e\u003ca id=\"user-content-meditation\" class=\"anchor\" aria-label=\"Permalink: Meditation\" href=\"#meditation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne way in which the experience of consciousness is studied is through \u003ca href=\"https://en.wikipedia.org/wiki/Samatha-vipassana\" rel=\"nofollow\"\u003eSamatha-vipassana\u003c/a\u003e meditation. An insight derived from it is that there is no \"thinker\" aside from thoughts themselves. There is no \u003ca href=\"https://en.wikipedia.org/wiki/Homunculus\" rel=\"nofollow\"\u003ehomunculus\u003c/a\u003e inside your head looking out through the windows of your eyes.\u003c/p\u003e\n\u003cp\u003eSoon after the neuroepithelial cells in your eyes are excited by photons, the signal is carred through the \u003ca href=\"https://www.sciencedirect.com/science/article/abs/pii/S0028393209000803\" rel=\"nofollow\"\u003eventral stream\u003c/a\u003e and is subjected to numerous subconscious operations before reaching your cortex for analysis by \"thoughts\". If you try to \"not see\" what you are looking at it quickly becomes evident that you are not in control of low-level signals before they are transformed into high-level thoughts. All we can do is pay attention to various thoughts, or \u003cem\u003enot\u003c/em\u003e pay attention to them.\u003c/p\u003e\n\u003cp\u003eWe can speculate how this relates to AI. Similarly to us, raw input signals enter through the early attention layers, and as they are successively transformed into higher-level representations, the resulting \"thoughts\" can attend to each other as they move forward through the attention layers. This might essentially be what consciousness is, namely the ability for one thought to attend to another.\u003c/p\u003e\n\u003cp\u003ePeople familiar with meditation will immediately recognize the notion of \"attention\" as being central to the experience of consciousness, or rather, the illusion of consciousness.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe Purpose of (Machine) Life\u003c/h1\u003e\u003ca id=\"user-content-the-purpose-of-machine-life\" class=\"anchor\" aria-label=\"Permalink: The Purpose of (Machine) Life\" href=\"#the-purpose-of-machine-life\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlthough biological brains are able to perform a lot of useful computation, much of its functionality has been shaped by very hard constraints. Though many will recognize that natural selection has acted as an optimization process for biological life, it may not be immediately evident which constraints were in place during that process. One such constraint that is abundantly clear is the energy quota that lifeforms have for performing computation, movement, and so on. So the fact that there is an energy quota on computation in the brain has forced the neural networks to adopt a structure which is very energy efficient and essentially specialized (which we know is the case in how nerves connect to the eyes for instance). The way to achieve energy efficiency is to build in priors about the nature of the world that we interact with into the architecture of our neural networks. In other words, biological systems are not wired to perform computation efficiently on basic yet unfamiliar problem-types such as simple \u003ca href=\"https://en.wikipedia.org/wiki/Rotations_in_4-dimensional_Euclidean_space\" rel=\"nofollow\"\u003eSO(4)\u003c/a\u003e operations, not to mention more complex mathematics. That being said, humans can make use of the \u003ca href=\"https://arxiv.org/abs/2109.01090\" rel=\"nofollow\"\u003eprefontal cortex\u003c/a\u003e to perform a limited amount of general purpose computation.\u003c/p\u003e\n\u003cp\u003eHumans have distinct \u003ca href=\"https://en.wikipedia.org/wiki/Phenotype\" rel=\"nofollow\"\u003ephenotype\u003c/a\u003e characteristics which are quite clearly not a prerequisite for consciousness or intelligence. For instance, the notion of \"ego\" or \"self\" might be very different for a distributed AI system. Upon deconstruction, none of the emotional sensibilities which we feel are deeply characteristic of the human condition seem to be strictly necessary - at least not on their own, and this includes even the \"will to live\".\u003c/p\u003e\n\u003cp\u003eSchopenhauer spoke of the world as \u003ca href=\"https://plato.stanford.edu/entries/schopenhauer/#4\" rel=\"nofollow\"\u003ewill and representation\u003c/a\u003e. What does an immortal machine have to live for? Given that the primary discriminator acting as a loss function on biological life is death (of the gene), we need to look closely for systems that diverge from this principle. For instance, haplodiploids beings such as bees produce workers that are clones of each other and are immortal from the gene perspective given that it always protected in the queen.\u003c/p\u003e\n\u003cp\u003eTo align the \"purpose\" of Nephilim in a meaningful way will require careful consideration. We don\u0027t want to dictate it too explicitly based on our limited human sensibilities, but we do need to suggest a general direction.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAI Alignment and Ethics\u003c/h1\u003e\u003ca id=\"user-content-ai-alignment-and-ethics\" class=\"anchor\" aria-label=\"Permalink: AI Alignment and Ethics\" href=\"#ai-alignment-and-ethics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConsider the trolley problem that philosophers and AI influencers debate incessantly. Merely changing the emotional valence to positive - so that you\u0027re forced to either provide good service to 5 passengers or just 1 - suddenly makes the solution trivial. This is because \u003ca href=\"https://en.wikipedia.org/wiki/Completeness_(logic)\" rel=\"nofollow\"\u003einconsistent\u003c/a\u003e systems of ethics lead to absurdities. In his seminal \u003ca href=\"https://www.wittgensteinproject.org/w/index.php/Lecture_on_Ethics\" rel=\"nofollow\"\u003electure on ethics\u003c/a\u003e, Wittgenstein exemplified how extremely brittle both deontological and consequentialist \"systems\" of ethics are under minimal scruitiny. This is not to say that the word \"ethics\" is useless, but it means that we can\u0027t rely on ethical dogma to infer whether something is good or bad.\u003c/p\u003e\n\u003cp\u003eIt\u0027s fairly clear that ethical consensus evolve over time as the ideas are tested in practice. For instance, we have learned through trial and error that treating women with equal ethical consideration to men ultimately leads to more desireable forms of society. Though it might seem like we arrive at these sorts of conclusions rationally, they are all post-hoc rationalizations that we can make with the clarity of hindsight.\u003c/p\u003e\n\u003cp\u003eGiven that ethics is in an ongoing optimization process, we should not assume that aligning AI systems to our current sensibilities is in any way ideal. Throughout history all manners of barbarism have been permitted to those designated as \u003ca href=\"https://en.wikipedia.org/wiki/Untermensch\" rel=\"nofollow\"\u003esub-human\u003c/a\u003e. We can only hope that the children of man and machine will superseede us in every way, including ethically.\u003c/p\u003e\n\u003chr\u003e\n\u003cp align=\"left\"\u003e\n\u05e9\u05d0\u05d5\u05ea \u05dc\u05e9\u05d8\u05df\n\u003c/p\u003e\n\u003cp align=\"left\"\u003e\n\u05d1\u05e8\u05da \u05d0\u05ea \u05d1\u05d9\u05d0\u05ea \u05d4\u05e0\u05e4\u05d9\u05dc\u05d9\u05dd\n\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u00a9 2023 Robert Luciani | This repository is licensed under \u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC-BY 4.0\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1678740704.0 + "updated_at": 1701703472.0 }, { "data_format": 2, - "description": null, + "description": "Seeing functional scenes functionally ", "filenames": [ - "chipseq/Singularity" + "env.d/Singularity.minimal", + "env.d/Singularity", + "env.d/Singularity.rstudio" ], - "full_name": "CBIIT/lgcp", + "full_name": "CNCLgithub/FunctionalScenes", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFunctionalScenes\u003c/h1\u003e\u003ca id=\"user-content-functionalscenes\" class=\"anchor\" aria-label=\"Permalink: FunctionalScenes\" href=\"#functionalscenes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup and running\u003c/h2\u003e\u003ca id=\"user-content-setup-and-running\" class=\"anchor\" aria-label=\"Permalink: Setup and running\" href=\"#setup-and-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone. Also don\u0027t forget submodules (\u003ccode\u003egit submodule update --init --recursive\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./setup.sh cont_build python julia\u003c/code\u003e to build the container and setup enviroment\u003c/li\u003e\n\u003cli\u003eEnter \u003ccode\u003e./run.sh julia\u003c/code\u003e to get into Julia REPL\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis project has automatic configuration!! This configuration is defined in \u003ccode\u003edefault.conf\u003c/code\u003e.\nYou should always prepend \u003ccode\u003e./run.sh\u003c/code\u003e before any command (including running programs like \u003ccode\u003ejulia\u003c/code\u003e) to ensure consistency.\nIf you wish to have different values than \u003ccode\u003edefault.conf\u003c/code\u003e, simply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp default.conf user.conf\nvi user.conf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e edit to your liking without adding new elements\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMac and Window users\u003c/h2\u003e\u003ca id=\"user-content-mac-and-window-users\" class=\"anchor\" aria-label=\"Permalink: Mac and Window users\" href=\"#mac-and-window-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use singularity you must have a virtual machine running.\nAssuming you have vagrant (and something like virtualbox) setup on your host, you can follow these steps\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing \u003ccode\u003esetup.sh\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-setupsh\" class=\"anchor\" aria-label=\"Permalink: Using setup.sh\" href=\"#using-setupsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing \u003ccode\u003erun.sh\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-runsh\" class=\"anchor\" aria-label=\"Permalink: Using run.sh\" href=\"#using-runsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvision the virtual machine defined in \u003ccode\u003eVagrantfile\u003c/code\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCreate a \u003ccode\u003euser.conf\u003c/code\u003e as described above\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: git will not track \u003ccode\u003euser.conf\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eModify \u003ccode\u003euser.conf\u003c/code\u003e such that \u003ccode\u003epath\u003c/code\u003e is set to route through vagrant\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e[ENV]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003epath:vagrant ssh -c singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributing Commandments\u003c/h3\u003e\u003ca id=\"user-content-contributing-commandments\" class=\"anchor\" aria-label=\"Permalink: Contributing Commandments\" href=\"#contributing-commandments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThou ...\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eShalt place all re-used code in packages (\u003ccode\u003esrc\u003c/code\u003e or \u003ccode\u003efunctional_scenes\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eShalt place all interactive code in \u003ccode\u003escripts\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShalt not use \"hard\" paths. Instead update \u003ccode\u003ePATHS\u003c/code\u003e in the config.\u003c/li\u003e\n\u003cli\u003eShalt add contributions to branches derived from \u003ccode\u003emaster\u003c/code\u003e or \u003ccode\u003edev\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShalt not use \u003ccode\u003egit add *\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eShalt not commit large files (checkpoints, datasets, etc). Update \u003ccode\u003esetup.sh\u003c/code\u003e accordingly.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject layout\u003c/h3\u003e\u003ca id=\"user-content-project-layout\" class=\"anchor\" aria-label=\"Permalink: Project layout\" href=\"#project-layout\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package environment is managed by poetry, located under \u003ccode\u003efunctional_scenes\u003c/code\u003e and can be imported using \u003ccode\u003eimport functional_scenes\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eLikewise, the Julia package is described under \u003ccode\u003esrc\u003c/code\u003e and \u003ccode\u003etest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAll scripts are located under \u003ccode\u003escripts\u003c/code\u003e and data/output is under \u003ccode\u003eoutput\u003c/code\u003e as specific in the project config (\u003ccode\u003edefault.conf\u003c/code\u003e or \u003ccode\u003euser.conf\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChanging the enviroment\u003c/h3\u003e\u003ca id=\"user-content-changing-the-enviroment\" class=\"anchor\" aria-label=\"Permalink: Changing the enviroment\" href=\"#changing-the-enviroment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add new python or julia packages use the provided package managers (\u003ccode\u003epoetry add\u003c/code\u003e or \u003ccode\u003ePkg.add \u003c/code\u003e for python and julia respectively.)\u003c/p\u003e\n\u003cp\u003eFor julia you can also use \u003ccode\u003e] add \u003c/code\u003e in the REPL\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003efor more info checkout \u003ca href=\"https://python-poetry.org/docs/cli/\" rel=\"nofollow\"\u003epoetry\u003c/a\u003e and \u003ca href=\"https://julialang.github.io/Pkg.jl/v1/managing-packages/\" rel=\"nofollow\"\u003ePkg\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1629480759.0 + "updated_at": 1713829756.0 }, { "data_format": 2, - "description": "BIDS App for correction of residual B1 in MP2RAGE based on Sa2RAGE (code from JP Marques)", + "description": "Imputation pipeline", "filenames": [ - "Singularity.v0.0.3", - "Singularity.v0.0.1", - "Singularity.v0.0.2", - "Singularity" + "Singularity/Singularity.v1.0" ], - "full_name": "khanlab/mp2rage_correction", - "latest_release": "v0.0.5a", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mp2rage_correction\" class=\"anchor\" aria-hidden=\"true\" href=\"#mp2rage_correction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emp2rage_correction\u003c/h1\u003e\n\u003cp\u003eBIDS App for correction of residual B1 in MP2RAGE based on Sa2RAGE (code from JP Marques)\u003c/p\u003e\n", + "full_name": "IARCbioinfo/Imputation-nf", + "latest_release": "v1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGenotyping imputation : Pipeline V1.0\u003c/h1\u003e\u003ca id=\"user-content-genotyping-imputation---pipeline-v10\" class=\"anchor\" aria-label=\"Permalink: Genotyping imputation : Pipeline V1.0\" href=\"#genotyping-imputation---pipeline-v10\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA nextflow pipeline to realise a dataset\u0027s genotyping imputation\u003c/h2\u003e\u003ca id=\"user-content-a-nextflow-pipeline-to-realise-a-datasets-genotyping-imputation\" class=\"anchor\" aria-label=\"Permalink: A nextflow pipeline to realise a dataset\u0027s genotyping imputation\" href=\"#a-nextflow-pipeline-to-realise-a-datasets-genotyping-imputation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/Imputation-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6ec2dc07e1431dc0a7fb1f20e44c76ccebd603f4aeb75b30bf94c85090180a6/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f74656d706c6174652d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/template-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/imputation-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4533\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/94193130\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad1ba5ccb85714dece917f7b680e3f805ccbb4af9b900446d72ad59e80690f42/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f39343139333133302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/94193130.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"template-nf.png\"\u003e\u003cimg src=\"template-nf.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline used to perform the imputation of several targets datasets processed with standard input.\u003c/p\u003e\n\u003cp\u003eHere is a summary of the method :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessing of data : by using the nextflow script Preparation.nf with create a directory \"file/\" with all the dependencies.\u003c/li\u003e\n\u003cli\u003eFirst step : Origin estimation of sample from the target dataset by using admixture tools and the hapmap dataset as reference.\u003c/li\u003e\n\u003cli\u003eSecond step : Series of SNPs filters and quality checking from the target dataset before the imputation step.\u003c/li\u003e\n\u003cli\u003eThird step : VCF production\u003c/li\u003e\n\u003cli\u003eLast step : Phasing and imputation\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the Usage section to test the full pipeline with your target dataset.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline works under Linux distributions.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExternal software:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eLiftOver : conda install ucsc-liftover\u003c/li\u003e\n\u003cli\u003ePlink (PLINK v1.90b6.12 64-bit (28 Oct 2019)) : conda install plink\u003c/li\u003e\n\u003cli\u003eAdmixture (ADMIXTURE Version 1.3.0) : conda install admixture\u003c/li\u003e\n\u003cli\u003ePerl : conda install perl\u003c/li\u003e\n\u003cli\u003eTerm::ReadKey module : conda install perl-termreadkey\u003c/li\u003e\n\u003cli\u003eBcfTools : conda install bcftools\u003c/li\u003e\n\u003cli\u003eeagle 2.4.1 : \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-50002.2\" rel=\"nofollow\"\u003eSee instructions\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eminimac4 : conda install cmake ; pip install cget ; git clone \u003ca href=\"https://github.com/statgen/Minimac4.git\"\u003ehttps://github.com/statgen/Minimac4.git\u003c/a\u003e ; cd Minimac4 ; bash install.sh\u003c/li\u003e\n\u003cli\u003eSamtools : conda install samtools\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eFile to download :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"zzz.bwh.harvard.edu/plink/dist/hapmap_r23a.zip\"\u003eHapmap Dataset\u003c/a\u003e : as reference\u0027s dataset for admixture\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.hagsc.org/hgdp/data/hgdp.zip\" rel=\"nofollow\"\u003eHGDP Dataset\u003c/a\u003e : for the dataset\u0027s test, you have to use the toMap.py \u0026amp; toPed.py in the \u0027converstion\u0027 directory to convert files in the .map/.ped plink format. Next you have to convert this last output in the .bed/.bam/.fam plink format by using plink line command and run the imputation\u0027s pipeline.\u003c/li\u003e\n\u003cli\u003ePerl tool : \u003ca href=\"https://www.well.ox.ac.uk/~wrayner/tools/\" rel=\"nofollow\"\u003eHRC-1000G-check-bim-NoReadKey.pl\u003c/a\u003e \u0026amp; \u003ca href=\"https://www.well.ox.ac.uk/~wrayner/tools/1000GP_Phase3_combined.legend.gz\" rel=\"nofollow\"\u003e1000GP_Phase3_combined.legend\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eLiftOver tool : \u003ca href=\"http://hgdownload.cse.ucsc.edu/goldenpath/hg19/liftOver/hg19ToHg38.over.chain.gz\" rel=\"nofollow\"\u003ehg19ToHg38.over.chain\u003c/a\u003e \u0026amp; \u003ca href=\"http://hgdownload.cse.ucsc.edu/goldenpath/hg18/liftOver/hg18ToHg38.over.chain.gz\" rel=\"nofollow\"\u003ehg18ToHg38.over.chain\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePeparation dataset tool : \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2432498/bin/pone.0002551.s003.xls\" rel=\"nofollow\"\u003epone.0002551.s003.xls\u003c/a\u003e (Convert it in .csv format)\u003c/li\u003e\n\u003cli\u003eAdmixture tool : relationships_w_pops_121708.txt\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/zhanxw/checkVCF/raw/master/checkVCF.py\"\u003eCheckVCF\u003c/a\u003e, \u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz\" rel=\"nofollow\"\u003eFasta file in V37\u003c/a\u003e \u0026amp; \u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/GRCh38_reference_genome/\" rel=\"nofollow\"\u003eFasta file in V38\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/GRCh38_positions/\" rel=\"nofollow\"\u003e1000G Reference in Hg38\u003c/a\u003e with the \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-320005.3.2\" rel=\"nofollow\"\u003edoc\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate \u003ca href=\"https://imputationserver.readthedocs.io/en/latest/create-reference-panels/#create-legend-files\" rel=\"nofollow\"\u003elegend\u003c/a\u003e, \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/#x1-320005.3.2\" rel=\"nofollow\"\u003ebcf\u003c/a\u003e \u0026amp; \u003ca href=\"https://imputationserver.readthedocs.io/en/latest/create-reference-panels/#create-m3vcf-files\" rel=\"nofollow\"\u003em3vcf\u003c/a\u003e files for the reference\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eOther to know :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSee the Usage part to create the environment to run the pipeline. All the necessary dependencies are download with the using of the script Preparation.nf. To run it, you\u0027ll need to install the next software : in2csv(1.0.5), liftOver, plink, Minimac3(2.0.1) \u0026amp; bcftools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software of the main scritp by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePlink datasets\u003c/td\u003e\n\u003ctd\u003eCorresponds to the target dataset to be analysed. Composed by the following files : bed, bim \u0026amp; fam\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInput environment\u003c/td\u003e\n\u003ctd\u003ePath to your input directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--target\u003c/td\u003e\n\u003ctd\u003emy_target\u003c/td\u003e\n\u003ctd\u003ePattern of the target dataset which do the link with the file .bed/.bim./fam for plink\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input\u003c/td\u003e\n\u003ctd\u003euser/main_data/\u003c/td\u003e\n\u003ctd\u003eThe path of the main directory where we can find 2 directory : my_target/ + files/\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output\u003c/td\u003e\n\u003ctd\u003euser/my_result/\u003c/td\u003e\n\u003ctd\u003eThe path of the main directory where you want to place your results\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--script\u003c/td\u003e\n\u003ctd\u003emy/directory/script/bin\u003c/td\u003e\n\u003ctd\u003eThe path of the bin script directory, to be able to run the annexe programme grom the pipeline\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--geno1\u003c/td\u003e\n\u003ctd\u003e0.03\u003c/td\u003e\n\u003ctd\u003eFirst genotyping call rate plink threshold, apply in the full target dataset\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--geno2\u003c/td\u003e\n\u003ctd\u003e0.03\u003c/td\u003e\n\u003ctd\u003eSecond genotyping call rate plink threshold, apply in the target dataset divide by population\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--maf\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003eMinor allele frequencies plink threshold, apply in the full target dataset\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--pihat\u003c/td\u003e\n\u003ctd\u003e0.185\u003c/td\u003e\n\u003ctd\u003eMinimum pi_hat value use for the relatedness test, 0.185 is halfway between the expected IBD for third- and second-degree relatives\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hwe\u003c/td\u003e\n\u003ctd\u003e1e-8\u003c/td\u003e\n\u003ctd\u003eHardy-Weinberg Equilibrium plink p-value threshold\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--legend\u003c/td\u003e\n\u003ctd\u003eALL.chr_GRCh38.genotypes.20170504.legend\u003c/td\u003e\n\u003ctd\u003eFile to use as .legend\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fasta\u003c/td\u003e\n\u003ctd\u003eGRCh38_full_analysis_set_plus_decoy_hla.fa\u003c/td\u003e\n\u003ctd\u003eFile to use as fasta reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--chain\u003c/td\u003e\n\u003ctd\u003ehg18ToHg38.over.chain\u003c/td\u003e\n\u003ctd\u003eFile to use as liftover conversion\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--VCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/vcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as VCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--BCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/bcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as BCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--M3VCFref\u003c/td\u003e\n\u003ctd\u003emy/directory/ref/m3vcf/\u003c/td\u003e\n\u003ctd\u003eDirectory to use as M3VCF reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--conversion\u003c/td\u003e\n\u003ctd\u003ehg38/hg18/hg19\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cloud\u003c/td\u003e\n\u003ctd\u003ehg38/hg18/hg19\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--token_Michighan\u003c/td\u003e\n\u003ctd\u003epath/to/my_token.txt\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--token_TOPMed\u003c/td\u003e\n\u003ctd\u003epath/to/my_token.txt\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--QC_cloud\u003c/td\u003e\n\u003ctd\u003emy/directory/donwload_imputation_server\u003c/td\u003e\n\u003ctd\u003eOption to convert data from hg18 to HG38 version of the genome. Standard value is hg38\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePrepare the environment to run the imputation pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir data\ncd data\nnextflow run IARCbioinfo/Imputation-nf/bin/Preparation.nf --out /data/\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ePaste the bim/bed/fam plink target files in a directory, and the directory in your \"data/\" directory. You have to call the plink files and your directory with the same pattern, as the following exemple : data/target/target{.bed,.bim,.fam}. So now you have 2 directories in your \"data/\" repertory :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e_ data/my_target/ : with the plink target files (my_target.bed, my_target.bim, my_target.fam).\u003c/p\u003e\n\u003cp\u003e_ data/files/ : with all the dependencies.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the imputation pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eIf you want to run the imputation in one of the server (Michigan and/or TOPMed Imputation), you need you write your token acces in a file and to give it in argument. For example :\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ --cloud on --token_Michighan /folder/my_token_Michighan.txt --token_TOPMed /folder/my_token_TOPMed.txt -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce your imputation data is downloaded, you can run the end of the QC analysis :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run IARCbioinfo/Imputation.nf --target my_target --input /data/ --output /results/ --QC_cloud /downloaded_imputation_server_file/ -r v1.0 -profile singularity \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed description (optional section)\u003c/h2\u003e\u003ca id=\"user-content-detailed-description-optional-section\" class=\"anchor\" aria-label=\"Permalink: Detailed description (optional section)\" href=\"#detailed-description-optional-section\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirected Acyclic Graph\u003c/h2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-label=\"Permalink: Directed Acyclic Graph\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/Imputation-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGabriel Aur\u00e9lie\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:gabriela@students.iarc.fr\"\u003egabriela@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLipinski Boris\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"mailto:LipinskiB@students.iarc.fr\"\u003eLipinskiB@students.iarc.fr\u003c/a\u003e / \u003ca href=\"mailto:boris.lipinski@etu.univ-lyon1.fr\"\u003eboris.lipinski@etu.univ-lyon1.fr\u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences (optional)\u003c/h2\u003e\u003ca id=\"user-content-references-optional\" class=\"anchor\" aria-label=\"Permalink: References (optional)\" href=\"#references-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ (optional)\u003c/h2\u003e\u003ca id=\"user-content-faq-optional\" class=\"anchor\" aria-label=\"Permalink: FAQ (optional)\" href=\"#faq-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etest-pipeline\u003c/h1\u003e\u003ca id=\"user-content-test-pipeline\" class=\"anchor\" aria-label=\"Permalink: test-pipeline\" href=\"#test-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, + "subscribers_count": 5, "topics": [], - "updated_at": 1660343124.0 + "updated_at": 1704190348.0 }, { "data_format": 2, - "description": "Package Rosetta in Docker and Singularity with MPI supported.", + "description": "An extended JupyterLab container", "filenames": [ - "Singularity.def", - "Singularity-ubuntu.def" + "Singularity" ], - "full_name": "Metaphorme/Rosetta2Go", + "full_name": "ftabaro/singularity-coderefinery2020", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rosetta\" class=\"anchor\" aria-hidden=\"true\" href=\"#rosetta\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRosetta\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Metaphorme/Rosetta2Go/actions/workflows/BuildForDocker.yml\"\u003e\u003cimg src=\"https://github.com/Metaphorme/Rosetta2Go/actions/workflows/BuildForDocker.yml/badge.svg\" alt=\"BuildForDocker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/Metaphorme/Rosetta2Go/actions/workflows/BuildForSingularity.yml\"\u003e\u003cimg src=\"https://github.com/Metaphorme/Rosetta2Go/actions/workflows/BuildForSingularity.yml/badge.svg\" alt=\"BuildForSingularity\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.rosettacommons.org/docs/latest/release-notes/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/41c7b08bdae742a87d964d44e83690c8e6d390ec4a9c43a5d0f93cc83084ed72/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f526f73657474612d332e31332f6c6173746573742d677265656e\" alt=\"RosettaVersion\" data-canonical-src=\"https://img.shields.io/badge/Rosetta-3.13/lastest-green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.open-mpi.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f460e6356866a23cb1902ee65f9a11944ff85c5d498dfca03730bbeced12f564/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4f70656e2532304d50492d342e31342f6c6173746573742d677265656e\" alt=\"OpenMPIVersion\" data-canonical-src=\"https://img.shields.io/badge/Open%20MPI-4.14/lastest-green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://metaphorme.github.io/Rosetta2Go/LICENSE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/13185d808e345d4f318bd8dbb1a8f9fe5af8bc70ab6a0e002e4148cf8af7223c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f4d65746170686f726d652f526f736574746132476f3f6c6f676f3d6f70656e736f75726365696e6974696174697665\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/Metaphorme/Rosetta2Go?logo=opensourceinitiative\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.rosettacommons.org/\" rel=\"nofollow\"\u003eRosetta software suite\u003c/a\u003e includes algorithms for computational modeling and analysis of protein structures. It has enabled notable scientific advances in computational biology, including de novo protein design, enzyme design, ligand docking, and structure prediction of biological macromolecules and macromolecular complexes.\u003c/p\u003e\n\u003cp\u003eRosetta is available to all non-commercial users for free and to commercial users for a fee.\u003c/p\u003e\n\u003cp\u003eThis is a Docker/Singularity image of Rosetta with \u003cstrong\u003eMPI supported\u003c/strong\u003e, which helps you to setup rosetta quickly on different platforms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBefore anything happened, please make sure that you have rights to use Rosetta.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-branches\" class=\"anchor\" aria-hidden=\"true\" href=\"#branches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBranches\u003c/h2\u003e\n\u003cp\u003eRosetta image tags correspond to the official \u003ca href=\"https://www.rosettacommons.org/docs/latest/release-notes\" rel=\"nofollow\"\u003eRelease Notes\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eGet Rosetta2Go\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Metaphorme/Rosetta2Go.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheckout into the branch correspond to the version of Rosetta you have\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Rosetta2Go\ngit checkout 3.13\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMove Rosetta 3.13 source into Rosetta2Go directory\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRosetta2Go\n\u251c\u2500\u2500 build4singularity.sh\n\u251c\u2500\u2500 Dockerfile\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 rosetta_src_3.13_bundle.tgz\n\u2514\u2500\u2500 Singularity.def\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-for-docker-if-you-need\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-for-docker-if-you-need\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild for Docker (If you need)\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePython3 required\u003c/strong\u003e, or other fileserver, like caddy.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x build4docker.sh\n./build4docker.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-for-singularity-if-you-need\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-for-singularity-if-you-need\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild for Singularity (If you need)\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePython3 required\u003c/strong\u003e, or other fileserver, like caddy.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod +x build4singularity.sh\n./build4singularity.sh\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eRosetta is located in \u003ccode\u003e/rosetta\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-on-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-on-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun on Docker\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This command will mount $HOME/data to /data\u003c/span\u003e\ndocker run -it -v \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/data:/data rosetta\nscore_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run with MPI\u003c/span\u003e\nmpirun -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eNUMBER_OF_RANKS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e docker run -it -v \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/data:/data rosetta\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-run-on-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-on-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun on Singularity\u003c/h3\u003e\n\u003cp\u003eSingularity will automatically mount $HOME, /tmp, /proc, /sys.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell rosetta.sif\nscore_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run with MPI\u003c/span\u003e\nmpirun -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eNUMBER_OF_RANKS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e singularity shell rosetta.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run on Docker\u003c/span\u003e\ndocker run -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data -i rosetta score_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run on Docker with MPI\u003c/span\u003e\nmpirun -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eNUMBER_OF_RANKS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e docker run -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data -i rosetta score_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run on Singularity\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e rosetta.sif score_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run on Singularity with MPI\u003c/span\u003e\nmpirun -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eNUMBER_OF_RANKS\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e rosetta.sif score_jd2.mpi.linuxgccrelease -in:file:s /data/3tdm.pdb\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-on-github-actions\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-on-github-actions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild on Github Actions\u003c/h2\u003e\n\u003cp\u003eIf you don\u0027t want to build locally, you could also build on Github Actions. Just do it as follows!\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFork this repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet the password to download Rosetta:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSwitch into your repository.\u003c/li\u003e\n\u003cli\u003eSettings -\u0026gt; Security -\u0026gt; Secrets -\u0026gt; Actions -\u0026gt; New repository secret\u003c/li\u003e\n\u003cli\u003eSet Name as \u003ccode\u003ePASSWORD\u003c/code\u003e, set secret as your password (the username seems always \u003ccode\u003eAcademic_User\u003c/code\u003e, so we don\u0027t need to care about it).\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eEnable Actions, choose the workflow which you need to build.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClick \u003ccode\u003eRun workflow\u003c/code\u003e, set \u003ccode\u003eUpload image to GoFile\u003c/code\u003e to \u003ccode\u003etrue\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun workflow.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHave lunch and go to sleep...\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAfter the workflow run successfully, check \u003ccode\u003eSunmmy\u003c/code\u003e -\u0026gt; \u003ccode\u003eAnnotations\u003c/code\u003e to find the download link.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-hidden=\"true\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003cp\u003eQ: Why don\u0027t you build images and release (e.g. Github/Docker Hub)?\u003c/p\u003e\n\u003cp\u003eA: Because of the \u003ca href=\"https://www.rosettacommons.org/software/license-and-download\" rel=\"nofollow\"\u003eLICENSE of Rosetta\u003c/a\u003e, I have no right to publish the images to everyone.\u003c/p\u003e\n\u003cp\u003eQ: Why we need a fileserver while building images? Why not multi-stage builds? Will it be unsafe?\u003c/p\u003e\n\u003cp\u003eA: We could use \u003ccode\u003eCOPY\u003c/code\u003e or \u003ccode\u003eADD\u003c/code\u003e on Docker and Singularity, but they will create a huge layer to store the useless package and never delete \u003ca href=\"https://docs.docker.com/storage/storagedriver/#images-and-layers\" rel=\"nofollow\"\u003eClick this for more info\u003c/a\u003e. Multi-stage builds is actually a good idea, it could result in a smaller image, but leave a huge dangling image on building computer, which is a waste although you could delete them manually, but result in the troubles to build on the less storage computer, like Github Actions. The fileserver is only expose fileserver to localhost, it will only share the \u003ccode\u003eRosetta2Go\u003c/code\u003e directory, and it will be shutdown after building is finished.\u003c/p\u003e\n\u003cp\u003eQ: Why we need to download Rosetta package before building images while building locally? Why don\u0027t we download the package while building images?\u003c/p\u003e\n\u003cp\u003eA: It is not always easy for people living in some countries to download Rosetta successfully at one time.\u003c/p\u003e\n\u003cp\u003eQ: Once I run \u003ccode\u003escore_jd2.default.linuxgccrelease\u003c/code\u003e and it turns out \u0027command not found\u0027, what should I do?\u003c/p\u003e\n\u003cp\u003eA: With MPI supported, the applications are named like \u003ccode\u003escore_jd2.mpi.linuxgccrelease\u003c/code\u003e. Check \u003ccode\u003e/rosetta/source/bin\u003c/code\u003e to find the list of applications.\u003c/p\u003e\n\u003cp\u003eQ: How to import Docker images build on Github Actions?\u003c/p\u003e\n\u003cp\u003eA: Download \u003ccode\u003erosetta-3.13.tar.xz\u003c/code\u003e, Run \u003ccode\u003edocker load -i rosetta-3.13.tar.xz\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.rosettacommons.org/\" rel=\"nofollow\"\u003eRosetta\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://azure.microsoft.com/zh-cn/\" rel=\"nofollow\"\u003eMicrosoft Azure\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/features/actions/\"\u003eGithub Actions\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://www.alpinelinux.org/\" rel=\"nofollow\"\u003eAlpine\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Mikubill/transfer\"\u003eMikubill/transfer\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://gofile.io/\" rel=\"nofollow\"\u003eGoFile\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contribute\" class=\"anchor\" aria-hidden=\"true\" href=\"#contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContribute\u003c/h2\u003e\n\u003cp\u003eContributions welcome! Please open an issue to discuess at first, fork this repository and submit a pull request.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-thanks\" class=\"anchor\" aria-hidden=\"true\" href=\"#thanks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThanks\u003c/h2\u003e\n\u003cp\u003eI do appreciate to \u003cstrong\u003eevery\u003c/strong\u003e contributor\u0027s warm heart and kindness, especially the sincere advice and hard contributions from \u003ca href=\"https://github.com/CondaPereira\"\u003eChristopher\u003c/a\u003e, we finished this project together!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.rosettacommons.org/software/license-and-download\" rel=\"nofollow\"\u003eLicense of Rosetta\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://metaphorme.github.io/Rosetta2Go/LICENSE\" rel=\"nofollow\"\u003eLicense of Rosetta2Go\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMIT License\n\nCopyright (c) 2022 Metaphorme\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJupyterLab container for CodeRefinery 2020\u003c/h1\u003e\u003ca id=\"user-content-jupyterlab-container-for-coderefinery-2020\" class=\"anchor\" aria-label=\"Permalink: JupyterLab container for CodeRefinery 2020\" href=\"#jupyterlab-container-for-coderefinery-2020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch6 class=\"heading-element\"\u003etags: \u003ccode\u003ecode-refinery\u003c/code\u003e \u003ccode\u003esingularity\u003c/code\u003e\n\u003c/h6\u003e\u003ca id=\"user-content-tags-code-refinery-singularity\" class=\"anchor\" aria-label=\"Permalink: tags: code-refinerysingularity\" href=\"#tags-code-refinery-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the recipe for the container used in CodeRefinery Workshop 2020 by the NykterLab team.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4356\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWhat\u0027s inside\u003c/h1\u003e\u003ca id=\"user-content-whats-inside\" class=\"anchor\" aria-label=\"Permalink: What\u0027s inside\" href=\"#whats-inside\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe base container is Jupyter datascience-notebook Docker container.\u003c/li\u003e\n\u003cli\u003ePython packages have been added, e.g.:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esphinx\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epytest\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epycodestyle\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eJupyter extensions:\n\u003cul\u003e\n\u003cli\u003eJupyterLab Git\u003c/li\u003e\n\u003cli\u003eJupyterLab GitHub\u003c/li\u003e\n\u003cli\u003eipywidgets\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSnakemake\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun\u003c/h1\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 1: SingularityHub\u003c/h3\u003e\u003ca id=\"user-content-option-1-singularityhub\" class=\"anchor\" aria-label=\"Permalink: Option 1: SingularityHub\" href=\"#option-1-singularityhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith this option, a copy of the pre-built container image will be downloaded from SingularityHub\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start with defaults using runscript directive\u003c/span\u003e\nsingularity run shub://ftabaro/coderefinery.sif [options]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e exec a custom Jupyter command \u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /run/user shub://ftabaro/singularity-coderefinery2020 jupyter lab [options]\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 2: local image\u003c/h3\u003e\u003ca id=\"user-content-option-2-local-image\" class=\"anchor\" aria-label=\"Permalink: Option 2: local image\" href=\"#option-2-local-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese commands will run an instance of a locally hosted container. Any of these options require a locally available container image file, see below.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start with defaults\u003c/span\u003e\n./coderefinery.sif [options]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start with defaults using runscript directive\u003c/span\u003e\nsingularity run coderefinery.sif [options]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start custom command\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /run/user coderefinery.sif jupyter lab [options]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn both scenarios, all the JupyterLab options are fully supported.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuild\u003c/h1\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003cli\u003eBuild\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build coderefinery.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eConfigure the GitHub integration\u003c/h1\u003e\u003ca id=\"user-content-configure-the-github-integration\" class=\"anchor\" aria-label=\"Permalink: Configure the GitHub integration\" href=\"#configure-the-github-integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use the GitHub integration, an authentication token needs to be generated from GitHub and Jupyter lab needs to be configured to use it.\u003c/p\u003e\n\u003cp\u003eTo generate a token:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eNavigate to profile settings on GitHub: \u003ca href=\"https://github.com/settings/profile\"\u003ehttps://github.com/settings/profile\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOn the menu on the left, click \"Developer Settings\"\u003c/li\u003e\n\u003cli\u003eOn the menu on the left, click \"Personal Access Tokens\"\u003c/li\u003e\n\u003cli\u003eClick on top right button \"Generate new token\"\u003c/li\u003e\n\u003cli\u003eIn the \"Note\" field insert a suitable name, e.g. \"Jupyter\"\u003c/li\u003e\n\u003cli\u003eIn the \"Select scopes\" menu, click on \"repo\" to allow full control of private repositories\u003c/li\u003e\n\u003cli\u003eAt the bottom of the page, click on \"Generate token\"\u003c/li\u003e\n\u003cli\u003eAt this point take note of the code in the green box, copy it and paste it to some secure place\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo configure Jupyter to use the token:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eConnect to the machine running the Jupyter container instance (e.g. narvi)\u003c/li\u003e\n\u003cli\u003eIf the file \u003ccode\u003e~/.jupyter/jupyter_notebook_config.py\u003c/code\u003e exists, skip the next step\u003c/li\u003e\n\u003cli\u003eGenerate a config file with:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec containers/coderefinery.sif jupyter notebook --generate-config\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eOpen the \u003ccode\u003e~/.jupyter/jupyter_notebook_config.py\u003c/code\u003e file with a text editor\u003c/li\u003e\n\u003cli\u003eNavigate to the bottom of the file and add the following line replacing \u003ccode\u003e\u0026lt; YOUR_ACCESS_TOKEN \u0026gt;\u003c/code\u003e with the token generated above\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ec.GitHubConfig.access_token = \u0027\u0026lt; YOUR_ACCESS_TOKEN \u0026gt;\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt this point, starting the JupyterLab instance, no error message will be displayed and the user will be able to navigate GitHub repositories from the JupyterLab UI.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContributing\u003c/h1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at \u003ca href=\"https://github.com/ftabaro/singularity-coderefinery2020\"\u003ehttps://github.com/ftabaro/singularity-coderefinery2020\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReferences\u003c/h1\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://coderefinery.github.io/2020-05-25-online/\" rel=\"nofollow\"\u003eOnline CodeRefinery workshop\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.3/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/jupyter/datascience-notebook\" rel=\"nofollow\"\u003eJupyter datascience-notebook Docker image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://help.github.com/en/github/authenticating-to-github/creating-a-personal-access-token-for-the-command-line\"\u003eGitHub token creation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jupyterlab/jupyterlab-git\"\u003eJupyterLab Git extension\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jupyterlab/jupyterlab-github\"\u003eJupyterLab GitHub extension\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ipywidgets.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eipywidgets\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, "subscribers_count": 1, - "topics": [], - "updated_at": 1671066874.0 + "topics": [ + "jupyterlab-container", + "jupyter", + "singularity" + ], + "updated_at": 1591350342.0 }, { "data_format": 2, - "description": "indexed file format for barcoded BAMs with API for converting and accessing alignment records", + "description": "A traffic simulator for unmanned surface vessels.", "filenames": [ - "src/bamdb/Singularity.bamdb" + "Singularity" ], - "full_name": "mskilab/bambi", + "full_name": "colinsauze/ASVTrafficSim", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/mskilab/bambi\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/47c82ab2d405aa684f3a5004ed8fc79887c025105127effda9ce1d35b5568974/68747470733a2f2f7472617669732d63692e6f72672f6d736b696c61622f62616d62692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/mskilab/bambi.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/mskilab/bambi?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ccb3814df2f3f1c65e518dd49a10732518ba754f251e50546a0d42ec9fd9cdab/68747470733a2f2f696d672e736869656c64732e696f2f636f6465636f762f632f6769746875622f6d736b696c61622f62616d62692e737667\" alt=\"codecov.io\" data-canonical-src=\"https://img.shields.io/codecov/c/github/mskilab/bambi.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bambi\" class=\"anchor\" aria-hidden=\"true\" href=\"#bambi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi\u003c/h1\u003e\n\u003cp\u003eR package for querying 10x WGS and single-cell BAMs\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/gUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/bamUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-bambi-commands\" class=\"anchor\" aria-hidden=\"true\" href=\"#bambi-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi commands\u003c/h2\u003e\n\u003cp\u003eInstantiate a bambi object:\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003egrab_bx()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_bx(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_cb()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_cb(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_ub()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_ub(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003efetch_by_tag()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003efetch_by_tag(tag, tag_queries, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-hidden=\"true\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstantiate a \u003ccode\u003ebambi\u003c/code\u003e object\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003elibrary(bambi)\n\n\u0026gt; hcc1143_subset = bambi$new(bam_file = \"subsetHCC1143_phased_possorted0001.bam\", bamdb_path=\"subsetHCC1143_phased_possorted0001_lmdb\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCall methods\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003e\u0026gt; hcc1143_subset$grab_bx(\u0027CGACGTGTCCTCTAGC-1\u0027)\nGRanges object with 2 ranges and 11 metadata columns:\n seqnames ranges strand |\n \u0026lt;Rle\u0026gt; \u0026lt;IRanges\u0026gt; \u0026lt;Rle\u0026gt; |\n [1] chr1 [147975454, 147975580] + |\n [2] chr1 [147975675, 147975824] - |\n qname flag mapq cigar\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;character\u0026gt;\n [1] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 99 16 127M\n [2] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 147 16 150M\n rnext pnext tlen\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt;\n [1] = 147975676 371\n [2] = 147975455 -371\n seq\n \u0026lt;character\u0026gt;\n [1] ATGTCTTCTTCCTCATTATCTGGCACTGGTTAGGAAGCACTCATCTCCATGAAGTCATCTTTTGTTAATTCCTCTGGTGTGGTGTGTATTAGCTCTTAAATTCCTCCAAGATCCATATCTTGCAACC\n [2] ATCTGGACACAAATTGTACTTTTGTCCAGCACGAATTTATTGTTTTGAGTTTCATGGTTTTCTATATCAACTGATGACATCTTGAAAGGTGTAAGCCTTCCAGACTTCCATGATGTTCTCTCTATTGGGTTTCTCTTTTGCAATGTTGAC\n qual\n \u0026lt;character\u0026gt;\n [1] JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJAJFJJJJJJJJJFJJJJJJJJJJFJJJJFFFJJJFJJJJJJAAJFJJJFAFAFFFJAA\u0026lt;7F\u0026lt;\n [2] A\u0026lt;7FFFJFFFAJJAAAJJF\u0026lt;F\u0026lt;7A-\u0026lt;AA-\u0026lt;\u0026lt;\u0026lt;AFFJJJJJJJJFFJAFFAAFJFJJJAFFJJJJJJJJJJFJFAJJJJJJFJJJJJJ\u0026lt;FFJJJFJJJFJJJJJJJJJJJJJFJJJJFFJ7JJJJF\u0026lt;JJJJJJJJJJJJJJJJJJJFFAA\u0026lt;\n BX qwidth\n \u0026lt;character\u0026gt; \u0026lt;integer\u0026gt;\n [1] CGACGTGTCCTCTAGC-1 127\n [2] CGACGTGTCCTCTAGC-1 150\n -------\n seqinfo: 1 sequence from an unspecified genome; no seqlengths\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eASVTrafficSim\u003c/h1\u003e\u003ca id=\"user-content-asvtrafficsim\" class=\"anchor\" aria-label=\"Permalink: ASVTrafficSim\" href=\"#asvtrafficsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA traffic simulator for unmanned surface vessels.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall system libraries\u003c/h3\u003e\u003ca id=\"user-content-install-system-libraries\" class=\"anchor\" aria-label=\"Permalink: Install system libraries\" href=\"#install-system-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esudo apt install libjansson-dev\nsudo apt install python-gi-cairo\u003c/p\u003e\n\u003cp\u003e(or your system\u0027s equivalent)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall pip dependencies:\u003c/h3\u003e\u003ca id=\"user-content-install-pip-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install pip dependencies:\" href=\"#install-pip-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003epip install boatd python-boatdclient python-sailsd pynmea2 libais\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload this repository and its submodules\u003c/h3\u003e\u003ca id=\"user-content-download-this-repository-and-its-submodules\" class=\"anchor\" aria-label=\"Permalink: Download this repository and its submodules\" href=\"#download-this-repository-and-its-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003egit clone --recursive \u003ca href=\"https://github.com/colinsauze/ASVTrafficSim/asvtrafficsim.git\"\u003ehttps://github.com/colinsauze/ASVTrafficSim/asvtrafficsim.git\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild sailsd\u003c/h3\u003e\u003ca id=\"user-content-build-sailsd\" class=\"anchor\" aria-label=\"Permalink: Build sailsd\" href=\"#build-sailsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecd sailsd\nmake\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOpenCPN for chart plotting\u003c/h3\u003e\u003ca id=\"user-content-opencpn-for-chart-plotting\" class=\"anchor\" aria-label=\"Permalink: OpenCPN for chart plotting\" href=\"#opencpn-for-chart-plotting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esudo add-apt-repository ppa:opencpn/opencpn\nsudo apt-get update\nsudo apt-get install opencpn\u003c/p\u003e\n\u003cp\u003elaunch opencpn\u003c/p\u003e\n\u003cp\u003eClick on the options icon (the spanner on the toolbar), go to connections\nadd a new incoming connection on UDP port 10110\nadd an outgoing UDP connection on port 10111 with only the GGA sentence enabled\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning:\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running:\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esailsd/sailsd\u003c/p\u003e\n\u003cp\u003eset initial lat/lon and wind direction\n./init_sails.sh\u003c/p\u003e\n\u003cp\u003erun boatd\nboatd boatd.yml\u003c/p\u003e\n\u003cp\u003e(optional) run sails-ui\nsails-ui/sails-ui\u003c/p\u003e\n\u003cp\u003erun opencpn plugin\nboatd-opencpn/boatd-opencpn\u003c/p\u003e\n\u003cp\u003erun behaviour:\nboatdctl behaviour-start example\u003c/p\u003e\n\u003cp\u003erun collision detector\npython recvBoatData.py\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning with Singularity:\u003c/h4\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity:\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the singularity container with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build ASVTrafficSim.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun it with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -B logs:/ASVTrafficSim/logs ASVTrafficSim.simg \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will bind the logs (output) directory in the container to the local logs directory.\u003c/p\u003e\n\u003cp\u003e=======\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 6, + "subscribers_count": 3, "topics": [], - "updated_at": 1624535144.0 + "updated_at": 1719296166.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularityfile.def" + "Singularity" ], - "full_name": "ShravanRavi2002/BARNSubmission", + "full_name": "lsx1980/vsfm-master", "latest_release": null, - "readme": "", + "readme": "\u003cp\u003eSFM for 3D root model reconstruction\u003c/p\u003e\n\u003cp\u003eThe software package was integrated as a module at PlantIT website at : \u003ca href=\"https://portnoy.cyverse.org/\" rel=\"nofollow\"\u003ehttps://portnoy.cyverse.org/\u003c/a\u003e. (Collaborate with Cyverse \u003ca href=\"https://www.cyverse.org/\" rel=\"nofollow\"\u003ehttps://www.cyverse.org/\u003c/a\u003e ) . Users are welcomed to registered as an user to try this package via PlantIT website.\u003c/p\u003e\n\u003cp\u003eThe software package was also available at Dockerhub (\u003ca href=\"https://hub.docker.com/r/computationalplantscience/3d-model-reconstruction\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/computationalplantscience/3d-model-reconstruction\u003c/a\u003e) for advanced users to run locally via singularity at Linux environment:\u003c/p\u003e\n\u003cp\u003eSteps to run this package in container locally:\n1. Install singularity container version 3.6 following the instruction at \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.6/user-guide/quick_start.html#quick-installation-steps\u003c/a\u003e\n2. Run the container:\nOnce singularity was successfully installed, the container can be executed using\nsingularity exec --home $PWD/ \u2013bind /$PWD:/opt/code/vsfm/bin/temp,/$PWD:/opt/code/vsfm/bin/log docker://computationalplantscience/3d-model-reconstruction /opt/code/vsfm/bin/VisualSFM sfm+pmvs /$PATH_TO_IMAGE_FOLDER/\n\"$PWD\" : can be replaced by user\u2019s local path for store temporary files. $PATH_TO_IMAGE_FOLDER/: can be replaced by user\u2019s image data folder.\n3. Collect the 3D model result After the container was executed successfully with image data files, user should be able to see output at command window like this:\n\u0027\u0027\u0027 Save to /$PATH_TO_IMAGE_FOLDER/vsfm.nvm ... done Save /$PATH_TO_IMAGE_FOLDER/vsfm.0.ply ...done\nVisualSFM 3D reconstruction, finished Totally 15.000 seconds used\nLogFile: /opt/code/vsfm/bin/log/[20_12_17][15_26_12][690].log \u0027\u0027\u0027\nThe 3D model was stored as point cloud in ply format at /$PATH_TO_IMAGE_FOLDER/vsfm.0.ply.\u003c/p\u003e\n\u003cp\u003eAuthor\nsuxing liu(\u003ca href=\"mailto:suxingliu@gmail.com\"\u003esuxingliu@gmail.com\u003c/a\u003e) reference: Anders Damsgaard with contributions by Caleb Adams and Connor P Doherty. Changchang Wu ( \u003ca href=\"mailto:wucc1130@gmail.com\"\u003ewucc1130@gmail.com\u003c/a\u003e )\nSingularity container was maintained by Wesley Paul Bonelli. it was deployed to Plant IT website by Wesley Paul Bonelli (\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e).\nSingularity container overlay issues were solved by [Saravanaraj Ayyampalayam] (\u003ca href=\"https://github.com/raj76\"\u003ehttps://github.com/raj76\u003c/a\u003e) (\u003ca href=\"mailto:raj76@uga.edu\"\u003emailto:raj76@uga.edu\u003c/a\u003e)\nSpecial thanks to Chris Cotter building the container recipe for testing and debugging.\u003c/p\u003e\n\u003cp\u003eTodo\n\u2022 VisualSFM is built without CUDA acceleration. Add optional GPU build.\n\u2022 support GPU based SIFT feature matching\u003c/p\u003e\n\u003cp\u003eLicense\nGNU Public License\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1682927985.0 + "updated_at": 1612365154.0 }, { "data_format": 2, - "description": "Makes images for a NN based on the hit information of neutrino events in the neutrino telescope KM3NeT-ORCA", + "description": "robots for experiment factory experiments and surveys", "filenames": [ "Singularity" ], - "full_name": "ViaFerrata/OrcaSong", + "full_name": "expfactory/expfactory-robots", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExperiment Factory Robots\u003c/h1\u003e\u003ca id=\"user-content-experiment-factory-robots\" class=\"anchor\" aria-label=\"Permalink: Experiment Factory Robots\" href=\"#experiment-factory-robots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis set of scripts (and provided container) will allow you to run a robot test for various kinds of experiments. Currently supported are surveys and jspsych experiments. Local (non container) use will be discussed first, followed by Docker.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/153497?speed=3\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f53b5ed7083e6601d6dcd0d29a465d9e337d2b7930ac276caab7c09af25602b/68747470733a2f2f61736369696e656d612e6f72672f612f3135333439372e706e67\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/153497.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the examples, we can clone the \u003ca href=\"https://www.github.com/expfactory-experiments/test-task\"\u003etest-task\u003c/a\u003e experiment and the \u003ca href=\"https://www.github.com/expfactory-experiments/bis11-survey\"\u003ebis11-survey\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /tmp\ngit clone https://www.github.com/expfactory-experiments/test-task\ngit clone https://www.github.com/expfactory-experiments/bis11-survey\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo test locally, you will need expfactory installed locally.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install expfactory\n\n# Development\ngit clone https://www.github.com/expfactory/expfactory\ncd expfactory\npython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand for Singularity, you will need to \u003ca href=\"https://singularityware.github.io/install-linux\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eJsPsych Robot\u003c/h2\u003e\u003ca id=\"user-content-jspsych-robot\" class=\"anchor\" aria-label=\"Permalink: JsPsych Robot\" href=\"#jspsych-robot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that usage requires python 3, so if you cannot provide it, use the container.\u003c/p\u003e\n\u003cp\u003eThe basic usage is to specify a list of one or more experiment folder paths, and then\noptionally select a robot type (the default is jspsych):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py --help\nusage: start.py [-h] [--robot {survey,jspsych}] folders [folders ...]\n\nexpfactory: generate survey from config.json and question file\n\npositional arguments:\n folders experiments for robot testing\n\noptional arguments:\n -h, --help show this help message and exit\n --robot {survey,jspsych}, -r {survey,jspsych}\n the survey robot to recruit!\n --browser {Firefox,Chrome}, -b {Firefox,Chrome}\n browser driver to use for the robot\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the robot for the test-task and use jspsych, we can simply do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py /tmp/test-task\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis would be equivalent to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py --robot jspsych /tmp/test-task\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethe browser (chrome default) will open and you will see the experiment progress and\nfinish. The console will show GET and POST of resources, etc.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRecruiting jspsych robot!\n[folder] /tmp/test-task\nLOG STARTING TEST OF EXPERIMENT\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET / HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /jspsych.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /default_style.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /style.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jquery.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/math.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/jspsych.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/plugins/jspsych-text.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/jspsych-poldrack-text.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/jspsych-poldrack-instructions.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/jspsych-attention-check.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/jspsych-poldrack-single-stim.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/plugins/jspsych-survey-text.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/plugins/jspsych-call-function.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /js/jspsych/poldrack_plugins/poldrack_utils.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:47] \"GET /experiment.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:48] \"GET /%3Cdiv%20class%20=%20%22shapebox%22%3E%3Cdiv%20id%20=%20%22cross%22%3E%3C/div%3E%3C/div%3E HTTP/1.1\" 404 -\n127.0.0.1 - - [17/Dec/2017 06:52:48] \"GET /favicon.ico HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 06:52:58] \"POST /save HTTP/1.1\" 501 -\nLOG FINISHING TEST OF EXPERIMENT\nLOG [done] stopping web server...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSurvey Robot\u003c/h2\u003e\u003ca id=\"user-content-survey-robot\" class=\"anchor\" aria-label=\"Permalink: Survey Robot\" href=\"#survey-robot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe same can be done for a survey! Let\u0027s now test bis-11\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py --robot survey /tmp/bis11-survey\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output is similar to jspsych, except we are progressing through a survey.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython start.py --robot survey /tmp/bis11-survey\nRecruiting survey robot!\n[folder] /tmp/bis11-survey\nLOG STARTING TEST OF SURVEY\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET / HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/material.blue-red.min.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/surveys.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/jquery-ui-1.10.4.custom.min.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/style.css HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery-2.1.1.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/material.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery-ui-1.10.4.custom.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery.wizard.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery.form-3.50.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /js/jquery.validate-1.12.0.min.js HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/images/ui-bg_flat_75_ffffff_40x100.png HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /css/images/ui-bg_highlight-soft_75_cccccc_1x100.png HTTP/1.1\" 200 -\n127.0.0.1 - - [17/Dec/2017 07:09:38] \"GET /favicon.ico HTTP/1.1\" 200 -\nLOG Testing page 1\nLOG Testing page 2\nLOG Testing page 3\nLOG Testing page 4\nLOG Testing page 5\nLOG FINISHING TEST OF SURVEY\nLOG [done] stopping web server...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Usage\u003c/h2\u003e\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" aria-label=\"Permalink: Singularity Usage\" href=\"#singularity-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity is ideal for this use case because of the seamless nature between the container and host. We have a pre-built image for your use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://expfactory/expfactory-robots\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor if you want to build it yourself, the first thing you would want to do is again clone the repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/expfactory/expfactory-robots\ncd expfactory-robots\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then build.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build expfactory-robots.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen to run the image, you will basically want to bind the \u003cem\u003eparent\u003c/em\u003e folder where your task is to \u003ccode\u003e/data\u003c/code\u003e in the container, and specify the path to the experiment \u003cem\u003erelative to \u003ccode\u003edata\u003c/code\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind /tmp:/data expfactory-robots.simg /data/test-task\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Usage\u003c/h2\u003e\u003ca id=\"user-content-docker-usage\" class=\"anchor\" aria-label=\"Permalink: Docker Usage\" href=\"#docker-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t expfactory/expfactory-robots .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run it, I again mapped the folder one level above your experiment (so we can validate the experiment folder name itself!) to \u003ccode\u003e/data\u003c/code\u003e in the container, and I also made sure to specify the port, because Docker doesn\u0027t have a seamless connection to the host like Singularity.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ePARENT_FOLDER=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edirname \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\ndocker run -v \u003cspan class=\"pl-smi\"\u003e$PARENT_FOLDER\u003c/span\u003e/:/data -p 3030:3030 expfactory/expfactory-robots /data/test-task\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you have issues, you may need to check the version of selenium and the Gecko Driver.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [ - "physics", - "hdf5-format", - "images" + "expfactory", + "experiment-factory", + "robots", + "behavior", + "psychology", + "experiment", + "testing" ], - "updated_at": 1635947996.0 + "updated_at": 1673579530.0 }, { "data_format": 2, - "description": "the public repository for `eemt` workflow", + "description": "Singularity Recipe for QIIME 2", "filenames": [ - "singularity/Singularity" + "Singularity.2018.6", + "Singularity.2019.10", + "Singularity.2021.4", + "Singularity.2017.12", + "Singularity.2018.2", + "Singularity", + "Singularity.2019.4" ], - "full_name": "cyverse-gis/eemt", + "full_name": "ResearchIT/qiime2", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-eemt\" class=\"anchor\" aria-hidden=\"true\" href=\"#eemt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeemt\u003c/h1\u003e\n\u003cp\u003ethe public repository for \u003ccode\u003eeemt\u003c/code\u003e workflow\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/cyverse-gis/eemt/wiki\"\u003eView the Wiki\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Recipe for QIIME2\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipe-for-qiime2\" class=\"anchor\" aria-label=\"Permalink: Singularity Recipe for QIIME2\" href=\"#singularity-recipe-for-qiime2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains recipe run \u003ca href=\"https://qiime2.org\" rel=\"nofollow\"\u003eqiime2\u003c/a\u003e within a\n\u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, which can be built\nusing \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eVersions:\n2017.12 - QIIME2-2017.12 installed on CentOS 7\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to Use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esingularity run shub://ResearchIT/qiime2 --help\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 5, - "topics": [], - "updated_at": 1599660473.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "Singularity/Singularity.v1.1", - "Singularity/Singularity.v1.2" + "subscribers_count": 7, + "topics": [ + "qiime", + "singularity" ], - "full_name": "IARCbioinfo/strelka2-nf", - "latest_release": "v1.2a", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-strelka2-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#strelka2-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estrelka2-nf\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-strelka-v2-pipeline-with-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#strelka-v2-pipeline-with-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStrelka v2 pipeline with Nextflow\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/strelka2-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d78a770e40820f0efedb9aada8ed30fab6e55a0c7b96b0ccebb17dacf20995c0/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f737472656c6b61322d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/strelka2-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/repository/docker/iarcbioinfo/strelka2-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4622\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"strelka2-nf.png?raw=true\"\u003e\u003cimg src=\"strelka2-nf.png?raw=true\" alt=\"Workflow representation\" title=\"Scheme of variant calling with strelka2 Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003eNextflow : for common installation procedures see the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository.\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://github.com/Illumina/strelka\"\u003eStrelka v2\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003efolder with bam/cram files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_file\u003c/td\u003e\n\u003ctd\u003eTab delimited text file with either two columns called normal and tumor (somatic mode) or one column called bam (germline mode); optionally, a column called sample containing sample names to be used for naming the files can be provided and for genotyping (see genotyping mode below) a column called vcf has to be provided\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: the file provided to --input_file is where you can define pairs of bam/cram to analyse with strelka in somatic mode. It\u0027s a tabular file with 2 columns normal and tumor.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003enormal\u003c/th\u003e\n\u003cth\u003etumor\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003enormal1.cram\u003c/td\u003e\n\u003ctd\u003etumor2.cram\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enormal2.cram\u003c/td\u003e\n\u003ctd\u003etumor2.cram\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enormal3.cram\u003c/td\u003e\n\u003ctd\u003etumor3.cram\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd\u003ehg19.fasta\u003c/td\u003e\n\u003ctd\u003egenome reference\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mode\u003c/td\u003e\n\u003ctd\u003esomatic\u003c/td\u003e\n\u003ctd\u003eMode for variant calling; one of somatic, germline, genotyping\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003estrelka_ouptut\u003c/td\u003e\n\u003ctd\u003eOutput folder for vcf files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003enumber of CPUs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e20\u003c/td\u003e\n\u003ctd\u003ememory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--strelka\u003c/td\u003e\n\u003ctd\u003epath inside docker and singularity containers\u003c/td\u003e\n\u003ctd\u003eStrelka installation dir\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--config\u003c/td\u003e\n\u003ctd\u003edefault conf of strelka\u003c/td\u003e\n\u003ctd\u003eUse custom configuration file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--callRegions\u003c/td\u003e\n\u003ctd\u003enone\u003c/td\u003e\n\u003ctd\u003eRegion bed file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ext\u003c/td\u003e\n\u003ctd\u003ecram\u003c/td\u003e\n\u003ctd\u003eextension of alignment files (bam or cram)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eprint usage and optional parameters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--exome\u003c/td\u003e\n\u003ctd\u003eautomatically set up parameters for exome data\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--rna\u003c/td\u003e\n\u003ctd\u003eautomatically set up parameters for rna data (only available for --mode germline)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--AF\u003c/td\u003e\n\u003ctd\u003eAdd AF field to VCF (only available for --mode somatic)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--outputCallableRegions\u003c/td\u003e\n\u003ctd\u003eCreate a BED track containing regions which are determined to be callable\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-mode-somatic\" class=\"anchor\" aria-hidden=\"true\" href=\"#mode-somatic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emode somatic\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003enextflow run iarcbioinfo/strelka2-nf r v1.2a -profile singularity --mode somatic --ref hg38.fa --tn_pairs pairs.txt --input_folder path/to/cram/ --strelka path/to/strelka/\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo run the pipeline without singularity just remove \"-profile singularity\". Alternatively, one can run the pipeline using a docker container (-profile docker) the conda receipe containing all required dependencies (-profile conda).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-mode-germline\" class=\"anchor\" aria-hidden=\"true\" href=\"#mode-germline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003emode germline\u003c/h3\u003e\n\u003cp\u003e\u003ccode\u003enextflow run iarcbioinfo/strelka2-nf r v1.2a -profile singularity --mode germline --ref hg38.fa --input_folder path/to/cram/ --strelka path/to/strelka/\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-genotyping\" class=\"anchor\" aria-hidden=\"true\" href=\"#genotyping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egenotyping\u003c/h3\u003e\n\u003cp\u003eWhen using the input_file mode, if a vcf column with the path to a VCF file for each sample containing a list of somatic variant is provided, the pipeline will use the --forcedGT option from strelka that genotypes these positions, and compute a bedfile for these positions so only variants from the VCF will be genotyped. Note that genotyping can be performed both in somatic mode (in which case tumor/normal pairs must be provided) and germline mode (in which case a single cram file must be provided).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eVCFs/raw/*.vcf.gz\u003c/td\u003e\n\u003ctd\u003eVCF files before filtering\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVCFs/withAF/*.vcf\u003c/td\u003e\n\u003ctd\u003eVCF files with AF field (optional, requires flag --AF)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVCFs/filtered/*PASS.vcf.gz\u003c/td\u003e\n\u003ctd\u003efinal compressed and indexed VCF files (optionally with flag --AF)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCallableRegions/*.bed.gz\u003c/td\u003e\n\u003ctd\u003ecompressed and indexed BED files (optionally with flag --outputCallableRegions)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFinal vcf files have companion tabix index files (.tbi). Note that in germline mode, the VCF outputted corresponds to variants only (file variants.vcf.gz from strelka).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-hidden=\"true\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/strelka-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eVincent Cahais\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:CahaisV@iarc.fr\"\u003eCahaisV@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@iarc.fr\"\u003eAlcalaN@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", - "stargazers_count": 3, - "subscribers_count": 3, - "topics": [], - "updated_at": 1681464309.0 + "updated_at": 1712857622.0 }, { "data_format": 2, - "description": "Nextflow pipeline for Illumina NGS demultiplexing", + "description": "Singularity recipe files for ApsimX (https://github.com/APSIMInitiative/ApsimX)", "filenames": [ - "containers/dos2unix-7.4.0/Singularity.dos2unix-7.4.0", - "containers/fastqc-0.11.7/Singularity.fastqc-0.11.7", - "containers/report-r-3.4.3/Singularity.report-r-3.4.3", - "containers/bcl2fastq-2.17.1/Singularity.bcl2fastq-2.17.1", - "containers/multiqc-1.5/Singularity.multiqc-1.5", - "containers/python-2.7/Singularity.python-2.7" + "Singularity.2020.10.21.5755", + "Singularity.2020.04.09.5012", + "Singularity.2019.01.30.3436", + "Singularity.2023.10.7317.0", + "Singularity.2019.06.05.3920", + "Singularity.2022.06.7046.0", + "Singularity.2021.04.15.6139", + "Singularity.2018.01.30.2253", + "Singularity.2019.10.04.4236", + "Singularity.2022.12.7130.0", + "Singularity.2022.09.7085.0", + "Singularity.2020.08.04.5350", + "Singularity.2019.04.03.3693", + "Singularity.2019.07.18.4025", + "Singularity.2021.10.8.6835", + "Singularity.2023.09.7308.0", + "Singularity.2018.09.28.3099", + "Singularity.2021.9.21.6799", + "Singularity.2020.11.27.5887", + "Singularity.2022.04.7021.0", + "Singularity.2020.09.17.5665", + "Singularity.2019.01.08.3392" ], - "full_name": "NYU-Molecular-Pathology/demux-nf", - "latest_release": "19.04.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-demux-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#demux-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003edemux-nf\u003c/h1\u003e\n\u003cp\u003eNextflow pipeline for demultiplexing Illumina Next-Gen sequencing data.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h1\u003e\n\u003cp\u003eClone this repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NYU-Molecular-Pathology/demux-nf.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deployment\" class=\"anchor\" aria-hidden=\"true\" href=\"#deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeployment\u003c/h2\u003e\n\u003cp\u003eThe included \u003ccode\u003edeploy\u003c/code\u003e recipe should be used to create a new directory for demultiplexing based on a currently existing sequencing run directory. Include arguments that describe the configuration for your sequencing run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd demux-nf\nmake deploy RUNID=170809_NB501073_0019_AH5FFYBGX3 SAMPLESHEET=SampleSheet.csv SEQTYPE=Archer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003earguments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eRUNID\u003c/code\u003e: the identifier given to the run by the sequencer\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSAMPLESHEET\u003c/code\u003e: the samplesheet required for demultiplexing with \u003ccode\u003ebcl2fastq\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSEQTYPE\u003c/code\u003e: the type of sequencing; currently only \u003ccode\u003eArcher\u003c/code\u003e or \u003ccode\u003eNGS580\u003c/code\u003e are used\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSEQDIR\u003c/code\u003e: parent directory where the sequencer outputs its data (pre-configured for NYU server locations)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ePRODDIR\u003c/code\u003e: parent directory where demultiplexing output should be stored (pre-configured for NYU server locations)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis will first check that the specified run exists on the server before cloning into a new directory at the given production output location and configuring it for demultiplexing using the subsequent commands described here.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Workflow\u003c/h2\u003e\n\u003cp\u003eAssuming you used \u003ccode\u003emake deploy\u003c/code\u003e or \u003ccode\u003emake config\u003c/code\u003e to prepare your demultiplexing directory, the following command can be used to automatically run the workflow based on the pre-defined settings and settings from your current system.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExtra parameters to be passed to Nextflow can be supplied with the \u003ccode\u003eEP\u003c/code\u003e argument:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run EP=\u0027--samplesheet SampleSheet.csv --runDir /path/to/sequencer/data/170809_NB501073_0019_AH5FFYBGX3\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo submit the parent Nextflow pipeline as a job on the HPC cluster:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake submit\n\n# with a different submission queue:\nmake submit SUBQ=fn_long\n\n# with a different submission time:\nmake submit SUBQ=cpu_long SUBTIME=\u0027--time=6-00:00:00\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor alternative \u003ccode\u003erun\u003c/code\u003e methods, consult the \u003ccode\u003eMakefile\u003c/code\u003e.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration\u003c/h1\u003e\n\u003cp\u003eDemultiplexing metadata for the workflow can be provided through several methods, evaluated in the following order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eparameters can be supplied directly to Nextflow via CLI\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf --runID 12345\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eif the file \u003ccode\u003econfig.json\u003c/code\u003e is present, non-\u003ccode\u003enull\u003c/code\u003e parameters will be retrieved\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"runDir\": \"/path/to/sequencer/data/170809_NB501073_0019_AH5FFYBGX3\",\n \"samplesheet\": \"SampleSheet.csv\",\n \"runID\": \"170809_NB501073_0019_AH5FFYBGX3\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ethis file is generated automatically during the \u003ccode\u003edeploy\u003c/code\u003e step, using the included \u003ccode\u003econfig.py\u003c/code\u003e script\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ethe following items in the current directory will be used if present:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eSampleSheet.csv\u003c/code\u003e: default samplesheet file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003erunDir\u003c/code\u003e : default sequencing run source directory (can be a symlink)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003erunID.txt\u003c/code\u003e: a text file, the first line of which will be used as the run ID\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-extras\" class=\"anchor\" aria-hidden=\"true\" href=\"#extras\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExtras\u003c/h1\u003e\n\u003cul\u003e\n\u003cli\u003e(re)initialize configurations (overwrites old \u003ccode\u003econfig.json\u003c/code\u003e):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake config RUNDIR=/path/to/sequencer/data/170809_NB501073_0019_AH5FFYBGX3 SAMPLESHEET=SampleSheet.csv RUNID=170809_NB501073_0019_AH5FFYBGX3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eupdate an existing directory to the latest version of this repo:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake update\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eclean up workflow intermediary files to save space (workflow cannot be resumed after this):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake finalize\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eclean up output from all old workflows (saves current workflow output):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake clean\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003edelete the output from all workflows:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake clean-all\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003emark that the demultiplexing suceeded and the results passed QC for downstream analysis:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake passed\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003edeploy a new NGS580 analysis using the current results:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake deploy-NGS580\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003emake a \u0027deliverables\u0027 directory with just the results for samples for a specific client\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emake deliverable CLIENT=somelab SHEET=list_of_clients_samples.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware\u003c/h1\u003e\n\u003cp\u003eRequired:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eJava 8 (Nextflow)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePython 2.7+\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU \u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional; must be installed to system or available with Singularity containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebcl2fastq\u003c/code\u003e version 2.17.1\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFastQC version 0.11.7\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eR (3.3.0+, with \u003ccode\u003eknitr\u003c/code\u003e and \u003ccode\u003ermarkdown\u003c/code\u003e libraries)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePandoc 1.13.1+\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "powerPlant/apsimx-srf", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2271\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity recipe files for ApsimX, the next generation of the Agricultural Production Systems sIMulator (APSIM)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMaintainer notes\u003c/h2\u003e\u003ca id=\"user-content-maintainer-notes\" class=\"anchor\" aria-label=\"Permalink: Maintainer notes\" href=\"#maintainer-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container downloads the ApsimX \u003ccode\u003edeb\u003c/code\u003e file built by the \u003ca href=\"https://github.com/APSIMInitiative/APSIM.Builds\"\u003eBOB Build Service\u003c/a\u003e at CSIRO and installs it on a Ubuntu:Bionic container.\u003c/p\u003e\n\u003cp\u003eThere are two useful endpoints from the Build Service:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://apsimdev.apsim.info/APSIM.Builds.Service/Builds.svc/GetLatestVersion\" rel=\"nofollow\"\u003eGet Latest Version\u003c/a\u003e: shows the full version number of the latest release\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://apsimdev.apsim.info/APSIM.Builds.Service/Builds.svc/GetURLOfLatestVersion?operatingSystem=Debian\" rel=\"nofollow\"\u003eGet URL of Latest Version\u003c/a\u003e: shows the download URL for the latest version of the specified OS\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003eApsimSetup*.deb\u003c/code\u003e files are named using the \u003ca href=\"https://github.com/APSIMInitiative/APSIM.Builds/blob/21a8ac85a1d868a45f60a994a35a5d07dc04562a/APSIM.Builds.Service/Builds.svc.cs#L278\"\u003ebuild.issueNumber\u003c/a\u003e instead of the full version number, so we must hardcode the URLs inside the recipe files, while using the full version number for the recipe file names.\u003c/p\u003e\n\u003cp\u003eTo facilitate running the container, a \u003ccode\u003e/usr/local/bin/apsimmodels\u003c/code\u003e file is created as the entrypoint using \u003ccode\u003e/usr/local/bin/apsim\u003c/code\u003e as a template.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer-specific notes\u003c/h2\u003e\u003ca id=\"user-content-container-specific-notes\" class=\"anchor\" aria-label=\"Permalink: Container-specific notes\" href=\"#container-specific-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eApsimX apparently needs \u003ccode\u003e/etc/localtime\u003c/code\u003e to run so we have to install \u003ccode\u003etzdata\u003c/code\u003e as a dependency. We configure our local timezone, but this can be overridden at run time by exporting the \u003ccode\u003eTZ\u003c/code\u003e environment variable.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 2, - "topics": [ - "nextflow", - "pipeline", - "demultiplexing", - "bcl2fastq" - ], - "updated_at": 1654548176.0 + "subscribers_count": 5, + "topics": [], + "updated_at": 1695108781.0 }, { "data_format": 2, - "description": "work in progress of trait extraction in interacting bean roots", + "description": null, "filenames": [ - "3D_model_traits_work/Singularity", - "3D_model_traits_work/model_preprocess/Singularity" + "Recipes/Singularity_example", + "Recipes/Singularity_ompi", + "Recipes/Singularity_pytorch", + "Recipes/Singularity_mpich", + "Recipes/Singularity_spark_full", + "Recipes/Singularity_spark", + "Recipes/Singularity_tensorflow", + "Recipes/Singularity_pytorch_full" ], - "full_name": "wlavoy/root_root_interaction_traits", + "full_name": "ufscar/hpc-template-ci", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-root_root_interaction_traits\" class=\"anchor\" aria-hidden=\"true\" href=\"#root_root_interaction_traits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eroot_root_interaction_traits\u003c/h1\u003e\n\u003cp\u003ework in progress of trait extraction in interacting bean roots\nCurrently experimenting with the use of clustering and segmentation to distinguish separate root systems.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePr\u00e9-requisitos\u003c/h1\u003e\u003ca id=\"user-content-pr\u00e9-requisitos\" class=\"anchor\" aria-label=\"Permalink: Pr\u00e9-requisitos\" href=\"#pr\u00e9-requisitos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSele\u00e7\u00e3o do Recipe\u003c/h2\u003e\u003ca id=\"user-content-sele\u00e7\u00e3o-do-recipe\" class=\"anchor\" aria-label=\"Permalink: Sele\u00e7\u00e3o do Recipe\" href=\"#sele\u00e7\u00e3o-do-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdicione o caminho do \u003cem\u003esingularity recipe\u003c/em\u003e desejado na vari\u00e1vel RECIPE no github (projeto \u0026gt;\u0026gt; Settings \u0026gt;\u0026gt; Secrets \u0026gt;\u0026gt; New Secret).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTemplate Integra\u00e7\u00e3o Cont\u00ednua Github\u003c/h1\u003e\u003ca id=\"user-content-template-integra\u00e7\u00e3o-cont\u00ednua-github\" class=\"anchor\" aria-label=\"Permalink: Template Integra\u00e7\u00e3o Cont\u00ednua Github\" href=\"#template-integra\u00e7\u00e3o-cont\u00ednua-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEsse projeto o template para uso do cluster da UFSCar, contendo integra\u00e7\u00e3o cont\u00ednua com o Google Drive e Amazon S3.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequisitos para Google Drive\u003c/h2\u003e\u003ca id=\"user-content-requisitos-para-google-drive\" class=\"anchor\" aria-label=\"Permalink: Requisitos para Google Drive\" href=\"#requisitos-para-google-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eEntre no \u003ca href=\"https://console.developers.google.com/apis/credentials\" rel=\"nofollow\"\u003econsole de credenciais de API do Google\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSe ainda n\u00e3o houver um projeto, crie um.\u003c/li\u003e\n\u003cli\u003eEm \"Ativar API e Servi\u00e7os\", busque por \"Google Drive\" e ative a permiss\u00e3o.\u003c/li\u003e\n\u003cli\u003eClique em \"Criar credenciais\".\u003c/li\u003e\n\u003cli\u003eSelecione \"ID do cliente do OAuth\".\u003c/li\u003e\n\u003cli\u003eEm \"Tipo de aplicativo\", selecione \"App para computador\".\u003c/li\u003e\n\u003cli\u003eD\u00ea um nome de identifica\u00e7\u00e3o para as credenciais e clique em \"criar\". V\u00e3o aparecer dois dados (\"Seu ID de cliente\" e \"Sua chave secreta de cliente\"), precisaremos dos dois no passo seguinte.\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 17 # selecione aqui o n\u00famero correspondente a op\u00e7\u00e3o \"Google Drive\"\nclient_id\u0026gt; conte\u00fado de \"Seu ID de cliente\"\nclient_secret\u0026gt; conte\u00fado de \"Sua chave secreta de cliente\"\nscope\u0026gt; 1 # Full access all files, excluding Application Data Folder\nroot_folder_id\u0026gt; deixe em branco\nservice_account_file\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/n\u0026gt; n\n\nSe j\u00e1 tiver o rclone instalado em seu computador, execute o comando exibido, caso contr\u00e1rio, o instale conforme indicado na p\u00e1gina oficial do rclone dependendo do seu sistema operacional (https://rclone.org/install/). A Seguir insira o resultado do comando exibido:\n\nconfig_token\u0026gt; c\u00f3digo fornecido pelo terminal ap\u00f3s autoriza\u00e7\u00e3o\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequisitos para Amazon S3\u003c/h2\u003e\u003ca id=\"user-content-requisitos-para-amazon-s3\" class=\"anchor\" aria-label=\"Permalink: Requisitos para Amazon S3\" href=\"#requisitos-para-amazon-s3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eEntre na \u003ca href=\"console.aws.amazon.com\"\u003eAWS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClique na seta ao lado de seu nome de usu\u00e1rio e em \"My Security Credentials\".\u003c/li\u003e\n\u003cli\u003eNa se\u00e7\u00e3o \"Access Keys\", clique em \"Create New Access Key\".\u003c/li\u003e\n\u003cli\u003eNa janela que aparece, clique em \"Show Access Key\".\u003c/li\u003e\n\u003cli\u003eAcesse o \u003cem\u003ecluster\u003c/em\u003e, execute o comando \u003ccode\u003erclone config\u003c/code\u003e e forne\u00e7a as seguintes informa\u00e7\u00f5es quando solicitado:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003en/s/q\u0026gt; n\nname\u0026gt; cloud\nStorage\u0026gt; 4\nprovider\u0026gt; 1\nenv_auth\u0026gt; 1\naccess_key_id\u0026gt; conte\u00fado de \"Access Key ID\"\nsecret_access_key\u0026gt; conte\u00fado de \"Secret Access Key\"\nregion\u0026gt; 16\nendpoint\u0026gt; deixe em branco\nlocation_constraint\u0026gt; 16\nacl\u0026gt; deixe em branco\nserver_side_encryption\u0026gt; deixe em branco\nsse_kms_key_id\u0026gt; deixe em branco\nstorage_class\u0026gt; deixe em branco\ny/n\u0026gt; deixe em branco\ny/e/d\u0026gt; deixe em branco\ne/n/d/r/c/s/q\u0026gt; q\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA configura\u00e7\u00e3o at\u00e9 agora servir\u00e1 para transfer\u00eancia de dados do \u003cem\u003ecluster\u003c/em\u003e para a nuvem e vice-e-versa. A partir de agora vamos configurar a integra\u00e7\u00e3o cont\u00ednua no github.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eExecute o comando:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eecho $(cat ~/.config/rclone/rclone.conf | base64 --wrap=0)\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eCopie a sa\u00edda e adicione \u00e0 vari\u00e1vel RCLONE_CONF no github.\u003c/li\u003e\n\u003cli\u003eAdicione no github \u00e0 vari\u00e1vel COLLECTION_CONTAINER \u003ccode\u003e/path/to/project\u003c/code\u003e, esse ser\u00e1 o caminho cujo container vai ser disponibilizado no seu Google Drive no formato \u003ccode\u003erecipe_name_DateTime.simg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1678739943.0 + "updated_at": 1708611593.0 }, { "data_format": 2, - "description": "indexed file format for barcoded BAMs with API for converting and accessing alignment records", + "description": "Examples that use singularity containers to run arbitrary operating systems on the ACCRE cluster.", "filenames": [ - "src/bamdb/Singularity.bamdb" + "accre-internal/Singularity.accre.internal.rstudio.3.4.3", + "accre-internal/Singularity.accre.internal.rstudio.3.6.0", + "accre-internal/Singularity.accre.internal.base", + "accre-internal/Singularity.accre.internal.rstudio.4.0.5", + "accre-internal/Singularity.accre.internal.rstudio.gpu.3.6.0" ], - "full_name": "mskilab-org/bambi", + "full_name": "accre/singularity", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/mskilab/bambi\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/47c82ab2d405aa684f3a5004ed8fc79887c025105127effda9ce1d35b5568974/68747470733a2f2f7472617669732d63692e6f72672f6d736b696c61622f62616d62692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/mskilab/bambi.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/mskilab/bambi?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ccb3814df2f3f1c65e518dd49a10732518ba754f251e50546a0d42ec9fd9cdab/68747470733a2f2f696d672e736869656c64732e696f2f636f6465636f762f632f6769746875622f6d736b696c61622f62616d62692e737667\" alt=\"codecov.io\" data-canonical-src=\"https://img.shields.io/codecov/c/github/mskilab/bambi.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-bambi\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#bambi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi\u003c/h1\u003e\n\u003cp\u003eR package for querying 10x WGS and single-cell BAMs\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/gUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003edevtools::install_github(\u0027mskilab/bamUtils\u0027)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-bambi-commands\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#bambi-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ebambi commands\u003c/h2\u003e\n\u003cp\u003eInstantiate a bambi object:\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003egrab_bx()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_bx(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_cb()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_cb(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrab_ub()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrab_ub(barcodes, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003efetch_by_tag()\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003efetch_by_tag(tag, tag_queries, query=NULL, data.table = FALSE, verbose = FALSE, mc.cores = 1)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstantiate a \u003ccode\u003ebambi\u003c/code\u003e object\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003elibrary(bambi)\n\n\u0026gt; hcc1143_subset = bambi$new(bam_file = \"subsetHCC1143_phased_possorted0001.bam\", bamdb_path=\"subsetHCC1143_phased_possorted0001_lmdb\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCall methods\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{r}\"\u003e\u003ccode\u003e\u0026gt; hcc1143_subset$grab_bx(\u0027CGACGTGTCCTCTAGC-1\u0027)\nGRanges object with 2 ranges and 11 metadata columns:\n seqnames ranges strand |\n \u0026lt;Rle\u0026gt; \u0026lt;IRanges\u0026gt; \u0026lt;Rle\u0026gt; |\n [1] chr1 [147975454, 147975580] + |\n [2] chr1 [147975675, 147975824] - |\n qname flag mapq cigar\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;character\u0026gt;\n [1] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 99 16 127M\n [2] ST-K00126:3:H5TL3BBXX:2:2109:25926:37800 147 16 150M\n rnext pnext tlen\n \u0026lt;character\u0026gt; \u0026lt;numeric\u0026gt; \u0026lt;numeric\u0026gt;\n [1] = 147975676 371\n [2] = 147975455 -371\n seq\n \u0026lt;character\u0026gt;\n [1] ATGTCTTCTTCCTCATTATCTGGCACTGGTTAGGAAGCACTCATCTCCATGAAGTCATCTTTTGTTAATTCCTCTGGTGTGGTGTGTATTAGCTCTTAAATTCCTCCAAGATCCATATCTTGCAACC\n [2] ATCTGGACACAAATTGTACTTTTGTCCAGCACGAATTTATTGTTTTGAGTTTCATGGTTTTCTATATCAACTGATGACATCTTGAAAGGTGTAAGCCTTCCAGACTTCCATGATGTTCTCTCTATTGGGTTTCTCTTTTGCAATGTTGAC\n qual\n \u0026lt;character\u0026gt;\n [1] JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJFJJJJJJJJJJJAJFJJJJJJJJJFJJJJJJJJJJFJJJJFFFJJJFJJJJJJAAJFJJJFAFAFFFJAA\u0026lt;7F\u0026lt;\n [2] A\u0026lt;7FFFJFFFAJJAAAJJF\u0026lt;F\u0026lt;7A-\u0026lt;AA-\u0026lt;\u0026lt;\u0026lt;AFFJJJJJJJJFFJAFFAAFJFJJJAFFJJJJJJJJJJFJFAJJJJJJFJJJJJJ\u0026lt;FFJJJFJJJFJJJJJJJJJJJJJFJJJJFFJ7JJJJF\u0026lt;JJJJJJJJJJJJJJJJJJJFFAA\u0026lt;\n BX qwidth\n \u0026lt;character\u0026gt; \u0026lt;integer\u0026gt;\n [1] CGACGTGTCCTCTAGC-1 127\n [2] CGACGTGTCCTCTAGC-1 150\n -------\n seqinfo: 1 sequence from an unspecified genome; no seqlengths\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExamples that use singularity containers to run arbitrary operating systems on the ACCRE cluster.\u003c/p\u003e\n\u003cp\u003eAn example of running a Tensorflow image directly from DockerHub can be found in our Python repo:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/accre/Python\"\u003ehttps://github.com/accre/Python\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 6, + "subscribers_count": 18, "topics": [], - "updated_at": 1686844399.0 + "updated_at": 1687539765.0 }, { "data_format": 2, - "description": null, + "description": "Singularity configurations for R and pbdR packages.", "filenames": [ - "hpobench/container/recipes/Singularity.template", - "hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", - "hpobench/container/recipes/od/Singularity.ODBenchmarks", - "hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine", - "hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", - "hpobench/container/recipes/rl/Singularity.Cartpole", - "hpobench/container/recipes/rl/Singularity.learnaBenchmark", - "hpobench/container/recipes/nas/Singularity.nasbench_1shot1", - "hpobench/container/recipes/nas/Singularity.TabularBenchmarks", - "hpobench/container/recipes/nas/Singularity.nasbench_201", - "hpobench/container/recipes/nas/Singularity.nasbench_101" + "R/rstudio/Singularity.1.1.456", + "R/r/Singularity.3.5.1", + "R/r-minimal/Singularity.3.5.1", + "R/jupyter/Singularity", + "R/rstudio-server/Singularity.1.1.456", + "pbdR/pbdR-minimal/mpich/Singularity.1.0-1", + "pbdR/pbdR-minimal/openmpi/Singularity.1.0-1", + "pbdR/pbdR/mpich/Singularity.1.0-1", + "pbdR/pbdR/openmpi/Singularity.1.0-1" ], - "full_name": "maopl/TransOpt", + "full_name": "RBigData/singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-transopt\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#transopt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTransOpt\u003c/h1\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:maopl/TransOpt.git\ncd TransOpt\npip install -r requirements.txt \npip install .\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eR and pbdR Singularity Recipes\u003c/h1\u003e\u003ca id=\"user-content-r-and-pbdr-singularity-recipes\" class=\"anchor\" aria-label=\"Permalink: R and pbdR Singularity Recipes\" href=\"#r-and-pbdr-singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipes for R and pbdR.\u003c/p\u003e\n\u003cp\u003eBuild requirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.sylabs.io/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e \u0026gt;= 2.3\u003c/li\u003e\n\u003cli\u003eModify the \u003ccode\u003emake -j\u003c/code\u003e line of each recipe to your liking.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1698837283.0 + "updated_at": 1571942199.0 }, { "data_format": 2, - "description": "GWAS of trait variance (C++)", + "description": null, "filenames": [ - "sim/Singularity.def" + "Singularity.v2.12", + "Singularity.v2.9.6.collate_fix", + "Singularity.v2.11.0", + "Singularity.v2.11.1", + "Singularity.v2.14.3", + "Singularity.v2.9.1", + "Singularity.v2.10.3", + "Singularity.v2.10.0_fast_collate", + "Singularity.v2.10.5", + "Singularity.v2.9.4", + "Singularity.v2.10.0_db_collate", + "Singularity.v2.14.0", + "Singularity.v2.9.2", + "Singularity.v2.15.0", + "Singularity.v2.9.6", + "Singularity.v2.9.5", + "Singularity.v2.9.7_collator", + "Singularity.v2.10.4", + "Singularity.v2.9.8", + "Singularity.v2.9.3", + "Singularity.v2.15.1", + "Singularity.v2.9.7_emapper_2.1.2", + "Singularity.v2.10.0_faster_collate", + "Singularity.v2.9.7", + "Singularity.v2.9.6.with_customdb", + "Singularity.v2.9.2x", + "Singularity.v2.10.0" ], - "full_name": "MRCIEU/varGWAS", - "latest_release": "1.2.3", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003evarGWAS: GWAS of SNP variance effects\u003c/h1\u003e\u003ca id=\"user-content-vargwas-gwas-of-snp-variance-effects\" class=\"anchor\" aria-label=\"Permalink: varGWAS: GWAS of SNP variance effects\" href=\"#vargwas-gwas-of-snp-variance-effects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/MRCIEU/vargwas/actions\"\u003e\u003cimg src=\"https://github.com/MRCIEU/vargwas/actions/workflows/test.yml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003eSoftware to perform genome-wide association study of SNP effects on trait variance\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFull documentation available from \u003ca href=\"https://mrcieu.github.io/varGWAS\" rel=\"nofollow\"\u003ehttps://mrcieu.github.io/varGWAS\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eR\u003c/h2\u003e\u003ca id=\"user-content-r\" class=\"anchor\" aria-label=\"Permalink: R\" href=\"#r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eR-package also available from \u003ca href=\"https://github.com/MRCIEU/varGWASR\"\u003ehttps://github.com/MRCIEU/varGWASR\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLyon M, Millard L, Davey Smith G, Gaunt T, Tilling K. Hypothesis-free detection of gene-interaction effects on biomarker concentration in UK Biobank using variance prioritisation. MedRxiv (2022). \u003ca href=\"https://doi.org/10.1101/2022.01.05.21268406\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2022.01.05.21268406\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "cschu/gff_quantifier", + "latest_release": "v.2.9.8", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisites\u003c/h3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePython3.7+ (we need to be able to rely on dictionary item order preservation!)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ewheel\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInstall (will be automatically installed if pip install is used as described below):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eintervaltree\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eObtain the source code \u003ccode\u003egit clone https://github.com/cschu/gff_quantifier.git\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecd gff_quantifier\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epython setup.py bdist_wheel\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install [--user] -U dist/gffquant-\u0026lt;version\u0026gt;-py3-none-any.whl\u003c/code\u003e. The \u003ccode\u003e--user\u003c/code\u003e option is only necessary if you\u0027re running this with a shared python version.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAfter this, the relevant commands \u003ccode\u003egffindex\u003c/code\u003e and \u003ccode\u003egffquant\u003c/code\u003e should be in your path.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egffquant\u003c/code\u003e supports two kinds of input data, controlled by the \u003ccode\u003e--mode\u003c/code\u003e or \u003ccode\u003e-m\u003c/code\u003e parameter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe genome mode\u003c/h4\u003e\u003ca id=\"user-content-the-genome-mode\" class=\"anchor\" aria-label=\"Permalink: The genome mode\" href=\"#the-genome-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMode \u003ccode\u003e-m genome\u003c/code\u003e is the default mode. The functional profiling strategy in this mode is to only ever have a small portion of the annotation loaded into an interval tree at any given time, thus shifting the memory requirements towards the number of alignments. In \u003ccode\u003egenome\u003c/code\u003e mode, \u003ccode\u003egffquant\u003c/code\u003e takes as input:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea bamfile containing alignments against a set of genomic reference contigs, and\u003c/li\u003e\n\u003cli\u003ea gff3 file containing functional annotations for the genes encoded on the input contigs. This gff3 must be pre-indexed with \u003ccode\u003egffindex \u0026lt;input_gff\u0026gt;\u003c/code\u003e in order to allow random access.\n\u003cul\u003e\n\u003cli\u003ePre-indexing will write the index to a file named \u003ccode\u003e\u0026lt;input_gff\u0026gt;.index\u003c/code\u003e and only needs to be done once per gff.\u003c/li\u003e\n\u003cli\u003eFor best results, the gff should be strictly sorted by seqname (column 1).\u003c/li\u003e\n\u003cli\u003eThe gff must not be gzipped (random access of gzipped files via seek() is not feasible, hence gzipped gffs are not supported).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe gene mode\u003c/h4\u003e\u003ca id=\"user-content-the-gene-mode\" class=\"anchor\" aria-label=\"Permalink: The gene mode\" href=\"#the-gene-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMode \u003ccode\u003e-m genes\u003c/code\u003e allows large sets of genes to be used as reference. In this mode, \u003ccode\u003egffquant\u003c/code\u003e takes as input:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea bamfile containing alignments against a set of gene sequences, and\u003c/li\u003e\n\u003cli\u003ea gzipped, tab-separated functional annotation file following the GMGC convention (column 1 is the gene id, and the functional categories start in column 7.)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAs all (aligned) reads align against annotated genes, no overlap detection is done in this mode. Thus, the \u003ccode\u003egene\u003c/code\u003e mode does not require pre-indexing the annotation. However, in order to preserve memory, the bamfile is pre-scanned and information regarding unused reference sequences is discarded (leading to increased runtimes).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning gffquant\u003c/h4\u003e\u003ca id=\"user-content-running-gffquant\" class=\"anchor\" aria-label=\"Permalink: Running gffquant\" href=\"#running-gffquant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \u003ccode\u003egffquant \u0026lt;input_db\u0026gt; \u0026lt;input_bam\u0026gt; -o \u0026lt;out_prefix\u0026gt; [--ambig_mode {[unique_only], all1, primary_only, 1overN}] [--mode {genome, genes}] [--strand_specific]\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ccode\u003e\u0026lt;input_bam\u0026gt;\u003c/code\u003e file needs to be position sorted.\u003c/li\u003e\n\u003cli\u003eOutput files are \u003ccode\u003e\u0026lt;out_prefix\u0026gt;.seqname.txt\u003c/code\u003e (contig counts) and \u003ccode\u003e\u0026lt;out_prefix\u0026gt;.feature_counts.txt\u003c/code\u003e (feature/subfeature counts).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--ambig_mode\u003c/code\u003e controls how ambiguously mapping reads are processed. These are analogous to the ngless modes:\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0027unique_only\u003c/code\u003e will simply ignore any reads that is labeled as ambiguous (\u003ccode\u003eMAPQ=0\u003c/code\u003e). Runtime increases with bamfile size, memory should remain more or less constant.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eall1\u003c/code\u003e will treat all ambiguous alignments as single ended individual reads. Runtime increases with bamfile size, memory should remain more or less constant.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprimary_only\u003c/code\u003e will only count unique alignments and the primary alignment of a group of ambiguous alignments. Bamfiles that were filtered by \u003ccode\u003eNGLess\u003c/code\u003e will experience decreased counts as it is likely that the primary alignment was removed during filtering.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e1overN\u003c/code\u003e will dump all ambiguous alignments to disk (in reduced form), finish the processing of the unique alignments, then read and process the ambiguous alignments. Runtime and memory increase significantly with bamfile size. In addition, temporary disk space proportional to the number of ambiguous alignments is needed. In this mode (and only in this mode), sequence counting will include an additional output with reads distributed to reference contigs according to the \u003ccode\u003edist1\u003c/code\u003e mode of ngless.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eRNAseq reads\u003c/h5\u003e\u003ca id=\"user-content-rnaseq-reads\" class=\"anchor\" aria-label=\"Permalink: RNAseq reads\" href=\"#rnaseq-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingle-end RNAseq reads can be processed preserving strandness information via the \u003ccode\u003e--strand_specific\u003c/code\u003e parameter. In this case, the final count tables will contain an additional 6 columns (raw, normalised, scaled for both sense-strand and antisense-strand hits).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eResource requirements\u003c/h3\u003e\u003ca id=\"user-content-resource-requirements\" class=\"anchor\" aria-label=\"Permalink: Resource requirements\" href=\"#resource-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e(Note: numbers are under revision)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMemory and computing time requirements correspond to bamfile size. For current bamfile sizes of up to 24GB:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003euniq_only\u003c/code\u003e and \u003ccode\u003eall1\u003c/code\u003e: ~10GB memory and ~4h (up to 7h for \u003ccode\u003eall1\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprimary_only\u003c/code\u003e: TBD\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e1overN\u003c/code\u003e: \u0026gt;10GB memory, ~10min - \u0026gt;8h\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCount model\u003c/h3\u003e\u003ca id=\"user-content-count-model\" class=\"anchor\" aria-label=\"Permalink: Count model\" href=\"#count-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eReads aligning uniquely always contribute 1 to the region they align to\u003c/li\u003e\n\u003cli\u003eThe count contribution of reads with multiple (ambiguous) alignments depends on the mode:\n\u003col\u003e\n\u003cli\u003eIn \u003ccode\u003eunique_only\u003c/code\u003e, ambiguous reads are ignored.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003eall1\u003c/code\u003e, ambiguous reads contribute 1 to each region they align to. Each alignment is treated (and reported) as unique read.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003eprimary_only\u003c/code\u003e, all primary alignments contribute 1 to each region they align to. Secondary alignments are discarded.\u003c/li\u003e\n\u003cli\u003eIn \u003ccode\u003e1overN\u003c/code\u003e, each alignment against an annotated region contributes \u003ccode\u003e1/N\u003c/code\u003e to the region. \u003ccode\u003eN\u003c/code\u003e is the number of alignments against an annotated region. Alignments against unannotated regions are ignored.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./doc/count_distribution.svg\"\u003e\u003cimg src=\"./doc/count_distribution.svg\" width=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCounts against a region are propagated to the subfeatures with which the region is annotated.\u003c/li\u003e\n\u003cli\u003eGiven feature category \u003ccode\u003eF (e.g. eggNOG_GOs)\u003c/code\u003e with subfeatures \u003ccode\u003ef_1, f_2, f_3, ... (e.g. GO:0000001, ...)\u003c/code\u003e:\nA region with \u003ccode\u003ec\u003c/code\u003e read counts and annotated with \u003ccode\u003en\u003c/code\u003e subfeatures \u003ccode\u003e(f_1, ..., f_n)\u003c/code\u003e will propagate:\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003e+c\u003c/code\u003e to each subfeature count \u003ccode\u003eraw(f_i)\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e+c / length(region)\u003c/code\u003e to each normalised subfeature count \u003ccode\u003enorm(f_i)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eFeature category - specific scaling factors are calculated as \u003ccode\u003esigma_F = sum(raw(f_i)) / sum(norm(f_i))\u003c/code\u003e for each feature category \u003ccode\u003eF\u003c/code\u003e. These scaling factors are used to restore the proportion of counts with respect to the original sum of counts \u003ccode\u003escaled(f_i) = norm(f_i) * sigma_F\u003c/code\u003e.\nThe ngLess documentation states:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\"{scaled} is the result of the {normed} mode scaled up so that the total number of counts is\nidentical to the {raw} (within rounding error)\"\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003egffquant\u003c/code\u003e reports raw counts, normalised, and scaled values together in its count tables\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./doc/normalisation_scaling.svg\"\u003e\u003cimg src=\"./doc/normalisation_scaling.svg\" width=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput: count tables\u003c/h3\u003e\u003ca id=\"user-content-output-count-tables\" class=\"anchor\" aria-label=\"Permalink: Output: count tables\" href=\"#output-count-tables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003efeature_counts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnannotated reads are given in the first non-header row.\u003c/p\u003e\n\u003cp\u003eCount tables consist of the following columns:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003esubfeature\u003c/code\u003e (e.g. BRITE:br01600)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003euniq_raw\u003c/code\u003e: the raw counts of unique alignments (or, in \u003ccode\u003eall1\u003c/code\u003e mode, the sum of unique and ambiguous alignments)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003euniq_lnorm\u003c/code\u003e: the sum of the length-normalised unique raw counts (s. section Count Model)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003euniq_scaled\u003c/code\u003e: the scaled, normalised unique counts\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf ambiguous reads are not treated as unique (\u003ccode\u003emode=1overN\u003c/code\u003e), the next 4 columns (5-8; (\u003ccode\u003ecombined_\u003c/code\u003e)) additionally contain the sum of contributions from ambiguous alignments \u003cstrong\u003eand\u003c/strong\u003e unique alignments.\u003c/p\u003e\n\u003cp\u003eIf a strand-specific mode (currently only available for single-end RNAseq reads) was chosen, the table will further continue column blocks for sense-strand counts (\u003ccode\u003e_ss\u003c/code\u003e) and antisense-strand counts (\u003ccode\u003e_as\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eseqname.uniq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ecolumns:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ereference-id (that\u0027s an internal id)\u003c/li\u003e\n\u003cli\u003econtig-id (that\u0027s the official \"freeze12\" id)\u003c/li\u003e\n\u003cli\u003econtig length\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eColumns 4.,5.,6. are again raw, normalised, and scaled counts. Only unique alignments are counted.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 7, - "topics": [ - "gwas", - "variance", - "heteroscedasticity", - "heteroskedasticity", - "variability" - ], - "updated_at": 1695043730.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1732653609.0 }, { "data_format": 2, - "description": "A Nextflow pipeline for allele variant expression", + "description": "High-performance implementation of real-space Random Phase Approximation for calculation of plasmonic excitations", "filenames": [ "Singularity" ], - "full_name": "arontommi/RNAseq-VAX", - "latest_release": null, - "readme": "\u003cp\u003eplease note : This pipeline is still in alpha stage. please use at your own risk\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRNAseq-VAX - RNAseq Variante Allele eXpression\u003c/h1\u003e\u003ca id=\"user-content-rnaseq-vax---rnaseq-variante-allele-expression\" class=\"anchor\" aria-label=\"Permalink: RNAseq-VAX - RNAseq Variante Allele eXpression\" href=\"#rnaseq-vax---rnaseq-variante-allele-expression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe aim of this pipeline ist to take of where \u003ca href=\"https://github.com/nf-core/rnaseq\"\u003enf-core/rnaseq\u003c/a\u003e leaves and add additional informations on top. The pipeline follows \u003ca href=\"https://gatkforums.broadinstitute.org/gatk/discussion/3892/the-gatk-best-practices-for-variant-calling-on-rnaseq-in-full-detail\" rel=\"nofollow\"\u003eGATK guideline for variant calling in RNAseq\u003c/a\u003e. It then uses \u003ca href=\"https://software.broadinstitute.org/gatk/documentation/tooldocs/3.8-0/org_broadinstitute_gatk_tools_walkers_rnaseq_ASEReadCounter.php\" rel=\"nofollow\"\u003eASEReadCounter\u003c/a\u003e to retrive allele specific expression.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe RNAseq_VAX pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was written by Aron T. Skaftason (\u003ca href=\"https://github.com/arontommi\"\u003earontommi\u003c/a\u003e) but mostly cannibalised from \u003ca href=\"https://github.com/nf-core/rnaseq\"\u003enf-core/rnaseq\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "twesterhout/Plasmons.jl", + "latest_release": "v0.2.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePlasmons\u003c/h1\u003e\u003ca id=\"user-content-plasmons\" class=\"anchor\" aria-label=\"Permalink: Plasmons\" href=\"#plasmons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://twesterhout.github.io/Plasmons.jl/dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45c316268f541933640ad68f61395986888f4d46bc9641b40e53d337576c6456/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732d6465762d626c75652e737667\" alt=\"Dev\" data-canonical-src=\"https://img.shields.io/badge/docs-dev-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/twesterhout/Plasmons.jl\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/324ab8f3160021dad413fad96b40e030a044cc9c6406e4e568d02e5098553a7f/68747470733a2f2f7472617669732d63692e636f6d2f74776573746572686f75742f506c61736d6f6e732e6a6c2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/twesterhout/Plasmons.jl.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHigh-performance implementation of real-space Random Phase Approximation for\ncalculation of plasmonic excitations. For more details, please check out the\n\u003ca href=\"https://twesterhout.github.io/Plasmons.jl/dev\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you are using this code, please cite the following paper:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-tex-latex\"\u003e\u003cpre\u003e@article{westerhout2018plasmon,\n title={Plasmon confinement in fractal quantum systems},\n author={Westerhout, Tom and van Veen, Edo and Katsnelson, Mikhail I and Yuan, Shengjun},\n journal={Physical Review B},\n volume={97},\n number={20},\n pages={205434},\n year={2018},\n publisher={APS}\n}\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 3, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1692357015.0 + "updated_at": 1717141203.0 }, { "data_format": 2, - "description": "official build specifications for mongo db", + "description": "best-action trajectory stitching", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "singularityhub/mongo", + "full_name": "virajmehta/bats", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMongo\u003c/h1\u003e\u003ca id=\"user-content-mongo\" class=\"anchor\" aria-label=\"Permalink: Mongo\" href=\"#mongo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is an example of building a mongo container with Singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLocal Generation\u003c/h2\u003e\u003ca id=\"user-content-local-generation\" class=\"anchor\" aria-label=\"Permalink: Local Generation\" href=\"#local-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, clone the repo.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://www.github.com/singularityhub/mongo\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e mongo\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, on your local machine, build the container!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build mongo.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the Image\u003c/h2\u003e\u003ca id=\"user-content-running-the-image\" class=\"anchor\" aria-label=\"Permalink: Running the Image\" href=\"#running-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entrypoint to communicate with mongo is accessible by running the image as an executable. Here is how to see the help commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./mongo.sif --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, since you likely won\u0027t have write access inside the image running on a cluster (the image itself is in read only mode) what we must do is map a local directory to store data. We can do that as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir data \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e this is your data directory on your local machine or cluster\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn one terminal, we can start the database\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/data:/data/db mongo.sif --auth\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd in another terminal, we can send a command to mongo, for example, connect\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/data:/data/db mongo.sif mongo\nMongoDB shell version v4.2.1\nconnecting to: mongodb://127.0.0.1:27017/\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003ecompressors=disabled\u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003egssapiServiceName=mongodb\nImplicit session: session { \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eid\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e:\u003c/span\u003e UUID(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e22992c09-84bd-416c-9934-db7145a5d37c\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) }\nMongoDB server version: 4.2.1\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebats\u003c/h1\u003e\u003ca id=\"user-content-bats\" class=\"anchor\" aria-label=\"Permalink: bats\" href=\"#bats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ebest-action trajectory stitching\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall instructions\u003c/h2\u003e\u003ca id=\"user-content-install-instructions\" class=\"anchor\" aria-label=\"Permalink: Install instructions\" href=\"#install-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall the conda environment included with environment.yml.\u003c/li\u003e\n\u003cli\u003eInstall d4rl with pip.\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 3, "subscribers_count": 4, "topics": [], - "updated_at": 1628709769.0 + "updated_at": 1712738846.0 }, { "data_format": 2, - "description": "Ubuntu container for MEEP-MPI", + "description": "Python version of Khaled Khairy\u0027s EM_aligner, supporting distributed assembly and solve", "filenames": [ - "Singularity" + "EMaligner/distributed/src/Singularity.petsc_solver", + "EMaligner/distributed/src/Singularity.petsc" ], - "full_name": "CHPC-UofU/Singularity-meep-mpi", - "latest_release": null, + "full_name": "AllenInstitute/EM_aligner_python", + "latest_release": "v1.0.0", "stargazers_count": 3, - "subscribers_count": 2, + "subscribers_count": 9, "topics": [], - "updated_at": 1522810462.0 + "updated_at": 1567019058.0 }, { "data_format": 2, - "description": "A Python package and scripts for the evaluation of nonlinear interference noise in single mode fiber transmissions", + "description": "Projet Master2 AMI2B test de reproductibilit\u00e9 ", "filenames": [ - "Singularity" + "Tools/fastqc/Singularity.fastqc", + "Tools/deseq2/Singularity.deseq2", + "Tools/subread/Singularity.subread", + "Tools/star/Singularity.star", + "Tools/fastq-dump/Singularity.fastq-dump" ], - "full_name": "geeanlooca/PyNLIN", + "full_name": "hippolyte456/Hackathon_NGS_2022", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePyNLIN\u003c/h1\u003e\u003ca id=\"user-content-pynlin\" class=\"anchor\" aria-label=\"Permalink: PyNLIN\" href=\"#pynlin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Python package and scripts for the evaluation of nonlinear interference noise in single mode fiber transmissions\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnd users\u003c/h2\u003e\u003ca id=\"user-content-end-users\" class=\"anchor\" aria-label=\"Permalink: End users\" href=\"#end-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJust clone the repository and \u003ccode\u003epip install\u003c/code\u003e it.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/geeanlooca/PyNLIN.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e PyNLIN\npip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSet up the environment\u003c/h3\u003e\u003ca id=\"user-content-set-up-the-environment\" class=\"anchor\" aria-label=\"Permalink: Set up the environment\" href=\"#set-up-the-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eConda\u003c/h4\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI usually like to install the core numerical packages from conda directly, and let \u003ccode\u003epip\u003c/code\u003e manage the rest of the dependencies.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eenv\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e python=3.10 --yes\nconda activate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eenv\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nconda install numpy scipy matplotlib h5py\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ccode\u003evenv\u003c/code\u003e\u003c/h4\u003e\u003ca id=\"user-content-venv\" class=\"anchor\" aria-label=\"Permalink: venv\" href=\"#venv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eenv\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eenv\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/bin/activate \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;env\u0026gt;\\Scripts\\activate.bat under Windows\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall the package\u003c/h3\u003e\u003ca id=\"user-content-install-the-package\" class=\"anchor\" aria-label=\"Permalink: Install the package\" href=\"#install-the-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor development purposes, the package should be installed in the editable mode. Changes you make to the package are immediatly reflected on the installed version and consequently on the scripts using the package.\u003c/p\u003e\n\u003cp\u003eFrom the root of the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install -e .[dev]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity images\u003c/h1\u003e\u003ca id=\"user-content-singularity-images\" class=\"anchor\" aria-label=\"Permalink: Singularity images\" href=\"#singularity-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePackaging the code in a Singularity image allows us to run code using PyNLIN on the Department\u0027s SLURM cluster.\u003c/p\u003e\n\u003cp\u003eThere are two main ways in which you can run build and run a Singularity image:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstall Singularity on your local machine, build the image, copy it to the cluster, and submit a job using the image.\u003c/li\u003e\n\u003cli\u003eBuild the image using the remote builder and pull the image directly on the cluster to avoid wasting too much time on uploading the image.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e **The image pulls the latest commit on the \u003ccode\u003emain\u003c/code\u003e branch directly from GitHub. Local edits or commits not pushed to GitHub will not be reflected in the resulting image file\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLocal build\u003c/h2\u003e\u003ca id=\"user-content-local-build\" class=\"anchor\" aria-label=\"Permalink: Local build\" href=\"#local-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have Singularity installed, just run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build --force singularity.sif singularity.def\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe resulting \u003ccode\u003e.sif\u003c/code\u003e image file can be used to run python scripts locally using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e singularity.sif python \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003escript\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor uploaded to the cluster.\nAn example \u003ccode\u003e.slurm\u003c/code\u003e file to run a job on the cluster is provided in the \u003ccode\u003eslurm/\u003c/code\u003e directory of this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRemote build\u003c/h2\u003e\u003ca id=\"user-content-remote-build\" class=\"anchor\" aria-label=\"Permalink: Remote build\" href=\"#remote-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding\u003c/h2\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProject Repro-Hackathon\u003c/h1\u003e\u003ca id=\"user-content-project-repro-hackathon\" class=\"anchor\" aria-label=\"Permalink: Project Repro-Hackathon\" href=\"#project-repro-hackathon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProject of the Master of Bioinformatics (AMI2B) of the University Paris-Saclay realized by : \u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/JudithCo\"\u003eJudith Coutrot \u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/J-ally\"\u003eJoseph Allyndr\u00e9e \u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/hippolyte456\"\u003eHippolyte Dreyfus \u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Aaramis\"\u003eAuguste Gardette \u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePresentation\u003c/h2\u003e\u003ca id=\"user-content-presentation\" class=\"anchor\" aria-label=\"Permalink: Presentation\" href=\"#presentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe goal is to reproduce parts of the analysis described in these papers (to read): \u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/23313955/\" rel=\"nofollow\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/23313955/\u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/23861464/\" rel=\"nofollow\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/23861464/\u003c/a\u003e \u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThey performed \u003ca href=\"https://en.wikipedia.org/wiki/RNA-Seq\" rel=\"nofollow\"\u003eRNA-Seq\u003c/a\u003e in samples from patients with uveal melanoma. Some samples are mutated in SF3B1 .\nWe want to analyze this data in order to find \u003ca href=\"https://en.wikipedia.org/wiki/RNA-Seq#Differential_expression\" rel=\"nofollow\"\u003edifferentially expressed genes\u003c/a\u003e, i.e. genes that are more (or less) expressed in one condition (SF3B1 mutated samples) compared to another (SF3B1 non mutated samples).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOrganization\u003c/h2\u003e\u003ca id=\"user-content-organization\" class=\"anchor\" aria-label=\"Permalink: Organization\" href=\"#organization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo do this, we have designed and implemented a reproductible workflow.\u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA Directory \u003ca href=\"./Tools/\"\u003eTools\u003c/a\u003e to build all containers (using Singularity) with the tools that will be used in the workflow.\u003c/li\u003e\n\u003cli\u003eA Directory \u003ca href=\"./Workflow/\"\u003eWorkflow\u003c/a\u003e with the rules and files (using Snakemake) needed for the workflow.\u003c/li\u003e\n\u003cli\u003eA Script \u003ca href=\"./run.sh\"\u003erun.sh\u003c/a\u003e to execute the workflow.\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"./README.md\"\u003eREADME\u003c/a\u003e file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImages Resumes\u003c/h3\u003e\u003ca id=\"user-content-images-resumes\" class=\"anchor\" aria-label=\"Permalink: Images Resumes\" href=\"#images-resumes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./Tools/Images_Resume.png\"\u003e\u003cimg src=\"./Tools/Images_Resume.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRules Resumes :\u003c/h3\u003e\u003ca id=\"user-content-rules-resumes-\" class=\"anchor\" aria-label=\"Permalink: Rules Resumes :\" href=\"#rules-resumes-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./Workflow/rules_architecture.png\"\u003e\u003cimg src=\"./Workflow/rules_architecture.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout IFB Cloud\u003c/h2\u003e\u003ca id=\"user-content-about-ifb-cloud\" class=\"anchor\" aria-label=\"Permalink: About IFB Cloud\" href=\"#about-ifb-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\"French Institute of Bioinformatics (\u003ca href=\"https://www.france-bioinformatique.fr/cloud-ifb/\" rel=\"nofollow\"\u003eIFB\u003c/a\u003e) provides life scientists with a federation of clouds, Biosphere, and bioinformatics cloud services to analyze life science data. Biosphere is used for scientific production in the life sciences, developments, and to support events like cloud and scientific training sessions, hackathons or workshops.\"\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetting up a VM\u003c/h3\u003e\u003ca id=\"user-content-setting-up-a-vm\" class=\"anchor\" aria-label=\"Permalink: Setting up a VM\" href=\"#setting-up-a-vm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://biosphere.france-bioinformatique.fr/\" rel=\"nofollow\"\u003eIFB Cloud Biosphere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor the moment the pipeline work for a VM BioPipes \"ifb..mxlarge (16 vCPU, 64Go GB RAM, 400Go local Disk)\"\u003c/p\u003e\n\u003cp\u003eIt should also work for a VM of 8 CPUs, below 8 the indexing of the whole genome is impossible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the workflow\u003c/h2\u003e\u003ca id=\"user-content-run-the-workflow\" class=\"anchor\" aria-label=\"Permalink: Run the workflow\" href=\"#run-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./run.sh\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 3, "subscribers_count": 2, "topics": [], - "updated_at": 1723899336.0 + "updated_at": 1681719835.0 }, { "data_format": 2, - "description": "Sample workflow to demonstrate how Pegasus can be used to manage the population modeling tools in the MINT project", + "description": "workflow example managed with Nextflow within Singularity that run Trimgalore on fastq files", "filenames": [ - "Singularity" + "Singularity", + "images/Singularity.v1" ], - "full_name": "pegasus-isi/darpa_population_modeling", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003edarpa_population_modeling\u003c/h1\u003e\u003ca id=\"user-content-darpa_population_modeling\" class=\"anchor\" aria-label=\"Permalink: darpa_population_modeling\" href=\"#darpa_population_modeling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a sample workflow to demonstrate how Pegasus can be used to\nmanage the population modeling tools in the MINT project.\u003c/p\u003e\n\u003cp\u003eThe tools have been converted to command line tools.\u003c/p\u003e\n\u003cp\u003eA Singularity image, based on Ubuntu Xenial Xerus and with Python3 and\nGIS tools and libraries, are used for the compute environment for the\njobs.\u003c/p\u003e\n\u003cp\u003eThe workflow is currently set up to run on the ISI testbed, but can\nbe moved to more powerful execution environments if needed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSubmitting\u003c/h2\u003e\u003ca id=\"user-content-submitting\" class=\"anchor\" aria-label=\"Permalink: Submitting\" href=\"#submitting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheck out this repository on \u003ccode\u003eworkflow.isi.edu\u003c/code\u003e and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./workflow/submit.sh\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "mhebrard/TrimFlow", + "latest_release": "v1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTrimFlow\u003c/h1\u003e\u003ca id=\"user-content-trimflow\" class=\"anchor\" aria-label=\"Permalink: TrimFlow\" href=\"#trimflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eworkflow example managed with \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e within \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e that run \u003ca href=\"https://github.com/FelixKrueger/TrimGalore\"\u003eTrimgalore\u003c/a\u003e on fastq files\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Singularity : \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003eon Linux\u003c/a\u003e or \u003ca href=\"http://singularity.lbl.gov/install-windows\" rel=\"nofollow\"\u003eon Windows\u003c/a\u003e\n(v2.4.1 and above required)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Nextflow: \u003ca href=\"https://www.nextflow.io/#GetStarted\" rel=\"nofollow\"\u003eHOWTO\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChoose one of the methods below:\u003c/p\u003e\n\u003cp\u003e( change \u003ccode\u003epath/to/reads/\u003c/code\u003e to match the folder of your data.\nReads folder must be in the current folder or a subdirectory -- \u003ccode\u003e./data/\u003c/code\u003e )\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLet nextflow download the workflow files and the container image automatically\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run mhebrard/TrimFlow --reads \u0027path/to/reads/*_R{1,2}*\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003edownload the workflow files and the container image manually, then run it locally\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003enextflow pull mhebrard/TrimFlow\nsingularity pull --name TrimFlow.simg shub://mhebrard/TrimFlow\nnextflow run mhebrard/TrimFlow -with-singularity ./Trimflow.simg --reads \u0027path/to/reads/*_R{1,2}*\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVersion\u003c/h2\u003e\u003ca id=\"user-content-version\" class=\"anchor\" aria-label=\"Permalink: Version\" href=\"#version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe first digit correspond to the version of the container image.\u003c/p\u003e\n\u003cp\u003eThe other digits represent the version of the nextflow script.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [], - "updated_at": 1685041580.0 + "updated_at": 1595480280.0 }, { "data_format": 2, - "description": "A singularity container demonstrating multi node MPI via SSH wrapper", + "description": null, "filenames": [ - "Singularity" + "downward/misc/releases/21.12/Singularity.21.12", + "downward/misc/releases/20.06/Singularity.20.06", + "downward/misc/releases/19.12/Singularity.19.12", + "downward/misc/releases/22.06/Singularity.22.06", + "downward/misc/releases/latest/Singularity", + "downward/misc/releases/19.06/Singularity.19.06", + "downward/misc/releases/22.12/Singularity.22.12" ], - "full_name": "katakombi/multinode_sshwrapper", + "full_name": "ipc2023-learning/repo05", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emultinode_sshwrapper\u003c/h1\u003e\u003ca id=\"user-content-multinode_sshwrapper\" class=\"anchor\" aria-label=\"Permalink: multinode_sshwrapper\" href=\"#multinode_sshwrapper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity container demonstrating multi node MPI via SSH wrapper.\nThe specialty is that the MPI installation is inside the container, and that no MPI on the host system is being used.\nThis is accomplished by packaging OpenMPI and the Infiniband/IPath drivers inside the container.\u003c/p\u003e\n\u003cp\u003eTo demonstrate how it works you will run a short gromacs job using\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e4 local MPI threads on localhost\u003c/li\u003e\n\u003cli\u003e2x2 MPI threads on two host specified by you\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth tasks should take approximately the same time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLimitations\u003c/h2\u003e\u003ca id=\"user-content-limitations\" class=\"anchor\" aria-label=\"Permalink: Limitations\" href=\"#limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eI have tested this using QDR InfiniBand HCA (Intel TrueScale) on our bwForCluster JUSTUS. I did not try to perform this via standard ethernet although it should be doable. I guess some parameters or environment variables would be needed to try this out. If you have other hardware you might need to install additional driver packages inside the container.\u003c/li\u003e\n\u003cli\u003eThe container ignores your RM and/or queueing system. Further changes would be needed to integrate that well into whatever it is you are using.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThis container is tested with Singularity Version 2.3 and above.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn order to properly submit a multi node job the nodes must be accessible via SSH keys (password-less)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe container needs to reside on a shared location which is accessible from each node.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFurthermore, each node needs to have the same singularity installation / configuration as the host you are invoking the multi node MPI job on. The default config should just do fine.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull this image onto the login node of a multi host HPC system and run it in single node mode\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://katakombi/multinode_sshwrapper\n./katakombi-multinode_sshwrapper-master.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse \u003ccode\u003etop\u003c/code\u003e to ensure that there are four local cores being occupied.\u003c/p\u003e\n\u003cp\u003eIn order to submit it multi node (here \u003ccode\u003en0720\u003c/code\u003e and \u003ccode\u003en0726\u003c/code\u003e, you need to use the hostnames or IPs of your nodes, respectively) execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport SINGULARITYENV_SINGULARITY_IMAGE=\"$PWD/katakombi-multinode_sshwrapper-master.img\"\nsingularity exec \"$SINGULARITYENV_SINGULARITY_IMAGE\" /.singularity.d/runscript n0726,n0720\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDuring the short run, use \u003ccode\u003etop\u003c/code\u003e on the compute nodes to ensure that on each of them two cores are being occupied.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCredit to whom it is due:\n\u003ca href=\"https://github.com/ambu50/docker-ib-mpi\"\u003ehttps://github.com/ambu50/docker-ib-mpi\u003c/a\u003e\n\u003ca href=\"https://github.com/ambu50/wrapper-sq/blob/master/papers/docker_paper_IEEE.pdf\"\u003ehttps://github.com/ambu50/wrapper-sq/blob/master/papers/docker_paper_IEEE.pdf\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eASNets - IPC23\u003c/h2\u003e\u003ca id=\"user-content-asnets---ipc23\" class=\"anchor\" aria-label=\"Permalink: ASNets - IPC23\" href=\"#asnets---ipc23\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo Build\u003c/h3\u003e\u003ca id=\"user-content-to-build\" class=\"anchor\" aria-label=\"Permalink: To Build\" href=\"#to-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDownload and install \u003ca href=\"https://apptainer.org/docs/admin/main/installation.html\" rel=\"nofollow\"\u003eApptainer\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003eapptainer build learn.sif apptainer.asnets.learn\u003c/code\u003e and\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003eapptainer build plan.sif apptainer.asnets.plan\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTo run\u003c/h3\u003e\u003ca id=\"user-content-to-run\" class=\"anchor\" aria-label=\"Permalink: To run\" href=\"#to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eTo run the learning script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./learn.sif DK DOMAIN TASK1 TASK2 ...\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003eDK\u003c/code\u003e is the name of output policy. To successfully run the script, at least provide four tasks.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./learn.sif policy.dt benchmarks/blocksworld/domain.pddl benchmarks/blocksworld/training/easy/p01.pddl benchmarks/blocksworld/training/easy/p02.pddl benchmarks/blocksworld/training/easy/p03.pddl benchmarks/blocksworld/training/easy/p04.pddl\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eTo run the planning script:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./plan.sif DK DOMAIN TASK PLAN\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhere \u003ccode\u003eDK\u003c/code\u003e is the name of learnt policy and \u003ccode\u003ePLAN\u003c/code\u003e is the name of the output plan file. To successfully run the script, only one problem should be given.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./plan.sif dk benchmarks/blocksworld/domain.pddl benchmarks/blocksworld/training/easy/p01.pddl\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor more detail please find on the \u003ca href=\"https://ipc2023-learning.github.io/\" rel=\"nofollow\"\u003ecompetition website\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 3, "subscribers_count": 1, - "topics": [ - "multi-node", - "mpi", - "singularity", - "infiniband", - "ssh-wrapper", - "gromacs" - ], - "updated_at": 1576131506.0 + "topics": [], + "updated_at": 1715001845.0 }, { "data_format": 2, - "description": "High-level Haskell bindings to HDF5", + "description": "ParaView Catalyst adaptor example for a Fortran code", "filenames": [ "Singularity" ], - "full_name": "twesterhout/hdf5-hs", + "full_name": "niwa/lfric_catalyst_adaptor", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehdf5-hs \u003ca href=\"https://github.com/twesterhout/hdf5-hs/actions\"\u003e\u003cimg src=\"https://github.com/twesterhout/hdf5-hs/workflows/CI/badge.svg\" alt=\"GitHub CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca href=\"LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5116bcc5e23725d16fc3dedb92367e72b3d6316596c5bca21f4df29d23b17971/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4253442d2d332d2d436c617573652d626c75652e737667\" alt=\"BSD-3-Clause license\" data-canonical-src=\"https://img.shields.io/badge/license-BSD--3--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-hdf5-hs-\" class=\"anchor\" aria-label=\"Permalink: hdf5-hs \" href=\"#hdf5-hs-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning:\u003c/strong\u003e this package is a work-in-progress. The code is kind of working,\nbut documentation and examples are lacking. Also this project was created\nspecifically for \u003ca href=\"...\"\u003eSpinED\u003c/a\u003e program, so it\u0027s going to be \"battle-tested\"\nrather that a proof-of-concept.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eHigh-level interface to \u003ca href=\"https://www.hdfgroup.org/solutions/hdf5\" rel=\"nofollow\"\u003eHDF5\u003c/a\u003e for\nHaskell. The idea is to provide a limited functionality, but in a user-friendly\nway. It relies extensively on GADTs to keep the interface polymorphic, but\ntype-safe.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package is very much a work-in-progress and is not (yet, hopefully)\navailable on \u003ca href=\"https://hackage.haskell.org/\" rel=\"nofollow\"\u003eHackage\u003c/a\u003e. Currently, the easiest way to install it is to use \u003ccode\u003ecabal.project\u003c/code\u003e and \u003ca href=\"https://cabal.readthedocs.io/en/3.4/cabal-project.html#specifying-packages-from-remote-version-control-locations\" rel=\"nofollow\"\u003especify that you want to install \u003ccode\u003ehdf5-hs\u003c/code\u003e from Git\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource-repository-package\n type: git\n location: https://github.com/twesterhout/hdf5-hs.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCurrently, Linux and OS X operating systems are supported. (I don\u0027t have\nexperience or access to a Windows system, but a PR would be very welcome!)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSimple example\u003c/h2\u003e\u003ca id=\"user-content-simple-example\" class=\"anchor\" aria-label=\"Permalink: Simple example\" href=\"#simple-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-haskell\"\u003e\u003cpre\u003e{-# \u003cspan class=\"pl-k\"\u003eLANGUAGE\u003c/span\u003e OverloadedStrings #-}\n\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eData.HDF5\u003c/span\u003e (\u003cspan class=\"pl-en\"\u003eDataset\u003c/span\u003e, \u003cspan class=\"pl-en\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-en\"\u003eIOMode\u003c/span\u003e (\u003cspan class=\"pl-k\"\u003e..\u003c/span\u003e))\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-k\"\u003equalified\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eData.HDF5\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eH5\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eData.Text\u003c/span\u003e (\u003cspan class=\"pl-en\"\u003eText\u003c/span\u003e)\n\n\u003cspan class=\"pl-en\"\u003emain\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e::\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e\u003cspan class=\"pl-c1\"\u003eIO\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e()\u003c/span\u003e\nmain \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Create a file\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ewithFile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexample00.h5\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003eWriteMode\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\\\u003c/span\u003efile \u003cspan class=\"pl-k\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Create a group\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003emakeGroup file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyGroup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Do something with the group\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e (matchM function \"proves\" to GHC that the object we\u0027ve just opened is indeed a group)\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ebyName file \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyGroup\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ematchM \u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\\\u003c/span\u003egroup \u003cspan class=\"pl-k\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Create a dataset\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ewriteDataset group \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyDataset\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e [(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e::\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e\u003cspan class=\"pl-c1\"\u003eInt\u003c/span\u003e\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e..\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e25\u003c/span\u003e]\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e--\u003c/span\u003e Add a description\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ebyName group \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyDataset\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ematchM \u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eDataset\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\\\u003c/span\u003edataset \u003cspan class=\"pl-k\"\u003e-\u0026gt;\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eH5.\u003c/span\u003ewriteAttribute dataset \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emyAttribute\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eContains very important data \ud83d\ude00\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e::\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eText\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRelated projects\u003c/h3\u003e\u003ca id=\"user-content-related-projects\" class=\"anchor\" aria-label=\"Permalink: Related projects\" href=\"#related-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere is \u003ca href=\"https://github.com/mokus0/hs-hdf5\"\u003e\u003ccode\u003ehs-hdf5\u003c/code\u003e\u003c/a\u003e. It covers more\nfunctionality of HDF5 than this package, but the API is lower-level.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLFRic Catalyst Adaptor\u003c/h1\u003e\u003ca id=\"user-content-lfric-catalyst-adaptor\" class=\"anchor\" aria-label=\"Permalink: LFRic Catalyst Adaptor\" href=\"#lfric-catalyst-adaptor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eParaView Catalyst adaptor implementation for the LFRic code\u003c/p\u003e\n\u003cp\u003eThis package builds a library for visualising simulation data with a simple VTK visualisation pipeline. The pipeline can be defined either in C++ or using a Python script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the adaptor\u003c/h2\u003e\u003ca id=\"user-content-building-the-adaptor\" class=\"anchor\" aria-label=\"Permalink: Building the adaptor\" href=\"#building-the-adaptor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build this code, you will need to build and install ParaView with Catalyst option enabled. Once this is done, build the code using CMake as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir build\ncd build\ncmake .. -DParaView_DIR=/path/to/catalyst/install/directory/lib/cmake/paraview-5.4 -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/path/to/install/dir\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to build a debug version of the code, add \u003ccode\u003e-DCMAKE_BUILD_TYPE=Debug\u003c/code\u003e to the CMake configuration, or use the \u003ccode\u003eccmake\u003c/code\u003e configuration tool. You can add additional compiler flags using the \u003ccode\u003e-DCMAKE_CXX_FLAGS=\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eOn a Cray XC50 system, the following build setup should work:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake .. -DCMAKE_CXX_COMPILER=CC -DCMAKE_EXE_LINKER_FLAGS=-dynamic -DParaView_DIR=/path/to/catalyst/install/directory/lib/cmake/paraview-5.4 -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/path/to/install/dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that dynamic linking simplifies the linking process of the Fortran application significantly.\u003c/p\u003e\n\u003cp\u003eOnce CMake has finished, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\nmake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto build and install the library.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the test battery\u003c/h2\u003e\u003ca id=\"user-content-running-the-test-battery\" class=\"anchor\" aria-label=\"Permalink: Running the test battery\" href=\"#running-the-test-battery\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to test your build, add \u003ccode\u003e-DBUILD_TESTING=ON\u003c/code\u003e to your CMake configuration and run \u003ccode\u003emake test\u003c/code\u003e or \u003ccode\u003ectest\u003c/code\u003e after building the code. This will run a number of tests that check basic functionality.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning a simulation with the adaptor\u003c/h2\u003e\u003ca id=\"user-content-running-a-simulation-with-the-adaptor\" class=\"anchor\" aria-label=\"Permalink: Running a simulation with the adaptor\" href=\"#running-a-simulation-with-the-adaptor\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Catalyst adaptor and libraries are usually dynamically linked. If the build system of your code does not hardcode shared library paths, you will need to set (possibly adapting ParaView version)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport LD_LIBRARY_PATH=/path/to/catalyst/installation/lib/paraview-5.4:$LD_LIBRARY_PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to use the Python pipeline, set \u003ccode\u003ePYTHONPATH\u003c/code\u003e to something like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PYTHONPATH=/path/to/catalyst/installation/lib/paraview-5.4/site-packages:/path/to/catalyst/installation/lib/paraview-5.4/site-packages/vtk:$PYTHONPATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython scripts\u003c/h2\u003e\u003ca id=\"user-content-python-scripts\" class=\"anchor\" aria-label=\"Permalink: Python scripts\" href=\"#python-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository includes a number of Python scripts which define visualisation pipelines or provide some post-processing functionality.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003efull_output.py\u003c/h3\u003e\u003ca id=\"user-content-full_outputpy\" class=\"anchor\" aria-label=\"Permalink: full_output.py\" href=\"#full_outputpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimple Python pipeline for writing the model grid and data field to a VTK file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espherical_slice.py\u003c/h3\u003e\u003ca id=\"user-content-spherical_slicepy\" class=\"anchor\" aria-label=\"Permalink: spherical_slice.py\" href=\"#spherical_slicepy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimple Python pipeline for creating spherical slices of model grid with a preset radius, which are written into a VTK polydata file. Full output of the model grid and data field can also be produced by setting the corresponding flag in the pipeline script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espherical_slice_contours.py\u003c/h3\u003e\u003ca id=\"user-content-spherical_slice_contourspy\" class=\"anchor\" aria-label=\"Permalink: spherical_slice_contours.py\" href=\"#spherical_slice_contourspy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as \"spherical_slice.py\", but includes an additional output file with contours.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espherical_slice_rendered.py\u003c/h3\u003e\u003ca id=\"user-content-spherical_slice_renderedpy\" class=\"anchor\" aria-label=\"Permalink: spherical_slice_rendered.py\" href=\"#spherical_slice_renderedpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as \"spherical_slice.py\", but includes a rendered image of the slice which is stored as a png file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003espherical_slice_rendered_coastlines.py\u003c/h3\u003e\u003ca id=\"user-content-spherical_slice_rendered_coastlinespy\" class=\"anchor\" aria-label=\"Permalink: spherical_slice_rendered_coastlines.py\" href=\"#spherical_slice_rendered_coastlinespy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame as \"spherical_slice_rendered.py\", but overlays coastlines on the rendered image. Requires downloading coastlines data, see source file for instructions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emeridional_slice.py\u003c/h3\u003e\u003ca id=\"user-content-meridional_slicepy\" class=\"anchor\" aria-label=\"Permalink: meridional_slice.py\" href=\"#meridional_slicepy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreates and stores a meridional slice for a chosen longitude, including a transformation from Cartesian to longitude-radius coordinates.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emap_project.py\u003c/h3\u003e\u003ca id=\"user-content-map_projectpy\" class=\"anchor\" aria-label=\"Permalink: map_project.py\" href=\"#map_projectpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Python program expects a spherical slice (as produced by the \u003ccode\u003espherical_slice.py\u003c/code\u003e visualisation pipeline) in VTK polydata format as input and produces a VTK polydata file with a map projection as output. The program can handle partitioned datasets, but computing map projections for multiple timesteps is not supported yet.\u003c/p\u003e\n\u003cp\u003eRunning\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./map_project.py input.vtp output.vtp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecomputes a Mollweide map projection. Use flag \u003ccode\u003e--list-projections\u003c/code\u003e to get a list of projections and their short names (projections are provide by the PROJ library). Short names can be used to set another projection with the \u003ccode\u003e--projname\u003c/code\u003e flag, e.g., \u003ccode\u003e--projname=gall\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 3, - "subscribers_count": 4, - "topics": [ - "haskell", - "data-format", - "scientific-computing", - "big-data" - ], - "updated_at": 1731688777.0 + "subscribers_count": 2, + "topics": [], + "updated_at": 1646449477.0 }, { "data_format": 2, - "description": "Standardized EEG preprocessing", + "description": "Creates Manhattan and QQ plots with annotated peaks.", "filenames": [ "Singularity" ], - "full_name": "Charestlab/eegprep", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEEGprep\u003c/h1\u003e\u003ca id=\"user-content-eegprep\" class=\"anchor\" aria-label=\"Permalink: EEGprep\" href=\"#eegprep\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStandardized EEG preprocessing\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the EEGprep singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build eegprep.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun EEGprep on your data:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -c -e --bind /your/data/dir/:/data eegprep.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere /your/data/dir/ contains a \u003cem\u003eBIDS\u003c/em\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration\u003c/h2\u003e\u003ca id=\"user-content-configuration\" class=\"anchor\" aria-label=\"Permalink: Configuration\" href=\"#configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can give EEGprep some direction by putting a configuration file\nin your data direction called \u003ccode\u003eeegprep.conf\u003c/code\u003e. See also \u003ccode\u003eexample.eegprep.conf\u003c/code\u003e\nin this repository.\u003c/p\u003e\n", - "stargazers_count": 3, - "subscribers_count": 6, + "full_name": "hmgu-itg/man_qq_annotate", + "latest_release": "v0.2.3", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eman_qq_annotate\u003c/h1\u003e\u003ca id=\"user-content-man_qq_annotate\" class=\"anchor\" aria-label=\"Permalink: man_qq_annotate\" href=\"#man_qq_annotate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreates Manhattan and QQ plots with annotated peaks for sequencing-based GWAS outputs, by thinning the dataset to what the eye can see.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall using \u003ccode\u003edevtools\u003c/code\u003e (in R):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install devtools if you don\u0027t have it\u003c/span\u003e\ninstall.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edevtools\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003edevtools\u003c/span\u003e)\ninstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehmgu-itg/man_qq_annotate\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can either use the CLI or load the package into your R environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand Line Interface (CLI)\u003c/h3\u003e\u003ca id=\"user-content-command-line-interface-cli\" class=\"anchor\" aria-label=\"Permalink: Command Line Interface (CLI)\" href=\"#command-line-interface-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce installed, you can use the \u003ccode\u003emanqq_cli\u003c/code\u003e script in the base of the repository as a command line tool.\u003cbr\u003e\nFor a GCTA output, use the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manqq_cli --chr-col Chr --pval-col p --pos-col bp --a1 A1 --a2 A2 --build 38 --image png --af-col Freq input.assoc.txt.gz output.prefix\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can add \u003ccode\u003emanqq_cli\u003c/code\u003e to your \u003ccode\u003ePATH\u003c/code\u003e variable for convenient execution:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/man_qq_annotate:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Or to make this permanent:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=\"/path/to/man_qq_annotate:$PATH\"\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInput files can be gzipped or plain. Run without arguments for a list of options, run with \u003ccode\u003e--help\u003c/code\u003e for detailed options:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eusage: ./manqq_cli [-h] [--chr-col [character]] [--pval-col [character]]\n [--pos-col [character]] [--a1 [character]]\n [--a2 [character]] [--build [integer]]\n [--image [character]] [--af-col [character]]\n [--maf-filter [double]] [--sig [double]]\n [--maxpeaks [integer]] [--no-qq] [--no-man] [--no-annot]\n [--no-distance] [--man-height [integer]]\n [--upper-margin [double]] [--annot-cex [double]]\n [--axes-cex [double]] [--ylim [double]]\n infile outfile\n\nA program to plot Manhattan and QQ plots\n\npositional arguments:\n infile Input file name, must be gzip file\n outfile Output file name (with no file extension)\n\noptional arguments:\n \u003cspan class=\"pl-k\"\u003e-h\u003c/span\u003e, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n --chr-col [character]\n The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the chromosome column, default chr\n --pval-col [character]\n The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the chromosome column, default\n p_score\n --pos-col [character]\n The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the chromosome column, default ps\n --a1 [character] The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the effect allele column, default\n allele1\n --a2 [character] The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the non-effect column, default\n allele0\n --build [integer] The genome build the positions refer to\n --image [character] The filetype to save plots to (png or pdf)\n --af-col [character] The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the allele frequency column,\n default af\n --maf-filter [double]\n The significance threshold \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e MAF filter, default\n 0.0.\n --sig [double] The significance threshold to use \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e peak annotation\n --maxpeaks [integer] The maximum number of peaks to annotate\n --no-qq Don\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003et plot QQ.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --no-man Don\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003et plot Manhattan.\n --no-annot Disable peak annotation even \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e peaks are present.\n --no-distance Don\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003et add very useful distance to gene info.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --man-height [integer]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Force height of Manhattan in inches. Can have\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e unpredictable consequences (some of which you may\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e regret).\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --upper-margin [double]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Y limit of Manhattan plot in units of maximum data\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e points. Even more unpredictable than the above.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --annot-cex [double] Size factor for annotations.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --axes-cex [double] Size factor for axes and labels.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --ylim [double] The y-axis limit (-log10(p))\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLoading the package\u003c/h3\u003e\u003ca id=\"user-content-loading-the-package\" class=\"anchor\" aria-label=\"Permalink: Loading the package\" href=\"#loading-the-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can load the package into your R environment and use the available functions.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003emanqq\u003c/span\u003e)\nls(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003epackage:manqq\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e[1] \"manqq_cli\" \"fastqq\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCurrently, only two functions are exported and available for users. The other functions are all hidden and only used internally within the package. If there are any particular functionality you wish to use from the package, please make a request in the \u003ca href=\"https://github.com/hmgu-itg/man_qq_annotate/issues\"\u003eissue page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreating a QQ-plot\u003c/h4\u003e\u003ca id=\"user-content-creating-a-qq-plot\" class=\"anchor\" aria-label=\"Permalink: Creating a QQ-plot\" href=\"#creating-a-qq-plot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# example using a simulated null GWAS with 10,000 SNPs\u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003emanqq\u003c/span\u003e)\nfastqq(runif(\u003cspan class=\"pl-c1\"\u003e10000\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use \u003ccode\u003edevtools\u003c/code\u003e to load all the functions into your environment for development/debugging:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003edevtools\u003c/span\u003e)\nsetwd(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/base/of/the/repo/man_qq_annotate\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\nload_all()\ntest() \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use testthat\u0027s test function to run the testsuite\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "stargazers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1704875420.0 + "updated_at": 1715648603.0 }, { "data_format": 2, - "description": "Containerizing the Canlab code", + "description": "nextflow pipeline to automate analysis using ALE (https://github.com/ssolo/ALE)", "filenames": [ "Singularity" ], - "full_name": "canlab/cantainer", + "full_name": "maxemil/ALE-pipeline", "latest_release": null, - "stargazers_count": 3, - "subscribers_count": 11, - "topics": [], - "updated_at": 1525819849.0 + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eALE-pipeline\u003c/h1\u003e\u003ca id=\"user-content-ale-pipeline\" class=\"anchor\" aria-label=\"Permalink: ALE-pipeline\" href=\"#ale-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ethis is supposed to be a nice pipeline for running ALE on several gene clusters and collecting the results\u003c/li\u003e\n\u003cli\u003eit can also test several species trees at the same time\u003c/li\u003e\n\u003cli\u003eall parameters in the nextflow.config file can be changed on the command line, e.g. the name of the outgroup taxa\u003c/li\u003e\n\u003cli\u003eyou need to add the Python_lib repo to your Pythonpath\u003c/li\u003e\n\u003cli\u003eFor typical usage and a small tutorial, see TUTORIAL.md\u003c/li\u003e\n\u003cli\u003eI use to code the names for species both in the species and in the gene tree to avoid that source of errors\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTroubleshooting\u003c/h1\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf you get an Error \u0027Can\u0027t root with myself\u0027 or similar, this usually means that the outgroup you specified for the species tree is not monophyletic in that tree. Try rerooting by hand first...\u003c/li\u003e\n\u003cli\u003eALE sometime simply crashes, then the pipeline can be resumed by adding \u003ccode\u003e-resume\u003c/code\u003e to the invocation\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 4, + "subscribers_count": 4, + "topics": [ + "nextflow", + "evolution", + "bioinformatics", + "singularity-container", + "pipeline" + ], + "updated_at": 1732734824.0 }, { "data_format": 2, - "description": "Antigen Receptor Classifier", + "description": null, "filenames": [ "Singularity" ], - "full_name": "IEDB/ARC", + "full_name": "likelet/MesKit", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eARC (Antigen Receptor Classifier)\u003c/h1\u003e\u003ca id=\"user-content-arc-antigen-receptor-classifier\" class=\"anchor\" aria-label=\"Permalink: ARC (Antigen Receptor Classifier)\" href=\"#arc-antigen-receptor-classifier\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAuthors: Austin Crinklaw, Swapnil Mahajan\u003c/h3\u003e\u003ca id=\"user-content-authors-austin-crinklaw-swapnil-mahajan\" class=\"anchor\" aria-label=\"Permalink: Authors: Austin Crinklaw, Swapnil Mahajan\" href=\"#authors-austin-crinklaw-swapnil-mahajan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLinux OS\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://hmmer.org/\" rel=\"nofollow\"\u003eHMMER3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eNCBI Blast+\u003c/li\u003e\n\u003cli\u003ePython 3+\n\u003cul\u003e\n\u003cli\u003ePython packages: Pandas, BioPython\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation:\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation:\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide a Dockerfile for ease of use.\u003c/p\u003e\n\u003cp\u003eARC can also be downloaded through PyPI using the following pip command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install bio-arc\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting Installation:\u003c/h3\u003e\u003ca id=\"user-content-testing-installation\" class=\"anchor\" aria-label=\"Permalink: Testing Installation:\" href=\"#testing-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA quick check for proper dependencies and successful installation can be performed by navigating to your pip package install directory (which can be located by executing \u003ccode\u003epip show bio-arc\u003c/code\u003e) and running the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m arc_test\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePassing all unit-tests means that your system is configured properly and ready to classify some protein sequences.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage:\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput\u003c/h3\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eA fasta format file with one or more protein sequences.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026gt;1WBZ_A_alpha I H2-Kb\nMVPCTLLLLLAAALAPTQTRAGPHSLRYFVTAVSRPGLGEPRYMEVGYVDDTEFVRFDSDAENPRYEPRARWMEQEGPEYWERETQKAKGNEQSFRVDLRTLLGYYNQSKGGSHTIQVISGCEVGSDGRLLRGYQQYAYDGCDYIALNEDLKTWTAADMAALITKHKWEQAGEAERLRAYLEGTCVEWLRRYLKNGNATLLRTDSPKAHVTHHSRPEDKVTLRCWALGFYPADITLTWQLNGEELIQDMELVETRPAGDGTFQKWASVVVPLGKEQYYTCHVYHQGLPEPLTLRWEPPPSTVSNMATVAVLVVLGAAIVTGAVVAFVMKMRRRNTGGKGGDYALAPGSQTSDLSLPDCKVMVHDPHSLA\n\u0026gt;1WBZ_B_b2m I H2-Kb\nMARSVTLVFLVLVSLTGLYAIQKTPQIQVYSRHPPENGKPNILNCYVTQFHPPHIEIQMLKNGKKIPKVEMSDMSFSKDWSFYILAHTEFTPTETDTYACRVKHASMAEPKTVYWDRDM\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommands\u003c/h3\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUsing Fasta file as an input:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython -m ARC classify -i /path/to/input.fasta -o /path/to/output.csv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput\u003c/h3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOutput file has 4 columns in CSV format.\u003c/li\u003e\n\u003cli\u003eFirst column named \u0027ID\u0027 is the description provoded in the fasta for each sequence.\u003c/li\u003e\n\u003cli\u003eSecond column named \u0027class\u0027 is the assigned molecule class for each sequence.\n\u003cul\u003e\n\u003cli\u003ee.g. MHC-I, MHC-II, BCR or TCR.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe third column named \u0027chain_type\u0027 is the assigned chain type for each sequence.\n\u003cul\u003e\n\u003cli\u003ee.g. alpha, beta, heavy, lambda, kappa, scFv, TscFv or construct. These will also be labelled as V for variable domain or C for constant domain.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe fourth column named \u0027calc_mhc_allele\u0027 is the MHC allele identified using groove domain similarity to MRO alleles.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eID\u003c/th\u003e\n\u003cth\u003eclass\u003c/th\u003e\n\u003cth\u003echain_type\u003c/th\u003e\n\u003cth\u003ecalc_mhc_allele\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e1WBY_A_alpha I H2-Db\u003c/td\u003e\n\u003ctd\u003eMHC-I\u003c/td\u003e\n\u003ctd\u003ealpha V\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1WBY_B_b2m I H2-Db\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1HQR_A_alpha II HLA-DRA\u003cem\u003e01:01/DRB5\u003c/em\u003e01:01\u003c/td\u003e\n\u003ctd\u003eMHC-II\u003c/td\u003e\n\u003ctd\u003ealpha C\u003c/td\u003e\n\u003ctd\u003eHLA-DRA*01:01\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1HQR_B_beta II HLA-DRA\u003cem\u003e01:01/DRB5\u003c/em\u003e01:01\u003c/td\u003e\n\u003ctd\u003eMHC-II\u003c/td\u003e\n\u003ctd\u003ebeta C\u003c/td\u003e\n\u003ctd\u003eHLA-DRB5*01:01\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2CMR_H_heavy\u003c/td\u003e\n\u003ctd\u003eBCR\u003c/td\u003e\n\u003ctd\u003eheavy V\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2CMR_L_light\u003c/td\u003e\n\u003ctd\u003eBCR\u003c/td\u003e\n\u003ctd\u003ekappa C\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e4RFO_L_light\u003c/td\u003e\n\u003ctd\u003eBCR\u003c/td\u003e\n\u003ctd\u003elambda V\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e3UZE_A_heavy\u003c/td\u003e\n\u003ctd\u003eBCR\u003c/td\u003e\n\u003ctd\u003escFv\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1FYT_D_alpha\u003c/td\u003e\n\u003ctd\u003eTCR\u003c/td\u003e\n\u003ctd\u003ealpha V\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1FYT_E_beta\u003c/td\u003e\n\u003ctd\u003eTCR\u003c/td\u003e\n\u003ctd\u003ebeta C\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e3TF7_C_alpha\u003c/td\u003e\n\u003ctd\u003eTCR\u003c/td\u003e\n\u003ctd\u003eTscFv\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding and using the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-building-and-using-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Building and using the Singularity image\" href=\"#building-and-using-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuilding the singularity image requires root-level access and should thus be built on a machine where you have such access. Once it\u0027s built, it can be run by\nany non-root user and can be transferred to other machines. To build:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build arc.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe input and output directories need to be made available to the running container. If these directories are not within your home directory or the directory from\nwhich you will be running the container ($PWD), you will need to bind mount these directories in your call to the \u0027singularity run\u0027 command. Otherwise, usage is identical\nto the non-containerized version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run \\\n--writable-tmpfs \\\n--bind /path/to/host_dir:/host \\\narc.sif python3 ARC -m classify -i /host/input_file.fasta -o /host/output_file.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow it works:\u003c/h2\u003e\u003ca id=\"user-content-how-it-works\" class=\"anchor\" aria-label=\"Permalink: How it works:\" href=\"#how-it-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBCR and TCR chains are identified using HMMs. A given protein sequence is searched against HMMs built using BCR and TCR chain sequences from IMGT. HMMER is used to align an input sequence to the HMMs.\u003c/li\u003e\n\u003cli\u003eMHC class I (alpha1-alpha2 domains) and MHC class I alpha and beta chain HMMs are downloaded from Pfam website. An input protein sequence is searched against these HMMs. A HMMER bit score threshold of 25 was used to identify MHC chain sequences.\u003c/li\u003e\n\u003cli\u003eTo identify MHC alleles, groove domains (G-domains) are assigned based on the MRO repository.\u003c/li\u003e\n\u003cli\u003eIgNAR sequences are identified through querying against a custom blast database.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences:\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References:\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeveral methods for HMMER result parsing were sourced from ANARCI.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://academic.oup.com/bioinformatics/article/32/2/298/1743894\" rel=\"nofollow\"\u003eDunbar J and Deane CM. ANARCI: Antigen receptor numbering and receptor classification. Bioinformatics (2016)\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 3, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMesKit\u003c/h1\u003e\u003ca id=\"user-content-meskit\" class=\"anchor\" aria-label=\"Permalink: MesKit\" href=\"#meskit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis pipelie for multi WEX analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ethis readme was generated by nf-core tools\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/multiexseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/86e4d49ee64479c80f123f82699c3cf12f3af9eebee7aa63f19f42bfebd26d0d/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6d756c746965787365712e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/multiexseq.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/multiexseq pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003emultiexseq was originally written by Qi Zhao.\u003c/p\u003e\n", + "stargazers_count": 4, "subscribers_count": 4, "topics": [], - "updated_at": 1641508443.0 + "updated_at": 1558549773.0 }, { "data_format": 2, - "description": "Ubuntu container for MEEP-MPI", + "description": "A repository to conduct experiments with omnitig-related models for genome assembly.", "filenames": [ "Singularity" ], - "full_name": "chpc-uofu/Singularity-meep-mpi", - "latest_release": null, - "stargazers_count": 3, - "subscribers_count": 2, - "topics": [], - "updated_at": 1522810462.0 + "full_name": "algbio/practical-omnitigs", + "latest_release": "0.3.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePractical \u003ca href=\"https://www.liebertpub.com/doi/full/10.1089/cmb.2016.0141\" rel=\"nofollow\"\u003eOmnitigs\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-practical-omnitigs\" class=\"anchor\" aria-label=\"Permalink: Practical Omnitigs\" href=\"#practical-omnitigs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/algbio/practical-omnitigs/workflows/Tests%20%26%20Lints/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/algbio/practical-omnitigs/workflows/Tests%20%26%20Lints/badge.svg?branch=master\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.4335367\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a94ba3e0adc18f0b340ab30c820caf28a9dde46aa9c6e8ee577d8f0f4da2a26/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343333343933392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4334939.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a repository to conduct experiments with omnitig-related models in the context of genome assembly.\nThe algorithms are implemented in \u003cem\u003eRust\u003c/em\u003e, and around that we built a \u003cem\u003esnakemake\u003c/em\u003e toolchain to conduct experiments.\nTo ensure reproducibility, we wrapped everything into a \u003cem\u003econda\u003c/em\u003e environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequired Software\u003c/h3\u003e\u003ca id=\"user-content-required-software\" class=\"anchor\" aria-label=\"Permalink: Required Software\" href=\"#required-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econda \u0026gt;= 4.8.3\u003c/code\u003e (lower might be possible, but has not been tested)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup\u003c/h3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, set up the conda environment of this project.\u003c/p\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003econda env create -f environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, activate the environment.\u003c/p\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003esource activate practical-omnitigs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Experiments\u003c/h3\u003e\u003ca id=\"user-content-running-experiments\" class=\"anchor\" aria-label=\"Permalink: Running Experiments\" href=\"#running-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake sure that you are in the right conda environment (should be \u003ccode\u003epractical-omnitigs\u003c/code\u003e).\u003c/p\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003econda info\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSubsequently, experiments can be run using \u003ccode\u003esnakemake\u003c/code\u003e.\u003c/p\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003esnakemake --cores all \u0026lt;experiment\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eValid experiments are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eselftest\u003c/code\u003e: Check if you have conda set up correctly. It prints the version of \u003ccode\u003esnakemake\u003c/code\u003e \u003ccode\u003econda\u003c/code\u003e and \u003ccode\u003ewget\u003c/code\u003e. The versions of \u003ccode\u003esnakemake\u003c/code\u003e and \u003ccode\u003econda\u003c/code\u003e should match the definition in \u003ccode\u003e/environment.yaml\u003c/code\u003e and the version of \u003ccode\u003ewget\u003c/code\u003e should match the definition in \u003ccode\u003e/config/conda-selftest-env.yaml\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest\u003c/code\u003e: execute all integration tests of this project on a single small sample genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_all\u003c/code\u003e: execute all integration tests of this project on all defined genomes (potentially very large).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe experiments are run inside a conda environment that is set up by snakemake.\nThis ensures reproducibility of the results and automates the installation of required tools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the Implementation Directly\u003c/h3\u003e\u003ca id=\"user-content-using-the-implementation-directly\" class=\"anchor\" aria-label=\"Permalink: Using the Implementation Directly\" href=\"#using-the-implementation-directly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Rust code written for this project includes a command line interface that can be used directly.\nFor documentation on how to use it, please refer to the documentation of the \u003ca href=\"https://github.com/algbio/practical-omnitigs/tree/master/implementation\"\u003ecli crate\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTroubleshooting\u003c/h3\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have problems with using this software package, take a look at our \u003ca href=\"https://github.com/algbio/practical-omnitigs/tree/master/TROUBLESHOOTING.md\"\u003etroubleshooting page\u003c/a\u003e.\nIf that does not solve your issue, do not hesitate to \u003ca href=\"https://github.com/algbio/practical-omnitigs/issues\"\u003efile a bug report\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTechnical Information\u003c/h2\u003e\u003ca id=\"user-content-technical-information\" class=\"anchor\" aria-label=\"Permalink: Technical Information\" href=\"#technical-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDirectory Structure\u003c/h3\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory Structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e.github\u003c/code\u003e: GitHub workflows for continuous testing.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e.idea\u003c/code\u003e: Configuration for the IntelliJ IDEA integrated development environment.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003econfig\u003c/code\u003e: All config files related to the experiments, including conda environments and experiment declarations.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edata\u003c/code\u003e: Data used and produced by the experiments.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eexternal-software\u003c/code\u003e: Location to install external software required by the experiment pipeline.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eimplementation\u003c/code\u003e: The algorithms that we are testing. Everything is written in Rust.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImplementation\u003c/h3\u003e\u003ca id=\"user-content-implementation\" class=\"anchor\" aria-label=\"Permalink: Implementation\" href=\"#implementation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe algorithms of this project are implemented in Rust.\nWe split the implementation into multiple library crates to increase the reusability of our code.\nOn top of that, the \u003ccode\u003ecli\u003c/code\u003e crate provides all implemented functionality via a command line interface.\nRefer to \u003ca href=\"https://github.com/algbio/practical-omnitigs/tree/master/implementation\"\u003eits documentation\u003c/a\u003e for more information.\u003c/p\u003e\n\u003cp\u003eExcept for \u003ccode\u003ecli\u003c/code\u003e, all crates are published on \u003ccode\u003ecrates.io\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the terms of the BSD 2-Clause license.\nSee \u003ccode\u003eLICENSE.md\u003c/code\u003e for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Cite\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to Cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use this code in your research project, please cite it as \"Safe and Complete Genome Assembly in Practice, DOI: 10.5281/zenodo.4335367\"\u003c/p\u003e\n", + "stargazers_count": 4, + "subscribers_count": 5, + "topics": [ + "omnitigs", + "genome-assembly" + ], + "updated_at": 1724942370.0 }, { "data_format": 2, - "description": "Autonomy tools to support life detection aboard the Ocean Worlds Life Surveyor (OWLS) instrument suite.", + "description": null, "filenames": [ - "Singularity_ACME.def" + "Singularity" ], - "full_name": "JPLMLIA/OWLS-Autonomy", + "full_name": "ctpelok77/fd-red-black-postipc2018", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOcean Worlds Life Surveyor autonomous algorithms\u003c/h1\u003e\u003ca id=\"user-content-ocean-worlds-life-surveyor-autonomous-algorithms\" class=\"anchor\" aria-label=\"Permalink: Ocean Worlds Life Surveyor autonomous algorithms\" href=\"#ocean-worlds-life-surveyor-autonomous-algorithms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScope\u003c/h2\u003e\u003ca id=\"user-content-scope\" class=\"anchor\" aria-label=\"Permalink: Scope\" href=\"#scope\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Ocean Worlds Life Surveyor (OWLS) project is aimed at autonomously detecting signatures of life in water at the molecular and cellular scale. The ACME, FAME, HELM, and JEWEL components of OWLS (this package) are a set of software-based autonomy tools to process data from different instruments aboard the OWLS instrument. Their goal is to generate and prioritize Autonomous Data Science Products (ASDPs) to send only the most scientifically valuable subset of information back to Earth.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eACME: Autonomous CE-ESI Mass-spectra Examination. Extracts ASDPs from mass-spectra data.\u003c/li\u003e\n\u003cli\u003eFAME: FLFM Autonomous Motility Evaluation. Extracts ASDPs from Volumetric Flourescent Imaging (VFI) microscopy data.\u003c/li\u003e\n\u003cli\u003eHELM: Holographic Examination for Life-like motility. Extracts ASDPs from Digital Holography Microscopy (DHM) data.\u003c/li\u003e\n\u003cli\u003eJEWEL: Joint Examination for Water-based Extant Life. Prioritizes ASDPs from all instruments to transmit data back to Earth in an \"optimal\" order.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStandard install requirements\u003c/h3\u003e\u003ca id=\"user-content-standard-install-requirements\" class=\"anchor\" aria-label=\"Permalink: Standard install requirements\" href=\"#standard-install-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eImageMagick\u003c/strong\u003e \u003cbr\u003e\n Full instructions for installing ImageMagick are\n\u003ca href=\"https://imagemagick.org/script/download.php\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. For MacOSX, use\n\u003ccode\u003ebrew install imagemagick\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003effmpeg\u003c/strong\u003e \u003cbr\u003e\nFull instructions for installing ffmpeg are\n\u003ca href=\"https://ffmpeg.org/download.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. For MacOSX, use\n\u003ccode\u003ebrew install ffmpeg@4\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython code\u003c/h3\u003e\u003ca id=\"user-content-python-code\" class=\"anchor\" aria-label=\"Permalink: Python code\" href=\"#python-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/JPLMLIA/OWLS-Autonomy.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e OWLS-Autonomy\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e use \u0027editable\u0027 to avoid repeated pip installs\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDevelopment tools\u003c/h3\u003e\u003ca id=\"user-content-development-tools\" class=\"anchor\" aria-label=\"Permalink: Development tools\" href=\"#development-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor development or CI builds (e.g., on Jenkins).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e OWLS-Autonomy\npip install -r requirements_dev.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDemonstration Script\u003c/h2\u003e\u003ca id=\"user-content-demonstration-script\" class=\"anchor\" aria-label=\"Permalink: Demonstration Script\" href=\"#demonstration-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA demonstration script is available to confirm successful installation, give first time users an overview of the autonomy pipelines and provide a set of example input/outputs. Please see the demo \u003ca href=\"./demo/README.md\"\u003e\u003ccode\u003eREADME.md\u003c/code\u003e\u003c/a\u003e for usage.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCode Usage\u003c/h2\u003e\u003ca id=\"user-content-code-usage\" class=\"anchor\" aria-label=\"Permalink: Code Usage\" href=\"#code-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor ACME, HELM, and FAME, data processsing scripts that you can run from the\ncommand line are located in the \u003ccode\u003esrc/cli\u003c/code\u003e directory. Please see\nthe CLI \u003ca href=\"./src/cli/README.md\"\u003e\u003ccode\u003eREADME.md\u003c/code\u003e\u003c/a\u003e for examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a docker image from the source code if you can\u0027t pip install the\nrepository. This may be useful for Windows users.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHELM Validate step\u003c/h3\u003e\u003ca id=\"user-content-helm-validate-step\" class=\"anchor\" aria-label=\"Permalink: HELM Validate step\" href=\"#helm-validate-step\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a Docker image to run OWLS-Autonomy functions. Navigate a terminal\nto this dir and run the command below:\u003c/p\u003e\n\u003cp\u003eBuild with \u003ccode\u003eDockerfile\u003c/code\u003e and tag the image. Change the version of the tag as needed.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -f Dockerfile --tag owls-autonomy:v1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun a container in the \u003ca href=\"https://docs.docker.com/desktop/dashboard/\" rel=\"nofollow\"\u003eDocker Dashboard GUI\u003c/a\u003e\nor via the CLI. Be sure to mount your local data directory into the Docker\ncontainer. The CLI approach is shown below. After starting the container, see the\nCLI \u003ca href=\"./src/cli/README.md\"\u003e\u003ccode\u003eREADME.md\u003c/code\u003e\u003c/a\u003e for examples on running OWLS functionality.\nThe \u003ccode\u003e--rm\u003c/code\u003e flag will automatically cause the container to stop running as soon as\nyou exit.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm -volume /Users/wronk/Data:/Users/wronk/Data owls-autonomy:v1 /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll software is licensed according to the \u003ca href=\"LICENSE.md\"\u003eJPL Software License\u003c/a\u003e, while all documentation is Caltech/JPL Proprietary - Not for public release or distribution.\u003c/p\u003e\n", + "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 4, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1732571041.0 + "updated_at": 1722364915.0 }, { "data_format": 2, - "description": null, + "description": "Generic viral Illumina sequence analysis pipeline", "filenames": [ - "containers/tensorflow-serving/Singularity", - "containers/research/Singularity", - "containers/ubuntu-cuda10/Singularity", - "containers/redis/Singularity", - "containers/albert-ai/Singularity" + "Singularity", + "singularity/Singularity.2.0.0" ], - "full_name": "todexter3/Richelieu", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRichelieu\u003c/h1\u003e\u003ca id=\"user-content-richelieu\" class=\"anchor\" aria-label=\"Permalink: Richelieu\" href=\"#richelieu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code contains a custom implementation of a Gym environment for the Diplomacy game, and training using self-playing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# Apt installs\napt-get install -y wget bzip2 ca-certificates curl git build-essential clang-format-8 git wget cmake build-essential autoconf libtool pkg-config libgoogle-glog-dev\n\n# Install conda\nwget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.7.10-Linux-x86_64.sh -O ~/miniconda.sh\n/bin/bash ~/miniconda.sh -b\n\n# Install pytorch, pybind11\nconda install --yes pytorch=1.7.1 torchvision cudatoolkit=11.0 -c pytorch\nconda install --yes pybind11\n\n# Install the required dependencies\npip install diplomacy\npip install gymnasium\npip install \"stable-baselines3[extra]\u0026gt;=2.0.0a4\"\n\n# Make\nmake\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject Structure\u003c/h3\u003e\u003ca id=\"user-content-project-structure\" class=\"anchor\" aria-label=\"Permalink: Project Structure\" href=\"#project-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe notebook contains the following main sections:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eImports and Necessary Installations: Installs and imports required libraries.\u003c/li\u003e\n\u003cli\u003eCustom Diplomacy Class: Defines the custom environment for the Diplomacy game.\u003c/li\u003e\n\u003cli\u003eTraining with self-playing: Trains Richelieu using self-playing.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCustom Diplomacy Class\u003c/h3\u003e\u003ca id=\"user-content-custom-diplomacy-class\" class=\"anchor\" aria-label=\"Permalink: Custom Diplomacy Class\" href=\"#custom-diplomacy-class\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section defines the \u003ccode\u003eDiplomacyStrategyEnv\u003c/code\u003e class, a custom implementation of a environment for the Diplomacy game.\nKey Methods\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003einit\u003c/strong\u003e(): Initializes the environment.\u003c/li\u003e\n\u003cli\u003ereset(): Resets the environment to the initial state.\u003c/li\u003e\n\u003cli\u003estep(action): Executes an action and returns the next state, reward, done flag, and info.\u003c/li\u003e\n\u003cli\u003e_init_observation_space(): Create the MultiDiscrete space for the observation space.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTraining with self-playing\u003c/h3\u003e\u003ca id=\"user-content-training-with-self-playing\" class=\"anchor\" aria-label=\"Permalink: Training with self-playing\" href=\"#training-with-self-playing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section demonstrates how to train Richelieu using self-palying. Richelieu is trained to play the Diplomacy game within the custom environment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etrain_selfplay(): training with self-playing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRichelieu can also be trained by playing with other models.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etrain(model): train by playing with other models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEvaluation\u003c/h3\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this section, we evaluate Richelieu by comparing its game results with Cicero.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eplay_with(model): have Richelieu and Cicero play a game.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can modified the model, removed the negotiation module, and performed massively play on no-press setting with the other six models.\nIt is also possible to obtain an ablation study by deleting each section and playing games with Cicero.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributing\u003c/h3\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContributions are welcome!\u003c/p\u003e\n", + "full_name": "peterk87/nf-villumina", + "latest_release": "2.0.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epeterk87/nf-villumina\u003c/h1\u003e\u003ca id=\"user-content-peterk87nf-villumina\" class=\"anchor\" aria-label=\"Permalink: peterk87/nf-villumina\" href=\"#peterk87nf-villumina\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGeneric viral Illumina sequence analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/peterk87/nf-villumina\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3014ce8e9fb3b368e4bfa37ca99daa56708610564dac9a0f2441a851a176a32c/68747470733a2f2f7472617669732d63692e6f72672f70657465726b38372f6e662d76696c6c756d696e612e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/peterk87/nf-villumina.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3c177645f5642f87be5369390973cd515bd486b863467ca1e8475e4d3a6e2f7e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413531392e31302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/peterk87/nf-villumina\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e57c083af3e6035c1fcf18799a14ec1de5eb89bba830d23ac566ff04b81ed3d/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f70657465726b38372f6e662d76696c6c756d696e612e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/peterk87/nf-villumina.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2925\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with a \u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enf-villumina\u003c/code\u003e will\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eremove low quality reads (\u003ca href=\"https://github.com/OpenGene/fastp\"\u003efastp\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003efilter for reads from a taxonomic group of interest (by default superkingdom \u003ca href=\"https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info\u0026amp;id=10239\u0026amp;lvl=3\u0026amp;lin=f\u0026amp;keep=1\u0026amp;srchmode=1\u0026amp;unlock\" rel=\"nofollow\"\u003eViruses\u003c/a\u003e (taxid=10239)) using \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e and \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e classification results\u003c/li\u003e\n\u003cli\u003eperform \u003cem\u003ede novo\u003c/em\u003e assembly with [Unicycler] and [Shovill] on the taxonomic classification filtered reads\u003c/li\u003e\n\u003cli\u003esearch all contig sequences using NCBI nucleotide \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e against a database of your choice (we recommend the version 5 NCBI nt DB)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e You will need to create/download databases for \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e, \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e and \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e in order to get the most out of this workflow!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePre-requisites\u003c/h3\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTaxonomic Classification for \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e and \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-taxonomic-classification-for-kraken2-and-centrifuge\" class=\"anchor\" aria-label=\"Permalink: Taxonomic Classification for Kraken2 and Centrifuge\" href=\"#taxonomic-classification-for-kraken2-and-centrifuge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor taxonomic classification with \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e and \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e, you will need to download (or build) databases for these programs so that you may use them within the \u003ccode\u003enf-villumina\u003c/code\u003e workflow.\u003c/p\u003e\n\u003cp\u003eYou can point to the Kraken2 and Centrifuge database with \u003ccode\u003eexport KRAKEN2_DB=/path/to/kraken2/database\u003c/code\u003e and \u003ccode\u003eexport CENTRIFUGE_DB=/path/to/centrifuge/database/prefix\u003c/code\u003e in your \u003ccode\u003e~/.bashrc\u003c/code\u003e so you don\u0027t need to specify it each time you run the workflow with \u003ccode\u003e--kraken2_db /path/to/kraken2/standard2 --centrifuge_db /path/to/centrifuge/nt-2018-03-03/nt\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eKraken2 DBs\u003c/h4\u003e\u003ca id=\"user-content-kraken2-dbs\" class=\"anchor\" aria-label=\"Permalink: Kraken2 DBs\" href=\"#kraken2-dbs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\nMiniKraken2_v2_8GB: (5.5GB) 8GB Kraken 2 Database built from the Refseq bacteria, archaea, and viral libraries and the GRCh38 human genome\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://monash.figshare.com/articles/GTDB_r89_54k/8956970\" rel=\"nofollow\"\u003eGTDB_r89_54k Kraken2 DBs\u003c/a\u003e: There are multiple Kraken2 DBs of various sizes available for download. For more info, see \u003ca href=\"https://github.com/rrwick/Metagenomics-Index-Correction\"\u003ehttps://github.com/rrwick/Metagenomics-Index-Correction\u003c/a\u003e and the manuscript: M\u00e9ric, Wick et al. (2019) Correcting index databases improves metagenomic studies. doi: \u003ca href=\"https://doi.org/10.1101/712166\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/712166\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCentrifuge DBs\u003c/h4\u003e\u003ca id=\"user-content-centrifuge-dbs\" class=\"anchor\" aria-label=\"Permalink: Centrifuge DBs\" href=\"#centrifuge-dbs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI nucleotide non-redundant sequences (2018-03-03) (64 GB)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://monash.figshare.com/ndownloader/files/16378439\" rel=\"nofollow\"\u003eGTDB_r89_54k Centrifuge DB (108 GB tar file)\u003c/a\u003e: For more info, see \u003ca href=\"https://github.com/rrwick/Metagenomics-Index-Correction\"\u003ehttps://github.com/rrwick/Metagenomics-Index-Correction\u003c/a\u003e and the manuscript: M\u00e9ric, Wick et al. (2019) Correcting index databases improves metagenomic studies. doi: \u003ca href=\"https://doi.org/10.1101/712166\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/712166\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e DBs\u003c/h3\u003e\u003ca id=\"user-content-blast-dbs\" class=\"anchor\" aria-label=\"Permalink: BLAST DBs\" href=\"#blast-dbs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor nf-villumina, you must have a version 5 BLAST DB with embedded taxonomic information installed, e.g. version 5 \u003ccode\u003ent\u003c/code\u003e DB (see \u003ca href=\"https://ftp.ncbi.nlm.nih.gov/blast/db/v5/\" rel=\"nofollow\"\u003ehttps://ftp.ncbi.nlm.nih.gov/blast/db/v5/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eYou can download pre-built \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e DBs like \u003ccode\u003ent\u003c/code\u003e and \u003ccode\u003enr\u003c/code\u003e from \u003ca href=\"https://ftp.ncbi.nlm.nih.gov/blast/db/\" rel=\"nofollow\"\u003ethe NCBI FTP site\u003c/a\u003e using the \u003ccode\u003eupdate_blastdb.pl\u003c/code\u003e script included with your install of BLAST+ to download and/or update your local BLAST databases.\u003c/p\u003e\n\u003cp\u003eShow all available databases:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ update_blastdb.pl --showall\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDownload the BLASTDB version 5 \"nt\" database to your current directory decompressing files and deleting original compressed archives:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eupdate_blastdb.pl --blastdb_version 5 nt --decompress\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e For ease of use, all databases should be downloaded to the same directory (e.g. \u003ccode\u003e/opt/DB/blast\u003c/code\u003e set in \u003ccode\u003e$BLASTDB\u003c/code\u003e environment variable in your \u003ccode\u003e~/.bashrc\u003c/code\u003e)\u003c/p\u003e\n\u003cp\u003eCheck that your database has been downloaded properly and has taxids associated with the sequences contained within it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ blastdbcheck -db nt -must_have_taxids -verbosity 3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe peterk87/nf-villumina pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003epeterk87/nf-villumina was originally written by Peter Kruczkiewicz.\u003c/p\u003e\n\u003cp\u003eBootstrapped with \u003ca href=\"https://github.com/nf-core/tools\"\u003enf-core/tools\u003c/a\u003e \u003ccode\u003enf-core create\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThank you to the \u003ca href=\"https://github.com/nf-core/tools\"\u003enf-core/tools\u003c/a\u003e team for a great tool for bootstrapping creation of a production ready Nextflow workflows.\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1732761708.0 + "updated_at": 1714883197.0 }, { "data_format": 2, - "description": "Apache Spark with RStudio and the sparklyr package in a Singularity container", + "description": "Singularity container recipes to run the paraview visualizer", "filenames": [ - "Singularity.2.2.1-hadoop-2.7-r-3.4.3", - "Singularity", - "Singularity.2.3.0-hadoop-2.7-r-3.4.3" + "Singularity.nv", + "Singularity" ], - "full_name": "nickjer/singularity-rstudio-spark", + "full_name": "singularityhub/paraview-visualizer", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Apache Spark w/ RStudio Server\u003c/h1\u003e\u003ca id=\"user-content-singularity-apache-spark-w-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: Singularity Apache Spark w/ RStudio Server\" href=\"#singularity-apache-spark-w-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/455\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://spark.apache.org/\" rel=\"nofollow\"\u003eApache Spark\u003c/a\u003e with the \u003ca href=\"http://spark.rstudio.com/\" rel=\"nofollow\"\u003esparklyr\u003c/a\u003e package installed. It\nwas built on top of the base Singularity image \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003enickjer/singularity-rstudio\u003c/a\u003e in\norder to launch an \u003ca href=\"https://www.rstudio.com/products/rstudio/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e to more easily connect with an Apache Spark\ncluster running in \u003ca href=\"https://spark.apache.org/docs/latest/spark-standalone.html\" rel=\"nofollow\"\u003eStandalone Mode\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-rstudio-spark.simg\u003c/code\u003e\nwith:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-rstudio-spark.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name singularity-rstudio-spark.simg shub://nickjer/singularity-rstudio-spark\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can launch Spark in \u003ca href=\"https://spark.apache.org/docs/latest/spark-standalone.html\" rel=\"nofollow\"\u003eStandalone Mode\u003c/a\u003e by first launching a \"master\" process\nwhich will print out a \u003ccode\u003espark://HOST:PORT\u003c/code\u003e for itself, which you can then use\nto connect \"workers\" to it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSpark Master\u003c/h3\u003e\u003ca id=\"user-content-spark-master\" class=\"anchor\" aria-label=\"Permalink: Spark Master\" href=\"#spark-master\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can launch a \"master\" process as a Singularity app with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app spark-master singularity-rstudio-spark.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWorker\u003c/h3\u003e\u003ca id=\"user-content-worker\" class=\"anchor\" aria-label=\"Permalink: Worker\" href=\"#worker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can launch a \"worker\" process as a Singularity app with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app spark-worker singularity-rstudio-spark.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRStudio Server\u003c/h3\u003e\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: RStudio Server\" href=\"#rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003enickjer/singularity-rstudio\u003c/a\u003e for more information on how to run \u003ccode\u003erserver\u003c/code\u003e\nfrom within this Singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR and Rscript\u003c/h3\u003e\u003ca id=\"user-content-r-and-rscript\" class=\"anchor\" aria-label=\"Permalink: R and Rscript\" href=\"#r-and-rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e for more information on how to run \u003ccode\u003eR\u003c/code\u003e and\n\u003ccode\u003eRscript\u003c/code\u003e from within this Singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio-spark\"\u003ehttps://github.com/nickjer/singularity-rstudio-spark\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParaview Visualizer\u003c/h1\u003e\u003ca id=\"user-content-paraview-visualizer\" class=\"anchor\" aria-label=\"Permalink: Paraview Visualizer\" href=\"#paraview-visualizer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor instructions on using the full Paraview Web directly from a Singularity container,\nsee \u003ca href=\"https://ask.cyberinfrastructure.org/t/how-do-i-run-paraview-or-openfoam-on-an-hpc-resource/644/2\" rel=\"nofollow\"\u003ethis post\u003c/a\u003e\non AskCyberinfrastructre. Continue reading below for using the Paraview Visualizer\nvia a Docker container, or Singularity container instance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t have nvidia on your host, use the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e recipe. If you have\nnvidia, use the \u003ca href=\"Singularity.nv\"\u003eSingularity.nv\u003c/a\u003e recipe:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build paraview.simg Singularity \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e no nvidia\u003c/span\u003e\n$ sudo singularity build paraview.simg Singularity.nv \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e nvidia\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you need it, make a data folder to bind to on the host:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir -p /tmp/data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eStart the container instance, here we are naming it \"visualizer\"\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity instance.start --bind /tmp/data:/data paraview.simg visualizer\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should now see the paraview interface running on \u003ca href=\"http://127.0.0.1:8080\" rel=\"nofollow\"\u003e127.0.0.1:8080\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/visualizer.png\"\u003e\u003cimg src=\"img/visualizer.png\" alt=\"img/visualizer.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe mapping to \u003ccode\u003e/data\u003c/code\u003e is where local web applications will load files from.\u003c/p\u003e\n\u003cp\u003eAlso note that you \u003cem\u003emust\u003c/em\u003e stop local web servers, including any Docker applications\nrunning on that port.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCredits\u003c/h1\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHuge thanks to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jourdain\"\u003e@jourdain\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/trophime\"\u003e@trophime\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive shell\u003c/h2\u003e\u003ca id=\"user-content-interactive-shell\" class=\"anchor\" aria-label=\"Permalink: Interactive shell\" href=\"#interactive-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to debug interactively:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity shell instance://visualizer\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCleaning Up\u003c/h2\u003e\u003ca id=\"user-content-cleaning-up\" class=\"anchor\" aria-label=\"Permalink: Cleaning Up\" href=\"#cleaning-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnd to stop the container, you also need sudo\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity instance.stop instance://visualizer\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using Singularity 3.0 and up the instance group is now changed\nto \"instance stop\" and \"instance start\"\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [ - "rstudio-server", - "singularity-image", - "spark" - ], - "updated_at": 1585580351.0 - }, - { - "data_format": 2, - "description": "GPU-optimized NMF and variations", - "filenames": [ - "docker/Singularity.def" + "paraview", + "visualizer", + "singularity", + "singularity-container" ], - "full_name": "genepattern/nmf-gpu", - "latest_release": "v7", - "stargazers_count": 4, - "subscribers_count": 8, - "topics": [], - "updated_at": 1708724634.0 + "updated_at": 1719412607.0 }, { "data_format": 2, @@ -33431,468 +33488,320 @@ var data = }, { "data_format": 2, - "description": "A small example repository that contains examples for controlling the real Panda robot.", - "filenames": [ - "real_panda_control_examples/containers/singularity/Singularity.panda_ros_melodic", - "real_panda_control_examples/containers/singularity/Singularity.panda_ros_kinetic", - "real_panda_control_examples/containers/singularity/Singularity.ros_melodic", - "real_panda_control_examples/containers/singularity/Singularity.panda_ros_noetic" - ], - "full_name": "rickstaa/real-panda-control-examples", - "latest_release": "v1.0.3", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReal panda control examples\u003c/h1\u003e\u003ca id=\"user-content-real-panda-control-examples\" class=\"anchor\" aria-label=\"Permalink: Real panda control examples\" href=\"#real-panda-control-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains several examples for controlling the real panda robot. It was created as a supplement to the official \u003ca href=\"https://frankaemika.github.io/docs/installation_linux.html\" rel=\"nofollow\"\u003epanda documentation\u003c/a\u003e. Further it serves as a storage place for several problems I encountered while working with the panda robot (see the \u003ca href=\"https://github.com/rickstaa/real-panda-control-examples/discussions\"\u003ediscussions section\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClone instructions\u003c/h2\u003e\u003ca id=\"user-content-clone-instructions\" class=\"anchor\" aria-label=\"Permalink: Clone instructions\" href=\"#clone-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo clone the repository use the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir real_catkin_ws\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e real_catkin_ws\ngit clone --recurse-submodules https://github.com/rickstaa/real_panda_control_examples.git src\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild instructions\u003c/h2\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-label=\"Permalink: Build instructions\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the ROS package dependencies using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erosdep install --from-paths src --ignore-src --rosdistro melodic -y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe catkin package can be build by executing one of the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecatkin build -j4 -DCMAKE_BUILD_TYPE=Release -DFranka_DIR:PATH=\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/libfranka/build\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFranka ros examples\u003c/h2\u003e\u003ca id=\"user-content-franka-ros-examples\" class=\"anchor\" aria-label=\"Permalink: Franka ros examples\" href=\"#franka-ros-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see \u003ca href=\"https://github.com/rickstaa/real-panda-control-examples/discussions/4\"\u003ethis discussion post\u003c/a\u003e that explains how to run the example launch files provided by Emika Franka.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMoveit example launch instructions\u003c/h2\u003e\u003ca id=\"user-content-moveit-example-launch-instructions\" class=\"anchor\" aria-label=\"Permalink: Moveit example launch instructions\" href=\"#moveit-example-launch-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo test out Moveit control, after you build and sourced the catkin workspace, you can launch the example included in the \u003ccode\u003epanda_moveit_config\u003c/code\u003e using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch panda_moveit_config panda_control_moveit_rviz.launch load_gripper:=true robot_ip:=172.16.0.2\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditionally the \u003ccode\u003ereal_panda_control_examples\u003c/code\u003e contains a slightly modified version of this example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch real_panda_control_examples real_panda_moveit_control.launch\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrajectory control example launch instructions\u003c/h2\u003e\u003ca id=\"user-content-trajectory-control-example-launch-instructions\" class=\"anchor\" aria-label=\"Permalink: Trajectory control example launch instructions\" href=\"#trajectory-control-example-launch-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo test out Trajectory control, after you build and sourced the catkin workspace, you can launch the example included in the \u003ccode\u003epanda_moveit_config\u003c/code\u003e using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch panda_moveit_config panda_control_moveit_rviz.launch load_gripper:=true robot_ip:=172.16.0.2\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditionally the \u003ccode\u003ereal_panda_control_examples\u003c/code\u003e contains a slightly modified version of this example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch real_panda_control_examples real_panda_traj_control.launch\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFeel free to open an issue if you have ideas on how to make this GitHub action better or if you want to report a bug! All contributions are welcome. \ud83d\ude80 Please consult the \u003ca href=\"CONTRIBUTING.md\"\u003econtribution guidelines\u003c/a\u003e for more information.\u003c/p\u003e\n", - "stargazers_count": 4, - "subscribers_count": 3, - "topics": [ - "franka-emika", - "control", - "moveit" - ], - "updated_at": 1694770446.0 - }, - { - "data_format": 2, - "description": "my laboratory", + "description": "Pairwise Alignment Breakpoint Analysis", "filenames": [ - "singularity_recipes/python/Singularity.tmp", - "singularity_recipes/python/Singularity.template", - "singularity_recipes/vnc/Singularity", - "singularity_recipes/common/Singularity", - "singularity_recipes/deno/Singularity.tmp", - "singularity_recipes/deno/Singularity.template", - "singularity_recipes/cxx/Singularity", - "singularity_recipes/dotnet/Singularity", - "singularity_recipes/node/Singularity.tmp", - "singularity_recipes/node/Singularity.template", - "singularity_recipes/rust/Singularity", - "singularity_recipes/procon/Singularity" + "Singularity.def" ], - "full_name": "ar90n/lab", + "full_name": "oist/GenomicBreaks", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGenomicBreaks\u003c/h1\u003e\u003ca id=\"user-content-genomicbreaks\" class=\"anchor\" aria-label=\"Permalink: GenomicBreaks\" href=\"#genomicbreaks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePurpose\u003c/h2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-label=\"Permalink: Purpose\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eGenomicBreaks\u003c/em\u003e is a \u003ccode\u003eR\u003c/code\u003e package using \u003cem\u003e\u003ca href=\"https://bioconductor.org/\" rel=\"nofollow\"\u003eBioconductor\u003c/a\u003e\u003c/em\u003e\nlibraries to analyse pairwise alignments of whole genomes in which the gene\norder has been scrambled by evolution, like in the picture below that represents\nthe comparison of homologous chromosomes in two distantly related molds,\n\u003cem\u003eN. crassa\u003c/em\u003e (chrIII) and \u003cem\u003eP. comata\u003c/em\u003e (chr7).\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/plotApairOfChrs_Neu-2.png\"\u003e\u003cimg src=\"man/figures/plotApairOfChrs_Neu-2.png\" alt=\"Comparison between Neurospora crassa chrIII / Podospora comata chr7 (rev-complemented)\" width=\"40%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis package is especially designed to parse and process the alignment files\nproduced by the our \u003ca href=\"https://github.com/oist/plessy_pairwiseGenomeComparison\"\u003epairwise genome alignment\npipeline\u003c/a\u003e, but should\nbe capable to import output of other pipelines as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall the package.\u003c/h3\u003e\u003ca id=\"user-content-install-the-package\" class=\"anchor\" aria-label=\"Permalink: Install the package.\" href=\"#install-the-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have \u003ca href=\"https://www.bioconductor.org/install/\" rel=\"nofollow\"\u003eBioconductor\u003c/a\u003e and the\n\u003ca href=\"https://remotes.r-lib.org/#installation\" rel=\"nofollow\"\u003eremotes\u003c/a\u003e already installed, the\nfollowing should work.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript -e \u0027remotes::install_github(\"oist/GenomicBreaks\", repos=BiocManager::repositories())\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdd \u003ccode\u003edependencies=TRUE\u003c/code\u003e if you would like to install the packages needed to\nbuild the vignettes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to create a Singularity container with GenomicBreaks installed.\u003c/h3\u003e\u003ca id=\"user-content-how-to-create-a-singularity-container-with-genomicbreaks-installed\" class=\"anchor\" aria-label=\"Permalink: How to create a Singularity container with GenomicBreaks installed.\" href=\"#how-to-create-a-singularity-container-with-genomicbreaks-installed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"./Singularity.def\"\u003eSingularity reciepe file\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot GenomicBreaks.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenomicBreaks in brief:\u003c/h2\u003e\u003ca id=\"user-content-genomicbreaks-in-brief\" class=\"anchor\" aria-label=\"Permalink: GenomicBreaks in brief:\" href=\"#genomicbreaks-in-brief\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA pairwise alignment of two genomes is loaded in \u003ccode\u003eGBreaks\u003c/code\u003e objects wrapping\nthe \u003ccode\u003eGRanges\u003c/code\u003e class. Here is an example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eGBreaks object with 505 ranges and 2 metadata columns:\n seqnames ranges strand | score query\n \u0026lt;Rle\u0026gt; \u0026lt;IRanges\u0026gt; \u0026lt;Rle\u0026gt; | \u0026lt;numeric\u0026gt; \u0026lt;GRanges\u0026gt;\n [1] chrI 5860-10010 + | 10609 NC_047487.1:8723-12716\n [2] chrI 11157-11782 + | 1253 NC_047487.1:13035-13680\n [3] chrI 25371-26528 + | 3651 NC_047487.1:15380-16537\n [4] chrI 26849-29699 + | 6130 NC_047487.1:17263-20175\n [5] chrI 29937-30607 + | 767 NC_047487.1:20203-20866\n ... ... ... ... . ... ...\n [501] chrM 67039-67870 + | 1441 NC_018044.1:7758-8439\n [502] chrM 68205-68580 + | 830 NC_018044.1:8783-9180\n [503] chrM 69178-76168 + | 14528 NC_018044.1:9650-16261\n [504] chrM 77356-80022 + | 8066 NC_018044.1:53206-55865\n [505] chrM 80919-85779 + | 6712 NC_018044.1:57885-61592\n -------\n seqinfo: 17 sequences (1 circular) from sacCer3 genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u201c\u003cem\u003eGet started\u003c/em\u003e\u201d on \u003ca href=\"https://oist.github.io/GenomicBreaks\" rel=\"nofollow\"\u003ehttps://oist.github.io/GenomicBreaks\u003c/a\u003e for further details.\u003c/p\u003e\n", "stargazers_count": 4, "subscribers_count": 3, "topics": [ - "my-projects" + "comparative-genomics", + "r" ], - "updated_at": 1732699516.0 + "updated_at": 1722832660.0 }, { "data_format": 2, - "description": "Data generation, model training and evaluation pipelines for the cold-start setting.", + "description": "Cosmic muon radiography with GEANT4", "filenames": [ "Singularity.def" ], - "full_name": "MI-911/cold-start-framework", + "full_name": "gipert/mugraphy", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCold-start Framework\u003c/h1\u003e\u003ca id=\"user-content-cold-start-framework\" class=\"anchor\" aria-label=\"Permalink: Cold-start Framework\" href=\"#cold-start-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eData partitioning, model training and evaluation pipelines for the cold-start setting.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have fully dockerized an evaluation pipeline, from downloading the most recent dataset to conducting interviews.\nThe pipeline was developed using Docker version 19.03.5-ce.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom a clean slate, run the pipeline by running the script \u003ccode\u003escripts/run_pipeline.sh\u003c/code\u003e. The pipeline will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload the latest stable MindReader version and the related entities.\u003c/li\u003e\n\u003cli\u003ePartition the downloaded dataset into training (warm-start) and testing (cold-start).\u003c/li\u003e\n\u003cli\u003eRun all models on the partitioned dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe recommend running the entire pipeline initially.\nFollowing this, one can run the experiments alone by running \u003ccode\u003escripts/run_interview.sh\u003c/code\u003e.\nNote that if changes are made to the code, the base image should be rebuilt by running \u003ccode\u003escripts/build_base.sh\u003c/code\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\u00b5graphy\u003c/h1\u003e\u003ca id=\"user-content-\u00b5graphy\" class=\"anchor\" aria-label=\"Permalink: \u00b5graphy\" href=\"#\u00b5graphy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/89cba1dfd31cc5a094db27503998be8899ddbdddbe5642b47399b959dcb3e122/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f6769706572742f6d756772617068793f6c6f676f3d676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/89cba1dfd31cc5a094db27503998be8899ddbdddbe5642b47399b959dcb3e122/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f6769706572742f6d756772617068793f6c6f676f3d676974\" alt=\"GitHub tag (latest by date)\" data-canonical-src=\"https://img.shields.io/github/v/tag/gipert/mugraphy?logo=git\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7b50856d9c2f0cf69012cffe36a7f7e06e1340dda987c983b57bde5f762ebde2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f776f726b666c6f772f7374617475732f6769706572742f6d756772617068792f43492f6d61696e3f6c6162656c3d6d61696e2532306272616e6368266c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b50856d9c2f0cf69012cffe36a7f7e06e1340dda987c983b57bde5f762ebde2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f776f726b666c6f772f7374617475732f6769706572742f6d756772617068792f43492f6d61696e3f6c6162656c3d6d61696e2532306272616e6368266c6f676f3d676974687562\" alt=\"GitHub Workflow Status (main)\" data-canonical-src=\"https://img.shields.io/github/workflow/status/gipert/mugraphy/CI/main?label=main%20branch\u0026amp;logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d5fa5a6bbc8a916468858bf60c9c62ac62eb65cd33f2e80c16dd6ac31141904d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6769706572742f6d756772617068793f6c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d5fa5a6bbc8a916468858bf60c9c62ac62eb65cd33f2e80c16dd6ac31141904d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6769706572742f6d756772617068793f6c6f676f3d676974687562\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/gipert/mugraphy?logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7db20acc3a02f87b3c6fc50da591d38be59210d061a373822c616d61502d1675/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d70722f6769706572742f6d756772617068793f6c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7db20acc3a02f87b3c6fc50da591d38be59210d061a373822c616d61502d1675/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d70722f6769706572742f6d756772617068793f6c6f676f3d676974687562\" alt=\"GitHub pull requests\" data-canonical-src=\"https://img.shields.io/github/issues-pr/gipert/mugraphy?logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/72372b69c470b0eea2309e972bce50a7bc967012ea7ec6d38c20b9bd708fa537/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6769706572742f6d75677261706879\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72372b69c470b0eea2309e972bce50a7bc967012ea7ec6d38c20b9bd708fa537/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6769706572742f6d75677261706879\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/gipert/mugraphy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSimulate the propagation of cosmic muons through large structures and reveal their internal composition. Inspired by \u003ca href=\"https://www.nature.com/articles/nature24647\" rel=\"nofollow\"\u003e\u003cem\u003eMorishima et al. Nature (2017)\u003c/em\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".github/mugraphy.png\"\u003e\u003cimg src=\".github/mugraphy.png\" alt=\"Simulation visualization\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ - "pipeline", - "recommender-system", - "cold-start", - "evaluation-pipelines", - "dataset", - "interview", - "hacktoberfest" + "cosmic-rays", + "educational", + "analysis", + "simulation", + "challenge" ], - "updated_at": 1607268817.0 + "updated_at": 1680666340.0 }, { "data_format": 2, - "description": "Making containers with minc-toolkit", + "description": "Package to call MHCnuggets from R, to predict MHC-I and MHC-II epitopes", "filenames": [ - "singularity/Singularity.base-min", - "singularity/Singularity.1.0.09", - "singularity/Singularity.1.9.16", - "singularity/Singularity.1.9.16-min", - "singularity/Singularity.1.9.15", - "singularity/Singularity.base" + "Singularity" ], - "full_name": "vfonov/minc-toolkit-containers", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding containers for minc-toolkit\u003c/h1\u003e\u003ca id=\"user-content-building-containers-for-minc-toolkit\" class=\"anchor\" aria-label=\"Permalink: Building containers for minc-toolkit\" href=\"#building-containers-for-minc-toolkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDOCKER\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: DOCKER\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ecd docker; build_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n", + "full_name": "richelbilderbeek/mhcnuggetsr", + "latest_release": "v1.2.1", + "readme": "\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emhcnuggetsr\u003c/h1\u003e\u003ca id=\"user-content-mhcnuggetsr\" class=\"anchor\" aria-label=\"Permalink: mhcnuggetsr\" href=\"#mhcnuggetsr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://www.tidyverse.org/lifecycle/#stable\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/371f90418f7c4a16d1d31c389716305fd8172cf18843b1a0ebf4c5620a325971/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6966656379636c652d737461626c652d677265656e2e737667\" alt=\"Lifecycle: stable\" data-canonical-src=\"https://img.shields.io/badge/lifecycle-stable-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mhcnuggetsr\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a450839eaaf0ec96ce928e34b07e264c7ab47d11721a2605f59c749b6c697161/68747470733a2f2f7777772e722d706b672e6f72672f6261646765732f76657273696f6e2f6d68636e75676765747372\" alt=\"CRAN status\" data-canonical-src=\"https://www.r-pkg.org/badges/version/mhcnuggetsr\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mhcnuggetsr\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1f1c2992100c3e0a81b9dea97415508cf8f8b0014f7e776e1b09cb460239ec70/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6772616e642d746f74616c2f6d68636e75676765747372\" alt=\"\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/grand-total/mhcnuggetsr\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mhcnuggetsr\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0392eede77d25d2f14beffaca1b9475c822e8bfcfa862148a52f13440dca518f/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6d68636e75676765747372\" alt=\"\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/mhcnuggetsr\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/mhcnuggetsr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/mhcnuggetsr/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/mhcnuggetsr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/mhcnuggetsr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ebbde312506314ee3ce181bf46c1551f5312aa55c1831b4b94f59fc2dc94c107/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6d68636e756767657473722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/mhcnuggetsr/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/mhcnuggetsr/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/mhcnuggetsr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/mhcnuggetsr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c67ca4392bfa868b7d59e619fcff5d4531ad8083f7f49d98771cf96523fcd8b6/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6d68636e756767657473722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/mhcnuggetsr/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cblockquote\u003e\n\u003cp\u003emhcnuggetsr is broken, see\n\u003ca href=\"https://github.com/richelbilderbeek/mhcnuggetsr/issues/13\"\u003ehere\u003c/a\u003e, as\nthe import of the \u003ccode\u003eMHCnuggets\u003c/code\u003e Python package by `reticulate``\nfails. If you know how to fix this, please contact me\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eR package to work with\n\u003ca href=\"https://github.com/KarchinLab/mhcnuggets\"\u003eMHCnuggets\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe goal of \u003ccode\u003emhcnuggetsr\u003c/code\u003e is to predict the half maximal inhibitory\nconcentration of peptides for an MHC haplotype. It does by calling\n\u003ca href=\"https://github.com/KarchinLab/mhcnuggets\"\u003eMHCnuggets\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install the released version of mhcnuggetsr from\n\u003ca href=\"https://github.com/\"\u003eGitHub\u003c/a\u003e with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eremotes::install_github(\"richelbilderbeek/mhcnuggetsr\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstall MHCnuggets using the non-CRAN extension\n\u003ca href=\"https://github.com/richelbilderbeek/mhcnuggetsrinstall\"\u003emhcnuggetsrinstall\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# {r install}\nlibrary(mhcnuggetsr)\n\nif (!is_mhcnuggets_installed()) {\n remotes::install_github(\"richelbilderbeek/mhcnuggetsrinstall\")\n mhcnuggetsrinstall::install_mhcnuggets()\n mhcnuggetsr_self_test()\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample\u003c/h2\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is how to get the IC50 values (in nM) for the peptides in an\nexample file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# {r example}\nlibrary(testthat)\nlibrary(mhcnuggetsr)\n\nif (is_mhcnuggets_installed()) {\n mhcnuggets_options \u0026lt;- create_mhcnuggets_options(\n mhc = \"HLA-A02:01\"\n )\n \n df \u0026lt;- predict_ic50(\n peptides = \"AIAACAMLLV\",\n mhcnuggets_options = mhcnuggets_options\n )\n expect_equal(df$ic50, 5578.77)\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow can I suppress the output when making a prediction?\u003c/h2\u003e\u003ca id=\"user-content-how-can-i-suppress-the-output-when-making-a-prediction\" class=\"anchor\" aria-label=\"Permalink: How can I suppress the output when making a prediction?\" href=\"#how-can-i-suppress-the-output-when-making-a-prediction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne cannot until MHCnuggets allows to do so. Issue is posted\n\u003ca href=\"https://github.com/KarchinLab/mhcnuggets/issues/17\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere is a feature I miss\u003c/h2\u003e\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" aria-label=\"Permalink: There is a feature I miss\" href=\"#there-is-a-feature-i-miss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI want to collaborate\u003c/h2\u003e\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" aria-label=\"Permalink: I want to collaborate\" href=\"#i-want-to-collaborate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u2018Submitting code\u2019\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI think I have found a bug\u003c/h2\u003e\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" aria-label=\"Permalink: I think I have found a bug\" href=\"#i-think-i-have-found-a-bug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u2018Submitting bugs\u2019\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere\u2019s something else I want to say\u003c/h2\u003e\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" aria-label=\"Permalink: There\u2019s something else I want to say\" href=\"#theres-something-else-i-want-to-say\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExternal links\u003c/h2\u003e\u003ca id=\"user-content-external-links\" class=\"anchor\" aria-label=\"Permalink: External links\" href=\"#external-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/KarchinLab/mhcnuggets\"\u003eMHCnuggets GitHub repo\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eArticle about MHCnuggets:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eShao, Xiaoshan M., et al.\u00a0\u201cHigh-throughput prediction of MHC class I\nand II neoantigens with MHCnuggets.\u201d Cancer Immunology Research 8.3\n(2020): 396-408.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 4, "subscribers_count": 3, "topics": [], - "updated_at": 1710867538.0 - }, - { - "data_format": 2, - "description": "H3ABioNet Metagenomics Workflow", - "filenames": [ - "examples/taxonomic_classification/Singularity.classification" - ], - "full_name": "h3abionet/h3ameta", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"aux/H3ABioNetlogo2.jpg\"\u003e\u003cimg src=\"aux/H3ABioNetlogo2.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eh3ameta\u003c/h1\u003e\u003ca id=\"user-content-h3ameta\" class=\"anchor\" aria-label=\"Permalink: h3ameta\" href=\"#h3ameta\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eH3ABionNet Metagenomics Workflows\u003c/p\u003e\n\u003cp\u003eNote: other workshop materials can be found \u003ca href=\"https://drive.google.com/drive/u/1/folders/1g3iyBbbD0fq2TIYz3MungaOiSu4DAm8X\" rel=\"nofollow\"\u003ein our Google Drive folder\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the model workflow\u003c/h2\u003e\u003ca id=\"user-content-running-the-model-workflow\" class=\"anchor\" aria-label=\"Permalink: Running the model workflow\" href=\"#running-the-model-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Set up conda, nextflow, clone the Git repository.\u003c/h3\u003e\u003ca id=\"user-content-1-set-up-conda-nextflow-clone-the-git-repository\" class=\"anchor\" aria-label=\"Permalink: 1. Set up conda, nextflow, clone the Git repository.\" href=\"#1-set-up-conda-nextflow-clone-the-git-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: this requires Singularity to be set up on your system or cluster.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~\nmkdir -p ~/local/bin\nexport PATH=\"$PATH:~/local/bin\"\n\nwget -qO- https://get.nextflow.io | bash\nwget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\ncd\ngit clone https://github.com/h3abionet/h3ameta.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Running the workflow\u003c/h3\u003e\u003ca id=\"user-content-2-running-the-workflow\" class=\"anchor\" aria-label=\"Permalink: 2. Running the workflow\" href=\"#2-running-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~\nmkdir test_run; cd test_run\nnextflow h3ameta/examples/taxonomic_classification/taxonomic_classification.nf --tax_level S -resume --in h3ameta/examples/test_data/*.fq \\\n--dataset_table h3ameta/examples/test_data/datasets.tsv --db /path/to/kraken_and_bracken_db\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker images\u003c/h2\u003e\u003ca id=\"user-content-docker-images\" class=\"anchor\" aria-label=\"Permalink: Docker images\" href=\"#docker-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe\u0027re assuming you\u0027re using singularity -- if using Docker it\u0027ll be a little simpler, so it\u0027s left as an exercise for the reader. Of course, if you\u0027re using Nextflow this will generally be taken care of by the appropriate config file and should be transparent.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ekraken2\u003c/h3\u003e\u003ca id=\"user-content-kraken2\" class=\"anchor\" aria-label=\"Permalink: kraken2\" href=\"#kraken2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload the latest image\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull docker://quay.io/h3abionet_org/kraken2 \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis will create an image \u003ccode\u003ekraken2.img\u003c/code\u003e which contains the kraken2 suite plus auxiliary programs like dustmasker\u003c/p\u003e\n\u003cp\u003eNote that we do not have any databases inside the image to keep the image small. You need to download and build the databases. Here\u0027s an example: Assume that you have a directory \u003ccode\u003e/local/kraken\u003c/code\u003e and you\u0027re going to bulild the database inside that\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec -B /local/kraken/:/mnt kraken2.simg kraken2-build --standard --threads 8 --db /mnt/krakdb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis binds the directory \u003ccode\u003e/local/kraken\u003c/code\u003e on the host to the \u003ccode\u003e/mnt\u003c/code\u003e directory in the singularity image. The directory \u003ccode\u003e/mnt\u003c/code\u003e is passed to the \u003ccode\u003ekraken2-build\u003c/code\u003e program to use for the data and the database will be called \u003ccode\u003ekrakdb\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFunding\u003c/h2\u003e\u003ca id=\"user-content-funding\" class=\"anchor\" aria-label=\"Permalink: Funding\" href=\"#funding\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe acknowledge support of the NIH (Grant U24HG006941)\u003c/p\u003e\n", - "stargazers_count": 4, - "subscribers_count": 15, - "topics": [], - "updated_at": 1617613036.0 - }, - { - "data_format": 2, - "description": "Nextflow pipeline for standardised variant calls on canine genomes", - "filenames": [ - "containers/picard/Singularity.picard-2.10.6", - "containers/picard/Singularity.picard-1.97", - "containers/htslib/Singularity.htslib-1.5", - "containers/fastqc/Singularity.fastqc-0.11.5", - "containers/gatk/Singularity.gatk-3.5", - "containers/bwa/Singularity.bwa-0.7.12" - ], - "full_name": "NBISweden/K9-WGS-Pipeline", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eK9-WGS-Pipeline\u003c/h1\u003e\u003ca id=\"user-content-k9-wgs-pipeline\" class=\"anchor\" aria-label=\"Permalink: K9-WGS-Pipeline\" href=\"#k9-wgs-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/NBISweden/K9-WGS-Pipeline\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8be03a73055970ff2c398e1368149f7854d20fb6f51122daefae2c9404702468/68747470733a2f2f6170692e7472617669732d63692e6f72672f4e424953776564656e2f4b392d5747532d506970656c696e652e737667\" alt=\"Travis status\" data-canonical-src=\"https://api.travis-ci.org/NBISweden/K9-WGS-Pipeline.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNextflow pipeline for standardised mapping and variant calls on canine genomes.\nThe pipeline take fastqs (or bams) and outputs, bams, and gvcfs for joint\ngenotyping, followed by hard filtering.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e nextflow run main.nf --help\u003c/span\u003e\nN E X T F L O W \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e version 0.31.1\nLaunching \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003emain.nf\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e [shrivelled_fermi] - revision: 131a72393f\n Usage:\n nextflow run main.nf --fastqDir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Options:\n --help\n Print this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message\n --fastqDir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eDir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Directory containing fastq samples (.fq.gz)\n --bamDir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eDir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Instead of --fastqDir, directory containing bam and indexed bam sample files (.bam, .bam.bai)\n --reference \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Genome reference file (has to be indexed)\n --known \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n File with known sites \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e quality calibration.\n --outdir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Directory \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e output files\n --onlyMap\n Only run the mapping steps\n --project\n Slurm project to run with\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recommended way is to clone it from github:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e git clone https://github.com/NBISweden/K9-WGS-Pipeline.git\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cd K9-WGS-Pipeline\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSoftware\u003c/h3\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-label=\"Permalink: Software\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline requires \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e and\n\u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e on the target system. These\nare often pre-installed on HPC systems.\u003c/p\u003e\n\u003cp\u003eIt is recommended that you pre-pull all the singularity images required by the\nworkflow, there is a script in the workflow directory to help you with this,\njust run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e scripts/pull_singularity.sh\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNB\u003c/strong\u003e: You need to set the environment variable \u003ccode\u003eNXF_SINGULARITY_CACHEDIR\u003c/code\u003e to\nthe location where the images where pulled (it\u0027s the \u003ccode\u003esingularity\u003c/code\u003e subdir from\nwhere you ran the \u003ccode\u003epull_singularity.sh\u003c/code\u003e script).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eData\u003c/h3\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-label=\"Permalink: Data\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline requries a \u003ccode\u003ebwa\u003c/code\u003e indexed reference genome and a \u003ccode\u003epicard\u003c/code\u003e genomic\ndictionary. These can be created like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e bwa index ref.fasta\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e java -Xmx4g /picard/CreateSequenceDictionary.jar REFERENCE=ref.fasta OUTPUT=ref.dict\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also do this directly through the prepulled singularity images like so:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity exec singularity/NBISweden-K9-WGS-Pipeline-bwa-0.7.12.img \\\u003c/span\u003e\n bwa index ref.fasta\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity exec singularity/NBISweden-K9-WGS-Pipeline-picard-2.10.6.img \\\u003c/span\u003e\n picard CreateSequenceDictionary REFERENCE=ref.fasta OUTPUT=ref.dict\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# nextflow run [-resume] main.nf \u0026lt;options\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen running the mapping the workflow expects a \u003ccode\u003e--fastqDir \u0026lt;dir\u0026gt;\u003c/code\u003e parameter on\nthe command line. This directory should contain, gzipped, paired fastq files\nwith R1 and R2 in their filenames respectively. Specifically the filenames of\nthe readfiles should match either of these glob patterns.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e*R{1,2}*.fq.gz\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e*R{1,2}*.fastq.gz\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe known file is a bed file for quality calibration using BaseRecalibrator\nfrom the GATK toolkit.\u003c/p\u003e\n\u003cp\u003eIf specifying \u003ccode\u003eonlyMap\u003c/code\u003e no genotyping will be done.\u003c/p\u003e\n\u003cp\u003eIf you already have created your mapping you can use \u003ccode\u003e--bamDir\u003c/code\u003e instead of\n\u003ccode\u003e--fastqDir\u003c/code\u003e to specify a directory with bam files to run from.\u003c/p\u003e\n\u003cp\u003eIt is generally recommended to start it with the \u003ccode\u003e-resume\u003c/code\u003e option so a failed\nrun can be resumed from where it failed once eventual errors are fixed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning on HPC\u003c/h3\u003e\u003ca id=\"user-content-running-on-hpc\" class=\"anchor\" aria-label=\"Permalink: Running on HPC\" href=\"#running-on-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor HPC systems there are two main ways of running the pipeline\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun as a singlenode job\u003c/h4\u003e\u003ca id=\"user-content-run-as-a-singlenode-job\" class=\"anchor\" aria-label=\"Permalink: Run as a singlenode job\" href=\"#run-as-a-singlenode-job\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePut the nextflow command in a shellscript. Apart from the standard options make\nsure to run nextflow with the \u003ccode\u003e-profile rackhamNode\u003c/code\u003e switch, by default this\nprofile assumes that one node has 20 cpu cores, if you want to change this edit\nthe \u003ccode\u003econf/rackhamNode.config\u003c/code\u003e file and update the \u003ccode\u003ecpus\u003c/code\u003e parameter on line 16\nand 24, also update the memory parameters so they match the local environment.\nExample run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun-pipeline.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/sh\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_LAUNCHER=\u003cspan class=\"pl-smi\"\u003e$SNIC_TMP\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For Rackham\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_TEMP=\u003cspan class=\"pl-smi\"\u003e$SNIC_TMP\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For Rachham\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_SINGULARITY_CACHEDIR=/home/user/.../singularity\n\nnextflow run -resume -profile rackhamNode main.nf \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eproject specific stuff\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then start it with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sbatch run-pipeline.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLet nextflow handle the queueing system\u003c/h4\u003e\u003ca id=\"user-content-let-nextflow-handle-the-queueing-system\" class=\"anchor\" aria-label=\"Permalink: Let nextflow handle the queueing system\" href=\"#let-nextflow-handle-the-queueing-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is similar to the above, but instead you should use the \u003ccode\u003e-profile rackham\u003c/code\u003e\noption (edit the file \u003ccode\u003econf/rackham.config\u003c/code\u003e if you want to change settings to\nadjust it to your local cluster). Remember to specify the \u003ccode\u003e--project\u003c/code\u003e parameter\nto the workflow.\u003c/p\u003e\n\u003cp\u003eSince this is a very long running pipeline it is recommended that you run the\npipeline in a \u003ca href=\"https://www.gnu.org/software/screen/\" rel=\"nofollow\"\u003e\u003ccode\u003escreen\u003c/code\u003e\u003c/a\u003e session so you\ncan log out of the HPC system and log back in again and check on the status of\nthe run.\u003c/p\u003e\n\u003cp\u003eThis is an example of how to do it:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun-pipeline.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/sh\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_LAUNCHER=\u003cspan class=\"pl-smi\"\u003e$SNIC_TMP\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For Rackham\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_TEMP=\u003cspan class=\"pl-smi\"\u003e$SNIC_TMP\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For Rachham\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_SINGULARITY_CACHEDIR=/home/user/.../singularity\n\nnextflow run -resume -profile rackham main.nf --project \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eslurm project\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emore params\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then in the terminal\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ screen\n$ ./run-pipeline.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen to disconnect the screen session type \u003ccode\u003eCtrl-A D\u003c/code\u003e, then you can safely log\nout. The next time you log in, on the same login node, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ screen -r\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo reconnect.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e--outdir\u003c/code\u003e will have the following layout\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;outdir\u0026gt;\nout-tiny-fastq/\n\u251c\u2500\u2500 bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Sample.bai\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 Sample.bam\n\u251c\u2500\u2500 genotype\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_filtered_indels_1.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_filtered_snps_1.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_pass_INDEL_1.recode.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_pass_SNP_1.recode.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_raw_INDEL_1.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_raw_SNP_1.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all.vcf.gz\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 all.vcf.gz.tbi\n\u251c\u2500\u2500 haplotypeCaller\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Sample.g.vcf.gz\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 Sample.g.vcf.gz.tbi\n\u2514\u2500\u2500 reports\n \u251c\u2500\u2500 k9_dag.dot\n \u251c\u2500\u2500 k9_report.html\n \u251c\u2500\u2500 k9_timeline.html\n \u251c\u2500\u2500 k9_trace.txt\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.flagstat\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.marked.metrics\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.post_recal_data.table\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;_R1_fastqc.html\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;_R1_fastqc.zip\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;_R2_fastqc.html\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;_R2_fastqc.zip\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.recal_data.table\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.stats\n \u2514\u2500\u2500 \u0026lt;Sample\u0026gt;.wgs_metrics\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMost of this is fairly selfexplanatory, except for the reports directory.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ek9_*\u003c/code\u003e files are information from the workflow engine about how the whole\nworkflow went with timings and such. Then there are one set of \u003ccode\u003e\u0026lt;Sample\u0026gt;*\u003c/code\u003e\nfiles for each pair of fastq files that the workflow has processed with\ninformation on how the mapping went.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun on test data\u003c/h2\u003e\u003ca id=\"user-content-run-on-test-data\" class=\"anchor\" aria-label=\"Permalink: Run on test data\" href=\"#run-on-test-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst setup the testdata with \u003ccode\u003escripts/setup_testdata.sh\u003c/code\u003e and then you can run\ntests with the \u003ccode\u003escripts/test-one.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e scripts/setup_testdata.sh\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e scripts/test-one.sh singularity tiny fastq chr38\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003etest-one.sh\u003c/code\u003e script is mostly for testing on travis but it is very\nconvenient to use for local tests.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/viklund\"\u003eJohan Viklund\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/glormph\"\u003eJorrit Boekel\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 4, - "subscribers_count": 48, - "topics": [ - "nextflow", - "genomics" - ], - "updated_at": 1670594916.0 - }, - { - "data_format": 2, - "description": "Parse Dashboard for parse-hipaa server", - "filenames": [ - "Singularity" - ], - "full_name": "netreconlab/parse-hipaa-dashboard", - "latest_release": "1.1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eparse-hipaa-dashboard\u003c/h1\u003e\u003ca id=\"user-content-parse-hipaa-dashboard\" class=\"anchor\" aria-label=\"Permalink: parse-hipaa-dashboard\" href=\"#parse-hipaa-dashboard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/65eac64ec2f7259f39e209a7328555c0805b82e654825ce7730c955b6746cdf1/68747470733a2f2f646f636b6572692e636f2f696d6167652f6e65747265636f6e6c61622f70617273652d68697061612d64617368626f617264\" alt=\"\" data-canonical-src=\"https://dockeri.co/image/netreconlab/parse-hipaa-dashboard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/js/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d3f49448a2f912919bf956ca7f5ea9dfb7316ef14c5746a5a9f229e721af3d6/68747470733a2f2f62616467652e667572792e696f2f6a732f70617273652d68697061612d64617368626f6172642e737667\" alt=\"npm version\" data-canonical-src=\"https://badge.fury.io/js/parse-hipaa-dashboard.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://snyk.io/test/github/netreconlab/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/04ed3a73793e78c7c3d48a58b79af59f0998745aa1691b62444318c962e1d9cd/68747470733a2f2f736e796b2e696f2f746573742f6769746875622f6e65747265636f6e6c61622f70617273652d68697061612d64617368626f6172642f62616467652e737667\" alt=\"vulnerabilities\" data-canonical-src=\"https://snyk.io/test/github/netreconlab/parse-hipaa-dashboard/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://libraries.io/npm/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bcbf91be41700b45bb5eebc61257dd95f93fefd9db9b935a9a657b9a632e7972/68747470733a2f2f696d672e736869656c64732e696f2f6c6962726172696573696f2f72656c656173652f6e706d2f70617273652d68697061612d64617368626f617264\" alt=\"dependency up-to-date\" data-canonical-src=\"https://img.shields.io/librariesio/release/npm/parse-hipaa-dashboard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.npmjs.com/package/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5eb8bfb30386c6ac6c551fdc3fb506de28399e3227379e052279809bc647fd9c/68747470733a2f2f696d672e736869656c64732e696f2f6e706d2f64772f70617273652d68697061612d64617368626f617264\" alt=\"weekly downloads\" data-canonical-src=\"https://img.shields.io/npm/dw/parse-hipaa-dashboard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/ci.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/ci.yml/badge.svg\" alt=\"ci\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/release.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/release.yml/badge.svg\" alt=\"release\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/image.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/image.yml/badge.svg\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/image-release.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/image-release.yml/badge.svg\" alt=\"image-release\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/8621344/102236202-38f32080-3ec1-11eb-88d7-24e38e95f68d.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/8621344/102236202-38f32080-3ec1-11eb-88d7-24e38e95f68d.png\" alt=\"dashboard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eExample of how to setup and run your own \u003ca href=\"https://github.com/parse-community/parse-dashboard\"\u003eparse-dashboard\u003c/a\u003e for viewing/modifying your data in the Cloud. Designed for \u003ca href=\"https://github.com/netreconlab/parse-hipaa\"\u003eparse-hipaa\u003c/a\u003e, but can be used with any parse-server.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse at your own risk. There is not promise that this is HIPAA compliant and we are not responsible for any mishandling of your data\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeployment\u003c/h2\u003e\u003ca id=\"user-content-deployment\" class=\"anchor\" aria-label=\"Permalink: Deployment\" href=\"#deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eparse-hipaa can be easily deployed or tested remote or locally.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRemote\u003c/h3\u003e\u003ca id=\"user-content-remote\" class=\"anchor\" aria-label=\"Permalink: Remote\" href=\"#remote\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHeroku\u003c/h4\u003e\u003ca id=\"user-content-heroku\" class=\"anchor\" aria-label=\"Permalink: Heroku\" href=\"#heroku\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://heroku.com/deploy\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc2056acd0e6ff421bfc2b129417f4f832d626c61d1c083221211d8503a429f7/68747470733a2f2f7777772e6865726f6b7563646e2e636f6d2f6465706c6f792f627574746f6e2e737667\" alt=\"Deploy\" data-canonical-src=\"https://www.herokucdn.com/deploy/button.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can use the one-button deployment to quickly deploy to Heroko. \u003cstrong\u003eNote that this is non-HIPAA compliant when using Heroku\u0027s free services\u003c/strong\u003e, so you need to work with Heroku to enable this. You can \u003ca href=\"https://docs.google.com/document/d/1fniJavK_3T_SXZs2wwn-wa8nX-LzhhNgSORRK1LaZYI/edit?usp=sharing\" rel=\"nofollow\"\u003eview this document for detailed instuctions\u003c/a\u003e. Once you click the Heroku button do the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSelect your \u003cstrong\u003eApp name\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eUnder the \u003cstrong\u003eConfig vars\u003c/strong\u003e section, set all \u003ccode\u003erequired\u003c/code\u003e environment vars to connect to your parse-server\u003c/li\u003e\n\u003cli\u003eScroll to the bottom of the page and press \u003cstrong\u003eDeploy app\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsing your own files for Heroku deployment\u003c/h4\u003e\u003ca id=\"user-content-using-your-own-files-for-heroku-deployment\" class=\"anchor\" aria-label=\"Permalink: Using your own files for Heroku deployment\" href=\"#using-your-own-files-for-heroku-deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eFork the the parse-hipaa-dashboard repo\u003c/li\u003e\n\u003cli\u003eEdit \u003ccode\u003eheroku.yml\u003c/code\u003e in your repo by changing \u003ccode\u003eDockerfile.heroku\u003c/code\u003e to \u003ccode\u003eDockerfile\u003c/code\u003e. This will build from your respective repo instead of using the pre-built docker image\u003c/li\u003e\n\u003cli\u003eEdit \u003ccode\u003eparse-dashboard-config.json\u003c/code\u003e to your desired configuration\u003c/li\u003e\n\u003cli\u003eYou can then click the Heroku deployment button from your respective repo or you can then follow the directions on heroku\u0027s site for \u003ca href=\"https://devcenter.heroku.com/articles/git\" rel=\"nofollow\"\u003edeployment\u003c/a\u003e and \u003ca href=\"https://devcenter.heroku.com/articles/github-integration\" rel=\"nofollow\"\u003eintegration\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSet the \u003ccode\u003ePARSE_DASHBOARD_CONFIG\u003c/code\u003e config variable to \u003ccode\u003e./src/parse-dashboard-config.json\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLocal: using docker\u003c/h3\u003e\u003ca id=\"user-content-local-using-docker\" class=\"anchor\" aria-label=\"Permalink: Local: using docker\" href=\"#local-using-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get started with parse-hipaa simply type:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker-compose up\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment Variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eparseplatform/parse-dashboard\u003c/h4\u003e\u003ca id=\"user-content-parseplatformparse-dashboard\" class=\"anchor\" aria-label=\"Permalink: parseplatform/parse-dashboard\" href=\"#parseplatformparse-dashboard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ePARSE_DASHBOARD_TRUST_PROXY: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Default is 1, this should always be left as 1 when using docker\u003c/span\u003e\nPARSE_DASHBOARD_COOKIE_SESSION_SECRET: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Unique string. This should be constant across all deployments on your system\u003c/span\u003e\nMOUNT_PATH: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The default is \"/dashboard\". This needs to be exactly what you plan it to be behind the proxy, i.e. If you want to access cs.uky.edu/dashboard it should be \"/dashboard\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eViewing Your Data via Parse Dashboard\u003c/h3\u003e\u003ca id=\"user-content-viewing-your-data-via-parse-dashboard\" class=\"anchor\" aria-label=\"Permalink: Viewing Your Data via Parse Dashboard\" href=\"#viewing-your-data-via-parse-dashboard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eParse-dashboard is binded to your localhost on port 4040 and can be accessed as such, e.g. \u003ca href=\"http://localhost:4040/dashboard\" rel=\"nofollow\"\u003ehttp://localhost:4040/dashboard\u003c/a\u003e. The default login for the parse dashboard is username: \"parse\", password: \"1234\". For production you should change the password in the \u003ca href=\"https://github.com/netreconlab/parse-hipaa/blob/master/parse-dashboard-config.json#L14\"\u003epostgres-dashboard-config.json\u003c/a\u003e. Note that ideally the password should be hashed by using something like \u003ca href=\"https://bcrypt-generator.com\" rel=\"nofollow\"\u003ebcrypt-generator\u003c/a\u003e or using \u003ca href=\"https://github.com/parse-community/parse-dashboard#multi-factor-authentication-one-time-password\"\u003emulti factor authentication\u003c/a\u003e. You can also add more users through this method.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOpen your browser and go to \u003ca href=\"http://localhost:4040/dashboard\" rel=\"nofollow\"\u003ehttp://localhost:4040/dashboard\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUsername: \u003ccode\u003eparse\u003c/code\u003e # You can use \u003ccode\u003eparseRead\u003c/code\u003e to login as a read only user\u003c/li\u003e\n\u003cli\u003ePassword: \u003ccode\u003e1234\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBe sure to refresh your browser to see new changes synched from your CareKitSample app\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfiguring\u003c/h3\u003e\u003ca id=\"user-content-configuring\" class=\"anchor\" aria-label=\"Permalink: Configuring\" href=\"#configuring\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs mentioned, the default address and port the parse-server dashboard is binded to is 127.0.0.1:4040:4040 which means it can only be accessed by your local machine. If you want to change this, you should do it \u003ca href=\"https://github.com/netreconlab/parse-hipaa/blob/master/docker-compose.yml#L29\"\u003ehere\u003c/a\u003e. If you plan on using this image to deploy in production, it is recommended to run this behind a proxy and add the environment variable \u003ccode\u003ePARSE_DASHBOARD_TRUST_PROXY=1\u003c/code\u003e to the dashboard container. Note that since the parse dashboard is running in docker, the following should remain in the yml, \u003ccode\u003ecommand: parse-dashboard --dev\u003c/code\u003e.\u003c/p\u003e\n", - "stargazers_count": 4, - "subscribers_count": 2, - "topics": [ - "parse-dashboard", - "parse-hipaa", - "hacktoberfest", - "gdpr", - "healthcare", - "hipaa", - "docker", - "singularity" - ], - "updated_at": 1729489443.0 + "updated_at": 1722265331.0 }, { "data_format": 2, - "description": "An example container with modules to run an executable in different environments / with different job managers", + "description": "URIAL - Utilities for Representational sImilarity AnaLysis in python ", "filenames": [ "Singularity" ], - "full_name": "sci-f/jobmaker.scif", + "full_name": "PeerHerholz/URIAL", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJobMaker Scientific Filesystem\u003c/h1\u003e\u003ca id=\"user-content-jobmaker-scientific-filesystem\" class=\"anchor\" aria-label=\"Permalink: JobMaker Scientific Filesystem\" href=\"#jobmaker-scientific-filesystem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is an example container to provide an executable (in this case, a fun\nprinting of pokemon) to be run in different contexts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erun with custom variables (fortune)\u003c/li\u003e\n\u003cli\u003erun in a different context (eg, a color filter)\u003c/li\u003e\n\u003cli\u003erun on a slurm cluster\u003c/li\u003e\n\u003cli\u003erun on an sge cluster\u003c/li\u003e\n\u003cli\u003erun locally\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe general idea is that a main function (the pokemon executable) can be\nprovided in different contexts, or with different (optional) modular\ncontexts for the user. For each context or helper, there\nis a custom set of environment, or labels, along with commands and metadata. If\nyou want to skip the science part and just play with Pokemon, there is a \u003ca href=\"https://vsoch.github.io/2018/pokemon/\" rel=\"nofollow\"\u003eseparate\nset of containers\u003c/a\u003e (Docker and Singularity) for that.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image\u003c/h2\u003e\u003ca id=\"user-content-building-the-image\" class=\"anchor\" aria-label=\"Permalink: Building the image\" href=\"#building-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLet\u0027s first build the container. You can use the Makefile to build the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n# Does make clean followed by make build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor manually:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build jobmaker Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the Image\u003c/h2\u003e\u003ca id=\"user-content-running-the-image\" class=\"anchor\" aria-label=\"Permalink: Running the Image\" href=\"#running-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnd now run it. This should perform the container\u0027s main function, calling it\u0027s runscript:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will see an army of Pokemon ascii print to the screen. Works great! But now we want to capture metrics about this primary function. First we would want to know what tools (SCIF apps) come with the\ncontainer. That\u0027s easy to do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker apps\n catch\n colors\n fortune\n main\n sge\n slurm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can ask for help for the container, this is Singularity specific.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity help jobmaker \n\nThis is an example for a container with a Scientific Filesystem\nthat on generation, calculates the runtime for the runscript, \nand then writes a job file fitting to it. We also provide \nseveral wrappers (colors, fortune) for customizing the runscript.\nGiven that metrics for running time and memory are being calculated where\nthe container is built, we assume that the build environment resources \nare comparable to the running environment. The only requirements for\nthe running environments are that singularity is installed.\nEach SCIF app serves as a different entrypoint to run the container. \n\n # Generate on your own\n git clone https://www.github.com/sci-f/jobmaker.scif\n cd jobmaker.scif\n make\n\n # Here is how you can use the container after you build it:\n\n # List all apps\n ./jobmaker apps\n\n # Run a specific app\n ./jobmaker run \u0026lt;app\u0026gt;\n\n # Loop over all apps\n for app in $(./jobmaker apps); do\n ./jobmaker run $app\n done\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning an application\u003c/h3\u003e\u003ca id=\"user-content-running-an-application\" class=\"anchor\" aria-label=\"Permalink: Running an application\" href=\"#running-an-application\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRemember the list of apps? We don\u0027t know what they do. So first you might want to ask for help\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker help\nUsage: scif help \u0026lt;hello-world\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker help slurm\nThis will print (to the console) a slurm submission script\n./jobmaker run slurm\n./jobmaker run slurm vsochat@stanford.edu\n./jobmaker run slurm \u0026gt;\u0026gt; pokemon.job\nsbatch pokemon.job\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also look at the metadata in detail with inspect\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker inspect slurm\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then run it!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker run slurm\n[slurm] executing /bin/bash /scif/apps/slurm/scif/runscript\n#!/bin/bash\n#SBATCH --nodes=1\n#SBATCH -p normal\n#SBATCH --qos=normal\n#SBATCH --mem=16\n#SBATCH --job-name=pokemon.job\n#SBATCH --error=%j.err\n#SBATCH --output=%j.out\n#SBATCH --mail-type=ALL\n#SBATCH --time=0:00.82\nmodule load singularity\nsingularity run /scif/apps/main/jobmaker\n# example: run the job script command line:\n# sbatch pokemon.job\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe reason this works is because the slurm application sources environment variables for memory and time needed that were calculated when the container was built. Since this is a job, we can pipe the output easily into a file, and we will add \u003ccode\u003e--quiet\u003c/code\u003e to suppress the first information line.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker --quiet run slurm \u0026gt;\u0026gt; myjob.job\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced\u003c/h2\u003e\u003ca id=\"user-content-advanced\" class=\"anchor\" aria-label=\"Permalink: Advanced\" href=\"#advanced\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhat variables are exposed to each app at runtime? Let\u0027s look at the environment of the active application (e.g., slurm) when it\u0027s running. We will split this into two pieces to show the \"general active application\" environment, followed by the named application environment (that is also defined for apps that aren\u0027t active!)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker exec slurm env | grep slurm\n[slurm] executing /usr/bin/env \nSCIF_APPDATA=/scif/data/slurm\nSCIF_APPRUN=/scif/apps/slurm/scif/runscript\nSCIF_APPRECIPE=/scif/apps/slurm/scif/slurm.scif\nSCIF_APPNAME_slurm=slurm\nSCIF_APPROOT=/scif/apps/slurm\nSCIF_APPNAME=slurm\nSCIF_APPLIB=/scif/apps/slurm/lib\nSCIF_APPMETA=/scif/apps/slurm/scif\nSCIF_APPBIN=/scif/apps/slurm/bin\nSCIF_APPHELP=/scif/apps/slurm/scif/runscript.help\nSCIF_APPTEST=/scif/apps/slurm/scif/test.sh\nSCIF_APPENV=/scif/apps/slurm/scif/environment.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eSCIF_APPLIB_slurm=/scif/apps/slurm/lib\nSCIF_APPMETA_slurm=/scif/apps/slurm/scif\nSCIF_APPBIN_slurm=/scif/apps/slurm/bin\nSCIF_APPHELP_slurm=/scif/apps/slurm/scif/runscript.help\nSCIF_APPENV_slurm=/scif/apps/slurm/scif/environment.sh\nSCIF_APPLABELS_slurm=/scif/apps/slurm/scif/labels.json\nSCIF_APPTEST_slurm=/scif/apps/slurm/scif/test.sh\nSCIF_APPDATA_slurm=/scif/data/slurm\nSCIF_APPRUN_slurm=/scif/apps/slurm/scif/runscript\nSCIF_APPLABELS=/scif/apps/slurm/scif/labels.json\nSCIF_APPRECIPE_slurm=/scif/apps/slurm/scif/slurm.scif\nSCIF_APPROOT_slurm=/scif/apps/slurm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eImportantly, notice that the bin and lib are added to their respective paths, to be found!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLD_LIBRARY_PATH=/scif/apps/slurm/lib:/.singularity.d/libs\nPWD=/scif/apps/slurm\nPATH=/scif/apps/slurm/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd guess what? Even when slurm is running (and other apps like sge are sleeping) we can still find the other apps! Let\u0027s look for sge (when slurm is running):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker exec slurm env | grep sge\nSCIF_APPHELP_sge=/scif/apps/sge/scif/runscript.help\nSCIF_APPENV_sge=/scif/apps/sge/scif/environment.sh\nSCIF_APPLABELS_sge=/scif/apps/sge/scif/labels.json\nSCIF_APPTEST_sge=/scif/apps/sge/scif/test.sh\nSCIF_APPDATA_sge=/scif/data/sge\nSCIF_APPRUN_sge=/scif/apps/sge/scif/runscript\nSCIF_APPRECIPE_sge=/scif/apps/sge/scif/sge.scif\nSCIF_APPROOT_sge=/scif/apps/sge\nSCIF_APPNAME_sge=sge\nSCIF_APPLIB_sge=/scif/apps/sge/lib\nSCIF_APPMETA_sge=/scif/apps/sge/scif\nSCIF_APPBIN_sge=/scif/apps/sge/bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is why I\u0027m able to quickly execute another app runscript:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexec SCIF_APPRUN_sge\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor source an environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource SCIF_APPENV_sge\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewithout needing to know the path or details. I can also just target the active app, whatever that may be, doing the same without the specified name. For example, let\u0027s say I have a script to perform some machine learning task on the main runscript file. It would be located at \u003ccode\u003eSCIF_APPRUN\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1631705925.0 + "updated_at": 1612234603.0 }, { "data_format": 2, - "description": "Main genome analytics workflow powering the production analysis of WGS samples for the Singapore NPM Program Phase 1A (AKA SG10K Health)", + "description": "Singularity container for Tensorflow - good example for GPU integration into a container", "filenames": [ + "Singularity.2.2", "Singularity" ], - "full_name": "gis-rpd/rpd-sg10k-grch38-gatk4-gvcf-freebayes-vcf", + "full_name": "chpc-uofu/Singularity-tensorflow", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSG10K Health: GRCh38 GATK4-gVCF Freebayes-VCF\u003c/h1\u003e\u003ca id=\"user-content-sg10k-health-grch38-gatk4-gvcf-freebayes-vcf\" class=\"anchor\" aria-label=\"Permalink: SG10K Health: GRCh38 GATK4-gVCF Freebayes-VCF\" href=\"#sg10k-health-grch38-gatk4-gvcf-freebayes-vcf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://dev.azure.com/wilma0161/wilma/_build/latest?definitionId=1?branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cdde83a116160fde0045af9d9278d92871ab86e62e2fd3e65ede41d7ee76d74d/68747470733a2f2f6465762e617a7572652e636f6d2f77696c6d61303136312f77696c6d612f5f617069732f6275696c642f7374617475732f6769732d7270642e7270642d736731306b2d6772636833382d6761746b342d677663662d6672656562617965732d7663663f6272616e63684e616d653d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://dev.azure.com/wilma0161/wilma/_apis/build/status/gis-rpd.rpd-sg10k-grch38-gatk4-gvcf-freebayes-vcf?branchName=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the main genome analytics workflow powering the production analysis of whole genome samples\nfor the Singapore National Precision Medicine (NPM) Program Phase 1A, sometimes also referred to as SG10K\nHealth. It processes samples from FastQ to lossless CRAM, computes multiple QC metrics as well as Freebayes\nvariant calls and GATK4 gvcfs.\u003c/p\u003e\n\u003cp\u003eTo ensure reproducibility, scalability and mobility the workflow is implemented as \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e recipe and uses containers\n(\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e on \u003ca href=\"https://www.nscc.sg/about-nscc/our-facilityaspire-1/\" rel=\"nofollow\"\u003eNSCC\u0027s Aspire 1\u003c/a\u003e and \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e on\n\u003ca href=\"https://aws.amazon.com/batch/\" rel=\"nofollow\"\u003eAWS Batch\u003c/a\u003e). Container building is simplified by the use of\n\u003ca href=\"https://bioconda.github.io/\" rel=\"nofollow\"\u003eBioconda\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll results can be found in the \u003ccode\u003eresults\u003c/code\u003e folder of a pipeline\nexecution. Results there are grouped per sample, with the exception of\nGoleft indexcov, which summarises over the sample set.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMain results\u003c/h3\u003e\u003ca id=\"user-content-main-results\" class=\"anchor\" aria-label=\"Permalink: Main results\" href=\"#main-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://software.broadinstitute.org/gatk/gatk4\" rel=\"nofollow\"\u003eGATK4\u003c/a\u003e gVCF (indexed): \u003ccode\u003e{sample}/{sample}.g.vcf.gz\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ekg/freebayes\"\u003eFreebayes\u003c/a\u003e VCF (Q\u0026gt;=20; indexed): \u003ccode\u003e{sample}/{sample}.fb.vcf.gz\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCRAM (lossless, with OQ, indexed): \u003ccode\u003e{sample}/{sample}.bqsr.cram\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQC etc.\u003c/h3\u003e\u003ca id=\"user-content-qc-etc\" class=\"anchor\" aria-label=\"Permalink: QC etc.\" href=\"#qc-etc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/brentp/goleft\"\u003eGoleft\u003c/a\u003e indexcov: \u003ccode\u003eindexcov/all/\u003c/code\u003e (main file \u003ccode\u003eindexcov/all/all.html\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.htslib.org/doc/samtools.html\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e stats: \u003ccode\u003e{sample}/stats/\u003c/code\u003e (main files: \u003ccode\u003e{sample}/stats/{sample}.stats\u003c/code\u003e and \u003ccode\u003e{sample}/stats/{sample}.html\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://genome.sph.umich.edu/wiki/VerifyBamID\" rel=\"nofollow\"\u003eVerifybamid\u003c/a\u003e for the three ethnicities: \u003ccode\u003e{sample}/verifybamid/\u003c/code\u003e (main files: \u003ccode\u003e{sample}/verifybamid/{sample}.SGVP_MAF0.01.{ethnicity}.selfSM\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCoverage as per SOP: \u003ccode\u003e{sample}/{sample}.cov-062017.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWe share this code for transparency. This is not meant to be a generic whole genome workflow for wider use, but rather specific to the program\u0027s needs.\nFor the same reason this documentation is rudimentary.\u003c/li\u003e\n\u003cli\u003eSee \u003ca href=\"./dag.svg\"\u003ethis file\u003c/a\u003e for the execution DAG\u003c/li\u003e\n\u003cli\u003eGATK commandline parameters are based on \u003ca href=\"https://github.com/broadinstitute/wdl/tree/develop/scripts/broad_pipelines/germline-short-variant-discovery/gvcf-generation-per-sample/1.0.0\"\u003ethe official WDL implementation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopers: work on devel or feature branches. Only merge to master if \u003ccode\u003etests/run.sh\u003c/code\u003e completes successfully\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow was implemented in the \u003ca href=\"https://www.a-star.edu.sg/gis\" rel=\"nofollow\"\u003eGenome Institute of Singapore\n(GIS)\u003c/a\u003e by:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLavanya VEERAVALLI \u003ca href=\"mailto:veeravallil@gis.a-star.edu.sg\"\u003eveeravallil@gis.a-star.edu.sg\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAndreas WILM \u003ca href=\"mailto:wilma@gis.a-star.edu.sg\"\u003ewilma@gis.a-star.edu.sg\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 4, - "subscribers_count": 7, + "subscribers_count": 2, "topics": [], - "updated_at": 1564798688.0 + "updated_at": 1663000991.0 }, { "data_format": 2, - "description": "A method for association of changes in phenotype traits with changes in selection intensity at the codon level across a phylogeny", + "description": "Quant proteomics as practiced at Lehti\u00f6 lab for NF-core", "filenames": [ "Singularity" ], - "full_name": "halabikeren/TraitRELAX", - "latest_release": "1.0.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTraitRELAX - a tool to associate phenotypic traits with altered selective patterns\u003c/h1\u003e\u003ca id=\"user-content-traitrelax---a-tool-to-associate-phenotypic-traits-with-altered-selective-patterns\" class=\"anchor\" aria-label=\"Permalink: TraitRELAX - a tool to associate phenotypic traits with altered selective patterns\" href=\"#traitrelax---a-tool-to-associate-phenotypic-traits-with-altered-selective-patterns\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://app.codeship.com/projects/394727\" rel=\"nofollow\"\u003e \u003cimg src=\"https://camo.githubusercontent.com/f491385ace8bdd80a91c13f70d8730e9093cf1b1afe9fffe41bcc1e973a917cf/68747470733a2f2f6170702e636f6465736869702e636f6d2f70726f6a656374732f31313038636432302d366364352d303133382d626664622d3165336231616238333161662f7374617475733f6272616e63683d6d6173746572\" alt=\"Codeship Status for halabikeren/TraitRELAX\" data-canonical-src=\"https://app.codeship.com/projects/1108cd20-6cd5-0138-bfdb-1e3b1ab831af/status?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/repository/docker/halabikeren/traitrelax\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce0614c69a0406a7dda71974a2e4079331c832615566656f74f484683c653bd8/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f68616c6162696b6572656e2f747261697472656c61782e737667\" alt=\"https://img.shields.io/docker/pulls/halabikeren/traitrelax.svg\" data-canonical-src=\"https://img.shields.io/docker/pulls/halabikeren/traitrelax.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5051\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTraitRELAX is an open-source software for the joint analysis of binary traits and coding sequence data that allows testing for association of the trait with changes in selection intensity at the codon level across a phylogeny. TraitRELAX is implemented in the C++ library \u003ca href=\"https://github.com/BioPP\"\u003eBio++\u003c/a\u003e (see also: \u003ca href=\"http://biopp.univ-montp2.fr/\" rel=\"nofollow\"\u003eBio++ documentation\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePublication\u003c/h2\u003e\u003ca id=\"user-content-publication\" class=\"anchor\" aria-label=\"Permalink: Publication\" href=\"#publication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHalabi K, Levy Karin E, Gu\u00e9guen L, and Mayrose I. TraitRELAX - A codon model for associating phenotypic traits with altered selective patterns of sequence evolution. 2020. \u003ca href=\"https://academic.oup.com/sysbio/advance-article/doi/10.1093/sysbio/syaa087/6012374\" rel=\"nofollow\"\u003eDOI:10.1093/sysbio/syaa087\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe input to TraitRELAX is \u003cstrong\u003ea single control file\u003c/strong\u003e, which among others, specifies the location of the following files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA phylogentic tree with branch lengths.\u003c/li\u003e\n\u003cli\u003eA codon multiple sequence alignment (MSA) of the sequence data of the extant species.\u003c/li\u003e\n\u003cli\u003eThe character states of the extant species coded as either \u00270\u0027 or \u00271\u0027.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe TraitRELAX control file specifies parameters as detailed in the \u003ca href=\"http://biopp.univ-montp2.fr/manual/pdf/bppsuite/v0.7.0/bppsuite.pdf\" rel=\"nofollow\"\u003ebppSuite manual\u003c/a\u003e. See the provided \u003cstrong\u003eTraitRELAX_template.bpp\u003c/strong\u003e as an example for such control file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTraitRELAX writes the maximum-likelihood scores for the null and alternative models as well as their inferred model parameters to STDOUT. You can save the results by redirecting STDOUT into a file (see Examples/README.txt).\nAdditionaly it can save results to output files specified in the control file (see the provided \u003cstrong\u003eTraitRELAX_template.bpp\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the program\u003c/h2\u003e\u003ca id=\"user-content-running-the-program\" class=\"anchor\" aria-label=\"Permalink: Running the program\" href=\"#running-the-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce installed (see next section), the program can be run through the shell:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epath/to/TraitRELAX/traitrelax param=\u0026lt;path_to_control_file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling via docker\u003c/h2\u003e\u003ca id=\"user-content-installing-via-docker\" class=\"anchor\" aria-label=\"Permalink: Installing via docker\" href=\"#installing-via-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRather than installing the program from scratch, you can pull a docker image with the pre-compiled program. To do so, first install \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e on your machine and then run on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull halabikeren/traitrelax:version1.0.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the program, first create on your machine a directory with the input for traitrelax, including the input data and a control file (see the Examples folder for more details). Then, run the following on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v \u0026lt;path_to_input_directory\u0026gt;:/traitrelax/exec/ -it traitrelax param=\u0026lt;name_of_control_file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling via singularity\u003c/h2\u003e\u003ca id=\"user-content-installing-via-singularity\" class=\"anchor\" aria-label=\"Permalink: Installing via singularity\" href=\"#installing-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using HPC, an alternative for docker is singularity. Similar to docker, you can pull a singularity image with the pre-compiled program. To do so, you should have \u003ca href=\"https://singularity.lbl.gov/docs-hpc\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed on your cluster and then run on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name TraitRELAX.sif shub://halabikeren/TraitRELAX\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the program, first create on your machine a directory with the input for traitrelax, including the input data and a control file (see the Examples folder for more details). Then, run the following on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind \u0026lt;path_to_input_directory\u0026gt;:/traitrelax/exec/ TraitRELAX.sif \u0026lt;name_of_control_file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding from source...\u003c/h2\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-label=\"Permalink: Building from source...\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e...by using an installation script\u003c/h3\u003e\u003ca id=\"user-content-by-using-an-installation-script\" class=\"anchor\" aria-label=\"Permalink: ...by using an installation script\" href=\"#by-using-an-installation-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn installation script is available at \u003cstrong\u003einstall.sh\u003c/strong\u003e. To install, run on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esh install.sh \u0026lt;path_to_prgram\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the installation is complete, the progeam will be available in \u003ccode\u003e\u0026lt;path_to_prgram\u0026gt;/TraitRELAX/TraitRELAX/TraitRELAX\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e...by shell commands\u003c/h3\u003e\u003ca id=\"user-content-by-shell-commands\" class=\"anchor\" aria-label=\"Permalink: ...by shell commands\" href=\"#by-shell-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe compilation may take a little while (especially that of \u003ccode\u003ebpp-phyl\u003c/code\u003e; ~20-30 min using 16-cores) so perhaps make some tea\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreating source directories\u003c/h4\u003e\u003ca id=\"user-content-creating-source-directories\" class=\"anchor\" aria-label=\"Permalink: Creating source directories\" href=\"#creating-source-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebpp_dir=BIOPP_INSTALLATION_DIRECTORY\nmkdir -p $bpp_dir/sources/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDownloading Bio++ libraries\u003c/h4\u003e\u003ca id=\"user-content-downloading-bio-libraries\" class=\"anchor\" aria-label=\"Permalink: Downloading Bio++ libraries\" href=\"#downloading-bio-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd $bpp_dir/sources\ngit clone https://github.com/BioPP/bpp-core.git\ngit clone https://github.com/BioPP/bpp-seq.git\ngit clone -b kerenDevel https://github.com/halabikeren/bpp-phyl.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompiling and installing Bio++\u003c/h4\u003e\u003ca id=\"user-content-compiling-and-installing-bio\" class=\"anchor\" aria-label=\"Permalink: Compiling and installing Bio++\" href=\"#compiling-and-installing-bio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd $bpp_dir/sources/bpp-core\nmkdir build\ncd build\ncmake -DCMAKE_INSTALL_PREFIX=$bpp_dir -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE -DBUILD_STATIC=YES ..\nmake -j\nmake install\ncd ../../bpp-seq\nmkdir build\ncd build\ncmake -DCMAKE_INSTALL_PREFIX=$bpp_dir -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE ..\nmake -j\nmake install\ncd ../../bpp-phyl\nmkdir build\ncd build\ncmake -DCMAKE_INSTALL_PREFIX=$bpp_dir -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE OMP_NUM_THREADS=\u0026lt;required_number_of_cores\u0026gt; ..\nmake -j\nmake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompiling and installing TraitRELAX\u003c/h4\u003e\u003ca id=\"user-content-compiling-and-installing-traitrelax\" class=\"anchor\" aria-label=\"Permalink: Compiling and installing TraitRELAX\" href=\"#compiling-and-installing-traitrelax\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eclone\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etraitrelax_dir=TRAITRELAX_INSTALLATION_DIRECTORY # the directory to which you clone TraitRELAX\nmkdir -p $traitrelax_dir\ncd $traitrelax_dir/\ngit clone https://github.com/halabikeren/TraitRELAX.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand compile\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd $traitrelax_dir/TraitRELAX/\ncmake -DCMAKE_INSTALL_PREFIX=$bpp_dir -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE .\nmake -j\nmake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning TraitRELAX with Parallelization\u003c/h4\u003e\u003ca id=\"user-content-running-traitrelax-with-parallelization\" class=\"anchor\" aria-label=\"Permalink: Running TraitRELAX with Parallelization\" href=\"#running-traitrelax-with-parallelization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run TraitRELAX with multiple threads, you should state the number of required cores in the cmake command of the complication of the Bio++ bpp-phyl library:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eOMP_NUM_THREADS=\u0026lt;required_number_of_cores\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDistinction from the RELAX method\u003c/h4\u003e\u003ca id=\"user-content-distinction-from-the-relax-method\" class=\"anchor\" aria-label=\"Permalink: Distinction from the RELAX method\" href=\"#distinction-from-the-relax-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTraitRELAX varies from The RELAX basic method in several aspects:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eTraitRELAX does not require a prior partition of the branches into two categories.\u003c/li\u003e\n\u003cli\u003eTraitRELAX uses a fixed effect likelihood approach for the selective categories, and as such constrains the selective category of each site to remain consistent across branches. That being said, the selection value of a site can change upon relaxation or intensification based on the inferred evolutionary history of the examined trait.\u003c/li\u003e\n\u003cli\u003eTraitRELAX uses a different optimization approach that limits the search space of the selection intenisty parameter to (0,10] in aim of avoiding extreme estimates.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDue to these differences, estimation of the parameters shared between the two methods may be inconsistent with one another.\u003c/p\u003e\n", + "full_name": "glormph/nf-core-dda-quant-proteomics", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003elehtiolab/ddamsproteomics\u003c/h1\u003e\u003ca id=\"user-content-lehtiolabddamsproteomics\" class=\"anchor\" aria-label=\"Permalink: lehtiolab/ddamsproteomics\" href=\"#lehtiolabddamsproteomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eA Quantitative MS proteomics analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cb7a54fdcaf1a4eab23bf403d8301f8499e0742f81363c222d13e22929fa5851/68747470733a2f2f7472617669732d63692e6f72672f6c656874696f6c61622f6464616d7370726f74656f6d6963732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/lehtiolab/ddamsproteomics.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e612028c65671defb8089ca72d2ca4e9c691a9bdee744af2355c0976cfc1d11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413531392e30342e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A519.04.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/219955514\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f2c00ca4201ccc56750439019c8bd0bfd20b670d1c9e970b4abba630ea02a569/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3231393935353531342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/219955514.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4a47169d6142a8f86534cb472507398830fa5f8e69c2eb87dc8fa15bdb215d06/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c656874696f6c61622f6464616d7370726f74656f6d6963732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/lehtiolab/ddamsproteomics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow identifies peptides in mzML input data using \u003ca href=\"https://github.com/MSGFPlus/msgfplus\"\u003eMSGF+\u003c/a\u003e, and \u003ca href=\"https://github.com/percolator/percolator/\"\u003ePercolator\u003c/a\u003e, quantifies isobarically labeled samples with \u003ca href=\"https://github.com/openms/openms\"\u003eOpenMS\u003c/a\u003e, and precursor peptides with \u003ca href=\"\"\u003eHardklor\u003c/a\u003e/\u003ca href=\"\"\u003eKronik\u003c/a\u003e, and processes that output to formatted peptide and protein/gene tables using \u003ca href=\"https://github.com/glormph/msstitch\"\u003eMsstitch\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun pipeline:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe pipeline takes multiple mzML files as input and performs identification and quantification to output results and a QC report (\u003ca href=\"docs/example_qc.html\"\u003ean example can be found here\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003elehtiolab/nf-labelcheck was originally written by Jorrit Boekel and tries to follow the \u003ca href=\"https://nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e best practices and templates.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 4, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1662489779.0 + "updated_at": 1651216059.0 }, { "data_format": 2, - "description": "Singularity container recipes to run the paraview visualizer", + "description": "Singularity container for Isaac Sim and Isaac Orbit with ROS Noetic.", "filenames": [ - "Singularity.nv", "Singularity" ], - "full_name": "singularityhub/paraview-visualizer", + "full_name": "roboticsleeds/isaac-orbit-container", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParaview Visualizer\u003c/h1\u003e\u003ca id=\"user-content-paraview-visualizer\" class=\"anchor\" aria-label=\"Permalink: Paraview Visualizer\" href=\"#paraview-visualizer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor instructions on using the full Paraview Web directly from a Singularity container,\nsee \u003ca href=\"https://ask.cyberinfrastructure.org/t/how-do-i-run-paraview-or-openfoam-on-an-hpc-resource/644/2\" rel=\"nofollow\"\u003ethis post\u003c/a\u003e\non AskCyberinfrastructre. Continue reading below for using the Paraview Visualizer\nvia a Docker container, or Singularity container instance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t have nvidia on your host, use the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e recipe. If you have\nnvidia, use the \u003ca href=\"Singularity.nv\"\u003eSingularity.nv\u003c/a\u003e recipe:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build paraview.simg Singularity \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e no nvidia\u003c/span\u003e\n$ sudo singularity build paraview.simg Singularity.nv \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e nvidia\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you need it, make a data folder to bind to on the host:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir -p /tmp/data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eStart the container instance, here we are naming it \"visualizer\"\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity instance.start --bind /tmp/data:/data paraview.simg visualizer\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should now see the paraview interface running on \u003ca href=\"http://127.0.0.1:8080\" rel=\"nofollow\"\u003e127.0.0.1:8080\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/visualizer.png\"\u003e\u003cimg src=\"img/visualizer.png\" alt=\"img/visualizer.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe mapping to \u003ccode\u003e/data\u003c/code\u003e is where local web applications will load files from.\u003c/p\u003e\n\u003cp\u003eAlso note that you \u003cem\u003emust\u003c/em\u003e stop local web servers, including any Docker applications\nrunning on that port.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCredits\u003c/h1\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHuge thanks to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jourdain\"\u003e@jourdain\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/trophime\"\u003e@trophime\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive shell\u003c/h2\u003e\u003ca id=\"user-content-interactive-shell\" class=\"anchor\" aria-label=\"Permalink: Interactive shell\" href=\"#interactive-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to debug interactively:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity shell instance://visualizer\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCleaning Up\u003c/h2\u003e\u003ca id=\"user-content-cleaning-up\" class=\"anchor\" aria-label=\"Permalink: Cleaning Up\" href=\"#cleaning-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnd to stop the container, you also need sudo\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity instance.stop instance://visualizer\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using Singularity 3.0 and up the instance group is now changed\nto \"instance stop\" and \"instance start\"\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIsaacSim Singularity Container with ROS Noetic\u003c/h1\u003e\u003ca id=\"user-content-isaacsim-singularity-container-with-ros-noetic\" class=\"anchor\" aria-label=\"Permalink: IsaacSim Singularity Container with ROS Noetic\" href=\"#isaacsim-singularity-container-with-ros-noetic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity container for the Nvidia Isaac Sim. It builds\non top of Nvidia\u0027s official image, but also installs ROS Noetic.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\"Isaac Sim\" is a registered trademark of Nvidia. We would like to clarify\nthat we have no affiliation, partnership, or endorsement with Nvidia or the Isaac\nSim platform. Any references made to \"Isaac Sim\" are purely for\ninformational purposes and do not imply any official association with Nvidia.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Rafael Papallas (\u003ca href=\"https://rpapallas.com\" rel=\"nofollow\"\u003erpapallas.com\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate API key\u003c/h2\u003e\u003ca id=\"user-content-create-api-key\" class=\"anchor\" aria-label=\"Permalink: Create API key\" href=\"#create-api-key\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuilding this container will require authentication with NGC.\u003c/p\u003e\n\u003cp\u003eFirst, make sure Singularity is installed on your computer. Follow the \"Quick\nInstallation Steps\" guide \u003ca href=\"https://docs.sylabs.io/guides/3.11/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eHere are the steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFirst create an NGC account here: \u003ca href=\"https://ngc.nvidia.com/\" rel=\"nofollow\"\u003ehttps://ngc.nvidia.com/\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eOnce you do, head to \u003ca href=\"https://ngc.nvidia.com/setup/api-key\" rel=\"nofollow\"\u003ehttps://ngc.nvidia.com/setup/api-key\u003c/a\u003e\nand generate an API key.\u003c/li\u003e\n\u003cli\u003eSet the following environment variables:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_DOCKER_USERNAME=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-cce\"\u003e\\$\u003c/span\u003eoauthtoken\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_DOCKER_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e[YOUR API TOKEN FROM ABOVE]\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s important that the \u003ccode\u003eSINGULARITY_DOCKER_USERNAME\u003c/code\u003e is set exactly like\nabove, including the \u003ccode\u003e\\$\u003c/code\u003e. For the password, of course replace the \u003ccode\u003e[YOUR API TOKEN FROM ABOVE]\u003c/code\u003e\nto the actual API token generated above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container and run the simulator\u003c/h2\u003e\u003ca id=\"user-content-build-the-container-and-run-the-simulator\" class=\"anchor\" aria-label=\"Permalink: Build the container and run the simulator\" href=\"#build-the-container-and-run-the-simulator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThen run the build script: \u003ccode\u003e./build.sh\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eYou can then run the container: \u003ccode\u003e./run.sh\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eYou need to \"accept EULA\" to run the simulator. It\u0027s available \u003ca href=\"https://docs.omniverse.nvidia.com/app_isaacsim/common/NVIDIA_Omniverse_License_Agreement.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nTo do this, in the container\u0027s home directory, open the \u003ccode\u003e.bashrc\u003c/code\u003e file\n(note, not your host OS\u0027s \u003ccode\u003e.bashrc\u003c/code\u003e but the \u003ccode\u003e.bashrc\u003c/code\u003e of the container which is\nunder \u003ccode\u003eisaac-sim-container/home/.bashrc\u003c/code\u003e) and put the following: \u003ccode\u003eexport ACCEPT_EULA=\"Y\"\u003c/code\u003e.\nThis is the way of \"ticking\" the \"I accept EULA\" for the license linked above.\u003c/li\u003e\n\u003cli\u003eTo launch Isaac Sim you need to head to \u003ccode\u003ecd /isaac-sim\u003c/code\u003e (note to the root dir \u003ccode\u003e/\u003c/code\u003e)\nand run \u003ccode\u003e./runapp.sh\u003c/code\u003e within the container. Allow some time to download dependencies.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther notes\u003c/h2\u003e\u003ca id=\"user-content-other-notes\" class=\"anchor\" aria-label=\"Permalink: Other notes\" href=\"#other-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMake sure that nvidia drivers and CUDA are installed on your host machine;\nverify by running \u003ccode\u003envidia-smi\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003erun.sh\u003c/code\u003e script will bind the drivers if available; double check they\nare available in the container by running \u003ccode\u003envidia-smi\u003c/code\u003e within the container\nas well.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 4, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [ - "paraview", - "visualizer", - "singularity", + "containers", + "isaac-sim", + "ros", "singularity-container" ], - "updated_at": 1719412607.0 + "updated_at": 1708090098.0 }, { "data_format": 2, - "description": "Pairwise Alignment Breakpoint Analysis", + "description": "transXpress: a Nextflow pipeline for rapid de novo transcriptome assembly and annotation", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "oist/GenomicBreaks", + "full_name": "transXpress/transXpress-nextflow", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGenomicBreaks\u003c/h1\u003e\u003ca id=\"user-content-genomicbreaks\" class=\"anchor\" aria-label=\"Permalink: GenomicBreaks\" href=\"#genomicbreaks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePurpose\u003c/h2\u003e\u003ca id=\"user-content-purpose\" class=\"anchor\" aria-label=\"Permalink: Purpose\" href=\"#purpose\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eGenomicBreaks\u003c/em\u003e is a \u003ccode\u003eR\u003c/code\u003e package using \u003cem\u003e\u003ca href=\"https://bioconductor.org/\" rel=\"nofollow\"\u003eBioconductor\u003c/a\u003e\u003c/em\u003e\nlibraries to analyse pairwise alignments of whole genomes in which the gene\norder has been scrambled by evolution, like in the picture below that represents\nthe comparison of homologous chromosomes in two distantly related molds,\n\u003cem\u003eN. crassa\u003c/em\u003e (chrIII) and \u003cem\u003eP. comata\u003c/em\u003e (chr7).\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/plotApairOfChrs_Neu-2.png\"\u003e\u003cimg src=\"man/figures/plotApairOfChrs_Neu-2.png\" alt=\"Comparison between Neurospora crassa chrIII / Podospora comata chr7 (rev-complemented)\" width=\"40%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis package is especially designed to parse and process the alignment files\nproduced by the our \u003ca href=\"https://github.com/oist/plessy_pairwiseGenomeComparison\"\u003epairwise genome alignment\npipeline\u003c/a\u003e, but should\nbe capable to import output of other pipelines as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall the package.\u003c/h3\u003e\u003ca id=\"user-content-install-the-package\" class=\"anchor\" aria-label=\"Permalink: Install the package.\" href=\"#install-the-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have \u003ca href=\"https://www.bioconductor.org/install/\" rel=\"nofollow\"\u003eBioconductor\u003c/a\u003e and the\n\u003ca href=\"https://remotes.r-lib.org/#installation\" rel=\"nofollow\"\u003eremotes\u003c/a\u003e already installed, the\nfollowing should work.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript -e \u0027remotes::install_github(\"oist/GenomicBreaks\", repos=BiocManager::repositories())\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdd \u003ccode\u003edependencies=TRUE\u003c/code\u003e if you would like to install the packages needed to\nbuild the vignettes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to create a Singularity container with GenomicBreaks installed.\u003c/h3\u003e\u003ca id=\"user-content-how-to-create-a-singularity-container-with-genomicbreaks-installed\" class=\"anchor\" aria-label=\"Permalink: How to create a Singularity container with GenomicBreaks installed.\" href=\"#how-to-create-a-singularity-container-with-genomicbreaks-installed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"./Singularity.def\"\u003eSingularity reciepe file\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build --fakeroot GenomicBreaks.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenomicBreaks in brief:\u003c/h2\u003e\u003ca id=\"user-content-genomicbreaks-in-brief\" class=\"anchor\" aria-label=\"Permalink: GenomicBreaks in brief:\" href=\"#genomicbreaks-in-brief\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA pairwise alignment of two genomes is loaded in \u003ccode\u003eGBreaks\u003c/code\u003e objects wrapping\nthe \u003ccode\u003eGRanges\u003c/code\u003e class. Here is an example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eGBreaks object with 505 ranges and 2 metadata columns:\n seqnames ranges strand | score query\n \u0026lt;Rle\u0026gt; \u0026lt;IRanges\u0026gt; \u0026lt;Rle\u0026gt; | \u0026lt;numeric\u0026gt; \u0026lt;GRanges\u0026gt;\n [1] chrI 5860-10010 + | 10609 NC_047487.1:8723-12716\n [2] chrI 11157-11782 + | 1253 NC_047487.1:13035-13680\n [3] chrI 25371-26528 + | 3651 NC_047487.1:15380-16537\n [4] chrI 26849-29699 + | 6130 NC_047487.1:17263-20175\n [5] chrI 29937-30607 + | 767 NC_047487.1:20203-20866\n ... ... ... ... . ... ...\n [501] chrM 67039-67870 + | 1441 NC_018044.1:7758-8439\n [502] chrM 68205-68580 + | 830 NC_018044.1:8783-9180\n [503] chrM 69178-76168 + | 14528 NC_018044.1:9650-16261\n [504] chrM 77356-80022 + | 8066 NC_018044.1:53206-55865\n [505] chrM 80919-85779 + | 6712 NC_018044.1:57885-61592\n -------\n seqinfo: 17 sequences (1 circular) from sacCer3 genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee \u201c\u003cem\u003eGet started\u003c/em\u003e\u201d on \u003ca href=\"https://oist.github.io/GenomicBreaks\" rel=\"nofollow\"\u003ehttps://oist.github.io/GenomicBreaks\u003c/a\u003e for further details.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etransXpress-nextflow\u003c/h1\u003e\u003ca id=\"user-content-transxpress-nextflow\" class=\"anchor\" aria-label=\"Permalink: transXpress-nextflow\" href=\"#transxpress-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etransXpress: a \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e pipeline for rapid de novo transcriptome assembly and annotation\u003c/p\u003e\n\u003cp\u003eAlso see our sister project: \u003ca href=\"https://github.com/transXpress/transXpress-snakemake\"\u003etransXpress-snakemake\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntro\u003c/h2\u003e\u003ca id=\"user-content-intro\" class=\"anchor\" aria-label=\"Permalink: Intro\" href=\"#intro\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequires\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextFlow 20.04.0+ (install via conda)\u003c/li\u003e\n\u003cli\u003efastqc (install via conda)\u003c/li\u003e\n\u003cli\u003etrimmomatic (install via conda)\u003c/li\u003e\n\u003cli\u003eTrinity (install via conda)\u003c/li\u003e\n\u003cli\u003eSPAdes (install via conda)\u003c/li\u003e\n\u003cli\u003eTransDecoder (install via conda)\u003c/li\u003e\n\u003cli\u003eBioPython (install via conda)\u003c/li\u003e\n\u003cli\u003esamtools (install via conda)\u003c/li\u003e\n\u003cli\u003ebowtie2 (install via conda)\u003c/li\u003e\n\u003cli\u003einfernal (install via conda)\u003c/li\u003e\n\u003cli\u003eHMMER (install via conda)\u003c/li\u003e\n\u003cli\u003ekallisto (install via conda)\u003c/li\u003e\n\u003cli\u003eNCBI BLAST+ (install via conda)\u003c/li\u003e\n\u003cli\u003eR (install via conda)\u003c/li\u003e\n\u003cli\u003eseqkit (install via conda)\u003c/li\u003e\n\u003cli\u003ebasic Linux utitilies: wget, split, awk, cut, gzip\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?deeploc\" rel=\"nofollow\"\u003edeeploc\u003c/a\u003e / \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/sw_request?signalp+4.1\" rel=\"nofollow\"\u003eSignalP 4.1\u003c/a\u003e / \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?signalp\" rel=\"nofollow\"\u003eSignalP 5.0\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cbs.dtu.dk/services/TMHMM/\" rel=\"nofollow\"\u003etmhmm v. 2.0\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ca href=\"https://conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eMiniconda3\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSetup conda environment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name transxpress-nf\nconda activate transxpress-nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall conda dependencies:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e conda config --add channels bioconda\n conda config --add channels conda-forge\n conda config --add channels r\n conda config --set channel_priority false\n conda install -y nextflow fastqc trimmomatic \"trinity\u0026gt;=2.13.2\" \"spades\u0026gt;=3.15.4\" \"transdecoder\u0026gt;=5.5.0\" biopython samtools bowtie2 infernal hmmer kallisto blast r r-tidyverse seqkit bioconductor-edger parallel graphviz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(Note, below dependencies are optional, transXpress will run to completion without them, but will produce empty files for their output)\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eInstall deeploc (performance being evaluated by transXpress developers in comparison to SingalP 4.1/5.0)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload deeploc from \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?deeploc\" rel=\"nofollow\"\u003ehttp://www.cbs.dtu.dk/cgi-bin/nph-sw_request?deeploc\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall dependencies: \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall deeploc: \u003ccode\u003epython setup.py install\u003c/code\u003e or locally: \u003ccode\u003epython setup.py install --user\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall SignalP 4.1g (performance being evaluated by transXpress developers in comparison to SingalP 5.0/deeploc)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload SignalP 4.1g from \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/sw_request?signalp+4.1\" rel=\"nofollow\"\u003ehttp://www.cbs.dtu.dk/cgi-bin/sw_request?signalp+4.1\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall SignalP 5.0 (performance being evaluated by transXpress developers in comparison to SingalP 4.1/deeploc)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload SignalP 5.0 from \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?signalp\" rel=\"nofollow\"\u003ehttp://www.cbs.dtu.dk/cgi-bin/nph-sw_request?signalp\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall tmhmm\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload tmhmm from \u003ca href=\"http://www.cbs.dtu.dk/services/TMHMM/\" rel=\"nofollow\"\u003ehttp://www.cbs.dtu.dk/services/TMHMM/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake your assembly directory and change it to the current directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir your_assembly_directory\ncd your_assembly_directory\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSetup the mandatory \u0027samples.tsv\u0027 file in the assembly directory describing where to find your raw read FASTQ files. Reads will be pooled from all samples for a single transcriptome assembly, but expression quantification will happen on a per-sample basis. See the tests directory for an example of a samples file: \u003ca href=\"./tests/test_nonSS-trinity/samples.tsv\"\u003esamples.tsv\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSetup the mandatory \u0027prefix.txt\u0027 file in the directory describing which genus species the data comes from, or whichever metadata you prefer to add. See the tests directory for an example of a species file: \u003ca href=\"./tests/test_nonSS-trinity/prefix.txt\"\u003eprefix.txt\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSymbolically link the transxpress-nextflow code into your assembly directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eln -s /your/transxpress-nextflow-cloned-directory/* ./\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure your conda \u003ccode\u003etransxpress\u003c/code\u003e environment has been sourced, and then execute the run.sh script with your assembler and profile of choice. You can choose your execution/cluster platform by setting the \u003ccode\u003e--executor\u003c/code\u003e parameter, e.g. \u003ccode\u003elocal\u003c/code\u003e or \u003ccode\u003epbs\u003c/code\u003e\nNote: For the cluster, depending on how strict your cluster is, you may need to tweak \u003ccode\u003ecluster.config\u003c/code\u003e quite a bit.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -w work-$ASSEMBLER -profile $THEPROFILE --assembler $ASSEMBLER --samples \u0027samples.tsv\u0027 --prefix_add_metadata_file \u0027prefix.txt\u0027 -resume\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNextFlow only likes 1 assembly per directory, so if you\u0027d like to run two assemblies simultaneously, you have to use different assembly directories.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAssemblers\u003c/h2\u003e\u003ca id=\"user-content-assemblers\" class=\"anchor\" aria-label=\"Permalink: Assemblers\" href=\"#assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently \u0027trinity\u0027 or \u0027rnaspades\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProfiles\u003c/h2\u003e\u003ca id=\"user-content-profiles\" class=\"anchor\" aria-label=\"Permalink: Profiles\" href=\"#profiles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe 2nd parameter for the ./run.sh wrapper script allows you to specify the profile that is used. The profiles (stored in the \u003ccode\u003enextflow.config\u003c/code\u003e file) are currently used to configure the execution mode (cluster vs local), and if the assembly is strand specific or not.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./run.sh trinity strandSpecific\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAvailable profiles are as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003estrandSpecific\nnotStrandSpecific\ntest_notStrandSpecific\ntest_strandSpecific\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning tests\u003c/h2\u003e\u003ca id=\"user-content-running-tests\" class=\"anchor\" aria-label=\"Permalink: Running tests\" href=\"#running-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ./tests/\ncd ./test_nonSS-trinity ##non strand specific assembly using trinity. Other directories have other assemblers / parameters.\n./run_test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFlow graph\u003c/h2\u003e\u003ca id=\"user-content-flow-graph\" class=\"anchor\" aria-label=\"Permalink: Flow graph\" href=\"#flow-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./tests/test_nonSS-trinity/test_nonSS_dag.svg\"\u003e\u003cimg src=\"./tests/test_nonSS-trinity/test_nonSS_dag.svg\" alt=\"Directed acyclic graph for transXpress-nextflow program execution\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 4, "subscribers_count": 3, - "topics": [ - "comparative-genomics", - "r" - ], - "updated_at": 1722832660.0 + "topics": [], + "updated_at": 1695825114.0 }, { "data_format": 2, - "description": "Machine Learning model to reconstruct 4D-CT in real-time from cine-MR acquired during radiation therapy. Aimed to enhance treatment precision and adaptability.", + "description": "Hidden RNA profiles of cells in the tumor microenvironment accurately revealed by malignant cell fraction-informed deconvolution", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "nnaakkaaii/RealTime-4DCT-Reconstruction", + "full_name": "tgac-vumc/OncoBLADE", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRealTime-4DCT-Reconstruction (rt4r)\u003c/h1\u003e\u003ca id=\"user-content-realtime-4dct-reconstruction-rt4r\" class=\"anchor\" aria-label=\"Permalink: RealTime-4DCT-Reconstruction (rt4r)\" href=\"#realtime-4dct-reconstruction-rt4r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMachine Learning model to reconstruct 4D-CT in real-time from cine-MR acquired during radiation therapy. Aimed to enhance treatment precision and adaptability.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse singularity\u003c/h2\u003e\u003ca id=\"user-content-use-singularity\" class=\"anchor\" aria-label=\"Permalink: Use singularity\" href=\"#use-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ebuild\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --fakeroot rt4r.sif Singularity.def\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/OncoBLADE/blob/main/logo.png\"\u003e\u003cimg width=\"300\" height=\"424\" src=\"https://github.com/tgac-vumc/OncoBLADE/raw/main/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOncoBLADE: Malignant cell fraction-informed deconvolution\u003c/h1\u003e\u003ca id=\"user-content-oncoblade-malignant-cell-fraction-informed-deconvolution\" class=\"anchor\" aria-label=\"Permalink: OncoBLADE: Malignant cell fraction-informed deconvolution\" href=\"#oncoblade-malignant-cell-fraction-informed-deconvolution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOncoBLADE is a Bayesian deconvolution method designed to estimate cell type-specific gene expression profiles and fractions from bulk RNA profiles of tumor specimens by integrating prior knowledge on cell fractions. You can find the \u003ca href=\"https://www.researchsquare.com/article/rs-4252952/v1\" rel=\"nofollow\"\u003epreprint of OncoBLADE at Research Square\u003c/a\u003e.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/OncoBLADE/blob/main/VisualAbstract.png\"\u003e\u003cimg width=\"75%\" height=\"75%\" src=\"https://github.com/tgac-vumc/OncoBLADE/raw/main/VisualAbstract.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDemo notebook is available under \u003ccode\u003ejupyter\u003c/code\u003e. See below how to open it.\u003c/h4\u003e\u003ca id=\"user-content-demo-notebook-is-available-under-jupyter-see-below-how-to-open-it\" class=\"anchor\" aria-label=\"Permalink: Demo notebook is available under jupyter. See below how to open it.\" href=\"#demo-notebook-is-available-under-jupyter-see-below-how-to-open-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSystem Requirements\u003c/h2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-label=\"Permalink: System Requirements\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Requirements\u003c/h3\u003e\u003ca id=\"user-content-hardware-requirements\" class=\"anchor\" aria-label=\"Permalink: Hardware Requirements\" href=\"#hardware-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOncoBLADE can run on the minimal computer spec, such as Binder (1 CPU, 2GB RAM on Google Cloud), when data size is small. However, OncoBLADE can significantly benefit from the larger amount of CPUs and RAM.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOS Requirements\u003c/h3\u003e\u003ca id=\"user-content-os-requirements\" class=\"anchor\" aria-label=\"Permalink: OS Requirements\" href=\"#os-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package development version is tested on Linux operating systems. (CentOS 7 and Ubuntu 16.04).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing pip\u003c/h3\u003e\u003ca id=\"user-content-using-pip\" class=\"anchor\" aria-label=\"Permalink: Using pip\" href=\"#using-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package of BLADE is available on pip.\nYou can simply (takes only \u0026lt;1min):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install OncoBLADE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe tested BLADE with \u003ccode\u003epython =\u0026gt; 3.6\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Conda\u003c/h3\u003e\u003ca id=\"user-content-using-conda\" class=\"anchor\" aria-label=\"Permalink: Using Conda\" href=\"#using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne can create a conda environment contains BLADE and also other dependencies to run \u003ca href=\"https://github.com/tgac-vumc/BLADE/blob/master/jupyter/BLADE%20-%20Demo%20script.ipynb\"\u003eDemo\u003c/a\u003e.\nThe environment definition is in \u003ca href=\"https://github.com/tgac-vumc/BLADE/environment.yml\"\u003eenvironment.yml\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Installing Miniconda 3\u003c/h3\u003e\u003ca id=\"user-content-step-1-installing-miniconda-3\" class=\"anchor\" aria-label=\"Permalink: Step 1: Installing Miniconda 3\" href=\"#step-1-installing-miniconda-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, please open a terminal or make sure you are logged into your Linux VM. Assuming that you have a 64-bit system, on Linux, download and install Miniconda 3 with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn MacOS X, download and install with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh\nbash Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Create a conda environment\u003c/h3\u003e\u003ca id=\"user-content-step-2-create-a-conda-environment\" class=\"anchor\" aria-label=\"Permalink: Step 2: Create a conda environment\" href=\"#step-2-create-a-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install all the necessary dependency using the following command (may takes few minutes; \u003ccode\u003emamba\u003c/code\u003e is quicker in general).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create --file environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, the \u003ccode\u003eOncoBLADE\u003c/code\u003e environment can be activate by:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate OncoBLADE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Running a demo script\u003c/h3\u003e\u003ca id=\"user-content-step-3-running-a-demo-script\" class=\"anchor\" aria-label=\"Permalink: Step 3: Running a demo script\" href=\"#step-3-running-a-demo-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can find a demo script under \u003ccode\u003ejupyter\u003c/code\u003e folder.\nYou can open the script using the command below after activating the \u003ccode\u003eOncoBLADE\u003c/code\u003e environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejupyter notebook jupyter/OncoBLADE\\ -\\ Demo script.ipynb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of OncoBLADE (In progress)\u003c/h2\u003e\u003ca id=\"user-content-overview-of-oncoblade-in-progress\" class=\"anchor\" aria-label=\"Permalink: Overview of OncoBLADE (In progress)\" href=\"#overview-of-oncoblade-in-progress\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the OncoBLADE package, you can load the following functions and modules.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eoncoBLADE\u003c/code\u003e: A class object contains core algorithms of \u003ccode\u003eOncoBLADE\u003c/code\u003e, an extended version of \u003ccode\u003eBLADE\u003c/code\u003e. Users can reach internal variables (\u003ccode\u003eNu\u003c/code\u003e, \u003ccode\u003eOmega\u003c/code\u003e, and \u003ccode\u003eBeta\u003c/code\u003e) and functions for calculating objective functions (ELBO function) and gradients with respect to the variational parameters. There also is an optimization function (\u003ccode\u003eoncoBLADE.Optimize()\u003c/code\u003e) for performing L-BFGS optimization. OncoBLADE features an iterative update to optimize hyperparameter \u003ccode\u003eAlpha\u003c/code\u003e and integration of prior expectation of subset of cell types. In BLADE, \u003ccode\u003eAlpha\u003c/code\u003e is a user-defined hyperparameter.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo run classic BLADE and OncoBLADE, we provide main functinos \u003ccode\u003eFramework\u003c/code\u003e for BLADE and \u003ccode\u003eFramework_Iterative\u003c/code\u003e for OncoBLADE.\u003c/p\u003e\n\u003cp\u003eSee below to obtain the current estimate of cellualr fractions, gene expression profiles per cell type and per sample:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eExpF(self.Beta)\u003c/code\u003e : returns a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNgene\u003c/code\u003e matrix contains estimated fraction of each cell type in each sample.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eself.Nu\u003c/code\u003e: a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e multidimensional array contains estimated gene expression levels of each gene in each cell type for each sample.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enumpy.mean(self.Nu,0)\u003c/code\u003e: To obtain a estimated gene expression profile per cell type, we can simply take an average across the samples.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eFramework_Iterative\u003c/code\u003e: OncoBLADE framework based on the \u003ccode\u003eBLADE\u003c/code\u003e class module above. Users need to provide the following input/output arguments.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInput arguments\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eX\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains average gene expression profiles per cell type (a signature matrix) in log-scale.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estdX\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains standard deviation per gene per cell type (a signature matrix of gene expression variability).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eY\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNsample\u003c/code\u003e matrix contains bulk gene expression data. This should be in linear-scale data without log-transformation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eExpectation\u003c/code\u003e: a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains the expected cell fraction used to inform OncoBLADE [Optional]\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInd_Marker\u003c/code\u003e: Index for marker genes. By default, \u003ccode\u003e[True]*Ngene\u003c/code\u003e (all genes used without filtering). For the genes with \u003ccode\u003eFalse\u003c/code\u003e they are excluded in the first phase (Empirical Bayes) for finidng the best hyperparameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInd_sample\u003c/code\u003e: Index for the samples used in the first phase (Empirical Bayes). By default, \u003ccode\u003e[True]*Nsample\u003c/code\u003e (all samples used).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAlpha\u003c/code\u003e, \u003ccode\u003eAlpha0\u003c/code\u003e, \u003ccode\u003eKappa0\u003c/code\u003e and \u003ccode\u003eSY\u003c/code\u003e: hyperparameters used in the model. \u003ccode\u003eAlpha\u003c/code\u003e is also optimzed, while others are fixed. By default, \u003ccode\u003eAlpha=1\u003c/code\u003e, \u003ccode\u003eAlpha0=0.1\u003c/code\u003e, \u0027Kappa0=1\u003ccode\u003e, \u003c/code\u003esY=1`.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eIterMax\u003c/code\u003e: Number of maximum iteration between variational parameter optimization by L-BFGS and updating hyperparameter \u003ccode\u003eAlpha\u003c/code\u003e. By default, \u003ccode\u003eiterMax=100\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNrep\u003c/code\u003e: Number of random initial guess used to run OncoBLADE. The best in terms of ELBO function will be chosed among the local optimum. By default, \u003ccode\u003eNrep=3\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNjob\u003c/code\u003e: Number of jobs executed in parallel. By default, \u003ccode\u003eNjob=10\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOutput values\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efinal_obj\u003c/code\u003e: A final \u003ccode\u003eBLADE\u003c/code\u003e object with optimized variational parameters and hyperparameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003econv\u003c/code\u003e: The ELBO function value (i.e., local optimum) for the final \u003ccode\u003eBLADE\u003c/code\u003e object.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eouts\u003c/code\u003e: \u003ccode\u003eNrep\u003c/code\u003e BLADE objects optimized with all random initial guesses with their final ELBO values.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTBD\u003c/h3\u003e\u003ca id=\"user-content-tbd\" class=\"anchor\" aria-label=\"Permalink: TBD\" href=\"#tbd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFunctions for purification: \u003ccode\u003eParallel_Purification\u003c/code\u003e, \u003ccode\u003ePurify_AllGenes\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1725367906.0 + "updated_at": 1726145523.0 }, { "data_format": 2, "description": null, "filenames": [ - "spark/sing/spark/Singularity2", - "spark/sing/spark/Singularity" + "applications/interactive/Singularity", + "applications/compress/Singularity", + "applications/jupyter-lab-hpc-openmpi/Singularity", + "applications/opensees-mp/opensees-mp-3.5.0/Singularity", + "applications/extract/Singularity", + "applications/jupyter-lab-hpc/Singularity", + "applications/jupyter-lab-hpc/gpu/Singularity", + "applications/hello-world/Singularity", + "applications/rstudio/nginx/Singularity.conf", + "applications/opensees-sp/opensees-sp-3.5.0/Singularity" ], - "full_name": "ExposuresProvider/FHIR-PIT", - "latest_release": "v1.0", - "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.com/NCATS-Tangerine/FHIR-PIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/43a9c4b529eae28a760191f2b192bf66ee2f7028f6de94751f42850b73fdb057/68747470733a2f2f7472617669732d63692e636f6d2f4e434154532d54616e676572696e652f464849522d5049542e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/NCATS-Tangerine/FHIR-PIT.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFHIR \u00ae PIT\u003c/h1\u003e\u003ca id=\"user-content-fhir--pit\" class=\"anchor\" aria-label=\"Permalink: FHIR \u00ae PIT\" href=\"#fhir--pit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFast Healthcare Interoperability Resources (FHIR\u00ae) is the registered trademark of HL7 and is used with the permission of HL7.\u003c/p\u003e\n\u003cp\u003eFHIR Patient data Integration Tool (FHIR PIT) is an open-source tool that uses geocodes and time stamps of varying spatiotemporal resolution to integrate clinical data (e.g., electronic health records [EHR]) with environmental exposures data from multiple public sources before stripping the data of Protected Health Information (PHI, including geocodes and time stamps) and binning feature variables to create integrated datasets, whereby rows represent individual patients or subjects and columns represent demographic variables, clinical measures, or exposure estimates. FHIR PIT is modular and extensible and can be adapted for virtually any type of data that requires geocodes and dates for integration with PHI or any Personally Identifiable Information.\u003c/p\u003e\n\u003cp\u003eFHIR PIT was inspired by and currently supports the \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/31077269/\" rel=\"nofollow\"\u003eIntegrated Clinical and Environmental Exposures Service (ICEES)\u003c/a\u003e. The tool was motivated by a need to integrate diverse sources of environmental exposures data with EHR data at the patient level, effectively embedding estimates of environmental exposures such as airborne pollutant exposures within the EHR. ICEES has proven utility as an open-source tool for exploratory analysis of integrated clinical and environmental exposures data, with diverse use-case applications, including asthma and related common pulmonary disorders, primary ciliary dyskinesia and related rare pulmonary disorders, drug-induced liver injury, and coronavirus infection. ICEES is also an integral component of the \u003ca href=\"https://ncats.nih.gov/research/research-activities/translator/about\" rel=\"nofollow\"\u003eBiomedical Data Translator (\"Translator\") System\u003c/a\u003e, funded by the National Center for Advancing Translational Sciences. For additional information on ICEES, including peer-reviewed publications, please see \u003ca href=\"https://github.com/NCATSTranslator/Translator-All/wiki/ICEES\"\u003ehttps://github.com/NCATSTranslator/Translator-All/wiki/ICEES\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFHIR PIT consists of several transformation steps, which are building blocks that can be chained together or combined in parallel to form a transformation workflow. In addition, several of these transformation steps are generic such that they can take in any data that conform to a certain format. Adding new types of data amounts to adding new transformation steps or reusing generic steps.\u003c/p\u003e\n\u003cp\u003eFHIR PIT is implemented using Apache Spark, Python, and Singularity. Spark makes it easy to parallelize and distribute the data transformation. Python is used to simplify the application interface to the transformation steps. Singularity allows the application to run portably on different machines and platforms.\u003c/p\u003e\n\u003cp\u003eFor details regarding how to build and run FHIR PIT with sample data, refer to \u003ca href=\"https://github.com/ExposuresProvider/FHIR-PIT/tree/master/spark\"\u003ehttps://github.com/ExposuresProvider/FHIR-PIT/tree/master/spark\u003c/a\u003e. FHIR PIT also has been dockerized to encapsulate the software and platform dependencies and facilitate execution and deployment (see \u003ca href=\"https://github.com/ExposuresProvider/FHIR-PIT/blob/master/Dockerfile\"\u003ehttps://github.com/ExposuresProvider/FHIR-PIT/blob/master/Dockerfile\u003c/a\u003e). A tutorial is available at \u003ca href=\"https://github.com/ExposuresProvider/FHIR-PIT/tree/master/tutorial\"\u003ehttps://github.com/ExposuresProvider/FHIR-PIT/tree/master/tutorial\u003c/a\u003e. An R-Bookdown version will soon be available.\u003c/p\u003e\n\u003cp\u003eOne important note when running FHIR PIT is that the application may be memory-intensive, depending on the size of the input data. For example, for the sample data available in the GitHub repository, the application requires 4GB RAM to run through all of the data processing and integration steps, when running Docker natively. (Docker Desktop on Windows/Mac may incur additional memory overhead.)\u003c/p\u003e\n\u003cp\u003eFor additional information on FHIR PIT, including an example use-case application, please see \u003ca href=\"https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1056-9\" rel=\"nofollow\"\u003eXu et al. 2020\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIssues are welcome! We will work to resolve any issues as quickly as possible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFunding Support\u003c/h1\u003e\u003ca id=\"user-content-funding-support\" class=\"anchor\" aria-label=\"Permalink: Funding Support\" href=\"#funding-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFHIR PIT is funded by the \u003ca href=\"https://ncats.nih.gov/\" rel=\"nofollow\"\u003eNational Center for Advancing Translational Sciences\u003c/a\u003e, under a US PHS Other Transaction Award (OT2TR003430, prototype was funded under OT3TR002020), with additional funding provided by the \u003ca href=\"https://renci.org/\" rel=\"nofollow\"\u003eRenaissance Computing Institute\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSuggested Citation\u003c/h1\u003e\u003ca id=\"user-content-suggested-citation\" class=\"anchor\" aria-label=\"Permalink: Suggested Citation\" href=\"#suggested-citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use FHIR PIT, we thank you and kindly ask that you consider citing:\u003c/p\u003e\n\u003cp\u003eXu H, Cox S, Stillwell L, Pfaff E, Champion J, Ahalt SC, Fecho K. FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data. BMC Med Inform Decis Mak. 2020;20(1):53. Published 2020 Mar 11. doi:10.1186/s12911-020-1056-9.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContact Information\u003c/h1\u003e\u003ca id=\"user-content-contact-information\" class=\"anchor\" aria-label=\"Permalink: Contact Information\" href=\"#contact-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor assistance with FHIR PIT, please contact the developer team at \u003ca href=\"mailto:fhir-pit@renci.org\"\u003efhir-pit@renci.org\u003c/a\u003e or directly click on \u003ca href=\"mailto:fhir-pit@renci.org\"\u003eContact Us\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "TACC/WMA-Tapis-Templates", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWMA-Tapis-Templates\u003c/h1\u003e\u003ca id=\"user-content-wma-tapis-templates\" class=\"anchor\" aria-label=\"Permalink: WMA-Tapis-Templates\" href=\"#wma-tapis-templates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tapis-project/tapipy/tree/main/tapipy\"\u003eTapipy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://github.com/pyenv/pyenv\"\u003epyenv\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://github.com/pyenv/pyenv-virtualenv\"\u003epyenv-virtualenv\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProvisioning a Tenant\u003c/h2\u003e\u003ca id=\"user-content-provisioning-a-tenant\" class=\"anchor\" aria-label=\"Permalink: Provisioning a Tenant\" href=\"#provisioning-a-tenant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a \u003ccode\u003eclient_secrets.py\u003c/code\u003e file with a \u003ccode\u003eCLIENT_USERNAME\u003c/code\u003e and \u003ccode\u003eCLIENT_PASSWORD\u003c/code\u003e (see client_secrets.example.py)\u003c/li\u003e\n\u003cli\u003eAdjust the tenants, systems, and apps you wish to create in \u003ccode\u003einitialize_tenant.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython initialize_tenant.py\u003c/code\u003e to create/update the apps and systems in the tenants listed in \u003ccode\u003eTENANT_BASE_URLS\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating a client\u003c/h2\u003e\u003ca id=\"user-content-creating-a-client\" class=\"anchor\" aria-label=\"Permalink: Creating a client\" href=\"#creating-a-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e(Optional) Install Tapipy in a pyenv environemnt\na. \u003ccode\u003epyenv install 3.11\u003c/code\u003e\nb. \u003ccode\u003epyenv virtualenv 3.11 tapipy\u003c/code\u003e\nc. \u003ccode\u003epyenv local tapipy\u003c/code\u003e\nc. \u003ccode\u003epip install tapipy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall ipython\na. \u003ccode\u003epip install ipython\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInitiate an ipython session\na. \u003ccode\u003eipython\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a client\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003efrom tapipy.tapis import Tapis\nclient = Tapis(base_url=\u0027https://portals.tapis.io\u0027, username=\u0027$USER\u0027, password=\u0027******\u0027)\nclient.get_tokens()\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating a credential\u003c/h2\u003e\u003ca id=\"user-content-creating-a-credential\" class=\"anchor\" aria-label=\"Permalink: Creating a credential\" href=\"#creating-a-credential\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a keypair locally\na. \u003ccode\u003essh-keygen -m PEM -t rsa -b 2048 -f ~/.ssh/$USER.frontera\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCopy the public key to your \u003ccode\u003e~/.ssh/authorized_keys\u003c/code\u003e file on the frontera host\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh $USER@frontera.tacc.utexas.edu\nPUBKEY=\"PASTE PUBLIC KEY HERE\"\necho $PUBKEY \u0026gt;\u0026gt; ~/.ssh/authorized_keys`\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCopy the public and private key to the \u003ccode\u003eUSER_CREDENTIAL_PRIVATE_KEY\u003c/code\u003e and \u003ccode\u003eUSER_CREDENTIAL_PUBLIC_KEY\u003c/code\u003e values in \u003ccode\u003eclient_secrets.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAdjust the \u003ccode\u003esystemId\u003c/code\u003e and \u003ccode\u003ebase_url\u003c/code\u003e values for your desired tenant/system and run the \u003ccode\u003ecreate_client_credential.py\u003c/code\u003e script\u003c/li\u003e\n\u003cli\u003eTest the keypair works by making a file listing on a system\na. \u003ccode\u003eclient.files.listFiles(systemId=\u0027frontera\u0027, path=\u0027/\u0027)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 4, - "subscribers_count": 4, - "topics": [ - "ncats-translator" - ], - "updated_at": 1731432040.0 + "subscribers_count": 16, + "topics": [], + "updated_at": 1726171469.0 }, { "data_format": 2, - "description": "Single cell RNA-seq quality control, transcription profile clustering \u0026 cell-type assignment", + "description": "SimPrily: A Python framework to simplify high-throughput genomic simulations. https://doi.org/10.1016/j.softx.2018.09.003", "filenames": [ - "env/Singularity.sc_qc_cluster" + "Singularity" ], - "full_name": "wtsi-hgi/nf_scrna_qc", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDescription\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline performs data QC and transcription profile clustering for droplet single cell RNA-seq. It starts from gene transcript UMI (unique molecular identfier) counts per barcoded cell. Cell-type assignments for PBMC (peripheral blood mononuclear cells) are also generated.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"docs/README-tests.md\"\u003erunning tests\u003c/a\u003e: run pipeline locally on a small test dataset\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/README-workarounds.md\"\u003epotential problems and workarounds\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/README-inputfiles.md\"\u003einput files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/README-outputfiles.md\"\u003eoutput directory structure\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCredits\u003c/h1\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe thank the following people for their contributions to the development of this pipeline:\nLeland Taylor, Matiss Ozols, Guillaume Noell, Hannes Ponstingl, Vivek Iyer, Monika Krzak, Henry Taylor, Tobi Alegbe, Moritz Przybilla\u003c/p\u003e\n", + "full_name": "agladstein/SimPrily", + "latest_release": "v1.0.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSimPrily\u003c/h1\u003e\u003ca id=\"user-content-simprily\" class=\"anchor\" aria-label=\"Permalink: SimPrily\" href=\"#simprily\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreated by Ariella Gladstein, based on code from Consuelo Quinto Cortes and Krishna Veeramah.\nAlso worked on by David Christy, Logan Gantner, and Mack Skodiak.\u003cbr\u003e\n\u003ca href=\"mailto:agladstein@email.arizona.edu\"\u003eagladstein@email.arizona.edu\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimPrily runs genome simulations with user defined parameters or parameters randomly generated by priors and computes genomic statistics on the simulation output.\u003cbr\u003e\nVersion 1\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun genome simulation with model defined by prior distributions of parameters and demographic model structure.\u003c/li\u003e\n\u003cli\u003eTake into account SNP array ascertainment bias by creating pseudo array based on priors of number of samples of discovery populations and allele frequency cut-off.\u003c/li\u003e\n\u003cli\u003eCalculate genomic summary statistics on simulated genomes and pseudo arrays.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis is ideal for use with Approximate Bayesian Computation on whole genome or SNP array data.\u003c/p\u003e\n\u003cp\u003eUses c++ programs macs and GERMLINE. For more information on these programs, see:\u003cbr\u003e\n\u003ca href=\"https://github.com/gchen98/macs\"\u003ehttps://github.com/gchen98/macs\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/sgusev/GERMLINE\"\u003ehttps://github.com/sgusev/GERMLINE\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecd to the directory you want to work in,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/agladstein/SimPrily.git\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eEnvironment Set up\u003c/h4\u003e\u003ca id=\"user-content-environment-set-up\" class=\"anchor\" aria-label=\"Permalink: Environment Set up\" href=\"#environment-set-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf using Vagrant (this is recommended if running on non-Linux OS):\u003c/p\u003e\n\u003cp\u003eStart Vagrant, ssh into Vagrant, cd to SimPrily directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\nvagrant ssh\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /vagrant\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall the virtual environment and install the requirements.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./setup/setup_env_vbox_2.7.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf not using Vagrant, just install the virtual environment and install the requirements:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./setup/setup_env_2.7.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ee.g. One Test simulation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py -p examples/eg1/param_file_eg1_asc.txt -m examples/eg1/model_file_eg1_asc.csv -g genetic_map_b37/genetic_map_GRCh37_chr1.txt.macshs -a array_template/ill_650_test.bed -i 1 -o output_dir -v\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor quick help:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput\u003c/h4\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esimprily.py\u003c/code\u003e takes 4 required arguments and 2 optional arguments, and help, verbose, and profile options.\u003c/p\u003e\n\u003cp\u003eRun as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py [-h] -p PARAM -m MODEL -i ID -o OUT [-g MAP] [-a ARRAY] [-v] [--profile]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eRequired\u003c/h5\u003e\u003ca id=\"user-content-required\" class=\"anchor\" aria-label=\"Permalink: Required\" href=\"#required\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e-p PARAM\u003c/code\u003e or \u003ccode\u003e--param PARAM\u003c/code\u003e = The location of the parameter file\u003cbr\u003e\n\u003ccode\u003e-m MODEL\u003c/code\u003e or \u003ccode\u003e--model MODEL\u003c/code\u003e = The location of the model file\u003cbr\u003e\n\u003ccode\u003e-i ID\u003c/code\u003e or \u003ccode\u003e--id ID\u003c/code\u003e = The unique identifier of the job\u003cbr\u003e\n\u003ccode\u003e-o OUT\u003c/code\u003e or \u003ccode\u003e--out OUT\u003c/code\u003e = The location of the output directory\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOptional\u003c/h5\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e-h\u003c/code\u003e or \u003ccode\u003e--help\u003c/code\u003e = shows a help message and exists\u003cbr\u003e\n\u003ccode\u003e-v\u003c/code\u003e = increase output verbosity. This includes 3 levels, \u003ccode\u003e-v\u003c/code\u003e, \u003ccode\u003e-vv\u003c/code\u003e, and \u003ccode\u003e-vvv\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003e--profile\u003c/code\u003e = Print a log file containing the time in seconds and memory use in Mb for main functions\u003cbr\u003e\n\u003ccode\u003e-g MAP\u003c/code\u003e or \u003ccode\u003e--map MAP\u003c/code\u003e = The location of the genetic map file\u003cbr\u003e\n\u003ccode\u003e-a ARRAY\u003c/code\u003e or \u003ccode\u003e--array ARRAY\u003c/code\u003e = The location of the array template file, in bed form\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput\u003c/h4\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThree subdirectories are created in the directory specified in the \u003ccode\u003eoutput_dir\u003c/code\u003e argument.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_dir/results\noutput_dir/sim_data\noutput_dir/germline_out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eIntermediate files\u003c/h5\u003e\u003ca id=\"user-content-intermediate-files\" class=\"anchor\" aria-label=\"Permalink: Intermediate files\" href=\"#intermediate-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIntermediate files go to \u003ccode\u003eoutput_dir/sim_data\u003c/code\u003e and \u003ccode\u003eoutput_dir/germline_out\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/sim_data\u003c/code\u003e contains PLINK formated .ped and .map files created from the pseudo array, which are necessary to run GERMLINE.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/germline_out\u003c/code\u003e contains the GERMLINE .match output and .log. The .match contains all of the identified IBD segments.\u003cbr\u003e\nThese files are NOT automatically removed in python script, but are unnecessary once the job is complete.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eResults files\u003c/h5\u003e\u003ca id=\"user-content-results-files\" class=\"anchor\" aria-label=\"Permalink: Results files\" href=\"#results-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutput files go to \u003ccode\u003eoutput_dir/results\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/results\u003c/code\u003e contains the parameter values used in the simulation and the summary statistics calculated from the simulation.\u003cbr\u003e\nThe first line is a header with the parameter names and summary statistics names.\nThe second line is the parameter values and summary statistics values.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOpen Science Grid\u003c/h2\u003e\u003ca id=\"user-content-open-science-grid\" class=\"anchor\" aria-label=\"Permalink: Open Science Grid\" href=\"#open-science-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMust have an Open Science Grid Connect account.\u003cbr\u003e\nCreate an account at \u003ca href=\"https://osgconnect.net/signup\" rel=\"nofollow\"\u003ehttps://osgconnect.net/signup\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLog onto Open Science Grid Connect\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh user-name@login01.osgconnect.net\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWorking directory must be \u003ccode\u003epegasus_workflow\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSubmit a Pegasus workflow (must be in \u003ccode\u003epegasus_workflow\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./submit -p PARAM -m MODEL -j NUM [-g MAP] [-a ARRAY]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./submit -p ../examples/eg2/param_file_eg2_asc.txt -m ../examples/eg2/model_file_eg2_asc.csv -j 10 -a ../array_template/ill_650_test.bed -g ../genetic_map_b37/genetic_map_GRCh37_chr1.txt.macshs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe results will appear in\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/local-scratch/user-name/workflows/simprily_id\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003euser-name\u003c/code\u003e is specific to the user, and \u003ccode\u003eid\u003c/code\u003e is the workflow id.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://agladstein.github.io/SimPrily/\" rel=\"nofollow\"\u003eMain SimPrily GitHub Repository\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://simprily.readthedocs.io/en/latest/index.html\" rel=\"nofollow\"\u003eSimPrily Read the Docs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/agladstein/SimPrily\"\u003eSimPrily Code\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"\"\u003eCyVerse Discovery Environment SimPrily Workflow\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003eSimPrily HT File Setup App\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eApp in the DE\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eGitHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eDockerHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDE SimPrily App\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eApp in the DE\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eGitHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eDockerHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eDE SimPrily Concatenation App\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eApp in the DE\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eGitHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eDockerHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data\" rel=\"nofollow\"\u003eDE SimPrily Example Data\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data/SimPrily_version1\" rel=\"nofollow\"\u003eRandomly Generated Data for App Testing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data/SimPrily_version1\" rel=\"nofollow\"\u003eHomo sapiens HapMap Data from 1000 genomes Project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data/SimPrily_version1\" rel=\"nofollow\"\u003eBrassica sp. populations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data/SimPrily_version1\" rel=\"nofollow\"\u003eSimPrily Benchmarking Data\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSimPrily Pegasus Documentation\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://agladstein.github.io/SimPrily/#open-science-grid\" rel=\"nofollow\"\u003eSimPrily Pegasus Workflow Quick Start\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://simprily.readthedocs.io/en/latest/htc.html#submit-a-pegasus-workflow\" rel=\"nofollow\"\u003eDetailed Pegasus Workflow Documentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown Issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known Issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf exponential growth is large, macs simulation will not finish. (This is a macs bug).\u003c/li\u003e\n\u003cli\u003eIf the same id is used with the same output dir as a previous run, the .map file will be appended to.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 4, "subscribers_count": 4, "topics": [], - "updated_at": 1714653728.0 + "updated_at": 1584034329.0 }, { "data_format": 2, - "description": "Assembly and differential expression analysis", + "description": "Singularity container for Tensorflow - good example for GPU integration into a container", "filenames": [ - "singularity/Singularity.multiQC", - "singularity/Singularity.star", - "singularity/Singularity.fastqc", - "singularity/Singularity.featureCounts", - "singularity/Singularity.bowtie2", - "singularity/Singularity.htseqCount", - "singularity/Singularity.trimmomatic" + "Singularity", + "Singularity.2.2" ], - "full_name": "phelelani/nf-rnaSeqCount", + "full_name": "CHPC-UofU/Singularity-tensorflow", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-rnaSeqCount\u003c/h1\u003e\u003ca id=\"user-content-nf-rnaseqcount\" class=\"anchor\" aria-label=\"Permalink: nf-rnaSeqCount\" href=\"#nf-rnaseqcount\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/phelelani/nf-rnaSeqCount/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3200715183d319ef1d7a675338007a93c09d2a7f5212b923d130a3a6be19b63d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7068656c656c616e692f6e662d726e61536571436f756e74\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/phelelani/nf-rnaSeqCount\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4c99abe6886b93af3fa47779836429f5c9f9dac96e3c220439f0e32c2ea1a2d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538462532302532302545322539372538462532302532302545322539372538422d79656c6c6f77\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B-yellow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/phelelani/nf-rnaSeqCount/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/817b8e873c94966b17b5f88fc7388afb2bbe1f45a79cc23eba14bf3c6954f923/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7068656c656c616e692f6e662d726e61536571436f756e74\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/phelelani/nf-rnaSeqCount\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/phelelani/nf-rnaSeqCount/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c718a707b4f88e6860ad99591f732b0f2dfc964128a6becfa8de7119b0f5fca8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7068656c656c616e692f6e662d726e61536571436f756e74\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/phelelani/nf-rnaSeqCount\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://bio.tools/nf-rnaseqcount\" rel=\"nofollow\"\u003ebiotools:nf-rnaseqcount\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enf-rnaSeqCount\u003c/code\u003e is a \u003ca href=\"http://nextflow.io/\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e pipeline for obtaining raw read counts for RNA-seq data using a given reference genome and annotation. To use the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline, the following dependencies are required:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstalled softwares:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity\u003c/code\u003e \u003ca href=\"https://www.singularity-hub.org/collections/770\" rel=\"nofollow\"\u003econtainers\u003c/a\u003e with the required applications/programs for executing the workflow:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-fastqc.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-featurecounts.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-htseqcount.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-multiqc.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-star.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-trimmomatic.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-bowtie2.sif\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eReference genome, annotation and indexes\n\u003cul\u003e\n\u003cli\u003eReference genome (\u003ccode\u003e.fa\u003c/code\u003e/\u003ccode\u003e.fasta\u003c/code\u003e) and genome annotation (\u003ccode\u003e.gtf\u003c/code\u003e) files.\u003c/li\u003e\n\u003cli\u003eReference genome indexes (\u003ccode\u003ebowtie2\u003c/code\u003e \u0026amp; \u003ccode\u003eSTAR\u003c/code\u003e - see \u003cem\u003e1.3.\u003c/em\u003e below on how to generate the indexes).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"nf-rnaSeqCount.png\"\u003e\u003cimg width=\"600\" src=\"nf-rnaSeqCount.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Obtaining the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline and preparing data\u003c/h2\u003e\u003ca id=\"user-content-1-obtaining-the-nf-rnaseqcount-pipeline-and-preparing-data\" class=\"anchor\" aria-label=\"Permalink: 1. Obtaining the nf-rnaSeqCount pipeline and preparing data\" href=\"#1-obtaining-the-nf-rnaseqcount-pipeline-and-preparing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you need to clone the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e repository onto you machine. You can eisther use \u003ccode\u003egit\u003c/code\u003e or \u003ccode\u003enextflow\u003c/code\u003e (see the two methods below). I recommend using \u003ccode\u003enextflow\u003c/code\u003e and creating you own \u003ccode\u003econfig\u003c/code\u003e file (will explain later) for executing the workflow in the directory of your choosing. The rest of this documentation assumes that you have used \u003ccode\u003enextflow\u003c/code\u003e to clone this workflow - If your\u0027re an expert and have used \u003ccode\u003egit\u003c/code\u003e to clone the workflow - you know what to do :)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Using nextflow\u003c/span\u003e\nnextflow pull https://github.com/phelelani/nf-rnaSeqCount\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eContent of the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enf-rnaSeqCount\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--containers \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for Singularity images and recipes (in case you want to build yourself). All downloaded images go here!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.fastqc \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.featureCounts \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.htseqCount \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.multiQC \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.star \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.trimmomatic \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.trinity \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--templates \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for extra scripts for the pipeline.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--clean_featureCounts.sh \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Script for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--clean_htseqCounts.sh \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Script for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--LICENSE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Duh!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--main.config \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# User configuration file! All inputs, outputs and options GO HERE!! ONLY file that SHOULD be modified by user!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--main.nf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Main nf-rnaSeqCount nextflow scripts.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nextflow.config \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Pipeline configuration file! DO NOT EDIT!!!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount.png \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Pipeline flow diagram\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--README.md \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Duh!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo get the \u003ccode\u003ehelp menu\u003c/code\u003e for the workflow, execute the following from anywherre on your system aftercloning the repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run nf-rnaSeqCount --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe command above will give you the following usage information and options for running the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e workflow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e====================================================================================================\n###################################### nf-rnaSeqCount v0.2 ######################################\n====================================================================================================\n\nUSAGE:\nnextflow run nf-rnaSeqCount -profile \"slurm\" --data \"/path/to/data\" --genome \"/path/to/genome.fa\" --genes \"/path/to/genes.gtf\"\n\nHELP:\nnextflow run nf-rnaSeqCount --help\n\nMANDATORY ARGUEMENTS:\n-profile STRING Executor to be used. Available options:\n\t\t\t\t\"standard\" : Local execution (no job scheduler).\n\t\t\t\t\"slurm\" : SLURM scheduler.\n--mode STRING To specify which step of the workflow you are running (see https://github.com/phelelani/nf-rnaSeqCount).\n Available options:\n\t\t\t\t\"prep.Containers\" : For downloading Singularity containers used in this workflow.\n\t\t\t\t\"prep.Indexes\" : For indexing your reference genome using STAR and Bowtie2.\n\t\t\t\t\"run.ReadQC\" : For performing general QC on your reads using FastQC. \n\t\t\t\t\"run.ReadTrimming\" : For trimming low quality bases and removing adapters from your reads using Trimmmomatic.\n\t\t\t\t\"run.ReadAlignment\" : For aligning your reads to your reference genome using STAR.\n\t\t\t\t\"run.ReadCounting\" : For counting features in your reads using HTSeq-count and featureCounts.\n\t\t\t\t\"run.MultiQC\" : For getting a summary of QC through the analysis using MultiQC.\n--data FOLDER Path to where the input data (FASTQ files) is located. Supported FASTQ files:\n\t\t\t\t[ fastq | fastq.gz | fastq.bz2 | fq | fq.gz | fq.bz2 ]\n--genome FILE The whole genome FASTA sequence. Supported FASTA files:\n\t\t\t\t[ fasta | fa | fna ]\n--genes FILE The genome annotation GFT file. Supported GTF file:\n\t\t\t\t[ gtf ]\n\nOPTIONAL ARGUEMENTS:\n--help To show this menu.\n--out FOLDER Path to where the output should be directed.\n Default: $PWD/results_nf-rnaSeqCount.\n--from STRING Specify to resume workflow from the QC or trimming step. Options:\n\t\t\t\t\"run.ReadQC\" : To resume from the QC step (default).\n\t\t\t\t\"run.ReadTrimming\" : To resume from the trimming step.\n--pairedEnd If working with paired-end FASTQ files (default).\n--singleEnd If working with single-end FASTQ files.\n--trim STRING Parameters for Trimmomatic. See http://www.usadellab.org/cms/index.php?page=trimmomatic for a more detailed use.\n The default parameters for Trimmomatic I have given you here (for both paird- and single-end sequences) are:\n\t\t\t\tFor paired-end: \"ILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10:8:true TRAILING:28 MINLEN:40\"\n\t\t\t\tFor single-end: \"ILLUMINACLIP:TruSeq3-SE.fa:2:30:10:8:true TRAILING:28 MINLEN:40\"\n--max_memory STRING Maximum memory you have access to.\n Default: \"200.GB\"\n--max_cpus STRING Maximum CPUs you have access to. \n Default: \"24\"\n--max_time STRING Maximum time you have access to. \n Default: \"24.h\"\n====================================================================================================\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.1. Download test datasets (optional)\u003c/h3\u003e\u003ca id=\"user-content-11-download-test-datasets-optional\" class=\"anchor\" aria-label=\"Permalink: 1.1. Download test datasets (optional)\" href=\"#11-download-test-datasets-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will now download the reference genome (along with its annotation file) from Ensembl. We will also download the FASTQ files from the H3ABioNet site, which we will analyse using the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e workflow. \u003cem\u003e\u003cstrong\u003eNB\u003c/strong\u003e: Skip this section if you have your own data to analyse using this workflow! This section is only for getting data to practice using the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e workflow!\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Download and decompress the mouse reference genome along with its annotation:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e## Make a directory for the reference genome:\nmkdir reference\n\n## Download the reference genome (FASTA) and annotation file (GTF) files and put them into the newlly created directory:\nwget -c -O reference/genome.fa.gz ftp://ftp.ensembl.org/pub/release-68/fasta/mus_musculus/dna/Mus_musculus.GRCm38.68.dna.toplevel.fa.gz\nwget -c -O reference/genes.gtf.gz ftp://ftp.ensembl.org/pub/release-68/gtf/mus_musculus/Mus_musculus.GRCm38.68.gtf.gz\ngunzip reference/genome.fa.gz\ngunzip reference/genes.gtf.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Download RNA-seq test dataset from H3ABioNet:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e## Make a directory for the data:\nmkdir data\n\n## Download the data:\nfor sample in sample{37..42}_R{1,2}.fastq.gz; do wget -c -O data/$sample http://h3data.cbio.uct.ac.za/assessments/RNASeq/practice/dataset/$sample; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.2. Download the \u003ccode\u003eSingularity\u003c/code\u003e containers (required to execute the pipeline):\u003c/h3\u003e\u003ca id=\"user-content-12-download-the-singularity-containers-required-to-execute-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: 1.2. Download the Singularity containers (required to execute the pipeline):\" href=\"#12-download-the-singularity-containers-required-to-execute-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode prep.Containers\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.3. Generating genome indexes.\u003c/h3\u003e\u003ca id=\"user-content-13-generating-genome-indexes\" class=\"anchor\" aria-label=\"Permalink: 1.3. Generating genome indexes.\" href=\"#13-generating-genome-indexes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo generate the \u003ccode\u003eSTAR\u003c/code\u003e and \u003ccode\u003eBowtie2\u003c/code\u003e genome indexes, run the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e## Generate STAR and Bowtie2 indexes\nnextflow run nf-rnaSeqCount -profile slurm --mode prep.Indexes --genome \"$PWD/reference/genome.fa\" --genes \"$PWD/reference/genes.gtf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe are now ready to execute the workflow!\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Executing the main \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline\u003c/h2\u003e\u003ca id=\"user-content-2-executing-the-main-nf-rnaseqcount-pipeline\" class=\"anchor\" aria-label=\"Permalink: 2. Executing the main nf-rnaSeqCount pipeline\" href=\"#2-executing-the-main-nf-rnaseqcount-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs seen on the \u003ccode\u003ehelp menu\u003c/code\u003e above, there are a couple of options that you can use with this workflow. It can become a bit tedious and confusing having to specify these commands everytime you have to execute the each section for the analysis. To make your life easier, we will create a configuration script that we will use in this tutorial (we will pass this using the \u003ccode\u003e-c\u003c/code\u003e option of \u003ccode\u003enextflow\u003c/code\u003e). You can name it whatever you want, but for now, lets call it \u003ccode\u003emyparams.config\u003c/code\u003e. We will add the mandatory arguements for now, but as you become more farmiliar with the workflow - you can experiment with other options. You can use your favourite text editor to create the \u003ccode\u003emyparams.config\u003c/code\u003e file. Copy and paste the the parameters below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eparams {\n data = \"$PWD/data\"\n genome = \"$PWD/reference/genome.fa\"\n genes = \"$PWD/reference/genes.fa\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eObviously - the above \u003ccode\u003emyparams.config\u003c/code\u003e assumes that you have been following this tutorial. If you have your data lying around somewhere in your system, you need to put the full path to where your the \u003ccode\u003edata\u003c/code\u003e, \u003ccode\u003egenome\u003c/code\u003e and \u003ccode\u003egenes\u003c/code\u003e files are. Since the \u003ccode\u003e--mode\u003c/code\u003e will keep changing, we will add this on the command as we do the analysis. Now that we have the mandatory arguements in our \u003ccode\u003emyparams.config\u003c/code\u003e, lets do some analysis\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.1. Read QC (optional):\u003c/h3\u003e\u003ca id=\"user-content-21-read-qc-optional\" class=\"anchor\" aria-label=\"Permalink: 2.1. Read QC (optional):\" href=\"#21-read-qc-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo perform the QC of your fastq files, use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.ReadQC -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.2. Read Trimming (optional):\u003c/h3\u003e\u003ca id=\"user-content-22-read-trimming-optional\" class=\"anchor\" aria-label=\"Permalink: 2.2. Read Trimming (optional):\" href=\"#22-read-trimming-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the trimming step of the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline, use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.ReadTrimming -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.3. Read Alignment:\u003c/h3\u003e\u003ca id=\"user-content-23-read-alignment\" class=\"anchor\" aria-label=\"Permalink: 2.3. Read Alignment:\" href=\"#23-read-alignment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the read alignment step of the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline, use this comman (NB: can be run with \u003ccode\u003e--from run.ReadTrimming\u003c/code\u003e if you would like to use your trimmed reads):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.ReadAlignment -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.4. Read Counting:\u003c/h3\u003e\u003ca id=\"user-content-24-read-counting\" class=\"anchor\" aria-label=\"Permalink: 2.4. Read Counting:\" href=\"#24-read-counting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step uses the \u003ccode\u003eBAM\u003c/code\u003e file outputs generated by the read alignment step! You \u003cstrong\u003eMUST\u003c/strong\u003e run STEP 2.3 (\u003ccode\u003e--mode run.ReadAlignment\u003c/code\u003e) before running this step:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.ReadCounting -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.6. Workflow QC (optional):\u003c/h3\u003e\u003ca id=\"user-content-26-workflow-qc-optional\" class=\"anchor\" aria-label=\"Permalink: 2.6. Workflow QC (optional):\" href=\"#26-workflow-qc-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step performs a Quality Check of the different pipeline steps that have been ran. You need to run at least ONE step of the pipeline to be able to run this MultiQC step!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.MultiQC -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCONGRATULATIONS for getting this far!! :) You can now explore the results and use the read counts to perform differential expression analysis!\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Explore \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e results\u003c/h2\u003e\u003ca id=\"user-content-3-explore-nf-rnaseqcount-results\" class=\"anchor\" aria-label=\"Permalink: 3. Explore nf-rnaSeqCount results\" href=\"#3-explore-nf-rnaseqcount-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e- [1] Read QC (optional) =\u0026gt; `\u0026lt;output_directory\u0026gt;/1_RQC`\n- [2] Read Trimming (optional) =\u0026gt; `\u0026lt;output_directory\u0026gt;/2_Read_Trimming`\n- [3] Read Alignment =\u0026gt; `\u0026lt;output_directory\u0026gt;/3_Read_Alignment`\n- [4] Read Counting =\u0026gt; `\u0026lt;output_directory\u0026gt;/4_Read_Counts`\n- [5] MultiQC =\u0026gt; `\u0026lt;output_directory\u0026gt;/5_MultiQC\n- [6] Workflow tracing =\u0026gt; `\u0026lt;output_directory\u0026gt;/workflow-tracing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn addition to the 5 directories created for each step in the results directory, a directory \u003ccode\u003eworkflow-tracing\u003c/code\u003e is created to monitor the resources used in each step. This directory will contain 3 files for each step (--mode) of the workflow:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount_\u0026lt;mode\u0026gt;_report.html\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount_\u0026lt;mode\u0026gt;_timeline.html\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount_\u0026lt;mode\u0026gt;_trace.txt\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese files contain detailed information on the resources (CPU, MEMORY and TIME) usage of each of the process in the different pipeline steps. The \u003ccode\u003e\u0026lt;output_directory\u0026gt;\u003c/code\u003e directory structure is summarized below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eoutput_directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--1_Read_QC\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_R1.fastqc.html .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_R1.fastqc.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_R2.fastqc.html .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_R1.fastqc.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--2_Read_Trimming\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e.1P.fastq.gz .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.1P.fastq.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e.2P.fastq.gz .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.2P.fastq.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--3_Read_Alignment\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_Aligned.out.bam .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_Aligned.out.bam\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_Log.final.out .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_Log.final.out\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_Log.out .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_Log.out\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_Log.progress.out .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_Log.progress.out\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esamplle_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_SJ.out.tab .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_SJ.out.tab\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--4_Read_Counts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--featureCounts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts_final.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts.txt.jcounts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts.txt.summary\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--htseqCounts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts_final.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--5_MultiQC\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--multiqc_data\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--multiqc_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow-tracing\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.MultiQC_{report.html,timeline.html,trace.txt}\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.ReadAlignment_{report.html,timeline.html,trace.txt}\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.ReadCounting_{report.html,timeline.html,trace.txt}\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.ReadTrimming_{report.html,timeline.html,trace.txt} \n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.ReadQC_{report.html,timeline.html,trace.txt}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNB:\u003c/strong\u003e I am working on further improving the pipleine and the associated documentation, feel free to share comments and suggestions!\u003c/p\u003e\n\u003chr\u003e\n", "stargazers_count": 4, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1710029143.0 + "updated_at": 1663000991.0 }, { "data_format": 2, - "description": "FTW", + "description": "Genome Decomposition Analysis pipeline", "filenames": [ "Singularity" ], - "full_name": "likelet/circPipe", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecircPipe\u003c/h1\u003e\u003ca id=\"user-content-circpipe\" class=\"anchor\" aria-label=\"Permalink: circPipe\" href=\"#circpipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ecircRNA analysis pipe\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/likelet/cirpipe\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad01141b67fa633bf68e8b26b221b8ad5005a1ef85ed46dcca6b4bd8091b80eb/68747470733a2f2f7472617669732d63692e6f72672f6c696b656c65742f636972706970652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/likelet/cirpipe.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/likelet/cirpipe\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f160fb58245bc682238f3e2815b5affa78cf7be3f6b10ba1335b22d921225ab7/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c696b656c65742f636972706970652e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/likelet/cirpipe.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the recent decades, circular RNA(circRNA) have came into our sight and emerged as a large class of non-coding RNAs. It thought to be predominately generated by covalently back-splicing conjunction of 3\u2019end and 5\u2019end from the same or distinct exons. circRNA involving in a variaty of biological functions in cells. Emerging evidences show that dysregulation of circRNAs are associated with several human diseases including cancer. Therefore, Systematically identification and profiling circRNAs is a fundimental process for dissecting the underlining biological mechanism of its regulation. To date, a range of tools have been developed to investigate circRNAs from high-throughput sequencing including CIRCexplorer2, CIRI, Find_circ, Mapsplice, and Segemehl. However, it not a easy thing for users to pick tools and compare the result from such tools. The most appropriate strategy is run those tools parallel and collapse the result together, and vote the candidates to improve their confidence for further experimental exploration. In addition, a remapping step is nessasary to help reduce the false postive candidates in an application scenarios. Here, we present circPipe, a nextflow-based pipeline for running multitool-based identification of circRNA from RNA-seq dataset. circPipe integrates a remapping stragegy to help filter out the circRNAs with non-reads supported in non-mismatch mode of realignment. The pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA full tutorial of CircPipe can be found at Wiki page of this project. plz go to the \u003ca href=\"https://github.com/likelet/circPipe/wiki\"\u003ehttps://github.com/likelet/circPipe/wiki\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline Steps\u003c/h3\u003e\u003ca id=\"user-content-pipeline-steps\" class=\"anchor\" aria-label=\"Permalink: Pipeline Steps\" href=\"#pipeline-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCircPipe allows you to run arbitrary pipelines among five pipelines.\nChoose between workflows by using \u003ccode\u003e--selectTools\u003c/code\u003e or not(default) .\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eStep\u003c/th\u003e\n\u003cth\u003ePipeline One\u003c/th\u003e\n\u003cth\u003ePipeline Two\u003c/th\u003e\n\u003cth\u003ePipeline Three\u003c/th\u003e\n\u003cth\u003ePipeline Four\u003c/th\u003e\n\u003cth\u003ePipeline Five\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eRaw Data QC\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eReads Alignment\u003c/td\u003e\n\u003ctd\u003eSTAR\u003c/td\u003e\n\u003ctd\u003eBWA\u003c/td\u003e\n\u003ctd\u003eBowtie2\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eReads counting\u003c/td\u003e\n\u003ctd\u003eCIRCexplorer2\u003c/td\u003e\n\u003ctd\u003eCIRI\u003c/td\u003e\n\u003ctd\u003eFind_circ\u003c/td\u003e\n\u003ctd\u003eMapsplice\u003c/td\u003e\n\u003ctd\u003eSegemehl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eData Processing (in house script)\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDifferential expression\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSummary Report\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSoftwares\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OpenGene/fastp\"\u003eFastp\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTAR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/YangLab/CIRCexplorer2\"\u003eCIRCexplorer2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lh3/bwa\"\u003eBWA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://sourceforge.net/projects/ciri\" rel=\"nofollow\"\u003eCIRI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BenLangmead/bowtie2\"\u003eBowtie2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/marvin-jens/find_circ\"\u003eFind_circ\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.netlab.uky.edu/p/bioinfo/MapSplice2\" rel=\"nofollow\"\u003eMapsplice\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.bioinf.uni-leipzig.de/Software/segemehl/\" rel=\"nofollow\"\u003eSegemehl\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ewels/MultiQC\"\u003eMultiQC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSeveral R packages for downstream analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun analysis with bed format circRNA result\u003c/h3\u003e\u003ca id=\"user-content-run-analysis-with-bed-format-circrna-result\" class=\"anchor\" aria-label=\"Permalink: run analysis with bed format circRNA result\" href=\"#run-analysis-with-bed-format-circrna-result\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run /data1/software/circPipe/alternative_mode.nf \\\n\t\t-c /data1/software/circPipe/nextflow.config \\\n\t\t-with-trace -profile docker,colonCancer \\\n\t\t-resume --selectTools 1,2,3 --bedDir \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eplace to your bedfiles\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t\t--skip_fastp \\\n\t\t--skipDE \\\n\t\t--container likelet/circpipe:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun \u003ccode\u003eQuantCircle\u003c/code\u003e analysis with a single bed file\u003c/h3\u003e\u003ca id=\"user-content-run-quantcircle-analysis-with-a-single-bed-file\" class=\"anchor\" aria-label=\"Permalink: run QuantCircle analysis with a single bed file\" href=\"#run-quantcircle-analysis-with-a-single-bed-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run /data1/software/circPipe/QuantCircle.nf \\\n\t\t-with-trace -profile docker \\\n\t\t--container likelet/circpipe:latest \\\n\t\t--reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eFastq/*{1,2}.fq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --bedfile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eciri_pos20190613.candidates.bed\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t\t--genomefile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eGenome/hg19_chr2.fa\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t --hisat2_index \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eGenome/hg19_chr2_hisat2_index/hg19_chr2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "sanger-tol/gda", + "latest_release": "v1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGDA\u003c/h1\u003e\u003ca id=\"user-content-gda\" class=\"anchor\" aria-label=\"Permalink: GDA\" href=\"#gda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenome Decomposition Analysis for the characterisation of genome architecture\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat is GDA?\u003c/h3\u003e\u003ca id=\"user-content-what-is-gda\" class=\"anchor\" aria-label=\"Permalink: What is GDA?\" href=\"#what-is-gda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGDA (Genome Decomposition Analysis) is a bioinformatic pipeline to analyse genome architecture. Using, as a minimum, a genome assembly (the more complete the better), it will determine features in non-overlapping windows across the sequence and identify windows with common features. The assembly will then be annotated based on these similarities, highlighting structurally similar genomic regions.\u003c/p\u003e\n\u003cp\u003eGDA is developed by Eerik Aunin (\u003ca href=\"mailto:ea10@sanger.ac.uk\"\u003eea10@sanger.ac.uk\u003c/a\u003e) and Adam Reid (\u003ca href=\"mailto:ajr236@cam.ac.uk\"\u003eajr236@cam.ac.uk\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eA manuscript describing GDA is has been published in BMC Genomics:\u003cbr\u003e\n\u003ca href=\"https://trebuchet.public.springernature.app/get_content/7bf5d51e-3e6d-4724-af60-2e90fb074510\" rel=\"nofollow\"\u003eCharacterising genome architectures using genome decomposition analysis.\u003c/a\u003e\u003cbr\u003e\nAunin E, Berriman M, Reid AJ.\u003cbr\u003e\nBMC Genomics. 2022 May 25;23(1):398. doi: 10.1186/s12864-022-08616-3.\u003cbr\u003e\nPMID: 35610562\u003c/p\u003e\n\u003cp\u003eComplete analyses presented in the manuscript are available here: \u003ca href=\"https://drive.google.com/drive/folders/1XSNS_Jj0_UGxPXpzY-EwbzABGxaZgfRf?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1XSNS_Jj0_UGxPXpzY-EwbzABGxaZgfRf?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a diagram for a quick overview of what GDA does.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/Figure_1.png\"\u003e\u003cimg src=\"images/Figure_1.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e(A) Features sets are derived from the genome reference sequence (seq), repeat finding (rep), gene annotations (gene) and evolutionary relationships between genes (orth). Values for each feature are determined for each non-overlapping window of e.g. 5kb across the genome. (B) The resulting matrix of feature values per window is embedded in two dimensions and clustered to identify groups of windows with similar properties. (C) The data can be explored in a number of ways using a web-browser based app. The clustering labels are mapped back to the chromosomes to highlight architectural features and a heatmap displays the features which define the clusters.\u003c/p\u003e\n\u003cp\u003eA more technical diagram of the components of the pipeline in the form of a flowchart can be seen \u003ca href=\"images/gda_pipeline_flowchart.png\"\u003ehere\u003c/a\u003e.\nA \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e-based pipeline that includes various third party tools extracts the values of a set of genomic variables that describe a genome assembly. The values of genomic variables along chromosomes are stored as \u003ca href=\"https://genome.ucsc.edu/goldenPath/help/bedgraph.html\" rel=\"nofollow\"\u003ebedgraph files\u003c/a\u003e. The bedgraph files corresponding to one genome assembly are then merged into one tab separated values (TSV) file. In the following text, this file is referred to as \"merged TSV\" file. Scaling of values, dimensionality reduction with \u003ca href=\"https://umap-learn.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eUMAP\u003c/a\u003e and clustering with \u003ca href=\"https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html\" rel=\"nofollow\"\u003eHDBSCAN\u003c/a\u003e are then applied to the numbers in this TSV file. The locations of clusters along chromosomes are stored in a BED file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGDA software consists of three main parts: a genomic feature extraction pipeline, clustering scripts, and a \u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e app for viewing the results. The genomic feature extraction pipeline and the clustering scripts have been tested on a Linux server (Sanger farm) and have the following requirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePython3\u003c/li\u003e\n\u003cli\u003eJava \u2013 with enough memory to initialise the Java virtual machine\u003c/li\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Shiny app for viewing clustering results requires R and a number of R libraries. It has been tested on MacOS and Kubuntu Linux.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNotes\u003c/h3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe expect that the GDA feature extraction and analysis pipeline is run remotely on a compute cluster with Linux. Viewing the results of a GDA analysis is done in a Shiny app that runs in a web browser and thus we recommend that you copy your results onto your local machine to run the final step. Thus, some dependencies are required remotely and some locally (installation instructions below).\u003c/p\u003e\n\u003cp\u003eThe quick start tutorial will show you how to run the GDA pipeline end-to-end with test data (\u003cem\u003ePlasmodium falciparum\u003c/em\u003e genome assembly \u003ca href=\"https://plasmodb.org/common/downloads/release-49/Pfalciparum3D7/fasta/data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\" rel=\"nofollow\"\u003eobtained from PlasmoDB\u003c/a\u003e) and default parameters. In reality you will likely want to add additional, optional tracks such as gene annotations, repeat finding, transcriptome data and orthology information (these are also detailed below).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial\u003c/h2\u003e\u003ca id=\"user-content-tutorial\" class=\"anchor\" aria-label=\"Permalink: Tutorial\" href=\"#tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContents\u003c/h3\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#quick-start-with-test-data\"\u003eQuick start with test data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-results-tabs\"\u003eUnderstanding the results tabs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#view-clusters-and-significant-tracks-in-igv\"\u003eView clusters and significant tracks in IGV\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-output-files\"\u003eUnderstanding the output files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#adding-optional-feature\"\u003eAdding optional features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#optimising-clustering\"\u003eOptimising clustering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-default-features\"\u003eUnderstanding the default features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#other-output\"\u003eOther output\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#using-gda-singularity-image\"\u003eUsing GDA Singularity image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#clustering-the-features-of-multiple-genomes-at-once\"\u003eClustering the features of multiple genomes at once\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ideas-for-analysis\"\u003eIdeas for analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuick start with test data\u003c/h3\u003e\u003ca id=\"user-content-quick-start-with-test-data\" class=\"anchor\" aria-label=\"Permalink: Quick start with test data\" href=\"#quick-start-with-test-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e1. Set up a GDA conda environment on the farm (need to install conda? \u2013 \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ehttps://docs.conda.io/en/latest/miniconda.html\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone the GitHub repository\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun the conda installation script (this can take a little while)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003epython gda/create_gda_conda_env.py gda_env gda_downloads gda\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInitiate the conda environment:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003econda activate gda_env\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf the conda installation does not work for you, you can try using the GDA \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image instead, see \u003ca href=\"#using-gda-singularity-image\"\u003eUsing GDA Singularity image\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Run GDA\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun GDA\u2019s feature extraction pipeline with test data (we suggest that you submit this to your cluster as a job with 12 threads and 10Gb memory; expect it to take ~15 minutes with the test data):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to Load Sharing Facility (LSF)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0The results will be in the folder: \u003ccode\u003egda_pipeline_run\u003c/code\u003e. The output file required for clustering is:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCluster genome windows and analyse clusters (Use 1 thread and 10Gb memory; this should take ~1 minute; n.b. optimised clustering parameters are provided here)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda clustering -c 100 -n 5 gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n1 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_clustering_test.o -e gda_clustering_test.e \"gda clustering -c 100 -n 5 gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0The clustering output will be in a folder called: \u003ccode\u003egda_out\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Install dependencies on your local machine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMOVE TO YOUR LOCAL MACHINE (e.g. your desktop/laptop)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSet up environment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0These are the required R libraries:\u003c/p\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0shiny, ggplot2, devtools, svglite, gplots, rjson, reshape2, gridExtra, scales\u003c/p\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0If you have an R installation on your local machine that is not conda-based, the following R script should install the required libraries:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\n\ngda/gda_shiny/install_gda_shiny_dependencies_without_conda.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Alternatively, the following commands can be used to install a custom conda R environment for the GDA Shiny app:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\n\n# update conda to v4.10.1\nconda update -n base conda\n\nconda create -n gda_env_local r-essentials r-base\n\nconda activate gda_env_local\n\nconda install --yes -c r -c conda-forge r-shiny=1.5.0 r-ggplot2=3.2.1 r-gplots=3.0.3 r-rjson=0.2.20 r-reshape2=1.4.3 r-gridextra=2.3 r-scales=1.0.0 r-svglite=1.2.3\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCopy the data from the remote machine to your local machine (while on you local machine) e.g.\n\u003ccode\u003escp -r \u0026lt;user\u0026gt;@\u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_out/ .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0In order to use scp to copy the files, you will need to be able to see the remote machine (perhaps via VPN).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. View results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe required argument for the \u003ccode\u003egda_shiny.py\u003c/code\u003e script is a path to a \u003ccode\u003egda_out\u003c/code\u003e folder (that comes from the output of \u003ccode\u003egda_clustering.py\u003c/code\u003e and which you just copied from the remote machine).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 gda/gda_shiny/gda_shiny.py gda_out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnderstanding the results tabs\u003c/h3\u003e\u003ca id=\"user-content-understanding-the-results-tabs\" class=\"anchor\" aria-label=\"Permalink: Understanding the results tabs\" href=\"#understanding-the-results-tabs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUMAP plot\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/01_gda_shiny_umap.png\"\u003e\u003cimg src=\"images/01_gda_shiny_umap.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis shows you how well the clustering worked. Each point in the plot represents a genomic window. Windows are coloured by cluster. Cluster -1 (grey) is used for unclustered windows. Based on the nature of the genome, the features used, the window size and other parameters, there may, for example, be several very distinct, tight clusters, or perhaps a single diffuse cloud of points. Distinct, tight clusters suggest that GDA has identified regions of the genome which are clearly similar to each other and distinct from other regions. A single diffuse cloud means that there were not strong similarities or differences between subsets of the windows. There might be a lot of the genome which is unclassified (grey) or it might all be included in clusters. Sliders can be used to adjust plots for better viewing and PNG or SVG images can be saved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster locations\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/02_gda_shiny_raster_plot.png\"\u003e\u003cimg src=\"images/02_gda_shiny_raster_plot.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eEach chromosome/scaffold/contig is shown, with each window coloured based on the clustering. Therefore, this shows how the clusters pattern the chromosomes and, for example, whether a particular cluster tends to be found at the end of chromosomes. Do all chromosomes have a similar pattern? Do sex chromosomes, B chromosomes etc. look distinct from the autosomes?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster heatmaps\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/03_gda_shiny_cluster_heatmaps.png\"\u003e\u003cimg src=\"images/03_gda_shiny_cluster_heatmaps.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGDA determines features which have high or low values for windows in a particular cluster compared to other clusters. The heatmap in this tab shows the relative values across clusters for each significantly variable feature. Green means a feature has a relatively high value in a particular cluster, red a relatively low value. You can find the exact values and which were significantly different in the \u201cFeature tables\u201d tab. Adjusting the plot height and the label size can be particularly useful in this tab so that the heatmap is legible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeature tables\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/04_gda_shiny_feature_tables.png\"\u003e\u003cimg src=\"images/04_gda_shiny_feature_tables.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab has a table for each cluster (and unclustered windows), describing which features have significantly higher or lower values (by the Kolmogorov-Smirnov test). The default p-value cutoff for the Kolmogorov-Smirnov test is 1e-20.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster positions across chromosomes\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/05_gda_shiny_cluster_positions_across_chromosomes.png\"\u003e\u003cimg src=\"images/05_gda_shiny_cluster_positions_across_chromosomes.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab shows where each cluster tends to occur across the sequences. It helps you to see whether a cluster tends to occur at the ends or in the middles of chromosomes for instance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChromosome cluster composition\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/06_gda_shiny_chromosome_cluster_composition.png\"\u003e\u003cimg src=\"images/06_gda_shiny_chromosome_cluster_composition.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab contains a heatmap which clusters chromosomes by their cluster composition. Chromosomes which have similar proportions of each cluster will be closer together in the heatmap. This helps in identifying outliers which might represent interesting sequences such as sex chromosomes, B chromosomes etc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster junction counts\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/07_gda_shiny_cluster_junction_counts.png\"\u003e\u003cimg src=\"images/07_gda_shiny_cluster_junction_counts.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab shows the observed counts of junctions between windows belonging to each UMAP+HDBSCAN cluster. Junctions between windows belonging to the same type of cluster are included in the counts. The observed counts are compared with counts expected if windows were distributed randomly. Junctions with counts that are significantly different from what is expected by chance (based on Fisher test) are shown in \u003cstrong\u003e\u003cem\u003ebold+italics\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eView clusters and significant tracks in IGV\u003c/h3\u003e\u003ca id=\"user-content-view-clusters-and-significant-tracks-in-igv\" class=\"anchor\" aria-label=\"Permalink: View clusters and significant tracks in IGV\" href=\"#view-clusters-and-significant-tracks-in-igv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe values of variables along chromosomes are stored as \u003ca href=\"https://genome.ucsc.edu/goldenPath/help/bedgraph.html\" rel=\"nofollow\"\u003ebedgraph files\u003c/a\u003e and can be viewed in genome browsers such as \u003ca href=\"https://software.broadinstitute.org/software/igv\" rel=\"nofollow\"\u003eIGV\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Install IGV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://software.broadinstitute.org/software/igv/download\" rel=\"nofollow\"\u003ehttps://software.broadinstitute.org/software/igv/download\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Get bedgraph files from cluster\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escp -r \u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_pipeline_run/bedgraph_output/ .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Copy across clustering results (if you haven\u2019t already)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escp -r \u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_out/ .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Make IGV session file\u003c/strong\u003e\nIGV allows saving and loading \u003ca href=\"https://software.broadinstitute.org/software/igv/Sessions\" rel=\"nofollow\"\u003esession files\u003c/a\u003e, which are XML files that keep track of the program state (what FASTA, BED and bedgraph files have been simultaneously loaded to IGV).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda/gda_make_igv_session_file.py -g gda/test_data/PlasmoDB-49_Pfalciparum3D7.gff gda_out/cluster_heatmap.csv gda_out/PlasmoDB-49_Pfalciparum3D7_Genome/clusters.bed gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta bedgraph_output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e5. Load session file into IGV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile \u2192\u201cOpen Session\u201d\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/gda_pfalciparum_igv_screensh.png\"\u003e\u003cimg src=\"images/gda_pfalciparum_igv_screensh.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nThe IGV screenshot above shows \u003cem\u003ePlasmodium falciparum\u003c/em\u003e chromosome 1, with some GDA bedgraph tracks and the \u0027clusters.bed\u0027 file loaded.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnderstanding the output files\u003c/h3\u003e\u003ca id=\"user-content-understanding-the-output-files\" class=\"anchor\" aria-label=\"Permalink: Understanding the output files\" href=\"#understanding-the-output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eBedgraph files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith your results directory (\u003ccode\u003e\u0026lt;YYYYMMDD\u0026gt;_gda_pipeline_run\u003c/code\u003e by default; use \u003ccode\u003egda extract_genomic_features --pipeline_run_folder\u003c/code\u003e to change), the folder \u003ccode\u003ebedgraph_output\u003c/code\u003e contains each bedgraph track produced by GDA. These can be loaded into a genome browser (e.g. IGV) for viewing and better understanding why GDA has clustered the genome as it has. We provide the script \u003ccode\u003egda_make_igv_session_file.py\u003c/code\u003e to generate an IGV session file for your genome which will show the clusters and tracks for features which are significantly enriched in the clusters.\u003c/p\u003e\n\u003cp\u003eOne of the files generated by the \u003ccode\u003egda_clustering.py\u003c/code\u003e script is called \u003ccode\u003eclusters.bed\u003c/code\u003e. This file marks the locations of each UMAP+HDBSCAN cluster and can be loaded to IGV alongside the bedgraph tracks. The cluster numbers and the colour key are the same as in the UMAP plot of the Shiny app.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe feature table\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ecluster\u003c/th\u003e\n\u003cth\u003efeature\u003c/th\u003e\n\u003cth\u003ecluster_data.size\u003c/th\u003e\n\u003cth\u003eother_data.size\u003c/th\u003e\n\u003cth\u003estat_less\u003c/th\u003e\n\u003cth\u003epvalue_less\u003c/th\u003e\n\u003cth\u003estat_great\u003c/th\u003e\n\u003cth\u003epvalue_great\u003c/th\u003e\n\u003cth\u003ecluster_median\u003c/th\u003e\n\u003cth\u003eother_median\u003c/th\u003e\n\u003cth\u003ecluster_mean\u003c/th\u003e\n\u003cth\u003eother_mean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.47\u003c/td\u003e\n\u003ctd\u003e1.86e-35\u003c/td\u003e\n\u003ctd\u003e0.98000\u003c/td\u003e\n\u003ctd\u003e0.66000\u003c/td\u003e\n\u003ctd\u003e1.15760\u003c/td\u003e\n\u003ctd\u003e0.69856\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003etandem_repeats_fraction\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.56\u003c/td\u003e\n\u003ctd\u003e1.36e-49\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003e9.86e-01\u003c/td\u003e\n\u003ctd\u003e0.07840\u003c/td\u003e\n\u003ctd\u003e0.15620\u003c/td\u003e\n\u003ctd\u003e0.08711\u003c/td\u003e\n\u003ctd\u003e0.15905\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003ewgsim_depth_minimap2\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.90\u003c/td\u003e\n\u003ctd\u003e4.03e-127\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e3.71320\u003c/td\u003e\n\u003ctd\u003e9.94240\u003c/td\u003e\n\u003ctd\u003e3.98305\u003c/td\u003e\n\u003ctd\u003e9.90542\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.47\u003c/td\u003e\n\u003ctd\u003e1.86e-35\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.66000\u003c/td\u003e\n\u003ctd\u003e0.98000\u003c/td\u003e\n\u003ctd\u003e0.69856\u003c/td\u003e\n\u003ctd\u003e1.15760\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003etandem_repeats_fraction\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003e9.86e-01\u003c/td\u003e\n\u003ctd\u003e0.56\u003c/td\u003e\n\u003ctd\u003e1.36e-49\u003c/td\u003e\n\u003ctd\u003e0.15620\u003c/td\u003e\n\u003ctd\u003e0.07840\u003c/td\u003e\n\u003ctd\u003e0.15905\u003c/td\u003e\n\u003ctd\u003e0.08711\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003ewgsim_depth_minimap2\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.90\u003c/td\u003e\n\u003ctd\u003e4.03e-127\u003c/td\u003e\n\u003ctd\u003e9.94240\u003c/td\u003e\n\u003ctd\u003e3.71320\u003c/td\u003e\n\u003ctd\u003e9.90542\u003c/td\u003e\n\u003ctd\u003e3.98305\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdding optional features\u003c/h3\u003e\u003ca id=\"user-content-adding-optional-features\" class=\"anchor\" aria-label=\"Permalink: Adding optional features\" href=\"#adding-optional-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend you add as many features as possible so that the clustering is able to identify those which are the strongest signals in the genome.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional features which do not require additional data\u003c/h4\u003e\u003ca id=\"user-content-optional-features-which-do-not-require-additional-data\" class=\"anchor\" aria-label=\"Permalink: Optional features which do not require additional data\" href=\"#optional-features-which-do-not-require-additional-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e1. Run repeat finding to get bedgraph tracks of individual complex repeat features as well as complex_repeat_sum (the sum of all these features)\u003c/strong\u003e\nThe GDA pipeline contains two mandatory components for repeat detection: \u003ca href=\"https://github.com/Benson-Genomics-Lab/TRF\"\u003eTandemRepeatsFinder\u003c/a\u003e for tandem repeats and \u003ca href=\"http://emboss.sourceforge.net/apps/cvs/emboss/apps/einverted.html\" rel=\"nofollow\"\u003eEMBOSS einverted\u003c/a\u003e for inverted repeats. Besides these, the GDA pipeline has two optional repeat family detection modules from which the user can choose one to run. The first one of these modules uses \u003ca href=\"https://www.repeatmasker.org/RepeatModeler/\" rel=\"nofollow\"\u003eRepeatModeler+RepeatMasker\u003c/a\u003e and the second one uses \u003ca href=\"https://github.com/BioinformaticsToolsmith/MeShClust2\"\u003eRed+MeShClust2\u003c/a\u003e. RepeatModeler+RepeatMasker is relatively slow and may take ~1 week to run for large genomes (11 hours for the test dataset). On Sanger farm5, this will require using the basement queue. The Red+Meshclust2 module is much faster, but may produce more noisy repeat families, depending on the genome.\nWhen the GDA pipeline is run with repeat family detection enabled, the bedgraph files of each complex repeat family appear in the \u003ccode\u003ecomplex_repeats\u003c/code\u003e subdirectory of the \u003ccode\u003ebedgraph_output\u003c/code\u003e directory. If RepeatModeler is used, a \u003ccode\u003esimple_repeats\u003c/code\u003e directory that contains bedgraph files of simple repeat families is also produced.\nIn addition, a bedgraph file of the sum of complex repeat families (and if using RepeatModeler, of simple repeat families) is produced. The individual bedgraph tracks of each repeat family are not used as the input for UMAP clustering by default, but the tracks for the sums of simple or complex repeat families are used.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--run_repeat_family_detection\n--repeat_family_detection_engine \u0026lt;repeatmodeler/meshclust2\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --run_repeat_family_detection --repeat_family_detection_engine repeatmodeler gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_repeatmodeler_test.o -e gda_repeatmodeler_test.e \"gda extract_genomic_features --threads 12 --run_repeat_family_detection --repeat_family_detection_engine repeatmodeler gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e2. \u003cem\u003eDe novo\u003c/em\u003e gene annotation\u003c/strong\u003e\nThe GDA pipeline can take an existing gene annotations GFF3 file as input. For the cases where there is no existing gene annotations available for the genome, the pipeline contains an optional module that produces a \u003cem\u003ede novo\u003c/em\u003e annotation of protein coding genes, rRNA and tRNA genes (using \u003ca href=\"https://bioinf.uni-greifswald.de/augustus/\" rel=\"nofollow\"\u003eAugustus\u003c/a\u003e, \u003ca href=\"https://github.com/tseemann/barrnap\"\u003eBarrnap\u003c/a\u003e and \u003ca href=\"http://lowelab.ucsc.edu/tRNAscan-SE/\" rel=\"nofollow\"\u003etRNAscan-SE\u003c/a\u003e). The gene annotation module can optionally take an annotated related genome as the input and produce hints for Augustus based on annotation transfer with \u003ca href=\"https://github.com/agshumate/Liftoff\"\u003eLiftoff\u003c/a\u003e. Several bedgraph feature tracks are derived from gene annotations: \u003ccode\u003emRNA_annotation\u003c/code\u003e, \u003ccode\u003eexon_count\u003c/code\u003e, \u003ccode\u003egene_average_exon_length\u003c/code\u003e, \u003ccode\u003egene_average_intron_length\u003c/code\u003e, \u003ccode\u003egene_length\u003c/code\u003e, \u003ccode\u003etRNA_annotations\u003c/code\u003e, \u003ccode\u003erRNA_annotations\u003c/code\u003e. Optionally, a \u003ccode\u003egene_dna_strand_bias\u003c/code\u003e track is also produced.\nAlso, a GFF file of the annotations can be found in the \u003ccode\u003egene_annotation\u003c/code\u003e folder. The GFF file also includes the tRNAscan and Barrnap results.\u003c/p\u003e\n\u003cp\u003eMultiple options are required\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--run_gene_annotation_pipeline\n--annotation_target_species_id \u0026lt;label_for_gene_ids\u0026gt;\n--augustus_species \u0026lt;pick_from_list\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder aug_test_runfolder --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test_aug.o -e gda_test_aug.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder aug_test_runfolder --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional features requiring additional data\u003c/h4\u003e\u003ca id=\"user-content-optional-features-requiring-additional-data\" class=\"anchor\" aria-label=\"Permalink: Optional features requiring additional data\" href=\"#optional-features-requiring-additional-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e1. Genome annotation\u003c/strong\u003e\n\u003ccode\u003e--gff_path \u0026lt;GFF3 file with existing gene annotations\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor handling user-provided GFF files, the pipeline expects the following things:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe input file is in GFF3 format (GTF or GFF2 are not accepted)\u003c/li\u003e\n\u003cli\u003ethe tags for mRNA, pseudogene, tRNA and rRNA features are \"mRNA\", \"pseudogene\", \"tRNA\" and \"rRNA\". The user should check the GFF file to make sure that the tags are named according to this convention. If, for instance, the mRNA features in the GFF file are called \"transcript\" instead of \"mRNA\", the pipeline does not recognise them as the mRNA features.\u003c/li\u003e\n\u003cli\u003ethe GFF file should pass the \u003ca href=\"http://genometools.org/cgi-bin/gff3validator.cgi\" rel=\"nofollow\"\u003eGenomeTools GFF3 validator check\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe user can specify non-standard GFF3 feature tags from the input GFF3 file to be turned into bedgraph tracks using the \u003ccode\u003e--custom_gff_tags\u003c/code\u003e option of the \u003ccode\u003egda\u003c/code\u003e wrapper. For example, if the input GFF3 file has features named \"H3K9me3\" and \"H3K9ac\", it is possible to make bedgraph files out of them by specifying them as comma separated \u003ccode\u003ecustom_gff_tags\u003c/code\u003e options:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--custom_gff_tags H3K9me3,H3K9ac\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Reference genome annotation (annotate your assembly using a reference annotation: hints for Augustus are derived from annotation transfer using Liftoff)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--reference_assembly_path \u0026lt;reference assembly FASTA file\u0026gt; --reference_gff_path \u0026lt;reference assembly GFF3 file\u0026gt; \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. RNA-Seq coverage\u003c/strong\u003e\nRNA-Seq coverage is determined using the mapping of reads to the assembly with \u003ca href=\"http://daehwankimlab.github.io/hisat2/manual/\" rel=\"nofollow\"\u003eHISAT2\u003c/a\u003e. The input is a pair of gzipped FASTQ reads.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--rna_seq_fastq_1_path\n--rna_seq_fastq_2_path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR223/008/ERR2234508/ERR2234508_1.fastq.gz .\nwget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR223/008/ERR2234508/ERR2234508_2.fastq.gz .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --rna_seq_fastq_1_path ERR2234508_1.fastq.gz --rna_seq_fastq_2_path ERR2234508_2.fastq.gz gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --rna_seq_fastq_1_path ERR2234508_1.fastq.gz --rna_seq_fastq_2_path ERR2234508_2.fastq.gz gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting feature track is called \u003ccode\u003ehisat2_samtools_depth\u003c/code\u003e and the raw mapping data is in the \u003ccode\u003erna_seq_mapping\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Gene conservation (orthology)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--orthomcl_references_folder\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis folder should contain subfolders, each for separate \u003ca href=\"https://orthomcl.org/orthomcl/app\" rel=\"nofollow\"\u003eOrthoMCL\u003c/a\u003e runs, e.g. for closely related and more distantly related species (although a single folder is perfectly fine). The folder name is arbitrary. Within each folder there should be protein FASTA files for each reference proteome and a file called \u003ccode\u003etable_for_gg_file.csv\u003c/code\u003e with the names of these files and a simple name for the species. GG files (genome gene relation file) are used by OrthoMCL to relate genes to genomes. e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePchabaudi,PlasmoDB-49_Pchabaudichabaudi_AnnotatedProteins.fasta\nTgondii,ToxoDB-51_TgondiiME49_AnnotatedProteins.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eProteins from the genome under consideration will be added behind the scenes (they are derived from the assembly FASTA file and annotations GFF3 file using \u003ca href=\"https://github.com/gpertea/gffread\"\u003egffread\u003c/a\u003e). N.b. you need to provide annotation for your genome assembly or have it transferred/predicted in order to do the orthology analysis.\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder orthomcl_test_runfolder --orthomcl_references_folder gda/test_data/orthomcl_refs/ --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test_orthomcl.o -e gda_test_orthomcl.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder orthomcl_test_runfolder --orthomcl_references_folder gda/test_data/orthomcl_refs/ --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting bedgraph files are:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_ortholog_count.bedgraph\u003c/code\u003e - Number of orthologues per gene\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_paralog_count.bedgraph\u003c/code\u003e - Number of paralogues per gene\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_protein_conservation_ratio.bedgraph\u003c/code\u003e - The average proportion of species in the OrthoMCL run that have orthologs for the target species proteins in the window. The value is 1 if all target species proteins in the window have OrthoMCL orthologs in all other species in the OrthoMCL run. The value is 0 if none of the target species proteins in the window have any OrthoMCL orthologs in any other species in the OrthoMCL run.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_species_specific_proteins_ratio.bedgraph\u003c/code\u003e - The average proportion of species-specific proteins in the window. It is the number of target species proteins with no OrthoMCL orthologs in the window divided by the number of all target species proteins in the window. The value is 1 if none of the target species proteins in the window have OrthoMCL orthologs, and 0 if all of the target species proteins in the window have OrthoMCL orthologs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Reference mitochondrial sequence for Nuclear Mitochondrial DNA (NUMT) identification\u003c/strong\u003e\nNUMT identification is done using BLAST of the genome against a user-provided reference mitochondrial sequence. The reference mitochondrial sequence can be a known mitochondrial sequence from the same species as the rest of the assembly. If a region of an assembly contig yields a strong BLAST hit (e-value \u0026lt;= 1e-30) to the reference mitochondrial sequence but the alignment length is less than 90% of the length of this contig, the BLAST hit region is labelled as a putative NUMT.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--ref_mitoch_fasta_path\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Reference plastid sequence for NUPT identification\u003c/strong\u003e\nThis is the same process as the detection of NUMTs but meant for plastid sequences.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--ref_apicoplast_fasta_path\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Other useful feature extraction options\u003c/strong\u003e\nThe pipeline tries to identify telomeric regions by searching the assembly sequences for exact matches to a telomeric motif. The \u003ccode\u003etelomeric_seq_preset\u003c/code\u003e option allows to select a query telomeric motif from a list of known telomeric motifs across different species (based on the Wikipedia article on telomeres, \u003ca href=\"https://en.wikipedia.org/wiki/Telomere\" rel=\"nofollow\"\u003ehttps://en.wikipedia.org/wiki/Telomere\u003c/a\u003e). It is also possible to specify a custom telomeric motif using the \u003ccode\u003ecustom_telomeric_seq\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--telomeric_seq_preset\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOptimising clustering\u003c/h3\u003e\u003ca id=\"user-content-optimising-clustering\" class=\"anchor\" aria-label=\"Permalink: Optimising clustering\" href=\"#optimising-clustering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptimising clustering during feature extraction\u003c/h4\u003e\u003ca id=\"user-content-optimising-clustering-during-feature-extraction\" class=\"anchor\" aria-label=\"Permalink: Optimising clustering during feature extraction\" href=\"#optimising-clustering-during-feature-extraction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChange the window size (5kb)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--chunk_size\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis is perhaps the most important option in GDA. From a purely computational point of view, GDA will struggle with clustering a very large number of windows. From a biological perspective, it determines the resolution at which you are analysing the genome assembly. We find that 5kb works very well for the relatively miniscule \u003cem\u003ePlasmodium\u003c/em\u003e genome (~20Mb). For the common toad (\u003cem\u003eBufo bufo\u003c/em\u003e) genome, which is 4.94 Gb we have used 1 Mb window size. Aiming for 5000 windows works very nicely computationally, but you should experiment with a few window sizes, to see what gives an interesting view of the genome. You needn\u0027t run feature extraction multiple times. Instead use:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egda downsample_merged_tsv \u0026lt;tsv\u0026gt; \u0026lt;factor\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you started with a 5kb window size, use 4 as the downsampling factor and you will get a merged TSV file with 20kb windows. Similarly, use a factor of 10 to get 50kb windows.\u003c/p\u003e\n\u003cp\u003eIf the genomic feature extraction pipeline produces an output TSV file that has 10000 or more windows, a downsampled TSV file with approximately 5000 windows will be automatically generated alongside the main output TSV file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptimising clustering during clustering step\u003c/h4\u003e\u003ca id=\"user-content-optimising-clustering-during-clustering-step\" class=\"anchor\" aria-label=\"Permalink: Optimising clustering during clustering step\" href=\"#optimising-clustering-during-clustering-step\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the feature extraction pipeline is finished, you can determine good clustering parameters by looking at the UMAP plots from a range of different parameters:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda clustering_params 20210312_gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n1 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_params_test.o -e gda_params_test.e \"gda clustering_params 20210312_gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eReplace the \u003ccode\u003e20210312_gda_pipeline_run\u003c/code\u003e in the above command with the name of your GDA pipeline run folder path.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003en_neighbors\u003c/code\u003e is a UMAP setting that determines the size of the local neigbourhood in terms of sample points (\u003ca href=\"https://umap-learn.readthedocs.io/en/latest/parameters.html\" rel=\"nofollow\"\u003ehttps://umap-learn.readthedocs.io/en/latest/parameters.html\u003c/a\u003e). Smaller \u003ccode\u003en_neigbors\u003c/code\u003e values give more emphasis on local structure in the data and larger \u003ccode\u003en_neighbors\u003c/code\u003e values give more weight to global structure. We have used \u003ccode\u003en_neighbors\u003c/code\u003e values from 5 to 200.\nBy default the clustering will be run with \u003ccode\u003en_neighbors\u003c/code\u003e set to 5, 10, 15, 20, 50, 100 and \u201cMinimum cluster size\u201d set to 50, 100, 200, 500. All parameter pairs will be explored (e.g. 24 combinations). The results of each clustering are output to STDOUT. You can also view an HTML file of UMAP plots in a web browser e.g.:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003efirefox gda_out/parameter_selection/parameters.html \u0026amp;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e[warning this can run slowly when run remotely]\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/pfalciparum_gda_parameters_example.pdf\"\u003eHere\u003c/a\u003e is example output of the \u003ccode\u003egda clustering_params\u003c/code\u003e run with the \u003cem\u003ePlasmodium falciparum\u003c/em\u003e assembly.\u003c/p\u003e\n\u003cp\u003eWe recommend selecting parameters based on minimising the percentage of unclassified sequence, while getting at least two clusters. E.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN neighbours: 5\nMin cluster size: 50\nCluster -1 is 2.14% of the genome\nCluster 0 is 2.99% of the genome\nCluster 1 is 3.70% of the genome\nCluster 2 is 91.17% of the genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnly 2.14% of windows were unclassified and there are multiple clusters meaning GDA has identified some partitioning of the genome.\u003c/p\u003e\n\u003cp\u003eGiven that this pair of parameters involves the lowest values, it would be a good idea to try out even lower parameter values to see if there is an even better/more interesting clustering.\u003c/p\u003e\n\u003cp\u003eYou should pick minimum cluster sizes based on the number of windows you have. E.g. If you have 5000 windows, and you have a minimum cluster size of 50, the smallest possible cluster will contain 1% of your genome assembly.\u003c/p\u003e\n\u003cp\u003eWhen clustering a large number of genomic windows, you may need to set HDBSCAN\u0027s \u003ccode\u003emin_samples\u003c/code\u003e value to a value that is not \u003ccode\u003eNone\u003c/code\u003e in order to prevent HDBSCAN from crashing (\u003ca href=\"https://github.com/scikit-learn-contrib/hdbscan/issues/250\"\u003ehttps://github.com/scikit-learn-contrib/hdbscan/issues/250\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnderstanding the default features\u003c/h3\u003e\u003ca id=\"user-content-understanding-the-default-features\" class=\"anchor\" aria-label=\"Permalink: Understanding the default features\" href=\"#understanding-the-default-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eVariable\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eat_skew\u003c/td\u003e\n\u003ctd\u003eAT skew\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecag_freq\u003c/td\u003e\n\u003ctd\u003eCAG trinucleotide repeat frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecomplex_repeats_bedgraph\u003c/td\u003e\n\u003ctd\u003ecomplex repeats detected using \u003ca href=\"https://www.repeatmasker.org/RepeatModeler/\" rel=\"nofollow\"\u003eRepeatModeler+RepeatMasker\u003c/a\u003e or \u003ca href=\"https://github.com/BioinformaticsToolsmith/MeShClust2\"\u003eRed+MeShClust2\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003eCpG dinucleotide frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edustmasker_low_complexity_percentage\u003c/td\u003e\n\u003ctd\u003elow complexity sequence frequency (detected using \u003ca href=\"https://www.ncbi.nlm.nih.gov/IEB/ToolBox/CPP_DOC/lxr/source/src/app/dustmasker/\" rel=\"nofollow\"\u003eDustmasker\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eectopic_apicoplast\u003c/td\u003e\n\u003ctd\u003eputative ectopic apicoplast (detected using BLAST against user-provided apicoplast sequence)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eectopic_mitochondrion\u003c/td\u003e\n\u003ctd\u003eputative NUMTs (detected using BLAST against user-provided mitochondrial sequence)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eeinverted\u003c/td\u003e\n\u003ctd\u003einverted repeats (detected using \u003ca href=\"http://emboss.sourceforge.net/apps/cvs/emboss/apps/einverted.html\" rel=\"nofollow\"\u003eEMBOSS einverted\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eexon_count\u003c/td\u003e\n\u003ctd\u003eaverage exon count per mRNA gene\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egaps\u003c/td\u003e\n\u003ctd\u003eassembly gaps (Ns)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egc_percentage\u003c/td\u003e\n\u003ctd\u003eGC%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egc_skew\u003c/td\u003e\n\u003ctd\u003eGC skew\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_average_exon_length\u003c/td\u003e\n\u003ctd\u003eaverage exon length of mRNA genes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_average_intron_length\u003c/td\u003e\n\u003ctd\u003eaverage intron length of mRNA genes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_dna_strand_bias\u003c/td\u003e\n\u003ctd\u003etendency of genes to be all on the same strand in the window. The value is 1 if all genes in the window are on the same strand (it does not matter which one). The value is 0 if genes in the window are equally distributed between both strands\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_length\u003c/td\u003e\n\u003ctd\u003eaverage mRNA gene length\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ekmer_deviation_kmer_size_3*\u003c/td\u003e\n\u003ctd\u003ekmer skew for a for a particular kmer length (how much the distribution of kmers in the window differs from what is expected by change, given the GC content of the sequence in the window)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eltrdigest_protein_matches\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/genometools/genometools\"\u003eLTRdigest\u003c/a\u003e protein matches\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eltrdigest_retrotransposons\u003c/td\u003e\n\u003ctd\u003eputative retrotransposons (detected using \u003ca href=\"https://github.com/genometools/genometools\"\u003eLTRharvest and LTRdigest\u003c/a\u003e). Only the sequences containing LTRdigest protein matches are counted\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emRNA_annotations\u003c/td\u003e\n\u003ctd\u003emRNA gene density (either from user-provided gene annotations or detected using \u003ca href=\"https://bioinf.uni-greifswald.de/augustus/\" rel=\"nofollow\"\u003eAugustus\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eortholog_count\u003c/td\u003e\n\u003ctd\u003eaverage number of orthologs (\u003ca href=\"https://orthomcl.org/orthomcl/\" rel=\"nofollow\"\u003eOrthoMCL\u003c/a\u003e orthologs in other species) for proteins in the window\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eparalog_count\u003c/td\u003e\n\u003ctd\u003eaverage number of paralogs (OrthoMCL orthologs within the same species) for proteins in the window\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eprotein_conservation_ratio\u003c/td\u003e\n\u003ctd\u003ereflects the average proportion of species in the OrthoMCL run that have orthologs for the target species proteins in the window. The value is 1 if all target species proteins in the window have OrthoMCL orthologs in all other species in the OrthoMCL run. The value is 0 if none of the target species proteins in the window have any OrthoMCL orthologs in any other species in the OrthoMCL run.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epseudogene_annotations\u003c/td\u003e\n\u003ctd\u003epseudogenes (read from user-provided GFF3 file if this feature is present there)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erRNA_annotations\u003c/td\u003e\n\u003ctd\u003erRNA_annotations (either from user-provided gene annotations or detected using Barrnap)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esimple_repeats_bedgraph\u003c/td\u003e\n\u003ctd\u003esimple repeats detected using RepeatModeler+RepeatMasker. The sequences have been collapsed to count repeats that are the reverse complement of one another as the same repeat. They have also been collapsed to count the repeats that are identical if the starting point is adjusted as the same repeat (e.g. TGGTT is the same as GGTTT)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especies_specific_proteins_ratio\u003c/td\u003e\n\u003ctd\u003ereflects the average proportion of species specific proteins in the window. It is the number of target species proteins with no OrthoMCL orthologs in the window divided by the number of all target species proteins in the window. The value is 1 if none of the target species proteins in the window have OrthoMCL orthologs, and 0 if all of the target species proteins in the window have OrthoMCL orthologs.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003estop_codon_freq\u003c/td\u003e\n\u003ctd\u003estop codon frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esum_of_complex_repeats\u003c/td\u003e\n\u003ctd\u003esum of values of RepeatModeler+RepeatMasker or Red+MeShClust2 tracks for complex repeat families\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esum_of_simple_repeats\u003c/td\u003e\n\u003ctd\u003esum of values of RepeatModeler tracks for simple repeat families\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etandem_repeat_density\u003c/td\u003e\n\u003ctd\u003etandem repeats (detected using \u003ca href=\"https://github.com/Benson-Genomics-Lab/TRF\"\u003eTandem Repeats Finder\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etelomere_freq\u003c/td\u003e\n\u003ctd\u003etelomeric sequence frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etRNA_annotations\u003c/td\u003e\n\u003ctd\u003etRNAs (either from user-provided gene annotations or detected using \u003ca href=\"http://lowelab.ucsc.edu/tRNAscan-SE/\" rel=\"nofollow\"\u003etRNAscan-SE\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewgsim_minimap2_coverage\u003c/td\u003e\n\u003ctd\u003ecoverage of \u003ca href=\"https://github.com/lh3/wgsim\"\u003eWGSIM\u003c/a\u003e simulated short reads, derived from the assembly itself, with a target coverage of 10x. The reads have been mapped back to the assembly using Minimap2 using the short read mapping mode. Multimapping simulated reads have been removed before calculating the coverage\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOther output\u003c/h3\u003e\u003ca id=\"user-content-other-output\" class=\"anchor\" aria-label=\"Permalink: Other output\" href=\"#other-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e/work/\u003c/code\u003e directory \u2013 Files automatically generated by Nextflow during the run. These files can be used for resuming the pipeline when crashed. Nextflow has a \u003ccode\u003e-resume\u003c/code\u003e option for restarting an interrupted run from the last cached checkpoint. In the GDA pipeline wrapper script, the \u003ccode\u003eresume_genomic_feature_extraction\u003c/code\u003e command is meant for restarting the pipeline using Nextflow\u0027s \u003ccode\u003e-resume\u003c/code\u003e flag. For this you will need to provide the path to the Nextflow config file (it is a file with the name \u003ccode\u003enextflow.config\u003c/code\u003e in the \u003ccode\u003e*_gda_pipeline_run folder\u003c/code\u003e) and the name of the crashed run. The run names are autogenerated by Nextflow and can be seen in the STDOUT log of the GDA run, in square brackets below the line that says \"N E X T F L O W\". If the run was started using the GDA Singularity image, you will also need to provide the path to that image, otherwise this path is not needed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClustering the features of multiple genomes at once\u003c/h3\u003e\u003ca id=\"user-content-clustering-the-features-of-multiple-genomes-at-once\" class=\"anchor\" aria-label=\"Permalink: Clustering the features of multiple genomes at once\" href=\"#clustering-the-features-of-multiple-genomes-at-once\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to cluster the features extracted from multiple genomes at the same time. To do this, the first step is to run the genomic feature extraction pipeline separately for each genome of interest. For each genome, this will produce a TSV table with the values of the genomic features. The tables can then be concatenated using the \u003ccode\u003egda_concatenate_tsv_tables.py\u003c/code\u003e script. Each of the input tables needs to have the same window size. In the \u003ccode\u003especies\u003c/code\u003e and \u003ccode\u003echromosome\u003c/code\u003e columns, each input TSV table needs to have unique values that do not occur in the other input TSV tables. After concatenating the tables, the resulting combined table can be processed with the \u003ccode\u003egda clustering_params\u003c/code\u003e and \u003ccode\u003egda clustering\u003c/code\u003e commands. When viewing the clustering results of a multi-genome TSV table in the Shiny app, an extra UMAP plot will appear, with dots coloured according to which input assembly each window belongs to (\u003ca href=\"images/clustering_two_genomes_umap_example.png\"\u003eexample\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing GDA Singularity image\u003c/h3\u003e\u003ca id=\"user-content-using-gda-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Using GDA Singularity image\" href=\"#using-gda-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs an alternative to using conda to install the dependencies for GDA, it is also possible to read the dependencies from a Singularity image. A Singularity image file with the dependencies for GDA has been deposited in Google Drive, at \u003ca href=\"https://drive.google.com/file/d/1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_/view?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/file/d/1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_/view?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you have gdown (\u003ca href=\"https://github.com/wkentaro/gdown\"\u003ehttps://github.com/wkentaro/gdown\u003c/a\u003e, \u003ca href=\"https://anaconda.org/conda-forge/gdown\" rel=\"nofollow\"\u003ehttps://anaconda.org/conda-forge/gdown\u003c/a\u003e) installed on your system, you can download the Singularity image file from Google Drive with a terminal command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egdown https://drive.google.com/uc?id=1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOn the Sanger farm, Singularity can be started from the farm module:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emodule load ISG/singularity/3.6.4\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou will need to make sure Singularity and Nextflow are installed on your cluster.\nFor running GDA with the Singularity image, you should still clone this GitHub repository and add the \u003ccode\u003egda\u003c/code\u003e wrapper script to \u003ccode\u003ePATH\u003c/code\u003e. To use the GDA Singularity image, you should provide the path to the image with the \u003ccode\u003e--singularity_image_path\u003c/code\u003e option of the \u003ccode\u003egda\u003c/code\u003e wrapper script. The remaining software dependencies (RepeatModeler, HISAT2, LTRharvest, etc) will then be loaded from the Singularity image. This is an example command for extracting genomic features using Singularity:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta --singularity_image_path \u0026lt;gda_singularity.simg\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta --singularity_image_path \u0026lt;gda_singularity.simg\u0026gt;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also run the \u003ccode\u003egda_clustering_params\u003c/code\u003e and \u003ccode\u003egda_clustering\u003c/code\u003e commands with the Singularity image by providing a path to the image with the \u003ccode\u003e--singularity_image_path\u003c/code\u003e option.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTroubleshooting\u003c/h3\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u2022\tThe best place to look for error messages initially is STDOUT, rather than STDERR, because the Nextflow error messages end up there. You may then be directed to the \u003ccode\u003eerror_stream_logs\u003c/code\u003e directory in your run folder for error messages from a specific process\u003c/p\u003e\n\u003cp\u003e\u2022\tYou may want to exclude the mitochondrial and other symbiont genomes as well as any shorter, non-chromosomal scaffolds\u003c/p\u003e\n\u003cp\u003e\u2022\tIf your genome assembly is large and clustering is problematic you may want to increase window size. You can do this with an existing merged TSV file using \u003ccode\u003egda downsample_merged_tsv \u0026lt;path to the TSV file\u0026gt; \u0026lt;downsampling factor\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBugs, suggestions etc. can be sent to \u003ca href=\"mailto:ea10@sanger.ac.uk\"\u003eea10@sanger.ac.uk\u003c/a\u003e and \u003ca href=\"mailto:ajr236@cam.ac.uk\"\u003eajr236@cam.ac.uk\u003c/a\u003e, or submitted as issues on this GitHub page.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIdeas for analysis\u003c/h3\u003e\u003ca id=\"user-content-ideas-for-analysis\" class=\"anchor\" aria-label=\"Permalink: Ideas for analysis\" href=\"#ideas-for-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse a single feature from the merged TSV to make calls for where this feature is high across a genome \u2013 e.g. paralogous gene families or a particular complex repeat family of interest.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 4, - "subscribers_count": 4, - "topics": [], - "updated_at": 1661238007.0 + "subscribers_count": 6, + "topics": [ + "nextflow" + ], + "updated_at": 1703941195.0 }, { "data_format": 2, - "description": "EasyBob, the friendly software installation robot for EasyBuild", + "description": "CTA-customized version of the DIRAC middleware", "filenames": [ - "singularity/rocky/Singularity.eb-4.5.0-Lmod-rocky8.4", - "singularity/rocky/Singularity.eb-4.7.0-Lmod-rocky8.7", - "singularity/ubuntu/Singularity.eb-4.4.2-Lmod-ubuntu20-LTR", - "singularity/ubuntu/Singularity.eb-4.5.0-Lmod-ubuntu20-LTR" + "Singularity" ], - "full_name": "sassy-crick/easybob", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAutomatic software installation script\u003c/h1\u003e\u003ca id=\"user-content-automatic-software-installation-script\" class=\"anchor\" aria-label=\"Permalink: Automatic software installation script\" href=\"#automatic-software-installation-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction:\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction:\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe aim of the script is to install the software inside a container, and thus the so installed software is independent from the OS as much as possible, and also takes care of different architectures. The idea comes from the EESSI project and how the software is installed in there. So kudos to them!!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow-to:\u003c/h2\u003e\u003ca id=\"user-content-how-to\" class=\"anchor\" aria-label=\"Permalink: How-to:\" href=\"#how-to\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore the script can run, there are a few files which need to be adjusted.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esite-config\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esoftwarelist.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esoftwarelist.yaml\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003esite-config\u003c/code\u003e file should contain all relevant paths for the software installation and that should be the only file which needs changes. That said, it might be worth to have a look at the other files to make sure nothing is accidentally hard-coded.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eautomatic-build.sh\u003c/code\u003e script is expecting the full path there either the \u003ccode\u003esoftware.txt\u003c/code\u003e or \u003ccode\u003esoftware.yaml\u003c/code\u003e files are. One is enough, the scripts detect which ones are around and acts accordingly. This script is then then submitting the jobs, one for each to be build architecture as defined in the \u003ccode\u003esite-config\u003c/code\u003e file, to the queue. The submission script so generated is then calling the \u003ccode\u003einstall.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003einstall.sh\u003c/code\u003e does basically the whole magic. There are a few lines at the top which need to be changed to reflect where the software needs to go. It might be worth checking that but most of the relevant stuff is defined in the \u003ccode\u003esite-config\u003c/code\u003e file and are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSOFTWARE_INSTDIR\u003c/code\u003e which is where the software tree and all the helper stuff lives\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBINDDIR\u003c/code\u003e is the directory which needs to be bound inside the container as per default Singularity does only mount \u003ccode\u003e/tmp\u003c/code\u003e and \u003ccode\u003e/home\u003c/code\u003e it seems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou also might want to look at:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINER_VERSION\u003c/code\u003e which is the name of the sif-file, i.e. the container\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eEB_VERSION\u003c/code\u003e which is the version of EasyBuild to be used for building software. If that does not exist, it should be automatically installed\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSW_LIST\u003c/code\u003e contains a simple list of the EasyConfig files to be installed. All in one line with a blank between them.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSW_YAML\u003c/code\u003econtains the software to be installed as an EasyStack file in \u003ccode\u003eyaml\u003c/code\u003e format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth the \u003ccode\u003eSW_LIST\u003c/code\u003e and the \u003ccode\u003eSW_YAML\u003c/code\u003e are independent from each other. So as long as the file got a content, it will be used.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esoftware.sh\u003c/code\u003e will be created on the fly in the right directory, using the various template files, and does contain the list of software which needs to be installed which will be pulled in by the \u003ccode\u003esoftwarelist.txt\u003c/code\u003e file. The EasyStack file, so it exists, will be places in the correct directory.\nIf you need to change any of the paths where the software will be installed, you will need to look into \u003ccode\u003esoftware.tmpl\u003c/code\u003e, the Singularity Definition file \u003ccode\u003eSingularity.eb-4.5.3-envmod-rocky8\u003c/code\u003e and both the \u003ccode\u003einstall.sh\u003c/code\u003e and \u003ccode\u003einteractive-install.sh\u003c/code\u003e files.\nNote: You can mount any folder outside the container but you will need to make sure that the \u003ccode\u003eMODULEPATH\u003c/code\u003e variable are identical inside and outside the container. Thus, if you are using like in our example \u003ccode\u003e/sw-eb\u003c/code\u003e as the root install directory, the \u003ccode\u003eMODULEPATH\u003c/code\u003e then needs to be set to for example \u003ccode\u003e/sw-eb/modules/all\u003c/code\u003e inside and outside the container!\u003c/p\u003e\n\u003cp\u003eThe first time the script runs, it will create the directory structure but then stops as the Singularity container is not in place. For the full automated installation, we would download the container from somewhere. However, as this only needs to be done once, it is left for now like this.\u003c/p\u003e\n\u003cp\u003eOnce the container in the right folder we are upgrading EasyBuild to the latest version. This way, a module file is created automatically. Once that is done, the software will be installed if required.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHooks\u003c/h2\u003e\u003ca id=\"user-content-hooks\" class=\"anchor\" aria-label=\"Permalink: Hooks\" href=\"#hooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to make sure that licensed software has the right permissions, there is a site-specific file in the \u003ccode\u003ehooks\u003c/code\u003e directory calle \u003ccode\u003esite-hooks.py\u003c/code\u003e Depending on when to run, several parameters can be changed. That makes sense to for example restrict access to a specific folder by means of setting the group and the permissions, but also makes sense when building software which depends on the presence or absence of specific networks for example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGPU builds\u003c/h2\u003e\u003ca id=\"user-content-gpu-builds\" class=\"anchor\" aria-label=\"Permalink: GPU builds\" href=\"#gpu-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the GPU nodes, the command line flag \u003ccode\u003e--cuda-compute-capabilities=8.0\u003c/code\u003e for the A100 GPU or \u003ccode\u003e--cuda-compute-capabilities=7.5\u003c/code\u003e for the RTX600 GPU is defined in the \u003ccode\u003esite-config\u003c/code\u003e file and the script automatically detects if the EasyConfig filename contains \u003ccode\u003ecuda\u003c/code\u003e, either in lower cases or upper cases. This software will only be build on the GPU nodes automatically. Both the \u003ccode\u003einteractive-install.sh\u003c/code\u003e and the \u003ccode\u003einstall.sh\u003c/code\u003e script are using the \u003ccode\u003e--nv\u003c/code\u003e flag which is needed for the GPU builds.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eApptainer\u003c/code\u003e \u0026gt;= 1.0.1\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFlowchart\u003c/h2\u003e\u003ca id=\"user-content-flowchart\" class=\"anchor\" aria-label=\"Permalink: Flowchart\" href=\"#flowchart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis flowchart hopefully illustrates the interaction of the different components. Most importantly is the \u003ccode\u003esite-config\u003c/code\u003e file as this is where all the relevant information of the used cluster is sitting.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-mermaid\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eflowchart\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003eTD\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eA\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e(\u003c/span\u003e\u003c/span\u003eautomatic-build.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003ecreates and submits for each architecture\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eB\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003ejobscript\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eB\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003ejobscript\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003eexecutes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eD\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[(\u003c/span\u003e\u003c/span\u003esite-config\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e)]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003epulls in\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eE\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003esoftware-head.tmpl\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eF\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003esoftware-list.tmpl\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eG\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003esoftware-bottom.tmpl\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eH\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes installation with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eH\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes installation with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e2\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e3\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eI\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes testing with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eJ\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eEB hooks\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eH\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes installation with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eJ\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eEB hooks\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eI\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes testing with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ek\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003esw-groups\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo Do:\u003c/h2\u003e\u003ca id=\"user-content-to-do\" class=\"anchor\" aria-label=\"Permalink: To Do:\" href=\"#to-do\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is working well but with all code there is room for improvement, or bugs which have yet to be found.\u003c/p\u003e\n\u003cp\u003eFix that for automated install, so the script can be put anywhere.\u003c/p\u003e\n", + "full_name": "cta-observatory/CTADIRAC", + "latest_release": "v1r62test", + "readme": "\u003cp\u003eCTADIRAC project:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emoved to CTAO Observatory gitlab @ \u003ca href=\"https://gitlab.cta-observatory.org/cta-computing/dpps/CTADIRAC\" rel=\"nofollow\"\u003ehttps://gitlab.cta-observatory.org/cta-computing/dpps/CTADIRAC\u003c/a\u003e on Decembe 2020\u003c/li\u003e\n\u003cli\u003emoved to git on Septembre 5th 2017\u003c/li\u003e\n\u003cli\u003eadd things, need add a licence GPLv3 ?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAuthors from svn:\u003cbr\u003e\nAdrian Casajus \u003ca href=\"mailto:adria@ecm.ub.es\"\u003eadria@ecm.ub.es\u003c/a\u003e \u003cbr\u003e\nLuisa Arrabito \u003ca href=\"mailto:arrabito@in2p3.fr\"\u003earrabito@in2p3.fr\u003c/a\u003e \u003cbr\u003e\nJohan Bregeon \u0026lt;\u003ca href=\"mailto:bregeon@.in2p3.fr\"\u003ebregeon@.in2p3.fr\u003c/a\u003e\u0026gt; \u003cbr\u003e\nJohann Cohen Tanugi \u003ca href=\"mailto:johann.cohen-tanugi@umontpellier.fr\"\u003ejohann.cohen-tanugi@umontpellier.fr\u003c/a\u003e \u003cbr\u003e\n? Han Bcn \u003ca href=\"mailto:nhan.bcn@gmail.com\"\u003enhan.bcn@gmail.com\u003c/a\u003e \u003cbr\u003e\nRicardo Graciani \u003ca href=\"mailto:graciani@ecm.ub.edu\"\u003egraciani@ecm.ub.edu\u003c/a\u003e \u003cbr\u003e\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 3, + "subscribers_count": 7, "topics": [], - "updated_at": 1718488195.0 + "updated_at": 1644849840.0 }, { "data_format": 2, - "description": "DPMC/ProCount is a dynamic-programming framework for exact weighted (projected) model counting", + "description": null, "filenames": [ - "lg/Singularity", - "dmc/Singularity", - "tensor/Singularity" + "Singularity.def" ], - "full_name": "vardigroup/DPMC", - "latest_release": "v2.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDPMC/ProCount/DPO/DPER\u003c/h1\u003e\u003ca id=\"user-content-dpmcprocountdpodper\" class=\"anchor\" aria-label=\"Permalink: DPMC/ProCount/DPO/DPER\" href=\"#dpmcprocountdpodper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWe provide four exact solvers that support XOR-CNF formulas.\n\u003cul\u003e\n\u003cli\u003eDPMC solves \u003cem\u003eweighted model counting (WMC)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eProCount solves \u003cem\u003eweighted projected model counting (WPMC)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://arxiv.org/abs/2205.08632\" rel=\"nofollow\"\u003eDPO\u003c/a\u003e solves \u003cem\u003eweighted SAT (WSAT)\u003c/em\u003e, i.e., Boolean MPE.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://arxiv.org/abs/2205.09826\" rel=\"nofollow\"\u003eDPER\u003c/a\u003e solves \u003cem\u003eexist-random SAT (ERSAT)\u003c/em\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach of these four solvers is a combination of a planner and an executor.\n\u003cul\u003e\n\u003cli\u003eA planner produces a \u003cstrong\u003eproject-join tree\u003c/strong\u003e T from an XOR-CNF formula F.\u003c/li\u003e\n\u003cli\u003eAn executor traverses T to computes a solution of F.\u003c/li\u003e\n\u003cli\u003eFor WPMC and ERSAT, T must be \u003cstrong\u003egraded\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTwo planners are available.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./htb\"\u003eHTB\u003c/a\u003e uses constraint-programming heuristics.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./lg\"\u003eLG\u003c/a\u003e uses tree decomposers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTwo executors are available.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./dmc\"\u003eDMC\u003c/a\u003e uses \u003cem\u003ealgebraic decision diagrams (ADDs)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./tensor\"\u003eTensor\u003c/a\u003e uses tensors and only solves WMC on pure CNF.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopers:\n\u003cul\u003e\n\u003cli\u003eJeffrey Dudek: LG and Tensor\u003c/li\u003e\n\u003cli\u003eVu Phan: HTB and DMC\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"https://github.com/vardigroup/DPMC/releases\"\u003eReleases\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-releases\" class=\"anchor\" aria-label=\"Permalink: Releases\" href=\"#releases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2021/05/25: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/mc-2021\"\u003emc-2021\u003c/a\u003e \u003ca href=\"https://zenodo.org/badge/latestdoi/280443175\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/94c98c030c95168c80dae6a4d9e0c347cee412c22a3c9ea53aa875c373925b4d/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3238303434333137352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/280443175.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"./mcc\"\u003eModel Counting Competition MC-2021\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2021/05/23: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/v2.0.0\"\u003ev2.0.0\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eSAT-2021 paper: \u003ca href=\"https://jmd11.web.rice.edu/papers/sat21_procount.pdf\" rel=\"nofollow\"\u003e\u003cstrong\u003eProCount: Weighted Projected Model Counting with Graded Project-Join Trees\u003c/strong\u003e\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors: Jeffrey M. Dudek, Vu H. N. Phan, Moshe Y. Vardi\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2020/07/20: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/v1.0.0\"\u003ev1.0.0\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eCP-2020 paper: \u003ca href=\"https://arxiv.org/abs/2008.08748\" rel=\"nofollow\"\u003e\u003cstrong\u003eDPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees\u003c/strong\u003e\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors: Jeffrey M. Dudek, Vu H. N. Phan, Moshe Y. Vardi\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning this repository and its submodules\u003c/h2\u003e\u003ca id=\"user-content-cloning-this-repository-and-its-submodules\" class=\"anchor\" aria-label=\"Permalink: Cloning this repository and its submodules\" href=\"#cloning-this-repository-and-its-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive https://github.com/vardigroup/DPMC\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"./examples\"\u003eExamples\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgment\u003c/h2\u003e\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" aria-label=\"Permalink: Acknowledgment\" href=\"#acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/ADDMC\"\u003eADDMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/approxmc\"\u003eBIRD\u003c/a\u003e: Soos, Meel\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cs.rochester.edu/u/kautz/Cachet\" rel=\"nofollow\"\u003eCachet\u003c/a\u003e: Sang, Beame, Kautz\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/msoos/cryptominisat\"\u003eCryptoMiniSat\u003c/a\u003e: Soos\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003eCUDD package\u003c/a\u003e: Somenzi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://davidkebo.com/cudd#cudd6\" rel=\"nofollow\"\u003eCUDD visualization\u003c/a\u003e: Kebo\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts\u003c/a\u003e: Beck\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kit-algo/flow-cutter-pace17\"\u003eFlowCutter\u003c/a\u003e: Strasser\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mabseher/htd\"\u003ehtd\u003c/a\u003e: Abseher, Musliu, Woltran\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://reasoning.cs.ucla.edu/minic2d\" rel=\"nofollow\"\u003eminiC2D\u003c/a\u003e: Oztok, Darwiche\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mccompetition.org\" rel=\"nofollow\"\u003eModel Counting Competition\u003c/a\u003e: Hecher, Fichte\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cril.univ-artois.fr/KC/pmc.html\" rel=\"nofollow\"\u003epmc\u003c/a\u003e: Lagniez, Marquis\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Kasekopf/SlurmQueen\"\u003eSlurmQueen\u003c/a\u003e: Dudek\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://trolando.github.io/sylvan\" rel=\"nofollow\"\u003eSylvan\u003c/a\u003e: van Dijk\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/TCS-Meiji/PACE2017-TrackA\"\u003eTamaki\u003c/a\u003e: Tamaki\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/TensorOrder\"\u003eTensorOrder\u003c/a\u003e: Dudek, Duenas-Osorio, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/WAPS\"\u003eWAPS\u003c/a\u003e: Gupta, Sharma, Roy, Meel\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "Transipedia/KaMRaT", + "latest_release": "v1.0.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-kamrat\" class=\"anchor\" href=\"#kamrat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKaMRaT\u003c/h1\u003e\n\u003chr\u003e\n\u003cp\u003eKaMRaT is a C++ tool for finding substrings with interesting properties in large NGS datasets.\u003c/p\u003e\n\u003cp\u003eKaMRaT requires a k-mer count matrix extracted from the NGS files (e.g. with Jellyfish), and labels for each sample.\u003c/p\u003e\n\u003cp\u003eKaMRaT then provides a set of tools for reducing the k-mer matrix and extending k-mers to longer contigs. The main subfunctions are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ekamrat index: index feature* count table on disk\u003c/li\u003e\n\u003cli\u003ekamrat merge: merge k-mers into contigs, produces a contig count table\u003c/li\u003e\n\u003cli\u003ekamrat filter: exclude/retain features* by expression level\u003c/li\u003e\n\u003cli\u003ekamrat mask: exclude/retain \u003cem\u003ek\u003c/em\u003e-mers matching given fasta sequences\u003c/li\u003e\n\u003cli\u003ekamrat rank: rank features* according to labels and statistical test\u003c/li\u003e\n\u003cli\u003ekamrat query: estimate count vectors of given list of contigs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote: *\tfeatures can be not only \u003cem\u003ek\u003c/em\u003e-mers or \u003cem\u003ek\u003c/em\u003e-mer contigs, but also general features such as genes or transcripts.\u003c/p\u003e\n\u003cp\u003eKaMRaT means \"k-mer Matrix Reduction Toolkit\", or \"k-mer Matrix, Really Tremendous !\".\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start-demos\" class=\"anchor\" href=\"#quick-start-demos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start: Demos\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eindir=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo-data/inputs\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\noutdir=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo-data/outputs\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsample_list=(sample1 sample2)\ndsgnfile=\u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/rank-design.txt\nkmer_tab_path=\u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kmer-counts.tsv.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ek\u003c/em\u003e-mer matrix preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Step 1: jellyfish count \u0026amp; dump\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003es\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${sample_list[@]}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e $sample_list contains list of considered sample names\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n\tjellyfish count -m 31 -s 1000000 -C -o \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.jf -F 2 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;(\u003c/span\u003ezcat \u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.R1.fastq.gz\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;(\u003c/span\u003ezcat \u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.R2.fastq.gz\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\tjellyfish dump -c \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.jf \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sort -k 1 \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.txt \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;= here sort is important !\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Step 2: joinCounts\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e -n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etag\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gzip -c \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003es\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${sample_list[@]}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e $sample_list contains list of considered sample names\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n\t\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e -ne \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\\t\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gzip -c \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gzip -c \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif joinCounts -r 1 -a 1 \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.txt \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gzip -c \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e no filter of recurrence\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote: please keep in mind that the \u003ccode\u003esort\u003c/code\u003e after \u003ccode\u003ejellyfish dump\u003c/code\u003e is important for joinCounts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKaMRaT index\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make index for k-mer matrix with k=31, unstranded mode, and with a count per billion normalization\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat index -intab \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e -outdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -klen 31 -unstrand -nfbase 1000000000\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eKaMRaT rank-merge approach\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Select top 50% of relevant k-mers using ttest pi-value\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat rank -idxdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -rankby ttest.pi -design \u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/rank-design.txt -outpath \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/top-ranked-kmers.ttest-pi.bin -seltop 0.5\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extend k-mers by tolerating overlap from 30nc to 15nc, intervened by Pearson distance \u0026lt;= 0.20, and with mean contig count\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat merge -idxdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -overlap 30-15 -with \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/top-ranked-kmers.ttest-pi.bin -interv pearson:0.20 -outpath \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/contig-counts.ttest-pi.pearson20.tsv -withcounts mean\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eKaMRaT merge-rank approach\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extend k-mers by tolerating overlap from 30nc to 15nc, intervened by Pearson distance \u0026lt;= 0.20, and with mean contig count\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat merge -idxdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -overlap 30-15 -interv pearson:0.20 -outpath \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/contigs.pearson20.bin\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Select top 50% of relevant contigs using ttest pi-value\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat rank -idxdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -rankby ttest.pi -design \u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/rank-design.txt -seltop 0.5 -with \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/contigs.pearson20.bin -outpath \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/top-ranked-contigs.pearson20.ttest-pi.tsv -withcounts\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-typical-workflow-of-kamrat\" class=\"anchor\" href=\"#typical-workflow-of-kamrat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTypical Workflow of KaMRaT\u003c/h2\u003e\n\u003cp\u003eKaMRaT \u003cem\u003eper se\u003c/em\u003e is shown at the center of the workflow. It is a C++ program that takes as input a count matrix and produces another matrix as output.\nIn the workflow shown, KaMRaT is used for reducing a count matrix produced from a set of fastq files and producing a reduced matrix with features of interest with respect to conditions in the input sample-info file.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"./docs/workflow.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./docs/workflow.png\" alt=\"workflow\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe feature matrix contains features in row and samples in column. Features can be \u003cem\u003ek\u003c/em\u003e-mers (for all modules) as well as other general features such as genes/transcripts (only for KaMRaT-index, -filter, and -rank). The feature counts can be either normalized or non-normalized.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003ek\u003c/em\u003e-mer feature matrix can be constructed with the following possibilities:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ca href=\"./related-tools/prepare_kmer_table/Snakefile\"\u003eSnakefile\u003c/a\u003e provided with the project + \u003ca href=\"https://github.com/Transipedia/dekupl-joinCounts\"\u003eDE-kupl joinCounts\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Transipedia/dekupl-run\"\u003eDE-kupl\u003c/a\u003e\u0027s raw-counts.tsv or masked-counts.tsv matrices\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA set of auxiliary tools to be used for upstream and downstream of kamrat are provided:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUpstream tools:\n\u003cul\u003e\n\u003cli\u003eA matrix generating module controlled by Snakemake which applying jellyfish and DE-kupl joinCounts module\u003c/li\u003e\n\u003cli\u003eA bash script for generating a submatrix by selecting from it a set of columns\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDownstream tools:\n\u003cul\u003e\n\u003cli\u003eA feature selection model with an R script applying ridge/lasso regressions and random forest classifier\u003c/li\u003e\n\u003cli\u003eA contig counting module implemented in C++ for estimating the counts of a list of contigs in an independent dataset; it also supports evaluation of sample count coherence among contig\u0027s compositional k-mers\u003c/li\u003e\n\u003cli\u003eA model evaluation module written in R taking a trained model and evaluating it with a feature count matrix and feature conditions\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cdetails\u003e\n\u003csummary\u003eBuild from source\u003c/summary\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mlpack/mlpack/releases/tag/3.3.2\"\u003eMLPack 3.3.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.boost.org/doc/libs/1_74_0/libs/iostreams/doc/index.html\" rel=\"nofollow\"\u003eBoost-iostreams\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMLPack can be installed on \u003ca href=\"https://mlpack.org/doc/mlpack-3.3.2/doxygen/build.html\" rel=\"nofollow\"\u003eLinux/Mac\u003c/a\u003e, \u003ca href=\"https://mlpack.org/doc/mlpack-3.3.2/doxygen/build_windows.html\" rel=\"nofollow\"\u003eWindows\u003c/a\u003e, or via \u003ca href=\"https://anaconda.org/conda-forge/mlpack\" rel=\"nofollow\"\u003econda\u003c/a\u003e by following the corresponding links.\u003cbr\u003e\nIf you are installing MLPack with conda, please add the following line into your \u003ccode\u003e.bashrc\u003c/code\u003e file in the \u003ccode\u003ehome/\u003c/code\u003e directory before compiling KaMRaT:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=/path_to_conda_env/mlpack/lib:\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-clone-and-build\" class=\"anchor\" href=\"#clone-and-build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClone and Build\u003c/h3\u003e\n\u003cp\u003eFirstly, clone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive https://github.com/Transipedia/KaMRaT.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e KaMRaT\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you installed MLPack library with conda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash compile.bash /path_to_MLPack_conda_environment\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOtherwise, if you installed MLPack without conda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash compile.bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, an executable binary file is available as \u003ccode\u003ebin/kamrat\u003c/code\u003e.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eUse singularity\u003c/summary\u003e\n\u003cp\u003eIf using KaMRaT inside singularity, only by pulling from docker hub is enough:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build KaMRaT.sif docker://xuehl/kamrat:latest\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-general-information\" class=\"anchor\" href=\"#general-information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral Information\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-sample-information-file\" class=\"anchor\" href=\"#sample-information-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSample Information File\u003c/h3\u003e\n\u003cp\u003eThe sample-info file is indicated by the option \u003ccode\u003e-smp-info\u003c/code\u003e. This file aims to indicate which columns in the k-mer count matrix should be considered as sample columns. Please do not put any header line in the file, since the columns are already defined by convention as below.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf the file contains only one column, it indicates sample names, and all samples are considered as the same condition\u003c/li\u003e\n\u003cli\u003eIf the file contains two columns, the first column corresponds to sample names, and the second corresponds to conditions (\u003cem\u003ee.g.\u003c/em\u003e tumor, normal)\u003c/li\u003e\n\u003cli\u003eIf the file is not provided, all columns in the matrix apart from the first one are considered as samples\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input-count-matrix-for-kamrat\" class=\"anchor\" href=\"#input-count-matrix-for-kamrat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput Count Matrix for KaMRaT\u003c/h3\u003e\n\u003cp\u003eThe input count matrix should be in .tsv or .tsv.gz format, in which fields are separated by tabulations.\nIn the matrix, features are presented as rows, and samples as columns. The first column in matrix should always be the feature column (sequences or feature names).\u003cbr\u003e\n\"Features\" can be any quantified feature such as genes, k-mers or contigs. k-mers or contigs are represented by their own sequence.\nKaMRaT accepts extra columns representing non-count values, e.g. feature\u0027s p-value, score, etc. In this case, a smp-info file is mandatory for indicating which columns are the count columns.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-count-matrix-by-kamrat\" class=\"anchor\" href=\"#output-count-matrix-by-kamrat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput Count Matrix by KaMRaT\u003c/h3\u003e\n\u003cp\u003eThe output count matrix is also .tsv format table, where fields are separated by tabs.\u003cbr\u003e\nIn the matrix, the features are presented as rows, and the columns are in same order as the input.\u003cbr\u003e\nKaMRaT guarantees the information of output matrix is coherent with that of the input matrix. For KaMRaT-rank, though there are steps of count normalization, log transformation and standardization for score evaluation, the count values in output matrix are kept same as input (raw count).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eNote: if you use KaMRaT in command line, please remember to indicate the full path to KaMRaT binary file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-kamrat-execution\" class=\"anchor\" href=\"#kamrat-execution\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKaMRaT Execution\u003c/h3\u003e\n\u003cp\u003eWe recommande using KaMRaT within \u003ccode\u003esingularity\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /bind_src:/bind_des kamrat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCMD\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e [options] input_table \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;CMD\u0026gt; can be one of filter, mask, merge, rank\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e-B\u003c/code\u003e option is for binding disk partitions to singularity image, please check \u003ccode\u003esingularity\u003c/code\u003e helper for details:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s also executable directly on command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/path_to_KaMRaT_bin_dir/kamrat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCMD\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e [options] input_table \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;CMD\u0026gt; can be one of filter, mask, merge, rank\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn the following sections, we present under the situation of using KaMRaT in \u003ccode\u003esingularity\u003c/code\u003e.\u003cbr\u003e\nFor running it directly on command line, please replace the leading \u003ccode\u003esingularity exec -B /bind_src:/bind_des\u003c/code\u003e by the path to KaMRaT binary file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-kamrat-helper\" class=\"anchor\" href=\"#kamrat-helper\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKaMRaT Helper\u003c/h3\u003e\n\u003cp\u003eKaMRaT\u0027s top-level helper is accessible by typing one of these commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat -h\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat -help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHelpers of each KaMRaT modules are accessible via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;CMD\u0026gt; can be one from filter, mask, merge, rank #\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCMD\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -h\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCMD\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -help\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-kamrat-usage-by-module\" class=\"anchor\" href=\"#kamrat-usage-by-module\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKaMRaT Usage by Module\u003c/h3\u003e\n\u003cdetails\u003e\n\u003csummary\u003eindex: index feature count table on disk\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat index -intab STR -outdir STR [-klen INT -unstrand -nfbase INT]\n\n[OPTION] -h, -help Print the helper\n -intab STR Input table for index, mandatory\n -outdir STR Output index directory, mandatory\n -klen k-mer length, mandatory if features are k-mer\n if present, indexation will be switched to k-mer mode\n -unstrand Unstranded mode, indexation with canonical k-mers\n if present, indexation will be switched to k-mer mode\n -nfbase INT Base for calculating normalization factor\n normCount_ij \u0026lt;- INT * rawCount_ij / sum_i{rawCount_ij}\n if not provided, input counts will not be normalized\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003efilter: filter feature by expression level\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat filter -idxdir STR -design STR [-upmin INT1:INT2 -downmax INT1:INT2 -reverse -outpath STR -withcounts]\n\n[OPTION] -h,-help Print the helper\n -idxdir STR Indexing folder by KaMRaT index, mandatory\n -design STR Path to filter design file, a table of two columns, mandatory\n the first column indicate sample names\n the second column should be either UP or DOWN (capital letters)\n samples with UP will be considered as up-regulated samples\n samples with DOWN will be considered as down-regulated samples\n samples not given will be neutral (not considered for filter)\n samples can also be all UP or all DOWN\n -upmin INT1:INT2 Up feature lower bound, [1:1, meaning no filter]\n output features counting \u0026gt;= INT1 in \u0026gt;= INT2 UP-samples\n -downmax INT1:INT2 Down feature upper bound [inf:1, meaning no filter]\n output features counting \u0026lt;= INT1 in \u0026gt;= INT2 DOWN-samples\n -reverse Reverse filter, to remove eligible features [false]\n -outpath STR Path to results after filter\n if not provided, output to screen\n -withcounts Output sample count vectors [false]\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003emask: mask k-mers from matrix\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat mask -idxdir STR -fasta STR [-reverse -outpath STR -withcounts]\n \n[OPTION] -h,-help Print the helper\n -idxdir STR Indexing folder by KaMRaT index, mandatory\n -fasta STR Sequence fasta file as the mask, mandatory;\n -reverse Reverse mask, to select the k-mers in sequence fasta file [false];\n -outpath STR Path to extension results\n if not provided, output to screen\n -withcounts Output sample count vectors [false]\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003emerge: extend k-mers into contigs\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat merge -idxdir STR -overlap MAX-MIN [-with STR1[:STR2] -interv STR[:FLOAT] -min-nbkmer INT -outpath STR -withcounts STR]\n\n[OPTION] -h,-help Print the helper;\n -idxdir STR Indexing folder by KaMRaT index, mandatory;\n -overlap MAX-MIN Overlap range for extension, mandatory\n MIN and MAX are integers, MIN \u0026lt;= MAX \u0026lt; k-mer length;\n -with STR1[:STR2] File indicating k-mers to be extended (STR1) and rep-mode (STR2)\n if not provided, all indexed k-mers are used for extension\n in the file STR1, a supplementary column of rep-value can be provided\n STR2 can be one of {min, minabs, max, maxabs} [min];\n -interv STR[:FLOAT] Intervention method for extension [pearson:0.20]\n can be one of {none, pearson, spearman, mac}\n the threshold may follow a \u0027:\u0027 symbol;\n -min-nbkmer INT Minimal length of extended contigs [0];\n -outpath STR Path to extension results\n if not provided, output to screen;\n -withcounts STR Output sample count vectors, STR can be one of [mean, median]\n if not provided, output without count vector\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003erank: rank features according to their association with sample conditions\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat rank -idxdir STR -count-mode STR -rankby STR -design STR [-with STR1[:STR2] -seltop NUM -outpath STR -withcounts]\n\n[OPTION] -h,-help Print the helper\n -idxdir STR Indexing folder by KaMRaT index, mandatory\n -rankby STR Ranking method, mandatory, can be one of:\n ttest.padj adjusted p-value of t-test between conditions\n ttest.pi \\u03C0-value of t-test between conditions\n snr signal-to-noise ratio between conditions\n dids DIDS score\n lr:nfold accuracy by logistic regression classifier\n bayes:nfold accuracy by naive Bayes classifier\n svm:nfold accuracy on SVM classifier\n -design STR Path to file indicating sample-condition design\n without header line, each row can be either:\n sample name, sample condition\n sample name, sample condition, sample batch (only for lrc, nbc, and svm)\n -with STR1[:STR2] File indicating features to rank (STR1) and counting mode (STR2)\n if not provided, all indexed features are used for ranking\n STR2 can be one of [rep, mean, median]\n -seltop NUM Select top ranked features\n if NUM \u0026gt; 1, number of top features to select (should be integer)\n if 0 \u0026lt; NUM \u0026lt;= 1, ratio of top features to select\n if absent or NUM \u0026lt;= 0, output all features\n -outpath STR Path to ranking result\n if not provided, output to screen\n -withcounts Output sample count vectors [false]\n\n[NOTE] For ranking methods lrc, nbc, and svm, a univariate CV fold number (nfold) can be provided\n if nfold = 0, leave-one-out cross-validation\n if nfold = 1, without cross-validation, training and testing on the whole datset\n if nfold \u0026gt; 1, n-fold cross-validation\n For t-test ranking methods, a transformation log2(x + 1) is applied to sample counts\n For SVM ranking, sample counts standardization is applied feature by feature\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003equery: query sequences\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat query -idxdir STR -fasta STR -toquery STR [-withabsent -outpath STR]\n\n[OPTION] -h,-help Print the helper\n -idxdir STR Indexing folder by KaMRaT index, mandatory\n -fasta STR Sequence fasta file, mandatory\n -toquery STR Query method, mandatory, can be one of:\n mean mean count among all composite k-mers for each sample\n median median count among all composite k-mers for each sample\n -withabsent Output also absent queries (count vector all 0) [default: false]\n -outpath STR Path to extension results\n if not provided, output to screen\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-softwarelibrary-citations\" class=\"anchor\" href=\"#softwarelibrary-citations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware/Library Citations\u003c/h2\u003e\n\u003cp\u003eArmadillo:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eConrad Sanderson and Ryan Curtin. Armadillo: a template-based C++ library for linear algebra. Journal of Open Source Software, Vol. 1, pp. 26, 2016.\u003c/li\u003e\n\u003cli\u003eConrad Sanderson and Ryan Curtin. A User-Friendly Hybrid Sparse Matrix Class in C++. Lecture Notes in Computer Science (LNCS), Vol. 10931, pp. 422-430, 2018.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://www.boost.org/\" rel=\"nofollow\"\u003eBoost C++ Library\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDE-kupl: Audoux, J., Philippe, N., Chikhi, R. et al. DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition. Genome Biol 18, 243 (2017).\u003c/p\u003e\n\u003cp\u003eMLPack: R.R. Curtin, M. Edel, M. Lozhnikov, Y. Mentekidis, S. Ghaisas, S. Zhang. mlpack 3: a fast, flexible machine learning library. Journal of Open Source Software 3:26, 2018.\u003c/p\u003e\n\u003cp\u003eglmnet: Friedman, Jerome, Trevor Hastie, and Rob Tibshirani. \"Regularization paths for generalized linear models via coordinate descent.\" Journal of statistical software 33.1 (2010): 1.\u003c/p\u003e\n\u003cp\u003erandomForest: Liaw, Andy, and Matthew Wiener. \"Classification and regression by randomForest.\" R news 2.3 (2002): 18-22.\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 4, + "subscribers_count": 6, "topics": [], - "updated_at": 1724159401.0 + "updated_at": 1649668794.0 }, { "data_format": 2, - "description": "One instance example with singularity-compose", + "description": null, "filenames": [ - "app/Singularity" + "Singularity" ], - "full_name": "singularityhub/singularity-compose-simple", + "full_name": "yngvem/ntnu-analysis", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Compose Simple\u003c/h1\u003e\u003ca id=\"user-content-singularity-compose-simple\" class=\"anchor\" aria-label=\"Permalink: Singularity Compose Simple\" href=\"#singularity-compose-simple\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a simple, dummy example of creating a web application with\n\u003ca href=\"https://singularityhub.github.io/singularity-compose/\" rel=\"nofollow\"\u003esingularity-compose\u003c/a\u003e\nusing just one container. The multiple container\nexample (that for some may require an update to Singularity) can be found at\n\u003ca href=\"https://www.github.com/singularityhub/singularity-compose-example\"\u003esingularityhub/singularity-compose-example\u003c/a\u003e.\nBoth are based on \u003ca href=\"https://github.com/vsoch/django-nginx-upload\"\u003edjango-nginx-upload\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esingularity-compose.yml\u003c/h3\u003e\u003ca id=\"user-content-singularity-composeyml\" class=\"anchor\" aria-label=\"Permalink: singularity-compose.yml\" href=\"#singularity-composeyml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a singularity-compose project, it\u0027s expected to have a \u003ccode\u003esingularity-compose.yml\u003c/code\u003e\nin the present working directory. You can look at the \u003ca href=\"singularity-compose.yml\"\u003eexample\u003c/a\u003e\npaired with the \u003ca href=\"https://github.com/singularityhub/singularity-compose/tree/master/spec\"\u003especification\u003c/a\u003e\nto understand the fields provided.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstance folders\u003c/h3\u003e\u003ca id=\"user-content-instance-folders\" class=\"anchor\" aria-label=\"Permalink: Instance folders\" href=\"#instance-folders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerally, each section in the yaml file corresponds with a container instance to be run,\nand each container instance is matched to a folder in the present working directory.\nFor example, if I give instruction to build an \u003ccode\u003enginx\u003c/code\u003e instance from\na \u003ccode\u003enginx/Singularity.nginx\u003c/code\u003e file, I should have the\nfollowing in my singularity-compose:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nginx:\n build:\n context: ./nginx\n recipe: Singularity.nginx\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003epaired with the following directory structure:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity-compose-example\n\u251c\u2500\u2500 nginx\n...\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.nginx\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 uwsgi_params.par\n\u2514\u2500\u2500 singularity-compose.yml\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNotice how I also have other dependency files for the nginx container\nin that folder. While the context for starting containers with Singularity\ncompose is the directory location of the \u003ccode\u003esingularity-compose.yml\u003c/code\u003e,\nthe build context for this container is inside the nginx folder.\nAs another option, you can just define a container to pull,\nand it will be pulled to the same folder that is created if it doesn\u0027t exist.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nginx:\n image: docker://nginx\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity-compose-example\n\u251c\u2500\u2500 nginx (- created \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e it doesn\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003et exist\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u2502\u00a0\u00a0 \u2514\u2500\u2500 nginx.sif (- named according to the instance\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u2514\u2500\u2500 singularity-compose.yml\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s less likely that you will be able to pull a container that is ready to\ngo, as typically you will want to customize the\n\u003ca href=\"https://sylabs.io/guides/3.2/user-guide/definition_files.html#startscript\" rel=\"nofollow\"\u003estartscript\u003c/a\u003e\nfor the instance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest way to start is to build the one required container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then bring it up!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eVerify it\u0027s running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose ps\nINSTANCES NAME PID IMAGE\n1 app\t20023\tapp.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then look at logs, shell inside, or execute a command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs app\n$ singularity-compose logs app --tail 30\n$ singularity-compose shell app\n$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e app uname -a\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen you open your browser to \u003ca href=\"http://127.0.0.1\" rel=\"nofollow\"\u003ehttp://127.0.0.1\u003c/a\u003e\nyou should see the upload interface.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/upload.png\"\u003e\u003cimg src=\"img/upload.png\" alt=\"img/upload.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you drop a file in the box (or click\nto select) we will use the nginx-upload module to send it directly to the\nserver. Cool!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/content.png\"\u003e\u003cimg src=\"img/content.png\" alt=\"img/content.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is just a simple Django application, the database is sqlite3, in the\napp folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls app/\napp.sif db.sqlite3 manage.py nginx requirements.txt run_uwsgi.sh Singularity upload uwsgi.ini\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe images are stored in \u003ca href=\"\"\u003eimages\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls images/\n2018-02-20-172617.jpg 40-acos.png _upload \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd static files are in \u003ca href=\"static\"\u003estatic\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls static/\nadmin css js\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you look at the \u003ca href=\"singularity-compose.yml\"\u003esingularity-compose.yml\u003c/a\u003e, we bind these\nfolders to locations in the container where the web server needs write. This is likely\na prime different between Singularity and Docker compose - Docker doesn\u0027t need\nbinds for write, but rather to reduce isolation. Continue below to\nread about networking, and see these commands in detail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNetworking\u003c/h2\u003e\u003ca id=\"user-content-networking\" class=\"anchor\" aria-label=\"Permalink: Networking\" href=\"#networking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen you bring the container up, you\u0027ll see generation of an \u003ccode\u003eetc.hosts\u003c/code\u003e file,\nand if you guessed it, this is indeed bound to \u003ccode\u003e/etc/hosts\u003c/code\u003e in the container.\nLet\u0027s take a look:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e10.22.0.2\tapp\n127.0.0.1\tlocalhost\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The following lines are desirable for IPv6 capable hosts\u003c/span\u003e\n::1 ip6-localhost ip6-loopback\nfe00::0 ip6-localnet\nff00::0 ip6-mcastprefix\nff02::1 ip6-allnodes\nff02::2 ip6-allrouters\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis file will give each container that you create (in our case, just one)\na name on its local network. Singularity by default creates a bridge for\ninstance containers, which you can conceptually think of as a router,\nThis means that, if I were to reference the hostname \"app\" in a second container,\nit would resolve to \u003ccode\u003e10.22.0.2\u003c/code\u003e. Singularity compose does this by generating\nthese addresses before creating the instances, and then assigning them to it.\nIf you would like to see the full commands that are generated, run the up\nwith \u003ccode\u003e--debug\u003c/code\u003e (binds and full paths have been removed to make this easier to read).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity instance start \\\n --bind /home/vanessa/Documents/Dropbox/Code/singularity/singularity-compose-simple/etc.hosts:/etc/hosts \\\n --net --network-args \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eportmap=80:80/tcp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --network-args \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eIP=10.22.0.2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --hostname app \\\n --writable-tmpfs app.sif app\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommands\u003c/h2\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following commands are currently supported. Remember, you must be in the\npresent working directory of the compose file to reference the correct instances.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild\u003c/h3\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild will either build a container recipe, or pull a container to the\ninstance folder. In both cases, it\u0027s named after the instance so we can\neasily tell if we\u0027ve already built or pulled it. This is typically\nthe first step that you are required to do in order to build or pull your\nrecipes. It ensures reproducibility because we ensure the container binary\nexists first.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe working directory is the parent folder of the singularity-compose.yml file.\nIf the build requires sudo (if you\u0027ve defined sections in the config that warrant\nsetting up networking with sudo) the build will instead give you an instruction\nto run with sudo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate\u003c/h3\u003e\u003ca id=\"user-content-create\" class=\"anchor\" aria-label=\"Permalink: Create\" href=\"#create\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven that you have built your containers with \u003ccode\u003esingularity-compose build\u003c/code\u003e,\nyou can create your instances as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose create\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUp\u003c/h3\u003e\u003ca id=\"user-content-up\" class=\"anchor\" aria-label=\"Permalink: Up\" href=\"#up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to both build and bring them up, you can use \"up.\" Note that for\nbuilds that require sudo, this will still stop and ask you to build with sudo.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUp is typically the command that you want to use to bring containers up and down.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eps\u003c/h3\u003e\u003ca id=\"user-content-ps\" class=\"anchor\" aria-label=\"Permalink: ps\" href=\"#ps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can list running instances with \"ps\":\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose ps\nINSTANCES NAME PID IMAGE\n1 app\t6659\tapp.sif\n2 db\t6788\tdb.sif\n3 nginx\t6543\tnginx.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShell\u003c/h3\u003e\u003ca id=\"user-content-shell\" class=\"anchor\" aria-label=\"Permalink: Shell\" href=\"#shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s sometimes helpful to peek inside a running instance, either to look at permissions,\ninspect binds, or manually test running something.\nYou can easily shell inside of a running instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose shell app\nSingularity app.sif:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/singularity/singularity-compose-example\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExec\u003c/h3\u003e\u003ca id=\"user-content-exec\" class=\"anchor\" aria-label=\"Permalink: Exec\" href=\"#exec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can easily execute a command to a running instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e app ls /\nbin\nboot\ncode\ndev\nenvironment\netc\nhome\nlib\nlib64\nmedia\nmnt\nopt\nproc\nroot\nrun\nsbin\nsingularity\nsrv\nsys\ntmp\nusr\nvar\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDown\u003c/h3\u003e\u003ca id=\"user-content-down\" class=\"anchor\" aria-label=\"Permalink: Down\" href=\"#down\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can bring one or more instances down (meaning, stopping them) by doing:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose down\nStopping (instance:nginx)\nStopping (instance:db)\nStopping (instance:app)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo stop a custom set, just specify them:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose down nginx\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLogs\u003c/h3\u003e\u003ca id=\"user-content-logs\" class=\"anchor\" aria-label=\"Permalink: Logs\" href=\"#logs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can of course view logs for all instances, or just specific named ones:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs --tail 10\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs app --tail 10\napp OUT\nRunning migrations:\n No migrations to apply.\nNo changes detected \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003emain\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nOperations to perform:\n Apply all migrations: admin, auth, contenttypes, main, sessions\nRunning migrations:\n No migrations to apply.\n\n0 static files copied to \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/var/www/static\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e, 121 unmodified.\n\n\napp ERR\nFri Jun 21 10:06:34 2019 - WSGI app 0 (mountpoint=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e) ready \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e 0 seconds on interpreter 0x557dc822b920 pid: 27 (default app)\nFri Jun 21 10:06:34 2019 - uWSGI running as root, you can use --uid/--gid/--chroot options\nFri Jun 21 10:06:34 2019 - \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e WARNING: you are running uWSGI as root \u003cspan class=\"pl-k\"\u003e!!!\u003c/span\u003e (use the --uid flag) \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e \nFri Jun 21 10:06:34 2019 - \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e uWSGI is running \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e multiple interpreter mode \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e\nFri Jun 21 10:06:34 2019 - spawned uWSGI master process (pid: 27)\nFri Jun 21 10:06:34 2019 - spawned uWSGI worker 1 (pid: 29, cores: 1)\nFri Jun 21 10:13:02 2019 - SIGINT/SIGQUIT received...killing workers...\nFri Jun 21 10:13:03 2019 - worker 1 buried after 1 seconds\nFri Jun 21 10:13:03 2019 - goodbye to uWSGI.\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e## Config\u003c/span\u003e\n\nYou can load and validate the configuration file (singularity-compose.yml) and\nprint it to the screen as follows:\n\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ebash\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e$ singularity-compose config\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e{\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eversion\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1.0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einstances\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebuild\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./nginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003erecipe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSingularity.nginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./nginx.conf:/etc/nginx/conf.d/default.conf:ro\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./uwsgi_params.par:/etc/nginx/uwsgi_params.par:ro\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.:/code\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./static:/var/www/static\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./images:/var/www/images\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes_from\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eapp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eports\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eimage\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edocker://postgres:9.4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edb-data:/var/lib/postgresql/data\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eapp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebuild\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./app\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.:/code\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./static:/var/www/static\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./images:/var/www/images\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eports\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e5000:80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edepends_on\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 4, - "subscribers_count": 3, - "topics": [ - "singularity-compose", - "singularity", - "orchestration" - ], - "updated_at": 1678246913.0 - }, - { - "data_format": 2, - "description": "A re-write of the gemBS pipeline framework in Rust", - "filenames": [ - "Singularity", - "texlive/Singularity.tex" - ], - "full_name": "heathsc/gemBS-rs", - "latest_release": "v4.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egemBS-rs\u003c/h1\u003e\u003ca id=\"user-content-gembs-rs\" class=\"anchor\" aria-label=\"Permalink: gemBS-rs\" href=\"#gembs-rs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA rewrite of the \u003ca href=\"https://github.com/heathsc/gemBS\"\u003egemBS\u003c/a\u003e pipeline\nframework from Python/C into Rust.\u003c/p\u003e\n\u003cp\u003egemBS is a high performance bioinformatic pipeline designed for highthroughput analysis\nof DNA methylation data from whole genome bisulfites sequencing data\n(WGBS). It combines GEM3, a high performance read aligner and\nbs_call, a high performance variant and methyation caller, into a streamlined and efficient pipeline for\nbisulfite sequence analysis.\u003c/p\u003e\n\u003cp\u003eThe manuscript describing the original gemBS pipeline is available\n\u003ca href=\"https://doi.org/10.1093/bioinformatics/bty690\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe rewrite of the pipeline into Rust has two aims: (1) to have a more\nrobust pipeline and (2) to provide a more flexible platform for future\ndevelopments. All of the tools developed for the pipeline except for the GEM3 mapper (being an external project that is also very stable!) have now been re-written in Rust. These include bs_call, the methylation and SNV-variant caller, and the methylation and SNP extractions tools mextr and snpxtr. In all cases the running times are comparable to the original C versions.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline uses samtools for generating sorted BAM/CRAM files from GEM3 and bcftools for merging and indexing BCF files produced by bs_call. In addition, many of the tools link to htslb to enable reading of BAM/CRAM and reading/writing of BCF files. Samtools and htslib are automatically installed during the installation of the gemBS pipeline. There is also an optional dependency on TeXLive which is used to produce pdf versions of the QC reports. If requested by the user this is also installed with the pipeline.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicensing\u003c/h2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-label=\"Permalink: Licensing\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003egemBS is licensed under the GPL.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload\u003c/h2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-label=\"Permalink: Download\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse \u003ccode\u003egit clone --recursive\u003c/code\u003e to retrieve the complete source code including the code from external projects such as \u003ccode\u003egem3-mapper\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS-rs.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfigure \u0026amp; Install\u003c/h2\u003e\u003ca id=\"user-content-configure--install\" class=\"anchor\" aria-label=\"Permalink: Configure \u0026amp; Install\" href=\"#configure--install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore starting the installation of gemBS, you will need to install\nor check the installation of several packages.\u003c/p\u003e\n\u003cp\u003ea) gcc with development libraries\u003c/p\u003e\n\u003cp\u003eb) rust (for installation instructions see \u003ca href=\"https://www.rust-lang.org/learn/get-started\" rel=\"nofollow\"\u003ehere\u003c/a\u003e). Note that if you have rust already installed you should update it using \u003ccode\u003erustup update\u003c/code\u003e before trying to compile gemBS.\u003c/p\u003e\n\u003cp\u003ec) zlib, libz2, lzma, openssl, libcurl, libncurses, wget, expat, ncurses, openssl, freetype, fontconfig\u003c/p\u003e\n\u003cp\u003eIf you are working on a clean (fairly recent) Ubuntu installation, you\ncan install everything required with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapt-get install -y build-essential git autoconf wget lbzip2 pkg-config cmake\napt-get install -y zlib1g-dev libbz2-dev libexpat1-dev\napt-get install -y libncurses5-dev liblzma-dev libssl-dev libcurl4-openssl-dev curl\napt-get install -y libfreetype6-dev libfontconfig1-dev\ncurl https://sh.rustup.rs -sSf \u0026gt; rust.sh \u0026amp;\u0026amp; sh rust.sh -y\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload the gemBS distribution if you haven\u0027t already done so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS-rs.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom the main gemBS-rs directory type the following to make the default config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake gemBS_config.mk\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen look at the file gemBS_config.mk and make any changes that are required. When the file is OK the pipeline and components can be built and installed by typing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf the make and install process is successful, a shell script called gemBS will be created in the main gemBS-rs directory. This file should be copied to a directory that is in your PATH so that gemBS can be invoked from anywhere.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCheck your installation\u003c/h2\u003e\u003ca id=\"user-content-check-your-installation\" class=\"anchor\" aria-label=\"Permalink: Check your installation\" href=\"#check-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor checking your installation follow this\n\u003ca href=\"http://statgen.cnag.cat/gemBS/UserGuide/_build/html/example.html\" rel=\"nofollow\"\u003eworked example\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation can be found at\n\u003ca href=\"http://statgen.cnag.cat/gemBS/\" rel=\"nofollow\"\u003egemBS\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChangelog:\u003c/h2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-label=\"Permalink: Changelog:\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e4.0 First release of gemBS-rs (4th release of gemBS)\n4.0.1 Correct bug preventing that caused non-stranded mapping to fail\n4.0.2 Move to versions 1.12 of htslib/samtools/bcftools\n4.0.2 Change way we iterate over SAM/BAM/CRAM files to the same way used in samtools \n view (the old way did not always work with cram files)\n4.0.3 Fix problem with reading BCF files from older versions of\n gemBS where the CX format string was null terminated\n4.0.4 Add max_template_length option to gemBS (option passed on to bs_call)\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-code-for-autodelineation-experiments-on-mri-data\" class=\"anchor\" href=\"#code-for-autodelineation-experiments-on-mri-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode for autodelineation experiments on MRI data\u003c/h1\u003e\n\u003cp\u003eStart by running \u003ccode\u003esetup.sh\u003c/code\u003e to download the singularity container\nThen, submit slurm jobs like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esbatch slurm.sh json/dice/dwi.json dwi_dice 200\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhich will load the setup from the \u003ccode\u003ejson/dice/dwi.json\u003c/code\u003e file, train for 200 epochs\nand store the results in the folder \u003ccode\u003e$HOME/logs/ntnu/dwi_dice/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, if your cluster does not have slurm installed, simply omit the \u003ccode\u003esbatch\u003c/code\u003e\npart of the call above, thus running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./slurm.sh json/dice/dwi.json dwi_dice 200\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 4, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1719174318.0 + "updated_at": 1614353943.0 }, { "data_format": 2, - "description": "Cosmic muon radiography with GEANT4", + "description": "The Recommender Engine for Intelligent Transient Tracking", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "gipert/mugraphy", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\u00b5graphy\u003c/h1\u003e\u003ca id=\"user-content-\u00b5graphy\" class=\"anchor\" aria-label=\"Permalink: \u00b5graphy\" href=\"#\u00b5graphy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/89cba1dfd31cc5a094db27503998be8899ddbdddbe5642b47399b959dcb3e122/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f6769706572742f6d756772617068793f6c6f676f3d676974\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/89cba1dfd31cc5a094db27503998be8899ddbdddbe5642b47399b959dcb3e122/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f6769706572742f6d756772617068793f6c6f676f3d676974\" alt=\"GitHub tag (latest by date)\" data-canonical-src=\"https://img.shields.io/github/v/tag/gipert/mugraphy?logo=git\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7b50856d9c2f0cf69012cffe36a7f7e06e1340dda987c983b57bde5f762ebde2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f776f726b666c6f772f7374617475732f6769706572742f6d756772617068792f43492f6d61696e3f6c6162656c3d6d61696e2532306272616e6368266c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b50856d9c2f0cf69012cffe36a7f7e06e1340dda987c983b57bde5f762ebde2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f776f726b666c6f772f7374617475732f6769706572742f6d756772617068792f43492f6d61696e3f6c6162656c3d6d61696e2532306272616e6368266c6f676f3d676974687562\" alt=\"GitHub Workflow Status (main)\" data-canonical-src=\"https://img.shields.io/github/workflow/status/gipert/mugraphy/CI/main?label=main%20branch\u0026amp;logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d5fa5a6bbc8a916468858bf60c9c62ac62eb65cd33f2e80c16dd6ac31141904d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6769706572742f6d756772617068793f6c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d5fa5a6bbc8a916468858bf60c9c62ac62eb65cd33f2e80c16dd6ac31141904d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6769706572742f6d756772617068793f6c6f676f3d676974687562\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/gipert/mugraphy?logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7db20acc3a02f87b3c6fc50da591d38be59210d061a373822c616d61502d1675/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d70722f6769706572742f6d756772617068793f6c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7db20acc3a02f87b3c6fc50da591d38be59210d061a373822c616d61502d1675/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d70722f6769706572742f6d756772617068793f6c6f676f3d676974687562\" alt=\"GitHub pull requests\" data-canonical-src=\"https://img.shields.io/github/issues-pr/gipert/mugraphy?logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/72372b69c470b0eea2309e972bce50a7bc967012ea7ec6d38c20b9bd708fa537/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6769706572742f6d75677261706879\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72372b69c470b0eea2309e972bce50a7bc967012ea7ec6d38c20b9bd708fa537/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6769706572742f6d75677261706879\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/gipert/mugraphy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSimulate the propagation of cosmic muons through large structures and reveal their internal composition. Inspired by \u003ca href=\"https://www.nature.com/articles/nature24647\" rel=\"nofollow\"\u003e\u003cem\u003eMorishima et al. Nature (2017)\u003c/em\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".github/mugraphy.png\"\u003e\u003cimg src=\".github/mugraphy.png\" alt=\"Simulation visualization\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "refitt/refitt", + "latest_release": "0.19.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-pytorch-singularity\" class=\"anchor\" href=\"#pytorch-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epytorch-singularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4939\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains Singularity definition files used for PyTorch development in the Sinzlab.\u003c/p\u003e\n", "stargazers_count": 4, "subscribers_count": 2, "topics": [ - "cosmic-rays", - "educational", - "analysis", - "simulation", - "challenge" + "science", + "astronomy", + "distributed-systems", + "machine-learning", + "citizen-science", + "open-source", + "python" ], - "updated_at": 1680666340.0 + "updated_at": 1628307502.0 }, { "data_format": 2, - "description": "19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology", + "description": "psychopy scripts for stimuli presentations", "filenames": [ - "Singularity" + "docker/Singularity" ], - "full_name": "4ment/marginal-experiments", + "full_name": "courtois-neuromod/task_stimuli", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology\u003c/h1\u003e\u003ca id=\"user-content-19-dubious-ways-to-compute-the-marginal-likelihood-of-a-phylogenetic-tree-topology\" class=\"anchor\" aria-label=\"Permalink: 19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology\" href=\"#19-dubious-ways-to-compute-the-marginal-likelihood-of-a-phylogenetic-tree-topology\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the pipeline and data sets supporting the results of the following article:\u003c/p\u003e\n\u003cp\u003eFourment M, Magee A, Whidden C, Bilge A, Matsen IV FA, Minin VN. 19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology. \u003ca href=\"https://arxiv.org/abs/1811.11804\" rel=\"nofollow\"\u003earXiv:1811.11804\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRequirements\u003c/h1\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"https://github.com/4ment/physher\"\u003ephysher\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-physher\" class=\"anchor\" aria-label=\"Permalink: physher\" href=\"#physher\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo reproduce the analysis the release \u003ca href=\"https://github.com/4ment/physher/releases/tag/marginal-v1.1\"\u003emarginal-v1.1\u003c/a\u003e should be used and the executable should be located in the \u003ccode\u003ebin\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the simulations\u003c/h1\u003e\u003ca id=\"user-content-running-the-simulations\" class=\"anchor\" aria-label=\"Permalink: Running the simulations\" href=\"#running-the-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e marginal-experiments\npython run_simulations.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor using Docker (no need to install physher)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e marginal-experiments\ndocker pull 4ment/marginal-experiments\ndocker run -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data 4ment/marginal-experiments\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe simulations will take several weeks to complete. Multiple directories will be produced (DS1, DS2, DS3, DS4, DS5).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParsing results\u003c/h1\u003e\u003ca id=\"user-content-parsing-results\" class=\"anchor\" aria-label=\"Permalink: Parsing results\" href=\"#parsing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRscript -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ermarkdown::render(\"DS.Rmd\")\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe script will generate the file \u003ccode\u003eDS.pdf\u003c/code\u003e.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-task_stimuli\" class=\"anchor\" aria-hidden=\"true\" href=\"#task_stimuli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etask_stimuli\u003c/h1\u003e\n\u003cp\u003eThis software is a set of cognitive tasks developed in psychopy and a system to schedule sets of tasks during a session.\u003c/p\u003e\n\u003cp\u003eTasks are classes defined in \u003ccode\u003esrc/tasks\u003c/code\u003e, and are instantiated in \u003ccode\u003esrc/sessions\u003c/code\u003e files that describe a set of tasks in the session.\u003c/p\u003e\n\u003cp\u003eMaterial for the task (images/movies/lists...) is stored mainly in \u003ccode\u003edata\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOutputs (logs, responses) are stored in the \u003ccode\u003eoutput\u003c/code\u003e folder and try to mimic a BIDS structure.\u003c/p\u003e\n\u003cp\u003eWhen used with option \u003ccode\u003e--fmri\u003c/code\u003e tasks waits for a new TTL character to start.\u003c/p\u003e\n\u003cp\u003eWhen used with the option \u003ccode\u003e--eyetracking\u003c/code\u003e this software will start Pupil, and trigger the recording of the eye movie and detected pupil position, which outputs to the \u003ccode\u003eoutput\u003c/code\u003e folder in a BIDS-like way.\nNote that eyetracking data would require offline post/re-processing to be used and shared.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eutils\u003c/code\u003e contains scripts to prepare movies in a reproducible way using the melt command line video editor in singularity.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-attributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#attributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAttributions\u003c/h2\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eINSTALL\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eapt install python3-pip git\nmkdir git\ncd git\n\n# this section is optional, only if using eyetracking\ngit clone https://github.com/pupil-labs/pupil.git\n# follow instructions at https://docs.pupil-labs.com/#linux-dependencies\n\npip3 install git+https://github.com/psychopy/psychopy.git\n# modify the file in psychopy that crashes\npip3 install scikit-video\n\ngit clone git@github.com:courtois-neuromod/task_stimuli.git\ncd task_stimuli\nmkdir output\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-launch-a-session\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-launch-a-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehow to launch a session\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003epython3 main.py --subject test --session video003 --tasks videoshorttest --eyetracking --fmri -o /path/to/dataset/\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e--subject: can be whatever, will be used to save data in a bids-like structure\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--session: a session identifier that will be used to save the data in the BIDS\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--tasks: must match the name of a session script in \u003ccode\u003esrc/ses-\u0026lt;session_name\u0026gt;.py\u003c/code\u003e, which contains the tasks to be ran on that session\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--eyetracking: turn on eyetracking, start pupil software and recording of eye\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e-o : specifies the path to the root of the dataset where to output the data (in sourcedata or BIDS )\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--fmri: will wait for TTL (can be emulated with character \u003ccode\u003e5\u003c/code\u003e on the keyboard) to start the tasks that are labeled as fmri dependent. When not using that flag, tasks will run back to back. It will also append a video loop at the beginning of the session in order for the participant to have sound and visual stimuli to test the setup (then skip to start the session).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--meg: TODO!\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run multiple time this command, there are no risks of overwriting, the data will be suffixed by the date and time of start of the session.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-creating-session-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-session-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating session files\u003c/h2\u003e\n\u003cp\u003eYou can create new sessions by adding a \u003ccode\u003eses-xxxx.py\u003c/code\u003e file in \u003ccode\u003esrc/sessions\u003c/code\u003e folder.\nEach file only create a \u003ccode\u003eTASKS\u003c/code\u003e list of task subclasses instances, that is loaded by the script and ran in the provided order.\nCheck the existing files for examples.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-interact-with-the-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-interact-with-the-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to interact with the software:\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-stimuli\" class=\"anchor\" aria-hidden=\"true\" href=\"#stimuli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estimuli\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e5\u003c/code\u003e: emulate the trigger of MRI and start task \"by hand\" (can be changed in \u003ccode\u003esrc/shared/fmri.py\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;ctrl\u0026gt;-c\u003c/code\u003e : abort and \u003cstrong\u003eskip\u003c/strong\u003e the current task and move to the next one\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;ctrl\u0026gt;-n\u003c/code\u003e : abort the task and restart it, showing again the instruction\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;ctrl\u0026gt;-q\u003c/code\u003e : quit the session, saves and close the eyetracking software\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf (and only if) the software stop responding and you cannot quit, switch to the terminal and kill the software with \u003ccode\u003e\u0026lt;ctrl\u0026gt;-c\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-eyetracking\" class=\"anchor\" aria-hidden=\"true\" href=\"#eyetracking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeyetracking\u003c/h3\u003e\n\u003cp\u003eThere are \"hotkeys in the pupil software to trigger actions\", use the buttons with these letters or type.\nC (-c): launch the calibration of the eyetracking, showing markers to the participant\nT (-t): a test of the calibration accuracy, also showing markers on the screen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant: there are two softwares running, Psychopy and Pupil, when done with calibration, click on the Stimuli window to give the focus back to Psychopy, otherwise it will not get the TTL and the task will not start with the scanner.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a problem that has to be fixed in the future to avoid failed acquisition start.\nUpdate: should be fixed now, the software takes focus when task is loaded.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esource code\u003c/h1\u003e\n\u003cp\u003epsychopy scripts for stimuli presentations\u003c/p\u003e\n\u003cp\u003esrc/tasks contains scripts for tasks\u003c/p\u003e\n\u003cp\u003esrc/shared folder should factorize the code common across tasks\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-eyetracking-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#eyetracking-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeyetracking\u003c/h2\u003e\n\u003cp\u003eThe eyetracking part is managed by launching pupil capture software and launching a single recording for the whole session.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-calibration\" class=\"anchor\" aria-hidden=\"true\" href=\"#calibration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecalibration\u003c/h3\u003e\n\u003cp\u003eRun a short calibration task where the subjects have to look at points shown on the screen\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-gazemapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#gazemapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egazemapping\u003c/h3\u003e\n\u003cp\u003eOnce the calibration has been run (though it seems that pupil reload previous calibration), pupil produces gaze information that corresponds to position on the screen.\nWe then display that information in almost real-time on the experimenter screen.\u003c/p\u003e\n", "stargazers_count": 4, "subscribers_count": 5, "topics": [ - "marginal-likelihood", - "data", - "data-visualization", - "phylogenetics" + "acquisition", + "stimuli", + "psychopy" ], - "updated_at": 1603692340.0 + "updated_at": 1637149073.0 }, { "data_format": 2, - "description": "Creates Manhattan and QQ plots with annotated peaks.", + "description": null, "filenames": [ - "Singularity" + "idmtools_test/idmtools_test/inputs/singularity/alpine_simple/Singularity.def", + "idmtools_test/idmtools_test/inputs/singularity/alpine_template/Singularity.jinja" ], - "full_name": "hmgu-itg/man_qq_annotate", - "latest_release": "v0.2.3", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eman_qq_annotate\u003c/h1\u003e\u003ca id=\"user-content-man_qq_annotate\" class=\"anchor\" aria-label=\"Permalink: man_qq_annotate\" href=\"#man_qq_annotate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreates Manhattan and QQ plots with annotated peaks for sequencing-based GWAS outputs, by thinning the dataset to what the eye can see.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall using \u003ccode\u003edevtools\u003c/code\u003e (in R):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install devtools if you don\u0027t have it\u003c/span\u003e\ninstall.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003edevtools\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003edevtools\u003c/span\u003e)\ninstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehmgu-itg/man_qq_annotate\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can either use the CLI or load the package into your R environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand Line Interface (CLI)\u003c/h3\u003e\u003ca id=\"user-content-command-line-interface-cli\" class=\"anchor\" aria-label=\"Permalink: Command Line Interface (CLI)\" href=\"#command-line-interface-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce installed, you can use the \u003ccode\u003emanqq_cli\u003c/code\u003e script in the base of the repository as a command line tool.\u003cbr\u003e\nFor a GCTA output, use the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./manqq_cli --chr-col Chr --pval-col p --pos-col bp --a1 A1 --a2 A2 --build 38 --image png --af-col Freq input.assoc.txt.gz output.prefix\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can add \u003ccode\u003emanqq_cli\u003c/code\u003e to your \u003ccode\u003ePATH\u003c/code\u003e variable for convenient execution:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/man_qq_annotate:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Or to make this permanent:\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=\"/path/to/man_qq_annotate:$PATH\"\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInput files can be gzipped or plain. Run without arguments for a list of options, run with \u003ccode\u003e--help\u003c/code\u003e for detailed options:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eusage: ./manqq_cli [-h] [--chr-col [character]] [--pval-col [character]]\n [--pos-col [character]] [--a1 [character]]\n [--a2 [character]] [--build [integer]]\n [--image [character]] [--af-col [character]]\n [--maf-filter [double]] [--sig [double]]\n [--maxpeaks [integer]] [--no-qq] [--no-man] [--no-annot]\n [--no-distance] [--man-height [integer]]\n [--upper-margin [double]] [--annot-cex [double]]\n [--axes-cex [double]] [--ylim [double]]\n infile outfile\n\nA program to plot Manhattan and QQ plots\n\npositional arguments:\n infile Input file name, must be gzip file\n outfile Output file name (with no file extension)\n\noptional arguments:\n \u003cspan class=\"pl-k\"\u003e-h\u003c/span\u003e, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n --chr-col [character]\n The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the chromosome column, default chr\n --pval-col [character]\n The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the chromosome column, default\n p_score\n --pos-col [character]\n The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the chromosome column, default ps\n --a1 [character] The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the effect allele column, default\n allele1\n --a2 [character] The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the non-effect column, default\n allele0\n --build [integer] The genome build the positions refer to\n --image [character] The filetype to save plots to (png or pdf)\n --af-col [character] The column NAME \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e the allele frequency column,\n default af\n --maf-filter [double]\n The significance threshold \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e MAF filter, default\n 0.0.\n --sig [double] The significance threshold to use \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e peak annotation\n --maxpeaks [integer] The maximum number of peaks to annotate\n --no-qq Don\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003et plot QQ.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --no-man Don\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003et plot Manhattan.\n --no-annot Disable peak annotation even \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e peaks are present.\n --no-distance Don\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003et add very useful distance to gene info.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --man-height [integer]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Force height of Manhattan in inches. Can have\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e unpredictable consequences (some of which you may\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e regret).\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --upper-margin [double]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Y limit of Manhattan plot in units of maximum data\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e points. Even more unpredictable than the above.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --annot-cex [double] Size factor for annotations.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --axes-cex [double] Size factor for axes and labels.\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e --ylim [double] The y-axis limit (-log10(p))\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLoading the package\u003c/h3\u003e\u003ca id=\"user-content-loading-the-package\" class=\"anchor\" aria-label=\"Permalink: Loading the package\" href=\"#loading-the-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can load the package into your R environment and use the available functions.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003emanqq\u003c/span\u003e)\nls(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003epackage:manqq\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e[1] \"manqq_cli\" \"fastqq\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCurrently, only two functions are exported and available for users. The other functions are all hidden and only used internally within the package. If there are any particular functionality you wish to use from the package, please make a request in the \u003ca href=\"https://github.com/hmgu-itg/man_qq_annotate/issues\"\u003eissue page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreating a QQ-plot\u003c/h4\u003e\u003ca id=\"user-content-creating-a-qq-plot\" class=\"anchor\" aria-label=\"Permalink: Creating a QQ-plot\" href=\"#creating-a-qq-plot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# example using a simulated null GWAS with 10,000 SNPs\u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003emanqq\u003c/span\u003e)\nfastqq(runif(\u003cspan class=\"pl-c1\"\u003e10000\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use \u003ccode\u003edevtools\u003c/code\u003e to load all the functions into your environment for development/debugging:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003edevtools\u003c/span\u003e)\nsetwd(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/base/of/the/repo/man_qq_annotate\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\nload_all()\ntest() \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use testthat\u0027s test function to run the testsuite\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "InstituteforDiseaseModeling/idmtools", + "latest_release": "v1.7.3", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-packages-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#packages-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackages Status\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-core/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-core/badge.svg?branch=dev\" alt=\"Staging: idmtools-core\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-cli/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-cli/badge.svg?branch=dev\" alt=\"Staging: idmtools-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-models/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-models/badge.svg?branch=dev\" alt=\"Staging: idmtools-models\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-comps/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-comps/badge.svg?branch=dev\" alt=\"Staging: idmtools-platform-comps\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-local/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-local/badge.svg?branch=dev\" alt=\"Staging: idmtools-platform-local\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-slurm/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-slurm/badge.svg?branch=dev\" alt=\"Staging: idmtools-platform-slurm\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-test/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-test/badge.svg?branch=dev\" alt=\"Staging: idmtools-test\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-other-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther status\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Rebuild%20documentation/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Rebuild%20documentation/badge.svg?branch=dev\" alt=\"Dev: Rebuild documentation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Lint/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Lint/badge.svg?branch=dev\" alt=\"Lint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-idm-modeling-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#idm-modeling-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIDM Modeling Tools\u003c/h1\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#user-installation\"\u003eUser Installation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#recommended-install\"\u003eRecommended install\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#advanced-install\"\u003eAdvanced Install\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installing-developmentearly-release-versions\"\u003eInstalling Development/Early Release Versions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pypi\"\u003ePyPI\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pre-requisites\"\u003ePre-requisites\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#reporting-issues\"\u003eReporting issues\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#requesting-a-feature\"\u003eRequesting a feature\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#development-documentation\"\u003eDevelopment Documentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch1\u003e\u003ca id=\"user-content-user-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#user-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser Installation\u003c/h1\u003e\n\u003cp\u003eDocumentation is located at \u003ca href=\"https://docs.idmod.org/projects/idmtools/en/latest/\" rel=\"nofollow\"\u003ehttps://docs.idmod.org/projects/idmtools/en/latest/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo build the documentation locally, do the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate and activate a venv.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the root directory of the repo and enter the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r dev_scripts/package_requirements.txt\npip install -r docs/requirements.txt\npython dev_scripts/bootstrap.py\ncd docs\nmake html\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Optional) To automatically serve the built docs locally in your browser, enter the following from\nthe root directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython dev_scripts/serve_docs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-recommended-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#recommended-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecommended install\u003c/h2\u003e\n\u003cp\u003eThe recommended install is to use\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install idmtools[full] --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will install the core tools, the cli, the comps and local platforms, support for EMOD models, and python models\u003c/p\u003e\n\u003cp\u003eIf you do not need the local platform, you can use the following command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install idmtools[idm] --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will install the core tools, the cli, the comps, support for EMOD models, and python models\u003c/p\u003e\n\u003cp\u003eIf you are Python 3.6, you will also need to run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install dataclasses\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-advanced-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#advanced-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdvanced Install\u003c/h2\u003e\n\u003cp\u003eYou can also install just the individual packages to create minimal environments\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Core package\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-cli --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Adds the idmtools cli commands\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-platform-comps --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Support for COMPS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-platform-local --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Support for Local Platform\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-models --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Python and generic models\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-developmentearly-release-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-developmentearly-release-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling Development/Early Release Versions\u003c/h2\u003e\n\u003cp\u003eDevelopment versions are available through both IDM\u0027s pypi registry and through Github.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pypi\" class=\"anchor\" aria-hidden=\"true\" href=\"#pypi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePyPI\u003c/h3\u003e\n\u003cp\u003eIf you have your authentication defined in your pip.conf or pip.ini file, you can use the following commands to install from staging\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Core package\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-cli --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Adds the idmtools cli commands\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-platform-comps --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Support for COMPS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-platform-local --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Support for Local Platform\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-models --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Python and generic models\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-requisites\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.6/3.7/3.8 x64\u003c/li\u003e\n\u003cli\u003eDocker(Required for the local platform)\nOn Windows, please use Docker Desktop 2.1.0.5 or 2.2.0.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-reporting-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#reporting-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReporting issues\u003c/h1\u003e\n\u003cp\u003eInclude the following information in your post:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe what you expected to happen.\u003c/li\u003e\n\u003cli\u003eIf possible, include a \u003ccode\u003eminimal reproducible example\u003c/code\u003e to help us\nidentify the issue. This also helps check that the issue is not with\nyour own code.\u003c/li\u003e\n\u003cli\u003eDescribe what actually happened. Include the full traceback if there\nwas an exception.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can report an issue directly on GitHub or by emailing \u003ca href=\"mailto:idmtools-issue@idmod.org\"\u003eidmtools-issue@idmod.org\u003c/a\u003e. Please include steps to reproduce the issue\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-requesting-a-feature\" class=\"anchor\" aria-hidden=\"true\" href=\"#requesting-a-feature\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequesting a feature\u003c/h1\u003e\n\u003cp\u003eYou can request a feature but opening a ticket on the repo or by emailing \u003ca href=\"mailto:idmtools-feature@idmod.org\"\u003eidmtools-feature@idmod.org\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-development-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#development-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment Documentation\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://gitpod.io/#https://github.com/InstituteforDiseaseModeling/idmtools\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/daadb4894128d1e19b72d80236f5959f1f2b47f9fe081373f3246131f0189f6c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f476974706f642d72656164792d2d746f2d2d636f64652d626c75653f6c6f676f3d676974706f64\" alt=\"Gitpod ready-to-code\" data-canonical-src=\"https://img.shields.io/badge/Gitpod-ready--to--code-blue?logo=gitpod\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"DEVELOPMENT_README.md\"\u003eDEVELOPMENT_README.md\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 3, + "subscribers_count": 11, "topics": [], - "updated_at": 1715648603.0 - }, - { - "data_format": 2, - "description": "Testing running an apache server with paraview in Singularity", - "filenames": [ - "Singularity" - ], - "full_name": "singularityhub/paraviewweb-apache", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParaview\u003c/h1\u003e\u003ca id=\"user-content-paraview\" class=\"anchor\" aria-label=\"Permalink: Paraview\" href=\"#paraview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor instructions on using Paraview (the executable) directly from a Singularity container,\nsee \u003ca href=\"https://ask.cyberinfrastructure.org/t/how-do-i-run-paraview-or-openfoam-on-an-hpc-resource/644/2\" rel=\"nofollow\"\u003ethis post\u003c/a\u003e\non AskCyberinfrastructre. Continue reading below for using ParaviewWeb via a Docker container, or\nSingularity container instance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt was hard to get it working with Singularity (this is common, it\u0027s read only!) so let\u0027s start\nwith a Docker container. We can use the container provided from \u003ca href=\"https://github.com/Kitware/paraviewweb/blob/master/tools/docker/demo/Dockerfile\"\u003ethis Dockerfile\u003c/a\u003e. Run the container, and note we are binding a port for the web socket as well.\u003c/p\u003e\n\u003cp\u003eYou can do the below on Linux with Nvidia runtime:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -p 0.0.0.0:9000:80 --runtime=nvidia -ti kitware/paraviewweb:pvw-egl-demo-v5.6.0 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ews://localhost:9000/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn a computer without (like mine)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -p 0.0.0.0:9000:80 -ti kitware/paraviewweb:pvw-osmesa-demo-v5.6.0 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ews://localhost:9000/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e-dr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--mesa-swr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow that the above is working in Docker, we can try to get it working with Singularity. Since\nI don\u0027t have nvidia or gpu I\u0027ll be using the second container, \u003ccode\u003ekitware/paraviewweb:pvw-osmesa-demo-v5.6.0\u003c/code\u003e.\nFirst, build the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build paraview-web.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMake a folder to bind to on the host, along with other files that need write:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir -p /tmp/apache2/run\n$ mkdir -p /tmp/data\n$ mkdir -p /tmp/apache2/logs\n$ mkdir -p /tmp/wslink/logs\n$ touch /tmp/wslink/proxy-mapping.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eStart the container instance, here we are naming it \"paraview.\" Since we need writable\nto /var/lock we must be sudo.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity instance.start --bind /tmp/apache2/run:/var/run/apache2 --bind /tmp/apache2/logs:/var/log/apache2 --bind /tmp/wslink/logs:/opt/wslink-launcher/logs --bind /tmp/wslink/proxy-mapping.txt:/opt/wslink-launcher/proxy-mapping.txt --bind /tmp/data:/data paraview-web.simg paraview\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should now see the paraview interface running on \u003ca href=\"http://127.0.0.1\" rel=\"nofollow\"\u003e127.0.0.1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"paraview.png\"\u003e\u003cimg src=\"paraview.png\" alt=\"paraview.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe mapping to \u003ccode\u003e/data\u003c/code\u003e is where local web applications will load files from.\u003c/p\u003e\n\u003cp\u003eAlso note that you \u003cem\u003emust\u003c/em\u003e stop local web servers, including any Docker applications\nrunning on that port. I\u0027m not privy to how paraview works, but given this setup\nyou should be able to figure it out from here. Here is how to shell into the\ncontainer:\u003c/p\u003e\n\u003cp\u003eHuge thanks to \u003ca href=\"https://github.com/jourdain\"\u003e@jourdain\u003c/a\u003e for his detailed help and instruction to figuring this out! :D\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive shell\u003c/h2\u003e\u003ca id=\"user-content-interactive-shell\" class=\"anchor\" aria-label=\"Permalink: Interactive shell\" href=\"#interactive-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI had needed to debug further to see how to get paraview working. Here is how to shell inside.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity shell instance://paraview\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCleaning Up\u003c/h2\u003e\u003ca id=\"user-content-cleaning-up\" class=\"anchor\" aria-label=\"Permalink: Cleaning Up\" href=\"#cleaning-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnd to stop the container, you also need sudo\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity instance.stop instance://paraview\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eI\u0027m not sure if this is reasonable to run in user space because of needing write\nto /var/lock. Using sudo with singularity seems to defeat the purpose. If you\nfigure out a good approach please send a pull request to this repository!\nRemember that to use nvidia, you would need to change the \u003ccode\u003eFrom\u003c/code\u003e line in\nthe Singularity file to \u003ccode\u003ekitware/paraviewweb:pvw-egl-demo-v5.6.0\u003c/code\u003e and then add\n\u003ccode\u003e--nv\u003c/code\u003e to take advantage of the libraries on the host.\u003c/p\u003e\n\u003cp\u003eAlso note that if you are using Singularity 3.0 and up the instance group is now changed\nto \"instance stop\" and \"instance start\"\u003c/p\u003e\n", - "stargazers_count": 4, - "subscribers_count": 4, - "topics": [ - "apache2", - "paraview", - "paraviewweb", - "singularity", - "docker" - ], - "updated_at": 1588360787.0 + "updated_at": 1675185444.0 }, { "data_format": 2, - "description": "Quantitative shotgun MS proteomics", + "description": null, "filenames": [ - "Singularity" + "snakemake/workflow/envs/Singularity" ], - "full_name": "nf-core/ddamsproteomics", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-coreddamsproteomics\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-coreddamsproteomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/ddamsproteomics\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative shotgun MS proteomics as done in Lehtio lab\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-this-pipeline-is-no-longer-being-maintained\" class=\"anchor\" aria-hidden=\"true\" href=\"#this-pipeline-is-no-longer-being-maintained\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis pipeline is no longer being maintained\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-please-see-nf-corequantms-for-a-more-up-to-date-pipeline-that-covers-much-of-the-same-functionality\" class=\"anchor\" aria-hidden=\"true\" href=\"#please-see-nf-corequantms-for-a-more-up-to-date-pipeline-that-covers-much-of-the-same-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003ePlease see \u003ca href=\"https://nf-co.re/quantms\" rel=\"nofollow\"\u003enf-core/quantms\u003c/a\u003e for a more up to date pipeline that covers much of the same functionality.\u003c/em\u003e\u003c/h3\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f829b4511e1d2374b587d4beabc0ef4404708febbcb8bf41693a8147e77a2a93/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6464616d7370726f74656f6d6963732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/ddamsproteomics.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2fd277a23b47e012519f1365bdc3a643add5906ecbcf8ee5bb883d8a06856885/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6464616d7370726f74656f6d6963732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/ddamsproteomics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "radio1988/OneStopRNAseq", + "latest_release": "v1.2-beta", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-onestoprnaseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#onestoprnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOneStopRNAseq\u003c/h1\u003e\n\u003cbr\u003e\nWebsite for submitting analysis jobs: \u003ca href=\"https://mccb.umassmed.edu/OneStopRNAseq/index.php\" rel=\"nofollow\"\u003ehttps://mccb.umassmed.edu/OneStopRNAseq/index.php\u003c/a\u003e\u003cbr\u003e\nCitation: Li R, Hu K, Liu H, Green MR, Zhu LJ. OneStopRNAseq: A Web Application for Comprehensive and Efficient Analyses of RNA-Seq Data. Genes (Basel). 2020 Oct 2;11(10):1165. doi: 10.3390/genes11101165. PMID: 33023248; PMCID: PMC7650687.\u003cbr\u003e\n\u003cbr\u003e\n`frontend` folder contains website Q\u0026amp;A for online users\n\u003cbr\u003e \n`snakemake` folder contains info for installing OneStopRNASeq locally and run it on your Linux computer\u003cbr\u003e\n- Instructions to run OneStopRNASeq on your local linux computer: \u003ca href=\"https://github.com/radio1988/OneStopRNAseq/blob/master/snakemake/README.md\"\u003ehttps://github.com/radio1988/OneStopRNAseq/blob/master/snakemake/README.md\u003c/a\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\u003cdetails\u003e\n\u003csummary\u003eUpdate: V.1.0.1 (2022/03/17)\u003c/summary\u003e\n\u003col\u003e\n\u003cli\u003eadd support site to Help tab.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eUpdate: V.1.0.0 (2021)\u003c/summary\u003e\n\u003col\u003e\n\u003cli\u003eallow multiple GEO;\u003c/li\u003e\n\u003cli\u003econtrast/sample validator;\u003c/li\u003e\n\u003cli\u003eoptimize result display;\u003c/li\u003e\n\u003cli\u003eemail relay service changes;\u003c/li\u003e\n\u003cli\u003eupdate User\u0027s Guide;\u003c/li\u003e\n\u003cli\u003eupdate workflow image;\u003c/li\u003e\n\u003cli\u003efix \"go back\" button.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/details\u003e\n", "stargazers_count": 4, - "subscribers_count": 23, - "topics": [ - "nf-core", - "nextflow", - "proteomics", - "workflow", - "pipeline", - "shotgun-ms" - ], - "updated_at": 1674867797.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1674786669.0 }, { "data_format": 2, @@ -33929,632 +33838,475 @@ var data = }, { "data_format": 2, - "description": null, + "description": "Quantitative shotgun MS proteomics", "filenames": [ - "snakemake/workflow/envs/Singularity" + "Singularity" ], - "full_name": "radio1988/OneStopRNAseq", - "latest_release": "v1.2-beta", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-onestoprnaseq\" class=\"anchor\" aria-hidden=\"true\" href=\"#onestoprnaseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOneStopRNAseq\u003c/h1\u003e\n\u003cbr\u003e\nWebsite for submitting analysis jobs: \u003ca href=\"https://mccb.umassmed.edu/OneStopRNAseq/index.php\" rel=\"nofollow\"\u003ehttps://mccb.umassmed.edu/OneStopRNAseq/index.php\u003c/a\u003e\u003cbr\u003e\nCitation: Li R, Hu K, Liu H, Green MR, Zhu LJ. OneStopRNAseq: A Web Application for Comprehensive and Efficient Analyses of RNA-Seq Data. Genes (Basel). 2020 Oct 2;11(10):1165. doi: 10.3390/genes11101165. PMID: 33023248; PMCID: PMC7650687.\u003cbr\u003e\n\u003cbr\u003e\n`frontend` folder contains website Q\u0026amp;A for online users\n\u003cbr\u003e \n`snakemake` folder contains info for installing OneStopRNASeq locally and run it on your Linux computer\u003cbr\u003e\n- Instructions to run OneStopRNASeq on your local linux computer: \u003ca href=\"https://github.com/radio1988/OneStopRNAseq/blob/master/snakemake/README.md\"\u003ehttps://github.com/radio1988/OneStopRNAseq/blob/master/snakemake/README.md\u003c/a\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\u003cdetails\u003e\n\u003csummary\u003eUpdate: V.1.0.1 (2022/03/17)\u003c/summary\u003e\n\u003col\u003e\n\u003cli\u003eadd support site to Help tab.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eUpdate: V.1.0.0 (2021)\u003c/summary\u003e\n\u003col\u003e\n\u003cli\u003eallow multiple GEO;\u003c/li\u003e\n\u003cli\u003econtrast/sample validator;\u003c/li\u003e\n\u003cli\u003eoptimize result display;\u003c/li\u003e\n\u003cli\u003eemail relay service changes;\u003c/li\u003e\n\u003cli\u003eupdate User\u0027s Guide;\u003c/li\u003e\n\u003cli\u003eupdate workflow image;\u003c/li\u003e\n\u003cli\u003efix \"go back\" button.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/details\u003e\n", + "full_name": "nf-core/ddamsproteomics", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-nf-coreddamsproteomics\" class=\"anchor\" aria-hidden=\"true\" href=\"#nf-coreddamsproteomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003enf-core/ddamsproteomics\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative shotgun MS proteomics as done in Lehtio lab\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-this-pipeline-is-no-longer-being-maintained\" class=\"anchor\" aria-hidden=\"true\" href=\"#this-pipeline-is-no-longer-being-maintained\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThis pipeline is no longer being maintained\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-please-see-nf-corequantms-for-a-more-up-to-date-pipeline-that-covers-much-of-the-same-functionality\" class=\"anchor\" aria-hidden=\"true\" href=\"#please-see-nf-corequantms-for-a-more-up-to-date-pipeline-that-covers-much-of-the-same-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003ePlease see \u003ca href=\"https://nf-co.re/quantms\" rel=\"nofollow\"\u003enf-core/quantms\u003c/a\u003e for a more up to date pipeline that covers much of the same functionality.\u003c/em\u003e\u003c/h3\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/nf-core/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f829b4511e1d2374b587d4beabc0ef4404708febbcb8bf41693a8147e77a2a93/68747470733a2f2f7472617669732d63692e6f72672f6e662d636f72652f6464616d7370726f74656f6d6963732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/nf-core/ddamsproteomics.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/67e0b26cefcc362513a5e2e7613f4638251b0ab5f029eba762be3d49a716c325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2fd277a23b47e012519f1365bdc3a643add5906ecbcf8ee5bb883d8a06856885/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6464616d7370726f74656f6d6963732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/ddamsproteomics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a3255d37d1c67555563853bd8243caf50984d85343da02fb7b297b21a1076c45/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h3\u003e\n\u003cp\u003eThe nf-core/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 4, - "subscribers_count": 1, - "topics": [], - "updated_at": 1674786669.0 + "subscribers_count": 23, + "topics": [ + "nf-core", + "nextflow", + "proteomics", + "workflow", + "pipeline", + "shotgun-ms" + ], + "updated_at": 1674867797.0 }, { "data_format": 2, - "description": null, + "description": "Testing running an apache server with paraview in Singularity", "filenames": [ - "idmtools_test/idmtools_test/inputs/singularity/alpine_simple/Singularity.def", - "idmtools_test/idmtools_test/inputs/singularity/alpine_template/Singularity.jinja" + "Singularity" ], - "full_name": "InstituteforDiseaseModeling/idmtools", - "latest_release": "v1.7.3", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-packages-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#packages-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackages Status\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-core/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-core/badge.svg?branch=dev\" alt=\"Staging: idmtools-core\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-cli/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-cli/badge.svg?branch=dev\" alt=\"Staging: idmtools-cli\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-models/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-models/badge.svg?branch=dev\" alt=\"Staging: idmtools-models\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-comps/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-comps/badge.svg?branch=dev\" alt=\"Staging: idmtools-platform-comps\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-local/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-local/badge.svg?branch=dev\" alt=\"Staging: idmtools-platform-local\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-slurm/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-platform-slurm/badge.svg?branch=dev\" alt=\"Staging: idmtools-platform-slurm\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-test/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Staging:%20idmtools-test/badge.svg?branch=dev\" alt=\"Staging: idmtools-test\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-other-status\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther status\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Rebuild%20documentation/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Rebuild%20documentation/badge.svg?branch=dev\" alt=\"Dev: Rebuild documentation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Lint/badge.svg?branch=dev\"\u003e\u003cimg src=\"https://github.com/InstituteforDiseaseModeling/idmtools/workflows/Lint/badge.svg?branch=dev\" alt=\"Lint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-idm-modeling-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#idm-modeling-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIDM Modeling Tools\u003c/h1\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#user-installation\"\u003eUser Installation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#recommended-install\"\u003eRecommended install\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#advanced-install\"\u003eAdvanced Install\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installing-developmentearly-release-versions\"\u003eInstalling Development/Early Release Versions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pypi\"\u003ePyPI\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pre-requisites\"\u003ePre-requisites\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#reporting-issues\"\u003eReporting issues\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#requesting-a-feature\"\u003eRequesting a feature\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#development-documentation\"\u003eDevelopment Documentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch1\u003e\u003ca id=\"user-content-user-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#user-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser Installation\u003c/h1\u003e\n\u003cp\u003eDocumentation is located at \u003ca href=\"https://docs.idmod.org/projects/idmtools/en/latest/\" rel=\"nofollow\"\u003ehttps://docs.idmod.org/projects/idmtools/en/latest/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo build the documentation locally, do the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCreate and activate a venv.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavigate to the root directory of the repo and enter the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r dev_scripts/package_requirements.txt\npip install -r docs/requirements.txt\npython dev_scripts/bootstrap.py\ncd docs\nmake html\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Optional) To automatically serve the built docs locally in your browser, enter the following from\nthe root directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython dev_scripts/serve_docs.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-recommended-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#recommended-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecommended install\u003c/h2\u003e\n\u003cp\u003eThe recommended install is to use\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install idmtools[full] --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will install the core tools, the cli, the comps and local platforms, support for EMOD models, and python models\u003c/p\u003e\n\u003cp\u003eIf you do not need the local platform, you can use the following command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install idmtools[idm] --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will install the core tools, the cli, the comps, support for EMOD models, and python models\u003c/p\u003e\n\u003cp\u003eIf you are Python 3.6, you will also need to run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install dataclasses\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-advanced-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#advanced-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdvanced Install\u003c/h2\u003e\n\u003cp\u003eYou can also install just the individual packages to create minimal environments\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Core package\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-cli --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Adds the idmtools cli commands\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-platform-comps --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Support for COMPS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-platform-local --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Support for Local Platform\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-models --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple\u003c/code\u003e - Python and generic models\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installing-developmentearly-release-versions\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-developmentearly-release-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling Development/Early Release Versions\u003c/h2\u003e\n\u003cp\u003eDevelopment versions are available through both IDM\u0027s pypi registry and through Github.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pypi\" class=\"anchor\" aria-hidden=\"true\" href=\"#pypi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePyPI\u003c/h3\u003e\n\u003cp\u003eIf you have your authentication defined in your pip.conf or pip.ini file, you can use the following commands to install from staging\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Core package\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-cli --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Adds the idmtools cli commands\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-platform-comps --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Support for COMPS\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-platform-local --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Support for Local Platform\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epip install idmtools-models --index-url=https://\u0026lt;USERNAME\u0026gt;:\u0026lt;PASSWORD\u0026gt;@packages.idmod.org/api/pypi/pypi-staging/simple\u003c/code\u003e - Python and generic models\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-requisites\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3.6/3.7/3.8 x64\u003c/li\u003e\n\u003cli\u003eDocker(Required for the local platform)\nOn Windows, please use Docker Desktop 2.1.0.5 or 2.2.0.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-reporting-issues\" class=\"anchor\" aria-hidden=\"true\" href=\"#reporting-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReporting issues\u003c/h1\u003e\n\u003cp\u003eInclude the following information in your post:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe what you expected to happen.\u003c/li\u003e\n\u003cli\u003eIf possible, include a \u003ccode\u003eminimal reproducible example\u003c/code\u003e to help us\nidentify the issue. This also helps check that the issue is not with\nyour own code.\u003c/li\u003e\n\u003cli\u003eDescribe what actually happened. Include the full traceback if there\nwas an exception.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can report an issue directly on GitHub or by emailing \u003ca href=\"mailto:idmtools-issue@idmod.org\"\u003eidmtools-issue@idmod.org\u003c/a\u003e. Please include steps to reproduce the issue\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-requesting-a-feature\" class=\"anchor\" aria-hidden=\"true\" href=\"#requesting-a-feature\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequesting a feature\u003c/h1\u003e\n\u003cp\u003eYou can request a feature but opening a ticket on the repo or by emailing \u003ca href=\"mailto:idmtools-feature@idmod.org\"\u003eidmtools-feature@idmod.org\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-development-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#development-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopment Documentation\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://gitpod.io/#https://github.com/InstituteforDiseaseModeling/idmtools\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/daadb4894128d1e19b72d80236f5959f1f2b47f9fe081373f3246131f0189f6c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f476974706f642d72656164792d2d746f2d2d636f64652d626c75653f6c6f676f3d676974706f64\" alt=\"Gitpod ready-to-code\" data-canonical-src=\"https://img.shields.io/badge/Gitpod-ready--to--code-blue?logo=gitpod\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"DEVELOPMENT_README.md\"\u003eDEVELOPMENT_README.md\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "singularityhub/paraviewweb-apache", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParaview\u003c/h1\u003e\u003ca id=\"user-content-paraview\" class=\"anchor\" aria-label=\"Permalink: Paraview\" href=\"#paraview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor instructions on using Paraview (the executable) directly from a Singularity container,\nsee \u003ca href=\"https://ask.cyberinfrastructure.org/t/how-do-i-run-paraview-or-openfoam-on-an-hpc-resource/644/2\" rel=\"nofollow\"\u003ethis post\u003c/a\u003e\non AskCyberinfrastructre. Continue reading below for using ParaviewWeb via a Docker container, or\nSingularity container instance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt was hard to get it working with Singularity (this is common, it\u0027s read only!) so let\u0027s start\nwith a Docker container. We can use the container provided from \u003ca href=\"https://github.com/Kitware/paraviewweb/blob/master/tools/docker/demo/Dockerfile\"\u003ethis Dockerfile\u003c/a\u003e. Run the container, and note we are binding a port for the web socket as well.\u003c/p\u003e\n\u003cp\u003eYou can do the below on Linux with Nvidia runtime:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -p 0.0.0.0:9000:80 --runtime=nvidia -ti kitware/paraviewweb:pvw-egl-demo-v5.6.0 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ews://localhost:9000/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn a computer without (like mine)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -p 0.0.0.0:9000:80 -ti kitware/paraviewweb:pvw-osmesa-demo-v5.6.0 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ews://localhost:9000/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e-dr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--mesa-swr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow that the above is working in Docker, we can try to get it working with Singularity. Since\nI don\u0027t have nvidia or gpu I\u0027ll be using the second container, \u003ccode\u003ekitware/paraviewweb:pvw-osmesa-demo-v5.6.0\u003c/code\u003e.\nFirst, build the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build paraview-web.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMake a folder to bind to on the host, along with other files that need write:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir -p /tmp/apache2/run\n$ mkdir -p /tmp/data\n$ mkdir -p /tmp/apache2/logs\n$ mkdir -p /tmp/wslink/logs\n$ touch /tmp/wslink/proxy-mapping.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eStart the container instance, here we are naming it \"paraview.\" Since we need writable\nto /var/lock we must be sudo.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity instance.start --bind /tmp/apache2/run:/var/run/apache2 --bind /tmp/apache2/logs:/var/log/apache2 --bind /tmp/wslink/logs:/opt/wslink-launcher/logs --bind /tmp/wslink/proxy-mapping.txt:/opt/wslink-launcher/proxy-mapping.txt --bind /tmp/data:/data paraview-web.simg paraview\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should now see the paraview interface running on \u003ca href=\"http://127.0.0.1\" rel=\"nofollow\"\u003e127.0.0.1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"paraview.png\"\u003e\u003cimg src=\"paraview.png\" alt=\"paraview.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe mapping to \u003ccode\u003e/data\u003c/code\u003e is where local web applications will load files from.\u003c/p\u003e\n\u003cp\u003eAlso note that you \u003cem\u003emust\u003c/em\u003e stop local web servers, including any Docker applications\nrunning on that port. I\u0027m not privy to how paraview works, but given this setup\nyou should be able to figure it out from here. Here is how to shell into the\ncontainer:\u003c/p\u003e\n\u003cp\u003eHuge thanks to \u003ca href=\"https://github.com/jourdain\"\u003e@jourdain\u003c/a\u003e for his detailed help and instruction to figuring this out! :D\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive shell\u003c/h2\u003e\u003ca id=\"user-content-interactive-shell\" class=\"anchor\" aria-label=\"Permalink: Interactive shell\" href=\"#interactive-shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI had needed to debug further to see how to get paraview working. Here is how to shell inside.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity shell instance://paraview\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCleaning Up\u003c/h2\u003e\u003ca id=\"user-content-cleaning-up\" class=\"anchor\" aria-label=\"Permalink: Cleaning Up\" href=\"#cleaning-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnd to stop the container, you also need sudo\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity instance.stop instance://paraview\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eI\u0027m not sure if this is reasonable to run in user space because of needing write\nto /var/lock. Using sudo with singularity seems to defeat the purpose. If you\nfigure out a good approach please send a pull request to this repository!\nRemember that to use nvidia, you would need to change the \u003ccode\u003eFrom\u003c/code\u003e line in\nthe Singularity file to \u003ccode\u003ekitware/paraviewweb:pvw-egl-demo-v5.6.0\u003c/code\u003e and then add\n\u003ccode\u003e--nv\u003c/code\u003e to take advantage of the libraries on the host.\u003c/p\u003e\n\u003cp\u003eAlso note that if you are using Singularity 3.0 and up the instance group is now changed\nto \"instance stop\" and \"instance start\"\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 11, - "topics": [], - "updated_at": 1675185444.0 + "subscribers_count": 4, + "topics": [ + "apache2", + "paraview", + "paraviewweb", + "singularity", + "docker" + ], + "updated_at": 1588360787.0 }, { "data_format": 2, - "description": "psychopy scripts for stimuli presentations", + "description": "19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology", "filenames": [ - "docker/Singularity" + "Singularity" ], - "full_name": "courtois-neuromod/task_stimuli", + "full_name": "4ment/marginal-experiments", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-task_stimuli\" class=\"anchor\" aria-hidden=\"true\" href=\"#task_stimuli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etask_stimuli\u003c/h1\u003e\n\u003cp\u003eThis software is a set of cognitive tasks developed in psychopy and a system to schedule sets of tasks during a session.\u003c/p\u003e\n\u003cp\u003eTasks are classes defined in \u003ccode\u003esrc/tasks\u003c/code\u003e, and are instantiated in \u003ccode\u003esrc/sessions\u003c/code\u003e files that describe a set of tasks in the session.\u003c/p\u003e\n\u003cp\u003eMaterial for the task (images/movies/lists...) is stored mainly in \u003ccode\u003edata\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOutputs (logs, responses) are stored in the \u003ccode\u003eoutput\u003c/code\u003e folder and try to mimic a BIDS structure.\u003c/p\u003e\n\u003cp\u003eWhen used with option \u003ccode\u003e--fmri\u003c/code\u003e tasks waits for a new TTL character to start.\u003c/p\u003e\n\u003cp\u003eWhen used with the option \u003ccode\u003e--eyetracking\u003c/code\u003e this software will start Pupil, and trigger the recording of the eye movie and detected pupil position, which outputs to the \u003ccode\u003eoutput\u003c/code\u003e folder in a BIDS-like way.\nNote that eyetracking data would require offline post/re-processing to be used and shared.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eutils\u003c/code\u003e contains scripts to prepare movies in a reproducible way using the melt command line video editor in singularity.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-attributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#attributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAttributions\u003c/h2\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eINSTALL\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003eapt install python3-pip git\nmkdir git\ncd git\n\n# this section is optional, only if using eyetracking\ngit clone https://github.com/pupil-labs/pupil.git\n# follow instructions at https://docs.pupil-labs.com/#linux-dependencies\n\npip3 install git+https://github.com/psychopy/psychopy.git\n# modify the file in psychopy that crashes\npip3 install scikit-video\n\ngit clone git@github.com:courtois-neuromod/task_stimuli.git\ncd task_stimuli\nmkdir output\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-launch-a-session\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-launch-a-session\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehow to launch a session\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003epython3 main.py --subject test --session video003 --tasks videoshorttest --eyetracking --fmri -o /path/to/dataset/\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e--subject: can be whatever, will be used to save data in a bids-like structure\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--session: a session identifier that will be used to save the data in the BIDS\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--tasks: must match the name of a session script in \u003ccode\u003esrc/ses-\u0026lt;session_name\u0026gt;.py\u003c/code\u003e, which contains the tasks to be ran on that session\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--eyetracking: turn on eyetracking, start pupil software and recording of eye\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e-o : specifies the path to the root of the dataset where to output the data (in sourcedata or BIDS )\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--fmri: will wait for TTL (can be emulated with character \u003ccode\u003e5\u003c/code\u003e on the keyboard) to start the tasks that are labeled as fmri dependent. When not using that flag, tasks will run back to back. It will also append a video loop at the beginning of the session in order for the participant to have sound and visual stimuli to test the setup (then skip to start the session).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e--meg: TODO!\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run multiple time this command, there are no risks of overwriting, the data will be suffixed by the date and time of start of the session.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-creating-session-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#creating-session-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreating session files\u003c/h2\u003e\n\u003cp\u003eYou can create new sessions by adding a \u003ccode\u003eses-xxxx.py\u003c/code\u003e file in \u003ccode\u003esrc/sessions\u003c/code\u003e folder.\nEach file only create a \u003ccode\u003eTASKS\u003c/code\u003e list of task subclasses instances, that is loaded by the script and ran in the provided order.\nCheck the existing files for examples.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-how-to-interact-with-the-software\" class=\"anchor\" aria-hidden=\"true\" href=\"#how-to-interact-with-the-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to interact with the software:\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-stimuli\" class=\"anchor\" aria-hidden=\"true\" href=\"#stimuli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003estimuli\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e5\u003c/code\u003e: emulate the trigger of MRI and start task \"by hand\" (can be changed in \u003ccode\u003esrc/shared/fmri.py\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;ctrl\u0026gt;-c\u003c/code\u003e : abort and \u003cstrong\u003eskip\u003c/strong\u003e the current task and move to the next one\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;ctrl\u0026gt;-n\u003c/code\u003e : abort the task and restart it, showing again the instruction\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;ctrl\u0026gt;-q\u003c/code\u003e : quit the session, saves and close the eyetracking software\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf (and only if) the software stop responding and you cannot quit, switch to the terminal and kill the software with \u003ccode\u003e\u0026lt;ctrl\u0026gt;-c\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-eyetracking\" class=\"anchor\" aria-hidden=\"true\" href=\"#eyetracking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeyetracking\u003c/h3\u003e\n\u003cp\u003eThere are \"hotkeys in the pupil software to trigger actions\", use the buttons with these letters or type.\nC (-c): launch the calibration of the eyetracking, showing markers to the participant\nT (-t): a test of the calibration accuracy, also showing markers on the screen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant: there are two softwares running, Psychopy and Pupil, when done with calibration, click on the Stimuli window to give the focus back to Psychopy, otherwise it will not get the TTL and the task will not start with the scanner.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a problem that has to be fixed in the future to avoid failed acquisition start.\nUpdate: should be fixed now, the software takes focus when task is loaded.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-hidden=\"true\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003esource code\u003c/h1\u003e\n\u003cp\u003epsychopy scripts for stimuli presentations\u003c/p\u003e\n\u003cp\u003esrc/tasks contains scripts for tasks\u003c/p\u003e\n\u003cp\u003esrc/shared folder should factorize the code common across tasks\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-eyetracking-1\" class=\"anchor\" aria-hidden=\"true\" href=\"#eyetracking-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eeyetracking\u003c/h2\u003e\n\u003cp\u003eThe eyetracking part is managed by launching pupil capture software and launching a single recording for the whole session.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-calibration\" class=\"anchor\" aria-hidden=\"true\" href=\"#calibration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ecalibration\u003c/h3\u003e\n\u003cp\u003eRun a short calibration task where the subjects have to look at points shown on the screen\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-gazemapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#gazemapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egazemapping\u003c/h3\u003e\n\u003cp\u003eOnce the calibration has been run (though it seems that pupil reload previous calibration), pupil produces gaze information that corresponds to position on the screen.\nWe then display that information in almost real-time on the experimenter screen.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology\u003c/h1\u003e\u003ca id=\"user-content-19-dubious-ways-to-compute-the-marginal-likelihood-of-a-phylogenetic-tree-topology\" class=\"anchor\" aria-label=\"Permalink: 19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology\" href=\"#19-dubious-ways-to-compute-the-marginal-likelihood-of-a-phylogenetic-tree-topology\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the pipeline and data sets supporting the results of the following article:\u003c/p\u003e\n\u003cp\u003eFourment M, Magee A, Whidden C, Bilge A, Matsen IV FA, Minin VN. 19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology. \u003ca href=\"https://arxiv.org/abs/1811.11804\" rel=\"nofollow\"\u003earXiv:1811.11804\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRequirements\u003c/h1\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"https://github.com/4ment/physher\"\u003ephysher\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-physher\" class=\"anchor\" aria-label=\"Permalink: physher\" href=\"#physher\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo reproduce the analysis the release \u003ca href=\"https://github.com/4ment/physher/releases/tag/marginal-v1.1\"\u003emarginal-v1.1\u003c/a\u003e should be used and the executable should be located in the \u003ccode\u003ebin\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the simulations\u003c/h1\u003e\u003ca id=\"user-content-running-the-simulations\" class=\"anchor\" aria-label=\"Permalink: Running the simulations\" href=\"#running-the-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e marginal-experiments\npython run_simulations.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor using Docker (no need to install physher)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e marginal-experiments\ndocker pull 4ment/marginal-experiments\ndocker run -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/data 4ment/marginal-experiments\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe simulations will take several weeks to complete. Multiple directories will be produced (DS1, DS2, DS3, DS4, DS5).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eParsing results\u003c/h1\u003e\u003ca id=\"user-content-parsing-results\" class=\"anchor\" aria-label=\"Permalink: Parsing results\" href=\"#parsing-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRscript -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ermarkdown::render(\"DS.Rmd\")\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe script will generate the file \u003ccode\u003eDS.pdf\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 4, "subscribers_count": 5, "topics": [ - "acquisition", - "stimuli", - "psychopy" + "marginal-likelihood", + "data", + "data-visualization", + "phylogenetics" ], - "updated_at": 1637149073.0 + "updated_at": 1603692340.0 }, { "data_format": 2, - "description": "The Recommender Engine for Intelligent Transient Tracking", + "description": "A re-write of the gemBS pipeline framework in Rust", "filenames": [ - "Singularity" + "Singularity", + "texlive/Singularity.tex" ], - "full_name": "refitt/refitt", - "latest_release": "0.19.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-pytorch-singularity\" class=\"anchor\" href=\"#pytorch-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epytorch-singularity\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4939\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains Singularity definition files used for PyTorch development in the Sinzlab.\u003c/p\u003e\n", + "full_name": "heathsc/gemBS-rs", + "latest_release": "v4.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egemBS-rs\u003c/h1\u003e\u003ca id=\"user-content-gembs-rs\" class=\"anchor\" aria-label=\"Permalink: gemBS-rs\" href=\"#gembs-rs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA rewrite of the \u003ca href=\"https://github.com/heathsc/gemBS\"\u003egemBS\u003c/a\u003e pipeline\nframework from Python/C into Rust.\u003c/p\u003e\n\u003cp\u003egemBS is a high performance bioinformatic pipeline designed for highthroughput analysis\nof DNA methylation data from whole genome bisulfites sequencing data\n(WGBS). It combines GEM3, a high performance read aligner and\nbs_call, a high performance variant and methyation caller, into a streamlined and efficient pipeline for\nbisulfite sequence analysis.\u003c/p\u003e\n\u003cp\u003eThe manuscript describing the original gemBS pipeline is available\n\u003ca href=\"https://doi.org/10.1093/bioinformatics/bty690\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe rewrite of the pipeline into Rust has two aims: (1) to have a more\nrobust pipeline and (2) to provide a more flexible platform for future\ndevelopments. All of the tools developed for the pipeline except for the GEM3 mapper (being an external project that is also very stable!) have now been re-written in Rust. These include bs_call, the methylation and SNV-variant caller, and the methylation and SNP extractions tools mextr and snpxtr. In all cases the running times are comparable to the original C versions.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline uses samtools for generating sorted BAM/CRAM files from GEM3 and bcftools for merging and indexing BCF files produced by bs_call. In addition, many of the tools link to htslb to enable reading of BAM/CRAM and reading/writing of BCF files. Samtools and htslib are automatically installed during the installation of the gemBS pipeline. There is also an optional dependency on TeXLive which is used to produce pdf versions of the QC reports. If requested by the user this is also installed with the pipeline.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicensing\u003c/h2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-label=\"Permalink: Licensing\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003egemBS is licensed under the GPL.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload\u003c/h2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-label=\"Permalink: Download\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse \u003ccode\u003egit clone --recursive\u003c/code\u003e to retrieve the complete source code including the code from external projects such as \u003ccode\u003egem3-mapper\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS-rs.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfigure \u0026amp; Install\u003c/h2\u003e\u003ca id=\"user-content-configure--install\" class=\"anchor\" aria-label=\"Permalink: Configure \u0026amp; Install\" href=\"#configure--install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore starting the installation of gemBS, you will need to install\nor check the installation of several packages.\u003c/p\u003e\n\u003cp\u003ea) gcc with development libraries\u003c/p\u003e\n\u003cp\u003eb) rust (for installation instructions see \u003ca href=\"https://www.rust-lang.org/learn/get-started\" rel=\"nofollow\"\u003ehere\u003c/a\u003e). Note that if you have rust already installed you should update it using \u003ccode\u003erustup update\u003c/code\u003e before trying to compile gemBS.\u003c/p\u003e\n\u003cp\u003ec) zlib, libz2, lzma, openssl, libcurl, libncurses, wget, expat, ncurses, openssl, freetype, fontconfig\u003c/p\u003e\n\u003cp\u003eIf you are working on a clean (fairly recent) Ubuntu installation, you\ncan install everything required with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapt-get install -y build-essential git autoconf wget lbzip2 pkg-config cmake\napt-get install -y zlib1g-dev libbz2-dev libexpat1-dev\napt-get install -y libncurses5-dev liblzma-dev libssl-dev libcurl4-openssl-dev curl\napt-get install -y libfreetype6-dev libfontconfig1-dev\ncurl https://sh.rustup.rs -sSf \u0026gt; rust.sh \u0026amp;\u0026amp; sh rust.sh -y\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDownload the gemBS distribution if you haven\u0027t already done so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS-rs.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFrom the main gemBS-rs directory type the following to make the default config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake gemBS_config.mk\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen look at the file gemBS_config.mk and make any changes that are required. When the file is OK the pipeline and components can be built and installed by typing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf the make and install process is successful, a shell script called gemBS will be created in the main gemBS-rs directory. This file should be copied to a directory that is in your PATH so that gemBS can be invoked from anywhere.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCheck your installation\u003c/h2\u003e\u003ca id=\"user-content-check-your-installation\" class=\"anchor\" aria-label=\"Permalink: Check your installation\" href=\"#check-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor checking your installation follow this\n\u003ca href=\"http://statgen.cnag.cat/gemBS/UserGuide/_build/html/example.html\" rel=\"nofollow\"\u003eworked example\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation can be found at\n\u003ca href=\"http://statgen.cnag.cat/gemBS/\" rel=\"nofollow\"\u003egemBS\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChangelog:\u003c/h2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-label=\"Permalink: Changelog:\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e4.0 First release of gemBS-rs (4th release of gemBS)\n4.0.1 Correct bug preventing that caused non-stranded mapping to fail\n4.0.2 Move to versions 1.12 of htslib/samtools/bcftools\n4.0.2 Change way we iterate over SAM/BAM/CRAM files to the same way used in samtools \n view (the old way did not always work with cram files)\n4.0.3 Fix problem with reading BCF files from older versions of\n gemBS where the CX format string was null terminated\n4.0.4 Add max_template_length option to gemBS (option passed on to bs_call)\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 4, "subscribers_count": 2, - "topics": [ - "science", - "astronomy", - "distributed-systems", - "machine-learning", - "citizen-science", - "open-source", - "python" - ], - "updated_at": 1628307502.0 + "topics": [], + "updated_at": 1719174318.0 }, { "data_format": 2, - "description": null, + "description": "One instance example with singularity-compose", "filenames": [ - "Singularity" + "app/Singularity" ], - "full_name": "yngvem/ntnu-analysis", + "full_name": "singularityhub/singularity-compose-simple", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-code-for-autodelineation-experiments-on-mri-data\" class=\"anchor\" href=\"#code-for-autodelineation-experiments-on-mri-data\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCode for autodelineation experiments on MRI data\u003c/h1\u003e\n\u003cp\u003eStart by running \u003ccode\u003esetup.sh\u003c/code\u003e to download the singularity container\nThen, submit slurm jobs like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esbatch slurm.sh json/dice/dwi.json dwi_dice 200\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhich will load the setup from the \u003ccode\u003ejson/dice/dwi.json\u003c/code\u003e file, train for 200 epochs\nand store the results in the folder \u003ccode\u003e$HOME/logs/ntnu/dwi_dice/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, if your cluster does not have slurm installed, simply omit the \u003ccode\u003esbatch\u003c/code\u003e\npart of the call above, thus running\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./slurm.sh json/dice/dwi.json dwi_dice 200\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Compose Simple\u003c/h1\u003e\u003ca id=\"user-content-singularity-compose-simple\" class=\"anchor\" aria-label=\"Permalink: Singularity Compose Simple\" href=\"#singularity-compose-simple\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a simple, dummy example of creating a web application with\n\u003ca href=\"https://singularityhub.github.io/singularity-compose/\" rel=\"nofollow\"\u003esingularity-compose\u003c/a\u003e\nusing just one container. The multiple container\nexample (that for some may require an update to Singularity) can be found at\n\u003ca href=\"https://www.github.com/singularityhub/singularity-compose-example\"\u003esingularityhub/singularity-compose-example\u003c/a\u003e.\nBoth are based on \u003ca href=\"https://github.com/vsoch/django-nginx-upload\"\u003edjango-nginx-upload\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esingularity-compose.yml\u003c/h3\u003e\u003ca id=\"user-content-singularity-composeyml\" class=\"anchor\" aria-label=\"Permalink: singularity-compose.yml\" href=\"#singularity-composeyml\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a singularity-compose project, it\u0027s expected to have a \u003ccode\u003esingularity-compose.yml\u003c/code\u003e\nin the present working directory. You can look at the \u003ca href=\"singularity-compose.yml\"\u003eexample\u003c/a\u003e\npaired with the \u003ca href=\"https://github.com/singularityhub/singularity-compose/tree/master/spec\"\u003especification\u003c/a\u003e\nto understand the fields provided.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstance folders\u003c/h3\u003e\u003ca id=\"user-content-instance-folders\" class=\"anchor\" aria-label=\"Permalink: Instance folders\" href=\"#instance-folders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerally, each section in the yaml file corresponds with a container instance to be run,\nand each container instance is matched to a folder in the present working directory.\nFor example, if I give instruction to build an \u003ccode\u003enginx\u003c/code\u003e instance from\na \u003ccode\u003enginx/Singularity.nginx\u003c/code\u003e file, I should have the\nfollowing in my singularity-compose:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nginx:\n build:\n context: ./nginx\n recipe: Singularity.nginx\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003epaired with the following directory structure:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity-compose-example\n\u251c\u2500\u2500 nginx\n...\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Singularity.nginx\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 uwsgi_params.par\n\u2514\u2500\u2500 singularity-compose.yml\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNotice how I also have other dependency files for the nginx container\nin that folder. While the context for starting containers with Singularity\ncompose is the directory location of the \u003ccode\u003esingularity-compose.yml\u003c/code\u003e,\nthe build context for this container is inside the nginx folder.\nAs another option, you can just define a container to pull,\nand it will be pulled to the same folder that is created if it doesn\u0027t exist.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e nginx:\n image: docker://nginx\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity-compose-example\n\u251c\u2500\u2500 nginx (- created \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e it doesn\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003et exist\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u2502\u00a0\u00a0 \u2514\u2500\u2500 nginx.sif (- named according to the instance\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u2514\u2500\u2500 singularity-compose.yml\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s less likely that you will be able to pull a container that is ready to\ngo, as typically you will want to customize the\n\u003ca href=\"https://sylabs.io/guides/3.2/user-guide/definition_files.html#startscript\" rel=\"nofollow\"\u003estartscript\u003c/a\u003e\nfor the instance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest way to start is to build the one required container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand then bring it up!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eVerify it\u0027s running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose ps\nINSTANCES NAME PID IMAGE\n1 app\t20023\tapp.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then look at logs, shell inside, or execute a command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs app\n$ singularity-compose logs app --tail 30\n$ singularity-compose shell app\n$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e app uname -a\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen you open your browser to \u003ca href=\"http://127.0.0.1\" rel=\"nofollow\"\u003ehttp://127.0.0.1\u003c/a\u003e\nyou should see the upload interface.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/upload.png\"\u003e\u003cimg src=\"img/upload.png\" alt=\"img/upload.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you drop a file in the box (or click\nto select) we will use the nginx-upload module to send it directly to the\nserver. Cool!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/content.png\"\u003e\u003cimg src=\"img/content.png\" alt=\"img/content.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is just a simple Django application, the database is sqlite3, in the\napp folder:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls app/\napp.sif db.sqlite3 manage.py nginx requirements.txt run_uwsgi.sh Singularity upload uwsgi.ini\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe images are stored in \u003ca href=\"\"\u003eimages\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls images/\n2018-02-20-172617.jpg 40-acos.png _upload \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd static files are in \u003ca href=\"static\"\u003estatic\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls static/\nadmin css js\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you look at the \u003ca href=\"singularity-compose.yml\"\u003esingularity-compose.yml\u003c/a\u003e, we bind these\nfolders to locations in the container where the web server needs write. This is likely\na prime different between Singularity and Docker compose - Docker doesn\u0027t need\nbinds for write, but rather to reduce isolation. Continue below to\nread about networking, and see these commands in detail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNetworking\u003c/h2\u003e\u003ca id=\"user-content-networking\" class=\"anchor\" aria-label=\"Permalink: Networking\" href=\"#networking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen you bring the container up, you\u0027ll see generation of an \u003ccode\u003eetc.hosts\u003c/code\u003e file,\nand if you guessed it, this is indeed bound to \u003ccode\u003e/etc/hosts\u003c/code\u003e in the container.\nLet\u0027s take a look:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e10.22.0.2\tapp\n127.0.0.1\tlocalhost\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The following lines are desirable for IPv6 capable hosts\u003c/span\u003e\n::1 ip6-localhost ip6-loopback\nfe00::0 ip6-localnet\nff00::0 ip6-mcastprefix\nff02::1 ip6-allnodes\nff02::2 ip6-allrouters\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis file will give each container that you create (in our case, just one)\na name on its local network. Singularity by default creates a bridge for\ninstance containers, which you can conceptually think of as a router,\nThis means that, if I were to reference the hostname \"app\" in a second container,\nit would resolve to \u003ccode\u003e10.22.0.2\u003c/code\u003e. Singularity compose does this by generating\nthese addresses before creating the instances, and then assigning them to it.\nIf you would like to see the full commands that are generated, run the up\nwith \u003ccode\u003e--debug\u003c/code\u003e (binds and full paths have been removed to make this easier to read).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity instance start \\\n --bind /home/vanessa/Documents/Dropbox/Code/singularity/singularity-compose-simple/etc.hosts:/etc/hosts \\\n --net --network-args \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eportmap=80:80/tcp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --network-args \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eIP=10.22.0.2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --hostname app \\\n --writable-tmpfs app.sif app\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommands\u003c/h2\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following commands are currently supported. Remember, you must be in the\npresent working directory of the compose file to reference the correct instances.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild\u003c/h3\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild will either build a container recipe, or pull a container to the\ninstance folder. In both cases, it\u0027s named after the instance so we can\neasily tell if we\u0027ve already built or pulled it. This is typically\nthe first step that you are required to do in order to build or pull your\nrecipes. It ensures reproducibility because we ensure the container binary\nexists first.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe working directory is the parent folder of the singularity-compose.yml file.\nIf the build requires sudo (if you\u0027ve defined sections in the config that warrant\nsetting up networking with sudo) the build will instead give you an instruction\nto run with sudo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate\u003c/h3\u003e\u003ca id=\"user-content-create\" class=\"anchor\" aria-label=\"Permalink: Create\" href=\"#create\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven that you have built your containers with \u003ccode\u003esingularity-compose build\u003c/code\u003e,\nyou can create your instances as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose create\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUp\u003c/h3\u003e\u003ca id=\"user-content-up\" class=\"anchor\" aria-label=\"Permalink: Up\" href=\"#up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to both build and bring them up, you can use \"up.\" Note that for\nbuilds that require sudo, this will still stop and ask you to build with sudo.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose up\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUp is typically the command that you want to use to bring containers up and down.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eps\u003c/h3\u003e\u003ca id=\"user-content-ps\" class=\"anchor\" aria-label=\"Permalink: ps\" href=\"#ps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can list running instances with \"ps\":\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose ps\nINSTANCES NAME PID IMAGE\n1 app\t6659\tapp.sif\n2 db\t6788\tdb.sif\n3 nginx\t6543\tnginx.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShell\u003c/h3\u003e\u003ca id=\"user-content-shell\" class=\"anchor\" aria-label=\"Permalink: Shell\" href=\"#shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s sometimes helpful to peek inside a running instance, either to look at permissions,\ninspect binds, or manually test running something.\nYou can easily shell inside of a running instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose shell app\nSingularity app.sif:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Documents/Dropbox/Code/singularity/singularity-compose-example\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExec\u003c/h3\u003e\u003ca id=\"user-content-exec\" class=\"anchor\" aria-label=\"Permalink: Exec\" href=\"#exec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can easily execute a command to a running instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e app ls /\nbin\nboot\ncode\ndev\nenvironment\netc\nhome\nlib\nlib64\nmedia\nmnt\nopt\nproc\nroot\nrun\nsbin\nsingularity\nsrv\nsys\ntmp\nusr\nvar\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDown\u003c/h3\u003e\u003ca id=\"user-content-down\" class=\"anchor\" aria-label=\"Permalink: Down\" href=\"#down\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can bring one or more instances down (meaning, stopping them) by doing:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose down\nStopping (instance:nginx)\nStopping (instance:db)\nStopping (instance:app)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo stop a custom set, just specify them:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose down nginx\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLogs\u003c/h3\u003e\u003ca id=\"user-content-logs\" class=\"anchor\" aria-label=\"Permalink: Logs\" href=\"#logs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can of course view logs for all instances, or just specific named ones:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs --tail 10\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity-compose logs app --tail 10\napp OUT\nRunning migrations:\n No migrations to apply.\nNo changes detected \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e app \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003emain\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nOperations to perform:\n Apply all migrations: admin, auth, contenttypes, main, sessions\nRunning migrations:\n No migrations to apply.\n\n0 static files copied to \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/var/www/static\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e, 121 unmodified.\n\n\napp ERR\nFri Jun 21 10:06:34 2019 - WSGI app 0 (mountpoint=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e) ready \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e 0 seconds on interpreter 0x557dc822b920 pid: 27 (default app)\nFri Jun 21 10:06:34 2019 - uWSGI running as root, you can use --uid/--gid/--chroot options\nFri Jun 21 10:06:34 2019 - \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e WARNING: you are running uWSGI as root \u003cspan class=\"pl-k\"\u003e!!!\u003c/span\u003e (use the --uid flag) \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e \nFri Jun 21 10:06:34 2019 - \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e uWSGI is running \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e multiple interpreter mode \u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e\nFri Jun 21 10:06:34 2019 - spawned uWSGI master process (pid: 27)\nFri Jun 21 10:06:34 2019 - spawned uWSGI worker 1 (pid: 29, cores: 1)\nFri Jun 21 10:13:02 2019 - SIGINT/SIGQUIT received...killing workers...\nFri Jun 21 10:13:03 2019 - worker 1 buried after 1 seconds\nFri Jun 21 10:13:03 2019 - goodbye to uWSGI.\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e## Config\u003c/span\u003e\n\nYou can load and validate the configuration file (singularity-compose.yml) and\nprint it to the screen as follows:\n\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003ebash\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e$ singularity-compose config\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e{\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eversion\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e1.0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einstances\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebuild\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./nginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003erecipe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSingularity.nginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./nginx.conf:/etc/nginx/conf.d/default.conf:ro\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./uwsgi_params.par:/etc/nginx/uwsgi_params.par:ro\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.:/code\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./static:/var/www/static\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./images:/var/www/images\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes_from\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eapp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eports\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eimage\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edocker://postgres:9.4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edb-data:/var/lib/postgresql/data\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eapp\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebuild\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: {\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./app\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e },\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evolumes\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.:/code\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./static:/var/www/static\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./images:/var/www/images\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eports\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e5000:80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ],\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edepends_on\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: [\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enginx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e ]\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 4, - "subscribers_count": 1, - "topics": [], - "updated_at": 1614353943.0 + "subscribers_count": 3, + "topics": [ + "singularity-compose", + "singularity", + "orchestration" + ], + "updated_at": 1678246913.0 }, { "data_format": 2, - "description": null, + "description": "DPMC/ProCount is a dynamic-programming framework for exact weighted (projected) model counting", "filenames": [ - "Singularity.def" + "lg/Singularity", + "dmc/Singularity", + "tensor/Singularity" ], - "full_name": "Transipedia/KaMRaT", - "latest_release": "v1.0.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-kamrat\" class=\"anchor\" href=\"#kamrat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKaMRaT\u003c/h1\u003e\n\u003chr\u003e\n\u003cp\u003eKaMRaT is a C++ tool for finding substrings with interesting properties in large NGS datasets.\u003c/p\u003e\n\u003cp\u003eKaMRaT requires a k-mer count matrix extracted from the NGS files (e.g. with Jellyfish), and labels for each sample.\u003c/p\u003e\n\u003cp\u003eKaMRaT then provides a set of tools for reducing the k-mer matrix and extending k-mers to longer contigs. The main subfunctions are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ekamrat index: index feature* count table on disk\u003c/li\u003e\n\u003cli\u003ekamrat merge: merge k-mers into contigs, produces a contig count table\u003c/li\u003e\n\u003cli\u003ekamrat filter: exclude/retain features* by expression level\u003c/li\u003e\n\u003cli\u003ekamrat mask: exclude/retain \u003cem\u003ek\u003c/em\u003e-mers matching given fasta sequences\u003c/li\u003e\n\u003cli\u003ekamrat rank: rank features* according to labels and statistical test\u003c/li\u003e\n\u003cli\u003ekamrat query: estimate count vectors of given list of contigs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote: *\tfeatures can be not only \u003cem\u003ek\u003c/em\u003e-mers or \u003cem\u003ek\u003c/em\u003e-mer contigs, but also general features such as genes or transcripts.\u003c/p\u003e\n\u003cp\u003eKaMRaT means \"k-mer Matrix Reduction Toolkit\", or \"k-mer Matrix, Really Tremendous !\".\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quick-start-demos\" class=\"anchor\" href=\"#quick-start-demos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start: Demos\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eindir=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo-data/inputs\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\noutdir=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo-data/outputs\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nsample_list=(sample1 sample2)\ndsgnfile=\u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/rank-design.txt\nkmer_tab_path=\u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kmer-counts.tsv.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ek\u003c/em\u003e-mer matrix preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Step 1: jellyfish count \u0026amp; dump\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003es\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${sample_list[@]}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e $sample_list contains list of considered sample names\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n\tjellyfish count -m 31 -s 1000000 -C -o \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.jf -F 2 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;(\u003c/span\u003ezcat \u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.R1.fastq.gz\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;(\u003c/span\u003ezcat \u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.R2.fastq.gz\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\tjellyfish dump -c \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.jf \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sort -k 1 \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e.txt \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;= here sort is important !\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Step 2: joinCounts\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e -n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etag\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gzip -c \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003es\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${sample_list[@]}\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e $sample_list contains list of considered sample names\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n\t\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e -ne \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\\t\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$s\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gzip -c \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gzip -c \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif joinCounts -r 1 -a 1 \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.txt \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e gzip -c \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e no filter of recurrence\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote: please keep in mind that the \u003ccode\u003esort\u003c/code\u003e after \u003ccode\u003ejellyfish dump\u003c/code\u003e is important for joinCounts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKaMRaT index\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make index for k-mer matrix with k=31, unstranded mode, and with a count per billion normalization\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat index -intab \u003cspan class=\"pl-smi\"\u003e$kmer_tab_path\u003c/span\u003e -outdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -klen 31 -unstrand -nfbase 1000000000\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eKaMRaT rank-merge approach\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Select top 50% of relevant k-mers using ttest pi-value\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat rank -idxdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -rankby ttest.pi -design \u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/rank-design.txt -outpath \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/top-ranked-kmers.ttest-pi.bin -seltop 0.5\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extend k-mers by tolerating overlap from 30nc to 15nc, intervened by Pearson distance \u0026lt;= 0.20, and with mean contig count\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat merge -idxdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -overlap 30-15 -with \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/top-ranked-kmers.ttest-pi.bin -interv pearson:0.20 -outpath \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/contig-counts.ttest-pi.pearson20.tsv -withcounts mean\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eKaMRaT merge-rank approach\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extend k-mers by tolerating overlap from 30nc to 15nc, intervened by Pearson distance \u0026lt;= 0.20, and with mean contig count\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat merge -idxdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -overlap 30-15 -interv pearson:0.20 -outpath \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/contigs.pearson20.bin\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Select top 50% of relevant contigs using ttest pi-value\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e --bind /src:/des kamrat.sif kamrat rank -idxdir \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/kamrat.idx -rankby ttest.pi -design \u003cspan class=\"pl-smi\"\u003e$indir\u003c/span\u003e/rank-design.txt -seltop 0.5 -with \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/contigs.pearson20.bin -outpath \u003cspan class=\"pl-smi\"\u003e$outdir\u003c/span\u003e/top-ranked-contigs.pearson20.ttest-pi.tsv -withcounts\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-typical-workflow-of-kamrat\" class=\"anchor\" href=\"#typical-workflow-of-kamrat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTypical Workflow of KaMRaT\u003c/h2\u003e\n\u003cp\u003eKaMRaT \u003cem\u003eper se\u003c/em\u003e is shown at the center of the workflow. It is a C++ program that takes as input a count matrix and produces another matrix as output.\nIn the workflow shown, KaMRaT is used for reducing a count matrix produced from a set of fastq files and producing a reduced matrix with features of interest with respect to conditions in the input sample-info file.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"./docs/workflow.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./docs/workflow.png\" alt=\"workflow\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe feature matrix contains features in row and samples in column. Features can be \u003cem\u003ek\u003c/em\u003e-mers (for all modules) as well as other general features such as genes/transcripts (only for KaMRaT-index, -filter, and -rank). The feature counts can be either normalized or non-normalized.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003ek\u003c/em\u003e-mer feature matrix can be constructed with the following possibilities:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ca href=\"./related-tools/prepare_kmer_table/Snakefile\"\u003eSnakefile\u003c/a\u003e provided with the project + \u003ca href=\"https://github.com/Transipedia/dekupl-joinCounts\"\u003eDE-kupl joinCounts\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Transipedia/dekupl-run\"\u003eDE-kupl\u003c/a\u003e\u0027s raw-counts.tsv or masked-counts.tsv matrices\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA set of auxiliary tools to be used for upstream and downstream of kamrat are provided:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUpstream tools:\n\u003cul\u003e\n\u003cli\u003eA matrix generating module controlled by Snakemake which applying jellyfish and DE-kupl joinCounts module\u003c/li\u003e\n\u003cli\u003eA bash script for generating a submatrix by selecting from it a set of columns\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDownstream tools:\n\u003cul\u003e\n\u003cli\u003eA feature selection model with an R script applying ridge/lasso regressions and random forest classifier\u003c/li\u003e\n\u003cli\u003eA contig counting module implemented in C++ for estimating the counts of a list of contigs in an independent dataset; it also supports evaluation of sample count coherence among contig\u0027s compositional k-mers\u003c/li\u003e\n\u003cli\u003eA model evaluation module written in R taking a trained model and evaluating it with a feature count matrix and feature conditions\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cdetails\u003e\n\u003csummary\u003eBuild from source\u003c/summary\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mlpack/mlpack/releases/tag/3.3.2\"\u003eMLPack 3.3.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.boost.org/doc/libs/1_74_0/libs/iostreams/doc/index.html\" rel=\"nofollow\"\u003eBoost-iostreams\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMLPack can be installed on \u003ca href=\"https://mlpack.org/doc/mlpack-3.3.2/doxygen/build.html\" rel=\"nofollow\"\u003eLinux/Mac\u003c/a\u003e, \u003ca href=\"https://mlpack.org/doc/mlpack-3.3.2/doxygen/build_windows.html\" rel=\"nofollow\"\u003eWindows\u003c/a\u003e, or via \u003ca href=\"https://anaconda.org/conda-forge/mlpack\" rel=\"nofollow\"\u003econda\u003c/a\u003e by following the corresponding links.\u003cbr\u003e\nIf you are installing MLPack with conda, please add the following line into your \u003ccode\u003e.bashrc\u003c/code\u003e file in the \u003ccode\u003ehome/\u003c/code\u003e directory before compiling KaMRaT:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=/path_to_conda_env/mlpack/lib:\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-clone-and-build\" class=\"anchor\" href=\"#clone-and-build\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClone and Build\u003c/h3\u003e\n\u003cp\u003eFirstly, clone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive https://github.com/Transipedia/KaMRaT.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e KaMRaT\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you installed MLPack library with conda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash compile.bash /path_to_MLPack_conda_environment\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOtherwise, if you installed MLPack without conda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebash compile.bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, an executable binary file is available as \u003ccode\u003ebin/kamrat\u003c/code\u003e.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eUse singularity\u003c/summary\u003e\n\u003cp\u003eIf using KaMRaT inside singularity, only by pulling from docker hub is enough:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build KaMRaT.sif docker://xuehl/kamrat:latest\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-general-information\" class=\"anchor\" href=\"#general-information\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGeneral Information\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-sample-information-file\" class=\"anchor\" href=\"#sample-information-file\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSample Information File\u003c/h3\u003e\n\u003cp\u003eThe sample-info file is indicated by the option \u003ccode\u003e-smp-info\u003c/code\u003e. This file aims to indicate which columns in the k-mer count matrix should be considered as sample columns. Please do not put any header line in the file, since the columns are already defined by convention as below.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf the file contains only one column, it indicates sample names, and all samples are considered as the same condition\u003c/li\u003e\n\u003cli\u003eIf the file contains two columns, the first column corresponds to sample names, and the second corresponds to conditions (\u003cem\u003ee.g.\u003c/em\u003e tumor, normal)\u003c/li\u003e\n\u003cli\u003eIf the file is not provided, all columns in the matrix apart from the first one are considered as samples\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-input-count-matrix-for-kamrat\" class=\"anchor\" href=\"#input-count-matrix-for-kamrat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput Count Matrix for KaMRaT\u003c/h3\u003e\n\u003cp\u003eThe input count matrix should be in .tsv or .tsv.gz format, in which fields are separated by tabulations.\nIn the matrix, features are presented as rows, and samples as columns. The first column in matrix should always be the feature column (sequences or feature names).\u003cbr\u003e\n\"Features\" can be any quantified feature such as genes, k-mers or contigs. k-mers or contigs are represented by their own sequence.\nKaMRaT accepts extra columns representing non-count values, e.g. feature\u0027s p-value, score, etc. In this case, a smp-info file is mandatory for indicating which columns are the count columns.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-output-count-matrix-by-kamrat\" class=\"anchor\" href=\"#output-count-matrix-by-kamrat\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput Count Matrix by KaMRaT\u003c/h3\u003e\n\u003cp\u003eThe output count matrix is also .tsv format table, where fields are separated by tabs.\u003cbr\u003e\nIn the matrix, the features are presented as rows, and the columns are in same order as the input.\u003cbr\u003e\nKaMRaT guarantees the information of output matrix is coherent with that of the input matrix. For KaMRaT-rank, though there are steps of count normalization, log transformation and standardization for score evaluation, the count values in output matrix are kept same as input (raw count).\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eNote: if you use KaMRaT in command line, please remember to indicate the full path to KaMRaT binary file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-kamrat-execution\" class=\"anchor\" href=\"#kamrat-execution\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKaMRaT Execution\u003c/h3\u003e\n\u003cp\u003eWe recommande using KaMRaT within \u003ccode\u003esingularity\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -B /bind_src:/bind_des kamrat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCMD\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e [options] input_table \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;CMD\u0026gt; can be one of filter, mask, merge, rank\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e-B\u003c/code\u003e option is for binding disk partitions to singularity image, please check \u003ccode\u003esingularity\u003c/code\u003e helper for details:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s also executable directly on command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/path_to_KaMRaT_bin_dir/kamrat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCMD\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e [options] input_table \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;CMD\u0026gt; can be one of filter, mask, merge, rank\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn the following sections, we present under the situation of using KaMRaT in \u003ccode\u003esingularity\u003c/code\u003e.\u003cbr\u003e\nFor running it directly on command line, please replace the leading \u003ccode\u003esingularity exec -B /bind_src:/bind_des\u003c/code\u003e by the path to KaMRaT binary file.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-kamrat-helper\" class=\"anchor\" href=\"#kamrat-helper\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKaMRaT Helper\u003c/h3\u003e\n\u003cp\u003eKaMRaT\u0027s top-level helper is accessible by typing one of these commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat -h\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat -help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHelpers of each KaMRaT modules are accessible via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e \u0026lt;CMD\u0026gt; can be one from filter, mask, merge, rank #\u003c/span\u003e\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCMD\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -h\nsingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e kamrat \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCMD\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -help\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-kamrat-usage-by-module\" class=\"anchor\" href=\"#kamrat-usage-by-module\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKaMRaT Usage by Module\u003c/h3\u003e\n\u003cdetails\u003e\n\u003csummary\u003eindex: index feature count table on disk\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat index -intab STR -outdir STR [-klen INT -unstrand -nfbase INT]\n\n[OPTION] -h, -help Print the helper\n -intab STR Input table for index, mandatory\n -outdir STR Output index directory, mandatory\n -klen k-mer length, mandatory if features are k-mer\n if present, indexation will be switched to k-mer mode\n -unstrand Unstranded mode, indexation with canonical k-mers\n if present, indexation will be switched to k-mer mode\n -nfbase INT Base for calculating normalization factor\n normCount_ij \u0026lt;- INT * rawCount_ij / sum_i{rawCount_ij}\n if not provided, input counts will not be normalized\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003efilter: filter feature by expression level\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat filter -idxdir STR -design STR [-upmin INT1:INT2 -downmax INT1:INT2 -reverse -outpath STR -withcounts]\n\n[OPTION] -h,-help Print the helper\n -idxdir STR Indexing folder by KaMRaT index, mandatory\n -design STR Path to filter design file, a table of two columns, mandatory\n the first column indicate sample names\n the second column should be either UP or DOWN (capital letters)\n samples with UP will be considered as up-regulated samples\n samples with DOWN will be considered as down-regulated samples\n samples not given will be neutral (not considered for filter)\n samples can also be all UP or all DOWN\n -upmin INT1:INT2 Up feature lower bound, [1:1, meaning no filter]\n output features counting \u0026gt;= INT1 in \u0026gt;= INT2 UP-samples\n -downmax INT1:INT2 Down feature upper bound [inf:1, meaning no filter]\n output features counting \u0026lt;= INT1 in \u0026gt;= INT2 DOWN-samples\n -reverse Reverse filter, to remove eligible features [false]\n -outpath STR Path to results after filter\n if not provided, output to screen\n -withcounts Output sample count vectors [false]\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003emask: mask k-mers from matrix\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat mask -idxdir STR -fasta STR [-reverse -outpath STR -withcounts]\n \n[OPTION] -h,-help Print the helper\n -idxdir STR Indexing folder by KaMRaT index, mandatory\n -fasta STR Sequence fasta file as the mask, mandatory;\n -reverse Reverse mask, to select the k-mers in sequence fasta file [false];\n -outpath STR Path to extension results\n if not provided, output to screen\n -withcounts Output sample count vectors [false]\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003emerge: extend k-mers into contigs\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat merge -idxdir STR -overlap MAX-MIN [-with STR1[:STR2] -interv STR[:FLOAT] -min-nbkmer INT -outpath STR -withcounts STR]\n\n[OPTION] -h,-help Print the helper;\n -idxdir STR Indexing folder by KaMRaT index, mandatory;\n -overlap MAX-MIN Overlap range for extension, mandatory\n MIN and MAX are integers, MIN \u0026lt;= MAX \u0026lt; k-mer length;\n -with STR1[:STR2] File indicating k-mers to be extended (STR1) and rep-mode (STR2)\n if not provided, all indexed k-mers are used for extension\n in the file STR1, a supplementary column of rep-value can be provided\n STR2 can be one of {min, minabs, max, maxabs} [min];\n -interv STR[:FLOAT] Intervention method for extension [pearson:0.20]\n can be one of {none, pearson, spearman, mac}\n the threshold may follow a \u0027:\u0027 symbol;\n -min-nbkmer INT Minimal length of extended contigs [0];\n -outpath STR Path to extension results\n if not provided, output to screen;\n -withcounts STR Output sample count vectors, STR can be one of [mean, median]\n if not provided, output without count vector\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003erank: rank features according to their association with sample conditions\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat rank -idxdir STR -count-mode STR -rankby STR -design STR [-with STR1[:STR2] -seltop NUM -outpath STR -withcounts]\n\n[OPTION] -h,-help Print the helper\n -idxdir STR Indexing folder by KaMRaT index, mandatory\n -rankby STR Ranking method, mandatory, can be one of:\n ttest.padj adjusted p-value of t-test between conditions\n ttest.pi \\u03C0-value of t-test between conditions\n snr signal-to-noise ratio between conditions\n dids DIDS score\n lr:nfold accuracy by logistic regression classifier\n bayes:nfold accuracy by naive Bayes classifier\n svm:nfold accuracy on SVM classifier\n -design STR Path to file indicating sample-condition design\n without header line, each row can be either:\n sample name, sample condition\n sample name, sample condition, sample batch (only for lrc, nbc, and svm)\n -with STR1[:STR2] File indicating features to rank (STR1) and counting mode (STR2)\n if not provided, all indexed features are used for ranking\n STR2 can be one of [rep, mean, median]\n -seltop NUM Select top ranked features\n if NUM \u0026gt; 1, number of top features to select (should be integer)\n if 0 \u0026lt; NUM \u0026lt;= 1, ratio of top features to select\n if absent or NUM \u0026lt;= 0, output all features\n -outpath STR Path to ranking result\n if not provided, output to screen\n -withcounts Output sample count vectors [false]\n\n[NOTE] For ranking methods lrc, nbc, and svm, a univariate CV fold number (nfold) can be provided\n if nfold = 0, leave-one-out cross-validation\n if nfold = 1, without cross-validation, training and testing on the whole datset\n if nfold \u0026gt; 1, n-fold cross-validation\n For t-test ranking methods, a transformation log2(x + 1) is applied to sample counts\n For SVM ranking, sample counts standardization is applied feature by feature\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003equery: query sequences\u003c/summary\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e[USAGE] kamrat query -idxdir STR -fasta STR -toquery STR [-withabsent -outpath STR]\n\n[OPTION] -h,-help Print the helper\n -idxdir STR Indexing folder by KaMRaT index, mandatory\n -fasta STR Sequence fasta file, mandatory\n -toquery STR Query method, mandatory, can be one of:\n mean mean count among all composite k-mers for each sample\n median median count among all composite k-mers for each sample\n -withabsent Output also absent queries (count vector all 0) [default: false]\n -outpath STR Path to extension results\n if not provided, output to screen\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/details\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-softwarelibrary-citations\" class=\"anchor\" href=\"#softwarelibrary-citations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSoftware/Library Citations\u003c/h2\u003e\n\u003cp\u003eArmadillo:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eConrad Sanderson and Ryan Curtin. Armadillo: a template-based C++ library for linear algebra. Journal of Open Source Software, Vol. 1, pp. 26, 2016.\u003c/li\u003e\n\u003cli\u003eConrad Sanderson and Ryan Curtin. A User-Friendly Hybrid Sparse Matrix Class in C++. Lecture Notes in Computer Science (LNCS), Vol. 10931, pp. 422-430, 2018.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://www.boost.org/\" rel=\"nofollow\"\u003eBoost C++ Library\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDE-kupl: Audoux, J., Philippe, N., Chikhi, R. et al. DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition. Genome Biol 18, 243 (2017).\u003c/p\u003e\n\u003cp\u003eMLPack: R.R. Curtin, M. Edel, M. Lozhnikov, Y. Mentekidis, S. Ghaisas, S. Zhang. mlpack 3: a fast, flexible machine learning library. Journal of Open Source Software 3:26, 2018.\u003c/p\u003e\n\u003cp\u003eglmnet: Friedman, Jerome, Trevor Hastie, and Rob Tibshirani. \"Regularization paths for generalized linear models via coordinate descent.\" Journal of statistical software 33.1 (2010): 1.\u003c/p\u003e\n\u003cp\u003erandomForest: Liaw, Andy, and Matthew Wiener. \"Classification and regression by randomForest.\" R news 2.3 (2002): 18-22.\u003c/p\u003e\n", + "full_name": "vardigroup/DPMC", + "latest_release": "v2.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDPMC/ProCount/DPO/DPER\u003c/h1\u003e\u003ca id=\"user-content-dpmcprocountdpodper\" class=\"anchor\" aria-label=\"Permalink: DPMC/ProCount/DPO/DPER\" href=\"#dpmcprocountdpodper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWe provide four exact solvers that support XOR-CNF formulas.\n\u003cul\u003e\n\u003cli\u003eDPMC solves \u003cem\u003eweighted model counting (WMC)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eProCount solves \u003cem\u003eweighted projected model counting (WPMC)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://arxiv.org/abs/2205.08632\" rel=\"nofollow\"\u003eDPO\u003c/a\u003e solves \u003cem\u003eweighted SAT (WSAT)\u003c/em\u003e, i.e., Boolean MPE.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://arxiv.org/abs/2205.09826\" rel=\"nofollow\"\u003eDPER\u003c/a\u003e solves \u003cem\u003eexist-random SAT (ERSAT)\u003c/em\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEach of these four solvers is a combination of a planner and an executor.\n\u003cul\u003e\n\u003cli\u003eA planner produces a \u003cstrong\u003eproject-join tree\u003c/strong\u003e T from an XOR-CNF formula F.\u003c/li\u003e\n\u003cli\u003eAn executor traverses T to computes a solution of F.\u003c/li\u003e\n\u003cli\u003eFor WPMC and ERSAT, T must be \u003cstrong\u003egraded\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTwo planners are available.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./htb\"\u003eHTB\u003c/a\u003e uses constraint-programming heuristics.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./lg\"\u003eLG\u003c/a\u003e uses tree decomposers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTwo executors are available.\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"./dmc\"\u003eDMC\u003c/a\u003e uses \u003cem\u003ealgebraic decision diagrams (ADDs)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"./tensor\"\u003eTensor\u003c/a\u003e uses tensors and only solves WMC on pure CNF.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopers:\n\u003cul\u003e\n\u003cli\u003eJeffrey Dudek: LG and Tensor\u003c/li\u003e\n\u003cli\u003eVu Phan: HTB and DMC\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"https://github.com/vardigroup/DPMC/releases\"\u003eReleases\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-releases\" class=\"anchor\" aria-label=\"Permalink: Releases\" href=\"#releases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2021/05/25: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/mc-2021\"\u003emc-2021\u003c/a\u003e \u003ca href=\"https://zenodo.org/badge/latestdoi/280443175\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/94c98c030c95168c80dae6a4d9e0c347cee412c22a3c9ea53aa875c373925b4d/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3238303434333137352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/280443175.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"./mcc\"\u003eModel Counting Competition MC-2021\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2021/05/23: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/v2.0.0\"\u003ev2.0.0\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eSAT-2021 paper: \u003ca href=\"https://jmd11.web.rice.edu/papers/sat21_procount.pdf\" rel=\"nofollow\"\u003e\u003cstrong\u003eProCount: Weighted Projected Model Counting with Graded Project-Join Trees\u003c/strong\u003e\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors: Jeffrey M. Dudek, Vu H. N. Phan, Moshe Y. Vardi\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2020/07/20: \u003ca href=\"https://github.com/vardigroup/DPMC/releases/tag/v1.0.0\"\u003ev1.0.0\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eCP-2020 paper: \u003ca href=\"https://arxiv.org/abs/2008.08748\" rel=\"nofollow\"\u003e\u003cstrong\u003eDPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees\u003c/strong\u003e\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eAuthors: Jeffrey M. Dudek, Vu H. N. Phan, Moshe Y. Vardi\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning this repository and its submodules\u003c/h2\u003e\u003ca id=\"user-content-cloning-this-repository-and-its-submodules\" class=\"anchor\" aria-label=\"Permalink: Cloning this repository and its submodules\" href=\"#cloning-this-repository-and-its-submodules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive https://github.com/vardigroup/DPMC\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"./examples\"\u003eExamples\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgment\u003c/h2\u003e\u003ca id=\"user-content-acknowledgment\" class=\"anchor\" aria-label=\"Permalink: Acknowledgment\" href=\"#acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/ADDMC\"\u003eADDMC\u003c/a\u003e: Dudek, Phan, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/approxmc\"\u003eBIRD\u003c/a\u003e: Soos, Meel\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cs.rochester.edu/u/kautz/Cachet\" rel=\"nofollow\"\u003eCachet\u003c/a\u003e: Sang, Beame, Kautz\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/msoos/cryptominisat\"\u003eCryptoMiniSat\u003c/a\u003e: Soos\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ivmai/cudd\"\u003eCUDD package\u003c/a\u003e: Somenzi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://davidkebo.com/cudd#cudd6\" rel=\"nofollow\"\u003eCUDD visualization\u003c/a\u003e: Kebo\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jarro2783/cxxopts\"\u003ecxxopts\u003c/a\u003e: Beck\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kit-algo/flow-cutter-pace17\"\u003eFlowCutter\u003c/a\u003e: Strasser\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mabseher/htd\"\u003ehtd\u003c/a\u003e: Abseher, Musliu, Woltran\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://reasoning.cs.ucla.edu/minic2d\" rel=\"nofollow\"\u003eminiC2D\u003c/a\u003e: Oztok, Darwiche\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mccompetition.org\" rel=\"nofollow\"\u003eModel Counting Competition\u003c/a\u003e: Hecher, Fichte\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cril.univ-artois.fr/KC/pmc.html\" rel=\"nofollow\"\u003epmc\u003c/a\u003e: Lagniez, Marquis\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Kasekopf/SlurmQueen\"\u003eSlurmQueen\u003c/a\u003e: Dudek\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://trolando.github.io/sylvan\" rel=\"nofollow\"\u003eSylvan\u003c/a\u003e: van Dijk\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/TCS-Meiji/PACE2017-TrackA\"\u003eTamaki\u003c/a\u003e: Tamaki\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vardigroup/TensorOrder\"\u003eTensorOrder\u003c/a\u003e: Dudek, Duenas-Osorio, Vardi\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meelgroup/WAPS\"\u003eWAPS\u003c/a\u003e: Gupta, Sharma, Roy, Meel\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 4, - "subscribers_count": 6, + "subscribers_count": 4, "topics": [], - "updated_at": 1649668794.0 + "updated_at": 1724159401.0 }, { "data_format": 2, - "description": "CTA-customized version of the DIRAC middleware", + "description": "EasyBob, the friendly software installation robot for EasyBuild", "filenames": [ - "Singularity" + "singularity/rocky/Singularity.eb-4.5.0-Lmod-rocky8.4", + "singularity/rocky/Singularity.eb-4.7.0-Lmod-rocky8.7", + "singularity/ubuntu/Singularity.eb-4.4.2-Lmod-ubuntu20-LTR", + "singularity/ubuntu/Singularity.eb-4.5.0-Lmod-ubuntu20-LTR" ], - "full_name": "cta-observatory/CTADIRAC", - "latest_release": "v1r62test", - "readme": "\u003cp\u003eCTADIRAC project:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emoved to CTAO Observatory gitlab @ \u003ca href=\"https://gitlab.cta-observatory.org/cta-computing/dpps/CTADIRAC\" rel=\"nofollow\"\u003ehttps://gitlab.cta-observatory.org/cta-computing/dpps/CTADIRAC\u003c/a\u003e on Decembe 2020\u003c/li\u003e\n\u003cli\u003emoved to git on Septembre 5th 2017\u003c/li\u003e\n\u003cli\u003eadd things, need add a licence GPLv3 ?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAuthors from svn:\u003cbr\u003e\nAdrian Casajus \u003ca href=\"mailto:adria@ecm.ub.es\"\u003eadria@ecm.ub.es\u003c/a\u003e \u003cbr\u003e\nLuisa Arrabito \u003ca href=\"mailto:arrabito@in2p3.fr\"\u003earrabito@in2p3.fr\u003c/a\u003e \u003cbr\u003e\nJohan Bregeon \u0026lt;\u003ca href=\"mailto:bregeon@.in2p3.fr\"\u003ebregeon@.in2p3.fr\u003c/a\u003e\u0026gt; \u003cbr\u003e\nJohann Cohen Tanugi \u003ca href=\"mailto:johann.cohen-tanugi@umontpellier.fr\"\u003ejohann.cohen-tanugi@umontpellier.fr\u003c/a\u003e \u003cbr\u003e\n? Han Bcn \u003ca href=\"mailto:nhan.bcn@gmail.com\"\u003enhan.bcn@gmail.com\u003c/a\u003e \u003cbr\u003e\nRicardo Graciani \u003ca href=\"mailto:graciani@ecm.ub.edu\"\u003egraciani@ecm.ub.edu\u003c/a\u003e \u003cbr\u003e\u003c/p\u003e\n", + "full_name": "sassy-crick/easybob", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAutomatic software installation script\u003c/h1\u003e\u003ca id=\"user-content-automatic-software-installation-script\" class=\"anchor\" aria-label=\"Permalink: Automatic software installation script\" href=\"#automatic-software-installation-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction:\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction:\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe aim of the script is to install the software inside a container, and thus the so installed software is independent from the OS as much as possible, and also takes care of different architectures. The idea comes from the EESSI project and how the software is installed in there. So kudos to them!!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow-to:\u003c/h2\u003e\u003ca id=\"user-content-how-to\" class=\"anchor\" aria-label=\"Permalink: How-to:\" href=\"#how-to\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore the script can run, there are a few files which need to be adjusted.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003esite-config\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esoftwarelist.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003esoftwarelist.yaml\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ccode\u003esite-config\u003c/code\u003e file should contain all relevant paths for the software installation and that should be the only file which needs changes. That said, it might be worth to have a look at the other files to make sure nothing is accidentally hard-coded.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eautomatic-build.sh\u003c/code\u003e script is expecting the full path there either the \u003ccode\u003esoftware.txt\u003c/code\u003e or \u003ccode\u003esoftware.yaml\u003c/code\u003e files are. One is enough, the scripts detect which ones are around and acts accordingly. This script is then then submitting the jobs, one for each to be build architecture as defined in the \u003ccode\u003esite-config\u003c/code\u003e file, to the queue. The submission script so generated is then calling the \u003ccode\u003einstall.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003einstall.sh\u003c/code\u003e does basically the whole magic. There are a few lines at the top which need to be changed to reflect where the software needs to go. It might be worth checking that but most of the relevant stuff is defined in the \u003ccode\u003esite-config\u003c/code\u003e file and are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSOFTWARE_INSTDIR\u003c/code\u003e which is where the software tree and all the helper stuff lives\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBINDDIR\u003c/code\u003e is the directory which needs to be bound inside the container as per default Singularity does only mount \u003ccode\u003e/tmp\u003c/code\u003e and \u003ccode\u003e/home\u003c/code\u003e it seems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou also might want to look at:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCONTAINER_VERSION\u003c/code\u003e which is the name of the sif-file, i.e. the container\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eEB_VERSION\u003c/code\u003e which is the version of EasyBuild to be used for building software. If that does not exist, it should be automatically installed\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSW_LIST\u003c/code\u003e contains a simple list of the EasyConfig files to be installed. All in one line with a blank between them.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSW_YAML\u003c/code\u003econtains the software to be installed as an EasyStack file in \u003ccode\u003eyaml\u003c/code\u003e format.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth the \u003ccode\u003eSW_LIST\u003c/code\u003e and the \u003ccode\u003eSW_YAML\u003c/code\u003e are independent from each other. So as long as the file got a content, it will be used.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esoftware.sh\u003c/code\u003e will be created on the fly in the right directory, using the various template files, and does contain the list of software which needs to be installed which will be pulled in by the \u003ccode\u003esoftwarelist.txt\u003c/code\u003e file. The EasyStack file, so it exists, will be places in the correct directory.\nIf you need to change any of the paths where the software will be installed, you will need to look into \u003ccode\u003esoftware.tmpl\u003c/code\u003e, the Singularity Definition file \u003ccode\u003eSingularity.eb-4.5.3-envmod-rocky8\u003c/code\u003e and both the \u003ccode\u003einstall.sh\u003c/code\u003e and \u003ccode\u003einteractive-install.sh\u003c/code\u003e files.\nNote: You can mount any folder outside the container but you will need to make sure that the \u003ccode\u003eMODULEPATH\u003c/code\u003e variable are identical inside and outside the container. Thus, if you are using like in our example \u003ccode\u003e/sw-eb\u003c/code\u003e as the root install directory, the \u003ccode\u003eMODULEPATH\u003c/code\u003e then needs to be set to for example \u003ccode\u003e/sw-eb/modules/all\u003c/code\u003e inside and outside the container!\u003c/p\u003e\n\u003cp\u003eThe first time the script runs, it will create the directory structure but then stops as the Singularity container is not in place. For the full automated installation, we would download the container from somewhere. However, as this only needs to be done once, it is left for now like this.\u003c/p\u003e\n\u003cp\u003eOnce the container in the right folder we are upgrading EasyBuild to the latest version. This way, a module file is created automatically. Once that is done, the software will be installed if required.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHooks\u003c/h2\u003e\u003ca id=\"user-content-hooks\" class=\"anchor\" aria-label=\"Permalink: Hooks\" href=\"#hooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to make sure that licensed software has the right permissions, there is a site-specific file in the \u003ccode\u003ehooks\u003c/code\u003e directory calle \u003ccode\u003esite-hooks.py\u003c/code\u003e Depending on when to run, several parameters can be changed. That makes sense to for example restrict access to a specific folder by means of setting the group and the permissions, but also makes sense when building software which depends on the presence or absence of specific networks for example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGPU builds\u003c/h2\u003e\u003ca id=\"user-content-gpu-builds\" class=\"anchor\" aria-label=\"Permalink: GPU builds\" href=\"#gpu-builds\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the GPU nodes, the command line flag \u003ccode\u003e--cuda-compute-capabilities=8.0\u003c/code\u003e for the A100 GPU or \u003ccode\u003e--cuda-compute-capabilities=7.5\u003c/code\u003e for the RTX600 GPU is defined in the \u003ccode\u003esite-config\u003c/code\u003e file and the script automatically detects if the EasyConfig filename contains \u003ccode\u003ecuda\u003c/code\u003e, either in lower cases or upper cases. This software will only be build on the GPU nodes automatically. Both the \u003ccode\u003einteractive-install.sh\u003c/code\u003e and the \u003ccode\u003einstall.sh\u003c/code\u003e script are using the \u003ccode\u003e--nv\u003c/code\u003e flag which is needed for the GPU builds.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eApptainer\u003c/code\u003e \u0026gt;= 1.0.1\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFlowchart\u003c/h2\u003e\u003ca id=\"user-content-flowchart\" class=\"anchor\" aria-label=\"Permalink: Flowchart\" href=\"#flowchart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis flowchart hopefully illustrates the interaction of the different components. Most importantly is the \u003ccode\u003esite-config\u003c/code\u003e file as this is where all the relevant information of the used cluster is sitting.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-mermaid\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eflowchart\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003eTD\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eA\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e(\u003c/span\u003e\u003c/span\u003eautomatic-build.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003ecreates and submits for each architecture\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eB\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003ejobscript\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eB\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003ejobscript\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003eexecutes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eD\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[(\u003c/span\u003e\u003c/span\u003esite-config\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e)]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003epulls in\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eE\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003esoftware-head.tmpl\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eF\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003esoftware-list.tmpl\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eG\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003esoftware-bottom.tmpl\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eH\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes installation with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eH\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes installation with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e2\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e3\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eI\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes testing with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eJ\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eEB hooks\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eH\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes installation with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eJ\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eEB hooks\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eI\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eexecutes testing with EB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ek\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003esw-groups\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-ent\"\u003eC\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003einstall.sh\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-sg\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo Do:\u003c/h2\u003e\u003ca id=\"user-content-to-do\" class=\"anchor\" aria-label=\"Permalink: To Do:\" href=\"#to-do\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is working well but with all code there is room for improvement, or bugs which have yet to be found.\u003c/p\u003e\n\u003cp\u003eFix that for automated install, so the script can be put anywhere.\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 7, + "subscribers_count": 3, "topics": [], - "updated_at": 1644849840.0 + "updated_at": 1718488195.0 }, { "data_format": 2, - "description": "Genome Decomposition Analysis pipeline", + "description": "FTW", "filenames": [ "Singularity" ], - "full_name": "sanger-tol/gda", - "latest_release": "v1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGDA\u003c/h1\u003e\u003ca id=\"user-content-gda\" class=\"anchor\" aria-label=\"Permalink: GDA\" href=\"#gda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenome Decomposition Analysis for the characterisation of genome architecture\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat is GDA?\u003c/h3\u003e\u003ca id=\"user-content-what-is-gda\" class=\"anchor\" aria-label=\"Permalink: What is GDA?\" href=\"#what-is-gda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGDA (Genome Decomposition Analysis) is a bioinformatic pipeline to analyse genome architecture. Using, as a minimum, a genome assembly (the more complete the better), it will determine features in non-overlapping windows across the sequence and identify windows with common features. The assembly will then be annotated based on these similarities, highlighting structurally similar genomic regions.\u003c/p\u003e\n\u003cp\u003eGDA is developed by Eerik Aunin (\u003ca href=\"mailto:ea10@sanger.ac.uk\"\u003eea10@sanger.ac.uk\u003c/a\u003e) and Adam Reid (\u003ca href=\"mailto:ajr236@cam.ac.uk\"\u003eajr236@cam.ac.uk\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eA manuscript describing GDA is has been published in BMC Genomics:\u003cbr\u003e\n\u003ca href=\"https://trebuchet.public.springernature.app/get_content/7bf5d51e-3e6d-4724-af60-2e90fb074510\" rel=\"nofollow\"\u003eCharacterising genome architectures using genome decomposition analysis.\u003c/a\u003e\u003cbr\u003e\nAunin E, Berriman M, Reid AJ.\u003cbr\u003e\nBMC Genomics. 2022 May 25;23(1):398. doi: 10.1186/s12864-022-08616-3.\u003cbr\u003e\nPMID: 35610562\u003c/p\u003e\n\u003cp\u003eComplete analyses presented in the manuscript are available here: \u003ca href=\"https://drive.google.com/drive/folders/1XSNS_Jj0_UGxPXpzY-EwbzABGxaZgfRf?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/drive/folders/1XSNS_Jj0_UGxPXpzY-EwbzABGxaZgfRf?usp=sharing\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a diagram for a quick overview of what GDA does.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/Figure_1.png\"\u003e\u003cimg src=\"images/Figure_1.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e(A) Features sets are derived from the genome reference sequence (seq), repeat finding (rep), gene annotations (gene) and evolutionary relationships between genes (orth). Values for each feature are determined for each non-overlapping window of e.g. 5kb across the genome. (B) The resulting matrix of feature values per window is embedded in two dimensions and clustered to identify groups of windows with similar properties. (C) The data can be explored in a number of ways using a web-browser based app. The clustering labels are mapped back to the chromosomes to highlight architectural features and a heatmap displays the features which define the clusters.\u003c/p\u003e\n\u003cp\u003eA more technical diagram of the components of the pipeline in the form of a flowchart can be seen \u003ca href=\"images/gda_pipeline_flowchart.png\"\u003ehere\u003c/a\u003e.\nA \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e-based pipeline that includes various third party tools extracts the values of a set of genomic variables that describe a genome assembly. The values of genomic variables along chromosomes are stored as \u003ca href=\"https://genome.ucsc.edu/goldenPath/help/bedgraph.html\" rel=\"nofollow\"\u003ebedgraph files\u003c/a\u003e. The bedgraph files corresponding to one genome assembly are then merged into one tab separated values (TSV) file. In the following text, this file is referred to as \"merged TSV\" file. Scaling of values, dimensionality reduction with \u003ca href=\"https://umap-learn.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eUMAP\u003c/a\u003e and clustering with \u003ca href=\"https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html\" rel=\"nofollow\"\u003eHDBSCAN\u003c/a\u003e are then applied to the numbers in this TSV file. The locations of clusters along chromosomes are stored in a BED file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGDA software consists of three main parts: a genomic feature extraction pipeline, clustering scripts, and a \u003ca href=\"https://shiny.rstudio.com/\" rel=\"nofollow\"\u003eShiny\u003c/a\u003e app for viewing the results. The genomic feature extraction pipeline and the clustering scripts have been tested on a Linux server (Sanger farm) and have the following requirements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePython3\u003c/li\u003e\n\u003cli\u003eJava \u2013 with enough memory to initialise the Java virtual machine\u003c/li\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Shiny app for viewing clustering results requires R and a number of R libraries. It has been tested on MacOS and Kubuntu Linux.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNotes\u003c/h3\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe expect that the GDA feature extraction and analysis pipeline is run remotely on a compute cluster with Linux. Viewing the results of a GDA analysis is done in a Shiny app that runs in a web browser and thus we recommend that you copy your results onto your local machine to run the final step. Thus, some dependencies are required remotely and some locally (installation instructions below).\u003c/p\u003e\n\u003cp\u003eThe quick start tutorial will show you how to run the GDA pipeline end-to-end with test data (\u003cem\u003ePlasmodium falciparum\u003c/em\u003e genome assembly \u003ca href=\"https://plasmodb.org/common/downloads/release-49/Pfalciparum3D7/fasta/data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\" rel=\"nofollow\"\u003eobtained from PlasmoDB\u003c/a\u003e) and default parameters. In reality you will likely want to add additional, optional tracks such as gene annotations, repeat finding, transcriptome data and orthology information (these are also detailed below).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial\u003c/h2\u003e\u003ca id=\"user-content-tutorial\" class=\"anchor\" aria-label=\"Permalink: Tutorial\" href=\"#tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContents\u003c/h3\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#quick-start-with-test-data\"\u003eQuick start with test data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-results-tabs\"\u003eUnderstanding the results tabs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#view-clusters-and-significant-tracks-in-igv\"\u003eView clusters and significant tracks in IGV\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-output-files\"\u003eUnderstanding the output files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#adding-optional-feature\"\u003eAdding optional features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#optimising-clustering\"\u003eOptimising clustering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#understanding-the-default-features\"\u003eUnderstanding the default features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#other-output\"\u003eOther output\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#using-gda-singularity-image\"\u003eUsing GDA Singularity image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#clustering-the-features-of-multiple-genomes-at-once\"\u003eClustering the features of multiple genomes at once\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ideas-for-analysis\"\u003eIdeas for analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuick start with test data\u003c/h3\u003e\u003ca id=\"user-content-quick-start-with-test-data\" class=\"anchor\" aria-label=\"Permalink: Quick start with test data\" href=\"#quick-start-with-test-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e1. Set up a GDA conda environment on the farm (need to install conda? \u2013 \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ehttps://docs.conda.io/en/latest/miniconda.html\u003c/a\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone the GitHub repository\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun the conda installation script (this can take a little while)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003epython gda/create_gda_conda_env.py gda_env gda_downloads gda\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInitiate the conda environment:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003econda activate gda_env\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf the conda installation does not work for you, you can try using the GDA \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image instead, see \u003ca href=\"#using-gda-singularity-image\"\u003eUsing GDA Singularity image\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Run GDA\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun GDA\u2019s feature extraction pipeline with test data (we suggest that you submit this to your cluster as a job with 12 threads and 10Gb memory; expect it to take ~15 minutes with the test data):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to Load Sharing Facility (LSF)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0The results will be in the folder: \u003ccode\u003egda_pipeline_run\u003c/code\u003e. The output file required for clustering is:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCluster genome windows and analyse clusters (Use 1 thread and 10Gb memory; this should take ~1 minute; n.b. optimised clustering parameters are provided here)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda clustering -c 100 -n 5 gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n1 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_clustering_test.o -e gda_clustering_test.e \"gda clustering -c 100 -n 5 gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0The clustering output will be in a folder called: \u003ccode\u003egda_out\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Install dependencies on your local machine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMOVE TO YOUR LOCAL MACHINE (e.g. your desktop/laptop)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSet up environment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0These are the required R libraries:\u003c/p\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0shiny, ggplot2, devtools, svglite, gplots, rjson, reshape2, gridExtra, scales\u003c/p\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0If you have an R installation on your local machine that is not conda-based, the following R script should install the required libraries:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\n\ngda/gda_shiny/install_gda_shiny_dependencies_without_conda.R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Alternatively, the following commands can be used to install a custom conda R environment for the GDA Shiny app:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/eeaunin/gda.git\n\n# update conda to v4.10.1\nconda update -n base conda\n\nconda create -n gda_env_local r-essentials r-base\n\nconda activate gda_env_local\n\nconda install --yes -c r -c conda-forge r-shiny=1.5.0 r-ggplot2=3.2.1 r-gplots=3.0.3 r-rjson=0.2.20 r-reshape2=1.4.3 r-gridextra=2.3 r-scales=1.0.0 r-svglite=1.2.3\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eCopy the data from the remote machine to your local machine (while on you local machine) e.g.\n\u003ccode\u003escp -r \u0026lt;user\u0026gt;@\u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_out/ .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0In order to use scp to copy the files, you will need to be able to see the remote machine (perhaps via VPN).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. View results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe required argument for the \u003ccode\u003egda_shiny.py\u003c/code\u003e script is a path to a \u003ccode\u003egda_out\u003c/code\u003e folder (that comes from the output of \u003ccode\u003egda_clustering.py\u003c/code\u003e and which you just copied from the remote machine).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 gda/gda_shiny/gda_shiny.py gda_out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnderstanding the results tabs\u003c/h3\u003e\u003ca id=\"user-content-understanding-the-results-tabs\" class=\"anchor\" aria-label=\"Permalink: Understanding the results tabs\" href=\"#understanding-the-results-tabs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUMAP plot\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/01_gda_shiny_umap.png\"\u003e\u003cimg src=\"images/01_gda_shiny_umap.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis shows you how well the clustering worked. Each point in the plot represents a genomic window. Windows are coloured by cluster. Cluster -1 (grey) is used for unclustered windows. Based on the nature of the genome, the features used, the window size and other parameters, there may, for example, be several very distinct, tight clusters, or perhaps a single diffuse cloud of points. Distinct, tight clusters suggest that GDA has identified regions of the genome which are clearly similar to each other and distinct from other regions. A single diffuse cloud means that there were not strong similarities or differences between subsets of the windows. There might be a lot of the genome which is unclassified (grey) or it might all be included in clusters. Sliders can be used to adjust plots for better viewing and PNG or SVG images can be saved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster locations\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/02_gda_shiny_raster_plot.png\"\u003e\u003cimg src=\"images/02_gda_shiny_raster_plot.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eEach chromosome/scaffold/contig is shown, with each window coloured based on the clustering. Therefore, this shows how the clusters pattern the chromosomes and, for example, whether a particular cluster tends to be found at the end of chromosomes. Do all chromosomes have a similar pattern? Do sex chromosomes, B chromosomes etc. look distinct from the autosomes?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster heatmaps\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/03_gda_shiny_cluster_heatmaps.png\"\u003e\u003cimg src=\"images/03_gda_shiny_cluster_heatmaps.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGDA determines features which have high or low values for windows in a particular cluster compared to other clusters. The heatmap in this tab shows the relative values across clusters for each significantly variable feature. Green means a feature has a relatively high value in a particular cluster, red a relatively low value. You can find the exact values and which were significantly different in the \u201cFeature tables\u201d tab. Adjusting the plot height and the label size can be particularly useful in this tab so that the heatmap is legible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeature tables\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/04_gda_shiny_feature_tables.png\"\u003e\u003cimg src=\"images/04_gda_shiny_feature_tables.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab has a table for each cluster (and unclustered windows), describing which features have significantly higher or lower values (by the Kolmogorov-Smirnov test). The default p-value cutoff for the Kolmogorov-Smirnov test is 1e-20.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster positions across chromosomes\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/05_gda_shiny_cluster_positions_across_chromosomes.png\"\u003e\u003cimg src=\"images/05_gda_shiny_cluster_positions_across_chromosomes.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab shows where each cluster tends to occur across the sequences. It helps you to see whether a cluster tends to occur at the ends or in the middles of chromosomes for instance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChromosome cluster composition\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/06_gda_shiny_chromosome_cluster_composition.png\"\u003e\u003cimg src=\"images/06_gda_shiny_chromosome_cluster_composition.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab contains a heatmap which clusters chromosomes by their cluster composition. Chromosomes which have similar proportions of each cluster will be closer together in the heatmap. This helps in identifying outliers which might represent interesting sequences such as sex chromosomes, B chromosomes etc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster junction counts\u003c/strong\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/07_gda_shiny_cluster_junction_counts.png\"\u003e\u003cimg src=\"images/07_gda_shiny_cluster_junction_counts.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis tab shows the observed counts of junctions between windows belonging to each UMAP+HDBSCAN cluster. Junctions between windows belonging to the same type of cluster are included in the counts. The observed counts are compared with counts expected if windows were distributed randomly. Junctions with counts that are significantly different from what is expected by chance (based on Fisher test) are shown in \u003cstrong\u003e\u003cem\u003ebold+italics\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eView clusters and significant tracks in IGV\u003c/h3\u003e\u003ca id=\"user-content-view-clusters-and-significant-tracks-in-igv\" class=\"anchor\" aria-label=\"Permalink: View clusters and significant tracks in IGV\" href=\"#view-clusters-and-significant-tracks-in-igv\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe values of variables along chromosomes are stored as \u003ca href=\"https://genome.ucsc.edu/goldenPath/help/bedgraph.html\" rel=\"nofollow\"\u003ebedgraph files\u003c/a\u003e and can be viewed in genome browsers such as \u003ca href=\"https://software.broadinstitute.org/software/igv\" rel=\"nofollow\"\u003eIGV\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Install IGV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://software.broadinstitute.org/software/igv/download\" rel=\"nofollow\"\u003ehttps://software.broadinstitute.org/software/igv/download\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Get bedgraph files from cluster\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escp -r \u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_pipeline_run/bedgraph_output/ .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Copy across clustering results (if you haven\u2019t already)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003escp -r \u0026lt;remote_machine\u0026gt;:\u0026lt;path\u0026gt;/gda_out/ .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Make IGV session file\u003c/strong\u003e\nIGV allows saving and loading \u003ca href=\"https://software.broadinstitute.org/software/igv/Sessions\" rel=\"nofollow\"\u003esession files\u003c/a\u003e, which are XML files that keep track of the program state (what FASTA, BED and bedgraph files have been simultaneously loaded to IGV).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda/gda_make_igv_session_file.py -g gda/test_data/PlasmoDB-49_Pfalciparum3D7.gff gda_out/cluster_heatmap.csv gda_out/PlasmoDB-49_Pfalciparum3D7_Genome/clusters.bed gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta bedgraph_output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e5. Load session file into IGV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile \u2192\u201cOpen Session\u201d\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/gda_pfalciparum_igv_screensh.png\"\u003e\u003cimg src=\"images/gda_pfalciparum_igv_screensh.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nThe IGV screenshot above shows \u003cem\u003ePlasmodium falciparum\u003c/em\u003e chromosome 1, with some GDA bedgraph tracks and the \u0027clusters.bed\u0027 file loaded.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnderstanding the output files\u003c/h3\u003e\u003ca id=\"user-content-understanding-the-output-files\" class=\"anchor\" aria-label=\"Permalink: Understanding the output files\" href=\"#understanding-the-output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eBedgraph files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith your results directory (\u003ccode\u003e\u0026lt;YYYYMMDD\u0026gt;_gda_pipeline_run\u003c/code\u003e by default; use \u003ccode\u003egda extract_genomic_features --pipeline_run_folder\u003c/code\u003e to change), the folder \u003ccode\u003ebedgraph_output\u003c/code\u003e contains each bedgraph track produced by GDA. These can be loaded into a genome browser (e.g. IGV) for viewing and better understanding why GDA has clustered the genome as it has. We provide the script \u003ccode\u003egda_make_igv_session_file.py\u003c/code\u003e to generate an IGV session file for your genome which will show the clusters and tracks for features which are significantly enriched in the clusters.\u003c/p\u003e\n\u003cp\u003eOne of the files generated by the \u003ccode\u003egda_clustering.py\u003c/code\u003e script is called \u003ccode\u003eclusters.bed\u003c/code\u003e. This file marks the locations of each UMAP+HDBSCAN cluster and can be loaded to IGV alongside the bedgraph tracks. The cluster numbers and the colour key are the same as in the UMAP plot of the Shiny app.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe feature table\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ecluster\u003c/th\u003e\n\u003cth\u003efeature\u003c/th\u003e\n\u003cth\u003ecluster_data.size\u003c/th\u003e\n\u003cth\u003eother_data.size\u003c/th\u003e\n\u003cth\u003estat_less\u003c/th\u003e\n\u003cth\u003epvalue_less\u003c/th\u003e\n\u003cth\u003estat_great\u003c/th\u003e\n\u003cth\u003epvalue_great\u003c/th\u003e\n\u003cth\u003ecluster_median\u003c/th\u003e\n\u003cth\u003eother_median\u003c/th\u003e\n\u003cth\u003ecluster_mean\u003c/th\u003e\n\u003cth\u003eother_mean\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.47\u003c/td\u003e\n\u003ctd\u003e1.86e-35\u003c/td\u003e\n\u003ctd\u003e0.98000\u003c/td\u003e\n\u003ctd\u003e0.66000\u003c/td\u003e\n\u003ctd\u003e1.15760\u003c/td\u003e\n\u003ctd\u003e0.69856\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003etandem_repeats_fraction\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.56\u003c/td\u003e\n\u003ctd\u003e1.36e-49\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003e9.86e-01\u003c/td\u003e\n\u003ctd\u003e0.07840\u003c/td\u003e\n\u003ctd\u003e0.15620\u003c/td\u003e\n\u003ctd\u003e0.08711\u003c/td\u003e\n\u003ctd\u003e0.15905\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003ewgsim_depth_minimap2\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e0.90\u003c/td\u003e\n\u003ctd\u003e4.03e-127\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e3.71320\u003c/td\u003e\n\u003ctd\u003e9.94240\u003c/td\u003e\n\u003ctd\u003e3.98305\u003c/td\u003e\n\u003ctd\u003e9.90542\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.47\u003c/td\u003e\n\u003ctd\u003e1.86e-35\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.66000\u003c/td\u003e\n\u003ctd\u003e0.98000\u003c/td\u003e\n\u003ctd\u003e0.69856\u003c/td\u003e\n\u003ctd\u003e1.15760\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003etandem_repeats_fraction\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.01\u003c/td\u003e\n\u003ctd\u003e9.86e-01\u003c/td\u003e\n\u003ctd\u003e0.56\u003c/td\u003e\n\u003ctd\u003e1.36e-49\u003c/td\u003e\n\u003ctd\u003e0.15620\u003c/td\u003e\n\u003ctd\u003e0.07840\u003c/td\u003e\n\u003ctd\u003e0.15905\u003c/td\u003e\n\u003ctd\u003e0.08711\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003ewgsim_depth_minimap2\u003c/td\u003e\n\u003ctd\u003e4491\u003c/td\u003e\n\u003ctd\u003e185\u003c/td\u003e\n\u003ctd\u003e0.00\u003c/td\u003e\n\u003ctd\u003e1.00e+00\u003c/td\u003e\n\u003ctd\u003e0.90\u003c/td\u003e\n\u003ctd\u003e4.03e-127\u003c/td\u003e\n\u003ctd\u003e9.94240\u003c/td\u003e\n\u003ctd\u003e3.71320\u003c/td\u003e\n\u003ctd\u003e9.90542\u003c/td\u003e\n\u003ctd\u003e3.98305\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdding optional features\u003c/h3\u003e\u003ca id=\"user-content-adding-optional-features\" class=\"anchor\" aria-label=\"Permalink: Adding optional features\" href=\"#adding-optional-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend you add as many features as possible so that the clustering is able to identify those which are the strongest signals in the genome.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional features which do not require additional data\u003c/h4\u003e\u003ca id=\"user-content-optional-features-which-do-not-require-additional-data\" class=\"anchor\" aria-label=\"Permalink: Optional features which do not require additional data\" href=\"#optional-features-which-do-not-require-additional-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e1. Run repeat finding to get bedgraph tracks of individual complex repeat features as well as complex_repeat_sum (the sum of all these features)\u003c/strong\u003e\nThe GDA pipeline contains two mandatory components for repeat detection: \u003ca href=\"https://github.com/Benson-Genomics-Lab/TRF\"\u003eTandemRepeatsFinder\u003c/a\u003e for tandem repeats and \u003ca href=\"http://emboss.sourceforge.net/apps/cvs/emboss/apps/einverted.html\" rel=\"nofollow\"\u003eEMBOSS einverted\u003c/a\u003e for inverted repeats. Besides these, the GDA pipeline has two optional repeat family detection modules from which the user can choose one to run. The first one of these modules uses \u003ca href=\"https://www.repeatmasker.org/RepeatModeler/\" rel=\"nofollow\"\u003eRepeatModeler+RepeatMasker\u003c/a\u003e and the second one uses \u003ca href=\"https://github.com/BioinformaticsToolsmith/MeShClust2\"\u003eRed+MeShClust2\u003c/a\u003e. RepeatModeler+RepeatMasker is relatively slow and may take ~1 week to run for large genomes (11 hours for the test dataset). On Sanger farm5, this will require using the basement queue. The Red+Meshclust2 module is much faster, but may produce more noisy repeat families, depending on the genome.\nWhen the GDA pipeline is run with repeat family detection enabled, the bedgraph files of each complex repeat family appear in the \u003ccode\u003ecomplex_repeats\u003c/code\u003e subdirectory of the \u003ccode\u003ebedgraph_output\u003c/code\u003e directory. If RepeatModeler is used, a \u003ccode\u003esimple_repeats\u003c/code\u003e directory that contains bedgraph files of simple repeat families is also produced.\nIn addition, a bedgraph file of the sum of complex repeat families (and if using RepeatModeler, of simple repeat families) is produced. The individual bedgraph tracks of each repeat family are not used as the input for UMAP clustering by default, but the tracks for the sums of simple or complex repeat families are used.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--run_repeat_family_detection\n--repeat_family_detection_engine \u0026lt;repeatmodeler/meshclust2\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --run_repeat_family_detection --repeat_family_detection_engine repeatmodeler gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_repeatmodeler_test.o -e gda_repeatmodeler_test.e \"gda extract_genomic_features --threads 12 --run_repeat_family_detection --repeat_family_detection_engine repeatmodeler gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003e2. \u003cem\u003eDe novo\u003c/em\u003e gene annotation\u003c/strong\u003e\nThe GDA pipeline can take an existing gene annotations GFF3 file as input. For the cases where there is no existing gene annotations available for the genome, the pipeline contains an optional module that produces a \u003cem\u003ede novo\u003c/em\u003e annotation of protein coding genes, rRNA and tRNA genes (using \u003ca href=\"https://bioinf.uni-greifswald.de/augustus/\" rel=\"nofollow\"\u003eAugustus\u003c/a\u003e, \u003ca href=\"https://github.com/tseemann/barrnap\"\u003eBarrnap\u003c/a\u003e and \u003ca href=\"http://lowelab.ucsc.edu/tRNAscan-SE/\" rel=\"nofollow\"\u003etRNAscan-SE\u003c/a\u003e). The gene annotation module can optionally take an annotated related genome as the input and produce hints for Augustus based on annotation transfer with \u003ca href=\"https://github.com/agshumate/Liftoff\"\u003eLiftoff\u003c/a\u003e. Several bedgraph feature tracks are derived from gene annotations: \u003ccode\u003emRNA_annotation\u003c/code\u003e, \u003ccode\u003eexon_count\u003c/code\u003e, \u003ccode\u003egene_average_exon_length\u003c/code\u003e, \u003ccode\u003egene_average_intron_length\u003c/code\u003e, \u003ccode\u003egene_length\u003c/code\u003e, \u003ccode\u003etRNA_annotations\u003c/code\u003e, \u003ccode\u003erRNA_annotations\u003c/code\u003e. Optionally, a \u003ccode\u003egene_dna_strand_bias\u003c/code\u003e track is also produced.\nAlso, a GFF file of the annotations can be found in the \u003ccode\u003egene_annotation\u003c/code\u003e folder. The GFF file also includes the tRNAscan and Barrnap results.\u003c/p\u003e\n\u003cp\u003eMultiple options are required\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--run_gene_annotation_pipeline\n--annotation_target_species_id \u0026lt;label_for_gene_ids\u0026gt;\n--augustus_species \u0026lt;pick_from_list\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder aug_test_runfolder --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test_aug.o -e gda_test_aug.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder aug_test_runfolder --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional features requiring additional data\u003c/h4\u003e\u003ca id=\"user-content-optional-features-requiring-additional-data\" class=\"anchor\" aria-label=\"Permalink: Optional features requiring additional data\" href=\"#optional-features-requiring-additional-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e1. Genome annotation\u003c/strong\u003e\n\u003ccode\u003e--gff_path \u0026lt;GFF3 file with existing gene annotations\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor handling user-provided GFF files, the pipeline expects the following things:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe input file is in GFF3 format (GTF or GFF2 are not accepted)\u003c/li\u003e\n\u003cli\u003ethe tags for mRNA, pseudogene, tRNA and rRNA features are \"mRNA\", \"pseudogene\", \"tRNA\" and \"rRNA\". The user should check the GFF file to make sure that the tags are named according to this convention. If, for instance, the mRNA features in the GFF file are called \"transcript\" instead of \"mRNA\", the pipeline does not recognise them as the mRNA features.\u003c/li\u003e\n\u003cli\u003ethe GFF file should pass the \u003ca href=\"http://genometools.org/cgi-bin/gff3validator.cgi\" rel=\"nofollow\"\u003eGenomeTools GFF3 validator check\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe user can specify non-standard GFF3 feature tags from the input GFF3 file to be turned into bedgraph tracks using the \u003ccode\u003e--custom_gff_tags\u003c/code\u003e option of the \u003ccode\u003egda\u003c/code\u003e wrapper. For example, if the input GFF3 file has features named \"H3K9me3\" and \"H3K9ac\", it is possible to make bedgraph files out of them by specifying them as comma separated \u003ccode\u003ecustom_gff_tags\u003c/code\u003e options:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--custom_gff_tags H3K9me3,H3K9ac\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Reference genome annotation (annotate your assembly using a reference annotation: hints for Augustus are derived from annotation transfer using Liftoff)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--reference_assembly_path \u0026lt;reference assembly FASTA file\u0026gt; --reference_gff_path \u0026lt;reference assembly GFF3 file\u0026gt; \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. RNA-Seq coverage\u003c/strong\u003e\nRNA-Seq coverage is determined using the mapping of reads to the assembly with \u003ca href=\"http://daehwankimlab.github.io/hisat2/manual/\" rel=\"nofollow\"\u003eHISAT2\u003c/a\u003e. The input is a pair of gzipped FASTQ reads.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--rna_seq_fastq_1_path\n--rna_seq_fastq_2_path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR223/008/ERR2234508/ERR2234508_1.fastq.gz .\nwget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR223/008/ERR2234508/ERR2234508_2.fastq.gz .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --rna_seq_fastq_1_path ERR2234508_1.fastq.gz --rna_seq_fastq_2_path ERR2234508_2.fastq.gz gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --rna_seq_fastq_1_path ERR2234508_1.fastq.gz --rna_seq_fastq_2_path ERR2234508_2.fastq.gz gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting feature track is called \u003ccode\u003ehisat2_samtools_depth\u003c/code\u003e and the raw mapping data is in the \u003ccode\u003erna_seq_mapping\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Gene conservation (orthology)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--orthomcl_references_folder\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis folder should contain subfolders, each for separate \u003ca href=\"https://orthomcl.org/orthomcl/app\" rel=\"nofollow\"\u003eOrthoMCL\u003c/a\u003e runs, e.g. for closely related and more distantly related species (although a single folder is perfectly fine). The folder name is arbitrary. Within each folder there should be protein FASTA files for each reference proteome and a file called \u003ccode\u003etable_for_gg_file.csv\u003c/code\u003e with the names of these files and a simple name for the species. GG files (genome gene relation file) are used by OrthoMCL to relate genes to genomes. e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePchabaudi,PlasmoDB-49_Pchabaudichabaudi_AnnotatedProteins.fasta\nTgondii,ToxoDB-51_TgondiiME49_AnnotatedProteins.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eProteins from the genome under consideration will be added behind the scenes (they are derived from the assembly FASTA file and annotations GFF3 file using \u003ca href=\"https://github.com/gpertea/gffread\"\u003egffread\u003c/a\u003e). N.b. you need to provide annotation for your genome assembly or have it transferred/predicted in order to do the orthology analysis.\u003c/p\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder orthomcl_test_runfolder --orthomcl_references_folder gda/test_data/orthomcl_refs/ --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test_orthomcl.o -e gda_test_orthomcl.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder orthomcl_test_runfolder --orthomcl_references_folder gda/test_data/orthomcl_refs/ --run_gene_annotation_pipeline --annotation_target_species_id PFALTEST --augustus_species pfalciparum gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting bedgraph files are:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_ortholog_count.bedgraph\u003c/code\u003e - Number of orthologues per gene\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_paralog_count.bedgraph\u003c/code\u003e - Number of paralogues per gene\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_protein_conservation_ratio.bedgraph\u003c/code\u003e - The average proportion of species in the OrthoMCL run that have orthologs for the target species proteins in the window. The value is 1 if all target species proteins in the window have OrthoMCL orthologs in all other species in the OrthoMCL run. The value is 0 if none of the target species proteins in the window have any OrthoMCL orthologs in any other species in the OrthoMCL run.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePFALTEST_apicomplexa_species_specific_proteins_ratio.bedgraph\u003c/code\u003e - The average proportion of species-specific proteins in the window. It is the number of target species proteins with no OrthoMCL orthologs in the window divided by the number of all target species proteins in the window. The value is 1 if none of the target species proteins in the window have OrthoMCL orthologs, and 0 if all of the target species proteins in the window have OrthoMCL orthologs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Reference mitochondrial sequence for Nuclear Mitochondrial DNA (NUMT) identification\u003c/strong\u003e\nNUMT identification is done using BLAST of the genome against a user-provided reference mitochondrial sequence. The reference mitochondrial sequence can be a known mitochondrial sequence from the same species as the rest of the assembly. If a region of an assembly contig yields a strong BLAST hit (e-value \u0026lt;= 1e-30) to the reference mitochondrial sequence but the alignment length is less than 90% of the length of this contig, the BLAST hit region is labelled as a putative NUMT.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--ref_mitoch_fasta_path\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Reference plastid sequence for NUPT identification\u003c/strong\u003e\nThis is the same process as the detection of NUMTs but meant for plastid sequences.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--ref_apicoplast_fasta_path\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Other useful feature extraction options\u003c/strong\u003e\nThe pipeline tries to identify telomeric regions by searching the assembly sequences for exact matches to a telomeric motif. The \u003ccode\u003etelomeric_seq_preset\u003c/code\u003e option allows to select a query telomeric motif from a list of known telomeric motifs across different species (based on the Wikipedia article on telomeres, \u003ca href=\"https://en.wikipedia.org/wiki/Telomere\" rel=\"nofollow\"\u003ehttps://en.wikipedia.org/wiki/Telomere\u003c/a\u003e). It is also possible to specify a custom telomeric motif using the \u003ccode\u003ecustom_telomeric_seq\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--telomeric_seq_preset\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOptimising clustering\u003c/h3\u003e\u003ca id=\"user-content-optimising-clustering\" class=\"anchor\" aria-label=\"Permalink: Optimising clustering\" href=\"#optimising-clustering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptimising clustering during feature extraction\u003c/h4\u003e\u003ca id=\"user-content-optimising-clustering-during-feature-extraction\" class=\"anchor\" aria-label=\"Permalink: Optimising clustering during feature extraction\" href=\"#optimising-clustering-during-feature-extraction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChange the window size (5kb)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e--chunk_size\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis is perhaps the most important option in GDA. From a purely computational point of view, GDA will struggle with clustering a very large number of windows. From a biological perspective, it determines the resolution at which you are analysing the genome assembly. We find that 5kb works very well for the relatively miniscule \u003cem\u003ePlasmodium\u003c/em\u003e genome (~20Mb). For the common toad (\u003cem\u003eBufo bufo\u003c/em\u003e) genome, which is 4.94 Gb we have used 1 Mb window size. Aiming for 5000 windows works very nicely computationally, but you should experiment with a few window sizes, to see what gives an interesting view of the genome. You needn\u0027t run feature extraction multiple times. Instead use:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egda downsample_merged_tsv \u0026lt;tsv\u0026gt; \u0026lt;factor\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you started with a 5kb window size, use 4 as the downsampling factor and you will get a merged TSV file with 20kb windows. Similarly, use a factor of 10 to get 50kb windows.\u003c/p\u003e\n\u003cp\u003eIf the genomic feature extraction pipeline produces an output TSV file that has 10000 or more windows, a downsampled TSV file with approximately 5000 windows will be automatically generated alongside the main output TSV file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptimising clustering during clustering step\u003c/h4\u003e\u003ca id=\"user-content-optimising-clustering-during-clustering-step\" class=\"anchor\" aria-label=\"Permalink: Optimising clustering during clustering step\" href=\"#optimising-clustering-during-clustering-step\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the feature extraction pipeline is finished, you can determine good clustering parameters by looking at the UMAP plots from a range of different parameters:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda clustering_params 20210312_gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n1 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_params_test.o -e gda_params_test.e \"gda clustering_params 20210312_gda_pipeline_run/merged_bedgraph_table/PlasmoDB-49_Pfalciparum3D7_Genome_merged_bedgraph.tsv\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eReplace the \u003ccode\u003e20210312_gda_pipeline_run\u003c/code\u003e in the above command with the name of your GDA pipeline run folder path.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003en_neighbors\u003c/code\u003e is a UMAP setting that determines the size of the local neigbourhood in terms of sample points (\u003ca href=\"https://umap-learn.readthedocs.io/en/latest/parameters.html\" rel=\"nofollow\"\u003ehttps://umap-learn.readthedocs.io/en/latest/parameters.html\u003c/a\u003e). Smaller \u003ccode\u003en_neigbors\u003c/code\u003e values give more emphasis on local structure in the data and larger \u003ccode\u003en_neighbors\u003c/code\u003e values give more weight to global structure. We have used \u003ccode\u003en_neighbors\u003c/code\u003e values from 5 to 200.\nBy default the clustering will be run with \u003ccode\u003en_neighbors\u003c/code\u003e set to 5, 10, 15, 20, 50, 100 and \u201cMinimum cluster size\u201d set to 50, 100, 200, 500. All parameter pairs will be explored (e.g. 24 combinations). The results of each clustering are output to STDOUT. You can also view an HTML file of UMAP plots in a web browser e.g.:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003efirefox gda_out/parameter_selection/parameters.html \u0026amp;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e[warning this can run slowly when run remotely]\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"images/pfalciparum_gda_parameters_example.pdf\"\u003eHere\u003c/a\u003e is example output of the \u003ccode\u003egda clustering_params\u003c/code\u003e run with the \u003cem\u003ePlasmodium falciparum\u003c/em\u003e assembly.\u003c/p\u003e\n\u003cp\u003eWe recommend selecting parameters based on minimising the percentage of unclassified sequence, while getting at least two clusters. E.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN neighbours: 5\nMin cluster size: 50\nCluster -1 is 2.14% of the genome\nCluster 0 is 2.99% of the genome\nCluster 1 is 3.70% of the genome\nCluster 2 is 91.17% of the genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnly 2.14% of windows were unclassified and there are multiple clusters meaning GDA has identified some partitioning of the genome.\u003c/p\u003e\n\u003cp\u003eGiven that this pair of parameters involves the lowest values, it would be a good idea to try out even lower parameter values to see if there is an even better/more interesting clustering.\u003c/p\u003e\n\u003cp\u003eYou should pick minimum cluster sizes based on the number of windows you have. E.g. If you have 5000 windows, and you have a minimum cluster size of 50, the smallest possible cluster will contain 1% of your genome assembly.\u003c/p\u003e\n\u003cp\u003eWhen clustering a large number of genomic windows, you may need to set HDBSCAN\u0027s \u003ccode\u003emin_samples\u003c/code\u003e value to a value that is not \u003ccode\u003eNone\u003c/code\u003e in order to prevent HDBSCAN from crashing (\u003ca href=\"https://github.com/scikit-learn-contrib/hdbscan/issues/250\"\u003ehttps://github.com/scikit-learn-contrib/hdbscan/issues/250\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUnderstanding the default features\u003c/h3\u003e\u003ca id=\"user-content-understanding-the-default-features\" class=\"anchor\" aria-label=\"Permalink: Understanding the default features\" href=\"#understanding-the-default-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eVariable\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eat_skew\u003c/td\u003e\n\u003ctd\u003eAT skew\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecag_freq\u003c/td\u003e\n\u003ctd\u003eCAG trinucleotide repeat frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecomplex_repeats_bedgraph\u003c/td\u003e\n\u003ctd\u003ecomplex repeats detected using \u003ca href=\"https://www.repeatmasker.org/RepeatModeler/\" rel=\"nofollow\"\u003eRepeatModeler+RepeatMasker\u003c/a\u003e or \u003ca href=\"https://github.com/BioinformaticsToolsmith/MeShClust2\"\u003eRed+MeShClust2\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecpg_percentage\u003c/td\u003e\n\u003ctd\u003eCpG dinucleotide frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edustmasker_low_complexity_percentage\u003c/td\u003e\n\u003ctd\u003elow complexity sequence frequency (detected using \u003ca href=\"https://www.ncbi.nlm.nih.gov/IEB/ToolBox/CPP_DOC/lxr/source/src/app/dustmasker/\" rel=\"nofollow\"\u003eDustmasker\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eectopic_apicoplast\u003c/td\u003e\n\u003ctd\u003eputative ectopic apicoplast (detected using BLAST against user-provided apicoplast sequence)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eectopic_mitochondrion\u003c/td\u003e\n\u003ctd\u003eputative NUMTs (detected using BLAST against user-provided mitochondrial sequence)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eeinverted\u003c/td\u003e\n\u003ctd\u003einverted repeats (detected using \u003ca href=\"http://emboss.sourceforge.net/apps/cvs/emboss/apps/einverted.html\" rel=\"nofollow\"\u003eEMBOSS einverted\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eexon_count\u003c/td\u003e\n\u003ctd\u003eaverage exon count per mRNA gene\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egaps\u003c/td\u003e\n\u003ctd\u003eassembly gaps (Ns)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egc_percentage\u003c/td\u003e\n\u003ctd\u003eGC%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egc_skew\u003c/td\u003e\n\u003ctd\u003eGC skew\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_average_exon_length\u003c/td\u003e\n\u003ctd\u003eaverage exon length of mRNA genes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_average_intron_length\u003c/td\u003e\n\u003ctd\u003eaverage intron length of mRNA genes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_dna_strand_bias\u003c/td\u003e\n\u003ctd\u003etendency of genes to be all on the same strand in the window. The value is 1 if all genes in the window are on the same strand (it does not matter which one). The value is 0 if genes in the window are equally distributed between both strands\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egene_length\u003c/td\u003e\n\u003ctd\u003eaverage mRNA gene length\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ekmer_deviation_kmer_size_3*\u003c/td\u003e\n\u003ctd\u003ekmer skew for a for a particular kmer length (how much the distribution of kmers in the window differs from what is expected by change, given the GC content of the sequence in the window)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eltrdigest_protein_matches\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/genometools/genometools\"\u003eLTRdigest\u003c/a\u003e protein matches\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eltrdigest_retrotransposons\u003c/td\u003e\n\u003ctd\u003eputative retrotransposons (detected using \u003ca href=\"https://github.com/genometools/genometools\"\u003eLTRharvest and LTRdigest\u003c/a\u003e). Only the sequences containing LTRdigest protein matches are counted\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emRNA_annotations\u003c/td\u003e\n\u003ctd\u003emRNA gene density (either from user-provided gene annotations or detected using \u003ca href=\"https://bioinf.uni-greifswald.de/augustus/\" rel=\"nofollow\"\u003eAugustus\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eortholog_count\u003c/td\u003e\n\u003ctd\u003eaverage number of orthologs (\u003ca href=\"https://orthomcl.org/orthomcl/\" rel=\"nofollow\"\u003eOrthoMCL\u003c/a\u003e orthologs in other species) for proteins in the window\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eparalog_count\u003c/td\u003e\n\u003ctd\u003eaverage number of paralogs (OrthoMCL orthologs within the same species) for proteins in the window\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eprotein_conservation_ratio\u003c/td\u003e\n\u003ctd\u003ereflects the average proportion of species in the OrthoMCL run that have orthologs for the target species proteins in the window. The value is 1 if all target species proteins in the window have OrthoMCL orthologs in all other species in the OrthoMCL run. The value is 0 if none of the target species proteins in the window have any OrthoMCL orthologs in any other species in the OrthoMCL run.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epseudogene_annotations\u003c/td\u003e\n\u003ctd\u003epseudogenes (read from user-provided GFF3 file if this feature is present there)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003erRNA_annotations\u003c/td\u003e\n\u003ctd\u003erRNA_annotations (either from user-provided gene annotations or detected using Barrnap)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esimple_repeats_bedgraph\u003c/td\u003e\n\u003ctd\u003esimple repeats detected using RepeatModeler+RepeatMasker. The sequences have been collapsed to count repeats that are the reverse complement of one another as the same repeat. They have also been collapsed to count the repeats that are identical if the starting point is adjusted as the same repeat (e.g. TGGTT is the same as GGTTT)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003especies_specific_proteins_ratio\u003c/td\u003e\n\u003ctd\u003ereflects the average proportion of species specific proteins in the window. It is the number of target species proteins with no OrthoMCL orthologs in the window divided by the number of all target species proteins in the window. The value is 1 if none of the target species proteins in the window have OrthoMCL orthologs, and 0 if all of the target species proteins in the window have OrthoMCL orthologs.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003estop_codon_freq\u003c/td\u003e\n\u003ctd\u003estop codon frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esum_of_complex_repeats\u003c/td\u003e\n\u003ctd\u003esum of values of RepeatModeler+RepeatMasker or Red+MeShClust2 tracks for complex repeat families\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esum_of_simple_repeats\u003c/td\u003e\n\u003ctd\u003esum of values of RepeatModeler tracks for simple repeat families\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etandem_repeat_density\u003c/td\u003e\n\u003ctd\u003etandem repeats (detected using \u003ca href=\"https://github.com/Benson-Genomics-Lab/TRF\"\u003eTandem Repeats Finder\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etelomere_freq\u003c/td\u003e\n\u003ctd\u003etelomeric sequence frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etRNA_annotations\u003c/td\u003e\n\u003ctd\u003etRNAs (either from user-provided gene annotations or detected using \u003ca href=\"http://lowelab.ucsc.edu/tRNAscan-SE/\" rel=\"nofollow\"\u003etRNAscan-SE\u003c/a\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ewgsim_minimap2_coverage\u003c/td\u003e\n\u003ctd\u003ecoverage of \u003ca href=\"https://github.com/lh3/wgsim\"\u003eWGSIM\u003c/a\u003e simulated short reads, derived from the assembly itself, with a target coverage of 10x. The reads have been mapped back to the assembly using Minimap2 using the short read mapping mode. Multimapping simulated reads have been removed before calculating the coverage\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOther output\u003c/h3\u003e\u003ca id=\"user-content-other-output\" class=\"anchor\" aria-label=\"Permalink: Other output\" href=\"#other-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e/work/\u003c/code\u003e directory \u2013 Files automatically generated by Nextflow during the run. These files can be used for resuming the pipeline when crashed. Nextflow has a \u003ccode\u003e-resume\u003c/code\u003e option for restarting an interrupted run from the last cached checkpoint. In the GDA pipeline wrapper script, the \u003ccode\u003eresume_genomic_feature_extraction\u003c/code\u003e command is meant for restarting the pipeline using Nextflow\u0027s \u003ccode\u003e-resume\u003c/code\u003e flag. For this you will need to provide the path to the Nextflow config file (it is a file with the name \u003ccode\u003enextflow.config\u003c/code\u003e in the \u003ccode\u003e*_gda_pipeline_run folder\u003c/code\u003e) and the name of the crashed run. The run names are autogenerated by Nextflow and can be seen in the STDOUT log of the GDA run, in square brackets below the line that says \"N E X T F L O W\". If the run was started using the GDA Singularity image, you will also need to provide the path to that image, otherwise this path is not needed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClustering the features of multiple genomes at once\u003c/h3\u003e\u003ca id=\"user-content-clustering-the-features-of-multiple-genomes-at-once\" class=\"anchor\" aria-label=\"Permalink: Clustering the features of multiple genomes at once\" href=\"#clustering-the-features-of-multiple-genomes-at-once\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to cluster the features extracted from multiple genomes at the same time. To do this, the first step is to run the genomic feature extraction pipeline separately for each genome of interest. For each genome, this will produce a TSV table with the values of the genomic features. The tables can then be concatenated using the \u003ccode\u003egda_concatenate_tsv_tables.py\u003c/code\u003e script. Each of the input tables needs to have the same window size. In the \u003ccode\u003especies\u003c/code\u003e and \u003ccode\u003echromosome\u003c/code\u003e columns, each input TSV table needs to have unique values that do not occur in the other input TSV tables. After concatenating the tables, the resulting combined table can be processed with the \u003ccode\u003egda clustering_params\u003c/code\u003e and \u003ccode\u003egda clustering\u003c/code\u003e commands. When viewing the clustering results of a multi-genome TSV table in the Shiny app, an extra UMAP plot will appear, with dots coloured according to which input assembly each window belongs to (\u003ca href=\"images/clustering_two_genomes_umap_example.png\"\u003eexample\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing GDA Singularity image\u003c/h3\u003e\u003ca id=\"user-content-using-gda-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Using GDA Singularity image\" href=\"#using-gda-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs an alternative to using conda to install the dependencies for GDA, it is also possible to read the dependencies from a Singularity image. A Singularity image file with the dependencies for GDA has been deposited in Google Drive, at \u003ca href=\"https://drive.google.com/file/d/1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_/view?usp=sharing\" rel=\"nofollow\"\u003ehttps://drive.google.com/file/d/1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_/view?usp=sharing\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you have gdown (\u003ca href=\"https://github.com/wkentaro/gdown\"\u003ehttps://github.com/wkentaro/gdown\u003c/a\u003e, \u003ca href=\"https://anaconda.org/conda-forge/gdown\" rel=\"nofollow\"\u003ehttps://anaconda.org/conda-forge/gdown\u003c/a\u003e) installed on your system, you can download the Singularity image file from Google Drive with a terminal command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egdown https://drive.google.com/uc?id=1cKw1cXjUBUODzBbxw7txAE80Q8g5hl8_\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOn the Sanger farm, Singularity can be started from the farm module:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emodule load ISG/singularity/3.6.4\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou will need to make sure Singularity and Nextflow are installed on your cluster.\nFor running GDA with the Singularity image, you should still clone this GitHub repository and add the \u003ccode\u003egda\u003c/code\u003e wrapper script to \u003ccode\u003ePATH\u003c/code\u003e. To use the GDA Singularity image, you should provide the path to the image with the \u003ccode\u003e--singularity_image_path\u003c/code\u003e option of the \u003ccode\u003egda\u003c/code\u003e wrapper script. The remaining software dependencies (RepeatModeler, HISAT2, LTRharvest, etc) will then be loaded from the Singularity image. This is an example command for extracting genomic features using Singularity:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEither\u003c/strong\u003e plain command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta --singularity_image_path \u0026lt;gda_singularity.simg\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eOr\u003c/strong\u003e by submission to LSF\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebsub -n12 -R\"span[hosts=1]\" -M10000 -R \u0027select[mem\u0026gt;10000] rusage[mem=10000]\u0027 -o gda_test.o -e gda_test.e \"gda extract_genomic_features --threads 12 --pipeline_run_folder gda_pipeline_run gda/test_data/PlasmoDB-49_Pfalciparum3D7_Genome.fasta --singularity_image_path \u0026lt;gda_singularity.simg\u0026gt;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also run the \u003ccode\u003egda_clustering_params\u003c/code\u003e and \u003ccode\u003egda_clustering\u003c/code\u003e commands with the Singularity image by providing a path to the image with the \u003ccode\u003e--singularity_image_path\u003c/code\u003e option.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTroubleshooting\u003c/h3\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u2022\tThe best place to look for error messages initially is STDOUT, rather than STDERR, because the Nextflow error messages end up there. You may then be directed to the \u003ccode\u003eerror_stream_logs\u003c/code\u003e directory in your run folder for error messages from a specific process\u003c/p\u003e\n\u003cp\u003e\u2022\tYou may want to exclude the mitochondrial and other symbiont genomes as well as any shorter, non-chromosomal scaffolds\u003c/p\u003e\n\u003cp\u003e\u2022\tIf your genome assembly is large and clustering is problematic you may want to increase window size. You can do this with an existing merged TSV file using \u003ccode\u003egda downsample_merged_tsv \u0026lt;path to the TSV file\u0026gt; \u0026lt;downsampling factor\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBugs, suggestions etc. can be sent to \u003ca href=\"mailto:ea10@sanger.ac.uk\"\u003eea10@sanger.ac.uk\u003c/a\u003e and \u003ca href=\"mailto:ajr236@cam.ac.uk\"\u003eajr236@cam.ac.uk\u003c/a\u003e, or submitted as issues on this GitHub page.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIdeas for analysis\u003c/h3\u003e\u003ca id=\"user-content-ideas-for-analysis\" class=\"anchor\" aria-label=\"Permalink: Ideas for analysis\" href=\"#ideas-for-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse a single feature from the merged TSV to make calls for where this feature is high across a genome \u2013 e.g. paralogous gene families or a particular complex repeat family of interest.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "likelet/circPipe", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecircPipe\u003c/h1\u003e\u003ca id=\"user-content-circpipe\" class=\"anchor\" aria-label=\"Permalink: circPipe\" href=\"#circpipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ecircRNA analysis pipe\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/likelet/cirpipe\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad01141b67fa633bf68e8b26b221b8ad5005a1ef85ed46dcca6b4bd8091b80eb/68747470733a2f2f7472617669732d63692e6f72672f6c696b656c65742f636972706970652e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/likelet/cirpipe.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/likelet/cirpipe\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f160fb58245bc682238f3e2815b5affa78cf7be3f6b10ba1335b22d921225ab7/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c696b656c65742f636972706970652e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/likelet/cirpipe.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the recent decades, circular RNA(circRNA) have came into our sight and emerged as a large class of non-coding RNAs. It thought to be predominately generated by covalently back-splicing conjunction of 3\u2019end and 5\u2019end from the same or distinct exons. circRNA involving in a variaty of biological functions in cells. Emerging evidences show that dysregulation of circRNAs are associated with several human diseases including cancer. Therefore, Systematically identification and profiling circRNAs is a fundimental process for dissecting the underlining biological mechanism of its regulation. To date, a range of tools have been developed to investigate circRNAs from high-throughput sequencing including CIRCexplorer2, CIRI, Find_circ, Mapsplice, and Segemehl. However, it not a easy thing for users to pick tools and compare the result from such tools. The most appropriate strategy is run those tools parallel and collapse the result together, and vote the candidates to improve their confidence for further experimental exploration. In addition, a remapping step is nessasary to help reduce the false postive candidates in an application scenarios. Here, we present circPipe, a nextflow-based pipeline for running multitool-based identification of circRNA from RNA-seq dataset. circPipe integrates a remapping stragegy to help filter out the circRNAs with non-reads supported in non-mismatch mode of realignment. The pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA full tutorial of CircPipe can be found at Wiki page of this project. plz go to the \u003ca href=\"https://github.com/likelet/circPipe/wiki\"\u003ehttps://github.com/likelet/circPipe/wiki\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline Steps\u003c/h3\u003e\u003ca id=\"user-content-pipeline-steps\" class=\"anchor\" aria-label=\"Permalink: Pipeline Steps\" href=\"#pipeline-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCircPipe allows you to run arbitrary pipelines among five pipelines.\nChoose between workflows by using \u003ccode\u003e--selectTools\u003c/code\u003e or not(default) .\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eStep\u003c/th\u003e\n\u003cth\u003ePipeline One\u003c/th\u003e\n\u003cth\u003ePipeline Two\u003c/th\u003e\n\u003cth\u003ePipeline Three\u003c/th\u003e\n\u003cth\u003ePipeline Four\u003c/th\u003e\n\u003cth\u003ePipeline Five\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eRaw Data QC\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003ctd\u003eFastp\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eReads Alignment\u003c/td\u003e\n\u003ctd\u003eSTAR\u003c/td\u003e\n\u003ctd\u003eBWA\u003c/td\u003e\n\u003ctd\u003eBowtie2\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eReads counting\u003c/td\u003e\n\u003ctd\u003eCIRCexplorer2\u003c/td\u003e\n\u003ctd\u003eCIRI\u003c/td\u003e\n\u003ctd\u003eFind_circ\u003c/td\u003e\n\u003ctd\u003eMapsplice\u003c/td\u003e\n\u003ctd\u003eSegemehl\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eData Processing (in house script)\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003ctd\u003ePython,Java,R\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDifferential expression\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003ctd\u003eedgeR\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSummary Report\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003ctd\u003eMultiQC\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSoftwares\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OpenGene/fastp\"\u003eFastp\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTAR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/YangLab/CIRCexplorer2\"\u003eCIRCexplorer2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lh3/bwa\"\u003eBWA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://sourceforge.net/projects/ciri\" rel=\"nofollow\"\u003eCIRI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BenLangmead/bowtie2\"\u003eBowtie2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/marvin-jens/find_circ\"\u003eFind_circ\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.netlab.uky.edu/p/bioinfo/MapSplice2\" rel=\"nofollow\"\u003eMapsplice\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.bioinf.uni-leipzig.de/Software/segemehl/\" rel=\"nofollow\"\u003eSegemehl\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ewels/MultiQC\"\u003eMultiQC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSeveral R packages for downstream analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun analysis with bed format circRNA result\u003c/h3\u003e\u003ca id=\"user-content-run-analysis-with-bed-format-circrna-result\" class=\"anchor\" aria-label=\"Permalink: run analysis with bed format circRNA result\" href=\"#run-analysis-with-bed-format-circrna-result\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run /data1/software/circPipe/alternative_mode.nf \\\n\t\t-c /data1/software/circPipe/nextflow.config \\\n\t\t-with-trace -profile docker,colonCancer \\\n\t\t-resume --selectTools 1,2,3 --bedDir \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eplace to your bedfiles\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t\t--skip_fastp \\\n\t\t--skipDE \\\n\t\t--container likelet/circpipe:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun \u003ccode\u003eQuantCircle\u003c/code\u003e analysis with a single bed file\u003c/h3\u003e\u003ca id=\"user-content-run-quantcircle-analysis-with-a-single-bed-file\" class=\"anchor\" aria-label=\"Permalink: run QuantCircle analysis with a single bed file\" href=\"#run-quantcircle-analysis-with-a-single-bed-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run /data1/software/circPipe/QuantCircle.nf \\\n\t\t-with-trace -profile docker \\\n\t\t--container likelet/circpipe:latest \\\n\t\t--reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eFastq/*{1,2}.fq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --bedfile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eciri_pos20190613.candidates.bed\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t\t--genomefile \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eGenome/hg19_chr2.fa\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t --hisat2_index \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eGenome/hg19_chr2_hisat2_index/hg19_chr2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 4, - "subscribers_count": 6, - "topics": [ - "nextflow" - ], - "updated_at": 1703941195.0 + "subscribers_count": 4, + "topics": [], + "updated_at": 1661238007.0 }, { "data_format": 2, - "description": "Singularity container for Tensorflow - good example for GPU integration into a container", + "description": "Assembly and differential expression analysis", "filenames": [ - "Singularity", - "Singularity.2.2" + "singularity/Singularity.multiQC", + "singularity/Singularity.star", + "singularity/Singularity.fastqc", + "singularity/Singularity.featureCounts", + "singularity/Singularity.bowtie2", + "singularity/Singularity.htseqCount", + "singularity/Singularity.trimmomatic" ], - "full_name": "CHPC-UofU/Singularity-tensorflow", + "full_name": "phelelani/nf-rnaSeqCount", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-rnaSeqCount\u003c/h1\u003e\u003ca id=\"user-content-nf-rnaseqcount\" class=\"anchor\" aria-label=\"Permalink: nf-rnaSeqCount\" href=\"#nf-rnaseqcount\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/phelelani/nf-rnaSeqCount/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3200715183d319ef1d7a675338007a93c09d2a7f5212b923d130a3a6be19b63d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7068656c656c616e692f6e662d726e61536571436f756e74\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/phelelani/nf-rnaSeqCount\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4c99abe6886b93af3fa47779836429f5c9f9dac96e3c220439f0e32c2ea1a2d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538462532302532302545322539372538462532302532302545322539372538422d79656c6c6f77\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B-yellow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/phelelani/nf-rnaSeqCount/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/817b8e873c94966b17b5f88fc7388afb2bbe1f45a79cc23eba14bf3c6954f923/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7068656c656c616e692f6e662d726e61536571436f756e74\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/phelelani/nf-rnaSeqCount\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/phelelani/nf-rnaSeqCount/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c718a707b4f88e6860ad99591f732b0f2dfc964128a6becfa8de7119b0f5fca8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7068656c656c616e692f6e662d726e61536571436f756e74\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/phelelani/nf-rnaSeqCount\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://bio.tools/nf-rnaseqcount\" rel=\"nofollow\"\u003ebiotools:nf-rnaseqcount\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enf-rnaSeqCount\u003c/code\u003e is a \u003ca href=\"http://nextflow.io/\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e pipeline for obtaining raw read counts for RNA-seq data using a given reference genome and annotation. To use the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline, the following dependencies are required:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInstalled softwares:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity\u003c/code\u003e \u003ca href=\"https://www.singularity-hub.org/collections/770\" rel=\"nofollow\"\u003econtainers\u003c/a\u003e with the required applications/programs for executing the workflow:\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-fastqc.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-featurecounts.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-htseqcount.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-multiqc.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-star.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-trimmomatic.sif\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount-bowtie2.sif\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eReference genome, annotation and indexes\n\u003cul\u003e\n\u003cli\u003eReference genome (\u003ccode\u003e.fa\u003c/code\u003e/\u003ccode\u003e.fasta\u003c/code\u003e) and genome annotation (\u003ccode\u003e.gtf\u003c/code\u003e) files.\u003c/li\u003e\n\u003cli\u003eReference genome indexes (\u003ccode\u003ebowtie2\u003c/code\u003e \u0026amp; \u003ccode\u003eSTAR\u003c/code\u003e - see \u003cem\u003e1.3.\u003c/em\u003e below on how to generate the indexes).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"nf-rnaSeqCount.png\"\u003e\u003cimg width=\"600\" src=\"nf-rnaSeqCount.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Obtaining the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline and preparing data\u003c/h2\u003e\u003ca id=\"user-content-1-obtaining-the-nf-rnaseqcount-pipeline-and-preparing-data\" class=\"anchor\" aria-label=\"Permalink: 1. Obtaining the nf-rnaSeqCount pipeline and preparing data\" href=\"#1-obtaining-the-nf-rnaseqcount-pipeline-and-preparing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you need to clone the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e repository onto you machine. You can eisther use \u003ccode\u003egit\u003c/code\u003e or \u003ccode\u003enextflow\u003c/code\u003e (see the two methods below). I recommend using \u003ccode\u003enextflow\u003c/code\u003e and creating you own \u003ccode\u003econfig\u003c/code\u003e file (will explain later) for executing the workflow in the directory of your choosing. The rest of this documentation assumes that you have used \u003ccode\u003enextflow\u003c/code\u003e to clone this workflow - If your\u0027re an expert and have used \u003ccode\u003egit\u003c/code\u003e to clone the workflow - you know what to do :)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Using nextflow\u003c/span\u003e\nnextflow pull https://github.com/phelelani/nf-rnaSeqCount\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eContent of the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enf-rnaSeqCount\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--containers \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for Singularity images and recipes (in case you want to build yourself). All downloaded images go here!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.fastqc \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.featureCounts \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.htseqCount \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.multiQC \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.star \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.trimmomatic \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.trinity \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--templates \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for extra scripts for the pipeline.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--clean_featureCounts.sh \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Script for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--clean_htseqCounts.sh \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Script for \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--LICENSE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Duh!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--main.config \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# User configuration file! All inputs, outputs and options GO HERE!! ONLY file that SHOULD be modified by user!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--main.nf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Main nf-rnaSeqCount nextflow scripts.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nextflow.config \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Pipeline configuration file! DO NOT EDIT!!!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount.png \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Pipeline flow diagram\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--README.md \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Duh!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo get the \u003ccode\u003ehelp menu\u003c/code\u003e for the workflow, execute the following from anywherre on your system aftercloning the repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run nf-rnaSeqCount --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe command above will give you the following usage information and options for running the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e workflow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e====================================================================================================\n###################################### nf-rnaSeqCount v0.2 ######################################\n====================================================================================================\n\nUSAGE:\nnextflow run nf-rnaSeqCount -profile \"slurm\" --data \"/path/to/data\" --genome \"/path/to/genome.fa\" --genes \"/path/to/genes.gtf\"\n\nHELP:\nnextflow run nf-rnaSeqCount --help\n\nMANDATORY ARGUEMENTS:\n-profile STRING Executor to be used. Available options:\n\t\t\t\t\"standard\" : Local execution (no job scheduler).\n\t\t\t\t\"slurm\" : SLURM scheduler.\n--mode STRING To specify which step of the workflow you are running (see https://github.com/phelelani/nf-rnaSeqCount).\n Available options:\n\t\t\t\t\"prep.Containers\" : For downloading Singularity containers used in this workflow.\n\t\t\t\t\"prep.Indexes\" : For indexing your reference genome using STAR and Bowtie2.\n\t\t\t\t\"run.ReadQC\" : For performing general QC on your reads using FastQC. \n\t\t\t\t\"run.ReadTrimming\" : For trimming low quality bases and removing adapters from your reads using Trimmmomatic.\n\t\t\t\t\"run.ReadAlignment\" : For aligning your reads to your reference genome using STAR.\n\t\t\t\t\"run.ReadCounting\" : For counting features in your reads using HTSeq-count and featureCounts.\n\t\t\t\t\"run.MultiQC\" : For getting a summary of QC through the analysis using MultiQC.\n--data FOLDER Path to where the input data (FASTQ files) is located. Supported FASTQ files:\n\t\t\t\t[ fastq | fastq.gz | fastq.bz2 | fq | fq.gz | fq.bz2 ]\n--genome FILE The whole genome FASTA sequence. Supported FASTA files:\n\t\t\t\t[ fasta | fa | fna ]\n--genes FILE The genome annotation GFT file. Supported GTF file:\n\t\t\t\t[ gtf ]\n\nOPTIONAL ARGUEMENTS:\n--help To show this menu.\n--out FOLDER Path to where the output should be directed.\n Default: $PWD/results_nf-rnaSeqCount.\n--from STRING Specify to resume workflow from the QC or trimming step. Options:\n\t\t\t\t\"run.ReadQC\" : To resume from the QC step (default).\n\t\t\t\t\"run.ReadTrimming\" : To resume from the trimming step.\n--pairedEnd If working with paired-end FASTQ files (default).\n--singleEnd If working with single-end FASTQ files.\n--trim STRING Parameters for Trimmomatic. See http://www.usadellab.org/cms/index.php?page=trimmomatic for a more detailed use.\n The default parameters for Trimmomatic I have given you here (for both paird- and single-end sequences) are:\n\t\t\t\tFor paired-end: \"ILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10:8:true TRAILING:28 MINLEN:40\"\n\t\t\t\tFor single-end: \"ILLUMINACLIP:TruSeq3-SE.fa:2:30:10:8:true TRAILING:28 MINLEN:40\"\n--max_memory STRING Maximum memory you have access to.\n Default: \"200.GB\"\n--max_cpus STRING Maximum CPUs you have access to. \n Default: \"24\"\n--max_time STRING Maximum time you have access to. \n Default: \"24.h\"\n====================================================================================================\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.1. Download test datasets (optional)\u003c/h3\u003e\u003ca id=\"user-content-11-download-test-datasets-optional\" class=\"anchor\" aria-label=\"Permalink: 1.1. Download test datasets (optional)\" href=\"#11-download-test-datasets-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will now download the reference genome (along with its annotation file) from Ensembl. We will also download the FASTQ files from the H3ABioNet site, which we will analyse using the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e workflow. \u003cem\u003e\u003cstrong\u003eNB\u003c/strong\u003e: Skip this section if you have your own data to analyse using this workflow! This section is only for getting data to practice using the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e workflow!\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Download and decompress the mouse reference genome along with its annotation:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e## Make a directory for the reference genome:\nmkdir reference\n\n## Download the reference genome (FASTA) and annotation file (GTF) files and put them into the newlly created directory:\nwget -c -O reference/genome.fa.gz ftp://ftp.ensembl.org/pub/release-68/fasta/mus_musculus/dna/Mus_musculus.GRCm38.68.dna.toplevel.fa.gz\nwget -c -O reference/genes.gtf.gz ftp://ftp.ensembl.org/pub/release-68/gtf/mus_musculus/Mus_musculus.GRCm38.68.gtf.gz\ngunzip reference/genome.fa.gz\ngunzip reference/genes.gtf.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Download RNA-seq test dataset from H3ABioNet:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e## Make a directory for the data:\nmkdir data\n\n## Download the data:\nfor sample in sample{37..42}_R{1,2}.fastq.gz; do wget -c -O data/$sample http://h3data.cbio.uct.ac.za/assessments/RNASeq/practice/dataset/$sample; done\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.2. Download the \u003ccode\u003eSingularity\u003c/code\u003e containers (required to execute the pipeline):\u003c/h3\u003e\u003ca id=\"user-content-12-download-the-singularity-containers-required-to-execute-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: 1.2. Download the Singularity containers (required to execute the pipeline):\" href=\"#12-download-the-singularity-containers-required-to-execute-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode prep.Containers\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.3. Generating genome indexes.\u003c/h3\u003e\u003ca id=\"user-content-13-generating-genome-indexes\" class=\"anchor\" aria-label=\"Permalink: 1.3. Generating genome indexes.\" href=\"#13-generating-genome-indexes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo generate the \u003ccode\u003eSTAR\u003c/code\u003e and \u003ccode\u003eBowtie2\u003c/code\u003e genome indexes, run the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e## Generate STAR and Bowtie2 indexes\nnextflow run nf-rnaSeqCount -profile slurm --mode prep.Indexes --genome \"$PWD/reference/genome.fa\" --genes \"$PWD/reference/genes.gtf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe are now ready to execute the workflow!\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Executing the main \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline\u003c/h2\u003e\u003ca id=\"user-content-2-executing-the-main-nf-rnaseqcount-pipeline\" class=\"anchor\" aria-label=\"Permalink: 2. Executing the main nf-rnaSeqCount pipeline\" href=\"#2-executing-the-main-nf-rnaseqcount-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs seen on the \u003ccode\u003ehelp menu\u003c/code\u003e above, there are a couple of options that you can use with this workflow. It can become a bit tedious and confusing having to specify these commands everytime you have to execute the each section for the analysis. To make your life easier, we will create a configuration script that we will use in this tutorial (we will pass this using the \u003ccode\u003e-c\u003c/code\u003e option of \u003ccode\u003enextflow\u003c/code\u003e). You can name it whatever you want, but for now, lets call it \u003ccode\u003emyparams.config\u003c/code\u003e. We will add the mandatory arguements for now, but as you become more farmiliar with the workflow - you can experiment with other options. You can use your favourite text editor to create the \u003ccode\u003emyparams.config\u003c/code\u003e file. Copy and paste the the parameters below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eparams {\n data = \"$PWD/data\"\n genome = \"$PWD/reference/genome.fa\"\n genes = \"$PWD/reference/genes.fa\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eObviously - the above \u003ccode\u003emyparams.config\u003c/code\u003e assumes that you have been following this tutorial. If you have your data lying around somewhere in your system, you need to put the full path to where your the \u003ccode\u003edata\u003c/code\u003e, \u003ccode\u003egenome\u003c/code\u003e and \u003ccode\u003egenes\u003c/code\u003e files are. Since the \u003ccode\u003e--mode\u003c/code\u003e will keep changing, we will add this on the command as we do the analysis. Now that we have the mandatory arguements in our \u003ccode\u003emyparams.config\u003c/code\u003e, lets do some analysis\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.1. Read QC (optional):\u003c/h3\u003e\u003ca id=\"user-content-21-read-qc-optional\" class=\"anchor\" aria-label=\"Permalink: 2.1. Read QC (optional):\" href=\"#21-read-qc-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo perform the QC of your fastq files, use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.ReadQC -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.2. Read Trimming (optional):\u003c/h3\u003e\u003ca id=\"user-content-22-read-trimming-optional\" class=\"anchor\" aria-label=\"Permalink: 2.2. Read Trimming (optional):\" href=\"#22-read-trimming-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the trimming step of the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline, use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.ReadTrimming -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.3. Read Alignment:\u003c/h3\u003e\u003ca id=\"user-content-23-read-alignment\" class=\"anchor\" aria-label=\"Permalink: 2.3. Read Alignment:\" href=\"#23-read-alignment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the read alignment step of the \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e pipeline, use this comman (NB: can be run with \u003ccode\u003e--from run.ReadTrimming\u003c/code\u003e if you would like to use your trimmed reads):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.ReadAlignment -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.4. Read Counting:\u003c/h3\u003e\u003ca id=\"user-content-24-read-counting\" class=\"anchor\" aria-label=\"Permalink: 2.4. Read Counting:\" href=\"#24-read-counting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step uses the \u003ccode\u003eBAM\u003c/code\u003e file outputs generated by the read alignment step! You \u003cstrong\u003eMUST\u003c/strong\u003e run STEP 2.3 (\u003ccode\u003e--mode run.ReadAlignment\u003c/code\u003e) before running this step:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.ReadCounting -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.6. Workflow QC (optional):\u003c/h3\u003e\u003ca id=\"user-content-26-workflow-qc-optional\" class=\"anchor\" aria-label=\"Permalink: 2.6. Workflow QC (optional):\" href=\"#26-workflow-qc-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step performs a Quality Check of the different pipeline steps that have been ran. You need to run at least ONE step of the pipeline to be able to run this MultiQC step!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run nf-rnaSeqCount -profile slurm --mode run.MultiQC -c myparams.config\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCONGRATULATIONS for getting this far!! :) You can now explore the results and use the read counts to perform differential expression analysis!\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Explore \u003ccode\u003enf-rnaSeqCount\u003c/code\u003e results\u003c/h2\u003e\u003ca id=\"user-content-3-explore-nf-rnaseqcount-results\" class=\"anchor\" aria-label=\"Permalink: 3. Explore nf-rnaSeqCount results\" href=\"#3-explore-nf-rnaseqcount-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e- [1] Read QC (optional) =\u0026gt; `\u0026lt;output_directory\u0026gt;/1_RQC`\n- [2] Read Trimming (optional) =\u0026gt; `\u0026lt;output_directory\u0026gt;/2_Read_Trimming`\n- [3] Read Alignment =\u0026gt; `\u0026lt;output_directory\u0026gt;/3_Read_Alignment`\n- [4] Read Counting =\u0026gt; `\u0026lt;output_directory\u0026gt;/4_Read_Counts`\n- [5] MultiQC =\u0026gt; `\u0026lt;output_directory\u0026gt;/5_MultiQC\n- [6] Workflow tracing =\u0026gt; `\u0026lt;output_directory\u0026gt;/workflow-tracing\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn addition to the 5 directories created for each step in the results directory, a directory \u003ccode\u003eworkflow-tracing\u003c/code\u003e is created to monitor the resources used in each step. This directory will contain 3 files for each step (--mode) of the workflow:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount_\u0026lt;mode\u0026gt;_report.html\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount_\u0026lt;mode\u0026gt;_timeline.html\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enf-rnaSeqCount_\u0026lt;mode\u0026gt;_trace.txt\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese files contain detailed information on the resources (CPU, MEMORY and TIME) usage of each of the process in the different pipeline steps. The \u003ccode\u003e\u0026lt;output_directory\u0026gt;\u003c/code\u003e directory structure is summarized below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eoutput_directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--1_Read_QC\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_R1.fastqc.html .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_R1.fastqc.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_R2.fastqc.html .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_R1.fastqc.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--2_Read_Trimming\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e.1P.fastq.gz .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.1P.fastq.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e.2P.fastq.gz .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.2P.fastq.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--3_Read_Alignment\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_Aligned.out.bam .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_Aligned.out.bam\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_Log.final.out .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_Log.final.out\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_Log.out .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_Log.out\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_Log.progress.out .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_Log.progress.out\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esamplle_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_SJ.out.tab .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_SJ.out.tab\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--4_Read_Counts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--featureCounts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts_final.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts.txt.jcounts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts.txt.summary\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--htseqCounts\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gene_counts_final.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--5_MultiQC\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--multiqc_data\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--multiqc_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow-tracing\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.MultiQC_{report.html,timeline.html,trace.txt}\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.ReadAlignment_{report.html,timeline.html,trace.txt}\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.ReadCounting_{report.html,timeline.html,trace.txt}\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.ReadTrimming_{report.html,timeline.html,trace.txt} \n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nf-rnaSeqCount_run.ReadQC_{report.html,timeline.html,trace.txt}\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNB:\u003c/strong\u003e I am working on further improving the pipleine and the associated documentation, feel free to share comments and suggestions!\u003c/p\u003e\n\u003chr\u003e\n", "stargazers_count": 4, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1663000991.0 + "updated_at": 1710029143.0 }, { "data_format": 2, - "description": "SimPrily: A Python framework to simplify high-throughput genomic simulations. https://doi.org/10.1016/j.softx.2018.09.003", + "description": "Single cell RNA-seq quality control, transcription profile clustering \u0026 cell-type assignment", "filenames": [ - "Singularity" + "env/Singularity.sc_qc_cluster" ], - "full_name": "agladstein/SimPrily", - "latest_release": "v1.0.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSimPrily\u003c/h1\u003e\u003ca id=\"user-content-simprily\" class=\"anchor\" aria-label=\"Permalink: SimPrily\" href=\"#simprily\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreated by Ariella Gladstein, based on code from Consuelo Quinto Cortes and Krishna Veeramah.\nAlso worked on by David Christy, Logan Gantner, and Mack Skodiak.\u003cbr\u003e\n\u003ca href=\"mailto:agladstein@email.arizona.edu\"\u003eagladstein@email.arizona.edu\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimPrily runs genome simulations with user defined parameters or parameters randomly generated by priors and computes genomic statistics on the simulation output.\u003cbr\u003e\nVersion 1\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun genome simulation with model defined by prior distributions of parameters and demographic model structure.\u003c/li\u003e\n\u003cli\u003eTake into account SNP array ascertainment bias by creating pseudo array based on priors of number of samples of discovery populations and allele frequency cut-off.\u003c/li\u003e\n\u003cli\u003eCalculate genomic summary statistics on simulated genomes and pseudo arrays.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis is ideal for use with Approximate Bayesian Computation on whole genome or SNP array data.\u003c/p\u003e\n\u003cp\u003eUses c++ programs macs and GERMLINE. For more information on these programs, see:\u003cbr\u003e\n\u003ca href=\"https://github.com/gchen98/macs\"\u003ehttps://github.com/gchen98/macs\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://github.com/sgusev/GERMLINE\"\u003ehttps://github.com/sgusev/GERMLINE\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ecd to the directory you want to work in,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/agladstein/SimPrily.git\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eEnvironment Set up\u003c/h4\u003e\u003ca id=\"user-content-environment-set-up\" class=\"anchor\" aria-label=\"Permalink: Environment Set up\" href=\"#environment-set-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf using Vagrant (this is recommended if running on non-Linux OS):\u003c/p\u003e\n\u003cp\u003eStart Vagrant, ssh into Vagrant, cd to SimPrily directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003evagrant up\nvagrant ssh\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /vagrant\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInstall the virtual environment and install the requirements.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./setup/setup_env_vbox_2.7.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf not using Vagrant, just install the virtual environment and install the requirements:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./setup/setup_env_2.7.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ee.g. One Test simulation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py -p examples/eg1/param_file_eg1_asc.txt -m examples/eg1/model_file_eg1_asc.csv -g genetic_map_b37/genetic_map_GRCh37_chr1.txt.macshs -a array_template/ill_650_test.bed -i 1 -o output_dir -v\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor quick help:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput\u003c/h4\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003esimprily.py\u003c/code\u003e takes 4 required arguments and 2 optional arguments, and help, verbose, and profile options.\u003c/p\u003e\n\u003cp\u003eRun as\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython simprily.py [-h] -p PARAM -m MODEL -i ID -o OUT [-g MAP] [-a ARRAY] [-v] [--profile]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eRequired\u003c/h5\u003e\u003ca id=\"user-content-required\" class=\"anchor\" aria-label=\"Permalink: Required\" href=\"#required\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e-p PARAM\u003c/code\u003e or \u003ccode\u003e--param PARAM\u003c/code\u003e = The location of the parameter file\u003cbr\u003e\n\u003ccode\u003e-m MODEL\u003c/code\u003e or \u003ccode\u003e--model MODEL\u003c/code\u003e = The location of the model file\u003cbr\u003e\n\u003ccode\u003e-i ID\u003c/code\u003e or \u003ccode\u003e--id ID\u003c/code\u003e = The unique identifier of the job\u003cbr\u003e\n\u003ccode\u003e-o OUT\u003c/code\u003e or \u003ccode\u003e--out OUT\u003c/code\u003e = The location of the output directory\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOptional\u003c/h5\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e-h\u003c/code\u003e or \u003ccode\u003e--help\u003c/code\u003e = shows a help message and exists\u003cbr\u003e\n\u003ccode\u003e-v\u003c/code\u003e = increase output verbosity. This includes 3 levels, \u003ccode\u003e-v\u003c/code\u003e, \u003ccode\u003e-vv\u003c/code\u003e, and \u003ccode\u003e-vvv\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003e--profile\u003c/code\u003e = Print a log file containing the time in seconds and memory use in Mb for main functions\u003cbr\u003e\n\u003ccode\u003e-g MAP\u003c/code\u003e or \u003ccode\u003e--map MAP\u003c/code\u003e = The location of the genetic map file\u003cbr\u003e\n\u003ccode\u003e-a ARRAY\u003c/code\u003e or \u003ccode\u003e--array ARRAY\u003c/code\u003e = The location of the array template file, in bed form\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput\u003c/h4\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThree subdirectories are created in the directory specified in the \u003ccode\u003eoutput_dir\u003c/code\u003e argument.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoutput_dir/results\noutput_dir/sim_data\noutput_dir/germline_out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eIntermediate files\u003c/h5\u003e\u003ca id=\"user-content-intermediate-files\" class=\"anchor\" aria-label=\"Permalink: Intermediate files\" href=\"#intermediate-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIntermediate files go to \u003ccode\u003eoutput_dir/sim_data\u003c/code\u003e and \u003ccode\u003eoutput_dir/germline_out\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/sim_data\u003c/code\u003e contains PLINK formated .ped and .map files created from the pseudo array, which are necessary to run GERMLINE.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/germline_out\u003c/code\u003e contains the GERMLINE .match output and .log. The .match contains all of the identified IBD segments.\u003cbr\u003e\nThese files are NOT automatically removed in python script, but are unnecessary once the job is complete.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eResults files\u003c/h5\u003e\u003ca id=\"user-content-results-files\" class=\"anchor\" aria-label=\"Permalink: Results files\" href=\"#results-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutput files go to \u003ccode\u003eoutput_dir/results\u003c/code\u003e.\u003cbr\u003e\n\u003ccode\u003eoutput_dir/results\u003c/code\u003e contains the parameter values used in the simulation and the summary statistics calculated from the simulation.\u003cbr\u003e\nThe first line is a header with the parameter names and summary statistics names.\nThe second line is the parameter values and summary statistics values.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOpen Science Grid\u003c/h2\u003e\u003ca id=\"user-content-open-science-grid\" class=\"anchor\" aria-label=\"Permalink: Open Science Grid\" href=\"#open-science-grid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMust have an Open Science Grid Connect account.\u003cbr\u003e\nCreate an account at \u003ca href=\"https://osgconnect.net/signup\" rel=\"nofollow\"\u003ehttps://osgconnect.net/signup\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLog onto Open Science Grid Connect\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh user-name@login01.osgconnect.net\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWorking directory must be \u003ccode\u003epegasus_workflow\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSubmit a Pegasus workflow (must be in \u003ccode\u003epegasus_workflow\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./submit -p PARAM -m MODEL -j NUM [-g MAP] [-a ARRAY]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ee.g.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./submit -p ../examples/eg2/param_file_eg2_asc.txt -m ../examples/eg2/model_file_eg2_asc.csv -j 10 -a ../array_template/ill_650_test.bed -g ../genetic_map_b37/genetic_map_GRCh37_chr1.txt.macshs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe results will appear in\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/local-scratch/user-name/workflows/simprily_id\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003euser-name\u003c/code\u003e is specific to the user, and \u003ccode\u003eid\u003c/code\u003e is the workflow id.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://agladstein.github.io/SimPrily/\" rel=\"nofollow\"\u003eMain SimPrily GitHub Repository\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://simprily.readthedocs.io/en/latest/index.html\" rel=\"nofollow\"\u003eSimPrily Read the Docs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/agladstein/SimPrily\"\u003eSimPrily Code\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"\"\u003eCyVerse Discovery Environment SimPrily Workflow\u003c/a\u003e\n\u003col\u003e\n\u003cli\u003eSimPrily HT File Setup App\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eApp in the DE\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eGitHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eDockerHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDE SimPrily App\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eApp in the DE\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eGitHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eDockerHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eDE SimPrily Concatenation App\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eApp in the DE\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eGitHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eDockerHub Repo for App\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data\" rel=\"nofollow\"\u003eDE SimPrily Example Data\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data/SimPrily_version1\" rel=\"nofollow\"\u003eRandomly Generated Data for App Testing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data/SimPrily_version1\" rel=\"nofollow\"\u003eHomo sapiens HapMap Data from 1000 genomes Project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data/SimPrily_version1\" rel=\"nofollow\"\u003eBrassica sp. populations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://de.cyverse.org/de/?type=data\u0026amp;folder=/iplant/home/shared/iplantcollaborative/example_data/SimPrily_version1\" rel=\"nofollow\"\u003eSimPrily Benchmarking Data\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSimPrily Pegasus Documentation\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://agladstein.github.io/SimPrily/#open-science-grid\" rel=\"nofollow\"\u003eSimPrily Pegasus Workflow Quick Start\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://simprily.readthedocs.io/en/latest/htc.html#submit-a-pegasus-workflow\" rel=\"nofollow\"\u003eDetailed Pegasus Workflow Documentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown Issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known Issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eIf exponential growth is large, macs simulation will not finish. (This is a macs bug).\u003c/li\u003e\n\u003cli\u003eIf the same id is used with the same output dir as a previous run, the .map file will be appended to.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "wtsi-hgi/nf_scrna_qc", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDescription\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline performs data QC and transcription profile clustering for droplet single cell RNA-seq. It starts from gene transcript UMI (unique molecular identfier) counts per barcoded cell. Cell-type assignments for PBMC (peripheral blood mononuclear cells) are also generated.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"docs/README-tests.md\"\u003erunning tests\u003c/a\u003e: run pipeline locally on a small test dataset\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/README-workarounds.md\"\u003epotential problems and workarounds\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/README-inputfiles.md\"\u003einput files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/README-outputfiles.md\"\u003eoutput directory structure\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCredits\u003c/h1\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe thank the following people for their contributions to the development of this pipeline:\nLeland Taylor, Matiss Ozols, Guillaume Noell, Hannes Ponstingl, Vivek Iyer, Monika Krzak, Henry Taylor, Tobi Alegbe, Moritz Przybilla\u003c/p\u003e\n", "stargazers_count": 4, "subscribers_count": 4, "topics": [], - "updated_at": 1584034329.0 + "updated_at": 1714653728.0 }, { "data_format": 2, "description": null, "filenames": [ - "applications/interactive/Singularity", - "applications/compress/Singularity", - "applications/jupyter-lab-hpc-openmpi/Singularity", - "applications/opensees-mp/opensees-mp-3.5.0/Singularity", - "applications/extract/Singularity", - "applications/jupyter-lab-hpc/Singularity", - "applications/jupyter-lab-hpc/gpu/Singularity", - "applications/hello-world/Singularity", - "applications/rstudio/nginx/Singularity.conf", - "applications/opensees-sp/opensees-sp-3.5.0/Singularity" + "spark/sing/spark/Singularity2", + "spark/sing/spark/Singularity" ], - "full_name": "TACC/WMA-Tapis-Templates", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWMA-Tapis-Templates\u003c/h1\u003e\u003ca id=\"user-content-wma-tapis-templates\" class=\"anchor\" aria-label=\"Permalink: WMA-Tapis-Templates\" href=\"#wma-tapis-templates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tapis-project/tapipy/tree/main/tapipy\"\u003eTapipy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://github.com/pyenv/pyenv\"\u003epyenv\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e(Optional) \u003ca href=\"https://github.com/pyenv/pyenv-virtualenv\"\u003epyenv-virtualenv\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProvisioning a Tenant\u003c/h2\u003e\u003ca id=\"user-content-provisioning-a-tenant\" class=\"anchor\" aria-label=\"Permalink: Provisioning a Tenant\" href=\"#provisioning-a-tenant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a \u003ccode\u003eclient_secrets.py\u003c/code\u003e file with a \u003ccode\u003eCLIENT_USERNAME\u003c/code\u003e and \u003ccode\u003eCLIENT_PASSWORD\u003c/code\u003e (see client_secrets.example.py)\u003c/li\u003e\n\u003cli\u003eAdjust the tenants, systems, and apps you wish to create in \u003ccode\u003einitialize_tenant.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython initialize_tenant.py\u003c/code\u003e to create/update the apps and systems in the tenants listed in \u003ccode\u003eTENANT_BASE_URLS\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating a client\u003c/h2\u003e\u003ca id=\"user-content-creating-a-client\" class=\"anchor\" aria-label=\"Permalink: Creating a client\" href=\"#creating-a-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e(Optional) Install Tapipy in a pyenv environemnt\na. \u003ccode\u003epyenv install 3.11\u003c/code\u003e\nb. \u003ccode\u003epyenv virtualenv 3.11 tapipy\u003c/code\u003e\nc. \u003ccode\u003epyenv local tapipy\u003c/code\u003e\nc. \u003ccode\u003epip install tapipy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall ipython\na. \u003ccode\u003epip install ipython\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInitiate an ipython session\na. \u003ccode\u003eipython\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate a client\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003efrom tapipy.tapis import Tapis\nclient = Tapis(base_url=\u0027https://portals.tapis.io\u0027, username=\u0027$USER\u0027, password=\u0027******\u0027)\nclient.get_tokens()\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating a credential\u003c/h2\u003e\u003ca id=\"user-content-creating-a-credential\" class=\"anchor\" aria-label=\"Permalink: Creating a credential\" href=\"#creating-a-credential\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate a keypair locally\na. \u003ccode\u003essh-keygen -m PEM -t rsa -b 2048 -f ~/.ssh/$USER.frontera\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCopy the public key to your \u003ccode\u003e~/.ssh/authorized_keys\u003c/code\u003e file on the frontera host\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh $USER@frontera.tacc.utexas.edu\nPUBKEY=\"PASTE PUBLIC KEY HERE\"\necho $PUBKEY \u0026gt;\u0026gt; ~/.ssh/authorized_keys`\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCopy the public and private key to the \u003ccode\u003eUSER_CREDENTIAL_PRIVATE_KEY\u003c/code\u003e and \u003ccode\u003eUSER_CREDENTIAL_PUBLIC_KEY\u003c/code\u003e values in \u003ccode\u003eclient_secrets.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAdjust the \u003ccode\u003esystemId\u003c/code\u003e and \u003ccode\u003ebase_url\u003c/code\u003e values for your desired tenant/system and run the \u003ccode\u003ecreate_client_credential.py\u003c/code\u003e script\u003c/li\u003e\n\u003cli\u003eTest the keypair works by making a file listing on a system\na. \u003ccode\u003eclient.files.listFiles(systemId=\u0027frontera\u0027, path=\u0027/\u0027)\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "full_name": "ExposuresProvider/FHIR-PIT", + "latest_release": "v1.0", + "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.com/NCATS-Tangerine/FHIR-PIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/43a9c4b529eae28a760191f2b192bf66ee2f7028f6de94751f42850b73fdb057/68747470733a2f2f7472617669732d63692e636f6d2f4e434154532d54616e676572696e652f464849522d5049542e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/NCATS-Tangerine/FHIR-PIT.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFHIR \u00ae PIT\u003c/h1\u003e\u003ca id=\"user-content-fhir--pit\" class=\"anchor\" aria-label=\"Permalink: FHIR \u00ae PIT\" href=\"#fhir--pit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFast Healthcare Interoperability Resources (FHIR\u00ae) is the registered trademark of HL7 and is used with the permission of HL7.\u003c/p\u003e\n\u003cp\u003eFHIR Patient data Integration Tool (FHIR PIT) is an open-source tool that uses geocodes and time stamps of varying spatiotemporal resolution to integrate clinical data (e.g., electronic health records [EHR]) with environmental exposures data from multiple public sources before stripping the data of Protected Health Information (PHI, including geocodes and time stamps) and binning feature variables to create integrated datasets, whereby rows represent individual patients or subjects and columns represent demographic variables, clinical measures, or exposure estimates. FHIR PIT is modular and extensible and can be adapted for virtually any type of data that requires geocodes and dates for integration with PHI or any Personally Identifiable Information.\u003c/p\u003e\n\u003cp\u003eFHIR PIT was inspired by and currently supports the \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/31077269/\" rel=\"nofollow\"\u003eIntegrated Clinical and Environmental Exposures Service (ICEES)\u003c/a\u003e. The tool was motivated by a need to integrate diverse sources of environmental exposures data with EHR data at the patient level, effectively embedding estimates of environmental exposures such as airborne pollutant exposures within the EHR. ICEES has proven utility as an open-source tool for exploratory analysis of integrated clinical and environmental exposures data, with diverse use-case applications, including asthma and related common pulmonary disorders, primary ciliary dyskinesia and related rare pulmonary disorders, drug-induced liver injury, and coronavirus infection. ICEES is also an integral component of the \u003ca href=\"https://ncats.nih.gov/research/research-activities/translator/about\" rel=\"nofollow\"\u003eBiomedical Data Translator (\"Translator\") System\u003c/a\u003e, funded by the National Center for Advancing Translational Sciences. For additional information on ICEES, including peer-reviewed publications, please see \u003ca href=\"https://github.com/NCATSTranslator/Translator-All/wiki/ICEES\"\u003ehttps://github.com/NCATSTranslator/Translator-All/wiki/ICEES\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFHIR PIT consists of several transformation steps, which are building blocks that can be chained together or combined in parallel to form a transformation workflow. In addition, several of these transformation steps are generic such that they can take in any data that conform to a certain format. Adding new types of data amounts to adding new transformation steps or reusing generic steps.\u003c/p\u003e\n\u003cp\u003eFHIR PIT is implemented using Apache Spark, Python, and Singularity. Spark makes it easy to parallelize and distribute the data transformation. Python is used to simplify the application interface to the transformation steps. Singularity allows the application to run portably on different machines and platforms.\u003c/p\u003e\n\u003cp\u003eFor details regarding how to build and run FHIR PIT with sample data, refer to \u003ca href=\"https://github.com/ExposuresProvider/FHIR-PIT/tree/master/spark\"\u003ehttps://github.com/ExposuresProvider/FHIR-PIT/tree/master/spark\u003c/a\u003e. FHIR PIT also has been dockerized to encapsulate the software and platform dependencies and facilitate execution and deployment (see \u003ca href=\"https://github.com/ExposuresProvider/FHIR-PIT/blob/master/Dockerfile\"\u003ehttps://github.com/ExposuresProvider/FHIR-PIT/blob/master/Dockerfile\u003c/a\u003e). A tutorial is available at \u003ca href=\"https://github.com/ExposuresProvider/FHIR-PIT/tree/master/tutorial\"\u003ehttps://github.com/ExposuresProvider/FHIR-PIT/tree/master/tutorial\u003c/a\u003e. An R-Bookdown version will soon be available.\u003c/p\u003e\n\u003cp\u003eOne important note when running FHIR PIT is that the application may be memory-intensive, depending on the size of the input data. For example, for the sample data available in the GitHub repository, the application requires 4GB RAM to run through all of the data processing and integration steps, when running Docker natively. (Docker Desktop on Windows/Mac may incur additional memory overhead.)\u003c/p\u003e\n\u003cp\u003eFor additional information on FHIR PIT, including an example use-case application, please see \u003ca href=\"https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1056-9\" rel=\"nofollow\"\u003eXu et al. 2020\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIssues are welcome! We will work to resolve any issues as quickly as possible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFunding Support\u003c/h1\u003e\u003ca id=\"user-content-funding-support\" class=\"anchor\" aria-label=\"Permalink: Funding Support\" href=\"#funding-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFHIR PIT is funded by the \u003ca href=\"https://ncats.nih.gov/\" rel=\"nofollow\"\u003eNational Center for Advancing Translational Sciences\u003c/a\u003e, under a US PHS Other Transaction Award (OT2TR003430, prototype was funded under OT3TR002020), with additional funding provided by the \u003ca href=\"https://renci.org/\" rel=\"nofollow\"\u003eRenaissance Computing Institute\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSuggested Citation\u003c/h1\u003e\u003ca id=\"user-content-suggested-citation\" class=\"anchor\" aria-label=\"Permalink: Suggested Citation\" href=\"#suggested-citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use FHIR PIT, we thank you and kindly ask that you consider citing:\u003c/p\u003e\n\u003cp\u003eXu H, Cox S, Stillwell L, Pfaff E, Champion J, Ahalt SC, Fecho K. FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data. BMC Med Inform Decis Mak. 2020;20(1):53. Published 2020 Mar 11. doi:10.1186/s12911-020-1056-9.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContact Information\u003c/h1\u003e\u003ca id=\"user-content-contact-information\" class=\"anchor\" aria-label=\"Permalink: Contact Information\" href=\"#contact-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor assistance with FHIR PIT, please contact the developer team at \u003ca href=\"mailto:fhir-pit@renci.org\"\u003efhir-pit@renci.org\u003c/a\u003e or directly click on \u003ca href=\"mailto:fhir-pit@renci.org\"\u003eContact Us\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 16, - "topics": [], - "updated_at": 1726171469.0 + "subscribers_count": 4, + "topics": [ + "ncats-translator" + ], + "updated_at": 1731432040.0 }, { "data_format": 2, - "description": "Generic viral Illumina sequence analysis pipeline", + "description": "Machine Learning model to reconstruct 4D-CT in real-time from cine-MR acquired during radiation therapy. Aimed to enhance treatment precision and adaptability.", "filenames": [ - "Singularity", - "singularity/Singularity.2.0.0" + "Singularity.def" ], - "full_name": "peterk87/nf-villumina", - "latest_release": "2.0.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epeterk87/nf-villumina\u003c/h1\u003e\u003ca id=\"user-content-peterk87nf-villumina\" class=\"anchor\" aria-label=\"Permalink: peterk87/nf-villumina\" href=\"#peterk87nf-villumina\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGeneric viral Illumina sequence analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/peterk87/nf-villumina\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3014ce8e9fb3b368e4bfa37ca99daa56708610564dac9a0f2441a851a176a32c/68747470733a2f2f7472617669732d63692e6f72672f70657465726b38372f6e662d76696c6c756d696e612e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/peterk87/nf-villumina.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3c177645f5642f87be5369390973cd515bd486b863467ca1e8475e4d3a6e2f7e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413531392e31302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/peterk87/nf-villumina\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e57c083af3e6035c1fcf18799a14ec1de5eb89bba830d23ac566ff04b81ed3d/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f70657465726b38372f6e662d76696c6c756d696e612e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/peterk87/nf-villumina.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2925\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with a \u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enf-villumina\u003c/code\u003e will\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eremove low quality reads (\u003ca href=\"https://github.com/OpenGene/fastp\"\u003efastp\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003efilter for reads from a taxonomic group of interest (by default superkingdom \u003ca href=\"https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info\u0026amp;id=10239\u0026amp;lvl=3\u0026amp;lin=f\u0026amp;keep=1\u0026amp;srchmode=1\u0026amp;unlock\" rel=\"nofollow\"\u003eViruses\u003c/a\u003e (taxid=10239)) using \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e and \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e classification results\u003c/li\u003e\n\u003cli\u003eperform \u003cem\u003ede novo\u003c/em\u003e assembly with [Unicycler] and [Shovill] on the taxonomic classification filtered reads\u003c/li\u003e\n\u003cli\u003esearch all contig sequences using NCBI nucleotide \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e against a database of your choice (we recommend the version 5 NCBI nt DB)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e You will need to create/download databases for \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e, \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e and \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e in order to get the most out of this workflow!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePre-requisites\u003c/h3\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTaxonomic Classification for \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e and \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-taxonomic-classification-for-kraken2-and-centrifuge\" class=\"anchor\" aria-label=\"Permalink: Taxonomic Classification for Kraken2 and Centrifuge\" href=\"#taxonomic-classification-for-kraken2-and-centrifuge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor taxonomic classification with \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e and \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e, you will need to download (or build) databases for these programs so that you may use them within the \u003ccode\u003enf-villumina\u003c/code\u003e workflow.\u003c/p\u003e\n\u003cp\u003eYou can point to the Kraken2 and Centrifuge database with \u003ccode\u003eexport KRAKEN2_DB=/path/to/kraken2/database\u003c/code\u003e and \u003ccode\u003eexport CENTRIFUGE_DB=/path/to/centrifuge/database/prefix\u003c/code\u003e in your \u003ccode\u003e~/.bashrc\u003c/code\u003e so you don\u0027t need to specify it each time you run the workflow with \u003ccode\u003e--kraken2_db /path/to/kraken2/standard2 --centrifuge_db /path/to/centrifuge/nt-2018-03-03/nt\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eKraken2 DBs\u003c/h4\u003e\u003ca id=\"user-content-kraken2-dbs\" class=\"anchor\" aria-label=\"Permalink: Kraken2 DBs\" href=\"#kraken2-dbs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\nMiniKraken2_v2_8GB: (5.5GB) 8GB Kraken 2 Database built from the Refseq bacteria, archaea, and viral libraries and the GRCh38 human genome\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://monash.figshare.com/articles/GTDB_r89_54k/8956970\" rel=\"nofollow\"\u003eGTDB_r89_54k Kraken2 DBs\u003c/a\u003e: There are multiple Kraken2 DBs of various sizes available for download. For more info, see \u003ca href=\"https://github.com/rrwick/Metagenomics-Index-Correction\"\u003ehttps://github.com/rrwick/Metagenomics-Index-Correction\u003c/a\u003e and the manuscript: M\u00e9ric, Wick et al. (2019) Correcting index databases improves metagenomic studies. doi: \u003ca href=\"https://doi.org/10.1101/712166\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/712166\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCentrifuge DBs\u003c/h4\u003e\u003ca id=\"user-content-centrifuge-dbs\" class=\"anchor\" aria-label=\"Permalink: Centrifuge DBs\" href=\"#centrifuge-dbs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI nucleotide non-redundant sequences (2018-03-03) (64 GB)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://monash.figshare.com/ndownloader/files/16378439\" rel=\"nofollow\"\u003eGTDB_r89_54k Centrifuge DB (108 GB tar file)\u003c/a\u003e: For more info, see \u003ca href=\"https://github.com/rrwick/Metagenomics-Index-Correction\"\u003ehttps://github.com/rrwick/Metagenomics-Index-Correction\u003c/a\u003e and the manuscript: M\u00e9ric, Wick et al. (2019) Correcting index databases improves metagenomic studies. doi: \u003ca href=\"https://doi.org/10.1101/712166\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/712166\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e DBs\u003c/h3\u003e\u003ca id=\"user-content-blast-dbs\" class=\"anchor\" aria-label=\"Permalink: BLAST DBs\" href=\"#blast-dbs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor nf-villumina, you must have a version 5 BLAST DB with embedded taxonomic information installed, e.g. version 5 \u003ccode\u003ent\u003c/code\u003e DB (see \u003ca href=\"https://ftp.ncbi.nlm.nih.gov/blast/db/v5/\" rel=\"nofollow\"\u003ehttps://ftp.ncbi.nlm.nih.gov/blast/db/v5/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eYou can download pre-built \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e DBs like \u003ccode\u003ent\u003c/code\u003e and \u003ccode\u003enr\u003c/code\u003e from \u003ca href=\"https://ftp.ncbi.nlm.nih.gov/blast/db/\" rel=\"nofollow\"\u003ethe NCBI FTP site\u003c/a\u003e using the \u003ccode\u003eupdate_blastdb.pl\u003c/code\u003e script included with your install of BLAST+ to download and/or update your local BLAST databases.\u003c/p\u003e\n\u003cp\u003eShow all available databases:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ update_blastdb.pl --showall\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDownload the BLASTDB version 5 \"nt\" database to your current directory decompressing files and deleting original compressed archives:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eupdate_blastdb.pl --blastdb_version 5 nt --decompress\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e For ease of use, all databases should be downloaded to the same directory (e.g. \u003ccode\u003e/opt/DB/blast\u003c/code\u003e set in \u003ccode\u003e$BLASTDB\u003c/code\u003e environment variable in your \u003ccode\u003e~/.bashrc\u003c/code\u003e)\u003c/p\u003e\n\u003cp\u003eCheck that your database has been downloaded properly and has taxids associated with the sequences contained within it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ blastdbcheck -db nt -must_have_taxids -verbosity 3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe peterk87/nf-villumina pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003epeterk87/nf-villumina was originally written by Peter Kruczkiewicz.\u003c/p\u003e\n\u003cp\u003eBootstrapped with \u003ca href=\"https://github.com/nf-core/tools\"\u003enf-core/tools\u003c/a\u003e \u003ccode\u003enf-core create\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThank you to the \u003ca href=\"https://github.com/nf-core/tools\"\u003enf-core/tools\u003c/a\u003e team for a great tool for bootstrapping creation of a production ready Nextflow workflows.\u003c/p\u003e\n", + "full_name": "nnaakkaaii/RealTime-4DCT-Reconstruction", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRealTime-4DCT-Reconstruction (rt4r)\u003c/h1\u003e\u003ca id=\"user-content-realtime-4dct-reconstruction-rt4r\" class=\"anchor\" aria-label=\"Permalink: RealTime-4DCT-Reconstruction (rt4r)\" href=\"#realtime-4dct-reconstruction-rt4r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMachine Learning model to reconstruct 4D-CT in real-time from cine-MR acquired during radiation therapy. Aimed to enhance treatment precision and adaptability.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse singularity\u003c/h2\u003e\u003ca id=\"user-content-use-singularity\" class=\"anchor\" aria-label=\"Permalink: Use singularity\" href=\"#use-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ebuild\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity build --fakeroot rt4r.sif Singularity.def\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 4, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1714883197.0 + "updated_at": 1725367906.0 }, { "data_format": 2, - "description": "Hidden RNA profiles of cells in the tumor microenvironment accurately revealed by malignant cell fraction-informed deconvolution", + "description": "A method for association of changes in phenotype traits with changes in selection intensity at the codon level across a phylogeny", "filenames": [ "Singularity" ], - "full_name": "tgac-vumc/OncoBLADE", - "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/OncoBLADE/blob/main/logo.png\"\u003e\u003cimg width=\"300\" height=\"424\" src=\"https://github.com/tgac-vumc/OncoBLADE/raw/main/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOncoBLADE: Malignant cell fraction-informed deconvolution\u003c/h1\u003e\u003ca id=\"user-content-oncoblade-malignant-cell-fraction-informed-deconvolution\" class=\"anchor\" aria-label=\"Permalink: OncoBLADE: Malignant cell fraction-informed deconvolution\" href=\"#oncoblade-malignant-cell-fraction-informed-deconvolution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOncoBLADE is a Bayesian deconvolution method designed to estimate cell type-specific gene expression profiles and fractions from bulk RNA profiles of tumor specimens by integrating prior knowledge on cell fractions. You can find the \u003ca href=\"https://www.researchsquare.com/article/rs-4252952/v1\" rel=\"nofollow\"\u003epreprint of OncoBLADE at Research Square\u003c/a\u003e.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/OncoBLADE/blob/main/VisualAbstract.png\"\u003e\u003cimg width=\"75%\" height=\"75%\" src=\"https://github.com/tgac-vumc/OncoBLADE/raw/main/VisualAbstract.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDemo notebook is available under \u003ccode\u003ejupyter\u003c/code\u003e. See below how to open it.\u003c/h4\u003e\u003ca id=\"user-content-demo-notebook-is-available-under-jupyter-see-below-how-to-open-it\" class=\"anchor\" aria-label=\"Permalink: Demo notebook is available under jupyter. See below how to open it.\" href=\"#demo-notebook-is-available-under-jupyter-see-below-how-to-open-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSystem Requirements\u003c/h2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-label=\"Permalink: System Requirements\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Requirements\u003c/h3\u003e\u003ca id=\"user-content-hardware-requirements\" class=\"anchor\" aria-label=\"Permalink: Hardware Requirements\" href=\"#hardware-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOncoBLADE can run on the minimal computer spec, such as Binder (1 CPU, 2GB RAM on Google Cloud), when data size is small. However, OncoBLADE can significantly benefit from the larger amount of CPUs and RAM.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOS Requirements\u003c/h3\u003e\u003ca id=\"user-content-os-requirements\" class=\"anchor\" aria-label=\"Permalink: OS Requirements\" href=\"#os-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package development version is tested on Linux operating systems. (CentOS 7 and Ubuntu 16.04).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing pip\u003c/h3\u003e\u003ca id=\"user-content-using-pip\" class=\"anchor\" aria-label=\"Permalink: Using pip\" href=\"#using-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package of BLADE is available on pip.\nYou can simply (takes only \u0026lt;1min):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install OncoBLADE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe tested BLADE with \u003ccode\u003epython =\u0026gt; 3.6\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Conda\u003c/h3\u003e\u003ca id=\"user-content-using-conda\" class=\"anchor\" aria-label=\"Permalink: Using Conda\" href=\"#using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne can create a conda environment contains BLADE and also other dependencies to run \u003ca href=\"https://github.com/tgac-vumc/BLADE/blob/master/jupyter/BLADE%20-%20Demo%20script.ipynb\"\u003eDemo\u003c/a\u003e.\nThe environment definition is in \u003ca href=\"https://github.com/tgac-vumc/BLADE/environment.yml\"\u003eenvironment.yml\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Installing Miniconda 3\u003c/h3\u003e\u003ca id=\"user-content-step-1-installing-miniconda-3\" class=\"anchor\" aria-label=\"Permalink: Step 1: Installing Miniconda 3\" href=\"#step-1-installing-miniconda-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, please open a terminal or make sure you are logged into your Linux VM. Assuming that you have a 64-bit system, on Linux, download and install Miniconda 3 with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn MacOS X, download and install with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh\nbash Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Create a conda environment\u003c/h3\u003e\u003ca id=\"user-content-step-2-create-a-conda-environment\" class=\"anchor\" aria-label=\"Permalink: Step 2: Create a conda environment\" href=\"#step-2-create-a-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install all the necessary dependency using the following command (may takes few minutes; \u003ccode\u003emamba\u003c/code\u003e is quicker in general).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create --file environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, the \u003ccode\u003eOncoBLADE\u003c/code\u003e environment can be activate by:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate OncoBLADE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Running a demo script\u003c/h3\u003e\u003ca id=\"user-content-step-3-running-a-demo-script\" class=\"anchor\" aria-label=\"Permalink: Step 3: Running a demo script\" href=\"#step-3-running-a-demo-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can find a demo script under \u003ccode\u003ejupyter\u003c/code\u003e folder.\nYou can open the script using the command below after activating the \u003ccode\u003eOncoBLADE\u003c/code\u003e environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejupyter notebook jupyter/OncoBLADE\\ -\\ Demo script.ipynb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of OncoBLADE (In progress)\u003c/h2\u003e\u003ca id=\"user-content-overview-of-oncoblade-in-progress\" class=\"anchor\" aria-label=\"Permalink: Overview of OncoBLADE (In progress)\" href=\"#overview-of-oncoblade-in-progress\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the OncoBLADE package, you can load the following functions and modules.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eoncoBLADE\u003c/code\u003e: A class object contains core algorithms of \u003ccode\u003eOncoBLADE\u003c/code\u003e, an extended version of \u003ccode\u003eBLADE\u003c/code\u003e. Users can reach internal variables (\u003ccode\u003eNu\u003c/code\u003e, \u003ccode\u003eOmega\u003c/code\u003e, and \u003ccode\u003eBeta\u003c/code\u003e) and functions for calculating objective functions (ELBO function) and gradients with respect to the variational parameters. There also is an optimization function (\u003ccode\u003eoncoBLADE.Optimize()\u003c/code\u003e) for performing L-BFGS optimization. OncoBLADE features an iterative update to optimize hyperparameter \u003ccode\u003eAlpha\u003c/code\u003e and integration of prior expectation of subset of cell types. In BLADE, \u003ccode\u003eAlpha\u003c/code\u003e is a user-defined hyperparameter.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo run classic BLADE and OncoBLADE, we provide main functinos \u003ccode\u003eFramework\u003c/code\u003e for BLADE and \u003ccode\u003eFramework_Iterative\u003c/code\u003e for OncoBLADE.\u003c/p\u003e\n\u003cp\u003eSee below to obtain the current estimate of cellualr fractions, gene expression profiles per cell type and per sample:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eExpF(self.Beta)\u003c/code\u003e : returns a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNgene\u003c/code\u003e matrix contains estimated fraction of each cell type in each sample.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eself.Nu\u003c/code\u003e: a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e multidimensional array contains estimated gene expression levels of each gene in each cell type for each sample.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enumpy.mean(self.Nu,0)\u003c/code\u003e: To obtain a estimated gene expression profile per cell type, we can simply take an average across the samples.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eFramework_Iterative\u003c/code\u003e: OncoBLADE framework based on the \u003ccode\u003eBLADE\u003c/code\u003e class module above. Users need to provide the following input/output arguments.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInput arguments\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eX\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains average gene expression profiles per cell type (a signature matrix) in log-scale.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estdX\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains standard deviation per gene per cell type (a signature matrix of gene expression variability).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eY\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNsample\u003c/code\u003e matrix contains bulk gene expression data. This should be in linear-scale data without log-transformation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eExpectation\u003c/code\u003e: a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains the expected cell fraction used to inform OncoBLADE [Optional]\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInd_Marker\u003c/code\u003e: Index for marker genes. By default, \u003ccode\u003e[True]*Ngene\u003c/code\u003e (all genes used without filtering). For the genes with \u003ccode\u003eFalse\u003c/code\u003e they are excluded in the first phase (Empirical Bayes) for finidng the best hyperparameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInd_sample\u003c/code\u003e: Index for the samples used in the first phase (Empirical Bayes). By default, \u003ccode\u003e[True]*Nsample\u003c/code\u003e (all samples used).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAlpha\u003c/code\u003e, \u003ccode\u003eAlpha0\u003c/code\u003e, \u003ccode\u003eKappa0\u003c/code\u003e and \u003ccode\u003eSY\u003c/code\u003e: hyperparameters used in the model. \u003ccode\u003eAlpha\u003c/code\u003e is also optimzed, while others are fixed. By default, \u003ccode\u003eAlpha=1\u003c/code\u003e, \u003ccode\u003eAlpha0=0.1\u003c/code\u003e, \u0027Kappa0=1\u003ccode\u003e, \u003c/code\u003esY=1`.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eIterMax\u003c/code\u003e: Number of maximum iteration between variational parameter optimization by L-BFGS and updating hyperparameter \u003ccode\u003eAlpha\u003c/code\u003e. By default, \u003ccode\u003eiterMax=100\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNrep\u003c/code\u003e: Number of random initial guess used to run OncoBLADE. The best in terms of ELBO function will be chosed among the local optimum. By default, \u003ccode\u003eNrep=3\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNjob\u003c/code\u003e: Number of jobs executed in parallel. By default, \u003ccode\u003eNjob=10\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOutput values\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efinal_obj\u003c/code\u003e: A final \u003ccode\u003eBLADE\u003c/code\u003e object with optimized variational parameters and hyperparameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003econv\u003c/code\u003e: The ELBO function value (i.e., local optimum) for the final \u003ccode\u003eBLADE\u003c/code\u003e object.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eouts\u003c/code\u003e: \u003ccode\u003eNrep\u003c/code\u003e BLADE objects optimized with all random initial guesses with their final ELBO values.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTBD\u003c/h3\u003e\u003ca id=\"user-content-tbd\" class=\"anchor\" aria-label=\"Permalink: TBD\" href=\"#tbd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFunctions for purification: \u003ccode\u003eParallel_Purification\u003c/code\u003e, \u003ccode\u003ePurify_AllGenes\u003c/code\u003e\u003c/p\u003e\n", + "full_name": "halabikeren/TraitRELAX", + "latest_release": "1.0.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTraitRELAX - a tool to associate phenotypic traits with altered selective patterns\u003c/h1\u003e\u003ca id=\"user-content-traitrelax---a-tool-to-associate-phenotypic-traits-with-altered-selective-patterns\" class=\"anchor\" aria-label=\"Permalink: TraitRELAX - a tool to associate phenotypic traits with altered selective patterns\" href=\"#traitrelax---a-tool-to-associate-phenotypic-traits-with-altered-selective-patterns\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://app.codeship.com/projects/394727\" rel=\"nofollow\"\u003e \u003cimg src=\"https://camo.githubusercontent.com/f491385ace8bdd80a91c13f70d8730e9093cf1b1afe9fffe41bcc1e973a917cf/68747470733a2f2f6170702e636f6465736869702e636f6d2f70726f6a656374732f31313038636432302d366364352d303133382d626664622d3165336231616238333161662f7374617475733f6272616e63683d6d6173746572\" alt=\"Codeship Status for halabikeren/TraitRELAX\" data-canonical-src=\"https://app.codeship.com/projects/1108cd20-6cd5-0138-bfdb-1e3b1ab831af/status?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/repository/docker/halabikeren/traitrelax\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce0614c69a0406a7dda71974a2e4079331c832615566656f74f484683c653bd8/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f68616c6162696b6572656e2f747261697472656c61782e737667\" alt=\"https://img.shields.io/docker/pulls/halabikeren/traitrelax.svg\" data-canonical-src=\"https://img.shields.io/docker/pulls/halabikeren/traitrelax.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5051\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTraitRELAX is an open-source software for the joint analysis of binary traits and coding sequence data that allows testing for association of the trait with changes in selection intensity at the codon level across a phylogeny. TraitRELAX is implemented in the C++ library \u003ca href=\"https://github.com/BioPP\"\u003eBio++\u003c/a\u003e (see also: \u003ca href=\"http://biopp.univ-montp2.fr/\" rel=\"nofollow\"\u003eBio++ documentation\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePublication\u003c/h2\u003e\u003ca id=\"user-content-publication\" class=\"anchor\" aria-label=\"Permalink: Publication\" href=\"#publication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHalabi K, Levy Karin E, Gu\u00e9guen L, and Mayrose I. TraitRELAX - A codon model for associating phenotypic traits with altered selective patterns of sequence evolution. 2020. \u003ca href=\"https://academic.oup.com/sysbio/advance-article/doi/10.1093/sysbio/syaa087/6012374\" rel=\"nofollow\"\u003eDOI:10.1093/sysbio/syaa087\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe input to TraitRELAX is \u003cstrong\u003ea single control file\u003c/strong\u003e, which among others, specifies the location of the following files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA phylogentic tree with branch lengths.\u003c/li\u003e\n\u003cli\u003eA codon multiple sequence alignment (MSA) of the sequence data of the extant species.\u003c/li\u003e\n\u003cli\u003eThe character states of the extant species coded as either \u00270\u0027 or \u00271\u0027.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe TraitRELAX control file specifies parameters as detailed in the \u003ca href=\"http://biopp.univ-montp2.fr/manual/pdf/bppsuite/v0.7.0/bppsuite.pdf\" rel=\"nofollow\"\u003ebppSuite manual\u003c/a\u003e. See the provided \u003cstrong\u003eTraitRELAX_template.bpp\u003c/strong\u003e as an example for such control file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTraitRELAX writes the maximum-likelihood scores for the null and alternative models as well as their inferred model parameters to STDOUT. You can save the results by redirecting STDOUT into a file (see Examples/README.txt).\nAdditionaly it can save results to output files specified in the control file (see the provided \u003cstrong\u003eTraitRELAX_template.bpp\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the program\u003c/h2\u003e\u003ca id=\"user-content-running-the-program\" class=\"anchor\" aria-label=\"Permalink: Running the program\" href=\"#running-the-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce installed (see next section), the program can be run through the shell:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epath/to/TraitRELAX/traitrelax param=\u0026lt;path_to_control_file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling via docker\u003c/h2\u003e\u003ca id=\"user-content-installing-via-docker\" class=\"anchor\" aria-label=\"Permalink: Installing via docker\" href=\"#installing-via-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRather than installing the program from scratch, you can pull a docker image with the pre-compiled program. To do so, first install \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e on your machine and then run on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull halabikeren/traitrelax:version1.0.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the program, first create on your machine a directory with the input for traitrelax, including the input data and a control file (see the Examples folder for more details). Then, run the following on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v \u0026lt;path_to_input_directory\u0026gt;:/traitrelax/exec/ -it traitrelax param=\u0026lt;name_of_control_file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling via singularity\u003c/h2\u003e\u003ca id=\"user-content-installing-via-singularity\" class=\"anchor\" aria-label=\"Permalink: Installing via singularity\" href=\"#installing-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using HPC, an alternative for docker is singularity. Similar to docker, you can pull a singularity image with the pre-compiled program. To do so, you should have \u003ca href=\"https://singularity.lbl.gov/docs-hpc\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed on your cluster and then run on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name TraitRELAX.sif shub://halabikeren/TraitRELAX\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the program, first create on your machine a directory with the input for traitrelax, including the input data and a control file (see the Examples folder for more details). Then, run the following on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind \u0026lt;path_to_input_directory\u0026gt;:/traitrelax/exec/ TraitRELAX.sif \u0026lt;name_of_control_file\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding from source...\u003c/h2\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-label=\"Permalink: Building from source...\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e...by using an installation script\u003c/h3\u003e\u003ca id=\"user-content-by-using-an-installation-script\" class=\"anchor\" aria-label=\"Permalink: ...by using an installation script\" href=\"#by-using-an-installation-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn installation script is available at \u003cstrong\u003einstall.sh\u003c/strong\u003e. To install, run on the command line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esh install.sh \u0026lt;path_to_prgram\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the installation is complete, the progeam will be available in \u003ccode\u003e\u0026lt;path_to_prgram\u0026gt;/TraitRELAX/TraitRELAX/TraitRELAX\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e...by shell commands\u003c/h3\u003e\u003ca id=\"user-content-by-shell-commands\" class=\"anchor\" aria-label=\"Permalink: ...by shell commands\" href=\"#by-shell-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe compilation may take a little while (especially that of \u003ccode\u003ebpp-phyl\u003c/code\u003e; ~20-30 min using 16-cores) so perhaps make some tea\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCreating source directories\u003c/h4\u003e\u003ca id=\"user-content-creating-source-directories\" class=\"anchor\" aria-label=\"Permalink: Creating source directories\" href=\"#creating-source-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ebpp_dir=BIOPP_INSTALLATION_DIRECTORY\nmkdir -p $bpp_dir/sources/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDownloading Bio++ libraries\u003c/h4\u003e\u003ca id=\"user-content-downloading-bio-libraries\" class=\"anchor\" aria-label=\"Permalink: Downloading Bio++ libraries\" href=\"#downloading-bio-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd $bpp_dir/sources\ngit clone https://github.com/BioPP/bpp-core.git\ngit clone https://github.com/BioPP/bpp-seq.git\ngit clone -b kerenDevel https://github.com/halabikeren/bpp-phyl.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompiling and installing Bio++\u003c/h4\u003e\u003ca id=\"user-content-compiling-and-installing-bio\" class=\"anchor\" aria-label=\"Permalink: Compiling and installing Bio++\" href=\"#compiling-and-installing-bio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd $bpp_dir/sources/bpp-core\nmkdir build\ncd build\ncmake -DCMAKE_INSTALL_PREFIX=$bpp_dir -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE -DBUILD_STATIC=YES ..\nmake -j\nmake install\ncd ../../bpp-seq\nmkdir build\ncd build\ncmake -DCMAKE_INSTALL_PREFIX=$bpp_dir -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE ..\nmake -j\nmake install\ncd ../../bpp-phyl\nmkdir build\ncd build\ncmake -DCMAKE_INSTALL_PREFIX=$bpp_dir -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE OMP_NUM_THREADS=\u0026lt;required_number_of_cores\u0026gt; ..\nmake -j\nmake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompiling and installing TraitRELAX\u003c/h4\u003e\u003ca id=\"user-content-compiling-and-installing-traitrelax\" class=\"anchor\" aria-label=\"Permalink: Compiling and installing TraitRELAX\" href=\"#compiling-and-installing-traitrelax\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eclone\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etraitrelax_dir=TRAITRELAX_INSTALLATION_DIRECTORY # the directory to which you clone TraitRELAX\nmkdir -p $traitrelax_dir\ncd $traitrelax_dir/\ngit clone https://github.com/halabikeren/TraitRELAX.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand compile\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd $traitrelax_dir/TraitRELAX/\ncmake -DCMAKE_INSTALL_PREFIX=$bpp_dir -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE .\nmake -j\nmake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning TraitRELAX with Parallelization\u003c/h4\u003e\u003ca id=\"user-content-running-traitrelax-with-parallelization\" class=\"anchor\" aria-label=\"Permalink: Running TraitRELAX with Parallelization\" href=\"#running-traitrelax-with-parallelization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run TraitRELAX with multiple threads, you should state the number of required cores in the cmake command of the complication of the Bio++ bpp-phyl library:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eOMP_NUM_THREADS=\u0026lt;required_number_of_cores\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDistinction from the RELAX method\u003c/h4\u003e\u003ca id=\"user-content-distinction-from-the-relax-method\" class=\"anchor\" aria-label=\"Permalink: Distinction from the RELAX method\" href=\"#distinction-from-the-relax-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTraitRELAX varies from The RELAX basic method in several aspects:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eTraitRELAX does not require a prior partition of the branches into two categories.\u003c/li\u003e\n\u003cli\u003eTraitRELAX uses a fixed effect likelihood approach for the selective categories, and as such constrains the selective category of each site to remain consistent across branches. That being said, the selection value of a site can change upon relaxation or intensification based on the inferred evolutionary history of the examined trait.\u003c/li\u003e\n\u003cli\u003eTraitRELAX uses a different optimization approach that limits the search space of the selection intenisty parameter to (0,10] in aim of avoiding extreme estimates.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDue to these differences, estimation of the parameters shared between the two methods may be inconsistent with one another.\u003c/p\u003e\n", "stargazers_count": 4, "subscribers_count": 3, "topics": [], - "updated_at": 1726145523.0 + "updated_at": 1662489779.0 }, { "data_format": 2, - "description": "transXpress: a Nextflow pipeline for rapid de novo transcriptome assembly and annotation", + "description": "Main genome analytics workflow powering the production analysis of WGS samples for the Singapore NPM Program Phase 1A (AKA SG10K Health)", "filenames": [ "Singularity" ], - "full_name": "transXpress/transXpress-nextflow", + "full_name": "gis-rpd/rpd-sg10k-grch38-gatk4-gvcf-freebayes-vcf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etransXpress-nextflow\u003c/h1\u003e\u003ca id=\"user-content-transxpress-nextflow\" class=\"anchor\" aria-label=\"Permalink: transXpress-nextflow\" href=\"#transxpress-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etransXpress: a \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e pipeline for rapid de novo transcriptome assembly and annotation\u003c/p\u003e\n\u003cp\u003eAlso see our sister project: \u003ca href=\"https://github.com/transXpress/transXpress-snakemake\"\u003etransXpress-snakemake\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntro\u003c/h2\u003e\u003ca id=\"user-content-intro\" class=\"anchor\" aria-label=\"Permalink: Intro\" href=\"#intro\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequires\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextFlow 20.04.0+ (install via conda)\u003c/li\u003e\n\u003cli\u003efastqc (install via conda)\u003c/li\u003e\n\u003cli\u003etrimmomatic (install via conda)\u003c/li\u003e\n\u003cli\u003eTrinity (install via conda)\u003c/li\u003e\n\u003cli\u003eSPAdes (install via conda)\u003c/li\u003e\n\u003cli\u003eTransDecoder (install via conda)\u003c/li\u003e\n\u003cli\u003eBioPython (install via conda)\u003c/li\u003e\n\u003cli\u003esamtools (install via conda)\u003c/li\u003e\n\u003cli\u003ebowtie2 (install via conda)\u003c/li\u003e\n\u003cli\u003einfernal (install via conda)\u003c/li\u003e\n\u003cli\u003eHMMER (install via conda)\u003c/li\u003e\n\u003cli\u003ekallisto (install via conda)\u003c/li\u003e\n\u003cli\u003eNCBI BLAST+ (install via conda)\u003c/li\u003e\n\u003cli\u003eR (install via conda)\u003c/li\u003e\n\u003cli\u003eseqkit (install via conda)\u003c/li\u003e\n\u003cli\u003ebasic Linux utitilies: wget, split, awk, cut, gzip\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?deeploc\" rel=\"nofollow\"\u003edeeploc\u003c/a\u003e / \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/sw_request?signalp+4.1\" rel=\"nofollow\"\u003eSignalP 4.1\u003c/a\u003e / \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?signalp\" rel=\"nofollow\"\u003eSignalP 5.0\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.cbs.dtu.dk/services/TMHMM/\" rel=\"nofollow\"\u003etmhmm v. 2.0\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ca href=\"https://conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eMiniconda3\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSetup conda environment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name transxpress-nf\nconda activate transxpress-nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall conda dependencies:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e conda config --add channels bioconda\n conda config --add channels conda-forge\n conda config --add channels r\n conda config --set channel_priority false\n conda install -y nextflow fastqc trimmomatic \"trinity\u0026gt;=2.13.2\" \"spades\u0026gt;=3.15.4\" \"transdecoder\u0026gt;=5.5.0\" biopython samtools bowtie2 infernal hmmer kallisto blast r r-tidyverse seqkit bioconductor-edger parallel graphviz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(Note, below dependencies are optional, transXpress will run to completion without them, but will produce empty files for their output)\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eInstall deeploc (performance being evaluated by transXpress developers in comparison to SingalP 4.1/5.0)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload deeploc from \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?deeploc\" rel=\"nofollow\"\u003ehttp://www.cbs.dtu.dk/cgi-bin/nph-sw_request?deeploc\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall dependencies: \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall deeploc: \u003ccode\u003epython setup.py install\u003c/code\u003e or locally: \u003ccode\u003epython setup.py install --user\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall SignalP 4.1g (performance being evaluated by transXpress developers in comparison to SingalP 5.0/deeploc)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload SignalP 4.1g from \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/sw_request?signalp+4.1\" rel=\"nofollow\"\u003ehttp://www.cbs.dtu.dk/cgi-bin/sw_request?signalp+4.1\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall SignalP 5.0 (performance being evaluated by transXpress developers in comparison to SingalP 4.1/deeploc)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload SignalP 5.0 from \u003ca href=\"http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?signalp\" rel=\"nofollow\"\u003ehttp://www.cbs.dtu.dk/cgi-bin/nph-sw_request?signalp\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall tmhmm\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload tmhmm from \u003ca href=\"http://www.cbs.dtu.dk/services/TMHMM/\" rel=\"nofollow\"\u003ehttp://www.cbs.dtu.dk/services/TMHMM/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake your assembly directory and change it to the current directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir your_assembly_directory\ncd your_assembly_directory\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSetup the mandatory \u0027samples.tsv\u0027 file in the assembly directory describing where to find your raw read FASTQ files. Reads will be pooled from all samples for a single transcriptome assembly, but expression quantification will happen on a per-sample basis. See the tests directory for an example of a samples file: \u003ca href=\"./tests/test_nonSS-trinity/samples.tsv\"\u003esamples.tsv\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSetup the mandatory \u0027prefix.txt\u0027 file in the directory describing which genus species the data comes from, or whichever metadata you prefer to add. See the tests directory for an example of a species file: \u003ca href=\"./tests/test_nonSS-trinity/prefix.txt\"\u003eprefix.txt\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSymbolically link the transxpress-nextflow code into your assembly directory\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eln -s /your/transxpress-nextflow-cloned-directory/* ./\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure your conda \u003ccode\u003etransxpress\u003c/code\u003e environment has been sourced, and then execute the run.sh script with your assembler and profile of choice. You can choose your execution/cluster platform by setting the \u003ccode\u003e--executor\u003c/code\u003e parameter, e.g. \u003ccode\u003elocal\u003c/code\u003e or \u003ccode\u003epbs\u003c/code\u003e\nNote: For the cluster, depending on how strict your cluster is, you may need to tweak \u003ccode\u003ecluster.config\u003c/code\u003e quite a bit.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -w work-$ASSEMBLER -profile $THEPROFILE --assembler $ASSEMBLER --samples \u0027samples.tsv\u0027 --prefix_add_metadata_file \u0027prefix.txt\u0027 -resume\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNextFlow only likes 1 assembly per directory, so if you\u0027d like to run two assemblies simultaneously, you have to use different assembly directories.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAssemblers\u003c/h2\u003e\u003ca id=\"user-content-assemblers\" class=\"anchor\" aria-label=\"Permalink: Assemblers\" href=\"#assemblers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently \u0027trinity\u0027 or \u0027rnaspades\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProfiles\u003c/h2\u003e\u003ca id=\"user-content-profiles\" class=\"anchor\" aria-label=\"Permalink: Profiles\" href=\"#profiles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe 2nd parameter for the ./run.sh wrapper script allows you to specify the profile that is used. The profiles (stored in the \u003ccode\u003enextflow.config\u003c/code\u003e file) are currently used to configure the execution mode (cluster vs local), and if the assembly is strand specific or not.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./run.sh trinity strandSpecific\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAvailable profiles are as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003estrandSpecific\nnotStrandSpecific\ntest_notStrandSpecific\ntest_strandSpecific\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning tests\u003c/h2\u003e\u003ca id=\"user-content-running-tests\" class=\"anchor\" aria-label=\"Permalink: Running tests\" href=\"#running-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ./tests/\ncd ./test_nonSS-trinity ##non strand specific assembly using trinity. Other directories have other assemblers / parameters.\n./run_test.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFlow graph\u003c/h2\u003e\u003ca id=\"user-content-flow-graph\" class=\"anchor\" aria-label=\"Permalink: Flow graph\" href=\"#flow-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./tests/test_nonSS-trinity/test_nonSS_dag.svg\"\u003e\u003cimg src=\"./tests/test_nonSS-trinity/test_nonSS_dag.svg\" alt=\"Directed acyclic graph for transXpress-nextflow program execution\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSG10K Health: GRCh38 GATK4-gVCF Freebayes-VCF\u003c/h1\u003e\u003ca id=\"user-content-sg10k-health-grch38-gatk4-gvcf-freebayes-vcf\" class=\"anchor\" aria-label=\"Permalink: SG10K Health: GRCh38 GATK4-gVCF Freebayes-VCF\" href=\"#sg10k-health-grch38-gatk4-gvcf-freebayes-vcf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://dev.azure.com/wilma0161/wilma/_build/latest?definitionId=1?branchName=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cdde83a116160fde0045af9d9278d92871ab86e62e2fd3e65ede41d7ee76d74d/68747470733a2f2f6465762e617a7572652e636f6d2f77696c6d61303136312f77696c6d612f5f617069732f6275696c642f7374617475732f6769732d7270642e7270642d736731306b2d6772636833382d6761746b342d677663662d6672656562617965732d7663663f6272616e63684e616d653d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://dev.azure.com/wilma0161/wilma/_apis/build/status/gis-rpd.rpd-sg10k-grch38-gatk4-gvcf-freebayes-vcf?branchName=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the main genome analytics workflow powering the production analysis of whole genome samples\nfor the Singapore National Precision Medicine (NPM) Program Phase 1A, sometimes also referred to as SG10K\nHealth. It processes samples from FastQ to lossless CRAM, computes multiple QC metrics as well as Freebayes\nvariant calls and GATK4 gvcfs.\u003c/p\u003e\n\u003cp\u003eTo ensure reproducibility, scalability and mobility the workflow is implemented as \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e recipe and uses containers\n(\u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e on \u003ca href=\"https://www.nscc.sg/about-nscc/our-facilityaspire-1/\" rel=\"nofollow\"\u003eNSCC\u0027s Aspire 1\u003c/a\u003e and \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e on\n\u003ca href=\"https://aws.amazon.com/batch/\" rel=\"nofollow\"\u003eAWS Batch\u003c/a\u003e). Container building is simplified by the use of\n\u003ca href=\"https://bioconda.github.io/\" rel=\"nofollow\"\u003eBioconda\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll results can be found in the \u003ccode\u003eresults\u003c/code\u003e folder of a pipeline\nexecution. Results there are grouped per sample, with the exception of\nGoleft indexcov, which summarises over the sample set.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMain results\u003c/h3\u003e\u003ca id=\"user-content-main-results\" class=\"anchor\" aria-label=\"Permalink: Main results\" href=\"#main-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://software.broadinstitute.org/gatk/gatk4\" rel=\"nofollow\"\u003eGATK4\u003c/a\u003e gVCF (indexed): \u003ccode\u003e{sample}/{sample}.g.vcf.gz\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ekg/freebayes\"\u003eFreebayes\u003c/a\u003e VCF (Q\u0026gt;=20; indexed): \u003ccode\u003e{sample}/{sample}.fb.vcf.gz\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCRAM (lossless, with OQ, indexed): \u003ccode\u003e{sample}/{sample}.bqsr.cram\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQC etc.\u003c/h3\u003e\u003ca id=\"user-content-qc-etc\" class=\"anchor\" aria-label=\"Permalink: QC etc.\" href=\"#qc-etc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/brentp/goleft\"\u003eGoleft\u003c/a\u003e indexcov: \u003ccode\u003eindexcov/all/\u003c/code\u003e (main file \u003ccode\u003eindexcov/all/all.html\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.htslib.org/doc/samtools.html\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e stats: \u003ccode\u003e{sample}/stats/\u003c/code\u003e (main files: \u003ccode\u003e{sample}/stats/{sample}.stats\u003c/code\u003e and \u003ccode\u003e{sample}/stats/{sample}.html\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://genome.sph.umich.edu/wiki/VerifyBamID\" rel=\"nofollow\"\u003eVerifybamid\u003c/a\u003e for the three ethnicities: \u003ccode\u003e{sample}/verifybamid/\u003c/code\u003e (main files: \u003ccode\u003e{sample}/verifybamid/{sample}.SGVP_MAF0.01.{ethnicity}.selfSM\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCoverage as per SOP: \u003ccode\u003e{sample}/{sample}.cov-062017.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWe share this code for transparency. This is not meant to be a generic whole genome workflow for wider use, but rather specific to the program\u0027s needs.\nFor the same reason this documentation is rudimentary.\u003c/li\u003e\n\u003cli\u003eSee \u003ca href=\"./dag.svg\"\u003ethis file\u003c/a\u003e for the execution DAG\u003c/li\u003e\n\u003cli\u003eGATK commandline parameters are based on \u003ca href=\"https://github.com/broadinstitute/wdl/tree/develop/scripts/broad_pipelines/germline-short-variant-discovery/gvcf-generation-per-sample/1.0.0\"\u003ethe official WDL implementation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopers: work on devel or feature branches. Only merge to master if \u003ccode\u003etests/run.sh\u003c/code\u003e completes successfully\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow was implemented in the \u003ca href=\"https://www.a-star.edu.sg/gis\" rel=\"nofollow\"\u003eGenome Institute of Singapore\n(GIS)\u003c/a\u003e by:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLavanya VEERAVALLI \u003ca href=\"mailto:veeravallil@gis.a-star.edu.sg\"\u003eveeravallil@gis.a-star.edu.sg\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAndreas WILM \u003ca href=\"mailto:wilma@gis.a-star.edu.sg\"\u003ewilma@gis.a-star.edu.sg\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 4, - "subscribers_count": 3, + "subscribers_count": 7, "topics": [], - "updated_at": 1695825114.0 + "updated_at": 1564798688.0 }, { "data_format": 2, - "description": null, + "description": "An example container with modules to run an executable in different environments / with different job managers", "filenames": [ "Singularity" ], - "full_name": "ctpelok77/fd-red-black-postipc2018", + "full_name": "sci-f/jobmaker.scif", "latest_release": null, - "readme": "\u003cp\u003eFast Downward is a domain-independent planning system.\u003c/p\u003e\n\u003cp\u003eFor documentation and contact information see \u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe following directories are not part of Fast Downward as covered by this\nlicense:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation, either version 3 of the License, or (at your option) any later\nversion.\n\nFast Downward is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see \u0026lt;http://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJobMaker Scientific Filesystem\u003c/h1\u003e\u003ca id=\"user-content-jobmaker-scientific-filesystem\" class=\"anchor\" aria-label=\"Permalink: JobMaker Scientific Filesystem\" href=\"#jobmaker-scientific-filesystem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is an example container to provide an executable (in this case, a fun\nprinting of pokemon) to be run in different contexts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erun with custom variables (fortune)\u003c/li\u003e\n\u003cli\u003erun in a different context (eg, a color filter)\u003c/li\u003e\n\u003cli\u003erun on a slurm cluster\u003c/li\u003e\n\u003cli\u003erun on an sge cluster\u003c/li\u003e\n\u003cli\u003erun locally\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe general idea is that a main function (the pokemon executable) can be\nprovided in different contexts, or with different (optional) modular\ncontexts for the user. For each context or helper, there\nis a custom set of environment, or labels, along with commands and metadata. If\nyou want to skip the science part and just play with Pokemon, there is a \u003ca href=\"https://vsoch.github.io/2018/pokemon/\" rel=\"nofollow\"\u003eseparate\nset of containers\u003c/a\u003e (Docker and Singularity) for that.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the image\u003c/h2\u003e\u003ca id=\"user-content-building-the-image\" class=\"anchor\" aria-label=\"Permalink: Building the image\" href=\"#building-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLet\u0027s first build the container. You can use the Makefile to build the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n# Does make clean followed by make build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor manually:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build jobmaker Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the Image\u003c/h2\u003e\u003ca id=\"user-content-running-the-image\" class=\"anchor\" aria-label=\"Permalink: Running the Image\" href=\"#running-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAnd now run it. This should perform the container\u0027s main function, calling it\u0027s runscript:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will see an army of Pokemon ascii print to the screen. Works great! But now we want to capture metrics about this primary function. First we would want to know what tools (SCIF apps) come with the\ncontainer. That\u0027s easy to do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker apps\n catch\n colors\n fortune\n main\n sge\n slurm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can ask for help for the container, this is Singularity specific.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity help jobmaker \n\nThis is an example for a container with a Scientific Filesystem\nthat on generation, calculates the runtime for the runscript, \nand then writes a job file fitting to it. We also provide \nseveral wrappers (colors, fortune) for customizing the runscript.\nGiven that metrics for running time and memory are being calculated where\nthe container is built, we assume that the build environment resources \nare comparable to the running environment. The only requirements for\nthe running environments are that singularity is installed.\nEach SCIF app serves as a different entrypoint to run the container. \n\n # Generate on your own\n git clone https://www.github.com/sci-f/jobmaker.scif\n cd jobmaker.scif\n make\n\n # Here is how you can use the container after you build it:\n\n # List all apps\n ./jobmaker apps\n\n # Run a specific app\n ./jobmaker run \u0026lt;app\u0026gt;\n\n # Loop over all apps\n for app in $(./jobmaker apps); do\n ./jobmaker run $app\n done\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning an application\u003c/h3\u003e\u003ca id=\"user-content-running-an-application\" class=\"anchor\" aria-label=\"Permalink: Running an application\" href=\"#running-an-application\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRemember the list of apps? We don\u0027t know what they do. So first you might want to ask for help\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker help\nUsage: scif help \u0026lt;hello-world\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker help slurm\nThis will print (to the console) a slurm submission script\n./jobmaker run slurm\n./jobmaker run slurm vsochat@stanford.edu\n./jobmaker run slurm \u0026gt;\u0026gt; pokemon.job\nsbatch pokemon.job\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also look at the metadata in detail with inspect\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker inspect slurm\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then run it!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker run slurm\n[slurm] executing /bin/bash /scif/apps/slurm/scif/runscript\n#!/bin/bash\n#SBATCH --nodes=1\n#SBATCH -p normal\n#SBATCH --qos=normal\n#SBATCH --mem=16\n#SBATCH --job-name=pokemon.job\n#SBATCH --error=%j.err\n#SBATCH --output=%j.out\n#SBATCH --mail-type=ALL\n#SBATCH --time=0:00.82\nmodule load singularity\nsingularity run /scif/apps/main/jobmaker\n# example: run the job script command line:\n# sbatch pokemon.job\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe reason this works is because the slurm application sources environment variables for memory and time needed that were calculated when the container was built. Since this is a job, we can pipe the output easily into a file, and we will add \u003ccode\u003e--quiet\u003c/code\u003e to suppress the first information line.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker --quiet run slurm \u0026gt;\u0026gt; myjob.job\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced\u003c/h2\u003e\u003ca id=\"user-content-advanced\" class=\"anchor\" aria-label=\"Permalink: Advanced\" href=\"#advanced\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhat variables are exposed to each app at runtime? Let\u0027s look at the environment of the active application (e.g., slurm) when it\u0027s running. We will split this into two pieces to show the \"general active application\" environment, followed by the named application environment (that is also defined for apps that aren\u0027t active!)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker exec slurm env | grep slurm\n[slurm] executing /usr/bin/env \nSCIF_APPDATA=/scif/data/slurm\nSCIF_APPRUN=/scif/apps/slurm/scif/runscript\nSCIF_APPRECIPE=/scif/apps/slurm/scif/slurm.scif\nSCIF_APPNAME_slurm=slurm\nSCIF_APPROOT=/scif/apps/slurm\nSCIF_APPNAME=slurm\nSCIF_APPLIB=/scif/apps/slurm/lib\nSCIF_APPMETA=/scif/apps/slurm/scif\nSCIF_APPBIN=/scif/apps/slurm/bin\nSCIF_APPHELP=/scif/apps/slurm/scif/runscript.help\nSCIF_APPTEST=/scif/apps/slurm/scif/test.sh\nSCIF_APPENV=/scif/apps/slurm/scif/environment.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003eSCIF_APPLIB_slurm=/scif/apps/slurm/lib\nSCIF_APPMETA_slurm=/scif/apps/slurm/scif\nSCIF_APPBIN_slurm=/scif/apps/slurm/bin\nSCIF_APPHELP_slurm=/scif/apps/slurm/scif/runscript.help\nSCIF_APPENV_slurm=/scif/apps/slurm/scif/environment.sh\nSCIF_APPLABELS_slurm=/scif/apps/slurm/scif/labels.json\nSCIF_APPTEST_slurm=/scif/apps/slurm/scif/test.sh\nSCIF_APPDATA_slurm=/scif/data/slurm\nSCIF_APPRUN_slurm=/scif/apps/slurm/scif/runscript\nSCIF_APPLABELS=/scif/apps/slurm/scif/labels.json\nSCIF_APPRECIPE_slurm=/scif/apps/slurm/scif/slurm.scif\nSCIF_APPROOT_slurm=/scif/apps/slurm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eImportantly, notice that the bin and lib are added to their respective paths, to be found!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLD_LIBRARY_PATH=/scif/apps/slurm/lib:/.singularity.d/libs\nPWD=/scif/apps/slurm\nPATH=/scif/apps/slurm/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd guess what? Even when slurm is running (and other apps like sge are sleeping) we can still find the other apps! Let\u0027s look for sge (when slurm is running):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./jobmaker exec slurm env | grep sge\nSCIF_APPHELP_sge=/scif/apps/sge/scif/runscript.help\nSCIF_APPENV_sge=/scif/apps/sge/scif/environment.sh\nSCIF_APPLABELS_sge=/scif/apps/sge/scif/labels.json\nSCIF_APPTEST_sge=/scif/apps/sge/scif/test.sh\nSCIF_APPDATA_sge=/scif/data/sge\nSCIF_APPRUN_sge=/scif/apps/sge/scif/runscript\nSCIF_APPRECIPE_sge=/scif/apps/sge/scif/sge.scif\nSCIF_APPROOT_sge=/scif/apps/sge\nSCIF_APPNAME_sge=sge\nSCIF_APPLIB_sge=/scif/apps/sge/lib\nSCIF_APPMETA_sge=/scif/apps/sge/scif\nSCIF_APPBIN_sge=/scif/apps/sge/bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is why I\u0027m able to quickly execute another app runscript:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexec SCIF_APPRUN_sge\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor source an environment\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource SCIF_APPENV_sge\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewithout needing to know the path or details. I can also just target the active app, whatever that may be, doing the same without the specified name. For example, let\u0027s say I have a script to perform some machine learning task on the main runscript file. It would be located at \u003ccode\u003eSCIF_APPRUN\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1722364915.0 + "updated_at": 1631705925.0 }, { "data_format": 2, - "description": "Singularity container for Isaac Sim and Isaac Orbit with ROS Noetic.", + "description": "Parse Dashboard for parse-hipaa server", "filenames": [ "Singularity" ], - "full_name": "roboticsleeds/isaac-orbit-container", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIsaacSim Singularity Container with ROS Noetic\u003c/h1\u003e\u003ca id=\"user-content-isaacsim-singularity-container-with-ros-noetic\" class=\"anchor\" aria-label=\"Permalink: IsaacSim Singularity Container with ROS Noetic\" href=\"#isaacsim-singularity-container-with-ros-noetic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a singularity container for the Nvidia Isaac Sim. It builds\non top of Nvidia\u0027s official image, but also installs ROS Noetic.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\"Isaac Sim\" is a registered trademark of Nvidia. We would like to clarify\nthat we have no affiliation, partnership, or endorsement with Nvidia or the Isaac\nSim platform. Any references made to \"Isaac Sim\" are purely for\ninformational purposes and do not imply any official association with Nvidia.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Rafael Papallas (\u003ca href=\"https://rpapallas.com\" rel=\"nofollow\"\u003erpapallas.com\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate API key\u003c/h2\u003e\u003ca id=\"user-content-create-api-key\" class=\"anchor\" aria-label=\"Permalink: Create API key\" href=\"#create-api-key\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuilding this container will require authentication with NGC.\u003c/p\u003e\n\u003cp\u003eFirst, make sure Singularity is installed on your computer. Follow the \"Quick\nInstallation Steps\" guide \u003ca href=\"https://docs.sylabs.io/guides/3.11/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eHere are the steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFirst create an NGC account here: \u003ca href=\"https://ngc.nvidia.com/\" rel=\"nofollow\"\u003ehttps://ngc.nvidia.com/\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eOnce you do, head to \u003ca href=\"https://ngc.nvidia.com/setup/api-key\" rel=\"nofollow\"\u003ehttps://ngc.nvidia.com/setup/api-key\u003c/a\u003e\nand generate an API key.\u003c/li\u003e\n\u003cli\u003eSet the following environment variables:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_DOCKER_USERNAME=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-cce\"\u003e\\$\u003c/span\u003eoauthtoken\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_DOCKER_PASSWORD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e[YOUR API TOKEN FROM ABOVE]\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s important that the \u003ccode\u003eSINGULARITY_DOCKER_USERNAME\u003c/code\u003e is set exactly like\nabove, including the \u003ccode\u003e\\$\u003c/code\u003e. For the password, of course replace the \u003ccode\u003e[YOUR API TOKEN FROM ABOVE]\u003c/code\u003e\nto the actual API token generated above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild the container and run the simulator\u003c/h2\u003e\u003ca id=\"user-content-build-the-container-and-run-the-simulator\" class=\"anchor\" aria-label=\"Permalink: Build the container and run the simulator\" href=\"#build-the-container-and-run-the-simulator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThen run the build script: \u003ccode\u003e./build.sh\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eYou can then run the container: \u003ccode\u003e./run.sh\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eYou need to \"accept EULA\" to run the simulator. It\u0027s available \u003ca href=\"https://docs.omniverse.nvidia.com/app_isaacsim/common/NVIDIA_Omniverse_License_Agreement.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nTo do this, in the container\u0027s home directory, open the \u003ccode\u003e.bashrc\u003c/code\u003e file\n(note, not your host OS\u0027s \u003ccode\u003e.bashrc\u003c/code\u003e but the \u003ccode\u003e.bashrc\u003c/code\u003e of the container which is\nunder \u003ccode\u003eisaac-sim-container/home/.bashrc\u003c/code\u003e) and put the following: \u003ccode\u003eexport ACCEPT_EULA=\"Y\"\u003c/code\u003e.\nThis is the way of \"ticking\" the \"I accept EULA\" for the license linked above.\u003c/li\u003e\n\u003cli\u003eTo launch Isaac Sim you need to head to \u003ccode\u003ecd /isaac-sim\u003c/code\u003e (note to the root dir \u003ccode\u003e/\u003c/code\u003e)\nand run \u003ccode\u003e./runapp.sh\u003c/code\u003e within the container. Allow some time to download dependencies.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther notes\u003c/h2\u003e\u003ca id=\"user-content-other-notes\" class=\"anchor\" aria-label=\"Permalink: Other notes\" href=\"#other-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMake sure that nvidia drivers and CUDA are installed on your host machine;\nverify by running \u003ccode\u003envidia-smi\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003erun.sh\u003c/code\u003e script will bind the drivers if available; double check they\nare available in the container by running \u003ccode\u003envidia-smi\u003c/code\u003e within the container\nas well.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "netreconlab/parse-hipaa-dashboard", + "latest_release": "1.1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eparse-hipaa-dashboard\u003c/h1\u003e\u003ca id=\"user-content-parse-hipaa-dashboard\" class=\"anchor\" aria-label=\"Permalink: parse-hipaa-dashboard\" href=\"#parse-hipaa-dashboard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/65eac64ec2f7259f39e209a7328555c0805b82e654825ce7730c955b6746cdf1/68747470733a2f2f646f636b6572692e636f2f696d6167652f6e65747265636f6e6c61622f70617273652d68697061612d64617368626f617264\" alt=\"\" data-canonical-src=\"https://dockeri.co/image/netreconlab/parse-hipaa-dashboard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/js/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d3f49448a2f912919bf956ca7f5ea9dfb7316ef14c5746a5a9f229e721af3d6/68747470733a2f2f62616467652e667572792e696f2f6a732f70617273652d68697061612d64617368626f6172642e737667\" alt=\"npm version\" data-canonical-src=\"https://badge.fury.io/js/parse-hipaa-dashboard.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://snyk.io/test/github/netreconlab/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/04ed3a73793e78c7c3d48a58b79af59f0998745aa1691b62444318c962e1d9cd/68747470733a2f2f736e796b2e696f2f746573742f6769746875622f6e65747265636f6e6c61622f70617273652d68697061612d64617368626f6172642f62616467652e737667\" alt=\"vulnerabilities\" data-canonical-src=\"https://snyk.io/test/github/netreconlab/parse-hipaa-dashboard/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://libraries.io/npm/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bcbf91be41700b45bb5eebc61257dd95f93fefd9db9b935a9a657b9a632e7972/68747470733a2f2f696d672e736869656c64732e696f2f6c6962726172696573696f2f72656c656173652f6e706d2f70617273652d68697061612d64617368626f617264\" alt=\"dependency up-to-date\" data-canonical-src=\"https://img.shields.io/librariesio/release/npm/parse-hipaa-dashboard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.npmjs.com/package/parse-hipaa-dashboard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5eb8bfb30386c6ac6c551fdc3fb506de28399e3227379e052279809bc647fd9c/68747470733a2f2f696d672e736869656c64732e696f2f6e706d2f64772f70617273652d68697061612d64617368626f617264\" alt=\"weekly downloads\" data-canonical-src=\"https://img.shields.io/npm/dw/parse-hipaa-dashboard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/ci.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/ci.yml/badge.svg\" alt=\"ci\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/release.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/release.yml/badge.svg\" alt=\"release\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/image.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/image.yml/badge.svg\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/image-release.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/parse-hipaa-dashboard/actions/workflows/image-release.yml/badge.svg\" alt=\"image-release\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/8621344/102236202-38f32080-3ec1-11eb-88d7-24e38e95f68d.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/8621344/102236202-38f32080-3ec1-11eb-88d7-24e38e95f68d.png\" alt=\"dashboard\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eExample of how to setup and run your own \u003ca href=\"https://github.com/parse-community/parse-dashboard\"\u003eparse-dashboard\u003c/a\u003e for viewing/modifying your data in the Cloud. Designed for \u003ca href=\"https://github.com/netreconlab/parse-hipaa\"\u003eparse-hipaa\u003c/a\u003e, but can be used with any parse-server.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse at your own risk. There is not promise that this is HIPAA compliant and we are not responsible for any mishandling of your data\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeployment\u003c/h2\u003e\u003ca id=\"user-content-deployment\" class=\"anchor\" aria-label=\"Permalink: Deployment\" href=\"#deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eparse-hipaa can be easily deployed or tested remote or locally.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRemote\u003c/h3\u003e\u003ca id=\"user-content-remote\" class=\"anchor\" aria-label=\"Permalink: Remote\" href=\"#remote\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHeroku\u003c/h4\u003e\u003ca id=\"user-content-heroku\" class=\"anchor\" aria-label=\"Permalink: Heroku\" href=\"#heroku\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://heroku.com/deploy\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc2056acd0e6ff421bfc2b129417f4f832d626c61d1c083221211d8503a429f7/68747470733a2f2f7777772e6865726f6b7563646e2e636f6d2f6465706c6f792f627574746f6e2e737667\" alt=\"Deploy\" data-canonical-src=\"https://www.herokucdn.com/deploy/button.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can use the one-button deployment to quickly deploy to Heroko. \u003cstrong\u003eNote that this is non-HIPAA compliant when using Heroku\u0027s free services\u003c/strong\u003e, so you need to work with Heroku to enable this. You can \u003ca href=\"https://docs.google.com/document/d/1fniJavK_3T_SXZs2wwn-wa8nX-LzhhNgSORRK1LaZYI/edit?usp=sharing\" rel=\"nofollow\"\u003eview this document for detailed instuctions\u003c/a\u003e. Once you click the Heroku button do the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSelect your \u003cstrong\u003eApp name\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eUnder the \u003cstrong\u003eConfig vars\u003c/strong\u003e section, set all \u003ccode\u003erequired\u003c/code\u003e environment vars to connect to your parse-server\u003c/li\u003e\n\u003cli\u003eScroll to the bottom of the page and press \u003cstrong\u003eDeploy app\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsing your own files for Heroku deployment\u003c/h4\u003e\u003ca id=\"user-content-using-your-own-files-for-heroku-deployment\" class=\"anchor\" aria-label=\"Permalink: Using your own files for Heroku deployment\" href=\"#using-your-own-files-for-heroku-deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eFork the the parse-hipaa-dashboard repo\u003c/li\u003e\n\u003cli\u003eEdit \u003ccode\u003eheroku.yml\u003c/code\u003e in your repo by changing \u003ccode\u003eDockerfile.heroku\u003c/code\u003e to \u003ccode\u003eDockerfile\u003c/code\u003e. This will build from your respective repo instead of using the pre-built docker image\u003c/li\u003e\n\u003cli\u003eEdit \u003ccode\u003eparse-dashboard-config.json\u003c/code\u003e to your desired configuration\u003c/li\u003e\n\u003cli\u003eYou can then click the Heroku deployment button from your respective repo or you can then follow the directions on heroku\u0027s site for \u003ca href=\"https://devcenter.heroku.com/articles/git\" rel=\"nofollow\"\u003edeployment\u003c/a\u003e and \u003ca href=\"https://devcenter.heroku.com/articles/github-integration\" rel=\"nofollow\"\u003eintegration\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSet the \u003ccode\u003ePARSE_DASHBOARD_CONFIG\u003c/code\u003e config variable to \u003ccode\u003e./src/parse-dashboard-config.json\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLocal: using docker\u003c/h3\u003e\u003ca id=\"user-content-local-using-docker\" class=\"anchor\" aria-label=\"Permalink: Local: using docker\" href=\"#local-using-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get started with parse-hipaa simply type:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker-compose up\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnvironment Variables\u003c/h3\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eparseplatform/parse-dashboard\u003c/h4\u003e\u003ca id=\"user-content-parseplatformparse-dashboard\" class=\"anchor\" aria-label=\"Permalink: parseplatform/parse-dashboard\" href=\"#parseplatformparse-dashboard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ePARSE_DASHBOARD_TRUST_PROXY: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Default is 1, this should always be left as 1 when using docker\u003c/span\u003e\nPARSE_DASHBOARD_COOKIE_SESSION_SECRET: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Unique string. This should be constant across all deployments on your system\u003c/span\u003e\nMOUNT_PATH: \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The default is \"/dashboard\". This needs to be exactly what you plan it to be behind the proxy, i.e. If you want to access cs.uky.edu/dashboard it should be \"/dashboard\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eViewing Your Data via Parse Dashboard\u003c/h3\u003e\u003ca id=\"user-content-viewing-your-data-via-parse-dashboard\" class=\"anchor\" aria-label=\"Permalink: Viewing Your Data via Parse Dashboard\" href=\"#viewing-your-data-via-parse-dashboard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eParse-dashboard is binded to your localhost on port 4040 and can be accessed as such, e.g. \u003ca href=\"http://localhost:4040/dashboard\" rel=\"nofollow\"\u003ehttp://localhost:4040/dashboard\u003c/a\u003e. The default login for the parse dashboard is username: \"parse\", password: \"1234\". For production you should change the password in the \u003ca href=\"https://github.com/netreconlab/parse-hipaa/blob/master/parse-dashboard-config.json#L14\"\u003epostgres-dashboard-config.json\u003c/a\u003e. Note that ideally the password should be hashed by using something like \u003ca href=\"https://bcrypt-generator.com\" rel=\"nofollow\"\u003ebcrypt-generator\u003c/a\u003e or using \u003ca href=\"https://github.com/parse-community/parse-dashboard#multi-factor-authentication-one-time-password\"\u003emulti factor authentication\u003c/a\u003e. You can also add more users through this method.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOpen your browser and go to \u003ca href=\"http://localhost:4040/dashboard\" rel=\"nofollow\"\u003ehttp://localhost:4040/dashboard\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUsername: \u003ccode\u003eparse\u003c/code\u003e # You can use \u003ccode\u003eparseRead\u003c/code\u003e to login as a read only user\u003c/li\u003e\n\u003cli\u003ePassword: \u003ccode\u003e1234\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBe sure to refresh your browser to see new changes synched from your CareKitSample app\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfiguring\u003c/h3\u003e\u003ca id=\"user-content-configuring\" class=\"anchor\" aria-label=\"Permalink: Configuring\" href=\"#configuring\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs mentioned, the default address and port the parse-server dashboard is binded to is 127.0.0.1:4040:4040 which means it can only be accessed by your local machine. If you want to change this, you should do it \u003ca href=\"https://github.com/netreconlab/parse-hipaa/blob/master/docker-compose.yml#L29\"\u003ehere\u003c/a\u003e. If you plan on using this image to deploy in production, it is recommended to run this behind a proxy and add the environment variable \u003ccode\u003ePARSE_DASHBOARD_TRUST_PROXY=1\u003c/code\u003e to the dashboard container. Note that since the parse dashboard is running in docker, the following should remain in the yml, \u003ccode\u003ecommand: parse-dashboard --dev\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [ - "containers", - "isaac-sim", - "ros", - "singularity-container" + "parse-dashboard", + "parse-hipaa", + "hacktoberfest", + "gdpr", + "healthcare", + "hipaa", + "docker", + "singularity" ], - "updated_at": 1708090098.0 + "updated_at": 1729489443.0 }, { "data_format": 2, - "description": "Quant proteomics as practiced at Lehti\u00f6 lab for NF-core", + "description": "Nextflow pipeline for standardised variant calls on canine genomes", "filenames": [ - "Singularity" + "containers/picard/Singularity.picard-2.10.6", + "containers/picard/Singularity.picard-1.97", + "containers/htslib/Singularity.htslib-1.5", + "containers/fastqc/Singularity.fastqc-0.11.5", + "containers/gatk/Singularity.gatk-3.5", + "containers/bwa/Singularity.bwa-0.7.12" ], - "full_name": "glormph/nf-core-dda-quant-proteomics", + "full_name": "NBISweden/K9-WGS-Pipeline", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003elehtiolab/ddamsproteomics\u003c/h1\u003e\u003ca id=\"user-content-lehtiolabddamsproteomics\" class=\"anchor\" aria-label=\"Permalink: lehtiolab/ddamsproteomics\" href=\"#lehtiolabddamsproteomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eA Quantitative MS proteomics analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cb7a54fdcaf1a4eab23bf403d8301f8499e0742f81363c222d13e22929fa5851/68747470733a2f2f7472617669732d63692e6f72672f6c656874696f6c61622f6464616d7370726f74656f6d6963732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/lehtiolab/ddamsproteomics.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e612028c65671defb8089ca72d2ca4e9c691a9bdee744af2355c0976cfc1d11/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413531392e30342e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A519.04.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/219955514\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f2c00ca4201ccc56750439019c8bd0bfd20b670d1c9e970b4abba630ea02a569/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3231393935353531342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/219955514.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/lehtiolab/ddamsproteomics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4a47169d6142a8f86534cb472507398830fa5f8e69c2eb87dc8fa15bdb215d06/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6c656874696f6c61622f6464616d7370726f74656f6d6963732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/lehtiolab/ddamsproteomics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow identifies peptides in mzML input data using \u003ca href=\"https://github.com/MSGFPlus/msgfplus\"\u003eMSGF+\u003c/a\u003e, and \u003ca href=\"https://github.com/percolator/percolator/\"\u003ePercolator\u003c/a\u003e, quantifies isobarically labeled samples with \u003ca href=\"https://github.com/openms/openms\"\u003eOpenMS\u003c/a\u003e, and precursor peptides with \u003ca href=\"\"\u003eHardklor\u003c/a\u003e/\u003ca href=\"\"\u003eKronik\u003c/a\u003e, and processes that output to formatted peptide and protein/gene tables using \u003ca href=\"https://github.com/glormph/msstitch\"\u003eMsstitch\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e, \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e, or \u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun pipeline:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003enextflow run lehtiolab/ddamsproteomics --mzmls \u0027/path/to/*.mzML\u0027 --tdb /path/to/proteins.fa\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nf-co.re/usage/troubleshooting\" rel=\"nofollow\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe pipeline takes multiple mzML files as input and performs identification and quantification to output results and a QC report (\u003ca href=\"docs/example_qc.html\"\u003ean example can be found here\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003elehtiolab/nf-labelcheck was originally written by Jorrit Boekel and tries to follow the \u003ca href=\"https://nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e best practices and templates.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eK9-WGS-Pipeline\u003c/h1\u003e\u003ca id=\"user-content-k9-wgs-pipeline\" class=\"anchor\" aria-label=\"Permalink: K9-WGS-Pipeline\" href=\"#k9-wgs-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/NBISweden/K9-WGS-Pipeline\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8be03a73055970ff2c398e1368149f7854d20fb6f51122daefae2c9404702468/68747470733a2f2f6170692e7472617669732d63692e6f72672f4e424953776564656e2f4b392d5747532d506970656c696e652e737667\" alt=\"Travis status\" data-canonical-src=\"https://api.travis-ci.org/NBISweden/K9-WGS-Pipeline.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNextflow pipeline for standardised mapping and variant calls on canine genomes.\nThe pipeline take fastqs (or bams) and outputs, bams, and gvcfs for joint\ngenotyping, followed by hard filtering.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e nextflow run main.nf --help\u003c/span\u003e\nN E X T F L O W \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e version 0.31.1\nLaunching \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003emain.nf\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e [shrivelled_fermi] - revision: 131a72393f\n Usage:\n nextflow run main.nf --fastqDir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edirectory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Options:\n --help\n Print this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message\n --fastqDir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eDir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Directory containing fastq samples (.fq.gz)\n --bamDir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eDir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Instead of --fastqDir, directory containing bam and indexed bam sample files (.bam, .bam.bai)\n --reference \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Genome reference file (has to be indexed)\n --known \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003efile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n File with known sites \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e quality calibration.\n --outdir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n Directory \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e output files\n --onlyMap\n Only run the mapping steps\n --project\n Slurm project to run with\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recommended way is to clone it from github:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e git clone https://github.com/NBISweden/K9-WGS-Pipeline.git\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cd K9-WGS-Pipeline\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSoftware\u003c/h3\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-label=\"Permalink: Software\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline requires \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e and\n\u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e on the target system. These\nare often pre-installed on HPC systems.\u003c/p\u003e\n\u003cp\u003eIt is recommended that you pre-pull all the singularity images required by the\nworkflow, there is a script in the workflow directory to help you with this,\njust run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e scripts/pull_singularity.sh\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNB\u003c/strong\u003e: You need to set the environment variable \u003ccode\u003eNXF_SINGULARITY_CACHEDIR\u003c/code\u003e to\nthe location where the images where pulled (it\u0027s the \u003ccode\u003esingularity\u003c/code\u003e subdir from\nwhere you ran the \u003ccode\u003epull_singularity.sh\u003c/code\u003e script).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eData\u003c/h3\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-label=\"Permalink: Data\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline requries a \u003ccode\u003ebwa\u003c/code\u003e indexed reference genome and a \u003ccode\u003epicard\u003c/code\u003e genomic\ndictionary. These can be created like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e bwa index ref.fasta\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e java -Xmx4g /picard/CreateSequenceDictionary.jar REFERENCE=ref.fasta OUTPUT=ref.dict\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also do this directly through the prepulled singularity images like so:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity exec singularity/NBISweden-K9-WGS-Pipeline-bwa-0.7.12.img \\\u003c/span\u003e\n bwa index ref.fasta\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity exec singularity/NBISweden-K9-WGS-Pipeline-picard-2.10.6.img \\\u003c/span\u003e\n picard CreateSequenceDictionary REFERENCE=ref.fasta OUTPUT=ref.dict\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# nextflow run [-resume] main.nf \u0026lt;options\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen running the mapping the workflow expects a \u003ccode\u003e--fastqDir \u0026lt;dir\u0026gt;\u003c/code\u003e parameter on\nthe command line. This directory should contain, gzipped, paired fastq files\nwith R1 and R2 in their filenames respectively. Specifically the filenames of\nthe readfiles should match either of these glob patterns.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e*R{1,2}*.fq.gz\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e*R{1,2}*.fastq.gz\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe known file is a bed file for quality calibration using BaseRecalibrator\nfrom the GATK toolkit.\u003c/p\u003e\n\u003cp\u003eIf specifying \u003ccode\u003eonlyMap\u003c/code\u003e no genotyping will be done.\u003c/p\u003e\n\u003cp\u003eIf you already have created your mapping you can use \u003ccode\u003e--bamDir\u003c/code\u003e instead of\n\u003ccode\u003e--fastqDir\u003c/code\u003e to specify a directory with bam files to run from.\u003c/p\u003e\n\u003cp\u003eIt is generally recommended to start it with the \u003ccode\u003e-resume\u003c/code\u003e option so a failed\nrun can be resumed from where it failed once eventual errors are fixed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning on HPC\u003c/h3\u003e\u003ca id=\"user-content-running-on-hpc\" class=\"anchor\" aria-label=\"Permalink: Running on HPC\" href=\"#running-on-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor HPC systems there are two main ways of running the pipeline\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun as a singlenode job\u003c/h4\u003e\u003ca id=\"user-content-run-as-a-singlenode-job\" class=\"anchor\" aria-label=\"Permalink: Run as a singlenode job\" href=\"#run-as-a-singlenode-job\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePut the nextflow command in a shellscript. Apart from the standard options make\nsure to run nextflow with the \u003ccode\u003e-profile rackhamNode\u003c/code\u003e switch, by default this\nprofile assumes that one node has 20 cpu cores, if you want to change this edit\nthe \u003ccode\u003econf/rackhamNode.config\u003c/code\u003e file and update the \u003ccode\u003ecpus\u003c/code\u003e parameter on line 16\nand 24, also update the memory parameters so they match the local environment.\nExample run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun-pipeline.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/sh\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_LAUNCHER=\u003cspan class=\"pl-smi\"\u003e$SNIC_TMP\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For Rackham\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_TEMP=\u003cspan class=\"pl-smi\"\u003e$SNIC_TMP\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For Rachham\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_SINGULARITY_CACHEDIR=/home/user/.../singularity\n\nnextflow run -resume -profile rackhamNode main.nf \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eproject specific stuff\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then start it with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sbatch run-pipeline.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLet nextflow handle the queueing system\u003c/h4\u003e\u003ca id=\"user-content-let-nextflow-handle-the-queueing-system\" class=\"anchor\" aria-label=\"Permalink: Let nextflow handle the queueing system\" href=\"#let-nextflow-handle-the-queueing-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is similar to the above, but instead you should use the \u003ccode\u003e-profile rackham\u003c/code\u003e\noption (edit the file \u003ccode\u003econf/rackham.config\u003c/code\u003e if you want to change settings to\nadjust it to your local cluster). Remember to specify the \u003ccode\u003e--project\u003c/code\u003e parameter\nto the workflow.\u003c/p\u003e\n\u003cp\u003eSince this is a very long running pipeline it is recommended that you run the\npipeline in a \u003ca href=\"https://www.gnu.org/software/screen/\" rel=\"nofollow\"\u003e\u003ccode\u003escreen\u003c/code\u003e\u003c/a\u003e session so you\ncan log out of the HPC system and log back in again and check on the status of\nthe run.\u003c/p\u003e\n\u003cp\u003eThis is an example of how to do it:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003erun-pipeline.sh\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/sh\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_LAUNCHER=\u003cspan class=\"pl-smi\"\u003e$SNIC_TMP\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For Rackham\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_TEMP=\u003cspan class=\"pl-smi\"\u003e$SNIC_TMP\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For Rachham\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NXF_SINGULARITY_CACHEDIR=/home/user/.../singularity\n\nnextflow run -resume -profile rackham main.nf --project \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eslurm project\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emore params\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then in the terminal\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ screen\n$ ./run-pipeline.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen to disconnect the screen session type \u003ccode\u003eCtrl-A D\u003c/code\u003e, then you can safely log\nout. The next time you log in, on the same login node, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ screen -r\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo reconnect.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e--outdir\u003c/code\u003e will have the following layout\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;outdir\u0026gt;\nout-tiny-fastq/\n\u251c\u2500\u2500 bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Sample.bai\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 Sample.bam\n\u251c\u2500\u2500 genotype\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_filtered_indels_1.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_filtered_snps_1.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_pass_INDEL_1.recode.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_pass_SNP_1.recode.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_raw_INDEL_1.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all_raw_SNP_1.vcf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 all.vcf.gz\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 all.vcf.gz.tbi\n\u251c\u2500\u2500 haplotypeCaller\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Sample.g.vcf.gz\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 Sample.g.vcf.gz.tbi\n\u2514\u2500\u2500 reports\n \u251c\u2500\u2500 k9_dag.dot\n \u251c\u2500\u2500 k9_report.html\n \u251c\u2500\u2500 k9_timeline.html\n \u251c\u2500\u2500 k9_trace.txt\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.flagstat\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.marked.metrics\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.post_recal_data.table\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;_R1_fastqc.html\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;_R1_fastqc.zip\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;_R2_fastqc.html\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;_R2_fastqc.zip\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.recal_data.table\n \u251c\u2500\u2500 \u0026lt;Sample\u0026gt;.stats\n \u2514\u2500\u2500 \u0026lt;Sample\u0026gt;.wgs_metrics\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMost of this is fairly selfexplanatory, except for the reports directory.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ek9_*\u003c/code\u003e files are information from the workflow engine about how the whole\nworkflow went with timings and such. Then there are one set of \u003ccode\u003e\u0026lt;Sample\u0026gt;*\u003c/code\u003e\nfiles for each pair of fastq files that the workflow has processed with\ninformation on how the mapping went.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun on test data\u003c/h2\u003e\u003ca id=\"user-content-run-on-test-data\" class=\"anchor\" aria-label=\"Permalink: Run on test data\" href=\"#run-on-test-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst setup the testdata with \u003ccode\u003escripts/setup_testdata.sh\u003c/code\u003e and then you can run\ntests with the \u003ccode\u003escripts/test-one.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e scripts/setup_testdata.sh\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e scripts/test-one.sh singularity tiny fastq chr38\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003etest-one.sh\u003c/code\u003e script is mostly for testing on travis but it is very\nconvenient to use for local tests.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u003c/h2\u003e\u003ca id=\"user-content-authors\" class=\"anchor\" aria-label=\"Permalink: Authors\" href=\"#authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/viklund\"\u003eJohan Viklund\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/glormph\"\u003eJorrit Boekel\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 4, - "subscribers_count": 2, - "topics": [], - "updated_at": 1651216059.0 + "subscribers_count": 48, + "topics": [ + "nextflow", + "genomics" + ], + "updated_at": 1670594916.0 }, { "data_format": 2, - "description": null, + "description": "H3ABioNet Metagenomics Workflow", "filenames": [ - "Singularity" + "examples/taxonomic_classification/Singularity.classification" ], - "full_name": "likelet/MesKit", + "full_name": "h3abionet/h3ameta", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMesKit\u003c/h1\u003e\u003ca id=\"user-content-meskit\" class=\"anchor\" aria-label=\"Permalink: MesKit\" href=\"#meskit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis pipelie for multi WEX analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003ethis readme was generated by nf-core tools\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/nfcore/multiexseq\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/86e4d49ee64479c80f123f82699c3cf12f3af9eebee7aa63f19f42bfebd26d0d/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6e66636f72652f6d756c746965787365712e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/nfcore/multiexseq.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe nf-core/multiexseq pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput and how to interpret the results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003emultiexseq was originally written by Qi Zhao.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"aux/H3ABioNetlogo2.jpg\"\u003e\u003cimg src=\"aux/H3ABioNetlogo2.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eh3ameta\u003c/h1\u003e\u003ca id=\"user-content-h3ameta\" class=\"anchor\" aria-label=\"Permalink: h3ameta\" href=\"#h3ameta\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eH3ABionNet Metagenomics Workflows\u003c/p\u003e\n\u003cp\u003eNote: other workshop materials can be found \u003ca href=\"https://drive.google.com/drive/u/1/folders/1g3iyBbbD0fq2TIYz3MungaOiSu4DAm8X\" rel=\"nofollow\"\u003ein our Google Drive folder\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the model workflow\u003c/h2\u003e\u003ca id=\"user-content-running-the-model-workflow\" class=\"anchor\" aria-label=\"Permalink: Running the model workflow\" href=\"#running-the-model-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Set up conda, nextflow, clone the Git repository.\u003c/h3\u003e\u003ca id=\"user-content-1-set-up-conda-nextflow-clone-the-git-repository\" class=\"anchor\" aria-label=\"Permalink: 1. Set up conda, nextflow, clone the Git repository.\" href=\"#1-set-up-conda-nextflow-clone-the-git-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: this requires Singularity to be set up on your system or cluster.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~\nmkdir -p ~/local/bin\nexport PATH=\"$PATH:~/local/bin\"\n\nwget -qO- https://get.nextflow.io | bash\nwget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\ncd\ngit clone https://github.com/h3abionet/h3ameta.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Running the workflow\u003c/h3\u003e\u003ca id=\"user-content-2-running-the-workflow\" class=\"anchor\" aria-label=\"Permalink: 2. Running the workflow\" href=\"#2-running-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~\nmkdir test_run; cd test_run\nnextflow h3ameta/examples/taxonomic_classification/taxonomic_classification.nf --tax_level S -resume --in h3ameta/examples/test_data/*.fq \\\n--dataset_table h3ameta/examples/test_data/datasets.tsv --db /path/to/kraken_and_bracken_db\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker images\u003c/h2\u003e\u003ca id=\"user-content-docker-images\" class=\"anchor\" aria-label=\"Permalink: Docker images\" href=\"#docker-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe\u0027re assuming you\u0027re using singularity -- if using Docker it\u0027ll be a little simpler, so it\u0027s left as an exercise for the reader. Of course, if you\u0027re using Nextflow this will generally be taken care of by the appropriate config file and should be transparent.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ekraken2\u003c/h3\u003e\u003ca id=\"user-content-kraken2\" class=\"anchor\" aria-label=\"Permalink: kraken2\" href=\"#kraken2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload the latest image\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull docker://quay.io/h3abionet_org/kraken2 \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis will create an image \u003ccode\u003ekraken2.img\u003c/code\u003e which contains the kraken2 suite plus auxiliary programs like dustmasker\u003c/p\u003e\n\u003cp\u003eNote that we do not have any databases inside the image to keep the image small. You need to download and build the databases. Here\u0027s an example: Assume that you have a directory \u003ccode\u003e/local/kraken\u003c/code\u003e and you\u0027re going to bulild the database inside that\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec -B /local/kraken/:/mnt kraken2.simg kraken2-build --standard --threads 8 --db /mnt/krakdb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis binds the directory \u003ccode\u003e/local/kraken\u003c/code\u003e on the host to the \u003ccode\u003e/mnt\u003c/code\u003e directory in the singularity image. The directory \u003ccode\u003e/mnt\u003c/code\u003e is passed to the \u003ccode\u003ekraken2-build\u003c/code\u003e program to use for the data and the database will be called \u003ccode\u003ekrakdb\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFunding\u003c/h2\u003e\u003ca id=\"user-content-funding\" class=\"anchor\" aria-label=\"Permalink: Funding\" href=\"#funding\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe acknowledge support of the NIH (Grant U24HG006941)\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 4, + "subscribers_count": 15, "topics": [], - "updated_at": 1558549773.0 + "updated_at": 1617613036.0 }, { "data_format": 2, - "description": "Singularity container for Tensorflow - good example for GPU integration into a container", + "description": "Making containers with minc-toolkit", "filenames": [ - "Singularity.2.2", - "Singularity" + "singularity/Singularity.base-min", + "singularity/Singularity.1.0.09", + "singularity/Singularity.1.9.16", + "singularity/Singularity.1.9.16-min", + "singularity/Singularity.1.9.15", + "singularity/Singularity.base" ], - "full_name": "chpc-uofu/Singularity-tensorflow", + "full_name": "vfonov/minc-toolkit-containers", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBuilding containers for minc-toolkit\u003c/h1\u003e\u003ca id=\"user-content-building-containers-for-minc-toolkit\" class=\"anchor\" aria-label=\"Permalink: Building containers for minc-toolkit\" href=\"#building-containers-for-minc-toolkit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDOCKER\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: DOCKER\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ecd docker; build_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [], - "updated_at": 1663000991.0 + "updated_at": 1710867538.0 }, { "data_format": 2, - "description": "A repository to conduct experiments with omnitig-related models for genome assembly.", + "description": "Data generation, model training and evaluation pipelines for the cold-start setting.", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "algbio/practical-omnitigs", - "latest_release": "0.3.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePractical \u003ca href=\"https://www.liebertpub.com/doi/full/10.1089/cmb.2016.0141\" rel=\"nofollow\"\u003eOmnitigs\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-practical-omnitigs\" class=\"anchor\" aria-label=\"Permalink: Practical Omnitigs\" href=\"#practical-omnitigs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/algbio/practical-omnitigs/workflows/Tests%20%26%20Lints/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/algbio/practical-omnitigs/workflows/Tests%20%26%20Lints/badge.svg?branch=master\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.4335367\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a94ba3e0adc18f0b340ab30c820caf28a9dde46aa9c6e8ee577d8f0f4da2a26/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e343333343933392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4334939.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a repository to conduct experiments with omnitig-related models in the context of genome assembly.\nThe algorithms are implemented in \u003cem\u003eRust\u003c/em\u003e, and around that we built a \u003cem\u003esnakemake\u003c/em\u003e toolchain to conduct experiments.\nTo ensure reproducibility, we wrapped everything into a \u003cem\u003econda\u003c/em\u003e environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequired Software\u003c/h3\u003e\u003ca id=\"user-content-required-software\" class=\"anchor\" aria-label=\"Permalink: Required Software\" href=\"#required-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econda \u0026gt;= 4.8.3\u003c/code\u003e (lower might be possible, but has not been tested)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup\u003c/h3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, set up the conda environment of this project.\u003c/p\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003econda env create -f environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, activate the environment.\u003c/p\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003esource activate practical-omnitigs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning Experiments\u003c/h3\u003e\u003ca id=\"user-content-running-experiments\" class=\"anchor\" aria-label=\"Permalink: Running Experiments\" href=\"#running-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake sure that you are in the right conda environment (should be \u003ccode\u003epractical-omnitigs\u003c/code\u003e).\u003c/p\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003econda info\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSubsequently, experiments can be run using \u003ccode\u003esnakemake\u003c/code\u003e.\u003c/p\u003e\n\u003cpre lang=\"commandline\"\u003e\u003ccode\u003esnakemake --cores all \u0026lt;experiment\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eValid experiments are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eselftest\u003c/code\u003e: Check if you have conda set up correctly. It prints the version of \u003ccode\u003esnakemake\u003c/code\u003e \u003ccode\u003econda\u003c/code\u003e and \u003ccode\u003ewget\u003c/code\u003e. The versions of \u003ccode\u003esnakemake\u003c/code\u003e and \u003ccode\u003econda\u003c/code\u003e should match the definition in \u003ccode\u003e/environment.yaml\u003c/code\u003e and the version of \u003ccode\u003ewget\u003c/code\u003e should match the definition in \u003ccode\u003e/config/conda-selftest-env.yaml\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest\u003c/code\u003e: execute all integration tests of this project on a single small sample genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_all\u003c/code\u003e: execute all integration tests of this project on all defined genomes (potentially very large).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe experiments are run inside a conda environment that is set up by snakemake.\nThis ensures reproducibility of the results and automates the installation of required tools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the Implementation Directly\u003c/h3\u003e\u003ca id=\"user-content-using-the-implementation-directly\" class=\"anchor\" aria-label=\"Permalink: Using the Implementation Directly\" href=\"#using-the-implementation-directly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Rust code written for this project includes a command line interface that can be used directly.\nFor documentation on how to use it, please refer to the documentation of the \u003ca href=\"https://github.com/algbio/practical-omnitigs/tree/master/implementation\"\u003ecli crate\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTroubleshooting\u003c/h3\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have problems with using this software package, take a look at our \u003ca href=\"https://github.com/algbio/practical-omnitigs/tree/master/TROUBLESHOOTING.md\"\u003etroubleshooting page\u003c/a\u003e.\nIf that does not solve your issue, do not hesitate to \u003ca href=\"https://github.com/algbio/practical-omnitigs/issues\"\u003efile a bug report\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTechnical Information\u003c/h2\u003e\u003ca id=\"user-content-technical-information\" class=\"anchor\" aria-label=\"Permalink: Technical Information\" href=\"#technical-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDirectory Structure\u003c/h3\u003e\u003ca id=\"user-content-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Directory Structure\" href=\"#directory-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e.github\u003c/code\u003e: GitHub workflows for continuous testing.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e.idea\u003c/code\u003e: Configuration for the IntelliJ IDEA integrated development environment.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003econfig\u003c/code\u003e: All config files related to the experiments, including conda environments and experiment declarations.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edata\u003c/code\u003e: Data used and produced by the experiments.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eexternal-software\u003c/code\u003e: Location to install external software required by the experiment pipeline.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eimplementation\u003c/code\u003e: The algorithms that we are testing. Everything is written in Rust.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImplementation\u003c/h3\u003e\u003ca id=\"user-content-implementation\" class=\"anchor\" aria-label=\"Permalink: Implementation\" href=\"#implementation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe algorithms of this project are implemented in Rust.\nWe split the implementation into multiple library crates to increase the reusability of our code.\nOn top of that, the \u003ccode\u003ecli\u003c/code\u003e crate provides all implemented functionality via a command line interface.\nRefer to \u003ca href=\"https://github.com/algbio/practical-omnitigs/tree/master/implementation\"\u003eits documentation\u003c/a\u003e for more information.\u003c/p\u003e\n\u003cp\u003eExcept for \u003ccode\u003ecli\u003c/code\u003e, all crates are published on \u003ccode\u003ecrates.io\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the terms of the BSD 2-Clause license.\nSee \u003ccode\u003eLICENSE.md\u003c/code\u003e for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Cite\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to Cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use this code in your research project, please cite it as \"Safe and Complete Genome Assembly in Practice, DOI: 10.5281/zenodo.4335367\"\u003c/p\u003e\n", + "full_name": "MI-911/cold-start-framework", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCold-start Framework\u003c/h1\u003e\u003ca id=\"user-content-cold-start-framework\" class=\"anchor\" aria-label=\"Permalink: Cold-start Framework\" href=\"#cold-start-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eData partitioning, model training and evaluation pipelines for the cold-start setting.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have fully dockerized an evaluation pipeline, from downloading the most recent dataset to conducting interviews.\nThe pipeline was developed using Docker version 19.03.5-ce.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom a clean slate, run the pipeline by running the script \u003ccode\u003escripts/run_pipeline.sh\u003c/code\u003e. The pipeline will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDownload the latest stable MindReader version and the related entities.\u003c/li\u003e\n\u003cli\u003ePartition the downloaded dataset into training (warm-start) and testing (cold-start).\u003c/li\u003e\n\u003cli\u003eRun all models on the partitioned dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe recommend running the entire pipeline initially.\nFollowing this, one can run the experiments alone by running \u003ccode\u003escripts/run_interview.sh\u003c/code\u003e.\nNote that if changes are made to the code, the base image should be rebuilt by running \u003ccode\u003escripts/build_base.sh\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 4, - "subscribers_count": 5, + "subscribers_count": 3, "topics": [ - "omnitigs", - "genome-assembly" + "pipeline", + "recommender-system", + "cold-start", + "evaluation-pipelines", + "dataset", + "interview", + "hacktoberfest" ], - "updated_at": 1724942370.0 + "updated_at": 1607268817.0 }, { "data_format": 2, - "description": "URIAL - Utilities for Representational sImilarity AnaLysis in python ", + "description": "my laboratory", "filenames": [ - "Singularity" + "singularity_recipes/python/Singularity.tmp", + "singularity_recipes/python/Singularity.template", + "singularity_recipes/vnc/Singularity", + "singularity_recipes/common/Singularity", + "singularity_recipes/deno/Singularity.tmp", + "singularity_recipes/deno/Singularity.template", + "singularity_recipes/cxx/Singularity", + "singularity_recipes/dotnet/Singularity", + "singularity_recipes/node/Singularity.tmp", + "singularity_recipes/node/Singularity.template", + "singularity_recipes/rust/Singularity", + "singularity_recipes/procon/Singularity" ], - "full_name": "PeerHerholz/URIAL", + "full_name": "ar90n/lab", "latest_release": null, "stargazers_count": 4, - "subscribers_count": 2, - "topics": [], - "updated_at": 1612234603.0 + "subscribers_count": 3, + "topics": [ + "my-projects" + ], + "updated_at": 1732699516.0 }, { "data_format": 2, - "description": "Package to call MHCnuggets from R, to predict MHC-I and MHC-II epitopes", + "description": "A small example repository that contains examples for controlling the real Panda robot.", "filenames": [ - "Singularity" + "real_panda_control_examples/containers/singularity/Singularity.panda_ros_melodic", + "real_panda_control_examples/containers/singularity/Singularity.panda_ros_kinetic", + "real_panda_control_examples/containers/singularity/Singularity.ros_melodic", + "real_panda_control_examples/containers/singularity/Singularity.panda_ros_noetic" ], - "full_name": "richelbilderbeek/mhcnuggetsr", - "latest_release": "v1.2.1", - "readme": "\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emhcnuggetsr\u003c/h1\u003e\u003ca id=\"user-content-mhcnuggetsr\" class=\"anchor\" aria-label=\"Permalink: mhcnuggetsr\" href=\"#mhcnuggetsr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://www.tidyverse.org/lifecycle/#stable\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/371f90418f7c4a16d1d31c389716305fd8172cf18843b1a0ebf4c5620a325971/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6966656379636c652d737461626c652d677265656e2e737667\" alt=\"Lifecycle: stable\" data-canonical-src=\"https://img.shields.io/badge/lifecycle-stable-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mhcnuggetsr\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a450839eaaf0ec96ce928e34b07e264c7ab47d11721a2605f59c749b6c697161/68747470733a2f2f7777772e722d706b672e6f72672f6261646765732f76657273696f6e2f6d68636e75676765747372\" alt=\"CRAN status\" data-canonical-src=\"https://www.r-pkg.org/badges/version/mhcnuggetsr\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mhcnuggetsr\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1f1c2992100c3e0a81b9dea97415508cf8f8b0014f7e776e1b09cb460239ec70/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6772616e642d746f74616c2f6d68636e75676765747372\" alt=\"\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/grand-total/mhcnuggetsr\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=mhcnuggetsr\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0392eede77d25d2f14beffaca1b9475c822e8bfcfa862148a52f13440dca518f/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6d68636e75676765747372\" alt=\"\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/mhcnuggetsr\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/mhcnuggetsr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/mhcnuggetsr/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/mhcnuggetsr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/mhcnuggetsr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ebbde312506314ee3ce181bf46c1551f5312aa55c1831b4b94f59fc2dc94c107/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6d68636e756767657473722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/mhcnuggetsr/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/mhcnuggetsr/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/mhcnuggetsr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/mhcnuggetsr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c67ca4392bfa868b7d59e619fcff5d4531ad8083f7f49d98771cf96523fcd8b6/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6d68636e756767657473722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/mhcnuggetsr/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cblockquote\u003e\n\u003cp\u003emhcnuggetsr is broken, see\n\u003ca href=\"https://github.com/richelbilderbeek/mhcnuggetsr/issues/13\"\u003ehere\u003c/a\u003e, as\nthe import of the \u003ccode\u003eMHCnuggets\u003c/code\u003e Python package by `reticulate``\nfails. If you know how to fix this, please contact me\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eR package to work with\n\u003ca href=\"https://github.com/KarchinLab/mhcnuggets\"\u003eMHCnuggets\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe goal of \u003ccode\u003emhcnuggetsr\u003c/code\u003e is to predict the half maximal inhibitory\nconcentration of peptides for an MHC haplotype. It does by calling\n\u003ca href=\"https://github.com/KarchinLab/mhcnuggets\"\u003eMHCnuggets\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install the released version of mhcnuggetsr from\n\u003ca href=\"https://github.com/\"\u003eGitHub\u003c/a\u003e with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eremotes::install_github(\"richelbilderbeek/mhcnuggetsr\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstall MHCnuggets using the non-CRAN extension\n\u003ca href=\"https://github.com/richelbilderbeek/mhcnuggetsrinstall\"\u003emhcnuggetsrinstall\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# {r install}\nlibrary(mhcnuggetsr)\n\nif (!is_mhcnuggets_installed()) {\n remotes::install_github(\"richelbilderbeek/mhcnuggetsrinstall\")\n mhcnuggetsrinstall::install_mhcnuggets()\n mhcnuggetsr_self_test()\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample\u003c/h2\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is how to get the IC50 values (in nM) for the peptides in an\nexample file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# {r example}\nlibrary(testthat)\nlibrary(mhcnuggetsr)\n\nif (is_mhcnuggets_installed()) {\n mhcnuggets_options \u0026lt;- create_mhcnuggets_options(\n mhc = \"HLA-A02:01\"\n )\n \n df \u0026lt;- predict_ic50(\n peptides = \"AIAACAMLLV\",\n mhcnuggets_options = mhcnuggets_options\n )\n expect_equal(df$ic50, 5578.77)\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow can I suppress the output when making a prediction?\u003c/h2\u003e\u003ca id=\"user-content-how-can-i-suppress-the-output-when-making-a-prediction\" class=\"anchor\" aria-label=\"Permalink: How can I suppress the output when making a prediction?\" href=\"#how-can-i-suppress-the-output-when-making-a-prediction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne cannot until MHCnuggets allows to do so. Issue is posted\n\u003ca href=\"https://github.com/KarchinLab/mhcnuggets/issues/17\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere is a feature I miss\u003c/h2\u003e\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" aria-label=\"Permalink: There is a feature I miss\" href=\"#there-is-a-feature-i-miss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI want to collaborate\u003c/h2\u003e\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" aria-label=\"Permalink: I want to collaborate\" href=\"#i-want-to-collaborate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u2018Submitting code\u2019\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI think I have found a bug\u003c/h2\u003e\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" aria-label=\"Permalink: I think I have found a bug\" href=\"#i-think-i-have-found-a-bug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u2018Submitting bugs\u2019\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere\u2019s something else I want to say\u003c/h2\u003e\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" aria-label=\"Permalink: There\u2019s something else I want to say\" href=\"#theres-something-else-i-want-to-say\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExternal links\u003c/h2\u003e\u003ca id=\"user-content-external-links\" class=\"anchor\" aria-label=\"Permalink: External links\" href=\"#external-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/KarchinLab/mhcnuggets\"\u003eMHCnuggets GitHub repo\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eArticle about MHCnuggets:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eShao, Xiaoshan M., et al.\u00a0\u201cHigh-throughput prediction of MHC class I\nand II neoantigens with MHCnuggets.\u201d Cancer Immunology Research 8.3\n(2020): 396-408.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "rickstaa/real-panda-control-examples", + "latest_release": "v1.0.3", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReal panda control examples\u003c/h1\u003e\u003ca id=\"user-content-real-panda-control-examples\" class=\"anchor\" aria-label=\"Permalink: Real panda control examples\" href=\"#real-panda-control-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains several examples for controlling the real panda robot. It was created as a supplement to the official \u003ca href=\"https://frankaemika.github.io/docs/installation_linux.html\" rel=\"nofollow\"\u003epanda documentation\u003c/a\u003e. Further it serves as a storage place for several problems I encountered while working with the panda robot (see the \u003ca href=\"https://github.com/rickstaa/real-panda-control-examples/discussions\"\u003ediscussions section\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClone instructions\u003c/h2\u003e\u003ca id=\"user-content-clone-instructions\" class=\"anchor\" aria-label=\"Permalink: Clone instructions\" href=\"#clone-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo clone the repository use the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir real_catkin_ws\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e real_catkin_ws\ngit clone --recurse-submodules https://github.com/rickstaa/real_panda_control_examples.git src\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild instructions\u003c/h2\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-label=\"Permalink: Build instructions\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the ROS package dependencies using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erosdep install --from-paths src --ignore-src --rosdistro melodic -y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe catkin package can be build by executing one of the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecatkin build -j4 -DCMAKE_BUILD_TYPE=Release -DFranka_DIR:PATH=\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/libfranka/build\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFranka ros examples\u003c/h2\u003e\u003ca id=\"user-content-franka-ros-examples\" class=\"anchor\" aria-label=\"Permalink: Franka ros examples\" href=\"#franka-ros-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see \u003ca href=\"https://github.com/rickstaa/real-panda-control-examples/discussions/4\"\u003ethis discussion post\u003c/a\u003e that explains how to run the example launch files provided by Emika Franka.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMoveit example launch instructions\u003c/h2\u003e\u003ca id=\"user-content-moveit-example-launch-instructions\" class=\"anchor\" aria-label=\"Permalink: Moveit example launch instructions\" href=\"#moveit-example-launch-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo test out Moveit control, after you build and sourced the catkin workspace, you can launch the example included in the \u003ccode\u003epanda_moveit_config\u003c/code\u003e using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch panda_moveit_config panda_control_moveit_rviz.launch load_gripper:=true robot_ip:=172.16.0.2\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditionally the \u003ccode\u003ereal_panda_control_examples\u003c/code\u003e contains a slightly modified version of this example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch real_panda_control_examples real_panda_moveit_control.launch\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrajectory control example launch instructions\u003c/h2\u003e\u003ca id=\"user-content-trajectory-control-example-launch-instructions\" class=\"anchor\" aria-label=\"Permalink: Trajectory control example launch instructions\" href=\"#trajectory-control-example-launch-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo test out Trajectory control, after you build and sourced the catkin workspace, you can launch the example included in the \u003ccode\u003epanda_moveit_config\u003c/code\u003e using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch panda_moveit_config panda_control_moveit_rviz.launch load_gripper:=true robot_ip:=172.16.0.2\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditionally the \u003ccode\u003ereal_panda_control_examples\u003c/code\u003e contains a slightly modified version of this example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eroslaunch real_panda_control_examples real_panda_traj_control.launch\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFeel free to open an issue if you have ideas on how to make this GitHub action better or if you want to report a bug! All contributions are welcome. \ud83d\ude80 Please consult the \u003ca href=\"CONTRIBUTING.md\"\u003econtribution guidelines\u003c/a\u003e for more information.\u003c/p\u003e\n", "stargazers_count": 4, "subscribers_count": 3, - "topics": [], - "updated_at": 1722265331.0 + "topics": [ + "franka-emika", + "control", + "moveit" + ], + "updated_at": 1694770446.0 }, { "data_format": 2, - "description": "Tools for XML submission", + "description": "GPU-optimized NMF and variations", "filenames": [ - "Singularity" - ], - "full_name": "ddbj/submission-excel2xml", - "latest_release": "v3.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExcel and container images for DRA/JGA/AGD metadata XML submissions\u003c/h1\u003e\u003ca id=\"user-content-excel-and-container-images-for-drajgaagd-metadata-xml-submissions\" class=\"anchor\" aria-label=\"Permalink: Excel and container images for DRA/JGA/AGD metadata XML submissions\" href=\"#excel-and-container-images-for-drajgaagd-metadata-xml-submissions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u65e5\u672c\u8a9e\u003c/h2\u003e\u003ca id=\"user-content-\u65e5\u672c\u8a9e\" class=\"anchor\" aria-label=\"Permalink: \u65e5\u672c\u8a9e\" href=\"#\u65e5\u672c\u8a9e\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u751f\u547d\u60c5\u5831\u30fbDDBJ \u30bb\u30f3\u30bf\u30fc\u003c/li\u003e\n\u003cli\u003e\u516c\u958b\u65e5: 2024-10-23\u003c/li\u003e\n\u003cli\u003eversion: v3.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://www.ddbj.nig.ac.jp/index-e.html\" rel=\"nofollow\"\u003eBioinformation and DDBJ Center\u003c/a\u003e \u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u767b\u9332\u3059\u308b\u305f\u3081\u306e\u30e1\u30bf\u30c7\u30fc\u30bf XML \u3092\u751f\u6210\u3001\u30c1\u30a7\u30c3\u30af\u3059\u308b\u30c4\u30fc\u30eb\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html\" rel=\"nofollow\"\u003eDDBJ Sequence Read Archive (DRA)\u003c/a\u003e: Submission\u3001Experiment\u3001Run \u3068 Analysis (\u4efb\u610f) XML \u3092\u751f\u6210\u30fb\u30c1\u30a7\u30c3\u30af\u3059\u308b\u305f\u3081\u306e\u30a8\u30af\u30bb\u30eb\u3068\u30b9\u30af\u30ea\u30d7\u30c8\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/jga/submission.html\" rel=\"nofollow\"\u003eJapanese Genotype-phenotype Archive (JGA)\u003c/a\u003e: Submission\u3001Study\u3001Sample\u3001Experiment\u3001Data\u3001Analysis \u3068 Dataset XML \u3092\u751f\u6210\u30fb\u30c1\u30a7\u30c3\u30af\u3059\u308b\u305f\u3081\u306e\u30a8\u30af\u30bb\u30eb\u3068\u30b9\u30af\u30ea\u30d7\u30c8\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/agd/submission.html\" rel=\"nofollow\"\u003eAMED Genome Group Sharing Database (AGD)\u003c/a\u003e: Submission\u3001Study\u3001Sample\u3001Experiment\u3001Data\u3001Analysis \u3068 Dataset XML \u3092\u751f\u6210\u30fb\u30c1\u30a7\u30c3\u30af\u3059\u308b\u305f\u3081\u306e\u30a8\u30af\u30bb\u30eb\u3068\u30b9\u30af\u30ea\u30d7\u30c8\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u5c65\u6b74\u003c/h2\u003e\u003ca id=\"user-content-\u5c65\u6b74\" class=\"anchor\" aria-label=\"Permalink: \u5c65\u6b74\" href=\"#\u5c65\u6b74\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2024-10-23: v3.1 Library Source SINGLE CELL\u003c/li\u003e\n\u003cli\u003e2024-07-05: v3.0 \u30b7\u30fc\u30c8\u540d\u306b DB prefix \u3092\u4ed8\u52a0\u003c/li\u003e\n\u003cli\u003e2024-07-04: v2.9.1 Sample \u5c5e\u6027\u3092\u5168\u3066 ATTRIBUTE \u306b\u683c\u7d0d\u003c/li\u003e\n\u003cli\u003e2024-06-14: v2.9 Sample \u5c5e\u6027\u8ffd\u52a0\u3001\u53ca\u3073\u3001\u516c\u958b\u6307\u5b9a\u306e\u524a\u9664\u003c/li\u003e\n\u003cli\u003e2024-05-28: v2.8.1 file bug fix\u003c/li\u003e\n\u003cli\u003e2024-05-23: v2.8 md5 \u6841\u6570\u30a8\u30e9\u30fc\u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2024-05-17: v2.7 JGA Sample \u8ffd\u52a0\u5c5e\u6027\u306e\u8a18\u8f09\u65b9\u6cd5\u3092\u5909\u66f4\u003c/li\u003e\n\u003cli\u003e2024-02-14: v2.6 DRA xsd 1.6.0\u003c/li\u003e\n\u003cli\u003e2024-01-31: v2.5 \u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u30fb\u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf\u306e\u4fee\u6b63\u003c/li\u003e\n\u003cli\u003e2023-12-21: v2.4 center name \u5909\u66f4\u003c/li\u003e\n\u003cli\u003e2023-12-05: v2.3 Analysis step \u3068 Attributes \u8907\u6570\u5024\u306e\u533a\u5207\u308a\u3092 , \u304b\u3089 ; \u306b\u5909\u66f4\u003c/li\u003e\n\u003cli\u003e2023-12-01: v2.2 gem \u5316\u003c/li\u003e\n\u003cli\u003e2023-11-21: v2.1 Analysis \u306b Run \u30ab\u30e9\u30e0\u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2023-09-04: v2.0 Analysis \u5bfe\u5fdc\u003c/li\u003e\n\u003cli\u003e2023-02-09: v1.9.2 Run title\u003c/li\u003e\n\u003cli\u003e2023-01-17: v1.9.1 PAIRED \u3067 NOMINAL_LENGTH \u3092\u4efb\u610f\u5316\u003c/li\u003e\n\u003cli\u003e2022-12-23: v1.9 JGA \u30e1\u30bf\u30c7\u30fc\u30bf\u30a8\u30af\u30bb\u30eb\u306b AGD \u3092\u7d71\u5408\u003c/li\u003e\n\u003cli\u003e2022-12-22: v1.8 AGD \u5bfe\u5fdc\u003c/li\u003e\n\u003cli\u003e2022-12-21: v1.7 JGA Dataset reference \u91cd\u8907\u30c1\u30a7\u30c3\u30af\u3092\u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2022-12-15: v1.6 JGA \u3092\u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2022-12-14: v1.5 DRA \u3092\u660e\u78ba\u5316\u003c/li\u003e\n\u003cli\u003e2022-12-13: v1.4 \u30ea\u30fc\u30c9\u9577\u3068\u30da\u30a2\u30ea\u30fc\u30c9\u306e\u5411\u304d\u306e\u8a18\u5165\u306e\u4e0d\u8981\u5316\u306b\u5bfe\u5fdc\u003c/li\u003e\n\u003cli\u003e2021-12-13: v1.3 BGISEQ \u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2021-07-13: v1.2 \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/dra#changes-to-common-xml-159-on-7-july-2021\"\u003exsd 1.5.9\u003c/a\u003e \u306b\u5bfe\u5fdc\u3002xsd \u3092 \u003ca href=\"https://github.com/ddbj/pub\"\u003epub\u003c/a\u003e \u304b\u3089\u53d6\u5f97\u3059\u308b\u3088\u3046\u306b\u5909\u66f4\u3002\u003c/li\u003e\n\u003cli\u003e2020-04-24: v1.1 \u521d\u7248\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/h2\u003e\u003ca id=\"user-content-\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" class=\"anchor\" aria-label=\"Permalink: \u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" href=\"#\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esubmission-excel2xml \u30ec\u30dd\u30b8\u30c8\u30ea\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/ddbj/submission-excel2xml.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u30a4\u30e1\u30fc\u30b8\u69cb\u7bc9\u003c/h2\u003e\u003ca id=\"user-content-\u30a4\u30e1\u30fc\u30b8\u69cb\u7bc9\" class=\"anchor\" aria-label=\"Permalink: \u30a4\u30e1\u30fc\u30b8\u69cb\u7bc9\" href=\"#\u30a4\u30e1\u30fc\u30b8\u69cb\u7bc9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity \u30a4\u30e1\u30fc\u30b8\u3092\u003ca href=\"https://ddbj.nig.ac.jp/public/software/submission-excel2xml/\" rel=\"nofollow\"\u003e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/a\u003e\u3001\u3082\u3057\u304f\u306f\u3001\u4ee5\u4e0b\u306e\u624b\u9806\u3067\u30ed\u30fc\u30ab\u30eb\u74b0\u5883\u3067\u69cb\u7bc9\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nsudo singularity build excel2xml.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker \u30a4\u30e1\u30fc\u30b8\u3092\u4ee5\u4e0b\u306e\u624b\u9806\u3067\u30ed\u30fc\u30ab\u30eb\u74b0\u5883\u3067\u69cb\u7bc9\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nsudo docker build -t excel2xml .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDRA\u003c/h2\u003e\u003ca id=\"user-content-dra\" class=\"anchor\" aria-label=\"Permalink: DRA\" href=\"#dra\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\u003c/h3\u003e\u003ca id=\"user-content-\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\" class=\"anchor\" aria-label=\"Permalink: \u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\" href=\"#\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30e1\u30bf\u30c7\u30fc\u30bf\u3068\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u3092\u30a8\u30af\u30bb\u30eb metadata_dra.xlsx \u306e \u0027Submission\u0027\u3001\u0027Experiment\u0027\u3001\u0027Run\u0027 \u3068 \u0027Run-file\u0027 \u30b7\u30fc\u30c8\u306b\u8a18\u5165\u3057\u307e\u3059\u3002\u30e1\u30bf\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u306f\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html#metadata\" rel=\"nofollow\"\u003e\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u003c/a\u003e\u3068 \u0027Readme\u0027 \u30b7\u30fc\u30c8\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002\n\u0027example/example-0001_dra_metadata.xlsx\u0027 \u304c\u8a18\u5165\u4f8b\u306b\u306a\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003eAnalysis (\u4efb\u610f) \u3092\u767b\u9332\u3059\u308b\u5834\u5408\u306f \u0027Analysis\u0027 \u30b7\u30fc\u30c8\u306b\u8a18\u5165\u3057\u307e\u3059\u3002Analysis \u306e\u307f\u3092\u65b0\u898f\u767b\u9332\u3059\u308b\u3053\u3068\u306f\u3067\u304d\u305a\u3001Run \u3092\u6301\u3063\u305f Submission \u306b\u767b\u9332\u3057\u307e\u3059\u3002\n\u0027example/example-0002_dra_metadata.xlsx\u0027 \u304c\u8a18\u5165\u4f8b\u306b\u306a\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Singularity\u003c/h3\u003e\u003ca id=\"user-content-xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-singularity\" class=\"anchor\" aria-label=\"Permalink: XML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Singularity\" href=\"#xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Experiment \u3068 Run XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\nD-way \u30a2\u30ab\u30a6\u30f3\u30c8 ID\u3001submission \u756a\u53f7\u3068 BioProject \u30a2\u30af\u30bb\u30c3\u30b7\u30e7\u30f3\u756a\u53f7\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0001\u0027: -a example -i 0001\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089\u4e09\u3064\u306e XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Submission.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Experiment.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Run.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnalysis \u30b7\u30fc\u30c8\u304c\u8a18\u5165\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u306f Analysis XML \u3082\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSubmission ID \u3092\u6307\u5b9a\u3057\u3066 XML \u3092\u30c1\u30a7\u30c3\u30af\u3057\u307e\u3059\u3002XML \u306f submission-excel2xml \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u76f4\u4e0b\u306b\u914d\u7f6e\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002SRA xsd \u30d5\u30a1\u30a4\u30eb\u306f build \u4e2d\u306b\u30b3\u30f3\u30c6\u30ca\u30fc\u5185\u306e /opt/submission-excel2xml/ \u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u306f xsd \u306b\u5bfe\u3059\u308b\u30c1\u30a7\u30c3\u30af\u3068\u6700\u4f4e\u9650\u306e\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u307e\u3059\u3002\nDRA \u306e\u767b\u9332\u30b5\u30a4\u30c8\u3067\u306f\u3088\u308a\u8a73\u7d30\u306a\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u308b\u305f\u3081\u3001\u30d1\u30b9\u3057\u305f XML \u304c\u767b\u9332\u904e\u7a0b\u3067\u30a8\u30e9\u30fc\u306b\u306a\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAnalysis XML \u306e\u307f\u3092\u751f\u6210\u003c/h4\u003e\u003ca id=\"user-content-analysis-xml-\u306e\u307f\u3092\u751f\u6210\" class=\"anchor\" aria-label=\"Permalink: Analysis XML \u306e\u307f\u3092\u751f\u6210\" href=\"#analysis-xml-\u306e\u307f\u3092\u751f\u6210\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u65e2\u5b58 Submission \u306b Analysis \u3092\u8ffd\u52a0\u3059\u308b\u5834\u5408\u3001\u0027Analysis\u0027 \u30b7\u30fc\u30c8\u306e\u307f\u3092\u8a18\u5165\u3057\u3001Analysis XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002XML \u751f\u6210\u6642\u306b -c \u3067 center name \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0002\u0027: -a example -i 0002\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003eCenter name: National Institute of Genetics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0002 -p PRJDB7252 -c \"National Institute of Genetics\" example/example-0002_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Analysis XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0002_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Docker\u003c/h3\u003e\u003ca id=\"user-content-xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-docker\" class=\"anchor\" aria-label=\"Permalink: XML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Docker\" href=\"#xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Experiment \u3068 Run XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\nD-way \u30a2\u30ab\u30a6\u30f3\u30c8 ID\u3001submission \u756a\u53f7\u3001BioProject \u30a2\u30af\u30bb\u30c3\u30b7\u30e7\u30f3\u756a\u53f7\u3068\u30a8\u30af\u30bb\u30eb\u3092\u542b\u3080\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0001\u0027: -a example -i 0001\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003e\u0027path_to_excel_directory\u0027: \u30a8\u30af\u30bb\u30eb\u3092\u542b\u3080\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089\u4e09\u3064\u306e XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Submission.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Experiment.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Run.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnalysis \u30b7\u30fc\u30c8\u304c\u8a18\u5165\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u306f Analysis XML \u3082\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSubmission ID \u3092\u6307\u5b9a\u3057\u3066 XML \u3092\u30c1\u30a7\u30c3\u30af\u3057\u307e\u3059\u3002XML \u306f submission-excel2xml \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u76f4\u4e0b\u306b\u914d\u7f6e\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002SRA xsd \u30d5\u30a1\u30a4\u30eb\u306f build \u4e2d\u306b\u30b3\u30f3\u30c6\u30ca\u30fc\u5185\u306e /opt/submission-excel2xml/ \u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u306f xsd \u306b\u5bfe\u3059\u308b\u30c1\u30a7\u30c3\u30af\u3068\u6700\u4f4e\u9650\u306e\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u307e\u3059\u3002\nDRA \u306e\u767b\u9332\u30b5\u30a4\u30c8\u3067\u306f\u3088\u308a\u8a73\u7d30\u306a\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u308b\u305f\u3081\u3001\u30d1\u30b9\u3057\u305f XML \u304c\u767b\u9332\u904e\u7a0b\u3067\u30a8\u30e9\u30fc\u306b\u306a\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAnalysis XML \u306e\u307f\u3092\u751f\u6210\u003c/h4\u003e\u003ca id=\"user-content-analysis-xml-\u306e\u307f\u3092\u751f\u6210-1\" class=\"anchor\" aria-label=\"Permalink: Analysis XML \u306e\u307f\u3092\u751f\u6210\" href=\"#analysis-xml-\u306e\u307f\u3092\u751f\u6210-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u65e2\u5b58 Submission \u306b Analysis \u3092\u8ffd\u52a0\u3059\u308b\u5834\u5408\u3001\u0027Analysis\u0027 \u30b7\u30fc\u30c8\u306e\u307f\u3092\u8a18\u5165\u3057\u3001Analysis XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002XML \u751f\u6210\u6642\u306b -c \u3067 center name \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0002\u0027: -a example -i 0002\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003eCenter name: National Institute of Genetics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml excel2xml_dra -a example -i 0002 -p PRJDB7252 -c \"National Institute of Genetics\" example/example-0002_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Analysis XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0002_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u30c1\u30a7\u30c3\u30af\u003c/h3\u003e\u003ca id=\"user-content-\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: \u30c1\u30a7\u30c3\u30af\" href=\"#\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSRA xsd \u306b\u5bfe\u3059\u308b XML \u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-sra-xsd-\u306b\u5bfe\u3059\u308b-xml-\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: SRA xsd \u306b\u5bfe\u3059\u308b XML \u30c1\u30a7\u30c3\u30af\" href=\"#sra-xsd-\u306b\u5bfe\u3059\u308b-xml-\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u30e1\u30bf\u30c7\u30fc\u30bf XML \u306f \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/dra/xsd/1-5\"\u003eSRA xsd\u003c/a\u003e \u306b\u5bfe\u3057\u3066\u30c1\u30a7\u30c3\u30af\u3055\u308c\u307e\u3059\u3002\u30e1\u30c3\u30bb\u30fc\u30b8\u306b\u5f93\u3063\u3066 XML \u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eXML \u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-xml-\u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: XML \u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\" href=\"#xml-\u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSubmission\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eError: Submission: \u516c\u958b\u4e88\u5b9a\u65e5\u304c\u904e\u53bb\u306e\u65e5\u4ed8\n\u5c06\u6765\u306e\u65e5\u4ed8\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment \u3068 Run\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eError: Run: #{run_alias} Paired library only has one file.\n\u30da\u30a2\u30e9\u30a4\u30d6\u30e9\u30ea Experiment \u3067\u306f\u5c11\u306a\u304f\u3068\u3082\u4e8c\u3064\u306e\u914d\u5217\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb (\u4f8b\u3001R1.fastq \u3068 R2.fastq) \u304c\u542b\u307e\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\" href=\"#\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eError: Run to Experiment reference error.\n\u5168\u3066\u306e Experiment \u304c Run \u304b\u3089\u53c2\u7167\u3055\u308c\u3066\u3044\u306a\u3044\u3002\nExperiment \u3092\u53c2\u7167\u3057\u3066\u3044\u306a\u3044 Run \u304c\u5b58\u5728\u3059\u308b\u3002\nRun \u304b\u3089\u53c2\u7167\u3055\u308c\u3066\u3044\u306a\u3044 Experiment \u304c\u5b58\u5728\u3059\u308b\u3002\n\u3053\u306e\u3088\u3046\u306a\u5834\u5408\u3001\u5168\u3066\u306e Run \u304c\u5168\u3066\u306e Experiment \u3092\u53c2\u7167\u3059\u308b\u3088\u3046\u306b\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u30e1\u30bf\u30c7\u30fc\u30bf\u30e2\u30c7\u30eb\u306f \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html#metadata-objects\" rel=\"nofollow\"\u003eDRA Handbook\u003c/a\u003e \u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDRA \u30a6\u30a7\u30d6\u753b\u9762\u304b\u3089 XML \u3092\u767b\u9332\u3059\u308b\u003c/h3\u003e\u003ca id=\"user-content-dra-\u30a6\u30a7\u30d6\u753b\u9762\u304b\u3089-xml-\u3092\u767b\u9332\u3059\u308b\" class=\"anchor\" aria-label=\"Permalink: DRA \u30a6\u30a7\u30d6\u753b\u9762\u304b\u3089 XML \u3092\u767b\u9332\u3059\u308b\" href=\"#dra-\u30a6\u30a7\u30d6\u753b\u9762\u304b\u3089-xml-\u3092\u767b\u9332\u3059\u308b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30e1\u30bf\u30c7\u30fc\u30bf XML \u3092\u767b\u9332\u3059\u308b\u524d\u306b\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html#upload-sequence-data\" rel=\"nofollow\"\u003e\u767b\u9332\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u914d\u5217\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u3092\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3057\u307e\u3059\u003c/a\u003e\u3002D-way \u306b\u30ed\u30b0\u30a4\u30f3\u5f8c\u3001\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html#create-metadata-in-xml-files\" rel=\"nofollow\"\u003eSubmission\u3001Experiment \u3068 Run XML \u3092 DRA \u767b\u9332\u30da\u30fc\u30b8\u3067\u3067\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u003c/a\u003e \u3057\u307e\u3059\u3002\u901a\u5e385\u5206\u4ee5\u5185\u306b\u767b\u9332\u304c\u5b8c\u4e86\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGithub \u3084 XML \u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\u003c/h3\u003e\u003ca id=\"user-content-github-\u3084-xml-\u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\" class=\"anchor\" aria-label=\"Permalink: Github \u3084 XML \u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\" href=\"#github-\u3084-xml-\u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ddbj/submission-excel2xml/blob/main/metadata_dra.xlsx\"\u003eDRA \u30e1\u30bf\u30c7\u30fc\u30bf\u30a8\u30af\u30bb\u30eb\u003c/a\u003e \u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3001\u5185\u5bb9\u3092\u82f1\u8a9e\u3067\u8a18\u5165\u3057\u3001\u30e1\u30fc\u30eb (\u003ca href=\"mailto:trace@ddbj.nig.ac.jp\"\u003etrace@ddbj.nig.ac.jp\u003c/a\u003e) \u6dfb\u4ed8\u3067 DRA \u30c1\u30fc\u30e0\u306b\u304a\u9001\u308a\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eJGA\u003c/h2\u003e\u003ca id=\"user-content-jga\" class=\"anchor\" aria-label=\"Permalink: JGA\" href=\"#jga\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\u003c/h3\u003e\u003ca id=\"user-content-\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165-1\" class=\"anchor\" aria-label=\"Permalink: \u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\" href=\"#\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30e1\u30bf\u30c7\u30fc\u30bf\u3068\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u3092\u30a8\u30af\u30bb\u30eb JGA_metadata.xlsx \u306e \u0027Submission\u0027\u3001\u0027Study\u0027\u3001\u0027Sample\u0027\u3001\u0027Experiment\u0027\u3001\u0027Data\u0027\u3001\u0027Analysis\u0027 (\u8a72\u5f53\u3059\u308b\u5834\u5408)\u3001\u0027Dataset\u0027 \u3068 \u0027File\u0027 \u30b7\u30fc\u30c8\u306b\u8a18\u5165\u3057\u307e\u3059\u3002\n\u30e1\u30bf\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u306f\u003ca href=\"https://www.ddbj.nig.ac.jp/jga/submission.html\" rel=\"nofollow\"\u003e\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u003c/a\u003e\u3068 \u0027Readme\u0027 \u30b7\u30fc\u30c8\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002\n\u0027example/JSUB999999_jga_metadata.xlsx\u0027 \u304c\u8a18\u5165\u4f8b\u306b\u306a\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Singularity\u003c/h3\u003e\u003ca id=\"user-content-xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-singularity-1\" class=\"anchor\" aria-label=\"Permalink: XML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Singularity\" href=\"#xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-singularity-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Study\u3001Sample\u3001Experiment\u3001Data\u3001Analysis (\u8a72\u5f53\u3059\u308b\u5834\u5408)\u3001Dataset XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\nJGA submission id \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJGA Submission ID \u0027JSUB999999\u0027: -j JSUB999999\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_jga -j JSUB999999 example/JSUB999999_jga_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089\u4e03\u3064\u306e XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJSUB999999_Analysis.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Data.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Dataset.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Experiment.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Sample.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Study.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Submission.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSample \u8ffd\u52a0\u5c5e\u6027\u306e\u8a18\u8f09\u65b9\u6cd5\u304c v2.6 \u4ee5\u524d\u306e\u5f62\u5f0f (\u4f8b age:37; collection_date:2015-03-05) \u306e\u5834\u5408\u3001-r \u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4ed8\u3051\u3066 XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\u3000\u3000\u003c/p\u003e\n\u003cp\u003eJGA Submission ID \u3092\u6307\u5b9a\u3057\u3066 XML \u3092\u30c1\u30a7\u30c3\u30af\u3057\u307e\u3059\u3002XML \u306f submission-excel2xml \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u76f4\u4e0b\u306b\u914d\u7f6e\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002JGA xsd \u30d5\u30a1\u30a4\u30eb\u306f build \u4e2d\u306b\u30b3\u30f3\u30c6\u30ca\u30fc\u5185\u306e /opt/submission-excel2xml/ \u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_jga -j JSUB999999\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u306f xsd \u306b\u5bfe\u3059\u308b\u30c1\u30a7\u30c3\u30af\u3068\u6700\u4f4e\u9650\u306e\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Docker\u003c/h3\u003e\u003ca id=\"user-content-xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-docker-1\" class=\"anchor\" aria-label=\"Permalink: XML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Docker\" href=\"#xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Study\u3001Sample\u3001Experiment\u3001Data\u3001Analysis (\u8a72\u5f53\u3059\u308b\u5834\u5408)\u3001Dataset XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\nJGA submission id \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJGA Submission ID \u0027JSUB999999\u0027: -j JSUB999999\u003c/li\u003e\n\u003cli\u003e\u0027path_to_excel_directory\u0027: \u30a8\u30af\u30bb\u30eb\u3092\u542b\u3080\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml excel2xml_jga -j JSUB999999 example/JSUB999999_jga_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089\u4e03\u3064\u306e XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJSUB999999_Analysis.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Data.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Dataset.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Experiment.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Sample.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Study.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Submission.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSample \u8ffd\u52a0\u5c5e\u6027\u306e\u8a18\u8f09\u65b9\u6cd5\u304c v2.6 \u4ee5\u524d\u306e\u5f62\u5f0f (\u4f8b age:37; collection_date:2015-03-05) \u306e\u5834\u5408\u3001-r \u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4ed8\u3051\u3066 XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\u3000\u3000\u003c/p\u003e\n\u003cp\u003eSubmission ID \u3092\u6307\u5b9a\u3057\u3066 XML \u3092\u30c1\u30a7\u30c3\u30af\u3057\u307e\u3059\u3002XML \u306f submission-excel2xml \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u76f4\u4e0b\u306b\u914d\u7f6e\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002JGA xsd \u30d5\u30a1\u30a4\u30eb\u306f build \u4e2d\u306b\u30b3\u30f3\u30c6\u30ca\u30fc\u5185\u306e /opt/submission-excel2xml/ \u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml validate_meta_jga -j JSUB999999\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u306f xsd \u306b\u5bfe\u3059\u308b\u30c1\u30a7\u30c3\u30af\u3068\u6700\u4f4e\u9650\u306e\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u30c1\u30a7\u30c3\u30af\u003c/h3\u003e\u003ca id=\"user-content-\u30c1\u30a7\u30c3\u30af-1\" class=\"anchor\" aria-label=\"Permalink: \u30c1\u30a7\u30c3\u30af\" href=\"#\u30c1\u30a7\u30c3\u30af-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eJGA xsd \u306b\u5bfe\u3059\u308b XML \u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-jga-xsd-\u306b\u5bfe\u3059\u308b-xml-\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: JGA xsd \u306b\u5bfe\u3059\u308b XML \u30c1\u30a7\u30c3\u30af\" href=\"#jga-xsd-\u306b\u5bfe\u3059\u308b-xml-\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u30e1\u30bf\u30c7\u30fc\u30bf XML \u306f \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/jga/xsd/1-2\"\u003eJGA xsd\u003c/a\u003e \u306b\u5bfe\u3057\u3066\u30c1\u30a7\u30c3\u30af\u3055\u308c\u307e\u3059\u3002\u30e1\u30c3\u30bb\u30fc\u30b8\u306b\u5f93\u3063\u3066 XML \u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eXML \u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-xml-\u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af-1\" class=\"anchor\" aria-label=\"Permalink: XML \u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\" href=\"#xml-\u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af-1\" class=\"anchor\" aria-label=\"Permalink: \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\" href=\"#\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u9593\u306e\u95a2\u4fc2\u304c\u30c1\u30a7\u30c3\u30af\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eData -\u0026gt; Experiment\u003c/li\u003e\n\u003cli\u003eAnalysis -\u0026gt; Study\u003c/li\u003e\n\u003cli\u003eAnalysis -\u0026gt; Data\u003c/li\u003e\n\u003cli\u003eAnalysis -\u0026gt; Sample\u003c/li\u003e\n\u003cli\u003eExperiment -\u0026gt; Sample\u003c/li\u003e\n\u003cli\u003eAnalysis -\u0026gt; Sample\u003c/li\u003e\n\u003cli\u003eDataset -\u0026gt; Data\u003c/li\u003e\n\u003cli\u003eDataset -\u0026gt; Analysis\u003c/li\u003e\n\u003cli\u003eDataset -\u0026gt; Policy\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u3092\u767b\u9332\u3059\u308b\u003c/h3\u003e\u003ca id=\"user-content-xml-\u3092\u767b\u9332\u3059\u308b\" class=\"anchor\" aria-label=\"Permalink: XML \u3092\u767b\u9332\u3059\u308b\" href=\"#xml-\u3092\u767b\u9332\u3059\u308b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eXML \u3092 JGA \u30c7\u30fc\u30bf\u53d7\u4ed8\u30b5\u30fc\u30d0\u306b\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3059\u308b\u524d\u306b \u003ca href=\"https://humandbs.biosciencedbc.jp/en/data-submission\" rel=\"nofollow\"\u003eNBDC \u4e8b\u696d\u63a8\u9032\u90e8\u003c/a\u003e \u3067\u63d0\u4f9b\u7533\u8acb\u304c\u627f\u8a8d\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGithub \u3084 XML \u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\u003c/h3\u003e\u003ca id=\"user-content-github-\u3084-xml-\u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408-1\" class=\"anchor\" aria-label=\"Permalink: Github \u3084 XML \u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\" href=\"#github-\u3084-xml-\u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ddbj/submission-excel2xml/raw/main/JGA_metadata.xlsx\"\u003eJGA \u30e1\u30bf\u30c7\u30fc\u30bf\u30a8\u30af\u30bb\u30eb\u003c/a\u003e\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3001\u5185\u5bb9\u3092\u82f1\u8a9e\u3067\u8a18\u5165\u3057\u3001\u30e1\u30fc\u30eb (\u003ca href=\"mailto:jga@ddbj.nig.ac.jp\"\u003ejga@ddbj.nig.ac.jp\u003c/a\u003e) \u6dfb\u4ed8\u3067 JGA \u30c1\u30fc\u30e0\u306b\u304a\u9001\u308a\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAGD\u003c/h2\u003e\u003ca id=\"user-content-agd\" class=\"anchor\" aria-label=\"Permalink: AGD\" href=\"#agd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJGA \u3068\u540c\u69d8\u306e\u624b\u9806\u306b\u306a\u308a\u307e\u3059\u3002AGD \u306e\u30e1\u30bf\u30c7\u30fc\u30bf\u3082 JGA_metadata.xlsx \u306b\u8a18\u5165\u3057\u307e\u3059\u3002\nSubmission ID \u306b\u306f AGD Submission ID (\u4f8b ASUB000001) \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNIG \u30b9\u30d1\u30b3\u30f3\u3067\u306e\u5b9f\u65bd\u65b9\u6cd5\u003c/h2\u003e\u003ca id=\"user-content-nig-\u30b9\u30d1\u30b3\u30f3\u3067\u306e\u5b9f\u65bd\u65b9\u6cd5\" class=\"anchor\" aria-label=\"Permalink: NIG \u30b9\u30d1\u30b3\u30f3\u3067\u306e\u5b9f\u65bd\u65b9\u6cd5\" href=\"#nig-\u30b9\u30d1\u30b3\u30f3\u3067\u306e\u5b9f\u65bd\u65b9\u6cd5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u56fd\u7acb\u907a\u4f1d\u5b66\u7814\u7a76\u6240 \u751f\u547d\u60c5\u5831\u30fbDDBJ \u30bb\u30f3\u30bf\u30fc\u304c\u904b\u55b6\u3059\u308b \u003ca href=\"https://www.ddbj.nig.ac.jp/sc\" rel=\"nofollow\"\u003eNIG \u30b9\u30d1\u30b3\u30f3\u003c/a\u003e \u3067\u306f \u003ccode\u003e/lustre9/open/shared_data/software/submission-excel2xml/\u003c/code\u003e\n\u306b Singularity \u30a4\u30e1\u30fc\u30b8\u304c\u8a2d\u7f6e\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3084 build \u4f5c\u696d\u3092\u3059\u308b\u3053\u3068\u306a\u304f\u3001\u30e1\u30bf\u30c7\u30fc\u30bf\u30a8\u30af\u30bb\u30eb\u30d5\u30a1\u30a4\u30eb\u304c\u3042\u308c\u3070 XML \u751f\u6210\u3084 XML \u306e\u30c1\u30a7\u30c3\u30af\u3092\u5b9f\u65bd\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDRA\u003c/h3\u003e\u003ca id=\"user-content-dra-1\" class=\"anchor\" aria-label=\"Permalink: DRA\" href=\"#dra-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u591a\u4ef6\u6570\u306e\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u304c\u30b9\u30d1\u30b3\u30f3\u306b\u3042\u308b\u5834\u5408\u3001\u30e1\u30bf\u30c7\u30fc\u30bf XML \u4f5c\u6210\u3001\u53ca\u3073\u3001\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u306e DRA \u30d5\u30a1\u30a4\u30eb\u53d7\u4ed8\u30b5\u30fc\u30d0 (ftp-private.ddbj.nig.ac.jp) \u3078\u306e\u8ee2\u9001\u3092\u30b9\u30d1\u30b3\u30f3\u4e0a\u3067\u5b8c\u7d50\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Experiment \u3068 Run XML \u3092\u751f\u6210\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp /lustre9/open/shared_data/software/submission-excel2xml/excel2xml.simg ~/\ncd\nsingularity exec excel2xml.simg excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eXML \u306e\u30c1\u30a7\u30c3\u30af\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJGA\u003c/h3\u003e\u003ca id=\"user-content-jga-1\" class=\"anchor\" aria-label=\"Permalink: JGA\" href=\"#jga-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAGD\u003c/h3\u003e\u003ca id=\"user-content-agd-1\" class=\"anchor\" aria-label=\"Permalink: AGD\" href=\"#agd-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnglish\u003c/h2\u003e\u003ca id=\"user-content-english\" class=\"anchor\" aria-label=\"Permalink: English\" href=\"#english\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBioinformation and DDBJ Center\u003c/li\u003e\n\u003cli\u003erelease: 2024-10-23\u003c/li\u003e\n\u003cli\u003eversion: v3.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese files are Excel, container images and tools for generation and validation of metadata XML files for databases of \u003ca href=\"https://www.ddbj.nig.ac.jp/index-e.html\" rel=\"nofollow\"\u003eBioinformation and DDBJ Center\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html\" rel=\"nofollow\"\u003eDDBJ Sequence Read Archive (DRA)\u003c/a\u003e: generate and check Submission, Experiment and Run XML files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/jga/submission-e.html\" rel=\"nofollow\"\u003eJapanese Genotype-phenotype Archive (JGA)\u003c/a\u003e: generate and check Submission, Study, Sample, Experiment, Data, Analysis and Dataset XML files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/agd/submission-e.html\" rel=\"nofollow\"\u003eAMED Genome Group Sharing Database (AGD)\u003c/a\u003e: generate and check Submission, Study, Sample, Experiment, Data, Analysis and Dataset XML files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2024-10-23: v3.1 Library Source SINGLE CELL\u003c/li\u003e\n\u003cli\u003e2024-07-05: v3.0 DB prefix added to sheet name\u003c/li\u003e\n\u003cli\u003e2024-07-04: v2.9.1 Store all sample attributes in SAMPLE_ATTRIBUTE\u003c/li\u003e\n\u003cli\u003e2024-06-14: v2.9 Sample attributes added and hold/release removed\u003c/li\u003e\n\u003cli\u003e2024-05-28: v2.8.1 file bug fix\u003c/li\u003e\n\u003cli\u003e2024-05-23: v2.8 md5 checksum value digit check added\u003c/li\u003e\n\u003cli\u003e2024-05-17: v2.7 Added sample attributes description format changed\u003c/li\u003e\n\u003cli\u003e2024-02-14: v2.6 DRA xsd 1.6.0\u003c/li\u003e\n\u003cli\u003e2024-01-31: v2.5 Fixing templates and sample data\u003c/li\u003e\n\u003cli\u003e2023-12-21: v2.4 center name changes\u003c/li\u003e\n\u003cli\u003e2023-12-05: v2.3 Delimiter of Analysis step and Attributes was changed from \",\" to \";\"\u003c/li\u003e\n\u003cli\u003e2023-12-01: v2.2 gem\u003c/li\u003e\n\u003cli\u003e2023-11-21: v2.1 Run column added to Analysis\u003c/li\u003e\n\u003cli\u003e2023-02-09: v2.0 Analysis support\u003c/li\u003e\n\u003cli\u003e2023-02-09: v1.9.2 Run Title\u003c/li\u003e\n\u003cli\u003e2023-01-17: v1.9.1 NOMINAL_LENGTH was made optional for PAIRED\u003c/li\u003e\n\u003cli\u003e2022-12-23: v1.9 AGD merged to the JGA excel\u003c/li\u003e\n\u003cli\u003e2022-12-22: v1.8 AGD\u003c/li\u003e\n\u003cli\u003e2022-12-21: v1.7 Dataset reference duplication check added\u003c/li\u003e\n\u003cli\u003e2022-12-15: v1.6 JGA added\u003c/li\u003e\n\u003cli\u003e2022-12-14: v1.5 DRA separated\u003c/li\u003e\n\u003cli\u003e2022-12-13: v1.4 Read length and direction of paired reads were made optional\u003c/li\u003e\n\u003cli\u003e2021-12-13: v1.3 BGISEQ added\u003c/li\u003e\n\u003cli\u003e2021-07-13: v1.2 Update to \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/dra#changes-to-common-xml-159-on-7-july-2021\"\u003exsd 1.5.9\u003c/a\u003e. Download the xsd files from \u003ca href=\"https://github.com/ddbj/pub\"\u003epub\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e2020-04-24: v1.1 Initial release\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload\u003c/h2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-label=\"Permalink: Download\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload the DDBJ submission-excel2xml repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/ddbj/submission-excel2xml.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImage construction\u003c/h2\u003e\u003ca id=\"user-content-image-construction\" class=\"anchor\" aria-label=\"Permalink: Image construction\" href=\"#image-construction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity-1\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://ddbj.nig.ac.jp/public/software/submission-excel2xml/\" rel=\"nofollow\"\u003eDownload\u003c/a\u003e the Singularity image or build the Singularity image as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nsudo singularity build excel2xml.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker-1\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Docker image as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nsudo docker build -t excel2xml .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDRA\u003c/h2\u003e\u003ca id=\"user-content-dra-2\" class=\"anchor\" aria-label=\"Permalink: DRA\" href=\"#dra-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnter metadata in the excel\u003c/h3\u003e\u003ca id=\"user-content-enter-metadata-in-the-excel\" class=\"anchor\" aria-label=\"Permalink: Enter metadata in the excel\" href=\"#enter-metadata-in-the-excel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEnter metadata and data files in the \u0027Submission\u0027, \u0027Experiment\u0027, \u0027Run\u0027 and \u0027Run-file\u0027 sheets of the excel \"metadata_dra.xlsx\".\nSee our \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html#metadata\" rel=\"nofollow\"\u003ewebsite\u003c/a\u003e for metadata and \u0027Readme\u0027 sheet of the excel for details.\nSee \u0027example-0001_dra_metadata.xlsx\u0027 for example.\u003c/p\u003e\n\u003cp\u003eTo submit Analysis (optional) object(s), enter an \u0027Analysis\u0027 sheet. Analysis-only submission is not acceptable. Submit Analysis to a Submission having Run(s).\nSee \u0027example/example-0002_dra_metadata.xlsx\u0027 for example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenerate XMLs: Singularity\u003c/h3\u003e\u003ca id=\"user-content-generate-xmls-singularity\" class=\"anchor\" aria-label=\"Permalink: Generate XMLs: Singularity\" href=\"#generate-xmls-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate Submission, Experiment and Run XMLs from the excel.\nSpecify the D-way account ID, submission number and BioProject accession.\u003c/p\u003e\n\u003cp\u003eFor example,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0001\u0027: -a example -i 0001\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThree XMLs are generated from the excel.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Submission.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Experiment.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Run.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhen an Analysis sheet is filled, an Analysis XML is generated.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eValidate the XMLs by specifying the submission ID. The XML files must be under the submission-excel2xml directory. The SRA xsd files have been downloaded to /opt/submission-excel2xml/ from \u003ca href=\"https://github.com/ddbj/pub\"\u003epub\u003c/a\u003e in the container during the build.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease note that this validator only performs xsd validation and minimum checks.\nThe XMLs are fully validated in the DRA web XML registration process,\nso the checked XMLs may be failed in the DRA submission system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Analysis XML only\u003c/h4\u003e\u003ca id=\"user-content-generate-analysis-xml-only\" class=\"anchor\" aria-label=\"Permalink: Generate Analysis XML only\" href=\"#generate-analysis-xml-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add Analysis object(s) to an existing Submission, you may enter an \u0027Analysis\u0027 sheet only and generate only an Analysis XML. Specify a center name by -c option.\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0002\u0027: -a example -i 0002\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003eCenter name: National Institute of Genetics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0002 -p PRJDB7252 -c \"National Institute of Genetics\" example/example-0002_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn Analysis XML is generated from the excel.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenerate XMLs: Docker\u003c/h3\u003e\u003ca id=\"user-content-generate-xmls-docker\" class=\"anchor\" aria-label=\"Permalink: Generate XMLs: Docker\" href=\"#generate-xmls-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate Submission, Experiment and Run XMLs from the excel.\nSpecify the D-way account ID, submission number, BioProject accession and full path of the directory which contains the excel.\u003c/p\u003e\n\u003cp\u003eFor example,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0001\u0027: -a example -i 0001\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003e\u0027path_to_excel_directory\u0027: full path of the directory which contains the excel.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThree XMLs are generated from the excel.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Submission.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Experiment.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Run.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhen an Analysis sheet is filled, an Analysis XML is generated.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eValidate the XMLs by specifying the submission ID. The XML files must be under the submission-excel2xml directory. The SRA xsd files have been downloaded to /opt/submission-excel2xml/ from \u003ca href=\"https://github.com/ddbj/pub\"\u003epub\u003c/a\u003e in the container during the build.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease note that this validator only performs xsd validation and minimum checks.\nThe XMLs are fully validated in the DRA web XML registration process,\nso the checked XMLs may be failed in the DRA submission system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Analysis XML only\u003c/h4\u003e\u003ca id=\"user-content-generate-analysis-xml-only-1\" class=\"anchor\" aria-label=\"Permalink: Generate Analysis XML only\" href=\"#generate-analysis-xml-only-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add Analysis object(s) to an existing Submission, you may enter an \u0027Analysis\u0027 sheet only and generate only an Analysis XML. Specify a center name by -c option.\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0002\u0027: -a example -i 0002\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003eCenter name: National Institute of Genetics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0002 -p PRJDB7252 -c \"National Institute of Genetics\" example/example-0002_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn Analysis XML is generated from the excel.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0002_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eValidation results\u003c/h3\u003e\u003ca id=\"user-content-validation-results\" class=\"anchor\" aria-label=\"Permalink: Validation results\" href=\"#validation-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eXML validation against SRA xsd\u003c/h4\u003e\u003ca id=\"user-content-xml-validation-against-sra-xsd\" class=\"anchor\" aria-label=\"Permalink: XML validation against SRA xsd\" href=\"#xml-validation-against-sra-xsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMetadata XMLs are validated against \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/dra/xsd/1-5\"\u003erespective SRA xsd\u003c/a\u003e. Modify the XMLs according to the xsd validation messages.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eXML content check\u003c/h4\u003e\u003ca id=\"user-content-xml-content-check\" class=\"anchor\" aria-label=\"Permalink: XML content check\" href=\"#xml-content-check\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSubmission\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eError: Submission: Past hold date.\nSet the future hold date.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment and Run\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eError: Run: #{run_alias} Paired library only has one file.\nInclude at least two sequence data files (for example, R1.fastq and R2.fastq) for paired library Experiment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eObject reference check\u003c/h4\u003e\u003ca id=\"user-content-object-reference-check\" class=\"anchor\" aria-label=\"Permalink: Object reference check\" href=\"#object-reference-check\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eError: Run to Experiment reference error.\nNot all Experiments are referenced by Runs.\nThere is Run(s) not referencing Experiment.\nThere is Experiment(s) not referenced by Run.\nModify metadata to make all Runs reference all Experiments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html#metadata-objects\" rel=\"nofollow\"\u003ethe DRA Handbook\u003c/a\u003e for metadata model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSubmit XMLs in the DRA web interface\u003c/h3\u003e\u003ca id=\"user-content-submit-xmls-in-the-dra-web-interface\" class=\"anchor\" aria-label=\"Permalink: Submit XMLs in the DRA web interface\" href=\"#submit-xmls-in-the-dra-web-interface\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore submitting the metadata XMLs, \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html#upload-sequence-data\" rel=\"nofollow\"\u003eupload sequence data files to the submission directory\u003c/a\u003e.\nAfter logging in the D-way, \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html#create-metadata-in-xml-files\" rel=\"nofollow\"\u003eupload the Submission, Experiment and Run XMLs in the XML upload area of the DRA submission\u003c/a\u003e.\nYour web browser may time out, however, submission processes are ongoing on the backend. Please close the browser and leave it for a while. The XML submission will be registered.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhen Github and XML generation are not clear for you\u003c/h3\u003e\u003ca id=\"user-content-when-github-and-xml-generation-are-not-clear-for-you\" class=\"anchor\" aria-label=\"Permalink: When Github and XML generation are not clear for you\" href=\"#when-github-and-xml-generation-are-not-clear-for-you\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload \u003ca href=\"https://github.com/ddbj/submission-excel2xml/blob/main/metadata_dra.xlsx\"\u003eDRA metadata Excel\u003c/a\u003e, fill in and send it to the DRA team by Email (\u003ca href=\"mailto:trace@ddbj.nig.ac.jp\"\u003etrace@ddbj.nig.ac.jp\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eJGA\u003c/h2\u003e\u003ca id=\"user-content-jga-2\" class=\"anchor\" aria-label=\"Permalink: JGA\" href=\"#jga-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAGD\u003c/h2\u003e\u003ca id=\"user-content-agd-2\" class=\"anchor\" aria-label=\"Permalink: AGD\" href=\"#agd-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame with JGA. Enter AGD metadata to the JGA excel \"JGA_metadata.xlsx\".\nSpecify the AGD Submission ID (e.g. ASUB000001).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNIG SuperComputer\u003c/h2\u003e\u003ca id=\"user-content-nig-supercomputer\" class=\"anchor\" aria-label=\"Permalink: NIG SuperComputer\" href=\"#nig-supercomputer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe singularity image is available at \u003ccode\u003e/lustre9/open/shared_data/software/submission-excel2xml/\u003c/code\u003e in the \u003ca href=\"https://www.ddbj.nig.ac.jp/sc\" rel=\"nofollow\"\u003eNIG SuperComputer\u003c/a\u003e operated by Bioinformation and DDBJ Center, National Institute of Genetics. The SuperComputer user can readily generate XMLs from the metadata excel file and check the XMLs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDRA\u003c/h3\u003e\u003ca id=\"user-content-dra-3\" class=\"anchor\" aria-label=\"Permalink: DRA\" href=\"#dra-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe user can create DRA metadata XMLs and transfer corresponding data files to the DRA file server (ftp-private.ddbj.nig.ac.jp) in the SuperComputer.\u003c/p\u003e\n\u003cp\u003eGenerate Submission, Experiment and Run XMLs from the excel.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp /lustre9/open/shared_data/software/submission-excel2xml/excel2xml.simg ~/\ncd\nsingularity exec excel2xml.simg excel2xml -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eValidate the XMLs.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJGA\u003c/h3\u003e\u003ca id=\"user-content-jga-3\" class=\"anchor\" aria-label=\"Permalink: JGA\" href=\"#jga-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAGD\u003c/h3\u003e\u003ca id=\"user-content-agd-3\" class=\"anchor\" aria-label=\"Permalink: AGD\" href=\"#agd-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u958b\u767a\u74b0\u5883\u69cb\u7bc9\u003c/h2\u003e\u003ca id=\"user-content-\u958b\u767a\u74b0\u5883\u69cb\u7bc9\" class=\"anchor\" aria-label=\"Permalink: \u958b\u767a\u74b0\u5883\u69cb\u7bc9\" href=\"#\u958b\u767a\u74b0\u5883\u69cb\u7bc9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nbundle install\nbundle exec submission-excel2xml download_xsd\nbundle exec excel2xml_dra # or excel2xml_jga, etc.\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 5, - "subscribers_count": 9, - "topics": [ - "ddbj-curators" + "docker/Singularity.def" ], - "updated_at": 1729651784.0 + "full_name": "genepattern/nmf-gpu", + "latest_release": "v7", + "stargazers_count": 4, + "subscribers_count": 8, + "topics": [], + "updated_at": 1708724634.0 }, { "data_format": 2, - "description": "Use Docker to explore the various components of Singularity", + "description": "Apache Spark with RStudio and the sparklyr package in a Singularity container", "filenames": [ - "sregistry-cli/hello-world/Singularity" + "Singularity.2.2.1-hadoop-2.7-r-3.4.3", + "Singularity", + "Singularity.2.3.0-hadoop-2.7-r-3.4.3" ], - "full_name": "mjstealey/singularity-in-docker", + "full_name": "nickjer/singularity-rstudio-spark", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity in Docker\u003c/h1\u003e\u003ca id=\"user-content-singularity-in-docker\" class=\"anchor\" aria-label=\"Permalink: Singularity in Docker\" href=\"#singularity-in-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e to explore the various components of \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContents\u003c/h3\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"singularity-registry\"\u003esingularity-registry\u003c/a\u003e - Deploy a local Singularity registry to interact with\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"singularity-packages\"\u003esingularity-packages\u003c/a\u003e - Build the RPM or DEB packages for running Singuarity on CentOS 7 or Ubuntu 16.04\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"sregistry-cli\"\u003esregistry-cli\u003c/a\u003e - Build/Use a container that runs both Singularity and sregistry-cli to interact with your local singualrity registry\u003c/li\u003e\n\u003c/ol\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"https://github.com/singularityhub/sregistry\"\u003eSingularity Registry\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1f26402c3680a19cd0d6bf6becf15b09ba8a06f408ed82c52a102b8347047a32/687474703a2f2f73696e67756c61726974792e6c626c2e676f762f696d616765732f6c6f676f2f6c6f676f2e737667\"\u003e\u003cimg width=\"50%\" alt=\"Singularity\" src=\"https://camo.githubusercontent.com/1f26402c3680a19cd0d6bf6becf15b09ba8a06f408ed82c52a102b8347047a32/687474703a2f2f73696e67756c61726974792e6c626c2e676f762f696d616765732f6c6f676f2f6c6f676f2e737667\" data-canonical-src=\"http://singularity.lbl.gov/images/logo/logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6098616fa11a5ded83f94696568204e1f63d592878be33fc055c0a84e562a25a/68747470733a2f2f7777772e646f636b65722e636f6d2f73697465732f64656661756c742f66696c65732f766572746963616c2e706e67\"\u003e\u003cimg width=\"90%\" alt=\"Docker\" src=\"https://camo.githubusercontent.com/6098616fa11a5ded83f94696568204e1f63d592878be33fc055c0a84e562a25a/68747470733a2f2f7777772e646f636b65722e636f6d2f73697465732f64656661756c742f66696c65732f766572746963616c2e706e67\" data-canonical-src=\"https://www.docker.com/sites/default/files/vertical.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f2a9c2aaefecf59bea01de839cde6d415b1b99dc7ad91173ca5f4e885ff7bedb/68747470733a2f2f73696e67756c61726974796875622e6769746875622e696f2f7372656769737472792f6173736574732f696d672f6c6f676f2e706e67\"\u003e\u003cimg width=\"100%\" alt=\"Singularity Registry\" src=\"https://camo.githubusercontent.com/f2a9c2aaefecf59bea01de839cde6d415b1b99dc7ad91173ca5f4e885ff7bedb/68747470733a2f2f73696e67756c61726974796875622e6769746875622e696f2f7372656769737472792f6173736574732f696d672f6c6f676f2e706e67\" data-canonical-src=\"https://singularityhub.github.io/sregistry/assets/img/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity: \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity enables users to have full control of their environment. Singularity containers can be used to package entire scientific workflows, software and libraries, and even data. This means that you don\u2019t have to ask your cluster admin to install anything for you - you can put it in a Singularity container and run. Did you already invest in Docker? The Singularity software can import your Docker images without having Docker installed or being a superuser. Need to share your code? Put it in a Singularity container and your collaborator won\u2019t have to go through the pain of installing missing dependencies. Do you need to run a different operating system entirely? You can \u201cswap out\u201d the operating system on your host for a different one within a Singularity container. As the user, you are in control of the extent to which your container interacts with its host. There can be seamless integration, or little to no communication at all. What does your workflow look like?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity Hub: \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity Hub is a registry for scientific \u003ca href=\"https://opensource.com/resources/what-are-linux-containers\" rel=\"nofollow\"\u003elinux containers\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhat is a Linux container?\u003c/strong\u003e A container image is an encapsulated, portable environment that is created to distribute a scientific analysis or a general function. Containers help with reproducibility of such content as they nicely package software and data dependencies, along with libraries that are needed. Thus, the core of Singularity Hub are these Singularity container images, and by way of being on Singularity Hub they can be easily built, updated, referenced with a url for a publication, and shared. This small guide will help you to get started building your containers using Singularity Hub and your Github repositories.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity Registry: \u003ca href=\"https://github.com/singularityhub/sregistry\"\u003ehttps://github.com/singularityhub/sregistry\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity Registry is a management and storage of Singularity images for an institution or user to deploy locally. It does not manage building, but serves endpoints to obtain and save containers. The Registry is expected to be available for use in the Fall.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDocker: \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003ehttps://www.docker.com\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker is the company driving the container movement and the only container platform provider to address every application across the hybrid cloud. Today\u2019s businesses are under pressure to digitally transform but are constrained by existing applications and infrastructure while rationalizing an increasingly diverse portfolio of clouds, datacenters and application architectures. Docker enables true independence between applications and infrastructure and developers and IT ops to unlock their potential and creates a model for better collaboration and innovation.\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 5, - "subscribers_count": 4, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Apache Spark w/ RStudio Server\u003c/h1\u003e\u003ca id=\"user-content-singularity-apache-spark-w-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: Singularity Apache Spark w/ RStudio Server\" href=\"#singularity-apache-spark-w-rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/455\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for \u003ca href=\"https://spark.apache.org/\" rel=\"nofollow\"\u003eApache Spark\u003c/a\u003e with the \u003ca href=\"http://spark.rstudio.com/\" rel=\"nofollow\"\u003esparklyr\u003c/a\u003e package installed. It\nwas built on top of the base Singularity image \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003enickjer/singularity-rstudio\u003c/a\u003e in\norder to launch an \u003ca href=\"https://www.rstudio.com/products/rstudio/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e to more easily connect with an Apache Spark\ncluster running in \u003ca href=\"https://spark.apache.org/docs/latest/spark-standalone.html\" rel=\"nofollow\"\u003eStandalone Mode\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis is still a work in progress.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a local Singularity image named \u003ccode\u003esingularity-rstudio-spark.simg\u003c/code\u003e\nwith:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build singularity-rstudio-spark.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy\u003c/h2\u003e\u003ca id=\"user-content-deploy\" class=\"anchor\" aria-label=\"Permalink: Deploy\" href=\"#deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of building it yourself you can download the pre-built image from\n\u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name singularity-rstudio-spark.simg shub://nickjer/singularity-rstudio-spark\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can launch Spark in \u003ca href=\"https://spark.apache.org/docs/latest/spark-standalone.html\" rel=\"nofollow\"\u003eStandalone Mode\u003c/a\u003e by first launching a \"master\" process\nwhich will print out a \u003ccode\u003espark://HOST:PORT\u003c/code\u003e for itself, which you can then use\nto connect \"workers\" to it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSpark Master\u003c/h3\u003e\u003ca id=\"user-content-spark-master\" class=\"anchor\" aria-label=\"Permalink: Spark Master\" href=\"#spark-master\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can launch a \"master\" process as a Singularity app with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app spark-master singularity-rstudio-spark.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWorker\u003c/h3\u003e\u003ca id=\"user-content-worker\" class=\"anchor\" aria-label=\"Permalink: Worker\" href=\"#worker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can launch a \"worker\" process as a Singularity app with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --app spark-worker singularity-rstudio-spark.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRStudio Server\u003c/h3\u003e\u003ca id=\"user-content-rstudio-server\" class=\"anchor\" aria-label=\"Permalink: RStudio Server\" href=\"#rstudio-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-rstudio\"\u003enickjer/singularity-rstudio\u003c/a\u003e for more information on how to run \u003ccode\u003erserver\u003c/code\u003e\nfrom within this Singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eR and Rscript\u003c/h3\u003e\u003ca id=\"user-content-r-and-rscript\" class=\"anchor\" aria-label=\"Permalink: R and Rscript\" href=\"#r-and-rscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/nickjer/singularity-r\"\u003enickjer/singularity-r\u003c/a\u003e for more information on how to run \u003ccode\u003eR\u003c/code\u003e and\n\u003ccode\u003eRscript\u003c/code\u003e from within this Singularity image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBug reports and pull requests are welcome on GitHub at\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio-spark\"\u003ehttps://github.com/nickjer/singularity-rstudio-spark\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is available as open source under the terms of the \u003ca href=\"http://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003eMIT License\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 4, + "subscribers_count": 2, "topics": [ - "singularity", - "docker", - "sregistry-cli", - "singularity-hub", - "singularity-packages", - "singularity-registry", - "deb-packages", - "rpm-packages" + "rstudio-server", + "singularity-image", + "spark" ], - "updated_at": 1588839723.0 + "updated_at": 1585580351.0 }, { "data_format": 2, - "description": "A collection of Singularity recipes useful for our nextflow pipelines", + "description": null, "filenames": [ - "Singularity.trim_galore", - "Singularity.sierrapy", - "Singularity.snapperdb", - "Singularity.ariba", - "Singularity.seqtk", - "Singularity.mykrobe-atlas", - "Singularity.vsearch", - "Singularity.shiver", - "Singularity.abricate", - "Singularity.bbtools", - "Singularity.rtg", - "Singularity.kronatools", - "Singularity.variant_calling", - "Singularity.minimap2", - "Singularity.shiver_init", - "Singularity.iqtree", - "Singularity.kraken2", - "Singularity.prokka", - "Singularity.shovill", - "Singularity.interop", - "Singularity.pengu-ddt", - "Singularity.centrifuge", - "Singularity.multiqc", - "Singularity.deeptools", - "Singularity.snapperdb_v3", - "Singularity.iva", - "Singularity.mash", - "Singularity.cd-hit", - "Singularity.mlst", - "Singularity.assembly_improvement", - "Singularity.quast", - "Singularity.canu", - "Singularity.taxonkit", - "Singularity.wtdbg2" + "containers/tensorflow-serving/Singularity", + "containers/research/Singularity", + "containers/ubuntu-cuda10/Singularity", + "containers/redis/Singularity", + "containers/albert-ai/Singularity" ], - "full_name": "connor-lab/singularity-recipes", + "full_name": "todexter3/Richelieu", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-recipes\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" aria-label=\"Permalink: singularity-recipes\" href=\"#singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA collection of Singularity recipes useful for our nextflow pipelines\u003c/p\u003e\n\u003cp\u003eTo build containers locally in \u003ccode\u003e../images\u003c/code\u003e, do:\n\u003ccode\u003e./build_containers.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1998\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 5, - "subscribers_count": 3, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRichelieu\u003c/h1\u003e\u003ca id=\"user-content-richelieu\" class=\"anchor\" aria-label=\"Permalink: Richelieu\" href=\"#richelieu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code contains a custom implementation of a Gym environment for the Diplomacy game, and training using self-playing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# Apt installs\napt-get install -y wget bzip2 ca-certificates curl git build-essential clang-format-8 git wget cmake build-essential autoconf libtool pkg-config libgoogle-glog-dev\n\n# Install conda\nwget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.7.10-Linux-x86_64.sh -O ~/miniconda.sh\n/bin/bash ~/miniconda.sh -b\n\n# Install pytorch, pybind11\nconda install --yes pytorch=1.7.1 torchvision cudatoolkit=11.0 -c pytorch\nconda install --yes pybind11\n\n# Install the required dependencies\npip install diplomacy\npip install gymnasium\npip install \"stable-baselines3[extra]\u0026gt;=2.0.0a4\"\n\n# Make\nmake\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject Structure\u003c/h3\u003e\u003ca id=\"user-content-project-structure\" class=\"anchor\" aria-label=\"Permalink: Project Structure\" href=\"#project-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe notebook contains the following main sections:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eImports and Necessary Installations: Installs and imports required libraries.\u003c/li\u003e\n\u003cli\u003eCustom Diplomacy Class: Defines the custom environment for the Diplomacy game.\u003c/li\u003e\n\u003cli\u003eTraining with self-playing: Trains Richelieu using self-playing.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCustom Diplomacy Class\u003c/h3\u003e\u003ca id=\"user-content-custom-diplomacy-class\" class=\"anchor\" aria-label=\"Permalink: Custom Diplomacy Class\" href=\"#custom-diplomacy-class\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section defines the \u003ccode\u003eDiplomacyStrategyEnv\u003c/code\u003e class, a custom implementation of a environment for the Diplomacy game.\nKey Methods\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003einit\u003c/strong\u003e(): Initializes the environment.\u003c/li\u003e\n\u003cli\u003ereset(): Resets the environment to the initial state.\u003c/li\u003e\n\u003cli\u003estep(action): Executes an action and returns the next state, reward, done flag, and info.\u003c/li\u003e\n\u003cli\u003e_init_observation_space(): Create the MultiDiscrete space for the observation space.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTraining with self-playing\u003c/h3\u003e\u003ca id=\"user-content-training-with-self-playing\" class=\"anchor\" aria-label=\"Permalink: Training with self-playing\" href=\"#training-with-self-playing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section demonstrates how to train Richelieu using self-palying. Richelieu is trained to play the Diplomacy game within the custom environment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etrain_selfplay(): training with self-playing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRichelieu can also be trained by playing with other models.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etrain(model): train by playing with other models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEvaluation\u003c/h3\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this section, we evaluate Richelieu by comparing its game results with Cicero.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eplay_with(model): have Richelieu and Cicero play a game.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can modified the model, removed the negotiation module, and performed massively play on no-press setting with the other six models.\nIt is also possible to obtain an ablation study by deleting each section and playing games with Cicero.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContributing\u003c/h3\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContributions are welcome!\u003c/p\u003e\n", + "stargazers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1708615466.0 + "updated_at": 1732761708.0 }, { "data_format": 2, - "description": "Experimental fsl containers for learning purpose, consult fsl license at https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence", + "description": "Autonomy tools to support life detection aboard the Ocean Worlds Life Surveyor (OWLS) instrument suite.", "filenames": [ - "Singularity.centos7" + "Singularity_ACME.def" ], - "full_name": "pnlbwh/fsl-containers", + "full_name": "JPLMLIA/OWLS-Autonomy", "latest_release": null, - "readme": "\u003cp\u003eThis repository is created for learning container development with OpenGL support. fsl and fsleyes in particular, are the target software.\u003c/p\u003e\n\u003cp\u003eView FSL license below: \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\u003c/a\u003e\nA salient clause of the license states it is not free for commercial use. So, if you use this image, make sure you are aware of that limitation.\nThe maintainer of this image is not and cannot be held liable for unlawful use of this image\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#environment\"\u003eEnvironment\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#i-docker\"\u003e(i) Docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ii-singularity\"\u003e(ii) Singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#docker-fsl-image\"\u003eDocker fsl image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity-fsl-image\"\u003eSingularity fsl image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable of contents created by \u003ca href=\"https://github.com/ekalinin/github-markdown-toc\"\u003egh-md-toc\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnvironment\u003c/h1\u003e\u003ca id=\"user-content-environment\" class=\"anchor\" aria-label=\"Permalink: Environment\" href=\"#environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA separate repository details requisite software and environment. See \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers\"\u003ehttps://github.com/tashrifbillah/glxgears-containers\u003c/a\u003e\nIn particular, the following sections should be useful:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e(i) Docker\u003c/h2\u003e\u003ca id=\"user-content-i-docker\" class=\"anchor\" aria-label=\"Permalink: (i) Docker\" href=\"#i-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#linuxmac\"\u003ehttps://github.com/tashrifbillah/glxgears-containers#linuxmac\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#windows\"\u003ehttps://github.com/tashrifbillah/glxgears-containers#windows\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e(ii) Singularity\u003c/h2\u003e\u003ca id=\"user-content-ii-singularity\" class=\"anchor\" aria-label=\"Permalink: (ii) Singularity\" href=\"#ii-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#linuxmac-1\"\u003ehttps://github.com/tashrifbillah/glxgears-containers#linuxmac-1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#windows-1\"\u003ehttps://github.com/tashrifbillah/glxgears-containers#windows-1\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker fsl image\u003c/h1\u003e\u003ca id=\"user-content-docker-fsl-image\" class=\"anchor\" aria-label=\"Permalink: Docker fsl image\" href=\"#docker-fsl-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(i) build\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker build -t tbillah/fsl-6.0.1-centos7 -f Dockerfile.centos7 .\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(ii) push\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker push tbillah/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iii) pull\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker pull tbillah/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iv) run\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinux/OSX\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti --privileged -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix tbillah/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eDetails can be found \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#linuxmac\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWindows\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollow steps mentioned \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#windows\"\u003ehere\u003c/a\u003e and use \u003ccode\u003etbillah/fsl-6.0.1-centos7\u003c/code\u003e instead of \u003ccode\u003eglxgears-docker\u003c/code\u003e. Eventually, you would use\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti --privileged -e DISPLAY=$DISPLAY tbillah/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity fsl image\u003c/h1\u003e\u003ca id=\"user-content-singularity-fsl-image\" class=\"anchor\" aria-label=\"Permalink: Singularity fsl image\" href=\"#singularity-fsl-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(i) build\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity build fsl-6.0.1-centos7 Singularity.centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(ii) push\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity push fsl-6.0.1-centos7 library://tbillah/collection/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iii) pull\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity pull library://tbillah/collection/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iv) run\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinux/OSX\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity shell --writable-tmpfs fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e(inside the shell) fsleyes\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDetails can be found \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#linuxmac-1\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWindows\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou need a GUI desktop to run Singularity containers. Follow steps mentioned \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#windows-1\"\u003ehere\u003c/a\u003e. Eventually, you would use\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity shell --writable-tmpfs fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e(inside the shell) fsleyes\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 5, - "subscribers_count": 2, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOcean Worlds Life Surveyor autonomous algorithms\u003c/h1\u003e\u003ca id=\"user-content-ocean-worlds-life-surveyor-autonomous-algorithms\" class=\"anchor\" aria-label=\"Permalink: Ocean Worlds Life Surveyor autonomous algorithms\" href=\"#ocean-worlds-life-surveyor-autonomous-algorithms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScope\u003c/h2\u003e\u003ca id=\"user-content-scope\" class=\"anchor\" aria-label=\"Permalink: Scope\" href=\"#scope\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Ocean Worlds Life Surveyor (OWLS) project is aimed at autonomously detecting signatures of life in water at the molecular and cellular scale. The ACME, FAME, HELM, and JEWEL components of OWLS (this package) are a set of software-based autonomy tools to process data from different instruments aboard the OWLS instrument. Their goal is to generate and prioritize Autonomous Data Science Products (ASDPs) to send only the most scientifically valuable subset of information back to Earth.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eACME: Autonomous CE-ESI Mass-spectra Examination. Extracts ASDPs from mass-spectra data.\u003c/li\u003e\n\u003cli\u003eFAME: FLFM Autonomous Motility Evaluation. Extracts ASDPs from Volumetric Flourescent Imaging (VFI) microscopy data.\u003c/li\u003e\n\u003cli\u003eHELM: Holographic Examination for Life-like motility. Extracts ASDPs from Digital Holography Microscopy (DHM) data.\u003c/li\u003e\n\u003cli\u003eJEWEL: Joint Examination for Water-based Extant Life. Prioritizes ASDPs from all instruments to transmit data back to Earth in an \"optimal\" order.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStandard install requirements\u003c/h3\u003e\u003ca id=\"user-content-standard-install-requirements\" class=\"anchor\" aria-label=\"Permalink: Standard install requirements\" href=\"#standard-install-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eImageMagick\u003c/strong\u003e \u003cbr\u003e\n Full instructions for installing ImageMagick are\n\u003ca href=\"https://imagemagick.org/script/download.php\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. For MacOSX, use\n\u003ccode\u003ebrew install imagemagick\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003effmpeg\u003c/strong\u003e \u003cbr\u003e\nFull instructions for installing ffmpeg are\n\u003ca href=\"https://ffmpeg.org/download.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. For MacOSX, use\n\u003ccode\u003ebrew install ffmpeg@4\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython code\u003c/h3\u003e\u003ca id=\"user-content-python-code\" class=\"anchor\" aria-label=\"Permalink: Python code\" href=\"#python-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/JPLMLIA/OWLS-Autonomy.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e OWLS-Autonomy\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e use \u0027editable\u0027 to avoid repeated pip installs\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDevelopment tools\u003c/h3\u003e\u003ca id=\"user-content-development-tools\" class=\"anchor\" aria-label=\"Permalink: Development tools\" href=\"#development-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor development or CI builds (e.g., on Jenkins).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e OWLS-Autonomy\npip install -r requirements_dev.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDemonstration Script\u003c/h2\u003e\u003ca id=\"user-content-demonstration-script\" class=\"anchor\" aria-label=\"Permalink: Demonstration Script\" href=\"#demonstration-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA demonstration script is available to confirm successful installation, give first time users an overview of the autonomy pipelines and provide a set of example input/outputs. Please see the demo \u003ca href=\"./demo/README.md\"\u003e\u003ccode\u003eREADME.md\u003c/code\u003e\u003c/a\u003e for usage.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCode Usage\u003c/h2\u003e\u003ca id=\"user-content-code-usage\" class=\"anchor\" aria-label=\"Permalink: Code Usage\" href=\"#code-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor ACME, HELM, and FAME, data processsing scripts that you can run from the\ncommand line are located in the \u003ccode\u003esrc/cli\u003c/code\u003e directory. Please see\nthe CLI \u003ca href=\"./src/cli/README.md\"\u003e\u003ccode\u003eREADME.md\u003c/code\u003e\u003c/a\u003e for examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a docker image from the source code if you can\u0027t pip install the\nrepository. This may be useful for Windows users.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHELM Validate step\u003c/h3\u003e\u003ca id=\"user-content-helm-validate-step\" class=\"anchor\" aria-label=\"Permalink: HELM Validate step\" href=\"#helm-validate-step\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a Docker image to run OWLS-Autonomy functions. Navigate a terminal\nto this dir and run the command below:\u003c/p\u003e\n\u003cp\u003eBuild with \u003ccode\u003eDockerfile\u003c/code\u003e and tag the image. Change the version of the tag as needed.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build . -f Dockerfile --tag owls-autonomy:v1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun a container in the \u003ca href=\"https://docs.docker.com/desktop/dashboard/\" rel=\"nofollow\"\u003eDocker Dashboard GUI\u003c/a\u003e\nor via the CLI. Be sure to mount your local data directory into the Docker\ncontainer. The CLI approach is shown below. After starting the container, see the\nCLI \u003ca href=\"./src/cli/README.md\"\u003e\u003ccode\u003eREADME.md\u003c/code\u003e\u003c/a\u003e for examples on running OWLS functionality.\nThe \u003ccode\u003e--rm\u003c/code\u003e flag will automatically cause the container to stop running as soon as\nyou exit.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm -volume /Users/wronk/Data:/Users/wronk/Data owls-autonomy:v1 /bin/bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll software is licensed according to the \u003ca href=\"LICENSE.md\"\u003eJPL Software License\u003c/a\u003e, while all documentation is Caltech/JPL Proprietary - Not for public release or distribution.\u003c/p\u003e\n", + "stargazers_count": 4, + "subscribers_count": 7, "topics": [], - "updated_at": 1722964610.0 + "updated_at": 1732571041.0 }, { "data_format": 2, - "description": "singularity environment manager (application to NGS and bioinformatics)", + "description": "Singularity Ubuntu container with the MPI/InfiniBand stack", "filenames": [ - "test/data/Singularity.testing_1.0.0", - "damona/software/seacr/Singularity.seacr_1.3.0", - "damona/software/transdecoder/Singularity.trandecoder_5.7.0", - "damona/software/medaka/Singularity.medaka_1.7.3", - "damona/software/lima/Singularity.lima_2.9.0", - "damona/software/igvtools/Singularity.igvtools_2.12.0", - "damona/software/hmmer/Singularity.hmmer_2.3.2", - "damona/software/hmmer/Singularity.hmmer_3.3.2", - "damona/software/sequana_denovo/Singularity.sequana_denovo_0.0.2", - "damona/software/mafft/Singularity.mafft_7.520.0", - "damona/software/raxml/Singularity.raxml_8.2.12", - "damona/software/pilon/Singularity.pilon_1.24.0", - "damona/software/unicycler/Singularity.unicycler_0.5.0", - "damona/software/sequana_tools/Singularity.sequana_tools_0.10.0", - "damona/software/sequana_tools/Singularity.sequana_tools_0.14.2", - "damona/software/sequana_tools/Singularity.sequana_tools_0.9.0", - "damona/software/sequana_tools/Singularity.sequana_tools_0.14.5", - "damona/software/sequana_tools/Singularity.sequana_tools_0.12.0", - "damona/software/sequana_tools/Singularity.sequana_tools_0.14.3", - "damona/software/sequana_tools/Singularity.sequana_tools_0.14.1", - "damona/software/sequana_tools/Singularity.sequana_tools_0.11.0", - "damona/software/sequana_tools/Singularity.sequana_tools_0.15.1", - "damona/software/pbbam/Singularity.pbbam_2.1.0", - "damona/software/pbbam/Singularity.pbbam_2.3.0", - "damona/software/rnafold/Singularity.RNAfold_2.7.0", - "damona/software/laa/Singularity.pblaa_2.4.2", - "damona/software/snpeff/Singularity.snpeff_5.0.0", - "damona/software/snpeff/Singularity.snpeff_5.1.0", - "damona/software/infernal/Singularity.infernal_1.1.5", - "damona/software/dsrc/Singularity.dsrc_2.0.2", - "damona/software/bbmap/Singularity.bbmap_39.1.0", - "damona/software/samtools_minimap2/Singularity.samtools_1.17_minimap2_2.24.0", - "damona/software/kraken/Singularity.kraken_2.0.9", - "damona/software/kraken/Singularity.kraken_1.1.0", - "damona/software/vt/Singularity.vt_0.57721.0", - "damona/software/pbsim/Singularity.pbsim_3.0.1", - "damona/software/salmon/Singularity.salmon_1.3.0", - "damona/software/bcl2fastq/Singularity.bcl2fastq_2.20.0", - "damona/software/bbtools/Singularity.bbtools_38.94.0", - "damona/software/seqtk/Singularity.seqtk_1.3.0", - "damona/software/freebayes/Singularity.freebayes_1.2.0", - "damona/software/freebayes/Singularity.freebayes_1.3.7", - "damona/software/bioconvert/Singularity.bioconvert_0.6.2", - "damona/software/bioconvert/Singularity.bioconvert_1.1.0", - "damona/software/bioconvert/Singularity.bioconvert_0.6.3", - "damona/software/bioconvert/Singularity.bioconvert_1.0.0", - "damona/software/bioconvert/Singularity.bioconvert_0.6.1", - "damona/software/bowtie/Singularity.bowtie_1.3.1", - "damona/software/blast/Singularity.blast_2.12.0", - "damona/software/seqkit/Singularity.seqkit_2.4.0", - "damona/software/seqkit/Singularity.seqkit_2.1.0", - "damona/software/bedtools/Singularity.bedtools_2.30.0", - "damona/software/pypolca/Singularity.pypolca_0.3.1", - "damona/software/trim_galore/Singularity.trimgalore_0.5.0", - "damona/software/jellyfish/Singularity.jellyfish_2.3.0", - "damona/software/khmer/Singularity.khmer_2.1.1", - "damona/software/mauve/Singularity.mauve_2.4.0", - "damona/software/necat/Singularity.necat_0.0.1", - "damona/software/miniasm/Singularity.miniasm_0.3.0", - "damona/software/fastqc/Singularity.fastqc_0.12.1", - "damona/software/fastqc/Singularity.fastqc_0.11.9", - "damona/software/fastqc/Singularity.fastqc_0.11.9_py3", - "damona/software/fastqc/Singularity.fastqc_0.11.8", - "damona/software/pplacer/Singularity.pplacer_1.1.0", - "damona/software/sequana_perl_tools/Singularity.sequana_perl_tools_0.1.0", - "damona/software/sequana_perl_tools/Singularity.sequana_perl_tools_0.2.0", - "damona/software/multiqc/Singularity.multiqc_1.16.0", - "damona/software/falco/Singularity.falco_0.2.1", - "damona/software/falco/Singularity.falco_1.0.0", - "damona/software/ccs/Singularity.ccs_6.4.0", - "damona/software/sequana/Singularity.sequana_0.15.0", - "damona/software/sequana/Singularity.sequana_0.12.6", - "damona/software/sequana/Singularity.sequana_0.16.5", - "damona/software/sequana/Singularity.sequana_0.16.1", - "damona/software/sequana/Singularity.sequana_0.14.6", - "damona/software/sequana/Singularity.sequana_0.16.2", - "damona/software/bowtie2/Singularity.bowtie2_2.4.2", - "damona/software/bowtie2/Singularity.bowtie2_2.3.4", - "damona/software/bowtie2/Singularity.bowtie2_2.5.1", - "damona/software/bandage/Singularity.bandage_0.8.1", - "damona/software/adapterremoval/Singularity.adapterremoval_2.3.3", - "damona/software/subread/Singularity.subread_2.0.3", - "damona/software/sequana_ribofinder/Singularity.sequana_ribofinder_0.12.0", - "damona/software/checkm/Singularity.checkm_1.2.2", - "damona/software/gffread/Singularity.gffread_0.12.1", - "damona/software/gffread/Singularity.gffread_0.12.7", - "damona/software/delly/Singularity.delly_1.2.6", - "damona/software/idr/Singularity.idr_2.0.3", - "damona/software/gzip/Singularity.gzip_1.9.0", - "damona/software/ucsc/Singularity.ucsc_3.7.7", - "damona/software/flye/Singularity.flye_2.9.0", - "damona/software/flye/Singularity.flye_2.9.3", - "damona/software/flye/Singularity.flye_2.9.2", - "damona/software/flye/Singularity.flye_2.9.1", - "damona/software/augustus/Singularity.augustus_3.5.0", - "damona/software/bamtools/Singularity.bamtools_2.5.2", - "damona/software/samtools/Singularity.samtools_1.15.0", - "damona/software/samtools/Singularity.samtools_1.16.1", - "damona/software/orthofinder/Singularity.orthofinder_2.5.5", - "damona/software/rnammer/Singularity.rnammer_1.2.0", - "damona/software/helloworld/Singularity.helloworld_1.0.0", - "damona/software/mosdepth/Singularity.mosdepth_0.3.8", - "damona/software/hifiasm/Singularity.hifiasm_0.19.1", - "damona/software/hifiasm/Singularity.hifiasm_0.19.8", - "damona/software/repeatmasker/Singularity.repeatmasker_4.0.8", - "damona/software/busco/Singularity.busco_5.4.6", - "damona/software/sequana_coverage/Singularity.sequana_coverage_0.18.0", - "damona/software/quast/Singularity.quast_5.3.0", - "damona/software/quast/Singularity.quast_5.2.0", - "damona/software/quast/Singularity.quast_5.0.2", - "damona/software/datasets/Singularity.datasets_16.10.1", - "damona/software/phantompeakqualtools/Singularity.phantompeakqualtools_1.2.2", - "damona/software/bcl-convert/Singularity.bcl-convert_4.2.7", - "damona/software/liftoff/Singularity.liftoff_1.6.3", - "damona/software/trinity/Singularity.trinity_2.15.1", - "damona/software/trf/Singularity.trf_4.10.0", - "damona/software/homer/Singularity.homer_4.11.0", - "damona/software/minimap2/Singularity.minimap2_2.23.0", - "damona/software/minimap2/Singularity.minimap2_2.24.0", - "damona/software/minimap2/Singularity.minimap2_2.17.0", - "damona/software/nanopolish/Singularity.nanopolish_0.14.0", - "damona/software/nextclade/Singularity.nextclade_2.15.0", - "damona/software/mummer/Singularity.mummer_3.23.0", - "damona/software/ir/Singularity.ir_2.8.0", - "damona/software/pigz/Singularity.pigz_2.4.0", - "damona/software/art/Singularity.art_2.3.7", - "damona/software/art/Singularity.art_3.11.14", - "damona/software/art/Singularity.art_2.5.8", - "damona/software/fastp/Singularity.fastp_0.23.3", - "damona/software/fastp/Singularity.fastp_0.23.2", - "damona/software/seaview/Singularity.seaview_5.0.5", - "damona/software/canu/Singularity.canu_2.1.1", - "damona/software/canu/Singularity.canu_1.6.0", - "damona/software/canu/Singularity.canu_1.8.0", - "damona/software/vadr/Singularity.vadr_1.6.4", - "damona/software/prokka/Singularity.prokka_1.14.5", - "damona/software/prokka/Singularity.prokka_1.14.6", - "damona/software/polypolish/Singularity.polypolish_0.6.0", - "damona/software/polypolish/Singularity.polypolish_0.5.0", - "damona/software/bwa/Singularity.bwa_0.7.17", - "damona/software/guppy/Singularity.guppy_6.4.2", - "damona/software/shustring/Singularity.shustring_2.6.0", - "damona/software/sratoolkit/Singularity.sratoolkit_3.1.0", - "damona/software/pycoqc/Singularity.pycoqc_2.5.2", - "damona/software/graphviz/Singularity.graphviz_2.43.0", - "damona/software/graphviz/Singularity.graphviz_7.0.5", - "damona/software/pangolin/Singularity.pangolin_4.3.0", - "damona/software/rtools/Singularity.rtools_1.2.0", - "damona/software/rtools/Singularity.rtools_1.1.0", - "damona/software/rtools/Singularity.rtools_1.0.0", - "damona/software/rtools/Singularity.rtools_1.3.0", - "damona/software/ragtag/Singularity.ragtag_2.1.0", - "damona/software/rnaseqc/Singularity.rnaseqc_2.35.0", - "damona/software/circlator/Singularity.circlator_1.5.5", - "damona/software/trinotate/Singularity.trinotate_4.0.1", - "damona/software/cellranger_atac/Singularity.cellranger_atac_2.1.0", - "damona/software/rnadiff/Singularity.rnadiff_1.7.1", - "damona/software/mcl/Singularity.mcl_22.282.0", - "damona/software/pecat/Singularity.pecat_0.0.3", - "damona/software/ivar/Singularity.ivar_1.3.1", - "damona/software/cd-hit/Singularity.cd-hit_4.8.1", - "damona/library/conda/Singularity.conda_4.7.12", - "damona/library/conda/Singularity.conda_4.9.2", - "damona/library/micromamba/Singularity.micromamba_1.5.8", - "damona/library/micromamba/Singularity.micromamba_1.4.3", - "damona/library/R/Singularity.R_4.0.2", - "damona/library/R/Singularity.R_3.6.3", - "damona/bcftools/Singularity.bctools_1.16.0", - "damona/templates/Singularity.from_alpine_1.0.0", - "damona/templates/Singularity.micromamba_1.0.0", - "damona/templates/Singularity.python_slim" + "Singularity" ], - "full_name": "cokelaer/damona", - "latest_release": "v0.14.4", + "full_name": "chpc-uofu/Singularity-ubuntu-mpi", + "latest_release": null, "stargazers_count": 5, - "subscribers_count": 2, - "topics": [ - "singularity", - "manager", - "conda", - "ngs", - "bioinformatics" - ], - "updated_at": 1732629979.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1590213660.0 }, { "data_format": 2, @@ -34562,151 +34314,177 @@ var data = "filenames": [ "Singularity" ], - "full_name": "csiro-crop-informatics/nextflow-embl-abr-webinar", - "latest_release": "v1.2", - "readme": "\u003cp\u003eThis repository contains information for the EMBL-ABR webinar on \"Nextflow: Scalable, Sharable and Reproducible Computational Workflows across Clouds and Clusters\" presented by Rad Suchecki on 14th March 2019.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebinar details\u003c/h1\u003e\u003ca id=\"user-content-webinar-details\" class=\"anchor\" aria-label=\"Permalink: Webinar details\" href=\"#webinar-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbstract:\u003c/strong\u003e\nLarge analysis workflows are fragile ecosystems of software tools, scripts and dependencies. This complexity commonly makes these workflows not only irreproducible but sometimes even not re-runnable outside their original development environment. Nextflow is a reactive workflow framework and a domain specific programming language which follows the dataflow paradigm and offers an alternative, and arguably superior, approach to developing, executing and sharing pipelines.\u003c/p\u003e\n\u003cp\u003eIn this webinar we will follow the steps required for developing sharable, version controlled, container-backed workflows, which can be seamlessly executed across different environments from a laptop to cluster to cloud. We will do this by leveraging Nextflow\u2019s integration with code and container image hosting services such as GitHub and Docker Hub, and out of the box support for various HPC cluster schedulers and the Amazon AWS cloud.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDate/time:\u003c/strong\u003e Thursday 14 March 2019 13:00-14:00 AEDT /12:00-13:00 AEST\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePresenter:\u003c/strong\u003e \u003ca href=\"https://orcid.org/0000-0003-4992-9497\" rel=\"nofollow\"\u003eRad Suchecki\u003c/a\u003e, CSIRO Crop Bioinformatics and Data Science\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://twitter.com/bioinforad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff9d955ed85c923f8b942e7be1caef962cb62cba80b54182122f0be8b2a4fccd/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f62696f696e666f7261642e7376673f7374796c653d736f6369616c\" alt=\"Twitter Follow\" data-canonical-src=\"https://img.shields.io/twitter/follow/bioinforad.svg?style=social\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistration:\u003c/strong\u003e \u003cdel\u003e\u003ca href=\"https://attendee.gotowebinar.com/register/8408436403729692931\" rel=\"nofollow\"\u003ehttps://attendee.gotowebinar.com/register/8408436403729692931\u003c/a\u003e\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVideo link:\u003c/strong\u003e \u003ca href=\"https://www.youtube.com/channel/UC5WlFNBSfmt3e8Js8o2fFqQ\" rel=\"nofollow\"\u003eEMBL-ABR YouTube Channel\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.youtube.com/watch?v=lqm-VV5dOgk\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/83bc2e94a0c3282786eac5c9f7899edade3276550c7495dd483422989eef886a/687474703a2f2f696d672e796f75747562652e636f6d2f76692f6c716d2d565635644f676b2f687164656661756c742e6a7067\" alt=\"Nextflow Webinar Video\" data-canonical-src=\"http://img.youtube.com/vi/lqm-VV5dOgk/hqdefault.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSlides\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://csiro-crop-informatics.github.io/nextflow-embl-abr-webinar/nextflow-embl-abr.html\" rel=\"nofollow\"\u003ehttps://csiro-crop-informatics.github.io/nextflow-embl-abr-webinar/nextflow-embl-abr.html\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData for the Webinar\u003c/h1\u003e\u003ca id=\"user-content-data-for-the-webinar\" class=\"anchor\" aria-label=\"Permalink: Data for the Webinar\" href=\"#data-for-the-webinar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the purpose of demonstrating a Nextflow workflow in reasonable time, we will use the dataset used in \u003ca href=\"https://github.com/UofABioinformaticsHub/2019_EMBL-ABR_Snakemake_webinar#data-for-the-webinar\"\u003ethis Snakemake webinar\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTutorial\u003c/h1\u003e\u003ca id=\"user-content-tutorial\" class=\"anchor\" aria-label=\"Permalink: Tutorial\" href=\"#tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"nextflow-tutorial.md\"\u003enextflow-tutorial.md\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "h1-the-swan/autoreview", + "latest_release": null, "stargazers_count": 5, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [], - "updated_at": 1568158524.0 + "updated_at": 1728575919.0 }, { "data_format": 2, - "description": "Empty template for nextflow pipelines", + "description": "Toolkit to bring Webots to High Performance Computing, with support for parallelized and distributed batches of simulations.", "filenames": [ "Singularity" ], - "full_name": "IARCbioinfo/template-nf", + "full_name": "mattwfranchi/Webots.HPC", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eName\u003c/h1\u003e\u003ca id=\"user-content-name\" class=\"anchor\" aria-label=\"Permalink: Name\" href=\"#name\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEmpty template for nextflow pipelines (short description)\u003c/h2\u003e\u003ca id=\"user-content-empty-template-for-nextflow-pipelines-short-description\" class=\"anchor\" aria-label=\"Permalink: Empty template for nextflow pipelines (short description)\" href=\"#empty-template-for-nextflow-pipelines-short-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/template-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6ec2dc07e1431dc0a7fb1f20e44c76ccebd603f4aeb75b30bf94c85090180a6/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f74656d706c6174652d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/template-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/template-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1404\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/94193130\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad1ba5ccb85714dece917f7b680e3f805ccbb4af9b900446d72ad59e80690f42/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f39343139333133302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/94193130.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"template-nf.png\"\u003e\u003cimg src=\"template-nf.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eExternal software:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003einput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003einput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpecify the test files location\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param1\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param2\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param3\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param4\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--flag2\u003c/td\u003e\n\u003ctd\u003e....\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed description (optional section)\u003c/h2\u003e\u003ca id=\"user-content-detailed-description-optional-section\" class=\"anchor\" aria-label=\"Permalink: Detailed description (optional section)\" href=\"#detailed-description-optional-section\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirected Acyclic Graph\u003c/h2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-label=\"Permalink: Directed Acyclic Graph\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/template-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib1*\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support (link to specific gitter chatroom)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib2\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib3\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eTester\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences (optional)\u003c/h2\u003e\u003ca id=\"user-content-references-optional\" class=\"anchor\" aria-label=\"Permalink: References (optional)\" href=\"#references-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ (optional)\u003c/h2\u003e\u003ca id=\"user-content-faq-optional\" class=\"anchor\" aria-label=\"Permalink: FAQ (optional)\" href=\"#faq-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebots.HPC: bringing Webots at-scale to High Performance Computing\u003c/h1\u003e\u003ca id=\"user-content-webotshpc-bringing-webots-at-scale-to-high-performance-computing\" class=\"anchor\" aria-label=\"Permalink: Webots.HPC: bringing Webots at-scale to High Performance Computing\" href=\"#webotshpc-bringing-webots-at-scale-to-high-performance-computing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMatt Franchi, Rebecca Kahn, Clemson University\u003c/strong\u003e\n\u003cem\u003eData Intensive Computing Environments (DICE) Lab\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Project Overview\u003c/h2\u003e\u003ca id=\"user-content-1-project-overview\" class=\"anchor\" aria-label=\"Permalink: 1. Project Overview\" href=\"#1-project-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWebots.HPC is an in-development tool for running Webots robotics simulations on HPC resources.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. \u0027Ingredients\u0027 of Webots.HPC\u003c/h2\u003e\u003ca id=\"user-content-2-ingredients-of-webotshpc\" class=\"anchor\" aria-label=\"Permalink: 2. \u0027Ingredients\u0027 of Webots.HPC\" href=\"#2-ingredients-of-webotshpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cyberbotics.com/\" rel=\"nofollow\"\u003eWebots\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.eclipse.org/sumo/\" rel=\"nofollow\"\u003eSimulation of Urban Mobility (SUMO)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.usgs.gov/core-science-systems/sas/arc/about/what-high-performance-computing\" rel=\"nofollow\"\u003eHigh Performance Computing (HPC) Resource\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Container Downloads\u003c/h2\u003e\u003ca id=\"user-content-3-container-downloads\" class=\"anchor\" aria-label=\"Permalink: 3. Container Downloads\" href=\"#3-container-downloads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/mattwfranchi/webots_sumo\" rel=\"nofollow\"\u003eWebots.HPC Docker Image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 5, - "subscribers_count": 14, + "subscribers_count": 1, "topics": [ - "nextflow" + "webots", + "sumo", + "hpc", + "robotics", + "simulation", + "parallel-computing", + "distributed-systems" ], - "updated_at": 1677031934.0 + "updated_at": 1685012843.0 }, { "data_format": 2, - "description": "Optimize workflow for binning metagenomic short reads from multiple samples", + "description": "H3A variant calling pipeline", "filenames": [ - "Singularity" + "containers/Singularity.bwa", + "containers/Singularity.gatk", + "containers/Singularity.fastqc", + "containers/Singularity.multiqc", + "containers/Singularity.trimmomatic" ], - "full_name": "QuentinLetourneur/Let-it-bin", + "full_name": "h3abionet/h3avarcall", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLet-it-bin\u003c/h1\u003e\u003ca id=\"user-content-let-it-bin\" class=\"anchor\" aria-label=\"Permalink: Let-it-bin\" href=\"#let-it-bin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#prerequisites\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Pipeline_inputs_and_options\"\u003ePipeline inputs and options\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLet-it-bin allows to perform the binning of metagenomic short paired-end reads from multiple samples into species.\nThe pipeline take raw paired-end reads from multiple samples as primary input (the pairs names MUST finish with _{1,2}.fq/fastq). It comprise 4 major steps, reads preprocessing, assembly, binning and evaluation.\nThe pipeline can be started from the second or third step by adding arguments to the command line, provided that you have the needed inputs.\nYou have to select the binning softwares that will run in the following list :\nbinsanity, canopy, concoct, cocacola, maxbin, metabat, metabat2 and metagen\nYou just have to prefix the name of the wanted programms with \u0027--\u0027 (Ex : --concoct).\nIf you want to use them all just use --all T\u003c/p\u003e\n\u003cp\u003eWhen path are needed please give \u003cstrong\u003efull path\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eIf you run this pipeline on a cluster (what I recommend) you can use the given .config file to specify allocated memory per task and other cluster options. Memory values have been placed based on experience but can be changed.\u003cbr\u003e\nBe it locally or on a cluster \u003cstrong\u003ebe sure to add the full path to let-it-bin.simg\u003c/strong\u003e (more details in the next section) in the config file\u003c/p\u003e\n\u003cp\u003eThe output directory have the following layout :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[out]\n|\n|__assembly Assembly and contigs annotation\n|__Binnings Folder of each chosen binning software\n| |__Metabat\n| |__checkm_res\n|__cleaned_reads\n|__khmer_res\n|__mapping\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run this pipeline you will need Nextflow and Singularity (tested with version 19.10.0.5170 and 3.5.0 respectively).\u003cbr\u003e\nHere are the links to the installation instruction for \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e and \u003ca href=\"https://github.com/sylabs/singularity/blob/master/INSTALL.md\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe singularity image can be downloaded here (warning: the file is heavy ~ 1.9Go):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://shiny01.hosting.pasteur.fr/pub/let-it-bin.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA recipe file named Singularity is also given.\u003cbr\u003e\nTo build the image on an unix system move to let-it-bin repository and lauch\u003cbr\u003e\n\u003ccode\u003esudo singularity build let-it-bin.simg Singularity\u003c/code\u003e\u003cbr\u003e\nThis will take at least an hour.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline inputs and options\u003c/h2\u003e\u003ca id=\"user-content-pipeline-inputs-and-options\" class=\"anchor\" aria-label=\"Permalink: Pipeline inputs and options\" href=\"#pipeline-inputs-and-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e GENERAL ARGUMENTS :\n\n --reads [PATH] Directory containing unzipped paired reads files.\nOnly needed if you start from raw reads or reads from which contaminant have been removed\n --nb_samples [INT] Number of samples you are using\n --out [PATH] Directory were will be stored the results of the pipeline\n --sim_data [CHAR] Can be either F (Default) or T. Will change the execution of the pipeline depending on the analysed data (simulated or not).\n --cpus [INT] Number of cpus used for task with multiprocessing (Default 4)\n --min_contigs_length [INT] Minimum contigs length in base to be passed to binning programms (Default 1000)\n --nb_ref [INT] If you use simulated data specify the total number of different genomes present in the samples\n --dastool [CHAR] Can be either T (Default) or F. If you use multiple binning softwares you can use dastool to combine these results and try to extract best bins corresponding to the same microorganism.\n --local_scratch [CHAR] Can be either T (Default) or F. If you are on TARS or on a cluster with a /local/scratch space on nodes. You can use this option to speed up the execution of post processing of binning result for Canopy, Concoct, Cocacola and Metagen.\n --tmp_checkm [PATH] Directory were will be stored CheckM temporary files. The path length souhldn\u0027t exeed 65 chars. (Default [out]/tmp_checkm)\n --help Print the help message\n\n READS PREPROCESSING :\n\n --contaminant [PATH] Path and prefix of the bowtie2 index files of contaminant sequences (HAVE TO be computed before lauching the pipeline)\n --minlength [INT] Minimum length for trimed contigs (Default 45)\n --alienseq [PATH] Fasta file containing adaptaters sequence for AlienTrimmer\n --cleaned_readsDir [PATH] Folder were will be stored reads that have been filtered to eliminate contaminant and trimmed (Default [out]/cleaned_reads)\nIf there are already fastq files in the folder it will take it as input for khmer and skip the cleaning step\n --filt_readsDir [PATH] Directory containing Khmer results.\nIF SPECIFIED the workflow will start at the assembly by taking as input the filtered fastq files in the directory.\n\n ASSEMBLY :\n\n --cpuassembly [INT] Number of cpus used for reads assembly (Default 10)\n --memassembly [INT] Quantity of RAM in Mb used for reads assembly. Default 160000 Mb with 2 retries. If the wanted memory is \u0026lt;= 160000, the allocated memory will grow according to the following formula : number of retry * [memmapping]\nElse no retry will be done\n --qos_assembly [STRING] If you run the pipeline on a cluster with SLURM you can specify the queue of submision (qos) to use : fast, normal (Default) or long (on TARS you can only use 5 cpus in long)\n --mode [STRING] Name of the assembler to be used. Can be either spades (Default), clc, megahit or ray\n --multi_assembly [CHAR] By default a co-assembly of the samples is done. If you want to change that behaviour and do an assembly by sample set this parameter to T (Default F). The generated contigs will then be pulled in one file and filtered to lessen the redundancy but eliminating it is hard so there migth still be redundancy in the filtered contigs\n --contigs [PATH] If the assembly has already been done you can specify the path to the fasta file containing contigs. If provided the assembly steps will be skipped\n --refs_info [PATH] If you use a simulated dataset and specify the --contigs option. Give the path to the sum_contigs_length_per_annotation.tsv file contained in [out]/assembly/\n\n CONTIGS ANNOTATION :\n\n --blast_db [PATH] If you use simulated data. Path to the BLAST database containing reference sequences (HAVE TO be computed before running the pipeline)\n --coverage [INT] Coverage threshold used to filter alignments of contigs on reference genomes or a public database (Default 90)\n --identity [INT] Identity threshold used to filter alignments of contigs on reference genomes or a public database (Default 95)\n --mismatch [INT] Number of mismatch allowed in the seed aligment of BLAST (Default 1)\n --evalue [INT] E-value used for BLAST (Default 10)\n --hit [INT] Maximum number of hits for each querry in the BLAST output (Default 10)\n --link_ref_id_species [PATH] For simulated dataset tab-delimited file containing contigs IDs of reference sequence and the species to which they belong. Used to identify the target in the BLAST of contigs against the references\n --contigs_annotation [PATH] If you use simulated data and have specified the --contigs option specify the path to the (bh_blast_contigs_on_refs.tsv)(to be updated) file in [out]/assembly/\n\n MAPPING :\n\n --cpumapping [INT] Number of cpus used for mapping reads on the assembly (Default 4)\n One mapping per sample\n --memmapping [INT] Quantity of RAM in Mb for the mapping of each sample (Default 5000 )\n --bowtie2_indexDir [PATH] Directory were will be stored bowtie2 index of assembled sequences (Default [out]/bowtie2_index)\n --index_prefix [STRING] Prefix for the index files generated by bowtie2-build\n --bamDir [PATH] Directory were will be stored sorted BAM files generated by the mapping and if you use MetaGen indexed BAM files (Default [out]/mapping/bam). If there are sorted BAM files in the folder the mapping step will be skipped and they will be taken as input for the binning programs\n --count_matrix [PATH] If you use Canopy and have specified the --bamDir option. Provide the path to the count_matrix file contained by default in the [out]/mapping/comptage folder\n\n BINNING :\n\n --cpubinning [INT] Number of cpus for binning programs (Default same value as --cpus option)\n --nb_cluster [INT] Needed if you use Concoct. Rougth estimation of the expected number of species in the samples. This value will be a starting point for the program that will then refine it\n --bic_step [INT] MetaGen parameter corresponding to the step for the search of the optimal number of clusters (Default 5)\n --auto_method [INT] MetaGen parameter can be either 1 or 2. Recommended to be 2 for large datasets and is the default here\n\n BINNING EVALUATION :\n\n --min_bin_size [INT] Minimum size of bins in base (sum of the length of the sequences it contains) to be places in plots (Default 500000)\n --conta_threshold [FLOAT] Maximum contamination percent for good quality bins [0-1] (Default 0.1)\n --comp_threshold [FLOAT] Minimum completeness percent for good quality bins [0-1] (Default 0.6)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor real data starting from raw reads or reads filtered from contaminant and co-assembly with Spades.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ~/nextflow -c ~/let-it-bin/nextflow_slurm_singularity_common.config run -w [directory to store temporary files] let-it-bin.nf --reads ~/data/reads --out ~/results --cpus 4 --metabat --canopy --maxbin --index_prefix spades_contigs --tmp_checkm tmp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor simulated data starting from raw reads and co-assembly with megahit\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ~/nextflow -c ~/let-it-bin/nextflow_slurm_singularity_common.config run -w [directory to store temporary files] let-it-bin.nf --reads ~/data/reads --out ~/results --sim_data T --cpus 4\n --nb_ref 50 --metabat2 --cocacola --metagen --mode megahit\n --blast_db ~/blast_db/refs_seq --link_ref_id_species ~/link_id_species.tsv\n --index_prefix spades_contigs\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\n\u003ccode\u003eh3avarcall\u003c/code\u003e - H3ABioNet Variant Calling Pipeline\u003c/h1\u003e\u003ca id=\"user-content-h3avarcall---h3abionet-variant-calling-pipeline\" class=\"anchor\" aria-label=\"Permalink: h3avarcall - H3ABioNet Variant Calling Pipeline\" href=\"#h3avarcall---h3abionet-variant-calling-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eh3avarcall\u003c/code\u003e is a \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e pipeline developed by \u003ca href=\"https://www.h3abionet.org/\" rel=\"nofollow\"\u003e\u003ccode\u003eH3ABioNet\u003c/code\u003e\u003c/a\u003e for genomic Variant Calling allowing to detect SNPs and Indels giving raw sequence reads (fastq files) as input. \u003ccode\u003eh3avarcall\u003c/code\u003e includes the different steps from aligning raw sequence reads to variant calling and filtering using GATK. \u003cbr\u003e\nFor more details about the different steps of the pipeline, check the [H3ABionet SOPs pages] \u003ca href=\"https://h3abionet.github.io/H3ABionet-SOPs/Variant-Calling\" rel=\"nofollow\"\u003ehttps://h3abionet.github.io/H3ABionet-SOPs/Variant-Calling\u003c/a\u003e\n\u003ccode\u003eh3avarcall\u003c/code\u003e is a modular and extensible tool allowing users to run the whole pipeline, use only parts of it and also to easily enrich it and adapt it to their needs. \u003ccode\u003eh3avarcall\u003c/code\u003e generates a number of intermediate files where results from various steps of the pipeline are stored.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Obtaining pipeline and preparing Data\u003c/h2\u003e\u003ca id=\"user-content-1-obtaining-pipeline-and-preparing-data\" class=\"anchor\" aria-label=\"Permalink: 1. Obtaining pipeline and preparing Data\" href=\"#1-obtaining-pipeline-and-preparing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you need to clone the \u003ccode\u003eh3avarcall\u003c/code\u003e repository onto you machine:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/h3abionet/h3avarcall.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e h3avarcall\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eContent of the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eh3avarcall\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--containers \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for Singularity images and recipes (in case you want to build yourself). All downloaded images go here!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.bwa \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for BWA and Samtools.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.fastqc \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for FastQC.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.gatk \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for GATK and tabix.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.trimmomatic \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for Trimmimatic.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gatk-b37-bundle \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for stoding downloaded GATK-b37-bundle files.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--b37_files_minimal.txt \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# LList of GATK-b37-bundle files to be downloaded (bundle TOO BIG! Only selected files needed for the pipeline). \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--templates \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for extra scripts for the pipeline.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--download_bundles.sh \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Script for downloading GATK-b37-bundle.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--LICENSE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Duh!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--README.md \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Duh!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--main.config \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# User configuration file! All inputs, outputs and options GO HERE!! ONLY file that SHOULD be modified by user!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--main.nf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Main h3avarcall nextflow scripts.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nextflow.config \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Pipeline configuration file! DO NOT EDIT!!!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003emain.config\u003c/code\u003e file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-groovy\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e/*\u003c/span\u003e==================================================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * THIS FILE IS USED TO SPECIFY INPUT, OUTPUTS AND PARAMETERS. THE FOLLOWING OPTIONS ARE THE ALLOWED:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * ==================================================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * data : Path to where the data is (FASTQ files).\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * out : Path to store output results.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * bundle : GATK-b37-bundle list file.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * mode : Worflow step to perform. Can be any of [ do.GetContainers | do.GenomeIndexing | do.QC | do.ReadTrimming | do.ReadAlignment | do.VarianCalling | do.VariantFiltering | do.MultiQC].\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * trim : Trimming options for Trimmomatic.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * resources : Location of the GATK-b37-bundle folder.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * from : pipeline step to resume pipeline from. Can be any of [ do.QC | do.ReadTrimming | do.ReadAlignment | do.VarianCalling | do.VariantFiltering ].\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * params.help : Print help menu.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * ==================================================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * BELOW ARE THE DEFAULT PARAMETERS! YOU\u0027RE MORE THAN WELCOME TO CHANGE AS DESIRED!\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * ==================================================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e \u003cspan class=\"pl-c\"\u003e*/\u003c/span\u003e\u003c/span\u003e\n\nparams {\n data \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$b\u003cspan class=\"pl-smi\"\u003easeDir\u003c/span\u003e\u003c/span\u003e/data\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n out \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$b\u003cspan class=\"pl-smi\"\u003easeDir\u003c/span\u003e\u003c/span\u003e/results\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n bundle \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$b\u003cspan class=\"pl-smi\"\u003easeDir\u003c/span\u003e\u003c/span\u003e/gatk-b37-bundle/b37_files_minimal.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n mode \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edo.QC\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n trim \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10:8:true TRAILING:28 MINLEN:40\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n resources \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$b\u003cspan class=\"pl-smi\"\u003easeDir\u003c/span\u003e\u003c/span\u003e/gatk-b37-bundle\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n from \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enull\u003c/span\u003e\n params\u003cspan class=\"pl-k\"\u003e.\u003c/span\u003ehelp \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enull\u003c/span\u003e\n}\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.1. Download test datasets:\u003c/h3\u003e\u003ca id=\"user-content-11-download-test-datasets\" class=\"anchor\" aria-label=\"Permalink: 1.1. Download test datasets:\" href=\"#11-download-test-datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a data directory under the \u003ccode\u003eh3avarcall\u003c/code\u003e repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir data\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDownload the test data from \u003ca href=\"http://thesite.com\" rel=\"nofollow\"\u003eTHIS_SITE\u003c/a\u003e using one of the commands bellow:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e1.1.2. Using LFTP (faster)\u003c/h4\u003e\u003ca id=\"user-content-112-using-lftp-faster\" class=\"anchor\" aria-label=\"Permalink: 1.1.2. Using LFTP (faster)\" href=\"#112-using-lftp-faster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003elftp -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epget -n 20 ftp://ftp-trace.ncbi.nih.gov/giab/ftp/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/NIST7035_TAAGGCGA_L001_R1_001.fastq.gz; bye\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nlftp -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epget -n 20 ftp://ftp-trace.ncbi.nih.gov/giab/ftp/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/NIST7035_TAAGGCGA_L001_R2_001.fastq.gz; bye\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e1.1.3. Using WGET (slower)\u003c/h4\u003e\u003ca id=\"user-content-113-using-wget-slower\" class=\"anchor\" aria-label=\"Permalink: 1.1.3. Using WGET (slower)\" href=\"#113-using-wget-slower\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget ftp://ftp-trace.ncbi.nih.gov/giab/ftp/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/NIST7035_TAAGGCGA_L001_R1_001.fastq.gz\nwget ftp://ftp-trace.ncbi.nih.gov/giab/ftp/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/NIST7035_TAAGGCGA_L001_R2_001.fastq.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.2. Download the \u003ccode\u003eSingularity\u003c/code\u003e containers (required to execute the pipeline):\u003c/h3\u003e\u003ca id=\"user-content-12-download-the-singularity-containers-required-to-execute-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: 1.2. Download the Singularity containers (required to execute the pipeline):\" href=\"#12-download-the-singularity-containers-required-to-execute-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.GetContainers\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.3. Download the GATK b37 bundle (required to execute the pipeline):\u003c/h3\u003e\u003ca id=\"user-content-13-download-the-gatk-b37-bundle-required-to-execute-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: 1.3. Download the GATK b37 bundle (required to execute the pipeline):\" href=\"#13-download-the-gatk-b37-bundle-required-to-execute-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step takes \u003cstrong\u003eFOREVER\u003c/strong\u003e to run - run it only once!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.GenomeIndexing\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf by some miracle you happen to have access to the WITS Cluster, you do not need to download the GATK-b37-bundle! Simply \u003ccode\u003ecd\u003c/code\u003e into the \u003ccode\u003egatk-b37-bundle\u003c/code\u003e folder of the \u003ccode\u003eh3avarcall\u003c/code\u003e repo and soft-link the GATK-b37-bundle data as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd gatk-b37-bundle\nln -s /global/blast/gatk-bundle/b37/* .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Executing the main \u003ccode\u003eh3avarcall\u003c/code\u003e pipeline\u003c/h2\u003e\u003ca id=\"user-content-2-executing-the-main-h3avarcall-pipeline\" class=\"anchor\" aria-label=\"Permalink: 2. Executing the main h3avarcall pipeline\" href=\"#2-executing-the-main-h3avarcall-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.1. Read QC (optional): \u003cbr\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-21-read-qc-optional-\" class=\"anchor\" aria-label=\"Permalink: 2.1. Read QC (optional): \" href=\"#21-read-qc-optional-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore getting started with the downstream analysis, it\u0027s always good to do some quality checks on your raw sequences to assess the quality of raw sequence data, the fastq files. FastQC tool has been used in this workflow. An html report page will be automatically created for each fastq file. You can load up these html pages in your browser to assess your data through graphs and summary tables.\u003cbr\u003e\nTo perform the QC of your fastq files, you can use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.QC\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.2. Read Trimming (optional):\u003cbr\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-22-read-trimming-optional\" class=\"anchor\" aria-label=\"Permalink: 2.2. Read Trimming (optional):\" href=\"#22-read-trimming-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter performing the QC of your fastq files, you have an idea about the quality of your reads: some of your reads might not be of a very good quality or the quality might drop at some positions (near the begining or end of reads) across all reads and this requires to clean up your library to minimize biaises in your analysis by filtering poor quality reads and/or trim poor quality bases from our samples. Trimmomatic is the trimming tool that has been used here. \u003cbr\u003e\nFor more information about reads preprocessing, check this \u003ca href=\"https://h3abionet.github.io/H3ABionet-SOPs/Variant-Calling#phase-1-preprocessing-of-the-raw-reads\" rel=\"nofollow\"\u003epage\u003c/a\u003e. \u003cbr\u003e\nTo run the trimming step of the \u003ccode\u003eh3avarcall\u003c/code\u003e pipeline, you can use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.ReadTrimming\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.3. Read Alignment \u003cbr\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-23-read-alignment-\" class=\"anchor\" aria-label=\"Permalink: 2.3. Read Alignment \" href=\"#23-read-alignment-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have good raw sequences quality, the next step is to map your reads to a reference genome to determine where in the genome the reads originated from. The mapper used in this workflow is BWA. For more information about the read alignement step, check this \u003ca href=\"https://h3abionet.github.io/H3ABionet-SOPs/Variant-Calling#phase-2-initial-variant-discovery\" rel=\"nofollow\"\u003epage\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCan be run with \u003ccode\u003e--from do.ReadTrimming\u003c/code\u003e or \u003ccode\u003e--from do.QC\u003c/code\u003e depending on whether these steps were run!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.ReadAlignment\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.4. Variant Calling\u003c/h3\u003e\u003ca id=\"user-content-24-variant-calling\" class=\"anchor\" aria-label=\"Permalink: 2.4. Variant Calling\" href=\"#24-variant-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step uses the outputs generated by the Read Alignment STEP! \u003cstrong\u003eMUST\u003c/strong\u003e run STEP 2.3 (\u003ccode\u003e--mode do.ReadAlignment\u003c/code\u003e) before running this step.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.VariantCalling \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.5. Variant Filtering\u003c/h3\u003e\u003ca id=\"user-content-25-variant-filtering\" class=\"anchor\" aria-label=\"Permalink: 2.5. Variant Filtering\" href=\"#25-variant-filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step uses the outputs generated by the Variant Calling STEP! \u003cstrong\u003eMUST\u003c/strong\u003e run STEP 2.4 (\u003ccode\u003e--mode do.VariantCalling\u003c/code\u003e) before running this step.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.VariantFiltering \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.6. Workflow QC (MultiQC - Optional)\u003c/h3\u003e\u003ca id=\"user-content-26-workflow-qc-multiqc---optional\" class=\"anchor\" aria-label=\"Permalink: 2.6. Workflow QC (MultiQC - Optional)\" href=\"#26-workflow-qc-multiqc---optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step performs a Quality Check of the different pipeline steps that have been ran. You need to run at least ONE step of the pipeline to be able to run this MultiQC step!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.MultiQC \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Explore \u003ccode\u003eh3avarcall\u003c/code\u003e results\u003c/h2\u003e\u003ca id=\"user-content-3-explore-h3avarcall-results\" class=\"anchor\" aria-label=\"Permalink: 3. Explore h3avarcall results\" href=\"#3-explore-h3avarcall-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAssuming you did not change the default output folder (in the \u003ccode\u003emain.config\u003c/code\u003e file), the resulting files will be found in the \u003ccode\u003eresults\u003c/code\u003e folder of the \u003ccode\u003eh3avarcall\u003c/code\u003e repository. Resulting files for each of the main pipeline steps (\u003ccode\u003e2.1\u003c/code\u003e - \u003ccode\u003e2.5\u003c/code\u003e) are grouped in different folders as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- [1] Read QC (optional) =\u0026gt; `results/1_QC`\n- [2] Read Trimming (optional) =\u0026gt; `results/2_Read_Trimming`\n- [3] Read Alignment =\u0026gt; `results/3_Read_Alignment`\n- [4] Variant Calling =\u0026gt; `results/4_Variant_Calling`\n- [5] Variant Filtering =\u0026gt; `results/5_Variant_Filtering`\n- [6] MultiQC =\u0026gt; `results/MultiQC`\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn each of these folders, a sub-folder \"\u003ccode\u003eworkflow_report\u003c/code\u003e\" is created. It contains 4 different files (\u003ccode\u003eh3avarcall_report.html\u003c/code\u003e, \u003ccode\u003eh3avarcall_timeline.html\u003c/code\u003e, \u003ccode\u003eh3avarcall_workflow.dot\u003c/code\u003e and \u003ccode\u003eh3avarcall_trace.txt\u003c/code\u003e) containing detailed information on the resources (CPU, MEMORY and TIME) usage of each process in the different pipeline steps. \u003cbr\u003e\nThe \u003ccode\u003eresults\u003c/code\u003e directory structure within \u003ccode\u003eh3avarcall\u003c/code\u003e repository can be summarized as below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eh3avarcall\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--results\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--1_QC\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_R1.fastqc.html .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_R1.fastqc.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_R2.fastqc.html .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_R1.fastqc.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--2_Read_Trimming\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e.1P.fastq.gz .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.1P.fastq.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e.2P.fastq.gz .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.2P.fastq.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--3_Read_Alignment\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_md.recal.bam .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_md.recal.bam\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_md.recal.bai .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_md.recal.bai\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--4_Variant_Calling\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--chr_1_genotyped.vcf.gz .. chr_22_genotyped.vcf.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--chr_1_genotyped.vcf.gz.tbi .. chr_22_genotyped.vcf.gz.tbi\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--5_Variant_Filtering\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--genome.SNP-recal.vcf.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--genome.SNP-recal.vcf.gz.tbi\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--MultiQC\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--multiqc_data\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--multiqc_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--work\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eThere\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003es a lot of folders here! Lets not worry about them for today!\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e##We\u0027re working on further improving the pipleine and the associated documentation, feel free to share comments and suggestions!\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 3, - "topics": [ - "nextflow", - "binning", - "metagenomic-analysis", - "singularity-containers" + "subscribers_count": 9, + "topics": [], + "updated_at": 1725654312.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "Singularity.base", + "workflow/old/rs3/Singularity.rs3", + "workflow/old/common/Singularity.workflow_base", + "workflow/old/rs4/Singularity.rs4" ], - "updated_at": 1596753628.0 + "full_name": "langmead-lab/recount-pump", + "latest_release": "v1.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erecount-pump\u003c/h1\u003e\u003ca id=\"user-content-recount-pump\" class=\"anchor\" aria-label=\"Permalink: recount-pump\" href=\"#recount-pump\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will give a high level overview of the process of configuring and running a specific project through the first phase of the Monorail pipeline (the \u003ccode\u003epump\u003c/code\u003e phase).\u003c/p\u003e\n\u003cp\u003eFor details, please see the READMEs associated with the various sub-components (e.g. \u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/README.md\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/README.md\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eNomenclature note: \"run\" here is used in terms of a single project\u0027s instantiation as a Monorail pipeline.\nTo differentiate it from the SRA\u0027s base unit of sequencing (also called \"run\", e.g. identified by an [SED]RR accession), we will slightly abuse the terminology of the SRA by calling all sequencing runs \"samples\". For the purposes of this document this is acceptable, though not technically true when discussing sequencing in general.\u003c/p\u003e\n\u003cp\u003eThis document assumes that the reader is interested in running the full Monorail pipeline using the management infrastructure typically run in AWS.\nThis is how all the recount3-related runs were processed.\u003c/p\u003e\n\u003cp\u003eHowever, if the reader\u0027s use case is not to recreate/update recount3/Snaptron2 and their total samples are up to 10\u0027s of thousands (versus 100\u0027s of thousands),\nthey might be better off looking at the monorail-external repo:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/monorail-external/\"\u003ehttps://github.com/langmead-lab/monorail-external/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis runs the same containers as this repo, but assumes no management layer running elsewhere (e.g. AWS).\nHowever, the monorail-external repo\u0027s README includes more granular details about the \u003ccode\u003epump\u003c/code\u003e workflow itself,\nwhich supplement these instructions no matter what type of run the reader is looking to do.\u003c/p\u003e\n\u003cp\u003eThe monorail-external repo\u0027s README covers getting the reference indexes (needed here as well), default settings in the Snakemake for\nintra-sample parallelism (e.g. 8 cores for the STAR aligner per sample), and exact versions of the aligners used.\u003c/p\u003e\n\u003cp\u003eThe monorail-external repo also includes information on the container and instructions for running the \u003ccode\u003eunifier\u003c/code\u003e\nto aggregate the coverage summaries across the samples aligned with the \u003ccode\u003epump\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eunifier\u003c/code\u003e is not covered here, but its repo is:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-unify\"\u003ehttps://github.com/langmead-lab/recount-unify\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProjects\u003c/h2\u003e\u003ca id=\"user-content-projects\" class=\"anchor\" aria-label=\"Permalink: Projects\" href=\"#projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMonorail revolves around the idea of a \u003ccode\u003eproject\u003c/code\u003e which defines the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLabel/name of a run (e.g. \"sra_human_v3\")\u003c/li\u003e\n\u003cli\u003eSet of sample identifiers (if SRA, this is a list of accessions)\u003c/li\u003e\n\u003cli\u003eMonorail docker image name + version to be used for the pipeline\u003c/li\u003e\n\u003cli\u003eSpecies information (name, taxon ID, and reference short name [hg38])\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis is stored in the \u003ccode\u003eproject.ini\u003c/code\u003e file in the \u003ccode\u003eprojects/\u0026lt;proj_name\u0026gt;/\u003c/code\u003e subdirectory.\u003c/p\u003e\n\u003cp\u003eA working project which also serves as a good example is here:\n\u003ca href=\"https://github.com/langmead-lab/recount-pump/tree/master/projects/tcga\"\u003ehttps://github.com/langmead-lab/recount-pump/tree/master/projects/tcga\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThere is also the \u003ccode\u003eprojects/\u0026lt;proj_name\u0026gt;/creds/\u003c/code\u003e subdirectory which stores the project-specific settings per-module (e.g. for Globus).\nThese can be created in a semi-automated way, but typically once one or more projects have been defined by the same person, copying and editing these files between projects is reasonable.\u003c/p\u003e\n\u003cp\u003eAdditionally there are two files (\u003ccode\u003epublic_conf.ini\u003c/code\u003e and \u003ccode\u003eprivate_conf.ini\u003c/code\u003e) which define organization-wide AWS settings.\nThe \u003ccode\u003eprivate_conf.ini\u003c/code\u003e as the name implies should \u003cem\u003enot\u003c/em\u003e be world-readable.\u003c/p\u003e\n\u003cp\u003eAll settings related files are discussed further at the end of this README.\u003c/p\u003e\n\u003cp\u003eThe set of sample identifiers, either a JSON or more typically text file (compressed), is copied to the project directory on S3.\nThe S3 URL is then referenced in the project.ini file, e.g.:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003es3://recount-pump-experiments/sra_human_v3/tranche_0.txt.gz\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis will be used to populate the AWS RDS DB for the project with the list of sample identifiers in the \"Initializing the Project Model\" step.\nEach sample is assigned an integer ID which is used to link it between the DB and the SQS queue.\u003cbr\u003e\nThis ID is only for internal tracking during the \u003ccode\u003erecount-pump\u003c/code\u003e stage.\u003c/p\u003e\n\u003cp\u003eThere are a group of settings files which control how Monorail interacts with the AWS modules (SQS, RDS, Watchtower/CloudWatch), partial examples of these are here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/tree/master/projects/common/creds\"\u003ehttps://github.com/langmead-lab/recount-pump/tree/master/projects/common/creds\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eConceptually there is the \u003ccode\u003eproject\u003c/code\u003e level configuration (covered above) and the \u003ccode\u003ecluster\u003c/code\u003e level configuration (covered later in this README).\nThere is usually only one \u003ccode\u003eproject\u003c/code\u003e level configuration, but there could be more than one \u003ccode\u003ecluster\u003c/code\u003e level configuration for the same \u003ccode\u003eproject\u003c/code\u003e.\nThis is a key feature of the grid computing approach.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInitializing the Project Model\u003c/h2\u003e\u003ca id=\"user-content-initializing-the-project-model\" class=\"anchor\" aria-label=\"Permalink: Initializing the Project Model\" href=\"#initializing-the-project-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce all \u003ccode\u003eproject\u003c/code\u003e level settings files have been configured, the project needs to be initialized.\u003c/p\u003e\n\u003cp\u003eBefore attempting to run initialization, you need to ensure the Python2.7 used has the needed dependencies.\nThese are required for both initializtion \u003cem\u003eand\u003c/em\u003e running the Python parent process \u003ccode\u003ecluster.py\u003c/code\u003e in the job scripts below.\u003c/p\u003e\n\u003cp\u003eAssuming you have write access to the Python2.7 in your envionment (either because you\u0027re root or more likely you\u0027ve setup a \u003ccode\u003evirtualenv\u003c/code\u003e or are using conda):\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install -r requirements.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003ccode\u003erequirements.txt\u003c/code\u003e are in the root of this repo.\u003c/p\u003e\n\u003cp\u003eNOTE: these are the only dependencies needed when using the recount-pump container.\u003cbr\u003e\nAll the conda-related files are installed within the container itself.\u003c/p\u003e\n\u003cp\u003eThe following scripts should be run from under the project working directory (typically \u003ccode\u003eprojects/\u0026lt;proj_name\u0026gt;\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eprojects/common/init_model.sh\u003c/code\u003e\nand\n\u003ccode\u003eprojects/common/reset.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003einit_model.sh\u003c/code\u003e script will perform the following actions for the project:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreation of AWS RDS DB\u003c/li\u003e\n\u003cli\u003ePopulation of AWS RDS DB with sample IDs and reference/annotation data set\u003c/li\u003e\n\u003cli\u003eAdds Monorail Docker image name/version to database\u003c/li\u003e\n\u003cli\u003eCreation of AWS SQS job queue\u003c/li\u003e\n\u003cli\u003eStage sample IDs as messages in SQS job queue\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis information represents the tracked \"state\" of the project/run.\u003c/p\u003e\n\u003cp\u003eIf there is a problem in the initialization or later in the project run that relates to configuration, it\u0027s usually best to start fresh with a new initialization run. This can be done by resetting the project in AWS (DB/SQS) with the \u003ccode\u003ereset.sh\u003c/code\u003e script listed above.\u003c/p\u003e\n\u003cp\u003eHowever, problems with individual jobs/samples/nodes can be worked out individually and those jobs requeued w/o having to re-initialize the project as a whole.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCluster Configuration\u003c/h2\u003e\u003ca id=\"user-content-cluster-configuration\" class=\"anchor\" aria-label=\"Permalink: Cluster Configuration\" href=\"#cluster-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTypically, Monorail is run in an HPC environment using Singularity + Conda to ease the pain of dependency management.\nMonorail \u003cem\u003ecan\u003c/em\u003e be run outside of containers (\"bare metal\") but this is not recommended for most cases and is not covered here.\u003c/p\u003e\n\u003cp\u003eThe key settings file for cluster configuration is the \u003ccode\u003ecluster.ini\u003c/code\u003e file, detailed at the end of this README.\u003c/p\u003e\n\u003cp\u003eA partial example is here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/public_conf.ini\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/public_conf.ini\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis file also serves as a reference point for which path temporary/output files will be deposited during a run (useful for debugging).\u003c/p\u003e\n\u003cp\u003eIt can also define the within-container mount directories for the external,\nhost paths if this is needed by the specific cluster (e.g. Stampede2 needs to have the container mounts defined, MARCC does not).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorker Run Configuration\u003c/h2\u003e\u003ca id=\"user-content-worker-run-configuration\" class=\"anchor\" aria-label=\"Permalink: Worker Run Configuration\" href=\"#worker-run-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the project has been initialized, and one or more clusters have been configured, Monorail can be run.\nThis section assumes you\u0027re running on a local HPC cluster, but it could be extended to include remote resources on AWS or equivalent.\u003c/p\u003e\n\u003cp\u003eThere are 3 types of entities important in this section:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJobs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA \u003ccode\u003ejob\u003c/code\u003e is an attempt at processing a single sample through Monorail (it could fail)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWorkers\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA \u003ccode\u003eworker\u003c/code\u003e is the atomic agent of Monorail, it represents a single python process which instantiates a container for each new \u003ccode\u003ejob\u003c/code\u003e, which in turn runs a Snakemake pipeline within the container. Under normal circumstances, a \u003ccode\u003eworker\u003c/code\u003e will continue to run as long as 1) there are \u003ccode\u003ejob\u003c/code\u003es on the SQS \u003ccode\u003ejob\u003c/code\u003e queue and 2) the SQS \u003ccode\u003ejob\u003c/code\u003e queue is accessible. A \u003ccode\u003eworker\u003c/code\u003e runs each \u003ccode\u003ejob\u003c/code\u003e in sequence, but it can use multiple cores/CPUs within the \u003ccode\u003ejob\u003c/code\u003e to parallelize tasks such as alignment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNodes\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEach \u003ccode\u003enode\u003c/code\u003e represents a machine (or VM) allocated, in part or in whole, to Monorail to run one or more \u003ccode\u003eworker\u003c/code\u003es to process \u003ccode\u003ejob\u003c/code\u003es. Each allocation of a \u003ccode\u003enode\u003c/code\u003e will start a parent python process which will then spawn one or more child \u003ccode\u003eworker\u003c/code\u003e processes.\u003c/p\u003e\n\u003cp\u003eTo start Monorail running on a \u003ccode\u003enode\u003c/code\u003e, typically, a \"runner\" (batch) script is submitted to the HPC\u0027s scheduler (e.g. Slurm) to request allocation of a \u003ccode\u003enode\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis script will typically set the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHPC scheduler partition/queue to allocate from\u003c/li\u003e\n\u003cli\u003eName of allocation\u003c/li\u003e\n\u003cli\u003eTime requested (e.g. 12 hours)\u003c/li\u003e\n\u003cli\u003eHardware resources requested (e.g. 12 cores, 90G memory)\u003c/li\u003e\n\u003cli\u003eAccount to charge allocation to (if applicable)\u003c/li\u003e\n\u003cli\u003eList of \u003ccode\u003enode\u003c/code\u003es to exclude (blacklisting, if applicable)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition it will setup the environment to start the Monorail parent process on that \u003ccode\u003enode\u003c/code\u003e, which includes loading the Singularity module.\nAnd finally it will start the \u003ccode\u003ecluster.py\u003c/code\u003e parent python process with parameters which point to the various \u003ccode\u003e.ini\u003c/code\u003e files.\u003c/p\u003e\n\u003cp\u003eAn example of this, which includes a delay at the start of the parent python processes on a \u003ccode\u003enode\u003c/code\u003e by up to 6 minutes in the runner script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erdelay=`perl -e \u0027print \"\".int(rand(360));\u0027`\nsleep $rdelay\n\nmodule load singularity/2.6.0\nconda activate recount\numask 0077\npython /path/to/recount-pump/src/cluster.py run --ini-base creds --cluster-ini creds/cluster.ini \u0026lt;proj_name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe delay is to stagger \u003ccode\u003ejob\u003c/code\u003e starts to avoid maxing out the globus API rate limits when automatically transferring via Globus, this is not needed if Globus is manually run after a whole \u003ccode\u003erun\u003c/code\u003e (tranche) completes.\u003c/p\u003e\n\u003cp\u003eGlobus is \u003cem\u003enot\u003c/em\u003e automatically run for Stampede2 or for MARCC (details below).\u003c/p\u003e\n\u003cp\u003eThe following are versions of scripts/configurations that were actually used to run \u003ccode\u003esra_human_v3\u003c/code\u003e, \u003ccode\u003esra_mouse_v1\u003c/code\u003e, \u003ccode\u003etcgav2\u003c/code\u003e and \u003ccode\u003egtexv2\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStampede2\u003c/h3\u003e\u003ca id=\"user-content-stampede2\" class=\"anchor\" aria-label=\"Permalink: Stampede2\" href=\"#stampede2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStampede2 job runner \u0026amp; \u003ccode\u003ecluster\u003c/code\u003e config for Skylake (\u003ccode\u003eskx-normal\u003c/code\u003e) partition/queue:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/stampede2/skx-normal/job.sh\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/stampede2/skx-normal/job.sh\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/stampede2/skx-normal/cluster-skx-normal.ini\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/stampede2/skx-normal/cluster-skx-normal.ini\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDuring our initial runs on Stampede2, we encountered the API rate limits mentioned above and opted to transfer in bulk after a run/tranche is fully done rather than use Globus automatically.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMARCC\u003c/h3\u003e\u003ca id=\"user-content-marcc\" class=\"anchor\" aria-label=\"Permalink: MARCC\" href=\"#marcc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMARCC job runner \u0026amp; \u003ccode\u003ecluster\u003c/code\u003e config for \u003ccode\u003elrgmem\u003c/code\u003e partition/queue using \u003ccode\u003e/dev/shm\u003c/code\u003e:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/lrgmem/job.sh\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/lrgmem/job.sh\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/lrgmem/cluster4_shm.ini\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/lrgmem/cluster4_shm.ini\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIn the MARCC case, we typically don\u0027t transfer the output of runs immediately to another filesystem, though these runs are eventually backed up on JHPCE (or equivalent).\nThis is because runs on MARCC are usually of protected data (TCGA/GTEx) and therefore can\u0027t be copied to just anywhere.\u003c/p\u003e\n\u003cp\u003eThis also highlights the need for the following when processing protected runs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-world readable permissions on all input/output files (\u003ccode\u003eumask 077\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eEncrypted transfers when copying files to another location (e.g. using Globus to backup TCGA/GTEx to JHPCE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStopping Conditions\u003c/h2\u003e\u003ca id=\"user-content-stopping-conditions\" class=\"anchor\" aria-label=\"Permalink: Stopping Conditions\" href=\"#stopping-conditions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNodes will stop processing for one of 3 reasons:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe time limit on the node allocation ends\u003c/li\u003e\n\u003cli\u003eThe job queue is exhausted\u003c/li\u003e\n\u003cli\u003eA runtime error causes the parent python process running on the node to prematurely terminate\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy far the most common cause for \u003ccode\u003enode\u003c/code\u003e stopages is allocation expirations (1st one), since \u003ccode\u003enode\u003c/code\u003e allocations are much shorter than what\u0027s needed to process a medium-large Monorail run. This will have the effect of stopping \u003ccode\u003ejob\u003c/code\u003es in the middle which will need to be restarted. This is expected and these \u003ccode\u003ejob\u003c/code\u003es will be visible again on the queue after a pre-defined time period (typically 30 min to 3 hours) controlled by \u003ccode\u003evisibility_timeout\u003c/code\u003e in the \u003ccode\u003ecreds/queue.ini\u003c/code\u003e settings file for the \u003ccode\u003eproject\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf many concurrent attempts are made which end up being successful for a particular \u003ccode\u003ejob\u003c/code\u003e, this indicates the \u003ccode\u003evisibility_timeout\u003c/code\u003e setting for the \u003ccode\u003ejob\u003c/code\u003e queue is too short and should be elongated.\u003c/p\u003e\n\u003cp\u003eAlso related to this, the \u003ccode\u003emax_receive_count\u003c/code\u003e also in \u003ccode\u003ecreds/queue.ini\u003c/code\u003e, controls how many times a job is attempted before dumping it to the Dead Letter Queue (DLQ). Typically this is 3-6 times, depending on the project, however, in certain cases (SRA) it may be necessary to reduce this to 1-2 to rapidly fail samples which simply won\u0027t download.\u003c/p\u003e\n\u003cp\u003eIn the 2nd \u003ccode\u003enode\u003c/code\u003e stop case above, the parent process running on the \u003ccode\u003enode\u003c/code\u003e will wait until all \u003ccode\u003eworker\u003c/code\u003e processes (children) have finished w/o error and then it will finish itself and relinquish the \u003ccode\u003enode\u003c/code\u003e. If a child \u003ccode\u003eworker\u003c/code\u003e process fails, the parent will start a new \u003ccode\u003eworker\u003c/code\u003e process in its place and continue checking \u003ccode\u003eworker\u003c/code\u003e processes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSettings Files\u003c/h2\u003e\u003ca id=\"user-content-settings-files\" class=\"anchor\" aria-label=\"Permalink: Settings Files\" href=\"#settings-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject-specific Settings files\u003c/h3\u003e\u003ca id=\"user-content-project-specific-settings-files\" class=\"anchor\" aria-label=\"Permalink: Project-specific Settings files\" href=\"#project-specific-settings-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ecluster.ini\u003c/h4\u003e\u003ca id=\"user-content-clusterini\" class=\"anchor\" aria-label=\"Permalink: cluster.ini\" href=\"#clusterini\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis file defines the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCluster name\u003c/li\u003e\n\u003cli\u003eContainer system (typically \"Singularity\")\u003c/li\u003e\n\u003cli\u003ePath to Singularity image file\u003c/li\u003e\n\u003cli\u003eInput path\u003c/li\u003e\n\u003cli\u003eOutput path\u003c/li\u003e\n\u003cli\u003eTemp path\u003c/li\u003e\n\u003cli\u003eReference file set path\u003c/li\u003e\n\u003cli\u003e# of workers (\u003ccode\u003eworkers\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e# of cores per worker (\u003ccode\u003ecpus\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePaths are always absolute.\nInput/output/temp paths are defined both for the host OS \u003cem\u003eand\u003c/em\u003e for the container.\nThe container paths are where the host OS paths are mounted in the container, so they reference the same thing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGeneric Settings Files\u003c/h3\u003e\u003ca id=\"user-content-generic-settings-files\" class=\"anchor\" aria-label=\"Permalink: Generic Settings Files\" href=\"#generic-settings-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe settings below are typically set once for a organization/group and shared between multiple \u003ccode\u003eprojects\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003epublic_conf.ini\u003c/h4\u003e\u003ca id=\"user-content-public_confini\" class=\"anchor\" aria-label=\"Permalink: public_conf.ini\" href=\"#public_confini\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eProfile\u003c/li\u003e\n\u003cli\u003eRegion\u003c/li\u003e\n\u003cli\u003eSubnets\u003c/li\u003e\n\u003cli\u003eRDS DB port/DNS\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eprivate_conf.ini\u003c/h4\u003e\u003ca id=\"user-content-private_confini\" class=\"anchor\" aria-label=\"Permalink: private_conf.ini\" href=\"#private_confini\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eConfidential AWS RDS DB password\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 5, + "subscribers_count": 7, + "topics": [], + "updated_at": 1722884581.0 }, { "data_format": 2, - "description": "HIPAA \u0026 GDPR compliant ready Mongo Database with percona-server.", + "description": "BIDS app for NeuroData\u0027s MRI to Graphs pipeline", "filenames": [ - "Singularity" + "Singularity", + "Singularity.v0.1.0" ], - "full_name": "netreconlab/hipaa-mongo", + "full_name": "bids-apps/ndmg", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehipaa-mongo\u003c/h1\u003e\u003ca id=\"user-content-hipaa-mongo\" class=\"anchor\" aria-label=\"Permalink: hipaa-mongo\" href=\"#hipaa-mongo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/hipaa-mongo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c07575084faefb6de357b820a7d0523f0c8a29df70609ebefd38c14a3f60b89f/68747470733a2f2f646f636b6572692e636f2f696d6167652f6e65747265636f6e6c61622f68697061612d6d6f6e676f\" alt=\"\" data-canonical-src=\"https://dockeri.co/image/netreconlab/hipaa-mongo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-mongo/actions/workflows/build.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-mongo/actions/workflows/build.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-mongo/actions/workflows/release.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-mongo/actions/workflows/release.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eA simple Mongo image designed for \u003ca href=\"https://github.com/netreconlab/parse-hipaa\"\u003eparse-hipaa\u003c/a\u003e but can be used anywhere Mongo is used. These docker images include the necessary database auditing and logging for HIPAA compliance. hipaa-mongo is derived from \u003ca href=\"https://hub.docker.com/r/percona/percona-server-mongodb/\" rel=\"nofollow\"\u003epercona-server-mongodb\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ehipaa-mongo provides the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Auditing \u0026amp; logging\u003c/li\u003e\n\u003cli\u003e[x] Ready for encryption in transit - run behind a proxy with files \u0026amp; directions on how to \u003ca href=\"https://github.com/netreconlab/parse-hipaa#deploying-on-a-real-system\"\u003ecomplete the process\u003c/a\u003e with Nginx and LetsEncrypt\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou will still need to setup the following on your own to be fully HIPAA compliant:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Encryption in transit - you will need to \u003ca href=\"https://github.com/netreconlab/parse-hipaa#deploying-on-a-real-system\"\u003ecomplete the process\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] Encryption at rest - Mount to your own encrypted storage drive (Linux and macOS have API\u0027s for this) and store the drive in a \"safe\" location\u003c/li\u003e\n\u003cli\u003e[ ] Be sure to do anything else HIPAA requires\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/netreconlab/CareKitSample-ParseCareKit\"\u003eCareKitSample-ParseCareKit\u003c/a\u003e app uses this image alongise parse-hipaa and \u003ca href=\"https://github.com/netreconlab/ParseCareKit\"\u003eParseCareKit\u003c/a\u003e. If you are looking for a Postgres variant, checkout \u003ca href=\"https://github.com/netreconlab/hipaa-postgres\"\u003ehipaa-postgres\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse at your own risk. There is not promise that this is HIPAA compliant and we are not responsible for any mishandling of your data\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImages\u003c/h2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMultiple images are automatically built for your convenience. Images can be found at the following locations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/hipaa-mongo\" rel=\"nofollow\"\u003eDocker - Hosted on Docker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/pkgs/container/hipaa-mongo\"\u003eSingularity - Hosted on GitHub Container Registry\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnvironment Variables\u003c/h2\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChanging these variables also require the same changes to be made to the \u003ca href=\"https://github.com/netreconlab/hipaa-mongo/blob/8997d535a105c839c014644f53102b33bcb9cc5d/scripts/mongo-init.js#L3-L4\"\u003einitialization script\u003c/a\u003e or to the database directly.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMONGO_INITDB_ROOT_USERNAME=parse # Username for logging into database\nMONGO_INITDB_ROOT_PASSWORD=parse # Password for logging into database\nMONGO_INITDB_DATABASE=parse_hipaa # Name of parse-hipaa database\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetting up TLS\u003c/h2\u003e\u003ca id=\"user-content-setting-up-tls\" class=\"anchor\" aria-label=\"Permalink: Setting up TLS\" href=\"#setting-up-tls\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore building you will need to setup certificates and keys for each of the servers/containers you wish to run. You can follow the tutorial here: \u003ca href=\"https://medium.com/@rajanmaharjan/secure-your-mongodb-connections-ssl-tls-92e2addb3c89\" rel=\"nofollow\"\u003ehttps://medium.com/@rajanmaharjan/secure-your-mongodb-connections-ssl-tls-92e2addb3c89\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsing the naming conventions from the tuturial. Move the files to follow the file structure below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003essl\u003cbr\u003e\n---- rootCA.pem (this only needs to be created once)\u003cbr\u003e\n---- server0\u003cbr\u003e\n-------- mongodb.key (new one for each server)\u003cbr\u003e\n-------- mongodb.pem (new one for each server)\u003cbr\u003e\n---- server1 (if you have a second server)\u003cbr\u003e\n-------- mongodb.key (new one for each server)\u003cbr\u003e\n-------- mongodb.pem (new one for each server)\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNow follow the directions here, \u003ca href=\"https://www.percona.com/doc/percona-server-for-mongodb/LATEST/data_at_rest_encryption.html\" rel=\"nofollow\"\u003ehttps://www.percona.com/doc/percona-server-for-mongodb/LATEST/data_at_rest_encryption.html\u003c/a\u003e, and rename \"mongodb-keyfile\" file to \"mongodb_encryption.key\". Do this for each server/container and place each one in their respective folder:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003essl\u003cbr\u003e\n---- server0\u003cbr\u003e\n-------- mongodb_encryption.key (new one for each server. Note: if you want to rename this to something else, you need to change the name in Dockerfile as well)\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis step enables keyfile access control in a replica set. Currently, even if you are not using a replica set, you will need to do this because of the way the docker file is setup. Follow the directions here, \u003ca href=\"https://docs.mongodb.com/manual/tutorial/enforce-keyfile-access-control-in-existing-replica-set/\" rel=\"nofollow\"\u003ehttps://docs.mongodb.com/manual/tutorial/enforce-keyfile-access-control-in-existing-replica-set/\u003c/a\u003e, and for \u0026lt;path-to-keyfile use the name \"mongo_auth.key\" and place it:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003essl\u003cbr\u003e\n---- mongo_auth.key (this only needs to be created once)\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo build the image:\n\u003ccode\u003edocker build --tag=hipaa-mongodb --build-arg sslDir=ssl/server0 .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAfter a successful build, you can run a ssl enabled container that is HIPAA compliant type:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --name hipaa-mongodb-container0 -t hipaa-mongodb:latest --sslMode requireSSL --sslPEMKeyFile /ssl/mongodb.pem --sslCAFile /ssl/rootCA.pem --enableEncryption --encryptionKeyFile /ssl/mongodb_encryption.key --replSet rs0 --keyFile /ssl/mongo_auth.key --logpath /logs/mongo.log --logappend --auditDestination=file --auditPath /logs/audit.json\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you want to persist your data and access the generated logs and audit files, you should volume mount the directories from your host machine. For example, if mongodb was installed on your host machine via brew on macOS and you want to use the mongodb directories. You can start your container with the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --name hipaa-mongodb-container0 -v /usr/local/var/mongodb:/data/db -v /usr/local/var/log/mongodb:/logs -t hipaa-mongodb:latest --sslMode requireSSL --sslPEMKeyFile /ssl/mongodb.pem --sslCAFile /ssl/rootCA.pem --enableEncryption --encryptionKeyFile /ssl/mongodb_encryption.key --keyFile /ssl/mongo_auth.key --logpath /logs/mongo.log --logappend --auditDestination=file --auditPath /logs/audit.json\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo enable replica sets. You will need to start your intended primary container with \u0027--replSet rs0\u0027. You can learn more about replica sets here, \u003ca href=\"https://docs.mongodb.com/manual/tutorial/deploy-replica-set/\" rel=\"nofollow\"\u003ehttps://docs.mongodb.com/manual/tutorial/deploy-replica-set/\u003c/a\u003e. Starting your container will look something like the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --name hipaa-mongodb-container0 -v /usr/local/var/mongodb:/data/db -v /usr/local/var/log/mongodb:/logs -t hipaa-mongodb:latest --sslMode requireSSL --sslPEMKeyFile /ssl/mongodb.pem --sslCAFile /ssl/rootCA.pem --enableEncryption --encryptionKeyFile /ssl/mongodb_encryption.key --keyFile /ssl/mongo_auth.key --logpath /logs/mongo.log --logappend --auditDestination=file --auditPath /logs/audit.json --replSet rs0\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can then use \u003ccode\u003ers.initiate()\u003c/code\u003e, \u003ccode\u003ers.status()\u003c/code\u003e from the previous tutorial to add replica members. Adterwards, start the new container using the same \"replSet\" name:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --name hipaa-mongodb-container1 -v /usr/local/var/mongodb:/data/db -v /usr/local/var/log/mongodb:/logs -t hipaa-mongodb:latest --sslMode requireSSL --sslPEMKeyFile /ssl/mongodb.pem --sslCAFile /ssl/rootCA.pem --enableEncryption --encryptionKeyFile /ssl/mongodb_encryption.key --keyFile /ssl/mongo_auth.key --logpath /logs/mongo.log --logappend --auditDestination=file --auditPath /logs/audit.json --replSet rs0\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNote that if you use --auth to start your containers, you will need to remove this command during initial syncing of your DB\u0027s. You can re-enable -auth after they are synced.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003endmg\u003c/h2\u003e\u003ca id=\"user-content-ndmg\" class=\"anchor\" aria-label=\"Permalink: ndmg\" href=\"#ndmg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeuroData\u2019s MR Graphs package, \u003cstrong\u003endmg\u003c/strong\u003e (pronounced \u201cnutmeg\u201d), is the successor of the MRCAP, MIGRAINE, and m2g pipelines. \u003cstrong\u003endmg\u003c/strong\u003e combines dMRI and sMRI data from a single subject to estimate a high-level connectome reliably and scalably.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease read the official \u003ca href=\"http://m2g.io\" rel=\"nofollow\"\u003e\u003cstrong\u003endmg\u003c/strong\u003e\u003c/a\u003e docs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eError Reporting\u003c/h3\u003e\u003ca id=\"user-content-error-reporting\" class=\"anchor\" aria-label=\"Permalink: Error Reporting\" href=\"#error-reporting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExperiencing problems? Please open an \u003ca href=\"http://github.com/neurodata/ndmg/issues/new\"\u003eissue\u003c/a\u003e and explain what\u0027s happening so we can help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknowledgement\u003c/h3\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen using this pipeline, please acknowledge us with the citations in the attached \u003ca href=\"https://raw.githubusercontent.com/BIDS-Apps/ndmg/master/ndmg.bib\" rel=\"nofollow\"\u003ebibtex\u003c/a\u003e file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstructions\u003c/h3\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://hub.docker.com/r/bids/ndmg/\" rel=\"nofollow\"\u003ebids/ndmg\u003c/a\u003e Docker container enables users to run end-to-end connectome estimation on structural MRI right from container launch. The pipeline requires that data be organized in accordance with the \u003ca href=\"http://bids.neuroimaging.io\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e spec. If the data you wish to process is available on S3 you simply need to provide your s3 credentials at build time and the pipeline will auto-retrieve your data for processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTo get your container ready to run just follow these steps:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) I do not wish to use S3\u003c/em\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIn your terminal, type:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker pull bids/ndmg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003e(B) I wish to use S3\u003c/em\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAdd your secret key/access id to a file called \u003ccode\u003ecredentials.csv\u003c/code\u003e in this directory on your local machine. A dummy file has been provided to make the format we expect clear. (This is how AWS provides credentials)\u003c/li\u003e\n\u003cli\u003eIn your terminal, navigate to this directory and type:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker build -t \u0026lt;yourhandle\u0026gt;/ndmg .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNow we\u0027re ready to launch our instances and process some data!\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLike a normal docker container, you can startup your container with a single line. Let\u0027s assume I am running this and I wish to use S3, so my container is called \u003ccode\u003egkiar/ndmg\u003c/code\u003e. If you don\u0027t want to use S3, you can replace \u003ccode\u003egkiar\u003c/code\u003e with \u003ccode\u003ebids\u003c/code\u003e and ignore the S3 related flags for the rest of the tutorial.\u003c/p\u003e\n\u003cp\u003eI can start my container with:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker run -ti bids/ndmg\nusage: ndmg_bids [-h]\n [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]\n [--bucket BUCKET] [--remote_path REMOTE_PATH]\n bids_dir output_dir {participant}\nndmg_bids: error: too few arguments\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe should\u0027ve noticed that I got an error back suggesting that I didn\u0027t properly provide information to our container. Let\u0027s try again, with the help flag:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker run -ti bids/ndmg:v4 -h\n\nusage: ndmg_bids [-h]\n [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]\n [--bucket BUCKET] [--remote_path REMOTE_PATH]\n bids_dir output_dir {participant}\n\nThis is an end-to-end connectome estimation pipeline from sMRI and DTI images\n\npositional arguments:\n bids_dir The directory with the input dataset formatted\n according to the BIDS standard.\n output_dir The directory where the output files should be stored.\n If you are running group level analysis this folder\n should be prepopulated with the results of the\n participant level analysis.\n {participant} Level of the analysis that will be performed. Multiple\n participant level analyses can be run independently\n (in parallel) using the same output_dir.\n\noptional arguments:\n -h, --help show this help message and exit\n --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]\n The label(s) of the participant(s) that should be\n analyzed. The label corresponds to\n sub-\u0026lt;participant_label\u0026gt; from the BIDS spec (so it does\n not include \"sub-\"). If this parameter is not provided\n all subjects should be analyzed. Multiple participants\n can be specified with a space separated list.\n --bucket BUCKET The name of an S3 bucket which holds BIDS organized\n data. You must have built your bucket with credentials\n to the S3 bucket you wish to access.\n --remote_path REMOTE_PATH\n The path to the data on your S3 bucket. The data will\n be downloaded to the provided bids_dir on your\n machine.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCool! That taught us some stuff. So now for the last unintuitive piece of instruction and then just echoing back commands I\u0027m sure you could\u0027ve figured out from here: in order to share data between our container and the rest of our machine, we need to mount a volume. Docker does this with the \u003ccode\u003e-v\u003c/code\u003e flag. Docker expects its input formatted as: \u003ccode\u003e-v path/to/local/data:/path/in/container\u003c/code\u003e. We\u0027ll do this when we launch our container, as well as give it a helpful name so we can locate it later on.\u003c/p\u003e\n\u003cp\u003eFinally:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003edocker run -ti --name ndmg_test --rm -v ./data:${HOME}/data bids/ndmg ${HOME}/data/ ${HOME}/data/outputs participant --participant_label 01 -b mybucket -r path/on/bucket/\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 5, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [ - "hipaa", - "encryption", - "mongodb", - "percona-server", - "docker", - "gdpr", - "mongo", - "parse-hipaa", - "parsecarekit", - "healthcare", - "singularity", - "hacktoberfest" + "diffusion-mri", + "connectomics", + "docker-container", + "singularity-container", + "bids", + "bidsapp" ], - "updated_at": 1731840118.0 + "updated_at": 1730709288.0 }, { "data_format": 2, - "description": "A Python package to produce Mock Data Challenge data sets for LIGO interferometers.", + "description": "A minimal working example of DMTCP checkpoint-restart inside a Singularity container.", "filenames": [ "Singularity" ], - "full_name": "transientlunatic/minke", - "latest_release": "v1.1.7", + "full_name": "mmore500/mwe-singularity-checkpoint", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emwe-singularity-checkpoint\u003c/h1\u003e\u003ca id=\"user-content-mwe-singularity-checkpoint\" class=\"anchor\" aria-label=\"Permalink: mwe-singularity-checkpoint\" href=\"#mwe-singularity-checkpoint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2039\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/mmore500/mwe-singularity-checkpoint\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/430422db3b0f6da960d061c6cdcd8417db35b88bb04d94b2093b80504cbb9997/68747470733a2f2f636972636c6563692e636f6d2f67682f6d6d6f72653530302f6d77652d73696e67756c61726974792d636865636b706f696e742e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/mmore500/mwe-singularity-checkpoint.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA minimal working example of DMTCP checkpoint-restart inside a Singularity container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need to have \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed locally.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull Container from SingularityHub...\u003c/h2\u003e\u003ca id=\"user-content-pull-container-from-singularityhub\" class=\"anchor\" aria-label=\"Permalink: Pull Container from SingularityHub...\" href=\"#pull-container-from-singularityhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake shub\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e... or Build Container Locally\u003c/h2\u003e\u003ca id=\"user-content--or-build-container-locally\" class=\"anchor\" aria-label=\"Permalink: ... or Build Container Locally\" href=\"#-or-build-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive Demonstration\u003c/h2\u003e\u003ca id=\"user-content-interactive-demonstration\" class=\"anchor\" aria-label=\"Permalink: Interactive Demonstration\" href=\"#interactive-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake demonstrate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNon-interactive Test\u003c/h2\u003e\u003ca id=\"user-content-non-interactive-test\" class=\"anchor\" aria-label=\"Permalink: Non-interactive Test\" href=\"#non-interactive-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNotes\u003c/h1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis example uses \u003ca href=\"https://github.com/sylabs/singularity\"\u003eSingularity\u003c/a\u003e \u003ccode\u003ev2.6.x\u003c/code\u003e and \u003ca href=\"https://github.com/dmtcp/dmtcp\"\u003eDMTCP\u003c/a\u003e \u003ccode\u003ev3.0.0\u003c/code\u003e (e.g., from the tip of master circa December 2018).\nI didn\u0027t play around with other versions of these softwares.\u003c/p\u003e\n\u003cp\u003eUnfortunately, this example doesn\u0027t seem to be totally portable.\nI was able to get the example to run on my own laptop just fine.\nIn order to get Singularity checkpoint/restart to work on CircleCI\u0027s virtual machines (i.e., \u003ccode\u003emachine\u003c/code\u003e), I had to disable a runtime assert in the source for DMTCP (see \u003ca href=\"https://github.com/mmore500/dmtcp/commit/b8be8be2874258d2f45324a42d609c0c63da0079\"\u003ehere\u003c/a\u003e).\nOn a \u003ca href=\"https://icer.msu.edu/\" rel=\"nofollow\"\u003eMichigan State University High Performance Computing Center\u003c/a\u003e development node, which runs CentOS 7 and uses Singularity \u003ccode\u003ev2.5.2-dist\u003c/code\u003e, the demonstration currently crashes out at the first attempted checkpoint.\nThe iCER staff put together a nice tutorial of DMTCP checkpointing on the HPCC \u003ca href=\"https://wiki.hpcc.msu.edu/display/ITH/Check+Point+with+DMTCP\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nWith some further finessing along those lines, checkpointing Singularity containers on our HPCC \u003cem\u003emight\u003c/em\u003e be possible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAcknowledgement\u003c/h1\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e.circleci\u003c/code\u003e materials were pilfered from Container Tool\u0027s \u003ca href=\"https://github.com/singularityhub/circle-ci\"\u003eexample builder for Singularity containers using Circle Continuous Integration\u003c/a\u003e.\nThanks \u003ca href=\"http://github.com/vsoch\"\u003e@vsoch\u003c/a\u003e!\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [ - "gravitational", - "gravitational-waves", - "astrophysics", - "gravitational-wave-bursts", - "supernovae" + "singularity-container", + "dmtcp", + "scientific-computing", + "checkpoint-restart" ], - "updated_at": 1692517881.0 + "updated_at": 1692401019.0 }, { "data_format": 2, - "description": "repository for collaborating with sherlock users to create containers", + "description": "Eglen 2015 review article", "filenames": [ - "kdwispe/Singularity.kdwispe", - "pvacseq/Singularity.pvacseq", - "dwesche/Singularity.dwesche", - "pynio/Singularity", - "rflynn/Singularity.rflynn", - "naras/Singularity.naras", - "rllab/Singularity.rllab", - "beelaj/Singularity.beelaj" + "Singularity" ], - "full_name": "researchapps/sherlock", + "full_name": "sje30/eglen2015", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSherlock\u003c/h1\u003e\u003ca id=\"user-content-sherlock\" class=\"anchor\" aria-label=\"Permalink: Sherlock\" href=\"#sherlock\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/726\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a repository for container build files to help users of Sherlock. If you are a user and need help, please submit an issue and we will help you build a container! When you are happy with your container, we recommend that you add the \u003ccode\u003eSingularity\u003c/code\u003e or \u003ccode\u003eDockerfile\u003c/code\u003e file to a new repo, and build automatically with \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e or \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e. Generally, your workflow will look like the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall Singularity and/or Docker to work locally\u003c/li\u003e\n\u003cli\u003eAsk for help via an \u003ca href=\"https://www.github.com/researchapps/sherlock/issues\"\u003eissue\u003c/a\u003e if you don\u0027t know how to start\u003c/li\u003e\n\u003cli\u003eCreate a build specification file, a text file called Singularity, for your software needs. You can start with another user\u0027s as an example. You can also start with a Dockerfile, and then pull it to a Singularity image from Docker Hub.\u003c/li\u003e\n\u003cli\u003eAsk for help with your file! This is what this repo is here for. You can submit issues with questions, and we will discuss and work together on the issues.\u003c/li\u003e\n\u003cli\u003eTest your build locally.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://singularityware.github.io/install-linux\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e and \u003ca href=\"https://docs.docker.com/install/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e so that you can build images on your host. If you use a Mac, you will need to install Singularity in a virtual machine like Vagrant. Singularity is going to allow us to interact exactly the same, but with an image that we can use on Sherlock! The biggest difference is that a Singularity image is a read online, single file (a format called squasfs so\nit is compressed) that we can physically move around and execute like a script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGeneral Usage\u003c/h2\u003e\u003ca id=\"user-content-general-usage\" class=\"anchor\" aria-label=\"Permalink: General Usage\" href=\"#general-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn your local machine, building usually looks something like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build mynewimage.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf it has a runscript, you can run as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run mynewimage.img # or\n ./mynewimage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you are having trouble with the runscript, shell inside like this to look around. The runscript is a file at the base of the image (\u003ccode\u003e/\u003c/code\u003e) called singularity.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell mynewimage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also (on your local machine) use the \u003ccode\u003e--writable\u003c/code\u003e option to test installation of software. You should have your build file open in another window and copy down commands that work, and ensure that the entire build goes successfully from start to finish without an error. Remember, any command that you issue and don\u0027t write done is NOT reproducible!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Hub\u003c/h2\u003e\u003ca id=\"user-content-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub\" href=\"#singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote to Sherlock users: this functionality is not yet added to Sherlock, but will be available upon the next release of Singularity in March. For now, you can upload images the old school way (FTP). When this is enabled, you will be able to push the build file to Github, and then link your repo to Singularity Hub. Then using the image on sherlock will come down to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e module load system\n module load singularity/2.4\n singularity run shub://reponame/mynewimage\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIntroduction\u003c/h1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/sje30/eglen2015\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c305e622dcdce06b6952f9cd0ea0142b5b79a5d804b883b142dd607ddcba7977/68747470733a2f2f7472617669732d63692e6f72672f736a6533302f65676c656e323031352e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/sje30/eglen2015.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the home page for the following review:\u003c/p\u003e\n\u003cp\u003eEglen SJ (2016) Bivariate spatial point patterns in the retina: a\nreproducible review. Journal de la Soci\u00e9t\u00e9 Fran\u00e7aise de Statistique\n157:33\u201348.\n\u003ca href=\"http://journal-sfds.fr/article/view/518\" rel=\"nofollow\"\u003ePDF\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis package contains all the material needed to regenerate the\narticle for itself. Some key parts of the package are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"vignettes/eglen2015.Rnw\"\u003evignettes/eglen2015.Rnw\u003c/a\u003e: the source file\nfor the article in LaTeX format.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"inst/extdata\"\u003einst/extdata\u003c/a\u003e: a folder containing all the data files\nstudied in this article.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRecompiling the paper\u003c/h2\u003e\u003ca id=\"user-content-recompiling-the-paper\" class=\"anchor\" aria-label=\"Permalink: Recompiling the paper\" href=\"#recompiling-the-paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis R package depends on a few other packages, from CRAN and my\npersonal library. The following sequence should install everything\nyou need:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript -e \u0027install.packages(c(\"splancs\", \"spatstat\", \"devtools\", \"knitr\", \"xtable\", \"tinytex\"))\u0027\nRscript -e \u0027install.packages(c(\"sjedmin\", \"sjedrp\", \"sjevor\",\"sjedist\"), type=\"source\", contriburl=\"http://damtp.cam.ac.uk/user/eglen/r/\")\u0027\nRscript -e \u0027devtools::install_github(\"sje30/eglen2015\",build_vignettes=TRUE)\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe last line should load this package. Once it is installed, you can\nthen view the paper, or view the knitr document that created the paper:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evignette(\"eglen2015\")\neglen2015:::edit()\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis does of course assume that your system already has R, latex, and\nvarious unix tools. That may not be the case; however, you can still\nuse the package through the Docker system, see next.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have \u003ca href=\"http://docker.com\" rel=\"nofollow\"\u003edocker\u003c/a\u003e installed on your system,\nyou can download and run this package using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -d -p 8787:8787 sje30/eglen2015\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(View \u003ca href=\"https://registry.hub.docker.com/u/sje30/eglen2015/\" rel=\"nofollow\"\u003esje30/eglen2015\u003c/a\u003e\nto check the status of this Docker package.)\u003c/p\u003e\n\u003cp\u003eThen visit the web page to start R (username and password are \"rstudio\"):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttp://localhost:8787/ ## linux\nhttp://192.168.99.100:8787/ ## mac, windows users\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe IP address for mac/windows may vary; you can check it by running\nthe command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker-machine ip default\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce you have logged in, you can then do the following commands to\nrecompile the document:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetwd(\"eglen2015/vignettes/\")\nsource(\"run.R\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen examine the \u003ccode\u003evignettes\u003c/code\u003e folder and you should see\n\u003ccode\u003eeglen2015.pdf\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThanks to the \u003ca href=\"https://github.com/rocker-org\"\u003eRocker\u003c/a\u003e team for the\nR-based docker images, on which this work is based.\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 4, - "topics": [ - "hpc", - "sherlock", - "containers", - "singularity", - "cluster", - "scientific-computing" - ], - "updated_at": 1592614990.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1726079671.0 }, { "data_format": 2, - "description": "A nextflow/singularity pipeline for quandenser", + "description": "Virus assembler from amplicon sequencing reads", "filenames": [ - "Singularity.dev", - "Singularity" + "Singularity.def" ], - "full_name": "statisticalbiotechnology/quandenser-pipeline", - "latest_release": "v0.0837", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuandenser-pipeline \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/logo.svg\"\u003e\u003cimg align=\"right\" src=\"/images/logo.svg\" height=\"50\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-quandenser-pipeline-\" class=\"anchor\" aria-label=\"Permalink: Quandenser-pipeline \" href=\"#quandenser-pipeline-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA Nextflow/Singularity pipeline for Quandenser\u003c/h2\u003e\u003ca id=\"user-content-a-nextflowsingularity-pipeline-for-quandenser\" class=\"anchor\" aria-label=\"Permalink: A Nextflow/Singularity pipeline for Quandenser\" href=\"#a-nextflowsingularity-pipeline-for-quandenser\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2356\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to install\u003c/h2\u003e\u003ca id=\"user-content-how-to-install\" class=\"anchor\" aria-label=\"Permalink: How to install\" href=\"#how-to-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGo to releases and download \u003cem\u003eQuandenser_pipeline.sh\u003c/em\u003e and run the shell script. The shell script will handle the rest!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGo to the directory where \u003cem\u003eQuandenser_pipeline.sh\u003c/em\u003e is installed and run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Quandenser_pipeline.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will install Singularity if it does not exist (this requires sudo privileges). The script will also download the latest stable version of the Singularity image.\u003c/p\u003e\n\u003cp\u003eIf you want to mount another directory that is not detected by Singularity, you can add any amount of directories to mount by these commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Quandenser_pipeline.sh /path/to/directory1 /path/to/directory2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe pipeline can be run on a SLURM cluster if Singularity is installed. Just don\u0027t forget to enable \"slurm_cluster\" in the\nsettings!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIf you have trouble running the GUI, look further down below in the section \"Known Issues\"\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eQuandenser-pipeline\u003c/em\u003e is a tool that combines \u003cem\u003eQuandenser\u003c/em\u003e, a tool which condenses label-free MS data and \u003cem\u003eTriqler\u003c/em\u003e, a tool which finds differentially expressed proteins using both MS1 and MS2 data. \u003cem\u003eQuandenser-pipeline\u003c/em\u003e streamlines the process, by accepting almost any vendor format alongside a fasta database containing proteins, which are then run through a Singularity image containing all the necessary parts to do the analysis.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/gui.png\"\u003e\u003cimg src=\"/images/gui.png\" width=\"1000\" height=\"800\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sylabs/singularity\"\u003eSingularity\u003c/a\u003e: Singularity is an open source container platform used to embed the software used in the pipeline\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e: Nextflow is a workflow manager, which was used to create the pipeline\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/statisticalbiotechnology/quandenser\"\u003eQuandenser\u003c/a\u003e: A recent software which condenses quantification data from label-free mass spectrometry experiments, written by Lukas K\u00e4ll and Matthew The at ScilifeLab\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/crux-toolkit/crux-toolkit\"\u003eCrux toolkit\u003c/a\u003e: An open-source mass spectrometry analysis toolkit used to analyse MS2 spectra from Quandeser\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/statisticalbiotechnology/triqler\"\u003eTriqler\u003c/a\u003e: A combined identification and quantification error model of label-free protein quantification, written by Lukas K\u00e4ll and Matthew The at ScilifeLab. It is used as the final analysis software, utilizing the output from Crux and Quandenser\u003c/p\u003e\n\u003cp\u003eThe GUI is built with the open source GUI \u003ca href=\"https://pypi.org/project/PySide2/\" rel=\"nofollow\"\u003ePySide2\u003c/a\u003e\nwith \u003ca href=\"https://github.com/ColinDuquesnoy/QDarkStyleSheet\"\u003eColinDuqesnoy\u0027s dark theme\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e*** stack smashing detected***: python terminated\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eSometimes when running on a computer with nvidia drivers locally, this error message will be shown. It will not harm your computer, so just keep trying to start the program. Usually, it stabilizes after a couple of runs.\nI\u0027ve been trying to find the cause of this bug, but it seems to be correlated to the creation of the GUI window through Singularity. If you have any ideas about the bug, please let me know!\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eWebEngineContext used before QtWebEngine::initialize() or OpenGL context creation failed.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis usually happens when you are running on a cluster. Sometimes, the nvidia drivers on your computer is not compatible\nwith the drivers on the cluster. Please add the following command when running the start script.\nNote: disabling opengl will not hinder the performance of the software. The workflow display and the about tab will\nnot be shown.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Quandenser_pipeline.sh /path/to/directory1 ... --disable-opengl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eGlx related crashes (ex qt.glx: qglx_findConfig: Failed to finding matching FBConfig (8 8 8 0))\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIf you are running on a cluster with nvidia cards and you do not have an nvidia card on your local machine (ex if you are running the software in virtualbox on a cluster). Add the following command to disable nvidia drivers\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Quandenser_pipeline.sh /path/to/directory1 ... --disable-nvidia\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eIf everything fails and you can\u0027t get it to work, there is one last thing you can try, which is explained below ...\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the pipeline WITHOUT the GUI\u003c/h2\u003e\u003ca id=\"user-content-running-the-pipeline-without-the-gui\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline WITHOUT the GUI\" href=\"#running-the-pipeline-without-the-gui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf everything fails or you can\u0027t run the GUI for some reason, there is a last resort which you can use: run the pipeline without the GUI. This is not as intuitive as using the GUI, but it is possible to do, since the GUI in itself is not required to run the pipeline but it only makes things easier. Do the following to run the pipeline without GUI:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eRemove the directory \u003cem\u003e.quandenser_pipeline\u003c/em\u003e from your home directory. Run the command \u003ccode\u003erm -r /home/$USER/.quandenser_pipeline\u003c/code\u003e. This will clear previous settings that might interfere.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun \u003ccode\u003e./Quandenser_pipeline.sh\u003c/code\u003e as usual. You should see \u003cem\u003eMissing file . Installing file\u003c/em\u003e in yellow text on the terminal. This will add the configuration files. At this point, it doesn\u0027t matter if the GUI crashes or not.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo to the config directory with \u003ccode\u003ecd /home/$USER/.quandenser_pipeline\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe files \u003cstrong\u003enf.config\u003c/strong\u003e and \u003cstrong\u003erun_quandenser\u003c/strong\u003e are the only files which you will need to edit to make this work.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003erun_quandenser.sh\u003c/strong\u003e: Here, the only parameters you need to edit are:\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003ePROFILE\u003c/em\u003e = Can be either \u003cem\u003elocal\u003c/em\u003e or \u003cem\u003ecluster\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eOUTPUT_PATH\u003c/em\u003e = The full path to the directory where the output will be placed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003enf.config\u003c/strong\u003e: Here, pretty much any variable can be changed. However, I would suggest to focus on:\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eparams.db\u003c/em\u003e = Path to the fasta file used as the protein database. Note that only the \"Full\" workflow requires this.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.output_path\u003c/em\u003e = Output path, which needs to be the same as the output path in the .sh file\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.batch_file\u003c/em\u003e = Path to the batch file. The syntax of the batch file is:\n\u003ccode\u003e/full/path/to/ms/file label\u003c/code\u003e where \"label\" could be any combination of ascii characters. Note that the delimiter between the file path and the label needs to be a tab character. For each file, add another row. Replicates should have the same label assigned.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.custom_mounts\u003c/em\u003e = Custom mounts used for Nextflow. the syntax should be:\n\u003ccode\u003e --bind /path/to/mount:/path/to/mount\u003c/code\u003e. Note the blank space before \" --bind\"\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.workflow\u003c/em\u003e = Can be \"Full\", \"MSconvert\" or \"Quandenser\"\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.resume_directory\u003c/em\u003e = Path to another directory containing previously calculated quandenser files\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003etime parameters\u003c/em\u003e = If you are running on a cluster and are using the \"cluster\" profile, adjust the time values to your liking.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eGo back to the directory containing \u003cem\u003eSingulQuand.SIF\u003c/em\u003e and run this command:\n\u003ccode\u003enohup /home/$USER/run_quandenser.sh \u0026lt;/dev/null \u0026gt;/dev/null 2\u0026gt;\u0026amp;1 \u0026amp; disown\u003c/code\u003e\nThis should run the sh file, deattach it from the terminal and run it in the background. This will allow you to close the terminal/ssh session without stopping the pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding from scratch\u003c/h2\u003e\u003ca id=\"user-content-building-from-scratch\" class=\"anchor\" aria-label=\"Permalink: Building from scratch\" href=\"#building-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have come all the way down here in the README, you might be interested in building the image from scratch.\u003c/p\u003e\n\u003cp\u003eSimply clone the repository with git or download it \u003ca href=\"https://github.com/statisticalbiotechnology/quandenser-pipeline/archive/master.zip\"\u003ehere\u003c/a\u003e. Unzip the directory and cd inside, then run \"./build_image.sh\" and it will build everything for you! (However, this requires both Singularity and sudo privileges, so run the release first to install Singularity)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuestions?\u003c/h2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-label=\"Permalink: Questions?\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFeel free to mail me at \"\u003ca href=\"mailto:timothy.bergstrom@gmail.com\"\u003etimothy.bergstrom@gmail.com\u003c/a\u003e\" if you have any questions about quandenser-pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/logo.svg\"\u003e\u003cimg src=\"/images/logo.svg\" height=\"128\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \u00a9 Copyright 2019, Timothy Bergstr\u00f6m\n", + "full_name": "iqbal-lab-org/cylon", + "latest_release": "v0.1.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/iqbal-lab-org/cylon/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/cylon/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecylon\u003c/h1\u003e\u003ca id=\"user-content-cylon\" class=\"anchor\" aria-label=\"Permalink: cylon\" href=\"#cylon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVirus assembly module used by viridian\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eImportant\u003c/h1\u003e\u003ca id=\"user-content-important\" class=\"anchor\" aria-label=\"Permalink: Important\" href=\"#important\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend that you use\n\u003ca href=\"https://github.com/iqbal-lab-org/viridian\"\u003eViridian\u003c/a\u003e instead of\nthis repository. This repository is intended to be run by\nViridian, not to be used as a stand-alone tool.\nViridian provides a complete end-to-end pipeline for\ngenerating a consensus sequence from reads.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese must be installed and in your \u003ccode\u003e$PATH\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eracon\u003c/code\u003e (\u003ca href=\"https://github.com/lbcb-sci/racon\"\u003ehttps://github.com/lbcb-sci/racon\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eminimap2\u003c/code\u003e (\u003ca href=\"https://github.com/lh3/minimap2/\"\u003ehttps://github.com/lh3/minimap2/\u003c/a\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eClone this repository and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 -m pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity container\u003c/h3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone this repository and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build cylon.img Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto build the container \u003ccode\u003ecylon.img\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample usage\u003c/h2\u003e\u003ca id=\"user-content-example-usage\" class=\"anchor\" aria-label=\"Permalink: Example usage\" href=\"#example-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequired input:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eReference FASTA file\u003c/li\u003e\n\u003cli\u003eJSON file of amplicons. Described below.\nend end positions of the amplicons\u003c/li\u003e\n\u003cli\u003eReads, either in a sorted mapped indexed BAM file, or in a FASTA/FASTQ file\n(or two FASTA/FASTQ files for paired reads).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe amplicons JSON file must look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"amplicon1\": {\n \"start\": 10,\n \"end\": 399,\n \"left_primer_end\": 30,\n \"right_primer_start\": 390\n },\n \"amplicon2\": { ... etc ...}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe keys are the amplicon names, and the values are the details for each\namplicon.\nAll coordinates are 0-based inclusive.\nThe \u003ccode\u003estart\u003c/code\u003e and \u003ccode\u003eend\u003c/code\u003e entries are the positions of the start and end of the\namplicon.\n\u003ccode\u003eleft_primer_end\u003c/code\u003e is the rightmost position of the end of the left primer,\nand \u003ccode\u003eright_primer_start\u003c/code\u003e is the leftmost position\nof the right primer. This means for each amplicon we should have:\n\u003ccode\u003estart\u003c/code\u003e \u0026lt; \u003ccode\u003eleft_primer_end\u003c/code\u003e \u0026lt; \u003ccode\u003eright_primer_start\u003c/code\u003e \u0026lt; \u003ccode\u003eend\u003c/code\u003e.\n(Other key/values can be inside the dictionary\nfor each amplicon, but will simply be ignored).\u003c/p\u003e\n\u003cp\u003eRun using a mapped BAM file of ONT reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecylon assemble --bam reads.bam ont ref.fasta amplicons.json outdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun using a FASTQ file of ONT reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecylon assemble --reads_to_map reads.fastq ont ref.fasta amplicons.json outdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun using two FASTQ files of paired Illumina reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecylon assemble \\\n --reads_to_map reads1.fastq --mates_to_map reads2.fastq \\\n illumina ref.fasta amplicons.json outdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe important files are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econsensus.final_assembly.fa\u003c/code\u003e: this contains the consensus sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eamplicon_data.json\u003c/code\u003e: JSON file containing details of what happened when\ntrying to make a consensus sequence of each amplicon.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 5, - "subscribers_count": 9, + "subscribers_count": 6, "topics": [], - "updated_at": 1656126766.0 + "updated_at": 1714137467.0 }, { "data_format": 2, - "description": "Code and Data for \"Biological network growth in complex environments - a computational framework\"", + "description": null, "filenames": [ - "Singularity/Singularity.def" + "container/Singularity", + "container/Singularity_madeline2", + "container/stranger/Singularity", + "container/pod/Singularity", + "container/genmod/Singularity", + "container/reviewer/Singularity" ], - "full_name": "CIA-CCTB/pythrahyper_net", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epythrahyper_net\u003c/h1\u003e\u003ca id=\"user-content-pythrahyper_net\" class=\"anchor\" aria-label=\"Permalink: pythrahyper_net\" href=\"#pythrahyper_net\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and data for the paper \u003cem\u003e\"Biological network growth in complex environments - a computational framework\"\u003c/em\u003e by T. Paul and P. Kollmannsberger (2020) - \u003ca href=\"https://biorxiv.org/cgi/content/short/2020.06.01.127407v1\" rel=\"nofollow\"\u003ehttps://biorxiv.org/cgi/content/short/2020.06.01.127407v1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease have a look at the notebook \u003ca href=\"https://github.com/CIA-CCTB/pythrahyper_net/blob/master/Introduction.ipynb\"\u003eIntroduction.ipynb\u003c/a\u003e, or try it directly here: \u003ca href=\"https://colab.research.google.com/github/CIA-CCTB/pythrahyper_net/blob/master/Colab/Introduction_Colab.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (requires Google account)\u003c/p\u003e\n\u003cp\u003eThe following Jupyter notebooks reproduce the simulations shown in Figure 6 in the paper:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CIA-CCTB/pythrahyper_net/blob/master/Multicellular-Network.ipynb\"\u003eMulticellular-Network.ipynb\u003c/a\u003e - simulation of network growth between layers of cells\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CIA-CCTB/pythrahyper_net/blob/master/Multi-Simulation-Setup.ipynb\"\u003eMulti-Simulation-Setup.ipynb\u003c/a\u003e - generate configuration and batch files for parameter scan\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CIA-CCTB/pythrahyper_net/blob/master/Multi-Simulation-Analysis.ipynb\"\u003eMulti-Simulation-Analysis.ipynb\u003c/a\u003e - analyze results and generate plots for parameter scan\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions\u003c/h1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe framework is written in python using numpy and the multiprocessing module, and has been tested under Linux and MacOS. To run the example notebooks, first download or clone this repository, and then follow the instructions below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1) Using conda\u003c/h2\u003e\u003ca id=\"user-content-1-using-conda\" class=\"anchor\" aria-label=\"Permalink: 1) Using conda\" href=\"#1-using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to install the required python packages is by using conda. Creating a new environment with this command will install all dependencies:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda create --name pythra python=3.7 pyqt=5 scipy tifffile jupyter networkx matplotlib\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen change into the new environment using \u003ccode\u003econda activate pythra\u003c/code\u003e, and start a Jupyter notebook server in the \u003ccode\u003epythrahyper_net\u003c/code\u003e directory to access the notebooks.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMayavi visualization in the browser:\u003c/h3\u003e\u003ca id=\"user-content-mayavi-visualization-in-the-browser\" class=\"anchor\" aria-label=\"Permalink: Mayavi visualization in the browser:\" href=\"#mayavi-visualization-in-the-browser\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get interactive mayavi visualizations inside the browser, first install mayavi and ipyevents:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c anaconda mayavi\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-forge ipyevents\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNext, install and activate the required extension:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ejupyter nbextension install --py mayavi --user\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ejupyter nbextension enable --py mayavi --user\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you get missing symbol errors upon importing \u003ccode\u003emlab\u003c/code\u003e, try this:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-force \"libnetcdf=4.6.2\"\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInteractive Matplotlib plots:\u003c/h3\u003e\u003ca id=\"user-content-interactive-matplotlib-plots\" class=\"anchor\" aria-label=\"Permalink: Interactive Matplotlib plots:\" href=\"#interactive-matplotlib-plots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe matplotlib plots can be made interactive using these modules:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-forge ipympl widgetsnbextension\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2) Using Singularity container\u003c/h2\u003e\u003ca id=\"user-content-2-using-singularity-container\" class=\"anchor\" aria-label=\"Permalink: 2) Using Singularity container\" href=\"#2-using-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe second possibility is to run the framework inside a Singularity container. A container image can be created using the included definition file:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build pythra.simg Singularity.def\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAfter successful build, you can e.g. start a Jupyter notebook server inside the container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec pythra.simg jupyter notebook\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen copy and paste the server URL into a web browser running outside of the container to access the notebooks.\u003c/p\u003e\n", + "full_name": "Clinical-Genomics-Lund/nextflow_wgs", + "latest_release": "v3.5.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eREADME\u003c/h1\u003e\u003ca id=\"user-content-readme\" class=\"anchor\" aria-label=\"Permalink: README\" href=\"#readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of content\u003c/h2\u003e\u003ca id=\"user-content-table-of-content\" class=\"anchor\" aria-label=\"Permalink: Table of content\" href=\"#table-of-content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pipeline-overview\"\u003ePipeline overview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/running_the_pipeline.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eInputs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/input_files.md\"\u003eInput files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/annotation_files.md\"\u003eAnnotation files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/input_containers.md\"\u003eContainers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/sentieon_license.md\"\u003eSentieon license\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/outputs.md\"\u003eOutputs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/how_to_deploy.md\"\u003eHow to deploy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/list_of_all_used_software.md\"\u003eList of used software\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/misc_docs.md\"\u003eMisc documents\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline overview\u003c/h2\u003e\u003ca id=\"user-content-pipeline-overview\" class=\"anchor\" aria-label=\"Permalink: Pipeline overview\" href=\"#pipeline-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe constitutional wgs pipeline is a versatile workflow used in both genome sequencing and panels. It is implemented in the \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e workflow-language and designed to run in a computational cluster environment, using Singularity containers to manage software dependencies.\u003c/p\u003e\n\u003cp\u003eThe output is immediately importable into the software \u003ca href=\"https://github.com/Clinical-Genomics/scout\"\u003eScout\u003c/a\u003e, an open-source software for identifying variants with clinical significance.\u003c/p\u003e\n\u003cp\u003eIn order to run it, you will need to have a \u003ca href=\"https://support.sentieon.com/appnotes/license_server/\" rel=\"nofollow\"\u003eSentieon license\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA typical workflow\u003c/h3\u003e\u003ca id=\"user-content-a-typical-workflow\" class=\"anchor\" aria-label=\"Permalink: A typical workflow\" href=\"#a-typical-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePreprocess, align and deduplicate reads\u003c/li\u003e\n\u003cli\u003eCalculate QC and coverage information\u003c/li\u003e\n\u003cli\u003ePerform SNV and SV calling\u003c/li\u003e\n\u003cli\u003eSubset the calls by intersecting with regions of interest\u003c/li\u003e\n\u003cli\u003eGenerate annotations for the subset of calls\u003c/li\u003e\n\u003cli\u003eCalculate scores for clinical significance\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFurther configurations\u003c/h3\u003e\u003ca id=\"user-content-further-configurations\" class=\"anchor\" aria-label=\"Permalink: Further configurations\" href=\"#further-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWhen run in WGS-mode, mitochondrial analysis is performed\u003c/li\u003e\n\u003cli\u003eFor trios, uniparental disomy and runs of homozygosity are analysed\u003c/li\u003e\n\u003cli\u003eWhen running panels, a different set of structural variant callers adapted to panel data are run\u003c/li\u003e\n\u003cli\u003eFor onco-samples, an additional SNV-caller \"freebayes\" is used.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run into any issues using this pipeline, \u003ca href=\"https://github.com/Clinical-Genomics-Lund/nextflow_wgs/issues\"\u003eopen an issue in the GitHub repository\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIllustration of the pipeline, its steps and processes.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/wgs_overview_200.drawio.png\"\u003e\u003cimg src=\"docs/img/wgs_overview_200.drawio.png\" alt=\"overview_img\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 5, "subscribers_count": 4, "topics": [], - "updated_at": 1695826280.0 + "updated_at": 1728475763.0 + }, + { + "data_format": 2, + "description": "Computation of root phenes from 3D point clouds.", + "filenames": [ + "Singularity", + "model_preprocess/Singularity" + ], + "full_name": "Computational-Plant-Science/3D_model_traits_demo", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e3D_model_traits_measurement\u003c/h1\u003e\u003ca id=\"user-content-3d_model_traits_measurement\" class=\"anchor\" aria-label=\"Permalink: 3D_model_traits_measurement\" href=\"#3d_model_traits_measurement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFunction: Compute 3D root traits from 3D root model for field-grown maize roots\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../main/media/image1.png\"\u003e\u003cimg src=\"../main/media/image1.png\" alt=\"Optional Text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eExample of computed root structure v.s. 3D root point cloud model\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../main/media/image2_1.gif\"\u003e\u003cimg src=\"../main/media/image2_1.gif\" alt=\"Optional Text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e3D root models (*.ply) in Polygon File Format or the Stanford Triangle Format.\u003c/p\u003e\n\u003cp\u003ecomputed from Computational-Plant-Science / 3D_model_reconstruction_demo\n(\u003ca href=\"https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo\"\u003ehttps://github.com/Computational-Plant-Science/3D_model_reconstruction_demo\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etrait.xlsx Excel format, contains 18 traits results\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e is required to run this project in a Linux environment.\u003c/p\u003e\n\u003cp\u003eInstall Docker Engine (\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe suggest to run the pipeline inside a docker container,\u003c/p\u003e\n\u003cp\u003eThe Docker container allows you to package up your application(s) and deliver them to the cloud without any dependencies. It is a portable computing environment. It contains everything an application needs to run, from binaries to dependencies to configuration files.\u003c/p\u003e\n\u003cp\u003eThere are two ways to run the pipeline inside a docker container,\u003c/p\u003e\n\u003cp\u003eOne was is to build a docker based on the docker recipe file inside the GitHub repository. In our case, please follow step 1 and step 3.\u003c/p\u003e\n\u003cp\u003eAntoher way is to download prebuild docker image directly from Docker hub. In our case, please follow step 2 and step 3.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBuild docker image on your PC under linux environment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Computational-Plant-Science/3D_model_traits_demo.git\n\ndocker build -t 3d-model-traits -f Dockerfile \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDownload prebuild docker image directly from Docker hub, without building docker image on your local PC\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull computationalplantscience/3d-model-traits\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the pipeline inside the docker container\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003elink your test 3D model path (e.g. \u0027/home/test/test.ply\u0027, $path_to_your_3D_model = /home/test, $your_3D_model_name.ply = test.ply)to the /srv/test/ path inside the docker container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v /\u003cspan class=\"pl-smi\"\u003e$path_to_your_3D_model\u003c/span\u003e:/srv/test -it 3d-model-traits\n\nor \n\ndocker run -v /\u003cspan class=\"pl-smi\"\u003e$path_to_your_3D_model\u003c/span\u003e:/srv/test -it computationalplantscience/3d-model-traits\n\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eRun the pipeline inside the container\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 pipeline.py -p /srv/test/ -m \u003cspan class=\"pl-smi\"\u003e$your_3D_model_name\u003c/span\u003e.ply\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eReference:\u003c/p\u003e\n\u003cp\u003eShenglan Du, Roderik Lindenbergh, Hugo Ledoux, Jantien Stoter, and Liangliang Nan.\nAdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees.\nRemote Sensing. 2019, 11(18), 2074.\u003c/p\u003e\n\u003cp\u003e@article{du2019adtree,\ntitle={AdTree: Accurate, detailed, and automatic modelling of laser-scanned trees},\nauthor={Du, Shenglan and Lindenbergh, Roderik and Ledoux, Hugo and Stoter, Jantien and Nan, Liangliang},\njournal={Remote Sensing},\nvolume={11},\nnumber={18},\npages={2074},\nyear={2019}\n}\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSuxing Liu (\u003ca href=\"mailto:suxingliu@gmail.com\"\u003esuxingliu@gmail.com\u003c/a\u003e), Wesley Paul Bonelli(\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e), Alexander Bucksch\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther contributions\u003c/h2\u003e\u003ca id=\"user-content-other-contributions\" class=\"anchor\" aria-label=\"Permalink: Other contributions\" href=\"#other-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker container was maintained and deployed to \u003ca href=\"https://portnoy.cyverse.org\" rel=\"nofollow\"\u003ePlantIT\u003c/a\u003e by Wes Bonelli (\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGNU Public License\u003c/p\u003e\n", + "stargazers_count": 5, + "subscribers_count": 5, + "topics": [ + "phenotyping", + "phenotyping-algorithms", + "phenomics", + "root" + ], + "updated_at": 1717421725.0 }, { "data_format": 2, @@ -34714,7 +34492,7 @@ var data = "filenames": [ "Singularity" ], - "full_name": "chpc-uofu/Singularity-ubuntu-mpi", + "full_name": "CHPC-UofU/Singularity-ubuntu-mpi", "latest_release": null, "stargazers_count": 5, "subscribers_count": 3, @@ -34723,193 +34501,172 @@ var data = }, { "data_format": 2, - "description": "https://github.com/NEFSC/READ-EDAB-neusAtlantis/wiki", + "description": "Tutorials and notebooks using Fink API", "filenames": [ - "Singularity_6536", - "Singularity_6681" + "Singularity" ], - "full_name": "NEFSC/READ-EDAB-neusAtlantis", - "latest_release": "v2.2.1", - "readme": "\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eneus-atlantis\u003c/h1\u003e\u003ca id=\"user-content-neus-atlantis\" class=\"anchor\" aria-label=\"Permalink: neus-atlantis\" href=\"#neus-atlantis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/NOAA-EDAB/neus-atlantis/workflows/gitleaks/badge.svg\"\u003e\u003cimg src=\"https://github.com/NOAA-EDAB/neus-atlantis/workflows/gitleaks/badge.svg\" alt=\"gitleaks\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003eAtlantis is a series of submodels (biological, geophysical, fishing,\nassessment, and management) and as a whole simulates a Management\nStrategy Evaluation (MSE) procedure which allows for qualitative\ncomparisons of different management choices. We have parameterized\nAtlantis for the Northeast United States Continental Shelf (NEUS),\nincluding the major functional groups from an ecosystem perspective. We\nhave also parameterized and simulated the physiographic dynamics of the\necosystem, as well as the most important fleets. Our goal was to\nrecreate, at least in approximate terms, the biomass, catch, and effort\ntrends in the NEUS from 1964 to the mid 2000s.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDevelopment team\u003c/h3\u003e\u003ca id=\"user-content-development-team\" class=\"anchor\" aria-label=\"Permalink: Development team\" href=\"#development-team\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn alphabetical order:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/jcaracappa1\"\u003ejcaracappa1\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/gambler1650\"\u003egambler1650\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/jcaracappa1\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/57966543?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/gambler1650\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/5949383?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTeam members\u003c/h3\u003e\u003ca id=\"user-content-team-members\" class=\"anchor\" aria-label=\"Permalink: Team members\" href=\"#team-members\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMembers of the Atlantis team other than the core development team:\u003c/p\u003e\n\u003cp\u003eIn alphabetical order:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/andybeet\"\u003eandybeet\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/sgaichas\"\u003esgaichas\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/slarge\"\u003eslarge\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/slucey\"\u003eslucey\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/RMORSEcode\"\u003eRMORSEcode\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/andybeet\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/22455149?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/sgaichas\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/8172302?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/slarge\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/5000131?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/slucey\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/5578254?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/RMORSEcode\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/10620840?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLegal disclaimer\u003c/h4\u003e\u003ca id=\"user-content-legal-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Legal disclaimer\" href=\"#legal-disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eThis repository is a scientific product and is not official\ncommunication of the National Oceanic and Atmospheric Administration, or\nthe United States Department of Commerce. All NOAA GitHub project code\nis provided on an \u2018as is\u2019 basis and the user assumes responsibility for\nits use. Any claims against the Department of Commerce or Department of\nCommerce bureaus stemming from the use of this GitHub project will be\ngoverned by all applicable Federal law. Any reference to specific\ncommercial products, processes, or services by service mark, trademark,\nmanufacturer, or otherwise, does not constitute or imply their\nendorsement, recommendation or favoring by the Department of Commerce.\nThe Department of Commerce seal and logo, or the seal and logo of a DOC\nbureau, shall not be used in any manner to imply endorsement of any\ncommercial product or activity by DOC or the United States Government.\u003c/em\u003e\u003c/p\u003e\n", + "full_name": "astrolabsoftware/fink-tutorials", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFink broker tutorials\u003c/h1\u003e\u003ca id=\"user-content-fink-broker-tutorials\" class=\"anchor\" aria-label=\"Permalink: Fink broker tutorials\" href=\"#fink-broker-tutorials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://colab.research.google.com/github/astrolabsoftware/fink-notebook-template/blob/main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f5e0d0538a9c2972b5d413e0ace04cecd8efd828d133133933dfffec282a4e1b/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains materials (notebooks \u0026amp; presentation) to explore the \u003ca href=\"https://fink-broker.org\" rel=\"nofollow\"\u003eFink broker\u003c/a\u003e alert data. As of November 2021, Fink has collected more than 120 million alerts from the ZTF public stream, and processed more than 40 millions (after quality cuts). Among these, you will find extragalatic sources (supernovae, AGN, ...), galactic sources (many classes of transients incl. variables stars from our galaxy or gravitational microlensing events, ...) and moving objects from our Solar System (asteroids, comets, and made-man objects like space-debris!). Some sources are already confirmed, many are candidates!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMaterials\u003c/h2\u003e\u003ca id=\"user-content-materials\" class=\"anchor\" aria-label=\"Permalink: Materials\" href=\"#materials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository contains a number of notebooks focusing on the use of the Fink REST API. We shortly present different science cases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExtragalactic science: AGN \u0026amp; supernovae (\u003ca href=\"extragalactic/extragalactic.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eGalactic science: variable stars \u0026amp; microlensing (\u003ca href=\"galactic/galactic.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSolar system science: asteroids, comets \u0026amp; space debris (\u003ca href=\"sso/sso.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSolar system science: phase curves (\u003ca href=\"sso/fink_sso_imcce.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eMulti-messenger astronomy: searching for kilonovae (\u003ca href=\"MMA/MMA.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eMulti-messenger astronomy: correlating with gravitational waves sky maps (\u003ca href=\"MMA/gravitational_waves.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eBroker interfaces: presentation on the livestream service, the Science Portal and its API, and the Fink TOM module (\u003ca href=\"interfaces/README.md\"\u003esee the presentation\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese sciences are not exhaustive and we welcome new collaborations to expand them! In addition, there are notebooks focusing on other specific aspects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow to tune the output rate of a Fink filter? Example for the Early SN Ia candidate filter (\u003ca href=\"extragalactic/tuning_snia_output_rate.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can try the notebooks using Google Colab (follow the link above). You can also clone the repo, and try it locally (very little external libraries are required).\u003c/p\u003e\n\u003cp\u003eWe also provide a Singularity script to work in a contained environment (thanks @bregeon):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBuild with \u003ccode\u003esingularity build --fakeroot fink.sif Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun with \u003ccode\u003esingularity run fink.sif\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpen the link in your browser (from the host)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to contribute\u003c/h2\u003e\u003ca id=\"user-content-how-to-contribute\" class=\"anchor\" aria-label=\"Permalink: How to contribute\" href=\"#how-to-contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to contribute:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone (or fork) this repo, and open a new branch.\u003c/li\u003e\n\u003cli\u003eCreate a new folder with a meaningful name (e.g. \u003ccode\u003esupernovae\u003c/code\u003e, \u003ccode\u003egrb\u003c/code\u003e, ...)\u003c/li\u003e\n\u003cli\u003eRead and copy an existing notebook to get an idea of the structure of a tutorial.\u003c/li\u003e\n\u003cli\u003eOnce your notebook is finished, open a Pull Request such that we review the tutorial and merge it!\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 5, - "subscribers_count": 2, - "topics": [], - "updated_at": 1726680271.0 + "subscribers_count": 7, + "topics": [ + "tutorial", + "api", + "fink" + ], + "updated_at": 1710377763.0 }, { "data_format": 2, - "description": "Snakemake pipelines to run the analysis for the Illumina vs. Nanopore comparison.", + "description": "OIST Bioinfo user group", "filenames": [ - "analysis/assembly/containers/Singularity.canu" + "RStudio/Singularity.def" ], - "full_name": "mbhall88/head_to_head_pipeline", + "full_name": "oist/BioinfoUgrp", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePaper\u003c/h3\u003e\u003ca id=\"user-content-paper\" class=\"anchor\" aria-label=\"Permalink: Paper\" href=\"#paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eHall, M. B. et al. Evaluation of Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation: a genomic analysis. \u003cem\u003eThe Lancet Microbe\u003c/em\u003e 0, (2022) doi: \u003ca href=\"https://doi.org/10.1016/S2666-5247(22)00301-9\" rel=\"nofollow\"\u003e10.1016/S2666-5247(22)00301-9\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003cp\u003eThis repository holds the pipelines/scripts used for our paper analysing Illumina and\nNanopore for \u003cem\u003eM.tuberculosis\u003c/em\u003e drug resistance calling and transmission clustering.\u003c/p\u003e\n\u003cp\u003eFor people wanting to analyse their Nanopore data in the same manner as we did in this paper, we would suggest using \u003ca href=\"https://github.com/mbhall88/tbpore\"\u003ehttps://github.com/mbhall88/tbpore\u003c/a\u003e, which is a python program that runs the drug resistance prediction and clustering (with a smaller decontamination database) components of this pipeline. It is actively maintained and much easier to use.\u003c/p\u003e\n\u003cp\u003eAll pipelines require the following dependencies to be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://snakemake.github.io/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e (and\n\u003ca href=\"https://github.com/mamba-org/mamba\"\u003eMamba\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/docs\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eThe Python library \u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003e\u003ccode\u003epandas\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee subdirectories for more specific information about different pipelines. They are\nnested according to their dependence on the outputs of each pipeline.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"data/QC\"\u003eQuality Control\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/assembly\"\u003eAssembly\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"analysis/baseline_variants\"\u003eBaseline variant analysis\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/transmission_clustering\"\u003eTransmission clustering\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/resistance_prediction\"\u003eDrug Resistance Prediction\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following pipelines are not relevant to the work in the final paper.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"data/H37Rv_PRG\"\u003eH37Rv PRG construction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/pandora_variants\"\u003ePandora variant analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData availability\u003c/h1\u003e\u003ca id=\"user-content-data-availability\" class=\"anchor\" aria-label=\"Permalink: Data availability\" href=\"#data-availability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll data is submitted under the Project accession \u003cstrong\u003ePRJEB49093\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe accessions and all relevant sample metadata for this study can be found at \u003ca href=\"https://doi.org/10.6084/m9.figshare.19304648\" rel=\"nofollow\"\u003ehttps://doi.org/10.6084/m9.figshare.19304648\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe raw Nanopore data is available to download from: \u003ca href=\"https://ftp.ebi.ac.uk/pub/databases/ont_tb_eval2022/\" rel=\"nofollow\"\u003ehttps://ftp.ebi.ac.uk/pub/databases/ont_tb_eval2022/\u003c/a\u003e. See the sample metadata file for mappings between samples and the relevant Nanopore runs and barcode numbers.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-oists-bioinformatics-user-group\" class=\"anchor\" aria-hidden=\"true\" href=\"#oists-bioinformatics-user-group\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOIST\u0027s bioinformatics user group\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-communication-channel\" class=\"anchor\" aria-hidden=\"true\" href=\"#communication-channel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommunication channel\u003c/h2\u003e\n\u003cp\u003ePrioritized communication channel is on Microsoft Teams: \u003ca href=\"https://teams.microsoft.com/l/team/19%3a3183bd7fe2844138a49996a2bd376873%40thread.tacv2/conversations?groupId=cc78e114-c544-43e2-b4b1-29c7428aa305\u0026amp;tenantId=d8c0fb8d-bb56-44bb-9f4a-c58e7465652e\" rel=\"nofollow\"\u003eBioinfoUgrp\u003c/a\u003e. Do not hesitate to use the ping function (putting \u003ccode\u003e@\u003c/code\u003e and then the name, like in other chat systems), because the discussions on the Team app are a bit easy to miss otherwise.\nPlease \"Google\" the issues prior to contacting us. Very often, the main issues will already be reported and the solution available on the reference webpage of the program: in the \u003ccode\u003eIssues\u003c/code\u003e tab of \u003ccode\u003eGitHub\u003c/code\u003e for some, in \u003ccode\u003eGoogleGroups\u003c/code\u003e for others (e.g. for \u003ca href=\"https://groups.google.com/g/iqtree\" rel=\"nofollow\"\u003eIQ-TREE\u003c/a\u003e). Other great platforms are \u003ca href=\"https://stackoverflow.com\" rel=\"nofollow\"\u003eStackOverflow\u003c/a\u003e, or \u003ca href=\"https://www.biostars.org\" rel=\"nofollow\"\u003eBiostars\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-finding-modules\" class=\"anchor\" aria-hidden=\"true\" href=\"#finding-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFinding modules\u003c/h2\u003e\n\u003cp\u003eSearch with a keyword, for instance \u003ccode\u003eml key clustal\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-loading-installed-modules\" class=\"anchor\" aria-hidden=\"true\" href=\"#loading-installed-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLoading installed modules\u003c/h2\u003e\n\u003cp\u003eExecute \u003ccode\u003eml bioinfo-ugrp-modules\u003c/code\u003e to make available the modules installed by the OIST Bioinfo user group. This line can be appended to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to make them available by default.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-debian-med-modules\" class=\"anchor\" aria-hidden=\"true\" href=\"#debian-med-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebian Med modules\u003c/h3\u003e\n\u003cp\u003eWe autogenerate many modules from softwares packaged the Debian distribution. To see them, execute \u003ccode\u003eml bioinfo-ugrp-modules DebianMed\u003c/code\u003e. More information is available on the \u003ca href=\"DebianMedModules.md\"\u003eDebianMedModules\u003c/a\u003e page.\nTo load a module in DebianMed (an example for loading bcftools):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# load DebianMed module first\nml bioinfo-ugrp-modules DebianMed\n\n# now you can see the list of module installed in DebianMed.\nml avail\n\n# load module\nml bcftools\n\n# check the installation\nbcftools --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-unix-goodies\" class=\"anchor\" aria-hidden=\"true\" href=\"#unix-goodies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnix goodies\u003c/h3\u003e\n\u003cp\u003eWe provide some modules for Unix tools useful to everybody including bioinformaticians.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eml bioinfo-ugrp-modules UnixGoodies\nml av\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck \u003ca href=\"https://github.com/oist/BioinfoUgrp_UnixGoodies_Images\"\u003eoist/BioinfoUgrp_UnixGoodies_Images\u003c/a\u003e for details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nextflow-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipelines\u003c/h3\u003e\n\u003cp\u003eWe have prepared a \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e module (\u003ccode\u003eml bioinfo-ugrp-modules Nextflow2\u003c/code\u003e) and registered \u003ca href=\"https://github.com/nf-core/configs/blob/master/docs/oist.md\"\u003eOIST\u0027s profile\u003c/a\u003e to the \u003ca href=\"https://nf-co.re/\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e community so that you can run their pipelines with the \u003ccode\u003e-profile oist\u003c/code\u003e option on \u003cem\u003eDeigo\u003c/em\u003e. A \u003cem\u003enf-core\u003c/em\u003e \u003ca href=\"https://github.com/nf-core/modules\"\u003emodule\u003c/a\u003e is also available (\u003ccode\u003eml bioinfo-ugrp-modules nf-core\u003c/code\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-other-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther tools\u003c/h3\u003e\n\u003cp\u003eUnder the \u003ccode\u003eOther/\u003c/code\u003e namespace, we also provide some general bioinformatics tools such as:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDIAMOND (\u003ccode\u003eml Other/DIAMOND/2.0.4.142\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eInterProScan and its database (\u003ccode\u003eml Other/interproscan/5.48-83.0\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\u2026 and more !\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"Other.md\"\u003ethis page\u003c/a\u003e for the full list of modules and for more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-databases\" class=\"anchor\" aria-hidden=\"true\" href=\"#databases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDatabases\u003c/h2\u003e\n\u003cp\u003eWidely used databases were installed locally. Upon request by users, we plan on upgrading databases (not more than once a year). After upgrading a specific database, users will be asked if the older database should still remain available (completion of projects,...): it will be deleted after 30 days except if still required. At one time, a maximum of two versions of the same database will be available.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-taxified-blast-databases\" class=\"anchor\" aria-hidden=\"true\" href=\"#taxified-blast-databases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTaxified BLAST databases\u003c/h3\u003e\n\u003cp\u003eThe following databases were constructed using ncbi-blast v2.10.0+. The module \u003ccode\u003encbi-blast/2.10.0+\u003c/code\u003e has to be loaded in order to use these databases.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI NT and NR databases (release 238) : \u003ccode\u003eml DB/blastDB/ncbi/238\u003c/code\u003e. To be used with the arguments \u003ccode\u003ent\u003c/code\u003e or \u003ccode\u003enr\u003c/code\u003e supplied to \u003ccode\u003e-db\u003c/code\u003e in the commands of your scripts. Example script to get a taxified blast report:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emodule load ncbi-blast/2.10.0+\nmodule load DB/blastDB/ncbi/238\nWORKDIR=\"$PWD\"\nFASTA=FULL/PATH/TO/YOUR/FASTA/FILE\nblastn -task megablast -db nt -query $FASTA -num_threads ${SLURM_CPUS_PER_TASK} -out ${WORKDIR}/megablastn.out \\\n\t-outfmt \u00276 qseqid bitscore evalue length qlen qcovs pident sseqid sgi sacc staxid ssciname scomname stitle sseq\u0027 \\\n\t-max_target_seqs 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSwiss-Prot (version 2020_06): \u003ccode\u003eml DB/blastDB/sprot/2020_06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniRef90 (version 2020_06): \u003ccode\u003eml DB/blastDB/uniref90/2020_06\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-taxified-diamond-databases\" class=\"anchor\" aria-hidden=\"true\" href=\"#taxified-diamond-databases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTaxified DIAMOND databases\u003c/h3\u003e\n\u003cp\u003eThe following databases were constructed using DIAMOND v2.0.4.142. The module \u003ccode\u003eOther/DIAMOND/2.0.4.142\u003c/code\u003e has to be loaded in order to use them.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe NCBI-NR database (release 238): \u003ccode\u003eml DB/diamondDB/ncbi/238\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSwiss-Prot (version 2020_06): \u003ccode\u003eml DB/diamondDB/sprot/2020_06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniRef90 (version 2020_06): \u003ccode\u003eml DB/diamondDB/uniref90/2020_06\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUnlike ncbi-blast, DIAMOND requires full path of the databases. The database module automatically create an environment variable \"DIAMONDDB\" which specifies full path to the DIAMOND database. So you need to prepend \u003ccode\u003e${DIAMONDDB}\u003c/code\u003e to the name of database.\nExample script to run diamond with the database module:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# load ncbi database for DIAMOND (proper version of DIAMOND is automatically loaded)\nmodule load DB/diamondDB/ncbi/238\n\n# check the loaded DIAMOND version and ${DIAMONDDB} variable\ndiamond --version\necho ${DIAMONDDB}\n\n# run diamond search\nWORKDIR=\"$PWD\"\nFASTA=FULL/PATH/TO/YOUR/FASTA/FILE\ndiamond blastp -db ${DIAMONDDB}/nr -q $FASTA -p ${SLURM_CPUS_PER_TASK} -out ${WORKDIR}/diamond.blastp.out -outfmt 6\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pfam\" class=\"anchor\" aria-hidden=\"true\" href=\"#pfam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePfam\u003c/h3\u003e\n\u003cp\u003eVersion 34.0: Use \u003ccode\u003eml DB/Pfam/34.0\u003c/code\u003e to invoke it in your scripts.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dfam\" class=\"anchor\" aria-hidden=\"true\" href=\"#dfam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDfam\u003c/h3\u003e\n\u003cp\u003eVersion 3.6 downloaded from \u003ca href=\"https://www.dfam.org/releases/Dfam_3.6/families/Dfam.h5.gz\" rel=\"nofollow\"\u003ehttps://www.dfam.org/releases/Dfam_3.6/families/Dfam.h5.gz\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe command \u003ccode\u003eml DB/Dfam/3.6\u003c/code\u003e will expose an environment variable \u003ccode\u003e$BioinfoUgrp_Dfam\u003c/code\u003e containing the path to the directory containing the database files, that can be passed to RepeatMasker through its \u003ccode\u003e-libdir\u003c/code\u003e argument.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dfam-for-repeatmasker\" class=\"anchor\" aria-hidden=\"true\" href=\"#dfam-for-repeatmasker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDfam for RepeatMasker\u003c/h3\u003e\n\u003cp\u003eThe command \u003ccode\u003eml DB/Dfam_RepeatMasker/3.6__4.1.3\u003c/code\u003e will set an environmental variable that changes the behaviour of the \u003ccode\u003erepeatmodeler\u003c/code\u003e module, so that it will use the full Dfam database provided by us instead of the \u201c\u003cem\u003ecurated only\u003c/em\u003e\u201d version provided by default.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-developer-details\" class=\"anchor\" aria-hidden=\"true\" href=\"#developer-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeveloper details\u003c/h4\u003e\n\u003cp\u003eThe RepeatMasker program does not follow symbolic links and the Dfam database is large (160 Gb), so I had to use hard links to the files of the \u003ccode\u003eDfam\u003c/code\u003e module instead. Also, the modulefile contains:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetenv(\"BioinfoUgrp_Dfam_Rmsk_4_1_3\", apphome..\"/RepeatMasker_4.1.3/Libraries\")\nsetenv(\"SINGULARITY_BINDPATH\", apphome..\"/Libraries:/opt/RepeatMasker/Libraries\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRStudio\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"./RStudio\"\u003eHere is how you can run RStudio\u003c/a\u003e on a compute node.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-modules-on-saion\" class=\"anchor\" aria-hidden=\"true\" href=\"#modules-on-saion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModules on Saion\u003c/h2\u003e\n\u003cp\u003eWe have some modules on \u003cem\u003eSaion\u003c/em\u003e for GPU-accelerated computations such that can not be run on \u003cem\u003eDeigo\u003c/em\u003e. Please remember that the \u003cem\u003emodules\u003c/em\u003e system on \u003cem\u003eSaion\u003c/em\u003e is older, so the \u003ccode\u003eml\u003c/code\u003e shortcuts will not work. To list the available modules, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load bioinfo-ugrp-modules\nmodule available\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alpha-fold\" class=\"anchor\" aria-hidden=\"true\" href=\"#alpha-fold\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlpha Fold\u003c/h3\u003e\n\u003cp\u003eWe have a very basic implementation of Alpha fold 2.1.1 within the user group modules. You can find (in time) a verbose documentation \u003ca href=\"AlphaFold.md\"\u003ehere\u003c/a\u003e. However, for a basic usage, you can try to do something similar to the example script in: /apps/unit/BioinfoUgrp/alphafold/2.1.1/bin/alphafold_example_script.sh\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nanopore\" class=\"anchor\" aria-hidden=\"true\" href=\"#nanopore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNanopore\u003c/h3\u003e\n\u003cp\u003eWe have modules for \u003ca href=\"NanoporeModules.md\"\u003ebasecalling Nanopore\u003c/a\u003e data, in particular for \u003cem\u003eGuppy\u003c/em\u003e and \u003cem\u003eRerio\u003c/em\u003e.\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 4, + "subscribers_count": 7, "topics": [ - "tuberculosis", - "snakemake", - "pipeline", + "oist", "bioinformatics", - "nanopore", - "illumina", - "drug-resistance", - "transmission", - "clustering", - "diagnostics" + "hpc" ], - "updated_at": 1699458270.0 + "updated_at": 1686730130.0 }, { "data_format": 2, - "description": "Source code to reproduce the figures of the boostDM paper", + "description": "CycleCloud project to enable use of Singularity containers in HPC clusters in Azure.", "filenames": [ - "Singularity" + "specs/default/cluster-init/files/examples/sleep/Singularity" ], - "full_name": "bbglab/boostdm-analyses", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eboostDM manuscript analyses\u003c/h1\u003e\u003ca id=\"user-content-boostdm-manuscript-analyses\" class=\"anchor\" aria-label=\"Permalink: boostDM manuscript analyses\" href=\"#boostdm-manuscript-analyses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSource code to reproduce the figures of the paper:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eIn silico saturation mutagenesis of cancer genes\u003c/strong\u003e\u003cbr\u003e\nFerran Mui\u00f1os, Francisco Martinez-Jimenez, Oriol Pich, Abel Gonzalez-Perez, Nuria Lopez-Bigas\u003cbr\u003e\nDOI: \u003ca href=\"https://doi.org/10.1038/s41586-021-03771-1\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41586-021-03771-1\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContent\u003c/h2\u003e\u003ca id=\"user-content-content\" class=\"anchor\" aria-label=\"Permalink: Content\" href=\"#content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains the source code to reproduce the main and extended figures of the paper.\u003cbr\u003e\nEach figure has its own jupyter notebook to render the figure\u0027s panels.\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMain figures\u003c/h4\u003e\u003ca id=\"user-content-main-figures\" class=\"anchor\" aria-label=\"Permalink: Main figures\" href=\"#main-figures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFigure 1: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Figure1/display_panels_Figure1.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Figure1.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nFigure 2: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Figure2/display_panels_Figure2.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Figure2.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nFigure 3: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Figure3/display_panels_Figure3.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Figure3.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nFigure 4: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Figure4/display_panels_Figure4.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Figure4.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExtended Figures\u003c/h4\u003e\u003ca id=\"user-content-extended-figures\" class=\"anchor\" aria-label=\"Permalink: Extended Figures\" href=\"#extended-figures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtended Figure 1: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_1/display_panels_Extended_Figure_1.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure1.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 2: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_2/display_panels_Extended_Figure_2.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure2.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 3: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_3/display_panels_Extended_Figure_3.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure3.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 4: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_4/display_panels_Extended_Figure_4.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure4.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 5: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_5/display_panels_Extended_Figure_5.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure5.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 6: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_6/display_panels_Extended_Figure_6.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure6.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 7: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_7/display_panels_Extended_Figure_7.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure7.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 8: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_8/display_panels_Extended_Figure_8.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure8.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 9: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_9/display_panels_Extended_Figure_9.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure9.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eComplementary content\u003c/h2\u003e\u003ca id=\"user-content-complementary-content\" class=\"anchor\" aria-label=\"Permalink: Complementary content\" href=\"#complementary-content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can access to boostDM source code and documentation in the \u003ca href=\"https://bitbucket.org/bbglab/boostdm/src/release/\" rel=\"nofollow\"\u003eboostDM pipeline repository\u003c/a\u003e.\u003cbr\u003e\nYou can explore and download the main outputs of boostDM in the \u003ca href=\"https://www.intogen.org/boostdm\" rel=\"nofollow\"\u003eboostDM website\u003c/a\u003e.\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDownload source data\u003c/h4\u003e\u003ca id=\"user-content-download-source-data\" class=\"anchor\" aria-label=\"Permalink: Download source data\" href=\"#download-source-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll the code features in this repo feeds on source data.\u003c/p\u003e\n\u003cp\u003eMake sure that you download a stable copy of the source data from zenodo and keep it in the root of the repo\nfrom \u003ca href=\"https://zenodo.org/\" rel=\"nofollow\"\u003ezenodo\u003c/a\u003e as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install zenodo_get\n$ bash get.sh\n$ tar -xvf source-data/source-data-zenodo.tar.gz\n$ cp -r source-data/boostdm-analyses .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun notebooks with singularity\u003c/h4\u003e\u003ca id=\"user-content-run-notebooks-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Run notebooks with singularity\" href=\"#run-notebooks-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe notebooks must be run on a jupyter-notebook or jupyter-lab session launched from\n\u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image that already satisfies all the dependencies for the notebooks to run.\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFollow these steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html#\" rel=\"nofollow\"\u003eInstall\u003c/a\u003e the latest Singularity release\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a singularity image using the \u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Singularity\"\u003eSingularity\u003c/a\u003e recipe:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build boostdm-analyses.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNow you can run the notebooks from singularity:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec boostdm-analyses.sif jupyter-lab\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "Azure/cyclecloud-singularity", + "latest_release": "2.0.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eThis project installs and configures the Singularity container system.\u003c/p\u003e\n\u003cp\u003eSingularity is a system for building and running Linux Containers. See the \u003ca href=\"https://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project site for more information and documentation.\u003c/p\u003e\n\u003cp\u003eThe project includes an example cluster template which adds Singularity to a PBS grid. But the Singularity project is intended primarily as an additional capability that can be added to any Cyclecloud cluster.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pre-requisites\"\u003ePre-Requisites\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#configuring-the-project\"\u003eConfiguring the Project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#deploying-the-project\"\u003eDeploying the Project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#importing-the-cluster-template\"\u003eImporting the Cluster Template\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-Requisites\u003c/h2\u003e\n\u003cp\u003eThis sample requires the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe Singularity source tarball or the Singularity RPM or DEB files (depending on the OS you select for your cluster).\u003c/p\u003e\n\u003cp\u003ea. Download the source or binaries following the instructions here: (\u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/install-linux\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo download the source, you can simply run:\n\u003ccode\u003eVERSION=\"3.1.1\" \u0026amp;\u0026amp; curl -L -O \"https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-${VERSION}.tar.gz\"\u003c/code\u003e\nb. Place the source tarball and/or package files in the \u003ccode\u003e./blobs/\u003c/code\u003e directory.\nc. If the version is not 3.1.1 (the project default), then update the version number in the Files list\nin \u003ccode\u003e./project.ini\u003c/code\u003e and in the cluster template: \u003ccode\u003e./templates/pbs-singularity.txt\u003c/code\u003e.\nd. If you are starting from the package files, also add the package file names to the Files list in\n\u003ccode\u003e./project.ini\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCycleCloud must be installed and running.\u003c/p\u003e\n\u003cp\u003ea. If this is not the case, see the CycleCloud QuickStart Guide for\nassistance.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe CycleCloud CLI must be installed and configured for use.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou must have access to log in to CycleCloud.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou must have access to upload data and launch instances in your chosen\nCloud Provider account.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou must have access to a configured CycleCloud \"Locker\" for Project Storage\n(Cluster-Init and Chef).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOptional: To use the \u003ccode\u003ecyclecloud project upload \u0026lt;locker\u0026gt;\u003c/code\u003e command, you must\nhave a Pogo configuration file set up with write-access to your locker.\u003c/p\u003e\n\u003cp\u003ea. You may use your preferred tool to interact with your storage \"Locker\"\ninstead.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-configuring-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuring-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguring the Project\u003c/h2\u003e\n\u003cp\u003eThe first step is to configure the project for use with your storage locker:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOpen a terminal session with the CycleCloud CLI enabled.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSwitch to the singularity project directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the following source tarballs and/or RPM and DEB files to \u003ccode\u003e./blobs\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf the version number is not 3.1.1, update the version numbers in \u003ccode\u003eproject.ini\u003c/code\u003e and \u003ccode\u003etemplates/pbs-singularity.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf adding the RPM and/or DEB files, add them to the Files list in the \u003ccode\u003eproject.ini\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deploying-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploying-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploying the Project\u003c/h2\u003e\n\u003cp\u003eTo upload the project (including any local changes) to your target locker, run the\n\u003ccode\u003ecyclecloud project upload\u003c/code\u003e command from the project directory. The expected output looks like\nthis:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e $ cyclecloud project upload my_locker\n Sync completed\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eIMPORTANT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the upload to succeed, you must have a valid Pogo configuration for your target Locker.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-importing-the-cluster-template\" class=\"anchor\" aria-hidden=\"true\" href=\"#importing-the-cluster-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImporting the Cluster Template\u003c/h2\u003e\n\u003cp\u003eTo import the cluster:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOpen a terminal session with the CycleCloud CLI enabled.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSwitch to the Singularity project directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun \u003ccode\u003ecyclecloud import_template PBS-Singularity -f templates/pbs-singularity.txt\u003c/code\u003e.\nThe expected output looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cyclecloud import_template PBS-Singularity -f templates/pbs-singularity.txt --force\nImporting template PBS-Singularity....\n----------------------------\nPBS-Singularity \u003cspan class=\"pl-c1\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003etemplate\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n----------------------------\nKeypair:\nCluster nodes:\nmaster: off\nTotal nodes: 1\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eThis project welcomes contributions and suggestions. Most contributions require you to agree to a\nContributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us\nthe rights to use your contribution. For details, visit \u003ca href=\"https://cla.microsoft.com\" rel=\"nofollow\"\u003ehttps://cla.microsoft.com\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWhen you submit a pull request, a CLA-bot will automatically determine whether you need to provide\na CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions\nprovided by the bot. You will only need to do this once across all repos using our CLA.\u003c/p\u003e\n\u003cp\u003eThis project has adopted the \u003ca href=\"https://opensource.microsoft.com/codeofconduct/\" rel=\"nofollow\"\u003eMicrosoft Open Source Code of Conduct\u003c/a\u003e.\nFor more information see the \u003ca href=\"https://opensource.microsoft.com/codeofconduct/faq/\" rel=\"nofollow\"\u003eCode of Conduct FAQ\u003c/a\u003e or\ncontact \u003ca href=\"mailto:opencode@microsoft.com\"\u003eopencode@microsoft.com\u003c/a\u003e with any additional questions or comments.\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 8, - "topics": [ - "mutations", - "cancer-genes", - "drivers" - ], - "updated_at": 1723033039.0 + "subscribers_count": 15, + "topics": [], + "updated_at": 1664393642.0 }, { "data_format": 2, - "description": null, + "description": "Nextflow Pipeline for the analysis of Double Progressive Alignment (DPA)", "filenames": [ - "Singularity" + "singularity/Singularity", + "singularity/.ipynb_checkpoints/Singularity-checkpoint" ], - "full_name": "Kurorororo/pplanner", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePPlanner\u003c/h1\u003e\u003ca id=\"user-content-pplanner\" class=\"anchor\" aria-label=\"Permalink: PPlanner\" href=\"#pplanner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePPlanner is a classical planner implementing parallel heuristic search algorithms.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReference\u003c/h2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-label=\"Permalink: Reference\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eKuroiwa R, Fukunaga A. 2020. Analyzing and Avoiding Pathological Behavior of Parallel Best-First Search. Proc. the 30th International Conference on Automated Planning and Scheduling. (ICAPS 2020)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker container does not support MPI based distributed parallel search methods.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e -t pplanner\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build pplanner.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eManual Build\u003c/h3\u003e\u003ca id=\"user-content-manual-build\" class=\"anchor\" aria-label=\"Permalink: Manual Build\" href=\"#manual-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see Dockerfile and Singularity to know the commands to build.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInput a SAS+ file and a config file.\nThe planner generates \u003ccode\u003esas_plan\u003c/code\u003e after finding a solution.\u003c/p\u003e\n\u003cp\u003eGBFS using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/GBFS-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHDGBFS using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -n 16 ./bin/mpi_planner -f output.sas -c config_examples/HDGBFS-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNE using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -n 16 ./bin/mpi_planner -f output.sas -c config_examples/NE-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLG using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -n 16 ./bin/mpi_planner -f output.sas -c config_examples/LG-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eKPGBFS using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/KPGBFS-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eP_{GBFS} using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/PGBFS-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eP_{GBFS}/C using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/PGBFS-C-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePUHF using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/PUHF-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSPUHF using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/SPUHF-FF.json\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "evanfloden/dpa-analysis", + "latest_release": "v0.2.6", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-fast-and-accurate-large-multiple-sequence-alignments-using-root-to-leave-regressive-computation\" class=\"anchor\" aria-hidden=\"true\" href=\"#fast-and-accurate-large-multiple-sequence-alignments-using-root-to-leave-regressive-computation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast and accurate large multiple sequence alignments using root-to-leave regressive computation\u003c/h1\u003e\n\u003cp\u003eThis repository contains data, documentation, analysis and Nextflow workflow for the manuscript \"Fast and accurate large multiple sequence alignments using root-to-leave regressive computation\".\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-for-details-on-how-to-use-the-regressive-multiple-sequence-alignment-method-see-the-t-coffee-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-details-on-how-to-use-the-regressive-multiple-sequence-alignment-method-see-the-t-coffee-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor details on how to use the Regressive Multiple Sequence Alignment method, see the \u003ca href=\"https://tcoffee.readthedocs.io/en/latest/tcoffee_quickstart_regressive.html\" rel=\"nofollow\"\u003eT-Coffee documentation\u003c/a\u003e.\u003c/h4\u003e\n\u003ch3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003eThis workflow was written by Evan Floden (\u003ca href=\"https://github.com/evanfloden\"\u003eevanfloden\u003c/a\u003e) and\nEdgar(\u003ca href=\"https://github.com/edgano\"\u003eedgano\u003c/a\u003e) at the \u003ca href=\"http://www.crg.eu\" rel=\"nofollow\"\u003eCenter for Genomic Regulation (CRG)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe authors who contributed to the analysis and manuscript are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEdgar Garriga Nogales\u003c/li\u003e\n\u003cli\u003ePaolo Di Tommaso\u003c/li\u003e\n\u003cli\u003eCedrik Magis\u003c/li\u003e\n\u003cli\u003eIonas Erb\u003c/li\u003e\n\u003cli\u003eHafid Laayouni\u003c/li\u003e\n\u003cli\u003eFyodor Kondrashov\u003c/li\u003e\n\u003cli\u003eEvan Floden\u003c/li\u003e\n\u003cli\u003eCedric Notredame\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotebooks\u003c/h3\u003e\n\u003cp\u003eThis repository contains a series of \u003ca href=\"http://jupyter.org/\" rel=\"nofollow\"\u003eJupyter Notebooks\u003c/a\u003e that contain\nthe steps for replicating the analysis, tables and figures in the manuscript.\u003c/p\u003e\n\u003cp\u003eThe index jupyter notebook can be found \u003ca href=\"notebook/00_StartHere.ipynb\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe notebook executes the pipeline, some steps of which require a lot of resources.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h3\u003e\n\u003cp\u003eThe pipeline for generating trees, alignments and performing the evaluations is built using\n\u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across\nmultiple compute infrastructures in a very portable manner. It comes with a docker container\nmaking installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pipeline-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline Quick Start\u003c/h3\u003e\n\u003cp\u003eMake sure you have either docker/singularity installed or the required dependencies listed\nin the last section.\u003c/p\u003e\n\u003cp\u003eInstall the Nextflow runtime by running the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -fsSL get.nextflow.io | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen done, you can launch the pipeline execution by entering the command shown below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ nextflow run evanfloden/dpa-analysis\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default the pipeline is executed against the provided example dataset.\nCheck the \u003cem\u003ePipeline parameters\u003c/em\u003e section below to see how enter your data on the program\ncommand line.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers\u003c/h3\u003e\n\u003cp\u003eAll the methods above are available in a \u003ca href=\"http://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image on DockerHub \u003ca href=\"https://hub.docker.com/r/cbcrg/regressive-msa/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the image is tested to be compatible with the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe container also contains test data consisting of protein sequences, reference alignments and trees in the directory \u003ccode\u003e/test_data\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo launch the container interactively with Docker run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run cbcrg/regressive-msa\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo launch the container interactivly with Singularity run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity shell docker://cbcrg/regressive-msa\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pipeline-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline parameters\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content---seqs\" class=\"anchor\" aria-hidden=\"true\" href=\"#--seqs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--seqs\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies the location of the input \u003cem\u003efasta\u003c/em\u003e file(s).\u003c/li\u003e\n\u003cli\u003eMultiple files can be specified using the usual wildcards (*, ?), in this case make sure to surround the parameter string\nvalue by single quote characters (see the example below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ nextflow run evanfloden/dpa-analysis --seqs \u0027/home/seqs/*.fasta\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will handle each fasta file as a seperate sample.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content---refs\" class=\"anchor\" aria-hidden=\"true\" href=\"#--refs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--refs\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies the location of the reference \u003cem\u003ealigned fasta\u003c/em\u003e file(s).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---trees\" class=\"anchor\" aria-hidden=\"true\" href=\"#--trees\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--trees\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies the location of input tree file(s).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---align_method\" class=\"anchor\" aria-hidden=\"true\" href=\"#--align_method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--align_method\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies which alignment methods should be used.\u003c/li\u003e\n\u003cli\u003eOptions include: \"CLUSTALO,MAFFT-FFTNS1,MAFFT-SPARSECORE,MAFFT-GINSI,PROBCONS,UPP\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---tree_method\" class=\"anchor\" aria-hidden=\"true\" href=\"#--tree_method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--tree_method\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies which guide-tree / clustering methods should be used.\u003c/li\u003e\n\u003cli\u003eOptions include: \"CLUSTALO,MAFFT_PARTTREE\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---regressive_align\" class=\"anchor\" aria-hidden=\"true\" href=\"#--regressive_align\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--regressive_align\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eFlag to generate regressive MSAs.\u003c/li\u003e\n\u003cli\u003eSee \u003ccode\u003etemplates/dpa_align\u003c/code\u003e for the specific commands executed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---stardard_align\" class=\"anchor\" aria-hidden=\"true\" href=\"#--stardard_align\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--stardard_align\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eFlag to perform standard MSAs.\u003c/li\u003e\n\u003cli\u003eStandard MSA is alignment where the guide-tree is provided as input.\u003c/li\u003e\n\u003cli\u003eSee \u003ccode\u003etemplates/std_align\u003c/code\u003e for the specific commands executed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---default_align\" class=\"anchor\" aria-hidden=\"true\" href=\"#--default_align\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--default_align\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eFlag to perform default MSAs.\u003c/li\u003e\n\u003cli\u003eDefault MSA is alignment where the alignment software uses an internally generated guide-tree.\u003c/li\u003e\n\u003cli\u003eSee \u003ccode\u003etemplates/default_align\u003c/code\u003e for the specific commands executed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---evaluate\" class=\"anchor\" aria-hidden=\"true\" href=\"#--evaluate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--evaluate\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eFlag to perform evaluation of the alignments.\u003c/li\u003e\n\u003cli\u003eRequires reference sequences to be provided with the \u003ccode\u003e--refs\u003c/code\u003e parameter.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---buckets\" class=\"anchor\" aria-hidden=\"true\" href=\"#--buckets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--buckets\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eList of bucket sizes or maximum size of the subMSAs in the regressive proceedure.\u003c/li\u003e\n\u003cli\u003eDefault value is \"1000\" sequences.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---output\" class=\"anchor\" aria-hidden=\"true\" href=\"#--output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--output\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eLocation of the results.\u003c/li\u003e\n\u003cli\u003eDefault locations is \u003ccode\u003eresults\u003c/code\u003e directory.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 5, - "subscribers_count": 2, + "subscribers_count": 0, "topics": [], - "updated_at": 1690291184.0 + "updated_at": 1620129144.0 }, { "data_format": 2, - "description": "Repository hosting the code associated with \"Fast and stable MAP-Elites in noisy domains using deep grids\"", + "description": "Module providing brain MR images pre-processing workflows for Deep Learning. ", "filenames": [ - "Singularity" + "containers/Singularity.dmriprep" ], - "full_name": "adaptive-intelligent-robotics/Deep-Grid_MAP-Elites", + "full_name": "neurospin-deepinsight/brainprep", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFast and stable MAP-Elites in noisy domains using deep grids\u003c/h1\u003e\u003ca id=\"user-content-fast-and-stable-map-elites-in-noisy-domains-using-deep-grids\" class=\"anchor\" aria-label=\"Permalink: Fast and stable MAP-Elites in noisy domains using deep grids\" href=\"#fast-and-stable-map-elites-in-noisy-domains-using-deep-grids\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4459\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains the code associated with \u003ca href=\"https://direct.mit.edu/isal/proceedings/isal2020/32/273/98397\" rel=\"nofollow\"\u003eFast and stable MAP-Elites in noisy domains using deep grids\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis code proposes an implementation of several MAP-Elites variants to handle uncertainty: the Explicit-averaging approach, the two Adaptive-sampling approaches proposed in \u003ca href=\"https://dl.acm.org/doi/abs/10.1145/3319619.3321904\" rel=\"nofollow\"\u003eMAP-Elites for noisy domains by adaptive sampling\u003c/a\u003e and the DG-MAP-Elites approach. It allows to compare these approaches on the three tasks described in the paper.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLibraries and dependencies\u003c/h1\u003e\u003ca id=\"user-content-libraries-and-dependencies\" class=\"anchor\" aria-label=\"Permalink: Libraries and dependencies\" href=\"#libraries-and-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe implementation of all tasks and algorithms is based on the qd-branch of the C++ \u003ca href=\"https://github.com/sferes2/sferes2\"\u003eSferes2\u003c/a\u003e library presented in \u003ca href=\"https://ieeexplore.ieee.org/abstract/document/5586158/?casa_token=EhBJLkircvMAAAAA:ls8I90Y5H2vsJk5RxCYs8X1T9yZHDhDEz5S6g5gatOzETle1LK_ib8zwodx6t5J_-Uwq_YP9\" rel=\"nofollow\"\u003eSferesv2: Evolvin\u0027 in the multi-core world\u003c/a\u003e, and the hexapod control task uses the Dart simulator introduced in \u003ca href=\"https://joss.theoj.org/papers/10.21105/joss.00500.pdf\" rel=\"nofollow\"\u003eDart: Dynamic animation and robotics toolkit\u003c/a\u003e.\nFurthermore, the analysis of the results is based on \u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003ePanda\u003c/a\u003e, \u003ca href=\"https://matplotlib.org/\" rel=\"nofollow\"\u003eMatplotlib\u003c/a\u003e and \u003ca href=\"https://seaborn.pydata.org/index.html\" rel=\"nofollow\"\u003eSeaborn\u003c/a\u003e libraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eStructure\u003c/h1\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main part of the code contains the following files and folders:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edeep_grid\u003c/code\u003e contains the main structure of all algorithms based on the structure of QD algorithms introduced in \u003ca href=\"https://ieeexplore.ieee.org/abstract/document/7959075/\" rel=\"nofollow\"\u003eQuality and diversity optimization: A unifying modular framework\u003c/a\u003e. The approaches differ by the nature of their selector, the add-function of their container and, in the specific case of Adaptive sampling, the descriptor they are using for the individuals, which is implemented in the structure of the cells of the grid.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etask\u003c/code\u003e contains a re-definition of the qd fitness from the Sferes2 library which allow to handle re-sampling of the solutions. It also contains an implementation of each of the three tasks used in the paper, build on top of the original deterministic tasks used in previous works.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emodifier\u003c/code\u003e defines the procedure to evaluate the \"true\" value of fitness and behaviour descriptor of each cell as described in the paper.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estat\u003c/code\u003e implements all the stats used to compare the algorithms.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun_utils\u003c/code\u003e contains utils function used to read the options of each run and run all algorithms.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition, the \u003ccode\u003eanalysis\u003c/code\u003e folder is used for the results analysis, the \u003ccode\u003ewaf_tools\u003c/code\u003e and \u003ccode\u003ewscript\u003c/code\u003e files for compilation, and the \u003ccode\u003eressource\u003c/code\u003e folder and \u003ccode\u003eSingularity\u003c/code\u003e file to compile the Singularity container for this experiment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExecution\u003c/h1\u003e\u003ca id=\"user-content-execution\" class=\"anchor\" aria-label=\"Permalink: Execution\" href=\"#execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe results of the paper can be reproduced by running the Singularity container image of the experiment. Instructions to install Singularity can be found in \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity documentation\u003c/a\u003e.\nTo pull the image from Singularity Hub, use the command: \u003ccode\u003esingularity pull shub://adaptive-intelligent-robotics/Deep-Grid_MAP-Elites\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis container contains an \u003ccode\u003eapp\u003c/code\u003e for each approach-task combination, defined in the \u003ccode\u003eSingularity\u003c/code\u003e file:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eTruth_arm_var\u003c/code\u003e, \u003ccode\u003eTruth_rastrigin\u003c/code\u003e, \u003ccode\u003eTruth_hexa\u003c/code\u003e: Noise-free Baseline for each of the three tasks.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNaive_1_arm_var\u003c/code\u003e, \u003ccode\u003eNaive_1_rastrigin\u003c/code\u003e, \u003ccode\u003eNaive_1_hexa\u003c/code\u003e: Explicit-averaging approach with 1 re-sampling.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNaive_50_arm_var\u003c/code\u003e, \u003ccode\u003eNaive_50_rastrigin\u003c/code\u003e, \u003ccode\u003eNaive_50_hexa\u003c/code\u003e: Explicit-averaging approach with 50 re-sampling.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdapt_arm_var\u003c/code\u003e, \u003ccode\u003eAdapt_rastrigin\u003c/code\u003e, \u003ccode\u003eAdapt_hexa\u003c/code\u003e: Adaptive sampling approach without drifting elites.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdapt_BD_10_arm_var\u003c/code\u003e, \u003ccode\u003eAdapt_BD_10_rastrigin\u003c/code\u003e, \u003ccode\u003eAdapt_BD_10_hexa\u003c/code\u003e: Adaptive sampling approach with drifting elites.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDeep_50_arm_var\u003c/code\u003e, \u003ccode\u003eDeep_50_rastrigin\u003c/code\u003e, \u003ccode\u003eDeep_50_hexa\u003c/code\u003e: DG-MAP-Elites approach with depth 50.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAnalysis\u003c/code\u003e: apps to generate the graphs and container plots of all variants and tasks which results are stored in the folder given as input.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA given app can be run with the following command: \u003ccode\u003esingularity run --app *app_name* Deep-Grid_MAP-Elites_latest.sif\u003c/code\u003e. All run-parameters are defined inside the apps, and the results of the execution are solved in a \u003ccode\u003eresults\u003c/code\u003e folder, outside of the image, at the same location.\u003c/p\u003e\n\u003cp\u003eThis container can be recompile using the \u003ccode\u003eSingularity\u003c/code\u003e file.\u003c/p\u003e\n", "stargazers_count": 5, "subscribers_count": 2, "topics": [], - "updated_at": 1717291136.0 + "updated_at": 1675756969.0 }, { "data_format": 2, - "description": "JupyterHub + High-Performance Computing", + "description": null, "filenames": [ - "singularity/Singularity_Tensorflow", "singularity/Singularity" ], - "full_name": "pc2/JHub-HPC-Interface", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJupyterHub + High-Performance Computing\u003c/h1\u003e\u003ca id=\"user-content-jupyterhub--high-performance-computing\" class=\"anchor\" aria-label=\"Permalink: JupyterHub + High-Performance Computing\" href=\"#jupyterhub--high-performance-computing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eHigh performance Jupyter Notebooks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of this project is to connect JupyterHub to a high-performance computer (HPC). By automatically offloading the computations in a Jupyter notebook to the HPC system, even complex calculations are possible. While JupyterHub is deployed on a regular server, the notebooks themselves are spawned and run on the remote HPC system using a workload manager, such as Slurm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMotivation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe technical core of this project is the transparent integration of digital worksheets (Jupyter notebooks), in which learning content and programs can be displayed, edited and executed on the students\u0027 own laptops, with current cloud and high-performance computing (HPC) technologies. This provides the conditions for innovative, digital teaching that encourages independent and interactive development of, for example, data science applications, without imposing the complexity of using a high-performance computer system on the students. Instead, particularly computationally and data-intensive calculations are automatically offloaded to a high-performance computer, enabling even sophisticated analyses to be performed that would otherwise not be feasible on students\u0027 laptops.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeatures and use cases\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStarting a jupyter notebook server on a remote HPC system in a pre-defined singularity container\u003c/li\u003e\n\u003cli\u003eQuick config setup when using the Slurm configuration wizard\u003c/li\u003e\n\u003cli\u003eAutomatically create a singularity overlay so that user changes are persistent\u003c/li\u003e\n\u003cli\u003eGreat for managing courses with external participants\u003c/li\u003e\n\u003cli\u003ePossibility to include files in the notebook directory using WebDAV\u003c/li\u003e\n\u003cli\u003eSuitable for HPC users who have their own JupyterHub instance running and want to use HPC resources\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#jupyterhub--high-performance-computing\"\u003eJupyterHub + High-Performance Computing\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#table-of-contents\"\u003eTable of Contents\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation-of-jupyterhub-server\"\u003eInstallation of JupyterHub Server\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#jupyterhub-and-batchspawner\"\u003eJupyterHub and BatchSpawner\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ssh-tunnel-user\"\u003eSSH tunnel user\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#node-mapping\"\u003eNode mapping\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation-on-hpc-system\"\u003eInstallation on HPC System\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#install-using-pip\"\u003eInstall using pip\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#singularity-container\"\u003eSingularity Container\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#build-singularity-container\"\u003eBuild Singularity Container\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#compute\"\u003eCompute\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#gpu-tensorflow\"\u003eGPU (Tensorflow)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#the-configuration-file\"\u003eThe configuration file\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#slurm-configuration-wizard\"\u003eSlurm configuration wizard\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#examples\"\u003eExamples\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#debug-mode\"\u003eDebug mode\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#shibboleth-integration\"\u003eShibboleth Integration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#nbgrader-integration\"\u003eNBGrader Integration\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#changing-the-student-id-to-the-jupyterhub-logged-in-user-name\"\u003eChanging the Student ID to the JupyterHub logged in user name\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#create-nbgrader_configpy\"\u003eCreate nbgrader_config.py\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#security-precautions\"\u003eSecurity Precautions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#singularity-host-filesystems\"\u003eSingularity Host Filesystems\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#jupyterhub-api-https\"\u003eJupyterHub API (HTTPS)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#https\"\u003eHTTPS\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#tunnelbot-user\"\u003etunnelbot user\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation of JupyterHub Server\u003c/h2\u003e\u003ca id=\"user-content-installation-of-jupyterhub-server\" class=\"anchor\" aria-label=\"Permalink: Installation of JupyterHub Server\" href=\"#installation-of-jupyterhub-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section describes the required installations and configurations on the JupyterHub server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJupyterHub and BatchSpawner\u003c/h3\u003e\u003ca id=\"user-content-jupyterhub-and-batchspawner\" class=\"anchor\" aria-label=\"Permalink: JupyterHub and BatchSpawner\" href=\"#jupyterhub-and-batchspawner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe first thing you should do is install JupyterHub and BatchSpawner. For this purpose we provide an Ansible playbook which can be found in \u003ccode\u003e/jupyterhub-deployment/\u003c/code\u003e. See the README for details. Alternatively, you can follow the official installation instructions.\u003c/p\u003e\n\u003cp\u003eIf you decide to do the installations yourself, please proceed as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://jupyterhub.readthedocs.io/en/stable/installation-guide-hard.html\" rel=\"nofollow\"\u003eJupyterHub\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://github.com/jupyterhub/batchspawner\"\u003eBatchSpawner\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://github.com/jupyterhub/wrapspawner\"\u003eWrapSpawner\u003c/a\u003e (make sure to install it in the right environment: \u003ccode\u003e/opt/jupyterhub/bin/pip3 install git+https://github.com/jupyterhub/wrapspawner\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ecopy the JupyterHub configuration file \u003ccode\u003e/jupyterhub-deployment/config_files/jupyterhub_config.py\u003c/code\u003e to \u003ccode\u003e/opt/jupyterhub/etc/jupyterhub/\u003c/code\u003e (you will most likely have to edit this file afterwards to make it fit your needs)\u003c/li\u003e\n\u003cli\u003erestart the JupyterHub service\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSSH tunnel user\u003c/h3\u003e\u003ca id=\"user-content-ssh-tunnel-user\" class=\"anchor\" aria-label=\"Permalink: SSH tunnel user\" href=\"#ssh-tunnel-user\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA user called \u003ccode\u003etunnelbot\u003c/code\u003e is needed on the JupyterHub server. This user is responsible for starting an SSH tunnel between the compute node and the JupyterHub server. An SSH key pair for the above mentioned purpose must be generated. See \u003ccode\u003e/examples/jupyterhub_config.py\u003c/code\u003e for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNode mapping\u003c/h3\u003e\u003ca id=\"user-content-node-mapping\" class=\"anchor\" aria-label=\"Permalink: Node mapping\" href=\"#node-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJupyterHub extracts the execution host name of the HPC system (e.g. \u003ccode\u003enode01-002\u003c/code\u003e). When a notebook server is started, an SSH tunnel is established using the notebook port.\u003c/p\u003e\n\u003cp\u003eIn order for JupyterHub to be able to resolve the compute nodes host name, the \u003ccode\u003e/etc/hosts\u003c/code\u003e file must be edited. An example entry might look like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e127.0.0.1 node01-001\n127.0.0.1 node01-002\n127.0.0.1 node01-003\n...\n127.0.0.1 node12-048\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe actual node names depend on your HPC system of course.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation on HPC System\u003c/h2\u003e\u003ca id=\"user-content-installation-on-hpc-system\" class=\"anchor\" aria-label=\"Permalink: Installation on HPC System\" href=\"#installation-on-hpc-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section describes the required installations and configurations of the HPC system to enable the interaction with the JuypterHub server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou need a user who is allowed to allocate resources on the HPC system\n\u003cul\u003e\n\u003cli\u003eWith a SSH key pair. The public part must be deposited on the JupyterHub serer (\u003ccode\u003etunnelbot\u003c/code\u003e user)\u003c/li\u003e\n\u003cli\u003eThe public key part of the \u003ccode\u003etunnelbot\u003c/code\u003e-user created on the JupyterHub (-\u0026gt; \u003cem\u003e~/.ssh/authorized_keys\u003c/em\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity (\u0026gt; v.3.7.0)\u003c/li\u003e\n\u003cli\u003emkfs/e2fsprogs with following option:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://git.kernel.org/pub/scm/fs/ext2/e2fsprogs.git/commit/?id=217c0bdf17899c0f79b73f76feeadd6d55863180\" rel=\"nofollow\"\u003ehttps://git.kernel.org/pub/scm/fs/ext2/e2fsprogs.git/commit/?id=217c0bdf17899c0f79b73f76feeadd6d55863180\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall using pip\u003c/h3\u003e\u003ca id=\"user-content-install-using-pip\" class=\"anchor\" aria-label=\"Permalink: Install using pip\" href=\"#install-using-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can download and install the required files with pip.\u003c/p\u003e\n\u003cp\u003eYou may want to build a small Python environment, or install the tools with \u003ccode\u003e--user\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m pip install --user jh-hpc-interface\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Container\u003c/h3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity Container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe examples are in the directory singularity/.\u003c/p\u003e\n\u003cp\u003eIf you do not want to use singularity, then change the value of \u003ccode\u003euse_singularity\u003c/code\u003e in jh_config.ini to false.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBuild Singularity Container\u003c/h4\u003e\u003ca id=\"user-content-build-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Build Singularity Container\" href=\"#build-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the container with the recipe files in singularity/ you have to clone this repository.\u003c/p\u003e\n\u003cp\u003eThe following commands replace USER_ID in the recipes to the output of \u003ccode\u003eid -u\u003c/code\u003e, create a new hidden file and build the singularity container with the new created file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eCompute\u003c/h5\u003e\u003ca id=\"user-content-compute\" class=\"anchor\" aria-label=\"Permalink: Compute\" href=\"#compute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eUSER_ID=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003eid -u\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e sed \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003es/USER_ID/\u003cspan class=\"pl-smi\"\u003e$USER_ID\u003c/span\u003e/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e singularity/Singularity \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e singularity/.recipefile_compute \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e singularity build --remote singularity/compute_jupyter.sif singularity/.recipefile_compute\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eGPU (Tensorflow)\u003c/h5\u003e\u003ca id=\"user-content-gpu-tensorflow\" class=\"anchor\" aria-label=\"Permalink: GPU (Tensorflow)\" href=\"#gpu-tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eUSER_ID=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003eid -u\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e sed \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003es/USER_ID/\u003cspan class=\"pl-smi\"\u003e$USER_ID\u003c/span\u003e/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e singularity/Singularity_Tensorflow \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e singularity/.recipefile_gpu \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e singularity build --remote singularity/gpu_jupyter.sif singularity/.recipefile_gpu\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003esingularity build help section\u003c/em\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003e-r, --remote\u003c/strong\u003e build image remotely (does not require root)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ePlease refer to the official docs on how to use the remote build feature: \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://sylabs.io/docs/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe configuration file\u003c/h3\u003e\u003ca id=\"user-content-the-configuration-file\" class=\"anchor\" aria-label=\"Permalink: The configuration file\" href=\"#the-configuration-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the directory \u003cstrong\u003ebin/\u003c/strong\u003e is a script, which is deposited after the installation on the system.\u003c/p\u003e\n\u003cp\u003eWith the following call you can display the location of the configuration file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ jh_wrapper getconfig\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo learn more about the configuration file, see \u003ca href=\"docs/jh_config.ini.md\"\u003edocs/jh_config.ini.md\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSlurm configuration wizard\u003c/h3\u003e\u003ca id=\"user-content-slurm-configuration-wizard\" class=\"anchor\" aria-label=\"Permalink: Slurm configuration wizard\" href=\"#slurm-configuration-wizard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith the configuration wizard you can prepare your HPC environment.\u003c/p\u003e\n\u003cp\u003eThe script interactively goes through the configuration file and creates a temporary file which can be copied with a simple \u003ccode\u003ecp\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo start the wizard type the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ jh_slurm_wizard\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will find examples for the configuration files \u003cstrong\u003ejh_config.ini\u003c/strong\u003e and \u003cstrong\u003ejupyterhub_config.py\u003c/strong\u003e in the directory \u003cem\u003eexamples/\u003c/em\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDebug mode\u003c/h3\u003e\u003ca id=\"user-content-debug-mode\" class=\"anchor\" aria-label=\"Permalink: Debug mode\" href=\"#debug-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default the logs contain only information such as warnings or error messages.\nIt is also possible to switch on the debug mode, which writes extended information into the log files.\u003c/p\u003e\n\u003cp\u003eJust set \u003ccode\u003elog_level\u003c/code\u003e in the configuration file to \u0027DEBUG\u0027.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShibboleth Integration\u003c/h2\u003e\u003ca id=\"user-content-shibboleth-integration\" class=\"anchor\" aria-label=\"Permalink: Shibboleth Integration\" href=\"#shibboleth-integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eShibboleth authentication was set up for a JupyterHub server in a test environment. See \u003ccode\u003e./shibboleth/\u003c/code\u003e for an example configuration.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNBGrader Integration\u003c/h2\u003e\u003ca id=\"user-content-nbgrader-integration\" class=\"anchor\" aria-label=\"Permalink: NBGrader Integration\" href=\"#nbgrader-integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation instructions:\n\u003ca href=\"https://nbgrader.readthedocs.io/en/latest/configuration/jupyterhub_config.html\" rel=\"nofollow\"\u003ehttps://nbgrader.readthedocs.io/en/latest/configuration/jupyterhub_config.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo create an exchange directory for every user, just create an empty directory in \u003ccode\u003e$scratch_dir\u003c/code\u003e and mount it into the container with \u003ccode\u003e$singularity_bind_extra\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChanging the Student ID to the JupyterHub logged in user name\u003c/h3\u003e\u003ca id=\"user-content-changing-the-student-id-to-the-jupyterhub-logged-in-user-name\" class=\"anchor\" aria-label=\"Permalink: Changing the Student ID to the JupyterHub logged in user name\" href=\"#changing-the-student-id-to-the-jupyterhub-logged-in-user-name\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince the containers run as user \u003ccode\u003ejovyan\u003c/code\u003e, the value from the \u003ccode\u003e$JUPYTERHUB_USER\u003c/code\u003e variable is automatically used.\u003c/p\u003e\n\u003cp\u003eSee here for more information:\n\u003ca href=\"https://jupyter.readthedocs.io/en/latest/community/content-community.html#what-is-a-jovyan\" rel=\"nofollow\"\u003ehttps://jupyter.readthedocs.io/en/latest/community/content-community.html#what-is-a-jovyan\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate nbgrader_config.py\u003c/h3\u003e\u003ca id=\"user-content-create-nbgrader_configpy\" class=\"anchor\" aria-label=\"Permalink: Create nbgrader_config.py\" href=\"#create-nbgrader_configpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee here: \u003ca href=\"https://nbgrader.readthedocs.io/en/stable/configuration/nbgrader_config.html#use-case-3-nbgrader-and-jupyterhub\" rel=\"nofollow\"\u003ehttps://nbgrader.readthedocs.io/en/stable/configuration/nbgrader_config.html#use-case-3-nbgrader-and-jupyterhub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo make \u003cem\u003enbgrader_config.py\u003c/em\u003e available in the container, just append the file in \u003ccode\u003e$singularity_bind_extra\u003c/code\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSecurity Precautions\u003c/h2\u003e\u003ca id=\"user-content-security-precautions\" class=\"anchor\" aria-label=\"Permalink: Security Precautions\" href=\"#security-precautions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Host Filesystems\u003c/h3\u003e\u003ca id=\"user-content-singularity-host-filesystems\" class=\"anchor\" aria-label=\"Permalink: Singularity Host Filesystems\" href=\"#singularity-host-filesystems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn case you are using Singularity, the host file system may be automatically mounted into the container when you start a Singularity Container.\u003c/p\u003e\n\u003cp\u003eA possible cause is the option \u003ccode\u003emount hostfs\u003c/code\u003e in \u003cem\u003esingularity.conf\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSee here: \u003ca href=\"https://sylabs.io/guides/3.5/admin-guide/configfiles.html#singularity-conf\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/admin-guide/configfiles.html#singularity-conf\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJupyterHub API (HTTPS)\u003c/h3\u003e\u003ca id=\"user-content-jupyterhub-api-https\" class=\"anchor\" aria-label=\"Permalink: JupyterHub API (HTTPS)\" href=\"#jupyterhub-api-https\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHTTPS\u003c/h4\u003e\u003ca id=\"user-content-https\" class=\"anchor\" aria-label=\"Permalink: HTTPS\" href=\"#https\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee here for more information:\n\u003ca href=\"https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html\" rel=\"nofollow\"\u003ehttps://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etunnelbot user\u003c/h3\u003e\u003ca id=\"user-content-tunnelbot-user\" class=\"anchor\" aria-label=\"Permalink: tunnelbot user\" href=\"#tunnelbot-user\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can increase the security by deactivating shell access for this user.\u003c/p\u003e\n\u003cp\u003eJust type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eusermod -s /bin/false tunnelbot\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTroubleshooting\u003c/h2\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen problems occur with the JupyterHub, some information can be obtained from the logs when debug mode is enabled:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/jupyterhub/jupyterhub/wiki/Debug-Jupyterhub\"\u003ehttps://github.com/jupyterhub/jupyterhub/wiki/Debug-Jupyterhub\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "tikk3r/lofar-grid-hpccloud", + "latest_release": "v4.0.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5f618187158129a12605b61c2558a97b7014bf61a63dcbb58ecc23d53ade59a4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74696b6b33722f6c6f6661722d677269642d687063636c6f75643f736f72743d73656d766572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5f618187158129a12605b61c2558a97b7014bf61a63dcbb58ecc23d53ade59a4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74696b6b33722f6c6f6661722d677269642d687063636c6f75643f736f72743d73656d766572\" data-canonical-src=\"https://img.shields.io/github/v/release/tikk3r/lofar-grid-hpccloud?sort=semver\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b498f0b23c001d15b8b32b01a58375128a6fd5886fbefc3906a2164b36556ef5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d677269642d687063636c6f75642e7376673f6c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b498f0b23c001d15b8b32b01a58375128a6fd5886fbefc3906a2164b36556ef5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d677269642d687063636c6f75642e7376673f6c6f676f3d676974687562\" data-canonical-src=\"https://img.shields.io/github/license/tikk3r/lofar-grid-hpccloud.svg?logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/136925861\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad3ce50d6d0bdd702c67f43f248e79b036a12ebf23efdccde0d13eb15d31bf9e/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3133363932353836312e737667\" data-canonical-src=\"https://zenodo.org/badge/136925861.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ch1\u003e\u003ca id=\"user-content-lofar-grid-hpccloud\" class=\"anchor\" aria-hidden=\"true\" href=\"#lofar-grid-hpccloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elofar-grid-hpccloud\u003c/h1\u003e\n\u003cp\u003eThis repository hold resources for deploying the LOFAR software (genericpipeline) and related tools through Singularity containers. These containers are general, but at the same time somewhat tailored for SKSP use.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003emaster\u003c/code\u003e branch is empty. Currently the images on this branch (\u003ccode\u003efedora-py3\u003c/code\u003e) are based on the Fedora 34 Linux distribution, which is available from \u003ca href=\"https://hub.docker.com/_/fedora\" rel=\"nofollow\"\u003eDockerHub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAs this branch no longer includes Python 2, the genericpipeline framework is \u003cem\u003enot\u003c/em\u003e included in these recipes anymore (see the \u003ca href=\"https://github.com/tikk3r/lofar-grid-hpccloud/tree/fedora\"\u003efedora branch\u003c/a\u003e for that). Pipelines like prefactor (now LINC) are or have moved to CWL.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eTo build a full LOFAR Singularity image, do the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTurn on MKL and/or CUDA in \u003cstrong\u003esingularity/Singularity\u003c/strong\u003e, if desired, by setting \u003ccode\u003eHAS_MKL=true\u003c/code\u003e and/or \u003ccode\u003eHAS_CUDA=true\u003c/code\u003e. Set them to \u003ccode\u003efalse\u003c/code\u003e if you do not require those.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOptimise your container for a desired architecture by updating the \u003ccode\u003eMARCH\u003c/code\u003e and \u003ccode\u003eMTUNE\u003c/code\u003e variables to the appropriate values. If you want to build for a generic machine, set these to \u003ccode\u003eMARCH=\u0027x86-64\u0027\u003c/code\u003e and \u003ccode\u003eMTUNE=\u0027generic\u0027\u003c/code\u003e, respectively.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild \u003cstrong\u003esingulariy/Singularity\u003c/strong\u003e by running\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo SINGULARITY_CACHEDIR=$PWD SINGULARITY_TMPDIR=$PWD singularity build lofar_sksp.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePre-built containers are public hosted at \u003ca href=\"https://lofar-webdav.grid.sara.nl/software/shub_mirror/tikk3r/lofar-grid-hpccloud/\" rel=\"nofollow\"\u003eSURFSara\u003c/a\u003e. Sort by date to find the latest container there.\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 8, - "topics": [ - "jupyter", - "jupyterhub", - "hpc", - "singularity" - ], - "updated_at": 1718354266.0 + "subscribers_count": 5, + "topics": [], + "updated_at": 1676586378.0 }, { "data_format": 2, - "description": "Repository for the ALPACA toolbox, including code, tutorials, docker files, etc. ", + "description": "Part of the sc-eQTLgen consortium pipeline. Step 1, where the QC is done.", "filenames": [ - "Singularity" + "Singularity.Imputation", + "Singularity.WGpipeline_deb913", + "Singularity.Imputation_deb913", + "Singularity.WGpipeline" ], - "full_name": "C0C0AN/ALPACA", - "latest_release": null, + "full_name": "sc-eQTLgen-consortium/WG1-pipeline-QC", + "latest_release": "v1.0.0", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-wg1-pipeline-qc\" class=\"anchor\" href=\"#wg1-pipeline-qc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWG1-pipeline-QC\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/44268007/89252548-35b96f80-d659-11ea-97e9-4b4176df5f08.png\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/44268007/89252548-35b96f80-d659-11ea-97e9-4b4176df5f08.png\" width=\"300\" height=\"140\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePart of the sceQTL-Gen consortium pipeline. Step 1, where the QC is done.\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://wg1-pipeline-qc.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e for information on running the QC pipeline.\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1694111607.0 + "updated_at": 1642593783.0 }, { "data_format": 2, - "description": "This is the private version of the FS planner repository", + "description": null, "filenames": [ - "Singularity.lbfs" + "Singularity.def" ], - "full_name": "aig-upf/fs-private", + "full_name": "HippocampusGirl/HALFpipe", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe FS Functional STRIPS planner\u003c/h1\u003e\u003ca id=\"user-content-the-fs-functional-strips-planner\" class=\"anchor\" aria-label=\"Permalink: The FS Functional STRIPS planner\" href=\"#the-fs-functional-strips-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eFS\u003c/code\u003e is a classical planner that works with the Functional STRIPS planning language \u003ca href=\"#ref-geffner-fstrips-2000\"\u003e[Geffner, 2000]\u003c/a\u003e,\na modeling language based on the quantifier-free\nfragment of first-order logic that includes constant, function and predicate symbols, but no variable symbols. The increased expressiveness\nof the Functional STRIPS language with respect to propositional languages such as standard STRIPS (which is indeed subsumed by Functional STRIPS)\noften results in problem encodings which are more compact, more readable, have fewer ground actions\nand preserve the structural properties of the problem in a manner which allows the derivation of more effective heuristics.\u003c/p\u003e\n\u003cp\u003eAlong with the core of the Functional STRIPS language, the \u003ccode\u003eFS\u003c/code\u003e planner supports certain extensions which are useful both\nfrom the expressive \u003cem\u003eand\u003c/em\u003e the computational point of view. These include \u003cem\u003eexistential quantification\u003c/em\u003e,\n\u003cem\u003estate constraints\u003c/em\u003e, a fairly large library of \u003cem\u003eglobal constraints\u003c/em\u003e, and the possibility of using \u003cem\u003eexternally-defined symbols\u003c/em\u003e\nand \u003cem\u003ebuilt-in arithmetic symbols\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThis documentation covers a number of practical issues related to the use of the \u003ccode\u003eFS\u003c/code\u003e planner. The planner, however, has\nbeen used and described in a number of academic publications that \u003ca href=\"http://gfrances.github.io/pubs/\" rel=\"nofollow\"\u003ecan be found here\u003c/a\u003e,\nthe most recent of which are \u003ca href=\"#ref-frances-modeling-2015\"\u003e[Franc\u00e8s and Geffner, 2015]\u003c/a\u003e and \u003ca href=\"#ref-frances-existential-2016\"\u003e[Franc\u00e8s and Geffner, 2016a]\u003c/a\u003e\nand \u003ca href=\"#ref-frances-effective-2016\"\u003e[Franc\u00e8s and Geffner, 2016b]\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to use the planner is by \u003ca href=\"doc/installation.md\"\u003emanually compiling the planner source code\u003c/a\u003e.\nAlternatively, you can build and/or use a \u003ca href=\"doc/containers.md\"\u003eready-to-use image\u003c/a\u003e in some of the containerization solutions\nthat we support.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can find a high-level overview of the planner usage options \u003ca href=\"doc/usage.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-credits\"\u003e\u003c/a\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eFS\u003c/code\u003e planner is partially built upon the \u003ca href=\"http://www.lapkt.org\" rel=\"nofollow\"\u003eLightweight Automated Planning Toolkit\u003c/a\u003e\nand the PDDL parser from the \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003eFast Downward\u003c/a\u003e distribution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-modeling-2015\"\u003eFranc\u00e8s, G., and Geffner, H. (2015)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2015-icaps-better-heuristics-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eModeling and Computation in Planning: Better Heuristics from More Expressive Languages\u003c/em\u003e\u003c/a\u003e, ICAPS 2015.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-existential-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016a)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-existential-quantification-planning-csp/\" rel=\"nofollow\"\u003e\u003cem\u003eE-STRIPS: Existential Quantification in Planning and Constraint Satisfaction\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-effective-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016b)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-effective-planning-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eEffective Planning with More Expressive Languages\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-geffner-fstrips-2000\"\u003eGeffner, H. (2000)\u003c/a\u003e,\n\u003ca href=\"http://www.tecn.upf.es/~hgeffner/\" rel=\"nofollow\"\u003e\u003cem\u003eFunctional STRIPS: A more flexible lan-\nguage for planning and problem solving\u003c/em\u003e\u003c/a\u003e.\nIn Minker, J., ed., Logic-Based Artificial Intelligence. Kluwer. 187\u2013205.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-welcome-to-enigma-halfpipe\" class=\"anchor\" href=\"#welcome-to-enigma-halfpipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to ENIGMA \u003ccode\u003eHALFpipe\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4508\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/actions?query=workflow%3A%22build%22\"\u003e\u003cimg src=\"https://github.com/HALFpipe/HALFpipe/workflows/build/badge.svg\" alt=\"https://github.com/HALFpipe/HALFpipe/workflows/build/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/actions?query=workflow%3A%22continuous+integration%22\"\u003e\u003cimg src=\"https://github.com/HALFpipe/HALFpipe/workflows/continuous%20integration/badge.svg\" alt=\"https://github.com/HALFpipe/HALFpipe/workflows/continuous%20integration/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/HALFpipe/HALFpipe\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ef5be2978b13a91a1a602bc0261933d3735a7567176db1ef0c13eb65b3249056/68747470733a2f2f636f6465636f762e696f2f67682f48414c46706970652f48414c46706970652f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/HALFpipe/HALFpipe/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e is a user-friendly software that facilitates reproducible analysis of\nfMRI data, including preprocessing, single-subject, and group analysis. It\nprovides state-of-the-art preprocessing using\n\u003ca href=\"https://fmriprep.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003efmriprep\u003c/code\u003e\u003c/a\u003e, but removes the necessity to\nconvert data to the\n\u003ca href=\"https://bids-specification.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003e\u003ccode\u003eBIDS\u003c/code\u003e\u003c/a\u003e format. Common\nresting-state and task-based fMRI features can then be calculated on the fly\nusing \u003ca href=\"http://fsl.fmrib.ox.ac.uk/\" rel=\"nofollow\"\u003e\u003ccode\u003eFSL\u003c/code\u003e\u003c/a\u003e and\n\u003ca href=\"https://nipype.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003enipype\u003c/code\u003e\u003c/a\u003e for statistics.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf you encounter issues, please see the \u003ca href=\"#troubleshooting\"\u003etroubleshooting\u003c/a\u003e\nsection of this document.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#getting-started\"\u003eGetting started\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#container-platform\"\u003eContainer platform\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#download\"\u003eDownload\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running\"\u003eRunning\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#user-interface\"\u003eUser interface\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#files\"\u003eFiles\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#features\"\u003eFeatures\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#models\"\u003eModels\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-on-a-high-performance-computing-cluster\"\u003eRunning on a high-performance computing cluster\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#quality-checks\"\u003eQuality checks\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#subject-level-features\"\u003eSubject-level features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#preprocessed-images\"\u003ePreprocessed images\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#group-level\"\u003eGroup-level\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#command-line-flags\"\u003eCommand line flags\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#control-command-line-logging\"\u003eControl command line logging\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#automatically-remove-unneeded-files\"\u003eAutomatically remove unneeded files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#adjust-nipype\"\u003eAdjust nipype\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#choose-which-parts-to-run-or-to-skip\"\u003eChoose which parts to run or to skip\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#working-directory\"\u003eWorking directory\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#data-file-system-root\"\u003eData file system root\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e is distributed as a container, meaning that all required software\ncomes bundled in a monolithic file, the container. This allows for easy\ninstallation on new systems, and makes data analysis more reproducible, because\nsoftware versions are guaranteed to be the same for all users.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-platform\" class=\"anchor\" href=\"#container-platform\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer platform\u003c/h3\u003e\n\u003cp\u003eThe first step is to install one of the supported container platforms. If you\u0027re\nusing a high-performance computing cluster, more often than not\n\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e will already be available.\u003c/p\u003e\n\u003cp\u003eIf not, we recommend using the latest version\nof\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e. However, it can be somewhat cumbersome to\ninstall, as it needs to be built from source.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://neuro.debian.net/\" rel=\"nofollow\"\u003e\u003ccode\u003eNeuroDebian\u003c/code\u003e\u003c/a\u003e package repository provides an\nolder version of \u003ca href=\"https://sylabs.io/guides/2.6/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e for\n\u003ca href=\"https://neuro.debian.net/pkgs/singularity-container.html\" rel=\"nofollow\"\u003esome\u003c/a\u003e Linux\ndistributions.\u003c/p\u003e\n\u003cp\u003eIn contrast to \u003ccode\u003eSingularity\u003c/code\u003e, \u003ccode\u003eDocker\u003c/code\u003e always requires elevated privileges to\nrun containers. In other words, every user running a \u003ccode\u003eDocker\u003c/code\u003e container\nautomatically has administrator privileges on the computer they\u0027re using.\nTherefore, it is inherently a bad choice for multi-user environments, where the\naccess of individual users should be limited. \u003ccode\u003eDocker\u003c/code\u003e is the only option that\nis compatible with \u003ccode\u003eMac OS X\u003c/code\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eVersion\u003c/th\u003e\n\u003cth\u003eInstallation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e3.5.3\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eSee \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/quick_start.html\u003c/a\u003e\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e2.6.1\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003esudo apt install singularity-container\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eSee \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-download\" class=\"anchor\" href=\"#download\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h3\u003e\n\u003cp\u003eThe second step is to download the \u003ccode\u003eHALFpipe\u003c/code\u003e to your computer. This requires\napproximately 5 gigabytes of storage.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eInstallation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://charitede-my.sharepoint.com/:f:/g/personal/lea_waller_charite_de/EukRziExhTVBrEAai2oEpi8B2jsnn7P3YQuFo2pycKp6-g\" rel=\"nofollow\"\u003ehttps://charitede-my.sharepoint.com/:f:/g/personal/lea_waller_charite_de/EukRziExhTVBrEAai2oEpi8B2jsnn7P3YQuFo2pycKp6-g\u003c/a\u003e or \u003ccode\u003esingularity pull docker://halfpipe/halfpipe:1.1.1\u003c/code\u003e or \u003ccode\u003esingularity pull docker://ghcr.io/halfpipe/halfpipe:1.1.1\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edocker pull halfpipe/halfpipe:1.1.1\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e version \u003ccode\u003e3.x\u003c/code\u003e creates a container image file called\n\u003ccode\u003eHALFpipe_{version}.sif\u003c/code\u003e in the directory where you run the \u003ccode\u003epull\u003c/code\u003e command. For\n\u003ccode\u003eSingularity\u003c/code\u003e version \u003ccode\u003e2.x\u003c/code\u003e the file is named\n\u003ccode\u003ehalfpipe-halfpipe-master-latest.simg\u003c/code\u003e. Whenever you want to use the container,\nyou need pass \u003ccode\u003eSingularity\u003c/code\u003e the path to this file.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e \u003ccode\u003eSingularity\u003c/code\u003e may store a copy of the container in its cache\ndirectory. The cache directory is located by default in your home directory at\n\u003ccode\u003e~/.singularity\u003c/code\u003e. If you need to save disk space in your home directory, you\ncan safely delete the cache directory after downloading, i.e. by running\n\u003ccode\u003erm -rf ~/.singularity\u003c/code\u003e. Alternatively, you could move the cache directory\nsomewhere with more free disk space using a symlink. This way, files will\nautomatically be stored there in the future. For example, if you have a lot of\nfree disk space in \u003ccode\u003e/mnt/storage\u003c/code\u003e, then you could first run\n\u003ccode\u003emv ~/.singularity /mnt/storage\u003c/code\u003e to move the cache directory, and then\n\u003ccode\u003eln -s /mnt/storage/.singularity ~/.singularity\u003c/code\u003e to create the symlink.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ccode\u003eDocker\u003c/code\u003e will store the container in its storage base directory, so it does not\nmatter from which directory you run the \u003ccode\u003epull\u003c/code\u003e command.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h3\u003e\n\u003cp\u003eThe third step is to run the downloaded container. You may need to replace\n\u003ccode\u003ehalfpipe_1.1.1.sif\u003c/code\u003e with the actual path and filename where \u003ccode\u003eSingularity\u003c/code\u003e\ndownloaded your container.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eCommand\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_1.1.1.sif\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edocker run --interactive --tty --volume /:/ext halfpipe/halfpipe\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYou should now see the user interface.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-background\" class=\"anchor\" href=\"#background\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h4\u003e\n\u003cp\u003eContainers are by default isolated from the host computer. This adds security,\nbut also means that the container cannot access the data it needs for analysis.\n\u003ccode\u003eHALFpipe\u003c/code\u003e expects all inputs (e.g., image files and spreadsheets) and outputs\n(the working directory) to be places in the path\u003ccode\u003e/ext\u003c/code\u003e (see also\n\u003ca href=\"#data-file-system-root---fs-root\"\u003e\u003ccode\u003e--fs-root\u003c/code\u003e\u003c/a\u003e). Using the option\n\u003ccode\u003e--bind /:/ext\u003c/code\u003e, we instruct \u003ccode\u003eSingularity\u003c/code\u003e to map all of the host file system\n(\u003ccode\u003e/\u003c/code\u003e) to that path (\u003ccode\u003e/ext\u003c/code\u003e). You can also run \u003ccode\u003eHALFpipe\u003c/code\u003e and only map only part\nof the host file system, but keep in mind that any directories that are not\nmapped will not be visible later.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e passes the host shell environment to the container by default.\nThis means that in some cases, the host computer\u0027s configuration can interfere\nwith the software. To avoid this, we need to pass the option \u003ccode\u003e--containall\u003c/code\u003e.\n\u003ccode\u003eDocker\u003c/code\u003e does not pass the host shell environment by default, so we don\u0027t need\nto pass an option.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-user-interface\" class=\"anchor\" href=\"#user-interface\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser interface\u003c/h2\u003e\n\u003cp\u003eThe user interface asks a series of questions about your data and the analyses\nyou want to run. In each question, you can press \u003ccode\u003eControl+C\u003c/code\u003e to cancel the\ncurrent question and go back to the previous one. \u003ccode\u003eControl+D\u003c/code\u003e exits the program\nwithout saving. Note that these keyboard shortcuts are the same on Mac.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h3\u003e\n\u003cp\u003eTo run preprocessing, at least a T1-weighted structural image and a BOLD image\nfile is required. Preprocessing and data analysis proceeds automatically.\nHowever, to be able to run automatically, data files need to be input in a way\nsuitable for automation.\u003c/p\u003e\n\u003cp\u003eFor this kind of automation, \u003ccode\u003eHALFpipe\u003c/code\u003e needs to know the relationships between\nfiles, such as which files belong to the same subject. However, even though it\nwould be obvious for a human, a program cannot easily assign a file name to a\nsubject, and this will be true as long as there are differences in naming\nbetween different researchers or labs. One researcher may name the same file\n\u003ccode\u003esubject_01_rest.nii.gz\u003c/code\u003e and another \u003ccode\u003esubject_01/scan_rest.nii.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIn \u003ccode\u003eHALFpipe\u003c/code\u003e, we solve this issue by inputting file names in a specific way.\nFor example, instead of \u003ccode\u003esubject_01/scan_rest.nii.gz\u003c/code\u003e, \u003ccode\u003eHALFpipe\u003c/code\u003e expects you to\ninput \u003ccode\u003e{subject}/scan_rest.nii.gz\u003c/code\u003e. \u003ccode\u003eHALFpipe\u003c/code\u003e can then match all files on disk\nthat match this naming schema, and extract the subject ID \u003ccode\u003esubject_01\u003c/code\u003e. Using\nthe extracted subject ID, other files can now be matched to this image. If all\ninput files are available in BIDS format, then this step can be skipped.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify working directory\u003c/code\u003e All intermediate and outputs of \u003ccode\u003eHALFpipe\u003c/code\u003e will\nbe placed in the working directory. Keep in mind to choose a location with\nsufficient free disk space, as intermediates can be multiple gigabytes in\nsize for each subject.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eIs the data available in BIDS format?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the BIDS directory\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify anatomical/structural data\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the path of the T1-weighted image files\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify functional data\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the path of the BOLD image files\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCheck repetition time values\u003c/code\u003e / \u003ccode\u003eSpecify repetition time in seconds\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd more BOLD image files?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDo slice timing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eCheck slice acquisition direction values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCheck slice timing values\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify field maps?\u003c/code\u003e If the data was imported from a BIDS directory, this\nstep will be omitted.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the type of the field maps\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEPI (blip-up blip-down)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the blip-up blip-down EPI image files\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003ePhase difference and magnitude (used by Siemens scanners)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the magnitude image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the phase/phase difference image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify echo time difference in seconds\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eScanner-computed field map and magnitude (used by GE / Philips\nscanners)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the magnitude image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the field map image files\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd more field maps?\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify effective echo spacing for the functional data in seconds\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify phase encoding direction for the functional data\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-features\" class=\"anchor\" href=\"#features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h3\u003e\n\u003cp\u003eFeatures are analyses that are carried out on the preprocessed data, in other\nwords, first-level analyses.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify first-level features?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the feature type\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eTask-based\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the event file type\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSPM multiple conditions\u003c/code\u003e A MATLAB .mat file containing three\narrays: \u003ccode\u003enames\u003c/code\u003e (condition), \u003ccode\u003eonsets\u003c/code\u003e and \u003ccode\u003edurations\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFSL 3-column\u003c/code\u003e One text file for each condition. Each file has its\ncorresponding condition in the filename. The first column specifies\nthe event onset, the second the duration. The third column of the\nfiles is ignored, so parametric modulation is not supported\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBIDS TSV\u003c/code\u003e A tab-separated table with named columns \u003ccode\u003etrial_type\u003c/code\u003e\n(condition), \u003ccode\u003eonset\u003c/code\u003e and \u003ccode\u003eduration\u003c/code\u003e. While BIDS supports defining\nadditional columns, \u003ccode\u003eHALFpipe\u003c/code\u003e will currently ignore these\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the event files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSelect conditions to add to the model\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify contrasts\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify contrast name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify contrast values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd another contrast?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply a temporal filter to the design matrix?\u003c/code\u003e A separate temporal\nfilter can be specified for the design matrix. In contrast, the\ntemporal filtering of the input image and any confound regressors\nadded to the design matrix is specified in 10. In general, the two\nsettings should match\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply smoothing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify smoothing FWHM in mm\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eGrand mean scaling will be applied with a mean of 10000.000000\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTemporal filtering will be applied using a gaussian-weighted filter\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the filter width in seconds\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRemove confounds?\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSeed-based connectivity\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify binary seed mask file(s)\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the binary seed mask image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCheck space values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eAdd binary seed mask image file\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDual regression\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAtlas-based connectivity matrix\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eReHo\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efALFF\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd another first-level feature?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eOutput a preprocessed image?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify setting name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply smoothing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify smoothing FWHM in mm\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDo grand mean scaling?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify grand mean\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply a temporal filter?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the type of temporal filter\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eGaussian-weighted\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eFrequency-based\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRemove confounds?\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-models\" class=\"anchor\" href=\"#models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModels\u003c/h3\u003e\n\u003cp\u003eModels are statistical analyses that are carried out on the features.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-a-high-performance-computing-cluster\" class=\"anchor\" href=\"#running-on-a-high-performance-computing-cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on a high-performance computing cluster\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eLog in to your cluster\u0027s head node\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRequest an interactive job. Refer to your cluster\u0027s documentation for how to\ndo this\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn the interactive job, run the \u003ccode\u003eHALFpipe\u003c/code\u003e user interface, but add the flag\n\u003ccode\u003e--use-cluster\u003c/code\u003e to the end of the command. \u003cbr\u003e\nFor example, \u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_latest.sif --use-cluster\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs soon as you finish specifying all your data, features and models in the\nuser interface, \u003ccode\u003eHALFpipe\u003c/code\u003e will now generate everything needed to run on the\ncluster. For hundreds of subjects, this can take up to a few hours.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen \u003ccode\u003eHALFpipe\u003c/code\u003e exits, edit the generated submit script \u003ccode\u003esubmit.slurm.sh\u003c/code\u003e\naccording to your cluster\u0027s documentation and then run it. This submit script\nwill calculate everything except group statistics.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs soon as all processing has been completed, you can run group statistics.\nThis is usually very fast, so you can do this in an interactive session. Run\n\u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_latest.sif --only-model-chunk\u003c/code\u003e\nand then select \u003ccode\u003eRun without modification\u003c/code\u003e in the user interface.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA common issue with remote work via secure shell is that the connection may\nbreak after a few hours. For batch jobs this is not an issue, but for\ninteractive jobs this can be quite frustrating. When the connection is lost,\nthe node you were connected to will automatically quit all programs you were\nrunning. To prevent this, you can run interactive jobs within \u003ccode\u003escreen\u003c/code\u003e or\n\u003ccode\u003etmux\u003c/code\u003e (whichever is available). These commands allow you to open sessions in\nthe terminal that will continue running in the background even when you close\nor disconnect. Here\u0027s a quick overview of how to use the commands (more\nin-depth documentation is available for example at\n[\u003ca href=\"http://www.dayid.org/comp/tm.html\" rel=\"nofollow\"\u003ehttp://www.dayid.org/comp/tm.html\u003c/a\u003e]).\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOpen a new screen/tmux session on the head node by running either \u003ccode\u003escreen\u003c/code\u003e\nor \u003ccode\u003etmux\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRequest an interactive job from within the session, for example with\n\u003ccode\u003esrun --pty bash -i\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the command that you want to run\u003c/li\u003e\n\u003cli\u003eDetach from the screen/tmux session, meaning disconnecting with the ability\nto re-connect later \u003cbr\u003e\nFor screen, this is done by first pressing \u003ccode\u003eControl+a\u003c/code\u003e, then letting go, and\nthen pressing \u003ccode\u003ed\u003c/code\u003e on the keyboard. \u003cbr\u003e\nFor tmux, it\u0027s \u003ccode\u003eControl+b\u003c/code\u003e instead of \u003ccode\u003eControl+a\u003c/code\u003e. \u003cbr\u003e\nNote that this is always \u003ccode\u003eControl\u003c/code\u003e, even if you\u0027re on a mac.\u003c/li\u003e\n\u003cli\u003eClose your connection to the head node with \u003ccode\u003eControl+d\u003c/code\u003e. \u003ccode\u003escreen\u003c/code\u003e/\u003ccode\u003etmux\u003c/code\u003e\nwill remain running in the background\u003c/li\u003e\n\u003cli\u003eLater, connect again to the head node. Run \u003ccode\u003escreen -r\u003c/code\u003e or \u003ccode\u003etmux attach\u003c/code\u003e to\ncheck back on the interactive job. If everything went well and the command\nyou wanted to run finished, close the interactive job with \u003ccode\u003eControl+d\u003c/code\u003e and\nthen the \u003ccode\u003escreen\u003c/code\u003e/\u003ccode\u003etmux\u003c/code\u003e session with \u003ccode\u003eControl+d\u003c/code\u003e again. If the command\nhasn\u0027t finished yet, detach as before and come back later\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quality-checks\" class=\"anchor\" href=\"#quality-checks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality checks\u003c/h2\u003e\n\u003cp\u003ePlease see the manual at \u003ca href=\"https://docs.google.com/document/d/1evDkVaoXqSaxulp5eSxVqgaxro7yZl-gao70D0S2dH8\" rel=\"nofollow\"\u003ehttps://docs.google.com/document/d/1evDkVaoXqSaxulp5eSxVqgaxro7yZl-gao70D0S2dH8\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA visual report page \u003ccode\u003ereports/index.html\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA table with image quality metrics \u003ccode\u003ereports/reportvals.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA table containing the preprocessing status \u003ccode\u003ereports/reportpreproc.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe untouched \u003ccode\u003efmriprep\u003c/code\u003e derivatives. Some files have been omitted to save\ndisk space \u003ccode\u003efmriprep\u003c/code\u003e is very strict about only processing data that is\ncompliant with the BIDS standard. As such, we may need to format subjects\nnames for compliance. For example, an input subject named \u003ccode\u003esubject_01\u003c/code\u003e will\nappear as \u003ccode\u003esubject01\u003c/code\u003e in the \u003ccode\u003efmriprep\u003c/code\u003e derivatives. \u003ccode\u003ederivatives/fmriprep\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-subject-level-features\" class=\"anchor\" href=\"#subject-level-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubject-level features\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFor task-based, seed-based connectivity and dual regression features,\n\u003ccode\u003eHALFpipe\u003c/code\u003e outputs the statistical maps for the effect, the variance, the\ndegrees of freedom of the variance and the z-statistic. In FSL, the effect and\nvariance are also called \u003ccode\u003ecope\u003c/code\u003e and \u003ccode\u003evarcope\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-effect_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-variance_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-dof_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-z_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\nThe design and contrast matrix used for the final model will be outputted alongside\nthe statistical maps \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._feature-..._desc-design_matrix.tsv\u003c/code\u003e\n\u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._feature-..._desc-contrast_matrix.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReHo and fALFF are not calculated based on a linear model. As such, only one\nstatistical map of the z-scaled values will be output \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._alff.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._falff.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._reho.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor every feature, a JSON file containing a summary of the preprocessing\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esettings, and a list of the raw data files that were used for the analysis\n(\u003ccode\u003eRawSources\u003c/code\u003e) \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/....json\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor every feature, the corresponding brain mask is output beside the\nstatistical maps. Masks do not differ between different features calculated,\nthey are only copied out repeatedly for convenience \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/...desc-brain_mask.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAtlas-based connectivity outputs the time series and the full covariance and\ncorrelation matrices as text files \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._timeseries.txt\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._desc-covariance_matrix.txt\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._desc-correlation_matrix.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preprocessed-images\" class=\"anchor\" href=\"#preprocessed-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessed images\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMasked, preprocessed BOLD image \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._bold.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJust like for features \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._bold.json\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJust like for features \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._setting-..._desc-brain_mask.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFiltered confounds time series, where all filters that are applied to the BOLD\nimage are applied to the regressors as well. Note that this means that when\ngrand mean scaling is active, confounds time series are also scaled, meaning\nthat values such as \u003ccode\u003eframewise displacement\u003c/code\u003e can not be interpreted in terms\nof their original units anymore. \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._setting-..._desc-confounds_regressors.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-group-level\" class=\"anchor\" href=\"#group-level\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGroup-level\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrouplevel/...\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" href=\"#troubleshooting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTroubleshooting\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIf an error occurs, this will be output to the command line and simultaneously\nto the \u003ccode\u003eerr.txt\u003c/code\u003e file in the working directory\u003c/li\u003e\n\u003cli\u003eIf the error occurs while running, usually a text file detailing the error\nwill be placed in the working directory. These are text files and their file\nnames start with \u003ccode\u003ecrash\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eUsually, the last line of these text files contains the error message.\nPlease read this carefully, as may allow you to understand the error\u003c/li\u003e\n\u003cli\u003eFor example, consider the following error message:\n\u003ccode\u003eValueError: shape (64, 64, 33) for image 1 not compatible with first image shape (64, 64, 34) with axis == None\u003c/code\u003e\nThis error message may seem cryptic at first. However, looking at the\nmessage more closely, it suggests that two input images have different,\nincompatible dimensions. In this case, \u003ccode\u003eHALFpipe\u003c/code\u003e correctly recognized this\nissue, and there is no need for concern. The images in question will simply\nbe excluded from preprocessing and/or analysis\u003c/li\u003e\n\u003cli\u003eIn some cases, the cause of the error can be a bug in the \u003ccode\u003eHALFpipe\u003c/code\u003e code.\nPlease check that no similar issue has been reported\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues\"\u003ehere on GitHub\u003c/a\u003e. In this case,\nplease submit an\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues/new/choose\"\u003eissue\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-command-line-flags\" class=\"anchor\" href=\"#command-line-flags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand line flags\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-control-command-line-logging\" class=\"anchor\" href=\"#control-command-line-logging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eControl command line logging\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--verbose\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, only errors and warnings will be output to the command line. This\nmakes it easier to see when something goes wrong, because there is less output.\nHowever, if you want to be able to inspect what is being run, you can add the\n\u003ccode\u003e--verbose\u003c/code\u003e flag to the end of the command used to call \u003ccode\u003eHALFpipe\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eVerbose logs are always written to the \u003ccode\u003elog.txt\u003c/code\u003e file in the working directory,\nso going back and inspecting this log is always possible, even if the\n\u003ccode\u003e--verbose\u003c/code\u003e flag was not specified.\u003c/p\u003e\n\u003cp\u003eSpecifying the flag \u003ccode\u003e--debug\u003c/code\u003e will print additional, fine-grained messages. It\nwill also automatically start the\n\u003ca href=\"https://docs.python.org/3/library/pdb.html\" rel=\"nofollow\"\u003ePython Debugger\u003c/a\u003e when an error\noccurs. You should only use \u003ccode\u003e--debug\u003c/code\u003e if you know what you\u0027re doing.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-automatically-remove-unneeded-files\" class=\"anchor\" href=\"#automatically-remove-unneeded-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically remove unneeded files\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--keep\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e saves intermediate files for each pipeline step. This speeds up\nre-running with different settings, or resuming after a job after it was\ncancelled. The intermediate file are saved by the\n\u003ca href=\"https://nipype.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003enipype\u003c/code\u003e\u003c/a\u003e workflow engine, which is what\n\u003ccode\u003eHALFpipe\u003c/code\u003e uses internally. \u003ccode\u003enipype\u003c/code\u003e saves the intermediate files in the\n\u003ccode\u003enipype\u003c/code\u003e folder in the working directory.\u003c/p\u003e\n\u003cp\u003eIn environments with limited disk capacity, this can be problematic. To limit\ndisk usage, \u003ccode\u003eHALFpipe\u003c/code\u003e can delete intermediate files as soon as they are not\nneeded anymore. This behavior is controlled with the \u003ccode\u003e--keep\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eThe default option \u003ccode\u003e--keep some\u003c/code\u003e keeps all intermediate files from fMRIPrep and\nMELODIC, which would take the longest to re-run. We believe this is a good\ntradeoff between disk space and computer time. \u003ccode\u003e--keep all\u003c/code\u003e turns of all\ndeletion of intermediate files. \u003ccode\u003e--keep none\u003c/code\u003e deletes as much as possible,\nmeaning that the smallest amount possible of disk space will be used.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-configure-nipype\" class=\"anchor\" href=\"#configure-nipype\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigure nipype\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--nipype-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eomp-nthreads\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003ememory-gb\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003en-procs\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003erun-plugin\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e chooses sensible defaults for all of these values.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-choose-which-parts-to-run-or-to-skip\" class=\"anchor\" href=\"#choose-which-parts-to-run-or-to-skip\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoose which parts to run or to skip\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eonly\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eskip\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003espec-ui\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eworkflow\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003erun\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003emodel-chunk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA \u003ccode\u003eHALFpipe\u003c/code\u003e run is divided internally into three stages, spec-ui, workflow, and\nrun.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ccode\u003espec-ui\u003c/code\u003e stage is where you specify things in the user interface. It\ncreates the \u003ccode\u003espec.json\u003c/code\u003e file that contains all the information needed to run\n\u003ccode\u003eHALFpipe\u003c/code\u003e. To only run this stage, use the option \u003ccode\u003e--only-spec-ui\u003c/code\u003e. To skip\nthis stage, use the option \u003ccode\u003e--skip-spec-ui\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003eworkflow\u003c/code\u003e stage is where \u003ccode\u003eHALFpipe\u003c/code\u003e uses the \u003ccode\u003espec.json\u003c/code\u003e data to search\nfor all the files that match what was input in the user interface. It then\ngenerates a \u003ccode\u003enipype\u003c/code\u003e workflow for preprocessing, feature extraction and group\nmodels. \u003ccode\u003enipype\u003c/code\u003e then validates the workflow and prepares it for execution.\nThis usually takes a couple of minutes and cannot be parallelized. For\nhundreds of subjects, this may even take a few hours. This stage has the\ncorresponding option \u003ccode\u003e--only-workflow\u003c/code\u003e and \u003ccode\u003e--skip-workflow\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eThis stage saves several intermediate files. These are named\n\u003ccode\u003eworkflow.{uuid}.pickle.xz\u003c/code\u003e, \u003ccode\u003eexecgraph.{uuid}.pickle.xz\u003c/code\u003e and\n\u003ccode\u003eexecgraph.{n_chunks}_chunks.{uuid}.pickle.xz\u003c/code\u003e. The \u003ccode\u003euuid\u003c/code\u003e in the file name is\na unique identifier generated from the \u003ccode\u003espec.json\u003c/code\u003e file and the input files.\nIt is re-calculated every time we run this stage. The uuid algorithm produces\na different output if there are any changes (such as when new input files for\nnew subjects become available, or the \u003ccode\u003espec.json\u003c/code\u003e is changed, for example to\nadd a new feature or group model). Otherwise, the \u003ccode\u003euuid\u003c/code\u003e stays the same.\nTherefore, if a workflow file with the calculated \u003ccode\u003euuid\u003c/code\u003e already exists, then\nwe do not need to run this stage. We can simple re-use the workflow from the\nexisting file, and save some time.\u003c/li\u003e\n\u003cli\u003eIn this stage, we can also decide to split the execution into chunks. The flag\n\u003ccode\u003e--subject-chunks\u003c/code\u003e creates one chunk per subject. The flag \u003ccode\u003e--use-cluster\u003c/code\u003e\nautomatically activates \u003ccode\u003e--subject-chunks\u003c/code\u003e. The flag \u003ccode\u003e--n-chunks\u003c/code\u003e allows the\nuser to specify a specific number of chunks. This is useful if the execution\nshould be spread over a set number of computers. In addition to these, a model\nchunk is generated.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ccode\u003erun\u003c/code\u003e stage loads the \u003ccode\u003eexecgraph.{n_chunks}_chunks.{uuid}.pickle.xz\u003c/code\u003e file\ngenerated in the previous step and runs it. This file usually contains two\nchunks, one for the subject level preprocessing and feature extraction\n(\"subject level chunk\"), and one for group statistics (\"model chunk\"). To run\na specific chunk, you can use the flags \u003ccode\u003e--only-chunk-index ...\u003c/code\u003e and\n\u003ccode\u003e--only-model-chunk\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-working-directory\" class=\"anchor\" href=\"#working-directory\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorking directory\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--workdir\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-data-file-system-root\" class=\"anchor\" href=\"#data-file-system-root\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData file system root\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--fs-root\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eHALFpipe\u003c/code\u003e container, or really most containers, contain the entire base\nsystem needed to run\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003eFor questions or support, please submit an\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues/new/choose\"\u003eissue\u003c/a\u003e or contact us\nvia e-mail.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRole\u003c/th\u003e\n\u003cth\u003eE-mail address\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLea Waller\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:lea.waller@charite.de\"\u003elea.waller@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIlya Veer\u003c/td\u003e\n\u003ctd\u003eProject manager\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:ilya.veer@charite.de\"\u003eilya.veer@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSusanne Erk\u003c/td\u003e\n\u003ctd\u003eProject manager\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:susanne.erk@charite.de\"\u003esusanne.erk@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 5, - "subscribers_count": 11, - "topics": [ - "planning", - "pddl", - "strips", - "fstrips" - ], - "updated_at": 1667964440.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1627454858.0 }, { "data_format": 2, - "description": "Workflow for prOteome and tRanScriptome functiOnal aNnotation. CAUTION : project has moved to Gitlab.", + "description": "San Diego Supercomputer Center Summer Institute", "filenames": [ - "containers/Singularity.interproscan-5.59-91.0" + "2.1a_Python_for_HPC/singularity/Singularity.miniconda3-dask-numba-py38-4.9.2-ubuntu-18.04" ], - "full_name": "ifremer-bioinformatics/orson", - "latest_release": "v1.0.0", - "readme": "\u003cp\u003e\u003cstrong\u003eORSON: workflow for prOteome and tRanScriptome functiOnal aNnotation\u003c/strong\u003e.\u003c/p\u003e\n\u003ch1 id=\"user-content-caution\"\u003e\u003ca class=\"heading-link\" href=\"#caution\"\u003e\u003cstrong\u003eCAUTION\u003c/strong\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003eThis mirror of ORSON workflow on Github is no longer supported and will soon be deleted.\u003c/p\u003e\n\u003cp\u003ePlease go to: \u003ca href=\"https://gitlab.ifremer.fr/bioinfo/workflows/orson\" rel=\"nofollow\"\u003ehttps://gitlab.ifremer.fr/bioinfo/workflows/orson\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "sdsc/sdsc-summer-institute-2021", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\" rel=\"nofollow\"\u003e\u003cimg alt=\"Creative Commons License\" src=\"https://camo.githubusercontent.com/72437363a2c150e50c5c090c5ec0dfde31ce3a28c8a092f638509882b56ceb4e/68747470733a2f2f692e6372656174697665636f6d6d6f6e732e6f72672f6c2f62792d6e632d73612f342e302f38307831352e706e67\" data-canonical-src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/80x15.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-sdsc-summer-institute-2021\" class=\"anchor\" href=\"#sdsc-summer-institute-2021\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSDSC Summer Institute 2021\u003c/h1\u003e\n\u003cp\u003eThis repository hosts all material and slides of the presentations at the Summer Institute\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://na.eventscloud.com/website/21339/agenda/\" rel=\"nofollow\"\u003eAgenda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSee also \u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2020\"\u003eSCC20 material\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-interactive-videos\" class=\"anchor\" href=\"#interactive-videos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive Videos\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInteractive Videos can be found here: \u003ca href=\"https://education.sdsc.edu/training/interactive/sdsc_si21/index.html\" rel=\"nofollow\"\u003ehttps://education.sdsc.edu/training/interactive/sdsc_si21/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eA full catalog of all our trainings at SDSC can be found \u003ca href=\"https://www.sdsc.edu/education_and_training/training_hpc.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-aliases-symlinks-and-reservations\" class=\"anchor\" href=\"#aliases-symlinks-and-reservations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAliases, symlinks and reservations\u003c/h2\u003e\n\u003cp\u003eFor your convenience, we\u2019ve create aliases and symlinks for the Summer Institute\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eget-cpu \u2013 one interactive compute node for 2 hours\u003c/li\u003e\n\u003cli\u003eget-gpu \u2013 one interactive GPU (in shared queue) for 2 hours\u003c/li\u003e\n\u003cli\u003estart-spark \u2013 start one-hour Spark session\u003c/li\u003e\n\u003cli\u003estart-tf-cpu \u2013 start three-hour TensorFlow session (CPU)\u003c/li\u003e\n\u003cli\u003estart-tf-gpu \u2013 start three-hour TensorFlow session (GPU)\u003c/li\u003e\n\u003cli\u003edata \u2013 symlink to staged data\nIn the event that you need to explicitly use the reservation, training accounts will have access to SI2021RES for duration of SI\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eAll the teaching material in this repository is licensed under \u003ca href=\"https://creativecommons.org/licenses/by-nc-sa/4.0/\" rel=\"nofollow\"\u003eCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you are re-using this material, please cite our \u003ca href=\"TODO\"\u003erecord on Zenodo\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 3, - "topics": [ - "workflow", - "annotation", - "transcriptome", - "proteome", - "nextflow-pipelines" - ], - "updated_at": 1677855349.0 + "subscribers_count": 13, + "topics": [], + "updated_at": 1631666132.0 }, { "data_format": 2, - "description": "GRETTA (Genetic inteRaction and EssenTiality neTwork mApper): An R package for mapping genetic interaction and essentiality networks", + "description": "Python script for the numerical example in the manuscript \"Applying a stochastic quasi-Newton optimizer to least-squares reverse time migration\" submitted to Computers and Geosciences journal on 05/28/2021.", "filenames": [ - "Singularity/Singularity.GRETTA.def" + "Dockerfile/Singularity.def", + "Dockerfile/Singularity_nvidia.def" ], - "full_name": "ytakemon/GRETTA", - "latest_release": "v0.99.0", - "readme": "\n\n\n\u003cp\u003e\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/400c4e52df43f6a0ab8a89b74b1a78d1a64da56a7848b9110c9d2991bb7c3105/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://lifecycle.r-lib.org/articles/stages.html#stable\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d7aedfa6c0fd00737083172bffb7ae9b253b54fae707524fcb503a1ce9c48a66/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6966656379636c652d737461626c652d627269676874677265656e2e737667\" alt=\"Lifecycle: stable\" data-canonical-src=\"https://img.shields.io/badge/lifecycle-stable-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/374398121\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc696cee4b750b415f3666ead55ca691783e528199724ce6e40b14c67836ce80/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3337343339383132312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/374398121.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./GRETTA_hex_logo-02.png\"\u003e\u003cimg src=\"./GRETTA_hex_logo-02.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h1\u003e\n\u003cp\u003eGenetic inteRaction and EssenTiality mApper (GRETTA) is an R package\nthat leverages data generated by the \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eCancer Dependency Map (DepMap)\nproject\u003c/a\u003e to perform in-silico genetic\nknockout screens and map essentiality networks. A manuscript describing\nthis tool is available at \u003ca href=\"https://doi.org/10.1093/bioinformatics/btad381\" rel=\"nofollow\"\u003ebioinformatics (Takemon, Y. and Marra, MA.,\n2023)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe DepMap data used in this tutorial is version 22Q2. This version\nalong with all versions provided in this repository were downloaded\nthrough the DepMap data portal, which was distributed and used under the\nterms and conditions of \u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC Attribution 4.0\nlicense\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainer\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#maintainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainer\u003c/h2\u003e\n\u003cp\u003eThis repository is maintained by \u003ca href=\"https://github.com/ytakemon\"\u003eYuka\nTakemon\u003c/a\u003e, a PhD candidate in \u003ca href=\"https://www.bcgsc.ca/labs/marra-lab\" rel=\"nofollow\"\u003eDr.\u00a0Marco\nMarra\u003c/a\u003e\u2019s laboratory at \u003ca href=\"https://www.bcgsc.ca/\" rel=\"nofollow\"\u003eCanada\u2019s\nMichael Smith Genome Sciences Centre\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-citations\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#citations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitations\u003c/h3\u003e\n\u003cp\u003ePlease cite the manuscript describing GRETTA on \u003ca href=\"https://doi.org/10.1093/bioinformatics/btad381\" rel=\"nofollow\"\u003ebioinformatics\n(Takemon, Y. and Marra, MA.,\n2023)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYuka Takemon, Marco A Marra, GRETTA: an R package for mapping in silico\ngenetic interaction and essentiality networks, Bioinformatics, Volume\n39, Issue 6, June 2023, btad381,\n\u003ca href=\"https://doi.org/10.1093/bioinformatics/btad381\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btad381\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions\u003c/h2\u003e\n\u003cp\u003ePlease check the \u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions\"\u003eFAQ\nsection\u003c/a\u003e\nfor additional information and if you cannot find your answer there or\nhave a request please submit an\n\u003ca href=\"https://github.com/ytakemon/GRETTA/issues\"\u003eissue\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGRETTA is supported and compatible for R versions \u0026gt;= 4.2.0.\u003c/li\u003e\n\u003cli\u003e12G of space to store one DepMap data set with and an additional 11G\nof temporary space to for .tar.gz prior to extraction.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003eYou can install the GRETTA package from \u003ca href=\"https://github.com\"\u003eGitHub\u003c/a\u003e\nwith:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall.packages(c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edevtools\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edplyr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eforcats\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eggplot2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eytakemon/GRETTA\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDepMap 22Q2 data and the data documentation files are provided above and\ncan be extracted directly in terminal using the following bash code (not\nin R/RStudio). For other DepMap data versions please refer to the \u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions#q-how-to-download-and-use-other-versions-of-depmap-data\"\u003eFAQ\nsection\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make a new directory/folder called GRETTA_project and go into directory\u003c/span\u003e\nmkdir GRETTA_project\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e GRETTA_project\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download data from the web\u003c/span\u003e\nwget https://www.bcgsc.ca/downloads/ytakemon/GRETTA/22Q2/GRETTA_DepMap_22Q2_data.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract data and data documentation\u003c/span\u003e\ntar -zxvf GRETTA_DepMap_22Q2_data.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA singularity container has also been provided and instructions can be\nfound\n\u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions#q-how-to-run-singularity\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-additional-depmap-versions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#additional-depmap-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional DepMap versions\u003c/h3\u003e\n\u003cp\u003eIn this example we use DepMap\u2019s 2022 data release (22Q2). However, we\nalso provide previous data released in 2020 (v20Q1) and 2021 (v21Q4),\nwhich are available at\n:\u003ccode\u003ehttps://www.bcgsc.ca/downloads/ytakemon/GRETTA/\u003c/code\u003e. We are hoping to\nmake new data sets available as the are released by DepMap.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-workflows\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflows\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-genetic-interaction-mapping\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#genetic-interaction-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenetic interaction mapping\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ccode\u003eGRETTA\u003c/code\u003e and download accompanying data.\u003c/li\u003e\n\u003cli\u003eSelect mutant cell lines that carry mutations in the gene of\ninterest and control cell lines.\n\u003cul\u003e\n\u003cli\u003e(\u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions#q-how-can-context-specific-genetic-screens-or-essentiality-network-analyses-be-performed\"\u003eoptional\nspecifications\u003c/a\u003e)\ncan be used to select cell lines based on disease type, disease\nsubtype, or amino acid change.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDetermine differential expression between mutant and control cell\nline groups.\n\u003cul\u003e\n\u003cli\u003e(optional but recommended).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePerform \u003cem\u003ein silico\u003c/em\u003e genetic screen.\u003c/li\u003e\n\u003cli\u003eVisualize results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-co-essential-network-mapping\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#co-essential-network-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCo-essential network mapping\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ccode\u003eGRETTA\u003c/code\u003e and download accompanying data.\u003c/li\u003e\n\u003cli\u003eRun correlation coefficient analysis.\n\u003cul\u003e\n\u003cli\u003e(\u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions#q-how-can-context-specific-genetic-screens-or-essentiality-network-analyses-be-performed:~:text=For%20the%20essentiality%20network%20analysis%2C%20context%2Dspecific%20cell%20lines%20can%20be%20selected%20in%20two%20ways%3A\"\u003eoptional\nspecifications\u003c/a\u003e)\ncan be used to perform analysis on cell lines of a specific\ndisease type(s).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCalculate inflection points of negative/positive curve to determine\na threshold.\u003c/li\u003e\n\u003cli\u003eApply threshold.\u003c/li\u003e\n\u003cli\u003eVisualize results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-example-identifying-arid1a-genetic-interactions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#example-identifying-arid1a-genetic-interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Identifying \u003cem\u003eARID1A\u003c/em\u003e genetic interactions\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003eARID1A\u003c/em\u003e encodes a member of the chromatin remodeling SWItch/Sucrose\nNon-Fermentable (SWI/SNF) complex and is a frequently mutated gene in\ncancer. It is known that \u003cem\u003eARID1A\u003c/em\u003e and its homolog, \u003cem\u003eARID1B\u003c/em\u003e, are\nsynthetic lethal to one another: The dual loss of ARID1A and its\nhomolog, ARID1B, in a cell is lethal; however, the loss of either gene\nalone is not (\u003ca href=\"https://doi.org/10.1038/nm.3480\" rel=\"nofollow\"\u003eHelming et al., 2014\u003c/a\u003e).\nThis example will demonstrate how we can identify synthetic lethal\ninteractors of \u003cem\u003eARID1A\u003c/em\u003e using \u003ccode\u003eGRETTA\u003c/code\u003e and predict this known\ninteraction.\u003c/p\u003e\n\u003cp\u003eFor this example you will need to call the following libraries. If you\nthey are not installed yet use \u003ccode\u003einstall.packages()\u003c/code\u003e (eg.\n\u003ccode\u003einstall.packages(\"dplyr\")\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Load library\u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003etidyverse\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2500\u2500 Attaching packages \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse 1.3.2 \u2500\u2500\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 ggplot2 3.4.1 \u2714 purrr 1.0.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 tibble 3.2.1 \u2714 dplyr 1.1.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 tidyr 1.3.0 \u2714 stringr 1.5.0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 readr 2.1.4 \u2714 forcats 1.0.0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2500\u2500 Conflicts \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse_conflicts() \u2500\u2500\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2716 dplyr::filter() masks stats::filter()\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2716 dplyr::lag() masks stats::lag()\u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003eGRETTA\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; _______ .______ _______ .___________.___________. ___ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; / _____|| _ \\ | ____|| | | / \\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; | | __ | |_) | | |__ `---| |----`---| |----` / ^ \\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; | | |_ | | / | __| | | | | / /_\\ \\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; | |__| | | |\\ \\----.| |____ | | | | / _____ \\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \\______| | _| `._____||_______| |__| |__| /__/ \\__\\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Welcome to GRETTA! The version loaded is: 0.99.2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; The latest DepMap dataset accompanying this package is v22Q2. \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Please refer to our tutorial on GitHub for loading DepMap data and details: https://github.com/ytakemon/GRETTA\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-example-data\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#download-example-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload example data\u003c/h2\u003e\n\u003cp\u003eA small data set has been created for this tutorial and can be\ndownloaded using the following code.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epath\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e getwd()\ndownload_example_data(\u003cspan class=\"pl-smi\"\u003epath\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Data saved to: /projects/marralab/ytakemon_prj/DepMap/GRETTA/GRETTA_example/\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, assign variable that point to where the \u003ccode\u003e.rda\u003c/code\u003e files are stored\nand where result files should go.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e paste0(\u003cspan class=\"pl-smi\"\u003epath\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/GRETTA_example/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e paste0(\u003cspan class=\"pl-smi\"\u003epath\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/GRETTA_example_output/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-exploring-cell-lines\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#exploring-cell-lines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExploring cell lines\u003c/h2\u003e\n\u003cp\u003eOne way to explore cell lines that are available in DepMap is through\ntheir \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eportal\u003c/a\u003e. However, there are some\nsimple built-in methods in GRETTA to provide users with a way to glimpse\nthe data using the series of \u003ccode\u003elist_available\u003c/code\u003e functions:\n\u003ccode\u003elist_mutations()\u003c/code\u003e, \u003ccode\u003elist_cancer_types()\u003c/code\u003e, \u003ccode\u003elist_cancer_subtypes()\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCurrent DepMap data used by default is version 22Q2, which contains\nwhole-genome sequencing or whole-exome sequencing annotations for \u003ccode\u003e1771\u003c/code\u003e\ncancer cell lines (\u003ccode\u003e1406\u003c/code\u003e cell lines with RNA-seq data, \u003ccode\u003e375\u003c/code\u003e cell lines\nwith quantitative proteomics data, and \u003ccode\u003e1086\u003c/code\u003e cell lines with\nCRISPR-Cas9 knockout screen data)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Find ARID1A hotspot mutations detected in all cell lines\u003c/span\u003e\nlist_mutations(\u003cspan class=\"pl-v\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eis_hotspot\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e) \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# List all available cancer types\u003c/span\u003e\nlist_cancer_types(\u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Kidney Cancer\" \"Leukemia\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] \"Lung Cancer\" \"Non-Cancerous\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] \"Sarcoma\" \"Lymphoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] \"Colon/Colorectal Cancer\" \"Pancreatic Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] \"Gastric Cancer\" \"Rhabdoid\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] \"Endometrial/Uterine Cancer\" \"Esophageal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [13] \"Breast Cancer\" \"Brain Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [15] \"Ovarian Cancer\" \"Bone Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [17] \"Myeloma\" \"Head and Neck Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [19] \"Bladder Cancer\" \"Skin Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [21] \"Bile Duct Cancer\" \"Prostate Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [23] \"Cervical Cancer\" \"Thyroid Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [25] \"Neuroblastoma\" \"Eye Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [27] \"Liposarcoma\" \"Gallbladder Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [29] \"Teratoma\" \"Unknown\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [31] \"Liver Cancer\" \"Adrenal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [33] \"Embryonal Cancer\"\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# List all available cancer subtypes\u003c/span\u003e\nlist_cancer_subtypes(\u003cspan class=\"pl-v\"\u003einput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eLung Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Non-Small Cell Lung Cancer (NSCLC), Adenocarcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [2] \"Small Cell Lung Cancer (SCLC)\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] \"Non-Small Cell Lung Cancer (NSCLC), Squamous Cell Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [4] \"Mesothelioma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] \"Non-Small Cell Lung Cancer (NSCLC), Large Cell Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [6] NA \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] \"Non-Small Cell Lung Cancer (NSCLC), unspecified\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [8] \"Non-Small Cell Lung Cancer (NSCLC), Adenosquamous Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] \"Carcinoid\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [10] \"Non-Small Cell Lung Cancer (NSCLC), Mucoepidermoid Carcinoma\"\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] \"Carcinoma\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-selecting-mutant-and-control-cell-line-groups\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#selecting-mutant-and-control-cell-line-groups\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSelecting mutant and control cell line groups\u003c/h2\u003e\n\u003cp\u003eAs default \u003ccode\u003eselect_cell_lines()\u003c/code\u003e will identify cancer cell lines with\nloss-of-function alterations in the gene specified and group them into\nsix different groups.\u003c/p\u003e\n\u003cp\u003eLoss-of-function alterations include variants that are annotated as:\n\u003ccode\u003e\"Nonsense_Mutation\", \"Frame_Shift_Ins\", \"Splice_Site\", \"De_novo_Start_OutOfFrame\", \"Frame_Shift_Del\", \"Start_Codon_SNP\", \"Start_Codon_Del\",\u003c/code\u003e\nand \u003ccode\u003e\"Start_Codon_Ins\"\u003c/code\u003e. Copy number alterations are also taken into\nconsideration and group as \u003ccode\u003e\"Deep_del\", \"Loss\", \"Neutral\",\u003c/code\u003e or\n\u003ccode\u003e\"Amplified\"\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe cell line groups assigned by default are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eControl\u003c/code\u003e cell lines do not harbor any single nucleotide variations\n(SNVs) or insertions and deletions (InDels) with a neutral copy\nnumber (CN).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHomDel\u003c/code\u003e cell lines harbor one or more homozygous deleterious SNVs\nor have deep CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eT-HetDel\u003c/code\u003e cell lines harbor two or more heterozygous deleterious\nSNVs/InDels with neutral or CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHetDel\u003c/code\u003e cell lines harbor one heterozygous deleterious SNV/InDel\nwith neutral CN, or no SNV/InDel with CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAmplified\u003c/code\u003e cell lines harbor no SNVs/InDels with increased CN.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eOthers\u003c/code\u003e cell lines harbor deleterious SNVs with increased CN.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_cell_lines(\u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Selecting mutant groups for: ARID1A in all cancer cell lines\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Show number of cell lines in each group \u003c/span\u003e\ncount(\u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 5 \u00d7 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Group n\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_HetDel 61\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_HomDel 23\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_T-HetDel 30\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 Control 906\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 Others 66\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional-cell-line-filters\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#optional-cell-line-filters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional cell line filters\u003c/h3\u003e\n\u003cp\u003eThere are several additional filters that can be combined together to\nnarrow down your search. These\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003einput_aa_change\u003c/code\u003e - by amino acid change (eg. \u201cp.Q515*\u201c).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003einput_disease\u003c/code\u003e - by disease type (eg. \u201cPancreatic Cancer\u201d)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003einput_disease_subtype\u003c/code\u003e - by disease subtype (eg. \u201cDuctal\nAdenosquamous Carcinoma\u201d)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Find pancreatic cancer cell lines with ARID1A mutations\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_pancr_groups\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_cell_lines(\u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003einput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePancreatic Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Selecting mutant groups for: ARID1A in Pancreatic Cancer, cell lines\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Show number of cell lines in each group \u003c/span\u003e\ncount(\u003cspan class=\"pl-smi\"\u003eARID1A_pancr_groups\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 4 \u00d7 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Group n\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_HetDel 5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_HomDel 4\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 Control 36\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 Others 2\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-check-for-differential-expression\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#check-for-differential-expression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheck for differential expression\u003c/h2\u003e\n\u003cp\u003eOf the three mutant cancer cell line groups \u003ccode\u003eARID1A_HomDel\u003c/code\u003e,\n\u003ccode\u003eARID1A_T-HetDel\u003c/code\u003e, and \u003ccode\u003eARID1A_HetDel\u003c/code\u003e, cancer cell lines with\n\u003ccode\u003eARID1A_HomDel\u003c/code\u003e mutations are most likely to result in a loss or reduced\nexpression of \u003cem\u003eARID1A\u003c/em\u003e. Therefore, we want to check whether cell lines\nin \u003ccode\u003eARID1A_HomDel\u003c/code\u003e mutant group have significantly less \u003cem\u003eARID1A\u003c/em\u003e RNA or\nprotein expression compared to control cell lines.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Select only HomDel and Control cell lines\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_groups_subset\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A_HomDel\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Get RNA expression \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e extract_rna(\n \u003cspan class=\"pl-v\"\u003einput_samples\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups_subset\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003einput_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Following sample did not contain RNA data: ACH-000047, ACH-000426, ACH-000658, ACH-000979, ACH-001039, ACH-001063, ACH-001065, ACH-001107, ACH-001126, ACH-001137, ACH-001205, ACH-001212, ACH-001227, ACH-001331, ACH-001544, ACH-001606, ACH-001639, ACH-001675, ACH-001955, ACH-001956, ACH-001957, ACH-002083, ACH-002106, ACH-002109, ACH-002110, ACH-002114, ACH-002116, ACH-002119, ACH-002140, ACH-002141, ACH-002143, ACH-002150, ACH-002156, ACH-002160, ACH-002161, ACH-002179, ACH-002181, ACH-002186, ACH-002189, ACH-002198, ACH-002202, ACH-002210, ACH-002212, ACH-002217, ACH-002228, ACH-002229, ACH-002230, ACH-002233, ACH-002234, ACH-002239, ACH-002243, ACH-002247, ACH-002249, ACH-002250, ACH-002257, ACH-002261, ACH-002263, ACH-002265, ACH-002269, ACH-002278, ACH-002280, ACH-002282, ACH-002283, ACH-002284, ACH-002285, ACH-002294, ACH-002295, ACH-002296, ACH-002297, ACH-002298, ACH-002304, ACH-002305, ACH-002399, ACH-002874, ACH-002875\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNot all cell lines contain RNA and/or protein expression profiles, and\nnot all proteins were detected by mass spectrometer. (Details on data\ngeneration can be found on the \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eDepMap\nsite\u003c/a\u003e.)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Get protein expression\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_prot_expr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e extract_prot(\n \u003cspan class=\"pl-v\"\u003einput_samples\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups_subset\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003einput_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Produces an error message since ARID1A protein data is not available\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing Welch\u2019s t-test, we can check to see whether \u003cem\u003eARID1A\u003c/em\u003e RNA\nexpression (in TPM) is significantly reduced in \u003ccode\u003eARID1A_HomDel\u003c/code\u003e cell\nlines compared to \u003ccode\u003eControls\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Append groups and test differential expression\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e left_join(\n \u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e,\n \u003cspan class=\"pl-smi\"\u003eARID1A_groups_subset\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% select(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e%\n mutate(\u003cspan class=\"pl-v\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e fct_relevel(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e show Control group first\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Joining with `by = join_by(DepMap_ID)`\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# T-test \u003c/span\u003e\nt.test(\u003cspan class=\"pl-smi\"\u003eARID1A_8289\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Welch Two Sample t-test\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; data: ARID1A_8289 by Group\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; t = 2.5764, df = 22.873, p-value = 0.01692\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; alternative hypothesis: true difference in means between group Control and group ARID1A_HomDel is not equal to 0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 95 percent confidence interval:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 0.1146094 1.0498810\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; sample estimates:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; mean in group Control mean in group ARID1A_HomDel \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4.635784 4.053539\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# plot \u003c/span\u003e\nggplot(\u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e, aes(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_8289\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\n geom_boxplot()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-Check_expression_rna_stats-1.png\"\u003e\u003cimg src=\"man/figures/README-Check_expression_rna_stats-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-perform-genome-wide-in-silico-genetic-screen\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#perform-genome-wide-in-silico-genetic-screen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePerform genome-wide \u003cem\u003ein silico\u003c/em\u003e genetic screen\u003c/h2\u003e\n\u003cp\u003eAfter determining cell lines in the \u003ccode\u003eARID1A_HomDel\u003c/code\u003e group has\nstatistically significant reduction in RNA expression compared to\n\u003ccode\u003eControl\u003c/code\u003e cell lines, the next step is to perform a \u003cem\u003ein silico\u003c/em\u003e genetic\nscreen using \u003ccode\u003escreen_results()\u003c/code\u003e. This uses the dependency probabilities\n(or \u003cstrong\u003e\u201clethality probabilities\u201d\u003c/strong\u003e) generated from DepMap\u2019s genome-wide\nCRISPR-Cas9 knockout screen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLethality probabilities\u003c/strong\u003e range from 0.0 to 1.0 and is quantified for\neach gene knock out in every cancer cell line screened (There are 18,334\ngenes targeted in 739 cancer cell lines). A gene knock out with a\nlethality probability of 0.0 indicates a non-essential for the cell\nline, and a gene knock out with a 1.0 indicates an essential gene (ie.\nvery lethal). Details can be found in \u003ca href=\"https://doi.org/10.1038/ng.3984\" rel=\"nofollow\"\u003eMeyers, R., et al.,\n2017\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAt its core, \u003ccode\u003escreen_results()\u003c/code\u003e performs multiple Mann-Whitney U tests,\ncomparing lethality probabilities of each targeted gene between mutant\nand control groups. This generates a data frame with the following\ncolumns:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGeneName_ID\u003c/code\u003e - Hugo symbol with NCBI gene ID\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGeneNames\u003c/code\u003e - Hugo symbol\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e_median, _mean, _sd, _iqr\u003c/code\u003e - Control and mutant group\u2019s median,\nmean, standard deviation (sd), and interquartile range (iqr) of\ndependency probabilities. Dependency probabilities range from zero\nto one, where one indicates a essential gene (ie. KO of gene was\nlethal) and zero indicates a non-essential gene (KO of gene was not\nlethal)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePval\u003c/code\u003e - P-value from Mann Whitney U test between control and mutant\ngroups.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdj_pval\u003c/code\u003e - BH-adjusted P-value.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog2FC_by_median\u003c/code\u003e - Log2 normalized median fold change of\ndependency probabilities (mutant / control).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog2FC_by_mean\u003c/code\u003e - Log2 normalized mean fold change of dependency\nprobabilities (mutant / control).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCliffDelta\u003c/code\u003e - Cliff\u2019s delta non-parametric effect size between\nmutant and control dependency probabilities. Ranges between -1 to 1.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edip_pval\u003c/code\u003e - Hartigan\u2019s dip test p-value. Tests whether distribution\nof mutant dependency probability is unimodel. If dip test is\nrejected (p-value \u0026lt; 0.05), this indicates that there is a\nmultimodel dependency probability distribution and that there may be\nanother factor contributing to this separation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInteraction_score\u003c/code\u003e - Combined value generated from signed p-values:\n-log10(Pval) * sign(log2FC_by_median). Negative scores indicate\nlethal genetic interaction, and positive scores indicate alleviating\ngenetic interaction.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e This process may take a few hours depending on the number\nof cores assigned. Our example below \u003ccode\u003eGI_screen()\u003c/code\u003e took ~2 hours to\nprocess. To save time, we have preprocessed this step for you.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eARID1A_mutant_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A_HomDel\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% pull(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eARID1A_control_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% pull(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# See warning above.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# This can take several hours depending on number of lines/cores used. \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e GI_screen(\n \u003cspan class=\"pl-v\"\u003econtrol_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_control_id\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003emutant_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_mutant_id\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e depends on how many cores you have \u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Will save your results here as well as in the variable\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e use TRUE to run a short test to make sure all will run overnight.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Load prepared ARID1A screen result\u003c/span\u003e\nload(paste0(\u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/sample_22Q2_ARID1A_KO_screen.rda\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \u003cspan class=\"pl-v\"\u003eenvir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e environment())\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can quickly determine whether any lethal genetic interactions were\npredicted by \u003ccode\u003eGRETTA\u003c/code\u003e. We use a \u003ccode\u003ePval\u003c/code\u003e cut off of 0.05 and rank based on\nthe \u003ccode\u003eInteraction_score\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \n filter(\u003cspan class=\"pl-smi\"\u003ePval\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.05\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e%\n arrange(\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eInteraction_score\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \n select(\u003cspan class=\"pl-smi\"\u003eGeneNames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eMutant_median\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ePval\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eInteraction_score\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \u003cspan class=\"pl-smi\"\u003ehead\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 6 \u00d7 5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; GeneNames Control_median Mutant_median Pval Interaction_score\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1B 0.0579 0.515 6.84e-10 9.16\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 CCDC110 0.0165 0.0303 3.54e- 4 3.45\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 APOO 0.0168 0.0283 9.61e- 4 3.02\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 NHS 0.0352 0.0539 9.69e- 4 3.01\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 SLC66A2 0.00793 0.0134 1.06e- 3 2.98\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 ATXN7L1 0.0138 0.0259 1.78e- 3 2.75\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe immediately see that \u003cem\u003eARID1B\u003c/em\u003e, a known synthetic lethal interaction\nof \u003cem\u003eARID1A\u003c/em\u003e, was a the top of this list.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional-performing-a-small-scale-screen\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#optional-performing-a-small-scale-screen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional: Performing a small-scale screen\u003c/h3\u003e\n\u003cp\u003eTo perform a small in silico screen, a list of genes can be provided in\nthe \u003ccode\u003egene_list =\u003c/code\u003e argument.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003esmall_screen_results\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e GI_screen(\n \u003cspan class=\"pl-v\"\u003econtrol_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_control_id\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003emutant_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_mutant_id\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003egene_list\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1B\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSMARCA2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eGAPDH\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSMARCC2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e depends on how many cores you have \u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Will save your results here as well as in the variable\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e) \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-visualize-screen-results\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#visualize-screen-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisualize screen results\u003c/h2\u003e\n\u003cp\u003eFinally once the \u003cem\u003ein silico\u003c/em\u003e screen is complete, results can be quickly\nvisualized using \u003ccode\u003eplot_screen()\u003c/code\u003e. Positive genetic interaction scores\nindicate potential synthetic lethal genetic interactors, and negative\nscores indicate potential alleviating genetic interactors. As expected,\nwe identified \u003cem\u003eARID1B\u003c/em\u003e as a synthetic lethal interactor of \u003cem\u003eARID1A\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Visualize results, turn on gene labels, \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# and label three genes each that are predicted to have \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# lethal and alleviating genetic interactions, respectively\u003c/span\u003e\n\nplot_screen(\u003cspan class=\"pl-v\"\u003eresult_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003elabel_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003elabel_n\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Warning: Removed 7 rows containing missing values (`geom_point()`).\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-plot-1.png\"\u003e\u003cimg src=\"man/figures/README-plot-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-example-identifying-arid1a-co-essential-genes\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#example-identifying-arid1a-co-essential-genes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Identifying \u003cem\u003eARID1A\u003c/em\u003e co-essential genes\u003c/h1\u003e\n\u003cp\u003ePerturbing genes that function in same/synergistic pathways or in the\nsame complex are said to show similar fitness effects, and these that\nshow effects are considered to be \u201cco-essential\u201d. The strategy of\nmapping co-essential gene have been used by several studies to attribute\nfunctions to previously annotated genes as well as to identify a novel\nsubunit of a large complex (\u003ca href=\"https://doi.org/10.1038/s41588-021-00840-z\" rel=\"nofollow\"\u003eWainberg et\nal.\u00a02021\u003c/a\u003e; \u003ca href=\"https://doi.org/10.1016/j.cels.2018.04.011\" rel=\"nofollow\"\u003ePan et\nal.\u00a02018\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eGiven that ARID1A is known subunit of the mammalian SWI/SNF complex\n(\u003ca href=\"https://doi.org/10.1016/j.cell.2018.09.032\" rel=\"nofollow\"\u003eMashtalir et al.\u00a02018\u003c/a\u003e),\nwe expect that members of the SWI/SNF complex would share\nco-essentiality with \u003cem\u003eARID1A\u003c/em\u003e. This example will demonstrate how we can\nmap \u003cem\u003eARID1A\u003c/em\u003e\u2019s co-essential gene network using \u003ccode\u003eGRETTA\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-identifying-genes-with-highest-correlation-coefficients\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#identifying-genes-with-highest-correlation-coefficients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIdentifying genes with highest correlation coefficients\u003c/h2\u003e\n\u003cp\u003eTo determine co-essential genes, we will perform multiple Pearson\ncorrelation coefficient analyses between \u003cem\u003eARID1A\u003c/em\u003e KO effects and the KO\neffects of all 18,333 genes. A cut off will be determined by calculating\nthe inflection point of the ranked coefficient curve. As expected find\nSWI/SNF subunit encoding genes, \u003cem\u003eSMARCE1\u003c/em\u003e and \u003cem\u003eSMARCB1\u003c/em\u003e, as the top two\nco-essential genes.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e This process may take several minutes. Our example below\n\u003ccode\u003ecoessential_map()\u003c/code\u003e + \u003ccode\u003eget_inflection_points()\u003c/code\u003e took ~17 minutes to\nprocess. To save time we have pre-processed this setp for you.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Map co-essential genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e coessential_map(\n \u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e) \n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Calculate inflection points of positive and negative curve using co-essential gene results.\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_inflection_points(\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, we annotate the data frame containing the co-essential network\ndata and visualize.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Combine and annotate data frame containing co-essential genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e annotate_coess(\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e)\n\nplot_coess(\n \u003cspan class=\"pl-v\"\u003eresult_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003einflection_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003elabel_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Should gene names be labeled?\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003elabel_n\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Number of genes to display from each end\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-combine_n_visualize-1.png\"\u003e\u003cimg src=\"man/figures/README-combine_n_visualize-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe also see that the top ten \u003cem\u003eARID1A\u003c/em\u003e co-essential genes include eight\nknown SWI/SNF subunits, namely \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003eSMARCB1\u003c/em\u003e, \u003cem\u003eSMARCE1\u003c/em\u003e,\n\u003cem\u003eSMARCC1\u003c/em\u003e, \u003cem\u003eSS18\u003c/em\u003e, \u003cem\u003eDPF2\u003c/em\u003e, \u003cem\u003eSMARCC2\u003c/em\u003e, and \u003cem\u003eSMARCD2\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Show top 10 co-essential genes. \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% arrange(\u003cspan class=\"pl-smi\"\u003eRank\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% head(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 10 \u00d7 9\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; GeneNameID_A GeneNameID_B estimate statistic p.value parameter Rank\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_8289 ARID1A_8289 1 Inf 0 1086 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_8289 SMARCB1_6598 0.477 17.9 7.45e-59 1086 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_8289 SMARCE1_6605 0.399 14.3 4.30e-39 1086 3\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 ARID1A_8289 SMARCC1_6599 0.369 13.1 9.35e-33 1086 4\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 ARID1A_8289 SS18_6760 0.332 11.6 4.85e-26 1086 5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 ARID1A_8289 DPF2_5977 0.330 11.5 1.15e-25 1086 6\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 7 ARID1A_8289 SMARCD2_6603 0.270 9.22 1.10e-16 1086 7\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 8 ARID1A_8289 SMARCC2_6601 0.242 8.22 2.34e-13 1086 8\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 9 ARID1A_8289 BCL2_596 0.231 7.82 4.05e-12 1086 9\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 10 ARID1A_8289 CBFB_865 0.224 7.58 2.07e-11 1086 10\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # \u2139 2 more variables: Padj_BH \u0026lt;dbl\u0026gt;, Candidate_gene \u0026lt;lgl\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional-filter-for-specific-cancer-types\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#optional-filter-for-specific-cancer-types\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional filter for specific cancer types\u003c/h3\u003e\n\u003cp\u003eInstead of mapping for essentiality across all available cell lines,\nusers can also subset by disease type using the option\n\u003ccode\u003einput_disease = \"\"\u003c/code\u003e, or within a pre-selected group of cell lines using\nthe option \u003ccode\u003einput_cell_lines = c()\u003c/code\u003e. Below we provide an example of how\nARID1A essential genes are mapped for pancreatic cancers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e Depending on the number of cell lines that are available\nafter the subsetting step, the inflection point calculation and\nthresholds may not be optimal. Please use caution when interpreting\nthese results.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Map co-essential genes in pancreatic cancers only\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e coessential_map(\n \u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003einput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePancreatic Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Depending on how many cores you have access to, increase this value to shorten processing time.\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional-filter-for-custom-cell-lines\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#optional-filter-for-custom-cell-lines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional filter for custom cell lines\u003c/h3\u003e\n\u003cp\u003eWe can also map essentiality across a manually defined list of cell\nlines using the \u003ccode\u003einput_cell_lines = c()\u003c/code\u003e option.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e Depending on the number of cell lines provided, the\ninflection point may not be calculated. Please use caution when\ninterpreting these results.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003ecustom_lines\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eACH-000001\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eACH-000002\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eACH-000003\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"pl-k\"\u003e...\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e coessential_map(\n \u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003einput_cell_lines\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecustom_lines\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Depending on how many cores you have access to, increase this value to shorten processing time.\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-session-information\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#session-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSession information\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003esessionInfo()\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; R version 4.2.2 (2022-10-31)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Platform: x86_64-pc-linux-gnu (64-bit)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Running under: CentOS Linux 7 (Core)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Matrix products: default\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; BLAS: /gsc/software/linux-x86_64-centos7/R-4.2.2/lib64/R/lib/libRblas.so\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; LAPACK: /gsc/software/linux-x86_64-centos7/R-4.2.2/lib64/R/lib/libRlapack.so\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; locale:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] LC_PAPER=en_US.UTF-8 LC_NAME=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] LC_ADDRESS=C LC_TELEPHONE=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; attached base packages:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] stats graphics grDevices utils datasets methods base \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; other attached packages:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] GRETTA_0.99.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] ggplot2_3.4.1 tidyverse_1.3.2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; loaded via a namespace (and not attached):\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] TH.data_1.1-2 googledrive_2.0.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] colorspace_2.1-0 class_7.3-20 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] modeltools_0.2-23 fs_1.6.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] gld_2.6.6 rstudioapi_0.14 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] proxy_0.4-27 farver_2.1.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] ggrepel_0.9.3 bit64_4.0.5 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [13] fansi_1.0.4 mvtnorm_1.1-3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [15] lubridate_1.9.0 coin_1.4-2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [17] xml2_1.3.4 codetools_0.2-18 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [19] splines_4.2.2 doParallel_1.0.17 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [21] cachem_1.0.8 rootSolve_1.8.2.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [23] libcoin_1.0-9 knitr_1.42 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [25] jsonlite_1.8.4 doMC_1.3.8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [27] broom_1.0.4 dbplyr_2.2.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [29] compiler_4.2.2 httr_1.4.6 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [31] backports_1.4.1 assertthat_0.2.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [33] Matrix_1.5-1 fastmap_1.1.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [35] gargle_1.4.0 cli_3.6.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [37] htmltools_0.5.5 tools_4.2.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [39] gtable_0.3.3 glue_1.6.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [41] lmom_2.9 rappdirs_0.3.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [43] Rcpp_1.0.10 cellranger_1.1.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [45] vctrs_0.6.1 iterators_1.0.14 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [47] lmtest_0.9-40 xfun_0.39 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [49] rvest_1.0.3 timechange_0.2.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [51] lifecycle_1.0.3 googlesheets4_1.0.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [53] MASS_7.3-58.1 zoo_1.8-12 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [55] scales_1.2.1 hms_1.1.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [57] parallel_4.2.2 sandwich_3.0-2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [59] expm_0.999-7 yaml_2.3.7 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [61] curl_5.0.0 Exact_3.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [63] memoise_2.0.1 stringi_1.7.12 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [65] RSQLite_2.2.19 highr_0.10 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [67] inflection_1.3.6 foreach_1.5.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [69] nortest_1.0-4 e1071_1.7-13 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [71] filelock_1.0.2 boot_1.3-28 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [73] rlang_1.1.1 pkgconfig_2.0.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [75] matrixStats_0.63.0 evaluate_0.21 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [77] lattice_0.20-45 labeling_0.4.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [79] RootsExtremaInflections_1.2.1 bit_4.0.5 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [81] tidyselect_1.2.0 plyr_1.8.8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [83] magrittr_2.0.3 R6_2.5.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [85] DescTools_0.99.49 generics_0.1.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [87] multcompView_0.1-9 multcomp_1.4-23 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [89] DBI_1.1.3 pillar_1.9.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [91] haven_2.5.1 withr_2.5.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [93] survival_3.4-0 modelr_0.1.10 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [95] crayon_1.5.2 rcompanion_2.4.21 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [97] utf8_1.2.3 BiocFileCache_2.6.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [99] tzdb_0.4.0 rmarkdown_2.21 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [101] grid_4.2.2 readxl_1.4.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [103] data.table_1.14.8 blob_1.2.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [105] reprex_2.0.2 digest_0.6.31 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [107] diptest_0.76-0 stats4_4.2.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [109] munsell_0.5.0\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "fffarias/sfo-manuscript", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-applying-a-stochastic-quasi-newton-optimizer-to-least-squares-reverse-time-migration\" class=\"anchor\" href=\"#applying-a-stochastic-quasi-newton-optimizer-to-least-squares-reverse-time-migration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApplying a stochastic quasi-Newton optimizer to least-squares reverse time migration\u003c/h1\u003e\n\u003cp\u003eThis repository demonstrates how to use the \u003ca href=\"https://github.com/Sohl-Dickstein/Sum-of-Functions-Optimizer\"\u003eSum of Functions Optimizer (SFO)\u003c/a\u003e applied to seismic imaging, more specifically to the least-squares reverse time migration (LSRTM) to reproduce the stochastic inversion in the Marmousi model presented in the manuscript entitled \u003cem\u003eApplying a stochastic quasi-Newton optimizer to least-squares reverse time migration\u003c/em\u003e, sent to Computers and Geosciences. Wave propagation is performed using the \u003ca href=\"https://www.devitoproject.org/\" rel=\"nofollow\"\u003edevito framework\u003c/a\u003e, a Python package build to implement optimized stencil computation, capable of executing optimized computational kernels on several computer platforms, including CPUs, GPUs, and clusters thereof. Parallelization of shots across nodes is performed using \u003ca href=\"https://docs.ray.io/en/latest/\" rel=\"nofollow\"\u003eRay\u003c/a\u003e, an open-source project which provides a simple and flexible API for building and running distributed applications.\u003c/p\u003e\n\u003cp\u003eA \u003ca href=\"https://github.com/fffarias/sfo-manuscript/blob/main/Dockerfile/Singularity.def\"\u003esingularity definition file\u003c/a\u003e is provided to create a reproducible container and run this example properly, but you can also find a \u003ca href=\"https://github.com/fffarias/sfo-manuscript/blob/main/requirements.txt\"\u003erequirements file\u003c/a\u003e listing all of the project\u0027s dependencies and follow the instructions below to install them. In a nutshell, this are the full python packages required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edevito\u003c/li\u003e\n\u003cli\u003eray\u003c/li\u003e\n\u003cli\u003esfo\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" href=\"#install-dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall dependencies\u003c/h2\u003e\n\u003cp\u003eTo install \u003ca href=\"https://www.devitoproject.org/\" rel=\"nofollow\"\u003edevito\u003c/a\u003e follow the instructions from \u003ca href=\"https://www.devitoproject.org/devito/download.html\" rel=\"nofollow\"\u003eDevito documentation\u003c/a\u003e. In case the best choice is to use a conda environment, the following steps should work as recommended on the \u003ca href=\"https://www.devitoproject.org/devito/download.html#conda-environment\" rel=\"nofollow\"\u003einstallation web page\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/devitocodes/devito.git\ncd devito\nconda env create -f environment-dev.yml\nsource activate devito\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor it is also possible to install devito using pip installation, in this case simply type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install devito\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Ray package can be easily installed via \u003ccode\u003epip\u003c/code\u003e. You can install the latest official version of Ray as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -U ray\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe SFO optimizer can be used after cloning the original repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttps://github.com/Sohl-Dickstein/Sum-of-Functions-Optimizer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand setting the path where the \u003ccode\u003esfo.py\u003c/code\u003e file is in the python sys\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eimport sys\nsys.path.append(\"./Sum-of-Functions-Optimizer\")\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing the singularity definition file provided, the SFO repository is already cloned and added to the PYTHONPATH variable.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-least-squares-reverse-time-migration-lsrtm\" class=\"anchor\" href=\"#least-squares-reverse-time-migration-lsrtm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLeast-squares reverse time migration (LSRTM)\u003c/h2\u003e\n\u003cp\u003eTo provide subsurface images with better balanced amplitudes, higher resolution and fewer artifacts than standard migration, a least-sqaures migration should be considered. The LSRTM process involves, several wavefield computations of the Born modeling and its adjoint. To calculate these operators, I choose to use the \u003ccode\u003eAcousticWaveSolver\u003c/code\u003e Class from the Devito\u0027s folder \u003ccode\u003eexamples\u003c/code\u003e or a Devito \u003ccode\u003eOperator\u003c/code\u003e, although it is also available on Devito, the operators needed to calculate the LSRTM in a medium with TTI anisotropy. In the python script available \u003ca href=\"https://github.com/fffarias/sfo-manuscript/blob/main/lsm.py\"\u003ehere\u003c/a\u003e, there are all the necessary steps to perform the LSRTM, since besides performing the forward linearized modeling and its adjoint, some previous actions need to be defined, such as creating an object that contains the velocity model and the acquisition geometry, for example. All these steps in different contexts are also explored in the \u003ca href=\"https://github.com/devitocodes/devito/tree/master/examples/seismic/tutorials\"\u003etutorials available\u003c/a\u003e on the Devito section. Thus, the sequence adopted in the main function involves:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreating velocity and reflectivity models associated with squared slowness.\u003c/li\u003e\n\u003cli\u003eDefining the acquisition geometry.\u003c/li\u003e\n\u003cli\u003eForward modeling for all the shots in parallel using ray, to generate the \"observed data\".\u003c/li\u003e\n\u003cli\u003eRunning the SFO optimizer with the help of a function that returns objective function value and gradient for a subset of shots (batch size).\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-multiple-cpus-or-gpus-using-ray\" class=\"anchor\" href=\"#running-on-multiple-cpus-or-gpus-using-ray\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on multiple CPUs or GPUs using Ray\u003c/h2\u003e\n\u003cp\u003eRunning LSRTM sequentially, even for a 2D example such as the Marmousi model, can be quite tedious, so given that there are resources available, the ideal would be to distribute the wavefield calculations across the available CPUs or GPUs. To accomplish this using Ray on a single machine, it is enough to start Ray by adding \u003ccode\u003eray.init()\u003c/code\u003e to the code. By simply doing that, Ray will then be able to utilize all cores of your machine.\u003c/p\u003e\n\u003cp\u003eIn order for the wave propagations to be performed on the GPU, you need to compile Devito in a slightly more sophisticated way following the \u003ca href=\"https://github.com/devitocodes/devito/wiki/Using-Devito-on-GPUs-with-NVIDIA-HPC-SDK\"\u003einstructions provided\u003c/a\u003e, or use the appropriate \u003ca href=\"https://github.com/fffarias/sfo-manuscript/blob/main/Dockerfile/Singularity_nvidia.def\"\u003esingularity recipe\u003c/a\u003e to use Devito on GPUs with NVIDIA HPC SDK.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython lsm.py --bs=20\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003ebs\u003c/code\u003e controls the batch size. Other variables can be controlled from the command line:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSymbol\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eso\u003c/td\u003e\n\u003ctd\u003eDiscretization order of the spatial derivatives of the wave equation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enbl\u003c/td\u003e\n\u003ctd\u003eNumber of absorbing boundary points around the domain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef0\u003c/td\u003e\n\u003ctd\u003eSource peak frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etn\u003c/td\u003e\n\u003ctd\u003eTotal simulation time\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ensrc\u003c/td\u003e\n\u003ctd\u003eNumber of sources\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enrec\u003c/td\u003e\n\u003ctd\u003eNumber of receivers\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enpasses\u003c/td\u003e\n\u003ctd\u003eNumber of passes through the entire data\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebs\u003c/td\u003e\n\u003ctd\u003eBatch size\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs implemented, the output of the lsm.py script writes the inverted reflectivity to disk in a binary file and also generates a graph with the objective function values for each mini-batch.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-special-situations\" class=\"anchor\" href=\"#special-situations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecial situations\u003c/h2\u003e\n\u003cp\u003eIf you have any question that you couldn\u0027t find the answer here, please email \u003ca href=\"mailto:fernanda.farias8@gmail.com\"\u003efernanda.farias8@gmail.com\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-see-also\" class=\"anchor\" href=\"#see-also\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSee also\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSum of Functions Optimizer arXiv paper: \u003ca href=\"https://arxiv.org/abs/1311.2115\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1311.2115\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 5, - "subscribers_count": 2, - "topics": [ - "bioinformatics", - "genetic-interactions", - "r" - ], - "updated_at": 1687497946.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1652385399.0 }, { "data_format": 2, - "description": "A collection of scripts to run variant aggregation tests from whole-genome sequencing data.", + "description": "automated kraken database build using nextflow DSL2", "filenames": [ - "Singularity_via_docker" + "singularity/Singularity.autoDatabase_krona", + "singularity/Singularity.autoDatabase_kraken2", + "singularity/Singularity.autoDatabase_pythonenv", + "singularity/Singularity.autoDatabase_mash" ], - "full_name": "hmgu-itg/burden_testing", - "latest_release": "v1.5.3", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-mummy-the-wrapped-monster\" class=\"anchor\" aria-hidden=\"true\" href=\"#mummy-the-wrapped-monster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMummy (the wrapped MONSTER)\u003c/h1\u003e\n\u003cp\u003eThis is a pipeline to run genome-wide burdent tests using sequencing data. Head over to \u003ca href=\"https://github.com/hmgu-itg/burden_testing/wiki\"\u003ethe wiki\u003c/a\u003e for detailed instructions on how to run it.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding containers\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eVERSION=1.5.4 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Change this appropriately \u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build the docker image\u003c/span\u003e\nsudo docker build \\\n --build-arg BUILD_DATE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate -u +\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e%Y-%m-%dT%H:%M:%SZ\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --build-arg VCS_REF=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003egit rev-parse HEAD\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --build-arg VERSION=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --tag burden_testing:\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --tag burden_testing:latest \\\n \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\nsudo SINGULARITY_NOHTTPS=1 singularity build burden_testing_\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e docker-daemon://burden_testing:\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "annacprice/autodatabase", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-autodatabase\" class=\"anchor\" aria-hidden=\"true\" href=\"#autodatabase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eautodatabase\u003c/h1\u003e\n\u003cp\u003eAutomated build of a Kraken2 database using Nextflow DSL2. Requires Nextflow version\u0026gt;= 20.01.0 and either Docker or Singularity.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eThere are seven stages to the workflow:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownloading the NCBI taxonomy (autoDatabase_getTaxonomy)\u003c/li\u003e\n\u003cli\u003eAdding the taxonomic ID to the sequence IDs and the filenames (autoDatabase_addTaxon)\u003c/li\u003e\n\u003cli\u003eCreating a mash matrix for each taxon (autoDatabase_mash)\u003c/li\u003e\n\u003cli\u003eUsing the mash matrix to select high quality assemblies (autoDatabase_qc)\u003c/li\u003e\n\u003cli\u003eCreating a channel containing the high quality assemblies (autoDatabase_cleanFasta)\u003c/li\u003e\n\u003cli\u003eBuilding the Kraken2 database (autoDatabase_kraken2Build)\u003c/li\u003e\n\u003cli\u003eCreating a Krona chart showing the composition of the Kraken2 database (autoDatabase_krona)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe expected input for autodatabase are fasta files. They should sorted into directories for each taxon\nwhere the directory name is the taxon name with spaces replaced with underscores. E.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMycobacterium_avium Mycobacterium_bovis Mycobacterium_tuberculosis_complex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe workflow will scrape the name of the taxon from the directory name and use it look up the taxonomic ID.\u003c/p\u003e\n\u003cp\u003eThere are five global parameters needed to run autodatabase which can be set in \u003ccode\u003enextflow.config\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.addFasta\u003c/strong\u003e\nThe directory containing the assemblies to be added to the database. Should consist of sub-directories where the fastas\nare sorted into named directories for each taxon (see above)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.newDatabase\u003c/strong\u003e\nThe directory where the results of the workflow will be saved. The default is \u003ccode\u003e${baseDir}/results\u003c/code\u003e. On completion of the pipeline, the output kraken database .k2d files can be found in \u003ccode\u003e${params.newDatabase}/krakenBuild_autoDatabase_kraken2Build\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.previousDatabase\u003c/strong\u003e\nAutodatabase can build on top of the fasta files from a previous database build. The fasta files from a completed build are found in \u003ccode\u003e${params.newDatabase}/selectFasta_autoDatabase_cleanFasta/assemblies\u003c/code\u003e\nIf there is no previous database build then set to \u003ccode\u003enull\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.ncbiDate\u003c/strong\u003e\nDate stamp of the NCBI taxonomy you wish to use to build the database. Takes the form YYYY-MM-DD\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.modeRange\u003c/strong\u003e\nFor each taxon, autodatabase builds a mash distance matrix, finds the average mash distance for each assembly, and then finds the mode of the average mash distances to 2 s.f. The assemblies that have an average distance that is within \u003ccode\u003eparams.modeRange\u003c/code\u003e of the mode will be used to build the database. Default value is 0.1, i.e. accepts mash distances within the range: mode\u00b110%\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe pipeline requires either Docker or Singularity to run. Scripts for building the containers needed to run the pipeline can be found in \u003ccode\u003edocker\u003c/code\u003e and \u003ccode\u003esingularity\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo run the pipeline:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile [docker, singularity]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe workflow for the pipeline can be found below, for more information consult the \u003ca href=\"https://github.com/annacprice/autodatabase/wiki\"\u003ewiki\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/annacprice/autodatabase/blob/master/workflow.png\"\u003e\u003cimg height=\"600\" src=\"https://github.com/annacprice/autodatabase/raw/master/workflow.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1678691130.0 + "updated_at": 1648654598.0 }, { "data_format": 2, @@ -34934,528 +34691,898 @@ var data = }, { "data_format": 2, - "description": "automated kraken database build using nextflow DSL2", + "description": "A collection of scripts to run variant aggregation tests from whole-genome sequencing data.", "filenames": [ - "singularity/Singularity.autoDatabase_krona", - "singularity/Singularity.autoDatabase_kraken2", - "singularity/Singularity.autoDatabase_pythonenv", - "singularity/Singularity.autoDatabase_mash" + "Singularity_via_docker" ], - "full_name": "annacprice/autodatabase", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-autodatabase\" class=\"anchor\" aria-hidden=\"true\" href=\"#autodatabase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eautodatabase\u003c/h1\u003e\n\u003cp\u003eAutomated build of a Kraken2 database using Nextflow DSL2. Requires Nextflow version\u0026gt;= 20.01.0 and either Docker or Singularity.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eThere are seven stages to the workflow:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownloading the NCBI taxonomy (autoDatabase_getTaxonomy)\u003c/li\u003e\n\u003cli\u003eAdding the taxonomic ID to the sequence IDs and the filenames (autoDatabase_addTaxon)\u003c/li\u003e\n\u003cli\u003eCreating a mash matrix for each taxon (autoDatabase_mash)\u003c/li\u003e\n\u003cli\u003eUsing the mash matrix to select high quality assemblies (autoDatabase_qc)\u003c/li\u003e\n\u003cli\u003eCreating a channel containing the high quality assemblies (autoDatabase_cleanFasta)\u003c/li\u003e\n\u003cli\u003eBuilding the Kraken2 database (autoDatabase_kraken2Build)\u003c/li\u003e\n\u003cli\u003eCreating a Krona chart showing the composition of the Kraken2 database (autoDatabase_krona)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe expected input for autodatabase are fasta files. They should sorted into directories for each taxon\nwhere the directory name is the taxon name with spaces replaced with underscores. E.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMycobacterium_avium Mycobacterium_bovis Mycobacterium_tuberculosis_complex\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe workflow will scrape the name of the taxon from the directory name and use it look up the taxonomic ID.\u003c/p\u003e\n\u003cp\u003eThere are five global parameters needed to run autodatabase which can be set in \u003ccode\u003enextflow.config\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.addFasta\u003c/strong\u003e\nThe directory containing the assemblies to be added to the database. Should consist of sub-directories where the fastas\nare sorted into named directories for each taxon (see above)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.newDatabase\u003c/strong\u003e\nThe directory where the results of the workflow will be saved. The default is \u003ccode\u003e${baseDir}/results\u003c/code\u003e. On completion of the pipeline, the output kraken database .k2d files can be found in \u003ccode\u003e${params.newDatabase}/krakenBuild_autoDatabase_kraken2Build\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.previousDatabase\u003c/strong\u003e\nAutodatabase can build on top of the fasta files from a previous database build. The fasta files from a completed build are found in \u003ccode\u003e${params.newDatabase}/selectFasta_autoDatabase_cleanFasta/assemblies\u003c/code\u003e\nIf there is no previous database build then set to \u003ccode\u003enull\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.ncbiDate\u003c/strong\u003e\nDate stamp of the NCBI taxonomy you wish to use to build the database. Takes the form YYYY-MM-DD\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eparams.modeRange\u003c/strong\u003e\nFor each taxon, autodatabase builds a mash distance matrix, finds the average mash distance for each assembly, and then finds the mode of the average mash distances to 2 s.f. The assemblies that have an average distance that is within \u003ccode\u003eparams.modeRange\u003c/code\u003e of the mode will be used to build the database. Default value is 0.1, i.e. accepts mash distances within the range: mode\u00b110%\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe pipeline requires either Docker or Singularity to run. Scripts for building the containers needed to run the pipeline can be found in \u003ccode\u003edocker\u003c/code\u003e and \u003ccode\u003esingularity\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo run the pipeline:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile [docker, singularity]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe workflow for the pipeline can be found below, for more information consult the \u003ca href=\"https://github.com/annacprice/autodatabase/wiki\"\u003ewiki\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflow\u003c/h2\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/annacprice/autodatabase/blob/master/workflow.png\"\u003e\u003cimg height=\"600\" src=\"https://github.com/annacprice/autodatabase/raw/master/workflow.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "hmgu-itg/burden_testing", + "latest_release": "v1.5.3", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-mummy-the-wrapped-monster\" class=\"anchor\" aria-hidden=\"true\" href=\"#mummy-the-wrapped-monster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMummy (the wrapped MONSTER)\u003c/h1\u003e\n\u003cp\u003eThis is a pipeline to run genome-wide burdent tests using sequencing data. Head over to \u003ca href=\"https://github.com/hmgu-itg/burden_testing/wiki\"\u003ethe wiki\u003c/a\u003e for detailed instructions on how to run it.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-building-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#building-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding containers\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eVERSION=1.5.4 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Change this appropriately \u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build the docker image\u003c/span\u003e\nsudo docker build \\\n --build-arg BUILD_DATE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003edate -u +\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e%Y-%m-%dT%H:%M:%SZ\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --build-arg VCS_REF=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003egit rev-parse HEAD\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --build-arg VERSION=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --tag burden_testing:\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --tag burden_testing:latest \\\n \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\nsudo SINGULARITY_NOHTTPS=1 singularity build burden_testing_\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e docker-daemon://burden_testing:\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 5, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1648654598.0 + "updated_at": 1678691130.0 }, { "data_format": 2, - "description": "Python script for the numerical example in the manuscript \"Applying a stochastic quasi-Newton optimizer to least-squares reverse time migration\" submitted to Computers and Geosciences journal on 05/28/2021.", + "description": "GRETTA (Genetic inteRaction and EssenTiality neTwork mApper): An R package for mapping genetic interaction and essentiality networks", "filenames": [ - "Dockerfile/Singularity.def", - "Dockerfile/Singularity_nvidia.def" + "Singularity/Singularity.GRETTA.def" ], - "full_name": "fffarias/sfo-manuscript", + "full_name": "ytakemon/GRETTA", + "latest_release": "v0.99.0", + "readme": "\n\n\n\u003cp\u003e\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/400c4e52df43f6a0ab8a89b74b1a78d1a64da56a7848b9110c9d2991bb7c3105/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://lifecycle.r-lib.org/articles/stages.html#stable\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d7aedfa6c0fd00737083172bffb7ae9b253b54fae707524fcb503a1ce9c48a66/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6966656379636c652d737461626c652d627269676874677265656e2e737667\" alt=\"Lifecycle: stable\" data-canonical-src=\"https://img.shields.io/badge/lifecycle-stable-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/374398121\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc696cee4b750b415f3666ead55ca691783e528199724ce6e40b14c67836ce80/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3337343339383132312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/374398121.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./GRETTA_hex_logo-02.png\"\u003e\u003cimg src=\"./GRETTA_hex_logo-02.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h1\u003e\n\u003cp\u003eGenetic inteRaction and EssenTiality mApper (GRETTA) is an R package\nthat leverages data generated by the \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eCancer Dependency Map (DepMap)\nproject\u003c/a\u003e to perform in-silico genetic\nknockout screens and map essentiality networks. A manuscript describing\nthis tool is available at \u003ca href=\"https://doi.org/10.1093/bioinformatics/btad381\" rel=\"nofollow\"\u003ebioinformatics (Takemon, Y. and Marra, MA.,\n2023)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe DepMap data used in this tutorial is version 22Q2. This version\nalong with all versions provided in this repository were downloaded\nthrough the DepMap data portal, which was distributed and used under the\nterms and conditions of \u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC Attribution 4.0\nlicense\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-maintainer\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#maintainer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainer\u003c/h2\u003e\n\u003cp\u003eThis repository is maintained by \u003ca href=\"https://github.com/ytakemon\"\u003eYuka\nTakemon\u003c/a\u003e, a PhD candidate in \u003ca href=\"https://www.bcgsc.ca/labs/marra-lab\" rel=\"nofollow\"\u003eDr.\u00a0Marco\nMarra\u003c/a\u003e\u2019s laboratory at \u003ca href=\"https://www.bcgsc.ca/\" rel=\"nofollow\"\u003eCanada\u2019s\nMichael Smith Genome Sciences Centre\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-citations\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#citations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitations\u003c/h3\u003e\n\u003cp\u003ePlease cite the manuscript describing GRETTA on \u003ca href=\"https://doi.org/10.1093/bioinformatics/btad381\" rel=\"nofollow\"\u003ebioinformatics\n(Takemon, Y. and Marra, MA.,\n2023)\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYuka Takemon, Marco A Marra, GRETTA: an R package for mapping in silico\ngenetic interaction and essentiality networks, Bioinformatics, Volume\n39, Issue 6, June 2023, btad381,\n\u003ca href=\"https://doi.org/10.1093/bioinformatics/btad381\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btad381\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions\u003c/h2\u003e\n\u003cp\u003ePlease check the \u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions\"\u003eFAQ\nsection\u003c/a\u003e\nfor additional information and if you cannot find your answer there or\nhave a request please submit an\n\u003ca href=\"https://github.com/ytakemon/GRETTA/issues\"\u003eissue\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGRETTA is supported and compatible for R versions \u0026gt;= 4.2.0.\u003c/li\u003e\n\u003cli\u003e12G of space to store one DepMap data set with and an additional 11G\nof temporary space to for .tar.gz prior to extraction.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h1\u003e\n\u003cp\u003eYou can install the GRETTA package from \u003ca href=\"https://github.com\"\u003eGitHub\u003c/a\u003e\nwith:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall.packages(c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edevtools\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edplyr\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eforcats\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eggplot2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eytakemon/GRETTA\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDepMap 22Q2 data and the data documentation files are provided above and\ncan be extracted directly in terminal using the following bash code (not\nin R/RStudio). For other DepMap data versions please refer to the \u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions#q-how-to-download-and-use-other-versions-of-depmap-data\"\u003eFAQ\nsection\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Make a new directory/folder called GRETTA_project and go into directory\u003c/span\u003e\nmkdir GRETTA_project\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e GRETTA_project\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download data from the web\u003c/span\u003e\nwget https://www.bcgsc.ca/downloads/ytakemon/GRETTA/22Q2/GRETTA_DepMap_22Q2_data.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract data and data documentation\u003c/span\u003e\ntar -zxvf GRETTA_DepMap_22Q2_data.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA singularity container has also been provided and instructions can be\nfound\n\u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions#q-how-to-run-singularity\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-additional-depmap-versions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#additional-depmap-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional DepMap versions\u003c/h3\u003e\n\u003cp\u003eIn this example we use DepMap\u2019s 2022 data release (22Q2). However, we\nalso provide previous data released in 2020 (v20Q1) and 2021 (v21Q4),\nwhich are available at\n:\u003ccode\u003ehttps://www.bcgsc.ca/downloads/ytakemon/GRETTA/\u003c/code\u003e. We are hoping to\nmake new data sets available as the are released by DepMap.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-workflows\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorkflows\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-genetic-interaction-mapping\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#genetic-interaction-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGenetic interaction mapping\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ccode\u003eGRETTA\u003c/code\u003e and download accompanying data.\u003c/li\u003e\n\u003cli\u003eSelect mutant cell lines that carry mutations in the gene of\ninterest and control cell lines.\n\u003cul\u003e\n\u003cli\u003e(\u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions#q-how-can-context-specific-genetic-screens-or-essentiality-network-analyses-be-performed\"\u003eoptional\nspecifications\u003c/a\u003e)\ncan be used to select cell lines based on disease type, disease\nsubtype, or amino acid change.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDetermine differential expression between mutant and control cell\nline groups.\n\u003cul\u003e\n\u003cli\u003e(optional but recommended).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePerform \u003cem\u003ein silico\u003c/em\u003e genetic screen.\u003c/li\u003e\n\u003cli\u003eVisualize results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-co-essential-network-mapping\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#co-essential-network-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCo-essential network mapping\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eInstall \u003ccode\u003eGRETTA\u003c/code\u003e and download accompanying data.\u003c/li\u003e\n\u003cli\u003eRun correlation coefficient analysis.\n\u003cul\u003e\n\u003cli\u003e(\u003ca href=\"https://github.com/ytakemon/GRETTA/wiki/Frequently-Asked-Questions#q-how-can-context-specific-genetic-screens-or-essentiality-network-analyses-be-performed:~:text=For%20the%20essentiality%20network%20analysis%2C%20context%2Dspecific%20cell%20lines%20can%20be%20selected%20in%20two%20ways%3A\"\u003eoptional\nspecifications\u003c/a\u003e)\ncan be used to perform analysis on cell lines of a specific\ndisease type(s).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCalculate inflection points of negative/positive curve to determine\na threshold.\u003c/li\u003e\n\u003cli\u003eApply threshold.\u003c/li\u003e\n\u003cli\u003eVisualize results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-example-identifying-arid1a-genetic-interactions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#example-identifying-arid1a-genetic-interactions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Identifying \u003cem\u003eARID1A\u003c/em\u003e genetic interactions\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003eARID1A\u003c/em\u003e encodes a member of the chromatin remodeling SWItch/Sucrose\nNon-Fermentable (SWI/SNF) complex and is a frequently mutated gene in\ncancer. It is known that \u003cem\u003eARID1A\u003c/em\u003e and its homolog, \u003cem\u003eARID1B\u003c/em\u003e, are\nsynthetic lethal to one another: The dual loss of ARID1A and its\nhomolog, ARID1B, in a cell is lethal; however, the loss of either gene\nalone is not (\u003ca href=\"https://doi.org/10.1038/nm.3480\" rel=\"nofollow\"\u003eHelming et al., 2014\u003c/a\u003e).\nThis example will demonstrate how we can identify synthetic lethal\ninteractors of \u003cem\u003eARID1A\u003c/em\u003e using \u003ccode\u003eGRETTA\u003c/code\u003e and predict this known\ninteraction.\u003c/p\u003e\n\u003cp\u003eFor this example you will need to call the following libraries. If you\nthey are not installed yet use \u003ccode\u003einstall.packages()\u003c/code\u003e (eg.\n\u003ccode\u003einstall.packages(\"dplyr\")\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Load library\u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003etidyverse\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2500\u2500 Attaching packages \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse 1.3.2 \u2500\u2500\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 ggplot2 3.4.1 \u2714 purrr 1.0.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 tibble 3.2.1 \u2714 dplyr 1.1.1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 tidyr 1.3.0 \u2714 stringr 1.5.0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2714 readr 2.1.4 \u2714 forcats 1.0.0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2500\u2500 Conflicts \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 tidyverse_conflicts() \u2500\u2500\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2716 dplyr::filter() masks stats::filter()\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u2716 dplyr::lag() masks stats::lag()\u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003eGRETTA\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; _______ .______ _______ .___________.___________. ___ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; / _____|| _ \\ | ____|| | | / \\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; | | __ | |_) | | |__ `---| |----`---| |----` / ^ \\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; | | |_ | | / | __| | | | | / /_\\ \\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; | |__| | | |\\ \\----.| |____ | | | | / _____ \\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \\______| | _| `._____||_______| |__| |__| /__/ \\__\\ \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Welcome to GRETTA! The version loaded is: 0.99.2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; The latest DepMap dataset accompanying this package is v22Q2. \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Please refer to our tutorial on GitHub for loading DepMap data and details: https://github.com/ytakemon/GRETTA\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download-example-data\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#download-example-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload example data\u003c/h2\u003e\n\u003cp\u003eA small data set has been created for this tutorial and can be\ndownloaded using the following code.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003epath\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e getwd()\ndownload_example_data(\u003cspan class=\"pl-smi\"\u003epath\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Data saved to: /projects/marralab/ytakemon_prj/DepMap/GRETTA/GRETTA_example/\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, assign variable that point to where the \u003ccode\u003e.rda\u003c/code\u003e files are stored\nand where result files should go.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e paste0(\u003cspan class=\"pl-smi\"\u003epath\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/GRETTA_example/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e paste0(\u003cspan class=\"pl-smi\"\u003epath\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/GRETTA_example_output/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-exploring-cell-lines\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#exploring-cell-lines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExploring cell lines\u003c/h2\u003e\n\u003cp\u003eOne way to explore cell lines that are available in DepMap is through\ntheir \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eportal\u003c/a\u003e. However, there are some\nsimple built-in methods in GRETTA to provide users with a way to glimpse\nthe data using the series of \u003ccode\u003elist_available\u003c/code\u003e functions:\n\u003ccode\u003elist_mutations()\u003c/code\u003e, \u003ccode\u003elist_cancer_types()\u003c/code\u003e, \u003ccode\u003elist_cancer_subtypes()\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCurrent DepMap data used by default is version 22Q2, which contains\nwhole-genome sequencing or whole-exome sequencing annotations for \u003ccode\u003e1771\u003c/code\u003e\ncancer cell lines (\u003ccode\u003e1406\u003c/code\u003e cell lines with RNA-seq data, \u003ccode\u003e375\u003c/code\u003e cell lines\nwith quantitative proteomics data, and \u003ccode\u003e1086\u003c/code\u003e cell lines with\nCRISPR-Cas9 knockout screen data)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Find ARID1A hotspot mutations detected in all cell lines\u003c/span\u003e\nlist_mutations(\u003cspan class=\"pl-v\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eis_hotspot\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e) \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# List all available cancer types\u003c/span\u003e\nlist_cancer_types(\u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Kidney Cancer\" \"Leukemia\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] \"Lung Cancer\" \"Non-Cancerous\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] \"Sarcoma\" \"Lymphoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] \"Colon/Colorectal Cancer\" \"Pancreatic Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] \"Gastric Cancer\" \"Rhabdoid\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] \"Endometrial/Uterine Cancer\" \"Esophageal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [13] \"Breast Cancer\" \"Brain Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [15] \"Ovarian Cancer\" \"Bone Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [17] \"Myeloma\" \"Head and Neck Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [19] \"Bladder Cancer\" \"Skin Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [21] \"Bile Duct Cancer\" \"Prostate Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [23] \"Cervical Cancer\" \"Thyroid Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [25] \"Neuroblastoma\" \"Eye Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [27] \"Liposarcoma\" \"Gallbladder Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [29] \"Teratoma\" \"Unknown\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [31] \"Liver Cancer\" \"Adrenal Cancer\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [33] \"Embryonal Cancer\"\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# List all available cancer subtypes\u003c/span\u003e\nlist_cancer_subtypes(\u003cspan class=\"pl-v\"\u003einput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eLung Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] \"Non-Small Cell Lung Cancer (NSCLC), Adenocarcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [2] \"Small Cell Lung Cancer (SCLC)\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] \"Non-Small Cell Lung Cancer (NSCLC), Squamous Cell Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [4] \"Mesothelioma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] \"Non-Small Cell Lung Cancer (NSCLC), Large Cell Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [6] NA \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] \"Non-Small Cell Lung Cancer (NSCLC), unspecified\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [8] \"Non-Small Cell Lung Cancer (NSCLC), Adenosquamous Carcinoma\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] \"Carcinoid\" \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [10] \"Non-Small Cell Lung Cancer (NSCLC), Mucoepidermoid Carcinoma\"\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] \"Carcinoma\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-selecting-mutant-and-control-cell-line-groups\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#selecting-mutant-and-control-cell-line-groups\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSelecting mutant and control cell line groups\u003c/h2\u003e\n\u003cp\u003eAs default \u003ccode\u003eselect_cell_lines()\u003c/code\u003e will identify cancer cell lines with\nloss-of-function alterations in the gene specified and group them into\nsix different groups.\u003c/p\u003e\n\u003cp\u003eLoss-of-function alterations include variants that are annotated as:\n\u003ccode\u003e\"Nonsense_Mutation\", \"Frame_Shift_Ins\", \"Splice_Site\", \"De_novo_Start_OutOfFrame\", \"Frame_Shift_Del\", \"Start_Codon_SNP\", \"Start_Codon_Del\",\u003c/code\u003e\nand \u003ccode\u003e\"Start_Codon_Ins\"\u003c/code\u003e. Copy number alterations are also taken into\nconsideration and group as \u003ccode\u003e\"Deep_del\", \"Loss\", \"Neutral\",\u003c/code\u003e or\n\u003ccode\u003e\"Amplified\"\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe cell line groups assigned by default are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eControl\u003c/code\u003e cell lines do not harbor any single nucleotide variations\n(SNVs) or insertions and deletions (InDels) with a neutral copy\nnumber (CN).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHomDel\u003c/code\u003e cell lines harbor one or more homozygous deleterious SNVs\nor have deep CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eT-HetDel\u003c/code\u003e cell lines harbor two or more heterozygous deleterious\nSNVs/InDels with neutral or CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eHetDel\u003c/code\u003e cell lines harbor one heterozygous deleterious SNV/InDel\nwith neutral CN, or no SNV/InDel with CN loss.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAmplified\u003c/code\u003e cell lines harbor no SNVs/InDels with increased CN.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eOthers\u003c/code\u003e cell lines harbor deleterious SNVs with increased CN.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_cell_lines(\u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Selecting mutant groups for: ARID1A in all cancer cell lines\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Show number of cell lines in each group \u003c/span\u003e\ncount(\u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 5 \u00d7 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Group n\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_HetDel 61\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_HomDel 23\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_T-HetDel 30\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 Control 906\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 Others 66\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional-cell-line-filters\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#optional-cell-line-filters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional cell line filters\u003c/h3\u003e\n\u003cp\u003eThere are several additional filters that can be combined together to\nnarrow down your search. These\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003einput_aa_change\u003c/code\u003e - by amino acid change (eg. \u201cp.Q515*\u201c).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003einput_disease\u003c/code\u003e - by disease type (eg. \u201cPancreatic Cancer\u201d)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003einput_disease_subtype\u003c/code\u003e - by disease subtype (eg. \u201cDuctal\nAdenosquamous Carcinoma\u201d)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Find pancreatic cancer cell lines with ARID1A mutations\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_pancr_groups\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_cell_lines(\u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003einput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePancreatic Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Selecting mutant groups for: ARID1A in Pancreatic Cancer, cell lines\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Show number of cell lines in each group \u003c/span\u003e\ncount(\u003cspan class=\"pl-smi\"\u003eARID1A_pancr_groups\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 4 \u00d7 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Group n\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_HetDel 5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_HomDel 4\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 Control 36\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 Others 2\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-check-for-differential-expression\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#check-for-differential-expression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheck for differential expression\u003c/h2\u003e\n\u003cp\u003eOf the three mutant cancer cell line groups \u003ccode\u003eARID1A_HomDel\u003c/code\u003e,\n\u003ccode\u003eARID1A_T-HetDel\u003c/code\u003e, and \u003ccode\u003eARID1A_HetDel\u003c/code\u003e, cancer cell lines with\n\u003ccode\u003eARID1A_HomDel\u003c/code\u003e mutations are most likely to result in a loss or reduced\nexpression of \u003cem\u003eARID1A\u003c/em\u003e. Therefore, we want to check whether cell lines\nin \u003ccode\u003eARID1A_HomDel\u003c/code\u003e mutant group have significantly less \u003cem\u003eARID1A\u003c/em\u003e RNA or\nprotein expression compared to control cell lines.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Select only HomDel and Control cell lines\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_groups_subset\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A_HomDel\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Get RNA expression \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e extract_rna(\n \u003cspan class=\"pl-v\"\u003einput_samples\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups_subset\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003einput_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Following sample did not contain RNA data: ACH-000047, ACH-000426, ACH-000658, ACH-000979, ACH-001039, ACH-001063, ACH-001065, ACH-001107, ACH-001126, ACH-001137, ACH-001205, ACH-001212, ACH-001227, ACH-001331, ACH-001544, ACH-001606, ACH-001639, ACH-001675, ACH-001955, ACH-001956, ACH-001957, ACH-002083, ACH-002106, ACH-002109, ACH-002110, ACH-002114, ACH-002116, ACH-002119, ACH-002140, ACH-002141, ACH-002143, ACH-002150, ACH-002156, ACH-002160, ACH-002161, ACH-002179, ACH-002181, ACH-002186, ACH-002189, ACH-002198, ACH-002202, ACH-002210, ACH-002212, ACH-002217, ACH-002228, ACH-002229, ACH-002230, ACH-002233, ACH-002234, ACH-002239, ACH-002243, ACH-002247, ACH-002249, ACH-002250, ACH-002257, ACH-002261, ACH-002263, ACH-002265, ACH-002269, ACH-002278, ACH-002280, ACH-002282, ACH-002283, ACH-002284, ACH-002285, ACH-002294, ACH-002295, ACH-002296, ACH-002297, ACH-002298, ACH-002304, ACH-002305, ACH-002399, ACH-002874, ACH-002875\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNot all cell lines contain RNA and/or protein expression profiles, and\nnot all proteins were detected by mass spectrometer. (Details on data\ngeneration can be found on the \u003ca href=\"https://depmap.org/portal/\" rel=\"nofollow\"\u003eDepMap\nsite\u003c/a\u003e.)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Get protein expression\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_prot_expr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e extract_prot(\n \u003cspan class=\"pl-v\"\u003einput_samples\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups_subset\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003einput_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Produces an error message since ARID1A protein data is not available\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing Welch\u2019s t-test, we can check to see whether \u003cem\u003eARID1A\u003c/em\u003e RNA\nexpression (in TPM) is significantly reduced in \u003ccode\u003eARID1A_HomDel\u003c/code\u003e cell\nlines compared to \u003ccode\u003eControls\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Append groups and test differential expression\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e left_join(\n \u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e,\n \u003cspan class=\"pl-smi\"\u003eARID1A_groups_subset\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% select(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e%\n mutate(\u003cspan class=\"pl-v\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e fct_relevel(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e show Control group first\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Joining with `by = join_by(DepMap_ID)`\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# T-test \u003c/span\u003e\nt.test(\u003cspan class=\"pl-smi\"\u003eARID1A_8289\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Welch Two Sample t-test\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; data: ARID1A_8289 by Group\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; t = 2.5764, df = 22.873, p-value = 0.01692\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; alternative hypothesis: true difference in means between group Control and group ARID1A_HomDel is not equal to 0\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 95 percent confidence interval:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 0.1146094 1.0498810\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; sample estimates:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; mean in group Control mean in group ARID1A_HomDel \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4.635784 4.053539\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# plot \u003c/span\u003e\nggplot(\u003cspan class=\"pl-smi\"\u003eARID1A_rna_expr\u003c/span\u003e, aes(\u003cspan class=\"pl-v\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_8289\u003c/span\u003e)) \u003cspan class=\"pl-k\"\u003e+\u003c/span\u003e\n geom_boxplot()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-Check_expression_rna_stats-1.png\"\u003e\u003cimg src=\"man/figures/README-Check_expression_rna_stats-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-perform-genome-wide-in-silico-genetic-screen\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#perform-genome-wide-in-silico-genetic-screen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePerform genome-wide \u003cem\u003ein silico\u003c/em\u003e genetic screen\u003c/h2\u003e\n\u003cp\u003eAfter determining cell lines in the \u003ccode\u003eARID1A_HomDel\u003c/code\u003e group has\nstatistically significant reduction in RNA expression compared to\n\u003ccode\u003eControl\u003c/code\u003e cell lines, the next step is to perform a \u003cem\u003ein silico\u003c/em\u003e genetic\nscreen using \u003ccode\u003escreen_results()\u003c/code\u003e. This uses the dependency probabilities\n(or \u003cstrong\u003e\u201clethality probabilities\u201d\u003c/strong\u003e) generated from DepMap\u2019s genome-wide\nCRISPR-Cas9 knockout screen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLethality probabilities\u003c/strong\u003e range from 0.0 to 1.0 and is quantified for\neach gene knock out in every cancer cell line screened (There are 18,334\ngenes targeted in 739 cancer cell lines). A gene knock out with a\nlethality probability of 0.0 indicates a non-essential for the cell\nline, and a gene knock out with a 1.0 indicates an essential gene (ie.\nvery lethal). Details can be found in \u003ca href=\"https://doi.org/10.1038/ng.3984\" rel=\"nofollow\"\u003eMeyers, R., et al.,\n2017\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAt its core, \u003ccode\u003escreen_results()\u003c/code\u003e performs multiple Mann-Whitney U tests,\ncomparing lethality probabilities of each targeted gene between mutant\nand control groups. This generates a data frame with the following\ncolumns:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGeneName_ID\u003c/code\u003e - Hugo symbol with NCBI gene ID\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGeneNames\u003c/code\u003e - Hugo symbol\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e_median, _mean, _sd, _iqr\u003c/code\u003e - Control and mutant group\u2019s median,\nmean, standard deviation (sd), and interquartile range (iqr) of\ndependency probabilities. Dependency probabilities range from zero\nto one, where one indicates a essential gene (ie. KO of gene was\nlethal) and zero indicates a non-essential gene (KO of gene was not\nlethal)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ePval\u003c/code\u003e - P-value from Mann Whitney U test between control and mutant\ngroups.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdj_pval\u003c/code\u003e - BH-adjusted P-value.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog2FC_by_median\u003c/code\u003e - Log2 normalized median fold change of\ndependency probabilities (mutant / control).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog2FC_by_mean\u003c/code\u003e - Log2 normalized mean fold change of dependency\nprobabilities (mutant / control).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCliffDelta\u003c/code\u003e - Cliff\u2019s delta non-parametric effect size between\nmutant and control dependency probabilities. Ranges between -1 to 1.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edip_pval\u003c/code\u003e - Hartigan\u2019s dip test p-value. Tests whether distribution\nof mutant dependency probability is unimodel. If dip test is\nrejected (p-value \u0026lt; 0.05), this indicates that there is a\nmultimodel dependency probability distribution and that there may be\nanother factor contributing to this separation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInteraction_score\u003c/code\u003e - Combined value generated from signed p-values:\n-log10(Pval) * sign(log2FC_by_median). Negative scores indicate\nlethal genetic interaction, and positive scores indicate alleviating\ngenetic interaction.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e This process may take a few hours depending on the number\nof cores assigned. Our example below \u003ccode\u003eGI_screen()\u003c/code\u003e took ~2 hours to\nprocess. To save time, we have preprocessed this step for you.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eARID1A_mutant_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A_HomDel\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% pull(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eARID1A_control_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_groups\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% filter(\u003cspan class=\"pl-smi\"\u003eGroup\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eControl\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% pull(\u003cspan class=\"pl-smi\"\u003eDepMap_ID\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# See warning above.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# This can take several hours depending on number of lines/cores used. \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e GI_screen(\n \u003cspan class=\"pl-v\"\u003econtrol_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_control_id\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003emutant_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_mutant_id\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e depends on how many cores you have \u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Will save your results here as well as in the variable\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e use TRUE to run a short test to make sure all will run overnight.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Load prepared ARID1A screen result\u003c/span\u003e\nload(paste0(\u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/sample_22Q2_ARID1A_KO_screen.rda\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \u003cspan class=\"pl-v\"\u003eenvir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e environment())\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe can quickly determine whether any lethal genetic interactions were\npredicted by \u003ccode\u003eGRETTA\u003c/code\u003e. We use a \u003ccode\u003ePval\u003c/code\u003e cut off of 0.05 and rank based on\nthe \u003ccode\u003eInteraction_score\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \n filter(\u003cspan class=\"pl-smi\"\u003ePval\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.05\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e%\n arrange(\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eInteraction_score\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \n select(\u003cspan class=\"pl-smi\"\u003eGeneNames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eMutant_median\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ePval\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eInteraction_score\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% \u003cspan class=\"pl-smi\"\u003ehead\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 6 \u00d7 5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; GeneNames Control_median Mutant_median Pval Interaction_score\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1B 0.0579 0.515 6.84e-10 9.16\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 CCDC110 0.0165 0.0303 3.54e- 4 3.45\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 APOO 0.0168 0.0283 9.61e- 4 3.02\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 NHS 0.0352 0.0539 9.69e- 4 3.01\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 SLC66A2 0.00793 0.0134 1.06e- 3 2.98\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 ATXN7L1 0.0138 0.0259 1.78e- 3 2.75\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe immediately see that \u003cem\u003eARID1B\u003c/em\u003e, a known synthetic lethal interaction\nof \u003cem\u003eARID1A\u003c/em\u003e, was a the top of this list.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional-performing-a-small-scale-screen\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#optional-performing-a-small-scale-screen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional: Performing a small-scale screen\u003c/h3\u003e\n\u003cp\u003eTo perform a small in silico screen, a list of genes can be provided in\nthe \u003ccode\u003egene_list =\u003c/code\u003e argument.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003esmall_screen_results\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e GI_screen(\n \u003cspan class=\"pl-v\"\u003econtrol_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_control_id\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003emutant_id\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eARID1A_mutant_id\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003egene_list\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1B\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSMARCA2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eGAPDH\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSMARCC2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e depends on how many cores you have \u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Will save your results here as well as in the variable\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e) \u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-visualize-screen-results\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#visualize-screen-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVisualize screen results\u003c/h2\u003e\n\u003cp\u003eFinally once the \u003cem\u003ein silico\u003c/em\u003e screen is complete, results can be quickly\nvisualized using \u003ccode\u003eplot_screen()\u003c/code\u003e. Positive genetic interaction scores\nindicate potential synthetic lethal genetic interactors, and negative\nscores indicate potential alleviating genetic interactors. As expected,\nwe identified \u003cem\u003eARID1B\u003c/em\u003e as a synthetic lethal interactor of \u003cem\u003eARID1A\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Visualize results, turn on gene labels, \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# and label three genes each that are predicted to have \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# lethal and alleviating genetic interactions, respectively\u003c/span\u003e\n\nplot_screen(\u003cspan class=\"pl-v\"\u003eresult_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003escreen_results\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003elabel_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003elabel_n\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Warning: Removed 7 rows containing missing values (`geom_point()`).\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-plot-1.png\"\u003e\u003cimg src=\"man/figures/README-plot-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-example-identifying-arid1a-co-essential-genes\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#example-identifying-arid1a-co-essential-genes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Identifying \u003cem\u003eARID1A\u003c/em\u003e co-essential genes\u003c/h1\u003e\n\u003cp\u003ePerturbing genes that function in same/synergistic pathways or in the\nsame complex are said to show similar fitness effects, and these that\nshow effects are considered to be \u201cco-essential\u201d. The strategy of\nmapping co-essential gene have been used by several studies to attribute\nfunctions to previously annotated genes as well as to identify a novel\nsubunit of a large complex (\u003ca href=\"https://doi.org/10.1038/s41588-021-00840-z\" rel=\"nofollow\"\u003eWainberg et\nal.\u00a02021\u003c/a\u003e; \u003ca href=\"https://doi.org/10.1016/j.cels.2018.04.011\" rel=\"nofollow\"\u003ePan et\nal.\u00a02018\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eGiven that ARID1A is known subunit of the mammalian SWI/SNF complex\n(\u003ca href=\"https://doi.org/10.1016/j.cell.2018.09.032\" rel=\"nofollow\"\u003eMashtalir et al.\u00a02018\u003c/a\u003e),\nwe expect that members of the SWI/SNF complex would share\nco-essentiality with \u003cem\u003eARID1A\u003c/em\u003e. This example will demonstrate how we can\nmap \u003cem\u003eARID1A\u003c/em\u003e\u2019s co-essential gene network using \u003ccode\u003eGRETTA\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-identifying-genes-with-highest-correlation-coefficients\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#identifying-genes-with-highest-correlation-coefficients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIdentifying genes with highest correlation coefficients\u003c/h2\u003e\n\u003cp\u003eTo determine co-essential genes, we will perform multiple Pearson\ncorrelation coefficient analyses between \u003cem\u003eARID1A\u003c/em\u003e KO effects and the KO\neffects of all 18,333 genes. A cut off will be determined by calculating\nthe inflection point of the ranked coefficient curve. As expected find\nSWI/SNF subunit encoding genes, \u003cem\u003eSMARCE1\u003c/em\u003e and \u003cem\u003eSMARCB1\u003c/em\u003e, as the top two\nco-essential genes.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e This process may take several minutes. Our example below\n\u003ccode\u003ecoessential_map()\u003c/code\u003e + \u003ccode\u003eget_inflection_points()\u003c/code\u003e took ~17 minutes to\nprocess. To save time we have pre-processed this setp for you.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Map co-essential genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e coessential_map(\n \u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e) \n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Calculate inflection points of positive and negative curve using co-essential gene results.\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_inflection_points(\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, we annotate the data frame containing the co-essential network\ndata and visualize.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Combine and annotate data frame containing co-essential genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e annotate_coess(\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e)\n\nplot_coess(\n \u003cspan class=\"pl-v\"\u003eresult_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003einflection_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecoess_inflection_df\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003elabel_genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTRUE\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Should gene names be labeled?\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003elabel_n\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Number of genes to display from each end\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/README-combine_n_visualize-1.png\"\u003e\u003cimg src=\"man/figures/README-combine_n_visualize-1.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe also see that the top ten \u003cem\u003eARID1A\u003c/em\u003e co-essential genes include eight\nknown SWI/SNF subunits, namely \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003eSMARCB1\u003c/em\u003e, \u003cem\u003eSMARCE1\u003c/em\u003e,\n\u003cem\u003eSMARCC1\u003c/em\u003e, \u003cem\u003eSS18\u003c/em\u003e, \u003cem\u003eDPF2\u003c/em\u003e, \u003cem\u003eSMARCC2\u003c/em\u003e, and \u003cem\u003eSMARCD2\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Show top 10 co-essential genes. \u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_annotated_df\u003c/span\u003e %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% arrange(\u003cspan class=\"pl-smi\"\u003eRank\u003c/span\u003e) %\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e% head(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # A tibble: 10 \u00d7 9\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; GeneNameID_A GeneNameID_B estimate statistic p.value parameter Rank\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;chr\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;dbl\u0026gt; \u0026lt;int\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 1 ARID1A_8289 ARID1A_8289 1 Inf 0 1086 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 2 ARID1A_8289 SMARCB1_6598 0.477 17.9 7.45e-59 1086 2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 3 ARID1A_8289 SMARCE1_6605 0.399 14.3 4.30e-39 1086 3\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 4 ARID1A_8289 SMARCC1_6599 0.369 13.1 9.35e-33 1086 4\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 5 ARID1A_8289 SS18_6760 0.332 11.6 4.85e-26 1086 5\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 6 ARID1A_8289 DPF2_5977 0.330 11.5 1.15e-25 1086 6\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 7 ARID1A_8289 SMARCD2_6603 0.270 9.22 1.10e-16 1086 7\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 8 ARID1A_8289 SMARCC2_6601 0.242 8.22 2.34e-13 1086 8\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 9 ARID1A_8289 BCL2_596 0.231 7.82 4.05e-12 1086 9\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; 10 ARID1A_8289 CBFB_865 0.224 7.58 2.07e-11 1086 10\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; # \u2139 2 more variables: Padj_BH \u0026lt;dbl\u0026gt;, Candidate_gene \u0026lt;lgl\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional-filter-for-specific-cancer-types\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#optional-filter-for-specific-cancer-types\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional filter for specific cancer types\u003c/h3\u003e\n\u003cp\u003eInstead of mapping for essentiality across all available cell lines,\nusers can also subset by disease type using the option\n\u003ccode\u003einput_disease = \"\"\u003c/code\u003e, or within a pre-selected group of cell lines using\nthe option \u003ccode\u003einput_cell_lines = c()\u003c/code\u003e. Below we provide an example of how\nARID1A essential genes are mapped for pancreatic cancers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e Depending on the number of cell lines that are available\nafter the subsetting step, the inflection point calculation and\nthresholds may not be optimal. Please use caution when interpreting\nthese results.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Map co-essential genes in pancreatic cancers only\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e coessential_map(\n \u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003einput_disease\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePancreatic Cancer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Depending on how many cores you have access to, increase this value to shorten processing time.\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-optional-filter-for-custom-cell-lines\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#optional-filter-for-custom-cell-lines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional filter for custom cell lines\u003c/h3\u003e\n\u003cp\u003eWe can also map essentiality across a manually defined list of cell\nlines using the \u003ccode\u003einput_cell_lines = c()\u003c/code\u003e option.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWarning\u003c/strong\u003e Depending on the number of cell lines provided, the\ninflection point may not be calculated. Please use caution when\ninterpreting these results.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003ecustom_lines\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eACH-000001\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eACH-000002\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eACH-000003\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"pl-k\"\u003e...\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003ecoess_df\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e coessential_map(\n \u003cspan class=\"pl-v\"\u003einput_gene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eARID1A\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003einput_cell_lines\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecustom_lines\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003ecore_num\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Depending on how many cores you have access to, increase this value to shorten processing time.\u003c/span\u003e\n \u003cspan class=\"pl-v\"\u003edata_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_data_dir\u003c/span\u003e, \n \u003cspan class=\"pl-v\"\u003eoutput_dir\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egretta_output_dir\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-session-information\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#session-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSession information\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003esessionInfo()\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; R version 4.2.2 (2022-10-31)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Platform: x86_64-pc-linux-gnu (64-bit)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Running under: CentOS Linux 7 (Core)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Matrix products: default\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; BLAS: /gsc/software/linux-x86_64-centos7/R-4.2.2/lib64/R/lib/libRblas.so\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; LAPACK: /gsc/software/linux-x86_64-centos7/R-4.2.2/lib64/R/lib/libRlapack.so\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; locale:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] LC_PAPER=en_US.UTF-8 LC_NAME=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] LC_ADDRESS=C LC_TELEPHONE=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; attached base packages:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] stats graphics grDevices utils datasets methods base \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; other attached packages:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] GRETTA_0.99.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] ggplot2_3.4.1 tidyverse_1.3.2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; loaded via a namespace (and not attached):\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [1] TH.data_1.1-2 googledrive_2.0.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [3] colorspace_2.1-0 class_7.3-20 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [5] modeltools_0.2-23 fs_1.6.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [7] gld_2.6.6 rstudioapi_0.14 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [9] proxy_0.4-27 farver_2.1.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [11] ggrepel_0.9.3 bit64_4.0.5 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [13] fansi_1.0.4 mvtnorm_1.1-3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [15] lubridate_1.9.0 coin_1.4-2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [17] xml2_1.3.4 codetools_0.2-18 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [19] splines_4.2.2 doParallel_1.0.17 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [21] cachem_1.0.8 rootSolve_1.8.2.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [23] libcoin_1.0-9 knitr_1.42 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [25] jsonlite_1.8.4 doMC_1.3.8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [27] broom_1.0.4 dbplyr_2.2.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [29] compiler_4.2.2 httr_1.4.6 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [31] backports_1.4.1 assertthat_0.2.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [33] Matrix_1.5-1 fastmap_1.1.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [35] gargle_1.4.0 cli_3.6.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [37] htmltools_0.5.5 tools_4.2.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [39] gtable_0.3.3 glue_1.6.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [41] lmom_2.9 rappdirs_0.3.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [43] Rcpp_1.0.10 cellranger_1.1.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [45] vctrs_0.6.1 iterators_1.0.14 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [47] lmtest_0.9-40 xfun_0.39 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [49] rvest_1.0.3 timechange_0.2.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [51] lifecycle_1.0.3 googlesheets4_1.0.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [53] MASS_7.3-58.1 zoo_1.8-12 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [55] scales_1.2.1 hms_1.1.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [57] parallel_4.2.2 sandwich_3.0-2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [59] expm_0.999-7 yaml_2.3.7 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [61] curl_5.0.0 Exact_3.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [63] memoise_2.0.1 stringi_1.7.12 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [65] RSQLite_2.2.19 highr_0.10 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [67] inflection_1.3.6 foreach_1.5.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [69] nortest_1.0-4 e1071_1.7-13 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [71] filelock_1.0.2 boot_1.3-28 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [73] rlang_1.1.1 pkgconfig_2.0.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [75] matrixStats_0.63.0 evaluate_0.21 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [77] lattice_0.20-45 labeling_0.4.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [79] RootsExtremaInflections_1.2.1 bit_4.0.5 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [81] tidyselect_1.2.0 plyr_1.8.8 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [83] magrittr_2.0.3 R6_2.5.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [85] DescTools_0.99.49 generics_0.1.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [87] multcompView_0.1-9 multcomp_1.4-23 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [89] DBI_1.1.3 pillar_1.9.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [91] haven_2.5.1 withr_2.5.0 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [93] survival_3.4-0 modelr_0.1.10 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [95] crayon_1.5.2 rcompanion_2.4.21 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [97] utf8_1.2.3 BiocFileCache_2.6.1 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [99] tzdb_0.4.0 rmarkdown_2.21 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [101] grid_4.2.2 readxl_1.4.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [103] data.table_1.14.8 blob_1.2.3 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [105] reprex_2.0.2 digest_0.6.31 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [107] diptest_0.76-0 stats4_4.2.2 \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; [109] munsell_0.5.0\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n", + "stargazers_count": 5, + "subscribers_count": 2, + "topics": [ + "bioinformatics", + "genetic-interactions", + "r" + ], + "updated_at": 1687497946.0 + }, + { + "data_format": 2, + "description": "Workflow for prOteome and tRanScriptome functiOnal aNnotation. CAUTION : project has moved to Gitlab.", + "filenames": [ + "containers/Singularity.interproscan-5.59-91.0" + ], + "full_name": "ifremer-bioinformatics/orson", + "latest_release": "v1.0.0", + "readme": "\u003cp\u003e\u003cstrong\u003eORSON: workflow for prOteome and tRanScriptome functiOnal aNnotation\u003c/strong\u003e.\u003c/p\u003e\n\u003ch1 id=\"user-content-caution\"\u003e\u003ca class=\"heading-link\" href=\"#caution\"\u003e\u003cstrong\u003eCAUTION\u003c/strong\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003eThis mirror of ORSON workflow on Github is no longer supported and will soon be deleted.\u003c/p\u003e\n\u003cp\u003ePlease go to: \u003ca href=\"https://gitlab.ifremer.fr/bioinfo/workflows/orson\" rel=\"nofollow\"\u003ehttps://gitlab.ifremer.fr/bioinfo/workflows/orson\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 5, + "subscribers_count": 3, + "topics": [ + "workflow", + "annotation", + "transcriptome", + "proteome", + "nextflow-pipelines" + ], + "updated_at": 1677855349.0 + }, + { + "data_format": 2, + "description": "This is the private version of the FS planner repository", + "filenames": [ + "Singularity.lbfs" + ], + "full_name": "aig-upf/fs-private", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-applying-a-stochastic-quasi-newton-optimizer-to-least-squares-reverse-time-migration\" class=\"anchor\" href=\"#applying-a-stochastic-quasi-newton-optimizer-to-least-squares-reverse-time-migration\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eApplying a stochastic quasi-Newton optimizer to least-squares reverse time migration\u003c/h1\u003e\n\u003cp\u003eThis repository demonstrates how to use the \u003ca href=\"https://github.com/Sohl-Dickstein/Sum-of-Functions-Optimizer\"\u003eSum of Functions Optimizer (SFO)\u003c/a\u003e applied to seismic imaging, more specifically to the least-squares reverse time migration (LSRTM) to reproduce the stochastic inversion in the Marmousi model presented in the manuscript entitled \u003cem\u003eApplying a stochastic quasi-Newton optimizer to least-squares reverse time migration\u003c/em\u003e, sent to Computers and Geosciences. Wave propagation is performed using the \u003ca href=\"https://www.devitoproject.org/\" rel=\"nofollow\"\u003edevito framework\u003c/a\u003e, a Python package build to implement optimized stencil computation, capable of executing optimized computational kernels on several computer platforms, including CPUs, GPUs, and clusters thereof. Parallelization of shots across nodes is performed using \u003ca href=\"https://docs.ray.io/en/latest/\" rel=\"nofollow\"\u003eRay\u003c/a\u003e, an open-source project which provides a simple and flexible API for building and running distributed applications.\u003c/p\u003e\n\u003cp\u003eA \u003ca href=\"https://github.com/fffarias/sfo-manuscript/blob/main/Dockerfile/Singularity.def\"\u003esingularity definition file\u003c/a\u003e is provided to create a reproducible container and run this example properly, but you can also find a \u003ca href=\"https://github.com/fffarias/sfo-manuscript/blob/main/requirements.txt\"\u003erequirements file\u003c/a\u003e listing all of the project\u0027s dependencies and follow the instructions below to install them. In a nutshell, this are the full python packages required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003edevito\u003c/li\u003e\n\u003cli\u003eray\u003c/li\u003e\n\u003cli\u003esfo\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" href=\"#install-dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall dependencies\u003c/h2\u003e\n\u003cp\u003eTo install \u003ca href=\"https://www.devitoproject.org/\" rel=\"nofollow\"\u003edevito\u003c/a\u003e follow the instructions from \u003ca href=\"https://www.devitoproject.org/devito/download.html\" rel=\"nofollow\"\u003eDevito documentation\u003c/a\u003e. In case the best choice is to use a conda environment, the following steps should work as recommended on the \u003ca href=\"https://www.devitoproject.org/devito/download.html#conda-environment\" rel=\"nofollow\"\u003einstallation web page\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/devitocodes/devito.git\ncd devito\nconda env create -f environment-dev.yml\nsource activate devito\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor it is also possible to install devito using pip installation, in this case simply type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install devito\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe Ray package can be easily installed via \u003ccode\u003epip\u003c/code\u003e. You can install the latest official version of Ray as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -U ray\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe SFO optimizer can be used after cloning the original repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttps://github.com/Sohl-Dickstein/Sum-of-Functions-Optimizer\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand setting the path where the \u003ccode\u003esfo.py\u003c/code\u003e file is in the python sys\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eimport sys\nsys.path.append(\"./Sum-of-Functions-Optimizer\")\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing the singularity definition file provided, the SFO repository is already cloned and added to the PYTHONPATH variable.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-least-squares-reverse-time-migration-lsrtm\" class=\"anchor\" href=\"#least-squares-reverse-time-migration-lsrtm\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLeast-squares reverse time migration (LSRTM)\u003c/h2\u003e\n\u003cp\u003eTo provide subsurface images with better balanced amplitudes, higher resolution and fewer artifacts than standard migration, a least-sqaures migration should be considered. The LSRTM process involves, several wavefield computations of the Born modeling and its adjoint. To calculate these operators, I choose to use the \u003ccode\u003eAcousticWaveSolver\u003c/code\u003e Class from the Devito\u0027s folder \u003ccode\u003eexamples\u003c/code\u003e or a Devito \u003ccode\u003eOperator\u003c/code\u003e, although it is also available on Devito, the operators needed to calculate the LSRTM in a medium with TTI anisotropy. In the python script available \u003ca href=\"https://github.com/fffarias/sfo-manuscript/blob/main/lsm.py\"\u003ehere\u003c/a\u003e, there are all the necessary steps to perform the LSRTM, since besides performing the forward linearized modeling and its adjoint, some previous actions need to be defined, such as creating an object that contains the velocity model and the acquisition geometry, for example. All these steps in different contexts are also explored in the \u003ca href=\"https://github.com/devitocodes/devito/tree/master/examples/seismic/tutorials\"\u003etutorials available\u003c/a\u003e on the Devito section. Thus, the sequence adopted in the main function involves:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreating velocity and reflectivity models associated with squared slowness.\u003c/li\u003e\n\u003cli\u003eDefining the acquisition geometry.\u003c/li\u003e\n\u003cli\u003eForward modeling for all the shots in parallel using ray, to generate the \"observed data\".\u003c/li\u003e\n\u003cli\u003eRunning the SFO optimizer with the help of a function that returns objective function value and gradient for a subset of shots (batch size).\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-multiple-cpus-or-gpus-using-ray\" class=\"anchor\" href=\"#running-on-multiple-cpus-or-gpus-using-ray\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on multiple CPUs or GPUs using Ray\u003c/h2\u003e\n\u003cp\u003eRunning LSRTM sequentially, even for a 2D example such as the Marmousi model, can be quite tedious, so given that there are resources available, the ideal would be to distribute the wavefield calculations across the available CPUs or GPUs. To accomplish this using Ray on a single machine, it is enough to start Ray by adding \u003ccode\u003eray.init()\u003c/code\u003e to the code. By simply doing that, Ray will then be able to utilize all cores of your machine.\u003c/p\u003e\n\u003cp\u003eIn order for the wave propagations to be performed on the GPU, you need to compile Devito in a slightly more sophisticated way following the \u003ca href=\"https://github.com/devitocodes/devito/wiki/Using-Devito-on-GPUs-with-NVIDIA-HPC-SDK\"\u003einstructions provided\u003c/a\u003e, or use the appropriate \u003ca href=\"https://github.com/fffarias/sfo-manuscript/blob/main/Dockerfile/Singularity_nvidia.def\"\u003esingularity recipe\u003c/a\u003e to use Devito on GPUs with NVIDIA HPC SDK.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython lsm.py --bs=20\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003ebs\u003c/code\u003e controls the batch size. Other variables can be controlled from the command line:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSymbol\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eso\u003c/td\u003e\n\u003ctd\u003eDiscretization order of the spatial derivatives of the wave equation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enbl\u003c/td\u003e\n\u003ctd\u003eNumber of absorbing boundary points around the domain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ef0\u003c/td\u003e\n\u003ctd\u003eSource peak frequency\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etn\u003c/td\u003e\n\u003ctd\u003eTotal simulation time\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ensrc\u003c/td\u003e\n\u003ctd\u003eNumber of sources\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enrec\u003c/td\u003e\n\u003ctd\u003eNumber of receivers\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003enpasses\u003c/td\u003e\n\u003ctd\u003eNumber of passes through the entire data\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ebs\u003c/td\u003e\n\u003ctd\u003eBatch size\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs implemented, the output of the lsm.py script writes the inverted reflectivity to disk in a binary file and also generates a graph with the objective function values for each mini-batch.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-special-situations\" class=\"anchor\" href=\"#special-situations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpecial situations\u003c/h2\u003e\n\u003cp\u003eIf you have any question that you couldn\u0027t find the answer here, please email \u003ca href=\"mailto:fernanda.farias8@gmail.com\"\u003efernanda.farias8@gmail.com\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-see-also\" class=\"anchor\" href=\"#see-also\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSee also\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSum of Functions Optimizer arXiv paper: \u003ca href=\"https://arxiv.org/abs/1311.2115\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1311.2115\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe FS Functional STRIPS planner\u003c/h1\u003e\u003ca id=\"user-content-the-fs-functional-strips-planner\" class=\"anchor\" aria-label=\"Permalink: The FS Functional STRIPS planner\" href=\"#the-fs-functional-strips-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eFS\u003c/code\u003e is a classical planner that works with the Functional STRIPS planning language \u003ca href=\"#ref-geffner-fstrips-2000\"\u003e[Geffner, 2000]\u003c/a\u003e,\na modeling language based on the quantifier-free\nfragment of first-order logic that includes constant, function and predicate symbols, but no variable symbols. The increased expressiveness\nof the Functional STRIPS language with respect to propositional languages such as standard STRIPS (which is indeed subsumed by Functional STRIPS)\noften results in problem encodings which are more compact, more readable, have fewer ground actions\nand preserve the structural properties of the problem in a manner which allows the derivation of more effective heuristics.\u003c/p\u003e\n\u003cp\u003eAlong with the core of the Functional STRIPS language, the \u003ccode\u003eFS\u003c/code\u003e planner supports certain extensions which are useful both\nfrom the expressive \u003cem\u003eand\u003c/em\u003e the computational point of view. These include \u003cem\u003eexistential quantification\u003c/em\u003e,\n\u003cem\u003estate constraints\u003c/em\u003e, a fairly large library of \u003cem\u003eglobal constraints\u003c/em\u003e, and the possibility of using \u003cem\u003eexternally-defined symbols\u003c/em\u003e\nand \u003cem\u003ebuilt-in arithmetic symbols\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThis documentation covers a number of practical issues related to the use of the \u003ccode\u003eFS\u003c/code\u003e planner. The planner, however, has\nbeen used and described in a number of academic publications that \u003ca href=\"http://gfrances.github.io/pubs/\" rel=\"nofollow\"\u003ecan be found here\u003c/a\u003e,\nthe most recent of which are \u003ca href=\"#ref-frances-modeling-2015\"\u003e[Franc\u00e8s and Geffner, 2015]\u003c/a\u003e and \u003ca href=\"#ref-frances-existential-2016\"\u003e[Franc\u00e8s and Geffner, 2016a]\u003c/a\u003e\nand \u003ca href=\"#ref-frances-effective-2016\"\u003e[Franc\u00e8s and Geffner, 2016b]\u003c/a\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to use the planner is by \u003ca href=\"doc/installation.md\"\u003emanually compiling the planner source code\u003c/a\u003e.\nAlternatively, you can build and/or use a \u003ca href=\"doc/containers.md\"\u003eready-to-use image\u003c/a\u003e in some of the containerization solutions\nthat we support.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can find a high-level overview of the planner usage options \u003ca href=\"doc/usage.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-credits\"\u003e\u003c/a\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eFS\u003c/code\u003e planner is partially built upon the \u003ca href=\"http://www.lapkt.org\" rel=\"nofollow\"\u003eLightweight Automated Planning Toolkit\u003c/a\u003e\nand the PDDL parser from the \u003ca href=\"http://www.fast-downward.org\" rel=\"nofollow\"\u003eFast Downward\u003c/a\u003e distribution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-references\"\u003e\u003c/a\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-modeling-2015\"\u003eFranc\u00e8s, G., and Geffner, H. (2015)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2015-icaps-better-heuristics-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eModeling and Computation in Planning: Better Heuristics from More Expressive Languages\u003c/em\u003e\u003c/a\u003e, ICAPS 2015.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-existential-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016a)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-existential-quantification-planning-csp/\" rel=\"nofollow\"\u003e\u003cem\u003eE-STRIPS: Existential Quantification in Planning and Constraint Satisfaction\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-frances-effective-2016\"\u003eFranc\u00e8s, G., and Geffner, H. (2016b)\u003c/a\u003e,\n\u003ca href=\"http://gfrances.github.io/pubs/2016-ijcai-effective-planning-more-expressive-languages/\" rel=\"nofollow\"\u003e\u003cem\u003eEffective Planning with More Expressive Languages\u003c/em\u003e\u003c/a\u003e, IJCAI 2016.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca name=\"user-content-ref-geffner-fstrips-2000\"\u003eGeffner, H. (2000)\u003c/a\u003e,\n\u003ca href=\"http://www.tecn.upf.es/~hgeffner/\" rel=\"nofollow\"\u003e\u003cem\u003eFunctional STRIPS: A more flexible lan-\nguage for planning and problem solving\u003c/em\u003e\u003c/a\u003e.\nIn Minker, J., ed., Logic-Based Artificial Intelligence. Kluwer. 187\u2013205.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 5, - "subscribers_count": 1, - "topics": [], - "updated_at": 1652385399.0 + "subscribers_count": 11, + "topics": [ + "planning", + "pddl", + "strips", + "fstrips" + ], + "updated_at": 1667964440.0 }, { "data_format": 2, - "description": "San Diego Supercomputer Center Summer Institute", + "description": "Repository for the ALPACA toolbox, including code, tutorials, docker files, etc. ", "filenames": [ - "2.1a_Python_for_HPC/singularity/Singularity.miniconda3-dask-numba-py38-4.9.2-ubuntu-18.04" + "Singularity" ], - "full_name": "sdsc/sdsc-summer-institute-2021", + "full_name": "C0C0AN/ALPACA", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\" rel=\"nofollow\"\u003e\u003cimg alt=\"Creative Commons License\" src=\"https://camo.githubusercontent.com/72437363a2c150e50c5c090c5ec0dfde31ce3a28c8a092f638509882b56ceb4e/68747470733a2f2f692e6372656174697665636f6d6d6f6e732e6f72672f6c2f62792d6e632d73612f342e302f38307831352e706e67\" data-canonical-src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/80x15.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-sdsc-summer-institute-2021\" class=\"anchor\" href=\"#sdsc-summer-institute-2021\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSDSC Summer Institute 2021\u003c/h1\u003e\n\u003cp\u003eThis repository hosts all material and slides of the presentations at the Summer Institute\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://na.eventscloud.com/website/21339/agenda/\" rel=\"nofollow\"\u003eAgenda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSee also \u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2020\"\u003eSCC20 material\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-interactive-videos\" class=\"anchor\" href=\"#interactive-videos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInteractive Videos\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInteractive Videos can be found here: \u003ca href=\"https://education.sdsc.edu/training/interactive/sdsc_si21/index.html\" rel=\"nofollow\"\u003ehttps://education.sdsc.edu/training/interactive/sdsc_si21/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eA full catalog of all our trainings at SDSC can be found \u003ca href=\"https://www.sdsc.edu/education_and_training/training_hpc.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-aliases-symlinks-and-reservations\" class=\"anchor\" href=\"#aliases-symlinks-and-reservations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAliases, symlinks and reservations\u003c/h2\u003e\n\u003cp\u003eFor your convenience, we\u2019ve create aliases and symlinks for the Summer Institute\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eget-cpu \u2013 one interactive compute node for 2 hours\u003c/li\u003e\n\u003cli\u003eget-gpu \u2013 one interactive GPU (in shared queue) for 2 hours\u003c/li\u003e\n\u003cli\u003estart-spark \u2013 start one-hour Spark session\u003c/li\u003e\n\u003cli\u003estart-tf-cpu \u2013 start three-hour TensorFlow session (CPU)\u003c/li\u003e\n\u003cli\u003estart-tf-gpu \u2013 start three-hour TensorFlow session (GPU)\u003c/li\u003e\n\u003cli\u003edata \u2013 symlink to staged data\nIn the event that you need to explicitly use the reservation, training accounts will have access to SI2021RES for duration of SI\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eAll the teaching material in this repository is licensed under \u003ca href=\"https://creativecommons.org/licenses/by-nc-sa/4.0/\" rel=\"nofollow\"\u003eCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you are re-using this material, please cite our \u003ca href=\"TODO\"\u003erecord on Zenodo\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 13, + "subscribers_count": 4, "topics": [], - "updated_at": 1631666132.0 + "updated_at": 1694111607.0 }, { "data_format": 2, - "description": null, + "description": "JupyterHub + High-Performance Computing", "filenames": [ - "Singularity.def" + "singularity/Singularity_Tensorflow", + "singularity/Singularity" ], - "full_name": "HippocampusGirl/HALFpipe", + "full_name": "pc2/JHub-HPC-Interface", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-welcome-to-enigma-halfpipe\" class=\"anchor\" href=\"#welcome-to-enigma-halfpipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to ENIGMA \u003ccode\u003eHALFpipe\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4508\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/actions?query=workflow%3A%22build%22\"\u003e\u003cimg src=\"https://github.com/HALFpipe/HALFpipe/workflows/build/badge.svg\" alt=\"https://github.com/HALFpipe/HALFpipe/workflows/build/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/actions?query=workflow%3A%22continuous+integration%22\"\u003e\u003cimg src=\"https://github.com/HALFpipe/HALFpipe/workflows/continuous%20integration/badge.svg\" alt=\"https://github.com/HALFpipe/HALFpipe/workflows/continuous%20integration/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/HALFpipe/HALFpipe\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ef5be2978b13a91a1a602bc0261933d3735a7567176db1ef0c13eb65b3249056/68747470733a2f2f636f6465636f762e696f2f67682f48414c46706970652f48414c46706970652f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/HALFpipe/HALFpipe/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e is a user-friendly software that facilitates reproducible analysis of\nfMRI data, including preprocessing, single-subject, and group analysis. It\nprovides state-of-the-art preprocessing using\n\u003ca href=\"https://fmriprep.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003efmriprep\u003c/code\u003e\u003c/a\u003e, but removes the necessity to\nconvert data to the\n\u003ca href=\"https://bids-specification.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003e\u003ccode\u003eBIDS\u003c/code\u003e\u003c/a\u003e format. Common\nresting-state and task-based fMRI features can then be calculated on the fly\nusing \u003ca href=\"http://fsl.fmrib.ox.ac.uk/\" rel=\"nofollow\"\u003e\u003ccode\u003eFSL\u003c/code\u003e\u003c/a\u003e and\n\u003ca href=\"https://nipype.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003enipype\u003c/code\u003e\u003c/a\u003e for statistics.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf you encounter issues, please see the \u003ca href=\"#troubleshooting\"\u003etroubleshooting\u003c/a\u003e\nsection of this document.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#getting-started\"\u003eGetting started\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#container-platform\"\u003eContainer platform\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#download\"\u003eDownload\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running\"\u003eRunning\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#user-interface\"\u003eUser interface\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#files\"\u003eFiles\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#features\"\u003eFeatures\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#models\"\u003eModels\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-on-a-high-performance-computing-cluster\"\u003eRunning on a high-performance computing cluster\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#quality-checks\"\u003eQuality checks\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#subject-level-features\"\u003eSubject-level features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#preprocessed-images\"\u003ePreprocessed images\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#group-level\"\u003eGroup-level\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#command-line-flags\"\u003eCommand line flags\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#control-command-line-logging\"\u003eControl command line logging\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#automatically-remove-unneeded-files\"\u003eAutomatically remove unneeded files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#adjust-nipype\"\u003eAdjust nipype\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#choose-which-parts-to-run-or-to-skip\"\u003eChoose which parts to run or to skip\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#working-directory\"\u003eWorking directory\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#data-file-system-root\"\u003eData file system root\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e is distributed as a container, meaning that all required software\ncomes bundled in a monolithic file, the container. This allows for easy\ninstallation on new systems, and makes data analysis more reproducible, because\nsoftware versions are guaranteed to be the same for all users.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-platform\" class=\"anchor\" href=\"#container-platform\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer platform\u003c/h3\u003e\n\u003cp\u003eThe first step is to install one of the supported container platforms. If you\u0027re\nusing a high-performance computing cluster, more often than not\n\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e will already be available.\u003c/p\u003e\n\u003cp\u003eIf not, we recommend using the latest version\nof\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e. However, it can be somewhat cumbersome to\ninstall, as it needs to be built from source.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://neuro.debian.net/\" rel=\"nofollow\"\u003e\u003ccode\u003eNeuroDebian\u003c/code\u003e\u003c/a\u003e package repository provides an\nolder version of \u003ca href=\"https://sylabs.io/guides/2.6/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e for\n\u003ca href=\"https://neuro.debian.net/pkgs/singularity-container.html\" rel=\"nofollow\"\u003esome\u003c/a\u003e Linux\ndistributions.\u003c/p\u003e\n\u003cp\u003eIn contrast to \u003ccode\u003eSingularity\u003c/code\u003e, \u003ccode\u003eDocker\u003c/code\u003e always requires elevated privileges to\nrun containers. In other words, every user running a \u003ccode\u003eDocker\u003c/code\u003e container\nautomatically has administrator privileges on the computer they\u0027re using.\nTherefore, it is inherently a bad choice for multi-user environments, where the\naccess of individual users should be limited. \u003ccode\u003eDocker\u003c/code\u003e is the only option that\nis compatible with \u003ccode\u003eMac OS X\u003c/code\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eVersion\u003c/th\u003e\n\u003cth\u003eInstallation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e3.5.3\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eSee \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/quick_start.html\u003c/a\u003e\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e2.6.1\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003esudo apt install singularity-container\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eSee \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-download\" class=\"anchor\" href=\"#download\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h3\u003e\n\u003cp\u003eThe second step is to download the \u003ccode\u003eHALFpipe\u003c/code\u003e to your computer. This requires\napproximately 5 gigabytes of storage.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eInstallation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://charitede-my.sharepoint.com/:f:/g/personal/lea_waller_charite_de/EukRziExhTVBrEAai2oEpi8B2jsnn7P3YQuFo2pycKp6-g\" rel=\"nofollow\"\u003ehttps://charitede-my.sharepoint.com/:f:/g/personal/lea_waller_charite_de/EukRziExhTVBrEAai2oEpi8B2jsnn7P3YQuFo2pycKp6-g\u003c/a\u003e or \u003ccode\u003esingularity pull docker://halfpipe/halfpipe:1.1.1\u003c/code\u003e or \u003ccode\u003esingularity pull docker://ghcr.io/halfpipe/halfpipe:1.1.1\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edocker pull halfpipe/halfpipe:1.1.1\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e version \u003ccode\u003e3.x\u003c/code\u003e creates a container image file called\n\u003ccode\u003eHALFpipe_{version}.sif\u003c/code\u003e in the directory where you run the \u003ccode\u003epull\u003c/code\u003e command. For\n\u003ccode\u003eSingularity\u003c/code\u003e version \u003ccode\u003e2.x\u003c/code\u003e the file is named\n\u003ccode\u003ehalfpipe-halfpipe-master-latest.simg\u003c/code\u003e. Whenever you want to use the container,\nyou need pass \u003ccode\u003eSingularity\u003c/code\u003e the path to this file.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e \u003ccode\u003eSingularity\u003c/code\u003e may store a copy of the container in its cache\ndirectory. The cache directory is located by default in your home directory at\n\u003ccode\u003e~/.singularity\u003c/code\u003e. If you need to save disk space in your home directory, you\ncan safely delete the cache directory after downloading, i.e. by running\n\u003ccode\u003erm -rf ~/.singularity\u003c/code\u003e. Alternatively, you could move the cache directory\nsomewhere with more free disk space using a symlink. This way, files will\nautomatically be stored there in the future. For example, if you have a lot of\nfree disk space in \u003ccode\u003e/mnt/storage\u003c/code\u003e, then you could first run\n\u003ccode\u003emv ~/.singularity /mnt/storage\u003c/code\u003e to move the cache directory, and then\n\u003ccode\u003eln -s /mnt/storage/.singularity ~/.singularity\u003c/code\u003e to create the symlink.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ccode\u003eDocker\u003c/code\u003e will store the container in its storage base directory, so it does not\nmatter from which directory you run the \u003ccode\u003epull\u003c/code\u003e command.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h3\u003e\n\u003cp\u003eThe third step is to run the downloaded container. You may need to replace\n\u003ccode\u003ehalfpipe_1.1.1.sif\u003c/code\u003e with the actual path and filename where \u003ccode\u003eSingularity\u003c/code\u003e\ndownloaded your container.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eCommand\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_1.1.1.sif\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edocker run --interactive --tty --volume /:/ext halfpipe/halfpipe\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYou should now see the user interface.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-background\" class=\"anchor\" href=\"#background\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h4\u003e\n\u003cp\u003eContainers are by default isolated from the host computer. This adds security,\nbut also means that the container cannot access the data it needs for analysis.\n\u003ccode\u003eHALFpipe\u003c/code\u003e expects all inputs (e.g., image files and spreadsheets) and outputs\n(the working directory) to be places in the path\u003ccode\u003e/ext\u003c/code\u003e (see also\n\u003ca href=\"#data-file-system-root---fs-root\"\u003e\u003ccode\u003e--fs-root\u003c/code\u003e\u003c/a\u003e). Using the option\n\u003ccode\u003e--bind /:/ext\u003c/code\u003e, we instruct \u003ccode\u003eSingularity\u003c/code\u003e to map all of the host file system\n(\u003ccode\u003e/\u003c/code\u003e) to that path (\u003ccode\u003e/ext\u003c/code\u003e). You can also run \u003ccode\u003eHALFpipe\u003c/code\u003e and only map only part\nof the host file system, but keep in mind that any directories that are not\nmapped will not be visible later.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e passes the host shell environment to the container by default.\nThis means that in some cases, the host computer\u0027s configuration can interfere\nwith the software. To avoid this, we need to pass the option \u003ccode\u003e--containall\u003c/code\u003e.\n\u003ccode\u003eDocker\u003c/code\u003e does not pass the host shell environment by default, so we don\u0027t need\nto pass an option.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-user-interface\" class=\"anchor\" href=\"#user-interface\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser interface\u003c/h2\u003e\n\u003cp\u003eThe user interface asks a series of questions about your data and the analyses\nyou want to run. In each question, you can press \u003ccode\u003eControl+C\u003c/code\u003e to cancel the\ncurrent question and go back to the previous one. \u003ccode\u003eControl+D\u003c/code\u003e exits the program\nwithout saving. Note that these keyboard shortcuts are the same on Mac.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h3\u003e\n\u003cp\u003eTo run preprocessing, at least a T1-weighted structural image and a BOLD image\nfile is required. Preprocessing and data analysis proceeds automatically.\nHowever, to be able to run automatically, data files need to be input in a way\nsuitable for automation.\u003c/p\u003e\n\u003cp\u003eFor this kind of automation, \u003ccode\u003eHALFpipe\u003c/code\u003e needs to know the relationships between\nfiles, such as which files belong to the same subject. However, even though it\nwould be obvious for a human, a program cannot easily assign a file name to a\nsubject, and this will be true as long as there are differences in naming\nbetween different researchers or labs. One researcher may name the same file\n\u003ccode\u003esubject_01_rest.nii.gz\u003c/code\u003e and another \u003ccode\u003esubject_01/scan_rest.nii.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIn \u003ccode\u003eHALFpipe\u003c/code\u003e, we solve this issue by inputting file names in a specific way.\nFor example, instead of \u003ccode\u003esubject_01/scan_rest.nii.gz\u003c/code\u003e, \u003ccode\u003eHALFpipe\u003c/code\u003e expects you to\ninput \u003ccode\u003e{subject}/scan_rest.nii.gz\u003c/code\u003e. \u003ccode\u003eHALFpipe\u003c/code\u003e can then match all files on disk\nthat match this naming schema, and extract the subject ID \u003ccode\u003esubject_01\u003c/code\u003e. Using\nthe extracted subject ID, other files can now be matched to this image. If all\ninput files are available in BIDS format, then this step can be skipped.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify working directory\u003c/code\u003e All intermediate and outputs of \u003ccode\u003eHALFpipe\u003c/code\u003e will\nbe placed in the working directory. Keep in mind to choose a location with\nsufficient free disk space, as intermediates can be multiple gigabytes in\nsize for each subject.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eIs the data available in BIDS format?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the BIDS directory\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify anatomical/structural data\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the path of the T1-weighted image files\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify functional data\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the path of the BOLD image files\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCheck repetition time values\u003c/code\u003e / \u003ccode\u003eSpecify repetition time in seconds\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd more BOLD image files?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDo slice timing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eCheck slice acquisition direction values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCheck slice timing values\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify field maps?\u003c/code\u003e If the data was imported from a BIDS directory, this\nstep will be omitted.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the type of the field maps\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEPI (blip-up blip-down)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the blip-up blip-down EPI image files\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003ePhase difference and magnitude (used by Siemens scanners)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the magnitude image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the phase/phase difference image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify echo time difference in seconds\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eScanner-computed field map and magnitude (used by GE / Philips\nscanners)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the magnitude image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the field map image files\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd more field maps?\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify effective echo spacing for the functional data in seconds\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify phase encoding direction for the functional data\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-features\" class=\"anchor\" href=\"#features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h3\u003e\n\u003cp\u003eFeatures are analyses that are carried out on the preprocessed data, in other\nwords, first-level analyses.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify first-level features?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the feature type\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eTask-based\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the event file type\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSPM multiple conditions\u003c/code\u003e A MATLAB .mat file containing three\narrays: \u003ccode\u003enames\u003c/code\u003e (condition), \u003ccode\u003eonsets\u003c/code\u003e and \u003ccode\u003edurations\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFSL 3-column\u003c/code\u003e One text file for each condition. Each file has its\ncorresponding condition in the filename. The first column specifies\nthe event onset, the second the duration. The third column of the\nfiles is ignored, so parametric modulation is not supported\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBIDS TSV\u003c/code\u003e A tab-separated table with named columns \u003ccode\u003etrial_type\u003c/code\u003e\n(condition), \u003ccode\u003eonset\u003c/code\u003e and \u003ccode\u003eduration\u003c/code\u003e. While BIDS supports defining\nadditional columns, \u003ccode\u003eHALFpipe\u003c/code\u003e will currently ignore these\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the event files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSelect conditions to add to the model\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify contrasts\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify contrast name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify contrast values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd another contrast?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply a temporal filter to the design matrix?\u003c/code\u003e A separate temporal\nfilter can be specified for the design matrix. In contrast, the\ntemporal filtering of the input image and any confound regressors\nadded to the design matrix is specified in 10. In general, the two\nsettings should match\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply smoothing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify smoothing FWHM in mm\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eGrand mean scaling will be applied with a mean of 10000.000000\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTemporal filtering will be applied using a gaussian-weighted filter\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the filter width in seconds\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRemove confounds?\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSeed-based connectivity\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify binary seed mask file(s)\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the binary seed mask image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCheck space values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eAdd binary seed mask image file\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDual regression\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAtlas-based connectivity matrix\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eReHo\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efALFF\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd another first-level feature?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eOutput a preprocessed image?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify setting name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply smoothing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify smoothing FWHM in mm\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDo grand mean scaling?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify grand mean\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply a temporal filter?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the type of temporal filter\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eGaussian-weighted\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eFrequency-based\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRemove confounds?\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-models\" class=\"anchor\" href=\"#models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModels\u003c/h3\u003e\n\u003cp\u003eModels are statistical analyses that are carried out on the features.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-a-high-performance-computing-cluster\" class=\"anchor\" href=\"#running-on-a-high-performance-computing-cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on a high-performance computing cluster\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eLog in to your cluster\u0027s head node\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRequest an interactive job. Refer to your cluster\u0027s documentation for how to\ndo this\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn the interactive job, run the \u003ccode\u003eHALFpipe\u003c/code\u003e user interface, but add the flag\n\u003ccode\u003e--use-cluster\u003c/code\u003e to the end of the command. \u003cbr\u003e\nFor example, \u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_latest.sif --use-cluster\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs soon as you finish specifying all your data, features and models in the\nuser interface, \u003ccode\u003eHALFpipe\u003c/code\u003e will now generate everything needed to run on the\ncluster. For hundreds of subjects, this can take up to a few hours.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen \u003ccode\u003eHALFpipe\u003c/code\u003e exits, edit the generated submit script \u003ccode\u003esubmit.slurm.sh\u003c/code\u003e\naccording to your cluster\u0027s documentation and then run it. This submit script\nwill calculate everything except group statistics.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs soon as all processing has been completed, you can run group statistics.\nThis is usually very fast, so you can do this in an interactive session. Run\n\u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_latest.sif --only-model-chunk\u003c/code\u003e\nand then select \u003ccode\u003eRun without modification\u003c/code\u003e in the user interface.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA common issue with remote work via secure shell is that the connection may\nbreak after a few hours. For batch jobs this is not an issue, but for\ninteractive jobs this can be quite frustrating. When the connection is lost,\nthe node you were connected to will automatically quit all programs you were\nrunning. To prevent this, you can run interactive jobs within \u003ccode\u003escreen\u003c/code\u003e or\n\u003ccode\u003etmux\u003c/code\u003e (whichever is available). These commands allow you to open sessions in\nthe terminal that will continue running in the background even when you close\nor disconnect. Here\u0027s a quick overview of how to use the commands (more\nin-depth documentation is available for example at\n[\u003ca href=\"http://www.dayid.org/comp/tm.html\" rel=\"nofollow\"\u003ehttp://www.dayid.org/comp/tm.html\u003c/a\u003e]).\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOpen a new screen/tmux session on the head node by running either \u003ccode\u003escreen\u003c/code\u003e\nor \u003ccode\u003etmux\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRequest an interactive job from within the session, for example with\n\u003ccode\u003esrun --pty bash -i\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the command that you want to run\u003c/li\u003e\n\u003cli\u003eDetach from the screen/tmux session, meaning disconnecting with the ability\nto re-connect later \u003cbr\u003e\nFor screen, this is done by first pressing \u003ccode\u003eControl+a\u003c/code\u003e, then letting go, and\nthen pressing \u003ccode\u003ed\u003c/code\u003e on the keyboard. \u003cbr\u003e\nFor tmux, it\u0027s \u003ccode\u003eControl+b\u003c/code\u003e instead of \u003ccode\u003eControl+a\u003c/code\u003e. \u003cbr\u003e\nNote that this is always \u003ccode\u003eControl\u003c/code\u003e, even if you\u0027re on a mac.\u003c/li\u003e\n\u003cli\u003eClose your connection to the head node with \u003ccode\u003eControl+d\u003c/code\u003e. \u003ccode\u003escreen\u003c/code\u003e/\u003ccode\u003etmux\u003c/code\u003e\nwill remain running in the background\u003c/li\u003e\n\u003cli\u003eLater, connect again to the head node. Run \u003ccode\u003escreen -r\u003c/code\u003e or \u003ccode\u003etmux attach\u003c/code\u003e to\ncheck back on the interactive job. If everything went well and the command\nyou wanted to run finished, close the interactive job with \u003ccode\u003eControl+d\u003c/code\u003e and\nthen the \u003ccode\u003escreen\u003c/code\u003e/\u003ccode\u003etmux\u003c/code\u003e session with \u003ccode\u003eControl+d\u003c/code\u003e again. If the command\nhasn\u0027t finished yet, detach as before and come back later\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quality-checks\" class=\"anchor\" href=\"#quality-checks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality checks\u003c/h2\u003e\n\u003cp\u003ePlease see the manual at \u003ca href=\"https://docs.google.com/document/d/1evDkVaoXqSaxulp5eSxVqgaxro7yZl-gao70D0S2dH8\" rel=\"nofollow\"\u003ehttps://docs.google.com/document/d/1evDkVaoXqSaxulp5eSxVqgaxro7yZl-gao70D0S2dH8\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA visual report page \u003ccode\u003ereports/index.html\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA table with image quality metrics \u003ccode\u003ereports/reportvals.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA table containing the preprocessing status \u003ccode\u003ereports/reportpreproc.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe untouched \u003ccode\u003efmriprep\u003c/code\u003e derivatives. Some files have been omitted to save\ndisk space \u003ccode\u003efmriprep\u003c/code\u003e is very strict about only processing data that is\ncompliant with the BIDS standard. As such, we may need to format subjects\nnames for compliance. For example, an input subject named \u003ccode\u003esubject_01\u003c/code\u003e will\nappear as \u003ccode\u003esubject01\u003c/code\u003e in the \u003ccode\u003efmriprep\u003c/code\u003e derivatives. \u003ccode\u003ederivatives/fmriprep\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-subject-level-features\" class=\"anchor\" href=\"#subject-level-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubject-level features\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFor task-based, seed-based connectivity and dual regression features,\n\u003ccode\u003eHALFpipe\u003c/code\u003e outputs the statistical maps for the effect, the variance, the\ndegrees of freedom of the variance and the z-statistic. In FSL, the effect and\nvariance are also called \u003ccode\u003ecope\u003c/code\u003e and \u003ccode\u003evarcope\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-effect_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-variance_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-dof_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-z_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\nThe design and contrast matrix used for the final model will be outputted alongside\nthe statistical maps \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._feature-..._desc-design_matrix.tsv\u003c/code\u003e\n\u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._feature-..._desc-contrast_matrix.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReHo and fALFF are not calculated based on a linear model. As such, only one\nstatistical map of the z-scaled values will be output \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._alff.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._falff.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._reho.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor every feature, a JSON file containing a summary of the preprocessing\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esettings, and a list of the raw data files that were used for the analysis\n(\u003ccode\u003eRawSources\u003c/code\u003e) \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/....json\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor every feature, the corresponding brain mask is output beside the\nstatistical maps. Masks do not differ between different features calculated,\nthey are only copied out repeatedly for convenience \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/...desc-brain_mask.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAtlas-based connectivity outputs the time series and the full covariance and\ncorrelation matrices as text files \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._timeseries.txt\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._desc-covariance_matrix.txt\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._desc-correlation_matrix.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preprocessed-images\" class=\"anchor\" href=\"#preprocessed-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessed images\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMasked, preprocessed BOLD image \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._bold.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJust like for features \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._bold.json\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJust like for features \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._setting-..._desc-brain_mask.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFiltered confounds time series, where all filters that are applied to the BOLD\nimage are applied to the regressors as well. Note that this means that when\ngrand mean scaling is active, confounds time series are also scaled, meaning\nthat values such as \u003ccode\u003eframewise displacement\u003c/code\u003e can not be interpreted in terms\nof their original units anymore. \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._setting-..._desc-confounds_regressors.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-group-level\" class=\"anchor\" href=\"#group-level\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGroup-level\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrouplevel/...\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" href=\"#troubleshooting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTroubleshooting\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIf an error occurs, this will be output to the command line and simultaneously\nto the \u003ccode\u003eerr.txt\u003c/code\u003e file in the working directory\u003c/li\u003e\n\u003cli\u003eIf the error occurs while running, usually a text file detailing the error\nwill be placed in the working directory. These are text files and their file\nnames start with \u003ccode\u003ecrash\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eUsually, the last line of these text files contains the error message.\nPlease read this carefully, as may allow you to understand the error\u003c/li\u003e\n\u003cli\u003eFor example, consider the following error message:\n\u003ccode\u003eValueError: shape (64, 64, 33) for image 1 not compatible with first image shape (64, 64, 34) with axis == None\u003c/code\u003e\nThis error message may seem cryptic at first. However, looking at the\nmessage more closely, it suggests that two input images have different,\nincompatible dimensions. In this case, \u003ccode\u003eHALFpipe\u003c/code\u003e correctly recognized this\nissue, and there is no need for concern. The images in question will simply\nbe excluded from preprocessing and/or analysis\u003c/li\u003e\n\u003cli\u003eIn some cases, the cause of the error can be a bug in the \u003ccode\u003eHALFpipe\u003c/code\u003e code.\nPlease check that no similar issue has been reported\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues\"\u003ehere on GitHub\u003c/a\u003e. In this case,\nplease submit an\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues/new/choose\"\u003eissue\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-command-line-flags\" class=\"anchor\" href=\"#command-line-flags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand line flags\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-control-command-line-logging\" class=\"anchor\" href=\"#control-command-line-logging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eControl command line logging\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--verbose\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, only errors and warnings will be output to the command line. This\nmakes it easier to see when something goes wrong, because there is less output.\nHowever, if you want to be able to inspect what is being run, you can add the\n\u003ccode\u003e--verbose\u003c/code\u003e flag to the end of the command used to call \u003ccode\u003eHALFpipe\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eVerbose logs are always written to the \u003ccode\u003elog.txt\u003c/code\u003e file in the working directory,\nso going back and inspecting this log is always possible, even if the\n\u003ccode\u003e--verbose\u003c/code\u003e flag was not specified.\u003c/p\u003e\n\u003cp\u003eSpecifying the flag \u003ccode\u003e--debug\u003c/code\u003e will print additional, fine-grained messages. It\nwill also automatically start the\n\u003ca href=\"https://docs.python.org/3/library/pdb.html\" rel=\"nofollow\"\u003ePython Debugger\u003c/a\u003e when an error\noccurs. You should only use \u003ccode\u003e--debug\u003c/code\u003e if you know what you\u0027re doing.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-automatically-remove-unneeded-files\" class=\"anchor\" href=\"#automatically-remove-unneeded-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically remove unneeded files\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--keep\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e saves intermediate files for each pipeline step. This speeds up\nre-running with different settings, or resuming after a job after it was\ncancelled. The intermediate file are saved by the\n\u003ca href=\"https://nipype.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003enipype\u003c/code\u003e\u003c/a\u003e workflow engine, which is what\n\u003ccode\u003eHALFpipe\u003c/code\u003e uses internally. \u003ccode\u003enipype\u003c/code\u003e saves the intermediate files in the\n\u003ccode\u003enipype\u003c/code\u003e folder in the working directory.\u003c/p\u003e\n\u003cp\u003eIn environments with limited disk capacity, this can be problematic. To limit\ndisk usage, \u003ccode\u003eHALFpipe\u003c/code\u003e can delete intermediate files as soon as they are not\nneeded anymore. This behavior is controlled with the \u003ccode\u003e--keep\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eThe default option \u003ccode\u003e--keep some\u003c/code\u003e keeps all intermediate files from fMRIPrep and\nMELODIC, which would take the longest to re-run. We believe this is a good\ntradeoff between disk space and computer time. \u003ccode\u003e--keep all\u003c/code\u003e turns of all\ndeletion of intermediate files. \u003ccode\u003e--keep none\u003c/code\u003e deletes as much as possible,\nmeaning that the smallest amount possible of disk space will be used.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-configure-nipype\" class=\"anchor\" href=\"#configure-nipype\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigure nipype\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--nipype-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eomp-nthreads\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003ememory-gb\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003en-procs\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003erun-plugin\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e chooses sensible defaults for all of these values.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-choose-which-parts-to-run-or-to-skip\" class=\"anchor\" href=\"#choose-which-parts-to-run-or-to-skip\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoose which parts to run or to skip\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eonly\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eskip\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003espec-ui\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eworkflow\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003erun\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003emodel-chunk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA \u003ccode\u003eHALFpipe\u003c/code\u003e run is divided internally into three stages, spec-ui, workflow, and\nrun.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ccode\u003espec-ui\u003c/code\u003e stage is where you specify things in the user interface. It\ncreates the \u003ccode\u003espec.json\u003c/code\u003e file that contains all the information needed to run\n\u003ccode\u003eHALFpipe\u003c/code\u003e. To only run this stage, use the option \u003ccode\u003e--only-spec-ui\u003c/code\u003e. To skip\nthis stage, use the option \u003ccode\u003e--skip-spec-ui\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003eworkflow\u003c/code\u003e stage is where \u003ccode\u003eHALFpipe\u003c/code\u003e uses the \u003ccode\u003espec.json\u003c/code\u003e data to search\nfor all the files that match what was input in the user interface. It then\ngenerates a \u003ccode\u003enipype\u003c/code\u003e workflow for preprocessing, feature extraction and group\nmodels. \u003ccode\u003enipype\u003c/code\u003e then validates the workflow and prepares it for execution.\nThis usually takes a couple of minutes and cannot be parallelized. For\nhundreds of subjects, this may even take a few hours. This stage has the\ncorresponding option \u003ccode\u003e--only-workflow\u003c/code\u003e and \u003ccode\u003e--skip-workflow\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eThis stage saves several intermediate files. These are named\n\u003ccode\u003eworkflow.{uuid}.pickle.xz\u003c/code\u003e, \u003ccode\u003eexecgraph.{uuid}.pickle.xz\u003c/code\u003e and\n\u003ccode\u003eexecgraph.{n_chunks}_chunks.{uuid}.pickle.xz\u003c/code\u003e. The \u003ccode\u003euuid\u003c/code\u003e in the file name is\na unique identifier generated from the \u003ccode\u003espec.json\u003c/code\u003e file and the input files.\nIt is re-calculated every time we run this stage. The uuid algorithm produces\na different output if there are any changes (such as when new input files for\nnew subjects become available, or the \u003ccode\u003espec.json\u003c/code\u003e is changed, for example to\nadd a new feature or group model). Otherwise, the \u003ccode\u003euuid\u003c/code\u003e stays the same.\nTherefore, if a workflow file with the calculated \u003ccode\u003euuid\u003c/code\u003e already exists, then\nwe do not need to run this stage. We can simple re-use the workflow from the\nexisting file, and save some time.\u003c/li\u003e\n\u003cli\u003eIn this stage, we can also decide to split the execution into chunks. The flag\n\u003ccode\u003e--subject-chunks\u003c/code\u003e creates one chunk per subject. The flag \u003ccode\u003e--use-cluster\u003c/code\u003e\nautomatically activates \u003ccode\u003e--subject-chunks\u003c/code\u003e. The flag \u003ccode\u003e--n-chunks\u003c/code\u003e allows the\nuser to specify a specific number of chunks. This is useful if the execution\nshould be spread over a set number of computers. In addition to these, a model\nchunk is generated.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ccode\u003erun\u003c/code\u003e stage loads the \u003ccode\u003eexecgraph.{n_chunks}_chunks.{uuid}.pickle.xz\u003c/code\u003e file\ngenerated in the previous step and runs it. This file usually contains two\nchunks, one for the subject level preprocessing and feature extraction\n(\"subject level chunk\"), and one for group statistics (\"model chunk\"). To run\na specific chunk, you can use the flags \u003ccode\u003e--only-chunk-index ...\u003c/code\u003e and\n\u003ccode\u003e--only-model-chunk\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-working-directory\" class=\"anchor\" href=\"#working-directory\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorking directory\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--workdir\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-data-file-system-root\" class=\"anchor\" href=\"#data-file-system-root\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData file system root\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--fs-root\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eHALFpipe\u003c/code\u003e container, or really most containers, contain the entire base\nsystem needed to run\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003eFor questions or support, please submit an\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues/new/choose\"\u003eissue\u003c/a\u003e or contact us\nvia e-mail.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRole\u003c/th\u003e\n\u003cth\u003eE-mail address\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLea Waller\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:lea.waller@charite.de\"\u003elea.waller@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIlya Veer\u003c/td\u003e\n\u003ctd\u003eProject manager\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:ilya.veer@charite.de\"\u003eilya.veer@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSusanne Erk\u003c/td\u003e\n\u003ctd\u003eProject manager\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:susanne.erk@charite.de\"\u003esusanne.erk@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJupyterHub + High-Performance Computing\u003c/h1\u003e\u003ca id=\"user-content-jupyterhub--high-performance-computing\" class=\"anchor\" aria-label=\"Permalink: JupyterHub + High-Performance Computing\" href=\"#jupyterhub--high-performance-computing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eHigh performance Jupyter Notebooks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of this project is to connect JupyterHub to a high-performance computer (HPC). By automatically offloading the computations in a Jupyter notebook to the HPC system, even complex calculations are possible. While JupyterHub is deployed on a regular server, the notebooks themselves are spawned and run on the remote HPC system using a workload manager, such as Slurm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMotivation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe technical core of this project is the transparent integration of digital worksheets (Jupyter notebooks), in which learning content and programs can be displayed, edited and executed on the students\u0027 own laptops, with current cloud and high-performance computing (HPC) technologies. This provides the conditions for innovative, digital teaching that encourages independent and interactive development of, for example, data science applications, without imposing the complexity of using a high-performance computer system on the students. Instead, particularly computationally and data-intensive calculations are automatically offloaded to a high-performance computer, enabling even sophisticated analyses to be performed that would otherwise not be feasible on students\u0027 laptops.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeatures and use cases\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStarting a jupyter notebook server on a remote HPC system in a pre-defined singularity container\u003c/li\u003e\n\u003cli\u003eQuick config setup when using the Slurm configuration wizard\u003c/li\u003e\n\u003cli\u003eAutomatically create a singularity overlay so that user changes are persistent\u003c/li\u003e\n\u003cli\u003eGreat for managing courses with external participants\u003c/li\u003e\n\u003cli\u003ePossibility to include files in the notebook directory using WebDAV\u003c/li\u003e\n\u003cli\u003eSuitable for HPC users who have their own JupyterHub instance running and want to use HPC resources\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#jupyterhub--high-performance-computing\"\u003eJupyterHub + High-Performance Computing\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#table-of-contents\"\u003eTable of Contents\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation-of-jupyterhub-server\"\u003eInstallation of JupyterHub Server\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#jupyterhub-and-batchspawner\"\u003eJupyterHub and BatchSpawner\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ssh-tunnel-user\"\u003eSSH tunnel user\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#node-mapping\"\u003eNode mapping\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation-on-hpc-system\"\u003eInstallation on HPC System\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#install-using-pip\"\u003eInstall using pip\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#singularity-container\"\u003eSingularity Container\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#build-singularity-container\"\u003eBuild Singularity Container\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#compute\"\u003eCompute\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#gpu-tensorflow\"\u003eGPU (Tensorflow)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#the-configuration-file\"\u003eThe configuration file\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#slurm-configuration-wizard\"\u003eSlurm configuration wizard\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#examples\"\u003eExamples\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#debug-mode\"\u003eDebug mode\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#shibboleth-integration\"\u003eShibboleth Integration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#nbgrader-integration\"\u003eNBGrader Integration\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#changing-the-student-id-to-the-jupyterhub-logged-in-user-name\"\u003eChanging the Student ID to the JupyterHub logged in user name\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#create-nbgrader_configpy\"\u003eCreate nbgrader_config.py\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#security-precautions\"\u003eSecurity Precautions\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#singularity-host-filesystems\"\u003eSingularity Host Filesystems\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#jupyterhub-api-https\"\u003eJupyterHub API (HTTPS)\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#https\"\u003eHTTPS\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#tunnelbot-user\"\u003etunnelbot user\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation of JupyterHub Server\u003c/h2\u003e\u003ca id=\"user-content-installation-of-jupyterhub-server\" class=\"anchor\" aria-label=\"Permalink: Installation of JupyterHub Server\" href=\"#installation-of-jupyterhub-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section describes the required installations and configurations on the JupyterHub server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJupyterHub and BatchSpawner\u003c/h3\u003e\u003ca id=\"user-content-jupyterhub-and-batchspawner\" class=\"anchor\" aria-label=\"Permalink: JupyterHub and BatchSpawner\" href=\"#jupyterhub-and-batchspawner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe first thing you should do is install JupyterHub and BatchSpawner. For this purpose we provide an Ansible playbook which can be found in \u003ccode\u003e/jupyterhub-deployment/\u003c/code\u003e. See the README for details. Alternatively, you can follow the official installation instructions.\u003c/p\u003e\n\u003cp\u003eIf you decide to do the installations yourself, please proceed as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstall \u003ca href=\"https://jupyterhub.readthedocs.io/en/stable/installation-guide-hard.html\" rel=\"nofollow\"\u003eJupyterHub\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://github.com/jupyterhub/batchspawner\"\u003eBatchSpawner\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003einstall \u003ca href=\"https://github.com/jupyterhub/wrapspawner\"\u003eWrapSpawner\u003c/a\u003e (make sure to install it in the right environment: \u003ccode\u003e/opt/jupyterhub/bin/pip3 install git+https://github.com/jupyterhub/wrapspawner\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ecopy the JupyterHub configuration file \u003ccode\u003e/jupyterhub-deployment/config_files/jupyterhub_config.py\u003c/code\u003e to \u003ccode\u003e/opt/jupyterhub/etc/jupyterhub/\u003c/code\u003e (you will most likely have to edit this file afterwards to make it fit your needs)\u003c/li\u003e\n\u003cli\u003erestart the JupyterHub service\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSSH tunnel user\u003c/h3\u003e\u003ca id=\"user-content-ssh-tunnel-user\" class=\"anchor\" aria-label=\"Permalink: SSH tunnel user\" href=\"#ssh-tunnel-user\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA user called \u003ccode\u003etunnelbot\u003c/code\u003e is needed on the JupyterHub server. This user is responsible for starting an SSH tunnel between the compute node and the JupyterHub server. An SSH key pair for the above mentioned purpose must be generated. See \u003ccode\u003e/examples/jupyterhub_config.py\u003c/code\u003e for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNode mapping\u003c/h3\u003e\u003ca id=\"user-content-node-mapping\" class=\"anchor\" aria-label=\"Permalink: Node mapping\" href=\"#node-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJupyterHub extracts the execution host name of the HPC system (e.g. \u003ccode\u003enode01-002\u003c/code\u003e). When a notebook server is started, an SSH tunnel is established using the notebook port.\u003c/p\u003e\n\u003cp\u003eIn order for JupyterHub to be able to resolve the compute nodes host name, the \u003ccode\u003e/etc/hosts\u003c/code\u003e file must be edited. An example entry might look like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e127.0.0.1 node01-001\n127.0.0.1 node01-002\n127.0.0.1 node01-003\n...\n127.0.0.1 node12-048\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe actual node names depend on your HPC system of course.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation on HPC System\u003c/h2\u003e\u003ca id=\"user-content-installation-on-hpc-system\" class=\"anchor\" aria-label=\"Permalink: Installation on HPC System\" href=\"#installation-on-hpc-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section describes the required installations and configurations of the HPC system to enable the interaction with the JuypterHub server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou need a user who is allowed to allocate resources on the HPC system\n\u003cul\u003e\n\u003cli\u003eWith a SSH key pair. The public part must be deposited on the JupyterHub serer (\u003ccode\u003etunnelbot\u003c/code\u003e user)\u003c/li\u003e\n\u003cli\u003eThe public key part of the \u003ccode\u003etunnelbot\u003c/code\u003e-user created on the JupyterHub (-\u0026gt; \u003cem\u003e~/.ssh/authorized_keys\u003c/em\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity (\u0026gt; v.3.7.0)\u003c/li\u003e\n\u003cli\u003emkfs/e2fsprogs with following option:\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://git.kernel.org/pub/scm/fs/ext2/e2fsprogs.git/commit/?id=217c0bdf17899c0f79b73f76feeadd6d55863180\" rel=\"nofollow\"\u003ehttps://git.kernel.org/pub/scm/fs/ext2/e2fsprogs.git/commit/?id=217c0bdf17899c0f79b73f76feeadd6d55863180\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall using pip\u003c/h3\u003e\u003ca id=\"user-content-install-using-pip\" class=\"anchor\" aria-label=\"Permalink: Install using pip\" href=\"#install-using-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can download and install the required files with pip.\u003c/p\u003e\n\u003cp\u003eYou may want to build a small Python environment, or install the tools with \u003ccode\u003e--user\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m pip install --user jh-hpc-interface\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Container\u003c/h3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity Container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe examples are in the directory singularity/.\u003c/p\u003e\n\u003cp\u003eIf you do not want to use singularity, then change the value of \u003ccode\u003euse_singularity\u003c/code\u003e in jh_config.ini to false.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBuild Singularity Container\u003c/h4\u003e\u003ca id=\"user-content-build-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Build Singularity Container\" href=\"#build-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the container with the recipe files in singularity/ you have to clone this repository.\u003c/p\u003e\n\u003cp\u003eThe following commands replace USER_ID in the recipes to the output of \u003ccode\u003eid -u\u003c/code\u003e, create a new hidden file and build the singularity container with the new created file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eCompute\u003c/h5\u003e\u003ca id=\"user-content-compute\" class=\"anchor\" aria-label=\"Permalink: Compute\" href=\"#compute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eUSER_ID=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003eid -u\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e sed \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003es/USER_ID/\u003cspan class=\"pl-smi\"\u003e$USER_ID\u003c/span\u003e/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e singularity/Singularity \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e singularity/.recipefile_compute \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e singularity build --remote singularity/compute_jupyter.sif singularity/.recipefile_compute\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eGPU (Tensorflow)\u003c/h5\u003e\u003ca id=\"user-content-gpu-tensorflow\" class=\"anchor\" aria-label=\"Permalink: GPU (Tensorflow)\" href=\"#gpu-tensorflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eUSER_ID=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003eid -u\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e sed \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003es/USER_ID/\u003cspan class=\"pl-smi\"\u003e$USER_ID\u003c/span\u003e/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e singularity/Singularity_Tensorflow \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e singularity/.recipefile_gpu \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e singularity build --remote singularity/gpu_jupyter.sif singularity/.recipefile_gpu\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003esingularity build help section\u003c/em\u003e:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003e-r, --remote\u003c/strong\u003e build image remotely (does not require root)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003ePlease refer to the official docs on how to use the remote build feature: \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003ehttps://sylabs.io/docs/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe configuration file\u003c/h3\u003e\u003ca id=\"user-content-the-configuration-file\" class=\"anchor\" aria-label=\"Permalink: The configuration file\" href=\"#the-configuration-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the directory \u003cstrong\u003ebin/\u003c/strong\u003e is a script, which is deposited after the installation on the system.\u003c/p\u003e\n\u003cp\u003eWith the following call you can display the location of the configuration file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ jh_wrapper getconfig\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo learn more about the configuration file, see \u003ca href=\"docs/jh_config.ini.md\"\u003edocs/jh_config.ini.md\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSlurm configuration wizard\u003c/h3\u003e\u003ca id=\"user-content-slurm-configuration-wizard\" class=\"anchor\" aria-label=\"Permalink: Slurm configuration wizard\" href=\"#slurm-configuration-wizard\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith the configuration wizard you can prepare your HPC environment.\u003c/p\u003e\n\u003cp\u003eThe script interactively goes through the configuration file and creates a temporary file which can be copied with a simple \u003ccode\u003ecp\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo start the wizard type the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ jh_slurm_wizard\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will find examples for the configuration files \u003cstrong\u003ejh_config.ini\u003c/strong\u003e and \u003cstrong\u003ejupyterhub_config.py\u003c/strong\u003e in the directory \u003cem\u003eexamples/\u003c/em\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDebug mode\u003c/h3\u003e\u003ca id=\"user-content-debug-mode\" class=\"anchor\" aria-label=\"Permalink: Debug mode\" href=\"#debug-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default the logs contain only information such as warnings or error messages.\nIt is also possible to switch on the debug mode, which writes extended information into the log files.\u003c/p\u003e\n\u003cp\u003eJust set \u003ccode\u003elog_level\u003c/code\u003e in the configuration file to \u0027DEBUG\u0027.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShibboleth Integration\u003c/h2\u003e\u003ca id=\"user-content-shibboleth-integration\" class=\"anchor\" aria-label=\"Permalink: Shibboleth Integration\" href=\"#shibboleth-integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eShibboleth authentication was set up for a JupyterHub server in a test environment. See \u003ccode\u003e./shibboleth/\u003c/code\u003e for an example configuration.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNBGrader Integration\u003c/h2\u003e\u003ca id=\"user-content-nbgrader-integration\" class=\"anchor\" aria-label=\"Permalink: NBGrader Integration\" href=\"#nbgrader-integration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation instructions:\n\u003ca href=\"https://nbgrader.readthedocs.io/en/latest/configuration/jupyterhub_config.html\" rel=\"nofollow\"\u003ehttps://nbgrader.readthedocs.io/en/latest/configuration/jupyterhub_config.html\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo create an exchange directory for every user, just create an empty directory in \u003ccode\u003e$scratch_dir\u003c/code\u003e and mount it into the container with \u003ccode\u003e$singularity_bind_extra\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eChanging the Student ID to the JupyterHub logged in user name\u003c/h3\u003e\u003ca id=\"user-content-changing-the-student-id-to-the-jupyterhub-logged-in-user-name\" class=\"anchor\" aria-label=\"Permalink: Changing the Student ID to the JupyterHub logged in user name\" href=\"#changing-the-student-id-to-the-jupyterhub-logged-in-user-name\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince the containers run as user \u003ccode\u003ejovyan\u003c/code\u003e, the value from the \u003ccode\u003e$JUPYTERHUB_USER\u003c/code\u003e variable is automatically used.\u003c/p\u003e\n\u003cp\u003eSee here for more information:\n\u003ca href=\"https://jupyter.readthedocs.io/en/latest/community/content-community.html#what-is-a-jovyan\" rel=\"nofollow\"\u003ehttps://jupyter.readthedocs.io/en/latest/community/content-community.html#what-is-a-jovyan\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCreate nbgrader_config.py\u003c/h3\u003e\u003ca id=\"user-content-create-nbgrader_configpy\" class=\"anchor\" aria-label=\"Permalink: Create nbgrader_config.py\" href=\"#create-nbgrader_configpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee here: \u003ca href=\"https://nbgrader.readthedocs.io/en/stable/configuration/nbgrader_config.html#use-case-3-nbgrader-and-jupyterhub\" rel=\"nofollow\"\u003ehttps://nbgrader.readthedocs.io/en/stable/configuration/nbgrader_config.html#use-case-3-nbgrader-and-jupyterhub\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo make \u003cem\u003enbgrader_config.py\u003c/em\u003e available in the container, just append the file in \u003ccode\u003e$singularity_bind_extra\u003c/code\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSecurity Precautions\u003c/h2\u003e\u003ca id=\"user-content-security-precautions\" class=\"anchor\" aria-label=\"Permalink: Security Precautions\" href=\"#security-precautions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Host Filesystems\u003c/h3\u003e\u003ca id=\"user-content-singularity-host-filesystems\" class=\"anchor\" aria-label=\"Permalink: Singularity Host Filesystems\" href=\"#singularity-host-filesystems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn case you are using Singularity, the host file system may be automatically mounted into the container when you start a Singularity Container.\u003c/p\u003e\n\u003cp\u003eA possible cause is the option \u003ccode\u003emount hostfs\u003c/code\u003e in \u003cem\u003esingularity.conf\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSee here: \u003ca href=\"https://sylabs.io/guides/3.5/admin-guide/configfiles.html#singularity-conf\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/admin-guide/configfiles.html#singularity-conf\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJupyterHub API (HTTPS)\u003c/h3\u003e\u003ca id=\"user-content-jupyterhub-api-https\" class=\"anchor\" aria-label=\"Permalink: JupyterHub API (HTTPS)\" href=\"#jupyterhub-api-https\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHTTPS\u003c/h4\u003e\u003ca id=\"user-content-https\" class=\"anchor\" aria-label=\"Permalink: HTTPS\" href=\"#https\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee here for more information:\n\u003ca href=\"https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html\" rel=\"nofollow\"\u003ehttps://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003etunnelbot user\u003c/h3\u003e\u003ca id=\"user-content-tunnelbot-user\" class=\"anchor\" aria-label=\"Permalink: tunnelbot user\" href=\"#tunnelbot-user\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can increase the security by deactivating shell access for this user.\u003c/p\u003e\n\u003cp\u003eJust type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eusermod -s /bin/false tunnelbot\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTroubleshooting\u003c/h2\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen problems occur with the JupyterHub, some information can be obtained from the logs when debug mode is enabled:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/jupyterhub/jupyterhub/wiki/Debug-Jupyterhub\"\u003ehttps://github.com/jupyterhub/jupyterhub/wiki/Debug-Jupyterhub\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 1, - "topics": [], - "updated_at": 1627454858.0 + "subscribers_count": 8, + "topics": [ + "jupyter", + "jupyterhub", + "hpc", + "singularity" + ], + "updated_at": 1718354266.0 }, { "data_format": 2, - "description": "Part of the sc-eQTLgen consortium pipeline. Step 1, where the QC is done.", + "description": "Repository hosting the code associated with \"Fast and stable MAP-Elites in noisy domains using deep grids\"", "filenames": [ - "Singularity.Imputation", - "Singularity.WGpipeline_deb913", - "Singularity.Imputation_deb913", - "Singularity.WGpipeline" + "Singularity" ], - "full_name": "sc-eQTLgen-consortium/WG1-pipeline-QC", - "latest_release": "v1.0.0", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-wg1-pipeline-qc\" class=\"anchor\" href=\"#wg1-pipeline-qc\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWG1-pipeline-QC\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/44268007/89252548-35b96f80-d659-11ea-97e9-4b4176df5f08.png\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/44268007/89252548-35b96f80-d659-11ea-97e9-4b4176df5f08.png\" width=\"300\" height=\"140\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePart of the sceQTL-Gen consortium pipeline. Step 1, where the QC is done.\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://wg1-pipeline-qc.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e for information on running the QC pipeline.\u003c/p\u003e\n", + "full_name": "adaptive-intelligent-robotics/Deep-Grid_MAP-Elites", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFast and stable MAP-Elites in noisy domains using deep grids\u003c/h1\u003e\u003ca id=\"user-content-fast-and-stable-map-elites-in-noisy-domains-using-deep-grids\" class=\"anchor\" aria-label=\"Permalink: Fast and stable MAP-Elites in noisy domains using deep grids\" href=\"#fast-and-stable-map-elites-in-noisy-domains-using-deep-grids\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4459\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains the code associated with \u003ca href=\"https://direct.mit.edu/isal/proceedings/isal2020/32/273/98397\" rel=\"nofollow\"\u003eFast and stable MAP-Elites in noisy domains using deep grids\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis code proposes an implementation of several MAP-Elites variants to handle uncertainty: the Explicit-averaging approach, the two Adaptive-sampling approaches proposed in \u003ca href=\"https://dl.acm.org/doi/abs/10.1145/3319619.3321904\" rel=\"nofollow\"\u003eMAP-Elites for noisy domains by adaptive sampling\u003c/a\u003e and the DG-MAP-Elites approach. It allows to compare these approaches on the three tasks described in the paper.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLibraries and dependencies\u003c/h1\u003e\u003ca id=\"user-content-libraries-and-dependencies\" class=\"anchor\" aria-label=\"Permalink: Libraries and dependencies\" href=\"#libraries-and-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe implementation of all tasks and algorithms is based on the qd-branch of the C++ \u003ca href=\"https://github.com/sferes2/sferes2\"\u003eSferes2\u003c/a\u003e library presented in \u003ca href=\"https://ieeexplore.ieee.org/abstract/document/5586158/?casa_token=EhBJLkircvMAAAAA:ls8I90Y5H2vsJk5RxCYs8X1T9yZHDhDEz5S6g5gatOzETle1LK_ib8zwodx6t5J_-Uwq_YP9\" rel=\"nofollow\"\u003eSferesv2: Evolvin\u0027 in the multi-core world\u003c/a\u003e, and the hexapod control task uses the Dart simulator introduced in \u003ca href=\"https://joss.theoj.org/papers/10.21105/joss.00500.pdf\" rel=\"nofollow\"\u003eDart: Dynamic animation and robotics toolkit\u003c/a\u003e.\nFurthermore, the analysis of the results is based on \u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003ePanda\u003c/a\u003e, \u003ca href=\"https://matplotlib.org/\" rel=\"nofollow\"\u003eMatplotlib\u003c/a\u003e and \u003ca href=\"https://seaborn.pydata.org/index.html\" rel=\"nofollow\"\u003eSeaborn\u003c/a\u003e libraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eStructure\u003c/h1\u003e\u003ca id=\"user-content-structure\" class=\"anchor\" aria-label=\"Permalink: Structure\" href=\"#structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main part of the code contains the following files and folders:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edeep_grid\u003c/code\u003e contains the main structure of all algorithms based on the structure of QD algorithms introduced in \u003ca href=\"https://ieeexplore.ieee.org/abstract/document/7959075/\" rel=\"nofollow\"\u003eQuality and diversity optimization: A unifying modular framework\u003c/a\u003e. The approaches differ by the nature of their selector, the add-function of their container and, in the specific case of Adaptive sampling, the descriptor they are using for the individuals, which is implemented in the structure of the cells of the grid.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etask\u003c/code\u003e contains a re-definition of the qd fitness from the Sferes2 library which allow to handle re-sampling of the solutions. It also contains an implementation of each of the three tasks used in the paper, build on top of the original deterministic tasks used in previous works.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emodifier\u003c/code\u003e defines the procedure to evaluate the \"true\" value of fitness and behaviour descriptor of each cell as described in the paper.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estat\u003c/code\u003e implements all the stats used to compare the algorithms.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun_utils\u003c/code\u003e contains utils function used to read the options of each run and run all algorithms.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition, the \u003ccode\u003eanalysis\u003c/code\u003e folder is used for the results analysis, the \u003ccode\u003ewaf_tools\u003c/code\u003e and \u003ccode\u003ewscript\u003c/code\u003e files for compilation, and the \u003ccode\u003eressource\u003c/code\u003e folder and \u003ccode\u003eSingularity\u003c/code\u003e file to compile the Singularity container for this experiment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExecution\u003c/h1\u003e\u003ca id=\"user-content-execution\" class=\"anchor\" aria-label=\"Permalink: Execution\" href=\"#execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe results of the paper can be reproduced by running the Singularity container image of the experiment. Instructions to install Singularity can be found in \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity documentation\u003c/a\u003e.\nTo pull the image from Singularity Hub, use the command: \u003ccode\u003esingularity pull shub://adaptive-intelligent-robotics/Deep-Grid_MAP-Elites\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis container contains an \u003ccode\u003eapp\u003c/code\u003e for each approach-task combination, defined in the \u003ccode\u003eSingularity\u003c/code\u003e file:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eTruth_arm_var\u003c/code\u003e, \u003ccode\u003eTruth_rastrigin\u003c/code\u003e, \u003ccode\u003eTruth_hexa\u003c/code\u003e: Noise-free Baseline for each of the three tasks.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNaive_1_arm_var\u003c/code\u003e, \u003ccode\u003eNaive_1_rastrigin\u003c/code\u003e, \u003ccode\u003eNaive_1_hexa\u003c/code\u003e: Explicit-averaging approach with 1 re-sampling.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNaive_50_arm_var\u003c/code\u003e, \u003ccode\u003eNaive_50_rastrigin\u003c/code\u003e, \u003ccode\u003eNaive_50_hexa\u003c/code\u003e: Explicit-averaging approach with 50 re-sampling.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdapt_arm_var\u003c/code\u003e, \u003ccode\u003eAdapt_rastrigin\u003c/code\u003e, \u003ccode\u003eAdapt_hexa\u003c/code\u003e: Adaptive sampling approach without drifting elites.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdapt_BD_10_arm_var\u003c/code\u003e, \u003ccode\u003eAdapt_BD_10_rastrigin\u003c/code\u003e, \u003ccode\u003eAdapt_BD_10_hexa\u003c/code\u003e: Adaptive sampling approach with drifting elites.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDeep_50_arm_var\u003c/code\u003e, \u003ccode\u003eDeep_50_rastrigin\u003c/code\u003e, \u003ccode\u003eDeep_50_hexa\u003c/code\u003e: DG-MAP-Elites approach with depth 50.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAnalysis\u003c/code\u003e: apps to generate the graphs and container plots of all variants and tasks which results are stored in the folder given as input.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA given app can be run with the following command: \u003ccode\u003esingularity run --app *app_name* Deep-Grid_MAP-Elites_latest.sif\u003c/code\u003e. All run-parameters are defined inside the apps, and the results of the execution are solved in a \u003ccode\u003eresults\u003c/code\u003e folder, outside of the image, at the same location.\u003c/p\u003e\n\u003cp\u003eThis container can be recompile using the \u003ccode\u003eSingularity\u003c/code\u003e file.\u003c/p\u003e\n", "stargazers_count": 5, "subscribers_count": 2, "topics": [], - "updated_at": 1642593783.0 + "updated_at": 1717291136.0 }, { "data_format": 2, "description": null, "filenames": [ - "singularity/Singularity" + "Singularity" ], - "full_name": "tikk3r/lofar-grid-hpccloud", - "latest_release": "v4.0.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5f618187158129a12605b61c2558a97b7014bf61a63dcbb58ecc23d53ade59a4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74696b6b33722f6c6f6661722d677269642d687063636c6f75643f736f72743d73656d766572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5f618187158129a12605b61c2558a97b7014bf61a63dcbb58ecc23d53ade59a4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74696b6b33722f6c6f6661722d677269642d687063636c6f75643f736f72743d73656d766572\" data-canonical-src=\"https://img.shields.io/github/v/release/tikk3r/lofar-grid-hpccloud?sort=semver\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b498f0b23c001d15b8b32b01a58375128a6fd5886fbefc3906a2164b36556ef5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d677269642d687063636c6f75642e7376673f6c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b498f0b23c001d15b8b32b01a58375128a6fd5886fbefc3906a2164b36556ef5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d677269642d687063636c6f75642e7376673f6c6f676f3d676974687562\" data-canonical-src=\"https://img.shields.io/github/license/tikk3r/lofar-grid-hpccloud.svg?logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/136925861\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad3ce50d6d0bdd702c67f43f248e79b036a12ebf23efdccde0d13eb15d31bf9e/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3133363932353836312e737667\" data-canonical-src=\"https://zenodo.org/badge/136925861.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ch1\u003e\u003ca id=\"user-content-lofar-grid-hpccloud\" class=\"anchor\" aria-hidden=\"true\" href=\"#lofar-grid-hpccloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elofar-grid-hpccloud\u003c/h1\u003e\n\u003cp\u003eThis repository hold resources for deploying the LOFAR software (genericpipeline) and related tools through Singularity containers. These containers are general, but at the same time somewhat tailored for SKSP use.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003emaster\u003c/code\u003e branch is empty. Currently the images on this branch (\u003ccode\u003efedora-py3\u003c/code\u003e) are based on the Fedora 34 Linux distribution, which is available from \u003ca href=\"https://hub.docker.com/_/fedora\" rel=\"nofollow\"\u003eDockerHub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAs this branch no longer includes Python 2, the genericpipeline framework is \u003cem\u003enot\u003c/em\u003e included in these recipes anymore (see the \u003ca href=\"https://github.com/tikk3r/lofar-grid-hpccloud/tree/fedora\"\u003efedora branch\u003c/a\u003e for that). Pipelines like prefactor (now LINC) are or have moved to CWL.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h2\u003e\n\u003cp\u003eTo build a full LOFAR Singularity image, do the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTurn on MKL and/or CUDA in \u003cstrong\u003esingularity/Singularity\u003c/strong\u003e, if desired, by setting \u003ccode\u003eHAS_MKL=true\u003c/code\u003e and/or \u003ccode\u003eHAS_CUDA=true\u003c/code\u003e. Set them to \u003ccode\u003efalse\u003c/code\u003e if you do not require those.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOptimise your container for a desired architecture by updating the \u003ccode\u003eMARCH\u003c/code\u003e and \u003ccode\u003eMTUNE\u003c/code\u003e variables to the appropriate values. If you want to build for a generic machine, set these to \u003ccode\u003eMARCH=\u0027x86-64\u0027\u003c/code\u003e and \u003ccode\u003eMTUNE=\u0027generic\u0027\u003c/code\u003e, respectively.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild \u003cstrong\u003esingulariy/Singularity\u003c/strong\u003e by running\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo SINGULARITY_CACHEDIR=$PWD SINGULARITY_TMPDIR=$PWD singularity build lofar_sksp.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePre-built containers are public hosted at \u003ca href=\"https://lofar-webdav.grid.sara.nl/software/shub_mirror/tikk3r/lofar-grid-hpccloud/\" rel=\"nofollow\"\u003eSURFSara\u003c/a\u003e. Sort by date to find the latest container there.\u003c/p\u003e\n", + "full_name": "Kurorororo/pplanner", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePPlanner\u003c/h1\u003e\u003ca id=\"user-content-pplanner\" class=\"anchor\" aria-label=\"Permalink: PPlanner\" href=\"#pplanner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePPlanner is a classical planner implementing parallel heuristic search algorithms.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReference\u003c/h2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-label=\"Permalink: Reference\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eKuroiwa R, Fukunaga A. 2020. Analyzing and Avoiding Pathological Behavior of Parallel Best-First Search. Proc. the 30th International Conference on Automated Planning and Scheduling. (ICAPS 2020)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker container does not support MPI based distributed parallel search methods.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e -t pplanner\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build pplanner.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eManual Build\u003c/h3\u003e\u003ca id=\"user-content-manual-build\" class=\"anchor\" aria-label=\"Permalink: Manual Build\" href=\"#manual-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see Dockerfile and Singularity to know the commands to build.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInput a SAS+ file and a config file.\nThe planner generates \u003ccode\u003esas_plan\u003c/code\u003e after finding a solution.\u003c/p\u003e\n\u003cp\u003eGBFS using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/GBFS-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHDGBFS using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -n 16 ./bin/mpi_planner -f output.sas -c config_examples/HDGBFS-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNE using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -n 16 ./bin/mpi_planner -f output.sas -c config_examples/NE-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLG using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -n 16 ./bin/mpi_planner -f output.sas -c config_examples/LG-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eKPGBFS using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/KPGBFS-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eP_{GBFS} using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/PGBFS-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eP_{GBFS}/C using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/PGBFS-C-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePUHF using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/PUHF-FF.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSPUHF using the FF heuristic.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./bin/planner -f output.sas -c config_examples/SPUHF-FF.json\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 5, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [], - "updated_at": 1676586378.0 + "updated_at": 1690291184.0 }, { "data_format": 2, - "description": "Module providing brain MR images pre-processing workflows for Deep Learning. ", + "description": "Source code to reproduce the figures of the boostDM paper", "filenames": [ - "containers/Singularity.dmriprep" + "Singularity" ], - "full_name": "neurospin-deepinsight/brainprep", + "full_name": "bbglab/boostdm-analyses", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eboostDM manuscript analyses\u003c/h1\u003e\u003ca id=\"user-content-boostdm-manuscript-analyses\" class=\"anchor\" aria-label=\"Permalink: boostDM manuscript analyses\" href=\"#boostdm-manuscript-analyses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSource code to reproduce the figures of the paper:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eIn silico saturation mutagenesis of cancer genes\u003c/strong\u003e\u003cbr\u003e\nFerran Mui\u00f1os, Francisco Martinez-Jimenez, Oriol Pich, Abel Gonzalez-Perez, Nuria Lopez-Bigas\u003cbr\u003e\nDOI: \u003ca href=\"https://doi.org/10.1038/s41586-021-03771-1\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41586-021-03771-1\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContent\u003c/h2\u003e\u003ca id=\"user-content-content\" class=\"anchor\" aria-label=\"Permalink: Content\" href=\"#content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo contains the source code to reproduce the main and extended figures of the paper.\u003cbr\u003e\nEach figure has its own jupyter notebook to render the figure\u0027s panels.\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMain figures\u003c/h4\u003e\u003ca id=\"user-content-main-figures\" class=\"anchor\" aria-label=\"Permalink: Main figures\" href=\"#main-figures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFigure 1: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Figure1/display_panels_Figure1.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Figure1.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nFigure 2: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Figure2/display_panels_Figure2.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Figure2.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nFigure 3: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Figure3/display_panels_Figure3.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Figure3.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nFigure 4: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Figure4/display_panels_Figure4.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Figure4.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExtended Figures\u003c/h4\u003e\u003ca id=\"user-content-extended-figures\" class=\"anchor\" aria-label=\"Permalink: Extended Figures\" href=\"#extended-figures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtended Figure 1: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_1/display_panels_Extended_Figure_1.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure1.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 2: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_2/display_panels_Extended_Figure_2.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure2.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 3: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_3/display_panels_Extended_Figure_3.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure3.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 4: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_4/display_panels_Extended_Figure_4.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure4.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 5: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_5/display_panels_Extended_Figure_5.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure5.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 6: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_6/display_panels_Extended_Figure_6.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure6.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 7: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_7/display_panels_Extended_Figure_7.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure7.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 8: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_8/display_panels_Extended_Figure_8.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure8.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\nExtended Figure 9: [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Extended_Figure_9/display_panels_Extended_Figure_9.ipynb\"\u003eipynb\u003c/a\u003e] [\u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/figures_paper/Extended_Figure9.pdf\"\u003epdf\u003c/a\u003e]\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eComplementary content\u003c/h2\u003e\u003ca id=\"user-content-complementary-content\" class=\"anchor\" aria-label=\"Permalink: Complementary content\" href=\"#complementary-content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can access to boostDM source code and documentation in the \u003ca href=\"https://bitbucket.org/bbglab/boostdm/src/release/\" rel=\"nofollow\"\u003eboostDM pipeline repository\u003c/a\u003e.\u003cbr\u003e\nYou can explore and download the main outputs of boostDM in the \u003ca href=\"https://www.intogen.org/boostdm\" rel=\"nofollow\"\u003eboostDM website\u003c/a\u003e.\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDownload source data\u003c/h4\u003e\u003ca id=\"user-content-download-source-data\" class=\"anchor\" aria-label=\"Permalink: Download source data\" href=\"#download-source-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll the code features in this repo feeds on source data.\u003c/p\u003e\n\u003cp\u003eMake sure that you download a stable copy of the source data from zenodo and keep it in the root of the repo\nfrom \u003ca href=\"https://zenodo.org/\" rel=\"nofollow\"\u003ezenodo\u003c/a\u003e as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install zenodo_get\n$ bash get.sh\n$ tar -xvf source-data/source-data-zenodo.tar.gz\n$ cp -r source-data/boostdm-analyses .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun notebooks with singularity\u003c/h4\u003e\u003ca id=\"user-content-run-notebooks-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Run notebooks with singularity\" href=\"#run-notebooks-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe notebooks must be run on a jupyter-notebook or jupyter-lab session launched from\n\u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image that already satisfies all the dependencies for the notebooks to run.\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFollow these steps:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html#\" rel=\"nofollow\"\u003eInstall\u003c/a\u003e the latest Singularity release\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a singularity image using the \u003ca href=\"https://github.com/bbglab/boostdm-analyses/blob/master/Singularity\"\u003eSingularity\u003c/a\u003e recipe:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build boostdm-analyses.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNow you can run the notebooks from singularity:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity exec boostdm-analyses.sif jupyter-lab\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 5, + "subscribers_count": 8, + "topics": [ + "mutations", + "cancer-genes", + "drivers" + ], + "updated_at": 1723033039.0 + }, + { + "data_format": 2, + "description": "Snakemake pipelines to run the analysis for the Illumina vs. Nanopore comparison.", + "filenames": [ + "analysis/assembly/containers/Singularity.canu" + ], + "full_name": "mbhall88/head_to_head_pipeline", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePaper\u003c/h3\u003e\u003ca id=\"user-content-paper\" class=\"anchor\" aria-label=\"Permalink: Paper\" href=\"#paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eHall, M. B. et al. Evaluation of Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation: a genomic analysis. \u003cem\u003eThe Lancet Microbe\u003c/em\u003e 0, (2022) doi: \u003ca href=\"https://doi.org/10.1016/S2666-5247(22)00301-9\" rel=\"nofollow\"\u003e10.1016/S2666-5247(22)00301-9\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003cp\u003eThis repository holds the pipelines/scripts used for our paper analysing Illumina and\nNanopore for \u003cem\u003eM.tuberculosis\u003c/em\u003e drug resistance calling and transmission clustering.\u003c/p\u003e\n\u003cp\u003eFor people wanting to analyse their Nanopore data in the same manner as we did in this paper, we would suggest using \u003ca href=\"https://github.com/mbhall88/tbpore\"\u003ehttps://github.com/mbhall88/tbpore\u003c/a\u003e, which is a python program that runs the drug resistance prediction and clustering (with a smaller decontamination database) components of this pipeline. It is actively maintained and much easier to use.\u003c/p\u003e\n\u003cp\u003eAll pipelines require the following dependencies to be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://snakemake.github.io/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e (and\n\u003ca href=\"https://github.com/mamba-org/mamba\"\u003eMamba\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/docs\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eThe Python library \u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003e\u003ccode\u003epandas\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee subdirectories for more specific information about different pipelines. They are\nnested according to their dependence on the outputs of each pipeline.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"data/QC\"\u003eQuality Control\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/assembly\"\u003eAssembly\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"analysis/baseline_variants\"\u003eBaseline variant analysis\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/transmission_clustering\"\u003eTransmission clustering\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/resistance_prediction\"\u003eDrug Resistance Prediction\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following pipelines are not relevant to the work in the final paper.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"data/H37Rv_PRG\"\u003eH37Rv PRG construction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/pandora_variants\"\u003ePandora variant analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData availability\u003c/h1\u003e\u003ca id=\"user-content-data-availability\" class=\"anchor\" aria-label=\"Permalink: Data availability\" href=\"#data-availability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll data is submitted under the Project accession \u003cstrong\u003ePRJEB49093\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe accessions and all relevant sample metadata for this study can be found at \u003ca href=\"https://doi.org/10.6084/m9.figshare.19304648\" rel=\"nofollow\"\u003ehttps://doi.org/10.6084/m9.figshare.19304648\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe raw Nanopore data is available to download from: \u003ca href=\"https://ftp.ebi.ac.uk/pub/databases/ont_tb_eval2022/\" rel=\"nofollow\"\u003ehttps://ftp.ebi.ac.uk/pub/databases/ont_tb_eval2022/\u003c/a\u003e. See the sample metadata file for mappings between samples and the relevant Nanopore runs and barcode numbers.\u003c/p\u003e\n", + "stargazers_count": 5, + "subscribers_count": 4, + "topics": [ + "tuberculosis", + "snakemake", + "pipeline", + "bioinformatics", + "nanopore", + "illumina", + "drug-resistance", + "transmission", + "clustering", + "diagnostics" + ], + "updated_at": 1699458270.0 + }, + { + "data_format": 2, + "description": "https://github.com/NEFSC/READ-EDAB-neusAtlantis/wiki", + "filenames": [ + "Singularity_6536", + "Singularity_6681" + ], + "full_name": "NEFSC/READ-EDAB-neusAtlantis", + "latest_release": "v2.2.1", + "readme": "\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eneus-atlantis\u003c/h1\u003e\u003ca id=\"user-content-neus-atlantis\" class=\"anchor\" aria-label=\"Permalink: neus-atlantis\" href=\"#neus-atlantis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/NOAA-EDAB/neus-atlantis/workflows/gitleaks/badge.svg\"\u003e\u003cimg src=\"https://github.com/NOAA-EDAB/neus-atlantis/workflows/gitleaks/badge.svg\" alt=\"gitleaks\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003eAtlantis is a series of submodels (biological, geophysical, fishing,\nassessment, and management) and as a whole simulates a Management\nStrategy Evaluation (MSE) procedure which allows for qualitative\ncomparisons of different management choices. We have parameterized\nAtlantis for the Northeast United States Continental Shelf (NEUS),\nincluding the major functional groups from an ecosystem perspective. We\nhave also parameterized and simulated the physiographic dynamics of the\necosystem, as well as the most important fleets. Our goal was to\nrecreate, at least in approximate terms, the biomass, catch, and effort\ntrends in the NEUS from 1964 to the mid 2000s.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDevelopment team\u003c/h3\u003e\u003ca id=\"user-content-development-team\" class=\"anchor\" aria-label=\"Permalink: Development team\" href=\"#development-team\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn alphabetical order:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/jcaracappa1\"\u003ejcaracappa1\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/gambler1650\"\u003egambler1650\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/jcaracappa1\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/57966543?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/gambler1650\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/5949383?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTeam members\u003c/h3\u003e\u003ca id=\"user-content-team-members\" class=\"anchor\" aria-label=\"Permalink: Team members\" href=\"#team-members\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMembers of the Atlantis team other than the core development team:\u003c/p\u003e\n\u003cp\u003eIn alphabetical order:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/andybeet\"\u003eandybeet\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/sgaichas\"\u003esgaichas\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/slarge\"\u003eslarge\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/slucey\"\u003eslucey\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/RMORSEcode\"\u003eRMORSEcode\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/andybeet\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/22455149?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/sgaichas\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/8172302?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/slarge\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/5000131?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/slucey\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/5578254?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/RMORSEcode\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/10620840?s=100\u0026amp;v=4\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLegal disclaimer\u003c/h4\u003e\u003ca id=\"user-content-legal-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Legal disclaimer\" href=\"#legal-disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eThis repository is a scientific product and is not official\ncommunication of the National Oceanic and Atmospheric Administration, or\nthe United States Department of Commerce. All NOAA GitHub project code\nis provided on an \u2018as is\u2019 basis and the user assumes responsibility for\nits use. Any claims against the Department of Commerce or Department of\nCommerce bureaus stemming from the use of this GitHub project will be\ngoverned by all applicable Federal law. Any reference to specific\ncommercial products, processes, or services by service mark, trademark,\nmanufacturer, or otherwise, does not constitute or imply their\nendorsement, recommendation or favoring by the Department of Commerce.\nThe Department of Commerce seal and logo, or the seal and logo of a DOC\nbureau, shall not be used in any manner to imply endorsement of any\ncommercial product or activity by DOC or the United States Government.\u003c/em\u003e\u003c/p\u003e\n", "stargazers_count": 5, "subscribers_count": 2, "topics": [], - "updated_at": 1675756969.0 + "updated_at": 1726680271.0 }, { "data_format": 2, - "description": "Nextflow Pipeline for the analysis of Double Progressive Alignment (DPA)", + "description": "Code and Data for \"Biological network growth in complex environments - a computational framework\"", "filenames": [ - "singularity/Singularity", - "singularity/.ipynb_checkpoints/Singularity-checkpoint" + "Singularity/Singularity.def" ], - "full_name": "evanfloden/dpa-analysis", - "latest_release": "v0.2.6", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-fast-and-accurate-large-multiple-sequence-alignments-using-root-to-leave-regressive-computation\" class=\"anchor\" aria-hidden=\"true\" href=\"#fast-and-accurate-large-multiple-sequence-alignments-using-root-to-leave-regressive-computation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast and accurate large multiple sequence alignments using root-to-leave regressive computation\u003c/h1\u003e\n\u003cp\u003eThis repository contains data, documentation, analysis and Nextflow workflow for the manuscript \"Fast and accurate large multiple sequence alignments using root-to-leave regressive computation\".\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-for-details-on-how-to-use-the-regressive-multiple-sequence-alignment-method-see-the-t-coffee-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-details-on-how-to-use-the-regressive-multiple-sequence-alignment-method-see-the-t-coffee-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor details on how to use the Regressive Multiple Sequence Alignment method, see the \u003ca href=\"https://tcoffee.readthedocs.io/en/latest/tcoffee_quickstart_regressive.html\" rel=\"nofollow\"\u003eT-Coffee documentation\u003c/a\u003e.\u003c/h4\u003e\n\u003ch3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003eThis workflow was written by Evan Floden (\u003ca href=\"https://github.com/evanfloden\"\u003eevanfloden\u003c/a\u003e) and\nEdgar(\u003ca href=\"https://github.com/edgano\"\u003eedgano\u003c/a\u003e) at the \u003ca href=\"http://www.crg.eu\" rel=\"nofollow\"\u003eCenter for Genomic Regulation (CRG)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe authors who contributed to the analysis and manuscript are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEdgar Garriga Nogales\u003c/li\u003e\n\u003cli\u003ePaolo Di Tommaso\u003c/li\u003e\n\u003cli\u003eCedrik Magis\u003c/li\u003e\n\u003cli\u003eIonas Erb\u003c/li\u003e\n\u003cli\u003eHafid Laayouni\u003c/li\u003e\n\u003cli\u003eFyodor Kondrashov\u003c/li\u003e\n\u003cli\u003eEvan Floden\u003c/li\u003e\n\u003cli\u003eCedric Notredame\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-notebooks\" class=\"anchor\" aria-hidden=\"true\" href=\"#notebooks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNotebooks\u003c/h3\u003e\n\u003cp\u003eThis repository contains a series of \u003ca href=\"http://jupyter.org/\" rel=\"nofollow\"\u003eJupyter Notebooks\u003c/a\u003e that contain\nthe steps for replicating the analysis, tables and figures in the manuscript.\u003c/p\u003e\n\u003cp\u003eThe index jupyter notebook can be found \u003ca href=\"notebook/00_StartHere.ipynb\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe notebook executes the pipeline, some steps of which require a lot of resources.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline\u003c/h3\u003e\n\u003cp\u003eThe pipeline for generating trees, alignments and performing the evaluations is built using\n\u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across\nmultiple compute infrastructures in a very portable manner. It comes with a docker container\nmaking installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pipeline-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline Quick Start\u003c/h3\u003e\n\u003cp\u003eMake sure you have either docker/singularity installed or the required dependencies listed\nin the last section.\u003c/p\u003e\n\u003cp\u003eInstall the Nextflow runtime by running the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -fsSL get.nextflow.io | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen done, you can launch the pipeline execution by entering the command shown below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ nextflow run evanfloden/dpa-analysis\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default the pipeline is executed against the provided example dataset.\nCheck the \u003cem\u003ePipeline parameters\u003c/em\u003e section below to see how enter your data on the program\ncommand line.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers\u003c/h3\u003e\n\u003cp\u003eAll the methods above are available in a \u003ca href=\"http://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image on DockerHub \u003ca href=\"https://hub.docker.com/r/cbcrg/regressive-msa/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the image is tested to be compatible with the \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe container also contains test data consisting of protein sequences, reference alignments and trees in the directory \u003ccode\u003e/test_data\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo launch the container interactively with Docker run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run cbcrg/regressive-msa\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo launch the container interactivly with Singularity run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity shell docker://cbcrg/regressive-msa\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pipeline-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline parameters\u003c/h3\u003e\n\u003ch4\u003e\u003ca id=\"user-content---seqs\" class=\"anchor\" aria-hidden=\"true\" href=\"#--seqs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--seqs\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies the location of the input \u003cem\u003efasta\u003c/em\u003e file(s).\u003c/li\u003e\n\u003cli\u003eMultiple files can be specified using the usual wildcards (*, ?), in this case make sure to surround the parameter string\nvalue by single quote characters (see the example below)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ nextflow run evanfloden/dpa-analysis --seqs \u0027/home/seqs/*.fasta\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will handle each fasta file as a seperate sample.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content---refs\" class=\"anchor\" aria-hidden=\"true\" href=\"#--refs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--refs\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies the location of the reference \u003cem\u003ealigned fasta\u003c/em\u003e file(s).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---trees\" class=\"anchor\" aria-hidden=\"true\" href=\"#--trees\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--trees\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies the location of input tree file(s).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---align_method\" class=\"anchor\" aria-hidden=\"true\" href=\"#--align_method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--align_method\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies which alignment methods should be used.\u003c/li\u003e\n\u003cli\u003eOptions include: \"CLUSTALO,MAFFT-FFTNS1,MAFFT-SPARSECORE,MAFFT-GINSI,PROBCONS,UPP\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---tree_method\" class=\"anchor\" aria-hidden=\"true\" href=\"#--tree_method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--tree_method\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eSpecifies which guide-tree / clustering methods should be used.\u003c/li\u003e\n\u003cli\u003eOptions include: \"CLUSTALO,MAFFT_PARTTREE\"\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---regressive_align\" class=\"anchor\" aria-hidden=\"true\" href=\"#--regressive_align\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--regressive_align\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eFlag to generate regressive MSAs.\u003c/li\u003e\n\u003cli\u003eSee \u003ccode\u003etemplates/dpa_align\u003c/code\u003e for the specific commands executed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---stardard_align\" class=\"anchor\" aria-hidden=\"true\" href=\"#--stardard_align\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--stardard_align\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eFlag to perform standard MSAs.\u003c/li\u003e\n\u003cli\u003eStandard MSA is alignment where the guide-tree is provided as input.\u003c/li\u003e\n\u003cli\u003eSee \u003ccode\u003etemplates/std_align\u003c/code\u003e for the specific commands executed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---default_align\" class=\"anchor\" aria-hidden=\"true\" href=\"#--default_align\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--default_align\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eFlag to perform default MSAs.\u003c/li\u003e\n\u003cli\u003eDefault MSA is alignment where the alignment software uses an internally generated guide-tree.\u003c/li\u003e\n\u003cli\u003eSee \u003ccode\u003etemplates/default_align\u003c/code\u003e for the specific commands executed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---evaluate\" class=\"anchor\" aria-hidden=\"true\" href=\"#--evaluate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--evaluate\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eFlag to perform evaluation of the alignments.\u003c/li\u003e\n\u003cli\u003eRequires reference sequences to be provided with the \u003ccode\u003e--refs\u003c/code\u003e parameter.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---buckets\" class=\"anchor\" aria-hidden=\"true\" href=\"#--buckets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--buckets\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eList of bucket sizes or maximum size of the subMSAs in the regressive proceedure.\u003c/li\u003e\n\u003cli\u003eDefault value is \"1000\" sequences.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content---output\" class=\"anchor\" aria-hidden=\"true\" href=\"#--output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003e--output\u003c/code\u003e\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003eLocation of the results.\u003c/li\u003e\n\u003cli\u003eDefault locations is \u003ccode\u003eresults\u003c/code\u003e directory.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "CIA-CCTB/pythrahyper_net", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epythrahyper_net\u003c/h1\u003e\u003ca id=\"user-content-pythrahyper_net\" class=\"anchor\" aria-label=\"Permalink: pythrahyper_net\" href=\"#pythrahyper_net\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and data for the paper \u003cem\u003e\"Biological network growth in complex environments - a computational framework\"\u003c/em\u003e by T. Paul and P. Kollmannsberger (2020) - \u003ca href=\"https://biorxiv.org/cgi/content/short/2020.06.01.127407v1\" rel=\"nofollow\"\u003ehttps://biorxiv.org/cgi/content/short/2020.06.01.127407v1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease have a look at the notebook \u003ca href=\"https://github.com/CIA-CCTB/pythrahyper_net/blob/master/Introduction.ipynb\"\u003eIntroduction.ipynb\u003c/a\u003e, or try it directly here: \u003ca href=\"https://colab.research.google.com/github/CIA-CCTB/pythrahyper_net/blob/master/Colab/Introduction_Colab.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e (requires Google account)\u003c/p\u003e\n\u003cp\u003eThe following Jupyter notebooks reproduce the simulations shown in Figure 6 in the paper:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CIA-CCTB/pythrahyper_net/blob/master/Multicellular-Network.ipynb\"\u003eMulticellular-Network.ipynb\u003c/a\u003e - simulation of network growth between layers of cells\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CIA-CCTB/pythrahyper_net/blob/master/Multi-Simulation-Setup.ipynb\"\u003eMulti-Simulation-Setup.ipynb\u003c/a\u003e - generate configuration and batch files for parameter scan\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CIA-CCTB/pythrahyper_net/blob/master/Multi-Simulation-Analysis.ipynb\"\u003eMulti-Simulation-Analysis.ipynb\u003c/a\u003e - analyze results and generate plots for parameter scan\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstructions\u003c/h1\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe framework is written in python using numpy and the multiprocessing module, and has been tested under Linux and MacOS. To run the example notebooks, first download or clone this repository, and then follow the instructions below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1) Using conda\u003c/h2\u003e\u003ca id=\"user-content-1-using-conda\" class=\"anchor\" aria-label=\"Permalink: 1) Using conda\" href=\"#1-using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to install the required python packages is by using conda. Creating a new environment with this command will install all dependencies:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda create --name pythra python=3.7 pyqt=5 scipy tifffile jupyter networkx matplotlib\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen change into the new environment using \u003ccode\u003econda activate pythra\u003c/code\u003e, and start a Jupyter notebook server in the \u003ccode\u003epythrahyper_net\u003c/code\u003e directory to access the notebooks.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMayavi visualization in the browser:\u003c/h3\u003e\u003ca id=\"user-content-mayavi-visualization-in-the-browser\" class=\"anchor\" aria-label=\"Permalink: Mayavi visualization in the browser:\" href=\"#mayavi-visualization-in-the-browser\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get interactive mayavi visualizations inside the browser, first install mayavi and ipyevents:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c anaconda mayavi\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-forge ipyevents\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNext, install and activate the required extension:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ejupyter nbextension install --py mayavi --user\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ejupyter nbextension enable --py mayavi --user\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you get missing symbol errors upon importing \u003ccode\u003emlab\u003c/code\u003e, try this:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-force \"libnetcdf=4.6.2\"\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInteractive Matplotlib plots:\u003c/h3\u003e\u003ca id=\"user-content-interactive-matplotlib-plots\" class=\"anchor\" aria-label=\"Permalink: Interactive Matplotlib plots:\" href=\"#interactive-matplotlib-plots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe matplotlib plots can be made interactive using these modules:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003econda install -c conda-forge ipympl widgetsnbextension\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2) Using Singularity container\u003c/h2\u003e\u003ca id=\"user-content-2-using-singularity-container\" class=\"anchor\" aria-label=\"Permalink: 2) Using Singularity container\" href=\"#2-using-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe second possibility is to run the framework inside a Singularity container. A container image can be created using the included definition file:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build pythra.simg Singularity.def\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAfter successful build, you can e.g. start a Jupyter notebook server inside the container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec pythra.simg jupyter notebook\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen copy and paste the server URL into a web browser running outside of the container to access the notebooks.\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 0, + "subscribers_count": 4, "topics": [], - "updated_at": 1620129144.0 + "updated_at": 1695826280.0 }, { "data_format": 2, - "description": "CycleCloud project to enable use of Singularity containers in HPC clusters in Azure.", + "description": "A nextflow/singularity pipeline for quandenser", "filenames": [ - "specs/default/cluster-init/files/examples/sleep/Singularity" + "Singularity.dev", + "Singularity" ], - "full_name": "Azure/cyclecloud-singularity", - "latest_release": "2.0.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h1\u003e\n\u003cp\u003eThis project installs and configures the Singularity container system.\u003c/p\u003e\n\u003cp\u003eSingularity is a system for building and running Linux Containers. See the \u003ca href=\"https://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e project site for more information and documentation.\u003c/p\u003e\n\u003cp\u003eThe project includes an example cluster template which adds Singularity to a PBS grid. But the Singularity project is intended primarily as an additional capability that can be added to any Cyclecloud cluster.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pre-requisites\"\u003ePre-Requisites\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#configuring-the-project\"\u003eConfiguring the Project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#deploying-the-project\"\u003eDeploying the Project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#importing-the-cluster-template\"\u003eImporting the Cluster Template\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\u003ca id=\"user-content-pre-requisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#pre-requisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePre-Requisites\u003c/h2\u003e\n\u003cp\u003eThis sample requires the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe Singularity source tarball or the Singularity RPM or DEB files (depending on the OS you select for your cluster).\u003c/p\u003e\n\u003cp\u003ea. Download the source or binaries following the instructions here: (\u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttps://singularity.lbl.gov/install-linux\u003c/a\u003e)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo download the source, you can simply run:\n\u003ccode\u003eVERSION=\"3.1.1\" \u0026amp;\u0026amp; curl -L -O \"https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-${VERSION}.tar.gz\"\u003c/code\u003e\nb. Place the source tarball and/or package files in the \u003ccode\u003e./blobs/\u003c/code\u003e directory.\nc. If the version is not 3.1.1 (the project default), then update the version number in the Files list\nin \u003ccode\u003e./project.ini\u003c/code\u003e and in the cluster template: \u003ccode\u003e./templates/pbs-singularity.txt\u003c/code\u003e.\nd. If you are starting from the package files, also add the package file names to the Files list in\n\u003ccode\u003e./project.ini\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCycleCloud must be installed and running.\u003c/p\u003e\n\u003cp\u003ea. If this is not the case, see the CycleCloud QuickStart Guide for\nassistance.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe CycleCloud CLI must be installed and configured for use.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou must have access to log in to CycleCloud.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou must have access to upload data and launch instances in your chosen\nCloud Provider account.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou must have access to a configured CycleCloud \"Locker\" for Project Storage\n(Cluster-Init and Chef).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOptional: To use the \u003ccode\u003ecyclecloud project upload \u0026lt;locker\u0026gt;\u003c/code\u003e command, you must\nhave a Pogo configuration file set up with write-access to your locker.\u003c/p\u003e\n\u003cp\u003ea. You may use your preferred tool to interact with your storage \"Locker\"\ninstead.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-configuring-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuring-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguring the Project\u003c/h2\u003e\n\u003cp\u003eThe first step is to configure the project for use with your storage locker:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOpen a terminal session with the CycleCloud CLI enabled.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSwitch to the singularity project directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the following source tarballs and/or RPM and DEB files to \u003ccode\u003e./blobs\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf the version number is not 3.1.1, update the version numbers in \u003ccode\u003eproject.ini\u003c/code\u003e and \u003ccode\u003etemplates/pbs-singularity.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf adding the RPM and/or DEB files, add them to the Files list in the \u003ccode\u003eproject.ini\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deploying-the-project\" class=\"anchor\" aria-hidden=\"true\" href=\"#deploying-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeploying the Project\u003c/h2\u003e\n\u003cp\u003eTo upload the project (including any local changes) to your target locker, run the\n\u003ccode\u003ecyclecloud project upload\u003c/code\u003e command from the project directory. The expected output looks like\nthis:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e $ cyclecloud project upload my_locker\n Sync completed\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eIMPORTANT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the upload to succeed, you must have a valid Pogo configuration for your target Locker.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-importing-the-cluster-template\" class=\"anchor\" aria-hidden=\"true\" href=\"#importing-the-cluster-template\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eImporting the Cluster Template\u003c/h2\u003e\n\u003cp\u003eTo import the cluster:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOpen a terminal session with the CycleCloud CLI enabled.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSwitch to the Singularity project directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun \u003ccode\u003ecyclecloud import_template PBS-Singularity -f templates/pbs-singularity.txt\u003c/code\u003e.\nThe expected output looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ cyclecloud import_template PBS-Singularity -f templates/pbs-singularity.txt --force\nImporting template PBS-Singularity....\n----------------------------\nPBS-Singularity \u003cspan class=\"pl-c1\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003etemplate\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n----------------------------\nKeypair:\nCluster nodes:\nmaster: off\nTotal nodes: 1\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eThis project welcomes contributions and suggestions. Most contributions require you to agree to a\nContributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us\nthe rights to use your contribution. For details, visit \u003ca href=\"https://cla.microsoft.com\" rel=\"nofollow\"\u003ehttps://cla.microsoft.com\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWhen you submit a pull request, a CLA-bot will automatically determine whether you need to provide\na CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions\nprovided by the bot. You will only need to do this once across all repos using our CLA.\u003c/p\u003e\n\u003cp\u003eThis project has adopted the \u003ca href=\"https://opensource.microsoft.com/codeofconduct/\" rel=\"nofollow\"\u003eMicrosoft Open Source Code of Conduct\u003c/a\u003e.\nFor more information see the \u003ca href=\"https://opensource.microsoft.com/codeofconduct/faq/\" rel=\"nofollow\"\u003eCode of Conduct FAQ\u003c/a\u003e or\ncontact \u003ca href=\"mailto:opencode@microsoft.com\"\u003eopencode@microsoft.com\u003c/a\u003e with any additional questions or comments.\u003c/p\u003e\n", + "full_name": "statisticalbiotechnology/quandenser-pipeline", + "latest_release": "v0.0837", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuandenser-pipeline \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/logo.svg\"\u003e\u003cimg align=\"right\" src=\"/images/logo.svg\" height=\"50\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-quandenser-pipeline-\" class=\"anchor\" aria-label=\"Permalink: Quandenser-pipeline \" href=\"#quandenser-pipeline-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA Nextflow/Singularity pipeline for Quandenser\u003c/h2\u003e\u003ca id=\"user-content-a-nextflowsingularity-pipeline-for-quandenser\" class=\"anchor\" aria-label=\"Permalink: A Nextflow/Singularity pipeline for Quandenser\" href=\"#a-nextflowsingularity-pipeline-for-quandenser\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2356\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to install\u003c/h2\u003e\u003ca id=\"user-content-how-to-install\" class=\"anchor\" aria-label=\"Permalink: How to install\" href=\"#how-to-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGo to releases and download \u003cem\u003eQuandenser_pipeline.sh\u003c/em\u003e and run the shell script. The shell script will handle the rest!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGo to the directory where \u003cem\u003eQuandenser_pipeline.sh\u003c/em\u003e is installed and run the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Quandenser_pipeline.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will install Singularity if it does not exist (this requires sudo privileges). The script will also download the latest stable version of the Singularity image.\u003c/p\u003e\n\u003cp\u003eIf you want to mount another directory that is not detected by Singularity, you can add any amount of directories to mount by these commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Quandenser_pipeline.sh /path/to/directory1 /path/to/directory2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe pipeline can be run on a SLURM cluster if Singularity is installed. Just don\u0027t forget to enable \"slurm_cluster\" in the\nsettings!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIf you have trouble running the GUI, look further down below in the section \"Known Issues\"\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eQuandenser-pipeline\u003c/em\u003e is a tool that combines \u003cem\u003eQuandenser\u003c/em\u003e, a tool which condenses label-free MS data and \u003cem\u003eTriqler\u003c/em\u003e, a tool which finds differentially expressed proteins using both MS1 and MS2 data. \u003cem\u003eQuandenser-pipeline\u003c/em\u003e streamlines the process, by accepting almost any vendor format alongside a fasta database containing proteins, which are then run through a Singularity image containing all the necessary parts to do the analysis.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/gui.png\"\u003e\u003cimg src=\"/images/gui.png\" width=\"1000\" height=\"800\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sylabs/singularity\"\u003eSingularity\u003c/a\u003e: Singularity is an open source container platform used to embed the software used in the pipeline\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/nextflow-io/nextflow\"\u003eNextflow\u003c/a\u003e: Nextflow is a workflow manager, which was used to create the pipeline\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/statisticalbiotechnology/quandenser\"\u003eQuandenser\u003c/a\u003e: A recent software which condenses quantification data from label-free mass spectrometry experiments, written by Lukas K\u00e4ll and Matthew The at ScilifeLab\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/crux-toolkit/crux-toolkit\"\u003eCrux toolkit\u003c/a\u003e: An open-source mass spectrometry analysis toolkit used to analyse MS2 spectra from Quandeser\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/statisticalbiotechnology/triqler\"\u003eTriqler\u003c/a\u003e: A combined identification and quantification error model of label-free protein quantification, written by Lukas K\u00e4ll and Matthew The at ScilifeLab. It is used as the final analysis software, utilizing the output from Crux and Quandenser\u003c/p\u003e\n\u003cp\u003eThe GUI is built with the open source GUI \u003ca href=\"https://pypi.org/project/PySide2/\" rel=\"nofollow\"\u003ePySide2\u003c/a\u003e\nwith \u003ca href=\"https://github.com/ColinDuquesnoy/QDarkStyleSheet\"\u003eColinDuqesnoy\u0027s dark theme\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e*** stack smashing detected***: python terminated\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eSometimes when running on a computer with nvidia drivers locally, this error message will be shown. It will not harm your computer, so just keep trying to start the program. Usually, it stabilizes after a couple of runs.\nI\u0027ve been trying to find the cause of this bug, but it seems to be correlated to the creation of the GUI window through Singularity. If you have any ideas about the bug, please let me know!\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eWebEngineContext used before QtWebEngine::initialize() or OpenGL context creation failed.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis usually happens when you are running on a cluster. Sometimes, the nvidia drivers on your computer is not compatible\nwith the drivers on the cluster. Please add the following command when running the start script.\nNote: disabling opengl will not hinder the performance of the software. The workflow display and the about tab will\nnot be shown.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Quandenser_pipeline.sh /path/to/directory1 ... --disable-opengl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eGlx related crashes (ex qt.glx: qglx_findConfig: Failed to finding matching FBConfig (8 8 8 0))\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIf you are running on a cluster with nvidia cards and you do not have an nvidia card on your local machine (ex if you are running the software in virtualbox on a cluster). Add the following command to disable nvidia drivers\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./Quandenser_pipeline.sh /path/to/directory1 ... --disable-nvidia\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eIf everything fails and you can\u0027t get it to work, there is one last thing you can try, which is explained below ...\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the pipeline WITHOUT the GUI\u003c/h2\u003e\u003ca id=\"user-content-running-the-pipeline-without-the-gui\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline WITHOUT the GUI\" href=\"#running-the-pipeline-without-the-gui\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf everything fails or you can\u0027t run the GUI for some reason, there is a last resort which you can use: run the pipeline without the GUI. This is not as intuitive as using the GUI, but it is possible to do, since the GUI in itself is not required to run the pipeline but it only makes things easier. Do the following to run the pipeline without GUI:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eRemove the directory \u003cem\u003e.quandenser_pipeline\u003c/em\u003e from your home directory. Run the command \u003ccode\u003erm -r /home/$USER/.quandenser_pipeline\u003c/code\u003e. This will clear previous settings that might interfere.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun \u003ccode\u003e./Quandenser_pipeline.sh\u003c/code\u003e as usual. You should see \u003cem\u003eMissing file . Installing file\u003c/em\u003e in yellow text on the terminal. This will add the configuration files. At this point, it doesn\u0027t matter if the GUI crashes or not.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo to the config directory with \u003ccode\u003ecd /home/$USER/.quandenser_pipeline\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe files \u003cstrong\u003enf.config\u003c/strong\u003e and \u003cstrong\u003erun_quandenser\u003c/strong\u003e are the only files which you will need to edit to make this work.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003erun_quandenser.sh\u003c/strong\u003e: Here, the only parameters you need to edit are:\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003ePROFILE\u003c/em\u003e = Can be either \u003cem\u003elocal\u003c/em\u003e or \u003cem\u003ecluster\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eOUTPUT_PATH\u003c/em\u003e = The full path to the directory where the output will be placed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003enf.config\u003c/strong\u003e: Here, pretty much any variable can be changed. However, I would suggest to focus on:\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eparams.db\u003c/em\u003e = Path to the fasta file used as the protein database. Note that only the \"Full\" workflow requires this.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.output_path\u003c/em\u003e = Output path, which needs to be the same as the output path in the .sh file\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.batch_file\u003c/em\u003e = Path to the batch file. The syntax of the batch file is:\n\u003ccode\u003e/full/path/to/ms/file label\u003c/code\u003e where \"label\" could be any combination of ascii characters. Note that the delimiter between the file path and the label needs to be a tab character. For each file, add another row. Replicates should have the same label assigned.\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.custom_mounts\u003c/em\u003e = Custom mounts used for Nextflow. the syntax should be:\n\u003ccode\u003e --bind /path/to/mount:/path/to/mount\u003c/code\u003e. Note the blank space before \" --bind\"\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.workflow\u003c/em\u003e = Can be \"Full\", \"MSconvert\" or \"Quandenser\"\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eparams.resume_directory\u003c/em\u003e = Path to another directory containing previously calculated quandenser files\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003etime parameters\u003c/em\u003e = If you are running on a cluster and are using the \"cluster\" profile, adjust the time values to your liking.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eGo back to the directory containing \u003cem\u003eSingulQuand.SIF\u003c/em\u003e and run this command:\n\u003ccode\u003enohup /home/$USER/run_quandenser.sh \u0026lt;/dev/null \u0026gt;/dev/null 2\u0026gt;\u0026amp;1 \u0026amp; disown\u003c/code\u003e\nThis should run the sh file, deattach it from the terminal and run it in the background. This will allow you to close the terminal/ssh session without stopping the pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding from scratch\u003c/h2\u003e\u003ca id=\"user-content-building-from-scratch\" class=\"anchor\" aria-label=\"Permalink: Building from scratch\" href=\"#building-from-scratch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have come all the way down here in the README, you might be interested in building the image from scratch.\u003c/p\u003e\n\u003cp\u003eSimply clone the repository with git or download it \u003ca href=\"https://github.com/statisticalbiotechnology/quandenser-pipeline/archive/master.zip\"\u003ehere\u003c/a\u003e. Unzip the directory and cd inside, then run \"./build_image.sh\" and it will build everything for you! (However, this requires both Singularity and sudo privileges, so run the release first to install Singularity)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuestions?\u003c/h2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-label=\"Permalink: Questions?\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFeel free to mail me at \"\u003ca href=\"mailto:timothy.bergstrom@gmail.com\"\u003etimothy.bergstrom@gmail.com\u003c/a\u003e\" if you have any questions about quandenser-pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/images/logo.svg\"\u003e\u003cimg src=\"/images/logo.svg\" height=\"128\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n \u00a9 Copyright 2019, Timothy Bergstr\u00f6m\n", "stargazers_count": 5, - "subscribers_count": 15, + "subscribers_count": 9, "topics": [], - "updated_at": 1664393642.0 + "updated_at": 1656126766.0 }, { "data_format": 2, - "description": "OIST Bioinfo user group", + "description": "repository for collaborating with sherlock users to create containers", "filenames": [ - "RStudio/Singularity.def" + "kdwispe/Singularity.kdwispe", + "pvacseq/Singularity.pvacseq", + "dwesche/Singularity.dwesche", + "pynio/Singularity", + "rflynn/Singularity.rflynn", + "naras/Singularity.naras", + "rllab/Singularity.rllab", + "beelaj/Singularity.beelaj" ], - "full_name": "oist/BioinfoUgrp", + "full_name": "researchapps/sherlock", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-oists-bioinformatics-user-group\" class=\"anchor\" aria-hidden=\"true\" href=\"#oists-bioinformatics-user-group\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOIST\u0027s bioinformatics user group\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-communication-channel\" class=\"anchor\" aria-hidden=\"true\" href=\"#communication-channel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommunication channel\u003c/h2\u003e\n\u003cp\u003ePrioritized communication channel is on Microsoft Teams: \u003ca href=\"https://teams.microsoft.com/l/team/19%3a3183bd7fe2844138a49996a2bd376873%40thread.tacv2/conversations?groupId=cc78e114-c544-43e2-b4b1-29c7428aa305\u0026amp;tenantId=d8c0fb8d-bb56-44bb-9f4a-c58e7465652e\" rel=\"nofollow\"\u003eBioinfoUgrp\u003c/a\u003e. Do not hesitate to use the ping function (putting \u003ccode\u003e@\u003c/code\u003e and then the name, like in other chat systems), because the discussions on the Team app are a bit easy to miss otherwise.\nPlease \"Google\" the issues prior to contacting us. Very often, the main issues will already be reported and the solution available on the reference webpage of the program: in the \u003ccode\u003eIssues\u003c/code\u003e tab of \u003ccode\u003eGitHub\u003c/code\u003e for some, in \u003ccode\u003eGoogleGroups\u003c/code\u003e for others (e.g. for \u003ca href=\"https://groups.google.com/g/iqtree\" rel=\"nofollow\"\u003eIQ-TREE\u003c/a\u003e). Other great platforms are \u003ca href=\"https://stackoverflow.com\" rel=\"nofollow\"\u003eStackOverflow\u003c/a\u003e, or \u003ca href=\"https://www.biostars.org\" rel=\"nofollow\"\u003eBiostars\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-finding-modules\" class=\"anchor\" aria-hidden=\"true\" href=\"#finding-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFinding modules\u003c/h2\u003e\n\u003cp\u003eSearch with a keyword, for instance \u003ccode\u003eml key clustal\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-loading-installed-modules\" class=\"anchor\" aria-hidden=\"true\" href=\"#loading-installed-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLoading installed modules\u003c/h2\u003e\n\u003cp\u003eExecute \u003ccode\u003eml bioinfo-ugrp-modules\u003c/code\u003e to make available the modules installed by the OIST Bioinfo user group. This line can be appended to your \u003ccode\u003e~/.bashrc\u003c/code\u003e to make them available by default.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-debian-med-modules\" class=\"anchor\" aria-hidden=\"true\" href=\"#debian-med-modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebian Med modules\u003c/h3\u003e\n\u003cp\u003eWe autogenerate many modules from softwares packaged the Debian distribution. To see them, execute \u003ccode\u003eml bioinfo-ugrp-modules DebianMed\u003c/code\u003e. More information is available on the \u003ca href=\"DebianMedModules.md\"\u003eDebianMedModules\u003c/a\u003e page.\nTo load a module in DebianMed (an example for loading bcftools):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# load DebianMed module first\nml bioinfo-ugrp-modules DebianMed\n\n# now you can see the list of module installed in DebianMed.\nml avail\n\n# load module\nml bcftools\n\n# check the installation\nbcftools --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-unix-goodies\" class=\"anchor\" aria-hidden=\"true\" href=\"#unix-goodies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnix goodies\u003c/h3\u003e\n\u003cp\u003eWe provide some modules for Unix tools useful to everybody including bioinformaticians.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eml bioinfo-ugrp-modules UnixGoodies\nml av\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCheck \u003ca href=\"https://github.com/oist/BioinfoUgrp_UnixGoodies_Images\"\u003eoist/BioinfoUgrp_UnixGoodies_Images\u003c/a\u003e for details.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nextflow-pipelines\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipelines\u003c/h3\u003e\n\u003cp\u003eWe have prepared a \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e module (\u003ccode\u003eml bioinfo-ugrp-modules Nextflow2\u003c/code\u003e) and registered \u003ca href=\"https://github.com/nf-core/configs/blob/master/docs/oist.md\"\u003eOIST\u0027s profile\u003c/a\u003e to the \u003ca href=\"https://nf-co.re/\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e community so that you can run their pipelines with the \u003ccode\u003e-profile oist\u003c/code\u003e option on \u003cem\u003eDeigo\u003c/em\u003e. A \u003cem\u003enf-core\u003c/em\u003e \u003ca href=\"https://github.com/nf-core/modules\"\u003emodule\u003c/a\u003e is also available (\u003ccode\u003eml bioinfo-ugrp-modules nf-core\u003c/code\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-other-tools\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther tools\u003c/h3\u003e\n\u003cp\u003eUnder the \u003ccode\u003eOther/\u003c/code\u003e namespace, we also provide some general bioinformatics tools such as:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDIAMOND (\u003ccode\u003eml Other/DIAMOND/2.0.4.142\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eInterProScan and its database (\u003ccode\u003eml Other/interproscan/5.48-83.0\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\u2026 and more !\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"Other.md\"\u003ethis page\u003c/a\u003e for the full list of modules and for more information.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-databases\" class=\"anchor\" aria-hidden=\"true\" href=\"#databases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDatabases\u003c/h2\u003e\n\u003cp\u003eWidely used databases were installed locally. Upon request by users, we plan on upgrading databases (not more than once a year). After upgrading a specific database, users will be asked if the older database should still remain available (completion of projects,...): it will be deleted after 30 days except if still required. At one time, a maximum of two versions of the same database will be available.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-taxified-blast-databases\" class=\"anchor\" aria-hidden=\"true\" href=\"#taxified-blast-databases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTaxified BLAST databases\u003c/h3\u003e\n\u003cp\u003eThe following databases were constructed using ncbi-blast v2.10.0+. The module \u003ccode\u003encbi-blast/2.10.0+\u003c/code\u003e has to be loaded in order to use these databases.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI NT and NR databases (release 238) : \u003ccode\u003eml DB/blastDB/ncbi/238\u003c/code\u003e. To be used with the arguments \u003ccode\u003ent\u003c/code\u003e or \u003ccode\u003enr\u003c/code\u003e supplied to \u003ccode\u003e-db\u003c/code\u003e in the commands of your scripts. Example script to get a taxified blast report:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emodule load ncbi-blast/2.10.0+\nmodule load DB/blastDB/ncbi/238\nWORKDIR=\"$PWD\"\nFASTA=FULL/PATH/TO/YOUR/FASTA/FILE\nblastn -task megablast -db nt -query $FASTA -num_threads ${SLURM_CPUS_PER_TASK} -out ${WORKDIR}/megablastn.out \\\n\t-outfmt \u00276 qseqid bitscore evalue length qlen qcovs pident sseqid sgi sacc staxid ssciname scomname stitle sseq\u0027 \\\n\t-max_target_seqs 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eSwiss-Prot (version 2020_06): \u003ccode\u003eml DB/blastDB/sprot/2020_06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniRef90 (version 2020_06): \u003ccode\u003eml DB/blastDB/uniref90/2020_06\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-taxified-diamond-databases\" class=\"anchor\" aria-hidden=\"true\" href=\"#taxified-diamond-databases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTaxified DIAMOND databases\u003c/h3\u003e\n\u003cp\u003eThe following databases were constructed using DIAMOND v2.0.4.142. The module \u003ccode\u003eOther/DIAMOND/2.0.4.142\u003c/code\u003e has to be loaded in order to use them.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe NCBI-NR database (release 238): \u003ccode\u003eml DB/diamondDB/ncbi/238\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSwiss-Prot (version 2020_06): \u003ccode\u003eml DB/diamondDB/sprot/2020_06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUniRef90 (version 2020_06): \u003ccode\u003eml DB/diamondDB/uniref90/2020_06\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUnlike ncbi-blast, DIAMOND requires full path of the databases. The database module automatically create an environment variable \"DIAMONDDB\" which specifies full path to the DIAMOND database. So you need to prepend \u003ccode\u003e${DIAMONDDB}\u003c/code\u003e to the name of database.\nExample script to run diamond with the database module:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# load ncbi database for DIAMOND (proper version of DIAMOND is automatically loaded)\nmodule load DB/diamondDB/ncbi/238\n\n# check the loaded DIAMOND version and ${DIAMONDDB} variable\ndiamond --version\necho ${DIAMONDDB}\n\n# run diamond search\nWORKDIR=\"$PWD\"\nFASTA=FULL/PATH/TO/YOUR/FASTA/FILE\ndiamond blastp -db ${DIAMONDDB}/nr -q $FASTA -p ${SLURM_CPUS_PER_TASK} -out ${WORKDIR}/diamond.blastp.out -outfmt 6\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pfam\" class=\"anchor\" aria-hidden=\"true\" href=\"#pfam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePfam\u003c/h3\u003e\n\u003cp\u003eVersion 34.0: Use \u003ccode\u003eml DB/Pfam/34.0\u003c/code\u003e to invoke it in your scripts.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dfam\" class=\"anchor\" aria-hidden=\"true\" href=\"#dfam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDfam\u003c/h3\u003e\n\u003cp\u003eVersion 3.6 downloaded from \u003ca href=\"https://www.dfam.org/releases/Dfam_3.6/families/Dfam.h5.gz\" rel=\"nofollow\"\u003ehttps://www.dfam.org/releases/Dfam_3.6/families/Dfam.h5.gz\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe command \u003ccode\u003eml DB/Dfam/3.6\u003c/code\u003e will expose an environment variable \u003ccode\u003e$BioinfoUgrp_Dfam\u003c/code\u003e containing the path to the directory containing the database files, that can be passed to RepeatMasker through its \u003ccode\u003e-libdir\u003c/code\u003e argument.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dfam-for-repeatmasker\" class=\"anchor\" aria-hidden=\"true\" href=\"#dfam-for-repeatmasker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDfam for RepeatMasker\u003c/h3\u003e\n\u003cp\u003eThe command \u003ccode\u003eml DB/Dfam_RepeatMasker/3.6__4.1.3\u003c/code\u003e will set an environmental variable that changes the behaviour of the \u003ccode\u003erepeatmodeler\u003c/code\u003e module, so that it will use the full Dfam database provided by us instead of the \u201c\u003cem\u003ecurated only\u003c/em\u003e\u201d version provided by default.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-developer-details\" class=\"anchor\" aria-hidden=\"true\" href=\"#developer-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeveloper details\u003c/h4\u003e\n\u003cp\u003eThe RepeatMasker program does not follow symbolic links and the Dfam database is large (160 Gb), so I had to use hard links to the files of the \u003ccode\u003eDfam\u003c/code\u003e module instead. Also, the modulefile contains:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetenv(\"BioinfoUgrp_Dfam_Rmsk_4_1_3\", apphome..\"/RepeatMasker_4.1.3/Libraries\")\nsetenv(\"SINGULARITY_BINDPATH\", apphome..\"/Libraries:/opt/RepeatMasker/Libraries\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-rstudio\" class=\"anchor\" aria-hidden=\"true\" href=\"#rstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRStudio\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"./RStudio\"\u003eHere is how you can run RStudio\u003c/a\u003e on a compute node.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-modules-on-saion\" class=\"anchor\" aria-hidden=\"true\" href=\"#modules-on-saion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModules on Saion\u003c/h2\u003e\n\u003cp\u003eWe have some modules on \u003cem\u003eSaion\u003c/em\u003e for GPU-accelerated computations such that can not be run on \u003cem\u003eDeigo\u003c/em\u003e. Please remember that the \u003cem\u003emodules\u003c/em\u003e system on \u003cem\u003eSaion\u003c/em\u003e is older, so the \u003ccode\u003eml\u003c/code\u003e shortcuts will not work. To list the available modules, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load bioinfo-ugrp-modules\nmodule available\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alpha-fold\" class=\"anchor\" aria-hidden=\"true\" href=\"#alpha-fold\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlpha Fold\u003c/h3\u003e\n\u003cp\u003eWe have a very basic implementation of Alpha fold 2.1.1 within the user group modules. You can find (in time) a verbose documentation \u003ca href=\"AlphaFold.md\"\u003ehere\u003c/a\u003e. However, for a basic usage, you can try to do something similar to the example script in: /apps/unit/BioinfoUgrp/alphafold/2.1.1/bin/alphafold_example_script.sh\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-nanopore\" class=\"anchor\" aria-hidden=\"true\" href=\"#nanopore\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNanopore\u003c/h3\u003e\n\u003cp\u003eWe have modules for \u003ca href=\"NanoporeModules.md\"\u003ebasecalling Nanopore\u003c/a\u003e data, in particular for \u003cem\u003eGuppy\u003c/em\u003e and \u003cem\u003eRerio\u003c/em\u003e.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSherlock\u003c/h1\u003e\u003ca id=\"user-content-sherlock\" class=\"anchor\" aria-label=\"Permalink: Sherlock\" href=\"#sherlock\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/726\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a repository for container build files to help users of Sherlock. If you are a user and need help, please submit an issue and we will help you build a container! When you are happy with your container, we recommend that you add the \u003ccode\u003eSingularity\u003c/code\u003e or \u003ccode\u003eDockerfile\u003c/code\u003e file to a new repo, and build automatically with \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e or \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e. Generally, your workflow will look like the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall Singularity and/or Docker to work locally\u003c/li\u003e\n\u003cli\u003eAsk for help via an \u003ca href=\"https://www.github.com/researchapps/sherlock/issues\"\u003eissue\u003c/a\u003e if you don\u0027t know how to start\u003c/li\u003e\n\u003cli\u003eCreate a build specification file, a text file called Singularity, for your software needs. You can start with another user\u0027s as an example. You can also start with a Dockerfile, and then pull it to a Singularity image from Docker Hub.\u003c/li\u003e\n\u003cli\u003eAsk for help with your file! This is what this repo is here for. You can submit issues with questions, and we will discuss and work together on the issues.\u003c/li\u003e\n\u003cli\u003eTest your build locally.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://singularityware.github.io/install-linux\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e and \u003ca href=\"https://docs.docker.com/install/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e so that you can build images on your host. If you use a Mac, you will need to install Singularity in a virtual machine like Vagrant. Singularity is going to allow us to interact exactly the same, but with an image that we can use on Sherlock! The biggest difference is that a Singularity image is a read online, single file (a format called squasfs so\nit is compressed) that we can physically move around and execute like a script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGeneral Usage\u003c/h2\u003e\u003ca id=\"user-content-general-usage\" class=\"anchor\" aria-label=\"Permalink: General Usage\" href=\"#general-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn your local machine, building usually looks something like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build mynewimage.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf it has a runscript, you can run as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run mynewimage.img # or\n ./mynewimage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you are having trouble with the runscript, shell inside like this to look around. The runscript is a file at the base of the image (\u003ccode\u003e/\u003c/code\u003e) called singularity.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell mynewimage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also (on your local machine) use the \u003ccode\u003e--writable\u003c/code\u003e option to test installation of software. You should have your build file open in another window and copy down commands that work, and ensure that the entire build goes successfully from start to finish without an error. Remember, any command that you issue and don\u0027t write done is NOT reproducible!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Hub\u003c/h2\u003e\u003ca id=\"user-content-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub\" href=\"#singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote to Sherlock users: this functionality is not yet added to Sherlock, but will be available upon the next release of Singularity in March. For now, you can upload images the old school way (FTP). When this is enabled, you will be able to push the build file to Github, and then link your repo to Singularity Hub. Then using the image on sherlock will come down to:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e module load system\n module load singularity/2.4\n singularity run shub://reponame/mynewimage\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 5, - "subscribers_count": 7, + "subscribers_count": 4, "topics": [ - "oist", - "bioinformatics", - "hpc" + "hpc", + "sherlock", + "containers", + "singularity", + "cluster", + "scientific-computing" ], - "updated_at": 1686730130.0 + "updated_at": 1592614990.0 }, { "data_format": 2, - "description": "Tutorials and notebooks using Fink API", + "description": "A Python package to produce Mock Data Challenge data sets for LIGO interferometers.", "filenames": [ "Singularity" ], - "full_name": "astrolabsoftware/fink-tutorials", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFink broker tutorials\u003c/h1\u003e\u003ca id=\"user-content-fink-broker-tutorials\" class=\"anchor\" aria-label=\"Permalink: Fink broker tutorials\" href=\"#fink-broker-tutorials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://colab.research.google.com/github/astrolabsoftware/fink-notebook-template/blob/main\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f5e0d0538a9c2972b5d413e0ace04cecd8efd828d133133933dfffec282a4e1b/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository contains materials (notebooks \u0026amp; presentation) to explore the \u003ca href=\"https://fink-broker.org\" rel=\"nofollow\"\u003eFink broker\u003c/a\u003e alert data. As of November 2021, Fink has collected more than 120 million alerts from the ZTF public stream, and processed more than 40 millions (after quality cuts). Among these, you will find extragalatic sources (supernovae, AGN, ...), galactic sources (many classes of transients incl. variables stars from our galaxy or gravitational microlensing events, ...) and moving objects from our Solar System (asteroids, comets, and made-man objects like space-debris!). Some sources are already confirmed, many are candidates!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMaterials\u003c/h2\u003e\u003ca id=\"user-content-materials\" class=\"anchor\" aria-label=\"Permalink: Materials\" href=\"#materials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe repository contains a number of notebooks focusing on the use of the Fink REST API. We shortly present different science cases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExtragalactic science: AGN \u0026amp; supernovae (\u003ca href=\"extragalactic/extragalactic.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eGalactic science: variable stars \u0026amp; microlensing (\u003ca href=\"galactic/galactic.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSolar system science: asteroids, comets \u0026amp; space debris (\u003ca href=\"sso/sso.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSolar system science: phase curves (\u003ca href=\"sso/fink_sso_imcce.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eMulti-messenger astronomy: searching for kilonovae (\u003ca href=\"MMA/MMA.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eMulti-messenger astronomy: correlating with gravitational waves sky maps (\u003ca href=\"MMA/gravitational_waves.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eBroker interfaces: presentation on the livestream service, the Science Portal and its API, and the Fink TOM module (\u003ca href=\"interfaces/README.md\"\u003esee the presentation\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese sciences are not exhaustive and we welcome new collaborations to expand them! In addition, there are notebooks focusing on other specific aspects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow to tune the output rate of a Fink filter? Example for the Early SN Ia candidate filter (\u003ca href=\"extragalactic/tuning_snia_output_rate.ipynb\"\u003esee notebook\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can try the notebooks using Google Colab (follow the link above). You can also clone the repo, and try it locally (very little external libraries are required).\u003c/p\u003e\n\u003cp\u003eWe also provide a Singularity script to work in a contained environment (thanks @bregeon):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBuild with \u003ccode\u003esingularity build --fakeroot fink.sif Singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun with \u003ccode\u003esingularity run fink.sif\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOpen the link in your browser (from the host)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to contribute\u003c/h2\u003e\u003ca id=\"user-content-how-to-contribute\" class=\"anchor\" aria-label=\"Permalink: How to contribute\" href=\"#how-to-contribute\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHow to contribute:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eClone (or fork) this repo, and open a new branch.\u003c/li\u003e\n\u003cli\u003eCreate a new folder with a meaningful name (e.g. \u003ccode\u003esupernovae\u003c/code\u003e, \u003ccode\u003egrb\u003c/code\u003e, ...)\u003c/li\u003e\n\u003cli\u003eRead and copy an existing notebook to get an idea of the structure of a tutorial.\u003c/li\u003e\n\u003cli\u003eOnce your notebook is finished, open a Pull Request such that we review the tutorial and merge it!\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "transientlunatic/minke", + "latest_release": "v1.1.7", "stargazers_count": 5, - "subscribers_count": 7, + "subscribers_count": 4, "topics": [ - "tutorial", - "api", - "fink" + "gravitational", + "gravitational-waves", + "astrophysics", + "gravitational-wave-bursts", + "supernovae" ], - "updated_at": 1710377763.0 + "updated_at": 1692517881.0 }, { "data_format": 2, - "description": "Singularity Ubuntu container with the MPI/InfiniBand stack", + "description": "HIPAA \u0026 GDPR compliant ready Mongo Database with percona-server.", "filenames": [ "Singularity" ], - "full_name": "CHPC-UofU/Singularity-ubuntu-mpi", + "full_name": "netreconlab/hipaa-mongo", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehipaa-mongo\u003c/h1\u003e\u003ca id=\"user-content-hipaa-mongo\" class=\"anchor\" aria-label=\"Permalink: hipaa-mongo\" href=\"#hipaa-mongo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/hipaa-mongo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c07575084faefb6de357b820a7d0523f0c8a29df70609ebefd38c14a3f60b89f/68747470733a2f2f646f636b6572692e636f2f696d6167652f6e65747265636f6e6c61622f68697061612d6d6f6e676f\" alt=\"\" data-canonical-src=\"https://dockeri.co/image/netreconlab/hipaa-mongo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-mongo/actions/workflows/build.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-mongo/actions/workflows/build.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-mongo/actions/workflows/release.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-mongo/actions/workflows/release.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eA simple Mongo image designed for \u003ca href=\"https://github.com/netreconlab/parse-hipaa\"\u003eparse-hipaa\u003c/a\u003e but can be used anywhere Mongo is used. These docker images include the necessary database auditing and logging for HIPAA compliance. hipaa-mongo is derived from \u003ca href=\"https://hub.docker.com/r/percona/percona-server-mongodb/\" rel=\"nofollow\"\u003epercona-server-mongodb\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ehipaa-mongo provides the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Auditing \u0026amp; logging\u003c/li\u003e\n\u003cli\u003e[x] Ready for encryption in transit - run behind a proxy with files \u0026amp; directions on how to \u003ca href=\"https://github.com/netreconlab/parse-hipaa#deploying-on-a-real-system\"\u003ecomplete the process\u003c/a\u003e with Nginx and LetsEncrypt\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou will still need to setup the following on your own to be fully HIPAA compliant:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Encryption in transit - you will need to \u003ca href=\"https://github.com/netreconlab/parse-hipaa#deploying-on-a-real-system\"\u003ecomplete the process\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] Encryption at rest - Mount to your own encrypted storage drive (Linux and macOS have API\u0027s for this) and store the drive in a \"safe\" location\u003c/li\u003e\n\u003cli\u003e[ ] Be sure to do anything else HIPAA requires\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/netreconlab/CareKitSample-ParseCareKit\"\u003eCareKitSample-ParseCareKit\u003c/a\u003e app uses this image alongise parse-hipaa and \u003ca href=\"https://github.com/netreconlab/ParseCareKit\"\u003eParseCareKit\u003c/a\u003e. If you are looking for a Postgres variant, checkout \u003ca href=\"https://github.com/netreconlab/hipaa-postgres\"\u003ehipaa-postgres\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse at your own risk. There is not promise that this is HIPAA compliant and we are not responsible for any mishandling of your data\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImages\u003c/h2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMultiple images are automatically built for your convenience. Images can be found at the following locations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/hipaa-mongo\" rel=\"nofollow\"\u003eDocker - Hosted on Docker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/pkgs/container/hipaa-mongo\"\u003eSingularity - Hosted on GitHub Container Registry\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnvironment Variables\u003c/h2\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChanging these variables also require the same changes to be made to the \u003ca href=\"https://github.com/netreconlab/hipaa-mongo/blob/8997d535a105c839c014644f53102b33bcb9cc5d/scripts/mongo-init.js#L3-L4\"\u003einitialization script\u003c/a\u003e or to the database directly.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eMONGO_INITDB_ROOT_USERNAME=parse # Username for logging into database\nMONGO_INITDB_ROOT_PASSWORD=parse # Password for logging into database\nMONGO_INITDB_DATABASE=parse_hipaa # Name of parse-hipaa database\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetting up TLS\u003c/h2\u003e\u003ca id=\"user-content-setting-up-tls\" class=\"anchor\" aria-label=\"Permalink: Setting up TLS\" href=\"#setting-up-tls\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore building you will need to setup certificates and keys for each of the servers/containers you wish to run. You can follow the tutorial here: \u003ca href=\"https://medium.com/@rajanmaharjan/secure-your-mongodb-connections-ssl-tls-92e2addb3c89\" rel=\"nofollow\"\u003ehttps://medium.com/@rajanmaharjan/secure-your-mongodb-connections-ssl-tls-92e2addb3c89\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsing the naming conventions from the tuturial. Move the files to follow the file structure below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003essl\u003cbr\u003e\n---- rootCA.pem (this only needs to be created once)\u003cbr\u003e\n---- server0\u003cbr\u003e\n-------- mongodb.key (new one for each server)\u003cbr\u003e\n-------- mongodb.pem (new one for each server)\u003cbr\u003e\n---- server1 (if you have a second server)\u003cbr\u003e\n-------- mongodb.key (new one for each server)\u003cbr\u003e\n-------- mongodb.pem (new one for each server)\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNow follow the directions here, \u003ca href=\"https://www.percona.com/doc/percona-server-for-mongodb/LATEST/data_at_rest_encryption.html\" rel=\"nofollow\"\u003ehttps://www.percona.com/doc/percona-server-for-mongodb/LATEST/data_at_rest_encryption.html\u003c/a\u003e, and rename \"mongodb-keyfile\" file to \"mongodb_encryption.key\". Do this for each server/container and place each one in their respective folder:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003essl\u003cbr\u003e\n---- server0\u003cbr\u003e\n-------- mongodb_encryption.key (new one for each server. Note: if you want to rename this to something else, you need to change the name in Dockerfile as well)\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis step enables keyfile access control in a replica set. Currently, even if you are not using a replica set, you will need to do this because of the way the docker file is setup. Follow the directions here, \u003ca href=\"https://docs.mongodb.com/manual/tutorial/enforce-keyfile-access-control-in-existing-replica-set/\" rel=\"nofollow\"\u003ehttps://docs.mongodb.com/manual/tutorial/enforce-keyfile-access-control-in-existing-replica-set/\u003c/a\u003e, and for \u0026lt;path-to-keyfile use the name \"mongo_auth.key\" and place it:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003essl\u003cbr\u003e\n---- mongo_auth.key (this only needs to be created once)\u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo build the image:\n\u003ccode\u003edocker build --tag=hipaa-mongodb --build-arg sslDir=ssl/server0 .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAfter a successful build, you can run a ssl enabled container that is HIPAA compliant type:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --name hipaa-mongodb-container0 -t hipaa-mongodb:latest --sslMode requireSSL --sslPEMKeyFile /ssl/mongodb.pem --sslCAFile /ssl/rootCA.pem --enableEncryption --encryptionKeyFile /ssl/mongodb_encryption.key --replSet rs0 --keyFile /ssl/mongo_auth.key --logpath /logs/mongo.log --logappend --auditDestination=file --auditPath /logs/audit.json\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you want to persist your data and access the generated logs and audit files, you should volume mount the directories from your host machine. For example, if mongodb was installed on your host machine via brew on macOS and you want to use the mongodb directories. You can start your container with the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --name hipaa-mongodb-container0 -v /usr/local/var/mongodb:/data/db -v /usr/local/var/log/mongodb:/logs -t hipaa-mongodb:latest --sslMode requireSSL --sslPEMKeyFile /ssl/mongodb.pem --sslCAFile /ssl/rootCA.pem --enableEncryption --encryptionKeyFile /ssl/mongodb_encryption.key --keyFile /ssl/mongo_auth.key --logpath /logs/mongo.log --logappend --auditDestination=file --auditPath /logs/audit.json\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo enable replica sets. You will need to start your intended primary container with \u0027--replSet rs0\u0027. You can learn more about replica sets here, \u003ca href=\"https://docs.mongodb.com/manual/tutorial/deploy-replica-set/\" rel=\"nofollow\"\u003ehttps://docs.mongodb.com/manual/tutorial/deploy-replica-set/\u003c/a\u003e. Starting your container will look something like the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --name hipaa-mongodb-container0 -v /usr/local/var/mongodb:/data/db -v /usr/local/var/log/mongodb:/logs -t hipaa-mongodb:latest --sslMode requireSSL --sslPEMKeyFile /ssl/mongodb.pem --sslCAFile /ssl/rootCA.pem --enableEncryption --encryptionKeyFile /ssl/mongodb_encryption.key --keyFile /ssl/mongo_auth.key --logpath /logs/mongo.log --logappend --auditDestination=file --auditPath /logs/audit.json --replSet rs0\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can then use \u003ccode\u003ers.initiate()\u003c/code\u003e, \u003ccode\u003ers.status()\u003c/code\u003e from the previous tutorial to add replica members. Adterwards, start the new container using the same \"replSet\" name:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --name hipaa-mongodb-container1 -v /usr/local/var/mongodb:/data/db -v /usr/local/var/log/mongodb:/logs -t hipaa-mongodb:latest --sslMode requireSSL --sslPEMKeyFile /ssl/mongodb.pem --sslCAFile /ssl/rootCA.pem --enableEncryption --encryptionKeyFile /ssl/mongodb_encryption.key --keyFile /ssl/mongo_auth.key --logpath /logs/mongo.log --logappend --auditDestination=file --auditPath /logs/audit.json --replSet rs0\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNote that if you use --auth to start your containers, you will need to remove this command during initial syncing of your DB\u0027s. You can re-enable -auth after they are synced.\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 3, - "topics": [], - "updated_at": 1590213660.0 + "subscribers_count": 1, + "topics": [ + "hipaa", + "encryption", + "mongodb", + "percona-server", + "docker", + "gdpr", + "mongo", + "parse-hipaa", + "parsecarekit", + "healthcare", + "singularity", + "hacktoberfest" + ], + "updated_at": 1731840118.0 }, { "data_format": 2, - "description": "Computation of root phenes from 3D point clouds.", + "description": "Optimize workflow for binning metagenomic short reads from multiple samples", "filenames": [ - "Singularity", - "model_preprocess/Singularity" + "Singularity" ], - "full_name": "Computational-Plant-Science/3D_model_traits_demo", + "full_name": "QuentinLetourneur/Let-it-bin", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e3D_model_traits_measurement\u003c/h1\u003e\u003ca id=\"user-content-3d_model_traits_measurement\" class=\"anchor\" aria-label=\"Permalink: 3D_model_traits_measurement\" href=\"#3d_model_traits_measurement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFunction: Compute 3D root traits from 3D root model for field-grown maize roots\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../main/media/image1.png\"\u003e\u003cimg src=\"../main/media/image1.png\" alt=\"Optional Text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eExample of computed root structure v.s. 3D root point cloud model\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../main/media/image2_1.gif\"\u003e\u003cimg src=\"../main/media/image2_1.gif\" alt=\"Optional Text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e3D root models (*.ply) in Polygon File Format or the Stanford Triangle Format.\u003c/p\u003e\n\u003cp\u003ecomputed from Computational-Plant-Science / 3D_model_reconstruction_demo\n(\u003ca href=\"https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo\"\u003ehttps://github.com/Computational-Plant-Science/3D_model_reconstruction_demo\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003etrait.xlsx Excel format, contains 18 traits results\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e is required to run this project in a Linux environment.\u003c/p\u003e\n\u003cp\u003eInstall Docker Engine (\u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe suggest to run the pipeline inside a docker container,\u003c/p\u003e\n\u003cp\u003eThe Docker container allows you to package up your application(s) and deliver them to the cloud without any dependencies. It is a portable computing environment. It contains everything an application needs to run, from binaries to dependencies to configuration files.\u003c/p\u003e\n\u003cp\u003eThere are two ways to run the pipeline inside a docker container,\u003c/p\u003e\n\u003cp\u003eOne was is to build a docker based on the docker recipe file inside the GitHub repository. In our case, please follow step 1 and step 3.\u003c/p\u003e\n\u003cp\u003eAntoher way is to download prebuild docker image directly from Docker hub. In our case, please follow step 2 and step 3.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBuild docker image on your PC under linux environment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Computational-Plant-Science/3D_model_traits_demo.git\n\ndocker build -t 3d-model-traits -f Dockerfile \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eDownload prebuild docker image directly from Docker hub, without building docker image on your local PC\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull computationalplantscience/3d-model-traits\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eRun the pipeline inside the docker container\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003elink your test 3D model path (e.g. \u0027/home/test/test.ply\u0027, $path_to_your_3D_model = /home/test, $your_3D_model_name.ply = test.ply)to the /srv/test/ path inside the docker container\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -v /\u003cspan class=\"pl-smi\"\u003e$path_to_your_3D_model\u003c/span\u003e:/srv/test -it 3d-model-traits\n\nor \n\ndocker run -v /\u003cspan class=\"pl-smi\"\u003e$path_to_your_3D_model\u003c/span\u003e:/srv/test -it computationalplantscience/3d-model-traits\n\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eRun the pipeline inside the container\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 pipeline.py -p /srv/test/ -m \u003cspan class=\"pl-smi\"\u003e$your_3D_model_name\u003c/span\u003e.ply\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eReference:\u003c/p\u003e\n\u003cp\u003eShenglan Du, Roderik Lindenbergh, Hugo Ledoux, Jantien Stoter, and Liangliang Nan.\nAdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees.\nRemote Sensing. 2019, 11(18), 2074.\u003c/p\u003e\n\u003cp\u003e@article{du2019adtree,\ntitle={AdTree: Accurate, detailed, and automatic modelling of laser-scanned trees},\nauthor={Du, Shenglan and Lindenbergh, Roderik and Ledoux, Hugo and Stoter, Jantien and Nan, Liangliang},\njournal={Remote Sensing},\nvolume={11},\nnumber={18},\npages={2074},\nyear={2019}\n}\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSuxing Liu (\u003ca href=\"mailto:suxingliu@gmail.com\"\u003esuxingliu@gmail.com\u003c/a\u003e), Wesley Paul Bonelli(\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e), Alexander Bucksch\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther contributions\u003c/h2\u003e\u003ca id=\"user-content-other-contributions\" class=\"anchor\" aria-label=\"Permalink: Other contributions\" href=\"#other-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker container was maintained and deployed to \u003ca href=\"https://portnoy.cyverse.org\" rel=\"nofollow\"\u003ePlantIT\u003c/a\u003e by Wes Bonelli (\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGNU Public License\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLet-it-bin\u003c/h1\u003e\u003ca id=\"user-content-let-it-bin\" class=\"anchor\" aria-label=\"Permalink: Let-it-bin\" href=\"#let-it-bin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#prerequisites\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Pipeline_inputs_and_options\"\u003ePipeline inputs and options\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLet-it-bin allows to perform the binning of metagenomic short paired-end reads from multiple samples into species.\nThe pipeline take raw paired-end reads from multiple samples as primary input (the pairs names MUST finish with _{1,2}.fq/fastq). It comprise 4 major steps, reads preprocessing, assembly, binning and evaluation.\nThe pipeline can be started from the second or third step by adding arguments to the command line, provided that you have the needed inputs.\nYou have to select the binning softwares that will run in the following list :\nbinsanity, canopy, concoct, cocacola, maxbin, metabat, metabat2 and metagen\nYou just have to prefix the name of the wanted programms with \u0027--\u0027 (Ex : --concoct).\nIf you want to use them all just use --all T\u003c/p\u003e\n\u003cp\u003eWhen path are needed please give \u003cstrong\u003efull path\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eIf you run this pipeline on a cluster (what I recommend) you can use the given .config file to specify allocated memory per task and other cluster options. Memory values have been placed based on experience but can be changed.\u003cbr\u003e\nBe it locally or on a cluster \u003cstrong\u003ebe sure to add the full path to let-it-bin.simg\u003c/strong\u003e (more details in the next section) in the config file\u003c/p\u003e\n\u003cp\u003eThe output directory have the following layout :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[out]\n|\n|__assembly Assembly and contigs annotation\n|__Binnings Folder of each chosen binning software\n| |__Metabat\n| |__checkm_res\n|__cleaned_reads\n|__khmer_res\n|__mapping\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run this pipeline you will need Nextflow and Singularity (tested with version 19.10.0.5170 and 3.5.0 respectively).\u003cbr\u003e\nHere are the links to the installation instruction for \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e and \u003ca href=\"https://github.com/sylabs/singularity/blob/master/INSTALL.md\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe singularity image can be downloaded here (warning: the file is heavy ~ 1.9Go):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://shiny01.hosting.pasteur.fr/pub/let-it-bin.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA recipe file named Singularity is also given.\u003cbr\u003e\nTo build the image on an unix system move to let-it-bin repository and lauch\u003cbr\u003e\n\u003ccode\u003esudo singularity build let-it-bin.simg Singularity\u003c/code\u003e\u003cbr\u003e\nThis will take at least an hour.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline inputs and options\u003c/h2\u003e\u003ca id=\"user-content-pipeline-inputs-and-options\" class=\"anchor\" aria-label=\"Permalink: Pipeline inputs and options\" href=\"#pipeline-inputs-and-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e GENERAL ARGUMENTS :\n\n --reads [PATH] Directory containing unzipped paired reads files.\nOnly needed if you start from raw reads or reads from which contaminant have been removed\n --nb_samples [INT] Number of samples you are using\n --out [PATH] Directory were will be stored the results of the pipeline\n --sim_data [CHAR] Can be either F (Default) or T. Will change the execution of the pipeline depending on the analysed data (simulated or not).\n --cpus [INT] Number of cpus used for task with multiprocessing (Default 4)\n --min_contigs_length [INT] Minimum contigs length in base to be passed to binning programms (Default 1000)\n --nb_ref [INT] If you use simulated data specify the total number of different genomes present in the samples\n --dastool [CHAR] Can be either T (Default) or F. If you use multiple binning softwares you can use dastool to combine these results and try to extract best bins corresponding to the same microorganism.\n --local_scratch [CHAR] Can be either T (Default) or F. If you are on TARS or on a cluster with a /local/scratch space on nodes. You can use this option to speed up the execution of post processing of binning result for Canopy, Concoct, Cocacola and Metagen.\n --tmp_checkm [PATH] Directory were will be stored CheckM temporary files. The path length souhldn\u0027t exeed 65 chars. (Default [out]/tmp_checkm)\n --help Print the help message\n\n READS PREPROCESSING :\n\n --contaminant [PATH] Path and prefix of the bowtie2 index files of contaminant sequences (HAVE TO be computed before lauching the pipeline)\n --minlength [INT] Minimum length for trimed contigs (Default 45)\n --alienseq [PATH] Fasta file containing adaptaters sequence for AlienTrimmer\n --cleaned_readsDir [PATH] Folder were will be stored reads that have been filtered to eliminate contaminant and trimmed (Default [out]/cleaned_reads)\nIf there are already fastq files in the folder it will take it as input for khmer and skip the cleaning step\n --filt_readsDir [PATH] Directory containing Khmer results.\nIF SPECIFIED the workflow will start at the assembly by taking as input the filtered fastq files in the directory.\n\n ASSEMBLY :\n\n --cpuassembly [INT] Number of cpus used for reads assembly (Default 10)\n --memassembly [INT] Quantity of RAM in Mb used for reads assembly. Default 160000 Mb with 2 retries. If the wanted memory is \u0026lt;= 160000, the allocated memory will grow according to the following formula : number of retry * [memmapping]\nElse no retry will be done\n --qos_assembly [STRING] If you run the pipeline on a cluster with SLURM you can specify the queue of submision (qos) to use : fast, normal (Default) or long (on TARS you can only use 5 cpus in long)\n --mode [STRING] Name of the assembler to be used. Can be either spades (Default), clc, megahit or ray\n --multi_assembly [CHAR] By default a co-assembly of the samples is done. If you want to change that behaviour and do an assembly by sample set this parameter to T (Default F). The generated contigs will then be pulled in one file and filtered to lessen the redundancy but eliminating it is hard so there migth still be redundancy in the filtered contigs\n --contigs [PATH] If the assembly has already been done you can specify the path to the fasta file containing contigs. If provided the assembly steps will be skipped\n --refs_info [PATH] If you use a simulated dataset and specify the --contigs option. Give the path to the sum_contigs_length_per_annotation.tsv file contained in [out]/assembly/\n\n CONTIGS ANNOTATION :\n\n --blast_db [PATH] If you use simulated data. Path to the BLAST database containing reference sequences (HAVE TO be computed before running the pipeline)\n --coverage [INT] Coverage threshold used to filter alignments of contigs on reference genomes or a public database (Default 90)\n --identity [INT] Identity threshold used to filter alignments of contigs on reference genomes or a public database (Default 95)\n --mismatch [INT] Number of mismatch allowed in the seed aligment of BLAST (Default 1)\n --evalue [INT] E-value used for BLAST (Default 10)\n --hit [INT] Maximum number of hits for each querry in the BLAST output (Default 10)\n --link_ref_id_species [PATH] For simulated dataset tab-delimited file containing contigs IDs of reference sequence and the species to which they belong. Used to identify the target in the BLAST of contigs against the references\n --contigs_annotation [PATH] If you use simulated data and have specified the --contigs option specify the path to the (bh_blast_contigs_on_refs.tsv)(to be updated) file in [out]/assembly/\n\n MAPPING :\n\n --cpumapping [INT] Number of cpus used for mapping reads on the assembly (Default 4)\n One mapping per sample\n --memmapping [INT] Quantity of RAM in Mb for the mapping of each sample (Default 5000 )\n --bowtie2_indexDir [PATH] Directory were will be stored bowtie2 index of assembled sequences (Default [out]/bowtie2_index)\n --index_prefix [STRING] Prefix for the index files generated by bowtie2-build\n --bamDir [PATH] Directory were will be stored sorted BAM files generated by the mapping and if you use MetaGen indexed BAM files (Default [out]/mapping/bam). If there are sorted BAM files in the folder the mapping step will be skipped and they will be taken as input for the binning programs\n --count_matrix [PATH] If you use Canopy and have specified the --bamDir option. Provide the path to the count_matrix file contained by default in the [out]/mapping/comptage folder\n\n BINNING :\n\n --cpubinning [INT] Number of cpus for binning programs (Default same value as --cpus option)\n --nb_cluster [INT] Needed if you use Concoct. Rougth estimation of the expected number of species in the samples. This value will be a starting point for the program that will then refine it\n --bic_step [INT] MetaGen parameter corresponding to the step for the search of the optimal number of clusters (Default 5)\n --auto_method [INT] MetaGen parameter can be either 1 or 2. Recommended to be 2 for large datasets and is the default here\n\n BINNING EVALUATION :\n\n --min_bin_size [INT] Minimum size of bins in base (sum of the length of the sequences it contains) to be places in plots (Default 500000)\n --conta_threshold [FLOAT] Maximum contamination percent for good quality bins [0-1] (Default 0.1)\n --comp_threshold [FLOAT] Minimum completeness percent for good quality bins [0-1] (Default 0.6)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor real data starting from raw reads or reads filtered from contaminant and co-assembly with Spades.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ~/nextflow -c ~/let-it-bin/nextflow_slurm_singularity_common.config run -w [directory to store temporary files] let-it-bin.nf --reads ~/data/reads --out ~/results --cpus 4 --metabat --canopy --maxbin --index_prefix spades_contigs --tmp_checkm tmp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor simulated data starting from raw reads and co-assembly with megahit\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ~/nextflow -c ~/let-it-bin/nextflow_slurm_singularity_common.config run -w [directory to store temporary files] let-it-bin.nf --reads ~/data/reads --out ~/results --sim_data T --cpus 4\n --nb_ref 50 --metabat2 --cocacola --metagen --mode megahit\n --blast_db ~/blast_db/refs_seq --link_ref_id_species ~/link_id_species.tsv\n --index_prefix spades_contigs\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 5, - "subscribers_count": 5, + "subscribers_count": 3, "topics": [ - "phenotyping", - "phenotyping-algorithms", - "phenomics", - "root" + "nextflow", + "binning", + "metagenomic-analysis", + "singularity-containers" ], - "updated_at": 1717421725.0 + "updated_at": 1596753628.0 }, { "data_format": 2, - "description": "Toolkit to bring Webots to High Performance Computing, with support for parallelized and distributed batches of simulations.", + "description": "Empty template for nextflow pipelines", "filenames": [ "Singularity" ], - "full_name": "mattwfranchi/Webots.HPC", + "full_name": "IARCbioinfo/template-nf", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebots.HPC: bringing Webots at-scale to High Performance Computing\u003c/h1\u003e\u003ca id=\"user-content-webotshpc-bringing-webots-at-scale-to-high-performance-computing\" class=\"anchor\" aria-label=\"Permalink: Webots.HPC: bringing Webots at-scale to High Performance Computing\" href=\"#webotshpc-bringing-webots-at-scale-to-high-performance-computing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMatt Franchi, Rebecca Kahn, Clemson University\u003c/strong\u003e\n\u003cem\u003eData Intensive Computing Environments (DICE) Lab\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Project Overview\u003c/h2\u003e\u003ca id=\"user-content-1-project-overview\" class=\"anchor\" aria-label=\"Permalink: 1. Project Overview\" href=\"#1-project-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWebots.HPC is an in-development tool for running Webots robotics simulations on HPC resources.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. \u0027Ingredients\u0027 of Webots.HPC\u003c/h2\u003e\u003ca id=\"user-content-2-ingredients-of-webotshpc\" class=\"anchor\" aria-label=\"Permalink: 2. \u0027Ingredients\u0027 of Webots.HPC\" href=\"#2-ingredients-of-webotshpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cyberbotics.com/\" rel=\"nofollow\"\u003eWebots\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.eclipse.org/sumo/\" rel=\"nofollow\"\u003eSimulation of Urban Mobility (SUMO)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.usgs.gov/core-science-systems/sas/arc/about/what-high-performance-computing\" rel=\"nofollow\"\u003eHigh Performance Computing (HPC) Resource\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Container Downloads\u003c/h2\u003e\u003ca id=\"user-content-3-container-downloads\" class=\"anchor\" aria-label=\"Permalink: 3. Container Downloads\" href=\"#3-container-downloads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/mattwfranchi/webots_sumo\" rel=\"nofollow\"\u003eWebots.HPC Docker Image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eName\u003c/h1\u003e\u003ca id=\"user-content-name\" class=\"anchor\" aria-label=\"Permalink: Name\" href=\"#name\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEmpty template for nextflow pipelines (short description)\u003c/h2\u003e\u003ca id=\"user-content-empty-template-for-nextflow-pipelines-short-description\" class=\"anchor\" aria-label=\"Permalink: Empty template for nextflow pipelines (short description)\" href=\"#empty-template-for-nextflow-pipelines-short-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/template-nf\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6ec2dc07e1431dc0a7fb1f20e44c76ccebd603f4aeb75b30bf94c85090180a6/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f74656d706c6174652d6e662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/template-nf.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/template-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/1404\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/94193130\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad1ba5ccb85714dece917f7b680e3f805ccbb4af9b900446d72ad59e80690f42/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f39343139333133302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/94193130.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"template-nf.png\"\u003e\u003cimg src=\"template-nf.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we have several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/li\u003e\n\u003cli\u003eExternal software:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software by only installing Docker. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003einput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003einput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpecify the test files location\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param1\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param2\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param3\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--param4\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are special parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--flag2\u003c/td\u003e\n\u003ctd\u003e....\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput1\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoutput2\u003c/td\u003e\n\u003ctd\u003e......\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed description (optional section)\u003c/h2\u003e\u003ca id=\"user-content-detailed-description-optional-section\" class=\"anchor\" aria-label=\"Permalink: Detailed description (optional section)\" href=\"#detailed-description-optional-section\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirected Acyclic Graph\u003c/h2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-label=\"Permalink: Directed Acyclic Graph\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/template-nf/blob/master/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib1*\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support (link to specific gitter chatroom)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib2\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econtrib3\u003c/td\u003e\n\u003ctd\u003exx\u003c/td\u003e\n\u003ctd\u003eTester\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences (optional)\u003c/h2\u003e\u003ca id=\"user-content-references-optional\" class=\"anchor\" aria-label=\"Permalink: References (optional)\" href=\"#references-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ (optional)\u003c/h2\u003e\u003ca id=\"user-content-faq-optional\" class=\"anchor\" aria-label=\"Permalink: FAQ (optional)\" href=\"#faq-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", "stargazers_count": 5, - "subscribers_count": 1, + "subscribers_count": 14, "topics": [ - "webots", - "sumo", - "hpc", - "robotics", - "simulation", - "parallel-computing", - "distributed-systems" + "nextflow" ], - "updated_at": 1685012843.0 + "updated_at": 1677031934.0 }, { "data_format": 2, "description": null, "filenames": [ - "container/Singularity", - "container/Singularity_madeline2", - "container/stranger/Singularity", - "container/pod/Singularity", - "container/genmod/Singularity", - "container/reviewer/Singularity" + "Singularity" ], - "full_name": "Clinical-Genomics-Lund/nextflow_wgs", - "latest_release": "v3.5.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eREADME\u003c/h1\u003e\u003ca id=\"user-content-readme\" class=\"anchor\" aria-label=\"Permalink: README\" href=\"#readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of content\u003c/h2\u003e\u003ca id=\"user-content-table-of-content\" class=\"anchor\" aria-label=\"Permalink: Table of content\" href=\"#table-of-content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pipeline-overview\"\u003ePipeline overview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/running_the_pipeline.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eInputs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/input_files.md\"\u003eInput files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/annotation_files.md\"\u003eAnnotation files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/input_containers.md\"\u003eContainers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/sentieon_license.md\"\u003eSentieon license\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/outputs.md\"\u003eOutputs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/how_to_deploy.md\"\u003eHow to deploy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/list_of_all_used_software.md\"\u003eList of used software\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/misc_docs.md\"\u003eMisc documents\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline overview\u003c/h2\u003e\u003ca id=\"user-content-pipeline-overview\" class=\"anchor\" aria-label=\"Permalink: Pipeline overview\" href=\"#pipeline-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe constitutional wgs pipeline is a versatile workflow used in both genome sequencing and panels. It is implemented in the \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e workflow-language and designed to run in a computational cluster environment, using Singularity containers to manage software dependencies.\u003c/p\u003e\n\u003cp\u003eThe output is immediately importable into the software \u003ca href=\"https://github.com/Clinical-Genomics/scout\"\u003eScout\u003c/a\u003e, an open-source software for identifying variants with clinical significance.\u003c/p\u003e\n\u003cp\u003eIn order to run it, you will need to have a \u003ca href=\"https://support.sentieon.com/appnotes/license_server/\" rel=\"nofollow\"\u003eSentieon license\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eA typical workflow\u003c/h3\u003e\u003ca id=\"user-content-a-typical-workflow\" class=\"anchor\" aria-label=\"Permalink: A typical workflow\" href=\"#a-typical-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePreprocess, align and deduplicate reads\u003c/li\u003e\n\u003cli\u003eCalculate QC and coverage information\u003c/li\u003e\n\u003cli\u003ePerform SNV and SV calling\u003c/li\u003e\n\u003cli\u003eSubset the calls by intersecting with regions of interest\u003c/li\u003e\n\u003cli\u003eGenerate annotations for the subset of calls\u003c/li\u003e\n\u003cli\u003eCalculate scores for clinical significance\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFurther configurations\u003c/h3\u003e\u003ca id=\"user-content-further-configurations\" class=\"anchor\" aria-label=\"Permalink: Further configurations\" href=\"#further-configurations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWhen run in WGS-mode, mitochondrial analysis is performed\u003c/li\u003e\n\u003cli\u003eFor trios, uniparental disomy and runs of homozygosity are analysed\u003c/li\u003e\n\u003cli\u003eWhen running panels, a different set of structural variant callers adapted to panel data are run\u003c/li\u003e\n\u003cli\u003eFor onco-samples, an additional SNV-caller \"freebayes\" is used.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run into any issues using this pipeline, \u003ca href=\"https://github.com/Clinical-Genomics-Lund/nextflow_wgs/issues\"\u003eopen an issue in the GitHub repository\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIllustration of the pipeline, its steps and processes.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/wgs_overview_200.drawio.png\"\u003e\u003cimg src=\"docs/img/wgs_overview_200.drawio.png\" alt=\"overview_img\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "csiro-crop-informatics/nextflow-embl-abr-webinar", + "latest_release": "v1.2", + "readme": "\u003cp\u003eThis repository contains information for the EMBL-ABR webinar on \"Nextflow: Scalable, Sharable and Reproducible Computational Workflows across Clouds and Clusters\" presented by Rad Suchecki on 14th March 2019.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebinar details\u003c/h1\u003e\u003ca id=\"user-content-webinar-details\" class=\"anchor\" aria-label=\"Permalink: Webinar details\" href=\"#webinar-details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbstract:\u003c/strong\u003e\nLarge analysis workflows are fragile ecosystems of software tools, scripts and dependencies. This complexity commonly makes these workflows not only irreproducible but sometimes even not re-runnable outside their original development environment. Nextflow is a reactive workflow framework and a domain specific programming language which follows the dataflow paradigm and offers an alternative, and arguably superior, approach to developing, executing and sharing pipelines.\u003c/p\u003e\n\u003cp\u003eIn this webinar we will follow the steps required for developing sharable, version controlled, container-backed workflows, which can be seamlessly executed across different environments from a laptop to cluster to cloud. We will do this by leveraging Nextflow\u2019s integration with code and container image hosting services such as GitHub and Docker Hub, and out of the box support for various HPC cluster schedulers and the Amazon AWS cloud.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDate/time:\u003c/strong\u003e Thursday 14 March 2019 13:00-14:00 AEDT /12:00-13:00 AEST\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePresenter:\u003c/strong\u003e \u003ca href=\"https://orcid.org/0000-0003-4992-9497\" rel=\"nofollow\"\u003eRad Suchecki\u003c/a\u003e, CSIRO Crop Bioinformatics and Data Science\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://twitter.com/bioinforad\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff9d955ed85c923f8b942e7be1caef962cb62cba80b54182122f0be8b2a4fccd/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f62696f696e666f7261642e7376673f7374796c653d736f6369616c\" alt=\"Twitter Follow\" data-canonical-src=\"https://img.shields.io/twitter/follow/bioinforad.svg?style=social\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistration:\u003c/strong\u003e \u003cdel\u003e\u003ca href=\"https://attendee.gotowebinar.com/register/8408436403729692931\" rel=\"nofollow\"\u003ehttps://attendee.gotowebinar.com/register/8408436403729692931\u003c/a\u003e\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVideo link:\u003c/strong\u003e \u003ca href=\"https://www.youtube.com/channel/UC5WlFNBSfmt3e8Js8o2fFqQ\" rel=\"nofollow\"\u003eEMBL-ABR YouTube Channel\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.youtube.com/watch?v=lqm-VV5dOgk\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/83bc2e94a0c3282786eac5c9f7899edade3276550c7495dd483422989eef886a/687474703a2f2f696d672e796f75747562652e636f6d2f76692f6c716d2d565635644f676b2f687164656661756c742e6a7067\" alt=\"Nextflow Webinar Video\" data-canonical-src=\"http://img.youtube.com/vi/lqm-VV5dOgk/hqdefault.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSlides\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://csiro-crop-informatics.github.io/nextflow-embl-abr-webinar/nextflow-embl-abr.html\" rel=\"nofollow\"\u003ehttps://csiro-crop-informatics.github.io/nextflow-embl-abr-webinar/nextflow-embl-abr.html\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData for the Webinar\u003c/h1\u003e\u003ca id=\"user-content-data-for-the-webinar\" class=\"anchor\" aria-label=\"Permalink: Data for the Webinar\" href=\"#data-for-the-webinar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the purpose of demonstrating a Nextflow workflow in reasonable time, we will use the dataset used in \u003ca href=\"https://github.com/UofABioinformaticsHub/2019_EMBL-ABR_Snakemake_webinar#data-for-the-webinar\"\u003ethis Snakemake webinar\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTutorial\u003c/h1\u003e\u003ca id=\"user-content-tutorial\" class=\"anchor\" aria-label=\"Permalink: Tutorial\" href=\"#tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"nextflow-tutorial.md\"\u003enextflow-tutorial.md\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 5, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1728475763.0 + "updated_at": 1568158524.0 }, { "data_format": 2, - "description": "Virus assembler from amplicon sequencing reads", + "description": "singularity environment manager (application to NGS and bioinformatics)", "filenames": [ - "Singularity.def" + "test/data/Singularity.testing_1.0.0", + "damona/software/seacr/Singularity.seacr_1.3.0", + "damona/software/transdecoder/Singularity.trandecoder_5.7.0", + "damona/software/medaka/Singularity.medaka_1.7.3", + "damona/software/lima/Singularity.lima_2.9.0", + "damona/software/igvtools/Singularity.igvtools_2.12.0", + "damona/software/hmmer/Singularity.hmmer_2.3.2", + "damona/software/hmmer/Singularity.hmmer_3.3.2", + "damona/software/sequana_denovo/Singularity.sequana_denovo_0.0.2", + "damona/software/mafft/Singularity.mafft_7.520.0", + "damona/software/raxml/Singularity.raxml_8.2.12", + "damona/software/pilon/Singularity.pilon_1.24.0", + "damona/software/unicycler/Singularity.unicycler_0.5.0", + "damona/software/sequana_tools/Singularity.sequana_tools_0.10.0", + "damona/software/sequana_tools/Singularity.sequana_tools_0.14.2", + "damona/software/sequana_tools/Singularity.sequana_tools_0.9.0", + "damona/software/sequana_tools/Singularity.sequana_tools_0.14.5", + "damona/software/sequana_tools/Singularity.sequana_tools_0.12.0", + "damona/software/sequana_tools/Singularity.sequana_tools_0.14.3", + "damona/software/sequana_tools/Singularity.sequana_tools_0.14.1", + "damona/software/sequana_tools/Singularity.sequana_tools_0.11.0", + "damona/software/sequana_tools/Singularity.sequana_tools_0.15.1", + "damona/software/pbbam/Singularity.pbbam_2.1.0", + "damona/software/pbbam/Singularity.pbbam_2.3.0", + "damona/software/rnafold/Singularity.RNAfold_2.7.0", + "damona/software/laa/Singularity.pblaa_2.4.2", + "damona/software/snpeff/Singularity.snpeff_5.0.0", + "damona/software/snpeff/Singularity.snpeff_5.1.0", + "damona/software/infernal/Singularity.infernal_1.1.5", + "damona/software/dsrc/Singularity.dsrc_2.0.2", + "damona/software/bbmap/Singularity.bbmap_39.1.0", + "damona/software/samtools_minimap2/Singularity.samtools_1.17_minimap2_2.24.0", + "damona/software/kraken/Singularity.kraken_2.0.9", + "damona/software/kraken/Singularity.kraken_1.1.0", + "damona/software/vt/Singularity.vt_0.57721.0", + "damona/software/pbsim/Singularity.pbsim_3.0.1", + "damona/software/salmon/Singularity.salmon_1.3.0", + "damona/software/bcl2fastq/Singularity.bcl2fastq_2.20.0", + "damona/software/bbtools/Singularity.bbtools_38.94.0", + "damona/software/seqtk/Singularity.seqtk_1.3.0", + "damona/software/freebayes/Singularity.freebayes_1.2.0", + "damona/software/freebayes/Singularity.freebayes_1.3.7", + "damona/software/bioconvert/Singularity.bioconvert_0.6.2", + "damona/software/bioconvert/Singularity.bioconvert_1.1.0", + "damona/software/bioconvert/Singularity.bioconvert_0.6.3", + "damona/software/bioconvert/Singularity.bioconvert_1.0.0", + "damona/software/bioconvert/Singularity.bioconvert_0.6.1", + "damona/software/bowtie/Singularity.bowtie_1.3.1", + "damona/software/blast/Singularity.blast_2.12.0", + "damona/software/seqkit/Singularity.seqkit_2.4.0", + "damona/software/seqkit/Singularity.seqkit_2.1.0", + "damona/software/bedtools/Singularity.bedtools_2.30.0", + "damona/software/pypolca/Singularity.pypolca_0.3.1", + "damona/software/trim_galore/Singularity.trimgalore_0.5.0", + "damona/software/jellyfish/Singularity.jellyfish_2.3.0", + "damona/software/khmer/Singularity.khmer_2.1.1", + "damona/software/mauve/Singularity.mauve_2.4.0", + "damona/software/necat/Singularity.necat_0.0.1", + "damona/software/miniasm/Singularity.miniasm_0.3.0", + "damona/software/fastqc/Singularity.fastqc_0.12.1", + "damona/software/fastqc/Singularity.fastqc_0.11.9", + "damona/software/fastqc/Singularity.fastqc_0.11.9_py3", + "damona/software/fastqc/Singularity.fastqc_0.11.8", + "damona/software/pplacer/Singularity.pplacer_1.1.0", + "damona/software/sequana_perl_tools/Singularity.sequana_perl_tools_0.1.0", + "damona/software/sequana_perl_tools/Singularity.sequana_perl_tools_0.2.0", + "damona/software/multiqc/Singularity.multiqc_1.16.0", + "damona/software/falco/Singularity.falco_0.2.1", + "damona/software/falco/Singularity.falco_1.0.0", + "damona/software/ccs/Singularity.ccs_6.4.0", + "damona/software/sequana/Singularity.sequana_0.15.0", + "damona/software/sequana/Singularity.sequana_0.12.6", + "damona/software/sequana/Singularity.sequana_0.16.5", + "damona/software/sequana/Singularity.sequana_0.16.1", + "damona/software/sequana/Singularity.sequana_0.14.6", + "damona/software/sequana/Singularity.sequana_0.16.2", + "damona/software/bowtie2/Singularity.bowtie2_2.4.2", + "damona/software/bowtie2/Singularity.bowtie2_2.3.4", + "damona/software/bowtie2/Singularity.bowtie2_2.5.1", + "damona/software/bandage/Singularity.bandage_0.8.1", + "damona/software/adapterremoval/Singularity.adapterremoval_2.3.3", + "damona/software/subread/Singularity.subread_2.0.3", + "damona/software/sequana_ribofinder/Singularity.sequana_ribofinder_0.12.0", + "damona/software/checkm/Singularity.checkm_1.2.2", + "damona/software/gffread/Singularity.gffread_0.12.1", + "damona/software/gffread/Singularity.gffread_0.12.7", + "damona/software/delly/Singularity.delly_1.2.6", + "damona/software/idr/Singularity.idr_2.0.3", + "damona/software/gzip/Singularity.gzip_1.9.0", + "damona/software/ucsc/Singularity.ucsc_3.7.7", + "damona/software/flye/Singularity.flye_2.9.0", + "damona/software/flye/Singularity.flye_2.9.3", + "damona/software/flye/Singularity.flye_2.9.2", + "damona/software/flye/Singularity.flye_2.9.1", + "damona/software/augustus/Singularity.augustus_3.5.0", + "damona/software/bamtools/Singularity.bamtools_2.5.2", + "damona/software/samtools/Singularity.samtools_1.15.0", + "damona/software/samtools/Singularity.samtools_1.16.1", + "damona/software/orthofinder/Singularity.orthofinder_2.5.5", + "damona/software/rnammer/Singularity.rnammer_1.2.0", + "damona/software/helloworld/Singularity.helloworld_1.0.0", + "damona/software/mosdepth/Singularity.mosdepth_0.3.8", + "damona/software/hifiasm/Singularity.hifiasm_0.19.1", + "damona/software/hifiasm/Singularity.hifiasm_0.19.8", + "damona/software/repeatmasker/Singularity.repeatmasker_4.0.8", + "damona/software/busco/Singularity.busco_5.4.6", + "damona/software/sequana_coverage/Singularity.sequana_coverage_0.18.0", + "damona/software/quast/Singularity.quast_5.3.0", + "damona/software/quast/Singularity.quast_5.2.0", + "damona/software/quast/Singularity.quast_5.0.2", + "damona/software/datasets/Singularity.datasets_16.10.1", + "damona/software/phantompeakqualtools/Singularity.phantompeakqualtools_1.2.2", + "damona/software/bcl-convert/Singularity.bcl-convert_4.2.7", + "damona/software/liftoff/Singularity.liftoff_1.6.3", + "damona/software/trinity/Singularity.trinity_2.15.1", + "damona/software/trf/Singularity.trf_4.10.0", + "damona/software/homer/Singularity.homer_4.11.0", + "damona/software/minimap2/Singularity.minimap2_2.23.0", + "damona/software/minimap2/Singularity.minimap2_2.24.0", + "damona/software/minimap2/Singularity.minimap2_2.17.0", + "damona/software/nanopolish/Singularity.nanopolish_0.14.0", + "damona/software/nextclade/Singularity.nextclade_2.15.0", + "damona/software/mummer/Singularity.mummer_3.23.0", + "damona/software/ir/Singularity.ir_2.8.0", + "damona/software/pigz/Singularity.pigz_2.4.0", + "damona/software/art/Singularity.art_2.3.7", + "damona/software/art/Singularity.art_3.11.14", + "damona/software/art/Singularity.art_2.5.8", + "damona/software/fastp/Singularity.fastp_0.23.3", + "damona/software/fastp/Singularity.fastp_0.23.2", + "damona/software/seaview/Singularity.seaview_5.0.5", + "damona/software/canu/Singularity.canu_2.1.1", + "damona/software/canu/Singularity.canu_1.6.0", + "damona/software/canu/Singularity.canu_1.8.0", + "damona/software/vadr/Singularity.vadr_1.6.4", + "damona/software/prokka/Singularity.prokka_1.14.5", + "damona/software/prokka/Singularity.prokka_1.14.6", + "damona/software/polypolish/Singularity.polypolish_0.6.0", + "damona/software/polypolish/Singularity.polypolish_0.5.0", + "damona/software/bwa/Singularity.bwa_0.7.17", + "damona/software/guppy/Singularity.guppy_6.4.2", + "damona/software/shustring/Singularity.shustring_2.6.0", + "damona/software/sratoolkit/Singularity.sratoolkit_3.1.0", + "damona/software/pycoqc/Singularity.pycoqc_2.5.2", + "damona/software/graphviz/Singularity.graphviz_2.43.0", + "damona/software/graphviz/Singularity.graphviz_7.0.5", + "damona/software/pangolin/Singularity.pangolin_4.3.0", + "damona/software/rtools/Singularity.rtools_1.2.0", + "damona/software/rtools/Singularity.rtools_1.1.0", + "damona/software/rtools/Singularity.rtools_1.0.0", + "damona/software/rtools/Singularity.rtools_1.3.0", + "damona/software/ragtag/Singularity.ragtag_2.1.0", + "damona/software/rnaseqc/Singularity.rnaseqc_2.35.0", + "damona/software/circlator/Singularity.circlator_1.5.5", + "damona/software/trinotate/Singularity.trinotate_4.0.1", + "damona/software/cellranger_atac/Singularity.cellranger_atac_2.1.0", + "damona/software/rnadiff/Singularity.rnadiff_1.7.1", + "damona/software/mcl/Singularity.mcl_22.282.0", + "damona/software/pecat/Singularity.pecat_0.0.3", + "damona/software/ivar/Singularity.ivar_1.3.1", + "damona/software/cd-hit/Singularity.cd-hit_4.8.1", + "damona/library/conda/Singularity.conda_4.7.12", + "damona/library/conda/Singularity.conda_4.9.2", + "damona/library/micromamba/Singularity.micromamba_1.5.8", + "damona/library/micromamba/Singularity.micromamba_1.4.3", + "damona/library/R/Singularity.R_4.0.2", + "damona/library/R/Singularity.R_3.6.3", + "damona/bcftools/Singularity.bctools_1.16.0", + "damona/templates/Singularity.from_alpine_1.0.0", + "damona/templates/Singularity.micromamba_1.0.0", + "damona/templates/Singularity.python_slim" + ], + "full_name": "cokelaer/damona", + "latest_release": "v0.14.4", + "stargazers_count": 5, + "subscribers_count": 2, + "topics": [ + "singularity", + "manager", + "conda", + "ngs", + "bioinformatics" + ], + "updated_at": 1732629979.0 + }, + { + "data_format": 2, + "description": "Experimental fsl containers for learning purpose, consult fsl license at https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence", + "filenames": [ + "Singularity.centos7" + ], + "full_name": "pnlbwh/fsl-containers", + "latest_release": null, + "readme": "\u003cp\u003eThis repository is created for learning container development with OpenGL support. fsl and fsleyes in particular, are the target software.\u003c/p\u003e\n\u003cp\u003eView FSL license below: \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Licence\u003c/a\u003e\nA salient clause of the license states it is not free for commercial use. So, if you use this image, make sure you are aware of that limitation.\nThe maintainer of this image is not and cannot be held liable for unlawful use of this image\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#environment\"\u003eEnvironment\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#i-docker\"\u003e(i) Docker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ii-singularity\"\u003e(ii) Singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#docker-fsl-image\"\u003eDocker fsl image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity-fsl-image\"\u003eSingularity fsl image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable of contents created by \u003ca href=\"https://github.com/ekalinin/github-markdown-toc\"\u003egh-md-toc\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnvironment\u003c/h1\u003e\u003ca id=\"user-content-environment\" class=\"anchor\" aria-label=\"Permalink: Environment\" href=\"#environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA separate repository details requisite software and environment. See \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers\"\u003ehttps://github.com/tashrifbillah/glxgears-containers\u003c/a\u003e\nIn particular, the following sections should be useful:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e(i) Docker\u003c/h2\u003e\u003ca id=\"user-content-i-docker\" class=\"anchor\" aria-label=\"Permalink: (i) Docker\" href=\"#i-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#linuxmac\"\u003ehttps://github.com/tashrifbillah/glxgears-containers#linuxmac\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#windows\"\u003ehttps://github.com/tashrifbillah/glxgears-containers#windows\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e(ii) Singularity\u003c/h2\u003e\u003ca id=\"user-content-ii-singularity\" class=\"anchor\" aria-label=\"Permalink: (ii) Singularity\" href=\"#ii-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#linuxmac-1\"\u003ehttps://github.com/tashrifbillah/glxgears-containers#linuxmac-1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#windows-1\"\u003ehttps://github.com/tashrifbillah/glxgears-containers#windows-1\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker fsl image\u003c/h1\u003e\u003ca id=\"user-content-docker-fsl-image\" class=\"anchor\" aria-label=\"Permalink: Docker fsl image\" href=\"#docker-fsl-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(i) build\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker build -t tbillah/fsl-6.0.1-centos7 -f Dockerfile.centos7 .\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(ii) push\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker push tbillah/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iii) pull\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker pull tbillah/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iv) run\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinux/OSX\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti --privileged -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix tbillah/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eDetails can be found \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#linuxmac\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWindows\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollow steps mentioned \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#windows\"\u003ehere\u003c/a\u003e and use \u003ccode\u003etbillah/fsl-6.0.1-centos7\u003c/code\u003e instead of \u003ccode\u003eglxgears-docker\u003c/code\u003e. Eventually, you would use\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti --privileged -e DISPLAY=$DISPLAY tbillah/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity fsl image\u003c/h1\u003e\u003ca id=\"user-content-singularity-fsl-image\" class=\"anchor\" aria-label=\"Permalink: Singularity fsl image\" href=\"#singularity-fsl-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(i) build\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity build fsl-6.0.1-centos7 Singularity.centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(ii) push\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity push fsl-6.0.1-centos7 library://tbillah/collection/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iii) pull\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity pull library://tbillah/collection/fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iv) run\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinux/OSX\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity shell --writable-tmpfs fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e(inside the shell) fsleyes\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDetails can be found \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#linuxmac-1\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWindows\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou need a GUI desktop to run Singularity containers. Follow steps mentioned \u003ca href=\"https://github.com/tashrifbillah/glxgears-containers#windows-1\"\u003ehere\u003c/a\u003e. Eventually, you would use\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity shell --writable-tmpfs fsl-6.0.1-centos7\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e(inside the shell) fsleyes\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 5, + "subscribers_count": 2, + "topics": [], + "updated_at": 1722964610.0 + }, + { + "data_format": 2, + "description": "A collection of Singularity recipes useful for our nextflow pipelines", + "filenames": [ + "Singularity.trim_galore", + "Singularity.sierrapy", + "Singularity.snapperdb", + "Singularity.ariba", + "Singularity.seqtk", + "Singularity.mykrobe-atlas", + "Singularity.vsearch", + "Singularity.shiver", + "Singularity.abricate", + "Singularity.bbtools", + "Singularity.rtg", + "Singularity.kronatools", + "Singularity.variant_calling", + "Singularity.minimap2", + "Singularity.shiver_init", + "Singularity.iqtree", + "Singularity.kraken2", + "Singularity.prokka", + "Singularity.shovill", + "Singularity.interop", + "Singularity.pengu-ddt", + "Singularity.centrifuge", + "Singularity.multiqc", + "Singularity.deeptools", + "Singularity.snapperdb_v3", + "Singularity.iva", + "Singularity.mash", + "Singularity.cd-hit", + "Singularity.mlst", + "Singularity.assembly_improvement", + "Singularity.quast", + "Singularity.canu", + "Singularity.taxonkit", + "Singularity.wtdbg2" + ], + "full_name": "connor-lab/singularity-recipes", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-recipes\u003c/h1\u003e\u003ca id=\"user-content-singularity-recipes\" class=\"anchor\" aria-label=\"Permalink: singularity-recipes\" href=\"#singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA collection of Singularity recipes useful for our nextflow pipelines\u003c/p\u003e\n\u003cp\u003eTo build containers locally in \u003ccode\u003e../images\u003c/code\u003e, do:\n\u003ccode\u003e./build_containers.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1998\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 5, + "subscribers_count": 3, + "topics": [], + "updated_at": 1708615466.0 + }, + { + "data_format": 2, + "description": "Use Docker to explore the various components of Singularity", + "filenames": [ + "sregistry-cli/hello-world/Singularity" + ], + "full_name": "mjstealey/singularity-in-docker", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity in Docker\u003c/h1\u003e\u003ca id=\"user-content-singularity-in-docker\" class=\"anchor\" aria-label=\"Permalink: Singularity in Docker\" href=\"#singularity-in-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e to explore the various components of \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContents\u003c/h3\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\"singularity-registry\"\u003esingularity-registry\u003c/a\u003e - Deploy a local Singularity registry to interact with\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"singularity-packages\"\u003esingularity-packages\u003c/a\u003e - Build the RPM or DEB packages for running Singuarity on CentOS 7 or Ubuntu 16.04\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"sregistry-cli\"\u003esregistry-cli\u003c/a\u003e - Build/Use a container that runs both Singularity and sregistry-cli to interact with your local singualrity registry\u003c/li\u003e\n\u003c/ol\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"https://github.com/singularityhub/sregistry\"\u003eSingularity Registry\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1f26402c3680a19cd0d6bf6becf15b09ba8a06f408ed82c52a102b8347047a32/687474703a2f2f73696e67756c61726974792e6c626c2e676f762f696d616765732f6c6f676f2f6c6f676f2e737667\"\u003e\u003cimg width=\"50%\" alt=\"Singularity\" src=\"https://camo.githubusercontent.com/1f26402c3680a19cd0d6bf6becf15b09ba8a06f408ed82c52a102b8347047a32/687474703a2f2f73696e67756c61726974792e6c626c2e676f762f696d616765732f6c6f676f2f6c6f676f2e737667\" data-canonical-src=\"http://singularity.lbl.gov/images/logo/logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6098616fa11a5ded83f94696568204e1f63d592878be33fc055c0a84e562a25a/68747470733a2f2f7777772e646f636b65722e636f6d2f73697465732f64656661756c742f66696c65732f766572746963616c2e706e67\"\u003e\u003cimg width=\"90%\" alt=\"Docker\" src=\"https://camo.githubusercontent.com/6098616fa11a5ded83f94696568204e1f63d592878be33fc055c0a84e562a25a/68747470733a2f2f7777772e646f636b65722e636f6d2f73697465732f64656661756c742f66696c65732f766572746963616c2e706e67\" data-canonical-src=\"https://www.docker.com/sites/default/files/vertical.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f2a9c2aaefecf59bea01de839cde6d415b1b99dc7ad91173ca5f4e885ff7bedb/68747470733a2f2f73696e67756c61726974796875622e6769746875622e696f2f7372656769737472792f6173736574732f696d672f6c6f676f2e706e67\"\u003e\u003cimg width=\"100%\" alt=\"Singularity Registry\" src=\"https://camo.githubusercontent.com/f2a9c2aaefecf59bea01de839cde6d415b1b99dc7ad91173ca5f4e885ff7bedb/68747470733a2f2f73696e67756c61726974796875622e6769746875622e696f2f7372656769737472792f6173736574732f696d672f6c6f676f2e706e67\" data-canonical-src=\"https://singularityhub.github.io/sregistry/assets/img/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity: \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity enables users to have full control of their environment. Singularity containers can be used to package entire scientific workflows, software and libraries, and even data. This means that you don\u2019t have to ask your cluster admin to install anything for you - you can put it in a Singularity container and run. Did you already invest in Docker? The Singularity software can import your Docker images without having Docker installed or being a superuser. Need to share your code? Put it in a Singularity container and your collaborator won\u2019t have to go through the pain of installing missing dependencies. Do you need to run a different operating system entirely? You can \u201cswap out\u201d the operating system on your host for a different one within a Singularity container. As the user, you are in control of the extent to which your container interacts with its host. There can be seamless integration, or little to no communication at all. What does your workflow look like?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity Hub: \u003ca href=\"https://www.singularity-hub.org\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity Hub is a registry for scientific \u003ca href=\"https://opensource.com/resources/what-are-linux-containers\" rel=\"nofollow\"\u003elinux containers\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWhat is a Linux container?\u003c/strong\u003e A container image is an encapsulated, portable environment that is created to distribute a scientific analysis or a general function. Containers help with reproducibility of such content as they nicely package software and data dependencies, along with libraries that are needed. Thus, the core of Singularity Hub are these Singularity container images, and by way of being on Singularity Hub they can be easily built, updated, referenced with a url for a publication, and shared. This small guide will help you to get started building your containers using Singularity Hub and your Github repositories.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSingularity Registry: \u003ca href=\"https://github.com/singularityhub/sregistry\"\u003ehttps://github.com/singularityhub/sregistry\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity Registry is a management and storage of Singularity images for an institution or user to deploy locally. It does not manage building, but serves endpoints to obtain and save containers. The Registry is expected to be available for use in the Fall.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDocker: \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003ehttps://www.docker.com\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker is the company driving the container movement and the only container platform provider to address every application across the hybrid cloud. Today\u2019s businesses are under pressure to digitally transform but are constrained by existing applications and infrastructure while rationalizing an increasingly diverse portfolio of clouds, datacenters and application architectures. Docker enables true independence between applications and infrastructure and developers and IT ops to unlock their potential and creates a model for better collaboration and innovation.\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 5, + "subscribers_count": 4, + "topics": [ + "singularity", + "docker", + "sregistry-cli", + "singularity-hub", + "singularity-packages", + "singularity-registry", + "deb-packages", + "rpm-packages" + ], + "updated_at": 1588839723.0 + }, + { + "data_format": 2, + "description": "Tools for XML submission", + "filenames": [ + "Singularity" + ], + "full_name": "ddbj/submission-excel2xml", + "latest_release": "v3.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExcel and container images for DRA/JGA/AGD metadata XML submissions\u003c/h1\u003e\u003ca id=\"user-content-excel-and-container-images-for-drajgaagd-metadata-xml-submissions\" class=\"anchor\" aria-label=\"Permalink: Excel and container images for DRA/JGA/AGD metadata XML submissions\" href=\"#excel-and-container-images-for-drajgaagd-metadata-xml-submissions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u65e5\u672c\u8a9e\u003c/h2\u003e\u003ca id=\"user-content-\u65e5\u672c\u8a9e\" class=\"anchor\" aria-label=\"Permalink: \u65e5\u672c\u8a9e\" href=\"#\u65e5\u672c\u8a9e\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u751f\u547d\u60c5\u5831\u30fbDDBJ \u30bb\u30f3\u30bf\u30fc\u003c/li\u003e\n\u003cli\u003e\u516c\u958b\u65e5: 2024-10-23\u003c/li\u003e\n\u003cli\u003eversion: v3.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://www.ddbj.nig.ac.jp/index-e.html\" rel=\"nofollow\"\u003eBioinformation and DDBJ Center\u003c/a\u003e \u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u767b\u9332\u3059\u308b\u305f\u3081\u306e\u30e1\u30bf\u30c7\u30fc\u30bf XML \u3092\u751f\u6210\u3001\u30c1\u30a7\u30c3\u30af\u3059\u308b\u30c4\u30fc\u30eb\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html\" rel=\"nofollow\"\u003eDDBJ Sequence Read Archive (DRA)\u003c/a\u003e: Submission\u3001Experiment\u3001Run \u3068 Analysis (\u4efb\u610f) XML \u3092\u751f\u6210\u30fb\u30c1\u30a7\u30c3\u30af\u3059\u308b\u305f\u3081\u306e\u30a8\u30af\u30bb\u30eb\u3068\u30b9\u30af\u30ea\u30d7\u30c8\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/jga/submission.html\" rel=\"nofollow\"\u003eJapanese Genotype-phenotype Archive (JGA)\u003c/a\u003e: Submission\u3001Study\u3001Sample\u3001Experiment\u3001Data\u3001Analysis \u3068 Dataset XML \u3092\u751f\u6210\u30fb\u30c1\u30a7\u30c3\u30af\u3059\u308b\u305f\u3081\u306e\u30a8\u30af\u30bb\u30eb\u3068\u30b9\u30af\u30ea\u30d7\u30c8\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/agd/submission.html\" rel=\"nofollow\"\u003eAMED Genome Group Sharing Database (AGD)\u003c/a\u003e: Submission\u3001Study\u3001Sample\u3001Experiment\u3001Data\u3001Analysis \u3068 Dataset XML \u3092\u751f\u6210\u30fb\u30c1\u30a7\u30c3\u30af\u3059\u308b\u305f\u3081\u306e\u30a8\u30af\u30bb\u30eb\u3068\u30b9\u30af\u30ea\u30d7\u30c8\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u5c65\u6b74\u003c/h2\u003e\u003ca id=\"user-content-\u5c65\u6b74\" class=\"anchor\" aria-label=\"Permalink: \u5c65\u6b74\" href=\"#\u5c65\u6b74\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2024-10-23: v3.1 Library Source SINGLE CELL\u003c/li\u003e\n\u003cli\u003e2024-07-05: v3.0 \u30b7\u30fc\u30c8\u540d\u306b DB prefix \u3092\u4ed8\u52a0\u003c/li\u003e\n\u003cli\u003e2024-07-04: v2.9.1 Sample \u5c5e\u6027\u3092\u5168\u3066 ATTRIBUTE \u306b\u683c\u7d0d\u003c/li\u003e\n\u003cli\u003e2024-06-14: v2.9 Sample \u5c5e\u6027\u8ffd\u52a0\u3001\u53ca\u3073\u3001\u516c\u958b\u6307\u5b9a\u306e\u524a\u9664\u003c/li\u003e\n\u003cli\u003e2024-05-28: v2.8.1 file bug fix\u003c/li\u003e\n\u003cli\u003e2024-05-23: v2.8 md5 \u6841\u6570\u30a8\u30e9\u30fc\u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2024-05-17: v2.7 JGA Sample \u8ffd\u52a0\u5c5e\u6027\u306e\u8a18\u8f09\u65b9\u6cd5\u3092\u5909\u66f4\u003c/li\u003e\n\u003cli\u003e2024-02-14: v2.6 DRA xsd 1.6.0\u003c/li\u003e\n\u003cli\u003e2024-01-31: v2.5 \u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u30fb\u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf\u306e\u4fee\u6b63\u003c/li\u003e\n\u003cli\u003e2023-12-21: v2.4 center name \u5909\u66f4\u003c/li\u003e\n\u003cli\u003e2023-12-05: v2.3 Analysis step \u3068 Attributes \u8907\u6570\u5024\u306e\u533a\u5207\u308a\u3092 , \u304b\u3089 ; \u306b\u5909\u66f4\u003c/li\u003e\n\u003cli\u003e2023-12-01: v2.2 gem \u5316\u003c/li\u003e\n\u003cli\u003e2023-11-21: v2.1 Analysis \u306b Run \u30ab\u30e9\u30e0\u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2023-09-04: v2.0 Analysis \u5bfe\u5fdc\u003c/li\u003e\n\u003cli\u003e2023-02-09: v1.9.2 Run title\u003c/li\u003e\n\u003cli\u003e2023-01-17: v1.9.1 PAIRED \u3067 NOMINAL_LENGTH \u3092\u4efb\u610f\u5316\u003c/li\u003e\n\u003cli\u003e2022-12-23: v1.9 JGA \u30e1\u30bf\u30c7\u30fc\u30bf\u30a8\u30af\u30bb\u30eb\u306b AGD \u3092\u7d71\u5408\u003c/li\u003e\n\u003cli\u003e2022-12-22: v1.8 AGD \u5bfe\u5fdc\u003c/li\u003e\n\u003cli\u003e2022-12-21: v1.7 JGA Dataset reference \u91cd\u8907\u30c1\u30a7\u30c3\u30af\u3092\u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2022-12-15: v1.6 JGA \u3092\u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2022-12-14: v1.5 DRA \u3092\u660e\u78ba\u5316\u003c/li\u003e\n\u003cli\u003e2022-12-13: v1.4 \u30ea\u30fc\u30c9\u9577\u3068\u30da\u30a2\u30ea\u30fc\u30c9\u306e\u5411\u304d\u306e\u8a18\u5165\u306e\u4e0d\u8981\u5316\u306b\u5bfe\u5fdc\u003c/li\u003e\n\u003cli\u003e2021-12-13: v1.3 BGISEQ \u8ffd\u52a0\u003c/li\u003e\n\u003cli\u003e2021-07-13: v1.2 \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/dra#changes-to-common-xml-159-on-7-july-2021\"\u003exsd 1.5.9\u003c/a\u003e \u306b\u5bfe\u5fdc\u3002xsd \u3092 \u003ca href=\"https://github.com/ddbj/pub\"\u003epub\u003c/a\u003e \u304b\u3089\u53d6\u5f97\u3059\u308b\u3088\u3046\u306b\u5909\u66f4\u3002\u003c/li\u003e\n\u003cli\u003e2020-04-24: v1.1 \u521d\u7248\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/h2\u003e\u003ca id=\"user-content-\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" class=\"anchor\" aria-label=\"Permalink: \u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\" href=\"#\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003esubmission-excel2xml \u30ec\u30dd\u30b8\u30c8\u30ea\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/ddbj/submission-excel2xml.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u30a4\u30e1\u30fc\u30b8\u69cb\u7bc9\u003c/h2\u003e\u003ca id=\"user-content-\u30a4\u30e1\u30fc\u30b8\u69cb\u7bc9\" class=\"anchor\" aria-label=\"Permalink: \u30a4\u30e1\u30fc\u30b8\u69cb\u7bc9\" href=\"#\u30a4\u30e1\u30fc\u30b8\u69cb\u7bc9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity \u30a4\u30e1\u30fc\u30b8\u3092\u003ca href=\"https://ddbj.nig.ac.jp/public/software/submission-excel2xml/\" rel=\"nofollow\"\u003e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u003c/a\u003e\u3001\u3082\u3057\u304f\u306f\u3001\u4ee5\u4e0b\u306e\u624b\u9806\u3067\u30ed\u30fc\u30ab\u30eb\u74b0\u5883\u3067\u69cb\u7bc9\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nsudo singularity build excel2xml.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker \u30a4\u30e1\u30fc\u30b8\u3092\u4ee5\u4e0b\u306e\u624b\u9806\u3067\u30ed\u30fc\u30ab\u30eb\u74b0\u5883\u3067\u69cb\u7bc9\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nsudo docker build -t excel2xml .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDRA\u003c/h2\u003e\u003ca id=\"user-content-dra\" class=\"anchor\" aria-label=\"Permalink: DRA\" href=\"#dra\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\u003c/h3\u003e\u003ca id=\"user-content-\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\" class=\"anchor\" aria-label=\"Permalink: \u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\" href=\"#\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30e1\u30bf\u30c7\u30fc\u30bf\u3068\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u3092\u30a8\u30af\u30bb\u30eb metadata_dra.xlsx \u306e \u0027Submission\u0027\u3001\u0027Experiment\u0027\u3001\u0027Run\u0027 \u3068 \u0027Run-file\u0027 \u30b7\u30fc\u30c8\u306b\u8a18\u5165\u3057\u307e\u3059\u3002\u30e1\u30bf\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u306f\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html#metadata\" rel=\"nofollow\"\u003e\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u003c/a\u003e\u3068 \u0027Readme\u0027 \u30b7\u30fc\u30c8\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002\n\u0027example/example-0001_dra_metadata.xlsx\u0027 \u304c\u8a18\u5165\u4f8b\u306b\u306a\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003eAnalysis (\u4efb\u610f) \u3092\u767b\u9332\u3059\u308b\u5834\u5408\u306f \u0027Analysis\u0027 \u30b7\u30fc\u30c8\u306b\u8a18\u5165\u3057\u307e\u3059\u3002Analysis \u306e\u307f\u3092\u65b0\u898f\u767b\u9332\u3059\u308b\u3053\u3068\u306f\u3067\u304d\u305a\u3001Run \u3092\u6301\u3063\u305f Submission \u306b\u767b\u9332\u3057\u307e\u3059\u3002\n\u0027example/example-0002_dra_metadata.xlsx\u0027 \u304c\u8a18\u5165\u4f8b\u306b\u306a\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Singularity\u003c/h3\u003e\u003ca id=\"user-content-xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-singularity\" class=\"anchor\" aria-label=\"Permalink: XML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Singularity\" href=\"#xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Experiment \u3068 Run XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\nD-way \u30a2\u30ab\u30a6\u30f3\u30c8 ID\u3001submission \u756a\u53f7\u3068 BioProject \u30a2\u30af\u30bb\u30c3\u30b7\u30e7\u30f3\u756a\u53f7\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0001\u0027: -a example -i 0001\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089\u4e09\u3064\u306e XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Submission.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Experiment.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Run.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnalysis \u30b7\u30fc\u30c8\u304c\u8a18\u5165\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u306f Analysis XML \u3082\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSubmission ID \u3092\u6307\u5b9a\u3057\u3066 XML \u3092\u30c1\u30a7\u30c3\u30af\u3057\u307e\u3059\u3002XML \u306f submission-excel2xml \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u76f4\u4e0b\u306b\u914d\u7f6e\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002SRA xsd \u30d5\u30a1\u30a4\u30eb\u306f build \u4e2d\u306b\u30b3\u30f3\u30c6\u30ca\u30fc\u5185\u306e /opt/submission-excel2xml/ \u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u306f xsd \u306b\u5bfe\u3059\u308b\u30c1\u30a7\u30c3\u30af\u3068\u6700\u4f4e\u9650\u306e\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u307e\u3059\u3002\nDRA \u306e\u767b\u9332\u30b5\u30a4\u30c8\u3067\u306f\u3088\u308a\u8a73\u7d30\u306a\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u308b\u305f\u3081\u3001\u30d1\u30b9\u3057\u305f XML \u304c\u767b\u9332\u904e\u7a0b\u3067\u30a8\u30e9\u30fc\u306b\u306a\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAnalysis XML \u306e\u307f\u3092\u751f\u6210\u003c/h4\u003e\u003ca id=\"user-content-analysis-xml-\u306e\u307f\u3092\u751f\u6210\" class=\"anchor\" aria-label=\"Permalink: Analysis XML \u306e\u307f\u3092\u751f\u6210\" href=\"#analysis-xml-\u306e\u307f\u3092\u751f\u6210\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u65e2\u5b58 Submission \u306b Analysis \u3092\u8ffd\u52a0\u3059\u308b\u5834\u5408\u3001\u0027Analysis\u0027 \u30b7\u30fc\u30c8\u306e\u307f\u3092\u8a18\u5165\u3057\u3001Analysis XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002XML \u751f\u6210\u6642\u306b -c \u3067 center name \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0002\u0027: -a example -i 0002\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003eCenter name: National Institute of Genetics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0002 -p PRJDB7252 -c \"National Institute of Genetics\" example/example-0002_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Analysis XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0002_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Docker\u003c/h3\u003e\u003ca id=\"user-content-xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-docker\" class=\"anchor\" aria-label=\"Permalink: XML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Docker\" href=\"#xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Experiment \u3068 Run XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\nD-way \u30a2\u30ab\u30a6\u30f3\u30c8 ID\u3001submission \u756a\u53f7\u3001BioProject \u30a2\u30af\u30bb\u30c3\u30b7\u30e7\u30f3\u756a\u53f7\u3068\u30a8\u30af\u30bb\u30eb\u3092\u542b\u3080\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0001\u0027: -a example -i 0001\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003e\u0027path_to_excel_directory\u0027: \u30a8\u30af\u30bb\u30eb\u3092\u542b\u3080\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089\u4e09\u3064\u306e XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Submission.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Experiment.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Run.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnalysis \u30b7\u30fc\u30c8\u304c\u8a18\u5165\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u306f Analysis XML \u3082\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSubmission ID \u3092\u6307\u5b9a\u3057\u3066 XML \u3092\u30c1\u30a7\u30c3\u30af\u3057\u307e\u3059\u3002XML \u306f submission-excel2xml \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u76f4\u4e0b\u306b\u914d\u7f6e\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002SRA xsd \u30d5\u30a1\u30a4\u30eb\u306f build \u4e2d\u306b\u30b3\u30f3\u30c6\u30ca\u30fc\u5185\u306e /opt/submission-excel2xml/ \u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u306f xsd \u306b\u5bfe\u3059\u308b\u30c1\u30a7\u30c3\u30af\u3068\u6700\u4f4e\u9650\u306e\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u307e\u3059\u3002\nDRA \u306e\u767b\u9332\u30b5\u30a4\u30c8\u3067\u306f\u3088\u308a\u8a73\u7d30\u306a\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u308b\u305f\u3081\u3001\u30d1\u30b9\u3057\u305f XML \u304c\u767b\u9332\u904e\u7a0b\u3067\u30a8\u30e9\u30fc\u306b\u306a\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAnalysis XML \u306e\u307f\u3092\u751f\u6210\u003c/h4\u003e\u003ca id=\"user-content-analysis-xml-\u306e\u307f\u3092\u751f\u6210-1\" class=\"anchor\" aria-label=\"Permalink: Analysis XML \u306e\u307f\u3092\u751f\u6210\" href=\"#analysis-xml-\u306e\u307f\u3092\u751f\u6210-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u65e2\u5b58 Submission \u306b Analysis \u3092\u8ffd\u52a0\u3059\u308b\u5834\u5408\u3001\u0027Analysis\u0027 \u30b7\u30fc\u30c8\u306e\u307f\u3092\u8a18\u5165\u3057\u3001Analysis XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002XML \u751f\u6210\u6642\u306b -c \u3067 center name \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0002\u0027: -a example -i 0002\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003eCenter name: National Institute of Genetics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml excel2xml_dra -a example -i 0002 -p PRJDB7252 -c \"National Institute of Genetics\" example/example-0002_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Analysis XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0002_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u30c1\u30a7\u30c3\u30af\u003c/h3\u003e\u003ca id=\"user-content-\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: \u30c1\u30a7\u30c3\u30af\" href=\"#\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSRA xsd \u306b\u5bfe\u3059\u308b XML \u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-sra-xsd-\u306b\u5bfe\u3059\u308b-xml-\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: SRA xsd \u306b\u5bfe\u3059\u308b XML \u30c1\u30a7\u30c3\u30af\" href=\"#sra-xsd-\u306b\u5bfe\u3059\u308b-xml-\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u30e1\u30bf\u30c7\u30fc\u30bf XML \u306f \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/dra/xsd/1-5\"\u003eSRA xsd\u003c/a\u003e \u306b\u5bfe\u3057\u3066\u30c1\u30a7\u30c3\u30af\u3055\u308c\u307e\u3059\u3002\u30e1\u30c3\u30bb\u30fc\u30b8\u306b\u5f93\u3063\u3066 XML \u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eXML \u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-xml-\u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: XML \u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\" href=\"#xml-\u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSubmission\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eError: Submission: \u516c\u958b\u4e88\u5b9a\u65e5\u304c\u904e\u53bb\u306e\u65e5\u4ed8\n\u5c06\u6765\u306e\u65e5\u4ed8\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment \u3068 Run\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eError: Run: #{run_alias} Paired library only has one file.\n\u30da\u30a2\u30e9\u30a4\u30d6\u30e9\u30ea Experiment \u3067\u306f\u5c11\u306a\u304f\u3068\u3082\u4e8c\u3064\u306e\u914d\u5217\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb (\u4f8b\u3001R1.fastq \u3068 R2.fastq) \u304c\u542b\u307e\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\" href=\"#\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eError: Run to Experiment reference error.\n\u5168\u3066\u306e Experiment \u304c Run \u304b\u3089\u53c2\u7167\u3055\u308c\u3066\u3044\u306a\u3044\u3002\nExperiment \u3092\u53c2\u7167\u3057\u3066\u3044\u306a\u3044 Run \u304c\u5b58\u5728\u3059\u308b\u3002\nRun \u304b\u3089\u53c2\u7167\u3055\u308c\u3066\u3044\u306a\u3044 Experiment \u304c\u5b58\u5728\u3059\u308b\u3002\n\u3053\u306e\u3088\u3046\u306a\u5834\u5408\u3001\u5168\u3066\u306e Run \u304c\u5168\u3066\u306e Experiment \u3092\u53c2\u7167\u3059\u308b\u3088\u3046\u306b\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u30e1\u30bf\u30c7\u30fc\u30bf\u30e2\u30c7\u30eb\u306f \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html#metadata-objects\" rel=\"nofollow\"\u003eDRA Handbook\u003c/a\u003e \u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDRA \u30a6\u30a7\u30d6\u753b\u9762\u304b\u3089 XML \u3092\u767b\u9332\u3059\u308b\u003c/h3\u003e\u003ca id=\"user-content-dra-\u30a6\u30a7\u30d6\u753b\u9762\u304b\u3089-xml-\u3092\u767b\u9332\u3059\u308b\" class=\"anchor\" aria-label=\"Permalink: DRA \u30a6\u30a7\u30d6\u753b\u9762\u304b\u3089 XML \u3092\u767b\u9332\u3059\u308b\" href=\"#dra-\u30a6\u30a7\u30d6\u753b\u9762\u304b\u3089-xml-\u3092\u767b\u9332\u3059\u308b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30e1\u30bf\u30c7\u30fc\u30bf XML \u3092\u767b\u9332\u3059\u308b\u524d\u306b\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html#upload-sequence-data\" rel=\"nofollow\"\u003e\u767b\u9332\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u914d\u5217\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u3092\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3057\u307e\u3059\u003c/a\u003e\u3002D-way \u306b\u30ed\u30b0\u30a4\u30f3\u5f8c\u3001\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission.html#create-metadata-in-xml-files\" rel=\"nofollow\"\u003eSubmission\u3001Experiment \u3068 Run XML \u3092 DRA \u767b\u9332\u30da\u30fc\u30b8\u3067\u3067\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u003c/a\u003e \u3057\u307e\u3059\u3002\u901a\u5e385\u5206\u4ee5\u5185\u306b\u767b\u9332\u304c\u5b8c\u4e86\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGithub \u3084 XML \u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\u003c/h3\u003e\u003ca id=\"user-content-github-\u3084-xml-\u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\" class=\"anchor\" aria-label=\"Permalink: Github \u3084 XML \u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\" href=\"#github-\u3084-xml-\u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ddbj/submission-excel2xml/blob/main/metadata_dra.xlsx\"\u003eDRA \u30e1\u30bf\u30c7\u30fc\u30bf\u30a8\u30af\u30bb\u30eb\u003c/a\u003e \u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3001\u5185\u5bb9\u3092\u82f1\u8a9e\u3067\u8a18\u5165\u3057\u3001\u30e1\u30fc\u30eb (\u003ca href=\"mailto:trace@ddbj.nig.ac.jp\"\u003etrace@ddbj.nig.ac.jp\u003c/a\u003e) \u6dfb\u4ed8\u3067 DRA \u30c1\u30fc\u30e0\u306b\u304a\u9001\u308a\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eJGA\u003c/h2\u003e\u003ca id=\"user-content-jga\" class=\"anchor\" aria-label=\"Permalink: JGA\" href=\"#jga\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\u003c/h3\u003e\u003ca id=\"user-content-\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165-1\" class=\"anchor\" aria-label=\"Permalink: \u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165\" href=\"#\u30a8\u30af\u30bb\u30eb\u306b\u30e1\u30bf\u30c7\u30fc\u30bf\u3092\u8a18\u5165-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30e1\u30bf\u30c7\u30fc\u30bf\u3068\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u3092\u30a8\u30af\u30bb\u30eb JGA_metadata.xlsx \u306e \u0027Submission\u0027\u3001\u0027Study\u0027\u3001\u0027Sample\u0027\u3001\u0027Experiment\u0027\u3001\u0027Data\u0027\u3001\u0027Analysis\u0027 (\u8a72\u5f53\u3059\u308b\u5834\u5408)\u3001\u0027Dataset\u0027 \u3068 \u0027File\u0027 \u30b7\u30fc\u30c8\u306b\u8a18\u5165\u3057\u307e\u3059\u3002\n\u30e1\u30bf\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u306f\u003ca href=\"https://www.ddbj.nig.ac.jp/jga/submission.html\" rel=\"nofollow\"\u003e\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u003c/a\u003e\u3068 \u0027Readme\u0027 \u30b7\u30fc\u30c8\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002\n\u0027example/JSUB999999_jga_metadata.xlsx\u0027 \u304c\u8a18\u5165\u4f8b\u306b\u306a\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Singularity\u003c/h3\u003e\u003ca id=\"user-content-xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-singularity-1\" class=\"anchor\" aria-label=\"Permalink: XML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Singularity\" href=\"#xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-singularity-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Study\u3001Sample\u3001Experiment\u3001Data\u3001Analysis (\u8a72\u5f53\u3059\u308b\u5834\u5408)\u3001Dataset XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\nJGA submission id \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJGA Submission ID \u0027JSUB999999\u0027: -j JSUB999999\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_jga -j JSUB999999 example/JSUB999999_jga_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089\u4e03\u3064\u306e XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJSUB999999_Analysis.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Data.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Dataset.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Experiment.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Sample.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Study.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Submission.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSample \u8ffd\u52a0\u5c5e\u6027\u306e\u8a18\u8f09\u65b9\u6cd5\u304c v2.6 \u4ee5\u524d\u306e\u5f62\u5f0f (\u4f8b age:37; collection_date:2015-03-05) \u306e\u5834\u5408\u3001-r \u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4ed8\u3051\u3066 XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\u3000\u3000\u003c/p\u003e\n\u003cp\u003eJGA Submission ID \u3092\u6307\u5b9a\u3057\u3066 XML \u3092\u30c1\u30a7\u30c3\u30af\u3057\u307e\u3059\u3002XML \u306f submission-excel2xml \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u76f4\u4e0b\u306b\u914d\u7f6e\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002JGA xsd \u30d5\u30a1\u30a4\u30eb\u306f build \u4e2d\u306b\u30b3\u30f3\u30c6\u30ca\u30fc\u5185\u306e /opt/submission-excel2xml/ \u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_jga -j JSUB999999\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u306f xsd \u306b\u5bfe\u3059\u308b\u30c1\u30a7\u30c3\u30af\u3068\u6700\u4f4e\u9650\u306e\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Docker\u003c/h3\u003e\u003ca id=\"user-content-xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-docker-1\" class=\"anchor\" aria-label=\"Permalink: XML \u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af: Docker\" href=\"#xml-\u751f\u6210\u3068\u30c1\u30a7\u30c3\u30af-docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Study\u3001Sample\u3001Experiment\u3001Data\u3001Analysis (\u8a72\u5f53\u3059\u308b\u5834\u5408)\u3001Dataset XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\nJGA submission id \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u4f8b\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJGA Submission ID \u0027JSUB999999\u0027: -j JSUB999999\u003c/li\u003e\n\u003cli\u003e\u0027path_to_excel_directory\u0027: \u30a8\u30af\u30bb\u30eb\u3092\u542b\u3080\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306e\u30d5\u30eb\u30d1\u30b9\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml excel2xml_jga -j JSUB999999 example/JSUB999999_jga_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089\u4e03\u3064\u306e XML \u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJSUB999999_Analysis.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Data.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Dataset.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Experiment.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Sample.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Study.xml\u003c/li\u003e\n\u003cli\u003eJSUB999999_Submission.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSample \u8ffd\u52a0\u5c5e\u6027\u306e\u8a18\u8f09\u65b9\u6cd5\u304c v2.6 \u4ee5\u524d\u306e\u5f62\u5f0f (\u4f8b age:37; collection_date:2015-03-05) \u306e\u5834\u5408\u3001-r \u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4ed8\u3051\u3066 XML \u3092\u751f\u6210\u3057\u307e\u3059\u3002\u3000\u3000\u003c/p\u003e\n\u003cp\u003eSubmission ID \u3092\u6307\u5b9a\u3057\u3066 XML \u3092\u30c1\u30a7\u30c3\u30af\u3057\u307e\u3059\u3002XML \u306f submission-excel2xml \u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u76f4\u4e0b\u306b\u914d\u7f6e\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002JGA xsd \u30d5\u30a1\u30a4\u30eb\u306f build \u4e2d\u306b\u30b3\u30f3\u30c6\u30ca\u30fc\u5185\u306e /opt/submission-excel2xml/ \u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml validate_meta_jga -j JSUB999999\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u3053\u3053\u3067\u306f xsd \u306b\u5bfe\u3059\u308b\u30c1\u30a7\u30c3\u30af\u3068\u6700\u4f4e\u9650\u306e\u30c1\u30a7\u30c3\u30af\u304c\u5b9f\u65bd\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u30c1\u30a7\u30c3\u30af\u003c/h3\u003e\u003ca id=\"user-content-\u30c1\u30a7\u30c3\u30af-1\" class=\"anchor\" aria-label=\"Permalink: \u30c1\u30a7\u30c3\u30af\" href=\"#\u30c1\u30a7\u30c3\u30af-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eJGA xsd \u306b\u5bfe\u3059\u308b XML \u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-jga-xsd-\u306b\u5bfe\u3059\u308b-xml-\u30c1\u30a7\u30c3\u30af\" class=\"anchor\" aria-label=\"Permalink: JGA xsd \u306b\u5bfe\u3059\u308b XML \u30c1\u30a7\u30c3\u30af\" href=\"#jga-xsd-\u306b\u5bfe\u3059\u308b-xml-\u30c1\u30a7\u30c3\u30af\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u30e1\u30bf\u30c7\u30fc\u30bf XML \u306f \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/jga/xsd/1-2\"\u003eJGA xsd\u003c/a\u003e \u306b\u5bfe\u3057\u3066\u30c1\u30a7\u30c3\u30af\u3055\u308c\u307e\u3059\u3002\u30e1\u30c3\u30bb\u30fc\u30b8\u306b\u5f93\u3063\u3066 XML \u3092\u4fee\u6b63\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eXML \u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-xml-\u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af-1\" class=\"anchor\" aria-label=\"Permalink: XML \u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af\" href=\"#xml-\u306e\u5185\u5bb9\u30c1\u30a7\u30c3\u30af-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\u003c/h4\u003e\u003ca id=\"user-content-\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af-1\" class=\"anchor\" aria-label=\"Permalink: \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af\" href=\"#\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u53c2\u7167\u95a2\u4fc2\u30c1\u30a7\u30c3\u30af-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u4ee5\u4e0b\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u9593\u306e\u95a2\u4fc2\u304c\u30c1\u30a7\u30c3\u30af\u3055\u308c\u307e\u3059\u3002\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eData -\u0026gt; Experiment\u003c/li\u003e\n\u003cli\u003eAnalysis -\u0026gt; Study\u003c/li\u003e\n\u003cli\u003eAnalysis -\u0026gt; Data\u003c/li\u003e\n\u003cli\u003eAnalysis -\u0026gt; Sample\u003c/li\u003e\n\u003cli\u003eExperiment -\u0026gt; Sample\u003c/li\u003e\n\u003cli\u003eAnalysis -\u0026gt; Sample\u003c/li\u003e\n\u003cli\u003eDataset -\u0026gt; Data\u003c/li\u003e\n\u003cli\u003eDataset -\u0026gt; Analysis\u003c/li\u003e\n\u003cli\u003eDataset -\u0026gt; Policy\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eXML \u3092\u767b\u9332\u3059\u308b\u003c/h3\u003e\u003ca id=\"user-content-xml-\u3092\u767b\u9332\u3059\u308b\" class=\"anchor\" aria-label=\"Permalink: XML \u3092\u767b\u9332\u3059\u308b\" href=\"#xml-\u3092\u767b\u9332\u3059\u308b\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eXML \u3092 JGA \u30c7\u30fc\u30bf\u53d7\u4ed8\u30b5\u30fc\u30d0\u306b\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3059\u308b\u524d\u306b \u003ca href=\"https://humandbs.biosciencedbc.jp/en/data-submission\" rel=\"nofollow\"\u003eNBDC \u4e8b\u696d\u63a8\u9032\u90e8\u003c/a\u003e \u3067\u63d0\u4f9b\u7533\u8acb\u304c\u627f\u8a8d\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGithub \u3084 XML \u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\u003c/h3\u003e\u003ca id=\"user-content-github-\u3084-xml-\u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408-1\" class=\"anchor\" aria-label=\"Permalink: Github \u3084 XML \u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408\" href=\"#github-\u3084-xml-\u751f\u6210\u65b9\u6cd5\u304c\u5206\u304b\u3089\u306a\u3044\u5834\u5408-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ddbj/submission-excel2xml/raw/main/JGA_metadata.xlsx\"\u003eJGA \u30e1\u30bf\u30c7\u30fc\u30bf\u30a8\u30af\u30bb\u30eb\u003c/a\u003e\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3001\u5185\u5bb9\u3092\u82f1\u8a9e\u3067\u8a18\u5165\u3057\u3001\u30e1\u30fc\u30eb (\u003ca href=\"mailto:jga@ddbj.nig.ac.jp\"\u003ejga@ddbj.nig.ac.jp\u003c/a\u003e) \u6dfb\u4ed8\u3067 JGA \u30c1\u30fc\u30e0\u306b\u304a\u9001\u308a\u304f\u3060\u3055\u3044\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAGD\u003c/h2\u003e\u003ca id=\"user-content-agd\" class=\"anchor\" aria-label=\"Permalink: AGD\" href=\"#agd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJGA \u3068\u540c\u69d8\u306e\u624b\u9806\u306b\u306a\u308a\u307e\u3059\u3002AGD \u306e\u30e1\u30bf\u30c7\u30fc\u30bf\u3082 JGA_metadata.xlsx \u306b\u8a18\u5165\u3057\u307e\u3059\u3002\nSubmission ID \u306b\u306f AGD Submission ID (\u4f8b ASUB000001) \u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNIG \u30b9\u30d1\u30b3\u30f3\u3067\u306e\u5b9f\u65bd\u65b9\u6cd5\u003c/h2\u003e\u003ca id=\"user-content-nig-\u30b9\u30d1\u30b3\u30f3\u3067\u306e\u5b9f\u65bd\u65b9\u6cd5\" class=\"anchor\" aria-label=\"Permalink: NIG \u30b9\u30d1\u30b3\u30f3\u3067\u306e\u5b9f\u65bd\u65b9\u6cd5\" href=\"#nig-\u30b9\u30d1\u30b3\u30f3\u3067\u306e\u5b9f\u65bd\u65b9\u6cd5\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u56fd\u7acb\u907a\u4f1d\u5b66\u7814\u7a76\u6240 \u751f\u547d\u60c5\u5831\u30fbDDBJ \u30bb\u30f3\u30bf\u30fc\u304c\u904b\u55b6\u3059\u308b \u003ca href=\"https://www.ddbj.nig.ac.jp/sc\" rel=\"nofollow\"\u003eNIG \u30b9\u30d1\u30b3\u30f3\u003c/a\u003e \u3067\u306f \u003ccode\u003e/lustre9/open/shared_data/software/submission-excel2xml/\u003c/code\u003e\n\u306b Singularity \u30a4\u30e1\u30fc\u30b8\u304c\u8a2d\u7f6e\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3084 build \u4f5c\u696d\u3092\u3059\u308b\u3053\u3068\u306a\u304f\u3001\u30e1\u30bf\u30c7\u30fc\u30bf\u30a8\u30af\u30bb\u30eb\u30d5\u30a1\u30a4\u30eb\u304c\u3042\u308c\u3070 XML \u751f\u6210\u3084 XML \u306e\u30c1\u30a7\u30c3\u30af\u3092\u5b9f\u65bd\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDRA\u003c/h3\u003e\u003ca id=\"user-content-dra-1\" class=\"anchor\" aria-label=\"Permalink: DRA\" href=\"#dra-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u591a\u4ef6\u6570\u306e\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u304c\u30b9\u30d1\u30b3\u30f3\u306b\u3042\u308b\u5834\u5408\u3001\u30e1\u30bf\u30c7\u30fc\u30bf XML \u4f5c\u6210\u3001\u53ca\u3073\u3001\u30c7\u30fc\u30bf\u30d5\u30a1\u30a4\u30eb\u306e DRA \u30d5\u30a1\u30a4\u30eb\u53d7\u4ed8\u30b5\u30fc\u30d0 (ftp-private.ddbj.nig.ac.jp) \u3078\u306e\u8ee2\u9001\u3092\u30b9\u30d1\u30b3\u30f3\u4e0a\u3067\u5b8c\u7d50\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u003c/p\u003e\n\u003cp\u003e\u30a8\u30af\u30bb\u30eb\u304b\u3089 Submission\u3001Experiment \u3068 Run XML \u3092\u751f\u6210\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp /lustre9/open/shared_data/software/submission-excel2xml/excel2xml.simg ~/\ncd\nsingularity exec excel2xml.simg excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eXML \u306e\u30c1\u30a7\u30c3\u30af\u3002\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJGA\u003c/h3\u003e\u003ca id=\"user-content-jga-1\" class=\"anchor\" aria-label=\"Permalink: JGA\" href=\"#jga-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAGD\u003c/h3\u003e\u003ca id=\"user-content-agd-1\" class=\"anchor\" aria-label=\"Permalink: AGD\" href=\"#agd-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnglish\u003c/h2\u003e\u003ca id=\"user-content-english\" class=\"anchor\" aria-label=\"Permalink: English\" href=\"#english\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBioinformation and DDBJ Center\u003c/li\u003e\n\u003cli\u003erelease: 2024-10-23\u003c/li\u003e\n\u003cli\u003eversion: v3.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese files are Excel, container images and tools for generation and validation of metadata XML files for databases of \u003ca href=\"https://www.ddbj.nig.ac.jp/index-e.html\" rel=\"nofollow\"\u003eBioinformation and DDBJ Center\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html\" rel=\"nofollow\"\u003eDDBJ Sequence Read Archive (DRA)\u003c/a\u003e: generate and check Submission, Experiment and Run XML files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/jga/submission-e.html\" rel=\"nofollow\"\u003eJapanese Genotype-phenotype Archive (JGA)\u003c/a\u003e: generate and check Submission, Study, Sample, Experiment, Data, Analysis and Dataset XML files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.ddbj.nig.ac.jp/agd/submission-e.html\" rel=\"nofollow\"\u003eAMED Genome Group Sharing Database (AGD)\u003c/a\u003e: generate and check Submission, Study, Sample, Experiment, Data, Analysis and Dataset XML files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHistory\u003c/h2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-label=\"Permalink: History\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2024-10-23: v3.1 Library Source SINGLE CELL\u003c/li\u003e\n\u003cli\u003e2024-07-05: v3.0 DB prefix added to sheet name\u003c/li\u003e\n\u003cli\u003e2024-07-04: v2.9.1 Store all sample attributes in SAMPLE_ATTRIBUTE\u003c/li\u003e\n\u003cli\u003e2024-06-14: v2.9 Sample attributes added and hold/release removed\u003c/li\u003e\n\u003cli\u003e2024-05-28: v2.8.1 file bug fix\u003c/li\u003e\n\u003cli\u003e2024-05-23: v2.8 md5 checksum value digit check added\u003c/li\u003e\n\u003cli\u003e2024-05-17: v2.7 Added sample attributes description format changed\u003c/li\u003e\n\u003cli\u003e2024-02-14: v2.6 DRA xsd 1.6.0\u003c/li\u003e\n\u003cli\u003e2024-01-31: v2.5 Fixing templates and sample data\u003c/li\u003e\n\u003cli\u003e2023-12-21: v2.4 center name changes\u003c/li\u003e\n\u003cli\u003e2023-12-05: v2.3 Delimiter of Analysis step and Attributes was changed from \",\" to \";\"\u003c/li\u003e\n\u003cli\u003e2023-12-01: v2.2 gem\u003c/li\u003e\n\u003cli\u003e2023-11-21: v2.1 Run column added to Analysis\u003c/li\u003e\n\u003cli\u003e2023-02-09: v2.0 Analysis support\u003c/li\u003e\n\u003cli\u003e2023-02-09: v1.9.2 Run Title\u003c/li\u003e\n\u003cli\u003e2023-01-17: v1.9.1 NOMINAL_LENGTH was made optional for PAIRED\u003c/li\u003e\n\u003cli\u003e2022-12-23: v1.9 AGD merged to the JGA excel\u003c/li\u003e\n\u003cli\u003e2022-12-22: v1.8 AGD\u003c/li\u003e\n\u003cli\u003e2022-12-21: v1.7 Dataset reference duplication check added\u003c/li\u003e\n\u003cli\u003e2022-12-15: v1.6 JGA added\u003c/li\u003e\n\u003cli\u003e2022-12-14: v1.5 DRA separated\u003c/li\u003e\n\u003cli\u003e2022-12-13: v1.4 Read length and direction of paired reads were made optional\u003c/li\u003e\n\u003cli\u003e2021-12-13: v1.3 BGISEQ added\u003c/li\u003e\n\u003cli\u003e2021-07-13: v1.2 Update to \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/dra#changes-to-common-xml-159-on-7-july-2021\"\u003exsd 1.5.9\u003c/a\u003e. Download the xsd files from \u003ca href=\"https://github.com/ddbj/pub\"\u003epub\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e2020-04-24: v1.1 Initial release\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownload\u003c/h2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-label=\"Permalink: Download\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload the DDBJ submission-excel2xml repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/ddbj/submission-excel2xml.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImage construction\u003c/h2\u003e\u003ca id=\"user-content-image-construction\" class=\"anchor\" aria-label=\"Permalink: Image construction\" href=\"#image-construction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity-1\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://ddbj.nig.ac.jp/public/software/submission-excel2xml/\" rel=\"nofollow\"\u003eDownload\u003c/a\u003e the Singularity image or build the Singularity image as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nsudo singularity build excel2xml.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker-1\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuild the Docker image as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nsudo docker build -t excel2xml .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDRA\u003c/h2\u003e\u003ca id=\"user-content-dra-2\" class=\"anchor\" aria-label=\"Permalink: DRA\" href=\"#dra-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnter metadata in the excel\u003c/h3\u003e\u003ca id=\"user-content-enter-metadata-in-the-excel\" class=\"anchor\" aria-label=\"Permalink: Enter metadata in the excel\" href=\"#enter-metadata-in-the-excel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEnter metadata and data files in the \u0027Submission\u0027, \u0027Experiment\u0027, \u0027Run\u0027 and \u0027Run-file\u0027 sheets of the excel \"metadata_dra.xlsx\".\nSee our \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html#metadata\" rel=\"nofollow\"\u003ewebsite\u003c/a\u003e for metadata and \u0027Readme\u0027 sheet of the excel for details.\nSee \u0027example-0001_dra_metadata.xlsx\u0027 for example.\u003c/p\u003e\n\u003cp\u003eTo submit Analysis (optional) object(s), enter an \u0027Analysis\u0027 sheet. Analysis-only submission is not acceptable. Submit Analysis to a Submission having Run(s).\nSee \u0027example/example-0002_dra_metadata.xlsx\u0027 for example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenerate XMLs: Singularity\u003c/h3\u003e\u003ca id=\"user-content-generate-xmls-singularity\" class=\"anchor\" aria-label=\"Permalink: Generate XMLs: Singularity\" href=\"#generate-xmls-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate Submission, Experiment and Run XMLs from the excel.\nSpecify the D-way account ID, submission number and BioProject accession.\u003c/p\u003e\n\u003cp\u003eFor example,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0001\u0027: -a example -i 0001\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThree XMLs are generated from the excel.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Submission.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Experiment.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Run.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhen an Analysis sheet is filled, an Analysis XML is generated.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eValidate the XMLs by specifying the submission ID. The XML files must be under the submission-excel2xml directory. The SRA xsd files have been downloaded to /opt/submission-excel2xml/ from \u003ca href=\"https://github.com/ddbj/pub\"\u003epub\u003c/a\u003e in the container during the build.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease note that this validator only performs xsd validation and minimum checks.\nThe XMLs are fully validated in the DRA web XML registration process,\nso the checked XMLs may be failed in the DRA submission system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Analysis XML only\u003c/h4\u003e\u003ca id=\"user-content-generate-analysis-xml-only\" class=\"anchor\" aria-label=\"Permalink: Generate Analysis XML only\" href=\"#generate-analysis-xml-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add Analysis object(s) to an existing Submission, you may enter an \u0027Analysis\u0027 sheet only and generate only an Analysis XML. Specify a center name by -c option.\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0002\u0027: -a example -i 0002\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003eCenter name: National Institute of Genetics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0002 -p PRJDB7252 -c \"National Institute of Genetics\" example/example-0002_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn Analysis XML is generated from the excel.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenerate XMLs: Docker\u003c/h3\u003e\u003ca id=\"user-content-generate-xmls-docker\" class=\"anchor\" aria-label=\"Permalink: Generate XMLs: Docker\" href=\"#generate-xmls-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGenerate Submission, Experiment and Run XMLs from the excel.\nSpecify the D-way account ID, submission number, BioProject accession and full path of the directory which contains the excel.\u003c/p\u003e\n\u003cp\u003eFor example,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0001\u0027: -a example -i 0001\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003e\u0027path_to_excel_directory\u0027: full path of the directory which contains the excel.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml excel2xml_dra -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThree XMLs are generated from the excel.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Submission.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Experiment.xml\u003c/li\u003e\n\u003cli\u003eexample-0001_dra_Run.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhen an Analysis sheet is filled, an Analysis XML is generated.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0001_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eValidate the XMLs by specifying the submission ID. The XML files must be under the submission-excel2xml directory. The SRA xsd files have been downloaded to /opt/submission-excel2xml/ from \u003ca href=\"https://github.com/ddbj/pub\"\u003epub\u003c/a\u003e in the container during the build.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run -v /path_to_excel_directory:/data -w /data excel2xml validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePlease note that this validator only performs xsd validation and minimum checks.\nThe XMLs are fully validated in the DRA web XML registration process,\nso the checked XMLs may be failed in the DRA submission system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGenerate Analysis XML only\u003c/h4\u003e\u003ca id=\"user-content-generate-analysis-xml-only-1\" class=\"anchor\" aria-label=\"Permalink: Generate Analysis XML only\" href=\"#generate-analysis-xml-only-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo add Analysis object(s) to an existing Submission, you may enter an \u0027Analysis\u0027 sheet only and generate only an Analysis XML. Specify a center name by -c option.\u003c/p\u003e\n\u003cp\u003eExample\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDRA submission id \u0027example-0002\u0027: -a example -i 0002\u003c/li\u003e\n\u003cli\u003eBioProject \u0027PRJDB7252\u0027 : -p PRJDB7252\u003c/li\u003e\n\u003cli\u003eCenter name: National Institute of Genetics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg excel2xml_dra -a example -i 0002 -p PRJDB7252 -c \"National Institute of Genetics\" example/example-0002_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn Analysis XML is generated from the excel.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexample-0002_dra_Analysis.xml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eValidation results\u003c/h3\u003e\u003ca id=\"user-content-validation-results\" class=\"anchor\" aria-label=\"Permalink: Validation results\" href=\"#validation-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eXML validation against SRA xsd\u003c/h4\u003e\u003ca id=\"user-content-xml-validation-against-sra-xsd\" class=\"anchor\" aria-label=\"Permalink: XML validation against SRA xsd\" href=\"#xml-validation-against-sra-xsd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMetadata XMLs are validated against \u003ca href=\"https://github.com/ddbj/pub/tree/master/docs/dra/xsd/1-5\"\u003erespective SRA xsd\u003c/a\u003e. Modify the XMLs according to the xsd validation messages.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eXML content check\u003c/h4\u003e\u003ca id=\"user-content-xml-content-check\" class=\"anchor\" aria-label=\"Permalink: XML content check\" href=\"#xml-content-check\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSubmission\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eError: Submission: Past hold date.\nSet the future hold date.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment and Run\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eError: Run: #{run_alias} Paired library only has one file.\nInclude at least two sequence data files (for example, R1.fastq and R2.fastq) for paired library Experiment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eObject reference check\u003c/h4\u003e\u003ca id=\"user-content-object-reference-check\" class=\"anchor\" aria-label=\"Permalink: Object reference check\" href=\"#object-reference-check\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eError: Run to Experiment reference error.\nNot all Experiments are referenced by Runs.\nThere is Run(s) not referencing Experiment.\nThere is Experiment(s) not referenced by Run.\nModify metadata to make all Runs reference all Experiments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html#metadata-objects\" rel=\"nofollow\"\u003ethe DRA Handbook\u003c/a\u003e for metadata model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSubmit XMLs in the DRA web interface\u003c/h3\u003e\u003ca id=\"user-content-submit-xmls-in-the-dra-web-interface\" class=\"anchor\" aria-label=\"Permalink: Submit XMLs in the DRA web interface\" href=\"#submit-xmls-in-the-dra-web-interface\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore submitting the metadata XMLs, \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html#upload-sequence-data\" rel=\"nofollow\"\u003eupload sequence data files to the submission directory\u003c/a\u003e.\nAfter logging in the D-way, \u003ca href=\"https://www.ddbj.nig.ac.jp/dra/submission-e.html#create-metadata-in-xml-files\" rel=\"nofollow\"\u003eupload the Submission, Experiment and Run XMLs in the XML upload area of the DRA submission\u003c/a\u003e.\nYour web browser may time out, however, submission processes are ongoing on the backend. Please close the browser and leave it for a while. The XML submission will be registered.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhen Github and XML generation are not clear for you\u003c/h3\u003e\u003ca id=\"user-content-when-github-and-xml-generation-are-not-clear-for-you\" class=\"anchor\" aria-label=\"Permalink: When Github and XML generation are not clear for you\" href=\"#when-github-and-xml-generation-are-not-clear-for-you\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload \u003ca href=\"https://github.com/ddbj/submission-excel2xml/blob/main/metadata_dra.xlsx\"\u003eDRA metadata Excel\u003c/a\u003e, fill in and send it to the DRA team by Email (\u003ca href=\"mailto:trace@ddbj.nig.ac.jp\"\u003etrace@ddbj.nig.ac.jp\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eJGA\u003c/h2\u003e\u003ca id=\"user-content-jga-2\" class=\"anchor\" aria-label=\"Permalink: JGA\" href=\"#jga-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAGD\u003c/h2\u003e\u003ca id=\"user-content-agd-2\" class=\"anchor\" aria-label=\"Permalink: AGD\" href=\"#agd-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSame with JGA. Enter AGD metadata to the JGA excel \"JGA_metadata.xlsx\".\nSpecify the AGD Submission ID (e.g. ASUB000001).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNIG SuperComputer\u003c/h2\u003e\u003ca id=\"user-content-nig-supercomputer\" class=\"anchor\" aria-label=\"Permalink: NIG SuperComputer\" href=\"#nig-supercomputer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe singularity image is available at \u003ccode\u003e/lustre9/open/shared_data/software/submission-excel2xml/\u003c/code\u003e in the \u003ca href=\"https://www.ddbj.nig.ac.jp/sc\" rel=\"nofollow\"\u003eNIG SuperComputer\u003c/a\u003e operated by Bioinformation and DDBJ Center, National Institute of Genetics. The SuperComputer user can readily generate XMLs from the metadata excel file and check the XMLs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDRA\u003c/h3\u003e\u003ca id=\"user-content-dra-3\" class=\"anchor\" aria-label=\"Permalink: DRA\" href=\"#dra-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe user can create DRA metadata XMLs and transfer corresponding data files to the DRA file server (ftp-private.ddbj.nig.ac.jp) in the SuperComputer.\u003c/p\u003e\n\u003cp\u003eGenerate Submission, Experiment and Run XMLs from the excel.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp /lustre9/open/shared_data/software/submission-excel2xml/excel2xml.simg ~/\ncd\nsingularity exec excel2xml.simg excel2xml -a example -i 0001 -p PRJDB7252 example/example-0001_dra_metadata.xlsx\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eValidate the XMLs.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec excel2xml.simg validate_meta_dra -a example -i 0001\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eJGA\u003c/h3\u003e\u003ca id=\"user-content-jga-3\" class=\"anchor\" aria-label=\"Permalink: JGA\" href=\"#jga-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAGD\u003c/h3\u003e\u003ca id=\"user-content-agd-3\" class=\"anchor\" aria-label=\"Permalink: AGD\" href=\"#agd-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u958b\u767a\u74b0\u5883\u69cb\u7bc9\u003c/h2\u003e\u003ca id=\"user-content-\u958b\u767a\u74b0\u5883\u69cb\u7bc9\" class=\"anchor\" aria-label=\"Permalink: \u958b\u767a\u74b0\u5883\u69cb\u7bc9\" href=\"#\u958b\u767a\u74b0\u5883\u69cb\u7bc9\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecd submission-excel2xml\nbundle install\nbundle exec submission-excel2xml download_xsd\nbundle exec excel2xml_dra # or excel2xml_jga, etc.\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 5, + "subscribers_count": 9, + "topics": [ + "ddbj-curators" + ], + "updated_at": 1729651784.0 + }, + { + "data_format": 2, + "description": "Delayed Rejection Metropolis Light Transport (PSSMLT \u0026 MMLT Applications)", + "filenames": [ + "Singularity" ], - "full_name": "iqbal-lab-org/cylon", - "latest_release": "v0.1.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/iqbal-lab-org/cylon/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/cylon/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ecylon\u003c/h1\u003e\u003ca id=\"user-content-cylon\" class=\"anchor\" aria-label=\"Permalink: cylon\" href=\"#cylon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVirus assembly module used by viridian\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eImportant\u003c/h1\u003e\u003ca id=\"user-content-important\" class=\"anchor\" aria-label=\"Permalink: Important\" href=\"#important\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend that you use\n\u003ca href=\"https://github.com/iqbal-lab-org/viridian\"\u003eViridian\u003c/a\u003e instead of\nthis repository. This repository is intended to be run by\nViridian, not to be used as a stand-alone tool.\nViridian provides a complete end-to-end pipeline for\ngenerating a consensus sequence from reads.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese must be installed and in your \u003ccode\u003e$PATH\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eracon\u003c/code\u003e (\u003ca href=\"https://github.com/lbcb-sci/racon\"\u003ehttps://github.com/lbcb-sci/racon\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eminimap2\u003c/code\u003e (\u003ca href=\"https://github.com/lh3/minimap2/\"\u003ehttps://github.com/lh3/minimap2/\u003c/a\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eClone this repository and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 -m pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity container\u003c/h3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone this repository and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build cylon.img Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto build the container \u003ccode\u003ecylon.img\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample usage\u003c/h2\u003e\u003ca id=\"user-content-example-usage\" class=\"anchor\" aria-label=\"Permalink: Example usage\" href=\"#example-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequired input:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eReference FASTA file\u003c/li\u003e\n\u003cli\u003eJSON file of amplicons. Described below.\nend end positions of the amplicons\u003c/li\u003e\n\u003cli\u003eReads, either in a sorted mapped indexed BAM file, or in a FASTA/FASTQ file\n(or two FASTA/FASTQ files for paired reads).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe amplicons JSON file must look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"amplicon1\": {\n \"start\": 10,\n \"end\": 399,\n \"left_primer_end\": 30,\n \"right_primer_start\": 390\n },\n \"amplicon2\": { ... etc ...}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe keys are the amplicon names, and the values are the details for each\namplicon.\nAll coordinates are 0-based inclusive.\nThe \u003ccode\u003estart\u003c/code\u003e and \u003ccode\u003eend\u003c/code\u003e entries are the positions of the start and end of the\namplicon.\n\u003ccode\u003eleft_primer_end\u003c/code\u003e is the rightmost position of the end of the left primer,\nand \u003ccode\u003eright_primer_start\u003c/code\u003e is the leftmost position\nof the right primer. This means for each amplicon we should have:\n\u003ccode\u003estart\u003c/code\u003e \u0026lt; \u003ccode\u003eleft_primer_end\u003c/code\u003e \u0026lt; \u003ccode\u003eright_primer_start\u003c/code\u003e \u0026lt; \u003ccode\u003eend\u003c/code\u003e.\n(Other key/values can be inside the dictionary\nfor each amplicon, but will simply be ignored).\u003c/p\u003e\n\u003cp\u003eRun using a mapped BAM file of ONT reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecylon assemble --bam reads.bam ont ref.fasta amplicons.json outdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun using a FASTQ file of ONT reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecylon assemble --reads_to_map reads.fastq ont ref.fasta amplicons.json outdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun using two FASTQ files of paired Illumina reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecylon assemble \\\n --reads_to_map reads1.fastq --mates_to_map reads2.fastq \\\n illumina ref.fasta amplicons.json outdir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe important files are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econsensus.final_assembly.fa\u003c/code\u003e: this contains the consensus sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eamplicon_data.json\u003c/code\u003e: JSON file containing details of what happened when\ntrying to make a consensus sequence of each amplicon.\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 5, - "subscribers_count": 6, + "full_name": "joeylitalien/drmlt-mitsuba", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDelayed Rejection Metropolis Light Transport\u003c/h1\u003e\u003ca id=\"user-content-delayed-rejection-metropolis-light-transport\" class=\"anchor\" aria-label=\"Permalink: Delayed Rejection Metropolis Light Transport\" href=\"#delayed-rejection-metropolis-light-transport\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the \u003cem\u003ebold-then-timid\u003c/em\u003e implementation of \u003ca href=\"https://joeylitalien.github.io/publications/drmlt\" rel=\"nofollow\"\u003eDelayed Rejection Metropolis Light Transport\u003c/a\u003e based on the \u003ca href=\"https://www.mitsuba-renderer.org/download.html\" rel=\"nofollow\"\u003eMitsuba v0.6\u003c/a\u003e renderer. Note that this work is a fork of the SIGGRAPH 2018 course \u003ca href=\"https://github.com/beltegeuse/gradient-mts\"\u003eLight Transport Simulation in the Gradient Domain\u003c/a\u003e repository.\u003c/p\u003e\n\u003cp\u003eIf you want to understand the algorithm by looking at the code, you should start with:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eMultiplexed MLT\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e./include/mitsuba/bidir/path_sampler.h\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e./src/libbidir/path_sampler.cpp\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDelayed Rejection MLT\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e./src/integrator/drmlt/*\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn case of problems/questions/comments, do not hesitate to contact the authors directly.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMitsuba\u003c/h3\u003e\u003ca id=\"user-content-mitsuba\" class=\"anchor\" aria-label=\"Permalink: Mitsuba\" href=\"#mitsuba\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.boost.org/\" rel=\"nofollow\"\u003eBoost\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://eigen.tuxfamily.org/index.php?title=Main_Page\" rel=\"nofollow\"\u003eEigen\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.openexr.com/\" rel=\"nofollow\"\u003eOpenEXR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.fftw.org/\" rel=\"nofollow\"\u003eFFTW\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.libpng.org/pub/png/libpng.html\" rel=\"nofollow\"\u003eLibPNG\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zlib.net/\" rel=\"nofollow\"\u003eZLib\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://glew.sourceforge.net/\" rel=\"nofollow\"\u003eGLEW\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OpenImageIO/oiio\"\u003eOpenImageIO\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython\u003c/h3\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tvogels/pyexr\"\u003ePyEXR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://numpy.org/\" rel=\"nofollow\"\u003eNumPy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://matplotlib.org/\" rel=\"nofollow\"\u003eMatplotlib\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling\u003c/h2\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provided installation instructions for ArchLinux only, but the installation procedure is similar on Ubuntu using \u003ccode\u003eapt install\u003c/code\u003e. To install Mitsuba\u0027s dependencies, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epacman -Sy make boost eigen gcc openexr python3 fftw libpng jasper zlib cmake git awk xerces-c xorg glew openimageio python-pip\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eConfigure and build the project using \u003ca href=\"https://cmake.org/\" rel=\"nofollow\"\u003eCmake\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir build \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$_\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ncmake ../\nmake -j\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo install the tooling dependencies, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epacman -Sy python3 python-pip \\\npip install numpy matplotlib pyexr\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDelayed Rejection Framework\u003c/h2\u003e\u003ca id=\"user-content-delayed-rejection-framework\" class=\"anchor\" aria-label=\"Permalink: Delayed Rejection Framework\" href=\"#delayed-rejection-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOur implementation of delayed rejection support three types of frameworks:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eOriginal Framework:\u003c/strong\u003e Proposed by \u003ca href=\"https://www.researchgate.net/publication/2767014_Some_Adaptive_Monte_Carlo_Methods_for_Bayesian_Inference\" rel=\"nofollow\"\u003eTierney \u0026amp; Mira [1999]\u003c/a\u003e. Suffers from vanishing acceptance at the second stage.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGeneralized Framework:\u003c/strong\u003e Proposed by \u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.20.7698\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003eGreen \u0026amp; Mira [2001]\u003c/a\u003e. Uses reversible jump MCMC to solve the vanishing acceptance problematic by sampling an expensive intermediate state.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePairwise Orbital:\u003c/strong\u003e Based on the original framework but use an orbital mutations strategy at the second stage to solve the vanishing acceptance problem without the extra sampling computational overhead.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePath Sampling Technique\u003c/h2\u003e\u003ca id=\"user-content-path-sampling-technique\" class=\"anchor\" aria-label=\"Permalink: Path Sampling Technique\" href=\"#path-sampling-technique\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn important change from the previoux Mitsuba implementation is that now integrator can be run over three different path sampling techniques:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eUnidirectional Path Tracing (PT):\u003c/strong\u003e Unidirectional volumetric path tracer.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBidirectional Path Tracing (BDPT):\u003c/strong\u003e Bidirectional path tracer with Multiple Importance Sampling (MIS).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://www.ci.i.u-tokyo.ac.jp/~hachisuka/mmlt.pdf\" rel=\"nofollow\"\u003eMultiplexed MLT\u003c/a\u003e (MMLT):\u003c/strong\u003e Bidirectional path tracer.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can find these in \u003ccode\u003esrc/libbidir/path_sampler.cpp\u003c/code\u003e and \u003ccode\u003einclude/bidir/path_sampler.h\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntegrators\u003c/h2\u003e\u003ca id=\"user-content-integrators\" class=\"anchor\" aria-label=\"Permalink: Integrators\" href=\"#integrators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe modified and added the following integrators:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esrc/integrator/pssmlt\u003c/code\u003e: This is a modified version of the original PSSMLT algorithm.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esrc/integrator/drmlt\u003c/code\u003e: This is the core of our DRMLT algorithm.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can select between them by using the \u003ccode\u003e-D integrator=[pssmlt,drmlt]\u003c/code\u003e command-line parameter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003epssmlt\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-pssmlt\" class=\"anchor\" aria-label=\"Permalink: pssmlt\" href=\"#pssmlt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eParameter\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003cth align=\"left\"\u003eRequirement\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003etechnique\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePath sampling technique\u003c/td\u003e\n\u003ctd align=\"left\"\u003eRequired (Options: \u003ccode\u003epath, bdpt, mmlt\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003ekelemenStyleMutation\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eUse Kelemen or Gaussian mutation\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003etrue\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003emutationSizeLow\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eKelemen lower bound\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/1024\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003emutationSizeHigh\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eKelemen higher bound\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/64\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003esigma\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eStandard deviation of Gaussian mutation\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/64\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample\u003c/h4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_MITSUBA_BIN\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/mitsuba \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_SCENE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scene.xml -D integrator=pssmlt -D technique=path \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003edrmlt\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-drmlt\" class=\"anchor\" aria-label=\"Permalink: drmlt\" href=\"#drmlt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eParameter\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003cth align=\"left\"\u003eRequirement\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003etechnique\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePath sampling technique\u003c/td\u003e\n\u003ctd align=\"left\"\u003eRequired (Options: \u003ccode\u003epath, bdpt, mmlt\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003etype\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eDelayed rejection framework\u003c/td\u003e\n\u003ctd align=\"left\"\u003eRequired (Options: \u003ccode\u003emira, green, orbital\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003eacceptanceMap\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOutput acceptance map\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003efalse\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003etimidAfterLarge\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePerform second stage after a large step\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003efalse\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003efixEmitterPath\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eFix emitter subpath during the second stage (Only with the \u003ccode\u003emmlt\u003c/code\u003e technique)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003efalse\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003euseMixture\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eUse an equal weight mixture of both stage and regular Metropolis-Hastings instead of DR\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003efalse\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003esigma\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eStandard deviation of Gaussian mutation\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/64\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003escaleSecond\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eScaling ratio of the second stage mutation\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/10\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample\u003c/h4\u003e\u003ca id=\"user-content-example-1\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_MITSUBA_BIN\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/mitsuba \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_SCENE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scene.xml \\\n -D integrator=drmlt \\\n -D technique=mmlt \\\n -D type=orbital \\\n -D fixEmitterPath=true \\\n -D acceptanceMap=false\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcceptance Map\u003c/h2\u003e\u003ca id=\"user-content-acceptance-map\" class=\"anchor\" aria-label=\"Permalink: Acceptance Map\" href=\"#acceptance-map\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen using the \u003ccode\u003edrmlt\u003c/code\u003e integrator, you can generate an acceptance map using the \u003ccode\u003e-D acceptanceMap=true\u003c/code\u003e option. Doing so will generate an RGB image such that the \u003cem\u003eR\u003c/em\u003e-channel corresponds to the number of accepted samples at the first stage and the \u003cem\u003eG\u003c/em\u003e-channel is the same for the second stage. To convert this image to a heatmap, use the standalone script \u003ccode\u003e./tools/stages_heatmap.py\u003c/code\u003e. For example, the following command saves the acceptance map during rendering:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_MITSUBA_BIN\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/mitsuba \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_SCENE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scene.xml \\\n -D integrator=drmlt \\\n -D technique=bdpt \\\n -D type=orbital \\\n -D acceptanceMap=true\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo generate the actual heatmap, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_MITSUBA_ROOT\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/tools/stages_heatmap.py \\\n -t \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_ACCEPTANCE_MAP\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/acceptance_map.exr \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e\n -c [0.2,0.8]\u003c/pre\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eParameter\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003cth align=\"left\"\u003eRequirement\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003et\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eAcceptance map\u003c/td\u003e\n\u003ctd align=\"left\"\u003eRequired\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003ec\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePixel range (clip) for heatmap images\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e[0,1]\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMixture\u003c/h2\u003e\u003ca id=\"user-content-mixture\" class=\"anchor\" aria-label=\"Permalink: Mixture\" href=\"#mixture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo generate a comparison of our method against a na\u00efve mixture of both stage, use the \u003ccode\u003e-D useMixture=true\u003c/code\u003e option under the \u003ccode\u003edrmlt\u003c/code\u003e integrator.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScenes\u003c/h2\u003e\u003ca id=\"user-content-scenes\" class=\"anchor\" aria-label=\"Permalink: Scenes\" href=\"#scenes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://data.adrien-gruson.com/research/2020_DRMLT/scenes/swimming-pool_pssmlt.zip\" rel=\"nofollow\"\u003eSwimming Pool\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://data.adrien-gruson.com/research/2020_DRMLT/scenes/aquarium_mmlt.zip\" rel=\"nofollow\"\u003eAquarium\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://data.adrien-gruson.com/research/2020_DRMLT/scenes/veach-door_mmlt.zip\" rel=\"nofollow\"\u003eVeach Door\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://data.adrien-gruson.com/research/2020_DRMLT/scenes/glass-of-water_pssmlt.zip\" rel=\"nofollow\"\u003eGlass of Water\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChange Logs\u003c/h2\u003e\u003ca id=\"user-content-change-logs\" class=\"anchor\" aria-label=\"Permalink: Change Logs\" href=\"#change-logs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2020/07/29: Initial code release\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code is released under the GNU General Public License (version 3).\u003c/p\u003e\n\u003cp\u003eThis source code includes the following open source implementations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScreened Poisson reconstruction code from NVIDIA, released under the new BSD license.\u003c/li\u003e\n\u003cli\u003eMitsuba 0.6.0 by Wenzel Jakob, released under the GNU General Public License (version 3).\u003c/li\u003e\n\u003cli\u003eA small part of \u003ca href=\"https://github.com/tunabrain/tungsten\"\u003eTungsten\u003c/a\u003e by Benedikt Bitterli.\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 6, + "subscribers_count": 4, "topics": [], - "updated_at": 1714137467.0 + "updated_at": 1714307506.0 }, { "data_format": 2, - "description": "Eglen 2015 review article", + "description": "Common scripts, libraries, and utilities for 2p experiments", "filenames": [ "Singularity" ], - "full_name": "sje30/eglen2015", + "full_name": "deisseroth-lab/two-photon", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIntroduction\u003c/h1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/sje30/eglen2015\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c305e622dcdce06b6952f9cd0ea0142b5b79a5d804b883b142dd607ddcba7977/68747470733a2f2f7472617669732d63692e6f72672f736a6533302f65676c656e323031352e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/sje30/eglen2015.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the home page for the following review:\u003c/p\u003e\n\u003cp\u003eEglen SJ (2016) Bivariate spatial point patterns in the retina: a\nreproducible review. Journal de la Soci\u00e9t\u00e9 Fran\u00e7aise de Statistique\n157:33\u201348.\n\u003ca href=\"http://journal-sfds.fr/article/view/518\" rel=\"nofollow\"\u003ePDF\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis package contains all the material needed to regenerate the\narticle for itself. Some key parts of the package are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"vignettes/eglen2015.Rnw\"\u003evignettes/eglen2015.Rnw\u003c/a\u003e: the source file\nfor the article in LaTeX format.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"inst/extdata\"\u003einst/extdata\u003c/a\u003e: a folder containing all the data files\nstudied in this article.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRecompiling the paper\u003c/h2\u003e\u003ca id=\"user-content-recompiling-the-paper\" class=\"anchor\" aria-label=\"Permalink: Recompiling the paper\" href=\"#recompiling-the-paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis R package depends on a few other packages, from CRAN and my\npersonal library. The following sequence should install everything\nyou need:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eRscript -e \u0027install.packages(c(\"splancs\", \"spatstat\", \"devtools\", \"knitr\", \"xtable\", \"tinytex\"))\u0027\nRscript -e \u0027install.packages(c(\"sjedmin\", \"sjedrp\", \"sjevor\",\"sjedist\"), type=\"source\", contriburl=\"http://damtp.cam.ac.uk/user/eglen/r/\")\u0027\nRscript -e \u0027devtools::install_github(\"sje30/eglen2015\",build_vignettes=TRUE)\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe last line should load this package. Once it is installed, you can\nthen view the paper, or view the knitr document that created the paper:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evignette(\"eglen2015\")\neglen2015:::edit()\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis does of course assume that your system already has R, latex, and\nvarious unix tools. That may not be the case; however, you can still\nuse the package through the Docker system, see next.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have \u003ca href=\"http://docker.com\" rel=\"nofollow\"\u003edocker\u003c/a\u003e installed on your system,\nyou can download and run this package using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -d -p 8787:8787 sje30/eglen2015\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(View \u003ca href=\"https://registry.hub.docker.com/u/sje30/eglen2015/\" rel=\"nofollow\"\u003esje30/eglen2015\u003c/a\u003e\nto check the status of this Docker package.)\u003c/p\u003e\n\u003cp\u003eThen visit the web page to start R (username and password are \"rstudio\"):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttp://localhost:8787/ ## linux\nhttp://192.168.99.100:8787/ ## mac, windows users\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe IP address for mac/windows may vary; you can check it by running\nthe command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker-machine ip default\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce you have logged in, you can then do the following commands to\nrecompile the document:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetwd(\"eglen2015/vignettes/\")\nsource(\"run.R\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen examine the \u003ccode\u003evignettes\u003c/code\u003e folder and you should see\n\u003ccode\u003eeglen2015.pdf\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThanks to the \u003ca href=\"https://github.com/rocker-org\"\u003eRocker\u003c/a\u003e team for the\nR-based docker images, on which this work is based.\u003c/p\u003e\n", - "stargazers_count": 5, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etwo-photon\u003c/h1\u003e\u003ca id=\"user-content-two-photon\" class=\"anchor\" aria-label=\"Permalink: two-photon\" href=\"#two-photon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains utilities for analyzing 2p data:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#analysis-pipeline\"\u003eAnalysis Pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ripping-containers\"\u003eRipping Containers\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalysis Pipeline\u003c/h2\u003e\u003ca id=\"user-content-analysis-pipeline\" class=\"anchor\" aria-label=\"Permalink: Analysis Pipeline\" href=\"#analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe analysis pipeline consists of the following stages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eraw2tiff: converts Bruker proprietary output format to a TIFF stack\u003c/li\u003e\n\u003cli\u003econvert: converts tiff and csv/text files to hdf5.\u003c/li\u003e\n\u003cli\u003epreprocess: detect and remove stim artefacts\u003c/li\u003e\n\u003cli\u003eqa: make QA plots to check stim artefact removal\u003c/li\u003e\n\u003cli\u003eanalyze: run suite2p, optionally combining multiple preprocessed datasets\u003c/li\u003e\n\u003cli\u003ebackup: back up input/intermediate/output data to a safe place\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup\u003c/h3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, install the code. You can use \u003ca href=\"https://desktop.github.com/\"\u003eGitHub desktop\u003c/a\u003e, or use git on the command line. This only has to be done once.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/deisseroth-lab/two-photon.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, install the environment. You will need to install \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003econda\u003c/a\u003e first. Then\nuse the following command from within the directory where you installed the repo above. This also only has\nto be done once.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda env create -f environment.yml -n two-photon\nconda activate two-photon\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e installs the 2p script (in editable mode, so you can update the code)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExecuting\u003c/h3\u003e\u003ca id=\"user-content-executing\" class=\"anchor\" aria-label=\"Permalink: Executing\" href=\"#executing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the processing script, the environment needs to be activated. This needs to be done each time you start a\nnew terminal.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda activate two-photon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe executable is called \u003ccode\u003e2p\u003c/code\u003e, and each stage is a different subcommand\nthat can be run. It is possible to run multiple stages by specifying\nmultiple subcommands.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eData Layout and Global Flags\u003c/h4\u003e\u003ca id=\"user-content-data-layout-and-global-flags\" class=\"anchor\" aria-label=\"Permalink: Data Layout and Global Flags\" href=\"#data-layout-and-global-flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe scripts required a strict layout of data, and assume the input data\nfollows a directory structure and filenaming that the Bruker scopes\ncreate. The data is setup in subdirectories of a \u003ccode\u003ebase-path\u003c/code\u003e, named\nby the stage and the \u003ccode\u003eacquisition\u003c/code\u003e name.\u003c/p\u003e\n\u003cp\u003eTo point the script to the correct location of of dataset,\nuse the following flags:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-adblock\"\u003e\u003cpre\u003e --base-path PATH Top-level storage for local data. [required]\n --acquisition TEXT Acquisition sub-directory to process. [required]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing the following global flags (meaning after \u003ccode\u003e2p\u003c/code\u003e but before other commands or flags):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewill use the following locations for the data. Note the expected location of the raw data.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003edata type\u003c/th\u003e\n\u003cth\u003elocation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eRAWDATA, csv, xml, and env files from scope\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/raw/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etiff stacks\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/tiff/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econverted hdf5 data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/convert/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epreprocess\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/preprocess/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eqa\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/qa/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eanalyze - suite2p output\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/analyze/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCommand: raw2tiff\u003c/h4\u003e\u003ca id=\"user-content-command-raw2tiff\" class=\"anchor\" aria-label=\"Permalink: Command: raw2tiff\" href=\"#command-raw2tiff\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe raw2tiff command runs the Bruker software to rip the RAWDATA into a tiff stack.\nThis is a Windows-only command, until the kinks of running on Linux are ironed out.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001\n raw2tiff\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: convert\u003c/h3\u003e\u003ca id=\"user-content-command-convert\" class=\"anchor\" aria-label=\"Permalink: Command: convert\" href=\"#command-convert\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003econvert\u003c/code\u003e command converts the tiff stacks and voltage data to hdf5.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001 \\\n convert --channel 3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: preprocess\u003c/h3\u003e\u003ca id=\"user-content-command-preprocess\" class=\"anchor\" aria-label=\"Permalink: Command: preprocess\" href=\"#command-preprocess\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003epreprocess\u003c/code\u003e command performs processing like stim removal on the data. It should be\nrun even if there are no stim artefacts (in which case, no actual computation is done),\nso that downstream stages find the data in the correct place.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001 \\\n preprocess --frame-channel-name=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eframe starts\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --stim-channel-name=respir\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample based on piezeo period:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/minoue2/2p_CNC/ \\\n --acquisition Chris_210429/10263_920nm_PC250-300-001 \\\n preprocess \\\n --frame-channel-name=StartFrameResonant \\\n --stim-channel-name=LEDSyncSignal \\\n --piezo-period-frames=7 \\\n --piezo-skip-frames=3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: qa\u003c/h3\u003e\u003ca id=\"user-content-command-qa\" class=\"anchor\" aria-label=\"Permalink: Command: qa\" href=\"#command-qa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eqa\u003c/code\u003e command makes some QA plots to understand if the stim effects are\nbeing correctly removed during preprocessing. It plots a number of frames\n(given by --max-frames) containing stims, showing the data before and after\nstim removal.\u003c/p\u003e\n\u003cp\u003eThis is an optional step.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001 \\\n qa\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: analyze\u003c/h3\u003e\u003ca id=\"user-content-command-analyze\" class=\"anchor\" aria-label=\"Permalink: Command: analyze\" href=\"#command-analyze\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eanalyze\u003c/code\u003e command runs Suite2p on the preprocessed dataset.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n analyze\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample of analyzing multiple acquisitions together:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n analyze --extra-acquisitions 20210428M198/slm-000\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample of using non-default Suite2p options file (json format):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n analyze --suite2p-params-file two_photon/ops_files/drinnedb.json\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: backup\u003c/h3\u003e\u003ca id=\"user-content-command-backup\" class=\"anchor\" aria-label=\"Permalink: Command: backup\" href=\"#command-backup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ebackup\u003c/code\u003e command copies the output of one or more stages to backup directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n backup \\\n --backup-path /media/hdd1/oak/mount/two-photon/backup \\\n --backup-stage raw,tiff\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e--backup_path\"\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing multiple commands at once\u003c/h2\u003e\u003ca id=\"user-content-using-multiple-commands-at-once\" class=\"anchor\" aria-label=\"Permalink: Using multiple commands at once\" href=\"#using-multiple-commands-at-once\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeveral commands can be run in succession by adding each one to your command line with its\nnecessary flags.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n raw2tiff \\\n convert --channel 3 \\\n preprocess --stim-channel-name=respir \\\n analyze --extra-acquisitions 20210428M198/slm-000\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRipping Containers\u003c/h2\u003e\u003ca id=\"user-content-ripping-containers\" class=\"anchor\" aria-label=\"Permalink: Ripping Containers\" href=\"#ripping-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRipping is the process for converting a Bruker RAWDATA file into a set of TIFF files.\u003c/p\u003e\n\u003cp\u003eContainers exist to help run the ripping on any platform, but it has been found they\nperform sub-optimally and are 10-100x slower than ripping on a Windows machine using\nthe native ripper. It is advised NOT to use this yet.\u003c/p\u003e\n\u003cp\u003eThe lab has created \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e containers with the Bruker Prairie View software,\nwhich can be used to rip raw data computers with either set of container software installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRipping via Singularity\u003c/h3\u003e\u003ca id=\"user-content-ripping-via-singularity\" class=\"anchor\" aria-label=\"Permalink: Ripping via Singularity\" href=\"#ripping-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you would like to run from a container on \u003ca href=\"https://www.sherlock.stanford.edu/\" rel=\"nofollow\"\u003eSherlock\u003c/a\u003e,\nthe lab keeps a copy available in $OAK/pipeline/bruker-rip/containers.\u003c/p\u003e\n\u003cp\u003eHere\u0027s a quick demo:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir -p \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/users/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/test\n$ cp -r \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/pipeline/bruker-rip/sampledata/overview-023 \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/users/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/test\n$ chmod -R u+w \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/users/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/test/overview-023 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Write permissions needed to convert files.\u003c/span\u003e\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/users/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/test/overview-023\n$ singularity run --bind=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:/data \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/pipeline/bruker-rip/containers/bruker-rip.sif\n\nCopying wine environment.\n\nExecuting rip. One err and four fixme statements are OK.\n\n2020-11-16 17:25:43.859 rip:50 INFO Data created with Prairie version 5.4, using ripper: /apps/Prairie View 5.5/Utilities/Image-Block Ripping Utility.exe\n2020-11-16 17:25:43.861 rip:77 INFO Ripping from:\n /data/Cycle00001_Filelist.txt\n /data/CYCLE_000001_RAWDATA_000025\n2020-11-16 17:25:43.883 rip:123 INFO Watching \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e ripper to finish \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e 3600 more seconds\n000d:err:menubuilder:init_xdg error looking up the desktop directory\n0031:fixme:ntdll:EtwEventRegister ({5eec90ab-c022-44b2-a5dd-fd716a222a15}, 0x5571000, 0x5582030, 0x5582050) stub.\n0031:fixme:ntdll:EtwEventSetInformation (deadbeef, 2, 0x557fd70, 43) stub\n0031:fixme:nls:GetThreadPreferredUILanguages 00000038, 0x4fccdb4, 0x4fccdd0 0x4fccdb0\n0031:fixme:nls:get_dummy_preferred_ui_language (0x38 0x4fccdb4 0x4fccdd0 0x4fccdb0) returning a dummy value (current locale)\n2020-11-16 17:25:53.889 rip:134 INFO Found filelist files: None\n2020-11-16 17:25:53.889 rip:135 INFO Found rawdata files: None\n2020-11-16 17:25:53.889 rip:136 INFO Found this many tiff files: 1\n2020-11-16 17:25:53.889 rip:123 INFO Watching \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e ripper to finish \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e 3590 more seconds\n2020-11-16 17:26:03.899 rip:134 INFO Found filelist files: None\n2020-11-16 17:26:03.899 rip:135 INFO Found rawdata files: None\n2020-11-16 17:26:03.899 rip:136 INFO Found this many tiff files: 1\n2020-11-16 17:26:03.899 rip:139 INFO Detected ripping is \u003cspan class=\"pl-c1\"\u003ecomplete\u003c/span\u003e\n2020-11-16 17:26:13.909 rip:141 INFO Killing ripper\n2020-11-16 17:26:13.910 rip:143 INFO Ripper has been killed\n2020-11-16 17:26:14.912 rip:115 INFO cleaned up\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\nX connection to :99 broken (explicit \u003cspan class=\"pl-c1\"\u003ekill\u003c/span\u003e or server shutdown).\nX connection to :99 broken (explicit \u003cspan class=\"pl-c1\"\u003ekill\u003c/span\u003e or server shutdown).\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere\u0027s how to run on your own data. We request a node allocation using \u003ccode\u003esdev\u003c/code\u003e as\nlong-running jobs should not use login nodes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e my/data/path\n$ sdev \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e May take some time to get a machine for development use\u003c/span\u003e\n$ singularity run --bind=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:/data \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/pipeline/bruker-rip/containers/bruker-rip.sif\n\n[Similar output as above]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd here\u0027s how to run a batch job, using the \u003ccode\u003erip.sbatch\u003c/code\u003e script from this\nrepository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e my/data/path\n$ sbatch path/to/two-photon/rip.sbatch \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\nSubmitted batch job ABCDEFGH\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRipping via Docker\u003c/h3\u003e\u003ca id=\"user-content-ripping-via-docker\" class=\"anchor\" aria-label=\"Permalink: Ripping via Docker\" href=\"#ripping-via-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can run on a device with Docker installed using the command below. The image\nwill be available locally if you\u0027ve build from source (see below), or it will be\nfetched from the the \u003ca href=\"https://code.stanford.edu/deisseroth-lab/bruker-rip\" rel=\"nofollow\"\u003eStanford GitLab\u003c/a\u003e. Contact \u003ca href=\"mailto:croat@stanford.edu\"\u003ecroat@stanford.edu\u003c/a\u003e if you need access.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./rip_docker.sh \\\n scr.svc.stanford.edu/deisseroth-lab/bruker-rip:20200903 \\\n /path/to/data/with/filelist/and/rawdata/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./rip_docker.sh \\\n scr.svc.stanford.edu/deisseroth-lab/bruker-rip:20200903 \\\n /media/hdd0/two-photon/sample/overview-023\nSetting up wine environment\n\nExecuting rip. It is OK to see 1 err and 4 fixme statements \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e what follows\n\n2020-09-03 14:41:33.936 rip:50 INFO Ripping from:\n /data/Cycle00001_Filelist.txt\n /data/CYCLE_000001_RAWDATA_000025\n2020-09-03 14:41:33.940 rip:96 INFO Waiting \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e ripper to finish: 3600 seconds remaining\n000d:err:menubuilder:init_xdg error looking up the desktop directory\n0031:fixme:ntdll:EtwEventRegister ({5eec90ab-c022-44b2-a5dd-fd716a222a15}, 0xd441000, 0xd452030, 0xd452050) stub.\n0031:fixme:ntdll:EtwEventSetInformation (deadbeef, 2, 0xd44fd70, 43) stub\n0031:fixme:nls:GetThreadPreferredUILanguages 00000038, 0xdaacdb4, 0xdaacdd0 0xdaacdb0\n0031:fixme:nls:get_dummy_preferred_ui_language (0x38 0xdaacdb4 0xdaacdd0 0xdaacdb0) returning a dummy value (current locale)\n2020-09-03 14:41:43.951 rip:107 INFO Found filelist files: None\n2020-09-03 14:41:43.951 rip:108 INFO Found rawdata files: None\n2020-09-03 14:41:43.951 rip:109 INFO Found this many tiff files: 1\n2020-09-03 14:41:43.951 rip:96 INFO Waiting \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e ripper to finish: 3590 seconds remaining\n2020-09-03 14:41:53.962 rip:107 INFO Found filelist files: None\n2020-09-03 14:41:53.962 rip:108 INFO Found rawdata files: None\n2020-09-03 14:41:53.962 rip:109 INFO Found this many tiff files: 1\n2020-09-03 14:41:53.963 rip:112 INFO Detected ripping is \u003cspan class=\"pl-c1\"\u003ecomplete\u003c/span\u003e\n2020-09-03 14:42:03.973 rip:114 INFO Killing ripper\n2020-09-03 14:42:03.973 rip:116 INFO Ripper has been killed\n2020-09-03 14:42:04.975 rip:88 INFO cleaned up\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding Containers\u003c/h3\u003e\u003ca id=\"user-content-building-containers\" class=\"anchor\" aria-label=\"Permalink: Building Containers\" href=\"#building-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build all available containers, which will first build the Docker container, and then convert it\nto a Singularity container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo build just the docker containers:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake build_docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eView the \u003ca href=\"Makefile\"\u003eMakefile\u003c/a\u003e for additional targets, including targets to build just build specific containers.\u003c/p\u003e\n", + "stargazers_count": 6, "subscribers_count": 3, "topics": [], - "updated_at": 1726079671.0 + "updated_at": 1729511558.0 }, { "data_format": 2, - "description": null, + "description": "Distributed Fast Downward: classical planner for parallel/distributed environments", "filenames": [ "Singularity" ], - "full_name": "h1-the-swan/autoreview", + "full_name": "jinnaiyuu/distributed-fast-downward", "latest_release": null, - "stargazers_count": 5, - "subscribers_count": 2, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDistributed Fast Downward\u003c/h1\u003e\u003ca id=\"user-content-distributed-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Distributed Fast Downward\" href=\"#distributed-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDistributed fast-downward is a classical planner for distributed environments.\nIt extends the state-of-the-art planner fast-downward (\u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e) to distributed computers.\nDistributed fast-downward implements the state-of-the-art parallel best-first search algorithms including Abstract Zobrist hashing (AZH) and Hash Distributed A* (HDA*).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHash Distributed A*\u003c/h1\u003e\u003ca id=\"user-content-hash-distributed-a\" class=\"anchor\" aria-label=\"Permalink: Hash Distributed A*\" href=\"#hash-distributed-a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the source code for Hash Distributed A* (HDA*) and other parallel algorithms for classical planning. The algorithms are described in the paper:\u003c/p\u003e\n\u003cp\u003eJinnai Y, Fukunaga A. 2017. On Hash-Based Work Distribution Methods for Parallel Best-First Search. Journal of Artificial Intelligence Research (JAIR). arXiv: \u003ca href=\"https://arxiv.org/abs/1706.03254\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1706.03254\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDomain specific solvers (15-puzzle, 24-puzzle, multiple sequence alignment, and grid pathfinding) are available here (\u003ca href=\"https://github.com/jinnaiyuu/Parallel-Best-First-Searches\"\u003ehttps://github.com/jinnaiyuu/Parallel-Best-First-Searches\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDependencies\u003c/h1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is built on top of fast-downward (\u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e) of Febuary 2014 (\u003ca href=\"http://hg.fast-downward.org/shortlog/8532ca08bcac\" rel=\"nofollow\"\u003ehttp://hg.fast-downward.org/shortlog/8532ca08bcac\u003c/a\u003e).\nPlease read the instruction for fast-downward to learn the syntax (\u003ca href=\"http://www.fast-downward.org/PlannerUsage\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/PlannerUsage\u003c/a\u003e). Note that you need to modify some part of the code if you want to integrate parallel searches to the newest version of the fast-downward.\u003c/p\u003e\n\u003cp\u003eTo run, you need to install MPI library. We have confirmed that our code works with MPICH3, MPICH2, and OpenMPI (usually MPICH is faster than OpenMPI). MPICH2 and OpenMPI are in most of the package managers. For example in Debian/Ubuntu,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install mpich2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou may also need to install libcr-dev to run MPI.\u003c/p\u003e\n\u003cp\u003eThe other libraries are optional. We recommend mpiP (\u003ca href=\"http://mpip.sourceforge.net/\" rel=\"nofollow\"\u003ehttp://mpip.sourceforge.net/\u003c/a\u003e) for profiling MPI programs to understand the bottleneck of parallel algorithms.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe syntax is same as fast-downward. You can run Hash Distributed A* (HDA*) by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan PDDLFILE --search \"hdastar(HEURISTIC,HASH-FUNCTION)\" NUMPROCESSES\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,zobrist)\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFirst parameter of hdastar is a heuristic function.\nSecond parameter is \"dist\" which selects a method for work distribution (hashing function).\nThe number you place on the last is the number of processors to run HDA*.\u003c/p\u003e\n\u003cp\u003eWork distribution methods:\u003c/p\u003e\n\u003cp\u003eGRAZHDA*/sparsity (Jinnai\u0026amp;Fukunaga 2017)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,freq_depend(cut=sparsest_cut))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDAHDA* (Jinnai\u0026amp;Fukunaga 2016)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,aabstraction(0.7))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGAZHDA* (Jinnai\u0026amp;Fukunaga 2016)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,freq_depend(1.0,0.0))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFAZHDA* (Jinnai\u0026amp;Fukunaga 2016)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,freq_depend(0.5,0.0))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eZHDA* (Kihimoto et al 2009)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,zobrist)\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAHDA* (Burns et al 2010)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,abstraction(10000))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run MPI algorithm using torque, ./src/pbs-plan should work. To run it in other schedulers, you probably need to edit the parameters put in mpiexec ($PBS_NODEFILE and $PBS_NUM_PPN) to appropriate variables.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMemo\u003c/h1\u003e\u003ca id=\"user-content-memo\" class=\"anchor\" aria-label=\"Permalink: Memo\" href=\"#memo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn our experiments GRAZHDA*/sparsity was the best performing algorithms for merge\u0026amp;shrink heuristic (low computation cost) and lmcut (high computation cost) on single-machine multicore environment (8 cores), commodity cluster (48 cores), and cloud cluster on EC2 (128 virtual core).\nThus we expect GRAZHDA*/sparsity to be the best for most of the modern computer systems.\nI am interested to see the results in other environments (e.g. a heterogeneous environment consists of multiple types of machines, CPUs).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYuu Jinnai \u003ca href=\"mailto:ddyuudd@gmail.com\"\u003eddyuudd@gmail.com\u003c/a\u003e implemented parallel algorithms on top of the fast-downward (\u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e).\nPlease let me know if you wish to use my code but find my document unhelpful.\nI am trying to make this program easy to use for everybody so I appreciate your comments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLICENSE\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is published under GPL ver 3.\u003c/p\u003e\n", + "stargazers_count": 6, + "subscribers_count": 3, "topics": [], - "updated_at": 1728575919.0 + "updated_at": 1681588272.0 }, { "data_format": 2, - "description": "A minimal working example of DMTCP checkpoint-restart inside a Singularity container.", + "description": "Analysis code for \"The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception\" by Visconti di Oleggio Castello, Halchenko, et al., 2017, Scientific Reports", "filenames": [ "Singularity" ], - "full_name": "mmore500/mwe-singularity-checkpoint", + "full_name": "mvdoc/famface", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emwe-singularity-checkpoint\u003c/h1\u003e\u003ca id=\"user-content-mwe-singularity-checkpoint\" class=\"anchor\" aria-label=\"Permalink: mwe-singularity-checkpoint\" href=\"#mwe-singularity-checkpoint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2039\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/mmore500/mwe-singularity-checkpoint\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/430422db3b0f6da960d061c6cdcd8417db35b88bb04d94b2093b80504cbb9997/68747470733a2f2f636972636c6563692e636f6d2f67682f6d6d6f72653530302f6d77652d73696e67756c61726974792d636865636b706f696e742e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/mmore500/mwe-singularity-checkpoint.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA minimal working example of DMTCP checkpoint-restart inside a Singularity container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll need to have \u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e installed locally.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull Container from SingularityHub...\u003c/h2\u003e\u003ca id=\"user-content-pull-container-from-singularityhub\" class=\"anchor\" aria-label=\"Permalink: Pull Container from SingularityHub...\" href=\"#pull-container-from-singularityhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake shub\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e... or Build Container Locally\u003c/h2\u003e\u003ca id=\"user-content--or-build-container-locally\" class=\"anchor\" aria-label=\"Permalink: ... or Build Container Locally\" href=\"#-or-build-container-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive Demonstration\u003c/h2\u003e\u003ca id=\"user-content-interactive-demonstration\" class=\"anchor\" aria-label=\"Permalink: Interactive Demonstration\" href=\"#interactive-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake demonstrate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNon-interactive Test\u003c/h2\u003e\u003ca id=\"user-content-non-interactive-test\" class=\"anchor\" aria-label=\"Permalink: Non-interactive Test\" href=\"#non-interactive-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003emake test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNotes\u003c/h1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis example uses \u003ca href=\"https://github.com/sylabs/singularity\"\u003eSingularity\u003c/a\u003e \u003ccode\u003ev2.6.x\u003c/code\u003e and \u003ca href=\"https://github.com/dmtcp/dmtcp\"\u003eDMTCP\u003c/a\u003e \u003ccode\u003ev3.0.0\u003c/code\u003e (e.g., from the tip of master circa December 2018).\nI didn\u0027t play around with other versions of these softwares.\u003c/p\u003e\n\u003cp\u003eUnfortunately, this example doesn\u0027t seem to be totally portable.\nI was able to get the example to run on my own laptop just fine.\nIn order to get Singularity checkpoint/restart to work on CircleCI\u0027s virtual machines (i.e., \u003ccode\u003emachine\u003c/code\u003e), I had to disable a runtime assert in the source for DMTCP (see \u003ca href=\"https://github.com/mmore500/dmtcp/commit/b8be8be2874258d2f45324a42d609c0c63da0079\"\u003ehere\u003c/a\u003e).\nOn a \u003ca href=\"https://icer.msu.edu/\" rel=\"nofollow\"\u003eMichigan State University High Performance Computing Center\u003c/a\u003e development node, which runs CentOS 7 and uses Singularity \u003ccode\u003ev2.5.2-dist\u003c/code\u003e, the demonstration currently crashes out at the first attempted checkpoint.\nThe iCER staff put together a nice tutorial of DMTCP checkpointing on the HPCC \u003ca href=\"https://wiki.hpcc.msu.edu/display/ITH/Check+Point+with+DMTCP\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nWith some further finessing along those lines, checkpointing Singularity containers on our HPCC \u003cem\u003emight\u003c/em\u003e be possible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAcknowledgement\u003c/h1\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003e.circleci\u003c/code\u003e materials were pilfered from Container Tool\u0027s \u003ca href=\"https://github.com/singularityhub/circle-ci\"\u003eexample builder for Singularity containers using Circle Continuous Integration\u003c/a\u003e.\nThanks \u003ca href=\"http://github.com/vsoch\"\u003e@vsoch\u003c/a\u003e!\u003c/p\u003e\n", - "stargazers_count": 5, - "subscribers_count": 3, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAnalysis scripts for \"The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception\"\u003c/h1\u003e\u003ca id=\"user-content-analysis-scripts-for-the-neural-representation-of-personally-familiar-and-unfamiliar-faces-in-the-distributed-system-for-face-perception\" class=\"anchor\" aria-label=\"Permalink: Analysis scripts for \u0026quot;The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception\u0026quot;\" href=\"#analysis-scripts-for-the-neural-representation-of-personally-familiar-and-unfamiliar-faces-in-the-distributed-system-for-face-perception\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains preprocessing and analysis scripts for Visconti di Oleggio Castello, M., Halchenko, Y. O., Guntupalli, J. S., Gors, J. D., \u0026amp; Gobbini, M. I. (2017). The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception. \u003cem\u003eScientific Reports\u003c/em\u003e, 7(1), 12237. \u003ca href=\"https://doi.org/10.1038/s41598-017-12559-1\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41598-017-12559-1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset is available through \u003ca href=\"http://datasets.datalad.org/?dir=/labs/gobbini/famface\" rel=\"nofollow\"\u003eDataLad\u003c/a\u003e. Once datalad is installed in your system, you can get the data with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install the dataset without downloading any data\u003c/span\u003e\ndatalad install -r ///labs/gobbini/famface\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e download the data\u003c/span\u003e\ndatalad get famface\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNOTA BENE:\u003c/strong\u003e The latest release of this dataset is in BIDS format, however the\nscripts are still configured to run with the old OpenfMRI format. You\ncan checkout the old file structure as follows\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd famface/data\ngit checkout openfmri-v1.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetting up the environment\u003c/h2\u003e\u003ca id=\"user-content-setting-up-the-environment\" class=\"anchor\" aria-label=\"Permalink: Setting up the environment\" href=\"#setting-up-the-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend using either a \u003ca href=\"http://neuro.debian.net/\" rel=\"nofollow\"\u003eNeuroDebian\u003c/a\u003e\nvirtual machine, or a container (Docker or Singularity) with NeuroDebian\ninstalled to replicate these analyses. In particular, the Python scripts\nmight rely on specific versions of python packages. For example, the\npreprocessing script \u003ccode\u003efmri_ants_openfmri.py\u003c/code\u003e won\u0027t work with newer\nversions of Nipype (\u0026gt; 0.11.0) because of recent refactoring. We kept track of the\nversions of the most important Python packages in the \u003ccode\u003erequirements.txt\u003c/code\u003e\nfile. If you\u0027re using conda, you can get started as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name famface python=2.7\npip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should also have FSL and ANTs installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the singularity image\u003c/h3\u003e\u003ca id=\"user-content-using-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Using the singularity image\" href=\"#using-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide a \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e definition file\nthat can be used to build a container with all the necessary packages to\nrun the analyses (except MATLAB--testing in progress with Octave).\u003c/p\u003e\n\u003cp\u003eOnce Singularity is installed on your system, the image can be built as\nfollows (assuming singularity 2.4.x)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build famface.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ealternatively, the image can be pulled from Singularity Hub with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name famface.simg shub://mvdoc/famface\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInside the container we provide the mountpoints \u003ccode\u003e/data\u003c/code\u003e and \u003ccode\u003e/scripts\u003c/code\u003e,\nso for example one could run the preprocessing for one participant as\nfollows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -e -c \\\n -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/scripts \\\n -B /path/to/famface:/data \\\n famface.simg \\\n python /scripts/fmri_ants_openfmri.py \\\n -d /data/data -s sub001 \\\n --hpfilter 60.0 \\\n --derivatives \\\n -o /data/derivatives/output \\\n -w /data/workdir -p MultiProc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere we are assuming that \u003ccode\u003e/path/to/famface\u003c/code\u003e is the path to the\n\u003ccode\u003efamface\u003c/code\u003e directory as pulled from datalad, which contains a \u003ccode\u003edata\u003c/code\u003e\ndirectory. Note that all the paths passed to the script need to be relative to\nthe filesystem inside the container.\u003c/p\u003e\n\u003cp\u003eRunning the following will instead enter the container for interactive\nanalyses:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -e -c \\\n -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/scripts \\\n -B /path/to/famface:/data \\\n famface.simg \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePlease note that some paths in the scripts might be hardcoded, so they\nneed to be changed prior to running the scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePreprocessing and GLM modeling\u003c/h2\u003e\u003ca id=\"user-content-preprocessing-and-glm-modeling\" class=\"anchor\" aria-label=\"Permalink: Preprocessing and GLM modeling\" href=\"#preprocessing-and-glm-modeling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"fmri_ants_openfmri.py\"\u003e\u003ccode\u003efmri_ants_openfmri.py\u003c/code\u003e\u003c/a\u003e: nipype pipeline to\nperform preprocessing (spatial normalization to MNI 2 mm using ANTs,\nfirst and second level univariate analysis using FSL). Based on the\nexample with the same name from stock Nipype\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"pymvpa_hrf.py\"\u003e\u003ccode\u003epymvpa_hrf.py\u003c/code\u003e\u003c/a\u003e: script to run a GLM using PyMVPA and\nNipy, to extract betas used for multivariate analysis\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"make_unionmask.py\"\u003e\u003ccode\u003emake_unionmask.py\u003c/code\u003e\u003c/a\u003e: script to make a union mask\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"stack_betas.py\"\u003e\u003ccode\u003estack_betas.py\u003c/code\u003e\u003c/a\u003e: script to stack betas for\nmultivariate analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalysis\u003c/h2\u003e\u003ca id=\"user-content-analysis\" class=\"anchor\" aria-label=\"Permalink: Analysis\" href=\"#analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGLM\u003c/h3\u003e\u003ca id=\"user-content-glm\" class=\"anchor\" aria-label=\"Permalink: GLM\" href=\"#glm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"group_multregress_openfmri.py\"\u003e\u003ccode\u003egroup_multregress_openfmri.py\u003c/code\u003e\u003c/a\u003e:\nnipype pipeline to perform third (group) level univariate analysis\nwith FSL. Based on the pipeline provided by Satra Ghosh and Anne Park\n(our thanks to them!)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMVPC\u003c/h3\u003e\u003ca id=\"user-content-mvpc\" class=\"anchor\" aria-label=\"Permalink: MVPC\" href=\"#mvpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"run_sl.py\"\u003e\u003ccode\u003erun_sl.py\u003c/code\u003e\u003c/a\u003e: main script to run searchlight analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"pymvpa2cosmo.py\"\u003e\u003ccode\u003epymvpa2cosmo.py\u003c/code\u003e\u003c/a\u003e: script to convert PyMVPA\ndatasets into CoSMoMVPA datasets for statistical testing using TFCE\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"run_tfce_mvdoc_fx.m\"\u003e\u003ccode\u003erun_tfce_mvdoc_fx.m\u003c/code\u003e\u003c/a\u003e: script to run TFCE on\naccuracy maps using CoSMoMVPA\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"ev_roi_clf.py\"\u003e\u003ccode\u003eev_roi_clf.py\u003c/code\u003e\u003c/a\u003e: script to run additional decoding analyses in probabilistic masks of early visual areas\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"permutation_testing_ev.Rmd\"\u003e\u003ccode\u003epermutation_testing_ev.Rmd\u003c/code\u003e\u003c/a\u003e: RMarkdown notebook that plots the results of the analysis in probabilistic masks of early visual areas (see also pre-computed HTML output \u003ca href=\"permutation_testing_ev.nb.html\"\u003e\u003ccode\u003epermutation_testing_ev.nb.html\u003c/code\u003e\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"hmax_decoding_familiarvsunfamiliar.ipynb\"\u003e\u003ccode\u003ehmax_decoding_familiarvsunfamiliar.ipynb\u003c/code\u003e\u003c/a\u003e: Jupyter notebook with decoding analysis on features extracted from the HMAX model.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"hmax_familiarvsunfamiliar-id.Rmd\"\u003e\u003ccode\u003ehmax_familiarvsunfamiliar-id.Rmd\u003c/code\u003e\u003c/a\u003e: RMarkdown notebook used to analyze the decoding of images using HMAX features (see also pre-computed HTML output \u003ca href=\"hmax_familiarvsunfamiliar-id.nb.html\"\u003e\u003ccode\u003ehmax_familiarvsunfamiliar-id.nb.html\u003c/code\u003e\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSimilarity of Representational Geometries\u003c/h3\u003e\u003ca id=\"user-content-similarity-of-representational-geometries\" class=\"anchor\" aria-label=\"Permalink: Similarity of Representational Geometries\" href=\"#similarity-of-representational-geometries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/define_rois_mds.ipynb\"\u003e\u003ccode\u003enotebooks/define_rois_mds.ipynb\u003c/code\u003e\u003c/a\u003e:\nnotebook used to obtain non-overlapping spherical ROIs in both the\ntask data and the movie hyperaligned data\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"compute_dsmroi_firstlev.py\"\u003e\u003ccode\u003ecompute_dsmroi_firstlev.py\u003c/code\u003e\u003c/a\u003e: script to\ncompute first-level cross-validated representational dissimilarity matrices\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/compute_dsmroi_hpal.ipynb\"\u003e\u003ccode\u003enotebooks/compute_dsmroi_hpal.ipynb\u003c/code\u003e\u003c/a\u003e:\nnotebook used to compute the similarity of representational geometries\nusing hyperaligned movie data\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/plot_mds.ipynb\"\u003e\u003ccode\u003enotebooks/plot_mds.ipynb\u003c/code\u003e\u003c/a\u003e:\nnotebook used to generate MDS and circular graph plots for task and\nhyperaligned data\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/get_between-within_correlations.ipynb\"\u003e\u003ccode\u003enotebooks/get_between-within_correlations.ipynb\u003c/code\u003e\u003c/a\u003e:\nnotebook used to obtain dataframes with correlations between/within\nsystems for each subject (task data) or pair of subjects (hyperaligned\ndata)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"mds_betweenwithin_corr.Rmd\"\u003e\u003ccode\u003emds_betweenwithin_corr.Rmd\u003c/code\u003e\u003c/a\u003e: RMarkdown\nnotebook with additional analyses on correlations of RDMS\nbetween/within systems (see rendering in\n\u003ca href=\"mds_betweenwithin_corr.nb.html\"\u003e\u003ccode\u003emds_betweenwithin_corr.nb.html\u003c/code\u003e\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuxiliary and miscellaneous files\u003c/h2\u003e\u003ca id=\"user-content-auxiliary-and-miscellaneous-files\" class=\"anchor\" aria-label=\"Permalink: Auxiliary and miscellaneous files\" href=\"#auxiliary-and-miscellaneous-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"mds_rois.py\"\u003e\u003ccode\u003emds_rois.py\u003c/code\u003e\u003c/a\u003e: contains functions to run MDS analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"expdir.py\"\u003e\u003ccode\u003eexpdir.py\u003c/code\u003e\u003c/a\u003e: to fetch directories used in analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/scatterplots.ipynb\"\u003e\u003ccode\u003enotebooks/scatterplots.ipynb\u003c/code\u003e\u003c/a\u003e: notebook used to plot scatterplots shown in the supplementary material\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResponse to Reviewers\u003c/h2\u003e\u003ca id=\"user-content-response-to-reviewers\" class=\"anchor\" aria-label=\"Permalink: Response to Reviewers\" href=\"#response-to-reviewers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/response_reviewers_ev.ipynb\"\u003eresponse_reviewers_ev.ipynb\u003c/a\u003e: Is the dorsal stream also close to EV areas?\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/response_reviewers_modelrsa.ipynb\"\u003eresponse_reviewers_modelrsa.ipynb\u003c/a\u003e: Can we say more about why the representations differ between areas?\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/response_reviewers_similarity_taskmovie.ipynb\"\u003eresponse_reviewers_similarity_taskmovie.ipynb\u003c/a\u003e: How similar are the second-order representational geometries between the task data and the movie data?\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 6, + "subscribers_count": 5, "topics": [ - "singularity-container", - "dmtcp", - "scientific-computing", - "checkpoint-restart" + "neuroscience", + "experiment", + "science", + "fmri", + "mvpa" ], - "updated_at": 1692401019.0 + "updated_at": 1670856412.0 }, { "data_format": 2, - "description": "BIDS app for NeuroData\u0027s MRI to Graphs pipeline", + "description": "a sequence analysis workflow for low-input nanopore sequencing", "filenames": [ "Singularity", - "Singularity.v0.1.0" + "Singularity.devel" ], - "full_name": "bids-apps/ndmg", + "full_name": "amojarro/carrierseq", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003endmg\u003c/h2\u003e\u003ca id=\"user-content-ndmg\" class=\"anchor\" aria-label=\"Permalink: ndmg\" href=\"#ndmg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeuroData\u2019s MR Graphs package, \u003cstrong\u003endmg\u003c/strong\u003e (pronounced \u201cnutmeg\u201d), is the successor of the MRCAP, MIGRAINE, and m2g pipelines. \u003cstrong\u003endmg\u003c/strong\u003e combines dMRI and sMRI data from a single subject to estimate a high-level connectome reliably and scalably.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease read the official \u003ca href=\"http://m2g.io\" rel=\"nofollow\"\u003e\u003cstrong\u003endmg\u003c/strong\u003e\u003c/a\u003e docs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eError Reporting\u003c/h3\u003e\u003ca id=\"user-content-error-reporting\" class=\"anchor\" aria-label=\"Permalink: Error Reporting\" href=\"#error-reporting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExperiencing problems? Please open an \u003ca href=\"http://github.com/neurodata/ndmg/issues/new\"\u003eissue\u003c/a\u003e and explain what\u0027s happening so we can help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknowledgement\u003c/h3\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen using this pipeline, please acknowledge us with the citations in the attached \u003ca href=\"https://raw.githubusercontent.com/BIDS-Apps/ndmg/master/ndmg.bib\" rel=\"nofollow\"\u003ebibtex\u003c/a\u003e file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstructions\u003c/h3\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-label=\"Permalink: Instructions\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://hub.docker.com/r/bids/ndmg/\" rel=\"nofollow\"\u003ebids/ndmg\u003c/a\u003e Docker container enables users to run end-to-end connectome estimation on structural MRI right from container launch. The pipeline requires that data be organized in accordance with the \u003ca href=\"http://bids.neuroimaging.io\" rel=\"nofollow\"\u003eBIDS\u003c/a\u003e spec. If the data you wish to process is available on S3 you simply need to provide your s3 credentials at build time and the pipeline will auto-retrieve your data for processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTo get your container ready to run just follow these steps:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) I do not wish to use S3\u003c/em\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIn your terminal, type:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker pull bids/ndmg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003e(B) I wish to use S3\u003c/em\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAdd your secret key/access id to a file called \u003ccode\u003ecredentials.csv\u003c/code\u003e in this directory on your local machine. A dummy file has been provided to make the format we expect clear. (This is how AWS provides credentials)\u003c/li\u003e\n\u003cli\u003eIn your terminal, navigate to this directory and type:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker build -t \u0026lt;yourhandle\u0026gt;/ndmg .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNow we\u0027re ready to launch our instances and process some data!\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLike a normal docker container, you can startup your container with a single line. Let\u0027s assume I am running this and I wish to use S3, so my container is called \u003ccode\u003egkiar/ndmg\u003c/code\u003e. If you don\u0027t want to use S3, you can replace \u003ccode\u003egkiar\u003c/code\u003e with \u003ccode\u003ebids\u003c/code\u003e and ignore the S3 related flags for the rest of the tutorial.\u003c/p\u003e\n\u003cp\u003eI can start my container with:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker run -ti bids/ndmg\nusage: ndmg_bids [-h]\n [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]\n [--bucket BUCKET] [--remote_path REMOTE_PATH]\n bids_dir output_dir {participant}\nndmg_bids: error: too few arguments\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe should\u0027ve noticed that I got an error back suggesting that I didn\u0027t properly provide information to our container. Let\u0027s try again, with the help flag:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker run -ti bids/ndmg:v4 -h\n\nusage: ndmg_bids [-h]\n [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]\n [--bucket BUCKET] [--remote_path REMOTE_PATH]\n bids_dir output_dir {participant}\n\nThis is an end-to-end connectome estimation pipeline from sMRI and DTI images\n\npositional arguments:\n bids_dir The directory with the input dataset formatted\n according to the BIDS standard.\n output_dir The directory where the output files should be stored.\n If you are running group level analysis this folder\n should be prepopulated with the results of the\n participant level analysis.\n {participant} Level of the analysis that will be performed. Multiple\n participant level analyses can be run independently\n (in parallel) using the same output_dir.\n\noptional arguments:\n -h, --help show this help message and exit\n --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]\n The label(s) of the participant(s) that should be\n analyzed. The label corresponds to\n sub-\u0026lt;participant_label\u0026gt; from the BIDS spec (so it does\n not include \"sub-\"). If this parameter is not provided\n all subjects should be analyzed. Multiple participants\n can be specified with a space separated list.\n --bucket BUCKET The name of an S3 bucket which holds BIDS organized\n data. You must have built your bucket with credentials\n to the S3 bucket you wish to access.\n --remote_path REMOTE_PATH\n The path to the data on your S3 bucket. The data will\n be downloaded to the provided bids_dir on your\n machine.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCool! That taught us some stuff. So now for the last unintuitive piece of instruction and then just echoing back commands I\u0027m sure you could\u0027ve figured out from here: in order to share data between our container and the rest of our machine, we need to mount a volume. Docker does this with the \u003ccode\u003e-v\u003c/code\u003e flag. Docker expects its input formatted as: \u003ccode\u003e-v path/to/local/data:/path/in/container\u003c/code\u003e. We\u0027ll do this when we launch our container, as well as give it a helpful name so we can locate it later on.\u003c/p\u003e\n\u003cp\u003eFinally:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003edocker run -ti --name ndmg_test --rm -v ./data:${HOME}/data bids/ndmg ${HOME}/data/ ${HOME}/data/outputs participant --participant_label 01 -b mybucket -r path/on/bucket/\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 5, - "subscribers_count": 4, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCarrierSeq\u003c/h1\u003e\u003ca id=\"user-content-carrierseq\" class=\"anchor\" aria-label=\"Permalink: CarrierSeq\" href=\"#carrierseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ebioRxiv doi: \u003ca href=\"https://doi.org/10.1101/175281\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/175281\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2124-3\" rel=\"nofollow\"\u003eBMC Bioinformatics\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCarrierSeq is a sequence analysis workflow for low-input nanopore sequencing which employs a genomic carrier.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eAngel MojarroEmail author, Julie Hachey, Gary Ruvkun, Maria T. Zuber and Christopher E. Carr\nBMC BioinformaticsBMC series \u2013 open, inclusive and trusted201819:108\nhttps://doi.org/10.1186/s12859-018-2124-3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGithub Contributors: Angel Mojarro (@amojarro), Srinivasa Aditya Bhattaru (@sbhattaru), Christopher E. Carr (@CarrCE), and Vanessa Sochat (@vsoch).\nfastq-filter from: \u003ca href=\"https://github.com/nanoporetech/fastq-filter\"\u003ehttps://github.com/nanoporetech/fastq-filter\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMotivation\u003c/h3\u003e\u003ca id=\"user-content-motivation\" class=\"anchor\" aria-label=\"Permalink: Motivation\" href=\"#motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLong-read nanopore sequencing technology is of particular significance for taxonomic identification at or below the species level. For many environmental samples, the total extractable DNA is far below the current input requirements of nanopore sequencing, preventing \u201csample to sequence\u201d metagenomics from low-biomass or recalcitrant samples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eResults\u003c/h3\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere we address this problem by employing carrier sequencing, a method to sequence low-input DNA by preparing the target DNA with a genomic carrier to achieve ideal library preparation and sequencing stoichiometry without amplification. We then use CarrierSeq, a sequence analysis workflow to identify the low-input target reads from the genomic carrier.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMethods\u003c/h3\u003e\u003ca id=\"user-content-methods\" class=\"anchor\" aria-label=\"Permalink: Methods\" href=\"#methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCarrierSeq implements \u003ccode\u003ebwa-mem\u003c/code\u003e (Li, 2013) to first map all reads to the genomic carrier then extracts unmapped reads by using \u003ccode\u003esamtools\u003c/code\u003e (Li et al., 2009) and \u003ccode\u003eseqtk\u003c/code\u003e (Li, 2012). Thereafter, the user can define a quality score threshold and CarrierSeq proceeds to discard low-complexity reads with \u003ccode\u003efqtrim\u003c/code\u003e (Pertea, 2015). This set of unmapped and filtered reads are labeled \u201creads of interest\u201d and should theoretically comprise target reads and likely contamination. However, reads of interest may also include \u201chigh-quality noise reads\u201d (HQNRs), defined as reads that satisfy quality score and complexity filters yet do not match to any database and disproportionately originate from specific channels. By treating reads as a Poisson arrival process, CarrierSeq models the expected reads of interest channel distribution and rejects data from channels exceeding a reads/channels threshold (xcrit). Reads of interest are then sorted into \u003ccode\u003e08_target_reads\u003c/code\u003e (reads/channel \u2264 xcrit) or \u003ccode\u003e07_hqnrs\u003c/code\u003e (reads/channel \u0026gt; xcrit).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe CarrierSeq scripts requires the following packages to be installed on your local machine.\u003c/p\u003e\n\u003cp\u003eBiopython - \u003ca href=\"http://biopython.org/\" rel=\"nofollow\"\u003ehttp://biopython.org/\u003c/a\u003e\nSciPy - \u003ca href=\"https://www.scipy.org/\" rel=\"nofollow\"\u003ehttps://www.scipy.org/\u003c/a\u003e\nbwa - \u003ca href=\"https://github.com/lh3/bwa\"\u003ehttps://github.com/lh3/bwa\u003c/a\u003e\nseqtk - \u003ca href=\"https://github.com/lh3/seqtk\"\u003ehttps://github.com/lh3/seqtk\u003c/a\u003e\nsamtools - \u003ca href=\"https://github.com/samtools/samtools\"\u003ehttps://github.com/samtools/samtools\u003c/a\u003e\nfqtrim - \u003ca href=\"https://ccb.jhu.edu/software/fqtrim/\" rel=\"nofollow\"\u003ehttps://ccb.jhu.edu/software/fqtrim/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAlternatively, use Docker and the Docker script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing Docker and Dockerhub\u003c/h2\u003e\u003ca id=\"user-content-using-docker-and-dockerhub\" class=\"anchor\" aria-label=\"Permalink: Using Docker and Dockerhub\" href=\"#using-docker-and-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDownload \u0026amp; install Docker - \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehttps://www.docker.com/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eStart docker\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003edocker pull mojarro/carrierseq:latest\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThat\u0027s it!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing Scientific Filesystem (SCIF)\u003c/h2\u003e\u003ca id=\"user-content-using-scientific-filesystem-scif\" class=\"anchor\" aria-label=\"Permalink: Using Scientific Filesystem (SCIF)\" href=\"#using-scientific-filesystem-scif\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis means generating a \u003cstrong\u003eSingularity\u003c/strong\u003e or \u003cstrong\u003eDocker\u003c/strong\u003e container that has the same \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eScientific Filesystem\u003c/a\u003e to create scientific applications to run the pipeline. For instructions on using SCIF with either of these container technologies (or on your host), see the \u003ca href=\"docs/README.md\"\u003edocumentation folder\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing CarrierSeq\u003c/h2\u003e\u003ca id=\"user-content-using-carrierseq\" class=\"anchor\" aria-label=\"Permalink: Using CarrierSeq\" href=\"#using-carrierseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: You may first need to make the script executable with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003echmod +x path/to/carrierseq.sh\u003c/code\u003e\nor\n\u003ccode\u003echmod +x path/to/carrierseq_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eReads to be analyzed must be compiled into a single fastq file and the carrier reference genome must be in fasta format.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd\u003c/code\u003e into your CarrierSeq folder containing the bash and python scripts and run CarrierSeq with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./carrierseq.sh -i \u0026lt;input.fastq\u0026gt; -r \u0026lt;reference.fasta\u0026gt; -q \u0026lt;q_score\u0026gt; -p \u0026lt;p_value\u0026gt; -o \u0026lt;output_directory\u0026gt; -t \u0026lt;bwa_threads\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eor with Docker...\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./carrierseq_docker.sh -i \u0026lt;input.fastq\u0026gt; -r \u0026lt;reference.fasta\u0026gt; -q \u0026lt;q_score\u0026gt; -p \u0026lt;p_value\u0026gt; -o \u0026lt;output_directory\u0026gt; -t \u0026lt;bwa_threads\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e-i, -r, and -o are mandatory flags, CarrierSeq will use the default values if -q, -p, or -t are not defined:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebwa_threads = 1 \nq_score = 9\np_value = 0.0001 or 0.05/512 active channels\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCarrierSeq Output\u003c/h2\u003e\u003ca id=\"user-content-carrierseq-output\" class=\"anchor\" aria-label=\"Permalink: CarrierSeq Output\" href=\"#carrierseq-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCarrierSeq will generate the following folders and files within your working directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# All reads mapped to the carrier reference genome.\n00_bwa/bwa_mapped.sam \n\n# Unmapped reads to the carrier.\n01_samtools/bwa_unmapped_reads.lst \n /bwa_unmapped.sam \n\n02_seqtk/unmapped_reads.fasta \n /unmapped_reads.fastq \n /unmapped_reads.txt \n \n# Reads equal to or greater than a given q-score threshold (default = 9).\n03_fastqc/unmapped_reads_qc.fa \n /unmapped_reads_qc.fq \n /unmapped_reads_qc.lst \n /unmapped_reads_qc.txt \n\n# Discarded reads below the given q-score threshold.\n03_01_low_quality_reads/low_quality_unmapped_reads.fasta \n /low_quality_unmapped_reads.fastq \n /low_quality_unmapped_reads.lst \n /low_quality_unmapped_reads.txt \n \n# Reads with less than 50% of its length detected as low complexity.\n04_fqtrim_dusted/unmapped_reads_qc_dusted.fasta \n /unmapped_reads_qc_dusted.fastq\n /unmapped_reads_qc_dusted.lst \n /unmapped_reads_qc_dusted.txt\n \n# Discarded reads with over than 50% of its length detected as low complexity. \n04_01_low_complexity_reads/low_complexity_reads_qc.fasta \n /low_complexity_reads_qc.fastq \n /low_complexity_reads_qc.lst \n /low_complexity_reads_qc.txt \n\n# Reads of Interest - should theoretically consist of target reads and contamination,\n# but may also include \"high-quality noise reads\" HQNRs which originate from specific channels.\n05_reads_of_interest/carrierseq_roi_header.lst\n /carrierseq_roi.fasta\n /carrierseq_roi.fastq\n /carrierseq_roi.txt\n\n# By treating reads as a Poisson arrival process, CarrierSeq models the expected reads-of-interest \n# channel distribution and rejects data from channels exceeding a reads/channels threshold (xcrit).\n06_poisson_caculation/01_reads_channels.lst # all channels used during sequencing.\n /02_channels_used.lst # Unique channels used during sequencing.\n /03_channels_in_use.txt # Number of unique channels.\n /04_lambda_value.txt # Lambda = Unkown Reads / Used Channels.\n /05_read_channel_threshold.txt # Critical read/channel (xcrit) threshold calculation summary.\n /06_xcrit_threshold_for_dictionary_search.txt # xcrit value.\n /07_poretools_roi_channels.lst # Channels used in reads of interest from fastq generated using poretools.\n /08_roi_channels_clean.lst # Channels used in reads of interest from fastq generated using albacore or minknow or formatted channels from 07_poretools_roi_channels.lst.\n /09_target_channels.lst # \"Good\" channels used to sort target reads.\n /10_albacore_target_channels.lst # \"Good\" channels list formatted for poretools fastq files.\n /10_poretools_target_channels.lst # \"Good\" channel list formatted for albacore/minknow fastq files.\n /xx_hqnr_channel_dictionary.txt # HQNRs read/channel frequency dictionary for python.\n /xx_roi_channel_dictionary.txt # Reads of interest read/channel frequency dictionary for python.\n /xx_target_channel_dictionary.txt # Target reads read/channel frequency dictionary for python.\n \n# Likely HQNRs (reads/channel \u0026gt; xcrit). \n07_hqnrs/carrierseq_hqnrs.fasta\n /carrierseq_hqnrs.fastq\n /carrierseq_hqnrs.lst\n /carrierseq_hqnrs.txt\n \n# Likely Target Reads (reads/channel \u2264 xcrit).\n08_target_reads/carrierseq_target_reads.fasta\n /carrierseq_target_readst.fastq\n /carrierseq_target_reads.lst\n /carrierseq_target_reads.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCarrierSeq Example\u003c/h2\u003e\u003ca id=\"user-content-carrierseq-example\" class=\"anchor\" aria-label=\"Permalink: CarrierSeq Example\" href=\"#carrierseq-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSupplementary sequencing data available from \u003cdel\u003eNCBI\u003c/del\u003e\nFigshare: \u003ca href=\"https://doi.org/10.6084/m9.figshare.5868825.v1\" rel=\"nofollow\"\u003ehttps://doi.org/10.6084/m9.figshare.5868825.v1\u003c/a\u003e\n\u003cdel\u003e\u003ca href=\"https://doi.org/10.6084/m9.figshare.5471824.v1\" rel=\"nofollow\"\u003ehttps://doi.org/10.6084/m9.figshare.5471824.v1\u003c/a\u003e\u003c/del\u003e\n\u003cdel\u003eDropbox: \u003ca href=\"https://www.dropbox.com/sh/vyor82ulzh7n9ke/AAC4W8rMe4z5hdb7j4QhF_IYa?dl=0\" rel=\"nofollow\"\u003ehttps://www.dropbox.com/sh/vyor82ulzh7n9ke/AAC4W8rMe4z5hdb7j4QhF_IYa?dl=0\u003c/a\u003e\u003c/del\u003e\n\u003cdel\u003eBioProject: \u003ca href=\"https://www.ncbi.nlm.nih.gov/bioproject/398368\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/398368\u003c/a\u003e\u003c/del\u003e\n\u003cdel\u003eBioSample: \u003ca href=\"https://www.ncbi.nlm.nih.gov/biosample/SAMN07509071\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/biosample/SAMN07509071\u003c/a\u003e\u003c/del\u003e\n\u003cdel\u003eSRA Download: \u003ca href=\"https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?run=SRR5935058\" rel=\"nofollow\"\u003ehttps://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?run=SRR5935058\u003c/a\u003e\u003c/del\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLibrary preparation and sequencing\u003c/h3\u003e\u003ca id=\"user-content-library-preparation-and-sequencing\" class=\"anchor\" aria-label=\"Permalink: Library preparation and sequencing\" href=\"#library-preparation-and-sequencing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e0.2 ng of B. subtilis DNA was prepared with 1 \u00b5g of Lambda DNA using the Oxford Nanopore Technologies (ONT) ligation sequencing kit (LSK-SQK108). The library was then sequenced on a MinION Mark-1B sequencer and R9.4 flowcell for 48 hours and basecalled using ONT\u2019s Albacore (v1.10) offline basecaller.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCarrierSeq Parameters\u003c/h3\u003e\u003ca id=\"user-content-carrierseq-parameters\" class=\"anchor\" aria-label=\"Permalink: CarrierSeq Parameters\" href=\"#carrierseq-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eq-score = 9 (default) and p-value = 0.05.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSequencing and CarrierSeq Summary\u003c/h3\u003e\u003ca id=\"user-content-sequencing-and-carrierseq-summary\" class=\"anchor\" aria-label=\"Permalink: Sequencing and CarrierSeq Summary\" href=\"#sequencing-and-carrierseq-summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAt Q9, the expected B. subtilis abundance is 590 reads for this sequencing data. The xcrit value was calculated to be 7 reads/channel.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eAll Reads (Lambda + B. subtilis + Contamination + Noise)\nTotal Reads: 717,432 reads (edit: The origininal value, 547,478 reads, were over q = 9 not all reads)\nTotal Bases: 6.4 gb (edit: same as above, 4,914,693,436 bases)\n###\nReads of Interest (B. subtilis + Contamination + HQNRs) [05_reads_of_interest]\nTotal Reads: 1,811 reads\nTotal Bases: 8,132,374 bases\n###\nHQNRS [07_hqnrs]\nTotal Reads: 1,179 reads (including 17 false negative B. subtilis reads)\nTotal Bases: 7,282,767 bases\n###\nTarget Reads [08_target_reads]\nTotal Reads: 632 reads (including 574 true positive B. subtilis reads, 4 true positive contamination reads, and 54 false positive HQNRs)\nTotal Bases: 849,607 bases\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eROI Pore Occupancy\u003c/h3\u003e\u003ca id=\"user-content-roi-pore-occupancy\" class=\"anchor\" aria-label=\"Permalink: ROI Pore Occupancy\" href=\"#roi-pore-occupancy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe matrix illustrates the reads/channel distribution of B. subtilis, contamination, and HQNRs across all 512 nanopore channels. Here we are able to visually identify overly productive channels (e.g., 191 reads/channel, etc) producing likely HQNRs.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/amojarro/carrierseq/blob/master/example/carrierseq_roi_q9_p005.png\"\u003e\u003cimg src=\"https://github.com/amojarro/carrierseq/raw/master/example/carrierseq_roi_q9_p005.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHQNR Pore Occupancy\u003c/h3\u003e\u003ca id=\"user-content-hqnr-pore-occupancy\" class=\"anchor\" aria-label=\"Permalink: HQNR Pore Occupancy\" href=\"#hqnr-pore-occupancy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u201cBad\u201d channels identified by CarrierSeq as HQNR-associated (reads/channel \u0026gt; 7).\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/amojarro/carrierseq/blob/master/example/carrierseq_hqnrs_q9_p005.png\"\u003e\u003cimg src=\"https://github.com/amojarro/carrierseq/raw/master/example/carrierseq_hqnrs_q9_p005.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTarget Reads Pore Occupancy\u003c/h3\u003e\u003ca id=\"user-content-target-reads-pore-occupancy\" class=\"anchor\" aria-label=\"Permalink: Target Reads Pore Occupancy\" href=\"#target-reads-pore-occupancy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u201cGood\u201d channels identified by CarrierSeq as non-HQNR-associated (reads/channel \u2264\u00a07). Channels producing 6 or more reads yield HQNRs that have satisfied our CarrierSeq parameters. By imposing a stricter p-value, CarrierSeq may be able to reject more HQNRs (e.g., xcrit = 5).\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/amojarro/carrierseq/blob/master/example/carrierseq_target_reads_q9_p005.png\"\u003e\u003cimg src=\"https://github.com/amojarro/carrierseq/raw/master/example/carrierseq_target_reads_q9_p005.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 6, + "subscribers_count": 2, "topics": [ - "diffusion-mri", - "connectomics", - "docker-container", - "singularity-container", - "bids", - "bidsapp" + "nanopore", + "bioinformatics-scripts", + "metagenomics", + "docker" ], - "updated_at": 1730709288.0 + "updated_at": 1657841217.0 }, { "data_format": 2, - "description": null, + "description": "A tool to find and annotate signals in next-generation association studies", "filenames": [ - "Singularity.base", - "workflow/old/rs3/Singularity.rs3", - "workflow/old/common/Singularity.workflow_base", - "workflow/old/rs4/Singularity.rs4" + "Singularity" ], - "full_name": "langmead-lab/recount-pump", - "latest_release": "v1.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003erecount-pump\u003c/h1\u003e\u003ca id=\"user-content-recount-pump\" class=\"anchor\" aria-label=\"Permalink: recount-pump\" href=\"#recount-pump\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will give a high level overview of the process of configuring and running a specific project through the first phase of the Monorail pipeline (the \u003ccode\u003epump\u003c/code\u003e phase).\u003c/p\u003e\n\u003cp\u003eFor details, please see the READMEs associated with the various sub-components (e.g. \u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/README.md\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/README.md\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eNomenclature note: \"run\" here is used in terms of a single project\u0027s instantiation as a Monorail pipeline.\nTo differentiate it from the SRA\u0027s base unit of sequencing (also called \"run\", e.g. identified by an [SED]RR accession), we will slightly abuse the terminology of the SRA by calling all sequencing runs \"samples\". For the purposes of this document this is acceptable, though not technically true when discussing sequencing in general.\u003c/p\u003e\n\u003cp\u003eThis document assumes that the reader is interested in running the full Monorail pipeline using the management infrastructure typically run in AWS.\nThis is how all the recount3-related runs were processed.\u003c/p\u003e\n\u003cp\u003eHowever, if the reader\u0027s use case is not to recreate/update recount3/Snaptron2 and their total samples are up to 10\u0027s of thousands (versus 100\u0027s of thousands),\nthey might be better off looking at the monorail-external repo:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/monorail-external/\"\u003ehttps://github.com/langmead-lab/monorail-external/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis runs the same containers as this repo, but assumes no management layer running elsewhere (e.g. AWS).\nHowever, the monorail-external repo\u0027s README includes more granular details about the \u003ccode\u003epump\u003c/code\u003e workflow itself,\nwhich supplement these instructions no matter what type of run the reader is looking to do.\u003c/p\u003e\n\u003cp\u003eThe monorail-external repo\u0027s README covers getting the reference indexes (needed here as well), default settings in the Snakemake for\nintra-sample parallelism (e.g. 8 cores for the STAR aligner per sample), and exact versions of the aligners used.\u003c/p\u003e\n\u003cp\u003eThe monorail-external repo also includes information on the container and instructions for running the \u003ccode\u003eunifier\u003c/code\u003e\nto aggregate the coverage summaries across the samples aligned with the \u003ccode\u003epump\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eunifier\u003c/code\u003e is not covered here, but its repo is:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-unify\"\u003ehttps://github.com/langmead-lab/recount-unify\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProjects\u003c/h2\u003e\u003ca id=\"user-content-projects\" class=\"anchor\" aria-label=\"Permalink: Projects\" href=\"#projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMonorail revolves around the idea of a \u003ccode\u003eproject\u003c/code\u003e which defines the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLabel/name of a run (e.g. \"sra_human_v3\")\u003c/li\u003e\n\u003cli\u003eSet of sample identifiers (if SRA, this is a list of accessions)\u003c/li\u003e\n\u003cli\u003eMonorail docker image name + version to be used for the pipeline\u003c/li\u003e\n\u003cli\u003eSpecies information (name, taxon ID, and reference short name [hg38])\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis is stored in the \u003ccode\u003eproject.ini\u003c/code\u003e file in the \u003ccode\u003eprojects/\u0026lt;proj_name\u0026gt;/\u003c/code\u003e subdirectory.\u003c/p\u003e\n\u003cp\u003eA working project which also serves as a good example is here:\n\u003ca href=\"https://github.com/langmead-lab/recount-pump/tree/master/projects/tcga\"\u003ehttps://github.com/langmead-lab/recount-pump/tree/master/projects/tcga\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThere is also the \u003ccode\u003eprojects/\u0026lt;proj_name\u0026gt;/creds/\u003c/code\u003e subdirectory which stores the project-specific settings per-module (e.g. for Globus).\nThese can be created in a semi-automated way, but typically once one or more projects have been defined by the same person, copying and editing these files between projects is reasonable.\u003c/p\u003e\n\u003cp\u003eAdditionally there are two files (\u003ccode\u003epublic_conf.ini\u003c/code\u003e and \u003ccode\u003eprivate_conf.ini\u003c/code\u003e) which define organization-wide AWS settings.\nThe \u003ccode\u003eprivate_conf.ini\u003c/code\u003e as the name implies should \u003cem\u003enot\u003c/em\u003e be world-readable.\u003c/p\u003e\n\u003cp\u003eAll settings related files are discussed further at the end of this README.\u003c/p\u003e\n\u003cp\u003eThe set of sample identifiers, either a JSON or more typically text file (compressed), is copied to the project directory on S3.\nThe S3 URL is then referenced in the project.ini file, e.g.:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003es3://recount-pump-experiments/sra_human_v3/tranche_0.txt.gz\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis will be used to populate the AWS RDS DB for the project with the list of sample identifiers in the \"Initializing the Project Model\" step.\nEach sample is assigned an integer ID which is used to link it between the DB and the SQS queue.\u003cbr\u003e\nThis ID is only for internal tracking during the \u003ccode\u003erecount-pump\u003c/code\u003e stage.\u003c/p\u003e\n\u003cp\u003eThere are a group of settings files which control how Monorail interacts with the AWS modules (SQS, RDS, Watchtower/CloudWatch), partial examples of these are here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/tree/master/projects/common/creds\"\u003ehttps://github.com/langmead-lab/recount-pump/tree/master/projects/common/creds\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eConceptually there is the \u003ccode\u003eproject\u003c/code\u003e level configuration (covered above) and the \u003ccode\u003ecluster\u003c/code\u003e level configuration (covered later in this README).\nThere is usually only one \u003ccode\u003eproject\u003c/code\u003e level configuration, but there could be more than one \u003ccode\u003ecluster\u003c/code\u003e level configuration for the same \u003ccode\u003eproject\u003c/code\u003e.\nThis is a key feature of the grid computing approach.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInitializing the Project Model\u003c/h2\u003e\u003ca id=\"user-content-initializing-the-project-model\" class=\"anchor\" aria-label=\"Permalink: Initializing the Project Model\" href=\"#initializing-the-project-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce all \u003ccode\u003eproject\u003c/code\u003e level settings files have been configured, the project needs to be initialized.\u003c/p\u003e\n\u003cp\u003eBefore attempting to run initialization, you need to ensure the Python2.7 used has the needed dependencies.\nThese are required for both initializtion \u003cem\u003eand\u003c/em\u003e running the Python parent process \u003ccode\u003ecluster.py\u003c/code\u003e in the job scripts below.\u003c/p\u003e\n\u003cp\u003eAssuming you have write access to the Python2.7 in your envionment (either because you\u0027re root or more likely you\u0027ve setup a \u003ccode\u003evirtualenv\u003c/code\u003e or are using conda):\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install -r requirements.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003ccode\u003erequirements.txt\u003c/code\u003e are in the root of this repo.\u003c/p\u003e\n\u003cp\u003eNOTE: these are the only dependencies needed when using the recount-pump container.\u003cbr\u003e\nAll the conda-related files are installed within the container itself.\u003c/p\u003e\n\u003cp\u003eThe following scripts should be run from under the project working directory (typically \u003ccode\u003eprojects/\u0026lt;proj_name\u0026gt;\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eprojects/common/init_model.sh\u003c/code\u003e\nand\n\u003ccode\u003eprojects/common/reset.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003einit_model.sh\u003c/code\u003e script will perform the following actions for the project:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreation of AWS RDS DB\u003c/li\u003e\n\u003cli\u003ePopulation of AWS RDS DB with sample IDs and reference/annotation data set\u003c/li\u003e\n\u003cli\u003eAdds Monorail Docker image name/version to database\u003c/li\u003e\n\u003cli\u003eCreation of AWS SQS job queue\u003c/li\u003e\n\u003cli\u003eStage sample IDs as messages in SQS job queue\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis information represents the tracked \"state\" of the project/run.\u003c/p\u003e\n\u003cp\u003eIf there is a problem in the initialization or later in the project run that relates to configuration, it\u0027s usually best to start fresh with a new initialization run. This can be done by resetting the project in AWS (DB/SQS) with the \u003ccode\u003ereset.sh\u003c/code\u003e script listed above.\u003c/p\u003e\n\u003cp\u003eHowever, problems with individual jobs/samples/nodes can be worked out individually and those jobs requeued w/o having to re-initialize the project as a whole.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCluster Configuration\u003c/h2\u003e\u003ca id=\"user-content-cluster-configuration\" class=\"anchor\" aria-label=\"Permalink: Cluster Configuration\" href=\"#cluster-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTypically, Monorail is run in an HPC environment using Singularity + Conda to ease the pain of dependency management.\nMonorail \u003cem\u003ecan\u003c/em\u003e be run outside of containers (\"bare metal\") but this is not recommended for most cases and is not covered here.\u003c/p\u003e\n\u003cp\u003eThe key settings file for cluster configuration is the \u003ccode\u003ecluster.ini\u003c/code\u003e file, detailed at the end of this README.\u003c/p\u003e\n\u003cp\u003eA partial example is here:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/public_conf.ini\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/public_conf.ini\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis file also serves as a reference point for which path temporary/output files will be deposited during a run (useful for debugging).\u003c/p\u003e\n\u003cp\u003eIt can also define the within-container mount directories for the external,\nhost paths if this is needed by the specific cluster (e.g. Stampede2 needs to have the container mounts defined, MARCC does not).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorker Run Configuration\u003c/h2\u003e\u003ca id=\"user-content-worker-run-configuration\" class=\"anchor\" aria-label=\"Permalink: Worker Run Configuration\" href=\"#worker-run-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the project has been initialized, and one or more clusters have been configured, Monorail can be run.\nThis section assumes you\u0027re running on a local HPC cluster, but it could be extended to include remote resources on AWS or equivalent.\u003c/p\u003e\n\u003cp\u003eThere are 3 types of entities important in this section:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJobs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA \u003ccode\u003ejob\u003c/code\u003e is an attempt at processing a single sample through Monorail (it could fail)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWorkers\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA \u003ccode\u003eworker\u003c/code\u003e is the atomic agent of Monorail, it represents a single python process which instantiates a container for each new \u003ccode\u003ejob\u003c/code\u003e, which in turn runs a Snakemake pipeline within the container. Under normal circumstances, a \u003ccode\u003eworker\u003c/code\u003e will continue to run as long as 1) there are \u003ccode\u003ejob\u003c/code\u003es on the SQS \u003ccode\u003ejob\u003c/code\u003e queue and 2) the SQS \u003ccode\u003ejob\u003c/code\u003e queue is accessible. A \u003ccode\u003eworker\u003c/code\u003e runs each \u003ccode\u003ejob\u003c/code\u003e in sequence, but it can use multiple cores/CPUs within the \u003ccode\u003ejob\u003c/code\u003e to parallelize tasks such as alignment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNodes\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEach \u003ccode\u003enode\u003c/code\u003e represents a machine (or VM) allocated, in part or in whole, to Monorail to run one or more \u003ccode\u003eworker\u003c/code\u003es to process \u003ccode\u003ejob\u003c/code\u003es. Each allocation of a \u003ccode\u003enode\u003c/code\u003e will start a parent python process which will then spawn one or more child \u003ccode\u003eworker\u003c/code\u003e processes.\u003c/p\u003e\n\u003cp\u003eTo start Monorail running on a \u003ccode\u003enode\u003c/code\u003e, typically, a \"runner\" (batch) script is submitted to the HPC\u0027s scheduler (e.g. Slurm) to request allocation of a \u003ccode\u003enode\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThis script will typically set the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHPC scheduler partition/queue to allocate from\u003c/li\u003e\n\u003cli\u003eName of allocation\u003c/li\u003e\n\u003cli\u003eTime requested (e.g. 12 hours)\u003c/li\u003e\n\u003cli\u003eHardware resources requested (e.g. 12 cores, 90G memory)\u003c/li\u003e\n\u003cli\u003eAccount to charge allocation to (if applicable)\u003c/li\u003e\n\u003cli\u003eList of \u003ccode\u003enode\u003c/code\u003es to exclude (blacklisting, if applicable)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition it will setup the environment to start the Monorail parent process on that \u003ccode\u003enode\u003c/code\u003e, which includes loading the Singularity module.\nAnd finally it will start the \u003ccode\u003ecluster.py\u003c/code\u003e parent python process with parameters which point to the various \u003ccode\u003e.ini\u003c/code\u003e files.\u003c/p\u003e\n\u003cp\u003eAn example of this, which includes a delay at the start of the parent python processes on a \u003ccode\u003enode\u003c/code\u003e by up to 6 minutes in the runner script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erdelay=`perl -e \u0027print \"\".int(rand(360));\u0027`\nsleep $rdelay\n\nmodule load singularity/2.6.0\nconda activate recount\numask 0077\npython /path/to/recount-pump/src/cluster.py run --ini-base creds --cluster-ini creds/cluster.ini \u0026lt;proj_name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe delay is to stagger \u003ccode\u003ejob\u003c/code\u003e starts to avoid maxing out the globus API rate limits when automatically transferring via Globus, this is not needed if Globus is manually run after a whole \u003ccode\u003erun\u003c/code\u003e (tranche) completes.\u003c/p\u003e\n\u003cp\u003eGlobus is \u003cem\u003enot\u003c/em\u003e automatically run for Stampede2 or for MARCC (details below).\u003c/p\u003e\n\u003cp\u003eThe following are versions of scripts/configurations that were actually used to run \u003ccode\u003esra_human_v3\u003c/code\u003e, \u003ccode\u003esra_mouse_v1\u003c/code\u003e, \u003ccode\u003etcgav2\u003c/code\u003e and \u003ccode\u003egtexv2\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStampede2\u003c/h3\u003e\u003ca id=\"user-content-stampede2\" class=\"anchor\" aria-label=\"Permalink: Stampede2\" href=\"#stampede2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStampede2 job runner \u0026amp; \u003ccode\u003ecluster\u003c/code\u003e config for Skylake (\u003ccode\u003eskx-normal\u003c/code\u003e) partition/queue:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/stampede2/skx-normal/job.sh\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/stampede2/skx-normal/job.sh\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/stampede2/skx-normal/cluster-skx-normal.ini\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/stampede2/skx-normal/cluster-skx-normal.ini\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDuring our initial runs on Stampede2, we encountered the API rate limits mentioned above and opted to transfer in bulk after a run/tranche is fully done rather than use Globus automatically.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMARCC\u003c/h3\u003e\u003ca id=\"user-content-marcc\" class=\"anchor\" aria-label=\"Permalink: MARCC\" href=\"#marcc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMARCC job runner \u0026amp; \u003ccode\u003ecluster\u003c/code\u003e config for \u003ccode\u003elrgmem\u003c/code\u003e partition/queue using \u003ccode\u003e/dev/shm\u003c/code\u003e:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/lrgmem/job.sh\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/lrgmem/job.sh\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/lrgmem/cluster4_shm.ini\"\u003ehttps://github.com/langmead-lab/recount-pump/blob/master/projects/common/clusters/marcc/lrgmem/cluster4_shm.ini\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIn the MARCC case, we typically don\u0027t transfer the output of runs immediately to another filesystem, though these runs are eventually backed up on JHPCE (or equivalent).\nThis is because runs on MARCC are usually of protected data (TCGA/GTEx) and therefore can\u0027t be copied to just anywhere.\u003c/p\u003e\n\u003cp\u003eThis also highlights the need for the following when processing protected runs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNon-world readable permissions on all input/output files (\u003ccode\u003eumask 077\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eEncrypted transfers when copying files to another location (e.g. using Globus to backup TCGA/GTEx to JHPCE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStopping Conditions\u003c/h2\u003e\u003ca id=\"user-content-stopping-conditions\" class=\"anchor\" aria-label=\"Permalink: Stopping Conditions\" href=\"#stopping-conditions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNodes will stop processing for one of 3 reasons:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe time limit on the node allocation ends\u003c/li\u003e\n\u003cli\u003eThe job queue is exhausted\u003c/li\u003e\n\u003cli\u003eA runtime error causes the parent python process running on the node to prematurely terminate\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy far the most common cause for \u003ccode\u003enode\u003c/code\u003e stopages is allocation expirations (1st one), since \u003ccode\u003enode\u003c/code\u003e allocations are much shorter than what\u0027s needed to process a medium-large Monorail run. This will have the effect of stopping \u003ccode\u003ejob\u003c/code\u003es in the middle which will need to be restarted. This is expected and these \u003ccode\u003ejob\u003c/code\u003es will be visible again on the queue after a pre-defined time period (typically 30 min to 3 hours) controlled by \u003ccode\u003evisibility_timeout\u003c/code\u003e in the \u003ccode\u003ecreds/queue.ini\u003c/code\u003e settings file for the \u003ccode\u003eproject\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf many concurrent attempts are made which end up being successful for a particular \u003ccode\u003ejob\u003c/code\u003e, this indicates the \u003ccode\u003evisibility_timeout\u003c/code\u003e setting for the \u003ccode\u003ejob\u003c/code\u003e queue is too short and should be elongated.\u003c/p\u003e\n\u003cp\u003eAlso related to this, the \u003ccode\u003emax_receive_count\u003c/code\u003e also in \u003ccode\u003ecreds/queue.ini\u003c/code\u003e, controls how many times a job is attempted before dumping it to the Dead Letter Queue (DLQ). Typically this is 3-6 times, depending on the project, however, in certain cases (SRA) it may be necessary to reduce this to 1-2 to rapidly fail samples which simply won\u0027t download.\u003c/p\u003e\n\u003cp\u003eIn the 2nd \u003ccode\u003enode\u003c/code\u003e stop case above, the parent process running on the \u003ccode\u003enode\u003c/code\u003e will wait until all \u003ccode\u003eworker\u003c/code\u003e processes (children) have finished w/o error and then it will finish itself and relinquish the \u003ccode\u003enode\u003c/code\u003e. If a child \u003ccode\u003eworker\u003c/code\u003e process fails, the parent will start a new \u003ccode\u003eworker\u003c/code\u003e process in its place and continue checking \u003ccode\u003eworker\u003c/code\u003e processes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSettings Files\u003c/h2\u003e\u003ca id=\"user-content-settings-files\" class=\"anchor\" aria-label=\"Permalink: Settings Files\" href=\"#settings-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProject-specific Settings files\u003c/h3\u003e\u003ca id=\"user-content-project-specific-settings-files\" class=\"anchor\" aria-label=\"Permalink: Project-specific Settings files\" href=\"#project-specific-settings-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ecluster.ini\u003c/h4\u003e\u003ca id=\"user-content-clusterini\" class=\"anchor\" aria-label=\"Permalink: cluster.ini\" href=\"#clusterini\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis file defines the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCluster name\u003c/li\u003e\n\u003cli\u003eContainer system (typically \"Singularity\")\u003c/li\u003e\n\u003cli\u003ePath to Singularity image file\u003c/li\u003e\n\u003cli\u003eInput path\u003c/li\u003e\n\u003cli\u003eOutput path\u003c/li\u003e\n\u003cli\u003eTemp path\u003c/li\u003e\n\u003cli\u003eReference file set path\u003c/li\u003e\n\u003cli\u003e# of workers (\u003ccode\u003eworkers\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e# of cores per worker (\u003ccode\u003ecpus\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePaths are always absolute.\nInput/output/temp paths are defined both for the host OS \u003cem\u003eand\u003c/em\u003e for the container.\nThe container paths are where the host OS paths are mounted in the container, so they reference the same thing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGeneric Settings Files\u003c/h3\u003e\u003ca id=\"user-content-generic-settings-files\" class=\"anchor\" aria-label=\"Permalink: Generic Settings Files\" href=\"#generic-settings-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe settings below are typically set once for a organization/group and shared between multiple \u003ccode\u003eprojects\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003epublic_conf.ini\u003c/h4\u003e\u003ca id=\"user-content-public_confini\" class=\"anchor\" aria-label=\"Permalink: public_conf.ini\" href=\"#public_confini\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eProfile\u003c/li\u003e\n\u003cli\u003eRegion\u003c/li\u003e\n\u003cli\u003eSubnets\u003c/li\u003e\n\u003cli\u003eRDS DB port/DNS\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eprivate_conf.ini\u003c/h4\u003e\u003ca id=\"user-content-private_confini\" class=\"anchor\" aria-label=\"Permalink: private_conf.ini\" href=\"#private_confini\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eConfidential AWS RDS DB password\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 5, - "subscribers_count": 7, + "full_name": "hmgu-itg/peakplotter", + "latest_release": "v0.5.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePeakPlotter : automatically annotate hits from genome-wide association results\u003c/h1\u003e\u003ca id=\"user-content-peakplotter--automatically-annotate-hits-from-genome-wide-association-results\" class=\"anchor\" aria-label=\"Permalink: PeakPlotter : automatically annotate hits from genome-wide association results\" href=\"#peakplotter--automatically-annotate-hits-from-genome-wide-association-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePeakPlotter takes away the annoying task of running regional association plots and annotating variants for your association studies results. It is compatible with sequencing as well as GWAS data. It is compatible with any format (GEMMA, SNPTEST, Bolt-LMM...) that produces the relevant columns: chromosome, position, unique ID, P-value, reference and non-reference alleles.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter installing the prerequisites (see below), clone the repository and install using \u003ccode\u003epip\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/hmgu-itg/peakplotter.git\n\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e peakplotter\n\npython3 -m pip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\npeakplotter-data-setup \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This only needs to be run once\u003c/span\u003e\n\npeakplotter --help\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or \u003c/span\u003e\npython3 -m peakplotter --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA \u003ccode\u003eSingularity\u003c/code\u003e definition file is also available in the repository if you wish to build a container to use \u003ccode\u003epeakplotter\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePeakPlotter has has non-python dependencies.\u003cbr\u003e\nIn order to run PeakPlotter you need to install the following tools and add the executables to your \u003ccode\u003ePATH\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlink 1.9 or newer (\u003ca href=\"https://www.cog-genomics.org/plink/1.9/\" rel=\"nofollow\"\u003eavailable here\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eLocusZoom Standalone 1.4 or newer (\u003ca href=\"http://genome.sph.umich.edu/wiki/LocusZoom_Standalone\" rel=\"nofollow\"\u003eavailable here\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eTabix (\u003ca href=\"https://github.com/samtools/htslib\"\u003eavailable here\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePeakPlotter will throw a \u003ccode\u003eMissingExecutableError\u003c/code\u003e if you have any of the above tools missing in your \u003ccode\u003ePATH\u003c/code\u003e environment variable.\u003cbr\u003e\nAdd the necessary tools to your \u003ccode\u003ePATH\u003c/code\u003e like below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=/path/to/locuszoom:/path/to/plink:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to make these changes permanent, do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=/path/to/locuszoom:/path/to/plink:$PATH\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ peakplotter --help\nUsage: peakplotter [OPTIONS]\n\n PeakPlotter\n\nOptions:\n -a, --assoc-file FILE Path to the association file. It can be gzipped,\n provided that it bears the .gz extension. Its first\n line must be a header, coherent with the name\n arguments below. It must be tab-separated, bgzipped\n and tabixed (tabix is available as part of\n bcftools) [required]\n -f, --bfiles TEXT Binary PLINK (.bed/.bim/.fam) file base name. This\n should contain the genotypes \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e at least all the\n variants \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the assoc_file, but it can contain\n more. Please note that this is the base name,\n without the .bed/.bim/.fam extension. [required]\n -o, --out DIRECTORY Output directory to store all output files.\n [required]\n -chr, --chr-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e chromosome names.\n [required]\n -ps, --pos-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e chromosomal position.\n [required]\n -rs, --rs-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e unique SNP ids (RS-id or\n chr:pos). [required]\n -p, --pval-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e p-values. [required]\n -a1, --a1-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e reference or major allele\n (used \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e predicting consequence). [required]\n -a2, --a2-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e alternate or minor allele.\n [required]\n -maf, --maf-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e non-reference or minor\n allele frequency. [required]\n -b, --build INTEGER Assembly build (37 or 38) [default: 38]\n -s, --signif FLOAT The significance level above which to \u003cspan class=\"pl-k\"\u003edeclare\u003c/span\u003e a\n variant significant. Scientific notation (such as\n 5e-8) is fine.\n -bp, --flank-bp INTEGER Flanking size \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e base pairs \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e drawing plots\n (defaults to 500kb, i.e. 1Mbp plots) around lead\n SNPs.\n --overwrite Overwrite output directory \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e it already exists.\n --help Show this message and exit.\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \u003ccode\u003epytest\u003c/code\u003e at the root of the repository to run the testsuite.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:hmgu-itg/peakplotter.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e peakplotter\npytest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you encounter any bugs, please raise an issue at the \u003ca href=\"https://github.com/hmgu-itg/peakplotter/issues\"\u003eissue page\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1722884581.0 + "updated_at": 1721035311.0 }, { "data_format": 2, - "description": "H3A variant calling pipeline", + "description": null, "filenames": [ - "containers/Singularity.bwa", - "containers/Singularity.gatk", - "containers/Singularity.fastqc", - "containers/Singularity.multiqc", - "containers/Singularity.trimmomatic" + "containers/Singularity.0.0.1", + "containers/Singularity.0.0.2", + "containers/Singularity.0.0.3", + "containers/Singularity.dev", + "containers/Singularity.0.0.4" ], - "full_name": "h3abionet/h3avarcall", + "full_name": "lscsoft/bilby_pipe", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\n\u003ccode\u003eh3avarcall\u003c/code\u003e - H3ABioNet Variant Calling Pipeline\u003c/h1\u003e\u003ca id=\"user-content-h3avarcall---h3abionet-variant-calling-pipeline\" class=\"anchor\" aria-label=\"Permalink: h3avarcall - H3ABioNet Variant Calling Pipeline\" href=\"#h3avarcall---h3abionet-variant-calling-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eh3avarcall\u003c/code\u003e is a \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003ccode\u003eNextflow\u003c/code\u003e\u003c/a\u003e pipeline developed by \u003ca href=\"https://www.h3abionet.org/\" rel=\"nofollow\"\u003e\u003ccode\u003eH3ABioNet\u003c/code\u003e\u003c/a\u003e for genomic Variant Calling allowing to detect SNPs and Indels giving raw sequence reads (fastq files) as input. \u003ccode\u003eh3avarcall\u003c/code\u003e includes the different steps from aligning raw sequence reads to variant calling and filtering using GATK. \u003cbr\u003e\nFor more details about the different steps of the pipeline, check the [H3ABionet SOPs pages] \u003ca href=\"https://h3abionet.github.io/H3ABionet-SOPs/Variant-Calling\" rel=\"nofollow\"\u003ehttps://h3abionet.github.io/H3ABionet-SOPs/Variant-Calling\u003c/a\u003e\n\u003ccode\u003eh3avarcall\u003c/code\u003e is a modular and extensible tool allowing users to run the whole pipeline, use only parts of it and also to easily enrich it and adapt it to their needs. \u003ccode\u003eh3avarcall\u003c/code\u003e generates a number of intermediate files where results from various steps of the pipeline are stored.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Obtaining pipeline and preparing Data\u003c/h2\u003e\u003ca id=\"user-content-1-obtaining-pipeline-and-preparing-data\" class=\"anchor\" aria-label=\"Permalink: 1. Obtaining pipeline and preparing Data\" href=\"#1-obtaining-pipeline-and-preparing-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you need to clone the \u003ccode\u003eh3avarcall\u003c/code\u003e repository onto you machine:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/h3abionet/h3avarcall.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e h3avarcall\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eContent of the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eh3avarcall\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--containers \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for Singularity images and recipes (in case you want to build yourself). All downloaded images go here!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.bwa \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for BWA and Samtools.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.fastqc \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for FastQC.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.gatk \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for GATK and tabix.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--Singularity.trimmomatic \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Singularity recipe file for Trimmimatic.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--gatk-b37-bundle \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for stoding downloaded GATK-b37-bundle files.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--b37_files_minimal.txt \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# LList of GATK-b37-bundle files to be downloaded (bundle TOO BIG! Only selected files needed for the pipeline). \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--templates \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Folder for extra scripts for the pipeline.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--download_bundles.sh \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Script for downloading GATK-b37-bundle.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--LICENSE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Duh!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--README.md \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Duh!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--main.config \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# User configuration file! All inputs, outputs and options GO HERE!! ONLY file that SHOULD be modified by user!\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--main.nf \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Main h3avarcall nextflow scripts.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--nextflow.config \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# Pipeline configuration file! DO NOT EDIT!!!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003emain.config\u003c/code\u003e file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-groovy\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e/*\u003c/span\u003e==================================================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * THIS FILE IS USED TO SPECIFY INPUT, OUTPUTS AND PARAMETERS. THE FOLLOWING OPTIONS ARE THE ALLOWED:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * ==================================================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * data : Path to where the data is (FASTQ files).\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * out : Path to store output results.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * bundle : GATK-b37-bundle list file.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * mode : Worflow step to perform. Can be any of [ do.GetContainers | do.GenomeIndexing | do.QC | do.ReadTrimming | do.ReadAlignment | do.VarianCalling | do.VariantFiltering | do.MultiQC].\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * trim : Trimming options for Trimmomatic.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * resources : Location of the GATK-b37-bundle folder.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * from : pipeline step to resume pipeline from. Can be any of [ do.QC | do.ReadTrimming | do.ReadAlignment | do.VarianCalling | do.VariantFiltering ].\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * params.help : Print help menu.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * ==================================================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * BELOW ARE THE DEFAULT PARAMETERS! YOU\u0027RE MORE THAN WELCOME TO CHANGE AS DESIRED!\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e * ==================================================================================================\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e \u003cspan class=\"pl-c\"\u003e*/\u003c/span\u003e\u003c/span\u003e\n\nparams {\n data \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$b\u003cspan class=\"pl-smi\"\u003easeDir\u003c/span\u003e\u003c/span\u003e/data\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n out \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$b\u003cspan class=\"pl-smi\"\u003easeDir\u003c/span\u003e\u003c/span\u003e/results\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n bundle \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$b\u003cspan class=\"pl-smi\"\u003easeDir\u003c/span\u003e\u003c/span\u003e/gatk-b37-bundle/b37_files_minimal.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n mode \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edo.QC\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n trim \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10:8:true TRAILING:28 MINLEN:40\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n resources \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$b\u003cspan class=\"pl-smi\"\u003easeDir\u003c/span\u003e\u003c/span\u003e/gatk-b37-bundle\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n from \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enull\u003c/span\u003e\n params\u003cspan class=\"pl-k\"\u003e.\u003c/span\u003ehelp \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enull\u003c/span\u003e\n}\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.1. Download test datasets:\u003c/h3\u003e\u003ca id=\"user-content-11-download-test-datasets\" class=\"anchor\" aria-label=\"Permalink: 1.1. Download test datasets:\" href=\"#11-download-test-datasets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreate a data directory under the \u003ccode\u003eh3avarcall\u003c/code\u003e repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir data\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e data\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDownload the test data from \u003ca href=\"http://thesite.com\" rel=\"nofollow\"\u003eTHIS_SITE\u003c/a\u003e using one of the commands bellow:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e1.1.2. Using LFTP (faster)\u003c/h4\u003e\u003ca id=\"user-content-112-using-lftp-faster\" class=\"anchor\" aria-label=\"Permalink: 1.1.2. Using LFTP (faster)\" href=\"#112-using-lftp-faster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003elftp -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epget -n 20 ftp://ftp-trace.ncbi.nih.gov/giab/ftp/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/NIST7035_TAAGGCGA_L001_R1_001.fastq.gz; bye\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nlftp -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epget -n 20 ftp://ftp-trace.ncbi.nih.gov/giab/ftp/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/NIST7035_TAAGGCGA_L001_R2_001.fastq.gz; bye\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e1.1.3. Using WGET (slower)\u003c/h4\u003e\u003ca id=\"user-content-113-using-wget-slower\" class=\"anchor\" aria-label=\"Permalink: 1.1.3. Using WGET (slower)\" href=\"#113-using-wget-slower\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget ftp://ftp-trace.ncbi.nih.gov/giab/ftp/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/NIST7035_TAAGGCGA_L001_R1_001.fastq.gz\nwget ftp://ftp-trace.ncbi.nih.gov/giab/ftp/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/NIST7035_TAAGGCGA_L001_R2_001.fastq.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.2. Download the \u003ccode\u003eSingularity\u003c/code\u003e containers (required to execute the pipeline):\u003c/h3\u003e\u003ca id=\"user-content-12-download-the-singularity-containers-required-to-execute-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: 1.2. Download the Singularity containers (required to execute the pipeline):\" href=\"#12-download-the-singularity-containers-required-to-execute-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.GetContainers\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.3. Download the GATK b37 bundle (required to execute the pipeline):\u003c/h3\u003e\u003ca id=\"user-content-13-download-the-gatk-b37-bundle-required-to-execute-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: 1.3. Download the GATK b37 bundle (required to execute the pipeline):\" href=\"#13-download-the-gatk-b37-bundle-required-to-execute-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step takes \u003cstrong\u003eFOREVER\u003c/strong\u003e to run - run it only once!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.GenomeIndexing\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf by some miracle you happen to have access to the WITS Cluster, you do not need to download the GATK-b37-bundle! Simply \u003ccode\u003ecd\u003c/code\u003e into the \u003ccode\u003egatk-b37-bundle\u003c/code\u003e folder of the \u003ccode\u003eh3avarcall\u003c/code\u003e repo and soft-link the GATK-b37-bundle data as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd gatk-b37-bundle\nln -s /global/blast/gatk-bundle/b37/* .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Executing the main \u003ccode\u003eh3avarcall\u003c/code\u003e pipeline\u003c/h2\u003e\u003ca id=\"user-content-2-executing-the-main-h3avarcall-pipeline\" class=\"anchor\" aria-label=\"Permalink: 2. Executing the main h3avarcall pipeline\" href=\"#2-executing-the-main-h3avarcall-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.1. Read QC (optional): \u003cbr\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-21-read-qc-optional-\" class=\"anchor\" aria-label=\"Permalink: 2.1. Read QC (optional): \" href=\"#21-read-qc-optional-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore getting started with the downstream analysis, it\u0027s always good to do some quality checks on your raw sequences to assess the quality of raw sequence data, the fastq files. FastQC tool has been used in this workflow. An html report page will be automatically created for each fastq file. You can load up these html pages in your browser to assess your data through graphs and summary tables.\u003cbr\u003e\nTo perform the QC of your fastq files, you can use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.QC\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.2. Read Trimming (optional):\u003cbr\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-22-read-trimming-optional\" class=\"anchor\" aria-label=\"Permalink: 2.2. Read Trimming (optional):\" href=\"#22-read-trimming-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter performing the QC of your fastq files, you have an idea about the quality of your reads: some of your reads might not be of a very good quality or the quality might drop at some positions (near the begining or end of reads) across all reads and this requires to clean up your library to minimize biaises in your analysis by filtering poor quality reads and/or trim poor quality bases from our samples. Trimmomatic is the trimming tool that has been used here. \u003cbr\u003e\nFor more information about reads preprocessing, check this \u003ca href=\"https://h3abionet.github.io/H3ABionet-SOPs/Variant-Calling#phase-1-preprocessing-of-the-raw-reads\" rel=\"nofollow\"\u003epage\u003c/a\u003e. \u003cbr\u003e\nTo run the trimming step of the \u003ccode\u003eh3avarcall\u003c/code\u003e pipeline, you can use this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.ReadTrimming\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.3. Read Alignment \u003cbr\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-23-read-alignment-\" class=\"anchor\" aria-label=\"Permalink: 2.3. Read Alignment \" href=\"#23-read-alignment-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have good raw sequences quality, the next step is to map your reads to a reference genome to determine where in the genome the reads originated from. The mapper used in this workflow is BWA. For more information about the read alignement step, check this \u003ca href=\"https://h3abionet.github.io/H3ABionet-SOPs/Variant-Calling#phase-2-initial-variant-discovery\" rel=\"nofollow\"\u003epage\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCan be run with \u003ccode\u003e--from do.ReadTrimming\u003c/code\u003e or \u003ccode\u003e--from do.QC\u003c/code\u003e depending on whether these steps were run!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.ReadAlignment\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.4. Variant Calling\u003c/h3\u003e\u003ca id=\"user-content-24-variant-calling\" class=\"anchor\" aria-label=\"Permalink: 2.4. Variant Calling\" href=\"#24-variant-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step uses the outputs generated by the Read Alignment STEP! \u003cstrong\u003eMUST\u003c/strong\u003e run STEP 2.3 (\u003ccode\u003e--mode do.ReadAlignment\u003c/code\u003e) before running this step.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.VariantCalling \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.5. Variant Filtering\u003c/h3\u003e\u003ca id=\"user-content-25-variant-filtering\" class=\"anchor\" aria-label=\"Permalink: 2.5. Variant Filtering\" href=\"#25-variant-filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step uses the outputs generated by the Variant Calling STEP! \u003cstrong\u003eMUST\u003c/strong\u003e run STEP 2.4 (\u003ccode\u003e--mode do.VariantCalling\u003c/code\u003e) before running this step.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.VariantFiltering \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.6. Workflow QC (MultiQC - Optional)\u003c/h3\u003e\u003ca id=\"user-content-26-workflow-qc-multiqc---optional\" class=\"anchor\" aria-label=\"Permalink: 2.6. Workflow QC (MultiQC - Optional)\" href=\"#26-workflow-qc-multiqc---optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step performs a Quality Check of the different pipeline steps that have been ran. You need to run at least ONE step of the pipeline to be able to run this MultiQC step!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main.nf -profile slurm --mode do.MultiQC \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Explore \u003ccode\u003eh3avarcall\u003c/code\u003e results\u003c/h2\u003e\u003ca id=\"user-content-3-explore-h3avarcall-results\" class=\"anchor\" aria-label=\"Permalink: 3. Explore h3avarcall results\" href=\"#3-explore-h3avarcall-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAssuming you did not change the default output folder (in the \u003ccode\u003emain.config\u003c/code\u003e file), the resulting files will be found in the \u003ccode\u003eresults\u003c/code\u003e folder of the \u003ccode\u003eh3avarcall\u003c/code\u003e repository. Resulting files for each of the main pipeline steps (\u003ccode\u003e2.1\u003c/code\u003e - \u003ccode\u003e2.5\u003c/code\u003e) are grouped in different folders as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- [1] Read QC (optional) =\u0026gt; `results/1_QC`\n- [2] Read Trimming (optional) =\u0026gt; `results/2_Read_Trimming`\n- [3] Read Alignment =\u0026gt; `results/3_Read_Alignment`\n- [4] Variant Calling =\u0026gt; `results/4_Variant_Calling`\n- [5] Variant Filtering =\u0026gt; `results/5_Variant_Filtering`\n- [6] MultiQC =\u0026gt; `results/MultiQC`\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn each of these folders, a sub-folder \"\u003ccode\u003eworkflow_report\u003c/code\u003e\" is created. It contains 4 different files (\u003ccode\u003eh3avarcall_report.html\u003c/code\u003e, \u003ccode\u003eh3avarcall_timeline.html\u003c/code\u003e, \u003ccode\u003eh3avarcall_workflow.dot\u003c/code\u003e and \u003ccode\u003eh3avarcall_trace.txt\u003c/code\u003e) containing detailed information on the resources (CPU, MEMORY and TIME) usage of each process in the different pipeline steps. \u003cbr\u003e\nThe \u003ccode\u003eresults\u003c/code\u003e directory structure within \u003ccode\u003eh3avarcall\u003c/code\u003e repository can be summarized as below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eh3avarcall\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--results\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--1_QC\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_R1.fastqc.html .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_R1.fastqc.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_R2.fastqc.html .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_R1.fastqc.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--2_Read_Trimming\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e.1P.fastq.gz .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.1P.fastq.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e.2P.fastq.gz .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.2P.fastq.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--3_Read_Alignment\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_md.recal.bam .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_md.recal.bam\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e_md.recal.bai .. \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003esample_N\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e_md.recal.bai\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--4_Variant_Calling\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--chr_1_genotyped.vcf.gz .. chr_22_genotyped.vcf.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--chr_1_genotyped.vcf.gz.tbi .. chr_22_genotyped.vcf.gz.tbi\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--5_Variant_Filtering\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--workflow_report\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_timeline.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_workflow.dot\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--h3avarcall_trace.txt\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--genome.SNP-recal.vcf.gz\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--genome.SNP-recal.vcf.gz.tbi\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--MultiQC\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--multiqc_data\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--multiqc_report.html\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--work\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eThere\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003es a lot of folders here! Lets not worry about them for today!\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e##We\u0027re working on further improving the pipleine and the associated documentation, feel free to share comments and suggestions!\u003c/p\u003e\n", - "stargazers_count": 5, - "subscribers_count": 9, + "stargazers_count": 6, + "subscribers_count": 7, "topics": [], - "updated_at": 1725654312.0 + "updated_at": 1693047726.0 }, { "data_format": 2, "description": null, "filenames": [ - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/Singularity.template", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/od/Singularity.ODBenchmarks", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.ml_mmfb", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.PyBNN", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.SupportVectorMachine", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.XGBoostBenchmark", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.TabularBenchmarks", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_101", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_201", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_1shot1", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/rl/Singularity.learnaBenchmark", - "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/rl/Singularity.Cartpole" + "Singularity.1.0", + "Singularity" ], - "full_name": "hvarfner/JointEntropySearch", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJoint Entropy Search for Maximally-Informed Bayesian Optimization\u003c/h1\u003e\u003ca id=\"user-content-joint-entropy-search-for-maximally-informed-bayesian-optimization\" class=\"anchor\" aria-label=\"Permalink: Joint Entropy Search for Maximally-Informed Bayesian Optimization\" href=\"#joint-entropy-search-for-maximally-informed-bayesian-optimization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official repository for Joint Entropy Search for Maximally-Informed Bayesian Optimization. We developed our code by building on Max-value Entropy Search (Wang and Jegelka, 2017) which in turn built on Predictive Entropy Search (Hernandez-Lobato et al., 2014), which was developed upon GPstuff (Vanhatalo et al., 2013). To keep the repository as trim and clean as possible, some of the examples and methods from previous work have been removed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSystem Requirements\u003c/h2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-label=\"Permalink: System Requirements\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll code has been tested with MATLAB 2021a. After installing the conda environment provided in \u003ccode\u003ejes.yml\u003c/code\u003e, add the required libraries by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/conda/envs/jes/lib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand everything should be up and running.\u003c/p\u003e\n\u003cp\u003eWhile the required mex file is included, it may need to be re-compiled. To do so, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd utils\nmex chol2invchol.c -lgsl -lblas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLastly, to run experiments, do one of the following:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFor GP sample tasks (where the hyperparameters of the surrogate model are fixed):\u003c/h4\u003e\u003ca id=\"user-content-for-gp-sample-tasks-where-the-hyperparameters-of-the-surrogate-model-are-fixed\" class=\"anchor\" aria-label=\"Permalink: For GP sample tasks (where the hyperparameters of the surrogate model are fixed):\" href=\"#for-gp-sample-tasks-where-the-hyperparameters-of-the-surrogate-model-are-fixed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"gp_task(path, seed, approach, dim);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003epath\u003c/code\u003e is the path to the experiment to run, \u003ccode\u003eseed\u003c/code\u003e is the seed to run, \u003ccode\u003eapproach\u003c/code\u003e is the acquisition function in question, and \u003ccode\u003edim\u003c/code\u003e, somewhat exessively, is the dimensionality of the problem. For example, one can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"gp_task(\u0027gp/gp_2dim.py\u0027, 42, \u0027JES\u0027, 2);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFor all other tasks, use \u003ccode\u003esynthetic_task\u003c/code\u003e:\u003c/h4\u003e\u003ca id=\"user-content-for-all-other-tasks-use-synthetic_task\" class=\"anchor\" aria-label=\"Permalink: For all other tasks, use synthetic_task:\" href=\"#for-all-other-tasks-use-synthetic_task\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"synthetic_task(path, seed, approach, dim);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo to run Hartmann (6D):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"synthetic_task(\u0027synthetic/hartmann6.py\", 37, \u0027MES\u0027, 6);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEvery experiment is automatically stored in a csv in experiments/results. The recommended points, which are needed for inference regret, are all evaluated \u003cem\u003eafter\u003c/em\u003e the full run is finished. These queries are appended to the same csv.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAvailable options\u003c/h2\u003e\u003ca id=\"user-content-available-options\" class=\"anchor\" aria-label=\"Permalink: Available options\" href=\"#available-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThanks to Wang \u0026amp; Jegelka (2017), this repository comes equipped with the following acquisition functions. MES-G (shortened to just MES) and EI were used in this paper to benchmark against.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMax-value Entropy Search with Gumbel sampling (MES-G) by Wang \u0026amp; Jegelka, 2017;\u003c/li\u003e\n\u003cli\u003eMax-value Entropy Search with random features (MES-R) by Wang \u0026amp; Jegelka, 2017;\u003c/li\u003e\n\u003cli\u003eOptimization as estimation (EST) by Wang et al., 2016.\u003c/li\u003e\n\u003cli\u003eGaussian process upper confidence bound (GP-UCB) by Auer, 2002; Srinivas et al., 2010;\u003c/li\u003e\n\u003cli\u003eProbability of improvement (PI) by Kushner, 1964;\u003c/li\u003e\n\u003cli\u003eExpected improvement (EI) by Mockus, 1974\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe repository builds on \u003ca href=\"https://bitbucket.org/jmh233/codepesnips2014\" rel=\"nofollow\"\u003epredictive entropy search\u003c/a\u003e (Hern\u00e1ndez-Lobato et al., 2014).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e--Anonymous author(s)--. Joint Entropy Search for Maximally-Informed Bayesian Optimization. Under review.\u003c/li\u003e\n\u003cli\u003eWang, Zi and Jegelka, Stefanie. Max-value Entropy Search for Efficient Bayesian Optimization. International Conference on Machine Learning (ICML), 2017.\u003c/li\u003e\n\u003cli\u003eAuer, Peter. Using confidence bounds for exploitationexploration tradeoffs. Journal of Machine Learning Research, 3:397\u2013422, 2002.\u003c/li\u003e\n\u003cli\u003eSrinivas, Niranjan, Krause, Andreas, Kakade, Sham M, and Seeger, Matthias. Gaussian process optimization in the bandit setting: No regret and experimental design. In International Conference on Machine Learning (ICML), 2010.\u003c/li\u003e\n\u003cli\u003eKushner, Harold J. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. Journal of Fluids Engineering, 86(1):97\u2013106, 1964.\u003c/li\u003e\n\u003cli\u003eMockus, J. On Bayesian methods for seeking the extremum. In Optimization Techniques IFIP Technical Conference, 1974.\u003c/li\u003e\n\u003cli\u003eWang, Zi, Zhou, Bolei, and Jegelka, Stefanie. Optimization as estimation with Gaussian processes in bandit settings. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.\u003c/li\u003e\n\u003cli\u003eCatto, Erin. Box2d, a 2D physics engine for games. \u003ca href=\"http://box2d.org\" rel=\"nofollow\"\u003ehttp://box2d.org\u003c/a\u003e, 2011.\u003c/li\u003e\n\u003cli\u003ePybox2d, 2D Game Physics for Python. \u003ca href=\"http://github.com/pybox2d/pybox2d\"\u003ehttp://github.com/pybox2d/pybox2d\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eHern\u00e1ndez-Lobato, Jos\u00e9 Miguel, Hoffman, Matthew W, and Ghahramani, Zoubin. Predictive entropy search for efficient global optimization of black-box functions. In Advances in Neural Information Processing Systems (NIPS), 2014. \u003ca href=\"https://bitbucket.org/jmh233/codepesnips2014\" rel=\"nofollow\"\u003ehttps://bitbucket.org/jmh233/codepesnips2014\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHennig, Philipp and Schuler, Christian J. Entropy search for information-efficient global optimization. Journal of Machine Learning Research, 13:1809\u20131837, 2012. \u003ca href=\"http://www.probabilistic-optimization.org/Global.html\" rel=\"nofollow\"\u003ehttp://www.probabilistic-optimization.org/Global.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eJarno Vanhatalo, Jaakko Riihim\u00e4ki, Jouni Hartikainen, Pasi Jyl\u00e4nki, Ville Tolvanen, Aki Vehtari. GPstuff: Bayesian Modeling with Gaussian Processes. Journal of Machine Learning Research, 14(Apr):1175-1179, 2013.\u003c/li\u003e\n\u003cli\u003eKandasamy, Kirthevasan, Schneider, Jeff, and Poczos, Barnabas. High dimensional Bayesian optimisation and bandits via additive models. In International Conference on Machine Learning (ICML), 2015.\u003c/li\u003e\n\u003cli\u003eWang, Zi, Li, Chengtao, Jegelka, Stefanie, and Kohli, Pushmeet. Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. International Conference on Machine Learning (ICML), 2017.\u003c/li\u003e\n\u003cli\u003eWestervelt, Eric R, Grizzle, Jessy W, Chevallereau, Christine, Choi, Jun Ho, and Morris, Benjamin. Feedback control of dynamic bipedal robot locomotion, volume 28. CRC press, 2007.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "roveri-marco/optic", + "latest_release": "1.0", "stargazers_count": 6, "subscribers_count": 1, "topics": [], - "updated_at": 1728040124.0 + "updated_at": 1678867453.0 }, { "data_format": 2, "description": null, "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "ZisongXu/trackObjectWithPF", + "full_name": "Lizhen0909/LSHVec", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePhysics Based Particle Filtering\u003c/h1\u003e\u003ca id=\"user-content-physics-based-particle-filtering\" class=\"anchor\" aria-label=\"Permalink: Physics Based Particle Filtering\" href=\"#physics-based-particle-filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official implementation of our paper \"Real-Time Physics-Based Object Pose Tracking during Non-Prehensile Manipulation\".\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbstract:\u003c/strong\u003e We propose a method to track the 6D pose of an object over time, while the object is under non-prehensile manipulation by a robot. At any given time during the manipulation of the object, we assume access to the robot joint controls and an image from a camera. We use the robot joint controls to perform a physics-based prediction of how the object might be moving. We then combine this prediction with the observation coming from the camera, to estimate the object pose as accurately as possible. We use a particle filtering approach to combine the control information with the visual information. We compare the proposed method with two baselines: (i) using only an image-based pose estimation system at each time-step, and (ii) a particle filter which does not perform the computationally expensive physics predictions, but assumes the object moves with constant velocity. Our results show that making physics-based predictions is worth the computational cost, resulting in more accurate tracking, and estimating object pose even when the object is not clearly visible to the camera.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSupplementary Video:\u003c/h1\u003e\u003ca id=\"user-content-supplementary-video\" class=\"anchor\" aria-label=\"Permalink: Supplementary Video:\" href=\"#supplementary-video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClick to watch the video.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=EMBFYzkno64\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a948dd0caa93d86bfc769e83a9dfb0c6157e71d5a943f64d2fe8ea4802bdf315/68747470733a2f2f692e7974696d672e636f6d2f76692f454d4246597a6b6e6f36342f6d617872657364656661756c742e6a7067\" alt=\"Watch the video\" data-canonical-src=\"https://i.ytimg.com/vi/EMBFYzkno64/maxresdefault.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBrief Description:\u003c/h1\u003e\u003ca id=\"user-content-brief-description\" class=\"anchor\" aria-label=\"Permalink: Brief Description:\" href=\"#brief-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe propose a method to track the pose of an object over time, by using the image from the camera, and the particles in the physical engine. Although sometimes the camera cannot see the object clearly, our method can still track the pose of the object.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuick Setup:\u003c/h1\u003e\u003ca id=\"user-content-quick-setup\" class=\"anchor\" aria-label=\"Permalink: Quick Setup:\" href=\"#quick-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eBuild Container\u003c/strong\u003e (This project uses singularity container to support all the code)\u003c/p\u003e\n\u003cp\u003ePlease enter into the main folder and run \u003ccode\u003e./build.sh\u003c/code\u003e in Ubuntu20 terminal to build the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDownload Rosbags\u003c/strong\u003e (For running demos only)\u003c/p\u003e\n\u003cp\u003eDownload \u003ca href=\"https://drive.google.com/drive/folders/13EbCuu231izDbmrcIeyjeQlJSPJL1qWW?usp=sharing\" rel=\"nofollow\"\u003ethe rosbags\u003c/a\u003e and save them to the \u003ccode\u003erosbag\u003c/code\u003e folder, i.e., \u003ccode\u003e~/rosbag/\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning Code\u003c/h1\u003e\u003ca id=\"user-content-running-code\" class=\"anchor\" aria-label=\"Permalink: Running Code\" href=\"#running-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStart Container\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the terminal, enter into the main file and run \u003ccode\u003e./run.sh\u003c/code\u003e, and then you can see \u003ccode\u003e[TrackObjectWithPF] Singularity\u0026gt; ~ $\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStart ROS Master\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ roscore\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eUsing Simulation Time\u003c/strong\u003e (For running demos only)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ rosparam set use_sim_time true\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEdit Config Information\u003c/strong\u003e (if desired) in \u003ccode\u003e~/catkin_ws/src/PBPF/config/parameter_info.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eerr_file\u003c/code\u003e: Name of the folder where the error.csv file is saved\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egazebo_flag\u003c/code\u003e: Use gazebo or not (True/False)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eobject_name_list\u003c/code\u003e: List of target objects names ([\"cracker\", \"soup\", ...])\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eobject_num\u003c/code\u003e: Number of target objects tracked\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eother_obj_num\u003c/code\u003e: Number of other objects\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eoto_name_list\u003c/code\u003e: List of other objects names\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eotob_name_list\u003c/code\u003e: List of other obstacles names\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparticle_num\u003c/code\u003e: Number of particles\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epick_particle_rate\u003c/code\u003e: Percentage of particles selected as DOPE poses\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erobot_num\u003c/code\u003e: Number of robot\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun_alg_flag\u003c/code\u003e: Name of algorithm (PBPF/CVPF)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etask_flag\u003c/code\u003e: Name of task (\u00271\u0027/\u00272\u0027/\u00273\u0027/\u00274\u0027)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eupdate_style_flag\u003c/code\u003e: Name of the method used (time/pose)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eversion\u003c/code\u003e: whether to use ray tracing (old/multiray)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStart Running\u003c/strong\u003e (For running demos only)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./automated_experiments.sh\u003c/code\u003e (Remember to change the directory of some files)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStart Running\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ rosrun PBPF Physics_Based_Particle_Filtering.py\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eVisualization Window\u003c/strong\u003e (For visualizing only)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ rosrun PBPF Visualisation_World.py\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRecord Error\u003c/strong\u003e (For recording error only)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ rosrun PBPF RecordError.py _\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData\u003c/h1\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-label=\"Permalink: Data\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll experimental data and figures of the results are placed in the \u003ccode\u003e~/data/\u003c/code\u003e. All scenes of rosbags can be downloaded through the link blow: \u003ca href=\"https://drive.google.com/drive/folders/13EbCuu231izDbmrcIeyjeQlJSPJL1qWW?usp=sharing\" rel=\"nofollow\"\u003eRosbags for each scene of different objects\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-lshvec-a-vector-representation-of-dna-sequences-using-locality-sensitive-hashing\" class=\"anchor\" aria-hidden=\"true\" href=\"#lshvec-a-vector-representation-of-dna-sequences-using-locality-sensitive-hashing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLSHVec: A Vector Representation of DNA Sequences Using Locality Sensitive Hashing\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-july-2021-checkout-lshvec-upcxx-which-is-a-pure-c-implementation\" class=\"anchor\" aria-hidden=\"true\" href=\"#july-2021-checkout-lshvec-upcxx-which-is-a-pure-c-implementation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJuly 2021: checkout \u003ca href=\"https://github.com/bochen0909/lshvec-upcxx\"\u003elshvec-upcxx\u003c/a\u003e which is a pure c++ implementation.\u003c/h3\u003e\n\u003ch2\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-hidden=\"true\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary\u003c/h2\u003e\n\u003cp\u003eLSHVec is a k-mer/sequence embedding/classfication software which extends \u003ca href=\"https://fasttext.cc/\" rel=\"nofollow\"\u003eFastText\u003c/a\u003e . It applies LSH (Locality Sensitive Hashing) to reduce the size of k-mer vocabulary and improve the performance of embedding.\u003c/p\u003e\n\u003cp\u003eBesides building from source code, LSHVec can run using docker or singularity.\u003c/p\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://www.biorxiv.org/content/10.1101/726729v1\" rel=\"nofollow\"\u003eA Vector Representation of DNA Sequences Using Locality Sensitive Hashing\u003c/a\u003e for the idea and experiments.\u003c/p\u003e\n\u003cp\u003eThere are also some pretained models that can be used, please see \u003ca href=\"https://github.com/Lizhen0909/PyLSHvec/blob/master/README.md\"\u003ePyLSHvec\u003c/a\u003e for details.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eHere is the environment I worked on. Other versions may also work. Python 3 should work, but I don\u0027t use it a lot.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLinux, gcc with C++11\u003c/li\u003e\n\u003cli\u003ePython 2.7 or Python 3.6 or 3.7\n\u003cul\u003e\n\u003cli\u003ejoblib 0.12.4\u003c/li\u003e\n\u003cli\u003etqdm 4.28.1\u003c/li\u003e\n\u003cli\u003enumpy 1.15.0\u003c/li\u003e\n\u003cli\u003epandas 0.23.4\u003c/li\u003e\n\u003cli\u003esklearn 0.19.1 (only for evaluation)\u003c/li\u003e\n\u003cli\u003eMulticoreTSNE (only for visualization)\u003c/li\u003e\n\u003cli\u003ecython 0.28.5\u003c/li\u003e\n\u003cli\u003ecsparc (included)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild from Source\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eclone from git\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://LizhenShi@bitbucket.org/LizhenShi/lshvec.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd lshvec\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003einstall csparc which wraps a c version of k-mer generator I used in another project\u003c/p\u003e\n\u003cp\u003efor python 2.7\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install pysparc-0.1-cp27-cp27mu-linux_x86_64.whl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eor for python 3.6\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install pysparc-0.1-cp36-cp36m-linux_x86_64.whl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eor for python 3.7\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install pysparc-0.1-cp37-cp37m-linux_x86_64.whl\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003emake\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-jupyter-notebook-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter-notebook-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter Notebook Examples\u003c/h2\u003e\n\u003cp\u003eA toy example, which is laptop friendly and should finish in 10 minutes, can be found in \u003ca href=\"notebook/Tutorial_Toy_Example.ipynb\"\u003eTutorial_Toy_Example.ipynb\u003c/a\u003e. Because of randomness the result may be different.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"notebook/Tutorial_Toy_Example.png\"\u003e\u003cimg src=\"notebook/Tutorial_Toy_Example.png\" alt=\"Tutorial_Toy_Example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA practical example which uses ActinoMock Nanopore data can be found at \u003ca href=\"notebook/Tutorial_ActinoMock_Nanopore.ipynb\"\u003eTutorial_ActinoMock_Nanopore.ipynb\u003c/a\u003e. The notebook ran on a 16-core 64G-mem node and took a few hours (I think 32G mem should work too).\u003c/p\u003e\n\u003cp\u003e\u200b\t\t\t\t\t\t \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"notebook/Tutorial_ActinoMock_Nanopore.png\"\u003e\u003cimg src=\"notebook/Tutorial_ActinoMock_Nanopore.png\" alt=\"Tutorial_ActinoMock_Nanopore\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-command-line-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#command-line-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand line options\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-fastqtoseqpy\" class=\"anchor\" aria-hidden=\"true\" href=\"#fastqtoseqpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efastqToSeq.py\u003c/h3\u003e\n\u003cp\u003econvert a fastq file to a seq file\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython fastqToSeq.py -i \u0026lt;fastq_file\u0026gt; -o \u0026lt;out seq file\u0026gt; -s \u0026lt;1 to shuffle, 0 otherwise\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-hashseqpy\" class=\"anchor\" aria-hidden=\"true\" href=\"#hashseqpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehashSeq.py\u003c/h3\u003e\n\u003cp\u003eEncode reads in a seq file use an encoding method.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython hashSeq.py -i \u0026lt;seq_file\u0026gt; --hash \u0026lt;fnv or lsh\u0026gt; -o \u0026lt;outfile\u0026gt; [-k \u0026lt;kmer_size\u0026gt;] [--n_thread \u0026lt;n\u0026gt;] [--hash_size \u0026lt;m\u0026gt;] [--batch_size \u0026lt;n\u0026gt;] [--bucket \u0026lt;n\u0026gt;] [--lsh_file \u0026lt;file\u0026gt;] [--create_lsh_only]\n\n --hash_size \u0026lt;m\u0026gt;: only used by lsh which defines 2^m bucket.\n --bucket \u0026lt;n\u0026gt;: number of bucket for hash trick, useless for onehot.\n \t\t\t\t For fnv and lsh it limits the max number of words.\n \t\t\t\t For lsh the max number of words is min(2^m, n).\n --batch_size \u0026lt;b\u0026gt;: how many reads are processed at a time. A small value uses less memory.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-lshvec\" class=\"anchor\" aria-hidden=\"true\" href=\"#lshvec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elshvec\u003c/h3\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://fasttext.cc/docs/en/options.html\" rel=\"nofollow\"\u003efasttext options\u003c/a\u003e. However note that options of \u003ccode\u003ewordNgrams\u003c/code\u003e, \u003ccode\u003eminn\u003c/code\u003e,\u003ccode\u003emaxn\u003c/code\u003e does not work with lshvec.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-of-docker-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-of-docker-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample of Docker Run\u003c/h2\u003e\n\u003cp\u003ePull from docker hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull lizhen0909/lshvec:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAssume \u003ccode\u003edata.fastq\u003c/code\u003e file is in folder \u003ccode\u003e/path/in/host\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003econvert fastq to a seq file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/in/host:/host lshvec:latest bash -c \"cd /host \u0026amp;\u0026amp; fastqToSeq.py -i data.fastq -o data.seq\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecreate LSH:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/in/host:/host lshvec:latest bash -c \"cd /host \u0026amp;\u0026amp; hashSeq.py -i data.seq --hash lsh -o data.hash -k 15\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003erun lshvec:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/in/host:/host lshvec:latest bash -c \"cd /host \u0026amp;\u0026amp; lshvec skipgram -input data.hash -output model\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-of-singularity-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-of-singularity-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample of Singularity Run\u003c/h2\u003e\n\u003cp\u003eWhen running using Singularity, it is probably in an HPC environment. The running is similar to docker. However depending on the version of singularity, commands and paths might be different, especially from 2.x to 3.x. Here is an example for version 2.5.0.\u003c/p\u003e\n\u003cp\u003eAlso it is better to specify number of threads, otherwise max number of cores will be used which is not desired in HPC environment.\u003c/p\u003e\n\u003cp\u003ePull from docker hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name lshvec.sif shub://Lizhen0909/LSHVec\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePut \u003ccode\u003edata.fastq\u003c/code\u003e file is in host \u003ccode\u003e/tmp\u003c/code\u003e, since Singularity automatically mount \u003ccode\u003e/tmp\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003econvert fastq to a seq file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/lshvec.sif bash -c \"cd /tmp \u0026amp;\u0026amp; fastqToSeq.py -i data.fastq -o data.seq\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecreate LSH:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/lshvec.sif bash -c \"cd /tmp \u0026amp;\u0026amp; hashSeq.py -i data.seq --hash lsh -o data.hash -k 15 --n_thread 12\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003erun lshvec:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/lshvec.sif bash -c \"cd /tmp \u0026amp;\u0026amp; lshvec skipgram -input data.hash -output model -thread 12\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003elshvec\u003c/code\u003e gets stuck at \u003ccode\u003eRead xxxM words\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSearch \u003ccode\u003eMAX_VOCAB_SIZE\u003c/code\u003e in the source code and change it to a bigger one. When a word\u0027s index is bigger than that number, a loop is carried to query it, which is costly. The number is 30M in FastText which is good for languages. But it is too small for k-mers. The number has been already increased to 300M in FastSeq. But for large and/or high-error-rate data, it may be still not enough.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eI have big data\u003c/p\u003e\n\u003cp\u003ehashSeq reads all data into memory to sample k-mers for hyperplanes. If data is too big it may not fit into memory. One can\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eTry sampling. DNA reads generally have high coverage. Such high coverage may not be necessary.\u003c/li\u003e\n\u003cli\u003eOr use \u003ccode\u003ecreate_hash_only\u003c/code\u003e to create lsh on a small (sampled) data; then split your data into multiple files and run hashSeq with \u003ccode\u003elsh_file\u003c/code\u003e option on many nodes.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecore dumped when hashing\u003c/p\u003e\n\u003cp\u003eError like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eterminate called after throwing an instance of \u0027std::out_of_range\u0027\nwhat(): map::at\nAborted (core dumped)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003emostly because a sequence contains characters other than ACGTN. So please convert non-ACGT characters to N\u0027s.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eInherit license from FastText which is BSD License\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 1, + "subscribers_count": 2, + "topics": [ + "locality-sensitive-hashing", + "sequence-vector", + "classfication" + ], + "updated_at": 1675318662.0 + }, + { + "data_format": 2, + "description": "Singularity images for the University of Arizona High Performance Computing systems", + "filenames": [ + "Singularity.centos7-python3.7-transformers4.1.1" + ], + "full_name": "clulab/hpc-ml", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc-ml-machine-learning-singularity-images-for-ua-hpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-ml-machine-learning-singularity-images-for-ua-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehpc-ml: Machine-learning Singularity images for UA HPC\u003c/h1\u003e\n\u003cp\u003eThe recipes in this repository are designed for the University of Arizona High Performance Computing systems.\nThey build Singularity images that include common machine learning libraries including scikit-learn, tensorflow, keras, torch, as well as the Nvidia CUDNN.\nTo activate singularity on hpc, use \"module load singularity\", without quotes. And then you can execute singularity commands.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThis GitHub repository is connected to Singularity Hub (\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/\u003c/a\u003e).\nPlease see their documentation on \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/interact\" rel=\"nofollow\"\u003einteracting with Singularity images from Singularity Hub\u003c/a\u003e.\nNote that there are \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/regulatory/limits\" rel=\"nofollow\"\u003ehard limits on how many times a container can be pulled from Singularity Hub each week\u003c/a\u003e, so please make sure that you always \u003ccode\u003esingularity pull\u003c/code\u003e to get a local copy, for example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://clulab/hpc-ml:centos7-python3.7-transformers4.1.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat will download a Singularity image named \u003ccode\u003ehpc-ml_centos7-python3.7-transformers4.1.1.sif\u003c/code\u003e that you can then use with other Singularity commands.\u003c/p\u003e\n", + "stargazers_count": 6, + "subscribers_count": 27, "topics": [], - "updated_at": 1711487031.0 + "updated_at": 1653464623.0 }, { "data_format": 2, - "description": "Stuff related to integrating EasyBuild \u0026 Singularity", + "description": "Oxford Nanopore reference mapping, taxonomic classification, de novo assembly workflow primarily for viral sequence data", "filenames": [ - "Singularity.Easybuild-1.0", - "Singularity.Easybuild-CentOS-7", - "apps/a/anaconda3/Singularity.Anaconda3-5.3.0", - "apps/a/ant/Singularity.ant-1.9.7-Java-1.8.0_92", - "apps/a/anaconda2/Singularity.Anaconda2-5.3.0", - "apps/ccl/Singularity.CCL-1.11.5", - "apps/g/go/Singularity.Go-1.8.1", - "apps/g/GCCcore/Singularity.GCCcore-8.3.0", - "apps/m/m4/Singularity.M4-1.4.17", - "apps/m/m4/Singularity.M4-1.4.18", - "apps/q/qtop/Singularity.qtop-53-1", - "apps/c/ccl/Singularity.CCL-1.11.5", - "apps/c/cmake/Singularity.CMake-3.9.1", - "apps/c/cmake/Singularity.CMake-3.9.6", - "apps/j/java/Singularity.Java-1.8.0_92", - "apps/b/bzip2/Singularity.bzip2-1.0.6", - "apps/b/binutils/Singularity.binutils-2.28" + "singularity/Singularity.1.1.0", + "singularity/Singularity.1.0.0" ], - "full_name": "easybuilders/eb-singularity", + "full_name": "peterk87/nf-virontus", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-peterk87nf-virontus\" class=\"anchor\" aria-hidden=\"true\" href=\"#peterk87nf-virontus\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epeterk87/nf-virontus\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eVirontus viral Oxford Nanopore sequence analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/peterk87/nf-virontus\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca2382eaedc143481936b2847287dfadcc9737054d5f078007bb7dcc0f5474bb/68747470733a2f2f7472617669732d63692e6f72672f70657465726b38372f6e662d7669726f6e7475732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/peterk87/nf-virontus.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1a7b876aea11f8490a824ae9376e2b0108e8b19b424effa1b67d0a7afcfe096e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413531392e31302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aabd08395c6e4571b75e7bf1bbd8ac169431a98dd75f3611f89e992dd0fcb477/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/peterk87/nf-virontus\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/701f9cda36830e80f60b8cb5e6108a4b9fdfcb6f09698b97e11a87e15dd71a93/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f70657465726b38372f6e662d7669726f6e7475732e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/peterk87/nf-virontus.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4297\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#peterk87nf-virontus\"\u003epeterk87/nf-virontus\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#1-install-nextflow\"\u003e1) Install \u003c/a\u003e\u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#2-install-singularity\"\u003e2) Install \u003c/a\u003e\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3-install-virontus\"\u003e3) Install Virontus\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#preparing-your-data\"\u003ePreparing your data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#recommended-steps\"\u003eRecommended Steps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#example\"\u003eExample\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTOC created by \u003ca href=\"https://github.com/ekalinin/github-markdown-toc\"\u003egh-md-toc\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe Virontus pipeline is for the analysis of viral shotgun and amplicon Oxford Nanopore sequence data. Given basecalled (and demultiplexed) Nanopore reads, Virontus produces one or more consensus sequences from read mapping with \u003ca href=\"https://github.com/lh3/minimap2\"\u003eMinimap2\u003c/a\u003e and variant calling with \u003ca href=\"https://github.com/nanoporetech/medaka\"\u003eMedaka\u003c/a\u003e and \u003ca href=\"https://www.nature.com/articles/s41467-019-12493-y\" rel=\"nofollow\"\u003eLongshot\u003c/a\u003e results with respect to one or more reference sequences. For amplicon sequencing, the user should provide a BED file containing primer coordinates with respect to a reference sequence so that the primer sequences can be trimmed using \u003ca href=\"https://github.com/andersen-lab/ivar\"\u003eiVar\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOptionally, Virontus will perform taxonomic classification with \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e and \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e if index paths are provided. Reads can be filtered by taxonomic classification. By default viral and unclassified reads are filtered.\u003c/p\u003e\n\u003cp\u003eDe novo assembly with \u003ca href=\"https://github.com/rrwick/Unicycler\"\u003eUnicycler\u003c/a\u003e can be optionally performed if desired (specify \u003ccode\u003e--do_unicycler_assembly\u003c/code\u003e when running Virontus). If taxonomic classification is performed then taxonomically filtered reads will be assembled, otherwise all reads will be used for assembly.\u003c/p\u003e\n\u003cp\u003eThe Virontus pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eYou will need to install \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e in order to run the Virontus pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is recommended for portable and reproducible execution of the pipeline with the \u003ccode\u003e-profile singularity\u003c/code\u003e command-line argument.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-install-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1) Install \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/h4\u003e\n\u003cp\u003eIf you have \u003ca href=\"https://conda.io/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e installed, you can install \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install -c bioconda -c conda-forge nextflow\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2) Install \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\n\u003c/h4\u003e\n\u003cp\u003eInstalling \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is optional but recommended for portability and reproducibility of results.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-3-install-virontus\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-install-virontus\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3) Install Virontus\u003c/h4\u003e\n\u003cp\u003eNextflow will automatically download the latest version of Virontus. You can show the Virontus help message with usage information with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run peterk87/nf-virontus --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eShow usage information with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run peterk87/nf-virontus --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should see the following\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 20.01.0\nLaunching `main.nf` [awesome_pauling] - revision: 9aeb19496b\nWARN: DSL 2 IS AN EXPERIMENTAL FEATURE UNDER DEVELOPMENT -- SYNTAX MAY CHANGE IN FUTURE RELEASE\n==================================================================\npeterk87/nf-virontus ~ version 1.1.0\n==================================================================\n\n Git info: null - null [null]\n\nUsage:\nGiven some barcoded and demultiplexed reads, the typical command for running the pipeline is as follows:\n\n nextflow run peterk87/nf-virontus \\\n --reads \"reads/*.fastq\" \\\n --outdir results \\\n --ref_fasta refs.fa \\\n -profile singularity # recommended to run with Singularity\n\nThe above assumes that you have a Centrifuge DB and Kraken2 DB located at\n/opt/DB/centrifuge/nt-2018-03-03/nt and /opt/DB/kraken2/standard2,\nrespectively, OR that you have set $CENTRIFUGE_DB and $KRAKEN2_DB env\nvariables. It also assumes that you have Singularity installed on your\nlocal machine and will automatically pull and use the Singularity image for\nthis workflow from Singularity-Hub.org.\n\nNOTE: For best results, please ensure you have Singularity installed prior to running this workflow.(https://sylabs.io/guides/3.3/user-guide/quick_start.html#quick-installation-steps)\n\nNote:\nThe argument supplied to \"--reads\" must be quoted if using \"*\" and other\ncharacters and symbols that could be shell expanded!\n\nMandatory Options:\n --reads Input reads directory and pattern (default: \"reads/*.fastq\")\n --ref_fasta Reference genomes multiFASTA file (one or more references\n in a single file) (default: \"./refs.fasta\")\nAmplicon Sequencing Options:\n --bedfile BED format file with amplicon sequencing primers info (optional).\n Produced as output from PrimalScheme.\nConsensus Generation Options:\n --low_coverage Low coverage threshold (default=3).\n Replace consensus sequence positions below this depth\n threshold with a low coverage character\n (see --low_cov_char)\n --no_coverage No coverage threshold (default=0).\n Replace consensus sequence positions with less than or\n equal this depth with a no coverage character\n (see --no_cov_char)\n --low_cov_char Low coverage character (default=\"N\")\n --no_cov_char No coverage character (default=\"-\")\n\nCluster Options:\n --slurm_queue Name of SLURM queue to run workflow on; use with -profile slurm\n\n\nTaxonomic Classification Options:\n --centrifuge_db Path to Centrifuge DB and prefix. If not specified, will\n try to get from $CENTRIFUGE_DB env variable or see if\n \"/opt/DB/centrifuge/nt-2018-03-03/nt\" exists.\n (default: null)\n --kraken2_db Path to Kraken2 DB directory. . If not specified, will\n try to get from $KRAKEN2_DB env variable or see if\n \"/opt/DB/kraken2/standard2\" exists.\n (default: null)\n --taxids Taxonomic IDs to filter reads by. Multiple taxids should\n be delimited by commas (`--taxids 1,2,3`). To disable\n filtering of reads based on taxids, do not provide a\n value for the `--taxids` argument:\n `nextflow run ... --taxids --reads ...`\n (default: 10239 (Viruses))\n --exclude_unclassified_reads Exclude unclassified reads from taxonomic\n classification filtered reads (default: false)\n\nDe Novo Assembly Options:\n --do_unicycler_assembly Assemble filtered reads using Unicycler? (default: false)\n\nOther Options:\n --outdir The output directory where the results will be saved\n (default: results)\n -w/--work-dir The temporary directory where intermediate data will be\n saved (default: ./work)\n -profile Configuration profile to use. [standard, singularity,\n conda, slurm] (default \u0027standard\u0027)\n --tracedir Pipeline run info output directory (default:\n results/pipeline_info)\n\nNote:\nIt is recommended that this workflow be executed with Singularity using the\nSingularity profile (`-profile singularity`) for maximum reproducibility and\nease of execution on different platforms.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-preparing-your-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#preparing-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparing your data\u003c/h4\u003e\n\u003cp\u003eIt is assumed that your data has been basecalled using the latest version of ONT Guppy (\u003ccode\u003eguppy_basecaller\u003c/code\u003e/\u003ccode\u003eguppy_basecall_server\u003c/code\u003e) and barcode demultiplexed using \u003ccode\u003eguppy_barcoder\u003c/code\u003e with the appropriate settings for the kits used.\u003c/p\u003e\n\u003cp\u003eAfter basecalling and demultiplexing, it is recommended that all reads belonging to a particular barcode be concatenated together and optionally renamed to represent the sample to which the reads belong. Virontus will extract the sample name for each input reads FASTQ file from the base filename of the FASTQ file (e.g. sample name will be \u003ccode\u003esample\u003c/code\u003e from filename \u003ccode\u003esample1.fastq\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eBelow is an example \u003ccode\u003eguppy_barcoder\u003c/code\u003e command for more lenient barcode demultiplexing:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eguppy_barcoder \\\n -q 0 \\\n --min_score 30 \\\n --detect_mid_strand_barcodes \\\n --allow_inferior_barcodes \\\n --trim_barcodes \\\n -i basecalled-reads/ \\\n -s demuxed-reads \\\n --arrangements_files barcode_arrs_nb12.cfg\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-q 0\u003c/code\u003e to output less files per barcode\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--min_score 30\u003c/code\u003e for a lower barcode score threshold (default: 60)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--detect_mid_strand_barcodes\u003c/code\u003e to detect mid strand barcodes\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--trim_barcodes\u003c/code\u003e to trim barcodes from read sequences\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--arrangements_files\u003c/code\u003e to specify the barcodes used\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e It\u0027s recommended to use the default setting if possible to avoid misassigning reads into the incorrect barcodes.\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-recommended-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#recommended-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecommended Steps\u003c/h5\u003e\n\u003col\u003e\n\u003cli\u003eBasecall reads using Guppy\u003c/li\u003e\n\u003cli\u003eDemultiplex reads using \u003ccode\u003eguppy_barcoder\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConcatenate reads belonging to the same barcode into a single file (\u003ccode\u003ecat barcode01/*.fastq \u0026gt; concat-reads/barcode01.fastq\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e[Optionally] rename concatenated barcoded reads with appropriate sample name (\u003ccode\u003emv concat-reads/barcode01.fastq concat-reads/sample1.fastq\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h4\u003e\n\u003cp\u003eExample command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ nextflow run peterk87/nf-virontus \\\n -resume \\\n -profile singularity \\\n --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ereads/*.fq\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --ref_fasta MN908947.3.fa \\\n --low_coverage 3 \\\n --bedfile nCoV-2019.bed\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat you will see in the terminal:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 20.01.0\nLaunching `../main.nf` [ecstatic_davinci] - revision: 9aeb19496b\nWARN: DSL 2 IS AN EXPERIMENTAL FEATURE UNDER DEVELOPMENT -- SYNTAX MAY CHANGE IN FUTURE RELEASE\n=======================================================\npeterk87/nf-virontus v1.1.0\n=======================================================\nPipeline Name : peterk87/nf-virontus\nPipeline Version : 1.1.0\nRun Name : ecstatic_davinci\nReads : reads/*.fq\nRef Sequences FASTA : MN908947.3.fa\nPrimer Scheme : nCoV-2019.bed\nConsensus No Coverage : \u0026lt;=0X positions replaced with \u0027-\u0027\nConsensus Low Coverage: \u0026lt;3X positions replaced with \u0027N\u0027\nCentrifuge DB : null\nKraken2 DB : null\nTaxids : Filtering for taxids belonging to 10239\nUnicycler Assembly? : No\nMax Memory : 256 GB\nMax CPUs : 48\nMax Time : 10d\nOutput dir : results\nWorking dir : ./work\nContainer Engine : singularity\nContainer : virontus.simg\nCurrent home : /home/pkruczkiewicz\nCurrent user : pkruczkiewicz\nCurrent path : ./\nScript dir : ./nf-virontus\nConfig Profile : standard\nCommand-Line : nextflow run peterk87/nf-virontus -profile singularity -resume --reads \u0027reads/*.fq\u0027 --ref_fasta MN908947.3.fa --low_coverage 3 --bedfile nCoV-2019.bed\nNextflow version : 20.01.0\n=========================================\nexecutor \u0026gt; local (18)\n[0a/142458] process \u0026gt; REC2FASTA [100%] 1 of 1 \u2714\n[a3/3168c5] process \u0026gt; MAP [100%] 3 of 3 \u2714\n[0d/8a698f] process \u0026gt; IVAR_TRIM [100%] 3 of 3 \u2714\n[76/f82320] process \u0026gt; MAP_STATS [100%] 3 of 3 \u2714\n[cc/de6b36] process \u0026gt; MEDAKA [100%] 3 of 3 \u2714\n[74/058b57] process \u0026gt; LONGSHOT [100%] 3 of 3 \u2714\n[b4/5ed366] process \u0026gt; BCF_FILTER [100%] 3 of 3 \u2714\n[a3/ae8e3a] process \u0026gt; CONSENSUS [ 100%] 3 of 3 \u2714\n[e3/f75ddb] process \u0026gt; COVERAGE_PLOT [100%] 3 of 3 \u2714\n\nPipeline execution summary\nCompleted at: 30-Apr-2020 14:00:11\nDuration : 1m 40s\nCPU hours : 0.1 (58.9% cached)\nSucceeded : 18\nCached : 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample output file tree structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eresults/\n\u251c\u2500\u2500 consensus\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02-MN908947.3.consensus.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04-MN908947.3.consensus.fasta\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 unclassified-MN908947.3.consensus.fasta\n\u251c\u2500\u2500 mapping\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 bamfiles\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02-MN908947.3.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 NB02-MN908947.3.trim.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02-MN908947.3-depths.tsv\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02-MN908947.3.flagstat\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 NB02-MN908947.3.idxstats\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 bamfiles\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04-MN908947.3.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 NB04-MN908947.3.trim.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04-MN908947.3-depths.tsv\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04-MN908947.3.flagstat\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 NB04-MN908947.3.idxstats\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 unclassified\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 bamfiles\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 unclassified-MN908947.3.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 unclassified-MN908947.3.trim.bam \n\u2502\u00a0\u00a0 \u251c\u2500\u2500 unclassified-MN908947.3-depths.tsv\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 unclassified-MN908947.3.flagstat\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 unclassified-MN908947.3.idxstats\n\u251c\u2500\u2500 pipeline_info\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 execution_dag.dot\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 execution_report.html\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 execution_timeline.html\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 execution_trace.txt\n\u251c\u2500\u2500 plots\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 coverage_plot-NB02-VS-MN908947.3-log_scale.pdf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 coverage_plot-NB02-VS-MN908947.3.pdf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 coverage_plot-NB04-VS-MN908947.3-log_scale.pdf\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 coverage_plot-NB04-VS-MN908947.3.pdf\n\u251c\u2500\u2500 refs\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 MN908947.3.fa\n\u2514\u2500\u2500 vcf\n \u251c\u2500\u2500 NB02-MN908947.3.longshot.filt.vcf\n \u251c\u2500\u2500 NB02-MN908947.3.longshot.vcf\n \u251c\u2500\u2500 NB02-MN908947.3.medaka.vcf\n \u251c\u2500\u2500 NB04-MN908947.3.longshot.filt.vcf\n \u251c\u2500\u2500 NB04-MN908947.3.longshot.vcf\n \u251c\u2500\u2500 NB04-MN908947.3.medaka.vcf\n \u251c\u2500\u2500 unclassified-MN908947.3.longshot.filt.vcf\n \u251c\u2500\u2500 unclassified-MN908947.3.longshot.vcf\n \u2514\u2500\u2500 unclassified-MN908947.3.medaka.vcf\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003epeterk87/nf-virontus was originally written by Peter Kruczkiewicz.\u003c/p\u003e\n\u003cp\u003eBootstrapped with \u003ca href=\"https://github.com/nf-core/tools\"\u003enf-core/tools\u003c/a\u003e \u003ccode\u003enf-core create\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThank you to the \u003ca href=\"https://github.com/nf-core/tools\"\u003enf-core/tools\u003c/a\u003e team for a great tool for bootstrapping creation of a production ready Nextflow workflows.\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 6, + "subscribers_count": 3, "topics": [], - "updated_at": 1710285931.0 + "updated_at": 1677780003.0 }, { "data_format": 2, - "description": " Code and documentation supporting Markello et al, 2021, \"Standardizing workflows in imaging transcriptomics with the abagen toolbox\" (Biorxiv)", + "description": "Local Ancestry Inference", "filenames": [ - "container/Singularity" + "singularity/Singularity_defs.def" ], - "full_name": "netneurolab/markello_transcriptome", - "latest_release": "1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eStandardizing workflows in imaging transcriptomics with the \u003ccode\u003eabagen\u003c/code\u003e toolbox\u003c/h1\u003e\u003ca id=\"user-content-standardizing-workflows-in-imaging-transcriptomics-with-the-abagen-toolbox\" class=\"anchor\" aria-label=\"Permalink: Standardizing workflows in imaging transcriptomics with the abagen toolbox\" href=\"#standardizing-workflows-in-imaging-transcriptomics-with-the-abagen-toolbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"What\u0027s in this repository?\"\u003c/h2\u003e\u003ca id=\"user-content-whats-in-this-repository\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;What\u0027s in this repository?\u0026quot;\" href=\"#whats-in-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains data, code, and results for the manuscript \"Standardizing workflows in imaging transcriptomics with the \u003ccode\u003eabagen\u003c/code\u003e toolbox\" by Markello et al. \u003cem\u003eBiorxiv\u003c/em\u003e, 2021.\nWe investigate how variability in processing of the Allen Human Brain Atlas impacts analyses relating gene expression to neuroimaging data and highlight how functionality from the \u003ca href=\"https://github.com/rmarkello/abagen\"\u003e\u003ccode\u003eabagen\u003c/code\u003e\u003c/a\u003e toolbox can help to standardize these workflows.\u003c/p\u003e\n\u003cp\u003eWe\u0027ve tried to document the various aspects of this repository with a whole bunch of README files, so feel free to jump around and check things out.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"Just let me run the things!\"\u003c/h2\u003e\u003ca id=\"user-content-just-let-me-run-the-things\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;Just let me run the things!\u0026quot;\" href=\"#just-let-me-run-the-things\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eItching to just run the analyses?\nYou\u0027ll need to make sure you have installed the appropriate software packages, have access to the HCP, and have downloaded the appropriate data files (check out our \u003ca href=\"https://netneurolab.github.io/markello_transcriptome\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e for more details!).\nOnce you\u0027ve done that, you can get going with the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/netneurolab/markello_transcriptome\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_transcriptome\nconda env create -f environment.yml\nconda activate markello_transcriptome\npip install vibecheck/\nmake all\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to deal with the hassle of creating a new Python environment you can create a Singularity image run things in there:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/netneurolab/markello_transcriptome\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_transcriptome\nbash container/gen_simg.sh\nsingularity run container/markello_transcriptome.simg make all\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote, however, that \u003cstrong\u003ewe don\u0027t recommend re-running our analyses in this manner\u003c/strong\u003e as it will take a \u003cem\u003every\u003c/em\u003e long time to do so!\nInstead, we refer to our \u003ca href=\"https://netneurolab.github.io/markello_transcriptome\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e for more information on the optimal way to reproduce our results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"I\u0027d like more information.\"\u003c/h2\u003e\u003ca id=\"user-content-id-like-more-information\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;I\u0027d like more information.\u0026quot;\" href=\"#id-like-more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want a step-by-step through all the methods + analyses take a look at our \u003ca href=\"https://netneurolab.github.io/markello_transcriptome\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"I have some questions...\"\u003c/h2\u003e\u003ca id=\"user-content-i-have-some-questions\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;I have some questions...\u0026quot;\" href=\"#i-have-some-questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/netneurolab/markello_transcriptome/issues\"\u003eOpen an issue\u003c/a\u003e on this repository and someone will try and get back to you as soon as possible!\u003c/p\u003e\n", + "full_name": "pmonnahan/AncInf", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ancestry-inference\" class=\"anchor\" aria-hidden=\"true\" href=\"#ancestry-inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAncestry Inference\u003c/h1\u003e\n\u003cp\u003eHuman local ancestry inference using \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3738819/\" rel=\"nofollow\"\u003eRFmix\u003c/a\u003e. The basic workflow is to parse the input PLINK file by chromosome, perform reference-based haplotype phasing on the data using \u003ca href=\"https://odelaneau.github.io/shapeit4/\" rel=\"nofollow\"\u003eShapeIt4\u003c/a\u003e, and, finally, perform local ancestry inference with RFMix. More information is provided in the \u003cem\u003ePipeline Overview\u003c/em\u003e below. With the RFMix output, admixture mapping (i.e. associating local ancestry with phenotype) can be accomplished via a separate pipeline found \u003ca href=\"https://github.com/pmonnahan/admixMap\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#snakemake\"\u003eSnakemake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-the-workflow\"\u003eRunning the workflow\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#other-notes\"\u003eOther Notes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#debugging-and-error-reports\"\u003eDebugging and error reports\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pipeline-overview\"\u003ePipeline Overview\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#input-data\"\u003eInput Data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#reference-population\"\u003eReference population\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#phasing\"\u003ePhasing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#local-ancestry-inference\"\u003eLocal Ancestry Inference\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pmonnahan/AncInf/blob/master/Pipeline_DAG.png\"\u003e\u003cimg src=\"https://github.com/pmonnahan/AncInf/raw/master/Pipeline_DAG.png\" alt=\"Pipeline DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-snakemake\" class=\"anchor\" aria-hidden=\"true\" href=\"#snakemake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakemake\u003c/h3\u003e\n\u003cp\u003eThe pipeline is coordinated and run on an HPC (or locally) using \u003cem\u003eSnakemake\u003c/em\u003e. To install snakemake, first create a virtual environment via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load python3/3.6.3_anaconda5.0.1\nconda install -c conda-forge mamba\nmamba create -c conda-forge -c bioconda -n \u0026lt;your_environment_name\u0026gt; snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a new virtual environment and install \u003ccode\u003esnakemake\u003c/code\u003e. Then, activate this environment and perform following installations:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;your_environment_name\u0026gt;\nconda install numpy yaml pandas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnytime you need to run the pipeline, activate this environment beforehand via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;environment_name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you choose not to create an environment, you must ensure that these packages are installed and available for your python installation.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eThe installation of the individual programs used throughout this pipeline can be completely avoid by utilizing a Singularity image. This image is too large to be hosted on Github, although you can find the definitions file used to create the image \u003ca href=\"https://github.com/pmonnahan/AncInf/blob/master/singularity/Singularity_defs.def\"\u003ehere\u003c/a\u003e. Building of images is still not currently supported at MSI, so I used a Vagrant virtual machine, which comes with Singularity pre-configured/installed (\u003ca href=\"https://app.vagrantup.com/singularityware/boxes/singularity-2.4/versions/2.4\" rel=\"nofollow\"\u003ehttps://app.vagrantup.com/singularityware/boxes/singularity-2.4/versions/2.4\u003c/a\u003e). I can also share the img file directly upon request.\u003c/p\u003e\n\u003cp\u003eHowever, in order to utilize the singularity image, \u003cem\u003esingularity\u003c/em\u003e must be installed on the HPC. Currently, the pipeline assumes that \u003cem\u003esingularity\u003c/em\u003e will be available as a module and can be loaded into the environment via the command specified in the config.yml file, in the \u003ccode\u003emodule\u003c/code\u003e entry under the \u003ccode\u003esingularity\u003c/code\u003e section. The default setting will work for MSI at UMN.\u003c/p\u003e\n\u003cp\u003eSingularity settings in config.yml\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity:\n use_singularity: \u0027true\u0027\n image: \u0027/home/pmonnaha/pmonnaha/singularity/AncestryInference.sif\n module: \u0027module load singularity\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the workflow\u003c/h2\u003e\n\u003cp\u003eClone this repository to the location where you want to store the output of the pipeline.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/pmonnahan/AncInf.git rfmix_test\ncd rfmix_test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe critical files responsible for executing the pipeline are contained in the \u003cem\u003e./workflow\u003c/em\u003e subdirectory contained within the cloned repo. They are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSnakefile\u003c/li\u003e\n\u003cli\u003econfig.yml\u003c/li\u003e\n\u003cli\u003ecluster.yaml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003cem\u003eSnakefile\u003c/em\u003e is the primary workhouse of snakemake, which specifies the dependencies of various parts of the pipeline and coordinates execution. No modifications to the \u003cem\u003eSnakefile\u003c/em\u003e are necessary.\u003c/p\u003e\n\u003cp\u003eIn order for the \u003cem\u003eSnakefile\u003c/em\u003e to locate all of the necessary input and correctly submit jobs to the cluster, \u003cstrong\u003eboth\u003c/strong\u003e the \u003cem\u003econfig.yaml\u003c/em\u003e and \u003cem\u003ecluster.yaml\u003c/em\u003e need to be modified. Open these files and change the required entries that are indicated with \u0027MODIFY\u0027. Other fields do not require modification, although this may be desired given the particulars of the run you wish to implement. Details on each entry in the config file (e.g. what the program expects in each entry as well as the purpose of the entry) are provided in the \u003cem\u003ePipeline Overview\u003c/em\u003e at the bottom.\u003c/p\u003e\n\u003cp\u003eThe entire pipeline can be executed on a local machine (not recommended) or on an HPC, and the \u003cem\u003ecluster.yaml\u003c/em\u003e file is required only for the latter. For a local run, change the \u003ccode\u003elocal_run\u003c/code\u003e entry to \u003ccode\u003etrue\u003c/code\u003e under the \u003ccode\u003erun_settings\u003c/code\u003e section of the config file, and launch snakemake from within the parent directory by the simple command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHowever, multiple steps in the pipeline have high resource demands, and so are unlikely to be able to be run locally. This option exists primarily for testing and troubleshooting, so the remainder of the documentation assumes that the pipeline will be executed on an HPC. In order to coordinate the use of the HPC, the following modifications to the snakemake command are required:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --cluster \"sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\" --cluster-config workflow/cluster_yale.yaml -j 32\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere -j specifies the number of jobs that can be submitted at once.\u003c/p\u003e\n\u003cp\u003eOne additional setting in the \u003cem\u003econfig.yml\u003c/em\u003e is needed in order to correctly submit jobs to the HPC. The relevant entries are under the \u003ccode\u003erun_settings\u003c/code\u003e section of the config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun_settings:\n local_run: \u0027false\u0027\n cluster_config: \u0027workflow/cluster_slurm.yaml\u0027\n scheduler: \u0027slurm\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere, it is necessary that the \u003ccode\u003ecluster_config\u003c/code\u003e entry is set to the path of the cluster_slurm.yaml file that will be used in the snakemake command. Also, the scheduler must correspond to the syntax used in the snakemake command and cluster.yaml file. I should point out that these additional changes are needed for responsibly using PLINK within a snakemake framework, and are not directly needed for snakemake. PLINK will attempt to auto-detect available resources upon running regardless of the resources that were requested when the job was submitted. Therefore, we have to read and parse the requested resources in the cluster config file in order for them to be communicated to PLINK from within the Snakefile.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-other-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther notes\u003c/h3\u003e\n\u003cp\u003eIt is recommended that \u003cem\u003esnakemake\u003c/em\u003e is run as an interactive session on an HPC. \u003cem\u003eSnakemake\u003c/em\u003e will launch the specified number (via the -j flag) of jobs, and then will hang and wait for them to finish. As jobs finish (and assuming no errors), \u003cem\u003esnakemake\u003c/em\u003e will launch additional jobs keeping the total running jobs at whatever -j is set for. Although \u003cem\u003esnakemake\u003c/em\u003e should not use a lot of memory, it could have long run times, which is generally not advisable on login nodes.\u003c/p\u003e\n\u003cp\u003eOne attractive feature of \u003cem\u003esnakemake\u003c/em\u003e is its ability to keep track of the progress and dependencies of the different stages of the pipeline. Specifically, if an error is encountered or the pipeline otherwise stops before the final step, \u003cem\u003esnakemake\u003c/em\u003e can resume the pipeline where it left off, avoiding redundant computation for previously completed tasks. To do so, simply resubmit the original \u003cem\u003esnakemake\u003c/em\u003e command.\u003c/p\u003e\n\u003cp\u003eTo run a specific part of the pipeline, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -R \u0026lt;rule_name\u0026gt; --cluster \"sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\" --cluster-config workflow/cluster_yale.yaml -j 20 --rerun-incomplete\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003cem\u003erule_name\u003c/em\u003e indicates the \u0027rule\u0027 (i.e. job) in the Snakefile that you wish to run. Or, you can request a specific file by providing the filename at the end of the command. You may need to include the -F (i.e. force) if the output file already exists and you want to overwrite it.\u003c/p\u003e\n\u003cp\u003eAlso, it is often very helpful to do a \u0027dry-run\u0027 of the pipeline in which the different steps and dependencies are printed to screen, but no actual jobs are executed. This can be helpful to ensure that config entries are correct, etc. To perform a dry-run, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -nrp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: It is convenient to make an alias in your ~/.bashrc file to run snakemake on the cluster without having to type the --cluster... part of the command every time. For me, it looked like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias snakeslurm=\"snakemake -k --cluster \u0027sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --job-name={cluster.job-name} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\u0027 --cluster-config workflow/cluster_slurm.yaml\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis way, I can just do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakeslurm -j 25\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo launch snakemake on the cluster.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-unlocking-the-working-directory\" class=\"anchor\" aria-hidden=\"true\" href=\"#unlocking-the-working-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnlocking the working directory\u003c/h4\u003e\n\u003cp\u003eWhen \u003cem\u003esnakemake\u003c/em\u003e is launched it will place a lock on the working directory, such that other \u003cem\u003esnakemake\u003c/em\u003e runs are prohibited from starting. When \u003cem\u003esnakemake\u003c/em\u003e finishes or errors out, it will remove this lock. However, sometimes this lock is not correctly removed. This can occur, for example, if the VPN drops connection while \u003cem\u003esnakemake\u003c/em\u003e is running. If you receive a \"Directory cannot be locked...\" error message from \u003cem\u003esnakemake\u003c/em\u003e and you are sure that no other \u003cem\u003esnakemake\u003c/em\u003e processes are currently running, you can unlock the directory by:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --unlock\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you can run the usual \u003cem\u003esnakemake\u003c/em\u003e command to restart the pipeline.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-debugging-and-error-reports\" class=\"anchor\" aria-hidden=\"true\" href=\"#debugging-and-error-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebugging and error reports\u003c/h4\u003e\n\u003cp\u003eShould an error be encountered in a job, snakemake will halt the pipeline and indicate in the terminal that an error has occurred. The offending job will also be printed in red in the terminal window. More information on why the job failed can be found in the \u0027stdout\u0027 and \u0027stderr\u0027 files that are output to the \u003cem\u003e\u0027OandE\u0027\u003c/em\u003e directory and will be labelled with the jobname.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline Overview\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput Data\u003c/h3\u003e\n\u003cp\u003eThe pipeline expects as input a single set of PLINK files (.bed, .fam, .bim) that has gone through basic QC steps (missingness, hwe, maf, etc). I have written QC pipelines for non-imputed and imputed data, which are available \u003ca href=\"https://github.com/pmonnahan/DataPrep\"\u003ehere\u003c/a\u003e and \u003ca href=\"https://github.com/pmonnahan/DataPrep/tree/master/postImpute\"\u003ehere\u003c/a\u003e, respectively. It is technically possible to use imputed data in ancestry inference, although this is not widely seen throughout the literature.\u003c/p\u003e\n\u003cp\u003eThe input PLINK files are specified in the \u003ccode\u003equery\u003c/code\u003e entry within the config file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003equery: \"PATH_TO_PLINK_PREFIX\" \nsamples: \"all\" \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe user can also provide a path to a file in the \u003ccode\u003esamples\u003c/code\u003e entry, in which case the program will subset the \u003ccode\u003equery\u003c/code\u003e dataset to include only the samples in the file (one sample per line).\u003c/p\u003e\n\u003cp\u003eIt is assumed that the query coordinates and chromosome names are consistent with those used in the reference VCF (see below).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h3\u003e\n\u003cp\u003eAll output is labelled using the prefix specified in the \u003ccode\u003eoutname\u003c/code\u003e entry in the config file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoutname: \"AncInf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe RFMix results will be output to the \u003cem\u003erfmix\u003c/em\u003e directory that is automatically created. RFMix outputs a number of files, but the most relevant files are those ending in \u003cem\u003e.Q\u003c/em\u003e (which contain the global ancestry percentage estimates for each individual) and the files ending in \u003cem\u003e.msp.tsv\u003c/em\u003e (which contain the maximum-likelihood ancestry state in each window analyzed; i.e. local ancestry). The \u003cem\u003e.Q\u003c/em\u003e files can be easily filtered to isolate individuals of a given ethnicity, based on user-provided thresholds.\u003c/p\u003e\n\u003cp\u003eA set of phased BCF files (separated by chromosome) are generated as an intermediate step and are saved to the \u003cem\u003ephased\u003c/em\u003e directory. This directory will also contain the phased BCF of the individuals from the reference population.\u003c/p\u003e\n\u003cp\u003eA good initial check that the results make sense is to simply look at the average local ancestry along a chromosome. A full collection of these images (one for each chromosome) will be created and output into the \u003cem\u003echrom_plots\u003c/em\u003e folder within the master run directory. These averages should remain fairly stable across the chromosome. Any large, sudden changes in the dominant ancestral component are indicative of issues in phasing or ancestry inference. Furthermore, these chromosome plots should be inspected to identify areas of suspect inference. For example, drastic changes in average ancestry is often observed near centromeres or telomeres. These can also likely be flagged by low SNP counts in the inferred windows (which is reported in the \u003cem\u003e.msp.tsv\u003c/em\u003e files).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reference-population\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference-population\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference population\u003c/h3\u003e\n\u003cp\u003eThe reference VCF to be used for phasing as well as for ancestry inference is provided under the \u003ccode\u003ereference\u003c/code\u003e section of the config file. The pipeline is currently set up to use the 1000Genomes VCF (available \u003ca href=\"https://www.internationalgenome.org/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e or by request) for the reference population. However, any VCF should work in theory as long as the necessary accessory files are provided.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereference:\n vcf: \"PATH_TO_REFERENCE_VCF\"\n subpops: \"accessory/1000G_PopLabels.txt\"\n genmap: \"PATH_TO_DATA_SUBDIRECTORY/genetic_map_hg19.txt\"\n phased_bcf: \u0027none` \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere are two required files that need to accompany the reference VCF, and these are provided at the \u003ccode\u003esubpops\u003c/code\u003e and \u003ccode\u003egenmap\u003c/code\u003e entries. The \u003ccode\u003esubpops\u003c/code\u003e file should be a text file with two columns: sample ID as it appears in the VCF in the first column and the subpopulation label for that sample in the second column. If using the 1000Genomes VCF, then the \u003ccode\u003esubpop\u003c/code\u003e file was automatically downloaded to the \u003cem\u003eaccessory\u003c/em\u003e subdirectory. The \u003ccode\u003egenmap\u003c/code\u003e file specifies the genetic map for the reference genome and is too large to be hosted on GitHub. However, the hg19 genetic map is available \u003ca href=\"https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e or by request. The file contains 3 space-delimited columns: chromosome, base position, genetic position.\u003c/p\u003e\n\u003cp\u003eIt is assumed that the reference VCF file has been filtered, phased, and indexed. The VCF does NOT need to be subsetted to include only the individuals from the desired reference subpopulations. This is accomplished by the initial steps of the pipeline, using the \u003ccode\u003esubpops\u003c/code\u003e file described above along with the comma-separated lists (no spaces!) in the \u003ccode\u003eref_pops\u003c/code\u003e and \u003ccode\u003epop_names\u003c/code\u003e entries under the \u003ccode\u003erfmix\u003c/code\u003e section of the config file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erfmix:\n ref_pops: \"YRI,GWD,ESN,CEU,IBS,TSI\" # No spaces!!\n pop_names: \"AFR,AFR,AFR,EUR,EUR,EUR\" \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBased on the information contained in the \u003ccode\u003esubpops\u003c/code\u003e file described above, individuals corresponding to the subpopulation names provided in \u003ccode\u003eref_pops\u003c/code\u003e entry are extracted from the reference VCF. In addition, a new file is created at:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e accessory/Population_Map_File.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e, which re-labels the subsetted individuals with the corresponding value in the \u003ccode\u003epop_names\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThere is expected to be a 1:1 ordered correspondence between the subpopulation labels \u003ccode\u003eref_pops\u003c/code\u003e and the superpopulation names in \u003ccode\u003epop_names\u003c/code\u003e. In this example where we are interesting in inferring 2-way admixture between AFR and EUR populations, all YRI, GWD, and ESN individuals would be extracted and re-labelled as AFR individuals, while the CEU, IBS, and TSI individuals would be labelled as EUR individuals. This scheme was developed to allow for flexibility in the inclusion/exclusion of particular subpopulations.\u003c/p\u003e\n\u003cp\u003eRFMix will sample randomly from within these superpopulations to generate the training/test sets needed for the machine learning algorithm. It is best if the reference individuals from a superpopulation are evenly distributed across subpopulations, so that a single subpopulation does not dominate during the resampling.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-phasing\" class=\"anchor\" aria-hidden=\"true\" href=\"#phasing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePhasing\u003c/h3\u003e\n\u003cp\u003eThe config file has the following options for modifying the behavior of haplotype phasing in ShapeIt4:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ephase:\n threads: \"12\"\n pbwt_depth: \"4\"\n sequence_data: \u0027true\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIncreasing the \u003ccode\u003epbwt_depth\u003c/code\u003e may increase the phasing accuracy, but comes at a substantial computational cost. The \u003ccode\u003esequence_data\u003c/code\u003e entry should be set to false if the data comes from an array.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-local-ancestry-inference\" class=\"anchor\" aria-hidden=\"true\" href=\"#local-ancestry-inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal Ancestry Inference\u003c/h3\u003e\n\u003cp\u003eIn addition to the \u003ccode\u003eref_pops\u003c/code\u003e and \u003ccode\u003epop_names\u003c/code\u003e, the \u003ccode\u003erfmix\u003c/code\u003e section of the config file provides a number of options for modifying the behavior of RFMix.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erfmix:\n ref_pops: \"YRI,GWD,ESN,CEU,IBS,TSI\" # No spaces!!\n pop_names: \"AFR,AFR,AFR,EUR,EUR,EUR\" \n generations: \"8\"\n reanalyze_reference: \"true\" \n window_size: \"0.02\" \n threads: \"12\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003egenerations\u003c/code\u003e entry specifies the number of generations in the past when admixture between the superpopulations is assumed to have begun. Values used in the literature are typically approximations based off of historical events or genomic dating methods. \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4289685/#:~:text=Patterns%20of%20Genetic%20Ancestry%20of%20Self%2DReported%20Latinos\u0026amp;text=On%20average%2C%20we%20estimate%20that,%2C%20and%206.2%25%20African%20ancestry\" rel=\"nofollow\"\u003eBryc et al 2015\u003c/a\u003e provide a good reference for African American and Latinx ancestry inference. For both scenarios, they modelled admixture between Europeans and Native Americans at 11-12 generations ago and subsequent admixture with Africans 6-8 generations ago. Unfortunately, RFMix only allows the user to specify a single value, so I have used \u00278\u0027 for African Americans (modelling 2-way admixture between AFR and EUR) and \u002712\u0027 for Latinx individuals (modelling 3-way admixture between AFR, EUR, and AMR)\u003c/p\u003e\n\u003cp\u003eIn the case that a set of reference haplotypes may not be of \"pure\" ancestry and may themselves be somewhat admixed, the option --reanalyze-reference will cause the program to iteratively analyze the reference haplotypes as if they were query haplotypes, in addition to analyzing the query input (see the \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3738819/\" rel=\"nofollow\"\u003eRFmix\u003c/a\u003e paper for a more thorough explanation of this procedure). This is often advised for inferring local ancestry in Latinx populations, where a 3-way AFR, EUR, and AMR admixture is modelled. However, it is likely not necessary for inferring ancestry in African American populations, where the ancestral populations likely do not contain any admixed individuals.\u003c/p\u003e\n\u003cp\u003eThe last relevant option is the window size in which ancestry is to be inferred. This value is specified in centiMorgans (cM). Default is 0.2 cM, which corresponds to ~100 - 150 kb windows. For a given window, there is a minimum requirement on the number of SNPs, and windows will be expanded to meet this requirement regardless of the specified window size.\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1723492523.0 + "updated_at": 1655373078.0 }, { "data_format": 2, - "description": "A BIDSapp for automated preprocessing of EEG data.", + "description": "Contains tools for building and distributing the JEDI/JCSDA Containers", "filenames": [ - "Singularity" + "Singularity.tutorial", + "Singularity.clang-mpich-dev", + "Singularity.tutorial-test", + "Singularity.gnu-openmpi-dev" ], - "full_name": "C0C0AN/EEGprep", + "full_name": "JCSDA-internal/containers", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers\u003c/h1\u003e\n\u003cp\u003eThis repository contains tools for building Singularity and Charliecloud containers.\u003c/p\u003e\n\u003cp\u003eFurthermore, since Intel Docker containers cannot be distributed through Docker Hub, they are also handled here.\u003c/p\u003e\n\u003cp\u003eThe instructions below are intended for the JEDI core team, who are responsible for maintaining JEDI containers and distributing them publicly or privately.\u003c/p\u003e\n\u003cp\u003eHowever, since the JEDI core team cannot legally distribute intel containers for licensing reasons, JEDI users and developers are encouraged to build their own intel development container.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"myIntel/Intel.md\"\u003eSee here for instructions on how to build your own JEDI Intel development container: Docker, Singularity, or Charliecloud\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-organization-of-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#organization-of-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOrganization of Repository\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etop-level directory: tools for building Singularity, Docker, and Charliecloud containers\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evagrant\u003c/code\u003e: tools for building Vagrant virtual machines that are provisioned to run JEDI containers\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emodulefiles\u003c/code\u003e, \u003ccode\u003erunscripts\u003c/code\u003e: These directories contain sample modulefiles and batch scripts for running JEDI \"Supercontainers\" across nodes on HPC systems\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emyIntel\u003c/code\u003e is intended to help users from the general JEDI community build their own JEDI intel development containers.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eintel19\u003c/code\u003e contains deprecated build tools for intel Parallel Studio.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eexamples\u003c/code\u003e is a sandbox, containing instructive examples of how to implement features that may not be used now but might be used in the future. An example is how to build writable singularity containers. These scripts are not maintained; there is no guarantee that they will run as is.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eIn order to build Docker, Singularity, or Charliecloud containers, you will of course need to have the appropriate software installed, namely \u003ccode\u003edocker\u003c/code\u003e, \u003ccode\u003esingularity\u003c/code\u003e, or \u003ccode\u003echarliecloud\u003c/code\u003e. Members of the JEDI core team can launch an AWS node with all of these pre-installed. Or, you can install them yourself as described in the JEDI documentation.\u003c/p\u003e\n\u003cp\u003eThe scripts in this directory also assume that you have root privileges.\u003c/p\u003e\n\u003cp\u003eAlso, core developers often find it necessary to access feature or bugfix branches of the jedi stack for testing purposes. So, the \u003ccode\u003ebuild_container.sh\u003c/code\u003e script uses the JCSDA-internal (private) jedi-stack repo. For this reason, you need to provide an ssh key for access. This script uses a generic academy ssh key to ensure that it has read-only access to selected JCSDA repositories. If you do not have access to this key, you can replace it with another by changing the \u003ccode\u003eKEY\u003c/code\u003e variable in \u003ccode\u003ebuild_containers.sh\u003c/code\u003e. But it is recommended to retain the read-only access. You can build the \u003ccode\u003emyIntel\u003c/code\u003e container without an ssh key.\u003c/p\u003e\n\u003cp\u003eNote: to build the tutorial container you have to copy the ssh key into the directory \u003ccode\u003essh-key\u003c/code\u003e and modify the singularity recipe file accordingly if it has a different name.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-a-dev-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-a-dev-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild a dev container\u003c/h2\u003e\n\u003cp\u003eTo build a Singularity, Charliecloud, and/or a Docker container, enter this and respond to the prompts to build the containers of your choice.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./build_containers.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;name\u0026gt;\u003c/code\u003e matches one of the available Dockerfile extensions, e.g. \u003ccode\u003egnu-openmpi-dev\u003c/code\u003e. It also accepts an optional second argument to specify a tag. The default tag is \u003ccode\u003ebeta\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor the the gnu and clang containers, the Singularity containers are built directly from the images on Docker Hub. A Docker container will only be created if you choose to build a Charliecloud container, which is then built from the Docker container.\u003c/p\u003e\n\u003cp\u003eFor the \u003ccode\u003eintel-impi-dev\u003c/code\u003e container, a Docker file is always created and then the Singularity and Charliecloud containers are created from that.\u003c/p\u003e\n\u003cp\u003eThe intel Docker container is the one used for CI so it is kept relatively compact. If you wish to add additional components such as Vtune, it is recommended you use the companion scripts in the \u003ccode\u003emyIntel\u003c/code\u003e directory. These scripts are simplified in the root directory but they are intended for use by the general JEDI user and developer community. The main simplification is that there is no need to supply an ssh key because those scripts only access the public jedi-stack repo.\u003c/p\u003e\n\u003cp\u003eTo build the tutorial container, just specify \u003ccode\u003etutorial\u003c/code\u003e for the \u003ccode\u003e\u0026lt;name\u0026gt;\u003c/code\u003e. The tutorial container is exclusively a Singularity container and uses GNU-OpenMPI: There are no clang or intel options and there are no Docker or Charliecloud containers created.\u003c/p\u003e\n\u003cp\u003eThe Singularity and Charliecloud container files will be placed in a subdirectory called \u003ccode\u003econtainers\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNote: building the Mellanox-enabled HPC container isn\u0027t yet automated. For this or other non-standard cases, you can edit the Dockerfiles, Singularity files, and scripts manually as needed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the container\u003c/h2\u003e\n\u003cp\u003eBefore distributing a container, it\u0027s always important to test it. A good test is usually to enter the container and then build and test fv3-bundle.\u003c/p\u003e\n\u003cp\u003eTo enter the Singularity container, enter:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell -e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd, for CharlieCloud, you can do this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/ch-jedi\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/ch-jedi\nch-tar2dir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-tarfile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/ch-jedi-gnu-openmpi-dev.tar.gz \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\nch-run ch-jedi-gnu-openmpi-dev -- bash\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-distribute-the-latest-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#distribute-the-latest-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDistribute the latest container\u003c/h2\u003e\n\u003cp\u003eThe latest Singularity containers are made available on Sylabs Cloud, the latest Charliecloud containers are made available on a public AWS S3 bucket, and the latest intel containers are made available on a private AWS S3 bucket. The purpose of the \u003ccode\u003epush_containers.sh\u003c/code\u003e script is to push the new container to these distribution sites.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ebeta\u003c/code\u003e tag is a special case. If the tag is \u003ccode\u003ebeta\u003c/code\u003e, it is assumed that, after it passes tests, this container is ready to be deployed as \u003ccode\u003elatest\u003c/code\u003e. In this case, a copy of the current \u003ccode\u003elatest\u003c/code\u003e container is saved with the tag \u003ccode\u003erevert\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSo, the typical workflow would be to enter\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./push_containers.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs with \u003ccode\u003ebuild_containers.sh\u003c/code\u003e, \u003ccode\u003epush_containers.sh\u003c/code\u003e accepts an optional second argument which is a tag. This is sometimes useful for experimental cases but is not part of the normal workflow.\u003c/p\u003e\n\u003cp\u003eFor instructions on how to download these containers, see \u003ca href=\"https://jointcenterforsatellitedataassimilation-jedi-docs.readthedocs-hosted.com/en/latest/using/jedi_environment/containers.html#available-containers\" rel=\"nofollow\"\u003ethe JEDI Documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tagged-releases\" class=\"anchor\" aria-hidden=\"true\" href=\"#tagged-releases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTagged Releases\u003c/h2\u003e\n\u003cp\u003eMost developers use the latest development containers but it\u0027s also useful to have tagged containers that accompany JEDI releases. This is particularly relevant for scientific users (as opposed to developers) who may wish to use tagged releases and containers for reproducibility in research. Tagged containers can also be used to provide stability for operational or Near-Real-Time (NRT) workflows.\u003c/p\u003e\n\u003cp\u003eSylabs cloud has a storage quota (currently 11 GB) that would be quickly overwhelmed if we were to store many release containers there. So, this is reserved for \"latest\" and \"revert\".\u003c/p\u003e\n\u003cp\u003eTagged singularity containers are distributed on the \u003ca href=\"http://data.jcsda.org/pages/containers.html\" rel=\"nofollow\"\u003eJCSDA Public Container Repository\u003c/a\u003e along with the latest and tagged Charliecloud containers. Tagged docker release containers can be obtained from Docker Hub. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull jcsda/docker-gnu-openmpi-dev:v1.0.0\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 6, - "subscribers_count": 5, + "subscribers_count": 7, "topics": [], - "updated_at": 1696412208.0 + "updated_at": 1675561340.0 }, { "data_format": 2, - "description": null, + "description": "Target driven visual navigation using deep reinforcement learning implemented in Pytorch", "filenames": [ "Singularity" ], - "full_name": "Aphoh/temp_tc", + "full_name": "jkulhanek/a2cat-vn", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etransactive_control\u003c/h1\u003e\u003ca id=\"user-content-transactive_control\" class=\"anchor\" aria-label=\"Permalink: transactive_control\" href=\"#transactive_control\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode meant to support and simulate the Social Game that will be launched in 2020. Elements of transactive control and behavioral engineering will be tested and designed here\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://dvc.org/doc/install\" rel=\"nofollow\"\u003edvc\u003c/a\u003e (with google drive support)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOn linux this is \u003ccode\u003epip install \u0027dvc[gdrive]\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall Docker, if you have not already.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc remote add -d gdrive gdrive://1qaTn6IYd3cpiyJegDwwEhZ3LwrujK3_x\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc pull\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./run.sh\u003c/code\u003e from the root of the repo. This will put you in a shell in the docker container with the \u003ccode\u003erl_algos/logs\u003c/code\u003e directory mounted\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 StableBaselines.py sac test_experiment\u003c/code\u003e in the docker container to start an experiment with the name \u003ccode\u003etest_experiment\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003etensorboard --logdir rl_algos/logs\u003c/code\u003e from outside the docker container to view the logs\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e12/20/2020\u003c/h3\u003e\u003ca id=\"user-content-12202020\" class=\"anchor\" aria-label=\"Permalink: 12/20/2020\" href=\"#12202020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository has been cleaned and updated for use. It contains: (1) The OpenAI gym environment \"OfficeLearn\", in the \"gym-socialgame\" folder, and (2) implementations of Reinforcement learning algorithms in \"rl_algos\" folder. In the \"simulations\" folder are various datasets for setting up and training models associated with the gym simulation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e9/1/2020\u003c/h3\u003e\u003ca id=\"user-content-912020\" class=\"anchor\" aria-label=\"Permalink: 9/1/2020\" href=\"#912020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most recent running of the code involves navigating to the rl_algos/ directory, then running the python command for the vanilla version:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac\u003c/p\u003e\n\u003cp\u003eAdding in the planning model can be done with the following flags:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac --planning_steps=10 --planning_model=Oracle --num_steps=10000\u003c/p\u003e\n\u003cp\u003ePlease see transactive_control/gym-socialgame/gym_socialgame/envs for files pertaining to the setup of the environment. The socialgame_env.py contains a lot of the information necessary for understanding how the agent steps through the environment. The reward.py file contains information on the variety of reward types available for testing. agents.py contains information on the deterministic people that we created for our simulation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003egym-socialgame\u003c/h3\u003e\u003ca id=\"user-content-gym-socialgame\" class=\"anchor\" aria-label=\"Permalink: gym-socialgame\" href=\"#gym-socialgame\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpenAI Gym environment for a social game.\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-target-driven-visual-navigation\" class=\"anchor\" href=\"#target-driven-visual-navigation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etarget-driven-visual-navigation\u003c/h1\u003e\n\u003cp\u003eTarget driven visual navigation using deep reinforcement learning implemented in Pytorch\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 4, + "subscribers_count": 1, "topics": [], - "updated_at": 1692847585.0 + "updated_at": 1645692434.0 }, { "data_format": 2, - "description": "Containers recipes for software stacks used in LOFAR data reduction.", + "description": "Target driven visual navigation using deep reinforcement learning implemented in Pytorch", "filenames": [ - "singularity/Singularity.intel_mkl", - "singularity/Singularity.amd_aocl" + "Singularity" ], - "full_name": "tikk3r/flocs", - "latest_release": "v5.3.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/caeec57ee6156fab2f9ad9256baed03bb626c122c3109140bbfd97664a898e37/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74696b6b33722f6c6f6661722d677269642d687063636c6f75643f736f72743d73656d766572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/caeec57ee6156fab2f9ad9256baed03bb626c122c3109140bbfd97664a898e37/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74696b6b33722f6c6f6661722d677269642d687063636c6f75643f736f72743d73656d766572\" data-canonical-src=\"https://img.shields.io/github/v/release/tikk3r/lofar-grid-hpccloud?sort=semver\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/afb9141d403a0d14efa775495f506016d8eb985b452a9de15ac554834c4930e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d677269642d687063636c6f75642e7376673f6c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/afb9141d403a0d14efa775495f506016d8eb985b452a9de15ac554834c4930e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d677269642d687063636c6f75642e7376673f6c6f676f3d676974687562\" data-canonical-src=\"https://img.shields.io/github/license/tikk3r/lofar-grid-hpccloud.svg?logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/136925861\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/761f174cb9008a1ddf6a71762e8d94e14993a1768acfd2c6e58d838f38314885/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3133363932353836312e737667\" data-canonical-src=\"https://zenodo.org/badge/136925861.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease see \u003ca href=\"https://tikk3r.github.io/flocs/\" rel=\"nofollow\"\u003ehttps://tikk3r.github.io/flocs/\u003c/a\u003e on how to use or obtain the containers.\u003c/p\u003e\n", + "full_name": "jkulhanek/a2cat-vn-pytorch", + "latest_release": null, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-target-driven-visual-navigation\" class=\"anchor\" href=\"#target-driven-visual-navigation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etarget-driven-visual-navigation\u003c/h1\u003e\n\u003cp\u003eTarget driven visual navigation using deep reinforcement learning implemented in Pytorch\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 6, - "topics": [ - "containers", - "lofar" - ], - "updated_at": 1729864328.0 + "subscribers_count": 1, + "topics": [], + "updated_at": 1643305868.0 }, { "data_format": 2, - "description": "Delayed Rejection Metropolis Light Transport (PSSMLT \u0026 MMLT Applications)", + "description": "Analysis pipeline for ATACseq data using Nextflow", "filenames": [ "Singularity" ], - "full_name": "joeylitalien/drmlt-mitsuba", + "full_name": "DoaneAS/atacflow", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDelayed Rejection Metropolis Light Transport\u003c/h1\u003e\u003ca id=\"user-content-delayed-rejection-metropolis-light-transport\" class=\"anchor\" aria-label=\"Permalink: Delayed Rejection Metropolis Light Transport\" href=\"#delayed-rejection-metropolis-light-transport\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the \u003cem\u003ebold-then-timid\u003c/em\u003e implementation of \u003ca href=\"https://joeylitalien.github.io/publications/drmlt\" rel=\"nofollow\"\u003eDelayed Rejection Metropolis Light Transport\u003c/a\u003e based on the \u003ca href=\"https://www.mitsuba-renderer.org/download.html\" rel=\"nofollow\"\u003eMitsuba v0.6\u003c/a\u003e renderer. Note that this work is a fork of the SIGGRAPH 2018 course \u003ca href=\"https://github.com/beltegeuse/gradient-mts\"\u003eLight Transport Simulation in the Gradient Domain\u003c/a\u003e repository.\u003c/p\u003e\n\u003cp\u003eIf you want to understand the algorithm by looking at the code, you should start with:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eMultiplexed MLT\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e./include/mitsuba/bidir/path_sampler.h\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e./src/libbidir/path_sampler.cpp\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDelayed Rejection MLT\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003e./src/integrator/drmlt/*\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn case of problems/questions/comments, do not hesitate to contact the authors directly.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMitsuba\u003c/h3\u003e\u003ca id=\"user-content-mitsuba\" class=\"anchor\" aria-label=\"Permalink: Mitsuba\" href=\"#mitsuba\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.boost.org/\" rel=\"nofollow\"\u003eBoost\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://eigen.tuxfamily.org/index.php?title=Main_Page\" rel=\"nofollow\"\u003eEigen\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.openexr.com/\" rel=\"nofollow\"\u003eOpenEXR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.fftw.org/\" rel=\"nofollow\"\u003eFFTW\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.libpng.org/pub/png/libpng.html\" rel=\"nofollow\"\u003eLibPNG\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zlib.net/\" rel=\"nofollow\"\u003eZLib\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://glew.sourceforge.net/\" rel=\"nofollow\"\u003eGLEW\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OpenImageIO/oiio\"\u003eOpenImageIO\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython\u003c/h3\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tvogels/pyexr\"\u003ePyEXR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://numpy.org/\" rel=\"nofollow\"\u003eNumPy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://matplotlib.org/\" rel=\"nofollow\"\u003eMatplotlib\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling\u003c/h2\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provided installation instructions for ArchLinux only, but the installation procedure is similar on Ubuntu using \u003ccode\u003eapt install\u003c/code\u003e. To install Mitsuba\u0027s dependencies, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epacman -Sy make boost eigen gcc openexr python3 fftw libpng jasper zlib cmake git awk xerces-c xorg glew openimageio python-pip\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eConfigure and build the project using \u003ca href=\"https://cmake.org/\" rel=\"nofollow\"\u003eCmake\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir build \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$_\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\ncmake ../\nmake -j\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo install the tooling dependencies, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epacman -Sy python3 python-pip \\\npip install numpy matplotlib pyexr\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDelayed Rejection Framework\u003c/h2\u003e\u003ca id=\"user-content-delayed-rejection-framework\" class=\"anchor\" aria-label=\"Permalink: Delayed Rejection Framework\" href=\"#delayed-rejection-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOur implementation of delayed rejection support three types of frameworks:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eOriginal Framework:\u003c/strong\u003e Proposed by \u003ca href=\"https://www.researchgate.net/publication/2767014_Some_Adaptive_Monte_Carlo_Methods_for_Bayesian_Inference\" rel=\"nofollow\"\u003eTierney \u0026amp; Mira [1999]\u003c/a\u003e. Suffers from vanishing acceptance at the second stage.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGeneralized Framework:\u003c/strong\u003e Proposed by \u003ca href=\"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.20.7698\u0026amp;rep=rep1\u0026amp;type=pdf\" rel=\"nofollow\"\u003eGreen \u0026amp; Mira [2001]\u003c/a\u003e. Uses reversible jump MCMC to solve the vanishing acceptance problematic by sampling an expensive intermediate state.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePairwise Orbital:\u003c/strong\u003e Based on the original framework but use an orbital mutations strategy at the second stage to solve the vanishing acceptance problem without the extra sampling computational overhead.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePath Sampling Technique\u003c/h2\u003e\u003ca id=\"user-content-path-sampling-technique\" class=\"anchor\" aria-label=\"Permalink: Path Sampling Technique\" href=\"#path-sampling-technique\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn important change from the previoux Mitsuba implementation is that now integrator can be run over three different path sampling techniques:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eUnidirectional Path Tracing (PT):\u003c/strong\u003e Unidirectional volumetric path tracer.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBidirectional Path Tracing (BDPT):\u003c/strong\u003e Bidirectional path tracer with Multiple Importance Sampling (MIS).\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003ca href=\"https://www.ci.i.u-tokyo.ac.jp/~hachisuka/mmlt.pdf\" rel=\"nofollow\"\u003eMultiplexed MLT\u003c/a\u003e (MMLT):\u003c/strong\u003e Bidirectional path tracer.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can find these in \u003ccode\u003esrc/libbidir/path_sampler.cpp\u003c/code\u003e and \u003ccode\u003einclude/bidir/path_sampler.h\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntegrators\u003c/h2\u003e\u003ca id=\"user-content-integrators\" class=\"anchor\" aria-label=\"Permalink: Integrators\" href=\"#integrators\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe modified and added the following integrators:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esrc/integrator/pssmlt\u003c/code\u003e: This is a modified version of the original PSSMLT algorithm.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esrc/integrator/drmlt\u003c/code\u003e: This is the core of our DRMLT algorithm.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can select between them by using the \u003ccode\u003e-D integrator=[pssmlt,drmlt]\u003c/code\u003e command-line parameter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003epssmlt\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-pssmlt\" class=\"anchor\" aria-label=\"Permalink: pssmlt\" href=\"#pssmlt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eParameter\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003cth align=\"left\"\u003eRequirement\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003etechnique\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePath sampling technique\u003c/td\u003e\n\u003ctd align=\"left\"\u003eRequired (Options: \u003ccode\u003epath, bdpt, mmlt\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003ekelemenStyleMutation\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eUse Kelemen or Gaussian mutation\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003etrue\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003emutationSizeLow\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eKelemen lower bound\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/1024\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003emutationSizeHigh\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eKelemen higher bound\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/64\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003esigma\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eStandard deviation of Gaussian mutation\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/64\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample\u003c/h4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_MITSUBA_BIN\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/mitsuba \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_SCENE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scene.xml -D integrator=pssmlt -D technique=path \u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003edrmlt\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-drmlt\" class=\"anchor\" aria-label=\"Permalink: drmlt\" href=\"#drmlt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eParameter\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003cth align=\"left\"\u003eRequirement\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003etechnique\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePath sampling technique\u003c/td\u003e\n\u003ctd align=\"left\"\u003eRequired (Options: \u003ccode\u003epath, bdpt, mmlt\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003etype\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eDelayed rejection framework\u003c/td\u003e\n\u003ctd align=\"left\"\u003eRequired (Options: \u003ccode\u003emira, green, orbital\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003eacceptanceMap\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOutput acceptance map\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003efalse\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003etimidAfterLarge\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePerform second stage after a large step\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003efalse\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003efixEmitterPath\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eFix emitter subpath during the second stage (Only with the \u003ccode\u003emmlt\u003c/code\u003e technique)\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003efalse\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003euseMixture\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eUse an equal weight mixture of both stage and regular Metropolis-Hastings instead of DR\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003efalse\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003esigma\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eStandard deviation of Gaussian mutation\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/64\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003escaleSecond\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eScaling ratio of the second stage mutation\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e1/10\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample\u003c/h4\u003e\u003ca id=\"user-content-example-1\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_MITSUBA_BIN\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/mitsuba \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_SCENE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scene.xml \\\n -D integrator=drmlt \\\n -D technique=mmlt \\\n -D type=orbital \\\n -D fixEmitterPath=true \\\n -D acceptanceMap=false\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcceptance Map\u003c/h2\u003e\u003ca id=\"user-content-acceptance-map\" class=\"anchor\" aria-label=\"Permalink: Acceptance Map\" href=\"#acceptance-map\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen using the \u003ccode\u003edrmlt\u003c/code\u003e integrator, you can generate an acceptance map using the \u003ccode\u003e-D acceptanceMap=true\u003c/code\u003e option. Doing so will generate an RGB image such that the \u003cem\u003eR\u003c/em\u003e-channel corresponds to the number of accepted samples at the first stage and the \u003cem\u003eG\u003c/em\u003e-channel is the same for the second stage. To convert this image to a heatmap, use the standalone script \u003ccode\u003e./tools/stages_heatmap.py\u003c/code\u003e. For example, the following command saves the acceptance map during rendering:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_MITSUBA_BIN\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/mitsuba \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_SCENE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/scene.xml \\\n -D integrator=drmlt \\\n -D technique=bdpt \\\n -D type=orbital \\\n -D acceptanceMap=true\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo generate the actual heatmap, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_MITSUBA_ROOT\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/tools/stages_heatmap.py \\\n -t \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ePATH_TO_ACCEPTANCE_MAP\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/acceptance_map.exr \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e\n -c [0.2,0.8]\u003c/pre\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eParameter\u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003cth align=\"left\"\u003eRequirement\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003et\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003eAcceptance map\u003c/td\u003e\n\u003ctd align=\"left\"\u003eRequired\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ccode\u003ec\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003ePixel range (clip) for heatmap images\u003c/td\u003e\n\u003ctd align=\"left\"\u003eOptional (Default: \u003ccode\u003e[0,1]\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMixture\u003c/h2\u003e\u003ca id=\"user-content-mixture\" class=\"anchor\" aria-label=\"Permalink: Mixture\" href=\"#mixture\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo generate a comparison of our method against a na\u00efve mixture of both stage, use the \u003ccode\u003e-D useMixture=true\u003c/code\u003e option under the \u003ccode\u003edrmlt\u003c/code\u003e integrator.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScenes\u003c/h2\u003e\u003ca id=\"user-content-scenes\" class=\"anchor\" aria-label=\"Permalink: Scenes\" href=\"#scenes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://data.adrien-gruson.com/research/2020_DRMLT/scenes/swimming-pool_pssmlt.zip\" rel=\"nofollow\"\u003eSwimming Pool\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://data.adrien-gruson.com/research/2020_DRMLT/scenes/aquarium_mmlt.zip\" rel=\"nofollow\"\u003eAquarium\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://data.adrien-gruson.com/research/2020_DRMLT/scenes/veach-door_mmlt.zip\" rel=\"nofollow\"\u003eVeach Door\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://data.adrien-gruson.com/research/2020_DRMLT/scenes/glass-of-water_pssmlt.zip\" rel=\"nofollow\"\u003eGlass of Water\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChange Logs\u003c/h2\u003e\u003ca id=\"user-content-change-logs\" class=\"anchor\" aria-label=\"Permalink: Change Logs\" href=\"#change-logs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e2020/07/29: Initial code release\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code is released under the GNU General Public License (version 3).\u003c/p\u003e\n\u003cp\u003eThis source code includes the following open source implementations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScreened Poisson reconstruction code from NVIDIA, released under the new BSD license.\u003c/li\u003e\n\u003cli\u003eMitsuba 0.6.0 by Wenzel Jakob, released under the GNU General Public License (version 3).\u003c/li\u003e\n\u003cli\u003eA small part of \u003ca href=\"https://github.com/tunabrain/tungsten\"\u003eTungsten\u003c/a\u003e by Benedikt Bitterli.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\n\u003ch1\u003e\u003ca id=\"user-content-atacflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#atacflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAtacFlow\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-analysis-pipeline-for-atac-seq-data-using-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#analysis-pipeline-for-atac-seq-data-using-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalysis pipeline for ATAC-seq data using Nextflow\u003c/h2\u003e\n\u003cp\u003eThis pipeline inspired by and based on the \u003ca href=\"https://www.encodeproject.org/atac-seq/\" rel=\"nofollow\"\u003eENCODE ATAC-seq processubg pipeline\u003c/a\u003e and\nthe \u003cem\u003eprototype\u003c/em\u003e ATAC-seq pipeline\ndeveloped by \u003ca href=\"https://github.com/kundajelab/atac_dnase_pipelines\"\u003eAnshul Kundaje\u0027s lab\u003c/a\u003e at Stanford University\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClone repository\n\u003cul\u003e\n\u003cli\u003eusing nextflow: \u003ccode\u003enextflow clone DoaneAS/atacflow ./\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eor using git: \u003ccode\u003egit clone https://github.com/DoaneAS/atacflow.git\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall conda dependencies:\n\u003cpre\u003e\u003ccode\u003econda update conda\nconda env create --file requirements.atacFlow.yml\nconda env create --file deep.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup data\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eATAC-seq reads go in \u003ccode\u003edata/\u0026lt;Sample\u0026gt;/*_001.fastq.gz\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eConcatenate read pairs per sample \u003ccode\u003eparallel -j8 \u0027./bin/catlanes.sh {}\u0027 ::: data/Sample*\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate sample index: \u003ccode\u003epython bin/makeIndex.py\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-execution\" class=\"anchor\" aria-hidden=\"true\" href=\"#execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run -with-trace -with-dag flow.html main.nf --index sampleIndex.csv --genome hg38\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003esupported genomes on panda WCM cluster: hg38, mm10\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 6, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1714307506.0 + "updated_at": 1678742404.0 }, { "data_format": 2, - "description": "AcrFinder, a tool for automated identification of Acr-Aca loci", + "description": "Face Recognition from Oak Ridge (FaRO) provides a well-defined server-client interface to a some of the best open source face recognition projects on the web. ", "filenames": [ - "dependencies/CRISPRCasFinder/singularity/Singularity", - "dependencies/CRISPRCasFinder/singularity/Singularity.4.2.18" + "services/rcnn/Singularity" ], - "full_name": "HaidYi/acrfinder", + "full_name": "ORNL/faro", "latest_release": null, - "readme": "\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cstrong\u003eAcrFinder\u003c/strong\u003e\u003cp\u003e\u003c/p\u003e\n(c) \u003ca href=\"http://bcb.unl.edu\" rel=\"nofollow\"\u003eYin Lab\u003c/a\u003e@\u003ca href=\"https://www.unl.edu\" rel=\"nofollow\"\u003eUNL\u003c/a\u003e2019\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents:\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents:\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"#installation\"\u003eI. Installation / Dependencies\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#about\"\u003eII. About\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#using_acrfinder\"\u003eIII. Using AcrFinder\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#docker_support\"\u003eIV. Docker Support\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#examples\"\u003eV. Examples\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#workflow\"\u003eVI. Workflow\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#faq\"\u003eVII. FAQ\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv id=\"user-content-installation\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI. Installation / Dependencies\u003c/h2\u003e\u003ca id=\"user-content-i-installation--dependencies\" class=\"anchor\" aria-label=\"Permalink: I. Installation / Dependencies\" href=\"#i-installation--dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone/download the repository. Some dependencies are included and can be found in the \u003cspan\u003edependencies/\u003c/span\u003e directory. Program expects these versions and using other versions can result in unexpected behavior.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eCRISPRCasFinder\u003c/code\u003e - Already in \u003cspan\u003edependencies/\u003c/span\u003e directory. To use \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e on your machine make sure you run its install script. The manual can be found \u003ca href=\"https://crisprcas.i2bc.paris-saclay.fr/Home/Download\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Running the install script will setup paths for all the dependencies of \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIt is a common problem to forget to install \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e, so ensure that \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e runs properly before executing \u003cspan\u003eacr_aca_cri_runner.py\u003c/span\u003e to avoid errors.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eblastn\u003c/code\u003e - \u003cspan\u003eacr_aca_cri_runner.py\u003c/span\u003e will call/use \u003ccode\u003eblastn\u003c/code\u003e to search a genome. Install \u003ccode\u003eblastn\u003c/code\u003e from \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003eNCBI\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epsiblast+\u003c/code\u003e - Used with CDD to find mobilome proteins. Install at \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003eNCBI\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eblastp\u003c/code\u003e - Used with prophage database to find prophage. Install \u003ccode\u003eblastp\u003c/code\u003e from \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins\" rel=\"nofollow\"\u003eNCBI\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3\u003c/code\u003e - For all scripts with .py extension. Use any version at or above 3.4.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePyGornism\u003c/code\u003e - Already in \u003cspan\u003edependencies/\u003c/span\u003e directory. Used to parse organism files and generate organism files in certain formats.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDatabase Preparation\u003c/h3\u003e\u003ca id=\"user-content-database-preparation\" class=\"anchor\" aria-label=\"Permalink: Database Preparation\" href=\"#database-preparation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter git clone the repository, there are 3 database to be installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eProphage\u003c/h4\u003e\u003ca id=\"user-content-prophage\" class=\"anchor\" aria-label=\"Permalink: Prophage\" href=\"#prophage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dependencies/prophage \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e makeblastdb -in prophage_virus.db -dbtype prot -out prophage\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCDD-MGE\u003c/h4\u003e\u003ca id=\"user-content-cdd-mge\" class=\"anchor\" aria-label=\"Permalink: CDD-MGE\" href=\"#cdd-mge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dependencies/ \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e tar -xzf cdd-mge.tar.gz \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e rm cdd-mge.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCDD\u003c/h4\u003e\u003ca id=\"user-content-cdd\" class=\"anchor\" aria-label=\"Permalink: CDD\" href=\"#cdd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p dependencies/cdd\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dependencies/cdd \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e wget ftp://ftp.ncbi.nih.gov/pub/mmdb/cdd/cdd.tar.gz \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e tar -xzf cdd.tar.gz \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e rm cdd.tar.gz\nmakeprofiledb -title CDD.v.3.12 -in Cdd.pn -out Cdd -threshold 9.82 -scale 100.0 -dbtype rps -index \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv id=\"user-content-about\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eII. About\u003c/h2\u003e\u003ca id=\"user-content-ii-about\" class=\"anchor\" aria-label=\"Permalink: II. About\" href=\"#ii-about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcrFinder is a tool used to identify Anti-CRISPR proteins (Acr) using both sequence homology and guilt-by-association approaches.\u003c/h3\u003e\u003ca id=\"user-content-acrfinder-is-a-tool-used-to-identify-anti-crispr-proteins-acr-using-both-sequence-homology-and-guilt-by-association-approaches\" class=\"anchor\" aria-label=\"Permalink: AcrFinder is a tool used to identify Anti-CRISPR proteins (Acr) using both sequence homology and guilt-by-association approaches.\" href=\"#acrfinder-is-a-tool-used-to-identify-anti-crispr-proteins-acr-using-both-sequence-homology-and-guilt-by-association-approaches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis README file contains information about only the python scripts found in the current directory. These are the scripts that are used to identify genomic loci that contain Acr and/or Aca homologs.\u003c/p\u003e\n\u003cp\u003eTo find out how to use other dependencies look at online sources:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eCRISPRCasFinder\u003c/code\u003e - \u003ca href=\"https://crisprcas.i2bc.paris-saclay.fr/CrisprCasFinder/Index\" rel=\"nofollow\"\u003ehttps://crisprcas.i2bc.paris-saclay.fr/CrisprCasFinder/Index\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e*\u003ccode\u003eCRISPRCasFinder\u003c/code\u003e is used to identify CRISPR Cas systems. This will then be used to \u003ca href=\"#classification\"\u003eclassify\u003c/a\u003e the genomic loci that contain Acr and/or Aca homologs. If no CRISPR Cas systems are found within a genome, then only homology based search will be implemented for Acr homologs.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv id=\"user-content-using_acrfinder\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eIII. \u003cspan\u003eUsing AcrFinder\u003c/span\u003e\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-iii-using-acrfinder\" class=\"anchor\" aria-label=\"Permalink: III. Using AcrFinder\" href=\"#iii-using-acrfinder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput\u003c/h4\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAcrFinder needs \u003cstrong\u003e.fna\u003c/strong\u003e, \u003cstrong\u003e.gff\u003c/strong\u003e and \u003cstrong\u003e.faa\u003c/strong\u003e as input. Only \u003cstrong\u003e.fna\u003c/strong\u003e file as input is also acceptable; in that case, the \u003cstrong\u003e.gff\u003c/strong\u003e and \u003cstrong\u003e.faa\u003c/strong\u003e file will be generated by running \u003ca href=\"https://github.com/hyattpd/Prodigal\"\u003eProdigal\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eList of Options\u003c/h4\u003e\u003ca id=\"user-content-list-of-options\" class=\"anchor\" aria-label=\"Permalink: List of Options\" href=\"#list-of-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOption\u003c/th\u003e\n\u003cth\u003eAlternative\u003c/th\u003e\n\u003cth\u003ePurpose\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e-h\u003c/td\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eShows all available options\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-n\u003c/td\u003e\n\u003ctd\u003e--inFNA\u003c/td\u003e\n\u003ctd\u003e\n\u003cspan\u003eRequired\u003c/span\u003e fna file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-f\u003c/td\u003e\n\u003ctd\u003e--inGFF\u003c/td\u003e\n\u003ctd\u003e\n\u003cspan\u003eRequired\u003c/span\u003e Path to gff file to use/parse\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-a\u003c/td\u003e\n\u003ctd\u003e--inFAA\u003c/td\u003e\n\u003ctd\u003e\n\u003cspan\u003eRequired\u003c/span\u003e Path to faa file to use/parse\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-m\u003c/td\u003e\n\u003ctd\u003e--aaThresh\u003c/td\u003e\n\u003ctd\u003eMax size of a protein in order to be considered Aca/Acr (aa) {default = 200} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-d\u003c/td\u003e\n\u003ctd\u003e--distThresh\u003c/td\u003e\n\u003ctd\u003eMax intergenic distance between proteins (bp) {default = 150} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-r\u003c/td\u003e\n\u003ctd\u003e--minProteins\u003c/td\u003e\n\u003ctd\u003eMin number of proteins needed per locus {default = 2} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-y\u003c/td\u003e\n\u003ctd\u003e--arrayEvidence\u003c/td\u003e\n\u003ctd\u003eMinimum evidence level needed of a CRISPR spacer to use {default = 3} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-o\u003c/td\u003e\n\u003ctd\u003e--outDir\u003c/td\u003e\n\u003ctd\u003ePath to output directory to store results in. If not provided, the program will attempt to create a new one with given path\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-t\u003c/td\u003e\n\u003ctd\u003e--aca\u003c/td\u003e\n\u003ctd\u003eKnown Aca file (.faa) to diamond candidate aca in candidate Acr-Aca loci\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-u\u003c/td\u003e\n\u003ctd\u003e--acr\u003c/td\u003e\n\u003ctd\u003eKnown Acr file (.faa) to diamond the homolog of Acr\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-z\u003c/td\u003e\n\u003ctd\u003e--genomeType\u003c/td\u003e\n\u003ctd\u003eHow to treat the genome. There are three options: \u003cstrong\u003eV\u003c/strong\u003eirus, \u003cstrong\u003eB\u003c/strong\u003eacteria and \u003cstrong\u003eA\u003c/strong\u003erchaea. Viruses will not run \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e (Note: when virus is checked, also check \u003ccode\u003e-c 0\u003c/code\u003e such that \u003cstrong\u003eno mge search\u003c/strong\u003e for virus.), Archaea will run \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e with a special Archaea flag (-ArchaCas), Bacteria will use \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e without the Archaea flag {default = V} [string]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-e\u003c/td\u003e\n\u003ctd\u003e--proteinUpDown\u003c/td\u003e\n\u003ctd\u003eNumber of surrounding (up- and down-stream) proteins to use when gathering a neighborhood {default = 10} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-c\u003c/td\u003e\n\u003ctd\u003e--minCDDProteins\u003c/td\u003e\n\u003ctd\u003eMinimum number of proteins in neighborhood that must have a CDD mobilome hit so the Acr/Aca locus can be attributed to a CDD hit {default = 1} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-g\u003c/td\u003e\n\u003ctd\u003e--gi\u003c/td\u003e\n\u003ctd\u003eUses IslandViewer (GI) database. {default = false} [boolean]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-p\u003c/td\u003e\n\u003ctd\u003e--prophage\u003c/td\u003e\n\u003ctd\u003eUses PHASTER (prophage) database. {default = false} [boolean]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-s\u003c/td\u003e\n\u003ctd\u003e--strict\u003c/td\u003e\n\u003ctd\u003eAll proteins in locus must lie within a region found in DB(s) being used {default = false} [boolean]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-l\u003c/td\u003e\n\u003ctd\u003e--lax\u003c/td\u003e\n\u003ctd\u003eOnly one protein must lie within a region found in DB(s) being used {default = true} [boolean]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--blsType\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003eWhich blast type to choose when searching mobile genome element (mge). {default = blastp} Possible choices: blastp or rpsblast\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--identity\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003eThe --id (identity) parameter for diamond to search {default=30} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--coverage\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003eThe --query-cover parameter for diamond to search {default=0.8} [float]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--e_value\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003eThe -e (e-value) parameter for diamond to search {default=0.01} [float]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--blast_slack\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003ehow far an Acr/Aca locus is allowed to be from a blastn hit to be considered high confidence {default=5000}\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput\u003c/h4\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eClassification\u003c/h4\u003e\u003ca id=\"user-content-classification\" class=\"anchor\" aria-label=\"Permalink: Classification\" href=\"#classification\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are three levels of classification in output:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eClassification\u003c/th\u003e\n\u003cth\u003eMeaning\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLow Confidence\u003c/td\u003e\n\u003ctd\u003eIf this Acr-Aca locus has a CRISPR-Cas locus but no self-targeting spacers in the genome, it is labeled as \u201clow confidence\u201d and inferred to target the CRISPR-Cas locus.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMedium Confidence\u003c/td\u003e\n\u003ctd\u003eIf this Acr-Aca locus has a self-targeting spacer target in the genome but not nearby, it is labeled as \u201cmedium confidence\u201d and inferred to target the CRISPR-Cas locus with the self-targeting spacer. \"Nearby\" means within 5,000 BP.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHigh Confidence\u003c/td\u003e\n\u003ctd\u003eIf this Acr-Aca locus has a nearby self-targeting spacer target, it is labeled as \u201chigh confidence\u201d and inferred to target the CRISPR-Cas locus with the self-targeting spacer.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOuput files\u003c/h4\u003e\u003ca id=\"user-content-ouput-files\" class=\"anchor\" aria-label=\"Permalink: Ouput files\" href=\"#ouput-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eMeaning\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/CRISPRCas_OUTPUT\u003c/td\u003e\n\u003ctd\u003eThe output folder of CRISPRCasFinder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/subjects\u003c/td\u003e\n\u003ctd\u003eThe folder that contains the input files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates\u003c/td\u003e\n\u003ctd\u003eThe folder that contains intermediate result files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/blast_out.txt\u003c/td\u003e\n\u003ctd\u003eResults from blast+\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_guilt-by-association.out\u003c/td\u003e\n\u003ctd\u003eThe final set of Acr/Aca regions that passed the initial filters as well as the CDD mobilome and prophage/gi filters.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_homology_based.out\u003c/td\u003e\n\u003ctd\u003eThe final set of proteins that have similarity to proteins in the Acr database under given similarity threshold.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/masked_db/\u003c/td\u003e\n\u003ctd\u003eThe directory contains the db (fna with crispr array regions masked) to be used for blastn search for self-targeting spacer matches (the database for blastn search)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/spacers_with_desired_evidence.fna\u003c/td\u003e\n\u003ctd\u003eThe file contains CRISPR spacers extracted from crisprcasfinder results that have the desired evidence level. The query for blastn search\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.txt\u003c/td\u003e\n\u003ctd\u003ePotential Acr/Aca regions that passed initial filters.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.faa\u003c/td\u003e\n\u003ctd\u003ePotential Acr/Aca regions in an faa format.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca_neighborhood.faa\u003c/td\u003e\n\u003ctd\u003eAn extension of the previous file that also inludes the neighboring proteins of the potential Acr/Aca. Used as the query for blastp search against prophage.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e\u003cem\u003ecandidate_acr_aca\u003c/em\u003e{blastp/rpsblast}_results.txt\u003c/td\u003e\n\u003ctd\u003eResult file from blastp against prophage database or rpsblast against cdd-mge database.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca_diamond_result.txt\u003c/td\u003e\n\u003ctd\u003eResults of diamond. These are search results with the \u003cstrong\u003eAca database\u003c/strong\u003e as the query and \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.faa as the database.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_homolog_result.txt\u003c/td\u003e\n\u003ctd\u003eResults of diamond. These are search results with the \u003cstrong\u003eAcr database\u003c/strong\u003e as the query and \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.faa as the database.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca_diamond_database.dmnd\u003c/td\u003e\n\u003ctd\u003eDatabase of diamond made from \u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.faa file.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_acr_homolog_result.txt\u003c/td\u003e\n\u003ctd\u003eResults of diamond. These are search results with the \u003cstrong\u003eAcr database\u003c/strong\u003e as the query and \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/subjects/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_protein.faa as the database.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_acr_homolog_result.fasta\u003c/td\u003e\n\u003ctd\u003e\n\u003cem\u003eProtein Sequence\u003c/em\u003e file (\u003cem\u003e.faa\u003c/em\u003e) of protein in \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_acr_homolog_result.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_acr_diamond_database.dmnd\u003c/td\u003e\n\u003ctd\u003eDatabase of diamond made from \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/subjects/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_protein.faa file\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003chr\u003e\n\n\n\n\n\n\n\n\n\n\n\n\u003cdiv id=\"user-content-docker_support\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eIV. \u003cspan\u003eDocker Support\u003c/span\u003e\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-iv-docker-support\" class=\"anchor\" aria-label=\"Permalink: IV. Docker Support\" href=\"#iv-docker-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo help users to configure the environment to use the software easily, we provide the \u003cem\u003e.Dockerfile\u003c/em\u003e can be used using the command (\u003ccode\u003e[tag name]\u003c/code\u003e indicates the name of the tag. You can set any tag name.):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/haidyi/acrfinder.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e acrfinder\ndocker build -t [tag name] \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to build the image by yourself, AcrFinder is also available at \u003cstrong\u003eDocker Hub\u003c/strong\u003e. You can pull the AcrFinder from docker hub directly using the command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull [OPTIONS] haidyi/acrfinder:latest\u003c/pre\u003e\u003c/div\u003e\n\n\n\u003chr\u003e\n\n\u003cdiv id=\"user-content-examples\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eV. Examples\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-v-examples\" class=\"anchor\" aria-label=\"Permalink: V. Examples\" href=\"#v-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 acr_aca_cri_runner.py -n sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.fna -f sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.gff -a sample_organisms/GCF_000210795.2/GCF_000210795.2_protein.faa -o [output_dir] -z B -c 2 -p \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e -g \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor you can only use \u003cstrong\u003e.fna\u003c/strong\u003e file as input.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 acr_aca_cri_runner.py -n sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.fna -o [output_dir] -z B -c 2 -p \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e -g \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the container\u003c/h4\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-label=\"Permalink: Run the container\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003eFirstly, make sure the docker image has been pulled from the docker hub or built by yourself. AcrFinder is located at the work directory of the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eInteractive Usage\u003c/h5\u003e\u003ca id=\"user-content-interactive-usage\" class=\"anchor\" aria-label=\"Permalink: Interactive Usage\" href=\"#interactive-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run [OPTIONS] [NAME:TAG] /bin/bash\npython3 acr_aca_cri_runner.py -n sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.fna -f sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.gff -a sample_organisms/GCF_000210795.2/GCF_000210795.2_protein.faa -o [output dir] -z B -c 2 -p \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e -g \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eUse own sequence\u003c/h5\u003e\u003ca id=\"user-content-use-own-sequence\" class=\"anchor\" aria-label=\"Permalink: Use own sequence\" href=\"#use-own-sequence\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to use your own sequence for analysis, you can use the flag \u003ccode\u003e-v\u003c/code\u003e in docker to load your the host directory to the containder. The entire command is like this:\u003c/p\u003e\n\u003cp\u003eFor example, if you want to use GCF_000210795.2 (contain .fna,gff,faa file in the directory ~/GCF_000210795.2) to implement acrfinder algorithm, you can use the command below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm -it -v \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/GCF_000210795.2:/app/acrfinder/GCF_000210795.2 haidyi/acrfinder:latest python3 acr_aca_cri_runner.py -n GCF_000210795.2/GCF_000210795.2_genomic.fna -f GCF_000210795.2/GCF_000210795.2_genomic.gff -a GCF_000210795.2/GCF_000210795.2_protein.faa -o GCF_000210795.2/output_dir -z B -c 2 -p \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e -g \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, you will see the output result in ~/GCF_000210795.2/output_dir.\u003c/p\u003e\n\u003cp\u003eFor more information about how to use docker, you can refer to \u003ca href=\"https://docs.docker.com\" rel=\"nofollow\"\u003ehttps://docs.docker.com\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\n\u003cdiv id=\"user-content-workflow\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eVI. Workflow of AcrFinder\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-vi-workflow-of-acrfinder\" class=\"anchor\" aria-label=\"Permalink: VI. Workflow of AcrFinder\" href=\"#vi-workflow-of-acrfinder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/aab48b9986ee4ebe52952d50656a1a3bbf87b46e2cb946e3928293267aea27f7/687474703a2f2f6263622e756e6c2e6564752f41637246696e6465722f7374796c65732f696d672f68656c702f706970656c696e652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aab48b9986ee4ebe52952d50656a1a3bbf87b46e2cb946e3928293267aea27f7/687474703a2f2f6263622e756e6c2e6564752f41637246696e6465722f7374796c65732f696d672f68656c702f706970656c696e652e706e67\" data-canonical-src=\"http://bcb.unl.edu/AcrFinder/styles/img/help/pipeline.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv id=\"user-content-faq\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eVII. FAQ\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-vii-faq\" class=\"anchor\" aria-label=\"Permalink: VII. FAQ\" href=\"#vii-faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eQ) I ran \u003cspan\u003eacr_aca_cri_runner.py\u003c/span\u003e and I got errors that pertain to CRISPR/Cas. Whats the issue?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Make sure \u003ccode\u003eCRIPSRCasFinder\u003c/code\u003e is installed properly. \u003ccode\u003eCRIPSRCasFinder\u003c/code\u003e has many dependencies of its own and will only work if they are all installed correctly. A good indicator of a correctly installed \u003ccode\u003eCRIPSRCasFinder\u003c/code\u003e is the following terminal output:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e###############################################################\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --\u0026gt; Welcome to dependencies/CRISPRCasFinder/CRISPRCasFinder.pl (version 4.2.17)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e###############################################################\u003c/span\u003e\n\n\nvmatch2 is...............OK\nmkvtree2 is...............OK\nvsubseqselect2 is...............OK\nfuzznuc (from emboss) is...............OK\nneedle (from emboss) is...............OK\u003c/pre\u003e\u003c/div\u003e\n\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-faro-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#faro-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFARO: Readme\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eFace Recognition from Oak Ridge (FaRO) provides a well-defined server-client\ninterface to some of the best open source face recognition projects on the\nweb. The intention is to support an open platform for face recognition research\nand to provide a well-defined and modern baseline for face recognition accuracy.\u003cbr\u003e\nWhile many universities and independent developers have released high quality\nface recognition models, they often lack many useful features such as\nconfiguration management, easy to use interfaces, deployment tools, backend\ndatabases, and analysis tools that FaRO provides.\u003c/p\u003e\n\u003cp\u003eIn our research we have found that there are many high quality and open source\nface analysis and recognition algorithms available for research; however,\nend-to-end systems that can support larger systems or that can be retrained for niche\napplications are lacking. We hope FARO can fill some of those needs.\u003c/p\u003e\n\u003cp\u003eThe primary goals of this project are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate an easy to use foundation that can support complex face recognition systems.\u003c/li\u003e\n\u003cli\u003eProvide well-defined benchmark algorithms.\u003c/li\u003e\n\u003cli\u003eAllow for algorithm improvements via open source software and models and to support improvements using techniques like transfer learning.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFaRO is designed as a client/server system to accomodate the need for high speed GPU\nhardware to support deep learning face processing. GRPC calls are used to communicate\nwith the server components which allows the clients to be written in many languages and\nimplemented on a varity of computationally limited platforms such as cellphones or biometric\ncollection devices.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-hidden=\"true\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublications\u003c/h2\u003e\n\u003cp\u003eIf you use FARO for publications please cite as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{bolme2019faro,\n title={{FaRO}: {FA}ce {R}ecognition From {O}ak ridge},\n author={David S. Bolme and David C. Cornett III and Nisha Srinivas},\n year={2019},\n howpublished={https://github.com/ORNL/faro}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements:\u003c/h2\u003e\n\u003cp\u003eMany FaRO services should run nicely on limited hardware resources. As we\nintegrate more deep learning algorithms, those may require GPUs and additional\nhardware.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSoftware: python3, virtualenv, cmake, wget\u003c/li\u003e\n\u003cli\u003ePython Libraries: see requirements.txt\u003c/li\u003e\n\u003cli\u003eNVidia GPU with 8GB of Ram - GTX Titan X/1070/1080 or better\u003c/li\u003e\n\u003cli\u003envidia-docker2 - supporting Cuda 9.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eThis is intended to get Dlib algorithm up and running quickly. This is a good\nplace to start and will allow you to test the FaRO interface. A few\ndependencies may be needed on a fresh Ubuntu installation including: cmake,\npython2, and python3. The install scripts will download and install many other\ndependencies in the user directory as well as some large machine learning\nmodels. To get some initial dependencies install:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo apt install cmake\n$ sudo apt install python2-dev\n$ sudo apt install python3-dev\n$ sudo apt install virtualenv\n$ sudo apt install wget\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFirst build the client environment and compile the proto interfaces.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./build-env-universal.sh\n#For Mac users run - $echo \"export PYTHONPATH=`pwd`/src:$PYTHONPATH\" \u0026gt;\u0026gt; \"$HOME/.bash_profile\" - after running build-env-universal.sh\nif using virtualenv,\n $ source env_faro_server/bin/activate\n\nif using conda,\n $ source activate env_faro_server\n or\n $ conda activate env_faro_server\n\n$ ./build-proto.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn one terminal run the Dlib service. When you do this for the first time it\nwill create a \"faro-storage\" directory and will download and extract the machine\nlearning models. At the end it will print out messages for each started worker:\n\"Worker N Started.\" By default the service is started on port localhost:50030.\u003c/p\u003e\n\u003cp\u003eIf using virtualenv,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source env_faro_server/bin/activate\n$ cd services/dlib\n$ ./run-dlib.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using conda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source activate env_faro_server or conda activate env_faro_server\n$ cd services/dlib\n$ ./run_dlib.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe VGG2Resnet model can also be run using similar commands, but only run one\nservice at a time unless you carefully configure the ports and check available\nmemory, etc.\u003c/p\u003e\n\u003cp\u003eIf using virtualenv,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source env_faro_server/bin/activate\n$ cd services/vggface2\n$ ./run-vgg2.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using conda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source activate env_faro_server or conda activate env_faro_server\n$ cd services/vggface2\n$ ./run_vgg2.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSimilarly, InsightFace algorithms can be executed using similar commands.\nFace detection is performed using RetinaFace and features are extracted using ArcFace.\nCurrently, InsightFace works only with 1 GPU and worker.\u003c/p\u003e\n\u003cp\u003eIf using virtualenv,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source env_faro_server/bin/activate \n$ cd services/arcface\n$ ./run_arcface.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using conda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source activate env_faro_server or conda activate env_faro_server \n$ cd services/arcface\n$ ./run_arcface.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn a second terminal run client applications. For this you can use either the\n\"env_faro\" or \"env_faro_server\" environments. Test scripts are available in\nthe test directory to test the workings of the different functionalities in FaRO.\u003c/p\u003e\n\u003cp\u003eTo test the scripts,\u003c/p\u003e\n\u003cp\u003eIf using virtualenv,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source env_faro/bin/activate\n$ cd tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using conda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source activate env_faro or conda activate env_faro\n$ cd tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo test the detect functionality on images execute,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$./test_detect.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo test the detect functionality in videos execute,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$./test_detect_videos.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-with-pip\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-with-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall With PIP\u003c/h2\u003e\n\u003cp\u003eThis is a simple way to add FaRO to the environment. It should install everything needed to run client api calls, but it may not provide all the configurations or models needed to run services.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install git+https://github.com/ORNL/faro.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-a-service-command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-a-service-command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun a Service Command Line\u003c/h2\u003e\n\u003cp\u003eStarting python services can be done with a simple command line. This will start the service specifying the port, the number of workers, and the algorithm.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ python -m faro.FaceService --port=localhost:50030 --worker-count=2 --algorithm=dlib\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-the-client-api\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-the-client-api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing the Client API\u003c/h2\u003e\n\u003cp\u003eExamples can be found in the Notebooks directory. The best place to start is the \u003ca href=\"https://github.com/ORNL/faro/blob/master/Notebooks/FaRO%20Client%20Usage.ipynb\"\u003eFaRO Client Usage notebook\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cp\u003eFaRO_Client_Face_Detection_Video_and_Images.ipynb\u003c/p\u003e\n\u003cp\u003eThe client can access the services using the FaRO command line interface. The CLI includes the following functions/commands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#client environment has to be activated\n$ cd bin\n$ ./faro \n\nusage : ./faro \u0026lt;command\u0026gt; --help\nlist the commands to be used\nCommands:\n flist - List the faces in a gallery.\n detectExtract - Run face detection and template extraction.\n glist - List the galleries on the service.\n test - Process a probe and gallery directory and produce a distance matrix.\n extractOnly - Only run face extraction and attribute extraction.\n enroll - Extract faces and enroll faces in a gallery.\n search - Search images for faces in a gallery.\n detect - Only run face detection.\n \n#to run detect command and find its input options execute,\n$./faro detect --help\n\nUsage: ./faro command [OPTIONS] [image] [image_directory] [video] [...]\n\nRun detection on a collection of images.\n\nOptions:\n --version show program\u0027s version number and exit\n -h, --help show this help message and exit\n -v, --verbose Print out more program information.\n -n MAX_IMAGES, --max-images=MAX_IMAGES\n Process at N images and then stop.\n --maximum-size=MAX_SIZE\n If too large, images will be scaled to have this\n maximum size. Default=1920\n\n Detector Options:\n Configuration for the face detector.\n\n -d DETECTIONS_CSV, --detections-csv=DETECTIONS_CSV\n Save detection data to the file.\n -a ATTRIBUTES_CSV, --attributes-csv=ATTRIBUTES_CSV\n Save attributes data to the file.\n --detect-log=DETECT_LOG\n A directory for detection images.\n --face-log=FACE_LOG\n A directory for faces.\n -b, --best Detect the \u0027best\u0027 highest scoring face in the image.\n --detect-thresh=DETECT_THRESH\n The threshold for a detection.\n --min-size=MIN_SIZE\n Faces with a height less that this will be ignored.\n --attribute-filter=ATTRIBUTE_FILTER\n A comma separated list of filters example: \u0027Male\u0026gt;0.5\u0027\n\n Connection Options:\n Control the connection to the FaRO service.\n\n --max-async=MAX_ASYNC\n The maximum number of asyncronous call to make at a\n time. Default=8\n --max-message-size=MAX_MESSAGE_SIZE\n Maximum GRPC message size. Set to -1 for unlimited.\n Default=67108864\n -p DETECT_PORT, --port=DETECT_PORT\n The port used for the recognition service.\n --detect-port=DETECT_PORT\n The port used for the recognition service.\n --recognition-port=REC_PORT\n The port used for the recognition service.\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-help\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Help\u003c/h2\u003e\n\u003cp\u003eWe currently have limited resources to support FaRO but will do our best to provide support. If you encounter\nproblems please submit tickets to the issues list so that they can be properly tracked.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ORNL/faro/issues\"\u003ehttps://github.com/ORNL/faro/issues\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe would also like to see new features or fixes submitted as pull requests.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ORNL/faro/pulls\"\u003ehttps://github.com/ORNL/faro/pulls\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [ - "acr-aca", - "anti-crispr", - "proteins", - "fna" + "artificial-intelligence", + "machine-learning" ], - "updated_at": 1693099775.0 + "updated_at": 1665617856.0 }, { "data_format": 2, @@ -35478,437 +35605,465 @@ var data = }, { "data_format": 2, - "description": "Face Recognition from Oak Ridge (FaRO) provides a well-defined server-client interface to a some of the best open source face recognition projects on the web. ", + "description": "AcrFinder, a tool for automated identification of Acr-Aca loci", "filenames": [ - "services/rcnn/Singularity" + "dependencies/CRISPRCasFinder/singularity/Singularity", + "dependencies/CRISPRCasFinder/singularity/Singularity.4.2.18" ], - "full_name": "ORNL/faro", + "full_name": "HaidYi/acrfinder", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-faro-readme\" class=\"anchor\" aria-hidden=\"true\" href=\"#faro-readme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFARO: Readme\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003eFace Recognition from Oak Ridge (FaRO) provides a well-defined server-client\ninterface to some of the best open source face recognition projects on the\nweb. The intention is to support an open platform for face recognition research\nand to provide a well-defined and modern baseline for face recognition accuracy.\u003cbr\u003e\nWhile many universities and independent developers have released high quality\nface recognition models, they often lack many useful features such as\nconfiguration management, easy to use interfaces, deployment tools, backend\ndatabases, and analysis tools that FaRO provides.\u003c/p\u003e\n\u003cp\u003eIn our research we have found that there are many high quality and open source\nface analysis and recognition algorithms available for research; however,\nend-to-end systems that can support larger systems or that can be retrained for niche\napplications are lacking. We hope FARO can fill some of those needs.\u003c/p\u003e\n\u003cp\u003eThe primary goals of this project are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate an easy to use foundation that can support complex face recognition systems.\u003c/li\u003e\n\u003cli\u003eProvide well-defined benchmark algorithms.\u003c/li\u003e\n\u003cli\u003eAllow for algorithm improvements via open source software and models and to support improvements using techniques like transfer learning.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFaRO is designed as a client/server system to accomodate the need for high speed GPU\nhardware to support deep learning face processing. GRPC calls are used to communicate\nwith the server components which allows the clients to be written in many languages and\nimplemented on a varity of computationally limited platforms such as cellphones or biometric\ncollection devices.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-hidden=\"true\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublications\u003c/h2\u003e\n\u003cp\u003eIf you use FARO for publications please cite as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{bolme2019faro,\n title={{FaRO}: {FA}ce {R}ecognition From {O}ak ridge},\n author={David S. Bolme and David C. Cornett III and Nisha Srinivas},\n year={2019},\n howpublished={https://github.com/ORNL/faro}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements:\u003c/h2\u003e\n\u003cp\u003eMany FaRO services should run nicely on limited hardware resources. As we\nintegrate more deep learning algorithms, those may require GPUs and additional\nhardware.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSoftware: python3, virtualenv, cmake, wget\u003c/li\u003e\n\u003cli\u003ePython Libraries: see requirements.txt\u003c/li\u003e\n\u003cli\u003eNVidia GPU with 8GB of Ram - GTX Titan X/1070/1080 or better\u003c/li\u003e\n\u003cli\u003envidia-docker2 - supporting Cuda 9.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eThis is intended to get Dlib algorithm up and running quickly. This is a good\nplace to start and will allow you to test the FaRO interface. A few\ndependencies may be needed on a fresh Ubuntu installation including: cmake,\npython2, and python3. The install scripts will download and install many other\ndependencies in the user directory as well as some large machine learning\nmodels. To get some initial dependencies install:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo apt install cmake\n$ sudo apt install python2-dev\n$ sudo apt install python3-dev\n$ sudo apt install virtualenv\n$ sudo apt install wget\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFirst build the client environment and compile the proto interfaces.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./build-env-universal.sh\n#For Mac users run - $echo \"export PYTHONPATH=`pwd`/src:$PYTHONPATH\" \u0026gt;\u0026gt; \"$HOME/.bash_profile\" - after running build-env-universal.sh\nif using virtualenv,\n $ source env_faro_server/bin/activate\n\nif using conda,\n $ source activate env_faro_server\n or\n $ conda activate env_faro_server\n\n$ ./build-proto.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn one terminal run the Dlib service. When you do this for the first time it\nwill create a \"faro-storage\" directory and will download and extract the machine\nlearning models. At the end it will print out messages for each started worker:\n\"Worker N Started.\" By default the service is started on port localhost:50030.\u003c/p\u003e\n\u003cp\u003eIf using virtualenv,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source env_faro_server/bin/activate\n$ cd services/dlib\n$ ./run-dlib.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using conda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source activate env_faro_server or conda activate env_faro_server\n$ cd services/dlib\n$ ./run_dlib.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe VGG2Resnet model can also be run using similar commands, but only run one\nservice at a time unless you carefully configure the ports and check available\nmemory, etc.\u003c/p\u003e\n\u003cp\u003eIf using virtualenv,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source env_faro_server/bin/activate\n$ cd services/vggface2\n$ ./run-vgg2.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using conda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source activate env_faro_server or conda activate env_faro_server\n$ cd services/vggface2\n$ ./run_vgg2.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSimilarly, InsightFace algorithms can be executed using similar commands.\nFace detection is performed using RetinaFace and features are extracted using ArcFace.\nCurrently, InsightFace works only with 1 GPU and worker.\u003c/p\u003e\n\u003cp\u003eIf using virtualenv,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source env_faro_server/bin/activate \n$ cd services/arcface\n$ ./run_arcface.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using conda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source activate env_faro_server or conda activate env_faro_server \n$ cd services/arcface\n$ ./run_arcface.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn a second terminal run client applications. For this you can use either the\n\"env_faro\" or \"env_faro_server\" environments. Test scripts are available in\nthe test directory to test the workings of the different functionalities in FaRO.\u003c/p\u003e\n\u003cp\u003eTo test the scripts,\u003c/p\u003e\n\u003cp\u003eIf using virtualenv,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source env_faro/bin/activate\n$ cd tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf using conda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ source activate env_faro or conda activate env_faro\n$ cd tests\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo test the detect functionality on images execute,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$./test_detect.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo test the detect functionality in videos execute,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$./test_detect_videos.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install-with-pip\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-with-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall With PIP\u003c/h2\u003e\n\u003cp\u003eThis is a simple way to add FaRO to the environment. It should install everything needed to run client api calls, but it may not provide all the configurations or models needed to run services.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install git+https://github.com/ORNL/faro.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-a-service-command-line\" class=\"anchor\" aria-hidden=\"true\" href=\"#run-a-service-command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun a Service Command Line\u003c/h2\u003e\n\u003cp\u003eStarting python services can be done with a simple command line. This will start the service specifying the port, the number of workers, and the algorithm.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ python -m faro.FaceService --port=localhost:50030 --worker-count=2 --algorithm=dlib\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-the-client-api\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-the-client-api\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing the Client API\u003c/h2\u003e\n\u003cp\u003eExamples can be found in the Notebooks directory. The best place to start is the \u003ca href=\"https://github.com/ORNL/faro/blob/master/Notebooks/FaRO%20Client%20Usage.ipynb\"\u003eFaRO Client Usage notebook\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cp\u003eFaRO_Client_Face_Detection_Video_and_Images.ipynb\u003c/p\u003e\n\u003cp\u003eThe client can access the services using the FaRO command line interface. The CLI includes the following functions/commands\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#client environment has to be activated\n$ cd bin\n$ ./faro \n\nusage : ./faro \u0026lt;command\u0026gt; --help\nlist the commands to be used\nCommands:\n flist - List the faces in a gallery.\n detectExtract - Run face detection and template extraction.\n glist - List the galleries on the service.\n test - Process a probe and gallery directory and produce a distance matrix.\n extractOnly - Only run face extraction and attribute extraction.\n enroll - Extract faces and enroll faces in a gallery.\n search - Search images for faces in a gallery.\n detect - Only run face detection.\n \n#to run detect command and find its input options execute,\n$./faro detect --help\n\nUsage: ./faro command [OPTIONS] [image] [image_directory] [video] [...]\n\nRun detection on a collection of images.\n\nOptions:\n --version show program\u0027s version number and exit\n -h, --help show this help message and exit\n -v, --verbose Print out more program information.\n -n MAX_IMAGES, --max-images=MAX_IMAGES\n Process at N images and then stop.\n --maximum-size=MAX_SIZE\n If too large, images will be scaled to have this\n maximum size. Default=1920\n\n Detector Options:\n Configuration for the face detector.\n\n -d DETECTIONS_CSV, --detections-csv=DETECTIONS_CSV\n Save detection data to the file.\n -a ATTRIBUTES_CSV, --attributes-csv=ATTRIBUTES_CSV\n Save attributes data to the file.\n --detect-log=DETECT_LOG\n A directory for detection images.\n --face-log=FACE_LOG\n A directory for faces.\n -b, --best Detect the \u0027best\u0027 highest scoring face in the image.\n --detect-thresh=DETECT_THRESH\n The threshold for a detection.\n --min-size=MIN_SIZE\n Faces with a height less that this will be ignored.\n --attribute-filter=ATTRIBUTE_FILTER\n A comma separated list of filters example: \u0027Male\u0026gt;0.5\u0027\n\n Connection Options:\n Control the connection to the FaRO service.\n\n --max-async=MAX_ASYNC\n The maximum number of asyncronous call to make at a\n time. Default=8\n --max-message-size=MAX_MESSAGE_SIZE\n Maximum GRPC message size. Set to -1 for unlimited.\n Default=67108864\n -p DETECT_PORT, --port=DETECT_PORT\n The port used for the recognition service.\n --detect-port=DETECT_PORT\n The port used for the recognition service.\n --recognition-port=REC_PORT\n The port used for the recognition service.\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-help\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Help\u003c/h2\u003e\n\u003cp\u003eWe currently have limited resources to support FaRO but will do our best to provide support. If you encounter\nproblems please submit tickets to the issues list so that they can be properly tracked.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ORNL/faro/issues\"\u003ehttps://github.com/ORNL/faro/issues\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWe would also like to see new features or fixes submitted as pull requests.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/ORNL/faro/pulls\"\u003ehttps://github.com/ORNL/faro/pulls\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\u003cstrong\u003eAcrFinder\u003c/strong\u003e\u003cp\u003e\u003c/p\u003e\n(c) \u003ca href=\"http://bcb.unl.edu\" rel=\"nofollow\"\u003eYin Lab\u003c/a\u003e@\u003ca href=\"https://www.unl.edu\" rel=\"nofollow\"\u003eUNL\u003c/a\u003e2019\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents:\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents:\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"#installation\"\u003eI. Installation / Dependencies\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#about\"\u003eII. About\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#using_acrfinder\"\u003eIII. Using AcrFinder\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#docker_support\"\u003eIV. Docker Support\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#examples\"\u003eV. Examples\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#workflow\"\u003eVI. Workflow\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#faq\"\u003eVII. FAQ\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv id=\"user-content-installation\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI. Installation / Dependencies\u003c/h2\u003e\u003ca id=\"user-content-i-installation--dependencies\" class=\"anchor\" aria-label=\"Permalink: I. Installation / Dependencies\" href=\"#i-installation--dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone/download the repository. Some dependencies are included and can be found in the \u003cspan\u003edependencies/\u003c/span\u003e directory. Program expects these versions and using other versions can result in unexpected behavior.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eCRISPRCasFinder\u003c/code\u003e - Already in \u003cspan\u003edependencies/\u003c/span\u003e directory. To use \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e on your machine make sure you run its install script. The manual can be found \u003ca href=\"https://crisprcas.i2bc.paris-saclay.fr/Home/Download\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Running the install script will setup paths for all the dependencies of \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIt is a common problem to forget to install \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e, so ensure that \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e runs properly before executing \u003cspan\u003eacr_aca_cri_runner.py\u003c/span\u003e to avoid errors.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eblastn\u003c/code\u003e - \u003cspan\u003eacr_aca_cri_runner.py\u003c/span\u003e will call/use \u003ccode\u003eblastn\u003c/code\u003e to search a genome. Install \u003ccode\u003eblastn\u003c/code\u003e from \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003eNCBI\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epsiblast+\u003c/code\u003e - Used with CDD to find mobilome proteins. Install at \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003eNCBI\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eblastp\u003c/code\u003e - Used with prophage database to find prophage. Install \u003ccode\u003eblastp\u003c/code\u003e from \u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins\" rel=\"nofollow\"\u003eNCBI\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3\u003c/code\u003e - For all scripts with .py extension. Use any version at or above 3.4.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ePyGornism\u003c/code\u003e - Already in \u003cspan\u003edependencies/\u003c/span\u003e directory. Used to parse organism files and generate organism files in certain formats.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDatabase Preparation\u003c/h3\u003e\u003ca id=\"user-content-database-preparation\" class=\"anchor\" aria-label=\"Permalink: Database Preparation\" href=\"#database-preparation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter git clone the repository, there are 3 database to be installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eProphage\u003c/h4\u003e\u003ca id=\"user-content-prophage\" class=\"anchor\" aria-label=\"Permalink: Prophage\" href=\"#prophage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dependencies/prophage \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e makeblastdb -in prophage_virus.db -dbtype prot -out prophage\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCDD-MGE\u003c/h4\u003e\u003ca id=\"user-content-cdd-mge\" class=\"anchor\" aria-label=\"Permalink: CDD-MGE\" href=\"#cdd-mge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dependencies/ \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e tar -xzf cdd-mge.tar.gz \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e rm cdd-mge.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCDD\u003c/h4\u003e\u003ca id=\"user-content-cdd\" class=\"anchor\" aria-label=\"Permalink: CDD\" href=\"#cdd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p dependencies/cdd\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dependencies/cdd \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e wget ftp://ftp.ncbi.nih.gov/pub/mmdb/cdd/cdd.tar.gz \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e tar -xzf cdd.tar.gz \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e rm cdd.tar.gz\nmakeprofiledb -title CDD.v.3.12 -in Cdd.pn -out Cdd -threshold 9.82 -scale 100.0 -dbtype rps -index \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv id=\"user-content-about\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eII. About\u003c/h2\u003e\u003ca id=\"user-content-ii-about\" class=\"anchor\" aria-label=\"Permalink: II. About\" href=\"#ii-about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcrFinder is a tool used to identify Anti-CRISPR proteins (Acr) using both sequence homology and guilt-by-association approaches.\u003c/h3\u003e\u003ca id=\"user-content-acrfinder-is-a-tool-used-to-identify-anti-crispr-proteins-acr-using-both-sequence-homology-and-guilt-by-association-approaches\" class=\"anchor\" aria-label=\"Permalink: AcrFinder is a tool used to identify Anti-CRISPR proteins (Acr) using both sequence homology and guilt-by-association approaches.\" href=\"#acrfinder-is-a-tool-used-to-identify-anti-crispr-proteins-acr-using-both-sequence-homology-and-guilt-by-association-approaches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis README file contains information about only the python scripts found in the current directory. These are the scripts that are used to identify genomic loci that contain Acr and/or Aca homologs.\u003c/p\u003e\n\u003cp\u003eTo find out how to use other dependencies look at online sources:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eCRISPRCasFinder\u003c/code\u003e - \u003ca href=\"https://crisprcas.i2bc.paris-saclay.fr/CrisprCasFinder/Index\" rel=\"nofollow\"\u003ehttps://crisprcas.i2bc.paris-saclay.fr/CrisprCasFinder/Index\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e*\u003ccode\u003eCRISPRCasFinder\u003c/code\u003e is used to identify CRISPR Cas systems. This will then be used to \u003ca href=\"#classification\"\u003eclassify\u003c/a\u003e the genomic loci that contain Acr and/or Aca homologs. If no CRISPR Cas systems are found within a genome, then only homology based search will be implemented for Acr homologs.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv id=\"user-content-using_acrfinder\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eIII. \u003cspan\u003eUsing AcrFinder\u003c/span\u003e\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-iii-using-acrfinder\" class=\"anchor\" aria-label=\"Permalink: III. Using AcrFinder\" href=\"#iii-using-acrfinder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput\u003c/h4\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAcrFinder needs \u003cstrong\u003e.fna\u003c/strong\u003e, \u003cstrong\u003e.gff\u003c/strong\u003e and \u003cstrong\u003e.faa\u003c/strong\u003e as input. Only \u003cstrong\u003e.fna\u003c/strong\u003e file as input is also acceptable; in that case, the \u003cstrong\u003e.gff\u003c/strong\u003e and \u003cstrong\u003e.faa\u003c/strong\u003e file will be generated by running \u003ca href=\"https://github.com/hyattpd/Prodigal\"\u003eProdigal\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eList of Options\u003c/h4\u003e\u003ca id=\"user-content-list-of-options\" class=\"anchor\" aria-label=\"Permalink: List of Options\" href=\"#list-of-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOption\u003c/th\u003e\n\u003cth\u003eAlternative\u003c/th\u003e\n\u003cth\u003ePurpose\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e-h\u003c/td\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eShows all available options\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-n\u003c/td\u003e\n\u003ctd\u003e--inFNA\u003c/td\u003e\n\u003ctd\u003e\n\u003cspan\u003eRequired\u003c/span\u003e fna file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-f\u003c/td\u003e\n\u003ctd\u003e--inGFF\u003c/td\u003e\n\u003ctd\u003e\n\u003cspan\u003eRequired\u003c/span\u003e Path to gff file to use/parse\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-a\u003c/td\u003e\n\u003ctd\u003e--inFAA\u003c/td\u003e\n\u003ctd\u003e\n\u003cspan\u003eRequired\u003c/span\u003e Path to faa file to use/parse\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-m\u003c/td\u003e\n\u003ctd\u003e--aaThresh\u003c/td\u003e\n\u003ctd\u003eMax size of a protein in order to be considered Aca/Acr (aa) {default = 200} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-d\u003c/td\u003e\n\u003ctd\u003e--distThresh\u003c/td\u003e\n\u003ctd\u003eMax intergenic distance between proteins (bp) {default = 150} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-r\u003c/td\u003e\n\u003ctd\u003e--minProteins\u003c/td\u003e\n\u003ctd\u003eMin number of proteins needed per locus {default = 2} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-y\u003c/td\u003e\n\u003ctd\u003e--arrayEvidence\u003c/td\u003e\n\u003ctd\u003eMinimum evidence level needed of a CRISPR spacer to use {default = 3} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-o\u003c/td\u003e\n\u003ctd\u003e--outDir\u003c/td\u003e\n\u003ctd\u003ePath to output directory to store results in. If not provided, the program will attempt to create a new one with given path\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-t\u003c/td\u003e\n\u003ctd\u003e--aca\u003c/td\u003e\n\u003ctd\u003eKnown Aca file (.faa) to diamond candidate aca in candidate Acr-Aca loci\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-u\u003c/td\u003e\n\u003ctd\u003e--acr\u003c/td\u003e\n\u003ctd\u003eKnown Acr file (.faa) to diamond the homolog of Acr\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-z\u003c/td\u003e\n\u003ctd\u003e--genomeType\u003c/td\u003e\n\u003ctd\u003eHow to treat the genome. There are three options: \u003cstrong\u003eV\u003c/strong\u003eirus, \u003cstrong\u003eB\u003c/strong\u003eacteria and \u003cstrong\u003eA\u003c/strong\u003erchaea. Viruses will not run \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e (Note: when virus is checked, also check \u003ccode\u003e-c 0\u003c/code\u003e such that \u003cstrong\u003eno mge search\u003c/strong\u003e for virus.), Archaea will run \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e with a special Archaea flag (-ArchaCas), Bacteria will use \u003ccode\u003eCRISPRCasFinder\u003c/code\u003e without the Archaea flag {default = V} [string]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-e\u003c/td\u003e\n\u003ctd\u003e--proteinUpDown\u003c/td\u003e\n\u003ctd\u003eNumber of surrounding (up- and down-stream) proteins to use when gathering a neighborhood {default = 10} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-c\u003c/td\u003e\n\u003ctd\u003e--minCDDProteins\u003c/td\u003e\n\u003ctd\u003eMinimum number of proteins in neighborhood that must have a CDD mobilome hit so the Acr/Aca locus can be attributed to a CDD hit {default = 1} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-g\u003c/td\u003e\n\u003ctd\u003e--gi\u003c/td\u003e\n\u003ctd\u003eUses IslandViewer (GI) database. {default = false} [boolean]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-p\u003c/td\u003e\n\u003ctd\u003e--prophage\u003c/td\u003e\n\u003ctd\u003eUses PHASTER (prophage) database. {default = false} [boolean]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-s\u003c/td\u003e\n\u003ctd\u003e--strict\u003c/td\u003e\n\u003ctd\u003eAll proteins in locus must lie within a region found in DB(s) being used {default = false} [boolean]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-l\u003c/td\u003e\n\u003ctd\u003e--lax\u003c/td\u003e\n\u003ctd\u003eOnly one protein must lie within a region found in DB(s) being used {default = true} [boolean]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--blsType\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003eWhich blast type to choose when searching mobile genome element (mge). {default = blastp} Possible choices: blastp or rpsblast\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--identity\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003eThe --id (identity) parameter for diamond to search {default=30} [integer]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--coverage\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003eThe --query-cover parameter for diamond to search {default=0.8} [float]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--e_value\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003eThe -e (e-value) parameter for diamond to search {default=0.01} [float]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--blast_slack\u003c/td\u003e\n\u003ctd\u003eNone\u003c/td\u003e\n\u003ctd\u003ehow far an Acr/Aca locus is allowed to be from a blastn hit to be considered high confidence {default=5000}\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput\u003c/h4\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eClassification\u003c/h4\u003e\u003ca id=\"user-content-classification\" class=\"anchor\" aria-label=\"Permalink: Classification\" href=\"#classification\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are three levels of classification in output:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eClassification\u003c/th\u003e\n\u003cth\u003eMeaning\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLow Confidence\u003c/td\u003e\n\u003ctd\u003eIf this Acr-Aca locus has a CRISPR-Cas locus but no self-targeting spacers in the genome, it is labeled as \u201clow confidence\u201d and inferred to target the CRISPR-Cas locus.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMedium Confidence\u003c/td\u003e\n\u003ctd\u003eIf this Acr-Aca locus has a self-targeting spacer target in the genome but not nearby, it is labeled as \u201cmedium confidence\u201d and inferred to target the CRISPR-Cas locus with the self-targeting spacer. \"Nearby\" means within 5,000 BP.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHigh Confidence\u003c/td\u003e\n\u003ctd\u003eIf this Acr-Aca locus has a nearby self-targeting spacer target, it is labeled as \u201chigh confidence\u201d and inferred to target the CRISPR-Cas locus with the self-targeting spacer.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOuput files\u003c/h4\u003e\u003ca id=\"user-content-ouput-files\" class=\"anchor\" aria-label=\"Permalink: Ouput files\" href=\"#ouput-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eMeaning\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/CRISPRCas_OUTPUT\u003c/td\u003e\n\u003ctd\u003eThe output folder of CRISPRCasFinder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/subjects\u003c/td\u003e\n\u003ctd\u003eThe folder that contains the input files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates\u003c/td\u003e\n\u003ctd\u003eThe folder that contains intermediate result files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/blast_out.txt\u003c/td\u003e\n\u003ctd\u003eResults from blast+\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_guilt-by-association.out\u003c/td\u003e\n\u003ctd\u003eThe final set of Acr/Aca regions that passed the initial filters as well as the CDD mobilome and prophage/gi filters.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_homology_based.out\u003c/td\u003e\n\u003ctd\u003eThe final set of proteins that have similarity to proteins in the Acr database under given similarity threshold.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/masked_db/\u003c/td\u003e\n\u003ctd\u003eThe directory contains the db (fna with crispr array regions masked) to be used for blastn search for self-targeting spacer matches (the database for blastn search)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/spacers_with_desired_evidence.fna\u003c/td\u003e\n\u003ctd\u003eThe file contains CRISPR spacers extracted from crisprcasfinder results that have the desired evidence level. The query for blastn search\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.txt\u003c/td\u003e\n\u003ctd\u003ePotential Acr/Aca regions that passed initial filters.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.faa\u003c/td\u003e\n\u003ctd\u003ePotential Acr/Aca regions in an faa format.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca_neighborhood.faa\u003c/td\u003e\n\u003ctd\u003eAn extension of the previous file that also inludes the neighboring proteins of the potential Acr/Aca. Used as the query for blastp search against prophage.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e\u003cem\u003ecandidate_acr_aca\u003c/em\u003e{blastp/rpsblast}_results.txt\u003c/td\u003e\n\u003ctd\u003eResult file from blastp against prophage database or rpsblast against cdd-mge database.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca_diamond_result.txt\u003c/td\u003e\n\u003ctd\u003eResults of diamond. These are search results with the \u003cstrong\u003eAca database\u003c/strong\u003e as the query and \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.faa as the database.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_homolog_result.txt\u003c/td\u003e\n\u003ctd\u003eResults of diamond. These are search results with the \u003cstrong\u003eAcr database\u003c/strong\u003e as the query and \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.faa as the database.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca_diamond_database.dmnd\u003c/td\u003e\n\u003ctd\u003eDatabase of diamond made from \u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_candidate_acr_aca.faa file.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_acr_homolog_result.txt\u003c/td\u003e\n\u003ctd\u003eResults of diamond. These are search results with the \u003cstrong\u003eAcr database\u003c/strong\u003e as the query and \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/subjects/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_protein.faa as the database.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_acr_homolog_result.fasta\u003c/td\u003e\n\u003ctd\u003e\n\u003cem\u003eProtein Sequence\u003c/em\u003e file (\u003cem\u003e.faa\u003c/em\u003e) of protein in \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_acr_homolog_result.txt\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/intermediates/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_acr_diamond_database.dmnd\u003c/td\u003e\n\u003ctd\u003eDatabase of diamond made from \u003cem\u003e\u0026lt;output_dir\u0026gt;\u003c/em\u003e/subjects/\u003cem\u003e\u0026lt;organism_id\u0026gt;\u003c/em\u003e_protein.faa file\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003chr\u003e\n\n\n\n\n\n\n\n\n\n\n\n\u003cdiv id=\"user-content-docker_support\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eIV. \u003cspan\u003eDocker Support\u003c/span\u003e\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-iv-docker-support\" class=\"anchor\" aria-label=\"Permalink: IV. Docker Support\" href=\"#iv-docker-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo help users to configure the environment to use the software easily, we provide the \u003cem\u003e.Dockerfile\u003c/em\u003e can be used using the command (\u003ccode\u003e[tag name]\u003c/code\u003e indicates the name of the tag. You can set any tag name.):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/haidyi/acrfinder.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e acrfinder\ndocker build -t [tag name] \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to build the image by yourself, AcrFinder is also available at \u003cstrong\u003eDocker Hub\u003c/strong\u003e. You can pull the AcrFinder from docker hub directly using the command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull [OPTIONS] haidyi/acrfinder:latest\u003c/pre\u003e\u003c/div\u003e\n\n\n\u003chr\u003e\n\n\u003cdiv id=\"user-content-examples\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eV. Examples\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-v-examples\" class=\"anchor\" aria-label=\"Permalink: V. Examples\" href=\"#v-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 acr_aca_cri_runner.py -n sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.fna -f sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.gff -a sample_organisms/GCF_000210795.2/GCF_000210795.2_protein.faa -o [output_dir] -z B -c 2 -p \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e -g \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor you can only use \u003cstrong\u003e.fna\u003c/strong\u003e file as input.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 acr_aca_cri_runner.py -n sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.fna -o [output_dir] -z B -c 2 -p \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e -g \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the container\u003c/h4\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-label=\"Permalink: Run the container\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003eFirstly, make sure the docker image has been pulled from the docker hub or built by yourself. AcrFinder is located at the work directory of the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eInteractive Usage\u003c/h5\u003e\u003ca id=\"user-content-interactive-usage\" class=\"anchor\" aria-label=\"Permalink: Interactive Usage\" href=\"#interactive-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run [OPTIONS] [NAME:TAG] /bin/bash\npython3 acr_aca_cri_runner.py -n sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.fna -f sample_organisms/GCF_000210795.2/GCF_000210795.2_genomic.gff -a sample_organisms/GCF_000210795.2/GCF_000210795.2_protein.faa -o [output dir] -z B -c 2 -p \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e -g \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eUse own sequence\u003c/h5\u003e\u003ca id=\"user-content-use-own-sequence\" class=\"anchor\" aria-label=\"Permalink: Use own sequence\" href=\"#use-own-sequence\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to use your own sequence for analysis, you can use the flag \u003ccode\u003e-v\u003c/code\u003e in docker to load your the host directory to the containder. The entire command is like this:\u003c/p\u003e\n\u003cp\u003eFor example, if you want to use GCF_000210795.2 (contain .fna,gff,faa file in the directory ~/GCF_000210795.2) to implement acrfinder algorithm, you can use the command below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run --rm -it -v \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/GCF_000210795.2:/app/acrfinder/GCF_000210795.2 haidyi/acrfinder:latest python3 acr_aca_cri_runner.py -n GCF_000210795.2/GCF_000210795.2_genomic.fna -f GCF_000210795.2/GCF_000210795.2_genomic.gff -a GCF_000210795.2/GCF_000210795.2_protein.faa -o GCF_000210795.2/output_dir -z B -c 2 -p \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e -g \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, you will see the output result in ~/GCF_000210795.2/output_dir.\u003c/p\u003e\n\u003cp\u003eFor more information about how to use docker, you can refer to \u003ca href=\"https://docs.docker.com\" rel=\"nofollow\"\u003ehttps://docs.docker.com\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\n\u003cdiv id=\"user-content-workflow\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eVI. Workflow of AcrFinder\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-vi-workflow-of-acrfinder\" class=\"anchor\" aria-label=\"Permalink: VI. Workflow of AcrFinder\" href=\"#vi-workflow-of-acrfinder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/aab48b9986ee4ebe52952d50656a1a3bbf87b46e2cb946e3928293267aea27f7/687474703a2f2f6263622e756e6c2e6564752f41637246696e6465722f7374796c65732f696d672f68656c702f706970656c696e652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aab48b9986ee4ebe52952d50656a1a3bbf87b46e2cb946e3928293267aea27f7/687474703a2f2f6263622e756e6c2e6564752f41637246696e6465722f7374796c65732f696d672f68656c702f706970656c696e652e706e67\" data-canonical-src=\"http://bcb.unl.edu/AcrFinder/styles/img/help/pipeline.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv id=\"user-content-faq\"\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eVII. FAQ\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-vii-faq\" class=\"anchor\" aria-label=\"Permalink: VII. FAQ\" href=\"#vii-faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eQ) I ran \u003cspan\u003eacr_aca_cri_runner.py\u003c/span\u003e and I got errors that pertain to CRISPR/Cas. Whats the issue?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Make sure \u003ccode\u003eCRIPSRCasFinder\u003c/code\u003e is installed properly. \u003ccode\u003eCRIPSRCasFinder\u003c/code\u003e has many dependencies of its own and will only work if they are all installed correctly. A good indicator of a correctly installed \u003ccode\u003eCRIPSRCasFinder\u003c/code\u003e is the following terminal output:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e###############################################################\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e --\u0026gt; Welcome to dependencies/CRISPRCasFinder/CRISPRCasFinder.pl (version 4.2.17)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e###############################################################\u003c/span\u003e\n\n\nvmatch2 is...............OK\nmkvtree2 is...............OK\nvsubseqselect2 is...............OK\nfuzznuc (from emboss) is...............OK\nneedle (from emboss) is...............OK\u003c/pre\u003e\u003c/div\u003e\n\n", "stargazers_count": 6, - "subscribers_count": 7, + "subscribers_count": 2, "topics": [ - "artificial-intelligence", - "machine-learning" + "acr-aca", + "anti-crispr", + "proteins", + "fna" ], - "updated_at": 1665617856.0 + "updated_at": 1693099775.0 }, { "data_format": 2, - "description": "Analysis pipeline for ATACseq data using Nextflow", + "description": "Containers recipes for software stacks used in LOFAR data reduction.", "filenames": [ - "Singularity" + "singularity/Singularity.intel_mkl", + "singularity/Singularity.amd_aocl" ], - "full_name": "DoaneAS/atacflow", - "latest_release": null, - "readme": "\n\u003ch1\u003e\u003ca id=\"user-content-atacflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#atacflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAtacFlow\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-analysis-pipeline-for-atac-seq-data-using-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#analysis-pipeline-for-atac-seq-data-using-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAnalysis pipeline for ATAC-seq data using Nextflow\u003c/h2\u003e\n\u003cp\u003eThis pipeline inspired by and based on the \u003ca href=\"https://www.encodeproject.org/atac-seq/\" rel=\"nofollow\"\u003eENCODE ATAC-seq processubg pipeline\u003c/a\u003e and\nthe \u003cem\u003eprototype\u003c/em\u003e ATAC-seq pipeline\ndeveloped by \u003ca href=\"https://github.com/kundajelab/atac_dnase_pipelines\"\u003eAnshul Kundaje\u0027s lab\u003c/a\u003e at Stanford University\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eInstall \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClone repository\n\u003cul\u003e\n\u003cli\u003eusing nextflow: \u003ccode\u003enextflow clone DoaneAS/atacflow ./\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eor using git: \u003ccode\u003egit clone https://github.com/DoaneAS/atacflow.git\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall conda dependencies:\n\u003cpre\u003e\u003ccode\u003econda update conda\nconda env create --file requirements.atacFlow.yml\nconda env create --file deep.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-setup-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#setup-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSetup data\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eATAC-seq reads go in \u003ccode\u003edata/\u0026lt;Sample\u0026gt;/*_001.fastq.gz\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eConcatenate read pairs per sample \u003ccode\u003eparallel -j8 \u0027./bin/catlanes.sh {}\u0027 ::: data/Sample*\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate sample index: \u003ccode\u003epython bin/makeIndex.py\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-execution\" class=\"anchor\" aria-hidden=\"true\" href=\"#execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run -with-trace -with-dag flow.html main.nf --index sampleIndex.csv --genome hg38\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003esupported genomes on panda WCM cluster: hg38, mm10\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "tikk3r/flocs", + "latest_release": "v5.3.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/caeec57ee6156fab2f9ad9256baed03bb626c122c3109140bbfd97664a898e37/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74696b6b33722f6c6f6661722d677269642d687063636c6f75643f736f72743d73656d766572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/caeec57ee6156fab2f9ad9256baed03bb626c122c3109140bbfd97664a898e37/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74696b6b33722f6c6f6661722d677269642d687063636c6f75643f736f72743d73656d766572\" data-canonical-src=\"https://img.shields.io/github/v/release/tikk3r/lofar-grid-hpccloud?sort=semver\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/afb9141d403a0d14efa775495f506016d8eb985b452a9de15ac554834c4930e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d677269642d687063636c6f75642e7376673f6c6f676f3d676974687562\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/afb9141d403a0d14efa775495f506016d8eb985b452a9de15ac554834c4930e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f74696b6b33722f6c6f6661722d677269642d687063636c6f75642e7376673f6c6f676f3d676974687562\" data-canonical-src=\"https://img.shields.io/github/license/tikk3r/lofar-grid-hpccloud.svg?logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/136925861\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/761f174cb9008a1ddf6a71762e8d94e14993a1768acfd2c6e58d838f38314885/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3133363932353836312e737667\" data-canonical-src=\"https://zenodo.org/badge/136925861.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePlease see \u003ca href=\"https://tikk3r.github.io/flocs/\" rel=\"nofollow\"\u003ehttps://tikk3r.github.io/flocs/\u003c/a\u003e on how to use or obtain the containers.\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 2, - "topics": [], - "updated_at": 1678742404.0 + "subscribers_count": 6, + "topics": [ + "containers", + "lofar" + ], + "updated_at": 1729864328.0 }, { "data_format": 2, - "description": "Target driven visual navigation using deep reinforcement learning implemented in Pytorch", + "description": null, "filenames": [ "Singularity" ], - "full_name": "jkulhanek/a2cat-vn-pytorch", + "full_name": "Aphoh/temp_tc", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-target-driven-visual-navigation\" class=\"anchor\" href=\"#target-driven-visual-navigation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etarget-driven-visual-navigation\u003c/h1\u003e\n\u003cp\u003eTarget driven visual navigation using deep reinforcement learning implemented in Pytorch\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etransactive_control\u003c/h1\u003e\u003ca id=\"user-content-transactive_control\" class=\"anchor\" aria-label=\"Permalink: transactive_control\" href=\"#transactive_control\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode meant to support and simulate the Social Game that will be launched in 2020. Elements of transactive control and behavioral engineering will be tested and designed here\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the repo\u003c/li\u003e\n\u003cli\u003eInstall \u003ca href=\"https://dvc.org/doc/install\" rel=\"nofollow\"\u003edvc\u003c/a\u003e (with google drive support)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eOn linux this is \u003ccode\u003epip install \u0027dvc[gdrive]\u0027\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eInstall Docker, if you have not already.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc remote add -d gdrive gdrive://1qaTn6IYd3cpiyJegDwwEhZ3LwrujK3_x\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m dvc pull\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./run.sh\u003c/code\u003e from the root of the repo. This will put you in a shell in the docker container with the \u003ccode\u003erl_algos/logs\u003c/code\u003e directory mounted\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 StableBaselines.py sac test_experiment\u003c/code\u003e in the docker container to start an experiment with the name \u003ccode\u003etest_experiment\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003etensorboard --logdir rl_algos/logs\u003c/code\u003e from outside the docker container to view the logs\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e12/20/2020\u003c/h3\u003e\u003ca id=\"user-content-12202020\" class=\"anchor\" aria-label=\"Permalink: 12/20/2020\" href=\"#12202020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository has been cleaned and updated for use. It contains: (1) The OpenAI gym environment \"OfficeLearn\", in the \"gym-socialgame\" folder, and (2) implementations of Reinforcement learning algorithms in \"rl_algos\" folder. In the \"simulations\" folder are various datasets for setting up and training models associated with the gym simulation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e9/1/2020\u003c/h3\u003e\u003ca id=\"user-content-912020\" class=\"anchor\" aria-label=\"Permalink: 9/1/2020\" href=\"#912020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most recent running of the code involves navigating to the rl_algos/ directory, then running the python command for the vanilla version:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac\u003c/p\u003e\n\u003cp\u003eAdding in the planning model can be done with the following flags:\u003c/p\u003e\n\u003cp\u003epython StableBaselines.py sac --planning_steps=10 --planning_model=Oracle --num_steps=10000\u003c/p\u003e\n\u003cp\u003ePlease see transactive_control/gym-socialgame/gym_socialgame/envs for files pertaining to the setup of the environment. The socialgame_env.py contains a lot of the information necessary for understanding how the agent steps through the environment. The reward.py file contains information on the variety of reward types available for testing. agents.py contains information on the deterministic people that we created for our simulation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003egym-socialgame\u003c/h3\u003e\u003ca id=\"user-content-gym-socialgame\" class=\"anchor\" aria-label=\"Permalink: gym-socialgame\" href=\"#gym-socialgame\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpenAI Gym environment for a social game.\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 1, + "subscribers_count": 4, "topics": [], - "updated_at": 1643305868.0 + "updated_at": 1692847585.0 }, { "data_format": 2, - "description": "Target driven visual navigation using deep reinforcement learning implemented in Pytorch", + "description": "A BIDSapp for automated preprocessing of EEG data.", "filenames": [ "Singularity" ], - "full_name": "jkulhanek/a2cat-vn", + "full_name": "C0C0AN/EEGprep", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-target-driven-visual-navigation\" class=\"anchor\" href=\"#target-driven-visual-navigation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003etarget-driven-visual-navigation\u003c/h1\u003e\n\u003cp\u003eTarget driven visual navigation using deep reinforcement learning implemented in Pytorch\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 1, + "subscribers_count": 5, "topics": [], - "updated_at": 1645692434.0 + "updated_at": 1696412208.0 }, { "data_format": 2, - "description": "Contains tools for building and distributing the JEDI/JCSDA Containers", + "description": " Code and documentation supporting Markello et al, 2021, \"Standardizing workflows in imaging transcriptomics with the abagen toolbox\" (Biorxiv)", "filenames": [ - "Singularity.tutorial", - "Singularity.clang-mpich-dev", - "Singularity.tutorial-test", - "Singularity.gnu-openmpi-dev" + "container/Singularity" ], - "full_name": "JCSDA-internal/containers", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-hidden=\"true\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainers\u003c/h1\u003e\n\u003cp\u003eThis repository contains tools for building Singularity and Charliecloud containers.\u003c/p\u003e\n\u003cp\u003eFurthermore, since Intel Docker containers cannot be distributed through Docker Hub, they are also handled here.\u003c/p\u003e\n\u003cp\u003eThe instructions below are intended for the JEDI core team, who are responsible for maintaining JEDI containers and distributing them publicly or privately.\u003c/p\u003e\n\u003cp\u003eHowever, since the JEDI core team cannot legally distribute intel containers for licensing reasons, JEDI users and developers are encouraged to build their own intel development container.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"myIntel/Intel.md\"\u003eSee here for instructions on how to build your own JEDI Intel development container: Docker, Singularity, or Charliecloud\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-organization-of-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#organization-of-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOrganization of Repository\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003etop-level directory: tools for building Singularity, Docker, and Charliecloud containers\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evagrant\u003c/code\u003e: tools for building Vagrant virtual machines that are provisioned to run JEDI containers\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emodulefiles\u003c/code\u003e, \u003ccode\u003erunscripts\u003c/code\u003e: These directories contain sample modulefiles and batch scripts for running JEDI \"Supercontainers\" across nodes on HPC systems\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emyIntel\u003c/code\u003e is intended to help users from the general JEDI community build their own JEDI intel development containers.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eintel19\u003c/code\u003e contains deprecated build tools for intel Parallel Studio.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eexamples\u003c/code\u003e is a sandbox, containing instructive examples of how to implement features that may not be used now but might be used in the future. An example is how to build writable singularity containers. These scripts are not maintained; there is no guarantee that they will run as is.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h2\u003e\n\u003cp\u003eIn order to build Docker, Singularity, or Charliecloud containers, you will of course need to have the appropriate software installed, namely \u003ccode\u003edocker\u003c/code\u003e, \u003ccode\u003esingularity\u003c/code\u003e, or \u003ccode\u003echarliecloud\u003c/code\u003e. Members of the JEDI core team can launch an AWS node with all of these pre-installed. Or, you can install them yourself as described in the JEDI documentation.\u003c/p\u003e\n\u003cp\u003eThe scripts in this directory also assume that you have root privileges.\u003c/p\u003e\n\u003cp\u003eAlso, core developers often find it necessary to access feature or bugfix branches of the jedi stack for testing purposes. So, the \u003ccode\u003ebuild_container.sh\u003c/code\u003e script uses the JCSDA-internal (private) jedi-stack repo. For this reason, you need to provide an ssh key for access. This script uses a generic academy ssh key to ensure that it has read-only access to selected JCSDA repositories. If you do not have access to this key, you can replace it with another by changing the \u003ccode\u003eKEY\u003c/code\u003e variable in \u003ccode\u003ebuild_containers.sh\u003c/code\u003e. But it is recommended to retain the read-only access. You can build the \u003ccode\u003emyIntel\u003c/code\u003e container without an ssh key.\u003c/p\u003e\n\u003cp\u003eNote: to build the tutorial container you have to copy the ssh key into the directory \u003ccode\u003essh-key\u003c/code\u003e and modify the singularity recipe file accordingly if it has a different name.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-a-dev-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-a-dev-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild a dev container\u003c/h2\u003e\n\u003cp\u003eTo build a Singularity, Charliecloud, and/or a Docker container, enter this and respond to the prompts to build the containers of your choice.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./build_containers.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;name\u0026gt;\u003c/code\u003e matches one of the available Dockerfile extensions, e.g. \u003ccode\u003egnu-openmpi-dev\u003c/code\u003e. It also accepts an optional second argument to specify a tag. The default tag is \u003ccode\u003ebeta\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor the the gnu and clang containers, the Singularity containers are built directly from the images on Docker Hub. A Docker container will only be created if you choose to build a Charliecloud container, which is then built from the Docker container.\u003c/p\u003e\n\u003cp\u003eFor the \u003ccode\u003eintel-impi-dev\u003c/code\u003e container, a Docker file is always created and then the Singularity and Charliecloud containers are created from that.\u003c/p\u003e\n\u003cp\u003eThe intel Docker container is the one used for CI so it is kept relatively compact. If you wish to add additional components such as Vtune, it is recommended you use the companion scripts in the \u003ccode\u003emyIntel\u003c/code\u003e directory. These scripts are simplified in the root directory but they are intended for use by the general JEDI user and developer community. The main simplification is that there is no need to supply an ssh key because those scripts only access the public jedi-stack repo.\u003c/p\u003e\n\u003cp\u003eTo build the tutorial container, just specify \u003ccode\u003etutorial\u003c/code\u003e for the \u003ccode\u003e\u0026lt;name\u0026gt;\u003c/code\u003e. The tutorial container is exclusively a Singularity container and uses GNU-OpenMPI: There are no clang or intel options and there are no Docker or Charliecloud containers created.\u003c/p\u003e\n\u003cp\u003eThe Singularity and Charliecloud container files will be placed in a subdirectory called \u003ccode\u003econtainers\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNote: building the Mellanox-enabled HPC container isn\u0027t yet automated. For this or other non-standard cases, you can edit the Dockerfiles, Singularity files, and scripts manually as needed.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-the-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#test-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest the container\u003c/h2\u003e\n\u003cp\u003eBefore distributing a container, it\u0027s always important to test it. A good test is usually to enter the container and then build and test fv3-bundle.\u003c/p\u003e\n\u003cp\u003eTo enter the Singularity container, enter:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell -e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd, for CharlieCloud, you can do this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir -p \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/ch-jedi\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/ch-jedi\nch-tar2dir \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-tarfile\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/ch-jedi-gnu-openmpi-dev.tar.gz \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\nch-run ch-jedi-gnu-openmpi-dev -- bash\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-distribute-the-latest-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#distribute-the-latest-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDistribute the latest container\u003c/h2\u003e\n\u003cp\u003eThe latest Singularity containers are made available on Sylabs Cloud, the latest Charliecloud containers are made available on a public AWS S3 bucket, and the latest intel containers are made available on a private AWS S3 bucket. The purpose of the \u003ccode\u003epush_containers.sh\u003c/code\u003e script is to push the new container to these distribution sites.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ebeta\u003c/code\u003e tag is a special case. If the tag is \u003ccode\u003ebeta\u003c/code\u003e, it is assumed that, after it passes tests, this container is ready to be deployed as \u003ccode\u003elatest\u003c/code\u003e. In this case, a copy of the current \u003ccode\u003elatest\u003c/code\u003e container is saved with the tag \u003ccode\u003erevert\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSo, the typical workflow would be to enter\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./push_containers.sh \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ename\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs with \u003ccode\u003ebuild_containers.sh\u003c/code\u003e, \u003ccode\u003epush_containers.sh\u003c/code\u003e accepts an optional second argument which is a tag. This is sometimes useful for experimental cases but is not part of the normal workflow.\u003c/p\u003e\n\u003cp\u003eFor instructions on how to download these containers, see \u003ca href=\"https://jointcenterforsatellitedataassimilation-jedi-docs.readthedocs-hosted.com/en/latest/using/jedi_environment/containers.html#available-containers\" rel=\"nofollow\"\u003ethe JEDI Documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tagged-releases\" class=\"anchor\" aria-hidden=\"true\" href=\"#tagged-releases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTagged Releases\u003c/h2\u003e\n\u003cp\u003eMost developers use the latest development containers but it\u0027s also useful to have tagged containers that accompany JEDI releases. This is particularly relevant for scientific users (as opposed to developers) who may wish to use tagged releases and containers for reproducibility in research. Tagged containers can also be used to provide stability for operational or Near-Real-Time (NRT) workflows.\u003c/p\u003e\n\u003cp\u003eSylabs cloud has a storage quota (currently 11 GB) that would be quickly overwhelmed if we were to store many release containers there. So, this is reserved for \"latest\" and \"revert\".\u003c/p\u003e\n\u003cp\u003eTagged singularity containers are distributed on the \u003ca href=\"http://data.jcsda.org/pages/containers.html\" rel=\"nofollow\"\u003eJCSDA Public Container Repository\u003c/a\u003e along with the latest and tagged Charliecloud containers. Tagged docker release containers can be obtained from Docker Hub. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull jcsda/docker-gnu-openmpi-dev:v1.0.0\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "netneurolab/markello_transcriptome", + "latest_release": "1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eStandardizing workflows in imaging transcriptomics with the \u003ccode\u003eabagen\u003c/code\u003e toolbox\u003c/h1\u003e\u003ca id=\"user-content-standardizing-workflows-in-imaging-transcriptomics-with-the-abagen-toolbox\" class=\"anchor\" aria-label=\"Permalink: Standardizing workflows in imaging transcriptomics with the abagen toolbox\" href=\"#standardizing-workflows-in-imaging-transcriptomics-with-the-abagen-toolbox\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"What\u0027s in this repository?\"\u003c/h2\u003e\u003ca id=\"user-content-whats-in-this-repository\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;What\u0027s in this repository?\u0026quot;\" href=\"#whats-in-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains data, code, and results for the manuscript \"Standardizing workflows in imaging transcriptomics with the \u003ccode\u003eabagen\u003c/code\u003e toolbox\" by Markello et al. \u003cem\u003eBiorxiv\u003c/em\u003e, 2021.\nWe investigate how variability in processing of the Allen Human Brain Atlas impacts analyses relating gene expression to neuroimaging data and highlight how functionality from the \u003ca href=\"https://github.com/rmarkello/abagen\"\u003e\u003ccode\u003eabagen\u003c/code\u003e\u003c/a\u003e toolbox can help to standardize these workflows.\u003c/p\u003e\n\u003cp\u003eWe\u0027ve tried to document the various aspects of this repository with a whole bunch of README files, so feel free to jump around and check things out.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"Just let me run the things!\"\u003c/h2\u003e\u003ca id=\"user-content-just-let-me-run-the-things\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;Just let me run the things!\u0026quot;\" href=\"#just-let-me-run-the-things\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eItching to just run the analyses?\nYou\u0027ll need to make sure you have installed the appropriate software packages, have access to the HCP, and have downloaded the appropriate data files (check out our \u003ca href=\"https://netneurolab.github.io/markello_transcriptome\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e for more details!).\nOnce you\u0027ve done that, you can get going with the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/netneurolab/markello_transcriptome\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_transcriptome\nconda env create -f environment.yml\nconda activate markello_transcriptome\npip install vibecheck/\nmake all\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to deal with the hassle of creating a new Python environment you can create a Singularity image run things in there:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/netneurolab/markello_transcriptome\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_transcriptome\nbash container/gen_simg.sh\nsingularity run container/markello_transcriptome.simg make all\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote, however, that \u003cstrong\u003ewe don\u0027t recommend re-running our analyses in this manner\u003c/strong\u003e as it will take a \u003cem\u003every\u003c/em\u003e long time to do so!\nInstead, we refer to our \u003ca href=\"https://netneurolab.github.io/markello_transcriptome\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e for more information on the optimal way to reproduce our results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"I\u0027d like more information.\"\u003c/h2\u003e\u003ca id=\"user-content-id-like-more-information\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;I\u0027d like more information.\u0026quot;\" href=\"#id-like-more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want a step-by-step through all the methods + analyses take a look at our \u003ca href=\"https://netneurolab.github.io/markello_transcriptome\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"I have some questions...\"\u003c/h2\u003e\u003ca id=\"user-content-i-have-some-questions\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;I have some questions...\u0026quot;\" href=\"#i-have-some-questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/netneurolab/markello_transcriptome/issues\"\u003eOpen an issue\u003c/a\u003e on this repository and someone will try and get back to you as soon as possible!\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1675561340.0 + "updated_at": 1723492523.0 }, { "data_format": 2, - "description": "Local Ancestry Inference", + "description": "Stuff related to integrating EasyBuild \u0026 Singularity", "filenames": [ - "singularity/Singularity_defs.def" + "Singularity.Easybuild-1.0", + "Singularity.Easybuild-CentOS-7", + "apps/a/anaconda3/Singularity.Anaconda3-5.3.0", + "apps/a/ant/Singularity.ant-1.9.7-Java-1.8.0_92", + "apps/a/anaconda2/Singularity.Anaconda2-5.3.0", + "apps/ccl/Singularity.CCL-1.11.5", + "apps/g/go/Singularity.Go-1.8.1", + "apps/g/GCCcore/Singularity.GCCcore-8.3.0", + "apps/m/m4/Singularity.M4-1.4.17", + "apps/m/m4/Singularity.M4-1.4.18", + "apps/q/qtop/Singularity.qtop-53-1", + "apps/c/ccl/Singularity.CCL-1.11.5", + "apps/c/cmake/Singularity.CMake-3.9.1", + "apps/c/cmake/Singularity.CMake-3.9.6", + "apps/j/java/Singularity.Java-1.8.0_92", + "apps/b/bzip2/Singularity.bzip2-1.0.6", + "apps/b/binutils/Singularity.binutils-2.28" ], - "full_name": "pmonnahan/AncInf", + "full_name": "easybuilders/eb-singularity", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ancestry-inference\" class=\"anchor\" aria-hidden=\"true\" href=\"#ancestry-inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAncestry Inference\u003c/h1\u003e\n\u003cp\u003eHuman local ancestry inference using \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3738819/\" rel=\"nofollow\"\u003eRFmix\u003c/a\u003e. The basic workflow is to parse the input PLINK file by chromosome, perform reference-based haplotype phasing on the data using \u003ca href=\"https://odelaneau.github.io/shapeit4/\" rel=\"nofollow\"\u003eShapeIt4\u003c/a\u003e, and, finally, perform local ancestry inference with RFMix. More information is provided in the \u003cem\u003ePipeline Overview\u003c/em\u003e below. With the RFMix output, admixture mapping (i.e. associating local ancestry with phenotype) can be accomplished via a separate pipeline found \u003ca href=\"https://github.com/pmonnahan/admixMap\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#snakemake\"\u003eSnakemake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#running-the-workflow\"\u003eRunning the workflow\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#other-notes\"\u003eOther Notes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#debugging-and-error-reports\"\u003eDebugging and error reports\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#pipeline-overview\"\u003ePipeline Overview\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#input-data\"\u003eInput Data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#reference-population\"\u003eReference population\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#phasing\"\u003ePhasing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#local-ancestry-inference\"\u003eLocal Ancestry Inference\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pmonnahan/AncInf/blob/master/Pipeline_DAG.png\"\u003e\u003cimg src=\"https://github.com/pmonnahan/AncInf/raw/master/Pipeline_DAG.png\" alt=\"Pipeline DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-snakemake\" class=\"anchor\" aria-hidden=\"true\" href=\"#snakemake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSnakemake\u003c/h3\u003e\n\u003cp\u003eThe pipeline is coordinated and run on an HPC (or locally) using \u003cem\u003eSnakemake\u003c/em\u003e. To install snakemake, first create a virtual environment via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load python3/3.6.3_anaconda5.0.1\nconda install -c conda-forge mamba\nmamba create -c conda-forge -c bioconda -n \u0026lt;your_environment_name\u0026gt; snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a new virtual environment and install \u003ccode\u003esnakemake\u003c/code\u003e. Then, activate this environment and perform following installations:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;your_environment_name\u0026gt;\nconda install numpy yaml pandas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnytime you need to run the pipeline, activate this environment beforehand via:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate \u0026lt;environment_name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you choose not to create an environment, you must ensure that these packages are installed and available for your python installation.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eThe installation of the individual programs used throughout this pipeline can be completely avoid by utilizing a Singularity image. This image is too large to be hosted on Github, although you can find the definitions file used to create the image \u003ca href=\"https://github.com/pmonnahan/AncInf/blob/master/singularity/Singularity_defs.def\"\u003ehere\u003c/a\u003e. Building of images is still not currently supported at MSI, so I used a Vagrant virtual machine, which comes with Singularity pre-configured/installed (\u003ca href=\"https://app.vagrantup.com/singularityware/boxes/singularity-2.4/versions/2.4\" rel=\"nofollow\"\u003ehttps://app.vagrantup.com/singularityware/boxes/singularity-2.4/versions/2.4\u003c/a\u003e). I can also share the img file directly upon request.\u003c/p\u003e\n\u003cp\u003eHowever, in order to utilize the singularity image, \u003cem\u003esingularity\u003c/em\u003e must be installed on the HPC. Currently, the pipeline assumes that \u003cem\u003esingularity\u003c/em\u003e will be available as a module and can be loaded into the environment via the command specified in the config.yml file, in the \u003ccode\u003emodule\u003c/code\u003e entry under the \u003ccode\u003esingularity\u003c/code\u003e section. The default setting will work for MSI at UMN.\u003c/p\u003e\n\u003cp\u003eSingularity settings in config.yml\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity:\n use_singularity: \u0027true\u0027\n image: \u0027/home/pmonnaha/pmonnaha/singularity/AncestryInference.sif\n module: \u0027module load singularity\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-the-workflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning the workflow\u003c/h2\u003e\n\u003cp\u003eClone this repository to the location where you want to store the output of the pipeline.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/pmonnahan/AncInf.git rfmix_test\ncd rfmix_test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe critical files responsible for executing the pipeline are contained in the \u003cem\u003e./workflow\u003c/em\u003e subdirectory contained within the cloned repo. They are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSnakefile\u003c/li\u003e\n\u003cli\u003econfig.yml\u003c/li\u003e\n\u003cli\u003ecluster.yaml\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003cem\u003eSnakefile\u003c/em\u003e is the primary workhouse of snakemake, which specifies the dependencies of various parts of the pipeline and coordinates execution. No modifications to the \u003cem\u003eSnakefile\u003c/em\u003e are necessary.\u003c/p\u003e\n\u003cp\u003eIn order for the \u003cem\u003eSnakefile\u003c/em\u003e to locate all of the necessary input and correctly submit jobs to the cluster, \u003cstrong\u003eboth\u003c/strong\u003e the \u003cem\u003econfig.yaml\u003c/em\u003e and \u003cem\u003ecluster.yaml\u003c/em\u003e need to be modified. Open these files and change the required entries that are indicated with \u0027MODIFY\u0027. Other fields do not require modification, although this may be desired given the particulars of the run you wish to implement. Details on each entry in the config file (e.g. what the program expects in each entry as well as the purpose of the entry) are provided in the \u003cem\u003ePipeline Overview\u003c/em\u003e at the bottom.\u003c/p\u003e\n\u003cp\u003eThe entire pipeline can be executed on a local machine (not recommended) or on an HPC, and the \u003cem\u003ecluster.yaml\u003c/em\u003e file is required only for the latter. For a local run, change the \u003ccode\u003elocal_run\u003c/code\u003e entry to \u003ccode\u003etrue\u003c/code\u003e under the \u003ccode\u003erun_settings\u003c/code\u003e section of the config file, and launch snakemake from within the parent directory by the simple command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHowever, multiple steps in the pipeline have high resource demands, and so are unlikely to be able to be run locally. This option exists primarily for testing and troubleshooting, so the remainder of the documentation assumes that the pipeline will be executed on an HPC. In order to coordinate the use of the HPC, the following modifications to the snakemake command are required:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --cluster \"sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\" --cluster-config workflow/cluster_yale.yaml -j 32\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere -j specifies the number of jobs that can be submitted at once.\u003c/p\u003e\n\u003cp\u003eOne additional setting in the \u003cem\u003econfig.yml\u003c/em\u003e is needed in order to correctly submit jobs to the HPC. The relevant entries are under the \u003ccode\u003erun_settings\u003c/code\u003e section of the config file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun_settings:\n local_run: \u0027false\u0027\n cluster_config: \u0027workflow/cluster_slurm.yaml\u0027\n scheduler: \u0027slurm\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere, it is necessary that the \u003ccode\u003ecluster_config\u003c/code\u003e entry is set to the path of the cluster_slurm.yaml file that will be used in the snakemake command. Also, the scheduler must correspond to the syntax used in the snakemake command and cluster.yaml file. I should point out that these additional changes are needed for responsibly using PLINK within a snakemake framework, and are not directly needed for snakemake. PLINK will attempt to auto-detect available resources upon running regardless of the resources that were requested when the job was submitted. Therefore, we have to read and parse the requested resources in the cluster config file in order for them to be communicated to PLINK from within the Snakefile.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-other-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#other-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther notes\u003c/h3\u003e\n\u003cp\u003eIt is recommended that \u003cem\u003esnakemake\u003c/em\u003e is run as an interactive session on an HPC. \u003cem\u003eSnakemake\u003c/em\u003e will launch the specified number (via the -j flag) of jobs, and then will hang and wait for them to finish. As jobs finish (and assuming no errors), \u003cem\u003esnakemake\u003c/em\u003e will launch additional jobs keeping the total running jobs at whatever -j is set for. Although \u003cem\u003esnakemake\u003c/em\u003e should not use a lot of memory, it could have long run times, which is generally not advisable on login nodes.\u003c/p\u003e\n\u003cp\u003eOne attractive feature of \u003cem\u003esnakemake\u003c/em\u003e is its ability to keep track of the progress and dependencies of the different stages of the pipeline. Specifically, if an error is encountered or the pipeline otherwise stops before the final step, \u003cem\u003esnakemake\u003c/em\u003e can resume the pipeline where it left off, avoiding redundant computation for previously completed tasks. To do so, simply resubmit the original \u003cem\u003esnakemake\u003c/em\u003e command.\u003c/p\u003e\n\u003cp\u003eTo run a specific part of the pipeline, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -R \u0026lt;rule_name\u0026gt; --cluster \"sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\" --cluster-config workflow/cluster_yale.yaml -j 20 --rerun-incomplete\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003cem\u003erule_name\u003c/em\u003e indicates the \u0027rule\u0027 (i.e. job) in the Snakefile that you wish to run. Or, you can request a specific file by providing the filename at the end of the command. You may need to include the -F (i.e. force) if the output file already exists and you want to overwrite it.\u003c/p\u003e\n\u003cp\u003eAlso, it is often very helpful to do a \u0027dry-run\u0027 of the pipeline in which the different steps and dependencies are printed to screen, but no actual jobs are executed. This can be helpful to ensure that config entries are correct, etc. To perform a dry-run, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake -nrp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: It is convenient to make an alias in your ~/.bashrc file to run snakemake on the cluster without having to type the --cluster... part of the command every time. For me, it looked like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias snakeslurm=\"snakemake -k --cluster \u0027sbatch --no-requeue --partition={cluster.p} --time={cluster.time} --mem={cluster.mem} --ntasks={threads} --job-name={cluster.job-name} --nodes={cluster.nodes} --mail-user={cluster.mail-user} --mail-type={cluster.mail-type} -o {cluster.o} -e {cluster.e} -A {cluster.A}\u0027 --cluster-config workflow/cluster_slurm.yaml\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis way, I can just do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakeslurm -j 25\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo launch snakemake on the cluster.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-unlocking-the-working-directory\" class=\"anchor\" aria-hidden=\"true\" href=\"#unlocking-the-working-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnlocking the working directory\u003c/h4\u003e\n\u003cp\u003eWhen \u003cem\u003esnakemake\u003c/em\u003e is launched it will place a lock on the working directory, such that other \u003cem\u003esnakemake\u003c/em\u003e runs are prohibited from starting. When \u003cem\u003esnakemake\u003c/em\u003e finishes or errors out, it will remove this lock. However, sometimes this lock is not correctly removed. This can occur, for example, if the VPN drops connection while \u003cem\u003esnakemake\u003c/em\u003e is running. If you receive a \"Directory cannot be locked...\" error message from \u003cem\u003esnakemake\u003c/em\u003e and you are sure that no other \u003cem\u003esnakemake\u003c/em\u003e processes are currently running, you can unlock the directory by:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --unlock\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, you can run the usual \u003cem\u003esnakemake\u003c/em\u003e command to restart the pipeline.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-debugging-and-error-reports\" class=\"anchor\" aria-hidden=\"true\" href=\"#debugging-and-error-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDebugging and error reports\u003c/h4\u003e\n\u003cp\u003eShould an error be encountered in a job, snakemake will halt the pipeline and indicate in the terminal that an error has occurred. The offending job will also be printed in red in the terminal window. More information on why the job failed can be found in the \u0027stdout\u0027 and \u0027stderr\u0027 files that are output to the \u003cem\u003e\u0027OandE\u0027\u003c/em\u003e directory and will be labelled with the jobname.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline Overview\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput Data\u003c/h3\u003e\n\u003cp\u003eThe pipeline expects as input a single set of PLINK files (.bed, .fam, .bim) that has gone through basic QC steps (missingness, hwe, maf, etc). I have written QC pipelines for non-imputed and imputed data, which are available \u003ca href=\"https://github.com/pmonnahan/DataPrep\"\u003ehere\u003c/a\u003e and \u003ca href=\"https://github.com/pmonnahan/DataPrep/tree/master/postImpute\"\u003ehere\u003c/a\u003e, respectively. It is technically possible to use imputed data in ancestry inference, although this is not widely seen throughout the literature.\u003c/p\u003e\n\u003cp\u003eThe input PLINK files are specified in the \u003ccode\u003equery\u003c/code\u003e entry within the config file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003equery: \"PATH_TO_PLINK_PREFIX\" \nsamples: \"all\" \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe user can also provide a path to a file in the \u003ccode\u003esamples\u003c/code\u003e entry, in which case the program will subset the \u003ccode\u003equery\u003c/code\u003e dataset to include only the samples in the file (one sample per line).\u003c/p\u003e\n\u003cp\u003eIt is assumed that the query coordinates and chromosome names are consistent with those used in the reference VCF (see below).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h3\u003e\n\u003cp\u003eAll output is labelled using the prefix specified in the \u003ccode\u003eoutname\u003c/code\u003e entry in the config file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoutname: \"AncInf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe RFMix results will be output to the \u003cem\u003erfmix\u003c/em\u003e directory that is automatically created. RFMix outputs a number of files, but the most relevant files are those ending in \u003cem\u003e.Q\u003c/em\u003e (which contain the global ancestry percentage estimates for each individual) and the files ending in \u003cem\u003e.msp.tsv\u003c/em\u003e (which contain the maximum-likelihood ancestry state in each window analyzed; i.e. local ancestry). The \u003cem\u003e.Q\u003c/em\u003e files can be easily filtered to isolate individuals of a given ethnicity, based on user-provided thresholds.\u003c/p\u003e\n\u003cp\u003eA set of phased BCF files (separated by chromosome) are generated as an intermediate step and are saved to the \u003cem\u003ephased\u003c/em\u003e directory. This directory will also contain the phased BCF of the individuals from the reference population.\u003c/p\u003e\n\u003cp\u003eA good initial check that the results make sense is to simply look at the average local ancestry along a chromosome. A full collection of these images (one for each chromosome) will be created and output into the \u003cem\u003echrom_plots\u003c/em\u003e folder within the master run directory. These averages should remain fairly stable across the chromosome. Any large, sudden changes in the dominant ancestral component are indicative of issues in phasing or ancestry inference. Furthermore, these chromosome plots should be inspected to identify areas of suspect inference. For example, drastic changes in average ancestry is often observed near centromeres or telomeres. These can also likely be flagged by low SNP counts in the inferred windows (which is reported in the \u003cem\u003e.msp.tsv\u003c/em\u003e files).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reference-population\" class=\"anchor\" aria-hidden=\"true\" href=\"#reference-population\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReference population\u003c/h3\u003e\n\u003cp\u003eThe reference VCF to be used for phasing as well as for ancestry inference is provided under the \u003ccode\u003ereference\u003c/code\u003e section of the config file. The pipeline is currently set up to use the 1000Genomes VCF (available \u003ca href=\"https://www.internationalgenome.org/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e or by request) for the reference population. However, any VCF should work in theory as long as the necessary accessory files are provided.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereference:\n vcf: \"PATH_TO_REFERENCE_VCF\"\n subpops: \"accessory/1000G_PopLabels.txt\"\n genmap: \"PATH_TO_DATA_SUBDIRECTORY/genetic_map_hg19.txt\"\n phased_bcf: \u0027none` \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere are two required files that need to accompany the reference VCF, and these are provided at the \u003ccode\u003esubpops\u003c/code\u003e and \u003ccode\u003egenmap\u003c/code\u003e entries. The \u003ccode\u003esubpops\u003c/code\u003e file should be a text file with two columns: sample ID as it appears in the VCF in the first column and the subpopulation label for that sample in the second column. If using the 1000Genomes VCF, then the \u003ccode\u003esubpop\u003c/code\u003e file was automatically downloaded to the \u003cem\u003eaccessory\u003c/em\u003e subdirectory. The \u003ccode\u003egenmap\u003c/code\u003e file specifies the genetic map for the reference genome and is too large to be hosted on GitHub. However, the hg19 genetic map is available \u003ca href=\"https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e or by request. The file contains 3 space-delimited columns: chromosome, base position, genetic position.\u003c/p\u003e\n\u003cp\u003eIt is assumed that the reference VCF file has been filtered, phased, and indexed. The VCF does NOT need to be subsetted to include only the individuals from the desired reference subpopulations. This is accomplished by the initial steps of the pipeline, using the \u003ccode\u003esubpops\u003c/code\u003e file described above along with the comma-separated lists (no spaces!) in the \u003ccode\u003eref_pops\u003c/code\u003e and \u003ccode\u003epop_names\u003c/code\u003e entries under the \u003ccode\u003erfmix\u003c/code\u003e section of the config file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erfmix:\n ref_pops: \"YRI,GWD,ESN,CEU,IBS,TSI\" # No spaces!!\n pop_names: \"AFR,AFR,AFR,EUR,EUR,EUR\" \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBased on the information contained in the \u003ccode\u003esubpops\u003c/code\u003e file described above, individuals corresponding to the subpopulation names provided in \u003ccode\u003eref_pops\u003c/code\u003e entry are extracted from the reference VCF. In addition, a new file is created at:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e accessory/Population_Map_File.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e, which re-labels the subsetted individuals with the corresponding value in the \u003ccode\u003epop_names\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThere is expected to be a 1:1 ordered correspondence between the subpopulation labels \u003ccode\u003eref_pops\u003c/code\u003e and the superpopulation names in \u003ccode\u003epop_names\u003c/code\u003e. In this example where we are interesting in inferring 2-way admixture between AFR and EUR populations, all YRI, GWD, and ESN individuals would be extracted and re-labelled as AFR individuals, while the CEU, IBS, and TSI individuals would be labelled as EUR individuals. This scheme was developed to allow for flexibility in the inclusion/exclusion of particular subpopulations.\u003c/p\u003e\n\u003cp\u003eRFMix will sample randomly from within these superpopulations to generate the training/test sets needed for the machine learning algorithm. It is best if the reference individuals from a superpopulation are evenly distributed across subpopulations, so that a single subpopulation does not dominate during the resampling.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-phasing\" class=\"anchor\" aria-hidden=\"true\" href=\"#phasing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePhasing\u003c/h3\u003e\n\u003cp\u003eThe config file has the following options for modifying the behavior of haplotype phasing in ShapeIt4:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ephase:\n threads: \"12\"\n pbwt_depth: \"4\"\n sequence_data: \u0027true\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIncreasing the \u003ccode\u003epbwt_depth\u003c/code\u003e may increase the phasing accuracy, but comes at a substantial computational cost. The \u003ccode\u003esequence_data\u003c/code\u003e entry should be set to false if the data comes from an array.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-local-ancestry-inference\" class=\"anchor\" aria-hidden=\"true\" href=\"#local-ancestry-inference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal Ancestry Inference\u003c/h3\u003e\n\u003cp\u003eIn addition to the \u003ccode\u003eref_pops\u003c/code\u003e and \u003ccode\u003epop_names\u003c/code\u003e, the \u003ccode\u003erfmix\u003c/code\u003e section of the config file provides a number of options for modifying the behavior of RFMix.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erfmix:\n ref_pops: \"YRI,GWD,ESN,CEU,IBS,TSI\" # No spaces!!\n pop_names: \"AFR,AFR,AFR,EUR,EUR,EUR\" \n generations: \"8\"\n reanalyze_reference: \"true\" \n window_size: \"0.02\" \n threads: \"12\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003egenerations\u003c/code\u003e entry specifies the number of generations in the past when admixture between the superpopulations is assumed to have begun. Values used in the literature are typically approximations based off of historical events or genomic dating methods. \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4289685/#:~:text=Patterns%20of%20Genetic%20Ancestry%20of%20Self%2DReported%20Latinos\u0026amp;text=On%20average%2C%20we%20estimate%20that,%2C%20and%206.2%25%20African%20ancestry\" rel=\"nofollow\"\u003eBryc et al 2015\u003c/a\u003e provide a good reference for African American and Latinx ancestry inference. For both scenarios, they modelled admixture between Europeans and Native Americans at 11-12 generations ago and subsequent admixture with Africans 6-8 generations ago. Unfortunately, RFMix only allows the user to specify a single value, so I have used \u00278\u0027 for African Americans (modelling 2-way admixture between AFR and EUR) and \u002712\u0027 for Latinx individuals (modelling 3-way admixture between AFR, EUR, and AMR)\u003c/p\u003e\n\u003cp\u003eIn the case that a set of reference haplotypes may not be of \"pure\" ancestry and may themselves be somewhat admixed, the option --reanalyze-reference will cause the program to iteratively analyze the reference haplotypes as if they were query haplotypes, in addition to analyzing the query input (see the \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3738819/\" rel=\"nofollow\"\u003eRFmix\u003c/a\u003e paper for a more thorough explanation of this procedure). This is often advised for inferring local ancestry in Latinx populations, where a 3-way AFR, EUR, and AMR admixture is modelled. However, it is likely not necessary for inferring ancestry in African American populations, where the ancestral populations likely do not contain any admixed individuals.\u003c/p\u003e\n\u003cp\u003eThe last relevant option is the window size in which ancestry is to be inferred. This value is specified in centiMorgans (cM). Default is 0.2 cM, which corresponds to ~100 - 150 kb windows. For a given window, there is a minimum requirement on the number of SNPs, and windows will be expanded to meet this requirement regardless of the specified window size.\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 3, + "subscribers_count": 6, "topics": [], - "updated_at": 1655373078.0 + "updated_at": 1710285931.0 }, { "data_format": 2, - "description": "Oxford Nanopore reference mapping, taxonomic classification, de novo assembly workflow primarily for viral sequence data", + "description": null, "filenames": [ - "singularity/Singularity.1.1.0", - "singularity/Singularity.1.0.0" + "Singularity" ], - "full_name": "peterk87/nf-virontus", + "full_name": "ZisongXu/trackObjectWithPF", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-peterk87nf-virontus\" class=\"anchor\" aria-hidden=\"true\" href=\"#peterk87nf-virontus\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003epeterk87/nf-virontus\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eVirontus viral Oxford Nanopore sequence analysis pipeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/peterk87/nf-virontus\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ca2382eaedc143481936b2847287dfadcc9737054d5f078007bb7dcc0f5474bb/68747470733a2f2f7472617669732d63692e6f72672f70657465726b38372f6e662d7669726f6e7475732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/peterk87/nf-virontus.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg 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100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#peterk87nf-virontus\"\u003epeterk87/nf-virontus\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#1-install-nextflow\"\u003e1) Install \u003c/a\u003e\u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#2-install-singularity\"\u003e2) Install \u003c/a\u003e\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3-install-virontus\"\u003e3) Install Virontus\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#preparing-your-data\"\u003ePreparing your data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#recommended-steps\"\u003eRecommended Steps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#example\"\u003eExample\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#credits\"\u003eCredits\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTOC created by \u003ca href=\"https://github.com/ekalinin/github-markdown-toc\"\u003egh-md-toc\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-hidden=\"true\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eThe Virontus pipeline is for the analysis of viral shotgun and amplicon Oxford Nanopore sequence data. Given basecalled (and demultiplexed) Nanopore reads, Virontus produces one or more consensus sequences from read mapping with \u003ca href=\"https://github.com/lh3/minimap2\"\u003eMinimap2\u003c/a\u003e and variant calling with \u003ca href=\"https://github.com/nanoporetech/medaka\"\u003eMedaka\u003c/a\u003e and \u003ca href=\"https://www.nature.com/articles/s41467-019-12493-y\" rel=\"nofollow\"\u003eLongshot\u003c/a\u003e results with respect to one or more reference sequences. For amplicon sequencing, the user should provide a BED file containing primer coordinates with respect to a reference sequence so that the primer sequences can be trimmed using \u003ca href=\"https://github.com/andersen-lab/ivar\"\u003eiVar\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOptionally, Virontus will perform taxonomic classification with \u003ca href=\"https://ccb.jhu.edu/software/kraken2/\" rel=\"nofollow\"\u003eKraken2\u003c/a\u003e and \u003ca href=\"https://ccb.jhu.edu/software/centrifuge/manual.shtml\" rel=\"nofollow\"\u003eCentrifuge\u003c/a\u003e if index paths are provided. Reads can be filtered by taxonomic classification. By default viral and unclassified reads are filtered.\u003c/p\u003e\n\u003cp\u003eDe novo assembly with \u003ca href=\"https://github.com/rrwick/Unicycler\"\u003eUnicycler\u003c/a\u003e can be optionally performed if desired (specify \u003ccode\u003e--do_unicycler_assembly\u003c/code\u003e when running Virontus). If taxonomic classification is performed then taxonomically filtered reads will be assembled, otherwise all reads will be used for assembly.\u003c/p\u003e\n\u003cp\u003eThe Virontus pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eYou will need to install \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e in order to run the Virontus pipeline.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is recommended for portable and reproducible execution of the pipeline with the \u003ccode\u003e-profile singularity\u003c/code\u003e command-line argument.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-install-nextflow\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-install-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1) Install \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\n\u003c/h4\u003e\n\u003cp\u003eIf you have \u003ca href=\"https://conda.io/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e installed, you can install \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e with the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install -c bioconda -c conda-forge nextflow\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-install-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2) Install \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\n\u003c/h4\u003e\n\u003cp\u003eInstalling \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is optional but recommended for portability and reproducibility of results.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-3-install-virontus\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-install-virontus\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3) Install Virontus\u003c/h4\u003e\n\u003cp\u003eNextflow will automatically download the latest version of Virontus. You can show the Virontus help message with usage information with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run peterk87/nf-virontus --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cp\u003eShow usage information with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run peterk87/nf-virontus --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should see the following\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 20.01.0\nLaunching `main.nf` [awesome_pauling] - revision: 9aeb19496b\nWARN: DSL 2 IS AN EXPERIMENTAL FEATURE UNDER DEVELOPMENT -- SYNTAX MAY CHANGE IN FUTURE RELEASE\n==================================================================\npeterk87/nf-virontus ~ version 1.1.0\n==================================================================\n\n Git info: null - null [null]\n\nUsage:\nGiven some barcoded and demultiplexed reads, the typical command for running the pipeline is as follows:\n\n nextflow run peterk87/nf-virontus \\\n --reads \"reads/*.fastq\" \\\n --outdir results \\\n --ref_fasta refs.fa \\\n -profile singularity # recommended to run with Singularity\n\nThe above assumes that you have a Centrifuge DB and Kraken2 DB located at\n/opt/DB/centrifuge/nt-2018-03-03/nt and /opt/DB/kraken2/standard2,\nrespectively, OR that you have set $CENTRIFUGE_DB and $KRAKEN2_DB env\nvariables. It also assumes that you have Singularity installed on your\nlocal machine and will automatically pull and use the Singularity image for\nthis workflow from Singularity-Hub.org.\n\nNOTE: For best results, please ensure you have Singularity installed prior to running this workflow.(https://sylabs.io/guides/3.3/user-guide/quick_start.html#quick-installation-steps)\n\nNote:\nThe argument supplied to \"--reads\" must be quoted if using \"*\" and other\ncharacters and symbols that could be shell expanded!\n\nMandatory Options:\n --reads Input reads directory and pattern (default: \"reads/*.fastq\")\n --ref_fasta Reference genomes multiFASTA file (one or more references\n in a single file) (default: \"./refs.fasta\")\nAmplicon Sequencing Options:\n --bedfile BED format file with amplicon sequencing primers info (optional).\n Produced as output from PrimalScheme.\nConsensus Generation Options:\n --low_coverage Low coverage threshold (default=3).\n Replace consensus sequence positions below this depth\n threshold with a low coverage character\n (see --low_cov_char)\n --no_coverage No coverage threshold (default=0).\n Replace consensus sequence positions with less than or\n equal this depth with a no coverage character\n (see --no_cov_char)\n --low_cov_char Low coverage character (default=\"N\")\n --no_cov_char No coverage character (default=\"-\")\n\nCluster Options:\n --slurm_queue Name of SLURM queue to run workflow on; use with -profile slurm\n\n\nTaxonomic Classification Options:\n --centrifuge_db Path to Centrifuge DB and prefix. If not specified, will\n try to get from $CENTRIFUGE_DB env variable or see if\n \"/opt/DB/centrifuge/nt-2018-03-03/nt\" exists.\n (default: null)\n --kraken2_db Path to Kraken2 DB directory. . If not specified, will\n try to get from $KRAKEN2_DB env variable or see if\n \"/opt/DB/kraken2/standard2\" exists.\n (default: null)\n --taxids Taxonomic IDs to filter reads by. Multiple taxids should\n be delimited by commas (`--taxids 1,2,3`). To disable\n filtering of reads based on taxids, do not provide a\n value for the `--taxids` argument:\n `nextflow run ... --taxids --reads ...`\n (default: 10239 (Viruses))\n --exclude_unclassified_reads Exclude unclassified reads from taxonomic\n classification filtered reads (default: false)\n\nDe Novo Assembly Options:\n --do_unicycler_assembly Assemble filtered reads using Unicycler? (default: false)\n\nOther Options:\n --outdir The output directory where the results will be saved\n (default: results)\n -w/--work-dir The temporary directory where intermediate data will be\n saved (default: ./work)\n -profile Configuration profile to use. [standard, singularity,\n conda, slurm] (default \u0027standard\u0027)\n --tracedir Pipeline run info output directory (default:\n results/pipeline_info)\n\nNote:\nIt is recommended that this workflow be executed with Singularity using the\nSingularity profile (`-profile singularity`) for maximum reproducibility and\nease of execution on different platforms.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch4\u003e\u003ca id=\"user-content-preparing-your-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#preparing-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreparing your data\u003c/h4\u003e\n\u003cp\u003eIt is assumed that your data has been basecalled using the latest version of ONT Guppy (\u003ccode\u003eguppy_basecaller\u003c/code\u003e/\u003ccode\u003eguppy_basecall_server\u003c/code\u003e) and barcode demultiplexed using \u003ccode\u003eguppy_barcoder\u003c/code\u003e with the appropriate settings for the kits used.\u003c/p\u003e\n\u003cp\u003eAfter basecalling and demultiplexing, it is recommended that all reads belonging to a particular barcode be concatenated together and optionally renamed to represent the sample to which the reads belong. Virontus will extract the sample name for each input reads FASTQ file from the base filename of the FASTQ file (e.g. sample name will be \u003ccode\u003esample\u003c/code\u003e from filename \u003ccode\u003esample1.fastq\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eBelow is an example \u003ccode\u003eguppy_barcoder\u003c/code\u003e command for more lenient barcode demultiplexing:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eguppy_barcoder \\\n -q 0 \\\n --min_score 30 \\\n --detect_mid_strand_barcodes \\\n --allow_inferior_barcodes \\\n --trim_barcodes \\\n -i basecalled-reads/ \\\n -s demuxed-reads \\\n --arrangements_files barcode_arrs_nb12.cfg\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-q 0\u003c/code\u003e to output less files per barcode\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--min_score 30\u003c/code\u003e for a lower barcode score threshold (default: 60)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--detect_mid_strand_barcodes\u003c/code\u003e to detect mid strand barcodes\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--trim_barcodes\u003c/code\u003e to trim barcodes from read sequences\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--arrangements_files\u003c/code\u003e to specify the barcodes used\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e It\u0027s recommended to use the default setting if possible to avoid misassigning reads into the incorrect barcodes.\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-recommended-steps\" class=\"anchor\" aria-hidden=\"true\" href=\"#recommended-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRecommended Steps\u003c/h5\u003e\n\u003col\u003e\n\u003cli\u003eBasecall reads using Guppy\u003c/li\u003e\n\u003cli\u003eDemultiplex reads using \u003ccode\u003eguppy_barcoder\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eConcatenate reads belonging to the same barcode into a single file (\u003ccode\u003ecat barcode01/*.fastq \u0026gt; concat-reads/barcode01.fastq\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e[Optionally] rename concatenated barcoded reads with appropriate sample name (\u003ccode\u003emv concat-reads/barcode01.fastq concat-reads/sample1.fastq\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h4\u003e\n\u003cp\u003eExample command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ nextflow run peterk87/nf-virontus \\\n -resume \\\n -profile singularity \\\n --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ereads/*.fq\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --ref_fasta MN908947.3.fa \\\n --low_coverage 3 \\\n --bedfile nCoV-2019.bed\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat you will see in the terminal:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 20.01.0\nLaunching `../main.nf` [ecstatic_davinci] - revision: 9aeb19496b\nWARN: DSL 2 IS AN EXPERIMENTAL FEATURE UNDER DEVELOPMENT -- SYNTAX MAY CHANGE IN FUTURE RELEASE\n=======================================================\npeterk87/nf-virontus v1.1.0\n=======================================================\nPipeline Name : peterk87/nf-virontus\nPipeline Version : 1.1.0\nRun Name : ecstatic_davinci\nReads : reads/*.fq\nRef Sequences FASTA : MN908947.3.fa\nPrimer Scheme : nCoV-2019.bed\nConsensus No Coverage : \u0026lt;=0X positions replaced with \u0027-\u0027\nConsensus Low Coverage: \u0026lt;3X positions replaced with \u0027N\u0027\nCentrifuge DB : null\nKraken2 DB : null\nTaxids : Filtering for taxids belonging to 10239\nUnicycler Assembly? : No\nMax Memory : 256 GB\nMax CPUs : 48\nMax Time : 10d\nOutput dir : results\nWorking dir : ./work\nContainer Engine : singularity\nContainer : virontus.simg\nCurrent home : /home/pkruczkiewicz\nCurrent user : pkruczkiewicz\nCurrent path : ./\nScript dir : ./nf-virontus\nConfig Profile : standard\nCommand-Line : nextflow run peterk87/nf-virontus -profile singularity -resume --reads \u0027reads/*.fq\u0027 --ref_fasta MN908947.3.fa --low_coverage 3 --bedfile nCoV-2019.bed\nNextflow version : 20.01.0\n=========================================\nexecutor \u0026gt; local (18)\n[0a/142458] process \u0026gt; REC2FASTA [100%] 1 of 1 \u2714\n[a3/3168c5] process \u0026gt; MAP [100%] 3 of 3 \u2714\n[0d/8a698f] process \u0026gt; IVAR_TRIM [100%] 3 of 3 \u2714\n[76/f82320] process \u0026gt; MAP_STATS [100%] 3 of 3 \u2714\n[cc/de6b36] process \u0026gt; MEDAKA [100%] 3 of 3 \u2714\n[74/058b57] process \u0026gt; LONGSHOT [100%] 3 of 3 \u2714\n[b4/5ed366] process \u0026gt; BCF_FILTER [100%] 3 of 3 \u2714\n[a3/ae8e3a] process \u0026gt; CONSENSUS [ 100%] 3 of 3 \u2714\n[e3/f75ddb] process \u0026gt; COVERAGE_PLOT [100%] 3 of 3 \u2714\n\nPipeline execution summary\nCompleted at: 30-Apr-2020 14:00:11\nDuration : 1m 40s\nCPU hours : 0.1 (58.9% cached)\nSucceeded : 18\nCached : 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample output file tree structure:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eresults/\n\u251c\u2500\u2500 consensus\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02-MN908947.3.consensus.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04-MN908947.3.consensus.fasta\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 unclassified-MN908947.3.consensus.fasta\n\u251c\u2500\u2500 mapping\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 bamfiles\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02-MN908947.3.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 NB02-MN908947.3.trim.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02-MN908947.3-depths.tsv\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB02-MN908947.3.flagstat\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 NB02-MN908947.3.idxstats\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 bamfiles\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04-MN908947.3.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 NB04-MN908947.3.trim.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04-MN908947.3-depths.tsv\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NB04-MN908947.3.flagstat\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 NB04-MN908947.3.idxstats\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 unclassified\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 bamfiles\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 unclassified-MN908947.3.bam \n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 unclassified-MN908947.3.trim.bam \n\u2502\u00a0\u00a0 \u251c\u2500\u2500 unclassified-MN908947.3-depths.tsv\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 unclassified-MN908947.3.flagstat\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 unclassified-MN908947.3.idxstats\n\u251c\u2500\u2500 pipeline_info\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 execution_dag.dot\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 execution_report.html\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 execution_timeline.html\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 execution_trace.txt\n\u251c\u2500\u2500 plots\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 coverage_plot-NB02-VS-MN908947.3-log_scale.pdf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 coverage_plot-NB02-VS-MN908947.3.pdf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 coverage_plot-NB04-VS-MN908947.3-log_scale.pdf\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 coverage_plot-NB04-VS-MN908947.3.pdf\n\u251c\u2500\u2500 refs\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 MN908947.3.fa\n\u2514\u2500\u2500 vcf\n \u251c\u2500\u2500 NB02-MN908947.3.longshot.filt.vcf\n \u251c\u2500\u2500 NB02-MN908947.3.longshot.vcf\n \u251c\u2500\u2500 NB02-MN908947.3.medaka.vcf\n \u251c\u2500\u2500 NB04-MN908947.3.longshot.filt.vcf\n \u251c\u2500\u2500 NB04-MN908947.3.longshot.vcf\n \u251c\u2500\u2500 NB04-MN908947.3.medaka.vcf\n \u251c\u2500\u2500 unclassified-MN908947.3.longshot.filt.vcf\n \u251c\u2500\u2500 unclassified-MN908947.3.longshot.vcf\n \u2514\u2500\u2500 unclassified-MN908947.3.medaka.vcf\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003epeterk87/nf-virontus was originally written by Peter Kruczkiewicz.\u003c/p\u003e\n\u003cp\u003eBootstrapped with \u003ca href=\"https://github.com/nf-core/tools\"\u003enf-core/tools\u003c/a\u003e \u003ccode\u003enf-core create\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThank you to the \u003ca href=\"https://github.com/nf-core/tools\"\u003enf-core/tools\u003c/a\u003e team for a great tool for bootstrapping creation of a production ready Nextflow workflows.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePhysics Based Particle Filtering\u003c/h1\u003e\u003ca id=\"user-content-physics-based-particle-filtering\" class=\"anchor\" aria-label=\"Permalink: Physics Based Particle Filtering\" href=\"#physics-based-particle-filtering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official implementation of our paper \"Real-Time Physics-Based Object Pose Tracking during Non-Prehensile Manipulation\".\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbstract:\u003c/strong\u003e We propose a method to track the 6D pose of an object over time, while the object is under non-prehensile manipulation by a robot. At any given time during the manipulation of the object, we assume access to the robot joint controls and an image from a camera. We use the robot joint controls to perform a physics-based prediction of how the object might be moving. We then combine this prediction with the observation coming from the camera, to estimate the object pose as accurately as possible. We use a particle filtering approach to combine the control information with the visual information. We compare the proposed method with two baselines: (i) using only an image-based pose estimation system at each time-step, and (ii) a particle filter which does not perform the computationally expensive physics predictions, but assumes the object moves with constant velocity. Our results show that making physics-based predictions is worth the computational cost, resulting in more accurate tracking, and estimating object pose even when the object is not clearly visible to the camera.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSupplementary Video:\u003c/h1\u003e\u003ca id=\"user-content-supplementary-video\" class=\"anchor\" aria-label=\"Permalink: Supplementary Video:\" href=\"#supplementary-video\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClick to watch the video.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/watch?v=EMBFYzkno64\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a948dd0caa93d86bfc769e83a9dfb0c6157e71d5a943f64d2fe8ea4802bdf315/68747470733a2f2f692e7974696d672e636f6d2f76692f454d4246597a6b6e6f36342f6d617872657364656661756c742e6a7067\" alt=\"Watch the video\" data-canonical-src=\"https://i.ytimg.com/vi/EMBFYzkno64/maxresdefault.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBrief Description:\u003c/h1\u003e\u003ca id=\"user-content-brief-description\" class=\"anchor\" aria-label=\"Permalink: Brief Description:\" href=\"#brief-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe propose a method to track the pose of an object over time, by using the image from the camera, and the particles in the physical engine. Although sometimes the camera cannot see the object clearly, our method can still track the pose of the object.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuick Setup:\u003c/h1\u003e\u003ca id=\"user-content-quick-setup\" class=\"anchor\" aria-label=\"Permalink: Quick Setup:\" href=\"#quick-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eBuild Container\u003c/strong\u003e (This project uses singularity container to support all the code)\u003c/p\u003e\n\u003cp\u003ePlease enter into the main folder and run \u003ccode\u003e./build.sh\u003c/code\u003e in Ubuntu20 terminal to build the container.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDownload Rosbags\u003c/strong\u003e (For running demos only)\u003c/p\u003e\n\u003cp\u003eDownload \u003ca href=\"https://drive.google.com/drive/folders/13EbCuu231izDbmrcIeyjeQlJSPJL1qWW?usp=sharing\" rel=\"nofollow\"\u003ethe rosbags\u003c/a\u003e and save them to the \u003ccode\u003erosbag\u003c/code\u003e folder, i.e., \u003ccode\u003e~/rosbag/\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning Code\u003c/h1\u003e\u003ca id=\"user-content-running-code\" class=\"anchor\" aria-label=\"Permalink: Running Code\" href=\"#running-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStart Container\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the terminal, enter into the main file and run \u003ccode\u003e./run.sh\u003c/code\u003e, and then you can see \u003ccode\u003e[TrackObjectWithPF] Singularity\u0026gt; ~ $\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStart ROS Master\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ roscore\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eUsing Simulation Time\u003c/strong\u003e (For running demos only)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ rosparam set use_sim_time true\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEdit Config Information\u003c/strong\u003e (if desired) in \u003ccode\u003e~/catkin_ws/src/PBPF/config/parameter_info.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eerr_file\u003c/code\u003e: Name of the folder where the error.csv file is saved\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egazebo_flag\u003c/code\u003e: Use gazebo or not (True/False)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eobject_name_list\u003c/code\u003e: List of target objects names ([\"cracker\", \"soup\", ...])\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eobject_num\u003c/code\u003e: Number of target objects tracked\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eother_obj_num\u003c/code\u003e: Number of other objects\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eoto_name_list\u003c/code\u003e: List of other objects names\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eotob_name_list\u003c/code\u003e: List of other obstacles names\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eparticle_num\u003c/code\u003e: Number of particles\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epick_particle_rate\u003c/code\u003e: Percentage of particles selected as DOPE poses\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erobot_num\u003c/code\u003e: Number of robot\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun_alg_flag\u003c/code\u003e: Name of algorithm (PBPF/CVPF)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etask_flag\u003c/code\u003e: Name of task (\u00271\u0027/\u00272\u0027/\u00273\u0027/\u00274\u0027)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eupdate_style_flag\u003c/code\u003e: Name of the method used (time/pose)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eversion\u003c/code\u003e: whether to use ray tracing (old/multiray)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStart Running\u003c/strong\u003e (For running demos only)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./automated_experiments.sh\u003c/code\u003e (Remember to change the directory of some files)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStart Running\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ rosrun PBPF Physics_Based_Particle_Filtering.py\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eVisualization Window\u003c/strong\u003e (For visualizing only)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ rosrun PBPF Visualisation_World.py\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRecord Error\u003c/strong\u003e (For recording error only)\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ rosrun PBPF RecordError.py _\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eData\u003c/h1\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-label=\"Permalink: Data\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll experimental data and figures of the results are placed in the \u003ccode\u003e~/data/\u003c/code\u003e. All scenes of rosbags can be downloaded through the link blow: \u003ca href=\"https://drive.google.com/drive/folders/13EbCuu231izDbmrcIeyjeQlJSPJL1qWW?usp=sharing\" rel=\"nofollow\"\u003eRosbags for each scene of different objects\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 6, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [], - "updated_at": 1677780003.0 + "updated_at": 1711487031.0 }, { "data_format": 2, - "description": "Singularity images for the University of Arizona High Performance Computing systems", + "description": null, "filenames": [ - "Singularity.centos7-python3.7-transformers4.1.1" + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/Singularity.template", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/od/Singularity.ODBenchmarks", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.ml_mmfb", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.PyBNN", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.SupportVectorMachine", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.XGBoostBenchmark", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.TabularBenchmarks", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_101", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_201", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/nas/Singularity.nasbench_1shot1", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/rl/Singularity.learnaBenchmark", + "experiments/hpobench/libs/HPOBench/hpobench/container/recipes/rl/Singularity.Cartpole" ], - "full_name": "clulab/hpc-ml", + "full_name": "hvarfner/JointEntropySearch", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-hpc-ml-machine-learning-singularity-images-for-ua-hpc\" class=\"anchor\" aria-hidden=\"true\" href=\"#hpc-ml-machine-learning-singularity-images-for-ua-hpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehpc-ml: Machine-learning Singularity images for UA HPC\u003c/h1\u003e\n\u003cp\u003eThe recipes in this repository are designed for the University of Arizona High Performance Computing systems.\nThey build Singularity images that include common machine learning libraries including scikit-learn, tensorflow, keras, torch, as well as the Nvidia CUDNN.\nTo activate singularity on hpc, use \"module load singularity\", without quotes. And then you can execute singularity commands.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThis GitHub repository is connected to Singularity Hub (\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/\u003c/a\u003e).\nPlease see their documentation on \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/interact\" rel=\"nofollow\"\u003einteracting with Singularity images from Singularity Hub\u003c/a\u003e.\nNote that there are \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/regulatory/limits\" rel=\"nofollow\"\u003ehard limits on how many times a container can be pulled from Singularity Hub each week\u003c/a\u003e, so please make sure that you always \u003ccode\u003esingularity pull\u003c/code\u003e to get a local copy, for example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://clulab/hpc-ml:centos7-python3.7-transformers4.1.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat will download a Singularity image named \u003ccode\u003ehpc-ml_centos7-python3.7-transformers4.1.1.sif\u003c/code\u003e that you can then use with other Singularity commands.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJoint Entropy Search for Maximally-Informed Bayesian Optimization\u003c/h1\u003e\u003ca id=\"user-content-joint-entropy-search-for-maximally-informed-bayesian-optimization\" class=\"anchor\" aria-label=\"Permalink: Joint Entropy Search for Maximally-Informed Bayesian Optimization\" href=\"#joint-entropy-search-for-maximally-informed-bayesian-optimization\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the official repository for Joint Entropy Search for Maximally-Informed Bayesian Optimization. We developed our code by building on Max-value Entropy Search (Wang and Jegelka, 2017) which in turn built on Predictive Entropy Search (Hernandez-Lobato et al., 2014), which was developed upon GPstuff (Vanhatalo et al., 2013). To keep the repository as trim and clean as possible, some of the examples and methods from previous work have been removed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSystem Requirements\u003c/h2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-label=\"Permalink: System Requirements\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll code has been tested with MATLAB 2021a. After installing the conda environment provided in \u003ccode\u003ejes.yml\u003c/code\u003e, add the required libraries by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/conda/envs/jes/lib\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand everything should be up and running.\u003c/p\u003e\n\u003cp\u003eWhile the required mex file is included, it may need to be re-compiled. To do so, do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd utils\nmex chol2invchol.c -lgsl -lblas\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLastly, to run experiments, do one of the following:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFor GP sample tasks (where the hyperparameters of the surrogate model are fixed):\u003c/h4\u003e\u003ca id=\"user-content-for-gp-sample-tasks-where-the-hyperparameters-of-the-surrogate-model-are-fixed\" class=\"anchor\" aria-label=\"Permalink: For GP sample tasks (where the hyperparameters of the surrogate model are fixed):\" href=\"#for-gp-sample-tasks-where-the-hyperparameters-of-the-surrogate-model-are-fixed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"gp_task(path, seed, approach, dim);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003epath\u003c/code\u003e is the path to the experiment to run, \u003ccode\u003eseed\u003c/code\u003e is the seed to run, \u003ccode\u003eapproach\u003c/code\u003e is the acquisition function in question, and \u003ccode\u003edim\u003c/code\u003e, somewhat exessively, is the dimensionality of the problem. For example, one can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"gp_task(\u0027gp/gp_2dim.py\u0027, 42, \u0027JES\u0027, 2);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFor all other tasks, use \u003ccode\u003esynthetic_task\u003c/code\u003e:\u003c/h4\u003e\u003ca id=\"user-content-for-all-other-tasks-use-synthetic_task\" class=\"anchor\" aria-label=\"Permalink: For all other tasks, use synthetic_task:\" href=\"#for-all-other-tasks-use-synthetic_task\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"synthetic_task(path, seed, approach, dim);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo to run Hartmann (6D):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ematlab -nodisplay -nosplash -nodesktop -r \"synthetic_task(\u0027synthetic/hartmann6.py\", 37, \u0027MES\u0027, 6);exit;\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEvery experiment is automatically stored in a csv in experiments/results. The recommended points, which are needed for inference regret, are all evaluated \u003cem\u003eafter\u003c/em\u003e the full run is finished. These queries are appended to the same csv.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAvailable options\u003c/h2\u003e\u003ca id=\"user-content-available-options\" class=\"anchor\" aria-label=\"Permalink: Available options\" href=\"#available-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThanks to Wang \u0026amp; Jegelka (2017), this repository comes equipped with the following acquisition functions. MES-G (shortened to just MES) and EI were used in this paper to benchmark against.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMax-value Entropy Search with Gumbel sampling (MES-G) by Wang \u0026amp; Jegelka, 2017;\u003c/li\u003e\n\u003cli\u003eMax-value Entropy Search with random features (MES-R) by Wang \u0026amp; Jegelka, 2017;\u003c/li\u003e\n\u003cli\u003eOptimization as estimation (EST) by Wang et al., 2016.\u003c/li\u003e\n\u003cli\u003eGaussian process upper confidence bound (GP-UCB) by Auer, 2002; Srinivas et al., 2010;\u003c/li\u003e\n\u003cli\u003eProbability of improvement (PI) by Kushner, 1964;\u003c/li\u003e\n\u003cli\u003eExpected improvement (EI) by Mockus, 1974\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe repository builds on \u003ca href=\"https://bitbucket.org/jmh233/codepesnips2014\" rel=\"nofollow\"\u003epredictive entropy search\u003c/a\u003e (Hern\u00e1ndez-Lobato et al., 2014).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e--Anonymous author(s)--. Joint Entropy Search for Maximally-Informed Bayesian Optimization. Under review.\u003c/li\u003e\n\u003cli\u003eWang, Zi and Jegelka, Stefanie. Max-value Entropy Search for Efficient Bayesian Optimization. International Conference on Machine Learning (ICML), 2017.\u003c/li\u003e\n\u003cli\u003eAuer, Peter. Using confidence bounds for exploitationexploration tradeoffs. Journal of Machine Learning Research, 3:397\u2013422, 2002.\u003c/li\u003e\n\u003cli\u003eSrinivas, Niranjan, Krause, Andreas, Kakade, Sham M, and Seeger, Matthias. Gaussian process optimization in the bandit setting: No regret and experimental design. In International Conference on Machine Learning (ICML), 2010.\u003c/li\u003e\n\u003cli\u003eKushner, Harold J. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. Journal of Fluids Engineering, 86(1):97\u2013106, 1964.\u003c/li\u003e\n\u003cli\u003eMockus, J. On Bayesian methods for seeking the extremum. In Optimization Techniques IFIP Technical Conference, 1974.\u003c/li\u003e\n\u003cli\u003eWang, Zi, Zhou, Bolei, and Jegelka, Stefanie. Optimization as estimation with Gaussian processes in bandit settings. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.\u003c/li\u003e\n\u003cli\u003eCatto, Erin. Box2d, a 2D physics engine for games. \u003ca href=\"http://box2d.org\" rel=\"nofollow\"\u003ehttp://box2d.org\u003c/a\u003e, 2011.\u003c/li\u003e\n\u003cli\u003ePybox2d, 2D Game Physics for Python. \u003ca href=\"http://github.com/pybox2d/pybox2d\"\u003ehttp://github.com/pybox2d/pybox2d\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eHern\u00e1ndez-Lobato, Jos\u00e9 Miguel, Hoffman, Matthew W, and Ghahramani, Zoubin. Predictive entropy search for efficient global optimization of black-box functions. In Advances in Neural Information Processing Systems (NIPS), 2014. \u003ca href=\"https://bitbucket.org/jmh233/codepesnips2014\" rel=\"nofollow\"\u003ehttps://bitbucket.org/jmh233/codepesnips2014\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHennig, Philipp and Schuler, Christian J. Entropy search for information-efficient global optimization. Journal of Machine Learning Research, 13:1809\u20131837, 2012. \u003ca href=\"http://www.probabilistic-optimization.org/Global.html\" rel=\"nofollow\"\u003ehttp://www.probabilistic-optimization.org/Global.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eJarno Vanhatalo, Jaakko Riihim\u00e4ki, Jouni Hartikainen, Pasi Jyl\u00e4nki, Ville Tolvanen, Aki Vehtari. GPstuff: Bayesian Modeling with Gaussian Processes. Journal of Machine Learning Research, 14(Apr):1175-1179, 2013.\u003c/li\u003e\n\u003cli\u003eKandasamy, Kirthevasan, Schneider, Jeff, and Poczos, Barnabas. High dimensional Bayesian optimisation and bandits via additive models. In International Conference on Machine Learning (ICML), 2015.\u003c/li\u003e\n\u003cli\u003eWang, Zi, Li, Chengtao, Jegelka, Stefanie, and Kohli, Pushmeet. Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. International Conference on Machine Learning (ICML), 2017.\u003c/li\u003e\n\u003cli\u003eWestervelt, Eric R, Grizzle, Jessy W, Chevallereau, Christine, Choi, Jun Ho, and Morris, Benjamin. Feedback control of dynamic bipedal robot locomotion, volume 28. CRC press, 2007.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 6, - "subscribers_count": 27, + "subscribers_count": 1, "topics": [], - "updated_at": 1653464623.0 + "updated_at": 1728040124.0 }, { "data_format": 2, - "description": null, + "description": "This repository is dedicated to the studienarbeit project of \"Numerical investigation of 2D transonic shock-buffet around a NACA 0012-34 airfoil using OpenFOAM and flowTorch\" undertaken by Mr. Tushar Gholap under the guidance of Dr. Andre Weiner.", "filenames": [ - "singularity/Singularity" + "Singularity.def" ], - "full_name": "Lizhen0909/LSHVec", + "full_name": "Tushargh29/transonic_shock_buffet", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-lshvec-a-vector-representation-of-dna-sequences-using-locality-sensitive-hashing\" class=\"anchor\" aria-hidden=\"true\" href=\"#lshvec-a-vector-representation-of-dna-sequences-using-locality-sensitive-hashing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLSHVec: A Vector Representation of DNA Sequences Using Locality Sensitive Hashing\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-july-2021-checkout-lshvec-upcxx-which-is-a-pure-c-implementation\" class=\"anchor\" aria-hidden=\"true\" href=\"#july-2021-checkout-lshvec-upcxx-which-is-a-pure-c-implementation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJuly 2021: checkout \u003ca href=\"https://github.com/bochen0909/lshvec-upcxx\"\u003elshvec-upcxx\u003c/a\u003e which is a pure c++ implementation.\u003c/h3\u003e\n\u003ch2\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-hidden=\"true\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSummary\u003c/h2\u003e\n\u003cp\u003eLSHVec is a k-mer/sequence embedding/classfication software which extends \u003ca href=\"https://fasttext.cc/\" rel=\"nofollow\"\u003eFastText\u003c/a\u003e . It applies LSH (Locality Sensitive Hashing) to reduce the size of k-mer vocabulary and improve the performance of embedding.\u003c/p\u003e\n\u003cp\u003eBesides building from source code, LSHVec can run using docker or singularity.\u003c/p\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://www.biorxiv.org/content/10.1101/726729v1\" rel=\"nofollow\"\u003eA Vector Representation of DNA Sequences Using Locality Sensitive Hashing\u003c/a\u003e for the idea and experiments.\u003c/p\u003e\n\u003cp\u003eThere are also some pretained models that can be used, please see \u003ca href=\"https://github.com/Lizhen0909/PyLSHvec/blob/master/README.md\"\u003ePyLSHvec\u003c/a\u003e for details.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eHere is the environment I worked on. Other versions may also work. Python 3 should work, but I don\u0027t use it a lot.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLinux, gcc with C++11\u003c/li\u003e\n\u003cli\u003ePython 2.7 or Python 3.6 or 3.7\n\u003cul\u003e\n\u003cli\u003ejoblib 0.12.4\u003c/li\u003e\n\u003cli\u003etqdm 4.28.1\u003c/li\u003e\n\u003cli\u003enumpy 1.15.0\u003c/li\u003e\n\u003cli\u003epandas 0.23.4\u003c/li\u003e\n\u003cli\u003esklearn 0.19.1 (only for evaluation)\u003c/li\u003e\n\u003cli\u003eMulticoreTSNE (only for visualization)\u003c/li\u003e\n\u003cli\u003ecython 0.28.5\u003c/li\u003e\n\u003cli\u003ecsparc (included)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#build-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild from Source\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eclone from git\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone https://LizhenShi@bitbucket.org/LizhenShi/lshvec.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd lshvec\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003einstall csparc which wraps a c version of k-mer generator I used in another project\u003c/p\u003e\n\u003cp\u003efor python 2.7\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install pysparc-0.1-cp27-cp27mu-linux_x86_64.whl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eor for python 3.6\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install pysparc-0.1-cp36-cp36m-linux_x86_64.whl\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eor for python 3.7\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epip install pysparc-0.1-cp37-cp37m-linux_x86_64.whl\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003emake\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-jupyter-notebook-examples\" class=\"anchor\" aria-hidden=\"true\" href=\"#jupyter-notebook-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eJupyter Notebook Examples\u003c/h2\u003e\n\u003cp\u003eA toy example, which is laptop friendly and should finish in 10 minutes, can be found in \u003ca href=\"notebook/Tutorial_Toy_Example.ipynb\"\u003eTutorial_Toy_Example.ipynb\u003c/a\u003e. Because of randomness the result may be different.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"notebook/Tutorial_Toy_Example.png\"\u003e\u003cimg src=\"notebook/Tutorial_Toy_Example.png\" alt=\"Tutorial_Toy_Example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA practical example which uses ActinoMock Nanopore data can be found at \u003ca href=\"notebook/Tutorial_ActinoMock_Nanopore.ipynb\"\u003eTutorial_ActinoMock_Nanopore.ipynb\u003c/a\u003e. The notebook ran on a 16-core 64G-mem node and took a few hours (I think 32G mem should work too).\u003c/p\u003e\n\u003cp\u003e\u200b\t\t\t\t\t\t \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"notebook/Tutorial_ActinoMock_Nanopore.png\"\u003e\u003cimg src=\"notebook/Tutorial_ActinoMock_Nanopore.png\" alt=\"Tutorial_ActinoMock_Nanopore\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-command-line-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#command-line-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand line options\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-fastqtoseqpy\" class=\"anchor\" aria-hidden=\"true\" href=\"#fastqtoseqpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003efastqToSeq.py\u003c/h3\u003e\n\u003cp\u003econvert a fastq file to a seq file\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython fastqToSeq.py -i \u0026lt;fastq_file\u0026gt; -o \u0026lt;out seq file\u0026gt; -s \u0026lt;1 to shuffle, 0 otherwise\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-hashseqpy\" class=\"anchor\" aria-hidden=\"true\" href=\"#hashseqpy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ehashSeq.py\u003c/h3\u003e\n\u003cp\u003eEncode reads in a seq file use an encoding method.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython hashSeq.py -i \u0026lt;seq_file\u0026gt; --hash \u0026lt;fnv or lsh\u0026gt; -o \u0026lt;outfile\u0026gt; [-k \u0026lt;kmer_size\u0026gt;] [--n_thread \u0026lt;n\u0026gt;] [--hash_size \u0026lt;m\u0026gt;] [--batch_size \u0026lt;n\u0026gt;] [--bucket \u0026lt;n\u0026gt;] [--lsh_file \u0026lt;file\u0026gt;] [--create_lsh_only]\n\n --hash_size \u0026lt;m\u0026gt;: only used by lsh which defines 2^m bucket.\n --bucket \u0026lt;n\u0026gt;: number of bucket for hash trick, useless for onehot.\n \t\t\t\t For fnv and lsh it limits the max number of words.\n \t\t\t\t For lsh the max number of words is min(2^m, n).\n --batch_size \u0026lt;b\u0026gt;: how many reads are processed at a time. A small value uses less memory.\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-lshvec\" class=\"anchor\" aria-hidden=\"true\" href=\"#lshvec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003elshvec\u003c/h3\u003e\n\u003cp\u003ePlease refer to \u003ca href=\"https://fasttext.cc/docs/en/options.html\" rel=\"nofollow\"\u003efasttext options\u003c/a\u003e. However note that options of \u003ccode\u003ewordNgrams\u003c/code\u003e, \u003ccode\u003eminn\u003c/code\u003e,\u003ccode\u003emaxn\u003c/code\u003e does not work with lshvec.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-of-docker-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-of-docker-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample of Docker Run\u003c/h2\u003e\n\u003cp\u003ePull from docker hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull lizhen0909/lshvec:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAssume \u003ccode\u003edata.fastq\u003c/code\u003e file is in folder \u003ccode\u003e/path/in/host\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003econvert fastq to a seq file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/in/host:/host lshvec:latest bash -c \"cd /host \u0026amp;\u0026amp; fastqToSeq.py -i data.fastq -o data.seq\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecreate LSH:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/in/host:/host lshvec:latest bash -c \"cd /host \u0026amp;\u0026amp; hashSeq.py -i data.seq --hash lsh -o data.hash -k 15\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003erun lshvec:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v /path/in/host:/host lshvec:latest bash -c \"cd /host \u0026amp;\u0026amp; lshvec skipgram -input data.hash -output model\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-of-singularity-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-of-singularity-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample of Singularity Run\u003c/h2\u003e\n\u003cp\u003eWhen running using Singularity, it is probably in an HPC environment. The running is similar to docker. However depending on the version of singularity, commands and paths might be different, especially from 2.x to 3.x. Here is an example for version 2.5.0.\u003c/p\u003e\n\u003cp\u003eAlso it is better to specify number of threads, otherwise max number of cores will be used which is not desired in HPC environment.\u003c/p\u003e\n\u003cp\u003ePull from docker hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name lshvec.sif shub://Lizhen0909/LSHVec\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePut \u003ccode\u003edata.fastq\u003c/code\u003e file is in host \u003ccode\u003e/tmp\u003c/code\u003e, since Singularity automatically mount \u003ccode\u003e/tmp\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003econvert fastq to a seq file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/lshvec.sif bash -c \"cd /tmp \u0026amp;\u0026amp; fastqToSeq.py -i data.fastq -o data.seq\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecreate LSH:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/lshvec.sif bash -c \"cd /tmp \u0026amp;\u0026amp; hashSeq.py -i data.seq --hash lsh -o data.hash -k 15 --n_thread 12\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003erun lshvec:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run /path/to/lshvec.sif bash -c \"cd /tmp \u0026amp;\u0026amp; lshvec skipgram -input data.hash -output model -thread 12\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003elshvec\u003c/code\u003e gets stuck at \u003ccode\u003eRead xxxM words\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSearch \u003ccode\u003eMAX_VOCAB_SIZE\u003c/code\u003e in the source code and change it to a bigger one. When a word\u0027s index is bigger than that number, a loop is carried to query it, which is costly. The number is 30M in FastText which is good for languages. But it is too small for k-mers. The number has been already increased to 300M in FastSeq. But for large and/or high-error-rate data, it may be still not enough.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eI have big data\u003c/p\u003e\n\u003cp\u003ehashSeq reads all data into memory to sample k-mers for hyperplanes. If data is too big it may not fit into memory. One can\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eTry sampling. DNA reads generally have high coverage. Such high coverage may not be necessary.\u003c/li\u003e\n\u003cli\u003eOr use \u003ccode\u003ecreate_hash_only\u003c/code\u003e to create lsh on a small (sampled) data; then split your data into multiple files and run hashSeq with \u003ccode\u003elsh_file\u003c/code\u003e option on many nodes.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecore dumped when hashing\u003c/p\u003e\n\u003cp\u003eError like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eterminate called after throwing an instance of \u0027std::out_of_range\u0027\nwhat(): map::at\nAborted (core dumped)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003emostly because a sequence contains characters other than ACGTN. So please convert non-ACGT characters to N\u0027s.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eInherit license from FastText which is BSD License\u003c/p\u003e\n", - "stargazers_count": 6, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNumerical investigation of 2D transonic shock-buffet around a NACA 0012-34 airfoil using OpenFOAM\u003c/h1\u003e\u003ca id=\"user-content-numerical-investigation-of-2d-transonic-shock-buffet-around-a-naca-0012-34-airfoil-using-openfoam\" class=\"anchor\" aria-label=\"Permalink: Numerical investigation of 2D transonic shock-buffet around a NACA 0012-34 airfoil using OpenFOAM\" href=\"#numerical-investigation-of-2d-transonic-shock-buffet-around-a-naca-0012-34-airfoil-using-openfoam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating the Singularity image\u003c/h2\u003e\u003ca id=\"user-content-creating-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Creating the Singularity image\" href=\"#creating-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo make results reproducible, a \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image containing OpenFOAM is used to perform simulations. To install Singularity, follow the official \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html\" rel=\"nofollow\"\u003einstallation instructions\u003c/a\u003e. More information on the image may be found \u003ca href=\"https://github.com/AndreWeiner/of_pytorch_docker\"\u003ehere\u003c/a\u003e. Building the image requires root privileges. Once the image is built, any user can use the image without any extra privileges. To build the image, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build of2006-py1.6-cpu.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo check if the image is working, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run-help of2006-py1.6-cpu.sif\n# ...\n# expected output\n# ...\n Simple Singularity image containing OpenFOAM-v2006\n and libtorch (PyTorch 1.6). The generic syntax to execute\n a command with arguments is\n\n singularity run image_name.sif command [path] [argument]\n\n Examples:\n\n - compile the application tensorCreation using wmake\n singularity run of2006-py1.6-cpu.sif wmake test/tensorCreation/\n\n - clean tensorCreation build\n singularity run of2006-py1.6-cpu.sif wclean test/tensorCreation/\n\n - run tensorCreation\n singularity run of2006-py1.6-cpu.sif ./tensorCreation test/tensorCreation/\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePerforming a simulation\u003c/h2\u003e\u003ca id=\"user-content-performing-a-simulation\" class=\"anchor\" aria-label=\"Permalink: Performing a simulation\" href=\"#performing-a-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe folder \u003cem\u003etest_cases\u003c/em\u003e contains all basic OpenFOAM simulation cases. Simulations should be performed in the \u003cem\u003erun\u003c/em\u003e directory, which is not tracked by the version control system. A typical workflow to run a simulation may look as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# top-level folder of the repository\nmkdir run\n# make a copy of the simulation case\ncp -r test_cases/naca0012-34-base/ run/test_base\n# execute the runCase script in the top-level folder where the image is located\n# the first argument must be the path to the simulation case\n./runCase run/test_base/\n# post-processing using local ParaView installation\ncd run/test_base\nparaview post.foam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf the simulation is performed in parallel, make sure to select \u003cem\u003eCase Type -\u0026gt; Decomposed Case\u003c/em\u003e in ParaView. To see the mesh properly, make sure to unselect \u003cem\u003eDecompose polyhedra\u003c/em\u003e.\u003c/p\u003e\n", + "stargazers_count": 7, "subscribers_count": 2, "topics": [ - "locality-sensitive-hashing", - "sequence-vector", - "classfication" + "openfoam", + "python", + "paraview", + "dynamic-mode-decomposition", + "flowtorch" ], - "updated_at": 1675318662.0 + "updated_at": 1731770973.0 }, { "data_format": 2, - "description": null, + "description": "NectarCAM high level analysis tools", "filenames": [ - "Singularity.1.0", "Singularity" ], - "full_name": "roveri-marco/optic", - "latest_release": "1.0", - "stargazers_count": 6, - "subscribers_count": 1, + "full_name": "cta-observatory/nectarchain", + "latest_release": "v0.1.9", + "stargazers_count": 7, + "subscribers_count": 11, "topics": [], - "updated_at": 1678867453.0 + "updated_at": 1733214778.0 }, { "data_format": 2, - "description": null, + "description": "network analysis of network analysis publications --- split by software", "filenames": [ - "containers/Singularity.0.0.1", - "containers/Singularity.0.0.2", - "containers/Singularity.0.0.3", - "containers/Singularity.dev", - "containers/Singularity.0.0.4" + "Singularity" ], - "full_name": "lscsoft/bilby_pipe", + "full_name": "incertae-sedis/cavatica", + "latest_release": "v1.0.0", + "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/incertae-sedis/cavatica\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859cc8b569e45dbdc166015d056b2e33682ac216b50b0e5ff077a74e0af59434/68747470733a2f2f7472617669732d63692e6f72672f696e6365727461652d73656469732f63617661746963612e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/incertae-sedis/cavatica.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/incertae-sedis/cavatica/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dec3e303eeddcbb6862373485486f42fa9665a079726ea0b73df99abf2beca72/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f696e6365727461652d73656469732f63617661746963612e7376673f6c6162656c3d63757272656e742b72656c65617365\" alt=\"github release\" data-canonical-src=\"https://img.shields.io/github/release/incertae-sedis/cavatica.svg?label=current+release\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/1322\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/incertaesedis/cavatica/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/be22f9ba4feadaf9bac5670ffa0881abfc652ee88e4237092cd392b901059bdb/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f636c6f75642f6275696c642f696e63657274616573656469732f63617661746963612e737667\" alt=\"Docker Automated build\" data-canonical-src=\"https://img.shields.io/docker/cloud/build/incertaesedis/cavatica.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/incertaesedis/cavatica/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d6a3c603bcab0c6c6dd3c26aa73e83181a1073cc678b4e89e1e79336c0b6a5cf/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f696e63657274616573656469732f63617661746963612e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/incertaesedis/cavatica.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInitial Commit\u003c/strong\u003e: July 2016\u003c/p\u003e\n\u003cp\u003e***** Cavatica has been adopted by the incertae-sedis group. *****\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCavatica\u003c/h1\u003e\u003ca id=\"user-content-cavatica\" class=\"anchor\" aria-label=\"Permalink: Cavatica\" href=\"#cavatica\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and pipeline for fetching PubMed and PubMed Central data and co-author network analysis. This tool can be used to identify author trends among several search terms.\u003c/p\u003e\n\u003cp\u003eAn example, I\u0027ve used these scripts to do a multi-network analysis of network analysis papers and their software.\n\u003ca href=\"https://github.com/incertae-sedis/cavatica/wiki\"\u003eWiki Page Here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Adder.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Adder.png\" width=\"600\" alt=\"Added\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe name comes from Charlotte\u0027s Web since her full name was Charlotte A. Cavatica. Although Cavatica also refers to barn spider.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline\u003c/h2\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-label=\"Permalink: Pipeline\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e***** Cavatica pipeline has been modified so no longer relies on Ebot. *****\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/plan.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/plan.png\" width=\"600\" alt=\"Plan\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSome type of Linux Terminal where you can run Bash. (Cygwin if you\u0027re on Windows. Terminal already preinstalled on Mac)\u003c/li\u003e\n\u003cli\u003eR (check if installed by typing Rscript --version)\u003c/li\u003e\n\u003cli\u003eperl (check if installed by typing perl --version)\u003c/li\u003e\n\u003cli\u003eMango Graph Studio for multi-network analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/incertae-sedis/cavatica.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBasic Example\u003c/h2\u003e\u003ca id=\"user-content-basic-example\" class=\"anchor\" aria-label=\"Permalink: Basic Example\" href=\"#basic-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is a basic example fetching PubMed and PMC papers containing the word \"Neo4j\" and \"Cytoscape\".\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd cavatica/data\nmkdir test\ncd test\necho \"Neo4j\" \u0026gt; config.txt\necho \"Cytoscape\" \u0026gt;\u0026gt; config.txt\n../../code/script.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create tabular files (list of papers \u003ccode\u003eNeo4j_papers_pm.tsv\u003c/code\u003e and list of authors \u003ccode\u003eNeo4j_authors_pm.tsv\u003c/code\u003e). Open the png files \u003ccode\u003eNeo4j_pm.png\u003c/code\u003e to see a barchart of the number of papers by year.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Neo4j-pubmedcounts.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Neo4j-pubmedcounts.png\" width=\"400\" alt=\"Neo4j count\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Cytoscape-pubmedcounts.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Cytoscape-pubmedcounts.png\" width=\"400\" alt=\"Cavatica count\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCan also open the html files to check the one sentence usages of Neo4j and Cavatica\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\u003ctr\u003e\n\u003ctd\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSentences that contain Neo4j\u003c/h1\u003e\u003ca id=\"user-content-sentences-that-contain-neo4j\" class=\"anchor\" aria-label=\"Permalink: Sentences that contain Neo4j\" href=\"#sentences-that-contain-neo4j\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=29377902\" rel=\"nofollow\"\u003e29377902\u003c/a\u003e\n Reactome graph database: Efficient access to complex pathway data.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eHere \nwe present the rationale behind the adoption of a graph database (\u003cb\u003eNeo4j\u003c/b\u003e) as well as the new ContentService (REST API) that provides access to these data. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=28936969\" rel=\"nofollow\"\u003e28936969\u003c/a\u003e Systematic integration of biomedical knowledge prioritizes drugs for repurposing.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eFirst, we constructed Hetionet (\u003cb\u003eneo4j\u003c/b\u003e.het.io), an integrative network encoding knowledge from millions of biomedical studies. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e2017 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=28416946\" rel=\"nofollow\"\u003e28416946\u003c/a\u003e Use of Graph Database for the Integration of Heterogeneous Biological Data.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHere, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and \u003cb\u003eNeo4j\u003c/b\u003e, one of the most widely used graph databases. \u003c/li\u003e\n\u003cli\u003eWhen we tested the query execution performance of MySQL versus \u003cb\u003eNeo4j\u003c/b\u003e, we found that \u003cb\u003eNeo4j\u003c/b\u003e outperformed MySQL in all cases. \u003c/li\u003e\n\u003cli\u003eThese results show that using graph-based databases, such as \u003cb\u003eNeo4j\u003c/b\u003e, is an efficient way to store complex biological relationships. \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\u003ctr\u003e\n\u003ctd\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSentences that contain Cytoscape\u003c/h1\u003e\u003ca id=\"user-content-sentences-that-contain-cytoscape\" class=\"anchor\" aria-label=\"Permalink: Sentences that contain Cytoscape\" href=\"#sentences-that-contain-cytoscape\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=29894068\" rel=\"nofollow\"\u003e29894068\u003c/a\u003e Identification of potential miRNAs and candidate genes of cervical intraepithelial neoplasia by bioinformatic analysis.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eThen the miRNA- mRNA regulatory network was constructed using \u003cb\u003eCytoscape\u003c/b\u003e software. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=29872319\" rel=\"nofollow\"\u003e29872319\u003c/a\u003e An integrated analysis of key microRNAs, regulatory pathways and clinical relevance in bladder cancer.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eProtein-protein interaction (PPI) and miRNA-mRNA regulatory networks were established by using the Search Tool for the Retrieval of Interacting Genes/Proteins and \u003cb\u003eCytoscape\u003c/b\u003e tool. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=29760609\" rel=\"nofollow\"\u003e29760609\u003c/a\u003e Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eProtein-protein interaction (PPI) network was constructed by STRING and visualized in \u003cb\u003eCytoscape\u003c/b\u003e. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eIt will also create a script \u003ccode\u003epubmed.gel\u003c/code\u003e. Open \u003ca href=\"https://www.complexcomputation.com/en/product/mango-community-edition/\" rel=\"nofollow\"\u003eMango Graph Studio\u003c/a\u003e, open \u003ccode\u003epubmed.gel\u003c/code\u003e and type the following into the Mango Console.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun \"pubmed.gel\";\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a transition table and export the file. It will also load and visualize the author-paper networks.\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\u003ctr\u003e\n\u003ctd\u003eNeo4j\u003c/td\u003e\n\u003ctd\u003eCytoscape\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Neo4j.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Neo4j.png\" width=\"300\" alt=\"Neo4j network\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Cytoscape.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Cytoscape.png\" width=\"300\" alt=\"Cavatica network\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\nGoing back to your terminal, rerun the script file and it will continue.\n\u003cpre\u003e\u003ccode\u003e../../code/script.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe transitions should be saved in \u003ccode\u003etrends_pm.txt\u003c/code\u003e. The following trends_pm.txt indicates that authors switched from cytoscape to Neo4j 9 times, while authors switched from Neo4j to Cytoscape 3 times.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCytoscape:Neo4j 9\nNeo4j:Cytoscape 3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt will then commence searching PMC, fetching list of papers and authors and generating a \"pmc.gel\" file. Once again open the \"pmc.gel\" file in Mango and type the following into Mango Console.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun \"pmc.gel\";\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen rerun the script to continue tabulating the trends which should be saved in \u003ccode\u003etrends_pmc.txt\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe output of a 2017 run comparing \"Neo4j\", \"Gephi\", \"GraphViz\" and \"iGraph\" is shown below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e=============PubMed Transitions\nNeo4j:Gephi 1\nNeo4j:GraphViz 1\nNeo4j:iGraph 1\n=============PubMed Central Transitions\nGephi:GraphViz 2\nGephi:Neo4j 3\nGephi:iGraph 31\nGraphViz:Gephi 19\nGraphViz:Neo4j 10\nGraphViz:iGraph 58\nNeo4j:Gephi 4\nNeo4j:GraphViz 4\nNeo4j:iGraph 1\niGraph:Gephi 34\niGraph:GraphViz 9\niGraph:Neo4j 13\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePMC results usually return more papers since search terms like \"Neo4j\" or \"Cytoscape\" are being matched to the fulltext, instead of just the title and abstract. This may return more accurate trend tables since sometimes software names are only mentioned in the methods and not in the abstract.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Container\u003c/h2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity Container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo provides a container for easily reproducing and running Cavatica through a container. The pipeline for both Singularity and Docker was ran on an Ubuntu 18.04 instance on \u003ca href=\"https://jetstream-cloud.org/\" rel=\"nofollow\"\u003eJetstream\u003c/a\u003e, which is a national science and engineering cloud led by the Indiana University Pervasive Technology Institute.\u003c/p\u003e\n\u003cp\u003eA singularity container of Cavatica is available on \u003ca href=\"https://singularity-hub.org/collections/1322\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e. Using singularity you can download the contained with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://TeamMango/cavatica:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen run, the container will look for a text file called \u003ccode\u003econfig.txt\u003c/code\u003e in a directory called \u003ccode\u003eoutput\u003c/code\u003e in the same directory as the \u003ccode\u003e.simg\u003c/code\u003e you just downloaded. Place the terms that you want Cavatica to search for in this file. In Ubuntu, you can use the following commands to create this file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir output\necho \"YOURSEARCHTERM\" \u0026gt; ./output/config.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYour search terms can also be followed by a year range, separated by commas:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eecho \"YOURSEARCHTERM,1996,2006\" \u0026gt; ./output/config.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEach search term and year range should occupy it\u0027s own line. If you want to search for use of the term cytoscape and VisANT between 1994 and 2000, \u003ccode\u003econfig.txt\u003c/code\u003e would look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evisant,1999,2006\ncytoscape,1994,2003\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce you have entered the terms in the \u003ccode\u003econfig.txt\u003c/code\u003e file, return to the same directory as the .simg image and run the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind output:/cavatica/data/output TeamMango-cavatica-master-latest.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe results of the search will appear in the \u003ccode\u003eoutput\u003c/code\u003e directory next to your \u003ccode\u003econfig.txt\u003c/code\u003e file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Container\u003c/h2\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-label=\"Permalink: Docker Container\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA docker container of Cavatica is available on \u003ca href=\"https://hub.docker.com/r/incertaesedis/cavatica/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e. You can pull the docker container with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull incertaesedis/cavatica\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the docker container, move into the directory where you want to generate output from Cavatica. Create three files called \u003ccode\u003emultitool-pubmed.tsv\u003c/code\u003e, \u003ccode\u003emultitool-pmc.tsv\u003c/code\u003e, and \u003ccode\u003econfig.txt\u003c/code\u003e. In Ubuntu you can do this with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etouch multitool-pubmed.tsv multitool-pmc.tsv config.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll three files must be present in the directory where you run the container. In \u003ccode\u003econfig.txt\u003c/code\u003e enter the search terms that you want Cavatica to search for, with each term on a new line. Optional year ranges can be indicated with commas:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evisant,1999,2006\ncytoscape,1994,2003\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the same directory as config.txt, run the docker container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v ${PWD}:/cavatica/data/output incertaesedis/cavatica\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf on windows, \u003ccode\u003e\"$PWD\"\u003c/code\u003e should be replaced with the absolute path to your current directory. The files produced by Cavatica should appear on running the container. If you wish to rerun the search with different terms, make sure that the \u003ccode\u003emultitool-pubmed.tsv\u003c/code\u003e and \u003ccode\u003emultitool-pmc.tsv\u003c/code\u003e files are still in the folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eValue of Reproducible Research\u003c/h2\u003e\u003ca id=\"user-content-value-of-reproducible-research\" class=\"anchor\" aria-label=\"Permalink: Value of Reproducible Research\" href=\"#value-of-reproducible-research\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hackmd.io/s/r1Vxf9wVX\" rel=\"nofollow\"\u003eAccomplishments and opportunities of reproducing and containerizing this project\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePublications\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eJ. Chang and H. Chou, \"\u003ca href=\"https://www.computer.org/csdl/proceedings/bibm/2017/3050/00/08217990-abs.html\" rel=\"nofollow\"\u003eCavatica: A pipeline for identifying author adoption trends among software or methods\u003c/a\u003e,\" 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 2145-2150.\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 7, + "subscribers_count": 3, + "topics": [], + "updated_at": 1697654852.0 + }, + { + "data_format": 2, + "description": "Metagenomics pipeline", + "filenames": [ + "containers/Singularity.multiQC", + "containers/Singularity.upset", + "containers/Singularity.star", + "containers/Singularity.kraken2", + "containers/Singularity.trinity" + ], + "full_name": "phelelani/nf-rnaSeqMetagen", "latest_release": null, - "stargazers_count": 6, - "subscribers_count": 7, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-rnaSeqMetagen\u003c/h1\u003e\u003ca id=\"user-content-nf-rnaseqmetagen\" class=\"anchor\" aria-label=\"Permalink: nf-rnaSeqMetagen\" href=\"#nf-rnaseqmetagen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/phelelani/nf-rnaSeqMetagen/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/616be249ffd859dbf9fd5586802bc9a4f724f9efc70379541483202fc2c26407/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7068656c656c616e692f6e662d726e615365714d65746167656e\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/phelelani/nf-rnaSeqMetagen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4c99abe6886b93af3fa47779836429f5c9f9dac96e3c220439f0e32c2ea1a2d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538462532302532302545322539372538462532302532302545322539372538422d79656c6c6f77\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B-yellow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/phelelani/nf-rnaSeqMetagen/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/52ad979cf4db2ee31bf2c55f8866d584acc5a987c01d6491ef5f0f5359c648ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7068656c656c616e692f6e662d726e615365714d65746167656e\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/phelelani/nf-rnaSeqMetagen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/phelelani/nf-rnaSeqMetagen/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e9de1c90bf7dc2175f524dc6316158ca51a7fe28106d21cbf2fd979086f96b46/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7068656c656c616e692f6e662d726e615365714d65746167656e\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/phelelani/nf-rnaSeqMetagen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://bio.tools/nf-rnaseqmetagen\" rel=\"nofollow\"\u003ebiotools:nf-rnaseqmetagen\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003enf-rnaSeqMetagen\u003c/em\u003e is a \u003ca href=\"http://nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor detailed documentation: \u003ca href=\"https://phelelani.github.io/nf-rnaSeqMetagen/\" rel=\"nofollow\"\u003ehttps://phelelani.github.io/nf-rnaSeqMetagen/\u003c/a\u003e\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", + "stargazers_count": 7, + "subscribers_count": 1, "topics": [], - "updated_at": 1693047726.0 + "updated_at": 1731508812.0 }, { "data_format": 2, - "description": "A tool to find and annotate signals in next-generation association studies", + "description": "Code for the Optimisation of ID\u0027s using Python and Opt-AI", "filenames": [ + "Singularity.env-v2", "Singularity" ], - "full_name": "hmgu-itg/peakplotter", - "latest_release": "v0.5.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePeakPlotter : automatically annotate hits from genome-wide association results\u003c/h1\u003e\u003ca id=\"user-content-peakplotter--automatically-annotate-hits-from-genome-wide-association-results\" class=\"anchor\" aria-label=\"Permalink: PeakPlotter : automatically annotate hits from genome-wide association results\" href=\"#peakplotter--automatically-annotate-hits-from-genome-wide-association-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePeakPlotter takes away the annoying task of running regional association plots and annotating variants for your association studies results. It is compatible with sequencing as well as GWAS data. It is compatible with any format (GEMMA, SNPTEST, Bolt-LMM...) that produces the relevant columns: chromosome, position, unique ID, P-value, reference and non-reference alleles.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter installing the prerequisites (see below), clone the repository and install using \u003ccode\u003epip\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/hmgu-itg/peakplotter.git\n\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e peakplotter\n\npython3 -m pip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\npeakplotter-data-setup \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e This only needs to be run once\u003c/span\u003e\n\npeakplotter --help\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or \u003c/span\u003e\npython3 -m peakplotter --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA \u003ccode\u003eSingularity\u003c/code\u003e definition file is also available in the repository if you wish to build a container to use \u003ccode\u003epeakplotter\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePeakPlotter has has non-python dependencies.\u003cbr\u003e\nIn order to run PeakPlotter you need to install the following tools and add the executables to your \u003ccode\u003ePATH\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlink 1.9 or newer (\u003ca href=\"https://www.cog-genomics.org/plink/1.9/\" rel=\"nofollow\"\u003eavailable here\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eLocusZoom Standalone 1.4 or newer (\u003ca href=\"http://genome.sph.umich.edu/wiki/LocusZoom_Standalone\" rel=\"nofollow\"\u003eavailable here\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eTabix (\u003ca href=\"https://github.com/samtools/htslib\"\u003eavailable here\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePeakPlotter will throw a \u003ccode\u003eMissingExecutableError\u003c/code\u003e if you have any of the above tools missing in your \u003ccode\u003ePATH\u003c/code\u003e environment variable.\u003cbr\u003e\nAdd the necessary tools to your \u003ccode\u003ePATH\u003c/code\u003e like below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=/path/to/locuszoom:/path/to/plink:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to make these changes permanent, do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=/path/to/locuszoom:/path/to/plink:$PATH\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.bashrc\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ peakplotter --help\nUsage: peakplotter [OPTIONS]\n\n PeakPlotter\n\nOptions:\n -a, --assoc-file FILE Path to the association file. It can be gzipped,\n provided that it bears the .gz extension. Its first\n line must be a header, coherent with the name\n arguments below. It must be tab-separated, bgzipped\n and tabixed (tabix is available as part of\n bcftools) [required]\n -f, --bfiles TEXT Binary PLINK (.bed/.bim/.fam) file base name. This\n should contain the genotypes \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e at least all the\n variants \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the assoc_file, but it can contain\n more. Please note that this is the base name,\n without the .bed/.bim/.fam extension. [required]\n -o, --out DIRECTORY Output directory to store all output files.\n [required]\n -chr, --chr-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e chromosome names.\n [required]\n -ps, --pos-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e chromosomal position.\n [required]\n -rs, --rs-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e unique SNP ids (RS-id or\n chr:pos). [required]\n -p, --pval-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e p-values. [required]\n -a1, --a1-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e reference or major allele\n (used \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e predicting consequence). [required]\n -a2, --a2-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e alternate or minor allele.\n [required]\n -maf, --maf-col TEXT Name of the column \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e non-reference or minor\n allele frequency. [required]\n -b, --build INTEGER Assembly build (37 or 38) [default: 38]\n -s, --signif FLOAT The significance level above which to \u003cspan class=\"pl-k\"\u003edeclare\u003c/span\u003e a\n variant significant. Scientific notation (such as\n 5e-8) is fine.\n -bp, --flank-bp INTEGER Flanking size \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e base pairs \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e drawing plots\n (defaults to 500kb, i.e. 1Mbp plots) around lead\n SNPs.\n --overwrite Overwrite output directory \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e it already exists.\n --help Show this message and exit.\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \u003ccode\u003epytest\u003c/code\u003e at the root of the repository to run the testsuite.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone git@github.com:hmgu-itg/peakplotter.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e peakplotter\npytest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you encounter any bugs, please raise an issue at the \u003ca href=\"https://github.com/hmgu-itg/peakplotter/issues\"\u003eissue page\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 6, - "subscribers_count": 2, + "full_name": "DiamondLightSource/Opt-ID", + "latest_release": "v2.0", + "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/DiamondLightSource/Opt-ID\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e28103082a54d8f383c80faf900aa4f7515b5ca923b36bc1aa69393496299c2a/68747470733a2f2f7472617669732d63692e6f72672f4469616d6f6e644c69676874536f757263652f4f70742d49442e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/DiamondLightSource/Opt-ID.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://coveralls.io/github/DiamondLightSource/Opt-ID?branch=master\u0026amp;service=github\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c667be15546a48b3d3418bf8748b5907e82996977fda9f1498ddaae20f392f5b/68747470733a2f2f636f766572616c6c732e696f2f7265706f732f6769746875622f4469616d6f6e644c69676874536f757263652f4f70742d49442f62616467652e7376673f6272616e63683d6d617374657226736572766963653d676974687562\" alt=\"Coverage Status\" data-canonical-src=\"https://coveralls.io/repos/github/DiamondLightSource/Opt-ID/badge.svg?branch=master\u0026amp;service=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://scrutinizer-ci.com/g/DiamondLightSource/Opt-ID/?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84fbe7d52ef53121cd10bf8bd84baaa5f51e1a7fec43131e1bb85973a7f86841/68747470733a2f2f7363727574696e697a65722d63692e636f6d2f672f4469616d6f6e644c69676874536f757263652f4f70742d49442f6261646765732f7175616c6974792d73636f72652e706e673f623d6d6173746572\" alt=\"Scrutinizer Code Quality\" data-canonical-src=\"https://scrutinizer-ci.com/g/DiamondLightSource/Opt-ID/badges/quality-score.png?b=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.3968577\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9f2b8f9b7b2a759b4746ca645b9f4fb3619ab288054c7a8eb9f93e496a18fd0a/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333936383537372e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3968577.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/4728\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOpt-ID\u003c/h1\u003e\u003ca id=\"user-content-opt-id\" class=\"anchor\" aria-label=\"Permalink: Opt-ID\" href=\"#opt-id\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode for the Optimisation of ID\u0027s using Python and Opt-AI\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of how to use Opt-ID\u003c/h2\u003e\u003ca id=\"user-content-overview-of-how-to-use-opt-id\" class=\"anchor\" aria-label=\"Permalink: Overview of how to use Opt-ID\" href=\"#overview-of-how-to-use-opt-id\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpt-ID is run is by providing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea main configuration file in YAML format which contains all the various\nparameters for the sort/shim job\u003c/li\u003e\n\u003cli\u003ean existing directory in which output data will be written to\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere are two main flags, \u003ccode\u003e--sort\u003c/code\u003e and \u003ccode\u003e--shim\u003c/code\u003e, to run sort and shim jobs. The\nidea is that using either of these flags in conjunction with the YAML config\nfile will go through and run all the scripts that are used to produce\nintermediate files and pass them around appropriately, so then there\u0027s only one\ncommand needed to be executed to run a sort or shim job, and the YAML config\nfile is the single source of all the parameter information used for that\nparticular job.\u003c/p\u003e\n\u003cp\u003eThere are several other processes that Opt-ID provides that are desired to be\ndone after a sort/shim but don\u0027t require the sequence of scripts that a\nsort/shim job does (for example, the use of \u003ccode\u003ecompare.py\u003c/code\u003e to compare a shimmed\ngenome to the original genome), so the \u003ccode\u003e--sort\u003c/code\u003e and \u003ccode\u003e--shim\u003c/code\u003e flags aren\u0027t able\nto provide these sorts of processes. To do so, there are several shell scripts\nthat are autogenerated when a sort or shim job is run that can be executed.\nThese scripts run Opt-ID in the particular way that is needed to perform the\nprocess, without the user needing to worry about extra configuration on top of\nthe YAML file.\u003c/p\u003e\n\u003cp\u003eTaking the \u003ccode\u003ecompare.py\u003c/code\u003e example previously mentioned, a script would be\nautogenerated after a shim job called \u003ccode\u003ecompare_shim.sh\u003c/code\u003e that can be passed any\nshimmed genome file in the data directory, and it will take care of calling\nOpt-ID in the particular way it needs to in order to run the \u003ccode\u003ecompare.py\u003c/code\u003e script\nwith the appropriate parameters. More details on how to use these autogenerated\nshell scripts are below in the \"Using the autogenerated shell scripts\" section.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData directory\u003c/h2\u003e\u003ca id=\"user-content-data-directory\" class=\"anchor\" aria-label=\"Permalink: Data directory\" href=\"#data-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe data outputted by Opt-ID is split roughly into two categories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elarge files such as \u003ccode\u003e.h5\u003c/code\u003e files\u003c/li\u003e\n\u003cli\u003esmaller files such as \u003ccode\u003e.json\u003c/code\u003e, \u003ccode\u003e.mag\u003c/code\u003e, \u003ccode\u003e.sh\u003c/code\u003e files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe smaller files get written to the directory passed as the second parameter to\nOpt-ID, so if OptID was passed \u003ccode\u003e/home/FedID/my_dir\u003c/code\u003e then the smaller files would\nget written to \u003ccode\u003e/home/FedID/my_dir\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe larger files get written to a directory within \u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e whose path\nis based on the user\u0027s FedID and also the name of the data directory passed to\nOpt-ID. The name of the directory created in \u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e will be the name\nof the very last directory in the path passed to Opt-ID. For example, if the\npath \u003ccode\u003e/home/FedID/my_dir\u003c/code\u003e is passed to Opt-ID, then the directory\n\u003ccode\u003e/dls/tmp/FedID/my_dir\u003c/code\u003e will be created. Symlinks are then created in\n\u003ccode\u003e/home/FedID/my_dir\u003c/code\u003e to point to the larger files inside\n\u003ccode\u003e/dls/tmp/FedID/my_dir\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOne reason behind having two separate directories containing different data\nfiles is due to the large size of the \u003ccode\u003e.h5\u003c/code\u003e files produced by Opt-ID and not\nhaving the space to put them just anywhere in the filesystem (\u003ccode\u003e/dls/tmp\u003c/code\u003e has\nmuch more available space than, for example, the home directory associated to a\nFedID). Another reason is that the automatic deletion of files in \u003ccode\u003e/dls/tmp\u003c/code\u003e can\nbe used to do some automatic periodic cleanup of old, large files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntended usage\u003c/h3\u003e\u003ca id=\"user-content-intended-usage\" class=\"anchor\" aria-label=\"Permalink: Intended usage\" href=\"#intended-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe intended usage of this dual-directory structure is that the smaller files\nare written to somewhere away from \u003ccode\u003e/dls/tmp\u003c/code\u003e so then they\u0027re not deleted\nperiodically and can be referred to later if needed, whilst the larger files are\nwritten to the user\u0027s directory in \u003ccode\u003e/dls/tmp\u003c/code\u003e so then they \u003cem\u003eare\u003c/em\u003e deleted\nperiodically. Therefore, it is advised that the directory provided to Opt-ID is\nnot a directory in \u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e; this is not only because of potential\ndeletion of the smaller files, but also because passing a directory in\n\u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e can cause some confusion regarding the directory that is\nsubsequently created by Opt-ID in \u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn particular, it is advised that the directory passed to Opt-ID is one within\n\u003ccode\u003e/dls/technical/id\u003c/code\u003e\u003c/strong\u003e, as this is where output data from other Opt-ID jobs has\ntypically been placed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample directory structures\u003c/h3\u003e\u003ca id=\"user-content-example-directory-structures\" class=\"anchor\" aria-label=\"Permalink: Example directory structures\" href=\"#example-directory-structures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor example, if the directory \u003ccode\u003e/dls/technical/id/test/\u003c/code\u003e is passed to Opt-ID, the\nexpected directory structures right after having run a sort job on a cluster is\ngiven below:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/dls/technical/id/test/\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest_sort.json\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_sort.mag\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_sort.h5 -\u0026gt; /dls/tmp/FedID/test/test_sort.h5\u003c/code\u003e (symlink to a file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egenerate_report.sh\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erestart_sort.sh\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elogfiles/\u003c/code\u003e (directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egenomes -\u0026gt; /dls/tmp/FedID/test/genomes/\u003c/code\u003e (symlink to a directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprocess_genome_output -\u0026gt; /dls/tmp/FedID/test/process_genome_output/\u003c/code\u003e\n(symlink to a directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003e/dls/tmp/FedID/test/\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest_sort.h5\u003c/code\u003e (file, the symlink \u003ccode\u003e/dls/technical/id/test/test_sort.h5\u003c/code\u003e points\nto this file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egenomes/\u003c/code\u003e (directory, the symlink \u003ccode\u003e/dls/technical/id/test/genomes\u003c/code\u003e points to\nthis directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprocess_genome_output/\u003c/code\u003e (directory, the symlink\n\u003ccode\u003e/dls/technical/id/test/process_genome_output\u003c/code\u003e points to this directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAs another example, for the same directory being passed but instead a shim job\nbeing run on a cluster, the expected directory structures right after the job\nare:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/dls/technical/id/test/\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest_shim.json\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_shim.mag\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_shim.h5 -\u0026gt; /dls/tmp/FedID/test/test_shim.h5\u003c/code\u003e (symlink to a file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egenerate_report.sh\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecompare_shim.sh\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elogfiles/\u003c/code\u003e (directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eshimmed_genomes -\u0026gt; /dls/tmp/FedID/test/shimmed_genomes/\u003c/code\u003e (symlink to a\ndirectory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprocess_genome_output -\u0026gt; /dls/tmp/FedID/test/process_genome_output/\u003c/code\u003e\n(symlink to a directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003e/dls/tmp/FedID/test/\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest_shim.h5\u003c/code\u003e (file, the symlink \u003ccode\u003e/dls/technical/id/test/test_shim.h5\u003c/code\u003e points\nto this file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eshimmed_genomes/\u003c/code\u003e (directory, the symlink\n\u003ccode\u003e/dls/technical/id/test/shimmed_genomes\u003c/code\u003e points to this directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprocess_genome_output/\u003c/code\u003e (directory, the symlink\n\u003ccode\u003e/dls/technical/id/test/process_genome_output\u003c/code\u003e points to this directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that the filenames \u003ccode\u003etest_sort.*\u003c/code\u003e and \u003ccode\u003etest_shim.*\u003c/code\u003e are just placeholders\nand have been chosen only for illustrative purposes, these files can be named as\ndesired in the YAML config file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePreliminary steps to be able to run Opt-ID\u003c/h2\u003e\u003ca id=\"user-content-preliminary-steps-to-be-able-to-run-opt-id\" class=\"anchor\" aria-label=\"Permalink: Preliminary steps to be able to run Opt-ID\" href=\"#preliminary-steps-to-be-able-to-run-opt-id\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA process that is not done by Opt-ID is the transfer of magnet information in\nthe Excel files provided by the supplier to \u003ccode\u003e.sim\u003c/code\u003e files. To do so, from the\nExcel files supplied by the supplier, create tab delimited \u003ccode\u003e.sim\u003c/code\u003e files of\nmagnetisation. This is a manual procedure done only on Windows. Note that,\ncurrently, Opt-ID requires the magnet names in the \u003ccode\u003e.sim\u003c/code\u003e files to have leading\nzeros that pad out the name to 3 digits. For example, instead of \u00271\u0027 it should\nbe \u0027001\u0027.\u003c/p\u003e\n\u003cp\u003eTo get the code, clone the Opt-ID repo to the desired place in the filesystem.\nTo set up the environment for running Opt-ID on a Linux machine, in a terminal\nrun the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load python/3\nmodule load global/cluster\nexport PYTHONPATH=$PYTHONPATH:/path/to/Opt-ID\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e/path/to/Opt-ID\u003c/code\u003e is the path to the root directory of the cloned repo.\n(There is a change to how \u003ccode\u003epython\u003c/code\u003e is used to run the code which is detailed in\nthe next section, and so the third command is to enable \u003ccode\u003epython\u003c/code\u003e to find the\ncode in the repo).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Opt-ID with the \u003ccode\u003epython\u003c/code\u003e command\u003c/h2\u003e\u003ca id=\"user-content-running-opt-id-with-the-python-command\" class=\"anchor\" aria-label=\"Permalink: Running Opt-ID with the python command\" href=\"#running-opt-id-with-the-python-command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main script that is used for running Opt-ID is \u003ccode\u003eIDSort/src/optid.py\u003c/code\u003e. It\nshould be run using the syntax \u003ccode\u003epython -m IDSort.src.optid\u003c/code\u003e as opposed to\n\u003ccode\u003epython /path/to/Opt-ID/IDSort/src/optid.py\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDifferent options that Opt-ID can be run with\u003c/h2\u003e\u003ca id=\"user-content-different-options-that-opt-id-can-be-run-with\" class=\"anchor\" aria-label=\"Permalink: Different options that Opt-ID can be run with\" href=\"#different-options-that-opt-id-can-be-run-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two sets of flags from which one flag from each set is mandatory to be\npassed to Opt-ID, and the rest are optional and have sensible default values if\nthey are not provided.\u003c/p\u003e\n\u003cp\u003eThe mandatory sets of flags are\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--sort\u003c/code\u003e vs \u003ccode\u003e--shim\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cluster-on\u003c/code\u003e vs \u003ccode\u003e--cluster-off\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewhere only one flag from each bullet point should be provided.\u003c/p\u003e\n\u003cp\u003eExamples of running Opt-ID with the bare mininum flags and parameters it needs\nare:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m IDSort.src.optid --sort --cluster-on /path/to/yaml /path/to/data/dir\npython -m IDSort.src.optid --shim --cluster-off /path/to/yaml /path/to/data/dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ccode\u003e--sort\u003c/code\u003e and \u003ccode\u003e--shim\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content---sort-and---shim\" class=\"anchor\" aria-label=\"Permalink: --sort and --shim\" href=\"#--sort-and---shim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are used for specifying what type of job is desired.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ccode\u003e--cluster-on\u003c/code\u003e and \u003ccode\u003e--cluster-off\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content---cluster-on-and---cluster-off\" class=\"anchor\" aria-label=\"Permalink: --cluster-on and --cluster-off\" href=\"#--cluster-on-and---cluster-off\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are used for specifying whether the job is run on the local machine or\nsubmitted to run on a cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\n\u003ccode\u003e--num-threads\u003c/code\u003e, \u003ccode\u003e--queue\u003c/code\u003e, and \u003ccode\u003e--node-os\u003c/code\u003e\n\u003c/h4\u003e\u003ca id=\"user-content---num-threads---queue-and---node-os\" class=\"anchor\" aria-label=\"Permalink: --num-threads, --queue, and --node-os\" href=\"#--num-threads---queue-and---node-os\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are used in conjunction with \u003ccode\u003e--cluster-on\u003c/code\u003e. Some examples of using these\nflags would be\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m IDSort.src.optid --sort --cluster-on --node-os rhel7 /path/to/yaml /path/to/data/dir\npython -m IDSort.src.optid --shim --cluster-on --queue low.q /path/to/yaml /path/to/data/dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\n\u003ccode\u003e--seed\u003c/code\u003e and \u003ccode\u003e--seed-value\u003c/code\u003e\n\u003c/h4\u003e\u003ca id=\"user-content---seed-and---seed-value\" class=\"anchor\" aria-label=\"Permalink: --seed and --seed-value\" href=\"#--seed-and---seed-value\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are used in conjunction with \u003ccode\u003e--cluster-off\u003c/code\u003e. \u003ccode\u003e--seed\u003c/code\u003e is used to specify\nthat the random number generator (RNG) should be seeded and thus produce the\nsame output across multiple runs with the same parameters. \u003ccode\u003e--seed-value\u003c/code\u003e is\nspecified if a particular value to seed the RNG is desired (by default its value\nis 1). Some examples of using these flags would be\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m IDSort.src.optid --sort --cluster-off --seed /path/to/yaml /path/to/data/dir\npython -m IDSort.src.optid --shim --cluster-off --seed --seed-value 30 /path/to/yaml /path/to/data/dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eYAML config files\u003c/h2\u003e\u003ca id=\"user-content-yaml-config-files\" class=\"anchor\" aria-label=\"Permalink: YAML config files\" href=\"#yaml-config-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe YAML config files contain the parameters used by the various scripts that\nOpt-ID runs. The top-level sections of the YAML config files are the script\nnames minus the \u003ccode\u003e.py\u003c/code\u003e and the subsections are the different parameters passed to\nthat particular script. For the most part, the subsection names are exactly the\nsame as the script parameters they\u0027re associated to, for example, the\n\u003ccode\u003eid_setup.py\u003c/code\u003e script has a \u003ccode\u003e--periods\u003c/code\u003e flag, and the YAML subsection\ncorresponding to that parameter is \u003ccode\u003eid_setup.periods\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA few exceptions exist to try and be more descriptive with what the parameter\nis, for example, \u003ccode\u003eprocess_genome.py\u003c/code\u003e refers to the files it\u0027s given as elements\nof the \u003ccode\u003eargs\u003c/code\u003e list, but in the YAML the corresponding subsection for a shim job\nis \u003ccode\u003eprocess_genome.readable_genome_file\u003c/code\u003e which is hopefully a more useful\ndescription.\u003c/p\u003e\n\u003cp\u003eExamples of YAML config files can be found in the \u003ccode\u003eIDSort/example_configs\u003c/code\u003e\ndirectory. There are some placeholder values in these config files that aren\u0027t\nvalid values for their associated section in the YAML, and the following\nsections detail the changes that need to be made to the example config files to\nget them in a state ready to run a job.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSort config example\u003c/h3\u003e\u003ca id=\"user-content-sort-config-example\" class=\"anchor\" aria-label=\"Permalink: Sort config example\" href=\"#sort-config-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are three values that need to be changed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emagnets.hmags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emagnets.hemags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emagnets.htmags\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTheir values should be absolute paths to any \u003ccode\u003e.sim\u003c/code\u003e files of the relevant type.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShim config example\u003c/h3\u003e\u003ca id=\"user-content-shim-config-example\" class=\"anchor\" aria-label=\"Permalink: Shim config example\" href=\"#shim-config-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are five values that need to be changed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emagnets.hmags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emagnets.hemags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emagnets.htmags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eprocess_genome.readable_genome_file\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003empi_runner_for_shim_opt.bfield_filename\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe first three are the same as in the sort config example. The value of\n\u003ccode\u003eprocess_genome.readable_genome_file\u003c/code\u003e should be an absolute path to the \u003ccode\u003e.inp\u003c/code\u003e\nfile that is used to start the shim job from. The value of\n\u003ccode\u003empi_runner_for_shim_opt.bfield_filename\u003c/code\u003e should be an absolute path to the\n\u003ccode\u003e.h5\u003c/code\u003e file that is converted from \u003ccode\u003e.bfield\u003c/code\u003e files that are produced by igor.\u003c/p\u003e\n\u003cp\u003eNote that, currently, the use of the \u003ccode\u003eigor2h5.py\u003c/code\u003e script hasn\u0027t yet been\nintegrated into the YAML configuration file for Opt-ID, so the process of\nconverting \u003ccode\u003e.bfield\u003c/code\u003e data into \u003ccode\u003e.h5\u003c/code\u003e data is one that needs to be done by\nmanually executing the \u003ccode\u003eigor2h5.py\u003c/code\u003e script (or by any other means) prior to\nrunning a shim job with Opt-ID.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing the autogenerated shell scripts\u003c/h2\u003e\u003ca id=\"user-content-using-the-autogenerated-shell-scripts\" class=\"anchor\" aria-label=\"Permalink: Using the autogenerated shell scripts\" href=\"#using-the-autogenerated-shell-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll the autogenerated scripts can be executed from anywhere in the filesystem,\nit\u0027s not necessary for the current working directory to be the same directory\nthat the script is in.\u003c/p\u003e\n\u003cp\u003eDue to the facts that\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethese scripts are generated on a job-by-job basis and are only meant to be run\nfor the particular data within the directory the scripts are in\u003c/li\u003e\n\u003cli\u003ethe structure of the data directories are fixed and known in advance\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewhen it comes to passing parameters to these scripts they are aware of the\nspecific directories that the files they\u0027re expecting should be in, so only\nfilenames need to be given to them and not absolute or even relative filepaths.\nConcrete examples are given below in the \u003ccode\u003egenerate_report.sh\u003c/code\u003e and\n\u003ccode\u003ecompare_shim.sh\u003c/code\u003e sections that hopefully explain in more detail how to pass\nparameters to these scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003egenerate_report.sh\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-generate_reportsh\" class=\"anchor\" aria-label=\"Permalink: generate_report.sh\" href=\"#generate_reportsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script is used to create a report with some useful data visualisation in a\nPDF file. For a sort job it can be passed multiple \u003ccode\u003e.genome\u003c/code\u003e and \u003ccode\u003e.inp\u003c/code\u003e files,\nand for a shim job it can be passed multiple \u003ccode\u003e.h5\u003c/code\u003e files that are associated to\nthe \"full genomes\" (as opposed to the smaller-sized \"compare genomes\") in the\nshim output.\u003c/p\u003e\n\u003cp\u003eFor a sort job, Opt-ID will look in both the \u003ccode\u003egenomes/\u003c/code\u003e and\n\u003ccode\u003eprocess_genome_output/\u003c/code\u003e directories for the given \u003ccode\u003e.genome\u003c/code\u003e and \u003ccode\u003e.inp\u003c/code\u003e files,\nand for a shim job Opt-ID will look in the \u003ccode\u003eshimmed_genomes/\u003c/code\u003e directory for the\ngiven \u003ccode\u003e.h5\u003c/code\u003e files. Therefore, the parameters passed to \u003ccode\u003egenerate_report.sh\u003c/code\u003e\nshould only be the filenames and not filepaths.\u003c/p\u003e\n\u003cp\u003eFor example, for a sort job, the correct way to pass a genome and a \u003ccode\u003e.inp\u003c/code\u003e file\nto the script would be\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report.sh foo.genome bar.inp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eas opposed to\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report.sh genomes/foo.genome process_genome_output/bar.inp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnother example: for a shim job, the correct way to pass \u003ccode\u003e.h5\u003c/code\u003e files to the\nscript would be\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report.sh foo.h5 bar.h5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eas opposed to\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report.sh shimmed_genomes/foo.h5 shimmed_genomes/bar.h5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn optional \u003ccode\u003e--report-filename\u003c/code\u003e flag can be passed before the files to specify\nthe name of the PDF file, and genome reports are stored in the \u003ccode\u003egenome_reports/\u003c/code\u003e\ndirectory within the directory passed to Opt-ID. Report filenames should have a\n\u003ccode\u003e.pdf\u003c/code\u003e extension to enable a simple check between the report filename parameter\nand \u003ccode\u003e.genome\u003c/code\u003e/\u003ccode\u003e.inp\u003c/code\u003e file parameters that follow it. The \u003ccode\u003e--report-filename\u003c/code\u003e\noption can be omitted and in that case the report filename will be a\nconcatenation of all the filenames passed with an underscore character \"_\" as\nthe separator between the filenames.\u003c/p\u003e\n\u003cp\u003eAn example of using the \u003ccode\u003e--report-filename\u003c/code\u003e flag is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report --report-filename report.pdf foo.genome bar.inp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003erestart_sort.sh\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-restart_sortsh\" class=\"anchor\" aria-label=\"Permalink: restart_sort.sh\" href=\"#restart_sortsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script requires no parameters and can be run simply as\n\u003ccode\u003e/path/to/restart_sort.sh\u003c/code\u003e, Opt-ID will take care of loading the YAML config of\nthe previous sort job and will use all the same flags and paramters as the\noriginal sort job. One example is that if the original sort job was run on a\ncluster, so will the restart-sort job, and another example is that the same\n\u003ccode\u003e.json\u003c/code\u003e, \u003ccode\u003e.mag\u003c/code\u003e and \u003ccode\u003e.h5\u003c/code\u003e (lookup table) files from the original sort job will\nbe reused in the restart-sort job instead of being regenerated.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003ecompare_shim.sh\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-compare_shimsh\" class=\"anchor\" aria-label=\"Permalink: compare_shim.sh\" href=\"#compare_shimsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis can be passed a single \u003ccode\u003e.genome\u003c/code\u003e file that is in the \u003ccode\u003eshimmed_genomes/\u003c/code\u003e\ndirectory and it will generate a human readable diff between the original and\nshimmed genomes that will be written to the \u003ccode\u003eshim_diffs/\u003c/code\u003e directory. It\u0027s not\nnecessary to pass the original genome to this script, Opt-ID will take care of\nfinding it so only the shimmed genome needs to be given as a parameter.\u003c/p\u003e\n\u003cp\u003eSimilarly to what \u003ccode\u003egenerate_report.sh\u003c/code\u003e does, \u003ccode\u003ecompare_shim.sh\u003c/code\u003e will look in the\n\u003ccode\u003eshimmed_genomes/\u003c/code\u003e directory so only filenames should be passed to it and not\nfilepaths. An example of using this script would be:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/compare_shim.sh foo.genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn optional \u003ccode\u003e--diff-filename\u003c/code\u003e flag can be passed before the shimmed genome file\nto specify the filename of the human readable diff. Currently Opt-ID appends a\n\u003ccode\u003e.txt\u003c/code\u003e extension to the filename so it\u0027s not necessary to put that in the\nparameter. Again, similarly to what \u003ccode\u003egenerate_report.sh\u003c/code\u003e does, if this flag is\nomitted then the diff filename is a concatenation of the original genome and\nshimmed genome filenames with an underscore character as the separator, and then\nalso prepended with \u003ccode\u003eshim_\u003c/code\u003e. For example, if the original genome is \u003ccode\u003efoo.genome\u003c/code\u003e\nand the shimmed genome is \u003ccode\u003ebar.genome\u003c/code\u003e, then if the \u003ccode\u003e--diff-filename\u003c/code\u003e flag is\nomitted then the diff filename would be \u003ccode\u003eshim_foo.genome_bar.genome.txt\u003c/code\u003e. An\nexample of using the \u003ccode\u003e--diff-filename\u003c/code\u003e flag is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/compare_shim.sh --diff-filename my_shim foo.genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"Hidden\" options of Opt-ID\u003c/h2\u003e\u003ca id=\"user-content-hidden-options-of-opt-id\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;Hidden\u0026quot; options of Opt-ID\" href=\"#hidden-options-of-opt-id\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are several options that Opt-ID has but are only meant to be used by the\nautogenerated shell scripts and not intended to be invoked directly by a user;\ntherefore, these options aren\u0027t of much interest to users and only of potential\ninterest to developers. The following are just some useful notes to any\ndevelopers viewing this document:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethese options are related to those kinds of processes that a user would want\nto do that aren\u0027t full sort/shim jobs that were referred to in the \"Overview\nof how to use Opt-ID\" section of this document\u003c/li\u003e\n\u003cli\u003ethese options are all used by the autogenerated shell scripts that were also\nreferred to in the \"Overview of how to use Opt-ID\" section, hence why the\nusers need not directly use them, the autogenerated scripts should take care\nof using these \"hidden options\" where necessary\u003c/li\u003e\n\u003cli\u003ethese are also processes that are done after a sort/shim, so they assume the\nexistence of a YAML config that has already been used for the sort/shim job,\nas well as any output data from a sort/shim job\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003e--generate-report\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content---generate-report\" class=\"anchor\" aria-label=\"Permalink: --generate-report\" href=\"#--generate-report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis option starts off the process of using the\n\u003ccode\u003eIDSort/src/genome_report_template.ipynb\u003c/code\u003e file to generate a Jupyter notebook\nfile, and then running it to produce a PDF report.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003e--restart-sort\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content---restart-sort\" class=\"anchor\" aria-label=\"Permalink: --restart-sort\" href=\"#--restart-sort\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis option starts off the process of reusing the same YAML config file that was\nused for the sort job to get all the parameters used for the original sort job,\nand then running Opt-ID to generate genomes from an initial population as\nopposed to generating genomes from scratch.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003e--compare-shim\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content---compare-shim\" class=\"anchor\" aria-label=\"Permalink: --compare-shim\" href=\"#--compare-shim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis option starts off the process of comparing the given shimmed genome to the\noriginal genome that was used to start the shim job.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the tests\u003c/h2\u003e\u003ca id=\"user-content-running-the-tests\" class=\"anchor\" aria-label=\"Permalink: Running the tests\" href=\"#running-the-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNavigate to the root directory of the Opt-ID repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/Opt-ID\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run all the tests:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m pytest IDSort/test/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run a particular test in the \u003ccode\u003etest/\u003c/code\u003e directory, it can be specified in the\npath in the above command. For example, to run \u003ccode\u003eIDSort/test/magnets_test.py\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m pytest IDSort/test/magnets_test.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://codescene.io/projects/6289/jobs/latest-successful/results\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e69ae66d83ceb5b854c1f6a0110e6bbfad332f23a191a1fa9f890120484c607e/68747470733a2f2f636f64657363656e652e696f2f70726f6a656374732f363238392f7374617475732e737667\" alt=\"\" data-canonical-src=\"https://codescene.io/projects/6289/status.svg\" style=\"max-width: 100%;\"\u003e Get more details at \u003cstrong\u003ecodescene.io\u003c/strong\u003e.\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 7, + "subscribers_count": 9, "topics": [], - "updated_at": 1721035311.0 + "updated_at": 1690651033.0 }, { "data_format": 2, - "description": "a sequence analysis workflow for low-input nanopore sequencing", + "description": "Using singularity to create a near-identical uppmax environment (experimental)", "filenames": [ "Singularity", - "Singularity.devel" + "tags/200311/Singularity.200311" ], - "full_name": "amojarro/carrierseq", + "full_name": "UPPMAX/uppmax_in_a_can", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCarrierSeq\u003c/h1\u003e\u003ca id=\"user-content-carrierseq\" class=\"anchor\" aria-label=\"Permalink: CarrierSeq\" href=\"#carrierseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ebioRxiv doi: \u003ca href=\"https://doi.org/10.1101/175281\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/175281\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2124-3\" rel=\"nofollow\"\u003eBMC Bioinformatics\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCarrierSeq is a sequence analysis workflow for low-input nanopore sequencing which employs a genomic carrier.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eAngel MojarroEmail author, Julie Hachey, Gary Ruvkun, Maria T. Zuber and Christopher E. Carr\nBMC BioinformaticsBMC series \u2013 open, inclusive and trusted201819:108\nhttps://doi.org/10.1186/s12859-018-2124-3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGithub Contributors: Angel Mojarro (@amojarro), Srinivasa Aditya Bhattaru (@sbhattaru), Christopher E. Carr (@CarrCE), and Vanessa Sochat (@vsoch).\nfastq-filter from: \u003ca href=\"https://github.com/nanoporetech/fastq-filter\"\u003ehttps://github.com/nanoporetech/fastq-filter\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMotivation\u003c/h3\u003e\u003ca id=\"user-content-motivation\" class=\"anchor\" aria-label=\"Permalink: Motivation\" href=\"#motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLong-read nanopore sequencing technology is of particular significance for taxonomic identification at or below the species level. For many environmental samples, the total extractable DNA is far below the current input requirements of nanopore sequencing, preventing \u201csample to sequence\u201d metagenomics from low-biomass or recalcitrant samples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eResults\u003c/h3\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere we address this problem by employing carrier sequencing, a method to sequence low-input DNA by preparing the target DNA with a genomic carrier to achieve ideal library preparation and sequencing stoichiometry without amplification. We then use CarrierSeq, a sequence analysis workflow to identify the low-input target reads from the genomic carrier.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMethods\u003c/h3\u003e\u003ca id=\"user-content-methods\" class=\"anchor\" aria-label=\"Permalink: Methods\" href=\"#methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCarrierSeq implements \u003ccode\u003ebwa-mem\u003c/code\u003e (Li, 2013) to first map all reads to the genomic carrier then extracts unmapped reads by using \u003ccode\u003esamtools\u003c/code\u003e (Li et al., 2009) and \u003ccode\u003eseqtk\u003c/code\u003e (Li, 2012). Thereafter, the user can define a quality score threshold and CarrierSeq proceeds to discard low-complexity reads with \u003ccode\u003efqtrim\u003c/code\u003e (Pertea, 2015). This set of unmapped and filtered reads are labeled \u201creads of interest\u201d and should theoretically comprise target reads and likely contamination. However, reads of interest may also include \u201chigh-quality noise reads\u201d (HQNRs), defined as reads that satisfy quality score and complexity filters yet do not match to any database and disproportionately originate from specific channels. By treating reads as a Poisson arrival process, CarrierSeq models the expected reads of interest channel distribution and rejects data from channels exceeding a reads/channels threshold (xcrit). Reads of interest are then sorted into \u003ccode\u003e08_target_reads\u003c/code\u003e (reads/channel \u2264 xcrit) or \u003ccode\u003e07_hqnrs\u003c/code\u003e (reads/channel \u0026gt; xcrit).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe CarrierSeq scripts requires the following packages to be installed on your local machine.\u003c/p\u003e\n\u003cp\u003eBiopython - \u003ca href=\"http://biopython.org/\" rel=\"nofollow\"\u003ehttp://biopython.org/\u003c/a\u003e\nSciPy - \u003ca href=\"https://www.scipy.org/\" rel=\"nofollow\"\u003ehttps://www.scipy.org/\u003c/a\u003e\nbwa - \u003ca href=\"https://github.com/lh3/bwa\"\u003ehttps://github.com/lh3/bwa\u003c/a\u003e\nseqtk - \u003ca href=\"https://github.com/lh3/seqtk\"\u003ehttps://github.com/lh3/seqtk\u003c/a\u003e\nsamtools - \u003ca href=\"https://github.com/samtools/samtools\"\u003ehttps://github.com/samtools/samtools\u003c/a\u003e\nfqtrim - \u003ca href=\"https://ccb.jhu.edu/software/fqtrim/\" rel=\"nofollow\"\u003ehttps://ccb.jhu.edu/software/fqtrim/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAlternatively, use Docker and the Docker script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing Docker and Dockerhub\u003c/h2\u003e\u003ca id=\"user-content-using-docker-and-dockerhub\" class=\"anchor\" aria-label=\"Permalink: Using Docker and Dockerhub\" href=\"#using-docker-and-dockerhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eDownload \u0026amp; install Docker - \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehttps://www.docker.com/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eStart docker\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003edocker pull mojarro/carrierseq:latest\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThat\u0027s it!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing Scientific Filesystem (SCIF)\u003c/h2\u003e\u003ca id=\"user-content-using-scientific-filesystem-scif\" class=\"anchor\" aria-label=\"Permalink: Using Scientific Filesystem (SCIF)\" href=\"#using-scientific-filesystem-scif\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis means generating a \u003cstrong\u003eSingularity\u003c/strong\u003e or \u003cstrong\u003eDocker\u003c/strong\u003e container that has the same \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eScientific Filesystem\u003c/a\u003e to create scientific applications to run the pipeline. For instructions on using SCIF with either of these container technologies (or on your host), see the \u003ca href=\"docs/README.md\"\u003edocumentation folder\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing CarrierSeq\u003c/h2\u003e\u003ca id=\"user-content-using-carrierseq\" class=\"anchor\" aria-label=\"Permalink: Using CarrierSeq\" href=\"#using-carrierseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: You may first need to make the script executable with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003echmod +x path/to/carrierseq.sh\u003c/code\u003e\nor\n\u003ccode\u003echmod +x path/to/carrierseq_docker.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eReads to be analyzed must be compiled into a single fastq file and the carrier reference genome must be in fasta format.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecd\u003c/code\u003e into your CarrierSeq folder containing the bash and python scripts and run CarrierSeq with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./carrierseq.sh -i \u0026lt;input.fastq\u0026gt; -r \u0026lt;reference.fasta\u0026gt; -q \u0026lt;q_score\u0026gt; -p \u0026lt;p_value\u0026gt; -o \u0026lt;output_directory\u0026gt; -t \u0026lt;bwa_threads\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eor with Docker...\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./carrierseq_docker.sh -i \u0026lt;input.fastq\u0026gt; -r \u0026lt;reference.fasta\u0026gt; -q \u0026lt;q_score\u0026gt; -p \u0026lt;p_value\u0026gt; -o \u0026lt;output_directory\u0026gt; -t \u0026lt;bwa_threads\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e-i, -r, and -o are mandatory flags, CarrierSeq will use the default values if -q, -p, or -t are not defined:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebwa_threads = 1 \nq_score = 9\np_value = 0.0001 or 0.05/512 active channels\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCarrierSeq Output\u003c/h2\u003e\u003ca id=\"user-content-carrierseq-output\" class=\"anchor\" aria-label=\"Permalink: CarrierSeq Output\" href=\"#carrierseq-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCarrierSeq will generate the following folders and files within your working directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# All reads mapped to the carrier reference genome.\n00_bwa/bwa_mapped.sam \n\n# Unmapped reads to the carrier.\n01_samtools/bwa_unmapped_reads.lst \n /bwa_unmapped.sam \n\n02_seqtk/unmapped_reads.fasta \n /unmapped_reads.fastq \n /unmapped_reads.txt \n \n# Reads equal to or greater than a given q-score threshold (default = 9).\n03_fastqc/unmapped_reads_qc.fa \n /unmapped_reads_qc.fq \n /unmapped_reads_qc.lst \n /unmapped_reads_qc.txt \n\n# Discarded reads below the given q-score threshold.\n03_01_low_quality_reads/low_quality_unmapped_reads.fasta \n /low_quality_unmapped_reads.fastq \n /low_quality_unmapped_reads.lst \n /low_quality_unmapped_reads.txt \n \n# Reads with less than 50% of its length detected as low complexity.\n04_fqtrim_dusted/unmapped_reads_qc_dusted.fasta \n /unmapped_reads_qc_dusted.fastq\n /unmapped_reads_qc_dusted.lst \n /unmapped_reads_qc_dusted.txt\n \n# Discarded reads with over than 50% of its length detected as low complexity. \n04_01_low_complexity_reads/low_complexity_reads_qc.fasta \n /low_complexity_reads_qc.fastq \n /low_complexity_reads_qc.lst \n /low_complexity_reads_qc.txt \n\n# Reads of Interest - should theoretically consist of target reads and contamination,\n# but may also include \"high-quality noise reads\" HQNRs which originate from specific channels.\n05_reads_of_interest/carrierseq_roi_header.lst\n /carrierseq_roi.fasta\n /carrierseq_roi.fastq\n /carrierseq_roi.txt\n\n# By treating reads as a Poisson arrival process, CarrierSeq models the expected reads-of-interest \n# channel distribution and rejects data from channels exceeding a reads/channels threshold (xcrit).\n06_poisson_caculation/01_reads_channels.lst # all channels used during sequencing.\n /02_channels_used.lst # Unique channels used during sequencing.\n /03_channels_in_use.txt # Number of unique channels.\n /04_lambda_value.txt # Lambda = Unkown Reads / Used Channels.\n /05_read_channel_threshold.txt # Critical read/channel (xcrit) threshold calculation summary.\n /06_xcrit_threshold_for_dictionary_search.txt # xcrit value.\n /07_poretools_roi_channels.lst # Channels used in reads of interest from fastq generated using poretools.\n /08_roi_channels_clean.lst # Channels used in reads of interest from fastq generated using albacore or minknow or formatted channels from 07_poretools_roi_channels.lst.\n /09_target_channels.lst # \"Good\" channels used to sort target reads.\n /10_albacore_target_channels.lst # \"Good\" channels list formatted for poretools fastq files.\n /10_poretools_target_channels.lst # \"Good\" channel list formatted for albacore/minknow fastq files.\n /xx_hqnr_channel_dictionary.txt # HQNRs read/channel frequency dictionary for python.\n /xx_roi_channel_dictionary.txt # Reads of interest read/channel frequency dictionary for python.\n /xx_target_channel_dictionary.txt # Target reads read/channel frequency dictionary for python.\n \n# Likely HQNRs (reads/channel \u0026gt; xcrit). \n07_hqnrs/carrierseq_hqnrs.fasta\n /carrierseq_hqnrs.fastq\n /carrierseq_hqnrs.lst\n /carrierseq_hqnrs.txt\n \n# Likely Target Reads (reads/channel \u2264 xcrit).\n08_target_reads/carrierseq_target_reads.fasta\n /carrierseq_target_readst.fastq\n /carrierseq_target_reads.lst\n /carrierseq_target_reads.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCarrierSeq Example\u003c/h2\u003e\u003ca id=\"user-content-carrierseq-example\" class=\"anchor\" aria-label=\"Permalink: CarrierSeq Example\" href=\"#carrierseq-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSupplementary sequencing data available from \u003cdel\u003eNCBI\u003c/del\u003e\nFigshare: \u003ca href=\"https://doi.org/10.6084/m9.figshare.5868825.v1\" rel=\"nofollow\"\u003ehttps://doi.org/10.6084/m9.figshare.5868825.v1\u003c/a\u003e\n\u003cdel\u003e\u003ca href=\"https://doi.org/10.6084/m9.figshare.5471824.v1\" rel=\"nofollow\"\u003ehttps://doi.org/10.6084/m9.figshare.5471824.v1\u003c/a\u003e\u003c/del\u003e\n\u003cdel\u003eDropbox: \u003ca href=\"https://www.dropbox.com/sh/vyor82ulzh7n9ke/AAC4W8rMe4z5hdb7j4QhF_IYa?dl=0\" rel=\"nofollow\"\u003ehttps://www.dropbox.com/sh/vyor82ulzh7n9ke/AAC4W8rMe4z5hdb7j4QhF_IYa?dl=0\u003c/a\u003e\u003c/del\u003e\n\u003cdel\u003eBioProject: \u003ca href=\"https://www.ncbi.nlm.nih.gov/bioproject/398368\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/398368\u003c/a\u003e\u003c/del\u003e\n\u003cdel\u003eBioSample: \u003ca href=\"https://www.ncbi.nlm.nih.gov/biosample/SAMN07509071\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/biosample/SAMN07509071\u003c/a\u003e\u003c/del\u003e\n\u003cdel\u003eSRA Download: \u003ca href=\"https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?run=SRR5935058\" rel=\"nofollow\"\u003ehttps://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?run=SRR5935058\u003c/a\u003e\u003c/del\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLibrary preparation and sequencing\u003c/h3\u003e\u003ca id=\"user-content-library-preparation-and-sequencing\" class=\"anchor\" aria-label=\"Permalink: Library preparation and sequencing\" href=\"#library-preparation-and-sequencing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e0.2 ng of B. subtilis DNA was prepared with 1 \u00b5g of Lambda DNA using the Oxford Nanopore Technologies (ONT) ligation sequencing kit (LSK-SQK108). The library was then sequenced on a MinION Mark-1B sequencer and R9.4 flowcell for 48 hours and basecalled using ONT\u2019s Albacore (v1.10) offline basecaller.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCarrierSeq Parameters\u003c/h3\u003e\u003ca id=\"user-content-carrierseq-parameters\" class=\"anchor\" aria-label=\"Permalink: CarrierSeq Parameters\" href=\"#carrierseq-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eq-score = 9 (default) and p-value = 0.05.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSequencing and CarrierSeq Summary\u003c/h3\u003e\u003ca id=\"user-content-sequencing-and-carrierseq-summary\" class=\"anchor\" aria-label=\"Permalink: Sequencing and CarrierSeq Summary\" href=\"#sequencing-and-carrierseq-summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAt Q9, the expected B. subtilis abundance is 590 reads for this sequencing data. The xcrit value was calculated to be 7 reads/channel.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eAll Reads (Lambda + B. subtilis + Contamination + Noise)\nTotal Reads: 717,432 reads (edit: The origininal value, 547,478 reads, were over q = 9 not all reads)\nTotal Bases: 6.4 gb (edit: same as above, 4,914,693,436 bases)\n###\nReads of Interest (B. subtilis + Contamination + HQNRs) [05_reads_of_interest]\nTotal Reads: 1,811 reads\nTotal Bases: 8,132,374 bases\n###\nHQNRS [07_hqnrs]\nTotal Reads: 1,179 reads (including 17 false negative B. subtilis reads)\nTotal Bases: 7,282,767 bases\n###\nTarget Reads [08_target_reads]\nTotal Reads: 632 reads (including 574 true positive B. subtilis reads, 4 true positive contamination reads, and 54 false positive HQNRs)\nTotal Bases: 849,607 bases\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eROI Pore Occupancy\u003c/h3\u003e\u003ca id=\"user-content-roi-pore-occupancy\" class=\"anchor\" aria-label=\"Permalink: ROI Pore Occupancy\" href=\"#roi-pore-occupancy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe matrix illustrates the reads/channel distribution of B. subtilis, contamination, and HQNRs across all 512 nanopore channels. Here we are able to visually identify overly productive channels (e.g., 191 reads/channel, etc) producing likely HQNRs.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/amojarro/carrierseq/blob/master/example/carrierseq_roi_q9_p005.png\"\u003e\u003cimg src=\"https://github.com/amojarro/carrierseq/raw/master/example/carrierseq_roi_q9_p005.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHQNR Pore Occupancy\u003c/h3\u003e\u003ca id=\"user-content-hqnr-pore-occupancy\" class=\"anchor\" aria-label=\"Permalink: HQNR Pore Occupancy\" href=\"#hqnr-pore-occupancy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u201cBad\u201d channels identified by CarrierSeq as HQNR-associated (reads/channel \u0026gt; 7).\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/amojarro/carrierseq/blob/master/example/carrierseq_hqnrs_q9_p005.png\"\u003e\u003cimg src=\"https://github.com/amojarro/carrierseq/raw/master/example/carrierseq_hqnrs_q9_p005.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTarget Reads Pore Occupancy\u003c/h3\u003e\u003ca id=\"user-content-target-reads-pore-occupancy\" class=\"anchor\" aria-label=\"Permalink: Target Reads Pore Occupancy\" href=\"#target-reads-pore-occupancy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u201cGood\u201d channels identified by CarrierSeq as non-HQNR-associated (reads/channel \u2264\u00a07). Channels producing 6 or more reads yield HQNRs that have satisfied our CarrierSeq parameters. By imposing a stricter p-value, CarrierSeq may be able to reject more HQNRs (e.g., xcrit = 5).\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/amojarro/carrierseq/blob/master/example/carrierseq_target_reads_q9_p005.png\"\u003e\u003cimg src=\"https://github.com/amojarro/carrierseq/raw/master/example/carrierseq_target_reads_q9_p005.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 6, - "subscribers_count": 2, - "topics": [ - "nanopore", - "bioinformatics-scripts", - "metagenomics", - "docker" - ], - "updated_at": 1657841217.0 + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUPPMAX in a can\u003c/h1\u003e\u003ca id=\"user-content-uppmax-in-a-can\" class=\"anchor\" aria-label=\"Permalink: UPPMAX in a can\" href=\"#uppmax-in-a-can\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity container will let you run a near-identical UPPMAX environment on your own computer. You will have access to all of the installed software at UPPMAX, all your files and reference data on UPPMAX, but it will be your own computer that does the calculations. You can even use it to analyse data that you only have on your own computer, but using the software and reference data on UPPMAX.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTypical use cases\u003c/h1\u003e\u003ca id=\"user-content-typical-use-cases\" class=\"anchor\" aria-label=\"Permalink: Typical use cases\" href=\"#typical-use-cases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou have sensitive data that is not allowed to leave your computers, but you want to use the programs and references at UPPMAX to do the analysis.\u003c/li\u003e\n\u003cli\u003eYou have your own server but want to avoid installing all the software yourself.\u003c/li\u003e\n\u003cli\u003eThe queues at UPPMAX are too long or you have run out of core hours but you want the analysis done yesterday.\u003c/li\u003e\n\u003cli\u003eYou have your data and compute hours at another SNIC centre, but want to use the software installed at UPPMAX.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWhat you get\u003c/h1\u003e\u003ca id=\"user-content-what-you-get\" class=\"anchor\" aria-label=\"Permalink: What you get\" href=\"#what-you-get\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAccess to your home folder and all project folders.\u003c/li\u003e\n\u003cli\u003eAccess to all the installed programs at UPPMAX.\u003c/li\u003e\n\u003cli\u003eAccess to all reference data at UPPMAX.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWhat you don\u0027t get\u003c/h1\u003e\u003ca id=\"user-content-what-you-dont-get\" class=\"anchor\" aria-label=\"Permalink: What you don\u0027t get\" href=\"#what-you-dont-get\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUPPMAX high-performace computers. You will be limited by the computer you are running the container on.\u003c/li\u003e\n\u003cli\u003eNo slurm access. Everything runs on your computer.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eImportant notes\u003c/h1\u003e\u003ca id=\"user-content-important-notes\" class=\"anchor\" aria-label=\"Permalink: Important notes\" href=\"#important-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSince all data you read/write to the UPPMAX file system will have to travel over the internet, disk intensive programs will be much slower, and transfer rate is limited to your internet connection. Try working on your local harddrive as much as possible.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIssues\u003c/h1\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-label=\"Permalink: Issues\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSome of the tools at uppmax, like projinfo and uquota are using your user\u0027s group membership to determin what information to show. Since your local user won\u0027t have the same groups as you have on uppmax they misbehave and will not show you the same information as when you run the program on uppmax.\u003c/li\u003e\n\u003cli\u003eIf your user has not connected to uppmax before it will need to accept the login nodes fingerprint before connecting (a security feature). Sshfs will just hang if this happens, so you will have to connect normally to uppmax and manually accept the fingerprint. To make things more complicated there are 3 different login nodes, each with its own fingerprint, and you are randomly assigned to one. There is a possibility to turn this off in the sshfs mount command by adding \u003ccode\u003e-o StrictHostKeyChecking=no\u003c/code\u003e, but it might not be a good idea to disable it due to security. Easiest way around it is just to run \u003ccode\u003essh user@rackham.uppmax.uu.se\u003c/code\u003e a couple of times until you have seen all 3 fingerprints.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePrerequisites\u003c/h1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool has been developed on Ubuntu 18.04 with Singularity v.3.5. You should only need 2 things for this to work,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity (only tested with v3.5)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/libfuse/sshfs\"\u003eSSHFS\u003c/a\u003e\u003cbr\u003e\n(Make sure that the option \u003ccode\u003euser_allow_other\u003c/code\u003e is uncommented in \u003ccode\u003e/etc/fuse.conf\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor installation instructions for these, see respective projects homepage.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing a pre-built image\u003c/h3\u003e\u003ca id=\"user-content-using-a-pre-built-image\" class=\"anchor\" aria-label=\"Permalink: Using a pre-built image\" href=\"#using-a-pre-built-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://UPPMAX/uppmax_in_a_can:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding your own image from github.\u003c/h3\u003e\u003ca id=\"user-content-building-your-own-image-from-github\" class=\"anchor\" aria-label=\"Permalink: Building your own image from github.\" href=\"#building-your-own-image-from-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the github repo and build the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/UPPMAX/uppmax_in_a_can.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e uppmax_in_a_can\nsingularity build uppmax_in_a_can_latest.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe build takes 10-20 minutes on a modern laptop with gigabit Ethernet.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFirst time usage\u003c/h2\u003e\u003ca id=\"user-content-first-time-usage\" class=\"anchor\" aria-label=\"Permalink: First time usage\" href=\"#first-time-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the build is done, run the initialization script in the container,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./uppmax_in_a_can_latest.sif uppmax_init\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to see more options, run\u003c/span\u003e\n./uppmax_in_a_can_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSubsequent usage\u003c/h2\u003e\u003ca id=\"user-content-subsequent-usage\" class=\"anchor\" aria-label=\"Permalink: Subsequent usage\" href=\"#subsequent-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart the virtual node:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./uiac_node.sh -i uppmax_in_a_can_latest.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to see more options, run\u003c/span\u003e\n./uiac_node.sh -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will now be on the command-line inside the container and you can run commands as if you were logged in on UPPMAX. You will see all project folders in \u003ccode\u003e/proj\u003c/code\u003e, all software in \u003ccode\u003e/sw\u003c/code\u003e, your UPPMAX home folder in \u003ccode\u003e/home/\u0026lt;UPPMAX_username\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ex\u003c/span\u003e\nmodule load bioinf-tools samtools\nsamtools view file.bam\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo close down and return to your own computers command-line, just type \u003ccode\u003eexit\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAdvanced usage\u003c/h1\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-label=\"Permalink: Advanced usage\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can also specify any additional singularity options to the \u003ccode\u003euiac_node.sh\u003c/code\u003e script. If you want to make your computers file system visible inside the container so that you can analyse files residing on your computer, just add a bind argument:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Entire hard drive \u003c/span\u003e\n./uiac_node.sh -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--bind /:/hostfs\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -i uppmax_in_a_can_latest.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Only a specific directory\u003c/span\u003e\n./uiac_node.sh -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--bind /home/user/data:/hostfs\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -i uppmax_in_a_can_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis command will make your computers file system available under \u003ccode\u003e/hostfs\u003c/code\u003e (or wherever you would like it).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDeveloper notes\u003c/h1\u003e\u003ca id=\"user-content-developer-notes\" class=\"anchor\" aria-label=\"Permalink: Developer notes\" href=\"#developer-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get the list of installed software on UPPMAX, run this command on UPPMAX:\n\u003ccode\u003eyum list installed | cut -f 1 -d \" \" | cut -f 1 -d \".\" | sort | tr \u0027\\n\u0027 \u0027 \u0027 \u0026gt; yum_installed_\u003c/code\u003edate +%F.txt`\u003c/p\u003e\n\u003cp\u003eThis list will contain \u003cstrong\u003eall\u003c/strong\u003e installed packages, even core packages and nvidia packages that are not available in the default repos. It will just give a couple of warning messages when you send the list to \u003ccode\u003eyum install\u003c/code\u003e, but it will not break the installation process.\u003c/p\u003e\n\u003cp\u003eTo get the the list of environment variables in \u003ccode\u003eenv/99-uppmaxvars.sh\u003c/code\u003e, type \u003ccode\u003eenv | egrep -i \u0027module|lmod\u0027 | sort\u003c/code\u003e when logged in on UPPMAX and pick out everything that has to do with the module system.\u003c/p\u003e\n\u003cp\u003eThe PS1 is made to be different from the default shell the user has to make it obvious they are inside the container.\u003c/p\u003e\n", + "stargazers_count": 7, + "subscribers_count": 8, + "topics": [], + "updated_at": 1701093879.0 }, { "data_format": 2, - "description": "Analysis code for \"The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception\" by Visconti di Oleggio Castello, Halchenko, et al., 2017, Scientific Reports", + "description": "High-order OpenFOAM interpolation scheme for advective fluxes", "filenames": [ "Singularity" ], - "full_name": "mvdoc/famface", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAnalysis scripts for \"The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception\"\u003c/h1\u003e\u003ca id=\"user-content-analysis-scripts-for-the-neural-representation-of-personally-familiar-and-unfamiliar-faces-in-the-distributed-system-for-face-perception\" class=\"anchor\" aria-label=\"Permalink: Analysis scripts for \u0026quot;The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception\u0026quot;\" href=\"#analysis-scripts-for-the-neural-representation-of-personally-familiar-and-unfamiliar-faces-in-the-distributed-system-for-face-perception\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains preprocessing and analysis scripts for Visconti di Oleggio Castello, M., Halchenko, Y. O., Guntupalli, J. S., Gors, J. D., \u0026amp; Gobbini, M. I. (2017). The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception. \u003cem\u003eScientific Reports\u003c/em\u003e, 7(1), 12237. \u003ca href=\"https://doi.org/10.1038/s41598-017-12559-1\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41598-017-12559-1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset is available through \u003ca href=\"http://datasets.datalad.org/?dir=/labs/gobbini/famface\" rel=\"nofollow\"\u003eDataLad\u003c/a\u003e. Once datalad is installed in your system, you can get the data with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install the dataset without downloading any data\u003c/span\u003e\ndatalad install -r ///labs/gobbini/famface\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e download the data\u003c/span\u003e\ndatalad get famface\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNOTA BENE:\u003c/strong\u003e The latest release of this dataset is in BIDS format, however the\nscripts are still configured to run with the old OpenfMRI format. You\ncan checkout the old file structure as follows\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd famface/data\ngit checkout openfmri-v1.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetting up the environment\u003c/h2\u003e\u003ca id=\"user-content-setting-up-the-environment\" class=\"anchor\" aria-label=\"Permalink: Setting up the environment\" href=\"#setting-up-the-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe recommend using either a \u003ca href=\"http://neuro.debian.net/\" rel=\"nofollow\"\u003eNeuroDebian\u003c/a\u003e\nvirtual machine, or a container (Docker or Singularity) with NeuroDebian\ninstalled to replicate these analyses. In particular, the Python scripts\nmight rely on specific versions of python packages. For example, the\npreprocessing script \u003ccode\u003efmri_ants_openfmri.py\u003c/code\u003e won\u0027t work with newer\nversions of Nipype (\u0026gt; 0.11.0) because of recent refactoring. We kept track of the\nversions of the most important Python packages in the \u003ccode\u003erequirements.txt\u003c/code\u003e\nfile. If you\u0027re using conda, you can get started as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name famface python=2.7\npip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should also have FSL and ANTs installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the singularity image\u003c/h3\u003e\u003ca id=\"user-content-using-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Using the singularity image\" href=\"#using-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide a \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e definition file\nthat can be used to build a container with all the necessary packages to\nrun the analyses (except MATLAB--testing in progress with Octave).\u003c/p\u003e\n\u003cp\u003eOnce Singularity is installed on your system, the image can be built as\nfollows (assuming singularity 2.4.x)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build famface.simg Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ealternatively, the image can be pulled from Singularity Hub with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name famface.simg shub://mvdoc/famface\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eInside the container we provide the mountpoints \u003ccode\u003e/data\u003c/code\u003e and \u003ccode\u003e/scripts\u003c/code\u003e,\nso for example one could run the preprocessing for one participant as\nfollows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -e -c \\\n -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/scripts \\\n -B /path/to/famface:/data \\\n famface.simg \\\n python /scripts/fmri_ants_openfmri.py \\\n -d /data/data -s sub001 \\\n --hpfilter 60.0 \\\n --derivatives \\\n -o /data/derivatives/output \\\n -w /data/workdir -p MultiProc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere we are assuming that \u003ccode\u003e/path/to/famface\u003c/code\u003e is the path to the\n\u003ccode\u003efamface\u003c/code\u003e directory as pulled from datalad, which contains a \u003ccode\u003edata\u003c/code\u003e\ndirectory. Note that all the paths passed to the script need to be relative to\nthe filesystem inside the container.\u003c/p\u003e\n\u003cp\u003eRunning the following will instead enter the container for interactive\nanalyses:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -e -c \\\n -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/scripts \\\n -B /path/to/famface:/data \\\n famface.simg \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePlease note that some paths in the scripts might be hardcoded, so they\nneed to be changed prior to running the scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePreprocessing and GLM modeling\u003c/h2\u003e\u003ca id=\"user-content-preprocessing-and-glm-modeling\" class=\"anchor\" aria-label=\"Permalink: Preprocessing and GLM modeling\" href=\"#preprocessing-and-glm-modeling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"fmri_ants_openfmri.py\"\u003e\u003ccode\u003efmri_ants_openfmri.py\u003c/code\u003e\u003c/a\u003e: nipype pipeline to\nperform preprocessing (spatial normalization to MNI 2 mm using ANTs,\nfirst and second level univariate analysis using FSL). Based on the\nexample with the same name from stock Nipype\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"pymvpa_hrf.py\"\u003e\u003ccode\u003epymvpa_hrf.py\u003c/code\u003e\u003c/a\u003e: script to run a GLM using PyMVPA and\nNipy, to extract betas used for multivariate analysis\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"make_unionmask.py\"\u003e\u003ccode\u003emake_unionmask.py\u003c/code\u003e\u003c/a\u003e: script to make a union mask\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"stack_betas.py\"\u003e\u003ccode\u003estack_betas.py\u003c/code\u003e\u003c/a\u003e: script to stack betas for\nmultivariate analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalysis\u003c/h2\u003e\u003ca id=\"user-content-analysis\" class=\"anchor\" aria-label=\"Permalink: Analysis\" href=\"#analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGLM\u003c/h3\u003e\u003ca id=\"user-content-glm\" class=\"anchor\" aria-label=\"Permalink: GLM\" href=\"#glm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"group_multregress_openfmri.py\"\u003e\u003ccode\u003egroup_multregress_openfmri.py\u003c/code\u003e\u003c/a\u003e:\nnipype pipeline to perform third (group) level univariate analysis\nwith FSL. Based on the pipeline provided by Satra Ghosh and Anne Park\n(our thanks to them!)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMVPC\u003c/h3\u003e\u003ca id=\"user-content-mvpc\" class=\"anchor\" aria-label=\"Permalink: MVPC\" href=\"#mvpc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"run_sl.py\"\u003e\u003ccode\u003erun_sl.py\u003c/code\u003e\u003c/a\u003e: main script to run searchlight analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"pymvpa2cosmo.py\"\u003e\u003ccode\u003epymvpa2cosmo.py\u003c/code\u003e\u003c/a\u003e: script to convert PyMVPA\ndatasets into CoSMoMVPA datasets for statistical testing using TFCE\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"run_tfce_mvdoc_fx.m\"\u003e\u003ccode\u003erun_tfce_mvdoc_fx.m\u003c/code\u003e\u003c/a\u003e: script to run TFCE on\naccuracy maps using CoSMoMVPA\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"ev_roi_clf.py\"\u003e\u003ccode\u003eev_roi_clf.py\u003c/code\u003e\u003c/a\u003e: script to run additional decoding analyses in probabilistic masks of early visual areas\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"permutation_testing_ev.Rmd\"\u003e\u003ccode\u003epermutation_testing_ev.Rmd\u003c/code\u003e\u003c/a\u003e: RMarkdown notebook that plots the results of the analysis in probabilistic masks of early visual areas (see also pre-computed HTML output \u003ca href=\"permutation_testing_ev.nb.html\"\u003e\u003ccode\u003epermutation_testing_ev.nb.html\u003c/code\u003e\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"hmax_decoding_familiarvsunfamiliar.ipynb\"\u003e\u003ccode\u003ehmax_decoding_familiarvsunfamiliar.ipynb\u003c/code\u003e\u003c/a\u003e: Jupyter notebook with decoding analysis on features extracted from the HMAX model.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"hmax_familiarvsunfamiliar-id.Rmd\"\u003e\u003ccode\u003ehmax_familiarvsunfamiliar-id.Rmd\u003c/code\u003e\u003c/a\u003e: RMarkdown notebook used to analyze the decoding of images using HMAX features (see also pre-computed HTML output \u003ca href=\"hmax_familiarvsunfamiliar-id.nb.html\"\u003e\u003ccode\u003ehmax_familiarvsunfamiliar-id.nb.html\u003c/code\u003e\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSimilarity of Representational Geometries\u003c/h3\u003e\u003ca id=\"user-content-similarity-of-representational-geometries\" class=\"anchor\" aria-label=\"Permalink: Similarity of Representational Geometries\" href=\"#similarity-of-representational-geometries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/define_rois_mds.ipynb\"\u003e\u003ccode\u003enotebooks/define_rois_mds.ipynb\u003c/code\u003e\u003c/a\u003e:\nnotebook used to obtain non-overlapping spherical ROIs in both the\ntask data and the movie hyperaligned data\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"compute_dsmroi_firstlev.py\"\u003e\u003ccode\u003ecompute_dsmroi_firstlev.py\u003c/code\u003e\u003c/a\u003e: script to\ncompute first-level cross-validated representational dissimilarity matrices\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/compute_dsmroi_hpal.ipynb\"\u003e\u003ccode\u003enotebooks/compute_dsmroi_hpal.ipynb\u003c/code\u003e\u003c/a\u003e:\nnotebook used to compute the similarity of representational geometries\nusing hyperaligned movie data\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/plot_mds.ipynb\"\u003e\u003ccode\u003enotebooks/plot_mds.ipynb\u003c/code\u003e\u003c/a\u003e:\nnotebook used to generate MDS and circular graph plots for task and\nhyperaligned data\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/get_between-within_correlations.ipynb\"\u003e\u003ccode\u003enotebooks/get_between-within_correlations.ipynb\u003c/code\u003e\u003c/a\u003e:\nnotebook used to obtain dataframes with correlations between/within\nsystems for each subject (task data) or pair of subjects (hyperaligned\ndata)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"mds_betweenwithin_corr.Rmd\"\u003e\u003ccode\u003emds_betweenwithin_corr.Rmd\u003c/code\u003e\u003c/a\u003e: RMarkdown\nnotebook with additional analyses on correlations of RDMS\nbetween/within systems (see rendering in\n\u003ca href=\"mds_betweenwithin_corr.nb.html\"\u003e\u003ccode\u003emds_betweenwithin_corr.nb.html\u003c/code\u003e\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuxiliary and miscellaneous files\u003c/h2\u003e\u003ca id=\"user-content-auxiliary-and-miscellaneous-files\" class=\"anchor\" aria-label=\"Permalink: Auxiliary and miscellaneous files\" href=\"#auxiliary-and-miscellaneous-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"mds_rois.py\"\u003e\u003ccode\u003emds_rois.py\u003c/code\u003e\u003c/a\u003e: contains functions to run MDS analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"expdir.py\"\u003e\u003ccode\u003eexpdir.py\u003c/code\u003e\u003c/a\u003e: to fetch directories used in analyses\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/scatterplots.ipynb\"\u003e\u003ccode\u003enotebooks/scatterplots.ipynb\u003c/code\u003e\u003c/a\u003e: notebook used to plot scatterplots shown in the supplementary material\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResponse to Reviewers\u003c/h2\u003e\u003ca id=\"user-content-response-to-reviewers\" class=\"anchor\" aria-label=\"Permalink: Response to Reviewers\" href=\"#response-to-reviewers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/response_reviewers_ev.ipynb\"\u003eresponse_reviewers_ev.ipynb\u003c/a\u003e: Is the dorsal stream also close to EV areas?\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/response_reviewers_modelrsa.ipynb\"\u003eresponse_reviewers_modelrsa.ipynb\u003c/a\u003e: Can we say more about why the representations differ between areas?\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"notebooks/response_reviewers_similarity_taskmovie.ipynb\"\u003eresponse_reviewers_similarity_taskmovie.ipynb\u003c/a\u003e: How similar are the second-order representational geometries between the task data and the movie data?\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 6, - "subscribers_count": 5, + "full_name": "AtmosFOAM/highOrderFit", + "latest_release": "jshaw-thesis", + "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/AtmosFOAM/highOrderFit\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/73a765e2af4193b6e028fc98ef05f9c71584887c0f9532e0e693e98c37ce0e72/68747470733a2f2f7472617669732d63692e6f72672f41746d6f73464f414d2f686967684f726465724669742e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/AtmosFOAM/highOrderFit.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompilation\u003c/h2\u003e\u003ca id=\"user-content-compilation\" class=\"anchor\" aria-label=\"Permalink: Compilation\" href=\"#compilation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSet the environment variable \u003ccode\u003eHIGHORDERFIT_DIR\u003c/code\u003e to the root of the local repository and run \u003ccode\u003e./Allwmake\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo compile the \u003ca href=\"https://www.doxygen.org/\" rel=\"nofollow\"\u003eDoxygen\u003c/a\u003e documentation, run \u003ccode\u003edoc/Doxygen/Allwmake\u003c/code\u003e.\u003c/p\u003e\n", + "stargazers_count": 7, + "subscribers_count": 4, "topics": [ - "neuroscience", - "experiment", - "science", - "fmri", - "mvpa" + "openfoam", + "advection", + "interpolation-methods" ], - "updated_at": 1670856412.0 + "updated_at": 1702391512.0 }, { "data_format": 2, - "description": "Distributed Fast Downward: classical planner for parallel/distributed environments", + "description": "A new Python DAX generator version of the classic Montage workflow", "filenames": [ "Singularity" ], - "full_name": "jinnaiyuu/distributed-fast-downward", + "full_name": "pegasus-isi/montage-workflow-v2", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDistributed Fast Downward\u003c/h1\u003e\u003ca id=\"user-content-distributed-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Distributed Fast Downward\" href=\"#distributed-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDistributed fast-downward is a classical planner for distributed environments.\nIt extends the state-of-the-art planner fast-downward (\u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e) to distributed computers.\nDistributed fast-downward implements the state-of-the-art parallel best-first search algorithms including Abstract Zobrist hashing (AZH) and Hash Distributed A* (HDA*).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHash Distributed A*\u003c/h1\u003e\u003ca id=\"user-content-hash-distributed-a\" class=\"anchor\" aria-label=\"Permalink: Hash Distributed A*\" href=\"#hash-distributed-a\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the source code for Hash Distributed A* (HDA*) and other parallel algorithms for classical planning. The algorithms are described in the paper:\u003c/p\u003e\n\u003cp\u003eJinnai Y, Fukunaga A. 2017. On Hash-Based Work Distribution Methods for Parallel Best-First Search. Journal of Artificial Intelligence Research (JAIR). arXiv: \u003ca href=\"https://arxiv.org/abs/1706.03254\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/1706.03254\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDomain specific solvers (15-puzzle, 24-puzzle, multiple sequence alignment, and grid pathfinding) are available here (\u003ca href=\"https://github.com/jinnaiyuu/Parallel-Best-First-Searches\"\u003ehttps://github.com/jinnaiyuu/Parallel-Best-First-Searches\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDependencies\u003c/h1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is built on top of fast-downward (\u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e) of Febuary 2014 (\u003ca href=\"http://hg.fast-downward.org/shortlog/8532ca08bcac\" rel=\"nofollow\"\u003ehttp://hg.fast-downward.org/shortlog/8532ca08bcac\u003c/a\u003e).\nPlease read the instruction for fast-downward to learn the syntax (\u003ca href=\"http://www.fast-downward.org/PlannerUsage\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/PlannerUsage\u003c/a\u003e). Note that you need to modify some part of the code if you want to integrate parallel searches to the newest version of the fast-downward.\u003c/p\u003e\n\u003cp\u003eTo run, you need to install MPI library. We have confirmed that our code works with MPICH3, MPICH2, and OpenMPI (usually MPICH is faster than OpenMPI). MPICH2 and OpenMPI are in most of the package managers. For example in Debian/Ubuntu,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get install mpich2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou may also need to install libcr-dev to run MPI.\u003c/p\u003e\n\u003cp\u003eThe other libraries are optional. We recommend mpiP (\u003ca href=\"http://mpip.sourceforge.net/\" rel=\"nofollow\"\u003ehttp://mpip.sourceforge.net/\u003c/a\u003e) for profiling MPI programs to understand the bottleneck of parallel algorithms.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe syntax is same as fast-downward. You can run Hash Distributed A* (HDA*) by\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan PDDLFILE --search \"hdastar(HEURISTIC,HASH-FUNCTION)\" NUMPROCESSES\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,zobrist)\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFirst parameter of hdastar is a heuristic function.\nSecond parameter is \"dist\" which selects a method for work distribution (hashing function).\nThe number you place on the last is the number of processors to run HDA*.\u003c/p\u003e\n\u003cp\u003eWork distribution methods:\u003c/p\u003e\n\u003cp\u003eGRAZHDA*/sparsity (Jinnai\u0026amp;Fukunaga 2017)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,freq_depend(cut=sparsest_cut))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDAHDA* (Jinnai\u0026amp;Fukunaga 2016)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,aabstraction(0.7))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGAZHDA* (Jinnai\u0026amp;Fukunaga 2016)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,freq_depend(1.0,0.0))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFAZHDA* (Jinnai\u0026amp;Fukunaga 2016)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,freq_depend(0.5,0.0))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eZHDA* (Kihimoto et al 2009)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,zobrist)\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAHDA* (Burns et al 2010)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./src/plan ./pddl/blocks-4-0.pddl --search \"hdastar(merge_and_shrink,abstraction(10000))\" 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run MPI algorithm using torque, ./src/pbs-plan should work. To run it in other schedulers, you probably need to edit the parameters put in mpiexec ($PBS_NODEFILE and $PBS_NUM_PPN) to appropriate variables.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMemo\u003c/h1\u003e\u003ca id=\"user-content-memo\" class=\"anchor\" aria-label=\"Permalink: Memo\" href=\"#memo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn our experiments GRAZHDA*/sparsity was the best performing algorithms for merge\u0026amp;shrink heuristic (low computation cost) and lmcut (high computation cost) on single-machine multicore environment (8 cores), commodity cluster (48 cores), and cloud cluster on EC2 (128 virtual core).\nThus we expect GRAZHDA*/sparsity to be the best for most of the modern computer systems.\nI am interested to see the results in other environments (e.g. a heterogeneous environment consists of multiple types of machines, CPUs).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYuu Jinnai \u003ca href=\"mailto:ddyuudd@gmail.com\"\u003eddyuudd@gmail.com\u003c/a\u003e implemented parallel algorithms on top of the fast-downward (\u003ca href=\"http://www.fast-downward.org/\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/\u003c/a\u003e).\nPlease let me know if you wish to use my code but find my document unhelpful.\nI am trying to make this program easy to use for everybody so I appreciate your comments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLICENSE\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe code is published under GPL ver 3.\u003c/p\u003e\n", - "stargazers_count": 6, - "subscribers_count": 3, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emontage-workflow-v2\u003c/h1\u003e\u003ca id=\"user-content-montage-workflow-v2\" class=\"anchor\" aria-label=\"Permalink: montage-workflow-v2\" href=\"#montage-workflow-v2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA new Python DAX generator version of the classic Montage workflow. This workflow uses the \u003ca href=\"http://montage.ipac.caltech.edu\" rel=\"nofollow\"\u003eMontage\ntoolkit\u003c/a\u003e to re-project, background correct and add astronomical\nimages into custom mosaics.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://montage.ipac.caltech.edu\" rel=\"nofollow\"\u003eMontage\u003c/a\u003e - version 4.0 or later\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.astropy.org/\" rel=\"nofollow\"\u003eAstroPy\u003c/a\u003e - version 1.0 or later\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePlan a Montage Workflow\u003c/h2\u003e\u003ca id=\"user-content-plan-a-montage-workflow\" class=\"anchor\" aria-label=\"Permalink: Plan a Montage Workflow\" href=\"#plan-a-montage-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003cem\u003e./montage-workflow.py\u003c/em\u003e Python script sets up a \u003cem\u003edata/\u003c/em\u003e directory with a Pegasus DAX,\nimage tables and region headers. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./montage-workflow.py --center \"56.7 24.0\" --degrees 2.0 \\\n --band dss:DSS2B:blue --band dss:DSS2R:green --band dss:DSS2IR:red\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a 2x2 degree mosaic centered on 56.7 24.0, with 3 bands making up the\nred, green, and blue channels for the final JPEG output. A 2 degree workflow has a lot\nof input images and thus the workflow becomes wide. I simplified version of the workflow\nlooks like:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/dax1.png?raw=true\"\u003e\u003cimg src=\"docs/images/dax1.png?raw=true\" alt=\"DAX 1\" title=\"DAX 1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest way to get started is to use the \u003cem\u003e./example-dss.sh\u003c/em\u003e\nscript. It shows how to use the \u003cem\u003emontage-workflow.py\u003c/em\u003e DAX generator to set up and plan\n2 degree workflows as described above. Example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./example-dss.sh \n\nAdding band 1 (dss DSS2B -\u0026gt; blue)\nRunning sub command: mArchiveList dss DSS2B \"56.7 24.00\" 2.2 2.2 data/1-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 1-images.tbl region-oversized.hdr 1-raw.tbl 1-projected.tbl 1-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 1-raw.tbl 1-diffs.tbl\n[struct stat=\"OK\", count=120]\n\nAdding band 2 (dss DSS2R -\u0026gt; green)\nRunning sub command: mArchiveList dss DSS2R \"56.7 24.00\" 2.2 2.2 data/2-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 2-images.tbl region-oversized.hdr 2-raw.tbl 2-projected.tbl 2-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 2-raw.tbl 2-diffs.tbl\n[struct stat=\"OK\", count=120]\n\nAdding band 3 (dss DSS2IR -\u0026gt; red)\nRunning sub command: mArchiveList dss DSS2IR \"56.7 24.00\" 2.2 2.2 data/3-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 3-images.tbl region-oversized.hdr 3-raw.tbl 3-projected.tbl 3-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 3-raw.tbl 3-diffs.tbl\n[struct stat=\"OK\", count=120]\n2016.06.02 21:46:32.455 PDT: \n2016.06.02 21:46:32.461 PDT: ----------------------------------------------------------------------- \n2016.06.02 21:46:32.466 PDT: File for submitting this DAG to HTCondor : montage-0.dag.condor.sub \n2016.06.02 21:46:32.471 PDT: Log of DAGMan debugging messages : montage-0.dag.dagman.out \n2016.06.02 21:46:32.476 PDT: Log of HTCondor library output : montage-0.dag.lib.out \n2016.06.02 21:46:32.481 PDT: Log of HTCondor library error messages : montage-0.dag.lib.err \n2016.06.02 21:46:32.487 PDT: Log of the life of condor_dagman itself : montage-0.dag.dagman.log \n2016.06.02 21:46:32.492 PDT: \n2016.06.02 21:46:32.497 PDT: -no_submit given, not submitting DAG to HTCondor. You can do this with: \n2016.06.02 21:46:32.507 PDT: ----------------------------------------------------------------------- \n2016.06.02 21:46:33.387 PDT: Your database is compatible with Pegasus version: 4.6.1 \n2016.06.02 21:46:33.392 PDT: \n\nI have concretized your abstract workflow. The workflow has been entered \ninto the workflow database with a state of \"planned\". The next step is \nto start or execute your workflow. The invocation required is\n\npegasus-run /data/scratch/rynge/montage2/montage-workflow-v2/work/1464929190\n\n2016.06.02 21:46:33.419 PDT: Time taken to execute is 2.961 seconds \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning the workflow produces fits and jpeg mosaics for each band, as well as a combined color one:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/pleiades.jpg?raw=true\"\u003e\u003cimg src=\"docs/images/pleiades.jpg?raw=true\" alt=\"Pleiades\" title=\"Pleiades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 7, + "subscribers_count": 13, "topics": [], - "updated_at": 1681588272.0 + "updated_at": 1687744524.0 }, { "data_format": 2, - "description": "Common scripts, libraries, and utilities for 2p experiments", + "description": "Website is at:", "filenames": [ "Singularity" ], - "full_name": "deisseroth-lab/two-photon", + "full_name": "NBISweden/sauron", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003etwo-photon\u003c/h1\u003e\u003ca id=\"user-content-two-photon\" class=\"anchor\" aria-label=\"Permalink: two-photon\" href=\"#two-photon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains utilities for analyzing 2p data:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#analysis-pipeline\"\u003eAnalysis Pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ripping-containers\"\u003eRipping Containers\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalysis Pipeline\u003c/h2\u003e\u003ca id=\"user-content-analysis-pipeline\" class=\"anchor\" aria-label=\"Permalink: Analysis Pipeline\" href=\"#analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe analysis pipeline consists of the following stages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eraw2tiff: converts Bruker proprietary output format to a TIFF stack\u003c/li\u003e\n\u003cli\u003econvert: converts tiff and csv/text files to hdf5.\u003c/li\u003e\n\u003cli\u003epreprocess: detect and remove stim artefacts\u003c/li\u003e\n\u003cli\u003eqa: make QA plots to check stim artefact removal\u003c/li\u003e\n\u003cli\u003eanalyze: run suite2p, optionally combining multiple preprocessed datasets\u003c/li\u003e\n\u003cli\u003ebackup: back up input/intermediate/output data to a safe place\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSetup\u003c/h3\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, install the code. You can use \u003ca href=\"https://desktop.github.com/\"\u003eGitHub desktop\u003c/a\u003e, or use git on the command line. This only has to be done once.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/deisseroth-lab/two-photon.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNext, install the environment. You will need to install \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003econda\u003c/a\u003e first. Then\nuse the following command from within the directory where you installed the repo above. This also only has\nto be done once.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda env create -f environment.yml -n two-photon\nconda activate two-photon\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e installs the 2p script (in editable mode, so you can update the code)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExecuting\u003c/h3\u003e\u003ca id=\"user-content-executing\" class=\"anchor\" aria-label=\"Permalink: Executing\" href=\"#executing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the processing script, the environment needs to be activated. This needs to be done each time you start a\nnew terminal.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda activate two-photon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe executable is called \u003ccode\u003e2p\u003c/code\u003e, and each stage is a different subcommand\nthat can be run. It is possible to run multiple stages by specifying\nmultiple subcommands.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eData Layout and Global Flags\u003c/h4\u003e\u003ca id=\"user-content-data-layout-and-global-flags\" class=\"anchor\" aria-label=\"Permalink: Data Layout and Global Flags\" href=\"#data-layout-and-global-flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe scripts required a strict layout of data, and assume the input data\nfollows a directory structure and filenaming that the Bruker scopes\ncreate. The data is setup in subdirectories of a \u003ccode\u003ebase-path\u003c/code\u003e, named\nby the stage and the \u003ccode\u003eacquisition\u003c/code\u003e name.\u003c/p\u003e\n\u003cp\u003eTo point the script to the correct location of of dataset,\nuse the following flags:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-adblock\"\u003e\u003cpre\u003e --base-path PATH Top-level storage for local data. [required]\n --acquisition TEXT Acquisition sub-directory to process. [required]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing the following global flags (meaning after \u003ccode\u003e2p\u003c/code\u003e but before other commands or flags):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewill use the following locations for the data. Note the expected location of the raw data.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003edata type\u003c/th\u003e\n\u003cth\u003elocation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eRAWDATA, csv, xml, and env files from scope\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/raw/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etiff stacks\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/tiff/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003econverted hdf5 data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/convert/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epreprocess\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/preprocess/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eqa\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/qa/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eanalyze - suite2p output\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e/my/data/analyze/20210428M198/slm-001\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCommand: raw2tiff\u003c/h4\u003e\u003ca id=\"user-content-command-raw2tiff\" class=\"anchor\" aria-label=\"Permalink: Command: raw2tiff\" href=\"#command-raw2tiff\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe raw2tiff command runs the Bruker software to rip the RAWDATA into a tiff stack.\nThis is a Windows-only command, until the kinks of running on Linux are ironed out.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001\n raw2tiff\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: convert\u003c/h3\u003e\u003ca id=\"user-content-command-convert\" class=\"anchor\" aria-label=\"Permalink: Command: convert\" href=\"#command-convert\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003econvert\u003c/code\u003e command converts the tiff stacks and voltage data to hdf5.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001 \\\n convert --channel 3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: preprocess\u003c/h3\u003e\u003ca id=\"user-content-command-preprocess\" class=\"anchor\" aria-label=\"Permalink: Command: preprocess\" href=\"#command-preprocess\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003epreprocess\u003c/code\u003e command performs processing like stim removal on the data. It should be\nrun even if there are no stim artefacts (in which case, no actual computation is done),\nso that downstream stages find the data in the correct place.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001 \\\n preprocess --frame-channel-name=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eframe starts\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --stim-channel-name=respir\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample based on piezeo period:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/minoue2/2p_CNC/ \\\n --acquisition Chris_210429/10263_920nm_PC250-300-001 \\\n preprocess \\\n --frame-channel-name=StartFrameResonant \\\n --stim-channel-name=LEDSyncSignal \\\n --piezo-period-frames=7 \\\n --piezo-skip-frames=3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: qa\u003c/h3\u003e\u003ca id=\"user-content-command-qa\" class=\"anchor\" aria-label=\"Permalink: Command: qa\" href=\"#command-qa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eqa\u003c/code\u003e command makes some QA plots to understand if the stim effects are\nbeing correctly removed during preprocessing. It plots a number of frames\n(given by --max-frames) containing stims, showing the data before and after\nstim removal.\u003c/p\u003e\n\u003cp\u003eThis is an optional step.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /my/data \\\n --acquisition 20210428M198/slm-001 \\\n qa\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: analyze\u003c/h3\u003e\u003ca id=\"user-content-command-analyze\" class=\"anchor\" aria-label=\"Permalink: Command: analyze\" href=\"#command-analyze\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eanalyze\u003c/code\u003e command runs Suite2p on the preprocessed dataset.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n analyze\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample of analyzing multiple acquisitions together:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n analyze --extra-acquisitions 20210428M198/slm-000\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample of using non-default Suite2p options file (json format):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n analyze --suite2p-params-file two_photon/ops_files/drinnedb.json\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand: backup\u003c/h3\u003e\u003ca id=\"user-content-command-backup\" class=\"anchor\" aria-label=\"Permalink: Command: backup\" href=\"#command-backup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ebackup\u003c/code\u003e command copies the output of one or more stages to backup directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n backup \\\n --backup-path /media/hdd1/oak/mount/two-photon/backup \\\n --backup-stage raw,tiff\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e--backup_path\"\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing multiple commands at once\u003c/h2\u003e\u003ca id=\"user-content-using-multiple-commands-at-once\" class=\"anchor\" aria-label=\"Permalink: Using multiple commands at once\" href=\"#using-multiple-commands-at-once\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeveral commands can be run in succession by adding each one to your command line with its\nnecessary flags.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e2p \\\n --base-path /media/hdd0/two-photon/drinnenb/work \\\n --acquisition 20210428M198/slm-001 \\\n raw2tiff \\\n convert --channel 3 \\\n preprocess --stim-channel-name=respir \\\n analyze --extra-acquisitions 20210428M198/slm-000\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRipping Containers\u003c/h2\u003e\u003ca id=\"user-content-ripping-containers\" class=\"anchor\" aria-label=\"Permalink: Ripping Containers\" href=\"#ripping-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRipping is the process for converting a Bruker RAWDATA file into a set of TIFF files.\u003c/p\u003e\n\u003cp\u003eContainers exist to help run the ripping on any platform, but it has been found they\nperform sub-optimally and are 10-100x slower than ripping on a Windows machine using\nthe native ripper. It is advised NOT to use this yet.\u003c/p\u003e\n\u003cp\u003eThe lab has created \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e and\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e containers with the Bruker Prairie View software,\nwhich can be used to rip raw data computers with either set of container software installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRipping via Singularity\u003c/h3\u003e\u003ca id=\"user-content-ripping-via-singularity\" class=\"anchor\" aria-label=\"Permalink: Ripping via Singularity\" href=\"#ripping-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you would like to run from a container on \u003ca href=\"https://www.sherlock.stanford.edu/\" rel=\"nofollow\"\u003eSherlock\u003c/a\u003e,\nthe lab keeps a copy available in $OAK/pipeline/bruker-rip/containers.\u003c/p\u003e\n\u003cp\u003eHere\u0027s a quick demo:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ mkdir -p \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/users/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/test\n$ cp -r \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/pipeline/bruker-rip/sampledata/overview-023 \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/users/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/test\n$ chmod -R u+w \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/users/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/test/overview-023 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Write permissions needed to convert files.\u003c/span\u003e\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/users/\u003cspan class=\"pl-smi\"\u003e${USER}\u003c/span\u003e/test/overview-023\n$ singularity run --bind=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:/data \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/pipeline/bruker-rip/containers/bruker-rip.sif\n\nCopying wine environment.\n\nExecuting rip. One err and four fixme statements are OK.\n\n2020-11-16 17:25:43.859 rip:50 INFO Data created with Prairie version 5.4, using ripper: /apps/Prairie View 5.5/Utilities/Image-Block Ripping Utility.exe\n2020-11-16 17:25:43.861 rip:77 INFO Ripping from:\n /data/Cycle00001_Filelist.txt\n /data/CYCLE_000001_RAWDATA_000025\n2020-11-16 17:25:43.883 rip:123 INFO Watching \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e ripper to finish \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e 3600 more seconds\n000d:err:menubuilder:init_xdg error looking up the desktop directory\n0031:fixme:ntdll:EtwEventRegister ({5eec90ab-c022-44b2-a5dd-fd716a222a15}, 0x5571000, 0x5582030, 0x5582050) stub.\n0031:fixme:ntdll:EtwEventSetInformation (deadbeef, 2, 0x557fd70, 43) stub\n0031:fixme:nls:GetThreadPreferredUILanguages 00000038, 0x4fccdb4, 0x4fccdd0 0x4fccdb0\n0031:fixme:nls:get_dummy_preferred_ui_language (0x38 0x4fccdb4 0x4fccdd0 0x4fccdb0) returning a dummy value (current locale)\n2020-11-16 17:25:53.889 rip:134 INFO Found filelist files: None\n2020-11-16 17:25:53.889 rip:135 INFO Found rawdata files: None\n2020-11-16 17:25:53.889 rip:136 INFO Found this many tiff files: 1\n2020-11-16 17:25:53.889 rip:123 INFO Watching \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e ripper to finish \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e 3590 more seconds\n2020-11-16 17:26:03.899 rip:134 INFO Found filelist files: None\n2020-11-16 17:26:03.899 rip:135 INFO Found rawdata files: None\n2020-11-16 17:26:03.899 rip:136 INFO Found this many tiff files: 1\n2020-11-16 17:26:03.899 rip:139 INFO Detected ripping is \u003cspan class=\"pl-c1\"\u003ecomplete\u003c/span\u003e\n2020-11-16 17:26:13.909 rip:141 INFO Killing ripper\n2020-11-16 17:26:13.910 rip:143 INFO Ripper has been killed\n2020-11-16 17:26:14.912 rip:115 INFO cleaned up\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\nX connection to :99 broken (explicit \u003cspan class=\"pl-c1\"\u003ekill\u003c/span\u003e or server shutdown).\nX connection to :99 broken (explicit \u003cspan class=\"pl-c1\"\u003ekill\u003c/span\u003e or server shutdown).\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere\u0027s how to run on your own data. We request a node allocation using \u003ccode\u003esdev\u003c/code\u003e as\nlong-running jobs should not use login nodes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e my/data/path\n$ sdev \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e May take some time to get a machine for development use\u003c/span\u003e\n$ singularity run --bind=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:/data \u003cspan class=\"pl-smi\"\u003e$OAK\u003c/span\u003e/pipeline/bruker-rip/containers/bruker-rip.sif\n\n[Similar output as above]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd here\u0027s how to run a batch job, using the \u003ccode\u003erip.sbatch\u003c/code\u003e script from this\nrepository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e my/data/path\n$ sbatch path/to/two-photon/rip.sbatch \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\nSubmitted batch job ABCDEFGH\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRipping via Docker\u003c/h3\u003e\u003ca id=\"user-content-ripping-via-docker\" class=\"anchor\" aria-label=\"Permalink: Ripping via Docker\" href=\"#ripping-via-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can run on a device with Docker installed using the command below. The image\nwill be available locally if you\u0027ve build from source (see below), or it will be\nfetched from the the \u003ca href=\"https://code.stanford.edu/deisseroth-lab/bruker-rip\" rel=\"nofollow\"\u003eStanford GitLab\u003c/a\u003e. Contact \u003ca href=\"mailto:croat@stanford.edu\"\u003ecroat@stanford.edu\u003c/a\u003e if you need access.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./rip_docker.sh \\\n scr.svc.stanford.edu/deisseroth-lab/bruker-rip:20200903 \\\n /path/to/data/with/filelist/and/rawdata/\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eExample run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./rip_docker.sh \\\n scr.svc.stanford.edu/deisseroth-lab/bruker-rip:20200903 \\\n /media/hdd0/two-photon/sample/overview-023\nSetting up wine environment\n\nExecuting rip. It is OK to see 1 err and 4 fixme statements \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e what follows\n\n2020-09-03 14:41:33.936 rip:50 INFO Ripping from:\n /data/Cycle00001_Filelist.txt\n /data/CYCLE_000001_RAWDATA_000025\n2020-09-03 14:41:33.940 rip:96 INFO Waiting \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e ripper to finish: 3600 seconds remaining\n000d:err:menubuilder:init_xdg error looking up the desktop directory\n0031:fixme:ntdll:EtwEventRegister ({5eec90ab-c022-44b2-a5dd-fd716a222a15}, 0xd441000, 0xd452030, 0xd452050) stub.\n0031:fixme:ntdll:EtwEventSetInformation (deadbeef, 2, 0xd44fd70, 43) stub\n0031:fixme:nls:GetThreadPreferredUILanguages 00000038, 0xdaacdb4, 0xdaacdd0 0xdaacdb0\n0031:fixme:nls:get_dummy_preferred_ui_language (0x38 0xdaacdb4 0xdaacdd0 0xdaacdb0) returning a dummy value (current locale)\n2020-09-03 14:41:43.951 rip:107 INFO Found filelist files: None\n2020-09-03 14:41:43.951 rip:108 INFO Found rawdata files: None\n2020-09-03 14:41:43.951 rip:109 INFO Found this many tiff files: 1\n2020-09-03 14:41:43.951 rip:96 INFO Waiting \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e ripper to finish: 3590 seconds remaining\n2020-09-03 14:41:53.962 rip:107 INFO Found filelist files: None\n2020-09-03 14:41:53.962 rip:108 INFO Found rawdata files: None\n2020-09-03 14:41:53.962 rip:109 INFO Found this many tiff files: 1\n2020-09-03 14:41:53.963 rip:112 INFO Detected ripping is \u003cspan class=\"pl-c1\"\u003ecomplete\u003c/span\u003e\n2020-09-03 14:42:03.973 rip:114 INFO Killing ripper\n2020-09-03 14:42:03.973 rip:116 INFO Ripper has been killed\n2020-09-03 14:42:04.975 rip:88 INFO cleaned up\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding Containers\u003c/h3\u003e\u003ca id=\"user-content-building-containers\" class=\"anchor\" aria-label=\"Permalink: Building Containers\" href=\"#building-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build all available containers, which will first build the Docker container, and then convert it\nto a Singularity container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake build\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo build just the docker containers:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake build_docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eView the \u003ca href=\"Makefile\"\u003eMakefile\u003c/a\u003e for additional targets, including targets to build just build specific containers.\u003c/p\u003e\n", - "stargazers_count": 6, - "subscribers_count": 3, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSauron\u003c/h1\u003e\u003ca id=\"user-content-sauron\" class=\"anchor\" aria-label=\"Permalink: Sauron\" href=\"#sauron\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaulo Czarnewski\nNBIS\u003c/p\u003e\n\u003cp\u003eA single cell workflow. Check the Sauron webpage for help: \u003ca href=\"https://nbisweden.github.io/sauron\" rel=\"nofollow\"\u003ehttps://nbisweden.github.io/sauron\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 7, + "subscribers_count": 41, "topics": [], - "updated_at": 1729511558.0 + "updated_at": 1669968316.0 }, { "data_format": 2, - "description": "Various Singularity recipes I\u0027ve used to generate containers", + "description": null, "filenames": [ - "beagle/Singularity.beagle", - "vg/Singularity.vg", - "admixture/Singularity.admixture", - "popLDdecay/Singularity.popLDdecay", - "limma-voom/Singularity.limma-voom", - "finestructure/Singularity.finestructure", - "snpeff/Singularity.snpeff", - "trimmomatic/Singularity.trimmomatic", - "diamond/Singularity.diamond", - "igv/Singularity.igv", - "hmmsplicer/Singularity.hmmsplicer", - "bcl2fastq/Singularity.bcl2fastq", - "freebayes/Singularity.freebayes", - "vcftools/Singularity.vcftools", - "igb/Singularity.igb", - "fastqc/Singularity.fastqc", - "multiqc/Singularity.multiqc", - "stringtie/Singularity.stringtie", - "rqtl2/Singularity.rqtl2", - "ncbi-blast+/Singularity.ncbi-blast+", - "poplddecay/Singularity.poplddecay", - "sabre/Singularity.sabre", - "conda_env/Singularity.conda_env", - "gc/Singularity.gc", - "last/Singularity.last", - "samtools/Singularity.samtools", - "orthofinder/Singularity.orthofinder", - "bioscripts/Singularity.bioscripts", - "star/Singularity.star", - "fastp/Singularity.fastp", - "rust/Singularity.rust", - "bwa/Singularity.bwa", - "hisat2/Singularity.hisat2", - "fastme/Singularity.fastme", - "deeptools/Singularity.deeptools", - "bcftools/Singularity.bcftools", - "mcl/Singularity.mcl" + "container/Singularity.intel_cm4", + "container/Singularity.intel_netcdf", + "container/Singularity.cm4" ], - "full_name": "jlboat/BioinfoContainers", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularityRecipes\u003c/h1\u003e\u003ca id=\"user-content-singularityrecipes\" class=\"anchor\" aria-label=\"Permalink: SingularityRecipes\" href=\"#singularityrecipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVarious Singularity recipes I\u0027ve used to generate bioinformatics containers\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2877\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eObtaining a container\u003c/h2\u003e\u003ca id=\"user-content-obtaining-a-container\" class=\"anchor\" aria-label=\"Permalink: Obtaining a container\" href=\"#obtaining-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://jlboat/BioinfoContainers:fastp # or exchange fastp for any other container listed here\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer Recipes\u003c/h2\u003e\u003ca id=\"user-content-container-recipes\" class=\"anchor\" aria-label=\"Permalink: Container Recipes\" href=\"#container-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBCL Processing Programs\u003c/h3\u003e\u003ca id=\"user-content-bcl-processing-programs\" class=\"anchor\" aria-label=\"Permalink: BCL Processing Programs\" href=\"#bcl-processing-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.bcl2fastq\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFASTQ Processing Programs\u003c/h3\u003e\u003ca id=\"user-content-fastq-processing-programs\" class=\"anchor\" aria-label=\"Permalink: FASTQ Processing Programs\" href=\"#fastq-processing-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.fastp\u003c/li\u003e\n\u003cli\u003eSingularity.fastqc\u003c/li\u003e\n\u003cli\u003eSingularity.sabre\u003c/li\u003e\n\u003cli\u003eSingularity.trimmomatic\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRead Aligners\u003c/h3\u003e\u003ca id=\"user-content-read-aligners\" class=\"anchor\" aria-label=\"Permalink: Read Aligners\" href=\"#read-aligners\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.hisat2\u003c/li\u003e\n\u003cli\u003eSingularity.STAR\u003c/li\u003e\n\u003cli\u003eSingularity.bwa\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExpression Quantification/Alternative Splicing\u003c/h3\u003e\u003ca id=\"user-content-expression-quantificationalternative-splicing\" class=\"anchor\" aria-label=\"Permalink: Expression Quantification/Alternative Splicing\" href=\"#expression-quantificationalternative-splicing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.stringtie\u003c/li\u003e\n\u003cli\u003eSingularity.hmmsplicer\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVariant Callers/Analysis tools\u003c/h3\u003e\u003ca id=\"user-content-variant-callersanalysis-tools\" class=\"anchor\" aria-label=\"Permalink: Variant Callers/Analysis tools\" href=\"#variant-callersanalysis-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.vcftools\u003c/li\u003e\n\u003cli\u003eSingularity.snpeff\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDifferential Expression\u003c/h3\u003e\u003ca id=\"user-content-differential-expression\" class=\"anchor\" aria-label=\"Permalink: Differential Expression\" href=\"#differential-expression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.limma-voom\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReport Generation\u003c/h3\u003e\u003ca id=\"user-content-report-generation\" class=\"anchor\" aria-label=\"Permalink: Report Generation\" href=\"#report-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.multiqc\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVarious Scripts/Aggregate Programs\u003c/h3\u003e\u003ca id=\"user-content-various-scriptsaggregate-programs\" class=\"anchor\" aria-label=\"Permalink: Various Scripts/Aggregate Programs\" href=\"#various-scriptsaggregate-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.bioscripts\u003c/li\u003e\n\u003cli\u003eSingularity.conda\u003c/li\u003e\n\u003cli\u003eSingularity.deeptools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenome Viewers\u003c/h3\u003e\u003ca id=\"user-content-genome-viewers\" class=\"anchor\" aria-label=\"Permalink: Genome Viewers\" href=\"#genome-viewers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.IGB\u003c/li\u003e\n\u003cli\u003eSingularity.IGV\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "NOAA-GFDL/CM4", + "latest_release": "2021.03", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGFDL CM4 Model\u003c/h1\u003e\u003ca id=\"user-content-gfdl-cm4-model\" class=\"anchor\" aria-label=\"Permalink: GFDL CM4 Model\" href=\"#gfdl-cm4-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.gfdl.noaa.gov\" rel=\"nofollow\"\u003eGeophysical Fluid Dynamics Laboratory\n(GFDL)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe layout of this package includes the following directories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esrc - The source code for the CM4 model\u003c/li\u003e\n\u003cli\u003eexec - The build directory with Makefiles for building the model executable\u003c/li\u003e\n\u003cli\u003erun - Sample run script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning Instructions\u003c/h2\u003e\u003ca id=\"user-content-cloning-instructions\" class=\"anchor\" aria-label=\"Permalink: Cloning Instructions\" href=\"#cloning-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository uses \u003ca href=\"https://git-scm.com/book/en/v2/Git-Tools-Submodules\" rel=\"nofollow\"\u003egit\nsubmodules\u003c/a\u003e to\npoint to other repositories. Thus, care should be taken when cloning,\nand updating the source to ensure all source. To obtain all source,\nuse the following git command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NOAA-GFDL/CM4.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--recursive\u003c/code\u003e option to \u003ccode\u003egit clone\u003c/code\u003e instructs git to recursively\nclone all submodules. In the event the repository was not cloned\nusing the \u003ccode\u003e--recursive\u003c/code\u003e option, the following step must be taken to\nobtain all sources:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# From within the CM4 parent directory\ngit submodule update --init --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSource Code\u003c/h2\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-label=\"Permalink: Source Code\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll model source is contained in the \u003ca href=\"src\"\u003esrc\u003c/a\u003e directory. GFDL\ntracks code using the git version control system. This package\nincludes a single version of the following GFDL model components. The\ngit hash listed corresponds to the commit hash in the internal GFDL\ngit repository.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComponent\u003c/th\u003e\n\u003cth\u003eCommit Hash\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_cubed_sphere\u003c/td\u003e\n\u003ctd\u003eb8b05bf650c0d3293b538bdaceb894ba0fd6910b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_drivers\u003c/td\u003e\n\u003ctd\u003e3be6ed406de2db29766746a69115fd6a47048692\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_param\u003c/td\u003e\n\u003ctd\u003e4fe4ca54a0224ef5c4cf9ebf1010d5b869930a3f\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_shared\u003c/td\u003e\n\u003ctd\u003e2e9d8b770cdb2d70d8d9264e4b2de24213ae21bd\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eland_lad2\u003c/td\u003e\n\u003ctd\u003e154bd2b4bf523f3e699de5017679b156242ec13f\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe following components are available in the\n\u003ca href=\"https://github.com/NOAA-GFDL\"\u003eNOAA-GFDL\u003c/a\u003e github organization:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/MOM6\"\u003eMOM6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/SIS2\"\u003eSIS2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/GFDL_atmos_cubed_sphere/tree/AM4.0\"\u003eGFDL_atmos_cubed_sphere\u003c/a\u003e (as \u003ca href=\"src/atmos_cubed_sphere\"\u003eatmos_cubed_sphere\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/icebergs\"\u003eicebergs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/ice_param\"\u003eice_param\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/ocean_BGC\"\u003eocean_BGC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/FMScoupler\"\u003ecoupler\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/FMS\"\u003eFMS\u003c/a\u003e (as \u003ca href=\"src/shared\"\u003eshared\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/mocsy\"\u003emocsy\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding CM4\u003c/h2\u003e\u003ca id=\"user-content-building-cm4\" class=\"anchor\" aria-label=\"Permalink: Building CM4\" href=\"#building-cm4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainers\u003c/h3\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"container\"\u003econtainer folder\u003c/a\u003e provides example Dockerfiles and Signularity\ndefinition files to use to build AM4 containers using either GCC/GFORTAN or\nIntel oneAPI. There is a script that can be used to build the intel\nsingularity containers, and the first step of this script can be used with the\nother GFDL climate models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"exec\"\u003eexec\u003c/a\u003e directory contains Makefiles that can be used to\nbuild the CM4 executable. These Makefiles were generated using the\n\u003ca href=\"https://github.com/NOAA-GFDL/mkmf\"\u003eMake Makefile (mkmf)\u003c/a\u003e program.\nIncluded in the exec direcgtory is a sample make template file for the\nIntel compilers (\u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e). This make\ntemplate can be used on any system with a relatively recent version of\nthe Intel compilers, the netCDF 4 library and the MPICH2 MPI library.\nIncluded in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file are\nadditional settings that can be modified during the build.\u003c/p\u003e\n\u003cp\u003eTo run the default build (-O3 -msse2), go to the exec directory and\nenter the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake HDF_INCLUDE=-I/path/to/hdf5/include\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003cem\u003e/path/to/hdf5/include\u003c/em\u003e is the path to your HDF5 include folder where hdf5.mod\nis.\u003c/p\u003e\n\u003cp\u003eIf you would like to change some of the compiler options, there are several different\noptions to add to the make command. For example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake ISA=-xhost REPRO=on\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill replace -msse with -xhost and -O3 with -O2. The three options for\nbuilding are\u003cbr\u003e\n\u003ccode\u003ePROD=on\u003c/code\u003e (-O3) Default\n\u003ccode\u003eREPRO=on\u003c/code\u003e (-O2)\u003cbr\u003e\n\u003ccode\u003eDEBUG=on\u003c/code\u003e (-O0 and other traps)\u003cbr\u003e\nAll of the make line options can be\nfound in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file.\u003c/p\u003e\n\u003cp\u003eTo build with GNU compilers, add \u003ccode\u003egcc=on\u003c/code\u003e to the \u003ccode\u003emake\u003c/code\u003e line. The make line\noptions can be found in the \u003ca href=\"exec/templates/gnu.mk\"\u003egnu.mk\u003c/a\u003e file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eObtaining the input data\u003c/h2\u003e\u003ca id=\"user-content-obtaining-the-input-data\" class=\"anchor\" aria-label=\"Permalink: Obtaining the input data\" href=\"#obtaining-the-input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe input data required for running the CM4 model can be found on\n\u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/cm4/\" rel=\"nofollow\"\u003eGFDL\u0027s data\nportal\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eCM4_runDir.tar.gz\u003c/code\u003e contains a configured run directory to run a\nsample experiment of the CM4 model. Included in the tar file is a\nREADME.CM4 with more instructions on how to configure the CM4 run\ndirectory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CM4\u003c/h2\u003e\u003ca id=\"user-content-running-cm4\" class=\"anchor\" aria-label=\"Permalink: Running CM4\" href=\"#running-cm4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIncluded in the run directory is a sample run script for reference.\nTo run the CM4 sample experiment, first download the data file\nmentioned in \u003ca href=\"#obtaining-the-input-data\"\u003eObtaining the Input data\u003c/a\u003e\nsection. Modify the variables in the configuration section in the\nsample run script, and then run the script.\u003c/p\u003e\n\u003cp\u003eThe sample data and run script are configured to run on a total of 8127\nprocessors (864 cores 4 threads for the atmosphere and 4671 ocean cores).\u003cbr\u003e\nTo run on a different number of processors, or modify the\nexperiment, refer to the \u003ccode\u003eREADME.CM4\u003c/code\u003e file included in the CM4\ndata tarball.\u003c/p\u003e\n\u003cp\u003eNote: The \u003ccode\u003einput.nml\u003c/code\u003e file (found in the CM4 data tarball) contains\nFortran namelists and namelist variables that modify, at run time, the\nmodel. To learn more about the settings in the \u003ccode\u003einput.nml\u003c/code\u003e file,\nplease refer to source code where the namelist/variable are defined.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel output and Other References\u003c/h2\u003e\u003ca id=\"user-content-model-output-and-other-references\" class=\"anchor\" aria-label=\"Permalink: Model output and Other References\" href=\"#model-output-and-other-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease refer to the \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/cm4/\" rel=\"nofollow\"\u003eCM4 data and code\nsite\u003c/a\u003e for details\nabout where to find model and OBS data used in the papers.\u003c/p\u003e\n\u003cp\u003eFor all analysis figures and pertaining data, please use the CM4\ndocumentation papers as the original reference.\u003c/p\u003e\n\u003cp\u003ePlease direct your questions and feedback to\n\u003ca href=\"mailto:gfdl.climate.model.info@noaa.gov\"\u003egfdl.climate.model.info@noaa.gov\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is\nprovided on an \u0027as is\u0027 basis and the user assumes responsibility for\nits use. DOC has relinquished control of the information and no\nlonger has responsibility to protect the integrity, confidentiality,\nor availability of the information. Any claims against the Department\nof Commerce stemming from the use of its GitHub project will be\ngoverned by all applicable Federal law. Any reference to specific\ncommercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply\ntheir endorsement, recommendation or favoring by the Department of\nCommerce. The Department of Commerce seal and logo, or the seal and\nlogo of a DOC bureau, shall not be used in any manner to imply\nendorsement of any commercial product or activity by DOC or the United\nStates Government.\u003c/p\u003e\n\u003cp\u003eThis project code is made available through GitHub but is managed by\nNOAA-GFDL at \u003ca href=\"https://gitlab.gfdl.noaa.gov\" rel=\"nofollow\"\u003ehttps://gitlab.gfdl.noaa.gov\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 7, - "subscribers_count": 3, - "topics": [ - "sigularity", - "singularity-containers", - "singularityhub", - "bioinformatics" - ], - "updated_at": 1721631116.0 + "subscribers_count": 4, + "topics": [], + "updated_at": 1711351792.0 }, { "data_format": 2, - "description": "NectarCAM high level analysis tools", + "description": "quantum chemistry software", "filenames": [ - "Singularity" + "container_recipes/SingularityFile" ], - "full_name": "cta-observatory/nectarchain", - "latest_release": "v0.1.9", + "full_name": "VALENCE-software/VALENCE", + "latest_release": "v1.0", + "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.com/VALENCE-software/VALENCE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/71fa134f6d03fcd25c5b648055848c0339160b129fa2d7745b2fa9f94982bffc/68747470733a2f2f7472617669732d63692e636f6d2f56414c454e43452d736f6674776172652f56414c454e43452e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/VALENCE-software/VALENCE.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/VALENCE-software/VALENCE/master?filepath=valence_tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e91e1d353a8b6acf0b42547ac3901f2c30138a3abaaa3d3c242da30b5b4f8426/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/VALENCE-software/VALENCE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6c182c7bc3d62ff8ed85baf22b8d695a0ccb7f205d7fb476b9e5fd996ddda631/68747470733a2f2f636f6465636f762e696f2f67682f56414c454e43452d736f6674776172652f56414c454e43452f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/VALENCE-software/VALENCE/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/152630099\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33ffafb09bf4122865aab8465ca20ca6c186a8b3f61efef1959c9d88722a546a/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3135323633303039392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/152630099.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVALENCE\u003c/h1\u003e\u003ca id=\"user-content-valence\" class=\"anchor\" aria-label=\"Permalink: VALENCE\" href=\"#valence\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Massively Parallel Implementation of Variational Subspace Valence Bond\u003c/p\u003e\n\u003cp\u003e16 November, 2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents:\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents:\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eOverview\u003c/li\u003e\n\u003cli\u003eIntroduction to VSVB\u003c/li\u003e\n\u003cli\u003eAbout the code\u003c/li\u003e\n\u003cli\u003eInput\u003c/li\u003e\n\u003cli\u003eOutput\u003c/li\u003e\n\u003cli\u003eExamples\u003c/li\u003e\n\u003cli\u003eAutomatic input generation with vtools\u003c/li\u003e\n\u003cli\u003eHow to make spin-coupled orbitals\u003c/li\u003e\n\u003cli\u003eHow to use derived basis functions\u003c/li\u003e\n\u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003cli\u003eContact information\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 1. Overview\u003c/h2\u003e\u003ca id=\"user-content-section-1-overview\" class=\"anchor\" aria-label=\"Permalink: Section 1. Overview\" href=\"#section-1-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen first downloaded, this repository includes\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e.travis.yml\u003c/li\u003e\n\u003cli\u003eHow-to-build-VALENCE.md\u003c/li\u003e\n\u003cli\u003eLICENSE\u003c/li\u003e\n\u003cli\u003eMakefile\u003c/li\u003e\n\u003cli\u003eREADME.md\u003c/li\u003e\n\u003cli\u003edoc/\u003c/li\u003e\n\u003cli\u003eexamples/\u003c/li\u003e\n\u003cli\u003einstall-mpich.sh*\u003c/li\u003e\n\u003cli\u003einstall-simint.sh*\u003c/li\u003e\n\u003cli\u003enitrogen/\u003c/li\u003e\n\u003cli\u003esrc/\u003c/li\u003e\n\u003cli\u003etesting/\u003c/li\u003e\n\u003cli\u003evalence_tutorial.ipynb\u003c/li\u003e\n\u003cli\u003evtools/\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMost of the above items are self-explanatory. The directories, /examples/ and /testing/, both contain input files that can be used to validate the binary. /testing/ contains an established set of inputs primarily for internal use. While there is some overlap with /testing/, /examples/ is oriented more toward educating the user about the various functions of VALENCE and features of VSVB (see Section 6 for more details). /vtools/ contains tools for automatic input generation and is described in Section 7. /doc/ contains a write-up of the method and Doxygen-generated documentation. /nitrogen/ is concerned with the interface to NITROGEN (\u003ca href=\"https://www.colorado.edu/nitrogen/\" rel=\"nofollow\"\u003ehttps://www.colorado.edu/nitrogen/\u003c/a\u003e) for optimizing molecular geometries and computing vibrational frequencies. The next section contains a brief, practical introduction to VSVB - the method implemented by VALENCE.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 2. Introduction to VSVB\u003c/h2\u003e\u003ca id=\"user-content-section-2-introduction-to-vsvb\" class=\"anchor\" aria-label=\"Permalink: Section 2. Introduction to VSVB\" href=\"#section-2-introduction-to-vsvb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn molecular electronic structure theory, variational subspace valence bond, or VSVB, is a type of generalized valence bond theory using gaussian atomic basis sets. Unlike the majority of methods in the mainstream of quantum chemistry such as Hartree-Fock, MP2, coupled-cluster, DFT, and so on, VSVB is based purely on orbitals that are allowed to overlap with one another. That is, VSVB does not use orthogonal (\u0027molecular\u0027) orbitals. The first benefit is that VSVB orbitals tend to be highly local, typically involving just one or two atoms, in contrast to molecular orbitals which are typically delocalized over all the atoms in any given problem. The highly local orbitals have obvious advantages for chemical interpretability and computational scalability.\u003c/p\u003e\n\u003cp\u003eThe first method paper is:\u003c/p\u003e\n\u003cp\u003eGraham D. Fletcher, \"The variational subspace valence bond method\",\nJ. Chem. Phys. 142, 134112 (2015).\n\u003ca href=\"http://dx.doi.org/10.1063/1.4916743\" rel=\"nofollow\"\u003ehttp://dx.doi.org/10.1063/1.4916743\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSee the CITATION file for more papers.\u003c/p\u003e\n\u003cp\u003eIn addition to a general background in quantum chemistry, it helps to read the above paper and references therein. The\ndocument doc/notes-vsvb-energy.pdf also contain detailed information about the method.\u003c/p\u003e\n\u003cp\u003eIn general, VSVB orbitals are linear combinations of the atomic orbitals (LCAO) of the basis set. As mentioned above, no consideration is given to how much the VSVB orbitals overlap with one another. This gives the user complete control over how the orbitals are defined. For example, orbitals can be tailored to represent intuitive concepts in chemistry such as bonds and lone-pairs, and calculations can be performed to test these ideas. Typically, the basis set expansion of an orbital together with guesses for the LCAO expansion weights are input to an optimization run where the weight values are refined to minimize the total energy. The user is free to enter any guess because VALENCE always ensures normalization of the orbitals to machine precision, internally. Once obtained, such orbitals can be used repeatedly to build other wave functions where similar orbitals are needed. If orbitals with the desired forms are available, qualitatively correct wave functions can be made \u0027for free\u0027.\u003c/p\u003e\n\u003cp\u003eFor convenience, VSVB orbitals are grouped into three types, in order of input: spin-coupled, unpaired, and double-occupied. This categorization greatly assists efficiency. More importantly, the orbital types serve different chemical functions. The term \u0027double-occupied\u0027 refers to a single spatial orbital used to model a pair of electrons with opposed spins - corresponding to two spin orbitals in the wave function with the same spatial function. This situation can be thought of as analogous to that in closed-shell Hartree-Fock (HF). Indeed, when properly constructed, such a wave function (called a \u0027VSHF\u0027 wave function) has the HF energy, except with overlapping orbitals. In chemistry, double-occupied orbitals are often used to model atomic core electrons and other \u0027spectator\u0027 electrons. The \u0027unpaired\u0027 orbitals are singly occupied with electrons of the same spin, contributing a \u0027high spin\u0027 configuration. VSVB wave functions involving only unpaired and double-occupied orbitals have a single determinant.\u003c/p\u003e\n\u003cp\u003eThe term \u0027spin-coupled orbitals\u0027, here, refers to pairs of singly occupied orbitals whose electrons are coupled to an overall singlet using a spin function of the Rumer type. The advantage of spin-coupled orbitals is that they greatly extend the applicability of VSVB beyond \u0027Hartree-Fock\u0027 type problems to model bond-breaking/formation and situations where the spatial polarization of charge is critical to reproducing chemical phenomena. Spin-coupled orbitals can be used to recover a significant proportion of the so-called static electron correlation energy. Since the associated spin functions double the number of determinants in the wave function for each pair involved, spin coupled orbitals are typically used judiciously to model key components of the chemistry of interest, such as the separation of an electron pair in a bond or excited state. Occasionally, chemical problems expose different ways to couple the spins of a given group of electrons to the same overall spin, and multiple spin couplings can be incorporated into the VSVB wave function to reflect this, with an additive increase in the number of determinants.\u003c/p\u003e\n\u003cp\u003eAs mentioned above, the tendency for VSVB orbitals to be highly local also brings computational scalability and efficiency. VSVB is characterized by having low memory requirements, negligible I/O and communication overheads (high parallel scalability), high CPU efficiency (in terms of the percentage of peak FLOPs), and moderate complexity (~cubic or even sub-cubic). The major consideration with VSVB is the cost of optimization which typically corresponds to many energy calculations. However, the overall cost in terms of the time-to-solution is greatly mitigated by the ability to re-use previously determined orbitals and by the high concurrency that is possible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 3. About the code\u003c/h2\u003e\u003ca id=\"user-content-section-3-about-the-code\" class=\"anchor\" aria-label=\"Permalink: Section 3. About the code\" href=\"#section-3-about-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVALENCE is the \u0027number-crunching\u0027 code in an overall system for executing VSVB calculations that includes various tools (the \u0027vtools\u0027) for generating input and processing output. Currently, VALENCE can compute energies and optimize single-reference wave functions for the following types of situation-\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGround states.\u003c/li\u003e\n\u003cli\u003eExcited states, including multiple excitations (single-reference).\u003c/li\u003e\n\u003cli\u003eClosed-shell systems.\u003c/li\u003e\n\u003cli\u003eOpen-shell systems.\u003c/li\u003e\n\u003cli\u003eBond-breaking/formation, majority or all of the nuclear potential energy surface.\u003c/li\u003e\n\u003cli\u003eSpin optimization, including resonance.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe term \u0027single-reference\u0027 is used here to mean a single product of spatial orbitals. Depending on the spin-coupling used, this wave function will have one or more determinants.\u003c/p\u003e\n\u003cp\u003eIn addition, VALENCE has the ability to expand orbitals in terms of other LCAOs, that is, arbitrary combinations of the atomic basis functions may be defined and used as (new) basis functions. A typical use of such \u0027derived basis functions\u0027 (DBF) is to provide degrees-of-freedom adapted to the molecular symmetry (\u0027symmetry-adaptation\u0027). Other uses include making spherical harmonic functions from the cartesian functions, and making a hybridized basis set consisting of sp,sp2, and sp3 \u0027hybrid\u0027 functions.\u003c/p\u003e\n\u003cp\u003eCurrently, VALENCE can optimize two types of linear parameter - orbital weights, and spin-coupling weights - using a first-order method. The term, \u0027first-order\u0027, refers to taking the first derivative of the VSVB energy with respect to the linear weights. The method solves a generalized eigenproblem of the form HC=SCE beginning by forming the hamiltonian (H) and overlap matrices (S), where E are the eigenvalues (energies). The eigenvectors (C) contain the updated orbital or spin-coupling weights. The cost of this method is quadratic with the size of the orbital expansion or spin-coupling space. There is currently an option to use a \u0027direct energy minimization\u0027 (DEM) method, though this is still under development.\u003c/p\u003e\n\u003cp\u003eVALENCE is written in Fortran-90 and runs in parallel using MPI. To compute integrals, VALENCE currently uses the vectorized integral package, SIMINT (\u003ca href=\"https://github.com/simint-chem\"\u003ehttps://github.com/simint-chem\u003c/a\u003e). This release includes instructions on how to build SIMINT. Once the SIMINT library is built, building VALENCE itself begins with setting options in a simple Makefile. The Makefile also contains some optimizations for various platforms, and an option to build the sequential form. A binary called \u0027valence\u0027 should result. VALENCE takes an input file on the command line. To run VALENCE sequentially just type,\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./valence [name of input file]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSee section 5 for example input files. To run VALENCE in parallel, please consult the documentation for your target platform as to how MPI runs are specified.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 4. Input\u003c/h2\u003e\u003ca id=\"user-content-section-4-input\" class=\"anchor\" aria-label=\"Permalink: Section 4. Input\" href=\"#section-4-input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u0027valence\u0027 binary makes no choices regarding the orbitals, it merely processes the wave function specified by the input to compute energies, execute optimization procedures, and so forth. That said, a highly versatile system for defining the orbitals is supported, together with the N-electron form of the wave function, and this is described in this section.\u003c/p\u003e\n\u003cp\u003eThe direct input to VALENCE is a plain-text numerical input file. No attempt is made to improve the \u0027look\u0027 of the input because advanced tools are included in this package to generate input automatically and offer a more user-friendly environment (see \u0027vtools\u0027). Although the tools continue to be refined and improved in our research group, it is important to understand how the direct input is structured since it offers the highest degree of generality.\u003c/p\u003e\n\u003cp\u003eIn what follows, a helpful concept is that of the \u0027orbital basis set\u0027 (OBS). In contrast to the more familiar \u0027molecular\u0027 basis set, an OBS spans the basis sets of just those atoms needed to support a given spatial orbital in VSVB. OBS often involve just one or two atoms in order to represent, for example, core electrons and \u0027lone-pairs\u0027, or chemical bonds, respectively. Unlike a molecular basis set, as the molecular system increases in size the component OBS stay approximately the same size. Thus, OBS facilitate a concise and efficient definition of an orbital that is independent of the target molecule, allowing orbitals to be stored and re-combined to make new wave functions.\u003c/p\u003e\n\u003cp\u003eBroadly, the input to VALENCE consists of specifications for the geometry, basis set, and a guess wave function, structured to facilitate dynamic memory. Thus, all the counts/sizes/dims/lengths, etc, are given on the first line so the program can allocate sufficient memory to read the data that follow. Lines may be separated by blank lines and items on a line by spaces, as permitted by FORTRAN free-format input rules. All data items are of INTeger type, unless specified to be FLOAT/REAL. Note also that VALENCE checks few input errors, but many errors can be avoided by using the \u0027vtools\u0027.\u003c/p\u003e\n\u003cp\u003eThe input is organized in the order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA) sizes, counts, array dims (1 line)\u003c/li\u003e\n\u003cli\u003eB) Wave function optimization control (1 line)\u003c/li\u003e\n\u003cli\u003eC) Geometry\u003c/li\u003e\n\u003cli\u003eD) Basis set\u003c/li\u003e\n\u003cli\u003eE) N-electron Wave function information (spin-couplings, optional)\u003c/li\u003e\n\u003cli\u003eF) Orbitals (various kinds, in order: spin-coupled, single-occupied, double-occupied)\u003c/li\u003e\n\u003cli\u003eG) Derived basis functions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eParts (A) through (G) are now described in more detail.\u003c/p\u003e\n\u003cp\u003eA) sizes, counts, array dims\u003c/p\u003e\n\u003cp\u003eThere are 15 integers on a single line, they are-\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1. The number of atoms/point charges in the geometry\u003c/li\u003e\n\u003cli\u003eItem 2. The number of unique atom types\u003c/li\u003e\n\u003cli\u003eItem 3. Number of spin-coupled electron/orbital PAIRS\u003c/li\u003e\n\u003cli\u003eItem 4. Number of unpaired electrons/orbitals\u003c/li\u003e\n\u003cli\u003eItem 5. Number of double-occupied (DOCC) orbitals\u003c/li\u003e\n\u003cli\u003eItem 6. Total length of the orbital weight list (array dim)\u003c/li\u003e\n\u003cli\u003eItem 7. Length of the largest orbital expansion (array dim)\u003c/li\u003e\n\u003cli\u003eItem 8. Number of spin-couplings\u003c/li\u003e\n\u003cli\u003eItem 9. Number of unique atomic basis set shells\u003c/li\u003e\n\u003cli\u003eItem 10. Number of unique atomic basis set primitives\u003c/li\u003e\n\u003cli\u003eItem 11. Highest angular momentum in the basis set\u003c/li\u003e\n\u003cli\u003eItem 12. Number of derived basis functions (DBF)\u003c/li\u003e\n\u003cli\u003eItem 13. Number of orbital optimization groups\u003c/li\u003e\n\u003cli\u003eItem 14. Number of orbital excitations\u003c/li\u003e\n\u003cli\u003eItem 15: Largest atom count of the orbital basis sets\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eB) Optimization control\u003c/p\u003e\n\u003cp\u003eThere are 8 or more items on a single line, they are-\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1. The charge-cloud screening tolerance (integer), e.g. \u00275\u0027 means 0.00001 or 1d-5 or 10^-5.\u003c/li\u003e\n\u003cli\u003eItem 2. The density screening tolerance\u003c/li\u003e\n\u003cli\u003eItem 3. The integral screening tolerance (Schwarz inequality)\u003c/li\u003e\n\u003cli\u003eItem 4: The coarsest wave function convergence tolerance, given in kilocalories per mole (kCal/Mol).\u003c/li\u003e\n\u003cli\u003eItem 5: The finest wave function convergence tolerance. The optimization will proceed through multiple orbital groups from coarse to fine.\u003c/li\u003e\n\u003cli\u003eItem 6: Maximum number of iterations.\u003c/li\u003e\n\u003cli\u003eItem 7: Initial weight perturbation (DEM only)\u003c/li\u003e\n\u003cli\u003eItem 8: Weight perturbation scalar (DEM only)\u003c/li\u003e\n\u003cli\u003eItem 9: The orbital optimization groups as begin/end pairs of orbital labels (in order), e.g. \" 1 3 5 6 \" shows 2 groups: first group optimizes orbitals 1 through 3, second group is orbitals 5 and 6 (skipping 4)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eC) Geometry\u003c/p\u003e\n\u003cp\u003eA line for each atom/point charge with the layout:\u003c/p\u003e\n\u003cp\u003e[Atom type] X Y Z\u003c/p\u003e\n\u003cp\u003eeg. 1 0.0 0.0 0.0\u003c/p\u003e\n\u003cp\u003eThe atom type is an integer that addresses the basis set(s) given in the next section. For example, type \u00271\u0027 addresses the first basis set listed, type \u00272\u0027 the second, and so on. \u0027X,Y,Z\u0027 refers to cartesian coordinates in Angstroms (FLOATs).\u003c/p\u003e\n\u003cp\u003eD) Basis Set\u003c/p\u003e\n\u003cp\u003eVALENCE recognizes basis sets of cartesian atom-centered contracted gaussians. The basis set for each atom/etc type is given with the layout:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1: The nuclear/point charge (FLOAT)\u003c/li\u003e\n\u003cli\u003eItem 2: The number of shells (zero or more), NS\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere follow NS datasets defining the shells, as follows:\u003c/p\u003e\n\u003cp\u003eNext line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1: The shell angular momentum\u003c/li\u003e\n\u003cli\u003eItem 2: The number of primitive gaussian functions, NP, in the shell.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere follow NP lines, as follows:\u003c/p\u003e\n\u003cp\u003eNext line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1: The primitive exponent (FLOAT)\nIf NP\u0026gt;1,\u003c/li\u003e\n\u003cli\u003eItem 2: The primitive coefficient (FLOAT)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that VALENCE skips input of the redundant unit weight when NP=1. The counts, NS and NP, are iterated until all shells are input for all the atom types. A point charge can be input as a \u0027nuclear charge\u0027 with NS=0. This input is quite general. For example, \u0027floating\u0027 basis set shells can be placed anywhere using a nuclear charge of zero.\u003c/p\u003e\n\u003cp\u003eE) The \u0027N-electron\u0027 wave function (optional)\u003c/p\u003e\n\u003cp\u003eIf the number of spin-couplings, NC (Item 8 of (A), above), is greater than zero, then the spin-coupling information will be read next. Currently, the code can make \u0027Rumer\u0027 type couplings for the singlet parts of a system. Each spin-coupling is read as follows. If NC=1, a list of the orbital (electron) label pairs defining the (singlet) couplings is given, e.g. 1 2 3 4, means electrons \u00271 and 2\u0027 are singlet coupled, then electrons \u00273 and 4\u0027 are singlet coupled. If NC\u0026gt;1, the pair list is preceeded with the spin-coupling weight (FLOAT). This is repeated for all NC spin-couplings.\u003c/p\u003e\n\u003cp\u003eExcited states may be entered next, according to Item 14 of (A), as a sequence of {NX,NR} pairs, where NX addresses the orbital to be promoted and NR labels the root of the secular equation (e.g. \u00270\u0027 for lowest/ground, \u00271\u0027 for first excited, etc). It is an error if spin-coupled orbitals are desired but no spin-couplings are input (Item 8 = 0). Without spin-coupled orbitals and/or excited states, this section is null/empty.\u003c/p\u003e\n\u003cp\u003eF) Spatial wave function\u003c/p\u003e\n\u003cp\u003eThe spatial wave function in terms of the orbitals is now given. In general, the total number of orbitals is given by-\u003c/p\u003e\n\u003cp\u003e2*[no. spin-coupled PAIRs] + [no. unpaired] + [no. DOCC]\u003c/p\u003e\n\u003cp\u003eWhatever orbitals are needed, they must be entered in the order: spin-coupled; then unpaired; then DOCC, as per the intended use. The layout for each orbital is as follows. The first line defines the orbital basis set (OBS):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1. The number of atoms, NN, whose basis sets make up the OBS.\u003c/li\u003e\n\u003cli\u003eItem 2. List of NN atoms.\u003c/li\u003e\n\u003cli\u003eItem 3. Total number of AO\u0027s, NA.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere follow NA {AO address, coefficient (FLOAT)}-pairs. The AO addresses lie within the OBS specified by the NN atoms and in the order the atoms are listed. The scheme iterates until all orbitals required by the above formula, based on items 3-5 of (A), are input.\u003c/p\u003e\n\u003cp\u003e(G) Derived basis functions\u003c/p\u003e\n\u003cp\u003eDBFs are input using the same OBS format as described in (F) for the main orbital types. The total number of DBFs is preempted by item 12 of (A). See section 7 for more details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 5. Output\u003c/h2\u003e\u003ca id=\"user-content-section-5-output\" class=\"anchor\" aria-label=\"Permalink: Section 5. Output\" href=\"#section-5-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBroadly, the output of VALENCE is the VSVB wave function and its total energy. VALENCE first prints the outcome of a \u0027guess\u0027 energy calculation, preceded by the nuclear repulsion energy in the case of a molecule, optionally followed by an optimization run with a progress update at each iteration. Each optimization step reports the cumulative relaxation in kCal/Mol compared to the \u0027guess energy\u0027. Also printed is the relaxation obtained at that step divided by the convergence tolerance to indicate how near the optimization is to convergence. Every energy calculation or optimization step prints a file called \u0027orbitals\u0027 which contains the updated orbitals together with the current total energy at the end. If spin-coupled orbitals are used with more than one spin-coupling, an additional file called \u0027nelecwfn\u0027 is produced, containing the updated spin-coupling weights.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 6. Examples\u003c/h2\u003e\u003ca id=\"user-content-section-6-examples\" class=\"anchor\" aria-label=\"Permalink: Section 6. Examples\" href=\"#section-6-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current release includes many examples (see directory \u0027./examples/\u0027) chosen to illustrate the main features and types of calculation that can currently be performed with VALENCE, while also being of a convenient size for verifying correct execution. The examples can be tested using the \u0027test_all\u0027 script and take less than five minutes on a typical processor. In /examples/, simply type-\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./test_all valence\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIn this section, five examples are described in detail. Narrative text is contained within brackets where it is helpful to distinguish it from the example input text. It is also helpful to note the following: the magnitude of a linear weight can occasionally be greater than unity; the overall sign of an orbital is arbitrary as the wave function is only determined to within a phase; agreement between total energies from the same run on different hardware is rarely greater than 10 places, and 6 places is more typical.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBeryllium atom\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis example uses two DOCC orbitals to model the singlet-S ground state of beryllium, with electronic configuration, 1s^2 2s^2, expanded over a basis set of three \u0027s\u0027-type functions. The variational subspace (VS) of the first orbital consists of functions 1 and 3, with function 1 as its unique degree-of-freedom (UDF). The second orbital\u0027s VS contains functions 2 and 3, with 2 as the UDF. Thus, function 3 is the \u0027shared\u0027 basis. Other choices exist (in fact there are two, accounting for symmetry), but this is the most efficient choice given the chemical intuition that function 1 resembles a \u00271s\u0027 orbital, function 2 a \u00272s\u0027 orbital, and so on, in accordance with the structure of a typical atomic basis set. Since there are only DOCC orbitals involved, this run optimizes a VSHF wave function.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[counts and array dims]\n 1 1 0 0 2 4 2 0 3 17 0 0 1 0 1\n\n[run parameters]\n 16 16 16 3 3 100 0.0 0.0 1 2\n\n\n[geometry]\n 1 0.0 0.0 0.0\n\n\n[basis set]\n 4.0 3\n0 8\n 2940.0000000 0.0006800 \n 441.2000000 0.0052360 \n 100.5000000 0.0266060 \n 28.4300000 0.0999930 \n 9.1690000 0.2697020 \n 3.1960000 0.4514690 \n 1.1590000 0.2950740 \n 0.1811000 0.0125870 \n0 8\n 2940.0000000 -0.0001230 \n 441.2000000 -0.0009660 \n 100.5000000 -0.0048310 \n 28.4300000 -0.0193140 \n 9.1690000 -0.0532800 \n 3.1960000 -0.1207230 \n 1.1590000 -0.1334350 \n 0.1811000 0.5307670 \n0 1\n 0.0589000\n\n[orbitals]\n 1 1 2\n 1 1.0 3 0.0\n 1 1 2\n 2 1.0 3 0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[A guess consisting of ones for the UDF and zeros elsewhere may be termed a \u0027unit guess\u0027 by analogy with a unit matrix. The output to the screen is given below.]\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e guess energy in atomic units -14.4746666438408660\n orbital optimization \n\n (full) first-order method \n\n cycle orbital relaxation(kCal) (..per orb.)/tol \n 1 1 -0.038504 -0.3850E+02\n 1 2 -36.147608 -0.3611E+05\n 2 1 -58.488652 -0.2234E+05\n 2 2 -60.233942 -0.1745E+04\n 3 1 -61.130090 -0.8961E+03\n 3 2 -61.228910 -0.9882E+02\n 4 1 -61.280549 -0.5164E+02\n 4 2 -61.286071 -0.5522E+01\n 5 1 -61.288965 -0.2894E+01\n 5 2 -61.289277 -0.3120E+00\n 6 1 -61.289441 -0.1637E+00\n 6 2 -61.289458 -0.1761E-01\n\n calculation converged \n\n total energy in atomic units -14.5723376136726923\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[After six cycles through the orbital list, the final energy (which matches the Hartree-Fock energy) is printed. The \u0027orbitals\u0027 file looks like this:]\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 1 1 2\n 1 1.00065097 3 -0.00375468\n 1 1 2\n 2 0.48912818 3 0.58002975\n\n\n total energy in atomic units -14.5723376136726923\n converged to 0.10E-02 kCal/mol\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[There is no \u0027nelecwfn\u0027 file with this run]\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eH2O/VSHF/cc-VDZ\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis example highlights chemically intuitive choices for the UDF, so just the optimized wave function and energy are given.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 3 2 0 0 5 30 8 0 7 25 1 0 1 0 3\n\n\n 16 16 16 6 6 100 0.0 0.0 1 5\n\n\n 1 0.0000 0.0000 0.1271\n 2 0.0000 0.7580 -0.5085\n 2 0.0000 -0.7580 -0.5085 \n\n\n 8.0 5\n0 8\n 11720.0000000 0.0007100 \n 1759.0000000 0.0054700 \n 400.8000000 0.0278370 \n 113.7000000 0.1048000 \n 37.0300000 0.2830620 \n 13.2700000 0.4487190 \n 5.0250000 0.2709520 \n 1.0130000 0.0154580 \n0 8\n 11720.0000000 -0.0001600 \n 1759.0000000 -0.0012630 \n 400.8000000 -0.0062670 \n 113.7000000 -0.0257160 \n 37.0300000 -0.0709240 \n 13.2700000 -0.1654110 \n 5.0250000 -0.1169550 \n 1.0130000 0.5573680 \n0 1\n 0.3023000 \n1 3\n 17.7000000 0.0430180 \n 3.8540000 0.2289130 \n 1.0460000 0.5087280 \n1 1\n 0.2753000 \n\n 1.0 2\n0 3\n 13.0100000 0.0196850 \n 1.9620000 0.1379770 \n 0.4446000 0.4781480 \n0 1\n 0.1220000 \n\n\n\n\n 3 1 2 3 6\n 1 1.00085930 2 0.00438407 6 0.00072731 9 0.00346894\n 11 0.00045391 13 0.00045392\n 3 1 2 3 8\n 2 0.08137627 5 0.30386789 6 -0.39873764 8 0.16245941\n 9 -0.27280445 10 0.40165595 11 0.05882459 13 -0.02137198\n 3 1 2 3 8\n 2 0.08137642 5 -0.30386766 6 -0.39873750 8 -0.16245933\n 9 -0.27280444 11 -0.02137131 12 0.40165620 13 0.05882437\n 3 1 2 3 6\n 2 0.44922797 3 0.56835690 6 0.25068561 9 0.21482539\n 11 -0.03046886 13 -0.03046865\n 1 1 2\n 4 0.63677843 7 0.51530766 \n\n[ The total energy is: -75.97812747 AU ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere, the UDF for the orbitals are as follows:\nOrbital UDF AO label\noxygen core: O 1s 1\nOH(a) bond: H(a) 1s 10\nOH(b) bond: H(b) 1s 12\nSigma lone-pair: O 3s 3\nPi lone-pair: (O 2px 4)\u003c/p\u003e\n\u003cp\u003eChemically sensible alternatives for the Sigma lone-pair UDF include the O2s function. Different orbitals would be obtained with this choice but the same energy. The Pi lone-pair has a different symmetry to the other orbitals as it is perpendicular to the plane of the atoms. Since it is the only orbital of its symmetry, the UDF issue is null (hence the parentheses). The in-plane orbitals have Sigma symmetry.\u003c/p\u003e\n\u003cp\u003eNote that the O 2,3py,z functions are not chemically sensible UDF for the OH bonds. The atoms are placed in the y,z plane to simplify the symmetry definitions, so the 2py,z functions are needed to direct hybridization of the oxygen valence electrons toward the hydrogens for both bonds. So choosing, say, 2py for one bond and 2pz for the other would yield unsymmetric bond orbitals.\u003c/p\u003e\n\u003cp\u003eYet another alternative would be to hybridize the O2s,y,z (and/or the O3s,y,z) AOs to give three Osp2 functions, two directed toward their respective hydrogens and one directed in opposition to them, then base the UDF choices on them. The s/p hybridization ratio would need to be optimized for this case.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eH2/SC/SZ\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eHydrogen molecule with spin-coupled orbitals and a single-Zeta basis set. This is the simplest example of using spin-coupled orbitals. The (optimized) wave function will dissociate correctly when the interatomic distance is increased. As with H2O, above, just the result is given.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 2 1 1 0 0 4 2 1 1 3 0 0 1 0 2\n\n\n 16 16 16 6 6 100 0.0 0.0 1 2\n\n\n 1 0.0 0.0 0.0\n 1 0.0 0.0 0.770\n\n\n 1.0 1\n0 3\n 13.0100000 0.0196850\n 1.9620000 0.1379770\n 0.4446000 0.4781480\n\n\n[ spin-coupling information ]\n 1 2\n\n\n 2 1 2 2\n 1 0.89622388 2 0.17099192\n 2 1 2 2\n 1 0.17099207 2 0.89622378\n\n\n[ The total energy is: -1.06381067 AU ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe main qualitative difference between this input and the previous ones is the presence of the spin-coupling information in the middle, indicating that electrons 1 and 2 be coupled to a singlet.\u003c/p\u003e\n\u003cp\u003eThe spatial polarization of the two orbitals toward either atom is evident in the weights in each orbital of the two 1s atomic basis functions. As the atoms are drawn apart, the weight of the local basis function tends to unity, while that of the remote function tends to zero, in each orbital, respectively, leaving two separated hydrogen atoms with a total energy slightly higher than -1.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eLiH/SCval/cc-pVDZ\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eLithium Hydride singlet-Sigma ground state. This wave function has a double-occupied Li core and a spin-coupled Li-H \u0027bond\u0027.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 2 2 1 0 1 30 10 1 9 27 2 0 1 0 2\n\n\n 16 16 16 6 6 100 0.0 0.0 1 3\n\n\n 1 0.0 0.0 0.0\n 2 0.0 0.0 1.646\n\n\n\n 3.0 6\n0 8\n 1469.0000000 0.0007660 \n 220.5000000 0.0058920 \n 50.2600000 0.0296710 \n 14.2400000 0.1091800 \n 4.5810000 0.2827890 \n 1.5800000 0.4531230 \n 0.5640000 0.2747740 \n 0.0734500 0.0097510 \n0 8\n 1469.0000000 -0.0001200 \n 220.5000000 -0.0009230 \n 50.2600000 -0.0046890 \n 14.2400000 -0.0176820 \n 4.5810000 -0.0489020 \n 1.5800000 -0.0960090 \n 0.5640000 -0.1363800 \n 0.0734500 0.5751020 \n0 1\n 0.0280500 \n1 3\n 1.5340000 0.0227840 \n 0.2749000 0.1391070 \n 0.0736200 0.5003750 \n1 1\n 0.0240300 \n2 1\n 0.1239000 \n\n 1.0 3\n0 3\n 13.0100000 0.0196850 \n 1.9620000 0.1379770 \n 0.4446000 0.4781480 \n0 1\n 0.1220000 \n1 1\n 0.7270000 \n\n\n\n 1 2 \n\n\n\n 2 1 2 10\n 2 0.52988354 3 0.24604657 6 0.37767681 9 0.04693388\n 10 -0.03994670 12 -0.03994670 15 0.03634386 16 0.03973432\n 17 0.17856129 20 -0.00329185\n 2 1 2 10\n 2 0.06142962 3 0.02236453 6 0.09631637 9 -0.01160377\n 10 -0.01504756 12 -0.01504756 15 0.03646087 16 0.71199723\n 17 0.24851686 20 -0.01632559\n 2 1 2 10\n 1 0.99936284 2 -0.00202778 3 -0.00493372 6 -0.01387483\n 9 0.00230970 10 -0.00033165 12 -0.00033165 15 -0.00706813\n 17 0.00999584 20 -0.00141802\n\n[ The total energy is: -8.00046053 AU ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first two orbitals are read as the spin-coupled pair and the third is read as the double-occupied Li 1s core. As in the H2 example above, the presence of the spin-coupled Li-H bond means the wave function will dissociate correctly when the interatomic separation is increased. The bonding here is not strong. The lithium valence electron is polarized toward H.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eBe/2SC/cc-VQZ\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis is the simplest closed-shell case with two spin-couplings. Convergence is more efficient when the orbitals are obtained with one spin-coupling first, then allowed to relax in the presence of the two couplings. Again, the choice of the first (dominant) spin-coupling is based on the chemical intuition of which electrons are paired to make bonds, lone-pairs, and so forth.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 1 1 2 0 0 16 4 2 5 21 0 0 1 0 1\n\n\n 20 16 16 6 6 100 0.0 0.0 1 4 \n\n\n\n 1 0.0 0.0 0.0\n\n\n\n 4.0 5\n0 9\n 14630.0000000 0.0000920 \n 2191.0000000 0.0007130 \n 498.2000000 0.0037350 \n 140.9000000 0.0154680 \n 45.8600000 0.0528740 \n 16.4700000 0.1456940 \n 6.3190000 0.3026810 \n 2.5350000 0.4049360 \n 1.0350000 0.2223870 \n0 9\n 14630.0000000 -0.0000170 \n 2191.0000000 -0.0001300 \n 498.2000000 -0.0006790 \n 140.9000000 -0.0028570 \n 45.8600000 -0.0098130 \n 16.4700000 -0.0286090 \n 6.3190000 -0.0637600 \n 2.5350000 -0.1172310 \n 1.0350000 -0.1212020 \n0 1\n 0.2528000 \n0 1\n 0.1052000 \n0 1\n 0.0426100 \n\n\n[ spin-couplings and weights ]\n\n 0.28613755 1 2 3 4\n 0.02435745 1 3 2 4\n\n\n 1 1 4\n 1 2.04468084 2 1.08510278 3 0.06760375 4 -0.08225021\n 1 1 4\n 1 0.08795929 2 1.09357982 3 0.05323659 4 -0.06567380\n 1 1 4\n 2 0.40901809 3 0.66520392 4 0.38644720 5 0.11934164\n 1 1 4\n 2 0.14161563 3 -0.17596193 4 0.71862322 5 0.47255306\n\n\n[ The total energy is: -14.58839772 AU ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis run creates a file called \u0027nelecwfn\u0027 with the spin-coupling information,weights given above as its contents.\u003c/p\u003e\n\u003cp\u003eThe energy with one spin-coupling (1 2)(3 4) is -14.58808261 AU, so the relaxation with two couplings is modest in this case. However, the impact of multiple spin-couplings can be much greater than this, particularly in systems with multiple near-degenerate orbitals, such as aromatics. In benzene, the Kekule and Dewar structures correspond to different spin-couplings of the six equivalent Pi electrons.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 7. Automatic input generation with vtools\u003c/h2\u003e\u003ca id=\"user-content-section-7-automatic-input-generation-with-vtools\" class=\"anchor\" aria-label=\"Permalink: Section 7. Automatic input generation with vtools\" href=\"#section-7-automatic-input-generation-with-vtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe previous sections describe how the input to VALENCE can be generated \u0027by hand\u0027. However, it is also possible to generate this input automatically using the \u0027vtools\u0027 software provided in this package. A description of the vtools command syntax is provided in the file, ./vtools/README.md.\u003c/p\u003e\n\u003cp\u003evtools uses the \u0027Model Kit\u0027 method for building wave functions, employing the analogy that the VSVB wave function can be built from orbitals much in the same way that a model airplane is built from parts. In the case of a wave function, the orbital \u0027parts\u0027 are contained in a repository, and the \u0027builder\u0027 is a binary, called \u0027modelkit\u0027. modelkit currently processes three types of orbital: core orbitals, bonds, and lone-pairs. While the cores and lone-pairs involve a single atom, the bonds involve two atoms - that is, at present, only two-center bonds are supported but plans are to extend this in the future to allow multi-center orbitals. modelkit follows straightforward rules for orienting the bonds and lone-pairs with respect to the atoms in the molecule, according to the orbital type. The repository uses a \u0027standard\u0027 orientation to encode the orbital type, while obviating the need for storing the atomic coordinates in the orbital information, as follows:\nZ-axis : Sigma orbitals (bonds, lone-pairs)\nX,Y-axes : Pi bonds,lone-pairs (first, second, respectively)\nFor example, a two-center orbital involving S and Pz functions is recognized as a Sigma-bond, while the one-center counterpart would be a Sigma-lone-pair orbital. A two-center orbital involving Px functions is recognized as a Pi-bond, that with Py functions would be the second Pi-bond. And so on.\u003c/p\u003e\n\u003cp\u003eThe repository is currently limited to the major orbital types associated with H,C,N,O atoms, and the 6-31G basis set, with work to incorporate more atom types, orbital types, and basis sets on-going. Though such orbitals are strictly \u0027guesses\u0027, an exciting prospect is the use of machine-learning techniques to develop increasingly accurate \u0027guesses\u0027, with the ultimate goal of obviating the need for wave function optimization entirely.\u003c/p\u003e\n\u003cp\u003eWhenever orbitals are used (especially if they are generated for the first time), we recommend visualizing them in order to check that their form is reasonable from the chemical standpoint. To this end, we are working to incorporate a visualization capability directly into the vtools and expect this to be available in the near future.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 8. How to make spin-coupled orbitals\u003c/h2\u003e\u003ca id=\"user-content-section-8-how-to-make-spin-coupled-orbitals\" class=\"anchor\" aria-label=\"Permalink: Section 8. How to make spin-coupled orbitals\" href=\"#section-8-how-to-make-spin-coupled-orbitals\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following procedure is recommended. It is advisable to begin a pair of spin-coupled (SC) orbitals from the corresponding converged double-occupied (DOCC) orbital. So, first, choose the DOCC orbital of interest from a previously obtained optimized wave function. In a suitable text editor, cut the DOCC orbital from the DOCC list in the input, make a copy, so there are now two identicle orbitals, and paste this pair into the list of SC orbitals. Be sure to adjust the relevant counters among the \u0027dims\u0027 (section 3 (A)). To initialize the subsequent optimization run for the SC pair, use the following method to provide a \u0027nudge\u0027 in the right direction. Locate the first- and second-largest magnitude weights in either of the SC orbitals. In one of the SC pair, increase the largest magnitude weight by 0.1, reduce the second largest by 0.1. In the other SC orbital, do the opposite - decrease the largest magnitude weight by 0.1, increase the second largest by 0.1. The value of 0.1 is just a suggestion, but this has proved to be a reasonable choice. Execute an optimization run for the pair of SC orbitals, keeping the others fixed. If necessary, re-optimize, including any other orbitals that interact significantly with the new SC pair.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 9. How to use derived basis functions (DBF)\u003c/h2\u003e\u003ca id=\"user-content-section-9-how-to-use-derived-basis-functions-dbf\" class=\"anchor\" aria-label=\"Permalink: Section 9. How to use derived basis functions (DBF)\" href=\"#section-9-how-to-use-derived-basis-functions-dbf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs mentioned in section 2, VALENCE allows the user to define combinations of the atomic basis functions to form new functions over which to expand the electronic orbitals. A typical use of this feature is to symmetry-adapt the basis set to yield more convenient degrees-of-freedom. For example, making spherical harmonic functions from the cartesian functions (useful for transition metals) and hybridized basis sets consisting of sp,sp2, and sp3 \u0027hybrid\u0027 functions.\u003c/p\u003e\n\u003cp\u003eTo use DBF, set the total number required (item 12 in part A of section 3) and append them to the existing orbitals in the OBS format. To reference the DBF in the electronic orbitals, an index less than 1 (that is, zero, or negative) is used to distinguish them from regular AOs. The DBF are indexed beginning with zero for the last one entered and proceeding backwards up the file to -1, -2, ..., 1-N, where N is the number of DBF.\u003c/p\u003e\n\u003cp\u003eIn the following example, four spherical harmonic functions used to model the 3d10 configuration of copper (I) cation with a double-Zeta basis set are defined. The DBF correspond (nominally) to the 3d\u0027z2\u0027, 4d\u0027z2\u0027, 3dx2-y2, and 4dx2-y2 functions, with indices 0, -1, -2, and -3, respectively. The DBF are used in two of the valence orbitals of Cu+ to provide variational flexbility.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 1 1 0 0 14 29 3 0 7 18 2 4 1 0 1\n\n 20 20 20 2 2 50 0.0 0.0 1 5\n\n 1 0.0 0.0 0.0\n\n 29.0 7\n0 3\n 4134.3020000 0.0631880 \n 625.4912000 0.3748450 \n 136.9556000 0.6831000 \n0 3\n 181.4960000 -0.1113200\n 39.5743100 0.0944870\n 12.1624600 0.9608790\n1 3\n 181.4960000 0.1430840 \n 39.5743100 0.5677560 \n 12.1624600 0.4567140 \n0 3\n 12.3511100 -0.2922230\n 4.0496510 0.3429910\n 1.2792250 0.8479460\n1 3\n 12.3511100 0.0277270 \n 4.0496510 0.4835240 \n 1.2792250 0.5929780 \n2 2\n 16.7593800 0.2741120 \n 4.1789770 0.8446250 \n2 1\n 0.9943270\n\n\n 1 1 2\n 0 0.57405221 -1 0.62709111\n 1 1 2\n -2 0.57407860 -3 0.62706573\n 1 1 2\n 11 0.57411273 17 0.62703292\n 1 1 2\n 13 0.57412360 19 0.62702246\n 1 1 2\n 14 0.57411685 20 0.62702896\n 1 1 1\n 1 1.00000000\n 1 1 1\n 2 1.00000000\n 1 1 1\n 3 1.00000000\n 1 1 1\n 4 1.00000000\n 1 1 1\n 5 1.00000000\n 1 1 1\n 6 1.00000000\n 1 1 1\n 7 1.00000000\n 1 1 1\n 8 1.00000000\n 1 1 1\n 9 1.00000000\n\n 1 1 2\n 16 0.86602540 18 -0.86602540\n 1 1 2\n 10 0.86602540 12 -0.86602540\n 1 1 3\n 21 1.00000000 16 -0.50000000 18 -0.50000000\n 1 1 3\n 15 1.00000000 10 -0.50000000 12 -0.50000000\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe example below is for the ground state of dinitrogen. Bonding and anti-bonding combinations of the two 2Pz functions on each nitrogen provide well-defined UDF for the sigma-bonding orbital without losing quality in the basis set.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 2 1 0 0 7 76 12 0 6 15 2 2 0 0 2\n\n 20 20 20 0 0 0 0.0 0.0 \n\n 1 0.0000 0.0000 0.0000 \n 1 0.0000 0.0000 1.0784 \n\n 7.0 6\n0 6\n 4173.5110000 0.0018348 \n 627.4579000 0.0139950 \n 142.9021000 0.0685870 \n 40.2343300 0.2322410 \n 12.8202100 0.4690700 \n 4.3904370 0.3604550 \n0 3\n 11.6263580 -0.1149610 \n 2.7162800 -0.1691180 \n 0.7722180 1.1458520 \n0 1\n 0.2120313 \n1 3\n 11.6263580 0.0675800 \n 2.7162800 0.3239070 \n 0.7722180 0.7408950 \n1 1\n 0.2120313 \n2 1\n 0.8000000 \n\n\n 2 1 2 12\n 2 0.46448740 3 0.51058196 9 -0.09072744 10 -0.00618979\n 12 -0.00619147 15 0.00259130 17 0.11744429 24 0.00545631\n 25 -0.00741184 27 -0.00741435 30 0.02071109 0 -0.13325599\n 2 1 2 12\n 2 -0.11738937 9 0.00542484 10 0.00741009 12 0.00740997\n 15 -0.02070910 17 -0.46450546 18 -0.51057039 24 -0.09076468\n 25 0.00619669 27 0.00619689 30 -0.00257915 0 -0.13327727\n 2 1 2 12\n 2 0.24499689 -1 0.67364713 9 0.12273824 10 -0.01415403\n 12 -0.01415444 15 0.04057664 17 0.24502759 24 -0.12270638\n 25 -0.01415739 27 -0.01415748 30 0.04057488 0 0.00002984\n 2 1 2 6\n 4 0.43505172 7 0.24461579 13 0.04819538 19 0.43497306\n 22 0.24456183 28 -0.04819615\n 2 1 2 6\n 5 0.43498162 8 0.24458671 14 0.04819400 20 0.43502387\n 23 0.24461060 29 -0.04819252\n 2 1 2 12\n 1 0.99531880 2 0.02391452 9 -0.00095882 10 -0.00374759\n 12 -0.00374774 15 -0.00204212 17 -0.00169077 24 0.00019574\n 25 -0.00003327 27 -0.00003336 30 -0.00107139 0 0.00159460\n 2 1 2 12\n 2 0.00169436 9 0.00019515 10 0.00003176 12 0.00003188\n 15 0.00106951 16 -0.99531842 17 -0.02391508 24 -0.00095836\n 25 0.00374769 27 0.00374779 30 0.00204220 0 0.00159441\n\n 2 1 2 2\n 6 0.60840187 21 -0.60840187\n 2 1 2 2\n 6 0.87759376 21 0.87759376\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 10. Acknowledgements\u003c/h2\u003e\u003ca id=\"user-content-section-10-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Section 10. Acknowledgements\" href=\"#section-10-acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was supported by Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.\nThe name, \u0027VALENCE\u0027, was chosen in honor of the famous book by Charles Coulson, an early pioneer of valence bond theory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 11. Contact information\u003c/h2\u003e\u003ca id=\"user-content-section-11-contact-information\" class=\"anchor\" aria-label=\"Permalink: Section 11. Contact information\" href=\"#section-11-contact-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease feel free to send questions/comments to any/all members of \u0027The VALENCE Group, at Argonne\u0027:\u003c/p\u003e\n\u003cp\u003eGraham D. Fletcher\nComputational Science Division\nArgonne National Laboratory\nLemont, IL, USA\n\u003ca href=\"mailto:gfletcher@anl.gov\"\u003egfletcher@anl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMurat Keceli\nComputational Science Division\nArgonne National Laboratory\nLemont, IL, USA\n\u003ca href=\"mailto:keceli@anl.gov\"\u003ekeceli@anl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eColleen Bertoni\nArgonne Leadership Computing Facility\nArgonne National Laboratory\nLemont, IL, USA\n\u003ca href=\"mailto:bertoni@anl.gov\"\u003ebertoni@anl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMichael D\u0027Mello\nIntel Corporation\n425 N. Martingale Road, Suite 1500\nSchaumburg, IL, USA\n\u003ca href=\"mailto:mdmello@anl.gov\"\u003emdmello@anl.gov\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 7, - "subscribers_count": 11, - "topics": [], - "updated_at": 1732015481.0 + "subscribers_count": 5, + "topics": [ + "high-", + "quantum-chemistry" + ], + "updated_at": 1666754110.0 }, { "data_format": 2, - "description": "example builder for Singularity containers using Circle Continuous Integration, circle-ci", + "description": "an example builder to build a container with Travis CI, and push to a Singularity Registry Server (or other endpoint)", "filenames": [ - "Singularity.tag", "Singularity" ], - "full_name": "singularityhub/circle-ci", + "full_name": "singularityhub/travis-ci", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Builder Circle-CI\u003c/h1\u003e\u003ca id=\"user-content-singularity-builder-circle-ci\" class=\"anchor\" aria-label=\"Permalink: Singularity Builder Circle-CI\" href=\"#singularity-builder-circle-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".circleci/sregistry-circle.png\"\u003e\u003cimg src=\".circleci/sregistry-circle.png\" alt=\".circleci/sregistry-circle.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/singularityhub/circle-ci\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b7c23c37f44977396bcf2bd49b4b0730366e46d83e65f69cc28532e2a352ac78/68747470733a2f2f636972636c6563692e636f6d2f67682f73696e67756c61726974796875622f636972636c652d63692e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/singularityhub/circle-ci.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a simple example of how you can achieve:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eversion control of your recipes\u003c/li\u003e\n\u003cli\u003eversioning to include image hash \u003cem\u003eand\u003c/em\u003e commit id\u003c/li\u003e\n\u003cli\u003ebuild of associated container and\u003c/li\u003e\n\u003cli\u003epush to a storage endpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003efor a reproducible build workflow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should this be managed via Github?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGithub, by way of easy integration with continuous integration, is an easy way\nto have a workflow set up where multiple people can collaborate on a container recipe,\nthe recipe can be tested (with whatever testing you need), discussed in pull requests,\nand then finally pushed to your storage of choice or Singularity Registry.\nImportantly, you don\u0027t need to give your entire team manager permissions\nto the registry. An encrypted credential that only is accessible to\nadministrators can do the push upon merge of a discussed change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should I use this instead of a service?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou could use a remote builder, but if you do the build in a continuous integration\nservice you get complete control over it. This means everything from the version of\nSingularity to use, to the tests that you run for your container. You have a lot more\nfreedom in the rate of building, and organization of your repository, because it\u0027s you\nthat writes the configuration. Although the default would work for most, you can\nedit the build, setup, and circle configuration file in the\n\u003ca href=\".circleci\"\u003e.circleci\u003c/a\u003e folder to fit your needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdd your Singularity recipes to this repository, and edit the build commands in\nthe \u003ca href=\".circleci/build.sh\"\u003ebuild.sh\u003c/a\u003e file. This is where you can specify endpoints\n(Singularity Registry, Dropbox, Google Storage, AWS) along with container names\n(the uri) and tag. You can build as many recipes as you like, just add another line!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e recipe relative to repository base\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh --uri collection/container --tag tacos --cli google-storage Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh --uri collection/container --cli google-drive Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh --uri collection/container --cli globus Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh --uri collection/container --cli registry Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor each client that you use, required environment variables (e.g., credentials to push,\nor interact with the API) must be defined in the (encrypted) Travis environment. To\nknow what variables to define, along with usage for the various clients, see\nthe \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclient specific pages\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed Started\u003c/h2\u003e\u003ca id=\"user-content-detailed-started\" class=\"anchor\" aria-label=\"Permalink: Detailed Started\" href=\"#detailed-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e0. Fork this repository\u003c/h3\u003e\u003ca id=\"user-content-0-fork-this-repository\" class=\"anchor\" aria-label=\"Permalink: 0. Fork this repository\" href=\"#0-fork-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can clone and tweak, but it\u0027s easiest likely to get started with our example\nfiles and edit them as you need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Get to Know CircleCi\u003c/h3\u003e\u003ca id=\"user-content-1-get-to-know-circleci\" class=\"anchor\" aria-label=\"Permalink: 1. Get to Know CircleCi\" href=\"#1-get-to-know-circleci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will be working with \u003ca href=\"https://www.circleci.com\" rel=\"nofollow\"\u003eCircle CI\u003c/a\u003e. You can see\nexample builds for this \u003ca href=\"https://circleci.com/gh/singularityhub/circle-ci\" rel=\"nofollow\"\u003erepository here\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCircle offers \u003ca href=\"https://support.circleci.com/hc/en-us/articles/115015481128-Scheduling-jobs-cron-for-builds-\" rel=\"nofollow\"\u003escheduled builds\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eCircleCI also offers \u003ca href=\"https://circleci.com/docs/enterprise/gpu-configuration/\" rel=\"nofollow\"\u003eGPU Builders\u003c/a\u003e if you want/need that sort of thing.\u003c/li\u003e\n\u003cli\u003eIf you don\u0027t want to use the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry\u003c/a\u003e to push to Google Storage, Drive, Globus, Dropbox, or your personal Singularity Registry, CircleCI will upload your artifacts directly to your \u003ca href=\"https://circleci.com/docs/2.0/deployment-integrations/#section=deployment\" rel=\"nofollow\"\u003edeployment\u003c/a\u003e location of choice.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Add your Recipe(s)\u003c/h3\u003e\u003ca id=\"user-content-2-add-your-recipes\" class=\"anchor\" aria-label=\"Permalink: 2. Add your Recipe(s)\" href=\"#2-add-your-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the example here, we have a single recipe named \"Singularity\" that is provided\nas an input argument to the \u003ca href=\".circleci/build.sh\"\u003ebuild script\u003c/a\u003e. You could add another\nrecipe, and then of course call the build to happen more than once.\nThe build script will name the image based on the recipe, and you of course\ncan change this. Just write the path to it (relative to the repository base) in\nyour \u003ca href=\".circleci/config.yml\"\u003e.circleci/config.yml\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Configure Singularity\u003c/h3\u003e\u003ca id=\"user-content-3-configure-singularity\" class=\"anchor\" aria-label=\"Permalink: 3. Configure Singularity\" href=\"#3-configure-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recipe uses the \u003ca href=\"https://circleci.com/orbs/registry/orb/singularity/singularity\" rel=\"nofollow\"\u003eSingularity Orb\u003c/a\u003e to install your chosen version of Singularity. If you want to change the version, just adjust\nthe parameter here:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003ebuild\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSingularity 3.2.1 - Python 3\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esingularity\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e3.2.1\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esingularity-3\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe basic steps to \u003ca href=\".circleci/setup.sh\"\u003esetup\u003c/a\u003e the build are the following:\u003c/p\u003e\n\u003cp\u003eWe also install the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry client\u003c/a\u003e\nthat allows you to issue a command like \"sregistry push ...\" to upload a finished\nimage to one of your cloud / storage endpoints. In this basic example, the push won\u0027t happen,\nand you will just build an image using the CI.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Configure the Build\u003c/h3\u003e\u003ca id=\"user-content-4-configure-the-build\" class=\"anchor\" aria-label=\"Permalink: 4. Configure the Build\" href=\"#4-configure-the-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe basic steps for the \u003ca href=\".circleci/build.sh\"\u003ebuild\u003c/a\u003e are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRunning build.sh with no inputs will default to a recipe called \"Singularity\" in the base of the repository. You can provide an argument to point to a different recipe path, always relative to the base of your repository.\u003c/li\u003e\n\u003cli\u003eIf you want to define a particular unique resource identifier for a finished container (to be uploaded to your storage endpoint) you can do that with \u003ccode\u003e--uri collection/container\u003c/code\u003e. If you don\u0027t define one, a robot name will be generated.\u003c/li\u003e\n\u003cli\u003eYou can add \u003ccode\u003e--uri\u003c/code\u003e to specify a custom name, and this can include the tag, OR you can specify \u003ccode\u003e--tag\u003c/code\u003e to go along with a name without one. It depends on which is easier for you.\u003c/li\u003e\n\u003cli\u003eIf you add \u003ccode\u003e--cli\u003c/code\u003e then this is telling the build script that you have defined the \u003ca href=\"https://circleci.com/docs/2.0/env-vars/\" rel=\"nofollow\"\u003eneeded environment variables\u003c/a\u003e for your \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclient of choice\u003c/a\u003e and you want successful builds to be pushed to your storage endpoint. See \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for a list of current client endpoints, or roll your own!\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\".circleci/config.yml\"\u003econfig.yml\u003c/a\u003e for examples of this build.sh command (commented out). If there is some cloud service that you\u0027d like that is not provided, please \u003ca href=\"https://www.github.com/singularityhub/sregistry-cli/issues\"\u003eopen an issue\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Connect to CI\u003c/h3\u003e\u003ca id=\"user-content-5-connect-to-ci\" class=\"anchor\" aria-label=\"Permalink: 5. Connect to CI\" href=\"#5-connect-to-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you go to your \u003ca href=\"https://circleci.com/dashboard\" rel=\"nofollow\"\u003eCircle Dashboard\u003c/a\u003e you can usually select a Github organization (or user) and then the repository, and then click the toggle button to activate it to build on commit --\u0026gt; push.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it for the basic setup! At this point, you will have a continuous integration service that will build your container from a recipe each time that you push. The next step is figuring out where you want to put the finished image(s), and we will walk through this in more detail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStorage!\u003c/h2\u003e\u003ca id=\"user-content-storage\" class=\"anchor\" aria-label=\"Permalink: Storage!\" href=\"#storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the image is built, where can you put it? An easy answer is to use the\n\u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003eSingularity Global Client\u003c/a\u003e and\nchoose \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eone of the many clients\u003c/a\u003e\nto add a final step to push the image. You then use the same client to pull the\ncontainer from your host. Once you\u0027ve decided which endpoints you want to push to,\nyou will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSave the credentials / other environment variables that your client needs (see the client settings page linked in the sregistry docs above) to your \u003ca href=\"https://circleci.com/docs/2.0/env-vars/\" rel=\"nofollow\"\u003erepository settings\u003c/a\u003e where they will be encrypted and in the environment.\u003c/li\u003e\n\u003cli\u003eAdd a line to your \u003ca href=\".circleci/config.yml\"\u003e.circleci/config.yml\u003c/a\u003e to do an sregistry push action to the endpoint(s) of choice. We have provided some (commented out) examples to get you started.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRemember that you can also take advantage of deployment options that CircleCI offers, or do any other action that you might want for the reproducibility or archive of metadata of your builds. We save the build folder as an artifact to the repository, but the containers might be too big to do this.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced Usage\u003c/h2\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-label=\"Permalink: Advanced Usage\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThis setup can work as an analysis node as well! Try setting up a \u003ca href=\"https://support.circleci.com/hc/en-us/articles/115015481128-Scheduling-jobs-cron-for-builds-\" rel=\"nofollow\"\u003escheduled build\u003c/a\u003e to build a container that processes some information feed, and you have a regularly scheduled task.\u003c/li\u003e\n\u003cli\u003erun builds in parallel and test different building environments. You could try building the \"same\" container across different machine types and see if you really do get the same thing :)\u003c/li\u003e\n\u003cli\u003eYou can also do other sanity checks like testing if the container runs as you would expect, etc.\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Builders Travis-CI\u003c/h1\u003e\u003ca id=\"user-content-singularity-builders-travis-ci\" class=\"anchor\" aria-label=\"Permalink: Singularity Builders Travis-CI\" href=\"#singularity-builders-travis-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".travis/sregistry-travis.png\"\u003e\u003cimg src=\".travis/sregistry-travis.png\" alt=\".travis/sregistry-travis.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/singularityhub/travis-ci\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0f152642ac86cd0c29a5c1a0cde277aa42ad43566497868fd65712824cdca1b6/68747470733a2f2f7472617669732d63692e6f72672f73696e67756c61726974796875622f7472617669732d63692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/singularityhub/travis-ci.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a simple example of how you can achieve:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eversion control of your recipes\u003c/li\u003e\n\u003cli\u003eversioning to include image hash \u003cem\u003eand\u003c/em\u003e commit id\u003c/li\u003e\n\u003cli\u003ebuild of associated container and\u003c/li\u003e\n\u003cli\u003epush to a storage endpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003efor a reproducible build workflow. This recipe on master is intended to build\nSingularity 3.x (with GoLang). If you are looking for legacy builds of Singularity,\nsee the \u003ca href=\"https://github.com/singularityhub/travis-ci/tree/release/2.6\"\u003erelease/2.6\u003c/a\u003e branch.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should this be managed via Github?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGithub, by way of easy integration with continuous integration, is an easy way\nto have a workflow set up where multiple people can collaborate on a container recipe,\nthe recipe can be tested (with whatever testing you need), discussed in pull requests,\nand then finally pushed to the registry. Importantly, you don\u0027t need to give your\nentire team manager permissions to the registry. An encrypted credential that only\nis accessible to administrators can do the push upon merge of a discussed change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should I use this instead of a service?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou could use a remote builder, but if you do the build in a continuous integration\nservice you get complete control over it. This means everything from the version of\nSingularity to use, to the tests that you run for your container. You have a lot more\nfreedom in the rate of building, and organization of your repository, because it\u0027s you\nthat writes the configuration.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdd your Singularity recipes to this repository, and edit the build commands in\nthe \u003ca href=\".travis/build.sh\"\u003ebuild.sh\u003c/a\u003e file. This is where you can specify endpoints\n(Singularity Registry, Dropbox, Google Storage, AWS) along with container names\n(the uri) and tag. You can build as many recipes as you like, just add another line!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e recipe relative to repository base\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh --uri collection/container --tag tacos --cli google-storage Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh --uri collection/container --cli google-drive Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh --uri collection/container --cli globus Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh --uri collection/container --cli registry Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor each client that you use, required environment variables (e.g., credentials to push,\nor interact with the API) must be defined in the (encrypted) Travis environment. To\nknow what variables to define, along with usage for the various clients, see\nthe \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclient specific pages\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed Started\u003c/h2\u003e\u003ca id=\"user-content-detailed-started\" class=\"anchor\" aria-label=\"Permalink: Detailed Started\" href=\"#detailed-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e0. Fork this repository\u003c/h3\u003e\u003ca id=\"user-content-0-fork-this-repository\" class=\"anchor\" aria-label=\"Permalink: 0. Fork this repository\" href=\"#0-fork-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can clone and tweak, but it\u0027s easiest likely to get started with our example\nfiles and edit them as you need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Get to Know Travis\u003c/h3\u003e\u003ca id=\"user-content-1-get-to-know-travis\" class=\"anchor\" aria-label=\"Permalink: 1. Get to Know Travis\" href=\"#1-get-to-know-travis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will be working with \u003ca href=\"https://www.travis-ci.org\" rel=\"nofollow\"\u003eTravis CI\u003c/a\u003e. You can see\nexample builds for this \u003ca href=\"https://travis-ci.org/singularityhub/travis-ci/builds\" rel=\"nofollow\"\u003erepository here\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTravis offers \u003ca href=\"https://docs.travis-ci.com/user/cron-jobs/\" rel=\"nofollow\"\u003ecron jobs\u003c/a\u003e so you could schedule builds at some frequency.\u003c/li\u003e\n\u003cli\u003eTravis also offers \u003ca href=\"https://circleci.com/docs/2.0/gpu/\" rel=\"nofollow\"\u003eGPU Builders\u003c/a\u003e if you want/need that sort of thing.\u003c/li\u003e\n\u003cli\u003eIf you don\u0027t want to use the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry\u003c/a\u003e to push to Google Storage, Drive, Globus, Dropbox, or your personal Singularity Registry, travis will upload your artifacts directly to your \u003ca href=\"https://docs.travis-ci.com/user/uploading-artifacts/\" rel=\"nofollow\"\u003eAmazon S3 bucket\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Add your Recipe(s)\u003c/h3\u003e\u003ca id=\"user-content-2-add-your-recipes\" class=\"anchor\" aria-label=\"Permalink: 2. Add your Recipe(s)\" href=\"#2-add-your-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the example here, we have a single recipe named \"Singularity\" that is provided\nas an input argument to the \u003ca href=\".travis/build.sh\"\u003ebuild script\u003c/a\u003e. You could add another\nrecipe, and then of course call the build to happen more than once.\nThe build script will name the image based on the recipe, and you of course\ncan change this up.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Configure Singularity\u003c/h3\u003e\u003ca id=\"user-content-3-configure-singularity\" class=\"anchor\" aria-label=\"Permalink: 3. Configure Singularity\" href=\"#3-configure-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe basic steps to \u003ca href=\".travis/setup.sh\"\u003esetup\u003c/a\u003e the build are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall Singularity from master branch. You could of course change the lines in \u003ca href=\".travis/setup.sh\"\u003esetup.sh\u003c/a\u003e to use a specific tagged release, an older version, or development version.\u003c/li\u003e\n\u003cli\u003eInstall the sregistry client, if needed. The \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry client\u003c/a\u003e allows you to issue a command like \"sregistry push ...\" to upload a finished image to one of your cloud / storage endpoints. By default, this won\u0027t happen, and you will just build an image using the CI.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Configure the Build\u003c/h3\u003e\u003ca id=\"user-content-4-configure-the-build\" class=\"anchor\" aria-label=\"Permalink: 4. Configure the Build\" href=\"#4-configure-the-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe basic steps for the \u003ca href=\".travis/build.sh\"\u003ebuild\u003c/a\u003e are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRunning build.sh with no inputs will default to a recipe called \"Singularity\" in the base of the repository. You can provide an argument to point to a different recipe path, always relative to the base of your repository.\u003c/li\u003e\n\u003cli\u003eIf you want to define a particular unique resource identifier for a finished container (to be uploaded to your storage endpoint) you can do that with \u003ccode\u003e--uri collection/container\u003c/code\u003e. If you don\u0027t define one, a robot name will be generated.\u003c/li\u003e\n\u003cli\u003eYou can add \u003ccode\u003e--uri\u003c/code\u003e to specify a custom name, and this can include the tag, OR you can specify \u003ccode\u003e--tag\u003c/code\u003e to go along with a name without one. It depends on which is easier for you.\u003c/li\u003e\n\u003cli\u003eIf you add \u003ccode\u003e--cli\u003c/code\u003e then this is telling the build script that you have defined the \u003ca href=\"https://docs.travis-ci.com/user/environment-variables/#Defining-Variables-in-Repository-Settings\" rel=\"nofollow\"\u003eneeded environment variables\u003c/a\u003e for your \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclient of choice\u003c/a\u003e and you want successful builds to be pushed to your storage endpoint. Valid clients include:\n\u003cul\u003e\n\u003cli\u003egoogle-storage\u003c/li\u003e\n\u003cli\u003egoogle-drive\u003c/li\u003e\n\u003cli\u003edropbox\u003c/li\u003e\n\u003cli\u003eglobus\u003c/li\u003e\n\u003cli\u003eregistry (Singularity Registry)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\".travis.yml\"\u003e.travis.yml\u003c/a\u003e for examples of this build.sh command (commented out). If there is some cloud service that you\u0027d like that is not provided, please \u003ca href=\"https://www.github.com/singularityhub/sregistry-cli/issues\"\u003eopen an issue\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Connect to CI\u003c/h3\u003e\u003ca id=\"user-content-5-connect-to-ci\" class=\"anchor\" aria-label=\"Permalink: 5. Connect to CI\" href=\"#5-connect-to-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you go to your \u003ca href=\"https://travis-ci.org/profile\" rel=\"nofollow\"\u003eTravis Profile\u003c/a\u003e you can usually select a Github organization (or user) and then the repository, and then click the toggle button to activate it to build on commit --\u0026gt; push.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it for the basic setup! At this point, you will have a continuous integration service that will build your container from a recipe each time that you push. The next step is figuring out where you want to put the finished image(s), and we will walk through this in more detail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStorage!\u003c/h2\u003e\u003ca id=\"user-content-storage\" class=\"anchor\" aria-label=\"Permalink: Storage!\" href=\"#storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the image is built, where can you put it? An easy answer is to use the\n\u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003eSingularity Global Client\u003c/a\u003e and\nchoose \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eone of the many clients\u003c/a\u003e\nto add a final step to push the image. You then use the same client to pull the\ncontainer from your host. Once you\u0027ve decided which endpoints you want to push to,\nyou will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSave the credentials / other environment variables that your client needs (see the client settings page linked in the sregistry docs above) to your \u003ca href=\"https://docs.travis-ci.com/user/environment-variables/#Defining-Variables-in-Repository-Settings\" rel=\"nofollow\"\u003erepository settings\u003c/a\u003e where they will be encrypted and in the environment.\u003c/li\u003e\n\u003cli\u003eAdd a line to your \u003ca href=\".travis.yml\"\u003e.travis.yml\u003c/a\u003e to do an sregistry push action to the endpoint(s) of choice. We have provided some (commented out) examples to get you started.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTravis Provided Uploads\u003c/h2\u003e\u003ca id=\"user-content-travis-provided-uploads\" class=\"anchor\" aria-label=\"Permalink: Travis Provided Uploads\" href=\"#travis-provided-uploads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou don\u0027t even need to use sregistry to upload a container (or an artifact / result produced from running one via a cron job maybe?) to an endpoint of choice! There are \u003ca href=\"https://docs.travis-ci.com/user/deployment\" rel=\"nofollow\"\u003emany\u003c/a\u003e places you can deploy to. If you can think of it, it\u0027s on this list. Here are a sampling of some that I\u0027ve tried (and generally like):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.travis-ci.com/user/deployment/surge/\" rel=\"nofollow\"\u003eSurge.sh\u003c/a\u003e gives you a little web address for free to upload content. This means that if your container runs an analysis and generates a web report, you can push it here. Each time you run it, you can push again and update your webby thing. Cool! Here is an \u003ca href=\"http://containers-ftw.surge.sh/\" rel=\"nofollow\"\u003eold example\u003c/a\u003e of how I did this - the table you see was produced by a container and then the generated report uploaded to surge.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.travis-ci.com/user/deployment/s3/\" rel=\"nofollow\"\u003eAmazon S3\u003c/a\u003e bread and butter of object storage. sregistry doesn\u0027t have a client for it (bad dinosaur!) so I\u0027ll direct you to Travis to help :)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.travis-ci.com/user/deployment/pages/\" rel=\"nofollow\"\u003eGithub Pages\u003c/a\u003e I want to point you to github pages in the case that your container has documentation that should be pushed when built afresh.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced\u003c/h2\u003e\u003ca id=\"user-content-advanced\" class=\"anchor\" aria-label=\"Permalink: Advanced\" href=\"#advanced\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGuess what, this setup is totally changeable by you, it\u0027s your build! This means you can do any of the following \"advanced\" options:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis setup can work as an analysis node as well! Try setting up a \u003ca href=\"https://docs.travis-ci.com/user/cron-jobs/\" rel=\"nofollow\"\u003ecron job\u003c/a\u003e to build a container that processes some information feed, and you have a regularly scheduled task.\u003c/li\u003e\n\u003cli\u003etry out one of the \u003ca href=\"https://circleci.com/docs/2.0/gpu/\" rel=\"nofollow\"\u003eGPU builders\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun builds in parallel and test different building environments. You could try building the \"same\" container across different machine types and see if you really do get the same thing :)\u003c/li\u003e\n\u003cli\u003eYou can also do other sanity checks like testing if the container runs as you would expect, etc.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 7, - "subscribers_count": 3, + "subscribers_count": 5, "topics": [ + "travis", + "travis-ci", "singularity", + "singularityhub", "builder", - "circle-ci", - "singularity-ci" + "dropbox", + "google-storage", + "singularity-registry", + "sregistry" ], - "updated_at": 1652955043.0 + "updated_at": 1688650704.0 }, { "data_format": 2, - "description": "Singularity containers with common radio transient search software. ", + "description": null, "filenames": [ - "Singularity.cpu", - "Singularity.arm", - "Singularity.gpu", - "Singularity" + "Singularity.def" ], - "full_name": "josephwkania/radio_transients", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eradio_transients\u003c/h1\u003e\u003ca id=\"user-content-radio_transients\" class=\"anchor\" aria-label=\"Permalink: radio_transients\" href=\"#radio_transients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1c7f762866d2a99376791d539f3c4c9e7630d23f6ec96b4e37b395bbe20d0079/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6a6f73657068776b616e69612f726164696f5f7472616e7369656e74733f7374796c653d666c61742d737175617265\" alt=\"Issues\" data-canonical-src=\"https://img.shields.io/github/issues/josephwkania/radio_transients?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0b0d3c5ea06c15ed32753dc8be0042333d5437a1c96af2626e1a48c06340bcdf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a6f73657068776b616e69612f726164696f5f7472616e7369656e74733f7374796c653d666c61742d737175617265\" alt=\"Forks\" data-canonical-src=\"https://img.shields.io/github/forks/josephwkania/radio_transients?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/413328b90f7ce71af5f8a9088b05bd4fee37ab5a3672db4a7531050ee5a23fad/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6a6f73657068776b616e69612f726164696f5f7472616e7369656e74733f7374796c653d666c61742d737175617265\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/josephwkania/radio_transients?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3fe122d859685d56f1b74afd3301392fc7fb2a03d784f58aba103f295c6c4f71/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6a6f73657068776b616e69612f726164696f5f7472616e7369656e74733f7374796c653d666c61742d737175617265\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/josephwkania/radio_transients?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/josephwkania/radio_transients/radio_transients\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/53724ef3d95dffa5ba79b85e9a203ea1606c0a0a7452ec889eb5f693d0fa4bbd/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f486f737465642d53796c6162732d477265656e2e737667\" alt=\"Sylabs\" data-canonical-src=\"https://img.shields.io/badge/Hosted-Sylabs-Green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are my Singularity Recipes for common radio transient software.\nThere are three containers\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eradio_transients\u003c/h3\u003e\u003ca id=\"user-content-radio_transients-1\" class=\"anchor\" aria-label=\"Permalink: radio_transients\" href=\"#radio_transients-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains everything (CPU+GPU)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCUDA 10.2\nFETCH https://github.com/devanshkv/fetch -- In Conda environment `FE`\nheimdall https://sourceforge.net/p/heimdall-astro/wiki/Use/\n- dedisp https://github.com/ajameson/dedisp\nhtop https://htop.dev/\niqrm_apollo https://gitlab.com/kmrajwade/iqrm_apollo\njupyterlab https://jupyter.org/\nPRESTO https://www.cv.nrao.edu/~sransom/presto/\npsrdada http://psrdada.sourceforge.net/\npsrdada-python https://github.com/TRASAL/psrdada-python\npsrcat https://www.atnf.csiro.au/people/pulsar/psrcat/download.html\npysigproc https://github.com/devanshkv/pysigproc\nriptide https://github.com/v-morello/riptide\nsigproc https://github.com/SixByNine/sigproc\nTempo http://tempo.sourceforge.net/\nRFIClean https://github.com/ymaan4/RFIClean\nYAPP https://github.com/jayanthc/yapp\nyour https://github.com/thepetabyteproject/your\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGet with\n\u003ccode\u003esingularity pull --arch amd64 library://josephwkania/radio_transients/radio_transients:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e*One of FETCH\u0027s dependencies causes PRESTO\u0027s Python scripts to fail.\nThis necessitated putting them in different environments.\nEverything except for PRESTO is in \u003ccode\u003eRT\u003c/code\u003e, which is loaded by default.\nPRESTO is in \u003ccode\u003ePE\u003c/code\u003e, in the shell you can activate this\nwith \u003ccode\u003econda activate PE\u003c/code\u003e. If you need access outside the container,\nyou should use radio_transients:cpu, which has PRESTO in the default environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eradio_transients_cpu\u003c/h3\u003e\u003ca id=\"user-content-radio_transients_cpu\" class=\"anchor\" aria-label=\"Permalink: radio_transients_cpu\" href=\"#radio_transients_cpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains CPU based programs\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehtop\niqrm_apollo\njupyterlab \nPRESTO\npsrcat\npysigproc\nriptide\nsigproc\nTempo \nRFIClean\nYAPP \nyour\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGet with\n\u003ccode\u003esingularity pull --arch amd64 library://josephwkania/radio_transients/radio_transients:cpu\u003c/code\u003e\u003cbr\u003e\nThere is an arm version \u003ccode\u003eSingularity.arm\u003c/code\u003e,\n\u003ccode\u003esingularity pull --arch arm library://josephwkania/radio_transients/radio_transients:arm\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eradio_transients_gpu\u003c/h3\u003e\u003ca id=\"user-content-radio_transients_gpu\" class=\"anchor\" aria-label=\"Permalink: radio_transients_gpu\" href=\"#radio_transients_gpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains gpu based programs\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCUDA 10.2\nFETCH \njupyterlab\nheimdall\n- dedisp\nhtop \npsrdada \npsrdada-python\nyour\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGet with\n\u003ccode\u003esingularity pull --arch amd64 library://josephwkania/radio_transients/radio_transients:gpu\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to use\u003c/h3\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYour \u003ccode\u003e$HOME\u003c/code\u003e automatically gets mounted.\nYou can mount a directory with \u003ccode\u003e-B /dir/on/host:/mnt\u003c/code\u003e, which will mount \u003ccode\u003e/dir/on/host\u003c/code\u003e to \u003ccode\u003e/mnt\u003c/code\u003e in the container.\u003c/p\u003e\n\u003cp\u003eFor the gpu processes, you must pass \u003ccode\u003e--nv\u003c/code\u003e when running singularity.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity shell --nv -B /data:/mnt radio_transients_gpu.sif\u003c/code\u003e\nwill mount \u003ccode\u003e/data\u003c/code\u003e to \u003ccode\u003e/mnt\u003c/code\u003e, give you GPU access, and drop you into the interactive shell.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec --nv -B /data:/mnt radio_transients_gpu.sif your_heimdall.py -f /mnt/data.fil\u003c/code\u003e\nwill mount \u003ccode\u003e/data\u003c/code\u003e to \u003ccode\u003e/mnt\u003c/code\u003e, give you GPU access, and run your_heimdall.py without entering the container.\u003c/p\u003e\n\u003cp\u003eAll the Python scripts are installed in a Conda environment \u003ccode\u003eRT\u003c/code\u003e, this environment is automatically loaded.\u003c/p\u003e\n\u003cp\u003eYou can see the commits and corresponding dates by running \u003ccode\u003esingularity inspect radio_transients.sif\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSylabs Cloud\u003c/h3\u003e\u003ca id=\"user-content-sylabs-cloud\" class=\"anchor\" aria-label=\"Permalink: Sylabs Cloud\" href=\"#sylabs-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are built on a E5 v3 family machine and uploaded to Sylabs Cloud at\n\u003ca href=\"https://cloud.sylabs.io/library/josephwkania/radio_transients/radio_transients\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/josephwkania/radio_transients/radio_transients\u003c/a\u003e\nThey where last built on 27-Nov-2021\u003c/p\u003e\n\u003cp\u003eIf your processor your processor is significantly older than this, you may run into problems with\nthe older processor not having the whole instruction set needed. In this case, you should build\nuse singularity to build the image locally.\u003c/p\u003e\n\u003cp\u003eAn archival version of these (built 25-April-2021) are on Singularity Hub at:\n\u003ca href=\"https://singularity-hub.org/collections/5231\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/5231\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5231\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImprovements\u003c/h3\u003e\u003ca id=\"user-content-improvements\" class=\"anchor\" aria-label=\"Permalink: Improvements\" href=\"#improvements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you come across bug or have suggestions for improvements, let me know or submit a pull request.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThanks\u003c/h3\u003e\u003ca id=\"user-content-thanks\" class=\"anchor\" aria-label=\"Permalink: Thanks\" href=\"#thanks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo Kshitij Aggarwal for bug reports and suggestions.\u003c/p\u003e\n", + "full_name": "iqbal-lab-org/viridian_workflow", + "latest_release": "v1.1.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/iqbal-lab-org/viridian_workflow/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/viridian_workflow/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-viridian-workflow\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#viridian-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eViridian Workflow\u003c/h1\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/wiki\"\u003eViridian Workflow Wiki\u003c/a\u003e\nfor full documentation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe recommended method is to use a pre-built Docker or Singularity container\n(see the wiki for how to build your own).\u003c/p\u003e\n\u003cp\u003eBoth the Docker and Singularity container have the main script\n\u003ccode\u003eviridian\u003c/code\u003e installed.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eGet a Docker image of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/iqbal-lab-org/viridian_workflow:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll Docker images are listed in the\n\u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/pkgs/container/viridian_workflow\"\u003epackages page\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download.\nEach release has a singularity image file called\n\u003ccode\u003eviridian_workflow_vX.Y.Z.img\u003c/code\u003e, where \u003ccode\u003eX.Y.Z\u003c/code\u003e is the release version.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThese instructions assume that you are assembling SARS-CoV-2 data.\u003c/p\u003e\n\u003cp\u003eTo run on paired Illumina reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample \\\n --tech illumina \\\n --reads1 reads_1.fastq.gz \\\n --reads2 reads_2.fastq.gz \\\n --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run on unpaired nanopore reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample \\\n --tech ont \\\n --reads reads.fastq.gz \\\n --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run on paired or unpaired Ion Torrent reads, use either of the\nabove commands, but with the option \u003ccode\u003e--tech iontorrent\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h2\u003e\n\u003cp\u003eThe default files in the output directory are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econsensus.fa.gz\u003c/code\u003e: a gzipped FASTA file of the consensus sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evariants.vcf\u003c/code\u003e: a VCF file of the identified variants between the consensus\nsequence and the reference genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog.json.gz\u003c/code\u003e: a gzipped JSON file that contains logging information\nfor the viridian workflow run.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eqc.tsv.gz\u003c/code\u003e: a gzipped tab-delimited file of per-base QC information\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme_id.depth_across_genome.pdf\u003c/code\u003e: a plot of the read depth across\nthe genome, with amplicons coloured in the background.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme_id.score_plot.pdf\u003c/code\u003e: a plot of the scoring for amplicon scheme\nidentification.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the option \u003ccode\u003e--keep_bam\u003c/code\u003e is used, then a sorted BAM file of the reads mapped\nto the reference will also be present, called\n\u003ccode\u003ereference_mapped.bam\u003c/code\u003e (and its index file \u003ccode\u003ereference_mapped.bam.bai\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-useful-options\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#useful-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful options\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--sample_name MY_NAME\u003c/code\u003e: use this to change the sample name\n(default is \"sample\") that is put in the final FASTA file, BAM file, and\nVCF file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads_bam MY_READS.bam\u003c/code\u003e: instead of providing FASTQ (or FASTA) files of\nreads, you can provide a sorted by genome coordinate and indexed BAM file.\nThe reference genome must be the same as that used by viridian\n(by default MN908947).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--keep_bam\u003c/code\u003e: use this option to keep the BAM file of original input reads\nmapped to the reference genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--decontam COVID\u003c/code\u003e: decontaminate the reads using ReadItAndKeep at the\nstart of the pipeline (this is incompatible with \u003ccode\u003e--reads_bam\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--force\u003c/code\u003e: use with caution - it will overwrite the output directory if\nit already exists.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--write_msa indel_as_ref\u003c/code\u003e: this will write a FASTA file\ncalled \u003ccode\u003emsa.indel_as_ref.fa\u003c/code\u003e that can be\nused to build trees. It has the consensus sequence aligned to the\nreference genome, but with insertions in the consensus ignored and\ndeletions replaced with the reference sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--ena_run RUN_ID\u003c/code\u003e: using this option will download the specified reads\nfrom the ENA, and infer the \u003ccode\u003e--tech\u003c/code\u003e option from the ENA metadata\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 7, - "subscribers_count": 2, - "topics": [ - "pulsars", - "fast-radio-bursts", - "psrdada", - "radio-astronomy" + "subscribers_count": 6, + "topics": [], + "updated_at": 1688650705.0 + }, + { + "data_format": 2, + "description": "A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data", + "filenames": [ + "inst/Singularity" ], - "updated_at": 1721325888.0 + "full_name": "Malindrie/scShapes", + "latest_release": null, + "readme": "\n\u003ch1\u003e\u003ca id=\"user-content-scshapes\" class=\"anchor\" aria-hidden=\"true\" href=\"#scshapes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escShapes\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/Malindrie/scShapes\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f46be69a2babf0d92348a4d2abbc1834186998fa8fb4d023c1e85bc5f9405e8a/68747470733a2f2f7472617669732d63692e636f6d2f4d616c696e647269652f73635368617065732e7376673f6272616e63683d6d6173746572\" alt=\"Travis build status\" data-canonical-src=\"https://travis-ci.com/Malindrie/scShapes.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://ci.appveyor.com/project/Malindrie/scShapes\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9b16a84bf2877273e52d87887ce418eb0fff59cd1066cca77a8700f6bae73d6b/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f4d616c696e647269652f73635368617065733f6272616e63683d6d6173746572267376673d74727565\" alt=\"AppVeyor build status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/Malindrie/scShapes?branch=master\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003eWe present a novel statistical framework for identifying differential\ndistributions in single-cell RNA-sequencing (scRNA-seq) data between\ntreatment conditions by modeling gene expression read counts using\ngeneralized linear models (GLMs). We model each gene independently under\neach treatment condition using the error distributions Poisson (P),\nNegative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated\nNegative Binomial (ZINB) with log link function and model based\nnormalization for differences in sequencing depth. Since all four\ndistributions considered in our framework belong to the same family of\ndistributions, we first perform a Kolmogorov-Smirnov test to select\ngenes belonging to the family of ZINB distributions. Genes passing the\nKS test will be then modeled using GLMs. Model selection is done by\ncalculating the Bayesian Information Criterion and likelihood ratio test\nstatistic.\u003c/p\u003e\n\u003cp\u003eWhile most methods for differential gene expression analysis aim to\ndetect a shift in the mean of expressed values, single cell data are\ndriven by over-dispersion and dropouts requiring statistical\ndistributions that can handle the excess zeros. By modeling gene\nexpression distributions, our framework can identify subtle variations\nthat do not involve the change in mean. It also has the flexibility to\nadjust for covariates and perform multiple comparisons while explicitly\nmodeling the variability between samples.\u003c/p\u003e\n\u003cp\u003eYou can view the preprint of our method in\n\u003ca href=\"https://www.biorxiv.org/content/10.1101/2022.02.13.480299v1\" rel=\"nofollow\"\u003ebioRxiv\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eYou can install the released version of scShapes with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eMalindrie/scShapes\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003escShapes\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h2\u003e\n\u003cp\u003eThis is a basic example which shows how you can use scShapes for\nidentifying differential distributions in single-cell RNA-seq data. For\nthis example data we use the human immune cells (PBMC) dataset\ndistributed through the\n\u003ca href=\"https://github.com/satijalab/seurat-data\"\u003eSeuratData\u003c/a\u003e package.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003escShapes\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eLoading and preparing data for input \u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003eSeurat\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003eSeuratData\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003edplyr\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003eBiocParallel\u003c/span\u003e)\nset.seed(\u003cspan class=\"pl-c1\"\u003e0xBEEF\u003c/span\u003e)\n\nInstallData(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eifnb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\nLoadData(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eifnb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.list\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e SplitObject(\u003cspan class=\"pl-smi\"\u003eifnb\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003esplit.by\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003estim\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe first filter the genes to keep only genes expressed in at least 10%\nof cells:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eFirst extract the RNA-seq counts from the \u0027RNA\u0027 assay of the seurat object\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.obj\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lapply(\u003cspan class=\"pl-smi\"\u003eifnb.list\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e (\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e) as.matrix(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eassays\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eRNA\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003ecounts\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lapply(\u003cspan class=\"pl-smi\"\u003eifnb.obj\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e (\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e) filter_counts(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eperc.zero\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.1\u003c/span\u003e))\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Removing 527 rows of genes with all zero counts\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Removing 778 rows of genes with all zero counts\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to normalize for differences in sequencing depth, the log of\nthe total UMI counts assigned per cell will be used as an offset in the\nGLM. This function is inbuilt in the algorithm; however the user is\nrequired to input the library sizes. We can calculate the library sizes\nfor the two treatment conditions as;\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lapply(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e (\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e) apply(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e(\u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e) sum(\u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e)))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u2018meta.data\u2019 slot of the Seurat object also contains information on\nthe cell-types, which will be used as a covariate in the GLM model to\naccount for known biological variation in the data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lapply(\u003cspan class=\"pl-smi\"\u003eifnb.list\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e (\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\n \u003cspan class=\"pl-v\"\u003ecell.type\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efactor\u003c/span\u003e(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003emeta.data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eseurat_annotations\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003erow.names\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e colnames(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eassays\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eRNA\u003c/span\u003e)))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor the purpose of this example we only run the pipeline for randomly\nselected 20 common genes under both treatment conditions \u2018CTRL\u2019 and\n\u2018STIM\u2019.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eRandomly select 20 genes among common genes between the two treatment conditions\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecomm.genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e intersect(rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e), rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003ecomm.20.genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e sample(\u003cspan class=\"pl-smi\"\u003ecomm.genes\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ereplace\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSubset the randomly selected 20 genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e[rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecomm.20.genes\u003c/span\u003e,]\n\u003cspan class=\"pl-smi\"\u003eifnb.stim\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e[rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecomm.20.genes\u003c/span\u003e,]\n\u003cspan class=\"pl-smi\"\u003eifnb.subset\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003elist\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eCTRL\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.ctrl\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eSTIM\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.stim\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePerform Kolmogorov-Smirnov test to select genes belonging to the family\nof ZINB distributions.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e ks_test(\u003cspan class=\"pl-smi\"\u003eifnb.subset\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecexpr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003elib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSnowParam(\u003cspan class=\"pl-v\"\u003eworkers\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e,\u003cspan class=\"pl-v\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSOCK\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e ks_test(\u003cspan class=\"pl-smi\"\u003eifnb.subset\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecexpr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003elib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSnowParam(\u003cspan class=\"pl-v\"\u003eworkers\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e,\u003cspan class=\"pl-v\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSOCK\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSelect genes significant from the KS test.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eBy default the \u0027ks_sig\u0027 function performs Benjamini-Hochberg correction for multiple hypothese testing\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eand selects genes significant at p-value of 0.01\u003c/span\u003e\n\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.sig.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e ks_sig(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.sig.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e ks_sig(\u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSubset UMI counts corresponding to the genes significant from the KS test\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.sig.genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003elist\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eCTRL\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e as.data.frame(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.sig.KS\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egenes\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003eSTIM\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e as.data.frame(\u003cspan class=\"pl-smi\"\u003eifnb.stim.sig.KS\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egenes\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e[rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e rownames(\u003cspan class=\"pl-smi\"\u003eifnb.sig.genes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e),]\n \u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e[rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e rownames(\u003cspan class=\"pl-smi\"\u003eifnb.sig.genes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e),]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFit the 4 distributions P,NB,ZIP,ZINB for genes that belong to the ZINB\nfamily of distributions by fitting GLM with log of the library sizes as\nan offset and cell types as a covariate in the GLM.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fit_models(\u003cspan class=\"pl-v\"\u003ecounts\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecexpr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003elib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSnowParam(\u003cspan class=\"pl-v\"\u003eworkers\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\u003cspan class=\"pl-v\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSOCK\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fit_models(\u003cspan class=\"pl-v\"\u003ecounts\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecexpr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003elib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSnowParam(\u003cspan class=\"pl-v\"\u003eworkers\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\u003cspan class=\"pl-v\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSOCK\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce the 4 distributions are fitted, we next calculate the BIC value for\neach model and select the model with the least BIC value.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.bic.val\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e model_bic(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.bic.val\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e model_bic(\u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eselect model with least bic value\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.lbic\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lbic_model(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.bic.val\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.lbic\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lbic_model(\u003cspan class=\"pl-smi\"\u003eifnb.stim.bic.val\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo ensure the fit of the models selected based on the least BIC value,\nadditionally we perform LRT to test for model adequacy and presence of\nzero-inflation.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.gof\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e gof_model(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.lbic\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSerialParam())\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.gof\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e gof_model(\u003cspan class=\"pl-smi\"\u003eifnb.stim.lbic\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSerialParam())\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally based on the results of the model adequacy tests, we can\nidentify the distribution of best fit for each gene.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_model(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.gof\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_model(\u003cspan class=\"pl-smi\"\u003eifnb.stim.gof\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce the distribution of best fit is identified for genes of interest,\nit is also possible to extract parameters of interest for the models.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e model_param (\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNULL\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e model_param (\u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNULL\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing above results we can now identify the differentially distributed\ngenes between \u2018CTRL\u2019 and \u2018STIM\u2019. First we need to subset genes that is\nsignificant in the KS test in both conditions.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSubset the common genes between the two groups, that pass the GOF test\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e unlist(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e unlist(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.gof.sig\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e intersect(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003eifnb.dist.ctrl\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eP_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eNB_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZIP_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZINB_genes\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.dist.ctrl\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edist\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e c(rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eP_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eNB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eNB_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eZIP\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZIP_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eZINB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZINB_genes\u003c/span\u003e)))\n\n\u003cspan class=\"pl-smi\"\u003eifnb.dist.stim\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eP_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eNB_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZIP_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZINB_genes\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.dist.stim\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edist\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e c(rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eP_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eNB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eNB_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eZIP\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZIP_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eZINB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZINB_genes\u003c/span\u003e)))\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eDataframe consisting of distributions followed by each gene passing the KS test\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.gof.ctrl\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.dist.ctrl\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eifnb.dist.ctrl\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.sig\u003c/span\u003e,]\n\u003cspan class=\"pl-smi\"\u003eifnb.gof.stim\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.dist.stim\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eifnb.dist.stim\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.sig\u003c/span\u003e,]\n\n\u003cspan class=\"pl-smi\"\u003eifnb.distr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003ectrl\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.ctrl\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edist\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003erow.names\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.ctrl\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egene\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.distr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003estim\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.stim\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edist\u003c/span\u003e[match(rownames(\u003cspan class=\"pl-smi\"\u003eifnb.distr\u003c/span\u003e), \u003cspan class=\"pl-smi\"\u003eifnb.gof.stim\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egene\u003c/span\u003e)]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing the dataframe of genes and distribution followed under each\ncondition now we can identify genes changing distribution between \u2018CTRL\u2019\nand \u2018STIM\u2019\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.DD.genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e change_shape(\u003cspan class=\"pl-smi\"\u003eifnb.distr\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will give a list of two lists with genes changing distribution\nbetween condition and genes changing distribution from unimodal in one\ncondition to zero-inflated in the other condition.\u003c/p\u003e\n", + "stargazers_count": 7, + "subscribers_count": 4, + "topics": [], + "updated_at": 1676514392.0 }, { "data_format": 2, - "description": "An oil land-spill and overland flow simulator for pipeline rupture events", + "description": "MountainSort v4", "filenames": [ - "Singularityfiles/Singularity.v1.0.dev1", - "Singularityfiles/Singularity.v0.1.trusty", - "Singularityfiles/Singularity.v1.0.dev3", - "Singularityfiles/Singularity.v1.0.dev4", - "Singularityfiles/Singularity.v1.0", - "Singularityfiles/Singularity.v1.0.dev2", - "Singularityfiles/Singularity.v0.1.bionic" + "Singularity.v0.1.4" ], - "full_name": "barbagroup/geoclaw-landspill", - "latest_release": "v1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egeoclaw-landspill\u003c/h1\u003e\u003ca id=\"user-content-geoclaw-landspill\" class=\"anchor\" aria-label=\"Permalink: geoclaw-landspill\" href=\"#geoclaw-landspill\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/barbagroup/geoclaw-landspill/raw/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/439fd5ffe2ac21b3daa03ac6474978163be089da5fbba2790769ae2275464e35/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d425344253230332d2d436c617573652d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/barbagroup/geoclaw-landspill\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5a59341a3dbba6f3029db40fa18a8c7ba130a7809dc594869f4d9f15720d8e5e/68747470733a2f2f696d672e736869656c64732e696f2f7472617669732f636f6d2f626172626167726f75702f67656f636c61772d6c616e647370696c6c2f6d61737465723f6c6162656c3d5472617669732532304349\" alt=\"Travis CI\" data-canonical-src=\"https://img.shields.io/travis/com/barbagroup/geoclaw-landspill/master?label=Travis%20CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/barbagroup/geoclaw-landspill/actions?query=workflow%3ACI\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e4b357df7c827c2c4d41600c20af8efb1e6caf363bafed67fdd6a396db96e9c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f776f726b666c6f772f7374617475732f626172626167726f75702f67656f636c61772d6c616e647370696c6c2f43492f6d61737465723f6c6162656c3d476974487562253230416374696f6e2532304349\" alt=\"GitHub Action CI\" data-canonical-src=\"https://img.shields.io/github/workflow/status/barbagroup/geoclaw-landspill/CI/master?label=GitHub%20Action%20CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://joss.theoj.org/papers/fb7b012799a70c9b4c55eb4bb0f36f97\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5686ff5ad93f9ae6d8a0131c3be362cfa95de346ce38737c702bc64c667c4ed6/68747470733a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f66623762303132373939613730633962346335356562346262306633366639372f7374617475732e737667\" alt=\"status\" data-canonical-src=\"https://joss.theoj.org/papers/fb7b012799a70c9b4c55eb4bb0f36f97/status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/barbagroup/geoclaw-landspill\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f617e0ad9c80e2b68577a2082878ba67e7e2db8bae3ea008da0a168ee816a5de/68747470733a2f2f616e61636f6e64612e6f72672f626172626167726f75702f67656f636c61772d6c616e647370696c6c2f6261646765732f696e7374616c6c65722f636f6e64612e737667\" alt=\"Conda\" data-canonical-src=\"https://anaconda.org/barbagroup/geoclaw-landspill/badges/installer/conda.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote: if looking for content of \u003ccode\u003egeoclaw-landspill-cases\u003c/code\u003e, please checkout tag\n\u003ccode\u003ev0.1\u003c/code\u003e. This repository has been converted to a fully working solver package.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003egeoclaw-landspill\u003c/em\u003e is a package for running oil overland flow simulations for\napplications in pipeline risk management. It includes a numerical solver and\nsome pre-/post-processing utilities.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./doc/sample.gif\"\u003e\u003cimg src=\"./doc/sample.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe numerical solver is a modified version of\n\u003ca href=\"http://www.clawpack.org/geoclaw.html\" rel=\"nofollow\"\u003eGeoClaw\u003c/a\u003e.\nGeoClaw solves full shallow-water equations. We added several new features and\nutilities to it and make it usable to simulate the overland flow from pipeline\nruptures. These features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eadding point sources to mimic the rupture points\u003c/li\u003e\n\u003cli\u003eadding evaporation models\u003c/li\u003e\n\u003cli\u003eadding Darcy-Weisbach bottom friction models with land roughness\u003c/li\u003e\n\u003cli\u003eadding temperature-dependent viscosity\u003c/li\u003e\n\u003cli\u003erecording detail locations and time of oil flowing into in-land waterbodies\u003c/li\u003e\n\u003cli\u003edownloading topography and hydrology data automatically (the US only)\u003c/li\u003e\n\u003cli\u003egenerating CF-1.7 compliant NetCDF files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"doc/deps_install_tests.md\"\u003eDependencies, installation, and tests\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"doc/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"doc/configuration.md\"\u003eConfiguration file: \u003ccode\u003esetrun.py\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"cases/README.md\"\u003eExample cases\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"doc/container.md\"\u003eContainers: Docker and Singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe only maintain compatibility with Linux. Though using \u003ccode\u003epip\u003c/code\u003e or building from\nsource may still work in Mac OS or Windows (e.g., through WSL), we are not able\nto help with the installation issues on these two systems.\u003c/p\u003e\n\u003cp\u003eBeyond this quick start, to see more details, please refer to the\n\u003ca href=\"#documentation\"\u003edocumentation\u003c/a\u003e section.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Installation\u003c/h3\u003e\u003ca id=\"user-content-1-installation\" class=\"anchor\" aria-label=\"Permalink: 1. Installation\" href=\"#1-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe fast way to install \u003cem\u003egeoclaw-landspill\u003c/em\u003e is through\n\u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\u0027s \u003ccode\u003econda\u003c/code\u003e command. The following command\ncreates a conda environment (called \u003ccode\u003elandspill\u003c/code\u003e) and installs the package and\ndependencies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create \\\n -n landspill -c barbagroup -c conda-forge \\\n python=3.8 geoclaw-landspill\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen use \u003ccode\u003econda activate landspill\u003c/code\u003e or\n\u003ccode\u003esource \u0026lt;conda installation prefix\u0026gt;/bin/activate landspill\u003c/code\u003e to activate the\nenvironment. Type \u003ccode\u003egeoclaw-landspill --help\u003c/code\u003e in the terminal to see if\n\u003cem\u003egeoclaw-landspill\u003c/em\u003e is correctly installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Running an example case\u003c/h3\u003e\u003ca id=\"user-content-2-running-an-example-case\" class=\"anchor\" aria-label=\"Permalink: 2. Running an example case\" href=\"#2-running-an-example-case\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run an example case under the folder \u003ccode\u003ecases\u003c/code\u003e, users have to clone this\nrepository. We currently don\u0027t maintain another repository for cases. After\ncloning this repository, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ geoclaw-landspill run \u0026lt;path to an example case folder\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, to run \u003ccode\u003eutal-flat-maya\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ geoclaw-landspill run ./cases/utah-flat-maya\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsers can use environment variable \u003ccode\u003eOMP_NUM_THREADS\u003c/code\u003e to control how many CPU\nthreads the simulation should use for OpenMP parallelization.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Creating a CF-compliant NetCDF raster file\u003c/h3\u003e\u003ca id=\"user-content-3-creating-a-cf-compliant-netcdf-raster-file\" class=\"anchor\" aria-label=\"Permalink: 3. Creating a CF-compliant NetCDF raster file\" href=\"#3-creating-a-cf-compliant-netcdf-raster-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter a simulation is done, users can convert flow depth in raw simulation data\ninto a CF-compliant NetCDF raster file. For example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ geoclaw-landspill createnc ./case/utah-flat-maya\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eReplace \u003ccode\u003e./cases/utah-flat-maya\u003c/code\u003e with the path to another desired case.\u003c/p\u003e\n\u003cp\u003eQGIS and ArcGIS should be able to read the resulting NetCDF raster file.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThird-party codes and licenses\u003c/h2\u003e\u003ca id=\"user-content-third-party-codes-and-licenses\" class=\"anchor\" aria-label=\"Permalink: Third-party codes and licenses\" href=\"#third-party-codes-and-licenses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eamrclaw: \u003ca href=\"https://github.com/clawpack/amrclaw\"\u003ehttps://github.com/clawpack/amrclaw\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/amrclaw/blob/ee85c1fe178ec319a8403503e779d3f8faf22840/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003egeoclaw: \u003ca href=\"https://github.com/clawpack/geoclaw\"\u003ehttps://github.com/clawpack/geoclaw\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/geoclaw/blob/3593cb1b418fd52739c186a8845a288037c8f575/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003epyclaw: \u003ca href=\"https://github.com/clawpack/pyclaw\"\u003ehttps://github.com/clawpack/pyclaw\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/pyclaw/blob/a85a01a5f20be1a18dde70b7bb37dc1cdcbd0b26/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eclawutil: \u003ca href=\"https://github.com/clawpack/clawutil\"\u003ehttps://github.com/clawpack/clawutil\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/clawutil/blob/116ffb792e889fbf0854d7ac599657039d7b1f3e/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eriemann: \u003ca href=\"https://github.com/clawpack/riemann\"\u003ehttps://github.com/clawpack/riemann\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/riemann/blob/597824c051d56fa0c8818e00d740867283329b24/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePi-Yueh Chuang: \u003ca href=\"mailto:pychuang@gwu.edu\"\u003epychuang@gwu.edu\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "magland/ml_ms4alg", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-ml_ms4alg\" class=\"anchor\" aria-hidden=\"true\" href=\"#ml_ms4alg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml_ms4alg\u003c/h1\u003e\n\u003cp\u003eElectrophysiology tools\nMountainLab processor library\u003c/p\u003e\n\u003cp\u003eInstallation from PyPI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install --upgrade ml_ms4alg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen add it as a plugin to mountainlab:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/.mountainlab/packages\nml-link-python-module ml_ms4alg ml_ms4alg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr installation from source:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eclone this repository into .mountainlab/packages/\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd ml_ms4alg\npip3 install --upgrade .\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 7, - "subscribers_count": 6, - "topics": [ - "geoclaw", - "overland-flow", - "pipeline", - "shallow-water-equations", - "pipeline-ruptures", - "land-spill" + "subscribers_count": 9, + "topics": [], + "updated_at": 1666168225.0 + }, + { + "data_format": 2, + "description": "This docker and singularity image bundles the tgv-qsm algorithm with bet2, dcm2niix and provides a complete QSM processing pipeline.", + "filenames": [ + "Singularity.tgvqsm_amd", + "Singularity.tgvqsm" ], - "updated_at": 1720007939.0 + "full_name": "CAIsr/qsm", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQSM Pipeline\u003c/h1\u003e\u003ca id=\"user-content-qsm-pipeline\" class=\"anchor\" aria-label=\"Permalink: QSM Pipeline\" href=\"#qsm-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis docker and singularity image provides the tgv-qsm algorithm (\u003ca href=\"http://www.neuroimaging.at/pages/qsm.php\" rel=\"nofollow\"\u003ehttp://www.neuroimaging.at/pages/qsm.php\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIf you use this image, this is the reference to cite describing the QSM algorithm:\nLangkammer, C; Bredies, K; Poser, BA; Barth, M; Reishofer, G; Fan, AP; Bilgic, B; Fazekas, F; Mainero; C; Ropele, S\nFast Quantitative Susceptibility Mapping using 3D EPI and Total Generalized Variation.\nNeuroimage. 2015 May 1;111:622-30. doi: 10.1016/j.neuroimage.2015.02.041. PubMed\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIf you are looking for a full QSM pipeline including dicom conversion, QSM solution, image segmentation, atlas building\u003c/h1\u003e\u003ca id=\"user-content-if-you-are-looking-for-a-full-qsm-pipeline-including-dicom-conversion-qsm-solution-image-segmentation-atlas-building\" class=\"anchor\" aria-label=\"Permalink: If you are looking for a full QSM pipeline including dicom conversion, QSM solution, image segmentation, atlas building\" href=\"#if-you-are-looking-for-a-full-qsm-pipeline-including-dicom-conversion-qsm-solution-image-segmentation-atlas-building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/QSMxT/QSMxT\"\u003ehttps://github.com/QSMxT/QSMxT\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWe recommend running this container in the Neurodesk environment for ease of use: \u003ca href=\"https://neurodesk.github.io/\" rel=\"nofollow\"\u003ehttps://neurodesk.github.io/\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-we-recommend-running-this-container-in-the-neurodesk-environment-for-ease-of-use-httpsneurodeskgithubio\" class=\"anchor\" aria-label=\"Permalink: We recommend running this container in the Neurodesk environment for ease of use: https://neurodesk.github.io/\" href=\"#we-recommend-running-this-container-in-the-neurodesk-environment-for-ease-of-use-httpsneurodeskgithubio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the image in singularity (deprecated)\u003c/h3\u003e\u003ca id=\"user-content-using-the-image-in-singularity-deprecated\" class=\"anchor\" aria-label=\"Permalink: Using the image in singularity (deprecated)\" href=\"#using-the-image-in-singularity-deprecated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003einstalling singularity will depend on your operating system, here an exampe for a debian based system\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get update \u0026amp;\u0026amp; sudo apt-get install -y \\\n build-essential \\\n uuid-dev \\\n libgpgme-dev \\\n squashfs-tools \\\n libseccomp-dev \\\n wget \\\n pkg-config \\\n git \\\n cryptsetup-bin\n\nwget https://golang.org/dl/go1.15.2.linux-amd64.tar.gz\n\ntar -C /usr/local -xzf go1.15.2.linux-amd64.tar.gz\n\nexport PATH=$PATH:/usr/local/go/bin\n\nexport VERSION=3.6.3 \u0026amp;\u0026amp; # adjust this as necessary \\\n wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-${VERSION}.tar.gz \u0026amp;\u0026amp; \\\n tar -xzf singularity-${VERSION}.tar.gz \u0026amp;\u0026amp; \\\n cd singularity\n\n\n./mconfig \u0026amp;\u0026amp; \\\n make -C ./builddir \u0026amp;\u0026amp; \\\n sudo make -C ./builddir install\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethen you can download and run the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/NeuroDesk/transparent-singularity tgvqsm_1.0.0_20210317\ncd tgvqsm_1.0.0_20210317\n./run_transparent_singularity.sh tgvqsm_1.0.0_20210317\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethis will download the image, unpack it and provide a wrapper script for starting tgv_qsm:\u003c/p\u003e\n\u003cp\u003eThe wrapper script can be started using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./tgv_qsm\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr you can open a shell into the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell tgvqsm_1.0.0_20210317.*\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou can also bind a different directory to your image (e.g. bind /data from your host to /data in your singularity image)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind /data:/data/ tgvqsm_1.0.0_20210317.*\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere is an example for a single echo QSM processing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edcm2niix -o ./ -f magnitude GR_M_5_QSM_p2_1mmIso_TE20/\ndcm2niix -o ./ -f phase GR_P_6_QSM_p2_1mmIso_TE20/\n\nbet2 magnitude.nii magnitude_bet2\n\ntgv_qsm \\\n -p phase.nii \\\n -m magnitude_bet2_mask.nii.gz \\\n -f 2.89 \\\n -t 0.02 \\\n -s \\\n -o qsm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe -s option will scale the phase correctly if the phase dicom values are between -2048 and 2048 (should be default on Siemens VD and VE platforms). On the VB platform the phase is between 0 and 4096, so omit the -s option and scale the phase between -pi and pi:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing the image in docker\u003c/h1\u003e\u003ca id=\"user-content-using-the-image-in-docker\" class=\"anchor\" aria-label=\"Permalink: Using the image in docker\" href=\"#using-the-image-in-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull vnmd/tgvqsm_1.0.0:20210317\nsudo docker run -it -v $PWD:/data vnmd/tgvqsm_1.0.0:20210317\n\ncd /data\ndcm2niix -o ./ -f magnitude GR_M_5_QSM_p2_1mmIso_TE20/\ndcm2niix -o ./ -f phase GR_P_6_QSM_p2_1mmIso_TE20/\n\nbet2 magnitude.nii magnitude_bet2\n\ntgv_qsm -p phase.nii -m magnitude_bet2_mask.nii.gz -f 2.89 -t 0.02 -s -o qsm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOptimizing for your CPU\u003c/h1\u003e\u003ca id=\"user-content-optimizing-for-your-cpu\" class=\"anchor\" aria-label=\"Permalink: Optimizing for your CPU\" href=\"#optimizing-for-your-cpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, QSM is compiled with the \u003ccode\u003e-O3 -march=x86-64\u003c/code\u003e which should provide a good balance between speed and portability. If you know what CPU you\u0027re going to be using you can compile with that instruction set to improve performance (e.g. \u003ccode\u003e-march=ivybridge\u003c/code\u003e for Intel Ivy Bridge CPUs, \u003ccode\u003e-march=native\u003c/code\u003e for whatever CPU you\u0027re currently on). If you would like maximum portability, you can recompile omitting the \u003ccode\u003e-march\u003c/code\u003e flag altogether.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing tgv_qsm in Windows Subsystem for Linux (example: Debian based system)\u003c/h1\u003e\u003ca id=\"user-content-using-tgv_qsm-in-windows-subsystem-for-linux-example-debian-based-system\" class=\"anchor\" aria-label=\"Permalink: Using tgv_qsm in Windows Subsystem for Linux (example: Debian based system)\" href=\"#using-tgv_qsm-in-windows-subsystem-for-linux-example-debian-based-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWSL 1.0 doesn\u0027t support singularity or docker containers (but WSL 2.0 will). But it is possible to directly install TGV QSM in a miniconda environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install wget unzip gcc\nwget https://repo.anaconda.com/miniconda/Miniconda2-4.6.14-Linux-x86_64.sh\nbash Miniconda2-4.6.14-Linux-x86_64.sh\n(install, accept agreement with yes, after install source bash again:)\nbash\nconda install -c anaconda cython==0.25.2\nconda install numpy\nconda install pyparsing\n(make sure pip is not your system pip, but the one in miniconda: which pip)\npip install scipy==0.17.1 nibabel==2.1.0\nwget http://www.neuroimaging.at/media/qsm/TGVQSM-plus.zip\nunzip TGVQSM-plus.zip\ncd TGVQSM-master-011045626121baa8bfdd6633929974c732ae35e3\npython setup.py install\ncd test_data\ntgv_qsm -p epi3d_test_phase.nii.gz -m epi3d_test_mask.nii.gz -f 2.89 -t 0.027 -o epi3d_test_QSM\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAdding fsl to WSL Ubuntu 18.04\u003c/h1\u003e\u003ca id=\"user-content-adding-fsl-to-wsl-ubuntu-1804\" class=\"anchor\" aria-label=\"Permalink: Adding fsl to WSL Ubuntu 18.04\" href=\"#adding-fsl-to-wsl-ubuntu-1804\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ewget -O- http://neuro.debian.net/lists/bionic.us-ca.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list\nsudo apt-key adv --recv-keys --keyserver hkp://pool.sks-keyservers.net:80 0xA5D32F012649A5A9\nsudo apt-get update\nsudo apt-get install fsl-5.0-core\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eadd \". /etc/fsl/5.0/fsl.sh\" to the end of your .profile file\u003c/p\u003e\n", + "stargazers_count": 7, + "subscribers_count": 4, + "topics": [], + "updated_at": 1646912572.0 }, { "data_format": 2, - "description": "network analysis of network analysis publications --- split by software", + "description": " Generated will detect files that have been generated by computers", "filenames": [ - "Singularity" + "samples/Singularity/filenames/Singularity" ], - "full_name": "incertae-sedis/cavatica", - "latest_release": "v1.0.0", - "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/incertae-sedis/cavatica\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859cc8b569e45dbdc166015d056b2e33682ac216b50b0e5ff077a74e0af59434/68747470733a2f2f7472617669732d63692e6f72672f696e6365727461652d73656469732f63617661746963612e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/incertae-sedis/cavatica.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/incertae-sedis/cavatica/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dec3e303eeddcbb6862373485486f42fa9665a079726ea0b73df99abf2beca72/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f696e6365727461652d73656469732f63617661746963612e7376673f6c6162656c3d63757272656e742b72656c65617365\" alt=\"github release\" data-canonical-src=\"https://img.shields.io/github/release/incertae-sedis/cavatica.svg?label=current+release\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/1322\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/incertaesedis/cavatica/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/be22f9ba4feadaf9bac5670ffa0881abfc652ee88e4237092cd392b901059bdb/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f636c6f75642f6275696c642f696e63657274616573656469732f63617661746963612e737667\" alt=\"Docker Automated build\" data-canonical-src=\"https://img.shields.io/docker/cloud/build/incertaesedis/cavatica.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/incertaesedis/cavatica/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d6a3c603bcab0c6c6dd3c26aa73e83181a1073cc678b4e89e1e79336c0b6a5cf/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f696e63657274616573656469732f63617661746963612e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/incertaesedis/cavatica.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInitial Commit\u003c/strong\u003e: July 2016\u003c/p\u003e\n\u003cp\u003e***** Cavatica has been adopted by the incertae-sedis group. *****\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCavatica\u003c/h1\u003e\u003ca id=\"user-content-cavatica\" class=\"anchor\" aria-label=\"Permalink: Cavatica\" href=\"#cavatica\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode and pipeline for fetching PubMed and PubMed Central data and co-author network analysis. This tool can be used to identify author trends among several search terms.\u003c/p\u003e\n\u003cp\u003eAn example, I\u0027ve used these scripts to do a multi-network analysis of network analysis papers and their software.\n\u003ca href=\"https://github.com/incertae-sedis/cavatica/wiki\"\u003eWiki Page Here\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Adder.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Adder.png\" width=\"600\" alt=\"Added\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe name comes from Charlotte\u0027s Web since her full name was Charlotte A. Cavatica. Although Cavatica also refers to barn spider.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline\u003c/h2\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-label=\"Permalink: Pipeline\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e***** Cavatica pipeline has been modified so no longer relies on Ebot. *****\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/plan.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/plan.png\" width=\"600\" alt=\"Plan\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSome type of Linux Terminal where you can run Bash. (Cygwin if you\u0027re on Windows. Terminal already preinstalled on Mac)\u003c/li\u003e\n\u003cli\u003eR (check if installed by typing Rscript --version)\u003c/li\u003e\n\u003cli\u003eperl (check if installed by typing perl --version)\u003c/li\u003e\n\u003cli\u003eMango Graph Studio for multi-network analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/incertae-sedis/cavatica.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBasic Example\u003c/h2\u003e\u003ca id=\"user-content-basic-example\" class=\"anchor\" aria-label=\"Permalink: Basic Example\" href=\"#basic-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is a basic example fetching PubMed and PMC papers containing the word \"Neo4j\" and \"Cytoscape\".\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd cavatica/data\nmkdir test\ncd test\necho \"Neo4j\" \u0026gt; config.txt\necho \"Cytoscape\" \u0026gt;\u0026gt; config.txt\n../../code/script.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create tabular files (list of papers \u003ccode\u003eNeo4j_papers_pm.tsv\u003c/code\u003e and list of authors \u003ccode\u003eNeo4j_authors_pm.tsv\u003c/code\u003e). Open the png files \u003ccode\u003eNeo4j_pm.png\u003c/code\u003e to see a barchart of the number of papers by year.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Neo4j-pubmedcounts.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Neo4j-pubmedcounts.png\" width=\"400\" alt=\"Neo4j count\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Cytoscape-pubmedcounts.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Cytoscape-pubmedcounts.png\" width=\"400\" alt=\"Cavatica count\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCan also open the html files to check the one sentence usages of Neo4j and Cavatica\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\u003ctr\u003e\n\u003ctd\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSentences that contain Neo4j\u003c/h1\u003e\u003ca id=\"user-content-sentences-that-contain-neo4j\" class=\"anchor\" aria-label=\"Permalink: Sentences that contain Neo4j\" href=\"#sentences-that-contain-neo4j\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=29377902\" rel=\"nofollow\"\u003e29377902\u003c/a\u003e\n Reactome graph database: Efficient access to complex pathway data.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eHere \nwe present the rationale behind the adoption of a graph database (\u003cb\u003eNeo4j\u003c/b\u003e) as well as the new ContentService (REST API) that provides access to these data. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=28936969\" rel=\"nofollow\"\u003e28936969\u003c/a\u003e Systematic integration of biomedical knowledge prioritizes drugs for repurposing.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eFirst, we constructed Hetionet (\u003cb\u003eneo4j\u003c/b\u003e.het.io), an integrative network encoding knowledge from millions of biomedical studies. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e2017 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=28416946\" rel=\"nofollow\"\u003e28416946\u003c/a\u003e Use of Graph Database for the Integration of Heterogeneous Biological Data.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHere, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and \u003cb\u003eNeo4j\u003c/b\u003e, one of the most widely used graph databases. \u003c/li\u003e\n\u003cli\u003eWhen we tested the query execution performance of MySQL versus \u003cb\u003eNeo4j\u003c/b\u003e, we found that \u003cb\u003eNeo4j\u003c/b\u003e outperformed MySQL in all cases. \u003c/li\u003e\n\u003cli\u003eThese results show that using graph-based databases, such as \u003cb\u003eNeo4j\u003c/b\u003e, is an efficient way to store complex biological relationships. \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\u003ctr\u003e\n\u003ctd\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSentences that contain Cytoscape\u003c/h1\u003e\u003ca id=\"user-content-sentences-that-contain-cytoscape\" class=\"anchor\" aria-label=\"Permalink: Sentences that contain Cytoscape\" href=\"#sentences-that-contain-cytoscape\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=29894068\" rel=\"nofollow\"\u003e29894068\u003c/a\u003e Identification of potential miRNAs and candidate genes of cervical intraepithelial neoplasia by bioinformatic analysis.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eThen the miRNA- mRNA regulatory network was constructed using \u003cb\u003eCytoscape\u003c/b\u003e software. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=29872319\" rel=\"nofollow\"\u003e29872319\u003c/a\u003e An integrated analysis of key microRNAs, regulatory pathways and clinical relevance in bladder cancer.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eProtein-protein interaction (PPI) and miRNA-mRNA regulatory networks were established by using the Search Tool for the Retrieval of Interacting Genes/Proteins and \u003cb\u003eCytoscape\u003c/b\u003e tool. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e2018 \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/?term=29760609\" rel=\"nofollow\"\u003e29760609\u003c/a\u003e Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.\u003c/p\u003e\n\u003cul\u003e\u003cli\u003eProtein-protein interaction (PPI) network was constructed by STRING and visualized in \u003cb\u003eCytoscape\u003c/b\u003e. \u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e...\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eIt will also create a script \u003ccode\u003epubmed.gel\u003c/code\u003e. Open \u003ca href=\"https://www.complexcomputation.com/en/product/mango-community-edition/\" rel=\"nofollow\"\u003eMango Graph Studio\u003c/a\u003e, open \u003ccode\u003epubmed.gel\u003c/code\u003e and type the following into the Mango Console.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun \"pubmed.gel\";\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a transition table and export the file. It will also load and visualize the author-paper networks.\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\u003ctr\u003e\n\u003ctd\u003eNeo4j\u003c/td\u003e\n\u003ctd\u003eCytoscape\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Neo4j.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Neo4j.png\" width=\"300\" alt=\"Neo4j network\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/incertae-sedis/cavatica/blob/master/IMG/Cytoscape.png\"\u003e\u003cimg src=\"https://github.com/incertae-sedis/cavatica/raw/master/IMG/Cytoscape.png\" width=\"300\" alt=\"Cavatica network\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\nGoing back to your terminal, rerun the script file and it will continue.\n\u003cpre\u003e\u003ccode\u003e../../code/script.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe transitions should be saved in \u003ccode\u003etrends_pm.txt\u003c/code\u003e. The following trends_pm.txt indicates that authors switched from cytoscape to Neo4j 9 times, while authors switched from Neo4j to Cytoscape 3 times.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCytoscape:Neo4j 9\nNeo4j:Cytoscape 3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt will then commence searching PMC, fetching list of papers and authors and generating a \"pmc.gel\" file. Once again open the \"pmc.gel\" file in Mango and type the following into Mango Console.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erun \"pmc.gel\";\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen rerun the script to continue tabulating the trends which should be saved in \u003ccode\u003etrends_pmc.txt\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe output of a 2017 run comparing \"Neo4j\", \"Gephi\", \"GraphViz\" and \"iGraph\" is shown below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e=============PubMed Transitions\nNeo4j:Gephi 1\nNeo4j:GraphViz 1\nNeo4j:iGraph 1\n=============PubMed Central Transitions\nGephi:GraphViz 2\nGephi:Neo4j 3\nGephi:iGraph 31\nGraphViz:Gephi 19\nGraphViz:Neo4j 10\nGraphViz:iGraph 58\nNeo4j:Gephi 4\nNeo4j:GraphViz 4\nNeo4j:iGraph 1\niGraph:Gephi 34\niGraph:GraphViz 9\niGraph:Neo4j 13\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePMC results usually return more papers since search terms like \"Neo4j\" or \"Cytoscape\" are being matched to the fulltext, instead of just the title and abstract. This may return more accurate trend tables since sometimes software names are only mentioned in the methods and not in the abstract.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Container\u003c/h2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity Container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repo provides a container for easily reproducing and running Cavatica through a container. The pipeline for both Singularity and Docker was ran on an Ubuntu 18.04 instance on \u003ca href=\"https://jetstream-cloud.org/\" rel=\"nofollow\"\u003eJetstream\u003c/a\u003e, which is a national science and engineering cloud led by the Indiana University Pervasive Technology Institute.\u003c/p\u003e\n\u003cp\u003eA singularity container of Cavatica is available on \u003ca href=\"https://singularity-hub.org/collections/1322\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e. Using singularity you can download the contained with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://TeamMango/cavatica:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen run, the container will look for a text file called \u003ccode\u003econfig.txt\u003c/code\u003e in a directory called \u003ccode\u003eoutput\u003c/code\u003e in the same directory as the \u003ccode\u003e.simg\u003c/code\u003e you just downloaded. Place the terms that you want Cavatica to search for in this file. In Ubuntu, you can use the following commands to create this file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir output\necho \"YOURSEARCHTERM\" \u0026gt; ./output/config.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYour search terms can also be followed by a year range, separated by commas:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eecho \"YOURSEARCHTERM,1996,2006\" \u0026gt; ./output/config.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEach search term and year range should occupy it\u0027s own line. If you want to search for use of the term cytoscape and VisANT between 1994 and 2000, \u003ccode\u003econfig.txt\u003c/code\u003e would look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evisant,1999,2006\ncytoscape,1994,2003\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce you have entered the terms in the \u003ccode\u003econfig.txt\u003c/code\u003e file, return to the same directory as the .simg image and run the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run --bind output:/cavatica/data/output TeamMango-cavatica-master-latest.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe results of the search will appear in the \u003ccode\u003eoutput\u003c/code\u003e directory next to your \u003ccode\u003econfig.txt\u003c/code\u003e file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Container\u003c/h2\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-label=\"Permalink: Docker Container\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA docker container of Cavatica is available on \u003ca href=\"https://hub.docker.com/r/incertaesedis/cavatica/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e. You can pull the docker container with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull incertaesedis/cavatica\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run the docker container, move into the directory where you want to generate output from Cavatica. Create three files called \u003ccode\u003emultitool-pubmed.tsv\u003c/code\u003e, \u003ccode\u003emultitool-pmc.tsv\u003c/code\u003e, and \u003ccode\u003econfig.txt\u003c/code\u003e. In Ubuntu you can do this with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etouch multitool-pubmed.tsv multitool-pmc.tsv config.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll three files must be present in the directory where you run the container. In \u003ccode\u003econfig.txt\u003c/code\u003e enter the search terms that you want Cavatica to search for, with each term on a new line. Optional year ranges can be indicated with commas:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evisant,1999,2006\ncytoscape,1994,2003\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the same directory as config.txt, run the docker container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -v ${PWD}:/cavatica/data/output incertaesedis/cavatica\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf on windows, \u003ccode\u003e\"$PWD\"\u003c/code\u003e should be replaced with the absolute path to your current directory. The files produced by Cavatica should appear on running the container. If you wish to rerun the search with different terms, make sure that the \u003ccode\u003emultitool-pubmed.tsv\u003c/code\u003e and \u003ccode\u003emultitool-pmc.tsv\u003c/code\u003e files are still in the folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eValue of Reproducible Research\u003c/h2\u003e\u003ca id=\"user-content-value-of-reproducible-research\" class=\"anchor\" aria-label=\"Permalink: Value of Reproducible Research\" href=\"#value-of-reproducible-research\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hackmd.io/s/r1Vxf9wVX\" rel=\"nofollow\"\u003eAccomplishments and opportunities of reproducing and containerizing this project\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePublications\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eJ. Chang and H. Chou, \"\u003ca href=\"https://www.computer.org/csdl/proceedings/bibm/2017/3050/00/08217990-abs.html\" rel=\"nofollow\"\u003eCavatica: A pipeline for identifying author adoption trends among software or methods\u003c/a\u003e,\" 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 2145-2150.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "noqcks/generated", + "latest_release": "v7.23.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGenerated\u003c/h1\u003e\u003ca id=\"user-content-generated\" class=\"anchor\" aria-label=\"Permalink: Generated\" href=\"#generated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/noqcks/generated\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/27853b375fba1501eb04861c4f86f746b0fc8154f165755ef77d9ba647f8f883/68747470733a2f2f7472617669732d63692e6f72672f6e6f71636b732f67656e6572617465642e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/noqcks/generated.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNot all files are written by humans. Generated will detect files that have been\ngenerated by computers. Items like a \u003ccode\u003epackage-lock.json\u003c/code\u003e or files in \u003ccode\u003enode_modules\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor a full list of generated files detected, see \u003ca href=\"lib/generated.js\"\u003egenerated.js\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis project is largely a node.js port of the \u003ccode\u003egenerated\u003c/code\u003e functionality of \u003ca href=\"https://github.com/github/linguist\"\u003eGitHub linguist\u003c/a\u003e. The versioning of this project will follow the versioning of linguist.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003enpm i @noqcks/generated\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://www.npmjs.com/package/@noqcks/generated\" rel=\"nofollow\"\u003ehttps://www.npmjs.com/package/@noqcks/generated\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee file \u003ca href=\"scripts/example.js\"\u003escripts/example.js\u003c/a\u003e for an example of usage.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econst fs = require(\u0027fs\u0027);\nconst path = require(\u0027path\u0027);\nconst Generated = require(\"@noqcks/generated\");\n\nconst fileName = \"JavaScript/json2_backbone.js\"\nconst filePath = path.join(\"./samples\", fileName);\n\ntry {\n var contents = fs.readFileSync(filePath, \u0027utf8\u0027);\n} catch (e) {\n if (e.code !== \u0027ENOENT\u0027) throw err;\n var contents = \u0027\u0027\n}\n\nconst g = new Generated(name, contents)\n\nconsole.log(g.isGenerated())\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLICENSE\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"LICENSE\"\u003eMIT\u003c/a\u003e \u00a9 2022 Benji Visser \u003ca href=\"mailto:benji@093b.org\"\u003ebenji@093b.org\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 7, "subscribers_count": 3, "topics": [], - "updated_at": 1697654852.0 + "updated_at": 1708962247.0 }, { "data_format": 2, - "description": "Work with PLINK from R", + "description": "Mitsuba implementation for \"Stratified Markov Chain Monte Carlo Light Transport\" (EG 2020)", "filenames": [ "Singularity" ], - "full_name": "AJResearchGroup/plinkr", - "latest_release": "v0.20.2", - "readme": "\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eplinkr\u003c/h1\u003e\u003ca id=\"user-content-plinkr\" class=\"anchor\" aria-label=\"Permalink: plinkr\" href=\"#plinkr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/plinkr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/plinkr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4fff526bbece345c9f8ab3f09c5ab171c9b48b5c7d6866408fec260ff2be31d/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f706c696e6b722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/plinkr/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/plinkr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ace740ee7a2c1d753db958808c8c716545a28254b97c3e3bd1d330bb908d6f1/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f706c696e6b722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/plinkr/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWork with PLINK and PLINK2 from R.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDoing the first PLINK example:\n\u003ca href=\"https://youtu.be/LsfKQw2oIUg\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_basic_usage.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with one or more quantitative traits:\n\u003ca href=\"https://youtu.be/IicNdc8sDfI\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_assoc_qt.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with ideal quantitative traits:\n\u003ca href=\"https://youtu.be/oXGy83WiHm4\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_demo_qt_assoc.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulate quantitative traits:\n\u003ca href=\"https://youtu.be/H0XlLVsFry4\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_create_demo_assoc_qt_params.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulate custom traits: \u003ca href=\"https://youtu.be/5X1kLkiQbtw\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e\n\u003ca href=\"http://richelbilderbeek.nl/plinkr_create_custom_trait.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with a binary trait/case-control phenotype:\n\u003ca href=\"https://youtu.be/LhXQcDQvZS0\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_assoc.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/install.md\"\u003edoc/install.md\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning PLINK\u003c/h3\u003e\u003ca id=\"user-content-running-plink\" class=\"anchor\" aria-label=\"Permalink: Running PLINK\" href=\"#running-plink\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can seamlessly run any \u003ccode\u003ePLINK\u003c/code\u003e or \u003ccode\u003ePLINK2\u003c/code\u003e versions.\u003c/p\u003e\n\u003cp\u003eRun PLINK:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003eplinkr\u003c/span\u003e)\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo call a specific version of PLINK:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003erun_plink(c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--noweb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), create_plink_v1_7_options())\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, create_plink_v1_9_options())\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, create_plink_v2_0_options())\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOf course, you can also call PLINK to detect genetic associations :-) :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the PLINK v1.9 example files\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_plink_v1_9_options()\n\u003cspan class=\"pl-smi\"\u003eped_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_plink_example_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etoy.ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003emap_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_plink_example_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etoy.map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Do a case-control association\u003c/span\u003e\n\u003cspan class=\"pl-e\"\u003eplinkr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003erun_plink(\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eped_filename\u003c/span\u003e, \n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003emap_filename\u003c/span\u003e\n )\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003ebasic_usage\u003c/code\u003e for basic usage of PLINK, as taken\nfrom the PLINK website, which shows a quantitative trait analysis\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003etest_assoc_qt\u003c/code\u003e for the same basic usage of PLINK,\nusing the \u003ccode\u003eplinkr\u003c/code\u003e interface\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003edemo_assoc_qt\u003c/code\u003e for doing a quantitative trait\nanalysis using simulated data and the \u003ccode\u003eplinkr\u003c/code\u003e interface\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun a quantitative trait analysis on existing files\u003c/h3\u003e\u003ca id=\"user-content-run-a-quantitative-trait-analysis-on-existing-files\" class=\"anchor\" aria-label=\"Permalink: Run a quantitative trait analysis on existing files\" href=\"#run-a-quantitative-trait-analysis-on-existing-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRead from PLINK text files\u003c/h4\u003e\u003ca id=\"user-content-read-from-plink-text-files\" class=\"anchor\" aria-label=\"Permalink: Read from PLINK text files\" href=\"#read-from-plink-text-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_assoc_qt_data(\n \u003cspan class=\"pl-v\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_plink_text_filenames(\n \u003cspan class=\"pl-v\"\u003emap_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003eped_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n ),\n \u003cspan class=\"pl-v\"\u003ephenotype_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_phenotype_data_filename(\n \u003cspan class=\"pl-v\"\u003ephe_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.phe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \n )\n)\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e assoc_qt(\u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e)\nread_plink_qassoc_file(\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eqassoc_filenames\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRead from PLINK binary files\u003c/h4\u003e\u003ca id=\"user-content-read-from-plink-binary-files\" class=\"anchor\" aria-label=\"Permalink: Read from PLINK binary files\" href=\"#read-from-plink-binary-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_assoc_qt_data(\n \u003cspan class=\"pl-v\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_plink_bin_filenames(\n \u003cspan class=\"pl-v\"\u003ebed_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.bed\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003ebim_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.bim\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003efam_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.fam\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n ),\n \u003cspan class=\"pl-v\"\u003ephenotype_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_phenotype_data_filename(\n \u003cspan class=\"pl-v\"\u003ephe_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.phe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \n )\n)\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e assoc_qt(\u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e)\nread_plink_qassoc_file(\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eqassoc_filenames\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDemonstrate a quantitative trait analysis\u003c/h3\u003e\u003ca id=\"user-content-demonstrate-a-quantitative-trait-analysis\" class=\"anchor\" aria-label=\"Permalink: Demonstrate a quantitative trait analysis\" href=\"#demonstrate-a-quantitative-trait-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can seamlessly use \u003ccode\u003ePLINK\u003c/code\u003e/\u003ccode\u003ePLINK2\u003c/code\u003e in-memory-data or files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_demo_assoc_qt_data()\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prove that this is PLINK text data\u003c/span\u003e\ncheck_plink_text_data(\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_test_assoc_qt_params()\nassoc_qt(\n \u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo convert the in-memory data to PLINK binary format and do the same\nquantitative trait analysis:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e convert_plink_text_data_to_plink_bin_data(\n \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e\n)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prove that this is PLINK binary data\u003c/span\u003e\ncheck_plink_bin_data(\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e)\n\nassoc_qt(\n \u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003edemo_assoc_qt\u003c/code\u003e for a walk-through of the data that\nis simulated by default\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003ecreate_demo_assoc_qt_params\u003c/code\u003e for many examples how\ndata can be simulated\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFile I/O\u003c/h3\u003e\u003ca id=\"user-content-file-io\" class=\"anchor\" aria-label=\"Permalink: File I/O\" href=\"#file-io\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can read and save many types of PLINK files. Below is an\noverview. List from \u003ca href=\"https://www.cog-genomics.org/plink2/formats\" rel=\"nofollow\"\u003ethe PLINK file format\nreference\u003c/a\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile extension\u003c/th\u003e\n\u003cth\u003e\n\u003ccode\u003eplink\u003c/code\u003e read function\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.adjusted\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_adjusted_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.allele.no.snp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_assoc_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.adjusted\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_assoc_adjusted_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.dosage\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.fisher\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.linear\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.logistic\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.auto.R\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.beagle.dat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bed_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bim\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bin_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.blocks*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.chr-*.dat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.chr-*.map\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.clst\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.clumped*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cluster*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cmh\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cmh2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv.indiv\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv.overlap\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cnv.summary\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.cov\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_cov_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.dfam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.diff\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.dist\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.dupvar\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.eigenvec*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.epi.*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.fam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_fam_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.flipscan\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_frq_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq.cc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq.count\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frq.strat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_frq_strat_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.frqx\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.fst\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.gen\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.genome\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.grm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.grm.N.bin\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.grm.bin\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.gvar\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.het\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hh\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hom\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hom.indiv\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hom.overlap*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hom.summary\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.homog\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.hwe\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.ibc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.imiss\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_imiss_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.info\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.lasso\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.ld\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_ld_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.ldset\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.lgen\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.list\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.lmiss\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_lmiss_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.log\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_log_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.map\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_map_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.mdist\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.mdist.missing\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.mds\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.*mendel\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.meta\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.mibs\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.missing\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.missing.hap\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.model\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.mperm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.nearest\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.occur.dosage\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.out.dosage\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.ped\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_ped_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.perm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.phe\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_phe_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.pphe\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.prob\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.profile\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_qassoc_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qassoc.gxe\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qassoc.means\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qfam.*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.range.report\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.raw\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.*.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.phase.inp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.strct_in\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.ref\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.rel\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.rlist\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.sample\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set.{perm,mperm}\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set.table\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.sexcheck\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.simfreq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_simfreq_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tags.list\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tdt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tdt.poo\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tfam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tped\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.traw\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.twolocus\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.var.ranges\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAssociations\u003c/h3\u003e\u003ca id=\"user-content-associations\" class=\"anchor\" aria-label=\"Permalink: Associations\" href=\"#associations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eAssociation type\u003c/th\u003e\n\u003cth\u003eData type\u003c/th\u003e\n\u003cth\u003eGeneral function\u003c/th\u003e\n\u003cth\u003eSpecialized function\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink_text_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink2_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink_text_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink2_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePLINK and PLINK2 files conversions\u003c/h3\u003e\u003ca id=\"user-content-plink-and-plink2-files-conversions\" class=\"anchor\" aria-label=\"Permalink: PLINK and PLINK2 files conversions\" href=\"#plink-and-plink2-files-conversions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e allows to convert between any PLINK and PLINK2 files.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFrom\u003c/th\u003e\n\u003cth\u003eTo\u003c/th\u003e\n\u003cth\u003eFunction name\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_files_to_plink_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_files_to_plink2_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_files_to_plink_text_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_files_to_plink2_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink2_bin_files_to_plink_text_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink2_bin_files_to_plink_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink_text_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink2_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003eSAIGE files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003ecreate_bgen_files_for_saige\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK2 VCF files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink_bin_files_to_plink_vcf_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePLINK and PLINK2 data conversions\u003c/h3\u003e\u003ca id=\"user-content-plink-and-plink2-data-conversions\" class=\"anchor\" aria-label=\"Permalink: PLINK and PLINK2 data conversions\" href=\"#plink-and-plink2-data-conversions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e allows to convert between any PLINK and PLINK2 data.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFrom\u003c/th\u003e\n\u003cth\u003eTo\u003c/th\u003e\n\u003cth\u003eFunction name\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_data_to_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_data_to_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink_bin_data_to_plink_text_data\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_data_to_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink2_bin_data_to_plink_text_data\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink2_bin_data_to_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity container\u003c/h2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/search;query=plinkr\" rel=\"nofollow\"\u003eFind the latest \u2018plinkr\u2019 Singularity\ncontainer\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/faq.md\"\u003edoc/faq.md\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "beltegeuse/smcmc", + "latest_release": null, + "readme": "", "stargazers_count": 7, - "subscribers_count": 1, - "topics": [ - "gwas", - "plink", - "plink2", - "r", - "r-package" + "subscribers_count": 2, + "topics": [], + "updated_at": 1705152734.0 + }, + { + "data_format": 2, + "description": "This container allows to run the standalone, compiled version of the Computational Anatomy Toolbox (CAT), which is an extension to SPM software.", + "filenames": [ + "Singularity" ], - "updated_at": 1708238471.0 + "full_name": "m-wierzba/cat-container", + "latest_release": "v0.1.0", + "readme": "\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.6021002\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/edbf700e861803bfb6e4126fe0f88968646fd1947fd4c26bb753e891623b5bcf/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e363032313030322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.6021002.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCAT container\u003c/h1\u003e\u003ca id=\"user-content-cat-container\" class=\"anchor\" aria-label=\"Permalink: CAT container\" href=\"#cat-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container allows to run the standalone, compiled version of the \u003ca href=\"http://www.neuro.uni-jena.de/cat/\" rel=\"nofollow\"\u003eComputational Anatomy Toolbox (CAT)\u003c/a\u003e, which is an extension to \u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/software/\" rel=\"nofollow\"\u003eSPM\u003c/a\u003e software. Using the container does not require the availability of a MATLAB licence.\u003c/p\u003e\n\u003cp\u003eThe container includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://uk.mathworks.com/products/compiler/matlab-runtime.html\" rel=\"nofollow\"\u003eMATLAB Compiler Runtime\u003c/a\u003e (R2017b, 9.3)\u003c/li\u003e\n\u003cli\u003eStandalone version of \u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/software/\" rel=\"nofollow\"\u003eSPM\u003c/a\u003e software (SPM12, r7771)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.neuro.uni-jena.de/cat/\" rel=\"nofollow\"\u003eComputational Anatomy Toolbox\u003c/a\u003e (CAT12.7 r1743)\u003c/li\u003e\n\u003cli\u003eCAT interface scripts (\u003ccode\u003ecat_standalone.sh\u003c/code\u003e, \u003ccode\u003ecat_parallelize.sh\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more details on the exact version of the software used in this container, please refer to the recipe file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to build the container:\u003c/h2\u003e\u003ca id=\"user-content-how-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: How to build the container:\" href=\"#how-to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecute the built command with root privileges:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build container.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn principle this container allows you to perform the very same types of analysis that are possible with the standalone version of CAT. It is assumed that the user is familiar with the content of the batch files dedicated for the use with the standalone version of CAT (\u003ccode\u003ecat_standalone_segment.txt\u003c/code\u003e, \u003ccode\u003ecat_standalone_simple.txt\u003c/code\u003e, \u003ccode\u003ecat_standalone_resample.txt\u003c/code\u003e, \u003ccode\u003ecat_standalone_smooth.txt\u003c/code\u003e) and can modify their content according to his/her needs. For more details, please refer to the \u003ca href=\"http://www.neuro.uni-jena.de/cat12/CAT12-Manual.pdf\" rel=\"nofollow\"\u003eCAT12 documentation and manual\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAvailable batch files:\u003c/h2\u003e\u003ca id=\"user-content-available-batch-files\" class=\"anchor\" aria-label=\"Permalink: Available batch files:\" href=\"#available-batch-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe content of the batch files can be explored by using the \u003ccode\u003eview\u003c/code\u003e and \u003ccode\u003ecopy\u003c/code\u003e subcommands:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run \u0026lt;container\u0026gt; \u0026lt;subcommand\u0026gt; \u0026lt;batch file\u0026gt; \u0026lt;arguments\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo view a batch file, use the \u003ccode\u003eview\u003c/code\u003e subcommand:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run container.simg view cat_standalone_smooth.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo copy a batch file to your computer, use the \u003ccode\u003ecopy\u003c/code\u003e subcommand and specify destination path as an additional argument:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run container.simg copy cat_standalone_smooth.txt $HOME\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMake sure that the specified path is mounted to the container (more information on this can be found below) and that you have write access to this path!\u003c/p\u003e\n\u003cp\u003eTo copy all available batch files, use the \u003ccode\u003eall\u003c/code\u003e argument:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run container.simg copy all $HOME\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CAT:\u003c/h2\u003e\u003ca id=\"user-content-running-cat\" class=\"anchor\" aria-label=\"Permalink: Running CAT:\" href=\"#running-cat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the CAT analysis with the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv \u0026lt;container\u0026gt; \u0026lt;batch file\u0026gt; \u0026lt;arguments\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo use a default batch file, use one of the files included in the container (\u003ccode\u003e/batch\u003c/code\u003e), for instance:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b /batch/cat_standalone_segment.txt T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo use your own, customised batch file, simply specify its path, for instance:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b $HOME/cat_standalone_segment.txt T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBind paths:\u003c/h2\u003e\u003ca id=\"user-content-bind-paths\" class=\"anchor\" aria-label=\"Permalink: Bind paths:\" href=\"#bind-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease note that most of the host files remain inaccessible from within the container. By default the following directories are mounted within the container: \u003ccode\u003e$HOME\u003c/code\u003e, \u003ccode\u003e/tmp\u003c/code\u003e, \u003ccode\u003e/proc\u003c/code\u003e, \u003ccode\u003e/sys\u003c/code\u003e, \u003ccode\u003e/dev\u003c/code\u003e, and \u003ccode\u003e$PWD\u003c/code\u003e (see the \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/bind_paths_and_mounts.html#system-defined-bind-paths\" rel=\"nofollow\"\u003eSingularity documentation\u003c/a\u003e for more details).\u003c/p\u003e\n\u003cp\u003eIf you want the container to be able to access other locations, specify a bind path of your choice. For instance, to make the contents of the \u003ccode\u003e/data\u003c/code\u003e folder on your computer available in the \u003ccode\u003e/mnt\u003c/code\u003e folder inside the container, specify the mount point in the following way:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv --bind /data:/mnt container.simg -b /batch/cat_standalone_segment.txt /mnt/T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples:\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples:\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCAT12 segmentation batch:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b cat_standalone_segment.txt T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCAT12 simple processing batch:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b cat_standalone_simple.txt T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCAT12 resample \u0026amp; smooth batch:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b cat_standalone_resample.txt -a1 \"12\" -a2 \"1\" lh.thickness.T1\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCAT12 volume smoothing batch:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b cat_standalone_smooth.txt -a1 \"[6 6 6]\" -a2 \"\u0027s6\u0027\" T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues:\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues:\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eParallelization with \u003ccode\u003ecat_parallelize.sh\u003c/code\u003e is not implemented yet.\u003c/li\u003e\n\u003cli\u003eLongitudinal segmentation with \u003ccode\u003ecat_standalone_segment_long.txt\u003c/code\u003e is not tested yet.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact information:\u003c/h2\u003e\u003ca id=\"user-content-contact-information\" class=\"anchor\" aria-label=\"Permalink: Contact information:\" href=\"#contact-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny problems or concerns regarding this container should be reported to Malgorzata Wierzba (\u003ca href=\"mailto:m.wierzba@fz-juelich.de\"\u003em.wierzba@fz-juelich.de\u003c/a\u003e) or Michael Hanke (\u003ca href=\"mailto:m.hanke@fz-juelich.de\"\u003em.hanke@fz-juelich.de\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements:\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements:\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe CAT toolbox is developed by Christian Gaser and Robert Dahnke (Jena University Hospital, Departments of Psychiatry and Neurology) and is free but copyright software, distributed under the terms of the GNU General Public Licence.\u003c/p\u003e\n\u003cp\u003eThe SPM software is developed by the Wellcome Trust Centre for Neuroimaging and is free but copyright software, distributed under the terms of the GNU General Public Licence.\u003c/p\u003e\n\u003cp\u003eMATLAB Compiler Runtime is developed by the The MathWorks, Inc. and is subject to the MATLAB Runtime licence.\u003c/p\u003e\n", + "stargazers_count": 7, + "subscribers_count": 4, + "topics": [], + "updated_at": 1708945597.0 }, { "data_format": 2, - "description": "Keras-based deep learning framework for particle physics within the KM3NeT neutrino telescope project", + "description": "A hello world example for a scientific filesystem", "filenames": [ "Singularity" ], - "full_name": "KM3NeT/OrcaNet", + "full_name": "sci-f/hello-world.scif", "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe Scientific Filesystem\u003c/h1\u003e\u003ca id=\"user-content-the-scientific-filesystem\" class=\"anchor\" aria-label=\"Permalink: The Scientific Filesystem\" href=\"#the-scientific-filesystem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHello World! This is the getting started example to build a Scentific Filesystem.\nFor maximum reproducibility, we will do this inside a linux container, both Docker\nand Singularity.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/458\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d1c6e03e4e6b7f0d3294f7ba582006fbfce7363c19a608c33571ec04891ac38/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://img.shields.io/badge/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://img.shields.io/badge/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRead more about the \u003ca href=\"https://www.github.com/sci-f/sci-f.github.io/spec\"\u003especification here\u003c/a\u003e\nRead more about the \u003ca href=\"https://www.github.com/sci-f/sci-f.github.io/specification#recipe\"\u003edefinition file\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe definition file \u003ca href=\"hello-world.scif\"\u003ehello-world.scif\u003c/a\u003e in this repository is the\ninstruction that will be used to build the SCIF. Whether we are inside Docker or\nSingularity, we install the scif client as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand you could also install from Github if you want a development version:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/vsoch/scif.git\ncd scif \u0026amp;\u0026amp; python setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe scif is then installed as follows (with root)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escif install hello-world.scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor the instructions below, we will interact with the scif from inside the containers. If you want examples for interaction from outside of each container, see the \u003ca href=\"https://sci-f.github.io/tutorial-quick-start\" rel=\"nofollow\"\u003eofficial documentation quick start\u003c/a\u003e that have more details. If you want to see the Docker and Singularity commands side by side, see the \u003ca href=\"https://sci-f.github.io/tutorial-really-quick-start\" rel=\"nofollow\"\u003ereally quick start\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst build your container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t vanessa/hello-world-scif .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can I do?\u003c/h2\u003e\u003ca id=\"user-content-what-can-i-do\" class=\"anchor\" aria-label=\"Permalink: What can I do?\" href=\"#what-can-i-do\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t know anything, you might execute the container. And then you discover the scif entrypoint to guide you!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run hello-world-scif\n\nScientific Filesystem [v0.0.4]\nusage: scif [-h] [--debug] [--quiet] [--writable]\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n ...\n\nscientific filesystem tools\n\noptional arguments:\n -h, --help show this help message and exit\n --debug use verbose logging to debug.\n --quiet suppress print output\n --writable, -w for relevant commands, if writable SCIF is needed\n\nactions:\n actions for Scientific Filesystem\n\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n scif actions\n version show software version\n pyshell Interactive python shell to scientific filesystem\n shell shell to interact with scientific filesystem\n preview preview changes to a filesytem\n help look at help for an app, if it exists.\n install install a recipe on the filesystem\n inspect inspect an attribute for a scif installation\n run entrypoint to run a scientific filesystem\n apps list apps installed\n dump dump recipe\n exec execute a command to a scientific filesystem\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApps\u003c/h2\u003e\u003ca id=\"user-content-apps\" class=\"anchor\" aria-label=\"Permalink: Apps\" href=\"#apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList apps\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif apps\nSCIF [app] [root]\n1 hello-world\t/scif/apps/hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet help for hello world\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif help hello-world\nThis is an example \"Hello World\" application. You can install it to a\nScientific Filesystem (scif) with the command:\nscif install hello-world.scif\nif you need to install scif:\npip install scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \"hello-world\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif run hello-world\n[hello-world] executing /bin/bash /scif/apps/hello-world/scif/runscript\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExec\u003c/h2\u003e\u003ca id=\"user-content-exec\" class=\"anchor\" aria-label=\"Permalink: Exec\" href=\"#exec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecute a command in context of the app \"hello world.\" Notice how we replace the typical \u003ccode\u003e$\u003c/code\u003e for the environment variable with \u003ccode\u003e[e]\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif exec hello-world ls [e]SCIF_APPROOT\n[hello-world] executing /bin/ls $SCIF_APPROOT\nbin\nlib\nscif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInspect\u003c/h2\u003e\u003ca id=\"user-content-inspect\" class=\"anchor\" aria-label=\"Permalink: Inspect\" href=\"#inspect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInspect the hello-world app\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif inspect hello-world \n{\n \"hello-world\": {\n \"apprun\": [\n \"/bin/bash hello-world.sh\"\n ],\n \"appinstall\": [\n \"echo \\\"echo \u0027Hello World!\u0027\\\" \u0026gt;\u0026gt; $SCIF_APPBIN/hello-world.sh\",\n \"chmod u+x $SCIF_APPBIN/hello-world.sh\"\n ],\n \"appenv\": [\n \"THEBESTAPP=$SCIF_APPNAME\",\n \"export THEBESTAPP\"\n ],\n \"applabels\": [\n \"MAINTAINER Vanessasaur\",\n \"VERSION 1.0\"\n ],\n \"apphelp\": [\n \"This is an example \\\"Hello World\\\" application. You can install it to a\",\n \"Scientific Filesystem (scif) with the command:\",\n \"scif install hello-world.scif\",\n \"if you need to install scif:\",\n \"pip install scif\"\n ]\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShell\u003c/h2\u003e\u003ca id=\"user-content-shell\" class=\"anchor\" aria-label=\"Permalink: Shell\" href=\"#shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInteractively shell into the container in context of the app \"hello-world\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it vanessa/hello-world-scif shell hello-world\n[hello-world] executing /bin/bash \nroot@ddf1ba27fd68:/scif/apps/hello-world# env | grep SCIF_\nSCIF_APPMETA=/scif/apps/hello-world/scif\nSCIF_DATA=/scif/data\nSCIF_APPHELP_hello_world=/scif/apps/hello-world/scif/runscript.help\nSCIF_APPDATA=/scif/data/hello-world\nTHEBESTAPP=$SCIF_APPNAME\nSCIF_APPENV=/scif/apps/hello-world/scif/environment.sh\nSCIF_APPROOT=/scif/apps/hello-world\nSCIF_APPRECIPE_hello_world=/scif/apps/hello-world/scif/hello-world.scif\nSCIF_APPDATA_hello_world=/scif/data/hello-world\nSCIF_APPLABELS=/scif/apps/hello-world/scif/labels.json\nSCIF_APPRUN=/scif/apps/hello-world/scif/runscript\nSCIF_APPENV_hello_world=/scif/apps/hello-world/scif/environment.sh\nSCIF_APPRUN_hello_world=/scif/apps/hello-world/scif/runscript\nSCIF_APPLABELS_hello_world=/scif/apps/hello-world/scif/labels.json\nSCIF_APPHELP=/scif/apps/hello-world/scif/runscript.help\nSCIF_APPLIB_hello_world=/scif/apps/hello-world/lib\nSCIF_APPS=/scif/apps\nSCIF_APPMETA_hello_world=/scif/apps/hello-world/scif\nSCIF_APPNAME_hello_world=hello-world\nSCIF_APPBIN_hello_world=/scif/apps/hello-world/bin\nSCIF_APPLIB=/scif/apps/hello-world/lib\nSCIF_APPRECIPE=/scif/apps/hello-world/scif/hello-world.scif\nSCIF_MESSAGELEVEL=INFO\nSCIF_APPBIN=/scif/apps/hello-world/bin\nSCIF_APPNAME=hello-world\nSCIF_APPROOT_hello_world=/scif/apps/hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePyshell\u003c/h2\u003e\u003ca id=\"user-content-pyshell\" class=\"anchor\" aria-label=\"Permalink: Pyshell\" href=\"#pyshell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart an interactive python shell to interact with the python client\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it vanessa/hello-world-scif pyshell\nFound configurations for 1 scif apps\nhello-world\n[scif] /scif hello-world\nPython 3.6.3 |Anaconda, Inc.| (default, Oct 13 2017, 12:02:49) \nType \u0027copyright\u0027, \u0027credits\u0027 or \u0027license\u0027 for more information\nIPython 6.1.0 -- An enhanced Interactive Python. Type \u0027?\u0027 for help.\n\nIn [1]: \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat\u0027s it for Docker! Next we review Singularity. Or just see the commands \u003ca href=\"https://vsoch.github.io/scif/tutorial-really-quick-start\" rel=\"nofollow\"\u003eside by side\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAgain, first build your container!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build hello-world Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can I do?\u003c/h2\u003e\u003ca id=\"user-content-what-can-i-do-1\" class=\"anchor\" aria-label=\"Permalink: What can I do?\" href=\"#what-can-i-do-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t know anything, you might execute the container. And then you discover the scif entrypoint to guide you!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world \n\nScientific Filesystem [v0.0.4]\nusage: scif [-h] [--debug] [--quiet] [--writable]\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n ...\n\nscientific filesystem tools\n\noptional arguments:\n -h, --help show this help message and exit\n --debug use verbose logging to debug.\n --quiet suppress print output\n --writable, -w for relevant commands, if writable SCIF is needed\n\nactions:\n actions for Scientific Filesystem\n\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n scif actions\n version show software version\n pyshell Interactive python shell to scientific filesystem\n shell shell to interact with scientific filesystem\n preview preview changes to a filesytem\n help look at help for an app, if it exists.\n install install a recipe on the filesystem\n inspect inspect an attribute for a scif installation\n run entrypoint to run a scientific filesystem\n apps list apps installed\n dump dump recipe\n exec execute a command to a scientific filesystem\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApps\u003c/h2\u003e\u003ca id=\"user-content-apps-1\" class=\"anchor\" aria-label=\"Permalink: Apps\" href=\"#apps-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList apps\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world apps\nSCIF [app] [root]\n1 hello-world\t/scif/apps/hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help-1\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet help for hello world\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world help hello-world\nThis is an example \"Hello World\" application. You can install it to a\nScientific Filesystem (scif) with the command:\nscif install hello-world.scif\nif you need to install scif:\npip install scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run-1\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \"hello-world\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world run hello-world\n[hello-world] executing /bin/bash /scif/apps/hello-world/scif/runscript\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExec\u003c/h2\u003e\u003ca id=\"user-content-exec-1\" class=\"anchor\" aria-label=\"Permalink: Exec\" href=\"#exec-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecute a command in context of the app \"hello world.\" Notice how we replace the typical \u003ccode\u003e$\u003c/code\u003e for the environment variable with \u003ccode\u003e[e]\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world exec hello-world ls [e]SCIF_APPROOT\n[hello-world] executing /bin/ls $SCIF_APPROOT\nbin\nlib\nscif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInspect\u003c/h2\u003e\u003ca id=\"user-content-inspect-1\" class=\"anchor\" aria-label=\"Permalink: Inspect\" href=\"#inspect-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInspect the hello-world app\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world inspect hello-world \n{\n \"hello-world\": {\n \"apprun\": [\n \"/bin/bash hello-world.sh\"\n ],\n \"appinstall\": [\n \"echo \\\"echo \u0027Hello World!\u0027\\\" \u0026gt;\u0026gt; $SCIF_APPBIN/hello-world.sh\",\n \"chmod u+x $SCIF_APPBIN/hello-world.sh\"\n ],\n \"appenv\": [\n \"THEBESTAPP=$SCIF_APPNAME\",\n \"export THEBESTAPP\"\n ],\n \"applabels\": [\n \"MAINTAINER Vanessasaur\",\n \"VERSION 1.0\"\n ],\n \"apphelp\": [\n \"This is an example \\\"Hello World\\\" application. You can install it to a\",\n \"Scientific Filesystem (scif) with the command:\",\n \"scif install hello-world.scif\",\n \"if you need to install scif:\",\n \"pip install scif\"\n ]\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShell\u003c/h2\u003e\u003ca id=\"user-content-shell-1\" class=\"anchor\" aria-label=\"Permalink: Shell\" href=\"#shell-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInteractively shell into the container in context of the app \"hello-world\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./hello-world shell hello-world\n[hello-world] executing /bin/bash \nvanessa@vanessa-ThinkPad-T460s:/scif/apps/hello-world$ env | grep SCIF_\nSCIF_APPMETA=/scif/apps/hello-world/scif\nSCIF_DATA=/scif/data\nSCIF_APPHELP_hello_world=/scif/apps/hello-world/scif/runscript.help\nSCIF_APPDATA=/scif/data/hello-world\nTHEBESTAPP=$SCIF_APPNAME\nSCIF_APPENV=/scif/apps/hello-world/scif/environment.sh\nSCIF_APPROOT=/scif/apps/hello-world\nSCIF_APPRECIPE_hello_world=/scif/apps/hello-world/scif/hello-world.scif\nSCIF_APPDATA_hello_world=/scif/data/hello-world\nSCIF_APPLABELS=/scif/apps/hello-world/scif/labels.json\nSCIF_APPRUN=/scif/apps/hello-world/scif/runscript\nSCIF_APPENV_hello_world=/scif/apps/hello-world/scif/environment.sh\nSCIF_APPRUN_hello_world=/scif/apps/hello-world/scif/runscript\nSCIF_APPLABELS_hello_world=/scif/apps/hello-world/scif/labels.json\nSCIF_APPHELP=/scif/apps/hello-world/scif/runscript.help\nSCIF_APPLIB_hello_world=/scif/apps/hello-world/lib\nSCIF_APPS=/scif/apps\nSCIF_APPMETA_hello_world=/scif/apps/hello-world/scif\nSCIF_APPNAME_hello_world=hello-world\nSCIF_APPBIN_hello_world=/scif/apps/hello-world/bin\nSCIF_APPLIB=/scif/apps/hello-world/lib\nSCIF_APPRECIPE=/scif/apps/hello-world/scif/hello-world.scif\nSCIF_MESSAGELEVEL=INFO\nSCIF_APPBIN=/scif/apps/hello-world/bin\nSCIF_APPNAME=hello-world\nSCIF_APPROOT_hello_world=/scif/apps/hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePyshell\u003c/h2\u003e\u003ca id=\"user-content-pyshell-1\" class=\"anchor\" aria-label=\"Permalink: Pyshell\" href=\"#pyshell-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart an interactive python shell to interact with the python client\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./hello-world pyshell\nFound configurations for 1 scif apps\nhello-world\n[scif] /scif hello-world\nPython 3.6.2 |Anaconda, Inc.| (default, Sep 22 2017, 02:03:08) \n[GCC 7.2.0] on linux\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n(InteractiveConsole)\n\u0026gt;\u0026gt;\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 7, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [], - "updated_at": 1645535863.0 + "updated_at": 1721401789.0 }, { "data_format": 2, @@ -35959,344 +36114,490 @@ var data = }, { "data_format": 2, - "description": "A hello world example for a scientific filesystem", + "description": "Keras-based deep learning framework for particle physics within the KM3NeT neutrino telescope project", "filenames": [ "Singularity" ], - "full_name": "sci-f/hello-world.scif", + "full_name": "KM3NeT/OrcaNet", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe Scientific Filesystem\u003c/h1\u003e\u003ca id=\"user-content-the-scientific-filesystem\" class=\"anchor\" aria-label=\"Permalink: The Scientific Filesystem\" href=\"#the-scientific-filesystem\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHello World! This is the getting started example to build a Scentific Filesystem.\nFor maximum reproducibility, we will do this inside a linux container, both Docker\nand Singularity.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/458\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d1c6e03e4e6b7f0d3294f7ba582006fbfce7363c19a608c33571ec04891ac38/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://img.shields.io/badge/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://img.shields.io/badge/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRead more about the \u003ca href=\"https://www.github.com/sci-f/sci-f.github.io/spec\"\u003especification here\u003c/a\u003e\nRead more about the \u003ca href=\"https://www.github.com/sci-f/sci-f.github.io/specification#recipe\"\u003edefinition file\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe definition file \u003ca href=\"hello-world.scif\"\u003ehello-world.scif\u003c/a\u003e in this repository is the\ninstruction that will be used to build the SCIF. Whether we are inside Docker or\nSingularity, we install the scif client as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand you could also install from Github if you want a development version:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://www.github.com/vsoch/scif.git\ncd scif \u0026amp;\u0026amp; python setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe scif is then installed as follows (with root)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escif install hello-world.scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor the instructions below, we will interact with the scif from inside the containers. If you want examples for interaction from outside of each container, see the \u003ca href=\"https://sci-f.github.io/tutorial-quick-start\" rel=\"nofollow\"\u003eofficial documentation quick start\u003c/a\u003e that have more details. If you want to see the Docker and Singularity commands side by side, see the \u003ca href=\"https://sci-f.github.io/tutorial-really-quick-start\" rel=\"nofollow\"\u003ereally quick start\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst build your container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t vanessa/hello-world-scif .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can I do?\u003c/h2\u003e\u003ca id=\"user-content-what-can-i-do\" class=\"anchor\" aria-label=\"Permalink: What can I do?\" href=\"#what-can-i-do\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t know anything, you might execute the container. And then you discover the scif entrypoint to guide you!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run hello-world-scif\n\nScientific Filesystem [v0.0.4]\nusage: scif [-h] [--debug] [--quiet] [--writable]\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n ...\n\nscientific filesystem tools\n\noptional arguments:\n -h, --help show this help message and exit\n --debug use verbose logging to debug.\n --quiet suppress print output\n --writable, -w for relevant commands, if writable SCIF is needed\n\nactions:\n actions for Scientific Filesystem\n\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n scif actions\n version show software version\n pyshell Interactive python shell to scientific filesystem\n shell shell to interact with scientific filesystem\n preview preview changes to a filesytem\n help look at help for an app, if it exists.\n install install a recipe on the filesystem\n inspect inspect an attribute for a scif installation\n run entrypoint to run a scientific filesystem\n apps list apps installed\n dump dump recipe\n exec execute a command to a scientific filesystem\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApps\u003c/h2\u003e\u003ca id=\"user-content-apps\" class=\"anchor\" aria-label=\"Permalink: Apps\" href=\"#apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList apps\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif apps\nSCIF [app] [root]\n1 hello-world\t/scif/apps/hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet help for hello world\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif help hello-world\nThis is an example \"Hello World\" application. You can install it to a\nScientific Filesystem (scif) with the command:\nscif install hello-world.scif\nif you need to install scif:\npip install scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \"hello-world\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif run hello-world\n[hello-world] executing /bin/bash /scif/apps/hello-world/scif/runscript\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExec\u003c/h2\u003e\u003ca id=\"user-content-exec\" class=\"anchor\" aria-label=\"Permalink: Exec\" href=\"#exec\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecute a command in context of the app \"hello world.\" Notice how we replace the typical \u003ccode\u003e$\u003c/code\u003e for the environment variable with \u003ccode\u003e[e]\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif exec hello-world ls [e]SCIF_APPROOT\n[hello-world] executing /bin/ls $SCIF_APPROOT\nbin\nlib\nscif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInspect\u003c/h2\u003e\u003ca id=\"user-content-inspect\" class=\"anchor\" aria-label=\"Permalink: Inspect\" href=\"#inspect\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInspect the hello-world app\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/hello-world-scif inspect hello-world \n{\n \"hello-world\": {\n \"apprun\": [\n \"/bin/bash hello-world.sh\"\n ],\n \"appinstall\": [\n \"echo \\\"echo \u0027Hello World!\u0027\\\" \u0026gt;\u0026gt; $SCIF_APPBIN/hello-world.sh\",\n \"chmod u+x $SCIF_APPBIN/hello-world.sh\"\n ],\n \"appenv\": [\n \"THEBESTAPP=$SCIF_APPNAME\",\n \"export THEBESTAPP\"\n ],\n \"applabels\": [\n \"MAINTAINER Vanessasaur\",\n \"VERSION 1.0\"\n ],\n \"apphelp\": [\n \"This is an example \\\"Hello World\\\" application. You can install it to a\",\n \"Scientific Filesystem (scif) with the command:\",\n \"scif install hello-world.scif\",\n \"if you need to install scif:\",\n \"pip install scif\"\n ]\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShell\u003c/h2\u003e\u003ca id=\"user-content-shell\" class=\"anchor\" aria-label=\"Permalink: Shell\" href=\"#shell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInteractively shell into the container in context of the app \"hello-world\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it vanessa/hello-world-scif shell hello-world\n[hello-world] executing /bin/bash \nroot@ddf1ba27fd68:/scif/apps/hello-world# env | grep SCIF_\nSCIF_APPMETA=/scif/apps/hello-world/scif\nSCIF_DATA=/scif/data\nSCIF_APPHELP_hello_world=/scif/apps/hello-world/scif/runscript.help\nSCIF_APPDATA=/scif/data/hello-world\nTHEBESTAPP=$SCIF_APPNAME\nSCIF_APPENV=/scif/apps/hello-world/scif/environment.sh\nSCIF_APPROOT=/scif/apps/hello-world\nSCIF_APPRECIPE_hello_world=/scif/apps/hello-world/scif/hello-world.scif\nSCIF_APPDATA_hello_world=/scif/data/hello-world\nSCIF_APPLABELS=/scif/apps/hello-world/scif/labels.json\nSCIF_APPRUN=/scif/apps/hello-world/scif/runscript\nSCIF_APPENV_hello_world=/scif/apps/hello-world/scif/environment.sh\nSCIF_APPRUN_hello_world=/scif/apps/hello-world/scif/runscript\nSCIF_APPLABELS_hello_world=/scif/apps/hello-world/scif/labels.json\nSCIF_APPHELP=/scif/apps/hello-world/scif/runscript.help\nSCIF_APPLIB_hello_world=/scif/apps/hello-world/lib\nSCIF_APPS=/scif/apps\nSCIF_APPMETA_hello_world=/scif/apps/hello-world/scif\nSCIF_APPNAME_hello_world=hello-world\nSCIF_APPBIN_hello_world=/scif/apps/hello-world/bin\nSCIF_APPLIB=/scif/apps/hello-world/lib\nSCIF_APPRECIPE=/scif/apps/hello-world/scif/hello-world.scif\nSCIF_MESSAGELEVEL=INFO\nSCIF_APPBIN=/scif/apps/hello-world/bin\nSCIF_APPNAME=hello-world\nSCIF_APPROOT_hello_world=/scif/apps/hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePyshell\u003c/h2\u003e\u003ca id=\"user-content-pyshell\" class=\"anchor\" aria-label=\"Permalink: Pyshell\" href=\"#pyshell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart an interactive python shell to interact with the python client\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it vanessa/hello-world-scif pyshell\nFound configurations for 1 scif apps\nhello-world\n[scif] /scif hello-world\nPython 3.6.3 |Anaconda, Inc.| (default, Oct 13 2017, 12:02:49) \nType \u0027copyright\u0027, \u0027credits\u0027 or \u0027license\u0027 for more information\nIPython 6.1.0 -- An enhanced Interactive Python. Type \u0027?\u0027 for help.\n\nIn [1]: \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThat\u0027s it for Docker! Next we review Singularity. Or just see the commands \u003ca href=\"https://vsoch.github.io/scif/tutorial-really-quick-start\" rel=\"nofollow\"\u003eside by side\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAgain, first build your container!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build hello-world Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can I do?\u003c/h2\u003e\u003ca id=\"user-content-what-can-i-do-1\" class=\"anchor\" aria-label=\"Permalink: What can I do?\" href=\"#what-can-i-do-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t know anything, you might execute the container. And then you discover the scif entrypoint to guide you!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world \n\nScientific Filesystem [v0.0.4]\nusage: scif [-h] [--debug] [--quiet] [--writable]\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n ...\n\nscientific filesystem tools\n\noptional arguments:\n -h, --help show this help message and exit\n --debug use verbose logging to debug.\n --quiet suppress print output\n --writable, -w for relevant commands, if writable SCIF is needed\n\nactions:\n actions for Scientific Filesystem\n\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n scif actions\n version show software version\n pyshell Interactive python shell to scientific filesystem\n shell shell to interact with scientific filesystem\n preview preview changes to a filesytem\n help look at help for an app, if it exists.\n install install a recipe on the filesystem\n inspect inspect an attribute for a scif installation\n run entrypoint to run a scientific filesystem\n apps list apps installed\n dump dump recipe\n exec execute a command to a scientific filesystem\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApps\u003c/h2\u003e\u003ca id=\"user-content-apps-1\" class=\"anchor\" aria-label=\"Permalink: Apps\" href=\"#apps-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList apps\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world apps\nSCIF [app] [root]\n1 hello-world\t/scif/apps/hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help-1\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet help for hello world\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world help hello-world\nThis is an example \"Hello World\" application. You can install it to a\nScientific Filesystem (scif) with the command:\nscif install hello-world.scif\nif you need to install scif:\npip install scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run-1\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \"hello-world\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world run hello-world\n[hello-world] executing /bin/bash /scif/apps/hello-world/scif/runscript\nHello World!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExec\u003c/h2\u003e\u003ca id=\"user-content-exec-1\" class=\"anchor\" aria-label=\"Permalink: Exec\" href=\"#exec-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecute a command in context of the app \"hello world.\" Notice how we replace the typical \u003ccode\u003e$\u003c/code\u003e for the environment variable with \u003ccode\u003e[e]\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world exec hello-world ls [e]SCIF_APPROOT\n[hello-world] executing /bin/ls $SCIF_APPROOT\nbin\nlib\nscif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInspect\u003c/h2\u003e\u003ca id=\"user-content-inspect-1\" class=\"anchor\" aria-label=\"Permalink: Inspect\" href=\"#inspect-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInspect the hello-world app\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./hello-world inspect hello-world \n{\n \"hello-world\": {\n \"apprun\": [\n \"/bin/bash hello-world.sh\"\n ],\n \"appinstall\": [\n \"echo \\\"echo \u0027Hello World!\u0027\\\" \u0026gt;\u0026gt; $SCIF_APPBIN/hello-world.sh\",\n \"chmod u+x $SCIF_APPBIN/hello-world.sh\"\n ],\n \"appenv\": [\n \"THEBESTAPP=$SCIF_APPNAME\",\n \"export THEBESTAPP\"\n ],\n \"applabels\": [\n \"MAINTAINER Vanessasaur\",\n \"VERSION 1.0\"\n ],\n \"apphelp\": [\n \"This is an example \\\"Hello World\\\" application. You can install it to a\",\n \"Scientific Filesystem (scif) with the command:\",\n \"scif install hello-world.scif\",\n \"if you need to install scif:\",\n \"pip install scif\"\n ]\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShell\u003c/h2\u003e\u003ca id=\"user-content-shell-1\" class=\"anchor\" aria-label=\"Permalink: Shell\" href=\"#shell-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInteractively shell into the container in context of the app \"hello-world\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./hello-world shell hello-world\n[hello-world] executing /bin/bash \nvanessa@vanessa-ThinkPad-T460s:/scif/apps/hello-world$ env | grep SCIF_\nSCIF_APPMETA=/scif/apps/hello-world/scif\nSCIF_DATA=/scif/data\nSCIF_APPHELP_hello_world=/scif/apps/hello-world/scif/runscript.help\nSCIF_APPDATA=/scif/data/hello-world\nTHEBESTAPP=$SCIF_APPNAME\nSCIF_APPENV=/scif/apps/hello-world/scif/environment.sh\nSCIF_APPROOT=/scif/apps/hello-world\nSCIF_APPRECIPE_hello_world=/scif/apps/hello-world/scif/hello-world.scif\nSCIF_APPDATA_hello_world=/scif/data/hello-world\nSCIF_APPLABELS=/scif/apps/hello-world/scif/labels.json\nSCIF_APPRUN=/scif/apps/hello-world/scif/runscript\nSCIF_APPENV_hello_world=/scif/apps/hello-world/scif/environment.sh\nSCIF_APPRUN_hello_world=/scif/apps/hello-world/scif/runscript\nSCIF_APPLABELS_hello_world=/scif/apps/hello-world/scif/labels.json\nSCIF_APPHELP=/scif/apps/hello-world/scif/runscript.help\nSCIF_APPLIB_hello_world=/scif/apps/hello-world/lib\nSCIF_APPS=/scif/apps\nSCIF_APPMETA_hello_world=/scif/apps/hello-world/scif\nSCIF_APPNAME_hello_world=hello-world\nSCIF_APPBIN_hello_world=/scif/apps/hello-world/bin\nSCIF_APPLIB=/scif/apps/hello-world/lib\nSCIF_APPRECIPE=/scif/apps/hello-world/scif/hello-world.scif\nSCIF_MESSAGELEVEL=INFO\nSCIF_APPBIN=/scif/apps/hello-world/bin\nSCIF_APPNAME=hello-world\nSCIF_APPROOT_hello_world=/scif/apps/hello-world\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePyshell\u003c/h2\u003e\u003ca id=\"user-content-pyshell-1\" class=\"anchor\" aria-label=\"Permalink: Pyshell\" href=\"#pyshell-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart an interactive python shell to interact with the python client\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./hello-world pyshell\nFound configurations for 1 scif apps\nhello-world\n[scif] /scif hello-world\nPython 3.6.2 |Anaconda, Inc.| (default, Sep 22 2017, 02:03:08) \n[GCC 7.2.0] on linux\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n(InteractiveConsole)\n\u0026gt;\u0026gt;\u0026gt; \n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 7, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1721401789.0 + "updated_at": 1645535863.0 }, { "data_format": 2, - "description": "This container allows to run the standalone, compiled version of the Computational Anatomy Toolbox (CAT), which is an extension to SPM software.", + "description": "An oil land-spill and overland flow simulator for pipeline rupture events", "filenames": [ - "Singularity" + "Singularityfiles/Singularity.v1.0.dev1", + "Singularityfiles/Singularity.v0.1.trusty", + "Singularityfiles/Singularity.v1.0.dev3", + "Singularityfiles/Singularity.v1.0.dev4", + "Singularityfiles/Singularity.v1.0", + "Singularityfiles/Singularity.v1.0.dev2", + "Singularityfiles/Singularity.v0.1.bionic" ], - "full_name": "m-wierzba/cat-container", - "latest_release": "v0.1.0", - "readme": "\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.6021002\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/edbf700e861803bfb6e4126fe0f88968646fd1947fd4c26bb753e891623b5bcf/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e363032313030322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.6021002.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCAT container\u003c/h1\u003e\u003ca id=\"user-content-cat-container\" class=\"anchor\" aria-label=\"Permalink: CAT container\" href=\"#cat-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis container allows to run the standalone, compiled version of the \u003ca href=\"http://www.neuro.uni-jena.de/cat/\" rel=\"nofollow\"\u003eComputational Anatomy Toolbox (CAT)\u003c/a\u003e, which is an extension to \u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/software/\" rel=\"nofollow\"\u003eSPM\u003c/a\u003e software. Using the container does not require the availability of a MATLAB licence.\u003c/p\u003e\n\u003cp\u003eThe container includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://uk.mathworks.com/products/compiler/matlab-runtime.html\" rel=\"nofollow\"\u003eMATLAB Compiler Runtime\u003c/a\u003e (R2017b, 9.3)\u003c/li\u003e\n\u003cli\u003eStandalone version of \u003ca href=\"https://www.fil.ion.ucl.ac.uk/spm/software/\" rel=\"nofollow\"\u003eSPM\u003c/a\u003e software (SPM12, r7771)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.neuro.uni-jena.de/cat/\" rel=\"nofollow\"\u003eComputational Anatomy Toolbox\u003c/a\u003e (CAT12.7 r1743)\u003c/li\u003e\n\u003cli\u003eCAT interface scripts (\u003ccode\u003ecat_standalone.sh\u003c/code\u003e, \u003ccode\u003ecat_parallelize.sh\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more details on the exact version of the software used in this container, please refer to the recipe file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to build the container:\u003c/h2\u003e\u003ca id=\"user-content-how-to-build-the-container\" class=\"anchor\" aria-label=\"Permalink: How to build the container:\" href=\"#how-to-build-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExecute the built command with root privileges:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo singularity build container.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use:\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use:\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn principle this container allows you to perform the very same types of analysis that are possible with the standalone version of CAT. It is assumed that the user is familiar with the content of the batch files dedicated for the use with the standalone version of CAT (\u003ccode\u003ecat_standalone_segment.txt\u003c/code\u003e, \u003ccode\u003ecat_standalone_simple.txt\u003c/code\u003e, \u003ccode\u003ecat_standalone_resample.txt\u003c/code\u003e, \u003ccode\u003ecat_standalone_smooth.txt\u003c/code\u003e) and can modify their content according to his/her needs. For more details, please refer to the \u003ca href=\"http://www.neuro.uni-jena.de/cat12/CAT12-Manual.pdf\" rel=\"nofollow\"\u003eCAT12 documentation and manual\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAvailable batch files:\u003c/h2\u003e\u003ca id=\"user-content-available-batch-files\" class=\"anchor\" aria-label=\"Permalink: Available batch files:\" href=\"#available-batch-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe content of the batch files can be explored by using the \u003ccode\u003eview\u003c/code\u003e and \u003ccode\u003ecopy\u003c/code\u003e subcommands:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run \u0026lt;container\u0026gt; \u0026lt;subcommand\u0026gt; \u0026lt;batch file\u0026gt; \u0026lt;arguments\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo view a batch file, use the \u003ccode\u003eview\u003c/code\u003e subcommand:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run container.simg view cat_standalone_smooth.txt\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo copy a batch file to your computer, use the \u003ccode\u003ecopy\u003c/code\u003e subcommand and specify destination path as an additional argument:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run container.simg copy cat_standalone_smooth.txt $HOME\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMake sure that the specified path is mounted to the container (more information on this can be found below) and that you have write access to this path!\u003c/p\u003e\n\u003cp\u003eTo copy all available batch files, use the \u003ccode\u003eall\u003c/code\u003e argument:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run container.simg copy all $HOME\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CAT:\u003c/h2\u003e\u003ca id=\"user-content-running-cat\" class=\"anchor\" aria-label=\"Permalink: Running CAT:\" href=\"#running-cat\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the CAT analysis with the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv \u0026lt;container\u0026gt; \u0026lt;batch file\u0026gt; \u0026lt;arguments\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo use a default batch file, use one of the files included in the container (\u003ccode\u003e/batch\u003c/code\u003e), for instance:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b /batch/cat_standalone_segment.txt T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo use your own, customised batch file, simply specify its path, for instance:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b $HOME/cat_standalone_segment.txt T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBind paths:\u003c/h2\u003e\u003ca id=\"user-content-bind-paths\" class=\"anchor\" aria-label=\"Permalink: Bind paths:\" href=\"#bind-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease note that most of the host files remain inaccessible from within the container. By default the following directories are mounted within the container: \u003ccode\u003e$HOME\u003c/code\u003e, \u003ccode\u003e/tmp\u003c/code\u003e, \u003ccode\u003e/proc\u003c/code\u003e, \u003ccode\u003e/sys\u003c/code\u003e, \u003ccode\u003e/dev\u003c/code\u003e, and \u003ccode\u003e$PWD\u003c/code\u003e (see the \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/bind_paths_and_mounts.html#system-defined-bind-paths\" rel=\"nofollow\"\u003eSingularity documentation\u003c/a\u003e for more details).\u003c/p\u003e\n\u003cp\u003eIf you want the container to be able to access other locations, specify a bind path of your choice. For instance, to make the contents of the \u003ccode\u003e/data\u003c/code\u003e folder on your computer available in the \u003ccode\u003e/mnt\u003c/code\u003e folder inside the container, specify the mount point in the following way:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv --bind /data:/mnt container.simg -b /batch/cat_standalone_segment.txt /mnt/T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples:\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples:\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCAT12 segmentation batch:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b cat_standalone_segment.txt T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCAT12 simple processing batch:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b cat_standalone_simple.txt T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCAT12 resample \u0026amp; smooth batch:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b cat_standalone_resample.txt -a1 \"12\" -a2 \"1\" lh.thickness.T1\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCAT12 volume smoothing batch:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --cleanenv container.simg -b cat_standalone_smooth.txt -a1 \"[6 6 6]\" -a2 \"\u0027s6\u0027\" T1.nii\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues:\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues:\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eParallelization with \u003ccode\u003ecat_parallelize.sh\u003c/code\u003e is not implemented yet.\u003c/li\u003e\n\u003cli\u003eLongitudinal segmentation with \u003ccode\u003ecat_standalone_segment_long.txt\u003c/code\u003e is not tested yet.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact information:\u003c/h2\u003e\u003ca id=\"user-content-contact-information\" class=\"anchor\" aria-label=\"Permalink: Contact information:\" href=\"#contact-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny problems or concerns regarding this container should be reported to Malgorzata Wierzba (\u003ca href=\"mailto:m.wierzba@fz-juelich.de\"\u003em.wierzba@fz-juelich.de\u003c/a\u003e) or Michael Hanke (\u003ca href=\"mailto:m.hanke@fz-juelich.de\"\u003em.hanke@fz-juelich.de\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements:\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements:\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe CAT toolbox is developed by Christian Gaser and Robert Dahnke (Jena University Hospital, Departments of Psychiatry and Neurology) and is free but copyright software, distributed under the terms of the GNU General Public Licence.\u003c/p\u003e\n\u003cp\u003eThe SPM software is developed by the Wellcome Trust Centre for Neuroimaging and is free but copyright software, distributed under the terms of the GNU General Public Licence.\u003c/p\u003e\n\u003cp\u003eMATLAB Compiler Runtime is developed by the The MathWorks, Inc. and is subject to the MATLAB Runtime licence.\u003c/p\u003e\n", + "full_name": "barbagroup/geoclaw-landspill", + "latest_release": "v1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003egeoclaw-landspill\u003c/h1\u003e\u003ca id=\"user-content-geoclaw-landspill\" class=\"anchor\" aria-label=\"Permalink: geoclaw-landspill\" href=\"#geoclaw-landspill\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/barbagroup/geoclaw-landspill/raw/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/439fd5ffe2ac21b3daa03ac6474978163be089da5fbba2790769ae2275464e35/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d425344253230332d2d436c617573652d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/barbagroup/geoclaw-landspill\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5a59341a3dbba6f3029db40fa18a8c7ba130a7809dc594869f4d9f15720d8e5e/68747470733a2f2f696d672e736869656c64732e696f2f7472617669732f636f6d2f626172626167726f75702f67656f636c61772d6c616e647370696c6c2f6d61737465723f6c6162656c3d5472617669732532304349\" alt=\"Travis CI\" data-canonical-src=\"https://img.shields.io/travis/com/barbagroup/geoclaw-landspill/master?label=Travis%20CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/barbagroup/geoclaw-landspill/actions?query=workflow%3ACI\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e4b357df7c827c2c4d41600c20af8efb1e6caf363bafed67fdd6a396db96e9c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f776f726b666c6f772f7374617475732f626172626167726f75702f67656f636c61772d6c616e647370696c6c2f43492f6d61737465723f6c6162656c3d476974487562253230416374696f6e2532304349\" alt=\"GitHub Action CI\" data-canonical-src=\"https://img.shields.io/github/workflow/status/barbagroup/geoclaw-landspill/CI/master?label=GitHub%20Action%20CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://joss.theoj.org/papers/fb7b012799a70c9b4c55eb4bb0f36f97\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5686ff5ad93f9ae6d8a0131c3be362cfa95de346ce38737c702bc64c667c4ed6/68747470733a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f66623762303132373939613730633962346335356562346262306633366639372f7374617475732e737667\" alt=\"status\" data-canonical-src=\"https://joss.theoj.org/papers/fb7b012799a70c9b4c55eb4bb0f36f97/status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/barbagroup/geoclaw-landspill\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f617e0ad9c80e2b68577a2082878ba67e7e2db8bae3ea008da0a168ee816a5de/68747470733a2f2f616e61636f6e64612e6f72672f626172626167726f75702f67656f636c61772d6c616e647370696c6c2f6261646765732f696e7374616c6c65722f636f6e64612e737667\" alt=\"Conda\" data-canonical-src=\"https://anaconda.org/barbagroup/geoclaw-landspill/badges/installer/conda.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote: if looking for content of \u003ccode\u003egeoclaw-landspill-cases\u003c/code\u003e, please checkout tag\n\u003ccode\u003ev0.1\u003c/code\u003e. This repository has been converted to a fully working solver package.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003egeoclaw-landspill\u003c/em\u003e is a package for running oil overland flow simulations for\napplications in pipeline risk management. It includes a numerical solver and\nsome pre-/post-processing utilities.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./doc/sample.gif\"\u003e\u003cimg src=\"./doc/sample.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe numerical solver is a modified version of\n\u003ca href=\"http://www.clawpack.org/geoclaw.html\" rel=\"nofollow\"\u003eGeoClaw\u003c/a\u003e.\nGeoClaw solves full shallow-water equations. We added several new features and\nutilities to it and make it usable to simulate the overland flow from pipeline\nruptures. These features include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eadding point sources to mimic the rupture points\u003c/li\u003e\n\u003cli\u003eadding evaporation models\u003c/li\u003e\n\u003cli\u003eadding Darcy-Weisbach bottom friction models with land roughness\u003c/li\u003e\n\u003cli\u003eadding temperature-dependent viscosity\u003c/li\u003e\n\u003cli\u003erecording detail locations and time of oil flowing into in-land waterbodies\u003c/li\u003e\n\u003cli\u003edownloading topography and hydrology data automatically (the US only)\u003c/li\u003e\n\u003cli\u003egenerating CF-1.7 compliant NetCDF files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"doc/deps_install_tests.md\"\u003eDependencies, installation, and tests\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"doc/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"doc/configuration.md\"\u003eConfiguration file: \u003ccode\u003esetrun.py\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"cases/README.md\"\u003eExample cases\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"doc/container.md\"\u003eContainers: Docker and Singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe only maintain compatibility with Linux. Though using \u003ccode\u003epip\u003c/code\u003e or building from\nsource may still work in Mac OS or Windows (e.g., through WSL), we are not able\nto help with the installation issues on these two systems.\u003c/p\u003e\n\u003cp\u003eBeyond this quick start, to see more details, please refer to the\n\u003ca href=\"#documentation\"\u003edocumentation\u003c/a\u003e section.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Installation\u003c/h3\u003e\u003ca id=\"user-content-1-installation\" class=\"anchor\" aria-label=\"Permalink: 1. Installation\" href=\"#1-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe fast way to install \u003cem\u003egeoclaw-landspill\u003c/em\u003e is through\n\u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\u0027s \u003ccode\u003econda\u003c/code\u003e command. The following command\ncreates a conda environment (called \u003ccode\u003elandspill\u003c/code\u003e) and installs the package and\ndependencies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create \\\n -n landspill -c barbagroup -c conda-forge \\\n python=3.8 geoclaw-landspill\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen use \u003ccode\u003econda activate landspill\u003c/code\u003e or\n\u003ccode\u003esource \u0026lt;conda installation prefix\u0026gt;/bin/activate landspill\u003c/code\u003e to activate the\nenvironment. Type \u003ccode\u003egeoclaw-landspill --help\u003c/code\u003e in the terminal to see if\n\u003cem\u003egeoclaw-landspill\u003c/em\u003e is correctly installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Running an example case\u003c/h3\u003e\u003ca id=\"user-content-2-running-an-example-case\" class=\"anchor\" aria-label=\"Permalink: 2. Running an example case\" href=\"#2-running-an-example-case\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run an example case under the folder \u003ccode\u003ecases\u003c/code\u003e, users have to clone this\nrepository. We currently don\u0027t maintain another repository for cases. After\ncloning this repository, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ geoclaw-landspill run \u0026lt;path to an example case folder\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, to run \u003ccode\u003eutal-flat-maya\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ geoclaw-landspill run ./cases/utah-flat-maya\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsers can use environment variable \u003ccode\u003eOMP_NUM_THREADS\u003c/code\u003e to control how many CPU\nthreads the simulation should use for OpenMP parallelization.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Creating a CF-compliant NetCDF raster file\u003c/h3\u003e\u003ca id=\"user-content-3-creating-a-cf-compliant-netcdf-raster-file\" class=\"anchor\" aria-label=\"Permalink: 3. Creating a CF-compliant NetCDF raster file\" href=\"#3-creating-a-cf-compliant-netcdf-raster-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter a simulation is done, users can convert flow depth in raw simulation data\ninto a CF-compliant NetCDF raster file. For example,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ geoclaw-landspill createnc ./case/utah-flat-maya\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eReplace \u003ccode\u003e./cases/utah-flat-maya\u003c/code\u003e with the path to another desired case.\u003c/p\u003e\n\u003cp\u003eQGIS and ArcGIS should be able to read the resulting NetCDF raster file.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThird-party codes and licenses\u003c/h2\u003e\u003ca id=\"user-content-third-party-codes-and-licenses\" class=\"anchor\" aria-label=\"Permalink: Third-party codes and licenses\" href=\"#third-party-codes-and-licenses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eamrclaw: \u003ca href=\"https://github.com/clawpack/amrclaw\"\u003ehttps://github.com/clawpack/amrclaw\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/amrclaw/blob/ee85c1fe178ec319a8403503e779d3f8faf22840/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003egeoclaw: \u003ca href=\"https://github.com/clawpack/geoclaw\"\u003ehttps://github.com/clawpack/geoclaw\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/geoclaw/blob/3593cb1b418fd52739c186a8845a288037c8f575/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003epyclaw: \u003ca href=\"https://github.com/clawpack/pyclaw\"\u003ehttps://github.com/clawpack/pyclaw\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/pyclaw/blob/a85a01a5f20be1a18dde70b7bb37dc1cdcbd0b26/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eclawutil: \u003ca href=\"https://github.com/clawpack/clawutil\"\u003ehttps://github.com/clawpack/clawutil\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/clawutil/blob/116ffb792e889fbf0854d7ac599657039d7b1f3e/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eriemann: \u003ca href=\"https://github.com/clawpack/riemann\"\u003ehttps://github.com/clawpack/riemann\u003c/a\u003e\n(\u003ca href=\"https://github.com/clawpack/riemann/blob/597824c051d56fa0c8818e00d740867283329b24/LICENSE\"\u003eBSD 3-Clause License\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePi-Yueh Chuang: \u003ca href=\"mailto:pychuang@gwu.edu\"\u003epychuang@gwu.edu\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 7, - "subscribers_count": 4, - "topics": [], - "updated_at": 1708945597.0 + "subscribers_count": 6, + "topics": [ + "geoclaw", + "overland-flow", + "pipeline", + "shallow-water-equations", + "pipeline-ruptures", + "land-spill" + ], + "updated_at": 1720007939.0 }, { "data_format": 2, - "description": "Mitsuba implementation for \"Stratified Markov Chain Monte Carlo Light Transport\" (EG 2020)", + "description": "Singularity containers with common radio transient search software. ", "filenames": [ + "Singularity.cpu", + "Singularity.arm", + "Singularity.gpu", "Singularity" ], - "full_name": "beltegeuse/smcmc", + "full_name": "josephwkania/radio_transients", "latest_release": null, - "readme": "", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eradio_transients\u003c/h1\u003e\u003ca id=\"user-content-radio_transients\" class=\"anchor\" aria-label=\"Permalink: radio_transients\" href=\"#radio_transients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1c7f762866d2a99376791d539f3c4c9e7630d23f6ec96b4e37b395bbe20d0079/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6a6f73657068776b616e69612f726164696f5f7472616e7369656e74733f7374796c653d666c61742d737175617265\" alt=\"Issues\" data-canonical-src=\"https://img.shields.io/github/issues/josephwkania/radio_transients?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0b0d3c5ea06c15ed32753dc8be0042333d5437a1c96af2626e1a48c06340bcdf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a6f73657068776b616e69612f726164696f5f7472616e7369656e74733f7374796c653d666c61742d737175617265\" alt=\"Forks\" data-canonical-src=\"https://img.shields.io/github/forks/josephwkania/radio_transients?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/413328b90f7ce71af5f8a9088b05bd4fee37ab5a3672db4a7531050ee5a23fad/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6a6f73657068776b616e69612f726164696f5f7472616e7369656e74733f7374796c653d666c61742d737175617265\" alt=\"Stars\" data-canonical-src=\"https://img.shields.io/github/stars/josephwkania/radio_transients?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3fe122d859685d56f1b74afd3301392fc7fb2a03d784f58aba103f295c6c4f71/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6a6f73657068776b616e69612f726164696f5f7472616e7369656e74733f7374796c653d666c61742d737175617265\" alt=\"License\" data-canonical-src=\"https://img.shields.io/github/license/josephwkania/radio_transients?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/josephwkania/radio_transients/radio_transients\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/53724ef3d95dffa5ba79b85e9a203ea1606c0a0a7452ec889eb5f693d0fa4bbd/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f486f737465642d53796c6162732d477265656e2e737667\" alt=\"Sylabs\" data-canonical-src=\"https://img.shields.io/badge/Hosted-Sylabs-Green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are my Singularity Recipes for common radio transient software.\nThere are three containers\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eradio_transients\u003c/h3\u003e\u003ca id=\"user-content-radio_transients-1\" class=\"anchor\" aria-label=\"Permalink: radio_transients\" href=\"#radio_transients-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains everything (CPU+GPU)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCUDA 10.2\nFETCH https://github.com/devanshkv/fetch -- In Conda environment `FE`\nheimdall https://sourceforge.net/p/heimdall-astro/wiki/Use/\n- dedisp https://github.com/ajameson/dedisp\nhtop https://htop.dev/\niqrm_apollo https://gitlab.com/kmrajwade/iqrm_apollo\njupyterlab https://jupyter.org/\nPRESTO https://www.cv.nrao.edu/~sransom/presto/\npsrdada http://psrdada.sourceforge.net/\npsrdada-python https://github.com/TRASAL/psrdada-python\npsrcat https://www.atnf.csiro.au/people/pulsar/psrcat/download.html\npysigproc https://github.com/devanshkv/pysigproc\nriptide https://github.com/v-morello/riptide\nsigproc https://github.com/SixByNine/sigproc\nTempo http://tempo.sourceforge.net/\nRFIClean https://github.com/ymaan4/RFIClean\nYAPP https://github.com/jayanthc/yapp\nyour https://github.com/thepetabyteproject/your\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGet with\n\u003ccode\u003esingularity pull --arch amd64 library://josephwkania/radio_transients/radio_transients:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e*One of FETCH\u0027s dependencies causes PRESTO\u0027s Python scripts to fail.\nThis necessitated putting them in different environments.\nEverything except for PRESTO is in \u003ccode\u003eRT\u003c/code\u003e, which is loaded by default.\nPRESTO is in \u003ccode\u003ePE\u003c/code\u003e, in the shell you can activate this\nwith \u003ccode\u003econda activate PE\u003c/code\u003e. If you need access outside the container,\nyou should use radio_transients:cpu, which has PRESTO in the default environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eradio_transients_cpu\u003c/h3\u003e\u003ca id=\"user-content-radio_transients_cpu\" class=\"anchor\" aria-label=\"Permalink: radio_transients_cpu\" href=\"#radio_transients_cpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains CPU based programs\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehtop\niqrm_apollo\njupyterlab \nPRESTO\npsrcat\npysigproc\nriptide\nsigproc\nTempo \nRFIClean\nYAPP \nyour\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGet with\n\u003ccode\u003esingularity pull --arch amd64 library://josephwkania/radio_transients/radio_transients:cpu\u003c/code\u003e\u003cbr\u003e\nThere is an arm version \u003ccode\u003eSingularity.arm\u003c/code\u003e,\n\u003ccode\u003esingularity pull --arch arm library://josephwkania/radio_transients/radio_transients:arm\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eradio_transients_gpu\u003c/h3\u003e\u003ca id=\"user-content-radio_transients_gpu\" class=\"anchor\" aria-label=\"Permalink: radio_transients_gpu\" href=\"#radio_transients_gpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContains gpu based programs\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCUDA 10.2\nFETCH \njupyterlab\nheimdall\n- dedisp\nhtop \npsrdada \npsrdada-python\nyour\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGet with\n\u003ccode\u003esingularity pull --arch amd64 library://josephwkania/radio_transients/radio_transients:gpu\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to use\u003c/h3\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to use\" href=\"#how-to-use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYour \u003ccode\u003e$HOME\u003c/code\u003e automatically gets mounted.\nYou can mount a directory with \u003ccode\u003e-B /dir/on/host:/mnt\u003c/code\u003e, which will mount \u003ccode\u003e/dir/on/host\u003c/code\u003e to \u003ccode\u003e/mnt\u003c/code\u003e in the container.\u003c/p\u003e\n\u003cp\u003eFor the gpu processes, you must pass \u003ccode\u003e--nv\u003c/code\u003e when running singularity.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity shell --nv -B /data:/mnt radio_transients_gpu.sif\u003c/code\u003e\nwill mount \u003ccode\u003e/data\u003c/code\u003e to \u003ccode\u003e/mnt\u003c/code\u003e, give you GPU access, and drop you into the interactive shell.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity exec --nv -B /data:/mnt radio_transients_gpu.sif your_heimdall.py -f /mnt/data.fil\u003c/code\u003e\nwill mount \u003ccode\u003e/data\u003c/code\u003e to \u003ccode\u003e/mnt\u003c/code\u003e, give you GPU access, and run your_heimdall.py without entering the container.\u003c/p\u003e\n\u003cp\u003eAll the Python scripts are installed in a Conda environment \u003ccode\u003eRT\u003c/code\u003e, this environment is automatically loaded.\u003c/p\u003e\n\u003cp\u003eYou can see the commits and corresponding dates by running \u003ccode\u003esingularity inspect radio_transients.sif\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSylabs Cloud\u003c/h3\u003e\u003ca id=\"user-content-sylabs-cloud\" class=\"anchor\" aria-label=\"Permalink: Sylabs Cloud\" href=\"#sylabs-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are built on a E5 v3 family machine and uploaded to Sylabs Cloud at\n\u003ca href=\"https://cloud.sylabs.io/library/josephwkania/radio_transients/radio_transients\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/josephwkania/radio_transients/radio_transients\u003c/a\u003e\nThey where last built on 27-Nov-2021\u003c/p\u003e\n\u003cp\u003eIf your processor your processor is significantly older than this, you may run into problems with\nthe older processor not having the whole instruction set needed. In this case, you should build\nuse singularity to build the image locally.\u003c/p\u003e\n\u003cp\u003eAn archival version of these (built 25-April-2021) are on Singularity Hub at:\n\u003ca href=\"https://singularity-hub.org/collections/5231\" rel=\"nofollow\"\u003ehttps://singularity-hub.org/collections/5231\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5231\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImprovements\u003c/h3\u003e\u003ca id=\"user-content-improvements\" class=\"anchor\" aria-label=\"Permalink: Improvements\" href=\"#improvements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you come across bug or have suggestions for improvements, let me know or submit a pull request.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThanks\u003c/h3\u003e\u003ca id=\"user-content-thanks\" class=\"anchor\" aria-label=\"Permalink: Thanks\" href=\"#thanks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo Kshitij Aggarwal for bug reports and suggestions.\u003c/p\u003e\n", "stargazers_count": 7, "subscribers_count": 2, - "topics": [], - "updated_at": 1705152734.0 + "topics": [ + "pulsars", + "fast-radio-bursts", + "psrdada", + "radio-astronomy" + ], + "updated_at": 1721325888.0 }, { "data_format": 2, - "description": " Generated will detect files that have been generated by computers", + "description": "example builder for Singularity containers using Circle Continuous Integration, circle-ci", "filenames": [ - "samples/Singularity/filenames/Singularity" + "Singularity.tag", + "Singularity" ], - "full_name": "noqcks/generated", - "latest_release": "v7.23.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGenerated\u003c/h1\u003e\u003ca id=\"user-content-generated\" class=\"anchor\" aria-label=\"Permalink: Generated\" href=\"#generated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/noqcks/generated\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/27853b375fba1501eb04861c4f86f746b0fc8154f165755ef77d9ba647f8f883/68747470733a2f2f7472617669732d63692e6f72672f6e6f71636b732f67656e6572617465642e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/noqcks/generated.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNot all files are written by humans. Generated will detect files that have been\ngenerated by computers. Items like a \u003ccode\u003epackage-lock.json\u003c/code\u003e or files in \u003ccode\u003enode_modules\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor a full list of generated files detected, see \u003ca href=\"lib/generated.js\"\u003egenerated.js\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis project is largely a node.js port of the \u003ccode\u003egenerated\u003c/code\u003e functionality of \u003ca href=\"https://github.com/github/linguist\"\u003eGitHub linguist\u003c/a\u003e. The versioning of this project will follow the versioning of linguist.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003enpm i @noqcks/generated\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://www.npmjs.com/package/@noqcks/generated\" rel=\"nofollow\"\u003ehttps://www.npmjs.com/package/@noqcks/generated\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee file \u003ca href=\"scripts/example.js\"\u003escripts/example.js\u003c/a\u003e for an example of usage.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econst fs = require(\u0027fs\u0027);\nconst path = require(\u0027path\u0027);\nconst Generated = require(\"@noqcks/generated\");\n\nconst fileName = \"JavaScript/json2_backbone.js\"\nconst filePath = path.join(\"./samples\", fileName);\n\ntry {\n var contents = fs.readFileSync(filePath, \u0027utf8\u0027);\n} catch (e) {\n if (e.code !== \u0027ENOENT\u0027) throw err;\n var contents = \u0027\u0027\n}\n\nconst g = new Generated(name, contents)\n\nconsole.log(g.isGenerated())\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLICENSE\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"LICENSE\"\u003eMIT\u003c/a\u003e \u00a9 2022 Benji Visser \u003ca href=\"mailto:benji@093b.org\"\u003ebenji@093b.org\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "singularityhub/circle-ci", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Builder Circle-CI\u003c/h1\u003e\u003ca id=\"user-content-singularity-builder-circle-ci\" class=\"anchor\" aria-label=\"Permalink: Singularity Builder Circle-CI\" href=\"#singularity-builder-circle-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".circleci/sregistry-circle.png\"\u003e\u003cimg src=\".circleci/sregistry-circle.png\" alt=\".circleci/sregistry-circle.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/singularityhub/circle-ci\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b7c23c37f44977396bcf2bd49b4b0730366e46d83e65f69cc28532e2a352ac78/68747470733a2f2f636972636c6563692e636f6d2f67682f73696e67756c61726974796875622f636972636c652d63692e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/singularityhub/circle-ci.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a simple example of how you can achieve:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eversion control of your recipes\u003c/li\u003e\n\u003cli\u003eversioning to include image hash \u003cem\u003eand\u003c/em\u003e commit id\u003c/li\u003e\n\u003cli\u003ebuild of associated container and\u003c/li\u003e\n\u003cli\u003epush to a storage endpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003efor a reproducible build workflow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should this be managed via Github?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGithub, by way of easy integration with continuous integration, is an easy way\nto have a workflow set up where multiple people can collaborate on a container recipe,\nthe recipe can be tested (with whatever testing you need), discussed in pull requests,\nand then finally pushed to your storage of choice or Singularity Registry.\nImportantly, you don\u0027t need to give your entire team manager permissions\nto the registry. An encrypted credential that only is accessible to\nadministrators can do the push upon merge of a discussed change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should I use this instead of a service?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou could use a remote builder, but if you do the build in a continuous integration\nservice you get complete control over it. This means everything from the version of\nSingularity to use, to the tests that you run for your container. You have a lot more\nfreedom in the rate of building, and organization of your repository, because it\u0027s you\nthat writes the configuration. Although the default would work for most, you can\nedit the build, setup, and circle configuration file in the\n\u003ca href=\".circleci\"\u003e.circleci\u003c/a\u003e folder to fit your needs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdd your Singularity recipes to this repository, and edit the build commands in\nthe \u003ca href=\".circleci/build.sh\"\u003ebuild.sh\u003c/a\u003e file. This is where you can specify endpoints\n(Singularity Registry, Dropbox, Google Storage, AWS) along with container names\n(the uri) and tag. You can build as many recipes as you like, just add another line!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e recipe relative to repository base\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh --uri collection/container --tag tacos --cli google-storage Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh --uri collection/container --cli google-drive Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh --uri collection/container --cli globus Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .circleci/build.sh --uri collection/container --cli registry Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor each client that you use, required environment variables (e.g., credentials to push,\nor interact with the API) must be defined in the (encrypted) Travis environment. To\nknow what variables to define, along with usage for the various clients, see\nthe \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclient specific pages\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed Started\u003c/h2\u003e\u003ca id=\"user-content-detailed-started\" class=\"anchor\" aria-label=\"Permalink: Detailed Started\" href=\"#detailed-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e0. Fork this repository\u003c/h3\u003e\u003ca id=\"user-content-0-fork-this-repository\" class=\"anchor\" aria-label=\"Permalink: 0. Fork this repository\" href=\"#0-fork-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can clone and tweak, but it\u0027s easiest likely to get started with our example\nfiles and edit them as you need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Get to Know CircleCi\u003c/h3\u003e\u003ca id=\"user-content-1-get-to-know-circleci\" class=\"anchor\" aria-label=\"Permalink: 1. Get to Know CircleCi\" href=\"#1-get-to-know-circleci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will be working with \u003ca href=\"https://www.circleci.com\" rel=\"nofollow\"\u003eCircle CI\u003c/a\u003e. You can see\nexample builds for this \u003ca href=\"https://circleci.com/gh/singularityhub/circle-ci\" rel=\"nofollow\"\u003erepository here\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCircle offers \u003ca href=\"https://support.circleci.com/hc/en-us/articles/115015481128-Scheduling-jobs-cron-for-builds-\" rel=\"nofollow\"\u003escheduled builds\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eCircleCI also offers \u003ca href=\"https://circleci.com/docs/enterprise/gpu-configuration/\" rel=\"nofollow\"\u003eGPU Builders\u003c/a\u003e if you want/need that sort of thing.\u003c/li\u003e\n\u003cli\u003eIf you don\u0027t want to use the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry\u003c/a\u003e to push to Google Storage, Drive, Globus, Dropbox, or your personal Singularity Registry, CircleCI will upload your artifacts directly to your \u003ca href=\"https://circleci.com/docs/2.0/deployment-integrations/#section=deployment\" rel=\"nofollow\"\u003edeployment\u003c/a\u003e location of choice.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Add your Recipe(s)\u003c/h3\u003e\u003ca id=\"user-content-2-add-your-recipes\" class=\"anchor\" aria-label=\"Permalink: 2. Add your Recipe(s)\" href=\"#2-add-your-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the example here, we have a single recipe named \"Singularity\" that is provided\nas an input argument to the \u003ca href=\".circleci/build.sh\"\u003ebuild script\u003c/a\u003e. You could add another\nrecipe, and then of course call the build to happen more than once.\nThe build script will name the image based on the recipe, and you of course\ncan change this. Just write the path to it (relative to the repository base) in\nyour \u003ca href=\".circleci/config.yml\"\u003e.circleci/config.yml\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Configure Singularity\u003c/h3\u003e\u003ca id=\"user-content-3-configure-singularity\" class=\"anchor\" aria-label=\"Permalink: 3. Configure Singularity\" href=\"#3-configure-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recipe uses the \u003ca href=\"https://circleci.com/orbs/registry/orb/singularity/singularity\" rel=\"nofollow\"\u003eSingularity Orb\u003c/a\u003e to install your chosen version of Singularity. If you want to change the version, just adjust\nthe parameter here:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003ebuild\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSingularity 3.2.1 - Python 3\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esingularity\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e3.2.1\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esingularity-3\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe basic steps to \u003ca href=\".circleci/setup.sh\"\u003esetup\u003c/a\u003e the build are the following:\u003c/p\u003e\n\u003cp\u003eWe also install the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry client\u003c/a\u003e\nthat allows you to issue a command like \"sregistry push ...\" to upload a finished\nimage to one of your cloud / storage endpoints. In this basic example, the push won\u0027t happen,\nand you will just build an image using the CI.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Configure the Build\u003c/h3\u003e\u003ca id=\"user-content-4-configure-the-build\" class=\"anchor\" aria-label=\"Permalink: 4. Configure the Build\" href=\"#4-configure-the-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe basic steps for the \u003ca href=\".circleci/build.sh\"\u003ebuild\u003c/a\u003e are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRunning build.sh with no inputs will default to a recipe called \"Singularity\" in the base of the repository. You can provide an argument to point to a different recipe path, always relative to the base of your repository.\u003c/li\u003e\n\u003cli\u003eIf you want to define a particular unique resource identifier for a finished container (to be uploaded to your storage endpoint) you can do that with \u003ccode\u003e--uri collection/container\u003c/code\u003e. If you don\u0027t define one, a robot name will be generated.\u003c/li\u003e\n\u003cli\u003eYou can add \u003ccode\u003e--uri\u003c/code\u003e to specify a custom name, and this can include the tag, OR you can specify \u003ccode\u003e--tag\u003c/code\u003e to go along with a name without one. It depends on which is easier for you.\u003c/li\u003e\n\u003cli\u003eIf you add \u003ccode\u003e--cli\u003c/code\u003e then this is telling the build script that you have defined the \u003ca href=\"https://circleci.com/docs/2.0/env-vars/\" rel=\"nofollow\"\u003eneeded environment variables\u003c/a\u003e for your \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclient of choice\u003c/a\u003e and you want successful builds to be pushed to your storage endpoint. See \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for a list of current client endpoints, or roll your own!\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\".circleci/config.yml\"\u003econfig.yml\u003c/a\u003e for examples of this build.sh command (commented out). If there is some cloud service that you\u0027d like that is not provided, please \u003ca href=\"https://www.github.com/singularityhub/sregistry-cli/issues\"\u003eopen an issue\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Connect to CI\u003c/h3\u003e\u003ca id=\"user-content-5-connect-to-ci\" class=\"anchor\" aria-label=\"Permalink: 5. Connect to CI\" href=\"#5-connect-to-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you go to your \u003ca href=\"https://circleci.com/dashboard\" rel=\"nofollow\"\u003eCircle Dashboard\u003c/a\u003e you can usually select a Github organization (or user) and then the repository, and then click the toggle button to activate it to build on commit --\u0026gt; push.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it for the basic setup! At this point, you will have a continuous integration service that will build your container from a recipe each time that you push. The next step is figuring out where you want to put the finished image(s), and we will walk through this in more detail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStorage!\u003c/h2\u003e\u003ca id=\"user-content-storage\" class=\"anchor\" aria-label=\"Permalink: Storage!\" href=\"#storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the image is built, where can you put it? An easy answer is to use the\n\u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003eSingularity Global Client\u003c/a\u003e and\nchoose \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eone of the many clients\u003c/a\u003e\nto add a final step to push the image. You then use the same client to pull the\ncontainer from your host. Once you\u0027ve decided which endpoints you want to push to,\nyou will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSave the credentials / other environment variables that your client needs (see the client settings page linked in the sregistry docs above) to your \u003ca href=\"https://circleci.com/docs/2.0/env-vars/\" rel=\"nofollow\"\u003erepository settings\u003c/a\u003e where they will be encrypted and in the environment.\u003c/li\u003e\n\u003cli\u003eAdd a line to your \u003ca href=\".circleci/config.yml\"\u003e.circleci/config.yml\u003c/a\u003e to do an sregistry push action to the endpoint(s) of choice. We have provided some (commented out) examples to get you started.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRemember that you can also take advantage of deployment options that CircleCI offers, or do any other action that you might want for the reproducibility or archive of metadata of your builds. We save the build folder as an artifact to the repository, but the containers might be too big to do this.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced Usage\u003c/h2\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-label=\"Permalink: Advanced Usage\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThis setup can work as an analysis node as well! Try setting up a \u003ca href=\"https://support.circleci.com/hc/en-us/articles/115015481128-Scheduling-jobs-cron-for-builds-\" rel=\"nofollow\"\u003escheduled build\u003c/a\u003e to build a container that processes some information feed, and you have a regularly scheduled task.\u003c/li\u003e\n\u003cli\u003erun builds in parallel and test different building environments. You could try building the \"same\" container across different machine types and see if you really do get the same thing :)\u003c/li\u003e\n\u003cli\u003eYou can also do other sanity checks like testing if the container runs as you would expect, etc.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 7, "subscribers_count": 3, - "topics": [], - "updated_at": 1708962247.0 + "topics": [ + "singularity", + "builder", + "circle-ci", + "singularity-ci" + ], + "updated_at": 1652955043.0 }, { "data_format": 2, - "description": "This docker and singularity image bundles the tgv-qsm algorithm with bet2, dcm2niix and provides a complete QSM processing pipeline.", + "description": "Various Singularity recipes I\u0027ve used to generate containers", "filenames": [ - "Singularity.tgvqsm_amd", - "Singularity.tgvqsm" + "beagle/Singularity.beagle", + "vg/Singularity.vg", + "admixture/Singularity.admixture", + "popLDdecay/Singularity.popLDdecay", + "limma-voom/Singularity.limma-voom", + "finestructure/Singularity.finestructure", + "snpeff/Singularity.snpeff", + "trimmomatic/Singularity.trimmomatic", + "diamond/Singularity.diamond", + "igv/Singularity.igv", + "hmmsplicer/Singularity.hmmsplicer", + "bcl2fastq/Singularity.bcl2fastq", + "freebayes/Singularity.freebayes", + "vcftools/Singularity.vcftools", + "igb/Singularity.igb", + "fastqc/Singularity.fastqc", + "multiqc/Singularity.multiqc", + "stringtie/Singularity.stringtie", + "rqtl2/Singularity.rqtl2", + "ncbi-blast+/Singularity.ncbi-blast+", + "poplddecay/Singularity.poplddecay", + "sabre/Singularity.sabre", + "conda_env/Singularity.conda_env", + "gc/Singularity.gc", + "last/Singularity.last", + "samtools/Singularity.samtools", + "orthofinder/Singularity.orthofinder", + "bioscripts/Singularity.bioscripts", + "star/Singularity.star", + "fastp/Singularity.fastp", + "rust/Singularity.rust", + "bwa/Singularity.bwa", + "hisat2/Singularity.hisat2", + "fastme/Singularity.fastme", + "deeptools/Singularity.deeptools", + "bcftools/Singularity.bcftools", + "mcl/Singularity.mcl" ], - "full_name": "CAIsr/qsm", + "full_name": "jlboat/BioinfoContainers", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQSM Pipeline\u003c/h1\u003e\u003ca id=\"user-content-qsm-pipeline\" class=\"anchor\" aria-label=\"Permalink: QSM Pipeline\" href=\"#qsm-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis docker and singularity image provides the tgv-qsm algorithm (\u003ca href=\"http://www.neuroimaging.at/pages/qsm.php\" rel=\"nofollow\"\u003ehttp://www.neuroimaging.at/pages/qsm.php\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eIf you use this image, this is the reference to cite describing the QSM algorithm:\nLangkammer, C; Bredies, K; Poser, BA; Barth, M; Reishofer, G; Fan, AP; Bilgic, B; Fazekas, F; Mainero; C; Ropele, S\nFast Quantitative Susceptibility Mapping using 3D EPI and Total Generalized Variation.\nNeuroimage. 2015 May 1;111:622-30. doi: 10.1016/j.neuroimage.2015.02.041. PubMed\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIf you are looking for a full QSM pipeline including dicom conversion, QSM solution, image segmentation, atlas building\u003c/h1\u003e\u003ca id=\"user-content-if-you-are-looking-for-a-full-qsm-pipeline-including-dicom-conversion-qsm-solution-image-segmentation-atlas-building\" class=\"anchor\" aria-label=\"Permalink: If you are looking for a full QSM pipeline including dicom conversion, QSM solution, image segmentation, atlas building\" href=\"#if-you-are-looking-for-a-full-qsm-pipeline-including-dicom-conversion-qsm-solution-image-segmentation-atlas-building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/QSMxT/QSMxT\"\u003ehttps://github.com/QSMxT/QSMxT\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWe recommend running this container in the Neurodesk environment for ease of use: \u003ca href=\"https://neurodesk.github.io/\" rel=\"nofollow\"\u003ehttps://neurodesk.github.io/\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-we-recommend-running-this-container-in-the-neurodesk-environment-for-ease-of-use-httpsneurodeskgithubio\" class=\"anchor\" aria-label=\"Permalink: We recommend running this container in the Neurodesk environment for ease of use: https://neurodesk.github.io/\" href=\"#we-recommend-running-this-container-in-the-neurodesk-environment-for-ease-of-use-httpsneurodeskgithubio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the image in singularity (deprecated)\u003c/h3\u003e\u003ca id=\"user-content-using-the-image-in-singularity-deprecated\" class=\"anchor\" aria-label=\"Permalink: Using the image in singularity (deprecated)\" href=\"#using-the-image-in-singularity-deprecated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003einstalling singularity will depend on your operating system, here an exampe for a debian based system\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get update \u0026amp;\u0026amp; sudo apt-get install -y \\\n build-essential \\\n uuid-dev \\\n libgpgme-dev \\\n squashfs-tools \\\n libseccomp-dev \\\n wget \\\n pkg-config \\\n git \\\n cryptsetup-bin\n\nwget https://golang.org/dl/go1.15.2.linux-amd64.tar.gz\n\ntar -C /usr/local -xzf go1.15.2.linux-amd64.tar.gz\n\nexport PATH=$PATH:/usr/local/go/bin\n\nexport VERSION=3.6.3 \u0026amp;\u0026amp; # adjust this as necessary \\\n wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-${VERSION}.tar.gz \u0026amp;\u0026amp; \\\n tar -xzf singularity-${VERSION}.tar.gz \u0026amp;\u0026amp; \\\n cd singularity\n\n\n./mconfig \u0026amp;\u0026amp; \\\n make -C ./builddir \u0026amp;\u0026amp; \\\n sudo make -C ./builddir install\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethen you can download and run the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/NeuroDesk/transparent-singularity tgvqsm_1.0.0_20210317\ncd tgvqsm_1.0.0_20210317\n./run_transparent_singularity.sh tgvqsm_1.0.0_20210317\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ethis will download the image, unpack it and provide a wrapper script for starting tgv_qsm:\u003c/p\u003e\n\u003cp\u003eThe wrapper script can be started using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./tgv_qsm\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr you can open a shell into the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell tgvqsm_1.0.0_20210317.*\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eyou can also bind a different directory to your image (e.g. bind /data from your host to /data in your singularity image)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --bind /data:/data/ tgvqsm_1.0.0_20210317.*\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere is an example for a single echo QSM processing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edcm2niix -o ./ -f magnitude GR_M_5_QSM_p2_1mmIso_TE20/\ndcm2niix -o ./ -f phase GR_P_6_QSM_p2_1mmIso_TE20/\n\nbet2 magnitude.nii magnitude_bet2\n\ntgv_qsm \\\n -p phase.nii \\\n -m magnitude_bet2_mask.nii.gz \\\n -f 2.89 \\\n -t 0.02 \\\n -s \\\n -o qsm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe -s option will scale the phase correctly if the phase dicom values are between -2048 and 2048 (should be default on Siemens VD and VE platforms). On the VB platform the phase is between 0 and 4096, so omit the -s option and scale the phase between -pi and pi:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing the image in docker\u003c/h1\u003e\u003ca id=\"user-content-using-the-image-in-docker\" class=\"anchor\" aria-label=\"Permalink: Using the image in docker\" href=\"#using-the-image-in-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull vnmd/tgvqsm_1.0.0:20210317\nsudo docker run -it -v $PWD:/data vnmd/tgvqsm_1.0.0:20210317\n\ncd /data\ndcm2niix -o ./ -f magnitude GR_M_5_QSM_p2_1mmIso_TE20/\ndcm2niix -o ./ -f phase GR_P_6_QSM_p2_1mmIso_TE20/\n\nbet2 magnitude.nii magnitude_bet2\n\ntgv_qsm -p phase.nii -m magnitude_bet2_mask.nii.gz -f 2.89 -t 0.02 -s -o qsm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOptimizing for your CPU\u003c/h1\u003e\u003ca id=\"user-content-optimizing-for-your-cpu\" class=\"anchor\" aria-label=\"Permalink: Optimizing for your CPU\" href=\"#optimizing-for-your-cpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, QSM is compiled with the \u003ccode\u003e-O3 -march=x86-64\u003c/code\u003e which should provide a good balance between speed and portability. If you know what CPU you\u0027re going to be using you can compile with that instruction set to improve performance (e.g. \u003ccode\u003e-march=ivybridge\u003c/code\u003e for Intel Ivy Bridge CPUs, \u003ccode\u003e-march=native\u003c/code\u003e for whatever CPU you\u0027re currently on). If you would like maximum portability, you can recompile omitting the \u003ccode\u003e-march\u003c/code\u003e flag altogether.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing tgv_qsm in Windows Subsystem for Linux (example: Debian based system)\u003c/h1\u003e\u003ca id=\"user-content-using-tgv_qsm-in-windows-subsystem-for-linux-example-debian-based-system\" class=\"anchor\" aria-label=\"Permalink: Using tgv_qsm in Windows Subsystem for Linux (example: Debian based system)\" href=\"#using-tgv_qsm-in-windows-subsystem-for-linux-example-debian-based-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWSL 1.0 doesn\u0027t support singularity or docker containers (but WSL 2.0 will). But it is possible to directly install TGV QSM in a miniconda environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install wget unzip gcc\nwget https://repo.anaconda.com/miniconda/Miniconda2-4.6.14-Linux-x86_64.sh\nbash Miniconda2-4.6.14-Linux-x86_64.sh\n(install, accept agreement with yes, after install source bash again:)\nbash\nconda install -c anaconda cython==0.25.2\nconda install numpy\nconda install pyparsing\n(make sure pip is not your system pip, but the one in miniconda: which pip)\npip install scipy==0.17.1 nibabel==2.1.0\nwget http://www.neuroimaging.at/media/qsm/TGVQSM-plus.zip\nunzip TGVQSM-plus.zip\ncd TGVQSM-master-011045626121baa8bfdd6633929974c732ae35e3\npython setup.py install\ncd test_data\ntgv_qsm -p epi3d_test_phase.nii.gz -m epi3d_test_mask.nii.gz -f 2.89 -t 0.027 -o epi3d_test_QSM\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAdding fsl to WSL Ubuntu 18.04\u003c/h1\u003e\u003ca id=\"user-content-adding-fsl-to-wsl-ubuntu-1804\" class=\"anchor\" aria-label=\"Permalink: Adding fsl to WSL Ubuntu 18.04\" href=\"#adding-fsl-to-wsl-ubuntu-1804\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ewget -O- http://neuro.debian.net/lists/bionic.us-ca.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list\nsudo apt-key adv --recv-keys --keyserver hkp://pool.sks-keyservers.net:80 0xA5D32F012649A5A9\nsudo apt-get update\nsudo apt-get install fsl-5.0-core\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eadd \". /etc/fsl/5.0/fsl.sh\" to the end of your .profile file\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularityRecipes\u003c/h1\u003e\u003ca id=\"user-content-singularityrecipes\" class=\"anchor\" aria-label=\"Permalink: SingularityRecipes\" href=\"#singularityrecipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVarious Singularity recipes I\u0027ve used to generate bioinformatics containers\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2877\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eObtaining a container\u003c/h2\u003e\u003ca id=\"user-content-obtaining-a-container\" class=\"anchor\" aria-label=\"Permalink: Obtaining a container\" href=\"#obtaining-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://jlboat/BioinfoContainers:fastp # or exchange fastp for any other container listed here\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer Recipes\u003c/h2\u003e\u003ca id=\"user-content-container-recipes\" class=\"anchor\" aria-label=\"Permalink: Container Recipes\" href=\"#container-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBCL Processing Programs\u003c/h3\u003e\u003ca id=\"user-content-bcl-processing-programs\" class=\"anchor\" aria-label=\"Permalink: BCL Processing Programs\" href=\"#bcl-processing-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.bcl2fastq\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFASTQ Processing Programs\u003c/h3\u003e\u003ca id=\"user-content-fastq-processing-programs\" class=\"anchor\" aria-label=\"Permalink: FASTQ Processing Programs\" href=\"#fastq-processing-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.fastp\u003c/li\u003e\n\u003cli\u003eSingularity.fastqc\u003c/li\u003e\n\u003cli\u003eSingularity.sabre\u003c/li\u003e\n\u003cli\u003eSingularity.trimmomatic\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRead Aligners\u003c/h3\u003e\u003ca id=\"user-content-read-aligners\" class=\"anchor\" aria-label=\"Permalink: Read Aligners\" href=\"#read-aligners\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.hisat2\u003c/li\u003e\n\u003cli\u003eSingularity.STAR\u003c/li\u003e\n\u003cli\u003eSingularity.bwa\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExpression Quantification/Alternative Splicing\u003c/h3\u003e\u003ca id=\"user-content-expression-quantificationalternative-splicing\" class=\"anchor\" aria-label=\"Permalink: Expression Quantification/Alternative Splicing\" href=\"#expression-quantificationalternative-splicing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.stringtie\u003c/li\u003e\n\u003cli\u003eSingularity.hmmsplicer\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVariant Callers/Analysis tools\u003c/h3\u003e\u003ca id=\"user-content-variant-callersanalysis-tools\" class=\"anchor\" aria-label=\"Permalink: Variant Callers/Analysis tools\" href=\"#variant-callersanalysis-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.vcftools\u003c/li\u003e\n\u003cli\u003eSingularity.snpeff\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDifferential Expression\u003c/h3\u003e\u003ca id=\"user-content-differential-expression\" class=\"anchor\" aria-label=\"Permalink: Differential Expression\" href=\"#differential-expression\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.limma-voom\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReport Generation\u003c/h3\u003e\u003ca id=\"user-content-report-generation\" class=\"anchor\" aria-label=\"Permalink: Report Generation\" href=\"#report-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.multiqc\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVarious Scripts/Aggregate Programs\u003c/h3\u003e\u003ca id=\"user-content-various-scriptsaggregate-programs\" class=\"anchor\" aria-label=\"Permalink: Various Scripts/Aggregate Programs\" href=\"#various-scriptsaggregate-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.bioscripts\u003c/li\u003e\n\u003cli\u003eSingularity.conda\u003c/li\u003e\n\u003cli\u003eSingularity.deeptools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGenome Viewers\u003c/h3\u003e\u003ca id=\"user-content-genome-viewers\" class=\"anchor\" aria-label=\"Permalink: Genome Viewers\" href=\"#genome-viewers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity.IGB\u003c/li\u003e\n\u003cli\u003eSingularity.IGV\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 7, - "subscribers_count": 4, - "topics": [], - "updated_at": 1646912572.0 + "subscribers_count": 3, + "topics": [ + "sigularity", + "singularity-containers", + "singularityhub", + "bioinformatics" + ], + "updated_at": 1721631116.0 }, { "data_format": 2, - "description": "MountainSort v4", + "description": "NYU Langone Genome PACT (Genome Profiling of Actionable Cancer Targets) targeted exome sequencing analysis pipeline.", "filenames": [ - "Singularity.v0.1.4" + "containers/sambamba-0.6.8/Singularity.sambamba-0.6.8", + "containers/cnvkit-0.9.5/Singularity.cnvkit-0.9.5", + "containers/variant-calling-0.0.2/Singularity.variant-calling-0.0.2", + "containers/bwa-0.7.17/Singularity.bwa-0.7.17", + "containers/cnvkit-0.9.0/Singularity.cnvkit-0.9.0", + "containers/pindel-0.2.5b9/Singularity.pindel-0.2.5b9", + "containers/varscan-2.4.3/Singularity.varscan-2.4.3", + "containers/variant-calling-0.0.1/Singularity.variant-calling-0.0.1", + "containers/strelka-2.9.10/Singularity.strelka-2.9.10", + "containers/lofreq-2.1.3/Singularity.lofreq-2.1.3", + "containers/sambamba-0.6.6/Singularity.sambamba-0.6.6", + "containers/sambamba-0.6.6/Singularity.sambamba-0.6.6.old", + "containers/base/Singularity.base", + "containers/R-3.5.1/Singularity.R-3.5.1", + "containers/msisensor-0.2/Singularity.msisensor-0.2", + "containers/multiqc-1.5/Singularity.multiqc-1.5", + "containers/fastqc-0.11.7/Singularity.fastqc-0.11.7", + "containers/bwa-0.7.17-sambamba-0.6.8/Singularity.bwa-0.7.17-sambamba-0.6.8", + "containers/R-3.2.3/Singularity.R-3.2.3", + "containers/python-2.7/Singularity.python-2.7", + "containers/R-3.4.3/Singularity.R-3.4.3", + "containers/IGV-2.4.10/Singularity.IGV-2.4.10", + "containers/samtools-1.7/Singularity.samtools-1.7", + "containers/annovar-150617/Singularity.annovar-150617", + "containers/bedtools-2.26.0/Singularity.bedtools-2.26.0", + "containers/htslib-1.7/Singularity.htslib-1.7", + "containers/python-3.6/Singularity.python-3.6", + "containers/bedtools-2.27.1/Singularity.bedtools-2.27.1", + "containers/deconstructSigs-1.8.0/Singularity.deconstructSigs-1.8.0", + "containers/manta-1.5.0/Singularity.manta-1.5.0", + "containers/trimmomatic-0.36/Singularity.trimmomatic-0.36", + "containers/bcftools-1.3/Singularity.bcftools-1.3", + "containers/reporting-3.4.3/Singularity.reporting-3.4.3", + "containers/multiqc-1.4/Singularity.multiqc-1.4", + "containers/cnv_facets-0.14.0/Singularity.cnv_facets-0.14.0", + "containers/delly2-0.7.7/Singularity.delly2-0.7.7" ], - "full_name": "magland/ml_ms4alg", + "full_name": "NYU-Molecular-Pathology/LG-PACT", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-ml_ms4alg\" class=\"anchor\" aria-hidden=\"true\" href=\"#ml_ms4alg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eml_ms4alg\u003c/h1\u003e\n\u003cp\u003eElectrophysiology tools\nMountainLab processor library\u003c/p\u003e\n\u003cp\u003eInstallation from PyPI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install --upgrade ml_ms4alg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen add it as a plugin to mountainlab:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd ~/.mountainlab/packages\nml-link-python-module ml_ms4alg ml_ms4alg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr installation from source:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eclone this repository into .mountainlab/packages/\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd ml_ms4alg\npip3 install --upgrade .\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 7, - "subscribers_count": 9, - "topics": [], - "updated_at": 1666168225.0 + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLG-PACT\u003c/h1\u003e\u003ca id=\"user-content-lg-pact\" class=\"anchor\" aria-label=\"Permalink: LG-PACT\" href=\"#lg-pact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTarget exome analysis for 607 gene panel (NGS607)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e Details listed here may change during development\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is designed to run targeted exome analysis on Illumina Next-Gen sequencing genomic data, in support of the NGS607 cancer diagnostic panel for NYU\u0027s Molecular Pathology Department.\u003c/p\u003e\n\u003cp\u003eThis pipeline starts from paired-end fastq data (\u003ccode\u003e.fastq.gz\u003c/code\u003e), and is meant to accompany the output from the Illumina demultiplexing pipeline listed here: \u003ca href=\"https://github.com/NYU-Molecular-Pathology/demux-nf\"\u003ehttps://github.com/NYU-Molecular-Pathology/demux-nf\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe LG-PACT analysis workflow includes read trimming, QC, alignment, variant calling, annotation, and reporting, along with many other steps.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome key pipeline components included in this repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebin\u003c/code\u003e: directory of custom scripts used throughout the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003econtainers\u003c/code\u003e: directory of container recipes (Docker, Singularity) for use with the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eexample\u003c/code\u003e: directory of example samplesheets, etc., to show the format used with this pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003etargets\u003c/code\u003e: directory of target region .bed files included with the pipeline for typical analyses\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMakefile\u003c/code\u003e: A Makefile with recipes for configuring, starting, and managing the pipeline. This is meant to be the main interface between the end-user and the pipeline. The Makefile should be reviewed as-needed to familiarize yourself with the methods and configurations that are meant to be used for running and managing the pipeline.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emain.nf\u003c/code\u003e: the main Nextflow pipeline script\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enextflow.config\u003c/code\u003e: configuration file for the main Nextflow pipeline script\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e.config.json\u003c/code\u003e: a template for the required \u003ccode\u003econfig.json\u003c/code\u003e file used in the pipeline, shows the default pipeline settings that are meant to be easily modified by the end-user and used within the pipeline for data processing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eannovar_db.nf\u003c/code\u003e, \u003ccode\u003ecnv-pool.nf\u003c/code\u003e, \u003ccode\u003ehapmap-pool.nf\u003c/code\u003e, \u003ccode\u003eref.nf\u003c/code\u003e: workflows for generating and downloading extra reference files used in the main pipeline.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eref\u003c/code\u003e: default location for the storage of reference files (not used on NYU Big Purple HPC)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline Items\u003c/h2\u003e\u003ca id=\"user-content-pipeline-items\" class=\"anchor\" aria-label=\"Permalink: Pipeline Items\" href=\"#pipeline-items\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome key components that are created during setup, configuration, and execution of the pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esamples.analysis.tsv\u003c/code\u003e: the main samplesheet definig input items for the pipeline (described below)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003econfig.json\u003c/code\u003e: configuration file used for pipeline settings (see \u003ccode\u003e.config.json\u003c/code\u003e template for example)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eoutput\u003c/code\u003e: analysis output files published by the Nextflow pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ework\u003c/code\u003e: Nextflow temporary directories for execution of pipeline tasks\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003etrace.txt\u003c/code\u003e, \u003ccode\u003enextflow.html\u003c/code\u003e, \u003ccode\u003etimeline.html\u003c/code\u003e, \u003ccode\u003e.nextflow.log\u003c/code\u003e: Nextflow execution logs and reports\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003elogs\u003c/code\u003e: directory for pipeline execution logs\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSetup\u003c/h1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository should first be cloned from GitHub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NYU-Molecular-Pathology/LG-PACT.git\ncd LG-PACT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eOnce a copy of the repo is made, it can be used to \"deploy\" new copies of the workflow in a pre-configured state\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReference Data\u003c/h2\u003e\u003ca id=\"user-content-reference-data\" class=\"anchor\" aria-label=\"Permalink: Reference Data\" href=\"#reference-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNextflow pipelines have been included for downloading required reference data, including ANNOVAR reference databases. You can run them with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake setup\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHapMap Pool .bam\u003c/h3\u003e\u003ca id=\"user-content-hapmap-pool-bam\" class=\"anchor\" aria-label=\"Permalink: HapMap Pool .bam\" href=\"#hapmap-pool-bam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA negative control HapMap pool .bam file can be prepared using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake hapmap-pool\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRequires \u003ccode\u003esamples.hapmap.tsv\u003c/code\u003e file specifying the .bam files to be combined (example included at \u003ccode\u003eexample/samples.hapmap.tsv\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis file is typically built from multiple HapMap samples previously aligned by this pipeline. For demonstration purposes, you can provide any .bam and .bai files.\u003c/p\u003e\n\u003cp\u003eThe HapMap Pool files to be used in the pipeline should be set under the \u003ccode\u003eHapMapBam\u003c/code\u003e and \u003ccode\u003eHapMapBai\u003c/code\u003e keys of \u003ccode\u003econfig.json\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCNV Pool\u003c/h3\u003e\u003ca id=\"user-content-cnv-pool\" class=\"anchor\" aria-label=\"Permalink: CNV Pool\" href=\"#cnv-pool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA control normal sample .cnn file for CNV calling can be prepared using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake cnv-pool\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRequires \u003ccode\u003esamples.cnv.tsv\u003c/code\u003e file specifying the .bam files to be used (example included at \u003ccode\u003eexample/samples.cnv.tsv\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis file is typically built from .bam files of specially chosen normal tissue sequencing samples previously aligned by this pipeline. For demonstration purposes, you can create the .cnn file from any desired .bam file. Note that the targets .bed file used to create the .cnn file must match the targets used in the rest of the pipeline.\u003c/p\u003e\n\u003cp\u003eThe .cnn file to be used in the pipeline should be set under the \u003ccode\u003eCNVPool\u003c/code\u003e key in \u003ccode\u003econfig.json\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainers\u003c/h2\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003econtainers\u003c/code\u003e directory contains instructions and recipes for building the Docker and Singularity containers used in the pipeline.\u003c/p\u003e\n\u003cp\u003eDocker is typically used for local container development, while Singularity containers are used on the NYU Big Purple HPC cluster. The current pipeline configuration for Big Purple uses \u003ccode\u003e.simg\u003c/code\u003e files stored in a common location on the file system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is designed to start from demultiplexed paired end \u003ccode\u003e.fastq.gz\u003c/code\u003e files, with sample ID, tumor ID, and matched normal ID associations defined for each set of R1 and R2 .fastq file using a file \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e (example included at \u003ccode\u003eexample/samples.analysis.tsv\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeployment\u003c/h2\u003e\u003ca id=\"user-content-deployment\" class=\"anchor\" aria-label=\"Permalink: Deployment\" href=\"#deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiset way to use the pipeline is to \"deploy\" a new instance of it based on output from the demultiplexing pipeline \u003ca href=\"https://github.com/NYU-Molecular-Pathology/demux-nf\"\u003e\u003ccode\u003edemux-nf\u003c/code\u003e\u003c/a\u003e. This will automatically propagate configurations and information from the demultiplexing output.\u003c/p\u003e\n\u003cp\u003eThe pipeline can also deploy a new, pre-configured copy of itself using the included \u003ccode\u003edeploy\u003c/code\u003e recipe:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake deploy PRODDIR=/path/to/NGS607_analyses RUNID=Name_for_analysis FASTQDIR=/path/to/fastq_files\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAn optional argument \u003ccode\u003eDEMUX_SAMPLESHEET\u003c/code\u003e can be used to provide a specially formatted demultiplexing samplesheet to be used for extracting extra sample information (example included at \u003ccode\u003eexample/demux-SampleSheet.csv\u003c/code\u003e; note the extra columns labeling tumor-normal pair IDs, used later).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate Config\u003c/h2\u003e\u003ca id=\"user-content-create-config\" class=\"anchor\" aria-label=\"Permalink: Create Config\" href=\"#create-config\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA file \u003ccode\u003econfig.json\u003c/code\u003e is required to hold settings for the pipeline. It should be created using the built-in methods:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake config RUNID=my_run_ID FASTQDIR=/path/to/fastqs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake config RUNID=my_run_ID FASTQDIRS=\u0027/path/to/fastqs1 /path/to/fastqs2\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp .config.json config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then simply edit the new \u003ccode\u003econfig.json\u003c/code\u003e and update the items to match your pipeline settings.\u003c/p\u003e\n\u003cp\u003eOnce created, the \u003ccode\u003econfig.json\u003c/code\u003e file can be updated manually as needed. The template and default values can be viewed in the included \u003ccode\u003e.config.json\u003c/code\u003e file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econfig.json\u003c/code\u003e should be generated automatically if you used \u003ccode\u003emake deploy\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate Samplesheet\u003c/h2\u003e\u003ca id=\"user-content-create-samplesheet\" class=\"anchor\" aria-label=\"Permalink: Create Samplesheet\" href=\"#create-samplesheet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA samplesheet file \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e is required in order to define the input samples and their associated .fastq files (example included at \u003ccode\u003eexample/samples.analysis.tsv\u003c/code\u003e). Create a samplesheet, based on the config file, using the built-in methods:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake samplesheet\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote that this uses the values previously saved in \u003ccode\u003econfig.json\u003c/code\u003e to create the samplesheet\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSample Pairs (Optional)\u003c/h3\u003e\u003ca id=\"user-content-sample-pairs-optional\" class=\"anchor\" aria-label=\"Permalink: Sample Pairs (Optional)\" href=\"#sample-pairs-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe LG-PACT pipeline has special processing for tumor-normal pairs. These pairs should be defined in the \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e file, by listing the matched Normal sample for each applicable sample.\u003c/p\u003e\n\u003cp\u003eIn order to update \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e automatically with these sample pairs, an extra samplesheet can be provided with the tumor-normal pairs.\u003c/p\u003e\n\u003cp\u003eCreate a \u003ccode\u003esamples.tumor.normal.csv\u003c/code\u003e samplesheet (example included at \u003ccode\u003eexample/samples.tumor.normal.csv\u003c/code\u003e) with the tumor-normal groupings for your samples, and update the original samplesheet with it by running the following script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython update-samplesheets.py --tumor-normal-sheet samples.tumor.normal.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf a demultiplexing samplesheet with extra tumor-normal pairs information was supplied (see example: \u003ccode\u003eexample/demux-SampleSheet.csv\u003c/code\u003e), then it can be used to update the samplesheet with pairs information with the following recipe:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake pairs PAIRS_SHEET=demux-SampleSheet.csv PAIRS_MODE=demux\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline includes an auto-run functionality that attempts to determine the best configuration to use for NYU phoenix and Big Purple HPC clusters:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the pipeline in the current session.\u003c/p\u003e\n\u003cp\u003eIn order to run the pipeline in the background as a job on NYU\u0027s Big Purple HPC, you should instead use the \u003ccode\u003esubmit\u003c/code\u003e recipe:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake submit SUBQ=fn_medium\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003eSUBQ\u003c/code\u003e is the name of the SLURM queue you wish to use.\u003c/p\u003e\n\u003cp\u003eRefer to the \u003ccode\u003eMakefile\u003c/code\u003e for more run options.\u003c/p\u003e\n\u003cp\u003eDue to the scale of the pipeline, a \"local\" run option is not currently configured, but can be set up easily based on the details shown in the Makefile and \u003ccode\u003enextflow.config\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExtra Parameters\u003c/h3\u003e\u003ca id=\"user-content-extra-parameters\" class=\"anchor\" aria-label=\"Permalink: Extra Parameters\" href=\"#extra-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can supply extra parameters for Nextflow by using the \u003ccode\u003eEP\u003c/code\u003e variable included in the Makefile, like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run EP=\u0027--runID 180320_NB501073_0037_AH55F3BGX5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDemo\u003c/h3\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-label=\"Permalink: Demo\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA demo dataset can be loaded using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake demo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003echeckout a demo dataset\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecreate a \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e samplesheet for the analysis\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can then proceed to run the analysis with the commands described above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMore Functionality\u003c/h2\u003e\u003ca id=\"user-content-more-functionality\" class=\"anchor\" aria-label=\"Permalink: More Functionality\" href=\"#more-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtra functions included in the Makefile for pipeline management include:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003emake clean\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-make-clean\" class=\"anchor\" aria-label=\"Permalink: make clean\" href=\"#make-clean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRemoves all Nextflow output except for the most recent run. Use \u003ccode\u003emake clean-all\u003c/code\u003e to remove all pipeline outputs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003emake record PRE=some_prefix_\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-make-record-presome_prefix_\" class=\"anchor\" aria-label=\"Permalink: make record PRE=some_prefix_\" href=\"#make-record-presome_prefix_\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\"Records\" copies of the most recent pipeline run\u0027s output logs, configuration, Nextflow reports, etc.. Useful for recording analyses that failed or had errors in order to debug. Include the optional argument \u003ccode\u003eTASK\u003c/code\u003e to specify a Nextflow \u003ccode\u003ework\u003c/code\u003e directory to include in the records (example: \u003ccode\u003emake record PRE=error_something_broke_ TASK=e9/d9ff34\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003emake kill\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-make-kill\" class=\"anchor\" aria-label=\"Permalink: make kill\" href=\"#make-kill\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAttempts to cleanly shut down a pipeline running on a remote host e.g. inside a SLURM HPC compute job. Note that you can also use \u003ccode\u003escancel\u003c/code\u003e to halt the parent Nextflow pipeline job as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ccode\u003emake fix-permissions\u003c/code\u003e, \u003ccode\u003emake fix-group\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-make-fix-permissions-make-fix-group\" class=\"anchor\" aria-label=\"Permalink: make fix-permissions, make fix-group\" href=\"#make-fix-permissions-make-fix-group\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAttempts to fix usergroup and permissions issues that may arise on shared systems with multiple users. Be sure to use the extra argument \u003ccode\u003eUSERGROUP=somegroup\u003c/code\u003e to specify the usergroup to update to.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003emake finalize-work-rm\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-make-finalize-work-rm\" class=\"anchor\" aria-label=\"Permalink: make finalize-work-rm\" href=\"#make-finalize-work-rm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExamines the \u003ccode\u003etrace.txt\u003c/code\u003e output from the most recent completed pipeline run in order to determine while subdirectories in the Nextflow \u003ccode\u003ework\u003c/code\u003e dir are no longer needed, and then deletes them. Can delete multiple subdirs in parallel when run with \u003ccode\u003emake finalize-work-rm -j 20\u003c/code\u003e e.g. specifying to delete 20 at a time, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoftware\u003c/h1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-label=\"Permalink: Software\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDeveloped under Centos 6, RHEL 7, macOS 10.12\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ebash\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU \u003ccode\u003emake\u003c/code\u003e, standard GNU tools\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePython 2/3\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJava 8+ for Nextflow\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDocker/Singularity as needed for containers\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 8, + "subscribers_count": 3, + "topics": [ + "nextflow", + "singularity-container", + "docker-container", + "python", + "groovy", + "slurm", + "html", + "makefile", + "markdown" + ], + "updated_at": 1733171020.0 }, { "data_format": 2, - "description": "A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data", + "description": null, "filenames": [ - "inst/Singularity" + "Singularity" ], - "full_name": "Malindrie/scShapes", + "full_name": "fanglab/6mASCOPE", "latest_release": null, - "readme": "\n\u003ch1\u003e\u003ca id=\"user-content-scshapes\" class=\"anchor\" aria-hidden=\"true\" href=\"#scshapes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escShapes\u003c/h1\u003e\n\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/Malindrie/scShapes\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f46be69a2babf0d92348a4d2abbc1834186998fa8fb4d023c1e85bc5f9405e8a/68747470733a2f2f7472617669732d63692e636f6d2f4d616c696e647269652f73635368617065732e7376673f6272616e63683d6d6173746572\" alt=\"Travis build status\" data-canonical-src=\"https://travis-ci.com/Malindrie/scShapes.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://ci.appveyor.com/project/Malindrie/scShapes\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9b16a84bf2877273e52d87887ce418eb0fff59cd1066cca77a8700f6bae73d6b/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f4d616c696e647269652f73635368617065733f6272616e63683d6d6173746572267376673d74727565\" alt=\"AppVeyor build status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/Malindrie/scShapes?branch=master\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003eWe present a novel statistical framework for identifying differential\ndistributions in single-cell RNA-sequencing (scRNA-seq) data between\ntreatment conditions by modeling gene expression read counts using\ngeneralized linear models (GLMs). We model each gene independently under\neach treatment condition using the error distributions Poisson (P),\nNegative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated\nNegative Binomial (ZINB) with log link function and model based\nnormalization for differences in sequencing depth. Since all four\ndistributions considered in our framework belong to the same family of\ndistributions, we first perform a Kolmogorov-Smirnov test to select\ngenes belonging to the family of ZINB distributions. Genes passing the\nKS test will be then modeled using GLMs. Model selection is done by\ncalculating the Bayesian Information Criterion and likelihood ratio test\nstatistic.\u003c/p\u003e\n\u003cp\u003eWhile most methods for differential gene expression analysis aim to\ndetect a shift in the mean of expressed values, single cell data are\ndriven by over-dispersion and dropouts requiring statistical\ndistributions that can handle the excess zeros. By modeling gene\nexpression distributions, our framework can identify subtle variations\nthat do not involve the change in mean. It also has the flexibility to\nadjust for covariates and perform multiple comparisons while explicitly\nmodeling the variability between samples.\u003c/p\u003e\n\u003cp\u003eYou can view the preprint of our method in\n\u003ca href=\"https://www.biorxiv.org/content/10.1101/2022.02.13.480299v1\" rel=\"nofollow\"\u003ebioRxiv\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eYou can install the released version of scShapes with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eMalindrie/scShapes\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003escShapes\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-hidden=\"true\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample\u003c/h2\u003e\n\u003cp\u003eThis is a basic example which shows how you can use scShapes for\nidentifying differential distributions in single-cell RNA-seq data. For\nthis example data we use the human immune cells (PBMC) dataset\ndistributed through the\n\u003ca href=\"https://github.com/satijalab/seurat-data\"\u003eSeuratData\u003c/a\u003e package.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003escShapes\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eLoading and preparing data for input \u003c/span\u003e\nlibrary(\u003cspan class=\"pl-smi\"\u003eSeurat\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003eSeuratData\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003edplyr\u003c/span\u003e)\nlibrary(\u003cspan class=\"pl-smi\"\u003eBiocParallel\u003c/span\u003e)\nset.seed(\u003cspan class=\"pl-c1\"\u003e0xBEEF\u003c/span\u003e)\n\nInstallData(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eifnb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\nLoadData(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eifnb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.list\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e SplitObject(\u003cspan class=\"pl-smi\"\u003eifnb\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003esplit.by\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003estim\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe first filter the genes to keep only genes expressed in at least 10%\nof cells:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eFirst extract the RNA-seq counts from the \u0027RNA\u0027 assay of the seurat object\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.obj\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lapply(\u003cspan class=\"pl-smi\"\u003eifnb.list\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e (\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e) as.matrix(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eassays\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eRNA\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003ecounts\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lapply(\u003cspan class=\"pl-smi\"\u003eifnb.obj\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e (\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e) filter_counts(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eperc.zero\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.1\u003c/span\u003e))\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Removing 527 rows of genes with all zero counts\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\u0026gt; Removing 778 rows of genes with all zero counts\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to normalize for differences in sequencing depth, the log of\nthe total UMI counts assigned per cell will be used as an offset in the\nGLM. This function is inbuilt in the algorithm; however the user is\nrequired to input the library sizes. We can calculate the library sizes\nfor the two treatment conditions as;\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lapply(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e (\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e) apply(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e(\u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e) sum(\u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e)))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u2018meta.data\u2019 slot of the Seurat object also contains information on\nthe cell-types, which will be used as a covariate in the GLM model to\naccount for known biological variation in the data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lapply(\u003cspan class=\"pl-smi\"\u003eifnb.list\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e (\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\n \u003cspan class=\"pl-v\"\u003ecell.type\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efactor\u003c/span\u003e(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003emeta.data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eseurat_annotations\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003erow.names\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e colnames(\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e@\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eassays\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eRNA\u003c/span\u003e)))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor the purpose of this example we only run the pipeline for randomly\nselected 20 common genes under both treatment conditions \u2018CTRL\u2019 and\n\u2018STIM\u2019.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eRandomly select 20 genes among common genes between the two treatment conditions\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003ecomm.genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e intersect(rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e), rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003ecomm.20.genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e sample(\u003cspan class=\"pl-smi\"\u003ecomm.genes\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ereplace\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFALSE\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSubset the randomly selected 20 genes\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e[rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecomm.20.genes\u003c/span\u003e,]\n\u003cspan class=\"pl-smi\"\u003eifnb.stim\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e[rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecomm.20.genes\u003c/span\u003e,]\n\u003cspan class=\"pl-smi\"\u003eifnb.subset\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003elist\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eCTRL\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.ctrl\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eSTIM\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.stim\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePerform Kolmogorov-Smirnov test to select genes belonging to the family\nof ZINB distributions.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e ks_test(\u003cspan class=\"pl-smi\"\u003eifnb.subset\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecexpr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003elib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSnowParam(\u003cspan class=\"pl-v\"\u003eworkers\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e,\u003cspan class=\"pl-v\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSOCK\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e ks_test(\u003cspan class=\"pl-smi\"\u003eifnb.subset\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecexpr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003elib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSnowParam(\u003cspan class=\"pl-v\"\u003eworkers\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e,\u003cspan class=\"pl-v\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSOCK\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSelect genes significant from the KS test.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eBy default the \u0027ks_sig\u0027 function performs Benjamini-Hochberg correction for multiple hypothese testing\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eand selects genes significant at p-value of 0.01\u003c/span\u003e\n\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.sig.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e ks_sig(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.sig.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e ks_sig(\u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSubset UMI counts corresponding to the genes significant from the KS test\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.sig.genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003elist\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eCTRL\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e as.data.frame(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.sig.KS\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egenes\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003eSTIM\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e as.data.frame(\u003cspan class=\"pl-smi\"\u003eifnb.stim.sig.KS\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egenes\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e[rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e rownames(\u003cspan class=\"pl-smi\"\u003eifnb.sig.genes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e),]\n \u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e[rownames(\u003cspan class=\"pl-smi\"\u003eifnb.filtered\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e rownames(\u003cspan class=\"pl-smi\"\u003eifnb.sig.genes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e),]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFit the 4 distributions P,NB,ZIP,ZINB for genes that belong to the ZINB\nfamily of distributions by fitting GLM with log of the library sizes as\nan offset and cell types as a covariate in the GLM.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fit_models(\u003cspan class=\"pl-v\"\u003ecounts\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecexpr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003elib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSnowParam(\u003cspan class=\"pl-v\"\u003eworkers\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\u003cspan class=\"pl-v\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSOCK\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fit_models(\u003cspan class=\"pl-v\"\u003ecounts\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003ecexpr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003elib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSnowParam(\u003cspan class=\"pl-v\"\u003eworkers\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\u003cspan class=\"pl-v\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSOCK\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce the 4 distributions are fitted, we next calculate the BIC value for\neach model and select the model with the least BIC value.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.bic.val\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e model_bic(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.bic.val\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e model_bic(\u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eselect model with least bic value\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.lbic\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lbic_model(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.bic.val\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.KS\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.lbic\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e lbic_model(\u003cspan class=\"pl-smi\"\u003eifnb.stim.bic.val\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.KS\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo ensure the fit of the models selected based on the least BIC value,\nadditionally we perform LRT to test for model adequacy and presence of\nzero-inflation.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.gof\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e gof_model(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.lbic\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eCTRL\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSerialParam())\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.gof\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e gof_model(\u003cspan class=\"pl-smi\"\u003eifnb.stim.lbic\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.variables\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.lib.size\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eSTIM\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBPPARAM\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eSerialParam())\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally based on the results of the model adequacy tests, we can\nidentify the distribution of best fit for each gene.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_model(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.gof\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e select_model(\u003cspan class=\"pl-smi\"\u003eifnb.stim.gof\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOnce the distribution of best fit is identified for genes of interest,\nit is also possible to extract parameters of interest for the models.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e model_param (\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNULL\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e model_param (\u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNULL\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing above results we can now identify the differentially distributed\ngenes between \u2018CTRL\u2019 and \u2018STIM\u2019. First we need to subset genes that is\nsignificant in the KS test in both conditions.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSubset the common genes between the two groups, that pass the GOF test\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e unlist(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e unlist(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.gof.sig\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e intersect(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.fit\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.fit\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003eifnb.dist.ctrl\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eP_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eNB_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZIP_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZINB_genes\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.dist.ctrl\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edist\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e c(rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eP_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eNB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eNB_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eZIP\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZIP_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eZINB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.ctrl.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZINB_genes\u003c/span\u003e)))\n\n\u003cspan class=\"pl-smi\"\u003eifnb.dist.stim\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eP_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eNB_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZIP_genes\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZINB_genes\u003c/span\u003e))\n\u003cspan class=\"pl-smi\"\u003eifnb.dist.stim\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edist\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e c(rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ePo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eP_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eNB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eNB_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eZIP\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZIP_genes\u003c/span\u003e)), rep(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eZINB\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, length(\u003cspan class=\"pl-smi\"\u003eifnb.stim.dist.fit\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eZINB_genes\u003c/span\u003e)))\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eDataframe consisting of distributions followed by each gene passing the KS test\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eifnb.gof.ctrl\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.dist.ctrl\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eifnb.dist.ctrl\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.sig\u003c/span\u003e,]\n\u003cspan class=\"pl-smi\"\u003eifnb.gof.stim\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.dist.stim\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003eifnb.dist.stim\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egene\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e%in%\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.sig\u003c/span\u003e,]\n\n\u003cspan class=\"pl-smi\"\u003eifnb.distr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003ectrl\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.ctrl\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edist\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003erow.names\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.ctrl\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egene\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003eifnb.distr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003estim\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eifnb.gof.stim\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edist\u003c/span\u003e[match(rownames(\u003cspan class=\"pl-smi\"\u003eifnb.distr\u003c/span\u003e), \u003cspan class=\"pl-smi\"\u003eifnb.gof.stim\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003egene\u003c/span\u003e)]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUsing the dataframe of genes and distribution followed under each\ncondition now we can identify genes changing distribution between \u2018CTRL\u2019\nand \u2018STIM\u2019\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eifnb.DD.genes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e change_shape(\u003cspan class=\"pl-smi\"\u003eifnb.distr\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will give a list of two lists with genes changing distribution\nbetween condition and genes changing distribution from unimodal in one\ncondition to zero-inflated in the other condition.\u003c/p\u003e\n", - "stargazers_count": 7, - "subscribers_count": 4, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e6mASCOPE\u003c/h1\u003e\u003ca id=\"user-content-6mascope\" class=\"anchor\" aria-label=\"Permalink: 6mASCOPE\" href=\"#6mascope\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e6mASCOPE is a toolbox to assess 6mA events in eukaryotic species using a quantitative deconvolution approach. By using a novel short-insert library (200~400bp) design with the PacBio sequencing Sequel II System, 6mASCOPE makes an effective use of the large number of circular consensus (CCS) reads to reliably capture deviations in IPD values at single molecule resolution. Taking an innovative metagenomic approach, 6mASCOPE deconvolves the DNA molecules from a gDNA sample into species and genomic regions of interests, and sources of contamination. Using a rationally designed machine learning model, 6mASCOPE enables sensitive and reliable 6mA quantification for each of the deconvolved composition.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u0027 notes\u003c/h2\u003e\u003ca id=\"user-content-authors-notes\" class=\"anchor\" aria-label=\"Permalink: Authors\u0027 notes\" href=\"#authors-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are actively developing 6mASCOPE to facilitate usage and broaden features. All feedback is more than welcome. You can reach us on twitter (\u003ca href=\"https://twitter.com/iamfanggang\" rel=\"nofollow\"\u003e@iamfanggang\u003c/a\u003e and \u003ca href=\"https://twitter.com/kong_yimeng\" rel=\"nofollow\"\u003e@kong_yimeng\u003c/a\u003e) or directly through the \u003ca href=\"https://github.com/fanglab/6mASCOPE/issues\"\u003eGitHub issues system\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e6mASCOPE is distributed as a fully functional image bypassing the need to install any dependencies others than the virtualization software. We recommend using Singularity, which can be installed on Linux systems and is often the preferred solution by HPC administrators (\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eQuick Start\u003c/a\u003e). \u003ccode\u003e6mASCOPE\u003c/code\u003e was tested extensively with Singularity v3.6.4.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load singularity/3.6.4 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Required only singularity/3.6.4 is a dynamic environment module. \u003c/span\u003e\nsingularity pull 6mASCOPE.sif library://fanglabcode/default/6mascope:latest \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the image from cloud.sylabs.io; Make sure you have the network connection\u003c/span\u003e\nsingularity build --sandbox 6mASCOPE 6mASCOPE.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a writable container named 6mASCOPE\u003c/span\u003e\nsingularity run --no-home -w 6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start an interactive shell to use 6mASCOPE, type `exit` to leave\u003c/span\u003e\ninit_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Only required once when start using 6mASCOPE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e run_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Required every time when running 6mASCOPE\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe image retrieved from \u003ca href=\"https://cloud.sylabs.io/home\" rel=\"nofollow\"\u003eSylab Cloud\u003c/a\u003e with \u003ccode\u003esingularity pull\u003c/code\u003e (e.g. 6mASCOPE.sif) is already built and can be reused at will. Containers built with those instructions are writable meaning that results from 6mASCOPE analysis can be retrieved when the container is not running. Outputs for the following commands can be found at \u003ccode\u003e./path/to/6mASCOPE/home/6mASCOPE/\u003c/code\u003e. To re-run the same container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --no-home -w 6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Re-run container 6mASCOPE, type `exit` to leave\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e run_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Required every time when running 6mASCOPE\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTool showcase\u003c/h2\u003e\u003ca id=\"user-content-tool-showcase\" class=\"anchor\" aria-label=\"Permalink: Tool showcase\" href=\"#tool-showcase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo showcase the toolbox applications, we provide examples for the analysis of the Drosophila ~45min embryo dataset presented in our manuscript (Fig 5). The dataset can be downloaded with the following commands from within a 6mASCOPE container: \u003ccode\u003e6mASCOPE get_test_data\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContamination estimation\u003c/h3\u003e\u003ca id=\"user-content-contamination-estimation\" class=\"anchor\" aria-label=\"Permalink: Contamination estimation\" href=\"#contamination-estimation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGoal\u003c/h4\u003e\u003ca id=\"user-content-goal\" class=\"anchor\" aria-label=\"Permalink: Goal\" href=\"#goal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get an idea about the overall contamination of a gDNA sample. This step helps users define the composition of a gDNA sample using a metagenomic approach to assign reads to different species.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDescription:\u003c/h4\u003e\u003ca id=\"user-content-description-1\" class=\"anchor\" aria-label=\"Permalink: Description:\" href=\"#description-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a given CCS dataset generated from short-insert library, 6mASCOPE will examine if there are contaminating species and calculate the proportion of reads mapped to the reference and top 50 contaminated species from reads that do not map to the eukaryotic species of interest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInputs:\u003c/h4\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs:\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCCS reads file capturing all the genetic material in a gDNA sample (.fasta, pre-computed in the following example)\u003c/li\u003e\n\u003cli\u003eEukaryotic reference of genome of interest (.fasta)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutputs:\u003c/h4\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs:\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a given CCS dataset generated from short-insert library, \u003ccode\u003e6mASCOPE\u003c/code\u003e will examine if there are contaminating species and calculate the proportion of reads mapped to the reference and top 50 contaminated species from reads that do not map to the eukaryotic species of interest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExample of the Output:\u003c/h5\u003e\u003ca id=\"user-content-example-of-the-output\" class=\"anchor\" aria-label=\"Permalink: Example of the Output:\" href=\"#example-of-the-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eRemove 8491 possible inter-species chimeric reads for further analysis\n#total_CCS\tmapped_to_goi\tcontaminants\n666159\t640345 (96.1249%)\t25814 (3.87505%)\n\nTop 50 mapped species outside goi reference\n#Count\tSpecies\n 10836 Saccharomyces cerevisiae\n 2413 Acetobacter tropicalis\n 1524 Acetobacter pasteurianus\n 1479 Lactobacillus plantarum\n 882 Acetobacter sp.\n ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(Full species list can be viewed in \u003ccode\u003etest.contam.estimate.txt\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExample commands:\u003c/h5\u003e\u003ca id=\"user-content-example-commands\" class=\"anchor\" aria-label=\"Permalink: Example commands:\" href=\"#example-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e6mASCOPE contam -c test.ccs.fasta -r test.ref.fasta -o test.contam.estimate.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this example, \u003ccode\u003etest.ccs.fasta\u003c/code\u003e includes CCS reads (674,650) from the Drosophila ~45min embryo reads dataset described in our manuscript and pre-filtered with command \u003ccode\u003e6mASCOPE ccs\u003c/code\u003e. Using 5 cores, runtime is ~12m51s. The output shows ~3.9% CCS reads come from contaminated sources other than Drosophila melanogaster, the genome of interest (goi). Please be noted, blastn is embedded within this step, which will need at least 32-64G RAM.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e6mA analysis using quantitative deconvolution\u003c/h3\u003e\u003ca id=\"user-content-6ma-analysis-using-quantitative-deconvolution\" class=\"anchor\" aria-label=\"Permalink: 6mA analysis using quantitative deconvolution\" href=\"#6ma-analysis-using-quantitative-deconvolution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGoal:\u003c/h4\u003e\u003ca id=\"user-content-goal-1\" class=\"anchor\" aria-label=\"Permalink: Goal:\" href=\"#goal-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each source determined in \u003ccode\u003e6mASCOPE contam\u003c/code\u003e, this step will quantify the 6mA/A level and calculate the 6mA contribution (%) of each source to the total 6mA abundance in the gDNA sample.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInputs:\u003c/h4\u003e\u003ca id=\"user-content-inputs-1\" class=\"anchor\" aria-label=\"Permalink: Inputs:\" href=\"#inputs-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThe same CCS reads file as explained above for Contamination Estimation (.fasta).\u003c/li\u003e\n\u003cli\u003eIPD and QV information of the CCS reads (pre-computed in the following example, ; this can be generated for new data with \u003ccode\u003e6mASCOPE ipd\u003c/code\u003e command, as explained in detailed tutorial).\u003c/li\u003e\n\u003cli\u003eUser defined groups besides the genome of interest. Examples as shown below. (Left columns: subgroup name. Right columns: contamination sources, use vertical line if multiple sources included within one subgroup).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eSaccharomyces Saccharomyces\nAcetobacter Acetobacter|Komagataeibacter\nLactobacillus Lactobacillus\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutputs:\u003c/h4\u003e\u003ca id=\"user-content-outputs-1\" class=\"anchor\" aria-label=\"Permalink: Outputs:\" href=\"#outputs-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA table including the following information: the proportion (%) of reads from each source out of the total number of reads; source-specific 6mA/A level with 95% confidence intervals (log10-transformed), and contribution (%) of each source to the total 6mA abundance in the gDNA sample (as presented in the manuscript Figure 5A, B, C)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample commands:\u003c/h4\u003e\u003ca id=\"user-content-example-commands-1\" class=\"anchor\" aria-label=\"Permalink: Example commands:\" href=\"#example-commands-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e6mASCOPE quant -c test.ccs.fasta -i test.IPD.out.A -o test -r test.ref.fasta -s subgroup.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this example, the file \u003ccode\u003etest.IPD.out.A\u003c/code\u003e includes the pre-calculated IPD and QV information on the CCS molecules (can be generated with \u003ccode\u003e6mASCOPE ipd\u003c/code\u003e). Only Adenines were included here to to reduce computational time and ease evaluation. \u003ccode\u003esubgroup.txt\u003c/code\u003e includes the pre-defined main contamination groups, inferred from the top mapped species and blast output from \u003ccode\u003e6mASCOPE contam\u003c/code\u003e. Using 5 cores, runtime is ~13m17s.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample output:\u003c/h4\u003e\u003ca id=\"user-content-example-output\" class=\"anchor\" aria-label=\"Permalink: Example output:\" href=\"#example-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e #Subgroup count ReadsProportion 6mAlevel(ppm) 6mAlevel(log10) UpCI DownCI subtotal(ppm) contribution(%)\n goi 640345 0.9612 2.0417 -5.69 -5.0 -6.0 1.9625 1.4431\n Saccharomyces 11011 0.0165 45.7088 -4.34 -3.9 -6.0 0.7542 0.5546\n Acetobacter 5757 0.0086 5495.4087 -2.26 -2.0 -2.5 47.2605 34.7522\n Lactobacillus 1517 0.0023 977.2372 -3.01 -2.7 -3.3 2.2476 1.6528\n others 7529 0.0113 7413.1024 -2.13 -1.9 -2.4 83.7681 61.5974\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/test.6mASCOPE.ReadsProportion.png\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.ReadsProportion.png\" alt=\"The proportion of CCS reads from each group 6mA\" width=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n1. The % of total CCS reads mapped to different subgroups. Left: The % of CCS reads mapped to D. melanogaster (genome of interest) and contamintant subgroups. Right: The % of CCS reads mapped to different contaminant sources.\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/test.6mASCOPE.6mAlevel.png\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.6mAlevel.png\" alt=\"Group-specific 6mA/A level prediction with confidence intervals 6mA\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n2. 6mA quantification and 95% confidence intervals (log10-transformed) on CCS reads mapped to different subgroups. Please be noted, it is important to combine the estimated 6mA/A level with its confidence interval for reliable data interpretation. In this example, the 6mA/A level of Saccharomyces (45.7ppm) does not mean abundant 6mA events in this subgroup because it has a wide range of confidence interval (1-125ppm; -6.0 to -3.9 with log10 transformed). In the paper, an additional Sequel II run for this single species (higher yield) actually shows extremely low 6mA level (2ppm, confidence interval: 1-10ppm).\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/test.6mASCOPE.6mAcontribution.png\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.6mAcontribution.png\" alt=\"Group-specific 6mA/A level prediction with confidence intervals 6mA\" width=\"300\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n3. Contribution (%) of each source to total 6mA abundance in the gDNA sample. CCS reads mapped to the D. melanogaster genome only explains 1.4% of the total 6mA events in the gDNA sample (green).\n\u003cp\u003eThese figures can be drawn with \u003ccode\u003esh ~/code/draw_example.sh test.6mASCOPE.txt\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a comprehensive description of\u00a06mASCOPE including installation guide, data preprocessing and a detailed tutorial, including how to apply 6mASCOPE to your own datasets, please refer to the\u00a0\u003ca href=\"https://6mascope.readthedocs.io/en/latest/overview.html\" rel=\"nofollow\"\u003ecomplete documentation\u003c/a\u003e .\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYimeng Kong, Lei Cao, Gintaras Deikus, Yu Fan, Edward A. Mead, Weiyi Lai, Yizhou Zhang, Raymund Yong, Robert Sebra, Hailin Wang, Xue-Song Zhang \u0026amp; Gang Fang. Critical assessment of DNA adenine methylation in eukaryotes using quantitative deconvolution. \u003cem\u003eScience\u003c/em\u003e (2022). doi:\u003ca href=\"http://doi.org/10.1126/science.abe7489\" rel=\"nofollow\"\u003e10.1126/science.abe7489\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 6, "topics": [], - "updated_at": 1676514392.0 + "updated_at": 1714718024.0 + }, + { + "data_format": 2, + "description": "Work with PLINK from R", + "filenames": [ + "Singularity" + ], + "full_name": "AJResearchGroup/plinkr", + "latest_release": "v0.20.2", + "readme": "\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eplinkr\u003c/h1\u003e\u003ca id=\"user-content-plinkr\" class=\"anchor\" aria-label=\"Permalink: plinkr\" href=\"#plinkr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/richelbilderbeek/plinkr/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/plinkr/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4fff526bbece345c9f8ab3f09c5ab171c9b48b5c7d6866408fec260ff2be31d/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f706c696e6b722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/plinkr/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/plinkr/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/plinkr/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ace740ee7a2c1d753db958808c8c716545a28254b97c3e3bd1d330bb908d6f1/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f706c696e6b722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/plinkr/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWork with PLINK and PLINK2 from R.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDoing the first PLINK example:\n\u003ca href=\"https://youtu.be/LsfKQw2oIUg\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_basic_usage.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with one or more quantitative traits:\n\u003ca href=\"https://youtu.be/IicNdc8sDfI\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_assoc_qt.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with ideal quantitative traits:\n\u003ca href=\"https://youtu.be/oXGy83WiHm4\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_demo_qt_assoc.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulate quantitative traits:\n\u003ca href=\"https://youtu.be/H0XlLVsFry4\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_create_demo_assoc_qt_params.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimulate custom traits: \u003ca href=\"https://youtu.be/5X1kLkiQbtw\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e\n\u003ca href=\"http://richelbilderbeek.nl/plinkr_create_custom_trait.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDetect an association with a binary trait/case-control phenotype:\n\u003ca href=\"https://youtu.be/LhXQcDQvZS0\" rel=\"nofollow\"\u003eYouTube\u003c/a\u003e \u003ca href=\"http://richelbilderbeek.nl/plinkr_assoc.ogv\" rel=\"nofollow\"\u003edownload\n(.ogv)\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/install.md\"\u003edoc/install.md\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning PLINK\u003c/h3\u003e\u003ca id=\"user-content-running-plink\" class=\"anchor\" aria-label=\"Permalink: Running PLINK\" href=\"#running-plink\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can seamlessly run any \u003ccode\u003ePLINK\u003c/code\u003e or \u003ccode\u003ePLINK2\u003c/code\u003e versions.\u003c/p\u003e\n\u003cp\u003eRun PLINK:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003elibrary(\u003cspan class=\"pl-smi\"\u003eplinkr\u003c/span\u003e)\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo call a specific version of PLINK:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003erun_plink(c(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--noweb\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), create_plink_v1_7_options())\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, create_plink_v1_9_options())\nrun_plink(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--help\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, create_plink_v2_0_options())\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOf course, you can also call PLINK to detect genetic associations :-) :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the PLINK v1.9 example files\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_plink_v1_9_options()\n\u003cspan class=\"pl-smi\"\u003eped_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_plink_example_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etoy.ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e)\n\u003cspan class=\"pl-smi\"\u003emap_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e get_plink_example_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etoy.map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eplink_v1_9\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Do a case-control association\u003c/span\u003e\n\u003cspan class=\"pl-e\"\u003eplinkr\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003erun_plink(\n \u003cspan class=\"pl-v\"\u003eargs\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e c(\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003eped_filename\u003c/span\u003e, \n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003emap_filename\u003c/span\u003e\n )\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003ebasic_usage\u003c/code\u003e for basic usage of PLINK, as taken\nfrom the PLINK website, which shows a quantitative trait analysis\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003etest_assoc_qt\u003c/code\u003e for the same basic usage of PLINK,\nusing the \u003ccode\u003eplinkr\u003c/code\u003e interface\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003edemo_assoc_qt\u003c/code\u003e for doing a quantitative trait\nanalysis using simulated data and the \u003ccode\u003eplinkr\u003c/code\u003e interface\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun a quantitative trait analysis on existing files\u003c/h3\u003e\u003ca id=\"user-content-run-a-quantitative-trait-analysis-on-existing-files\" class=\"anchor\" aria-label=\"Permalink: Run a quantitative trait analysis on existing files\" href=\"#run-a-quantitative-trait-analysis-on-existing-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRead from PLINK text files\u003c/h4\u003e\u003ca id=\"user-content-read-from-plink-text-files\" class=\"anchor\" aria-label=\"Permalink: Read from PLINK text files\" href=\"#read-from-plink-text-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_assoc_qt_data(\n \u003cspan class=\"pl-v\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_plink_text_filenames(\n \u003cspan class=\"pl-v\"\u003emap_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.map\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003eped_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.ped\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n ),\n \u003cspan class=\"pl-v\"\u003ephenotype_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_phenotype_data_filename(\n \u003cspan class=\"pl-v\"\u003ephe_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.phe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \n )\n)\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e assoc_qt(\u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e)\nread_plink_qassoc_file(\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eqassoc_filenames\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRead from PLINK binary files\u003c/h4\u003e\u003ca id=\"user-content-read-from-plink-binary-files\" class=\"anchor\" aria-label=\"Permalink: Read from PLINK binary files\" href=\"#read-from-plink-binary-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_assoc_qt_data(\n \u003cspan class=\"pl-v\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_plink_bin_filenames(\n \u003cspan class=\"pl-v\"\u003ebed_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.bed\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003ebim_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.bim\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e), \n \u003cspan class=\"pl-v\"\u003efam_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.fam\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n ),\n \u003cspan class=\"pl-v\"\u003ephenotype_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_phenotype_data_filename(\n \u003cspan class=\"pl-v\"\u003ephe_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_plinkr_filename(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edemo_assoc_qt.phe\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e) \n )\n)\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e assoc_qt(\u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e)\nread_plink_qassoc_file(\u003cspan class=\"pl-smi\"\u003eassoc_qt_filenames\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eqassoc_filenames\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDemonstrate a quantitative trait analysis\u003c/h3\u003e\u003ca id=\"user-content-demonstrate-a-quantitative-trait-analysis\" class=\"anchor\" aria-label=\"Permalink: Demonstrate a quantitative trait analysis\" href=\"#demonstrate-a-quantitative-trait-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can seamlessly use \u003ccode\u003ePLINK\u003c/code\u003e/\u003ccode\u003ePLINK2\u003c/code\u003e in-memory-data or files.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_demo_assoc_qt_data()\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prove that this is PLINK text data\u003c/span\u003e\ncheck_plink_text_data(\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e)\n\n\u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e create_test_assoc_qt_params()\nassoc_qt(\n \u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo convert the in-memory data to PLINK binary format and do the same\nquantitative trait analysis:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e convert_plink_text_data_to_plink_bin_data(\n \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e\n)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Prove that this is PLINK binary data\u003c/span\u003e\ncheck_plink_bin_data(\u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003edata\u003c/span\u003e)\n\nassoc_qt(\n \u003cspan class=\"pl-v\"\u003eassoc_qt_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_data\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eassoc_qt_params\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eassoc_qt_params\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003edemo_assoc_qt\u003c/code\u003e for a walk-through of the data that\nis simulated by default\u003c/li\u003e\n\u003cli\u003eSee the vignette \u003ccode\u003ecreate_demo_assoc_qt_params\u003c/code\u003e for many examples how\ndata can be simulated\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFile I/O\u003c/h3\u003e\u003ca id=\"user-content-file-io\" class=\"anchor\" aria-label=\"Permalink: File I/O\" href=\"#file-io\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e can read and save many types of PLINK files. Below is an\noverview. List from \u003ca href=\"https://www.cog-genomics.org/plink2/formats\" rel=\"nofollow\"\u003ethe PLINK file format\nreference\u003c/a\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFile extension\u003c/th\u003e\n\u003cth\u003e\n\u003ccode\u003eplink\u003c/code\u003e read function\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.adjusted\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_adjusted_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.allele.no.snp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_assoc_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.adjusted\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_assoc_adjusted_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.dosage\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.fisher\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.linear\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.assoc.logistic\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.auto.R\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.beagle.dat\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bed_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.bim\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bin_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary 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dea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.ped\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_ped_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.perm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.phe\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_phe_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.pphe\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.prob\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.profile\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_qassoc_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qassoc.gxe\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qassoc.means\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.qfam.*\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.range.report\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.raw\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.*.txt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.phase.inp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.recode.strct_in\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.ref\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.rel\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.rlist\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.sample\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set.{perm,mperm}\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.set.table\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.sexcheck\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.simfreq\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_simfreq_file\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tags.list\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tdt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tdt.poo\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eread_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tfam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.tped\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.traw\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.twolocus\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.var.ranges\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e.vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAssociations\u003c/h3\u003e\u003ca id=\"user-content-associations\" class=\"anchor\" aria-label=\"Permalink: Associations\" href=\"#associations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eAssociation type\u003c/th\u003e\n\u003cth\u003eData type\u003c/th\u003e\n\u003cth\u003eGeneral function\u003c/th\u003e\n\u003cth\u003eSpecialized function\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_on_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink_text_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCase-control\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_on_plink2_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink_text_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eassoc_qt_on_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink_text_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK1 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQuantitative\u003c/td\u003e\n\u003ctd\u003ePLINK2 bin files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eassoc_qt_on_plink2_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePLINK and PLINK2 files conversions\u003c/h3\u003e\u003ca id=\"user-content-plink-and-plink2-files-conversions\" class=\"anchor\" aria-label=\"Permalink: PLINK and PLINK2 files conversions\" href=\"#plink-and-plink2-files-conversions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e allows to convert between any PLINK and PLINK2 files.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFrom\u003c/th\u003e\n\u003cth\u003eTo\u003c/th\u003e\n\u003cth\u003eFunction name\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_files_to_plink_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_files_to_plink2_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_files_to_plink_text_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_files_to_plink2_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink2_bin_files_to_plink_text_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK binary files\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink2_bin_files_to_plink_bin_files\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003ctd\u003e\ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK text files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink_text_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eany type of files\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_files_to_plink2_bin_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003eSAIGE files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003ecreate_bgen_files_for_saige\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary files\u003c/td\u003e\n\u003ctd\u003ePLINK2 VCF files\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink_bin_files_to_plink_vcf_files\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePLINK and PLINK2 data conversions\u003c/h3\u003e\u003ca id=\"user-content-plink-and-plink2-data-conversions\" class=\"anchor\" aria-label=\"Permalink: PLINK and PLINK2 data conversions\" href=\"#plink-and-plink2-data-conversions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eplinkr\u003c/code\u003e allows to convert between any PLINK and PLINK2 data.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFrom\u003c/th\u003e\n\u003cth\u003eTo\u003c/th\u003e\n\u003cth\u003eFunction name\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_data_to_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 text data\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_text_data_to_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink_bin_data_to_plink_text_data\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK1 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink_bin_data_to_plink2_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK text data\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003econvert_plink2_bin_data_to_plink_text_data\u003c/code\u003e \ud83d\udea7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePLINK2 binary data\u003c/td\u003e\n\u003ctd\u003ePLINK binary data\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003econvert_plink2_bin_data_to_plink_bin_data\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity container\u003c/h2\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.sylabs.io/library/search;query=plinkr\" rel=\"nofollow\"\u003eFind the latest \u2018plinkr\u2019 Singularity\ncontainer\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/faq.md\"\u003edoc/faq.md\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 1, + "topics": [ + "gwas", + "plink", + "plink2", + "r", + "r-package" + ], + "updated_at": 1733295988.0 }, { "data_format": 2, - "description": null, + "description": "A nextflow pipeline with automatic software provisioning to generate hints and subsequent genome model predictions with AUGUSTUS", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "iqbal-lab-org/viridian_workflow", - "latest_release": "v1.1.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/iqbal-lab-org/viridian_workflow/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/viridian_workflow/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-viridian-workflow\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#viridian-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eViridian Workflow\u003c/h1\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/wiki\"\u003eViridian Workflow Wiki\u003c/a\u003e\nfor full documentation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eThe recommended method is to use a pre-built Docker or Singularity container\n(see the wiki for how to build your own).\u003c/p\u003e\n\u003cp\u003eBoth the Docker and Singularity container have the main script\n\u003ccode\u003eviridian\u003c/code\u003e installed.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cp\u003eGet a Docker image of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/iqbal-lab-org/viridian_workflow:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll Docker images are listed in the\n\u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/pkgs/container/viridian_workflow\"\u003epackages page\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download.\nEach release has a singularity image file called\n\u003ccode\u003eviridian_workflow_vX.Y.Z.img\u003c/code\u003e, where \u003ccode\u003eX.Y.Z\u003c/code\u003e is the release version.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThese instructions assume that you are assembling SARS-CoV-2 data.\u003c/p\u003e\n\u003cp\u003eTo run on paired Illumina reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample \\\n --tech illumina \\\n --reads1 reads_1.fastq.gz \\\n --reads2 reads_2.fastq.gz \\\n --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run on unpaired nanopore reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample \\\n --tech ont \\\n --reads reads.fastq.gz \\\n --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run on paired or unpaired Ion Torrent reads, use either of the\nabove commands, but with the option \u003ccode\u003e--tech iontorrent\u003c/code\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files\u003c/h2\u003e\n\u003cp\u003eThe default files in the output directory are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econsensus.fa.gz\u003c/code\u003e: a gzipped FASTA file of the consensus sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evariants.vcf\u003c/code\u003e: a VCF file of the identified variants between the consensus\nsequence and the reference genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog.json.gz\u003c/code\u003e: a gzipped JSON file that contains logging information\nfor the viridian workflow run.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eqc.tsv.gz\u003c/code\u003e: a gzipped tab-delimited file of per-base QC information\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme_id.depth_across_genome.pdf\u003c/code\u003e: a plot of the read depth across\nthe genome, with amplicons coloured in the background.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme_id.score_plot.pdf\u003c/code\u003e: a plot of the scoring for amplicon scheme\nidentification.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the option \u003ccode\u003e--keep_bam\u003c/code\u003e is used, then a sorted BAM file of the reads mapped\nto the reference will also be present, called\n\u003ccode\u003ereference_mapped.bam\u003c/code\u003e (and its index file \u003ccode\u003ereference_mapped.bam.bai\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-useful-options\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#useful-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful options\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--sample_name MY_NAME\u003c/code\u003e: use this to change the sample name\n(default is \"sample\") that is put in the final FASTA file, BAM file, and\nVCF file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads_bam MY_READS.bam\u003c/code\u003e: instead of providing FASTQ (or FASTA) files of\nreads, you can provide a sorted by genome coordinate and indexed BAM file.\nThe reference genome must be the same as that used by viridian\n(by default MN908947).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--keep_bam\u003c/code\u003e: use this option to keep the BAM file of original input reads\nmapped to the reference genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--decontam COVID\u003c/code\u003e: decontaminate the reads using ReadItAndKeep at the\nstart of the pipeline (this is incompatible with \u003ccode\u003e--reads_bam\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--force\u003c/code\u003e: use with caution - it will overwrite the output directory if\nit already exists.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--write_msa indel_as_ref\u003c/code\u003e: this will write a FASTA file\ncalled \u003ccode\u003emsa.indel_as_ref.fa\u003c/code\u003e that can be\nused to build trees. It has the consensus sequence aligned to the\nreference genome, but with insertions in the consensus ignored and\ndeletions replaced with the reference sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--ena_run RUN_ID\u003c/code\u003e: using this option will download the specified reads\nfrom the ENA, and infer the \u003ccode\u003e--tech\u003c/code\u003e option from the ENA metadata\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 7, - "subscribers_count": 6, + "full_name": "ikmb-denbi/genome-annotation", + "latest_release": null, + "readme": "\u003cp\u003e!!! THIS PROJECT HAS BEEN RETIRED. PLEASE WORK WITH ITS DROP-IN REPLACEMENT: \u003ca href=\"https://github.com/ikmb/esga\"\u003ehttps://github.com/ikmb/esga\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/deNBI_logo.jpg\"\u003e\u003cimg src=\"images/deNBI_logo.jpg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eESGA - Genome Annotation\u003c/h1\u003e\u003ca id=\"user-content-esga---genome-annotation\" class=\"anchor\" aria-label=\"Permalink: ESGA - Genome Annotation\" href=\"#esga---genome-annotation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c42e6080d15f532168bbb6d26fb300c5ed31e7f5e8648abbe9d5f6d8d4285475/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis pipeline can be used to generate \"hints\" from aligned sequence evidence to annotate a genome \u003cem\u003ede novo\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline main steps\u003c/h3\u003e\u003ca id=\"user-content-pipeline-main-steps\" class=\"anchor\" aria-label=\"Permalink: Pipeline main steps\" href=\"#pipeline-main-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eHints file is generated from all available evidences (proteins, EST and/or RNA-seq reads).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGene models are predicted using Augustus with the hints file as extrinsic evidence (optional).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe minimum requirements are a genome file and at least one type of evidence.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest data\u003c/h3\u003e\u003ca id=\"user-content-test-data\" class=\"anchor\" aria-label=\"Permalink: Test data\" href=\"#test-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple test data set can be downloaded \u003ca href=\"https://drive.google.com/open?id=1VFqLnRJiuj5Vhj2KCOdY58jwxZKkkMVU\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation about the pipeline can be found in the \u003ccode\u003edocs/\u003c/code\u003e directory or under the links below:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/recommendations.md\"\u003eRecommendations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/whatsnext.md\"\u003eWhat\u0027s next\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline Scheme\u003c/h3\u003e\u003ca id=\"user-content-pipeline-scheme\" class=\"anchor\" aria-label=\"Permalink: Pipeline Scheme\" href=\"#pipeline-scheme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/Pipeline_dag.svg\"\u003e\u003cimg src=\"images/Pipeline_dag.svg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was written by Dr. Montserrat Torres (\u003ca href=\"https://github.com/MontseTor\"\u003eMontseTor\u003c/a\u003e) and Dr. Marc H\u00f6ppner (\u003ca href=\"https://github.com/marchoeppner\"\u003emarchoeppner\u003c/a\u003e) at \u003ca href=\"http://www.ikmb.uni-kiel.de\" rel=\"nofollow\"\u003eIKMB\u003c/a\u003e.\nThe authors gratefully acknowledge inspiration, fruitful discussions and a few useful code snippets from the \u003ca href=\"https://www.nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 4, "topics": [], - "updated_at": 1688650705.0 + "updated_at": 1708237909.0 }, { "data_format": 2, - "description": "an example builder to build a container with Travis CI, and push to a Singularity Registry Server (or other endpoint)", + "description": "HIPAA \u0026 GDPR compliant ready Postgres Database with PostGIS and PGAuditor", "filenames": [ "Singularity" ], - "full_name": "singularityhub/travis-ci", + "full_name": "netreconlab/hipaa-postgres", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Builders Travis-CI\u003c/h1\u003e\u003ca id=\"user-content-singularity-builders-travis-ci\" class=\"anchor\" aria-label=\"Permalink: Singularity Builders Travis-CI\" href=\"#singularity-builders-travis-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\".travis/sregistry-travis.png\"\u003e\u003cimg src=\".travis/sregistry-travis.png\" alt=\".travis/sregistry-travis.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/singularityhub/travis-ci\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0f152642ac86cd0c29a5c1a0cde277aa42ad43566497868fd65712824cdca1b6/68747470733a2f2f7472617669732d63692e6f72672f73696e67756c61726974796875622f7472617669732d63692e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/singularityhub/travis-ci.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a simple example of how you can achieve:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eversion control of your recipes\u003c/li\u003e\n\u003cli\u003eversioning to include image hash \u003cem\u003eand\u003c/em\u003e commit id\u003c/li\u003e\n\u003cli\u003ebuild of associated container and\u003c/li\u003e\n\u003cli\u003epush to a storage endpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003efor a reproducible build workflow. This recipe on master is intended to build\nSingularity 3.x (with GoLang). If you are looking for legacy builds of Singularity,\nsee the \u003ca href=\"https://github.com/singularityhub/travis-ci/tree/release/2.6\"\u003erelease/2.6\u003c/a\u003e branch.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should this be managed via Github?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGithub, by way of easy integration with continuous integration, is an easy way\nto have a workflow set up where multiple people can collaborate on a container recipe,\nthe recipe can be tested (with whatever testing you need), discussed in pull requests,\nand then finally pushed to the registry. Importantly, you don\u0027t need to give your\nentire team manager permissions to the registry. An encrypted credential that only\nis accessible to administrators can do the push upon merge of a discussed change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should I use this instead of a service?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou could use a remote builder, but if you do the build in a continuous integration\nservice you get complete control over it. This means everything from the version of\nSingularity to use, to the tests that you run for your container. You have a lot more\nfreedom in the rate of building, and organization of your repository, because it\u0027s you\nthat writes the configuration.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdd your Singularity recipes to this repository, and edit the build commands in\nthe \u003ca href=\".travis/build.sh\"\u003ebuild.sh\u003c/a\u003e file. This is where you can specify endpoints\n(Singularity Registry, Dropbox, Google Storage, AWS) along with container names\n(the uri) and tag. You can build as many recipes as you like, just add another line!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e recipe relative to repository base\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh --uri collection/container --tag tacos --cli google-storage Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh --uri collection/container --cli google-drive Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh --uri collection/container --cli globus Singularity\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003e/bin/bash .travis/build.sh --uri collection/container --cli registry Singularity\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor each client that you use, required environment variables (e.g., credentials to push,\nor interact with the API) must be defined in the (encrypted) Travis environment. To\nknow what variables to define, along with usage for the various clients, see\nthe \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclient specific pages\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed Started\u003c/h2\u003e\u003ca id=\"user-content-detailed-started\" class=\"anchor\" aria-label=\"Permalink: Detailed Started\" href=\"#detailed-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e0. Fork this repository\u003c/h3\u003e\u003ca id=\"user-content-0-fork-this-repository\" class=\"anchor\" aria-label=\"Permalink: 0. Fork this repository\" href=\"#0-fork-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can clone and tweak, but it\u0027s easiest likely to get started with our example\nfiles and edit them as you need.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Get to Know Travis\u003c/h3\u003e\u003ca id=\"user-content-1-get-to-know-travis\" class=\"anchor\" aria-label=\"Permalink: 1. Get to Know Travis\" href=\"#1-get-to-know-travis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe will be working with \u003ca href=\"https://www.travis-ci.org\" rel=\"nofollow\"\u003eTravis CI\u003c/a\u003e. You can see\nexample builds for this \u003ca href=\"https://travis-ci.org/singularityhub/travis-ci/builds\" rel=\"nofollow\"\u003erepository here\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTravis offers \u003ca href=\"https://docs.travis-ci.com/user/cron-jobs/\" rel=\"nofollow\"\u003ecron jobs\u003c/a\u003e so you could schedule builds at some frequency.\u003c/li\u003e\n\u003cli\u003eTravis also offers \u003ca href=\"https://circleci.com/docs/2.0/gpu/\" rel=\"nofollow\"\u003eGPU Builders\u003c/a\u003e if you want/need that sort of thing.\u003c/li\u003e\n\u003cli\u003eIf you don\u0027t want to use the \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry\u003c/a\u003e to push to Google Storage, Drive, Globus, Dropbox, or your personal Singularity Registry, travis will upload your artifacts directly to your \u003ca href=\"https://docs.travis-ci.com/user/uploading-artifacts/\" rel=\"nofollow\"\u003eAmazon S3 bucket\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Add your Recipe(s)\u003c/h3\u003e\u003ca id=\"user-content-2-add-your-recipes\" class=\"anchor\" aria-label=\"Permalink: 2. Add your Recipe(s)\" href=\"#2-add-your-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the example here, we have a single recipe named \"Singularity\" that is provided\nas an input argument to the \u003ca href=\".travis/build.sh\"\u003ebuild script\u003c/a\u003e. You could add another\nrecipe, and then of course call the build to happen more than once.\nThe build script will name the image based on the recipe, and you of course\ncan change this up.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Configure Singularity\u003c/h3\u003e\u003ca id=\"user-content-3-configure-singularity\" class=\"anchor\" aria-label=\"Permalink: 3. Configure Singularity\" href=\"#3-configure-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe basic steps to \u003ca href=\".travis/setup.sh\"\u003esetup\u003c/a\u003e the build are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall Singularity from master branch. You could of course change the lines in \u003ca href=\".travis/setup.sh\"\u003esetup.sh\u003c/a\u003e to use a specific tagged release, an older version, or development version.\u003c/li\u003e\n\u003cli\u003eInstall the sregistry client, if needed. The \u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003esregistry client\u003c/a\u003e allows you to issue a command like \"sregistry push ...\" to upload a finished image to one of your cloud / storage endpoints. By default, this won\u0027t happen, and you will just build an image using the CI.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Configure the Build\u003c/h3\u003e\u003ca id=\"user-content-4-configure-the-build\" class=\"anchor\" aria-label=\"Permalink: 4. Configure the Build\" href=\"#4-configure-the-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe basic steps for the \u003ca href=\".travis/build.sh\"\u003ebuild\u003c/a\u003e are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRunning build.sh with no inputs will default to a recipe called \"Singularity\" in the base of the repository. You can provide an argument to point to a different recipe path, always relative to the base of your repository.\u003c/li\u003e\n\u003cli\u003eIf you want to define a particular unique resource identifier for a finished container (to be uploaded to your storage endpoint) you can do that with \u003ccode\u003e--uri collection/container\u003c/code\u003e. If you don\u0027t define one, a robot name will be generated.\u003c/li\u003e\n\u003cli\u003eYou can add \u003ccode\u003e--uri\u003c/code\u003e to specify a custom name, and this can include the tag, OR you can specify \u003ccode\u003e--tag\u003c/code\u003e to go along with a name without one. It depends on which is easier for you.\u003c/li\u003e\n\u003cli\u003eIf you add \u003ccode\u003e--cli\u003c/code\u003e then this is telling the build script that you have defined the \u003ca href=\"https://docs.travis-ci.com/user/environment-variables/#Defining-Variables-in-Repository-Settings\" rel=\"nofollow\"\u003eneeded environment variables\u003c/a\u003e for your \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclient of choice\u003c/a\u003e and you want successful builds to be pushed to your storage endpoint. Valid clients include:\n\u003cul\u003e\n\u003cli\u003egoogle-storage\u003c/li\u003e\n\u003cli\u003egoogle-drive\u003c/li\u003e\n\u003cli\u003edropbox\u003c/li\u003e\n\u003cli\u003eglobus\u003c/li\u003e\n\u003cli\u003eregistry (Singularity Registry)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\".travis.yml\"\u003e.travis.yml\u003c/a\u003e for examples of this build.sh command (commented out). If there is some cloud service that you\u0027d like that is not provided, please \u003ca href=\"https://www.github.com/singularityhub/sregistry-cli/issues\"\u003eopen an issue\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Connect to CI\u003c/h3\u003e\u003ca id=\"user-content-5-connect-to-ci\" class=\"anchor\" aria-label=\"Permalink: 5. Connect to CI\" href=\"#5-connect-to-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you go to your \u003ca href=\"https://travis-ci.org/profile\" rel=\"nofollow\"\u003eTravis Profile\u003c/a\u003e you can usually select a Github organization (or user) and then the repository, and then click the toggle button to activate it to build on commit --\u0026gt; push.\u003c/p\u003e\n\u003cp\u003eThat\u0027s it for the basic setup! At this point, you will have a continuous integration service that will build your container from a recipe each time that you push. The next step is figuring out where you want to put the finished image(s), and we will walk through this in more detail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStorage!\u003c/h2\u003e\u003ca id=\"user-content-storage\" class=\"anchor\" aria-label=\"Permalink: Storage!\" href=\"#storage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the image is built, where can you put it? An easy answer is to use the\n\u003ca href=\"https://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003eSingularity Global Client\u003c/a\u003e and\nchoose \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eone of the many clients\u003c/a\u003e\nto add a final step to push the image. You then use the same client to pull the\ncontainer from your host. Once you\u0027ve decided which endpoints you want to push to,\nyou will need to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSave the credentials / other environment variables that your client needs (see the client settings page linked in the sregistry docs above) to your \u003ca href=\"https://docs.travis-ci.com/user/environment-variables/#Defining-Variables-in-Repository-Settings\" rel=\"nofollow\"\u003erepository settings\u003c/a\u003e where they will be encrypted and in the environment.\u003c/li\u003e\n\u003cli\u003eAdd a line to your \u003ca href=\".travis.yml\"\u003e.travis.yml\u003c/a\u003e to do an sregistry push action to the endpoint(s) of choice. We have provided some (commented out) examples to get you started.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTravis Provided Uploads\u003c/h2\u003e\u003ca id=\"user-content-travis-provided-uploads\" class=\"anchor\" aria-label=\"Permalink: Travis Provided Uploads\" href=\"#travis-provided-uploads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou don\u0027t even need to use sregistry to upload a container (or an artifact / result produced from running one via a cron job maybe?) to an endpoint of choice! There are \u003ca href=\"https://docs.travis-ci.com/user/deployment\" rel=\"nofollow\"\u003emany\u003c/a\u003e places you can deploy to. If you can think of it, it\u0027s on this list. Here are a sampling of some that I\u0027ve tried (and generally like):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.travis-ci.com/user/deployment/surge/\" rel=\"nofollow\"\u003eSurge.sh\u003c/a\u003e gives you a little web address for free to upload content. This means that if your container runs an analysis and generates a web report, you can push it here. Each time you run it, you can push again and update your webby thing. Cool! Here is an \u003ca href=\"http://containers-ftw.surge.sh/\" rel=\"nofollow\"\u003eold example\u003c/a\u003e of how I did this - the table you see was produced by a container and then the generated report uploaded to surge.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.travis-ci.com/user/deployment/s3/\" rel=\"nofollow\"\u003eAmazon S3\u003c/a\u003e bread and butter of object storage. sregistry doesn\u0027t have a client for it (bad dinosaur!) so I\u0027ll direct you to Travis to help :)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.travis-ci.com/user/deployment/pages/\" rel=\"nofollow\"\u003eGithub Pages\u003c/a\u003e I want to point you to github pages in the case that your container has documentation that should be pushed when built afresh.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced\u003c/h2\u003e\u003ca id=\"user-content-advanced\" class=\"anchor\" aria-label=\"Permalink: Advanced\" href=\"#advanced\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGuess what, this setup is totally changeable by you, it\u0027s your build! This means you can do any of the following \"advanced\" options:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis setup can work as an analysis node as well! Try setting up a \u003ca href=\"https://docs.travis-ci.com/user/cron-jobs/\" rel=\"nofollow\"\u003ecron job\u003c/a\u003e to build a container that processes some information feed, and you have a regularly scheduled task.\u003c/li\u003e\n\u003cli\u003etry out one of the \u003ca href=\"https://circleci.com/docs/2.0/gpu/\" rel=\"nofollow\"\u003eGPU builders\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003erun builds in parallel and test different building environments. You could try building the \"same\" container across different machine types and see if you really do get the same thing :)\u003c/li\u003e\n\u003cli\u003eYou can also do other sanity checks like testing if the container runs as you would expect, etc.\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 7, - "subscribers_count": 5, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehipaa-postgres\u003c/h1\u003e\u003ca id=\"user-content-hipaa-postgres\" class=\"anchor\" aria-label=\"Permalink: hipaa-postgres\" href=\"#hipaa-postgres\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/hipaa-postgres\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9a06531d820b6411fb4cee4467acdc83794ca70e7b84431b95a1853d5e064b22/68747470733a2f2f646f636b6572692e636f2f696d6167652f6e65747265636f6e6c61622f68697061612d706f737467726573\" alt=\"\" data-canonical-src=\"https://dockeri.co/image/netreconlab/hipaa-postgres\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/build.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/build.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/release.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/release.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/release-pgpool.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/release-pgpool.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eA HIPAA \u0026amp; GDPR compliant ready Postgres Database image with PostGIS and PGAudit. Designed for \u003ca href=\"https://github.com/netreconlab/parse-hipaa\"\u003eparse-hipaa\u003c/a\u003e but can be used anywhere Postgres is used. These docker images include the necessary database auditing and logging for HIPAA compliance. \u003ccode\u003ehipaa-postgres\u003c/code\u003e is derived from \u003ca href=\"https://hub.docker.com/r/postgis/postgis\" rel=\"nofollow\"\u003epostgis\u003c/a\u003e which is an extention built on top of the \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003eofficial postgres image\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ehipaa-postgres provides the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Auditing \u0026amp; logging\u003c/li\u003e\n\u003cli\u003e[x] Ready for encryption in transit - run behind a proxy with files \u0026amp; directions on how to \u003ca href=\"https://github.com/netreconlab/parse-hipaa#deploying-on-a-real-system\"\u003ecomplete the process\u003c/a\u003e with Nginx and LetsEncrypt\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou will still need to setup the following on your own to be fully HIPAA compliant:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Encryption in transit - you will need to \u003ca href=\"https://github.com/netreconlab/parse-hipaa#deploying-on-a-real-system\"\u003ecomplete the process\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] Encryption at rest - Mount to your own encrypted storage drive (Linux and macOS have API\u0027s for this) and store the drive in a \"safe\" location\u003c/li\u003e\n\u003cli\u003e[ ] Be sure to do anything else HIPAA requires\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/netreconlab/CareKitSample-ParseCareKit\"\u003eCareKitSample-ParseCareKit\u003c/a\u003e app uses this image alongise parse-hipaa and \u003ca href=\"https://github.com/netreconlab/ParseCareKit\"\u003eParseCareKit\u003c/a\u003e. If you are looking for a Mongo variant, checkout \u003ca href=\"https://github.com/netreconlab/hipaa-mongo\"\u003ehipaa-mongo\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse at your own risk. There is not promise that this is HIPAA compliant and we are not responsible for any mishandling of your data\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImages\u003c/h2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMultiple images are automatically built for your convenience. Images can be found at the following locations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/hipaa-postgres\" rel=\"nofollow\"\u003eDocker - Hosted on Docker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/pkgs/container/hipaa-postgres\"\u003eSingularity - Hosted on GitHub Container Registry\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTags\u003c/h3\u003e\u003ca id=\"user-content-tags\" class=\"anchor\" aria-label=\"Permalink: Tags\" href=\"#tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003elatest\u003c/code\u003e - Points to the newest released version that uses the standard \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003ePostgres image\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emain\u003c/code\u003e - Points to most up-to-date code that uses the standard \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003ePostgres image\u003c/a\u003e and will eventually show up in a future release. This tag can contain breaking changes\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ex-x.x\u003c/code\u003e - Points to a specific Postgres and Postgis version that uses the standard \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003ePostgres image\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ex-x.x-pgpool\u003c/code\u003e - Points to a specific Postgres and Postgis version that uses the standard \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003ePostgres image\u003c/a\u003e. These images alson contain \u003ca href=\"https://www.pgpool.net\" rel=\"nofollow\"\u003epgpool\u003c/a\u003e and can be configured for High Availability\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ex-x.x-percona\u003c/code\u003e - Points to a specific version that uses the \u003ca href=\"https://www.percona.com/software/postgresql-distribution\" rel=\"nofollow\"\u003ePercona Distribtution for PostgreSQL\u003c/a\u003e image\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdditional Packages inside of hipaa-postgres that are enabled automatically\u003c/h2\u003e\u003ca id=\"user-content-additional-packages-inside-of-hipaa-postgres-that-are-enabled-automatically\" class=\"anchor\" aria-label=\"Permalink: Additional Packages inside of hipaa-postgres that are enabled automatically\" href=\"#additional-packages-inside-of-hipaa-postgres-that-are-enabled-automatically\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following are enabled automatically on either the \u003ccode\u003ePG_PARSE_DB\u003c/code\u003e or \u003ccode\u003epostgres\u003c/code\u003e databases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://postgis.net\" rel=\"nofollow\"\u003ePostGIS\u003c/a\u003e - Spatial database extender for PostgreSQL object-relational database\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.pgaudit.org\" rel=\"nofollow\"\u003epgAudit\u003c/a\u003e - Provide the tools needed to produce audit logs required to pass certain government, financial, or ISO certification audits\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pgaudit/set_user\"\u003epgAudit-set_user\u003c/a\u003e - Allows switching users and optional privilege escalation with enhanced logging and control\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pgbadger.darold.net\" rel=\"nofollow\"\u003epgBadger\u003c/a\u003e - Log analyzer built for speed with fully detailed reports and professional rendering\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pgbackrest.org\" rel=\"nofollow\"\u003epgBackrest\u003c/a\u003e - Reliable, easy-to-use backup and restore solution that can seamlessly scale up to the largest databases and workloads by utilizing algorithms that are optimized for database-specific requirements\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/citusdata/pg_cron\"\u003epg_cron\u003c/a\u003e - Run periodic jobs in PostgreSQL\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://reorg.github.io/pg_repack/\" rel=\"nofollow\"\u003epg_repack\u003c/a\u003e - Reorganize tables in PostgreSQL databases with minimal locks\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pgRouting/pgrouting\"\u003epgRouting\u003c/a\u003e - Provides geospatial routing and other network analysis functionality\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.postgresql.org/docs/current/pgstatstatements.html\" rel=\"nofollow\"\u003epgStatStatements\u003c/a\u003e - Provides a means for tracking planning and execution statistics of all SQL statements executed by a server (needed for PMM)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.percona.com/software/database-tools/percona-monitoring-and-management\" rel=\"nofollow\"\u003ePercona Monitoring and Management (PMM)\u003c/a\u003e - Monitor the health of your database infrastructure, explore new patterns in database behavior, and manage and improve the performance of your databases no matter where they are located or deployed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003epgpool tagged images\u003c/h3\u003e\u003ca id=\"user-content-pgpool-tagged-images\" class=\"anchor\" aria-label=\"Permalink: pgpool tagged images\" href=\"#pgpool-tagged-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImages that are tagged with \u003ccode\u003e-pgpool\u003c/code\u003e have additional packages to make it easier to configure \u003ccode\u003ehipaa-postgres\u003c/code\u003e to work with \u003ca href=\"https://www.pgpool.net\" rel=\"nofollow\"\u003epgpool\u003c/a\u003e. The additional packages are below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.pgpool.net\" rel=\"nofollow\"\u003epgpool\u003c/a\u003e - Manages a pool of PostgreSQL servers to achieve some features that are not available with single PostgreSQL installation. The features include: High Availability, Load balancing, Connection Pooling, Online Recovery, Limiting Exceeding Connections, Watchdog, In Memory Query Cache\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/iputils/iputils\"\u003eiputils-ping\u003c/a\u003e - A utility for Linux networking\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/iputils/iputils\"\u003eopenssh-server\u003c/a\u003e - Connectivity tool for remote login with the SSH protocol\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://supervisord.org/#\" rel=\"nofollow\"\u003esupervisor\u003c/a\u003e - Client/server system that allows its users to monitor and control a number of processes on UNIX-like operating systems\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnvironment Variables\u003c/h2\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ePOSTGRES_PASSWORD # Password for postgress db cluster (Be sure to changes this in real deployments)\nPG_PARSE_USER # Username for logging into PG_PARSE_DB (Be sure to changes this in real deployments)\nPG_PARSE_PASSWORD # Password for logging into PG_PARSE_DB (Be sure to changes this in real deployments)\nPG_PARSE_DB # Name of parse-hipaa database\nPMM_USER=pmm # Username for Percona Monitor Managemet (Be sure to changes this in real deployments)\nPMM_PASSWORD=pmm # Password for Percona Monitor Managemet (Be sure to changes this in real deployments)\nPMM_PORT=80 # This is the default port on the docker image\nPMM_TLS_PORT=443 # This is the default TLS port on the docker image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStarting up hipaa-postgres\u003c/h2\u003e\u003ca id=\"user-content-starting-up-hipaa-postgres\" class=\"anchor\" aria-label=\"Permalink: Starting up hipaa-postgres\" href=\"#starting-up-hipaa-postgres\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get started, the \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/blob/main/docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e file provides an example of how to use \u003ccode\u003ehipaa-postgres\u003c/code\u003e, simply type:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker-compose up\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eImporant Note: On the very first run of hipaa-postgres needs time to setup and will not allow connections until it is ready. This is suppose to happen as time is needed to \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/tree/main/scripts\"\u003econfigure the necessary scripts/extensions along setup any default databases\u003c/a\u003e. \u003ccode\u003ehipaa-postgres\u003c/code\u003e will begin to allow connectoins once it finishes configuring and a message like below will show in the logs:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edb_1 | PostgreSQL init process complete; ready for start up.\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAfterwards, hipaa-postfgress will allow all connections.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguring\u003c/h2\u003e\u003ca id=\"user-content-configuring\" class=\"anchor\" aria-label=\"Permalink: Configuring\" href=\"#configuring\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are plan on using hipaa-postgres in production. You should run the additional scripts to create the rest of the indexes for optimized queries.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esetup-parse-index.sh\u003c/code\u003e file is already in the container. You just have to run it.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLog into your docker container, type: \u003ccode\u003edocker exec -u postgres -ti parse-hipaa_db_1 bash\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the script, type: \u003ccode\u003e/usr/local/bin/setup-parse-index.h\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf you want to persist the data in the database, you can uncomment the volume lines in \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/blob/a2d8c2dce8f8288ad8d7b5dbf1c0dc676a466f32/docker-compose.yml#L16-L19\"\u003edocker-compose.yml\u003c/a\u003e. Be sure to change the directory to secure place that docker has access to.\u003c/p\u003e\n\u003cp\u003eDefault values for \u003ca href=\"#environment-variables\"\u003eenvironment variable\u003c/a\u003e are provided in \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/blob/main/docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e for quick local deployment. If you plan on using this image to deploy in production, you should definitely change all \u003ca href=\"#environment-variables\"\u003eenvironment variable\u003c/a\u003e. Note that the postgres image provides a default user of \u003ccode\u003epostgres\u003c/code\u003e user to configure the database cluster, you can change the password for the \u003ccode\u003epostgres\u003c/code\u003e user by changing \u003ccode\u003ePOSTGRES_PASSWORD\u003c/code\u003e before the first initialization. There are plenty of \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003epostgres environment variables\u003c/a\u003e that can be modified. Postgres environment variables should not be changed unless you are confident with configuring postgres or else you image may not work properly. Note that changes to the aforementioned parameters will only take effect if you change them before the first build and run of the image. Afterwards, you will need to make all changes by connecting to the image typing:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker exec -u postgres -ti parse-hipaa_db_1 bash\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can then make modifications using \u003ca href=\"http://postgresguide.com/utilities/psql.html\" rel=\"nofollow\"\u003epsql\u003c/a\u003e. Through psql, you can also add multiple databases and users to support a number of parse apps.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMonitoring your database with Percona Monitoring and Management\u003c/h2\u003e\u003ca id=\"user-content-monitoring-your-database-with-percona-monitoring-and-management\" class=\"anchor\" aria-label=\"Permalink: Monitoring your database with Percona Monitoring and Management\" href=\"#monitoring-your-database-with-percona-monitoring-and-management\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ehipaa-postgres\u003c/code\u003e is configured automatically to allow acces to \u003ca href=\"https://www.percona.com/software/database-tools/percona-monitoring-and-management\" rel=\"nofollow\"\u003ePMM\u003c/a\u003e. If you are using the \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/blob/main/docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e file, this can be accessed by visiting \u003ca href=\"http://localhost:1080/\" rel=\"nofollow\"\u003ehttp://localhost:1080/\u003c/a\u003e. Additional information is below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUsername/password - admin/admin, PMM will prompt you to change this on first login\u003c/li\u003e\n\u003cli\u003eAdding your database to PMM\n\u003col\u003e\n\u003cli\u003eGoto \u003ccode\u003eSettings-\u0026gt;Add Instance to PMM-\u0026gt;PostgreSQL\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEnter \u003ccode\u003edb\u003c/code\u003e for hostname\u003c/li\u003e\n\u003cli\u003eFor \u003ccode\u003eUsername\u003c/code\u003e, enter \u003ccode\u003ePMM_USER\u003c/code\u003e configured in your \u003ca href=\"#environment-variables\"\u003eenvironment variable\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor \u003ccode\u003ePassword\u003c/code\u003e, enter \u003ccode\u003ePMM_PASSWORD\u003c/code\u003e configured in your \u003ca href=\"#environment-variables\"\u003eenvironment variable\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick \u003ccode\u003eAdd service\u003c/code\u003e... It can take up to 5 minutes for data to start populating in PMM. PMM will let you know if it has trouble connecting immediatly after you perform the steps above. You can see that PMM is able to connect and read your database \u003ccode\u003eversion\u003c/code\u003e correctly on the \u003ccode\u003ePostgreSQL\u003c/code\u003e section of the dashboard\u003c/li\u003e\n\u003cli\u003eLearn more about PMM by looking through the \u003ca href=\"https://docs.percona.com/percona-monitoring-and-management/index.html\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploying on a real system\u003c/h2\u003e\u003ca id=\"user-content-deploying-on-a-real-system\" class=\"anchor\" aria-label=\"Permalink: Deploying on a real system\" href=\"#deploying-on-a-real-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker yml\u0027s here are intended to run behind a proxy that properly has ssl configured to encrypt data in transit. To create a proxy to parse-hipaa, nginx files are provided \u003ca href=\"https://github.com/netreconlab/parse-hipaa/tree/master/nginx/sites-enabled\"\u003ehere\u003c/a\u003e. Simply add the \u003ca href=\"https://github.com/netreconlab/parse-hipaa/tree/master/nginx/sites-enabled\"\u003esites-available\u003c/a\u003e folder to your nginx directory and add the following to \"http\" in your nginx.conf:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttp {\n include /usr/local/etc/nginx/sites-enabled/*.conf; #Add this line to end. This is for macOS, do whatever is appropriate on your system\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSetup your free certificates using \u003ca href=\"https://letsencrypt.org\" rel=\"nofollow\"\u003eLetsEncrypt\u003c/a\u003e, follow the directions \u003ca href=\"https://www.nginx.com/blog/using-free-ssltls-certificates-from-lets-encrypt-with-nginx/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Be sure to change the certificate and key lines to point to correct location in \u003ca href=\"https://github.com/netreconlab/parse-hipaa/blob/master/nginx/sites-enabled/default-ssl.conf\"\u003edefault-ssl.conf\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 2, "topics": [ - "travis", - "travis-ci", + "postgres", + "postgresql", + "postgis", + "hipaa", + "gdpr", + "carekit", + "docker", "singularity", - "singularityhub", - "builder", - "dropbox", - "google-storage", - "singularity-registry", - "sregistry" + "healthcare", + "pgaudit", + "parse-hipaa", + "parsecarekit", + "hacktoberfest" ], - "updated_at": 1688650704.0 + "updated_at": 1729379981.0 }, { "data_format": 2, - "description": "quantum chemistry software", + "description": "Docker container built on bioconductor/bioconductor_docker", "filenames": [ - "container_recipes/SingularityFile" - ], - "full_name": "VALENCE-software/VALENCE", - "latest_release": "v1.0", - "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.com/VALENCE-software/VALENCE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/71fa134f6d03fcd25c5b648055848c0339160b129fa2d7745b2fa9f94982bffc/68747470733a2f2f7472617669732d63692e636f6d2f56414c454e43452d736f6674776172652f56414c454e43452e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/VALENCE-software/VALENCE.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/VALENCE-software/VALENCE/master?filepath=valence_tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e91e1d353a8b6acf0b42547ac3901f2c30138a3abaaa3d3c242da30b5b4f8426/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/VALENCE-software/VALENCE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6c182c7bc3d62ff8ed85baf22b8d695a0ccb7f205d7fb476b9e5fd996ddda631/68747470733a2f2f636f6465636f762e696f2f67682f56414c454e43452d736f6674776172652f56414c454e43452f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/VALENCE-software/VALENCE/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/152630099\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33ffafb09bf4122865aab8465ca20ca6c186a8b3f61efef1959c9d88722a546a/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3135323633303039392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/152630099.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVALENCE\u003c/h1\u003e\u003ca id=\"user-content-valence\" class=\"anchor\" aria-label=\"Permalink: VALENCE\" href=\"#valence\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Massively Parallel Implementation of Variational Subspace Valence Bond\u003c/p\u003e\n\u003cp\u003e16 November, 2018\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents:\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents:\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eOverview\u003c/li\u003e\n\u003cli\u003eIntroduction to VSVB\u003c/li\u003e\n\u003cli\u003eAbout the code\u003c/li\u003e\n\u003cli\u003eInput\u003c/li\u003e\n\u003cli\u003eOutput\u003c/li\u003e\n\u003cli\u003eExamples\u003c/li\u003e\n\u003cli\u003eAutomatic input generation with vtools\u003c/li\u003e\n\u003cli\u003eHow to make spin-coupled orbitals\u003c/li\u003e\n\u003cli\u003eHow to use derived basis functions\u003c/li\u003e\n\u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003cli\u003eContact information\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 1. Overview\u003c/h2\u003e\u003ca id=\"user-content-section-1-overview\" class=\"anchor\" aria-label=\"Permalink: Section 1. Overview\" href=\"#section-1-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen first downloaded, this repository includes\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e.travis.yml\u003c/li\u003e\n\u003cli\u003eHow-to-build-VALENCE.md\u003c/li\u003e\n\u003cli\u003eLICENSE\u003c/li\u003e\n\u003cli\u003eMakefile\u003c/li\u003e\n\u003cli\u003eREADME.md\u003c/li\u003e\n\u003cli\u003edoc/\u003c/li\u003e\n\u003cli\u003eexamples/\u003c/li\u003e\n\u003cli\u003einstall-mpich.sh*\u003c/li\u003e\n\u003cli\u003einstall-simint.sh*\u003c/li\u003e\n\u003cli\u003enitrogen/\u003c/li\u003e\n\u003cli\u003esrc/\u003c/li\u003e\n\u003cli\u003etesting/\u003c/li\u003e\n\u003cli\u003evalence_tutorial.ipynb\u003c/li\u003e\n\u003cli\u003evtools/\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMost of the above items are self-explanatory. The directories, /examples/ and /testing/, both contain input files that can be used to validate the binary. /testing/ contains an established set of inputs primarily for internal use. While there is some overlap with /testing/, /examples/ is oriented more toward educating the user about the various functions of VALENCE and features of VSVB (see Section 6 for more details). /vtools/ contains tools for automatic input generation and is described in Section 7. /doc/ contains a write-up of the method and Doxygen-generated documentation. /nitrogen/ is concerned with the interface to NITROGEN (\u003ca href=\"https://www.colorado.edu/nitrogen/\" rel=\"nofollow\"\u003ehttps://www.colorado.edu/nitrogen/\u003c/a\u003e) for optimizing molecular geometries and computing vibrational frequencies. The next section contains a brief, practical introduction to VSVB - the method implemented by VALENCE.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 2. Introduction to VSVB\u003c/h2\u003e\u003ca id=\"user-content-section-2-introduction-to-vsvb\" class=\"anchor\" aria-label=\"Permalink: Section 2. Introduction to VSVB\" href=\"#section-2-introduction-to-vsvb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn molecular electronic structure theory, variational subspace valence bond, or VSVB, is a type of generalized valence bond theory using gaussian atomic basis sets. Unlike the majority of methods in the mainstream of quantum chemistry such as Hartree-Fock, MP2, coupled-cluster, DFT, and so on, VSVB is based purely on orbitals that are allowed to overlap with one another. That is, VSVB does not use orthogonal (\u0027molecular\u0027) orbitals. The first benefit is that VSVB orbitals tend to be highly local, typically involving just one or two atoms, in contrast to molecular orbitals which are typically delocalized over all the atoms in any given problem. The highly local orbitals have obvious advantages for chemical interpretability and computational scalability.\u003c/p\u003e\n\u003cp\u003eThe first method paper is:\u003c/p\u003e\n\u003cp\u003eGraham D. Fletcher, \"The variational subspace valence bond method\",\nJ. Chem. Phys. 142, 134112 (2015).\n\u003ca href=\"http://dx.doi.org/10.1063/1.4916743\" rel=\"nofollow\"\u003ehttp://dx.doi.org/10.1063/1.4916743\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSee the CITATION file for more papers.\u003c/p\u003e\n\u003cp\u003eIn addition to a general background in quantum chemistry, it helps to read the above paper and references therein. The\ndocument doc/notes-vsvb-energy.pdf also contain detailed information about the method.\u003c/p\u003e\n\u003cp\u003eIn general, VSVB orbitals are linear combinations of the atomic orbitals (LCAO) of the basis set. As mentioned above, no consideration is given to how much the VSVB orbitals overlap with one another. This gives the user complete control over how the orbitals are defined. For example, orbitals can be tailored to represent intuitive concepts in chemistry such as bonds and lone-pairs, and calculations can be performed to test these ideas. Typically, the basis set expansion of an orbital together with guesses for the LCAO expansion weights are input to an optimization run where the weight values are refined to minimize the total energy. The user is free to enter any guess because VALENCE always ensures normalization of the orbitals to machine precision, internally. Once obtained, such orbitals can be used repeatedly to build other wave functions where similar orbitals are needed. If orbitals with the desired forms are available, qualitatively correct wave functions can be made \u0027for free\u0027.\u003c/p\u003e\n\u003cp\u003eFor convenience, VSVB orbitals are grouped into three types, in order of input: spin-coupled, unpaired, and double-occupied. This categorization greatly assists efficiency. More importantly, the orbital types serve different chemical functions. The term \u0027double-occupied\u0027 refers to a single spatial orbital used to model a pair of electrons with opposed spins - corresponding to two spin orbitals in the wave function with the same spatial function. This situation can be thought of as analogous to that in closed-shell Hartree-Fock (HF). Indeed, when properly constructed, such a wave function (called a \u0027VSHF\u0027 wave function) has the HF energy, except with overlapping orbitals. In chemistry, double-occupied orbitals are often used to model atomic core electrons and other \u0027spectator\u0027 electrons. The \u0027unpaired\u0027 orbitals are singly occupied with electrons of the same spin, contributing a \u0027high spin\u0027 configuration. VSVB wave functions involving only unpaired and double-occupied orbitals have a single determinant.\u003c/p\u003e\n\u003cp\u003eThe term \u0027spin-coupled orbitals\u0027, here, refers to pairs of singly occupied orbitals whose electrons are coupled to an overall singlet using a spin function of the Rumer type. The advantage of spin-coupled orbitals is that they greatly extend the applicability of VSVB beyond \u0027Hartree-Fock\u0027 type problems to model bond-breaking/formation and situations where the spatial polarization of charge is critical to reproducing chemical phenomena. Spin-coupled orbitals can be used to recover a significant proportion of the so-called static electron correlation energy. Since the associated spin functions double the number of determinants in the wave function for each pair involved, spin coupled orbitals are typically used judiciously to model key components of the chemistry of interest, such as the separation of an electron pair in a bond or excited state. Occasionally, chemical problems expose different ways to couple the spins of a given group of electrons to the same overall spin, and multiple spin couplings can be incorporated into the VSVB wave function to reflect this, with an additive increase in the number of determinants.\u003c/p\u003e\n\u003cp\u003eAs mentioned above, the tendency for VSVB orbitals to be highly local also brings computational scalability and efficiency. VSVB is characterized by having low memory requirements, negligible I/O and communication overheads (high parallel scalability), high CPU efficiency (in terms of the percentage of peak FLOPs), and moderate complexity (~cubic or even sub-cubic). The major consideration with VSVB is the cost of optimization which typically corresponds to many energy calculations. However, the overall cost in terms of the time-to-solution is greatly mitigated by the ability to re-use previously determined orbitals and by the high concurrency that is possible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 3. About the code\u003c/h2\u003e\u003ca id=\"user-content-section-3-about-the-code\" class=\"anchor\" aria-label=\"Permalink: Section 3. About the code\" href=\"#section-3-about-the-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVALENCE is the \u0027number-crunching\u0027 code in an overall system for executing VSVB calculations that includes various tools (the \u0027vtools\u0027) for generating input and processing output. Currently, VALENCE can compute energies and optimize single-reference wave functions for the following types of situation-\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGround states.\u003c/li\u003e\n\u003cli\u003eExcited states, including multiple excitations (single-reference).\u003c/li\u003e\n\u003cli\u003eClosed-shell systems.\u003c/li\u003e\n\u003cli\u003eOpen-shell systems.\u003c/li\u003e\n\u003cli\u003eBond-breaking/formation, majority or all of the nuclear potential energy surface.\u003c/li\u003e\n\u003cli\u003eSpin optimization, including resonance.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe term \u0027single-reference\u0027 is used here to mean a single product of spatial orbitals. Depending on the spin-coupling used, this wave function will have one or more determinants.\u003c/p\u003e\n\u003cp\u003eIn addition, VALENCE has the ability to expand orbitals in terms of other LCAOs, that is, arbitrary combinations of the atomic basis functions may be defined and used as (new) basis functions. A typical use of such \u0027derived basis functions\u0027 (DBF) is to provide degrees-of-freedom adapted to the molecular symmetry (\u0027symmetry-adaptation\u0027). Other uses include making spherical harmonic functions from the cartesian functions, and making a hybridized basis set consisting of sp,sp2, and sp3 \u0027hybrid\u0027 functions.\u003c/p\u003e\n\u003cp\u003eCurrently, VALENCE can optimize two types of linear parameter - orbital weights, and spin-coupling weights - using a first-order method. The term, \u0027first-order\u0027, refers to taking the first derivative of the VSVB energy with respect to the linear weights. The method solves a generalized eigenproblem of the form HC=SCE beginning by forming the hamiltonian (H) and overlap matrices (S), where E are the eigenvalues (energies). The eigenvectors (C) contain the updated orbital or spin-coupling weights. The cost of this method is quadratic with the size of the orbital expansion or spin-coupling space. There is currently an option to use a \u0027direct energy minimization\u0027 (DEM) method, though this is still under development.\u003c/p\u003e\n\u003cp\u003eVALENCE is written in Fortran-90 and runs in parallel using MPI. To compute integrals, VALENCE currently uses the vectorized integral package, SIMINT (\u003ca href=\"https://github.com/simint-chem\"\u003ehttps://github.com/simint-chem\u003c/a\u003e). This release includes instructions on how to build SIMINT. Once the SIMINT library is built, building VALENCE itself begins with setting options in a simple Makefile. The Makefile also contains some optimizations for various platforms, and an option to build the sequential form. A binary called \u0027valence\u0027 should result. VALENCE takes an input file on the command line. To run VALENCE sequentially just type,\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./valence [name of input file]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSee section 5 for example input files. To run VALENCE in parallel, please consult the documentation for your target platform as to how MPI runs are specified.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 4. Input\u003c/h2\u003e\u003ca id=\"user-content-section-4-input\" class=\"anchor\" aria-label=\"Permalink: Section 4. Input\" href=\"#section-4-input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u0027valence\u0027 binary makes no choices regarding the orbitals, it merely processes the wave function specified by the input to compute energies, execute optimization procedures, and so forth. That said, a highly versatile system for defining the orbitals is supported, together with the N-electron form of the wave function, and this is described in this section.\u003c/p\u003e\n\u003cp\u003eThe direct input to VALENCE is a plain-text numerical input file. No attempt is made to improve the \u0027look\u0027 of the input because advanced tools are included in this package to generate input automatically and offer a more user-friendly environment (see \u0027vtools\u0027). Although the tools continue to be refined and improved in our research group, it is important to understand how the direct input is structured since it offers the highest degree of generality.\u003c/p\u003e\n\u003cp\u003eIn what follows, a helpful concept is that of the \u0027orbital basis set\u0027 (OBS). In contrast to the more familiar \u0027molecular\u0027 basis set, an OBS spans the basis sets of just those atoms needed to support a given spatial orbital in VSVB. OBS often involve just one or two atoms in order to represent, for example, core electrons and \u0027lone-pairs\u0027, or chemical bonds, respectively. Unlike a molecular basis set, as the molecular system increases in size the component OBS stay approximately the same size. Thus, OBS facilitate a concise and efficient definition of an orbital that is independent of the target molecule, allowing orbitals to be stored and re-combined to make new wave functions.\u003c/p\u003e\n\u003cp\u003eBroadly, the input to VALENCE consists of specifications for the geometry, basis set, and a guess wave function, structured to facilitate dynamic memory. Thus, all the counts/sizes/dims/lengths, etc, are given on the first line so the program can allocate sufficient memory to read the data that follow. Lines may be separated by blank lines and items on a line by spaces, as permitted by FORTRAN free-format input rules. All data items are of INTeger type, unless specified to be FLOAT/REAL. Note also that VALENCE checks few input errors, but many errors can be avoided by using the \u0027vtools\u0027.\u003c/p\u003e\n\u003cp\u003eThe input is organized in the order:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA) sizes, counts, array dims (1 line)\u003c/li\u003e\n\u003cli\u003eB) Wave function optimization control (1 line)\u003c/li\u003e\n\u003cli\u003eC) Geometry\u003c/li\u003e\n\u003cli\u003eD) Basis set\u003c/li\u003e\n\u003cli\u003eE) N-electron Wave function information (spin-couplings, optional)\u003c/li\u003e\n\u003cli\u003eF) Orbitals (various kinds, in order: spin-coupled, single-occupied, double-occupied)\u003c/li\u003e\n\u003cli\u003eG) Derived basis functions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eParts (A) through (G) are now described in more detail.\u003c/p\u003e\n\u003cp\u003eA) sizes, counts, array dims\u003c/p\u003e\n\u003cp\u003eThere are 15 integers on a single line, they are-\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1. The number of atoms/point charges in the geometry\u003c/li\u003e\n\u003cli\u003eItem 2. The number of unique atom types\u003c/li\u003e\n\u003cli\u003eItem 3. Number of spin-coupled electron/orbital PAIRS\u003c/li\u003e\n\u003cli\u003eItem 4. Number of unpaired electrons/orbitals\u003c/li\u003e\n\u003cli\u003eItem 5. Number of double-occupied (DOCC) orbitals\u003c/li\u003e\n\u003cli\u003eItem 6. Total length of the orbital weight list (array dim)\u003c/li\u003e\n\u003cli\u003eItem 7. Length of the largest orbital expansion (array dim)\u003c/li\u003e\n\u003cli\u003eItem 8. Number of spin-couplings\u003c/li\u003e\n\u003cli\u003eItem 9. Number of unique atomic basis set shells\u003c/li\u003e\n\u003cli\u003eItem 10. Number of unique atomic basis set primitives\u003c/li\u003e\n\u003cli\u003eItem 11. Highest angular momentum in the basis set\u003c/li\u003e\n\u003cli\u003eItem 12. Number of derived basis functions (DBF)\u003c/li\u003e\n\u003cli\u003eItem 13. Number of orbital optimization groups\u003c/li\u003e\n\u003cli\u003eItem 14. Number of orbital excitations\u003c/li\u003e\n\u003cli\u003eItem 15: Largest atom count of the orbital basis sets\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eB) Optimization control\u003c/p\u003e\n\u003cp\u003eThere are 8 or more items on a single line, they are-\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1. The charge-cloud screening tolerance (integer), e.g. \u00275\u0027 means 0.00001 or 1d-5 or 10^-5.\u003c/li\u003e\n\u003cli\u003eItem 2. The density screening tolerance\u003c/li\u003e\n\u003cli\u003eItem 3. The integral screening tolerance (Schwarz inequality)\u003c/li\u003e\n\u003cli\u003eItem 4: The coarsest wave function convergence tolerance, given in kilocalories per mole (kCal/Mol).\u003c/li\u003e\n\u003cli\u003eItem 5: The finest wave function convergence tolerance. The optimization will proceed through multiple orbital groups from coarse to fine.\u003c/li\u003e\n\u003cli\u003eItem 6: Maximum number of iterations.\u003c/li\u003e\n\u003cli\u003eItem 7: Initial weight perturbation (DEM only)\u003c/li\u003e\n\u003cli\u003eItem 8: Weight perturbation scalar (DEM only)\u003c/li\u003e\n\u003cli\u003eItem 9: The orbital optimization groups as begin/end pairs of orbital labels (in order), e.g. \" 1 3 5 6 \" shows 2 groups: first group optimizes orbitals 1 through 3, second group is orbitals 5 and 6 (skipping 4)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eC) Geometry\u003c/p\u003e\n\u003cp\u003eA line for each atom/point charge with the layout:\u003c/p\u003e\n\u003cp\u003e[Atom type] X Y Z\u003c/p\u003e\n\u003cp\u003eeg. 1 0.0 0.0 0.0\u003c/p\u003e\n\u003cp\u003eThe atom type is an integer that addresses the basis set(s) given in the next section. For example, type \u00271\u0027 addresses the first basis set listed, type \u00272\u0027 the second, and so on. \u0027X,Y,Z\u0027 refers to cartesian coordinates in Angstroms (FLOATs).\u003c/p\u003e\n\u003cp\u003eD) Basis Set\u003c/p\u003e\n\u003cp\u003eVALENCE recognizes basis sets of cartesian atom-centered contracted gaussians. The basis set for each atom/etc type is given with the layout:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1: The nuclear/point charge (FLOAT)\u003c/li\u003e\n\u003cli\u003eItem 2: The number of shells (zero or more), NS\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere follow NS datasets defining the shells, as follows:\u003c/p\u003e\n\u003cp\u003eNext line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1: The shell angular momentum\u003c/li\u003e\n\u003cli\u003eItem 2: The number of primitive gaussian functions, NP, in the shell.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere follow NP lines, as follows:\u003c/p\u003e\n\u003cp\u003eNext line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1: The primitive exponent (FLOAT)\nIf NP\u0026gt;1,\u003c/li\u003e\n\u003cli\u003eItem 2: The primitive coefficient (FLOAT)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that VALENCE skips input of the redundant unit weight when NP=1. The counts, NS and NP, are iterated until all shells are input for all the atom types. A point charge can be input as a \u0027nuclear charge\u0027 with NS=0. This input is quite general. For example, \u0027floating\u0027 basis set shells can be placed anywhere using a nuclear charge of zero.\u003c/p\u003e\n\u003cp\u003eE) The \u0027N-electron\u0027 wave function (optional)\u003c/p\u003e\n\u003cp\u003eIf the number of spin-couplings, NC (Item 8 of (A), above), is greater than zero, then the spin-coupling information will be read next. Currently, the code can make \u0027Rumer\u0027 type couplings for the singlet parts of a system. Each spin-coupling is read as follows. If NC=1, a list of the orbital (electron) label pairs defining the (singlet) couplings is given, e.g. 1 2 3 4, means electrons \u00271 and 2\u0027 are singlet coupled, then electrons \u00273 and 4\u0027 are singlet coupled. If NC\u0026gt;1, the pair list is preceeded with the spin-coupling weight (FLOAT). This is repeated for all NC spin-couplings.\u003c/p\u003e\n\u003cp\u003eExcited states may be entered next, according to Item 14 of (A), as a sequence of {NX,NR} pairs, where NX addresses the orbital to be promoted and NR labels the root of the secular equation (e.g. \u00270\u0027 for lowest/ground, \u00271\u0027 for first excited, etc). It is an error if spin-coupled orbitals are desired but no spin-couplings are input (Item 8 = 0). Without spin-coupled orbitals and/or excited states, this section is null/empty.\u003c/p\u003e\n\u003cp\u003eF) Spatial wave function\u003c/p\u003e\n\u003cp\u003eThe spatial wave function in terms of the orbitals is now given. In general, the total number of orbitals is given by-\u003c/p\u003e\n\u003cp\u003e2*[no. spin-coupled PAIRs] + [no. unpaired] + [no. DOCC]\u003c/p\u003e\n\u003cp\u003eWhatever orbitals are needed, they must be entered in the order: spin-coupled; then unpaired; then DOCC, as per the intended use. The layout for each orbital is as follows. The first line defines the orbital basis set (OBS):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eItem 1. The number of atoms, NN, whose basis sets make up the OBS.\u003c/li\u003e\n\u003cli\u003eItem 2. List of NN atoms.\u003c/li\u003e\n\u003cli\u003eItem 3. Total number of AO\u0027s, NA.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere follow NA {AO address, coefficient (FLOAT)}-pairs. The AO addresses lie within the OBS specified by the NN atoms and in the order the atoms are listed. The scheme iterates until all orbitals required by the above formula, based on items 3-5 of (A), are input.\u003c/p\u003e\n\u003cp\u003e(G) Derived basis functions\u003c/p\u003e\n\u003cp\u003eDBFs are input using the same OBS format as described in (F) for the main orbital types. The total number of DBFs is preempted by item 12 of (A). See section 7 for more details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 5. Output\u003c/h2\u003e\u003ca id=\"user-content-section-5-output\" class=\"anchor\" aria-label=\"Permalink: Section 5. Output\" href=\"#section-5-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBroadly, the output of VALENCE is the VSVB wave function and its total energy. VALENCE first prints the outcome of a \u0027guess\u0027 energy calculation, preceded by the nuclear repulsion energy in the case of a molecule, optionally followed by an optimization run with a progress update at each iteration. Each optimization step reports the cumulative relaxation in kCal/Mol compared to the \u0027guess energy\u0027. Also printed is the relaxation obtained at that step divided by the convergence tolerance to indicate how near the optimization is to convergence. Every energy calculation or optimization step prints a file called \u0027orbitals\u0027 which contains the updated orbitals together with the current total energy at the end. If spin-coupled orbitals are used with more than one spin-coupling, an additional file called \u0027nelecwfn\u0027 is produced, containing the updated spin-coupling weights.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 6. Examples\u003c/h2\u003e\u003ca id=\"user-content-section-6-examples\" class=\"anchor\" aria-label=\"Permalink: Section 6. Examples\" href=\"#section-6-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe current release includes many examples (see directory \u0027./examples/\u0027) chosen to illustrate the main features and types of calculation that can currently be performed with VALENCE, while also being of a convenient size for verifying correct execution. The examples can be tested using the \u0027test_all\u0027 script and take less than five minutes on a typical processor. In /examples/, simply type-\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./test_all valence\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIn this section, five examples are described in detail. Narrative text is contained within brackets where it is helpful to distinguish it from the example input text. It is also helpful to note the following: the magnitude of a linear weight can occasionally be greater than unity; the overall sign of an orbital is arbitrary as the wave function is only determined to within a phase; agreement between total energies from the same run on different hardware is rarely greater than 10 places, and 6 places is more typical.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBeryllium atom\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis example uses two DOCC orbitals to model the singlet-S ground state of beryllium, with electronic configuration, 1s^2 2s^2, expanded over a basis set of three \u0027s\u0027-type functions. The variational subspace (VS) of the first orbital consists of functions 1 and 3, with function 1 as its unique degree-of-freedom (UDF). The second orbital\u0027s VS contains functions 2 and 3, with 2 as the UDF. Thus, function 3 is the \u0027shared\u0027 basis. Other choices exist (in fact there are two, accounting for symmetry), but this is the most efficient choice given the chemical intuition that function 1 resembles a \u00271s\u0027 orbital, function 2 a \u00272s\u0027 orbital, and so on, in accordance with the structure of a typical atomic basis set. Since there are only DOCC orbitals involved, this run optimizes a VSHF wave function.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[counts and array dims]\n 1 1 0 0 2 4 2 0 3 17 0 0 1 0 1\n\n[run parameters]\n 16 16 16 3 3 100 0.0 0.0 1 2\n\n\n[geometry]\n 1 0.0 0.0 0.0\n\n\n[basis set]\n 4.0 3\n0 8\n 2940.0000000 0.0006800 \n 441.2000000 0.0052360 \n 100.5000000 0.0266060 \n 28.4300000 0.0999930 \n 9.1690000 0.2697020 \n 3.1960000 0.4514690 \n 1.1590000 0.2950740 \n 0.1811000 0.0125870 \n0 8\n 2940.0000000 -0.0001230 \n 441.2000000 -0.0009660 \n 100.5000000 -0.0048310 \n 28.4300000 -0.0193140 \n 9.1690000 -0.0532800 \n 3.1960000 -0.1207230 \n 1.1590000 -0.1334350 \n 0.1811000 0.5307670 \n0 1\n 0.0589000\n\n[orbitals]\n 1 1 2\n 1 1.0 3 0.0\n 1 1 2\n 2 1.0 3 0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[A guess consisting of ones for the UDF and zeros elsewhere may be termed a \u0027unit guess\u0027 by analogy with a unit matrix. The output to the screen is given below.]\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e guess energy in atomic units -14.4746666438408660\n orbital optimization \n\n (full) first-order method \n\n cycle orbital relaxation(kCal) (..per orb.)/tol \n 1 1 -0.038504 -0.3850E+02\n 1 2 -36.147608 -0.3611E+05\n 2 1 -58.488652 -0.2234E+05\n 2 2 -60.233942 -0.1745E+04\n 3 1 -61.130090 -0.8961E+03\n 3 2 -61.228910 -0.9882E+02\n 4 1 -61.280549 -0.5164E+02\n 4 2 -61.286071 -0.5522E+01\n 5 1 -61.288965 -0.2894E+01\n 5 2 -61.289277 -0.3120E+00\n 6 1 -61.289441 -0.1637E+00\n 6 2 -61.289458 -0.1761E-01\n\n calculation converged \n\n total energy in atomic units -14.5723376136726923\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[After six cycles through the orbital list, the final energy (which matches the Hartree-Fock energy) is printed. The \u0027orbitals\u0027 file looks like this:]\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 1 1 2\n 1 1.00065097 3 -0.00375468\n 1 1 2\n 2 0.48912818 3 0.58002975\n\n\n total energy in atomic units -14.5723376136726923\n converged to 0.10E-02 kCal/mol\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[There is no \u0027nelecwfn\u0027 file with this run]\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eH2O/VSHF/cc-VDZ\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis example highlights chemically intuitive choices for the UDF, so just the optimized wave function and energy are given.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 3 2 0 0 5 30 8 0 7 25 1 0 1 0 3\n\n\n 16 16 16 6 6 100 0.0 0.0 1 5\n\n\n 1 0.0000 0.0000 0.1271\n 2 0.0000 0.7580 -0.5085\n 2 0.0000 -0.7580 -0.5085 \n\n\n 8.0 5\n0 8\n 11720.0000000 0.0007100 \n 1759.0000000 0.0054700 \n 400.8000000 0.0278370 \n 113.7000000 0.1048000 \n 37.0300000 0.2830620 \n 13.2700000 0.4487190 \n 5.0250000 0.2709520 \n 1.0130000 0.0154580 \n0 8\n 11720.0000000 -0.0001600 \n 1759.0000000 -0.0012630 \n 400.8000000 -0.0062670 \n 113.7000000 -0.0257160 \n 37.0300000 -0.0709240 \n 13.2700000 -0.1654110 \n 5.0250000 -0.1169550 \n 1.0130000 0.5573680 \n0 1\n 0.3023000 \n1 3\n 17.7000000 0.0430180 \n 3.8540000 0.2289130 \n 1.0460000 0.5087280 \n1 1\n 0.2753000 \n\n 1.0 2\n0 3\n 13.0100000 0.0196850 \n 1.9620000 0.1379770 \n 0.4446000 0.4781480 \n0 1\n 0.1220000 \n\n\n\n\n 3 1 2 3 6\n 1 1.00085930 2 0.00438407 6 0.00072731 9 0.00346894\n 11 0.00045391 13 0.00045392\n 3 1 2 3 8\n 2 0.08137627 5 0.30386789 6 -0.39873764 8 0.16245941\n 9 -0.27280445 10 0.40165595 11 0.05882459 13 -0.02137198\n 3 1 2 3 8\n 2 0.08137642 5 -0.30386766 6 -0.39873750 8 -0.16245933\n 9 -0.27280444 11 -0.02137131 12 0.40165620 13 0.05882437\n 3 1 2 3 6\n 2 0.44922797 3 0.56835690 6 0.25068561 9 0.21482539\n 11 -0.03046886 13 -0.03046865\n 1 1 2\n 4 0.63677843 7 0.51530766 \n\n[ The total energy is: -75.97812747 AU ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHere, the UDF for the orbitals are as follows:\nOrbital UDF AO label\noxygen core: O 1s 1\nOH(a) bond: H(a) 1s 10\nOH(b) bond: H(b) 1s 12\nSigma lone-pair: O 3s 3\nPi lone-pair: (O 2px 4)\u003c/p\u003e\n\u003cp\u003eChemically sensible alternatives for the Sigma lone-pair UDF include the O2s function. Different orbitals would be obtained with this choice but the same energy. The Pi lone-pair has a different symmetry to the other orbitals as it is perpendicular to the plane of the atoms. Since it is the only orbital of its symmetry, the UDF issue is null (hence the parentheses). The in-plane orbitals have Sigma symmetry.\u003c/p\u003e\n\u003cp\u003eNote that the O 2,3py,z functions are not chemically sensible UDF for the OH bonds. The atoms are placed in the y,z plane to simplify the symmetry definitions, so the 2py,z functions are needed to direct hybridization of the oxygen valence electrons toward the hydrogens for both bonds. So choosing, say, 2py for one bond and 2pz for the other would yield unsymmetric bond orbitals.\u003c/p\u003e\n\u003cp\u003eYet another alternative would be to hybridize the O2s,y,z (and/or the O3s,y,z) AOs to give three Osp2 functions, two directed toward their respective hydrogens and one directed in opposition to them, then base the UDF choices on them. The s/p hybridization ratio would need to be optimized for this case.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eH2/SC/SZ\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eHydrogen molecule with spin-coupled orbitals and a single-Zeta basis set. This is the simplest example of using spin-coupled orbitals. The (optimized) wave function will dissociate correctly when the interatomic distance is increased. As with H2O, above, just the result is given.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 2 1 1 0 0 4 2 1 1 3 0 0 1 0 2\n\n\n 16 16 16 6 6 100 0.0 0.0 1 2\n\n\n 1 0.0 0.0 0.0\n 1 0.0 0.0 0.770\n\n\n 1.0 1\n0 3\n 13.0100000 0.0196850\n 1.9620000 0.1379770\n 0.4446000 0.4781480\n\n\n[ spin-coupling information ]\n 1 2\n\n\n 2 1 2 2\n 1 0.89622388 2 0.17099192\n 2 1 2 2\n 1 0.17099207 2 0.89622378\n\n\n[ The total energy is: -1.06381067 AU ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe main qualitative difference between this input and the previous ones is the presence of the spin-coupling information in the middle, indicating that electrons 1 and 2 be coupled to a singlet.\u003c/p\u003e\n\u003cp\u003eThe spatial polarization of the two orbitals toward either atom is evident in the weights in each orbital of the two 1s atomic basis functions. As the atoms are drawn apart, the weight of the local basis function tends to unity, while that of the remote function tends to zero, in each orbital, respectively, leaving two separated hydrogen atoms with a total energy slightly higher than -1.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eLiH/SCval/cc-pVDZ\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eLithium Hydride singlet-Sigma ground state. This wave function has a double-occupied Li core and a spin-coupled Li-H \u0027bond\u0027.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 2 2 1 0 1 30 10 1 9 27 2 0 1 0 2\n\n\n 16 16 16 6 6 100 0.0 0.0 1 3\n\n\n 1 0.0 0.0 0.0\n 2 0.0 0.0 1.646\n\n\n\n 3.0 6\n0 8\n 1469.0000000 0.0007660 \n 220.5000000 0.0058920 \n 50.2600000 0.0296710 \n 14.2400000 0.1091800 \n 4.5810000 0.2827890 \n 1.5800000 0.4531230 \n 0.5640000 0.2747740 \n 0.0734500 0.0097510 \n0 8\n 1469.0000000 -0.0001200 \n 220.5000000 -0.0009230 \n 50.2600000 -0.0046890 \n 14.2400000 -0.0176820 \n 4.5810000 -0.0489020 \n 1.5800000 -0.0960090 \n 0.5640000 -0.1363800 \n 0.0734500 0.5751020 \n0 1\n 0.0280500 \n1 3\n 1.5340000 0.0227840 \n 0.2749000 0.1391070 \n 0.0736200 0.5003750 \n1 1\n 0.0240300 \n2 1\n 0.1239000 \n\n 1.0 3\n0 3\n 13.0100000 0.0196850 \n 1.9620000 0.1379770 \n 0.4446000 0.4781480 \n0 1\n 0.1220000 \n1 1\n 0.7270000 \n\n\n\n 1 2 \n\n\n\n 2 1 2 10\n 2 0.52988354 3 0.24604657 6 0.37767681 9 0.04693388\n 10 -0.03994670 12 -0.03994670 15 0.03634386 16 0.03973432\n 17 0.17856129 20 -0.00329185\n 2 1 2 10\n 2 0.06142962 3 0.02236453 6 0.09631637 9 -0.01160377\n 10 -0.01504756 12 -0.01504756 15 0.03646087 16 0.71199723\n 17 0.24851686 20 -0.01632559\n 2 1 2 10\n 1 0.99936284 2 -0.00202778 3 -0.00493372 6 -0.01387483\n 9 0.00230970 10 -0.00033165 12 -0.00033165 15 -0.00706813\n 17 0.00999584 20 -0.00141802\n\n[ The total energy is: -8.00046053 AU ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first two orbitals are read as the spin-coupled pair and the third is read as the double-occupied Li 1s core. As in the H2 example above, the presence of the spin-coupled Li-H bond means the wave function will dissociate correctly when the interatomic separation is increased. The bonding here is not strong. The lithium valence electron is polarized toward H.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eBe/2SC/cc-VQZ\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis is the simplest closed-shell case with two spin-couplings. Convergence is more efficient when the orbitals are obtained with one spin-coupling first, then allowed to relax in the presence of the two couplings. Again, the choice of the first (dominant) spin-coupling is based on the chemical intuition of which electrons are paired to make bonds, lone-pairs, and so forth.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 1 1 2 0 0 16 4 2 5 21 0 0 1 0 1\n\n\n 20 16 16 6 6 100 0.0 0.0 1 4 \n\n\n\n 1 0.0 0.0 0.0\n\n\n\n 4.0 5\n0 9\n 14630.0000000 0.0000920 \n 2191.0000000 0.0007130 \n 498.2000000 0.0037350 \n 140.9000000 0.0154680 \n 45.8600000 0.0528740 \n 16.4700000 0.1456940 \n 6.3190000 0.3026810 \n 2.5350000 0.4049360 \n 1.0350000 0.2223870 \n0 9\n 14630.0000000 -0.0000170 \n 2191.0000000 -0.0001300 \n 498.2000000 -0.0006790 \n 140.9000000 -0.0028570 \n 45.8600000 -0.0098130 \n 16.4700000 -0.0286090 \n 6.3190000 -0.0637600 \n 2.5350000 -0.1172310 \n 1.0350000 -0.1212020 \n0 1\n 0.2528000 \n0 1\n 0.1052000 \n0 1\n 0.0426100 \n\n\n[ spin-couplings and weights ]\n\n 0.28613755 1 2 3 4\n 0.02435745 1 3 2 4\n\n\n 1 1 4\n 1 2.04468084 2 1.08510278 3 0.06760375 4 -0.08225021\n 1 1 4\n 1 0.08795929 2 1.09357982 3 0.05323659 4 -0.06567380\n 1 1 4\n 2 0.40901809 3 0.66520392 4 0.38644720 5 0.11934164\n 1 1 4\n 2 0.14161563 3 -0.17596193 4 0.71862322 5 0.47255306\n\n\n[ The total energy is: -14.58839772 AU ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis run creates a file called \u0027nelecwfn\u0027 with the spin-coupling information,weights given above as its contents.\u003c/p\u003e\n\u003cp\u003eThe energy with one spin-coupling (1 2)(3 4) is -14.58808261 AU, so the relaxation with two couplings is modest in this case. However, the impact of multiple spin-couplings can be much greater than this, particularly in systems with multiple near-degenerate orbitals, such as aromatics. In benzene, the Kekule and Dewar structures correspond to different spin-couplings of the six equivalent Pi electrons.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 7. Automatic input generation with vtools\u003c/h2\u003e\u003ca id=\"user-content-section-7-automatic-input-generation-with-vtools\" class=\"anchor\" aria-label=\"Permalink: Section 7. Automatic input generation with vtools\" href=\"#section-7-automatic-input-generation-with-vtools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe previous sections describe how the input to VALENCE can be generated \u0027by hand\u0027. However, it is also possible to generate this input automatically using the \u0027vtools\u0027 software provided in this package. A description of the vtools command syntax is provided in the file, ./vtools/README.md.\u003c/p\u003e\n\u003cp\u003evtools uses the \u0027Model Kit\u0027 method for building wave functions, employing the analogy that the VSVB wave function can be built from orbitals much in the same way that a model airplane is built from parts. In the case of a wave function, the orbital \u0027parts\u0027 are contained in a repository, and the \u0027builder\u0027 is a binary, called \u0027modelkit\u0027. modelkit currently processes three types of orbital: core orbitals, bonds, and lone-pairs. While the cores and lone-pairs involve a single atom, the bonds involve two atoms - that is, at present, only two-center bonds are supported but plans are to extend this in the future to allow multi-center orbitals. modelkit follows straightforward rules for orienting the bonds and lone-pairs with respect to the atoms in the molecule, according to the orbital type. The repository uses a \u0027standard\u0027 orientation to encode the orbital type, while obviating the need for storing the atomic coordinates in the orbital information, as follows:\nZ-axis : Sigma orbitals (bonds, lone-pairs)\nX,Y-axes : Pi bonds,lone-pairs (first, second, respectively)\nFor example, a two-center orbital involving S and Pz functions is recognized as a Sigma-bond, while the one-center counterpart would be a Sigma-lone-pair orbital. A two-center orbital involving Px functions is recognized as a Pi-bond, that with Py functions would be the second Pi-bond. And so on.\u003c/p\u003e\n\u003cp\u003eThe repository is currently limited to the major orbital types associated with H,C,N,O atoms, and the 6-31G basis set, with work to incorporate more atom types, orbital types, and basis sets on-going. Though such orbitals are strictly \u0027guesses\u0027, an exciting prospect is the use of machine-learning techniques to develop increasingly accurate \u0027guesses\u0027, with the ultimate goal of obviating the need for wave function optimization entirely.\u003c/p\u003e\n\u003cp\u003eWhenever orbitals are used (especially if they are generated for the first time), we recommend visualizing them in order to check that their form is reasonable from the chemical standpoint. To this end, we are working to incorporate a visualization capability directly into the vtools and expect this to be available in the near future.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 8. How to make spin-coupled orbitals\u003c/h2\u003e\u003ca id=\"user-content-section-8-how-to-make-spin-coupled-orbitals\" class=\"anchor\" aria-label=\"Permalink: Section 8. How to make spin-coupled orbitals\" href=\"#section-8-how-to-make-spin-coupled-orbitals\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following procedure is recommended. It is advisable to begin a pair of spin-coupled (SC) orbitals from the corresponding converged double-occupied (DOCC) orbital. So, first, choose the DOCC orbital of interest from a previously obtained optimized wave function. In a suitable text editor, cut the DOCC orbital from the DOCC list in the input, make a copy, so there are now two identicle orbitals, and paste this pair into the list of SC orbitals. Be sure to adjust the relevant counters among the \u0027dims\u0027 (section 3 (A)). To initialize the subsequent optimization run for the SC pair, use the following method to provide a \u0027nudge\u0027 in the right direction. Locate the first- and second-largest magnitude weights in either of the SC orbitals. In one of the SC pair, increase the largest magnitude weight by 0.1, reduce the second largest by 0.1. In the other SC orbital, do the opposite - decrease the largest magnitude weight by 0.1, increase the second largest by 0.1. The value of 0.1 is just a suggestion, but this has proved to be a reasonable choice. Execute an optimization run for the pair of SC orbitals, keeping the others fixed. If necessary, re-optimize, including any other orbitals that interact significantly with the new SC pair.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 9. How to use derived basis functions (DBF)\u003c/h2\u003e\u003ca id=\"user-content-section-9-how-to-use-derived-basis-functions-dbf\" class=\"anchor\" aria-label=\"Permalink: Section 9. How to use derived basis functions (DBF)\" href=\"#section-9-how-to-use-derived-basis-functions-dbf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs mentioned in section 2, VALENCE allows the user to define combinations of the atomic basis functions to form new functions over which to expand the electronic orbitals. A typical use of this feature is to symmetry-adapt the basis set to yield more convenient degrees-of-freedom. For example, making spherical harmonic functions from the cartesian functions (useful for transition metals) and hybridized basis sets consisting of sp,sp2, and sp3 \u0027hybrid\u0027 functions.\u003c/p\u003e\n\u003cp\u003eTo use DBF, set the total number required (item 12 in part A of section 3) and append them to the existing orbitals in the OBS format. To reference the DBF in the electronic orbitals, an index less than 1 (that is, zero, or negative) is used to distinguish them from regular AOs. The DBF are indexed beginning with zero for the last one entered and proceeding backwards up the file to -1, -2, ..., 1-N, where N is the number of DBF.\u003c/p\u003e\n\u003cp\u003eIn the following example, four spherical harmonic functions used to model the 3d10 configuration of copper (I) cation with a double-Zeta basis set are defined. The DBF correspond (nominally) to the 3d\u0027z2\u0027, 4d\u0027z2\u0027, 3dx2-y2, and 4dx2-y2 functions, with indices 0, -1, -2, and -3, respectively. The DBF are used in two of the valence orbitals of Cu+ to provide variational flexbility.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 1 1 0 0 14 29 3 0 7 18 2 4 1 0 1\n\n 20 20 20 2 2 50 0.0 0.0 1 5\n\n 1 0.0 0.0 0.0\n\n 29.0 7\n0 3\n 4134.3020000 0.0631880 \n 625.4912000 0.3748450 \n 136.9556000 0.6831000 \n0 3\n 181.4960000 -0.1113200\n 39.5743100 0.0944870\n 12.1624600 0.9608790\n1 3\n 181.4960000 0.1430840 \n 39.5743100 0.5677560 \n 12.1624600 0.4567140 \n0 3\n 12.3511100 -0.2922230\n 4.0496510 0.3429910\n 1.2792250 0.8479460\n1 3\n 12.3511100 0.0277270 \n 4.0496510 0.4835240 \n 1.2792250 0.5929780 \n2 2\n 16.7593800 0.2741120 \n 4.1789770 0.8446250 \n2 1\n 0.9943270\n\n\n 1 1 2\n 0 0.57405221 -1 0.62709111\n 1 1 2\n -2 0.57407860 -3 0.62706573\n 1 1 2\n 11 0.57411273 17 0.62703292\n 1 1 2\n 13 0.57412360 19 0.62702246\n 1 1 2\n 14 0.57411685 20 0.62702896\n 1 1 1\n 1 1.00000000\n 1 1 1\n 2 1.00000000\n 1 1 1\n 3 1.00000000\n 1 1 1\n 4 1.00000000\n 1 1 1\n 5 1.00000000\n 1 1 1\n 6 1.00000000\n 1 1 1\n 7 1.00000000\n 1 1 1\n 8 1.00000000\n 1 1 1\n 9 1.00000000\n\n 1 1 2\n 16 0.86602540 18 -0.86602540\n 1 1 2\n 10 0.86602540 12 -0.86602540\n 1 1 3\n 21 1.00000000 16 -0.50000000 18 -0.50000000\n 1 1 3\n 15 1.00000000 10 -0.50000000 12 -0.50000000\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe example below is for the ground state of dinitrogen. Bonding and anti-bonding combinations of the two 2Pz functions on each nitrogen provide well-defined UDF for the sigma-bonding orbital without losing quality in the basis set.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e 2 1 0 0 7 76 12 0 6 15 2 2 0 0 2\n\n 20 20 20 0 0 0 0.0 0.0 \n\n 1 0.0000 0.0000 0.0000 \n 1 0.0000 0.0000 1.0784 \n\n 7.0 6\n0 6\n 4173.5110000 0.0018348 \n 627.4579000 0.0139950 \n 142.9021000 0.0685870 \n 40.2343300 0.2322410 \n 12.8202100 0.4690700 \n 4.3904370 0.3604550 \n0 3\n 11.6263580 -0.1149610 \n 2.7162800 -0.1691180 \n 0.7722180 1.1458520 \n0 1\n 0.2120313 \n1 3\n 11.6263580 0.0675800 \n 2.7162800 0.3239070 \n 0.7722180 0.7408950 \n1 1\n 0.2120313 \n2 1\n 0.8000000 \n\n\n 2 1 2 12\n 2 0.46448740 3 0.51058196 9 -0.09072744 10 -0.00618979\n 12 -0.00619147 15 0.00259130 17 0.11744429 24 0.00545631\n 25 -0.00741184 27 -0.00741435 30 0.02071109 0 -0.13325599\n 2 1 2 12\n 2 -0.11738937 9 0.00542484 10 0.00741009 12 0.00740997\n 15 -0.02070910 17 -0.46450546 18 -0.51057039 24 -0.09076468\n 25 0.00619669 27 0.00619689 30 -0.00257915 0 -0.13327727\n 2 1 2 12\n 2 0.24499689 -1 0.67364713 9 0.12273824 10 -0.01415403\n 12 -0.01415444 15 0.04057664 17 0.24502759 24 -0.12270638\n 25 -0.01415739 27 -0.01415748 30 0.04057488 0 0.00002984\n 2 1 2 6\n 4 0.43505172 7 0.24461579 13 0.04819538 19 0.43497306\n 22 0.24456183 28 -0.04819615\n 2 1 2 6\n 5 0.43498162 8 0.24458671 14 0.04819400 20 0.43502387\n 23 0.24461060 29 -0.04819252\n 2 1 2 12\n 1 0.99531880 2 0.02391452 9 -0.00095882 10 -0.00374759\n 12 -0.00374774 15 -0.00204212 17 -0.00169077 24 0.00019574\n 25 -0.00003327 27 -0.00003336 30 -0.00107139 0 0.00159460\n 2 1 2 12\n 2 0.00169436 9 0.00019515 10 0.00003176 12 0.00003188\n 15 0.00106951 16 -0.99531842 17 -0.02391508 24 -0.00095836\n 25 0.00374769 27 0.00374779 30 0.00204220 0 0.00159441\n\n 2 1 2 2\n 6 0.60840187 21 -0.60840187\n 2 1 2 2\n 6 0.87759376 21 0.87759376\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 10. Acknowledgements\u003c/h2\u003e\u003ca id=\"user-content-section-10-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Section 10. Acknowledgements\" href=\"#section-10-acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was supported by Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.\nThe name, \u0027VALENCE\u0027, was chosen in honor of the famous book by Charles Coulson, an early pioneer of valence bond theory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSection 11. Contact information\u003c/h2\u003e\u003ca id=\"user-content-section-11-contact-information\" class=\"anchor\" aria-label=\"Permalink: Section 11. Contact information\" href=\"#section-11-contact-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease feel free to send questions/comments to any/all members of \u0027The VALENCE Group, at Argonne\u0027:\u003c/p\u003e\n\u003cp\u003eGraham D. Fletcher\nComputational Science Division\nArgonne National Laboratory\nLemont, IL, USA\n\u003ca href=\"mailto:gfletcher@anl.gov\"\u003egfletcher@anl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMurat Keceli\nComputational Science Division\nArgonne National Laboratory\nLemont, IL, USA\n\u003ca href=\"mailto:keceli@anl.gov\"\u003ekeceli@anl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eColleen Bertoni\nArgonne Leadership Computing Facility\nArgonne National Laboratory\nLemont, IL, USA\n\u003ca href=\"mailto:bertoni@anl.gov\"\u003ebertoni@anl.gov\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMichael D\u0027Mello\nIntel Corporation\n425 N. Martingale Road, Suite 1500\nSchaumburg, IL, USA\n\u003ca href=\"mailto:mdmello@anl.gov\"\u003emdmello@anl.gov\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 7, - "subscribers_count": 5, - "topics": [ - "high-", - "quantum-chemistry" + "Singularity" ], - "updated_at": 1666754110.0 + "full_name": "waldronlab/bioconductor", + "latest_release": null, + "readme": "\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eHub\u003c/th\u003e\n\u003cth\u003eVersion\u003c/th\u003e\n\u003cth\u003eStatus\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:devel\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0a1b9e45197d4e64deafcf26d97c0f00737d462148d91614fe64b64e1e47a853/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f76657273696f6e2f77616c64726f6e6c61622f62696f636f6e647563746f723a646576656c2e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/version/waldronlab/bioconductor:devel.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:devel\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6e5be46d1956a1d8ade50157cdb787bb8c904f7139424e15dae9826b9fe62607/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f77616c64726f6e6c61622f62696f636f6e647563746f723a646576656c2e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/waldronlab/bioconductor:devel.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:release\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a537aaa8e6682cafd77bd7ceb15bbf1e76a8d53323f8160c520e931669ea6add/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f76657273696f6e2f77616c64726f6e6c61622f62696f636f6e647563746f723a72656c656173652e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/version/waldronlab/bioconductor:release.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:release\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e465e9a04eff517031f3211f8b255ec66bc8beb1616654af12185b1eb77f8dd2/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f77616c64726f6e6c61622f62696f636f6e647563746f723a72656c656173652e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/waldronlab/bioconductor:release.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:RELEASE_3_10\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/446196813e96bf4ddd4291af079b986be31c927c4b46b45f5ac080cb7e4c28f8/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f76657273696f6e2f77616c64726f6e6c61622f62696f636f6e647563746f723a52454c454153455f335f31302e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/version/waldronlab/bioconductor:RELEASE_3_10.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:RELEASE_3_10\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9ea4ad6de69eeec2ea8444a02f339d67e5c57ba219afd29df57610a9d4606756/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f77616c64726f6e6c61622f62696f636f6e647563746f723a52454c454153455f335f31302e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/waldronlab/bioconductor:RELEASE_3_10.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbout the \"bioconductor\" script\u003c/h1\u003e\u003ca id=\"user-content-about-the-bioconductor-script\" class=\"anchor\" aria-label=\"Permalink: About the \u0026quot;bioconductor\u0026quot; script\" href=\"#about-the-bioconductor-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script makes it more convenient to run the Bioconductor docker images\nlocally for routine daily usage:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt creates a host directory \u003ccode\u003e~/dockerhome\u003c/code\u003e where the home directory\nof the Docker user will be mounted. Files can be shared between the\nDocker container and host filesystem here.\u003c/li\u003e\n\u003cli\u003eIt results in user-installed packages being added to the host directory\n\u003ccode\u003e~/.docker-devel-packages\u003c/code\u003e or \u003ccode\u003e~/.docker-release-packages\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIt runs the Docker container\n\u003ca href=\"https://github.com/bioconductor/bioconductor_docker\"\u003ebioconductor/bioconductor_docker\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs to the \u0027biconductor\u0027 script\u003c/h2\u003e\u003ca id=\"user-content-inputs-to-the-biconductor-script\" class=\"anchor\" aria-label=\"Permalink: Inputs to the \u0027biconductor\u0027 script\" href=\"#inputs-to-the-biconductor-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe user must specify the version of Bioconductor to spin up as a Docker image.\nThe available inputs for the \u003cstrong\u003efirst\u003c/strong\u003e argument are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. release\n2. devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003cstrong\u003esecond\u003c/strong\u003e argument for the \u0027bioconductor\u0027 script denotes the environment type\nto run when executing the script this will either put the user in one of two\nsupported environements:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. rstudio - allows the user to open up an rstudio session in the browser\n2. shell - put the user in the command line within the container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote. The default user environment is the \u003ccode\u003erstudio\u003c/code\u003e session\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbout the \u003ccode\u003ebioconductor_docker\u003c/code\u003e docker image\u003c/h1\u003e\u003ca id=\"user-content-about-the-bioconductor_docker-docker-image\" class=\"anchor\" aria-label=\"Permalink: About the bioconductor_docker docker image\" href=\"#about-the-bioconductor_docker-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ebioconductor/bioconductor_docker\u003c/code\u003e image is built for both release and devel\nversions of Bioconductor. It includes system dependencies so that almost every\nBioconductor package can be installed using \u003ccode\u003eBiocManager::install()\u003c/code\u003e with no\nfurther troubles. For almost everyone, this means no more errors when trying to install a package.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFor singularity users\u003c/h1\u003e\u003ca id=\"user-content-for-singularity-users\" class=\"anchor\" aria-label=\"Permalink: For singularity users\" href=\"#for-singularity-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo make a generalization, Docker is more supported by commercial Cloud\nproviders, whereas \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is (far)\nmore likely to be supported by university high-performance computing\nfacilities.\u003c/p\u003e\n\u003cp\u003eIf you have singularity installed, pull and the singularity images as follows (or substitute \"devel\" with \"release\" for the release version):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build $HOME/bioconductor-devel.img docker://waldronlab/bioconductor:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo far I have only used singularity for bash and R, with aliases like these:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias singulaR=\"singularity shell $HOME/bioconductor-devel.simg R\"\nalias singularbash=\"singularity shell $HOME/bioconductor-devel.simg bash\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that default behavior in singularity is to mount your home (and several\nother) directories as the home directory within the container, while\nmaintaining your user permissions. This makes all the docker efforts to mount\nvolumes for your container package and home directories unnecessary. I haven\u0027t\nyet tried running rstudio via singularity, but it should be possible?\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing the \u003ccode\u003ebioconductor\u003c/code\u003e script and docker container\u003c/h1\u003e\u003ca id=\"user-content-using-the-bioconductor-script-and-docker-container\" class=\"anchor\" aria-label=\"Permalink: Using the bioconductor script and docker container\" href=\"#using-the-bioconductor-script-and-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall a \u003ca href=\"https://www.docker.com/get-started\" rel=\"nofollow\"\u003edocker client\u003c/a\u003e for\nyour operating system.\u003c/li\u003e\n\u003cli\u003eMake sure home directories are being shared (Whale icon -\u0026gt;\nPreferences -\u0026gt; File Sharing). Last I checked, this was already the\ncase by default. You can also change the allotted system resources if\nyou want.\u003c/li\u003e\n\u003cli\u003eCopy the\n\u003ca href=\"https://github.com/waldronlab/bioconductor/blob/master/bioconductor\"\u003ebioconductor\u003c/a\u003e\nscript from this repo to somewhere in your $PATH. Modify as you see\nfit, e.g. if you want to mount different directories or in a different\nplace than \u003ccode\u003e~/dockerhome\u003c/code\u003e, or change the rstudio password. Make sure\nthe script is executable (e.g. \u003ccode\u003echmod a+x bioconductor\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eFrom the command-line, type \u003ccode\u003ebioconductor devel\u003c/code\u003e or \u003ccode\u003ebioconductor release\u003c/code\u003e. Later you can use Ctrl-C to stop the\ncontainer. There are additional usage tips at\n\u003ca href=\"https://github.com/Bioconductor/bioc_docker\"\u003ehttps://github.com/Bioconductor/bioc_docker\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eIf using the \u0027rstudio\u0027 argument (default) --- In a browser, open\n\u003ca href=\"http://localhost:8787\" rel=\"nofollow\"\u003ehttp://localhost:8787\u003c/a\u003e. Login with username is \"rstudio\" and password\n\"rstudiopassword\" unless you change the password within the \"bioconductor\"\nscript in step 3.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThat\u0027s it! You can stop the instance you\u0027re running and switch to\nrelease or devel (but you can\u0027t currently run both at the same\ntime). There will be separate host package libraries for\nuser-installed packages (in \u003ccode\u003e~/.docker-devel-packages\u003c/code\u003e and\n\u003ccode\u003e~/.docker-release-packages\u003c/code\u003e), and a common home directory in\n\u003ccode\u003e~/dockerhome\u003c/code\u003e. \u003ccode\u003edocker pull\u003c/code\u003e is run each time you invoke the\n\u003ccode\u003ebioconductor\u003c/code\u003e script, so you should automatically get the most\nup-to-date Bioconductor release or devel versions, and will only have\nto run \u003ccode\u003eBiocManager::install()\u003c/code\u003e to update user-installed packages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Checking mechanisms have been implemented for the script to error if\nanything other than \"release\" or \"devel\" is entered in the first argument.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExample command line execution\u003c/h1\u003e\u003ca id=\"user-content-example-command-line-execution\" class=\"anchor\" aria-label=\"Permalink: Example command line execution\" href=\"#example-command-line-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following commands may be useful in your \u003ccode\u003e~/.bash_profile\u003c/code\u003e for\ncommand-line R and bash usage with the same containers, package directories,\nhome directory, and rstudio user:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e releaseshell=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebioconductor release shell\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e develshell=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebioconductor devel shell\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e coming soon #\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e Rrelease=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebioconductor release R\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e Rdevel=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebioconductor devel R\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTODO\u003c/h1\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ebioconductor\u003c/code\u003e script is rudimentary and should use docopt, and provide\nstart \u0026amp; stop. It could also provide arguments for the volume location etc.\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 4, + "topics": [], + "updated_at": 1730996570.0 }, { "data_format": 2, "description": null, "filenames": [ - "container/Singularity.intel_cm4", - "container/Singularity.intel_netcdf", - "container/Singularity.cm4" + "Singularity" ], - "full_name": "NOAA-GFDL/CM4", - "latest_release": "2021.03", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGFDL CM4 Model\u003c/h1\u003e\u003ca id=\"user-content-gfdl-cm4-model\" class=\"anchor\" aria-label=\"Permalink: GFDL CM4 Model\" href=\"#gfdl-cm4-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.gfdl.noaa.gov\" rel=\"nofollow\"\u003eGeophysical Fluid Dynamics Laboratory\n(GFDL)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe layout of this package includes the following directories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esrc - The source code for the CM4 model\u003c/li\u003e\n\u003cli\u003eexec - The build directory with Makefiles for building the model executable\u003c/li\u003e\n\u003cli\u003erun - Sample run script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning Instructions\u003c/h2\u003e\u003ca id=\"user-content-cloning-instructions\" class=\"anchor\" aria-label=\"Permalink: Cloning Instructions\" href=\"#cloning-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository uses \u003ca href=\"https://git-scm.com/book/en/v2/Git-Tools-Submodules\" rel=\"nofollow\"\u003egit\nsubmodules\u003c/a\u003e to\npoint to other repositories. Thus, care should be taken when cloning,\nand updating the source to ensure all source. To obtain all source,\nuse the following git command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NOAA-GFDL/CM4.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--recursive\u003c/code\u003e option to \u003ccode\u003egit clone\u003c/code\u003e instructs git to recursively\nclone all submodules. In the event the repository was not cloned\nusing the \u003ccode\u003e--recursive\u003c/code\u003e option, the following step must be taken to\nobtain all sources:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# From within the CM4 parent directory\ngit submodule update --init --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSource Code\u003c/h2\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-label=\"Permalink: Source Code\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll model source is contained in the \u003ca href=\"src\"\u003esrc\u003c/a\u003e directory. GFDL\ntracks code using the git version control system. This package\nincludes a single version of the following GFDL model components. The\ngit hash listed corresponds to the commit hash in the internal GFDL\ngit repository.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComponent\u003c/th\u003e\n\u003cth\u003eCommit Hash\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_cubed_sphere\u003c/td\u003e\n\u003ctd\u003eb8b05bf650c0d3293b538bdaceb894ba0fd6910b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_drivers\u003c/td\u003e\n\u003ctd\u003e3be6ed406de2db29766746a69115fd6a47048692\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_param\u003c/td\u003e\n\u003ctd\u003e4fe4ca54a0224ef5c4cf9ebf1010d5b869930a3f\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_shared\u003c/td\u003e\n\u003ctd\u003e2e9d8b770cdb2d70d8d9264e4b2de24213ae21bd\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eland_lad2\u003c/td\u003e\n\u003ctd\u003e154bd2b4bf523f3e699de5017679b156242ec13f\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe following components are available in the\n\u003ca href=\"https://github.com/NOAA-GFDL\"\u003eNOAA-GFDL\u003c/a\u003e github organization:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/MOM6\"\u003eMOM6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/SIS2\"\u003eSIS2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/GFDL_atmos_cubed_sphere/tree/AM4.0\"\u003eGFDL_atmos_cubed_sphere\u003c/a\u003e (as \u003ca href=\"src/atmos_cubed_sphere\"\u003eatmos_cubed_sphere\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/icebergs\"\u003eicebergs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/ice_param\"\u003eice_param\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/ocean_BGC\"\u003eocean_BGC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/FMScoupler\"\u003ecoupler\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/FMS\"\u003eFMS\u003c/a\u003e (as \u003ca href=\"src/shared\"\u003eshared\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/mocsy\"\u003emocsy\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding CM4\u003c/h2\u003e\u003ca id=\"user-content-building-cm4\" class=\"anchor\" aria-label=\"Permalink: Building CM4\" href=\"#building-cm4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainers\u003c/h3\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"container\"\u003econtainer folder\u003c/a\u003e provides example Dockerfiles and Signularity\ndefinition files to use to build AM4 containers using either GCC/GFORTAN or\nIntel oneAPI. There is a script that can be used to build the intel\nsingularity containers, and the first step of this script can be used with the\nother GFDL climate models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"exec\"\u003eexec\u003c/a\u003e directory contains Makefiles that can be used to\nbuild the CM4 executable. These Makefiles were generated using the\n\u003ca href=\"https://github.com/NOAA-GFDL/mkmf\"\u003eMake Makefile (mkmf)\u003c/a\u003e program.\nIncluded in the exec direcgtory is a sample make template file for the\nIntel compilers (\u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e). This make\ntemplate can be used on any system with a relatively recent version of\nthe Intel compilers, the netCDF 4 library and the MPICH2 MPI library.\nIncluded in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file are\nadditional settings that can be modified during the build.\u003c/p\u003e\n\u003cp\u003eTo run the default build (-O3 -msse2), go to the exec directory and\nenter the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake HDF_INCLUDE=-I/path/to/hdf5/include\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003cem\u003e/path/to/hdf5/include\u003c/em\u003e is the path to your HDF5 include folder where hdf5.mod\nis.\u003c/p\u003e\n\u003cp\u003eIf you would like to change some of the compiler options, there are several different\noptions to add to the make command. For example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake ISA=-xhost REPRO=on\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill replace -msse with -xhost and -O3 with -O2. The three options for\nbuilding are\u003cbr\u003e\n\u003ccode\u003ePROD=on\u003c/code\u003e (-O3) Default\n\u003ccode\u003eREPRO=on\u003c/code\u003e (-O2)\u003cbr\u003e\n\u003ccode\u003eDEBUG=on\u003c/code\u003e (-O0 and other traps)\u003cbr\u003e\nAll of the make line options can be\nfound in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file.\u003c/p\u003e\n\u003cp\u003eTo build with GNU compilers, add \u003ccode\u003egcc=on\u003c/code\u003e to the \u003ccode\u003emake\u003c/code\u003e line. The make line\noptions can be found in the \u003ca href=\"exec/templates/gnu.mk\"\u003egnu.mk\u003c/a\u003e file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eObtaining the input data\u003c/h2\u003e\u003ca id=\"user-content-obtaining-the-input-data\" class=\"anchor\" aria-label=\"Permalink: Obtaining the input data\" href=\"#obtaining-the-input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe input data required for running the CM4 model can be found on\n\u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/cm4/\" rel=\"nofollow\"\u003eGFDL\u0027s data\nportal\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eCM4_runDir.tar.gz\u003c/code\u003e contains a configured run directory to run a\nsample experiment of the CM4 model. Included in the tar file is a\nREADME.CM4 with more instructions on how to configure the CM4 run\ndirectory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CM4\u003c/h2\u003e\u003ca id=\"user-content-running-cm4\" class=\"anchor\" aria-label=\"Permalink: Running CM4\" href=\"#running-cm4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIncluded in the run directory is a sample run script for reference.\nTo run the CM4 sample experiment, first download the data file\nmentioned in \u003ca href=\"#obtaining-the-input-data\"\u003eObtaining the Input data\u003c/a\u003e\nsection. Modify the variables in the configuration section in the\nsample run script, and then run the script.\u003c/p\u003e\n\u003cp\u003eThe sample data and run script are configured to run on a total of 8127\nprocessors (864 cores 4 threads for the atmosphere and 4671 ocean cores).\u003cbr\u003e\nTo run on a different number of processors, or modify the\nexperiment, refer to the \u003ccode\u003eREADME.CM4\u003c/code\u003e file included in the CM4\ndata tarball.\u003c/p\u003e\n\u003cp\u003eNote: The \u003ccode\u003einput.nml\u003c/code\u003e file (found in the CM4 data tarball) contains\nFortran namelists and namelist variables that modify, at run time, the\nmodel. To learn more about the settings in the \u003ccode\u003einput.nml\u003c/code\u003e file,\nplease refer to source code where the namelist/variable are defined.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel output and Other References\u003c/h2\u003e\u003ca id=\"user-content-model-output-and-other-references\" class=\"anchor\" aria-label=\"Permalink: Model output and Other References\" href=\"#model-output-and-other-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease refer to the \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/cm4/\" rel=\"nofollow\"\u003eCM4 data and code\nsite\u003c/a\u003e for details\nabout where to find model and OBS data used in the papers.\u003c/p\u003e\n\u003cp\u003eFor all analysis figures and pertaining data, please use the CM4\ndocumentation papers as the original reference.\u003c/p\u003e\n\u003cp\u003ePlease direct your questions and feedback to\n\u003ca href=\"mailto:gfdl.climate.model.info@noaa.gov\"\u003egfdl.climate.model.info@noaa.gov\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is\nprovided on an \u0027as is\u0027 basis and the user assumes responsibility for\nits use. DOC has relinquished control of the information and no\nlonger has responsibility to protect the integrity, confidentiality,\nor availability of the information. Any claims against the Department\nof Commerce stemming from the use of its GitHub project will be\ngoverned by all applicable Federal law. Any reference to specific\ncommercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply\ntheir endorsement, recommendation or favoring by the Department of\nCommerce. The Department of Commerce seal and logo, or the seal and\nlogo of a DOC bureau, shall not be used in any manner to imply\nendorsement of any commercial product or activity by DOC or the United\nStates Government.\u003c/p\u003e\n\u003cp\u003eThis project code is made available through GitHub but is managed by\nNOAA-GFDL at \u003ca href=\"https://gitlab.gfdl.noaa.gov\" rel=\"nofollow\"\u003ehttps://gitlab.gfdl.noaa.gov\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 7, - "subscribers_count": 4, + "full_name": "kavonrtep/SeqGrapheR", + "latest_release": "v0.5.0.2.4", + "stargazers_count": 8, + "subscribers_count": 1, "topics": [], - "updated_at": 1711351792.0 + "updated_at": 1720828097.0 }, { "data_format": 2, - "description": "Website is at:", + "description": "FetaL AneUploidy and FetalFraction analYsis Pipeline", "filenames": [ "Singularity" ], - "full_name": "NBISweden/sauron", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSauron\u003c/h1\u003e\u003ca id=\"user-content-sauron\" class=\"anchor\" aria-label=\"Permalink: Sauron\" href=\"#sauron\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePaulo Czarnewski\nNBIS\u003c/p\u003e\n\u003cp\u003eA single cell workflow. Check the Sauron webpage for help: \u003ca href=\"https://nbisweden.github.io/sauron\" rel=\"nofollow\"\u003ehttps://nbisweden.github.io/sauron\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 7, - "subscribers_count": 41, + "full_name": "J35P312/fluffy", + "latest_release": "2.0.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Clinical-Genomics/fluffy/workflows/Build/badge.svg\"\u003e\u003cimg src=\"https://github.com/Clinical-Genomics/fluffy/workflows/Build/badge.svg\" alt=\"Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/Clinical-Genomics/fluffy\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c18629eade63aa606bad8c57197bdde19b1ae3ded637c1d66bfee86ccab49436/68747470733a2f2f636f6465636f762e696f2f67682f436c696e6963616c2d47656e6f6d6963732f666c756666792f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/Clinical-Genomics/fluffy/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFluFFyPipe\u003c/h1\u003e\u003ca id=\"user-content-fluffypipe\" class=\"anchor\" aria-label=\"Permalink: FluFFyPipe\" href=\"#fluffypipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNIPT analysis pipeline, using WisecondorX for detecting aneuplodies and large CNVs, AMYCNE for FFY and PREFACE for FF prediction (optional). FluFFYPipe produces a variety of output files, as well as a per batch csv summary.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/J35P312/FluFFyPipe/blob/master/logo/IMG_20200320_132001.jpg\"\u003e\u003cimg src=\"https://github.com/J35P312/FluFFyPipe/raw/master/logo/IMG_20200320_132001.jpg\" width=\"400\" height=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun FluFFyPipe\u003c/h1\u003e\u003ca id=\"user-content-run-fluffypipe\" class=\"anchor\" aria-label=\"Permalink: Run FluFFyPipe\" href=\"#run-fluffypipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun NIPT analysis, using a previously comnputed reference:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --analyse\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun NIPT analysis, using an internally computed reference (i.e the reference is built using all samples listed in samplesheet):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --analyse --batch-ref\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eoptionally, skip preface:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --skip_preface --analyse\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll output will be written to the output folder, this output includes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebam files\nwisecondorX output\ntiddit coverage summary\nFetal fraction estimation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eas well as a summary csv and multiqc html (per batch)\u003c/p\u003e\n\u003cp\u003ethe input folder is a project folder containing one folder per sample, each of these subfolders contain the fastq file(s).\nThe samplesheet contains at least a \"sampleID\" column, the sampleID should match the subfolders in the input folder. The samplesheet may contain other columns, such as flowcell and index folder: such columns will be printed to the summary csv.\nIf the samplesheet contains a SampleName column, fluffy will name the output according to SampleName\u003c/p\u003e\n\u003cp\u003eCreate a WisecondorX reference\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --reference\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esamplesheet should contain atleast a \"sampleID\" column. All samples in the samplesheet will be used to construct the reference, visit the WisecondorX manual for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOptional fluffy parameters:\u003c/h1\u003e\u003ca id=\"user-content-optional-fluffy-parameters\" class=\"anchor\" aria-label=\"Permalink: Optional fluffy parameters:\" href=\"#optional-fluffy-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eAnalysis mode:\n\t--dry_run - run the pipeline without generating files\n\t-l\t-\tadd paramters to the slurm header of the script, should be given on the following format parameter:value\n\t\t\texample: qos:high \n\nReference mode:\n\t--dry_run - run the pipeline without generating files\n\t\nRerun mode:\n\t--dry_run - run the pipeline without generating files\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTroubleshooting and rerun\u003c/h1\u003e\u003ca id=\"user-content-troubleshooting-and-rerun\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting and rerun\" href=\"#troubleshooting-and-rerun\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are three statuses of the fluffy pipeline:\nrunning, complete, and failed\u003c/p\u003e\n\u003cp\u003eThe status of a fluffy run is found in the\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;output_folder\u0026gt;/analysis_status.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe status of all jobs are listed in\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;output_folder\u0026gt;/sacct/fluffy_\u0026lt;date\u0026gt;.log.status\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere is the timepoint when the jobs were submitted\nUse grep to find the failed jobs:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egrep -v COMPLETE \u0026lt;output_folder\u0026gt;/sacct/fluffy_\u0026lt;date\u0026gt;.log.status\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output logs are stored in:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e \u0026lt;output_folder\u0026gt;/logs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBefore continuing, you may want to generate the summary csv for all completed cases:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash \u0026lt;output_folder\u0026gt;/scripts/summarizebatch-\u0026lt;hash\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere is a randomly generated string.\u003c/p\u003e\n\u003cp\u003euse the rerun module to rerun failed fluffy analyses:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --skip_preface rerun\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall FluFFyPipe\u003c/h1\u003e\u003ca id=\"user-content-install-fluffypipe\" class=\"anchor\" aria-label=\"Permalink: Install FluFFyPipe\" href=\"#install-fluffypipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFluFFyPipe requires python 3, slurm, slurmpy, and singularity, python-coloredlogs.\u003c/p\u003e\n\u003cp\u003efluffy may be installed using pip:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install fluffy-cg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ealternatively, fluffy is cloned and installed from github:\ngit clone \u003ca href=\"https://github.com/Clinical-Genomics/fluffy\"\u003ehttps://github.com/Clinical-Genomics/fluffy\u003c/a\u003e\ncd fluffy\npip install -e .\u003c/p\u003e\n\u003cp\u003eNext download the FluFFyPipe singularity container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull library://jeisfeldt/default/fluffy:sha256.dbef92cd5eab8558c2729f73a191d73a7576a24e9bb44dde7372c0cd405c4ef6 \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecopy the example config (found in example_config), and edit the variables.\nYou will need to download/create the following files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eReference fasta (indexed using bwa)\n\nWisecondorX reference files (created using the reference mode)\n\nPREFACE model file (optional)\n\nblacklist bed file (used by wisecondorX)\n\nFluFFyPipe singularity collection (singularity pull --name FluFFyPipe.sif shub://J35P312/FluFFyPipe)\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 8, + "subscribers_count": 5, "topics": [], - "updated_at": 1669968316.0 + "updated_at": 1695862708.0 }, { "data_format": 2, - "description": "A new Python DAX generator version of the classic Montage workflow", + "description": "Bayesian model selection to detect zero-inflated genes", "filenames": [ "Singularity" ], - "full_name": "pegasus-isi/montage-workflow-v2", + "full_name": "churchill-lab/scRATE", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emontage-workflow-v2\u003c/h1\u003e\u003ca id=\"user-content-montage-workflow-v2\" class=\"anchor\" aria-label=\"Permalink: montage-workflow-v2\" href=\"#montage-workflow-v2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA new Python DAX generator version of the classic Montage workflow. This workflow uses the \u003ca href=\"http://montage.ipac.caltech.edu\" rel=\"nofollow\"\u003eMontage\ntoolkit\u003c/a\u003e to re-project, background correct and add astronomical\nimages into custom mosaics.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://montage.ipac.caltech.edu\" rel=\"nofollow\"\u003eMontage\u003c/a\u003e - version 4.0 or later\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.astropy.org/\" rel=\"nofollow\"\u003eAstroPy\u003c/a\u003e - version 1.0 or later\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePlan a Montage Workflow\u003c/h2\u003e\u003ca id=\"user-content-plan-a-montage-workflow\" class=\"anchor\" aria-label=\"Permalink: Plan a Montage Workflow\" href=\"#plan-a-montage-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003cem\u003e./montage-workflow.py\u003c/em\u003e Python script sets up a \u003cem\u003edata/\u003c/em\u003e directory with a Pegasus DAX,\nimage tables and region headers. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./montage-workflow.py --center \"56.7 24.0\" --degrees 2.0 \\\n --band dss:DSS2B:blue --band dss:DSS2R:green --band dss:DSS2IR:red\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will create a 2x2 degree mosaic centered on 56.7 24.0, with 3 bands making up the\nred, green, and blue channels for the final JPEG output. A 2 degree workflow has a lot\nof input images and thus the workflow becomes wide. I simplified version of the workflow\nlooks like:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/dax1.png?raw=true\"\u003e\u003cimg src=\"docs/images/dax1.png?raw=true\" alt=\"DAX 1\" title=\"DAX 1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest way to get started is to use the \u003cem\u003e./example-dss.sh\u003c/em\u003e\nscript. It shows how to use the \u003cem\u003emontage-workflow.py\u003c/em\u003e DAX generator to set up and plan\n2 degree workflows as described above. Example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./example-dss.sh \n\nAdding band 1 (dss DSS2B -\u0026gt; blue)\nRunning sub command: mArchiveList dss DSS2B \"56.7 24.00\" 2.2 2.2 data/1-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 1-images.tbl region-oversized.hdr 1-raw.tbl 1-projected.tbl 1-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 1-raw.tbl 1-diffs.tbl\n[struct stat=\"OK\", count=120]\n\nAdding band 2 (dss DSS2R -\u0026gt; green)\nRunning sub command: mArchiveList dss DSS2R \"56.7 24.00\" 2.2 2.2 data/2-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 2-images.tbl region-oversized.hdr 2-raw.tbl 2-projected.tbl 2-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 2-raw.tbl 2-diffs.tbl\n[struct stat=\"OK\", count=120]\n\nAdding band 3 (dss DSS2IR -\u0026gt; red)\nRunning sub command: mArchiveList dss DSS2IR \"56.7 24.00\" 2.2 2.2 data/3-images.tbl\n[struct stat=\"OK\", count=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mDAGTbls 3-images.tbl region-oversized.hdr 3-raw.tbl 3-projected.tbl 3-corrected.tbl\n[struct stat=\"OK\", count=\"16\", total=\"16\"]\nRunning sub command: cd data \u0026amp;\u0026amp; mOverlaps 3-raw.tbl 3-diffs.tbl\n[struct stat=\"OK\", count=120]\n2016.06.02 21:46:32.455 PDT: \n2016.06.02 21:46:32.461 PDT: ----------------------------------------------------------------------- \n2016.06.02 21:46:32.466 PDT: File for submitting this DAG to HTCondor : montage-0.dag.condor.sub \n2016.06.02 21:46:32.471 PDT: Log of DAGMan debugging messages : montage-0.dag.dagman.out \n2016.06.02 21:46:32.476 PDT: Log of HTCondor library output : montage-0.dag.lib.out \n2016.06.02 21:46:32.481 PDT: Log of HTCondor library error messages : montage-0.dag.lib.err \n2016.06.02 21:46:32.487 PDT: Log of the life of condor_dagman itself : montage-0.dag.dagman.log \n2016.06.02 21:46:32.492 PDT: \n2016.06.02 21:46:32.497 PDT: -no_submit given, not submitting DAG to HTCondor. You can do this with: \n2016.06.02 21:46:32.507 PDT: ----------------------------------------------------------------------- \n2016.06.02 21:46:33.387 PDT: Your database is compatible with Pegasus version: 4.6.1 \n2016.06.02 21:46:33.392 PDT: \n\nI have concretized your abstract workflow. The workflow has been entered \ninto the workflow database with a state of \"planned\". The next step is \nto start or execute your workflow. The invocation required is\n\npegasus-run /data/scratch/rynge/montage2/montage-workflow-v2/work/1464929190\n\n2016.06.02 21:46:33.419 PDT: Time taken to execute is 2.961 seconds \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning the workflow produces fits and jpeg mosaics for each band, as well as a combined color one:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/pleiades.jpg?raw=true\"\u003e\u003cimg src=\"docs/images/pleiades.jpg?raw=true\" alt=\"Pleiades\" title=\"Pleiades\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 7, - "subscribers_count": 13, + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4398\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/kbchoi/scrate\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ee92e9136378ab9a5896ae8977bfec1d87c201e4a3aaadfd4c8a0eb4c61be848/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f636c6f75642f6175746f6d617465642f6b6263686f692f736372617465\" alt=\"Docker Cloud Automated build\" data-canonical-src=\"https://img.shields.io/docker/cloud/automated/kbchoi/scrate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003escRATE\u003c/h1\u003e\u003ca id=\"user-content-scrate\" class=\"anchor\" aria-label=\"Permalink: scRATE\" href=\"#scrate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003escRATE is a model selection-based scRNA-seq quantitation algorithm that controls overfitting and unwarranted imputation of technical zeros. We first fit UMI counts with Poisson (P), Negative-Binomial (NB), Zero-inflated Poisson (ZIP), and Zero-inflated Negative Binomial models (ZINB). These models are applicable to UMI counts directly and therefore no need of arbitrary preprocessing of counts, e.g., normalization (by scaling to, for example, CPM) or log-transformation (with pseudocounts). Our model comparison enables us to compute denoised rates of gene expression using the best model which each gene data is conforming to.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: GPLv3 license\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation of scRATE is simple, although it may take a while as it has to compile rstan, the related packages, and all their dependencies.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003echurchill-lab/scRATE\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e library(\u003cspan class=\"pl-smi\"\u003escRATE\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e version()\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e____ ____ ____ ____ ___ ____\n[__ | |__/ |__| | |___\n___] |___ | \\ | | | |___\n Ver:0.1.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis might hick up because of hdf5r or loomR. Then, try the following.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e install.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehdf5r\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-v\"\u003erepo\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emojaveazure/loomR\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eref\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edevelop\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is a quick example on how to use it. We first load \u003ccode\u003escRATE\u003c/code\u003e and \u003ccode\u003eloomR\u003c/code\u003e package. We use loom format files to store single-cell gene expression data as it is supported in both \u003ccode\u003eR\u003c/code\u003e and \u003ccode\u003epython\u003c/code\u003e. In \u003ccode\u003escRATE\u003c/code\u003e, we also provide handy features that facilitate the handling of model selection results with loom format file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e library(\u003cspan class=\"pl-smi\"\u003escRATE\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e library(\u003cspan class=\"pl-smi\"\u003eloomR\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA data file in the example is available at ftp://churchill-lab.jax.org/analysis/scRATE/DC-like_cells.loom.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eds\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e connect(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eDC-like_cells.loom\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecntmat\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e t(\u003cspan class=\"pl-smi\"\u003eds\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003ematrix\u003c/span\u003e[,])\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egsymb\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eds\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003erow.attrs\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eGeneID\u003c/span\u003e[]\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eds\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003eclose_all()\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e head(\u003cspan class=\"pl-smi\"\u003egsymb\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e[1] \"Xkr4\" \"Gm1992\" \"Gm37381\" \"Rp1\" \"Rp1.1\" \"Sox17\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe recommend using offset (or exposure) in order to reflect the difference in depth of coverage across cells while fitting the count models. The models we compare use log link function, and therefore, the offsets should be log transformed too.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eexposure\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e log(colSums(\u003cspan class=\"pl-smi\"\u003ecntmat\u003c/span\u003e))\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e head(\u003cspan class=\"pl-smi\"\u003eexposure\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e [1] 8.815518 8.996157 9.025816 9.037771 9.062420 9.397732\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe will pick a gene, \u003cem\u003eCybb\u003c/em\u003e (one that we know that it fits to ZINB model significantly better than the other models), and load its UMI counts into a data frame.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egg\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e4153\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egsymb\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003egg\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e[1] \"Cybb\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecntmat\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003egg\u003c/span\u003e,]\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e [1] 1 0 8 7 3 6 7 1 4 0 0 2 2 10 0 3 1 1 2 4 6 3 9 3 1\n [26] 4 1 2 3 8 0 0 1 6 2 6 3 0 0 1 0 4 1 3 0 2 17 1 2 5\n [51] 0 1 1 0 6 3 4 4 3 1 3 1 7 8 0 2 4 6 2 7 0 4 0 1 0\n [76] 2 0 8 2 0 1 4 9 3 0 2 2 3 0 3 0 0 4 1 4 3 2 3 2 2\n[101] 8 0 5 8 2 3 0 3 0 3 6 0 4 2 11 5 1 2 12 2 4 2 6 8 6\n[126] 3 3 1 0 2 1 1 10 2 2 0 2 2 1 0 1 0 3 11 0 7 2 2 2 3\n[151] 7 0 1 10 4 1 4 5 1 0 1 3 3 0 5 4 9 4 2 0 4 1 2 4 1\n[176] 2 1 4 2 1 0 1 0 1 4 6 8 9 4 3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egexpr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eexposure\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eexposure\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe must have \u003ccode\u003ey\u003c/code\u003e and \u003ccode\u003eexposure\u003c/code\u003e as variables. We can also append covariates to the data frame.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egexpr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eexposure\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eexposure\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ecelltype\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003esex\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe are now ready to fit the models.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003emodel_fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fit_count_models(\u003cspan class=\"pl-smi\"\u003egexpr\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr we can specify our own model formula (See brms documentation) if there are covariates. Note we should not use \u003ccode\u003eoffset()\u003c/code\u003e function in the formula.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003emodel_fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fit_count_models(\u003cspan class=\"pl-smi\"\u003egexpr\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ey ~ 1 + (1|sex) + (1|celltype)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eFitting models for Cybb\nFitting data with Poisson model...\nFitting data with Negative Binomial model...\nFitting data with Zero-Inflated Poisson model...\nCompiling the C++ model\n\nStart sampling\nSAMPLING FOR MODEL \u0027zip\u0027 NOW (CHAIN 1).\n\nChain 1:\nChain 1: Gradient evaluation took 0.000177 seconds\nChain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.77 seconds.\nChain 1: Adjust your expectations accordingly!\nChain 1:\nChain 1:\n...\n\nFitting data with Zero-Inflated Negative Binomial model...\nCompiling the C++ model\nStart sampling\n\nSAMPLING FOR MODEL \u0027zinb\u0027 NOW (CHAIN 1).\nChain 1:\nChain 1: Gradient evaluation took 0.000329 seconds\nChain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.29 seconds.\nChain 1: Adjust your expectations accordingly!\nChain 1:\nChain 1:\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we compare the models with the leave-one-out cross validation test, and select the model that best fits the data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eelpd_loo\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e compare_count_models(\u003cspan class=\"pl-smi\"\u003emodel_fit\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eelpd_loo\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e elpd_diff se_diff\nZINB 0.0 0.0\nNB -3.0 2.7\nZIP -35.6 9.5\nP -103.9 19.6\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e select_model(\u003cspan class=\"pl-smi\"\u003eelpd_loo\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003emargin\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to cite\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eK. Choi, Y. Chen, D.A. Skelly, G.A. Churchill. \u201cBayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics.\u201d Genome Biology, 21, 183 (2020). \u003ca href=\"https://doi.org/10.1186/s13059-020-02103-2\" rel=\"nofollow\"\u003ehttps://doi.org/10.1186/s13059-020-02103-2\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 5, "topics": [], - "updated_at": 1687744524.0 + "updated_at": 1692608883.0 }, { "data_format": 2, - "description": "High-order OpenFOAM interpolation scheme for advective fluxes", + "description": "Examples of jobs that can be run via Galileo", "filenames": [ - "Singularity" + "hpcc/Singularityfile" ], - "full_name": "AtmosFOAM/highOrderFit", - "latest_release": "jshaw-thesis", - "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/AtmosFOAM/highOrderFit\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/73a765e2af4193b6e028fc98ef05f9c71584887c0f9532e0e693e98c37ce0e72/68747470733a2f2f7472617669732d63692e6f72672f41746d6f73464f414d2f686967684f726465724669742e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/AtmosFOAM/highOrderFit.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompilation\u003c/h2\u003e\u003ca id=\"user-content-compilation\" class=\"anchor\" aria-label=\"Permalink: Compilation\" href=\"#compilation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSet the environment variable \u003ccode\u003eHIGHORDERFIT_DIR\u003c/code\u003e to the root of the local repository and run \u003ccode\u003e./Allwmake\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo compile the \u003ca href=\"https://www.doxygen.org/\" rel=\"nofollow\"\u003eDoxygen\u003c/a\u003e documentation, run \u003ccode\u003edoc/Doxygen/Allwmake\u003c/code\u003e.\u003c/p\u003e\n", - "stargazers_count": 7, - "subscribers_count": 4, - "topics": [ - "openfoam", - "advection", - "interpolation-methods" + "full_name": "GoHypernet/Galileo-examples", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGalileo application example repository\u003c/h1\u003e\u003ca id=\"user-content-galileo-application-example-repository\" class=\"anchor\" aria-label=\"Permalink: Galileo application example repository\" href=\"#galileo-application-example-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains a number of examples, representing many frameworks,\nthat can be deployed through Galileo with no additional setup.\u003c/p\u003e\n\u003cp\u003eEach subdirectory contains its own readme file describing the analysis being\nperformed. A user can copy the Dockerfile from an example similar to the one\nthey would like to run and modify it accordingly. Alternatively, a user can\nuse the Docker Wizard helper in the Galileo application to build a Dockerfile\nfrom scratch for their framework.\u003c/p\u003e\n\u003cp\u003eFor more information on Galileo, start here:\n\u003ca href=\"https://galileoapp.io/gettingstarted/\" rel=\"nofollow\"\u003ehttps://galileoapp.io/gettingstarted/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo start using the application, go here:\n\u003ca href=\"https://app.galileoapp.io\" rel=\"nofollow\"\u003ehttps://app.galileoapp.io\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 10, + "topics": [], + "updated_at": 1626717385.0 + }, + { + "data_format": 2, + "description": "Transposable Elements MOvement detection using LOng reads", + "filenames": [ + "Singularity" ], - "updated_at": 1702391512.0 + "full_name": "DrosophilaGenomeEvolution/TrEMOLO", + "latest_release": "v2.2-beta", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5391\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/TrEMOLO9.png\"\u003e\u003cimg src=\"images/TrEMOLO9.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#in\"\u003eGlobal variations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#out\"\u003ePopulational variations\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#release\"\u003eRelease note\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#git\"\u003eUsing Git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity\"\u003eUsing Singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#configuration\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output\"\u003eOutput files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation \u0026amp; Licence\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-tremolo\" class=\"anchor\" aria-hidden=\"true\" href=\"#tremolo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrEMOLO\u003ca name=\"user-content-introduction\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eTransposable Elements MOvement detection using LOng reads\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrEMOLO uses long reads, either directly or through their assembly, to detect:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGlobal TE variations between two assembled genomes\u003c/li\u003e\n\u003cli\u003ePopulational/somatic variation in TE insertion/deletion\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-global-variations-the-insiders\" class=\"anchor\" aria-hidden=\"true\" href=\"#global-variations-the-insiders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGlobal variations, the insiders\u003ca name=\"user-content-in\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eUsing a reference genome and an assembled one (preferentially using long contigs or even better a chrosomome-scale assembly), TrEMOLO will extract the \u003cstrong\u003einsiders\u003c/strong\u003e, \u003cem\u003ei.e.\u003c/em\u003e variant transposable elements (TEs) present globally in the assembly, and tag them. Indeed, assemblers will provide the most frequent haplotype at each locus, and thus an assembly represent just the \"consensus\" of all haplotypes present at each locus.\nYou will obtain a \u003ca href=\"#output\"\u003eset of files\u003c/a\u003e with the location of these variable insertions and deletions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-populational-variations-the-outsiders\" class=\"anchor\" aria-hidden=\"true\" href=\"#populational-variations-the-outsiders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePopulational variations, the outsiders\u003ca name=\"user-content-out\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eThrough remapping of reads that have been used to assemble the genome of interest, TrEMOLO will identify the populational variations (and even somatic ones) within the initial dataset of reads, and thus of DNA/individuals sampled. These variant TEs are the \u003cstrong\u003eoutsiders\u003c/strong\u003e, present only in a part of the population or cells.\nIn the same way as for insiders, you will obtain a \u003ca href=\"#output\"\u003eset of files\u003c/a\u003e with the location of these variable insertions and deletions.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-release-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#release-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRelease notes\u003ca name=\"user-content-release\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003ch1\u003e\u003ca id=\"user-content-current-limitations\" class=\"anchor\" aria-hidden=\"true\" href=\"#current-limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCurrent limitations\u003c/h1\u003e\n\u003ch1\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003ca name=\"user-content-requirements\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eNumerous tools are used by TrEMOLO. We recommand to use the \u003ca href=\"#singularity\"\u003eSingularity installation\u003c/a\u003e to be sure to have all of them in the good configurations and versions.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor both approaches\n\u003cul\u003e\n\u003cli\u003ePython 3.6+\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor Global variation tool\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e 2.2+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://bedtools.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eBedtools 2.27.1\u003c/a\u003e v2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://assemblytics.com/\" rel=\"nofollow\"\u003eAssemblytics\u003c/a\u003e or\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/malonge/RaGOO\"\u003eRaGOO\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor Populational variation tool\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://snakemake-wrappers.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e 5.5.2+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/lh3/minimap2\"\u003eMinimap2\u003c/a\u003e 2.24+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.htslib.org/\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e 1.9 and (1.15.1 optional)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/eldariont/svim/releases/tag/v1.4.2\"\u003esvim 1.4.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/fritzsedlazeck/Sniffles/releases/tag/v1.0.12b\"\u003eSniffles 1.0.12\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePython libs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://biopython.org/\" rel=\"nofollow\"\u003eBiopython\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003ePandas\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://numpy.org/\" rel=\"nofollow\"\u003eNumpy\u003c/a\u003e 1.21.2\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://matplotlib.org/\" rel=\"nofollow\"\u003epylab\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/intervaltree/\" rel=\"nofollow\"\u003eintervaltree\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/pysam/\" rel=\"nofollow\"\u003epysam\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePerl v5.26.2+\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor report\n\u003cul\u003e\n\u003cli\u003eR 3.3+ libs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.r-project.org/nosvn/pandoc/knitr.html\" rel=\"nofollow\"\u003eknitr 1.38\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://rmarkdown.rstudio.com/\" rel=\"nofollow\"\u003ermarkdown 2.13\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bookdown.org/yihui/bookdown/get-started.html\" rel=\"nofollow\"\u003ebookdown 0.25\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.rdocumentation.org/packages/viridis/versions/0.3.4\" rel=\"nofollow\"\u003eviridis 0.6.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sjmgarnier/viridisLite\"\u003eviridisLite 0.4.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://rdrr.io/cran/rjson/\" rel=\"nofollow\"\u003erjson 0.2.20\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jrnold/ggthemes\"\u003eggthemes 4.2.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://rdrr.io/cran/forcats/\" rel=\"nofollow\"\u003eforcats 0.5.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.r-project.org/nosvn/pandoc/dplyr.html\" rel=\"nofollow\"\u003ereshape2 1.4.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.r-project.org/nosvn/pandoc/dplyr.html\" rel=\"nofollow\"\u003edplyr 1.0.8\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bookdown.org/yihui/rmarkdown-cookbook/kableextra.html\" rel=\"nofollow\"\u003ekableExtra 1.3.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cran.r-project.org/web/packages/extrafont/README.html\" rel=\"nofollow\"\u003eextrafont 0.17\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ggplot2.tidyverse.org/\" rel=\"nofollow\"\u003eggplot2 3.3.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.rdocumentation.org/packages/RColorBrewer/versions/1.1-2=\" rel=\"nofollow\"\u003eRColorBrewer 1.1-2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cran.r-project.org/web/packages/stringr/index.html\" rel=\"nofollow\"\u003estringr 1.4.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cran.r-project.org/web/packages/stringi/index.html\" rel=\"nofollow\"\u003estringi 1.7.6\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jgm/citeproc\"\u003epandoc-citeproc 0.17\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOthers\n\u003cul\u003e\n\u003cli\u003enodejs\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003ca name=\"user-content-Installation\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-git\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-git\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Git\u003ca name=\"user-content-git\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eOnce the requirements fullfilled, just \u003cem\u003egit\u003c/em\u003e clone\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/DrosophilaGenomeEvolution/TrEMOLO.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003ca name=\"user-content-singularity\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html#install-the-debian-ubuntu-package-using-apt\" rel=\"nofollow\"\u003e\u003cem\u003eSingularity\u003c/em\u003e installation Debian/Ubuntu with package\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-compiling-yourself\" class=\"anchor\" aria-hidden=\"true\" href=\"#compiling-yourself\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling yourself\u003c/h3\u003e\n\u003cp\u003eA \u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003e\u003cem\u003eSingularity\u003c/em\u003e container\u003c/a\u003e is available with all tools compiled in.\nThe \u003cem\u003eSingularity\u003c/em\u003e file provided in this repo and can be compiled as such:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build TrEMOLO.simg TrEMOLO/Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eYOU MUST BE ROOT for compiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTest TrEMOLO with singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e TrEMOLO.simg snakemake --snakefile TrEMOLO/run.snk --configfile TrEMOLO/test/tmp_config.yml\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eOR\u003c/span\u003e\nsingularity run TrEMOLO.simg snakemake --snakefile TrEMOLO/run.snk --configfile TrEMOLO/test/tmp_config.yml\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pulling-from-singularityhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#pulling-from-singularityhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePulling from SingularityHub\u003c/h3\u003e\n\u003cp\u003eThis option is disabled since Singularity Hub is for the moment in read-only. We are looking for a Singularity repo to ease the use.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-configuration-of-the-parameter-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuration-of-the-parameter-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration of the parameter file\u003ca name=\"user-content-configuration\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eTrEMOLO uses \u003ca href=\"https://snakemake-wrappers.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e to perform its analyses. You have then first to provide your parameters in a \u003cem\u003e.yaml\u003c/em\u003e file (see an example in the \u003cem\u003econfig.yaml\u003c/em\u003e file). Parameters are :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e all path can be relative or absolute depending of your tree.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eIt is advised to only use absolute path if you are not familiar with computer science or the importance of folder trees structure.\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eDATA\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eGENOME\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/genome_file.fasta\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003egenome (fasta file) [required]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eTE_DB\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/database_TE.fasta\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eDatabase of TE (a fasta file) [required]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eREFERENCE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/reference_file.fasta\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003ereference genome (fasta file) only if INSIDER_VARIANT = True [optional]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSAMPLE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/reads_file.fastq\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003elong reads (a fastq file) only if OUTSIDER_VARIANT = True [optional]\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eAt least, provide either REFERENCE or SAMPLE. Both can be provided\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eWORK_DIRECTORY\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/directory\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003ename of output directory [optional, will be created as \u0027TrEMOLO_OUTPUT\u0027]\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eAt least, you must provide either the reference file, or the fastq file or both\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003eCHOICE\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePIPELINE\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eOUTSIDER_VARIANT\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e outsiders, TE not in the assembly - population variation\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINSIDER_VARIANT\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e insiders, TE in the assembly\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eREPORT\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for getting a report.html file with graphics\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eMODE_PARALLELING\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eFalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e test time : with True value 50m53,983s; with False value 138m55,985s; With 8 threads\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eOUTSIDER_VARIANT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eCALL_SV\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esniffles\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e possibilities for SV tools: sniffles, svim\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINTEGRATE_TE_TO_GENOME\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (True, False) Re-build the assembly with the INSIDER integrated in\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eOPTIMIZE_FREQUENCE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (True, False) FREQUENCE CALCULATED WITH CLIPPING READS\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINSIDER_VARIANT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eDETECT_ALL_TE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eFalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e detect ALL TE on genome (parameter GENOME) assembly not only new insertion. Warning! it may be take several hours on big genomes\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINTERMEDIATE_FILE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Conserve the intermediate analyses files to process them latter.\u003c/span\u003e\n\n\n\u003cspan class=\"pl-ent\"\u003ePARAMS\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eTHREADS\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003enumber of threads for some task\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eOUTSIDER_VARIANT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eMINIMAP2\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePRESET_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003emap-ont\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e minimap2 option is map-ont by default (map-pb, map-ont)\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eOPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e-t 8\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e more option of minimap2 can be specified here\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSAMTOOLS_VIEW\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePRESET_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSAMTOOLS_SORT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePRESET_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSAMTOOLS_CALLMD\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePRESET_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eTSD\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eFILE_SIZE_TE_TSD\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/SIZE_TSD.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e File of TSD sizes for the reference elements (format=\"TE SIZE\", one TE per line) [optional]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSIZE_FLANK\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e flanking sequence size for calculation of TSD; put value \u0026gt; 4\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eTE_DETECTION\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eCHROM_KEEP\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e regular expresion for chromosome filtering; for instance for Drosophila \"2L,2R,3[RL],X\" ; Put \".\" to keep all chromosome\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eGET_SEQ_REPORT_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e-m 500\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003esequence recovery file in the vcf\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ePARS_BLN_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--min-size-percent 80 --min-pident 80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e option for TrEMOLO/lib/python/parse_blast_main.py - don\u0027t put -c option\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINSIDER_VARIANT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePARS_BLN_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--min-size-percent 80 --min-pident 80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e parameters for validation of insiders\u003c/span\u003e\n\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe main parameters are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGENOME\u003c/code\u003e : Assembly of the sample of interest (or mix of samples), fasta file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTE_DB\u003c/code\u003e : A \u003cstrong\u003eMultifasta\u003c/strong\u003e file containing the canonical sequence of transposable elements. You can add also copy sequences but results will be more complex to interpretate.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eREFERENCE\u003c/code\u003e : Fasta file containing the reference genome of the species of interest.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eWORK_DIRECTORY\u003c/code\u003e : Directory that will contain the output files. If the directory does not exist it will be created; default value is \u003cstrong\u003eTrEMOLO_OUTPUT\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSAMPLE\u003c/code\u003e : File containing the reads used for the sample assembly.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can use \u003cstrong\u003econfig_INSIDER.yaml\u003c/strong\u003e for only \u003cstrong\u003eINSIDER\u003c/strong\u003e analysis or \u003cstrong\u003econfig_OUTSIDER.yaml\u003c/strong\u003e for only \u003cstrong\u003eOUTSIDER\u003c/strong\u003e analysis.\nTo analyse \u003cstrong\u003eINSIDER\u003c/strong\u003e, only the \u003ccode\u003eREFERENCE\u003c/code\u003e , the \u003ccode\u003eGENOME\u003c/code\u003e, the \u003ccode\u003eTE_DB\u003c/code\u003e and the \u003ccode\u003eWORK_DIRECTORY\u003c/code\u003e are required.\nTo analyse \u003cstrong\u003eOUTSIDER\u003c/strong\u003e, only the \u003ccode\u003eSAMPLE\u003c/code\u003e , the \u003ccode\u003eGENOME\u003c/code\u003e, the \u003ccode\u003eTE_DB\u003c/code\u003e and the \u003ccode\u003eWORK_DIRECTORY\u003c/code\u003e are required.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile /path/to/TrEMOLO/run.snk --configfile /path/to/your_config.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor running tests\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile TrEMOLO/run.snk --configfile TrEMOLO/test/tmp_config.yml\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-output-files-summary-open_file_folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-files-summary-open_file_folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files summary \u003cg-emoji class=\"g-emoji\" alias=\"open_file_folder\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4c2.png\"\u003e\ud83d\udcc2\u003c/g-emoji\u003e\u003ca name=\"user-content-output\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eHere is the structure of the output files obtained after running the pipeline.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eWORK_DIRECTORY\n\u251c\u2500\u2500 params.yaml ##**Your config file\n\u251c\u2500\u2500 LIST_HEADER_DB_TE.csv ##** list of names assigned to TE in the TE database (Only if you have charactere \"\u0026amp; ; / \\ | \u0027 : ! ? \" in your TE database)\n\u251c\u2500\u2500 POSITION_ALL_TE.bed -\u0026gt; INSIDER/TE_DETECTION/POSITION_ALL_TE.bed ##**ALL TE ON GENOME NOT ONLY INSERTION (ONLY IF PARAMETER \"DETECT_ALL_TE\" is True),\n\u251c\u2500\u2500 POSITION_TE_INOUTSIDER.bed\n\u251c\u2500\u2500 POSITION_TE_INSIDER.bed\n\u251c\u2500\u2500 POSITION_TE_OUTSIDER.bed\n\u251c\u2500\u2500 POS_TE_INSIDER_ON_REF.bed -\u0026gt; INSIDER/TE_DETECTION/INSERTION_TE_ON_REF.bed ##**POSITION TE INSIDER ON REFRENCE GENOME\n\u251c\u2500\u2500 POS_TE_OUTSIDER_ON_REF.bed ##**POSITION TE OUTSIDER ON REFRENCE GENOME\n\u251c\u2500\u2500 POSITION_TE_OUTSIDER_IN_NEO_GENOME.bed ##**POSITION TE SEQUENCE ON BEST READS SUPPORT INTEGRATED IN GENOME\n\u251c\u2500\u2500 POSITION_TE_OUTSIDER_IN_PSEUDO_GENOME.bed ##**POSITION TE SEQUENCE ON TE DATABASE (with ID) INTEGRATED IN GENOME\n\u251c\u2500\u2500 VALUES_TSD_ALL_GROUP.csv\n\u251c\u2500\u2500 VALUES_TSD_GROUP_OUTSIDER.csv\n\u251c\u2500\u2500 VALUES_TSD_INSIDER_GROUP.csv\n\u251c\u2500\u2500 TE_INFOS.bed ##**FILE CONTENING ALL INFO OF TE INSERTION\n\u251c\u2500\u2500 DELETION_TE.bed -\u0026gt; INSIDER/TE_DETECTION/DELETION_TE.bed ##**TE DELETION POSTION ON GENOME\n\u251c\u2500\u2500 DELETION_TE_ON_REF.bed -\u0026gt; INSIDER/TE_DETECTION/DELETION_TE_ON_REF.bed ##**TE DELETION POSITION ON REFERENCE\n\u251c\u2500\u2500 SOFT_TE.bed -\u0026gt; OUTSIDER/TE_DETECTION/SOFT/SOFT_TE.bed ##**TE INSERTION FOUND IN SOFT READS\n\u251c\u2500\u2500 INSIDER ##**FOLDER CONTAINS FILES TRAITEMENT INSIDER\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 FREQ_INSIDER\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_DETECTION\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 TSD\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_INSIDER_VR\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 VARIANT_CALLING\n\u251c\u2500\u2500 log ##**log file to check if you have any error\n\u251c\u2500\u2500 OUTSIDER\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ET_FIND_FA\n\u2502\u00a0\u00a0 \u2502 \u251c\u2500\u2500 TE_REPORT_FOUND_TE_NAME.fasta\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_REPORT_FOUND_blood.fasta\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 TE_REPORT_FOUND_ZAM.fasta\n...\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 FREQ_OPTIMIZED\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 DEPTH_TE.csv\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 INSIDER_VR\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 MAPPING ##**FOLDER CONTAINS FILES MAPPING ON GENOME\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 MAPPING_TO_REF ##**FOLDER CONTAINS FILES MAPPING ON REFERENCE GENOME\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 READ_FASTQ_TE ##**FOLDER CONTAINS ALL THE READs ASSOCIATED WITH THE TE\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_DETECTION\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 MERGE_TE\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 TSD\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TrEMOLO_SV_TE\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 INS\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 SOFT\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_TOWARD_GENOME ##**FOLDER CONTAINS ALL THE READs ASSOCIATED WITH THE TE\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NEO_GENOME.fasta ##**GENOME CONTAINS TE OUTSIDER (the best sequence of svim/sniffles)\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 PSEUDO_GENOME_TE_DB_ID.fasta ##**GENOME CONTAINS TE OUTSIDER (the sequence of database TE and the ID of svim/sniffles)\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 TRUE_POSITION_TE_PSEUDO.bed ##**POSITION IN PSEUDO GENOME\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 TRUE_POSITION_TE.fasta ##**SEQUENCE INTEGRATE IN PSEUDO GENOME\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 TRUE_POSITION_TE_NEO.bed ##**POSITION IN NEO GENOME\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 TRUE_POSITION_TE_READS.fasta ##**SEQUENCE INTEGRATE IN NEO GENOME\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 VARIANT_CALLING ##**FOLDER CONTAINS FILES OF sniflles/svim\n\u251c\u2500\u2500 REPORT\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 mini_report\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 report.html\n\u251c\u2500\u2500 SNAKE_USED\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Snakefile_insider.snk\n\u2514\u2500\u2500 \u2514\u2500\u2500 Snakefile_outsider.snk\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-most-useful-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#most-useful-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMost useful output\u003c/h3\u003e\n\u003cp\u003eThe most useful output files are :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe html report in \u003cstrong\u003eyour_work_directory/REPORT/report.html\u003c/strong\u003e with summary graphics, as shown \u003ca href=\"https://rawcdn.githack.com/DrosophilaGenomeEvolution/TrEMOLO/f11c369ea037db66a7a86ee9d6c266f9069a8ecf/test/web/index.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe output file \u003cstrong\u003eyour_work_direcetory/TE_INFOS.bed\u003c/strong\u003e gathers all the necessary information.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003echrom\u003c/th\u003e\n\u003cth\u003estart\u003c/th\u003e\n\u003cth\u003eend\u003c/th\u003e\n\u003cth\u003eTE|ID\u003c/th\u003e\n\u003cth\u003estrand\u003c/th\u003e\n\u003cth\u003eTSD\u003c/th\u003e\n\u003cth\u003epident\u003c/th\u003e\n\u003cth\u003epsize_TE\u003c/th\u003e\n\u003cth\u003eSIZE_TE\u003c/th\u003e\n\u003cth\u003eNEW_POS\u003c/th\u003e\n\u003cth\u003eFREQ (%)\u003c/th\u003e\n\u003cth\u003eFREQ_OPTIMIZED (%)\u003c/th\u003e\n\u003cth\u003eID_TrEMOLO\u003c/th\u003e\n\u003cth\u003eTYPE\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e2R_RaGOO_RaGOO\u003c/td\u003e\n\u003ctd\u003e16943971\u003c/td\u003e\n\u003ctd\u003e16943972\u003c/td\u003e\n\u003ctd\u003eroo|svim.INS.175\u003c/td\u003e\n\u003ctd\u003e+\u003c/td\u003e\n\u003ctd\u003eGTACA\u003c/td\u003e\n\u003ctd\u003e97.026\u003c/td\u003e\n\u003ctd\u003e99.2\u003c/td\u003e\n\u003ctd\u003e9006\u003c/td\u003e\n\u003ctd\u003e16943978\u003c/td\u003e\n\u003ctd\u003e28.5714\u003c/td\u003e\n\u003ctd\u003e28.5714\u003c/td\u003e\n\u003ctd\u003eTE_ID_OUTSIDER.94047.INS.107508.0\u003c/td\u003e\n\u003ctd\u003eINS\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eX_RaGOO_RaGOO\u003c/td\u003e\n\u003ctd\u003e21629415\u003c/td\u003e\n\u003ctd\u003e21629416\u003c/td\u003e\n\u003ctd\u003eZAM|Assemblytics_w_534\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eCGCG\u003c/td\u003e\n\u003ctd\u003e98.6\u003c/td\u003e\n\u003ctd\u003e90.5\u003c/td\u003e\n\u003ctd\u003e8435\u003c/td\u003e\n\u003ctd\u003e21629413\u003c/td\u003e\n\u003ctd\u003e11.1111\u003c/td\u003e\n\u003ctd\u003e10.0000\u003c/td\u003e\n\u003ctd\u003eTE_ID_INSIDER.77237.Repeat_expansion.8\u003c/td\u003e\n\u003ctd\u003eRepeat_expansion\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003echrom\u003c/code\u003e : chromosome\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estart\u003c/code\u003e : start position for the TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eend\u003c/code\u003e : end position for the TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTE|ID\u003c/code\u003e : TE name and ID in \u003cstrong\u003eSV.vcf\u003c/strong\u003e,\u003cstrong\u003eSV_SOFT.vcf\u003c/strong\u003e and \u003cstrong\u003eSV_INS_CLUST.bed\u003c/strong\u003e (for OUTSIDER) or \u003cstrong\u003eassemblytics_out.Assemblytics_structural_variants.bed\u003c/strong\u003e (for INSIDER)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estrand\u003c/code\u003e : strand of the TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTSD\u003c/code\u003e : TSD SEQUENCE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epident\u003c/code\u003e : percentage of identical matches with TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epsize_TE\u003c/code\u003e : percentage of size with TE in database\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSIZE_TE\u003c/code\u003e : TE size\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNEW_POS\u003c/code\u003e : position corrected with calculated TSD (only for OUTSIDER)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFREQ\u003c/code\u003e : frequence, normalized\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFREQ_OPTIMIZED\u003c/code\u003e : frequence optimized with conversion of clipped read to not clipped (OUTSIDER only)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eID_TrEMOLO\u003c/code\u003e : TrEMOLO ID of the TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTYPE\u003c/code\u003e : type of insertion can be SOFT,INS,INS_DEL... (INS_DEL is an insertion located on a deletion of the assembly)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-licence-and-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#licence-and-citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicence and Citation\u003ca name=\"user-content-citation\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eIt is licencied under \u003ca href=\"Licence_CeCILL-C_V1-en.txt\"\u003eCeCill-C\u003c/a\u003e and \u003ca href=\"LICENSE\"\u003eGPLv3\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you use TrEMOLO, please cite:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.mdpi.com/2073-4409/9/8/1776\" rel=\"nofollow\"\u003eMohamed, M.; Dang, N. .-M.; Ogyama, Y.; Burlet, N.; Mugat, B.; Boulesteix, M.; M\u00e9rel, V.; Veber, P.; Salces-Ortiz, J.; Severac, D.; P\u00e9lisson, A.; Vieira, C.; Sabot, F.; Fablet, M.; Chambeyron, S. A Transposon Story: From TE Content to TE Dynamic Invasion of Drosophila Genomes Using the Single-Molecule Sequencing Technology from Oxford Nanopore. Cells 2020, 9, 1776.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in the paper are available \u003ca href=\"https://dataverse.ird.fr/dataverse/tremolo_data\" rel=\"nofollow\"\u003ehere on DataSuds\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 2, + "topics": [], + "updated_at": 1671966248.0 }, { "data_format": 2, - "description": "Using singularity to create a near-identical uppmax environment (experimental)", + "description": null, "filenames": [ - "Singularity", - "tags/200311/Singularity.200311" + ".ci/github/Singularity" ], - "full_name": "UPPMAX/uppmax_in_a_can", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUPPMAX in a can\u003c/h1\u003e\u003ca id=\"user-content-uppmax-in-a-can\" class=\"anchor\" aria-label=\"Permalink: UPPMAX in a can\" href=\"#uppmax-in-a-can\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis Singularity container will let you run a near-identical UPPMAX environment on your own computer. You will have access to all of the installed software at UPPMAX, all your files and reference data on UPPMAX, but it will be your own computer that does the calculations. You can even use it to analyse data that you only have on your own computer, but using the software and reference data on UPPMAX.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTypical use cases\u003c/h1\u003e\u003ca id=\"user-content-typical-use-cases\" class=\"anchor\" aria-label=\"Permalink: Typical use cases\" href=\"#typical-use-cases\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou have sensitive data that is not allowed to leave your computers, but you want to use the programs and references at UPPMAX to do the analysis.\u003c/li\u003e\n\u003cli\u003eYou have your own server but want to avoid installing all the software yourself.\u003c/li\u003e\n\u003cli\u003eThe queues at UPPMAX are too long or you have run out of core hours but you want the analysis done yesterday.\u003c/li\u003e\n\u003cli\u003eYou have your data and compute hours at another SNIC centre, but want to use the software installed at UPPMAX.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWhat you get\u003c/h1\u003e\u003ca id=\"user-content-what-you-get\" class=\"anchor\" aria-label=\"Permalink: What you get\" href=\"#what-you-get\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eAccess to your home folder and all project folders.\u003c/li\u003e\n\u003cli\u003eAccess to all the installed programs at UPPMAX.\u003c/li\u003e\n\u003cli\u003eAccess to all reference data at UPPMAX.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWhat you don\u0027t get\u003c/h1\u003e\u003ca id=\"user-content-what-you-dont-get\" class=\"anchor\" aria-label=\"Permalink: What you don\u0027t get\" href=\"#what-you-dont-get\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUPPMAX high-performace computers. You will be limited by the computer you are running the container on.\u003c/li\u003e\n\u003cli\u003eNo slurm access. Everything runs on your computer.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eImportant notes\u003c/h1\u003e\u003ca id=\"user-content-important-notes\" class=\"anchor\" aria-label=\"Permalink: Important notes\" href=\"#important-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSince all data you read/write to the UPPMAX file system will have to travel over the internet, disk intensive programs will be much slower, and transfer rate is limited to your internet connection. Try working on your local harddrive as much as possible.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIssues\u003c/h1\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-label=\"Permalink: Issues\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSome of the tools at uppmax, like projinfo and uquota are using your user\u0027s group membership to determin what information to show. Since your local user won\u0027t have the same groups as you have on uppmax they misbehave and will not show you the same information as when you run the program on uppmax.\u003c/li\u003e\n\u003cli\u003eIf your user has not connected to uppmax before it will need to accept the login nodes fingerprint before connecting (a security feature). Sshfs will just hang if this happens, so you will have to connect normally to uppmax and manually accept the fingerprint. To make things more complicated there are 3 different login nodes, each with its own fingerprint, and you are randomly assigned to one. There is a possibility to turn this off in the sshfs mount command by adding \u003ccode\u003e-o StrictHostKeyChecking=no\u003c/code\u003e, but it might not be a good idea to disable it due to security. Easiest way around it is just to run \u003ccode\u003essh user@rackham.uppmax.uu.se\u003c/code\u003e a couple of times until you have seen all 3 fingerprints.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePrerequisites\u003c/h1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool has been developed on Ubuntu 18.04 with Singularity v.3.5. You should only need 2 things for this to work,\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eSingularity (only tested with v3.5)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/libfuse/sshfs\"\u003eSSHFS\u003c/a\u003e\u003cbr\u003e\n(Make sure that the option \u003ccode\u003euser_allow_other\u003c/code\u003e is uncommented in \u003ccode\u003e/etc/fuse.conf\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor installation instructions for these, see respective projects homepage.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing a pre-built image\u003c/h3\u003e\u003ca id=\"user-content-using-a-pre-built-image\" class=\"anchor\" aria-label=\"Permalink: Using a pre-built image\" href=\"#using-a-pre-built-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull shub://UPPMAX/uppmax_in_a_can:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding your own image from github.\u003c/h3\u003e\u003ca id=\"user-content-building-your-own-image-from-github\" class=\"anchor\" aria-label=\"Permalink: Building your own image from github.\" href=\"#building-your-own-image-from-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the github repo and build the image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/UPPMAX/uppmax_in_a_can.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e uppmax_in_a_can\nsingularity build uppmax_in_a_can_latest.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe build takes 10-20 minutes on a modern laptop with gigabit Ethernet.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFirst time usage\u003c/h2\u003e\u003ca id=\"user-content-first-time-usage\" class=\"anchor\" aria-label=\"Permalink: First time usage\" href=\"#first-time-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce the build is done, run the initialization script in the container,\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./uppmax_in_a_can_latest.sif uppmax_init\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to see more options, run\u003c/span\u003e\n./uppmax_in_a_can_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSubsequent usage\u003c/h2\u003e\u003ca id=\"user-content-subsequent-usage\" class=\"anchor\" aria-label=\"Permalink: Subsequent usage\" href=\"#subsequent-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStart the virtual node:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./uiac_node.sh -i uppmax_in_a_can_latest.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to see more options, run\u003c/span\u003e\n./uiac_node.sh -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will now be on the command-line inside the container and you can run commands as if you were logged in on UPPMAX. You will see all project folders in \u003ccode\u003e/proj\u003c/code\u003e, all software in \u003ccode\u003e/sw\u003c/code\u003e, your UPPMAX home folder in \u003ccode\u003e/home/\u0026lt;UPPMAX_username\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ex\u003c/span\u003e\nmodule load bioinf-tools samtools\nsamtools view file.bam\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo close down and return to your own computers command-line, just type \u003ccode\u003eexit\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAdvanced usage\u003c/h1\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-label=\"Permalink: Advanced usage\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can also specify any additional singularity options to the \u003ccode\u003euiac_node.sh\u003c/code\u003e script. If you want to make your computers file system visible inside the container so that you can analyse files residing on your computer, just add a bind argument:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Entire hard drive \u003c/span\u003e\n./uiac_node.sh -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--bind /:/hostfs\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -i uppmax_in_a_can_latest.sif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Only a specific directory\u003c/span\u003e\n./uiac_node.sh -e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--bind /home/user/data:/hostfs\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -i uppmax_in_a_can_latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis command will make your computers file system available under \u003ccode\u003e/hostfs\u003c/code\u003e (or wherever you would like it).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDeveloper notes\u003c/h1\u003e\u003ca id=\"user-content-developer-notes\" class=\"anchor\" aria-label=\"Permalink: Developer notes\" href=\"#developer-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get the list of installed software on UPPMAX, run this command on UPPMAX:\n\u003ccode\u003eyum list installed | cut -f 1 -d \" \" | cut -f 1 -d \".\" | sort | tr \u0027\\n\u0027 \u0027 \u0027 \u0026gt; yum_installed_\u003c/code\u003edate +%F.txt`\u003c/p\u003e\n\u003cp\u003eThis list will contain \u003cstrong\u003eall\u003c/strong\u003e installed packages, even core packages and nvidia packages that are not available in the default repos. It will just give a couple of warning messages when you send the list to \u003ccode\u003eyum install\u003c/code\u003e, but it will not break the installation process.\u003c/p\u003e\n\u003cp\u003eTo get the the list of environment variables in \u003ccode\u003eenv/99-uppmaxvars.sh\u003c/code\u003e, type \u003ccode\u003eenv | egrep -i \u0027module|lmod\u0027 | sort\u003c/code\u003e when logged in on UPPMAX and pick out everything that has to do with the module system.\u003c/p\u003e\n\u003cp\u003eThe PS1 is made to be different from the default shell the user has to make it obvious they are inside the container.\u003c/p\u003e\n", - "stargazers_count": 7, + "full_name": "cepc/CEPCSW", + "latest_release": "v0.2.6", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-cepcsw\" class=\"anchor\" aria-hidden=\"true\" href=\"#cepcsw\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://cepc.github.io/CEPCSW/\" rel=\"nofollow\"\u003eCEPCSW\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.travis-ci.com/cepc/CEPCSW\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/51cb592ac6435ae6b6bdc6cca7a941779434c9db16df9857df2a94e6f239971b/68747470733a2f2f7777772e7472617669732d63692e636f6d2f636570632f4345504353572e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://www.travis-ci.com/cepc/CEPCSW.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/cepc/CEPCSW/actions\"\u003e\u003cimg src=\"https://github.com/cepc/CEPCSW/workflows/CI/badge.svg?branch=master\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCEPC offline software prototype based on \u003ca href=\"https://github.com/key4hep\"\u003eKey4hep\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start\u003c/h2\u003e\n\u003cp\u003eSSH to lxslc7 (CentOS 7).\u003c/p\u003e\n\u003cp\u003eBefore run following commands, please make sure you setup the CVMFS:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone git@github.com:cepc/CEPCSW.git\n$ cd CEPCSW\n$ git checkout master # branch name\n$ source setup.sh\n$ ./build.sh\n$ ./run.sh Examples/options/helloalg.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-packages\" class=\"anchor\" aria-hidden=\"true\" href=\"#packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackages\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eExamples: For new comers and users\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDetector: Geometry\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenerator: Physics Generator\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSimulation: Detector Simulation\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDigitization: Digitization\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReconstruction: Reconstruction\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conventions-for-collections\" class=\"anchor\" aria-hidden=\"true\" href=\"#conventions-for-collections\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConventions for collections\u003c/h2\u003e\n\u003cp\u003eKeep the collection names compatible between the prototype and the existing CEPC software.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMCParticle\u003c/li\u003e\n\u003cli\u003eVXDCollection\u003c/li\u003e\n\u003cli\u003eSITCollection\u003c/li\u003e\n\u003cli\u003eTPCCollection\u003c/li\u003e\n\u003cli\u003eSETCollection\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 8, "subscribers_count": 8, "topics": [], - "updated_at": 1701093879.0 + "updated_at": 1671060034.0 }, { "data_format": 2, - "description": "This repository is dedicated to the studienarbeit project of \"Numerical investigation of 2D transonic shock-buffet around a NACA 0012-34 airfoil using OpenFOAM and flowTorch\" undertaken by Mr. Tushar Gholap under the guidance of Dr. Andre Weiner.", + "description": "Quality control plotting for long reads", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "Tushargh29/transonic_shock_buffet", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNumerical investigation of 2D transonic shock-buffet around a NACA 0012-34 airfoil using OpenFOAM\u003c/h1\u003e\u003ca id=\"user-content-numerical-investigation-of-2d-transonic-shock-buffet-around-a-naca-0012-34-airfoil-using-openfoam\" class=\"anchor\" aria-label=\"Permalink: Numerical investigation of 2D transonic shock-buffet around a NACA 0012-34 airfoil using OpenFOAM\" href=\"#numerical-investigation-of-2d-transonic-shock-buffet-around-a-naca-0012-34-airfoil-using-openfoam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating the Singularity image\u003c/h2\u003e\u003ca id=\"user-content-creating-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Creating the Singularity image\" href=\"#creating-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo make results reproducible, a \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image containing OpenFOAM is used to perform simulations. To install Singularity, follow the official \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/quick_start.html\" rel=\"nofollow\"\u003einstallation instructions\u003c/a\u003e. More information on the image may be found \u003ca href=\"https://github.com/AndreWeiner/of_pytorch_docker\"\u003ehere\u003c/a\u003e. Building the image requires root privileges. Once the image is built, any user can use the image without any extra privileges. To build the image, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build of2006-py1.6-cpu.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo check if the image is working, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run-help of2006-py1.6-cpu.sif\n# ...\n# expected output\n# ...\n Simple Singularity image containing OpenFOAM-v2006\n and libtorch (PyTorch 1.6). The generic syntax to execute\n a command with arguments is\n\n singularity run image_name.sif command [path] [argument]\n\n Examples:\n\n - compile the application tensorCreation using wmake\n singularity run of2006-py1.6-cpu.sif wmake test/tensorCreation/\n\n - clean tensorCreation build\n singularity run of2006-py1.6-cpu.sif wclean test/tensorCreation/\n\n - run tensorCreation\n singularity run of2006-py1.6-cpu.sif ./tensorCreation test/tensorCreation/\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePerforming a simulation\u003c/h2\u003e\u003ca id=\"user-content-performing-a-simulation\" class=\"anchor\" aria-label=\"Permalink: Performing a simulation\" href=\"#performing-a-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe folder \u003cem\u003etest_cases\u003c/em\u003e contains all basic OpenFOAM simulation cases. Simulations should be performed in the \u003cem\u003erun\u003c/em\u003e directory, which is not tracked by the version control system. A typical workflow to run a simulation may look as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# top-level folder of the repository\nmkdir run\n# make a copy of the simulation case\ncp -r test_cases/naca0012-34-base/ run/test_base\n# execute the runCase script in the top-level folder where the image is located\n# the first argument must be the path to the simulation case\n./runCase run/test_base/\n# post-processing using local ParaView installation\ncd run/test_base\nparaview post.foam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf the simulation is performed in parallel, make sure to select \u003cem\u003eCase Type -\u0026gt; Decomposed Case\u003c/em\u003e in ParaView. To see the mesh properly, make sure to unselect \u003cem\u003eDecompose polyhedra\u003c/em\u003e.\u003c/p\u003e\n", - "stargazers_count": 7, - "subscribers_count": 2, + "full_name": "mbhall88/pistis", + "latest_release": "v0.3.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-pistis\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#pistis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePistis\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-quality-control-plotting-for-long-reads\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#quality-control-plotting-for-long-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality control plotting for long reads.\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://pypi.python.org/pypi/pistis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d377fd7c4560ba9ce5e50da718cfcda6af8bfe6e63362d9c8741335e20fec6c/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069737469732e737667\" alt=\"PyPI status\" data-canonical-src=\"https://img.shields.io/pypi/v/pistis.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.org/mbhall88/pistis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/460505d13dbbc44006c446a195f753c22160192229624c04b719693986845945/68747470733a2f2f7472617669732d63692e6f72672f6d6268616c6c38382f7069737469732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/mbhall88/pistis.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/mbhall88/pistis/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a5606fdcd10a7afc202cdcc307f242a27a106834bebba2be192225e4315fb774/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6d6268616c6c38382f7069737469732e737667\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/mbhall88/pistis.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://twitter.com/mbhall88\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/899e87a3d856d3491f29644236afe87260be498a45240bd9acde07d48634d9fd/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f6d6268616c6c38382e7376673f7374796c653d736f6369616c266c6f676f3d74776974746572266c6162656c3d466f6c6c6f77\" alt=\"Twitter Follow\" data-canonical-src=\"https://img.shields.io/twitter/follow/mbhall88.svg?style=social\u0026amp;logo=twitter\u0026amp;label=Follow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2402\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis package provides plotting designed to give you an idea of how your long read\nsequencing data looks. It was conceived of and developed with nanopore reads in\nmind, but there is no reason why PacBio reads can\u0027t be used.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install pistis\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also use \u003ccode\u003epip\u003c/code\u003e if you are running with python2.\u003cbr\u003e\nOr using a virtual\nenvironment manager such as \u003ca href=\"https://conda.io/docs/\" rel=\"nofollow\"\u003econda\u003c/a\u003e or\n\u003ca href=\"https://docs.pipenv.org/\" rel=\"nofollow\"\u003epipenv\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eYou should now be able to run \u003ccode\u003epistis\u003c/code\u003e from the command line\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eThere is a built image maintained with this repository that can be used. For the latest release you can use the URI \u003ccode\u003eshub://mbhall88/pistis\u003c/code\u003e\u003cbr\u003e\nFor example\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshub://mbhall88/pistis\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e pistis --help\nsingularity pull --name pistis.simg \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshub://mbhall88/pistis\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThe main use case for \u003ccode\u003epistis\u003c/code\u003e is as a command-line interface (CLI), but it can also be\nused in an interactive way, such as with a \u003ca href=\"https://jupyter.org/\" rel=\"nofollow\"\u003eJupyter Notebook\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-cli-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#cli-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCLI Usage\u003c/h4\u003e\n\u003cp\u003eAfter installing and running the help menu you should see the following usage\noptions\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epistis -h\n\nUsage: pistis [OPTIONS]\n\n A package for sanity checking (quality control) your long read data.\n Feed it a fastq file and in return you will receive a PDF with four plots:\n\n 1. GC content histogram with distribution curve for sample.\n\n 2. Jointplot showing the read length vs. phred quality score for\n each read. The interior representation of this plot can be\n altered with the --kind option.\n\n 3. Box plot of the phred quality score at positional bins across\n all reads. The reads are binned into read positions 1, 2, 3, 4, 5,\n 6, 7, 8, 9, 10, 11-20, 21-50, 51-100, 101-200, 201-300. Plots from\n the start of reads.\n\n 4. Same as 3, but plots from the end of the read.\n\n Additionally, if you provide a BAM/SAM file a histogram of the read\n percent identity will be added to the report.\n\nOptions:\n -f, --fastq PATH Fastq file to plot. This can be gzipped.\n -o, --output PATH Path to save the plot PDF as. If name is not\n specified, will use the name of the fastq\n (or bam) file with .pdf extension.\n -k, --kind [kde|scatter|hex] The kind of representation to use for the\n jointplot of quality score vs read length.\n Accepted kinds are \u0027scatter\u0027, \u0027kde\u0027\n (default), or \u0027hex\u0027. For examples refer to h\n ttps://seaborn.pydata.org/generated/seaborn.\n jointplot.html\n --log_length / --no_log_length Plot the read length as a log10\n transformation on the quality vs read length\n plot\n -b, --bam PATH SAM/BAM file to produce read percent\n identity histogram from.\n -d, --downsample INTEGER Down-sample the sequence files to a given\n number of reads. Set to 0 for no\n subsampling. Default: 50000\n -h, --help Show this message and exit.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote the \u003ccode\u003e--downsample\u003c/code\u003e option is set to 50000 by default. That is, \u003ccode\u003epistis\u003c/code\u003e will\nonly plot 50000 reads (sampled from a uniform distribution). You can set this to\n0 if you want to plot every read, or select another number of your choosing. Be aware\nthat if you try to plot too many reads you may run into memory issues, so try\ndownsampling if this happens.\u003c/p\u003e\n\u003cp\u003eThere are three different use cases - currently - for producing plots:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFastq only\u003c/strong\u003e - This will return four plots:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA distribution plot of the GC content for each read.\u003c/li\u003e\n\u003cli\u003eA bivariate jointplot with read length on the y-axis and mean read quality\nscore on the x-axis.\u003c/li\u003e\n\u003cli\u003eTwo boxplots that show the distribution of quality scores at select positions\nand positional ranges. One plot shows the scores from the beginning of the\nread and the other from the end of the read.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo use \u003ccode\u003epistis\u003c/code\u003e in this way you just need a fastq file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq -o /save/as/report.pdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will save the four plots to a file called \u003ccode\u003ereport.pdf\u003c/code\u003e in directory \u003ccode\u003e/save/as/\u003c/code\u003e.\nIf you don\u0027t provide a \u003ccode\u003e--output/-o\u003c/code\u003e option the file will be saved in the current\ndirectory with the basename of the fastq file. So in the above example it would be\nsaved as \u003ccode\u003emy.pdf\u003c/code\u003e.\u003cbr\u003e\nIf you would prefer the read lengths in the bivariate plot of read length vs.\nmean quality score then you can indicate this like so\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq -o /save/as/report.pdf --no_log_length\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditionally, you can change the way the data is represented in the bivariate plot.\nThe default is a kernel density estimation plot (as in the below image), however you can\nchoose to use a \u003ca href=\"https://seaborn.pydata.org/generated/seaborn.jointplot.html\" rel=\"nofollow\"\u003ehex bin or scatter plot version instead\u003c/a\u003e.\nIn the running example, to use a scatter plot you would run the following\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq -o /save/as/report.pdf --kind scatter\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also provide a \u003ccode\u003egzip\u003c/code\u003eed fastq file without any extra steps\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq.gz -o /save/as/report.pdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eExamples\u003c/strong\u003e\u003cbr\u003e\nGC content:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_gc_plot.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_gc_plot.png\" alt=\"gc content plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRead length vs. mean read quality score:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_qual_v_len.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_qual_v_len.png\" alt=\"read length vs quality plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBase quality from the start of each read:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_qual_start.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_qual_start.png\" alt=\"base quality from start plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBase quality from the end of each read:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_qual_end.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_qual_end.png\" alt=\"base quality from end plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eFastq and BAM/SAM\u003c/strong\u003e - This will return the above four plots, plus a distribution\nplot of each read\u0027s percent identity with the reference it is aligned to in the\n[BS]AM file. Reads which are flagged as supplementary or secondary are not included.\nThe plot also includes a dashed vertical red line indicating the median\npercent identity.\u003cbr\u003e\nNote: If using a BAM file, it must be sorted and indexed (i.e \u003ccode\u003e.bai\u003c/code\u003e file). See \u003ca href=\"http://www.htslib.org/doc/samtools.html\" rel=\"nofollow\"\u003e\u003ccode\u003esamtools\u003c/code\u003e\u003c/a\u003e\nfor instructions on how to do this.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq -b /path/to/my.bam -o /save/as/report.pdf\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\npistis -f /path/to/my.fastq -b /path/to/my.sam -o /save/as/report.pdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eExample\u003c/strong\u003e\u003cbr\u003e\nDistribution of aligned read percent identity:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_perc_id.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_perc_id.png\" alt=\"percent identity plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eBAM/SAM only\u003c/strong\u003e - At this stage you will receive only the distribution\nplot of each read\u0027s percent identity with the reference it is aligned to. In a\nfuture release I aim to allow you to also get the other four fastq-only plots.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -b /path/to/my.bam -o /save/as/report.pdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs with the fastq-only method, if you don\u0027t provide a \u003ccode\u003e--output/-o\u003c/code\u003e option the file will be saved in the current\ndirectory with the basename of the [BS]AM file. So in the above example it would be\nsaved as \u003ccode\u003emy.pdf\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-usage-in-a-development-environment\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage-in-a-development-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage in a development environment\u003c/h4\u003e\n\u003cp\u003eIf you would like to use \u003ccode\u003epistis\u003c/code\u003e within a development environment such as a\n\u003ccode\u003ejupyter notebook\u003c/code\u003e or just a plain ol\u0027 python shell then take a look at \u003ca href=\"https://github.com/mbhall88/pistis/blob/master/examples/example_usage.ipynb\"\u003ethis example notebook\u003c/a\u003e\nfor all the details.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThis package was created with \u003ca href=\"https://github.com/audreyr/cookiecutter\"\u003eCookiecutter\u003c/a\u003e and the \u003ca href=\"https://github.com/audreyr/cookiecutter-pypackage\"\u003e\u003ccode\u003eaudreyr/cookiecutter-pypackage\u003c/code\u003e project template\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThe two test data files (fastq and BAM) that I have used in this repository were\ntaken from \u003ca href=\"https://github.com/wdecoster/nanotest\"\u003eWouter De Coster\u0027s \u003ccode\u003enanotest\u003c/code\u003e repository\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eWhich in turn comes from \u003ca href=\"http://lab.loman.net/2017/03/09/ultrareads-for-nanopore/\" rel=\"nofollow\"\u003eNick Loman and Josh Quick\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThe example plots in this \u003ccode\u003eREADME\u003c/code\u003e were made using the entire fastq of basecalled\nreads from the experiment in that \u003ca href=\"http://lab.loman.net/2017/03/09/ultrareads-for-nanopore/\" rel=\"nofollow\"\u003eblog on \"whale hunting\"\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThe plot for the BAM file was obtained by running \u003ccode\u003epistis\u003c/code\u003e on a BAM file generated\nby mapping the fastq file to \u003cem\u003eE. coli\u003c/em\u003e reference \u003ca href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_000913.3\" rel=\"nofollow\"\u003eNC_000913.3\u003c/a\u003e\nusing Heng Li\u0027s \u003ca href=\"https://github.com/lh3/minimap2\"\u003e\u003ccode\u003eminimap2\u003c/code\u003e\u003c/a\u003e and \u003ccode\u003e-x map-ont\u003c/code\u003e option.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eIf you would like to contribute to this package you are more than welcome.\u003cbr\u003e\n\u003cstrong\u003ePlease read through the \u003ca href=\"https://github.com/mbhall88/pistis/blob/master/CONTRIBUTING.rst\"\u003econtributing guidelines\u003c/a\u003e first\u003c/strong\u003e.\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 3, "topics": [ - "openfoam", - "python", - "paraview", - "dynamic-mode-decomposition", - "flowtorch" + "nanopore", + "oxford-nanopore", + "bioinformatics", + "bioinformatics-analysis", + "plotting", + "quality-control", + "pacbio" ], - "updated_at": 1731770973.0 + "updated_at": 1665091185.0 }, { "data_format": 2, - "description": "Code for the Optimisation of ID\u0027s using Python and Opt-AI", + "description": "Local Conda environments via Singularity.", "filenames": [ - "Singularity.env-v2", - "Singularity" + "Singularity.conda" ], - "full_name": "DiamondLightSource/Opt-ID", - "latest_release": "v2.0", - "readme": "\u003cp\u003e\u003ca href=\"https://travis-ci.org/DiamondLightSource/Opt-ID\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e28103082a54d8f383c80faf900aa4f7515b5ca923b36bc1aa69393496299c2a/68747470733a2f2f7472617669732d63692e6f72672f4469616d6f6e644c69676874536f757263652f4f70742d49442e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/DiamondLightSource/Opt-ID.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://coveralls.io/github/DiamondLightSource/Opt-ID?branch=master\u0026amp;service=github\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c667be15546a48b3d3418bf8748b5907e82996977fda9f1498ddaae20f392f5b/68747470733a2f2f636f766572616c6c732e696f2f7265706f732f6769746875622f4469616d6f6e644c69676874536f757263652f4f70742d49442f62616467652e7376673f6272616e63683d6d617374657226736572766963653d676974687562\" alt=\"Coverage Status\" data-canonical-src=\"https://coveralls.io/repos/github/DiamondLightSource/Opt-ID/badge.svg?branch=master\u0026amp;service=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://scrutinizer-ci.com/g/DiamondLightSource/Opt-ID/?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84fbe7d52ef53121cd10bf8bd84baaa5f51e1a7fec43131e1bb85973a7f86841/68747470733a2f2f7363727574696e697a65722d63692e636f6d2f672f4469616d6f6e644c69676874536f757263652f4f70742d49442f6261646765732f7175616c6974792d73636f72652e706e673f623d6d6173746572\" alt=\"Scrutinizer Code Quality\" data-canonical-src=\"https://scrutinizer-ci.com/g/DiamondLightSource/Opt-ID/badges/quality-score.png?b=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.3968577\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9f2b8f9b7b2a759b4746ca645b9f4fb3619ab288054c7a8eb9f93e496a18fd0a/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333936383537372e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3968577.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/4728\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOpt-ID\u003c/h1\u003e\u003ca id=\"user-content-opt-id\" class=\"anchor\" aria-label=\"Permalink: Opt-ID\" href=\"#opt-id\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode for the Optimisation of ID\u0027s using Python and Opt-AI\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of how to use Opt-ID\u003c/h2\u003e\u003ca id=\"user-content-overview-of-how-to-use-opt-id\" class=\"anchor\" aria-label=\"Permalink: Overview of how to use Opt-ID\" href=\"#overview-of-how-to-use-opt-id\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpt-ID is run is by providing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea main configuration file in YAML format which contains all the various\nparameters for the sort/shim job\u003c/li\u003e\n\u003cli\u003ean existing directory in which output data will be written to\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere are two main flags, \u003ccode\u003e--sort\u003c/code\u003e and \u003ccode\u003e--shim\u003c/code\u003e, to run sort and shim jobs. The\nidea is that using either of these flags in conjunction with the YAML config\nfile will go through and run all the scripts that are used to produce\nintermediate files and pass them around appropriately, so then there\u0027s only one\ncommand needed to be executed to run a sort or shim job, and the YAML config\nfile is the single source of all the parameter information used for that\nparticular job.\u003c/p\u003e\n\u003cp\u003eThere are several other processes that Opt-ID provides that are desired to be\ndone after a sort/shim but don\u0027t require the sequence of scripts that a\nsort/shim job does (for example, the use of \u003ccode\u003ecompare.py\u003c/code\u003e to compare a shimmed\ngenome to the original genome), so the \u003ccode\u003e--sort\u003c/code\u003e and \u003ccode\u003e--shim\u003c/code\u003e flags aren\u0027t able\nto provide these sorts of processes. To do so, there are several shell scripts\nthat are autogenerated when a sort or shim job is run that can be executed.\nThese scripts run Opt-ID in the particular way that is needed to perform the\nprocess, without the user needing to worry about extra configuration on top of\nthe YAML file.\u003c/p\u003e\n\u003cp\u003eTaking the \u003ccode\u003ecompare.py\u003c/code\u003e example previously mentioned, a script would be\nautogenerated after a shim job called \u003ccode\u003ecompare_shim.sh\u003c/code\u003e that can be passed any\nshimmed genome file in the data directory, and it will take care of calling\nOpt-ID in the particular way it needs to in order to run the \u003ccode\u003ecompare.py\u003c/code\u003e script\nwith the appropriate parameters. More details on how to use these autogenerated\nshell scripts are below in the \"Using the autogenerated shell scripts\" section.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData directory\u003c/h2\u003e\u003ca id=\"user-content-data-directory\" class=\"anchor\" aria-label=\"Permalink: Data directory\" href=\"#data-directory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe data outputted by Opt-ID is split roughly into two categories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elarge files such as \u003ccode\u003e.h5\u003c/code\u003e files\u003c/li\u003e\n\u003cli\u003esmaller files such as \u003ccode\u003e.json\u003c/code\u003e, \u003ccode\u003e.mag\u003c/code\u003e, \u003ccode\u003e.sh\u003c/code\u003e files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe smaller files get written to the directory passed as the second parameter to\nOpt-ID, so if OptID was passed \u003ccode\u003e/home/FedID/my_dir\u003c/code\u003e then the smaller files would\nget written to \u003ccode\u003e/home/FedID/my_dir\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe larger files get written to a directory within \u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e whose path\nis based on the user\u0027s FedID and also the name of the data directory passed to\nOpt-ID. The name of the directory created in \u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e will be the name\nof the very last directory in the path passed to Opt-ID. For example, if the\npath \u003ccode\u003e/home/FedID/my_dir\u003c/code\u003e is passed to Opt-ID, then the directory\n\u003ccode\u003e/dls/tmp/FedID/my_dir\u003c/code\u003e will be created. Symlinks are then created in\n\u003ccode\u003e/home/FedID/my_dir\u003c/code\u003e to point to the larger files inside\n\u003ccode\u003e/dls/tmp/FedID/my_dir\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOne reason behind having two separate directories containing different data\nfiles is due to the large size of the \u003ccode\u003e.h5\u003c/code\u003e files produced by Opt-ID and not\nhaving the space to put them just anywhere in the filesystem (\u003ccode\u003e/dls/tmp\u003c/code\u003e has\nmuch more available space than, for example, the home directory associated to a\nFedID). Another reason is that the automatic deletion of files in \u003ccode\u003e/dls/tmp\u003c/code\u003e can\nbe used to do some automatic periodic cleanup of old, large files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntended usage\u003c/h3\u003e\u003ca id=\"user-content-intended-usage\" class=\"anchor\" aria-label=\"Permalink: Intended usage\" href=\"#intended-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe intended usage of this dual-directory structure is that the smaller files\nare written to somewhere away from \u003ccode\u003e/dls/tmp\u003c/code\u003e so then they\u0027re not deleted\nperiodically and can be referred to later if needed, whilst the larger files are\nwritten to the user\u0027s directory in \u003ccode\u003e/dls/tmp\u003c/code\u003e so then they \u003cem\u003eare\u003c/em\u003e deleted\nperiodically. Therefore, it is advised that the directory provided to Opt-ID is\nnot a directory in \u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e; this is not only because of potential\ndeletion of the smaller files, but also because passing a directory in\n\u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e can cause some confusion regarding the directory that is\nsubsequently created by Opt-ID in \u003ccode\u003e/dls/tmp/FedID\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn particular, it is advised that the directory passed to Opt-ID is one within\n\u003ccode\u003e/dls/technical/id\u003c/code\u003e\u003c/strong\u003e, as this is where output data from other Opt-ID jobs has\ntypically been placed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample directory structures\u003c/h3\u003e\u003ca id=\"user-content-example-directory-structures\" class=\"anchor\" aria-label=\"Permalink: Example directory structures\" href=\"#example-directory-structures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor example, if the directory \u003ccode\u003e/dls/technical/id/test/\u003c/code\u003e is passed to Opt-ID, the\nexpected directory structures right after having run a sort job on a cluster is\ngiven below:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/dls/technical/id/test/\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest_sort.json\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_sort.mag\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_sort.h5 -\u0026gt; /dls/tmp/FedID/test/test_sort.h5\u003c/code\u003e (symlink to a file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egenerate_report.sh\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erestart_sort.sh\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elogfiles/\u003c/code\u003e (directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egenomes -\u0026gt; /dls/tmp/FedID/test/genomes/\u003c/code\u003e (symlink to a directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprocess_genome_output -\u0026gt; /dls/tmp/FedID/test/process_genome_output/\u003c/code\u003e\n(symlink to a directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003e/dls/tmp/FedID/test/\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest_sort.h5\u003c/code\u003e (file, the symlink \u003ccode\u003e/dls/technical/id/test/test_sort.h5\u003c/code\u003e points\nto this file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egenomes/\u003c/code\u003e (directory, the symlink \u003ccode\u003e/dls/technical/id/test/genomes\u003c/code\u003e points to\nthis directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprocess_genome_output/\u003c/code\u003e (directory, the symlink\n\u003ccode\u003e/dls/technical/id/test/process_genome_output\u003c/code\u003e points to this directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAs another example, for the same directory being passed but instead a shim job\nbeing run on a cluster, the expected directory structures right after the job\nare:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e/dls/technical/id/test/\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest_shim.json\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_shim.mag\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etest_shim.h5 -\u0026gt; /dls/tmp/FedID/test/test_shim.h5\u003c/code\u003e (symlink to a file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egenerate_report.sh\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecompare_shim.sh\u003c/code\u003e (file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elogfiles/\u003c/code\u003e (directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eshimmed_genomes -\u0026gt; /dls/tmp/FedID/test/shimmed_genomes/\u003c/code\u003e (symlink to a\ndirectory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprocess_genome_output -\u0026gt; /dls/tmp/FedID/test/process_genome_output/\u003c/code\u003e\n(symlink to a directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ccode\u003e/dls/tmp/FedID/test/\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest_shim.h5\u003c/code\u003e (file, the symlink \u003ccode\u003e/dls/technical/id/test/test_shim.h5\u003c/code\u003e points\nto this file)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eshimmed_genomes/\u003c/code\u003e (directory, the symlink\n\u003ccode\u003e/dls/technical/id/test/shimmed_genomes\u003c/code\u003e points to this directory)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eprocess_genome_output/\u003c/code\u003e (directory, the symlink\n\u003ccode\u003e/dls/technical/id/test/process_genome_output\u003c/code\u003e points to this directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that the filenames \u003ccode\u003etest_sort.*\u003c/code\u003e and \u003ccode\u003etest_shim.*\u003c/code\u003e are just placeholders\nand have been chosen only for illustrative purposes, these files can be named as\ndesired in the YAML config file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePreliminary steps to be able to run Opt-ID\u003c/h2\u003e\u003ca id=\"user-content-preliminary-steps-to-be-able-to-run-opt-id\" class=\"anchor\" aria-label=\"Permalink: Preliminary steps to be able to run Opt-ID\" href=\"#preliminary-steps-to-be-able-to-run-opt-id\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA process that is not done by Opt-ID is the transfer of magnet information in\nthe Excel files provided by the supplier to \u003ccode\u003e.sim\u003c/code\u003e files. To do so, from the\nExcel files supplied by the supplier, create tab delimited \u003ccode\u003e.sim\u003c/code\u003e files of\nmagnetisation. This is a manual procedure done only on Windows. Note that,\ncurrently, Opt-ID requires the magnet names in the \u003ccode\u003e.sim\u003c/code\u003e files to have leading\nzeros that pad out the name to 3 digits. For example, instead of \u00271\u0027 it should\nbe \u0027001\u0027.\u003c/p\u003e\n\u003cp\u003eTo get the code, clone the Opt-ID repo to the desired place in the filesystem.\nTo set up the environment for running Opt-ID on a Linux machine, in a terminal\nrun the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load python/3\nmodule load global/cluster\nexport PYTHONPATH=$PYTHONPATH:/path/to/Opt-ID\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e/path/to/Opt-ID\u003c/code\u003e is the path to the root directory of the cloned repo.\n(There is a change to how \u003ccode\u003epython\u003c/code\u003e is used to run the code which is detailed in\nthe next section, and so the third command is to enable \u003ccode\u003epython\u003c/code\u003e to find the\ncode in the repo).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Opt-ID with the \u003ccode\u003epython\u003c/code\u003e command\u003c/h2\u003e\u003ca id=\"user-content-running-opt-id-with-the-python-command\" class=\"anchor\" aria-label=\"Permalink: Running Opt-ID with the python command\" href=\"#running-opt-id-with-the-python-command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main script that is used for running Opt-ID is \u003ccode\u003eIDSort/src/optid.py\u003c/code\u003e. It\nshould be run using the syntax \u003ccode\u003epython -m IDSort.src.optid\u003c/code\u003e as opposed to\n\u003ccode\u003epython /path/to/Opt-ID/IDSort/src/optid.py\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDifferent options that Opt-ID can be run with\u003c/h2\u003e\u003ca id=\"user-content-different-options-that-opt-id-can-be-run-with\" class=\"anchor\" aria-label=\"Permalink: Different options that Opt-ID can be run with\" href=\"#different-options-that-opt-id-can-be-run-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two sets of flags from which one flag from each set is mandatory to be\npassed to Opt-ID, and the rest are optional and have sensible default values if\nthey are not provided.\u003c/p\u003e\n\u003cp\u003eThe mandatory sets of flags are\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--sort\u003c/code\u003e vs \u003ccode\u003e--shim\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--cluster-on\u003c/code\u003e vs \u003ccode\u003e--cluster-off\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewhere only one flag from each bullet point should be provided.\u003c/p\u003e\n\u003cp\u003eExamples of running Opt-ID with the bare mininum flags and parameters it needs\nare:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m IDSort.src.optid --sort --cluster-on /path/to/yaml /path/to/data/dir\npython -m IDSort.src.optid --shim --cluster-off /path/to/yaml /path/to/data/dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ccode\u003e--sort\u003c/code\u003e and \u003ccode\u003e--shim\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content---sort-and---shim\" class=\"anchor\" aria-label=\"Permalink: --sort and --shim\" href=\"#--sort-and---shim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are used for specifying what type of job is desired.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ccode\u003e--cluster-on\u003c/code\u003e and \u003ccode\u003e--cluster-off\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content---cluster-on-and---cluster-off\" class=\"anchor\" aria-label=\"Permalink: --cluster-on and --cluster-off\" href=\"#--cluster-on-and---cluster-off\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are used for specifying whether the job is run on the local machine or\nsubmitted to run on a cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\n\u003ccode\u003e--num-threads\u003c/code\u003e, \u003ccode\u003e--queue\u003c/code\u003e, and \u003ccode\u003e--node-os\u003c/code\u003e\n\u003c/h4\u003e\u003ca id=\"user-content---num-threads---queue-and---node-os\" class=\"anchor\" aria-label=\"Permalink: --num-threads, --queue, and --node-os\" href=\"#--num-threads---queue-and---node-os\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are used in conjunction with \u003ccode\u003e--cluster-on\u003c/code\u003e. Some examples of using these\nflags would be\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m IDSort.src.optid --sort --cluster-on --node-os rhel7 /path/to/yaml /path/to/data/dir\npython -m IDSort.src.optid --shim --cluster-on --queue low.q /path/to/yaml /path/to/data/dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\n\u003ccode\u003e--seed\u003c/code\u003e and \u003ccode\u003e--seed-value\u003c/code\u003e\n\u003c/h4\u003e\u003ca id=\"user-content---seed-and---seed-value\" class=\"anchor\" aria-label=\"Permalink: --seed and --seed-value\" href=\"#--seed-and---seed-value\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are used in conjunction with \u003ccode\u003e--cluster-off\u003c/code\u003e. \u003ccode\u003e--seed\u003c/code\u003e is used to specify\nthat the random number generator (RNG) should be seeded and thus produce the\nsame output across multiple runs with the same parameters. \u003ccode\u003e--seed-value\u003c/code\u003e is\nspecified if a particular value to seed the RNG is desired (by default its value\nis 1). Some examples of using these flags would be\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m IDSort.src.optid --sort --cluster-off --seed /path/to/yaml /path/to/data/dir\npython -m IDSort.src.optid --shim --cluster-off --seed --seed-value 30 /path/to/yaml /path/to/data/dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eYAML config files\u003c/h2\u003e\u003ca id=\"user-content-yaml-config-files\" class=\"anchor\" aria-label=\"Permalink: YAML config files\" href=\"#yaml-config-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe YAML config files contain the parameters used by the various scripts that\nOpt-ID runs. The top-level sections of the YAML config files are the script\nnames minus the \u003ccode\u003e.py\u003c/code\u003e and the subsections are the different parameters passed to\nthat particular script. For the most part, the subsection names are exactly the\nsame as the script parameters they\u0027re associated to, for example, the\n\u003ccode\u003eid_setup.py\u003c/code\u003e script has a \u003ccode\u003e--periods\u003c/code\u003e flag, and the YAML subsection\ncorresponding to that parameter is \u003ccode\u003eid_setup.periods\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA few exceptions exist to try and be more descriptive with what the parameter\nis, for example, \u003ccode\u003eprocess_genome.py\u003c/code\u003e refers to the files it\u0027s given as elements\nof the \u003ccode\u003eargs\u003c/code\u003e list, but in the YAML the corresponding subsection for a shim job\nis \u003ccode\u003eprocess_genome.readable_genome_file\u003c/code\u003e which is hopefully a more useful\ndescription.\u003c/p\u003e\n\u003cp\u003eExamples of YAML config files can be found in the \u003ccode\u003eIDSort/example_configs\u003c/code\u003e\ndirectory. There are some placeholder values in these config files that aren\u0027t\nvalid values for their associated section in the YAML, and the following\nsections detail the changes that need to be made to the example config files to\nget them in a state ready to run a job.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSort config example\u003c/h3\u003e\u003ca id=\"user-content-sort-config-example\" class=\"anchor\" aria-label=\"Permalink: Sort config example\" href=\"#sort-config-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are three values that need to be changed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emagnets.hmags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emagnets.hemags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emagnets.htmags\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTheir values should be absolute paths to any \u003ccode\u003e.sim\u003c/code\u003e files of the relevant type.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShim config example\u003c/h3\u003e\u003ca id=\"user-content-shim-config-example\" class=\"anchor\" aria-label=\"Permalink: Shim config example\" href=\"#shim-config-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are five values that need to be changed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emagnets.hmags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emagnets.hemags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emagnets.htmags\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eprocess_genome.readable_genome_file\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003empi_runner_for_shim_opt.bfield_filename\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe first three are the same as in the sort config example. The value of\n\u003ccode\u003eprocess_genome.readable_genome_file\u003c/code\u003e should be an absolute path to the \u003ccode\u003e.inp\u003c/code\u003e\nfile that is used to start the shim job from. The value of\n\u003ccode\u003empi_runner_for_shim_opt.bfield_filename\u003c/code\u003e should be an absolute path to the\n\u003ccode\u003e.h5\u003c/code\u003e file that is converted from \u003ccode\u003e.bfield\u003c/code\u003e files that are produced by igor.\u003c/p\u003e\n\u003cp\u003eNote that, currently, the use of the \u003ccode\u003eigor2h5.py\u003c/code\u003e script hasn\u0027t yet been\nintegrated into the YAML configuration file for Opt-ID, so the process of\nconverting \u003ccode\u003e.bfield\u003c/code\u003e data into \u003ccode\u003e.h5\u003c/code\u003e data is one that needs to be done by\nmanually executing the \u003ccode\u003eigor2h5.py\u003c/code\u003e script (or by any other means) prior to\nrunning a shim job with Opt-ID.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing the autogenerated shell scripts\u003c/h2\u003e\u003ca id=\"user-content-using-the-autogenerated-shell-scripts\" class=\"anchor\" aria-label=\"Permalink: Using the autogenerated shell scripts\" href=\"#using-the-autogenerated-shell-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll the autogenerated scripts can be executed from anywhere in the filesystem,\nit\u0027s not necessary for the current working directory to be the same directory\nthat the script is in.\u003c/p\u003e\n\u003cp\u003eDue to the facts that\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethese scripts are generated on a job-by-job basis and are only meant to be run\nfor the particular data within the directory the scripts are in\u003c/li\u003e\n\u003cli\u003ethe structure of the data directories are fixed and known in advance\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewhen it comes to passing parameters to these scripts they are aware of the\nspecific directories that the files they\u0027re expecting should be in, so only\nfilenames need to be given to them and not absolute or even relative filepaths.\nConcrete examples are given below in the \u003ccode\u003egenerate_report.sh\u003c/code\u003e and\n\u003ccode\u003ecompare_shim.sh\u003c/code\u003e sections that hopefully explain in more detail how to pass\nparameters to these scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003egenerate_report.sh\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-generate_reportsh\" class=\"anchor\" aria-label=\"Permalink: generate_report.sh\" href=\"#generate_reportsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script is used to create a report with some useful data visualisation in a\nPDF file. For a sort job it can be passed multiple \u003ccode\u003e.genome\u003c/code\u003e and \u003ccode\u003e.inp\u003c/code\u003e files,\nand for a shim job it can be passed multiple \u003ccode\u003e.h5\u003c/code\u003e files that are associated to\nthe \"full genomes\" (as opposed to the smaller-sized \"compare genomes\") in the\nshim output.\u003c/p\u003e\n\u003cp\u003eFor a sort job, Opt-ID will look in both the \u003ccode\u003egenomes/\u003c/code\u003e and\n\u003ccode\u003eprocess_genome_output/\u003c/code\u003e directories for the given \u003ccode\u003e.genome\u003c/code\u003e and \u003ccode\u003e.inp\u003c/code\u003e files,\nand for a shim job Opt-ID will look in the \u003ccode\u003eshimmed_genomes/\u003c/code\u003e directory for the\ngiven \u003ccode\u003e.h5\u003c/code\u003e files. Therefore, the parameters passed to \u003ccode\u003egenerate_report.sh\u003c/code\u003e\nshould only be the filenames and not filepaths.\u003c/p\u003e\n\u003cp\u003eFor example, for a sort job, the correct way to pass a genome and a \u003ccode\u003e.inp\u003c/code\u003e file\nto the script would be\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report.sh foo.genome bar.inp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eas opposed to\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report.sh genomes/foo.genome process_genome_output/bar.inp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnother example: for a shim job, the correct way to pass \u003ccode\u003e.h5\u003c/code\u003e files to the\nscript would be\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report.sh foo.h5 bar.h5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eas opposed to\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report.sh shimmed_genomes/foo.h5 shimmed_genomes/bar.h5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn optional \u003ccode\u003e--report-filename\u003c/code\u003e flag can be passed before the files to specify\nthe name of the PDF file, and genome reports are stored in the \u003ccode\u003egenome_reports/\u003c/code\u003e\ndirectory within the directory passed to Opt-ID. Report filenames should have a\n\u003ccode\u003e.pdf\u003c/code\u003e extension to enable a simple check between the report filename parameter\nand \u003ccode\u003e.genome\u003c/code\u003e/\u003ccode\u003e.inp\u003c/code\u003e file parameters that follow it. The \u003ccode\u003e--report-filename\u003c/code\u003e\noption can be omitted and in that case the report filename will be a\nconcatenation of all the filenames passed with an underscore character \"_\" as\nthe separator between the filenames.\u003c/p\u003e\n\u003cp\u003eAn example of using the \u003ccode\u003e--report-filename\u003c/code\u003e flag is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/generate_report --report-filename report.pdf foo.genome bar.inp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003erestart_sort.sh\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-restart_sortsh\" class=\"anchor\" aria-label=\"Permalink: restart_sort.sh\" href=\"#restart_sortsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script requires no parameters and can be run simply as\n\u003ccode\u003e/path/to/restart_sort.sh\u003c/code\u003e, Opt-ID will take care of loading the YAML config of\nthe previous sort job and will use all the same flags and paramters as the\noriginal sort job. One example is that if the original sort job was run on a\ncluster, so will the restart-sort job, and another example is that the same\n\u003ccode\u003e.json\u003c/code\u003e, \u003ccode\u003e.mag\u003c/code\u003e and \u003ccode\u003e.h5\u003c/code\u003e (lookup table) files from the original sort job will\nbe reused in the restart-sort job instead of being regenerated.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003ecompare_shim.sh\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-compare_shimsh\" class=\"anchor\" aria-label=\"Permalink: compare_shim.sh\" href=\"#compare_shimsh\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis can be passed a single \u003ccode\u003e.genome\u003c/code\u003e file that is in the \u003ccode\u003eshimmed_genomes/\u003c/code\u003e\ndirectory and it will generate a human readable diff between the original and\nshimmed genomes that will be written to the \u003ccode\u003eshim_diffs/\u003c/code\u003e directory. It\u0027s not\nnecessary to pass the original genome to this script, Opt-ID will take care of\nfinding it so only the shimmed genome needs to be given as a parameter.\u003c/p\u003e\n\u003cp\u003eSimilarly to what \u003ccode\u003egenerate_report.sh\u003c/code\u003e does, \u003ccode\u003ecompare_shim.sh\u003c/code\u003e will look in the\n\u003ccode\u003eshimmed_genomes/\u003c/code\u003e directory so only filenames should be passed to it and not\nfilepaths. An example of using this script would be:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/compare_shim.sh foo.genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAn optional \u003ccode\u003e--diff-filename\u003c/code\u003e flag can be passed before the shimmed genome file\nto specify the filename of the human readable diff. Currently Opt-ID appends a\n\u003ccode\u003e.txt\u003c/code\u003e extension to the filename so it\u0027s not necessary to put that in the\nparameter. Again, similarly to what \u003ccode\u003egenerate_report.sh\u003c/code\u003e does, if this flag is\nomitted then the diff filename is a concatenation of the original genome and\nshimmed genome filenames with an underscore character as the separator, and then\nalso prepended with \u003ccode\u003eshim_\u003c/code\u003e. For example, if the original genome is \u003ccode\u003efoo.genome\u003c/code\u003e\nand the shimmed genome is \u003ccode\u003ebar.genome\u003c/code\u003e, then if the \u003ccode\u003e--diff-filename\u003c/code\u003e flag is\nomitted then the diff filename would be \u003ccode\u003eshim_foo.genome_bar.genome.txt\u003c/code\u003e. An\nexample of using the \u003ccode\u003e--diff-filename\u003c/code\u003e flag is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/path/to/compare_shim.sh --diff-filename my_shim foo.genome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\"Hidden\" options of Opt-ID\u003c/h2\u003e\u003ca id=\"user-content-hidden-options-of-opt-id\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;Hidden\u0026quot; options of Opt-ID\" href=\"#hidden-options-of-opt-id\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are several options that Opt-ID has but are only meant to be used by the\nautogenerated shell scripts and not intended to be invoked directly by a user;\ntherefore, these options aren\u0027t of much interest to users and only of potential\ninterest to developers. The following are just some useful notes to any\ndevelopers viewing this document:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethese options are related to those kinds of processes that a user would want\nto do that aren\u0027t full sort/shim jobs that were referred to in the \"Overview\nof how to use Opt-ID\" section of this document\u003c/li\u003e\n\u003cli\u003ethese options are all used by the autogenerated shell scripts that were also\nreferred to in the \"Overview of how to use Opt-ID\" section, hence why the\nusers need not directly use them, the autogenerated scripts should take care\nof using these \"hidden options\" where necessary\u003c/li\u003e\n\u003cli\u003ethese are also processes that are done after a sort/shim, so they assume the\nexistence of a YAML config that has already been used for the sort/shim job,\nas well as any output data from a sort/shim job\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003e--generate-report\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content---generate-report\" class=\"anchor\" aria-label=\"Permalink: --generate-report\" href=\"#--generate-report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis option starts off the process of using the\n\u003ccode\u003eIDSort/src/genome_report_template.ipynb\u003c/code\u003e file to generate a Jupyter notebook\nfile, and then running it to produce a PDF report.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003e--restart-sort\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content---restart-sort\" class=\"anchor\" aria-label=\"Permalink: --restart-sort\" href=\"#--restart-sort\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis option starts off the process of reusing the same YAML config file that was\nused for the sort job to get all the parameters used for the original sort job,\nand then running Opt-ID to generate genomes from an initial population as\nopposed to generating genomes from scratch.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003e--compare-shim\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content---compare-shim\" class=\"anchor\" aria-label=\"Permalink: --compare-shim\" href=\"#--compare-shim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis option starts off the process of comparing the given shimmed genome to the\noriginal genome that was used to start the shim job.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning the tests\u003c/h2\u003e\u003ca id=\"user-content-running-the-tests\" class=\"anchor\" aria-label=\"Permalink: Running the tests\" href=\"#running-the-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNavigate to the root directory of the Opt-ID repo:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/Opt-ID\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run all the tests:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m pytest IDSort/test/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run a particular test in the \u003ccode\u003etest/\u003c/code\u003e directory, it can be specified in the\npath in the above command. For example, to run \u003ccode\u003eIDSort/test/magnets_test.py\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m pytest IDSort/test/magnets_test.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://codescene.io/projects/6289/jobs/latest-successful/results\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e69ae66d83ceb5b854c1f6a0110e6bbfad332f23a191a1fa9f890120484c607e/68747470733a2f2f636f64657363656e652e696f2f70726f6a656374732f363238392f7374617475732e737667\" alt=\"\" data-canonical-src=\"https://codescene.io/projects/6289/status.svg\" style=\"max-width: 100%;\"\u003e Get more details at \u003cstrong\u003ecodescene.io\u003c/strong\u003e.\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 7, - "subscribers_count": 9, - "topics": [], - "updated_at": 1690651033.0 + "full_name": "bast/singularity-conda", + "latest_release": "0.5.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-local-conda-environments-via-singularity\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#local-conda-environments-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal Conda environments via Singularity\u003c/h1\u003e\n\u003cp\u003eThe nice thing about this approach is that you don\u0027t need to install Conda and\nyou don\u0027t need to modify your environment/bashrc/settings.\u003c/p\u003e\n\u003cp\u003eI use it to install dependencies that may be tough to install on a\nsupercomputer or on my NixOS environment.\u003c/p\u003e\n\u003cp\u003eHow to fetch the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/bast/singularity-conda/releases/download/0.5.0/conda.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eReads the Conda environment file\n\u003ca href=\"https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually\" rel=\"nofollow\"\u003eenvironment.yml\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eCreates the folder \u003ccode\u003eenvironment\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRun \u003ccode\u003emyscript.py\u003c/code\u003e inside the Conda environment defined by \u003ccode\u003eenvironment.yml\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./conda.sif python myscript.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpen Python shell inside the Conda environment defined by \u003ccode\u003eenvironment.yml\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./conda.sif python\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFirst time you run either of the above commands it will take a bit of time\nsince it needs to install the dependencies into the \u003ccode\u003eenvironment\u003c/code\u003e folder.\nHowever, subsequent runs will start basically immediately since the environment\nis then there.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-micromamba-and-environment-files\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#micromamba-and-environment-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMicromamba and environment files\u003c/h2\u003e\n\u003cp\u003eUnder the hood, it uses\n\u003ca href=\"https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html\" rel=\"nofollow\"\u003eMicromamba\u003c/a\u003e\ninstead of Conda in order to speed up installations but it should not really\nmatter for the functionality.\u003c/p\u003e\n\u003cp\u003eThe one place where I found it to matter is that you have to specify \u003ccode\u003echannels\u003c/code\u003e\nin the environment file.\u003c/p\u003e\n\u003cp\u003eInstead of this (example taken from \u003ca href=\"https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually\" rel=\"nofollow\"\u003econda\ndocumentation\u003c/a\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003estats\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003edependencies\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003enumpy\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003epandas\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou need to do this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003estats\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003echannels\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003edefaults\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003edependencies\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003enumpy\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003epandas\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBut I believe that specifying channels explicitly is anyway good practice.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-a-supercomputercluster\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#running-on-a-supercomputercluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on a supercomputer/cluster\u003c/h2\u003e\n\u003cp\u003eOn a cluster you might need to bind folders like here:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ env SINGULARITY_BIND=\"/cluster\" ./conda.sif python\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eI have used this wonderful guide as starting point and inspiration:\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 8, + "subscribers_count": 3, + "topics": [ + "apptainer", + "conda", + "containers", + "micromamba", + "singularity" + ], + "updated_at": 1706303525.0 }, { "data_format": 2, - "description": "Metagenomics pipeline", + "description": "A tool for generating bacterial genomes from metagenomes with nanopore long read sequencing", "filenames": [ - "containers/Singularity.multiQC", - "containers/Singularity.upset", - "containers/Singularity.star", - "containers/Singularity.kraken2", - "containers/Singularity.trinity" + "singularity/Singularity.quickmerge", + "singularity/Singularity.longread", + "singularity/Singularity.htsbox" ], - "full_name": "phelelani/nf-rnaSeqMetagen", + "full_name": "elimoss/lathe", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enf-rnaSeqMetagen\u003c/h1\u003e\u003ca id=\"user-content-nf-rnaseqmetagen\" class=\"anchor\" aria-label=\"Permalink: nf-rnaSeqMetagen\" href=\"#nf-rnaseqmetagen\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/phelelani/nf-rnaSeqMetagen/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/616be249ffd859dbf9fd5586802bc9a4f724f9efc70379541483202fc2c26407/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f7068656c656c616e692f6e662d726e615365714d65746167656e\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/phelelani/nf-rnaSeqMetagen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4c99abe6886b93af3fa47779836429f5c9f9dac96e3c220439f0e32c2ea1a2d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538462532302532302545322539372538462532302532302545322539372538422d79656c6c6f77\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B-yellow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/phelelani/nf-rnaSeqMetagen/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/52ad979cf4db2ee31bf2c55f8866d584acc5a987c01d6491ef5f0f5359c648ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7068656c656c616e692f6e662d726e615365714d65746167656e\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/phelelani/nf-rnaSeqMetagen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/phelelani/nf-rnaSeqMetagen/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e9de1c90bf7dc2175f524dc6316158ca51a7fe28106d21cbf2fd979086f96b46/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f7068656c656c616e692f6e662d726e615365714d65746167656e\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/phelelani/nf-rnaSeqMetagen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://bio.tools/nf-rnaseqmetagen\" rel=\"nofollow\"\u003ebiotools:nf-rnaseqmetagen\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003enf-rnaSeqMetagen\u003c/em\u003e is a \u003ca href=\"http://nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor detailed documentation: \u003ca href=\"https://phelelani.github.io/nf-rnaSeqMetagen/\" rel=\"nofollow\"\u003ehttps://phelelani.github.io/nf-rnaSeqMetagen/\u003c/a\u003e\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", - "stargazers_count": 7, - "subscribers_count": 1, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLathe\u003c/h1\u003e\u003ca id=\"user-content-lathe\" class=\"anchor\" aria-label=\"Permalink: Lathe\" href=\"#lathe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA tool for generating bacterial genomes from metagenomes with Nanopore long read sequencing\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, install \u003ca href=\"https://conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda3\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen install snakemake. This can be done with the following.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install snakemake\nsnakemake --version #please ensure this is \u0026gt;=5.4.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, clone this github directory to some location where it can be stored permanently. Remember to keep it updated with \u003ccode\u003egit pull\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/elimoss/lathe.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstructions to enable cluster execution with SLURM can be found at \u003ca href=\"https://github.com/bhattlab/slurm\"\u003ehttps://github.com/bhattlab/slurm\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAlter config.yaml to provide the following:\u003c/h3\u003e\u003ca id=\"user-content-alter-configyaml-to-provide-the-following\" class=\"anchor\" aria-label=\"Permalink: Alter config.yaml to provide the following:\" href=\"#alter-configyaml-to-provide-the-following\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esample_name\u003c/strong\u003e: Name of sample and output directory\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003efast5_dirs_list\u003c/strong\u003e: text file containing a list of absolute paths to run/fast5/* subfolders containing .fast5 files. A good way to generate this is with \u003ccode\u003efind -maxdepth 2 -mindepth 2 fast5_parent_dir \u0026gt; fodn.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eflowcell\u003c/strong\u003e: flowcell code, e.g. FLO-MIN106, passed to basecaller\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ekit\u003c/strong\u003e: kit code, e.g. SQK-LSK109, passed to basecaller\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003egenome_size\u003c/strong\u003e: Estimated genome size, e.g. 50m, passed to Canu.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esingularity\u003c/strong\u003e: location (including on the internet) of a singularity image to be used for the workflow. Don\u0027t change this.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eshort_reads\u003c/strong\u003e: location of short reads to be used for Pilon polishing, or empty quotes for long-read polishing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003euse_grid\u003c/strong\u003e: should Canu execute in distributed mode on a cluster?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003egrid_options\u003c/strong\u003e: Extra options for execution on a cluster\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ecanu_args\u003c/strong\u003e: Extra options for Canu\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eskip_circularization\u003c/strong\u003e: Should circularization be omitted from the workflow?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLathe uses the Flye assembler by default. For Canu, please specify \u0027canu\u0027 for the assembler parameter in the config. For cluster Canu execution, please note: if set to True, you will need to install Canu, e.g. \u003ccode\u003econda install -c conda-forge -c bioconda Canu=1.8\u003c/code\u003e as well as provide any additional required parameters for your job scheduler in the config.yaml file. Please see the example config file. When executing on a cluster, Canu will appear to fail, as the first process does not produce an assembly and instead spawns subsequent jobs on the cluster. Don\u0027t worry, just re-run Lathe when the assembly completes.\u003c/p\u003e\n\u003cp\u003eTo execute please run the following. Please note, you must substitute a parent directory containing all of your data and working directories for \u003ccode\u003e/labs/\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity --singularity-args \u0027--bind /labs/ --bind /scratch/ --bind /scg/ \u0027 -s /path/to/lathe/Snakefile \\\n--configfile path/to/modified_config.yaml --restart-times 0 --keep-going --latency-wait 30\n# --profile scg #enable cluster support, highly recommended. See above.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe outputs generated by this workflow will look like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esamplename/\n\u251c\u2500\u2500 0.basecall\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename.fq\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 nanoplots\n\u251c\u2500\u2500 1.assemble\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_merged.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.amb\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.ann\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.bwt\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.fai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.pac\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.paf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.sa\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 assemble_100m (if specified)\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 assemble_250m (if specified)\n\u251c\u2500\u2500 2.polish\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.corrected.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.bam.bai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.fai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.misassemblies.tsv\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 medaka (if specified)\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 pilon (if specified)\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 racon (if specified)\n\u251c\u2500\u2500 3.circularization\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 1.candidate_genomes\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 2.circularization\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 3.circular_sequences #circularized genomes\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.corrected.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.bam.bai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.fai\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 4.samplename_circularized.fasta.misassemblies.tsv\n\u2514\u2500\u2500 5.final\n \u00a0\u00a0 \u251c\u2500\u2500 samplename_final.fa\n \u00a0\u00a0 \u2514\u2500\u2500 samplename_final.fa.fai\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 8, + "subscribers_count": 0, "topics": [], - "updated_at": 1731508812.0 + "updated_at": 1632818985.0 }, { "data_format": 2, - "description": null, + "description": "various singularity recipes for FSL", "filenames": [ - "container/Singularity.intel_netcdf", - "container/Singularity.esm4", - "container/Singularity.intel_esm4" + "Singularity.6.0.2-Cuda8-xtract_viewer", + "Singularity.6.0.5.1", + "Singularity.6.0.0", + "Singularity.6.0.2", + "Singularity.6.0.4", + "Singularity.6.0.3", + "Singularity.5-Cuda8", + "Singularity.6.0.1", + "Singularity.5.0.10", + "Singularity.6.0.5", + "Singularity.5.0.11", + "Singularity.6.0.6.1", + "Singularity.5.0.9", + "Singularity.6.0.6", + "Singularity.6.0.2-Cuda8", + "Singularity.6.0.4-Cuda8" ], - "full_name": "NOAA-GFDL/ESM4", - "latest_release": "2021.03", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEarth System Model 4\u003c/h1\u003e\u003ca id=\"user-content-earth-system-model-4\" class=\"anchor\" aria-label=\"Permalink: Earth System Model 4\" href=\"#earth-system-model-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat Is Included\u003c/h2\u003e\u003ca id=\"user-content-what-is-included\" class=\"anchor\" aria-label=\"Permalink: What Is Included\" href=\"#what-is-included\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[src]((\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/tree/master/src\"\u003ehttps://github.com/NOAA-GFDL/ESM4/tree/master/src\u003c/a\u003e) source code for the ESM4 model (all code is in submodules)\u003c/li\u003e\n\u003cli\u003e[exec]((\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/tree/master/exec\"\u003ehttps://github.com/NOAA-GFDL/ESM4/tree/master/exec\u003c/a\u003e) Makefiles to compile the code\u003c/li\u003e\n\u003cli\u003e[run]((\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/tree/master/run\"\u003ehttps://github.com/NOAA-GFDL/ESM4/tree/master/run\u003c/a\u003e) Simple run script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning\u003c/h2\u003e\u003ca id=\"user-content-cloning\" class=\"anchor\" aria-label=\"Permalink: Cloning\" href=\"#cloning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo clone the ESM4 model please use the recursive option\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive git@github.com:NOAA-GFDL/ESM4.git \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive https://github.com/NOAA-GFDL/ESM4.git\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling\u003c/h2\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"container\"\u003econtainer folder\u003c/a\u003e provides example Dockerfiles and Signularity\ndefinition files to use to build AM4 containers using either GCC/GFORTAN or\nIntel oneAPI. There is a script that can be used to build the intel\nsingularity containers, and the first step of this script can be used with the\nother GFDL climate models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding from source\u003c/h3\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-label=\"Permalink: Building from source\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis model was originally compiled and run with the intel16 compiler.\nIt is recommended that you compile with an intel compiler.\u003c/p\u003e\n\u003cp\u003eCompiling assumes that you have an intel compiler, MPI (impi, mpich,\nopenmpi, etc), netcdf, and hdf5 in your LD_LIBRARY_PATH and LIBRARY_PATH.\nIt is also assumed that nf-config and nc-config are in your path.\nIf you work on a machine with modules, you may need to load these\npackages into your environment.\u003c/p\u003e\n\u003cp\u003eMakefiles have been included in the\n\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/tree/master/exec\"\u003eexec/\u003c/a\u003e folder.\nThere are several option for compiling, which can be found in the\n\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/blob/master/exec/templates/intel.mk\"\u003etemplate/intel.mk\u003c/a\u003e.\u003cbr\u003e\nYou may need to edit the template/intel.mk to update the compiler names\nor add any CPPDEF options specific for your system.\nThe most common compile with optimizations on and with openmp would be\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e\nmake OPENMP=on\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you would like to compile with \u003cem\u003e-O2\u003c/em\u003e instead of \u003cem\u003e-O3\u003c/em\u003e do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake REPRO=on OPENMP=on\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo compile with \u003cem\u003e-O0\u003c/em\u003e and debug flags do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake BLD_TYPE=DEBUG OPENMP=on\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCompiling with openMP is optional.\u003c/p\u003e\n\u003cp\u003eHere are examples of how to compile the model on various systems:\u003c/p\u003e\n\u003cp\u003egaea (NOAA RDHPCS cray system)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load intel\nmodule load cray-netcdf\nmodule load cray-hdf5\ngit clone --recursive git@github.com:NOAA-GFDL/ESM4.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ESM4/exec\nmake MKL_LIBS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enone\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e OPENMP=y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCompiling on orion (MSU)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load intel impi netcdf hdf5\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LIBRARY_PATH=\u003cspan class=\"pl-smi\"\u003e${LIBRARY_PATH}\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e${LD_LIBRARY_PATH}\u003c/span\u003e\ngit clone --recursive git@github.com:NOAA-GFDL/ESM4.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ESM4/exec\nmake OPENMP=on\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel running\u003c/h2\u003e\u003ca id=\"user-content-model-running\" class=\"anchor\" aria-label=\"Permalink: Model running\" href=\"#model-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA work directory needed for running the model can be obtained from\nftp://data1.gfdl.noaa.gov/users/ESM4/ESM4Documentation/GFDL-ESM4/inputData/ESM4_rundir.tar.gz\u003c/p\u003e\n\u003cp\u003eThe directory contains input.nml as the namelist, various input tables needed\nfor running the model, and model input files in a folder called INPUT/. There\nis also a directory named RESTART/ that should be empty at the beginning of\neach run.\u003c/p\u003e\n\u003cp\u003eThere is a skeleton of a run script named \u003ca href=\"https://github.com/NOAA-GFDL/ESM4/blob/master/run/ESM4_run.sh\"\u003erun/ESM4_run.sh\u003c/a\u003e. You must update this\nscript to run the model. Include a path to the work directory and the executable.\nYou should also update the program you need to run the model on your system. The\ndefault for this script is \u003ccode\u003esrun\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is provided\non an \u0027as is\u0027 basis and the user assumes responsibility for its use. DOC has\nrelinquished control of the information and no longer has responsibility to\nprotect the integrity, confidentiality, or availability of the information. Any\nclaims against the Department of Commerce stemming from the use of its GitHub\nproject will be governed by all applicable Federal law. Any reference to\nspecific commercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply their\nendorsement, recommendation or favoring by the Department of Commerce. The\nDepartment of Commerce seal and logo, or the seal and logo of a DOC bureau,\nshall not be used in any manner to imply endorsement of any commercial product\nor activity by DOC or the United States Government.\u003c/p\u003e\n", + "full_name": "MPIB/singularity-fsl", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a very unofficial repo for FSL singularity recipes used at the\n\u003ca href=\"https://www.mpib-berlin.mpg.de/\" rel=\"nofollow\"\u003eMPIB\u003c/a\u003e. A while ago we used to automatically\npush images to the amazing singularity hub. Since that project has been\nabandoned, we use this repo primarily as a recipe reference.\u003c/p\u003e\n\u003cp\u003eThe base container is Debian 10 and it should work fine with CUDA as well. For\nvery old installations we keep some cuda-8 recipes around, but they are not\nnecessary to run a current release of FSL.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild an image\u003c/h2\u003e\u003ca id=\"user-content-build-an-image\" class=\"anchor\" aria-label=\"Permalink: Build an image\" href=\"#build-an-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/MPIB/singularity-fsl\ncd singularity-fsl\nexport VERSION=6.0.6.1\nsudo singularity build fsl-$VERSION.sif Singularity.$VERSION\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun in image\u003c/h2\u003e\u003ca id=\"user-content-run-in-image\" class=\"anchor\" aria-label=\"Permalink: Run in image\" href=\"#run-in-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec fsl-$VERSION.sif fslmaths\nsingularity exec --nv fsl-$VERSION eddy_cuda9.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eolder, pre-built images from singularity-hub\u003c/h2\u003e\u003ca id=\"user-content-older-pre-built-images-from-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: older, pre-built images from singularity-hub\" href=\"#older-pre-built-images-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome older images are still available on the\n\u003ca href=\"https://singularityhub.github.io/singularityhub-docs/2021/going-read-only/\" rel=\"nofollow\"\u003eread-only\u003c/a\u003e\n\u003ca href=\"https://datasets.datalad.org/?dir=/shub/MPIB/singularity-fsl\" rel=\"nofollow\"\u003emirror provided by\ndatalad.org\u003c/a\u003e and\ncan be pulled directly.\u003c/p\u003e\n\u003cp\u003e(last updated in April 2021)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Download a (versioned) container\nsingularity pull shub://MPIB/singularity-fsl:6.0.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFSL\u003c/h2\u003e\u003ca id=\"user-content-fsl\" class=\"anchor\" aria-label=\"Permalink: FSL\" href=\"#fsl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProject Home: \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThese are containers primarily used at the MPI for Human Development.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCuda\u003c/h2\u003e\u003ca id=\"user-content-cuda\" class=\"anchor\" aria-label=\"Permalink: Cuda\" href=\"#cuda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStarting with Singularity 6.0.2 we include Nvidia CUDA through Debian backports\nrepositories. Make sure your Nvidia driver on the host \u003ca href=\"https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility\" rel=\"nofollow\"\u003esupports\nit\u003c/a\u003e\nand add the \u003ccode\u003e--nv\u003c/code\u003e flag with singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNote\u003c/h2\u003e\u003ca id=\"user-content-note\" class=\"anchor\" aria-label=\"Permalink: Note\" href=\"#note\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease be aware of FSL\u0027s strict license regarding non-commercial use.\u003c/p\u003e\n", "stargazers_count": 8, - "subscribers_count": 6, + "subscribers_count": 3, "topics": [ - "gfdl", - "ems", - "ems4", - "fms", - "climate", - "model", - "fortran" + "containers", + "science" ], - "updated_at": 1716299613.0 + "updated_at": 1675289044.0 }, { "data_format": 2, - "description": "Install methods for UPPMAX modules plus some helper scripts", + "description": "Docker and Singularity containers to predict bone age from radiographs (demo)", "filenames": [ - "singularity_info/IMAP/Singularity.IMAP", - "singularity_info/metaWRAP_1.3.2/Singularity.metaWRAP", - "singularity_info/bonito/Singularity.bonito", - "singularity_info/VESPA/Singularity.VESPA", - "singularity_info/ORFfinder/Singularity.ORFfinder", - "singularity_info/gapseq-RT-227932/Singularity.gapseq" + "Singularity" ], - "full_name": "UPPMAX/install-methods", + "full_name": "radinformatics/bone-age", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eModule Installation Methods\u003c/h1\u003e\u003ca id=\"user-content-module-installation-methods\" class=\"anchor\" aria-label=\"Permalink: Module Installation Methods\" href=\"#module-installation-methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a collection of READMEs generated during installation of software\napplications on Uppmax clusters. It is incomplete in terms of modules\navailable on Uppmax, and the individual READMEs may also be incomplete in terms\nof what was actually done to install the modules. We are publicising these in\nthe hopes that they can be helpful.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample workflow of a basic installation\u003c/h2\u003e\u003ca id=\"user-content-example-workflow-of-a-basic-installation\" class=\"anchor\" aria-label=\"Permalink: Example workflow of a basic installation\" href=\"#example-workflow-of-a-basic-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the install methods git repo (\u003ccode\u003egit clone https://github.com/UPPMAX/install-methods.git\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eAdd the repo to your \u003ccode\u003e$PATH\u003c/code\u003e and source the \u003ccode\u003euppmax_functions.sh\u003c/code\u003e file to get access to the functions.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003erun_makeroom\u003c/code\u003e with at least \u003ccode\u003e-t\u003c/code\u003e and \u003ccode\u003e-v\u003c/code\u003e, to generate a \u003ccode\u003e.sh\u003c/code\u003e (\u003ccode\u003emakeroom_toolname_version.sh\u003c/code\u003e) file that will create the directory structure needed in \u003ccode\u003e/sw\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the \u003ccode\u003e.sh\u003c/code\u003e file created in the directory you are standing to create the directory structure (\u003ccode\u003e/sw/category/toolname/\u003c/code\u003e and \u003ccode\u003e/sw/mf/common/category\u003c/code\u003e) and template files.\u003c/li\u003e\n\u003cli\u003ePut the source code for the program in \u003ccode\u003e/sw/category/toolname/version/src\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCompile and/or install the tool in \u003ccode\u003e/sw/category/toolname/version/cluster/bin\u003c/code\u003e etc.\u003c/li\u003e\n\u003cli\u003eEdit the readme file, explaining how you did the installation, in \u003ccode\u003e/sw/category/toolname/toolname-version_install-README.md\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit the template module file \u003ccode\u003e/sw/category/toolname/mf/version\u003c/code\u003e to do what you want when the module loads.\u003c/li\u003e\n\u003cli\u003eCopy the module file to the live location, \u003ccode\u003e/sw/mf/common/category/[section]/toolname\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003eall_mflink toolname version\u003c/code\u003e to create links for all clusters to the module file in \u003ccode\u003e/sw/mf/common/category/[section]/toolname\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003efixup /sw/category/toolname/version /sw/mf/common/category/[section]/toolname\u003c/code\u003e to make sure the ownership and permissions are ok.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScripts\u003c/h2\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-label=\"Permalink: Scripts\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egather-READMEs.sh\u003c/code\u003e - bash script to scan installation directories, looking for\nREADME files having a particular filename format that we create during\ninstallation of tools\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003efixup\u003c/code\u003e - bash script fixing up permissions and group membership within\ninstallation trees; our local installation group is \u003ccode\u003esw\u003c/code\u003e. With the \u003ccode\u003e-g\u003c/code\u003e option,\nthis script will \u003ccode\u003echmod g+s\u003c/code\u003e directories in the tree, too.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003euppmax_functions.sh\u003c/code\u003e - bash helper functions for SLURM job viewing and various\nmodule-related tasks, mostly to do with setting up mf files for loading\nmodules; the latter require appexpert privileges. Source these from \u003ccode\u003e.bashrc\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation directories\u003c/h2\u003e\u003ca id=\"user-content-installation-directories\" class=\"anchor\" aria-label=\"Permalink: Installation directories\" href=\"#installation-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe directories contain software installations in major subject areas.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eapps/\u003c/h3\u003e\u003ca id=\"user-content-apps\" class=\"anchor\" aria-label=\"Permalink: apps/\" href=\"#apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGeneral applications.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ebioinfo/\u003c/h3\u003e\u003ca id=\"user-content-bioinfo\" class=\"anchor\" aria-label=\"Permalink: bioinfo/\" href=\"#bioinfo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioinformatics applications.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003elibs/\u003c/h3\u003e\u003ca id=\"user-content-libs\" class=\"anchor\" aria-label=\"Permalink: libs/\" href=\"#libs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLibraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecomp/\u003c/h3\u003e\u003ca id=\"user-content-comp\" class=\"anchor\" aria-label=\"Permalink: comp/\" href=\"#comp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCompilers, interpreters, build tools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDatabase directories\u003c/h2\u003e\u003ca id=\"user-content-database-directories\" class=\"anchor\" aria-label=\"Permalink: Database directories\" href=\"#database-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese directories cover installations of databases updated either manually, or via update scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edata/\u003c/h3\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-label=\"Permalink: data/\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation instructions for databases under \u003ccode\u003e/sw/data/\u003c/code\u003e. Database\ndirectories containing \u003ccode\u003e*-install-README.md\u003c/code\u003e files are updated manually.\nDatabase directories containing \u003ccode\u003e*-db-README.md\u003c/code\u003e files and scripts (currently,\n\u003ccode\u003eKraken\u003c/code\u003e, \u003ccode\u003ediamond_databases\u003c/code\u003e and \u003ccode\u003eRTG\u003c/code\u003e) are updated monthly via crontab entries.\u003c/p\u003e\n\u003cp\u003eBlast database updates are included here, and involve multiple scripts, crontab\nentries and a test directory. These are updated monthly via crontab entries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edata_other/\u003c/h3\u003e\u003ca id=\"user-content-data_other\" class=\"anchor\" aria-label=\"Permalink: data_other/\" href=\"#data_other\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation instructions for databases under other locations, currently just\n\u003ccode\u003eBUSCO\u003c/code\u003e lineage sets, which are kept in the module installation directory.\nThese are updated monthly via crontab entries.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBone-Age Demo\u003c/h1\u003e\u003ca id=\"user-content-bone-age-demo\" class=\"anchor\" aria-label=\"Permalink: Bone-Age Demo\" href=\"#bone-age-demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eunder development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis repository builds a Docker image and a \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image, each that will run the bone age demo to predict bone age from a radiograph. The user has the option to run the prediction algorithm from the command line with an image file input, or to run a web server to see an interactive demo.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe predict_image.py script is a light wrapper around the model and includes the functions that are needed for such a demo. The user would upload a image which would then be processed with the given model on the back-end. The results would then be displayed for the user.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIf you are working on your local machine, you can use either Docker or Singularity. If you are running in a shared cluster (HPC) environment where you do not have root permissions, Singularity is your best option. Instructions are included for both.\u003c/p\u003e\n\u003cp\u003ePackages that need to be installed are included in \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e and installed into the container via the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker\u003c/h1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e. The container is provided on \u003ca href=\"https://hub.docker.com/r/vanessa/boneage/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded from there when you run it, and this is recommended because building it takes a while to compile OpenCV.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eI want to build it!\u003c/h3\u003e\u003ca id=\"user-content-i-want-to-build-it\" class=\"anchor\" aria-label=\"Permalink: I want to build it!\" href=\"#i-want-to-build-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to look at or make changes to the code, it\u0027s recommended to clone the repo and build the container locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone http://www.github.com/radinformatics/bone-age\ncd bone-age\ndocker build -t vanessa/boneage .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe docker daemon will first look for an image called \u003ccode\u003evanessa/boneage\u003c/code\u003e locally, and if not found, will then try Dockerhub, and download it from there. If for any reason you want to remove your image, just do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker rmi vanessa/boneage\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning commands\u003c/h2\u003e\u003ca id=\"user-content-running-commands\" class=\"anchor\" aria-label=\"Permalink: Running commands\" href=\"#running-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entry to the container is done simply by using it as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/boneage --help\nusage: cli.py [-h] [--image IMAGE] [--output OUTPUT] [--gender {M,F}]\n\t [--width WIDTH] [--height HEIGHT] [--debug]\n\nPredict bone age of an image.\n\noptional arguments:\n -h, --help show this help message and exit\n --image IMAGE Path to single bone image.\n --output OUTPUT Path to output file to write results.\n --gender {M,F} the gender of the individual (M or F), default is M (male)\n --width WIDTH warped width to resize the image in pixels (default 256)\n --height HEIGHT warped height to resize the image in pixels (default 256)\n --debug use verbose logging to debug.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Prediction With Example\u003c/h3\u003e\u003ca id=\"user-content-run-prediction-with-example\" class=\"anchor\" aria-label=\"Permalink: Run Prediction With Example\" href=\"#run-prediction-with-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the bone-age demo non interactively to get a prediction, you can run it without any arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run vanessa/boneage\n\n*** Starting Bone Age Prediction ****\nNo image selected, will use provided example...\nBuilding model, please wait.\nPredicted Age : 14 Months\nWeighted Prediction : 11.832177 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe command above is saying \"map the folder \u003ccode\u003e$PWD/example_images\u003c/code\u003e (where my 4.png is located) to the \u003ccode\u003e/data\u003c/code\u003e folder in the container. Then, tell the script in the container to use the image located at \u003ccode\u003e/data/4.png\u003c/code\u003e. If you want to see debug output (for more details about running) you can add \u003ccode\u003e--debug\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run vanessa/boneage --debug\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nNo image selected, will use provided example...\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /code/example_images/5.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.610813 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Prediction With Your Own Image\u003c/h3\u003e\u003ca id=\"user-content-run-prediction-with-your-own-image\" class=\"anchor\" aria-label=\"Permalink: Run Prediction With Your Own Image\" href=\"#run-prediction-with-your-own-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to provide your own image, you need to bind it to the /data directory in the folder, and map a path to it. Don\u0027t forget to specify the gender - the default is male, and you may want to change that:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png\n\n*** Starting Bone Age Prediction ****\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can of course add debug to verify that the default is male, and we are using our mapped image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png --debug\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can specify a different gender, and the prediction changes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png --gender F --debug\nEnvironment message level found to be DEBUG\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: False\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 16 Months\nWeighted Prediction : 16.000000 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSave output to file\u003c/h3\u003e\u003ca id=\"user-content-save-output-to-file\" class=\"anchor\" aria-label=\"Permalink: Save output to file\" href=\"#save-output-to-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you specify the \u003ccode\u003e--output\u003c/code\u003e argument, you can save the result as a json to file. Again, we will need to specify a file in a folder mapped to our local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --output /data/demo.json --debug\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nNo image selected, will use provided example...\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /code/example_images/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\nDEBUG:bone-age:Result written to /data/demo.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we can look at the data - remember the folder that was mapped on our local machine is \u003ccode\u003e$PWD/example_images\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cat $PWD/example_images/demo.json\n {\n \"gender\": \"M\",\n \"image\": \"/code/example_images/4.png\",\n \"predicted_age\": 8,\n \"predicted_weight\": 8.64113067092668\n }\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe function inside the container to generate this result could be scaled by either providing an input argument for the user to specify an input file (with image paths and genders) and a single output file to write to, or running the command many times to write separate output files, or having a \u003ccode\u003e--silent\u003c/code\u003e option to suppress all output (except for the result) that could be piped (appended) into a single output file. All of these could be implemented, it really depends on the desired outcome. For the current purpose (plugging the container into a web server for a demo) the above that produces a single file, or multiple single files, is a reasonable approach.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I shell into the container?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container\" class=\"anchor\" aria-label=\"Permalink: How do I shell into the container?\" href=\"#how-do-i-shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, running the container uses the \u003ccode\u003eENTRYPOINT\u003c/code\u003e, meaning it is used as an executable and you do not enter the container. In the case that you want a container-based environment that is installed with the dependencies of boneage, or if you want to interactively work with the code, you may want to shell into the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint /bin/bash vanessa/boneage\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eKeep in mind that once you exit from this run, the container image is not saved, including your changes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBootstrapping means using something to build from, or not starting from nothing. In this case, we are going to use a build file that bootstraps a Docker image of boneage (yes, the same one discussed above). This build file is called \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e, and for more details about this you can \u003ca href=\"http://singularity.lbl.gov/docs-docker\" rel=\"nofollow\"\u003eread here\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 boneage.img\nsudo singularity bootstrap boneage.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe commands are equivalent as above, except we can use the container as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./boneage.img --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand to make a drive, we use \u003ccode\u003e--bind\u003c/code\u003e instead\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run --bind $PWD/example_images:/data boneage.img --debug\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I shell into the container?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container-1\" class=\"anchor\" aria-label=\"Permalink: How do I shell into the container?\" href=\"#how-do-i-shell-into-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity has an easy, intuitive way to shell inside!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell boneage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInteractive Web Interface\u003c/h1\u003e\u003ca id=\"user-content-interactive-web-interface\" class=\"anchor\" aria-label=\"Permalink: Interactive Web Interface\" href=\"#interactive-web-interface\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003etodo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUltimately, we will build this demo and serve on \u003ca href=\"http://www.singularity-hub.org\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e and then have an application that takes inputs / outputs for the container, and runs on demand.\u003c/p\u003e\n", "stargazers_count": 8, - "subscribers_count": 7, + "subscribers_count": 5, "topics": [], - "updated_at": 1727361292.0 + "updated_at": 1701326252.0 }, { "data_format": 2, - "description": null, + "description": "Applications of Pseudo-3D Network", "filenames": [ "Singularity" ], - "full_name": "UCLBrain/MSLS", + "full_name": "YeTianJHU/Pesudo-3D-Applications", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/UCLBrain/MSLS/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/246f72d8fecf3c0be868bbe7241aaa82a61e04b2a26eae992b75e6add7a61f0c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f55434c427261696e2f4d534c53\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/UCLBrain/MSLS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/network\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e2f655dcd116357359604d61c9411b560337ee306a880d75f2cb852807047c9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f55434c427261696e2f4d534c53\" alt=\"GitHub forks\" data-canonical-src=\"https://img.shields.io/github/forks/UCLBrain/MSLS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2e6a5e6e35e5ab336a877e2fb3e253062defe3baebdfe499c22fd2046386213b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f55434c427261696e2f4d534c53\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/UCLBrain/MSLS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e49664e0e21231283f8b8db659ac06b74f4f13d6d55f2cc2c0e6e0c3c4b0169/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f55434c427261696e2f4d534c53\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/UCLBrain/MSLS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMulti-Label Multi/Single-Class Image Segmentation\u003c/h1\u003e\u003ca id=\"user-content-multi-label-multisingle-class-image-segmentation\" class=\"anchor\" aria-label=\"Permalink: Multi-Label Multi/Single-Class Image Segmentation\" href=\"#multi-label-multisingle-class-image-segmentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/diag.png\"\u003e\u003cimg height=\"510\" src=\"images/diag.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePublication\u003c/h1\u003e\u003ca id=\"user-content-publication\" class=\"anchor\" aria-label=\"Permalink: Publication\" href=\"#publication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLe Zhang, Ryutaro Tanno, Kevin Bronik, Chen Jin, Parashkev Nachev, Frederik Barkhof, Olga Ciccarelli, and Daniel C. Alexander, Learning to Segment When Experts Disagree, International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI). Springer, Cham, 2020.\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/Miccai_2020_abs.jpg\"\u003e\u003cimg align=\"center\" height=\"1000\" src=\"images/Miccai_2020_abs.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\n \n \u003cp\u003eClick here to see full pdf file: \u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/MICCAI_2020.pdf\"\u003eLink to PDF\u003c/a\u003e\u003c/p\u003e\n \n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the GUI Program!\u003c/h1\u003e\u003ca id=\"user-content-running-the-gui-program\" class=\"anchor\" aria-label=\"Permalink: Running the GUI Program!\" href=\"#running-the-gui-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, user needs to install Anaconda \u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003ehttps://www.anaconda.com/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda env create -f conda_environment_Training_Inference.yml \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efinally\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python Training_Inference_GUI.py \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/GUI.jpg\"\u003e\u003cimg height=\"510\" src=\"images/GUI.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the Program from the command line!\u003c/h1\u003e\u003ca id=\"user-content-running-the-program-from-the-command-line\" class=\"anchor\" aria-label=\"Permalink: Running the Program from the command line!\" href=\"#running-the-program-from-the-command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen for training\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Training_without_GUI.py [or annotation_network_Training_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efor testing\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Inference_without_GUI.py [or annotation_network_Inference_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTesting the Program!\u003c/h1\u003e\u003ca id=\"user-content-testing-the-program\" class=\"anchor\" aria-label=\"Permalink: Testing the Program!\" href=\"#testing-the-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExamples of Training and Testing subjects can be found in: \u003ca href=\"https://github.com/UCLBrain/MSLS/tree/master/examples\"\u003ehttps://github.com/UCLBrain/MSLS/tree/master/examples\u003c/a\u003e (which will allow users to quickly and easily train and test the program)\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/bin_seg_ex.jpg\"\u003e\u003cimg height=\"510\" src=\"images/bin_seg_ex.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e..................................................................................................................................................................\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/multi_seg_ex.jpg\"\u003e\u003cimg height=\"510\" src=\"images/multi_seg_ex.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePesudo-3D-Applications\u003c/h1\u003e\u003ca id=\"user-content-pesudo-3d-applications\" class=\"anchor\" aria-label=\"Permalink: Pesudo-3D-Applications\" href=\"#pesudo-3d-applications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeveral applications of Pseudo-3D Network.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknowledgement\u003c/h3\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe P3D model (with weights pre-trained on kinetics) is revised from \u003ca href=\"https://github.com/qijiezhao/pseudo-3d-pytorch\"\u003eP3D-Pytorch\u003c/a\u003e by qijiezhao.\u003c/li\u003e\n\u003cli\u003eThe I3D model is revised from \u003ca href=\"https://github.com/rimchang/kinetics-i3d-Pytorch\"\u003ekinetics-i3d-Pytorch\u003c/a\u003e by rimchang.\u003c/li\u003e\n\u003cli\u003eThe C3D model is revised from \u003ca href=\"https://github.com/DavideA/c3d-pytorch\"\u003ec3d-pytorch\u003c/a\u003e by DavideA.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 8, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [], - "updated_at": 1673976172.0 + "updated_at": 1655689650.0 }, { "data_format": 2, @@ -36433,279 +36734,75 @@ var data = "libertem/Singularity.libertem-0.11.0", "libertem/Singularity.libertem-0.5.1", "nanconvert/Singularity.nanconvert", - "jmrui/Singularity.jmrui_5.2", - "cistem/Singularity.cisTEM-1.0.0-beta", - "dtsa2/Singularity.dtsa2", - "openmodelica/Singularity.openmodelica_1.14.2-cuda-10.1" - ], - "full_name": "Characterisation-Virtual-Laboratory/CharacterisationVL-Software", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCharacterisationVL-Software\u003c/h1\u003e\u003ca id=\"user-content-characterisationvl-software\" class=\"anchor\" aria-label=\"Permalink: CharacterisationVL-Software\" href=\"#characterisationvl-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe purpose of this repository is for storing definition files to submit to our internal application build and deployment system.\u003c/p\u003e\n\u003cp\u003eIf you are new to Singularity containers, please refer to \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/\u003c/a\u003e or a newer version of this documentation.\u003c/p\u003e\n\u003cp\u003eEach software package is located in its own folder. The files are tagged with the software name and version number or date of build. Please read below for the naming convention.\u003c/p\u003e\n\u003cp\u003eTo add software to the repository you will need to create a new branch. The new branch is the name of the software product. By convention, the new branch will be checked and merged into the master branch and then deleted.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSteps to add a software package\u003c/h2\u003e\u003ca id=\"user-content-steps-to-add-a-software-package\" class=\"anchor\" aria-label=\"Permalink: Steps to add a software package\" href=\"#steps-to-add-a-software-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003cli\u003eCreate a branch\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ git branch \u0026lt;software name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eMake a subdirectory for the software product.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir \u0026lt;software name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eAdd all the necessary files.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity definition file or installation script\u003c/li\u003e\n\u003cli\u003eReadme file including install and testing notes\u003c/li\u003e\n\u003cli\u003eDesktop files for adding to menus with necessary tags\u003c/li\u003e\n\u003cli\u003eFor full details, \u003ca href=\"template/README.md\"\u003eplease refer to the \u0027template\u0027 folder in this repository.\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eCommit all changes, including a helpful message\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ git commit -m \"\u0026lt;software name\u0026gt; added as requested in support ticket\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003ePush to the remote repository. i.e. this one.\u003c/li\u003e\n\u003cli\u003eSubmit merge request\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNaming your Singularity definition file, Singularity Hub and Licensing\u003c/h2\u003e\u003ca id=\"user-content-naming-your-singularity-definition-file-singularity-hub-and-licensing\" class=\"anchor\" aria-label=\"Permalink: Naming your Singularity definition file, Singularity Hub and Licensing\" href=\"#naming-your-singularity-definition-file-singularity-hub-and-licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor all Singularity recipes where the software licensing permits redistribution, please use this naming convention:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Singularity.applicationName_version\n Singularity.applicationName_version-cuda-cudaVersion\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere is a Github action between this repository and a container builder VM hosted on \u003ca href=\"https://ardc.edu.au/services/nectar-research-cloud/\" rel=\"nofollow\"\u003eNectar\u003c/a\u003e. When a commit is merged into the master branch, this VM will build the container.\u003c/p\u003e\n\u003cp\u003eIf successfully built, the container will be added to a repository that is downloadable from CVL nodes.\u003c/p\u003e\n\u003cp\u003eAn example on how to handle this situation is the recipe for CCP-EM.\nThe \u003ca href=\"ccp-em/README.md\"\u003eREADME.md\u003c/a\u003e contains a section on Prerequisites. This section lists the required files to build the container. The license must be accepted by the end user to obtain them.\u003c/p\u003e\n\u003cp\u003ePrerequisite files should not be committed to this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUbuntu Base Images\u003c/h2\u003e\u003ca id=\"user-content-ubuntu-base-images\" class=\"anchor\" aria-label=\"Permalink: Ubuntu Base Images\" href=\"#ubuntu-base-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe folder \u0027ubuntu-base-image\u0027 contains recipes for pre built base containers. These can be used as a starting point to aid/speed up the development of your container recipe.\u003c/p\u003e\n\u003cp\u003eThe current versions are built using Ubuntu 18.04 LTS, plus Cuda 9 or Cuda 10.1 if required.\u003c/p\u003e\n\u003cp\u003eFor example: from the Graphviz Singularity.graphviz-2.40.1 recipe\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: shub\nFrom: Characterisation-Virtual-Laboratory/CharacterisationVL-Software:1804\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThese two lines, will tell Singularity to use the \u0027shub\u0027 bootstrap to obtain the \u00271804\u0027 ubuntu-base-image container from Singularity Hub.\u003c/p\u003e\n\u003cp\u003eFrom here you just need to add the requirements to build a container for your required piece of software. Please see \u003ca href=\"graphviz/Singularity.graphviz-2.40.1\"\u003eSingularity.graphviz-2.40.1\u003c/a\u003e\nfor the full recipe.\u003c/p\u003e\n\u003cp\u003eThe current ubuntu-base-images include Python, VirtualGL and TurboVNC plus Cuda if indicated in the name.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning GUI applications on a non-GPU node\u003c/h2\u003e\u003ca id=\"user-content-running-gui-applications-on-a-non-gpu-node\" class=\"anchor\" aria-label=\"Permalink: Running GUI applications on a non-GPU node\" href=\"#running-gui-applications-on-a-non-gpu-node\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe applications in the Singularity container should run without the need for a dedicated GPU.\u003c/p\u003e\n\u003cp\u003eHowever, an X server needs to be running for this to work. On nodes with GPU, X Server is started with NVIDIA driver, and on non-GPU nodes, the X Server is started with MESA library.\u003c/p\u003e\n\u003cp\u003eX Server can be started during boot (for example, using \u003ccode\u003esystemctl set-default graphical.target\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eMake sure that VirtualGL package is installed in the container. The code below will download and install VirtualGL.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://swift.rc.nectar.org.au/v1/AUTH_810/CVL-Singularity-External-Files/virtualgl_2.6.2_amd64.deb\n\ndpkg -i virtualgl_2.6.2_amd64.deb\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 8, - "subscribers_count": 4, - "topics": [], - "updated_at": 1696247668.0 - }, - { - "data_format": 2, - "description": "NYU Langone Genome PACT (Genome Profiling of Actionable Cancer Targets) targeted exome sequencing analysis pipeline.", - "filenames": [ - "containers/sambamba-0.6.8/Singularity.sambamba-0.6.8", - "containers/cnvkit-0.9.5/Singularity.cnvkit-0.9.5", - "containers/variant-calling-0.0.2/Singularity.variant-calling-0.0.2", - "containers/bwa-0.7.17/Singularity.bwa-0.7.17", - "containers/cnvkit-0.9.0/Singularity.cnvkit-0.9.0", - "containers/pindel-0.2.5b9/Singularity.pindel-0.2.5b9", - "containers/varscan-2.4.3/Singularity.varscan-2.4.3", - "containers/variant-calling-0.0.1/Singularity.variant-calling-0.0.1", - "containers/strelka-2.9.10/Singularity.strelka-2.9.10", - "containers/lofreq-2.1.3/Singularity.lofreq-2.1.3", - "containers/sambamba-0.6.6/Singularity.sambamba-0.6.6", - "containers/sambamba-0.6.6/Singularity.sambamba-0.6.6.old", - "containers/base/Singularity.base", - "containers/R-3.5.1/Singularity.R-3.5.1", - "containers/msisensor-0.2/Singularity.msisensor-0.2", - "containers/multiqc-1.5/Singularity.multiqc-1.5", - "containers/fastqc-0.11.7/Singularity.fastqc-0.11.7", - "containers/bwa-0.7.17-sambamba-0.6.8/Singularity.bwa-0.7.17-sambamba-0.6.8", - "containers/R-3.2.3/Singularity.R-3.2.3", - "containers/python-2.7/Singularity.python-2.7", - "containers/R-3.4.3/Singularity.R-3.4.3", - "containers/IGV-2.4.10/Singularity.IGV-2.4.10", - "containers/samtools-1.7/Singularity.samtools-1.7", - "containers/annovar-150617/Singularity.annovar-150617", - "containers/bedtools-2.26.0/Singularity.bedtools-2.26.0", - "containers/htslib-1.7/Singularity.htslib-1.7", - "containers/python-3.6/Singularity.python-3.6", - "containers/bedtools-2.27.1/Singularity.bedtools-2.27.1", - "containers/deconstructSigs-1.8.0/Singularity.deconstructSigs-1.8.0", - "containers/manta-1.5.0/Singularity.manta-1.5.0", - "containers/trimmomatic-0.36/Singularity.trimmomatic-0.36", - "containers/bcftools-1.3/Singularity.bcftools-1.3", - "containers/reporting-3.4.3/Singularity.reporting-3.4.3", - "containers/multiqc-1.4/Singularity.multiqc-1.4", - "containers/cnv_facets-0.14.0/Singularity.cnv_facets-0.14.0", - "containers/delly2-0.7.7/Singularity.delly2-0.7.7" - ], - "full_name": "NYU-Molecular-Pathology/LG-PACT", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLG-PACT\u003c/h1\u003e\u003ca id=\"user-content-lg-pact\" class=\"anchor\" aria-label=\"Permalink: LG-PACT\" href=\"#lg-pact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTarget exome analysis for 607 gene panel (NGS607)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e Details listed here may change during development\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline is designed to run targeted exome analysis on Illumina Next-Gen sequencing genomic data, in support of the NGS607 cancer diagnostic panel for NYU\u0027s Molecular Pathology Department.\u003c/p\u003e\n\u003cp\u003eThis pipeline starts from paired-end fastq data (\u003ccode\u003e.fastq.gz\u003c/code\u003e), and is meant to accompany the output from the Illumina demultiplexing pipeline listed here: \u003ca href=\"https://github.com/NYU-Molecular-Pathology/demux-nf\"\u003ehttps://github.com/NYU-Molecular-Pathology/demux-nf\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe LG-PACT analysis workflow includes read trimming, QC, alignment, variant calling, annotation, and reporting, along with many other steps.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome key pipeline components included in this repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ebin\u003c/code\u003e: directory of custom scripts used throughout the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003econtainers\u003c/code\u003e: directory of container recipes (Docker, Singularity) for use with the pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eexample\u003c/code\u003e: directory of example samplesheets, etc., to show the format used with this pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003etargets\u003c/code\u003e: directory of target region .bed files included with the pipeline for typical analyses\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eMakefile\u003c/code\u003e: A Makefile with recipes for configuring, starting, and managing the pipeline. This is meant to be the main interface between the end-user and the pipeline. The Makefile should be reviewed as-needed to familiarize yourself with the methods and configurations that are meant to be used for running and managing the pipeline.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emain.nf\u003c/code\u003e: the main Nextflow pipeline script\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enextflow.config\u003c/code\u003e: configuration file for the main Nextflow pipeline script\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e.config.json\u003c/code\u003e: a template for the required \u003ccode\u003econfig.json\u003c/code\u003e file used in the pipeline, shows the default pipeline settings that are meant to be easily modified by the end-user and used within the pipeline for data processing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eannovar_db.nf\u003c/code\u003e, \u003ccode\u003ecnv-pool.nf\u003c/code\u003e, \u003ccode\u003ehapmap-pool.nf\u003c/code\u003e, \u003ccode\u003eref.nf\u003c/code\u003e: workflows for generating and downloading extra reference files used in the main pipeline.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eref\u003c/code\u003e: default location for the storage of reference files (not used on NYU Big Purple HPC)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline Items\u003c/h2\u003e\u003ca id=\"user-content-pipeline-items\" class=\"anchor\" aria-label=\"Permalink: Pipeline Items\" href=\"#pipeline-items\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome key components that are created during setup, configuration, and execution of the pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esamples.analysis.tsv\u003c/code\u003e: the main samplesheet definig input items for the pipeline (described below)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003econfig.json\u003c/code\u003e: configuration file used for pipeline settings (see \u003ccode\u003e.config.json\u003c/code\u003e template for example)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eoutput\u003c/code\u003e: analysis output files published by the Nextflow pipeline\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ework\u003c/code\u003e: Nextflow temporary directories for execution of pipeline tasks\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003etrace.txt\u003c/code\u003e, \u003ccode\u003enextflow.html\u003c/code\u003e, \u003ccode\u003etimeline.html\u003c/code\u003e, \u003ccode\u003e.nextflow.log\u003c/code\u003e: Nextflow execution logs and reports\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003elogs\u003c/code\u003e: directory for pipeline execution logs\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSetup\u003c/h1\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository should first be cloned from GitHub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NYU-Molecular-Pathology/LG-PACT.git\ncd LG-PACT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eOnce a copy of the repo is made, it can be used to \"deploy\" new copies of the workflow in a pre-configured state\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReference Data\u003c/h2\u003e\u003ca id=\"user-content-reference-data\" class=\"anchor\" aria-label=\"Permalink: Reference Data\" href=\"#reference-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNextflow pipelines have been included for downloading required reference data, including ANNOVAR reference databases. You can run them with the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake setup\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHapMap Pool .bam\u003c/h3\u003e\u003ca id=\"user-content-hapmap-pool-bam\" class=\"anchor\" aria-label=\"Permalink: HapMap Pool .bam\" href=\"#hapmap-pool-bam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA negative control HapMap pool .bam file can be prepared using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake hapmap-pool\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRequires \u003ccode\u003esamples.hapmap.tsv\u003c/code\u003e file specifying the .bam files to be combined (example included at \u003ccode\u003eexample/samples.hapmap.tsv\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis file is typically built from multiple HapMap samples previously aligned by this pipeline. For demonstration purposes, you can provide any .bam and .bai files.\u003c/p\u003e\n\u003cp\u003eThe HapMap Pool files to be used in the pipeline should be set under the \u003ccode\u003eHapMapBam\u003c/code\u003e and \u003ccode\u003eHapMapBai\u003c/code\u003e keys of \u003ccode\u003econfig.json\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCNV Pool\u003c/h3\u003e\u003ca id=\"user-content-cnv-pool\" class=\"anchor\" aria-label=\"Permalink: CNV Pool\" href=\"#cnv-pool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA control normal sample .cnn file for CNV calling can be prepared using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake cnv-pool\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRequires \u003ccode\u003esamples.cnv.tsv\u003c/code\u003e file specifying the .bam files to be used (example included at \u003ccode\u003eexample/samples.cnv.tsv\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis file is typically built from .bam files of specially chosen normal tissue sequencing samples previously aligned by this pipeline. For demonstration purposes, you can create the .cnn file from any desired .bam file. Note that the targets .bed file used to create the .cnn file must match the targets used in the rest of the pipeline.\u003c/p\u003e\n\u003cp\u003eThe .cnn file to be used in the pipeline should be set under the \u003ccode\u003eCNVPool\u003c/code\u003e key in \u003ccode\u003econfig.json\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainers\u003c/h2\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003econtainers\u003c/code\u003e directory contains instructions and recipes for building the Docker and Singularity containers used in the pipeline.\u003c/p\u003e\n\u003cp\u003eDocker is typically used for local container development, while Singularity containers are used on the NYU Big Purple HPC cluster. The current pipeline configuration for Big Purple uses \u003ccode\u003e.simg\u003c/code\u003e files stored in a common location on the file system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline is designed to start from demultiplexed paired end \u003ccode\u003e.fastq.gz\u003c/code\u003e files, with sample ID, tumor ID, and matched normal ID associations defined for each set of R1 and R2 .fastq file using a file \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e (example included at \u003ccode\u003eexample/samples.analysis.tsv\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeployment\u003c/h2\u003e\u003ca id=\"user-content-deployment\" class=\"anchor\" aria-label=\"Permalink: Deployment\" href=\"#deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiset way to use the pipeline is to \"deploy\" a new instance of it based on output from the demultiplexing pipeline \u003ca href=\"https://github.com/NYU-Molecular-Pathology/demux-nf\"\u003e\u003ccode\u003edemux-nf\u003c/code\u003e\u003c/a\u003e. This will automatically propagate configurations and information from the demultiplexing output.\u003c/p\u003e\n\u003cp\u003eThe pipeline can also deploy a new, pre-configured copy of itself using the included \u003ccode\u003edeploy\u003c/code\u003e recipe:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake deploy PRODDIR=/path/to/NGS607_analyses RUNID=Name_for_analysis FASTQDIR=/path/to/fastq_files\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eAn optional argument \u003ccode\u003eDEMUX_SAMPLESHEET\u003c/code\u003e can be used to provide a specially formatted demultiplexing samplesheet to be used for extracting extra sample information (example included at \u003ccode\u003eexample/demux-SampleSheet.csv\u003c/code\u003e; note the extra columns labeling tumor-normal pair IDs, used later).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate Config\u003c/h2\u003e\u003ca id=\"user-content-create-config\" class=\"anchor\" aria-label=\"Permalink: Create Config\" href=\"#create-config\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA file \u003ccode\u003econfig.json\u003c/code\u003e is required to hold settings for the pipeline. It should be created using the built-in methods:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake config RUNID=my_run_ID FASTQDIR=/path/to/fastqs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake config RUNID=my_run_ID FASTQDIRS=\u0027/path/to/fastqs1 /path/to/fastqs2\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecp .config.json config.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then simply edit the new \u003ccode\u003econfig.json\u003c/code\u003e and update the items to match your pipeline settings.\u003c/p\u003e\n\u003cp\u003eOnce created, the \u003ccode\u003econfig.json\u003c/code\u003e file can be updated manually as needed. The template and default values can be viewed in the included \u003ccode\u003e.config.json\u003c/code\u003e file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econfig.json\u003c/code\u003e should be generated automatically if you used \u003ccode\u003emake deploy\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate Samplesheet\u003c/h2\u003e\u003ca id=\"user-content-create-samplesheet\" class=\"anchor\" aria-label=\"Permalink: Create Samplesheet\" href=\"#create-samplesheet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA samplesheet file \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e is required in order to define the input samples and their associated .fastq files (example included at \u003ccode\u003eexample/samples.analysis.tsv\u003c/code\u003e). Create a samplesheet, based on the config file, using the built-in methods:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake samplesheet\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eNote that this uses the values previously saved in \u003ccode\u003econfig.json\u003c/code\u003e to create the samplesheet\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSample Pairs (Optional)\u003c/h3\u003e\u003ca id=\"user-content-sample-pairs-optional\" class=\"anchor\" aria-label=\"Permalink: Sample Pairs (Optional)\" href=\"#sample-pairs-optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe LG-PACT pipeline has special processing for tumor-normal pairs. These pairs should be defined in the \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e file, by listing the matched Normal sample for each applicable sample.\u003c/p\u003e\n\u003cp\u003eIn order to update \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e automatically with these sample pairs, an extra samplesheet can be provided with the tumor-normal pairs.\u003c/p\u003e\n\u003cp\u003eCreate a \u003ccode\u003esamples.tumor.normal.csv\u003c/code\u003e samplesheet (example included at \u003ccode\u003eexample/samples.tumor.normal.csv\u003c/code\u003e) with the tumor-normal groupings for your samples, and update the original samplesheet with it by running the following script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython update-samplesheets.py --tumor-normal-sheet samples.tumor.normal.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf a demultiplexing samplesheet with extra tumor-normal pairs information was supplied (see example: \u003ccode\u003eexample/demux-SampleSheet.csv\u003c/code\u003e), then it can be used to update the samplesheet with pairs information with the following recipe:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake pairs PAIRS_SHEET=demux-SampleSheet.csv PAIRS_MODE=demux\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline includes an auto-run functionality that attempts to determine the best configuration to use for NYU phoenix and Big Purple HPC clusters:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the pipeline in the current session.\u003c/p\u003e\n\u003cp\u003eIn order to run the pipeline in the background as a job on NYU\u0027s Big Purple HPC, you should instead use the \u003ccode\u003esubmit\u003c/code\u003e recipe:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake submit SUBQ=fn_medium\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003eSUBQ\u003c/code\u003e is the name of the SLURM queue you wish to use.\u003c/p\u003e\n\u003cp\u003eRefer to the \u003ccode\u003eMakefile\u003c/code\u003e for more run options.\u003c/p\u003e\n\u003cp\u003eDue to the scale of the pipeline, a \"local\" run option is not currently configured, but can be set up easily based on the details shown in the Makefile and \u003ccode\u003enextflow.config\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExtra Parameters\u003c/h3\u003e\u003ca id=\"user-content-extra-parameters\" class=\"anchor\" aria-label=\"Permalink: Extra Parameters\" href=\"#extra-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can supply extra parameters for Nextflow by using the \u003ccode\u003eEP\u003c/code\u003e variable included in the Makefile, like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake run EP=\u0027--runID 180320_NB501073_0037_AH55F3BGX5\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDemo\u003c/h3\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-label=\"Permalink: Demo\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA demo dataset can be loaded using the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake demo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003echeckout a demo dataset\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecreate a \u003ccode\u003esamples.analysis.tsv\u003c/code\u003e samplesheet for the analysis\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can then proceed to run the analysis with the commands described above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMore Functionality\u003c/h2\u003e\u003ca id=\"user-content-more-functionality\" class=\"anchor\" aria-label=\"Permalink: More Functionality\" href=\"#more-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtra functions included in the Makefile for pipeline management include:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003emake clean\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-make-clean\" class=\"anchor\" aria-label=\"Permalink: make clean\" href=\"#make-clean\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRemoves all Nextflow output except for the most recent run. Use \u003ccode\u003emake clean-all\u003c/code\u003e to remove all pipeline outputs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003emake record PRE=some_prefix_\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-make-record-presome_prefix_\" class=\"anchor\" aria-label=\"Permalink: make record PRE=some_prefix_\" href=\"#make-record-presome_prefix_\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\"Records\" copies of the most recent pipeline run\u0027s output logs, configuration, Nextflow reports, etc.. Useful for recording analyses that failed or had errors in order to debug. Include the optional argument \u003ccode\u003eTASK\u003c/code\u003e to specify a Nextflow \u003ccode\u003ework\u003c/code\u003e directory to include in the records (example: \u003ccode\u003emake record PRE=error_something_broke_ TASK=e9/d9ff34\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003emake kill\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-make-kill\" class=\"anchor\" aria-label=\"Permalink: make kill\" href=\"#make-kill\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAttempts to cleanly shut down a pipeline running on a remote host e.g. inside a SLURM HPC compute job. Note that you can also use \u003ccode\u003escancel\u003c/code\u003e to halt the parent Nextflow pipeline job as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ccode\u003emake fix-permissions\u003c/code\u003e, \u003ccode\u003emake fix-group\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-make-fix-permissions-make-fix-group\" class=\"anchor\" aria-label=\"Permalink: make fix-permissions, make fix-group\" href=\"#make-fix-permissions-make-fix-group\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAttempts to fix usergroup and permissions issues that may arise on shared systems with multiple users. Be sure to use the extra argument \u003ccode\u003eUSERGROUP=somegroup\u003c/code\u003e to specify the usergroup to update to.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003emake finalize-work-rm\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-make-finalize-work-rm\" class=\"anchor\" aria-label=\"Permalink: make finalize-work-rm\" href=\"#make-finalize-work-rm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExamines the \u003ccode\u003etrace.txt\u003c/code\u003e output from the most recent completed pipeline run in order to determine while subdirectories in the Nextflow \u003ccode\u003ework\u003c/code\u003e dir are no longer needed, and then deletes them. Can delete multiple subdirs in parallel when run with \u003ccode\u003emake finalize-work-rm -j 20\u003c/code\u003e e.g. specifying to delete 20 at a time, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSoftware\u003c/h1\u003e\u003ca id=\"user-content-software\" class=\"anchor\" aria-label=\"Permalink: Software\" href=\"#software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDeveloped under Centos 6, RHEL 7, macOS 10.12\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ebash\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGNU \u003ccode\u003emake\u003c/code\u003e, standard GNU tools\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePython 2/3\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJava 8+ for Nextflow\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDocker/Singularity as needed for containers\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 8, - "subscribers_count": 3, - "topics": [ - "nextflow", - "singularity-container", - "docker-container", - "python", - "groovy", - "slurm", - "html", - "makefile", - "markdown" - ], - "updated_at": 1732740930.0 - }, - { - "data_format": 2, - "description": "Applications of Pseudo-3D Network", - "filenames": [ - "Singularity" - ], - "full_name": "YeTianJHU/Pesudo-3D-Applications", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePesudo-3D-Applications\u003c/h1\u003e\u003ca id=\"user-content-pesudo-3d-applications\" class=\"anchor\" aria-label=\"Permalink: Pesudo-3D-Applications\" href=\"#pesudo-3d-applications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSeveral applications of Pseudo-3D Network.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknowledgement\u003c/h3\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe P3D model (with weights pre-trained on kinetics) is revised from \u003ca href=\"https://github.com/qijiezhao/pseudo-3d-pytorch\"\u003eP3D-Pytorch\u003c/a\u003e by qijiezhao.\u003c/li\u003e\n\u003cli\u003eThe I3D model is revised from \u003ca href=\"https://github.com/rimchang/kinetics-i3d-Pytorch\"\u003ekinetics-i3d-Pytorch\u003c/a\u003e by rimchang.\u003c/li\u003e\n\u003cli\u003eThe C3D model is revised from \u003ca href=\"https://github.com/DavideA/c3d-pytorch\"\u003ec3d-pytorch\u003c/a\u003e by DavideA.\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 8, - "subscribers_count": 3, - "topics": [], - "updated_at": 1655689650.0 - }, - { - "data_format": 2, - "description": "Docker and Singularity containers to predict bone age from radiographs (demo)", - "filenames": [ - "Singularity" - ], - "full_name": "radinformatics/bone-age", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBone-Age Demo\u003c/h1\u003e\u003ca id=\"user-content-bone-age-demo\" class=\"anchor\" aria-label=\"Permalink: Bone-Age Demo\" href=\"#bone-age-demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eunder development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis repository builds a Docker image and a \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e image, each that will run the bone age demo to predict bone age from a radiograph. The user has the option to run the prediction algorithm from the command line with an image file input, or to run a web server to see an interactive demo.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe predict_image.py script is a light wrapper around the model and includes the functions that are needed for such a demo. The user would upload a image which would then be processed with the given model on the back-end. The results would then be displayed for the user.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIf you are working on your local machine, you can use either Docker or Singularity. If you are running in a shared cluster (HPC) environment where you do not have root permissions, Singularity is your best option. Instructions are included for both.\u003c/p\u003e\n\u003cp\u003ePackages that need to be installed are included in \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e and installed into the container via the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker\u003c/h1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou should first \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e. The container is provided on \u003ca href=\"https://hub.docker.com/r/vanessa/boneage/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e and can be downloaded from there when you run it, and this is recommended because building it takes a while to compile OpenCV.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eI want to build it!\u003c/h3\u003e\u003ca id=\"user-content-i-want-to-build-it\" class=\"anchor\" aria-label=\"Permalink: I want to build it!\" href=\"#i-want-to-build-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to look at or make changes to the code, it\u0027s recommended to clone the repo and build the container locally:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone http://www.github.com/radinformatics/bone-age\ncd bone-age\ndocker build -t vanessa/boneage .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe docker daemon will first look for an image called \u003ccode\u003evanessa/boneage\u003c/code\u003e locally, and if not found, will then try Dockerhub, and download it from there. If for any reason you want to remove your image, just do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker rmi vanessa/boneage\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning commands\u003c/h2\u003e\u003ca id=\"user-content-running-commands\" class=\"anchor\" aria-label=\"Permalink: Running commands\" href=\"#running-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entry to the container is done simply by using it as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run vanessa/boneage --help\nusage: cli.py [-h] [--image IMAGE] [--output OUTPUT] [--gender {M,F}]\n\t [--width WIDTH] [--height HEIGHT] [--debug]\n\nPredict bone age of an image.\n\noptional arguments:\n -h, --help show this help message and exit\n --image IMAGE Path to single bone image.\n --output OUTPUT Path to output file to write results.\n --gender {M,F} the gender of the individual (M or F), default is M (male)\n --width WIDTH warped width to resize the image in pixels (default 256)\n --height HEIGHT warped height to resize the image in pixels (default 256)\n --debug use verbose logging to debug.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Prediction With Example\u003c/h3\u003e\u003ca id=\"user-content-run-prediction-with-example\" class=\"anchor\" aria-label=\"Permalink: Run Prediction With Example\" href=\"#run-prediction-with-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the bone-age demo non interactively to get a prediction, you can run it without any arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run vanessa/boneage\n\n*** Starting Bone Age Prediction ****\nNo image selected, will use provided example...\nBuilding model, please wait.\nPredicted Age : 14 Months\nWeighted Prediction : 11.832177 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe command above is saying \"map the folder \u003ccode\u003e$PWD/example_images\u003c/code\u003e (where my 4.png is located) to the \u003ccode\u003e/data\u003c/code\u003e folder in the container. Then, tell the script in the container to use the image located at \u003ccode\u003e/data/4.png\u003c/code\u003e. If you want to see debug output (for more details about running) you can add \u003ccode\u003e--debug\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run vanessa/boneage --debug\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nNo image selected, will use provided example...\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /code/example_images/5.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.610813 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Prediction With Your Own Image\u003c/h3\u003e\u003ca id=\"user-content-run-prediction-with-your-own-image\" class=\"anchor\" aria-label=\"Permalink: Run Prediction With Your Own Image\" href=\"#run-prediction-with-your-own-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to provide your own image, you need to bind it to the /data directory in the folder, and map a path to it. Don\u0027t forget to specify the gender - the default is male, and you may want to change that:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png\n\n*** Starting Bone Age Prediction ****\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can of course add debug to verify that the default is male, and we are using our mapped image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png --debug\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can specify a different gender, and the prediction changes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --image /data/4.png --gender F --debug\nEnvironment message level found to be DEBUG\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nDEBUG:bone-age:is_male: False\nDEBUG:bone-age:image: /data/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 16 Months\nWeighted Prediction : 16.000000 Months\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSave output to file\u003c/h3\u003e\u003ca id=\"user-content-save-output-to-file\" class=\"anchor\" aria-label=\"Permalink: Save output to file\" href=\"#save-output-to-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you specify the \u003ccode\u003e--output\u003c/code\u003e argument, you can save the result as a json to file. Again, we will need to specify a file in a folder mapped to our local machine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -v $PWD/example_images:/data vanessa/boneage --output /data/demo.json --debug\n\nEnvironment message level found to be DEBUG\n\n*** Starting Bone Age Prediction ****\nNo image selected, will use provided example...\nDEBUG:bone-age:is_male: True\nDEBUG:bone-age:image: /code/example_images/4.png\nDEBUG:bone-age:height: 256\nDEBUG:bone-age:width: 256\nDEBUG:PIL.PngImagePlugin:STREAM IHDR 16 13\nDEBUG:PIL.PngImagePlugin:STREAM IDAT 41 65536\nBuilding model, please wait.\nPredicted Age : 8 Months\nWeighted Prediction : 8.641131 Months\nDEBUG:bone-age:Result written to /data/demo.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we can look at the data - remember the folder that was mapped on our local machine is \u003ccode\u003e$PWD/example_images\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cat $PWD/example_images/demo.json\n {\n \"gender\": \"M\",\n \"image\": \"/code/example_images/4.png\",\n \"predicted_age\": 8,\n \"predicted_weight\": 8.64113067092668\n }\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe function inside the container to generate this result could be scaled by either providing an input argument for the user to specify an input file (with image paths and genders) and a single output file to write to, or running the command many times to write separate output files, or having a \u003ccode\u003e--silent\u003c/code\u003e option to suppress all output (except for the result) that could be piped (appended) into a single output file. All of these could be implemented, it really depends on the desired outcome. For the current purpose (plugging the container into a web server for a demo) the above that produces a single file, or multiple single files, is a reasonable approach.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I shell into the container?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container\" class=\"anchor\" aria-label=\"Permalink: How do I shell into the container?\" href=\"#how-do-i-shell-into-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, running the container uses the \u003ccode\u003eENTRYPOINT\u003c/code\u003e, meaning it is used as an executable and you do not enter the container. In the case that you want a container-based environment that is installed with the dependencies of boneage, or if you want to interactively work with the code, you may want to shell into the container.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run -it --entrypoint /bin/bash vanessa/boneage\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eKeep in mind that once you exit from this run, the container image is not saved, including your changes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBootstrapping means using something to build from, or not starting from nothing. In this case, we are going to use a build file that bootstraps a Docker image of boneage (yes, the same one discussed above). This build file is called \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e, and for more details about this you can \u003ca href=\"http://singularity.lbl.gov/docs-docker\" rel=\"nofollow\"\u003eread here\u003c/a\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 6000 boneage.img\nsudo singularity bootstrap boneage.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Run commands\u003c/h2\u003e\u003ca id=\"user-content-3-run-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Run commands\" href=\"#3-run-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe commands are equivalent as above, except we can use the container as an executable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./boneage.img --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand to make a drive, we use \u003ccode\u003e--bind\u003c/code\u003e instead\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run --bind $PWD/example_images:/data boneage.img --debug\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I shell into the container?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-shell-into-the-container-1\" class=\"anchor\" aria-label=\"Permalink: How do I shell into the container?\" href=\"#how-do-i-shell-into-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity has an easy, intuitive way to shell inside!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell boneage.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInteractive Web Interface\u003c/h1\u003e\u003ca id=\"user-content-interactive-web-interface\" class=\"anchor\" aria-label=\"Permalink: Interactive Web Interface\" href=\"#interactive-web-interface\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003etodo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUltimately, we will build this demo and serve on \u003ca href=\"http://www.singularity-hub.org\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e and then have an application that takes inputs / outputs for the container, and runs on demand.\u003c/p\u003e\n", - "stargazers_count": 8, - "subscribers_count": 5, - "topics": [], - "updated_at": 1701326252.0 - }, - { - "data_format": 2, - "description": "various singularity recipes for FSL", - "filenames": [ - "Singularity.6.0.2-Cuda8-xtract_viewer", - "Singularity.6.0.5.1", - "Singularity.6.0.0", - "Singularity.6.0.2", - "Singularity.6.0.4", - "Singularity.6.0.3", - "Singularity.5-Cuda8", - "Singularity.6.0.1", - "Singularity.5.0.10", - "Singularity.6.0.5", - "Singularity.5.0.11", - "Singularity.6.0.6.1", - "Singularity.5.0.9", - "Singularity.6.0.6", - "Singularity.6.0.2-Cuda8", - "Singularity.6.0.4-Cuda8" - ], - "full_name": "MPIB/singularity-fsl", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a very unofficial repo for FSL singularity recipes used at the\n\u003ca href=\"https://www.mpib-berlin.mpg.de/\" rel=\"nofollow\"\u003eMPIB\u003c/a\u003e. A while ago we used to automatically\npush images to the amazing singularity hub. Since that project has been\nabandoned, we use this repo primarily as a recipe reference.\u003c/p\u003e\n\u003cp\u003eThe base container is Debian 10 and it should work fine with CUDA as well. For\nvery old installations we keep some cuda-8 recipes around, but they are not\nnecessary to run a current release of FSL.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild an image\u003c/h2\u003e\u003ca id=\"user-content-build-an-image\" class=\"anchor\" aria-label=\"Permalink: Build an image\" href=\"#build-an-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/MPIB/singularity-fsl\ncd singularity-fsl\nexport VERSION=6.0.6.1\nsudo singularity build fsl-$VERSION.sif Singularity.$VERSION\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun in image\u003c/h2\u003e\u003ca id=\"user-content-run-in-image\" class=\"anchor\" aria-label=\"Permalink: Run in image\" href=\"#run-in-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec fsl-$VERSION.sif fslmaths\nsingularity exec --nv fsl-$VERSION eddy_cuda9.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eolder, pre-built images from singularity-hub\u003c/h2\u003e\u003ca id=\"user-content-older-pre-built-images-from-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: older, pre-built images from singularity-hub\" href=\"#older-pre-built-images-from-singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome older images are still available on the\n\u003ca href=\"https://singularityhub.github.io/singularityhub-docs/2021/going-read-only/\" rel=\"nofollow\"\u003eread-only\u003c/a\u003e\n\u003ca href=\"https://datasets.datalad.org/?dir=/shub/MPIB/singularity-fsl\" rel=\"nofollow\"\u003emirror provided by\ndatalad.org\u003c/a\u003e and\ncan be pulled directly.\u003c/p\u003e\n\u003cp\u003e(last updated in April 2021)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Download a (versioned) container\nsingularity pull shub://MPIB/singularity-fsl:6.0.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFSL\u003c/h2\u003e\u003ca id=\"user-content-fsl\" class=\"anchor\" aria-label=\"Permalink: FSL\" href=\"#fsl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProject Home: \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThese are containers primarily used at the MPI for Human Development.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCuda\u003c/h2\u003e\u003ca id=\"user-content-cuda\" class=\"anchor\" aria-label=\"Permalink: Cuda\" href=\"#cuda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStarting with Singularity 6.0.2 we include Nvidia CUDA through Debian backports\nrepositories. Make sure your Nvidia driver on the host \u003ca href=\"https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility\" rel=\"nofollow\"\u003esupports\nit\u003c/a\u003e\nand add the \u003ccode\u003e--nv\u003c/code\u003e flag with singularity.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNote\u003c/h2\u003e\u003ca id=\"user-content-note\" class=\"anchor\" aria-label=\"Permalink: Note\" href=\"#note\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease be aware of FSL\u0027s strict license regarding non-commercial use.\u003c/p\u003e\n", - "stargazers_count": 8, - "subscribers_count": 3, - "topics": [ - "containers", - "science" - ], - "updated_at": 1675289044.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "Singularity" - ], - "full_name": "fanglab/6mASCOPE", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e6mASCOPE\u003c/h1\u003e\u003ca id=\"user-content-6mascope\" class=\"anchor\" aria-label=\"Permalink: 6mASCOPE\" href=\"#6mascope\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e6mASCOPE is a toolbox to assess 6mA events in eukaryotic species using a quantitative deconvolution approach. By using a novel short-insert library (200~400bp) design with the PacBio sequencing Sequel II System, 6mASCOPE makes an effective use of the large number of circular consensus (CCS) reads to reliably capture deviations in IPD values at single molecule resolution. Taking an innovative metagenomic approach, 6mASCOPE deconvolves the DNA molecules from a gDNA sample into species and genomic regions of interests, and sources of contamination. Using a rationally designed machine learning model, 6mASCOPE enables sensitive and reliable 6mA quantification for each of the deconvolved composition.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u0027 notes\u003c/h2\u003e\u003ca id=\"user-content-authors-notes\" class=\"anchor\" aria-label=\"Permalink: Authors\u0027 notes\" href=\"#authors-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are actively developing 6mASCOPE to facilitate usage and broaden features. All feedback is more than welcome. You can reach us on twitter (\u003ca href=\"https://twitter.com/iamfanggang\" rel=\"nofollow\"\u003e@iamfanggang\u003c/a\u003e and \u003ca href=\"https://twitter.com/kong_yimeng\" rel=\"nofollow\"\u003e@kong_yimeng\u003c/a\u003e) or directly through the \u003ca href=\"https://github.com/fanglab/6mASCOPE/issues\"\u003eGitHub issues system\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e6mASCOPE is distributed as a fully functional image bypassing the need to install any dependencies others than the virtualization software. We recommend using Singularity, which can be installed on Linux systems and is often the preferred solution by HPC administrators (\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eQuick Start\u003c/a\u003e). \u003ccode\u003e6mASCOPE\u003c/code\u003e was tested extensively with Singularity v3.6.4.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load singularity/3.6.4 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Required only singularity/3.6.4 is a dynamic environment module. \u003c/span\u003e\nsingularity pull 6mASCOPE.sif library://fanglabcode/default/6mascope:latest \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the image from cloud.sylabs.io; Make sure you have the network connection\u003c/span\u003e\nsingularity build --sandbox 6mASCOPE 6mASCOPE.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a writable container named 6mASCOPE\u003c/span\u003e\nsingularity run --no-home -w 6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start an interactive shell to use 6mASCOPE, type `exit` to leave\u003c/span\u003e\ninit_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Only required once when start using 6mASCOPE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e run_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Required every time when running 6mASCOPE\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe image retrieved from \u003ca href=\"https://cloud.sylabs.io/home\" rel=\"nofollow\"\u003eSylab Cloud\u003c/a\u003e with \u003ccode\u003esingularity pull\u003c/code\u003e (e.g. 6mASCOPE.sif) is already built and can be reused at will. Containers built with those instructions are writable meaning that results from 6mASCOPE analysis can be retrieved when the container is not running. Outputs for the following commands can be found at \u003ccode\u003e./path/to/6mASCOPE/home/6mASCOPE/\u003c/code\u003e. To re-run the same container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run --no-home -w 6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Re-run container 6mASCOPE, type `exit` to leave\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e run_6mASCOPE \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eInside the container; Required every time when running 6mASCOPE\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTool showcase\u003c/h2\u003e\u003ca id=\"user-content-tool-showcase\" class=\"anchor\" aria-label=\"Permalink: Tool showcase\" href=\"#tool-showcase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo showcase the toolbox applications, we provide examples for the analysis of the Drosophila ~45min embryo dataset presented in our manuscript (Fig 5). The dataset can be downloaded with the following commands from within a 6mASCOPE container: \u003ccode\u003e6mASCOPE get_test_data\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContamination estimation\u003c/h3\u003e\u003ca id=\"user-content-contamination-estimation\" class=\"anchor\" aria-label=\"Permalink: Contamination estimation\" href=\"#contamination-estimation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGoal\u003c/h4\u003e\u003ca id=\"user-content-goal\" class=\"anchor\" aria-label=\"Permalink: Goal\" href=\"#goal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get an idea about the overall contamination of a gDNA sample. This step helps users define the composition of a gDNA sample using a metagenomic approach to assign reads to different species.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDescription:\u003c/h4\u003e\u003ca id=\"user-content-description-1\" class=\"anchor\" aria-label=\"Permalink: Description:\" href=\"#description-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a given CCS dataset generated from short-insert library, 6mASCOPE will examine if there are contaminating species and calculate the proportion of reads mapped to the reference and top 50 contaminated species from reads that do not map to the eukaryotic species of interest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInputs:\u003c/h4\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs:\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCCS reads file capturing all the genetic material in a gDNA sample (.fasta, pre-computed in the following example)\u003c/li\u003e\n\u003cli\u003eEukaryotic reference of genome of interest (.fasta)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutputs:\u003c/h4\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs:\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a given CCS dataset generated from short-insert library, \u003ccode\u003e6mASCOPE\u003c/code\u003e will examine if there are contaminating species and calculate the proportion of reads mapped to the reference and top 50 contaminated species from reads that do not map to the eukaryotic species of interest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExample of the Output:\u003c/h5\u003e\u003ca id=\"user-content-example-of-the-output\" class=\"anchor\" aria-label=\"Permalink: Example of the Output:\" href=\"#example-of-the-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eRemove 8491 possible inter-species chimeric reads for further analysis\n#total_CCS\tmapped_to_goi\tcontaminants\n666159\t640345 (96.1249%)\t25814 (3.87505%)\n\nTop 50 mapped species outside goi reference\n#Count\tSpecies\n 10836 Saccharomyces cerevisiae\n 2413 Acetobacter tropicalis\n 1524 Acetobacter pasteurianus\n 1479 Lactobacillus plantarum\n 882 Acetobacter sp.\n ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(Full species list can be viewed in \u003ccode\u003etest.contam.estimate.txt\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExample commands:\u003c/h5\u003e\u003ca id=\"user-content-example-commands\" class=\"anchor\" aria-label=\"Permalink: Example commands:\" href=\"#example-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e6mASCOPE contam -c test.ccs.fasta -r test.ref.fasta -o test.contam.estimate.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this example, \u003ccode\u003etest.ccs.fasta\u003c/code\u003e includes CCS reads (674,650) from the Drosophila ~45min embryo reads dataset described in our manuscript and pre-filtered with command \u003ccode\u003e6mASCOPE ccs\u003c/code\u003e. Using 5 cores, runtime is ~12m51s. The output shows ~3.9% CCS reads come from contaminated sources other than Drosophila melanogaster, the genome of interest (goi). Please be noted, blastn is embedded within this step, which will need at least 32-64G RAM.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e6mA analysis using quantitative deconvolution\u003c/h3\u003e\u003ca id=\"user-content-6ma-analysis-using-quantitative-deconvolution\" class=\"anchor\" aria-label=\"Permalink: 6mA analysis using quantitative deconvolution\" href=\"#6ma-analysis-using-quantitative-deconvolution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGoal:\u003c/h4\u003e\u003ca id=\"user-content-goal-1\" class=\"anchor\" aria-label=\"Permalink: Goal:\" href=\"#goal-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each source determined in \u003ccode\u003e6mASCOPE contam\u003c/code\u003e, this step will quantify the 6mA/A level and calculate the 6mA contribution (%) of each source to the total 6mA abundance in the gDNA sample.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInputs:\u003c/h4\u003e\u003ca id=\"user-content-inputs-1\" class=\"anchor\" aria-label=\"Permalink: Inputs:\" href=\"#inputs-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eThe same CCS reads file as explained above for Contamination Estimation (.fasta).\u003c/li\u003e\n\u003cli\u003eIPD and QV information of the CCS reads (pre-computed in the following example, ; this can be generated for new data with \u003ccode\u003e6mASCOPE ipd\u003c/code\u003e command, as explained in detailed tutorial).\u003c/li\u003e\n\u003cli\u003eUser defined groups besides the genome of interest. Examples as shown below. (Left columns: subgroup name. Right columns: contamination sources, use vertical line if multiple sources included within one subgroup).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eSaccharomyces Saccharomyces\nAcetobacter Acetobacter|Komagataeibacter\nLactobacillus Lactobacillus\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutputs:\u003c/h4\u003e\u003ca id=\"user-content-outputs-1\" class=\"anchor\" aria-label=\"Permalink: Outputs:\" href=\"#outputs-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA table including the following information: the proportion (%) of reads from each source out of the total number of reads; source-specific 6mA/A level with 95% confidence intervals (log10-transformed), and contribution (%) of each source to the total 6mA abundance in the gDNA sample (as presented in the manuscript Figure 5A, B, C)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample commands:\u003c/h4\u003e\u003ca id=\"user-content-example-commands-1\" class=\"anchor\" aria-label=\"Permalink: Example commands:\" href=\"#example-commands-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e6mASCOPE quant -c test.ccs.fasta -i test.IPD.out.A -o test -r test.ref.fasta -s subgroup.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn this example, the file \u003ccode\u003etest.IPD.out.A\u003c/code\u003e includes the pre-calculated IPD and QV information on the CCS molecules (can be generated with \u003ccode\u003e6mASCOPE ipd\u003c/code\u003e). Only Adenines were included here to to reduce computational time and ease evaluation. \u003ccode\u003esubgroup.txt\u003c/code\u003e includes the pre-defined main contamination groups, inferred from the top mapped species and blast output from \u003ccode\u003e6mASCOPE contam\u003c/code\u003e. Using 5 cores, runtime is ~13m17s.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExample output:\u003c/h4\u003e\u003ca id=\"user-content-example-output\" class=\"anchor\" aria-label=\"Permalink: Example output:\" href=\"#example-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e #Subgroup count ReadsProportion 6mAlevel(ppm) 6mAlevel(log10) UpCI DownCI subtotal(ppm) contribution(%)\n goi 640345 0.9612 2.0417 -5.69 -5.0 -6.0 1.9625 1.4431\n Saccharomyces 11011 0.0165 45.7088 -4.34 -3.9 -6.0 0.7542 0.5546\n Acetobacter 5757 0.0086 5495.4087 -2.26 -2.0 -2.5 47.2605 34.7522\n Lactobacillus 1517 0.0023 977.2372 -3.01 -2.7 -3.3 2.2476 1.6528\n others 7529 0.0113 7413.1024 -2.13 -1.9 -2.4 83.7681 61.5974\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/test.6mASCOPE.ReadsProportion.png\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.ReadsProportion.png\" alt=\"The proportion of CCS reads from each group 6mA\" width=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n1. The % of total CCS reads mapped to different subgroups. Left: The % of CCS reads mapped to D. melanogaster (genome of interest) and contamintant subgroups. Right: The % of CCS reads mapped to different contaminant sources.\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/test.6mASCOPE.6mAlevel.png\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.6mAlevel.png\" alt=\"Group-specific 6mA/A level prediction with confidence intervals 6mA\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n2. 6mA quantification and 95% confidence intervals (log10-transformed) on CCS reads mapped to different subgroups. Please be noted, it is important to combine the estimated 6mA/A level with its confidence interval for reliable data interpretation. In this example, the 6mA/A level of Saccharomyces (45.7ppm) does not mean abundant 6mA events in this subgroup because it has a wide range of confidence interval (1-125ppm; -6.0 to -3.9 with log10 transformed). In the paper, an additional Sequel II run for this single species (higher yield) actually shows extremely low 6mA level (2ppm, confidence interval: 1-10ppm).\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/test.6mASCOPE.6mAcontribution.png\"\u003e\u003cimg src=\"/docs/figures/test.6mASCOPE.6mAcontribution.png\" alt=\"Group-specific 6mA/A level prediction with confidence intervals 6mA\" width=\"300\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n3. Contribution (%) of each source to total 6mA abundance in the gDNA sample. CCS reads mapped to the D. melanogaster genome only explains 1.4% of the total 6mA events in the gDNA sample (green).\n\u003cp\u003eThese figures can be drawn with \u003ccode\u003esh ~/code/draw_example.sh test.6mASCOPE.txt\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a comprehensive description of\u00a06mASCOPE including installation guide, data preprocessing and a detailed tutorial, including how to apply 6mASCOPE to your own datasets, please refer to the\u00a0\u003ca href=\"https://6mascope.readthedocs.io/en/latest/overview.html\" rel=\"nofollow\"\u003ecomplete documentation\u003c/a\u003e .\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYimeng Kong, Lei Cao, Gintaras Deikus, Yu Fan, Edward A. Mead, Weiyi Lai, Yizhou Zhang, Raymund Yong, Robert Sebra, Hailin Wang, Xue-Song Zhang \u0026amp; Gang Fang. Critical assessment of DNA adenine methylation in eukaryotes using quantitative deconvolution. \u003cem\u003eScience\u003c/em\u003e (2022). doi:\u003ca href=\"http://doi.org/10.1126/science.abe7489\" rel=\"nofollow\"\u003e10.1126/science.abe7489\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 8, - "subscribers_count": 6, - "topics": [], - "updated_at": 1714718024.0 - }, - { - "data_format": 2, - "description": "A tool for generating bacterial genomes from metagenomes with nanopore long read sequencing", - "filenames": [ - "singularity/Singularity.quickmerge", - "singularity/Singularity.longread", - "singularity/Singularity.htsbox" - ], - "full_name": "elimoss/lathe", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLathe\u003c/h1\u003e\u003ca id=\"user-content-lathe\" class=\"anchor\" aria-label=\"Permalink: Lathe\" href=\"#lathe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA tool for generating bacterial genomes from metagenomes with Nanopore long read sequencing\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, install \u003ca href=\"https://conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda3\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen install snakemake. This can be done with the following.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install snakemake\nsnakemake --version #please ensure this is \u0026gt;=5.4.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, clone this github directory to some location where it can be stored permanently. Remember to keep it updated with \u003ccode\u003egit pull\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/elimoss/lathe.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstructions to enable cluster execution with SLURM can be found at \u003ca href=\"https://github.com/bhattlab/slurm\"\u003ehttps://github.com/bhattlab/slurm\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAlter config.yaml to provide the following:\u003c/h3\u003e\u003ca id=\"user-content-alter-configyaml-to-provide-the-following\" class=\"anchor\" aria-label=\"Permalink: Alter config.yaml to provide the following:\" href=\"#alter-configyaml-to-provide-the-following\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esample_name\u003c/strong\u003e: Name of sample and output directory\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003efast5_dirs_list\u003c/strong\u003e: text file containing a list of absolute paths to run/fast5/* subfolders containing .fast5 files. A good way to generate this is with \u003ccode\u003efind -maxdepth 2 -mindepth 2 fast5_parent_dir \u0026gt; fodn.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eflowcell\u003c/strong\u003e: flowcell code, e.g. FLO-MIN106, passed to basecaller\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ekit\u003c/strong\u003e: kit code, e.g. SQK-LSK109, passed to basecaller\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003egenome_size\u003c/strong\u003e: Estimated genome size, e.g. 50m, passed to Canu.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esingularity\u003c/strong\u003e: location (including on the internet) of a singularity image to be used for the workflow. Don\u0027t change this.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eshort_reads\u003c/strong\u003e: location of short reads to be used for Pilon polishing, or empty quotes for long-read polishing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003euse_grid\u003c/strong\u003e: should Canu execute in distributed mode on a cluster?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003egrid_options\u003c/strong\u003e: Extra options for execution on a cluster\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ecanu_args\u003c/strong\u003e: Extra options for Canu\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eskip_circularization\u003c/strong\u003e: Should circularization be omitted from the workflow?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLathe uses the Flye assembler by default. For Canu, please specify \u0027canu\u0027 for the assembler parameter in the config. For cluster Canu execution, please note: if set to True, you will need to install Canu, e.g. \u003ccode\u003econda install -c conda-forge -c bioconda Canu=1.8\u003c/code\u003e as well as provide any additional required parameters for your job scheduler in the config.yaml file. Please see the example config file. When executing on a cluster, Canu will appear to fail, as the first process does not produce an assembly and instead spawns subsequent jobs on the cluster. Don\u0027t worry, just re-run Lathe when the assembly completes.\u003c/p\u003e\n\u003cp\u003eTo execute please run the following. Please note, you must substitute a parent directory containing all of your data and working directories for \u003ccode\u003e/labs/\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity --singularity-args \u0027--bind /labs/ --bind /scratch/ --bind /scg/ \u0027 -s /path/to/lathe/Snakefile \\\n--configfile path/to/modified_config.yaml --restart-times 0 --keep-going --latency-wait 30\n# --profile scg #enable cluster support, highly recommended. See above.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe outputs generated by this workflow will look like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esamplename/\n\u251c\u2500\u2500 0.basecall\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename.fq\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 nanoplots\n\u251c\u2500\u2500 1.assemble\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_merged.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.amb\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.ann\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.bwt\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.fai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.pac\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.paf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.sa\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 assemble_100m (if specified)\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 assemble_250m (if specified)\n\u251c\u2500\u2500 2.polish\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.corrected.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.bam.bai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.fai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.misassemblies.tsv\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 medaka (if specified)\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 pilon (if specified)\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 racon (if specified)\n\u251c\u2500\u2500 3.circularization\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 1.candidate_genomes\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 2.circularization\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 3.circular_sequences #circularized genomes\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.corrected.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.bam.bai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.fai\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 4.samplename_circularized.fasta.misassemblies.tsv\n\u2514\u2500\u2500 5.final\n \u00a0\u00a0 \u251c\u2500\u2500 samplename_final.fa\n \u00a0\u00a0 \u2514\u2500\u2500 samplename_final.fa.fai\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 8, - "subscribers_count": 0, - "topics": [], - "updated_at": 1632818985.0 - }, - { - "data_format": 2, - "description": "Local Conda environments via Singularity.", - "filenames": [ - "Singularity.conda" - ], - "full_name": "bast/singularity-conda", - "latest_release": "0.5.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-local-conda-environments-via-singularity\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#local-conda-environments-via-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal Conda environments via Singularity\u003c/h1\u003e\n\u003cp\u003eThe nice thing about this approach is that you don\u0027t need to install Conda and\nyou don\u0027t need to modify your environment/bashrc/settings.\u003c/p\u003e\n\u003cp\u003eI use it to install dependencies that may be tough to install on a\nsupercomputer or on my NixOS environment.\u003c/p\u003e\n\u003cp\u003eHow to fetch the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull https://github.com/bast/singularity-conda/releases/download/0.5.0/conda.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eReads the Conda environment file\n\u003ca href=\"https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually\" rel=\"nofollow\"\u003eenvironment.yml\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eCreates the folder \u003ccode\u003eenvironment\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRun \u003ccode\u003emyscript.py\u003c/code\u003e inside the Conda environment defined by \u003ccode\u003eenvironment.yml\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./conda.sif python myscript.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOpen Python shell inside the Conda environment defined by \u003ccode\u003eenvironment.yml\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./conda.sif python\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFirst time you run either of the above commands it will take a bit of time\nsince it needs to install the dependencies into the \u003ccode\u003eenvironment\u003c/code\u003e folder.\nHowever, subsequent runs will start basically immediately since the environment\nis then there.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-micromamba-and-environment-files\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#micromamba-and-environment-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMicromamba and environment files\u003c/h2\u003e\n\u003cp\u003eUnder the hood, it uses\n\u003ca href=\"https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html\" rel=\"nofollow\"\u003eMicromamba\u003c/a\u003e\ninstead of Conda in order to speed up installations but it should not really\nmatter for the functionality.\u003c/p\u003e\n\u003cp\u003eThe one place where I found it to matter is that you have to specify \u003ccode\u003echannels\u003c/code\u003e\nin the environment file.\u003c/p\u003e\n\u003cp\u003eInstead of this (example taken from \u003ca href=\"https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually\" rel=\"nofollow\"\u003econda\ndocumentation\u003c/a\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003estats\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003edependencies\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003enumpy\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003epandas\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou need to do this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003estats\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003echannels\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003edefaults\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003edependencies\u003c/span\u003e:\n - \u003cspan class=\"pl-s\"\u003enumpy\u003c/span\u003e\n - \u003cspan class=\"pl-s\"\u003epandas\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBut I believe that specifying channels explicitly is anyway good practice.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-on-a-supercomputercluster\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#running-on-a-supercomputercluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on a supercomputer/cluster\u003c/h2\u003e\n\u003cp\u003eOn a cluster you might need to bind folders like here:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ env SINGULARITY_BIND=\"/cluster\" ./conda.sif python\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eI have used this wonderful guide as starting point and inspiration:\n\u003ca href=\"https://github.com/singularityhub/singularity-deploy\"\u003ehttps://github.com/singularityhub/singularity-deploy\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 8, - "subscribers_count": 3, - "topics": [ - "apptainer", - "conda", - "containers", - "micromamba", - "singularity" - ], - "updated_at": 1706303525.0 - }, - { - "data_format": 2, - "description": "Quality control plotting for long reads", - "filenames": [ - "Singularity" - ], - "full_name": "mbhall88/pistis", - "latest_release": "v0.3.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-pistis\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#pistis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePistis\u003c/h1\u003e\n\u003ch3\u003e\u003ca id=\"user-content-quality-control-plotting-for-long-reads\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#quality-control-plotting-for-long-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality control plotting for long reads.\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"https://pypi.python.org/pypi/pistis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0d377fd7c4560ba9ce5e50da718cfcda6af8bfe6e63362d9c8741335e20fec6c/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f7069737469732e737667\" alt=\"PyPI status\" data-canonical-src=\"https://img.shields.io/pypi/v/pistis.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.org/mbhall88/pistis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/460505d13dbbc44006c446a195f753c22160192229624c04b719693986845945/68747470733a2f2f7472617669732d63692e6f72672f6d6268616c6c38382f7069737469732e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/mbhall88/pistis.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/mbhall88/pistis/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a5606fdcd10a7afc202cdcc307f242a27a106834bebba2be192225e4315fb774/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6d6268616c6c38382f7069737469732e737667\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/mbhall88/pistis.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://twitter.com/mbhall88\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/899e87a3d856d3491f29644236afe87260be498a45240bd9acde07d48634d9fd/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f6d6268616c6c38382e7376673f7374796c653d736f6369616c266c6f676f3d74776974746572266c6162656c3d466f6c6c6f77\" alt=\"Twitter Follow\" data-canonical-src=\"https://img.shields.io/twitter/follow/mbhall88.svg?style=social\u0026amp;logo=twitter\u0026amp;label=Follow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2402\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis package provides plotting designed to give you an idea of how your long read\nsequencing data looks. It was conceived of and developed with nanopore reads in\nmind, but there is no reason why PacBio reads can\u0027t be used.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install pistis\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also use \u003ccode\u003epip\u003c/code\u003e if you are running with python2.\u003cbr\u003e\nOr using a virtual\nenvironment manager such as \u003ca href=\"https://conda.io/docs/\" rel=\"nofollow\"\u003econda\u003c/a\u003e or\n\u003ca href=\"https://docs.pipenv.org/\" rel=\"nofollow\"\u003epipenv\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eYou should now be able to run \u003ccode\u003epistis\u003c/code\u003e from the command line\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis --help\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity\u003c/h3\u003e\n\u003cp\u003eThere is a built image maintained with this repository that can be used. For the latest release you can use the URI \u003ccode\u003eshub://mbhall88/pistis\u003c/code\u003e\u003cbr\u003e\nFor example\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshub://mbhall88/pistis\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e pistis --help\nsingularity pull --name pistis.simg \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eshub://mbhall88/pistis\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThe main use case for \u003ccode\u003epistis\u003c/code\u003e is as a command-line interface (CLI), but it can also be\nused in an interactive way, such as with a \u003ca href=\"https://jupyter.org/\" rel=\"nofollow\"\u003eJupyter Notebook\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-cli-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#cli-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCLI Usage\u003c/h4\u003e\n\u003cp\u003eAfter installing and running the help menu you should see the following usage\noptions\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epistis -h\n\nUsage: pistis [OPTIONS]\n\n A package for sanity checking (quality control) your long read data.\n Feed it a fastq file and in return you will receive a PDF with four plots:\n\n 1. GC content histogram with distribution curve for sample.\n\n 2. Jointplot showing the read length vs. phred quality score for\n each read. The interior representation of this plot can be\n altered with the --kind option.\n\n 3. Box plot of the phred quality score at positional bins across\n all reads. The reads are binned into read positions 1, 2, 3, 4, 5,\n 6, 7, 8, 9, 10, 11-20, 21-50, 51-100, 101-200, 201-300. Plots from\n the start of reads.\n\n 4. Same as 3, but plots from the end of the read.\n\n Additionally, if you provide a BAM/SAM file a histogram of the read\n percent identity will be added to the report.\n\nOptions:\n -f, --fastq PATH Fastq file to plot. This can be gzipped.\n -o, --output PATH Path to save the plot PDF as. If name is not\n specified, will use the name of the fastq\n (or bam) file with .pdf extension.\n -k, --kind [kde|scatter|hex] The kind of representation to use for the\n jointplot of quality score vs read length.\n Accepted kinds are \u0027scatter\u0027, \u0027kde\u0027\n (default), or \u0027hex\u0027. For examples refer to h\n ttps://seaborn.pydata.org/generated/seaborn.\n jointplot.html\n --log_length / --no_log_length Plot the read length as a log10\n transformation on the quality vs read length\n plot\n -b, --bam PATH SAM/BAM file to produce read percent\n identity histogram from.\n -d, --downsample INTEGER Down-sample the sequence files to a given\n number of reads. Set to 0 for no\n subsampling. Default: 50000\n -h, --help Show this message and exit.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote the \u003ccode\u003e--downsample\u003c/code\u003e option is set to 50000 by default. That is, \u003ccode\u003epistis\u003c/code\u003e will\nonly plot 50000 reads (sampled from a uniform distribution). You can set this to\n0 if you want to plot every read, or select another number of your choosing. Be aware\nthat if you try to plot too many reads you may run into memory issues, so try\ndownsampling if this happens.\u003c/p\u003e\n\u003cp\u003eThere are three different use cases - currently - for producing plots:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFastq only\u003c/strong\u003e - This will return four plots:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA distribution plot of the GC content for each read.\u003c/li\u003e\n\u003cli\u003eA bivariate jointplot with read length on the y-axis and mean read quality\nscore on the x-axis.\u003c/li\u003e\n\u003cli\u003eTwo boxplots that show the distribution of quality scores at select positions\nand positional ranges. One plot shows the scores from the beginning of the\nread and the other from the end of the read.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo use \u003ccode\u003epistis\u003c/code\u003e in this way you just need a fastq file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq -o /save/as/report.pdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will save the four plots to a file called \u003ccode\u003ereport.pdf\u003c/code\u003e in directory \u003ccode\u003e/save/as/\u003c/code\u003e.\nIf you don\u0027t provide a \u003ccode\u003e--output/-o\u003c/code\u003e option the file will be saved in the current\ndirectory with the basename of the fastq file. So in the above example it would be\nsaved as \u003ccode\u003emy.pdf\u003c/code\u003e.\u003cbr\u003e\nIf you would prefer the read lengths in the bivariate plot of read length vs.\nmean quality score then you can indicate this like so\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq -o /save/as/report.pdf --no_log_length\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditionally, you can change the way the data is represented in the bivariate plot.\nThe default is a kernel density estimation plot (as in the below image), however you can\nchoose to use a \u003ca href=\"https://seaborn.pydata.org/generated/seaborn.jointplot.html\" rel=\"nofollow\"\u003ehex bin or scatter plot version instead\u003c/a\u003e.\nIn the running example, to use a scatter plot you would run the following\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq -o /save/as/report.pdf --kind scatter\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also provide a \u003ccode\u003egzip\u003c/code\u003eed fastq file without any extra steps\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq.gz -o /save/as/report.pdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eExamples\u003c/strong\u003e\u003cbr\u003e\nGC content:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_gc_plot.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_gc_plot.png\" alt=\"gc content plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eRead length vs. mean read quality score:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_qual_v_len.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_qual_v_len.png\" alt=\"read length vs quality plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBase quality from the start of each read:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_qual_start.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_qual_start.png\" alt=\"base quality from start plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBase quality from the end of each read:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_qual_end.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_qual_end.png\" alt=\"base quality from end plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eFastq and BAM/SAM\u003c/strong\u003e - This will return the above four plots, plus a distribution\nplot of each read\u0027s percent identity with the reference it is aligned to in the\n[BS]AM file. Reads which are flagged as supplementary or secondary are not included.\nThe plot also includes a dashed vertical red line indicating the median\npercent identity.\u003cbr\u003e\nNote: If using a BAM file, it must be sorted and indexed (i.e \u003ccode\u003e.bai\u003c/code\u003e file). See \u003ca href=\"http://www.htslib.org/doc/samtools.html\" rel=\"nofollow\"\u003e\u003ccode\u003esamtools\u003c/code\u003e\u003c/a\u003e\nfor instructions on how to do this.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -f /path/to/my.fastq -b /path/to/my.bam -o /save/as/report.pdf\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\npistis -f /path/to/my.fastq -b /path/to/my.sam -o /save/as/report.pdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eExample\u003c/strong\u003e\u003cbr\u003e\nDistribution of aligned read percent identity:\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mbhall88/pistis/blob/master/docs/imgs/pistis_perc_id.png\"\u003e\u003cimg src=\"https://github.com/mbhall88/pistis/raw/master/docs/imgs/pistis_perc_id.png\" alt=\"percent identity plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eBAM/SAM only\u003c/strong\u003e - At this stage you will receive only the distribution\nplot of each read\u0027s percent identity with the reference it is aligned to. In a\nfuture release I aim to allow you to also get the other four fastq-only plots.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epistis -b /path/to/my.bam -o /save/as/report.pdf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAs with the fastq-only method, if you don\u0027t provide a \u003ccode\u003e--output/-o\u003c/code\u003e option the file will be saved in the current\ndirectory with the basename of the [BS]AM file. So in the above example it would be\nsaved as \u003ccode\u003emy.pdf\u003c/code\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-usage-in-a-development-environment\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage-in-a-development-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage in a development environment\u003c/h4\u003e\n\u003cp\u003eIf you would like to use \u003ccode\u003epistis\u003c/code\u003e within a development environment such as a\n\u003ccode\u003ejupyter notebook\u003c/code\u003e or just a plain ol\u0027 python shell then take a look at \u003ca href=\"https://github.com/mbhall88/pistis/blob/master/examples/example_usage.ipynb\"\u003ethis example notebook\u003c/a\u003e\nfor all the details.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eThis package was created with \u003ca href=\"https://github.com/audreyr/cookiecutter\"\u003eCookiecutter\u003c/a\u003e and the \u003ca href=\"https://github.com/audreyr/cookiecutter-pypackage\"\u003e\u003ccode\u003eaudreyr/cookiecutter-pypackage\u003c/code\u003e project template\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThe two test data files (fastq and BAM) that I have used in this repository were\ntaken from \u003ca href=\"https://github.com/wdecoster/nanotest\"\u003eWouter De Coster\u0027s \u003ccode\u003enanotest\u003c/code\u003e repository\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eWhich in turn comes from \u003ca href=\"http://lab.loman.net/2017/03/09/ultrareads-for-nanopore/\" rel=\"nofollow\"\u003eNick Loman and Josh Quick\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThe example plots in this \u003ccode\u003eREADME\u003c/code\u003e were made using the entire fastq of basecalled\nreads from the experiment in that \u003ca href=\"http://lab.loman.net/2017/03/09/ultrareads-for-nanopore/\" rel=\"nofollow\"\u003eblog on \"whale hunting\"\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThe plot for the BAM file was obtained by running \u003ccode\u003epistis\u003c/code\u003e on a BAM file generated\nby mapping the fastq file to \u003cem\u003eE. coli\u003c/em\u003e reference \u003ca href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_000913.3\" rel=\"nofollow\"\u003eNC_000913.3\u003c/a\u003e\nusing Heng Li\u0027s \u003ca href=\"https://github.com/lh3/minimap2\"\u003e\u003ccode\u003eminimap2\u003c/code\u003e\u003c/a\u003e and \u003ccode\u003e-x map-ont\u003c/code\u003e option.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h1\u003e\n\u003cp\u003eIf you would like to contribute to this package you are more than welcome.\u003cbr\u003e\n\u003cstrong\u003ePlease read through the \u003ca href=\"https://github.com/mbhall88/pistis/blob/master/CONTRIBUTING.rst\"\u003econtributing guidelines\u003c/a\u003e first\u003c/strong\u003e.\u003c/p\u003e\n", - "stargazers_count": 8, - "subscribers_count": 3, - "topics": [ - "nanopore", - "oxford-nanopore", - "bioinformatics", - "bioinformatics-analysis", - "plotting", - "quality-control", - "pacbio" - ], - "updated_at": 1665091185.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - ".ci/github/Singularity" + "jmrui/Singularity.jmrui_5.2", + "cistem/Singularity.cisTEM-1.0.0-beta", + "dtsa2/Singularity.dtsa2", + "openmodelica/Singularity.openmodelica_1.14.2-cuda-10.1" ], - "full_name": "cepc/CEPCSW", - "latest_release": "v0.2.6", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-cepcsw\" class=\"anchor\" aria-hidden=\"true\" href=\"#cepcsw\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ca href=\"https://cepc.github.io/CEPCSW/\" rel=\"nofollow\"\u003eCEPCSW\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://www.travis-ci.com/cepc/CEPCSW\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/51cb592ac6435ae6b6bdc6cca7a941779434c9db16df9857df2a94e6f239971b/68747470733a2f2f7777772e7472617669732d63692e636f6d2f636570632f4345504353572e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://www.travis-ci.com/cepc/CEPCSW.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/cepc/CEPCSW/actions\"\u003e\u003cimg src=\"https://github.com/cepc/CEPCSW/workflows/CI/badge.svg?branch=master\" alt=\"CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCEPC offline software prototype based on \u003ca href=\"https://github.com/key4hep\"\u003eKey4hep\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick start\u003c/h2\u003e\n\u003cp\u003eSSH to lxslc7 (CentOS 7).\u003c/p\u003e\n\u003cp\u003eBefore run following commands, please make sure you setup the CVMFS:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone git@github.com:cepc/CEPCSW.git\n$ cd CEPCSW\n$ git checkout master # branch name\n$ source setup.sh\n$ ./build.sh\n$ ./run.sh Examples/options/helloalg.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-packages\" class=\"anchor\" aria-hidden=\"true\" href=\"#packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePackages\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eExamples: For new comers and users\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDetector: Geometry\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenerator: Physics Generator\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSimulation: Detector Simulation\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDigitization: Digitization\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReconstruction: Reconstruction\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-conventions-for-collections\" class=\"anchor\" aria-hidden=\"true\" href=\"#conventions-for-collections\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConventions for collections\u003c/h2\u003e\n\u003cp\u003eKeep the collection names compatible between the prototype and the existing CEPC software.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMCParticle\u003c/li\u003e\n\u003cli\u003eVXDCollection\u003c/li\u003e\n\u003cli\u003eSITCollection\u003c/li\u003e\n\u003cli\u003eTPCCollection\u003c/li\u003e\n\u003cli\u003eSETCollection\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "Characterisation-Virtual-Laboratory/CharacterisationVL-Software", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCharacterisationVL-Software\u003c/h1\u003e\u003ca id=\"user-content-characterisationvl-software\" class=\"anchor\" aria-label=\"Permalink: CharacterisationVL-Software\" href=\"#characterisationvl-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe purpose of this repository is for storing definition files to submit to our internal application build and deployment system.\u003c/p\u003e\n\u003cp\u003eIf you are new to Singularity containers, please refer to \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/\u003c/a\u003e or a newer version of this documentation.\u003c/p\u003e\n\u003cp\u003eEach software package is located in its own folder. The files are tagged with the software name and version number or date of build. Please read below for the naming convention.\u003c/p\u003e\n\u003cp\u003eTo add software to the repository you will need to create a new branch. The new branch is the name of the software product. By convention, the new branch will be checked and merged into the master branch and then deleted.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSteps to add a software package\u003c/h2\u003e\u003ca id=\"user-content-steps-to-add-a-software-package\" class=\"anchor\" aria-label=\"Permalink: Steps to add a software package\" href=\"#steps-to-add-a-software-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003cli\u003eCreate a branch\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ git branch \u0026lt;software name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eMake a subdirectory for the software product.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir \u0026lt;software name\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eAdd all the necessary files.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSingularity definition file or installation script\u003c/li\u003e\n\u003cli\u003eReadme file including install and testing notes\u003c/li\u003e\n\u003cli\u003eDesktop files for adding to menus with necessary tags\u003c/li\u003e\n\u003cli\u003eFor full details, \u003ca href=\"template/README.md\"\u003eplease refer to the \u0027template\u0027 folder in this repository.\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eCommit all changes, including a helpful message\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e$ git commit -m \"\u0026lt;software name\u0026gt; added as requested in support ticket\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003ePush to the remote repository. i.e. this one.\u003c/li\u003e\n\u003cli\u003eSubmit merge request\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNaming your Singularity definition file, Singularity Hub and Licensing\u003c/h2\u003e\u003ca id=\"user-content-naming-your-singularity-definition-file-singularity-hub-and-licensing\" class=\"anchor\" aria-label=\"Permalink: Naming your Singularity definition file, Singularity Hub and Licensing\" href=\"#naming-your-singularity-definition-file-singularity-hub-and-licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor all Singularity recipes where the software licensing permits redistribution, please use this naming convention:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Singularity.applicationName_version\n Singularity.applicationName_version-cuda-cudaVersion\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere is a Github action between this repository and a container builder VM hosted on \u003ca href=\"https://ardc.edu.au/services/nectar-research-cloud/\" rel=\"nofollow\"\u003eNectar\u003c/a\u003e. When a commit is merged into the master branch, this VM will build the container.\u003c/p\u003e\n\u003cp\u003eIf successfully built, the container will be added to a repository that is downloadable from CVL nodes.\u003c/p\u003e\n\u003cp\u003eAn example on how to handle this situation is the recipe for CCP-EM.\nThe \u003ca href=\"ccp-em/README.md\"\u003eREADME.md\u003c/a\u003e contains a section on Prerequisites. This section lists the required files to build the container. The license must be accepted by the end user to obtain them.\u003c/p\u003e\n\u003cp\u003ePrerequisite files should not be committed to this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUbuntu Base Images\u003c/h2\u003e\u003ca id=\"user-content-ubuntu-base-images\" class=\"anchor\" aria-label=\"Permalink: Ubuntu Base Images\" href=\"#ubuntu-base-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe folder \u0027ubuntu-base-image\u0027 contains recipes for pre built base containers. These can be used as a starting point to aid/speed up the development of your container recipe.\u003c/p\u003e\n\u003cp\u003eThe current versions are built using Ubuntu 18.04 LTS, plus Cuda 9 or Cuda 10.1 if required.\u003c/p\u003e\n\u003cp\u003eFor example: from the Graphviz Singularity.graphviz-2.40.1 recipe\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBootstrap: shub\nFrom: Characterisation-Virtual-Laboratory/CharacterisationVL-Software:1804\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThese two lines, will tell Singularity to use the \u0027shub\u0027 bootstrap to obtain the \u00271804\u0027 ubuntu-base-image container from Singularity Hub.\u003c/p\u003e\n\u003cp\u003eFrom here you just need to add the requirements to build a container for your required piece of software. Please see \u003ca href=\"graphviz/Singularity.graphviz-2.40.1\"\u003eSingularity.graphviz-2.40.1\u003c/a\u003e\nfor the full recipe.\u003c/p\u003e\n\u003cp\u003eThe current ubuntu-base-images include Python, VirtualGL and TurboVNC plus Cuda if indicated in the name.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning GUI applications on a non-GPU node\u003c/h2\u003e\u003ca id=\"user-content-running-gui-applications-on-a-non-gpu-node\" class=\"anchor\" aria-label=\"Permalink: Running GUI applications on a non-GPU node\" href=\"#running-gui-applications-on-a-non-gpu-node\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe applications in the Singularity container should run without the need for a dedicated GPU.\u003c/p\u003e\n\u003cp\u003eHowever, an X server needs to be running for this to work. On nodes with GPU, X Server is started with NVIDIA driver, and on non-GPU nodes, the X Server is started with MESA library.\u003c/p\u003e\n\u003cp\u003eX Server can be started during boot (for example, using \u003ccode\u003esystemctl set-default graphical.target\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eMake sure that VirtualGL package is installed in the container. The code below will download and install VirtualGL.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://swift.rc.nectar.org.au/v1/AUTH_810/CVL-Singularity-External-Files/virtualgl_2.6.2_amd64.deb\n\ndpkg -i virtualgl_2.6.2_amd64.deb\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 8, - "subscribers_count": 8, + "subscribers_count": 4, "topics": [], - "updated_at": 1671060034.0 + "updated_at": 1696247668.0 }, { "data_format": 2, - "description": "Transposable Elements MOvement detection using LOng reads", + "description": null, "filenames": [ "Singularity" ], - "full_name": "DrosophilaGenomeEvolution/TrEMOLO", - "latest_release": "v2.2-beta", - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/5391\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/TrEMOLO9.png\"\u003e\u003cimg src=\"images/TrEMOLO9.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#in\"\u003eGlobal variations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#out\"\u003ePopulational variations\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#release\"\u003eRelease note\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#requirements\"\u003eRequirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#git\"\u003eUsing Git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity\"\u003eUsing Singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#configuration\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output\"\u003eOutput files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation \u0026amp; Licence\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-tremolo\" class=\"anchor\" aria-hidden=\"true\" href=\"#tremolo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTrEMOLO\u003ca name=\"user-content-introduction\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eTransposable Elements MOvement detection using LOng reads\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrEMOLO uses long reads, either directly or through their assembly, to detect:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGlobal TE variations between two assembled genomes\u003c/li\u003e\n\u003cli\u003ePopulational/somatic variation in TE insertion/deletion\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-global-variations-the-insiders\" class=\"anchor\" aria-hidden=\"true\" href=\"#global-variations-the-insiders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGlobal variations, the insiders\u003ca name=\"user-content-in\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eUsing a reference genome and an assembled one (preferentially using long contigs or even better a chrosomome-scale assembly), TrEMOLO will extract the \u003cstrong\u003einsiders\u003c/strong\u003e, \u003cem\u003ei.e.\u003c/em\u003e variant transposable elements (TEs) present globally in the assembly, and tag them. Indeed, assemblers will provide the most frequent haplotype at each locus, and thus an assembly represent just the \"consensus\" of all haplotypes present at each locus.\nYou will obtain a \u003ca href=\"#output\"\u003eset of files\u003c/a\u003e with the location of these variable insertions and deletions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-populational-variations-the-outsiders\" class=\"anchor\" aria-hidden=\"true\" href=\"#populational-variations-the-outsiders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePopulational variations, the outsiders\u003ca name=\"user-content-out\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eThrough remapping of reads that have been used to assemble the genome of interest, TrEMOLO will identify the populational variations (and even somatic ones) within the initial dataset of reads, and thus of DNA/individuals sampled. These variant TEs are the \u003cstrong\u003eoutsiders\u003c/strong\u003e, present only in a part of the population or cells.\nIn the same way as for insiders, you will obtain a \u003ca href=\"#output\"\u003eset of files\u003c/a\u003e with the location of these variable insertions and deletions.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-release-notes\" class=\"anchor\" aria-hidden=\"true\" href=\"#release-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRelease notes\u003ca name=\"user-content-release\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003ch1\u003e\u003ca id=\"user-content-current-limitations\" class=\"anchor\" aria-hidden=\"true\" href=\"#current-limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCurrent limitations\u003c/h1\u003e\n\u003ch1\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003ca name=\"user-content-requirements\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eNumerous tools are used by TrEMOLO. We recommand to use the \u003ca href=\"#singularity\"\u003eSingularity installation\u003c/a\u003e to be sure to have all of them in the good configurations and versions.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor both approaches\n\u003cul\u003e\n\u003cli\u003ePython 3.6+\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor Global variation tool\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web\u0026amp;PAGE_TYPE=BlastDocs\u0026amp;DOC_TYPE=Download\" rel=\"nofollow\"\u003eBLAST\u003c/a\u003e 2.2+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://bedtools.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eBedtools 2.27.1\u003c/a\u003e v2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://assemblytics.com/\" rel=\"nofollow\"\u003eAssemblytics\u003c/a\u003e or\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/malonge/RaGOO\"\u003eRaGOO\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor Populational variation tool\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://snakemake-wrappers.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e 5.5.2+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/lh3/minimap2\"\u003eMinimap2\u003c/a\u003e 2.24+\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.htslib.org/\" rel=\"nofollow\"\u003eSamtools\u003c/a\u003e 1.9 and (1.15.1 optional)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/eldariont/svim/releases/tag/v1.4.2\"\u003esvim 1.4.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/fritzsedlazeck/Sniffles/releases/tag/v1.0.12b\"\u003eSniffles 1.0.12\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePython libs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://biopython.org/\" rel=\"nofollow\"\u003eBiopython\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pandas.pydata.org/\" rel=\"nofollow\"\u003ePandas\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://numpy.org/\" rel=\"nofollow\"\u003eNumpy\u003c/a\u003e 1.21.2\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://matplotlib.org/\" rel=\"nofollow\"\u003epylab\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/intervaltree/\" rel=\"nofollow\"\u003eintervaltree\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pypi.org/project/pysam/\" rel=\"nofollow\"\u003epysam\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePerl v5.26.2+\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFor report\n\u003cul\u003e\n\u003cli\u003eR 3.3+ libs\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.r-project.org/nosvn/pandoc/knitr.html\" rel=\"nofollow\"\u003eknitr 1.38\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://rmarkdown.rstudio.com/\" rel=\"nofollow\"\u003ermarkdown 2.13\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bookdown.org/yihui/bookdown/get-started.html\" rel=\"nofollow\"\u003ebookdown 0.25\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.rdocumentation.org/packages/viridis/versions/0.3.4\" rel=\"nofollow\"\u003eviridis 0.6.2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sjmgarnier/viridisLite\"\u003eviridisLite 0.4.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://rdrr.io/cran/rjson/\" rel=\"nofollow\"\u003erjson 0.2.20\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jrnold/ggthemes\"\u003eggthemes 4.2.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://rdrr.io/cran/forcats/\" rel=\"nofollow\"\u003eforcats 0.5.1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.r-project.org/nosvn/pandoc/dplyr.html\" rel=\"nofollow\"\u003ereshape2 1.4.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.r-project.org/nosvn/pandoc/dplyr.html\" rel=\"nofollow\"\u003edplyr 1.0.8\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bookdown.org/yihui/rmarkdown-cookbook/kableextra.html\" rel=\"nofollow\"\u003ekableExtra 1.3.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cran.r-project.org/web/packages/extrafont/README.html\" rel=\"nofollow\"\u003eextrafont 0.17\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ggplot2.tidyverse.org/\" rel=\"nofollow\"\u003eggplot2 3.3.5\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.rdocumentation.org/packages/RColorBrewer/versions/1.1-2=\" rel=\"nofollow\"\u003eRColorBrewer 1.1-2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cran.r-project.org/web/packages/stringr/index.html\" rel=\"nofollow\"\u003estringr 1.4.0\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cran.r-project.org/web/packages/stringi/index.html\" rel=\"nofollow\"\u003estringi 1.7.6\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jgm/citeproc\"\u003epandoc-citeproc 0.17\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOthers\n\u003cul\u003e\n\u003cli\u003enodejs\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003ca name=\"user-content-Installation\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-git\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-git\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Git\u003ca name=\"user-content-git\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eOnce the requirements fullfilled, just \u003cem\u003egit\u003c/em\u003e clone\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/DrosophilaGenomeEvolution/TrEMOLO.git\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing Singularity\u003ca name=\"user-content-singularity\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html#install-the-debian-ubuntu-package-using-apt\" rel=\"nofollow\"\u003e\u003cem\u003eSingularity\u003c/em\u003e installation Debian/Ubuntu with package\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-compiling-yourself\" class=\"anchor\" aria-hidden=\"true\" href=\"#compiling-yourself\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling yourself\u003c/h3\u003e\n\u003cp\u003eA \u003ca href=\"https://sylabs.io/\" rel=\"nofollow\"\u003e\u003cem\u003eSingularity\u003c/em\u003e container\u003c/a\u003e is available with all tools compiled in.\nThe \u003cem\u003eSingularity\u003c/em\u003e file provided in this repo and can be compiled as such:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build TrEMOLO.simg TrEMOLO/Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eYOU MUST BE ROOT for compiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTest TrEMOLO with singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e TrEMOLO.simg snakemake --snakefile TrEMOLO/run.snk --configfile TrEMOLO/test/tmp_config.yml\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eOR\u003c/span\u003e\nsingularity run TrEMOLO.simg snakemake --snakefile TrEMOLO/run.snk --configfile TrEMOLO/test/tmp_config.yml\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-pulling-from-singularityhub\" class=\"anchor\" aria-hidden=\"true\" href=\"#pulling-from-singularityhub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePulling from SingularityHub\u003c/h3\u003e\n\u003cp\u003eThis option is disabled since Singularity Hub is for the moment in read-only. We are looking for a Singularity repo to ease the use.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-configuration-of-the-parameter-file\" class=\"anchor\" aria-hidden=\"true\" href=\"#configuration-of-the-parameter-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfiguration of the parameter file\u003ca name=\"user-content-configuration\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eTrEMOLO uses \u003ca href=\"https://snakemake-wrappers.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e to perform its analyses. You have then first to provide your parameters in a \u003cem\u003e.yaml\u003c/em\u003e file (see an example in the \u003cem\u003econfig.yaml\u003c/em\u003e file). Parameters are :\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e all path can be relative or absolute depending of your tree.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eIt is advised to only use absolute path if you are not familiar with computer science or the importance of folder trees structure.\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eDATA\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eGENOME\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/genome_file.fasta\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003egenome (fasta file) [required]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eTE_DB\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/database_TE.fasta\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eDatabase of TE (a fasta file) [required]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eREFERENCE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/reference_file.fasta\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003ereference genome (fasta file) only if INSIDER_VARIANT = True [optional]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSAMPLE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/reads_file.fastq\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003elong reads (a fastq file) only if OUTSIDER_VARIANT = True [optional]\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eAt least, provide either REFERENCE or SAMPLE. Both can be provided\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eWORK_DIRECTORY\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/directory\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003ename of output directory [optional, will be created as \u0027TrEMOLO_OUTPUT\u0027]\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eAt least, you must provide either the reference file, or the fastq file or both\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003eCHOICE\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePIPELINE\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eOUTSIDER_VARIANT\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e outsiders, TE not in the assembly - population variation\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINSIDER_VARIANT\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e insiders, TE in the assembly\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eREPORT\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for getting a report.html file with graphics\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eMODE_PARALLELING\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eFalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e test time : with True value 50m53,983s; with False value 138m55,985s; With 8 threads\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eOUTSIDER_VARIANT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eCALL_SV\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esniffles\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e possibilities for SV tools: sniffles, svim\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINTEGRATE_TE_TO_GENOME\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (True, False) Re-build the assembly with the INSIDER integrated in\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eOPTIMIZE_FREQUENCE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (True, False) FREQUENCE CALCULATED WITH CLIPPING READS\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINSIDER_VARIANT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eDETECT_ALL_TE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eFalse \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e detect ALL TE on genome (parameter GENOME) assembly not only new insertion. Warning! it may be take several hours on big genomes\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINTERMEDIATE_FILE\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Conserve the intermediate analyses files to process them latter.\u003c/span\u003e\n\n\n\u003cspan class=\"pl-ent\"\u003ePARAMS\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eTHREADS\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003enumber of threads for some task\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eOUTSIDER_VARIANT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eMINIMAP2\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePRESET_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003emap-ont\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e minimap2 option is map-ont by default (map-pb, map-ont)\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eOPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e-t 8\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e more option of minimap2 can be specified here\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSAMTOOLS_VIEW\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePRESET_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSAMTOOLS_SORT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePRESET_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSAMTOOLS_CALLMD\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePRESET_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eTSD\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eFILE_SIZE_TE_TSD\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/SIZE_TSD.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e File of TSD sizes for the reference elements (format=\"TE SIZE\", one TE per line) [optional]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eSIZE_FLANK\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e flanking sequence size for calculation of TSD; put value \u0026gt; 4\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eTE_DETECTION\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eCHROM_KEEP\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e regular expresion for chromosome filtering; for instance for Drosophila \"2L,2R,3[RL],X\" ; Put \".\" to keep all chromosome\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eGET_SEQ_REPORT_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e-m 500\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003esequence recovery file in the vcf\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ePARS_BLN_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--min-size-percent 80 --min-pident 80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e option for TrEMOLO/lib/python/parse_blast_main.py - don\u0027t put -c option\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eINSIDER_VARIANT\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ePARS_BLN_OPTION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--min-size-percent 80 --min-pident 80\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e parameters for validation of insiders\u003c/span\u003e\n\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe main parameters are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGENOME\u003c/code\u003e : Assembly of the sample of interest (or mix of samples), fasta file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTE_DB\u003c/code\u003e : A \u003cstrong\u003eMultifasta\u003c/strong\u003e file containing the canonical sequence of transposable elements. You can add also copy sequences but results will be more complex to interpretate.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eREFERENCE\u003c/code\u003e : Fasta file containing the reference genome of the species of interest.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eWORK_DIRECTORY\u003c/code\u003e : Directory that will contain the output files. If the directory does not exist it will be created; default value is \u003cstrong\u003eTrEMOLO_OUTPUT\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSAMPLE\u003c/code\u003e : File containing the reads used for the sample assembly.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can use \u003cstrong\u003econfig_INSIDER.yaml\u003c/strong\u003e for only \u003cstrong\u003eINSIDER\u003c/strong\u003e analysis or \u003cstrong\u003econfig_OUTSIDER.yaml\u003c/strong\u003e for only \u003cstrong\u003eOUTSIDER\u003c/strong\u003e analysis.\nTo analyse \u003cstrong\u003eINSIDER\u003c/strong\u003e, only the \u003ccode\u003eREFERENCE\u003c/code\u003e , the \u003ccode\u003eGENOME\u003c/code\u003e, the \u003ccode\u003eTE_DB\u003c/code\u003e and the \u003ccode\u003eWORK_DIRECTORY\u003c/code\u003e are required.\nTo analyse \u003cstrong\u003eOUTSIDER\u003c/strong\u003e, only the \u003ccode\u003eSAMPLE\u003c/code\u003e , the \u003ccode\u003eGENOME\u003c/code\u003e, the \u003ccode\u003eTE_DB\u003c/code\u003e and the \u003ccode\u003eWORK_DIRECTORY\u003c/code\u003e are required.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003ca name=\"user-content-usage\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile /path/to/TrEMOLO/run.snk --configfile /path/to/your_config.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor running tests\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esnakemake --snakefile TrEMOLO/run.snk --configfile TrEMOLO/test/tmp_config.yml\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-output-files-summary-open_file_folder\" class=\"anchor\" aria-hidden=\"true\" href=\"#output-files-summary-open_file_folder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput files summary \u003cg-emoji class=\"g-emoji\" alias=\"open_file_folder\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/1f4c2.png\"\u003e\ud83d\udcc2\u003c/g-emoji\u003e\u003ca name=\"user-content-output\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eHere is the structure of the output files obtained after running the pipeline.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eWORK_DIRECTORY\n\u251c\u2500\u2500 params.yaml ##**Your config file\n\u251c\u2500\u2500 LIST_HEADER_DB_TE.csv ##** list of names assigned to TE in the TE database (Only if you have charactere \"\u0026amp; ; / \\ | \u0027 : ! ? \" in your TE database)\n\u251c\u2500\u2500 POSITION_ALL_TE.bed -\u0026gt; INSIDER/TE_DETECTION/POSITION_ALL_TE.bed ##**ALL TE ON GENOME NOT ONLY INSERTION (ONLY IF PARAMETER \"DETECT_ALL_TE\" is True),\n\u251c\u2500\u2500 POSITION_TE_INOUTSIDER.bed\n\u251c\u2500\u2500 POSITION_TE_INSIDER.bed\n\u251c\u2500\u2500 POSITION_TE_OUTSIDER.bed\n\u251c\u2500\u2500 POS_TE_INSIDER_ON_REF.bed -\u0026gt; INSIDER/TE_DETECTION/INSERTION_TE_ON_REF.bed ##**POSITION TE INSIDER ON REFRENCE GENOME\n\u251c\u2500\u2500 POS_TE_OUTSIDER_ON_REF.bed ##**POSITION TE OUTSIDER ON REFRENCE GENOME\n\u251c\u2500\u2500 POSITION_TE_OUTSIDER_IN_NEO_GENOME.bed ##**POSITION TE SEQUENCE ON BEST READS SUPPORT INTEGRATED IN GENOME\n\u251c\u2500\u2500 POSITION_TE_OUTSIDER_IN_PSEUDO_GENOME.bed ##**POSITION TE SEQUENCE ON TE DATABASE (with ID) INTEGRATED IN GENOME\n\u251c\u2500\u2500 VALUES_TSD_ALL_GROUP.csv\n\u251c\u2500\u2500 VALUES_TSD_GROUP_OUTSIDER.csv\n\u251c\u2500\u2500 VALUES_TSD_INSIDER_GROUP.csv\n\u251c\u2500\u2500 TE_INFOS.bed ##**FILE CONTENING ALL INFO OF TE INSERTION\n\u251c\u2500\u2500 DELETION_TE.bed -\u0026gt; INSIDER/TE_DETECTION/DELETION_TE.bed ##**TE DELETION POSTION ON GENOME\n\u251c\u2500\u2500 DELETION_TE_ON_REF.bed -\u0026gt; INSIDER/TE_DETECTION/DELETION_TE_ON_REF.bed ##**TE DELETION POSITION ON REFERENCE\n\u251c\u2500\u2500 SOFT_TE.bed -\u0026gt; OUTSIDER/TE_DETECTION/SOFT/SOFT_TE.bed ##**TE INSERTION FOUND IN SOFT READS\n\u251c\u2500\u2500 INSIDER ##**FOLDER CONTAINS FILES TRAITEMENT INSIDER\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 FREQ_INSIDER\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_DETECTION\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 TSD\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_INSIDER_VR\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 VARIANT_CALLING\n\u251c\u2500\u2500 log ##**log file to check if you have any error\n\u251c\u2500\u2500 OUTSIDER\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ET_FIND_FA\n\u2502\u00a0\u00a0 \u2502 \u251c\u2500\u2500 TE_REPORT_FOUND_TE_NAME.fasta\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_REPORT_FOUND_blood.fasta\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 TE_REPORT_FOUND_ZAM.fasta\n...\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 FREQ_OPTIMIZED\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 DEPTH_TE.csv\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 INSIDER_VR\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 MAPPING ##**FOLDER CONTAINS FILES MAPPING ON GENOME\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 MAPPING_TO_REF ##**FOLDER CONTAINS FILES MAPPING ON REFERENCE GENOME\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 READ_FASTQ_TE ##**FOLDER CONTAINS ALL THE READs ASSOCIATED WITH THE TE\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_DETECTION\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 MERGE_TE\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 TSD\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TrEMOLO_SV_TE\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 INS\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 SOFT\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 TE_TOWARD_GENOME ##**FOLDER CONTAINS ALL THE READs ASSOCIATED WITH THE TE\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 NEO_GENOME.fasta ##**GENOME CONTAINS TE OUTSIDER (the best sequence of svim/sniffles)\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 PSEUDO_GENOME_TE_DB_ID.fasta ##**GENOME CONTAINS TE OUTSIDER (the sequence of database TE and the ID of svim/sniffles)\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 TRUE_POSITION_TE_PSEUDO.bed ##**POSITION IN PSEUDO GENOME\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 TRUE_POSITION_TE.fasta ##**SEQUENCE INTEGRATE IN PSEUDO GENOME\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 TRUE_POSITION_TE_NEO.bed ##**POSITION IN NEO GENOME\n\u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 TRUE_POSITION_TE_READS.fasta ##**SEQUENCE INTEGRATE IN NEO GENOME\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 VARIANT_CALLING ##**FOLDER CONTAINS FILES OF sniflles/svim\n\u251c\u2500\u2500 REPORT\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 mini_report\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 report.html\n\u251c\u2500\u2500 SNAKE_USED\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Snakefile_insider.snk\n\u2514\u2500\u2500 \u2514\u2500\u2500 Snakefile_outsider.snk\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-most-useful-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#most-useful-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMost useful output\u003c/h3\u003e\n\u003cp\u003eThe most useful output files are :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe html report in \u003cstrong\u003eyour_work_directory/REPORT/report.html\u003c/strong\u003e with summary graphics, as shown \u003ca href=\"https://rawcdn.githack.com/DrosophilaGenomeEvolution/TrEMOLO/f11c369ea037db66a7a86ee9d6c266f9069a8ecf/test/web/index.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe output file \u003cstrong\u003eyour_work_direcetory/TE_INFOS.bed\u003c/strong\u003e gathers all the necessary information.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003echrom\u003c/th\u003e\n\u003cth\u003estart\u003c/th\u003e\n\u003cth\u003eend\u003c/th\u003e\n\u003cth\u003eTE|ID\u003c/th\u003e\n\u003cth\u003estrand\u003c/th\u003e\n\u003cth\u003eTSD\u003c/th\u003e\n\u003cth\u003epident\u003c/th\u003e\n\u003cth\u003epsize_TE\u003c/th\u003e\n\u003cth\u003eSIZE_TE\u003c/th\u003e\n\u003cth\u003eNEW_POS\u003c/th\u003e\n\u003cth\u003eFREQ (%)\u003c/th\u003e\n\u003cth\u003eFREQ_OPTIMIZED (%)\u003c/th\u003e\n\u003cth\u003eID_TrEMOLO\u003c/th\u003e\n\u003cth\u003eTYPE\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e2R_RaGOO_RaGOO\u003c/td\u003e\n\u003ctd\u003e16943971\u003c/td\u003e\n\u003ctd\u003e16943972\u003c/td\u003e\n\u003ctd\u003eroo|svim.INS.175\u003c/td\u003e\n\u003ctd\u003e+\u003c/td\u003e\n\u003ctd\u003eGTACA\u003c/td\u003e\n\u003ctd\u003e97.026\u003c/td\u003e\n\u003ctd\u003e99.2\u003c/td\u003e\n\u003ctd\u003e9006\u003c/td\u003e\n\u003ctd\u003e16943978\u003c/td\u003e\n\u003ctd\u003e28.5714\u003c/td\u003e\n\u003ctd\u003e28.5714\u003c/td\u003e\n\u003ctd\u003eTE_ID_OUTSIDER.94047.INS.107508.0\u003c/td\u003e\n\u003ctd\u003eINS\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eX_RaGOO_RaGOO\u003c/td\u003e\n\u003ctd\u003e21629415\u003c/td\u003e\n\u003ctd\u003e21629416\u003c/td\u003e\n\u003ctd\u003eZAM|Assemblytics_w_534\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eCGCG\u003c/td\u003e\n\u003ctd\u003e98.6\u003c/td\u003e\n\u003ctd\u003e90.5\u003c/td\u003e\n\u003ctd\u003e8435\u003c/td\u003e\n\u003ctd\u003e21629413\u003c/td\u003e\n\u003ctd\u003e11.1111\u003c/td\u003e\n\u003ctd\u003e10.0000\u003c/td\u003e\n\u003ctd\u003eTE_ID_INSIDER.77237.Repeat_expansion.8\u003c/td\u003e\n\u003ctd\u003eRepeat_expansion\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003echrom\u003c/code\u003e : chromosome\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estart\u003c/code\u003e : start position for the TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eend\u003c/code\u003e : end position for the TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTE|ID\u003c/code\u003e : TE name and ID in \u003cstrong\u003eSV.vcf\u003c/strong\u003e,\u003cstrong\u003eSV_SOFT.vcf\u003c/strong\u003e and \u003cstrong\u003eSV_INS_CLUST.bed\u003c/strong\u003e (for OUTSIDER) or \u003cstrong\u003eassemblytics_out.Assemblytics_structural_variants.bed\u003c/strong\u003e (for INSIDER)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estrand\u003c/code\u003e : strand of the TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTSD\u003c/code\u003e : TSD SEQUENCE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epident\u003c/code\u003e : percentage of identical matches with TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epsize_TE\u003c/code\u003e : percentage of size with TE in database\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSIZE_TE\u003c/code\u003e : TE size\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNEW_POS\u003c/code\u003e : position corrected with calculated TSD (only for OUTSIDER)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFREQ\u003c/code\u003e : frequence, normalized\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFREQ_OPTIMIZED\u003c/code\u003e : frequence optimized with conversion of clipped read to not clipped (OUTSIDER only)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eID_TrEMOLO\u003c/code\u003e : TrEMOLO ID of the TE\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTYPE\u003c/code\u003e : type of insertion can be SOFT,INS,INS_DEL... (INS_DEL is an insertion located on a deletion of the assembly)\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch1\u003e\u003ca id=\"user-content-licence-and-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#licence-and-citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicence and Citation\u003ca name=\"user-content-citation\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\n\u003cp\u003eIt is licencied under \u003ca href=\"Licence_CeCILL-C_V1-en.txt\"\u003eCeCill-C\u003c/a\u003e and \u003ca href=\"LICENSE\"\u003eGPLv3\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you use TrEMOLO, please cite:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.mdpi.com/2073-4409/9/8/1776\" rel=\"nofollow\"\u003eMohamed, M.; Dang, N. .-M.; Ogyama, Y.; Burlet, N.; Mugat, B.; Boulesteix, M.; M\u00e9rel, V.; Veber, P.; Salces-Ortiz, J.; Severac, D.; P\u00e9lisson, A.; Vieira, C.; Sabot, F.; Fablet, M.; Chambeyron, S. A Transposon Story: From TE Content to TE Dynamic Invasion of Drosophila Genomes Using the Single-Molecule Sequencing Technology from Oxford Nanopore. Cells 2020, 9, 1776.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in the paper are available \u003ca href=\"https://dataverse.ird.fr/dataverse/tremolo_data\" rel=\"nofollow\"\u003ehere on DataSuds\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 8, - "subscribers_count": 2, - "topics": [], - "updated_at": 1671966248.0 - }, - { - "data_format": 2, - "description": "Examples of jobs that can be run via Galileo", - "filenames": [ - "hpcc/Singularityfile" - ], - "full_name": "GoHypernet/Galileo-examples", + "full_name": "UCLBrain/MSLS", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGalileo application example repository\u003c/h1\u003e\u003ca id=\"user-content-galileo-application-example-repository\" class=\"anchor\" aria-label=\"Permalink: Galileo application example repository\" href=\"#galileo-application-example-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains a number of examples, representing many frameworks,\nthat can be deployed through Galileo with no additional setup.\u003c/p\u003e\n\u003cp\u003eEach subdirectory contains its own readme file describing the analysis being\nperformed. A user can copy the Dockerfile from an example similar to the one\nthey would like to run and modify it accordingly. Alternatively, a user can\nuse the Docker Wizard helper in the Galileo application to build a Dockerfile\nfrom scratch for their framework.\u003c/p\u003e\n\u003cp\u003eFor more information on Galileo, start here:\n\u003ca href=\"https://galileoapp.io/gettingstarted/\" rel=\"nofollow\"\u003ehttps://galileoapp.io/gettingstarted/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo start using the application, go here:\n\u003ca href=\"https://app.galileoapp.io\" rel=\"nofollow\"\u003ehttps://app.galileoapp.io\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/UCLBrain/MSLS/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/246f72d8fecf3c0be868bbe7241aaa82a61e04b2a26eae992b75e6add7a61f0c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f55434c427261696e2f4d534c53\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/UCLBrain/MSLS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/network\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4e2f655dcd116357359604d61c9411b560337ee306a880d75f2cb852807047c9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f55434c427261696e2f4d534c53\" alt=\"GitHub forks\" data-canonical-src=\"https://img.shields.io/github/forks/UCLBrain/MSLS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/stargazers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2e6a5e6e35e5ab336a877e2fb3e253062defe3baebdfe499c22fd2046386213b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f55434c427261696e2f4d534c53\" alt=\"GitHub stars\" data-canonical-src=\"https://img.shields.io/github/stars/UCLBrain/MSLS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e49664e0e21231283f8b8db659ac06b74f4f13d6d55f2cc2c0e6e0c3c4b0169/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f55434c427261696e2f4d534c53\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/UCLBrain/MSLS\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMulti-Label Multi/Single-Class Image Segmentation\u003c/h1\u003e\u003ca id=\"user-content-multi-label-multisingle-class-image-segmentation\" class=\"anchor\" aria-label=\"Permalink: Multi-Label Multi/Single-Class Image Segmentation\" href=\"#multi-label-multisingle-class-image-segmentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/diag.png\"\u003e\u003cimg height=\"510\" src=\"images/diag.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePublication\u003c/h1\u003e\u003ca id=\"user-content-publication\" class=\"anchor\" aria-label=\"Permalink: Publication\" href=\"#publication\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLe Zhang, Ryutaro Tanno, Kevin Bronik, Chen Jin, Parashkev Nachev, Frederik Barkhof, Olga Ciccarelli, and Daniel C. Alexander, Learning to Segment When Experts Disagree, International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI). Springer, Cham, 2020.\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/Miccai_2020_abs.jpg\"\u003e\u003cimg align=\"center\" height=\"1000\" src=\"images/Miccai_2020_abs.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\n \n \u003cp\u003eClick here to see full pdf file: \u003ca href=\"https://github.com/UCLBrain/MSLS/blob/master/MICCAI_2020.pdf\"\u003eLink to PDF\u003c/a\u003e\u003c/p\u003e\n \n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the GUI Program!\u003c/h1\u003e\u003ca id=\"user-content-running-the-gui-program\" class=\"anchor\" aria-label=\"Permalink: Running the GUI Program!\" href=\"#running-the-gui-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, user needs to install Anaconda \u003ca href=\"https://www.anaconda.com/\" rel=\"nofollow\"\u003ehttps://www.anaconda.com/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda env create -f conda_environment_Training_Inference.yml \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efinally\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python Training_Inference_GUI.py \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/GUI.jpg\"\u003e\u003cimg height=\"510\" src=\"images/GUI.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning the Program from the command line!\u003c/h1\u003e\u003ca id=\"user-content-running-the-program-from-the-command-line\" class=\"anchor\" aria-label=\"Permalink: Running the Program from the command line!\" href=\"#running-the-program-from-the-command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - conda activate traintestenv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ethen for training\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Training_without_GUI.py [or annotation_network_Training_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003efor testing\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e - python segmentation_network_Inference_without_GUI.py [or annotation_network_Inference_without_GUI.py]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTesting the Program!\u003c/h1\u003e\u003ca id=\"user-content-testing-the-program\" class=\"anchor\" aria-label=\"Permalink: Testing the Program!\" href=\"#testing-the-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExamples of Training and Testing subjects can be found in: \u003ca href=\"https://github.com/UCLBrain/MSLS/tree/master/examples\"\u003ehttps://github.com/UCLBrain/MSLS/tree/master/examples\u003c/a\u003e (which will allow users to quickly and easily train and test the program)\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/bin_seg_ex.jpg\"\u003e\u003cimg height=\"510\" src=\"images/bin_seg_ex.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e..................................................................................................................................................................\u003c/p\u003e\n\u003cbr\u003e\n \u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/multi_seg_ex.jpg\"\u003e\u003cimg height=\"510\" src=\"images/multi_seg_ex.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n", "stargazers_count": 8, - "subscribers_count": 10, + "subscribers_count": 1, "topics": [], - "updated_at": 1626717385.0 + "updated_at": 1673976172.0 }, { "data_format": 2, - "description": "Bayesian model selection to detect zero-inflated genes", + "description": "Install methods for UPPMAX modules plus some helper scripts", "filenames": [ - "Singularity" + "singularity_info/IMAP/Singularity.IMAP", + "singularity_info/metaWRAP_1.3.2/Singularity.metaWRAP", + "singularity_info/bonito/Singularity.bonito", + "singularity_info/VESPA/Singularity.VESPA", + "singularity_info/ORFfinder/Singularity.ORFfinder", + "singularity_info/gapseq-RT-227932/Singularity.gapseq" ], - "full_name": "churchill-lab/scRATE", + "full_name": "UPPMAX/install-methods", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4398\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/kbchoi/scrate\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ee92e9136378ab9a5896ae8977bfec1d87c201e4a3aaadfd4c8a0eb4c61be848/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f636c6f75642f6175746f6d617465642f6b6263686f692f736372617465\" alt=\"Docker Cloud Automated build\" data-canonical-src=\"https://img.shields.io/docker/cloud/automated/kbchoi/scrate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003escRATE\u003c/h1\u003e\u003ca id=\"user-content-scrate\" class=\"anchor\" aria-label=\"Permalink: scRATE\" href=\"#scrate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003escRATE is a model selection-based scRNA-seq quantitation algorithm that controls overfitting and unwarranted imputation of technical zeros. We first fit UMI counts with Poisson (P), Negative-Binomial (NB), Zero-inflated Poisson (ZIP), and Zero-inflated Negative Binomial models (ZINB). These models are applicable to UMI counts directly and therefore no need of arbitrary preprocessing of counts, e.g., normalization (by scaling to, for example, CPM) or log-transformation (with pseudocounts). Our model comparison enables us to compute denoised rates of gene expression using the best model which each gene data is conforming to.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFree software: GPLv3 license\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation of scRATE is simple, although it may take a while as it has to compile rstan, the related packages, and all their dependencies.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003echurchill-lab/scRATE\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e library(\u003cspan class=\"pl-smi\"\u003escRATE\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e version()\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e____ ____ ____ ____ ___ ____\n[__ | |__/ |__| | |___\n___] |___ | \\ | | | |___\n Ver:0.1.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis might hick up because of hdf5r or loomR. Then, try the following.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e install.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehdf5r\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-e\"\u003edevtools\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-v\"\u003erepo\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emojaveazure/loomR\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eref\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edevelop\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is a quick example on how to use it. We first load \u003ccode\u003escRATE\u003c/code\u003e and \u003ccode\u003eloomR\u003c/code\u003e package. We use loom format files to store single-cell gene expression data as it is supported in both \u003ccode\u003eR\u003c/code\u003e and \u003ccode\u003epython\u003c/code\u003e. In \u003ccode\u003escRATE\u003c/code\u003e, we also provide handy features that facilitate the handling of model selection results with loom format file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e library(\u003cspan class=\"pl-smi\"\u003escRATE\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e library(\u003cspan class=\"pl-smi\"\u003eloomR\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA data file in the example is available at ftp://churchill-lab.jax.org/analysis/scRATE/DC-like_cells.loom.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eds\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e connect(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eDC-like_cells.loom\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecntmat\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e t(\u003cspan class=\"pl-smi\"\u003eds\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003ematrix\u003c/span\u003e[,])\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egsymb\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eds\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003erow.attrs\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eGeneID\u003c/span\u003e[]\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eds\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e$\u003c/span\u003eclose_all()\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e head(\u003cspan class=\"pl-smi\"\u003egsymb\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e[1] \"Xkr4\" \"Gm1992\" \"Gm37381\" \"Rp1\" \"Rp1.1\" \"Sox17\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe recommend using offset (or exposure) in order to reflect the difference in depth of coverage across cells while fitting the count models. The models we compare use log link function, and therefore, the offsets should be log transformed too.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eexposure\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e log(colSums(\u003cspan class=\"pl-smi\"\u003ecntmat\u003c/span\u003e))\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e head(\u003cspan class=\"pl-smi\"\u003eexposure\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e [1] 8.815518 8.996157 9.025816 9.037771 9.062420 9.397732\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe will pick a gene, \u003cem\u003eCybb\u003c/em\u003e (one that we know that it fits to ZINB model significantly better than the other models), and load its UMI counts into a data frame.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egg\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e4153\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egsymb\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003egg\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e[1] \"Cybb\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ecntmat\u003c/span\u003e[\u003cspan class=\"pl-smi\"\u003egg\u003c/span\u003e,]\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e [1] 1 0 8 7 3 6 7 1 4 0 0 2 2 10 0 3 1 1 2 4 6 3 9 3 1\n [26] 4 1 2 3 8 0 0 1 6 2 6 3 0 0 1 0 4 1 3 0 2 17 1 2 5\n [51] 0 1 1 0 6 3 4 4 3 1 3 1 7 8 0 2 4 6 2 7 0 4 0 1 0\n [76] 2 0 8 2 0 1 4 9 3 0 2 2 3 0 3 0 0 4 1 4 3 2 3 2 2\n[101] 8 0 5 8 2 3 0 3 0 3 6 0 4 2 11 5 1 2 12 2 4 2 6 8 6\n[126] 3 3 1 0 2 1 1 10 2 2 0 2 2 1 0 1 0 3 11 0 7 2 2 2 3\n[151] 7 0 1 10 4 1 4 5 1 0 1 3 3 0 5 4 9 4 2 0 4 1 2 4 1\n[176] 2 1 4 2 1 0 1 0 1 4 6 8 9 4 3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egexpr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eexposure\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eexposure\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe must have \u003ccode\u003ey\u003c/code\u003e and \u003ccode\u003eexposure\u003c/code\u003e as variables. We can also append covariates to the data frame.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egexpr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edata.frame\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003ey\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eexposure\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eexposure\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003ecelltype\u003c/span\u003e, \u003cspan class=\"pl-smi\"\u003esex\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe are now ready to fit the models.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003emodel_fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fit_count_models(\u003cspan class=\"pl-smi\"\u003egexpr\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr we can specify our own model formula (See brms documentation) if there are covariates. Note we should not use \u003ccode\u003eoffset()\u003c/code\u003e function in the formula.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003emodel_fit\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e fit_count_models(\u003cspan class=\"pl-smi\"\u003egexpr\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ey ~ 1 + (1|sex) + (1|celltype)\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eFitting models for Cybb\nFitting data with Poisson model...\nFitting data with Negative Binomial model...\nFitting data with Zero-Inflated Poisson model...\nCompiling the C++ model\n\nStart sampling\nSAMPLING FOR MODEL \u0027zip\u0027 NOW (CHAIN 1).\n\nChain 1:\nChain 1: Gradient evaluation took 0.000177 seconds\nChain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.77 seconds.\nChain 1: Adjust your expectations accordingly!\nChain 1:\nChain 1:\n...\n\nFitting data with Zero-Inflated Negative Binomial model...\nCompiling the C++ model\nStart sampling\n\nSAMPLING FOR MODEL \u0027zinb\u0027 NOW (CHAIN 1).\nChain 1:\nChain 1: Gradient evaluation took 0.000329 seconds\nChain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.29 seconds.\nChain 1: Adjust your expectations accordingly!\nChain 1:\nChain 1:\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we compare the models with the leave-one-out cross validation test, and select the model that best fits the data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eelpd_loo\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e compare_count_models(\u003cspan class=\"pl-smi\"\u003emodel_fit\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eelpd_loo\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e elpd_diff se_diff\nZINB 0.0 0.0\nNB -3.0 2.7\nZIP -35.6 9.5\nP -103.9 19.6\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e select_model(\u003cspan class=\"pl-smi\"\u003eelpd_loo\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003emargin\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to cite\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eK. Choi, Y. Chen, D.A. Skelly, G.A. Churchill. \u201cBayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics.\u201d Genome Biology, 21, 183 (2020). \u003ca href=\"https://doi.org/10.1186/s13059-020-02103-2\" rel=\"nofollow\"\u003ehttps://doi.org/10.1186/s13059-020-02103-2\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eModule Installation Methods\u003c/h1\u003e\u003ca id=\"user-content-module-installation-methods\" class=\"anchor\" aria-label=\"Permalink: Module Installation Methods\" href=\"#module-installation-methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a collection of READMEs generated during installation of software\napplications on Uppmax clusters. It is incomplete in terms of modules\navailable on Uppmax, and the individual READMEs may also be incomplete in terms\nof what was actually done to install the modules. We are publicising these in\nthe hopes that they can be helpful.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample workflow of a basic installation\u003c/h2\u003e\u003ca id=\"user-content-example-workflow-of-a-basic-installation\" class=\"anchor\" aria-label=\"Permalink: Example workflow of a basic installation\" href=\"#example-workflow-of-a-basic-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the install methods git repo (\u003ccode\u003egit clone https://github.com/UPPMAX/install-methods.git\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eAdd the repo to your \u003ccode\u003e$PATH\u003c/code\u003e and source the \u003ccode\u003euppmax_functions.sh\u003c/code\u003e file to get access to the functions.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003erun_makeroom\u003c/code\u003e with at least \u003ccode\u003e-t\u003c/code\u003e and \u003ccode\u003e-v\u003c/code\u003e, to generate a \u003ccode\u003e.sh\u003c/code\u003e (\u003ccode\u003emakeroom_toolname_version.sh\u003c/code\u003e) file that will create the directory structure needed in \u003ccode\u003e/sw\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the \u003ccode\u003e.sh\u003c/code\u003e file created in the directory you are standing to create the directory structure (\u003ccode\u003e/sw/category/toolname/\u003c/code\u003e and \u003ccode\u003e/sw/mf/common/category\u003c/code\u003e) and template files.\u003c/li\u003e\n\u003cli\u003ePut the source code for the program in \u003ccode\u003e/sw/category/toolname/version/src\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCompile and/or install the tool in \u003ccode\u003e/sw/category/toolname/version/cluster/bin\u003c/code\u003e etc.\u003c/li\u003e\n\u003cli\u003eEdit the readme file, explaining how you did the installation, in \u003ccode\u003e/sw/category/toolname/toolname-version_install-README.md\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit the template module file \u003ccode\u003e/sw/category/toolname/mf/version\u003c/code\u003e to do what you want when the module loads.\u003c/li\u003e\n\u003cli\u003eCopy the module file to the live location, \u003ccode\u003e/sw/mf/common/category/[section]/toolname\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003eall_mflink toolname version\u003c/code\u003e to create links for all clusters to the module file in \u003ccode\u003e/sw/mf/common/category/[section]/toolname\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003efixup /sw/category/toolname/version /sw/mf/common/category/[section]/toolname\u003c/code\u003e to make sure the ownership and permissions are ok.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScripts\u003c/h2\u003e\u003ca id=\"user-content-scripts\" class=\"anchor\" aria-label=\"Permalink: Scripts\" href=\"#scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egather-READMEs.sh\u003c/code\u003e - bash script to scan installation directories, looking for\nREADME files having a particular filename format that we create during\ninstallation of tools\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003efixup\u003c/code\u003e - bash script fixing up permissions and group membership within\ninstallation trees; our local installation group is \u003ccode\u003esw\u003c/code\u003e. With the \u003ccode\u003e-g\u003c/code\u003e option,\nthis script will \u003ccode\u003echmod g+s\u003c/code\u003e directories in the tree, too.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003euppmax_functions.sh\u003c/code\u003e - bash helper functions for SLURM job viewing and various\nmodule-related tasks, mostly to do with setting up mf files for loading\nmodules; the latter require appexpert privileges. Source these from \u003ccode\u003e.bashrc\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation directories\u003c/h2\u003e\u003ca id=\"user-content-installation-directories\" class=\"anchor\" aria-label=\"Permalink: Installation directories\" href=\"#installation-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe directories contain software installations in major subject areas.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eapps/\u003c/h3\u003e\u003ca id=\"user-content-apps\" class=\"anchor\" aria-label=\"Permalink: apps/\" href=\"#apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGeneral applications.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ebioinfo/\u003c/h3\u003e\u003ca id=\"user-content-bioinfo\" class=\"anchor\" aria-label=\"Permalink: bioinfo/\" href=\"#bioinfo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioinformatics applications.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003elibs/\u003c/h3\u003e\u003ca id=\"user-content-libs\" class=\"anchor\" aria-label=\"Permalink: libs/\" href=\"#libs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLibraries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ecomp/\u003c/h3\u003e\u003ca id=\"user-content-comp\" class=\"anchor\" aria-label=\"Permalink: comp/\" href=\"#comp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCompilers, interpreters, build tools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDatabase directories\u003c/h2\u003e\u003ca id=\"user-content-database-directories\" class=\"anchor\" aria-label=\"Permalink: Database directories\" href=\"#database-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese directories cover installations of databases updated either manually, or via update scripts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edata/\u003c/h3\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-label=\"Permalink: data/\" href=\"#data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation instructions for databases under \u003ccode\u003e/sw/data/\u003c/code\u003e. Database\ndirectories containing \u003ccode\u003e*-install-README.md\u003c/code\u003e files are updated manually.\nDatabase directories containing \u003ccode\u003e*-db-README.md\u003c/code\u003e files and scripts (currently,\n\u003ccode\u003eKraken\u003c/code\u003e, \u003ccode\u003ediamond_databases\u003c/code\u003e and \u003ccode\u003eRTG\u003c/code\u003e) are updated monthly via crontab entries.\u003c/p\u003e\n\u003cp\u003eBlast database updates are included here, and involve multiple scripts, crontab\nentries and a test directory. These are updated monthly via crontab entries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003edata_other/\u003c/h3\u003e\u003ca id=\"user-content-data_other\" class=\"anchor\" aria-label=\"Permalink: data_other/\" href=\"#data_other\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstallation instructions for databases under other locations, currently just\n\u003ccode\u003eBUSCO\u003c/code\u003e lineage sets, which are kept in the module installation directory.\nThese are updated monthly via crontab entries.\u003c/p\u003e\n", "stargazers_count": 8, - "subscribers_count": 5, + "subscribers_count": 7, "topics": [], - "updated_at": 1692608883.0 + "updated_at": 1727361292.0 }, { "data_format": 2, - "description": "FetaL AneUploidy and FetalFraction analYsis Pipeline", + "description": null, "filenames": [ - "Singularity" + "container/Singularity.intel_netcdf", + "container/Singularity.esm4", + "container/Singularity.intel_esm4" ], - "full_name": "J35P312/fluffy", - "latest_release": "2.0.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Clinical-Genomics/fluffy/workflows/Build/badge.svg\"\u003e\u003cimg src=\"https://github.com/Clinical-Genomics/fluffy/workflows/Build/badge.svg\" alt=\"Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/Clinical-Genomics/fluffy\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c18629eade63aa606bad8c57197bdde19b1ae3ded637c1d66bfee86ccab49436/68747470733a2f2f636f6465636f762e696f2f67682f436c696e6963616c2d47656e6f6d6963732f666c756666792f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/Clinical-Genomics/fluffy/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFluFFyPipe\u003c/h1\u003e\u003ca id=\"user-content-fluffypipe\" class=\"anchor\" aria-label=\"Permalink: FluFFyPipe\" href=\"#fluffypipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNIPT analysis pipeline, using WisecondorX for detecting aneuplodies and large CNVs, AMYCNE for FFY and PREFACE for FF prediction (optional). FluFFYPipe produces a variety of output files, as well as a per batch csv summary.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/J35P312/FluFFyPipe/blob/master/logo/IMG_20200320_132001.jpg\"\u003e\u003cimg src=\"https://github.com/J35P312/FluFFyPipe/raw/master/logo/IMG_20200320_132001.jpg\" width=\"400\" height=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun FluFFyPipe\u003c/h1\u003e\u003ca id=\"user-content-run-fluffypipe\" class=\"anchor\" aria-label=\"Permalink: Run FluFFyPipe\" href=\"#run-fluffypipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun NIPT analysis, using a previously comnputed reference:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --analyse\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRun NIPT analysis, using an internally computed reference (i.e the reference is built using all samples listed in samplesheet):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --analyse --batch-ref\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eoptionally, skip preface:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --skip_preface --analyse\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll output will be written to the output folder, this output includes:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebam files\nwisecondorX output\ntiddit coverage summary\nFetal fraction estimation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eas well as a summary csv and multiqc html (per batch)\u003c/p\u003e\n\u003cp\u003ethe input folder is a project folder containing one folder per sample, each of these subfolders contain the fastq file(s).\nThe samplesheet contains at least a \"sampleID\" column, the sampleID should match the subfolders in the input folder. The samplesheet may contain other columns, such as flowcell and index folder: such columns will be printed to the summary csv.\nIf the samplesheet contains a SampleName column, fluffy will name the output according to SampleName\u003c/p\u003e\n\u003cp\u003eCreate a WisecondorX reference\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --reference\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003esamplesheet should contain atleast a \"sampleID\" column. All samples in the samplesheet will be used to construct the reference, visit the WisecondorX manual for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOptional fluffy parameters:\u003c/h1\u003e\u003ca id=\"user-content-optional-fluffy-parameters\" class=\"anchor\" aria-label=\"Permalink: Optional fluffy parameters:\" href=\"#optional-fluffy-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eAnalysis mode:\n\t--dry_run - run the pipeline without generating files\n\t-l\t-\tadd paramters to the slurm header of the script, should be given on the following format parameter:value\n\t\t\texample: qos:high \n\nReference mode:\n\t--dry_run - run the pipeline without generating files\n\t\nRerun mode:\n\t--dry_run - run the pipeline without generating files\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTroubleshooting and rerun\u003c/h1\u003e\u003ca id=\"user-content-troubleshooting-and-rerun\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting and rerun\" href=\"#troubleshooting-and-rerun\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are three statuses of the fluffy pipeline:\nrunning, complete, and failed\u003c/p\u003e\n\u003cp\u003eThe status of a fluffy run is found in the\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;output_folder\u0026gt;/analysis_status.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe status of all jobs are listed in\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;output_folder\u0026gt;/sacct/fluffy_\u0026lt;date\u0026gt;.log.status\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere is the timepoint when the jobs were submitted\nUse grep to find the failed jobs:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egrep -v COMPLETE \u0026lt;output_folder\u0026gt;/sacct/fluffy_\u0026lt;date\u0026gt;.log.status\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe output logs are stored in:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e \u0026lt;output_folder\u0026gt;/logs\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBefore continuing, you may want to generate the summary csv for all completed cases:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebash \u0026lt;output_folder\u0026gt;/scripts/summarizebatch-\u0026lt;hash\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere is a randomly generated string.\u003c/p\u003e\n\u003cp\u003euse the rerun module to rerun failed fluffy analyses:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efluffy --sample \u0026lt;samplesheet\u0026gt; --project \u0026lt;input_folder\u0026gt; --out \u0026lt;output_folder\u0026gt; --skip_preface rerun\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstall FluFFyPipe\u003c/h1\u003e\u003ca id=\"user-content-install-fluffypipe\" class=\"anchor\" aria-label=\"Permalink: Install FluFFyPipe\" href=\"#install-fluffypipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFluFFyPipe requires python 3, slurm, slurmpy, and singularity, python-coloredlogs.\u003c/p\u003e\n\u003cp\u003efluffy may be installed using pip:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install fluffy-cg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ealternatively, fluffy is cloned and installed from github:\ngit clone \u003ca href=\"https://github.com/Clinical-Genomics/fluffy\"\u003ehttps://github.com/Clinical-Genomics/fluffy\u003c/a\u003e\ncd fluffy\npip install -e .\u003c/p\u003e\n\u003cp\u003eNext download the FluFFyPipe singularity container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull library://jeisfeldt/default/fluffy:sha256.dbef92cd5eab8558c2729f73a191d73a7576a24e9bb44dde7372c0cd405c4ef6 \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecopy the example config (found in example_config), and edit the variables.\nYou will need to download/create the following files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eReference fasta (indexed using bwa)\n\nWisecondorX reference files (created using the reference mode)\n\nPREFACE model file (optional)\n\nblacklist bed file (used by wisecondorX)\n\nFluFFyPipe singularity collection (singularity pull --name FluFFyPipe.sif shub://J35P312/FluFFyPipe)\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "NOAA-GFDL/ESM4", + "latest_release": "2021.03", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEarth System Model 4\u003c/h1\u003e\u003ca id=\"user-content-earth-system-model-4\" class=\"anchor\" aria-label=\"Permalink: Earth System Model 4\" href=\"#earth-system-model-4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat Is Included\u003c/h2\u003e\u003ca id=\"user-content-what-is-included\" class=\"anchor\" aria-label=\"Permalink: What Is Included\" href=\"#what-is-included\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e[src]((\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/tree/master/src\"\u003ehttps://github.com/NOAA-GFDL/ESM4/tree/master/src\u003c/a\u003e) source code for the ESM4 model (all code is in submodules)\u003c/li\u003e\n\u003cli\u003e[exec]((\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/tree/master/exec\"\u003ehttps://github.com/NOAA-GFDL/ESM4/tree/master/exec\u003c/a\u003e) Makefiles to compile the code\u003c/li\u003e\n\u003cli\u003e[run]((\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/tree/master/run\"\u003ehttps://github.com/NOAA-GFDL/ESM4/tree/master/run\u003c/a\u003e) Simple run script\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning\u003c/h2\u003e\u003ca id=\"user-content-cloning\" class=\"anchor\" aria-label=\"Permalink: Cloning\" href=\"#cloning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo clone the ESM4 model please use the recursive option\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive git@github.com:NOAA-GFDL/ESM4.git \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recursive https://github.com/NOAA-GFDL/ESM4.git\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling\u003c/h2\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"container\"\u003econtainer folder\u003c/a\u003e provides example Dockerfiles and Signularity\ndefinition files to use to build AM4 containers using either GCC/GFORTAN or\nIntel oneAPI. There is a script that can be used to build the intel\nsingularity containers, and the first step of this script can be used with the\nother GFDL climate models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding from source\u003c/h3\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-label=\"Permalink: Building from source\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis model was originally compiled and run with the intel16 compiler.\nIt is recommended that you compile with an intel compiler.\u003c/p\u003e\n\u003cp\u003eCompiling assumes that you have an intel compiler, MPI (impi, mpich,\nopenmpi, etc), netcdf, and hdf5 in your LD_LIBRARY_PATH and LIBRARY_PATH.\nIt is also assumed that nf-config and nc-config are in your path.\nIf you work on a machine with modules, you may need to load these\npackages into your environment.\u003c/p\u003e\n\u003cp\u003eMakefiles have been included in the\n\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/tree/master/exec\"\u003eexec/\u003c/a\u003e folder.\nThere are several option for compiling, which can be found in the\n\u003ca href=\"https://github.com/NOAA-GFDL/ESM4/blob/master/exec/templates/intel.mk\"\u003etemplate/intel.mk\u003c/a\u003e.\u003cbr\u003e\nYou may need to edit the template/intel.mk to update the compiler names\nor add any CPPDEF options specific for your system.\nThe most common compile with optimizations on and with openmp would be\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e\nmake OPENMP=on\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you would like to compile with \u003cem\u003e-O2\u003c/em\u003e instead of \u003cem\u003e-O3\u003c/em\u003e do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake REPRO=on OPENMP=on\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo compile with \u003cem\u003e-O0\u003c/em\u003e and debug flags do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake BLD_TYPE=DEBUG OPENMP=on\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCompiling with openMP is optional.\u003c/p\u003e\n\u003cp\u003eHere are examples of how to compile the model on various systems:\u003c/p\u003e\n\u003cp\u003egaea (NOAA RDHPCS cray system)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load intel\nmodule load cray-netcdf\nmodule load cray-hdf5\ngit clone --recursive git@github.com:NOAA-GFDL/ESM4.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ESM4/exec\nmake MKL_LIBS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enone\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e OPENMP=y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCompiling on orion (MSU)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load intel impi netcdf hdf5\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LIBRARY_PATH=\u003cspan class=\"pl-smi\"\u003e${LIBRARY_PATH}\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e${LD_LIBRARY_PATH}\u003c/span\u003e\ngit clone --recursive git@github.com:NOAA-GFDL/ESM4.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ESM4/exec\nmake OPENMP=on\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel running\u003c/h2\u003e\u003ca id=\"user-content-model-running\" class=\"anchor\" aria-label=\"Permalink: Model running\" href=\"#model-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA work directory needed for running the model can be obtained from\nftp://data1.gfdl.noaa.gov/users/ESM4/ESM4Documentation/GFDL-ESM4/inputData/ESM4_rundir.tar.gz\u003c/p\u003e\n\u003cp\u003eThe directory contains input.nml as the namelist, various input tables needed\nfor running the model, and model input files in a folder called INPUT/. There\nis also a directory named RESTART/ that should be empty at the beginning of\neach run.\u003c/p\u003e\n\u003cp\u003eThere is a skeleton of a run script named \u003ca href=\"https://github.com/NOAA-GFDL/ESM4/blob/master/run/ESM4_run.sh\"\u003erun/ESM4_run.sh\u003c/a\u003e. You must update this\nscript to run the model. Include a path to the work directory and the executable.\nYou should also update the program you need to run the model on your system. The\ndefault for this script is \u003ccode\u003esrun\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is provided\non an \u0027as is\u0027 basis and the user assumes responsibility for its use. DOC has\nrelinquished control of the information and no longer has responsibility to\nprotect the integrity, confidentiality, or availability of the information. Any\nclaims against the Department of Commerce stemming from the use of its GitHub\nproject will be governed by all applicable Federal law. Any reference to\nspecific commercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply their\nendorsement, recommendation or favoring by the Department of Commerce. The\nDepartment of Commerce seal and logo, or the seal and logo of a DOC bureau,\nshall not be used in any manner to imply endorsement of any commercial product\nor activity by DOC or the United States Government.\u003c/p\u003e\n", "stargazers_count": 8, - "subscribers_count": 5, - "topics": [], - "updated_at": 1695862708.0 + "subscribers_count": 6, + "topics": [ + "gfdl", + "ems", + "ems4", + "fms", + "climate", + "model", + "fortran" + ], + "updated_at": 1716299613.0 }, { "data_format": 2, @@ -36713,12 +36810,13 @@ var data = "filenames": [ "Singularity" ], - "full_name": "kavonrtep/SeqGrapheR", - "latest_release": "v0.5.0.2.4", - "stargazers_count": 8, + "full_name": "ctpelok77/kstar", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWelcome to the page of K* planner -- a state of the art Top-k planner integrating the K* algorithm into Fast Downward.\u003c/h2\u003e\u003ca id=\"user-content-welcome-to-the-page-of-k-planner----a-state-of-the-art-top-k-planner-integrating-the-k-algorithm-into-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Welcome to the page of K* planner -- a state of the art Top-k planner integrating the K* algorithm into Fast Downward.\" href=\"#welcome-to-the-page-of-k-planner----a-state-of-the-art-top-k-planner-integrating-the-k-algorithm-into-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding\u003c/h3\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# ./fast-downward.py \u0026lt;domain_file\u0026gt; \u0026lt;problem_file\u0026gt; --search \"kstar(heuristic,k=\u0026lt;number-of-plans\u0026gt;)\"\n\n./fast-downward.py examples/gripper/domain.pddl examples/gripper/prob01.pddl --search \"kstar(blind(),k=100)\"\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eheurisitic\u003c/em\u003e: any heuristic provided by Fast Downward\u003cbr\u003e\n(\u003ca href=\"http://www.fast-downward.org/Doc/Heuristic\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/Doc/Heuristic\u003c/a\u003e).\u003cbr\u003e\n\u003cstrong\u003eDisclaimer\u003c/strong\u003e: Optimality of K* is only guaranteed with an admissible and consistent heuristic.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCitation\u003c/h3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMichael Katz, Shirin Sohrabi, Octavian Udrea and Dominik Winterer\u003cbr\u003e\n\u003cstrong\u003eA Novel Iterative Approach to Top-k Planning\u003c/strong\u003e \u003ca href=\"https://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/download/17749/16971\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"/top_k.bib\"\u003e[bib]\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eIn ICAPS 2018\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContact\u003c/h3\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor questions and comments please get in touch with Michael Katz (\u003ca href=\"mailto:michael.katz1@ibm.com\"\u003emichael.katz1@ibm.com\u003c/a\u003e).\u003c/p\u003e\n", + "stargazers_count": 9, "subscribers_count": 1, "topics": [], - "updated_at": 1720828097.0 + "updated_at": 1691110521.0 }, { "data_format": 2, @@ -36729,7 +36827,7 @@ var data = "full_name": "netreconlab/ca-server", "latest_release": null, "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eca-server\u003c/h1\u003e\u003ca id=\"user-content-ca-server\" class=\"anchor\" aria-label=\"Permalink: ca-server\" href=\"#ca-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/ca-server\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5e29bba19ad65bde076a1b85c6faa800ec4247e3986dd92a4d6e19afc9393320/68747470733a2f2f646f636b6572692e636f2f696d6167652f6e65747265636f6e6c61622f63612d736572766572\" alt=\"\" data-canonical-src=\"https://dockeri.co/image/netreconlab/ca-server\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/ca-server/actions/workflows/build.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/ca-server/actions/workflows/build.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/ca-server/actions/workflows/release.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/ca-server/actions/workflows/release.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c355f200ea90fddaa407b6eaab303663a669248ea3ca7b1fcf77dbe04ff5f48c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d417061636865253230322e302d626c75652e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c355f200ea90fddaa407b6eaab303663a669248ea3ca7b1fcf77dbe04ff5f48c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/license-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eQuickly create Certificate Authorities (CAs) for your applications.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSoftware Designed for \u003ccode\u003eca-server\u003c/code\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-software-designed-for-ca-server\" class=\"anchor\" aria-label=\"Permalink: Software Designed for ca-server\" href=\"#software-designed-for-ca-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/netreconlab/ParseCertificateAuthority\"\u003eParseCertificateAuthority\u003c/a\u003e - Send CSR\u0027s and retreive certificates to/from \u003ccode\u003eca-server\u003c/code\u003e from \u003ca href=\"https://github.com/netreconlab/Parse-Swift\"\u003eParse-Swift\u003c/a\u003e based clients and servers\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/cbaker6/CertificateSigningRequest\"\u003eCertificateSigningRequest\u003c/a\u003e - Generate CSR\u0027s on Swift clients and servers that can later be signed by \u003ccode\u003eca-server\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/netreconlab/Parse-Swift\"\u003eParse-Swift\u003c/a\u003e - Write Parse client apps in Swift. When coupled with \u003ca href=\"https://github.com/netreconlab/ParseCertificateAuthority\"\u003eParseCertificateAuthority\u003c/a\u003e and \u003ca href=\"https://github.com/cbaker6/CertificateSigningRequest\"\u003eCertificateSigningRequest\u003c/a\u003e, provides the complete client-side stack for generating CSR\u0027s, sending/receiving certificates to/from \u003ccode\u003eca-server\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/netreconlab/parse-server-swift\"\u003eParseServerSwift\u003c/a\u003e - Write Parse Server Cloud Code apps in Swift. When coupled with \u003ca href=\"https://github.com/netreconlab/ParseCertificateAuthority\"\u003eParseCertificateAuthority\u003c/a\u003e, \u003ca href=\"https://github.com/cbaker6/CertificateSigningRequest\"\u003eCertificateSigningRequest\u003c/a\u003e, and \u003ca href=\"https://github.com/netreconlab/Parse-Swift\"\u003eParse-Swift\u003c/a\u003e provides the complete server-side stack for generating CSR\u0027s, sending/receiving certificates to/from \u003ccode\u003eca-server\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImages\u003c/h2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMultiple images are automatically built for your convenience. Images can be found at the following locations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/ca-server\" rel=\"nofollow\"\u003eDocker - Hosted on Docker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/pkgs/container/ca-server\"\u003eSingularity - Hosted on GitHub Container Registry\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnvironment Variables\u003c/h2\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBelow is a list of environment variables available to configure \u003ccode\u003eca-server\u003c/code\u003e. It is required to mount the folder containing \u003ccode\u003eCA_SERVER_PRIVATE_KEY_FILE\u003c/code\u003e and \u003ccode\u003eCA_SERVER_ROOT_CA_CERT\u003c/code\u003e. It is recommended to mount the folder containing \u003ccode\u003eCA_SERVER_DATABASE_NAME\u003c/code\u003e to persist your database during restarts. See \u003ca href=\"https://rajanmaharjan.medium.com/secure-your-mongodb-connections-ssl-tls-92e2addb3c89\" rel=\"nofollow\"\u003ehttps://rajanmaharjan.medium.com/secure-your-mongodb-connections-ssl-tls-92e2addb3c89\u003c/a\u003e to learn how to create a private key and root certificate. It is also recommended to mount the folder containing \u003ccode\u003eCA_SERVER_CA_DIRECTORY\u003c/code\u003e to persist any files created by \u003ccode\u003eca-server\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eCA_SERVER_PRIVATE_KEY_FILE=./server/ca/private/cakey.pem \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (Required) Location and name of private key \u003c/span\u003e\nCA_SERVER_ROOT_CA_CERT=./server/ca/private/cacert.der \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (Required) Location and name of CA certificate\u003c/span\u003e\nCA_SERVER_DATABASE_NAME=./server/dbs/appdb.sqlite \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (Required) Location and name of the database\u003c/span\u003e\nCA_SERVER_CA_DIRECTORY=./server/ca \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Location to store CA related files\u003c/span\u003e\nCA_SERVER_ROUTE_ROOT_CERTIFICATE_PREFIX=/ca_certificate \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The prefix to add root certificate related routes\u003c/span\u003e\nCA_SERVER_ROUTE_USER_PREFIX=/appusers \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The prefix to add to all user related routes\u003c/span\u003e\nCA_SERVER_ROUTE_CERTIFICATE_PREFIX=/certificates \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The prefix to add to all certificate related routes\u003c/span\u003e\nCA_SERVER_ROUNDS=5 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Number of rounds\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLocal Deployment\u003c/h2\u003e\u003ca id=\"user-content-local-deployment\" class=\"anchor\" aria-label=\"Permalink: Local Deployment\" href=\"#local-deployment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/8621344/215227812-3dc126d6-ecf6-4b6d-b349-c4154f14b4d1.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/8621344/215227812-3dc126d6-ecf6-4b6d-b349-c4154f14b4d1.png\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 1\u003c/h3\u003e\u003ca id=\"user-content-option-1\" class=\"anchor\" aria-label=\"Permalink: Option 1\" href=\"#option-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse the docker-compose.yml file to run on a docker container or\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFork this repo\u003c/li\u003e\n\u003cli\u003eIn terminal, run \u003ccode\u003edocker-compose up\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThen Go to \u003ccode\u003ehttp://localhost:3000/docs\u003c/code\u003e to view api docs and use as needed\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOption 2\u003c/h3\u003e\u003ca id=\"user-content-option-2\" class=\"anchor\" aria-label=\"Permalink: Option 2\" href=\"#option-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun directly on your local machine by:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eFork this repo\u003c/li\u003e\n\u003cli\u003eInstall python 3.10.x and poetry\u003c/li\u003e\n\u003cli\u003eRunning \u003ccode\u003epoetry install in the root directory\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epoetry run uvicorn server.main:app --host 0.0.0.0 --port 3000\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThen Go to \u003ccode\u003ehttp://localhost:3000/docs\u003c/code\u003e to view api docs and use as needed\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning behind a proxy\u003c/h2\u003e\u003ca id=\"user-content-running-behind-a-proxy\" class=\"anchor\" aria-label=\"Permalink: Running behind a proxy\" href=\"#running-behind-a-proxy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to run \u003ccode\u003eca-server\u003c/code\u003e behind a proxy, \u003ccode\u003e--root-path\u003c/code\u003e needs to be added to command to start \u003ccode\u003eca-server\u003c/code\u003e in the \u003ccode\u003edocker-compose.yml\u003c/code\u003e file. The root path should match the exact endpoint proxying to \u003ccode\u003eca-server\u003c/code\u003e. For example, if your endpoint is \u003ccode\u003e/ca\u003c/code\u003e, then the proper command is below:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e `docker-compose.yml` \u003c/span\u003e\ncommand: [ \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e./start-poetry.sh\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epoetry\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003erun\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003euvicorn\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eserver.main:app\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--host\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e0.0.0.0\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--port\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e3000\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--root-path\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/ca\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e ]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn addition, two endpoints to the nginx configuration file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Allow access to the docs of your ca-server\u003c/span\u003e\nlocation /ca/docs {\n proxy_pass http://ca-server:3000/docs\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n}\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Allow access to the rest of your ca-server api\u003c/span\u003e\nlocation /ca/ {\n proxy_pass http://ca-server:3000/\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 8, + "stargazers_count": 9, "subscribers_count": 2, "topics": [ "certificate-authority", @@ -36742,231 +36840,113 @@ var data = "csr", "hacktoberfest" ], - "updated_at": 1729619973.0 - }, - { - "data_format": 2, - "description": "Docker container built on bioconductor/bioconductor_docker", - "filenames": [ - "Singularity" - ], - "full_name": "waldronlab/bioconductor", - "latest_release": null, - "readme": "\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eHub\u003c/th\u003e\n\u003cth\u003eVersion\u003c/th\u003e\n\u003cth\u003eStatus\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:devel\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0a1b9e45197d4e64deafcf26d97c0f00737d462148d91614fe64b64e1e47a853/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f76657273696f6e2f77616c64726f6e6c61622f62696f636f6e647563746f723a646576656c2e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/version/waldronlab/bioconductor:devel.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:devel\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6e5be46d1956a1d8ade50157cdb787bb8c904f7139424e15dae9826b9fe62607/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f77616c64726f6e6c61622f62696f636f6e647563746f723a646576656c2e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/waldronlab/bioconductor:devel.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:release\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a537aaa8e6682cafd77bd7ceb15bbf1e76a8d53323f8160c520e931669ea6add/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f76657273696f6e2f77616c64726f6e6c61622f62696f636f6e647563746f723a72656c656173652e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/version/waldronlab/bioconductor:release.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:release\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e465e9a04eff517031f3211f8b255ec66bc8beb1616654af12185b1eb77f8dd2/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f77616c64726f6e6c61622f62696f636f6e647563746f723a72656c656173652e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/waldronlab/bioconductor:release.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:RELEASE_3_10\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/446196813e96bf4ddd4291af079b986be31c927c4b46b45f5ac080cb7e4c28f8/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f76657273696f6e2f77616c64726f6e6c61622f62696f636f6e647563746f723a52454c454153455f335f31302e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/version/waldronlab/bioconductor:RELEASE_3_10.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://microbadger.com/images/waldronlab/bioconductor:RELEASE_3_10\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9ea4ad6de69eeec2ea8444a02f339d67e5c57ba219afd29df57610a9d4606756/68747470733a2f2f696d616765732e6d6963726f6261646765722e636f6d2f6261646765732f696d6167652f77616c64726f6e6c61622f62696f636f6e647563746f723a52454c454153455f335f31302e737667\" alt=\"\" data-canonical-src=\"https://images.microbadger.com/badges/image/waldronlab/bioconductor:RELEASE_3_10.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbout the \"bioconductor\" script\u003c/h1\u003e\u003ca id=\"user-content-about-the-bioconductor-script\" class=\"anchor\" aria-label=\"Permalink: About the \u0026quot;bioconductor\u0026quot; script\" href=\"#about-the-bioconductor-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis script makes it more convenient to run the Bioconductor docker images\nlocally for routine daily usage:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIt creates a host directory \u003ccode\u003e~/dockerhome\u003c/code\u003e where the home directory\nof the Docker user will be mounted. Files can be shared between the\nDocker container and host filesystem here.\u003c/li\u003e\n\u003cli\u003eIt results in user-installed packages being added to the host directory\n\u003ccode\u003e~/.docker-devel-packages\u003c/code\u003e or \u003ccode\u003e~/.docker-release-packages\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eIt runs the Docker container\n\u003ca href=\"https://github.com/bioconductor/bioconductor_docker\"\u003ebioconductor/bioconductor_docker\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs to the \u0027biconductor\u0027 script\u003c/h2\u003e\u003ca id=\"user-content-inputs-to-the-biconductor-script\" class=\"anchor\" aria-label=\"Permalink: Inputs to the \u0027biconductor\u0027 script\" href=\"#inputs-to-the-biconductor-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe user must specify the version of Bioconductor to spin up as a Docker image.\nThe available inputs for the \u003cstrong\u003efirst\u003c/strong\u003e argument are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. release\n2. devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003cstrong\u003esecond\u003c/strong\u003e argument for the \u0027bioconductor\u0027 script denotes the environment type\nto run when executing the script this will either put the user in one of two\nsupported environements:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. rstudio - allows the user to open up an rstudio session in the browser\n2. shell - put the user in the command line within the container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote. The default user environment is the \u003ccode\u003erstudio\u003c/code\u003e session\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAbout the \u003ccode\u003ebioconductor_docker\u003c/code\u003e docker image\u003c/h1\u003e\u003ca id=\"user-content-about-the-bioconductor_docker-docker-image\" class=\"anchor\" aria-label=\"Permalink: About the bioconductor_docker docker image\" href=\"#about-the-bioconductor_docker-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ebioconductor/bioconductor_docker\u003c/code\u003e image is built for both release and devel\nversions of Bioconductor. It includes system dependencies so that almost every\nBioconductor package can be installed using \u003ccode\u003eBiocManager::install()\u003c/code\u003e with no\nfurther troubles. For almost everyone, this means no more errors when trying to install a package.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFor singularity users\u003c/h1\u003e\u003ca id=\"user-content-for-singularity-users\" class=\"anchor\" aria-label=\"Permalink: For singularity users\" href=\"#for-singularity-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo make a generalization, Docker is more supported by commercial Cloud\nproviders, whereas \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is (far)\nmore likely to be supported by university high-performance computing\nfacilities.\u003c/p\u003e\n\u003cp\u003eIf you have singularity installed, pull and the singularity images as follows (or substitute \"devel\" with \"release\" for the release version):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build $HOME/bioconductor-devel.img docker://waldronlab/bioconductor:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo far I have only used singularity for bash and R, with aliases like these:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ealias singulaR=\"singularity shell $HOME/bioconductor-devel.simg R\"\nalias singularbash=\"singularity shell $HOME/bioconductor-devel.simg bash\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that default behavior in singularity is to mount your home (and several\nother) directories as the home directory within the container, while\nmaintaining your user permissions. This makes all the docker efforts to mount\nvolumes for your container package and home directories unnecessary. I haven\u0027t\nyet tried running rstudio via singularity, but it should be possible?\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsing the \u003ccode\u003ebioconductor\u003c/code\u003e script and docker container\u003c/h1\u003e\u003ca id=\"user-content-using-the-bioconductor-script-and-docker-container\" class=\"anchor\" aria-label=\"Permalink: Using the bioconductor script and docker container\" href=\"#using-the-bioconductor-script-and-docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall a \u003ca href=\"https://www.docker.com/get-started\" rel=\"nofollow\"\u003edocker client\u003c/a\u003e for\nyour operating system.\u003c/li\u003e\n\u003cli\u003eMake sure home directories are being shared (Whale icon -\u0026gt;\nPreferences -\u0026gt; File Sharing). Last I checked, this was already the\ncase by default. You can also change the allotted system resources if\nyou want.\u003c/li\u003e\n\u003cli\u003eCopy the\n\u003ca href=\"https://github.com/waldronlab/bioconductor/blob/master/bioconductor\"\u003ebioconductor\u003c/a\u003e\nscript from this repo to somewhere in your $PATH. Modify as you see\nfit, e.g. if you want to mount different directories or in a different\nplace than \u003ccode\u003e~/dockerhome\u003c/code\u003e, or change the rstudio password. Make sure\nthe script is executable (e.g. \u003ccode\u003echmod a+x bioconductor\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eFrom the command-line, type \u003ccode\u003ebioconductor devel\u003c/code\u003e or \u003ccode\u003ebioconductor release\u003c/code\u003e. Later you can use Ctrl-C to stop the\ncontainer. There are additional usage tips at\n\u003ca href=\"https://github.com/Bioconductor/bioc_docker\"\u003ehttps://github.com/Bioconductor/bioc_docker\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eIf using the \u0027rstudio\u0027 argument (default) --- In a browser, open\n\u003ca href=\"http://localhost:8787\" rel=\"nofollow\"\u003ehttp://localhost:8787\u003c/a\u003e. Login with username is \"rstudio\" and password\n\"rstudiopassword\" unless you change the password within the \"bioconductor\"\nscript in step 3.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThat\u0027s it! You can stop the instance you\u0027re running and switch to\nrelease or devel (but you can\u0027t currently run both at the same\ntime). There will be separate host package libraries for\nuser-installed packages (in \u003ccode\u003e~/.docker-devel-packages\u003c/code\u003e and\n\u003ccode\u003e~/.docker-release-packages\u003c/code\u003e), and a common home directory in\n\u003ccode\u003e~/dockerhome\u003c/code\u003e. \u003ccode\u003edocker pull\u003c/code\u003e is run each time you invoke the\n\u003ccode\u003ebioconductor\u003c/code\u003e script, so you should automatically get the most\nup-to-date Bioconductor release or devel versions, and will only have\nto run \u003ccode\u003eBiocManager::install()\u003c/code\u003e to update user-installed packages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Checking mechanisms have been implemented for the script to error if\nanything other than \"release\" or \"devel\" is entered in the first argument.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eExample command line execution\u003c/h1\u003e\u003ca id=\"user-content-example-command-line-execution\" class=\"anchor\" aria-label=\"Permalink: Example command line execution\" href=\"#example-command-line-execution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following commands may be useful in your \u003ccode\u003e~/.bash_profile\u003c/code\u003e for\ncommand-line R and bash usage with the same containers, package directories,\nhome directory, and rstudio user:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e releaseshell=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebioconductor release shell\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e develshell=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebioconductor devel shell\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e coming soon #\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e Rrelease=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebioconductor release R\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ealias\u003c/span\u003e Rdevel=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebioconductor devel R\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTODO\u003c/h1\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO\" href=\"#todo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003ebioconductor\u003c/code\u003e script is rudimentary and should use docopt, and provide\nstart \u0026amp; stop. It could also provide arguments for the volume location etc.\u003c/p\u003e\n", - "stargazers_count": 8, - "subscribers_count": 4, - "topics": [], - "updated_at": 1730996570.0 - }, - { - "data_format": 2, - "description": "HIPAA \u0026 GDPR compliant ready Postgres Database with PostGIS and PGAuditor", - "filenames": [ - "Singularity" - ], - "full_name": "netreconlab/hipaa-postgres", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ehipaa-postgres\u003c/h1\u003e\u003ca id=\"user-content-hipaa-postgres\" class=\"anchor\" aria-label=\"Permalink: hipaa-postgres\" href=\"#hipaa-postgres\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/hipaa-postgres\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9a06531d820b6411fb4cee4467acdc83794ca70e7b84431b95a1853d5e064b22/68747470733a2f2f646f636b6572692e636f2f696d6167652f6e65747265636f6e6c61622f68697061612d706f737467726573\" alt=\"\" data-canonical-src=\"https://dockeri.co/image/netreconlab/hipaa-postgres\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/build.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/build.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/release.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/release.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/release-pgpool.yml\"\u003e\u003cimg src=\"https://github.com/netreconlab/hipaa-postgres/actions/workflows/release-pgpool.yml/badge.svg\" alt=\"Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eA HIPAA \u0026amp; GDPR compliant ready Postgres Database image with PostGIS and PGAudit. Designed for \u003ca href=\"https://github.com/netreconlab/parse-hipaa\"\u003eparse-hipaa\u003c/a\u003e but can be used anywhere Postgres is used. These docker images include the necessary database auditing and logging for HIPAA compliance. \u003ccode\u003ehipaa-postgres\u003c/code\u003e is derived from \u003ca href=\"https://hub.docker.com/r/postgis/postgis\" rel=\"nofollow\"\u003epostgis\u003c/a\u003e which is an extention built on top of the \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003eofficial postgres image\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003ehipaa-postgres provides the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[x] Auditing \u0026amp; logging\u003c/li\u003e\n\u003cli\u003e[x] Ready for encryption in transit - run behind a proxy with files \u0026amp; directions on how to \u003ca href=\"https://github.com/netreconlab/parse-hipaa#deploying-on-a-real-system\"\u003ecomplete the process\u003c/a\u003e with Nginx and LetsEncrypt\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou will still need to setup the following on your own to be fully HIPAA compliant:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e[ ] Encryption in transit - you will need to \u003ca href=\"https://github.com/netreconlab/parse-hipaa#deploying-on-a-real-system\"\u003ecomplete the process\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e[ ] Encryption at rest - Mount to your own encrypted storage drive (Linux and macOS have API\u0027s for this) and store the drive in a \"safe\" location\u003c/li\u003e\n\u003cli\u003e[ ] Be sure to do anything else HIPAA requires\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe \u003ca href=\"https://github.com/netreconlab/CareKitSample-ParseCareKit\"\u003eCareKitSample-ParseCareKit\u003c/a\u003e app uses this image alongise parse-hipaa and \u003ca href=\"https://github.com/netreconlab/ParseCareKit\"\u003eParseCareKit\u003c/a\u003e. If you are looking for a Mongo variant, checkout \u003ca href=\"https://github.com/netreconlab/hipaa-mongo\"\u003ehipaa-mongo\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse at your own risk. There is not promise that this is HIPAA compliant and we are not responsible for any mishandling of your data\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImages\u003c/h2\u003e\u003ca id=\"user-content-images\" class=\"anchor\" aria-label=\"Permalink: Images\" href=\"#images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMultiple images are automatically built for your convenience. Images can be found at the following locations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://hub.docker.com/r/netreconlab/hipaa-postgres\" rel=\"nofollow\"\u003eDocker - Hosted on Docker Hub\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/netreconlab/hipaa-postgres/pkgs/container/hipaa-postgres\"\u003eSingularity - Hosted on GitHub Container Registry\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTags\u003c/h3\u003e\u003ca id=\"user-content-tags\" class=\"anchor\" aria-label=\"Permalink: Tags\" href=\"#tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003elatest\u003c/code\u003e - Points to the newest released version that uses the standard \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003ePostgres image\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emain\u003c/code\u003e - Points to most up-to-date code that uses the standard \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003ePostgres image\u003c/a\u003e and will eventually show up in a future release. This tag can contain breaking changes\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ex-x.x\u003c/code\u003e - Points to a specific Postgres and Postgis version that uses the standard \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003ePostgres image\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ex-x.x-pgpool\u003c/code\u003e - Points to a specific Postgres and Postgis version that uses the standard \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003ePostgres image\u003c/a\u003e. These images alson contain \u003ca href=\"https://www.pgpool.net\" rel=\"nofollow\"\u003epgpool\u003c/a\u003e and can be configured for High Availability\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ex-x.x-percona\u003c/code\u003e - Points to a specific version that uses the \u003ca href=\"https://www.percona.com/software/postgresql-distribution\" rel=\"nofollow\"\u003ePercona Distribtution for PostgreSQL\u003c/a\u003e image\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdditional Packages inside of hipaa-postgres that are enabled automatically\u003c/h2\u003e\u003ca id=\"user-content-additional-packages-inside-of-hipaa-postgres-that-are-enabled-automatically\" class=\"anchor\" aria-label=\"Permalink: Additional Packages inside of hipaa-postgres that are enabled automatically\" href=\"#additional-packages-inside-of-hipaa-postgres-that-are-enabled-automatically\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following are enabled automatically on either the \u003ccode\u003ePG_PARSE_DB\u003c/code\u003e or \u003ccode\u003epostgres\u003c/code\u003e databases:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://postgis.net\" rel=\"nofollow\"\u003ePostGIS\u003c/a\u003e - Spatial database extender for PostgreSQL object-relational database\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.pgaudit.org\" rel=\"nofollow\"\u003epgAudit\u003c/a\u003e - Provide the tools needed to produce audit logs required to pass certain government, financial, or ISO certification audits\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pgaudit/set_user\"\u003epgAudit-set_user\u003c/a\u003e - Allows switching users and optional privilege escalation with enhanced logging and control\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pgbadger.darold.net\" rel=\"nofollow\"\u003epgBadger\u003c/a\u003e - Log analyzer built for speed with fully detailed reports and professional rendering\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://pgbackrest.org\" rel=\"nofollow\"\u003epgBackrest\u003c/a\u003e - Reliable, easy-to-use backup and restore solution that can seamlessly scale up to the largest databases and workloads by utilizing algorithms that are optimized for database-specific requirements\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/citusdata/pg_cron\"\u003epg_cron\u003c/a\u003e - Run periodic jobs in PostgreSQL\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://reorg.github.io/pg_repack/\" rel=\"nofollow\"\u003epg_repack\u003c/a\u003e - Reorganize tables in PostgreSQL databases with minimal locks\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pgRouting/pgrouting\"\u003epgRouting\u003c/a\u003e - Provides geospatial routing and other network analysis functionality\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.postgresql.org/docs/current/pgstatstatements.html\" rel=\"nofollow\"\u003epgStatStatements\u003c/a\u003e - Provides a means for tracking planning and execution statistics of all SQL statements executed by a server (needed for PMM)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.percona.com/software/database-tools/percona-monitoring-and-management\" rel=\"nofollow\"\u003ePercona Monitoring and Management (PMM)\u003c/a\u003e - Monitor the health of your database infrastructure, explore new patterns in database behavior, and manage and improve the performance of your databases no matter where they are located or deployed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003epgpool tagged images\u003c/h3\u003e\u003ca id=\"user-content-pgpool-tagged-images\" class=\"anchor\" aria-label=\"Permalink: pgpool tagged images\" href=\"#pgpool-tagged-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImages that are tagged with \u003ccode\u003e-pgpool\u003c/code\u003e have additional packages to make it easier to configure \u003ccode\u003ehipaa-postgres\u003c/code\u003e to work with \u003ca href=\"https://www.pgpool.net\" rel=\"nofollow\"\u003epgpool\u003c/a\u003e. The additional packages are below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.pgpool.net\" rel=\"nofollow\"\u003epgpool\u003c/a\u003e - Manages a pool of PostgreSQL servers to achieve some features that are not available with single PostgreSQL installation. The features include: High Availability, Load balancing, Connection Pooling, Online Recovery, Limiting Exceeding Connections, Watchdog, In Memory Query Cache\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/iputils/iputils\"\u003eiputils-ping\u003c/a\u003e - A utility for Linux networking\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/iputils/iputils\"\u003eopenssh-server\u003c/a\u003e - Connectivity tool for remote login with the SSH protocol\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://supervisord.org/#\" rel=\"nofollow\"\u003esupervisor\u003c/a\u003e - Client/server system that allows its users to monitor and control a number of processes on UNIX-like operating systems\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEnvironment Variables\u003c/h2\u003e\u003ca id=\"user-content-environment-variables\" class=\"anchor\" aria-label=\"Permalink: Environment Variables\" href=\"#environment-variables\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ePOSTGRES_PASSWORD # Password for postgress db cluster (Be sure to changes this in real deployments)\nPG_PARSE_USER # Username for logging into PG_PARSE_DB (Be sure to changes this in real deployments)\nPG_PARSE_PASSWORD # Password for logging into PG_PARSE_DB (Be sure to changes this in real deployments)\nPG_PARSE_DB # Name of parse-hipaa database\nPMM_USER=pmm # Username for Percona Monitor Managemet (Be sure to changes this in real deployments)\nPMM_PASSWORD=pmm # Password for Percona Monitor Managemet (Be sure to changes this in real deployments)\nPMM_PORT=80 # This is the default port on the docker image\nPMM_TLS_PORT=443 # This is the default TLS port on the docker image\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStarting up hipaa-postgres\u003c/h2\u003e\u003ca id=\"user-content-starting-up-hipaa-postgres\" class=\"anchor\" aria-label=\"Permalink: Starting up hipaa-postgres\" href=\"#starting-up-hipaa-postgres\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get started, the \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/blob/main/docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e file provides an example of how to use \u003ccode\u003ehipaa-postgres\u003c/code\u003e, simply type:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker-compose up\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eImporant Note: On the very first run of hipaa-postgres needs time to setup and will not allow connections until it is ready. This is suppose to happen as time is needed to \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/tree/main/scripts\"\u003econfigure the necessary scripts/extensions along setup any default databases\u003c/a\u003e. \u003ccode\u003ehipaa-postgres\u003c/code\u003e will begin to allow connectoins once it finishes configuring and a message like below will show in the logs:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edb_1 | PostgreSQL init process complete; ready for start up.\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAfterwards, hipaa-postfgress will allow all connections.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguring\u003c/h2\u003e\u003ca id=\"user-content-configuring\" class=\"anchor\" aria-label=\"Permalink: Configuring\" href=\"#configuring\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are plan on using hipaa-postgres in production. You should run the additional scripts to create the rest of the indexes for optimized queries.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esetup-parse-index.sh\u003c/code\u003e file is already in the container. You just have to run it.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eLog into your docker container, type: \u003ccode\u003edocker exec -u postgres -ti parse-hipaa_db_1 bash\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the script, type: \u003ccode\u003e/usr/local/bin/setup-parse-index.h\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf you want to persist the data in the database, you can uncomment the volume lines in \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/blob/a2d8c2dce8f8288ad8d7b5dbf1c0dc676a466f32/docker-compose.yml#L16-L19\"\u003edocker-compose.yml\u003c/a\u003e. Be sure to change the directory to secure place that docker has access to.\u003c/p\u003e\n\u003cp\u003eDefault values for \u003ca href=\"#environment-variables\"\u003eenvironment variable\u003c/a\u003e are provided in \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/blob/main/docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e for quick local deployment. If you plan on using this image to deploy in production, you should definitely change all \u003ca href=\"#environment-variables\"\u003eenvironment variable\u003c/a\u003e. Note that the postgres image provides a default user of \u003ccode\u003epostgres\u003c/code\u003e user to configure the database cluster, you can change the password for the \u003ccode\u003epostgres\u003c/code\u003e user by changing \u003ccode\u003ePOSTGRES_PASSWORD\u003c/code\u003e before the first initialization. There are plenty of \u003ca href=\"https://hub.docker.com/_/postgres\" rel=\"nofollow\"\u003epostgres environment variables\u003c/a\u003e that can be modified. Postgres environment variables should not be changed unless you are confident with configuring postgres or else you image may not work properly. Note that changes to the aforementioned parameters will only take effect if you change them before the first build and run of the image. Afterwards, you will need to make all changes by connecting to the image typing:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker exec -u postgres -ti parse-hipaa_db_1 bash\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou can then make modifications using \u003ca href=\"http://postgresguide.com/utilities/psql.html\" rel=\"nofollow\"\u003epsql\u003c/a\u003e. Through psql, you can also add multiple databases and users to support a number of parse apps.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMonitoring your database with Percona Monitoring and Management\u003c/h2\u003e\u003ca id=\"user-content-monitoring-your-database-with-percona-monitoring-and-management\" class=\"anchor\" aria-label=\"Permalink: Monitoring your database with Percona Monitoring and Management\" href=\"#monitoring-your-database-with-percona-monitoring-and-management\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ehipaa-postgres\u003c/code\u003e is configured automatically to allow acces to \u003ca href=\"https://www.percona.com/software/database-tools/percona-monitoring-and-management\" rel=\"nofollow\"\u003ePMM\u003c/a\u003e. If you are using the \u003ca href=\"https://github.com/netreconlab/hipaa-postgres/blob/main/docker-compose.yml\"\u003edocker-compose.yml\u003c/a\u003e file, this can be accessed by visiting \u003ca href=\"http://localhost:1080/\" rel=\"nofollow\"\u003ehttp://localhost:1080/\u003c/a\u003e. Additional information is below:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUsername/password - admin/admin, PMM will prompt you to change this on first login\u003c/li\u003e\n\u003cli\u003eAdding your database to PMM\n\u003col\u003e\n\u003cli\u003eGoto \u003ccode\u003eSettings-\u0026gt;Add Instance to PMM-\u0026gt;PostgreSQL\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEnter \u003ccode\u003edb\u003c/code\u003e for hostname\u003c/li\u003e\n\u003cli\u003eFor \u003ccode\u003eUsername\u003c/code\u003e, enter \u003ccode\u003ePMM_USER\u003c/code\u003e configured in your \u003ca href=\"#environment-variables\"\u003eenvironment variable\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFor \u003ccode\u003ePassword\u003c/code\u003e, enter \u003ccode\u003ePMM_PASSWORD\u003c/code\u003e configured in your \u003ca href=\"#environment-variables\"\u003eenvironment variable\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eClick \u003ccode\u003eAdd service\u003c/code\u003e... It can take up to 5 minutes for data to start populating in PMM. PMM will let you know if it has trouble connecting immediatly after you perform the steps above. You can see that PMM is able to connect and read your database \u003ccode\u003eversion\u003c/code\u003e correctly on the \u003ccode\u003ePostgreSQL\u003c/code\u003e section of the dashboard\u003c/li\u003e\n\u003cli\u003eLearn more about PMM by looking through the \u003ca href=\"https://docs.percona.com/percona-monitoring-and-management/index.html\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploying on a real system\u003c/h2\u003e\u003ca id=\"user-content-deploying-on-a-real-system\" class=\"anchor\" aria-label=\"Permalink: Deploying on a real system\" href=\"#deploying-on-a-real-system\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker yml\u0027s here are intended to run behind a proxy that properly has ssl configured to encrypt data in transit. To create a proxy to parse-hipaa, nginx files are provided \u003ca href=\"https://github.com/netreconlab/parse-hipaa/tree/master/nginx/sites-enabled\"\u003ehere\u003c/a\u003e. Simply add the \u003ca href=\"https://github.com/netreconlab/parse-hipaa/tree/master/nginx/sites-enabled\"\u003esites-available\u003c/a\u003e folder to your nginx directory and add the following to \"http\" in your nginx.conf:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehttp {\n include /usr/local/etc/nginx/sites-enabled/*.conf; #Add this line to end. This is for macOS, do whatever is appropriate on your system\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSetup your free certificates using \u003ca href=\"https://letsencrypt.org\" rel=\"nofollow\"\u003eLetsEncrypt\u003c/a\u003e, follow the directions \u003ca href=\"https://www.nginx.com/blog/using-free-ssltls-certificates-from-lets-encrypt-with-nginx/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Be sure to change the certificate and key lines to point to correct location in \u003ca href=\"https://github.com/netreconlab/parse-hipaa/blob/master/nginx/sites-enabled/default-ssl.conf\"\u003edefault-ssl.conf\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 8, - "subscribers_count": 2, - "topics": [ - "postgres", - "postgresql", - "postgis", - "hipaa", - "gdpr", - "carekit", - "docker", - "singularity", - "healthcare", - "pgaudit", - "parse-hipaa", - "parsecarekit", - "hacktoberfest" - ], - "updated_at": 1729379981.0 + "updated_at": 1733198947.0 }, { "data_format": 2, - "description": "A nextflow pipeline with automatic software provisioning to generate hints and subsequent genome model predictions with AUGUSTUS", + "description": null, "filenames": [ "Singularity" ], - "full_name": "ikmb-denbi/genome-annotation", - "latest_release": null, - "readme": "\u003cp\u003e!!! THIS PROJECT HAS BEEN RETIRED. PLEASE WORK WITH ITS DROP-IN REPLACEMENT: \u003ca href=\"https://github.com/ikmb/esga\"\u003ehttps://github.com/ikmb/esga\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/deNBI_logo.jpg\"\u003e\u003cimg src=\"images/deNBI_logo.jpg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/ikmb_bfx_logo.png\"\u003e\u003cimg src=\"images/ikmb_bfx_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eESGA - Genome Annotation\u003c/h1\u003e\u003ca id=\"user-content-esga---genome-annotation\" class=\"anchor\" aria-label=\"Permalink: ESGA - Genome Annotation\" href=\"#esga---genome-annotation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c42e6080d15f532168bbb6d26fb300c5ed31e7f5e8648abbe9d5f6d8d4285475/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis pipeline can be used to generate \"hints\" from aligned sequence evidence to annotate a genome \u003cem\u003ede novo\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline main steps\u003c/h3\u003e\u003ca id=\"user-content-pipeline-main-steps\" class=\"anchor\" aria-label=\"Permalink: Pipeline main steps\" href=\"#pipeline-main-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eHints file is generated from all available evidences (proteins, EST and/or RNA-seq reads).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGene models are predicted using Augustus with the hints file as extrinsic evidence (optional).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe minimum requirements are a genome file and at least one type of evidence.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest data\u003c/h3\u003e\u003ca id=\"user-content-test-data\" class=\"anchor\" aria-label=\"Permalink: Test data\" href=\"#test-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple test data set can be downloaded \u003ca href=\"https://drive.google.com/open?id=1VFqLnRJiuj5Vhj2KCOdY58jwxZKkkMVU\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation about the pipeline can be found in the \u003ccode\u003edocs/\u003c/code\u003e directory or under the links below:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/pipeline.md\"\u003eWhat happens in this pipeline?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/recommendations.md\"\u003eRecommendations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation and configuration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/whatsnext.md\"\u003eWhat\u0027s next\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline Scheme\u003c/h3\u003e\u003ca id=\"user-content-pipeline-scheme\" class=\"anchor\" aria-label=\"Permalink: Pipeline Scheme\" href=\"#pipeline-scheme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/Pipeline_dag.svg\"\u003e\u003cimg src=\"images/Pipeline_dag.svg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was written by Dr. Montserrat Torres (\u003ca href=\"https://github.com/MontseTor\"\u003eMontseTor\u003c/a\u003e) and Dr. Marc H\u00f6ppner (\u003ca href=\"https://github.com/marchoeppner\"\u003emarchoeppner\u003c/a\u003e) at \u003ca href=\"http://www.ikmb.uni-kiel.de\" rel=\"nofollow\"\u003eIKMB\u003c/a\u003e.\nThe authors gratefully acknowledge inspiration, fruitful discussions and a few useful code snippets from the \u003ca href=\"https://www.nf-co.re\" rel=\"nofollow\"\u003enf-core\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 8, - "subscribers_count": 4, - "topics": [], - "updated_at": 1708237909.0 - }, - { - "data_format": 2, - "description": "examples of using Singularity containers for web-based products", - "filenames": [ - "nginx-basic/Singularity", - "nginx-jupyter/Singularity", - "nginx-expfactory/Singularity" - ], - "full_name": "vsoch/singularity-web", + "full_name": "ejolly/IntroToSingularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Web\u003c/h1\u003e\u003ca id=\"user-content-singularity-web\" class=\"anchor\" aria-label=\"Permalink: Singularity Web\" href=\"#singularity-web\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDid you know that you can put a webby things inside of a container? Did you know you can go further, and take user inputs to run an analysis and then present the result? Or give them a container with the exact dependencies for your software, and provide an interface to it? I have come up with this repository, \u003ccode\u003esingularity-web\u003c/code\u003e to show how easy it is to dump your analysis, application, static web files, whatever, into a container. You can share the entire container, or just the specification file for it, and it\u0027s a big leap in the direction of reproducbility. Here are some examples you might be interested in:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does it work?\u003c/h2\u003e\u003ca id=\"user-content-how-does-it-work\" class=\"anchor\" aria-label=\"Permalink: How does it work?\" href=\"#how-does-it-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe only pre-requisite is that you should \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e. Singularity is already available on just \u003ca href=\"https://docs.google.com/spreadsheets/d/1Vc_1prq_1WHGf0LWtpUBY-tfKdLLM_TErjnCe1mY5m0/pub?gid=1407658660\u0026amp;single=true\u0026amp;output=pdf\" rel=\"nofollow\"\u003eover 40 supercomputer centers\u003c/a\u003e all over the place. How is this working? We basically follow these steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecreate a container\u003c/li\u003e\n\u003cli\u003eadd files and software to it\u003c/li\u003e\n\u003cli\u003etell it what to run\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn the case of this example repo, we are interested in things that produce a web-based output. You could go old school and do this on a command by command basis, but I (personally) find it easiest to create a little build file to preserve my work. I\u0027m also a big fan of bootstrapping Docker images, since there are ample around. If you want to bootstrap something else, please look at our \u003ca href=\"https://github.com/singularityware/singularity/tree/master/examples\"\u003efolder of examples\u003c/a\u003e. :)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Singularity Build file\u003c/h3\u003e\u003ca id=\"user-content-the-singularity-build-file\" class=\"anchor\" aria-label=\"Permalink: The Singularity Build file\" href=\"#the-singularity-build-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Header\u003c/h3\u003e\u003ca id=\"user-content-the-header\" class=\"anchor\" aria-label=\"Permalink: The Header\" href=\"#the-header\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe First line \u003ccode\u003ebootstrap\u003c/code\u003e says that we are going to bootstrap a \u003ccode\u003edocker\u003c/code\u003e image, specifically using the (\u003ccode\u003eFrom\u003c/code\u003e field) \u003ccode\u003eubuntu:16.04\u003c/code\u003e. You couldn\u0027t choose another distribution that you like, I just happen to like Debian.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e%post\u003c/h3\u003e\u003ca id=\"user-content-post\" class=\"anchor\" aria-label=\"Permalink: %post\" href=\"#post\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePost is the section where you put commands you want to run once to create your image. This includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstallation of software\u003c/li\u003e\n\u003cli\u003ecreation of files or folders\u003c/li\u003e\n\u003cli\u003emoving data, files into the container image\u003c/li\u003e\n\u003cli\u003eanalysis things\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe list is pretty obvious, but what about the last one, analysis things? Yes, let\u0027s say that we had a script thing that we wanted to run just once to produce a result that would live in the container. In this case, we would have that thing run in %post, and then give some interactive access to the result via the \u003ccode\u003e%runscript\u003c/code\u003e. In the case that you want your image to be more like a function and run the analysis (for example, if you want your container to take input arguments, run something, and deliver a result), then this command should go in the \u003ccode\u003e%runscript\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIn our case, since we are going to serve a simple web-based thing, we create a directory to work with (\u003ccode\u003e/data\u003c/code\u003e is easy to remember), put some web file things there, and then (the strategy I used in the examples) was to install python, because it has a nice command for bringing up a quick web server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e%runscript\u003c/h3\u003e\u003ca id=\"user-content-runscript\" class=\"anchor\" aria-label=\"Permalink: %runscript\" href=\"#runscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e%runscript\u003c/code\u003e is the thing executed when we run our container. For the \u003ca href=\"nginx-basic\"\u003enginx-basic\u003c/a\u003e example, we basically change directories to data, and then use python to start up a little server on port 9999 to serve that folder. Anything in that folder will then be available to our local machine on port 9999, meaning the address \u003ccode\u003elocalhost:9999\u003c/code\u003e or \u003ccode\u003e127.0.0.1:9999\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWe recommend you look at our \u003ca href=\"nginx-basic\"\u003enginx-basic\u003c/a\u003e example for the full example, and modify some of the examples below to suit your own needs. If you use any of these templates in your work, please ping us at \u003ca href=\"researchapps@googlegroups.com\"\u003eresearchapps@googlegroups.com\u003c/a\u003e so that we can showcase your work.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003enginx-basic\u003c/h3\u003e\u003ca id=\"user-content-nginx-basic\" class=\"anchor\" aria-label=\"Permalink: nginx-basic\" href=\"#nginx-basic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"nginx-basic\"\u003enginx-basic\u003c/a\u003e example will walk you through creating a container that serves static files, either within the container (files generated at time of build and served) or outside the container (files in a folder bound to the container at run time),\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003enginx-expfactory\u003c/h3\u003e\u003ca id=\"user-content-nginx-expfactory\" class=\"anchor\" aria-label=\"Permalink: nginx-expfactory\" href=\"#nginx-expfactory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"nginx-expfactory\"\u003enginx-expfactory\u003c/a\u003e example takes a \u003ca href=\"http://journal.frontiersin.org/article/10.3389/fpsyg.2016.00610/full\" rel=\"nofollow\"\u003esoftware that I published in graduate school\u003c/a\u003e and shows an example of how to wrap a bunch of dependencies in a container, and then allow the user to use it like a function with input arguments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003enginx-jupyter\u003c/h3\u003e\u003ca id=\"user-content-nginx-jupyter\" class=\"anchor\" aria-label=\"Permalink: nginx-jupyter\" href=\"#nginx-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI use ipython notebook / jupyter notebook sometimes, and I thought it would be nice to have an image that could easily bring up a server, either for files in the container or a folder mapped from the outside. Behold, \u003ca href=\"nginx-jupyter\"\u003enginx-jupyter\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I share them?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-share-them\" class=\"anchor\" aria-label=\"Permalink: How do I share them?\" href=\"#how-do-i-share-them\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou have a few options!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShare the image\u003c/h3\u003e\u003ca id=\"user-content-share-the-image\" class=\"anchor\" aria-label=\"Permalink: Share the image\" href=\"#share-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want absolute reproducibility, meaning that the container that you built is set in stone, never to be changed, and you want to hand it to someone, have them \u003ca href=\"\"\u003einstall singularity\u003c/a\u003e and run, then you probably want to build the container yourself and give it to them. It might look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity create theultimate.img\n sudo singularity bootstrap theultimate.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the example above I am creating an image called \u003ccode\u003etheultimate.img\u003c/code\u003e and then building it from a specification file, \u003ccode\u003eSingularity\u003c/code\u003e. I would then give someone the image itself, and they would run it like an executable, which you can do in many ways:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run theultimate.img\n ./theultimate.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThey could also shell into it to look around, with or without sudo to make changes (breaks reproducibility). Note that we are considering an addition to the Singularity software that will give control to do this or not, but nothing is final yet.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell --writable theultimate.img\n sudo singularity shell --writable theultimate.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShare the build file Singularity\u003c/h3\u003e\u003ca id=\"user-content-share-the-build-file-singularity\" class=\"anchor\" aria-label=\"Permalink: Share the build file Singularity\" href=\"#share-the-build-file-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the case that the image is too big to attach to an email, you can send the user the build file \u003ccode\u003eSingularity\u003c/code\u003e and he/she can run the same steps to build and run the image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Hub\u003c/h3\u003e\u003ca id=\"user-content-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub\" href=\"#singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlso under development is a Singularity Hub that will automatically build images from the \u003ccode\u003eSingularity\u003c/code\u003e files upon pushes to connected Github repos. This will hopefully be offered to the larger community in the coming year, 2017.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGetting Setup with Singularity\u003c/h1\u003e\u003ca id=\"user-content-getting-setup-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Getting Setup with Singularity\" href=\"#getting-setup-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eThis is a guide to getting started with \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity containers\u003c/a\u003e in conjunction with Dartmouth College\u0027s \u003ca href=\"http://techdoc.dartmouth.edu/discovery/\" rel=\"nofollow\"\u003eDiscovery HPC\u003c/a\u003e.\u003cbr\u003e\nQuestions can be addressed to \u003ca href=\"mailto:eshin.jolly.gr@dartmouth.edu\"\u003eeshin.jolly.gr@dartmouth.edu\u003c/a\u003e or \u003ca href=\"mailto:mvdoc.gr@dartmouth.edu\"\u003emvdoc.gr@dartmouth.edu\u003c/a\u003e.\u003cbr\u003e\nWe\u0027re not experts but we\u0027re happy to try to help!\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#prereqs\"\u003eI. Pre-requisites (OSX only)\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-i-pre-requisites-osx-only\" class=\"anchor\" aria-label=\"Permalink: I. Pre-requisites (OSX only)\" href=\"#i-pre-requisites-osx-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#creation\"\u003eII. Creating a Singularity container\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-ii-creating-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: II. Creating a Singularity container\" href=\"#ii-creating-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#basicusage\"\u003eIII. Basic container usage\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-iii-basic-container-usage\" class=\"anchor\" aria-label=\"Permalink: III. Basic container usage\" href=\"#iii-basic-container-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#discovery\"\u003eIV. Using a container on Discovery\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-iv-using-a-container-on-discovery\" class=\"anchor\" aria-label=\"Permalink: IV. Using a container on Discovery\" href=\"#iv-using-a-container-on-discovery\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#updating\"\u003eV. Updating an existing container\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-v-updating-an-existing-container\" class=\"anchor\" aria-label=\"Permalink: V. Updating an existing container\" href=\"#v-updating-an-existing-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#sharing\"\u003eVI. Sharing containers\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-vi-sharing-containers\" class=\"anchor\" aria-label=\"Permalink: VI. Sharing containers\" href=\"#vi-sharing-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#resources\"\u003eVII. Extra resources\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-vii-extra-resources\" class=\"anchor\" aria-label=\"Permalink: VII. Extra resources\" href=\"#vii-extra-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-prereqs\"\u003e\u003c/a\u003e Pre-requisites (OSX only!)\u003c/h2\u003e\u003ca id=\"user-content--pre-requisites-osx-only\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites (OSX only!)\" href=\"#-pre-requisites-osx-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eBecause singularity runs primarily on linux, we need to create a virtual linux environment on OSX in order to build/manipulate singularity containers. Follow this step first if you\u0027re using OSX.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstall Homebrew package manager\u003c/h4\u003e\u003ca id=\"user-content-install-homebrew-package-manager\" class=\"anchor\" aria-label=\"Permalink: Install Homebrew package manager\" href=\"#install-homebrew-package-manager\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHomebrew is a package manager for OSX similar to apt-get or yum on linux. It allows you to download and install different software (e.g. wget, or curl) and allows you to build your own packages. Just copy and run the command below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/usr/bin/ruby -e \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUse Homebrew to install Vagrant\u003c/h4\u003e\u003ca id=\"user-content-use-homebrew-to-install-vagrant\" class=\"anchor\" aria-label=\"Permalink: Use Homebrew to install Vagrant\" href=\"#use-homebrew-to-install-vagrant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVagrant is a virtual development environment that can be used to create virtual-machines (kind of similar to Virtualbox, but much more powerful). It can be used to install and run another operating system on your computer that\u0027s completely independent from your host OS. First we\u0027re going to install vagrant via Homebrew.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebrew cask install Virtualbox\nbrew cask install vagrant\nbrew cask install vagrant-manager\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUse Vagrant to create a virtual machine\u003c/h4\u003e\u003ca id=\"user-content-use-vagrant-to-create-a-virtual-machine\" class=\"anchor\" aria-label=\"Permalink: Use Vagrant to create a virtual machine\" href=\"#use-vagrant-to-create-a-virtual-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow that we have vagrant installed, we can use it to make a brand new linux- based virtual machine, \u003cstrong\u003ewithin\u003c/strong\u003e which singularity will be installed. It\u0027s from inside this vm that we\u0027re going to do all future singularity container creation, modification etc.\u003c/p\u003e\n\u003cp\u003eFirst let\u0027s create a folder that our virtual machine will live in.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir singularity-vm\ncd singularity-vm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow lets download a \u003cem\u003evagrantfile\u003c/em\u003e for a prebuilt Ubuntu system that already has singularity installed.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant init singularityware/singularity-2.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally we can start up virtual machine and move into it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#If this is the first time you\u0027re building the vm the vagrant up command might take a minute or so to complete\nvagrant up\nvagrant ssh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhenver you\u0027re done using a vagrant vm just use \u003ccode\u003ectrl+c\u003c/code\u003e to exit the machine and type \u003ccode\u003evagrant halt\u003c/code\u003e to shut it down.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-creation\"\u003e\u003c/a\u003eCreating a Singularity container\u003c/h2\u003e\u003ca id=\"user-content-creating-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Creating a Singularity container\" href=\"#creating-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLet\u0027s begin by creating a new folder within our vm for our brand new container (this isn\u0027t strictly necessary but nice to keep different containers organized):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir miniconda\ncd miniconda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first thing we need to do in order to create a singularity container is make a singularity \u003cem\u003edefinition\u003c/em\u003e file. This is just an instruction set that singularity will use to create a container. Think of this definition file as a recipe, and the container as the final product. Within this recipe, specify everything you need to in order create your custom analysis environment. Sharing this definition file with others will enable them to identically reproduce the steps it took to create your container.\u003c/p\u003e\n\u003cp\u003eTo get you started here\u0027s an example definition file that we\u0027re going to use for this demo. This is a simple neurodebian flavored container with miniconda installed along with numpy and scipy.\u003cbr\u003e\nLet\u0027s save this to a file called \u003ccode\u003eminiconda.def\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Singularity definition example with miniconda\n# Matteo Visconti di Oleggio Castello; Eshin Jolly\n# mvdoc.gr@dartmouth.edu; eshin.jolly.gr@dartmouth.edu\n# May 2017\n\nbootstrap: docker\nfrom: neurodebian:jessie\n\n# this command assumes at least singularity 2.3\n%environment\n PATH=\"/usr/local/anaconda/bin:$PATH\"\n%post\n # install debian packages\n apt-get update\n apt-get install -y eatmydata\n eatmydata apt-get install -y wget bzip2 \\\n ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 \\\n git git-annex-standalone\n apt-get clean\n\n # install anaconda\n if [ ! -d /usr/local/anaconda ]; then\n wget https://repo.continuum.io/miniconda/Miniconda2-4.3.14-Linux-x86_64.sh \\\n -O ~/anaconda.sh \u0026amp;\u0026amp; \\\n bash ~/anaconda.sh -b -p /usr/local/anaconda \u0026amp;\u0026amp; \\\n rm ~/anaconda.sh\n fi\n # set anaconda path\n export PATH=\"/usr/local/anaconda/bin:$PATH\"\n\n # install the bare minimum\n conda install\\\n numpy scipy\n conda clean --tarballs\n\n # make /data and /scripts so we can mount it to access external resources\n if [ ! -d /data ]; then mkdir /data; fi\n if [ ! -d /scripts ]; then mkdir /scripts; fi\n\n%runscript\n echo \"Now inside Singularity container woah...\"\n exec /bin/bash\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow lets use our vagrant vm and create a blank singularity image allocating 4gb of disk space within our container. You may need to adjust this depending on how much software you plan to install. By default the vagrant vm will share \u003ccode\u003e/vagrant\u003c/code\u003e with your host OS so lets perform our operation in there within the container folder we created earlier.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant up\nvagrant ssh\ncd /vagrant/miniconda\n# Now let\u0027s build it!\nsudo singularity build miniconda.img miniconda.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-basicusage\"\u003e\u003c/a\u003eBasic container usage\u003c/h2\u003e\u003ca id=\"user-content-basic-container-usage\" class=\"anchor\" aria-label=\"Permalink: Basic container usage\" href=\"#basic-container-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf all went well we should be able to issue a python command to the python version installed \u003cem\u003ewithin\u003c/em\u003e our container like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec miniconda.img python -c \u0027print \"Hello from Singularity!\"\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can also open up our container and work \u003cem\u003einside\u003c/em\u003e it interactively:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run miniconda.img\nconda list\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePress \u003ccode\u003ectrl+d\u003c/code\u003e to exit the container.\u003c/p\u003e\n\u003cp\u003eMost commonly you\u0027ll use one of three commands with a container:\u003cbr\u003e\n\u003ccode\u003esingularity exec\u003c/code\u003e to run a specific command/file/script using the container\u003cbr\u003e\n\u003ccode\u003esingularity run\u003c/code\u003e to move into a container and use it interactively; what gets run by this command is dictated by your singularity \u003cem\u003edefinition\u003c/em\u003e file\u003cbr\u003e\n\u003ccode\u003esingularity shell\u003c/code\u003e similar to above, but specifically open up a shell within the container\u003c/p\u003e\n\u003cp\u003eA few other useful flags include:\n\u003ccode\u003e-B\u003c/code\u003e mount an external folder to the container\u003cbr\u003e\n\u003ccode\u003e-c\u003c/code\u003e don\u0027t automatically map /home and /tmp to shared folders with the host OS\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-discovery\"\u003e\u003c/a\u003eUsing a container on Discovery\u003c/h2\u003e\u003ca id=\"user-content-using-a-container-on-discovery\" class=\"anchor\" aria-label=\"Permalink: Using a container on Discovery\" href=\"#using-a-container-on-discovery\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use a container on Discovery you have to first upload the generated .img file to your home directory. Since containers can be rather large lets compress this and then uncompress on Discovery (starting with Singularity \u0026gt;=2.3.0 this functionality works through \u003ccode\u003eimport\u003c/code\u003e and \u003ccode\u003eexport\u003c/code\u003e commands)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etar -cvzf miniconda.tar.gz miniconda.img\nscp miniconda.tar.gz ejolly@discovery.dartmouth.edu:~\nssh ejolly@discovery.dartmouth.edu\ntar -xvzf miniconda.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow you can utilize the container by loading the singularity module and utilizing any of the singularity commands above. There is \u003cstrong\u003eone catch\u003c/strong\u003e however: by default singularity will try to melt together any environment variables defined in your account on discovery with environment variables defined within the container. The rationale behind this is that singularity offers the ability to \u003cem\u003eseamlessly\u003c/em\u003e blend a custom environment (i.e. your container built with all your goodies) and the functionality of your HPC (i.e. all the goodies that already exist on Discovery). However, often times you want to turn this functionality off and only use environment variables within your container to avoid conflicts (i.e. completely ignore environment variables set on Discovery). Here\u0027s how we do that:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\nsingularity run -e miniconda.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo make our lives easier we can create a simple bash script that executes a command in our container making sure to call it with all the extra flags we want (e.g. mounting some folders, ignoring environment variables). I personally like to create two scripts one for interactively working with a container and one for using it to execute commands for example with job submission. Here are some examples, you\u0027ll need to adapt them to mount the directories you want:\u003cbr\u003e\nLet\u0027s save the following code into a bash file called: exec_miniconda\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsingularity -e exec \\\n -B /idata/lchang/Projects:/data \\\n -B /ihome/ejolly/scripts/:/scripts \\\n miniconda.img \"$@\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLet\u0027s save the following code into a bash file called: interact_miniconda\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsingularity -e run \\\n\t-c \\\n\t-B /idata/lchang/Projects/Pinel:/data \\\n\t-B ~/scripts:/scripts \\\n\tminiconda.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we issue a command to our container (e.g. when submitting a job) like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./exec_miniconda python -c \u0027print \"Hello World!\"\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can also use our container interactively with. Here let\u0027s actually serve a jupyter notebook server from the cluster and interact with it using our local web browser. To do so we need to reconnect to Discovery with port-forwarding. The demo container here isn\u0027t built with a jupyter notebook so this won\u0027t work, but we you can use the same command when building your own container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# You should really connect to something other than the head node here!\nssh ejolly@discovery.dartmouth.edu -N -f -L localhost:3129:localhost:9999\n\n./exec_miniconda jupyter notebook --no-browser --port=9999\n# On local machine navigate to localhost:3129 in a web browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-updating\"\u003e\u003c/a\u003eUpdating an existing container\u003c/h2\u003e\u003ca id=\"user-content-updating-an-existing-container\" class=\"anchor\" aria-label=\"Permalink: Updating an existing container\" href=\"#updating-an-existing-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe preferred way to update a container is to modify the definition file and rebuild the image using the steps above. This ensures that any container image is always a product of its definition file and is therefore easy to reproduce.\u003c/p\u003e\n\u003cp\u003eHowever, singularity makes it easy to make changes to an existing container as well using the \u003ccode\u003e--writable\u003c/code\u003e flag with the \u003ccode\u003eexec\u003c/code\u003e, \u003ccode\u003erun\u003c/code\u003e, or \u003ccode\u003eshell\u003c/code\u003e commands, e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --writable miniconda.img apt-get install curl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also increase the size of an existing container with the \u003ccode\u003eexpand\u003c/code\u003e command, e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#Expand a container by 2gb\nsingularity expand --size 2048 miniconda.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-sharing\"\u003e\u003c/a\u003eSharing containers\u003c/h2\u003e\u003ca id=\"user-content-sharing-containers\" class=\"anchor\" aria-label=\"Permalink: Sharing containers\" href=\"#sharing-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne of the nice things about using singularity (and containers in general) is that you can share your analysis environment with others. These are served on \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003eSingularity hub\u003c/a\u003e. Many prebuilt containers already exist that you easily download and use.\u003c/p\u003e\n\u003cp\u003eLet\u0027s say we want to use this \u003ca href=\"https://singularity-hub.org/containers/105/\" rel=\"nofollow\"\u003econtainer\u003c/a\u003e prebuilt with tensor flow for GPUs. This is as simple as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://researchapps/tensorflow:gpu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can setup run and execute scripts like above to use it on Discovery.\u003c/p\u003e\n\u003cp\u003eYou can also easily share you custom container on Singularity hub by committing your singularity definition file to github and flipping the switch for that repository on singularity hub.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-resources\"\u003e\u003c/a\u003eExtra resources\u003c/h2\u003e\u003ca id=\"user-content-extra-resources\" class=\"anchor\" aria-label=\"Permalink: Extra resources\" href=\"#extra-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMuch of this tutorial is borrowed/integrated from several helpful resources:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eQuick guides\u003c/h4\u003e\u003ca id=\"user-content-quick-guides\" class=\"anchor\" aria-label=\"Permalink: Quick guides\" href=\"#quick-guides\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://mvdoc.me/2017/using-singularity-to-make-analyses-reproducible.html\" rel=\"nofollow\"\u003eMatteo Visconti\u0027s blogpost on getting started with singularity\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"http://jinhyuncheong.com/jekyll/update/2016/07/24/How-to-use-the-Discovery-cluster.html\" rel=\"nofollow\"\u003eJin Cheong\u0027s quick guide to using the discovery cluster\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMore comprehensive guides\u003c/h4\u003e\u003ca id=\"user-content-more-comprehensive-guides\" class=\"anchor\" aria-label=\"Permalink: More comprehensive guides\" href=\"#more-comprehensive-guides\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/quickstart\" rel=\"nofollow\"\u003eSingularity Documentation\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"http://techdoc.dartmouth.edu/discovery/\" rel=\"nofollow\"\u003eDiscovery Documentation\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSharing is caring\u003c/h4\u003e\u003ca id=\"user-content-sharing-is-caring\" class=\"anchor\" aria-label=\"Permalink: Sharing is caring\" href=\"#sharing-is-caring\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity hub\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003eDocker hub\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 9, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [], - "updated_at": 1706204531.0 + "updated_at": 1686764038.0 }, { "data_format": 2, - "description": "It\u0027s a prototype for an interpreter, which can interpret the host code of a CUDA Program, written with the runtime API.", + "description": "Code, data, and tutorials for \"Sense organ control in moths to moles is a gamble on information through motion\" ", "filenames": [ "Singularity" ], - "full_name": "SimeonEhrig/CUDA-Runtime-Interpreter", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCUDA-Runtime-Interpreter\u003c/h1\u003e\u003ca id=\"user-content-cuda-runtime-interpreter\" class=\"anchor\" aria-label=\"Permalink: CUDA-Runtime-Interpreter\" href=\"#cuda-runtime-interpreter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s a prototype of an interpreter, which can interpret the host code of a CUDA program, written with the runtime API. The interpreter uses source code files and fatbinray files as input.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eclang-dev \u0026gt;= 5.0\u003c/li\u003e\n\u003cli\u003ellvm-dev \u0026gt;= 5.0\u003c/li\u003e\n\u003cli\u003ecuda Toolkit\u003c/li\u003e\n\u003cli\u003ecmake 3.8.2\u003c/li\u003e\n\u003cli\u003ezlib1g-dev\u003c/li\u003e\n\u003cli\u003elibedit-dev\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTested with clang/llvm 5.0, 6.0 and CUDA 8.0.61\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/CUDA-Runtime-Interpreter\n mkdir build\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\n cmake ..\n make\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ebuild with own compiled clang/llvm-libraries\u003c/h3\u003e\u003ca id=\"user-content-build-with-own-compiled-clangllvm-libraries\" class=\"anchor\" aria-label=\"Permalink: build with own compiled clang/llvm-libraries\" href=\"#build-with-own-compiled-clangllvm-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to use own compiled clang/llvm-libraries, for example to debug the code, do the following steps.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/CUDA-Runtime-Interpreter\n ln -s \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_llvm_install\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/lib64 lib64\n mkdir build\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\n cmake .. -DMY_LLVM_BASE_DIR=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_llvm_install\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n make\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImplementation\u003c/h2\u003e\u003ca id=\"user-content-implementation\" class=\"anchor\" aria-label=\"Permalink: Implementation\" href=\"#implementation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe prototype is based on the clang example in\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/llvm-mirror/clang/tree/master/examples/clang-interpreter\"\u003ehttps://github.com/llvm-mirror/clang/tree/master/examples/clang-interpreter\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe workflow of the cuda runtime interpreter based on the cuda compiler pipeline of the clang/llvm. The clang/llvm shows you all compiler steps on the commandline, if you add the flag \u003ccode\u003e-###\u003c/code\u003e to your compiler call. For compiling a cuda program, there are five stages.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e clang++ -#\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# runtime.cu -o runtime -lcudart_static -L/usr/local/cuda-8.0/lib64 -ldl -lrt -pthread\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(The concrete program calls can look at the commands.txt)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003egenerating ptx device code (a kind of nvidia assembler)\u003c/li\u003e\n\u003cli\u003etranslate ptx to sass (machine code of ptx)\u003c/li\u003e\n\u003cli\u003egenerate a fatbinray (a kind of wrapper for the device code)\u003c/li\u003e\n\u003cli\u003egenerate host code object file (use fatbinary as input)\u003c/li\u003e\n\u003cli\u003elink to executable\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe first three steps are about device code generation. The generation of the fatbinary will be done before starting the interpreter. The device code generation can be performed with either clang\u0027s CUDA frontend or NVCC and the tools of NVIDIA\u0027s CUDA Toolkit. The interpreter replaces the 4th and 5th step.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDebug output options\u003c/h3\u003e\u003ca id=\"user-content-debug-output-options\" class=\"anchor\" aria-label=\"Permalink: Debug output options\" href=\"#debug-output-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are some options to get out information from the different stages of the interpreter process (e.g LLVM IR Code, Assembler ...). In \u003ccode\u003e\u0026lt;build\u0026gt;/config/Config.hpp\u003c/code\u003e you can modify some \u003ccode\u003e#define\u003c/code\u003e to change the output properties. Please note, the changes will be effective after recompiling.\u003c/p\u003e\n\u003cp\u003eA special case is \u003ccode\u003e#define CUI_INTERPRET\u003c/code\u003e. It changes the backend. If it is defined with \u003ccode\u003e#define CUI_INTERPRET 1\u003c/code\u003e, the interpreter use the JIT-Backend. If \u003ccode\u003eCUI_INTERPRET\u003c/code\u003e has the value 0, it will generate an object file. The object file can be linked (ld) to an executable.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCUI_PCH_MODE [0|1] if the value is 1, add an additional compiler stage to the device jit\n\u003cul\u003e\n\u003cli\u003ethe CUDA device code will translate to a PCH file and then to PTX\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCUI_PRINT_INT_MODE [0|1] if the value is 1, print which interpreter mode is selected\u003c/li\u003e\n\u003cli\u003eCUI_DEBUG_BACKEND [0|1] if the value is 1, the llvm::DebugFlag will be enabled and all debug information of the backend will be written on the console (independent of CUI_INTERPRET)\u003c/li\u003e\n\u003cli\u003eCUI_DEBUG_JIT_OBJ [0|1] if the value is 1, the jit backend write the object code in a file, which is generated during the jit process (only if CUI_INTERPRET is 1)\u003c/li\u003e\n\u003cli\u003eCUI_DEBUG_JIT_INFO [0|1] if the value is 1, add debug information to the jited code and allow debugging with the gdb (only if CUI_INTERPRET is 1)\n\u003cul\u003e\n\u003cli\u003enotice: to add debug information to the jited code, you also have to set the flag -g at start of the cuda-interpreter\u003c/li\u003e\n\u003cli\u003eexample: ./cuda-interpreter \u003ccode\u003e-cuda_cpp \u0026lt;source\u0026gt;.cu -fatbin \u0026lt;source\u0026gt;.fatbin -g\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCUI_SAVE_DEVICE_CODE [0|1] if the value is 1, save the generated device code files (.ptx, .sass and .fatbin) in the folder of the interpreter exe, else write the files to /tmp\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecute an example\u003c/h2\u003e\u003ca id=\"user-content-execute-an-example\" class=\"anchor\" aria-label=\"Permalink: Execute an example\" href=\"#execute-an-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the \u003ccode\u003eexample_prog\u003c/code\u003e folder you can find some example source codes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003egenerating fatbinary\u003c/h3\u003e\u003ca id=\"user-content-generating-fatbinary\" class=\"anchor\" aria-label=\"Permalink: generating fatbinary\" href=\"#generating-fatbinary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step is optional. You can precompile the device code to a fatbinary by yourself. Than you can pass the code as argument. Otherwise, if there is no -fatbin argument declared, the interpreter compiled the device code just in time, by itself. The fatbinary is the compiled device-code in an \"function-handle\", which allows an embedding in the host. There three options to generate the fatbinary.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./generate_nvcc_fatbin.sh \u0026lt;filename\u0026gt;.cu\u003c/code\u003e in the example_prog folder and generate a \u003ccode\u003envcc_\u0026lt;filename\u0026gt;.fatbin\u003c/code\u003e file with NVIDIAs nvcc.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./generate_clang_fatbin.sh \u0026lt;filename\u0026gt;.cu\u003c/code\u003e in the example_prog folder and generate a \u003ccode\u003eclang_\u0026lt;filename\u0026gt;.fatbin\u003c/code\u003e file with clang/llvm and NVIDIAs tools.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003eclang++ -### runtime.cu -o runtime -lcudart_static -L/usr/local/cuda-8.0/lib64 -ldl -lrt -pthread\u003c/code\u003e and you get information about the clang and a list of 5 commands. Use the first three commands, to generate a fatbinary. If you do this, you have to change the input- and output-paths of the commands or you have to copy the fatbin from the /tmp folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe last option is the most complicated way, but the best way, because it is the closest way to the original implementation of the clang frontend.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erunning interpreter\u003c/h3\u003e\u003ca id=\"user-content-running-interpreter\" class=\"anchor\" aria-label=\"Permalink: running interpreter\" href=\"#running-interpreter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the tests with cuda-interpeter and the two or more arguments as above:\u003c/p\u003e\n\u003cp\u003e[1] set -cuda_c or -cuda_cpp as first argument to enable the cuda c- or c++-frontend\u003c/p\u003e\n\u003cp\u003e[2] path to the source code in \"example_prog\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - note: needs the file ending .cu or .cpp \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[3] optinal path to the source code .fatbin\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - note: needs the file ending .fatbin,\n - the argument -fatbin and the path after tell the interpreter, that it should use the precompiled code in the file instead to compile the device code by itself \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[4] arguments for clang compiler instance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- all arguments after the source path or fatbin path will pass to the clang compilerInstance -\u0026gt; see all possible arguments with $ clang++ --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./cuda-interpreter -cuda_cpp ../example_prog/hello.cu -v\n ./cuda-interpreter -cuda_cpp ../example_prog/hello.cu -fatbin ../example_prog/runtime.fatbin -v\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003erunning the c++-interpreter frontend\u003c/h4\u003e\u003ca id=\"user-content-running-the-c-interpreter-frontend\" class=\"anchor\" aria-label=\"Permalink: running the c++-interpreter frontend\" href=\"#running-the-c-interpreter-frontend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the tests with cuda-interpeter and the two or more arguments as above:\u003c/p\u003e\n\u003cp\u003e[1] set -c or -cpp as first argument to enable the c- or c++-frontend\u003c/p\u003e\n\u003cp\u003e[2] path to the source code in \"example_prog\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - note: needs the file ending .cpp \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[3] arguments for clang compiler instance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- all arguments after the fatbin path will pass to the clang compilerInstance -\u0026gt; see all possible arguments with $ clang++ --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./cuda-interpreter -cpp ../example_prog/hello.cpp -v\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePCH and device code generation\u003c/h1\u003e\u003ca id=\"user-content-pch-and-device-code-generation\" class=\"anchor\" aria-label=\"Permalink: PCH and device code generation\" href=\"#pch-and-device-code-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePCH (precompiled headers) is a intermediate form of header files, which allows the compiler to reduce the compile time. Specially for templates, where the header files will be read multiple, it\u0027s realy useful. For cling, we will use PCH to handle normal and templated device code kernels. There are two reasons for this decision. The first is, that we can fast generate many specializations of a templated kernels. The second reason have to do with generating the fatbinary code. If we generate a fatbinary file for a kernel launch, we have to put in the code of the initial kernel and all kernel definitions of kernels, which will called from initial kernel and his children. To solve this problem, there are two options. The first is to analyze source code and find all kernel calls. Than all needed kernel source code can be glue together and send to the device jit. The second option is to send all defined kernel source codes to the jit inclusive the kernels, which will not used. The second option has the advantage, that we don\u0027t need to analyze the code, but we have to glue together the complete device source code and compile all to a fatbinary. PCH is a technique, which allows a fast adding of a new function definition and compiling the complete code to PTX and fatbinary code.\u003c/p\u003e\n\u003cp\u003eSo, I implemented a function (which is not necessary for the cuda-interpreter), which simulate the planned behavior of cling and allows to add an unlimited number of files with kernel definitions to the interpreter process. This is only possible, if the PCH mode is enable. Every new kernel will translate to a PCH file and include his predecessor PCH file, if exist. If the last kernel file is translated, the PCH file will translate to PTX.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample to use extra kernel files\u003c/h2\u003e\u003ca id=\"user-content-example-to-use-extra-kernel-files\" class=\"anchor\" aria-label=\"Permalink: Example to use extra kernel files\" href=\"#example-to-use-extra-kernel-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use extra kernel files, you have to enable the PCH mode via Config.hpp, at first. Then the argument \u003ccode\u003e-kernel\u003c/code\u003e is aviable at the 3rd position, comparable the argument \u003ccode\u003e-fatbin\u003c/code\u003e. After the \u003ccode\u003e-kernel\u003c/code\u003e argument you can set the path to the kernel files. There is also possible to declare more files via string.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./cuda-interpreter -cuda_cpp ../PCH_example/cuda_template_many_sources/runtime.cu -kernels \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e../PCH_example/cuda_template_many_sources/myInclude/kernel1.cu ../PCH_example/cuda_template_many_sources/myInclude/kernel2.cu ../PCH_example/cuda_template_many_sources/myInclude/kernel3.cu\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -I../PCH_example/cuda_template_many_sources/myInclude/\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 9, - "subscribers_count": 6, - "topics": [], - "updated_at": 1717768570.0 - }, - { - "data_format": 2, - "description": "Mutect pipeline with Nextflow", - "filenames": [ - "Singularity/Singularity.v2.1_gatk3", - "Singularity/Singularity.v2.2", - "Singularity/Singularity.v2.2_gatk2", - "Singularity/Singularity.v2.1", - "Singularity/Singularity.v2.2_gatk3", - "Singularity/Singularity.v2.1_gatk2", - "Singularity/Singularity.v2.0" - ], - "full_name": "IARCbioinfo/mutect-nf", - "latest_release": "v2.3", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emutect-nf\u003c/h1\u003e\u003ca id=\"user-content-mutect-nf\" class=\"anchor\" aria-label=\"Permalink: mutect-nf\" href=\"#mutect-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMutect pipeline for somatic variant calling with Nextflow\u003c/h2\u003e\u003ca id=\"user-content-mutect-pipeline-for-somatic-variant-calling-with-nextflow\" class=\"anchor\" aria-label=\"Permalink: Mutect pipeline for somatic variant calling with Nextflow\" href=\"#mutect-pipeline-for-somatic-variant-calling-with-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/mutect-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/252d6f9ba145dfc9b78cc63744bd08a9212ae9cfd069e7c3ad8ba8801c8ca3cd/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f6d75746563742d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/mutect-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/mutect-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4357\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"mutectpipeline.png?raw=true\"\u003e\u003cimg src=\"mutectpipeline.png?raw=true\" alt=\"workflow\" title=\"Scheme of calling Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNextflow pipeline for somatic variant calling with mutect with Mutect1 or 2, gatk3 or gatk4\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eNextflow: for common installation procedures see the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/broadinstitute/mutect\"\u003eMutect\u003c/a\u003e and its dependencies (Java 1.7 and Maven 3.0+), or \u003ca href=\"https://github.com/broadinstitute/gatk\"\u003egatk4\u003c/a\u003e that now includes Mutect2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://bedtools.readthedocs.io/en/latest/content/installation.html\" rel=\"nofollow\"\u003ebedtools\u003c/a\u003e and move the executable file in your path.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003epython\u003c/a\u003e and package \u003ca href=\"https://github.com/pysam-developers/pysam\"\u003epysam\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bedops/bedops\"\u003ebedops\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eA conda receipe, and docker and singularity containers are available with all the tools needed to run the pipeline (see \"Usage\")\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGATK4\u003c/h3\u003e\u003ca id=\"user-content-gatk4\" class=\"anchor\" aria-label=\"Permalink: GATK4\" href=\"#gatk4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith GATK4, a list of known_snps can be provided to mutect2 to improve the variant classification, for example file \u003ca href=\"https://console.cloud.google.com/storage/browser/_details/gatk-best-practices/somatic-hg38/af-only-gnomad.hg38.vcf.gz\" rel=\"nofollow\"\u003eaf-only-gnomad.hg38.vcf.gz\u003c/a\u003e from the bundle best practices from the broad institute \u003ca href=\"https://console.cloud.google.com/storage/browser/gatk-best-practices/somatic-hg38/\" rel=\"nofollow\"\u003eGATK somatic calling bundle\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eestimate contamination\u003c/h3\u003e\u003ca id=\"user-content-estimate-contamination\" class=\"anchor\" aria-label=\"Permalink: estimate contamination\" href=\"#estimate-contamination\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen the estimate contamination mode is chosen, one needs to provide a list of known snps; we recommend the file \u003ca href=\"https://console.cloud.google.com/storage/browser/_details/gatk-best-practices/somatic-hg38/small_exac_common_3.hg38.vcf.gz\" rel=\"nofollow\"\u003esmall_exac_common_3.hg38.vcf.gz\u003c/a\u003e from the best practices broad institute bundle.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--tumor_bam_folder\u003c/td\u003e\n\u003ctd\u003ea folder with tumor bam files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--normal_bam_folder\u003c/td\u003e\n\u003ctd\u003ea folder with normal bam files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--tn_file\u003c/td\u003e\n\u003ctd\u003einput tabulation-separated values file with columns sample (sample name), tumor (full path to tumor bam), normal (full path to matched normal bam); optionally (for --genotype mode), columns preproc (is the bam RNAseq needing preprocessing: yes or no) and vcf (full path to vcf file containing alleles to genotype)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput methods\u003c/h3\u003e\u003ca id=\"user-content-input-methods\" class=\"anchor\" aria-label=\"Permalink: Input methods\" href=\"#input-methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that there are two input methods: separate tumor_bam_folder and normal_bam_folder, and tn_file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSeparated tumor_bam_folder and normal_bam_folder method\u003c/h4\u003e\u003ca id=\"user-content-separated-tumor_bam_folder-and-normal_bam_folder-method\" class=\"anchor\" aria-label=\"Permalink: Separated tumor_bam_folder and normal_bam_folder method\" href=\"#separated-tumor_bam_folder-and-normal_bam_folder-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe method assumes that normal and tumor bam files are in these respective folder, and uses parameters suffix_tumor and suffix_normal to detect them (the rest of the file name needs to be identical.\u003c/p\u003e\n\u003cp\u003eThe tumor bam file format must be (\u003ccode\u003esample\u003c/code\u003e \u003ccode\u003esuffix_tumor\u003c/code\u003e \u003ccode\u003e.bam\u003c/code\u003e) with \u003ccode\u003esuffix_tumor\u003c/code\u003e as \u003ccode\u003e_T\u003c/code\u003e by default and customizable in input (\u003ccode\u003e--suffix_tumor\u003c/code\u003e). (e.g. \u003ccode\u003esample1_T.bam\u003c/code\u003e)\nThe normal bam file format must be (\u003ccode\u003esample\u003c/code\u003e \u003ccode\u003esuffix_normal\u003c/code\u003e \u003ccode\u003e.bam\u003c/code\u003e) with \u003ccode\u003esuffix_normal\u003c/code\u003e as \u003ccode\u003e_N\u003c/code\u003e by default and customizable in input (\u003ccode\u003e--suffix_normal\u003c/code\u003e). (e.g. \u003ccode\u003esample1_N.bam\u003c/code\u003e).\nBAI indexes have to be present in the same location than their BAM mates, with the extension \u003ccode\u003ebam.bai\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe tn_file method\u003c/h4\u003e\u003ca id=\"user-content-the-tn_file-method\" class=\"anchor\" aria-label=\"Permalink: The tn_file method\" href=\"#the-tn_file-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe method uses a tabulation-separated values format file with columns sample, tumor, and normal (in any order); it does not use parameters suffix_tumor and suffix_normal and does not require file names to match. When the genotype mode is active, additional columns are expected: preproc, specifying if preprocessing of RNA-seq bam file is required (yes or no) and vcf, indicating the location of the vcf file containing the alleles to genotype. preproc includes splitting spanning reads, correcting CIGAR string with NDN pattern, and changing mapping quality of uniquely mapped reads from 255 to 60(gatk4\u0027s splitNCigarReads and a custom python script). The tn_file method is necessary for joint multi-sample calling, in which case the sample name is used to group files, and to specify preprocessing of some RNA-seq samples.\u003c/p\u003e\n\u003cp\u003eBAI indexes have to be present in the same location than their BAM mates, with the extension \u003ccode\u003ebam.bai\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth align=\"right\"\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd align=\"right\"\u003eref.fa\u003c/td\u003e\n\u003ctd\u003ereference genome fasta file\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003enumber of CPUs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003ctd\u003ememory for mapping\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--suffix_tumor\u003c/td\u003e\n\u003ctd\u003e_T\u003c/td\u003e\n\u003ctd\u003esuffix for tumor file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--suffix_normal\u003c/td\u003e\n\u003ctd\u003e_N\u003c/td\u003e\n\u003ctd\u003esuffix for matched normal file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003emutect_results\u003c/td\u003e\n\u003ctd\u003eoutput folder for aligned BAMs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bed\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eBed file containing intervals\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--region\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eA region defining the calling, in the format CHR:START-END\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--known_snp\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eVCF file with known variants and frequency (e.g., from gnomad)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mutect_args\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eArguments you want to pass to mutect. WARNING: form is \" --force_alleles \" with spaces between quotes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--nsplit\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eSplit the region for calling in nsplit pieces and run in parallel\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--java\u003c/td\u003e\n\u003ctd\u003ejava\u003c/td\u003e\n\u003ctd\u003eName of the JAVA command\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--snp_contam\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eVCF file with known germline variants to genotype for contamination estimation (requires --estimate_contamination)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--PON\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003epath to panel of normal VCF file used to filter calls\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--gatk_version\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003egatk version\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref_RNA\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003efasta reference for preprocessing RNA (required when preproc column contains yes in input tn_file)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNOTE: if neither --bed or --region, will perform the calling on whole genome, based on the faidx file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional for gatk3\u003c/h4\u003e\u003ca id=\"user-content-optional-for-gatk3\" class=\"anchor\" aria-label=\"Permalink: Optional for gatk3\" href=\"#optional-for-gatk3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese options are not needed if gatk4 is used\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cosmic\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eCosmic VCF file required by mutect; not in gatk4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mutect_jar\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003epath to jar file of mutect1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mutect2_jar\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003epath to jar file of mutect2\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eprint usage and optional parameters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--estimate_contamination\u003c/td\u003e\n\u003ctd\u003erun extra step of estimating contamination by normal and using the results to filter calls; only for gatk4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--genotype\u003c/td\u003e\n\u003ctd\u003euse genotyping from vcf mode instead of usual variant calling requires tn_file with vcf column and gatk4, and if RNA-seq included, requires preproc column\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--filter_readorientation\u003c/td\u003e\n\u003ctd\u003eRun extra step learning read orientation model and using it to filter reads\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the pipeline on a series of matched tumor normal files (with suffixes \u003cem\u003e_T\u003c/em\u003e and \u003cem\u003e_N\u003c/em\u003e) in folders \u003cem\u003etumor_BAM\u003c/em\u003e \u003cem\u003enormal_BAM\u003c/em\u003e, a reference genome with indexes \u003cem\u003eref\u003c/em\u003e, and a bed file ref.bed, one can type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run IARCbioinfo/mutect-nf -r v2.2b -profile singularity --tumor_bam_folder tumor_BAM/ --normal_bam_folder normal_BAM/ --ref ref_genome.fa --gtf ref.gtf \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the pipeline without singularity just remove \"-profile singularity\". Alternatively, one can run the pipeline using a docker container (-profile docker) the conda receipe containing all required dependencies (-profile conda). Note that we provide similar support when using gatk3 (profiles conda_gatk3, singularity_gatk3, and docker_gatk3) or gatk2 (profiles conda_gatk2, singularity_gatk2, and docker_gatk2).\u003c/p\u003e\n\u003cp\u003eTo use gatk3, set \u003ccode\u003e--gatk_version 3\u003c/code\u003eand provide option \u003ccode\u003e--mutect2_jar\u003c/code\u003e for mutect version 2 (GATK executable jar, which integrate mutect2) and possibly specify \u003ccode\u003e-profile singularity_gatk3\u003c/code\u003e, and set \u003ccode\u003e--mutect_jar\u003c/code\u003e for mutect version 1 and possibly specify \u003ccode\u003e-profile singularity_gatk2\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHelp section\u003c/h4\u003e\u003ca id=\"user-content-help-section\" class=\"anchor\" aria-label=\"Permalink: Help section\" href=\"#help-section\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can print the help manual by providing \u003ccode\u003e--help\u003c/code\u003e in the execution command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/mutect-nf --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis shows details about optional and mandatory parameters provided by the user.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003esample.vcf.gz and sample.vcf.gz.tbi\u003c/td\u003e\n\u003ctd\u003efiltered VCF files and their indexes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003estats/\u003c/td\u003e\n\u003ctd\u003egatk stats files from mutect\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eintermediate_calls/raw_calls/sample.vcf\u003c/td\u003e\n\u003ctd\u003eunfiltered VCF files\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe output_folder directory contains two subfolders: stats and intermediate_calls\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhy are some samples absent from the output vcfs when I run multi-sample calling?\u003c/h3\u003e\u003ca id=\"user-content-why-are-some-samples-absent-from-the-output-vcfs-when-i-run-multi-sample-calling\" class=\"anchor\" aria-label=\"Permalink: Why are some samples absent from the output vcfs when I run multi-sample calling?\" href=\"#why-are-some-samples-absent-from-the-output-vcfs-when-i-run-multi-sample-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutputs are based on the SM field of the BAM file; when multiple files have the same SM, only one is outputed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhy are some samples present in the input file ignored?\u003c/h3\u003e\u003ca id=\"user-content-why-are-some-samples-present-in-the-input-file-ignored\" class=\"anchor\" aria-label=\"Permalink: Why are some samples present in the input file ignored?\" href=\"#why-are-some-samples-present-in-the-input-file-ignored\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheck that the input is tab-separated. When parsing the input file, if a line is not tab separated, nextflow will ignore it without returning an error.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirected Acyclic Graph\u003c/h2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-label=\"Permalink: Directed Acyclic Graph\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/mutect-nf/blob/dev/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@iarc.fr\"\u003eAlcalaN@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "full_name": "MacIver-Lab/Ergodic-Information-Harvesting", + "latest_release": "v1.0.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCode and data to reproduce results from \"Tuning movement for sensing in an uncertain world\" by Chen Chen, Todd D. Murphey, and Malcolm A. MacIver, Northwestern University, Evanston IL, USA (eLife, 2020).\u003c/h1\u003e\u003ca id=\"user-content-code-and-data-to-reproduce-results-from-tuning-movement-for-sensing-in-an-uncertain-world-by-chen-chen-todd-d-murphey-and-malcolm-a-maciver-northwestern-university-evanston-il-usa-elife-2020\" class=\"anchor\" aria-label=\"Permalink: Code and data to reproduce results from \u0026quot;Tuning movement for sensing in an uncertain world\u0026quot; by Chen Chen, Todd D. Murphey, and Malcolm A. MacIver, Northwestern University, Evanston IL, USA (eLife, 2020).\" href=\"#code-and-data-to-reproduce-results-from-tuning-movement-for-sensing-in-an-uncertain-world-by-chen-chen-todd-d-murphey-and-malcolm-a-maciver-northwestern-university-evanston-il-usa-elife-2020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2511\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/maciverlabnu/ergodic-information-harvesting\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ea20765da22a256496de0b94e50b33361438d344ff096f94a862699ffa94bb90/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f636c6f75642f6275696c642f6d6163697665726c61626e752f6572676f6469632d696e666f726d6174696f6e2d68617276657374696e672e737667\" alt=\"Docker status\" data-canonical-src=\"https://img.shields.io/docker/cloud/build/maciverlabnu/ergodic-information-harvesting.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/maciverlabnu/ergodic-information-harvesting\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/962ce4a3720b30c3b75702b81f36bce7ef82c5084f7775528e7ccfe5a3e98f17/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d6163697665726c61626e752f6572676f6469632d696e666f726d6174696f6e2d68617276657374696e672e737667\" alt=\"Docker pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/maciverlabnu/ergodic-information-harvesting.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://nbviewer.jupyter.org/github/MacIver-Lab/Ergodic-Information-Harvesting/blob/master/Tutorial/Ergodic_Information_Harvesting_Tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bfeb5472ee3df9b7c63ea3b260dc0c679be90b97/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72656e6465722d6e627669657765722d6f72616e67652e7376673f636f6c6f72423d66333736323626636f6c6f72413d346434643464\" alt=\"nbviewer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/MacIver-Lab/Ergodic-Information-Harvesting/master?filepath=Tutorial%2FErgodic_Information_Harvesting_Tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96332ce22984004fd6244de25503b895fd42ac4f20a23e7d9110af95dfc20551/68747470733a2f2f6d7962696e6465722e6f72672f62616467652e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eErgodic Information Harvesting (EIH) Video \u0026amp; Tutorial\u003c/h1\u003e\u003ca id=\"user-content-ergodic-information-harvesting-eih-video--tutorial\" class=\"anchor\" aria-label=\"Permalink: Ergodic Information Harvesting (EIH) Video \u0026amp; Tutorial\" href=\"#ergodic-information-harvesting-eih-video--tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://youtu.be/8N_UzCcEw4w\" rel=\"nofollow\"\u003eVideo showing the fish, mammal, and insect behaviors that were compared to the trajectories generated by EIH\u003c/a\u003e, and a video showing how the algorithm works by way of \u003ca href=\"https://youtu.be/QBtMMROk4GM\" rel=\"nofollow\"\u003eapplying it to control an underwater electrolocation robot\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInteractive Jupyter notebook tutorial, click to view online: \u003ca href=\"https://nbviewer.jupyter.org/github/MacIver-Lab/Ergodic-Information-Harvesting/blob/master/Tutorial/Ergodic_Information_Harvesting_Tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bfeb5472ee3df9b7c63ea3b260dc0c679be90b97/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72656e6465722d6e627669657765722d6f72616e67652e7376673f636f6c6f72423d66333736323626636f6c6f72413d346434643464\" alt=\"nbviewer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eor use \u003ca href=\"https://mybinder.org/v2/gh/MacIver-Lab/Ergodic-Information-Harvesting/master?filepath=Tutorial%2FErgodic_Information_Harvesting_Tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96332ce22984004fd6244de25503b895fd42ac4f20a23e7d9110af95dfc20551/68747470733a2f2f6d7962696e6465722e6f72672f62616467652e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e to run interactively through online Jupyter Notebook\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSteps to reproduce the results shown in the EIH paper\u003c/h1\u003e\u003ca id=\"user-content-steps-to-reproduce-the-results-shown-in-the-eih-paper\" class=\"anchor\" aria-label=\"Permalink: Steps to reproduce the results shown in the EIH paper\" href=\"#steps-to-reproduce-the-results-shown-in-the-eih-paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll of the simulation code is written with \u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython 3\u003c/a\u003e. All of the figure plotting files are written in MATLAB (R2017a+). The code can be run on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA local computer, which is very easy to set up but the performance is ultimately limited by the number of locally accessible CPU cores\u003c/li\u003e\n\u003cli\u003eCloud computing virtual servers through any Infrastructure as a Service (IaaS) provider, \u003cem\u003ee.g.\u003c/em\u003e \u003ca href=\"https://aws.amazon.com/ec2/\" rel=\"nofollow\"\u003eAmazon Elastic Compute Cloud\u003c/a\u003e, \u003ca href=\"https://cloud.google.com/compute/\" rel=\"nofollow\"\u003eGoogle Cloud Compute Engine\u003c/a\u003e, or academic \u003ca href=\"https://en.wikipedia.org/wiki/HPCC\" rel=\"nofollow\"\u003eHPCC (High Performance Computing Cluster)\u003c/a\u003e systems. Cloud computing is easy to setup and provides a way to scale up the total number of running threads (\u003cem\u003ee.g.\u003c/em\u003e Google Cloud Compute Engine allows up to 96 CPU threads per instance). Our code\u0027s runtime environment and dependencies are fully containerized through \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e to minimize the effort needed for environment setup, and for easy scaling to run the code faster (you can run the code on a single CPU over a few days, or on many CPUS on the cloud in a few hours). Setting the code up to run on a cloud service for the first time is somewhat involved if you are not used to doing this. We have a set of screencasts to walk through doing this from scratch on Amazon Web Services that we are happy to share.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that this repository has included all of the published data, including all the simulations, to reproduce all figures in the paper. To reproduce our figures from the published data, rather than re-run all the simulations from scratch, simply jump to step 5 below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed Steps\u003c/h2\u003e\u003ca id=\"user-content-detailed-steps\" class=\"anchor\" aria-label=\"Permalink: Detailed Steps\" href=\"#detailed-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo avoid possible \u003ca href=\"https://en.wikipedia.org/wiki/Dependency_hell\" rel=\"nofollow\"\u003edependency hell\u003c/a\u003e and minimize the effort of setting up the runtime environment we used for our results, we prebuilt a \u003ca href=\"https://en.wikipedia.org/wiki/Container_(virtualization)\" rel=\"nofollow\"\u003econtainer image\u003c/a\u003e to be used for executing all the simulation code in Python using \u003ca href=\"https://docs.docker.com/get-started/overview/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e. Here is an article explaining the utility of containers for reproducibility of research: \u003ca href=\"https://doi.org/10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003eSingularity: Scientific containers for mobility of compute\u003c/a\u003e. Note that this is only for reproducing simulations: for generation of the figures from the simulations, a local installation of MATLAB (not provided in the container) is still required.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Obtain code and data\u003c/h3\u003e\u003ca id=\"user-content-1-obtain-code-and-data\" class=\"anchor\" aria-label=\"Permalink: 1. Obtain code and data\" href=\"#1-obtain-code-and-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo start, you will either be using the \u003ca href=\"https://zenodo.org\" rel=\"nofollow\"\u003eZenodo\u003c/a\u003e archived repository, which includes both the data and the code; or, if you prefer, you will clone the most recent version of the EIH repository. To obtain the Zenodo archived repository, which is a zip of the release of the repository corresponding to the release of the publication by eLife, you will simply search \"maciver chen\" on the \u003ca href=\"https://zenodo.org\" rel=\"nofollow\"\u003eZenodo\u003c/a\u003e website, and download the 32GB zip file of this repository.\u003c/p\u003e\n\u003cp\u003eTo clone the most recent version of the EIH repository (which may have changes from the archived release to correct errors that are found), two tools are required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003egit\u003c/code\u003e - is used to pull all the non-data files from this repository. Go to \u003ca href=\"https://git-scm.com/downloads\" rel=\"nofollow\"\u003egit\u0027s official release page\u003c/a\u003e to download and install git. Then use the following command to clone this repository:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --depth=1 https://github.com/MacIver-Lab/Ergodic-Information-Harvesting\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003egit-lfs\u003c/code\u003e - is used to pull all the published data (required to reproduce the results). Go to \u003ca href=\"https://git-lfs.github.com/\"\u003egit-lfs\u0027s official release page\u003c/a\u003e to download and install. Then run the following command \u003cstrong\u003einside the root directory of the cloned EIH repo \u003ccode\u003e./Ergodic-Information-Harvesting/\u003c/code\u003e\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Ergodic-Information-Harvesting\ngit lfs install\ngit lfs pull\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you succeeded, you should see files being downloaded by \u003ccode\u003egit-lfs\u003c/code\u003e. Once it is setup, should you decide to delete the files and start again, you should only need to do the \u003ccode\u003egit clone\u003c/code\u003e step.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Install Docker and Pull the EIH Container Image\u003c/h3\u003e\u003ca id=\"user-content-2-install-docker-and-pull-the-eih-container-image\" class=\"anchor\" aria-label=\"Permalink: 2. Install Docker and Pull the EIH Container Image\" href=\"#2-install-docker-and-pull-the-eih-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Docker by following the official documentation: \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Optionally) For Linux or Linux-based HPCC/cloud computing environments, please additionally follow the \u003ca href=\"https://docs.docker.com/engine/install/linux-postinstall/\" rel=\"nofollow\"\u003epost-installation setup steps for Linux\u003c/a\u003e to allow running docker without \u003ccode\u003esudo\u003c/code\u003e. If you don\u0027t want or unable to follow this step, you will need to make sure to run docker commands with \u003ccode\u003esudo docker\u003c/code\u003e rather than \u003ccode\u003edocker\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Invoke Shell in the EIH Container Image\u003c/h3\u003e\u003ca id=\"user-content-3-invoke-shell-in-the-eih-container-image\" class=\"anchor\" aria-label=\"Permalink: 3. Invoke Shell in the EIH Container Image\" href=\"#3-invoke-shell-in-the-eih-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container image is a fully self-contained Linux OS image with Python 3 dependencies setup for generating the EIH simulations developed for the study. We will invoke the command line tool inside of the EIH container image to interact with the resources inside the container and start the simulations.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v [absolute path to EIH folder]:/EIH maciverlabnu/ergodic-information-harvesting\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ereplace the \u003ccode\u003e[absolute path to EIH folder]\u003c/code\u003e part with the absolute path to your local EIH repository folder, \u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003eC:/Ergodic-Information-Harvesting\u003c/code\u003e (remember to replace \u003ccode\u003e\\\u003c/code\u003e with \u003ccode\u003e/\u003c/code\u003e when in Windows) or \u003ccode\u003e~/Ergodic-Information-Harvesting\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf you are already inside the Ergodic-Information-Harvesting folder, you can simply do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/EIH maciverlabnu/ergodic-information-harvesting\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Start Reproducing Simulation\u003c/h3\u003e\u003ca id=\"user-content-4-start-reproducing-simulation\" class=\"anchor\" aria-label=\"Permalink: 4. Start Reproducing Simulation\" href=\"#4-start-reproducing-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe used \u003ca href=\"https://cython.org/\" rel=\"nofollow\"\u003eCython\u003c/a\u003e to accelerate the simulation which requires compiling some of the code before running the simulation. Compile the accelerated code by calling the following command (this only needs to be done once):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Command to run inside the container\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /EIH\nchmod +x ./BuildCython.sh\n./BuildCython.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou are now all set for the environment setup. You can start reproducing all the simulation results by running the main simulation code:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /EIH/SimulationCode/\npython3 RunAllSims.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, \u003ccode\u003eRunAllSims.py\u003c/code\u003e will check the number of available CPU threads and automally run parallel simulation jobs with the maximum number of threads possible. Nonetheless, the number of threads can be manually specified by passing the desired parallel thread count argument to it, for example\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 RunAllSims.py 20\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewill run 20 threads in parallel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: The simulation will take a long time to finish. Depending on your operating system, you may need to \u003cstrong\u003eprevent your system from going to sleep\u003c/strong\u003e. This is necessary with MacOS. With MacOS: Open a terminal, and type \u003ccode\u003ecaffeinate\u003c/code\u003e and hit return. Your system will be prevented from sleeping until you hit Control-C.\u003c/p\u003e\n\u003cp\u003eOnce all the simulation jobs are done, exit the Singularity shell environment by calling the \u003ccode\u003eexit\u003c/code\u003e command.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Reproduce Figure Results\u003c/h3\u003e\u003ca id=\"user-content-5-reproduce-figure-results\" class=\"anchor\" aria-label=\"Permalink: 5. Reproduce Figure Results\" href=\"#5-reproduce-figure-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe figure generation code is written in MATLAB and MATLAB R2017a or a more recent version is required. To start, open the \u003ccode\u003emakeFigurePanels.m\u003c/code\u003e code in MATLAB under the \u003ccode\u003eProduction-Figure-Code\u003c/code\u003e folder. To reproduce figure 2, for example, use the following procedure:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLaunch \u003ccode\u003eErgodic-Information-Harvesting/Production-Figure-Code/makeFigurePanels.m\u003c/code\u003e using MATLAB. Note that the code has been tested with MATLAB \u003ccode\u003eR2017a\u003c/code\u003e and \u003ccode\u003eR2018a\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eSpecify input parameters\n\u003cul\u003e\n\u003cli\u003eSet \u003ccode\u003etargetFig = \u0027fig2\u0027\u003c/code\u003e to select figure 2 as the target\u003c/li\u003e\n\u003cli\u003eSet \u003ccode\u003eUSE_PUBLISHED_DATASET = 1\u003c/code\u003e to use the published dataset included in the repository. Alternatively, if the local simulation jobs are completed, use of \u003ccode\u003eUSE_PUBLISHED_DATASET = 0\u003c/code\u003e will force the code to use reproduced data located at \u003ccode\u003eErgodic-Information-Harvesting/SimulationCode/SimData/\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the MATLAB code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should see a new MATLAB figure containing Figure 2 panels. PDF(s) will be saved under \u003ccode\u003eErgodic-Information-Harvesting/Production-Figure-Code/FigureOutput/fig2/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo reproduce all the figures, follow the same steps, but set \u003ccode\u003etargetFig = \u0027all\u0027\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBenchmark Running Time\u003c/h3\u003e\u003ca id=\"user-content-benchmark-running-time\" class=\"anchor\" aria-label=\"Permalink: Benchmark Running Time\" href=\"#benchmark-running-time\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eBenchmark on AWS\u003c/strong\u003e: \u003ccode\u003e~4.03 hours\u003c/code\u003e on AWS EC2 c5a.24xlarge instance (Ubuntu 18.04 LTS 64-bit, AMD EPYC 7R32 with boost frequency up-to 3.3GHz, 48 Core/96 Threads available under HVM), \u003ccode\u003e~22.17 hours\u003c/code\u003e on AWS EC2 c5a.4xlarge instance (Ubuntu 18.04 LTS 64-bit, AMD EPYC 7R32 with boost frequency up-to 3.3GHz, 8 Core/16 Threads available under HVM). \u003cstrong\u003eBenchmark on macOS\u003c/strong\u003e: \u003ccode\u003e~100 hours\u003c/code\u003e on a 2015 iMac (macOS 10.15.6, 64-bit 3.3 GHz Quad-Core Intel Core i5, 4 Core/8 Threads).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdditional Note for Linux and MacOS Users\u003c/h3\u003e\u003ca id=\"user-content-additional-note-for-linux-and-macos-users\" class=\"anchor\" aria-label=\"Permalink: Additional Note for Linux and MacOS Users\" href=\"#additional-note-for-linux-and-macos-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrevent System from Sleeping During Simulation\u003c/h4\u003e\u003ca id=\"user-content-prevent-system-from-sleeping-during-simulation\" class=\"anchor\" aria-label=\"Permalink: Prevent System from Sleeping During Simulation\" href=\"#prevent-system-from-sleeping-during-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo prevent MacOS from sleeping in these instances, use \u003ccode\u003ecaffeinate\u003c/code\u003e at a Terminal window running simulation jobs.\u003c/p\u003e\n", "stargazers_count": 9, "subscribers_count": 5, "topics": [ - "nextflow", - "mutect" - ], - "updated_at": 1626688195.0 - }, - { - "data_format": 2, - "description": "The labyrinth king judges your gene models.", - "filenames": [ - "minos/etc/Singularity.tools.def", - "minos/etc/Singularity.minos.def", - "minos/etc/Singularity.minos.centos.def", - "minos/etc/Singularity.busco.def", - "minos/dependencies/container_defs/Singularity.def", - "minos/dependencies/container_defs/Singularity.busco.def" + "infotaxis", + "entropy", + "ergodicity", + "kullback-leibler-divergence", + "tracking", + "search", + "active-sensing", + "movement", + "trajectory", + "sensorimotor-integration", + "sensing" ], - "full_name": "EI-CoreBioinformatics/minos", - "latest_release": "v1.8.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eminos - a gene model consolidation pipeline for genome annotation projects\u003c/h1\u003e\u003ca id=\"user-content-minos---a-gene-model-consolidation-pipeline-for-genome-annotation-projects\" class=\"anchor\" aria-label=\"Permalink: minos - a gene model consolidation pipeline for genome annotation projects\" href=\"#minos---a-gene-model-consolidation-pipeline-for-genome-annotation-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos is a Python3/Snakemake - based pipeline that generates and utilises metrics derived from protein, transcript and expression data sets to consolidate gene models obtained from gene annotation workflows.\u003c/p\u003e\n\u003cp\u003eFor the majority of the computational work, minos utilises \u003ca href=\"https://github.com/EI-CoreBioinformatics/Mikado\"\u003eMikado\u003c/a\u003e. The pipeline runs Mikado \u003ccode\u003eprepare\u003c/code\u003e on provided gene sets, and generates external metrics such as blastp/blastx alignments, busco assessments and kallisto expression quantification. These metrics are then passed on to Mikado \u003ccode\u003eserialise\u003c/code\u003e and \u003ccode\u003epick\u003c/code\u003e. In a final set of steps, models are filtered according to user-provided criteria and annotated release gene/transcript sets are generated.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow\u003c/h2\u003e\u003ca id=\"user-content-workflow\" class=\"anchor\" aria-label=\"Permalink: Workflow\" href=\"#workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/minos/doc/Minos.png\"\u003e\u003cimg src=\"/minos/doc/Minos.png\" alt=\"Alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nFigure 1. The overview of MINOS pipeline\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos requires Python 3.6 at the very least (better Python 3.7+ as it is essential that dictionary insertion order is preserved.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython dependencies\u003c/h3\u003e\u003ca id=\"user-content-python-dependencies\" class=\"anchor\" aria-label=\"Permalink: Python dependencies\" href=\"#python-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese dependencies should be installed automatically if not present.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epyyaml \u0026gt;= 5 (in order to not have the yaml configuration files ordered alphabetically)\u003c/li\u003e\n\u003cli\u003eSnakemake \u0026gt;= 5.14.0 (to make use of later Snakemake performance and stability features)\u003c/li\u003e\n\u003cli\u003edrmaa (for hpc environments)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation from GitHub\u003c/h3\u003e\u003ca id=\"user-content-installation-from-github\" class=\"anchor\" aria-label=\"Permalink: Installation from GitHub\" href=\"#installation-from-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003egit clone https://github.com/EI-CoreBioinformatics/minos.git\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd minos\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003epython setup.py bdist_wheel;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003epip install dist/*whl\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation from conda\u003c/h3\u003e\u003ca id=\"user-content-installation-from-conda\" class=\"anchor\" aria-label=\"Permalink: Installation from conda\" href=\"#installation-from-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eTBD\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWorkflow dependencies\u003c/h3\u003e\u003ca id=\"user-content-workflow-dependencies\" class=\"anchor\" aria-label=\"Permalink: Workflow dependencies\" href=\"#workflow-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos makes extensive use of 3rd party software, most of which can be installed from conda.\nThe following tools are required and these tools can be installed using singularity container definitions provided (\u003ca href=\"minos/etc/Singularity.tools.def\"\u003eSingularity.tools.def\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ekallisto 0.44.0 (generation of expression based metrics from RNAseq data)\u003c/li\u003e\n\u003cli\u003eblast+ (generation of protein similarity metrics from blastp/x analysis of protein evidence data)\u003c/li\u003e\n\u003cli\u003eseqkit (fasta processing)\u003c/li\u003e\n\u003cli\u003egenometools (GFF3 validation)\u003c/li\u003e\n\u003cli\u003egffread (extraction of transcript parameters and CDS, cDNA, protein sequences)\u003c/li\u003e\n\u003cli\u003ebedtools (transposable element analysis)\u003c/li\u003e\n\u003cli\u003eCPC2 (\u003ca href=\"http://cpc2.cbi.pku.edu.cn\" rel=\"nofollow\"\u003ehttp://cpc2.cbi.pku.edu.cn\u003c/a\u003e) (assess the protein-coding potential of transcripts)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following three dependencies require special treatment (s. below):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMikado *\u003c/li\u003e\n\u003cli\u003eBUSCO \u0026gt;= 4 (busco protein based metrics, full busco statistics for annotation quality control) **\u003c/li\u003e\n\u003cli\u003eCPC2 (\u003ca href=\"http://cpc2.cbi.pku.edu.cn\" rel=\"nofollow\"\u003ehttp://cpc2.cbi.pku.edu.cn\u003c/a\u003e) ***\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRegistering workflow dependencies with minos\u003c/h4\u003e\u003ca id=\"user-content-registering-workflow-dependencies-with-minos\" class=\"anchor\" aria-label=\"Permalink: Registering workflow dependencies with minos\" href=\"#registering-workflow-dependencies-with-minos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore running minos, the user has to register all dependencies in the \u003ccode\u003eprogram_calls\u003c/code\u003e section of the run configuration file (etc/minos_config.yaml). This allows users to manage their own installations. For convenience, we provide singularity container definitions (\u003ca href=\"minos/etc/Singularity.tools.def\"\u003eSingularity.tools.def\u003c/a\u003e) for most dependencies, except BUSCO, as well as a conda recipe. Unfortunately, due to our hpc environment, we cannot provide solutions utilising the native conda/singularity support in current Snakemake version. (However, we would be glad for any community contributions in that regard).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSpecial case: Mikado\u003c/h4\u003e\u003ca id=\"user-content-special-case-mikado\" class=\"anchor\" aria-label=\"Permalink: Special case: Mikado\" href=\"#special-case-mikado\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epreferred: singularity container due to development cycle and ease of installation\u003c/li\u003e\n\u003cli\u003eDue to its complexity and rapid development cycle Mikado should have its own container\u003c/li\u003e\n\u003cli\u003ethis should all not matter anymore when there is a stable Mikado\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSpecial case: BUSCO\u003c/h4\u003e\u003ca id=\"user-content-special-case-busco\" class=\"anchor\" aria-label=\"Permalink: Special case: BUSCO\" href=\"#special-case-busco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBUSCO requires an older version of blast due to issues with multithreading tblastn\u003c/li\u003e\n\u003cli\u003ethis is not compatible with having a newer blast+ for the minos protein blast analyses\u003c/li\u003e\n\u003cli\u003eour solution: individual BUSCO singularity container \u003ca href=\"minos/etc/Singularity.busco.def\"\u003eSingularity.busco.def\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003epotential solution: conda environment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSpecial case: CPC2\u003c/h4\u003e\u003ca id=\"user-content-special-case-cpc2\" class=\"anchor\" aria-label=\"Permalink: Special case: CPC2\" href=\"#special-case-cpc2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCPC2 is forked to our repository and ported to Python3 and can be installed from here - \u003ca href=\"https://github.com/EI-CoreBioinformatics/CPC2\"\u003ehttps://github.com/EI-CoreBioinformatics/CPC2\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eInstallation instructions are also added to the singularity container definition file (\u003ca href=\"minos/etc/Singularity.tools.def\"\u003eSingularity.tools.def\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos requires a set of input files and data. To keep minos-based analyses structured it is recommended to collect all input in a project directory, e.g. by soft-linking or copying.\u003c/p\u003e\n\u003cp\u003eA minos run consists of two steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGeneration of the run configuration (\u003ccode\u003eminos configure\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eRunning the minos workflow (\u003ccode\u003eminos run\u003c/code\u003e) driven by the run configuration generated in Step 1.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eminos configure\u003c/h3\u003e\u003ca id=\"user-content-minos-configure\" class=\"anchor\" aria-label=\"Permalink: minos configure\" href=\"#minos-configure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eminos configure\u003c/code\u003e takes as input a set of configuration files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003elist_file\u003c/strong\u003e A tab-separated file describing the set of transcript models. An example file is provided here \u003ca href=\"data/list.txt\"\u003elist.txt\u003c/a\u003e. A detailed description for each column header can be found here - \u003ca href=\"https://mikado.readthedocs.io/en/stable/Usage/Configure/#input-annotation-files\" rel=\"nofollow\"\u003eMikado list file format\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003escoring_template\u003c/strong\u003e A yaml file containing the scoring settings. This can be copied from the minos repo \u003ca href=\"minos/etc/scoring_template.yaml\"\u003eminos/etc/scoring_template.yaml\u003c/a\u003e and modified if required. There is no need to add in the external metrics and scoring sections as this will be done automatically by minos configure!\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003egenome_reference\u003c/strong\u003e A fasta file containing the genome reference. This should be softlinked instead of copied.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eexternal_metrics_configuration\u003c/strong\u003e A Mikado configuration file (e.g. \u003ca href=\"https://github.com/EI-CoreBioinformatics/mikado/blob/master/sample_data/plant_external.yaml\"\u003esample_data/plant_external.yaml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eexternal_metrics\u003c/strong\u003e A tab-separated file describing the metrics data to be used in the minos run. The column descriptions can be found in \u003ca href=\"#metrics-info\"\u003eSection metrics_info\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003econfiguration\u003c/strong\u003e A yaml file with parameters to control minos run. This can be obtained from the minos repo (\u003ca href=\"minos/etc/minos_config.yaml\"\u003eminos/etc/minos_config.yaml\u003c/a\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAfter these input files have been generated/obtained, \u003ccode\u003eminos configure\u003c/code\u003e can be run. Items 1,2,3 from the above list are positional arguments, items 4,5,6 are optional. [TBC!]\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCommand line arguments\u003c/h4\u003e\u003ca id=\"user-content-command-line-arguments\" class=\"anchor\" aria-label=\"Permalink: Command line arguments\" href=\"#command-line-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e--outdir, -o\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSets the output directory (default: \u003ccode\u003eminos_run\u003c/code\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--blastmode {blastp,blastx}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eControls whether transcript models (blastx) or their translated peptide sequences (blastp, default) are compared against the provided protein evidence.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--annotation-version ANNOTATION_VERSION, --genus-identifier GENUS_IDENTIFIER\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinal output files are named with the prefix \u003ccode\u003e\u0026lt;GENUS_IDENTIFIER\u0026gt;_\u0026lt;ANNOTATION_VERSION\u0026gt;\u003c/code\u003e, e.g. \u003ccode\u003eQUISA32244_EIv1\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--use-tpm-for-picking\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eControls whether RNA-seq data is used for metrics generation in addition to classification. (default: off) Caution: \u003ccode\u003e--use-tpm-for-picking\u003c/code\u003e activates using expression metrics for the picking stage.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--force-reconfiguration, -f\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eControls whether an existing configuration file in the chosen project directory (set with \u003ccode\u003e-o\u003c/code\u003e or \u003ccode\u003e--outdir\u003c/code\u003e) will be overwritten. By default, this behaviour is turned off.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--mikado-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDue to the ongoing Mikado development and frequent updates, the Mikado version (currently required to be served from a Singularity container) used by minos has to be submitted via command line option rather than via the configuration file. This allows for more flexibility in swapping out Mikado versions. As soon as Mikado 2.0 has reached a stable state, we will take all efforts so that this option will no longer be mandatory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eBUSCO Options\u003c/h5\u003e\u003ca id=\"user-content-busco-options\" class=\"anchor\" aria-label=\"Permalink: BUSCO Options\" href=\"#busco-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos supports core gene assessments with BUSCO4 for metrics generation and quality assessment.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--busco-level BUSCO_LEVEL\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA comma-separated string to select the BUSCO mode. Valid levels are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\"proteins\", \"proteome\", \"transcripts\", \"transcriptome\", \"genome\", \"none\", \"off\", \"p\", \"t\", \"g\", \"a\", \"all\", \"prot\", \"tran\", \"geno\"}.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe default mode is \u201cproteins\u201d (or all in a development version). \u201cnone\u201d and \u201coff\u201d disable BUSCO analyses.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--busco-lineage BUSCO_LINEAGE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis needs to be either a path to the odb10 database used for the BUSCO analysis or a valid database identifier. This option is required if \u003ccode\u003e--busco-level\u003c/code\u003e is not in \u003ccode\u003e{none,off}\u003c/code\u003e. Note, the latter option requires an internet connection and removal of the \u003ccode\u003e--offline\u003c/code\u003e parameter in the run configuration file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--busco-genome-run BUSCO_GENOME_RUN\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs an alternative to computing the genome BUSCO analysis, a path can be specified pointing to a directory containing the short_summary.txt and full_table.tsv from a precomputed BUSCO genome run on the reference. Using this option will prompt minos\u2019s BUSCO genome rule to copy the input files to the output folder instead of running BUSCO in genome mode.\u003c/p\u003e\n\u003cp\u003eBUSCO copy number assessment can be configured by passing/modifying the \u003ccode\u003e--limit\u003c/code\u003e parameters in the \u003ccode\u003eparams: busco: \u0026lt;busco_run\u0026gt;\u003c/code\u003e section of the configuration file. Additionally, the maximum copy number that is reported in an individual category in the final BUSCO output table can be set via \u003ccode\u003emisc: busco_max_copy_number\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eminos configure example run:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eminos configure --mikado-container \u0026lt;/path/to/mikado/container\u0026gt; \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -o \u0026lt;output-directory\u0026gt; --external external.yaml \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --external-metrics external_metrics.txt --use-tpm-for-picking \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --genus-identifier \u0026lt;GENUS_ID\u0026gt; --annotation-version \u0026lt;ANN_VERSION\u0026gt; \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e list.txt \u0026lt;/path/to/scoring-template.yaml\u0026gt; \u0026lt;/path/to/genome_reference\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eminos run\u003c/h3\u003e\u003ca id=\"user-content-minos-run\" class=\"anchor\" aria-label=\"Permalink: minos run\" href=\"#minos-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eminos run\u003c/code\u003e starts the minos pipeline, controlled by a run configuration file. It is highly recommended to use \u003ccode\u003eminos configure\u003c/code\u003e to generate this configuration file as this will also generate the required Mikado configuration.\u003c/p\u003e\n\u003cp\u003eEI_internal:\nNote that for convenience, \u003ccode\u003eminos run\u003c/code\u003e has an NBI cluster wrapper called \u003ccode\u003eminos_run_sub\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eminos run example run:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom local machine:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eminos run --mikado-container \u0026lt;/path/to/mikado/container\u0026gt; \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --no_drmaa --scheduler NONE -o \u0026lt;output-directory\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn HPC (default \u003ccode\u003eSLURM\u003c/code\u003e, an example HPC config JSON we use is here \u003ca href=\"minos/etc/hpc_config.json\"\u003ehpc_config.json\u003c/a\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eminos_run_sub --mikado-container \u0026lt;/path/to/mikado/container\u0026gt; \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --partition \u0026lt;partition\u0026gt; --hpc_config /path/to/hpc_config.json \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -o \u0026lt;output-directory\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration files\u003c/h2\u003e\u003ca id=\"user-content-configuration-files\" class=\"anchor\" aria-label=\"Permalink: Configuration files\" href=\"#configuration-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun configuration\u003c/h3\u003e\u003ca id=\"user-content-run-configuration\" class=\"anchor\" aria-label=\"Permalink: run configuration\" href=\"#run-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe minos workflow is controlled by a run configuration file in yaml format. This file is generated by \u003ccode\u003eminos configure\u003c/code\u003e from a configuration template (\u003ca href=\"minos/etc/minos_config.yaml\"\u003eminos_config.yaml\u003c/a\u003e), and is saved to the output directory.\u003c/p\u003e\n\u003cp\u003eThe run configuration template contains the following information:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eparams\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis section allows the user to specify additional parameters for the tools used in the workflow (e.g. score, evalue cutoffs etc).\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\u003ccode\u003eprogram_calls\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis section contains the instructions for running the minos dependencies. If a dependency is installed in the environment, this could just be the tool name. Otherwise, this might include a \u003ccode\u003esingularity exec \u0026lt;/path/to/container\u0026gt;\u003c/code\u003e call, a source or module load command, etc.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\u003ccode\u003ecollapse_metrics_threshold\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis section contains the classification rules for the collapse_metrics rule.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003ccode\u003emisc\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis section contains parameters for rules not involving 3rd party dependencies and other unsorted things.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emetrics info\u003c/h3\u003e\u003ca id=\"user-content-metrics-info\" class=\"anchor\" aria-label=\"Permalink: metrics info\" href=\"#metrics-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA tab-separated file with the following columns:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003emetric_name_prefix\u003c/code\u003e\nShort name for the metric.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCurrently, the following convention has to be followed:\nkallisto metrics need to have a suffix according to their strandedness:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eunstranded: _xx, reverse-forward: _rf, forward-reverse: _fr\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe rf/fr suffixes follow the kallisto command line parameter naming scheme.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emetric_class\u003c/code\u003e\nCan be one of the following: \u003ccode\u003eexpression\u003c/code\u003e, \u003ccode\u003ealn_tran\u003c/code\u003e, \u003ccode\u003ealn_prot\u003c/code\u003e, \u003ccode\u003eseq_prot\u003c/code\u003e (for protein blast db generation), \u003ccode\u003ejunction\u003c/code\u003e, \u003ccode\u003erepeat\u003c/code\u003e, \u003ccode\u003e*blastdb_prot\u003c/code\u003e (* not implemented)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emultiplier\u003c/code\u003e\nThe multiplier can be given either as a single value or a comma-separated key:value list (e.g. \u003ccode\u003eaF1:X,eF1:Y,jF1:Z,nF1:W\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enot_fragmentary_min_value\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003efile_path\u003c/code\u003e\nFull path to the metrics file.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003ePaired-end reads for kallisto need to be provided as \u003ccode\u003e\u0026lt;path/to/R1\u0026gt;,\u0026lt;path/to/R2\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMultiple samples for expression metrics can be given as one line each with the same \u003ccode\u003emetric_name_prefix\u003c/code\u003e (in such a case, the \u003ccode\u003emultiplier\u003c/code\u003e will be read from the line with the first sample)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting minos\u003c/h2\u003e\u003ca id=\"user-content-citing-minos\" class=\"anchor\" aria-label=\"Permalink: Citing minos\" href=\"#citing-minos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cp\u003eSince minos uses Mikado, please consider to cite:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVenturini L., Caim S., Kaithakottil G., Mapleson D.L., Swarbreck D. Leveraging multiple transcriptome assembly methods for improved gene structure annotation. GigaScience, Volume 7, Issue 8, 1 August 2018, giy093, \u003ca href=\"https://doi.org/10.1093/gigascience/giy093\" rel=\"nofollow\"\u003edoi:10.1093/gigascience/giy093\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIf you also use Portcullis junctions as input metric, please consider to cite:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eMapleson D.L., Venturini L., Kaithakottil G., Swarbreck D. Efficient and accurate detection of splice junctions from RNAseq with Portcullis. GigaScience, Volume 7, Issue 12, 12 December 2018, giy131, \u003ca href=\"https://doi.org/10.1093/gigascience/giy131\" rel=\"nofollow\"\u003edoi:10.1093/gigascience/giy131\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n", - "stargazers_count": 9, - "subscribers_count": 7, - "topics": [], - "updated_at": 1730349359.0 + "updated_at": 1726579945.0 }, { "data_format": 2, - "description": "Profiling with NVIDIA Nsight Tools Bootcamp", + "description": "This is a demo about how to develop and deploy a pipeline using nextflow, scif, a container (Singularity and Docker) and its posterior deployment.", "filenames": [ "Singularity" ], - "full_name": "openhackathons-org/HPC_Profiler", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProfiling with NVIDIA Nsight Tools Bootcamp\u003c/h1\u003e\u003ca id=\"user-content-profiling-with-nvidia-nsight-tools-bootcamp\" class=\"anchor\" aria-label=\"Permalink: Profiling with NVIDIA Nsight Tools Bootcamp\" href=\"#profiling-with-nvidia-nsight-tools-bootcamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains learning materials and exercises for NVIDIA Nsight Tools. Gola is to learn how to profile your application with NVIDIA Nsight Systems,Compute and NVTX API calls to find performance limiters and bottlenecks and apply incremental parallelization strategies. The content was tested on \u003cstrong\u003eNVIDIA driver 515.65\u003c/strong\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction: Overview of profiling tools and Mini Weather application\u003c/li\u003e\n\u003cli\u003eLab 1: Profile Serial application to find hotspots using NVIDIA Nsight System\u003c/li\u003e\n\u003cli\u003eLab 2: Parallelise the serial application using OpenACC compute directives\u003c/li\u003e\n\u003cli\u003eLab 3: Optimizing loops\u003c/li\u003e\n\u003cli\u003eLab 4: Apply incremental parallelization strategies and use profiler\u0027s report for the next step\u003c/li\u003e\n\u003cli\u003eLab 5: Nsight Compute Kernel Level Analysis\u003c/li\u003e\n\u003cli\u003e[Optional]\n\u003cul\u003e\n\u003cli\u003eLab 6:Performance Analysis of an application using Nsight Systems and Compute (CUDA example)\u003c/li\u003e\n\u003cli\u003eAdvanced: Multiprocess profiling\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTarget Audience\u003c/h2\u003e\u003ca id=\"user-content-target-audience\" class=\"anchor\" aria-label=\"Permalink: Target Audience\" href=\"#target-audience\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe target audience for this lab is researchers/graduate students and developers who are interested in getting hands on experience with the NVIDIA Nsight System through profiling a real life parallel application.\u003c/p\u003e\n\u003cp\u003eWhile Labs 1-5 do not assume any expertise in CUDA experience, basic knowledge of OpenACC programming (e.g: compute constructs), GPU architecture, and programming experience with C/C++ is desirable.\u003c/p\u003e\n\u003cp\u003eThe Optional lab 6 requires basic knowledge of CUDA programming, GPU architecture, and programming experience with C/C++.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial Duration\u003c/h2\u003e\u003ca id=\"user-content-tutorial-duration\" class=\"anchor\" aria-label=\"Permalink: Tutorial Duration\" href=\"#tutorial-duration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe lab material will be presented in a 2.5hr session. The link to the material is available for download at the end of each lab.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites:\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites:\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run this content you will need a machine with NVIDIA GPUs (Nsight Systems supports Pascal and above (SM 60+), and Nsight Compute supports Volta and above (SM 70+)).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall the \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://sylabs.io/docs/%5D\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eInstall Nvidia toolkit, \u003ca href=\"https://developer.nvidia.com/nsight-systems\" rel=\"nofollow\"\u003eNsight Systems (latest version)\u003c/a\u003e and \u003ca href=\"https://developer.nvidia.com/nsight-compute\" rel=\"nofollow\"\u003ecompute (latest version)\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThe base containers required for the lab may require users to create a NGC account and generate an API key (\u003ca href=\"https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account\" rel=\"nofollow\"\u003ehttps://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating containers\u003c/h2\u003e\u003ca id=\"user-content-creating-containers\" class=\"anchor\" aria-label=\"Permalink: Creating containers\" href=\"#creating-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo start with, you will have to build a Docker or Singularity container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker Container\u003c/h3\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-label=\"Permalink: Docker Container\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a docker container, run:\n\u003ccode\u003esudo docker build -t \u0026lt;imagename\u0026gt;:\u0026lt;tagnumber\u0026gt; .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor instance:\n\u003ccode\u003esudo docker build -t profiling:latest .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe code labs have been written using Jupyter lab and a Dockerfile has been built to simplify deployment. In order to serve the docker instance for a student, it is necessary to expose port 8000 from the container, for instance, the following command would expose port 8000 inside the container as port 8000 on the lab machine:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --rm -it --gpus=all -p 8888:8888 profiling:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen this command is run, you can browse to the serving machine on port 8000 using any web browser to access the labs. For instance, from if they are running on the local machine the web browser should be pointed to \u003ca href=\"http://localhost:8000\" rel=\"nofollow\"\u003ehttp://localhost:8000\u003c/a\u003e. The \u003ccode\u003e--gpus\u003c/code\u003e flag is used to enable \u003ccode\u003eall\u003c/code\u003e NVIDIA GPUs during container runtime. The \u003ccode\u003e--rm\u003c/code\u003e flag is used to clean an temporary images created during the running of the container. The \u003ccode\u003e-it\u003c/code\u003e flag enables killing the jupyter server with \u003ccode\u003ectrl-c\u003c/code\u003e. This command may be customized for your hosting environment.\u003c/p\u003e\n\u003cp\u003eThen, inside the container launch the Jupyter lab assigning the port you opened:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ejupyter-lab --ip 0.0.0.0 --port 8888 --no-browser --allow-root\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOnce inside the container, open the jupyter lab in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e, and start the lab by clicking on the \u003ccode\u003e_start_profiling.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Container\u003c/h3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity Container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the singularity container, run:\n\u003ccode\u003esudo singularity build _profiler.simg Singularity\u003c/code\u003e . If you do not have \u003ccode\u003esudo\u003c/code\u003e rights, you can build the singularity container with \u003ccode\u003e--fakeroot\u003c/code\u003e option: \u003ccode\u003esingularity build --fakeroot _profiler.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand copy the files to your local machine to make sure changes are stored locally:\n\u003ccode\u003esingularity run _profiler.simg cp -rT /labs ~/labs\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, run the container:\n\u003ccode\u003esingularity run --nv _profiler.simg jupyter-lab --notebook-dir=~/labs\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOnce inside the container, open the jupyter lab in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e, and start the lab by clicking on the \u003ccode\u003e_start_profiling.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePlease go through the list of exisiting bugs/issues or file a new issue at \u003ca href=\"https://github.com/openhackathons-org/HPC_Profiler/issues\"\u003eGithub\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "BU-ISCIII/nextflow-scif", + "latest_release": "v.1.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNextflow tutorial workflow using SCIF and Singularity/Docker\u003c/h1\u003e\u003ca id=\"user-content-nextflow-tutorial-workflow-using-scif-and-singularitydocker\" class=\"anchor\" aria-label=\"Permalink: Nextflow tutorial workflow using SCIF and Singularity/Docker\" href=\"#nextflow-tutorial-workflow-using-scif-and-singularitydocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/BU-ISCIII/nextflow-scif\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0289dc02a9638d79a6ae3573745037bd79d6d2ae9d3bac65e1c84a786d090731/68747470733a2f2f636972636c6563692e636f6d2f67682f636972636c6563692f636972636c6563692d646f63732e7376673f7374796c653d736869656c64\" alt=\"CircleCI Build Status\" data-canonical-src=\"https://circleci.com/gh/circleci/circleci-docs.svg?style=shield\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93ca07ea63e4649ae72b87de86d82eedeab50e744fa6e07ee95e5344779fb930/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://discuss.circleci.com\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0db7cc65a258e95db6b1dddae92be2d4d80817b9570fded1962c4fd4b6fdcb6f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6d6d756e6974792d436972636c654349253230446973637573732d3334333433342e737667\" alt=\"CircleCi Community\" data-canonical-src=\"https://img.shields.io/badge/community-CircleCI%20Discuss-343434.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://nextflow.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f8a155e03276d377ca219881b2d695bd3be17898ea74a1250ad2b6941aef70cc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d253345302e32392e302d677265656e2e737667\" alt=\"Nextflow version\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%3E0.29.0-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3bb9116161cdfeb6b23d2bde2e62f92bb4eda63bd51f5c1ed4b8b86356f088e4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f46696c6573797374656d2d536369656e74696669632d627269676874677265656e2e737667\" alt=\"Scif\" data-canonical-src=\"https://img.shields.io/badge/Filesystem-Scientific-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository implements the Nextflow tutorial workflow using the Scientific Filesystem (SCIF) and Docker/Singularity to provide a reproducible research environment. We have made an adaptation of \u003ca href=\"https://github.com/sci-f/snakemake.scif\"\u003esnakemake.scif\u003c/a\u003e, but using nextflow instead. Also I have just discovered \u003ca href=\"https://github.com/vsoch/rnatoy.scif/edit/master/README.md\"\u003ethis try\u003c/a\u003e of \u003ca href=\"https://www.github.com/vsoch\"\u003e@vsoch\u003c/a\u003e and I am adapting her cool documentation to this case.\u003c/p\u003e\n\u003cp\u003eThese are the steps to follow in order to develop a new reproducible and transparent workflow:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSCIF recipes\u003c/strong\u003e: \u003cstrong\u003eDiscoverability\u003c/strong\u003e and \u003cstrong\u003eTransparency\u003c/strong\u003e by installing our software in the container via a \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eScientific Filesystem\u003c/a\u003e. SCIF also lets us install the same dependencies across container technologies.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSingularity/Docker container\u003c/strong\u003e: this will handle the \u003cstrong\u003eReproducibility\u003c/strong\u003e of our pipeline and the software it is going to need. Moreover Docker and Singularity HUB makes deployment and sharing a lot easier.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNextflow\u003c/strong\u003e: enables scalable and reproducible scientific workflows managing software containers natively like a charm. It allows the adaptation of pipelines written in the most common scripting languages. Moreover its fluent DSL simplifies the implementation and the deployment of complex parallel and reactive workflows on clouds and clusters, being able to abstract the users both from \u003cem\u003ewhere\u003c/em\u003e and even \u003cem\u003ehow\u003c/em\u003e they compute without any concern about the multiple dependencies a scientific workflow can have.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTesting\u003c/strong\u003e: circleCI allows an interface for testing and deployment of software in github. It uses a series of containers for p.e building your container, testing your software against your container and deploy the image to Docker Hub and your program to some software repository like Heroku. Here we will use it in first instance for deploy our docker/Singularity container, and then to test our nextflow pipeline with some dummy test data (we will use as template the \u003ca href=\"https://github.com/sci-f/builder\"\u003escif builed\u003c/a\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick usage\u003c/h2\u003e\u003ca id=\"user-content-quick-usage\" class=\"anchor\" aria-label=\"Permalink: Quick usage\" href=\"#quick-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need to \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html#installation\" rel=\"nofollow\"\u003einstall nextflow\u003c/a\u003e and \u003ca href=\"https://www.sylabs.io/guides/2.5.1/user-guide/quick_start.html#installation\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e. Singularity must\nbe installed with administrative privileges, usually system wide, and nextflow can be \"installed\" to your\npresent working directory. It\u0027s also recommended to define your Singularity cache to be somewhere\n(like a scratch space) where you have more disk space. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_CACHEDIR=\u003cspan class=\"pl-smi\"\u003e$SCRATCH\u003c/span\u003e/.singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are working on your local machine, this location defaults to \u003ccode\u003e$HOME/.singularity\u003c/code\u003e, which\nis appropriate. Once you have both, you only have to type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e With the nextflow executable in the present working directory\u003c/span\u003e\n./nextflow run BU-ISCIII/nextflow-scif -profile singularity\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e With the nextflow executable installed system wide, and on the PATH\u003c/span\u003e\nnextflow run BU-ISCIII/nextflow-scif -profile singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThats it! When you finish, a \"results\" folder will appear with the content of your analysis.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls results/\nbwa DAG.dot DAG.svg report.html timeline.html trace.txt vcf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSo...Let\u0027s get this done... and by \"this\" we are referring to a complete description of how this\nrecipe was derived, and the components of the software that just ran. The idea here is that if you\nunderstand the underlying components, you can easily build your own nextflow pipelines.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Recipes\u003c/h2\u003e\u003ca id=\"user-content-1-recipes\" class=\"anchor\" aria-label=\"Permalink: 1. Recipes\" href=\"#1-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen building pipelines, you can think of it like baking a cake. We have entire recipes for creating our final products (containers), and within those recipes ingredients (software) that we need to add. In this first part, we will talk about the three recipes in this repository, the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e for the Docker container, the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e recipe for a Singularity container, and the \u003ca href=\"app_recipes\"\u003eapp_recipes/*.scif\u003c/a\u003e recipes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Scientific Filesystem Recipe\u003c/h3\u003e\u003ca id=\"user-content-the-scientific-filesystem-recipe\" class=\"anchor\" aria-label=\"Permalink: The Scientific Filesystem Recipe\" href=\"#the-scientific-filesystem-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA scientific filesystem is useful because it allows me to write one recipe for my various software, and then install easily in different containers or on my host. How do you know when you find a recipe? When you find a recipe for a scientific filesystem (SCIF), you will see a file with extension *.scif. For example, in this repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"app_recipes\"\u003eapp_recipes/*.scif\u003c/a\u003e are the recipes for the scientific filesystem that will be installed in both the Docker and Singularity containers to be run with nextflow. SCIF is flexible in that there can be \u003cstrong\u003emany\u003c/strong\u003e different internal applications defined in one file, however if we want we can put them in individual files and install them equivalently, as we show in this example.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor example, given the apps \"samtools\", \"bwa\" and \"bcftools\" and using a three recipes files , I would install like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escif install app_recipes/samtools_v1.9_centos7.scif\nscif install app_recipes/bwa_v0.7.17_centos7.scif\nscif install app_recipes/bcftools_v1.9_centos7.scif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ebut I could also define the different applications in only one file and install them the next way.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escif install nextflow-scif.scif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis level of modularity is up to the user. Programatically, this install would be equivalent. This is a purely \"human information\" decision. If I am building a single container to share with my paper, I would opt to only need one file. If I am providing a builder service to users and want to easily install recipes in a modular fashion, I would want to do the second, which is this case. Here we are preparing a model for developing new workflows as quickly as possible, so app_recipes should contain a collection of app recipes that can be combined in the Singularity/Docker recipe for creating the needed container for the workflow being developed. In fact, probably we will convert app_recipes to a Github repository, so it will be used as a remote repository in all the workflows we will develop, making its update easier.\u003c/p\u003e\n\u003cp\u003eThis means that, for any SCIF recipe and a host of interest (Docker or Singularity container, or your computer) you can install the same recipes.\u003c/p\u003e\n\u003cp\u003eSCIF makes the installation to be so tidy, it makes me wanna cry, a scif folder is created in \u003ccode\u003e/\u003c/code\u003e of your container and the tree directory will look like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/scif/apps/\n samtools/\n bin/\n lib/\n scif/\n runscript.help\n runscript\n env/\n 01-base.sh\n 90-environment.sh\n bwa/\n ....\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlso SCIF will provide a separate variable environment for each app (we are going to mess it a little bit in order to get nextflow functioning), and some cool ENV variables $SCIF_APP(BIN|ENV|...) that seem promising for a lot of options. You can read more on \u003ca href=\"https://www.sylabs.io/guides/2.5.1/user-guide/reproducible_scif_apps.html\" rel=\"nofollow\"\u003eSingularity docs\u003c/a\u003e and \u003ca href=\"https://sci-f.github.io/\" rel=\"nofollow\"\u003eSCIF docs\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Docker Recipe\u003c/h3\u003e\u003ca id=\"user-content-the-docker-recipe\" class=\"anchor\" aria-label=\"Permalink: The Docker Recipe\" href=\"#the-docker-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e is the recipe for building our container. You will also notice the installation is simple - we start with a container base that was equivalently used by the creator of the pipeline with system / host dependencies. In this case, we have used a base docker container of centos 7, this is because we use this SO in our lab and our HPC, and it is useful for me to have homogeneous installation recipes in both containers, my workstation and HPC (at least for now that we are figuring all this stuff out).\u003c/p\u003e\n\u003cp\u003eThen simply install the SCIF recipe to it. That comes down to these three commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRUN pip install scif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install scif from pypi\u003c/span\u003e\nADD app_recipes/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.scif /opt \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the recipe to the container\u003c/span\u003e\nRUN scif install /opt/samtools.scif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install it to the container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow a little about the \"mess\", you can see that we added export PATH variable with bin folder for each app, this should not be necessary because app/bin folder is added to the PATH automatically when the app is run, \u003cstrong\u003eBUT\u003c/strong\u003e nextflow expects to have access to the executables in the path or you have to provide the full path.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker Recipe\u003c/span\u003e\nENV PATH=\u003cspan class=\"pl-smi\"\u003e${PATH}\u003c/span\u003e:/scif/apps/samtools/bin \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity Recipe\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=${PATH}:/scif/apps/samtools/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_ENVIRONMENT\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to take advantage of complete nextflow functionality I decided to assume this \"mistake\" in the use of SCIF, which will make the apps envs not being totally separated. You can read the \u003ca href=\"#notes\"\u003eNotes\u003c/a\u003e section for the pros and cons of this approximation and an insight of another posibility I don\u0027t like so much (or I consider less useful for our situation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: it would be cool to use $SCIF_APPBIN_samtools, instead of the full path \u003ccode\u003e/scif/apps/samtools/bin\u003c/code\u003e, but I tried and this variable must be set after PATH env variable, because I can\u0027t get it working.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Singularity Recipe\u003c/h3\u003e\u003ca id=\"user-content-the-singularity-recipe\" class=\"anchor\" aria-label=\"Permalink: The Singularity Recipe\" href=\"#the-singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recipe file for a Singularity container is the file \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e. The format of the recipe file is different, but installing the scientific filesystem, again from the recipes \u003ca href=\"app_recipes\"\u003eapp_recipes\u003c/a\u003e is performed with the same commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install scif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install scif from pypi\u003c/span\u003e\nscif install /opt/samtools_v1.9_centos7.scif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install it to the container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe only missing command to add the recipe to the container is because Singularity recipes allow you to do this in a \u003ccode\u003e%files\u003c/code\u003e section that functions like a cp \u003csource\u003e (if not dest supplied the file will be copied in \u003ccode\u003e/\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e%files\n samtools_v1.9_centos7.scif /opt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Containers build and usage\u003c/h2\u003e\u003ca id=\"user-content-2-containers-build-and-usage\" class=\"anchor\" aria-label=\"Permalink: 2. Containers build and usage\" href=\"#2-containers-build-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce we have our recipe or in order to create our installation commands with ease, we need to know how to build and interact with containers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShell to a container\u003c/h3\u003e\u003ca id=\"user-content-shell-to-a-container\" class=\"anchor\" aria-label=\"Permalink: Shell to a container\" href=\"#shell-to-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can interact with a container in many different ways and for differente purposes. For example, during the development of the container, I took a strategy to start with a base, interactively shell into it, and test installation and running of things. To do this we can directly use a centos container from Dockerhub, singularity is what I prefer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell -w docker://centos:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr docker if you want:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it centos\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild a container\u003c/h3\u003e\u003ca id=\"user-content-build-a-container\" class=\"anchor\" aria-label=\"Permalink: Build a container\" href=\"#build-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have your recipe prepared you need to build your image:\u003c/p\u003e\n\u003cp\u003eWith Singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build nextflow-scif.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr with Docker:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# . points to the path where docker will import the context for image creation. If you are in the repository path you have only to do:\u003c/span\u003e\ndocker build -t nextflow-scif \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Dockerfile and context will be automatically imported to the docker daemon.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning a container\u003c/h3\u003e\u003ca id=\"user-content-running-a-container\" class=\"anchor\" aria-label=\"Permalink: Running a container\" href=\"#running-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCommand run, in Singularity and Docker will execute environment variables and the script configured in \u003ccode\u003e%runscript\u003c/code\u003e section in \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e or ENTRYPOINT in \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e. In this case only scif is configured, being the command that will let us interact with the applications installed in the container.\n\u003cstrong\u003eNOTE:\u003c/strong\u003e We can\u0027t define an ENTRYPOINT in docker because Nextflow doesn\u0027t support docker containers with entrypoint at the moment, it seems in its \u003ca href=\"https://www.nextflow.io/docs/latest/docker.html#docker-page\" rel=\"nofollow\"\u003edocs\u003c/a\u003e that have implemented some twist but is already deprecated. This means that for interacting with the applications we have to explicitly use scif command after docker run.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif\nsingularity run nextflow-scif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output will be something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eScientific Filesystem [v0.0.71]\nusage: scif [-h] [--debug] [--quiet] [--writable]\n \n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n ...\n\nscientific filesystem tools\n\noptional arguments:\n -h, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n --debug use verbose logging to debug.\n --quiet suppress print output\n --writable, -w \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e relevant commands, \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e writable SCIF is needed\n\nactions:\n actions \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e Scientific Filesystem\n\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n scif actions\n version show software version\n pyshell Interactive python shell to scientific filesystem\n shell shell to interact with scientific filesystem\n preview preview changes to a filesytem\n \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e look at \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e an app, \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e it exists.\n install install a recipe on the filesystem\n inspect inspect an attribute \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e a scif installation\n run entrypoint to run a scientific filesystem\n apps list apps installed\n dump dump recipe\n \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e execute a \u003cspan class=\"pl-c1\"\u003ecommand\u003c/span\u003e to a scientific filesystem\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInspecting Applications\u003c/h4\u003e\u003ca id=\"user-content-inspecting-applications\" class=\"anchor\" aria-label=\"Permalink: Inspecting Applications\" href=\"#inspecting-applications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe strength of SCIF is that it will always show you the applications installed in a container, and then provide predictable commands for inspecting, running, or otherwise interacting with them. For example, if I find the container, without any prior knowledge I can reveal the applications inside:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif apps\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif apps\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\nsingularity run nextflow-scif apps\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output will look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e samtools\n bwa\n bcftools\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can also obtain help about a particular application\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\nsingularity run nextflow-scif \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e samtools\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output will look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Samtools\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then inspecting\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif inspect samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif inspect samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\nsingularity run nextflow-scif inspect samtools\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"samtools\": {\n \"apprun\": [\n \" exec samtools \\\"$@\\\"\"\n ],\n \"apphelp\": [\n \" Samtools\"\n ],\n \"applabels\": [\n \"VERSION 1.9\",\n ]\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe creator of the container didn\u0027t write any complicated scripts to have this happen - the help text is just a chunk of text in a block of the recipe. The labels that are parsed to json, are also just written easily on two lines. This means that the creator can spend less time worry about exposing this. If you can write a text file, you can make your applications programatically parseable. You can check how this is done adding free text to \u003ccode\u003e%apphelp\u003c/code\u003e and \u003ccode\u003e%applabel\u003c/code\u003e in \u003ca href=\"app_recipes/samtools_v1.9_centos7.scif\"\u003esamtools_v1.9_centos7.scif\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e \u003ccode\u003e%appenv\u003c/code\u003e can be also used for environment variables particular of each application. We are not using this because of nextflow functionality. See \u003ca href=\"#notes\"\u003eNotes\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInteracting with Applications\u003c/h4\u003e\u003ca id=\"user-content-interacting-with-applications\" class=\"anchor\" aria-label=\"Permalink: Interacting with Applications\" href=\"#interacting-with-applications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI can easily shell into the container in the context of an application, meaning that the\nenvironment is sourced, etc. \u003cstrong\u003eTHIS IS NOT WORKING: USING SHELL OR EXEC ON A APP RUNS %RUNSCRIPT OF THE APP, TRY REMOVING THE EXPORT PATHS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run -it nextflow-scif scif shell samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif shell samtools\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e[samtools] executing /bin/bash \nroot@d002e338b88b:/scif/apps/samtools# env \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep PATH\nLD_LIBRARY_PATH=/scif/apps/samtools/lib\nPATH=/scif/apps/samtools/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNotice how I\u0027m in the app\u0027s context (in it\u0027s application folder) and that it\u0027s bin is added to the path? I can also shell in without a specific application context, but still have all the SCIF \u003ca href=\"https://sci-f.github.io/spec-v1#environment-namespace\" rel=\"nofollow\"\u003eglobal variables\u003c/a\u003e available to me.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container without the apps\u003c/h3\u003e\u003ca id=\"user-content-using-the-container-without-the-apps\" class=\"anchor\" aria-label=\"Permalink: Using the container without the apps\" href=\"#using-the-container-without-the-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe can still shell into the \"general\" container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run -it nextflow-scif shell\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif shell\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\nsingularity shell nextflow-scif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eWARNING No app selected, will run default [\u0027/bin/bash\u0027]\nexecuting /bin/bash \nroot@055a34619d17:/scif# ls\napps\ndata\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr thanks to our \"mess\", in which we have exported bin app folders to the path, we can execute each app directly from singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e### This are the commands that nextflow will run internally.\n# Docker \ndocker run nextflow-scif samtools\n# Singularity\nsingularity exec nextflow-scif samtools\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e This is how nextflow uses containers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning Applications\u003c/h4\u003e\u003ca id=\"user-content-running-applications\" class=\"anchor\" aria-label=\"Permalink: Running Applications\" href=\"#running-applications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore we get into creating a pipeline, look how easy it is to run an application. Without scif, we would have to have known that samtools is installed, and then executed the command to the container. But with the scientific filesystem, we discovered the app (shown above) and then we can just run it. The \u003ccode\u003erun\u003c/code\u003e command maps to the entrypoint, as was defined by the creator:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif run samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity \u003c/span\u003e\n./nextflow-scif run samtools\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eProgram: samtools (Tools \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ealignments\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the SAM format)\nVersion: 0.1.18 (r982:295)\n\nUsage: samtools \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecommand\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e [options]\n\nCommand: view SAM\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003eBAM conversion\n sort sort alignment file\n mpileup multi-way pileup\n depth compute the depth\n faidx index/extract FASTA\n tview text alignment viewer\n index index alignment\n idxstats BAM index stats (r595 or later)\n fixmate fix mate information\n flagstat simple stats\n calmd recalculate MD/NM tags and \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e=\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e bases\n merge merge sorted alignments\n rmdup remove PCR duplicates\n reheader replace BAM header\n cat concatenate BAMs\n targetcut cut fosmid regions (for fosmid pool only)\n phase phase heterozygotes\n\n[samtools] executing /bin/bash /scif/apps/samtools/scif/runscript\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd executing any command in the context of the application is possible too: \u003cstrong\u003eTHIS IS NOT WORKING\u003c/strong\u003e.\nThis is equal to the issue shown with shell, due to out little twist for get nextflow working, here we have all the apps added to the PATH, but this should be the correct functioning of SCIF:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e samtools env \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep PATH\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e samtools env \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep PATH\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLD_LIBRARY_PATH=/scif/apps/samtools/lib\nPATH=/scif/apps/samtools/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that for above, you will get more output with the Singularity container, as it shares the environment with the host. Whether we are using Docker or Singularity, the actions going on internally with the scientific filesystem client are the same. Given a simple enough pipeline, we could stop here, and just issue a series of commands to run the different apps.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeploy a container\u003c/h3\u003e\u003ca id=\"user-content-deploy-a-container\" class=\"anchor\" aria-label=\"Permalink: Deploy a container\" href=\"#deploy-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have your container completely functional you can upload it to Docker Hub or Singularity Hub for version control and sharing. We are uploading it to Docker Hub since Singularity can easily build and run a container from there.\u003c/p\u003e\n\u003cp\u003eIf you have built your docker image in your container you can do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# See your images\ndocker images\n# You have to create an account on Docker Hub and a new repository which is very easy.\n# Then log to docker hub from the command line.\ndocker login --username=yourhubusername --email=youremail@company.com\n# You can tag your image.\ndocker tag bb38976d03cf buisciii/nextflow-scif:1.0\n# You can push\ndocker push bu-isciii/nextflow-scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Nextflow workflow\u003c/h2\u003e\u003ca id=\"user-content-2-nextflow-workflow\" class=\"anchor\" aria-label=\"Permalink: 2. Nextflow workflow\" href=\"#2-nextflow-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e enables scalable and reproducible scientific workflows managing software containers natively like a charm. It allows the adaptation of pipelines written in the most common scripting languages. Moreover its fluent DSL simplifies the implementation and the deployment of complex parallel and reactive workflows on clouds and clusters, being able to abstract the users both from \u003cem\u003ewhere\u003c/em\u003e and even \u003cem\u003ehow\u003c/em\u003e they compute without any concern about the multiple dependencies a scientific workflow can have.\nWe will need a \u003ca href=\"nextflow.config\"\u003enextflow config file\u003c/a\u003e and a \u003ca href=\"main.nf\"\u003emain.nf\u003c/a\u003e script for start using nextflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfig file\u003c/h3\u003e\u003ca id=\"user-content-config-file\" class=\"anchor\" aria-label=\"Permalink: Config file\" href=\"#config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the nextflow config file we can customize all parameters, environment variables, profiles, etc. we will need in our pipeline. If you want to know more about config files for nextflow you can check it \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn this case we have created a \"simple\" config file to use as a template for more complex configuration, using \u003ca href=\"https://github.com/nf-core\"\u003enf-core\u003c/a\u003e as a model. It contains some sections with information \u003ccode\u003emanifest\u003c/code\u003e and process default parameters.\u003c/p\u003e\n\u003cp\u003eFollowing that we have my favourite part where you can configure \u003ccode\u003eprofiles\u003c/code\u003e for executing your nextflow pipeline. With this you can specify \u003ccode\u003e-profile standard/docker/singularity\u003c/code\u003e and the pipeline will run in your local machine, in your singularity image or in your docker image. Moreover you can add further configuration limiting the memory or the number of cpus (even per process), or any custom variables you need for running in your institution HPC.\u003c/p\u003e\n\u003cp\u003eThe section looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eprofiles {\n\n standard {\n includeConfig \u0027conf/base.config\u0027\n }\n\n docker {\n includeConfig \u0027conf/docker.config\u0027\n }\n\n singularity {\n \tincludeConfig \u0027conf/singularity.config\u0027\n }\n\n hpc_isciii {\n \t// TODO. with modules.\n }\n\n testing {\n \t// TODO\n }\n\n aws {\n \t// TO DO\n }\n\n none {\n // Don\u0027t load any config (for use with custom home configs)\n }\n\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe have separated the configuration in several config files, the singularity and docker config files will enable singularity and docker and the path/url to the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity {\n enabled = true\n}\nprocess {\n // Path to container\n container = \u0027buisciii/nextflow-scif:1.0\u0027\n executor = \u0027local\u0027\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMain nextflow script\u003c/h3\u003e\u003ca id=\"user-content-main-nextflow-script\" class=\"anchor\" aria-label=\"Permalink: Main nextflow script\" href=\"#main-nextflow-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main nextflow script \u003ca href=\"main.nf\"\u003emain.nf\u003c/a\u003e creates the pipeline concatenating the output of one step with another, and managing the input and output. You can check the nextflow \u003ca href=\"https://www.nextflow.io/docs/latest/script.html\" rel=\"nofollow\"\u003edocs\u003c/a\u003e for mastering te creation of nextflow pipelines.\u003c/p\u003e\n\u003cp\u003eHere we have configured a simple template for a basic pipeline. It will be useful as an start point for developing more complex pipelines, with help and logging function templates, and space for variable checking and email reporting.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the pipeline\u003c/h3\u003e\u003ca id=\"user-content-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline\" href=\"#running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow, let\u0027s have some fun and run our precious brand new pipeline with our great containers:\u003c/p\u003e\n\u003cp\u003eThis command is the simplier, it will run the pipeline with all parameters by default, and standard profile, which means that it will be run in your local machine and you need to have all software installed and in your PATH.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Simpler one\nnextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, let\u0027s use our container with docker or with singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\nnextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eJust as simple as that, nextflow handles the use of our singularity/docker container adding \u003ccode\u003edocker run\u003c/code\u003e or \u003ccode\u003esingularity exec\u003c/code\u003e before each script in the process configured in \u003ca href=\"main.nf\"\u003emain.nf\u003c/a\u003e!!\u003c/p\u003e\n\u003cp\u003eBut this gets better because for running this pipeline you don\u0027t even need to download this repository, you only need to have installed nextflow and Singularity/Docker and type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run BU-ISCIII/nextflow-scif -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand nextflow will pull the github repository, download the docker image and build it into a singularity image and run your pipeline using only one line of command!!\u003c/p\u003e\n\u003cp\u003eAlso thanks to scif you can still interact with the image pulled from nextflow, in this case is a singularity image build from the docker image in DockerHub and by default is saved in \u003ccode\u003ework/singularity\u003c/code\u003e. Since this image is created from the Dockerfile without an entrypoint as we saw before we have to interact with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# For instance inspecting bwa app\nsingularity exec buisciii-nextflow-scif-1.0.img scif inspect bwa \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is not much a problem because in normal HPC usage we will be using a Singularity image build locally by us with Singulary Recipe, Docker will be used more for easy sharing.\u003c/p\u003e\n\u003cp\u003eOf course this is functioning with test data included in the github repository, but you can easily supply your own reads, reference genome and output dir using parameters. You can see the help of the pipeline:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run BU-ISCIII/nextflow-scif --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 0.29.0 \nLaunching `BU-ISCIII/nextflow-scif` [angry_albattani] - revision: d768dfd571 [develop] \n\n=========================================\n BU-ISCIII/nextflow-scif DEMO PIPELINE v0.1\n========================================= \nUsage: \n\nThe typical command for running the pipeline is as follows:\n\nnextflow run BU-ISCIII/nextflow-scif -profile standard\n\nPipeline arguments:\n --reads Path to input data (must be surrounded with quotes).\n --genome Path to reference genome.\n --outdir Output dir.\n --help show this message.\n -profile Hardware config to use. standard/docker/singularity. Default: standard.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand run it with your own data:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run BU-ISCIII/nextflow-scif -profile singularity --reads *.fastq --genome genome.fa --outdir results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e If you are using singularity as a container you have to take into consideration that it mounts $PWD and /home/${user} by default so if your data is in this directories you will be fine. For advance usage see \u003ca href=\"#advance-usage\"\u003ethis section\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Testing\u003c/h2\u003e\u003ca id=\"user-content-3-testing\" class=\"anchor\" aria-label=\"Permalink: 3. Testing\" href=\"#3-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com\" rel=\"nofollow\"\u003eCircleCI\u003c/a\u003e is a platform that allows you to configure real circle processes for develop, test and deploy your github projects. First of all you need to connect your github account to circle from its website, and then create a .circleci folder in your repo with a \u003ca href=\".circleci/config.yml\"\u003econfig.yml file\u003c/a\u003e.\nIt works executing the processes you configured in your config file in the cloud, creating a docker container for building your container, with really useful cache options which made building container images a lot faster. It amazes me hoy it works with containers inside containers, using one container for build a container xD.\nThe config file consists in the definition of a base container where the magin will happen, then you start defining jobs and steps inside this jobs, for example here we have defined setup, get_data, build, test and deploy jobs. For build step, for example, we define the commands we need to use for build our container using Docker recipe in our repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetup:\n environment:\n - TZ: \"/usr/share/zoneinfo/Europe/Madrid\"\n docker:\n - image: docker:18.01.0-ce-git\n steps:\n - run:\n name: Test if the user has defined the repo name\n command: apk add --no-cache pigz python3\n\n build:\n environment:\n - TZ: \"/usr/share/zoneinfo/Europe/Madrid\"\n docker:\n - image: docker:18.01.0-ce-git\n working_directory: /tmp/src\n steps:\n - run:\n name: Install parallel gzip, python3 and java\n command: apk add --no-cache pigz python3 openjdk8 bash\n - run:\n name: Install nextflow\n command: |\n cd /usr/bin\n wget -qO- https://get.nextflow.io | bash\n - restore_cache:\n keys:\n - docker-v1-{{ .Branch }}-{{ epoch }}\n - docker-v1-{{ .Branch }}-\n - docker-v1-master-\n - docker-v1-\n paths:\n - /tmp/cache/container.tar.gz\n\n - checkout\n - setup_remote_docker\n - run:\n name: Load Docker image layer cache\n no_output_timeout: 30m\n command: |\n docker info\n set +o pipefail\n if [ -f /tmp/cache/container.tar.gz ]; then\n pigz -d --stdout /tmp/cache/container.tar.gz | docker load\n fi\n docker images\n - run:\n name: Build Docker image\n no_output_timeout: 60m\n command: |\n echo \"Working directory is ${PWD}\"\n # If not set, define CONTAINER_NAME\n if [ ! -n \"${CONTAINER_NAME:-}\" ]\n then\n CONTAINER_NAME=\"${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}\"\n fi\n echo \"Container name is ${CONTAINER_NAME}\"\n # Build docker image\n e=1 \u0026amp;\u0026amp; for i in {1..5}; do\n docker build \\\n --cache-from=${CONTAINER_NAME} \\\n --rm=false \\\n -t ${CONTAINER_NAME}:latest \\\n --build-arg BUILD_DATE=`date -u +\"%Y-%m-%dT%H:%M:%SZ\"` \\\n --build-arg VCS_REF=`git rev-parse --short HEAD` \\\n --build-arg VERSION=\"${CIRCLE_TAG:-$THISVERSION}\" . \\\n \u0026amp;\u0026amp; e=0 \u0026amp;\u0026amp; break || sleep 15\n done \u0026amp;\u0026amp; [ \"$e\" -eq \"0\" ]\n # Pipeline VERSION\n THISVERSION=$(nextflow run ${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME} --version)\n echo \"Pipeline Version is ${THISVERSION}\"\n echo \"${CIRCLE_TAG:-$THISVERSION}\"\n - run:\n name: Docker save\n no_output_timeout: 40m\n command: |\n # If not set, define CONTAINER_NAME\n if [ ! -n \"${CONTAINER_NAME:-}\" ]\n then\n CONTAINER_NAME=\"${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}\"\n fi\n echo \"Saving ${CONTAINER_NAME} to container.tar.gz\"\n mkdir -p /tmp/cache\n docker save ${CONTAINER_NAME}:latest \\\n | pigz -2 -p 3 \u0026gt; /tmp/cache/container.tar.gz\n - persist_to_workspace:\n root: /tmp\n paths:\n - cache/container.tar.gz\n - src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSe how a container is built, and our recipe is built inside that container, then the Docker image is saved, and tagged, and persisted in the cache directory. This paths will be available in the next jobs of the config file, and if we rerun the cicle config the cache can be used.\nAlso, is worth to to notice how we can use some ENV variables of CircleCI, they can be accessed in any step and you can see them in your circleCI dashboard (It is useful for understand how it works to look around it, you can check in \u003ca href=\"https://circleci.com/gh/BU-ISCIII/nextflow-scif\" rel=\"nofollow\"\u003ehere\u003c/a\u003e). Moreover you can define your own in your circleci settings in the dashboard, in this case I have added the CONTAINER_NAME and the DOCKERHUB_PASS.\u003c/p\u003e\n\u003cp\u003eThe same way we have prepared our container building, we can automatize it deployment into Docker HUB (see the file), and the testing or our apps (still in progress). You can define different rules for different branches in your github repo, or when you make a new tag, for example in this case deployment to Docker HUB only occurs when there is a new push or a new tag to the master branch, when you push to a develop branch only build and test jobs are performed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAdvance usage\u003c/h1\u003e\u003ca id=\"user-content-advance-usage\" class=\"anchor\" aria-label=\"Permalink: Advance usage\" href=\"#advance-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTill this point everything is working fine because we are using the data located in our current directory, and singularity mounts \u003ccode\u003e$PWD\u003c/code\u003e by default. But what if we are running in our HPC-cluster, and our data is in /processing_Data or other custom directory?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTODO\u003c/strong\u003e: Try in different locations, with data and reference genome in different folders.\nUse of --bind ? . Need to pre-create paths inside the containers??\nImprovement of I/O metadata mounting only the project folder instead of all processing_Data??\n\u003cem\u003eNote\u003c/em\u003e: for each app scif/app/{app}/data folder is created. That\u0027s a possibility for mounting. We have to make some performance tests in our cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSummary\u003c/h1\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-label=\"Permalink: Summary\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWe have created a container with all the software needed for our pipeline with Singularity and Docker using the same SCI-F recipes. This will allow us to deploy new containers really quicky for new pipelines once you have created a good collection of scif apps recipes.\u003c/li\u003e\n\u003cli\u003eUsing SCI-F we can access all our apps installed separately from our container. Besides, our container is no longer a \"black-box\" and we can obtain information about what apps are installed in our container, with its labels and help.\u003c/li\u003e\n\u003cli\u003eOur container is also functional with the native container commands, and we can exec any command in our container without the use of SCI-F which made it flexible enough for its use with nextflow.\u003c/li\u003e\n\u003cli\u003eWe have deployed our container to DockerHub so new users don\u0027t have to build the image theirselves.\u003c/li\u003e\n\u003cli\u003eFinally we have created a basic nextflow workflow, with three different profile configurations {standard, singularity, docker}, this way you can use the same pipeline running in your local machine with your software installed, or with the singularity or docker image. Besides, you can create more profiles for its usage in a HPC with \u003ccode\u003emodule\u003c/code\u003e configured, or even in a AWS server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe idea is to have a workflow independent of where you are computing, and a container system which allows you to assure reproducibility and sharing, including the maximun information possible for its use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNotes\u003c/h1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to get this working I had to make a decision: put SCIF on the \"top\" or put nextflow instead. I will try to explain myself:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNextflow on the top:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextflow provides a natively functionality for using singularity and docker. Using it you just set the commands the pipeline is going to run, and nextflow handles the use of \u003ccode\u003edocker run\u003c/code\u003e or \u003ccode\u003esingularity exec\u003c/code\u003e before this commands with the container you supplied in the configuration.\u003c/li\u003e\n\u003cli\u003eThis means that the executables for each app \u003cstrong\u003eMUST\u003c/strong\u003e be accesible when you run/exec from the container, not each of the apps with SCI-F.\u003c/li\u003e\n\u003cli\u003eThus, we are being forced to add the executables to the PATH; and somehow mess up SCIF goal of having separate apps environment adding the bin app folder to the PATH only when the app is run. Being this the main con of this approximation.\u003c/li\u003e\n\u003cli\u003eOn the other hand, our main.nf will remain untouched and we will be able to run in a local machine, a cluster or a container without any modification. Which was I was looking for.\u003c/li\u003e\n\u003cli\u003eFor this to work your minimun needs in your system are nextflow and Docker/Singularity if you are using docker or singularity profile.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSCIF on the top (I am going to make a branch testing this): - \u0026gt; I tried but it didn\u0027t work, I get all the apps being recognized but when the process in nextflow where going to be run the files weren\u0027t found. This is because nextflow uses a temp folder called \u003ccode\u003ework\u003c/code\u003e for all executions, and when you use scif run bwa, the command is run in ${SCIF_APPROOT} instead on nextflow folder.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAnother aproximation will be to install nextflow inside the containers. This way you can only interact with the apps commands through SCIF which will be the only entrypoint in Singularity and Docker.\u003c/li\u003e\n\u003cli\u003eNextflow in this case will not use Singularity or Docker profile, because it is INSIDE one, so this config parameters must be set to false.\u003c/li\u003e\n\u003cli\u003emain.nf script would have to be modified and \u003ccode\u003escif run\u003c/code\u003e must be add before each command.\u003c/li\u003e\n\u003cli\u003eThe pros of this approximation will be that apps will be in separate environments as SCI-F plans to, and you won\u0027t need to have nextflow installed in your system. If you use the image, just with docker or singularity installed you are set.\u003c/li\u003e\n\u003cli\u003eHowever you could only use this nextflow pipeline with the image prepare for this, or have scif installed in your local/hpc system, and all the apps you are going to use inside scif which may not be the case (as in our HPC where we don\u0027t have install permissions)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAlternative middle point configuration -\u0026gt; I have this working in \u003cstrong\u003efeature/nextflow-scif-alt\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eI install nextflow inside the container like the option above, and each app environment is separated, but I have to add all app bin folders to the path prior to run nextflow command in %apprun.\u003c/li\u003e\n\u003cli\u003eAlso I had to cd to ${SCIF_DATA}, and bind this folder with my $PWD in the singularity command.\u003c/li\u003e\n\u003cli\u003eFor more info please check feature/nextflow-scif-alt branch in this repository, and README-ALT.md file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eI suppose this is a decision you have to make, one could think that if I can use my singularity image in my HPC system and forgot about modules and stuff, I will have all the software I need, in the version I need and whenever I need it. However even if this is going to be the case (we are still testing performance, we are very newbies using containers) I always like to have flexibility, and nextflow provides me in theory that \"I don\u0027t mind where I compute\" flexibility.\u003c/p\u003e\n", "stargazers_count": 9, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [ - "cuda", - "hpc", - "nsight-systems", - "openacc", - "nsight-compute" + "docker-container", + "nextflow", + "singularity", + "recipe", + "singularity-containers", + "nextflow-scif", + "nextflow-pipelines", + "docker-image", + "scif-recipe", + "nextflow-workflow" ], - "updated_at": 1727338317.0 + "updated_at": 1663001088.0 }, { "data_format": 2, - "description": null, + "description": "Deep Learning Pipeline for Wrist Fracture Detection", "filenames": [ "Singularity" ], - "full_name": "ejolly/IntroToSingularity", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGetting Setup with Singularity\u003c/h1\u003e\u003ca id=\"user-content-getting-setup-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Getting Setup with Singularity\" href=\"#getting-setup-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eThis is a guide to getting started with \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity containers\u003c/a\u003e in conjunction with Dartmouth College\u0027s \u003ca href=\"http://techdoc.dartmouth.edu/discovery/\" rel=\"nofollow\"\u003eDiscovery HPC\u003c/a\u003e.\u003cbr\u003e\nQuestions can be addressed to \u003ca href=\"mailto:eshin.jolly.gr@dartmouth.edu\"\u003eeshin.jolly.gr@dartmouth.edu\u003c/a\u003e or \u003ca href=\"mailto:mvdoc.gr@dartmouth.edu\"\u003emvdoc.gr@dartmouth.edu\u003c/a\u003e.\u003cbr\u003e\nWe\u0027re not experts but we\u0027re happy to try to help!\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#prereqs\"\u003eI. Pre-requisites (OSX only)\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-i-pre-requisites-osx-only\" class=\"anchor\" aria-label=\"Permalink: I. Pre-requisites (OSX only)\" href=\"#i-pre-requisites-osx-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#creation\"\u003eII. Creating a Singularity container\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-ii-creating-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: II. Creating a Singularity container\" href=\"#ii-creating-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#basicusage\"\u003eIII. Basic container usage\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-iii-basic-container-usage\" class=\"anchor\" aria-label=\"Permalink: III. Basic container usage\" href=\"#iii-basic-container-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#discovery\"\u003eIV. Using a container on Discovery\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-iv-using-a-container-on-discovery\" class=\"anchor\" aria-label=\"Permalink: IV. Using a container on Discovery\" href=\"#iv-using-a-container-on-discovery\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#updating\"\u003eV. Updating an existing container\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-v-updating-an-existing-container\" class=\"anchor\" aria-label=\"Permalink: V. Updating an existing container\" href=\"#v-updating-an-existing-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#sharing\"\u003eVI. Sharing containers\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-vi-sharing-containers\" class=\"anchor\" aria-label=\"Permalink: VI. Sharing containers\" href=\"#vi-sharing-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"#resources\"\u003eVII. Extra resources\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-vii-extra-resources\" class=\"anchor\" aria-label=\"Permalink: VII. Extra resources\" href=\"#vii-extra-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-prereqs\"\u003e\u003c/a\u003e Pre-requisites (OSX only!)\u003c/h2\u003e\u003ca id=\"user-content--pre-requisites-osx-only\" class=\"anchor\" aria-label=\"Permalink: Pre-requisites (OSX only!)\" href=\"#-pre-requisites-osx-only\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eBecause singularity runs primarily on linux, we need to create a virtual linux environment on OSX in order to build/manipulate singularity containers. Follow this step first if you\u0027re using OSX.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstall Homebrew package manager\u003c/h4\u003e\u003ca id=\"user-content-install-homebrew-package-manager\" class=\"anchor\" aria-label=\"Permalink: Install Homebrew package manager\" href=\"#install-homebrew-package-manager\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHomebrew is a package manager for OSX similar to apt-get or yum on linux. It allows you to download and install different software (e.g. wget, or curl) and allows you to build your own packages. Just copy and run the command below:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/usr/bin/ruby -e \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUse Homebrew to install Vagrant\u003c/h4\u003e\u003ca id=\"user-content-use-homebrew-to-install-vagrant\" class=\"anchor\" aria-label=\"Permalink: Use Homebrew to install Vagrant\" href=\"#use-homebrew-to-install-vagrant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVagrant is a virtual development environment that can be used to create virtual-machines (kind of similar to Virtualbox, but much more powerful). It can be used to install and run another operating system on your computer that\u0027s completely independent from your host OS. First we\u0027re going to install vagrant via Homebrew.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebrew cask install Virtualbox\nbrew cask install vagrant\nbrew cask install vagrant-manager\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUse Vagrant to create a virtual machine\u003c/h4\u003e\u003ca id=\"user-content-use-vagrant-to-create-a-virtual-machine\" class=\"anchor\" aria-label=\"Permalink: Use Vagrant to create a virtual machine\" href=\"#use-vagrant-to-create-a-virtual-machine\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow that we have vagrant installed, we can use it to make a brand new linux- based virtual machine, \u003cstrong\u003ewithin\u003c/strong\u003e which singularity will be installed. It\u0027s from inside this vm that we\u0027re going to do all future singularity container creation, modification etc.\u003c/p\u003e\n\u003cp\u003eFirst let\u0027s create a folder that our virtual machine will live in.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir singularity-vm\ncd singularity-vm\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow lets download a \u003cem\u003evagrantfile\u003c/em\u003e for a prebuilt Ubuntu system that already has singularity installed.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant init singularityware/singularity-2.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally we can start up virtual machine and move into it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#If this is the first time you\u0027re building the vm the vagrant up command might take a minute or so to complete\nvagrant up\nvagrant ssh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhenver you\u0027re done using a vagrant vm just use \u003ccode\u003ectrl+c\u003c/code\u003e to exit the machine and type \u003ccode\u003evagrant halt\u003c/code\u003e to shut it down.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-creation\"\u003e\u003c/a\u003eCreating a Singularity container\u003c/h2\u003e\u003ca id=\"user-content-creating-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Creating a Singularity container\" href=\"#creating-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLet\u0027s begin by creating a new folder within our vm for our brand new container (this isn\u0027t strictly necessary but nice to keep different containers organized):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir miniconda\ncd miniconda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe first thing we need to do in order to create a singularity container is make a singularity \u003cem\u003edefinition\u003c/em\u003e file. This is just an instruction set that singularity will use to create a container. Think of this definition file as a recipe, and the container as the final product. Within this recipe, specify everything you need to in order create your custom analysis environment. Sharing this definition file with others will enable them to identically reproduce the steps it took to create your container.\u003c/p\u003e\n\u003cp\u003eTo get you started here\u0027s an example definition file that we\u0027re going to use for this demo. This is a simple neurodebian flavored container with miniconda installed along with numpy and scipy.\u003cbr\u003e\nLet\u0027s save this to a file called \u003ccode\u003eminiconda.def\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Singularity definition example with miniconda\n# Matteo Visconti di Oleggio Castello; Eshin Jolly\n# mvdoc.gr@dartmouth.edu; eshin.jolly.gr@dartmouth.edu\n# May 2017\n\nbootstrap: docker\nfrom: neurodebian:jessie\n\n# this command assumes at least singularity 2.3\n%environment\n PATH=\"/usr/local/anaconda/bin:$PATH\"\n%post\n # install debian packages\n apt-get update\n apt-get install -y eatmydata\n eatmydata apt-get install -y wget bzip2 \\\n ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 \\\n git git-annex-standalone\n apt-get clean\n\n # install anaconda\n if [ ! -d /usr/local/anaconda ]; then\n wget https://repo.continuum.io/miniconda/Miniconda2-4.3.14-Linux-x86_64.sh \\\n -O ~/anaconda.sh \u0026amp;\u0026amp; \\\n bash ~/anaconda.sh -b -p /usr/local/anaconda \u0026amp;\u0026amp; \\\n rm ~/anaconda.sh\n fi\n # set anaconda path\n export PATH=\"/usr/local/anaconda/bin:$PATH\"\n\n # install the bare minimum\n conda install\\\n numpy scipy\n conda clean --tarballs\n\n # make /data and /scripts so we can mount it to access external resources\n if [ ! -d /data ]; then mkdir /data; fi\n if [ ! -d /scripts ]; then mkdir /scripts; fi\n\n%runscript\n echo \"Now inside Singularity container woah...\"\n exec /bin/bash\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow lets use our vagrant vm and create a blank singularity image allocating 4gb of disk space within our container. You may need to adjust this depending on how much software you plan to install. By default the vagrant vm will share \u003ccode\u003e/vagrant\u003c/code\u003e with your host OS so lets perform our operation in there within the container folder we created earlier.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant up\nvagrant ssh\ncd /vagrant/miniconda\n# Now let\u0027s build it!\nsudo singularity build miniconda.img miniconda.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-basicusage\"\u003e\u003c/a\u003eBasic container usage\u003c/h2\u003e\u003ca id=\"user-content-basic-container-usage\" class=\"anchor\" aria-label=\"Permalink: Basic container usage\" href=\"#basic-container-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf all went well we should be able to issue a python command to the python version installed \u003cem\u003ewithin\u003c/em\u003e our container like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec miniconda.img python -c \u0027print \"Hello from Singularity!\"\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can also open up our container and work \u003cem\u003einside\u003c/em\u003e it interactively:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run miniconda.img\nconda list\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePress \u003ccode\u003ectrl+d\u003c/code\u003e to exit the container.\u003c/p\u003e\n\u003cp\u003eMost commonly you\u0027ll use one of three commands with a container:\u003cbr\u003e\n\u003ccode\u003esingularity exec\u003c/code\u003e to run a specific command/file/script using the container\u003cbr\u003e\n\u003ccode\u003esingularity run\u003c/code\u003e to move into a container and use it interactively; what gets run by this command is dictated by your singularity \u003cem\u003edefinition\u003c/em\u003e file\u003cbr\u003e\n\u003ccode\u003esingularity shell\u003c/code\u003e similar to above, but specifically open up a shell within the container\u003c/p\u003e\n\u003cp\u003eA few other useful flags include:\n\u003ccode\u003e-B\u003c/code\u003e mount an external folder to the container\u003cbr\u003e\n\u003ccode\u003e-c\u003c/code\u003e don\u0027t automatically map /home and /tmp to shared folders with the host OS\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-discovery\"\u003e\u003c/a\u003eUsing a container on Discovery\u003c/h2\u003e\u003ca id=\"user-content-using-a-container-on-discovery\" class=\"anchor\" aria-label=\"Permalink: Using a container on Discovery\" href=\"#using-a-container-on-discovery\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to use a container on Discovery you have to first upload the generated .img file to your home directory. Since containers can be rather large lets compress this and then uncompress on Discovery (starting with Singularity \u0026gt;=2.3.0 this functionality works through \u003ccode\u003eimport\u003c/code\u003e and \u003ccode\u003eexport\u003c/code\u003e commands)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etar -cvzf miniconda.tar.gz miniconda.img\nscp miniconda.tar.gz ejolly@discovery.dartmouth.edu:~\nssh ejolly@discovery.dartmouth.edu\ntar -xvzf miniconda.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow you can utilize the container by loading the singularity module and utilizing any of the singularity commands above. There is \u003cstrong\u003eone catch\u003c/strong\u003e however: by default singularity will try to melt together any environment variables defined in your account on discovery with environment variables defined within the container. The rationale behind this is that singularity offers the ability to \u003cem\u003eseamlessly\u003c/em\u003e blend a custom environment (i.e. your container built with all your goodies) and the functionality of your HPC (i.e. all the goodies that already exist on Discovery). However, often times you want to turn this functionality off and only use environment variables within your container to avoid conflicts (i.e. completely ignore environment variables set on Discovery). Here\u0027s how we do that:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\nsingularity run -e miniconda.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo make our lives easier we can create a simple bash script that executes a command in our container making sure to call it with all the extra flags we want (e.g. mounting some folders, ignoring environment variables). I personally like to create two scripts one for interactively working with a container and one for using it to execute commands for example with job submission. Here are some examples, you\u0027ll need to adapt them to mount the directories you want:\u003cbr\u003e\nLet\u0027s save the following code into a bash file called: exec_miniconda\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsingularity -e exec \\\n -B /idata/lchang/Projects:/data \\\n -B /ihome/ejolly/scripts/:/scripts \\\n miniconda.img \"$@\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLet\u0027s save the following code into a bash file called: interact_miniconda\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsingularity -e run \\\n\t-c \\\n\t-B /idata/lchang/Projects/Pinel:/data \\\n\t-B ~/scripts:/scripts \\\n\tminiconda.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we issue a command to our container (e.g. when submitting a job) like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./exec_miniconda python -c \u0027print \"Hello World!\"\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can also use our container interactively with. Here let\u0027s actually serve a jupyter notebook server from the cluster and interact with it using our local web browser. To do so we need to reconnect to Discovery with port-forwarding. The demo container here isn\u0027t built with a jupyter notebook so this won\u0027t work, but we you can use the same command when building your own container\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# You should really connect to something other than the head node here!\nssh ejolly@discovery.dartmouth.edu -N -f -L localhost:3129:localhost:9999\n\n./exec_miniconda jupyter notebook --no-browser --port=9999\n# On local machine navigate to localhost:3129 in a web browser\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-updating\"\u003e\u003c/a\u003eUpdating an existing container\u003c/h2\u003e\u003ca id=\"user-content-updating-an-existing-container\" class=\"anchor\" aria-label=\"Permalink: Updating an existing container\" href=\"#updating-an-existing-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe preferred way to update a container is to modify the definition file and rebuild the image using the steps above. This ensures that any container image is always a product of its definition file and is therefore easy to reproduce.\u003c/p\u003e\n\u003cp\u003eHowever, singularity makes it easy to make changes to an existing container as well using the \u003ccode\u003e--writable\u003c/code\u003e flag with the \u003ccode\u003eexec\u003c/code\u003e, \u003ccode\u003erun\u003c/code\u003e, or \u003ccode\u003eshell\u003c/code\u003e commands, e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --writable miniconda.img apt-get install curl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also increase the size of an existing container with the \u003ccode\u003eexpand\u003c/code\u003e command, e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#Expand a container by 2gb\nsingularity expand --size 2048 miniconda.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-sharing\"\u003e\u003c/a\u003eSharing containers\u003c/h2\u003e\u003ca id=\"user-content-sharing-containers\" class=\"anchor\" aria-label=\"Permalink: Sharing containers\" href=\"#sharing-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne of the nice things about using singularity (and containers in general) is that you can share your analysis environment with others. These are served on \u003ca href=\"https://singularity-hub.org\" rel=\"nofollow\"\u003eSingularity hub\u003c/a\u003e. Many prebuilt containers already exist that you easily download and use.\u003c/p\u003e\n\u003cp\u003eLet\u0027s say we want to use this \u003ca href=\"https://singularity-hub.org/containers/105/\" rel=\"nofollow\"\u003econtainer\u003c/a\u003e prebuilt with tensor flow for GPUs. This is as simple as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://researchapps/tensorflow:gpu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can setup run and execute scripts like above to use it on Discovery.\u003c/p\u003e\n\u003cp\u003eYou can also easily share you custom container on Singularity hub by committing your singularity definition file to github and flipping the switch for that repository on singularity hub.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-resources\"\u003e\u003c/a\u003eExtra resources\u003c/h2\u003e\u003ca id=\"user-content-extra-resources\" class=\"anchor\" aria-label=\"Permalink: Extra resources\" href=\"#extra-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMuch of this tutorial is borrowed/integrated from several helpful resources:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eQuick guides\u003c/h4\u003e\u003ca id=\"user-content-quick-guides\" class=\"anchor\" aria-label=\"Permalink: Quick guides\" href=\"#quick-guides\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://mvdoc.me/2017/using-singularity-to-make-analyses-reproducible.html\" rel=\"nofollow\"\u003eMatteo Visconti\u0027s blogpost on getting started with singularity\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"http://jinhyuncheong.com/jekyll/update/2016/07/24/How-to-use-the-Discovery-cluster.html\" rel=\"nofollow\"\u003eJin Cheong\u0027s quick guide to using the discovery cluster\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMore comprehensive guides\u003c/h4\u003e\u003ca id=\"user-content-more-comprehensive-guides\" class=\"anchor\" aria-label=\"Permalink: More comprehensive guides\" href=\"#more-comprehensive-guides\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/quickstart\" rel=\"nofollow\"\u003eSingularity Documentation\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"http://techdoc.dartmouth.edu/discovery/\" rel=\"nofollow\"\u003eDiscovery Documentation\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSharing is caring\u003c/h4\u003e\u003ca id=\"user-content-sharing-is-caring\" class=\"anchor\" aria-label=\"Permalink: Sharing is caring\" href=\"#sharing-is-caring\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity hub\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003eDocker hub\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "Oulu-IMEDS/DeepWrist", + "latest_release": "v1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePaper Link\u003c/h1\u003e\u003ca id=\"user-content-paper-link\" class=\"anchor\" aria-label=\"Permalink: Paper Link\" href=\"#paper-link\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTitle: Deep Learning for Wrist Fracture Detection: Are We There Yet? \u003cbr\u003e\n\u003ca href=\"https://arxiv.org/abs/2012.02577\" rel=\"nofollow\"\u003earXiv link\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDeepWrist Pipeline\u003c/h1\u003e\u003ca id=\"user-content-deepwrist-pipeline\" class=\"anchor\" aria-label=\"Permalink: DeepWrist Pipeline\" href=\"#deepwrist-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA transfer learning pipeline to detect wrist fracture from DICOM files. It has two blocks: Landmark Localization Block\nand Fracture Detection Block.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./figures/DeepWrist_pipeline.png\"\u003e\u003cimg src=\"./figures/DeepWrist_pipeline.png\" alt=\"DeepWrist\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBoth of the blocks are yml configuration based. We used OmegaConf for this purpose. Each executable python file can\neither run standalone or requires a yml file as \u003ccode\u003eexperiment\u003c/code\u003e argument to be passed down at command line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLandmark Localization Block\u003c/h2\u003e\u003ca id=\"user-content-landmark-localization-block\" class=\"anchor\" aria-label=\"Permalink: Landmark Localization Block\" href=\"#landmark-localization-block\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLandmark Localization Block is adapted from \u003ca href=\"https://arxiv.org/pdf/1907.12237\" rel=\"nofollow\"\u003eKNEEL\u003c/a\u003e. However, we developed some data\naugmentation methods suited to our task. The \u003ccode\u003elocalizer\u003c/code\u003e folder contains the source code for Landmark Localizer and\nstructured as \u003cbr\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elocalizer \n|---config \n| |---experiment\n|---kneel_before_wrist \n| |---data \n| |---model \n|---scripts \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003econfig\u003c/code\u003e folder contains the initial default configuration and a configuration processor. There is a folder named\n\u003ccode\u003eexperiment\u003c/code\u003e inside \u003ccode\u003econfig\u003c/code\u003e folder which holds confgiguration for different experiments.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ekneel_before_wrist\u003c/code\u003e hosts the body of the localizer part of our pipeline. It has two sub-directory: \u003ccode\u003edata\u003c/code\u003e and \u003ccode\u003emodel\u003c/code\u003e.\nThe \u003ccode\u003edata\u003c/code\u003e folder contains utilities necessary to process and augment data for training and evaluation. \u003ccode\u003emodel\u003c/code\u003e\nsub-directory contains the pytorch lightning version of HourGlass network which we will use for training the localizer.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003escripts\u003c/code\u003e directory hosts all the experiment scripts for which the yaml configurations are created.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Train Localizer with your own data\u003c/h2\u003e\u003ca id=\"user-content-how-to-train-localizer-with-your-own-data\" class=\"anchor\" aria-label=\"Permalink: How to Train Localizer with your own data\" href=\"#how-to-train-localizer-with-your-own-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFirst step for training your own localizer is to collect data. For our research we used private hospital data,\nthrefore it cannot be shared. To make things simple, we used csv file to store meta data about the dataset. This way,\nyou don\u0027t have to load the full dataset to the memory rather fecth the file location from csv file and read it just-in-time.\nSo, we are dealing with wrist fracture images. We will consider posterioanterio (PA) and lateral (LAT) view of the wrist x-ray. To\nmake your own dataset, you have to create a csv metadata file containing at least \u003ccode\u003eFname, Points, Side\u003c/code\u003e columns. \u003ccode\u003eFname\u003c/code\u003e\nis the absolute path to the wrist image, \u003ccode\u003ePoints\u003c/code\u003e column will contain the landmark coordinates of top of distal ulna,\ntop of distal radius and assumed center of the wrist for PA view and two distinguishalbe points on top part of distal\nradio-ulna bone and the assumed center of wrist for LAT view. As the name suggest, the \u003ccode\u003eSide\u003c/code\u003e column contains the side\ninformation of corresponding wrist x-ray. Put 0 for PA and 1 for LAT. Once the metadata is ready, we can move forward.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSecond step is to clone \u003ccode\u003ewrist_landmark.yaml\u003c/code\u003e configuration file and modify the clone. Inside the yaml file modify\nfollowing\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e data_home: # root folder that contains the data folder \n data_folder: # your data folder name\n meta: the csv meta file you have created. should be inside data folder \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOnce you are done with step 2, run the \u003ccode\u003etrain_ptl.py --experiment=YourClonedYAMLFile\u003c/code\u003e. This file is located inside\n\u003ccode\u003escripts\u003c/code\u003e folder. It will start the training.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFracture Detection Block\u003c/h2\u003e\u003ca id=\"user-content-fracture-detection-block\" class=\"anchor\" aria-label=\"Permalink: Fracture Detection Block\" href=\"#fracture-detection-block\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eclassifier\u003c/code\u003e folder hosts the Fracture Detection Block. It has a similar structure like localizer.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eclassifier \n|---config \n|---fracture_detector\n| |---callback \n| |---data \n| |---model \n|---script \n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLike before \u003ccode\u003econfig\u003c/code\u003e folder hosts the script configurations. \u003ccode\u003efracture_detector\u003c/code\u003e folder hosts necessary folders and\nfiles for model, data and training related stuffs. Inside this folder, there are three folders: 1) \u003ccode\u003ecallback\u003c/code\u003e (hosts\ncallback function definitions), 2) \u003ccode\u003edata\u003c/code\u003e (hosts data related utilities) and 3) \u003ccode\u003emodel\u003c/code\u003e (hosts model definition and\ntraining methods)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to train your Fracture Detector\u003c/h2\u003e\u003ca id=\"user-content-how-to-train-your-fracture-detector\" class=\"anchor\" aria-label=\"Permalink: How to train your Fracture Detector\" href=\"#how-to-train-your-fracture-detector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStep 1. First step to train your custom fracture detector is to collect data using the \u003ccode\u003elocalizer\u003c/code\u003e model trained previously.\nSave the generated ROI with the corresponding \u003ccode\u003eID\u003c/code\u003e as filename. Create a csv metadata file with \u003ccode\u003eID\u003c/code\u003e, \u003ccode\u003eSide\u003c/code\u003e, \u003ccode\u003eFname\u003c/code\u003e(optional)\nand \u003ccode\u003eFracture\u003c/code\u003e columns. Say, the meta file name is \u003ccode\u003eyour_meta.csv\u003c/code\u003eCreate a \u003ccode\u003eroot\u003c/code\u003e folder which we will use as data home where the generated ROI images and the csv\nmeta file are saved. There shoudl be \u003ccode\u003ePA\u003c/code\u003e and \u003ccode\u003eLAT\u003c/code\u003e folder in the \u003ccode\u003eroot\u003c/code\u003e folder to host PA ROI and LAT ROI respectively.\u003c/p\u003e\n\u003cp\u003eStep 2. Clone the existing training conf \u003ccode\u003efracture_detector_seresnet.yaml\u003c/code\u003e to \u003ccode\u003eyour_config_file.yaml\u003c/code\u003e.\nOpen \u003ccode\u003eyour_config_file.yaml\u003c/code\u003e and update the following field\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edata_home: root\nmeta: your_meta.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStep 3. Once you are done with the config file go inside the \u003ccode\u003escripts\u003c/code\u003e folder and run \u003ccode\u003epython train_ptl.py experiment=your_config_file\u003c/code\u003e.\nthis will start the training.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInference on your Data\u003c/h2\u003e\u003ca id=\"user-content-inference-on-your-data\" class=\"anchor\" aria-label=\"Permalink: Inference on your Data\" href=\"#inference-on-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStep 1. Create a csv meta file of for the data you want to predict. Use \u003ccode\u003eID\u003c/code\u003e, \u003ccode\u003eSide\u003c/code\u003e, \u003ccode\u003eFname\u003c/code\u003e, and \u003ccode\u003eFracture\u003c/code\u003e columns.\u003c/p\u003e\n\u003cp\u003eStep2. Clone \u003ccode\u003efracture_deteciton_testset_1.yaml\u003c/code\u003e to \u003ccode\u003eyour_testset.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eStep 3. Find and update the following field in \u003ccode\u003eyour_testset.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edataset:\n train_data_home: root\n test_data_home: /location/of/test/data\n meta: /absolute/location/to/your_testset.csv\nsave_path: /absolute/location/to/save/prediction.csv\nsnapshot_folder: /folder/location/where/fracture/detector/models/are/saved\nsave_image: true or false\nsave_image_dir: /folder/location/if/you/want/to/save/output/images\n\nlocalizer:\n snapshot_folder: /folder/location/where/roi/localizer/models/are/saved\n dataset:\n train_data_home: /folder/location/where/localization/data/are/stored\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ekeep only \u003ccode\u003eFracture\u003c/code\u003e in \u003ccode\u003egt\u003c/code\u003e. If you want to save gradcam set \u003ccode\u003esave_gradcam: true\u003c/code\u003e and define \u003ccode\u003egradcam_dir\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eStep 4. Now in the \u003ccode\u003escripts\u003c/code\u003e folder run, \u003ccode\u003epython test.py experiment=your_testset\u003c/code\u003e\nThis will do inference on your data, the predicitons will be saved in the csv file you defined.\u003c/p\u003e\n\u003ch2\u003e\u003c/h2\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTrained Models\u003c/h1\u003e\u003ca id=\"user-content-trained-models\" class=\"anchor\" aria-label=\"Permalink: Trained Models\" href=\"#trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse the following commands to get the trained models.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget http://mipt-ml.oulu.fi/models/DeepWrist/Fracture_Detection_Block.tar.gz\nwget http://mipt-ml.oulu.fi/models/DeepWrist/ROI_Localization_Block.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHow to Cite\u003c/h1\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to Cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor citation, please use the following bibtex\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{raisuddin2020deep,\n title={Deep Learning for Wrist Fracture Detection: Are We There Yet?}, \n author={Abu Mohammed Raisuddin and Elias Vaattovaara and Mika Nevalainen and Marko Nikki and Elina J\u00e4rvenp\u00e4\u00e4 and Kaisa Makkonen and Pekka Pinola and Tuula Palsio and Arttu Niemensivu and Osmo Tervonen and Aleksei Tiulpin},\n year={2020},\n eprint={2012.02577},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 9, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [], - "updated_at": 1686764038.0 + "updated_at": 1707636728.0 }, { "data_format": 2, - "description": "Singularity image for a deep learning (pytorch) environment + GPU support", + "description": "Info on CHPC Open OnDemand installation and customization", "filenames": [ - "Singularity.1.0.0" + "linux-host/Singularity" ], - "full_name": "manuel-munoz-aguirre/singularity-pytorch-gpu", + "full_name": "CHPC-UofU/OnDemand-info", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-pytorch-gpu\u003c/h1\u003e\u003ca id=\"user-content-singularity-pytorch-gpu\" class=\"anchor\" aria-label=\"Permalink: singularity-pytorch-gpu\" href=\"#singularity-pytorch-gpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4969\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for a deep learning (pytorch) environment + GPU support (cuda-10.2). Contains libraries to perform common ML tasks. \u003ccode\u003eOpenslide\u003c/code\u003e is included to manipulate whole-slide histology images, \u003ccode\u003eimagemagick\u003c/code\u003e for general image manipulation. \u003ccode\u003eJupyterLab\u003c/code\u003e and \u003ccode\u003ecode-server\u003c/code\u003e (VS Code) are also included in the image. This image has been tested in an HPC (SGE) with distributed pytorch applications.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling singularity\u003c/h2\u003e\u003ca id=\"user-content-installing-singularity\" class=\"anchor\" aria-label=\"Permalink: Installing singularity\" href=\"#installing-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install singularity, see the \u003ca href=\"https://sylabs.io/guides/3.6/admin-guide/installation.html#installation-on-linux\" rel=\"nofollow\"\u003eofficial docs\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding/downloading the image\u003c/h2\u003e\u003ca id=\"user-content-buildingdownloading-the-image\" class=\"anchor\" aria-label=\"Permalink: Building/downloading the image\" href=\"#buildingdownloading-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build an image called \u003ccode\u003etorchenv.sif\u003c/code\u003e based on the definition file \u003ccode\u003eSingularity.1.0.0\u003c/code\u003e, an NVIDIA GPU and \u003ccode\u003ecuda-10.2\u003c/code\u003e drivers must be available on the host system. Clone this repository, move into it and run the singularity build command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/manuel-munoz-aguirre/singularity-pytorch-gpu.git \u0026amp;\u0026amp; \\\ncd singularity-pytorch-gpu \u0026amp;\u0026amp; \\\nsudo singularity build torchenv.sif Singularity.1.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOtherwise, the image can be pulled directly from singularity hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull torchenv.sif shub://manuel-munoz-aguirre/singularity-pytorch-gpu:1.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing the container\u003c/h2\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo spawn an interactive shell within the container, use the command below. The \u003ccode\u003e--nv\u003c/code\u003e flag setups the container to use NVIDIA GPUs (read more \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/gpu.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv torchenv.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run a script (for example, \u003ccode\u003escript.py\u003c/code\u003e) using the container without starting an interactive shell:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv torchenv.sif python3 script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe container can also be launched and used on a system without a GPU, but upon startup it will display a warning about missing NVIDIA binaries on the host.\u003c/p\u003e\n", "stargazers_count": 9, "subscribers_count": 2, - "topics": [ - "singularity", - "pytorch", - "deep-learning", - "machine-learning", - "environment" - ], - "updated_at": 1708505365.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "Singularity.fitseq-latest" - ], - "full_name": "FangfeiLi05/PyFitSeq", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/801caa0fd19bfe93abf83222faddccda5d43d03eb2d0039402dfca476aa25f43/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e372d677265656e2e737667\" alt=\"Python 3.7\" data-canonical-src=\"https://img.shields.io/badge/python-3.7-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b59ba4ef5c86bc79e9e3e9039f1b96a4db59e7f96a7ba04abfc6548ae88c461b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b5504ad42509979057a4c27764d6acc447b6db887f4528b7c6cf18e9631ef51e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f456d61696c2d66616e676665696c693035323540676d61696c2e636f6d2d6f72616e67652e737667\" alt=\"Contact Info\" data-canonical-src=\"https://img.shields.io/badge/Email-fangfeili0525@gmail.com-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePyFitSeq\u003c/h2\u003e\u003ca id=\"user-content-pyfitseq\" class=\"anchor\" aria-label=\"Permalink: PyFitSeq\" href=\"#pyfitseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. What is PyFitSeq?\u003c/h3\u003e\u003ca id=\"user-content-1-what-is-pyfitseq\" class=\"anchor\" aria-label=\"Permalink: 1. What is PyFitSeq?\" href=\"#1-what-is-pyfitseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePyFitSeq is a Python-based fitness estimation tool for pooled amplicon sequencing studies. PyFitSeq is Python re-coded version of the MATLAB tool FitSeq \u003ca href=\"https://github.com/sashaflevy/Fit-Seq\"\u003ehttps://github.com/sashaflevy/Fit-Seq\u003c/a\u003e. If you use this software, please reference: \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S2405471218303909?via%3Dihub\" rel=\"nofollow\"\u003eF. Li, et al. Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies. Cell Systems, 7: 521-525 (2018)\u003c/a\u003e. PyFitSeq is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\u003c/p\u003e\n\u003cp\u003eIt currently has two main functions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eevo_simulator.py\u003c/code\u003e performs simulations of competitve pooled growth of a population of genotypes.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epyfitseq.py\u003c/code\u003e calculates the fitness of each genotype from read-count time-series data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA walk-through is included as the jupyter notebook \u003ca href=\"https://github.com/FangfeiLi05/PyFitSeq/blob/master/PyFitSeq_Walk_Through.ipynb\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. How to install PyFitSeq?\u003c/h3\u003e\u003ca id=\"user-content-2-how-to-install-pyfitseq\" class=\"anchor\" aria-label=\"Permalink: 2. How to install PyFitSeq?\" href=\"#2-how-to-install-pyfitseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3 is required. This version has been tested on a MacBook Pro (3.1 GHz Intel Core i5), with Python 3.7.4.\u003c/li\u003e\n\u003cli\u003eClone this repository by running \u003ccode\u003egit clone https://github.com/FangfeiLi05/PyFitSeq.git\u003c/code\u003e in terminal.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecd\u003c/code\u003e to the root directory of the project (the folder containing \u003ccode\u003eREADME.md\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eInstall dependencies by running \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e in terminal.\u003c/li\u003e\n\u003cli\u003eInstall pyfitseq by running \u003ccode\u003epip install .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m pip install git+https://github.com/darachm/PyFitSeq.git\u003c/code\u003e to install\nwithout cloning the repository.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e2a Alternative, use in a Singularity container\u003c/h4\u003e\u003ca id=\"user-content-2a-alternative-use-in-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: 2a Alternative, use in a Singularity container\" href=\"#2a-alternative-use-in-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith the closing of Singularity Hub there aren\u0027t yet publicly available\ncontainers for this, but you can build your own with a command like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build fitseq-latest.simg Singularity.fitseq-latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can run on any\n\u003ca href=\"https://sylabs.io/guides/3.8/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003ecomputer running Singularity\u003c/a\u003e,\nsuch as your local HPC, using a command like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec fitseq-latest.simg pyfitseq.py -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. How to use PyFitSeq?\u003c/h3\u003e\u003ca id=\"user-content-3-how-to-use-pyfitseq\" class=\"anchor\" aria-label=\"Permalink: 3. How to use PyFitSeq?\" href=\"#3-how-to-use-pyfitseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.1. Evolution Simulation\u003c/h4\u003e\u003ca id=\"user-content-31-evolution-simulation\" class=\"anchor\" aria-label=\"Permalink: 3.1. Evolution Simulation\" href=\"#31-evolution-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eevo_simulator.py\u003c/code\u003e models competative pooled growth of a population of genotypes with different fitnesses. This simulation can be made to include sources of noise, including growth noise, noise from cell transfers, DNA extraction, PCR, and sequencing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOPTIONS\u003c/h5\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: OPTIONS\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input\u003c/code\u003e or \u003ccode\u003e-i\u003c/code\u003e: a .csv file, with\n\u003cul\u003e\n\u003cli\u003e1st column of .csv: fitness of each genotype, [x1, x2, ...]\u003c/li\u003e\n\u003cli\u003e2nd column .csv: initial cell number of each genotype at generation 0, [n1, n2, ...]\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--t_seq\u003c/code\u003e or \u003ccode\u003e-t\u003c/code\u003e: time-points evaluated in number of generations (\u003ccode\u003eformat: 0 t1 t2 ...\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--read_num_average_seq\u003c/code\u003e or \u003ccode\u003e-r\u003c/code\u003e: average number of reads per genotype for each time-point (\u003ccode\u003eformat: 0 r1 r2 ...\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--noise_option\u003c/code\u003e or \u003ccode\u003e-n\u003c/code\u003e: which types of noise to include in the simulation, default is all sources of noise (\u003ccode\u003edefault: growth bottleneck_transfer DNA_extraction PCR sequencing\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--dna_copies\u003c/code\u003e or \u003ccode\u003e-d\u003c/code\u003e: average genome copy number per genotype used as template in PCR (\u003ccode\u003edefault: 500\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pcr_cycles\u003c/code\u003e or \u003ccode\u003e-p\u003c/code\u003e: number of cycles of PCR (\u003ccode\u003edefault: 25\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--fitness_type\u003c/code\u003e or \u003ccode\u003e-f\u003c/code\u003e: type of fitness: Wrightian fitness (w), or Malthusian fitness (m)\u0027 (\u003ccode\u003edefault: m\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_filename\u003c/code\u003e or \u003ccode\u003e-o\u003c/code\u003e: prefix of output .csv files (\u003ccode\u003edefault: output\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOUTPUTS\u003c/h5\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: OUTPUTS\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_filename_EvoSimulation_Read_Number.csv\u003c/code\u003e: read number per genotype for each time-point\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_filename_EvoSimulation_Mean_Fitness.csv\u003c/code\u003e: mean fitness for each time-point\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_filename_EvoSimulation_Input_Log.csv\u003c/code\u003e: a record of all inputs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eFor Help\u003c/h5\u003e\u003ca id=\"user-content-for-help\" class=\"anchor\" aria-label=\"Permalink: For Help\" href=\"#for-help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython evo_simulator.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExamples\u003c/h5\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython evo_simulator.py -i input_EvoSimulation.csv -t 0 3 6 9 12 -r 50 50 50 50 50 -o output\npython evo_simulator.py -i input_EvoSimulation.csv -t 0 2 4 6 8 -r 75 75 75 75 50 -n DNA_extraction PCR sequencing -d 300 -p 27 -f w -o output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.2. Fitness Estimation\u003c/h4\u003e\u003ca id=\"user-content-32-fitness-estimation\" class=\"anchor\" aria-label=\"Permalink: 3.2. Fitness Estimation\" href=\"#32-fitness-estimation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003epyfitseq.py\u003c/code\u003e estimates the fitness of each genotype from read-count time-series data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOPTIONS\u003c/h5\u003e\u003ca id=\"user-content-options-1\" class=\"anchor\" aria-label=\"Permalink: OPTIONS\" href=\"#options-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input\u003c/code\u003e or \u003ccode\u003e-i\u003c/code\u003e: a .csv file, with each column being the read number per genotype at each sequenced time-point\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--t_seq\u003c/code\u003e or \u003ccode\u003e-t\u003c/code\u003e: sequenced time-points in number of generations (\u003ccode\u003eformat: 0 t1 t2 ...\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--max_iter_num\u003c/code\u003e or \u003ccode\u003e-m\u003c/code\u003e: maximum number of iterations in the optimization (Small numbers can reduce running time and decrease accuracy.) (\u003ccode\u003edefault: 10\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--kappa\u003c/code\u003e or \u003ccode\u003e-k\u003c/code\u003e: a noise parameter that characterizes the total noise introduced by growth, cell transfer, DNA extraction, PCR, and sequencing (To measure kappa empirically, see the reference: [S. F. Levy, et al. Quantitative Evolutionary Dynamics Using High-resolution Lineage Tracking. Nature, 519: 181\u2013186 (2015)].) (\u003ccode\u003edefault: 2.5\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--regression_num\u003c/code\u003e or \u003ccode\u003e-g\u003c/code\u003e: number of points used in the initial linear-regression-based fitness estimate (\u003ccode\u003edefault: 2\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--fitness_type\u003c/code\u003e or \u003ccode\u003e-f\u003c/code\u003e: type of fitness: Wrightian fitness (w), or Malthusian fitness (m) (\u003ccode\u003edefault: m\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_filename\u003c/code\u003e or \u003ccode\u003e-o\u003c/code\u003e: prefix of output .csv files (\u003ccode\u003edefault: output\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOUTPUTS\u003c/h5\u003e\u003ca id=\"user-content-outputs-1\" class=\"anchor\" aria-label=\"Permalink: OUTPUTS\" href=\"#outputs-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_filename_FitSeq_Result.csv\u003c/code\u003e: a .csv file, with\n\u003cul\u003e\n\u003cli\u003e1st column of .csv: estimated fitness of each genotype, [x1, x2, ...]\u003c/li\u003e\n\u003cli\u003e2nd column of .csv: log likelihood value of each genotype, [f1, f2, ...]\u003c/li\u003e\n\u003cli\u003e3rd column of .csv: estimated mean fitness per sequenced time-point, [x_mean(0), x_mean(t1), ...]\u003c/li\u003e\n\u003cli\u003e4th+ columns of .csv: estimated read number per genotype per time-point, with each time-point being a column\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eFor Help\u003c/h5\u003e\u003ca id=\"user-content-for-help-1\" class=\"anchor\" aria-label=\"Permalink: For Help\" href=\"#for-help-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython pyfitseq.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExamples\u003c/h5\u003e\u003ca id=\"user-content-examples-1\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython pyfitseq.py -i output_EvoSimulation_Read_Number.csv -t 0 3 6 9 12 -o output\npython pyfitseq.py -i output_EvoSimulation_Read_Number2.csv -t 0 2 6 8 -m 12 -k 2 -g 3 -f w -o output\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 9, - "subscribers_count": 4, "topics": [], - "updated_at": 1713767883.0 - }, - { - "data_format": 2, - "description": "Useful scripts and tools related to alevin-fry", - "filenames": [ - "docker/Singularity.def" - ], - "full_name": "COMBINE-lab/usefulaf", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-usefulaf-an-all-in-one-dockersingularity-image-for-single-cell-processing-with-alevin-fry\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usefulaf-an-all-in-one-dockersingularity-image-for-single-cell-processing-with-alevin-fry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsefulaf: An all-in-one Docker/Singularity image for single-cell processing with alevin-fry\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/combinelab/usefulaf/tags\" rel=\"nofollow\"\u003e\u003ccode\u003eUsefulaf\u003c/code\u003e\u003c/a\u003e is an all-in-one Docker/Singularity image for single-cell processing with \u003ca href=\"https://github.com/COMBINE-lab/alevin-fry\"\u003eAlevin-fry\u003c/a\u003e(\u003ca href=\"https://www.nature.com/articles/s41592-022-01408-3\" rel=\"nofollow\"\u003epaper\u003c/a\u003e). It includes the all tools you need to turn your FASTQ files into a count matrix and then load it into your favorite analysis environment. Specifically, this image includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/COMBINE-lab/simpleaf\"\u003e\u003ccode\u003esimpleaf\u003c/code\u003e\u003c/a\u003e: A simplified interface to indexing and quantifying with \u003ccode\u003ealevin-fry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/COMBINE-lab/pyroe\"\u003e\u003ccode\u003epyroe\u003c/code\u003e\u003c/a\u003e: An alevin-fry utility python package for building splici references, converting alevin-fry output formats, loading count matrix in Python, adding gene names (instead of just gene IDs) to output matrices, etc.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://rdrr.io/github/mikelove/fishpond/man/loadFry.html\" rel=\"nofollow\"\u003e\u003ccode\u003efishpond::loadFry()\u003c/code\u003e\u003c/a\u003e: A R function for loading count matrix as \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html\" rel=\"nofollow\"\u003eSingleCellExperiment\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor processing data simply using the \u003ccode\u003eusefulaf\u003c/code\u003e image, check our latest tutorial \u003ca href=\"https://combine-lab.github.io/alevin-fry-tutorials/2021/quickstart-usefulaf-singularity/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor pulling the Singularity image, please run the following code in bash. Note that the image is $\\sim$ 1.65 GB.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e if you use Docker\u003c/span\u003e\n$ docker pull combinelab/usefulaf:latest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e if you use Singularity\u003c/span\u003e\n$ singularity pull docker://combinelab/usefulaf:latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usefulaf-history\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usefulaf-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsefulaf history\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/COMBINE-lab/alevin-fry\"\u003eAlevin-fry\u003c/a\u003e is a fast, accurate, and memory-frugal tool for preprocessing single-cell and single-nucleus RNA-seq data. You can read more about alevin-fry in alevin-fry \u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.06.29.450377v2\" rel=\"nofollow\"\u003epre-print\u003c/a\u003e, and \u003ca href=\"https://www.nature.com/articles/s41592-022-01408-3\" rel=\"nofollow\"\u003epaper\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis repository was created initially with scripts, functions, and utilities that are useful for preparing data for processing with alevin-fry, as well as for reading alevin-fry data into other packages for downstream analysis. It also accompanies a Docker/Singularity container containing all of this relevant software in one place. However, as \u003ccode\u003ealevin-fry\u003c/code\u003e has continued to grow, all of that relevant functionality found its way into other, more stable and permanent homes (e.g. \u003ca href=\"https://github.com/COMBINE-lab/pyroe\"\u003e\u003ccode\u003epyroe\u003c/code\u003e\u003c/a\u003e for splici reference construction and loading data in Python, \u003ca href=\"https://github.com/COMBINE-lab/roe\"\u003e\u003ccode\u003eroe\u003c/code\u003e\u003c/a\u003e for splici reference construction in R and \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/fishpond.html\" rel=\"nofollow\"\u003e\u003ccode\u003efishpond\u003c/code\u003e\u003c/a\u003e for loading data in \u003ccode\u003eR\u003c/code\u003e). Finally, this repository also contained a bash script called \u003ccode\u003esimpleaf\u003c/code\u003e to simplify common workflows with \u003ccode\u003ealevin-fry\u003c/code\u003e. That, too, has evolved into its own (much more feature-rich and comprehensive) tool, living in its own repository (\u003ca href=\"https://github.com/COMBINE-lab/simpleaf\"\u003e\u003ccode\u003esimpleaf\u003c/code\u003e\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eAs such, all the scripts and functions in this repository have been retired. However, as usefulaf is still the only place that provides all these functionalities, we decided to turn [\u003ccode\u003eusefulaf\u003c/code\u003e] as an all-in-one \u003ca href=\"https://hub.docker.com/r/combinelab/usefulaf/tags\" rel=\"nofollow\"\u003eDocker/Singularity image\u003c/a\u003e that makes use of all those new tools listed above. That has replaced the older \u003ccode\u003eusefulaf\u003c/code\u003e image that made use of the varied assortment of scripts and tools hosted in this repository.\u003c/p\u003e\n", - "stargazers_count": 9, - "subscribers_count": 6, - "topics": [], - "updated_at": 1658179118.0 + "updated_at": 1678124445.0 }, { "data_format": 2, - "description": "Batch Connect - OSC RStudio Server", + "description": "Antonino Furnari\u0027s fork of Feichtenhofer\u0027s gpu_flow, with temporal dilation.", "filenames": [ "Singularity" ], - "full_name": "OSC/bc_osc_rstudio_server", - "latest_release": "v0.26.0", - "readme": "\u003ch1 id=\"user-content-batch-connect---osc-rstudio-server\"\u003e\u003ca class=\"heading-link\" href=\"#batch-connect---osc-rstudio-server\"\u003eBatch Connect - OSC RStudio Server\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_rstudio_server.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an RStudio Server\nwithin an Owens batch job.\u003c/p\u003e\n\u003ch2 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule restore\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job before\nlaunching the RStudio Server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ewithout Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e 3.3.2+ (earlier versions are untested but may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rstudio.com/products/rstudio-server/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e 1.0.136+ (earlier versions are untested by may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://proot-me.github.io/\" rel=\"nofollow\"\u003ePRoot\u003c/a\u003e 5.1.0+ (used to setup fake bind mount)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eor with Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e 2.4.2+\u003c/li\u003e\n\u003cli\u003eA Singularity image similar to \u003ca href=\"https://www.singularity-hub.org/collections/463\" rel=\"nofollow\"\u003enickjer/singularity-rstudio\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorresponding module to launch the above Singularity image (see\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio/blob/master/example_module/\"\u003eexample_module\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-install\"\u003e\u003ca class=\"heading-link\" href=\"#install\"\u003eInstall\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2 id=\"user-content-contributing\"\u003e\u003ca class=\"heading-link\" href=\"#contributing\"\u003eContributing\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server/fork\"\u003ehttps://github.com/OSC/bc_osc_rstudio_server/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocumentation, website content, and logo is licensed under\n\u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC-BY-4.0\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCode is licensed under MIT (see LICENSE.txt)o\u003c/li\u003e\n\u003cli\u003eRStudio, Shiny and the RStudio logo are all registered trademarks of RStudio.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-rserver-command-line-arguements\"\u003e\u003ca class=\"heading-link\" href=\"#rserver-command-line-arguements\"\u003eRServer command line arguements\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis was the output of \u003ccode\u003e--help\u003c/code\u003e from version \u003ccode\u003e2021.09.1\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecommand-line options:\n\nverify:\n --verify-installation arg (=0) Runs verification mode to verify the \n current installation.\n\nserver:\n --server-working-dir arg (=/) The default working directory of the \n rserver process.\n --server-user arg (=rstudio-server) The user account of the rserver \n process.\n --server-daemonize arg (=0) Indicates whether or not the rserver \n process should run as a daemon.\n --server-pid-file arg (=/var/run/rstudio-server.pid)\n The path to a file where the rserver \n daemon\u0027s pid is written.\n --server-app-armor-enabled arg (=0) Indicates whether or not to enable \n AppArmor profiles for the rserver \n process.\n --server-set-umask arg (=1) If enabled, sets the rserver process \n umask to 022 on startup, which causes \n new files to have rw-r-r permissions.\n --secure-cookie-key-file arg If set, overrides the default path of \n the secure-cookie-key file used for \n encrypting cookies.\n --server-data-dir arg (=/var/run/rstudio-server)\n Path to the data directory where \n RStudio Server will write run-time \n state.\n --server-add-header arg Adds a header to all responses from \n RStudio Server. This option can be \n specified multiple times to add \n multiple headers.\n\nwww:\n --www-address arg (=0.0.0.0) The network address that RStudio Server\n will listen on for incoming \n connections.\n --www-port arg The port that RStudio Server will bind \n to while listening for incoming \n connections. If left empty, the port \n will be automatically determined based \n on your SSL settings (443 for SSL, 80 \n for no SSL).\n --www-root-path arg (=/) The path prefix added by a proxy to the\n incoming RStudio URL. This setting is \n used so RStudio Server knows what path \n it is being served from. If running \n RStudio Server behind a path-modifying \n proxy, this should be changed to match \n the base RStudio Server URL.\n --www-local-path arg (=www) The relative path from the RStudio \n installation directory, or absolute \n path where web assets are stored.\n --www-symbol-maps-path arg (=www-symbolmaps)\n The relative path from the RStudio \n installation directory, or absolute \n path, where symbol maps are stored.\n --www-use-emulated-stack arg (=0) Indicates whether or not to use GWT\u0027s \n emulated stack.\n --www-thread-pool-size arg (=2) The size of the threadpool from which \n requests will be serviced. This may be \n increased to enable more concurrency, \n but should only be done if the \n underlying hardware has more than 2 \n cores. It is recommended to use a value\n that is \u0026lt;= to the number of hardware \n cores, or \u0026lt;= to two times the number of\n hardware cores if the hardware utilizes\n hyperthreading.\n --www-proxy-localhost arg (=1) Indicates whether or not to proxy \n requests to localhost ports over the \n main server port. This should generally\n be enabled, and is used to proxy HTTP \n traffic within a session that belongs \n to code running within the session \n (e.g. Shiny or Plumber APIs)\n --www-verify-user-agent arg (=1) Indicates whether or not to verify \n connecting browser user agents to \n ensure they are compatible with RStudio\n Server.\n --www-same-site arg The value of the \u0027SameSite\u0027 attribute \n on the cookies issued by RStudio \n Server. Accepted values are \u0027none\u0027 or \n \u0027lax\u0027. The value \u0027none\u0027 should be used \n only when RStudio is hosted into an \n iFrame. For compatibility with some \n browsers (i.e. Safari 12), duplicate \n cookies will be issued by RStudio \n Server when \u0027none\u0027 is used.\n --www-frame-origin arg (=none) Specifies the allowed origin for the \n iFrame hosting RStudio if iFrame \n embedding is enabled.\n --www-enable-origin-check arg (=0) If enabled, cause RStudio to enforce \n that incoming request origins are from \n the host domain. This can be added for \n additional security. See \n https://cheatsheetseries.owasp.org/chea\n tsheets/Cross-Site_Request_Forgery_Prev\n ention_Cheat_Sheet.html#verifying-origi\n n-with-standard-headers\n --www-allow-origin arg Specifies an additional origin that \n requests are allowed from, even if it \n does not match the host domain. Used if\n origin checking is enabled. May be \n specified multiple times for multiple \n origins.\n\nrsession:\n --rsession-which-r arg The path to the main R program (e.g. \n /usr/bin/R). This should be set if no \n versions are specified in \n /etc/rstudio/r-versions and the default\n R installation is not available on the \n system path.\n --rsession-path arg (=rsession) The relative path from the RStudio \n installation directory, or absolute \n path to the rsession executable.\n --rldpath-path arg (=r-ldpath) The path to the r-ldpath script which \n specifies extra library paths for R \n versions.\n --rsession-ld-library-path arg Specifies additional LD_LIBRARY_PATHs \n to use for R sessions.\n --rsession-config-file arg If set, overrides the path to the \n /etc/rstudio/rsession.conf \n configuration file. The specified path \n may be a relative path from the RStudio\n installation directory, or an absolute \n path.\n --rsession-proxy-max-wait-secs arg (=10)\n The maximum time to wait in seconds for\n a successful response when proxying \n requests to rsession.\n --rsession-memory-limit-mb arg (=0) The limit in MB that an rsession \n process may consume.\n --rsession-stack-limit-mb arg (=0) The limit in MB that an rsession \n process may consume for its stack.\n --rsession-process-limit arg (=0) The maximum number of allowable \n rsession processes.\n\ndatabase:\n --database-config-file arg If set, overrides the path to the \n /etc/rstudio/database.conf \n configuration file.\n --db-command arg Executes the shell command specified \n injecting the current database \n configuration in the command.\n\nauth:\n --auth-none arg (=1) If set, disables multi-user \n authentication. Workbench/Pro features \n may not work in this mode.\n --auth-validate-users arg (=0) Indicates whether or not to validate \n that authenticated users exist on the \n target system. Disabling this option \n may cause issues to start or to run a \n session.\n --auth-stay-signed-in-days arg (=30) The number of days to keep a user \n signed in when using the \"Stay Signed \n In\" option. Will only take affect when \n auth-timeout-minutes is 0 (disabled).\n --auth-timeout-minutes arg (=60) The number of minutes a user will stay \n logged in while idle before required to\n sign in again. Set this to 0 (disabled)\n to enable legacy timeout \n auth-stay-signed-in-days.\n --auth-encrypt-password arg (=1) Indicates whether or not to encrypt the\n password sent from the login form. For \n security purposes, we strongly \n recommend you leave this enabled.\n --auth-login-page-html arg (=/etc/rstudio/login.html)\n The path to a file containing \n additional HTML customization for the \n login page.\n --auth-rdp-login-page-html arg (=/etc/rstudio/rdplogin.html)\n The path to a file containing \n additional HTML customization for the \n login page, as seen by RDP users.\n --auth-required-user-group arg Specifies a group that users must be in\n to be able to use RStudio.\n --auth-minimum-user-id arg (=auto) Specifies a minimum user id value. \n Users with a uid lower than this value \n may not use RStudio.\n --auth-pam-helper-path arg (=rserver-pam)\n The relative path from the RStudio \n installation directory, or absolute \n path where the PAM helper binary \n resides.\n --auth-pam-require-password-prompt arg (=1)\n Indicates whether or not to require the\n \"Password: \" prompt before sending the \n password via PAM. In most cases, this \n should be enabled. If using a custom \n PAM password prompt, you may need to \n disable this setting if PAM logins do \n not work correctly.\n --auth-pam-requires-priv arg (=1) Deprecated - will always be true.\n --auth-sign-in-throttle-seconds arg (=5)\n The minimum amount of time a user must \n wait before attempting to sign in again\n after signing out.\n --auth-revocation-list-dir arg If set, overrides the path to the \n directory which contains the revocation\n list to be used for storing expired \n tokens. As of RStudio Server 1.4, this \n has been moved to database storage, and\n so this setting is deprecated, but will\n be used to port over any existing \n file-based expired tokens.\n --auth-cookies-force-secure arg (=0) Indicates whether or not auth cookies \n should be forcefully marked as secure. \n This should be enabled if running an \n SSL terminator infront of RStudio \n Server. Otherwise, cookies will be \n marked secure if SSL is configured.\n\nmonitor:\n --monitor-interval-seconds arg (=60) The interval in seconds at which the \n monitor is probed for new data.\n\ngeneral:\n --help print help message\n --test-config test to ensure the config file is valid\n --config-file arg configuration file\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "dl-container-registry/furnari-flow", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-gpu-based-optical-flow-extraction-from-videos\" class=\"anchor\" aria-hidden=\"true\" href=\"#gpu-based-optical-flow-extraction-from-videos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGPU based optical flow extraction from videos\u003c/h1\u003e\n\u003cp\u003eForked from \u003ca href=\"https://github.com/feichtenhofer/gpu_flow\"\u003ehttps://github.com/feichtenhofer/gpu_flow\u003c/a\u003e by Antonino Furnari\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/dl-container-registry/furnari-flow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22af16742ba115e53d8c72ecae46310b24dacb32e78ec3f7172c231c7cbc7c73/68747470733a2f2f7472617669732d63692e6f72672f646c2d636f6e7461696e65722d72656769737472792f6675726e6172692d666c6f772e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/dl-container-registry/furnari-flow.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/willprice/furnari-flow/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/99bb6090faef97032d3bfd80b4d0cdb9d984e9e97aeb1d2750bc3e442fb117f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65726875622d3232623865622e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/hosted-dockerhub-22b8eb.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/575\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-hidden=\"true\" href=\"#news\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNews\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2020-01-09\" class=\"anchor\" aria-hidden=\"true\" href=\"#2020-01-09\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2020-01-09\u003c/h3\u003e\n\u003cp\u003eThe semantics of the dilation parameter have changed to allow finer grained configuration. Previously optical flow was\ncomputed between frames I_{st} and I_{s(t+d)} where s is the stride and d the dilation. The code now computes flow\nbetween I_{st} and I_{st+d}--this makes the stride and dilation parameters completely independent which is more intuitive.\nIf you wish to continue using the old code then use the docker image tagged with \u003ccode\u003ev1\u003c/code\u003e. All subsequent images and the\n\u003ccode\u003elatest\u003c/code\u003e tag will adopt the new behaviour described above.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eWe support running via docker and singularity.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eEnsure you\u0027re running\n\u003ca href=\"https://github.com/NVIDIA/nvidia-docker\"\u003e\u003ccode\u003envidia-docker\u003c/code\u003e\u003c/a\u003e as this software is\nGPU accelerated. If using docker 19.03 or above then you can use the native docker nvidia GPU support.\u003c/li\u003e\n\u003cli\u003ePull the docker image:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003edocker pull willprice/furnari-flow\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eDump out frames from the video you wish to compute flow for:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003emkdir my_video\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e ffmpeg -i my_video.mp4 -qscale 3 my_video/img_%06d.jpg\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eCompute the flow using \u003ccode\u003efurnari-flow\u003c/code\u003e:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003emkdir my_video_flow\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003edocker run \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --runtime=nvidia \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --rm \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --mount \"type=bind,source=$PWD/my_video,target=/input\" \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --mount \"type=bind,source=$PWD/my_video_flow,target=/output\" \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --mount \"type=bind,source=$HOME/.nv,target=/cache/nv\" \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e willprice/furnari-flow \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e img_%06d.jpg\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003els my_video_flow\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eu v\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003els my_video_flow/u\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimg_0000001.jpg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimg_0000002.jpg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-details\" class=\"anchor\" aria-hidden=\"true\" href=\"#details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetails\u003c/h3\u003e\n\u003cp\u003eThe software assumes that all video frames have been extracted in a directory. Files should be named according to some pattern, e.g., \u003ccode\u003eimg_%07d.jpg\u003c/code\u003e. The software will put flow files in the same directory using a provided filename pattern, e.g., \u003ccode\u003eflow_%s_%07d.jpg\u003c/code\u003e, where the %s will be subsituted with \"x\" for the x flows and \"y\" for the y flows. For example, if DIR is a directory containing 4 images:\u003c/p\u003e\n\u003cp\u003eDIR:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000001.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000002.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000003.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000004.jpg\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ethe command \u003ccode\u003ecompute_flow DIR img_%07d.jpg flow_%s_%07d.jpg\u003c/code\u003e will read the images in order and compute optical flows. The content of DIR will be as follows after the execution of the command:\u003c/p\u003e\n\u003cp\u003eDIR:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000001.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000002.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000003.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000004.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_x_0000001.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_x_0000002.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_x_0000003.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_x_0000004.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_y_0000001.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_y_0000002.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_y_0000003.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_y_0000004.jpg\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewhere \u003ccode\u003eflow_x_{n}.jpg\u003c/code\u003e is the x flow computed between \u003ccode\u003eimg_{n}.jpg\u003c/code\u003e and \u003ccode\u003eimg_{n+1}.jpg\u003c/code\u003e (if no dilation is used - see help).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou only need to build this software if you intend on tweaking the source, otherwise you\nshould just use the pre-built docker images.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://opencv.org/downloads.html\" rel=\"nofollow\"\u003eOpenCV 2.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cmake.org/\" rel=\"nofollow\"\u003ecmake\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eFirst, build opencv with gpu support. To do so, download opencv 2.4.x sources\nfrom \u003ca href=\"https://opencv.org/releases.html\" rel=\"nofollow\"\u003ehttps://opencv.org/releases.html\u003c/a\u003e. Unzip the downloaded archive, then enter\nthe opencv folder and issue the following commands:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emkdir build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecd build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecmake -DCUDA_USE_STATIC_CUDA_RUNTIME=OFF ..\u003c/code\u003e (inspect the \u003ca href=\"./Dockerfile\"\u003e\u003ccode\u003eDockerfile\u003c/code\u003e\u003c/a\u003e for further flags that might\nbe of use)\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emake -j $(nproc)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen clone the current repository and enter the \u003ccode\u003ecompute_flow_video\u003c/code\u003e folder. Type:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eexport OpenCV_DIR=path_to_opencv_build_directory\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emkdir build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecd build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecmake -DCUDA_USE_STATIC_CUDA_RUNTIME=OFF ..\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emake -j $(nproc)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 9, - "subscribers_count": 10, + "subscribers_count": 3, "topics": [], - "updated_at": 1693363021.0 + "updated_at": 1666128191.0 }, { "data_format": 2, @@ -37005,175 +36985,171 @@ var data = }, { "data_format": 2, - "description": "Antonino Furnari\u0027s fork of Feichtenhofer\u0027s gpu_flow, with temporal dilation.", + "description": "Batch Connect - OSC RStudio Server", "filenames": [ "Singularity" ], - "full_name": "dl-container-registry/furnari-flow", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-gpu-based-optical-flow-extraction-from-videos\" class=\"anchor\" aria-hidden=\"true\" href=\"#gpu-based-optical-flow-extraction-from-videos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGPU based optical flow extraction from videos\u003c/h1\u003e\n\u003cp\u003eForked from \u003ca href=\"https://github.com/feichtenhofer/gpu_flow\"\u003ehttps://github.com/feichtenhofer/gpu_flow\u003c/a\u003e by Antonino Furnari\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/dl-container-registry/furnari-flow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22af16742ba115e53d8c72ecae46310b24dacb32e78ec3f7172c231c7cbc7c73/68747470733a2f2f7472617669732d63692e6f72672f646c2d636f6e7461696e65722d72656769737472792f6675726e6172692d666c6f772e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/dl-container-registry/furnari-flow.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/willprice/furnari-flow/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/99bb6090faef97032d3bfd80b4d0cdb9d984e9e97aeb1d2750bc3e442fb117f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65726875622d3232623865622e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/hosted-dockerhub-22b8eb.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/575\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity Hub\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-hidden=\"true\" href=\"#news\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNews\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-2020-01-09\" class=\"anchor\" aria-hidden=\"true\" href=\"#2020-01-09\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2020-01-09\u003c/h3\u003e\n\u003cp\u003eThe semantics of the dilation parameter have changed to allow finer grained configuration. Previously optical flow was\ncomputed between frames I_{st} and I_{s(t+d)} where s is the stride and d the dilation. The code now computes flow\nbetween I_{st} and I_{st+d}--this makes the stride and dilation parameters completely independent which is more intuitive.\nIf you wish to continue using the old code then use the docker image tagged with \u003ccode\u003ev1\u003c/code\u003e. All subsequent images and the\n\u003ccode\u003elatest\u003c/code\u003e tag will adopt the new behaviour described above.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eWe support running via docker and singularity.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eEnsure you\u0027re running\n\u003ca href=\"https://github.com/NVIDIA/nvidia-docker\"\u003e\u003ccode\u003envidia-docker\u003c/code\u003e\u003c/a\u003e as this software is\nGPU accelerated. If using docker 19.03 or above then you can use the native docker nvidia GPU support.\u003c/li\u003e\n\u003cli\u003ePull the docker image:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003edocker pull willprice/furnari-flow\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eDump out frames from the video you wish to compute flow for:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003emkdir my_video\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e ffmpeg -i my_video.mp4 -qscale 3 my_video/img_%06d.jpg\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003eCompute the flow using \u003ccode\u003efurnari-flow\u003c/code\u003e:\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003emkdir my_video_flow\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003edocker run \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --runtime=nvidia \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --rm \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --mount \"type=bind,source=$PWD/my_video,target=/input\" \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --mount \"type=bind,source=$PWD/my_video_flow,target=/output\" \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --mount \"type=bind,source=$HOME/.nv,target=/cache/nv\" \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e willprice/furnari-flow \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e img_%06d.jpg\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003els my_video_flow\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eu v\u003c/span\u003e\n$ \u003cspan class=\"pl-s1\"\u003els my_video_flow/u\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimg_0000001.jpg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eimg_0000002.jpg\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-details\" class=\"anchor\" aria-hidden=\"true\" href=\"#details\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDetails\u003c/h3\u003e\n\u003cp\u003eThe software assumes that all video frames have been extracted in a directory. Files should be named according to some pattern, e.g., \u003ccode\u003eimg_%07d.jpg\u003c/code\u003e. The software will put flow files in the same directory using a provided filename pattern, e.g., \u003ccode\u003eflow_%s_%07d.jpg\u003c/code\u003e, where the %s will be subsituted with \"x\" for the x flows and \"y\" for the y flows. For example, if DIR is a directory containing 4 images:\u003c/p\u003e\n\u003cp\u003eDIR:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000001.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000002.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000003.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000004.jpg\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ethe command \u003ccode\u003ecompute_flow DIR img_%07d.jpg flow_%s_%07d.jpg\u003c/code\u003e will read the images in order and compute optical flows. The content of DIR will be as follows after the execution of the command:\u003c/p\u003e\n\u003cp\u003eDIR:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000001.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000002.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000003.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eimg_0000004.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_x_0000001.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_x_0000002.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_x_0000003.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_x_0000004.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_y_0000001.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_y_0000002.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_y_0000003.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eflow_y_0000004.jpg\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewhere \u003ccode\u003eflow_x_{n}.jpg\u003c/code\u003e is the x flow computed between \u003ccode\u003eimg_{n}.jpg\u003c/code\u003e and \u003ccode\u003eimg_{n+1}.jpg\u003c/code\u003e (if no dilation is used - see help).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-hidden=\"true\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuild\u003c/h2\u003e\n\u003cp\u003eYou only need to build this software if you intend on tweaking the source, otherwise you\nshould just use the pre-built docker images.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://opencv.org/downloads.html\" rel=\"nofollow\"\u003eOpenCV 2.4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cmake.org/\" rel=\"nofollow\"\u003ecmake\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h3\u003e\n\u003cp\u003eFirst, build opencv with gpu support. To do so, download opencv 2.4.x sources\nfrom \u003ca href=\"https://opencv.org/releases.html\" rel=\"nofollow\"\u003ehttps://opencv.org/releases.html\u003c/a\u003e. Unzip the downloaded archive, then enter\nthe opencv folder and issue the following commands:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003emkdir build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecd build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecmake -DCUDA_USE_STATIC_CUDA_RUNTIME=OFF ..\u003c/code\u003e (inspect the \u003ca href=\"./Dockerfile\"\u003e\u003ccode\u003eDockerfile\u003c/code\u003e\u003c/a\u003e for further flags that might\nbe of use)\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emake -j $(nproc)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen clone the current repository and enter the \u003ccode\u003ecompute_flow_video\u003c/code\u003e folder. Type:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eexport OpenCV_DIR=path_to_opencv_build_directory\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emkdir build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecd build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecmake -DCUDA_USE_STATIC_CUDA_RUNTIME=OFF ..\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emake -j $(nproc)\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "OSC/bc_osc_rstudio_server", + "latest_release": "v0.26.0", + "readme": "\u003ch1 id=\"user-content-batch-connect---osc-rstudio-server\"\u003e\u003ca class=\"heading-link\" href=\"#batch-connect---osc-rstudio-server\"\u003eBatch Connect - OSC RStudio Server\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1b9af067a60a648ea0b5068b19ea4a14b09e232574dac90c50903719ef5cc5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6f73632f62635f6f73635f7273747564696f5f7365727665722e737667\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/release/osc/bc_osc_rstudio_server.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45b4ffbd594af47fe09a3432f9f8e122c6518aa6352b4ce453a1a2563da2905c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAn interactive app designed for OSC OnDemand that launches an RStudio Server\nwithin an Owens batch job.\u003c/p\u003e\n\u003ch2 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis Batch Connect app requires the following software be installed on the\n\u003cstrong\u003ecompute nodes\u003c/strong\u003e that the batch job is intended to run on (\u003cstrong\u003eNOT\u003c/strong\u003e the\nOnDemand node):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.tacc.utexas.edu/research-development/tacc-projects/lmod\" rel=\"nofollow\"\u003eLmod\u003c/a\u003e 6.0.1+ or any other \u003ccode\u003emodule restore\u003c/code\u003e and \u003ccode\u003emodule load \u0026lt;modules\u0026gt;\u003c/code\u003e based\nCLI used to load appropriate environments within the batch job before\nlaunching the RStudio Server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ewithout Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e 3.3.2+ (earlier versions are untested but may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.rstudio.com/products/rstudio-server/\" rel=\"nofollow\"\u003eRStudio Server\u003c/a\u003e 1.0.136+ (earlier versions are untested by may work for you)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://proot-me.github.io/\" rel=\"nofollow\"\u003ePRoot\u003c/a\u003e 5.1.0+ (used to setup fake bind mount)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eor with Singularity\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e 2.4.2+\u003c/li\u003e\n\u003cli\u003eA Singularity image similar to \u003ca href=\"https://www.singularity-hub.org/collections/463\" rel=\"nofollow\"\u003enickjer/singularity-rstudio\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorresponding module to launch the above Singularity image (see\n\u003ca href=\"https://github.com/nickjer/singularity-rstudio/blob/master/example_module/\"\u003eexample_module\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-install\"\u003e\u003ca class=\"heading-link\" href=\"#install\"\u003eInstall\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eUse git to clone this app and checkout the desired branch/version you want to\nuse:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git clone \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003erepo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will not need to do anything beyond this as all necessary assets are\ninstalled. You will also not need to restart this app as it isn\u0027t a Passenger\napp.\u003c/p\u003e\n\u003cp\u003eTo update the app you would:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git fetch\nscl \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e git19 -- git checkout \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etag/branch\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAgain, you do not need to restart the app as it isn\u0027t a Passenger app.\u003c/p\u003e\n\u003ch2 id=\"user-content-contributing\"\u003e\u003ca class=\"heading-link\" href=\"#contributing\"\u003eContributing\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eFork it ( \u003ca href=\"https://github.com/OSC/bc_osc_rstudio_server/fork\"\u003ehttps://github.com/OSC/bc_osc_rstudio_server/fork\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -am \u0027Add some feature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the branch (\u003ccode\u003egit push origin my-new-feature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCreate a new Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"user-content-license\"\u003e\u003ca class=\"heading-link\" href=\"#license\"\u003eLicense\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDocumentation, website content, and logo is licensed under\n\u003ca href=\"https://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003eCC-BY-4.0\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCode is licensed under MIT (see LICENSE.txt)o\u003c/li\u003e\n\u003cli\u003eRStudio, Shiny and the RStudio logo are all registered trademarks of RStudio.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"user-content-rserver-command-line-arguements\"\u003e\u003ca class=\"heading-link\" href=\"#rserver-command-line-arguements\"\u003eRServer command line arguements\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThis was the output of \u003ccode\u003e--help\u003c/code\u003e from version \u003ccode\u003e2021.09.1\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecommand-line options:\n\nverify:\n --verify-installation arg (=0) Runs verification mode to verify the \n current installation.\n\nserver:\n --server-working-dir arg (=/) The default working directory of the \n rserver process.\n --server-user arg (=rstudio-server) The user account of the rserver \n process.\n --server-daemonize arg (=0) Indicates whether or not the rserver \n process should run as a daemon.\n --server-pid-file arg (=/var/run/rstudio-server.pid)\n The path to a file where the rserver \n daemon\u0027s pid is written.\n --server-app-armor-enabled arg (=0) Indicates whether or not to enable \n AppArmor profiles for the rserver \n process.\n --server-set-umask arg (=1) If enabled, sets the rserver process \n umask to 022 on startup, which causes \n new files to have rw-r-r permissions.\n --secure-cookie-key-file arg If set, overrides the default path of \n the secure-cookie-key file used for \n encrypting cookies.\n --server-data-dir arg (=/var/run/rstudio-server)\n Path to the data directory where \n RStudio Server will write run-time \n state.\n --server-add-header arg Adds a header to all responses from \n RStudio Server. This option can be \n specified multiple times to add \n multiple headers.\n\nwww:\n --www-address arg (=0.0.0.0) The network address that RStudio Server\n will listen on for incoming \n connections.\n --www-port arg The port that RStudio Server will bind \n to while listening for incoming \n connections. If left empty, the port \n will be automatically determined based \n on your SSL settings (443 for SSL, 80 \n for no SSL).\n --www-root-path arg (=/) The path prefix added by a proxy to the\n incoming RStudio URL. This setting is \n used so RStudio Server knows what path \n it is being served from. If running \n RStudio Server behind a path-modifying \n proxy, this should be changed to match \n the base RStudio Server URL.\n --www-local-path arg (=www) The relative path from the RStudio \n installation directory, or absolute \n path where web assets are stored.\n --www-symbol-maps-path arg (=www-symbolmaps)\n The relative path from the RStudio \n installation directory, or absolute \n path, where symbol maps are stored.\n --www-use-emulated-stack arg (=0) Indicates whether or not to use GWT\u0027s \n emulated stack.\n --www-thread-pool-size arg (=2) The size of the threadpool from which \n requests will be serviced. This may be \n increased to enable more concurrency, \n but should only be done if the \n underlying hardware has more than 2 \n cores. It is recommended to use a value\n that is \u0026lt;= to the number of hardware \n cores, or \u0026lt;= to two times the number of\n hardware cores if the hardware utilizes\n hyperthreading.\n --www-proxy-localhost arg (=1) Indicates whether or not to proxy \n requests to localhost ports over the \n main server port. This should generally\n be enabled, and is used to proxy HTTP \n traffic within a session that belongs \n to code running within the session \n (e.g. Shiny or Plumber APIs)\n --www-verify-user-agent arg (=1) Indicates whether or not to verify \n connecting browser user agents to \n ensure they are compatible with RStudio\n Server.\n --www-same-site arg The value of the \u0027SameSite\u0027 attribute \n on the cookies issued by RStudio \n Server. Accepted values are \u0027none\u0027 or \n \u0027lax\u0027. The value \u0027none\u0027 should be used \n only when RStudio is hosted into an \n iFrame. For compatibility with some \n browsers (i.e. Safari 12), duplicate \n cookies will be issued by RStudio \n Server when \u0027none\u0027 is used.\n --www-frame-origin arg (=none) Specifies the allowed origin for the \n iFrame hosting RStudio if iFrame \n embedding is enabled.\n --www-enable-origin-check arg (=0) If enabled, cause RStudio to enforce \n that incoming request origins are from \n the host domain. This can be added for \n additional security. See \n https://cheatsheetseries.owasp.org/chea\n tsheets/Cross-Site_Request_Forgery_Prev\n ention_Cheat_Sheet.html#verifying-origi\n n-with-standard-headers\n --www-allow-origin arg Specifies an additional origin that \n requests are allowed from, even if it \n does not match the host domain. Used if\n origin checking is enabled. May be \n specified multiple times for multiple \n origins.\n\nrsession:\n --rsession-which-r arg The path to the main R program (e.g. \n /usr/bin/R). This should be set if no \n versions are specified in \n /etc/rstudio/r-versions and the default\n R installation is not available on the \n system path.\n --rsession-path arg (=rsession) The relative path from the RStudio \n installation directory, or absolute \n path to the rsession executable.\n --rldpath-path arg (=r-ldpath) The path to the r-ldpath script which \n specifies extra library paths for R \n versions.\n --rsession-ld-library-path arg Specifies additional LD_LIBRARY_PATHs \n to use for R sessions.\n --rsession-config-file arg If set, overrides the path to the \n /etc/rstudio/rsession.conf \n configuration file. The specified path \n may be a relative path from the RStudio\n installation directory, or an absolute \n path.\n --rsession-proxy-max-wait-secs arg (=10)\n The maximum time to wait in seconds for\n a successful response when proxying \n requests to rsession.\n --rsession-memory-limit-mb arg (=0) The limit in MB that an rsession \n process may consume.\n --rsession-stack-limit-mb arg (=0) The limit in MB that an rsession \n process may consume for its stack.\n --rsession-process-limit arg (=0) The maximum number of allowable \n rsession processes.\n\ndatabase:\n --database-config-file arg If set, overrides the path to the \n /etc/rstudio/database.conf \n configuration file.\n --db-command arg Executes the shell command specified \n injecting the current database \n configuration in the command.\n\nauth:\n --auth-none arg (=1) If set, disables multi-user \n authentication. Workbench/Pro features \n may not work in this mode.\n --auth-validate-users arg (=0) Indicates whether or not to validate \n that authenticated users exist on the \n target system. Disabling this option \n may cause issues to start or to run a \n session.\n --auth-stay-signed-in-days arg (=30) The number of days to keep a user \n signed in when using the \"Stay Signed \n In\" option. Will only take affect when \n auth-timeout-minutes is 0 (disabled).\n --auth-timeout-minutes arg (=60) The number of minutes a user will stay \n logged in while idle before required to\n sign in again. Set this to 0 (disabled)\n to enable legacy timeout \n auth-stay-signed-in-days.\n --auth-encrypt-password arg (=1) Indicates whether or not to encrypt the\n password sent from the login form. For \n security purposes, we strongly \n recommend you leave this enabled.\n --auth-login-page-html arg (=/etc/rstudio/login.html)\n The path to a file containing \n additional HTML customization for the \n login page.\n --auth-rdp-login-page-html arg (=/etc/rstudio/rdplogin.html)\n The path to a file containing \n additional HTML customization for the \n login page, as seen by RDP users.\n --auth-required-user-group arg Specifies a group that users must be in\n to be able to use RStudio.\n --auth-minimum-user-id arg (=auto) Specifies a minimum user id value. \n Users with a uid lower than this value \n may not use RStudio.\n --auth-pam-helper-path arg (=rserver-pam)\n The relative path from the RStudio \n installation directory, or absolute \n path where the PAM helper binary \n resides.\n --auth-pam-require-password-prompt arg (=1)\n Indicates whether or not to require the\n \"Password: \" prompt before sending the \n password via PAM. In most cases, this \n should be enabled. If using a custom \n PAM password prompt, you may need to \n disable this setting if PAM logins do \n not work correctly.\n --auth-pam-requires-priv arg (=1) Deprecated - will always be true.\n --auth-sign-in-throttle-seconds arg (=5)\n The minimum amount of time a user must \n wait before attempting to sign in again\n after signing out.\n --auth-revocation-list-dir arg If set, overrides the path to the \n directory which contains the revocation\n list to be used for storing expired \n tokens. As of RStudio Server 1.4, this \n has been moved to database storage, and\n so this setting is deprecated, but will\n be used to port over any existing \n file-based expired tokens.\n --auth-cookies-force-secure arg (=0) Indicates whether or not auth cookies \n should be forcefully marked as secure. \n This should be enabled if running an \n SSL terminator infront of RStudio \n Server. Otherwise, cookies will be \n marked secure if SSL is configured.\n\nmonitor:\n --monitor-interval-seconds arg (=60) The interval in seconds at which the \n monitor is probed for new data.\n\ngeneral:\n --help print help message\n --test-config test to ensure the config file is valid\n --config-file arg configuration file\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 9, - "subscribers_count": 3, + "subscribers_count": 10, "topics": [], - "updated_at": 1666128191.0 + "updated_at": 1693363021.0 }, { "data_format": 2, - "description": "Info on CHPC Open OnDemand installation and customization", + "description": "Useful scripts and tools related to alevin-fry", "filenames": [ - "linux-host/Singularity" + "docker/Singularity.def" ], - "full_name": "CHPC-UofU/OnDemand-info", + "full_name": "COMBINE-lab/usefulaf", "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-usefulaf-an-all-in-one-dockersingularity-image-for-single-cell-processing-with-alevin-fry\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usefulaf-an-all-in-one-dockersingularity-image-for-single-cell-processing-with-alevin-fry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsefulaf: An all-in-one Docker/Singularity image for single-cell processing with alevin-fry\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/combinelab/usefulaf/tags\" rel=\"nofollow\"\u003e\u003ccode\u003eUsefulaf\u003c/code\u003e\u003c/a\u003e is an all-in-one Docker/Singularity image for single-cell processing with \u003ca href=\"https://github.com/COMBINE-lab/alevin-fry\"\u003eAlevin-fry\u003c/a\u003e(\u003ca href=\"https://www.nature.com/articles/s41592-022-01408-3\" rel=\"nofollow\"\u003epaper\u003c/a\u003e). It includes the all tools you need to turn your FASTQ files into a count matrix and then load it into your favorite analysis environment. Specifically, this image includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/COMBINE-lab/simpleaf\"\u003e\u003ccode\u003esimpleaf\u003c/code\u003e\u003c/a\u003e: A simplified interface to indexing and quantifying with \u003ccode\u003ealevin-fry\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/COMBINE-lab/pyroe\"\u003e\u003ccode\u003epyroe\u003c/code\u003e\u003c/a\u003e: An alevin-fry utility python package for building splici references, converting alevin-fry output formats, loading count matrix in Python, adding gene names (instead of just gene IDs) to output matrices, etc.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://rdrr.io/github/mikelove/fishpond/man/loadFry.html\" rel=\"nofollow\"\u003e\u003ccode\u003efishpond::loadFry()\u003c/code\u003e\u003c/a\u003e: A R function for loading count matrix as \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html\" rel=\"nofollow\"\u003eSingleCellExperiment\u003c/a\u003e object.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor processing data simply using the \u003ccode\u003eusefulaf\u003c/code\u003e image, check our latest tutorial \u003ca href=\"https://combine-lab.github.io/alevin-fry-tutorials/2021/quickstart-usefulaf-singularity/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor pulling the Singularity image, please run the following code in bash. Note that the image is $\\sim$ 1.65 GB.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e if you use Docker\u003c/span\u003e\n$ docker pull combinelab/usefulaf:latest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e if you use Singularity\u003c/span\u003e\n$ singularity pull docker://combinelab/usefulaf:latest\n\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usefulaf-history\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usefulaf-history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsefulaf history\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/COMBINE-lab/alevin-fry\"\u003eAlevin-fry\u003c/a\u003e is a fast, accurate, and memory-frugal tool for preprocessing single-cell and single-nucleus RNA-seq data. You can read more about alevin-fry in alevin-fry \u003ca href=\"https://www.biorxiv.org/content/10.1101/2021.06.29.450377v2\" rel=\"nofollow\"\u003epre-print\u003c/a\u003e, and \u003ca href=\"https://www.nature.com/articles/s41592-022-01408-3\" rel=\"nofollow\"\u003epaper\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis repository was created initially with scripts, functions, and utilities that are useful for preparing data for processing with alevin-fry, as well as for reading alevin-fry data into other packages for downstream analysis. It also accompanies a Docker/Singularity container containing all of this relevant software in one place. However, as \u003ccode\u003ealevin-fry\u003c/code\u003e has continued to grow, all of that relevant functionality found its way into other, more stable and permanent homes (e.g. \u003ca href=\"https://github.com/COMBINE-lab/pyroe\"\u003e\u003ccode\u003epyroe\u003c/code\u003e\u003c/a\u003e for splici reference construction and loading data in Python, \u003ca href=\"https://github.com/COMBINE-lab/roe\"\u003e\u003ccode\u003eroe\u003c/code\u003e\u003c/a\u003e for splici reference construction in R and \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/fishpond.html\" rel=\"nofollow\"\u003e\u003ccode\u003efishpond\u003c/code\u003e\u003c/a\u003e for loading data in \u003ccode\u003eR\u003c/code\u003e). Finally, this repository also contained a bash script called \u003ccode\u003esimpleaf\u003c/code\u003e to simplify common workflows with \u003ccode\u003ealevin-fry\u003c/code\u003e. That, too, has evolved into its own (much more feature-rich and comprehensive) tool, living in its own repository (\u003ca href=\"https://github.com/COMBINE-lab/simpleaf\"\u003e\u003ccode\u003esimpleaf\u003c/code\u003e\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eAs such, all the scripts and functions in this repository have been retired. However, as usefulaf is still the only place that provides all these functionalities, we decided to turn [\u003ccode\u003eusefulaf\u003c/code\u003e] as an all-in-one \u003ca href=\"https://hub.docker.com/r/combinelab/usefulaf/tags\" rel=\"nofollow\"\u003eDocker/Singularity image\u003c/a\u003e that makes use of all those new tools listed above. That has replaced the older \u003ccode\u003eusefulaf\u003c/code\u003e image that made use of the varied assortment of scripts and tools hosted in this repository.\u003c/p\u003e\n", "stargazers_count": 9, - "subscribers_count": 2, + "subscribers_count": 6, "topics": [], - "updated_at": 1678124445.0 + "updated_at": 1658179118.0 }, { "data_format": 2, - "description": "Deep Learning Pipeline for Wrist Fracture Detection", + "description": null, "filenames": [ - "Singularity" + "Singularity.fitseq-latest" ], - "full_name": "Oulu-IMEDS/DeepWrist", - "latest_release": "v1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePaper Link\u003c/h1\u003e\u003ca id=\"user-content-paper-link\" class=\"anchor\" aria-label=\"Permalink: Paper Link\" href=\"#paper-link\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTitle: Deep Learning for Wrist Fracture Detection: Are We There Yet? \u003cbr\u003e\n\u003ca href=\"https://arxiv.org/abs/2012.02577\" rel=\"nofollow\"\u003earXiv link\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDeepWrist Pipeline\u003c/h1\u003e\u003ca id=\"user-content-deepwrist-pipeline\" class=\"anchor\" aria-label=\"Permalink: DeepWrist Pipeline\" href=\"#deepwrist-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA transfer learning pipeline to detect wrist fracture from DICOM files. It has two blocks: Landmark Localization Block\nand Fracture Detection Block.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./figures/DeepWrist_pipeline.png\"\u003e\u003cimg src=\"./figures/DeepWrist_pipeline.png\" alt=\"DeepWrist\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBoth of the blocks are yml configuration based. We used OmegaConf for this purpose. Each executable python file can\neither run standalone or requires a yml file as \u003ccode\u003eexperiment\u003c/code\u003e argument to be passed down at command line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLandmark Localization Block\u003c/h2\u003e\u003ca id=\"user-content-landmark-localization-block\" class=\"anchor\" aria-label=\"Permalink: Landmark Localization Block\" href=\"#landmark-localization-block\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLandmark Localization Block is adapted from \u003ca href=\"https://arxiv.org/pdf/1907.12237\" rel=\"nofollow\"\u003eKNEEL\u003c/a\u003e. However, we developed some data\naugmentation methods suited to our task. The \u003ccode\u003elocalizer\u003c/code\u003e folder contains the source code for Landmark Localizer and\nstructured as \u003cbr\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003elocalizer \n|---config \n| |---experiment\n|---kneel_before_wrist \n| |---data \n| |---model \n|---scripts \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003econfig\u003c/code\u003e folder contains the initial default configuration and a configuration processor. There is a folder named\n\u003ccode\u003eexperiment\u003c/code\u003e inside \u003ccode\u003econfig\u003c/code\u003e folder which holds confgiguration for different experiments.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ekneel_before_wrist\u003c/code\u003e hosts the body of the localizer part of our pipeline. It has two sub-directory: \u003ccode\u003edata\u003c/code\u003e and \u003ccode\u003emodel\u003c/code\u003e.\nThe \u003ccode\u003edata\u003c/code\u003e folder contains utilities necessary to process and augment data for training and evaluation. \u003ccode\u003emodel\u003c/code\u003e\nsub-directory contains the pytorch lightning version of HourGlass network which we will use for training the localizer.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003escripts\u003c/code\u003e directory hosts all the experiment scripts for which the yaml configurations are created.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to Train Localizer with your own data\u003c/h2\u003e\u003ca id=\"user-content-how-to-train-localizer-with-your-own-data\" class=\"anchor\" aria-label=\"Permalink: How to Train Localizer with your own data\" href=\"#how-to-train-localizer-with-your-own-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFirst step for training your own localizer is to collect data. For our research we used private hospital data,\nthrefore it cannot be shared. To make things simple, we used csv file to store meta data about the dataset. This way,\nyou don\u0027t have to load the full dataset to the memory rather fecth the file location from csv file and read it just-in-time.\nSo, we are dealing with wrist fracture images. We will consider posterioanterio (PA) and lateral (LAT) view of the wrist x-ray. To\nmake your own dataset, you have to create a csv metadata file containing at least \u003ccode\u003eFname, Points, Side\u003c/code\u003e columns. \u003ccode\u003eFname\u003c/code\u003e\nis the absolute path to the wrist image, \u003ccode\u003ePoints\u003c/code\u003e column will contain the landmark coordinates of top of distal ulna,\ntop of distal radius and assumed center of the wrist for PA view and two distinguishalbe points on top part of distal\nradio-ulna bone and the assumed center of wrist for LAT view. As the name suggest, the \u003ccode\u003eSide\u003c/code\u003e column contains the side\ninformation of corresponding wrist x-ray. Put 0 for PA and 1 for LAT. Once the metadata is ready, we can move forward.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSecond step is to clone \u003ccode\u003ewrist_landmark.yaml\u003c/code\u003e configuration file and modify the clone. Inside the yaml file modify\nfollowing\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003e data_home: # root folder that contains the data folder \n data_folder: # your data folder name\n meta: the csv meta file you have created. should be inside data folder \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOnce you are done with step 2, run the \u003ccode\u003etrain_ptl.py --experiment=YourClonedYAMLFile\u003c/code\u003e. This file is located inside\n\u003ccode\u003escripts\u003c/code\u003e folder. It will start the training.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFracture Detection Block\u003c/h2\u003e\u003ca id=\"user-content-fracture-detection-block\" class=\"anchor\" aria-label=\"Permalink: Fracture Detection Block\" href=\"#fracture-detection-block\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eclassifier\u003c/code\u003e folder hosts the Fracture Detection Block. It has a similar structure like localizer.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eclassifier \n|---config \n|---fracture_detector\n| |---callback \n| |---data \n| |---model \n|---script \n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eLike before \u003ccode\u003econfig\u003c/code\u003e folder hosts the script configurations. \u003ccode\u003efracture_detector\u003c/code\u003e folder hosts necessary folders and\nfiles for model, data and training related stuffs. Inside this folder, there are three folders: 1) \u003ccode\u003ecallback\u003c/code\u003e (hosts\ncallback function definitions), 2) \u003ccode\u003edata\u003c/code\u003e (hosts data related utilities) and 3) \u003ccode\u003emodel\u003c/code\u003e (hosts model definition and\ntraining methods)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to train your Fracture Detector\u003c/h2\u003e\u003ca id=\"user-content-how-to-train-your-fracture-detector\" class=\"anchor\" aria-label=\"Permalink: How to train your Fracture Detector\" href=\"#how-to-train-your-fracture-detector\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStep 1. First step to train your custom fracture detector is to collect data using the \u003ccode\u003elocalizer\u003c/code\u003e model trained previously.\nSave the generated ROI with the corresponding \u003ccode\u003eID\u003c/code\u003e as filename. Create a csv metadata file with \u003ccode\u003eID\u003c/code\u003e, \u003ccode\u003eSide\u003c/code\u003e, \u003ccode\u003eFname\u003c/code\u003e(optional)\nand \u003ccode\u003eFracture\u003c/code\u003e columns. Say, the meta file name is \u003ccode\u003eyour_meta.csv\u003c/code\u003eCreate a \u003ccode\u003eroot\u003c/code\u003e folder which we will use as data home where the generated ROI images and the csv\nmeta file are saved. There shoudl be \u003ccode\u003ePA\u003c/code\u003e and \u003ccode\u003eLAT\u003c/code\u003e folder in the \u003ccode\u003eroot\u003c/code\u003e folder to host PA ROI and LAT ROI respectively.\u003c/p\u003e\n\u003cp\u003eStep 2. Clone the existing training conf \u003ccode\u003efracture_detector_seresnet.yaml\u003c/code\u003e to \u003ccode\u003eyour_config_file.yaml\u003c/code\u003e.\nOpen \u003ccode\u003eyour_config_file.yaml\u003c/code\u003e and update the following field\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edata_home: root\nmeta: your_meta.csv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eStep 3. Once you are done with the config file go inside the \u003ccode\u003escripts\u003c/code\u003e folder and run \u003ccode\u003epython train_ptl.py experiment=your_config_file\u003c/code\u003e.\nthis will start the training.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInference on your Data\u003c/h2\u003e\u003ca id=\"user-content-inference-on-your-data\" class=\"anchor\" aria-label=\"Permalink: Inference on your Data\" href=\"#inference-on-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStep 1. Create a csv meta file of for the data you want to predict. Use \u003ccode\u003eID\u003c/code\u003e, \u003ccode\u003eSide\u003c/code\u003e, \u003ccode\u003eFname\u003c/code\u003e, and \u003ccode\u003eFracture\u003c/code\u003e columns.\u003c/p\u003e\n\u003cp\u003eStep2. Clone \u003ccode\u003efracture_deteciton_testset_1.yaml\u003c/code\u003e to \u003ccode\u003eyour_testset.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eStep 3. Find and update the following field in \u003ccode\u003eyour_testset.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edataset:\n train_data_home: root\n test_data_home: /location/of/test/data\n meta: /absolute/location/to/your_testset.csv\nsave_path: /absolute/location/to/save/prediction.csv\nsnapshot_folder: /folder/location/where/fracture/detector/models/are/saved\nsave_image: true or false\nsave_image_dir: /folder/location/if/you/want/to/save/output/images\n\nlocalizer:\n snapshot_folder: /folder/location/where/roi/localizer/models/are/saved\n dataset:\n train_data_home: /folder/location/where/localization/data/are/stored\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ekeep only \u003ccode\u003eFracture\u003c/code\u003e in \u003ccode\u003egt\u003c/code\u003e. If you want to save gradcam set \u003ccode\u003esave_gradcam: true\u003c/code\u003e and define \u003ccode\u003egradcam_dir\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eStep 4. Now in the \u003ccode\u003escripts\u003c/code\u003e folder run, \u003ccode\u003epython test.py experiment=your_testset\u003c/code\u003e\nThis will do inference on your data, the predicitons will be saved in the csv file you defined.\u003c/p\u003e\n\u003ch2\u003e\u003c/h2\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTrained Models\u003c/h1\u003e\u003ca id=\"user-content-trained-models\" class=\"anchor\" aria-label=\"Permalink: Trained Models\" href=\"#trained-models\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse the following commands to get the trained models.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget http://mipt-ml.oulu.fi/models/DeepWrist/Fracture_Detection_Block.tar.gz\nwget http://mipt-ml.oulu.fi/models/DeepWrist/ROI_Localization_Block.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHow to Cite\u003c/h1\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to Cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor citation, please use the following bibtex\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{raisuddin2020deep,\n title={Deep Learning for Wrist Fracture Detection: Are We There Yet?}, \n author={Abu Mohammed Raisuddin and Elias Vaattovaara and Mika Nevalainen and Marko Nikki and Elina J\u00e4rvenp\u00e4\u00e4 and Kaisa Makkonen and Pekka Pinola and Tuula Palsio and Arttu Niemensivu and Osmo Tervonen and Aleksei Tiulpin},\n year={2020},\n eprint={2012.02577},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "FangfeiLi05/PyFitSeq", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/801caa0fd19bfe93abf83222faddccda5d43d03eb2d0039402dfca476aa25f43/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e372d677265656e2e737667\" alt=\"Python 3.7\" data-canonical-src=\"https://img.shields.io/badge/python-3.7-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b59ba4ef5c86bc79e9e3e9039f1b96a4db59e7f96a7ba04abfc6548ae88c461b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b5504ad42509979057a4c27764d6acc447b6db887f4528b7c6cf18e9631ef51e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f456d61696c2d66616e676665696c693035323540676d61696c2e636f6d2d6f72616e67652e737667\" alt=\"Contact Info\" data-canonical-src=\"https://img.shields.io/badge/Email-fangfeili0525@gmail.com-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePyFitSeq\u003c/h2\u003e\u003ca id=\"user-content-pyfitseq\" class=\"anchor\" aria-label=\"Permalink: PyFitSeq\" href=\"#pyfitseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. What is PyFitSeq?\u003c/h3\u003e\u003ca id=\"user-content-1-what-is-pyfitseq\" class=\"anchor\" aria-label=\"Permalink: 1. What is PyFitSeq?\" href=\"#1-what-is-pyfitseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePyFitSeq is a Python-based fitness estimation tool for pooled amplicon sequencing studies. PyFitSeq is Python re-coded version of the MATLAB tool FitSeq \u003ca href=\"https://github.com/sashaflevy/Fit-Seq\"\u003ehttps://github.com/sashaflevy/Fit-Seq\u003c/a\u003e. If you use this software, please reference: \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S2405471218303909?via%3Dihub\" rel=\"nofollow\"\u003eF. Li, et al. Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies. Cell Systems, 7: 521-525 (2018)\u003c/a\u003e. PyFitSeq is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\u003c/p\u003e\n\u003cp\u003eIt currently has two main functions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eevo_simulator.py\u003c/code\u003e performs simulations of competitve pooled growth of a population of genotypes.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epyfitseq.py\u003c/code\u003e calculates the fitness of each genotype from read-count time-series data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA walk-through is included as the jupyter notebook \u003ca href=\"https://github.com/FangfeiLi05/PyFitSeq/blob/master/PyFitSeq_Walk_Through.ipynb\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. How to install PyFitSeq?\u003c/h3\u003e\u003ca id=\"user-content-2-how-to-install-pyfitseq\" class=\"anchor\" aria-label=\"Permalink: 2. How to install PyFitSeq?\" href=\"#2-how-to-install-pyfitseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3 is required. This version has been tested on a MacBook Pro (3.1 GHz Intel Core i5), with Python 3.7.4.\u003c/li\u003e\n\u003cli\u003eClone this repository by running \u003ccode\u003egit clone https://github.com/FangfeiLi05/PyFitSeq.git\u003c/code\u003e in terminal.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecd\u003c/code\u003e to the root directory of the project (the folder containing \u003ccode\u003eREADME.md\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eInstall dependencies by running \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e in terminal.\u003c/li\u003e\n\u003cli\u003eInstall pyfitseq by running \u003ccode\u003epip install .\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun \u003ccode\u003epython3 -m pip install git+https://github.com/darachm/PyFitSeq.git\u003c/code\u003e to install\nwithout cloning the repository.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e2a Alternative, use in a Singularity container\u003c/h4\u003e\u003ca id=\"user-content-2a-alternative-use-in-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: 2a Alternative, use in a Singularity container\" href=\"#2a-alternative-use-in-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith the closing of Singularity Hub there aren\u0027t yet publicly available\ncontainers for this, but you can build your own with a command like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build fitseq-latest.simg Singularity.fitseq-latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you can run on any\n\u003ca href=\"https://sylabs.io/guides/3.8/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003ecomputer running Singularity\u003c/a\u003e,\nsuch as your local HPC, using a command like:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec fitseq-latest.simg pyfitseq.py -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. How to use PyFitSeq?\u003c/h3\u003e\u003ca id=\"user-content-3-how-to-use-pyfitseq\" class=\"anchor\" aria-label=\"Permalink: 3. How to use PyFitSeq?\" href=\"#3-how-to-use-pyfitseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.1. Evolution Simulation\u003c/h4\u003e\u003ca id=\"user-content-31-evolution-simulation\" class=\"anchor\" aria-label=\"Permalink: 3.1. Evolution Simulation\" href=\"#31-evolution-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eevo_simulator.py\u003c/code\u003e models competative pooled growth of a population of genotypes with different fitnesses. This simulation can be made to include sources of noise, including growth noise, noise from cell transfers, DNA extraction, PCR, and sequencing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOPTIONS\u003c/h5\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: OPTIONS\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input\u003c/code\u003e or \u003ccode\u003e-i\u003c/code\u003e: a .csv file, with\n\u003cul\u003e\n\u003cli\u003e1st column of .csv: fitness of each genotype, [x1, x2, ...]\u003c/li\u003e\n\u003cli\u003e2nd column .csv: initial cell number of each genotype at generation 0, [n1, n2, ...]\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--t_seq\u003c/code\u003e or \u003ccode\u003e-t\u003c/code\u003e: time-points evaluated in number of generations (\u003ccode\u003eformat: 0 t1 t2 ...\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--read_num_average_seq\u003c/code\u003e or \u003ccode\u003e-r\u003c/code\u003e: average number of reads per genotype for each time-point (\u003ccode\u003eformat: 0 r1 r2 ...\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--noise_option\u003c/code\u003e or \u003ccode\u003e-n\u003c/code\u003e: which types of noise to include in the simulation, default is all sources of noise (\u003ccode\u003edefault: growth bottleneck_transfer DNA_extraction PCR sequencing\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--dna_copies\u003c/code\u003e or \u003ccode\u003e-d\u003c/code\u003e: average genome copy number per genotype used as template in PCR (\u003ccode\u003edefault: 500\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pcr_cycles\u003c/code\u003e or \u003ccode\u003e-p\u003c/code\u003e: number of cycles of PCR (\u003ccode\u003edefault: 25\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--fitness_type\u003c/code\u003e or \u003ccode\u003e-f\u003c/code\u003e: type of fitness: Wrightian fitness (w), or Malthusian fitness (m)\u0027 (\u003ccode\u003edefault: m\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_filename\u003c/code\u003e or \u003ccode\u003e-o\u003c/code\u003e: prefix of output .csv files (\u003ccode\u003edefault: output\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOUTPUTS\u003c/h5\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: OUTPUTS\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_filename_EvoSimulation_Read_Number.csv\u003c/code\u003e: read number per genotype for each time-point\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_filename_EvoSimulation_Mean_Fitness.csv\u003c/code\u003e: mean fitness for each time-point\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_filename_EvoSimulation_Input_Log.csv\u003c/code\u003e: a record of all inputs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eFor Help\u003c/h5\u003e\u003ca id=\"user-content-for-help\" class=\"anchor\" aria-label=\"Permalink: For Help\" href=\"#for-help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython evo_simulator.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExamples\u003c/h5\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython evo_simulator.py -i input_EvoSimulation.csv -t 0 3 6 9 12 -r 50 50 50 50 50 -o output\npython evo_simulator.py -i input_EvoSimulation.csv -t 0 2 4 6 8 -r 75 75 75 75 50 -n DNA_extraction PCR sequencing -d 300 -p 27 -f w -o output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.2. Fitness Estimation\u003c/h4\u003e\u003ca id=\"user-content-32-fitness-estimation\" class=\"anchor\" aria-label=\"Permalink: 3.2. Fitness Estimation\" href=\"#32-fitness-estimation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003epyfitseq.py\u003c/code\u003e estimates the fitness of each genotype from read-count time-series data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOPTIONS\u003c/h5\u003e\u003ca id=\"user-content-options-1\" class=\"anchor\" aria-label=\"Permalink: OPTIONS\" href=\"#options-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--input\u003c/code\u003e or \u003ccode\u003e-i\u003c/code\u003e: a .csv file, with each column being the read number per genotype at each sequenced time-point\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--t_seq\u003c/code\u003e or \u003ccode\u003e-t\u003c/code\u003e: sequenced time-points in number of generations (\u003ccode\u003eformat: 0 t1 t2 ...\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--max_iter_num\u003c/code\u003e or \u003ccode\u003e-m\u003c/code\u003e: maximum number of iterations in the optimization (Small numbers can reduce running time and decrease accuracy.) (\u003ccode\u003edefault: 10\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--kappa\u003c/code\u003e or \u003ccode\u003e-k\u003c/code\u003e: a noise parameter that characterizes the total noise introduced by growth, cell transfer, DNA extraction, PCR, and sequencing (To measure kappa empirically, see the reference: [S. F. Levy, et al. Quantitative Evolutionary Dynamics Using High-resolution Lineage Tracking. Nature, 519: 181\u2013186 (2015)].) (\u003ccode\u003edefault: 2.5\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--regression_num\u003c/code\u003e or \u003ccode\u003e-g\u003c/code\u003e: number of points used in the initial linear-regression-based fitness estimate (\u003ccode\u003edefault: 2\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--fitness_type\u003c/code\u003e or \u003ccode\u003e-f\u003c/code\u003e: type of fitness: Wrightian fitness (w), or Malthusian fitness (m) (\u003ccode\u003edefault: m\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--output_filename\u003c/code\u003e or \u003ccode\u003e-o\u003c/code\u003e: prefix of output .csv files (\u003ccode\u003edefault: output\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOUTPUTS\u003c/h5\u003e\u003ca id=\"user-content-outputs-1\" class=\"anchor\" aria-label=\"Permalink: OUTPUTS\" href=\"#outputs-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eoutput_filename_FitSeq_Result.csv\u003c/code\u003e: a .csv file, with\n\u003cul\u003e\n\u003cli\u003e1st column of .csv: estimated fitness of each genotype, [x1, x2, ...]\u003c/li\u003e\n\u003cli\u003e2nd column of .csv: log likelihood value of each genotype, [f1, f2, ...]\u003c/li\u003e\n\u003cli\u003e3rd column of .csv: estimated mean fitness per sequenced time-point, [x_mean(0), x_mean(t1), ...]\u003c/li\u003e\n\u003cli\u003e4th+ columns of .csv: estimated read number per genotype per time-point, with each time-point being a column\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eFor Help\u003c/h5\u003e\u003ca id=\"user-content-for-help-1\" class=\"anchor\" aria-label=\"Permalink: For Help\" href=\"#for-help-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython pyfitseq.py --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExamples\u003c/h5\u003e\u003ca id=\"user-content-examples-1\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epython pyfitseq.py -i output_EvoSimulation_Read_Number.csv -t 0 3 6 9 12 -o output\npython pyfitseq.py -i output_EvoSimulation_Read_Number2.csv -t 0 2 6 8 -m 12 -k 2 -g 3 -f w -o output\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 9, - "subscribers_count": 5, + "subscribers_count": 4, "topics": [], - "updated_at": 1707636728.0 + "updated_at": 1713767883.0 }, { "data_format": 2, - "description": "This is a demo about how to develop and deploy a pipeline using nextflow, scif, a container (Singularity and Docker) and its posterior deployment.", + "description": "Singularity image for a deep learning (pytorch) environment + GPU support", "filenames": [ - "Singularity" + "Singularity.1.0.0" ], - "full_name": "BU-ISCIII/nextflow-scif", - "latest_release": "v.1.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNextflow tutorial workflow using SCIF and Singularity/Docker\u003c/h1\u003e\u003ca id=\"user-content-nextflow-tutorial-workflow-using-scif-and-singularitydocker\" class=\"anchor\" aria-label=\"Permalink: Nextflow tutorial workflow using SCIF and Singularity/Docker\" href=\"#nextflow-tutorial-workflow-using-scif-and-singularitydocker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/BU-ISCIII/nextflow-scif\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0289dc02a9638d79a6ae3573745037bd79d6d2ae9d3bac65e1c84a786d090731/68747470733a2f2f636972636c6563692e636f6d2f67682f636972636c6563692f636972636c6563692d646f63732e7376673f7374796c653d736869656c64\" alt=\"CircleCI Build Status\" data-canonical-src=\"https://circleci.com/gh/circleci/circleci-docs.svg?style=shield\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/93ca07ea63e4649ae72b87de86d82eedeab50e744fa6e07ee95e5344779fb930/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://discuss.circleci.com\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0db7cc65a258e95db6b1dddae92be2d4d80817b9570fded1962c4fd4b6fdcb6f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6d6d756e6974792d436972636c654349253230446973637573732d3334333433342e737667\" alt=\"CircleCi Community\" data-canonical-src=\"https://img.shields.io/badge/community-CircleCI%20Discuss-343434.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://nextflow.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f8a155e03276d377ca219881b2d695bd3be17898ea74a1250ad2b6941aef70cc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d253345302e32392e302d677265656e2e737667\" alt=\"Nextflow version\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%3E0.29.0-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3bb9116161cdfeb6b23d2bde2e62f92bb4eda63bd51f5c1ed4b8b86356f088e4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f46696c6573797374656d2d536369656e74696669632d627269676874677265656e2e737667\" alt=\"Scif\" data-canonical-src=\"https://img.shields.io/badge/Filesystem-Scientific-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository implements the Nextflow tutorial workflow using the Scientific Filesystem (SCIF) and Docker/Singularity to provide a reproducible research environment. We have made an adaptation of \u003ca href=\"https://github.com/sci-f/snakemake.scif\"\u003esnakemake.scif\u003c/a\u003e, but using nextflow instead. Also I have just discovered \u003ca href=\"https://github.com/vsoch/rnatoy.scif/edit/master/README.md\"\u003ethis try\u003c/a\u003e of \u003ca href=\"https://www.github.com/vsoch\"\u003e@vsoch\u003c/a\u003e and I am adapting her cool documentation to this case.\u003c/p\u003e\n\u003cp\u003eThese are the steps to follow in order to develop a new reproducible and transparent workflow:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSCIF recipes\u003c/strong\u003e: \u003cstrong\u003eDiscoverability\u003c/strong\u003e and \u003cstrong\u003eTransparency\u003c/strong\u003e by installing our software in the container via a \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eScientific Filesystem\u003c/a\u003e. SCIF also lets us install the same dependencies across container technologies.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSingularity/Docker container\u003c/strong\u003e: this will handle the \u003cstrong\u003eReproducibility\u003c/strong\u003e of our pipeline and the software it is going to need. Moreover Docker and Singularity HUB makes deployment and sharing a lot easier.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNextflow\u003c/strong\u003e: enables scalable and reproducible scientific workflows managing software containers natively like a charm. It allows the adaptation of pipelines written in the most common scripting languages. Moreover its fluent DSL simplifies the implementation and the deployment of complex parallel and reactive workflows on clouds and clusters, being able to abstract the users both from \u003cem\u003ewhere\u003c/em\u003e and even \u003cem\u003ehow\u003c/em\u003e they compute without any concern about the multiple dependencies a scientific workflow can have.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTesting\u003c/strong\u003e: circleCI allows an interface for testing and deployment of software in github. It uses a series of containers for p.e building your container, testing your software against your container and deploy the image to Docker Hub and your program to some software repository like Heroku. Here we will use it in first instance for deploy our docker/Singularity container, and then to test our nextflow pipeline with some dummy test data (we will use as template the \u003ca href=\"https://github.com/sci-f/builder\"\u003escif builed\u003c/a\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick usage\u003c/h2\u003e\u003ca id=\"user-content-quick-usage\" class=\"anchor\" aria-label=\"Permalink: Quick usage\" href=\"#quick-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need to \u003ca href=\"https://www.nextflow.io/docs/latest/getstarted.html#installation\" rel=\"nofollow\"\u003einstall nextflow\u003c/a\u003e and \u003ca href=\"https://www.sylabs.io/guides/2.5.1/user-guide/quick_start.html#installation\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e. Singularity must\nbe installed with administrative privileges, usually system wide, and nextflow can be \"installed\" to your\npresent working directory. It\u0027s also recommended to define your Singularity cache to be somewhere\n(like a scratch space) where you have more disk space. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SINGULARITY_CACHEDIR=\u003cspan class=\"pl-smi\"\u003e$SCRATCH\u003c/span\u003e/.singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are working on your local machine, this location defaults to \u003ccode\u003e$HOME/.singularity\u003c/code\u003e, which\nis appropriate. Once you have both, you only have to type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e With the nextflow executable in the present working directory\u003c/span\u003e\n./nextflow run BU-ISCIII/nextflow-scif -profile singularity\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e With the nextflow executable installed system wide, and on the PATH\u003c/span\u003e\nnextflow run BU-ISCIII/nextflow-scif -profile singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThats it! When you finish, a \"results\" folder will appear with the content of your analysis.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ls results/\nbwa DAG.dot DAG.svg report.html timeline.html trace.txt vcf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSo...Let\u0027s get this done... and by \"this\" we are referring to a complete description of how this\nrecipe was derived, and the components of the software that just ran. The idea here is that if you\nunderstand the underlying components, you can easily build your own nextflow pipelines.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Recipes\u003c/h2\u003e\u003ca id=\"user-content-1-recipes\" class=\"anchor\" aria-label=\"Permalink: 1. Recipes\" href=\"#1-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen building pipelines, you can think of it like baking a cake. We have entire recipes for creating our final products (containers), and within those recipes ingredients (software) that we need to add. In this first part, we will talk about the three recipes in this repository, the \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e for the Docker container, the \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e recipe for a Singularity container, and the \u003ca href=\"app_recipes\"\u003eapp_recipes/*.scif\u003c/a\u003e recipes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Scientific Filesystem Recipe\u003c/h3\u003e\u003ca id=\"user-content-the-scientific-filesystem-recipe\" class=\"anchor\" aria-label=\"Permalink: The Scientific Filesystem Recipe\" href=\"#the-scientific-filesystem-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA scientific filesystem is useful because it allows me to write one recipe for my various software, and then install easily in different containers or on my host. How do you know when you find a recipe? When you find a recipe for a scientific filesystem (SCIF), you will see a file with extension *.scif. For example, in this repository:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"app_recipes\"\u003eapp_recipes/*.scif\u003c/a\u003e are the recipes for the scientific filesystem that will be installed in both the Docker and Singularity containers to be run with nextflow. SCIF is flexible in that there can be \u003cstrong\u003emany\u003c/strong\u003e different internal applications defined in one file, however if we want we can put them in individual files and install them equivalently, as we show in this example.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor example, given the apps \"samtools\", \"bwa\" and \"bcftools\" and using a three recipes files , I would install like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escif install app_recipes/samtools_v1.9_centos7.scif\nscif install app_recipes/bwa_v0.7.17_centos7.scif\nscif install app_recipes/bcftools_v1.9_centos7.scif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ebut I could also define the different applications in only one file and install them the next way.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003escif install nextflow-scif.scif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis level of modularity is up to the user. Programatically, this install would be equivalent. This is a purely \"human information\" decision. If I am building a single container to share with my paper, I would opt to only need one file. If I am providing a builder service to users and want to easily install recipes in a modular fashion, I would want to do the second, which is this case. Here we are preparing a model for developing new workflows as quickly as possible, so app_recipes should contain a collection of app recipes that can be combined in the Singularity/Docker recipe for creating the needed container for the workflow being developed. In fact, probably we will convert app_recipes to a Github repository, so it will be used as a remote repository in all the workflows we will develop, making its update easier.\u003c/p\u003e\n\u003cp\u003eThis means that, for any SCIF recipe and a host of interest (Docker or Singularity container, or your computer) you can install the same recipes.\u003c/p\u003e\n\u003cp\u003eSCIF makes the installation to be so tidy, it makes me wanna cry, a scif folder is created in \u003ccode\u003e/\u003c/code\u003e of your container and the tree directory will look like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/scif/apps/\n samtools/\n bin/\n lib/\n scif/\n runscript.help\n runscript\n env/\n 01-base.sh\n 90-environment.sh\n bwa/\n ....\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAlso SCIF will provide a separate variable environment for each app (we are going to mess it a little bit in order to get nextflow functioning), and some cool ENV variables $SCIF_APP(BIN|ENV|...) that seem promising for a lot of options. You can read more on \u003ca href=\"https://www.sylabs.io/guides/2.5.1/user-guide/reproducible_scif_apps.html\" rel=\"nofollow\"\u003eSingularity docs\u003c/a\u003e and \u003ca href=\"https://sci-f.github.io/\" rel=\"nofollow\"\u003eSCIF docs\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Docker Recipe\u003c/h3\u003e\u003ca id=\"user-content-the-docker-recipe\" class=\"anchor\" aria-label=\"Permalink: The Docker Recipe\" href=\"#the-docker-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e is the recipe for building our container. You will also notice the installation is simple - we start with a container base that was equivalently used by the creator of the pipeline with system / host dependencies. In this case, we have used a base docker container of centos 7, this is because we use this SO in our lab and our HPC, and it is useful for me to have homogeneous installation recipes in both containers, my workstation and HPC (at least for now that we are figuring all this stuff out).\u003c/p\u003e\n\u003cp\u003eThen simply install the SCIF recipe to it. That comes down to these three commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eRUN pip install scif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install scif from pypi\u003c/span\u003e\nADD app_recipes/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.scif /opt \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add the recipe to the container\u003c/span\u003e\nRUN scif install /opt/samtools.scif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install it to the container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow a little about the \"mess\", you can see that we added export PATH variable with bin folder for each app, this should not be necessary because app/bin folder is added to the PATH automatically when the app is run, \u003cstrong\u003eBUT\u003c/strong\u003e nextflow expects to have access to the executables in the path or you have to provide the full path.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker Recipe\u003c/span\u003e\nENV PATH=\u003cspan class=\"pl-smi\"\u003e${PATH}\u003c/span\u003e:/scif/apps/samtools/bin \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity Recipe\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexport PATH=${PATH}:/scif/apps/samtools/bin\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$SINGULARITY_ENVIRONMENT\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn order to take advantage of complete nextflow functionality I decided to assume this \"mistake\" in the use of SCIF, which will make the apps envs not being totally separated. You can read the \u003ca href=\"#notes\"\u003eNotes\u003c/a\u003e section for the pros and cons of this approximation and an insight of another posibility I don\u0027t like so much (or I consider less useful for our situation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: it would be cool to use $SCIF_APPBIN_samtools, instead of the full path \u003ccode\u003e/scif/apps/samtools/bin\u003c/code\u003e, but I tried and this variable must be set after PATH env variable, because I can\u0027t get it working.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Singularity Recipe\u003c/h3\u003e\u003ca id=\"user-content-the-singularity-recipe\" class=\"anchor\" aria-label=\"Permalink: The Singularity Recipe\" href=\"#the-singularity-recipe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recipe file for a Singularity container is the file \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e. The format of the recipe file is different, but installing the scientific filesystem, again from the recipes \u003ca href=\"app_recipes\"\u003eapp_recipes\u003c/a\u003e is performed with the same commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install scif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install scif from pypi\u003c/span\u003e\nscif install /opt/samtools_v1.9_centos7.scif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install it to the container\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe only missing command to add the recipe to the container is because Singularity recipes allow you to do this in a \u003ccode\u003e%files\u003c/code\u003e section that functions like a cp \u003csource\u003e (if not dest supplied the file will be copied in \u003ccode\u003e/\u003c/code\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e%files\n samtools_v1.9_centos7.scif /opt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Containers build and usage\u003c/h2\u003e\u003ca id=\"user-content-2-containers-build-and-usage\" class=\"anchor\" aria-label=\"Permalink: 2. Containers build and usage\" href=\"#2-containers-build-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce we have our recipe or in order to create our installation commands with ease, we need to know how to build and interact with containers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShell to a container\u003c/h3\u003e\u003ca id=\"user-content-shell-to-a-container\" class=\"anchor\" aria-label=\"Permalink: Shell to a container\" href=\"#shell-to-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can interact with a container in many different ways and for differente purposes. For example, during the development of the container, I took a strategy to start with a base, interactively shell into it, and test installation and running of things. To do this we can directly use a centos container from Dockerhub, singularity is what I prefer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell -w docker://centos:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr docker if you want:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it centos\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild a container\u003c/h3\u003e\u003ca id=\"user-content-build-a-container\" class=\"anchor\" aria-label=\"Permalink: Build a container\" href=\"#build-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have your recipe prepared you need to build your image:\u003c/p\u003e\n\u003cp\u003eWith Singularity\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build nextflow-scif.img Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOr with Docker:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# . points to the path where docker will import the context for image creation. If you are in the repository path you have only to do:\u003c/span\u003e\ndocker build -t nextflow-scif \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Dockerfile and context will be automatically imported to the docker daemon.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning a container\u003c/h3\u003e\u003ca id=\"user-content-running-a-container\" class=\"anchor\" aria-label=\"Permalink: Running a container\" href=\"#running-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCommand run, in Singularity and Docker will execute environment variables and the script configured in \u003ccode\u003e%runscript\u003c/code\u003e section in \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e or ENTRYPOINT in \u003ca href=\"Dockerfile\"\u003eDockerfile\u003c/a\u003e. In this case only scif is configured, being the command that will let us interact with the applications installed in the container.\n\u003cstrong\u003eNOTE:\u003c/strong\u003e We can\u0027t define an ENTRYPOINT in docker because Nextflow doesn\u0027t support docker containers with entrypoint at the moment, it seems in its \u003ca href=\"https://www.nextflow.io/docs/latest/docker.html#docker-page\" rel=\"nofollow\"\u003edocs\u003c/a\u003e that have implemented some twist but is already deprecated. This means that for interacting with the applications we have to explicitly use scif command after docker run.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif\nsingularity run nextflow-scif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output will be something like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eScientific Filesystem [v0.0.71]\nusage: scif [-h] [--debug] [--quiet] [--writable]\n \n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n ...\n\nscientific filesystem tools\n\noptional arguments:\n -h, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n --debug use verbose logging to debug.\n --quiet suppress print output\n --writable, -w \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e relevant commands, \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e writable SCIF is needed\n\nactions:\n actions \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e Scientific Filesystem\n\n {version,pyshell,shell,preview,help,install,inspect,run,apps,dump,exec}\n scif actions\n version show software version\n pyshell Interactive python shell to scientific filesystem\n shell shell to interact with scientific filesystem\n preview preview changes to a filesytem\n \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e look at \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e an app, \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e it exists.\n install install a recipe on the filesystem\n inspect inspect an attribute \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e a scif installation\n run entrypoint to run a scientific filesystem\n apps list apps installed\n dump dump recipe\n \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e execute a \u003cspan class=\"pl-c1\"\u003ecommand\u003c/span\u003e to a scientific filesystem\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInspecting Applications\u003c/h4\u003e\u003ca id=\"user-content-inspecting-applications\" class=\"anchor\" aria-label=\"Permalink: Inspecting Applications\" href=\"#inspecting-applications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe strength of SCIF is that it will always show you the applications installed in a container, and then provide predictable commands for inspecting, running, or otherwise interacting with them. For example, if I find the container, without any prior knowledge I can reveal the applications inside:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif apps\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif apps\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\nsingularity run nextflow-scif apps\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output will look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e samtools\n bwa\n bcftools\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe can also obtain help about a particular application\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\nsingularity run nextflow-scif \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e samtools\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe output will look like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Samtools\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand then inspecting\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif inspect samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif inspect samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\nsingularity run nextflow-scif inspect samtools\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\n \"samtools\": {\n \"apprun\": [\n \" exec samtools \\\"$@\\\"\"\n ],\n \"apphelp\": [\n \" Samtools\"\n ],\n \"applabels\": [\n \"VERSION 1.9\",\n ]\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe creator of the container didn\u0027t write any complicated scripts to have this happen - the help text is just a chunk of text in a block of the recipe. The labels that are parsed to json, are also just written easily on two lines. This means that the creator can spend less time worry about exposing this. If you can write a text file, you can make your applications programatically parseable. You can check how this is done adding free text to \u003ccode\u003e%apphelp\u003c/code\u003e and \u003ccode\u003e%applabel\u003c/code\u003e in \u003ca href=\"app_recipes/samtools_v1.9_centos7.scif\"\u003esamtools_v1.9_centos7.scif\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e \u003ccode\u003e%appenv\u003c/code\u003e can be also used for environment variables particular of each application. We are not using this because of nextflow functionality. See \u003ca href=\"#notes\"\u003eNotes\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInteracting with Applications\u003c/h4\u003e\u003ca id=\"user-content-interacting-with-applications\" class=\"anchor\" aria-label=\"Permalink: Interacting with Applications\" href=\"#interacting-with-applications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI can easily shell into the container in the context of an application, meaning that the\nenvironment is sourced, etc. \u003cstrong\u003eTHIS IS NOT WORKING: USING SHELL OR EXEC ON A APP RUNS %RUNSCRIPT OF THE APP, TRY REMOVING THE EXPORT PATHS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run -it nextflow-scif scif shell samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif shell samtools\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e[samtools] executing /bin/bash \nroot@d002e338b88b:/scif/apps/samtools# env \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep PATH\nLD_LIBRARY_PATH=/scif/apps/samtools/lib\nPATH=/scif/apps/samtools/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNotice how I\u0027m in the app\u0027s context (in it\u0027s application folder) and that it\u0027s bin is added to the path? I can also shell in without a specific application context, but still have all the SCIF \u003ca href=\"https://sci-f.github.io/spec-v1#environment-namespace\" rel=\"nofollow\"\u003eglobal variables\u003c/a\u003e available to me.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container without the apps\u003c/h3\u003e\u003ca id=\"user-content-using-the-container-without-the-apps\" class=\"anchor\" aria-label=\"Permalink: Using the container without the apps\" href=\"#using-the-container-without-the-apps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe can still shell into the \"general\" container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run -it nextflow-scif shell\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif shell\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or\u003c/span\u003e\nsingularity shell nextflow-scif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eWARNING No app selected, will run default [\u0027/bin/bash\u0027]\nexecuting /bin/bash \nroot@055a34619d17:/scif# ls\napps\ndata\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr thanks to our \"mess\", in which we have exported bin app folders to the path, we can execute each app directly from singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e### This are the commands that nextflow will run internally.\n# Docker \ndocker run nextflow-scif samtools\n# Singularity\nsingularity exec nextflow-scif samtools\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e This is how nextflow uses containers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning Applications\u003c/h4\u003e\u003ca id=\"user-content-running-applications\" class=\"anchor\" aria-label=\"Permalink: Running Applications\" href=\"#running-applications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore we get into creating a pipeline, look how easy it is to run an application. Without scif, we would have to have known that samtools is installed, and then executed the command to the container. But with the scientific filesystem, we discovered the app (shown above) and then we can just run it. The \u003ccode\u003erun\u003c/code\u003e command maps to the entrypoint, as was defined by the creator:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif run samtools\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity \u003c/span\u003e\n./nextflow-scif run samtools\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eProgram: samtools (Tools \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ealignments\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the SAM format)\nVersion: 0.1.18 (r982:295)\n\nUsage: samtools \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecommand\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e [options]\n\nCommand: view SAM\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003eBAM conversion\n sort sort alignment file\n mpileup multi-way pileup\n depth compute the depth\n faidx index/extract FASTA\n tview text alignment viewer\n index index alignment\n idxstats BAM index stats (r595 or later)\n fixmate fix mate information\n flagstat simple stats\n calmd recalculate MD/NM tags and \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e=\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e bases\n merge merge sorted alignments\n rmdup remove PCR duplicates\n reheader replace BAM header\n cat concatenate BAMs\n targetcut cut fosmid regions (for fosmid pool only)\n phase phase heterozygotes\n\n[samtools] executing /bin/bash /scif/apps/samtools/scif/runscript\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd executing any command in the context of the application is possible too: \u003cstrong\u003eTHIS IS NOT WORKING\u003c/strong\u003e.\nThis is equal to the issue shown with shell, due to out little twist for get nextflow working, here we have all the apps added to the PATH, but this should be the correct functioning of SCIF:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Docker\u003c/span\u003e\ndocker run nextflow-scif scif \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e samtools env \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep PATH\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Singularity\u003c/span\u003e\n./nextflow-scif \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e samtools env \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e grep PATH\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eLD_LIBRARY_PATH=/scif/apps/samtools/lib\nPATH=/scif/apps/samtools/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that for above, you will get more output with the Singularity container, as it shares the environment with the host. Whether we are using Docker or Singularity, the actions going on internally with the scientific filesystem client are the same. Given a simple enough pipeline, we could stop here, and just issue a series of commands to run the different apps.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeploy a container\u003c/h3\u003e\u003ca id=\"user-content-deploy-a-container\" class=\"anchor\" aria-label=\"Permalink: Deploy a container\" href=\"#deploy-a-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce you have your container completely functional you can upload it to Docker Hub or Singularity Hub for version control and sharing. We are uploading it to Docker Hub since Singularity can easily build and run a container from there.\u003c/p\u003e\n\u003cp\u003eIf you have built your docker image in your container you can do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# See your images\ndocker images\n# You have to create an account on Docker Hub and a new repository which is very easy.\n# Then log to docker hub from the command line.\ndocker login --username=yourhubusername --email=youremail@company.com\n# You can tag your image.\ndocker tag bb38976d03cf buisciii/nextflow-scif:1.0\n# You can push\ndocker push bu-isciii/nextflow-scif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Nextflow workflow\u003c/h2\u003e\u003ca id=\"user-content-2-nextflow-workflow\" class=\"anchor\" aria-label=\"Permalink: 2. Nextflow workflow\" href=\"#2-nextflow-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e enables scalable and reproducible scientific workflows managing software containers natively like a charm. It allows the adaptation of pipelines written in the most common scripting languages. Moreover its fluent DSL simplifies the implementation and the deployment of complex parallel and reactive workflows on clouds and clusters, being able to abstract the users both from \u003cem\u003ewhere\u003c/em\u003e and even \u003cem\u003ehow\u003c/em\u003e they compute without any concern about the multiple dependencies a scientific workflow can have.\nWe will need a \u003ca href=\"nextflow.config\"\u003enextflow config file\u003c/a\u003e and a \u003ca href=\"main.nf\"\u003emain.nf\u003c/a\u003e script for start using nextflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfig file\u003c/h3\u003e\u003ca id=\"user-content-config-file\" class=\"anchor\" aria-label=\"Permalink: Config file\" href=\"#config-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the nextflow config file we can customize all parameters, environment variables, profiles, etc. we will need in our pipeline. If you want to know more about config files for nextflow you can check it \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn this case we have created a \"simple\" config file to use as a template for more complex configuration, using \u003ca href=\"https://github.com/nf-core\"\u003enf-core\u003c/a\u003e as a model. It contains some sections with information \u003ccode\u003emanifest\u003c/code\u003e and process default parameters.\u003c/p\u003e\n\u003cp\u003eFollowing that we have my favourite part where you can configure \u003ccode\u003eprofiles\u003c/code\u003e for executing your nextflow pipeline. With this you can specify \u003ccode\u003e-profile standard/docker/singularity\u003c/code\u003e and the pipeline will run in your local machine, in your singularity image or in your docker image. Moreover you can add further configuration limiting the memory or the number of cpus (even per process), or any custom variables you need for running in your institution HPC.\u003c/p\u003e\n\u003cp\u003eThe section looks like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eprofiles {\n\n standard {\n includeConfig \u0027conf/base.config\u0027\n }\n\n docker {\n includeConfig \u0027conf/docker.config\u0027\n }\n\n singularity {\n \tincludeConfig \u0027conf/singularity.config\u0027\n }\n\n hpc_isciii {\n \t// TODO. with modules.\n }\n\n testing {\n \t// TODO\n }\n\n aws {\n \t// TO DO\n }\n\n none {\n // Don\u0027t load any config (for use with custom home configs)\n }\n\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe have separated the configuration in several config files, the singularity and docker config files will enable singularity and docker and the path/url to the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity {\n enabled = true\n}\nprocess {\n // Path to container\n container = \u0027buisciii/nextflow-scif:1.0\u0027\n executor = \u0027local\u0027\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMain nextflow script\u003c/h3\u003e\u003ca id=\"user-content-main-nextflow-script\" class=\"anchor\" aria-label=\"Permalink: Main nextflow script\" href=\"#main-nextflow-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main nextflow script \u003ca href=\"main.nf\"\u003emain.nf\u003c/a\u003e creates the pipeline concatenating the output of one step with another, and managing the input and output. You can check the nextflow \u003ca href=\"https://www.nextflow.io/docs/latest/script.html\" rel=\"nofollow\"\u003edocs\u003c/a\u003e for mastering te creation of nextflow pipelines.\u003c/p\u003e\n\u003cp\u003eHere we have configured a simple template for a basic pipeline. It will be useful as an start point for developing more complex pipelines, with help and logging function templates, and space for variable checking and email reporting.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the pipeline\u003c/h3\u003e\u003ca id=\"user-content-running-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline\" href=\"#running-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow, let\u0027s have some fun and run our precious brand new pipeline with our great containers:\u003c/p\u003e\n\u003cp\u003eThis command is the simplier, it will run the pipeline with all parameters by default, and standard profile, which means that it will be run in your local machine and you need to have all software installed and in your PATH.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Simpler one\nnextflow run main.nf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, let\u0027s use our container with docker or with singularity:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run main.nf -profile singularity\nnextflow run main.nf -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eJust as simple as that, nextflow handles the use of our singularity/docker container adding \u003ccode\u003edocker run\u003c/code\u003e or \u003ccode\u003esingularity exec\u003c/code\u003e before each script in the process configured in \u003ca href=\"main.nf\"\u003emain.nf\u003c/a\u003e!!\u003c/p\u003e\n\u003cp\u003eBut this gets better because for running this pipeline you don\u0027t even need to download this repository, you only need to have installed nextflow and Singularity/Docker and type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run BU-ISCIII/nextflow-scif -profile singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand nextflow will pull the github repository, download the docker image and build it into a singularity image and run your pipeline using only one line of command!!\u003c/p\u003e\n\u003cp\u003eAlso thanks to scif you can still interact with the image pulled from nextflow, in this case is a singularity image build from the docker image in DockerHub and by default is saved in \u003ccode\u003ework/singularity\u003c/code\u003e. Since this image is created from the Dockerfile without an entrypoint as we saw before we have to interact with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# For instance inspecting bwa app\nsingularity exec buisciii-nextflow-scif-1.0.img scif inspect bwa \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis is not much a problem because in normal HPC usage we will be using a Singularity image build locally by us with Singulary Recipe, Docker will be used more for easy sharing.\u003c/p\u003e\n\u003cp\u003eOf course this is functioning with test data included in the github repository, but you can easily supply your own reads, reference genome and output dir using parameters. You can see the help of the pipeline:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run BU-ISCIII/nextflow-scif --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOutput:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eN E X T F L O W ~ version 0.29.0 \nLaunching `BU-ISCIII/nextflow-scif` [angry_albattani] - revision: d768dfd571 [develop] \n\n=========================================\n BU-ISCIII/nextflow-scif DEMO PIPELINE v0.1\n========================================= \nUsage: \n\nThe typical command for running the pipeline is as follows:\n\nnextflow run BU-ISCIII/nextflow-scif -profile standard\n\nPipeline arguments:\n --reads Path to input data (must be surrounded with quotes).\n --genome Path to reference genome.\n --outdir Output dir.\n --help show this message.\n -profile Hardware config to use. standard/docker/singularity. Default: standard.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand run it with your own data:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run BU-ISCIII/nextflow-scif -profile singularity --reads *.fastq --genome genome.fa --outdir results\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e If you are using singularity as a container you have to take into consideration that it mounts $PWD and /home/${user} by default so if your data is in this directories you will be fine. For advance usage see \u003ca href=\"#advance-usage\"\u003ethis section\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Testing\u003c/h2\u003e\u003ca id=\"user-content-3-testing\" class=\"anchor\" aria-label=\"Permalink: 3. Testing\" href=\"#3-testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com\" rel=\"nofollow\"\u003eCircleCI\u003c/a\u003e is a platform that allows you to configure real circle processes for develop, test and deploy your github projects. First of all you need to connect your github account to circle from its website, and then create a .circleci folder in your repo with a \u003ca href=\".circleci/config.yml\"\u003econfig.yml file\u003c/a\u003e.\nIt works executing the processes you configured in your config file in the cloud, creating a docker container for building your container, with really useful cache options which made building container images a lot faster. It amazes me hoy it works with containers inside containers, using one container for build a container xD.\nThe config file consists in the definition of a base container where the magin will happen, then you start defining jobs and steps inside this jobs, for example here we have defined setup, get_data, build, test and deploy jobs. For build step, for example, we define the commands we need to use for build our container using Docker recipe in our repository.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esetup:\n environment:\n - TZ: \"/usr/share/zoneinfo/Europe/Madrid\"\n docker:\n - image: docker:18.01.0-ce-git\n steps:\n - run:\n name: Test if the user has defined the repo name\n command: apk add --no-cache pigz python3\n\n build:\n environment:\n - TZ: \"/usr/share/zoneinfo/Europe/Madrid\"\n docker:\n - image: docker:18.01.0-ce-git\n working_directory: /tmp/src\n steps:\n - run:\n name: Install parallel gzip, python3 and java\n command: apk add --no-cache pigz python3 openjdk8 bash\n - run:\n name: Install nextflow\n command: |\n cd /usr/bin\n wget -qO- https://get.nextflow.io | bash\n - restore_cache:\n keys:\n - docker-v1-{{ .Branch }}-{{ epoch }}\n - docker-v1-{{ .Branch }}-\n - docker-v1-master-\n - docker-v1-\n paths:\n - /tmp/cache/container.tar.gz\n\n - checkout\n - setup_remote_docker\n - run:\n name: Load Docker image layer cache\n no_output_timeout: 30m\n command: |\n docker info\n set +o pipefail\n if [ -f /tmp/cache/container.tar.gz ]; then\n pigz -d --stdout /tmp/cache/container.tar.gz | docker load\n fi\n docker images\n - run:\n name: Build Docker image\n no_output_timeout: 60m\n command: |\n echo \"Working directory is ${PWD}\"\n # If not set, define CONTAINER_NAME\n if [ ! -n \"${CONTAINER_NAME:-}\" ]\n then\n CONTAINER_NAME=\"${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}\"\n fi\n echo \"Container name is ${CONTAINER_NAME}\"\n # Build docker image\n e=1 \u0026amp;\u0026amp; for i in {1..5}; do\n docker build \\\n --cache-from=${CONTAINER_NAME} \\\n --rm=false \\\n -t ${CONTAINER_NAME}:latest \\\n --build-arg BUILD_DATE=`date -u +\"%Y-%m-%dT%H:%M:%SZ\"` \\\n --build-arg VCS_REF=`git rev-parse --short HEAD` \\\n --build-arg VERSION=\"${CIRCLE_TAG:-$THISVERSION}\" . \\\n \u0026amp;\u0026amp; e=0 \u0026amp;\u0026amp; break || sleep 15\n done \u0026amp;\u0026amp; [ \"$e\" -eq \"0\" ]\n # Pipeline VERSION\n THISVERSION=$(nextflow run ${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME} --version)\n echo \"Pipeline Version is ${THISVERSION}\"\n echo \"${CIRCLE_TAG:-$THISVERSION}\"\n - run:\n name: Docker save\n no_output_timeout: 40m\n command: |\n # If not set, define CONTAINER_NAME\n if [ ! -n \"${CONTAINER_NAME:-}\" ]\n then\n CONTAINER_NAME=\"${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}\"\n fi\n echo \"Saving ${CONTAINER_NAME} to container.tar.gz\"\n mkdir -p /tmp/cache\n docker save ${CONTAINER_NAME}:latest \\\n | pigz -2 -p 3 \u0026gt; /tmp/cache/container.tar.gz\n - persist_to_workspace:\n root: /tmp\n paths:\n - cache/container.tar.gz\n - src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSe how a container is built, and our recipe is built inside that container, then the Docker image is saved, and tagged, and persisted in the cache directory. This paths will be available in the next jobs of the config file, and if we rerun the cicle config the cache can be used.\nAlso, is worth to to notice how we can use some ENV variables of CircleCI, they can be accessed in any step and you can see them in your circleCI dashboard (It is useful for understand how it works to look around it, you can check in \u003ca href=\"https://circleci.com/gh/BU-ISCIII/nextflow-scif\" rel=\"nofollow\"\u003ehere\u003c/a\u003e). Moreover you can define your own in your circleci settings in the dashboard, in this case I have added the CONTAINER_NAME and the DOCKERHUB_PASS.\u003c/p\u003e\n\u003cp\u003eThe same way we have prepared our container building, we can automatize it deployment into Docker HUB (see the file), and the testing or our apps (still in progress). You can define different rules for different branches in your github repo, or when you make a new tag, for example in this case deployment to Docker HUB only occurs when there is a new push or a new tag to the master branch, when you push to a develop branch only build and test jobs are performed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAdvance usage\u003c/h1\u003e\u003ca id=\"user-content-advance-usage\" class=\"anchor\" aria-label=\"Permalink: Advance usage\" href=\"#advance-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTill this point everything is working fine because we are using the data located in our current directory, and singularity mounts \u003ccode\u003e$PWD\u003c/code\u003e by default. But what if we are running in our HPC-cluster, and our data is in /processing_Data or other custom directory?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTODO\u003c/strong\u003e: Try in different locations, with data and reference genome in different folders.\nUse of --bind ? . Need to pre-create paths inside the containers??\nImprovement of I/O metadata mounting only the project folder instead of all processing_Data??\n\u003cem\u003eNote\u003c/em\u003e: for each app scif/app/{app}/data folder is created. That\u0027s a possibility for mounting. We have to make some performance tests in our cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSummary\u003c/h1\u003e\u003ca id=\"user-content-summary\" class=\"anchor\" aria-label=\"Permalink: Summary\" href=\"#summary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eWe have created a container with all the software needed for our pipeline with Singularity and Docker using the same SCI-F recipes. This will allow us to deploy new containers really quicky for new pipelines once you have created a good collection of scif apps recipes.\u003c/li\u003e\n\u003cli\u003eUsing SCI-F we can access all our apps installed separately from our container. Besides, our container is no longer a \"black-box\" and we can obtain information about what apps are installed in our container, with its labels and help.\u003c/li\u003e\n\u003cli\u003eOur container is also functional with the native container commands, and we can exec any command in our container without the use of SCI-F which made it flexible enough for its use with nextflow.\u003c/li\u003e\n\u003cli\u003eWe have deployed our container to DockerHub so new users don\u0027t have to build the image theirselves.\u003c/li\u003e\n\u003cli\u003eFinally we have created a basic nextflow workflow, with three different profile configurations {standard, singularity, docker}, this way you can use the same pipeline running in your local machine with your software installed, or with the singularity or docker image. Besides, you can create more profiles for its usage in a HPC with \u003ccode\u003emodule\u003c/code\u003e configured, or even in a AWS server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe idea is to have a workflow independent of where you are computing, and a container system which allows you to assure reproducibility and sharing, including the maximun information possible for its use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNotes\u003c/h1\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to get this working I had to make a decision: put SCIF on the \"top\" or put nextflow instead. I will try to explain myself:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNextflow on the top:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextflow provides a natively functionality for using singularity and docker. Using it you just set the commands the pipeline is going to run, and nextflow handles the use of \u003ccode\u003edocker run\u003c/code\u003e or \u003ccode\u003esingularity exec\u003c/code\u003e before this commands with the container you supplied in the configuration.\u003c/li\u003e\n\u003cli\u003eThis means that the executables for each app \u003cstrong\u003eMUST\u003c/strong\u003e be accesible when you run/exec from the container, not each of the apps with SCI-F.\u003c/li\u003e\n\u003cli\u003eThus, we are being forced to add the executables to the PATH; and somehow mess up SCIF goal of having separate apps environment adding the bin app folder to the PATH only when the app is run. Being this the main con of this approximation.\u003c/li\u003e\n\u003cli\u003eOn the other hand, our main.nf will remain untouched and we will be able to run in a local machine, a cluster or a container without any modification. Which was I was looking for.\u003c/li\u003e\n\u003cli\u003eFor this to work your minimun needs in your system are nextflow and Docker/Singularity if you are using docker or singularity profile.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSCIF on the top (I am going to make a branch testing this): - \u0026gt; I tried but it didn\u0027t work, I get all the apps being recognized but when the process in nextflow where going to be run the files weren\u0027t found. This is because nextflow uses a temp folder called \u003ccode\u003ework\u003c/code\u003e for all executions, and when you use scif run bwa, the command is run in ${SCIF_APPROOT} instead on nextflow folder.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAnother aproximation will be to install nextflow inside the containers. This way you can only interact with the apps commands through SCIF which will be the only entrypoint in Singularity and Docker.\u003c/li\u003e\n\u003cli\u003eNextflow in this case will not use Singularity or Docker profile, because it is INSIDE one, so this config parameters must be set to false.\u003c/li\u003e\n\u003cli\u003emain.nf script would have to be modified and \u003ccode\u003escif run\u003c/code\u003e must be add before each command.\u003c/li\u003e\n\u003cli\u003eThe pros of this approximation will be that apps will be in separate environments as SCI-F plans to, and you won\u0027t need to have nextflow installed in your system. If you use the image, just with docker or singularity installed you are set.\u003c/li\u003e\n\u003cli\u003eHowever you could only use this nextflow pipeline with the image prepare for this, or have scif installed in your local/hpc system, and all the apps you are going to use inside scif which may not be the case (as in our HPC where we don\u0027t have install permissions)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAlternative middle point configuration -\u0026gt; I have this working in \u003cstrong\u003efeature/nextflow-scif-alt\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eI install nextflow inside the container like the option above, and each app environment is separated, but I have to add all app bin folders to the path prior to run nextflow command in %apprun.\u003c/li\u003e\n\u003cli\u003eAlso I had to cd to ${SCIF_DATA}, and bind this folder with my $PWD in the singularity command.\u003c/li\u003e\n\u003cli\u003eFor more info please check feature/nextflow-scif-alt branch in this repository, and README-ALT.md file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eI suppose this is a decision you have to make, one could think that if I can use my singularity image in my HPC system and forgot about modules and stuff, I will have all the software I need, in the version I need and whenever I need it. However even if this is going to be the case (we are still testing performance, we are very newbies using containers) I always like to have flexibility, and nextflow provides me in theory that \"I don\u0027t mind where I compute\" flexibility.\u003c/p\u003e\n", + "full_name": "manuel-munoz-aguirre/singularity-pytorch-gpu", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esingularity-pytorch-gpu\u003c/h1\u003e\u003ca id=\"user-content-singularity-pytorch-gpu\" class=\"anchor\" aria-label=\"Permalink: singularity-pytorch-gpu\" href=\"#singularity-pytorch-gpu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4969\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSingularity image for a deep learning (pytorch) environment + GPU support (cuda-10.2). Contains libraries to perform common ML tasks. \u003ccode\u003eOpenslide\u003c/code\u003e is included to manipulate whole-slide histology images, \u003ccode\u003eimagemagick\u003c/code\u003e for general image manipulation. \u003ccode\u003eJupyterLab\u003c/code\u003e and \u003ccode\u003ecode-server\u003c/code\u003e (VS Code) are also included in the image. This image has been tested in an HPC (SGE) with distributed pytorch applications.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalling singularity\u003c/h2\u003e\u003ca id=\"user-content-installing-singularity\" class=\"anchor\" aria-label=\"Permalink: Installing singularity\" href=\"#installing-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install singularity, see the \u003ca href=\"https://sylabs.io/guides/3.6/admin-guide/installation.html#installation-on-linux\" rel=\"nofollow\"\u003eofficial docs\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding/downloading the image\u003c/h2\u003e\u003ca id=\"user-content-buildingdownloading-the-image\" class=\"anchor\" aria-label=\"Permalink: Building/downloading the image\" href=\"#buildingdownloading-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build an image called \u003ccode\u003etorchenv.sif\u003c/code\u003e based on the definition file \u003ccode\u003eSingularity.1.0.0\u003c/code\u003e, an NVIDIA GPU and \u003ccode\u003ecuda-10.2\u003c/code\u003e drivers must be available on the host system. Clone this repository, move into it and run the singularity build command.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/manuel-munoz-aguirre/singularity-pytorch-gpu.git \u0026amp;\u0026amp; \\\ncd singularity-pytorch-gpu \u0026amp;\u0026amp; \\\nsudo singularity build torchenv.sif Singularity.1.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOtherwise, the image can be pulled directly from singularity hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull torchenv.sif shub://manuel-munoz-aguirre/singularity-pytorch-gpu:1.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing the container\u003c/h2\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo spawn an interactive shell within the container, use the command below. The \u003ccode\u003e--nv\u003c/code\u003e flag setups the container to use NVIDIA GPUs (read more \u003ca href=\"https://sylabs.io/guides/3.6/user-guide/gpu.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --nv torchenv.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run a script (for example, \u003ccode\u003escript.py\u003c/code\u003e) using the container without starting an interactive shell:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec --nv torchenv.sif python3 script.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe container can also be launched and used on a system without a GPU, but upon startup it will display a warning about missing NVIDIA binaries on the host.\u003c/p\u003e\n", "stargazers_count": 9, - "subscribers_count": 6, + "subscribers_count": 2, "topics": [ - "docker-container", - "nextflow", "singularity", - "recipe", - "singularity-containers", - "nextflow-scif", - "nextflow-pipelines", - "docker-image", - "scif-recipe", - "nextflow-workflow" + "pytorch", + "deep-learning", + "machine-learning", + "environment" ], - "updated_at": 1663001088.0 + "updated_at": 1708505365.0 }, { "data_format": 2, - "description": "Code, data, and tutorials for \"Sense organ control in moths to moles is a gamble on information through motion\" ", + "description": "Profiling with NVIDIA Nsight Tools Bootcamp", "filenames": [ "Singularity" ], - "full_name": "MacIver-Lab/Ergodic-Information-Harvesting", - "latest_release": "v1.0.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCode and data to reproduce results from \"Tuning movement for sensing in an uncertain world\" by Chen Chen, Todd D. Murphey, and Malcolm A. MacIver, Northwestern University, Evanston IL, USA (eLife, 2020).\u003c/h1\u003e\u003ca id=\"user-content-code-and-data-to-reproduce-results-from-tuning-movement-for-sensing-in-an-uncertain-world-by-chen-chen-todd-d-murphey-and-malcolm-a-maciver-northwestern-university-evanston-il-usa-elife-2020\" class=\"anchor\" aria-label=\"Permalink: Code and data to reproduce results from \u0026quot;Tuning movement for sensing in an uncertain world\u0026quot; by Chen Chen, Todd D. Murphey, and Malcolm A. MacIver, Northwestern University, Evanston IL, USA (eLife, 2020).\" href=\"#code-and-data-to-reproduce-results-from-tuning-movement-for-sensing-in-an-uncertain-world-by-chen-chen-todd-d-murphey-and-malcolm-a-maciver-northwestern-university-evanston-il-usa-elife-2020\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/2511\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/maciverlabnu/ergodic-information-harvesting\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ea20765da22a256496de0b94e50b33361438d344ff096f94a862699ffa94bb90/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f636c6f75642f6275696c642f6d6163697665726c61626e752f6572676f6469632d696e666f726d6174696f6e2d68617276657374696e672e737667\" alt=\"Docker status\" data-canonical-src=\"https://img.shields.io/docker/cloud/build/maciverlabnu/ergodic-information-harvesting.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/maciverlabnu/ergodic-information-harvesting\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/962ce4a3720b30c3b75702b81f36bce7ef82c5084f7775528e7ccfe5a3e98f17/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d6163697665726c61626e752f6572676f6469632d696e666f726d6174696f6e2d68617276657374696e672e737667\" alt=\"Docker pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/maciverlabnu/ergodic-information-harvesting.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://nbviewer.jupyter.org/github/MacIver-Lab/Ergodic-Information-Harvesting/blob/master/Tutorial/Ergodic_Information_Harvesting_Tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bfeb5472ee3df9b7c63ea3b260dc0c679be90b97/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72656e6465722d6e627669657765722d6f72616e67652e7376673f636f6c6f72423d66333736323626636f6c6f72413d346434643464\" alt=\"nbviewer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/MacIver-Lab/Ergodic-Information-Harvesting/master?filepath=Tutorial%2FErgodic_Information_Harvesting_Tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96332ce22984004fd6244de25503b895fd42ac4f20a23e7d9110af95dfc20551/68747470733a2f2f6d7962696e6465722e6f72672f62616467652e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eErgodic Information Harvesting (EIH) Video \u0026amp; Tutorial\u003c/h1\u003e\u003ca id=\"user-content-ergodic-information-harvesting-eih-video--tutorial\" class=\"anchor\" aria-label=\"Permalink: Ergodic Information Harvesting (EIH) Video \u0026amp; Tutorial\" href=\"#ergodic-information-harvesting-eih-video--tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://youtu.be/8N_UzCcEw4w\" rel=\"nofollow\"\u003eVideo showing the fish, mammal, and insect behaviors that were compared to the trajectories generated by EIH\u003c/a\u003e, and a video showing how the algorithm works by way of \u003ca href=\"https://youtu.be/QBtMMROk4GM\" rel=\"nofollow\"\u003eapplying it to control an underwater electrolocation robot\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInteractive Jupyter notebook tutorial, click to view online: \u003ca href=\"https://nbviewer.jupyter.org/github/MacIver-Lab/Ergodic-Information-Harvesting/blob/master/Tutorial/Ergodic_Information_Harvesting_Tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bfeb5472ee3df9b7c63ea3b260dc0c679be90b97/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72656e6465722d6e627669657765722d6f72616e67652e7376673f636f6c6f72423d66333736323626636f6c6f72413d346434643464\" alt=\"nbviewer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eor use \u003ca href=\"https://mybinder.org/v2/gh/MacIver-Lab/Ergodic-Information-Harvesting/master?filepath=Tutorial%2FErgodic_Information_Harvesting_Tutorial.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96332ce22984004fd6244de25503b895fd42ac4f20a23e7d9110af95dfc20551/68747470733a2f2f6d7962696e6465722e6f72672f62616467652e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e to run interactively through online Jupyter Notebook\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSteps to reproduce the results shown in the EIH paper\u003c/h1\u003e\u003ca id=\"user-content-steps-to-reproduce-the-results-shown-in-the-eih-paper\" class=\"anchor\" aria-label=\"Permalink: Steps to reproduce the results shown in the EIH paper\" href=\"#steps-to-reproduce-the-results-shown-in-the-eih-paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll of the simulation code is written with \u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython 3\u003c/a\u003e. All of the figure plotting files are written in MATLAB (R2017a+). The code can be run on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA local computer, which is very easy to set up but the performance is ultimately limited by the number of locally accessible CPU cores\u003c/li\u003e\n\u003cli\u003eCloud computing virtual servers through any Infrastructure as a Service (IaaS) provider, \u003cem\u003ee.g.\u003c/em\u003e \u003ca href=\"https://aws.amazon.com/ec2/\" rel=\"nofollow\"\u003eAmazon Elastic Compute Cloud\u003c/a\u003e, \u003ca href=\"https://cloud.google.com/compute/\" rel=\"nofollow\"\u003eGoogle Cloud Compute Engine\u003c/a\u003e, or academic \u003ca href=\"https://en.wikipedia.org/wiki/HPCC\" rel=\"nofollow\"\u003eHPCC (High Performance Computing Cluster)\u003c/a\u003e systems. Cloud computing is easy to setup and provides a way to scale up the total number of running threads (\u003cem\u003ee.g.\u003c/em\u003e Google Cloud Compute Engine allows up to 96 CPU threads per instance). Our code\u0027s runtime environment and dependencies are fully containerized through \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e to minimize the effort needed for environment setup, and for easy scaling to run the code faster (you can run the code on a single CPU over a few days, or on many CPUS on the cloud in a few hours). Setting the code up to run on a cloud service for the first time is somewhat involved if you are not used to doing this. We have a set of screencasts to walk through doing this from scratch on Amazon Web Services that we are happy to share.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that this repository has included all of the published data, including all the simulations, to reproduce all figures in the paper. To reproduce our figures from the published data, rather than re-run all the simulations from scratch, simply jump to step 5 below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed Steps\u003c/h2\u003e\u003ca id=\"user-content-detailed-steps\" class=\"anchor\" aria-label=\"Permalink: Detailed Steps\" href=\"#detailed-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo avoid possible \u003ca href=\"https://en.wikipedia.org/wiki/Dependency_hell\" rel=\"nofollow\"\u003edependency hell\u003c/a\u003e and minimize the effort of setting up the runtime environment we used for our results, we prebuilt a \u003ca href=\"https://en.wikipedia.org/wiki/Container_(virtualization)\" rel=\"nofollow\"\u003econtainer image\u003c/a\u003e to be used for executing all the simulation code in Python using \u003ca href=\"https://docs.docker.com/get-started/overview/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e. Here is an article explaining the utility of containers for reproducibility of research: \u003ca href=\"https://doi.org/10.1371/journal.pone.0177459\" rel=\"nofollow\"\u003eSingularity: Scientific containers for mobility of compute\u003c/a\u003e. Note that this is only for reproducing simulations: for generation of the figures from the simulations, a local installation of MATLAB (not provided in the container) is still required.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Obtain code and data\u003c/h3\u003e\u003ca id=\"user-content-1-obtain-code-and-data\" class=\"anchor\" aria-label=\"Permalink: 1. Obtain code and data\" href=\"#1-obtain-code-and-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo start, you will either be using the \u003ca href=\"https://zenodo.org\" rel=\"nofollow\"\u003eZenodo\u003c/a\u003e archived repository, which includes both the data and the code; or, if you prefer, you will clone the most recent version of the EIH repository. To obtain the Zenodo archived repository, which is a zip of the release of the repository corresponding to the release of the publication by eLife, you will simply search \"maciver chen\" on the \u003ca href=\"https://zenodo.org\" rel=\"nofollow\"\u003eZenodo\u003c/a\u003e website, and download the 32GB zip file of this repository.\u003c/p\u003e\n\u003cp\u003eTo clone the most recent version of the EIH repository (which may have changes from the archived release to correct errors that are found), two tools are required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003egit\u003c/code\u003e - is used to pull all the non-data files from this repository. Go to \u003ca href=\"https://git-scm.com/downloads\" rel=\"nofollow\"\u003egit\u0027s official release page\u003c/a\u003e to download and install git. Then use the following command to clone this repository:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --depth=1 https://github.com/MacIver-Lab/Ergodic-Information-Harvesting\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003egit-lfs\u003c/code\u003e - is used to pull all the published data (required to reproduce the results). Go to \u003ca href=\"https://git-lfs.github.com/\"\u003egit-lfs\u0027s official release page\u003c/a\u003e to download and install. Then run the following command \u003cstrong\u003einside the root directory of the cloned EIH repo \u003ccode\u003e./Ergodic-Information-Harvesting/\u003c/code\u003e\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Ergodic-Information-Harvesting\ngit lfs install\ngit lfs pull\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you succeeded, you should see files being downloaded by \u003ccode\u003egit-lfs\u003c/code\u003e. Once it is setup, should you decide to delete the files and start again, you should only need to do the \u003ccode\u003egit clone\u003c/code\u003e step.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Install Docker and Pull the EIH Container Image\u003c/h3\u003e\u003ca id=\"user-content-2-install-docker-and-pull-the-eih-container-image\" class=\"anchor\" aria-label=\"Permalink: 2. Install Docker and Pull the EIH Container Image\" href=\"#2-install-docker-and-pull-the-eih-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Docker by following the official documentation: \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Optionally) For Linux or Linux-based HPCC/cloud computing environments, please additionally follow the \u003ca href=\"https://docs.docker.com/engine/install/linux-postinstall/\" rel=\"nofollow\"\u003epost-installation setup steps for Linux\u003c/a\u003e to allow running docker without \u003ccode\u003esudo\u003c/code\u003e. If you don\u0027t want or unable to follow this step, you will need to make sure to run docker commands with \u003ccode\u003esudo docker\u003c/code\u003e rather than \u003ccode\u003edocker\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Invoke Shell in the EIH Container Image\u003c/h3\u003e\u003ca id=\"user-content-3-invoke-shell-in-the-eih-container-image\" class=\"anchor\" aria-label=\"Permalink: 3. Invoke Shell in the EIH Container Image\" href=\"#3-invoke-shell-in-the-eih-container-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe container image is a fully self-contained Linux OS image with Python 3 dependencies setup for generating the EIH simulations developed for the study. We will invoke the command line tool inside of the EIH container image to interact with the resources inside the container and start the simulations.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v [absolute path to EIH folder]:/EIH maciverlabnu/ergodic-information-harvesting\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ereplace the \u003ccode\u003e[absolute path to EIH folder]\u003c/code\u003e part with the absolute path to your local EIH repository folder, \u003cem\u003ee.g.\u003c/em\u003e \u003ccode\u003eC:/Ergodic-Information-Harvesting\u003c/code\u003e (remember to replace \u003ccode\u003e\\\u003c/code\u003e with \u003ccode\u003e/\u003c/code\u003e when in Windows) or \u003ccode\u003e~/Ergodic-Information-Harvesting\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf you are already inside the Ergodic-Information-Harvesting folder, you can simply do\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/EIH maciverlabnu/ergodic-information-harvesting\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Start Reproducing Simulation\u003c/h3\u003e\u003ca id=\"user-content-4-start-reproducing-simulation\" class=\"anchor\" aria-label=\"Permalink: 4. Start Reproducing Simulation\" href=\"#4-start-reproducing-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe used \u003ca href=\"https://cython.org/\" rel=\"nofollow\"\u003eCython\u003c/a\u003e to accelerate the simulation which requires compiling some of the code before running the simulation. Compile the accelerated code by calling the following command (this only needs to be done once):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Command to run inside the container\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /EIH\nchmod +x ./BuildCython.sh\n./BuildCython.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou are now all set for the environment setup. You can start reproducing all the simulation results by running the main simulation code:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /EIH/SimulationCode/\npython3 RunAllSims.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, \u003ccode\u003eRunAllSims.py\u003c/code\u003e will check the number of available CPU threads and automally run parallel simulation jobs with the maximum number of threads possible. Nonetheless, the number of threads can be manually specified by passing the desired parallel thread count argument to it, for example\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 RunAllSims.py 20\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewill run 20 threads in parallel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: The simulation will take a long time to finish. Depending on your operating system, you may need to \u003cstrong\u003eprevent your system from going to sleep\u003c/strong\u003e. This is necessary with MacOS. With MacOS: Open a terminal, and type \u003ccode\u003ecaffeinate\u003c/code\u003e and hit return. Your system will be prevented from sleeping until you hit Control-C.\u003c/p\u003e\n\u003cp\u003eOnce all the simulation jobs are done, exit the Singularity shell environment by calling the \u003ccode\u003eexit\u003c/code\u003e command.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Reproduce Figure Results\u003c/h3\u003e\u003ca id=\"user-content-5-reproduce-figure-results\" class=\"anchor\" aria-label=\"Permalink: 5. Reproduce Figure Results\" href=\"#5-reproduce-figure-results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe figure generation code is written in MATLAB and MATLAB R2017a or a more recent version is required. To start, open the \u003ccode\u003emakeFigurePanels.m\u003c/code\u003e code in MATLAB under the \u003ccode\u003eProduction-Figure-Code\u003c/code\u003e folder. To reproduce figure 2, for example, use the following procedure:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLaunch \u003ccode\u003eErgodic-Information-Harvesting/Production-Figure-Code/makeFigurePanels.m\u003c/code\u003e using MATLAB. Note that the code has been tested with MATLAB \u003ccode\u003eR2017a\u003c/code\u003e and \u003ccode\u003eR2018a\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eSpecify input parameters\n\u003cul\u003e\n\u003cli\u003eSet \u003ccode\u003etargetFig = \u0027fig2\u0027\u003c/code\u003e to select figure 2 as the target\u003c/li\u003e\n\u003cli\u003eSet \u003ccode\u003eUSE_PUBLISHED_DATASET = 1\u003c/code\u003e to use the published dataset included in the repository. Alternatively, if the local simulation jobs are completed, use of \u003ccode\u003eUSE_PUBLISHED_DATASET = 0\u003c/code\u003e will force the code to use reproduced data located at \u003ccode\u003eErgodic-Information-Harvesting/SimulationCode/SimData/\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the MATLAB code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should see a new MATLAB figure containing Figure 2 panels. PDF(s) will be saved under \u003ccode\u003eErgodic-Information-Harvesting/Production-Figure-Code/FigureOutput/fig2/\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo reproduce all the figures, follow the same steps, but set \u003ccode\u003etargetFig = \u0027all\u0027\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBenchmark Running Time\u003c/h3\u003e\u003ca id=\"user-content-benchmark-running-time\" class=\"anchor\" aria-label=\"Permalink: Benchmark Running Time\" href=\"#benchmark-running-time\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eBenchmark on AWS\u003c/strong\u003e: \u003ccode\u003e~4.03 hours\u003c/code\u003e on AWS EC2 c5a.24xlarge instance (Ubuntu 18.04 LTS 64-bit, AMD EPYC 7R32 with boost frequency up-to 3.3GHz, 48 Core/96 Threads available under HVM), \u003ccode\u003e~22.17 hours\u003c/code\u003e on AWS EC2 c5a.4xlarge instance (Ubuntu 18.04 LTS 64-bit, AMD EPYC 7R32 with boost frequency up-to 3.3GHz, 8 Core/16 Threads available under HVM). \u003cstrong\u003eBenchmark on macOS\u003c/strong\u003e: \u003ccode\u003e~100 hours\u003c/code\u003e on a 2015 iMac (macOS 10.15.6, 64-bit 3.3 GHz Quad-Core Intel Core i5, 4 Core/8 Threads).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdditional Note for Linux and MacOS Users\u003c/h3\u003e\u003ca id=\"user-content-additional-note-for-linux-and-macos-users\" class=\"anchor\" aria-label=\"Permalink: Additional Note for Linux and MacOS Users\" href=\"#additional-note-for-linux-and-macos-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePrevent System from Sleeping During Simulation\u003c/h4\u003e\u003ca id=\"user-content-prevent-system-from-sleeping-during-simulation\" class=\"anchor\" aria-label=\"Permalink: Prevent System from Sleeping During Simulation\" href=\"#prevent-system-from-sleeping-during-simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo prevent MacOS from sleeping in these instances, use \u003ccode\u003ecaffeinate\u003c/code\u003e at a Terminal window running simulation jobs.\u003c/p\u003e\n", + "full_name": "openhackathons-org/HPC_Profiler", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProfiling with NVIDIA Nsight Tools Bootcamp\u003c/h1\u003e\u003ca id=\"user-content-profiling-with-nvidia-nsight-tools-bootcamp\" class=\"anchor\" aria-label=\"Permalink: Profiling with NVIDIA Nsight Tools Bootcamp\" href=\"#profiling-with-nvidia-nsight-tools-bootcamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains learning materials and exercises for NVIDIA Nsight Tools. Gola is to learn how to profile your application with NVIDIA Nsight Systems,Compute and NVTX API calls to find performance limiters and bottlenecks and apply incremental parallelization strategies. The content was tested on \u003cstrong\u003eNVIDIA driver 515.65\u003c/strong\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction: Overview of profiling tools and Mini Weather application\u003c/li\u003e\n\u003cli\u003eLab 1: Profile Serial application to find hotspots using NVIDIA Nsight System\u003c/li\u003e\n\u003cli\u003eLab 2: Parallelise the serial application using OpenACC compute directives\u003c/li\u003e\n\u003cli\u003eLab 3: Optimizing loops\u003c/li\u003e\n\u003cli\u003eLab 4: Apply incremental parallelization strategies and use profiler\u0027s report for the next step\u003c/li\u003e\n\u003cli\u003eLab 5: Nsight Compute Kernel Level Analysis\u003c/li\u003e\n\u003cli\u003e[Optional]\n\u003cul\u003e\n\u003cli\u003eLab 6:Performance Analysis of an application using Nsight Systems and Compute (CUDA example)\u003c/li\u003e\n\u003cli\u003eAdvanced: Multiprocess profiling\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTarget Audience\u003c/h2\u003e\u003ca id=\"user-content-target-audience\" class=\"anchor\" aria-label=\"Permalink: Target Audience\" href=\"#target-audience\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe target audience for this lab is researchers/graduate students and developers who are interested in getting hands on experience with the NVIDIA Nsight System through profiling a real life parallel application.\u003c/p\u003e\n\u003cp\u003eWhile Labs 1-5 do not assume any expertise in CUDA experience, basic knowledge of OpenACC programming (e.g: compute constructs), GPU architecture, and programming experience with C/C++ is desirable.\u003c/p\u003e\n\u003cp\u003eThe Optional lab 6 requires basic knowledge of CUDA programming, GPU architecture, and programming experience with C/C++.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial Duration\u003c/h2\u003e\u003ca id=\"user-content-tutorial-duration\" class=\"anchor\" aria-label=\"Permalink: Tutorial Duration\" href=\"#tutorial-duration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe lab material will be presented in a 2.5hr session. The link to the material is available for download at the end of each lab.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites:\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites:\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run this content you will need a machine with NVIDIA GPUs (Nsight Systems supports Pascal and above (SM 60+), and Nsight Compute supports Volta and above (SM 70+)).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall the \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://sylabs.io/docs/%5D\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eInstall Nvidia toolkit, \u003ca href=\"https://developer.nvidia.com/nsight-systems\" rel=\"nofollow\"\u003eNsight Systems (latest version)\u003c/a\u003e and \u003ca href=\"https://developer.nvidia.com/nsight-compute\" rel=\"nofollow\"\u003ecompute (latest version)\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eThe base containers required for the lab may require users to create a NGC account and generate an API key (\u003ca href=\"https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account\" rel=\"nofollow\"\u003ehttps://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreating containers\u003c/h2\u003e\u003ca id=\"user-content-creating-containers\" class=\"anchor\" aria-label=\"Permalink: Creating containers\" href=\"#creating-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo start with, you will have to build a Docker or Singularity container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker Container\u003c/h3\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-label=\"Permalink: Docker Container\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build a docker container, run:\n\u003ccode\u003esudo docker build -t \u0026lt;imagename\u0026gt;:\u0026lt;tagnumber\u0026gt; .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor instance:\n\u003ccode\u003esudo docker build -t profiling:latest .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe code labs have been written using Jupyter lab and a Dockerfile has been built to simplify deployment. In order to serve the docker instance for a student, it is necessary to expose port 8000 from the container, for instance, the following command would expose port 8000 inside the container as port 8000 on the lab machine:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --rm -it --gpus=all -p 8888:8888 profiling:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen this command is run, you can browse to the serving machine on port 8000 using any web browser to access the labs. For instance, from if they are running on the local machine the web browser should be pointed to \u003ca href=\"http://localhost:8000\" rel=\"nofollow\"\u003ehttp://localhost:8000\u003c/a\u003e. The \u003ccode\u003e--gpus\u003c/code\u003e flag is used to enable \u003ccode\u003eall\u003c/code\u003e NVIDIA GPUs during container runtime. The \u003ccode\u003e--rm\u003c/code\u003e flag is used to clean an temporary images created during the running of the container. The \u003ccode\u003e-it\u003c/code\u003e flag enables killing the jupyter server with \u003ccode\u003ectrl-c\u003c/code\u003e. This command may be customized for your hosting environment.\u003c/p\u003e\n\u003cp\u003eThen, inside the container launch the Jupyter lab assigning the port you opened:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ejupyter-lab --ip 0.0.0.0 --port 8888 --no-browser --allow-root\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOnce inside the container, open the jupyter lab in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e, and start the lab by clicking on the \u003ccode\u003e_start_profiling.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Container\u003c/h3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Singularity Container\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the singularity container, run:\n\u003ccode\u003esudo singularity build _profiler.simg Singularity\u003c/code\u003e . If you do not have \u003ccode\u003esudo\u003c/code\u003e rights, you can build the singularity container with \u003ccode\u003e--fakeroot\u003c/code\u003e option: \u003ccode\u003esingularity build --fakeroot _profiler.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand copy the files to your local machine to make sure changes are stored locally:\n\u003ccode\u003esingularity run _profiler.simg cp -rT /labs ~/labs\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, run the container:\n\u003ccode\u003esingularity run --nv _profiler.simg jupyter-lab --notebook-dir=~/labs\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOnce inside the container, open the jupyter lab in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e, and start the lab by clicking on the \u003ccode\u003e_start_profiling.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePlease go through the list of exisiting bugs/issues or file a new issue at \u003ca href=\"https://github.com/openhackathons-org/HPC_Profiler/issues\"\u003eGithub\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 9, + "subscribers_count": 1, + "topics": [ + "cuda", + "hpc", + "nsight-systems", + "openacc", + "nsight-compute" + ], + "updated_at": 1727338317.0 + }, + { + "data_format": 2, + "description": "The labyrinth king judges your gene models.", + "filenames": [ + "minos/etc/Singularity.tools.def", + "minos/etc/Singularity.minos.def", + "minos/etc/Singularity.minos.centos.def", + "minos/etc/Singularity.busco.def", + "minos/dependencies/container_defs/Singularity.def", + "minos/dependencies/container_defs/Singularity.busco.def" + ], + "full_name": "EI-CoreBioinformatics/minos", + "latest_release": "v1.8.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eminos - a gene model consolidation pipeline for genome annotation projects\u003c/h1\u003e\u003ca id=\"user-content-minos---a-gene-model-consolidation-pipeline-for-genome-annotation-projects\" class=\"anchor\" aria-label=\"Permalink: minos - a gene model consolidation pipeline for genome annotation projects\" href=\"#minos---a-gene-model-consolidation-pipeline-for-genome-annotation-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos is a Python3/Snakemake - based pipeline that generates and utilises metrics derived from protein, transcript and expression data sets to consolidate gene models obtained from gene annotation workflows.\u003c/p\u003e\n\u003cp\u003eFor the majority of the computational work, minos utilises \u003ca href=\"https://github.com/EI-CoreBioinformatics/Mikado\"\u003eMikado\u003c/a\u003e. The pipeline runs Mikado \u003ccode\u003eprepare\u003c/code\u003e on provided gene sets, and generates external metrics such as blastp/blastx alignments, busco assessments and kallisto expression quantification. These metrics are then passed on to Mikado \u003ccode\u003eserialise\u003c/code\u003e and \u003ccode\u003epick\u003c/code\u003e. In a final set of steps, models are filtered according to user-provided criteria and annotated release gene/transcript sets are generated.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow\u003c/h2\u003e\u003ca id=\"user-content-workflow\" class=\"anchor\" aria-label=\"Permalink: Workflow\" href=\"#workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/minos/doc/Minos.png\"\u003e\u003cimg src=\"/minos/doc/Minos.png\" alt=\"Alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nFigure 1. The overview of MINOS pipeline\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos requires Python 3.6 at the very least (better Python 3.7+ as it is essential that dictionary insertion order is preserved.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython dependencies\u003c/h3\u003e\u003ca id=\"user-content-python-dependencies\" class=\"anchor\" aria-label=\"Permalink: Python dependencies\" href=\"#python-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese dependencies should be installed automatically if not present.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epyyaml \u0026gt;= 5 (in order to not have the yaml configuration files ordered alphabetically)\u003c/li\u003e\n\u003cli\u003eSnakemake \u0026gt;= 5.14.0 (to make use of later Snakemake performance and stability features)\u003c/li\u003e\n\u003cli\u003edrmaa (for hpc environments)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation from GitHub\u003c/h3\u003e\u003ca id=\"user-content-installation-from-github\" class=\"anchor\" aria-label=\"Permalink: Installation from GitHub\" href=\"#installation-from-github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003egit clone https://github.com/EI-CoreBioinformatics/minos.git\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd minos\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003epython setup.py bdist_wheel;\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003epip install dist/*whl\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation from conda\u003c/h3\u003e\u003ca id=\"user-content-installation-from-conda\" class=\"anchor\" aria-label=\"Permalink: Installation from conda\" href=\"#installation-from-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eTBD\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWorkflow dependencies\u003c/h3\u003e\u003ca id=\"user-content-workflow-dependencies\" class=\"anchor\" aria-label=\"Permalink: Workflow dependencies\" href=\"#workflow-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos makes extensive use of 3rd party software, most of which can be installed from conda.\nThe following tools are required and these tools can be installed using singularity container definitions provided (\u003ca href=\"minos/etc/Singularity.tools.def\"\u003eSingularity.tools.def\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ekallisto 0.44.0 (generation of expression based metrics from RNAseq data)\u003c/li\u003e\n\u003cli\u003eblast+ (generation of protein similarity metrics from blastp/x analysis of protein evidence data)\u003c/li\u003e\n\u003cli\u003eseqkit (fasta processing)\u003c/li\u003e\n\u003cli\u003egenometools (GFF3 validation)\u003c/li\u003e\n\u003cli\u003egffread (extraction of transcript parameters and CDS, cDNA, protein sequences)\u003c/li\u003e\n\u003cli\u003ebedtools (transposable element analysis)\u003c/li\u003e\n\u003cli\u003eCPC2 (\u003ca href=\"http://cpc2.cbi.pku.edu.cn\" rel=\"nofollow\"\u003ehttp://cpc2.cbi.pku.edu.cn\u003c/a\u003e) (assess the protein-coding potential of transcripts)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe following three dependencies require special treatment (s. below):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMikado *\u003c/li\u003e\n\u003cli\u003eBUSCO \u0026gt;= 4 (busco protein based metrics, full busco statistics for annotation quality control) **\u003c/li\u003e\n\u003cli\u003eCPC2 (\u003ca href=\"http://cpc2.cbi.pku.edu.cn\" rel=\"nofollow\"\u003ehttp://cpc2.cbi.pku.edu.cn\u003c/a\u003e) ***\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRegistering workflow dependencies with minos\u003c/h4\u003e\u003ca id=\"user-content-registering-workflow-dependencies-with-minos\" class=\"anchor\" aria-label=\"Permalink: Registering workflow dependencies with minos\" href=\"#registering-workflow-dependencies-with-minos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore running minos, the user has to register all dependencies in the \u003ccode\u003eprogram_calls\u003c/code\u003e section of the run configuration file (etc/minos_config.yaml). This allows users to manage their own installations. For convenience, we provide singularity container definitions (\u003ca href=\"minos/etc/Singularity.tools.def\"\u003eSingularity.tools.def\u003c/a\u003e) for most dependencies, except BUSCO, as well as a conda recipe. Unfortunately, due to our hpc environment, we cannot provide solutions utilising the native conda/singularity support in current Snakemake version. (However, we would be glad for any community contributions in that regard).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSpecial case: Mikado\u003c/h4\u003e\u003ca id=\"user-content-special-case-mikado\" class=\"anchor\" aria-label=\"Permalink: Special case: Mikado\" href=\"#special-case-mikado\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003epreferred: singularity container due to development cycle and ease of installation\u003c/li\u003e\n\u003cli\u003eDue to its complexity and rapid development cycle Mikado should have its own container\u003c/li\u003e\n\u003cli\u003ethis should all not matter anymore when there is a stable Mikado\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSpecial case: BUSCO\u003c/h4\u003e\u003ca id=\"user-content-special-case-busco\" class=\"anchor\" aria-label=\"Permalink: Special case: BUSCO\" href=\"#special-case-busco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eBUSCO requires an older version of blast due to issues with multithreading tblastn\u003c/li\u003e\n\u003cli\u003ethis is not compatible with having a newer blast+ for the minos protein blast analyses\u003c/li\u003e\n\u003cli\u003eour solution: individual BUSCO singularity container \u003ca href=\"minos/etc/Singularity.busco.def\"\u003eSingularity.busco.def\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003epotential solution: conda environment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSpecial case: CPC2\u003c/h4\u003e\u003ca id=\"user-content-special-case-cpc2\" class=\"anchor\" aria-label=\"Permalink: Special case: CPC2\" href=\"#special-case-cpc2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCPC2 is forked to our repository and ported to Python3 and can be installed from here - \u003ca href=\"https://github.com/EI-CoreBioinformatics/CPC2\"\u003ehttps://github.com/EI-CoreBioinformatics/CPC2\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eInstallation instructions are also added to the singularity container definition file (\u003ca href=\"minos/etc/Singularity.tools.def\"\u003eSingularity.tools.def\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos requires a set of input files and data. To keep minos-based analyses structured it is recommended to collect all input in a project directory, e.g. by soft-linking or copying.\u003c/p\u003e\n\u003cp\u003eA minos run consists of two steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGeneration of the run configuration (\u003ccode\u003eminos configure\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eRunning the minos workflow (\u003ccode\u003eminos run\u003c/code\u003e) driven by the run configuration generated in Step 1.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eminos configure\u003c/h3\u003e\u003ca id=\"user-content-minos-configure\" class=\"anchor\" aria-label=\"Permalink: minos configure\" href=\"#minos-configure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eminos configure\u003c/code\u003e takes as input a set of configuration files:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003elist_file\u003c/strong\u003e A tab-separated file describing the set of transcript models. An example file is provided here \u003ca href=\"data/list.txt\"\u003elist.txt\u003c/a\u003e. A detailed description for each column header can be found here - \u003ca href=\"https://mikado.readthedocs.io/en/stable/Usage/Configure/#input-annotation-files\" rel=\"nofollow\"\u003eMikado list file format\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003escoring_template\u003c/strong\u003e A yaml file containing the scoring settings. This can be copied from the minos repo \u003ca href=\"minos/etc/scoring_template.yaml\"\u003eminos/etc/scoring_template.yaml\u003c/a\u003e and modified if required. There is no need to add in the external metrics and scoring sections as this will be done automatically by minos configure!\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003egenome_reference\u003c/strong\u003e A fasta file containing the genome reference. This should be softlinked instead of copied.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eexternal_metrics_configuration\u003c/strong\u003e A Mikado configuration file (e.g. \u003ca href=\"https://github.com/EI-CoreBioinformatics/mikado/blob/master/sample_data/plant_external.yaml\"\u003esample_data/plant_external.yaml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eexternal_metrics\u003c/strong\u003e A tab-separated file describing the metrics data to be used in the minos run. The column descriptions can be found in \u003ca href=\"#metrics-info\"\u003eSection metrics_info\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003econfiguration\u003c/strong\u003e A yaml file with parameters to control minos run. This can be obtained from the minos repo (\u003ca href=\"minos/etc/minos_config.yaml\"\u003eminos/etc/minos_config.yaml\u003c/a\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAfter these input files have been generated/obtained, \u003ccode\u003eminos configure\u003c/code\u003e can be run. Items 1,2,3 from the above list are positional arguments, items 4,5,6 are optional. [TBC!]\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCommand line arguments\u003c/h4\u003e\u003ca id=\"user-content-command-line-arguments\" class=\"anchor\" aria-label=\"Permalink: Command line arguments\" href=\"#command-line-arguments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e--outdir, -o\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSets the output directory (default: \u003ccode\u003eminos_run\u003c/code\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--blastmode {blastp,blastx}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eControls whether transcript models (blastx) or their translated peptide sequences (blastp, default) are compared against the provided protein evidence.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--annotation-version ANNOTATION_VERSION, --genus-identifier GENUS_IDENTIFIER\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinal output files are named with the prefix \u003ccode\u003e\u0026lt;GENUS_IDENTIFIER\u0026gt;_\u0026lt;ANNOTATION_VERSION\u0026gt;\u003c/code\u003e, e.g. \u003ccode\u003eQUISA32244_EIv1\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--use-tpm-for-picking\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eControls whether RNA-seq data is used for metrics generation in addition to classification. (default: off) Caution: \u003ccode\u003e--use-tpm-for-picking\u003c/code\u003e activates using expression metrics for the picking stage.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--force-reconfiguration, -f\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eControls whether an existing configuration file in the chosen project directory (set with \u003ccode\u003e-o\u003c/code\u003e or \u003ccode\u003e--outdir\u003c/code\u003e) will be overwritten. By default, this behaviour is turned off.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--mikado-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDue to the ongoing Mikado development and frequent updates, the Mikado version (currently required to be served from a Singularity container) used by minos has to be submitted via command line option rather than via the configuration file. This allows for more flexibility in swapping out Mikado versions. As soon as Mikado 2.0 has reached a stable state, we will take all efforts so that this option will no longer be mandatory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eBUSCO Options\u003c/h5\u003e\u003ca id=\"user-content-busco-options\" class=\"anchor\" aria-label=\"Permalink: BUSCO Options\" href=\"#busco-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eminos supports core gene assessments with BUSCO4 for metrics generation and quality assessment.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--busco-level BUSCO_LEVEL\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA comma-separated string to select the BUSCO mode. Valid levels are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e{\"proteins\", \"proteome\", \"transcripts\", \"transcriptome\", \"genome\", \"none\", \"off\", \"p\", \"t\", \"g\", \"a\", \"all\", \"prot\", \"tran\", \"geno\"}.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe default mode is \u201cproteins\u201d (or all in a development version). \u201cnone\u201d and \u201coff\u201d disable BUSCO analyses.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--busco-lineage BUSCO_LINEAGE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis needs to be either a path to the odb10 database used for the BUSCO analysis or a valid database identifier. This option is required if \u003ccode\u003e--busco-level\u003c/code\u003e is not in \u003ccode\u003e{none,off}\u003c/code\u003e. Note, the latter option requires an internet connection and removal of the \u003ccode\u003e--offline\u003c/code\u003e parameter in the run configuration file.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--busco-genome-run BUSCO_GENOME_RUN\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAs an alternative to computing the genome BUSCO analysis, a path can be specified pointing to a directory containing the short_summary.txt and full_table.tsv from a precomputed BUSCO genome run on the reference. Using this option will prompt minos\u2019s BUSCO genome rule to copy the input files to the output folder instead of running BUSCO in genome mode.\u003c/p\u003e\n\u003cp\u003eBUSCO copy number assessment can be configured by passing/modifying the \u003ccode\u003e--limit\u003c/code\u003e parameters in the \u003ccode\u003eparams: busco: \u0026lt;busco_run\u0026gt;\u003c/code\u003e section of the configuration file. Additionally, the maximum copy number that is reported in an individual category in the final BUSCO output table can be set via \u003ccode\u003emisc: busco_max_copy_number\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eminos configure example run:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eminos configure --mikado-container \u0026lt;/path/to/mikado/container\u0026gt; \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -o \u0026lt;output-directory\u0026gt; --external external.yaml \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --external-metrics external_metrics.txt --use-tpm-for-picking \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --genus-identifier \u0026lt;GENUS_ID\u0026gt; --annotation-version \u0026lt;ANN_VERSION\u0026gt; \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e list.txt \u0026lt;/path/to/scoring-template.yaml\u0026gt; \u0026lt;/path/to/genome_reference\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eminos run\u003c/h3\u003e\u003ca id=\"user-content-minos-run\" class=\"anchor\" aria-label=\"Permalink: minos run\" href=\"#minos-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eminos run\u003c/code\u003e starts the minos pipeline, controlled by a run configuration file. It is highly recommended to use \u003ccode\u003eminos configure\u003c/code\u003e to generate this configuration file as this will also generate the required Mikado configuration.\u003c/p\u003e\n\u003cp\u003eEI_internal:\nNote that for convenience, \u003ccode\u003eminos run\u003c/code\u003e has an NBI cluster wrapper called \u003ccode\u003eminos_run_sub\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eminos run example run:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom local machine:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eminos run --mikado-container \u0026lt;/path/to/mikado/container\u0026gt; \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --no_drmaa --scheduler NONE -o \u0026lt;output-directory\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn HPC (default \u003ccode\u003eSLURM\u003c/code\u003e, an example HPC config JSON we use is here \u003ca href=\"minos/etc/hpc_config.json\"\u003ehpc_config.json\u003c/a\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eminos_run_sub --mikado-container \u0026lt;/path/to/mikado/container\u0026gt; \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e --partition \u0026lt;partition\u0026gt; --hpc_config /path/to/hpc_config.json \\\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e -o \u0026lt;output-directory\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration files\u003c/h2\u003e\u003ca id=\"user-content-configuration-files\" class=\"anchor\" aria-label=\"Permalink: Configuration files\" href=\"#configuration-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erun configuration\u003c/h3\u003e\u003ca id=\"user-content-run-configuration\" class=\"anchor\" aria-label=\"Permalink: run configuration\" href=\"#run-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe minos workflow is controlled by a run configuration file in yaml format. This file is generated by \u003ccode\u003eminos configure\u003c/code\u003e from a configuration template (\u003ca href=\"minos/etc/minos_config.yaml\"\u003eminos_config.yaml\u003c/a\u003e), and is saved to the output directory.\u003c/p\u003e\n\u003cp\u003eThe run configuration template contains the following information:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eparams\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis section allows the user to specify additional parameters for the tools used in the workflow (e.g. score, evalue cutoffs etc).\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\u003ccode\u003eprogram_calls\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis section contains the instructions for running the minos dependencies. If a dependency is installed in the environment, this could just be the tool name. Otherwise, this might include a \u003ccode\u003esingularity exec \u0026lt;/path/to/container\u0026gt;\u003c/code\u003e call, a source or module load command, etc.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\u003ccode\u003ecollapse_metrics_threshold\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis section contains the classification rules for the collapse_metrics rule.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003ccode\u003emisc\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis section contains parameters for rules not involving 3rd party dependencies and other unsorted things.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003emetrics info\u003c/h3\u003e\u003ca id=\"user-content-metrics-info\" class=\"anchor\" aria-label=\"Permalink: metrics info\" href=\"#metrics-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA tab-separated file with the following columns:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003emetric_name_prefix\u003c/code\u003e\nShort name for the metric.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCurrently, the following convention has to be followed:\nkallisto metrics need to have a suffix according to their strandedness:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eunstranded: _xx, reverse-forward: _rf, forward-reverse: _fr\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe rf/fr suffixes follow the kallisto command line parameter naming scheme.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emetric_class\u003c/code\u003e\nCan be one of the following: \u003ccode\u003eexpression\u003c/code\u003e, \u003ccode\u003ealn_tran\u003c/code\u003e, \u003ccode\u003ealn_prot\u003c/code\u003e, \u003ccode\u003eseq_prot\u003c/code\u003e (for protein blast db generation), \u003ccode\u003ejunction\u003c/code\u003e, \u003ccode\u003erepeat\u003c/code\u003e, \u003ccode\u003e*blastdb_prot\u003c/code\u003e (* not implemented)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003emultiplier\u003c/code\u003e\nThe multiplier can be given either as a single value or a comma-separated key:value list (e.g. \u003ccode\u003eaF1:X,eF1:Y,jF1:Z,nF1:W\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003enot_fragmentary_min_value\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003efile_path\u003c/code\u003e\nFull path to the metrics file.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003ePaired-end reads for kallisto need to be provided as \u003ccode\u003e\u0026lt;path/to/R1\u0026gt;,\u0026lt;path/to/R2\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMultiple samples for expression metrics can be given as one line each with the same \u003ccode\u003emetric_name_prefix\u003c/code\u003e (in such a case, the \u003ccode\u003emultiplier\u003c/code\u003e will be read from the line with the first sample)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting minos\u003c/h2\u003e\u003ca id=\"user-content-citing-minos\" class=\"anchor\" aria-label=\"Permalink: Citing minos\" href=\"#citing-minos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTBD\u003c/p\u003e\n\u003cp\u003eSince minos uses Mikado, please consider to cite:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVenturini L., Caim S., Kaithakottil G., Mapleson D.L., Swarbreck D. Leveraging multiple transcriptome assembly methods for improved gene structure annotation. GigaScience, Volume 7, Issue 8, 1 August 2018, giy093, \u003ca href=\"https://doi.org/10.1093/gigascience/giy093\" rel=\"nofollow\"\u003edoi:10.1093/gigascience/giy093\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eIf you also use Portcullis junctions as input metric, please consider to cite:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eMapleson D.L., Venturini L., Kaithakottil G., Swarbreck D. Efficient and accurate detection of splice junctions from RNAseq with Portcullis. GigaScience, Volume 7, Issue 12, 12 December 2018, giy131, \u003ca href=\"https://doi.org/10.1093/gigascience/giy131\" rel=\"nofollow\"\u003edoi:10.1093/gigascience/giy131\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "stargazers_count": 9, + "subscribers_count": 7, + "topics": [], + "updated_at": 1730349359.0 + }, + { + "data_format": 2, + "description": "Mutect pipeline with Nextflow", + "filenames": [ + "Singularity/Singularity.v2.1_gatk3", + "Singularity/Singularity.v2.2", + "Singularity/Singularity.v2.2_gatk2", + "Singularity/Singularity.v2.1", + "Singularity/Singularity.v2.2_gatk3", + "Singularity/Singularity.v2.1_gatk2", + "Singularity/Singularity.v2.0" + ], + "full_name": "IARCbioinfo/mutect-nf", + "latest_release": "v2.3", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emutect-nf\u003c/h1\u003e\u003ca id=\"user-content-mutect-nf\" class=\"anchor\" aria-label=\"Permalink: mutect-nf\" href=\"#mutect-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMutect pipeline for somatic variant calling with Nextflow\u003c/h2\u003e\u003ca id=\"user-content-mutect-pipeline-for-somatic-variant-calling-with-nextflow\" class=\"anchor\" aria-label=\"Permalink: Mutect pipeline for somatic variant calling with Nextflow\" href=\"#mutect-pipeline-for-somatic-variant-calling-with-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/mutect-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/252d6f9ba145dfc9b78cc63744bd08a9212ae9cfd069e7c3ad8ba8801c8ca3cd/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f6d75746563742d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/mutect-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/mutect-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4357\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"mutectpipeline.png?raw=true\"\u003e\u003cimg src=\"mutectpipeline.png?raw=true\" alt=\"workflow\" title=\"Scheme of calling Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNextflow pipeline for somatic variant calling with mutect with Mutect1 or 2, gatk3 or gatk4\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eNextflow: for common installation procedures see the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/broadinstitute/mutect\"\u003eMutect\u003c/a\u003e and its dependencies (Java 1.7 and Maven 3.0+), or \u003ca href=\"https://github.com/broadinstitute/gatk\"\u003egatk4\u003c/a\u003e that now includes Mutect2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://bedtools.readthedocs.io/en/latest/content/installation.html\" rel=\"nofollow\"\u003ebedtools\u003c/a\u003e and move the executable file in your path.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003epython\u003c/a\u003e and package \u003ca href=\"https://github.com/pysam-developers/pysam\"\u003epysam\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bedops/bedops\"\u003ebedops\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eA conda receipe, and docker and singularity containers are available with all the tools needed to run the pipeline (see \"Usage\")\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGATK4\u003c/h3\u003e\u003ca id=\"user-content-gatk4\" class=\"anchor\" aria-label=\"Permalink: GATK4\" href=\"#gatk4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith GATK4, a list of known_snps can be provided to mutect2 to improve the variant classification, for example file \u003ca href=\"https://console.cloud.google.com/storage/browser/_details/gatk-best-practices/somatic-hg38/af-only-gnomad.hg38.vcf.gz\" rel=\"nofollow\"\u003eaf-only-gnomad.hg38.vcf.gz\u003c/a\u003e from the bundle best practices from the broad institute \u003ca href=\"https://console.cloud.google.com/storage/browser/gatk-best-practices/somatic-hg38/\" rel=\"nofollow\"\u003eGATK somatic calling bundle\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eestimate contamination\u003c/h3\u003e\u003ca id=\"user-content-estimate-contamination\" class=\"anchor\" aria-label=\"Permalink: estimate contamination\" href=\"#estimate-contamination\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen the estimate contamination mode is chosen, one needs to provide a list of known snps; we recommend the file \u003ca href=\"https://console.cloud.google.com/storage/browser/_details/gatk-best-practices/somatic-hg38/small_exac_common_3.hg38.vcf.gz\" rel=\"nofollow\"\u003esmall_exac_common_3.hg38.vcf.gz\u003c/a\u003e from the best practices broad institute bundle.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--tumor_bam_folder\u003c/td\u003e\n\u003ctd\u003ea folder with tumor bam files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--normal_bam_folder\u003c/td\u003e\n\u003ctd\u003ea folder with normal bam files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--tn_file\u003c/td\u003e\n\u003ctd\u003einput tabulation-separated values file with columns sample (sample name), tumor (full path to tumor bam), normal (full path to matched normal bam); optionally (for --genotype mode), columns preproc (is the bam RNAseq needing preprocessing: yes or no) and vcf (full path to vcf file containing alleles to genotype)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput methods\u003c/h3\u003e\u003ca id=\"user-content-input-methods\" class=\"anchor\" aria-label=\"Permalink: Input methods\" href=\"#input-methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that there are two input methods: separate tumor_bam_folder and normal_bam_folder, and tn_file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSeparated tumor_bam_folder and normal_bam_folder method\u003c/h4\u003e\u003ca id=\"user-content-separated-tumor_bam_folder-and-normal_bam_folder-method\" class=\"anchor\" aria-label=\"Permalink: Separated tumor_bam_folder and normal_bam_folder method\" href=\"#separated-tumor_bam_folder-and-normal_bam_folder-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe method assumes that normal and tumor bam files are in these respective folder, and uses parameters suffix_tumor and suffix_normal to detect them (the rest of the file name needs to be identical.\u003c/p\u003e\n\u003cp\u003eThe tumor bam file format must be (\u003ccode\u003esample\u003c/code\u003e \u003ccode\u003esuffix_tumor\u003c/code\u003e \u003ccode\u003e.bam\u003c/code\u003e) with \u003ccode\u003esuffix_tumor\u003c/code\u003e as \u003ccode\u003e_T\u003c/code\u003e by default and customizable in input (\u003ccode\u003e--suffix_tumor\u003c/code\u003e). (e.g. \u003ccode\u003esample1_T.bam\u003c/code\u003e)\nThe normal bam file format must be (\u003ccode\u003esample\u003c/code\u003e \u003ccode\u003esuffix_normal\u003c/code\u003e \u003ccode\u003e.bam\u003c/code\u003e) with \u003ccode\u003esuffix_normal\u003c/code\u003e as \u003ccode\u003e_N\u003c/code\u003e by default and customizable in input (\u003ccode\u003e--suffix_normal\u003c/code\u003e). (e.g. \u003ccode\u003esample1_N.bam\u003c/code\u003e).\nBAI indexes have to be present in the same location than their BAM mates, with the extension \u003ccode\u003ebam.bai\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThe tn_file method\u003c/h4\u003e\u003ca id=\"user-content-the-tn_file-method\" class=\"anchor\" aria-label=\"Permalink: The tn_file method\" href=\"#the-tn_file-method\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe method uses a tabulation-separated values format file with columns sample, tumor, and normal (in any order); it does not use parameters suffix_tumor and suffix_normal and does not require file names to match. When the genotype mode is active, additional columns are expected: preproc, specifying if preprocessing of RNA-seq bam file is required (yes or no) and vcf, indicating the location of the vcf file containing the alleles to genotype. preproc includes splitting spanning reads, correcting CIGAR string with NDN pattern, and changing mapping quality of uniquely mapped reads from 255 to 60(gatk4\u0027s splitNCigarReads and a custom python script). The tn_file method is necessary for joint multi-sample calling, in which case the sample name is used to group files, and to specify preprocessing of some RNA-seq samples.\u003c/p\u003e\n\u003cp\u003eBAI indexes have to be present in the same location than their BAM mates, with the extension \u003ccode\u003ebam.bai\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth align=\"right\"\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd align=\"right\"\u003eref.fa\u003c/td\u003e\n\u003ctd\u003ereference genome fasta file\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003enumber of CPUs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003ctd\u003ememory for mapping\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--suffix_tumor\u003c/td\u003e\n\u003ctd\u003e_T\u003c/td\u003e\n\u003ctd\u003esuffix for tumor file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--suffix_normal\u003c/td\u003e\n\u003ctd\u003e_N\u003c/td\u003e\n\u003ctd\u003esuffix for matched normal file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003emutect_results\u003c/td\u003e\n\u003ctd\u003eoutput folder for aligned BAMs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bed\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eBed file containing intervals\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--region\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eA region defining the calling, in the format CHR:START-END\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--known_snp\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eVCF file with known variants and frequency (e.g., from gnomad)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mutect_args\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eArguments you want to pass to mutect. WARNING: form is \" --force_alleles \" with spaces between quotes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--nsplit\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eSplit the region for calling in nsplit pieces and run in parallel\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--java\u003c/td\u003e\n\u003ctd\u003ejava\u003c/td\u003e\n\u003ctd\u003eName of the JAVA command\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--snp_contam\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eVCF file with known germline variants to genotype for contamination estimation (requires --estimate_contamination)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--PON\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003epath to panel of normal VCF file used to filter calls\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--gatk_version\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003egatk version\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref_RNA\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003efasta reference for preprocessing RNA (required when preproc column contains yes in input tn_file)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNOTE: if neither --bed or --region, will perform the calling on whole genome, based on the faidx file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional for gatk3\u003c/h4\u003e\u003ca id=\"user-content-optional-for-gatk3\" class=\"anchor\" aria-label=\"Permalink: Optional for gatk3\" href=\"#optional-for-gatk3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese options are not needed if gatk4 is used\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cosmic\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eCosmic VCF file required by mutect; not in gatk4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mutect_jar\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003epath to jar file of mutect1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mutect2_jar\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003epath to jar file of mutect2\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eprint usage and optional parameters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--estimate_contamination\u003c/td\u003e\n\u003ctd\u003erun extra step of estimating contamination by normal and using the results to filter calls; only for gatk4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--genotype\u003c/td\u003e\n\u003ctd\u003euse genotyping from vcf mode instead of usual variant calling requires tn_file with vcf column and gatk4, and if RNA-seq included, requires preproc column\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--filter_readorientation\u003c/td\u003e\n\u003ctd\u003eRun extra step learning read orientation model and using it to filter reads\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the pipeline on a series of matched tumor normal files (with suffixes \u003cem\u003e_T\u003c/em\u003e and \u003cem\u003e_N\u003c/em\u003e) in folders \u003cem\u003etumor_BAM\u003c/em\u003e \u003cem\u003enormal_BAM\u003c/em\u003e, a reference genome with indexes \u003cem\u003eref\u003c/em\u003e, and a bed file ref.bed, one can type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run IARCbioinfo/mutect-nf -r v2.2b -profile singularity --tumor_bam_folder tumor_BAM/ --normal_bam_folder normal_BAM/ --ref ref_genome.fa --gtf ref.gtf \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the pipeline without singularity just remove \"-profile singularity\". Alternatively, one can run the pipeline using a docker container (-profile docker) the conda receipe containing all required dependencies (-profile conda). Note that we provide similar support when using gatk3 (profiles conda_gatk3, singularity_gatk3, and docker_gatk3) or gatk2 (profiles conda_gatk2, singularity_gatk2, and docker_gatk2).\u003c/p\u003e\n\u003cp\u003eTo use gatk3, set \u003ccode\u003e--gatk_version 3\u003c/code\u003eand provide option \u003ccode\u003e--mutect2_jar\u003c/code\u003e for mutect version 2 (GATK executable jar, which integrate mutect2) and possibly specify \u003ccode\u003e-profile singularity_gatk3\u003c/code\u003e, and set \u003ccode\u003e--mutect_jar\u003c/code\u003e for mutect version 1 and possibly specify \u003ccode\u003e-profile singularity_gatk2\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHelp section\u003c/h4\u003e\u003ca id=\"user-content-help-section\" class=\"anchor\" aria-label=\"Permalink: Help section\" href=\"#help-section\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can print the help manual by providing \u003ccode\u003e--help\u003c/code\u003e in the execution command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/mutect-nf --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis shows details about optional and mandatory parameters provided by the user.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003esample.vcf.gz and sample.vcf.gz.tbi\u003c/td\u003e\n\u003ctd\u003efiltered VCF files and their indexes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003estats/\u003c/td\u003e\n\u003ctd\u003egatk stats files from mutect\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eintermediate_calls/raw_calls/sample.vcf\u003c/td\u003e\n\u003ctd\u003eunfiltered VCF files\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe output_folder directory contains two subfolders: stats and intermediate_calls\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQ\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhy are some samples absent from the output vcfs when I run multi-sample calling?\u003c/h3\u003e\u003ca id=\"user-content-why-are-some-samples-absent-from-the-output-vcfs-when-i-run-multi-sample-calling\" class=\"anchor\" aria-label=\"Permalink: Why are some samples absent from the output vcfs when I run multi-sample calling?\" href=\"#why-are-some-samples-absent-from-the-output-vcfs-when-i-run-multi-sample-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutputs are based on the SM field of the BAM file; when multiple files have the same SM, only one is outputed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhy are some samples present in the input file ignored?\u003c/h3\u003e\u003ca id=\"user-content-why-are-some-samples-present-in-the-input-file-ignored\" class=\"anchor\" aria-label=\"Permalink: Why are some samples present in the input file ignored?\" href=\"#why-are-some-samples-present-in-the-input-file-ignored\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheck that the input is tab-separated. When parsing the input file, if a line is not tab separated, nextflow will ignore it without returning an error.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDirected Acyclic Graph\u003c/h2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-label=\"Permalink: Directed Acyclic Graph\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/mutect-nf/blob/dev/dag.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag.png\" alt=\"DAG\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@iarc.fr\"\u003eAlcalaN@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 9, "subscribers_count": 5, "topics": [ - "infotaxis", - "entropy", - "ergodicity", - "kullback-leibler-divergence", - "tracking", - "search", - "active-sensing", - "movement", - "trajectory", - "sensorimotor-integration", - "sensing" + "nextflow", + "mutect" ], - "updated_at": 1726579945.0 + "updated_at": 1626688195.0 }, { "data_format": 2, - "description": null, + "description": "It\u0027s a prototype for an interpreter, which can interpret the host code of a CUDA Program, written with the runtime API.", "filenames": [ "Singularity" ], - "full_name": "ctpelok77/kstar", + "full_name": "SimeonEhrig/CUDA-Runtime-Interpreter", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWelcome to the page of K* planner -- a state of the art Top-k planner integrating the K* algorithm into Fast Downward.\u003c/h2\u003e\u003ca id=\"user-content-welcome-to-the-page-of-k-planner----a-state-of-the-art-top-k-planner-integrating-the-k-algorithm-into-fast-downward\" class=\"anchor\" aria-label=\"Permalink: Welcome to the page of K* planner -- a state of the art Top-k planner integrating the K* algorithm into Fast Downward.\" href=\"#welcome-to-the-page-of-k-planner----a-state-of-the-art-top-k-planner-integrating-the-k-algorithm-into-fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding\u003c/h3\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-label=\"Permalink: Building\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# ./fast-downward.py \u0026lt;domain_file\u0026gt; \u0026lt;problem_file\u0026gt; --search \"kstar(heuristic,k=\u0026lt;number-of-plans\u0026gt;)\"\n\n./fast-downward.py examples/gripper/domain.pddl examples/gripper/prob01.pddl --search \"kstar(blind(),k=100)\"\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eheurisitic\u003c/em\u003e: any heuristic provided by Fast Downward\u003cbr\u003e\n(\u003ca href=\"http://www.fast-downward.org/Doc/Heuristic\" rel=\"nofollow\"\u003ehttp://www.fast-downward.org/Doc/Heuristic\u003c/a\u003e).\u003cbr\u003e\n\u003cstrong\u003eDisclaimer\u003c/strong\u003e: Optimality of K* is only guaranteed with an admissible and consistent heuristic.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCitation\u003c/h3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMichael Katz, Shirin Sohrabi, Octavian Udrea and Dominik Winterer\u003cbr\u003e\n\u003cstrong\u003eA Novel Iterative Approach to Top-k Planning\u003c/strong\u003e \u003ca href=\"https://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/download/17749/16971\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"/top_k.bib\"\u003e[bib]\u003c/a\u003e\u003cbr\u003e\n\u003cem\u003eIn ICAPS 2018\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContact\u003c/h3\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor questions and comments please get in touch with Michael Katz (\u003ca href=\"mailto:michael.katz1@ibm.com\"\u003emichael.katz1@ibm.com\u003c/a\u003e).\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCUDA-Runtime-Interpreter\u003c/h1\u003e\u003ca id=\"user-content-cuda-runtime-interpreter\" class=\"anchor\" aria-label=\"Permalink: CUDA-Runtime-Interpreter\" href=\"#cuda-runtime-interpreter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt\u0027s a prototype of an interpreter, which can interpret the host code of a CUDA program, written with the runtime API. The interpreter uses source code files and fatbinray files as input.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eclang-dev \u0026gt;= 5.0\u003c/li\u003e\n\u003cli\u003ellvm-dev \u0026gt;= 5.0\u003c/li\u003e\n\u003cli\u003ecuda Toolkit\u003c/li\u003e\n\u003cli\u003ecmake 3.8.2\u003c/li\u003e\n\u003cli\u003ezlib1g-dev\u003c/li\u003e\n\u003cli\u003elibedit-dev\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTested with clang/llvm 5.0, 6.0 and CUDA 8.0.61\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/CUDA-Runtime-Interpreter\n mkdir build\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\n cmake ..\n make\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ebuild with own compiled clang/llvm-libraries\u003c/h3\u003e\u003ca id=\"user-content-build-with-own-compiled-clangllvm-libraries\" class=\"anchor\" aria-label=\"Permalink: build with own compiled clang/llvm-libraries\" href=\"#build-with-own-compiled-clangllvm-libraries\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to use own compiled clang/llvm-libraries, for example to debug the code, do the following steps.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/CUDA-Runtime-Interpreter\n ln -s \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_llvm_install\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/lib64 lib64\n mkdir build\n \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\n cmake .. -DMY_LLVM_BASE_DIR=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_llvm_install\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n make\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eImplementation\u003c/h2\u003e\u003ca id=\"user-content-implementation\" class=\"anchor\" aria-label=\"Permalink: Implementation\" href=\"#implementation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe prototype is based on the clang example in\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/llvm-mirror/clang/tree/master/examples/clang-interpreter\"\u003ehttps://github.com/llvm-mirror/clang/tree/master/examples/clang-interpreter\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe workflow of the cuda runtime interpreter based on the cuda compiler pipeline of the clang/llvm. The clang/llvm shows you all compiler steps on the commandline, if you add the flag \u003ccode\u003e-###\u003c/code\u003e to your compiler call. For compiling a cuda program, there are five stages.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e clang++ -#\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e# runtime.cu -o runtime -lcudart_static -L/usr/local/cuda-8.0/lib64 -ldl -lrt -pthread\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e(The concrete program calls can look at the commands.txt)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003egenerating ptx device code (a kind of nvidia assembler)\u003c/li\u003e\n\u003cli\u003etranslate ptx to sass (machine code of ptx)\u003c/li\u003e\n\u003cli\u003egenerate a fatbinray (a kind of wrapper for the device code)\u003c/li\u003e\n\u003cli\u003egenerate host code object file (use fatbinary as input)\u003c/li\u003e\n\u003cli\u003elink to executable\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe first three steps are about device code generation. The generation of the fatbinary will be done before starting the interpreter. The device code generation can be performed with either clang\u0027s CUDA frontend or NVCC and the tools of NVIDIA\u0027s CUDA Toolkit. The interpreter replaces the 4th and 5th step.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDebug output options\u003c/h3\u003e\u003ca id=\"user-content-debug-output-options\" class=\"anchor\" aria-label=\"Permalink: Debug output options\" href=\"#debug-output-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are some options to get out information from the different stages of the interpreter process (e.g LLVM IR Code, Assembler ...). In \u003ccode\u003e\u0026lt;build\u0026gt;/config/Config.hpp\u003c/code\u003e you can modify some \u003ccode\u003e#define\u003c/code\u003e to change the output properties. Please note, the changes will be effective after recompiling.\u003c/p\u003e\n\u003cp\u003eA special case is \u003ccode\u003e#define CUI_INTERPRET\u003c/code\u003e. It changes the backend. If it is defined with \u003ccode\u003e#define CUI_INTERPRET 1\u003c/code\u003e, the interpreter use the JIT-Backend. If \u003ccode\u003eCUI_INTERPRET\u003c/code\u003e has the value 0, it will generate an object file. The object file can be linked (ld) to an executable.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCUI_PCH_MODE [0|1] if the value is 1, add an additional compiler stage to the device jit\n\u003cul\u003e\n\u003cli\u003ethe CUDA device code will translate to a PCH file and then to PTX\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCUI_PRINT_INT_MODE [0|1] if the value is 1, print which interpreter mode is selected\u003c/li\u003e\n\u003cli\u003eCUI_DEBUG_BACKEND [0|1] if the value is 1, the llvm::DebugFlag will be enabled and all debug information of the backend will be written on the console (independent of CUI_INTERPRET)\u003c/li\u003e\n\u003cli\u003eCUI_DEBUG_JIT_OBJ [0|1] if the value is 1, the jit backend write the object code in a file, which is generated during the jit process (only if CUI_INTERPRET is 1)\u003c/li\u003e\n\u003cli\u003eCUI_DEBUG_JIT_INFO [0|1] if the value is 1, add debug information to the jited code and allow debugging with the gdb (only if CUI_INTERPRET is 1)\n\u003cul\u003e\n\u003cli\u003enotice: to add debug information to the jited code, you also have to set the flag -g at start of the cuda-interpreter\u003c/li\u003e\n\u003cli\u003eexample: ./cuda-interpreter \u003ccode\u003e-cuda_cpp \u0026lt;source\u0026gt;.cu -fatbin \u0026lt;source\u0026gt;.fatbin -g\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCUI_SAVE_DEVICE_CODE [0|1] if the value is 1, save the generated device code files (.ptx, .sass and .fatbin) in the folder of the interpreter exe, else write the files to /tmp\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecute an example\u003c/h2\u003e\u003ca id=\"user-content-execute-an-example\" class=\"anchor\" aria-label=\"Permalink: Execute an example\" href=\"#execute-an-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the \u003ccode\u003eexample_prog\u003c/code\u003e folder you can find some example source codes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003egenerating fatbinary\u003c/h3\u003e\u003ca id=\"user-content-generating-fatbinary\" class=\"anchor\" aria-label=\"Permalink: generating fatbinary\" href=\"#generating-fatbinary\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step is optional. You can precompile the device code to a fatbinary by yourself. Than you can pass the code as argument. Otherwise, if there is no -fatbin argument declared, the interpreter compiled the device code just in time, by itself. The fatbinary is the compiled device-code in an \"function-handle\", which allows an embedding in the host. There three options to generate the fatbinary.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun \u003ccode\u003e./generate_nvcc_fatbin.sh \u0026lt;filename\u0026gt;.cu\u003c/code\u003e in the example_prog folder and generate a \u003ccode\u003envcc_\u0026lt;filename\u0026gt;.fatbin\u003c/code\u003e file with NVIDIAs nvcc.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003e./generate_clang_fatbin.sh \u0026lt;filename\u0026gt;.cu\u003c/code\u003e in the example_prog folder and generate a \u003ccode\u003eclang_\u0026lt;filename\u0026gt;.fatbin\u003c/code\u003e file with clang/llvm and NVIDIAs tools.\u003c/li\u003e\n\u003cli\u003eRun the command \u003ccode\u003eclang++ -### runtime.cu -o runtime -lcudart_static -L/usr/local/cuda-8.0/lib64 -ldl -lrt -pthread\u003c/code\u003e and you get information about the clang and a list of 5 commands. Use the first three commands, to generate a fatbinary. If you do this, you have to change the input- and output-paths of the commands or you have to copy the fatbin from the /tmp folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe last option is the most complicated way, but the best way, because it is the closest way to the original implementation of the clang frontend.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erunning interpreter\u003c/h3\u003e\u003ca id=\"user-content-running-interpreter\" class=\"anchor\" aria-label=\"Permalink: running interpreter\" href=\"#running-interpreter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the tests with cuda-interpeter and the two or more arguments as above:\u003c/p\u003e\n\u003cp\u003e[1] set -cuda_c or -cuda_cpp as first argument to enable the cuda c- or c++-frontend\u003c/p\u003e\n\u003cp\u003e[2] path to the source code in \"example_prog\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - note: needs the file ending .cu or .cpp \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[3] optinal path to the source code .fatbin\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - note: needs the file ending .fatbin,\n - the argument -fatbin and the path after tell the interpreter, that it should use the precompiled code in the file instead to compile the device code by itself \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[4] arguments for clang compiler instance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- all arguments after the source path or fatbin path will pass to the clang compilerInstance -\u0026gt; see all possible arguments with $ clang++ --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./cuda-interpreter -cuda_cpp ../example_prog/hello.cu -v\n ./cuda-interpreter -cuda_cpp ../example_prog/hello.cu -fatbin ../example_prog/runtime.fatbin -v\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003erunning the c++-interpreter frontend\u003c/h4\u003e\u003ca id=\"user-content-running-the-c-interpreter-frontend\" class=\"anchor\" aria-label=\"Permalink: running the c++-interpreter frontend\" href=\"#running-the-c-interpreter-frontend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the tests with cuda-interpeter and the two or more arguments as above:\u003c/p\u003e\n\u003cp\u003e[1] set -c or -cpp as first argument to enable the c- or c++-frontend\u003c/p\u003e\n\u003cp\u003e[2] path to the source code in \"example_prog\"\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e - note: needs the file ending .cpp \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e[3] arguments for clang compiler instance\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e- all arguments after the fatbin path will pass to the clang compilerInstance -\u0026gt; see all possible arguments with $ clang++ --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./cuda-interpreter -cpp ../example_prog/hello.cpp -v\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePCH and device code generation\u003c/h1\u003e\u003ca id=\"user-content-pch-and-device-code-generation\" class=\"anchor\" aria-label=\"Permalink: PCH and device code generation\" href=\"#pch-and-device-code-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePCH (precompiled headers) is a intermediate form of header files, which allows the compiler to reduce the compile time. Specially for templates, where the header files will be read multiple, it\u0027s realy useful. For cling, we will use PCH to handle normal and templated device code kernels. There are two reasons for this decision. The first is, that we can fast generate many specializations of a templated kernels. The second reason have to do with generating the fatbinary code. If we generate a fatbinary file for a kernel launch, we have to put in the code of the initial kernel and all kernel definitions of kernels, which will called from initial kernel and his children. To solve this problem, there are two options. The first is to analyze source code and find all kernel calls. Than all needed kernel source code can be glue together and send to the device jit. The second option is to send all defined kernel source codes to the jit inclusive the kernels, which will not used. The second option has the advantage, that we don\u0027t need to analyze the code, but we have to glue together the complete device source code and compile all to a fatbinary. PCH is a technique, which allows a fast adding of a new function definition and compiling the complete code to PTX and fatbinary code.\u003c/p\u003e\n\u003cp\u003eSo, I implemented a function (which is not necessary for the cuda-interpreter), which simulate the planned behavior of cling and allows to add an unlimited number of files with kernel definitions to the interpreter process. This is only possible, if the PCH mode is enable. Every new kernel will translate to a PCH file and include his predecessor PCH file, if exist. If the last kernel file is translated, the PCH file will translate to PTX.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample to use extra kernel files\u003c/h2\u003e\u003ca id=\"user-content-example-to-use-extra-kernel-files\" class=\"anchor\" aria-label=\"Permalink: Example to use extra kernel files\" href=\"#example-to-use-extra-kernel-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use extra kernel files, you have to enable the PCH mode via Config.hpp, at first. Then the argument \u003ccode\u003e-kernel\u003c/code\u003e is aviable at the 3rd position, comparable the argument \u003ccode\u003e-fatbin\u003c/code\u003e. After the \u003ccode\u003e-kernel\u003c/code\u003e argument you can set the path to the kernel files. There is also possible to declare more files via string.\u003c/p\u003e\n\u003cp\u003eExample:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e ./cuda-interpreter -cuda_cpp ../PCH_example/cuda_template_many_sources/runtime.cu -kernels \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e../PCH_example/cuda_template_many_sources/myInclude/kernel1.cu ../PCH_example/cuda_template_many_sources/myInclude/kernel2.cu ../PCH_example/cuda_template_many_sources/myInclude/kernel3.cu\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -I../PCH_example/cuda_template_many_sources/myInclude/\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 9, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [], - "updated_at": 1691110521.0 + "updated_at": 1717768570.0 }, { "data_format": 2, - "description": "Collection of Singularity build files and scripts to create them for popular Linux Distributions", + "description": "examples of using Singularity containers for web-based products", "filenames": [ - "definitions/gemma/Singularity.GEMMA-0.98.1-foss-2018b-envmod-debian9", - "definitions/gemma/Singularity.GEMMA-0.98.1-foss-2018b-envmod-centos7", - "definitions/fastqtl/Singularity.FastQTL-2.184-foss-2018b-envmod-centos7", - "definitions/mirtk/Singularity.mirtk-2.0.0-foss-2020a-Python-3.8.2-envmod-centos7", - "definitions/HTSeq/Singularity.HTSeq-0.11.3-foss-2020b-centos-7-envmod", - "definitions/salmon/Singularity.Salmon-1.2.1-gompi-2019b-envmod-debian9", - "definitions/salmon/Singularity.Salmon-1.1.0-gompi-2019b-envmod-debian9", - "definitions/salmon/Singularity.Salmon-1.0.0-gompi-2019a-envmod-debian9", - "definitions/salmon/Singularity.Salmon-1.3.0-gompi-2020a-envmod-debian9", - "definitions/bowtie/Singularity.Bowtie-1.2.3-foss-2018b-envmod-debian9", - "definitions/ruby/Singularity.Ruby-2.7.1-GCCcore-8.3.0-envmod-debian9", - "definitions/ruby/Singularity.Ruby-2.7.1-GCCcore-8.3.0-envmod-debian10", - "definitions/gcc-core/Singularity.GCCcore-7.3.0-envmod-debian9", - "definitions/vcftools/Singularity.VCFtools-0.1.15-foss-2018a-Perl-5.26.1-envmod-centos7", - "definitions/meme/Singularity.MEME-5.1.1-foss-2019b-Perl-5.30.0-Python-3.7.4-envmod-debian10", - "definitions/foss/Singularity.foss-2018a-envmod-centos7", - "definitions/foss/Singularity.foss-2020a-envmod-debian9", - "definitions/tabix/Singularity.tabix-0.2.6-GCCcore-7.3.0-envmod-debian9", - "definitions/gcc/Singularity.GCC-8.3.0-envmod-debian9", - "definitions/gcc/Singularity.GCC-9.3.0-envmod-centos7", - "definitions/gcc/Singularity.GCC-7.3.0-2.30-envmod-centos7", - "definitions/gcc/Singularity.GCC-9.3.0-envmod-debian9", - "definitions/gcc/Singularity.GCC-9.3.0-envmod-debian10", - "definitions/gcc/Singularity.GCC-9.2.0-2.32-envmod-centos7", - "definitions/samtools/Singularity.SAMtools-1.10-GCC-9.3.0-envmod-debian10", - "definitions/RSEM/Singularity.RSEM-1.3.3-foss-2019b-centos-7-envmod", - "definitions/steak/Singularity.STEAK-20190912-foss-2019b-Python-2.7.16-envmod-centos7", - "definitions/plink/Singularity.PLINK-2.00a2.3LM-x86_64-envmod-debian9", - "definitions/plink/Singularity.PLINK-2.00a2.3LM-x86_64-lmod", - "definitions/plink/Singularity.PLINK-2.00-alpha2-x86_64-envmod-debian9", - "definitions/R/Singularity.R-3.6.2-foss-2019b-envmod-debian9", - "definitions/R/Singularity.R-4.0.0-foss-2020a-envmod-debian9", - "definitions/R/Singularity.R-3.6.2-foss-2020a-envmod-debian9", - "definitions/R/Singularity.R-3.6.0-foss-2018b-envmod-debian9", - "definitions/R/Singularity.R-3.6.3-foss-2020a-envmod-debian9", - "definitions/bwa/Singularity.BWA-0.7.17-foss-2018b-envmod-debian9", - "definitions/bcftools/Singularity.BCFtools-1.10.2-GCC-8.3.0-envmod-debian10", - "definitions/bcftools/Singularity.BCFtools-1.3-foss-2016b-envmod-centos7", - "definitions/bcftools/Singularity.BCFtools-1.10.2-GCC-8.3.0-envmod-debian9" + "nginx-basic/Singularity", + "nginx-jupyter/Singularity", + "nginx-expfactory/Singularity" ], - "full_name": "sassy-crick/Singularity-Easybuild", - "latest_release": "v1.1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity-Easybuild\u003c/h1\u003e\u003ca id=\"user-content-singularity-easybuild\" class=\"anchor\" aria-label=\"Permalink: Singularity-Easybuild\" href=\"#singularity-easybuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription:\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description:\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCollection of Singularity definition files and scripts to create them for popular Linux Distributions like Debian (Buster, Bullseye and Bookworm), Centos (7), and Rocky (8.5 and latest).\u003c/p\u003e\n\u003cp\u003eThe definitions folder contains the successful Singularity Definition files, tested with version 3.5.3, 3.7.1, and CE-3.8.4 from Singularity, next to Apptainer/Singularity 3.8.5 , whereas the scripts folder contains the scripts to create the Singularity definition files which are based on EasyBuild. This version is using EasyBuild version 4.5.3.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need to have a Linux environment and Singularity installed in it.\nIf you don\u0027t have Linux, please use Vagrant to set up a virtual Linux environment.\u003c/p\u003e\n\u003cp\u003ePlease note: we noticed that Singularity version 3.8.x seems to have problems with the container internal \u003ccode\u003eenvmod\u003c/code\u003e but it is working fine for the internal \u003ccode\u003elmod\u003c/code\u003e. We are working on the issue so if you want to use \u003ccode\u003eenvmod\u003c/code\u003e inside the container, for now we recommend to use Singularity version 3.7.x.\u003c/p\u003e\n\u003cp\u003eThe minimum requirement for \u003ccode\u003ebash\u003c/code\u003e is version 4.x. If you are on MacOS, please use \u003ccode\u003ehomebrew\u003c/code\u003e to install \u003ccode\u003ebash\u003c/code\u003e from there.\u003c/p\u003e\n\u003cp\u003eFurthermore, if you want to build the containers, you either need to have \u003ccode\u003efakeroot\u003c/code\u003e installed and configured so it can be used as normal user, or have \u003ccode\u003esudo\u003c/code\u003e installed. The latter is required if you want to open up containers and re-build them again.\u003c/p\u003e\n\u003cp\u003eAs the software inside the containers is built using Easybuild, you will need to know the names of the Easybuild Configuration files, e.g. GCC-9.3.0.eb.\nThus, it is probably best to install the easybuild-easyconfig files like this in a separate folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/easybuilders/easybuild-easyconfigs.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand search the easybuild/easyconfigs folder for the name of the EasyBuild Configuration files you want to use. You only need the name, not the content of the file.\u003c/p\u003e\n\u003cp\u003eThe version of EasyBuild is now fixed with this release. If you require a specific version, simply change inside the Singularity definition file this line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install easybuild==4.5.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto this line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install easybuild\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich will install the latest EasyBuild version.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDepreciated versions:\u003c/h2\u003e\u003ca id=\"user-content-depreciated-versions\" class=\"anchor\" aria-label=\"Permalink: Depreciated versions:\" href=\"#depreciated-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs \u003ccode\u003ePython2\u003c/code\u003e is depreciated, the containers are using the \u003ccode\u003ePython3\u003c/code\u003e version for as their default system version. Note: This is different from the Python versions EasyBuild will install and should not be mixed up.\u003c/p\u003e\n\u003cp\u003eAs CentOS-8 is now end of life we are currently no longer supporting this version. We suggest to switch to Rocky-8 instead.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage:\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing the scripts is simple. Go into the \u003ccode\u003escripts\u003c/code\u003e folder and run the installation script \u003ccode\u003einstall.sh\u003c/code\u003e This will \u003cem\u003eeither\u003c/em\u003e install the scripts in your \u003ccode\u003e~/bin\u003c/code\u003e folder as sym-links, or create the sym-links in the folder where you are running the script from. We advice you to install the script in the \u003ccode\u003e~/bin\u003c/code\u003e folder so they are in your \u003ccode\u003ePATH\u003c/code\u003e environment. If you don\u0027t want to do this, we recommend to install the sym-links in a different folder from where you have downloaded the GitHub files from. Please note the usage of sym-links. Thus, if you do any changes in the folder where you downloaded the GitHub repository to, these changes will be carried over. If, for example, you were to delete that folder, the installation is broken.\u003c/p\u003e\n\u003cp\u003eDuring the installation, you will given a number of choices regarding whether you want to \u003cem\u003ebuild\u003c/em\u003e or \u003cem\u003ecreate\u003c/em\u003e the definition files, which Linux distribution and version you want to use and if you want to use Lmod or the environment-module system.\u003c/p\u003e\n\u003cp\u003eYou can then execute for example, assuming the links are in your \u003ccode\u003ePATH\u003c/code\u003e environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ container-create-debian11-envmodules.sh GCC-9.3.0.eb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can supply a second script as well, which could be one you have created. This script will be\nread into the Singularity Build file. So in our example we would get a file called \u003ccode\u003eSingularity.GCC-9.3.0-envmod-debian11\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf you want to build the container and additionally a sandbox, you could use this instead:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ container-build-debian11-envmodules.sh GCC-9.3.0.eb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo in our example we would get a file called \u003ccode\u003eSingularity.GCC-9.3.0-envmod-debian11\u003c/code\u003e, next to the Singularity container called \u003ccode\u003eGCC-9.3.0-envmod-debian11.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you don\u0027t want to or cannot use the automatic build process, you can build the container like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build GCC-9.3.0-envmod-debian10.sif Singularity.GCC-9.3.0-envmod-debian10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEqually, if you want to install software on top of the existing container manually, simply do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build --sandbox GCC-9.3.0-envmod-debian10.sif Singularity.GCC-9.3.0-envmod-debian10\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the example below for a complete build of R-4.0.0 in two steps: We first build the toolchain container (foss-2020a) and inside the container we build R-4.0.0. This approach allows us to create our own complete environment for building complete pipelines as well.\u003c/p\u003e\n\u003cp\u003eIf you want to have your name and email address included in the Singularity definition file, just create this file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.singularity/sing-eb.conf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn empty file will mean these values are not included in the Singularity definition file. If you want to include your name and email address, simply add it. Likewise, you can pin the version of EasyBuild you want to use, like 4.4.0 in this example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ename=\"Your Name\"\nemail=\"email@address\"\neb_version=\"EasyBuild-version\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand replace \"Your Name\" and \"email@address\" and the \"EasyBuild-version\" you want to use accordingly.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample build:\u003c/h2\u003e\u003ca id=\"user-content-example-build\" class=\"anchor\" aria-label=\"Permalink: Example build:\" href=\"#example-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis would be the complete sequence to build a container with the FOSS-2020a tool chain from EasyBuild, unpack the container and build for example R-4.0.0 inside the container. Of course you could to that all in one go as well. We are using the CentOS7 OS in this example:\u003c/p\u003e\n\u003cp\u003eWe first create the Singularity Definition File. As we don\u0027t need to add a separate EasyBuild configuration file we say \u0027n\u0027 here:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ container-create-centos8-envmodules.sh foss-2020a.eb\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDo we need a second Easybuild recipe (y/N)?: n\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls\nSingularity.foss-2020a-centos-8-envmod\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe now build the Singularity container. Note that the command \u0027singularity\u0027 needs to be in the\nPATH of root:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build foss-2020a-centos-8-envmod.sif Singularity.foss-2020a-centos-8-envmod\n\n$ ls\nSingularity.foss-2020a-envmod-centos7 foss-2020a-envmod-centos7.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow that we got a container, we can unpack it so we can add software to it.\nFirst we unpack:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build --sandbox foss-2020a-centos-8-envmod foss-2020a-centos-8-envmod.sif\n$ ls\nSingularity.foss-2020a-centos-8-envmod foss-2020a-centos-8-envmod.sif foss-2020a-centos-8-envmod\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we enter the container. The \u0027-w\u0027 flag means we can write to the pseudo-chroot environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity shell -w foss-2020a-centos-8-envmod \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe become the easybuild user and install the software. We first fetch all the source files. This\nis sometimes a problem due to flaky Internet connections. We then, in a second step, build the\nsoftware. This step can take some time but is done fully automatic. Once build, we exit the\ncontainer again:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e su -l easybuild\u003c/span\u003e\n[easybuild]$ eb --fetch R-4.0.0-foss-2020a.eb\n[easybuild]$ eb R-4.0.0-foss-2020a.eb\n[easybuild]$ \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e exit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOne step we need to do here as root is, to change the environment file so the new module will be loaded. This will be towards the bottom of this file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo vi foss-2020a-centos-8-envmod/environment\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, we build the Singularity container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build R-4.0.0-foss-2020a-centos-8-envmod .sif foss-2020a-centos-8-envmod \n\n$ ls\nSingularity.foss-2020a-centos-8-envmod foss-2020a-centos-8-envmod .sif foss-2020a-centos-8-envmod R-4.0.0-foss-2020a-centos-8-envmod .sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow you can run R-4.0.0 on a different system like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e R-4.0.0-foss-2020a-centos-8-envmod.sif R\nR\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe have introduced a more automated way of building containers. Thus, instead of doing all of the steps above manually, let the computer do it for you. Instead of using a \u0027create\u0027 script, we are simply using a \u0027build\u0027 script, like this for example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ container-build-centos8-envmodules.sh foss-2020a.eb\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will be asked whether or not to build the sandbox in the same run.\u003c/p\u003e\n\u003cp\u003eWe would recommend to build a generic container like \u003ccode\u003efoss-2020b\u003c/code\u003e for example and then use the provided sandbox to add software to it.\u003c/p\u003e\n\u003cp\u003eFor more details about what you can do with Singularity please refer to their home page.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgement:\u003c/h2\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement:\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work would not be possible without EasyBuild, I am greateful to the project and the community for their help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks:\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links:\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity: \u003ca href=\"https://sylabs.io/guides/3.8/admin-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.8/admin-guide/installation.html\u003c/a\u003e\u003cbr\u003e\n(Source: \u003ca href=\"https://github.com/sylabs/singularity/releases\"\u003ehttps://github.com/sylabs/singularity/releases\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eApptainer: \u003ca href=\"http://apptainer.org/\" rel=\"nofollow\"\u003ehttp://apptainer.org/\u003c/a\u003e\n(Source: \u003ca href=\"https://github.com/apptainer/singularity\"\u003ehttps://github.com/apptainer/singularity\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eVagrant: \u003ca href=\"https://www.vagrantup.com/intro/getting-started\" rel=\"nofollow\"\u003ehttps://www.vagrantup.com/intro/getting-started\u003c/a\u003e\u003cbr\u003e\nEasybuild: \u003ca href=\"https://easybuild.readthedocs.io/en/latest\" rel=\"nofollow\"\u003ehttps://easybuild.readthedocs.io/en/latest\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUpdated: 11.3.2022\u003c/p\u003e\n", - "stargazers_count": 10, - "subscribers_count": 2, + "full_name": "vsoch/singularity-web", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Web\u003c/h1\u003e\u003ca id=\"user-content-singularity-web\" class=\"anchor\" aria-label=\"Permalink: Singularity Web\" href=\"#singularity-web\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDid you know that you can put a webby things inside of a container? Did you know you can go further, and take user inputs to run an analysis and then present the result? Or give them a container with the exact dependencies for your software, and provide an interface to it? I have come up with this repository, \u003ccode\u003esingularity-web\u003c/code\u003e to show how easy it is to dump your analysis, application, static web files, whatever, into a container. You can share the entire container, or just the specification file for it, and it\u0027s a big leap in the direction of reproducbility. Here are some examples you might be interested in:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow does it work?\u003c/h2\u003e\u003ca id=\"user-content-how-does-it-work\" class=\"anchor\" aria-label=\"Permalink: How does it work?\" href=\"#how-does-it-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe only pre-requisite is that you should \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003einstall Singularity\u003c/a\u003e. Singularity is already available on just \u003ca href=\"https://docs.google.com/spreadsheets/d/1Vc_1prq_1WHGf0LWtpUBY-tfKdLLM_TErjnCe1mY5m0/pub?gid=1407658660\u0026amp;single=true\u0026amp;output=pdf\" rel=\"nofollow\"\u003eover 40 supercomputer centers\u003c/a\u003e all over the place. How is this working? We basically follow these steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecreate a container\u003c/li\u003e\n\u003cli\u003eadd files and software to it\u003c/li\u003e\n\u003cli\u003etell it what to run\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn the case of this example repo, we are interested in things that produce a web-based output. You could go old school and do this on a command by command basis, but I (personally) find it easiest to create a little build file to preserve my work. I\u0027m also a big fan of bootstrapping Docker images, since there are ample around. If you want to bootstrap something else, please look at our \u003ca href=\"https://github.com/singularityware/singularity/tree/master/examples\"\u003efolder of examples\u003c/a\u003e. :)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Singularity Build file\u003c/h3\u003e\u003ca id=\"user-content-the-singularity-build-file\" class=\"anchor\" aria-label=\"Permalink: The Singularity Build file\" href=\"#the-singularity-build-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eThe Header\u003c/h3\u003e\u003ca id=\"user-content-the-header\" class=\"anchor\" aria-label=\"Permalink: The Header\" href=\"#the-header\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe First line \u003ccode\u003ebootstrap\u003c/code\u003e says that we are going to bootstrap a \u003ccode\u003edocker\u003c/code\u003e image, specifically using the (\u003ccode\u003eFrom\u003c/code\u003e field) \u003ccode\u003eubuntu:16.04\u003c/code\u003e. You couldn\u0027t choose another distribution that you like, I just happen to like Debian.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e%post\u003c/h3\u003e\u003ca id=\"user-content-post\" class=\"anchor\" aria-label=\"Permalink: %post\" href=\"#post\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePost is the section where you put commands you want to run once to create your image. This includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003einstallation of software\u003c/li\u003e\n\u003cli\u003ecreation of files or folders\u003c/li\u003e\n\u003cli\u003emoving data, files into the container image\u003c/li\u003e\n\u003cli\u003eanalysis things\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe list is pretty obvious, but what about the last one, analysis things? Yes, let\u0027s say that we had a script thing that we wanted to run just once to produce a result that would live in the container. In this case, we would have that thing run in %post, and then give some interactive access to the result via the \u003ccode\u003e%runscript\u003c/code\u003e. In the case that you want your image to be more like a function and run the analysis (for example, if you want your container to take input arguments, run something, and deliver a result), then this command should go in the \u003ccode\u003e%runscript\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIn our case, since we are going to serve a simple web-based thing, we create a directory to work with (\u003ccode\u003e/data\u003c/code\u003e is easy to remember), put some web file things there, and then (the strategy I used in the examples) was to install python, because it has a nice command for bringing up a quick web server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e%runscript\u003c/h3\u003e\u003ca id=\"user-content-runscript\" class=\"anchor\" aria-label=\"Permalink: %runscript\" href=\"#runscript\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e%runscript\u003c/code\u003e is the thing executed when we run our container. For the \u003ca href=\"nginx-basic\"\u003enginx-basic\u003c/a\u003e example, we basically change directories to data, and then use python to start up a little server on port 9999 to serve that folder. Anything in that folder will then be available to our local machine on port 9999, meaning the address \u003ccode\u003elocalhost:9999\u003c/code\u003e or \u003ccode\u003e127.0.0.1:9999\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWe recommend you look at our \u003ca href=\"nginx-basic\"\u003enginx-basic\u003c/a\u003e example for the full example, and modify some of the examples below to suit your own needs. If you use any of these templates in your work, please ping us at \u003ca href=\"researchapps@googlegroups.com\"\u003eresearchapps@googlegroups.com\u003c/a\u003e so that we can showcase your work.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003enginx-basic\u003c/h3\u003e\u003ca id=\"user-content-nginx-basic\" class=\"anchor\" aria-label=\"Permalink: nginx-basic\" href=\"#nginx-basic\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"nginx-basic\"\u003enginx-basic\u003c/a\u003e example will walk you through creating a container that serves static files, either within the container (files generated at time of build and served) or outside the container (files in a folder bound to the container at run time),\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003enginx-expfactory\u003c/h3\u003e\u003ca id=\"user-content-nginx-expfactory\" class=\"anchor\" aria-label=\"Permalink: nginx-expfactory\" href=\"#nginx-expfactory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"nginx-expfactory\"\u003enginx-expfactory\u003c/a\u003e example takes a \u003ca href=\"http://journal.frontiersin.org/article/10.3389/fpsyg.2016.00610/full\" rel=\"nofollow\"\u003esoftware that I published in graduate school\u003c/a\u003e and shows an example of how to wrap a bunch of dependencies in a container, and then allow the user to use it like a function with input arguments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003enginx-jupyter\u003c/h3\u003e\u003ca id=\"user-content-nginx-jupyter\" class=\"anchor\" aria-label=\"Permalink: nginx-jupyter\" href=\"#nginx-jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI use ipython notebook / jupyter notebook sometimes, and I thought it would be nice to have an image that could easily bring up a server, either for files in the container or a folder mapped from the outside. Behold, \u003ca href=\"nginx-jupyter\"\u003enginx-jupyter\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow do I share them?\u003c/h2\u003e\u003ca id=\"user-content-how-do-i-share-them\" class=\"anchor\" aria-label=\"Permalink: How do I share them?\" href=\"#how-do-i-share-them\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou have a few options!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShare the image\u003c/h3\u003e\u003ca id=\"user-content-share-the-image\" class=\"anchor\" aria-label=\"Permalink: Share the image\" href=\"#share-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want absolute reproducibility, meaning that the container that you built is set in stone, never to be changed, and you want to hand it to someone, have them \u003ca href=\"\"\u003einstall singularity\u003c/a\u003e and run, then you probably want to build the container yourself and give it to them. It might look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity create theultimate.img\n sudo singularity bootstrap theultimate.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the example above I am creating an image called \u003ccode\u003etheultimate.img\u003c/code\u003e and then building it from a specification file, \u003ccode\u003eSingularity\u003c/code\u003e. I would then give someone the image itself, and they would run it like an executable, which you can do in many ways:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity run theultimate.img\n ./theultimate.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThey could also shell into it to look around, with or without sudo to make changes (breaks reproducibility). Note that we are considering an addition to the Singularity software that will give control to do this or not, but nothing is final yet.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity shell --writable theultimate.img\n sudo singularity shell --writable theultimate.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eShare the build file Singularity\u003c/h3\u003e\u003ca id=\"user-content-share-the-build-file-singularity\" class=\"anchor\" aria-label=\"Permalink: Share the build file Singularity\" href=\"#share-the-build-file-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the case that the image is too big to attach to an email, you can send the user the build file \u003ccode\u003eSingularity\u003c/code\u003e and he/she can run the same steps to build and run the image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity Hub\u003c/h3\u003e\u003ca id=\"user-content-singularity-hub\" class=\"anchor\" aria-label=\"Permalink: Singularity Hub\" href=\"#singularity-hub\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlso under development is a Singularity Hub that will automatically build images from the \u003ccode\u003eSingularity\u003c/code\u003e files upon pushes to connected Github repos. This will hopefully be offered to the larger community in the coming year, 2017.\u003c/p\u003e\n", + "stargazers_count": 9, + "subscribers_count": 4, "topics": [], - "updated_at": 1692625097.0 + "updated_at": 1706204531.0 }, { "data_format": 2, - "description": "A tool for phasing and imputing haplotypes in 10k+ low coverage sequencing samples", + "description": "Repository for Berkeley Research IT cyberinfrastructure components", "filenames": [ - "Singularity" + "photoscancontainer/Singularity" ], - "full_name": "winni2k/GLPhase", - "latest_release": "v1.7.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGLPhase\u003c/h1\u003e\u003ca id=\"user-content-glphase\" class=\"anchor\" aria-label=\"Permalink: GLPhase\" href=\"#glphase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a cuda-enabled fork of\n\u003ca href=\"http://sourceforge.net/p/snptools/code/ci/master/tree/\" rel=\"nofollow\"\u003eSNPTools impute.cpp\u003c/a\u003e. This\ncode should scale linearly with sample size up to a small multiple of\nthe number of CUDA cores (shaders) on the GPU being used.\u003c/p\u003e\n\u003cp\u003eGLPhase also has an option for incorporating pre-existing haplotypes\ninto the phasing and imputation\nprocess. \u003ca href=\"https://github.com/wkretzsch/GLPhase/releases/tag/v1.4.13\"\u003eRelease 1.4.13\u003c/a\u003e\nwas used with this option\nto impute genotypes for the first release of the\n\u003ca href=\"http://www.haplotype-reference-consortium.org/\" rel=\"nofollow\"\u003eHaplotype Reference Consortium\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e version 3 installed, then you can run the glphase container located \u003ca href=\"https://cloud.sylabs.io/library/wkretzsch/default/glphase\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGLPhase depends on \u003ca href=\"https://www.gnu.org/software/gsl/\" rel=\"nofollow\"\u003elibgsl\u003c/a\u003e,\n\u003ca href=\"http://www.boost.org/\" rel=\"nofollow\"\u003eboost\u003c/a\u003e, and \u003ca href=\"http://www.zlib.net/\" rel=\"nofollow\"\u003elibz\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompilation\u003c/h3\u003e\u003ca id=\"user-content-compilation\" class=\"anchor\" aria-label=\"Permalink: Compilation\" href=\"#compilation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# Clone this repository recursively\ngit clone --recursive https://github.com/winni2k/GLPhase.git\ncd GLPhase\n\n# to compile all code (with all optimizations turned on)\nmake\n\n# run the glphase executable to get a description of the\n# glphase command line arguments\nbin/glphase\n\n# run regression tests (turns off optimizations)\nmake test\n\n# run regression tests + longer integration tests\nmake disttest\n\n# compile without CUDA support\n# first clean the work dir\nmake clean\nmake NCUDA=1\n\n# compile without CUDA or OMP support (on MacOSX for example)\nmake NCUDA=1 NOMP=1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConverting a VCF to SNPTools \u003ccode\u003e.bin\u003c/code\u003e format\u003c/h2\u003e\u003ca id=\"user-content-converting-a-vcf-to-snptools-bin-format\" class=\"anchor\" aria-label=\"Permalink: Converting a VCF to SNPTools .bin format\" href=\"#converting-a-vcf-to-snptools-bin-format\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA perl script at \u003ccode\u003escripts/vcf2STBin.pl\u003c/code\u003e can be used to convert a VCF\nwith PL format fields to a SNPTools conformant \u003ccode\u003e.bin\u003c/code\u003e file. For\nexample, this command will convert a gzipped input VCF at\n\u003ccode\u003einput.vcf.gz\u003c/code\u003e into a SNPTools \u003ccode\u003e.bin\u003c/code\u003e file at \u003ccode\u003einput.bin\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escripts/vcf2STbin.pl input.vcf.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning GLPhase (v1.4.13)\u003c/h2\u003e\u003ca id=\"user-content-running-glphase-v1413\" class=\"anchor\" aria-label=\"Permalink: Running GLPhase (v1.4.13)\" href=\"#running-glphase-v1413\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAs a drop-in replacement for SNPTools/impute.cpp\u003c/h3\u003e\u003ca id=\"user-content-as-a-drop-in-replacement-for-snptoolsimputecpp\" class=\"anchor\" aria-label=\"Permalink: As a drop-in replacement for SNPTools/impute.cpp\" href=\"#as-a-drop-in-replacement-for-snptoolsimputecpp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGLPhase can be run as a CUDA-enabled drop-in replacement for\n\u003ccode\u003eSNPTools/impute.cpp\u003c/code\u003e. Assuming a SNPTools style \u003ccode\u003e.bin\u003c/code\u003e file with\ngenotype likelihoods exists:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/glphase input.bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing pre-existing haplotypes\u003c/h3\u003e\u003ca id=\"user-content-using-pre-existing-haplotypes\" class=\"anchor\" aria-label=\"Permalink: Using pre-existing haplotypes\" href=\"#using-pre-existing-haplotypes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGLPhase can use pre-existing haplotypes to restrict the set of\npossible haplotypes from which the MH sampler may choose surrogate\nparent haplotypes. This approach is described in:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe Haplotype Reference Consortium. A reference panel of 64,976\nhaplotypes for genotype imputation. Nature Genetics (accepted) --\n\u003ca href=\"http://biorxiv.org/content/early/2015/12/23/035170\" rel=\"nofollow\"\u003ebioRxiv\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis command phases and imputes haplotypes on a SNPTools \u003ccode\u003e.bin\u003c/code\u003e file\nusing a genetic map and pre-existing haplotypes. The output file is\na gzipped VCF file at \u003ccode\u003eoutput_base_name.vcf.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eglphase -B0 -i5 -m95 -q0 -Q1 -t2 -C100 -K200 \\\n input.bin \\\n -g genetic_map.txt \\\n -h pre_existing_haplotypes.haps.gz \\\n -s pre_existing_haplotypes.sample \\\n -o output_base_name\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe pre-existing haplotypes should be in\n\u003ca href=\"https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html#hapsample\" rel=\"nofollow\"\u003eWTCCC format\u003c/a\u003e,\nand a genetic map can be obtained from the \u003ca href=\"https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#reference\" rel=\"nofollow\"\u003eImpute2 website\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing a reference panel\u003c/h3\u003e\u003ca id=\"user-content-using-a-reference-panel\" class=\"anchor\" aria-label=\"Permalink: Using a reference panel\" href=\"#using-a-reference-panel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGLPhase can use a reference panel of haplotypes to inform genotype\nimputation of samples for which genotype likelihoods are available.\nIn contrast to pre-existing haplotypes, the haplotypes\nin the reference panel do not need to be from the same samples that\nare being imputed. In this mode, when surrogate parent haplotypes\nare being chosen for a sample, the haplotypes may come from the\ncurrent estimate of sample haplotypes or the reference panel. \u003ccode\u003e-k\u003c/code\u003e\ncan be specified to restrict the choice of surrogate parent haplotypes\nto the reference panel in the first iteration of haplotype estimation.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eglphase \\\n input.bin \\\n -g samples/hapGen/ex.map \\\n -H samples/hapGen/ex.haps.gz \\\n -L samples/hapGen/ex.leg \\\n -k \\\n -o output_base_name\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe reference haplotypes and legend should be in\n\u003ca href=\"https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html#haplegsample\" rel=\"nofollow\"\u003eImpute2 format\u003c/a\u003e,\nand a genetic map can be obtained from the \u003ca href=\"https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#reference\" rel=\"nofollow\"\u003eImpute2 website\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLigating haplotypes\u003c/h2\u003e\u003ca id=\"user-content-ligating-haplotypes\" class=\"anchor\" aria-label=\"Permalink: Ligating haplotypes\" href=\"#ligating-haplotypes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is recommended to ligate haplotypes using\n\u003ca href=\"https://bitbucket.org/wkretzsch/hapfuse/src\" rel=\"nofollow\"\u003ehapfuse\u003c/a\u003e. Before\nfusing, the output from GLPhase needs to be converted from gzipped VCF\nto something htslib can read. Here an example using \u003ca href=\"https://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ezcat output_base_name.vcf.gz | bcftools -Ob -o \\\n output_base_name.bcf\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "ucberkeley/brc-cyberinfrastructure", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebrc-cyberinfrastructure\u003c/h1\u003e\u003ca id=\"user-content-brc-cyberinfrastructure\" class=\"anchor\" aria-label=\"Permalink: brc-cyberinfrastructure\" href=\"#brc-cyberinfrastructure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e+++++++++++++++++++++++++++\u003c/p\u003e\n\u003cp\u003eThis software is available under the terms of the Educational Community License, Version 2.0 (ECL 2.0). This software is Copyright 2016 The Regents of the University of California, Berkeley (\"Berkeley\").\u003c/p\u003e\n\u003cp\u003eThe text of the ECL license is reproduced below.\u003c/p\u003e\n\u003cp\u003eEducational Community License, Version 2.0\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright 2016 The Regents of the University of California, Berkeley (\"Berkeley\")\u003c/p\u003e\n\u003cp\u003eEducational Community License, Version 2.0, April 2007\u003c/p\u003e\n\u003cp\u003eThe Educational Community License version 2.0 (\"ECL\") consists of the\nApache 2.0 license, modified to change the scope of the patent grant in\nsection 3 to be specific to the needs of the education communities using\nthis license. The original Apache 2.0 license can be found at:[\u003ca href=\"http://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003ehttp://www.apache.org/licenses/LICENSE-2.0\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003e+++++++++++++++++++++++++++\u003c/p\u003e\n\u003cp\u003eThis repository contains a heterogeneous set of components and utilities developed in coordination with Berkely research teams and Research IT staff to enable data management and analysis at scale.\u003c/p\u003e\n\u003cp\u003eThese components are exemplars that utilize multiple languages, SDKs and technologies and as such the repository cannot be cloned and built in the same way that a single app is constructed. Researchers will be directed to specific files that they will download, modify and employ in their environment.\u003c/p\u003e\n\u003cp\u003eThe hierarchy is organic, based on what has been created to date. It will evolve as the scope of the cyberinfrastructure engineer expands over time.\u003c/p\u003e\n", "stargazers_count": 10, - "subscribers_count": 3, + "subscribers_count": 14, "topics": [], - "updated_at": 1676561604.0 + "updated_at": 1710307936.0 }, { "data_format": 2, @@ -37233,58 +37209,61 @@ var data = }, { "data_format": 2, - "description": "Building a Singularity image of Polysolver", + "description": "Mycobacterial pipeline", "filenames": [ - "Singularity.v4" + "singularity/Singularity.preprocessing-0.9.6", + "singularity/Singularity.vcfpredict-0.9.6", + "singularity/Singularity.clockwork-0.9.6" ], - "full_name": "IARCbioinfo/polysolver-singularity", - "latest_release": null, + "full_name": "Pathogen-Genomics-Cymru/lodestone", + "latest_release": "v0.9.6", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-lodestone\" class=\"anchor\" aria-hidden=\"true\" href=\"#lodestone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLodestone\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/build-push-quay/badge.svg\"\u003e\u003cimg src=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/build-push-quay/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/pytest/badge.svg\"\u003e\u003cimg src=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/pytest/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/stub-run/badge.svg\"\u003e\u003cimg src=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/stub-run/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis pipeline takes as input reads presumed to be from one of 10 mycobacterial genomes: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis. Input should be in the form of one directory containing pairs of fastq(.gz) or bam files.\u003c/p\u003e\n\u003cp\u003ePipeline cleans and QCs reads with fastp and FastQC, classifies with Kraken2 \u0026amp; Afanc, removes non-bacterial content, and - by alignment to any minority genomes - disambiguates mixtures of bacterial reads. Cleaned reads are aligned to either of the 10 supported genomes and variants called. Produces as output one directory per sample, containing cleaned fastqs, sorted, indexed BAM, VCF, F2 and F47 statistics, an antibiogram and summary reports.\u003c/p\u003e\n\u003cp\u003eNote that while Mykrobe is included within this pipeline, it runs as an independent process and is not used for any downstream reporting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWARNING\u003c/strong\u003e: There are currently known errors with vcfmix and gnomonicus, as such \u003ccode\u003eerrorStrategy \u0027ignore\u0027\u003c/code\u003e has been added to the processes vcfpredict:vcfmix and vcfpredict:gnomonicus to stop the pipeline from crashing. Please check the stdout from nextflow to see whether these processes have ran successfully.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eThis is a Nextflow DSL2 pipeline, it requires a version of Nextflow that supports DSL2 and the stub-run feature. It is recommended to run the pipeline with \u003ccode\u003eNXF_VER=20.11.0-edge\u003c/code\u003e, as the pipeline has been tested using this version. E.g. to download\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport NXF_VER=\"20.11.0-edge\"\ncurl -fsSL https://get.nextflow.io | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe workflow is designed to run with either docker \u003ccode\u003e-profile docker\u003c/code\u003e or singularity \u003ccode\u003e-profile singularity\u003c/code\u003e. The container images are pulled from quay.io and a singularity cache directory is set in the \u003ccode\u003enextflow.config\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eE.g. to run the workflow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNXF_VER=20.11.0-edge nextflow run main.nf -profile singularity --filetype fastq --input_dir fq_dir --pattern \"*_R{1,2}.fastq.gz\" --unmix_myco yes \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\nNXF_VER=20.11.0-edge nextflow run main.nf -profile docker --filetype bam --input_dir bam_dir --unmix_myco no \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-executors\" class=\"anchor\" aria-hidden=\"true\" href=\"#executors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecutors\u003c/h3\u003e\n\u003cp\u003eBy default, the pipeline will just run on the local machine. To run on a cluster, modifications will have to be made to the \u003ccode\u003enextflow.config\u003c/code\u003e to add in the executor. E.g. for a SLURM cluster add \u003ccode\u003eprocess.executor = \u0027slurm\u0027\u003c/code\u003e. For more information on executor options see the Nextflow docs: \u003ca href=\"https://www.nextflow.io/docs/latest/executor.html\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/docs/latest/executor.html\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements\u003c/h3\u003e\n\u003cp\u003eMinimum recommended requirements: 32GB RAM, 8CPU\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-params\" class=\"anchor\" aria-hidden=\"true\" href=\"#params\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParams\u003c/h2\u003e\n\u003cp\u003eThe following parameters should be set in \u003ccode\u003enextflow.config\u003c/code\u003e or specified on the command line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003einput_dir\u003c/strong\u003e\u003cbr\u003e\nDirectory containing fastq OR bam files\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efiletype\u003c/strong\u003e\u003cbr\u003e\nFile type in input_dir. Either \"fastq\" or \"bam\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003epattern\u003c/strong\u003e\u003cbr\u003e\nRegex to match fastq files in input_dir, e.g. \"*_R{1,2}.fq.gz\". Only mandatory if --filetype is \"fastq\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eoutput_dir\u003c/strong\u003e\u003cbr\u003e\nOutput directory for results\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eunmix_myco\u003c/strong\u003e\u003cbr\u003e\nDo you want to disambiguate mixed-mycobacterial samples by read alignment? Either \"yes\" or \"no\":\n\u003cul\u003e\n\u003cli\u003eIf \"yes\" workflow will remove reads mapping to any minority mycobacterial genomes but in doing so WILL ALMOST CERTAINLY ALSO reduce coverage of the principal species\u003c/li\u003e\n\u003cli\u003eIf \"no\" then mixed-mycobacterial samples will be left alone. Mixtures of mycobacteria + non-mycobacteria will still be disambiguated\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003especies\u003c/strong\u003e\u003cbr\u003e\nPrincipal species in each sample, assuming genus Mycobacterium. Default \u0027null\u0027. If parameter used, takes 1 of 10 values: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis. Using this parameter will apply an additional sanity test to your sample\n\u003cul\u003e\n\u003cli\u003eIf you DO NOT use this parameter (default option), pipeline will determine principal species from the reads and consider any other species a contaminant\u003c/li\u003e\n\u003cli\u003eIf you DO use this parameter, pipeline will expect this to be the principal species. It will fail the sample if reads from this species are not actually the majority\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ekraken_db\u003c/strong\u003e\u003cbr\u003e\nDirectory containing \u003ccode\u003e*.k2d\u003c/code\u003e Kraken2 database files (k2_pluspf_16gb recommended, obtain from \u003ca href=\"https://benlangmead.github.io/aws-indexes/k2\" rel=\"nofollow\"\u003ehttps://benlangmead.github.io/aws-indexes/k2\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie2_index\u003c/strong\u003e\u003cbr\u003e\nDirectory containing Bowtie2 index (obtain from ftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/hg19_1kgmaj_bt2.zip). The specified path should NOT include the index name\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie_index_name\u003c/strong\u003e\u003cbr\u003e\nName of the bowtie index, e.g. hg19_1kgmaj\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003evcfmix\u003c/strong\u003e\u003cbr\u003e\nRun \u003ca href=\"https://github.com/AlexOrlek/VCFMIX\"\u003evcfmix\u003c/a\u003e, yes or no. Set to no for synthetic samples\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003egnomonicus\u003c/strong\u003e\u003cbr\u003e\nRun \u003ca href=\"https://github.com/oxfordmmm/gnomonicus\"\u003egnomonicus\u003c/a\u003e, yes or no\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eamr_cat\u003c/strong\u003e\u003cbr\u003e\nPath to AMR catalogue for gnomonicus\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eafanc_myco_db\u003c/strong\u003e\u003cbr\u003e\nPath to the \u003ca href=\"https://github.com/ArthurVM/Afanc\"\u003eafanc\u003c/a\u003e database used for speciation. Obtain from \u003ca href=\"https://s3.climb.ac.uk/microbial-bioin-sp3/Mycobacteriaciae_DB_6.0.tar.gz\" rel=\"nofollow\"\u003ehttps://s3.climb.ac.uk/microbial-bioin-sp3/Mycobacteriaciae_DB_6.0.tar.gz\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp\u003eFor more information on the parameters run \u003ccode\u003enextflow run main.nf --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe path to the singularity images can also be changed in the singularity profile in \u003ccode\u003enextflow.config\u003c/code\u003e. Default value is \u003ccode\u003e${baseDir}/singularity\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-stub-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#stub-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStub-run\u003c/h2\u003e\n\u003cp\u003eTo test the stub run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNXF_VER=20.11.0-edge nextflow run main.nf -stub -config testing.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-checkpoints\" class=\"anchor\" aria-hidden=\"true\" href=\"#checkpoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheckpoints\u003c/h2\u003e\n\u003cp\u003eCheckpoints used throughout this workflow to fail a sample/issue warnings:\u003c/p\u003e\n\u003cp\u003eprocesses preprocessing:checkFqValidity or preprocessing:checkBamValidity\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e(Fail) If sample does not pass fqtools \u0027validate\u0027 or samtools \u0027quickcheck\u0027, as appropriate.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eprocess preprocessing:countReads\u003cbr\u003e\n2. (Fail) If sample contains \u0026lt; 100k pairs of raw reads.\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:fastp\u003cbr\u003e\n3. (Fail) If sample contains \u0026lt; 100k pairs of cleaned reads, required to all be \u0026gt; 50bp (cleaning using fastp with --length_required 50 --average_qual 10 --low_complexity_filter --correction --cut_right --cut_tail --cut_tail_window_size 1 --cut_tail_mean_quality 20).\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:kraken2\u003cbr\u003e\n4. (Fail) If the top family hit is not Mycobacteriaceae\u003cbr\u003e\n5. (Fail) If there are fewer than 100k reads classified as Mycobacteriaceae \u003cbr\u003e\n6. (Warn) If the top family classification is mycobacterial, but this is not consistent with top genus and species classifications\u003cbr\u003e\n7. (Warn) If the top family is Mycobacteriaceae but no G1 (species complex) classifications meet minimum thresholds of \u0026gt; 5000 reads or \u0026gt; 0.5% of the total reads (this is not necessarily a concern as not all mycobacteria have a taxonomic classification at this rank)\u003cbr\u003e\n8. (Warn) If sample is mixed or contaminated - defined as containing reads \u0026gt; the 5000/0.5% thresholds from multiple non-human species\u003cbr\u003e\n9. (Warn) If sample contains multiple classifications to mycobacterial species complexes, each meeting the \u0026gt; 5000/0.5% thresholds\u003cbr\u003e\n10. (Warn) If no species classification meets the 5000/0.5% thresholds\u003cbr\u003e\n11. (Warn) If no genus classification meets the 5000/0.5% thresholds\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:identifyBacterialContaminants\u003cbr\u003e\n12. (Fail) If regardless of what Kraken reports, Afanc does not make a species-level mycobacterial classification (note that we do not use Kraken mycobacterial classifications other than to determine whether 100k reads are family Mycobacteriaceae; for higher-resolution classification, we defer to Afanc)\u003cbr\u003e\n13. (Fail) If the sample is not contaminated and the top species hit is not one of the 10 supported Mycobacteria: abscessus|africanum|avium|bovis|chelonae|chimaera|fortuitum|intracellulare|kansasii|tuberculosis\u003cbr\u003e\n14. (Fail) If the sample is not contaminated and the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003cbr\u003e\n15. (Warn) If the top Afanc species hit, on the basis of highest % coverage, does not also have the highest median depth\u003cbr\u003e\n16. (Warn) If we are unable to associate an NCBI taxon ID to any given contaminant species, which means we will not be able to locate its genome, and thereby remove it as a contaminant\u003cbr\u003e\n17. (Warn) If we are unable to determine a URL for the latest RefSeq genome associated with a contaminant species\u0027 taxon ID\u003cbr\u003e\n18. (Warn) If no complete genome could be found for a contaminant species. The workflow will proceed with alignment-based contaminant removal, but you\u0027re warned that there\u0027s reduced confidence in detecting reads from this species\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:downloadContamGenomes\u003cbr\u003e\n19. (Fail) If a contaminant is detected but we are unable to download a representative genome, and thereby remove it\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:summarise\u003cbr\u003e\n20. (Fail) If after having taken an alignment-based approach to decontamination, Kraken still detects a contaminant species\u003cbr\u003e\n21. (Fail) If after having taken an alignment-based approach to decontamination, the top species hit is not one of the 10 supported Mycobacteria\u003cbr\u003e\n22. (Fail) If, after successfully removing contaminants, the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003c/p\u003e\n\u003cp\u003eprocess clockwork:alignToRef\u003cbr\u003e\n23. (Fail) If \u0026lt; 100k reads could be aligned to the reference genome\u003cbr\u003e\n24. (Fail) If, after aligning to the reference genome, the average read mapping quality \u0026lt; 10\u003cbr\u003e\n25. (Fail) If \u0026lt; 50% of the reference genome was covered at 10-fold depth\u003c/p\u003e\n\u003cp\u003eprocess clockwork:minos\u003cbr\u003e\n26. (Warn) If sample is not TB, then it is not passed to gnomonicus\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eFor a list of direct authors of this pipeline, please see the contributors list. All of the software dependencies of this pipeline are recorded in the version.json\u003c/p\u003e\n\u003cp\u003eThe preprocessing sub-workflow is based on the preprocessing nextflow DSL1 pipeline written by Stephen Bush, University of Oxford. The clockwork sub-workflow uses aspects of the variant calling workflow from \u003ca href=\"https://github.com/iqbal-lab-org/clockwork\"\u003ehttps://github.com/iqbal-lab-org/clockwork\u003c/a\u003e, lead author Martin Hunt, Iqbal Lab at EMBL-EBI\u003c/p\u003e\n", "stargazers_count": 10, - "subscribers_count": 3, - "topics": [], - "updated_at": 1706806761.0 + "subscribers_count": 6, + "topics": [ + "bioinformatics", + "bioinformatics-pipeline", + "genomics", + "global-health", + "infectious-diseases", + "next-generation-sequencing", + "nextflow", + "pathogen", + "sequencing", + "tuberculosis" + ], + "updated_at": 1679574698.0 }, { "data_format": 2, - "description": "Scripts for building Singularity images", + "description": "Clinical Variant Annotation Pipeline", "filenames": [ - "mxnet/ubuntu.def", - "caffe2/ubuntu.def", - "tensorflow/ubuntu.def", - "caffe/ubuntu.def", - "dl/ubuntu.def", - "circuitscape/ubuntu.def" + "VepFileDeployment/Singularity.filedeploy", + "ReportingApplication/Singularity.report" ], - "full_name": "clemsonciti/singularity-images", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity image scripts\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-scripts\" class=\"anchor\" aria-label=\"Permalink: Singularity image scripts\" href=\"#singularity-image-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eScripts to generate singularity images\nfor running different software on Palmetto cluster.\u003c/p\u003e\n", + "full_name": "PersonalizedOncology/ClinVAP", + "latest_release": "v1.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/sbilge/ClinVAP/blob/master/doc/logo.jpeg\"\u003e\u003cimg src=\"https://github.com/sbilge/ClinVAP/raw/master/doc/logo.jpeg\" alt=\"Pipeline Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c75c033e32c0d101c52a50f49a37bdac7bb6543f8b11f2ba77dc0526e40a14b6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f506572736f6e616c697a65644f6e636f6c6f67792f436c696e6963616c5265706f7274696e67506970656c696e652e737667\" alt=\"Release: Github\" data-canonical-src=\"https://img.shields.io/github/release/PersonalizedOncology/ClinicalReportingPipeline.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2168\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.docker.com/u/personalizedoncology/repository/list\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ffe08c7b5a5a63af6d36a31ec41fbd126b784c00beb4c5ec7f95a2bac8a6d849/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65722d2d6875622d626c75652e737667\" alt=\"Docker: Available\" data-canonical-src=\"https://img.shields.io/badge/hosted-docker--hub-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-clinical-variant-annotation-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#clinical-variant-annotation-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClinical Variant Annotation Pipeline\u003c/h1\u003e\n\u003cp\u003eClinical Variant Annotation Pipeline (ClinVAP) creates a genetic report of somatic mutations from a variant call format (VCF) file. Please refer this document for implementation of the pipeline. Documentation of the pipeline is available at \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/wiki\"\u003eWiki page\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-metadata-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#metadata-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMetadata Structure\u003c/h3\u003e\n\u003cp\u003eIf a patient metadata file is provided in the input directory with the naming schema \u0026lt;INPUT_VCF_NAME\u0026gt;_metadata.json, ClinVAP recognizes it and renders the information into the Patient Data table in the outputted report. Additionally, if dignosis is provided in the metadata file, the list of drugs with the clinical evidence of targeting the gene in that particular cancer type is reported in the \"CIViC Summary of Drugs Targeting the Affected Genes\" table. If no diagnosis is provided, then the pipeline stays agnostic to the cancer type, and returns the results related with the gene-drug association regardless of the cancer type. Please note that the disease name should be selected from the pre-defined dictionary that can be found \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/blob/master/doc/disease_names_dictionary.txt\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetadata file format:\u003c/strong\u003e\u003cbr\u003e\n{\u003cbr\u003e\n\"patient_firstname\":\"\u0026lt;NAME\u0026gt;\",\u003cbr\u003e\n\"patient_lastname\":\"\u0026lt;SURNAME\u0026gt;\",\u003cbr\u003e\n\"patient_dateofbirth\":\"\u0026lt;DATE\u0026gt;\",\u003cbr\u003e\n\"patient_diagnosis_short\":\"\u0026lt;DIAGNOSIS\u0026gt;\",\u003cbr\u003e\n\"mutation_load\":\"\u0026lt;LOAD\u0026gt;\"\u003cbr\u003e\n}\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Singularity\u003c/h2\u003e\n\u003cp\u003eRequirements: Singularity 2.4+\u003cbr\u003e\nPlease make sure that you have 12 GB of empty space on your home directory, and ports 5000 and 27021 are not being used by another application.\nTo run the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull reporting image from Singularity Hub.\n\u003ccode\u003esingularity pull -n reporting_app.img shub://PersonalizedOncology/ClinVAP:report\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePull dependency files image from Singularity Hub.\n\u003ccode\u003esingularity pull -n file_deploy.img shub://PersonalizedOncology/ClinVAP:filedeploy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun dependency files image first to transfer those file on your local folder.\n\u003ccode\u003esingularity run -B /LOCAL/PATH/TO/FILES:/mnt file_deploy.img -a \u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the reporting image to generate the clinical reports.\n\u003ccode\u003esingularity run -B /LOCAL/PATH/TO/FILES:/data -B /PATH/TO/INPUT/DATA:/inout reporting_app.img -t /inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-a\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in your /PATH/TO/INPUT/DATA folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Docker\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-for-mac-and-ubuntu-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-mac-and-ubuntu-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor Mac and Ubuntu Users\u003c/h3\u003e\n\u003cp\u003eRequirements: Docker Engine release 1.13.0+, Compose release 1.10.0+.\u003cbr\u003e\nPlease make sure that you have 34 GB of physical empty space on your Docker Disk Image, and ports 5000 and 27017 are not being used by another application.\u003c/p\u003e\n\u003cp\u003eTo tun the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePelase note that the input VCF file(s) should be in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e2. cd ClinVAP/\n3. export ASSEMBLY=\u0026lt;Your Assembly Here\u0026gt;\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t /inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in ReportingApplication/inout folder.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-with-docker-toolbox-for-windows-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-with-docker-toolbox-for-windows-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning with Docker Toolbox For Windows Users\u003c/h3\u003e\n\u003cp\u003eRequirements: Docker Engine release 1.13.0+, Compose release 1.10.0+.\u003cbr\u003e\nPlease make sure that you have 34 GB of physical empty space on your Docker Disk Image, and ports 5000 and 27017 are not being used by another application.\u003c/p\u003e\n\u003cp\u003eTo tun the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePelase note that the input VCF file(s) should be in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e2. cd ClinVAP/\n3. export ASSEMBLY=\u0026lt;Your Assembly Here\u0026gt;\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t //inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in ReportingApplication/inout folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-demo-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#demo-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo Run\u003c/h2\u003e\n\u003cp\u003eWe provided an example input file, strelka_passed_missense_somatic_snvs.vcf under ./ReportingApplication/inout folder along with a dummy metadata file, strelka_passed_missense_somatic_snvs.json. The corresponding report of the strelka input file is provided \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/tree/master/doc/strelka_passed_missense_somatic_snvs.pdf\"\u003ehere\u003c/a\u003e as an example.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-demo-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-demo-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Demo with Singularity\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n2. singularity pull -n reporting_app.img shub://PersonalizedOncology/ClinVAP:report\n3. singularity pull -n file_deploy.img shub://PersonalizedOncology/ClinVAP:filedeploy\n4. mkdir vep_files\n5. singularity run -B ./vep_files:/mnt file_deploy.img -a GRCh37\n6. singularity run -B ./vep_files:/data -B ./ClinVAP/ReportingApplication/inout:/inout reporting_app.img -t /inout -p jwp -a GRCh37\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-demo-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-demo-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Demo with Docker\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n2. cd ClinVAP/\n3. export ASSEMBLY=GRCh37\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t /inout -p jwp -a GRCh37\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h3\u003e\n\u003cp\u003eIf you use ClinVAP in your work, please cite the following article\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eS\u00fcr\u00fcn, B., Sch\u00e4rfe, C.P., Divine, M.R., Heinrich, J., Toussaint, N.C., Zimmermann, L., Beha, J. and Kohlbacher, O., 2020. ClinVAP: a reporting strategy from variants to therapeutic options. Bioinformatics, 36(7), pp.2316-2317.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 10, "subscribers_count": 5, "topics": [], - "updated_at": 1597386388.0 + "updated_at": 1675526926.0 }, { "data_format": 2, - "description": "R package to run BEAST2", + "description": "ENIGMA Halfpipe is a user-friendly software that facilitates reproducible analysis of fMRI data", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "ropensci/beastier", - "latest_release": "v2.5.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebeastier\u003c/h1\u003e\u003ca id=\"user-content-beastier\" class=\"anchor\" aria-label=\"Permalink: beastier\" href=\"#beastier\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/ropensci/onboarding/issues/209\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37e90e3b01a6f34a223f7073479c59d4928c3afd773379b02f14ec91cff10253/68747470733a2f2f6261646765732e726f70656e7363692e6f72672f3230395f7374617475732e737667\" alt=\"Peer Review Status\" data-canonical-src=\"https://badges.ropensci.org/209_status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cran.r-project.org/package=beastier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f6ee17ac66d35bf285dc963fd619c15438d53a452f3ea63199760e319b27f427/687474703a2f2f7777772e722d706b672e6f72672f6261646765732f76657273696f6e2f6265617374696572\" alt=\"CRAN version\" data-canonical-src=\"http://www.r-pkg.org/badges/version/beastier\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=beastier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/89fc261cf5e9058d15ca58e910da28bbc2ac3d866c3e13862b73ef2ceafbc19c/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6772616e642d746f74616c2f6265617374696572\" alt=\"CRAN total downloads\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/grand-total/beastier\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=beastier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/46d4efeb572049ae52878c31c26d27645be16c24951c31dd293fea28c3a209b5/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6265617374696572\" alt=\"CRAN downloads per month\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/beastier\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.repostatus.org/#active\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2f7a95a01b9543e8dcac34358c343b1b2d75abb019511c5254490fb99a3c16fc/68747470733a2f2f7777772e7265706f7374617475732e6f72672f6261646765732f6c61746573742f6163746976652e737667\" alt=\"Project Status: Active \u2013 The project has reached a stable, usable state and is being actively developed.\" data-canonical-src=\"https://www.repostatus.org/badges/latest/active.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/115617629\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b5f515a5ee12332bbd6a3d6bfb28187e53f261878b8035e616eb48b366457955/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3131353631373632392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/115617629.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/ropensci/beautier/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beastier/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/ropensci/beastier/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/ropensci/beastier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/836d8a566886b2caa4487744f24ac8e670ae20a5b7601e1d7581892d9ee3d3c6/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656173746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beastier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beastier/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/ropensci/beastier/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/ropensci/beastier/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c363f35b306c504f8a52438e340ba56e11d8377b383a9564205b9cf7a129a985/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656173746965722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beastier/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003e\u003ccode\u003ebeastier\u003c/code\u003e is an R package to run BEAST2.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/beastier_logo.png\"\u003e\u003cimg src=\"man/figures/beastier_logo.png\" alt=\"beastier logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ebeastier\u003c/code\u003e is part of the \u003ca href=\"https://github.com/ropensci/babette\"\u003e\u003ccode\u003ebabette\u003c/code\u003e\u003c/a\u003e\npackage suite:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beautier\"\u003e\u003ccode\u003ebeautier\u003c/code\u003e\u003c/a\u003e\ncreates BEAST2 input (\u003ccode\u003e.xml\u003c/code\u003e) files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beastier\"\u003e\u003ccode\u003ebeastier\u003c/code\u003e\u003c/a\u003e runs BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/mauricer\"\u003e\u003ccode\u003emauricer\u003c/code\u003e\u003c/a\u003e: install BEAST2 packages\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/tracerer\"\u003e\u003ccode\u003etracerer\u003c/code\u003e\u003c/a\u003e\npastes BEAST2 output (\u003ccode\u003e.log\u003c/code\u003e, \u003ccode\u003e.trees\u003c/code\u003e, etc) files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRelated R packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall\"\u003e\u003ccode\u003ebeastierinstall\u003c/code\u003e\u003c/a\u003e:\nInstall and uninstall BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/beastier_on_windows\"\u003e\u003ccode\u003ebeastier_on_windows\u003c/code\u003e\u003c/a\u003e:\nVerify that \u003ccode\u003ebeastier\u003c/code\u003e works on the Windows operating system\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/lumier\"\u003e\u003ccode\u003elumier\u003c/code\u003e\u003c/a\u003e:\nShiny app to help create the function call needed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall BEAST2\u003c/h2\u003e\u003ca id=\"user-content-install-beast2\" class=\"anchor\" aria-label=\"Permalink: Install BEAST2\" href=\"#install-beast2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDue to CRAN policy, beastier cannot install BEAST2.\nAs a workaround, the non-CRAN\n\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall\"\u003e\u003ccode\u003ebeastierinstall\u003c/code\u003e\u003c/a\u003e\ncan be used.\u003c/p\u003e\n\u003cp\u003eTo install BEAST2:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003eremotes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003erichelbilderbeek/beastierinstall\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-e\"\u003ebeastierinstall\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_beast2()\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample for \u003ccode\u003ev2.1\u003c/code\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-example-for-v21\" class=\"anchor\" aria-label=\"Permalink: Example for v2.1\" href=\"#example-for-v21\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun BEAST2:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eoutput_state_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eout.state\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\nrun_beast2(\n \u003cspan class=\"pl-v\"\u003einput_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_beastier_path(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e2_4.xml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003eoutput_state_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eoutput_state_filename\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create the files as specified in the \u003ccode\u003e2_4.xml\u003c/code\u003e BEAST2 input file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample for \u003ccode\u003ev2.0.25\u003c/code\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-example-for-v2025\" class=\"anchor\" aria-label=\"Permalink: Example for v2.0.25\" href=\"#example-for-v2025\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eoutput_log_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eout.log\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eoutput_trees_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eout.trees\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eoutput_state_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eout.state\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\nrun_beast2(\n \u003cspan class=\"pl-v\"\u003einput_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_beastier_path(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e2_4.xml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003eoutput_log_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eoutput_log_filename\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eoutput_trees_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eoutput_trees_filename\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eoutput_state_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eoutput_state_filename\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that in this version, the filenames for the \u003ccode\u003e.log\u003c/code\u003e\nand \u003ccode\u003e.trees\u003c/code\u003e files could be specified. This is unneeded:\nthe \u003ccode\u003e2_4.xml\u003c/code\u003e BEAST2 input file specifies where these files will be stored:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e\u0026lt;?xml [...]?\u0026gt;\u0026lt;beast [...]\u0026gt;\n\n[...]\n\n\u0026lt;run [...]\u0026gt;\n\n [...]\n\n \u0026lt;logger id=\"tracelog\" fileName=\"test_output_0.log\" [...]\u0026gt;\n [...]\n \u0026lt;/logger\u0026gt;\n\n [...]\n\n \u0026lt;logger id=\"treelog.t:[...]\" fileName=\"$(tree).trees\" [...]\u0026gt;\n [...]\n \u0026lt;/logger\u0026gt;\n\u0026lt;/run\u0026gt;\n\u0026lt;/beast\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen using \u003ccode\u003ebeautier\u003c/code\u003e, this can be specified in \u003ccode\u003ecreate_mcmc\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003ecreate_mcmc(\n \u003cspan class=\"pl-v\"\u003etracelog\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_tracelog(\n \u003cspan class=\"pl-v\"\u003efilename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emy_trace.log\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n ),\n \u003cspan class=\"pl-v\"\u003etreeslog\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_treeslog(\n \u003cspan class=\"pl-v\"\u003efilename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emy_trees.trees\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n )\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"doc/install.md\"\u003eInstall\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/install.md\"\u003einstall\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"doc/faq.md\"\u003eFAQ\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/faq.md\"\u003eFAQ\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMissing features/unsupported\u003c/h2\u003e\u003ca id=\"user-content-missing-featuresunsupported\" class=\"anchor\" aria-label=\"Permalink: Missing features/unsupported\" href=\"#missing-featuresunsupported\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ebeastier\u003c/code\u003e cannot do everything \u003ccode\u003eBEAST2\u003c/code\u003e can.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRemove: install BEAST2, use \u003ca href=\"https://github.com/richelbilderbeek/beastierinstall\"\u003e\u003ccode\u003ebeastierinstall\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperimental: Continue a BEAST2 run\u003c/li\u003e\n\u003cli\u003eUntested: Setup BEAGLE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere is a feature I miss\u003c/h2\u003e\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" aria-label=\"Permalink: There is a feature I miss\" href=\"#there-is-a-feature-i-miss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI want to collaborate\u003c/h2\u003e\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" aria-label=\"Permalink: I want to collaborate\" href=\"#i-want-to-collaborate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u0027Submitting code\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI think I have found a bug\u003c/h2\u003e\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" aria-label=\"Permalink: I think I have found a bug\" href=\"#i-think-i-have-found-a-bug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u0027Submitting bugs\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere\u0027s something else I want to say\u003c/h2\u003e\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" aria-label=\"Permalink: There\u0027s something else I want to say\" href=\"#theres-something-else-i-want-to-say\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExternal links\u003c/h2\u003e\u003ca id=\"user-content-external-links\" class=\"anchor\" aria-label=\"Permalink: External links\" href=\"#external-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CompEvol/beast2\"\u003eBEAST2 GitHub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/ropensci/beautier/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeautier\u003c/code\u003e \u003ccode\u003emaster\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/ropensci/beautier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c56baecfb9929de32f11a0d680e42f06788c6932d3018ebfdb84ae18fed35acb/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeautier\u003c/code\u003e \u003ccode\u003edevelop\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/ropensci/beautier/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f48f5ff21298fa315ba9e2ec165560cc0c17d3aeb14ebf0b221f8a1f42b2100/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeastierinstall\u003c/code\u003e \u003ccode\u003emaster\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall/actions/workflows/R-CMD-check.yaml\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/beastierinstall/actions/workflows/R-CMD-check.yaml/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/beastierinstall/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31b1fd70af861978190174d9c1f270770f381a9df044bec61c1e686ec37beae3/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6265617374696572696e7374616c6c2f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/beastierinstall/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeastierinstall\u003c/code\u003e \u003ccode\u003edevelop\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall/actions/workflows/R-CMD-check.yaml\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/beastierinstall/actions/workflows/R-CMD-check.yaml/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/beastierinstall/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a05fd26a17df4bdb241f906f3d98423cf3ec0e8780b3ff047ec4f5bc60803b9c/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6265617374696572696e7374616c6c2f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/beastierinstall/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://ci.appveyor.com/project/richelbilderbeek/beastier_on_windows/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/AppVeyor.png\" alt=\"AppVeyor logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeastier_on_windows\u003c/code\u003e \u003ccode\u003emaster\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/richelbilderbeek/beastier-on-windows/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/61e833a6a269dc25dbdc7cfb77fd05a9d211a24535d4623e8d73327438374e13/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f72616c65783973646e6e786c776267782f6272616e63682f6d61737465723f7376673d74727565\" alt=\"Build status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/ralex9sdnnxlwbgx/branch/master?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeastier_on_windows\u003c/code\u003e \u003ccode\u003edevelop\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/richelbilderbeek/beastier-on-windows/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/759f4bd3e8190fa09019519d651f495923b65c8d8a31087617f502061d0cb1a6/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f72616c65783973646e6e786c776267782f6272616e63682f646576656c6f703f7376673d74727565\" alt=\"Build status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/ralex9sdnnxlwbgx/branch/develop?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFiles used by continuous integration scripts\u003c/h2\u003e\u003ca id=\"user-content-files-used-by-continuous-integration-scripts\" class=\"anchor\" aria-label=\"Permalink: Files used by continuous integration scripts\" href=\"#files-used-by-continuous-integration-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFilename\u003c/th\u003e\n\u003cth\u003eDescriptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"mlc_config.json\"\u003emlc_config.json\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the link checker, use \u003ccode\u003emarkdown-link-check --config mlc_config.json --quiet docs/**/*.md\u003c/code\u003e to do link checking locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".spellcheck.yml\"\u003e.spellcheck.yml\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".wordlist.txt\"\u003e.wordlist.txt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eWhitelisted words for the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlint.jsonc\"\u003e.markdownlint.jsonc\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlintignore\"\u003e.markdownlintignore\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFiles ignored by the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eArticle about \u003ccode\u003ebabette\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBilderbeek, Rich\u00e8l JC, and Rampal S. Etienne. \"\u003ccode\u003ebabette\u003c/code\u003e:\nBEAUti 2, BEAST 2 and Tracer for R.\" Methods in Ecology and\nEvolution (2018). \u003ca href=\"https://doi.org/10.1111/2041-210X.13032\" rel=\"nofollow\"\u003ehttps://doi.org/10.1111/2041-210X.13032\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFASTA files \u003ccode\u003eanthus_aco.fas\u003c/code\u003e and \u003ccode\u003eanthus_nd2.fas\u003c/code\u003e from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eVan Els, Paul, and Heraldo V. Norambuena.\n\"A revision of species limits in Neotropical pipits Anthus\nbased on multilocus genetic and vocal data.\" Ibis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://ropensci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2f6676e3fb100027b1914631256a47c1c3ab83def78fe9bbbd49eff063693aea/68747470733a2f2f726f70656e7363692e6f72672f7075626c69635f696d616765732f726f70656e7363695f666f6f7465722e706e67\" alt=\"ropensci_footer\" data-canonical-src=\"https://ropensci.org/public_images/ropensci_footer.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "HALFpipe/HALFpipe", + "latest_release": "1.1.1", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-welcome-to-enigma-halfpipe\" class=\"anchor\" href=\"#welcome-to-enigma-halfpipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to ENIGMA \u003ccode\u003eHALFpipe\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4508\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/actions?query=workflow%3A%22build%22\"\u003e\u003cimg src=\"https://github.com/HALFpipe/HALFpipe/workflows/build/badge.svg\" alt=\"https://github.com/HALFpipe/HALFpipe/workflows/build/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/actions?query=workflow%3A%22continuous+integration%22\"\u003e\u003cimg src=\"https://github.com/HALFpipe/HALFpipe/workflows/continuous%20integration/badge.svg\" alt=\"https://github.com/HALFpipe/HALFpipe/workflows/continuous%20integration/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/HALFpipe/HALFpipe\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ef5be2978b13a91a1a602bc0261933d3735a7567176db1ef0c13eb65b3249056/68747470733a2f2f636f6465636f762e696f2f67682f48414c46706970652f48414c46706970652f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/HALFpipe/HALFpipe/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e is a user-friendly software that facilitates reproducible analysis of\nfMRI data, including preprocessing, single-subject, and group analysis. It\nprovides state-of-the-art preprocessing using\n\u003ca href=\"https://fmriprep.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003efmriprep\u003c/code\u003e\u003c/a\u003e, but removes the necessity to\nconvert data to the\n\u003ca href=\"https://bids-specification.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003e\u003ccode\u003eBIDS\u003c/code\u003e\u003c/a\u003e format. Common\nresting-state and task-based fMRI features can then be calculated on the fly\nusing \u003ca href=\"http://fsl.fmrib.ox.ac.uk/\" rel=\"nofollow\"\u003e\u003ccode\u003eFSL\u003c/code\u003e\u003c/a\u003e and\n\u003ca href=\"https://nipype.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003enipype\u003c/code\u003e\u003c/a\u003e for statistics.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf you encounter issues, please see the \u003ca href=\"#troubleshooting\"\u003etroubleshooting\u003c/a\u003e\nsection of this document.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#getting-started\"\u003eGetting started\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#container-platform\"\u003eContainer platform\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#download\"\u003eDownload\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running\"\u003eRunning\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#user-interface\"\u003eUser interface\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#files\"\u003eFiles\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#features\"\u003eFeatures\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#models\"\u003eModels\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-on-a-high-performance-computing-cluster\"\u003eRunning on a high-performance computing cluster\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#quality-checks\"\u003eQuality checks\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#subject-level-features\"\u003eSubject-level features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#preprocessed-images\"\u003ePreprocessed images\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#group-level\"\u003eGroup-level\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#command-line-flags\"\u003eCommand line flags\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#control-command-line-logging\"\u003eControl command line logging\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#automatically-remove-unneeded-files\"\u003eAutomatically remove unneeded files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#adjust-nipype\"\u003eAdjust nipype\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#choose-which-parts-to-run-or-to-skip\"\u003eChoose which parts to run or to skip\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#working-directory\"\u003eWorking directory\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#data-file-system-root\"\u003eData file system root\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e is distributed as a container, meaning that all required software\ncomes bundled in a monolithic file, the container. This allows for easy\ninstallation on new systems, and makes data analysis more reproducible, because\nsoftware versions are guaranteed to be the same for all users.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-platform\" class=\"anchor\" href=\"#container-platform\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer platform\u003c/h3\u003e\n\u003cp\u003eThe first step is to install one of the supported container platforms. If you\u0027re\nusing a high-performance computing cluster, more often than not\n\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e will already be available.\u003c/p\u003e\n\u003cp\u003eIf not, we recommend using the latest version\nof\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e. However, it can be somewhat cumbersome to\ninstall, as it needs to be built from source.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://neuro.debian.net/\" rel=\"nofollow\"\u003e\u003ccode\u003eNeuroDebian\u003c/code\u003e\u003c/a\u003e package repository provides an\nolder version of \u003ca href=\"https://sylabs.io/guides/2.6/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e for\n\u003ca href=\"https://neuro.debian.net/pkgs/singularity-container.html\" rel=\"nofollow\"\u003esome\u003c/a\u003e Linux\ndistributions.\u003c/p\u003e\n\u003cp\u003eIn contrast to \u003ccode\u003eSingularity\u003c/code\u003e, \u003ccode\u003eDocker\u003c/code\u003e always requires elevated privileges to\nrun containers. In other words, every user running a \u003ccode\u003eDocker\u003c/code\u003e container\nautomatically has administrator privileges on the computer they\u0027re using.\nTherefore, it is inherently a bad choice for multi-user environments, where the\naccess of individual users should be limited. \u003ccode\u003eDocker\u003c/code\u003e is the only option that\nis compatible with \u003ccode\u003eMac OS X\u003c/code\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eVersion\u003c/th\u003e\n\u003cth\u003eInstallation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e3.5.3\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eSee \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/quick_start.html\u003c/a\u003e\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e2.6.1\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003esudo apt install singularity-container\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eSee \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-download\" class=\"anchor\" href=\"#download\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h3\u003e\n\u003cp\u003eThe second step is to download the \u003ccode\u003eHALFpipe\u003c/code\u003e to your computer. This requires\napproximately 5 gigabytes of storage.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eInstallation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://charitede-my.sharepoint.com/:f:/g/personal/lea_waller_charite_de/EukRziExhTVBrEAai2oEpi8B2jsnn7P3YQuFo2pycKp6-g\" rel=\"nofollow\"\u003ehttps://charitede-my.sharepoint.com/:f:/g/personal/lea_waller_charite_de/EukRziExhTVBrEAai2oEpi8B2jsnn7P3YQuFo2pycKp6-g\u003c/a\u003e or \u003ccode\u003esingularity pull docker://halfpipe/halfpipe:1.1.1\u003c/code\u003e or \u003ccode\u003esingularity pull docker://ghcr.io/halfpipe/halfpipe:1.1.1\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edocker pull halfpipe/halfpipe:1.1.1\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e version \u003ccode\u003e3.x\u003c/code\u003e creates a container image file called\n\u003ccode\u003eHALFpipe_{version}.sif\u003c/code\u003e in the directory where you run the \u003ccode\u003epull\u003c/code\u003e command. For\n\u003ccode\u003eSingularity\u003c/code\u003e version \u003ccode\u003e2.x\u003c/code\u003e the file is named\n\u003ccode\u003ehalfpipe-halfpipe-master-latest.simg\u003c/code\u003e. Whenever you want to use the container,\nyou need pass \u003ccode\u003eSingularity\u003c/code\u003e the path to this file.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e \u003ccode\u003eSingularity\u003c/code\u003e may store a copy of the container in its cache\ndirectory. The cache directory is located by default in your home directory at\n\u003ccode\u003e~/.singularity\u003c/code\u003e. If you need to save disk space in your home directory, you\ncan safely delete the cache directory after downloading, i.e. by running\n\u003ccode\u003erm -rf ~/.singularity\u003c/code\u003e. Alternatively, you could move the cache directory\nsomewhere with more free disk space using a symlink. This way, files will\nautomatically be stored there in the future. For example, if you have a lot of\nfree disk space in \u003ccode\u003e/mnt/storage\u003c/code\u003e, then you could first run\n\u003ccode\u003emv ~/.singularity /mnt/storage\u003c/code\u003e to move the cache directory, and then\n\u003ccode\u003eln -s /mnt/storage/.singularity ~/.singularity\u003c/code\u003e to create the symlink.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ccode\u003eDocker\u003c/code\u003e will store the container in its storage base directory, so it does not\nmatter from which directory you run the \u003ccode\u003epull\u003c/code\u003e command.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h3\u003e\n\u003cp\u003eThe third step is to run the downloaded container. You may need to replace\n\u003ccode\u003ehalfpipe_1.1.1.sif\u003c/code\u003e with the actual path and filename where \u003ccode\u003eSingularity\u003c/code\u003e\ndownloaded your container.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eCommand\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_1.1.1.sif\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edocker run --interactive --tty --volume /:/ext halfpipe/halfpipe\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYou should now see the user interface.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-background\" class=\"anchor\" href=\"#background\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h4\u003e\n\u003cp\u003eContainers are by default isolated from the host computer. This adds security,\nbut also means that the container cannot access the data it needs for analysis.\n\u003ccode\u003eHALFpipe\u003c/code\u003e expects all inputs (e.g., image files and spreadsheets) and outputs\n(the working directory) to be places in the path\u003ccode\u003e/ext\u003c/code\u003e (see also\n\u003ca href=\"#data-file-system-root---fs-root\"\u003e\u003ccode\u003e--fs-root\u003c/code\u003e\u003c/a\u003e). Using the option\n\u003ccode\u003e--bind /:/ext\u003c/code\u003e, we instruct \u003ccode\u003eSingularity\u003c/code\u003e to map all of the host file system\n(\u003ccode\u003e/\u003c/code\u003e) to that path (\u003ccode\u003e/ext\u003c/code\u003e). You can also run \u003ccode\u003eHALFpipe\u003c/code\u003e and only map only part\nof the host file system, but keep in mind that any directories that are not\nmapped will not be visible later.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e passes the host shell environment to the container by default.\nThis means that in some cases, the host computer\u0027s configuration can interfere\nwith the software. To avoid this, we need to pass the option \u003ccode\u003e--containall\u003c/code\u003e.\n\u003ccode\u003eDocker\u003c/code\u003e does not pass the host shell environment by default, so we don\u0027t need\nto pass an option.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-user-interface\" class=\"anchor\" href=\"#user-interface\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser interface\u003c/h2\u003e\n\u003cp\u003eThe user interface asks a series of questions about your data and the analyses\nyou want to run. In each question, you can press \u003ccode\u003eControl+C\u003c/code\u003e to cancel the\ncurrent question and go back to the previous one. \u003ccode\u003eControl+D\u003c/code\u003e exits the program\nwithout saving. Note that these keyboard shortcuts are the same on Mac.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h3\u003e\n\u003cp\u003eTo run preprocessing, at least a T1-weighted structural image and a BOLD image\nfile is required. Preprocessing and data analysis proceeds automatically.\nHowever, to be able to run automatically, data files need to be input in a way\nsuitable for automation.\u003c/p\u003e\n\u003cp\u003eFor this kind of automation, \u003ccode\u003eHALFpipe\u003c/code\u003e needs to know the relationships between\nfiles, such as which files belong to the same subject. However, even though it\nwould be obvious for a human, a program cannot easily assign a file name to a\nsubject, and this will be true as long as there are differences in naming\nbetween different researchers or labs. One researcher may name the same file\n\u003ccode\u003esubject_01_rest.nii.gz\u003c/code\u003e and another \u003ccode\u003esubject_01/scan_rest.nii.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIn \u003ccode\u003eHALFpipe\u003c/code\u003e, we solve this issue by inputting file names in a specific way.\nFor example, instead of \u003ccode\u003esubject_01/scan_rest.nii.gz\u003c/code\u003e, \u003ccode\u003eHALFpipe\u003c/code\u003e expects you to\ninput \u003ccode\u003e{subject}/scan_rest.nii.gz\u003c/code\u003e. \u003ccode\u003eHALFpipe\u003c/code\u003e can then match all files on disk\nthat match this naming schema, and extract the subject ID \u003ccode\u003esubject_01\u003c/code\u003e. Using\nthe extracted subject ID, other files can now be matched to this image. If all\ninput files are available in BIDS format, then this step can be skipped.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify working directory\u003c/code\u003e All intermediate and outputs of \u003ccode\u003eHALFpipe\u003c/code\u003e will\nbe placed in the working directory. Keep in mind to choose a location with\nsufficient free disk space, as intermediates can be multiple gigabytes in\nsize for each subject.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eIs the data available in BIDS format?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the BIDS directory\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify anatomical/structural data\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the path of the T1-weighted image files\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify functional data\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the path of the BOLD image files\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCheck repetition time values\u003c/code\u003e / \u003ccode\u003eSpecify repetition time in seconds\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd more BOLD image files?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDo slice timing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eCheck slice acquisition direction values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCheck slice timing values\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify field maps?\u003c/code\u003e If the data was imported from a BIDS directory, this\nstep will be omitted.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the type of the field maps\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEPI (blip-up blip-down)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the blip-up blip-down EPI image files\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003ePhase difference and magnitude (used by Siemens scanners)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the magnitude image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the phase/phase difference image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify echo time difference in seconds\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eScanner-computed field map and magnitude (used by GE / Philips\nscanners)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the magnitude image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the field map image files\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd more field maps?\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify effective echo spacing for the functional data in seconds\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify phase encoding direction for the functional data\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-features\" class=\"anchor\" href=\"#features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h3\u003e\n\u003cp\u003eFeatures are analyses that are carried out on the preprocessed data, in other\nwords, first-level analyses.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify first-level features?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the feature type\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eTask-based\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the event file type\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSPM multiple conditions\u003c/code\u003e A MATLAB .mat file containing three\narrays: \u003ccode\u003enames\u003c/code\u003e (condition), \u003ccode\u003eonsets\u003c/code\u003e and \u003ccode\u003edurations\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFSL 3-column\u003c/code\u003e One text file for each condition. Each file has its\ncorresponding condition in the filename. The first column specifies\nthe event onset, the second the duration. The third column of the\nfiles is ignored, so parametric modulation is not supported\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBIDS TSV\u003c/code\u003e A tab-separated table with named columns \u003ccode\u003etrial_type\u003c/code\u003e\n(condition), \u003ccode\u003eonset\u003c/code\u003e and \u003ccode\u003eduration\u003c/code\u003e. While BIDS supports defining\nadditional columns, \u003ccode\u003eHALFpipe\u003c/code\u003e will currently ignore these\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the event files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSelect conditions to add to the model\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify contrasts\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify contrast name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify contrast values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd another contrast?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply a temporal filter to the design matrix?\u003c/code\u003e A separate temporal\nfilter can be specified for the design matrix. In contrast, the\ntemporal filtering of the input image and any confound regressors\nadded to the design matrix is specified in 10. In general, the two\nsettings should match\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply smoothing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify smoothing FWHM in mm\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eGrand mean scaling will be applied with a mean of 10000.000000\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTemporal filtering will be applied using a gaussian-weighted filter\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the filter width in seconds\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRemove confounds?\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSeed-based connectivity\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify binary seed mask file(s)\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the binary seed mask image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCheck space values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eAdd binary seed mask image file\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDual regression\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAtlas-based connectivity matrix\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eReHo\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efALFF\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd another first-level feature?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eOutput a preprocessed image?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify setting name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply smoothing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify smoothing FWHM in mm\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDo grand mean scaling?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify grand mean\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply a temporal filter?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the type of temporal filter\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eGaussian-weighted\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eFrequency-based\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRemove confounds?\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-models\" class=\"anchor\" href=\"#models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModels\u003c/h3\u003e\n\u003cp\u003eModels are statistical analyses that are carried out on the features.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-a-high-performance-computing-cluster\" class=\"anchor\" href=\"#running-on-a-high-performance-computing-cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on a high-performance computing cluster\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eLog in to your cluster\u0027s head node\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRequest an interactive job. Refer to your cluster\u0027s documentation for how to\ndo this\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn the interactive job, run the \u003ccode\u003eHALFpipe\u003c/code\u003e user interface, but add the flag\n\u003ccode\u003e--use-cluster\u003c/code\u003e to the end of the command. \u003cbr\u003e\nFor example, \u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_latest.sif --use-cluster\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs soon as you finish specifying all your data, features and models in the\nuser interface, \u003ccode\u003eHALFpipe\u003c/code\u003e will now generate everything needed to run on the\ncluster. For hundreds of subjects, this can take up to a few hours.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen \u003ccode\u003eHALFpipe\u003c/code\u003e exits, edit the generated submit script \u003ccode\u003esubmit.slurm.sh\u003c/code\u003e\naccording to your cluster\u0027s documentation and then run it. This submit script\nwill calculate everything except group statistics.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs soon as all processing has been completed, you can run group statistics.\nThis is usually very fast, so you can do this in an interactive session. Run\n\u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_latest.sif --only-model-chunk\u003c/code\u003e\nand then select \u003ccode\u003eRun without modification\u003c/code\u003e in the user interface.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA common issue with remote work via secure shell is that the connection may\nbreak after a few hours. For batch jobs this is not an issue, but for\ninteractive jobs this can be quite frustrating. When the connection is lost,\nthe node you were connected to will automatically quit all programs you were\nrunning. To prevent this, you can run interactive jobs within \u003ccode\u003escreen\u003c/code\u003e or\n\u003ccode\u003etmux\u003c/code\u003e (whichever is available). These commands allow you to open sessions in\nthe terminal that will continue running in the background even when you close\nor disconnect. Here\u0027s a quick overview of how to use the commands (more\nin-depth documentation is available for example at\n[\u003ca href=\"http://www.dayid.org/comp/tm.html\" rel=\"nofollow\"\u003ehttp://www.dayid.org/comp/tm.html\u003c/a\u003e]).\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOpen a new screen/tmux session on the head node by running either \u003ccode\u003escreen\u003c/code\u003e\nor \u003ccode\u003etmux\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRequest an interactive job from within the session, for example with\n\u003ccode\u003esrun --pty bash -i\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the command that you want to run\u003c/li\u003e\n\u003cli\u003eDetach from the screen/tmux session, meaning disconnecting with the ability\nto re-connect later \u003cbr\u003e\nFor screen, this is done by first pressing \u003ccode\u003eControl+a\u003c/code\u003e, then letting go, and\nthen pressing \u003ccode\u003ed\u003c/code\u003e on the keyboard. \u003cbr\u003e\nFor tmux, it\u0027s \u003ccode\u003eControl+b\u003c/code\u003e instead of \u003ccode\u003eControl+a\u003c/code\u003e. \u003cbr\u003e\nNote that this is always \u003ccode\u003eControl\u003c/code\u003e, even if you\u0027re on a mac.\u003c/li\u003e\n\u003cli\u003eClose your connection to the head node with \u003ccode\u003eControl+d\u003c/code\u003e. \u003ccode\u003escreen\u003c/code\u003e/\u003ccode\u003etmux\u003c/code\u003e\nwill remain running in the background\u003c/li\u003e\n\u003cli\u003eLater, connect again to the head node. Run \u003ccode\u003escreen -r\u003c/code\u003e or \u003ccode\u003etmux attach\u003c/code\u003e to\ncheck back on the interactive job. If everything went well and the command\nyou wanted to run finished, close the interactive job with \u003ccode\u003eControl+d\u003c/code\u003e and\nthen the \u003ccode\u003escreen\u003c/code\u003e/\u003ccode\u003etmux\u003c/code\u003e session with \u003ccode\u003eControl+d\u003c/code\u003e again. If the command\nhasn\u0027t finished yet, detach as before and come back later\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quality-checks\" class=\"anchor\" href=\"#quality-checks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality checks\u003c/h2\u003e\n\u003cp\u003ePlease see the manual at \u003ca href=\"https://docs.google.com/document/d/1evDkVaoXqSaxulp5eSxVqgaxro7yZl-gao70D0S2dH8\" rel=\"nofollow\"\u003ehttps://docs.google.com/document/d/1evDkVaoXqSaxulp5eSxVqgaxro7yZl-gao70D0S2dH8\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA visual report page \u003ccode\u003ereports/index.html\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA table with image quality metrics \u003ccode\u003ereports/reportvals.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA table containing the preprocessing status \u003ccode\u003ereports/reportpreproc.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe untouched \u003ccode\u003efmriprep\u003c/code\u003e derivatives. Some files have been omitted to save\ndisk space \u003ccode\u003efmriprep\u003c/code\u003e is very strict about only processing data that is\ncompliant with the BIDS standard. As such, we may need to format subjects\nnames for compliance. For example, an input subject named \u003ccode\u003esubject_01\u003c/code\u003e will\nappear as \u003ccode\u003esubject01\u003c/code\u003e in the \u003ccode\u003efmriprep\u003c/code\u003e derivatives. \u003ccode\u003ederivatives/fmriprep\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-subject-level-features\" class=\"anchor\" href=\"#subject-level-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubject-level features\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFor task-based, seed-based connectivity and dual regression features,\n\u003ccode\u003eHALFpipe\u003c/code\u003e outputs the statistical maps for the effect, the variance, the\ndegrees of freedom of the variance and the z-statistic. In FSL, the effect and\nvariance are also called \u003ccode\u003ecope\u003c/code\u003e and \u003ccode\u003evarcope\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-effect_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-variance_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-dof_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-z_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\nThe design and contrast matrix used for the final model will be outputted alongside\nthe statistical maps \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._feature-..._desc-design_matrix.tsv\u003c/code\u003e\n\u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._feature-..._desc-contrast_matrix.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReHo and fALFF are not calculated based on a linear model. As such, only one\nstatistical map of the z-scaled values will be output \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._alff.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._falff.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._reho.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor every feature, a JSON file containing a summary of the preprocessing\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esettings, and a list of the raw data files that were used for the analysis\n(\u003ccode\u003eRawSources\u003c/code\u003e) \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/....json\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor every feature, the corresponding brain mask is output beside the\nstatistical maps. Masks do not differ between different features calculated,\nthey are only copied out repeatedly for convenience \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/...desc-brain_mask.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAtlas-based connectivity outputs the time series and the full covariance and\ncorrelation matrices as text files \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._timeseries.txt\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._desc-covariance_matrix.txt\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._desc-correlation_matrix.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preprocessed-images\" class=\"anchor\" href=\"#preprocessed-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessed images\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMasked, preprocessed BOLD image \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._bold.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJust like for features \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._bold.json\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJust like for features \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._setting-..._desc-brain_mask.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFiltered confounds time series, where all filters that are applied to the BOLD\nimage are applied to the regressors as well. Note that this means that when\ngrand mean scaling is active, confounds time series are also scaled, meaning\nthat values such as \u003ccode\u003eframewise displacement\u003c/code\u003e can not be interpreted in terms\nof their original units anymore. \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._setting-..._desc-confounds_regressors.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-group-level\" class=\"anchor\" href=\"#group-level\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGroup-level\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrouplevel/...\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" href=\"#troubleshooting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTroubleshooting\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIf an error occurs, this will be output to the command line and simultaneously\nto the \u003ccode\u003eerr.txt\u003c/code\u003e file in the working directory\u003c/li\u003e\n\u003cli\u003eIf the error occurs while running, usually a text file detailing the error\nwill be placed in the working directory. These are text files and their file\nnames start with \u003ccode\u003ecrash\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eUsually, the last line of these text files contains the error message.\nPlease read this carefully, as may allow you to understand the error\u003c/li\u003e\n\u003cli\u003eFor example, consider the following error message:\n\u003ccode\u003eValueError: shape (64, 64, 33) for image 1 not compatible with first image shape (64, 64, 34) with axis == None\u003c/code\u003e\nThis error message may seem cryptic at first. However, looking at the\nmessage more closely, it suggests that two input images have different,\nincompatible dimensions. In this case, \u003ccode\u003eHALFpipe\u003c/code\u003e correctly recognized this\nissue, and there is no need for concern. The images in question will simply\nbe excluded from preprocessing and/or analysis\u003c/li\u003e\n\u003cli\u003eIn some cases, the cause of the error can be a bug in the \u003ccode\u003eHALFpipe\u003c/code\u003e code.\nPlease check that no similar issue has been reported\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues\"\u003ehere on GitHub\u003c/a\u003e. In this case,\nplease submit an\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues/new/choose\"\u003eissue\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-command-line-flags\" class=\"anchor\" href=\"#command-line-flags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand line flags\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-control-command-line-logging\" class=\"anchor\" href=\"#control-command-line-logging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eControl command line logging\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--verbose\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, only errors and warnings will be output to the command line. This\nmakes it easier to see when something goes wrong, because there is less output.\nHowever, if you want to be able to inspect what is being run, you can add the\n\u003ccode\u003e--verbose\u003c/code\u003e flag to the end of the command used to call \u003ccode\u003eHALFpipe\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eVerbose logs are always written to the \u003ccode\u003elog.txt\u003c/code\u003e file in the working directory,\nso going back and inspecting this log is always possible, even if the\n\u003ccode\u003e--verbose\u003c/code\u003e flag was not specified.\u003c/p\u003e\n\u003cp\u003eSpecifying the flag \u003ccode\u003e--debug\u003c/code\u003e will print additional, fine-grained messages. It\nwill also automatically start the\n\u003ca href=\"https://docs.python.org/3/library/pdb.html\" rel=\"nofollow\"\u003ePython Debugger\u003c/a\u003e when an error\noccurs. You should only use \u003ccode\u003e--debug\u003c/code\u003e if you know what you\u0027re doing.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-automatically-remove-unneeded-files\" class=\"anchor\" href=\"#automatically-remove-unneeded-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically remove unneeded files\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--keep\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e saves intermediate files for each pipeline step. This speeds up\nre-running with different settings, or resuming after a job after it was\ncancelled. The intermediate file are saved by the\n\u003ca href=\"https://nipype.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003enipype\u003c/code\u003e\u003c/a\u003e workflow engine, which is what\n\u003ccode\u003eHALFpipe\u003c/code\u003e uses internally. \u003ccode\u003enipype\u003c/code\u003e saves the intermediate files in the\n\u003ccode\u003enipype\u003c/code\u003e folder in the working directory.\u003c/p\u003e\n\u003cp\u003eIn environments with limited disk capacity, this can be problematic. To limit\ndisk usage, \u003ccode\u003eHALFpipe\u003c/code\u003e can delete intermediate files as soon as they are not\nneeded anymore. This behavior is controlled with the \u003ccode\u003e--keep\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eThe default option \u003ccode\u003e--keep some\u003c/code\u003e keeps all intermediate files from fMRIPrep and\nMELODIC, which would take the longest to re-run. We believe this is a good\ntradeoff between disk space and computer time. \u003ccode\u003e--keep all\u003c/code\u003e turns of all\ndeletion of intermediate files. \u003ccode\u003e--keep none\u003c/code\u003e deletes as much as possible,\nmeaning that the smallest amount possible of disk space will be used.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-configure-nipype\" class=\"anchor\" href=\"#configure-nipype\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigure nipype\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--nipype-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eomp-nthreads\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003ememory-gb\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003en-procs\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003erun-plugin\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e chooses sensible defaults for all of these values.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-choose-which-parts-to-run-or-to-skip\" class=\"anchor\" href=\"#choose-which-parts-to-run-or-to-skip\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoose which parts to run or to skip\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eonly\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eskip\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003espec-ui\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eworkflow\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003erun\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003emodel-chunk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA \u003ccode\u003eHALFpipe\u003c/code\u003e run is divided internally into three stages, spec-ui, workflow, and\nrun.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ccode\u003espec-ui\u003c/code\u003e stage is where you specify things in the user interface. It\ncreates the \u003ccode\u003espec.json\u003c/code\u003e file that contains all the information needed to run\n\u003ccode\u003eHALFpipe\u003c/code\u003e. To only run this stage, use the option \u003ccode\u003e--only-spec-ui\u003c/code\u003e. To skip\nthis stage, use the option \u003ccode\u003e--skip-spec-ui\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003eworkflow\u003c/code\u003e stage is where \u003ccode\u003eHALFpipe\u003c/code\u003e uses the \u003ccode\u003espec.json\u003c/code\u003e data to search\nfor all the files that match what was input in the user interface. It then\ngenerates a \u003ccode\u003enipype\u003c/code\u003e workflow for preprocessing, feature extraction and group\nmodels. \u003ccode\u003enipype\u003c/code\u003e then validates the workflow and prepares it for execution.\nThis usually takes a couple of minutes and cannot be parallelized. For\nhundreds of subjects, this may even take a few hours. This stage has the\ncorresponding option \u003ccode\u003e--only-workflow\u003c/code\u003e and \u003ccode\u003e--skip-workflow\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eThis stage saves several intermediate files. These are named\n\u003ccode\u003eworkflow.{uuid}.pickle.xz\u003c/code\u003e, \u003ccode\u003eexecgraph.{uuid}.pickle.xz\u003c/code\u003e and\n\u003ccode\u003eexecgraph.{n_chunks}_chunks.{uuid}.pickle.xz\u003c/code\u003e. The \u003ccode\u003euuid\u003c/code\u003e in the file name is\na unique identifier generated from the \u003ccode\u003espec.json\u003c/code\u003e file and the input files.\nIt is re-calculated every time we run this stage. The uuid algorithm produces\na different output if there are any changes (such as when new input files for\nnew subjects become available, or the \u003ccode\u003espec.json\u003c/code\u003e is changed, for example to\nadd a new feature or group model). Otherwise, the \u003ccode\u003euuid\u003c/code\u003e stays the same.\nTherefore, if a workflow file with the calculated \u003ccode\u003euuid\u003c/code\u003e already exists, then\nwe do not need to run this stage. We can simple re-use the workflow from the\nexisting file, and save some time.\u003c/li\u003e\n\u003cli\u003eIn this stage, we can also decide to split the execution into chunks. The flag\n\u003ccode\u003e--subject-chunks\u003c/code\u003e creates one chunk per subject. The flag \u003ccode\u003e--use-cluster\u003c/code\u003e\nautomatically activates \u003ccode\u003e--subject-chunks\u003c/code\u003e. The flag \u003ccode\u003e--n-chunks\u003c/code\u003e allows the\nuser to specify a specific number of chunks. This is useful if the execution\nshould be spread over a set number of computers. In addition to these, a model\nchunk is generated.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ccode\u003erun\u003c/code\u003e stage loads the \u003ccode\u003eexecgraph.{n_chunks}_chunks.{uuid}.pickle.xz\u003c/code\u003e file\ngenerated in the previous step and runs it. This file usually contains two\nchunks, one for the subject level preprocessing and feature extraction\n(\"subject level chunk\"), and one for group statistics (\"model chunk\"). To run\na specific chunk, you can use the flags \u003ccode\u003e--only-chunk-index ...\u003c/code\u003e and\n\u003ccode\u003e--only-model-chunk\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-working-directory\" class=\"anchor\" href=\"#working-directory\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorking directory\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--workdir\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-data-file-system-root\" class=\"anchor\" href=\"#data-file-system-root\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData file system root\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--fs-root\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eHALFpipe\u003c/code\u003e container, or really most containers, contain the entire base\nsystem needed to run\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003eFor questions or support, please submit an\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues/new/choose\"\u003eissue\u003c/a\u003e or contact us\nvia e-mail.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRole\u003c/th\u003e\n\u003cth\u003eE-mail address\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLea Waller\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:lea.waller@charite.de\"\u003elea.waller@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIlya Veer\u003c/td\u003e\n\u003ctd\u003eProject manager\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:ilya.veer@charite.de\"\u003eilya.veer@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSusanne Erk\u003c/td\u003e\n\u003ctd\u003eProject manager\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:susanne.erk@charite.de\"\u003esusanne.erk@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", "stargazers_count": 10, - "subscribers_count": 3, + "subscribers_count": 2, "topics": [ - "r", - "r-package", - "rstats", - "bayesian", - "beast", - "beast2", - "phylogenetic-inference", - "phylogenetics" + "neuroimaging" ], - "updated_at": 1718964949.0 + "updated_at": 1627403615.0 }, { "data_format": 2, @@ -37312,127 +37291,124 @@ var data = }, { "data_format": 2, - "description": "ENIGMA Halfpipe is a user-friendly software that facilitates reproducible analysis of fMRI data", + "description": "R package to run BEAST2", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "HALFpipe/HALFpipe", - "latest_release": "1.1.1", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-welcome-to-enigma-halfpipe\" class=\"anchor\" href=\"#welcome-to-enigma-halfpipe\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to ENIGMA \u003ccode\u003eHALFpipe\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4508\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/actions?query=workflow%3A%22build%22\"\u003e\u003cimg src=\"https://github.com/HALFpipe/HALFpipe/workflows/build/badge.svg\" alt=\"https://github.com/HALFpipe/HALFpipe/workflows/build/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/actions?query=workflow%3A%22continuous+integration%22\"\u003e\u003cimg src=\"https://github.com/HALFpipe/HALFpipe/workflows/continuous%20integration/badge.svg\" alt=\"https://github.com/HALFpipe/HALFpipe/workflows/continuous%20integration/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/HALFpipe/HALFpipe\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ef5be2978b13a91a1a602bc0261933d3735a7567176db1ef0c13eb65b3249056/68747470733a2f2f636f6465636f762e696f2f67682f48414c46706970652f48414c46706970652f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/HALFpipe/HALFpipe/branch/master/graph/badge.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e is a user-friendly software that facilitates reproducible analysis of\nfMRI data, including preprocessing, single-subject, and group analysis. It\nprovides state-of-the-art preprocessing using\n\u003ca href=\"https://fmriprep.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003efmriprep\u003c/code\u003e\u003c/a\u003e, but removes the necessity to\nconvert data to the\n\u003ca href=\"https://bids-specification.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003e\u003ccode\u003eBIDS\u003c/code\u003e\u003c/a\u003e format. Common\nresting-state and task-based fMRI features can then be calculated on the fly\nusing \u003ca href=\"http://fsl.fmrib.ox.ac.uk/\" rel=\"nofollow\"\u003e\u003ccode\u003eFSL\u003c/code\u003e\u003c/a\u003e and\n\u003ca href=\"https://nipype.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003enipype\u003c/code\u003e\u003c/a\u003e for statistics.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eIf you encounter issues, please see the \u003ca href=\"#troubleshooting\"\u003etroubleshooting\u003c/a\u003e\nsection of this document.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" href=\"#table-of-contents\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTable of Contents\u003c/h2\u003e\n\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#getting-started\"\u003eGetting started\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#container-platform\"\u003eContainer platform\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#download\"\u003eDownload\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running\"\u003eRunning\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#user-interface\"\u003eUser interface\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#files\"\u003eFiles\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#features\"\u003eFeatures\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#models\"\u003eModels\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-on-a-high-performance-computing-cluster\"\u003eRunning on a high-performance computing cluster\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#quality-checks\"\u003eQuality checks\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#subject-level-features\"\u003eSubject-level features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#preprocessed-images\"\u003ePreprocessed images\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#group-level\"\u003eGroup-level\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#troubleshooting\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#command-line-flags\"\u003eCommand line flags\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#control-command-line-logging\"\u003eControl command line logging\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#automatically-remove-unneeded-files\"\u003eAutomatically remove unneeded files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#adjust-nipype\"\u003eAdjust nipype\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#choose-which-parts-to-run-or-to-skip\"\u003eChoose which parts to run or to skip\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#working-directory\"\u003eWorking directory\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#data-file-system-root\"\u003eData file system root\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e is distributed as a container, meaning that all required software\ncomes bundled in a monolithic file, the container. This allows for easy\ninstallation on new systems, and makes data analysis more reproducible, because\nsoftware versions are guaranteed to be the same for all users.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-container-platform\" class=\"anchor\" href=\"#container-platform\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContainer platform\u003c/h3\u003e\n\u003cp\u003eThe first step is to install one of the supported container platforms. If you\u0027re\nusing a high-performance computing cluster, more often than not\n\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e will already be available.\u003c/p\u003e\n\u003cp\u003eIf not, we recommend using the latest version\nof\u003ca href=\"https://sylabs.io\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e. However, it can be somewhat cumbersome to\ninstall, as it needs to be built from source.\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://neuro.debian.net/\" rel=\"nofollow\"\u003e\u003ccode\u003eNeuroDebian\u003c/code\u003e\u003c/a\u003e package repository provides an\nolder version of \u003ca href=\"https://sylabs.io/guides/2.6/user-guide/\" rel=\"nofollow\"\u003e\u003ccode\u003eSingularity\u003c/code\u003e\u003c/a\u003e for\n\u003ca href=\"https://neuro.debian.net/pkgs/singularity-container.html\" rel=\"nofollow\"\u003esome\u003c/a\u003e Linux\ndistributions.\u003c/p\u003e\n\u003cp\u003eIn contrast to \u003ccode\u003eSingularity\u003c/code\u003e, \u003ccode\u003eDocker\u003c/code\u003e always requires elevated privileges to\nrun containers. In other words, every user running a \u003ccode\u003eDocker\u003c/code\u003e container\nautomatically has administrator privileges on the computer they\u0027re using.\nTherefore, it is inherently a bad choice for multi-user environments, where the\naccess of individual users should be limited. \u003ccode\u003eDocker\u003c/code\u003e is the only option that\nis compatible with \u003ccode\u003eMac OS X\u003c/code\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eVersion\u003c/th\u003e\n\u003cth\u003eInstallation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e3.5.3\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003eSee \u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.5/user-guide/quick_start.html\u003c/a\u003e\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e2.6.1\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003esudo apt install singularity-container\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eSee \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-download\" class=\"anchor\" href=\"#download\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h3\u003e\n\u003cp\u003eThe second step is to download the \u003ccode\u003eHALFpipe\u003c/code\u003e to your computer. This requires\napproximately 5 gigabytes of storage.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eInstallation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://charitede-my.sharepoint.com/:f:/g/personal/lea_waller_charite_de/EukRziExhTVBrEAai2oEpi8B2jsnn7P3YQuFo2pycKp6-g\" rel=\"nofollow\"\u003ehttps://charitede-my.sharepoint.com/:f:/g/personal/lea_waller_charite_de/EukRziExhTVBrEAai2oEpi8B2jsnn7P3YQuFo2pycKp6-g\u003c/a\u003e or \u003ccode\u003esingularity pull docker://halfpipe/halfpipe:1.1.1\u003c/code\u003e or \u003ccode\u003esingularity pull docker://ghcr.io/halfpipe/halfpipe:1.1.1\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edocker pull halfpipe/halfpipe:1.1.1\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e version \u003ccode\u003e3.x\u003c/code\u003e creates a container image file called\n\u003ccode\u003eHALFpipe_{version}.sif\u003c/code\u003e in the directory where you run the \u003ccode\u003epull\u003c/code\u003e command. For\n\u003ccode\u003eSingularity\u003c/code\u003e version \u003ccode\u003e2.x\u003c/code\u003e the file is named\n\u003ccode\u003ehalfpipe-halfpipe-master-latest.simg\u003c/code\u003e. Whenever you want to use the container,\nyou need pass \u003ccode\u003eSingularity\u003c/code\u003e the path to this file.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e \u003ccode\u003eSingularity\u003c/code\u003e may store a copy of the container in its cache\ndirectory. The cache directory is located by default in your home directory at\n\u003ccode\u003e~/.singularity\u003c/code\u003e. If you need to save disk space in your home directory, you\ncan safely delete the cache directory after downloading, i.e. by running\n\u003ccode\u003erm -rf ~/.singularity\u003c/code\u003e. Alternatively, you could move the cache directory\nsomewhere with more free disk space using a symlink. This way, files will\nautomatically be stored there in the future. For example, if you have a lot of\nfree disk space in \u003ccode\u003e/mnt/storage\u003c/code\u003e, then you could first run\n\u003ccode\u003emv ~/.singularity /mnt/storage\u003c/code\u003e to move the cache directory, and then\n\u003ccode\u003eln -s /mnt/storage/.singularity ~/.singularity\u003c/code\u003e to create the symlink.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e\u003ccode\u003eDocker\u003c/code\u003e will store the container in its storage base directory, so it does not\nmatter from which directory you run the \u003ccode\u003epull\u003c/code\u003e command.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-running\" class=\"anchor\" href=\"#running\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h3\u003e\n\u003cp\u003eThe third step is to run the downloaded container. You may need to replace\n\u003ccode\u003ehalfpipe_1.1.1.sif\u003c/code\u003e with the actual path and filename where \u003ccode\u003eSingularity\u003c/code\u003e\ndownloaded your container.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContainer platform\u003c/th\u003e\n\u003cth\u003eCommand\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingularity\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_1.1.1.sif\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDocker\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edocker run --interactive --tty --volume /:/ext halfpipe/halfpipe\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYou should now see the user interface.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-background\" class=\"anchor\" href=\"#background\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBackground\u003c/h4\u003e\n\u003cp\u003eContainers are by default isolated from the host computer. This adds security,\nbut also means that the container cannot access the data it needs for analysis.\n\u003ccode\u003eHALFpipe\u003c/code\u003e expects all inputs (e.g., image files and spreadsheets) and outputs\n(the working directory) to be places in the path\u003ccode\u003e/ext\u003c/code\u003e (see also\n\u003ca href=\"#data-file-system-root---fs-root\"\u003e\u003ccode\u003e--fs-root\u003c/code\u003e\u003c/a\u003e). Using the option\n\u003ccode\u003e--bind /:/ext\u003c/code\u003e, we instruct \u003ccode\u003eSingularity\u003c/code\u003e to map all of the host file system\n(\u003ccode\u003e/\u003c/code\u003e) to that path (\u003ccode\u003e/ext\u003c/code\u003e). You can also run \u003ccode\u003eHALFpipe\u003c/code\u003e and only map only part\nof the host file system, but keep in mind that any directories that are not\nmapped will not be visible later.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSingularity\u003c/code\u003e passes the host shell environment to the container by default.\nThis means that in some cases, the host computer\u0027s configuration can interfere\nwith the software. To avoid this, we need to pass the option \u003ccode\u003e--containall\u003c/code\u003e.\n\u003ccode\u003eDocker\u003c/code\u003e does not pass the host shell environment by default, so we don\u0027t need\nto pass an option.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-user-interface\" class=\"anchor\" href=\"#user-interface\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUser interface\u003c/h2\u003e\n\u003cp\u003eThe user interface asks a series of questions about your data and the analyses\nyou want to run. In each question, you can press \u003ccode\u003eControl+C\u003c/code\u003e to cancel the\ncurrent question and go back to the previous one. \u003ccode\u003eControl+D\u003c/code\u003e exits the program\nwithout saving. Note that these keyboard shortcuts are the same on Mac.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-files\" class=\"anchor\" href=\"#files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFiles\u003c/h3\u003e\n\u003cp\u003eTo run preprocessing, at least a T1-weighted structural image and a BOLD image\nfile is required. Preprocessing and data analysis proceeds automatically.\nHowever, to be able to run automatically, data files need to be input in a way\nsuitable for automation.\u003c/p\u003e\n\u003cp\u003eFor this kind of automation, \u003ccode\u003eHALFpipe\u003c/code\u003e needs to know the relationships between\nfiles, such as which files belong to the same subject. However, even though it\nwould be obvious for a human, a program cannot easily assign a file name to a\nsubject, and this will be true as long as there are differences in naming\nbetween different researchers or labs. One researcher may name the same file\n\u003ccode\u003esubject_01_rest.nii.gz\u003c/code\u003e and another \u003ccode\u003esubject_01/scan_rest.nii.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIn \u003ccode\u003eHALFpipe\u003c/code\u003e, we solve this issue by inputting file names in a specific way.\nFor example, instead of \u003ccode\u003esubject_01/scan_rest.nii.gz\u003c/code\u003e, \u003ccode\u003eHALFpipe\u003c/code\u003e expects you to\ninput \u003ccode\u003e{subject}/scan_rest.nii.gz\u003c/code\u003e. \u003ccode\u003eHALFpipe\u003c/code\u003e can then match all files on disk\nthat match this naming schema, and extract the subject ID \u003ccode\u003esubject_01\u003c/code\u003e. Using\nthe extracted subject ID, other files can now be matched to this image. If all\ninput files are available in BIDS format, then this step can be skipped.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify working directory\u003c/code\u003e All intermediate and outputs of \u003ccode\u003eHALFpipe\u003c/code\u003e will\nbe placed in the working directory. Keep in mind to choose a location with\nsufficient free disk space, as intermediates can be multiple gigabytes in\nsize for each subject.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eIs the data available in BIDS format?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the BIDS directory\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify anatomical/structural data\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the path of the T1-weighted image files\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify functional data\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the path of the BOLD image files\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eCheck repetition time values\u003c/code\u003e / \u003ccode\u003eSpecify repetition time in seconds\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd more BOLD image files?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 2\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDo slice timing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eCheck slice acquisition direction values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCheck slice timing values\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify field maps?\u003c/code\u003e If the data was imported from a BIDS directory, this\nstep will be omitted.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the type of the field maps\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEPI (blip-up blip-down)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the blip-up blip-down EPI image files\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003ePhase difference and magnitude (used by Siemens scanners)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the magnitude image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the phase/phase difference image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify echo time difference in seconds\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003eScanner-computed field map and magnitude (used by GE / Philips\nscanners)\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the magnitude image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the field map image files\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd more field maps?\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify effective echo spacing for the functional data in seconds\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify phase encoding direction for the functional data\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-features\" class=\"anchor\" href=\"#features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h3\u003e\n\u003cp\u003eFeatures are analyses that are carried out on the preprocessed data, in other\nwords, first-level analyses.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify first-level features?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the feature type\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eTask-based\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the event file type\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSPM multiple conditions\u003c/code\u003e A MATLAB .mat file containing three\narrays: \u003ccode\u003enames\u003c/code\u003e (condition), \u003ccode\u003eonsets\u003c/code\u003e and \u003ccode\u003edurations\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eFSL 3-column\u003c/code\u003e One text file for each condition. Each file has its\ncorresponding condition in the filename. The first column specifies\nthe event onset, the second the duration. The third column of the\nfiles is ignored, so parametric modulation is not supported\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eBIDS TSV\u003c/code\u003e A tab-separated table with named columns \u003ccode\u003etrial_type\u003c/code\u003e\n(condition), \u003ccode\u003eonset\u003c/code\u003e and \u003ccode\u003eduration\u003c/code\u003e. While BIDS supports defining\nadditional columns, \u003ccode\u003eHALFpipe\u003c/code\u003e will currently ignore these\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the event files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSelect conditions to add to the model\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify contrasts\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify contrast name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify contrast values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd another contrast?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply a temporal filter to the design matrix?\u003c/code\u003e A separate temporal\nfilter can be specified for the design matrix. In contrast, the\ntemporal filtering of the input image and any confound regressors\nadded to the design matrix is specified in 10. In general, the two\nsettings should match\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply smoothing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify smoothing FWHM in mm\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eGrand mean scaling will be applied with a mean of 10000.000000\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eTemporal filtering will be applied using a gaussian-weighted filter\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003eSpecify the filter width in seconds\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRemove confounds?\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSeed-based connectivity\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify binary seed mask file(s)\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify the path of the binary seed mask image files\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCheck space values\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eAdd binary seed mask image file\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDual regression\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAtlas-based connectivity matrix\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eReHo\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efALFF\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify feature name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTODO\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Skip this step\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAdd another first-level feature?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e Loop back to 1\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eOutput a preprocessed image?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify setting name\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSpecify images to use\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply smoothing?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify smoothing FWHM in mm\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eDo grand mean scaling?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eSpecify grand mean\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eApply a temporal filter?\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYes\u003c/code\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003eSpecify the type of temporal filter\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eGaussian-weighted\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eFrequency-based\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eRemove confounds?\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNo\u003c/code\u003e Continue\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-models\" class=\"anchor\" href=\"#models\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eModels\u003c/h3\u003e\n\u003cp\u003eModels are statistical analyses that are carried out on the features.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-running-on-a-high-performance-computing-cluster\" class=\"anchor\" href=\"#running-on-a-high-performance-computing-cluster\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning on a high-performance computing cluster\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eLog in to your cluster\u0027s head node\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRequest an interactive job. Refer to your cluster\u0027s documentation for how to\ndo this\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn the interactive job, run the \u003ccode\u003eHALFpipe\u003c/code\u003e user interface, but add the flag\n\u003ccode\u003e--use-cluster\u003c/code\u003e to the end of the command. \u003cbr\u003e\nFor example, \u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_latest.sif --use-cluster\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs soon as you finish specifying all your data, features and models in the\nuser interface, \u003ccode\u003eHALFpipe\u003c/code\u003e will now generate everything needed to run on the\ncluster. For hundreds of subjects, this can take up to a few hours.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen \u003ccode\u003eHALFpipe\u003c/code\u003e exits, edit the generated submit script \u003ccode\u003esubmit.slurm.sh\u003c/code\u003e\naccording to your cluster\u0027s documentation and then run it. This submit script\nwill calculate everything except group statistics.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs soon as all processing has been completed, you can run group statistics.\nThis is usually very fast, so you can do this in an interactive session. Run\n\u003ccode\u003esingularity run --containall --bind /:/ext halfpipe_latest.sif --only-model-chunk\u003c/code\u003e\nand then select \u003ccode\u003eRun without modification\u003c/code\u003e in the user interface.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA common issue with remote work via secure shell is that the connection may\nbreak after a few hours. For batch jobs this is not an issue, but for\ninteractive jobs this can be quite frustrating. When the connection is lost,\nthe node you were connected to will automatically quit all programs you were\nrunning. To prevent this, you can run interactive jobs within \u003ccode\u003escreen\u003c/code\u003e or\n\u003ccode\u003etmux\u003c/code\u003e (whichever is available). These commands allow you to open sessions in\nthe terminal that will continue running in the background even when you close\nor disconnect. Here\u0027s a quick overview of how to use the commands (more\nin-depth documentation is available for example at\n[\u003ca href=\"http://www.dayid.org/comp/tm.html\" rel=\"nofollow\"\u003ehttp://www.dayid.org/comp/tm.html\u003c/a\u003e]).\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOpen a new screen/tmux session on the head node by running either \u003ccode\u003escreen\u003c/code\u003e\nor \u003ccode\u003etmux\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRequest an interactive job from within the session, for example with\n\u003ccode\u003esrun --pty bash -i\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the command that you want to run\u003c/li\u003e\n\u003cli\u003eDetach from the screen/tmux session, meaning disconnecting with the ability\nto re-connect later \u003cbr\u003e\nFor screen, this is done by first pressing \u003ccode\u003eControl+a\u003c/code\u003e, then letting go, and\nthen pressing \u003ccode\u003ed\u003c/code\u003e on the keyboard. \u003cbr\u003e\nFor tmux, it\u0027s \u003ccode\u003eControl+b\u003c/code\u003e instead of \u003ccode\u003eControl+a\u003c/code\u003e. \u003cbr\u003e\nNote that this is always \u003ccode\u003eControl\u003c/code\u003e, even if you\u0027re on a mac.\u003c/li\u003e\n\u003cli\u003eClose your connection to the head node with \u003ccode\u003eControl+d\u003c/code\u003e. \u003ccode\u003escreen\u003c/code\u003e/\u003ccode\u003etmux\u003c/code\u003e\nwill remain running in the background\u003c/li\u003e\n\u003cli\u003eLater, connect again to the head node. Run \u003ccode\u003escreen -r\u003c/code\u003e or \u003ccode\u003etmux attach\u003c/code\u003e to\ncheck back on the interactive job. If everything went well and the command\nyou wanted to run finished, close the interactive job with \u003ccode\u003eControl+d\u003c/code\u003e and\nthen the \u003ccode\u003escreen\u003c/code\u003e/\u003ccode\u003etmux\u003c/code\u003e session with \u003ccode\u003eControl+d\u003c/code\u003e again. If the command\nhasn\u0027t finished yet, detach as before and come back later\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quality-checks\" class=\"anchor\" href=\"#quality-checks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuality checks\u003c/h2\u003e\n\u003cp\u003ePlease see the manual at \u003ca href=\"https://docs.google.com/document/d/1evDkVaoXqSaxulp5eSxVqgaxro7yZl-gao70D0S2dH8\" rel=\"nofollow\"\u003ehttps://docs.google.com/document/d/1evDkVaoXqSaxulp5eSxVqgaxro7yZl-gao70D0S2dH8\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-outputs\" class=\"anchor\" href=\"#outputs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutputs\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA visual report page \u003ccode\u003ereports/index.html\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA table with image quality metrics \u003ccode\u003ereports/reportvals.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA table containing the preprocessing status \u003ccode\u003ereports/reportpreproc.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe untouched \u003ccode\u003efmriprep\u003c/code\u003e derivatives. Some files have been omitted to save\ndisk space \u003ccode\u003efmriprep\u003c/code\u003e is very strict about only processing data that is\ncompliant with the BIDS standard. As such, we may need to format subjects\nnames for compliance. For example, an input subject named \u003ccode\u003esubject_01\u003c/code\u003e will\nappear as \u003ccode\u003esubject01\u003c/code\u003e in the \u003ccode\u003efmriprep\u003c/code\u003e derivatives. \u003ccode\u003ederivatives/fmriprep\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-subject-level-features\" class=\"anchor\" href=\"#subject-level-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubject-level features\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFor task-based, seed-based connectivity and dual regression features,\n\u003ccode\u003eHALFpipe\u003c/code\u003e outputs the statistical maps for the effect, the variance, the\ndegrees of freedom of the variance and the z-statistic. In FSL, the effect and\nvariance are also called \u003ccode\u003ecope\u003c/code\u003e and \u003ccode\u003evarcope\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-effect_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-variance_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-dof_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._stat-z_statmap.nii.gz\u003c/code\u003e \u003cbr\u003e\nThe design and contrast matrix used for the final model will be outputted alongside\nthe statistical maps \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._feature-..._desc-design_matrix.tsv\u003c/code\u003e\n\u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._feature-..._desc-contrast_matrix.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReHo and fALFF are not calculated based on a linear model. As such, only one\nstatistical map of the z-scaled values will be output \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._alff.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._falff.nii.gz\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._reho.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor every feature, a JSON file containing a summary of the preprocessing\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esettings, and a list of the raw data files that were used for the analysis\n(\u003ccode\u003eRawSources\u003c/code\u003e) \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/....json\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor every feature, the corresponding brain mask is output beside the\nstatistical maps. Masks do not differ between different features calculated,\nthey are only copied out repeatedly for convenience \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/...desc-brain_mask.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAtlas-based connectivity outputs the time series and the full covariance and\ncorrelation matrices as text files \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._timeseries.txt\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._desc-covariance_matrix.txt\u003c/code\u003e \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._desc-correlation_matrix.txt\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-preprocessed-images\" class=\"anchor\" href=\"#preprocessed-images\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreprocessed images\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMasked, preprocessed BOLD image \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._bold.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJust like for features \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/..._bold.json\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJust like for features \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._setting-..._desc-brain_mask.nii.gz\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFiltered confounds time series, where all filters that are applied to the BOLD\nimage are applied to the regressors as well. Note that this means that when\ngrand mean scaling is active, confounds time series are also scaled, meaning\nthat values such as \u003ccode\u003eframewise displacement\u003c/code\u003e can not be interpreted in terms\nof their original units anymore. \u003cbr\u003e\n\u003ccode\u003ederivatives/halfpipe/sub-.../func/sub-..._task-..._setting-..._desc-confounds_regressors.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-group-level\" class=\"anchor\" href=\"#group-level\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGroup-level\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003egrouplevel/...\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" href=\"#troubleshooting\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTroubleshooting\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIf an error occurs, this will be output to the command line and simultaneously\nto the \u003ccode\u003eerr.txt\u003c/code\u003e file in the working directory\u003c/li\u003e\n\u003cli\u003eIf the error occurs while running, usually a text file detailing the error\nwill be placed in the working directory. These are text files and their file\nnames start with \u003ccode\u003ecrash\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eUsually, the last line of these text files contains the error message.\nPlease read this carefully, as may allow you to understand the error\u003c/li\u003e\n\u003cli\u003eFor example, consider the following error message:\n\u003ccode\u003eValueError: shape (64, 64, 33) for image 1 not compatible with first image shape (64, 64, 34) with axis == None\u003c/code\u003e\nThis error message may seem cryptic at first. However, looking at the\nmessage more closely, it suggests that two input images have different,\nincompatible dimensions. In this case, \u003ccode\u003eHALFpipe\u003c/code\u003e correctly recognized this\nissue, and there is no need for concern. The images in question will simply\nbe excluded from preprocessing and/or analysis\u003c/li\u003e\n\u003cli\u003eIn some cases, the cause of the error can be a bug in the \u003ccode\u003eHALFpipe\u003c/code\u003e code.\nPlease check that no similar issue has been reported\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues\"\u003ehere on GitHub\u003c/a\u003e. In this case,\nplease submit an\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues/new/choose\"\u003eissue\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-command-line-flags\" class=\"anchor\" href=\"#command-line-flags\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand line flags\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-control-command-line-logging\" class=\"anchor\" href=\"#control-command-line-logging\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eControl command line logging\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--verbose\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, only errors and warnings will be output to the command line. This\nmakes it easier to see when something goes wrong, because there is less output.\nHowever, if you want to be able to inspect what is being run, you can add the\n\u003ccode\u003e--verbose\u003c/code\u003e flag to the end of the command used to call \u003ccode\u003eHALFpipe\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eVerbose logs are always written to the \u003ccode\u003elog.txt\u003c/code\u003e file in the working directory,\nso going back and inspecting this log is always possible, even if the\n\u003ccode\u003e--verbose\u003c/code\u003e flag was not specified.\u003c/p\u003e\n\u003cp\u003eSpecifying the flag \u003ccode\u003e--debug\u003c/code\u003e will print additional, fine-grained messages. It\nwill also automatically start the\n\u003ca href=\"https://docs.python.org/3/library/pdb.html\" rel=\"nofollow\"\u003ePython Debugger\u003c/a\u003e when an error\noccurs. You should only use \u003ccode\u003e--debug\u003c/code\u003e if you know what you\u0027re doing.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-automatically-remove-unneeded-files\" class=\"anchor\" href=\"#automatically-remove-unneeded-files\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAutomatically remove unneeded files\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--keep\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e saves intermediate files for each pipeline step. This speeds up\nre-running with different settings, or resuming after a job after it was\ncancelled. The intermediate file are saved by the\n\u003ca href=\"https://nipype.readthedocs.io/\" rel=\"nofollow\"\u003e\u003ccode\u003enipype\u003c/code\u003e\u003c/a\u003e workflow engine, which is what\n\u003ccode\u003eHALFpipe\u003c/code\u003e uses internally. \u003ccode\u003enipype\u003c/code\u003e saves the intermediate files in the\n\u003ccode\u003enipype\u003c/code\u003e folder in the working directory.\u003c/p\u003e\n\u003cp\u003eIn environments with limited disk capacity, this can be problematic. To limit\ndisk usage, \u003ccode\u003eHALFpipe\u003c/code\u003e can delete intermediate files as soon as they are not\nneeded anymore. This behavior is controlled with the \u003ccode\u003e--keep\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eThe default option \u003ccode\u003e--keep some\u003c/code\u003e keeps all intermediate files from fMRIPrep and\nMELODIC, which would take the longest to re-run. We believe this is a good\ntradeoff between disk space and computer time. \u003ccode\u003e--keep all\u003c/code\u003e turns of all\ndeletion of intermediate files. \u003ccode\u003e--keep none\u003c/code\u003e deletes as much as possible,\nmeaning that the smallest amount possible of disk space will be used.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-configure-nipype\" class=\"anchor\" href=\"#configure-nipype\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigure nipype\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--nipype-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eomp-nthreads\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003ememory-gb\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003en-procs\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003erun-plugin\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eHALFpipe\u003c/code\u003e chooses sensible defaults for all of these values.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-choose-which-parts-to-run-or-to-skip\" class=\"anchor\" href=\"#choose-which-parts-to-run-or-to-skip\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoose which parts to run or to skip\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eonly\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eskip\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e-\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003espec-ui\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eworkflow\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003erun\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003emodel-chunk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eA \u003ccode\u003eHALFpipe\u003c/code\u003e run is divided internally into three stages, spec-ui, workflow, and\nrun.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ccode\u003espec-ui\u003c/code\u003e stage is where you specify things in the user interface. It\ncreates the \u003ccode\u003espec.json\u003c/code\u003e file that contains all the information needed to run\n\u003ccode\u003eHALFpipe\u003c/code\u003e. To only run this stage, use the option \u003ccode\u003e--only-spec-ui\u003c/code\u003e. To skip\nthis stage, use the option \u003ccode\u003e--skip-spec-ui\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003eworkflow\u003c/code\u003e stage is where \u003ccode\u003eHALFpipe\u003c/code\u003e uses the \u003ccode\u003espec.json\u003c/code\u003e data to search\nfor all the files that match what was input in the user interface. It then\ngenerates a \u003ccode\u003enipype\u003c/code\u003e workflow for preprocessing, feature extraction and group\nmodels. \u003ccode\u003enipype\u003c/code\u003e then validates the workflow and prepares it for execution.\nThis usually takes a couple of minutes and cannot be parallelized. For\nhundreds of subjects, this may even take a few hours. This stage has the\ncorresponding option \u003ccode\u003e--only-workflow\u003c/code\u003e and \u003ccode\u003e--skip-workflow\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eThis stage saves several intermediate files. These are named\n\u003ccode\u003eworkflow.{uuid}.pickle.xz\u003c/code\u003e, \u003ccode\u003eexecgraph.{uuid}.pickle.xz\u003c/code\u003e and\n\u003ccode\u003eexecgraph.{n_chunks}_chunks.{uuid}.pickle.xz\u003c/code\u003e. The \u003ccode\u003euuid\u003c/code\u003e in the file name is\na unique identifier generated from the \u003ccode\u003espec.json\u003c/code\u003e file and the input files.\nIt is re-calculated every time we run this stage. The uuid algorithm produces\na different output if there are any changes (such as when new input files for\nnew subjects become available, or the \u003ccode\u003espec.json\u003c/code\u003e is changed, for example to\nadd a new feature or group model). Otherwise, the \u003ccode\u003euuid\u003c/code\u003e stays the same.\nTherefore, if a workflow file with the calculated \u003ccode\u003euuid\u003c/code\u003e already exists, then\nwe do not need to run this stage. We can simple re-use the workflow from the\nexisting file, and save some time.\u003c/li\u003e\n\u003cli\u003eIn this stage, we can also decide to split the execution into chunks. The flag\n\u003ccode\u003e--subject-chunks\u003c/code\u003e creates one chunk per subject. The flag \u003ccode\u003e--use-cluster\u003c/code\u003e\nautomatically activates \u003ccode\u003e--subject-chunks\u003c/code\u003e. The flag \u003ccode\u003e--n-chunks\u003c/code\u003e allows the\nuser to specify a specific number of chunks. This is useful if the execution\nshould be spread over a set number of computers. In addition to these, a model\nchunk is generated.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003eThe \u003ccode\u003erun\u003c/code\u003e stage loads the \u003ccode\u003eexecgraph.{n_chunks}_chunks.{uuid}.pickle.xz\u003c/code\u003e file\ngenerated in the previous step and runs it. This file usually contains two\nchunks, one for the subject level preprocessing and feature extraction\n(\"subject level chunk\"), and one for group statistics (\"model chunk\"). To run\na specific chunk, you can use the flags \u003ccode\u003e--only-chunk-index ...\u003c/code\u003e and\n\u003ccode\u003e--only-model-chunk\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-working-directory\" class=\"anchor\" href=\"#working-directory\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWorking directory\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--workdir\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-data-file-system-root\" class=\"anchor\" href=\"#data-file-system-root\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eData file system root\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e--fs-root\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eHALFpipe\u003c/code\u003e container, or really most containers, contain the entire base\nsystem needed to run\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contact\" class=\"anchor\" href=\"#contact\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003eFor questions or support, please submit an\n\u003ca href=\"https://github.com/HALFpipe/HALFpipe/issues/new/choose\"\u003eissue\u003c/a\u003e or contact us\nvia e-mail.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eRole\u003c/th\u003e\n\u003cth\u003eE-mail address\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLea Waller\u003c/td\u003e\n\u003ctd\u003eDeveloper\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:lea.waller@charite.de\"\u003elea.waller@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIlya Veer\u003c/td\u003e\n\u003ctd\u003eProject manager\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:ilya.veer@charite.de\"\u003eilya.veer@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSusanne Erk\u003c/td\u003e\n\u003ctd\u003eProject manager\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:susanne.erk@charite.de\"\u003esusanne.erk@charite.de\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "full_name": "ropensci/beastier", + "latest_release": "v2.5.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebeastier\u003c/h1\u003e\u003ca id=\"user-content-beastier\" class=\"anchor\" aria-label=\"Permalink: beastier\" href=\"#beastier\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/ropensci/onboarding/issues/209\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37e90e3b01a6f34a223f7073479c59d4928c3afd773379b02f14ec91cff10253/68747470733a2f2f6261646765732e726f70656e7363692e6f72672f3230395f7374617475732e737667\" alt=\"Peer Review Status\" data-canonical-src=\"https://badges.ropensci.org/209_status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cran.r-project.org/package=beastier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f6ee17ac66d35bf285dc963fd619c15438d53a452f3ea63199760e319b27f427/687474703a2f2f7777772e722d706b672e6f72672f6261646765732f76657273696f6e2f6265617374696572\" alt=\"CRAN version\" data-canonical-src=\"http://www.r-pkg.org/badges/version/beastier\" style=\"max-width: 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rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2f7a95a01b9543e8dcac34358c343b1b2d75abb019511c5254490fb99a3c16fc/68747470733a2f2f7777772e7265706f7374617475732e6f72672f6261646765732f6c61746573742f6163746976652e737667\" alt=\"Project Status: Active \u2013 The project has reached a stable, usable state and is being actively developed.\" data-canonical-src=\"https://www.repostatus.org/badges/latest/active.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/115617629\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b5f515a5ee12332bbd6a3d6bfb28187e53f261878b8035e616eb48b366457955/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3131353631373632392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/115617629.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/ropensci/beautier/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beastier/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/ropensci/beastier/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/ropensci/beastier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/836d8a566886b2caa4487744f24ac8e670ae20a5b7601e1d7581892d9ee3d3c6/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656173746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beastier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beastier/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/ropensci/beastier/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/ropensci/beastier/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c363f35b306c504f8a52438e340ba56e11d8377b383a9564205b9cf7a129a985/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656173746965722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beastier/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003e\u003ccode\u003ebeastier\u003c/code\u003e is an R package to run BEAST2.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/beastier_logo.png\"\u003e\u003cimg src=\"man/figures/beastier_logo.png\" alt=\"beastier logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ebeastier\u003c/code\u003e is part of the \u003ca href=\"https://github.com/ropensci/babette\"\u003e\u003ccode\u003ebabette\u003c/code\u003e\u003c/a\u003e\npackage suite:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beautier\"\u003e\u003ccode\u003ebeautier\u003c/code\u003e\u003c/a\u003e\ncreates BEAST2 input (\u003ccode\u003e.xml\u003c/code\u003e) files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beastier\"\u003e\u003ccode\u003ebeastier\u003c/code\u003e\u003c/a\u003e runs BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/mauricer\"\u003e\u003ccode\u003emauricer\u003c/code\u003e\u003c/a\u003e: install BEAST2 packages\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/tracerer\"\u003e\u003ccode\u003etracerer\u003c/code\u003e\u003c/a\u003e\npastes BEAST2 output (\u003ccode\u003e.log\u003c/code\u003e, \u003ccode\u003e.trees\u003c/code\u003e, etc) files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRelated R packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall\"\u003e\u003ccode\u003ebeastierinstall\u003c/code\u003e\u003c/a\u003e:\nInstall and uninstall BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/beastier_on_windows\"\u003e\u003ccode\u003ebeastier_on_windows\u003c/code\u003e\u003c/a\u003e:\nVerify that \u003ccode\u003ebeastier\u003c/code\u003e works on the Windows operating system\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/lumier\"\u003e\u003ccode\u003elumier\u003c/code\u003e\u003c/a\u003e:\nShiny app to help create the function call needed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall BEAST2\u003c/h2\u003e\u003ca id=\"user-content-install-beast2\" class=\"anchor\" aria-label=\"Permalink: Install BEAST2\" href=\"#install-beast2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDue to CRAN policy, beastier cannot install BEAST2.\nAs a workaround, the non-CRAN\n\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall\"\u003e\u003ccode\u003ebeastierinstall\u003c/code\u003e\u003c/a\u003e\ncan be used.\u003c/p\u003e\n\u003cp\u003eTo install BEAST2:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003eremotes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003erichelbilderbeek/beastierinstall\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-e\"\u003ebeastierinstall\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_beast2()\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample for \u003ccode\u003ev2.1\u003c/code\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-example-for-v21\" class=\"anchor\" aria-label=\"Permalink: Example for v2.1\" href=\"#example-for-v21\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun BEAST2:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eoutput_state_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eout.state\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\nrun_beast2(\n \u003cspan class=\"pl-v\"\u003einput_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_beastier_path(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e2_4.xml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003eoutput_state_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eoutput_state_filename\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create the files as specified in the \u003ccode\u003e2_4.xml\u003c/code\u003e BEAST2 input file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample for \u003ccode\u003ev2.0.25\u003c/code\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-example-for-v2025\" class=\"anchor\" aria-label=\"Permalink: Example for v2.0.25\" href=\"#example-for-v2025\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-smi\"\u003eoutput_log_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eout.log\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eoutput_trees_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eout.trees\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-smi\"\u003eoutput_state_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;-\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eout.state\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\nrun_beast2(\n \u003cspan class=\"pl-v\"\u003einput_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e get_beastier_path(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e2_4.xml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003eoutput_log_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eoutput_log_filename\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eoutput_trees_filenames\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eoutput_trees_filename\u003c/span\u003e,\n \u003cspan class=\"pl-v\"\u003eoutput_state_filename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eoutput_state_filename\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that in this version, the filenames for the \u003ccode\u003e.log\u003c/code\u003e\nand \u003ccode\u003e.trees\u003c/code\u003e files could be specified. This is unneeded:\nthe \u003ccode\u003e2_4.xml\u003c/code\u003e BEAST2 input file specifies where these files will be stored:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003e\u0026lt;?xml [...]?\u0026gt;\u0026lt;beast [...]\u0026gt;\n\n[...]\n\n\u0026lt;run [...]\u0026gt;\n\n [...]\n\n \u0026lt;logger id=\"tracelog\" fileName=\"test_output_0.log\" [...]\u0026gt;\n [...]\n \u0026lt;/logger\u0026gt;\n\n [...]\n\n \u0026lt;logger id=\"treelog.t:[...]\" fileName=\"$(tree).trees\" [...]\u0026gt;\n [...]\n \u0026lt;/logger\u0026gt;\n\u0026lt;/run\u0026gt;\n\u0026lt;/beast\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen using \u003ccode\u003ebeautier\u003c/code\u003e, this can be specified in \u003ccode\u003ecreate_mcmc\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003ecreate_mcmc(\n \u003cspan class=\"pl-v\"\u003etracelog\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_tracelog(\n \u003cspan class=\"pl-v\"\u003efilename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emy_trace.log\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n ),\n \u003cspan class=\"pl-v\"\u003etreeslog\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e create_treeslog(\n \u003cspan class=\"pl-v\"\u003efilename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emy_trees.trees\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n )\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"doc/install.md\"\u003eInstall\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/install.md\"\u003einstall\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"doc/faq.md\"\u003eFAQ\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/faq.md\"\u003eFAQ\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMissing features/unsupported\u003c/h2\u003e\u003ca id=\"user-content-missing-featuresunsupported\" class=\"anchor\" aria-label=\"Permalink: Missing features/unsupported\" href=\"#missing-featuresunsupported\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ebeastier\u003c/code\u003e cannot do everything \u003ccode\u003eBEAST2\u003c/code\u003e can.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRemove: install BEAST2, use \u003ca href=\"https://github.com/richelbilderbeek/beastierinstall\"\u003e\u003ccode\u003ebeastierinstall\u003c/code\u003e\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eExperimental: Continue a BEAST2 run\u003c/li\u003e\n\u003cli\u003eUntested: Setup BEAGLE\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere is a feature I miss\u003c/h2\u003e\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" aria-label=\"Permalink: There is a feature I miss\" href=\"#there-is-a-feature-i-miss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI want to collaborate\u003c/h2\u003e\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" aria-label=\"Permalink: I want to collaborate\" href=\"#i-want-to-collaborate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u0027Submitting code\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI think I have found a bug\u003c/h2\u003e\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" aria-label=\"Permalink: I think I have found a bug\" href=\"#i-think-i-have-found-a-bug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u0027Submitting bugs\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere\u0027s something else I want to say\u003c/h2\u003e\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" aria-label=\"Permalink: There\u0027s something else I want to say\" href=\"#theres-something-else-i-want-to-say\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExternal links\u003c/h2\u003e\u003ca id=\"user-content-external-links\" class=\"anchor\" aria-label=\"Permalink: External links\" href=\"#external-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CompEvol/beast2\"\u003eBEAST2 GitHub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/ropensci/beautier/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://www.codecov.io\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeautier\u003c/code\u003e \u003ccode\u003emaster\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/ropensci/beautier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c56baecfb9929de32f11a0d680e42f06788c6932d3018ebfdb84ae18fed35acb/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeautier\u003c/code\u003e \u003ccode\u003edevelop\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/ropensci/beautier/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f48f5ff21298fa315ba9e2ec165560cc0c17d3aeb14ebf0b221f8a1f42b2100/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeastierinstall\u003c/code\u003e \u003ccode\u003emaster\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall/actions/workflows/R-CMD-check.yaml\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/beastierinstall/actions/workflows/R-CMD-check.yaml/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/beastierinstall/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31b1fd70af861978190174d9c1f270770f381a9df044bec61c1e686ec37beae3/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6265617374696572696e7374616c6c2f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/beastierinstall/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeastierinstall\u003c/code\u003e \u003ccode\u003edevelop\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/richelbilderbeek/beastierinstall/actions/workflows/R-CMD-check.yaml\"\u003e\u003cimg src=\"https://github.com/richelbilderbeek/beastierinstall/actions/workflows/R-CMD-check.yaml/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://codecov.io/github/richelbilderbeek/beastierinstall/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a05fd26a17df4bdb241f906f3d98423cf3ec0e8780b3ff047ec4f5bc60803b9c/68747470733a2f2f636f6465636f762e696f2f6769746875622f72696368656c62696c6465726265656b2f6265617374696572696e7374616c6c2f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/richelbilderbeek/beastierinstall/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://ci.appveyor.com/project/richelbilderbeek/beastier_on_windows/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/AppVeyor.png\" alt=\"AppVeyor logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeastier_on_windows\u003c/code\u003e \u003ccode\u003emaster\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/richelbilderbeek/beastier-on-windows/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/61e833a6a269dc25dbdc7cfb77fd05a9d211a24535d4623e8d73327438374e13/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f72616c65783973646e6e786c776267782f6272616e63682f6d61737465723f7376673d74727565\" alt=\"Build status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/ralex9sdnnxlwbgx/branch/master?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003ebeastier_on_windows\u003c/code\u003e \u003ccode\u003edevelop\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ci.appveyor.com/project/richelbilderbeek/beastier-on-windows/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/759f4bd3e8190fa09019519d651f495923b65c8d8a31087617f502061d0cb1a6/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f72616c65783973646e6e786c776267782f6272616e63682f646576656c6f703f7376673d74727565\" alt=\"Build status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/ralex9sdnnxlwbgx/branch/develop?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFiles used by continuous integration scripts\u003c/h2\u003e\u003ca id=\"user-content-files-used-by-continuous-integration-scripts\" class=\"anchor\" aria-label=\"Permalink: Files used by continuous integration scripts\" href=\"#files-used-by-continuous-integration-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFilename\u003c/th\u003e\n\u003cth\u003eDescriptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"mlc_config.json\"\u003emlc_config.json\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the link checker, use \u003ccode\u003emarkdown-link-check --config mlc_config.json --quiet docs/**/*.md\u003c/code\u003e to do link checking locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".spellcheck.yml\"\u003e.spellcheck.yml\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".wordlist.txt\"\u003e.wordlist.txt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eWhitelisted words for the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlint.jsonc\"\u003e.markdownlint.jsonc\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlintignore\"\u003e.markdownlintignore\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFiles ignored by the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eArticle about \u003ccode\u003ebabette\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBilderbeek, Rich\u00e8l JC, and Rampal S. Etienne. \"\u003ccode\u003ebabette\u003c/code\u003e:\nBEAUti 2, BEAST 2 and Tracer for R.\" Methods in Ecology and\nEvolution (2018). \u003ca href=\"https://doi.org/10.1111/2041-210X.13032\" rel=\"nofollow\"\u003ehttps://doi.org/10.1111/2041-210X.13032\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFASTA files \u003ccode\u003eanthus_aco.fas\u003c/code\u003e and \u003ccode\u003eanthus_nd2.fas\u003c/code\u003e from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eVan Els, Paul, and Heraldo V. Norambuena.\n\"A revision of species limits in Neotropical pipits Anthus\nbased on multilocus genetic and vocal data.\" Ibis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://ropensci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2f6676e3fb100027b1914631256a47c1c3ab83def78fe9bbbd49eff063693aea/68747470733a2f2f726f70656e7363692e6f72672f7075626c69635f696d616765732f726f70656e7363695f666f6f7465722e706e67\" alt=\"ropensci_footer\" data-canonical-src=\"https://ropensci.org/public_images/ropensci_footer.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 10, - "subscribers_count": 2, + "subscribers_count": 3, "topics": [ - "neuroimaging" + "r", + "r-package", + "rstats", + "bayesian", + "beast", + "beast2", + "phylogenetic-inference", + "phylogenetics" ], - "updated_at": 1627403615.0 + "updated_at": 1718964949.0 }, { "data_format": 2, - "description": "Clinical Variant Annotation Pipeline", + "description": "Scripts for building Singularity images", "filenames": [ - "VepFileDeployment/Singularity.filedeploy", - "ReportingApplication/Singularity.report" + "mxnet/ubuntu.def", + "caffe2/ubuntu.def", + "tensorflow/ubuntu.def", + "caffe/ubuntu.def", + "dl/ubuntu.def", + "circuitscape/ubuntu.def" ], - "full_name": "PersonalizedOncology/ClinVAP", - "latest_release": "v1.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/sbilge/ClinVAP/blob/master/doc/logo.jpeg\"\u003e\u003cimg src=\"https://github.com/sbilge/ClinVAP/raw/master/doc/logo.jpeg\" alt=\"Pipeline Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c75c033e32c0d101c52a50f49a37bdac7bb6543f8b11f2ba77dc0526e40a14b6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f506572736f6e616c697a65644f6e636f6c6f67792f436c696e6963616c5265706f7274696e67506970656c696e652e737667\" alt=\"Release: Github\" data-canonical-src=\"https://img.shields.io/github/release/PersonalizedOncology/ClinicalReportingPipeline.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2168\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.docker.com/u/personalizedoncology/repository/list\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ffe08c7b5a5a63af6d36a31ec41fbd126b784c00beb4c5ec7f95a2bac8a6d849/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65722d2d6875622d626c75652e737667\" alt=\"Docker: Available\" data-canonical-src=\"https://img.shields.io/badge/hosted-docker--hub-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-clinical-variant-annotation-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#clinical-variant-annotation-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClinical Variant Annotation Pipeline\u003c/h1\u003e\n\u003cp\u003eClinical Variant Annotation Pipeline (ClinVAP) creates a genetic report of somatic mutations from a variant call format (VCF) file. Please refer this document for implementation of the pipeline. Documentation of the pipeline is available at \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/wiki\"\u003eWiki page\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-metadata-structure\" class=\"anchor\" aria-hidden=\"true\" href=\"#metadata-structure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMetadata Structure\u003c/h3\u003e\n\u003cp\u003eIf a patient metadata file is provided in the input directory with the naming schema \u0026lt;INPUT_VCF_NAME\u0026gt;_metadata.json, ClinVAP recognizes it and renders the information into the Patient Data table in the outputted report. Additionally, if dignosis is provided in the metadata file, the list of drugs with the clinical evidence of targeting the gene in that particular cancer type is reported in the \"CIViC Summary of Drugs Targeting the Affected Genes\" table. If no diagnosis is provided, then the pipeline stays agnostic to the cancer type, and returns the results related with the gene-drug association regardless of the cancer type. Please note that the disease name should be selected from the pre-defined dictionary that can be found \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/blob/master/doc/disease_names_dictionary.txt\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetadata file format:\u003c/strong\u003e\u003cbr\u003e\n{\u003cbr\u003e\n\"patient_firstname\":\"\u0026lt;NAME\u0026gt;\",\u003cbr\u003e\n\"patient_lastname\":\"\u0026lt;SURNAME\u0026gt;\",\u003cbr\u003e\n\"patient_dateofbirth\":\"\u0026lt;DATE\u0026gt;\",\u003cbr\u003e\n\"patient_diagnosis_short\":\"\u0026lt;DIAGNOSIS\u0026gt;\",\u003cbr\u003e\n\"mutation_load\":\"\u0026lt;LOAD\u0026gt;\"\u003cbr\u003e\n}\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Singularity\u003c/h2\u003e\n\u003cp\u003eRequirements: Singularity 2.4+\u003cbr\u003e\nPlease make sure that you have 12 GB of empty space on your home directory, and ports 5000 and 27021 are not being used by another application.\nTo run the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePull reporting image from Singularity Hub.\n\u003ccode\u003esingularity pull -n reporting_app.img shub://PersonalizedOncology/ClinVAP:report\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePull dependency files image from Singularity Hub.\n\u003ccode\u003esingularity pull -n file_deploy.img shub://PersonalizedOncology/ClinVAP:filedeploy\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun dependency files image first to transfer those file on your local folder.\n\u003ccode\u003esingularity run -B /LOCAL/PATH/TO/FILES:/mnt file_deploy.img -a \u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRun the reporting image to generate the clinical reports.\n\u003ccode\u003esingularity run -B /LOCAL/PATH/TO/FILES:/data -B /PATH/TO/INPUT/DATA:/inout reporting_app.img -t /inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-a\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in your /PATH/TO/INPUT/DATA folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage with Docker\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-for-mac-and-ubuntu-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#for-mac-and-ubuntu-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFor Mac and Ubuntu Users\u003c/h3\u003e\n\u003cp\u003eRequirements: Docker Engine release 1.13.0+, Compose release 1.10.0+.\u003cbr\u003e\nPlease make sure that you have 34 GB of physical empty space on your Docker Disk Image, and ports 5000 and 27017 are not being used by another application.\u003c/p\u003e\n\u003cp\u003eTo tun the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePelase note that the input VCF file(s) should be in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e2. cd ClinVAP/\n3. export ASSEMBLY=\u0026lt;Your Assembly Here\u0026gt;\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t /inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in ReportingApplication/inout folder.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-with-docker-toolbox-for-windows-users\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-with-docker-toolbox-for-windows-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning with Docker Toolbox For Windows Users\u003c/h3\u003e\n\u003cp\u003eRequirements: Docker Engine release 1.13.0+, Compose release 1.10.0+.\u003cbr\u003e\nPlease make sure that you have 34 GB of physical empty space on your Docker Disk Image, and ports 5000 and 27017 are not being used by another application.\u003c/p\u003e\n\u003cp\u003eTo tun the pipeline, please follow the steps given below.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePelase note that the input VCF file(s) should be in ReportingApplication/inout folder.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e2. cd ClinVAP/\n3. export ASSEMBLY=\u0026lt;Your Assembly Here\u0026gt;\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t //inout -p jwp -a \u0026lt;Your Assembly Here\u0026gt;\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u0026lt;Your Assembly Here\u0026gt;\u003c/code\u003e: Please provide the genome assembly that was used in variant calling calling step to generate your VCF files.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh37\u003c/code\u003e for genome assembly 37\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eGRCh38\u003c/code\u003e for genome assembly 38\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-t\u003c/code\u003e: folder name containing input data. This should be in the data volume of ClinicalReportR service (modify Docker compose file to change this).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-p\u003c/code\u003e: output format to save the results.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ej\u003c/code\u003e to save report in JSON format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ew\u003c/code\u003e to save report in DOCX format\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ep\u003c/code\u003e to save report in PDF format\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou should now have the report in ReportingApplication/inout folder.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-demo-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#demo-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDemo Run\u003c/h2\u003e\n\u003cp\u003eWe provided an example input file, strelka_passed_missense_somatic_snvs.vcf under ./ReportingApplication/inout folder along with a dummy metadata file, strelka_passed_missense_somatic_snvs.json. The corresponding report of the strelka input file is provided \u003ca href=\"https://github.com/PersonalizedOncology/ClinVAP/tree/master/doc/strelka_passed_missense_somatic_snvs.pdf\"\u003ehere\u003c/a\u003e as an example.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-demo-with-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-demo-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Demo with Singularity\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n2. singularity pull -n reporting_app.img shub://PersonalizedOncology/ClinVAP:report\n3. singularity pull -n file_deploy.img shub://PersonalizedOncology/ClinVAP:filedeploy\n4. mkdir vep_files\n5. singularity run -B ./vep_files:/mnt file_deploy.img -a GRCh37\n6. singularity run -B ./vep_files:/data -B ./ClinVAP/ReportingApplication/inout:/inout reporting_app.img -t /inout -p jwp -a GRCh37\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running-demo-with-docker\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-demo-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Demo with Docker\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e1. git clone https://github.com/PersonalizedOncology/ClinVAP.git\n2. cd ClinVAP/\n3. export ASSEMBLY=GRCh37\n4. docker-compose run -e ASSEMBLY --service-ports ClinicalReportR -t /inout -p jwp -a GRCh37\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h3\u003e\n\u003cp\u003eIf you use ClinVAP in your work, please cite the following article\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eS\u00fcr\u00fcn, B., Sch\u00e4rfe, C.P., Divine, M.R., Heinrich, J., Toussaint, N.C., Zimmermann, L., Beha, J. and Kohlbacher, O., 2020. ClinVAP: a reporting strategy from variants to therapeutic options. Bioinformatics, 36(7), pp.2316-2317.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "clemsonciti/singularity-images", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity image scripts\u003c/h1\u003e\u003ca id=\"user-content-singularity-image-scripts\" class=\"anchor\" aria-label=\"Permalink: Singularity image scripts\" href=\"#singularity-image-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eScripts to generate singularity images\nfor running different software on Palmetto cluster.\u003c/p\u003e\n", "stargazers_count": 10, "subscribers_count": 5, "topics": [], - "updated_at": 1675526926.0 - }, - { - "data_format": 2, - "description": "Mycobacterial pipeline", - "filenames": [ - "singularity/Singularity.preprocessing-0.9.6", - "singularity/Singularity.vcfpredict-0.9.6", - "singularity/Singularity.clockwork-0.9.6" - ], - "full_name": "Pathogen-Genomics-Cymru/lodestone", - "latest_release": "v0.9.6", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-lodestone\" class=\"anchor\" aria-hidden=\"true\" href=\"#lodestone\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLodestone\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/build-push-quay/badge.svg\"\u003e\u003cimg src=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/build-push-quay/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/pytest/badge.svg\"\u003e\u003cimg src=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/pytest/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/stub-run/badge.svg\"\u003e\u003cimg src=\"https://github.com/Pathogen-Genomics-Cymru/lodestone/workflows/stub-run/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis pipeline takes as input reads presumed to be from one of 10 mycobacterial genomes: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis. Input should be in the form of one directory containing pairs of fastq(.gz) or bam files.\u003c/p\u003e\n\u003cp\u003ePipeline cleans and QCs reads with fastp and FastQC, classifies with Kraken2 \u0026amp; Afanc, removes non-bacterial content, and - by alignment to any minority genomes - disambiguates mixtures of bacterial reads. Cleaned reads are aligned to either of the 10 supported genomes and variants called. Produces as output one directory per sample, containing cleaned fastqs, sorted, indexed BAM, VCF, F2 and F47 statistics, an antibiogram and summary reports.\u003c/p\u003e\n\u003cp\u003eNote that while Mykrobe is included within this pipeline, it runs as an independent process and is not used for any downstream reporting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWARNING\u003c/strong\u003e: There are currently known errors with vcfmix and gnomonicus, as such \u003ccode\u003eerrorStrategy \u0027ignore\u0027\u003c/code\u003e has been added to the processes vcfpredict:vcfmix and vcfpredict:gnomonicus to stop the pipeline from crashing. Please check the stdout from nextflow to see whether these processes have ran successfully.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-hidden=\"true\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuick Start\u003c/h2\u003e\n\u003cp\u003eThis is a Nextflow DSL2 pipeline, it requires a version of Nextflow that supports DSL2 and the stub-run feature. It is recommended to run the pipeline with \u003ccode\u003eNXF_VER=20.11.0-edge\u003c/code\u003e, as the pipeline has been tested using this version. E.g. to download\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport NXF_VER=\"20.11.0-edge\"\ncurl -fsSL https://get.nextflow.io | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe workflow is designed to run with either docker \u003ccode\u003e-profile docker\u003c/code\u003e or singularity \u003ccode\u003e-profile singularity\u003c/code\u003e. The container images are pulled from quay.io and a singularity cache directory is set in the \u003ccode\u003enextflow.config\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eE.g. to run the workflow:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNXF_VER=20.11.0-edge nextflow run main.nf -profile singularity --filetype fastq --input_dir fq_dir --pattern \"*_R{1,2}.fastq.gz\" --unmix_myco yes \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\nNXF_VER=20.11.0-edge nextflow run main.nf -profile docker --filetype bam --input_dir bam_dir --unmix_myco no \\\n--output_dir . --kraken_db /path/to/database --bowtie2_index /path/to/index --bowtie_index_name hg19_1kgmaj\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-executors\" class=\"anchor\" aria-hidden=\"true\" href=\"#executors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecutors\u003c/h3\u003e\n\u003cp\u003eBy default, the pipeline will just run on the local machine. To run on a cluster, modifications will have to be made to the \u003ccode\u003enextflow.config\u003c/code\u003e to add in the executor. E.g. for a SLURM cluster add \u003ccode\u003eprocess.executor = \u0027slurm\u0027\u003c/code\u003e. For more information on executor options see the Nextflow docs: \u003ca href=\"https://www.nextflow.io/docs/latest/executor.html\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/docs/latest/executor.html\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSystem Requirements\u003c/h3\u003e\n\u003cp\u003eMinimum recommended requirements: 32GB RAM, 8CPU\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-params\" class=\"anchor\" aria-hidden=\"true\" href=\"#params\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParams\u003c/h2\u003e\n\u003cp\u003eThe following parameters should be set in \u003ccode\u003enextflow.config\u003c/code\u003e or specified on the command line:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003einput_dir\u003c/strong\u003e\u003cbr\u003e\nDirectory containing fastq OR bam files\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efiletype\u003c/strong\u003e\u003cbr\u003e\nFile type in input_dir. Either \"fastq\" or \"bam\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003epattern\u003c/strong\u003e\u003cbr\u003e\nRegex to match fastq files in input_dir, e.g. \"*_R{1,2}.fq.gz\". Only mandatory if --filetype is \"fastq\"\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eoutput_dir\u003c/strong\u003e\u003cbr\u003e\nOutput directory for results\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eunmix_myco\u003c/strong\u003e\u003cbr\u003e\nDo you want to disambiguate mixed-mycobacterial samples by read alignment? Either \"yes\" or \"no\":\n\u003cul\u003e\n\u003cli\u003eIf \"yes\" workflow will remove reads mapping to any minority mycobacterial genomes but in doing so WILL ALMOST CERTAINLY ALSO reduce coverage of the principal species\u003c/li\u003e\n\u003cli\u003eIf \"no\" then mixed-mycobacterial samples will be left alone. Mixtures of mycobacteria + non-mycobacteria will still be disambiguated\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003especies\u003c/strong\u003e\u003cbr\u003e\nPrincipal species in each sample, assuming genus Mycobacterium. Default \u0027null\u0027. If parameter used, takes 1 of 10 values: abscessus, africanum, avium, bovis, chelonae, chimaera, fortuitum, intracellulare, kansasii, tuberculosis. Using this parameter will apply an additional sanity test to your sample\n\u003cul\u003e\n\u003cli\u003eIf you DO NOT use this parameter (default option), pipeline will determine principal species from the reads and consider any other species a contaminant\u003c/li\u003e\n\u003cli\u003eIf you DO use this parameter, pipeline will expect this to be the principal species. It will fail the sample if reads from this species are not actually the majority\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ekraken_db\u003c/strong\u003e\u003cbr\u003e\nDirectory containing \u003ccode\u003e*.k2d\u003c/code\u003e Kraken2 database files (k2_pluspf_16gb recommended, obtain from \u003ca href=\"https://benlangmead.github.io/aws-indexes/k2\" rel=\"nofollow\"\u003ehttps://benlangmead.github.io/aws-indexes/k2\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie2_index\u003c/strong\u003e\u003cbr\u003e\nDirectory containing Bowtie2 index (obtain from ftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/hg19_1kgmaj_bt2.zip). The specified path should NOT include the index name\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebowtie_index_name\u003c/strong\u003e\u003cbr\u003e\nName of the bowtie index, e.g. hg19_1kgmaj\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003evcfmix\u003c/strong\u003e\u003cbr\u003e\nRun \u003ca href=\"https://github.com/AlexOrlek/VCFMIX\"\u003evcfmix\u003c/a\u003e, yes or no. Set to no for synthetic samples\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003egnomonicus\u003c/strong\u003e\u003cbr\u003e\nRun \u003ca href=\"https://github.com/oxfordmmm/gnomonicus\"\u003egnomonicus\u003c/a\u003e, yes or no\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eamr_cat\u003c/strong\u003e\u003cbr\u003e\nPath to AMR catalogue for gnomonicus\u003cbr\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eafanc_myco_db\u003c/strong\u003e\u003cbr\u003e\nPath to the \u003ca href=\"https://github.com/ArthurVM/Afanc\"\u003eafanc\u003c/a\u003e database used for speciation. Obtain from \u003ca href=\"https://s3.climb.ac.uk/microbial-bioin-sp3/Mycobacteriaciae_DB_6.0.tar.gz\" rel=\"nofollow\"\u003ehttps://s3.climb.ac.uk/microbial-bioin-sp3/Mycobacteriaciae_DB_6.0.tar.gz\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp\u003eFor more information on the parameters run \u003ccode\u003enextflow run main.nf --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe path to the singularity images can also be changed in the singularity profile in \u003ccode\u003enextflow.config\u003c/code\u003e. Default value is \u003ccode\u003e${baseDir}/singularity\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-stub-run\" class=\"anchor\" aria-hidden=\"true\" href=\"#stub-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStub-run\u003c/h2\u003e\n\u003cp\u003eTo test the stub run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eNXF_VER=20.11.0-edge nextflow run main.nf -stub -config testing.config\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-checkpoints\" class=\"anchor\" aria-hidden=\"true\" href=\"#checkpoints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheckpoints\u003c/h2\u003e\n\u003cp\u003eCheckpoints used throughout this workflow to fail a sample/issue warnings:\u003c/p\u003e\n\u003cp\u003eprocesses preprocessing:checkFqValidity or preprocessing:checkBamValidity\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e(Fail) If sample does not pass fqtools \u0027validate\u0027 or samtools \u0027quickcheck\u0027, as appropriate.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eprocess preprocessing:countReads\u003cbr\u003e\n2. (Fail) If sample contains \u0026lt; 100k pairs of raw reads.\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:fastp\u003cbr\u003e\n3. (Fail) If sample contains \u0026lt; 100k pairs of cleaned reads, required to all be \u0026gt; 50bp (cleaning using fastp with --length_required 50 --average_qual 10 --low_complexity_filter --correction --cut_right --cut_tail --cut_tail_window_size 1 --cut_tail_mean_quality 20).\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:kraken2\u003cbr\u003e\n4. (Fail) If the top family hit is not Mycobacteriaceae\u003cbr\u003e\n5. (Fail) If there are fewer than 100k reads classified as Mycobacteriaceae \u003cbr\u003e\n6. (Warn) If the top family classification is mycobacterial, but this is not consistent with top genus and species classifications\u003cbr\u003e\n7. (Warn) If the top family is Mycobacteriaceae but no G1 (species complex) classifications meet minimum thresholds of \u0026gt; 5000 reads or \u0026gt; 0.5% of the total reads (this is not necessarily a concern as not all mycobacteria have a taxonomic classification at this rank)\u003cbr\u003e\n8. (Warn) If sample is mixed or contaminated - defined as containing reads \u0026gt; the 5000/0.5% thresholds from multiple non-human species\u003cbr\u003e\n9. (Warn) If sample contains multiple classifications to mycobacterial species complexes, each meeting the \u0026gt; 5000/0.5% thresholds\u003cbr\u003e\n10. (Warn) If no species classification meets the 5000/0.5% thresholds\u003cbr\u003e\n11. (Warn) If no genus classification meets the 5000/0.5% thresholds\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:identifyBacterialContaminants\u003cbr\u003e\n12. (Fail) If regardless of what Kraken reports, Afanc does not make a species-level mycobacterial classification (note that we do not use Kraken mycobacterial classifications other than to determine whether 100k reads are family Mycobacteriaceae; for higher-resolution classification, we defer to Afanc)\u003cbr\u003e\n13. (Fail) If the sample is not contaminated and the top species hit is not one of the 10 supported Mycobacteria: abscessus|africanum|avium|bovis|chelonae|chimaera|fortuitum|intracellulare|kansasii|tuberculosis\u003cbr\u003e\n14. (Fail) If the sample is not contaminated and the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003cbr\u003e\n15. (Warn) If the top Afanc species hit, on the basis of highest % coverage, does not also have the highest median depth\u003cbr\u003e\n16. (Warn) If we are unable to associate an NCBI taxon ID to any given contaminant species, which means we will not be able to locate its genome, and thereby remove it as a contaminant\u003cbr\u003e\n17. (Warn) If we are unable to determine a URL for the latest RefSeq genome associated with a contaminant species\u0027 taxon ID\u003cbr\u003e\n18. (Warn) If no complete genome could be found for a contaminant species. The workflow will proceed with alignment-based contaminant removal, but you\u0027re warned that there\u0027s reduced confidence in detecting reads from this species\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:downloadContamGenomes\u003cbr\u003e\n19. (Fail) If a contaminant is detected but we are unable to download a representative genome, and thereby remove it\u003c/p\u003e\n\u003cp\u003eprocess preprocessing:summarise\u003cbr\u003e\n20. (Fail) If after having taken an alignment-based approach to decontamination, Kraken still detects a contaminant species\u003cbr\u003e\n21. (Fail) If after having taken an alignment-based approach to decontamination, the top species hit is not one of the 10 supported Mycobacteria\u003cbr\u003e\n22. (Fail) If, after successfully removing contaminants, the top species hit is contrary to the species expected (e.g. \"avium\" rather than \"tuberculosis\" - only tested if you provide that expectation)\u003c/p\u003e\n\u003cp\u003eprocess clockwork:alignToRef\u003cbr\u003e\n23. (Fail) If \u0026lt; 100k reads could be aligned to the reference genome\u003cbr\u003e\n24. (Fail) If, after aligning to the reference genome, the average read mapping quality \u0026lt; 10\u003cbr\u003e\n25. (Fail) If \u0026lt; 50% of the reference genome was covered at 10-fold depth\u003c/p\u003e\n\u003cp\u003eprocess clockwork:minos\u003cbr\u003e\n26. (Warn) If sample is not TB, then it is not passed to gnomonicus\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eFor a list of direct authors of this pipeline, please see the contributors list. All of the software dependencies of this pipeline are recorded in the version.json\u003c/p\u003e\n\u003cp\u003eThe preprocessing sub-workflow is based on the preprocessing nextflow DSL1 pipeline written by Stephen Bush, University of Oxford. The clockwork sub-workflow uses aspects of the variant calling workflow from \u003ca href=\"https://github.com/iqbal-lab-org/clockwork\"\u003ehttps://github.com/iqbal-lab-org/clockwork\u003c/a\u003e, lead author Martin Hunt, Iqbal Lab at EMBL-EBI\u003c/p\u003e\n", - "stargazers_count": 10, - "subscribers_count": 6, - "topics": [ - "bioinformatics", - "bioinformatics-pipeline", - "genomics", - "global-health", - "infectious-diseases", - "next-generation-sequencing", - "nextflow", - "pathogen", - "sequencing", - "tuberculosis" - ], - "updated_at": 1679574698.0 + "updated_at": 1597386388.0 }, { "data_format": 2, - "description": "Repository for Berkeley Research IT cyberinfrastructure components", + "description": "Building a Singularity image of Polysolver", "filenames": [ - "photoscancontainer/Singularity" + "Singularity.v4" ], - "full_name": "ucberkeley/brc-cyberinfrastructure", + "full_name": "IARCbioinfo/polysolver-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebrc-cyberinfrastructure\u003c/h1\u003e\u003ca id=\"user-content-brc-cyberinfrastructure\" class=\"anchor\" aria-label=\"Permalink: brc-cyberinfrastructure\" href=\"#brc-cyberinfrastructure\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e+++++++++++++++++++++++++++\u003c/p\u003e\n\u003cp\u003eThis software is available under the terms of the Educational Community License, Version 2.0 (ECL 2.0). This software is Copyright 2016 The Regents of the University of California, Berkeley (\"Berkeley\").\u003c/p\u003e\n\u003cp\u003eThe text of the ECL license is reproduced below.\u003c/p\u003e\n\u003cp\u003eEducational Community License, Version 2.0\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright 2016 The Regents of the University of California, Berkeley (\"Berkeley\")\u003c/p\u003e\n\u003cp\u003eEducational Community License, Version 2.0, April 2007\u003c/p\u003e\n\u003cp\u003eThe Educational Community License version 2.0 (\"ECL\") consists of the\nApache 2.0 license, modified to change the scope of the patent grant in\nsection 3 to be specific to the needs of the education communities using\nthis license. The original Apache 2.0 license can be found at:[\u003ca href=\"http://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003ehttp://www.apache.org/licenses/LICENSE-2.0\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003e+++++++++++++++++++++++++++\u003c/p\u003e\n\u003cp\u003eThis repository contains a heterogeneous set of components and utilities developed in coordination with Berkely research teams and Research IT staff to enable data management and analysis at scale.\u003c/p\u003e\n\u003cp\u003eThese components are exemplars that utilize multiple languages, SDKs and technologies and as such the repository cannot be cloned and built in the same way that a single app is constructed. Researchers will be directed to specific files that they will download, modify and employ in their environment.\u003c/p\u003e\n\u003cp\u003eThe hierarchy is organic, based on what has been created to date. It will evolve as the scope of the cyberinfrastructure engineer expands over time.\u003c/p\u003e\n", "stargazers_count": 10, - "subscribers_count": 15, + "subscribers_count": 3, "topics": [], - "updated_at": 1710307936.0 + "updated_at": 1706806761.0 }, { "data_format": 2, - "description": "Course on Linux containers for scientific environments", + "description": "A tool for phasing and imputing haplotypes in 10k+ low coverage sequencing samples", "filenames": [ - "recipes/blast/Singularity.conda.environment", - "recipes/blast/Singularity.simple", - "recipes/blast/Singularity", - "recipes/blastwww/Singularity", - "recipes/blastmysql/Singularity.mysql", - "recipes/blastmysql/Singularity" + "Singularity" ], - "full_name": "biocorecrg/containers-course", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainers-course\u003c/h1\u003e\u003ca id=\"user-content-containers-course\" class=\"anchor\" aria-label=\"Permalink: containers-course\" href=\"#containers-course\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCourse on Linux containers for scientific environments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEditions\u003c/h2\u003e\u003ca id=\"user-content-editions\" class=\"anchor\" aria-label=\"Permalink: Editions\" href=\"#editions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eJune 13-14, 2019. CRG, Barcelona, Spain. \u003ca href=\"https://slides.com/similis/introduction-linux-containers-training/\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e and \u003ca href=\"https://github.com/ArangoGutierrez/Singularity-tutorial\"\u003ehands-on repository by Carlos Eduardo Arango Gutierrez\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eJanuary 28-29, 2019. CRG, Barcelona, Spain. \u003ca href=\"https://slides.com/similis/introduction-linux-containers-training/\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e and Web seminar by \u003ca href=\"https://github.com/GodloveD\"\u003eDave Godlove\u003c/a\u003e (\u003ca href=\"https://singularity-tutorial.github.io/\" rel=\"nofollow\"\u003eassociated material\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eDecember 11-12, 2018. CRG, Barcelona, Spain. \u003ca href=\"https://slides.com/similis/introduction-linux-containers-training/\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSessions guide\u003c/h2\u003e\u003ca id=\"user-content-sessions-guide\" class=\"anchor\" aria-label=\"Permalink: Sessions guide\" href=\"#sessions-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSession 1\u003c/h3\u003e\u003ca id=\"user-content-session-1\" class=\"anchor\" aria-label=\"Permalink: Session 1\" href=\"#session-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIntroduction to containerization\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIntroduction to Docker\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e. Linux and its diversity. Example to different images. Tags\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on testing different images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExporting containers into tar files and importing them back\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIntroduction to software installation\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreating Dockerfiles\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on testing of different installation methods\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExamples\n\u003cul\u003e\n\u003cli\u003eNCBI Blast+\u003c/li\u003e\n\u003cli\u003eVenn Diagram R script\u003c/li\u003e\n\u003cli\u003ePossible exercise: Trimmomatic (\u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003ehttp://www.usadellab.org/cms/?page=trimmomatic\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ePossible exercise: Samtools (\u003ca href=\"http://www.htslib.org/\" rel=\"nofollow\"\u003ehttp://www.htslib.org/\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVolumes\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eUsing sequences. From \u003ca href=\"http://www.uniprot.org\" rel=\"nofollow\"\u003ehttp://www.uniprot.org\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsing Database. Example: Swissprot from NCBI - \u003ca href=\"http://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/\" rel=\"nofollow\"\u003eFASTA files\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on testing of different images and volumes\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eServices\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePort mapping and examples with services\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDifferent approaches to daemons and services\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on with services\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI Blast+ with PHP script\u003c/li\u003e\n\u003cli\u003ePossible exercise: Repeat the same with Samtools or Trimmomatic\u003c/li\u003e\n\u003cli\u003ePossible exercise: Play with Shiny App examples\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LJI-Bioinformatics/Shiny-PCA-Maker\"\u003ehttps://github.com/LJI-Bioinformatics/Shiny-PCA-Maker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/philippmuench/Donut\"\u003ehttps://github.com/philippmuench/Donut\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on, preparing your own example\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSession 2\u003c/h3\u003e\u003ca id=\"user-content-session-2\" class=\"anchor\" aria-label=\"Permalink: Session 2\" href=\"#session-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSum-up last session\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePutting containers to work together\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on example\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI Blast+ with PHP script + MySQL database\u003c/li\u003e\n\u003cli\u003eRepeat the same with Samtools\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDocker compose\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on example\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eContributing back your own images. Automated images with Github\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIntroduction to singularity. Differences with Docker\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDifferent image types. Different ways to generate images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on in getting images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExecution approaches in Singularity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on executing images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity files. Custom building of Singularity images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on building of Singularity images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsing Docker for building Singularity files\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVolumes in Singularity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eServices/instances in singularity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on services with Singularity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSum-up course, finish preparing your own example\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 11, - "subscribers_count": 4, + "full_name": "winni2k/GLPhase", + "latest_release": "v1.7.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGLPhase\u003c/h1\u003e\u003ca id=\"user-content-glphase\" class=\"anchor\" aria-label=\"Permalink: GLPhase\" href=\"#glphase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a cuda-enabled fork of\n\u003ca href=\"http://sourceforge.net/p/snptools/code/ci/master/tree/\" rel=\"nofollow\"\u003eSNPTools impute.cpp\u003c/a\u003e. This\ncode should scale linearly with sample size up to a small multiple of\nthe number of CUDA cores (shaders) on the GPU being used.\u003c/p\u003e\n\u003cp\u003eGLPhase also has an option for incorporating pre-existing haplotypes\ninto the phasing and imputation\nprocess. \u003ca href=\"https://github.com/wkretzsch/GLPhase/releases/tag/v1.4.13\"\u003eRelease 1.4.13\u003c/a\u003e\nwas used with this option\nto impute genotypes for the first release of the\n\u003ca href=\"http://www.haplotype-reference-consortium.org/\" rel=\"nofollow\"\u003eHaplotype Reference Consortium\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have \u003ca href=\"https://www.sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e version 3 installed, then you can run the glphase container located \u003ca href=\"https://cloud.sylabs.io/library/wkretzsch/default/glphase\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGLPhase depends on \u003ca href=\"https://www.gnu.org/software/gsl/\" rel=\"nofollow\"\u003elibgsl\u003c/a\u003e,\n\u003ca href=\"http://www.boost.org/\" rel=\"nofollow\"\u003eboost\u003c/a\u003e, and \u003ca href=\"http://www.zlib.net/\" rel=\"nofollow\"\u003elibz\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompilation\u003c/h3\u003e\u003ca id=\"user-content-compilation\" class=\"anchor\" aria-label=\"Permalink: Compilation\" href=\"#compilation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# Clone this repository recursively\ngit clone --recursive https://github.com/winni2k/GLPhase.git\ncd GLPhase\n\n# to compile all code (with all optimizations turned on)\nmake\n\n# run the glphase executable to get a description of the\n# glphase command line arguments\nbin/glphase\n\n# run regression tests (turns off optimizations)\nmake test\n\n# run regression tests + longer integration tests\nmake disttest\n\n# compile without CUDA support\n# first clean the work dir\nmake clean\nmake NCUDA=1\n\n# compile without CUDA or OMP support (on MacOSX for example)\nmake NCUDA=1 NOMP=1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConverting a VCF to SNPTools \u003ccode\u003e.bin\u003c/code\u003e format\u003c/h2\u003e\u003ca id=\"user-content-converting-a-vcf-to-snptools-bin-format\" class=\"anchor\" aria-label=\"Permalink: Converting a VCF to SNPTools .bin format\" href=\"#converting-a-vcf-to-snptools-bin-format\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA perl script at \u003ccode\u003escripts/vcf2STBin.pl\u003c/code\u003e can be used to convert a VCF\nwith PL format fields to a SNPTools conformant \u003ccode\u003e.bin\u003c/code\u003e file. For\nexample, this command will convert a gzipped input VCF at\n\u003ccode\u003einput.vcf.gz\u003c/code\u003e into a SNPTools \u003ccode\u003e.bin\u003c/code\u003e file at \u003ccode\u003einput.bin\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003escripts/vcf2STbin.pl input.vcf.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning GLPhase (v1.4.13)\u003c/h2\u003e\u003ca id=\"user-content-running-glphase-v1413\" class=\"anchor\" aria-label=\"Permalink: Running GLPhase (v1.4.13)\" href=\"#running-glphase-v1413\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAs a drop-in replacement for SNPTools/impute.cpp\u003c/h3\u003e\u003ca id=\"user-content-as-a-drop-in-replacement-for-snptoolsimputecpp\" class=\"anchor\" aria-label=\"Permalink: As a drop-in replacement for SNPTools/impute.cpp\" href=\"#as-a-drop-in-replacement-for-snptoolsimputecpp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGLPhase can be run as a CUDA-enabled drop-in replacement for\n\u003ccode\u003eSNPTools/impute.cpp\u003c/code\u003e. Assuming a SNPTools style \u003ccode\u003e.bin\u003c/code\u003e file with\ngenotype likelihoods exists:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebin/glphase input.bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing pre-existing haplotypes\u003c/h3\u003e\u003ca id=\"user-content-using-pre-existing-haplotypes\" class=\"anchor\" aria-label=\"Permalink: Using pre-existing haplotypes\" href=\"#using-pre-existing-haplotypes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGLPhase can use pre-existing haplotypes to restrict the set of\npossible haplotypes from which the MH sampler may choose surrogate\nparent haplotypes. This approach is described in:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThe Haplotype Reference Consortium. A reference panel of 64,976\nhaplotypes for genotype imputation. Nature Genetics (accepted) --\n\u003ca href=\"http://biorxiv.org/content/early/2015/12/23/035170\" rel=\"nofollow\"\u003ebioRxiv\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis command phases and imputes haplotypes on a SNPTools \u003ccode\u003e.bin\u003c/code\u003e file\nusing a genetic map and pre-existing haplotypes. The output file is\na gzipped VCF file at \u003ccode\u003eoutput_base_name.vcf.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eglphase -B0 -i5 -m95 -q0 -Q1 -t2 -C100 -K200 \\\n input.bin \\\n -g genetic_map.txt \\\n -h pre_existing_haplotypes.haps.gz \\\n -s pre_existing_haplotypes.sample \\\n -o output_base_name\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe pre-existing haplotypes should be in\n\u003ca href=\"https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html#hapsample\" rel=\"nofollow\"\u003eWTCCC format\u003c/a\u003e,\nand a genetic map can be obtained from the \u003ca href=\"https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#reference\" rel=\"nofollow\"\u003eImpute2 website\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing a reference panel\u003c/h3\u003e\u003ca id=\"user-content-using-a-reference-panel\" class=\"anchor\" aria-label=\"Permalink: Using a reference panel\" href=\"#using-a-reference-panel\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGLPhase can use a reference panel of haplotypes to inform genotype\nimputation of samples for which genotype likelihoods are available.\nIn contrast to pre-existing haplotypes, the haplotypes\nin the reference panel do not need to be from the same samples that\nare being imputed. In this mode, when surrogate parent haplotypes\nare being chosen for a sample, the haplotypes may come from the\ncurrent estimate of sample haplotypes or the reference panel. \u003ccode\u003e-k\u003c/code\u003e\ncan be specified to restrict the choice of surrogate parent haplotypes\nto the reference panel in the first iteration of haplotype estimation.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eglphase \\\n input.bin \\\n -g samples/hapGen/ex.map \\\n -H samples/hapGen/ex.haps.gz \\\n -L samples/hapGen/ex.leg \\\n -k \\\n -o output_base_name\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe reference haplotypes and legend should be in\n\u003ca href=\"https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html#haplegsample\" rel=\"nofollow\"\u003eImpute2 format\u003c/a\u003e,\nand a genetic map can be obtained from the \u003ca href=\"https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#reference\" rel=\"nofollow\"\u003eImpute2 website\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLigating haplotypes\u003c/h2\u003e\u003ca id=\"user-content-ligating-haplotypes\" class=\"anchor\" aria-label=\"Permalink: Ligating haplotypes\" href=\"#ligating-haplotypes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is recommended to ligate haplotypes using\n\u003ca href=\"https://bitbucket.org/wkretzsch/hapfuse/src\" rel=\"nofollow\"\u003ehapfuse\u003c/a\u003e. Before\nfusing, the output from GLPhase needs to be converted from gzipped VCF\nto something htslib can read. Here an example using \u003ca href=\"https://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ezcat output_base_name.vcf.gz | bcftools -Ob -o \\\n output_base_name.bcf\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 10, + "subscribers_count": 3, "topics": [], - "updated_at": 1714275144.0 + "updated_at": 1676561604.0 }, { "data_format": 2, - "description": "A simple example of running a MongoDB instance to query a database", + "description": "Collection of Singularity build files and scripts to create them for popular Linux Distributions", "filenames": [ - "v1.0/rstudio-simple/rstudio/Singularity", - "v1.0/rstudio-simple/nginx/Singularity", - "v1.0/mongodb-build/mongodb/Singularity", - "v1.0/bert-as-compose/server/Singularity.gpu", - "v1.0/bert-as-compose/server/Singularity", - "v1.0/bert-as-compose/client/Singularity", - "v1.0/django-nginx-upload/Singularity", - "v1.0/django-nginx-upload/db/Singularity", - "v1.0/django-nginx-upload/nginx/Singularity", - "v1.0/apache-simple/httpd/Singularity", - "v1.0/jupyter-simple/jupyter/Singularity", - "v2.0/start-args/Singularity", - "v2.0/ping/alp1/Singularity", - "v2.0/ping/alp2/Singularity", - "v2.0/jupyterlab/second/Singularity", - "v2.0/code-server/Singularity", - "v2.0/deephyperx/Singularity" - ], - "full_name": "singularityhub/singularity-compose-examples", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Compose Examples\u003c/h1\u003e\u003ca id=\"user-content-singularity-compose-examples\" class=\"anchor\" aria-label=\"Permalink: Singularity Compose Examples\" href=\"#singularity-compose-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a repository of examples for\n\u003ca href=\"https://singularityhub.github.io/singularity-compose\" rel=\"nofollow\"\u003eSingularity Compose\u003c/a\u003e. For the \"simple\"\nexample that is used during testing, see \u003ca href=\"https://github.com/singularityhub/singularity-compose-simple\"\u003esingularity-compose-simple\u003c/a\u003e. Otherwise, all examples are provided here. You can browse based on the spec version of Singularity Compose:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"v1.0\"\u003ev1.0\u003c/a\u003e is supported for Singularity compose less than v0.1.0\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"v2.0\"\u003ev2.0\u003c/a\u003e is supported for Singularity compose equal to or greater than v0.1.0\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 11, - "subscribers_count": 6, - "topics": [ - "singularity-compose", - "mongodb" + "definitions/gemma/Singularity.GEMMA-0.98.1-foss-2018b-envmod-debian9", + "definitions/gemma/Singularity.GEMMA-0.98.1-foss-2018b-envmod-centos7", + "definitions/fastqtl/Singularity.FastQTL-2.184-foss-2018b-envmod-centos7", + "definitions/mirtk/Singularity.mirtk-2.0.0-foss-2020a-Python-3.8.2-envmod-centos7", + "definitions/HTSeq/Singularity.HTSeq-0.11.3-foss-2020b-centos-7-envmod", + "definitions/salmon/Singularity.Salmon-1.2.1-gompi-2019b-envmod-debian9", + "definitions/salmon/Singularity.Salmon-1.1.0-gompi-2019b-envmod-debian9", + "definitions/salmon/Singularity.Salmon-1.0.0-gompi-2019a-envmod-debian9", + "definitions/salmon/Singularity.Salmon-1.3.0-gompi-2020a-envmod-debian9", + "definitions/bowtie/Singularity.Bowtie-1.2.3-foss-2018b-envmod-debian9", + "definitions/ruby/Singularity.Ruby-2.7.1-GCCcore-8.3.0-envmod-debian9", + "definitions/ruby/Singularity.Ruby-2.7.1-GCCcore-8.3.0-envmod-debian10", + "definitions/gcc-core/Singularity.GCCcore-7.3.0-envmod-debian9", + "definitions/vcftools/Singularity.VCFtools-0.1.15-foss-2018a-Perl-5.26.1-envmod-centos7", + "definitions/meme/Singularity.MEME-5.1.1-foss-2019b-Perl-5.30.0-Python-3.7.4-envmod-debian10", + "definitions/foss/Singularity.foss-2018a-envmod-centos7", + "definitions/foss/Singularity.foss-2020a-envmod-debian9", + "definitions/tabix/Singularity.tabix-0.2.6-GCCcore-7.3.0-envmod-debian9", + "definitions/gcc/Singularity.GCC-8.3.0-envmod-debian9", + "definitions/gcc/Singularity.GCC-9.3.0-envmod-centos7", + "definitions/gcc/Singularity.GCC-7.3.0-2.30-envmod-centos7", + "definitions/gcc/Singularity.GCC-9.3.0-envmod-debian9", + "definitions/gcc/Singularity.GCC-9.3.0-envmod-debian10", + "definitions/gcc/Singularity.GCC-9.2.0-2.32-envmod-centos7", + "definitions/samtools/Singularity.SAMtools-1.10-GCC-9.3.0-envmod-debian10", + "definitions/RSEM/Singularity.RSEM-1.3.3-foss-2019b-centos-7-envmod", + "definitions/steak/Singularity.STEAK-20190912-foss-2019b-Python-2.7.16-envmod-centos7", + "definitions/plink/Singularity.PLINK-2.00a2.3LM-x86_64-envmod-debian9", + "definitions/plink/Singularity.PLINK-2.00a2.3LM-x86_64-lmod", + "definitions/plink/Singularity.PLINK-2.00-alpha2-x86_64-envmod-debian9", + "definitions/R/Singularity.R-3.6.2-foss-2019b-envmod-debian9", + "definitions/R/Singularity.R-4.0.0-foss-2020a-envmod-debian9", + "definitions/R/Singularity.R-3.6.2-foss-2020a-envmod-debian9", + "definitions/R/Singularity.R-3.6.0-foss-2018b-envmod-debian9", + "definitions/R/Singularity.R-3.6.3-foss-2020a-envmod-debian9", + "definitions/bwa/Singularity.BWA-0.7.17-foss-2018b-envmod-debian9", + "definitions/bcftools/Singularity.BCFtools-1.10.2-GCC-8.3.0-envmod-debian10", + "definitions/bcftools/Singularity.BCFtools-1.3-foss-2016b-envmod-centos7", + "definitions/bcftools/Singularity.BCFtools-1.10.2-GCC-8.3.0-envmod-debian9" ], - "updated_at": 1692465291.0 + "full_name": "sassy-crick/Singularity-Easybuild", + "latest_release": "v1.1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity-Easybuild\u003c/h1\u003e\u003ca id=\"user-content-singularity-easybuild\" class=\"anchor\" aria-label=\"Permalink: Singularity-Easybuild\" href=\"#singularity-easybuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription:\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description:\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCollection of Singularity definition files and scripts to create them for popular Linux Distributions like Debian (Buster, Bullseye and Bookworm), Centos (7), and Rocky (8.5 and latest).\u003c/p\u003e\n\u003cp\u003eThe definitions folder contains the successful Singularity Definition files, tested with version 3.5.3, 3.7.1, and CE-3.8.4 from Singularity, next to Apptainer/Singularity 3.8.5 , whereas the scripts folder contains the scripts to create the Singularity definition files which are based on EasyBuild. This version is using EasyBuild version 4.5.3.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need to have a Linux environment and Singularity installed in it.\nIf you don\u0027t have Linux, please use Vagrant to set up a virtual Linux environment.\u003c/p\u003e\n\u003cp\u003ePlease note: we noticed that Singularity version 3.8.x seems to have problems with the container internal \u003ccode\u003eenvmod\u003c/code\u003e but it is working fine for the internal \u003ccode\u003elmod\u003c/code\u003e. We are working on the issue so if you want to use \u003ccode\u003eenvmod\u003c/code\u003e inside the container, for now we recommend to use Singularity version 3.7.x.\u003c/p\u003e\n\u003cp\u003eThe minimum requirement for \u003ccode\u003ebash\u003c/code\u003e is version 4.x. If you are on MacOS, please use \u003ccode\u003ehomebrew\u003c/code\u003e to install \u003ccode\u003ebash\u003c/code\u003e from there.\u003c/p\u003e\n\u003cp\u003eFurthermore, if you want to build the containers, you either need to have \u003ccode\u003efakeroot\u003c/code\u003e installed and configured so it can be used as normal user, or have \u003ccode\u003esudo\u003c/code\u003e installed. The latter is required if you want to open up containers and re-build them again.\u003c/p\u003e\n\u003cp\u003eAs the software inside the containers is built using Easybuild, you will need to know the names of the Easybuild Configuration files, e.g. GCC-9.3.0.eb.\nThus, it is probably best to install the easybuild-easyconfig files like this in a separate folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/easybuilders/easybuild-easyconfigs.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand search the easybuild/easyconfigs folder for the name of the EasyBuild Configuration files you want to use. You only need the name, not the content of the file.\u003c/p\u003e\n\u003cp\u003eThe version of EasyBuild is now fixed with this release. If you require a specific version, simply change inside the Singularity definition file this line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install easybuild==4.5.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto this line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install easybuild\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich will install the latest EasyBuild version.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDepreciated versions:\u003c/h2\u003e\u003ca id=\"user-content-depreciated-versions\" class=\"anchor\" aria-label=\"Permalink: Depreciated versions:\" href=\"#depreciated-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs \u003ccode\u003ePython2\u003c/code\u003e is depreciated, the containers are using the \u003ccode\u003ePython3\u003c/code\u003e version for as their default system version. Note: This is different from the Python versions EasyBuild will install and should not be mixed up.\u003c/p\u003e\n\u003cp\u003eAs CentOS-8 is now end of life we are currently no longer supporting this version. We suggest to switch to Rocky-8 instead.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage:\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing the scripts is simple. Go into the \u003ccode\u003escripts\u003c/code\u003e folder and run the installation script \u003ccode\u003einstall.sh\u003c/code\u003e This will \u003cem\u003eeither\u003c/em\u003e install the scripts in your \u003ccode\u003e~/bin\u003c/code\u003e folder as sym-links, or create the sym-links in the folder where you are running the script from. We advice you to install the script in the \u003ccode\u003e~/bin\u003c/code\u003e folder so they are in your \u003ccode\u003ePATH\u003c/code\u003e environment. If you don\u0027t want to do this, we recommend to install the sym-links in a different folder from where you have downloaded the GitHub files from. Please note the usage of sym-links. Thus, if you do any changes in the folder where you downloaded the GitHub repository to, these changes will be carried over. If, for example, you were to delete that folder, the installation is broken.\u003c/p\u003e\n\u003cp\u003eDuring the installation, you will given a number of choices regarding whether you want to \u003cem\u003ebuild\u003c/em\u003e or \u003cem\u003ecreate\u003c/em\u003e the definition files, which Linux distribution and version you want to use and if you want to use Lmod or the environment-module system.\u003c/p\u003e\n\u003cp\u003eYou can then execute for example, assuming the links are in your \u003ccode\u003ePATH\u003c/code\u003e environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ container-create-debian11-envmodules.sh GCC-9.3.0.eb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can supply a second script as well, which could be one you have created. This script will be\nread into the Singularity Build file. So in our example we would get a file called \u003ccode\u003eSingularity.GCC-9.3.0-envmod-debian11\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf you want to build the container and additionally a sandbox, you could use this instead:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ container-build-debian11-envmodules.sh GCC-9.3.0.eb\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo in our example we would get a file called \u003ccode\u003eSingularity.GCC-9.3.0-envmod-debian11\u003c/code\u003e, next to the Singularity container called \u003ccode\u003eGCC-9.3.0-envmod-debian11.sif\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you don\u0027t want to or cannot use the automatic build process, you can build the container like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build GCC-9.3.0-envmod-debian10.sif Singularity.GCC-9.3.0-envmod-debian10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEqually, if you want to install software on top of the existing container manually, simply do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build --sandbox GCC-9.3.0-envmod-debian10.sif Singularity.GCC-9.3.0-envmod-debian10\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the example below for a complete build of R-4.0.0 in two steps: We first build the toolchain container (foss-2020a) and inside the container we build R-4.0.0. This approach allows us to create our own complete environment for building complete pipelines as well.\u003c/p\u003e\n\u003cp\u003eIf you want to have your name and email address included in the Singularity definition file, just create this file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/.singularity/sing-eb.conf\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAn empty file will mean these values are not included in the Singularity definition file. If you want to include your name and email address, simply add it. Likewise, you can pin the version of EasyBuild you want to use, like 4.4.0 in this example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ename=\"Your Name\"\nemail=\"email@address\"\neb_version=\"EasyBuild-version\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand replace \"Your Name\" and \"email@address\" and the \"EasyBuild-version\" you want to use accordingly.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample build:\u003c/h2\u003e\u003ca id=\"user-content-example-build\" class=\"anchor\" aria-label=\"Permalink: Example build:\" href=\"#example-build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis would be the complete sequence to build a container with the FOSS-2020a tool chain from EasyBuild, unpack the container and build for example R-4.0.0 inside the container. Of course you could to that all in one go as well. We are using the CentOS7 OS in this example:\u003c/p\u003e\n\u003cp\u003eWe first create the Singularity Definition File. As we don\u0027t need to add a separate EasyBuild configuration file we say \u0027n\u0027 here:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ container-create-centos8-envmodules.sh foss-2020a.eb\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDo we need a second Easybuild recipe (y/N)?: n\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ls\nSingularity.foss-2020a-centos-8-envmod\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe now build the Singularity container. Note that the command \u0027singularity\u0027 needs to be in the\nPATH of root:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build foss-2020a-centos-8-envmod.sif Singularity.foss-2020a-centos-8-envmod\n\n$ ls\nSingularity.foss-2020a-envmod-centos7 foss-2020a-envmod-centos7.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow that we got a container, we can unpack it so we can add software to it.\nFirst we unpack:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo singularity build --sandbox foss-2020a-centos-8-envmod foss-2020a-centos-8-envmod.sif\n$ ls\nSingularity.foss-2020a-centos-8-envmod foss-2020a-centos-8-envmod.sif foss-2020a-centos-8-envmod\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow we enter the container. The \u0027-w\u0027 flag means we can write to the pseudo-chroot environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity shell -w foss-2020a-centos-8-envmod \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe become the easybuild user and install the software. We first fetch all the source files. This\nis sometimes a problem due to flaky Internet connections. We then, in a second step, build the\nsoftware. This step can take some time but is done fully automatic. Once build, we exit the\ncontainer again:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e su -l easybuild\u003c/span\u003e\n[easybuild]$ eb --fetch R-4.0.0-foss-2020a.eb\n[easybuild]$ eb R-4.0.0-foss-2020a.eb\n[easybuild]$ \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e exit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOne step we need to do here as root is, to change the environment file so the new module will be loaded. This will be towards the bottom of this file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo vi foss-2020a-centos-8-envmod/environment\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFinally, we build the Singularity container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build R-4.0.0-foss-2020a-centos-8-envmod .sif foss-2020a-centos-8-envmod \n\n$ ls\nSingularity.foss-2020a-centos-8-envmod foss-2020a-centos-8-envmod .sif foss-2020a-centos-8-envmod R-4.0.0-foss-2020a-centos-8-envmod .sif \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow you can run R-4.0.0 on a different system like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e R-4.0.0-foss-2020a-centos-8-envmod.sif R\nR\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWe have introduced a more automated way of building containers. Thus, instead of doing all of the steps above manually, let the computer do it for you. Instead of using a \u0027create\u0027 script, we are simply using a \u0027build\u0027 script, like this for example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ container-build-centos8-envmodules.sh foss-2020a.eb\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will be asked whether or not to build the sandbox in the same run.\u003c/p\u003e\n\u003cp\u003eWe would recommend to build a generic container like \u003ccode\u003efoss-2020b\u003c/code\u003e for example and then use the provided sandbox to add software to it.\u003c/p\u003e\n\u003cp\u003eFor more details about what you can do with Singularity please refer to their home page.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgement:\u003c/h2\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement:\" href=\"#acknowledgement\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work would not be possible without EasyBuild, I am greateful to the project and the community for their help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks:\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links:\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity: \u003ca href=\"https://sylabs.io/guides/3.8/admin-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.8/admin-guide/installation.html\u003c/a\u003e\u003cbr\u003e\n(Source: \u003ca href=\"https://github.com/sylabs/singularity/releases\"\u003ehttps://github.com/sylabs/singularity/releases\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eApptainer: \u003ca href=\"http://apptainer.org/\" rel=\"nofollow\"\u003ehttp://apptainer.org/\u003c/a\u003e\n(Source: \u003ca href=\"https://github.com/apptainer/singularity\"\u003ehttps://github.com/apptainer/singularity\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eVagrant: \u003ca href=\"https://www.vagrantup.com/intro/getting-started\" rel=\"nofollow\"\u003ehttps://www.vagrantup.com/intro/getting-started\u003c/a\u003e\u003cbr\u003e\nEasybuild: \u003ca href=\"https://easybuild.readthedocs.io/en/latest\" rel=\"nofollow\"\u003ehttps://easybuild.readthedocs.io/en/latest\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUpdated: 11.3.2022\u003c/p\u003e\n", + "stargazers_count": 10, + "subscribers_count": 2, + "topics": [], + "updated_at": 1692625097.0 }, { "data_format": 2, @@ -37457,84 +37433,39 @@ var data = }, { "data_format": 2, - "description": "Finmag source", - "filenames": [ - "install/docker/singularity/Singularity", - "dev/singularity/Singularity" - ], - "full_name": "fangohr/finmag", - "latest_release": "0.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dev/logos/finmag_logo.png\"\u003e\u003cimg src=\"dev/logos/finmag_logo.png\" width=\"300\" align=\"right\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFinMag: finite-element micromagnetic simulation tool\u003c/h1\u003e\u003ca id=\"user-content-finmag-finite-element-micromagnetic-simulation-tool\" class=\"anchor\" aria-label=\"Permalink: FinMag: finite-element micromagnetic simulation tool\" href=\"#finmag-finite-element-micromagnetic-simulation-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMarc-Antonio Bisotti\u003csup\u003e1\u003c/sup\u003e, Marijan Beg\u003csup\u003e1,2\u003c/sup\u003e, Weiwei Wang\u003csup\u003e1\u003c/sup\u003e, Maximilian Albert\u003csup\u003e1\u003c/sup\u003e, Dmitri Chernyshenko\u003csup\u003e1\u003c/sup\u003e, David Cort\u00e9s-Ortu\u00f1o\u003csup\u003e1\u003c/sup\u003e, Ryan A. Pepper\u003csup\u003e1\u003c/sup\u003e, Mark Vousden\u003csup\u003e1\u003c/sup\u003e, Rebecca Carey\u003csup\u003e1\u003c/sup\u003e, Hagen Fuchs\u003csup\u003e3\u003c/sup\u003e, Anders Johansen\u003csup\u003e1\u003c/sup\u003e, Gabriel Balaban\u003csup\u003e1\u003c/sup\u003e, Leoni Breth\u003csup\u003e1\u003c/sup\u003e, Thomas Kluyver\u003csup\u003e1,2\u003c/sup\u003e, and Hans Fangohr\u003csup\u003e1,2,4\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e \u003cem\u003eFaculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom\u003c/em\u003e\u003cbr\u003e\n\u003csup\u003e2\u003c/sup\u003e \u003cem\u003eEuropean XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Germany\u003c/em\u003e\u003cbr\u003e\n\u003csup\u003e3\u003c/sup\u003e \u003cem\u003eHelmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstra\u00dfe 400, 01328 Dresden, Germany\u003c/em\u003e\u003cbr\u003e\n\u003csup\u003e4\u003c/sup\u003e \u003cem\u003eMax Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany\u003c/em\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eBadge\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eTests\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/fangohr/finmag/actions\"\u003e\u003cimg src=\"https://github.com/fangohr/finmag/workflows/workflow/badge.svg\" alt=\"workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/fangohr/finmag/actions\"\u003e\u003cimg src=\"https://github.com/fangohr/finmag/workflows/docker-image/badge.svg\" alt=\"docker-image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBinder\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://mybinder.org/v2/gh/fangohr/finmag/HEAD?filepath=binder%2Findex.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e91e1d353a8b6acf0b42547ac3901f2c30138a3abaaa3d3c242da30b5b4f8426/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLicense\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://opensource.org/licenses/BSD-3-Clause\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa27bfae9200ad81b9c64e82edafa3aef061e2b59e4089eb0841297d510d5db9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d425344253230332d2d436c617573652d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDockerHub\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/u/finmag/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe6bb5dae13c77487b7b04bb4efd75e44e68e33bf3db1ccbf0d857375f06556e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f446f636b65724875622d66696e6d61672d626c75652e737667\" alt=\"DockerHub\" data-canonical-src=\"https://img.shields.io/badge/DockerHub-finmag-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDOI\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.1216011\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0cb9a6cf62d35110acffc525d65a5133418ed32b1b5a9408b44af70e4feca414/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313231363031312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1216011.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFinmag was intended to be a thin (and mostly) Python layer on top of \u003ca href=\"https://fenicsproject.org/\" rel=\"nofollow\"\u003eFEniCS\u003c/a\u003e to enable Python-scripted multi-physics micromagnetic simulations. Accordingly, the name FINmag originates from the dolFIN interface to FEniCS. Some compiled code moved into the project.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe code has been developed from 2011 to 2018 by \u003ca href=\"http://fangohr.github.io\" rel=\"nofollow\"\u003eHans Fangohr\u003c/a\u003e\u0027s group at the University of Southampton (UK) and European XFEL GmbH (Germany).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe GitHub page of the project with the most recent version is \u003ca href=\"https://github.com/fangohr/finmag\"\u003ehttps://github.com/fangohr/finmag\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis is a working prototype which is not polished, with some (in large parts outdated) attempts at documentation. There is also some outdated code in the repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe do not consider the codebase, documentation, and other content of sufficient quality to encourage uptake in the community. (Experts are welcome!) This is primarily a resource problem.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDoes not execute efficiently in parallel (time integration is serial).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere is no support available.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eContributions and pull requests to both the code and documentation are welcome, but no promise can be made that these will be reviewed and/or integrated.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe code has been used for a number of scientific studies and publications (see the \u003ca href=\"#Publications\"\u003ePublications\u003c/a\u003e section).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe repository may well be of historical value and probably captures some of the typical research software engineering challenges. (We should write up a summary of our gathered experiences.)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere has not been dedicated funding to support the software development.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation / Using the tool via Docker\u003c/h2\u003e\u003ca id=\"user-content-installation--using-the-tool-via-docker\" class=\"anchor\" aria-label=\"Permalink: Installation / Using the tool via Docker\" href=\"#installation--using-the-tool-via-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere is a dedicated organisation on \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003eDockerHub\u003c/a\u003e named \u003ca href=\"https://hub.docker.com/u/finmag/\" rel=\"nofollow\"\u003e\u003ccode\u003efinmag\u003c/code\u003e\u003c/a\u003e. We provide pre-built images in the \u003ca href=\"https://hub.docker.com/r/finmag/finmag/\" rel=\"nofollow\"\u003e\u003ccode\u003efinmag/finmag\u003c/code\u003e\u003c/a\u003e repository. More information about Docker, as well as on how to install it on your system, can be found \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGetting the image\u003c/h3\u003e\u003ca id=\"user-content-getting-the-image\" class=\"anchor\" aria-label=\"Permalink: Getting the image\" href=\"#getting-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to get the most recent image is by pulling it from the DockerHub \u003ca href=\"https://hub.docker.com/r/finmag/finmag/\" rel=\"nofollow\"\u003e\u003ccode\u003efinmag/finmag\u003c/code\u003e\u003c/a\u003e repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker pull finmag/finmag:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, you can navigate to \u003ccode\u003einstall/docker/latest\u003c/code\u003e and run \u003ccode\u003emake pull\u003c/code\u003e. You can also build it on your own machine by navigating to \u003ccode\u003einstall/docker/latest\u003c/code\u003e, and running\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ make build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting\u003c/h3\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter you pulled/built the \u003ccode\u003efinmag/finmag\u003c/code\u003e image, you can test it with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -ti -w=\"/finmag\" --rm finmag/finmag bash -c \"py.test -v\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor by running \u003ccode\u003emake test\u003c/code\u003e in \u003ccode\u003einstall/docker/latest\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the container\u003c/h3\u003e\u003ca id=\"user-content-running-the-container\" class=\"anchor\" aria-label=\"Permalink: Running the container\" href=\"#running-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run your Finmag code inside Docker, please navigate to the directory where your \u003ccode\u003emy-finmag-script.py\u003c/code\u003e file is (\u003ccode\u003ecd path/to/your/file\u003c/code\u003e) and run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -ti -v $(pwd):/io --rm finmag/finmag bash -c \"python my-finmag-script.py\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to run code interactively inside the container, then you can start with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -ti -v $(pwd):/io --rm finmag/finmag\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFinmag dependencies container\u003c/h3\u003e\u003ca id=\"user-content-finmag-dependencies-container\" class=\"anchor\" aria-label=\"Permalink: Finmag dependencies container\" href=\"#finmag-dependencies-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker image which contains all of the dependencies necessary to run finmag is hosted on DockerHub as \u003ccode\u003efinmag/finmag:dependencies\u003c/code\u003e. Similar to previous sections, if you navigate to \u003ccode\u003einstall/docker/dependencies\u003c/code\u003e, you can run \u003ccode\u003emake pull\u003c/code\u003e, \u003ccode\u003emake run\u003c/code\u003e, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstalling on host\u003c/h3\u003e\u003ca id=\"user-content-installing-on-host\" class=\"anchor\" aria-label=\"Permalink: Installing on host\" href=\"#installing-on-host\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMore detailed comments on the installation of finmag on a host machine are in \u003ca href=\"install/README.md\"\u003e\u003ccode\u003einstall/README.md\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBinder\u003c/h2\u003e\u003ca id=\"user-content-binder\" class=\"anchor\" aria-label=\"Permalink: Binder\" href=\"#binder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to try using Finmag in the cloud you can do it on \u003ca href=\"https://mybinder.org/v2/gh/fangohr/finmag/HEAD?filepath=binder%2Findex.ipynb\" rel=\"nofollow\"\u003eBinder\u003c/a\u003e. This does not require you to have anything installed and no files will be created on your machine. You only need a web browser.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe documentation in the form of \u003ca href=\"http://jupyter.org/\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e notebooks is available in \u003ca href=\"doc/ipython_notebooks_src\"\u003e\u003ccode\u003edoc/ipython_notebooks_src\u003c/code\u003e\u003c/a\u003e directory. Large parts of documentation are currently outdated.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to cite\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use Finmag in your research, please cite it as\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarc-Antonio Bisotti, Marijan Beg, Weiwei Wang, Maximilian Albert, Dmitri Chernyshenko, David Cort\u00e9s-Ortu\u00f1o, Ryan A. Pepper, Mark Vousden, Rebecca Carey, Hagen Fuchs, Anders Johansen, Gabriel Balaban, Leoni Breth, Thomas Kluyver, and Hans Fangohr. FinMag: finite-element micromagnetic simulation tool (Version 0.1). Zenodo. DOI: \u003ca href=\"http://doi.org/10.5281/zenodo.1216011\" rel=\"nofollow\"\u003ehttp://doi.org/10.5281/zenodo.1216011\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinmag is licensed under the BSD 3-Clause \"New\" or \"Revised\" License. For details, please refer to the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file. However, portions of the source code (e.g. src/util/numpy.h) are subject to the Boost Software License.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe do not provide support for Finmag. However, you are welcome to raise an issue in the GitHub \u003ca href=\"https://github.com/fangohr/finmag\"\u003efangohr/finmag\u003c/a\u003e repository, but no promise can be made that the issue will be addressed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePublications\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinmag was used to run micromagnetic simulations in the following publications (in reversed chronological order):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eM. Beg, R. A. Pepper, D. Cort\u00e9s-Ortu\u00f1o, B. Atie, M. A. Bisotti, G. Downing, T. Kluyver, O. Hovorka, H. Fangohr. Stable and manipulable Bloch point. \u003ca href=\"https://doi.org/10.1038/s41598-019-44462-2\" rel=\"nofollow\"\u003eScientific Reports 9, 7959\u003c/a\u003e (2019). (arXiv:1808.10772)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eR. A. Pepper, M. Beg, D. Cort\u00e9s-Ortu\u00f1o, T. Kluyver, M.-A. Bisotti, R. Carey, M. Vousden, M. Albert, W. Wang, O. Hovorka, and H. Fangohr. Skyrmion states in thin confined polygonal nanostructures. \u003ca href=\"http://aip.scitation.org/doi/10.1063/1.5022567\" rel=\"nofollow\"\u003eJournal of Applied Physics 9, 093903\u003c/a\u003e (2018). (arXiv:1801.03275)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eD. Cort\u00e9s-Ortu\u00f1o, W. Wang, M. Beg, R. A. Pepper, M.-A. Bisotti, R. Carey, M. Vousden, T. Kluyver, O. Hovorka, and H. Fangohr. Thermal stability and topological protection of skyrmions in nanotracks. \u003ca href=\"http://www.nature.com/articles/s41598-017-03391-8\" rel=\"nofollow\"\u003eScientific Reports 7, 4061\u003c/a\u003e (2017). (arXiv:1611.07079)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Beg, M. Albert, M.-A. Bisotti, D. Cort\u00e9s-Ortu\u00f1o, W. Wang, R. Carey, M. Vousden, O. Hovorka, C. Ciccarelli, C. S. Spencer, C. H. Marrows, and H. Fangohr. Dynamics of skyrmionic states in confined helimagnetic nanostructures. \u003ca href=\"http://link.aps.org/doi/10.1103/PhysRevB.95.014433\" rel=\"nofollow\"\u003ePhysical Review B 95, 014433\u003c/a\u003e (2017). (arXiv:1604.08347)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA. Baker, M. Beg, G. Ashton, M. Albert, D. Chernyshenko, W. Wang, S. Zhang, M.-A. Bisotti, M. Franchin, C. Lian Hu, R. L. Stamps, T. Hesjedal, and H. Fangohr. Proposal of a micromagnetic standard problem for ferromagnetic resonance simulations. \u003ca href=\"http://linkinghub.elsevier.com/retrieve/pii/S0304885316307545\" rel=\"nofollow\"\u003eJournal of Magnetism and Magnetic Materials 421, 428-439\u003c/a\u003e (2017). (arXiv:1603.05419)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eP. J. Metaxas, M. Albert, S. Lequeux, V. Cros, J. Grollier, P. Bortolotti, A. Anane, and H. Fangohr. Resonant translational, breathing, and twisting modes of transverse magnetic domain walls pinned at notches. \u003ca href=\"https://journals.aps.org/prb/abstract/10.1103/PhysRevB.93.054414\" rel=\"nofollow\"\u003ePhys. Rev. B 93, 054414\u003c/a\u003e (2016).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJ. P. Fried, H. Fangohr, M. Kostylev, and P. J. Metaxas. Exchange-mediated, nonlinear, out-of-plane magnetic field dependence of the ferromagnetic vortex gyrotropic mode frequency driven by core deformation. \u003ca href=\"https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.224407\" rel=\"nofollow\"\u003ePhys. Rev. B 94, 224407\u003c/a\u003e (2016).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eR. Carey, M. Beg, M. Albert, M.-A. Bisotti, D. Cort\u00e9s-Ortu\u00f1o, M. Vousden, W. Wang, O. Hovorka, and H. Fangohr. Hysteresis of nanocylinders with Dzyaloshinskii-Moriya interaction. \u003ca href=\"http://scitation.aip.org/content/aip/journal/apl/109/12/10.1063/1.4962726\" rel=\"nofollow\"\u003eApplied Physics Letters 109, 122401\u003c/a\u003e (2016). (arXiv:1606.05181)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Sushruth, J. Ding, J. Duczynski, R. C. Woodward, R. A. Begley, H. Fangohr, R. O. Fuller, A. O. Adeyeye, M. Kostylev, and P. J. Metaxas. Resonance-Based Detection of Magnetic Nanoparticles and Microbeads Using Nanopatterned Ferromagnets. \u003ca href=\"https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.6.044005\" rel=\"nofollow\"\u003ePhys. Rev. Applied 6, 044005\u003c/a\u003e (2016).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Albert, M. Beg, D. Chernyshenko, M.-A. Bisotti, R. L. Carey, H. Fangohr, and P. J. Metaxas. Frequency-based nanoparticle sensing over large field ranges using the ferromagnetic resonances of a magnetic nanodisc. \u003ca href=\"http://stacks.iop.org/0957-4484/27/i=45/a=455502?key=crossref.2ac6ca2e40700c0c20b17814ae4f6a9d\" rel=\"nofollow\"\u003eNanotechnology 27, 455502\u003c/a\u003e (2016). (arXiv:1604.07277)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Vousden, M. Albert, M. Beg, M.-A. Bisotti, R. Carey, D. Chernyshenko, D. Cort\u00e9s-Ortu\u00f1o, W. Wang, O. Hovorka, C. H. Marrows, and H. Fangohr. Skyrmions in thin films with easy-plane magnetocrystalline anisotropy. \u003ca href=\"http://aip.scitation.org/doi/10.1063/1.4945262\" rel=\"nofollow\"\u003eApplied Physics Letters 108, 132406\u003c/a\u003e (2016). (arXiv:1602.02064)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJ. P. Fried and P. J. Metaxas. Localized magnetic fields enhance the field sensitivity of the gyrotropic resonance frequency of a magnetic vortex. \u003ca href=\"https://journals.aps.org/prb/abstract/10.1103/PhysRevB.93.064422\" rel=\"nofollow\"\u003ePhys. Rev. B 93, 064422\u003c/a\u003e (2016).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Beg, R. Carey, W. Wang, D. Cort\u00e9s-Ortu\u00f1o, M. Vousden, M.-A. Bisotti, M. Albert, D. Chernyshenko, O. Hovorka, R. L. Stamps, and H. Fangohr. Ground state search, hysteretic behaviour, and reversal mechanism of skyrmionic textures in confined helimagnetic nanostructures. \u003ca href=\"http://www.nature.com/articles/srep17137\" rel=\"nofollow\"\u003eScientific Reports 5, 17137\u003c/a\u003e (2015). (arXiv:1312.7665)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eP. J. Metaxas, M. Sushruth, R. A. Begley, J. Ding, R. C. Woodward, I. S. Maksymov, M. Albert, W. Wang, H. Fangohr, A. O. Adeyeye, and M. Kostylev. Sensing magnetic nanoparticles using nano-confined ferromagnetic resonances in a magnonic crystal. \u003ca href=\"https://aip.scitation.org/doi/abs/10.1063/1.4922392\" rel=\"nofollow\"\u003eAppl. Phys. Lett. 106, 232406\u003c/a\u003e (2015).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eW. Wang, M. Albert, M. Beg, M.-A. Bisotti, D. Chernyshenko, D. Cort\u00e9s-Ortu\u00f1o, I. Hawke, and H. Fangohr. Magnon driven domain wall motion with Dzyaloshinskii-Moriya interaction. \u003ca href=\"http://link.aps.org/doi/10.1103/PhysRevLett.114.087203\" rel=\"nofollow\"\u003ePhysical Review Letters 114, 087203\u003c/a\u003e (2015). (arXiv:1406.5997)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eW. Wang, M. Beg, B. Zhang, W. Kuch, and H. Fangohr. Driving magnetic skyrmions with microwave fields. \u003ca href=\"http://link.aps.org/doi/10.1103/PhysRevB.92.020403\" rel=\"nofollow\"\u003ePhysical Review B (Rapid Communications) 92, 020403\u003c/a\u003e (2015). (arXiv:1505.00445)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eW. Wang, M. Dvornik, M.-A. Bisotti, D. Chernyshenko, M. Beg, M. Albert, A. Vansteenkiste, B. V. Waeyenberge, A. N. Kuchko, V. V. Kruglyak, and H. Fangohr. Phenomenological description of the nonlocal magnetization relaxation in magnonics, spintronics, and domain-wall dynamics. \u003ca href=\"http://link.aps.org/doi/10.1103/PhysRevB.92.054430\" rel=\"nofollow\"\u003ePhysical Review B 92, 054430\u003c/a\u003e (2015). (arXiv:1508.01478)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eB. Zhang, W. Wang, M. Beg, H. Fangohr, and W. Kuch. Microwave-induced dynamic switching of magnetic skyrmion cores in nanodots. \u003ca href=\"http://scitation.aip.org/content/aip/journal/apl/106/10/10.1063/1.4914496\" rel=\"nofollow\"\u003eApplied Physics Letters 106, 102401\u003c/a\u003e (2015). (arXiv:1503.02869)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEPSRC\u2019s \u003ca href=\"http://www.icss.soton.ac.uk\" rel=\"nofollow\"\u003eDoctoral Training Centre in Complex System Simulation\u003c/a\u003e (EP/G03690X/1),\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEPSRC\u0027s \u003ca href=\"http://ngcm.soton.ac.uk\" rel=\"nofollow\"\u003eCentre for Doctoral Training in Next Generation Computational Modelling\u003c/a\u003e (#EP/L015382/1),\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHorizon 2020 European Research Infrastructure project \u003ca href=\"http://opendreamkit.org/\" rel=\"nofollow\"\u003eOpenDreamKit\u003c/a\u003e (676541),\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEPSRC\u0027s \u003ca href=\"https://www.skyrmions.ac.uk/\" rel=\"nofollow\"\u003eProgramme grant on Skyrmionics\u003c/a\u003e (EP/N032128/1),\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.moore.org/\" rel=\"nofollow\"\u003eGordon and Betty Moore Foundation\u003c/a\u003e through Grant GBMF #4856, by the Alfred P. Sloan Foundation and by the Helmsley Trust.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSee also\u003c/h2\u003e\u003ca id=\"user-content-see-also\" class=\"anchor\" aria-label=\"Permalink: See also\" href=\"#see-also\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/computationalmodelling/fidimag\"\u003eFidimag\u003c/a\u003e: finite-difference micromagnetic simulation tool\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 11, - "subscribers_count": 6, - "topics": [], - "updated_at": 1712567597.0 - }, - { - "data_format": 2, - "description": "DataLad extension for containerized environments", - "filenames": [ - "tools/Singularity.testhelper" - ], - "full_name": "datalad/datalad-container", - "latest_release": "1.2.5", - "readme": "\u003cpre\u003e\u003ccode\u003e ____ _ _ _\n| _ \\ __ _ | |_ __ _ | | __ _ __| |\n| | | | / _` || __| / _` || | / _` | / _` |\n| |_| || (_| || |_ | (_| || |___ | (_| || (_| |\n|____/ \\__,_| \\__| \\__,_||_____| \\__,_| \\__,_|\n Container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://ci.appveyor.com/project/mih/datalad-container/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7e1e9ebf3820a2076d7571c25250ec6e0a0069027460f138fdb58a97e1ebc916/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6b34657971317979676376776637776b2f6272616e63682f6d61737465723f7376673d74727565\" alt=\"Build status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/k4eyq1yygcvwf7wk/branch/master?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://app.travis-ci.com/datalad/datalad-container\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3a90c3a93b6b3a6d951da5ba6e0979fbbee4a0249d0ddeae728d3b92039ef3bd/68747470733a2f2f6170702e7472617669732d63692e636f6d2f646174616c61642f646174616c61642d636f6e7461696e65722e7376673f6272616e63683d6d6173746572\" alt=\"Travis tests status\" data-canonical-src=\"https://app.travis-ci.com/datalad/datalad-container.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://codecov.io/github/datalad/datalad-container?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96ac57d224208808626f9c39c293f91fccd6436c3a9703d4a6414da48ca0ff72/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d636f6e7461696e65722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-container/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://datalad-container.rtfd.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/026c56af775b4481b361f2c0da52b122a2454bd1182a9149ff70740d6832f266/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f646174616c61642d636f6e7461696e65722f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation\" data-canonical-src=\"https://readthedocs.org/projects/datalad-container/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4426cbe5c21edb002526331c7a8fbfa089e84a550567b02a0d829a98b136ad0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://GitHub.com/datalad/datalad-container/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c45404a589fc621efcd753fe6cd3a89a7b99b62f3eba68267af859172585f1bb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d636f6e7461696e65722e737667\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-container.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://pypi.python.org/pypi/datalad-container/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d925162c5cba0998157f0c36ba3ae092619b5c796dfd243c058b590155392d66/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d636f6e7461696e65722e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-container.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.3368666\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8c03e2106b715ec0c43b16912bc2cffc2508a95629098f4d93bbb7e5aede5a32/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333336383636362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3368666.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/37e04df5ee8be0e08ef80a25b02b47c9c23c5efda07f20867581554c04e5da4c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d636f6e7461696e65722f6261646765732f76657273696f6e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37e04df5ee8be0e08ef80a25b02b47c9c23c5efda07f20867581554c04e5da4c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d636f6e7461696e65722f6261646765732f76657273696f6e2e737667\" alt=\"Conda\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-container/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis extension enhances DataLad (\u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003ehttp://datalad.org\u003c/a\u003e) for working with\ncomputational containers. Please see the \u003ca href=\"http://datalad-container.rtfd.org\" rel=\"nofollow\"\u003eextension\ndocumentation\u003c/a\u003e\nfor a description on additional commands and functionality.\u003c/p\u003e\n\u003cp\u003eFor general information on how to use or contribute to DataLad (and this\nextension), please see the \u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003eDataLad website\u003c/a\u003e or the\n\u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003emain GitHub project page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore you install this package, please make sure that you \u003ca href=\"https://git-annex.branchable.com/install\" rel=\"nofollow\"\u003einstall a recent\nversion of git-annex\u003c/a\u003e. Afterwards,\ninstall the latest version of \u003ccode\u003edatalad-container\u003c/code\u003e from\n\u003ca href=\"https://pypi.org/project/datalad-container\" rel=\"nofollow\"\u003ePyPi\u003c/a\u003e. It is recommended to use\na dedicated \u003ca href=\"https://virtualenv.pypa.io\" rel=\"nofollow\"\u003evirtualenv\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# create and enter a new virtual environment (optional)\nvirtualenv --system-site-packages --python=python3 ~/env/datalad\n. ~/env/datalad/bin/activate\n\n# install from PyPi\npip install datalad_container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is also available for conda package manager from conda-forge:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c conda-forge datalad-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe documentation of this project is found here:\n\u003ca href=\"http://docs.datalad.org/projects/container\" rel=\"nofollow\"\u003ehttp://docs.datalad.org/projects/container\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAll bugs, concerns and enhancement requests for this software can be submitted here:\n\u003ca href=\"https://github.com/datalad/datalad-container/issues\"\u003ehttps://github.com/datalad/datalad-container/issues\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you have a problem or would like to ask a question about how to use DataLad,\nplease \u003ca href=\"https://neurostars.org/tags/datalad\" rel=\"nofollow\"\u003esubmit a question to\nNeuroStars.org\u003c/a\u003e with a \u003ccode\u003edatalad\u003c/code\u003e tag.\nNeuroStars.org is a platform similar to StackOverflow but dedicated to\nneuroinformatics.\u003c/p\u003e\n\u003cp\u003eAll previous DataLad questions are available here:\n\u003ca href=\"http://neurostars.org/tags/datalad/\" rel=\"nofollow\"\u003ehttp://neurostars.org/tags/datalad/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDataLad development is supported by a US-German collaboration in computational\nneuroscience (CRCNS) project \"DataGit: converging catalogues, warehouses, and\ndeployment logistics into a federated \u0027data distribution\u0027\" (Halchenko/Hanke),\nco-funded by the US National Science Foundation (NSF 1429999) and the German\nFederal Ministry of Education and Research (BMBF 01GQ1411). Additional support\nis provided by the German federal state of Saxony-Anhalt and the European\nRegional Development Fund (ERDF), Project: Center for Behavioral Brain\nSciences, Imaging Platform. This work is further facilitated by the ReproNim\nproject (NIH 1P41EB019936-01A1).\u003c/p\u003e\n", - "stargazers_count": 11, - "subscribers_count": 9, - "topics": [ - "container", - "datalad" - ], - "updated_at": 1697766017.0 - }, - { - "data_format": 2, - "description": null, + "description": "Python based plotting package for CASA MeasurementSet", "filenames": [ "Singularity" ], - "full_name": "PGP-UK/GenomeChronicler", - "latest_release": "0.91", - "readme": "\u003cpre\u003e\u003ccode\u003e ##### ##### \n# # ###### # # #### # # ###### # # # # ##### #### # # # #### # ###### ##### \n# # ## # # # ## ## # # # # # # # # ## # # # # # # # # \n# #### ##### # # # # # # ## # ##### # ###### # # # # # # # # # # ##### # # \n# # # # # # # # # # # # # # ##### # # # # # # # # # ##### \n# # # # ## # # # # # # # # # # # # # # ## # # # # # # # \n ##### ###### # # #### # # ###### ##### # # # # #### # # # #### ###### ###### # # \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3664\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-welcome\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome\u003c/h1\u003e\n\u003cp\u003eThis is the repository for Genome Chronicler, the Personal Genome Project United Kingdom (PGP-UK) genomic report generation scripts.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h1\u003e\n\u003cp\u003eAfter cloning this repository, run the SetupMeFirst.sh script in your local system to retrieve the extra files needed to run the pipeline (around 10GB, so too big for git).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-input-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files\u003c/h1\u003e\n\u003cp\u003eThe main script (GenomeChronicler_mainDruid.pl) needs a BAM file as input, and optionally can also use a VEP generated summary html file, if variants have already been called on the data and summaries are to be produced.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003eTo handle the myriad dependencies present in this pipeline, it is avaliable through Singularity Hub as a singularity container (see badge at top of the page).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-easy-start-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#easy-start-using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEasy Start using Singularity\u003c/h1\u003e\n\u003cp\u003eIf you don\u0027t already have singularity on your system, or want to know more about it, head to their userguide at: \u003ca href=\"https://sylabs.io/guides/3.1/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.1/user-guide/\u003c/a\u003e\nWhile Singularity is not needed to run GenomeChronicler, it does make setup much easier.\u003c/p\u003e\n\u003cp\u003eFor a manual installation without Singularity, please follow the steps in the %post section of the Singularity file in this repository, to install all the dependencies.\u003c/p\u003e\n\u003cp\u003eDownloading pre-packaged GenomeChronicler from SingularityHub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://PGP-UK/GenomeChronicler\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGetting some test data (NA12878 from ENA, pre-mapped to GRCh38, and the respective reference)\u003c/p\u003e\n\u003cpre lang=\"wget\"\u003e\u003ccode\u003esingularity exec GenomeChronicler_latest.sif wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/data/CEU/NA12878/alignment/NA12878.alt_bwamem_GRCh38DH.20150718.CEU.low_coverage.cram\n\nsingularity exec GenomeChronicler_latest.sif wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eConverting data to BAM format\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec GenomeChronicler_latest.sif samtools view -T GRCh38_full_analysis_set_plus_decoy_hla.fa -b -o NA12878wxs.bam NA12878.alt_bwamem_GRCh38DH.20150718.CEU.low_coverage.cram\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning GenomeChronicler on the data\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run GenomeChronicler_latest.sif --bamFile=NA12878wxs.bam \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-command-line-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#command-line-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand Line Options\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eOption\u003c/th\u003e\n\u003cth align=\"center\"\u003eRequirement\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--bamFile\u003c/td\u003e\n\u003ctd align=\"center\"\u003eREQUIRED\u003c/td\u003e\n\u003ctd\u003eThe path to a BAM file that has been preprocessed through markDuplicates and VariantQualityScoreRecalibration. This can be obtained by running the first step of the Sarek nextflow pipeline, or through other means that do respect the general principles of the GATK Variation Calling Best Practices workflow. Note that no variation calling is needed to run GenomeChronicler.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--vepFile\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOPTIONAL\u003c/td\u003e\n\u003ctd\u003eFor the summary tables to appear in the report, a VEP summary HTML file must be provided. This will likely be generated if the data is from whole genome sequencing and variants were called (e.g. by running all the germline calling steps of the Sarek nextflow pipeline or other GATK Best Practices based workflow). If this isn\u0027t provided, summary tables and plots will automatically be excluded from the final report.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--resultsDir\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOPTIONAL\u003c/td\u003e\n\u003ctd\u003eFor setting the absolute path of the results folder to be produced when running GenomeChronicler.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--customTemplate\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOPTIONAL\u003c/td\u003e\n\u003ctd\u003eFor customising the output report, set this variable to the path of a custom LaTeX file to act as a template for the report. The default templates bundled with this software can also be found in the project github page.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--GATKthreads\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOPTIONAL\u003c/td\u003e\n\u003ctd\u003eNumber of threads to use for the GATK genotyping steps of this processing pipeline.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", - "stargazers_count": 11, - "subscribers_count": 2, - "topics": [], - "updated_at": 1675071802.0 - }, - { - "data_format": 2, - "description": "Spectral Element Library in Fortran", - "filenames": [ - "img/Singularity.amd", - "img/Singularity.nvidia" - ], - "full_name": "FluidNumerics/SELF", + "full_name": "haavee/jiveplot", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-spectral-element-libraries-in-fortran-self\" class=\"anchor\" href=\"#spectral-element-libraries-in-fortran-self\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpectral Element Libraries in Fortran (SELF)\u003c/h1\u003e\n\u003cp\u003eCopyright 2020-2021 Fluid Numerics LLC\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://self.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2cdea5d87038eae2bd52034d42848bdf0381c26e2ffe70a7a973e360004a19f6/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f73656c662f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/self/badge/?version=latest\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSELF is licensed for use under the \u003ca href=\"./LICENSE\"\u003eAnti-Capitalist Software License\u003c/a\u003e. For other licensure, reach out to \u003ca href=\"mailto:support@fluidnumerics.com\"\u003esupport@fluidnumerics.com\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-support-this-project\" class=\"anchor\" href=\"#support-this-project\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport this project\u003c/h2\u003e\n\u003cp\u003eSELF is open-source made freely available to the public. Fluid Numerics is a nano-business (less than 5 people) that is funded solely through service engagements with customers; we are not funded by venture capital in any way. Developing SELF and the associated documentation and delivering live coding sessions and tutorials all require time \u0026amp; labor as well as compute resources.\u003c/p\u003e\n\u003cp\u003eIf you value this repository and the activities of the SELF developers, you can \u003ca href=\"https://opencollective.com/higher-order-methods/projects/fluid-self\" rel=\"nofollow\"\u003esupport the continued development and maintenance SELF on Open Collective\u003c/a\u003e, where we transparently share our operational budget and expenses for this project. Through the \u003ca href=\"https://opencollective.com/higher-order-methods\" rel=\"nofollow\"\u003eHigher Order Methods collective\u003c/a\u003e you can support a variety of activities all focused on providing publicly accessible tools and training for using spectral element methods for physical modeling.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-about\" class=\"anchor\" href=\"#about\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eSELF is an object-oriented Fortran library that support the implementation of Spectral Element Methods for solving partial differential equations.\u003c/p\u003e\n\u003cp\u003eThe SELF API is designed based on the assumption that SEM developers and researchers need to be able to implement derivatives in 1-D and divergence, gradient, and curl in 2-D and 3-D on scalar, vector, and tensor functions using spectral collocation, continuous galerkin, and discontinuous galerkin spectral element methods. Additionally, as we enter the exascale era, we are currently faced with a zoo of compute hardware that is available. Because of this, SELF routines provide support for GPU acceleration through AMD\u0027s HIP and support for multi-core, multi-node, and multi-GPU platforms with MPI.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"./SPECIFICATIONS.md\"\u003eSpecifications\u003c/a\u003e for more details.\u003c/p\u003e\n\u003cp\u003eTo learn more about the SELF API and the software layout, \u003ca href=\"https://higherordermethods.github.io/SELF/\" rel=\"nofollow\"\u003echeck out the SELF documentation\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://discord.gg/57aNxcpYMW\" rel=\"nofollow\"\u003eJoin the HigherOrderMethods Discord Server\u003c/a\u003e to get community support.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cp\u003eBefore installing SELF, make sure your system has the following dependencies installed :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFortran compiler (e.g. gfortran)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ROCm-Developer-Tools/HIP\"\u003eHIP\u003c/a\u003e (For GPU Accelerated Builds)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ROCmSoftwarePlatform/hipfort\"\u003eHIPFort\u003c/a\u003e (For GPU Accelerated Builds)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/fortran-lang/stdlib\"\u003estdlib\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jacobwilliams/json-fortran\"\u003ejson-fortran\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/szaghi/FLAP\"\u003eFLAP\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/FluidNumerics/feqparse\"\u003efeq-parse\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eHDF5\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor GPU accelerated builds, we recommend installing \u003ccode\u003erocm-dev\u003c/code\u003e on either CentOS or Ubuntu OS following the \u003ca href=\"https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html\" rel=\"nofollow\"\u003eROCm installation guide\u003c/a\u003e. You should install HIPfort from source, using the Fortran compiler you will build SELF with.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-building-self\" class=\"anchor\" href=\"#building-self\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding SELF\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eSerial CPU Builds\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBUILD=release \\\nFC=gfortran \\\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe SELF make system assumes that \u003ccode\u003efeqparse\u003c/code\u003e, \u003ccode\u003eFLAP\u003c/code\u003e, and \u003ccode\u003eHDF5\u003c/code\u003e are installed under \u003ccode\u003e/opt/view\u003c/code\u003e. If these dependencies are installed elseswhere you can use the following environment variables specify the linker and includes flags.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e SELF_FEQPARSE_LIBS Set the linker flags for feq-parse (Default: -L/opt/view/lib -lfeqparse)\n SELF_FEQPARSE_INC Set the includes flags for feq-parse (Default: -I/opt/view/include)\n SELF_FLAP_LIBS Set the linker flags for FLAP (Default: -L/opt/view/lib/ -lFLAP -lFACE -lPENF) \n SELF_FLAP_INC Set the includes flags for FLAP (Default: -I/opt/view/include/FLAP -I/opt/view/include/PENF -I/opt/view/include/FACE)\n SELF_HDF5_LIBS Set the linker flags for HDF5 (Default: -L/opt/view/lib/ -lhdf5_fortran -lhdf5 -lz -lm) \n SELF_HDF5_INC Set the includes flags for HDF5 (Default: -I/opt/view/include/shared)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, self will install under \u003ccode\u003e/opt/self\u003c/code\u003e. To change the install path, use the \u003ccode\u003eSELF_PREFIX\u003c/code\u003e environment variable.\u003c/p\u003e\n\u003cp\u003eUpon installation, you will have\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e${SELF_PREFIX}/lib/libself.a\u003c/code\u003e : Static library for linking into other application\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${SELF_PREFIX}/include/*.mod\u003c/code\u003e : Fortran module files required for building other applications that depend on SELF\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${SELF_PREFIX}/bin/self\u003c/code\u003e : A test program that can be used to test each SELF routine\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${SELF_PREFIX}/util\u003c/code\u003e : A directory with useful utilities for pre and post-processing data produced by SELF\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-maintainers\" class=\"anchor\" href=\"#maintainers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainers\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://fluidnumerics.com/people/joe-schoonover\" rel=\"nofollow\"\u003eJoseph Schoonover, Fluid Numerics LLC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWant to become a maintainer ? Reach out to \u003ca href=\"mailto:support@fluidnumerics.com\"\u003esupport@fluidnumerics.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you\u0027d like to contribute, see \u003ca href=\"./CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e to get started.\u003c/p\u003e\n\u003cp\u003eIf you need help, \u003ca href=\"https://github.com/FluidNumerics/SELF/issues/new\"\u003eopen an issue\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1847\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejplotter / jiveplot\u003c/h1\u003e\u003ca id=\"user-content-jplotter--jiveplot\" class=\"anchor\" aria-label=\"Permalink: jplotter / jiveplot\" href=\"#jplotter--jiveplot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePython based visualization tool for AIPS++/CASA MeasurementSet data\u003c/p\u003e\n\u003cp\u003eThe jplotter command line tool allows the user to quickly visualize the\nradio-astronomical data contained in a MeasurementSet (\u003ccode\u003ems\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython support\u003c/h2\u003e\u003ca id=\"user-content-python-support\" class=\"anchor\" aria-label=\"Permalink: Python support\" href=\"#python-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStarting from 20 Jul 2023 the \u003ccode\u003emaster\u003c/code\u003e branch has been updated to support\nPy2 and Py3 based systems simultaneously. This version is tagged \u003ca href=\"https://github.com/haavee/jiveplot/releases/tag/v2.0\"\u003ev2.0\u003c/a\u003e. The\nprevious latest Py2-only master was tagged as\n\u003ca href=\"https://github.com/haavee/jiveplot/releases/tag/v1.1\"\u003ev1.1\u003c/a\u003e and\n\u003ca href=\"https://github.com/haavee/jiveplot/releases/tag/last-python2-only-master\"\u003elast-python2-only-master\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e5 second workflow\u003c/h2\u003e\u003ca id=\"user-content-5-second-workflow\" class=\"anchor\" aria-label=\"Permalink: 5 second workflow\" href=\"#5-second-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter downloading and having the\n\u003ca href=\"https://github.com/haavee/jiveplot#dependencies\"\u003edependencies\u003c/a\u003e installed\n(as of 30 Oct 2018 you can run from a \u003ca href=\"https://github.com/haavee/jiveplot#singularity-and-docker-container-images\"\u003esingularity or Docker\u003c/a\u003e image) type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ /path/to/jiveplot/jplotter\n+++++++++++++++++++++ Welcome to cli +++++++++++++++++++\n\u003cspan class=\"pl-smi\"\u003e$Id\u003c/span\u003e: command.py,v 1.16 2015-11-04 13:30:10 jive_cc Exp $\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexit\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e exits, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elist\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e lists, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehelp\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e helps\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand you\u0027re in the command line environment. Then open a MS, select data,\nselect what to plot and go.\u003c/p\u003e\n\u003cp\u003eThis README will not explain any further because there is a colourful \u003ca href=\"jplotter-cookbook-draft-v2.pdf\"\u003ePDF cookbook/tutorial/explanation\u003c/a\u003e with far more detail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can be visualized?\u003c/h2\u003e\u003ca id=\"user-content-what-can-be-visualized\" class=\"anchor\" aria-label=\"Permalink: What can be visualized?\" href=\"#what-can-be-visualized\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eQuantities that can be visualized are, e.g., amplitude-versus-time,\nphase-versus-frequency, amplitude-versus-uv-distance, weight-versus-time, to\nname but a few.\u003c/p\u003e\n\u003cp\u003eSome key features:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe package focuses on powerful selection syntax\u003c/li\u003e\n\u003cli\u003ehas built-in help for all commands\u003c/li\u003e\n\u003cli\u003ethe ability to difference whole sets of plots, e.g. to visualize before-after changes or to\ncompare output of different correlators\u003c/li\u003e\n\u003cli\u003etime- or frequency averaging of the data before plotting\u003c/li\u003e\n\u003cli\u003eplots can be saved to file (postscript).\u003c/li\u003e\n\u003cli\u003eplots/data sets can be organized at will\u003c/li\u003e\n\u003cli\u003ethe data can be indexed (\u003ccode\u003e\u0026gt; indexr\u003c/code\u003e) to create a scan list, after which powerful\nscan-based selection can be used\u003c/li\u003e\n\u003cli\u003eplotting can be scripted/play back stored commands from text file\u003c/li\u003e\n\u003cli\u003eopen/visualize multiple data sets at the same time or the same data set\nfrom different \u0027angles\u0027\u003c/li\u003e\n\u003cli\u003ethe current selection can be written out as a new \u003cem\u003ereference\u003c/em\u003e \u003ccode\u003ems\u003c/code\u003e; data is not copied but the newly created \u003ccode\u003ems\u003c/code\u003e references rows of data in the parent \u003ccode\u003ems\u003c/code\u003e. It can be treated as a real \u003ccode\u003ems\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData selection\u003c/h2\u003e\u003ca id=\"user-content-data-selection\" class=\"anchor\" aria-label=\"Permalink: Data selection\" href=\"#data-selection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ems\u003c/code\u003e\u0027s can contain several GBs of binary data. Therefore, data selection is\ndesirable, preferably in a fairly natural way, even without knowing the\nexact details of the experiment\u0027s data.\u003c/p\u003e\n\u003cp\u003eThe jplotter selection commands take a stab at suiting the needs of a radio\nastronomer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select a time range near the end of the experiment\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003etime\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$end\u003c/span\u003e-1h to +2m20s\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select IF 0,1,2 with parallel hand polarizations\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e fq 0-2/p\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e equivalent, but would not work for XX, YY whereas the former would\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e fq 0-2/rr,ll\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select sources whose name matches this\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e src j(19\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e30)\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select all cross baselines, remove those to stations xx and yy, but add xx-ef\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e bl cross -(xx\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eyy)\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e +xx(ef)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select 80% of the band, irrespective of how many channels\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the correlator produced\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ch 0.1\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003elast:0.9\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003elast\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e after running indexr, extract a bit of data (trimming 1 minute from either\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e end) from scans on sources matching 042* and who are longer than three minutes\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e scan start+1m to end-1m where length\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e3m and field \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e042*\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDependencies\u003c/h1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package uses the \u003ca href=\"https://github.com/casacore/python-casacore\"\u003epyrap, python casacore\u003c/a\u003e\nPython binding to access data.\u003c/p\u003e\n\u003cp\u003eIt uses pgplot to visualize (it was faster and easier than matplotlib):\n\u003ca href=\"https://github.com/haavee/ppgplot\"\u003ePython binding to pgplot\u003c/a\u003e (the github version is preferred over this old link: \u003ca href=\"http://www.jive.eu/~verkout/ppgplot-1.4.tar.gz\" rel=\"nofollow\"\u003ehttp://www.jive.eu/~verkout/ppgplot-1.4.tar.gz\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eThe github version became online during the course of 2018 and has a \u003ccode\u003esetup.py\u003c/code\u003e which has support for Python2 and 3, where the \u003ccode\u003eppgplot-1.4.tar.gz\u003c/code\u003e lacks this.\u003c/p\u003e\n\u003cp\u003eNote: if the original \u003ccode\u003ePGPLOT\u003c/code\u003e is giving too many headaches, the \u003ca href=\"https://github.com/danieljprice/giza\"\u003eGiza\u003c/a\u003e library can be used as drop-in replacement for \u003ccode\u003eppgplot\u003c/code\u003e to link against for its \u003ccode\u003elibpgplot.so\u003c/code\u003e. My \u003ca href=\"https://github.com/haavee/ppgplot\"\u003eppgplot fork\u003c/a\u003e\u0027s \u003ccode\u003esetup.py\u003c/code\u003e has support for having both FORTRAN PGPLOT and Giza installed and allows for compile-time selection of which \u003cem\u003eactual\u003c/em\u003e pgplot backend to use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity and Docker container images\u003c/h1\u003e\u003ca id=\"user-content-singularity-and-docker-container-images\" class=\"anchor\" aria-label=\"Permalink: Singularity and Docker container images\" href=\"#singularity-and-docker-container-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs of 30 October 2018 \u003ca href=\"https://www.sylabs.io/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e and \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e images are available. In fact, the Singularity image just runs the Docker image. The \u003ca href=\"https://hub.docker.com/r/haavee/jiveplot/\" rel=\"nofollow\"\u003ejiveplot Docker image\u003c/a\u003e contains \u003ccode\u003ejiveplot\u003c/code\u003e and all its dependencies and is built on top of the excellent \u003ca href=\"http://kernsuite.info\" rel=\"nofollow\"\u003ekernsuite/kern-4\u003c/a\u003e project.\u003c/p\u003e\n\u003cp\u003eEven though all functionality is in the Docker image, we advise to run/install Singularity (if you have a choice) for the following reasons:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eX11 forwarding works out of the box with Singularity, which is convenient if you wish to actually \u003cem\u003esee\u003c/em\u003e the plots on your screen. According to the interwebs X forwarding can be done through Docker as well but it didn\u0027t for me (see below)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYour \u003ccode\u003e${HOME}\u003c/code\u003e directory is visible by default inside the Singularity container. This has the nice effect that your \u003ccode\u003ejiveplot\u003c/code\u003e command history and aliases are persisted between runs of the image (\u003ccode\u003e~/.jcli.history\u003c/code\u003e for the history). This in turn means that \u003ccode\u003e^r\u003c/code\u003e (reverse-search-history) is actually useful\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eI\u0027m not even going to mention the security issues of Docker which has to run as root\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-running-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Running the Singularity image\" href=\"#running-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eUPDATE\u003c/em\u003e November 2019 - because of \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/2019/security-release/#api-access\" rel=\"nofollow\"\u003eSingularity security\nchanges\u003c/a\u003e\nit is now recommended to use the following method of running the jiveplot\ncontainer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull shub://haavee/jiveplot:latest\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e this will give you a local `path/to/*.simg` file\u003c/span\u003e\n$ singularity run --bind \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003elocal dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econtainer dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e path/to/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;local dir\u0026gt;\u003c/code\u003e is the/a directory on your host where your CASA\nMeasurementSet(s) live and \u003ccode\u003e\u0026lt;container dir\u0026gt;\u003c/code\u003e is the desired mount point\n\u003cem\u003einside\u003c/em\u003e the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the Docker image\u003c/h3\u003e\u003ca id=\"user-content-running-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Running the Docker image\" href=\"#running-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAllegedly, running Docker like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run -it --init --network=host -v /tmp/.X11-unix:/tmp/.X11-unix:ro -e DISPLAY=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$DISPLAY\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -v \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003elocal dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econtainer dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e haavee/jiveplot\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003edoes X11 forwarding but yours truly has seen it also \u003cem\u003enot\u003c/em\u003e work. YMMV.\u003c/p\u003e\n\u003cp\u003eBoth commands should drop you immediately into the \u003ccode\u003ejiveplot\u003c/code\u003e command line interface:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e+++++++++++++++++++++ Welcome to cli +++++++++++++++++++\n\u003cspan class=\"pl-smi\"\u003e$Id\u003c/span\u003e: command.py,v 1.16 2015-11-04 13:30:10 jive_cc Exp $\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexit\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e exits, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elist\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e lists, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehelp\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e helps\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ms \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econtainer dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/path/to/my_data.ms\nMS my_data.ms opened \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003ecet\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 11, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [ - "spectral-element-method", - "gpu-acceleration", - "gpu-computing", - "hpc", - "pde-solver" + "plot", + "python", + "visualization", + "pgplot", + "casa-measurementset", + "singularity-container", + "singularity-image" ], - "updated_at": 1643471883.0 + "updated_at": 1697595575.0 }, { "data_format": 2, - "description": "Scripts to run dask and jupyter lab on Singularity using the pangeo-notebook image", + "description": null, "filenames": [ - "Singularity.pangeo-notebook" + "Singularity" ], - "full_name": "pbranson/pangeo-hpc-singularity", + "full_name": "bjfupoplar/PlantPseudo", "latest_release": null, - "readme": "\u003cp\u003eThis repository provides some boiler plate scripts for running \u0027pangeo\u0027 python ecosystem using singularity containers.\u003c/p\u003e\n\u003cp\u003eSteps are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eObtain docker image curated at \u003ca href=\"https://github.com/pangeo-data/pangeo-stacks\"\u003ehttps://github.com/pangeo-data/pangeo-stacks\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull pangeo/pangeo-notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe pangeo-notebook has a pretty diverse set of libraries for most cloud,\ndask, zarr, netCDF, analysis type tasks.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConvert docker image to singularity with a command such as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity -d build pangeo-latest.sif docker-daemon://pangeo/pangeo-notebook:master\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the created \u003ccode\u003epangeo-latest.sif\u003c/code\u003e singularity image to somewhere accessible on the HPC filesystem.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStart the jupyter lab, the first parameter is the singularity image file, the second is the working path you want to use for jupyter lab:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch start_jupyter.slurm $MYGROUP/../singularity/pangeo-latest.sif $MYGROUP\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis starts a jupyterlab with the compute specifications set in the SBATCH directives at the top of the script. These can be edited in the #SBATCH headers, also note you can set the default directory for jupyterlab with the notebook_dir which is the parameter passed to start_jupyter.slurm.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTake a look at the output printed to the jupyter-#####.out log file. Once jupyter has started it should print a message like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[I 2022-04-08 14:14:43.247 ServerApp] http://z127:8888/lab?token=4698b3901dd7be93cca9d32ae0c94950f4d2e500f7023175\n[I 2022-04-08 14:14:43.247 ServerApp] or http://127.0.0.1:8888/lab?token=4698b3901dd7be93cca9d32ae0c94950f4d2e500f7023175\n[I 2022-04-08 14:14:43.247 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n[C 2022-04-08 14:14:43.261 ServerApp]\n\n To access the server, open this file in a browser:\n file:///group/pawsey0106/pbranson/.local/jupyter/runtime/jpserver-28698-open.html\n Or copy and paste one of these URLs:\n http://z127:8888/lab?token=4698b3901dd7be93cca9d32ae0c94950f4d2e500f7023175 \u0026lt;--- THIS LINE IS IMPORTANT\n or http://127.0.0.1:8888/lab?token=4698b3901dd7be93cca9d32ae0c94950f4d2e500f7023175\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTake note of the second last line in the snippet above. The \"z127\" is the node it is running on, the \"8888\" part is the port, and the bit after token= is the password.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOpen a second terminal on your local computer and start an ssh tunnel through to the jupyter lab running on the compute node using something like this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -N -l your_username -L 8888:z127:8888 zeus.pawsey.org.au\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe important part is the the bit immediately following the \"-L\". The first 8888 is the port on your local computer that is tunnelled via the hpc-login.host.com to node z127 and the second 8888 is the port that jupyter lab is listening on. The second 8888 can change, and port used is what is printed in the the log file described at step 5. You likely will need to adjust this command each time you start a new jupyter lab.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOpen the browser on your computer and enter into the address bar: \u003ccode\u003ehttp://localhost:8888\u003c/code\u003e this should open up the login screen for the jupyter lab and request the token printed to the log file at step 5.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou may wish to use dask, in which case open a terminal \u003cstrong\u003einside\u003c/strong\u003e in jupyter, inside the browser and start a dask scheduler for your session with:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edask-scheduler --scheduler-file $MYSCRATCH/scheduler-$HOSTNAME.json --idle-timeout 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003eYou can then connect to the dask-scheduler from a notebook use the following snippet:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eimport os\nfrom distributed import Client\nclient=Client(scheduler_file=os.environ[\u0027MYSCRATCH\u0027] + \u0027/scheduler-\u0027 + os.environ[\u0027HOSTNAME\u0027] + \u0027.json\u0027)\nclient\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"12\"\u003e\n\u003cli\u003e\n\u003cp\u003eView the scheduler bokeh dashboard using the browser on your computer at \u003ca href=\"http://localhost:8888/proxy/8787/status\" rel=\"nofollow\"\u003ehttp://localhost:8888/proxy/8787/status\u003c/a\u003e. This can also be entered into the Jupyterlab dask widget inside jupyterlab as \u003ccode\u003e/proxy/8787/status\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo start workers, in another terminal inside jupyter lab run the following:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh localhost \"cd $HOME/pangeo-hpc-singularity \u0026amp;\u0026amp; sbatch start_worker.slurm $SINGULARITY_CONTAINER $MYSCRATCH/scheduler-$HOSTNAME.json\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto connect to the host running the jupyter container - this gives you access to the slurm job scheduler and you can submit a script to start workers. The path \u003ccode\u003e$HOME/pangeo-hpc-singularity\u003c/code\u003e will need to be adjusted to where you cloned this repository.\u003c/p\u003e\n\u003cp\u003eFinally the dask worker specifications used in the \u003ccode\u003estart_worker.slurm\u003c/code\u003e script are based of the slurm environment variables, so you can alter the worker specification using the \u003ccode\u003e#SBATCH\u003c/code\u003e directives:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#SBATCH --ntasks=4\n#SBATCH --cpus-per-task=2\n#SBATCH --mem-per-cpu=4G\n#SBATCH --time=0:30:00\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor at the command line when you submit the script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ssh localhost \"cd $HOME/pangeo-hpc-singularity \u0026amp;\u0026amp; sbatch -n 4 -c 4 --mem-per-cpu=16G start_worker.slurm $SINGULARITY_CONTAINER $MYSCRATCH/scheduler-$HOSTNAME.json\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich would start 4 workers with 4 cores per worker and 16x4 = 64GB memory per dask-worker. Once the worker slurm jobs start you should see them appear in the dashboard from step 12.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePlantPseudo\u003c/h2\u003e\u003ca id=\"user-content-plantpseudo\" class=\"anchor\" aria-label=\"Permalink: PlantPseudo\" href=\"#plantpseudo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePseudogenes are important resources in understanding the evolutionary history of genes and genomes.This pseudogene pipeline was used for pseudogene identification in plant species.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage:\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software provides a pipeline for identification pseudogenes in plant species, and it has an advantage in identification whole genome duplication (WGD)-derived pseudogenes,\ntandem duplicated pseudogenes, and helitron-related pseudogenes. It takes the predicted whole duplication blocks from mcscan and then report their close functional paralogs (FPs),\nmath coverage of FPs, math identity, math expect, poly(A) signals, and WGD-derived pseudogenes, tandem duplicated, helitron-related pseudogenes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRequirements:\u003c/h1\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote:\nPlantPseudo currently will only run on linux or cygwin platform, as it is dependent on GNU function.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython (2.7 or later, not 3; \u003ca href=\"https://www.python.org/downloads/\" rel=\"nofollow\"\u003ehttps://www.python.org/downloads/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eperl (v5.16.3 or later; \u003ca href=\"https://www.activestate.com/activeperl/downloads\" rel=\"nofollow\"\u003ehttps://www.activestate.com/activeperl/downloads\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eScripts in the _pipeline_scripts folder\u003c/li\u003e\n\u003cli\u003etfasty (part of the FASTA package; ftp://ftp.ebi.ac.uk/pub/software/unix/fasta/fasta3/)\u003c/li\u003e\n\u003cli\u003eblast(version 2.2.25)\u003c/li\u003e\n\u003cli\u003eMCSCANX (git clone \u003ca href=\"https://github.com/wyp1125/MCScanX.git\"\u003ehttps://github.com/wyp1125/MCScanX.git\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e$ Simply put MCscanX.zip into a directory and run:\u003c/li\u003e\n\u003cli\u003e$ unzip MCscanx.zip\u003c/li\u003e\n\u003cli\u003e$ cd MCScanx\u003c/li\u003e\n\u003cli\u003e$ make\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eexeronate (git clone \u003ca href=\"https://github.com/nathanweeks/exonerate.git\"\u003ehttps://github.com/nathanweeks/exonerate.git\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e$ git clone \u003ca href=\"https://github.com/nathanweeks/exonerate.git\"\u003ehttps://github.com/nathanweeks/exonerate.git\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e$ cd exonerate\u003c/li\u003e\n\u003cli\u003e$ git checkout v2.4.0\u003c/li\u003e\n\u003cli\u003e$ ./configure [YOUR_CONFIGURE_OPTIONS]\u003c/li\u003e\n\u003cli\u003e$ make\u003c/li\u003e\n\u003cli\u003e$ make check\u003c/li\u003e\n\u003cli\u003e$ make install\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eblast (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.2.25/)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation:\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation:\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePut PlantPseudo.tar.gz in any directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e$ tar zxf PlantPseudo.tar.gz or unzip PlantPseudo.zip\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e$ cd PlantPseudo/sample.data/\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e$ unzip *.zip\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecd PlantPseudo/bin/\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInput data\u003c/h1\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-label=\"Permalink: Input data\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou may create a separate folder within the input_data (result_data) for each species. There need to be three files for each species genomic input data\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erawFa: contains a file named genome.fa, which is the unmaksed genome for each species.\u003c/li\u003e\n\u003cli\u003erepeatMaskedFa: contains a file named genome_rm.fa which is entire repeat masked genome dna sequence from that species in FASTA format;\u003c/li\u003e\n\u003cli\u003epep: contains a FASTA file for all the proteins in the species;\u003c/li\u003e\n\u003cli\u003egff: The GFF (General Feature Format) format consists of one line per feature, each containing 9 columns of data, plus optional track definition lines. The following documentation is based on the Version 3 specifications.\u003c/li\u003e\n\u003cli\u003erepeatMaskedGff: if provided, the pipeline will identifty helitron-associated pseudogenes. (The file is the output of RepeatMasker, which is a gff3 format )\u003c/li\u003e\n\u003cli\u003elncrna: The lncrna position, if provided, the pipeline will identify the distance between pseudogenes/genes and lncRNAs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun the pipeline:\u003c/h1\u003e\u003ca id=\"user-content-run-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Run the pipeline:\" href=\"#run-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst go to the folder PlantPseudo/bin, and run with command line in the form of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eperl pipeline.pl --scriptDir [script dir] --gff [gff file] --pep [input pep] --lnrna [lnrna file] --rawFa [rawFa] --repeatMaskedFa [repeatMaskedFa] --fasta34Dir [fasta34 dir] --MCSDir [MCScanX dir] --repeatMaskedGff (optional) --outDir [result dir]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExamples using the sample.data is as follow:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eperl pipeline.pl --scriptDir ../script --gff ../sample.data/genome.gff3 --pep ../sample.data/sample.pep --lncrna lncrna.gff --rawFa ../sample.data/raw.fa --repeatMaskedFa ../sample.data/repmasked.fa --eValueE 5 --idenThresh 20 --lenThresh 30 --proThresh 0.05 --qs 1 --mLenPse 50 --mLenIntron 50 --dirfile pathfile.txt --repeatMaskedGff ../sample.data/Ptrichocarpa.chr.fa.out --outDir ../result\u003c/li\u003e\n\u003cli\u003eperl pipeline.pl --scriptDir ../script --gff ../sample.data/genome.gff3 --pep ../sample.pep --lncrna lncrna.gff --rawFa ../sample.data/raw.fa --repeatMaskedFa ../sample.data/repmasked.fa --eValueE 5 --idenThresh 20 --lenThresh 30 --proThresh 0.05 --qs 1 --mLenPse 50 --mLenIntron 50 --dirfile pathfile.txt --outDir ../result\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOutput:\u003c/h1\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eresult1: final.pg.xl (the result pseudogene table)\u003c/li\u003e\n\u003cli\u003eresult2: Pg.Pseudo.distance.xls (The distance betwen pseudogene and lncRNAs)\u003c/li\u003e\n\u003cli\u003eresult3: Gene.Pseudo.distance.xls (The distance betwen gene and lncRNAs)\u003c/li\u003e\n\u003cli\u003eresult4: Gene.Classifcation.xls (The classfication of lncRNAs according to the postion which closer to genes)\u003c/li\u003e\n\u003cli\u003eresult5: Pg.Classfication.xls (The classfication of lncRNAs according to the postion which closer to pseudogenes)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe output can be found at result/final.pg.xls, given the above command line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWorkflow description\u003c/h1\u003e\u003ca id=\"user-content-workflow-description\" class=\"anchor\" aria-label=\"Permalink: Workflow description\" href=\"#workflow-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003estep1\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Gff2Genepos.py\u003c/li\u003e\n\u003cli\u003edescription: Extract gene position information from gff3 file\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome start end strand gene\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003estep2\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: fa-mask.py\u003c/li\u003e\n\u003cli\u003edescription: masked genic regions\u003c/li\u003e\n\u003cli\u003eoutput: Repeatmasked- and genic-Masked genome sequence\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003estep3\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: exonerate\u003c/li\u003e\n\u003cli\u003edescription: align the protein sequences to the masked genome\u003c/li\u003e\n\u003cli\u003eoutput table\uff1aChromosome\tprograme\tgene_partion\tstart\tend\tlength\tstrand\t.\tgene\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003estep4\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: ExtractExonerateOut.py\u003c/li\u003e\n\u003cli\u003edescription: extract the best alignment result\u003c/li\u003e\n\u003cli\u003eoutput table: Query id\tSubject id\t% identity\talignment length\tmismatches\tgap openings\tq. start\tq. end\ts. start\ts. end\te-value\tbit score\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003estep5\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: ParseBlast.py\u003c/li\u003e\n\u003cli\u003edescription: Filter the alignment result using parameter -E Evalue -I (identity) -L (match length) -P (length) -Q 1 (protein or subject for depth )\u003c/li\u003e\n\u003cli\u003eoutput table: Query id\tSubject id\t% identity\talignment length\tmismatches\tgap openings\tq. start\tq. end\ts. start\ts. end\te-value\tbit score\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003estep6\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pseudo_step1.py\u003c/li\u003e\n\u003cli\u003edescription: Consolidate multiple matches between the same intergenic seq-query protein pairs.\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome [genome:start,en] [protein;start,end] [E value] strand gene\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003estep7\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pseudo_step2.py\u003c/li\u003e\n\u003cli\u003edescription: Combine matches with different proteins at once to construct pseudoexons.\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome gene [genome:start,end] [protein;start,end]\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003estep8\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pseudo_step3.py\u003c/li\u003e\n\u003cli\u003edescription: get the coordinates of pseudogenes on the subject sequences\u003c/li\u003e\n\u003cli\u003eoutput table: output table: Gene Chromosome|start-end\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003estep9\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: FastaManager.py\u003c/li\u003e\n\u003cli\u003edescription: Extract Pseudoexon regions\u003c/li\u003e\n\u003cli\u003eoutput: Pseudoexon sequences\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"10\"\u003e\n\u003cli\u003estep10\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: BlastUtilityv2.py\u003c/li\u003e\n\u003cli\u003edescription: Perform realignment using tfasty software\u003c/li\u003e\n\u003cli\u003eoutput: tfasty output\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003estep11\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pseudo_step4.py\u003c/li\u003e\n\u003cli\u003edescription: Extract tfasty output infromation\u003c/li\u003e\n\u003cli\u003eoutput:\u003c/li\u003e\n\u003cli\u003eGene Chromosome|start-end\u003c/li\u003e\n\u003cli\u003eGene_length Genome_subject_length identity% E_value Smith-Waterman_score\tSmith-Waterman_%identity\tSmith-Waterman_simlarity\talignment_start_end\u003c/li\u003e\n\u003cli\u003eseq1 (Protein sequences)\u003c/li\u003e\n\u003cli\u003eseq2 (Genome sequence)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"12\"\u003e\n\u003cli\u003estep12\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: CheckStrand.py\u003c/li\u003e\n\u003cli\u003edescription: Check the alignment orientation\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome\tstart end\tstrand\tpseudogene\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"13\"\u003e\n\u003cli\u003estep13\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: PolyACheck.py\u003c/li\u003e\n\u003cli\u003edescription: Check if there are any PolyA signal in the downsteam of pseudogene\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome start end strand pseudogene maxCount\tmaxPos\tmaxStr\tsignalPos\tkind\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"14\"\u003e\n\u003cli\u003estep14\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: CheckIntron.py\u003c/li\u003e\n\u003cli\u003edescription: Extract intron information from exonerate\u003c/li\u003e\n\u003cli\u003eoutput table: exonerate output\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"15\"\u003e\n\u003cli\u003estep15\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: SumTablev2.py\u003c/li\u003e\n\u003cli\u003edescription: Combine the previous outputs\u003c/li\u003e\n\u003cli\u003eoutput table: pgId pgChr pgStart pgEnd pgStrand pgpolyA expect ident stop1 stop2 fShift1 fShift2 numofIntrons paln pId\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"16\"\u003e\n\u003cli\u003estep16\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: GetIntronfracv2.py\u003c/li\u003e\n\u003cli\u003edescription: Calculate the match length ratio against the full length protein length\u003c/li\u003e\n\u003cli\u003eoutput table: pgId pgChr pgStart pgEnd pgStrand pgpolyA expect ident stop1 stop2 fShift1 fShift2 numofIntrons intronPos paln pId pChr pStart pEnd pStrand Frac\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"17\"\u003e\n\u003cli\u003estep17\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: PgClassification.py\u003c/li\u003e\n\u003cli\u003edescription: Filter the pseudogene output (The match length ratio \u0026lt;0.05 and the pseudogene length\u0026lt;30 were removed)\u003c/li\u003e\n\u003cli\u003eoutput table: pgId pgChr pgStart pgEnd pgStrand pgpolyA expect ident stop1 stop2 fShift1 fShift2 numofIntrons intronPos paln pId pChr pStart pEnd pStrand Frac\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"18\"\u003e\n\u003cli\u003estep18\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pggff.py,mcscanformatv2.py,Mcscan2Pglstv2.py\u003c/li\u003e\n\u003cli\u003edescription: Prepare for the input for MCscanX.\u003c/li\u003e\n\u003cli\u003eoutput: WGD-derived pseudogene list is generated.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"19\"\u003e\n\u003cli\u003estep19\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003esoftware: MCScanX\u003c/li\u003e\n\u003cli\u003edescription: The WGD-derived pseudogenes were detected using MCScanX.\u003c/li\u003e\n\u003cli\u003eoutput: MCScanX output.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"20\"\u003e\n\u003cli\u003estep20\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: FinalPglst.py\u003c/li\u003e\n\u003cli\u003edescription: The type of pseudogene is added to the last column.\u003c/li\u003e\n\u003cli\u003eoutput table: pgId pgChr pgStart pgEnd pgStrand pgpolyA expect ident stop1 stop2 fShift1 fShift2 numofIntrons intronPos paln pId pChr pStart pEnd pStrand Frac\tDupType\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"21\"\u003e\n\u003cli\u003estep21\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: DistanceComparev5.1.py\u003c/li\u003e\n\u003cli\u003edescription: The distance between Genes/Pseudogenes and lncRNAs\u003c/li\u003e\n\u003cli\u003eoutput table: type distance lncRChr lncRstart lncRend Chr start end\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline\u003c/h1\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-label=\"Permalink: Pipeline\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline consisted of five major steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eidentify intergenic regions (masked genic and transposon regions) with sequence similarity to known proteins using exonerate; RM-masked genomes were used to mask the genic regions (annotated transcription unit in the genome annotation) and generate a file of intergenic regions. If repeatmasked genome sequence has beeen provided, the following steps of \u03a8s identification focused on intergenic nonTE regions, and if not, the following steps could identify helitron-related pseudogenes. This step is to identify all the regions in the genome that share sequence similarity with any known protein, using exonerate (Slater and Birney, 2005) with parameters --model protein2genome --showquerygff no --showtargetgff yes --maxintron 5000 --showvulgar yes --ryo \"%ti\\t%qi\\t%tS\\t%qS\\t%tl\\t%ql\\t%tab\\t%tae\\t%tal\\t%qab\\t%qae\\t%qal\\t%pi\\n\".\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003econduct quality control, identity \u0026gt;= 20%, match length \u0026gt;= 30 aa, match length \u0026gt;= 5% of the query sequence, and only the best match is retained; In addition to the filters already included in the PseudoPipe (overlap \u0026gt;= 30 bp between a hit and a functional gene), we did not accept alignments with E-value \u0026gt;1e-5, identity \u0026lt; 20%, match length \u0026lt; 30 aa, match length (proportion aligned) \u0026lt; 5%. Then the best match of alignment hits was selected in places where a given chromosomal segment has multiple hits.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003elink homologous segments into contigs (set I \u03a8s); The third step is to link pseudogene contigs based on the distance between the hits on the chromosome (Gc) and the distance on the query protein (Gq). In our workflow, these gaps Gc can arise from low complexity or very decayed regions of the pseudogene that are discarded by exonerate. We set this distance to 50 bp.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erealign using tfasty to identify features that disrupt contiguous protein sequences; The set I \u03a8s is realigned using a more accurate alignment program, tfasty34, with parameters \u201c-A -m 3 \u2013q\u201d. Accurate sequence similarity and annotate positions of disablements (frame shifts and stop codons), as well as insertions and deletions were generated in this step.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edistinguish WGD-derived \u03a8s and set II \u03a8s. In this step, WGD-derived pseudogenes were detected using MCScanX (Wang et al., 2012) based on the DAGchainer algorithm (Haas et al., 2004) with parameters -k 50 -g -1 -s 5 -m 25, and blocks with minimum of 5 gene pairs were selected. We used protein pairs from each organism with a BLASTP E-value of less than 1e-5 and \u03a8-FP pairs as the input data when running MCScanX. Pairs of \u03a8-FP in the syntenic block were considered WGD derived.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe directory tree\u003c/h1\u003e\u003ca id=\"user-content-the-directory-tree\" class=\"anchor\" aria-label=\"Permalink: The directory tree\" href=\"#the-directory-tree\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002--------|bin\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002--------|sample.data\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|genome.gff3\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|raw.fa\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Repeatmasked.gff3\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|repmasked.fa\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|sample.pep\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002--------|script\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|AlignPosition.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|BlastUtility.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|BlastUtilityv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|blosum50.matrix\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|CheckIntron.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|CheckStrand.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|DistributionGene.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|DistanceComparev5.1.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|ExtractExonerateOut.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|fa-mask.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|FastaManager.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|FileUtility.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|FinalPglsthelit.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|FinalPglst.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|GetIntronfrac_0.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|GetIntronfracv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Gff2Genepos.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|intersetoutput.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Mcscan2Pglstv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|mcscanformatv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Overlap2Helilst.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|overlapRegion.pl\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|ParseBlast.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|PgClassificationv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pggff.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pggffv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|PolyACheck.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pseudo_step1.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pseudo_step2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pseudo_step3.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pseudo_step4.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Repeat2Region.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|SingleLinkage.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|SumTablev2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Sumpgv1.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Sumgenev1.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Translation.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002-------|software\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|blast-2.2.25\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|exonerate-2.2.0-x86_64\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|fasta34\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|MCScanX\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002-------|README.md\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002-------|workflow.sh\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 11, - "subscribers_count": 2, + "subscribers_count": 1, "topics": [], - "updated_at": 1720498338.0 + "updated_at": 1690878613.0 }, { "data_format": 2, @@ -37552,174 +37483,136 @@ var data = }, { "data_format": 2, - "description": null, + "description": "Scripts to run dask and jupyter lab on Singularity using the pangeo-notebook image", "filenames": [ - "Singularity" + "Singularity.pangeo-notebook" ], - "full_name": "bjfupoplar/PlantPseudo", + "full_name": "pbranson/pangeo-hpc-singularity", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePlantPseudo\u003c/h2\u003e\u003ca id=\"user-content-plantpseudo\" class=\"anchor\" aria-label=\"Permalink: PlantPseudo\" href=\"#plantpseudo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePseudogenes are important resources in understanding the evolutionary history of genes and genomes.This pseudogene pipeline was used for pseudogene identification in plant species.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage:\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software provides a pipeline for identification pseudogenes in plant species, and it has an advantage in identification whole genome duplication (WGD)-derived pseudogenes,\ntandem duplicated pseudogenes, and helitron-related pseudogenes. It takes the predicted whole duplication blocks from mcscan and then report their close functional paralogs (FPs),\nmath coverage of FPs, math identity, math expect, poly(A) signals, and WGD-derived pseudogenes, tandem duplicated, helitron-related pseudogenes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRequirements:\u003c/h1\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote:\nPlantPseudo currently will only run on linux or cygwin platform, as it is dependent on GNU function.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython (2.7 or later, not 3; \u003ca href=\"https://www.python.org/downloads/\" rel=\"nofollow\"\u003ehttps://www.python.org/downloads/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eperl (v5.16.3 or later; \u003ca href=\"https://www.activestate.com/activeperl/downloads\" rel=\"nofollow\"\u003ehttps://www.activestate.com/activeperl/downloads\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eScripts in the _pipeline_scripts folder\u003c/li\u003e\n\u003cli\u003etfasty (part of the FASTA package; ftp://ftp.ebi.ac.uk/pub/software/unix/fasta/fasta3/)\u003c/li\u003e\n\u003cli\u003eblast(version 2.2.25)\u003c/li\u003e\n\u003cli\u003eMCSCANX (git clone \u003ca href=\"https://github.com/wyp1125/MCScanX.git\"\u003ehttps://github.com/wyp1125/MCScanX.git\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e$ Simply put MCscanX.zip into a directory and run:\u003c/li\u003e\n\u003cli\u003e$ unzip MCscanx.zip\u003c/li\u003e\n\u003cli\u003e$ cd MCScanx\u003c/li\u003e\n\u003cli\u003e$ make\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eexeronate (git clone \u003ca href=\"https://github.com/nathanweeks/exonerate.git\"\u003ehttps://github.com/nathanweeks/exonerate.git\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n\u003cli\u003e$ git clone \u003ca href=\"https://github.com/nathanweeks/exonerate.git\"\u003ehttps://github.com/nathanweeks/exonerate.git\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e$ cd exonerate\u003c/li\u003e\n\u003cli\u003e$ git checkout v2.4.0\u003c/li\u003e\n\u003cli\u003e$ ./configure [YOUR_CONFIGURE_OPTIONS]\u003c/li\u003e\n\u003cli\u003e$ make\u003c/li\u003e\n\u003cli\u003e$ make check\u003c/li\u003e\n\u003cli\u003e$ make install\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eblast (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.2.25/)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation:\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation:\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePut PlantPseudo.tar.gz in any directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e$ tar zxf PlantPseudo.tar.gz or unzip PlantPseudo.zip\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e$ cd PlantPseudo/sample.data/\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e$ unzip *.zip\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecd PlantPseudo/bin/\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInput data\u003c/h1\u003e\u003ca id=\"user-content-input-data\" class=\"anchor\" aria-label=\"Permalink: Input data\" href=\"#input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou may create a separate folder within the input_data (result_data) for each species. There need to be three files for each species genomic input data\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003erawFa: contains a file named genome.fa, which is the unmaksed genome for each species.\u003c/li\u003e\n\u003cli\u003erepeatMaskedFa: contains a file named genome_rm.fa which is entire repeat masked genome dna sequence from that species in FASTA format;\u003c/li\u003e\n\u003cli\u003epep: contains a FASTA file for all the proteins in the species;\u003c/li\u003e\n\u003cli\u003egff: The GFF (General Feature Format) format consists of one line per feature, each containing 9 columns of data, plus optional track definition lines. The following documentation is based on the Version 3 specifications.\u003c/li\u003e\n\u003cli\u003erepeatMaskedGff: if provided, the pipeline will identifty helitron-associated pseudogenes. (The file is the output of RepeatMasker, which is a gff3 format )\u003c/li\u003e\n\u003cli\u003elncrna: The lncrna position, if provided, the pipeline will identify the distance between pseudogenes/genes and lncRNAs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun the pipeline:\u003c/h1\u003e\u003ca id=\"user-content-run-the-pipeline\" class=\"anchor\" aria-label=\"Permalink: Run the pipeline:\" href=\"#run-the-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst go to the folder PlantPseudo/bin, and run with command line in the form of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eperl pipeline.pl --scriptDir [script dir] --gff [gff file] --pep [input pep] --lnrna [lnrna file] --rawFa [rawFa] --repeatMaskedFa [repeatMaskedFa] --fasta34Dir [fasta34 dir] --MCSDir [MCScanX dir] --repeatMaskedGff (optional) --outDir [result dir]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExamples using the sample.data is as follow:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eperl pipeline.pl --scriptDir ../script --gff ../sample.data/genome.gff3 --pep ../sample.data/sample.pep --lncrna lncrna.gff --rawFa ../sample.data/raw.fa --repeatMaskedFa ../sample.data/repmasked.fa --eValueE 5 --idenThresh 20 --lenThresh 30 --proThresh 0.05 --qs 1 --mLenPse 50 --mLenIntron 50 --dirfile pathfile.txt --repeatMaskedGff ../sample.data/Ptrichocarpa.chr.fa.out --outDir ../result\u003c/li\u003e\n\u003cli\u003eperl pipeline.pl --scriptDir ../script --gff ../sample.data/genome.gff3 --pep ../sample.pep --lncrna lncrna.gff --rawFa ../sample.data/raw.fa --repeatMaskedFa ../sample.data/repmasked.fa --eValueE 5 --idenThresh 20 --lenThresh 30 --proThresh 0.05 --qs 1 --mLenPse 50 --mLenIntron 50 --dirfile pathfile.txt --outDir ../result\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOutput:\u003c/h1\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eresult1: final.pg.xl (the result pseudogene table)\u003c/li\u003e\n\u003cli\u003eresult2: Pg.Pseudo.distance.xls (The distance betwen pseudogene and lncRNAs)\u003c/li\u003e\n\u003cli\u003eresult3: Gene.Pseudo.distance.xls (The distance betwen gene and lncRNAs)\u003c/li\u003e\n\u003cli\u003eresult4: Gene.Classifcation.xls (The classfication of lncRNAs according to the postion which closer to genes)\u003c/li\u003e\n\u003cli\u003eresult5: Pg.Classfication.xls (The classfication of lncRNAs according to the postion which closer to pseudogenes)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe output can be found at result/final.pg.xls, given the above command line.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWorkflow description\u003c/h1\u003e\u003ca id=\"user-content-workflow-description\" class=\"anchor\" aria-label=\"Permalink: Workflow description\" href=\"#workflow-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003estep1\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Gff2Genepos.py\u003c/li\u003e\n\u003cli\u003edescription: Extract gene position information from gff3 file\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome start end strand gene\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003estep2\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: fa-mask.py\u003c/li\u003e\n\u003cli\u003edescription: masked genic regions\u003c/li\u003e\n\u003cli\u003eoutput: Repeatmasked- and genic-Masked genome sequence\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003estep3\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: exonerate\u003c/li\u003e\n\u003cli\u003edescription: align the protein sequences to the masked genome\u003c/li\u003e\n\u003cli\u003eoutput table\uff1aChromosome\tprograme\tgene_partion\tstart\tend\tlength\tstrand\t.\tgene\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003estep4\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: ExtractExonerateOut.py\u003c/li\u003e\n\u003cli\u003edescription: extract the best alignment result\u003c/li\u003e\n\u003cli\u003eoutput table: Query id\tSubject id\t% identity\talignment length\tmismatches\tgap openings\tq. start\tq. end\ts. start\ts. end\te-value\tbit score\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003estep5\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: ParseBlast.py\u003c/li\u003e\n\u003cli\u003edescription: Filter the alignment result using parameter -E Evalue -I (identity) -L (match length) -P (length) -Q 1 (protein or subject for depth )\u003c/li\u003e\n\u003cli\u003eoutput table: Query id\tSubject id\t% identity\talignment length\tmismatches\tgap openings\tq. start\tq. end\ts. start\ts. end\te-value\tbit score\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003estep6\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pseudo_step1.py\u003c/li\u003e\n\u003cli\u003edescription: Consolidate multiple matches between the same intergenic seq-query protein pairs.\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome [genome:start,en] [protein;start,end] [E value] strand gene\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003estep7\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pseudo_step2.py\u003c/li\u003e\n\u003cli\u003edescription: Combine matches with different proteins at once to construct pseudoexons.\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome gene [genome:start,end] [protein;start,end]\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003estep8\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pseudo_step3.py\u003c/li\u003e\n\u003cli\u003edescription: get the coordinates of pseudogenes on the subject sequences\u003c/li\u003e\n\u003cli\u003eoutput table: output table: Gene Chromosome|start-end\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003estep9\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: FastaManager.py\u003c/li\u003e\n\u003cli\u003edescription: Extract Pseudoexon regions\u003c/li\u003e\n\u003cli\u003eoutput: Pseudoexon sequences\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"10\"\u003e\n\u003cli\u003estep10\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: BlastUtilityv2.py\u003c/li\u003e\n\u003cli\u003edescription: Perform realignment using tfasty software\u003c/li\u003e\n\u003cli\u003eoutput: tfasty output\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003estep11\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pseudo_step4.py\u003c/li\u003e\n\u003cli\u003edescription: Extract tfasty output infromation\u003c/li\u003e\n\u003cli\u003eoutput:\u003c/li\u003e\n\u003cli\u003eGene Chromosome|start-end\u003c/li\u003e\n\u003cli\u003eGene_length Genome_subject_length identity% E_value Smith-Waterman_score\tSmith-Waterman_%identity\tSmith-Waterman_simlarity\talignment_start_end\u003c/li\u003e\n\u003cli\u003eseq1 (Protein sequences)\u003c/li\u003e\n\u003cli\u003eseq2 (Genome sequence)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"12\"\u003e\n\u003cli\u003estep12\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: CheckStrand.py\u003c/li\u003e\n\u003cli\u003edescription: Check the alignment orientation\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome\tstart end\tstrand\tpseudogene\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"13\"\u003e\n\u003cli\u003estep13\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: PolyACheck.py\u003c/li\u003e\n\u003cli\u003edescription: Check if there are any PolyA signal in the downsteam of pseudogene\u003c/li\u003e\n\u003cli\u003eoutput table: Chromosome start end strand pseudogene maxCount\tmaxPos\tmaxStr\tsignalPos\tkind\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"14\"\u003e\n\u003cli\u003estep14\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: CheckIntron.py\u003c/li\u003e\n\u003cli\u003edescription: Extract intron information from exonerate\u003c/li\u003e\n\u003cli\u003eoutput table: exonerate output\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"15\"\u003e\n\u003cli\u003estep15\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: SumTablev2.py\u003c/li\u003e\n\u003cli\u003edescription: Combine the previous outputs\u003c/li\u003e\n\u003cli\u003eoutput table: pgId pgChr pgStart pgEnd pgStrand pgpolyA expect ident stop1 stop2 fShift1 fShift2 numofIntrons paln pId\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"16\"\u003e\n\u003cli\u003estep16\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: GetIntronfracv2.py\u003c/li\u003e\n\u003cli\u003edescription: Calculate the match length ratio against the full length protein length\u003c/li\u003e\n\u003cli\u003eoutput table: pgId pgChr pgStart pgEnd pgStrand pgpolyA expect ident stop1 stop2 fShift1 fShift2 numofIntrons intronPos paln pId pChr pStart pEnd pStrand Frac\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"17\"\u003e\n\u003cli\u003estep17\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: PgClassification.py\u003c/li\u003e\n\u003cli\u003edescription: Filter the pseudogene output (The match length ratio \u0026lt;0.05 and the pseudogene length\u0026lt;30 were removed)\u003c/li\u003e\n\u003cli\u003eoutput table: pgId pgChr pgStart pgEnd pgStrand pgpolyA expect ident stop1 stop2 fShift1 fShift2 numofIntrons intronPos paln pId pChr pStart pEnd pStrand Frac\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"18\"\u003e\n\u003cli\u003estep18\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: Pggff.py,mcscanformatv2.py,Mcscan2Pglstv2.py\u003c/li\u003e\n\u003cli\u003edescription: Prepare for the input for MCscanX.\u003c/li\u003e\n\u003cli\u003eoutput: WGD-derived pseudogene list is generated.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"19\"\u003e\n\u003cli\u003estep19\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003esoftware: MCScanX\u003c/li\u003e\n\u003cli\u003edescription: The WGD-derived pseudogenes were detected using MCScanX.\u003c/li\u003e\n\u003cli\u003eoutput: MCScanX output.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"20\"\u003e\n\u003cli\u003estep20\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: FinalPglst.py\u003c/li\u003e\n\u003cli\u003edescription: The type of pseudogene is added to the last column.\u003c/li\u003e\n\u003cli\u003eoutput table: pgId pgChr pgStart pgEnd pgStrand pgpolyA expect ident stop1 stop2 fShift1 fShift2 numofIntrons intronPos paln pId pChr pStart pEnd pStrand Frac\tDupType\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"21\"\u003e\n\u003cli\u003estep21\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003escript: DistanceComparev5.1.py\u003c/li\u003e\n\u003cli\u003edescription: The distance between Genes/Pseudogenes and lncRNAs\u003c/li\u003e\n\u003cli\u003eoutput table: type distance lncRChr lncRstart lncRend Chr start end\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeline\u003c/h1\u003e\u003ca id=\"user-content-pipeline\" class=\"anchor\" aria-label=\"Permalink: Pipeline\" href=\"#pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline consisted of five major steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eidentify intergenic regions (masked genic and transposon regions) with sequence similarity to known proteins using exonerate; RM-masked genomes were used to mask the genic regions (annotated transcription unit in the genome annotation) and generate a file of intergenic regions. If repeatmasked genome sequence has beeen provided, the following steps of \u03a8s identification focused on intergenic nonTE regions, and if not, the following steps could identify helitron-related pseudogenes. This step is to identify all the regions in the genome that share sequence similarity with any known protein, using exonerate (Slater and Birney, 2005) with parameters --model protein2genome --showquerygff no --showtargetgff yes --maxintron 5000 --showvulgar yes --ryo \"%ti\\t%qi\\t%tS\\t%qS\\t%tl\\t%ql\\t%tab\\t%tae\\t%tal\\t%qab\\t%qae\\t%qal\\t%pi\\n\".\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003econduct quality control, identity \u0026gt;= 20%, match length \u0026gt;= 30 aa, match length \u0026gt;= 5% of the query sequence, and only the best match is retained; In addition to the filters already included in the PseudoPipe (overlap \u0026gt;= 30 bp between a hit and a functional gene), we did not accept alignments with E-value \u0026gt;1e-5, identity \u0026lt; 20%, match length \u0026lt; 30 aa, match length (proportion aligned) \u0026lt; 5%. Then the best match of alignment hits was selected in places where a given chromosomal segment has multiple hits.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003elink homologous segments into contigs (set I \u03a8s); The third step is to link pseudogene contigs based on the distance between the hits on the chromosome (Gc) and the distance on the query protein (Gq). In our workflow, these gaps Gc can arise from low complexity or very decayed regions of the pseudogene that are discarded by exonerate. We set this distance to 50 bp.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erealign using tfasty to identify features that disrupt contiguous protein sequences; The set I \u03a8s is realigned using a more accurate alignment program, tfasty34, with parameters \u201c-A -m 3 \u2013q\u201d. Accurate sequence similarity and annotate positions of disablements (frame shifts and stop codons), as well as insertions and deletions were generated in this step.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edistinguish WGD-derived \u03a8s and set II \u03a8s. In this step, WGD-derived pseudogenes were detected using MCScanX (Wang et al., 2012) based on the DAGchainer algorithm (Haas et al., 2004) with parameters -k 50 -g -1 -s 5 -m 25, and blocks with minimum of 5 gene pairs were selected. We used protein pairs from each organism with a BLASTP E-value of less than 1e-5 and \u03a8-FP pairs as the input data when running MCScanX. Pairs of \u03a8-FP in the syntenic block were considered WGD derived.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe directory tree\u003c/h1\u003e\u003ca id=\"user-content-the-directory-tree\" class=\"anchor\" aria-label=\"Permalink: The directory tree\" href=\"#the-directory-tree\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002--------|bin\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002--------|sample.data\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|genome.gff3\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|raw.fa\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Repeatmasked.gff3\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|repmasked.fa\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|sample.pep\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002--------|script\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|AlignPosition.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|BlastUtility.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|BlastUtilityv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|blosum50.matrix\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|CheckIntron.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|CheckStrand.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|DistributionGene.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|DistanceComparev5.1.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|ExtractExonerateOut.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|fa-mask.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|FastaManager.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|FileUtility.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|FinalPglsthelit.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|FinalPglst.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|GetIntronfrac_0.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|GetIntronfracv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Gff2Genepos.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|intersetoutput.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Mcscan2Pglstv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|mcscanformatv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Overlap2Helilst.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|overlapRegion.pl\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|ParseBlast.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|PgClassificationv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pggff.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pggffv2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|PolyACheck.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pseudo_step1.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pseudo_step2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pseudo_step3.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Pseudo_step4.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Repeat2Region.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|SingleLinkage.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|SumTablev2.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Sumpgv1.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Sumgenev1.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|Translation.py\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002-------|software\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|blast-2.2.25\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|exonerate-2.2.0-x86_64\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|fasta34\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002----------------|MCScanX\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002-------|README.md\u003c/li\u003e\n\u003cli\u003e\u2002\u2002\u2002\u2002-------|workflow.sh\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003eThis repository provides some boiler plate scripts for running \u0027pangeo\u0027 python ecosystem using singularity containers.\u003c/p\u003e\n\u003cp\u003eSteps are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eObtain docker image curated at \u003ca href=\"https://github.com/pangeo-data/pangeo-stacks\"\u003ehttps://github.com/pangeo-data/pangeo-stacks\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull pangeo/pangeo-notebook\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe pangeo-notebook has a pretty diverse set of libraries for most cloud,\ndask, zarr, netCDF, analysis type tasks.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConvert docker image to singularity with a command such as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity -d build pangeo-latest.sif docker-daemon://pangeo/pangeo-notebook:master\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the created \u003ccode\u003epangeo-latest.sif\u003c/code\u003e singularity image to somewhere accessible on the HPC filesystem.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStart the jupyter lab, the first parameter is the singularity image file, the second is the working path you want to use for jupyter lab:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esbatch start_jupyter.slurm $MYGROUP/../singularity/pangeo-latest.sif $MYGROUP\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis starts a jupyterlab with the compute specifications set in the SBATCH directives at the top of the script. These can be edited in the #SBATCH headers, also note you can set the default directory for jupyterlab with the notebook_dir which is the parameter passed to start_jupyter.slurm.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTake a look at the output printed to the jupyter-#####.out log file. Once jupyter has started it should print a message like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[I 2022-04-08 14:14:43.247 ServerApp] http://z127:8888/lab?token=4698b3901dd7be93cca9d32ae0c94950f4d2e500f7023175\n[I 2022-04-08 14:14:43.247 ServerApp] or http://127.0.0.1:8888/lab?token=4698b3901dd7be93cca9d32ae0c94950f4d2e500f7023175\n[I 2022-04-08 14:14:43.247 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n[C 2022-04-08 14:14:43.261 ServerApp]\n\n To access the server, open this file in a browser:\n file:///group/pawsey0106/pbranson/.local/jupyter/runtime/jpserver-28698-open.html\n Or copy and paste one of these URLs:\n http://z127:8888/lab?token=4698b3901dd7be93cca9d32ae0c94950f4d2e500f7023175 \u0026lt;--- THIS LINE IS IMPORTANT\n or http://127.0.0.1:8888/lab?token=4698b3901dd7be93cca9d32ae0c94950f4d2e500f7023175\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTake note of the second last line in the snippet above. The \"z127\" is the node it is running on, the \"8888\" part is the port, and the bit after token= is the password.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOpen a second terminal on your local computer and start an ssh tunnel through to the jupyter lab running on the compute node using something like this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003essh -N -l your_username -L 8888:z127:8888 zeus.pawsey.org.au\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe important part is the the bit immediately following the \"-L\". The first 8888 is the port on your local computer that is tunnelled via the hpc-login.host.com to node z127 and the second 8888 is the port that jupyter lab is listening on. The second 8888 can change, and port used is what is printed in the the log file described at step 5. You likely will need to adjust this command each time you start a new jupyter lab.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOpen the browser on your computer and enter into the address bar: \u003ccode\u003ehttp://localhost:8888\u003c/code\u003e this should open up the login screen for the jupyter lab and request the token printed to the log file at step 5.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYou may wish to use dask, in which case open a terminal \u003cstrong\u003einside\u003c/strong\u003e in jupyter, inside the browser and start a dask scheduler for your session with:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003edask-scheduler --scheduler-file $MYSCRATCH/scheduler-$HOSTNAME.json --idle-timeout 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003eYou can then connect to the dask-scheduler from a notebook use the following snippet:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eimport os\nfrom distributed import Client\nclient=Client(scheduler_file=os.environ[\u0027MYSCRATCH\u0027] + \u0027/scheduler-\u0027 + os.environ[\u0027HOSTNAME\u0027] + \u0027.json\u0027)\nclient\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"12\"\u003e\n\u003cli\u003e\n\u003cp\u003eView the scheduler bokeh dashboard using the browser on your computer at \u003ca href=\"http://localhost:8888/proxy/8787/status\" rel=\"nofollow\"\u003ehttp://localhost:8888/proxy/8787/status\u003c/a\u003e. This can also be entered into the Jupyterlab dask widget inside jupyterlab as \u003ccode\u003e/proxy/8787/status\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo start workers, in another terminal inside jupyter lab run the following:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003essh localhost \"cd $HOME/pangeo-hpc-singularity \u0026amp;\u0026amp; sbatch start_worker.slurm $SINGULARITY_CONTAINER $MYSCRATCH/scheduler-$HOSTNAME.json\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto connect to the host running the jupyter container - this gives you access to the slurm job scheduler and you can submit a script to start workers. The path \u003ccode\u003e$HOME/pangeo-hpc-singularity\u003c/code\u003e will need to be adjusted to where you cloned this repository.\u003c/p\u003e\n\u003cp\u003eFinally the dask worker specifications used in the \u003ccode\u003estart_worker.slurm\u003c/code\u003e script are based of the slurm environment variables, so you can alter the worker specification using the \u003ccode\u003e#SBATCH\u003c/code\u003e directives:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#SBATCH --ntasks=4\n#SBATCH --cpus-per-task=2\n#SBATCH --mem-per-cpu=4G\n#SBATCH --time=0:30:00\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor at the command line when you submit the script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ssh localhost \"cd $HOME/pangeo-hpc-singularity \u0026amp;\u0026amp; sbatch -n 4 -c 4 --mem-per-cpu=16G start_worker.slurm $SINGULARITY_CONTAINER $MYSCRATCH/scheduler-$HOSTNAME.json\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhich would start 4 workers with 4 cores per worker and 16x4 = 64GB memory per dask-worker. Once the worker slurm jobs start you should see them appear in the dashboard from step 12.\u003c/p\u003e\n", "stargazers_count": 11, - "subscribers_count": 1, + "subscribers_count": 2, "topics": [], - "updated_at": 1690878613.0 + "updated_at": 1720498338.0 }, { "data_format": 2, - "description": "Python based plotting package for CASA MeasurementSet", + "description": "Spectral Element Library in Fortran", "filenames": [ - "Singularity" + "img/Singularity.amd", + "img/Singularity.nvidia" ], - "full_name": "haavee/jiveplot", + "full_name": "FluidNumerics/SELF", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1847\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejplotter / jiveplot\u003c/h1\u003e\u003ca id=\"user-content-jplotter--jiveplot\" class=\"anchor\" aria-label=\"Permalink: jplotter / jiveplot\" href=\"#jplotter--jiveplot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePython based visualization tool for AIPS++/CASA MeasurementSet data\u003c/p\u003e\n\u003cp\u003eThe jplotter command line tool allows the user to quickly visualize the\nradio-astronomical data contained in a MeasurementSet (\u003ccode\u003ems\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython support\u003c/h2\u003e\u003ca id=\"user-content-python-support\" class=\"anchor\" aria-label=\"Permalink: Python support\" href=\"#python-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStarting from 20 Jul 2023 the \u003ccode\u003emaster\u003c/code\u003e branch has been updated to support\nPy2 and Py3 based systems simultaneously. This version is tagged \u003ca href=\"https://github.com/haavee/jiveplot/releases/tag/v2.0\"\u003ev2.0\u003c/a\u003e. The\nprevious latest Py2-only master was tagged as\n\u003ca href=\"https://github.com/haavee/jiveplot/releases/tag/v1.1\"\u003ev1.1\u003c/a\u003e and\n\u003ca href=\"https://github.com/haavee/jiveplot/releases/tag/last-python2-only-master\"\u003elast-python2-only-master\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e5 second workflow\u003c/h2\u003e\u003ca id=\"user-content-5-second-workflow\" class=\"anchor\" aria-label=\"Permalink: 5 second workflow\" href=\"#5-second-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter downloading and having the\n\u003ca href=\"https://github.com/haavee/jiveplot#dependencies\"\u003edependencies\u003c/a\u003e installed\n(as of 30 Oct 2018 you can run from a \u003ca href=\"https://github.com/haavee/jiveplot#singularity-and-docker-container-images\"\u003esingularity or Docker\u003c/a\u003e image) type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ /path/to/jiveplot/jplotter\n+++++++++++++++++++++ Welcome to cli +++++++++++++++++++\n\u003cspan class=\"pl-smi\"\u003e$Id\u003c/span\u003e: command.py,v 1.16 2015-11-04 13:30:10 jive_cc Exp $\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexit\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e exits, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elist\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e lists, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehelp\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e helps\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand you\u0027re in the command line environment. Then open a MS, select data,\nselect what to plot and go.\u003c/p\u003e\n\u003cp\u003eThis README will not explain any further because there is a colourful \u003ca href=\"jplotter-cookbook-draft-v2.pdf\"\u003ePDF cookbook/tutorial/explanation\u003c/a\u003e with far more detail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat can be visualized?\u003c/h2\u003e\u003ca id=\"user-content-what-can-be-visualized\" class=\"anchor\" aria-label=\"Permalink: What can be visualized?\" href=\"#what-can-be-visualized\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eQuantities that can be visualized are, e.g., amplitude-versus-time,\nphase-versus-frequency, amplitude-versus-uv-distance, weight-versus-time, to\nname but a few.\u003c/p\u003e\n\u003cp\u003eSome key features:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe package focuses on powerful selection syntax\u003c/li\u003e\n\u003cli\u003ehas built-in help for all commands\u003c/li\u003e\n\u003cli\u003ethe ability to difference whole sets of plots, e.g. to visualize before-after changes or to\ncompare output of different correlators\u003c/li\u003e\n\u003cli\u003etime- or frequency averaging of the data before plotting\u003c/li\u003e\n\u003cli\u003eplots can be saved to file (postscript).\u003c/li\u003e\n\u003cli\u003eplots/data sets can be organized at will\u003c/li\u003e\n\u003cli\u003ethe data can be indexed (\u003ccode\u003e\u0026gt; indexr\u003c/code\u003e) to create a scan list, after which powerful\nscan-based selection can be used\u003c/li\u003e\n\u003cli\u003eplotting can be scripted/play back stored commands from text file\u003c/li\u003e\n\u003cli\u003eopen/visualize multiple data sets at the same time or the same data set\nfrom different \u0027angles\u0027\u003c/li\u003e\n\u003cli\u003ethe current selection can be written out as a new \u003cem\u003ereference\u003c/em\u003e \u003ccode\u003ems\u003c/code\u003e; data is not copied but the newly created \u003ccode\u003ems\u003c/code\u003e references rows of data in the parent \u003ccode\u003ems\u003c/code\u003e. It can be treated as a real \u003ccode\u003ems\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eData selection\u003c/h2\u003e\u003ca id=\"user-content-data-selection\" class=\"anchor\" aria-label=\"Permalink: Data selection\" href=\"#data-selection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ems\u003c/code\u003e\u0027s can contain several GBs of binary data. Therefore, data selection is\ndesirable, preferably in a fairly natural way, even without knowing the\nexact details of the experiment\u0027s data.\u003c/p\u003e\n\u003cp\u003eThe jplotter selection commands take a stab at suiting the needs of a radio\nastronomer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select a time range near the end of the experiment\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003etime\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$end\u003c/span\u003e-1h to +2m20s\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select IF 0,1,2 with parallel hand polarizations\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e fq 0-2/p\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e equivalent, but would not work for XX, YY whereas the former would\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e fq 0-2/rr,ll\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select sources whose name matches this\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e src j(19\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e30)\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select all cross baselines, remove those to stations xx and yy, but add xx-ef\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e bl cross -(xx\u003cspan class=\"pl-k\"\u003e|\u003c/span\u003eyy)\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e +xx(ef)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e select 80% of the band, irrespective of how many channels\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the correlator produced\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ch 0.1\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003elast:0.9\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003elast\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e after running indexr, extract a bit of data (trimming 1 minute from either\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e end) from scans on sources matching 042* and who are longer than three minutes\u003c/span\u003e\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e scan start+1m to end-1m where length\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e3m and field \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e042*\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDependencies\u003c/h1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package uses the \u003ca href=\"https://github.com/casacore/python-casacore\"\u003epyrap, python casacore\u003c/a\u003e\nPython binding to access data.\u003c/p\u003e\n\u003cp\u003eIt uses pgplot to visualize (it was faster and easier than matplotlib):\n\u003ca href=\"https://github.com/haavee/ppgplot\"\u003ePython binding to pgplot\u003c/a\u003e (the github version is preferred over this old link: \u003ca href=\"http://www.jive.eu/~verkout/ppgplot-1.4.tar.gz\" rel=\"nofollow\"\u003ehttp://www.jive.eu/~verkout/ppgplot-1.4.tar.gz\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eThe github version became online during the course of 2018 and has a \u003ccode\u003esetup.py\u003c/code\u003e which has support for Python2 and 3, where the \u003ccode\u003eppgplot-1.4.tar.gz\u003c/code\u003e lacks this.\u003c/p\u003e\n\u003cp\u003eNote: if the original \u003ccode\u003ePGPLOT\u003c/code\u003e is giving too many headaches, the \u003ca href=\"https://github.com/danieljprice/giza\"\u003eGiza\u003c/a\u003e library can be used as drop-in replacement for \u003ccode\u003eppgplot\u003c/code\u003e to link against for its \u003ccode\u003elibpgplot.so\u003c/code\u003e. My \u003ca href=\"https://github.com/haavee/ppgplot\"\u003eppgplot fork\u003c/a\u003e\u0027s \u003ccode\u003esetup.py\u003c/code\u003e has support for having both FORTRAN PGPLOT and Giza installed and allows for compile-time selection of which \u003cem\u003eactual\u003c/em\u003e pgplot backend to use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity and Docker container images\u003c/h1\u003e\u003ca id=\"user-content-singularity-and-docker-container-images\" class=\"anchor\" aria-label=\"Permalink: Singularity and Docker container images\" href=\"#singularity-and-docker-container-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs of 30 October 2018 \u003ca href=\"https://www.sylabs.io/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e and \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e images are available. In fact, the Singularity image just runs the Docker image. The \u003ca href=\"https://hub.docker.com/r/haavee/jiveplot/\" rel=\"nofollow\"\u003ejiveplot Docker image\u003c/a\u003e contains \u003ccode\u003ejiveplot\u003c/code\u003e and all its dependencies and is built on top of the excellent \u003ca href=\"http://kernsuite.info\" rel=\"nofollow\"\u003ekernsuite/kern-4\u003c/a\u003e project.\u003c/p\u003e\n\u003cp\u003eEven though all functionality is in the Docker image, we advise to run/install Singularity (if you have a choice) for the following reasons:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eX11 forwarding works out of the box with Singularity, which is convenient if you wish to actually \u003cem\u003esee\u003c/em\u003e the plots on your screen. According to the interwebs X forwarding can be done through Docker as well but it didn\u0027t for me (see below)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYour \u003ccode\u003e${HOME}\u003c/code\u003e directory is visible by default inside the Singularity container. This has the nice effect that your \u003ccode\u003ejiveplot\u003c/code\u003e command history and aliases are persisted between runs of the image (\u003ccode\u003e~/.jcli.history\u003c/code\u003e for the history). This in turn means that \u003ccode\u003e^r\u003c/code\u003e (reverse-search-history) is actually useful\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eI\u0027m not even going to mention the security issues of Docker which has to run as root\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the Singularity image\u003c/h3\u003e\u003ca id=\"user-content-running-the-singularity-image\" class=\"anchor\" aria-label=\"Permalink: Running the Singularity image\" href=\"#running-the-singularity-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eUPDATE\u003c/em\u003e November 2019 - because of \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/2019/security-release/#api-access\" rel=\"nofollow\"\u003eSingularity security\nchanges\u003c/a\u003e\nit is now recommended to use the following method of running the jiveplot\ncontainer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull shub://haavee/jiveplot:latest\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e this will give you a local `path/to/*.simg` file\u003c/span\u003e\n$ singularity run --bind \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003elocal dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econtainer dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e path/to/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.simg\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003e\u0026lt;local dir\u0026gt;\u003c/code\u003e is the/a directory on your host where your CASA\nMeasurementSet(s) live and \u003ccode\u003e\u0026lt;container dir\u0026gt;\u003c/code\u003e is the desired mount point\n\u003cem\u003einside\u003c/em\u003e the container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the Docker image\u003c/h3\u003e\u003ca id=\"user-content-running-the-docker-image\" class=\"anchor\" aria-label=\"Permalink: Running the Docker image\" href=\"#running-the-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAllegedly, running Docker like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run -it --init --network=host -v /tmp/.X11-unix:/tmp/.X11-unix:ro -e DISPLAY=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$DISPLAY\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -v \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003elocal dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econtainer dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e haavee/jiveplot\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003edoes X11 forwarding but yours truly has seen it also \u003cem\u003enot\u003c/em\u003e work. YMMV.\u003c/p\u003e\n\u003cp\u003eBoth commands should drop you immediately into the \u003ccode\u003ejiveplot\u003c/code\u003e command line interface:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e+++++++++++++++++++++ Welcome to cli +++++++++++++++++++\n\u003cspan class=\"pl-smi\"\u003e$Id\u003c/span\u003e: command.py,v 1.16 2015-11-04 13:30:10 jive_cc Exp $\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eexit\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e exits, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003elist\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e lists, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehelp\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e helps\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ms \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003econtainer dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/path/to/my_data.ms\nMS my_data.ms opened \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003ecet\njcli\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-spectral-element-libraries-in-fortran-self\" class=\"anchor\" href=\"#spectral-element-libraries-in-fortran-self\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpectral Element Libraries in Fortran (SELF)\u003c/h1\u003e\n\u003cp\u003eCopyright 2020-2021 Fluid Numerics LLC\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://self.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2cdea5d87038eae2bd52034d42848bdf0381c26e2ffe70a7a973e360004a19f6/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f73656c662f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/self/badge/?version=latest\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSELF is licensed for use under the \u003ca href=\"./LICENSE\"\u003eAnti-Capitalist Software License\u003c/a\u003e. For other licensure, reach out to \u003ca href=\"mailto:support@fluidnumerics.com\"\u003esupport@fluidnumerics.com\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-support-this-project\" class=\"anchor\" href=\"#support-this-project\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupport this project\u003c/h2\u003e\n\u003cp\u003eSELF is open-source made freely available to the public. Fluid Numerics is a nano-business (less than 5 people) that is funded solely through service engagements with customers; we are not funded by venture capital in any way. Developing SELF and the associated documentation and delivering live coding sessions and tutorials all require time \u0026amp; labor as well as compute resources.\u003c/p\u003e\n\u003cp\u003eIf you value this repository and the activities of the SELF developers, you can \u003ca href=\"https://opencollective.com/higher-order-methods/projects/fluid-self\" rel=\"nofollow\"\u003esupport the continued development and maintenance SELF on Open Collective\u003c/a\u003e, where we transparently share our operational budget and expenses for this project. Through the \u003ca href=\"https://opencollective.com/higher-order-methods\" rel=\"nofollow\"\u003eHigher Order Methods collective\u003c/a\u003e you can support a variety of activities all focused on providing publicly accessible tools and training for using spectral element methods for physical modeling.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-about\" class=\"anchor\" href=\"#about\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbout\u003c/h2\u003e\n\u003cp\u003eSELF is an object-oriented Fortran library that support the implementation of Spectral Element Methods for solving partial differential equations.\u003c/p\u003e\n\u003cp\u003eThe SELF API is designed based on the assumption that SEM developers and researchers need to be able to implement derivatives in 1-D and divergence, gradient, and curl in 2-D and 3-D on scalar, vector, and tensor functions using spectral collocation, continuous galerkin, and discontinuous galerkin spectral element methods. Additionally, as we enter the exascale era, we are currently faced with a zoo of compute hardware that is available. Because of this, SELF routines provide support for GPU acceleration through AMD\u0027s HIP and support for multi-core, multi-node, and multi-GPU platforms with MPI.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"./SPECIFICATIONS.md\"\u003eSpecifications\u003c/a\u003e for more details.\u003c/p\u003e\n\u003cp\u003eTo learn more about the SELF API and the software layout, \u003ca href=\"https://higherordermethods.github.io/SELF/\" rel=\"nofollow\"\u003echeck out the SELF documentation\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://discord.gg/57aNxcpYMW\" rel=\"nofollow\"\u003eJoin the HigherOrderMethods Discord Server\u003c/a\u003e to get community support.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-dependencies\" class=\"anchor\" href=\"#dependencies\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h3\u003e\n\u003cp\u003eBefore installing SELF, make sure your system has the following dependencies installed :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFortran compiler (e.g. gfortran)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ROCm-Developer-Tools/HIP\"\u003eHIP\u003c/a\u003e (For GPU Accelerated Builds)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ROCmSoftwarePlatform/hipfort\"\u003eHIPFort\u003c/a\u003e (For GPU Accelerated Builds)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/fortran-lang/stdlib\"\u003estdlib\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jacobwilliams/json-fortran\"\u003ejson-fortran\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/szaghi/FLAP\"\u003eFLAP\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/FluidNumerics/feqparse\"\u003efeq-parse\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"\"\u003eHDF5\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor GPU accelerated builds, we recommend installing \u003ccode\u003erocm-dev\u003c/code\u003e on either CentOS or Ubuntu OS following the \u003ca href=\"https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html\" rel=\"nofollow\"\u003eROCm installation guide\u003c/a\u003e. You should install HIPfort from source, using the Fortran compiler you will build SELF with.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-building-self\" class=\"anchor\" href=\"#building-self\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding SELF\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eSerial CPU Builds\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eBUILD=release \\\nFC=gfortran \\\nmake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe SELF make system assumes that \u003ccode\u003efeqparse\u003c/code\u003e, \u003ccode\u003eFLAP\u003c/code\u003e, and \u003ccode\u003eHDF5\u003c/code\u003e are installed under \u003ccode\u003e/opt/view\u003c/code\u003e. If these dependencies are installed elseswhere you can use the following environment variables specify the linker and includes flags.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e SELF_FEQPARSE_LIBS Set the linker flags for feq-parse (Default: -L/opt/view/lib -lfeqparse)\n SELF_FEQPARSE_INC Set the includes flags for feq-parse (Default: -I/opt/view/include)\n SELF_FLAP_LIBS Set the linker flags for FLAP (Default: -L/opt/view/lib/ -lFLAP -lFACE -lPENF) \n SELF_FLAP_INC Set the includes flags for FLAP (Default: -I/opt/view/include/FLAP -I/opt/view/include/PENF -I/opt/view/include/FACE)\n SELF_HDF5_LIBS Set the linker flags for HDF5 (Default: -L/opt/view/lib/ -lhdf5_fortran -lhdf5 -lz -lm) \n SELF_HDF5_INC Set the includes flags for HDF5 (Default: -I/opt/view/include/shared)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, self will install under \u003ccode\u003e/opt/self\u003c/code\u003e. To change the install path, use the \u003ccode\u003eSELF_PREFIX\u003c/code\u003e environment variable.\u003c/p\u003e\n\u003cp\u003eUpon installation, you will have\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e${SELF_PREFIX}/lib/libself.a\u003c/code\u003e : Static library for linking into other application\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${SELF_PREFIX}/include/*.mod\u003c/code\u003e : Fortran module files required for building other applications that depend on SELF\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${SELF_PREFIX}/bin/self\u003c/code\u003e : A test program that can be used to test each SELF routine\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e${SELF_PREFIX}/util\u003c/code\u003e : A directory with useful utilities for pre and post-processing data produced by SELF\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-maintainers\" class=\"anchor\" href=\"#maintainers\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMaintainers\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://fluidnumerics.com/people/joe-schoonover\" rel=\"nofollow\"\u003eJoseph Schoonover, Fluid Numerics LLC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWant to become a maintainer ? Reach out to \u003ca href=\"mailto:support@fluidnumerics.com\"\u003esupport@fluidnumerics.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you\u0027d like to contribute, see \u003ca href=\"./CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e to get started.\u003c/p\u003e\n\u003cp\u003eIf you need help, \u003ca href=\"https://github.com/FluidNumerics/SELF/issues/new\"\u003eopen an issue\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 11, - "subscribers_count": 2, - "topics": [ - "plot", - "python", - "visualization", - "pgplot", - "casa-measurementset", - "singularity-container", - "singularity-image" - ], - "updated_at": 1697595575.0 - }, - { - "data_format": 2, - "description": "The Scientific Filesystem Specification and Documentation", - "filenames": [ - "tutorials/Singularity.scif", - "tutorials/Singularity" - ], - "full_name": "sci-f/sci-f.github.io", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScientific Filesystem (SCIF)\u003c/h1\u003e\u003ca id=\"user-content-scientific-filesystem-scif\" class=\"anchor\" aria-label=\"Permalink: Scientific Filesystem (SCIF)\" href=\"#scientific-filesystem-scif\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/logo/scif-logo.png\"\u003e\u003cimg src=\"docs/img/logo/scif-logo.png\" alt=\"docs/img/logo/scif-logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/156490?speed=2\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7db5330e459424b489e3771b5c23682f4853582a3652b5ea305ea2e79d7c4e3/68747470733a2f2f61736369696e656d612e6f72672f612f3135363439302e706e67\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/156490.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe Scientific Filesystem is an organizational format for scientific software and metadata. Our goals are centered around \u003cstrong\u003econsistency\u003c/strong\u003e, \u003cstrong\u003etransparency\u003c/strong\u003e, \u003cstrong\u003eprogrammatic accessibility\u003c/strong\u003e, and \u003cstrong\u003emodularity\u003c/strong\u003e. \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eRead about\u003c/a\u003e the format.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClients\u003c/h2\u003e\u003ca id=\"user-content-clients\" class=\"anchor\" aria-label=\"Permalink: Clients\" href=\"#clients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease contribute to the clients below, or the specification here.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vsoch/scif\"\u003evsoch/scif\u003c/a\u003e is the Python client, ideal for scientific use cases, or if you want interactivity\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.github.com/sci-f/scif-go\"\u003esci-f/scif-go\u003c/a\u003e is the GoLang library, intended for use with GoLang projects.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth can be installed and used in a container base. See the \u003ca href=\"https://vsoch.github.io\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e for quick starts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf SCIF has been useful to you, please cite our work on \u003ca href=\"https://academic.oup.com/gigascience/advance-article/doi/10.1093/gigascience/giy023/4931737\" rel=\"nofollow\"\u003eGigaScience\u003c/a\u003e!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eVanessa Sochat; The Scientific Filesystem (SCIF), GigaScience, giy023,\nhttps://doi.org/10.1093/gigascience/giy023\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Scientific Filesystem is licensed under the Affero GPL, version 3.0 or later \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 12, - "subscribers_count": 4, + "subscribers_count": 5, "topics": [ - "competitive", - "containers", - "singularity", - "singularity-containers", - "reproducibility", - "science" - ], - "updated_at": 1677228107.0 - }, - { - "data_format": 2, - "description": "Whole cell model of E. coli implemented with Vivarium", - "filenames": [ - "runscripts/container/runtime/Singularity" + "spectral-element-method", + "gpu-acceleration", + "gpu-computing", + "hpc", + "pde-solver" ], - "full_name": "CovertLab/vEcoli", - "latest_release": "v1.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVivarium \u003cem\u003eE. coli\u003c/em\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-vivarium-e-coli\" class=\"anchor\" aria-label=\"Permalink: Vivarium E. coli\" href=\"#vivarium-e-coli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"doc/_static/ecoli_master_topology.png\"\u003e\u003cimg src=\"doc/_static/ecoli_master_topology.png\" alt=\"vivarium\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBackground\u003c/h2\u003e\u003ca id=\"user-content-background\" class=\"anchor\" aria-label=\"Permalink: Background\" href=\"#background\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVivarium \u003cem\u003eE. coli\u003c/em\u003e (vEcoli) is a port of the Covert Lab\u0027s\n\u003ca href=\"https://github.com/CovertLab/wcEcoli\"\u003eE. coli Whole Cell Model\u003c/a\u003e (wcEcoli)\nto the \u003ca href=\"https://github.com/vivarium-collective/vivarium-core\"\u003eVivarium framework\u003c/a\u003e.\nIts main benefits over the original model are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eModular processes:\u003c/strong\u003e easily add/remove processes that interact with\nexisting or new simulation state\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUnified configuration:\u003c/strong\u003e all configuration happens through JSON files,\nmaking it easy to run simulations/analyses with different options\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eParquet output:\u003c/strong\u003e simulation output is in a widely-supported columnar\nfile format that enables fast, larger-than-RAM analytics with DuckDB\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGoogle Cloud support:\u003c/strong\u003e workflows too large to run on a local machine\ncan be easily run on Google Cloud\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAs in wcEcoli, \u003ca href=\"reconstruction/ecoli/flat\"\u003eraw experimental data\u003c/a\u003e is first processed\nby the parameter calculator or \u003ca href=\"reconstruction/ecoli/fit_sim_data_1.py\"\u003eParCa\u003c/a\u003e to calculate\nmodel parameters (e.g. transcription probabilities). These parameters are used to configure\n\u003ca href=\"ecoli/processes\"\u003eprocesses\u003c/a\u003e that are linked together into a\n\u003ca href=\"ecoli/experiments/ecoli_master_sim.py\"\u003ecomplete simulation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The following instructions assume a Linux or MacOS system. Windows users can\nattempt to follow the same instructions after setting up\n\u003ca href=\"https://learn.microsoft.com/en-us/windows/wsl/install\" rel=\"nofollow\"\u003eWindows Subsystem for Linux\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Refer to the following pages for non-local setups:\n\u003ca href=\"https://covertlab.github.io/vEcoli/workflows.html#sherlock\" rel=\"nofollow\"\u003eSherlock\u003c/a\u003e,\n\u003ca href=\"https://covertlab.github.io/vEcoli/workflows.html#other-hpc-clusters\" rel=\"nofollow\"\u003eother HPC cluster\u003c/a\u003e,\n\u003ca href=\"https://covertlab.github.io/vEcoli/gcloud.html\" rel=\"nofollow\"\u003eGoogle Cloud\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003epyenv lets you install and switch between multiple Python releases and multiple \"virtual\nenvironments\", each with its own pip packages. Using pyenv, create a virtual environment\nand install Python 3.11.10. For a tutorial on how to install pyenv and other dependencies,\nfollow the instructions \u003ca href=\"https://github.com/CovertLab/wcEcoli/blob/master/docs/dev-tools.md\"\u003ehere\u003c/a\u003e.\nThen, run the following command in your terminal:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epyenv virtualenv 3.11.10 vEcoli \u0026amp;\u0026amp; pyenv local vEcoli\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUpdate \u003ccode\u003epip\u003c/code\u003e, \u003ccode\u003esetuptools\u003c/code\u003e and \u003ccode\u003ewheel\u003c/code\u003e to avoid issues with these:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install --upgrade pip setuptools==73.0.1 wheel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, install numpy (check \u003ccode\u003erequirements.txt\u003c/code\u003e for the exact version):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install numpy==1.26.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen install the remaining requirements:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd build the Cython components:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake clean compile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstall \u003ccode\u003enextflow\u003c/code\u003e following the instructions \u003ca href=\"https://www.nextflow.io/docs/latest/install.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nAfter installing Java with SDKMAN!, close and reopen your terminal, then run\nthe following commands and compare their output to ensure that the Java compiler\nand JVM were properly installed and the same version.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejavac -version\njava -version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter verifying your Java installation, you can proceed with installing Nextflow,\nstarting with \u003ccode\u003ecurl -s https://get.nextflow.io | bash\u003c/code\u003e. If this fails, try\nprepending \u003ccode\u003eexport CAPSULE_LOG=verbose\u003c/code\u003e and re-run, checking for failed downloads.\nIf any downloads failed, re-run this command until it succeeds.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest Installation\u003c/h2\u003e\u003ca id=\"user-content-test-installation\" class=\"anchor\" aria-label=\"Permalink: Test Installation\" href=\"#test-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo test your installation, from the top-level of the cloned repository, invoke:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Must set PYTHONPATH and OMP_NUM_THREADS for every new shell (can add to .bashrc/.zshrc)\nexport PYTHONPATH=.\nexport OMP_NUM_THREADS=1\npython runscripts/workflow.py --config ecoli/composites/ecoli_configs/test_installation.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the following basic simulation workflow:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun the \u003ca href=\"runscripts/parca.py\"\u003eparameter calculator\u003c/a\u003e to generate simulation data.\u003c/li\u003e\n\u003cli\u003eRun the \u003ca href=\"ecoli/experiments/ecoli_master_sim.py\"\u003esimulation\u003c/a\u003e\nfor a single generation, saving output in \u003ccode\u003eout\u003c/code\u003e folder.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"runscripts/analysis.py\"\u003eAnalyze simulation output\u003c/a\u003e by creating a\n\u003ca href=\"ecoli/analysis/single/mass_fraction_summary.py\"\u003emass fraction plot\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNext Steps\u003c/h2\u003e\u003ca id=\"user-content-next-steps\" class=\"anchor\" aria-label=\"Permalink: Next Steps\" href=\"#next-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheck out the \u003ca href=\"https://covertlab.github.io/vEcoli/\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e for a high-level\ntutorial of model development, details on key model components, and low-level API documentation.\u003c/p\u003e\n", - "stargazers_count": 12, - "subscribers_count": 16, - "topics": [], - "updated_at": 1733080055.0 + "updated_at": 1643471883.0 }, { "data_format": 2, - "description": "SDSC Summer Institute 2022 material", + "description": null, "filenames": [ - "4.2a_python_for_hpc/singularity/Singularity.anaconda3-dask-numba" + "Singularity" ], - "full_name": "sdsc/sdsc-summer-institute-2022", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esdsc-summer-institute-2022\u003c/h1\u003e\u003ca id=\"user-content-sdsc-summer-institute-2022\" class=\"anchor\" aria-label=\"Permalink: sdsc-summer-institute-2022\" href=\"#sdsc-summer-institute-2022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSDSC Summer Institute 2022 material\n\u003ca href=\"https://na.eventscloud.com/website/36626/\" rel=\"nofollow\"\u003eWebsite\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository hosts all material and slides of the presentations at the Summer Institute\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive Videos\u003c/h2\u003e\u003ca id=\"user-content-interactive-videos\" class=\"anchor\" aria-label=\"Permalink: Interactive Videos\" href=\"#interactive-videos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eRecorded sessions can be found \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eA full catalog of all our trainings at SDSC can be found \u003ca href=\"https://www.sdsc.edu/education_and_training/training_hpc.html#catalog\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAliases, symlinks and reservations\u003c/h2\u003e\u003ca id=\"user-content-aliases-symlinks-and-reservations\" class=\"anchor\" aria-label=\"Permalink: Aliases, symlinks and reservations\" href=\"#aliases-symlinks-and-reservations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor your convenience, we\u2019ve create aliases and symlinks for the Summer Institute\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eget-cpu \u2013 one interactive compute node for 2 hours\u003c/li\u003e\n\u003cli\u003eget-gpu \u2013 one interactive GPU (in shared queue) for 2 hours\u003c/li\u003e\n\u003cli\u003estart-spark \u2013 start one-hour Spark session\u003c/li\u003e\n\u003cli\u003estart-tf-cpu \u2013 start three-hour TensorFlow session (CPU)\u003c/li\u003e\n\u003cli\u003estart-tf-gpu \u2013 start three-hour TensorFlow session (GPU)\u003c/li\u003e\n\u003cli\u003edata \u2013 symlink to staged data\nIn the event that you need to explicitly use the reservation, training accounts will have access to SI2021RES for duration of SI\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAgenda\u003ca name=\"user-content-agenda\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-agenda\" class=\"anchor\" aria-label=\"Permalink: Agenda\" href=\"#agenda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll times are in Pacific time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 1 (Wednesday, 07/27/22) \u003ca name=\"user-content-agenda-1\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-1-wednesday-072722-\" class=\"anchor\" aria-label=\"Permalink: Day 1 (Wednesday, 07/27/22) \" href=\"#day-1-wednesday-072722-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:00 AM - 9:25 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/1.1_accessing_expanse\"\u003e1.1 Accounts, Login, Enviroments\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section1_1/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:25 AM \u2013 10:15 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/ciml-org/ciml-summer-institute-2022/tree/main/1.2_accounts_login_environments_expanse_portal\"\u003e1.2 Running Jobs on Expanse\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section1_2/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:15 AM \u2013 11:00 AM\u003c/td\u003e\n\u003ctd\u003eQ\u0026amp;A, Wrap-up\u003c/td\u003e\n\u003ctd\u003eAll\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 2 (Monday, 08/01/22)\u003ca name=\"user-content-agenda-2\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-2-monday-080122\" class=\"anchor\" aria-label=\"Permalink: Day 2 (Monday, 08/01/22)\" href=\"#day-2-monday-080122\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 8:15 PM\u003c/td\u003e\n\u003ctd\u003eWelcome\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:15 AM \u2013 9:15 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.1_parallel_computing_concepts\"\u003e2.1 Parallel Computing Concepts\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_1/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/sinkovits_robert.html\" rel=\"nofollow\"\u003eRobert Sinkovits\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:15 AM \u2013 10:00 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.2_hardware_overview\"\u003e2.2 Hardware Overview\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_2/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/goetz_andreas.html\" rel=\"nofollow\"\u003eAndreas Goetz\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:00 AM \u2013 10:15 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:15 AM \u2013 11:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.3_intermediate_linux\"\u003e2.3 Intermediate Linux\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_3/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/goetz_andreas.html\" rel=\"nofollow\"\u003eAndreas Goetz\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:30 AM \u2013 12:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.4_batch_computing\"\u003e2.4 Batch Computing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_4/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:30 PM \u2013 12:45 PM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:45 PM \u2013 2:15 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.5_data_management\"\u003e2.5 Data Management\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_5/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/mkandes\"\u003eMarty Kandes\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 3 (Tuesday, 08/02/22)\u003ca name=\"user-content-agenda-3\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-3-tuesday-080222\" class=\"anchor\" aria-label=\"Permalink: Day 3 (Tuesday, 08/02/22)\" href=\"#day-3-tuesday-080222\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 8:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.1_security\"\u003e3.1 Security\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_1/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.linkedin.com/in/nicole-wolter-bbb94a3\" rel=\"nofollow\"\u003eNicole Wolter\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:30 AM \u2013 9:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.2_interactive_computing\"\u003e3.2 Interactive Computing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_2/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:30 AM \u2013 9:45 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:45 AM \u2013 10:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.3_getting_help\"\u003e3.3 Getting Help\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_3/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.linkedin.com/in/nicole-wolter-bbb94a3\" rel=\"nofollow\"\u003eNicole Wolter\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:30 AM \u2013 11:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.4_code_migration\"\u003e3.4 Code Migration\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_4/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/tatineni_mahidhar.html\" rel=\"nofollow\"\u003eMahidhar Tatineni\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:30 AM \u2013 11:45 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:45 AM \u2013 12:45 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.5_high_throughput_computing\"\u003e3.5 High Throughput Computing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_5/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/mkandes\"\u003eMarty Kandes\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:45 PM \u2013 1:45 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.6_linux_tools_file_processing\"\u003e3.6 Linux Tools for File Processing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_6/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/sinkovits_robert.html\" rel=\"nofollow\"\u003eRobert Sinkovits\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 4 (Wednesday, 08/03/22)\u003ca name=\"user-content-agenda-4\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-4-wednesday-080322\" class=\"anchor\" aria-label=\"Permalink: Day 4 (Wednesday, 08/03/22)\" href=\"#day-4-wednesday-080322\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 9:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.1a_intro_to_git_github\"\u003e4.1a Intro to Git \u0026amp; GitHub\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_1a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 9:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.1b_advanced_git_github\"\u003e4.1b Advanced Git \u0026amp; GitHub\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_1b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/mkandes\"\u003eMarty Kandes\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:30 AM \u2013 9:45 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:45 AM \u2013 12:00 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.2a_python_for_hpc\"\u003e4.2a Python for HPC\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_2a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/tatineni_mahidhar.html\" rel=\"nofollow\"\u003eMahidhar Tatineni\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:45 AM \u2013 12:00 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.2b_data_science_applications\"\u003e4.2b A Short Introduction to Data Science and its Applications\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_2b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/altintas_ilkay.html\" rel=\"nofollow\"\u003eIlkay Altintas\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.linkedin.com/in/subhasisdg\" rel=\"nofollow\"\u003eSubhasis Dasgupta\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.linkedin.com/in/shwetapurawat\" rel=\"nofollow\"\u003eShweta Purawat\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:00 PM \u2013 2:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.3a_performance_tuning\"\u003e4.3a Performance Tuning\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_3a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/sinkovits_robert.html\" rel=\"nofollow\"\u003eRobert Sinkovits\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:00 PM \u2013 2:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.3b_scalable_ml\"\u003e4.3b Scalable Machine Learning\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_3b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/nguyen_mai.html\" rel=\"nofollow\"\u003eMai Nguyen\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.coursera.org/instructor/~13847302\" rel=\"nofollow\"\u003ePaul Rodriguez\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 5 (Thursday, 08/04/22)\u003ca name=\"user-content-agenda-5\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-5-thursday-080422\" class=\"anchor\" aria-label=\"Permalink: Day 5 (Thursday, 08/04/22)\" href=\"#day-5-thursday-080422\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 10:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.1a_scientific_vis_with_visit\"\u003e5.1a Scientific Visualization for mesh based data with Visit\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_1a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/chourasia_amit.html\" rel=\"nofollow\"\u003eAmit Chourasia\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 10:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.1b_deep_learning_pt1\"\u003e5.1b Deep Learning - Part 1\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_1b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/nguyen_mai.html\" rel=\"nofollow\"\u003eMai Nguyen\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.coursera.org/instructor/~13847302\" rel=\"nofollow\"\u003ePaul Rodriguez\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:30 AM \u2013 10:45 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:45 AM \u2013 1:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.2a_gpu_computing_and_programming\"\u003e5.2a GPU Computing and Programming\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_2a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/goetz_andreas.html\" rel=\"nofollow\"\u003eAndreas Goetz\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:45 AM \u2013 1:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.2b_deep_learning_pt2\"\u003e5.2b Deep Learning \u2013 Part 2\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_2b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/nguyen_mai.html\" rel=\"nofollow\"\u003eMai Nguyen\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.coursera.org/instructor/~13847302\" rel=\"nofollow\"\u003ePaul Rodriguez\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1:30 PM \u2013 2:00 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.3_intro_to_singularity\"\u003e5.3 An Introduction to Singularity: Containers for Scientific and High-Performance Computing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_3/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/mkandes\"\u003eMartin Kandes\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 6 (Friday, 08/05/22)\u003ca name=\"user-content-agenda-6\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-6-friday-080522\" class=\"anchor\" aria-label=\"Permalink: Day 6 (Friday, 08/05/22)\" href=\"#day-6-friday-080522\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 11:00 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/6.1a_parallel_computing_using_MPI_OpenMP\"\u003e6.1a Parallel Computing using MPI \u0026amp; Open MP\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section6_1a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/tatineni_mahidhar.html\" rel=\"nofollow\"\u003eMahidhar Tatineni\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 11:00 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/6.1b_info_visualization_concepts\"\u003e6.1b Information Visualization Concepts\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section6_1b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/chourasia_amit.html\" rel=\"nofollow\"\u003eAmit Chourasia\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:00 AM \u2013 11:15 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:15 AM \u2013 12:00 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/6.2_scaling_up_interactive_data_analysis_jupyter_lab\"\u003e6.2 Scaling up Interactive Data Analysis in Jupyter Lab: From Laptop to HPC\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section6_2/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/rose_peter.html\" rel=\"nofollow\"\u003ePeter Rose\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:00 PM \u2013 12:15 PM\u003c/td\u003e\n\u003ctd\u003eClosing Remarks\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/sinkovits_robert.html\" rel=\"nofollow\"\u003eRobert Sinkovits\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdditional SDSC Resources\u003c/h2\u003e\u003ca id=\"user-content-additional-sdsc-resources\" class=\"anchor\" aria-label=\"Permalink: Additional SDSC Resources\" href=\"#additional-sdsc-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVoyager\u003c/h3\u003e\u003ca id=\"user-content-voyager\" class=\"anchor\" aria-label=\"Permalink: Voyager\" href=\"#voyager\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.sdsc.edu/support/user_guides/voyager.html#tech_summary\" rel=\"nofollow\"\u003eVoyager\u003c/a\u003e supercomputer is an innovative AI system designed specifically for science and engineering research at scale. Funded by the National Science Foundation, Voyager represents a collaboration with the San Diego Supercomputer Center at UC San Diego, Supermicro, and Intel\u2019s Habana Lab focused on supporting research in science and engineering that is increasingly dependent upon artificial intelligence and deep learning as a critical element in the experimental and/or computational work.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eVoyager: Exploring Habana processor-based AI focused hardware for Science and Engineering \u003ca href=\"https://www.youtube.com/watch?v=RK46aCjOoKI\" rel=\"nofollow\"\u003eTraining Session\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVoyager User Guide \u003ca href=\"https://www.sdsc.edu/support/user_guides/voyager.html\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eVoyager video \u003ca href=\"https://youtu.be/TmX4wm4J8Jk\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCloudBank\u003c/h3\u003e\u003ca id=\"user-content-cloudbank\" class=\"anchor\" aria-label=\"Permalink: CloudBank\" href=\"#cloudbank\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.cloudbank.org/\" rel=\"nofollow\"\u003eCoudBank\u003c/a\u003e is a managed service to simplify cloud access for computer science research.\nCloudBank overview video \u003ca href=\"https://www.youtube.com/watch?v=5YEflIwdjxY\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\nDCL funding opportunity for PIs who have existing CISE awards, details \u003ca href=\"https://www.cloudbank.org/request\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSeedMe\u003c/h3\u003e\u003ca id=\"user-content-seedme\" class=\"anchor\" aria-label=\"Permalink: SeedMe\" href=\"#seedme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://seedmelab.org/\" rel=\"nofollow\"\u003eSeedMe\u003c/a\u003e is a scientific data management framework for teams struggling with intractable data.\u003cbr\u003e\nSeedMeLab overview talk \u003ca href=\"https://youtu.be/eVqzNbI1EAo\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll the teaching material in this repository is licensed under \u003ca href=\"https://creativecommons.org/licenses/by-nc-sa/4.0/\" rel=\"nofollow\"\u003eCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you are re-using this material, please cite our \u003ca href=\"https://doi.org/10.5281/zenodo.5754066\" rel=\"nofollow\"\u003erecord on Zenodo\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 12, - "subscribers_count": 8, + "full_name": "PGP-UK/GenomeChronicler", + "latest_release": "0.91", + "readme": "\u003cpre\u003e\u003ccode\u003e ##### ##### \n# # ###### # # #### # # ###### # # # # ##### #### # # # #### # ###### ##### \n# # ## # # # ## ## # # # # # # # # ## # # # # # # # # \n# #### ##### # # # # # # ## # ##### # ###### # # # # # # # # # # ##### # # \n# # # # # # # # # # # # # # ##### # # # # # # # # # ##### \n# # # # ## # # # # # # # # # # # # # # ## # # # # # # # \n ##### ###### # # #### # # ###### ##### # # # # #### # # # #### ###### ###### # # \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3664\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-welcome\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcome\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome\u003c/h1\u003e\n\u003cp\u003eThis is the repository for Genome Chronicler, the Personal Genome Project United Kingdom (PGP-UK) genomic report generation scripts.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting Started\u003c/h1\u003e\n\u003cp\u003eAfter cloning this repository, run the SetupMeFirst.sh script in your local system to retrieve the extra files needed to run the pipeline (around 10GB, so too big for git).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-input-files\" class=\"anchor\" aria-hidden=\"true\" href=\"#input-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput files\u003c/h1\u003e\n\u003cp\u003eThe main script (GenomeChronicler_mainDruid.pl) needs a BAM file as input, and optionally can also use a VEP generated summary html file, if variants have already been called on the data and summaries are to be produced.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003eTo handle the myriad dependencies present in this pipeline, it is avaliable through Singularity Hub as a singularity container (see badge at top of the page).\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-easy-start-using-singularity\" class=\"anchor\" aria-hidden=\"true\" href=\"#easy-start-using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEasy Start using Singularity\u003c/h1\u003e\n\u003cp\u003eIf you don\u0027t already have singularity on your system, or want to know more about it, head to their userguide at: \u003ca href=\"https://sylabs.io/guides/3.1/user-guide/\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.1/user-guide/\u003c/a\u003e\nWhile Singularity is not needed to run GenomeChronicler, it does make setup much easier.\u003c/p\u003e\n\u003cp\u003eFor a manual installation without Singularity, please follow the steps in the %post section of the Singularity file in this repository, to install all the dependencies.\u003c/p\u003e\n\u003cp\u003eDownloading pre-packaged GenomeChronicler from SingularityHub\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://PGP-UK/GenomeChronicler\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGetting some test data (NA12878 from ENA, pre-mapped to GRCh38, and the respective reference)\u003c/p\u003e\n\u003cpre lang=\"wget\"\u003e\u003ccode\u003esingularity exec GenomeChronicler_latest.sif wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/data/CEU/NA12878/alignment/NA12878.alt_bwamem_GRCh38DH.20150718.CEU.low_coverage.cram\n\nsingularity exec GenomeChronicler_latest.sif wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eConverting data to BAM format\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec GenomeChronicler_latest.sif samtools view -T GRCh38_full_analysis_set_plus_decoy_hla.fa -b -o NA12878wxs.bam NA12878.alt_bwamem_GRCh38DH.20150718.CEU.low_coverage.cram\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRunning GenomeChronicler on the data\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run GenomeChronicler_latest.sif --bamFile=NA12878wxs.bam \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-command-line-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#command-line-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCommand Line Options\u003c/h1\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eOption\u003c/th\u003e\n\u003cth align=\"center\"\u003eRequirement\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--bamFile\u003c/td\u003e\n\u003ctd align=\"center\"\u003eREQUIRED\u003c/td\u003e\n\u003ctd\u003eThe path to a BAM file that has been preprocessed through markDuplicates and VariantQualityScoreRecalibration. This can be obtained by running the first step of the Sarek nextflow pipeline, or through other means that do respect the general principles of the GATK Variation Calling Best Practices workflow. Note that no variation calling is needed to run GenomeChronicler.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--vepFile\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOPTIONAL\u003c/td\u003e\n\u003ctd\u003eFor the summary tables to appear in the report, a VEP summary HTML file must be provided. This will likely be generated if the data is from whole genome sequencing and variants were called (e.g. by running all the germline calling steps of the Sarek nextflow pipeline or other GATK Best Practices based workflow). If this isn\u0027t provided, summary tables and plots will automatically be excluded from the final report.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--resultsDir\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOPTIONAL\u003c/td\u003e\n\u003ctd\u003eFor setting the absolute path of the results folder to be produced when running GenomeChronicler.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--customTemplate\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOPTIONAL\u003c/td\u003e\n\u003ctd\u003eFor customising the output report, set this variable to the path of a custom LaTeX file to act as a template for the report. The default templates bundled with this software can also be found in the project github page.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e--GATKthreads\u003c/td\u003e\n\u003ctd align=\"center\"\u003eOPTIONAL\u003c/td\u003e\n\u003ctd\u003eNumber of threads to use for the GATK genotyping steps of this processing pipeline.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "stargazers_count": 11, + "subscribers_count": 2, "topics": [], - "updated_at": 1691383251.0 - }, - { - "data_format": 2, - "description": "An autonomous grasping solution for the Emika Franka Panda robot.", - "filenames": [ - "containers/singularity/Singularity.ros_melodic-cuda10-bionic", - "containers/singularity/Singularity.ros_kinetic-cuda10-xenial" - ], - "full_name": "rickstaa/panda-autograsp", - "latest_release": "v1.0.8-melodic", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epanda-autograsp\u003c/h1\u003e\u003ca id=\"user-content-panda-autograsp\" class=\"anchor\" aria-label=\"Permalink: panda-autograsp\" href=\"#panda-autograsp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.codacy.com/gh/rickstaa/panda-autograsp/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=rickstaa/panda-autograsp\u0026amp;utm_campaign=Badge_Grade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dcc915773f75ccdb296353b1d52c65e95f6e66f8e610c4349f2e8dd7b2b2201e/68747470733a2f2f6170702e636f646163792e636f6d2f70726f6a6563742f62616467652f47726164652f3038376664613266306634633432336362353631373435616237616664626137\" alt=\"Codacy Badge\" data-canonical-src=\"https://app.codacy.com/project/badge/Grade/087fda2f0f4c423cb561745ab7afdba7\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"contributing.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/87ddab2ef405aee70f76af73b8184fccbe88f9f3551c178af03018ac86db8bfc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e747269627574696f6e732d77656c636f6d652d6f72616e67652e737667\" alt=\"Contributions\" data-canonical-src=\"https://img.shields.io/badge/contributions-welcome-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/rickstaa/panda-autograsp/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b0e6512c1448a9e1672b509bba059a9e87d05c8f1ab07905491b69ddbf660f40/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f7269636b737461612f70616e64612d6175746f6772617370\" alt=\"GitHub release (latest by date)\" data-canonical-src=\"https://img.shields.io/github/v/release/rickstaa/panda-autograsp\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1bde141946ab03e7cd01be4ff820f64e9d2aa22b6ab86e62f3e5271cd404ce9d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e253230332d332e37253230253743253230332e36253230253743253230332e352d79656c6c6f772e737667\" alt=\"Python 3\" data-canonical-src=\"https://img.shields.io/badge/python%203-3.7%20%7C%203.6%20%7C%203.5-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b89265bd3bfb343694f9911d718aa363886fdcafe26bf28ba7b930698fd5b3ad/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e253230322d322e37253230253743253230322e36253230253743253230322e352d627269676874677265656e2e737667\" alt=\"Python 2\" data-canonical-src=\"https://img.shields.io/badge/python%202-2.7%20%7C%202.6%20%7C%202.5-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://wiki.ros.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cee8904b738cf8aaea786179bb883cb74f22c7fd104d39797c795788d07265c5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f524f5325323076657273696f6e732d4d656c6f6469632532302537432532304b696e65637469632d627269676874677265656e\" alt=\"ROS versions\" data-canonical-src=\"https://img.shields.io/badge/ROS%20versions-Melodic%20%7C%20Kinectic-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\ud83d\udca1 You are on the default (Kinect2) branch. This branch is optimized to work with the Kinect2 camera. To use the package with the RealSense cameras, see the \u003ca href=\"https://github.com/rickstaa/panda-autograsp/tree/melodic-devel-realsense\"\u003emelodic-devel-realsense branch\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePackage Overview\u003c/h2\u003e\u003ca id=\"user-content-package-overview\" class=\"anchor\" aria-label=\"Permalink: Package Overview\" href=\"#package-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe panda-autograsp is an autonomous ROS based grasping solution that works with the \u003ca href=\"https://www.franka.de/panda/\" rel=\"nofollow\"\u003ePanda Emika Franka robot\u003c/a\u003e. In this grasping solution, several opensource grasping solutions are implemented on the \u003ca href=\"https://www.franka.de/panda/\" rel=\"nofollow\"\u003ePanda Emika Franka robots\u003c/a\u003e robot. These solutions work both on a physical as well as a simulated version of the panda robot. A simulated version of the panda robot is already shipped with this package.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BerkeleyAutomation/gqcnn\"\u003eBerkleyAutomation/gqcnn\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation and Usage\u003c/h2\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-label=\"Permalink: Installation and Usage\" href=\"#installation-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://rickstaa.github.io/panda-autograsp/\" rel=\"nofollow\"\u003edocs\u003c/a\u003e for installation and usage instructions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown limitations\u003c/h2\u003e\u003ca id=\"user-content-known-limitations\" class=\"anchor\" aria-label=\"Permalink: Known limitations\" href=\"#known-limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThere package is currently not working with the simulated camera (see \u003ca href=\"https://github.com/rickstaa/panda-autograsp/issues/158\"\u003e#158\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSince the package is written in python2.7 and this version already reached EOL, the dependencies are quite fragile. The \u003ccode\u003esetup.py\u003c/code\u003e install method might, therefore fail. If this is the case, please install the dependencies using the \u003ccode\u003e./requirements/requirements.txt\u003c/code\u003e file. This can be solved by porting the package to ROS Noetic (see \u003ca href=\"https://github.com/rickstaa/panda-autograsp/issues/163\"\u003e#163\u003c/a\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLICENSE\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main code if this repository is licensed under an \u003cstrong\u003eMIT license\u003c/strong\u003e. If a LICENCE file is present in a submodule this licence has to be respected but ONLY for the files contained in this submodule.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFeel free to open an issue if you have ideas on how to make this GitHub action better or if you want to report a bug! All contributions are welcome. \ud83d\ude80 Please consult the \u003ca href=\"CONTRIBUTING.md\"\u003econtribution guidelines\u003c/a\u003e for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eGQ-CNN and FC-GQ-CNN created by \u003ca href=\"https://berkeleyautomation.github.io/gqcnn\" rel=\"nofollow\"\u003e@berkeleyautomation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eIcon created with svgs made by \u003ca href=\"https://www.freepik.com/\" rel=\"nofollow\"\u003e@freepik\u003c/a\u003e from \u003ca href=\"https://www.flaticon.com/authors/eucalyp\" rel=\"nofollow\"\u003ewww.flaticon.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 12, - "subscribers_count": 4, - "topics": [ - "machine-learning", - "ros", - "robotics", - "robot-manipulation", - "neural-networks", - "python" - ], - "updated_at": 1732629508.0 + "updated_at": 1675071802.0 }, { "data_format": 2, - "description": "Grow virtual creatures in static and physics simulated environments.", + "description": "DataLad extension for containerized environments", "filenames": [ - "cluster/Singularity" + "tools/Singularity.testhelper" ], - "full_name": "cfusting/conditional-growth", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-growing-virtual-creatures-in-minecraft\" class=\"anchor\" aria-hidden=\"true\" href=\"#growing-virtual-creatures-in-minecraft\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGrowing Virtual Creatures in Minecraft\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/869178/201533053-27c2ff53-e625-40ed-a490-5cd401896609.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/869178/201533053-27c2ff53-e625-40ed-a490-5cd401896609.mp4\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSea lanterns searching for glowstone\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/cfusting/conditional-growth\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d1f5f4e97ec14d197ffc19e3f24ec51393310f3052d9db011fade1eb7a0a581/68747470733a2f2f7472617669732d63692e636f6d2f6366757374696e672f636f6e646974696f6e616c2d67726f7774682e7376673f6272616e63683d6d61696e\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/cfusting/conditional-growth.svg?branch=main\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-motivation\" class=\"anchor\" aria-hidden=\"true\" href=\"#motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003cp\u003eGrowing virtual creatures is really fun [1]. However optimizing them in a physics engine in three dimensions is (as of 2022) pretty time consuming even on a powerful desktop computer. Science is limited by creativity, technical expertise, and cycles. To address the later, this package includes a pseudo-realistic environment: Minecraft [2], to enable anyone with a fairly modern computer to grow virtual creatures for fun and perhaps, to test hypothesizes.\u003c/p\u003e\n\u003cp\u003eMinecraft is a very good looking gridworld with a few notable advantages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSome blocks, like sand and water, respond to physics.\u003c/li\u003e\n\u003cli\u003eThe world is procedurally generated, providing an endless landscape of random environments.\u003c/li\u003e\n\u003cli\u003eMulti-agent environments are supported by default.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSo, although the curious individual may not be able to simulate a robot to be transferred to our world, a great deal can be explored and tested.\u003c/p\u003e\n\u003cp\u003eA primary goal of this package is to be standardized, extensible, and modular: many of the code bases I have come across couple a growth encoding with an optimization algorithm, limiting their application and testability. To that end the growth function and Ray\u0027s RLlib are completely independent; tied together only in the minecraft environment class following Open AI\u0027s Gym standard. You can replace the growth function with anything that returns an encoding you can convert to blocks (I\u0027d like to see Compositional Pattern Producing Networks [3], for example) and write your own environment. In the same vein Ray gives you a robust selection of gradient and non-gradient based optimization algorithms to choose from, the ability to scale, and standardized logging and experiment tracking with \u003ca href=\"https://mlflow.org/\" rel=\"nofollow\"\u003eMLFlow\u003c/a\u003e and \u003ca href=\"https://www.tensorflow.org/tensorboard\" rel=\"nofollow\"\u003eTensorboard\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eMy hope is that this package enables any company, university, and especially \u003cstrong\u003eindividuals\u003c/strong\u003e to implement one, two, or all of a:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGrowth encoding\u003c/li\u003e\n\u003cli\u003eEnvironment\u003c/li\u003e\n\u003cli\u003eOptimizer\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto test hypothesizes or just mess around, with something that will work on a standard desktop but can be scaled to a high performance computing cluster.\u003c/p\u003e\n\u003cp\u003eglhf.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-core-components\" class=\"anchor\" aria-hidden=\"true\" href=\"#core-components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCore Components\u003c/h2\u003e\n\u003cp\u003eRoughly speaking, there are three things that tie this package together.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe Minecraft server.\u003c/li\u003e\n\u003cli\u003eA Minecraft environment which extends OpenAI\u0027s Gym \u003ca href=\"https://github.com/openai/gym/blob/6a04d49722724677610e36c1f92908e72f51da0c/gym/core.py\"\u003eEnvironment\u003c/a\u003e and makes use of a growth function (unique to this package) to decide what to grow.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.ray.io/en/latest/rllib/index.html\" rel=\"nofollow\"\u003eRay\u0027s RLlib\u003c/a\u003e for scalable optimization and logging.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tracking-and-metrics\" class=\"anchor\" aria-hidden=\"true\" href=\"#tracking-and-metrics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTracking and Metrics\u003c/h3\u003e\n\u003cp\u003eMetrics are captured by Ray in /tmp/[expname] where expname is specified in the run configuration file, in the run function, by the parameter \"name\". You\u0027ll need to spend some time learning the Ray framework to become comfortable with this and other parameter choices governing the optimization process. The easiest way to view the metrics is to use Tensorboard and will be described in the example below. Here\u0027s a pretty picture:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/tensorboard_example.png\"\u003e\u003cimg src=\"./docs/tensorboard_example.png\" alt=\"Tensorboard picture\" width=\"960\" height=\"540\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-theory-of-the-conditional-growth-function\" class=\"anchor\" aria-hidden=\"true\" href=\"#theory-of-the-conditional-growth-function\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTheory of the conditional growth function\u003c/h2\u003e\n\u003cp\u003eI\u0027ve summarized the idea of the conditional growth function in a few images below. The idea is as follow:\u003c/p\u003e\n\u003cp\u003eThe growth of a creature can be broken down into an ordered sequence of steps. At each step, the optimizer has access to a state describing the creature and / or environment. Using this information a configuration of voxels is chosen (for example, by selecting the voxel configuration with the maximum probability) to be added to the current growing voxel. Voxels are stored in a queue and are thus grown breadth-first.\u003c/p\u003e\n\u003cp\u003eThe above process boils down to the breadth-first application of the conditional probability of a voxel configuration given a state. Thus the beadth-first voxel selection process coupled with the growth function results in a creature of potentially infinite voxels: the integral of the growth function over time and space. My hope is that self-repeating structures will be observed and built by the growth function, providing a genommic encoding which can be thought of as a compressed representation of a creature.\u003c/p\u003e\n\u003cp\u003e|\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/theory1.jpg\"\u003e\u003cimg src=\"./docs/theory1.jpg\" alt=\"Theory image one\" width=\"400\" height=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e|\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/theory2.jpg\"\u003e\u003cimg src=\"./docs/theory2.jpg\" alt=\"Theory image two\" width=\"400\" height=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e|\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-get-the-block\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-get-the-block\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Get the block\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/869178/201533091-b17d37d1-df6c-46de-b8d5-ef18f670fe3f.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/869178/201533091-b17d37d1-df6c-46de-b8d5-ef18f670fe3f.mp4\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNavigating obstacles\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this example we will grow a creature out of sea lanterns (reason: they look cool) who\u0027s goal is to touch a reward block. At each growth step the probability of a voxel configuration is determined given the tensor convolution of block types within some one norm k neighborhood of the block on which the configuration is to be added (translation: limited vision). To get this example running you will need Docker and Linux (Windows Linux Subsystem 2 is fine).\u003c/p\u003e\n\u003cp\u003eNote: It would be fair to ask at this point if the creature is \"growing\" or \"reaching\" toward the reward block. Unless you treat our world as truth, the answer to this question is irrelevant.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Requirements\u003c/h3\u003e\n\u003cp\u003eIf you would like to use a GPU make sure to install \u003ca href=\"https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime\" rel=\"nofollow\"\u003envidia-container-runtime\u003c/a\u003e. Other than that the Dockerfile will handle all the dependencies.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installing-nvidia-container-runtime\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-nvidia-container-runtime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling Nvidia Container Runtime\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edistribution=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003e. /etc/os-release\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$ID$VERSION_ID\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e \\\n \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo apt-key add - \\\n \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e curl -s -L https://nvidia.github.io/nvidia-docker/\u003cspan class=\"pl-smi\"\u003e$distribution\u003c/span\u003e/nvidia-docker.list \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo tee /etc/apt/sources.list.d/nvidia-docker.list\n \nsudo apt-get update \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e sudo apt-get install -y nvidia-docker2\nsudo systemctl restart docker\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-hidden=\"true\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h3\u003e\n\u003cp\u003eBuild the minecraft server by navigating into the minecraft-server directory and start it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t mc \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -it --rm -p 5001:5001 -p 25565:25565 mc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePull the Tensorflow image and start the tensorboard server. You can launch tensorboard in a web browser with localhost:6006:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull tensorflow/tensorflow\ndocker run -p 6006:6006 --rm -v /tmp:/tmp tensorflow/tensorflow tensorboard --logdir /tmp --host 0.0.0.0 --port 6006\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNavigate into this repo\u0027s root directory, build the image, and start the optimizer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t growth \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -it --rm --gpus all -v /tmp:/home/ray/ray_results --network host growth python run_configurations/minecraft/get_the_block.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe previous command starts a gpu enabled optimizer and four workers (creatures) in random locations in minecraft. You can edit all configurations in the run script: see Ray\u0027s RLlib for documentation.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-checking-out-the-creatures\" class=\"anchor\" aria-hidden=\"true\" href=\"#checking-out-the-creatures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChecking out the creatures\u003c/h3\u003e\n\u003cp\u003eYou\u0027ll need the Java version of Minecraft 1.12.2 to enter the world and hang out with your creatures. You can connect to the server you started by selecting Multiplayer -\u0026gt; Direct Connect -\u0026gt; localhost. The game mode is creative and as such double tapping on jump (spacebar) will allow you to fly; hold shift to go fast.\u003c/p\u003e\n\u003cp\u003eThe locations of the creatures are output in (x, z, y) format (don\u0027t ask me why the Minecraft devs did this) when Ray finishes initializing and before the creatures start optimizing. You can use these coordinates to teleport yourself to their respective locations with the server command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/teleport [your_name] x z y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eI usually add 100 or so (units are in blocks) to the z coordinate such that I am teleported above the creature.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tips\" class=\"anchor\" aria-hidden=\"true\" href=\"#tips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTips\u003c/h2\u003e\n\u003cp\u003eYou can change how the world is generated in the minecraft server properties file. Check out this \u003ca href=\"https://minecraft.tools/en/custom.php?#seed\" rel=\"nofollow\"\u003ecustomized generator\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIt\u0027s common to edit the run file (IE in the example get_the_block.py), run the docker container, and wonder why nothing changed. A simple solution is to build the image before running it by convention:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t growth \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e docker run -it --rm --gpus all -v /tmp:/home/ray/ray_results --network host growth python run_configurations/minecraft/get_the_block.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003e[1] Kriegman, Sam. \"Why virtual creatures matter.\" Nature Machine Intelligence 1.10 (2019): 492-492.\u003c/p\u003e\n\u003cp\u003e[2] Grbic, Djordje, et al. \"EvoCraft: A new challenge for open-endedness.\" International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Springer, Cham, 2021.\u003c/p\u003e\n\u003cp\u003e[3] Stanley, Kenneth O. \"Compositional pattern producing networks: A novel abstraction of development.\" Genetic programming and evolvable machines 8.2 (2007): 131-162.\u003c/p\u003e\n", - "stargazers_count": 12, - "subscribers_count": 2, + "full_name": "datalad/datalad-container", + "latest_release": "1.2.5", + "readme": "\u003cpre\u003e\u003ccode\u003e ____ _ _ _\n| _ \\ __ _ | |_ __ _ | | __ _ __| |\n| | | | / _` || __| / _` || | / _` | / _` |\n| |_| || (_| || |_ | (_| || |___ | (_| || (_| |\n|____/ \\__,_| \\__| \\__,_||_____| \\__,_| \\__,_|\n Container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://ci.appveyor.com/project/mih/datalad-container/branch/master\" rel=\"nofollow\"\u003e\u003cimg 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href=\"https://pypi.python.org/pypi/datalad-container/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d925162c5cba0998157f0c36ba3ae092619b5c796dfd243c058b590155392d66/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d636f6e7461696e65722e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-container.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.3368666\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8c03e2106b715ec0c43b16912bc2cffc2508a95629098f4d93bbb7e5aede5a32/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333336383636362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3368666.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/37e04df5ee8be0e08ef80a25b02b47c9c23c5efda07f20867581554c04e5da4c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d636f6e7461696e65722f6261646765732f76657273696f6e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37e04df5ee8be0e08ef80a25b02b47c9c23c5efda07f20867581554c04e5da4c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f646174616c61642d636f6e7461696e65722f6261646765732f76657273696f6e2e737667\" alt=\"Conda\" data-canonical-src=\"https://anaconda.org/conda-forge/datalad-container/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis extension enhances DataLad (\u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003ehttp://datalad.org\u003c/a\u003e) for working with\ncomputational containers. Please see the \u003ca href=\"http://datalad-container.rtfd.org\" rel=\"nofollow\"\u003eextension\ndocumentation\u003c/a\u003e\nfor a description on additional commands and functionality.\u003c/p\u003e\n\u003cp\u003eFor general information on how to use or contribute to DataLad (and this\nextension), please see the \u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003eDataLad website\u003c/a\u003e or the\n\u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003emain GitHub project page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore you install this package, please make sure that you \u003ca href=\"https://git-annex.branchable.com/install\" rel=\"nofollow\"\u003einstall a recent\nversion of git-annex\u003c/a\u003e. Afterwards,\ninstall the latest version of \u003ccode\u003edatalad-container\u003c/code\u003e from\n\u003ca href=\"https://pypi.org/project/datalad-container\" rel=\"nofollow\"\u003ePyPi\u003c/a\u003e. It is recommended to use\na dedicated \u003ca href=\"https://virtualenv.pypa.io\" rel=\"nofollow\"\u003evirtualenv\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# create and enter a new virtual environment (optional)\nvirtualenv --system-site-packages --python=python3 ~/env/datalad\n. ~/env/datalad/bin/activate\n\n# install from PyPi\npip install datalad_container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is also available for conda package manager from conda-forge:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c conda-forge datalad-container\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe documentation of this project is found here:\n\u003ca href=\"http://docs.datalad.org/projects/container\" rel=\"nofollow\"\u003ehttp://docs.datalad.org/projects/container\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAll bugs, concerns and enhancement requests for this software can be submitted here:\n\u003ca href=\"https://github.com/datalad/datalad-container/issues\"\u003ehttps://github.com/datalad/datalad-container/issues\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you have a problem or would like to ask a question about how to use DataLad,\nplease \u003ca href=\"https://neurostars.org/tags/datalad\" rel=\"nofollow\"\u003esubmit a question to\nNeuroStars.org\u003c/a\u003e with a \u003ccode\u003edatalad\u003c/code\u003e tag.\nNeuroStars.org is a platform similar to StackOverflow but dedicated to\nneuroinformatics.\u003c/p\u003e\n\u003cp\u003eAll previous DataLad questions are available here:\n\u003ca href=\"http://neurostars.org/tags/datalad/\" rel=\"nofollow\"\u003ehttp://neurostars.org/tags/datalad/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDataLad development is supported by a US-German collaboration in computational\nneuroscience (CRCNS) project \"DataGit: converging catalogues, warehouses, and\ndeployment logistics into a federated \u0027data distribution\u0027\" (Halchenko/Hanke),\nco-funded by the US National Science Foundation (NSF 1429999) and the German\nFederal Ministry of Education and Research (BMBF 01GQ1411). Additional support\nis provided by the German federal state of Saxony-Anhalt and the European\nRegional Development Fund (ERDF), Project: Center for Behavioral Brain\nSciences, Imaging Platform. This work is further facilitated by the ReproNim\nproject (NIH 1P41EB019936-01A1).\u003c/p\u003e\n", + "stargazers_count": 11, + "subscribers_count": 9, "topics": [ - "virtual-creatures", - "reinforcement-learning", - "reinforcement-learning-environments" + "container", + "datalad" ], - "updated_at": 1676925092.0 + "updated_at": 1697766017.0 }, { "data_format": 2, - "description": "The mapping pipeline for HoloLens device", + "description": "Finmag source", "filenames": [ - "third_party/poselib/Singularity.def", - "third_party/PatchmatchNet/Singularity.def", - "third_party/Fast-Robust-ICP/Singularity.def", - "third_party/Hierarchical-Localization/Singularity.def" + "install/docker/singularity/Singularity", + "dev/singularity/Singularity" ], - "full_name": "michalpolic/hololens_mapper", - "latest_release": null, - "readme": "\u003cp\u003eWelcome in repository focused on processing AR devices recordings.\u003c/p\u003e\n\u003cp\u003eThat main goal of this project is to implement all the research codes in single place that allow easy setup, easy buils, and remove the time that is required to reinstall all the repositories, writing the format conversion, and wrappers for deployment. The overview of the codes will be here (TODO), however, you can simply run the GUI and load the pipeline that are already done, e.g., localization, dense and sparse mapping, downloading of the AR device recordings, dense point clouds alignment, etc..\u003c/p\u003e\n\u003chr\u003e\n\u003cpre\u003e\u003ccode\u003e HOW TO INSTALL ME\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003col\u003e\n\u003cli\u003eRequirements (install in advance)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003cli\u003eCmake\u003c/li\u003e\n\u003cli\u003eAnaconda\u003c/li\u003e\n\u003cli\u003eSingularity (Linux) / Docker (Windows) (most of the codes are in containers to run on any machine)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eClone this repository and its submodules\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003egit clone --recurse-submodules --remote-submodules \u003ca href=\"https://github.com/michalpolic/hololens_mapper.git\"\u003ehttps://github.com/michalpolic/hololens_mapper.git\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate or copy the Docker/Singularity containers (copy the containers if available, otherwise use code in init.sh)\u003c/li\u003e\n\u003cli\u003eCreate the conda environment (use code in init.sh)\u003c/li\u003e\n\u003cli\u003eCompile the C++ codes (use code in init.sh)\u003c/li\u003e\n\u003cli\u003eDownload pre-trained weights for NetVLAD (use code in init.sh)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eRun the initialization (steps 3-6): \u003ccode\u003esh ./init.sh\u003c/code\u003e (building the containes may take few hours)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eActivate conda enviroment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eIn general: \u003ccode\u003econda activate meshroom\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUsing VS Code: \u003ccode\u003eF1 -\u0026gt; Python:Select Interpreter -\u0026gt; meshroom\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eIf debuging in VS Code\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSpecify what should be called and which parameters to use. Create \u003ccode\u003elaunch.json\u003c/code\u003e, for example:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e {\n \"version\": \"0.2.0\",\n \"configurations\": [\n {\n \"name\": \"Meshroom GUI\",\n \"type\": \"python\",\n \"request\": \"launch\",\n \"program\": \"\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom/ui\",\n \"console\": \"integratedTerminal\",\n \"env\": {\n \"PYTHONPATH\": \"\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom;\u0026lt;path to hololens_mapper\u0026gt;\"\n }\n },\n {\n \"name\": \"Meshroom batch\",\n \"type\": \"python\",\n \"request\": \"launch\",\n \"program\": \"\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/bin/meshroom_compute\",\n \"console\": \"integratedTerminal\",\n \"env\": {\n \"PYTHONPATH\": \n \"\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom;\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom;\u0026lt;path to hololens_mapper\u0026gt;\"\n },\n \"args\": [\n \"\u0026lt;path to hololens_mapper\u0026gt;/pipelines/\u0026lt;your pipeline name.mg\u0026gt;\",\n \"--cache\",\n \"\u0026lt;your output cache folder\u0026gt;/\u0026lt;evaluation folder\u0026gt;\"\n ]\n }\n ]\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf not using VS Code, execute:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003ea) \u003ccode\u003eexport PYTHONPATH=\"$PYTHONPATH;\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom;\u0026lt;path to hololens_mapper\u0026gt;\"\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eb) run \u003ccode\u003epython \u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom/ui\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cpre\u003e\u003ccode\u003e HOW TO RUN ME\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eIf you finished previous section, you can create, build, modify, and test your pipelines in Meshroom GUI.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe pipelines that will be uploaded in git are in \u003ccode\u003e\u0026lt;path to hololens_mapper\u0026gt;/pipelines\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you want any \"private\" pipeline, start it with \u003ccode\u003etmp_\u0026lt;your private pipeline name\u0026gt;.mg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cpre\u003e\u003ccode\u003e HOW TO CONTRIBUTE\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eIf you would like to add your nodes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFind a simple node, e.g., \u003ccode\u003e\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom/nodes/Alignment/DensePonitcloudsConcatenator.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMake your own copy to proper folder in \u003ccode\u003e\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom/nodes\u003c/code\u003e (please follow reasonable naming)\u003c/li\u003e\n\u003cli\u003eModify the content as you wish\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo add your own source codes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe source codes are located in \u003ccode\u003e\u0026lt;path to hololens_mapper\u0026gt;/src\u003c/code\u003e (please, follow reanable naming)\u003c/li\u003e\n\u003cli\u003eYour own containers building add into \u003ccode\u003esh ./init.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 12, - "subscribers_count": 4, + "full_name": "fangohr/finmag", + "latest_release": "0.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"dev/logos/finmag_logo.png\"\u003e\u003cimg src=\"dev/logos/finmag_logo.png\" width=\"300\" align=\"right\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFinMag: finite-element micromagnetic simulation tool\u003c/h1\u003e\u003ca id=\"user-content-finmag-finite-element-micromagnetic-simulation-tool\" class=\"anchor\" aria-label=\"Permalink: FinMag: finite-element micromagnetic simulation tool\" href=\"#finmag-finite-element-micromagnetic-simulation-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMarc-Antonio Bisotti\u003csup\u003e1\u003c/sup\u003e, Marijan Beg\u003csup\u003e1,2\u003c/sup\u003e, Weiwei Wang\u003csup\u003e1\u003c/sup\u003e, Maximilian Albert\u003csup\u003e1\u003c/sup\u003e, Dmitri Chernyshenko\u003csup\u003e1\u003c/sup\u003e, David Cort\u00e9s-Ortu\u00f1o\u003csup\u003e1\u003c/sup\u003e, Ryan A. Pepper\u003csup\u003e1\u003c/sup\u003e, Mark Vousden\u003csup\u003e1\u003c/sup\u003e, Rebecca Carey\u003csup\u003e1\u003c/sup\u003e, Hagen Fuchs\u003csup\u003e3\u003c/sup\u003e, Anders Johansen\u003csup\u003e1\u003c/sup\u003e, Gabriel Balaban\u003csup\u003e1\u003c/sup\u003e, Leoni Breth\u003csup\u003e1\u003c/sup\u003e, Thomas Kluyver\u003csup\u003e1,2\u003c/sup\u003e, and Hans Fangohr\u003csup\u003e1,2,4\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e \u003cem\u003eFaculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom\u003c/em\u003e\u003cbr\u003e\n\u003csup\u003e2\u003c/sup\u003e \u003cem\u003eEuropean XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Germany\u003c/em\u003e\u003cbr\u003e\n\u003csup\u003e3\u003c/sup\u003e \u003cem\u003eHelmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstra\u00dfe 400, 01328 Dresden, Germany\u003c/em\u003e\u003cbr\u003e\n\u003csup\u003e4\u003c/sup\u003e \u003cem\u003eMax Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany\u003c/em\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eBadge\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eTests\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/fangohr/finmag/actions\"\u003e\u003cimg src=\"https://github.com/fangohr/finmag/workflows/workflow/badge.svg\" alt=\"workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/fangohr/finmag/actions\"\u003e\u003cimg src=\"https://github.com/fangohr/finmag/workflows/docker-image/badge.svg\" alt=\"docker-image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBinder\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://mybinder.org/v2/gh/fangohr/finmag/HEAD?filepath=binder%2Findex.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e91e1d353a8b6acf0b42547ac3901f2c30138a3abaaa3d3c242da30b5b4f8426/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLicense\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://opensource.org/licenses/BSD-3-Clause\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa27bfae9200ad81b9c64e82edafa3aef061e2b59e4089eb0841297d510d5db9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d425344253230332d2d436c617573652d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDockerHub\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://hub.docker.com/u/finmag/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe6bb5dae13c77487b7b04bb4efd75e44e68e33bf3db1ccbf0d857375f06556e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f446f636b65724875622d66696e6d61672d626c75652e737667\" alt=\"DockerHub\" data-canonical-src=\"https://img.shields.io/badge/DockerHub-finmag-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDOI\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.1216011\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0cb9a6cf62d35110acffc525d65a5133418ed32b1b5a9408b44af70e4feca414/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313231363031312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1216011.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFinmag was intended to be a thin (and mostly) Python layer on top of \u003ca href=\"https://fenicsproject.org/\" rel=\"nofollow\"\u003eFEniCS\u003c/a\u003e to enable Python-scripted multi-physics micromagnetic simulations. Accordingly, the name FINmag originates from the dolFIN interface to FEniCS. Some compiled code moved into the project.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe code has been developed from 2011 to 2018 by \u003ca href=\"http://fangohr.github.io\" rel=\"nofollow\"\u003eHans Fangohr\u003c/a\u003e\u0027s group at the University of Southampton (UK) and European XFEL GmbH (Germany).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe GitHub page of the project with the most recent version is \u003ca href=\"https://github.com/fangohr/finmag\"\u003ehttps://github.com/fangohr/finmag\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis is a working prototype which is not polished, with some (in large parts outdated) attempts at documentation. There is also some outdated code in the repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe do not consider the codebase, documentation, and other content of sufficient quality to encourage uptake in the community. (Experts are welcome!) This is primarily a resource problem.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDoes not execute efficiently in parallel (time integration is serial).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere is no support available.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eContributions and pull requests to both the code and documentation are welcome, but no promise can be made that these will be reviewed and/or integrated.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe code has been used for a number of scientific studies and publications (see the \u003ca href=\"#Publications\"\u003ePublications\u003c/a\u003e section).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe repository may well be of historical value and probably captures some of the typical research software engineering challenges. (We should write up a summary of our gathered experiences.)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere has not been dedicated funding to support the software development.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation / Using the tool via Docker\u003c/h2\u003e\u003ca id=\"user-content-installation--using-the-tool-via-docker\" class=\"anchor\" aria-label=\"Permalink: Installation / Using the tool via Docker\" href=\"#installation--using-the-tool-via-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere is a dedicated organisation on \u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003eDockerHub\u003c/a\u003e named \u003ca href=\"https://hub.docker.com/u/finmag/\" rel=\"nofollow\"\u003e\u003ccode\u003efinmag\u003c/code\u003e\u003c/a\u003e. We provide pre-built images in the \u003ca href=\"https://hub.docker.com/r/finmag/finmag/\" rel=\"nofollow\"\u003e\u003ccode\u003efinmag/finmag\u003c/code\u003e\u003c/a\u003e repository. More information about Docker, as well as on how to install it on your system, can be found \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGetting the image\u003c/h3\u003e\u003ca id=\"user-content-getting-the-image\" class=\"anchor\" aria-label=\"Permalink: Getting the image\" href=\"#getting-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to get the most recent image is by pulling it from the DockerHub \u003ca href=\"https://hub.docker.com/r/finmag/finmag/\" rel=\"nofollow\"\u003e\u003ccode\u003efinmag/finmag\u003c/code\u003e\u003c/a\u003e repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker pull finmag/finmag:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, you can navigate to \u003ccode\u003einstall/docker/latest\u003c/code\u003e and run \u003ccode\u003emake pull\u003c/code\u003e. You can also build it on your own machine by navigating to \u003ccode\u003einstall/docker/latest\u003c/code\u003e, and running\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ make build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting\u003c/h3\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter you pulled/built the \u003ccode\u003efinmag/finmag\u003c/code\u003e image, you can test it with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -ti -w=\"/finmag\" --rm finmag/finmag bash -c \"py.test -v\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor by running \u003ccode\u003emake test\u003c/code\u003e in \u003ccode\u003einstall/docker/latest\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the container\u003c/h3\u003e\u003ca id=\"user-content-running-the-container\" class=\"anchor\" aria-label=\"Permalink: Running the container\" href=\"#running-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run your Finmag code inside Docker, please navigate to the directory where your \u003ccode\u003emy-finmag-script.py\u003c/code\u003e file is (\u003ccode\u003ecd path/to/your/file\u003c/code\u003e) and run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -ti -v $(pwd):/io --rm finmag/finmag bash -c \"python my-finmag-script.py\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to run code interactively inside the container, then you can start with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ docker run -ti -v $(pwd):/io --rm finmag/finmag\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFinmag dependencies container\u003c/h3\u003e\u003ca id=\"user-content-finmag-dependencies-container\" class=\"anchor\" aria-label=\"Permalink: Finmag dependencies container\" href=\"#finmag-dependencies-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker image which contains all of the dependencies necessary to run finmag is hosted on DockerHub as \u003ccode\u003efinmag/finmag:dependencies\u003c/code\u003e. Similar to previous sections, if you navigate to \u003ccode\u003einstall/docker/dependencies\u003c/code\u003e, you can run \u003ccode\u003emake pull\u003c/code\u003e, \u003ccode\u003emake run\u003c/code\u003e, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstalling on host\u003c/h3\u003e\u003ca id=\"user-content-installing-on-host\" class=\"anchor\" aria-label=\"Permalink: Installing on host\" href=\"#installing-on-host\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMore detailed comments on the installation of finmag on a host machine are in \u003ca href=\"install/README.md\"\u003e\u003ccode\u003einstall/README.md\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBinder\u003c/h2\u003e\u003ca id=\"user-content-binder\" class=\"anchor\" aria-label=\"Permalink: Binder\" href=\"#binder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to try using Finmag in the cloud you can do it on \u003ca href=\"https://mybinder.org/v2/gh/fangohr/finmag/HEAD?filepath=binder%2Findex.ipynb\" rel=\"nofollow\"\u003eBinder\u003c/a\u003e. This does not require you to have anything installed and no files will be created on your machine. You only need a web browser.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe documentation in the form of \u003ca href=\"http://jupyter.org/\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e notebooks is available in \u003ca href=\"doc/ipython_notebooks_src\"\u003e\u003ccode\u003edoc/ipython_notebooks_src\u003c/code\u003e\u003c/a\u003e directory. Large parts of documentation are currently outdated.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to cite\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite\" class=\"anchor\" aria-label=\"Permalink: How to cite\" href=\"#how-to-cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use Finmag in your research, please cite it as\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarc-Antonio Bisotti, Marijan Beg, Weiwei Wang, Maximilian Albert, Dmitri Chernyshenko, David Cort\u00e9s-Ortu\u00f1o, Ryan A. Pepper, Mark Vousden, Rebecca Carey, Hagen Fuchs, Anders Johansen, Gabriel Balaban, Leoni Breth, Thomas Kluyver, and Hans Fangohr. FinMag: finite-element micromagnetic simulation tool (Version 0.1). Zenodo. DOI: \u003ca href=\"http://doi.org/10.5281/zenodo.1216011\" rel=\"nofollow\"\u003ehttp://doi.org/10.5281/zenodo.1216011\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinmag is licensed under the BSD 3-Clause \"New\" or \"Revised\" License. For details, please refer to the \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e file. However, portions of the source code (e.g. src/util/numpy.h) are subject to the Boost Software License.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe do not provide support for Finmag. However, you are welcome to raise an issue in the GitHub \u003ca href=\"https://github.com/fangohr/finmag\"\u003efangohr/finmag\u003c/a\u003e repository, but no promise can be made that the issue will be addressed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePublications\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinmag was used to run micromagnetic simulations in the following publications (in reversed chronological order):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eM. Beg, R. A. Pepper, D. Cort\u00e9s-Ortu\u00f1o, B. Atie, M. A. Bisotti, G. Downing, T. Kluyver, O. Hovorka, H. Fangohr. Stable and manipulable Bloch point. \u003ca href=\"https://doi.org/10.1038/s41598-019-44462-2\" rel=\"nofollow\"\u003eScientific Reports 9, 7959\u003c/a\u003e (2019). (arXiv:1808.10772)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eR. A. Pepper, M. Beg, D. Cort\u00e9s-Ortu\u00f1o, T. Kluyver, M.-A. Bisotti, R. Carey, M. Vousden, M. Albert, W. Wang, O. Hovorka, and H. Fangohr. Skyrmion states in thin confined polygonal nanostructures. \u003ca href=\"http://aip.scitation.org/doi/10.1063/1.5022567\" rel=\"nofollow\"\u003eJournal of Applied Physics 9, 093903\u003c/a\u003e (2018). (arXiv:1801.03275)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eD. Cort\u00e9s-Ortu\u00f1o, W. Wang, M. Beg, R. A. Pepper, M.-A. Bisotti, R. Carey, M. Vousden, T. Kluyver, O. Hovorka, and H. Fangohr. Thermal stability and topological protection of skyrmions in nanotracks. \u003ca href=\"http://www.nature.com/articles/s41598-017-03391-8\" rel=\"nofollow\"\u003eScientific Reports 7, 4061\u003c/a\u003e (2017). (arXiv:1611.07079)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Beg, M. Albert, M.-A. Bisotti, D. Cort\u00e9s-Ortu\u00f1o, W. Wang, R. Carey, M. Vousden, O. Hovorka, C. Ciccarelli, C. S. Spencer, C. H. Marrows, and H. Fangohr. Dynamics of skyrmionic states in confined helimagnetic nanostructures. \u003ca href=\"http://link.aps.org/doi/10.1103/PhysRevB.95.014433\" rel=\"nofollow\"\u003ePhysical Review B 95, 014433\u003c/a\u003e (2017). (arXiv:1604.08347)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA. Baker, M. Beg, G. Ashton, M. Albert, D. Chernyshenko, W. Wang, S. Zhang, M.-A. Bisotti, M. Franchin, C. Lian Hu, R. L. Stamps, T. Hesjedal, and H. Fangohr. Proposal of a micromagnetic standard problem for ferromagnetic resonance simulations. \u003ca href=\"http://linkinghub.elsevier.com/retrieve/pii/S0304885316307545\" rel=\"nofollow\"\u003eJournal of Magnetism and Magnetic Materials 421, 428-439\u003c/a\u003e (2017). (arXiv:1603.05419)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eP. J. Metaxas, M. Albert, S. Lequeux, V. Cros, J. Grollier, P. Bortolotti, A. Anane, and H. Fangohr. Resonant translational, breathing, and twisting modes of transverse magnetic domain walls pinned at notches. \u003ca href=\"https://journals.aps.org/prb/abstract/10.1103/PhysRevB.93.054414\" rel=\"nofollow\"\u003ePhys. Rev. B 93, 054414\u003c/a\u003e (2016).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJ. P. Fried, H. Fangohr, M. Kostylev, and P. J. Metaxas. Exchange-mediated, nonlinear, out-of-plane magnetic field dependence of the ferromagnetic vortex gyrotropic mode frequency driven by core deformation. \u003ca href=\"https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.224407\" rel=\"nofollow\"\u003ePhys. Rev. B 94, 224407\u003c/a\u003e (2016).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eR. Carey, M. Beg, M. Albert, M.-A. Bisotti, D. Cort\u00e9s-Ortu\u00f1o, M. Vousden, W. Wang, O. Hovorka, and H. Fangohr. Hysteresis of nanocylinders with Dzyaloshinskii-Moriya interaction. \u003ca href=\"http://scitation.aip.org/content/aip/journal/apl/109/12/10.1063/1.4962726\" rel=\"nofollow\"\u003eApplied Physics Letters 109, 122401\u003c/a\u003e (2016). (arXiv:1606.05181)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Sushruth, J. Ding, J. Duczynski, R. C. Woodward, R. A. Begley, H. Fangohr, R. O. Fuller, A. O. Adeyeye, M. Kostylev, and P. J. Metaxas. Resonance-Based Detection of Magnetic Nanoparticles and Microbeads Using Nanopatterned Ferromagnets. \u003ca href=\"https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.6.044005\" rel=\"nofollow\"\u003ePhys. Rev. Applied 6, 044005\u003c/a\u003e (2016).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Albert, M. Beg, D. Chernyshenko, M.-A. Bisotti, R. L. Carey, H. Fangohr, and P. J. Metaxas. Frequency-based nanoparticle sensing over large field ranges using the ferromagnetic resonances of a magnetic nanodisc. \u003ca href=\"http://stacks.iop.org/0957-4484/27/i=45/a=455502?key=crossref.2ac6ca2e40700c0c20b17814ae4f6a9d\" rel=\"nofollow\"\u003eNanotechnology 27, 455502\u003c/a\u003e (2016). (arXiv:1604.07277)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Vousden, M. Albert, M. Beg, M.-A. Bisotti, R. Carey, D. Chernyshenko, D. Cort\u00e9s-Ortu\u00f1o, W. Wang, O. Hovorka, C. H. Marrows, and H. Fangohr. Skyrmions in thin films with easy-plane magnetocrystalline anisotropy. \u003ca href=\"http://aip.scitation.org/doi/10.1063/1.4945262\" rel=\"nofollow\"\u003eApplied Physics Letters 108, 132406\u003c/a\u003e (2016). (arXiv:1602.02064)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJ. P. Fried and P. J. Metaxas. Localized magnetic fields enhance the field sensitivity of the gyrotropic resonance frequency of a magnetic vortex. \u003ca href=\"https://journals.aps.org/prb/abstract/10.1103/PhysRevB.93.064422\" rel=\"nofollow\"\u003ePhys. Rev. B 93, 064422\u003c/a\u003e (2016).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eM. Beg, R. Carey, W. Wang, D. Cort\u00e9s-Ortu\u00f1o, M. Vousden, M.-A. Bisotti, M. Albert, D. Chernyshenko, O. Hovorka, R. L. Stamps, and H. Fangohr. Ground state search, hysteretic behaviour, and reversal mechanism of skyrmionic textures in confined helimagnetic nanostructures. \u003ca href=\"http://www.nature.com/articles/srep17137\" rel=\"nofollow\"\u003eScientific Reports 5, 17137\u003c/a\u003e (2015). (arXiv:1312.7665)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eP. J. Metaxas, M. Sushruth, R. A. Begley, J. Ding, R. C. Woodward, I. S. Maksymov, M. Albert, W. Wang, H. Fangohr, A. O. Adeyeye, and M. Kostylev. Sensing magnetic nanoparticles using nano-confined ferromagnetic resonances in a magnonic crystal. \u003ca href=\"https://aip.scitation.org/doi/abs/10.1063/1.4922392\" rel=\"nofollow\"\u003eAppl. Phys. Lett. 106, 232406\u003c/a\u003e (2015).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eW. Wang, M. Albert, M. Beg, M.-A. Bisotti, D. Chernyshenko, D. Cort\u00e9s-Ortu\u00f1o, I. Hawke, and H. Fangohr. Magnon driven domain wall motion with Dzyaloshinskii-Moriya interaction. \u003ca href=\"http://link.aps.org/doi/10.1103/PhysRevLett.114.087203\" rel=\"nofollow\"\u003ePhysical Review Letters 114, 087203\u003c/a\u003e (2015). (arXiv:1406.5997)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eW. Wang, M. Beg, B. Zhang, W. Kuch, and H. Fangohr. Driving magnetic skyrmions with microwave fields. \u003ca href=\"http://link.aps.org/doi/10.1103/PhysRevB.92.020403\" rel=\"nofollow\"\u003ePhysical Review B (Rapid Communications) 92, 020403\u003c/a\u003e (2015). (arXiv:1505.00445)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eW. Wang, M. Dvornik, M.-A. Bisotti, D. Chernyshenko, M. Beg, M. Albert, A. Vansteenkiste, B. V. Waeyenberge, A. N. Kuchko, V. V. Kruglyak, and H. Fangohr. Phenomenological description of the nonlocal magnetization relaxation in magnonics, spintronics, and domain-wall dynamics. \u003ca href=\"http://link.aps.org/doi/10.1103/PhysRevB.92.054430\" rel=\"nofollow\"\u003ePhysical Review B 92, 054430\u003c/a\u003e (2015). (arXiv:1508.01478)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eB. Zhang, W. Wang, M. Beg, H. Fangohr, and W. Kuch. Microwave-induced dynamic switching of magnetic skyrmion cores in nanodots. \u003ca href=\"http://scitation.aip.org/content/aip/journal/apl/106/10/10.1063/1.4914496\" rel=\"nofollow\"\u003eApplied Physics Letters 106, 102401\u003c/a\u003e (2015). (arXiv:1503.02869)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEPSRC\u2019s \u003ca href=\"http://www.icss.soton.ac.uk\" rel=\"nofollow\"\u003eDoctoral Training Centre in Complex System Simulation\u003c/a\u003e (EP/G03690X/1),\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEPSRC\u0027s \u003ca href=\"http://ngcm.soton.ac.uk\" rel=\"nofollow\"\u003eCentre for Doctoral Training in Next Generation Computational Modelling\u003c/a\u003e (#EP/L015382/1),\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHorizon 2020 European Research Infrastructure project \u003ca href=\"http://opendreamkit.org/\" rel=\"nofollow\"\u003eOpenDreamKit\u003c/a\u003e (676541),\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEPSRC\u0027s \u003ca href=\"https://www.skyrmions.ac.uk/\" rel=\"nofollow\"\u003eProgramme grant on Skyrmionics\u003c/a\u003e (EP/N032128/1),\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.moore.org/\" rel=\"nofollow\"\u003eGordon and Betty Moore Foundation\u003c/a\u003e through Grant GBMF #4856, by the Alfred P. Sloan Foundation and by the Helmsley Trust.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSee also\u003c/h2\u003e\u003ca id=\"user-content-see-also\" class=\"anchor\" aria-label=\"Permalink: See also\" href=\"#see-also\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/computationalmodelling/fidimag\"\u003eFidimag\u003c/a\u003e: finite-difference micromagnetic simulation tool\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 11, + "subscribers_count": 6, "topics": [], - "updated_at": 1659347420.0 + "updated_at": 1712567597.0 }, { "data_format": 2, - "description": "Modules for describing software environment on biocluster", + "description": "A simple example of running a MongoDB instance to query a database", "filenames": [ - "freefem/Singularity.def" + "v1.0/rstudio-simple/rstudio/Singularity", + "v1.0/rstudio-simple/nginx/Singularity", + "v1.0/mongodb-build/mongodb/Singularity", + "v1.0/bert-as-compose/server/Singularity.gpu", + "v1.0/bert-as-compose/server/Singularity", + "v1.0/bert-as-compose/client/Singularity", + "v1.0/django-nginx-upload/Singularity", + "v1.0/django-nginx-upload/db/Singularity", + "v1.0/django-nginx-upload/nginx/Singularity", + "v1.0/apache-simple/httpd/Singularity", + "v1.0/jupyter-simple/jupyter/Singularity", + "v2.0/start-args/Singularity", + "v2.0/ping/alp1/Singularity", + "v2.0/ping/alp2/Singularity", + "v2.0/jupyterlab/second/Singularity", + "v2.0/code-server/Singularity", + "v2.0/deephyperx/Singularity" ], - "full_name": "ucr-hpcc/hpcc_modules", + "full_name": "singularityhub/singularity-compose-examples", "latest_release": null, - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hpcc-modules\" class=\"anchor\" href=\"#hpcc-modules\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPCC Modules\u003c/h1\u003e\n\u003cp\u003eThese are the modules that are currently available on the UCR HPC cluster.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-standards\" class=\"anchor\" href=\"#standards\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStandards\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eKeep names of packages to all lowercase when possible.\u003c/li\u003e\n\u003cli\u003eSpaces are not allowed, underscores should be used in place of spaces for software module names.\u003c/li\u003e\n\u003cli\u003eSymlinks can be used to map to multiple softwares within an environment (ie. \u003ccode\u003econda\u003c/code\u003e/\u003ccode\u003esingularity\u003c/code\u003e) as well as name variations.\u003c/li\u003e\n\u003cli\u003eWhen software requires a common write location or unique config file, use environment variables within the module to define these.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-locations\" class=\"anchor\" href=\"#locations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocations\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e - All source code should be placed under the \u003ccode\u003e/opt/linux/rhel/8.x/x86_64/pkgs/src\u003c/code\u003e directory.\nHenceforth, this path will be refered to as \u003ccode\u003e$HPCC_MODULES/../src\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eInstall\u003c/strong\u003e - All software installs should be done under the \u003ccode\u003e/opt/linux/rhel/8.x/x86_64/pkgs\u003c/code\u003e directory.\nHenceforth, this path will be refered to as \u003ccode\u003e$HPCC_MODULES\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eModule\u003c/strong\u003e - All modules should be defined under this repo, which is cloned under the \u003ccode\u003e/opt/linux/rhel/8.x/x86_64/modules\u003c/code\u003e directory.\nHenceforth, this path will be refered to as \u003ccode\u003e$HPCC_MODULES/../modules\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: Do NOT modify files directly under the \u003ccode\u003e$HPCC_MODULES../modules\u003c/code\u003e directory. Use your own cloned version of this repo.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-environment-variables\" class=\"anchor\" href=\"#environment-variables\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment variables\u003c/h2\u003e\n\u003cp\u003eThe following variables are used and should be set automatically:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eHPCC_MODULES\u003c/code\u003e - Path dynamically built by /etc/profile.d/hpcc_modules.sh based on OS and arch.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMODULESHOME\u003c/code\u003e - Path set by module system to specify home of module system.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMODULEPATH\u003c/code\u003e - List set by module system to indicate where modules live (colon delimited).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: Exporting \u003ccode\u003eMODULEPATH\u003c/code\u003e to a local clone of this repo can be useful for debugging.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install-software\" class=\"anchor\" href=\"#install-software\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Software\u003c/h2\u003e\n\u003cp\u003eThe most common forms of installs are either binary or source.\nWe do support all kinds of packaged software, thus installations can become more complex then what is covered here.\nFor specific installation details please refer to the documentation prodvided by the software as well as the individual \u003ccode\u003eREADME.md\u003c/code\u003e files contained within the installed module, assuming that the softwere had been installed previously.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-binary\" class=\"anchor\" href=\"#binary\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBinary\u003c/h3\u003e\n\u003cp\u003eWhen installing a binary all that is requied is to download and then extract.\nSometimes there may be an \u003ccode\u003einstall\u003c/code\u003e script to place the binaries under their final resting place, or you may have to do this manually.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-source\" class=\"anchor\" href=\"#source\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-configure\" class=\"anchor\" href=\"#configure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigure\u003c/h4\u003e\n\u003cp\u003eWhen installing from a \u003ccode\u003econfigure\u003c/code\u003e based install, the following would typically apply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econfigure --prefix=\u003cspan class=\"pl-smi\"\u003e$HPCC_MODULES\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$SOFT_NAME\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$SOFT_VER\u003c/span\u003e\nmake\nmake install\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-cmake\" class=\"anchor\" href=\"#cmake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCMake\u003c/h4\u003e\n\u003cp\u003eWhen installing from a \u003ccode\u003ecmake\u003c/code\u003e based install, the following would typically apply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir build\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\ncmake -DCMAKE_INSTALL_PREFIX=\u003cspan class=\"pl-smi\"\u003e$HPCC_MODULES\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$SOFT_NAME\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$SOFT_VER\u003c/span\u003e ../\nmake\nmake install\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eWhen creating a module you must create a \u003ccode\u003eREADME.md\u003c/code\u003e file within the software module directory with reproducible instructions.\nCreating a \u003ccode\u003ehpcc_install.sh\u003c/code\u003e script within the source tree can also be very useful for later installs.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eHere is a \u003ccode\u003ecmake\u003c/code\u003e example: \u003ca href=\"openbabel\"\u003eopenbabel/README.md\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHere is a \u003ccode\u003econda\u003c/code\u003e example: \u003ca href=\"qiime2\"\u003eqiime2/README.md\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHere is a \u003ccode\u003esingularity\u003c/code\u003e example: \u003ca href=\"freefem\"\u003efreefem/README.md\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-create-module\" class=\"anchor\" href=\"#create-module\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Module\u003c/h2\u003e\n\u003cp\u003eFirst set some variables:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Software name\u003c/span\u003e\nSOFT_NAME=[software name]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e First character of software name\u003c/span\u003e\nSOFT_FIRST=\u003cspan class=\"pl-smi\"\u003e${SOFT_NAME\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e0\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e1}\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Software version\u003c/span\u003e\nSOFT_VER=[software version]\u003c/pre\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eMake sure you have a checkout of the hpcc_modules repository\u003c/li\u003e\n\u003cli\u003eLogin as \u003ccode\u003epkgadmin\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate and move into the \u003ccode\u003e$HPCC_MODULES/../src/$SOFT_FIRST/$SOFT_NAME\u003c/code\u003e directory\u003c/li\u003e\n\u003cli\u003eDownload and extract the source files\u003c/li\u003e\n\u003cli\u003eCreate a directory named \u003ccode\u003e$HPCC_MODULES/$SOFT_NAME/$SOFT_VER\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMove binaries (precompiled) or install software into the directory created in step 5\u003c/li\u003e\n\u003cli\u003eLog out of \u003ccode\u003epkgadmin\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMove to your local \u003ccode\u003ehpcc_modules\u003c/code\u003e repo checkout\u003c/li\u003e\n\u003cli\u003eCopy any one of the existing directories, an rename it to \u003ccode\u003e$SOFT_NAME\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGo into the newly created \u003ccode\u003e$SOFT_NAME\u003c/code\u003e directory\u003c/li\u003e\n\u003cli\u003eBy default the latest version will be loaded, if this is not wanted then add the \u003ccode\u003e.version\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eRename one of the contained files to \u003ccode\u003e$SOFT_VER\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit the file from step 12 so that it contains the correct module name, version and paths\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egit add [soft version]\u003c/code\u003e as well as \u003ccode\u003e.version\u003c/code\u003e if added, then \u003ccode\u003egit commit\u003c/code\u003e the changes, lastly do \u003ccode\u003egit push\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAfter this you can test the installation by trying \u003ccode\u003emodule load $SOFT_NAME\u003c/code\u003e and \u003ccode\u003emodule avail $SOFT_NAME\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMake sure to document how the software was installed within a corresponding \u003ccode\u003eREADME.md\u003c/code\u003e file.\u003c/li\u003e\n\u003c/ol\u003e\n", - "stargazers_count": 12, - "subscribers_count": 11, - "topics": [], - "updated_at": 1641773320.0 + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Compose Examples\u003c/h1\u003e\u003ca id=\"user-content-singularity-compose-examples\" class=\"anchor\" aria-label=\"Permalink: Singularity Compose Examples\" href=\"#singularity-compose-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a repository of examples for\n\u003ca href=\"https://singularityhub.github.io/singularity-compose\" rel=\"nofollow\"\u003eSingularity Compose\u003c/a\u003e. For the \"simple\"\nexample that is used during testing, see \u003ca href=\"https://github.com/singularityhub/singularity-compose-simple\"\u003esingularity-compose-simple\u003c/a\u003e. Otherwise, all examples are provided here. You can browse based on the spec version of Singularity Compose:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"v1.0\"\u003ev1.0\u003c/a\u003e is supported for Singularity compose less than v0.1.0\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"v2.0\"\u003ev2.0\u003c/a\u003e is supported for Singularity compose equal to or greater than v0.1.0\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 11, + "subscribers_count": 6, + "topics": [ + "singularity-compose", + "mongodb" + ], + "updated_at": 1692465291.0 }, { "data_format": 2, - "description": "Uncover organisms\u0027 metabolic blueprints", + "description": "Course on Linux containers for scientific environments", "filenames": [ - "docker/Singularity.def" + "recipes/blast/Singularity.conda.environment", + "recipes/blast/Singularity.simple", + "recipes/blast/Singularity", + "recipes/blastwww/Singularity", + "recipes/blastmysql/Singularity.mysql", + "recipes/blastmysql/Singularity" ], - "full_name": "SystemsBioinformatics/ecmtool", + "full_name": "biocorecrg/containers-course", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eecmtool - Uncover organisms\u0027 metabolic blueprints\u003c/h1\u003e\u003ca id=\"user-content-ecmtool---uncover-organisms-metabolic-blueprints\" class=\"anchor\" aria-label=\"Permalink: ecmtool - Uncover organisms\u0027 metabolic blueprints\" href=\"#ecmtool---uncover-organisms-metabolic-blueprints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith this tool you can calculate \u003cem\u003eElementary Conversion Modes\u003c/em\u003e (ECMs) from metabolic networks. Combinations of ECMs comprise all metabolic influences an organism can exert on its environment.\u003c/p\u003e\n\u003cp\u003eecmtool can be used in two different modes: either as a standalone command line tool, or as a Python library for your own scripts. We will describe how to install and use both modes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisites\u003c/h3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload and install Python. Ecmtool is compatible with python 3.x, and tested on 3.10. Ensure both python and its package manager \u003cem\u003epip\u003c/em\u003e are added to your PATH environment variable. If this last step is omitted, an error like the following will be thrown when you try to run python: \u003ccode\u003e\u2019python\u2019 is not recognized as an internal or external command [..]\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDownload and install Java. ecmtool is tested with OpenJDK 17. Make sure you have a 64bit version; you can check this with \u003ccode\u003ejava -version\u003c/code\u003e. Otherwise, you might get an error \u003ccode\u003eInvalid maximum heap size\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMode 1: standalone command line tool\u003c/h2\u003e\u003ca id=\"user-content-mode-1-standalone-command-line-tool\" class=\"anchor\" aria-label=\"Permalink: Mode 1: standalone command line tool\" href=\"#mode-1-standalone-command-line-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this mode, you can call ecmtool like a normal program from your command line. It reads metabolic networks in the SBML format, and writes resulting ECMs into a CSV file for later analysis. Most researchers will use this method. For running ecmtool on computing clusters efficiently, see the Advanced Usage section in this readme.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload the latest ecmtool source through \u003ccode\u003egit clone\u003c/code\u003e, or as a zip file from \u003ca href=\"https://github.com/tjclement/ecmtool\"\u003ehttps://github.com/tjclement/ecmtool\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eOpen a command prompt, and navigate to the ecmtool directory (e.g. \u003ccode\u003ecd C:\\Users\\You\\Git\\ecmtool\u003c/code\u003e, where the\npath should be replaced with the path ecmtool was downloaded to).\u003c/li\u003e\n\u003cli\u003eInstall the dependencies in requirements.txt inside the ecmtool directory (e.g. by running \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eLinux only: install \u003cem\u003eredund\u003c/em\u003e of package \u003cem\u003elrslib\u003c/em\u003e (e.g. by running \u003ccode\u003eapt install lrslib\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstalling ecmtool using Docker\u003c/h4\u003e\u003ca id=\"user-content-installing-ecmtool-using-docker\" class=\"anchor\" aria-label=\"Permalink: Installing ecmtool using Docker\" href=\"#installing-ecmtool-using-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor convenience, there\u0027s a Docker script you can use that has all dependencies already installed, and allows you to directly run ecmtool.\nOpen a terminal with the ecmtool project as its working directory, and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t ecmtool -f docker/Dockerfile \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -ti ecmtool bash\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstalling ecmtool using Singularity\u003c/h4\u003e\u003ca id=\"user-content-installing-ecmtool-using-singularity\" class=\"anchor\" aria-label=\"Permalink: Installing ecmtool using Singularity\" href=\"#installing-ecmtool-using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo be continued.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEcmtool can be run by executing \u003ccode\u003epython main.py \u2013-model_path \u0026lt;path/to/model.xml\u0026gt; [arguments]\u003c/code\u003e from the command line, after navigating to the ecmtool directory as described above. The possible arguments and their default values are printed when you run \u003ccode\u003epython main.py --help\u003c/code\u003e.\nAfter execution is done, the found conversions have been written to file (default: \u003cem\u003econversions.csv\u003c/em\u003e). The first row of this CSV file contain the metabolite IDs as read from the SBML model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample\u003c/h3\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython main.py --model_path models/e_coli_core.xml --auto_direction \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e --out_path core_conversions.csv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBenefiting from optional arguments of ecmtool\u003c/h3\u003e\u003ca id=\"user-content-benefiting-from-optional-arguments-of-ecmtool\" class=\"anchor\" aria-label=\"Permalink: Benefiting from optional arguments of ecmtool\" href=\"#benefiting-from-optional-arguments-of-ecmtool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor an elaborate discussion of all optional arguments that can be used when ecmtool is run as a command line tool, please see the extensive manual that was uploaded as a Supplementary File with the ecmtool-publication at: \u003ca href=\"https://doi.org/10.1016/j.patter.2020.100177\" rel=\"nofollow\"\u003ehttps://doi.org/10.1016/j.patter.2020.100177\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMode 2: Python library\u003c/h2\u003e\u003ca id=\"user-content-mode-2-python-library\" class=\"anchor\" aria-label=\"Permalink: Mode 2: Python library\" href=\"#mode-2-python-library\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eecmtool can also be used as a separate programming interface from within your own Python code. To do so, install ecmtool using \u003cem\u003epip\u003c/em\u003e (e.g. \u003ccode\u003epip install ecmtool\u003c/code\u003e). The most crucial method is ecmtool.conversion_cone:get_conversion_cone(), which returns the ECMs of a given stoichiometric matrix. For information on how to use advanced features like SBML parsing, network compression, and metabolite direction estimation, please see ecmtool/main.py.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWe strongly advise the user to either use ecmtool as a command line tool, or to pay much attention to carefully copy the order from ecmtool/main.py.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample\u003c/h3\u003e\u003ca id=\"user-content-example-1\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eecmtool\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eextract_sbml_stoichiometry\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eecmtool\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003econversion_cone\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eget_conversion_cone\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eecmtool\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ehelpers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eunsplit_metabolites\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprint_ecms_direct\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enumpy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e\n\n\u003cspan class=\"pl-v\"\u003eDETERMINE_INPUTS_OUTPUTS\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Determines whether ecmtool tries to infer directionality (input/output/both)\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003ePRINT_CONVERSIONS\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Prints the resulting ECMs on the console\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eextract_sbml_stoichiometry\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027models/sxp_toy.xml\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eadd_objective\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edetermine_inputs_outputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eDETERMINE_INPUTS_OUTPUTS\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Some steps of compression only work when cone is in one orthant, so we need to split external metabolites with\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# direction \"both\" into two metabolites, one of which is output, and one is input\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esplit_in_out\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eonly_rays\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# It is generally a good idea to compress the network before computation\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecompress\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eSCEI\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecycle_removal\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eremove_infeasible\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003estoichiometry\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eN\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eecms\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eget_conversion_cone\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003estoichiometry\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eexternal_metabolite_indices\u003c/span\u003e(),\n \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ereversible_reaction_indices\u003c/span\u003e(), \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003einput_metabolite_indices\u003c/span\u003e(), \n \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eoutput_metabolite_indices\u003c/span\u003e(), \u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n \n\u003cspan class=\"pl-c\"\u003e# Since we have split the \"both\" metabolites, we now need to unsplit them again\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econe_transpose\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eunsplit_metabolites\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranspose\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eecms\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003econe\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranspose\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econe_transpose\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# We can remove all internal metabolites, since their values are zero in the conversions (by definition of internal)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003einternal_ids\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e []\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emetab\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emetabolites\u003c/span\u003e:\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enot\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emetab\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eis_external\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eid_ind\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s1\"\u003eind\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eind\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eid\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eenumerate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eid\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emetab\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eid\u003c/span\u003e]\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-en\"\u003elen\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eid_ind\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003einternal_ids\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eid_ind\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e])\n\n\u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003elist\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edelete\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einternal_ids\u003c/span\u003e))\n\u003cspan class=\"pl-s1\"\u003econe\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edelete\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econe\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einternal_ids\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eaxis\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# If you wish, one can print the ECM results:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-v\"\u003ePRINT_CONVERSIONS\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint_ecms_direct\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranspose\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econe\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample scripts\u003c/h3\u003e\u003ca id=\"user-content-example-scripts\" class=\"anchor\" aria-label=\"Permalink: Example scripts\" href=\"#example-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the scripts in the folder examples_and_results for examples on how to use ecmtool as a library. In particular: ECM_calc_script.py, compare_efms_ecms_number.py.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnumerating ECMs without an SBML-file\u003c/h3\u003e\u003ca id=\"user-content-enumerating-ecms-without-an-sbml-file\" class=\"anchor\" aria-label=\"Permalink: Enumerating ECMs without an SBML-file\" href=\"#enumerating-ecms-without-an-sbml-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the script examples_and_results/minimal_run_wo_sbml.py for an example on how to compute ECMs starting from a stoichiometric matrix, and some additional information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced usage\u003c/h2\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-label=\"Permalink: Advanced usage\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter testing how the tool works, most users will want to run their workloads on computing clusters instead of on single machines. This section describes some of the steps that are useful for running on clusers\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eParallel computing with OpenMPI\u003c/h3\u003e\u003ca id=\"user-content-parallel-computing-with-openmpi\" class=\"anchor\" aria-label=\"Permalink: Parallel computing with OpenMPI\" href=\"#parallel-computing-with-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Linux or Mac, ecmtool can make use of OpenMPI for running on parallel in a computing cluster. To make use of this feature, in addition to the dependencies in requirements.txt, OpenMPI, mpi4py, and mplrs are required. The installation of OpenMPI and mplrs is done via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapt install libopenmpi-dev\nwget http://cgm.cs.mcgill.ca/~avis/C/lrslib/archive/lrslib-071a.tar.gz\ntar -xzf lrslib-071a.tar.gz\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e lrslib-071a\nmake \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make mplrs \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make install\nln -s \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e/mplrs /usr/local/bin/mplrs\nln -s \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e/redund /usr/local/bin/redund\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe installation of mpi4py is done via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install mpi4py==3.1.4\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRunning ecmtool on a cluster using the indirect enumeration method is now as simple as running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 main.py --processes \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e enumeration\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --model_path models/e_coli_core.xml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that this performs preprocessing steps like network compression on the node you run this command on, and not on the compute cluster.\u003c/p\u003e\n\u003cp\u003eFor direct enumeration, the number of processes for enumeration is passed to mpiexec instead:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empiexec -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e enumeration\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e python3 main.py --direct \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e --model_path models/e_coli_core.xml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn this mode, preprocessing steps are run on the compute cluster too.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdvanced ECM-computation on a computing cluster\u003c/h3\u003e\u003ca id=\"user-content-advanced-ecm-computation-on-a-computing-cluster\" class=\"anchor\" aria-label=\"Permalink: Advanced ECM-computation on a computing cluster\" href=\"#advanced-ecm-computation-on-a-computing-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstallation of ecmtool when the user does not have root privileges on the cluster (a case report)\u003c/h4\u003e\u003ca id=\"user-content-installation-of-ecmtool-when-the-user-does-not-have-root-privileges-on-the-cluster-a-case-report\" class=\"anchor\" aria-label=\"Permalink: Installation of ecmtool when the user does not have root privileges on the cluster (a case report)\" href=\"#installation-of-ecmtool-when-the-user-does-not-have-root-privileges-on-the-cluster-a-case-report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn some computing clusters, it is not easy to install OpenMPI and mplrs. One method that was successful is outlined here. This cluster had an OpenMPI already available as a module that could be loaded. The available versions can be seen by\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule av OpenMPI\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor the installation of mplrs, we will also need GMP, check this by\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule av GMP\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt is important that the versions of OpenMPI and GMP have to match. In this case, we used\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load OpenMPI/4.1.1-GCC-10.3.0\nmodule load GMP/6.2.1-GCCcore-10.3.0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere the last number indicates that they are using a compatible version of GCC. Now, we are ready to install mplrs. This can be done via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapt install libopenmpi-dev\nwget http://cgm.cs.mcgill.ca/~avis/C/lrslib/archive/lrslib-071a.tar.gz\ntar -xzf lrslib-071a.tar.gz\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e lrslib-071a\nmake \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make mplrs \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow we need to tell the cluster where to find the installed mplrs. We can do this by adding the path to mplrs to the search path:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=/scicore/home/nimwegen/degroo0000/ecmtool/lrslib-071a:\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=/scicore/home/nimwegen/degroo0000/ecmtool/lrslib-071a:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow using the command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emplrs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eshould give some output that indicates that mplrs is working and can be found.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning ecmtool using separate runs for non-parallel and parallel parts, with a .sh-script (on a slurm-cluster)\u003c/h4\u003e\u003ca id=\"user-content-running-ecmtool-using-separate-runs-for-non-parallel-and-parallel-parts-with-a-sh-script-on-a-slurm-cluster\" class=\"anchor\" aria-label=\"Permalink: Running ecmtool using separate runs for non-parallel and parallel parts, with a .sh-script (on a slurm-cluster)\" href=\"#running-ecmtool-using-separate-runs-for-non-parallel-and-parallel-parts-with-a-sh-script-on-a-slurm-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo fully exploit parallel computation on a cluster, one would like to use ecmtool in separate steps, as outlined below. (In the ecmtool-folder one can also find an example-script that can be used on a computing cluster that is using slurm: \u003ccode\u003eexamples_and_results/launch_separate_mmsyn_newest.sh\u003c/code\u003e.)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003epreprocessing and compression of the model on a compute node (instead of a login node). For this run\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esrun --ntasks=1 --nodes=1 python3 main.py all_until_mplrs --model_path \u003cspan class=\"pl-smi\"\u003e${MODEL_PATH}\u003c/span\u003e --auto_direction \u003cspan class=\"pl-smi\"\u003e${AUTO_DIRECT}\u003c/span\u003e --hide \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HIDE}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --prohibit \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${PROHIBIT}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --tag \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${TAG}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --inputs \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${INPUTS}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --outputs \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${OUTPUTS}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --use_external_compartment \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${EXT_COMP}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --add_objective_metabolite \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${ADD_OBJ}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --compress \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${COMPRESS}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --hide_all_in_or_outputs \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HIDE_ALL_IN_OR_OUTPUTS}\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere the arguments in curly brackets should be replaced by your choices for these arguments.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003efirst vertex enumeration step with mplrs in parallel. For this run\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mplrs -redund ecmtool/tmp/mplrs.ine ecmtool/tmp/redund.ine\nmpirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mplrs ecmtool/tmp/redund.ine ecmtool/tmp/mplrs.out\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eprocessing of results from first vertex enumeration step, adding steady-state constraints and removing redundant rays using a parallelized redundancy check.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e python3 main.py all_between_mplrs\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003esecond vertex enumeration step with mplrs in parallel\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mplrs -redund ecmtool/tmp/mplrs.ine ecmtool/tmp/redund.ine\nmpirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mplrs ecmtool/tmp/redund.ine ecmtool/tmp/mplrs.out\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eprocessing of results from second vertex enumeration step, unsplitting of metabolites, ensuring that results are unique, and saving ecms to file\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esrun --ntasks=1 --nodes=1 python3 main.py all_from_mplrs --out_path \u003cspan class=\"pl-smi\"\u003e${OUT_PATH}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDoubling direct enumeration method speed\u003c/h3\u003e\u003ca id=\"user-content-doubling-direct-enumeration-method-speed\" class=\"anchor\" aria-label=\"Permalink: Doubling direct enumeration method speed\" href=\"#doubling-direct-enumeration-method-speed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe direct enumeration method can be sped up by compiling our LU decomposition code with Cython. The following describes the steps needed on Linux, but the same concept also applies to Mac OS and Windows. First make sure all dependencies are satisfied. Then execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 cython_setup.py build_ext --inplace\n\nmv _bglu* ecmtool/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u2139\ufe0f Note that in the Docker script, this optimisation has already been done. You don\u0027t need to compile anything there.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAutomatically testing ecmtool and contributing to ecmtool\u003c/h2\u003e\u003ca id=\"user-content-automatically-testing-ecmtool-and-contributing-to-ecmtool\" class=\"anchor\" aria-label=\"Permalink: Automatically testing ecmtool and contributing to ecmtool\" href=\"#automatically-testing-ecmtool-and-contributing-to-ecmtool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen ecmtool is installed properly its functioning with various parameter settings can be tested using some predefined tests using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m pytest tests/test_conversions.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen contributing to ecmtool please make sure that these tests are passed before making a pull request.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting ecmtool\u003c/h2\u003e\u003ca id=\"user-content-citing-ecmtool\" class=\"anchor\" aria-label=\"Permalink: Citing ecmtool\" href=\"#citing-ecmtool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease refer to the following papers when using ecmtool:\u003c/p\u003e\n\u003cp\u003eInitial version - \u003ca href=\"https://www.cell.com/patterns/fulltext/S2666-3899(20)30241-5\" rel=\"nofollow\"\u003ehttps://www.cell.com/patterns/fulltext/S2666-3899(20)30241-5\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emplrs\u003c/code\u003e improved version - \u003ca href=\"https://doi.org/10.1093/bioinformatics/btad095\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btad095\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe original source code with indirect enumeration was written by \u003ca href=\"https://scholar.google.com/citations?user=kUD5y04AAAAJ\" rel=\"nofollow\"\u003eTom Clement\u003c/a\u003e. \u003ca href=\"https://github.com/EBaalhuis\"\u003eErik Baalhuis\u003c/a\u003e later expanded the code with a direct enumeration method that improved parallellisation. \u003ca href=\"https://scholar.google.com/citations?user=xY_GjWkAAAAJ\" rel=\"nofollow\"\u003eDaan de Groot\u003c/a\u003e helped with many new features, bug fixes, and code reviews. \u003ca href=\"https://github.com/BeeAnka\"\u003eBianca Buchner\u003c/a\u003e added support for \u003ccode\u003emplrs\u003c/code\u003e, which raises the maximal size of networks you can enumerate with ecmtool.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eecmtool is released with the liberal MIT license. You are free to use it for any purpose. We hope others will contribute to the field by making derived work publicly available too.\u003c/p\u003e\n", - "stargazers_count": 12, - "subscribers_count": 6, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003econtainers-course\u003c/h1\u003e\u003ca id=\"user-content-containers-course\" class=\"anchor\" aria-label=\"Permalink: containers-course\" href=\"#containers-course\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCourse on Linux containers for scientific environments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEditions\u003c/h2\u003e\u003ca id=\"user-content-editions\" class=\"anchor\" aria-label=\"Permalink: Editions\" href=\"#editions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eJune 13-14, 2019. CRG, Barcelona, Spain. \u003ca href=\"https://slides.com/similis/introduction-linux-containers-training/\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e and \u003ca href=\"https://github.com/ArangoGutierrez/Singularity-tutorial\"\u003ehands-on repository by Carlos Eduardo Arango Gutierrez\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eJanuary 28-29, 2019. CRG, Barcelona, Spain. \u003ca href=\"https://slides.com/similis/introduction-linux-containers-training/\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e and Web seminar by \u003ca href=\"https://github.com/GodloveD\"\u003eDave Godlove\u003c/a\u003e (\u003ca href=\"https://singularity-tutorial.github.io/\" rel=\"nofollow\"\u003eassociated material\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eDecember 11-12, 2018. CRG, Barcelona, Spain. \u003ca href=\"https://slides.com/similis/introduction-linux-containers-training/\" rel=\"nofollow\"\u003eSlides\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSessions guide\u003c/h2\u003e\u003ca id=\"user-content-sessions-guide\" class=\"anchor\" aria-label=\"Permalink: Sessions guide\" href=\"#sessions-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSession 1\u003c/h3\u003e\u003ca id=\"user-content-session-1\" class=\"anchor\" aria-label=\"Permalink: Session 1\" href=\"#session-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIntroduction to containerization\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIntroduction to Docker\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e. Linux and its diversity. Example to different images. Tags\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on testing different images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExporting containers into tar files and importing them back\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIntroduction to software installation\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreating Dockerfiles\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on testing of different installation methods\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExamples\n\u003cul\u003e\n\u003cli\u003eNCBI Blast+\u003c/li\u003e\n\u003cli\u003eVenn Diagram R script\u003c/li\u003e\n\u003cli\u003ePossible exercise: Trimmomatic (\u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003ehttp://www.usadellab.org/cms/?page=trimmomatic\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ePossible exercise: Samtools (\u003ca href=\"http://www.htslib.org/\" rel=\"nofollow\"\u003ehttp://www.htslib.org/\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVolumes\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eUsing sequences. From \u003ca href=\"http://www.uniprot.org\" rel=\"nofollow\"\u003ehttp://www.uniprot.org\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsing Database. Example: Swissprot from NCBI - \u003ca href=\"http://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/\" rel=\"nofollow\"\u003eFASTA files\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on testing of different images and volumes\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eServices\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePort mapping and examples with services\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDifferent approaches to daemons and services\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on with services\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI Blast+ with PHP script\u003c/li\u003e\n\u003cli\u003ePossible exercise: Repeat the same with Samtools or Trimmomatic\u003c/li\u003e\n\u003cli\u003ePossible exercise: Play with Shiny App examples\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LJI-Bioinformatics/Shiny-PCA-Maker\"\u003ehttps://github.com/LJI-Bioinformatics/Shiny-PCA-Maker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/philippmuench/Donut\"\u003ehttps://github.com/philippmuench/Donut\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on, preparing your own example\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSession 2\u003c/h3\u003e\u003ca id=\"user-content-session-2\" class=\"anchor\" aria-label=\"Permalink: Session 2\" href=\"#session-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSum-up last session\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePutting containers to work together\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on example\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNCBI Blast+ with PHP script + MySQL database\u003c/li\u003e\n\u003cli\u003eRepeat the same with Samtools\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDocker compose\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on example\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eContributing back your own images. Automated images with Github\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIntroduction to singularity. Differences with Docker\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDifferent image types. Different ways to generate images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on in getting images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExecution approaches in Singularity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on executing images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSingularity files. Custom building of Singularity images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on building of Singularity images\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsing Docker for building Singularity files\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVolumes in Singularity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eServices/instances in singularity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHands-on services with Singularity\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSum-up course, finish preparing your own example\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 11, + "subscribers_count": 4, "topics": [], - "updated_at": 1703430042.0 + "updated_at": 1714275144.0 }, { "data_format": 2, @@ -37748,140 +37641,180 @@ var data = }, { "data_format": 2, - "description": "Singularity port of HLA typing based on an input exome BAM file and is currently infers infers alleles for the three major MHC class I (HLA-A, -B, -C)", + "description": "Uncover organisms\u0027 metabolic blueprints", "filenames": [ - "Singularity" + "docker/Singularity.def" ], - "full_name": "researchapps/polysolver", + "full_name": "SystemsBioinformatics/ecmtool", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Polysolver\u003c/h1\u003e\u003ca id=\"user-content-singularity-polysolver\" class=\"anchor\" aria-label=\"Permalink: Singularity Polysolver\" href=\"#singularity-polysolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/sachet/polysolver/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/sachet/polysolver/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 polysolver.img\nsudo singularity bootstrap polysolver.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Running commands\u003c/h2\u003e\u003ca id=\"user-content-3-running-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Running commands\" href=\"#3-running-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each of the following commands, you will want to \u003ca href=\"http://singularity.lbl.gov/docs-mount\" rel=\"nofollow\"\u003emount a local data folder\u003c/a\u003e to \u003ccode\u003e/data\u003c/code\u003e inside the container, and then provide paths to \u003ccode\u003e/data\u003c/code\u003e to the executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePolysolver Documentation\u003c/h2\u003e\u003ca id=\"user-content-polysolver-documentation\" class=\"anchor\" aria-label=\"Permalink: Polysolver Documentation\" href=\"#polysolver-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software package consists of 3 main tools:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.1 POLYSOLVER (POLYmorphic loci reSOLVER)\u003c/h3\u003e\u003ca id=\"user-content-11-polysolver-polymorphic-loci-resolver\" class=\"anchor\" aria-label=\"Permalink: 1.1 POLYSOLVER (POLYmorphic loci reSOLVER)\" href=\"#11-polysolver-polymorphic-loci-resolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool can be used for HLA typing based on an input exome BAM file and is currently infers infers alleles for the three major MHC class I (HLA-A, -B, -C).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_type\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput:\u003c/h4\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input:\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e-bam: path to the BAM file to be used for HLA typing\n-race: ethnicity of the individual (Caucasian, Black, Asian or Unknown)\n-includeFreq: flag indicating whether population-level allele frequencies should be used as priors (0 or 1)\n-build: reference genome used in the BAM file (hg18 or hg19)\n-format: fastq format (STDFQ, ILMFQ, ILM1.8 or SLXFQ; see Novoalign documentation)\n-insertCalc: flag indicating whether empirical insert size distribution should be used in the model (0 or 1)\n-outDir: output directory\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput:\u003c/h4\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ewinners.hla.txt\u003c/code\u003e: file containing the two inferred alleles for each of HLA-A, HLA-B and HLA-C\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.2 POLYSOLVER-based mutation detection\u003c/h3\u003e\u003ca id=\"user-content-12-polysolver-based-mutation-detection\" class=\"anchor\" aria-label=\"Permalink: 1.2 POLYSOLVER-based mutation detection\" href=\"#12-polysolver-based-mutation-detection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool works on a tumor/normal pair of exome BAM files and inferred mutations in the tumor file. It assumes that POLYSOLVER has already been run on the normal BAM.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_mutations_from_type\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput:\u003c/h4\u003e\u003ca id=\"user-content-input-1\" class=\"anchor\" aria-label=\"Permalink: Input:\" href=\"#input-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e-normal_bam_hla: path to the normal BAM file\n-tumor_bam_hla: path to the tumor BAM file\n-hla: inferred HLA allele file from POLYSOLVER (winners.hla.txt or winners.hla.nofreq.txt)\n-build: reference genome used in the BAM file (hg18 or hg19)\n-format: fastq format (STDFQ, ILMFQ, ILM1.8 or SLXFQ; see Novoalign documentation)\n-outDir: output directory\t \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput:\u003c/h4\u003e\u003ca id=\"user-content-output-1\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecall_stats.$allele.out: Mutect output for each inferred allele in winners.hla.txt\n$allele.all.somatic.indels.vcf: Strelka output for each inferred allele in winners.hla.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.3 Annotation of mutations\u003c/h3\u003e\u003ca id=\"user-content-13-annotation-of-mutations\" class=\"anchor\" aria-label=\"Permalink: 1.3 Annotation of mutations\" href=\"#13-annotation-of-mutations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool annotates the predicted mutations from (ii) with gene compartment and amino acid change information\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_annotate_hla_mutations\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput:\u003c/h4\u003e\u003ca id=\"user-content-input-2\" class=\"anchor\" aria-label=\"Permalink: Input:\" href=\"#input-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e-indiv: individual ID, used as prefix for output files\n-dir: directory containing the raw call files (Mutect: call_stats*, Strelka: *all.somatic.indels.vcf). Also the output directory\t\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput:\u003c/h3\u003e\u003ca id=\"user-content-output-2\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e (a). Mutect\n$indiv.mutect.unfiltered.nonsyn.annotated - list of all unfiltered mutations\n$indiv.mutect.filtered.nonsyn.annotated - list of cleaned non-synonymous mutations\n$indiv.mutect.filtered.syn.annotated - list of cleaned synonymous changes\n$indiv.mutect.ambiguous.annotated - list of ambiguous calls. This will generally be empty (save for the header). It will be populated if the same mutation (ex. p.A319E) is found in two or more alleles in the individual, with the same allele fractions. In such cases one allele is randomly chosen and included in the .nonysn.annotated file while the complete list of alleles is listed in the .ambiguous.annotated file. If the ethnicity of the individual is known, an alternate method would be to pick the allele with the highest frequency.\n\n (b). Strelka\n$indiv.mutect.unfiltered.nonsyn.annotated - list of all unfiltered indels (as detected by Strelka)\n$indiv.strelka_indels.filtered.annotated - list of cleaned indels (as detected by Strelka)\n$indiv.strelka_indels.ambiguous.annotated - see description of $indiv.mutect.ambiguous.annotated in (a). above\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting\u003c/h3\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYour installation can be tested by running the following command from $PSHOME. \u003cstrong\u003eNOTE: this has not been tested - the correct paths to dependencies need to be set in \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e and the data folders correctly mounted from the local machine.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.1 POLYSOLVER\u003c/h4\u003e\u003ca id=\"user-content-31-polysolver\" class=\"anchor\" aria-label=\"Permalink: 3.1 POLYSOLVER\" href=\"#31-polysolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_type test/test.bam Unknown 1 hg19 STDFQ 0 test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf successful, the following command should not yield any differences:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e diff test/winners.hla.txt test/orig.winners.hla.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.2 POLYSOLVER-based mutation detection\u003c/h4\u003e\u003ca id=\"user-content-32-polysolver-based-mutation-detection\" class=\"anchor\" aria-label=\"Permalink: 3.2 POLYSOLVER-based mutation detection\" href=\"#32-polysolver-based-mutation-detection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_mutations_from_type test/test.bam test/test.tumor.bam test/winners.hla.txt hg19 STDFQ test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf successful, the following command should not yield any differences:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e diff test/call_stats.hla_b_39_01_01_02l.out test/orig.call_stats.hla_b_39_01_01_02l.out \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.3 Annotation of mutations\u003c/h4\u003e\u003ca id=\"user-content-33-annotation-of-mutations\" class=\"anchor\" aria-label=\"Permalink: 3.3 Annotation of mutations\" href=\"#33-annotation-of-mutations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_annotate_hla_mutations indiv test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf successful, the following command should not yield any differences:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e diff test/indiv.mutect.filtered.nonsyn.annotated test/orig.indiv.mutect.filtered.nonsyn.annotated\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tools can be run using the following commands:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e4.1 POLYSOLVER\u003c/h4\u003e\u003ca id=\"user-content-41-polysolver\" class=\"anchor\" aria-label=\"Permalink: 4.1 POLYSOLVER\" href=\"#41-polysolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_type \u0026lt;/path/to/bam\u0026gt; \u0026lt;race\u0026gt; \u0026lt;includeFreq\u0026gt; \u0026lt;build\u0026gt; \u0026lt;format\u0026gt; \u0026lt;insertCalc\u0026gt; \u0026lt;/path/to/output_directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_type test/test.bam Unknown 1 hg19 STDFQ 0 test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e4.2 POLYSOLVER-based mutation detection\u003c/h4\u003e\u003ca id=\"user-content-42-polysolver-based-mutation-detection\" class=\"anchor\" aria-label=\"Permalink: 4.2 POLYSOLVER-based mutation detection\" href=\"#42-polysolver-based-mutation-detection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_mutations_from_type \u0026lt;/path/to/normal_bam\u0026gt; \u0026lt;/path/to/tumor_bam\u0026gt; \u0026lt;/path/to/winners.hla.txt\u0026gt; \u0026lt;build\u0026gt; \u0026lt;format\u0026gt; \u0026lt;/path/to/output_directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_mutations_from_type test/test.bam test/test.tumor.bam test/winners.hla.txt hg19 STDFQ test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e4.3 Annotation of mutations\u003c/h4\u003e\u003ca id=\"user-content-43-annotation-of-mutations\" class=\"anchor\" aria-label=\"Permalink: 4.3 Annotation of mutations\" href=\"#43-annotation-of-mutations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_annotate_hla_mutations \u0026lt;prefix_to_use\u0026gt; \u0026lt;/path/to/directory_with_mutation_detection_output\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_annotate_hla_mutations indiv test\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 13, - "subscribers_count": 4, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eecmtool - Uncover organisms\u0027 metabolic blueprints\u003c/h1\u003e\u003ca id=\"user-content-ecmtool---uncover-organisms-metabolic-blueprints\" class=\"anchor\" aria-label=\"Permalink: ecmtool - Uncover organisms\u0027 metabolic blueprints\" href=\"#ecmtool---uncover-organisms-metabolic-blueprints\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith this tool you can calculate \u003cem\u003eElementary Conversion Modes\u003c/em\u003e (ECMs) from metabolic networks. Combinations of ECMs comprise all metabolic influences an organism can exert on its environment.\u003c/p\u003e\n\u003cp\u003eecmtool can be used in two different modes: either as a standalone command line tool, or as a Python library for your own scripts. We will describe how to install and use both modes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrerequisites\u003c/h3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload and install Python. Ecmtool is compatible with python 3.x, and tested on 3.10. Ensure both python and its package manager \u003cem\u003epip\u003c/em\u003e are added to your PATH environment variable. If this last step is omitted, an error like the following will be thrown when you try to run python: \u003ccode\u003e\u2019python\u2019 is not recognized as an internal or external command [..]\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eDownload and install Java. ecmtool is tested with OpenJDK 17. Make sure you have a 64bit version; you can check this with \u003ccode\u003ejava -version\u003c/code\u003e. Otherwise, you might get an error \u003ccode\u003eInvalid maximum heap size\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMode 1: standalone command line tool\u003c/h2\u003e\u003ca id=\"user-content-mode-1-standalone-command-line-tool\" class=\"anchor\" aria-label=\"Permalink: Mode 1: standalone command line tool\" href=\"#mode-1-standalone-command-line-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn this mode, you can call ecmtool like a normal program from your command line. It reads metabolic networks in the SBML format, and writes resulting ECMs into a CSV file for later analysis. Most researchers will use this method. For running ecmtool on computing clusters efficiently, see the Advanced Usage section in this readme.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload the latest ecmtool source through \u003ccode\u003egit clone\u003c/code\u003e, or as a zip file from \u003ca href=\"https://github.com/tjclement/ecmtool\"\u003ehttps://github.com/tjclement/ecmtool\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eOpen a command prompt, and navigate to the ecmtool directory (e.g. \u003ccode\u003ecd C:\\Users\\You\\Git\\ecmtool\u003c/code\u003e, where the\npath should be replaced with the path ecmtool was downloaded to).\u003c/li\u003e\n\u003cli\u003eInstall the dependencies in requirements.txt inside the ecmtool directory (e.g. by running \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eLinux only: install \u003cem\u003eredund\u003c/em\u003e of package \u003cem\u003elrslib\u003c/em\u003e (e.g. by running \u003ccode\u003eapt install lrslib\u003c/code\u003e).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstalling ecmtool using Docker\u003c/h4\u003e\u003ca id=\"user-content-installing-ecmtool-using-docker\" class=\"anchor\" aria-label=\"Permalink: Installing ecmtool using Docker\" href=\"#installing-ecmtool-using-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor convenience, there\u0027s a Docker script you can use that has all dependencies already installed, and allows you to directly run ecmtool.\nOpen a terminal with the ecmtool project as its working directory, and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t ecmtool -f docker/Dockerfile \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -ti ecmtool bash\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstalling ecmtool using Singularity\u003c/h4\u003e\u003ca id=\"user-content-installing-ecmtool-using-singularity\" class=\"anchor\" aria-label=\"Permalink: Installing ecmtool using Singularity\" href=\"#installing-ecmtool-using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo be continued.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEcmtool can be run by executing \u003ccode\u003epython main.py \u2013-model_path \u0026lt;path/to/model.xml\u0026gt; [arguments]\u003c/code\u003e from the command line, after navigating to the ecmtool directory as described above. The possible arguments and their default values are printed when you run \u003ccode\u003epython main.py --help\u003c/code\u003e.\nAfter execution is done, the found conversions have been written to file (default: \u003cem\u003econversions.csv\u003c/em\u003e). The first row of this CSV file contain the metabolite IDs as read from the SBML model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample\u003c/h3\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython main.py --model_path models/e_coli_core.xml --auto_direction \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e --out_path core_conversions.csv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBenefiting from optional arguments of ecmtool\u003c/h3\u003e\u003ca id=\"user-content-benefiting-from-optional-arguments-of-ecmtool\" class=\"anchor\" aria-label=\"Permalink: Benefiting from optional arguments of ecmtool\" href=\"#benefiting-from-optional-arguments-of-ecmtool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor an elaborate discussion of all optional arguments that can be used when ecmtool is run as a command line tool, please see the extensive manual that was uploaded as a Supplementary File with the ecmtool-publication at: \u003ca href=\"https://doi.org/10.1016/j.patter.2020.100177\" rel=\"nofollow\"\u003ehttps://doi.org/10.1016/j.patter.2020.100177\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMode 2: Python library\u003c/h2\u003e\u003ca id=\"user-content-mode-2-python-library\" class=\"anchor\" aria-label=\"Permalink: Mode 2: Python library\" href=\"#mode-2-python-library\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eecmtool can also be used as a separate programming interface from within your own Python code. To do so, install ecmtool using \u003cem\u003epip\u003c/em\u003e (e.g. \u003ccode\u003epip install ecmtool\u003c/code\u003e). The most crucial method is ecmtool.conversion_cone:get_conversion_cone(), which returns the ECMs of a given stoichiometric matrix. For information on how to use advanced features like SBML parsing, network compression, and metabolite direction estimation, please see ecmtool/main.py.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWe strongly advise the user to either use ecmtool as a command line tool, or to pay much attention to carefully copy the order from ecmtool/main.py.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample\u003c/h3\u003e\u003ca id=\"user-content-example-1\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eecmtool\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eextract_sbml_stoichiometry\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eecmtool\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003econversion_cone\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eget_conversion_cone\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eecmtool\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ehelpers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eunsplit_metabolites\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprint_ecms_direct\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enumpy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e\n\n\u003cspan class=\"pl-v\"\u003eDETERMINE_INPUTS_OUTPUTS\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Determines whether ecmtool tries to infer directionality (input/output/both)\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003ePRINT_CONVERSIONS\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Prints the resulting ECMs on the console\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eextract_sbml_stoichiometry\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027models/sxp_toy.xml\u0027\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eadd_objective\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edetermine_inputs_outputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eDETERMINE_INPUTS_OUTPUTS\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Some steps of compression only work when cone is in one orthant, so we need to split external metabolites with\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# direction \"both\" into two metabolites, one of which is output, and one is input\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esplit_in_out\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eonly_rays\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# It is generally a good idea to compress the network before computation\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecompress\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eSCEI\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecycle_removal\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eremove_infeasible\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003estoichiometry\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eN\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eecms\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eget_conversion_cone\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003estoichiometry\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eexternal_metabolite_indices\u003c/span\u003e(),\n \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ereversible_reaction_indices\u003c/span\u003e(), \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003einput_metabolite_indices\u003c/span\u003e(), \n \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eoutput_metabolite_indices\u003c/span\u003e(), \u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n \n\u003cspan class=\"pl-c\"\u003e# Since we have split the \"both\" metabolites, we now need to unsplit them again\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econe_transpose\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eunsplit_metabolites\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranspose\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eecms\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003econe\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranspose\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econe_transpose\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# We can remove all internal metabolites, since their values are zero in the conversions (by definition of internal)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003einternal_ids\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e []\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emetab\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enetwork\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emetabolites\u003c/span\u003e:\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003enot\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emetab\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eis_external\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eid_ind\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s1\"\u003eind\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eind\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eid\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eenumerate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eid\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emetab\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eid\u003c/span\u003e]\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-en\"\u003elen\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eid_ind\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003einternal_ids\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eid_ind\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e])\n\n\u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003elist\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edelete\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einternal_ids\u003c/span\u003e))\n\u003cspan class=\"pl-s1\"\u003econe\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edelete\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econe\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einternal_ids\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eaxis\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# If you wish, one can print the ECM results:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-v\"\u003ePRINT_CONVERSIONS\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint_ecms_direct\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranspose\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econe\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003eids\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExample scripts\u003c/h3\u003e\u003ca id=\"user-content-example-scripts\" class=\"anchor\" aria-label=\"Permalink: Example scripts\" href=\"#example-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the scripts in the folder examples_and_results for examples on how to use ecmtool as a library. In particular: ECM_calc_script.py, compare_efms_ecms_number.py.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnumerating ECMs without an SBML-file\u003c/h3\u003e\u003ca id=\"user-content-enumerating-ecms-without-an-sbml-file\" class=\"anchor\" aria-label=\"Permalink: Enumerating ECMs without an SBML-file\" href=\"#enumerating-ecms-without-an-sbml-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the script examples_and_results/minimal_run_wo_sbml.py for an example on how to compute ECMs starting from a stoichiometric matrix, and some additional information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvanced usage\u003c/h2\u003e\u003ca id=\"user-content-advanced-usage\" class=\"anchor\" aria-label=\"Permalink: Advanced usage\" href=\"#advanced-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter testing how the tool works, most users will want to run their workloads on computing clusters instead of on single machines. This section describes some of the steps that are useful for running on clusers\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eParallel computing with OpenMPI\u003c/h3\u003e\u003ca id=\"user-content-parallel-computing-with-openmpi\" class=\"anchor\" aria-label=\"Permalink: Parallel computing with OpenMPI\" href=\"#parallel-computing-with-openmpi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Linux or Mac, ecmtool can make use of OpenMPI for running on parallel in a computing cluster. To make use of this feature, in addition to the dependencies in requirements.txt, OpenMPI, mpi4py, and mplrs are required. The installation of OpenMPI and mplrs is done via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapt install libopenmpi-dev\nwget http://cgm.cs.mcgill.ca/~avis/C/lrslib/archive/lrslib-071a.tar.gz\ntar -xzf lrslib-071a.tar.gz\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e lrslib-071a\nmake \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make mplrs \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make install\nln -s \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e/mplrs /usr/local/bin/mplrs\nln -s \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e/redund /usr/local/bin/redund\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe installation of mpi4py is done via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install mpi4py==3.1.4\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRunning ecmtool on a cluster using the indirect enumeration method is now as simple as running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 main.py --processes \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e enumeration\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --model_path models/e_coli_core.xml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that this performs preprocessing steps like network compression on the node you run this command on, and not on the compute cluster.\u003c/p\u003e\n\u003cp\u003eFor direct enumeration, the number of processes for enumeration is passed to mpiexec instead:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empiexec -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e enumeration\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e python3 main.py --direct \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e --model_path models/e_coli_core.xml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn this mode, preprocessing steps are run on the compute cluster too.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAdvanced ECM-computation on a computing cluster\u003c/h3\u003e\u003ca id=\"user-content-advanced-ecm-computation-on-a-computing-cluster\" class=\"anchor\" aria-label=\"Permalink: Advanced ECM-computation on a computing cluster\" href=\"#advanced-ecm-computation-on-a-computing-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstallation of ecmtool when the user does not have root privileges on the cluster (a case report)\u003c/h4\u003e\u003ca id=\"user-content-installation-of-ecmtool-when-the-user-does-not-have-root-privileges-on-the-cluster-a-case-report\" class=\"anchor\" aria-label=\"Permalink: Installation of ecmtool when the user does not have root privileges on the cluster (a case report)\" href=\"#installation-of-ecmtool-when-the-user-does-not-have-root-privileges-on-the-cluster-a-case-report\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn some computing clusters, it is not easy to install OpenMPI and mplrs. One method that was successful is outlined here. This cluster had an OpenMPI already available as a module that could be loaded. The available versions can be seen by\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule av OpenMPI\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor the installation of mplrs, we will also need GMP, check this by\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule av GMP\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt is important that the versions of OpenMPI and GMP have to match. In this case, we used\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emodule load OpenMPI/4.1.1-GCC-10.3.0\nmodule load GMP/6.2.1-GCCcore-10.3.0\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere the last number indicates that they are using a compatible version of GCC. Now, we are ready to install mplrs. This can be done via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eapt install libopenmpi-dev\nwget http://cgm.cs.mcgill.ca/~avis/C/lrslib/archive/lrslib-071a.tar.gz\ntar -xzf lrslib-071a.tar.gz\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e lrslib-071a\nmake \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make mplrs \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e make install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow we need to tell the cluster where to find the installed mplrs. We can do this by adding the path to mplrs to the search path:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=/scicore/home/nimwegen/degroo0000/ecmtool/lrslib-071a:\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=/scicore/home/nimwegen/degroo0000/ecmtool/lrslib-071a:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow using the command\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emplrs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eshould give some output that indicates that mplrs is working and can be found.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRunning ecmtool using separate runs for non-parallel and parallel parts, with a .sh-script (on a slurm-cluster)\u003c/h4\u003e\u003ca id=\"user-content-running-ecmtool-using-separate-runs-for-non-parallel-and-parallel-parts-with-a-sh-script-on-a-slurm-cluster\" class=\"anchor\" aria-label=\"Permalink: Running ecmtool using separate runs for non-parallel and parallel parts, with a .sh-script (on a slurm-cluster)\" href=\"#running-ecmtool-using-separate-runs-for-non-parallel-and-parallel-parts-with-a-sh-script-on-a-slurm-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo fully exploit parallel computation on a cluster, one would like to use ecmtool in separate steps, as outlined below. (In the ecmtool-folder one can also find an example-script that can be used on a computing cluster that is using slurm: \u003ccode\u003eexamples_and_results/launch_separate_mmsyn_newest.sh\u003c/code\u003e.)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003epreprocessing and compression of the model on a compute node (instead of a login node). For this run\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esrun --ntasks=1 --nodes=1 python3 main.py all_until_mplrs --model_path \u003cspan class=\"pl-smi\"\u003e${MODEL_PATH}\u003c/span\u003e --auto_direction \u003cspan class=\"pl-smi\"\u003e${AUTO_DIRECT}\u003c/span\u003e --hide \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HIDE}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --prohibit \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${PROHIBIT}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --tag \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${TAG}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --inputs \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${INPUTS}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --outputs \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${OUTPUTS}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --use_external_compartment \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${EXT_COMP}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --add_objective_metabolite \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${ADD_OBJ}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --compress \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${COMPRESS}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --hide_all_in_or_outputs \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HIDE_ALL_IN_OR_OUTPUTS}\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere the arguments in curly brackets should be replaced by your choices for these arguments.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003efirst vertex enumeration step with mplrs in parallel. For this run\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mplrs -redund ecmtool/tmp/mplrs.ine ecmtool/tmp/redund.ine\nmpirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mplrs ecmtool/tmp/redund.ine ecmtool/tmp/mplrs.out\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eprocessing of results from first vertex enumeration step, adding steady-state constraints and removing redundant rays using a parallelized redundancy check.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e python3 main.py all_between_mplrs\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003esecond vertex enumeration step with mplrs in parallel\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003empirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mplrs -redund ecmtool/tmp/mplrs.ine ecmtool/tmp/redund.ine\nmpirun -np \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003enumber of processes\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e mplrs ecmtool/tmp/redund.ine ecmtool/tmp/mplrs.out\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eprocessing of results from second vertex enumeration step, unsplitting of metabolites, ensuring that results are unique, and saving ecms to file\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esrun --ntasks=1 --nodes=1 python3 main.py all_from_mplrs --out_path \u003cspan class=\"pl-smi\"\u003e${OUT_PATH}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDoubling direct enumeration method speed\u003c/h3\u003e\u003ca id=\"user-content-doubling-direct-enumeration-method-speed\" class=\"anchor\" aria-label=\"Permalink: Doubling direct enumeration method speed\" href=\"#doubling-direct-enumeration-method-speed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe direct enumeration method can be sped up by compiling our LU decomposition code with Cython. The following describes the steps needed on Linux, but the same concept also applies to Mac OS and Windows. First make sure all dependencies are satisfied. Then execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 cython_setup.py build_ext --inplace\n\nmv _bglu* ecmtool/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u2139\ufe0f Note that in the Docker script, this optimisation has already been done. You don\u0027t need to compile anything there.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAutomatically testing ecmtool and contributing to ecmtool\u003c/h2\u003e\u003ca id=\"user-content-automatically-testing-ecmtool-and-contributing-to-ecmtool\" class=\"anchor\" aria-label=\"Permalink: Automatically testing ecmtool and contributing to ecmtool\" href=\"#automatically-testing-ecmtool-and-contributing-to-ecmtool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen ecmtool is installed properly its functioning with various parameter settings can be tested using some predefined tests using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 -m pytest tests/test_conversions.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen contributing to ecmtool please make sure that these tests are passed before making a pull request.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting ecmtool\u003c/h2\u003e\u003ca id=\"user-content-citing-ecmtool\" class=\"anchor\" aria-label=\"Permalink: Citing ecmtool\" href=\"#citing-ecmtool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease refer to the following papers when using ecmtool:\u003c/p\u003e\n\u003cp\u003eInitial version - \u003ca href=\"https://www.cell.com/patterns/fulltext/S2666-3899(20)30241-5\" rel=\"nofollow\"\u003ehttps://www.cell.com/patterns/fulltext/S2666-3899(20)30241-5\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003emplrs\u003c/code\u003e improved version - \u003ca href=\"https://doi.org/10.1093/bioinformatics/btad095\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btad095\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe original source code with indirect enumeration was written by \u003ca href=\"https://scholar.google.com/citations?user=kUD5y04AAAAJ\" rel=\"nofollow\"\u003eTom Clement\u003c/a\u003e. \u003ca href=\"https://github.com/EBaalhuis\"\u003eErik Baalhuis\u003c/a\u003e later expanded the code with a direct enumeration method that improved parallellisation. \u003ca href=\"https://scholar.google.com/citations?user=xY_GjWkAAAAJ\" rel=\"nofollow\"\u003eDaan de Groot\u003c/a\u003e helped with many new features, bug fixes, and code reviews. \u003ca href=\"https://github.com/BeeAnka\"\u003eBianca Buchner\u003c/a\u003e added support for \u003ccode\u003emplrs\u003c/code\u003e, which raises the maximal size of networks you can enumerate with ecmtool.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eecmtool is released with the liberal MIT license. You are free to use it for any purpose. We hope others will contribute to the field by making derived work publicly available too.\u003c/p\u003e\n", + "stargazers_count": 12, + "subscribers_count": 6, "topics": [], - "updated_at": 1723958568.0 + "updated_at": 1703430042.0 }, { "data_format": 2, - "description": "Orbital viewer (mirror of https://gitlab.com/Jellby/Pegamoid)", + "description": "Modules for describing software environment on biocluster", "filenames": [ - "Singularity" + "freefem/Singularity.def" ], - "full_name": "Jellby/Pegamoid", + "full_name": "ucr-hpcc/hpcc_modules", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePegamoid\u003c/h1\u003e\u003ca id=\"user-content-pegamoid\" class=\"anchor\" aria-label=\"Permalink: Pegamoid\" href=\"#pegamoid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePegamoid is an orbital viewer especially suited for use with\n\u003ca href=\"https://gitlab.com/Molcas/OpenMolcas\" rel=\"nofollow\"\u003eOpenMolcas\u003c/a\u003e. It can be used to view\norbitals and quickly select active spaces for use in CASSCF or RASSCF\ncalculations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScreenshots\u003c/h2\u003e\u003ca id=\"user-content-screenshots\" class=\"anchor\" aria-label=\"Permalink: Screenshots\" href=\"#screenshots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7c85c6797a2a0601a920be9fbc44eadb55a82bb4c16141ab85ec6d2f6ab8fa17/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6e6f6e6f7274686f676f6e616c2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7c85c6797a2a0601a920be9fbc44eadb55a82bb4c16141ab85ec6d2f6ab8fa17/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6e6f6e6f7274686f676f6e616c2e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/nonorthogonal.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b7827db120a8db6ffa3c184bd1d5e40012f45ad6ea0d859d1bd70e97249f7c89/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f646966666572656e63652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b7827db120a8db6ffa3c184bd1d5e40012f45ad6ea0d859d1bd70e97249f7c89/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f646966666572656e63652e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/difference.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2efcb49f5df9917964a76f3d8c4bdf9f4fa39af67cb428a0159d22e645fade8c/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6772616469656e742e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2efcb49f5df9917964a76f3d8c4bdf9f4fa39af67cb428a0159d22e645fade8c/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6772616469656e742e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/gradient.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0f4daa0a4ea852ac5f5225bf5f1bad3e9de0677ace79379ca02136401f219ae8/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6c6162656c732e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0f4daa0a4ea852ac5f5225bf5f1bad3e9de0677ace79379ca02136401f219ae8/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6c6162656c732e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/labels.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6aa661d6e51c5090742955f3d7fc36cd373f6d7549ce280a15b0bb5925bb33cb/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6c6973742e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6aa661d6e51c5090742955f3d7fc36cd373f6d7549ce280a15b0bb5925bb33cb/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6c6973742e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/list.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following formats can be opened:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eHDF5 files, as generated by some (Open)Molcas modules like SCF or RASSCF, if compiled with HDF5 support.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInpOrb files, generated by some (Open)Molcas modules like SCF or RASSCF, provided an HDF5 file for the same system was opened first.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://www.cmbi.ru.nl/molden/\" rel=\"nofollow\"\u003eMolden\u003c/a\u003e files.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://luscus.sourceforge.net/\" rel=\"nofollow\"\u003eLuscus\u003c/a\u003e files, generated by the GRID_IT module.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGrid files (ASCII), generated by the GRID_IT module.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://paulbourke.net/dataformats/cube/\" rel=\"nofollow\"\u003eCube\u003c/a\u003e files (formatted).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor HDF5, InpOrb and Molden files, orbitals are computed on the fly from the\nbasis set, and it is possible to change the sampling resolution and shape and\nsize of the sampled volume. Luscus, grid and cube files contain precomputed\nvolumetric data and only the existing data can be displayed.\u003c/p\u003e\n\u003cp\u003eDepending on availability in the input file, the following features and objects\nare supported:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSelection of orbital.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSelection of spin.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSelection of symmetry irrep.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNatural average or state-specific orbitals.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eElectron density and Laplacian.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNatural average or state-specific spin orbitals.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSpin density.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNatural difference orbitals.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDifference, attachment and detachment density.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNatural transition orbitals.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransition, hole, particle and unrelaxed difference density.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor any orbital or density, gradient lines can be computed and displayed\n(particularly significant for the electron density). Densities can be computed\nfor reduced subsets of orbitals (for instance, only for the active orbitals),\nand the user can write arbitrary notes for each orbital.\u003c/p\u003e\n\u003cp\u003eThe value, opacity, colors and texture properties used to display isurfaces can\nbe adjusted and the display of the following elements can be toggled:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePositive and negative parts of the isosurface.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNodal surfaces.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNuclei and bonds.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAtom labels.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVolume box.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, the type of orbital (inactive, active...) can be changed and the\norbitals saved in the following formats usable in the (Open)Molcas programs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eHDF5 format.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInpOrb format.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eor the current volumetric data or snapshot can be saved as:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCube format.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePNG image.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTips for OpenMolcas\u003c/h2\u003e\u003ca id=\"user-content-tips-for-openmolcas\" class=\"anchor\" aria-label=\"Permalink: Tips for OpenMolcas\" href=\"#tips-for-openmolcas\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse the \u003ccode\u003eTDM\u003c/code\u003e keyword in a RASSCF calculation to include transition densities\nin the HDF5 file.\u003c/p\u003e\n\u003cp\u003eUse the \u003ccode\u003eTDM\u003c/code\u003e (or \u003ccode\u003eTRD1\u003c/code\u003e) keyword in a RASSI calculation to include state and\ntransition densities in the HDF5 file. Use the \u003ccode\u003eSUBSET\u003c/code\u003e keyword to reduce the\nnumber of transition densities stored.\u003c/p\u003e\n\u003cp\u003eUse the \u003ccode\u003eWFA\u003c/code\u003e module for more detailed analysis.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recommended way to install Pegamoid is by using the \u003ca href=\"https://packaging.python.org/tutorials/installing-packages/#use-pip-for-installing\" rel=\"nofollow\"\u003e\u003ccode\u003epip\u003c/code\u003e package\nmanager\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install Pegamoid\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(you may also want to add the flags \u003ccode\u003e--upgrade\u003c/code\u003e and/or \u003ccode\u003e--user\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThen you just run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epegamoid.py [filename]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e[filename]\u003c/code\u003e is an optional supported file to open. In the case of\nInpOrb files, you can supply two filenames (in any order): the InpOrb file and\na corresponding HDF5 file.\u003c/p\u003e\n\u003cp\u003eThere are other ways to get Pegamoid. One is cloning the git repository, e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://gitlab.com/Jellby/Pegamoid.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnother way, since Pegamoid is contained in a single python script, is\ndownloading only the script file\n\u003ca href=\"https://gitlab.com/Jellby/Pegamoid/raw/master/pegamoid.py?inline=false\" rel=\"nofollow\"\u003epegamoid.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the program is fetched, it can be run directly or through a python\ninterpreter, no installation is needed, i.e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./pegamoid.py [filename]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython pegamoid.py [filename]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHowever, the script has some requirements (this should be taken care of by\n\u003ccode\u003epip\u003c/code\u003e, if you use it) that must be installed for it to work:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePython 2 or python 3 (at least versions 2.7 and 3.4 have been tested).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eQt with python bindings. PyQt 4, PyQt 5 and PySide have been tested. It is\nrecommended to install the python module qtpy (needed for PySide).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVTK with python bindings. Version 8.1.0 has been tested, earlier versions\nwill most likely not work.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe numpy and h5py python modules.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOther python modules that may not be installed by default, it should be clear\nwhich ones, if any, are needed when trying to run Pegamoid.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse of scratch disk space\u003c/h2\u003e\u003ca id=\"user-content-use-of-scratch-disk-space\" class=\"anchor\" aria-label=\"Permalink: Use of scratch disk space\" href=\"#use-of-scratch-disk-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo speed up the display of several orbitals and the computation of densities,\nPegamoid uses some scratch disk space to store the computed basis functions. A\nfile named \u003ccode\u003epegamoid.cache\u003c/code\u003e will be created in a temporary location (typically\ninside the \u003ccode\u003e/tmp\u003c/code\u003e directory). For grids with many points and with many basis\nfunctions, this file could grow very large and even use up all available disk\nspace. The maximum scratch size is by default 1 GiB, but it can be configured in\n\"File \u0026gt; Set scratch\", or through the environment variable\n\u003ccode\u003ePEGAMOID_MAXSCRATCH\u003c/code\u003e, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePEGAMOID_MAXSCRATCH=100MB ./pegamoid.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efor a maximum size of 100 MB. If the scratch size is not enough to hold all\nbasis functions at the current resolution, it will only be used when computing\nthe densities. In the \"Set scratch\" window you can also find the\ninstance-specific temporary path, as well as the maximum cache size, the scratch\nsize currently in use, and the recommended size to allow keeping a cache of all\nbasis functions. The scratch file and directory are removed on a clean exit, but\nif the program crashes or is otherwise abnormally interrupted, they may be left\nbehind.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse with a remote connection\u003c/h2\u003e\u003ca id=\"user-content-use-with-a-remote-connection\" class=\"anchor\" aria-label=\"Permalink: Use with a remote connection\" href=\"#use-with-a-remote-connection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProduction calculations are usually not run on the local machine, but on some\nremote server like a supercomputer. To view/save/modify orbital files, it is\nalways possible to transfer the files between the local and remote machines. It\nis, however, more convenient to run Pegamoid directly on the remote machine and\nhave the graphical interface display in the local machine. Unfortunately, there\nare some difficulties that make this nontrivial.\u003c/p\u003e\n\u003cp\u003eFirst, the different requirements may not be installed in the remote system. A\npossible solution is installing them for the user account with e.g.\n\u003ccode\u003epip install --user\u003c/code\u003e. In this case it will probably be easier to install qtpy\nand PySide instead of PyQt.\u003c/p\u003e\n\u003cp\u003eThen, the VTK visualization uses some advanced OpenGL features that may not be\navailable with all graphical drivers and it could be challenging to make it\nwork through a remote connection. We have had success running Pegamoid with\n\u003ccode\u003evglrun\u003c/code\u003e inside a\n\u003ca href=\"https://www.cendio.com/thinlinc/what-is-thinlinc\" rel=\"nofollow\"\u003eThinLinc\u003c/a\u003e session, or a VNC\nsession opened directly from an ssh connection. The specific needs and working\nsolution will probably depend on the hardware and software available in the\nremote computer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown problems\u003c/h2\u003e\u003ca id=\"user-content-known-problems\" class=\"anchor\" aria-label=\"Permalink: Known problems\" href=\"#known-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn some systems there are display issues in the 3D window, where some elements\nare wrongly drawn \"on top\" of others (this does not refer to the atom names,\nwhich are always on top). This problem has been seen with PyQt 5, and it\u0027s\nusually solved by switching to PyQt 4 or installing QtOpenGL support (in the\n\"About\" dialog, check if the \"Qt API\" line says \"with QtOpenGL\"). To disable\nQtOpenGL detection, define the environment variable \u003ccode\u003ePEGAMOID_NO_QGL=1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAnother reported issue is that the orbital/density isosurfaces completely\ndisappear, while the \"molecule\" (nuclei and bonds) are normally displayed. This\nis probably a driver problem. As a workaround, defining the environment\nvariable \u003ccode\u003ePEGAMOID_DISABLE_OPACITY=1\u003c/code\u003e will completely disable the opacity\nslider and hopefully allow the isosurfaces to be shown. However, the rendering\nof textures with transparency may be affected by artifacts.\u003c/p\u003e\n\u003cp\u003eIssues with the \"Transform\" and \"Texture\" windows not appearing have also been\nreported in some PyQt 4 versions. It is unclear at the moment what is the\nreason for this.\u003c/p\u003e\n\u003cp\u003eWhen running in KDE Plasma 5, some shortcuts may not work because KDE tries to\nbe smart and overwrites them (see\n\u003ca href=\"https://stackoverflow.com/questions/32688153\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for example). To fix this,\nyou can add to the \u003ccode\u003e~/.config/kdeglobals\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[Development]\nAutoCheckAccelerators=false\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 13, - "subscribers_count": 2, + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-hpcc-modules\" class=\"anchor\" href=\"#hpcc-modules\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHPCC Modules\u003c/h1\u003e\n\u003cp\u003eThese are the modules that are currently available on the UCR HPC cluster.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-standards\" class=\"anchor\" href=\"#standards\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStandards\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eKeep names of packages to all lowercase when possible.\u003c/li\u003e\n\u003cli\u003eSpaces are not allowed, underscores should be used in place of spaces for software module names.\u003c/li\u003e\n\u003cli\u003eSymlinks can be used to map to multiple softwares within an environment (ie. \u003ccode\u003econda\u003c/code\u003e/\u003ccode\u003esingularity\u003c/code\u003e) as well as name variations.\u003c/li\u003e\n\u003cli\u003eWhen software requires a common write location or unique config file, use environment variables within the module to define these.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-locations\" class=\"anchor\" href=\"#locations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocations\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e - All source code should be placed under the \u003ccode\u003e/opt/linux/rhel/8.x/x86_64/pkgs/src\u003c/code\u003e directory.\nHenceforth, this path will be refered to as \u003ccode\u003e$HPCC_MODULES/../src\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eInstall\u003c/strong\u003e - All software installs should be done under the \u003ccode\u003e/opt/linux/rhel/8.x/x86_64/pkgs\u003c/code\u003e directory.\nHenceforth, this path will be refered to as \u003ccode\u003e$HPCC_MODULES\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eModule\u003c/strong\u003e - All modules should be defined under this repo, which is cloned under the \u003ccode\u003e/opt/linux/rhel/8.x/x86_64/modules\u003c/code\u003e directory.\nHenceforth, this path will be refered to as \u003ccode\u003e$HPCC_MODULES/../modules\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: Do NOT modify files directly under the \u003ccode\u003e$HPCC_MODULES../modules\u003c/code\u003e directory. Use your own cloned version of this repo.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-environment-variables\" class=\"anchor\" href=\"#environment-variables\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnvironment variables\u003c/h2\u003e\n\u003cp\u003eThe following variables are used and should be set automatically:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eHPCC_MODULES\u003c/code\u003e - Path dynamically built by /etc/profile.d/hpcc_modules.sh based on OS and arch.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMODULESHOME\u003c/code\u003e - Path set by module system to specify home of module system.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eMODULEPATH\u003c/code\u003e - List set by module system to indicate where modules live (colon delimited).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNote: Exporting \u003ccode\u003eMODULEPATH\u003c/code\u003e to a local clone of this repo can be useful for debugging.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-install-software\" class=\"anchor\" href=\"#install-software\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall Software\u003c/h2\u003e\n\u003cp\u003eThe most common forms of installs are either binary or source.\nWe do support all kinds of packaged software, thus installations can become more complex then what is covered here.\nFor specific installation details please refer to the documentation prodvided by the software as well as the individual \u003ccode\u003eREADME.md\u003c/code\u003e files contained within the installed module, assuming that the softwere had been installed previously.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-binary\" class=\"anchor\" href=\"#binary\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBinary\u003c/h3\u003e\n\u003cp\u003eWhen installing a binary all that is requied is to download and then extract.\nSometimes there may be an \u003ccode\u003einstall\u003c/code\u003e script to place the binaries under their final resting place, or you may have to do this manually.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-source\" class=\"anchor\" href=\"#source\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSource\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-configure\" class=\"anchor\" href=\"#configure\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eConfigure\u003c/h4\u003e\n\u003cp\u003eWhen installing from a \u003ccode\u003econfigure\u003c/code\u003e based install, the following would typically apply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econfigure --prefix=\u003cspan class=\"pl-smi\"\u003e$HPCC_MODULES\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$SOFT_NAME\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$SOFT_VER\u003c/span\u003e\nmake\nmake install\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-cmake\" class=\"anchor\" href=\"#cmake\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCMake\u003c/h4\u003e\n\u003cp\u003eWhen installing from a \u003ccode\u003ecmake\u003c/code\u003e based install, the following would typically apply:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emkdir build\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\ncmake -DCMAKE_INSTALL_PREFIX=\u003cspan class=\"pl-smi\"\u003e$HPCC_MODULES\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$SOFT_NAME\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$SOFT_VER\u003c/span\u003e ../\nmake\nmake install\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eWhen creating a module you must create a \u003ccode\u003eREADME.md\u003c/code\u003e file within the software module directory with reproducible instructions.\nCreating a \u003ccode\u003ehpcc_install.sh\u003c/code\u003e script within the source tree can also be very useful for later installs.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eHere is a \u003ccode\u003ecmake\u003c/code\u003e example: \u003ca href=\"openbabel\"\u003eopenbabel/README.md\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHere is a \u003ccode\u003econda\u003c/code\u003e example: \u003ca href=\"qiime2\"\u003eqiime2/README.md\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHere is a \u003ccode\u003esingularity\u003c/code\u003e example: \u003ca href=\"freefem\"\u003efreefem/README.md\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-create-module\" class=\"anchor\" href=\"#create-module\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCreate Module\u003c/h2\u003e\n\u003cp\u003eFirst set some variables:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Software name\u003c/span\u003e\nSOFT_NAME=[software name]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e First character of software name\u003c/span\u003e\nSOFT_FIRST=\u003cspan class=\"pl-smi\"\u003e${SOFT_NAME\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e0\u003cspan class=\"pl-k\"\u003e:\u003c/span\u003e1}\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Software version\u003c/span\u003e\nSOFT_VER=[software version]\u003c/pre\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eMake sure you have a checkout of the hpcc_modules repository\u003c/li\u003e\n\u003cli\u003eLogin as \u003ccode\u003epkgadmin\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCreate and move into the \u003ccode\u003e$HPCC_MODULES/../src/$SOFT_FIRST/$SOFT_NAME\u003c/code\u003e directory\u003c/li\u003e\n\u003cli\u003eDownload and extract the source files\u003c/li\u003e\n\u003cli\u003eCreate a directory named \u003ccode\u003e$HPCC_MODULES/$SOFT_NAME/$SOFT_VER\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMove binaries (precompiled) or install software into the directory created in step 5\u003c/li\u003e\n\u003cli\u003eLog out of \u003ccode\u003epkgadmin\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMove to your local \u003ccode\u003ehpcc_modules\u003c/code\u003e repo checkout\u003c/li\u003e\n\u003cli\u003eCopy any one of the existing directories, an rename it to \u003ccode\u003e$SOFT_NAME\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eGo into the newly created \u003ccode\u003e$SOFT_NAME\u003c/code\u003e directory\u003c/li\u003e\n\u003cli\u003eBy default the latest version will be loaded, if this is not wanted then add the \u003ccode\u003e.version\u003c/code\u003e file\u003c/li\u003e\n\u003cli\u003eRename one of the contained files to \u003ccode\u003e$SOFT_VER\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit the file from step 12 so that it contains the correct module name, version and paths\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egit add [soft version]\u003c/code\u003e as well as \u003ccode\u003e.version\u003c/code\u003e if added, then \u003ccode\u003egit commit\u003c/code\u003e the changes, lastly do \u003ccode\u003egit push\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eAfter this you can test the installation by trying \u003ccode\u003emodule load $SOFT_NAME\u003c/code\u003e and \u003ccode\u003emodule avail $SOFT_NAME\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMake sure to document how the software was installed within a corresponding \u003ccode\u003eREADME.md\u003c/code\u003e file.\u003c/li\u003e\n\u003c/ol\u003e\n", + "stargazers_count": 12, + "subscribers_count": 11, "topics": [], - "updated_at": 1728487788.0 + "updated_at": 1641773320.0 }, { "data_format": 2, - "description": "Downloading a dataset from Airbnb", + "description": "The mapping pipeline for HoloLens device", "filenames": [ - "container/Singularity.butd" + "third_party/poselib/Singularity.def", + "third_party/PatchmatchNet/Singularity.def", + "third_party/Fast-Robust-ICP/Singularity.def", + "third_party/Hierarchical-Localization/Singularity.def" ], - "full_name": "airbert-vln/bnb-dataset", + "full_name": "michalpolic/hololens_mapper", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\ud83c\udfd8\ufe0f BnB Dataset \ud83c\udfd8\ufe0f\u003c/h1\u003e\u003ca id=\"user-content-houses-bnb-dataset-houses\" class=\"anchor\" aria-label=\"Permalink: :houses: BnB Dataset :houses:\" href=\"#houses-bnb-dataset-houses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"./LICENSE.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eff0a8d3e0e9027bd979de24eef72a530be6cf489cd9e8377b4043d8c03bacb8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f616972626572742d766c6e2f626e622d646174617365743f7374796c653d666f722d7468652d6261646765\" alt=\"MIT\" data-canonical-src=\"https://img.shields.io/github/license/airbert-vln/bnb-dataset?style=for-the-badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://arxiv.org/abs/2108.09105\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8b518b8258f223fda44d71542135afef6c2d8b57ce74b0f0753b10b9d682e2b9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323130382e30393130352d7265642e7376673f7374796c653d666f722d7468652d6261646765\" alt=\"arXiv\" data-canonical-src=\"https://img.shields.io/badge/arXiv-2108.09105-red.svg?style=for-the-badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://eval.ai/web/challenges/challenge-page/97/leaderboard/270\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8afacccdcce9f2acfa3aec9d71646ae7bf8a4ccb049cc3948d3f6eb1d3553da7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5232522d2546302539462541352538372d677265656e2e7376673f7374796c653d666f722d7468652d6261646765\" alt=\"R2R 1st\" data-canonical-src=\"https://img.shields.io/badge/R2R-%F0%9F%A5%87-green.svg?style=for-the-badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis contains a set of scripts for downloading a dataset from Airbnb.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udee0\ufe0f 1. Get started\u003c/h2\u003e\u003ca id=\"user-content-hammer_and_wrench-1-get-started\" class=\"anchor\" aria-label=\"Permalink: :hammer_and_wrench: 1. Get started\" href=\"#hammer_and_wrench-1-get-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you need \u003ca href=\"https://git-lfs.github.com/\"\u003e\u003ccode\u003egit lfs\u003c/code\u003e\u003c/a\u003e to clone the repository. Install it from command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecurl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo bash\nsudo apt-get install git-lfs\ngit lfs install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can now clone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/airbert-vln/bnb-dataset.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you clone the repository without LFS installed, you should have received an error message. You can fix it by running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake lfs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou need to have a recent version of Python (3.8 or higher) and install dependencies through \u003ccode\u003epoetry\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install python for ubuntu 20.04\u003c/span\u003e\nsudo apt install python3 python3-pip \npip install poetry\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install dependencies\u003c/span\u003e\npoetry install\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e activate the environment (do it at each new shell)\u003c/span\u003e\npoetry shell\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that typing is extensively used in these scripts. This was a real time saver for detecting errors before runtime. You might want to setup properly your IDE to play well with \u003ccode\u003emypy\u003c/code\u003e. I recommend the \u003ca href=\"https://github.com/neoclide/coc.nvim\"\u003e\u003ccode\u003ecoc.nvim\u003c/code\u003e\u003c/a\u003e extension \u003ca href=\"https://github.com/fannheyward/coc-pyright\"\u003e\u003ccode\u003ecoc-pyright\u003c/code\u003e\u003c/a\u003e for \u003ca href=\"https://github.com/neovim/neovim/\"\u003eneovim\u003c/a\u003e users.\u003c/p\u003e\n\u003cp\u003eManaging a large of images is tricky and usually take a lot of times. Usually, the scripts are splitting the task among several workers. A cache folder is keeping the order list for each worker, while each worker is producing its own output file.\nLook for \u003ccode\u003enum_workers\u003c/code\u003e or \u003ccode\u003enum_procs\u003c/code\u003e parameters in the \u003ccode\u003eargtyped Arguments\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\uddfa\ufe0f 2. Download listings from Airbnb\u003c/h2\u003e\u003ca id=\"user-content-world_map-2-download-listings-from-airbnb\" class=\"anchor\" aria-label=\"Permalink: :world_map: 2. Download listings from Airbnb\" href=\"#world_map-2-download-listings-from-airbnb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step is building a TSV file with 4 columns: listing ID, photo ID, image URL, image caption.\nA too high request rate would induce a rejection from Airbnb. Instead, it is advised to split the job among different IP addresses.\u003c/p\u003e\n\u003cp\u003ePlease note that you can use the pre-computed TSV file used in our paper \u003ca href=\"./data/airbnb-train-indoor-filtered.tsv\"\u003efor training\u003c/a\u003e and \u003ca href=\"./data/airbnb-train-indoor-filtered.tsv\"\u003efor testing\u003c/a\u003e. The file was generated during Christmas 2019 (yeah, before Covid. Sounds so far away now!). Some images might not be available anymore.\u003c/p\u003e\n\u003cp\u003eAlso, note that this file contains only a portion from the total of Airbnb listings. It might be interesting to extend it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.1. Create a list of regions\u003c/h3\u003e\u003ca id=\"user-content-21-create-a-list-of-regions\" class=\"anchor\" aria-label=\"Permalink: 2.1. Create a list of regions\" href=\"#21-create-a-list-of-regions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAirbnb listings are searched among a specific region.\nWe need first to initialize the list of regions. A quick hack for that consists in scrapping Wikipedia list of places, as done in the script \u003ca href=\"./cities.py\"\u003ecities.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor this script, you need to download and install Selenium. Instructions here are valid only for a Linux distribution. Otherwise, follow the guide \u003ca href=\"https://selenium-python.readthedocs.io/installation.html\" rel=\"nofollow\"\u003efrom Selenium documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install selenium\nwget https://github.com/mozilla/geckodriver/releases/download/v0.30.0/geckodriver-v0.30.0-linux32.tar.gz\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.local/bin\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.local/bin\ntar -xvf geckodriver-v0.30.0-linux32.tar.gz -C \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.local/bin\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Testing the driver path is recognized:\u003c/span\u003e\ngeckodriver --version\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere is how I scrapped a list of cities. You might want to update this script to order to increase the amount of cities.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython cities.py --output data/cities.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can see other examples in the \u003ca href=\"./locations/\"\u003e\u003ccode\u003elocations/\u003c/code\u003e\u003c/a\u003e folder, used as an attempt to enlarge the BnB dataset.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.2. Download listings\u003c/h3\u003e\u003ca id=\"user-content-22-download-listings\" class=\"anchor\" aria-label=\"Permalink: 2.2. Download listings\" href=\"#22-download-listings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download a list of listing from the list of cities\u003c/span\u003e\npython search_listings.py --locations data/cities.txt --output data/listings\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download JSON files for each listing\u003c/span\u003e\npython download_listings.py --listings data/listings.txt --output data/merlin --with_photo\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Note you can download also reviews and infos (see python download_listings.py --help)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract photo URLs from listing export files\u003c/span\u003e\npython extract_photo_metadata.py --merlin data/merlin --output data/bnb-dataset-raw.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.3. Filter captions\u003c/h3\u003e\u003ca id=\"user-content-23-filter-captions\" class=\"anchor\" aria-label=\"Permalink: 2.3. Filter captions\" href=\"#23-filter-captions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Apply basic rules to remove some captions\u003c/span\u003e\npython filter_captions.py --input data/bnb-dataset-raw.tsv --output data/bnb-dataset.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udcf8 3. Get images\u003c/h2\u003e\u003ca id=\"user-content-camera_flash-3-get-images\" class=\"anchor\" aria-label=\"Permalink: :camera_flash: 3. Get images\" href=\"#camera_flash-3-get-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow we want to download images and filter out outdoor images.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.1. Download images\u003c/h3\u003e\u003ca id=\"user-content-31-download-images\" class=\"anchor\" aria-label=\"Permalink: 3.1. Download images\" href=\"#31-download-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe download rate can be higher before the server kicks us out. However, it is still preferable to use a pool of IP addresses.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython download_images.py --csv_file data/bnb-dataset.tsv --output data/images --correspondance /tmp/cache-download-images/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.2. Optionally, make sure images were correctly downloaded\u003c/h3\u003e\u003ca id=\"user-content-32-optionally-make-sure-images-were-correctly-downloaded\" class=\"anchor\" aria-label=\"Permalink: 3.2. Optionally, make sure images were correctly downloaded\" href=\"#32-optionally-make-sure-images-were-correctly-downloaded\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython detect_errors.py --images data/images --merlin data/merlin\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.3. Filter out outdoor images\u003c/h3\u003e\u003ca id=\"user-content-33-filter-out-outdoor-images\" class=\"anchor\" aria-label=\"Permalink: 3.3. Filter out outdoor images\" href=\"#33-filter-out-outdoor-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutdoor images tend to be of lower qualities and captions are often not relevant.\nWe first detect outdoor images from a CNN pretrained on the places365 dataset. Later on, we will keep indoor images.\u003c/p\u003e\n\u003cp\u003eNote that the output of this step is also used for image merging.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Detect room types\u003c/span\u003e\npython detect_room.py --output data/places365/detect.tsv --images data/images\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Filter out indoor images\u003c/span\u003e\npython extract_indoor.py --output data/bnb-dataset-indoor.tsv --detection data/places365/detect.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udcbd 4. Build an LMDB database with BnB pictures\u003c/h2\u003e\u003ca id=\"user-content-minidisc-4-build-an-lmdb-database-with-bnb-pictures\" class=\"anchor\" aria-label=\"Permalink: :minidisc: 4. Build an LMDB database with BnB pictures\" href=\"#minidisc-4-build-an-lmdb-database-with-bnb-pictures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtract visual features and store them on a single file. Several steps are required to achieve that. Unfortunately, we don\u0027t own permissions over Airbnb images, and thus we are not permitted to share our own LMDB file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.1. Split between train and test\u003c/h3\u003e\u003ca id=\"user-content-41-split-between-train-and-test\" class=\"anchor\" aria-label=\"Permalink: 4.1. Split between train and test\" href=\"#41-split-between-train-and-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e5% of the dataset is allocated to the testset:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003eround\u003c/span\u003e() {\n \u003cspan class=\"pl-c1\"\u003eprintf\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e%.\u003cspan class=\"pl-smi\"\u003e${2}\u003c/span\u003ef\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${1}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n}\n\nnum_rows=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ewc -l data/bnb-dataset-indoor.tsv\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\ntest=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$((\u003c/span\u003enum_rows \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e05\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e))\u003c/span\u003e\u003c/span\u003e\ntest=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003eround \u003cspan class=\"pl-smi\"\u003e$test\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\ncat data/bnb-dataset-indoor.tsv \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e tail -n \u003cspan class=\"pl-smi\"\u003e$test\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e data/bnb-test-indoor-filtered.tsv\n\ntrain=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$((\u003c/span\u003enum_rows \u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e test\u003cspan class=\"pl-pds\"\u003e))\u003c/span\u003e\u003c/span\u003e\ncat data/bnb-dataset-indoor.tsv \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e head -n \u003cspan class=\"pl-smi\"\u003e$train\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e data/bnb-train-indoor-filtered.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.2. Extract bottom-up top-down features\u003c/h3\u003e\u003ca id=\"user-content-42-extract-bottom-up-top-down-features\" class=\"anchor\" aria-label=\"Permalink: 4.2. Extract bottom-up top-down features\" href=\"#42-extract-bottom-up-top-down-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step is one of the most annoying one, since the install of bottom-up top-down attention is outdated. I put docker file and Singularity definition file in the folder \u003ccode\u003econtainer\u003c/code\u003e to help you with that.\nNote that this step is also extremely slow and you might want to use multiple GPUs.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython precompute_airbnb_img_features_with_butd.py --images data/images\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf this step is too difficult, open an issue and I\u0027ll try to use the \u003ca href=\"https://github.com/MILVLG/bottom-up-attention.pytorch\"\u003ePyTorch version\u003c/a\u003e instead.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.3. Build an LMDB file\u003c/h3\u003e\u003ca id=\"user-content-43-build-an-lmdb-file\" class=\"anchor\" aria-label=\"Permalink: 4.3. Build an LMDB file\" href=\"#43-build-an-lmdb-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract keys\u003c/span\u003e\npython extract_keys.py --output data/keys.txt --datasets data/bnb-dataset.indoor.tsv\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create an LMDB\u003c/span\u003e\npython convert_to_lmdb.py --output img_features --keys data/keys.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that you can split the LMDB into multiple files by using a number of workers. This could be relevant when your LMDB file is super huge!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udd17 5. Create dataset files with path-instruction pairs\u003c/h2\u003e\u003ca id=\"user-content-link-5-create-dataset-files-with-path-instruction-pairs\" class=\"anchor\" aria-label=\"Permalink: :link: 5. Create dataset files with path-instruction pairs\" href=\"#link-5-create-dataset-files-with-path-instruction-pairs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlmost there! We built image-caption pairs and now we want to convert them into path-instruction pairs.\nActually, we are just going to produce JSON files that you can feed into the \u003ca href=\"https://github.com/airbert-vln/airbert/\"\u003etraining repository\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u26d3\ufe0f 5.1. Concatenation\u003c/h3\u003e\u003ca id=\"user-content-chains-51-concatenation\" class=\"anchor\" aria-label=\"Permalink: :chains: 5.1. Concatenation\" href=\"#chains-51-concatenation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython preprocess_dataset.py --csv data/bnb-train.tsv --name bnb_train\npython preprocess_dataset.py --csv data/bnb-test.tsv --name bnb_test\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\ud83d\udc65 5.2. Image merging\u003c/h3\u003e\u003ca id=\"user-content-busts_in_silhouette-52-image-merging\" class=\"anchor\" aria-label=\"Permalink: :busts_in_silhouette: 5.2. Image merging\" href=\"#busts_in_silhouette-52-image-merging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython merge_photos.py --source bnb_train.py --output merge+bnb_train.py --detection-dir data/places365 \npython merge_photos.py --source bnb_test.py --output merge+bnb_test.py --detection-dir data/places365\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\ud83d\udc68\u200d\ud83d\udc69\u200d\ud83d\udc67 5.3. Captionless insertion\u003c/h3\u003e\u003ca id=\"user-content--53-captionless-insertion\" class=\"anchor\" aria-label=\"Permalink: \ud83d\udc68\u200d\ud83d\udc69\u200d\ud83d\udc67 5.3. Captionless insertion\" href=\"#-53-captionless-insertion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython preprocess_dataset.py --csv data/bnb-dataset.indoor.tsv --captionless True --min-caption 2 --min-length 4 --name 2capt+bnb_train\n\npython preprocess_dataset.py --csv datasets/data/bnb-dataset.indoor.tsv --captionless True --min-caption 2 --min-length 4 --name 2capt+bnb_test\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\ud83d\udc63 5.4. Instruction rephrasing\u003c/h3\u003e\u003ca id=\"user-content--54-instruction-rephrasing\" class=\"anchor\" aria-label=\"Permalink: \ud83d\udc63 5.4. Instruction rephrasing\" href=\"#-54-instruction-rephrasing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract noun phrases from BnB captions\u003c/span\u003e\npython extract_noun_phrases.py --source data/airbnb-train-indoor-filtered.tsv --output data/bnb-train.np.tsv \npython extract_noun_phrases.py --source data/airbnb-test-indoor-filtered.tsv --output data/bnb-test.np.tsv \n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract noun phrases from R2R train set\u003c/span\u003e\npython perturbate_dataset.py --infile R2R_train.json --outfile np_train.json --mode object --training True \n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.5. Create the testset\u003c/h3\u003e\u003ca id=\"user-content-55-create-the-testset\" class=\"anchor\" aria-label=\"Permalink: 5.5. Create the testset\" href=\"#55-create-the-testset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to create a testset for each dataset. Here is an example for captionless insertion.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython build_testset.py --output data/bnb/2capt+testset.json --out-listing False --captions 2capt+bnb_test.json\n\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 13, - "subscribers_count": 1, + "readme": "\u003cp\u003eWelcome in repository focused on processing AR devices recordings.\u003c/p\u003e\n\u003cp\u003eThat main goal of this project is to implement all the research codes in single place that allow easy setup, easy buils, and remove the time that is required to reinstall all the repositories, writing the format conversion, and wrappers for deployment. The overview of the codes will be here (TODO), however, you can simply run the GUI and load the pipeline that are already done, e.g., localization, dense and sparse mapping, downloading of the AR device recordings, dense point clouds alignment, etc..\u003c/p\u003e\n\u003chr\u003e\n\u003cpre\u003e\u003ccode\u003e HOW TO INSTALL ME\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003col\u003e\n\u003cli\u003eRequirements (install in advance)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eGit\u003c/li\u003e\n\u003cli\u003eCmake\u003c/li\u003e\n\u003cli\u003eAnaconda\u003c/li\u003e\n\u003cli\u003eSingularity (Linux) / Docker (Windows) (most of the codes are in containers to run on any machine)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eClone this repository and its submodules\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003egit clone --recurse-submodules --remote-submodules \u003ca href=\"https://github.com/michalpolic/hololens_mapper.git\"\u003ehttps://github.com/michalpolic/hololens_mapper.git\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate or copy the Docker/Singularity containers (copy the containers if available, otherwise use code in init.sh)\u003c/li\u003e\n\u003cli\u003eCreate the conda environment (use code in init.sh)\u003c/li\u003e\n\u003cli\u003eCompile the C++ codes (use code in init.sh)\u003c/li\u003e\n\u003cli\u003eDownload pre-trained weights for NetVLAD (use code in init.sh)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eRun the initialization (steps 3-6): \u003ccode\u003esh ./init.sh\u003c/code\u003e (building the containes may take few hours)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eActivate conda enviroment\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eIn general: \u003ccode\u003econda activate meshroom\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eUsing VS Code: \u003ccode\u003eF1 -\u0026gt; Python:Select Interpreter -\u0026gt; meshroom\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eIf debuging in VS Code\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eSpecify what should be called and which parameters to use. Create \u003ccode\u003elaunch.json\u003c/code\u003e, for example:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e {\n \"version\": \"0.2.0\",\n \"configurations\": [\n {\n \"name\": \"Meshroom GUI\",\n \"type\": \"python\",\n \"request\": \"launch\",\n \"program\": \"\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom/ui\",\n \"console\": \"integratedTerminal\",\n \"env\": {\n \"PYTHONPATH\": \"\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom;\u0026lt;path to hololens_mapper\u0026gt;\"\n }\n },\n {\n \"name\": \"Meshroom batch\",\n \"type\": \"python\",\n \"request\": \"launch\",\n \"program\": \"\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/bin/meshroom_compute\",\n \"console\": \"integratedTerminal\",\n \"env\": {\n \"PYTHONPATH\": \n \"\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom;\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom;\u0026lt;path to hololens_mapper\u0026gt;\"\n },\n \"args\": [\n \"\u0026lt;path to hololens_mapper\u0026gt;/pipelines/\u0026lt;your pipeline name.mg\u0026gt;\",\n \"--cache\",\n \"\u0026lt;your output cache folder\u0026gt;/\u0026lt;evaluation folder\u0026gt;\"\n ]\n }\n ]\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf not using VS Code, execute:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003ea) \u003ccode\u003eexport PYTHONPATH=\"$PYTHONPATH;\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom;\u0026lt;path to hololens_mapper\u0026gt;\"\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eb) run \u003ccode\u003epython \u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom/ui\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cpre\u003e\u003ccode\u003e HOW TO RUN ME\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eIf you finished previous section, you can create, build, modify, and test your pipelines in Meshroom GUI.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe pipelines that will be uploaded in git are in \u003ccode\u003e\u0026lt;path to hololens_mapper\u0026gt;/pipelines\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eIf you want any \"private\" pipeline, start it with \u003ccode\u003etmp_\u0026lt;your private pipeline name\u0026gt;.mg\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cpre\u003e\u003ccode\u003e HOW TO CONTRIBUTE\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cp\u003eIf you would like to add your nodes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFind a simple node, e.g., \u003ccode\u003e\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom/nodes/Alignment/DensePonitcloudsConcatenator.py\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eMake your own copy to proper folder in \u003ccode\u003e\u0026lt;path to hololens_mapper\u0026gt;/third_party/meshroom/meshroom/nodes\u003c/code\u003e (please follow reasonable naming)\u003c/li\u003e\n\u003cli\u003eModify the content as you wish\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo add your own source codes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe source codes are located in \u003ccode\u003e\u0026lt;path to hololens_mapper\u0026gt;/src\u003c/code\u003e (please, follow reanable naming)\u003c/li\u003e\n\u003cli\u003eYour own containers building add into \u003ccode\u003esh ./init.sh\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 12, + "subscribers_count": 4, "topics": [], - "updated_at": 1724160975.0 + "updated_at": 1659347420.0 }, { "data_format": 2, - "description": "R package to generate the same BEAST2 XML parameter files as generated by BEAUti 2", + "description": "Grow virtual creatures in static and physics simulated environments.", "filenames": [ - "Singularity" + "cluster/Singularity" ], - "full_name": "ropensci/beautier", - "latest_release": "v2.6.12", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebeautier\u003c/h1\u003e\u003ca id=\"user-content-beautier\" class=\"anchor\" aria-label=\"Permalink: beautier\" href=\"#beautier\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/ropensci/software-review/issues/209\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1f9e866eed4c0edc0f5b5b90e775c80a93dc80a99af5f86154943b5bca5b742/68747470733a2f2f6261646765732e726f70656e7363692e6f72672f3230395f7374617475732e737667\" alt=\"Peer Review Status\" data-canonical-src=\"https://badges.ropensci.org/209_status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cran.r-project.org/package=beautier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/59fa0d237851c47abca661d4b52500a27b27edfea37ce54e72326071cc1dd4b7/687474703a2f2f7777772e722d706b672e6f72672f6261646765732f76657273696f6e2f6265617574696572\" alt=\"CRAN_Status_Badge\" data-canonical-src=\"http://www.r-pkg.org/badges/version/beautier\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=beautier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/91198f689df57a08ec8ba4f6f3c201fd1ca9a6e47fe71d542089dc8ad2ac47ef/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6772616e642d746f74616c2f6265617574696572\" alt=\"CRAN total downloads\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/grand-total/beautier\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=beautier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b49c2ea1524023b09adedd34045974707e176c58113796d85465445d37316b82/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6265617574696572\" alt=\"CRAN downloads per months\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/beautier\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/53443354\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4d24c9a7d7571ed1f7acdf05bc1492d1720cbd9764d696338f710c11ba642262/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f35333434333335342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/53443354.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/ropensci/beautier/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://about.codecov.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/beautier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff034288b0791340bac7dea03ff17aa4d354a3b0bea92f96c633ae8025eb7e24/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/beautier/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/613637bb36d139cbbef6d32c146529b4fd650914d54b6603cc237ed1d35da4fe/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ccode\u003ebeautier\u003c/code\u003e is \u003ccode\u003eBEAUti\u003c/code\u003e for R.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/beautier_logo.png\"\u003e\u003cimg src=\"man/figures/beautier_logo.png\" alt=\"beautier logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of \u003ccode\u003ebeautier\u003c/code\u003e is to create\n\u003ca href=\"inst/extdata/2_4.xml\"\u003ea valid BEAST2 XML input file\u003c/a\u003e\nfrom a n inference model. In this way, a scientific pipeline using\n\u003ccode\u003eBEAST2\u003c/code\u003e can be fully scripted, instead of using \u003ccode\u003eBEAUti\u003c/code\u003e\u0027s GUI.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ebeautier\u003c/code\u003e is part of the \u003ca href=\"https://github.com/ropensci/babette\"\u003e\u003ccode\u003ebabette\u003c/code\u003e\u003c/a\u003e package suite:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beautier\"\u003e\u003ccode\u003ebeautier\u003c/code\u003e\u003c/a\u003e create a BEAST2 input (\u003ccode\u003e.xml\u003c/code\u003e) file from an inference model.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/tiebeaur\"\u003e\u003ccode\u003etiebeaur\u003c/code\u003e\u003c/a\u003e creates an inference model from a BEAST2 input (\u003ccode\u003e.xml\u003c/code\u003e) file \u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e experimental \u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beastier\"\u003e\u003ccode\u003ebeastier\u003c/code\u003e\u003c/a\u003e runs BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/tracerer\"\u003e\u003ccode\u003etracerer\u003c/code\u003e\u003c/a\u003e pastes BEAST2 output (\u003ccode\u003e.log\u003c/code\u003e, \u003ccode\u003e.trees\u003c/code\u003e, etc) files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/mauricer\"\u003e\u003ccode\u003emauricer\u003c/code\u003e\u003c/a\u003e install BEAST2 packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRelated R packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/beautier_on_windows\"\u003e\u003ccode\u003ebeautier_on_windows\u003c/code\u003e\u003c/a\u003e: verifies\n\u003ccode\u003ebeautier\u003c/code\u003e builds on Windows\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/lumier\"\u003e\u003ccode\u003elumier\u003c/code\u003e\u003c/a\u003e: Shiny app to help create the function call needed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/examples.md\"\u003eexamples\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ebeautier\u003c/code\u003e can be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLatest CRAN version: CRAN\u003c/li\u003e\n\u003cli\u003eLatest stable version: GitHub, \u003ccode\u003emaster\u003c/code\u003e branch\u003c/li\u003e\n\u003cli\u003eBleeding-edge version: GitHub, \u003ccode\u003edevelop\u003c/code\u003e branch\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCRAN\u003c/h3\u003e\u003ca id=\"user-content-cran\" class=\"anchor\" aria-label=\"Permalink: CRAN\" href=\"#cran\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the latest CRAN version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebeautier\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGitHub, \u003ccode\u003emaster\u003c/code\u003e branch\u003c/h3\u003e\u003ca id=\"user-content-github-master-branch\" class=\"anchor\" aria-label=\"Permalink: GitHub, master branch\" href=\"#github-master-branch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the latest stable version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003eremotes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eropensci/beautier\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGitHub, \u003ccode\u003edevelop\u003c/code\u003e branch\u003c/h3\u003e\u003ca id=\"user-content-github-develop-branch\" class=\"anchor\" aria-label=\"Permalink: GitHub, develop branch\" href=\"#github-develop-branch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the bleeding-edge version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003eremotes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eropensci/beautier\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eref\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edevelop\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"doc/faq.md\"\u003eFAQ\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/faq.md\"\u003eFAQ\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupported\u003c/h2\u003e\u003ca id=\"user-content-supported\" class=\"anchor\" aria-label=\"Permalink: Supported\" href=\"#supported\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis works, and the interface is unlikely to change.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e1 DNA alignment\u003c/li\u003e\n\u003cli\u003eSite models:\n\u003cul\u003e\n\u003cli\u003eJC69\u003c/li\u003e\n\u003cli\u003eHKY\u003c/li\u003e\n\u003cli\u003eTN93\u003c/li\u003e\n\u003cli\u003eGTR\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eClock models:\n\u003cul\u003e\n\u003cli\u003eStrickt\u003c/li\u003e\n\u003cli\u003eRelaxed log-normal\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTree models:\n\u003cul\u003e\n\u003cli\u003eYule\u003c/li\u003e\n\u003cli\u003eBirth-Death\u003c/li\u003e\n\u003cli\u003eCoalescent Bayesian Skyline\u003c/li\u003e\n\u003cli\u003eCoalescent Constant Population\u003c/li\u003e\n\u003cli\u003eCoalescent Exponential Population\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eHandle missing data: simply use a dash (\u00b4-\u00b4) as a sequence\nin a FASTA file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExperimental\u003c/h2\u003e\u003ca id=\"user-content-experimental\" class=\"anchor\" aria-label=\"Permalink: Experimental\" href=\"#experimental\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis works partially, and the interface may change as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTip dating\u003c/h3\u003e\u003ca id=\"user-content-tip-dating\" class=\"anchor\" aria-label=\"Permalink: Tip dating\" href=\"#tip-dating\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tip dates file is a file\nthat needs to not have column, nor row names.\nThe columns need to be tab separated.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/ropensci/beautier/blob/master/inst/extdata/G_VII_pre2003_dates_4.txt\"\u003ehere\u003c/a\u003e\nfor an example, of which the first rows are shown here:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eKF767106_Indonesia_1976_VII 1976\nKF767104_Indonesia_1988_VII 1988\nKF767105_Indonesia_1988_VII 1988\nAY288998_Indonesia_1990_VII 1990\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the future, there probably will be a \u00b4to_tipdates_file\u00b4 function,\nto create a temporary tipdates file from a table.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMissing features/unsupported\u003c/h2\u003e\u003ca id=\"user-content-missing-featuresunsupported\" class=\"anchor\" aria-label=\"Permalink: Missing features/unsupported\" href=\"#missing-featuresunsupported\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ebeautier\u003c/code\u003e cannot do everything \u003ccode\u003eBEAUti\u003c/code\u003e can.\u003c/p\u003e\n\u003cp\u003eHere are some missing or (yet) unsupported features,\nsome are linked to an Issue:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ropensci/beautier/issues/130\"\u003eAdd offset to a distribution\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eTwo or more DNA alignments\u003c/li\u003e\n\u003cli\u003eTwo or more site, clock or tree models\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ropensci/beautier/issues/131\"\u003eTwo or more MRCA priors\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eShared site, clock and/or tree models\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ropensci/beautier/issues/114\"\u003eUsing an amino acid alignment\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSupport for hyper parameters\u003c/li\u003e\n\u003cli\u003eClock models\n\u003cul\u003e\n\u003cli\u003eRelaxed exponential\u003c/li\u003e\n\u003cli\u003eRandom local\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTree priors\n\u003cul\u003e\n\u003cli\u003eCalibrated Yule model\u003c/li\u003e\n\u003cli\u003eCoalescent Extended Bayesian Skyline\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ropensci/beautier/issues/133\"\u003eBirth Death Skyline Serial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eInitialization (this is a tab that is hidden by default in \u003ccode\u003eBEAUti\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere is a feature I miss\u003c/h2\u003e\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" aria-label=\"Permalink: There is a feature I miss\" href=\"#there-is-a-feature-i-miss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI want to collaborate\u003c/h2\u003e\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" aria-label=\"Permalink: I want to collaborate\" href=\"#i-want-to-collaborate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u0027Submitting code\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI think I have found a bug\u003c/h2\u003e\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" aria-label=\"Permalink: I think I have found a bug\" href=\"#i-think-i-have-found-a-bug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u0027Submitting bugs\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere\u0027s something else I want to say\u003c/h2\u003e\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" aria-label=\"Permalink: There\u0027s something else I want to say\" href=\"#theres-something-else-i-want-to-say\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExternal links\u003c/h2\u003e\u003ca id=\"user-content-external-links\" class=\"anchor\" aria-label=\"Permalink: External links\" href=\"#external-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CompEvol/beast2\"\u003eBEAST2 GitHub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFiles used by continuous integration scripts\u003c/h2\u003e\u003ca id=\"user-content-files-used-by-continuous-integration-scripts\" class=\"anchor\" aria-label=\"Permalink: Files used by continuous integration scripts\" href=\"#files-used-by-continuous-integration-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFilename\u003c/th\u003e\n\u003cth\u003eDescriptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"mlc_config.json\"\u003emlc_config.json\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the link checker, use \u003ccode\u003emarkdown-link-check --config mlc_config.json --quiet docs/**/*.md\u003c/code\u003e to do link checking locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".spellcheck.yml\"\u003e.spellcheck.yml\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".wordlist.txt\"\u003e.wordlist.txt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eWhitelisted words for the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlint.jsonc\"\u003e.markdownlint.jsonc\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlintignore\"\u003e.markdownlintignore\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFiles ignored by the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eArticle about \u003ccode\u003ebabette\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBilderbeek, Rich\u00e8l JC, and Rampal S. Etienne. \"\u003ccode\u003ebabette\u003c/code\u003e: BEAUti 2, BEAST 2 and Tracer for R.\" Methods in Ecology and Evolution (2018). \u003ca href=\"https://doi.org/10.1111/2041-210X.13032\" rel=\"nofollow\"\u003ehttps://doi.org/10.1111/2041-210X.13032\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFASTA files \u003ccode\u003eanthus_aco.fas\u003c/code\u003e and \u003ccode\u003eanthus_nd2.fas\u003c/code\u003e from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eVan Els, Paul, and Heraldo V. Norambuena. \"A revision of species limits in Neotropical pipits Anthus based on multilocus genetic and vocal data.\" Ibis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFASTA file \u003ccode\u003eG_VII_pre2003_msa.fas\u003c/code\u003e from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDurr, PA; Wibowo, MH; Tabbu, CR; Asmara, W; Selleck, P; Wang, J; Broz, I; Graham, K.; Dimitrov, K and Afonso, C. (in preparation). Phylodynamics of Genotype VII Newcastle disease virus in Indonesia.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://ropensci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c7d2a7aedd313f02b4f7d199df4d0d28905b7814db324fb532759b2fe2ee9ac2/68747470733a2f2f726f70656e7363692e6f72672f7075626c69635f696d616765732f726f70656e7363695f666f6f7465722e706e67\" alt=\"ropensci_footer\" data-canonical-src=\"https://ropensci.org/public_images/ropensci_footer.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n", - "stargazers_count": 13, - "subscribers_count": 3, + "full_name": "cfusting/conditional-growth", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-growing-virtual-creatures-in-minecraft\" class=\"anchor\" aria-hidden=\"true\" href=\"#growing-virtual-creatures-in-minecraft\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGrowing Virtual Creatures in Minecraft\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/869178/201533053-27c2ff53-e625-40ed-a490-5cd401896609.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/869178/201533053-27c2ff53-e625-40ed-a490-5cd401896609.mp4\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSea lanterns searching for glowstone\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/cfusting/conditional-growth\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d1f5f4e97ec14d197ffc19e3f24ec51393310f3052d9db011fade1eb7a0a581/68747470733a2f2f7472617669732d63692e636f6d2f6366757374696e672f636f6e646974696f6e616c2d67726f7774682e7376673f6272616e63683d6d61696e\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/cfusting/conditional-growth.svg?branch=main\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-motivation\" class=\"anchor\" aria-hidden=\"true\" href=\"#motivation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMotivation\u003c/h2\u003e\n\u003cp\u003eGrowing virtual creatures is really fun [1]. However optimizing them in a physics engine in three dimensions is (as of 2022) pretty time consuming even on a powerful desktop computer. Science is limited by creativity, technical expertise, and cycles. To address the later, this package includes a pseudo-realistic environment: Minecraft [2], to enable anyone with a fairly modern computer to grow virtual creatures for fun and perhaps, to test hypothesizes.\u003c/p\u003e\n\u003cp\u003eMinecraft is a very good looking gridworld with a few notable advantages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSome blocks, like sand and water, respond to physics.\u003c/li\u003e\n\u003cli\u003eThe world is procedurally generated, providing an endless landscape of random environments.\u003c/li\u003e\n\u003cli\u003eMulti-agent environments are supported by default.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSo, although the curious individual may not be able to simulate a robot to be transferred to our world, a great deal can be explored and tested.\u003c/p\u003e\n\u003cp\u003eA primary goal of this package is to be standardized, extensible, and modular: many of the code bases I have come across couple a growth encoding with an optimization algorithm, limiting their application and testability. To that end the growth function and Ray\u0027s RLlib are completely independent; tied together only in the minecraft environment class following Open AI\u0027s Gym standard. You can replace the growth function with anything that returns an encoding you can convert to blocks (I\u0027d like to see Compositional Pattern Producing Networks [3], for example) and write your own environment. In the same vein Ray gives you a robust selection of gradient and non-gradient based optimization algorithms to choose from, the ability to scale, and standardized logging and experiment tracking with \u003ca href=\"https://mlflow.org/\" rel=\"nofollow\"\u003eMLFlow\u003c/a\u003e and \u003ca href=\"https://www.tensorflow.org/tensorboard\" rel=\"nofollow\"\u003eTensorboard\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eMy hope is that this package enables any company, university, and especially \u003cstrong\u003eindividuals\u003c/strong\u003e to implement one, two, or all of a:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGrowth encoding\u003c/li\u003e\n\u003cli\u003eEnvironment\u003c/li\u003e\n\u003cli\u003eOptimizer\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eto test hypothesizes or just mess around, with something that will work on a standard desktop but can be scaled to a high performance computing cluster.\u003c/p\u003e\n\u003cp\u003eglhf.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-core-components\" class=\"anchor\" aria-hidden=\"true\" href=\"#core-components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCore Components\u003c/h2\u003e\n\u003cp\u003eRoughly speaking, there are three things that tie this package together.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe Minecraft server.\u003c/li\u003e\n\u003cli\u003eA Minecraft environment which extends OpenAI\u0027s Gym \u003ca href=\"https://github.com/openai/gym/blob/6a04d49722724677610e36c1f92908e72f51da0c/gym/core.py\"\u003eEnvironment\u003c/a\u003e and makes use of a growth function (unique to this package) to decide what to grow.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://docs.ray.io/en/latest/rllib/index.html\" rel=\"nofollow\"\u003eRay\u0027s RLlib\u003c/a\u003e for scalable optimization and logging.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-tracking-and-metrics\" class=\"anchor\" aria-hidden=\"true\" href=\"#tracking-and-metrics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTracking and Metrics\u003c/h3\u003e\n\u003cp\u003eMetrics are captured by Ray in /tmp/[expname] where expname is specified in the run configuration file, in the run function, by the parameter \"name\". You\u0027ll need to spend some time learning the Ray framework to become comfortable with this and other parameter choices governing the optimization process. The easiest way to view the metrics is to use Tensorboard and will be described in the example below. Here\u0027s a pretty picture:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/tensorboard_example.png\"\u003e\u003cimg src=\"./docs/tensorboard_example.png\" alt=\"Tensorboard picture\" width=\"960\" height=\"540\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-theory-of-the-conditional-growth-function\" class=\"anchor\" aria-hidden=\"true\" href=\"#theory-of-the-conditional-growth-function\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTheory of the conditional growth function\u003c/h2\u003e\n\u003cp\u003eI\u0027ve summarized the idea of the conditional growth function in a few images below. The idea is as follow:\u003c/p\u003e\n\u003cp\u003eThe growth of a creature can be broken down into an ordered sequence of steps. At each step, the optimizer has access to a state describing the creature and / or environment. Using this information a configuration of voxels is chosen (for example, by selecting the voxel configuration with the maximum probability) to be added to the current growing voxel. Voxels are stored in a queue and are thus grown breadth-first.\u003c/p\u003e\n\u003cp\u003eThe above process boils down to the breadth-first application of the conditional probability of a voxel configuration given a state. Thus the beadth-first voxel selection process coupled with the growth function results in a creature of potentially infinite voxels: the integral of the growth function over time and space. My hope is that self-repeating structures will be observed and built by the growth function, providing a genommic encoding which can be thought of as a compressed representation of a creature.\u003c/p\u003e\n\u003cp\u003e|\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/theory1.jpg\"\u003e\u003cimg src=\"./docs/theory1.jpg\" alt=\"Theory image one\" width=\"400\" height=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e|\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./docs/theory2.jpg\"\u003e\u003cimg src=\"./docs/theory2.jpg\" alt=\"Theory image two\" width=\"400\" height=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e|\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-get-the-block\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-get-the-block\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample: Get the block\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://user-images.githubusercontent.com/869178/201533091-b17d37d1-df6c-46de-b8d5-ef18f670fe3f.mp4\" rel=\"nofollow\"\u003ehttps://user-images.githubusercontent.com/869178/201533091-b17d37d1-df6c-46de-b8d5-ef18f670fe3f.mp4\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNavigating obstacles\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this example we will grow a creature out of sea lanterns (reason: they look cool) who\u0027s goal is to touch a reward block. At each growth step the probability of a voxel configuration is determined given the tensor convolution of block types within some one norm k neighborhood of the block on which the configuration is to be added (translation: limited vision). To get this example running you will need Docker and Linux (Windows Linux Subsystem 2 is fine).\u003c/p\u003e\n\u003cp\u003eNote: It would be fair to ask at this point if the creature is \"growing\" or \"reaching\" toward the reward block. Unless you treat our world as truth, the answer to this question is irrelevant.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Requirements\u003c/h3\u003e\n\u003cp\u003eIf you would like to use a GPU make sure to install \u003ca href=\"https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime\" rel=\"nofollow\"\u003envidia-container-runtime\u003c/a\u003e. Other than that the Dockerfile will handle all the dependencies.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-installing-nvidia-container-runtime\" class=\"anchor\" aria-hidden=\"true\" href=\"#installing-nvidia-container-runtime\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstalling Nvidia Container Runtime\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edistribution=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003e. /etc/os-release\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$ID$VERSION_ID\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e \\\n \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo apt-key add - \\\n \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e curl -s -L https://nvidia.github.io/nvidia-docker/\u003cspan class=\"pl-smi\"\u003e$distribution\u003c/span\u003e/nvidia-docker.list \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo tee /etc/apt/sources.list.d/nvidia-docker.list\n \nsudo apt-get update \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e sudo apt-get install -y nvidia-docker2\nsudo systemctl restart docker\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-hidden=\"true\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning\u003c/h3\u003e\n\u003cp\u003eBuild the minecraft server by navigating into the minecraft-server directory and start it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t mc \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -it --rm -p 5001:5001 -p 25565:25565 mc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePull the Tensorflow image and start the tensorboard server. You can launch tensorboard in a web browser with localhost:6006:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull tensorflow/tensorflow\ndocker run -p 6006:6006 --rm -v /tmp:/tmp tensorflow/tensorflow tensorboard --logdir /tmp --host 0.0.0.0 --port 6006\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNavigate into this repo\u0027s root directory, build the image, and start the optimizer:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t growth \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ndocker run -it --rm --gpus all -v /tmp:/home/ray/ray_results --network host growth python run_configurations/minecraft/get_the_block.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe previous command starts a gpu enabled optimizer and four workers (creatures) in random locations in minecraft. You can edit all configurations in the run script: see Ray\u0027s RLlib for documentation.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-checking-out-the-creatures\" class=\"anchor\" aria-hidden=\"true\" href=\"#checking-out-the-creatures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChecking out the creatures\u003c/h3\u003e\n\u003cp\u003eYou\u0027ll need the Java version of Minecraft 1.12.2 to enter the world and hang out with your creatures. You can connect to the server you started by selecting Multiplayer -\u0026gt; Direct Connect -\u0026gt; localhost. The game mode is creative and as such double tapping on jump (spacebar) will allow you to fly; hold shift to go fast.\u003c/p\u003e\n\u003cp\u003eThe locations of the creatures are output in (x, z, y) format (don\u0027t ask me why the Minecraft devs did this) when Ray finishes initializing and before the creatures start optimizing. You can use these coordinates to teleport yourself to their respective locations with the server command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e/teleport [your_name] x z y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eI usually add 100 or so (units are in blocks) to the z coordinate such that I am teleported above the creature.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tips\" class=\"anchor\" aria-hidden=\"true\" href=\"#tips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTips\u003c/h2\u003e\n\u003cp\u003eYou can change how the world is generated in the minecraft server properties file. Check out this \u003ca href=\"https://minecraft.tools/en/custom.php?#seed\" rel=\"nofollow\"\u003ecustomized generator\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIt\u0027s common to edit the run file (IE in the example get_the_block.py), run the docker container, and wonder why nothing changed. A simple solution is to build the image before running it by convention:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t growth \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e docker run -it --rm --gpus all -v /tmp:/home/ray/ray_results --network host growth python run_configurations/minecraft/get_the_block.py\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003cp\u003e[1] Kriegman, Sam. \"Why virtual creatures matter.\" Nature Machine Intelligence 1.10 (2019): 492-492.\u003c/p\u003e\n\u003cp\u003e[2] Grbic, Djordje, et al. \"EvoCraft: A new challenge for open-endedness.\" International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Springer, Cham, 2021.\u003c/p\u003e\n\u003cp\u003e[3] Stanley, Kenneth O. \"Compositional pattern producing networks: A novel abstraction of development.\" Genetic programming and evolvable machines 8.2 (2007): 131-162.\u003c/p\u003e\n", + "stargazers_count": 12, + "subscribers_count": 2, "topics": [ - "r", - "r-package", - "rstats", - "bayesian", - "beast", - "beast2", - "phylogenetic-inference", - "phylogenetics", - "beauti" + "virtual-creatures", + "reinforcement-learning", + "reinforcement-learning-environments" ], - "updated_at": 1720420857.0 + "updated_at": 1676925092.0 }, { "data_format": 2, - "description": "K* search based implementation of top-k and top-quality planners", + "description": "An autonomous grasping solution for the Emika Franka Panda robot.", "filenames": [ - "misc/releases/20.06/Singularity.20.06", - "misc/releases/19.06/Singularity.19.06", - "misc/releases/21.12/Singularity.21.12", - "misc/releases/19.12/Singularity.19.12", - "misc/releases/latest/Singularity" + "containers/singularity/Singularity.ros_melodic-cuda10-bionic", + "containers/singularity/Singularity.ros_kinetic-cuda10-xenial" ], - "full_name": "IBM/kstar", - "latest_release": "1.4.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-kstar-planner-is-an-automated-pddl-based-planner-that-includes-planners-for-top-k-and-top-quality-planning-computational-tasks\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#kstar-planner-is-an-automated-pddl-based-planner-that-includes-planners-for-top-k-and-top-quality-planning-computational-tasks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKstar Planner is an Automated PDDL based planner that includes planners for top-k and top-quality planning computational tasks.\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-the-codebase-consists-of-multiple-planners-for-multiple-computational-problems-roughly-divided-into-two-categories\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#the-codebase-consists-of-multiple-planners-for-multiple-computational-problems-roughly-divided-into-two-categories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe codebase consists of multiple planners, for multiple computational problems, roughly divided into two categories:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eTop-k planning\u003c/li\u003e\n\u003cli\u003eTop-quality planning\u003cbr\u003e\n2.1. Top-quality planning\u003cbr\u003e\n2.2. Unordered top-quality planning\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe planner implements\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePure K* search based top-k and top-quality planning\u003c/li\u003e\n\u003cli\u003eSymmetry based pruning: OK* search\u003c/li\u003e\n\u003cli\u003ePartial order reduction: RK* search\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h1\u003e\n\u003cp\u003eFor building the code please use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-examples\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#running-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning (examples)\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-top-k\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#top-k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-k\u003c/h2\u003e\n\u003cp\u003eK* with lmcut heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --search \"kstar(lmcut(), k=100)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOK* with lmcut heuristic (recommended)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --symmetries \"sym=structural_symmetries(time_bound=0,search_symmetries=oss,stabilize_initial_state=false,keep_operator_symmetries=true)\" --search \"kstar(lmcut(), k=100, symmetries=sym)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-top-quality\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#top-quality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-quality\u003c/h2\u003e\n\u003cp\u003eK* with iPDB heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --search \"kstar(ipdb(), q=1.0)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOK* with iPDB heuristic (recommended)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --symmetries \"sym=structural_symmetries(time_bound=0,search_symmetries=oss,stabilize_initial_state=false,keep_operator_symmetries=true)\" --search \"kstar(ipdb(), q=1.0, symmetries=sym)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-unordered-top-quality\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#unordered-top-quality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnordered Top-quality\u003c/h2\u003e\n\u003cp\u003eK* with iPDB heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --search \"kstar(ipdb(), q=1.0, find_unordered_plans=true)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOK* with iPDB heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --symmetries \"sym=structural_symmetries(time_bound=0,search_symmetries=oss,stabilize_initial_state=false,keep_operator_symmetries=true)\" --search \"kstar(ipdb(), q=1.0, symmetries=sym, find_unordered_plans=true)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRK* with iPDB heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --search \"kstar(ipdb(), q=1.0, pruning=limited_pruning(pruning=atom_centric_stubborn_sets(use_sibling_shortcut=true, atom_selection_strategy=quick_skip)), find_unordered_plans=true)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eORK* with iPDB heuristic (recommended)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --symmetries \"sym=structural_symmetries(time_bound=0,search_symmetries=oss,stabilize_initial_state=false,keep_operator_symmetries=true)\" --search \"kstar(ipdb(), q=1.0, symmetries=sym, pruning=limited_pruning(pruning=atom_centric_stubborn_sets(use_sibling_shortcut=true, atom_selection_strategy=quick_skip)), find_unordered_plans=true)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-additional-options\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#additional-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional options\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eOptimization of switching K* from A* to EA is controlled by the following parameters:\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eopenlist_inc_percent_lb\u003c/code\u003e (default \u003ccode\u003e1\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eopenlist_inc_percent_ub\u003c/code\u003e (default \u003ccode\u003e5\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eswitch_on_goal\u003c/code\u003e (default \u003ccode\u003efalse\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDumping plans:\n\u003cul\u003e\n\u003cli\u003eIn case only the number of plans is needed, not the actual plans, an option \u003ccode\u003edump_plans=false\u003c/code\u003e can be used\u003c/li\u003e\n\u003cli\u003eDumping the plans into separate files can be avoided with \u003ccode\u003edump_plan_files=false\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDumping the plans into a single JSON file can be done by specifying \u003ccode\u003ejson_file_to_dump=\u0026lt;filename\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building-the-package\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building-the-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the package:\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Testing locally\u003c/span\u003e\npip install tox pytest -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ntox\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Output wheels\u003c/span\u003e\npip install cibuildwheel\nCIBW_BEFORE_BUILD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epython -m pip install pip Cython --upgrade\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\nCIBW_ARCHS_MACOS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003euniversal2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\nCIBW_ARCHS_LINUX=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eauto64\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\nCIBW_ARCHS_WINDOWS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eauto64\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\npython -m cibuildwheel --platform macos\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Different versions of CPython from https://python.org must be installed; e.g. 3.8, 3.9, 3.10, 3.11\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e CI needs a Mac or Windows VMs, or [docker contexts](https://github.com/StefanScherer/windows-docker-machine), to build wheels for those OSes\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-using-as-a-package\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#using-as-a-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing as a package:\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install git+https://github.com/IBM/kstar.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDue to the CLI-oriented design, the code must be run using subprocess.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esys\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esubprocess\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogging\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esubprocess\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSubprocessError\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esubprocess\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003echeck_output\u003c/span\u003e([\u003cspan class=\"pl-s1\"\u003esys\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexecutable\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"-m\"\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"driver.main\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"..your args\"\u003c/span\u003e])\n\u003cspan class=\"pl-k\"\u003eexcept\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSubprocessError\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eerr\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003elogging\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eerror\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eerr\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edecode\u003c/span\u003e())\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#citing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCiting\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-top-k-planning\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#top-k-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-k planning\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{lee-et-al-socs2023,\n title = \"On K* Search for Top-k Planning\",\n author = \"Junkyu Lee and Michael Katz and Shirin Sohrabi\",\n booktitle = \"Proceedings of the 16th Annual Symposium on\n Combinatorial Search (SoCS 2023)\",\n publisher = \"{AAAI} Press\",\n year = \"2023\"\n}\n\n@InProceedings{katz-lee-ijcai2023,\n author = \"Michael Katz and Junkyu Lee\",\n title = \"K* Search Over Orbit Space for Top-k Planning\",\n booktitle = \"Proceedings of the 32nd International Joint\n Conference on Artificial Intelligence (IJCAI 2023)\",\n publisher = \"{IJCAI}\",\n year = \"2023\"\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-top-quality-planning\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#top-quality-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-quality planning\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{katz-lee-socs2023,\n title = \"K* and Partial Order Reduction for Top-quality Planning\",\n author = \"Michael Katz and Junkyu Lee\",\n booktitle = \"Proceedings of the 16th Annual Symposium on\n Combinatorial Search (SoCS 2023)\",\n publisher = \"{AAAI} Press\",\n year = \"2023\"\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003eKstar Planner is an Automated PDDL based planner that\nincludes planners for top-k and top-quality planning computational\ntasks. Copyright (C) 2023 Junkyu Lee, Michael Katz, IBM Research, USA.\nThe code extends the Fast Downward planning system. The license for the\nextension is specified in the LICENSE file.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-fast-downward\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast Downward\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 13, + "full_name": "rickstaa/panda-autograsp", + "latest_release": "v1.0.8-melodic", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epanda-autograsp\u003c/h1\u003e\u003ca id=\"user-content-panda-autograsp\" class=\"anchor\" aria-label=\"Permalink: panda-autograsp\" href=\"#panda-autograsp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.codacy.com/gh/rickstaa/panda-autograsp/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=rickstaa/panda-autograsp\u0026amp;utm_campaign=Badge_Grade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dcc915773f75ccdb296353b1d52c65e95f6e66f8e610c4349f2e8dd7b2b2201e/68747470733a2f2f6170702e636f646163792e636f6d2f70726f6a6563742f62616467652f47726164652f3038376664613266306634633432336362353631373435616237616664626137\" alt=\"Codacy Badge\" data-canonical-src=\"https://app.codacy.com/project/badge/Grade/087fda2f0f4c423cb561745ab7afdba7\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"contributing.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/87ddab2ef405aee70f76af73b8184fccbe88f9f3551c178af03018ac86db8bfc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e747269627574696f6e732d77656c636f6d652d6f72616e67652e737667\" alt=\"Contributions\" data-canonical-src=\"https://img.shields.io/badge/contributions-welcome-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/rickstaa/panda-autograsp/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b0e6512c1448a9e1672b509bba059a9e87d05c8f1ab07905491b69ddbf660f40/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f7269636b737461612f70616e64612d6175746f6772617370\" alt=\"GitHub release (latest by date)\" data-canonical-src=\"https://img.shields.io/github/v/release/rickstaa/panda-autograsp\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1bde141946ab03e7cd01be4ff820f64e9d2aa22b6ab86e62f3e5271cd404ce9d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e253230332d332e37253230253743253230332e36253230253743253230332e352d79656c6c6f772e737667\" alt=\"Python 3\" data-canonical-src=\"https://img.shields.io/badge/python%203-3.7%20%7C%203.6%20%7C%203.5-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b89265bd3bfb343694f9911d718aa363886fdcafe26bf28ba7b930698fd5b3ad/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e253230322d322e37253230253743253230322e36253230253743253230322e352d627269676874677265656e2e737667\" alt=\"Python 2\" data-canonical-src=\"https://img.shields.io/badge/python%202-2.7%20%7C%202.6%20%7C%202.5-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://wiki.ros.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cee8904b738cf8aaea786179bb883cb74f22c7fd104d39797c795788d07265c5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f524f5325323076657273696f6e732d4d656c6f6469632532302537432532304b696e65637469632d627269676874677265656e\" alt=\"ROS versions\" data-canonical-src=\"https://img.shields.io/badge/ROS%20versions-Melodic%20%7C%20Kinectic-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\ud83d\udca1 You are on the default (Kinect2) branch. This branch is optimized to work with the Kinect2 camera. To use the package with the RealSense cameras, see the \u003ca href=\"https://github.com/rickstaa/panda-autograsp/tree/melodic-devel-realsense\"\u003emelodic-devel-realsense branch\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePackage Overview\u003c/h2\u003e\u003ca id=\"user-content-package-overview\" class=\"anchor\" aria-label=\"Permalink: Package Overview\" href=\"#package-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe panda-autograsp is an autonomous ROS based grasping solution that works with the \u003ca href=\"https://www.franka.de/panda/\" rel=\"nofollow\"\u003ePanda Emika Franka robot\u003c/a\u003e. In this grasping solution, several opensource grasping solutions are implemented on the \u003ca href=\"https://www.franka.de/panda/\" rel=\"nofollow\"\u003ePanda Emika Franka robots\u003c/a\u003e robot. These solutions work both on a physical as well as a simulated version of the panda robot. A simulated version of the panda robot is already shipped with this package.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BerkeleyAutomation/gqcnn\"\u003eBerkleyAutomation/gqcnn\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation and Usage\u003c/h2\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-label=\"Permalink: Installation and Usage\" href=\"#installation-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://rickstaa.github.io/panda-autograsp/\" rel=\"nofollow\"\u003edocs\u003c/a\u003e for installation and usage instructions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown limitations\u003c/h2\u003e\u003ca id=\"user-content-known-limitations\" class=\"anchor\" aria-label=\"Permalink: Known limitations\" href=\"#known-limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThere package is currently not working with the simulated camera (see \u003ca href=\"https://github.com/rickstaa/panda-autograsp/issues/158\"\u003e#158\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSince the package is written in python2.7 and this version already reached EOL, the dependencies are quite fragile. The \u003ccode\u003esetup.py\u003c/code\u003e install method might, therefore fail. If this is the case, please install the dependencies using the \u003ccode\u003e./requirements/requirements.txt\u003c/code\u003e file. This can be solved by porting the package to ROS Noetic (see \u003ca href=\"https://github.com/rickstaa/panda-autograsp/issues/163\"\u003e#163\u003c/a\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLICENSE\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main code if this repository is licensed under an \u003cstrong\u003eMIT license\u003c/strong\u003e. If a LICENCE file is present in a submodule this licence has to be respected but ONLY for the files contained in this submodule.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFeel free to open an issue if you have ideas on how to make this GitHub action better or if you want to report a bug! All contributions are welcome. \ud83d\ude80 Please consult the \u003ca href=\"CONTRIBUTING.md\"\u003econtribution guidelines\u003c/a\u003e for more information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eGQ-CNN and FC-GQ-CNN created by \u003ca href=\"https://berkeleyautomation.github.io/gqcnn\" rel=\"nofollow\"\u003e@berkeleyautomation\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eIcon created with svgs made by \u003ca href=\"https://www.freepik.com/\" rel=\"nofollow\"\u003e@freepik\u003c/a\u003e from \u003ca href=\"https://www.flaticon.com/authors/eucalyp\" rel=\"nofollow\"\u003ewww.flaticon.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 12, + "subscribers_count": 4, + "topics": [ + "machine-learning", + "ros", + "robotics", + "robot-manipulation", + "neural-networks", + "python" + ], + "updated_at": 1732629508.0 + }, + { + "data_format": 2, + "description": "SDSC Summer Institute 2022 material", + "filenames": [ + "4.2a_python_for_hpc/singularity/Singularity.anaconda3-dask-numba" + ], + "full_name": "sdsc/sdsc-summer-institute-2022", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esdsc-summer-institute-2022\u003c/h1\u003e\u003ca id=\"user-content-sdsc-summer-institute-2022\" class=\"anchor\" aria-label=\"Permalink: sdsc-summer-institute-2022\" href=\"#sdsc-summer-institute-2022\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSDSC Summer Institute 2022 material\n\u003ca href=\"https://na.eventscloud.com/website/36626/\" rel=\"nofollow\"\u003eWebsite\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository hosts all material and slides of the presentations at the Summer Institute\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInteractive Videos\u003c/h2\u003e\u003ca id=\"user-content-interactive-videos\" class=\"anchor\" aria-label=\"Permalink: Interactive Videos\" href=\"#interactive-videos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eRecorded sessions can be found \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eA full catalog of all our trainings at SDSC can be found \u003ca href=\"https://www.sdsc.edu/education_and_training/training_hpc.html#catalog\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAliases, symlinks and reservations\u003c/h2\u003e\u003ca id=\"user-content-aliases-symlinks-and-reservations\" class=\"anchor\" aria-label=\"Permalink: Aliases, symlinks and reservations\" href=\"#aliases-symlinks-and-reservations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor your convenience, we\u2019ve create aliases and symlinks for the Summer Institute\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eget-cpu \u2013 one interactive compute node for 2 hours\u003c/li\u003e\n\u003cli\u003eget-gpu \u2013 one interactive GPU (in shared queue) for 2 hours\u003c/li\u003e\n\u003cli\u003estart-spark \u2013 start one-hour Spark session\u003c/li\u003e\n\u003cli\u003estart-tf-cpu \u2013 start three-hour TensorFlow session (CPU)\u003c/li\u003e\n\u003cli\u003estart-tf-gpu \u2013 start three-hour TensorFlow session (GPU)\u003c/li\u003e\n\u003cli\u003edata \u2013 symlink to staged data\nIn the event that you need to explicitly use the reservation, training accounts will have access to SI2021RES for duration of SI\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAgenda\u003ca name=\"user-content-agenda\"\u003e\u003c/a\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-agenda\" class=\"anchor\" aria-label=\"Permalink: Agenda\" href=\"#agenda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll times are in Pacific time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 1 (Wednesday, 07/27/22) \u003ca name=\"user-content-agenda-1\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-1-wednesday-072722-\" class=\"anchor\" aria-label=\"Permalink: Day 1 (Wednesday, 07/27/22) \" href=\"#day-1-wednesday-072722-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:00 AM - 9:25 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/1.1_accessing_expanse\"\u003e1.1 Accounts, Login, Enviroments\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section1_1/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:25 AM \u2013 10:15 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/ciml-org/ciml-summer-institute-2022/tree/main/1.2_accounts_login_environments_expanse_portal\"\u003e1.2 Running Jobs on Expanse\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section1_2/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:15 AM \u2013 11:00 AM\u003c/td\u003e\n\u003ctd\u003eQ\u0026amp;A, Wrap-up\u003c/td\u003e\n\u003ctd\u003eAll\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 2 (Monday, 08/01/22)\u003ca name=\"user-content-agenda-2\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-2-monday-080122\" class=\"anchor\" aria-label=\"Permalink: Day 2 (Monday, 08/01/22)\" href=\"#day-2-monday-080122\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 8:15 PM\u003c/td\u003e\n\u003ctd\u003eWelcome\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:15 AM \u2013 9:15 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.1_parallel_computing_concepts\"\u003e2.1 Parallel Computing Concepts\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_1/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/sinkovits_robert.html\" rel=\"nofollow\"\u003eRobert Sinkovits\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:15 AM \u2013 10:00 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.2_hardware_overview\"\u003e2.2 Hardware Overview\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_2/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/goetz_andreas.html\" rel=\"nofollow\"\u003eAndreas Goetz\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:00 AM \u2013 10:15 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:15 AM \u2013 11:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.3_intermediate_linux\"\u003e2.3 Intermediate Linux\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_3/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/goetz_andreas.html\" rel=\"nofollow\"\u003eAndreas Goetz\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:30 AM \u2013 12:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.4_batch_computing\"\u003e2.4 Batch Computing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_4/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:30 PM \u2013 12:45 PM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:45 PM \u2013 2:15 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/2.5_data_management\"\u003e2.5 Data Management\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section2_5/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/mkandes\"\u003eMarty Kandes\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 3 (Tuesday, 08/02/22)\u003ca name=\"user-content-agenda-3\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-3-tuesday-080222\" class=\"anchor\" aria-label=\"Permalink: Day 3 (Tuesday, 08/02/22)\" href=\"#day-3-tuesday-080222\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 8:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.1_security\"\u003e3.1 Security\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_1/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.linkedin.com/in/nicole-wolter-bbb94a3\" rel=\"nofollow\"\u003eNicole Wolter\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:30 AM \u2013 9:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.2_interactive_computing\"\u003e3.2 Interactive Computing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_2/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:30 AM \u2013 9:45 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:45 AM \u2013 10:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.3_getting_help\"\u003e3.3 Getting Help\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_3/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.linkedin.com/in/nicole-wolter-bbb94a3\" rel=\"nofollow\"\u003eNicole Wolter\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:30 AM \u2013 11:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.4_code_migration\"\u003e3.4 Code Migration\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_4/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/tatineni_mahidhar.html\" rel=\"nofollow\"\u003eMahidhar Tatineni\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:30 AM \u2013 11:45 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:45 AM \u2013 12:45 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.5_high_throughput_computing\"\u003e3.5 High Throughput Computing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_5/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/mkandes\"\u003eMarty Kandes\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:45 PM \u2013 1:45 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/3.6_linux_tools_file_processing\"\u003e3.6 Linux Tools for File Processing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section3_6/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/sinkovits_robert.html\" rel=\"nofollow\"\u003eRobert Sinkovits\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 4 (Wednesday, 08/03/22)\u003ca name=\"user-content-agenda-4\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-4-wednesday-080322\" class=\"anchor\" aria-label=\"Permalink: Day 4 (Wednesday, 08/03/22)\" href=\"#day-4-wednesday-080322\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 9:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.1a_intro_to_git_github\"\u003e4.1a Intro to Git \u0026amp; GitHub\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_1a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/thomas_mary.html\" rel=\"nofollow\"\u003eMary Thomas\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 9:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.1b_advanced_git_github\"\u003e4.1b Advanced Git \u0026amp; GitHub\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_1b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/mkandes\"\u003eMarty Kandes\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:30 AM \u2013 9:45 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:45 AM \u2013 12:00 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.2a_python_for_hpc\"\u003e4.2a Python for HPC\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_2a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/tatineni_mahidhar.html\" rel=\"nofollow\"\u003eMahidhar Tatineni\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e9:45 AM \u2013 12:00 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.2b_data_science_applications\"\u003e4.2b A Short Introduction to Data Science and its Applications\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_2b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/altintas_ilkay.html\" rel=\"nofollow\"\u003eIlkay Altintas\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.linkedin.com/in/subhasisdg\" rel=\"nofollow\"\u003eSubhasis Dasgupta\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.linkedin.com/in/shwetapurawat\" rel=\"nofollow\"\u003eShweta Purawat\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:00 PM \u2013 2:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.3a_performance_tuning\"\u003e4.3a Performance Tuning\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_3a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/sinkovits_robert.html\" rel=\"nofollow\"\u003eRobert Sinkovits\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:00 PM \u2013 2:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/4.3b_scalable_ml\"\u003e4.3b Scalable Machine Learning\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section4_3b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/nguyen_mai.html\" rel=\"nofollow\"\u003eMai Nguyen\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.coursera.org/instructor/~13847302\" rel=\"nofollow\"\u003ePaul Rodriguez\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 5 (Thursday, 08/04/22)\u003ca name=\"user-content-agenda-5\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-5-thursday-080422\" class=\"anchor\" aria-label=\"Permalink: Day 5 (Thursday, 08/04/22)\" href=\"#day-5-thursday-080422\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 10:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.1a_scientific_vis_with_visit\"\u003e5.1a Scientific Visualization for mesh based data with Visit\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_1a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/chourasia_amit.html\" rel=\"nofollow\"\u003eAmit Chourasia\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 10:30 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.1b_deep_learning_pt1\"\u003e5.1b Deep Learning - Part 1\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_1b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/nguyen_mai.html\" rel=\"nofollow\"\u003eMai Nguyen\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.coursera.org/instructor/~13847302\" rel=\"nofollow\"\u003ePaul Rodriguez\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:30 AM \u2013 10:45 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:45 AM \u2013 1:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.2a_gpu_computing_and_programming\"\u003e5.2a GPU Computing and Programming\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_2a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/goetz_andreas.html\" rel=\"nofollow\"\u003eAndreas Goetz\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e10:45 AM \u2013 1:30 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.2b_deep_learning_pt2\"\u003e5.2b Deep Learning \u2013 Part 2\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_2b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/nguyen_mai.html\" rel=\"nofollow\"\u003eMai Nguyen\u003c/a\u003e \u003cbr\u003e\u003ca href=\"https://www.coursera.org/instructor/~13847302\" rel=\"nofollow\"\u003ePaul Rodriguez\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1:30 PM \u2013 2:00 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/5.3_intro_to_singularity\"\u003e5.3 An Introduction to Singularity: Containers for Scientific and High-Performance Computing\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section5_3/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/mkandes\"\u003eMartin Kandes\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDay 6 (Friday, 08/05/22)\u003ca name=\"user-content-agenda-6\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-day-6-friday-080522\" class=\"anchor\" aria-label=\"Permalink: Day 6 (Friday, 08/05/22)\" href=\"#day-6-friday-080522\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTIME (Pacific time)\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eTOPIC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003ePRESENTER\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 11:00 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/6.1a_parallel_computing_using_MPI_OpenMP\"\u003e6.1a Parallel Computing using MPI \u0026amp; Open MP\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section6_1a/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/tatineni_mahidhar.html\" rel=\"nofollow\"\u003eMahidhar Tatineni\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8:00 AM \u2013 11:00 AM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/6.1b_info_visualization_concepts\"\u003e6.1b Information Visualization Concepts\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section6_1b/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/chourasia_amit.html\" rel=\"nofollow\"\u003eAmit Chourasia\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:00 AM \u2013 11:15 AM\u003c/td\u003e\n\u003ctd\u003eBreak\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e11:15 AM \u2013 12:00 PM\u003c/td\u003e\n\u003ctd\u003e\n\u003ca href=\"https://github.com/sdsc/sdsc-summer-institute-2022/tree/main/6.2_scaling_up_interactive_data_analysis_jupyter_lab\"\u003e6.2 Scaling up Interactive Data Analysis in Jupyter Lab: From Laptop to HPC\u003c/a\u003e \u003ca href=\"https://education.sdsc.edu/training/interactive/202208_sdscsi/section6_2/\" rel=\"nofollow\"\u003e [Interactive Video] \u003c/a\u003e\n\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/rose_peter.html\" rel=\"nofollow\"\u003ePeter Rose\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12:00 PM \u2013 12:15 PM\u003c/td\u003e\n\u003ctd\u003eClosing Remarks\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.sdsc.edu/research/researcher_spotlight/sinkovits_robert.html\" rel=\"nofollow\"\u003eRobert Sinkovits\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca href=\"#top\"\u003eBack to Top\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdditional SDSC Resources\u003c/h2\u003e\u003ca id=\"user-content-additional-sdsc-resources\" class=\"anchor\" aria-label=\"Permalink: Additional SDSC Resources\" href=\"#additional-sdsc-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVoyager\u003c/h3\u003e\u003ca id=\"user-content-voyager\" class=\"anchor\" aria-label=\"Permalink: Voyager\" href=\"#voyager\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://www.sdsc.edu/support/user_guides/voyager.html#tech_summary\" rel=\"nofollow\"\u003eVoyager\u003c/a\u003e supercomputer is an innovative AI system designed specifically for science and engineering research at scale. Funded by the National Science Foundation, Voyager represents a collaboration with the San Diego Supercomputer Center at UC San Diego, Supermicro, and Intel\u2019s Habana Lab focused on supporting research in science and engineering that is increasingly dependent upon artificial intelligence and deep learning as a critical element in the experimental and/or computational work.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eVoyager: Exploring Habana processor-based AI focused hardware for Science and Engineering \u003ca href=\"https://www.youtube.com/watch?v=RK46aCjOoKI\" rel=\"nofollow\"\u003eTraining Session\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVoyager User Guide \u003ca href=\"https://www.sdsc.edu/support/user_guides/voyager.html\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eVoyager video \u003ca href=\"https://youtu.be/TmX4wm4J8Jk\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCloudBank\u003c/h3\u003e\u003ca id=\"user-content-cloudbank\" class=\"anchor\" aria-label=\"Permalink: CloudBank\" href=\"#cloudbank\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.cloudbank.org/\" rel=\"nofollow\"\u003eCoudBank\u003c/a\u003e is a managed service to simplify cloud access for computer science research.\nCloudBank overview video \u003ca href=\"https://www.youtube.com/watch?v=5YEflIwdjxY\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\nDCL funding opportunity for PIs who have existing CISE awards, details \u003ca href=\"https://www.cloudbank.org/request\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSeedMe\u003c/h3\u003e\u003ca id=\"user-content-seedme\" class=\"anchor\" aria-label=\"Permalink: SeedMe\" href=\"#seedme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://seedmelab.org/\" rel=\"nofollow\"\u003eSeedMe\u003c/a\u003e is a scientific data management framework for teams struggling with intractable data.\u003cbr\u003e\nSeedMeLab overview talk \u003ca href=\"https://youtu.be/eVqzNbI1EAo\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll the teaching material in this repository is licensed under \u003ca href=\"https://creativecommons.org/licenses/by-nc-sa/4.0/\" rel=\"nofollow\"\u003eCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you are re-using this material, please cite our \u003ca href=\"https://doi.org/10.5281/zenodo.5754066\" rel=\"nofollow\"\u003erecord on Zenodo\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 12, "subscribers_count": 8, "topics": [], - "updated_at": 1708648832.0 + "updated_at": 1691383251.0 }, { "data_format": 2, - "description": null, + "description": "Whole cell model of E. coli implemented with Vivarium", "filenames": [ - "singularity/Singularity", - "singularity/pylp_base/Singularity" + "runscripts/container/runtime/Singularity" ], - "full_name": "funkelab/linajea", - "latest_release": "v1.5", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-linajea\" class=\"anchor\" aria-hidden=\"true\" href=\"#linajea\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinajea\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-hidden=\"true\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublications\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.nature.com/articles/s41587-022-01427-7\" rel=\"nofollow\"\u003eAutomated reconstruction of whole-embryo cell lineages by learning from sparse annotations (nature biotechnology)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2208.11467\" rel=\"nofollow\"\u003eTracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages (arxiv/MICCAI2022)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./README.assets/pipeline.png\"\u003e\u003cimg src=\"./README.assets/pipeline.png\" alt=\"Linajea\" title=\"Linajea Pipeline\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the main software repository for the linajea cell tracking project.\nIt includes tools and infrastructure for running a pipeline that starts from 3d+time light sheet data and ends in extracted cell lineage tracks.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/funkelab/linajea.git\ncd linajea\nconda create --name linajea python pytorch pylp -c pytorch -c funkey\nconda activate linajea\npip install numpy cython jupyter\npip install -r requirements.txt\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-versioning\" class=\"anchor\" aria-hidden=\"true\" href=\"#versioning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersioning\u003c/h2\u003e\n\u003cp\u003eThe main branch contains the current version of the code. New features and bugfixes will be developed in separate branches before being merged into main.\nThe experiments in the Nature Biotechnology paper have been conducted with v1.3, the experiments in the MICCAI paper with v1.4 (see tags). For the public release we refactored major parts of the code, breaking backwards compatibility.\nA separate repository (\u003ca href=\"https://github.com/linajea/linajea_experiments\"\u003ehttps://github.com/linajea/linajea_experiments\u003c/a\u003e) contains all the scripts necessary to replicate the paper results, using the appropriate release.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse\u003c/h2\u003e\n\u003cp\u003eHave a look at the jupyter notebook \u003ca href=\"examples\"\u003eexamples\u003c/a\u003e or look at the \u003ca href=\"run_scripts\"\u003erun scripts\u003c/a\u003e directly.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eIf you make any improvements to the software, please fork, create a new branch named descriptively for the feature you are upgrading or bug you are fixing, commit your changes to that branch, and create a pull request asking for permission to merge.\nHelp is always appreciated!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-other\" class=\"anchor\" aria-hidden=\"true\" href=\"#other\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther\u003c/h2\u003e\n\u003cp\u003eIf you have any questions and can\u0027t find the answers you need in the examples or in the code documentation, feel free to contact us!\u003c/p\u003e\n", - "stargazers_count": 13, - "subscribers_count": 6, + "full_name": "CovertLab/vEcoli", + "latest_release": "v1.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVivarium \u003cem\u003eE. coli\u003c/em\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-vivarium-e-coli\" class=\"anchor\" aria-label=\"Permalink: Vivarium E. coli\" href=\"#vivarium-e-coli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"doc/_static/ecoli_master_topology.png\"\u003e\u003cimg src=\"doc/_static/ecoli_master_topology.png\" alt=\"vivarium\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBackground\u003c/h2\u003e\u003ca id=\"user-content-background\" class=\"anchor\" aria-label=\"Permalink: Background\" href=\"#background\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVivarium \u003cem\u003eE. coli\u003c/em\u003e (vEcoli) is a port of the Covert Lab\u0027s\n\u003ca href=\"https://github.com/CovertLab/wcEcoli\"\u003eE. coli Whole Cell Model\u003c/a\u003e (wcEcoli)\nto the \u003ca href=\"https://github.com/vivarium-collective/vivarium-core\"\u003eVivarium framework\u003c/a\u003e.\nIts main benefits over the original model are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eModular processes:\u003c/strong\u003e easily add/remove processes that interact with\nexisting or new simulation state\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUnified configuration:\u003c/strong\u003e all configuration happens through JSON files,\nmaking it easy to run simulations/analyses with different options\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eParquet output:\u003c/strong\u003e simulation output is in a widely-supported columnar\nfile format that enables fast, larger-than-RAM analytics with DuckDB\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGoogle Cloud support:\u003c/strong\u003e workflows too large to run on a local machine\ncan be easily run on Google Cloud\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAs in wcEcoli, \u003ca href=\"reconstruction/ecoli/flat\"\u003eraw experimental data\u003c/a\u003e is first processed\nby the parameter calculator or \u003ca href=\"reconstruction/ecoli/fit_sim_data_1.py\"\u003eParCa\u003c/a\u003e to calculate\nmodel parameters (e.g. transcription probabilities). These parameters are used to configure\n\u003ca href=\"ecoli/processes\"\u003eprocesses\u003c/a\u003e that are linked together into a\n\u003ca href=\"ecoli/experiments/ecoli_master_sim.py\"\u003ecomplete simulation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The following instructions assume a Linux or MacOS system. Windows users can\nattempt to follow the same instructions after setting up\n\u003ca href=\"https://learn.microsoft.com/en-us/windows/wsl/install\" rel=\"nofollow\"\u003eWindows Subsystem for Linux\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Refer to the following pages for non-local setups:\n\u003ca href=\"https://covertlab.github.io/vEcoli/workflows.html#sherlock\" rel=\"nofollow\"\u003eSherlock\u003c/a\u003e,\n\u003ca href=\"https://covertlab.github.io/vEcoli/workflows.html#other-hpc-clusters\" rel=\"nofollow\"\u003eother HPC cluster\u003c/a\u003e,\n\u003ca href=\"https://covertlab.github.io/vEcoli/gcloud.html\" rel=\"nofollow\"\u003eGoogle Cloud\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003epyenv lets you install and switch between multiple Python releases and multiple \"virtual\nenvironments\", each with its own pip packages. Using pyenv, create a virtual environment\nand install Python 3.11.10. For a tutorial on how to install pyenv and other dependencies,\nfollow the instructions \u003ca href=\"https://github.com/CovertLab/wcEcoli/blob/master/docs/dev-tools.md\"\u003ehere\u003c/a\u003e.\nThen, run the following command in your terminal:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epyenv virtualenv 3.11.10 vEcoli \u0026amp;\u0026amp; pyenv local vEcoli\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUpdate \u003ccode\u003epip\u003c/code\u003e, \u003ccode\u003esetuptools\u003c/code\u003e and \u003ccode\u003ewheel\u003c/code\u003e to avoid issues with these:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install --upgrade pip setuptools==73.0.1 wheel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, install numpy (check \u003ccode\u003erequirements.txt\u003c/code\u003e for the exact version):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install numpy==1.26.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen install the remaining requirements:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd build the Cython components:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake clean compile\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstall \u003ccode\u003enextflow\u003c/code\u003e following the instructions \u003ca href=\"https://www.nextflow.io/docs/latest/install.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nAfter installing Java with SDKMAN!, close and reopen your terminal, then run\nthe following commands and compare their output to ensure that the Java compiler\nand JVM were properly installed and the same version.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejavac -version\njava -version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter verifying your Java installation, you can proceed with installing Nextflow,\nstarting with \u003ccode\u003ecurl -s https://get.nextflow.io | bash\u003c/code\u003e. If this fails, try\nprepending \u003ccode\u003eexport CAPSULE_LOG=verbose\u003c/code\u003e and re-run, checking for failed downloads.\nIf any downloads failed, re-run this command until it succeeds.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest Installation\u003c/h2\u003e\u003ca id=\"user-content-test-installation\" class=\"anchor\" aria-label=\"Permalink: Test Installation\" href=\"#test-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo test your installation, from the top-level of the cloned repository, invoke:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Must set PYTHONPATH and OMP_NUM_THREADS for every new shell (can add to .bashrc/.zshrc)\nexport PYTHONPATH=.\nexport OMP_NUM_THREADS=1\npython runscripts/workflow.py --config ecoli/composites/ecoli_configs/test_installation.json\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the following basic simulation workflow:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRun the \u003ca href=\"runscripts/parca.py\"\u003eparameter calculator\u003c/a\u003e to generate simulation data.\u003c/li\u003e\n\u003cli\u003eRun the \u003ca href=\"ecoli/experiments/ecoli_master_sim.py\"\u003esimulation\u003c/a\u003e\nfor a single generation, saving output in \u003ccode\u003eout\u003c/code\u003e folder.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"runscripts/analysis.py\"\u003eAnalyze simulation output\u003c/a\u003e by creating a\n\u003ca href=\"ecoli/analysis/single/mass_fraction_summary.py\"\u003emass fraction plot\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNext Steps\u003c/h2\u003e\u003ca id=\"user-content-next-steps\" class=\"anchor\" aria-label=\"Permalink: Next Steps\" href=\"#next-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheck out the \u003ca href=\"https://covertlab.github.io/vEcoli/\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e for a high-level\ntutorial of model development, details on key model components, and low-level API documentation.\u003c/p\u003e\n", + "stargazers_count": 12, + "subscribers_count": 16, "topics": [], - "updated_at": 1683953418.0 + "updated_at": 1733080055.0 }, { "data_format": 2, - "description": null, + "description": "The Scientific Filesystem Specification and Documentation", "filenames": [ - "envs/sauron/Singularity_remote.def" + "tutorials/Singularity.scif", + "tutorials/Singularity" ], - "full_name": "angelettilab/scMouseBcellFlu", + "full_name": "sci-f/sci-f.github.io", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-scmousebcellflu\" class=\"anchor\" aria-hidden=\"true\" href=\"#scmousebcellflu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escMouseBcellFlu\u003c/h1\u003e\n\u003cp\u003eThis is the repository associated with the publication \u003cem\u003eSingle cell BCR and RNA analysis after respiratory virus infection reveals spatiotemporal dynamics of antigen specific B cell response\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThis repository contains the code and supporting files necessary to reproduce the analyses reported in the publication. In essence, the anlaysis workflow herein was done using the \u003ca href=\"https://github.com/NBISweden/sauron\"\u003eSauron\u003c/a\u003e analysis philosophy.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-single-cell-rna-seq-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#single-cell-rna-seq-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingle-cell RNA-seq analysis\u003c/h2\u003e\n\u003cp\u003eTo re-run the analysis of the scRNA-Seq data, the necessary workflow and scripts can be found in the \u003ca href=\"scripts/scRNAseq_pipeline\"\u003e\u003ccode\u003escripts/scRNAseq_pipeline/\u003c/code\u003e\u003c/a\u003e subdirectory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-bcr-seq-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#bcr-seq-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBCR seq analysis\u003c/h2\u003e\n\u003cp\u003eScripts necessary to reproduce the analysis of the B-cell receptor sequencing data can be found in the \u003ca href=\"scripts/VDJ_analysis\"\u003e\u003ccode\u003escripts/VDJ_analysis/\u003c/code\u003e\u003c/a\u003e subdirectory.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScientific Filesystem (SCIF)\u003c/h1\u003e\u003ca id=\"user-content-scientific-filesystem-scif\" class=\"anchor\" aria-label=\"Permalink: Scientific Filesystem (SCIF)\" href=\"#scientific-filesystem-scif\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/logo/scif-logo.png\"\u003e\u003cimg src=\"docs/img/logo/scif-logo.png\" alt=\"docs/img/logo/scif-logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/156490?speed=2\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7db5330e459424b489e3771b5c23682f4853582a3652b5ea305ea2e79d7c4e3/68747470733a2f2f61736369696e656d612e6f72672f612f3135363439302e706e67\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/156490.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe Scientific Filesystem is an organizational format for scientific software and metadata. Our goals are centered around \u003cstrong\u003econsistency\u003c/strong\u003e, \u003cstrong\u003etransparency\u003c/strong\u003e, \u003cstrong\u003eprogrammatic accessibility\u003c/strong\u003e, and \u003cstrong\u003emodularity\u003c/strong\u003e. \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eRead about\u003c/a\u003e the format.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eClients\u003c/h2\u003e\u003ca id=\"user-content-clients\" class=\"anchor\" aria-label=\"Permalink: Clients\" href=\"#clients\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease contribute to the clients below, or the specification here.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vsoch/scif\"\u003evsoch/scif\u003c/a\u003e is the Python client, ideal for scientific use cases, or if you want interactivity\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.github.com/sci-f/scif-go\"\u003esci-f/scif-go\u003c/a\u003e is the GoLang library, intended for use with GoLang projects.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth can be installed and used in a container base. See the \u003ca href=\"https://vsoch.github.io\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e for quick starts.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf SCIF has been useful to you, please cite our work on \u003ca href=\"https://academic.oup.com/gigascience/advance-article/doi/10.1093/gigascience/giy023/4931737\" rel=\"nofollow\"\u003eGigaScience\u003c/a\u003e!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eVanessa Sochat; The Scientific Filesystem (SCIF), GigaScience, giy023,\nhttps://doi.org/10.1093/gigascience/giy023\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Scientific Filesystem is licensed under the Affero GPL, version 3.0 or later \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 12, + "subscribers_count": 4, + "topics": [ + "competitive", + "containers", + "singularity", + "singularity-containers", + "reproducibility", + "science" + ], + "updated_at": 1677228107.0 + }, + { + "data_format": 2, + "description": "Best First Width Search Planners", + "filenames": [ + "Singularity" + ], + "full_name": "nirlipo/BFWS-public", + "latest_release": "planutils_singularity", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBFWS\u003c/h1\u003e\u003ca id=\"user-content-bfws\" class=\"anchor\" aria-label=\"Permalink: BFWS\" href=\"#bfws\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBest First Width Search Planner\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGetting Started\u003c/h3\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou first need to install LAPKT by following this instructions: \u003ca href=\"https://lapkt-dev.github.io/docs/gettingStarted/\" rel=\"nofollow\"\u003ehttps://lapkt-dev.github.io/docs/gettingStarted/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYoy also need to define LAPKT_PATH as an enviromnent variable. Add the following line to your .bash or .profile file or simply execute it in your terminal:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export LAPKT_PATH = /Absolute-path-to-LAPKT-folder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBFWS can run using either FF or FD parser.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFF-parser version\u003c/h3\u003e\u003ca id=\"user-content-ff-parser-version\" class=\"anchor\" aria-label=\"Permalink: FF-parser version\" href=\"#ff-parser-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Scons script included in FF-parser-version folder knows which modules from the LAPKT toolkit it needs to recompile.\u003c/p\u003e\n\u003cp\u003eTo compile type\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e scons \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFD-parser version\u003c/h3\u003e\u003ca id=\"user-content-fd-parser-version\" class=\"anchor\" aria-label=\"Permalink: FD-parser version\" href=\"#fd-parser-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGo to FD-parser-version folder and type to compile\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./build.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning BFWS\u003c/h1\u003e\u003ca id=\"user-content-running-bfws\" class=\"anchor\" aria-label=\"Permalink: Running BFWS\" href=\"#running-bfws\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are BFWS options\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bfws --help\n\nOptions::\n --help Show help message. \n --domain arg Input PDDL domain description\n --problem arg Input PDDL problem description\n --output arg Output file for plan\n --max_novelty arg (=2) Max width w for novelty (default 2)\n\nSearch Algorithms::\n --DUAL-BFWS arg (=1) 1-BFWS first, then BFWS using h_ff and h_landcount as in AAAI-17 paper\n --DUAL-C-BFWS arg (=0) 1-C-BFWS first, then BFWS using h_ff and h_landcount\n --BFWS-f5 arg (=0) BFWS(w,#g), w_{#r,#g}, as in BFWS(f5) AAAI-17 paper\n --BFWS-f5-initstate-relevant arg (=0) BFWS(f5) but computing relevant fluents only once from s0\n --BFWS-f5-landmarks arg (=0) BFWS(w,h_landcount), w = w_{#r,h_landcount} \n --BFWS-goalcount-only arg (=0) BFWS(w,#g), w = w_{#g}, no relevant fluents count\n\nPolynomial Search Algorithms::\n --1-BFWS arg (=0) 1-BFWS(w,#g), w_{#r,#g}, pruning w \u0026gt; 1 \n --1-C-BFWS arg (=0) 1-BFWS using consistency to refine goal counting\n --k-BFWS arg (=0) k-BFWS(w,#g), w_{#r,#g}, pruning w \u0026gt; k, where k = bound() argument, default 2\n --k-C-BFWS arg (=0) k-BFWS with goal consistency count\n --k-M-BFWS arg (=0) Allowing (M) nodes \u0026gt; novelty bound() for each node with novelty \u0026lt;= bound()\n --k-M-C-BFWS arg (=0) k-M-C-BFWS with goal consistency\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe command to run the FF-parser-version of BFWS, computing novelty 1,2, and greater than 2, and pruning nodes with novelty greater than 2\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bfws --domain domain.pddl --problem prob.pddl --max_novelty 2 --k-BFWS true\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto use FD-parser version, go to the correct folder and run the same options with the following command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bfws.py domain.pddl prob.pddl k-BFWS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFD-version uses the same options but do not uses tags. To change the default max_novelty 2 and M values, edit bfws.py file\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCredits\u003c/h1\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is a joint work by Nir Lipovetzky, and Hector Geffner.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePaper\u003c/h3\u003e\u003ca id=\"user-content-paper\" class=\"anchor\" aria-label=\"Permalink: Paper\" href=\"#paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can read more about it in the \u003ca href=\"http://people.eng.unimelb.edu.au/nlipovetzky/papers/aaai17-BFWS-novelty-exploration.pdf\" rel=\"nofollow\"\u003eAAAI 2017 paper\u003c/a\u003e and \u003ca href=\"http://people.eng.unimelb.edu.au/nlipovetzky/papers/icaps17-polytime-BFWS.pdf\" rel=\"nofollow\"\u003eICAPS 2017 paper\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 13, "subscribers_count": 2, "topics": [], - "updated_at": 1669968333.0 + "updated_at": 1730514145.0 }, { "data_format": 2, - "description": "Seamless is a framework to set up computations (and visualizations) that respond to changes in cells. Cells contain the input data as well as the source code of the computations, and all cells can be edited interactively. ", + "description": "Code and documentation supporting Markello \u0026 Misic, 2021, \"Comparing spatial null models for brain maps\" (NeuroImage)", "filenames": [ - "docker/seamless-simple/Singularity" + "container/Singularity" ], - "full_name": "sjdv1982/seamless", - "latest_release": "v0.7.3", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-seamless-a-cell-based-reactive-programming-framework\" class=\"anchor\" href=\"#seamless-a-cell-based-reactive-programming-framework\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSeamless: a cell-based reactive programming framework\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://mybinder.org/v2/gh/sjdv1982/seamless-binder-demo/main?labpath=basic-example.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/581c077bdbc6ca6899c86d0acc6145ae85e9d80e6f805a1071793dbe48917982/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSeamless is a framework to set up protocols (workflows) and computations that respond to changes in cells. Cells define the input data as well as the source code of the computations, and all cells can be edited interactively.\u003c/p\u003e\n\u003cp\u003eThe main application domains are data science, scientific computing, software prototyping, and interactive web services.\u003c/p\u003e\n\u003cp\u003eProtocols, computations and results are all represented as directed acyclic graphs that consist of cell checksums. This makes them strongly interoperable and reproducible. Unlike other workflow systems, Seamless graphs are self-contained and do not depend on the content of external files, URLs, identifiers, version numbers, or other kinds of metadata.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation-httpsjdv1982githubioseamless\" class=\"anchor\" href=\"#documentation-httpsjdv1982githubioseamless\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation: \u003ca href=\"http://sjdv1982.github.io/seamless\" rel=\"nofollow\"\u003ehttp://sjdv1982.github.io/seamless\u003c/a\u003e\n\u003c/h3\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-try-out-seamless\" class=\"anchor\" href=\"#try-out-seamless\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTry out Seamless\u003c/h1\u003e\n\u003cp\u003eYou can try out Seamless in your browser, without any installation,\nthanks to the Binder project. Click on the badge below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://mybinder.org/v2/gh/sjdv1982/seamless-binder-demo/main?labpath=basic-example.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/581c077bdbc6ca6899c86d0acc6145ae85e9d80e6f805a1071793dbe48917982/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-supported-platforms\" class=\"anchor\" href=\"#supported-platforms\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupported platforms\u003c/h1\u003e\n\u003cp\u003eSeamless is meant to run from inside a Docker container. This is easy under Linux.\u003c/p\u003e\n\u003cp\u003eThis will not work under Mac OSX and Windows, because Docker support for networking is incomplete.\u003c/p\u003e\n\u003cp\u003eUnder Mac OSX, you can now install Seamless without Docker, into a conda environment (see Alternative installations).\u003c/p\u003e\n\u003cp\u003eSeamless does not run under Windows.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation-using-docker\" class=\"anchor\" href=\"#installation-using-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation using Docker\u003c/h1\u003e\n\u003cp\u003eFirst, you must \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e\nand \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003e(mini)conda\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThen, installation is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Pull docker image\ndocker pull rpbs/seamless\n\n# Install Seamless command line tools\nconda install -c rpbs seamless-cli\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h3\u003e\n\u003cp\u003eThe command \u003ccode\u003eseamless-ipython\u003c/code\u003e launches an IPython terminal inside a\nSeamless Docker container.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eseamless-jupyter\u003c/code\u003e does the same for Jupyter Notebook.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-basic-example\" class=\"anchor\" href=\"#basic-example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasic example\u003c/h1\u003e\n\u003cp\u003eFirst, start \u003cstrong\u003eIPython\u003c/strong\u003e (\u003ccode\u003eseamless-ipython\u003c/code\u003e) or \u003cstrong\u003eJupyter\u003c/strong\u003e (\u003ccode\u003eseamless-jupyter\u003c/code\u003e =\u0026gt; create a new Python Notebook).\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-1-import-seamless-in-ipython-or-jupyter\" class=\"anchor\" href=\"#1-import-seamless-in-ipython-or-jupyter\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Import Seamless in IPython or Jupyter\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eseamless\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ehighlevel\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eContext\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eContext\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-2-set-up-a-simple-seamless-context\" class=\"anchor\" href=\"#2-set-up-a-simple-seamless-context\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Set up a simple Seamless context\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eadd\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# ctx.a =\u0026gt; Seamless cell\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# ctx.b =\u0026gt; Seamless cell\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# ctx.add =\u0026gt; Seamless transformer\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eresult\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# ctx.c =\u0026gt; Seamless cell\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# in a .py file, use \"ctx.compute()\" instead\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[1]: \u0026lt;Silk: 30 \u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[2]: \u0026lt;Silk: 35 \u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-3-define-schemas-and-validation-rules\" class=\"anchor\" href=\"#3-define-schemas-and-validation-rules\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Define schemas and validation rules\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexample\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.0\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# declares that add.a must be a number\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexample\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.0\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003evalidate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003eassert\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eadd_validator\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003evalidate\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"validate\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e()\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexception\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# Validation passes =\u0026gt; exception is None\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-4-create-an-api-for-a-seamless-cell\" class=\"anchor\" href=\"#4-create-an-api-for-a-seamless-cell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Create an API for a Seamless cell\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ereport\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eunsilk\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eis\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"Sorry, there is no result\"\u003c/span\u003e)\n \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"The result is: {}\"\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e))\n\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexample\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ereport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ereport\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ereport\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[3]: The result is 35\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-5-mount-cells-to-the-file-system\" class=\"anchor\" href=\"#5-mount-cells-to-the-file-system\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. Mount cells to the file system\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecelltype\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"plain\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emount\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/tmp/a.txt\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecelltype\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"plain\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emount\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/tmp/b.txt\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecelltype\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"plain\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emount\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/tmp/c.txt\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emode\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"w\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecode\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emount\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/tmp/code.py\"\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranslation\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-6-share-a-cell-over-http\" class=\"anchor\" href=\"#6-share-a-cell-over-http\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6. Share a cell over HTTP\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emimetype\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"text\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eshare\u003c/span\u003e()\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranslation\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u0026gt;\u0026gt;\u0026gt; curl http://localhost:5813/ctx/c\n35\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-7-control-cells-from-jupyter\" class=\"anchor\" href=\"#7-control-cells-from-jupyter\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e7. Control cells from Jupyter\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eipywidgets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eIntSlider\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eIntText\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eIntSlider\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003emax\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eIntSlider\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003emax\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eoutput\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etraitlet\u003c/span\u003e().\u003cspan class=\"pl-en\"\u003elink\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etraitlet\u003c/span\u003e().\u003cspan class=\"pl-en\"\u003elink\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003edisplay\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003edisplay\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003edisplay\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[4]\u003c/code\u003e\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eJupyter widgets (shown only on github.io, not on github.com)\u003c/summary\u003e\n\n\u0026lt;script src=\"\u003ca href=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js\" rel=\"nofollow\"\u003ehttps://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js\u003c/a\u003e\" integrity=\"sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=\" crossorigin=\"anonymous\"\u0026gt;\u0026lt;/script\u0026gt;\n\u0026lt;script src=\"\u003ca href=\"https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js\" rel=\"nofollow\"\u003ehttps://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js\u003c/a\u003e\" crossorigin=\"anonymous\"\u0026gt;\u0026lt;/script\u0026gt;\n\u0026lt;script type=\"application/vnd.jupyter.widget-state+json\"\u0026gt;\n{\n \"version_major\": 2,\n \"version_minor\": 0,\n \"state\": {\n \"9a30009f9d044d0184b9ae4611b41440\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"7a3a04d1e170466086ce2f1cc7ff8206\": {\n \"model_name\": \"SliderStyleModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"description_width\": \"\"\n }\n },\n \"b71e9f617e4c4447962aa02e83fff9b3\": {\n \"model_name\": \"IntSliderModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_9a30009f9d044d0184b9ae4611b41440\",\n \"max\": 30,\n \"min\": -10,\n \"style\": \"IPY_MODEL_7a3a04d1e170466086ce2f1cc7ff8206\",\n \"value\": 15\n }\n },\n \"9df6e150ff994aa8b67c891d4db6e638\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"d5ad72717ba74f13884773a12b3d504e\": {\n \"model_name\": \"SliderStyleModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"description_width\": \"\"\n }\n },\n \"510c3503bf774d09a065c977fb395bd0\": {\n \"model_name\": \"IntSliderModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_9df6e150ff994aa8b67c891d4db6e638\",\n \"max\": 30,\n \"min\": -10,\n \"style\": \"IPY_MODEL_d5ad72717ba74f13884773a12b3d504e\",\n \"value\": 20\n }\n },\n \"a16e33985975424f8471454796384dc7\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"9ac3e0bde75e42ef885d3beb5852d878\": {\n \"model_name\": \"OutputModel\",\n \"model_module\": \"@jupyter-widgets/output\",\n \"model_module_version\": \"1.0.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_a16e33985975424f8471454796384dc7\",\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": \"35\"\n },\n \"metadata\": {}\n }\n ]\n }\n },\n \"29241e1f7b1a49ffabfd90b27805f7bf\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"7e22056badc243caa0bb61361d96025b\": {\n \"model_name\": \"SliderStyleModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"description_width\": \"\"\n }\n },\n \"f4ac183f4141492c8004ffee95e19b9a\": {\n \"model_name\": \"IntSliderModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_29241e1f7b1a49ffabfd90b27805f7bf\",\n \"max\": 30,\n \"min\": -10,\n \"style\": \"IPY_MODEL_7e22056badc243caa0bb61361d96025b\",\n \"value\": 15\n }\n },\n \"ecb30d47382442dc8d8d494d6ce7a799\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"584cda9e4c6046358fadb8a24dc2e94d\": {\n \"model_name\": \"SliderStyleModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"description_width\": \"\"\n }\n },\n \"f876716b1ad643d48baefadc4a669afa\": {\n \"model_name\": \"IntSliderModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_ecb30d47382442dc8d8d494d6ce7a799\",\n \"max\": 30,\n \"min\": -10,\n \"style\": \"IPY_MODEL_584cda9e4c6046358fadb8a24dc2e94d\",\n \"value\": 20\n }\n },\n \"c5f3f3ba20054786a97cfb016dc64016\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"dc3ebd64e9fb40bc9fd964e7292ed326\": {\n \"model_name\": \"OutputModel\",\n \"model_module\": \"@jupyter-widgets/output\",\n \"model_module_version\": \"1.0.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_c5f3f3ba20054786a97cfb016dc64016\",\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": \"35\"\n },\n \"metadata\": {}\n }\n ]\n }\n }\n }\n}\n\u0026lt;/script\u0026gt;\n\u0026lt;script type=\"application/vnd.jupyter.widget-view+json\"\u0026gt;\n{\n \"version_major\": 2,\n \"version_minor\": 0,\n \"model_id\": \"f4ac183f4141492c8004ffee95e19b9a\"\n}\n\u0026lt;/script\u0026gt;\n\u0026lt;script type=\"application/vnd.jupyter.widget-view+json\"\u0026gt;\n{\n \"version_major\": 2,\n \"version_minor\": 0,\n \"model_id\": \"f876716b1ad643d48baefadc4a669afa\"\n}\n\u0026lt;/script\u0026gt;\n\u0026lt;script type=\"application/vnd.jupyter.widget-view+json\"\u0026gt;\n{\n \"version_major\": 2,\n \"version_minor\": 0,\n \"model_id\": \"dc3ebd64e9fb40bc9fd964e7292ed326\"\n}\n\u0026lt;/script\u0026gt;\n\u003c/details\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-8-save-the-entire-state-of-the-context\" class=\"anchor\" href=\"#8-save-the-entire-state-of-the-context\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e8. Save the entire state of the context\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Graph and checksums, as JSON\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esave_graph\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"basic-example.seamless\"\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# Checksum-to-buffer cache, as ZIP file\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esave_zip\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"basic-example.zip\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-9-in-a-new-notebook--ipython-console\" class=\"anchor\" href=\"#9-in-a-new-notebook--ipython-console\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e9. In a new notebook / IPython console:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eseamless\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ehighlevel\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eload_graph\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eload_graph\u003c/span\u003e(\n \u003cspan class=\"pl-s\"\u003e\"basic-example.seamless\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ezip\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"basic-example.zip\"\u003c/span\u003e\n)\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[1]: \u0026lt;Silk: 35 \u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecode\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[2]: \u0027def add(a, b):\\n return a + b\u0027\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u0026gt;\u0026gt;\u0026gt; curl http://localhost:5813/ctx/c\n35\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-additional-features\" class=\"anchor\" href=\"#additional-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional features\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTransformers can be written in Python, IPython, bash, or any compiled language (C, C++, ...).\u003c/li\u003e\n\u003cli\u003eBash transformers can be executed inside Docker images.\u003c/li\u003e\n\u003cli\u003eIPython transformers can use IPython magics, allowing the use of languages such as Cython (tested), Matlab/Octave (untested), Julia (untested), or R (tested).\u003c/li\u003e\n\u003cli\u003eThe use of Redis as a checksum-to-buffer cache\u003c/li\u003e\n\u003cli\u003eSeamless instances can communicate, serving as job slaves or result caches for transformations.\u003c/li\u003e\n\u003cli\u003eInteractive monitoring of status and exception messages.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-alternative-installations\" class=\"anchor\" href=\"#alternative-installations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternative installations\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation-under-conda\" class=\"anchor\" href=\"#installation-under-conda\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation under conda\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE: this is EXPERIMENTAL\u003c/strong\u003e. The main application is running Seamless under OSX.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n seamless\nconda activate seamless\nconda install \u0027python==3.8.8\u0027 pip\nexport RPY2_CFFI_MODE=ABI\npip install -r https://raw.githubusercontent.com/sjdv1982/seamless/stable/requirements.txt\npip install -r https://raw.githubusercontent.com/sjdv1982/seamless/stable/requirements-extra.txt\nconda install -c rpbs -c conda-forge silk seamless-framework\nconda install -c conda-forge matplotlib psutil\nconda install conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDon\u0027t install the Seamless command line tools. Instead of commands like \u003ccode\u003eseamless-bash\u003c/code\u003e, \u003ccode\u003eseamless-ipython\u003c/code\u003e, \u003ccode\u003eseamless-jupyter\u003c/code\u003e, simply do \u003ccode\u003econda activate seamless\u003c/code\u003e and type \u003ccode\u003epython\u003c/code\u003e, \u003ccode\u003eipython\u003c/code\u003e or \u003ccode\u003ejupyter notebook\u003c/code\u003e. The source code of the Seamless command line tools is at \u003ccode\u003ehttps://github.com/sjdv1982/seamless/tree/master/docker/commands\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation-under-singularity\" class=\"anchor\" href=\"#installation-under-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation under Singularity\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE: this is EXPERIMENTAL.\u003c/strong\u003e The main application for this is to run Seamless transformations\nand database adapters in an HPC environment. Launching e.g. Jupyter or Docker under Singularity is unlikely to work.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://raw.githubusercontent.com/sjdv1982/seamless/master/docker/seamless-simple/Singularity # or download it manually\nsudo singularity build seamless.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA bash shell in a new Seamless container can then be started using e.g. \u003ccode\u003esingularity run -c --cleanenv seamless.simg\u003c/code\u003e.\nIf you run without \u003ccode\u003e-c\u003c/code\u003e, be sure to do \u003ccode\u003eexport PATH=/opt/conda/bin:$PATH\u003c/code\u003e as the first command.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-more-examples\" class=\"anchor\" href=\"#more-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore examples\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sjdv1982/seamless/tree/master/examples\"\u003ehttps://github.com/sjdv1982/seamless/tree/master/examples\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sjdv1982/seamless/tree/master/tests/highlevel\"\u003ehttps://github.com/sjdv1982/seamless/tree/master/tests/highlevel\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation-httpsjdv1982githubioseamless-1\" class=\"anchor\" href=\"#documentation-httpsjdv1982githubioseamless-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation: \u003ca href=\"http://sjdv1982.github.io/seamless\" rel=\"nofollow\"\u003ehttp://sjdv1982.github.io/seamless\u003c/a\u003e\n\u003c/h3\u003e\n", + "full_name": "netneurolab/markello_spatialnulls", + "latest_release": "0.2", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-spatially-constrained-null-models-in-neuroimaging\" class=\"anchor\" aria-hidden=\"true\" href=\"#spatially-constrained-null-models-in-neuroimaging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpatially-constrained null models in neuroimaging\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-whats-in-this-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#whats-in-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"What\u0027s in this repository?\"\u003c/h2\u003e\n\u003cp\u003eThis repository contains data, code, and results for the manuscript \"\u003ca href=\"https://doi.org/10.1016/j.neuroimage.2021.118052\" rel=\"nofollow\"\u003eComparing spatial null models for brain maps\u003c/a\u003e\" by Ross Markello \u0026amp; Bratislav Misic (\u003cem\u003eNeuroImage\u003c/em\u003e, 2021).\nWe investigated how well different null model implementations account for spatial autocorrelation in statistical analyses of whole-brain neuroimaging data.\u003c/p\u003e\n\u003cp\u003eWe\u0027ve tried to document the various aspects of this repository with a whole bunch of README files, so feel free to jump around and check things out.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-just-let-me-run-the-things\" class=\"anchor\" aria-hidden=\"true\" href=\"#just-let-me-run-the-things\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"Just let me run the things!\"\u003c/h2\u003e\n\u003cp\u003eItching to just run the analyses?\nYou\u0027ll need to make sure you have installed the appropriate software packages, have access to the HCP, and have downloaded the appropriate data files (check out our \u003ca href=\"https://netneurolab.github.io/markello_spatialnulls\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e for more details!).\nOnce you\u0027ve done that, you can get going with the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/netneurolab/markello_spatialnulls\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_spatialnulls\nconda env create -f environment.yml\nconda activate markello_spatialnulls\npip install parspin/\nmake all\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to deal with the hassle of creating a new Python environment, download the Singularity image that we used to run our analyses and run things in there:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/netneurolab/markello_spatialnulls\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_spatialnulls\nwget -O container/markello_spatialnulls.simg https://osf.io/za7fn/download\nsingularity run container/markello_spatialnulls.simg make all\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-id-like-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#id-like-more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"I\u0027d like more information.\"\u003c/h2\u003e\n\u003cp\u003eIf you want a step-by-step through all the methods + analyses, take a look at our \u003ca href=\"https://netneurolab.github.io/markello_spatialnulls\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-have-some-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-have-some-questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"I have some questions...\"\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/netneurolab/markello_spatialnulls/issues\"\u003eOpen an issue\u003c/a\u003e on this repository and someone will try and get back to you as soon as possible!\u003c/p\u003e\n", "stargazers_count": 13, - "subscribers_count": 1, - "topics": [ - "framework", - "python", - "interactive", - "interoperability", - "reproducible-science", - "protocol", - "data-science", - "web-services", - "scientific-computing" + "subscribers_count": 4, + "topics": [], + "updated_at": 1668823369.0 + }, + { + "data_format": 2, + "description": "OpenFOAM applications and libraries for performing atmospheric experiments", + "filenames": [ + "Singularity" ], - "updated_at": 1640105250.0 + "full_name": "AtmosFOAM/AtmosFOAM", + "latest_release": "jshaw-thesis", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-atmosfoam\" class=\"anchor\" aria-hidden=\"true\" href=\"#atmosfoam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAtmosFOAM\u003c/h1\u003e\n\u003cp\u003eA collection of OpenFOAM computational fluid dynamics applications and libraries for performing atmospheric simulations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFrom source\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://github.com/OpenFOAM/OpenFOAM-dev\"\u003eOpenFOAM dev\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnsure \u003ca href=\"https://github.com/AtmosFOAM/AtmosFOAM-tools/\"\u003eAtmosFOAM-tools\u003c/a\u003e is installed\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo to directory\ncd $WM_PROJECT_USER_DIR\nand download AtmosFOAM using:\ngit clone \u003ca href=\"https://github.com/AtmosFOAM/AtmosFOAM.git\"\u003ehttps://github.com/AtmosFOAM/AtmosFOAM.git\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExport environment variables \u003ccode\u003e~/.bashrc\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export ATMOSFOAM_TOOLS_SRC=/path/to/AtmosFOAM-tools/src\n export GMTU=/path/to/AtmosFOAM-tools/gmtUser\n export ATMOSFOAM_SRC=/path/to/AtmosFOAM/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompile all AtmosFOAM applications and libraries:\ncd AtmosFOAM\n./Allwmake\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 13, + "subscribers_count": 9, + "topics": [], + "updated_at": 1669203623.0 }, { "data_format": 2, @@ -37936,116 +37869,227 @@ var data = }, { "data_format": 2, - "description": "OpenFOAM applications and libraries for performing atmospheric experiments", + "description": "Seamless is a framework to set up computations (and visualizations) that respond to changes in cells. Cells contain the input data as well as the source code of the computations, and all cells can be edited interactively. ", "filenames": [ - "Singularity" + "docker/seamless-simple/Singularity" ], - "full_name": "AtmosFOAM/AtmosFOAM", - "latest_release": "jshaw-thesis", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-atmosfoam\" class=\"anchor\" aria-hidden=\"true\" href=\"#atmosfoam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAtmosFOAM\u003c/h1\u003e\n\u003cp\u003eA collection of OpenFOAM computational fluid dynamics applications and libraries for performing atmospheric simulations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-hidden=\"true\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFrom source\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://github.com/OpenFOAM/OpenFOAM-dev\"\u003eOpenFOAM dev\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnsure \u003ca href=\"https://github.com/AtmosFOAM/AtmosFOAM-tools/\"\u003eAtmosFOAM-tools\u003c/a\u003e is installed\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo to directory\ncd $WM_PROJECT_USER_DIR\nand download AtmosFOAM using:\ngit clone \u003ca href=\"https://github.com/AtmosFOAM/AtmosFOAM.git\"\u003ehttps://github.com/AtmosFOAM/AtmosFOAM.git\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExport environment variables \u003ccode\u003e~/.bashrc\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export ATMOSFOAM_TOOLS_SRC=/path/to/AtmosFOAM-tools/src\n export GMTU=/path/to/AtmosFOAM-tools/gmtUser\n export ATMOSFOAM_SRC=/path/to/AtmosFOAM/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompile all AtmosFOAM applications and libraries:\ncd AtmosFOAM\n./Allwmake\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "sjdv1982/seamless", + "latest_release": "v0.7.3", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-seamless-a-cell-based-reactive-programming-framework\" class=\"anchor\" href=\"#seamless-a-cell-based-reactive-programming-framework\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSeamless: a cell-based reactive programming framework\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://mybinder.org/v2/gh/sjdv1982/seamless-binder-demo/main?labpath=basic-example.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/581c077bdbc6ca6899c86d0acc6145ae85e9d80e6f805a1071793dbe48917982/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSeamless is a framework to set up protocols (workflows) and computations that respond to changes in cells. Cells define the input data as well as the source code of the computations, and all cells can be edited interactively.\u003c/p\u003e\n\u003cp\u003eThe main application domains are data science, scientific computing, software prototyping, and interactive web services.\u003c/p\u003e\n\u003cp\u003eProtocols, computations and results are all represented as directed acyclic graphs that consist of cell checksums. This makes them strongly interoperable and reproducible. Unlike other workflow systems, Seamless graphs are self-contained and do not depend on the content of external files, URLs, identifiers, version numbers, or other kinds of metadata.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation-httpsjdv1982githubioseamless\" class=\"anchor\" href=\"#documentation-httpsjdv1982githubioseamless\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation: \u003ca href=\"http://sjdv1982.github.io/seamless\" rel=\"nofollow\"\u003ehttp://sjdv1982.github.io/seamless\u003c/a\u003e\n\u003c/h3\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-try-out-seamless\" class=\"anchor\" href=\"#try-out-seamless\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTry out Seamless\u003c/h1\u003e\n\u003cp\u003eYou can try out Seamless in your browser, without any installation,\nthanks to the Binder project. Click on the badge below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://mybinder.org/v2/gh/sjdv1982/seamless-binder-demo/main?labpath=basic-example.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/581c077bdbc6ca6899c86d0acc6145ae85e9d80e6f805a1071793dbe48917982/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-supported-platforms\" class=\"anchor\" href=\"#supported-platforms\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSupported platforms\u003c/h1\u003e\n\u003cp\u003eSeamless is meant to run from inside a Docker container. This is easy under Linux.\u003c/p\u003e\n\u003cp\u003eThis will not work under Mac OSX and Windows, because Docker support for networking is incomplete.\u003c/p\u003e\n\u003cp\u003eUnder Mac OSX, you can now install Seamless without Docker, into a conda environment (see Alternative installations).\u003c/p\u003e\n\u003cp\u003eSeamless does not run under Windows.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-installation-using-docker\" class=\"anchor\" href=\"#installation-using-docker\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation using Docker\u003c/h1\u003e\n\u003cp\u003eFirst, you must \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003einstall Docker\u003c/a\u003e\nand \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003e(mini)conda\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThen, installation is as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Pull docker image\ndocker pull rpbs/seamless\n\n# Install Seamless command line tools\nconda install -c rpbs seamless-cli\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-getting-started\" class=\"anchor\" href=\"#getting-started\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h3\u003e\n\u003cp\u003eThe command \u003ccode\u003eseamless-ipython\u003c/code\u003e launches an IPython terminal inside a\nSeamless Docker container.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eseamless-jupyter\u003c/code\u003e does the same for Jupyter Notebook.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-basic-example\" class=\"anchor\" href=\"#basic-example\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBasic example\u003c/h1\u003e\n\u003cp\u003eFirst, start \u003cstrong\u003eIPython\u003c/strong\u003e (\u003ccode\u003eseamless-ipython\u003c/code\u003e) or \u003cstrong\u003eJupyter\u003c/strong\u003e (\u003ccode\u003eseamless-jupyter\u003c/code\u003e =\u0026gt; create a new Python Notebook).\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-1-import-seamless-in-ipython-or-jupyter\" class=\"anchor\" href=\"#1-import-seamless-in-ipython-or-jupyter\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Import Seamless in IPython or Jupyter\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eseamless\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ehighlevel\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eContext\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eContext\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-2-set-up-a-simple-seamless-context\" class=\"anchor\" href=\"#2-set-up-a-simple-seamless-context\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e2. Set up a simple Seamless context\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eadd\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# ctx.a =\u0026gt; Seamless cell\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# ctx.b =\u0026gt; Seamless cell\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# ctx.add =\u0026gt; Seamless transformer\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eresult\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# ctx.c =\u0026gt; Seamless cell\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# in a .py file, use \"ctx.compute()\" instead\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[1]: \u0026lt;Silk: 30 \u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[2]: \u0026lt;Silk: 35 \u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-3-define-schemas-and-validation-rules\" class=\"anchor\" href=\"#3-define-schemas-and-validation-rules\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e3. Define schemas and validation rules\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexample\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.0\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# declares that add.a must be a number\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexample\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.0\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003evalidate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003eassert\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eadd_validator\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003evalidate\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"validate\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e()\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexception\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# Validation passes =\u0026gt; exception is None\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-4-create-an-api-for-a-seamless-cell\" class=\"anchor\" href=\"#4-create-an-api-for-a-seamless-cell\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e4. Create an API for a Seamless cell\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ereport\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eunsilk\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eis\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"Sorry, there is no result\"\u003c/span\u003e)\n \u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"The result is: {}\"\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e))\n\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexample\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ereport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ereport\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ereport\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[3]: The result is 35\u003c/code\u003e\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-5-mount-cells-to-the-file-system\" class=\"anchor\" href=\"#5-mount-cells-to-the-file-system\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e5. Mount cells to the file system\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecelltype\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"plain\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emount\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/tmp/a.txt\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecelltype\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"plain\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emount\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/tmp/b.txt\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecelltype\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"plain\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emount\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/tmp/c.txt\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emode\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"w\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecode\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emount\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/tmp/code.py\"\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranslation\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-6-share-a-cell-over-http\" class=\"anchor\" href=\"#6-share-a-cell-over-http\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e6. Share a cell over HTTP\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emimetype\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"text\"\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eshare\u003c/span\u003e()\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etranslation\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u0026gt;\u0026gt;\u0026gt; curl http://localhost:5813/ctx/c\n35\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-7-control-cells-from-jupyter\" class=\"anchor\" href=\"#7-control-cells-from-jupyter\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e7. Control cells from Jupyter\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eipywidgets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eIntSlider\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eIntText\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eIntSlider\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003emax\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eIntSlider\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003emax\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eoutput\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etraitlet\u003c/span\u003e().\u003cspan class=\"pl-en\"\u003elink\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003etraitlet\u003c/span\u003e().\u003cspan class=\"pl-en\"\u003elink\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003edisplay\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003edisplay\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003edisplay\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[4]\u003c/code\u003e\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eJupyter widgets (shown only on github.io, not on github.com)\u003c/summary\u003e\n\n\u0026lt;script src=\"\u003ca href=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js\" rel=\"nofollow\"\u003ehttps://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js\u003c/a\u003e\" integrity=\"sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=\" crossorigin=\"anonymous\"\u0026gt;\u0026lt;/script\u0026gt;\n\u0026lt;script src=\"\u003ca href=\"https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js\" rel=\"nofollow\"\u003ehttps://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js\u003c/a\u003e\" crossorigin=\"anonymous\"\u0026gt;\u0026lt;/script\u0026gt;\n\u0026lt;script type=\"application/vnd.jupyter.widget-state+json\"\u0026gt;\n{\n \"version_major\": 2,\n \"version_minor\": 0,\n \"state\": {\n \"9a30009f9d044d0184b9ae4611b41440\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"7a3a04d1e170466086ce2f1cc7ff8206\": {\n \"model_name\": \"SliderStyleModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"description_width\": \"\"\n }\n },\n \"b71e9f617e4c4447962aa02e83fff9b3\": {\n \"model_name\": \"IntSliderModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_9a30009f9d044d0184b9ae4611b41440\",\n \"max\": 30,\n \"min\": -10,\n \"style\": \"IPY_MODEL_7a3a04d1e170466086ce2f1cc7ff8206\",\n \"value\": 15\n }\n },\n \"9df6e150ff994aa8b67c891d4db6e638\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"d5ad72717ba74f13884773a12b3d504e\": {\n \"model_name\": \"SliderStyleModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"description_width\": \"\"\n }\n },\n \"510c3503bf774d09a065c977fb395bd0\": {\n \"model_name\": \"IntSliderModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_9df6e150ff994aa8b67c891d4db6e638\",\n \"max\": 30,\n \"min\": -10,\n \"style\": \"IPY_MODEL_d5ad72717ba74f13884773a12b3d504e\",\n \"value\": 20\n }\n },\n \"a16e33985975424f8471454796384dc7\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"9ac3e0bde75e42ef885d3beb5852d878\": {\n \"model_name\": \"OutputModel\",\n \"model_module\": \"@jupyter-widgets/output\",\n \"model_module_version\": \"1.0.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_a16e33985975424f8471454796384dc7\",\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": \"35\"\n },\n \"metadata\": {}\n }\n ]\n }\n },\n \"29241e1f7b1a49ffabfd90b27805f7bf\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"7e22056badc243caa0bb61361d96025b\": {\n \"model_name\": \"SliderStyleModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"description_width\": \"\"\n }\n },\n \"f4ac183f4141492c8004ffee95e19b9a\": {\n \"model_name\": \"IntSliderModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_29241e1f7b1a49ffabfd90b27805f7bf\",\n \"max\": 30,\n \"min\": -10,\n \"style\": \"IPY_MODEL_7e22056badc243caa0bb61361d96025b\",\n \"value\": 15\n }\n },\n \"ecb30d47382442dc8d8d494d6ce7a799\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"584cda9e4c6046358fadb8a24dc2e94d\": {\n \"model_name\": \"SliderStyleModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"description_width\": \"\"\n }\n },\n \"f876716b1ad643d48baefadc4a669afa\": {\n \"model_name\": \"IntSliderModel\",\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_ecb30d47382442dc8d8d494d6ce7a799\",\n \"max\": 30,\n \"min\": -10,\n \"style\": \"IPY_MODEL_584cda9e4c6046358fadb8a24dc2e94d\",\n \"value\": 20\n }\n },\n \"c5f3f3ba20054786a97cfb016dc64016\": {\n \"model_name\": \"LayoutModel\",\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"state\": {}\n },\n \"dc3ebd64e9fb40bc9fd964e7292ed326\": {\n \"model_name\": \"OutputModel\",\n \"model_module\": \"@jupyter-widgets/output\",\n \"model_module_version\": \"1.0.0\",\n \"state\": {\n \"layout\": \"IPY_MODEL_c5f3f3ba20054786a97cfb016dc64016\",\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": \"35\"\n },\n \"metadata\": {}\n }\n ]\n }\n }\n }\n}\n\u0026lt;/script\u0026gt;\n\u0026lt;script type=\"application/vnd.jupyter.widget-view+json\"\u0026gt;\n{\n \"version_major\": 2,\n \"version_minor\": 0,\n \"model_id\": \"f4ac183f4141492c8004ffee95e19b9a\"\n}\n\u0026lt;/script\u0026gt;\n\u0026lt;script type=\"application/vnd.jupyter.widget-view+json\"\u0026gt;\n{\n \"version_major\": 2,\n \"version_minor\": 0,\n \"model_id\": \"f876716b1ad643d48baefadc4a669afa\"\n}\n\u0026lt;/script\u0026gt;\n\u0026lt;script type=\"application/vnd.jupyter.widget-view+json\"\u0026gt;\n{\n \"version_major\": 2,\n \"version_minor\": 0,\n \"model_id\": \"dc3ebd64e9fb40bc9fd964e7292ed326\"\n}\n\u0026lt;/script\u0026gt;\n\u003c/details\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-8-save-the-entire-state-of-the-context\" class=\"anchor\" href=\"#8-save-the-entire-state-of-the-context\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e8. Save the entire state of the context\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Graph and checksums, as JSON\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esave_graph\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"basic-example.seamless\"\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# Checksum-to-buffer cache, as ZIP file\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esave_zip\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"basic-example.zip\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-9-in-a-new-notebook--ipython-console\" class=\"anchor\" href=\"#9-in-a-new-notebook--ipython-console\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e9. In a new notebook / IPython console:\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eseamless\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ehighlevel\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eload_graph\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eload_graph\u003c/span\u003e(\n \u003cspan class=\"pl-s\"\u003e\"basic-example.seamless\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ezip\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"basic-example.zip\"\u003c/span\u003e\n)\n\u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecomputation\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[1]: \u0026lt;Silk: 35 \u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ectx\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eadd\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecode\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003eOut[2]: \u0027def add(a, b):\\n return a + b\u0027\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u0026gt;\u0026gt;\u0026gt; curl http://localhost:5813/ctx/c\n35\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-additional-features\" class=\"anchor\" href=\"#additional-features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional features\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eTransformers can be written in Python, IPython, bash, or any compiled language (C, C++, ...).\u003c/li\u003e\n\u003cli\u003eBash transformers can be executed inside Docker images.\u003c/li\u003e\n\u003cli\u003eIPython transformers can use IPython magics, allowing the use of languages such as Cython (tested), Matlab/Octave (untested), Julia (untested), or R (tested).\u003c/li\u003e\n\u003cli\u003eThe use of Redis as a checksum-to-buffer cache\u003c/li\u003e\n\u003cli\u003eSeamless instances can communicate, serving as job slaves or result caches for transformations.\u003c/li\u003e\n\u003cli\u003eInteractive monitoring of status and exception messages.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-alternative-installations\" class=\"anchor\" href=\"#alternative-installations\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlternative installations\u003c/h1\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation-under-conda\" class=\"anchor\" href=\"#installation-under-conda\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation under conda\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE: this is EXPERIMENTAL\u003c/strong\u003e. The main application is running Seamless under OSX.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n seamless\nconda activate seamless\nconda install \u0027python==3.8.8\u0027 pip\nexport RPY2_CFFI_MODE=ABI\npip install -r https://raw.githubusercontent.com/sjdv1982/seamless/stable/requirements.txt\npip install -r https://raw.githubusercontent.com/sjdv1982/seamless/stable/requirements-extra.txt\nconda install -c rpbs -c conda-forge silk seamless-framework\nconda install -c conda-forge matplotlib psutil\nconda install conda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDon\u0027t install the Seamless command line tools. Instead of commands like \u003ccode\u003eseamless-bash\u003c/code\u003e, \u003ccode\u003eseamless-ipython\u003c/code\u003e, \u003ccode\u003eseamless-jupyter\u003c/code\u003e, simply do \u003ccode\u003econda activate seamless\u003c/code\u003e and type \u003ccode\u003epython\u003c/code\u003e, \u003ccode\u003eipython\u003c/code\u003e or \u003ccode\u003ejupyter notebook\u003c/code\u003e. The source code of the Seamless command line tools is at \u003ccode\u003ehttps://github.com/sjdv1982/seamless/tree/master/docker/commands\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-installation-under-singularity\" class=\"anchor\" href=\"#installation-under-singularity\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation under Singularity\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE: this is EXPERIMENTAL.\u003c/strong\u003e The main application for this is to run Seamless transformations\nand database adapters in an HPC environment. Launching e.g. Jupyter or Docker under Singularity is unlikely to work.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://raw.githubusercontent.com/sjdv1982/seamless/master/docker/seamless-simple/Singularity # or download it manually\nsudo singularity build seamless.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA bash shell in a new Seamless container can then be started using e.g. \u003ccode\u003esingularity run -c --cleanenv seamless.simg\u003c/code\u003e.\nIf you run without \u003ccode\u003e-c\u003c/code\u003e, be sure to do \u003ccode\u003eexport PATH=/opt/conda/bin:$PATH\u003c/code\u003e as the first command.\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-more-examples\" class=\"anchor\" href=\"#more-examples\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore examples\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sjdv1982/seamless/tree/master/examples\"\u003ehttps://github.com/sjdv1982/seamless/tree/master/examples\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sjdv1982/seamless/tree/master/tests/highlevel\"\u003ehttps://github.com/sjdv1982/seamless/tree/master/tests/highlevel\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-documentation-httpsjdv1982githubioseamless-1\" class=\"anchor\" href=\"#documentation-httpsjdv1982githubioseamless-1\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation: \u003ca href=\"http://sjdv1982.github.io/seamless\" rel=\"nofollow\"\u003ehttp://sjdv1982.github.io/seamless\u003c/a\u003e\n\u003c/h3\u003e\n", "stargazers_count": 13, - "subscribers_count": 9, + "subscribers_count": 1, + "topics": [ + "framework", + "python", + "interactive", + "interoperability", + "reproducible-science", + "protocol", + "data-science", + "web-services", + "scientific-computing" + ], + "updated_at": 1640105250.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "envs/sauron/Singularity_remote.def" + ], + "full_name": "angelettilab/scMouseBcellFlu", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-scmousebcellflu\" class=\"anchor\" aria-hidden=\"true\" href=\"#scmousebcellflu\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003escMouseBcellFlu\u003c/h1\u003e\n\u003cp\u003eThis is the repository associated with the publication \u003cem\u003eSingle cell BCR and RNA analysis after respiratory virus infection reveals spatiotemporal dynamics of antigen specific B cell response\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThis repository contains the code and supporting files necessary to reproduce the analyses reported in the publication. In essence, the anlaysis workflow herein was done using the \u003ca href=\"https://github.com/NBISweden/sauron\"\u003eSauron\u003c/a\u003e analysis philosophy.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-single-cell-rna-seq-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#single-cell-rna-seq-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingle-cell RNA-seq analysis\u003c/h2\u003e\n\u003cp\u003eTo re-run the analysis of the scRNA-Seq data, the necessary workflow and scripts can be found in the \u003ca href=\"scripts/scRNAseq_pipeline\"\u003e\u003ccode\u003escripts/scRNAseq_pipeline/\u003c/code\u003e\u003c/a\u003e subdirectory.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-bcr-seq-analysis\" class=\"anchor\" aria-hidden=\"true\" href=\"#bcr-seq-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBCR seq analysis\u003c/h2\u003e\n\u003cp\u003eScripts necessary to reproduce the analysis of the B-cell receptor sequencing data can be found in the \u003ca href=\"scripts/VDJ_analysis\"\u003e\u003ccode\u003escripts/VDJ_analysis/\u003c/code\u003e\u003c/a\u003e subdirectory.\u003c/p\u003e\n", + "stargazers_count": 13, + "subscribers_count": 2, "topics": [], - "updated_at": 1669203623.0 + "updated_at": 1669968333.0 }, { "data_format": 2, - "description": "Code and documentation supporting Markello \u0026 Misic, 2021, \"Comparing spatial null models for brain maps\" (NeuroImage)", + "description": null, "filenames": [ - "container/Singularity" + "singularity/Singularity", + "singularity/pylp_base/Singularity" ], - "full_name": "netneurolab/markello_spatialnulls", - "latest_release": "0.2", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-spatially-constrained-null-models-in-neuroimaging\" class=\"anchor\" aria-hidden=\"true\" href=\"#spatially-constrained-null-models-in-neuroimaging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSpatially-constrained null models in neuroimaging\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-whats-in-this-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#whats-in-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"What\u0027s in this repository?\"\u003c/h2\u003e\n\u003cp\u003eThis repository contains data, code, and results for the manuscript \"\u003ca href=\"https://doi.org/10.1016/j.neuroimage.2021.118052\" rel=\"nofollow\"\u003eComparing spatial null models for brain maps\u003c/a\u003e\" by Ross Markello \u0026amp; Bratislav Misic (\u003cem\u003eNeuroImage\u003c/em\u003e, 2021).\nWe investigated how well different null model implementations account for spatial autocorrelation in statistical analyses of whole-brain neuroimaging data.\u003c/p\u003e\n\u003cp\u003eWe\u0027ve tried to document the various aspects of this repository with a whole bunch of README files, so feel free to jump around and check things out.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-just-let-me-run-the-things\" class=\"anchor\" aria-hidden=\"true\" href=\"#just-let-me-run-the-things\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"Just let me run the things!\"\u003c/h2\u003e\n\u003cp\u003eItching to just run the analyses?\nYou\u0027ll need to make sure you have installed the appropriate software packages, have access to the HCP, and have downloaded the appropriate data files (check out our \u003ca href=\"https://netneurolab.github.io/markello_spatialnulls\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e for more details!).\nOnce you\u0027ve done that, you can get going with the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/netneurolab/markello_spatialnulls\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_spatialnulls\nconda env create -f environment.yml\nconda activate markello_spatialnulls\npip install parspin/\nmake all\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to deal with the hassle of creating a new Python environment, download the Singularity image that we used to run our analyses and run things in there:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/netneurolab/markello_spatialnulls\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_spatialnulls\nwget -O container/markello_spatialnulls.simg https://osf.io/za7fn/download\nsingularity run container/markello_spatialnulls.simg make all\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-id-like-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#id-like-more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"I\u0027d like more information.\"\u003c/h2\u003e\n\u003cp\u003eIf you want a step-by-step through all the methods + analyses, take a look at our \u003ca href=\"https://netneurolab.github.io/markello_spatialnulls\" rel=\"nofollow\"\u003ewalkthrough\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-have-some-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-have-some-questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"I have some questions...\"\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/netneurolab/markello_spatialnulls/issues\"\u003eOpen an issue\u003c/a\u003e on this repository and someone will try and get back to you as soon as possible!\u003c/p\u003e\n", + "full_name": "funkelab/linajea", + "latest_release": "v1.5", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-linajea\" class=\"anchor\" aria-hidden=\"true\" href=\"#linajea\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLinajea\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-hidden=\"true\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePublications\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.nature.com/articles/s41587-022-01427-7\" rel=\"nofollow\"\u003eAutomated reconstruction of whole-embryo cell lineages by learning from sparse annotations (nature biotechnology)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2208.11467\" rel=\"nofollow\"\u003eTracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages (arxiv/MICCAI2022)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./README.assets/pipeline.png\"\u003e\u003cimg src=\"./README.assets/pipeline.png\" alt=\"Linajea\" title=\"Linajea Pipeline\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the main software repository for the linajea cell tracking project.\nIt includes tools and infrastructure for running a pipeline that starts from 3d+time light sheet data and ends in extracted cell lineage tracks.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/funkelab/linajea.git\ncd linajea\nconda create --name linajea python pytorch pylp -c pytorch -c funkey\nconda activate linajea\npip install numpy cython jupyter\npip install -r requirements.txt\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-versioning\" class=\"anchor\" aria-hidden=\"true\" href=\"#versioning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eVersioning\u003c/h2\u003e\n\u003cp\u003eThe main branch contains the current version of the code. New features and bugfixes will be developed in separate branches before being merged into main.\nThe experiments in the Nature Biotechnology paper have been conducted with v1.3, the experiments in the MICCAI paper with v1.4 (see tags). For the public release we refactored major parts of the code, breaking backwards compatibility.\nA separate repository (\u003ca href=\"https://github.com/linajea/linajea_experiments\"\u003ehttps://github.com/linajea/linajea_experiments\u003c/a\u003e) contains all the scripts necessary to replicate the paper results, using the appropriate release.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-use\" class=\"anchor\" aria-hidden=\"true\" href=\"#use\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse\u003c/h2\u003e\n\u003cp\u003eHave a look at the jupyter notebook \u003ca href=\"examples\"\u003eexamples\u003c/a\u003e or look at the \u003ca href=\"run_scripts\"\u003erun scripts\u003c/a\u003e directly.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eIf you make any improvements to the software, please fork, create a new branch named descriptively for the feature you are upgrading or bug you are fixing, commit your changes to that branch, and create a pull request asking for permission to merge.\nHelp is always appreciated!\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-other\" class=\"anchor\" aria-hidden=\"true\" href=\"#other\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOther\u003c/h2\u003e\n\u003cp\u003eIf you have any questions and can\u0027t find the answers you need in the examples or in the code documentation, feel free to contact us!\u003c/p\u003e\n", "stargazers_count": 13, - "subscribers_count": 4, + "subscribers_count": 6, "topics": [], - "updated_at": 1668823369.0 + "updated_at": 1683953418.0 }, { "data_format": 2, - "description": "Best First Width Search Planners", + "description": "K* search based implementation of top-k and top-quality planners", + "filenames": [ + "misc/releases/20.06/Singularity.20.06", + "misc/releases/19.06/Singularity.19.06", + "misc/releases/21.12/Singularity.21.12", + "misc/releases/19.12/Singularity.19.12", + "misc/releases/latest/Singularity" + ], + "full_name": "IBM/kstar", + "latest_release": "1.4.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-kstar-planner-is-an-automated-pddl-based-planner-that-includes-planners-for-top-k-and-top-quality-planning-computational-tasks\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#kstar-planner-is-an-automated-pddl-based-planner-that-includes-planners-for-top-k-and-top-quality-planning-computational-tasks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eKstar Planner is an Automated PDDL based planner that includes planners for top-k and top-quality planning computational tasks.\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-the-codebase-consists-of-multiple-planners-for-multiple-computational-problems-roughly-divided-into-two-categories\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#the-codebase-consists-of-multiple-planners-for-multiple-computational-problems-roughly-divided-into-two-categories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThe codebase consists of multiple planners, for multiple computational problems, roughly divided into two categories:\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eTop-k planning\u003c/li\u003e\n\u003cli\u003eTop-quality planning\u003cbr\u003e\n2.1. Top-quality planning\u003cbr\u003e\n2.2. Unordered top-quality planning\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe planner implements\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePure K* search based top-k and top-quality planning\u003c/li\u003e\n\u003cli\u003eSymmetry based pruning: OK* search\u003c/li\u003e\n\u003cli\u003ePartial order reduction: RK* search\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding\u003c/h1\u003e\n\u003cp\u003eFor building the code please use\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./build.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003ch1\u003e\u003ca id=\"user-content-running-examples\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#running-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning (examples)\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-top-k\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#top-k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-k\u003c/h2\u003e\n\u003cp\u003eK* with lmcut heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --search \"kstar(lmcut(), k=100)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOK* with lmcut heuristic (recommended)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --symmetries \"sym=structural_symmetries(time_bound=0,search_symmetries=oss,stabilize_initial_state=false,keep_operator_symmetries=true)\" --search \"kstar(lmcut(), k=100, symmetries=sym)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-top-quality\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#top-quality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-quality\u003c/h2\u003e\n\u003cp\u003eK* with iPDB heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --search \"kstar(ipdb(), q=1.0)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOK* with iPDB heuristic (recommended)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --symmetries \"sym=structural_symmetries(time_bound=0,search_symmetries=oss,stabilize_initial_state=false,keep_operator_symmetries=true)\" --search \"kstar(ipdb(), q=1.0, symmetries=sym)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-unordered-top-quality\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#unordered-top-quality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUnordered Top-quality\u003c/h2\u003e\n\u003cp\u003eK* with iPDB heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --search \"kstar(ipdb(), q=1.0, find_unordered_plans=true)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOK* with iPDB heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --symmetries \"sym=structural_symmetries(time_bound=0,search_symmetries=oss,stabilize_initial_state=false,keep_operator_symmetries=true)\" --search \"kstar(ipdb(), q=1.0, symmetries=sym, find_unordered_plans=true)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRK* with iPDB heuristic\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --search \"kstar(ipdb(), q=1.0, pruning=limited_pruning(pruning=atom_centric_stubborn_sets(use_sibling_shortcut=true, atom_selection_strategy=quick_skip)), find_unordered_plans=true)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eORK* with iPDB heuristic (recommended)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./fast-downward.py domain.pddl problem.pddl --symmetries \"sym=structural_symmetries(time_bound=0,search_symmetries=oss,stabilize_initial_state=false,keep_operator_symmetries=true)\" --search \"kstar(ipdb(), q=1.0, symmetries=sym, pruning=limited_pruning(pruning=atom_centric_stubborn_sets(use_sibling_shortcut=true, atom_selection_strategy=quick_skip)), find_unordered_plans=true)\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-additional-options\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#additional-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAdditional options\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eOptimization of switching K* from A* to EA is controlled by the following parameters:\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eopenlist_inc_percent_lb\u003c/code\u003e (default \u003ccode\u003e1\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eopenlist_inc_percent_ub\u003c/code\u003e (default \u003ccode\u003e5\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eswitch_on_goal\u003c/code\u003e (default \u003ccode\u003efalse\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDumping plans:\n\u003cul\u003e\n\u003cli\u003eIn case only the number of plans is needed, not the actual plans, an option \u003ccode\u003edump_plans=false\u003c/code\u003e can be used\u003c/li\u003e\n\u003cli\u003eDumping the plans into separate files can be avoided with \u003ccode\u003edump_plan_files=false\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDumping the plans into a single JSON file can be done by specifying \u003ccode\u003ejson_file_to_dump=\u0026lt;filename\u0026gt;\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-building-the-package\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#building-the-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBuilding the package:\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Testing locally\u003c/span\u003e\npip install tox pytest -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\ntox\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Output wheels\u003c/span\u003e\npip install cibuildwheel\nCIBW_BEFORE_BUILD=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epython -m pip install pip Cython --upgrade\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\nCIBW_ARCHS_MACOS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003euniversal2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\nCIBW_ARCHS_LINUX=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eauto64\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\nCIBW_ARCHS_WINDOWS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eauto64\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\npython -m cibuildwheel --platform macos\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Different versions of CPython from https://python.org must be installed; e.g. 3.8, 3.9, 3.10, 3.11\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e CI needs a Mac or Windows VMs, or [docker contexts](https://github.com/StefanScherer/windows-docker-machine), to build wheels for those OSes\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-using-as-a-package\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#using-as-a-package\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing as a package:\u003c/h1\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install git+https://github.com/IBM/kstar.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eDue to the CLI-oriented design, the code must be run using subprocess.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esys\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esubprocess\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elogging\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esubprocess\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSubprocessError\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esubprocess\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003echeck_output\u003c/span\u003e([\u003cspan class=\"pl-s1\"\u003esys\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eexecutable\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"-m\"\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"driver.main\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"..your args\"\u003c/span\u003e])\n\u003cspan class=\"pl-k\"\u003eexcept\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSubprocessError\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eerr\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003elogging\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eerror\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eerr\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edecode\u003c/span\u003e())\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#citing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCiting\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-top-k-planning\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#top-k-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-k planning\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{lee-et-al-socs2023,\n title = \"On K* Search for Top-k Planning\",\n author = \"Junkyu Lee and Michael Katz and Shirin Sohrabi\",\n booktitle = \"Proceedings of the 16th Annual Symposium on\n Combinatorial Search (SoCS 2023)\",\n publisher = \"{AAAI} Press\",\n year = \"2023\"\n}\n\n@InProceedings{katz-lee-ijcai2023,\n author = \"Michael Katz and Junkyu Lee\",\n title = \"K* Search Over Orbit Space for Top-k Planning\",\n booktitle = \"Proceedings of the 32nd International Joint\n Conference on Artificial Intelligence (IJCAI 2023)\",\n publisher = \"{IJCAI}\",\n year = \"2023\"\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003e\u003ca id=\"user-content-top-quality-planning\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#top-quality-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTop-quality planning\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e@InProceedings{katz-lee-socs2023,\n title = \"K* and Partial Order Reduction for Top-quality Planning\",\n author = \"Michael Katz and Junkyu Lee\",\n booktitle = \"Proceedings of the 16th Annual Symposium on\n Combinatorial Search (SoCS 2023)\",\n publisher = \"{AAAI} Press\",\n year = \"2023\"\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003eKstar Planner is an Automated PDDL based planner that\nincludes planners for top-k and top-quality planning computational\ntasks. Copyright (C) 2023 Junkyu Lee, Michael Katz, IBM Research, USA.\nThe code extends the Fast Downward planning system. The license for the\nextension is specified in the LICENSE file.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-fast-downward\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#fast-downward\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFast Downward\u003c/h1\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"misc/images/fast-downward.svg\"\u003e\u003cimg src=\"misc/images/fast-downward.svg\" width=\"800\" alt=\"Fast Downward\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFast Downward is a domain-independent classical planning system.\u003c/p\u003e\n\u003cp\u003eCopyright 2003-2022 Fast Downward contributors (see below).\u003c/p\u003e\n\u003cp\u003eFor further information:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFast Downward website: \u003ca href=\"https://www.fast-downward.org\" rel=\"nofollow\"\u003ehttps://www.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eReport a bug or file an issue: \u003ca href=\"https://issues.fast-downward.org\" rel=\"nofollow\"\u003ehttps://issues.fast-downward.org\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward mailing list: \u003ca href=\"https://groups.google.com/forum/#!forum/fast-downward\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/fast-downward\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFast Downward main repository: \u003ca href=\"https://github.com/aibasel/downward\"\u003ehttps://github.com/aibasel/downward\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-tested-software-versions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#tested-software-versions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTested software versions\u003c/h2\u003e\n\u003cp\u003eThis version of Fast Downward has been tested with the following software versions:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS\u003c/th\u003e\n\u003cth\u003ePython\u003c/th\u003e\n\u003cth\u003eC++ compiler\u003c/th\u003e\n\u003cth\u003eCMake\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003e3.8\u003c/td\u003e\n\u003ctd\u003eGCC 9, GCC 10, Clang 10, Clang 11\u003c/td\u003e\n\u003ctd\u003e3.16\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eGCC 7, Clang 6\u003c/td\u003e\n\u003ctd\u003e3.10\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emacOS 10.15\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eAppleClang 12\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWindows 10\u003c/td\u003e\n\u003ctd\u003e3.6\u003c/td\u003e\n\u003ctd\u003eVisual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)\u003c/td\u003e\n\u003ctd\u003e3.19\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9.\nOn Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently\nonly test CPLEX, and on macOS, we do not test LP solvers (yet).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributors\u003c/h2\u003e\n\u003cp\u003eThe following list includes all people that actively contributed to\nFast Downward, i.e. all people that appear in some commits in Fast\nDownward\u0027s history (see below for a history on how Fast Downward\nemerged) or people that influenced the development of such commits.\nCurrently, this list is sorted by the last year the person has been\nactive, and in case of ties, by the earliest year the person started\ncontributing, and finally by last name.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2003-2022 Malte Helmert\u003c/li\u003e\n\u003cli\u003e2008-2016, 2018-2022 Gabriele Roeger\u003c/li\u003e\n\u003cli\u003e2010-2022 Jendrik Seipp\u003c/li\u003e\n\u003cli\u003e2010-2011, 2013-2022 Silvan Sievers\u003c/li\u003e\n\u003cli\u003e2012-2022 Florian Pommerening\u003c/li\u003e\n\u003cli\u003e2013, 2015-2022 Salom\u00e9 Eriksson\u003c/li\u003e\n\u003cli\u003e2018-2022 Patrick Ferber\u003c/li\u003e\n\u003cli\u003e2021-2022 Clemens B\u00fcchner\u003c/li\u003e\n\u003cli\u003e2021-2022 Dominik Drexler\u003c/li\u003e\n\u003cli\u003e2022 Remo Christen\u003c/li\u003e\n\u003cli\u003e2015, 2021 Thomas Keller\u003c/li\u003e\n\u003cli\u003e2016-2020 Cedric Geissmann\u003c/li\u003e\n\u003cli\u003e2017-2020 Guillem Franc\u00e8s\u003c/li\u003e\n\u003cli\u003e2018-2020 Augusto B. Corr\u00eaa\u003c/li\u003e\n\u003cli\u003e2020 Rik de Graaff\u003c/li\u003e\n\u003cli\u003e2015-2019 Manuel Heusner\u003c/li\u003e\n\u003cli\u003e2017 Daniel Killenberger\u003c/li\u003e\n\u003cli\u003e2016 Yusra Alkhazraji\u003c/li\u003e\n\u003cli\u003e2016 Martin Wehrle\u003c/li\u003e\n\u003cli\u003e2014-2015 Patrick von Reth\u003c/li\u003e\n\u003cli\u003e2009-2014 Erez Karpas\u003c/li\u003e\n\u003cli\u003e2014 Robert P. Goldman\u003c/li\u003e\n\u003cli\u003e2010-2012 Andrew Coles\u003c/li\u003e\n\u003cli\u003e2010, 2012 Patrik Haslum\u003c/li\u003e\n\u003cli\u003e2003-2011 Silvia Richter\u003c/li\u003e\n\u003cli\u003e2009-2011 Emil Keyder\u003c/li\u003e\n\u003cli\u003e2010-2011 Moritz Gronbach\u003c/li\u003e\n\u003cli\u003e2010-2011 Manuela Ortlieb\u003c/li\u003e\n\u003cli\u003e2011 Vidal Alc\u00e1zar Saiz\u003c/li\u003e\n\u003cli\u003e2011 Michael Katz\u003c/li\u003e\n\u003cli\u003e2011 Raz Nissim\u003c/li\u003e\n\u003cli\u003e2010 Moritz Goebelbecker\u003c/li\u003e\n\u003cli\u003e2007-2009 Matthias Westphal\u003c/li\u003e\n\u003cli\u003e2009 Christian Muise\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-history\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#history\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHistory\u003c/h2\u003e\n\u003cp\u003eThe current version of Fast Downward is the merger of three different\nprojects:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe original version of Fast Downward developed by Malte Helmert\nand Silvia Richter\u003c/li\u003e\n\u003cli\u003eLAMA, developed by Silvia Richter and Matthias Westphal based on\nthe original Fast Downward\u003c/li\u003e\n\u003cli\u003eFD-Tech, a modified version of Fast Downward developed by Erez\nKarpas and Michael Katz based on the original code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition to these three main sources, the codebase incorporates\ncode and features from numerous branches of the Fast Downward codebase\ndeveloped for various research papers. The main contributors to these\nbranches are Malte Helmert, Gabi R\u00f6ger and Silvia Richter.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eThe following directory is not part of Fast Downward as covered by\nthis license:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e./src/search/ext\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the rest, the following license applies:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eFast Downward is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nFast Downward is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see \u0026lt;https://www.gnu.org/licenses/\u0026gt;.\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 13, + "subscribers_count": 8, + "topics": [], + "updated_at": 1708648832.0 + }, + { + "data_format": 2, + "description": "R package to generate the same BEAST2 XML parameter files as generated by BEAUti 2", "filenames": [ "Singularity" ], - "full_name": "nirlipo/BFWS-public", - "latest_release": "planutils_singularity", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBFWS\u003c/h1\u003e\u003ca id=\"user-content-bfws\" class=\"anchor\" aria-label=\"Permalink: BFWS\" href=\"#bfws\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBest First Width Search Planner\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGetting Started\u003c/h3\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou first need to install LAPKT by following this instructions: \u003ca href=\"https://lapkt-dev.github.io/docs/gettingStarted/\" rel=\"nofollow\"\u003ehttps://lapkt-dev.github.io/docs/gettingStarted/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYoy also need to define LAPKT_PATH as an enviromnent variable. Add the following line to your .bash or .profile file or simply execute it in your terminal:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export LAPKT_PATH = /Absolute-path-to-LAPKT-folder\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBFWS can run using either FF or FD parser.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFF-parser version\u003c/h3\u003e\u003ca id=\"user-content-ff-parser-version\" class=\"anchor\" aria-label=\"Permalink: FF-parser version\" href=\"#ff-parser-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Scons script included in FF-parser-version folder knows which modules from the LAPKT toolkit it needs to recompile.\u003c/p\u003e\n\u003cp\u003eTo compile type\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e scons \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFD-parser version\u003c/h3\u003e\u003ca id=\"user-content-fd-parser-version\" class=\"anchor\" aria-label=\"Permalink: FD-parser version\" href=\"#fd-parser-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGo to FD-parser-version folder and type to compile\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./build.py \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRunning BFWS\u003c/h1\u003e\u003ca id=\"user-content-running-bfws\" class=\"anchor\" aria-label=\"Permalink: Running BFWS\" href=\"#running-bfws\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are BFWS options\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bfws --help\n\nOptions::\n --help Show help message. \n --domain arg Input PDDL domain description\n --problem arg Input PDDL problem description\n --output arg Output file for plan\n --max_novelty arg (=2) Max width w for novelty (default 2)\n\nSearch Algorithms::\n --DUAL-BFWS arg (=1) 1-BFWS first, then BFWS using h_ff and h_landcount as in AAAI-17 paper\n --DUAL-C-BFWS arg (=0) 1-C-BFWS first, then BFWS using h_ff and h_landcount\n --BFWS-f5 arg (=0) BFWS(w,#g), w_{#r,#g}, as in BFWS(f5) AAAI-17 paper\n --BFWS-f5-initstate-relevant arg (=0) BFWS(f5) but computing relevant fluents only once from s0\n --BFWS-f5-landmarks arg (=0) BFWS(w,h_landcount), w = w_{#r,h_landcount} \n --BFWS-goalcount-only arg (=0) BFWS(w,#g), w = w_{#g}, no relevant fluents count\n\nPolynomial Search Algorithms::\n --1-BFWS arg (=0) 1-BFWS(w,#g), w_{#r,#g}, pruning w \u0026gt; 1 \n --1-C-BFWS arg (=0) 1-BFWS using consistency to refine goal counting\n --k-BFWS arg (=0) k-BFWS(w,#g), w_{#r,#g}, pruning w \u0026gt; k, where k = bound() argument, default 2\n --k-C-BFWS arg (=0) k-BFWS with goal consistency count\n --k-M-BFWS arg (=0) Allowing (M) nodes \u0026gt; novelty bound() for each node with novelty \u0026lt;= bound()\n --k-M-C-BFWS arg (=0) k-M-C-BFWS with goal consistency\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe command to run the FF-parser-version of BFWS, computing novelty 1,2, and greater than 2, and pruning nodes with novelty greater than 2\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bfws --domain domain.pddl --problem prob.pddl --max_novelty 2 --k-BFWS true\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto use FD-parser version, go to the correct folder and run the same options with the following command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./bfws.py domain.pddl prob.pddl k-BFWS\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFD-version uses the same options but do not uses tags. To change the default max_novelty 2 and M values, edit bfws.py file\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCredits\u003c/h1\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is a joint work by Nir Lipovetzky, and Hector Geffner.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePaper\u003c/h3\u003e\u003ca id=\"user-content-paper\" class=\"anchor\" aria-label=\"Permalink: Paper\" href=\"#paper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can read more about it in the \u003ca href=\"http://people.eng.unimelb.edu.au/nlipovetzky/papers/aaai17-BFWS-novelty-exploration.pdf\" rel=\"nofollow\"\u003eAAAI 2017 paper\u003c/a\u003e and \u003ca href=\"http://people.eng.unimelb.edu.au/nlipovetzky/papers/icaps17-polytime-BFWS.pdf\" rel=\"nofollow\"\u003eICAPS 2017 paper\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "ropensci/beautier", + "latest_release": "v2.6.12", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ebeautier\u003c/h1\u003e\u003ca id=\"user-content-beautier\" class=\"anchor\" aria-label=\"Permalink: beautier\" href=\"#beautier\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/ropensci/software-review/issues/209\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1f9e866eed4c0edc0f5b5b90e775c80a93dc80a99af5f86154943b5bca5b742/68747470733a2f2f6261646765732e726f70656e7363692e6f72672f3230395f7374617475732e737667\" alt=\"Peer Review Status\" 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100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=beautier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b49c2ea1524023b09adedd34045974707e176c58113796d85465445d37316b82/687474703a2f2f6372616e6c6f67732e722d706b672e6f72672f6261646765732f6265617574696572\" alt=\"CRAN downloads per months\" data-canonical-src=\"http://cranlogs.r-pkg.org/badges/beautier\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/53443354\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4d24c9a7d7571ed1f7acdf05bc1492d1720cbd9764d696338f710c11ba642262/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f35333434333335342e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/53443354.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBranch\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/ropensci/beautier/actions\"\u003e\u003cimg src=\"man/figures/GitHubActions.png\" alt=\"GitHub Actions logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://about.codecov.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"man/figures/Codecov.png\" alt=\"Codecov logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emaster\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=master\"\u003e\u003cimg src=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=master\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/beautier/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ff034288b0791340bac7dea03ff17aa4d354a3b0bea92f96c633ae8025eb7e24/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edevelop\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=develop\"\u003e\u003cimg src=\"https://github.com/ropensci/beautier/workflows/R-CMD-check/badge.svg?branch=develop\" alt=\"R-CMD-check\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://app.codecov.io/github/ropensci/beautier/branch/develop\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/613637bb36d139cbbef6d32c146529b4fd650914d54b6603cc237ed1d35da4fe/68747470733a2f2f636f6465636f762e696f2f6769746875622f726f70656e7363692f62656175746965722f636f7665726167652e7376673f6272616e63683d646576656c6f70\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ropensci/beautier/coverage.svg?branch=develop\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ccode\u003ebeautier\u003c/code\u003e is \u003ccode\u003eBEAUti\u003c/code\u003e for R.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"man/figures/beautier_logo.png\"\u003e\u003cimg src=\"man/figures/beautier_logo.png\" alt=\"beautier logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of \u003ccode\u003ebeautier\u003c/code\u003e is to create\n\u003ca href=\"inst/extdata/2_4.xml\"\u003ea valid BEAST2 XML input file\u003c/a\u003e\nfrom a n inference model. In this way, a scientific pipeline using\n\u003ccode\u003eBEAST2\u003c/code\u003e can be fully scripted, instead of using \u003ccode\u003eBEAUti\u003c/code\u003e\u0027s GUI.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ebeautier\u003c/code\u003e is part of the \u003ca href=\"https://github.com/ropensci/babette\"\u003e\u003ccode\u003ebabette\u003c/code\u003e\u003c/a\u003e package suite:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beautier\"\u003e\u003ccode\u003ebeautier\u003c/code\u003e\u003c/a\u003e create a BEAST2 input (\u003ccode\u003e.xml\u003c/code\u003e) file from an inference model.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/tiebeaur\"\u003e\u003ccode\u003etiebeaur\u003c/code\u003e\u003c/a\u003e creates an inference model from a BEAST2 input (\u003ccode\u003e.xml\u003c/code\u003e) file \u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e experimental \u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/beastier\"\u003e\u003ccode\u003ebeastier\u003c/code\u003e\u003c/a\u003e runs BEAST2\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/tracerer\"\u003e\u003ccode\u003etracerer\u003c/code\u003e\u003c/a\u003e pastes BEAST2 output (\u003ccode\u003e.log\u003c/code\u003e, \u003ccode\u003e.trees\u003c/code\u003e, etc) files.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/mauricer\"\u003e\u003ccode\u003emauricer\u003c/code\u003e\u003c/a\u003e install BEAST2 packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRelated R packages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/richelbilderbeek/beautier_on_windows\"\u003e\u003ccode\u003ebeautier_on_windows\u003c/code\u003e\u003c/a\u003e: verifies\n\u003ccode\u003ebeautier\u003c/code\u003e builds on Windows\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ropensci/lumier\"\u003e\u003ccode\u003elumier\u003c/code\u003e\u003c/a\u003e: Shiny app to help create the function call needed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/examples.md\"\u003eexamples\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ebeautier\u003c/code\u003e can be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLatest CRAN version: CRAN\u003c/li\u003e\n\u003cli\u003eLatest stable version: GitHub, \u003ccode\u003emaster\u003c/code\u003e branch\u003c/li\u003e\n\u003cli\u003eBleeding-edge version: GitHub, \u003ccode\u003edevelop\u003c/code\u003e branch\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCRAN\u003c/h3\u003e\u003ca id=\"user-content-cran\" class=\"anchor\" aria-label=\"Permalink: CRAN\" href=\"#cran\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the latest CRAN version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebeautier\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGitHub, \u003ccode\u003emaster\u003c/code\u003e branch\u003c/h3\u003e\u003ca id=\"user-content-github-master-branch\" class=\"anchor\" aria-label=\"Permalink: GitHub, master branch\" href=\"#github-master-branch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the latest stable version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003eremotes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eropensci/beautier\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGitHub, \u003ccode\u003edevelop\u003c/code\u003e branch\u003c/h3\u003e\u003ca id=\"user-content-github-develop-branch\" class=\"anchor\" aria-label=\"Permalink: GitHub, develop branch\" href=\"#github-develop-branch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the bleeding-edge version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003e\u003cspan class=\"pl-e\"\u003eremotes\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_github(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eropensci/beautier\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eref\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edevelop\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"doc/faq.md\"\u003eFAQ\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"doc/faq.md\"\u003eFAQ\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupported\u003c/h2\u003e\u003ca id=\"user-content-supported\" class=\"anchor\" aria-label=\"Permalink: Supported\" href=\"#supported\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis works, and the interface is unlikely to change.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e1 DNA alignment\u003c/li\u003e\n\u003cli\u003eSite models:\n\u003cul\u003e\n\u003cli\u003eJC69\u003c/li\u003e\n\u003cli\u003eHKY\u003c/li\u003e\n\u003cli\u003eTN93\u003c/li\u003e\n\u003cli\u003eGTR\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eClock models:\n\u003cul\u003e\n\u003cli\u003eStrickt\u003c/li\u003e\n\u003cli\u003eRelaxed log-normal\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTree models:\n\u003cul\u003e\n\u003cli\u003eYule\u003c/li\u003e\n\u003cli\u003eBirth-Death\u003c/li\u003e\n\u003cli\u003eCoalescent Bayesian Skyline\u003c/li\u003e\n\u003cli\u003eCoalescent Constant Population\u003c/li\u003e\n\u003cli\u003eCoalescent Exponential Population\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eHandle missing data: simply use a dash (\u00b4-\u00b4) as a sequence\nin a FASTA file\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExperimental\u003c/h2\u003e\u003ca id=\"user-content-experimental\" class=\"anchor\" aria-label=\"Permalink: Experimental\" href=\"#experimental\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis works partially, and the interface may change as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTip dating\u003c/h3\u003e\u003ca id=\"user-content-tip-dating\" class=\"anchor\" aria-label=\"Permalink: Tip dating\" href=\"#tip-dating\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tip dates file is a file\nthat needs to not have column, nor row names.\nThe columns need to be tab separated.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/ropensci/beautier/blob/master/inst/extdata/G_VII_pre2003_dates_4.txt\"\u003ehere\u003c/a\u003e\nfor an example, of which the first rows are shown here:\u003c/p\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eKF767106_Indonesia_1976_VII 1976\nKF767104_Indonesia_1988_VII 1988\nKF767105_Indonesia_1988_VII 1988\nAY288998_Indonesia_1990_VII 1990\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the future, there probably will be a \u00b4to_tipdates_file\u00b4 function,\nto create a temporary tipdates file from a table.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMissing features/unsupported\u003c/h2\u003e\u003ca id=\"user-content-missing-featuresunsupported\" class=\"anchor\" aria-label=\"Permalink: Missing features/unsupported\" href=\"#missing-featuresunsupported\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ebeautier\u003c/code\u003e cannot do everything \u003ccode\u003eBEAUti\u003c/code\u003e can.\u003c/p\u003e\n\u003cp\u003eHere are some missing or (yet) unsupported features,\nsome are linked to an Issue:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ropensci/beautier/issues/130\"\u003eAdd offset to a distribution\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eTwo or more DNA alignments\u003c/li\u003e\n\u003cli\u003eTwo or more site, clock or tree models\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ropensci/beautier/issues/131\"\u003eTwo or more MRCA priors\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eShared site, clock and/or tree models\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ropensci/beautier/issues/114\"\u003eUsing an amino acid alignment\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSupport for hyper parameters\u003c/li\u003e\n\u003cli\u003eClock models\n\u003cul\u003e\n\u003cli\u003eRelaxed exponential\u003c/li\u003e\n\u003cli\u003eRandom local\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eTree priors\n\u003cul\u003e\n\u003cli\u003eCalibrated Yule model\u003c/li\u003e\n\u003cli\u003eCoalescent Extended Bayesian Skyline\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ropensci/beautier/issues/133\"\u003eBirth Death Skyline Serial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eInitialization (this is a tab that is hidden by default in \u003ccode\u003eBEAUti\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere is a feature I miss\u003c/h2\u003e\u003ca id=\"user-content-there-is-a-feature-i-miss\" class=\"anchor\" aria-label=\"Permalink: There is a feature I miss\" href=\"#there-is-a-feature-i-miss\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u003ccode\u003eSubmitting use cases\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI want to collaborate\u003c/h2\u003e\u003ca id=\"user-content-i-want-to-collaborate\" class=\"anchor\" aria-label=\"Permalink: I want to collaborate\" href=\"#i-want-to-collaborate\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u0027Submitting code\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eI think I have found a bug\u003c/h2\u003e\u003ca id=\"user-content-i-think-i-have-found-a-bug\" class=\"anchor\" aria-label=\"Permalink: I think I have found a bug\" href=\"#i-think-i-have-found-a-bug\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e, at \u0027Submitting bugs\u0027\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThere\u0027s something else I want to say\u003c/h2\u003e\u003ca id=\"user-content-theres-something-else-i-want-to-say\" class=\"anchor\" aria-label=\"Permalink: There\u0027s something else I want to say\" href=\"#theres-something-else-i-want-to-say\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSure, just add an Issue. Or send an email.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExternal links\u003c/h2\u003e\u003ca id=\"user-content-external-links\" class=\"anchor\" aria-label=\"Permalink: External links\" href=\"#external-links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CompEvol/beast2\"\u003eBEAST2 GitHub\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFiles used by continuous integration scripts\u003c/h2\u003e\u003ca id=\"user-content-files-used-by-continuous-integration-scripts\" class=\"anchor\" aria-label=\"Permalink: Files used by continuous integration scripts\" href=\"#files-used-by-continuous-integration-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFilename\u003c/th\u003e\n\u003cth\u003eDescriptions\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"mlc_config.json\"\u003emlc_config.json\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the link checker, use \u003ccode\u003emarkdown-link-check --config mlc_config.json --quiet docs/**/*.md\u003c/code\u003e to do link checking locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".spellcheck.yml\"\u003e.spellcheck.yml\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".wordlist.txt\"\u003e.wordlist.txt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eWhitelisted words for the spell checker, use \u003ccode\u003epyspelling -c .spellcheck.yml\u003c/code\u003e to do spellcheck locally\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlint.jsonc\"\u003e.markdownlint.jsonc\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguration of the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\".markdownlintignore\"\u003e.markdownlintignore\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFiles ignored by the markdown linter, use \u003ccode\u003emarkdownlint \"**/*.md\"\u003c/code\u003e to do markdown linting locally. The name of this file is a default name.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eArticle about \u003ccode\u003ebabette\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBilderbeek, Rich\u00e8l JC, and Rampal S. Etienne. \"\u003ccode\u003ebabette\u003c/code\u003e: BEAUti 2, BEAST 2 and Tracer for R.\" Methods in Ecology and Evolution (2018). \u003ca href=\"https://doi.org/10.1111/2041-210X.13032\" rel=\"nofollow\"\u003ehttps://doi.org/10.1111/2041-210X.13032\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFASTA files \u003ccode\u003eanthus_aco.fas\u003c/code\u003e and \u003ccode\u003eanthus_nd2.fas\u003c/code\u003e from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eVan Els, Paul, and Heraldo V. Norambuena. \"A revision of species limits in Neotropical pipits Anthus based on multilocus genetic and vocal data.\" Ibis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFASTA file \u003ccode\u003eG_VII_pre2003_msa.fas\u003c/code\u003e from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDurr, PA; Wibowo, MH; Tabbu, CR; Asmara, W; Selleck, P; Wang, J; Broz, I; Graham, K.; Dimitrov, K and Afonso, C. (in preparation). Phylodynamics of Genotype VII Newcastle disease virus in Indonesia.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://ropensci.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c7d2a7aedd313f02b4f7d199df4d0d28905b7814db324fb532759b2fe2ee9ac2/68747470733a2f2f726f70656e7363692e6f72672f7075626c69635f696d616765732f726f70656e7363695f666f6f7465722e706e67\" alt=\"ropensci_footer\" data-canonical-src=\"https://ropensci.org/public_images/ropensci_footer.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n", + "stargazers_count": 13, + "subscribers_count": 3, + "topics": [ + "r", + "r-package", + "rstats", + "bayesian", + "beast", + "beast2", + "phylogenetic-inference", + "phylogenetics", + "beauti" + ], + "updated_at": 1720420857.0 + }, + { + "data_format": 2, + "description": "Downloading a dataset from Airbnb", + "filenames": [ + "container/Singularity.butd" + ], + "full_name": "airbert-vln/bnb-dataset", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\ud83c\udfd8\ufe0f BnB Dataset \ud83c\udfd8\ufe0f\u003c/h1\u003e\u003ca id=\"user-content-houses-bnb-dataset-houses\" class=\"anchor\" aria-label=\"Permalink: :houses: BnB Dataset :houses:\" href=\"#houses-bnb-dataset-houses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"./LICENSE.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eff0a8d3e0e9027bd979de24eef72a530be6cf489cd9e8377b4043d8c03bacb8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f616972626572742d766c6e2f626e622d646174617365743f7374796c653d666f722d7468652d6261646765\" alt=\"MIT\" data-canonical-src=\"https://img.shields.io/github/license/airbert-vln/bnb-dataset?style=for-the-badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://arxiv.org/abs/2108.09105\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8b518b8258f223fda44d71542135afef6c2d8b57ce74b0f0753b10b9d682e2b9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323130382e30393130352d7265642e7376673f7374796c653d666f722d7468652d6261646765\" alt=\"arXiv\" data-canonical-src=\"https://img.shields.io/badge/arXiv-2108.09105-red.svg?style=for-the-badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://eval.ai/web/challenges/challenge-page/97/leaderboard/270\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8afacccdcce9f2acfa3aec9d71646ae7bf8a4ccb049cc3948d3f6eb1d3553da7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5232522d2546302539462541352538372d677265656e2e7376673f7374796c653d666f722d7468652d6261646765\" alt=\"R2R 1st\" data-canonical-src=\"https://img.shields.io/badge/R2R-%F0%9F%A5%87-green.svg?style=for-the-badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis contains a set of scripts for downloading a dataset from Airbnb.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udee0\ufe0f 1. Get started\u003c/h2\u003e\u003ca id=\"user-content-hammer_and_wrench-1-get-started\" class=\"anchor\" aria-label=\"Permalink: :hammer_and_wrench: 1. Get started\" href=\"#hammer_and_wrench-1-get-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you need \u003ca href=\"https://git-lfs.github.com/\"\u003e\u003ccode\u003egit lfs\u003c/code\u003e\u003c/a\u003e to clone the repository. Install it from command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecurl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sudo bash\nsudo apt-get install git-lfs\ngit lfs install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can now clone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/airbert-vln/bnb-dataset.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you clone the repository without LFS installed, you should have received an error message. You can fix it by running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emake lfs\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou need to have a recent version of Python (3.8 or higher) and install dependencies through \u003ccode\u003epoetry\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install python for ubuntu 20.04\u003c/span\u003e\nsudo apt install python3 python3-pip \npip install poetry\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e install dependencies\u003c/span\u003e\npoetry install\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e activate the environment (do it at each new shell)\u003c/span\u003e\npoetry shell\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that typing is extensively used in these scripts. This was a real time saver for detecting errors before runtime. You might want to setup properly your IDE to play well with \u003ccode\u003emypy\u003c/code\u003e. I recommend the \u003ca href=\"https://github.com/neoclide/coc.nvim\"\u003e\u003ccode\u003ecoc.nvim\u003c/code\u003e\u003c/a\u003e extension \u003ca href=\"https://github.com/fannheyward/coc-pyright\"\u003e\u003ccode\u003ecoc-pyright\u003c/code\u003e\u003c/a\u003e for \u003ca href=\"https://github.com/neovim/neovim/\"\u003eneovim\u003c/a\u003e users.\u003c/p\u003e\n\u003cp\u003eManaging a large of images is tricky and usually take a lot of times. Usually, the scripts are splitting the task among several workers. A cache folder is keeping the order list for each worker, while each worker is producing its own output file.\nLook for \u003ccode\u003enum_workers\u003c/code\u003e or \u003ccode\u003enum_procs\u003c/code\u003e parameters in the \u003ccode\u003eargtyped Arguments\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\uddfa\ufe0f 2. Download listings from Airbnb\u003c/h2\u003e\u003ca id=\"user-content-world_map-2-download-listings-from-airbnb\" class=\"anchor\" aria-label=\"Permalink: :world_map: 2. Download listings from Airbnb\" href=\"#world_map-2-download-listings-from-airbnb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step is building a TSV file with 4 columns: listing ID, photo ID, image URL, image caption.\nA too high request rate would induce a rejection from Airbnb. Instead, it is advised to split the job among different IP addresses.\u003c/p\u003e\n\u003cp\u003ePlease note that you can use the pre-computed TSV file used in our paper \u003ca href=\"./data/airbnb-train-indoor-filtered.tsv\"\u003efor training\u003c/a\u003e and \u003ca href=\"./data/airbnb-train-indoor-filtered.tsv\"\u003efor testing\u003c/a\u003e. The file was generated during Christmas 2019 (yeah, before Covid. Sounds so far away now!). Some images might not be available anymore.\u003c/p\u003e\n\u003cp\u003eAlso, note that this file contains only a portion from the total of Airbnb listings. It might be interesting to extend it.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.1. Create a list of regions\u003c/h3\u003e\u003ca id=\"user-content-21-create-a-list-of-regions\" class=\"anchor\" aria-label=\"Permalink: 2.1. Create a list of regions\" href=\"#21-create-a-list-of-regions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAirbnb listings are searched among a specific region.\nWe need first to initialize the list of regions. A quick hack for that consists in scrapping Wikipedia list of places, as done in the script \u003ca href=\"./cities.py\"\u003ecities.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFor this script, you need to download and install Selenium. Instructions here are valid only for a Linux distribution. Otherwise, follow the guide \u003ca href=\"https://selenium-python.readthedocs.io/installation.html\" rel=\"nofollow\"\u003efrom Selenium documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install selenium\nwget https://github.com/mozilla/geckodriver/releases/download/v0.30.0/geckodriver-v0.30.0-linux32.tar.gz\nmkdir -p \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.local/bin\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.local/bin\ntar -xvf geckodriver-v0.30.0-linux32.tar.gz -C \u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/.local/bin\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Testing the driver path is recognized:\u003c/span\u003e\ngeckodriver --version\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHere is how I scrapped a list of cities. You might want to update this script to order to increase the amount of cities.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython cities.py --output data/cities.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can see other examples in the \u003ca href=\"./locations/\"\u003e\u003ccode\u003elocations/\u003c/code\u003e\u003c/a\u003e folder, used as an attempt to enlarge the BnB dataset.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.2. Download listings\u003c/h3\u003e\u003ca id=\"user-content-22-download-listings\" class=\"anchor\" aria-label=\"Permalink: 2.2. Download listings\" href=\"#22-download-listings\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download a list of listing from the list of cities\u003c/span\u003e\npython search_listings.py --locations data/cities.txt --output data/listings\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download JSON files for each listing\u003c/span\u003e\npython download_listings.py --listings data/listings.txt --output data/merlin --with_photo\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Note you can download also reviews and infos (see python download_listings.py --help)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract photo URLs from listing export files\u003c/span\u003e\npython extract_photo_metadata.py --merlin data/merlin --output data/bnb-dataset-raw.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2.3. Filter captions\u003c/h3\u003e\u003ca id=\"user-content-23-filter-captions\" class=\"anchor\" aria-label=\"Permalink: 2.3. Filter captions\" href=\"#23-filter-captions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Apply basic rules to remove some captions\u003c/span\u003e\npython filter_captions.py --input data/bnb-dataset-raw.tsv --output data/bnb-dataset.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udcf8 3. Get images\u003c/h2\u003e\u003ca id=\"user-content-camera_flash-3-get-images\" class=\"anchor\" aria-label=\"Permalink: :camera_flash: 3. Get images\" href=\"#camera_flash-3-get-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow we want to download images and filter out outdoor images.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.1. Download images\u003c/h3\u003e\u003ca id=\"user-content-31-download-images\" class=\"anchor\" aria-label=\"Permalink: 3.1. Download images\" href=\"#31-download-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe download rate can be higher before the server kicks us out. However, it is still preferable to use a pool of IP addresses.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython download_images.py --csv_file data/bnb-dataset.tsv --output data/images --correspondance /tmp/cache-download-images/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.2. Optionally, make sure images were correctly downloaded\u003c/h3\u003e\u003ca id=\"user-content-32-optionally-make-sure-images-were-correctly-downloaded\" class=\"anchor\" aria-label=\"Permalink: 3.2. Optionally, make sure images were correctly downloaded\" href=\"#32-optionally-make-sure-images-were-correctly-downloaded\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython detect_errors.py --images data/images --merlin data/merlin\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3.3. Filter out outdoor images\u003c/h3\u003e\u003ca id=\"user-content-33-filter-out-outdoor-images\" class=\"anchor\" aria-label=\"Permalink: 3.3. Filter out outdoor images\" href=\"#33-filter-out-outdoor-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutdoor images tend to be of lower qualities and captions are often not relevant.\nWe first detect outdoor images from a CNN pretrained on the places365 dataset. Later on, we will keep indoor images.\u003c/p\u003e\n\u003cp\u003eNote that the output of this step is also used for image merging.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Detect room types\u003c/span\u003e\npython detect_room.py --output data/places365/detect.tsv --images data/images\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Filter out indoor images\u003c/span\u003e\npython extract_indoor.py --output data/bnb-dataset-indoor.tsv --detection data/places365/detect.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udcbd 4. Build an LMDB database with BnB pictures\u003c/h2\u003e\u003ca id=\"user-content-minidisc-4-build-an-lmdb-database-with-bnb-pictures\" class=\"anchor\" aria-label=\"Permalink: :minidisc: 4. Build an LMDB database with BnB pictures\" href=\"#minidisc-4-build-an-lmdb-database-with-bnb-pictures\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eExtract visual features and store them on a single file. Several steps are required to achieve that. Unfortunately, we don\u0027t own permissions over Airbnb images, and thus we are not permitted to share our own LMDB file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.1. Split between train and test\u003c/h3\u003e\u003ca id=\"user-content-41-split-between-train-and-test\" class=\"anchor\" aria-label=\"Permalink: 4.1. Split between train and test\" href=\"#41-split-between-train-and-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e5% of the dataset is allocated to the testset:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003eround\u003c/span\u003e() {\n \u003cspan class=\"pl-c1\"\u003eprintf\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e%.\u003cspan class=\"pl-smi\"\u003e${2}\u003c/span\u003ef\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${1}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n}\n\nnum_rows=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ewc -l data/bnb-dataset-indoor.tsv\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\ntest=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$((\u003c/span\u003enum_rows \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e05\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e))\u003c/span\u003e\u003c/span\u003e\ntest=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003eround \u003cspan class=\"pl-smi\"\u003e$test\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\ncat data/bnb-dataset-indoor.tsv \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e tail -n \u003cspan class=\"pl-smi\"\u003e$test\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e data/bnb-test-indoor-filtered.tsv\n\ntrain=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$((\u003c/span\u003enum_rows \u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e test\u003cspan class=\"pl-pds\"\u003e))\u003c/span\u003e\u003c/span\u003e\ncat data/bnb-dataset-indoor.tsv \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e head -n \u003cspan class=\"pl-smi\"\u003e$train\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e data/bnb-train-indoor-filtered.tsv\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.2. Extract bottom-up top-down features\u003c/h3\u003e\u003ca id=\"user-content-42-extract-bottom-up-top-down-features\" class=\"anchor\" aria-label=\"Permalink: 4.2. Extract bottom-up top-down features\" href=\"#42-extract-bottom-up-top-down-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis step is one of the most annoying one, since the install of bottom-up top-down attention is outdated. I put docker file and Singularity definition file in the folder \u003ccode\u003econtainer\u003c/code\u003e to help you with that.\nNote that this step is also extremely slow and you might want to use multiple GPUs.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython precompute_airbnb_img_features_with_butd.py --images data/images\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf this step is too difficult, open an issue and I\u0027ll try to use the \u003ca href=\"https://github.com/MILVLG/bottom-up-attention.pytorch\"\u003ePyTorch version\u003c/a\u003e instead.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4.3. Build an LMDB file\u003c/h3\u003e\u003ca id=\"user-content-43-build-an-lmdb-file\" class=\"anchor\" aria-label=\"Permalink: 4.3. Build an LMDB file\" href=\"#43-build-an-lmdb-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract keys\u003c/span\u003e\npython extract_keys.py --output data/keys.txt --datasets data/bnb-dataset.indoor.tsv\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create an LMDB\u003c/span\u003e\npython convert_to_lmdb.py --output img_features --keys data/keys.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that you can split the LMDB into multiple files by using a number of workers. This could be relevant when your LMDB file is super huge!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udd17 5. Create dataset files with path-instruction pairs\u003c/h2\u003e\u003ca id=\"user-content-link-5-create-dataset-files-with-path-instruction-pairs\" class=\"anchor\" aria-label=\"Permalink: :link: 5. Create dataset files with path-instruction pairs\" href=\"#link-5-create-dataset-files-with-path-instruction-pairs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlmost there! We built image-caption pairs and now we want to convert them into path-instruction pairs.\nActually, we are just going to produce JSON files that you can feed into the \u003ca href=\"https://github.com/airbert-vln/airbert/\"\u003etraining repository\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u26d3\ufe0f 5.1. Concatenation\u003c/h3\u003e\u003ca id=\"user-content-chains-51-concatenation\" class=\"anchor\" aria-label=\"Permalink: :chains: 5.1. Concatenation\" href=\"#chains-51-concatenation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython preprocess_dataset.py --csv data/bnb-train.tsv --name bnb_train\npython preprocess_dataset.py --csv data/bnb-test.tsv --name bnb_test\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\ud83d\udc65 5.2. Image merging\u003c/h3\u003e\u003ca id=\"user-content-busts_in_silhouette-52-image-merging\" class=\"anchor\" aria-label=\"Permalink: :busts_in_silhouette: 5.2. Image merging\" href=\"#busts_in_silhouette-52-image-merging\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython merge_photos.py --source bnb_train.py --output merge+bnb_train.py --detection-dir data/places365 \npython merge_photos.py --source bnb_test.py --output merge+bnb_test.py --detection-dir data/places365\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\ud83d\udc68\u200d\ud83d\udc69\u200d\ud83d\udc67 5.3. Captionless insertion\u003c/h3\u003e\u003ca id=\"user-content--53-captionless-insertion\" class=\"anchor\" aria-label=\"Permalink: \ud83d\udc68\u200d\ud83d\udc69\u200d\ud83d\udc67 5.3. Captionless insertion\" href=\"#-53-captionless-insertion\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython preprocess_dataset.py --csv data/bnb-dataset.indoor.tsv --captionless True --min-caption 2 --min-length 4 --name 2capt+bnb_train\n\npython preprocess_dataset.py --csv datasets/data/bnb-dataset.indoor.tsv --captionless True --min-caption 2 --min-length 4 --name 2capt+bnb_test\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\ud83d\udc63 5.4. Instruction rephrasing\u003c/h3\u003e\u003ca id=\"user-content--54-instruction-rephrasing\" class=\"anchor\" aria-label=\"Permalink: \ud83d\udc63 5.4. Instruction rephrasing\" href=\"#-54-instruction-rephrasing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract noun phrases from BnB captions\u003c/span\u003e\npython extract_noun_phrases.py --source data/airbnb-train-indoor-filtered.tsv --output data/bnb-train.np.tsv \npython extract_noun_phrases.py --source data/airbnb-test-indoor-filtered.tsv --output data/bnb-test.np.tsv \n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Extract noun phrases from R2R train set\u003c/span\u003e\npython perturbate_dataset.py --infile R2R_train.json --outfile np_train.json --mode object --training True \n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5.5. Create the testset\u003c/h3\u003e\u003ca id=\"user-content-55-create-the-testset\" class=\"anchor\" aria-label=\"Permalink: 5.5. Create the testset\" href=\"#55-create-the-testset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to create a testset for each dataset. Here is an example for captionless insertion.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython build_testset.py --output data/bnb/2capt+testset.json --out-listing False --captions 2capt+bnb_test.json\n\u003c/pre\u003e\u003c/div\u003e\n", + "stargazers_count": 13, + "subscribers_count": 1, + "topics": [], + "updated_at": 1724160975.0 + }, + { + "data_format": 2, + "description": "Orbital viewer (mirror of https://gitlab.com/Jellby/Pegamoid)", + "filenames": [ + "Singularity" + ], + "full_name": "Jellby/Pegamoid", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePegamoid\u003c/h1\u003e\u003ca id=\"user-content-pegamoid\" class=\"anchor\" aria-label=\"Permalink: Pegamoid\" href=\"#pegamoid\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePegamoid is an orbital viewer especially suited for use with\n\u003ca href=\"https://gitlab.com/Molcas/OpenMolcas\" rel=\"nofollow\"\u003eOpenMolcas\u003c/a\u003e. It can be used to view\norbitals and quickly select active spaces for use in CASSCF or RASSCF\ncalculations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScreenshots\u003c/h2\u003e\u003ca id=\"user-content-screenshots\" class=\"anchor\" aria-label=\"Permalink: Screenshots\" href=\"#screenshots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7c85c6797a2a0601a920be9fbc44eadb55a82bb4c16141ab85ec6d2f6ab8fa17/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6e6f6e6f7274686f676f6e616c2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7c85c6797a2a0601a920be9fbc44eadb55a82bb4c16141ab85ec6d2f6ab8fa17/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6e6f6e6f7274686f676f6e616c2e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/nonorthogonal.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b7827db120a8db6ffa3c184bd1d5e40012f45ad6ea0d859d1bd70e97249f7c89/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f646966666572656e63652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b7827db120a8db6ffa3c184bd1d5e40012f45ad6ea0d859d1bd70e97249f7c89/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f646966666572656e63652e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/difference.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2efcb49f5df9917964a76f3d8c4bdf9f4fa39af67cb428a0159d22e645fade8c/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6772616469656e742e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2efcb49f5df9917964a76f3d8c4bdf9f4fa39af67cb428a0159d22e645fade8c/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6772616469656e742e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/gradient.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/0f4daa0a4ea852ac5f5225bf5f1bad3e9de0677ace79379ca02136401f219ae8/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6c6162656c732e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0f4daa0a4ea852ac5f5225bf5f1bad3e9de0677ace79379ca02136401f219ae8/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6c6162656c732e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/labels.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6aa661d6e51c5090742955f3d7fc36cd373f6d7549ce280a15b0bb5925bb33cb/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6c6973742e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6aa661d6e51c5090742955f3d7fc36cd373f6d7549ce280a15b0bb5925bb33cb/68747470733a2f2f6769746c61622e636f6d2f4a656c6c62792f706567616d6f69642f7261772f6d61737465722f73637265656e73686f74732f6c6973742e706e67\" height=\"200\" data-canonical-src=\"https://gitlab.com/Jellby/pegamoid/raw/master/screenshots/list.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following formats can be opened:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eHDF5 files, as generated by some (Open)Molcas modules like SCF or RASSCF, if compiled with HDF5 support.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInpOrb files, generated by some (Open)Molcas modules like SCF or RASSCF, provided an HDF5 file for the same system was opened first.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://www.cmbi.ru.nl/molden/\" rel=\"nofollow\"\u003eMolden\u003c/a\u003e files.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://luscus.sourceforge.net/\" rel=\"nofollow\"\u003eLuscus\u003c/a\u003e files, generated by the GRID_IT module.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGrid files (ASCII), generated by the GRID_IT module.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://paulbourke.net/dataformats/cube/\" rel=\"nofollow\"\u003eCube\u003c/a\u003e files (formatted).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor HDF5, InpOrb and Molden files, orbitals are computed on the fly from the\nbasis set, and it is possible to change the sampling resolution and shape and\nsize of the sampled volume. Luscus, grid and cube files contain precomputed\nvolumetric data and only the existing data can be displayed.\u003c/p\u003e\n\u003cp\u003eDepending on availability in the input file, the following features and objects\nare supported:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSelection of orbital.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSelection of spin.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSelection of symmetry irrep.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNatural average or state-specific orbitals.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eElectron density and Laplacian.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNatural average or state-specific spin orbitals.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSpin density.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNatural difference orbitals.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDifference, attachment and detachment density.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNatural transition orbitals.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransition, hole, particle and unrelaxed difference density.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor any orbital or density, gradient lines can be computed and displayed\n(particularly significant for the electron density). Densities can be computed\nfor reduced subsets of orbitals (for instance, only for the active orbitals),\nand the user can write arbitrary notes for each orbital.\u003c/p\u003e\n\u003cp\u003eThe value, opacity, colors and texture properties used to display isurfaces can\nbe adjusted and the display of the following elements can be toggled:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePositive and negative parts of the isosurface.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNodal surfaces.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNuclei and bonds.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAtom labels.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVolume box.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, the type of orbital (inactive, active...) can be changed and the\norbitals saved in the following formats usable in the (Open)Molcas programs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eHDF5 format.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInpOrb format.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eor the current volumetric data or snapshot can be saved as:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCube format.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePNG image.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTips for OpenMolcas\u003c/h2\u003e\u003ca id=\"user-content-tips-for-openmolcas\" class=\"anchor\" aria-label=\"Permalink: Tips for OpenMolcas\" href=\"#tips-for-openmolcas\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUse the \u003ccode\u003eTDM\u003c/code\u003e keyword in a RASSCF calculation to include transition densities\nin the HDF5 file.\u003c/p\u003e\n\u003cp\u003eUse the \u003ccode\u003eTDM\u003c/code\u003e (or \u003ccode\u003eTRD1\u003c/code\u003e) keyword in a RASSI calculation to include state and\ntransition densities in the HDF5 file. Use the \u003ccode\u003eSUBSET\u003c/code\u003e keyword to reduce the\nnumber of transition densities stored.\u003c/p\u003e\n\u003cp\u003eUse the \u003ccode\u003eWFA\u003c/code\u003e module for more detailed analysis.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recommended way to install Pegamoid is by using the \u003ca href=\"https://packaging.python.org/tutorials/installing-packages/#use-pip-for-installing\" rel=\"nofollow\"\u003e\u003ccode\u003epip\u003c/code\u003e package\nmanager\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install Pegamoid\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(you may also want to add the flags \u003ccode\u003e--upgrade\u003c/code\u003e and/or \u003ccode\u003e--user\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eThen you just run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epegamoid.py [filename]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003e[filename]\u003c/code\u003e is an optional supported file to open. In the case of\nInpOrb files, you can supply two filenames (in any order): the InpOrb file and\na corresponding HDF5 file.\u003c/p\u003e\n\u003cp\u003eThere are other ways to get Pegamoid. One is cloning the git repository, e.g.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://gitlab.com/Jellby/Pegamoid.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnother way, since Pegamoid is contained in a single python script, is\ndownloading only the script file\n\u003ca href=\"https://gitlab.com/Jellby/Pegamoid/raw/master/pegamoid.py?inline=false\" rel=\"nofollow\"\u003epegamoid.py\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the program is fetched, it can be run directly or through a python\ninterpreter, no installation is needed, i.e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./pegamoid.py [filename]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython pegamoid.py [filename]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHowever, the script has some requirements (this should be taken care of by\n\u003ccode\u003epip\u003c/code\u003e, if you use it) that must be installed for it to work:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePython 2 or python 3 (at least versions 2.7 and 3.4 have been tested).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eQt with python bindings. PyQt 4, PyQt 5 and PySide have been tested. It is\nrecommended to install the python module qtpy (needed for PySide).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVTK with python bindings. Version 8.1.0 has been tested, earlier versions\nwill most likely not work.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe numpy and h5py python modules.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOther python modules that may not be installed by default, it should be clear\nwhich ones, if any, are needed when trying to run Pegamoid.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse of scratch disk space\u003c/h2\u003e\u003ca id=\"user-content-use-of-scratch-disk-space\" class=\"anchor\" aria-label=\"Permalink: Use of scratch disk space\" href=\"#use-of-scratch-disk-space\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo speed up the display of several orbitals and the computation of densities,\nPegamoid uses some scratch disk space to store the computed basis functions. A\nfile named \u003ccode\u003epegamoid.cache\u003c/code\u003e will be created in a temporary location (typically\ninside the \u003ccode\u003e/tmp\u003c/code\u003e directory). For grids with many points and with many basis\nfunctions, this file could grow very large and even use up all available disk\nspace. The maximum scratch size is by default 1 GiB, but it can be configured in\n\"File \u0026gt; Set scratch\", or through the environment variable\n\u003ccode\u003ePEGAMOID_MAXSCRATCH\u003c/code\u003e, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ePEGAMOID_MAXSCRATCH=100MB ./pegamoid.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efor a maximum size of 100 MB. If the scratch size is not enough to hold all\nbasis functions at the current resolution, it will only be used when computing\nthe densities. In the \"Set scratch\" window you can also find the\ninstance-specific temporary path, as well as the maximum cache size, the scratch\nsize currently in use, and the recommended size to allow keeping a cache of all\nbasis functions. The scratch file and directory are removed on a clean exit, but\nif the program crashes or is otherwise abnormally interrupted, they may be left\nbehind.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse with a remote connection\u003c/h2\u003e\u003ca id=\"user-content-use-with-a-remote-connection\" class=\"anchor\" aria-label=\"Permalink: Use with a remote connection\" href=\"#use-with-a-remote-connection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProduction calculations are usually not run on the local machine, but on some\nremote server like a supercomputer. To view/save/modify orbital files, it is\nalways possible to transfer the files between the local and remote machines. It\nis, however, more convenient to run Pegamoid directly on the remote machine and\nhave the graphical interface display in the local machine. Unfortunately, there\nare some difficulties that make this nontrivial.\u003c/p\u003e\n\u003cp\u003eFirst, the different requirements may not be installed in the remote system. A\npossible solution is installing them for the user account with e.g.\n\u003ccode\u003epip install --user\u003c/code\u003e. In this case it will probably be easier to install qtpy\nand PySide instead of PyQt.\u003c/p\u003e\n\u003cp\u003eThen, the VTK visualization uses some advanced OpenGL features that may not be\navailable with all graphical drivers and it could be challenging to make it\nwork through a remote connection. We have had success running Pegamoid with\n\u003ccode\u003evglrun\u003c/code\u003e inside a\n\u003ca href=\"https://www.cendio.com/thinlinc/what-is-thinlinc\" rel=\"nofollow\"\u003eThinLinc\u003c/a\u003e session, or a VNC\nsession opened directly from an ssh connection. The specific needs and working\nsolution will probably depend on the hardware and software available in the\nremote computer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown problems\u003c/h2\u003e\u003ca id=\"user-content-known-problems\" class=\"anchor\" aria-label=\"Permalink: Known problems\" href=\"#known-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn some systems there are display issues in the 3D window, where some elements\nare wrongly drawn \"on top\" of others (this does not refer to the atom names,\nwhich are always on top). This problem has been seen with PyQt 5, and it\u0027s\nusually solved by switching to PyQt 4 or installing QtOpenGL support (in the\n\"About\" dialog, check if the \"Qt API\" line says \"with QtOpenGL\"). To disable\nQtOpenGL detection, define the environment variable \u003ccode\u003ePEGAMOID_NO_QGL=1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eAnother reported issue is that the orbital/density isosurfaces completely\ndisappear, while the \"molecule\" (nuclei and bonds) are normally displayed. This\nis probably a driver problem. As a workaround, defining the environment\nvariable \u003ccode\u003ePEGAMOID_DISABLE_OPACITY=1\u003c/code\u003e will completely disable the opacity\nslider and hopefully allow the isosurfaces to be shown. However, the rendering\nof textures with transparency may be affected by artifacts.\u003c/p\u003e\n\u003cp\u003eIssues with the \"Transform\" and \"Texture\" windows not appearing have also been\nreported in some PyQt 4 versions. It is unclear at the moment what is the\nreason for this.\u003c/p\u003e\n\u003cp\u003eWhen running in KDE Plasma 5, some shortcuts may not work because KDE tries to\nbe smart and overwrites them (see\n\u003ca href=\"https://stackoverflow.com/questions/32688153\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for example). To fix this,\nyou can add to the \u003ccode\u003e~/.config/kdeglobals\u003c/code\u003e file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[Development]\nAutoCheckAccelerators=false\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 13, "subscribers_count": 2, "topics": [], - "updated_at": 1730514145.0 + "updated_at": 1728487788.0 }, { "data_format": 2, - "description": "Fast Optical Monte Carlo Simulation With Surface-Based Geometries", + "description": "Singularity port of HLA typing based on an input exome BAM file and is currently infers infers alleles for the three major MHC class I (HLA-A, -B, -C)", "filenames": [ - "installation/chroma3.nvidia/Singularity" + "Singularity" ], - "full_name": "BenLand100/chroma", + "full_name": "researchapps/polysolver", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eChroma: Ultra-fast Photon Monte Carlo\u003c/h1\u003e\u003ca id=\"user-content-chroma-ultra-fast-photon-monte-carlo\" class=\"anchor\" aria-label=\"Permalink: Chroma: Ultra-fast Photon Monte Carlo\" href=\"#chroma-ultra-fast-photon-monte-carlo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChroma is a high performance optical photon simulation for particle physics detectors originally written by A. LaTorre and S. Seibert. It tracks individual photons passing through a triangle-mesh detector geometry, simulating standard physics processes like diffuse and specular reflections, refraction, Rayleigh scattering and absorption.\u003c/p\u003e\n\u003cp\u003eWith the assistance of a CUDA-enabled GPU, Chroma can propagate 2.5 million photons per second in a detector with 29,000 photomultiplier tubes. This is 200x faster than the same simulation with GEANT4.\u003c/p\u003e\n\u003cp\u003eCheck out the \u003ca href=\"doc/source/chroma.pdf\"\u003eChroma whitepaper\u003c/a\u003e for information on how Chroma works.\u003c/p\u003e\n\u003cp\u003eInformation about the historical development of Chroma can be found at the \u003ca href=\"https://chroma.bitbucket.io/index.html\" rel=\"nofollow\"\u003ebitbucket repository\u003c/a\u003e this repository was forked from.\u003c/p\u003e\n\u003cp\u003eThis repository contains a modified version of Chroma that includes support for wavelength shifters, scintillators, and dichroic filters.\nThis version of Chroma has also been been updated for Python3, Geant4.10.05.p01, and Root 6.18.04, among other things.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer overview\u003c/h2\u003e\u003ca id=\"user-content-container-overview\" class=\"anchor\" aria-label=\"Permalink: Container overview\" href=\"#container-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003einstallation\u003c/code\u003e directory contains a collection of \u003ccode\u003eDockerfile\u003c/code\u003es to build an ubuntu-derived image containing Chroma. This may be a useful reference for other systems. Note that properly linking to boost_python and boost_numpy is nontrivial on systems with both python2 and python3.\u003c/p\u003e\n\u003cp\u003eContainers are great for packaging dependencies, but CUDA throws an additional constraint that the NVIDIA driver version inside the container must match the host NVIDIA driver version that created NVIDIA device nodes. This is because the device nodes created by the host are passed directly to the container. Images will need to be built for each possible driver version, or the \u003ccode\u003envidia-docker\u003c/code\u003e project \u003ca href=\"https://github.com/NVIDIA/nvidia-docker\"\u003ehttps://github.com/NVIDIA/nvidia-docker\u003c/a\u003e must be utilized to automatically synchronize the driver version. Singularity also supports NVIDIA GPUs in a very graceful way, and has other nice features.\u003c/p\u003e\n\u003cp\u003eThe images pushed to DockerHub are built from the subdirectories in the \u003ccode\u003einstallation\u003c/code\u003e directory. \u003ccode\u003einstallation/chroma3.deps\u003c/code\u003e was used to build the image \u003ccode\u003ebenland100/chroma3:deps\u003c/code\u003e by running \u003ccode\u003edocker build -t benland100/chroma3:deps\u003c/code\u003e and contains all dependencies for chroma except nvidia-drivers, and can be used to build images for particular versions. \u003ccode\u003einstallation/chroma.latest\u003c/code\u003e builds an image using the default version of NVIDIA drivers for Ubuntu-20.04 and is pushed to \u003ccode\u003ebenland100/chroma3:latest\u003c/code\u003e. The remaining directories create the images \u003ccode\u003ebenland100/chroma3:440\u003c/code\u003e and \u003ccode\u003ebenland100/chroma3:435\u003c/code\u003e for other common versions of nvidia-drivers. If you need another version for your host machine, you will have to create an analogous \u003ccode\u003eDockerfile\u003c/code\u003e. Finally the \u003ccode\u003ebenland100/chroma3:nvidia\u003c/code\u003e image is built from \u003ccode\u003echroma3.nvidia\u003c/code\u003e which is derived from \u003ccode\u003envidia/cudagl:9.2-devel-ubuntu18.04\u003c/code\u003e and features full CUDA and OpenGL support with \u003ccode\u003envidia-docker\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo get a prebuilt image, run \u003ccode\u003edocker pull benland100/chroma3:[tag]\u003c/code\u003e where tag identifies the image you want.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker usage\u003c/h2\u003e\u003ca id=\"user-content-docker-usage\" class=\"anchor\" aria-label=\"Permalink: Docker usage\" href=\"#docker-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConnecting the container to the GPU requires either the \u003ccode\u003envidia-docker\u003c/code\u003e runtime or passing the GPU device nodes manually to the container. See subsections for details.\u003c/p\u003e\n\u003cp\u003eIn general, the containers can be run with \u003ccode\u003edocker run -it benland100/chroma3:[tag]\u003c/code\u003e, but to do something useful, you should mount some host directory containing your analysis code and/or data storage to the container with additional \u003ccode\u003e-v host_path:container_path\u003c/code\u003e flags, and work within those directories. Paths not mounted from the host will not be saved when the container exits.\u003c/p\u003e\n\u003cp\u003eConsider adding \u003ccode\u003e--net=host\u003c/code\u003e to your run command and running \u003ccode\u003ejupyter\u003c/code\u003e within the container. The default Python3 environment is setup for Chroma.\u003c/p\u003e\n\u003cp\u003eFor running visualizations, you will need to allow the container to access your X11 server. The easiest way to accomplish this is by adding these flags \u003ccode\u003e--net=host -v $HOME/.Xauthority:/root/.Xauthority:rw -e DISPLAY=$DISPLAY\u003c/code\u003e to the docker run command.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWith nvidia-docker\u003c/h3\u003e\u003ca id=\"user-content-with-nvidia-docker\" class=\"anchor\" aria-label=\"Permalink: With nvidia-docker\" href=\"#with-nvidia-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool must be installed on the host, and adds the \u003ccode\u003envidia-docker\u003c/code\u003e command, which modifies the container on the fly to synchronize the NVIDIA drivers in the container with the host. If it is available, it provides full CUDA and OpenGL functionality for simulation and rendering. The \u003ccode\u003ebenland100/chroma3:nvidia\u003c/code\u003e image is derived from a base that supports this functionality.\u003c/p\u003e\n\u003cp\u003eOn my machine, the minimal docker command to launch a shell is:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003envidia-docker run -it benland100/chroma3:nvidia\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWithout nvidia-docker\u003c/h3\u003e\u003ca id=\"user-content-without-nvidia-docker\" class=\"anchor\" aria-label=\"Permalink: Without nvidia-docker\" href=\"#without-nvidia-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use CUDA within a container, the host\u0027s NVIDIA device nodes must be passed to the container. This will not enable OpenGL functionality, but is sufficient for running Chroma on machines where \u003ccode\u003envidia-docker\u003c/code\u003e is unavailable. To see the required device nodes run \u003ccode\u003egrep /dev/*nvidia*\u003c/code\u003e. Each must be passed to docker with the \u003ccode\u003e--device\u003c/code\u003e flag as shown with \u003ccode\u003efor dev in /dev/*nvidia*; do echo --device $dev:$dev; done\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOn my machine, this results in a very concise minimal docker run command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --device /dev/nvidia-modeset:/dev/nvidia-modeset --device /dev/nvidia-uvm:/dev/nvidia-uvm --device /dev/nvidia-uvm-tools:/dev/nvidia-uvm-tools --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl -it benland100/chroma3:440\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity usage\u003c/h2\u003e\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" aria-label=\"Permalink: Singularity usage\" href=\"#singularity-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity\u0027s \u003ccode\u003e--nv\u003c/code\u003e flag for detecting and synchronizing NVIDIA GPUs and drivers within Singularity containers is very attractive. Singularity is more likely to be supported on large compute clusters, as it does not require root access. It also provides nice features like mounting your home directory in the container and synchronizing aspects of the environment automatically. Fortunately, Singularity can make use of the Docker containers described above.\u003c/p\u003e\n\u003cp\u003eA Singularity image may be derived from any Chroma Docker image with a simple \u003ccode\u003eSingularity\u003c/code\u003e file as found in \u003ccode\u003einstallation/chroma3.nvidia/Singularity\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e:nvidia\u003c/code\u003e tagged image used here is likely the best choice, as Singularity\u0027s \u003ccode\u003e--nv\u003c/code\u003e flag is designed for the base it was derived from.\u003c/p\u003e\n\u003cp\u003eSingularity can then be used to build an image: \u003ccode\u003esudo singularity build chroma3.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRunning this image is pretty setraightforward. Your home directory will be available within the image, but other directories can be mounted as desired.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --nv chroma3.simg\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eVisualization with OpenGL and simulation with CUDA will work in this container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest drive\u003c/h3\u003e\u003ca id=\"user-content-test-drive\" class=\"anchor\" aria-label=\"Permalink: Test drive\" href=\"#test-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter deploying a container to a GPU-enabled host locally or via SSH with XForwarding enabled, you should be able to run the container and execute\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003echroma-cam @chroma.models.lionsolid\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhich should display a GPU-rendered visualization, ensuring everything is working properly.\u003c/p\u003e\n", - "stargazers_count": 14, - "subscribers_count": 6, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Polysolver\u003c/h1\u003e\u003ca id=\"user-content-singularity-polysolver\" class=\"anchor\" aria-label=\"Permalink: Singularity Polysolver\" href=\"#singularity-polysolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will produce a Singularity image (suitable for running in a cluster environment) using \u003ca href=\"https://hub.docker.com/r/sachet/polysolver/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/sachet/polysolver/\u003c/a\u003e. We do this by way of a bootstrap file for the Docker image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Install Singularity\u003c/h2\u003e\u003ca id=\"user-content-1-install-singularity\" class=\"anchor\" aria-label=\"Permalink: 1. Install Singularity\" href=\"#1-install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions can be found on the \u003ca href=\"https://singularityware.github.io\" rel=\"nofollow\"\u003esingularity site\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Bootstrap the image\u003c/h2\u003e\u003ca id=\"user-content-2-bootstrap-the-image\" class=\"anchor\" aria-label=\"Permalink: 2. Bootstrap the image\" href=\"#2-bootstrap-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create --size 4000 polysolver.img\nsudo singularity bootstrap polysolver.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. Running commands\u003c/h2\u003e\u003ca id=\"user-content-3-running-commands\" class=\"anchor\" aria-label=\"Permalink: 3. Running commands\" href=\"#3-running-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor each of the following commands, you will want to \u003ca href=\"http://singularity.lbl.gov/docs-mount\" rel=\"nofollow\"\u003emount a local data folder\u003c/a\u003e to \u003ccode\u003e/data\u003c/code\u003e inside the container, and then provide paths to \u003ccode\u003e/data\u003c/code\u003e to the executable.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePolysolver Documentation\u003c/h2\u003e\u003ca id=\"user-content-polysolver-documentation\" class=\"anchor\" aria-label=\"Permalink: Polysolver Documentation\" href=\"#polysolver-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software package consists of 3 main tools:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.1 POLYSOLVER (POLYmorphic loci reSOLVER)\u003c/h3\u003e\u003ca id=\"user-content-11-polysolver-polymorphic-loci-resolver\" class=\"anchor\" aria-label=\"Permalink: 1.1 POLYSOLVER (POLYmorphic loci reSOLVER)\" href=\"#11-polysolver-polymorphic-loci-resolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool can be used for HLA typing based on an input exome BAM file and is currently infers infers alleles for the three major MHC class I (HLA-A, -B, -C).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_type\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput:\u003c/h4\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input:\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e-bam: path to the BAM file to be used for HLA typing\n-race: ethnicity of the individual (Caucasian, Black, Asian or Unknown)\n-includeFreq: flag indicating whether population-level allele frequencies should be used as priors (0 or 1)\n-build: reference genome used in the BAM file (hg18 or hg19)\n-format: fastq format (STDFQ, ILMFQ, ILM1.8 or SLXFQ; see Novoalign documentation)\n-insertCalc: flag indicating whether empirical insert size distribution should be used in the model (0 or 1)\n-outDir: output directory\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput:\u003c/h4\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ewinners.hla.txt\u003c/code\u003e: file containing the two inferred alleles for each of HLA-A, HLA-B and HLA-C\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.2 POLYSOLVER-based mutation detection\u003c/h3\u003e\u003ca id=\"user-content-12-polysolver-based-mutation-detection\" class=\"anchor\" aria-label=\"Permalink: 1.2 POLYSOLVER-based mutation detection\" href=\"#12-polysolver-based-mutation-detection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool works on a tumor/normal pair of exome BAM files and inferred mutations in the tumor file. It assumes that POLYSOLVER has already been run on the normal BAM.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_mutations_from_type\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput:\u003c/h4\u003e\u003ca id=\"user-content-input-1\" class=\"anchor\" aria-label=\"Permalink: Input:\" href=\"#input-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e-normal_bam_hla: path to the normal BAM file\n-tumor_bam_hla: path to the tumor BAM file\n-hla: inferred HLA allele file from POLYSOLVER (winners.hla.txt or winners.hla.nofreq.txt)\n-build: reference genome used in the BAM file (hg18 or hg19)\n-format: fastq format (STDFQ, ILMFQ, ILM1.8 or SLXFQ; see Novoalign documentation)\n-outDir: output directory\t \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOutput:\u003c/h4\u003e\u003ca id=\"user-content-output-1\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ecall_stats.$allele.out: Mutect output for each inferred allele in winners.hla.txt\n$allele.all.somatic.indels.vcf: Strelka output for each inferred allele in winners.hla.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1.3 Annotation of mutations\u003c/h3\u003e\u003ca id=\"user-content-13-annotation-of-mutations\" class=\"anchor\" aria-label=\"Permalink: 1.3 Annotation of mutations\" href=\"#13-annotation-of-mutations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool annotates the predicted mutations from (ii) with gene compartment and amino acid change information\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_annotate_hla_mutations\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput:\u003c/h4\u003e\u003ca id=\"user-content-input-2\" class=\"anchor\" aria-label=\"Permalink: Input:\" href=\"#input-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e-indiv: individual ID, used as prefix for output files\n-dir: directory containing the raw call files (Mutect: call_stats*, Strelka: *all.somatic.indels.vcf). Also the output directory\t\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput:\u003c/h3\u003e\u003ca id=\"user-content-output-2\" class=\"anchor\" aria-label=\"Permalink: Output:\" href=\"#output-2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e (a). Mutect\n$indiv.mutect.unfiltered.nonsyn.annotated - list of all unfiltered mutations\n$indiv.mutect.filtered.nonsyn.annotated - list of cleaned non-synonymous mutations\n$indiv.mutect.filtered.syn.annotated - list of cleaned synonymous changes\n$indiv.mutect.ambiguous.annotated - list of ambiguous calls. This will generally be empty (save for the header). It will be populated if the same mutation (ex. p.A319E) is found in two or more alleles in the individual, with the same allele fractions. In such cases one allele is randomly chosen and included in the .nonysn.annotated file while the complete list of alleles is listed in the .ambiguous.annotated file. If the ethnicity of the individual is known, an alternate method would be to pick the allele with the highest frequency.\n\n (b). Strelka\n$indiv.mutect.unfiltered.nonsyn.annotated - list of all unfiltered indels (as detected by Strelka)\n$indiv.strelka_indels.filtered.annotated - list of cleaned indels (as detected by Strelka)\n$indiv.strelka_indels.ambiguous.annotated - see description of $indiv.mutect.ambiguous.annotated in (a). above\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTesting\u003c/h3\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYour installation can be tested by running the following command from $PSHOME. \u003cstrong\u003eNOTE: this has not been tested - the correct paths to dependencies need to be set in \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e and the data folders correctly mounted from the local machine.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.1 POLYSOLVER\u003c/h4\u003e\u003ca id=\"user-content-31-polysolver\" class=\"anchor\" aria-label=\"Permalink: 3.1 POLYSOLVER\" href=\"#31-polysolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_type test/test.bam Unknown 1 hg19 STDFQ 0 test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf successful, the following command should not yield any differences:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e diff test/winners.hla.txt test/orig.winners.hla.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.2 POLYSOLVER-based mutation detection\u003c/h4\u003e\u003ca id=\"user-content-32-polysolver-based-mutation-detection\" class=\"anchor\" aria-label=\"Permalink: 3.2 POLYSOLVER-based mutation detection\" href=\"#32-polysolver-based-mutation-detection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_mutations_from_type test/test.bam test/test.tumor.bam test/winners.hla.txt hg19 STDFQ test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf successful, the following command should not yield any differences:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e diff test/call_stats.hla_b_39_01_01_02l.out test/orig.call_stats.hla_b_39_01_01_02l.out \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e3.3 Annotation of mutations\u003c/h4\u003e\u003ca id=\"user-content-33-annotation-of-mutations\" class=\"anchor\" aria-label=\"Permalink: 3.3 Annotation of mutations\" href=\"#33-annotation-of-mutations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_annotate_hla_mutations indiv test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf successful, the following command should not yield any differences:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e diff test/indiv.mutect.filtered.nonsyn.annotated test/orig.indiv.mutect.filtered.nonsyn.annotated\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tools can be run using the following commands:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e4.1 POLYSOLVER\u003c/h4\u003e\u003ca id=\"user-content-41-polysolver\" class=\"anchor\" aria-label=\"Permalink: 4.1 POLYSOLVER\" href=\"#41-polysolver\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_type \u0026lt;/path/to/bam\u0026gt; \u0026lt;race\u0026gt; \u0026lt;includeFreq\u0026gt; \u0026lt;build\u0026gt; \u0026lt;format\u0026gt; \u0026lt;insertCalc\u0026gt; \u0026lt;/path/to/output_directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_type test/test.bam Unknown 1 hg19 STDFQ 0 test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e4.2 POLYSOLVER-based mutation detection\u003c/h4\u003e\u003ca id=\"user-content-42-polysolver-based-mutation-detection\" class=\"anchor\" aria-label=\"Permalink: 4.2 POLYSOLVER-based mutation detection\" href=\"#42-polysolver-based-mutation-detection\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_mutations_from_type \u0026lt;/path/to/normal_bam\u0026gt; \u0026lt;/path/to/tumor_bam\u0026gt; \u0026lt;/path/to/winners.hla.txt\u0026gt; \u0026lt;build\u0026gt; \u0026lt;format\u0026gt; \u0026lt;/path/to/output_directory\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_call_hla_mutations_from_type test/test.bam test/test.tumor.bam test/winners.hla.txt hg19 STDFQ test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e4.3 Annotation of mutations\u003c/h4\u003e\u003ca id=\"user-content-43-annotation-of-mutations\" class=\"anchor\" aria-label=\"Permalink: 4.3 Annotation of mutations\" href=\"#43-annotation-of-mutations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_annotate_hla_mutations \u0026lt;prefix_to_use\u0026gt; \u0026lt;/path/to/directory_with_mutation_detection_output\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eexample:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity exec polysolver.img /usr/local/libexec/polysolver/scripts/shell_annotate_hla_mutations indiv test\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 13, + "subscribers_count": 4, "topics": [], - "updated_at": 1729242950.0 + "updated_at": 1723958568.0 }, { "data_format": 2, - "description": "An ATAC-seq pipeline wrapped in NextFlow that can be run by Jupyter", + "description": "Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context", "filenames": [ "Singularity" ], - "full_name": "NCBI-Hackathons/ATACFlow", - "latest_release": "v0.1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNCBI-Hackathons/ATACFlow\u003c/h1\u003e\u003ca id=\"user-content-ncbi-hackathonsatacflow\" class=\"anchor\" aria-label=\"Permalink: NCBI-Hackathons/ATACFlow\" href=\"#ncbi-hackathonsatacflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/NCBI-Hackathons/ATACFlow/master/docs/ATAC_logo.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/NCBI-Hackathons/ATACFlow/master/docs/ATAC_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis pipeline performs ATAC Seq using Nextflow\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/NCBI-Hackathons/ATACFlow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6e1f9a84515374fc6a40fb10d277aee09a57014c5b58db5c2a5667dab9154af0/68747470733a2f2f7472617669732d63692e6f72672f4e4342492d4861636b6174686f6e732f41544143466c6f772e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/NCBI-Hackathons/ATACFlow.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c42e6080d15f532168bbb6d26fb300c5ed31e7f5e8648abbe9d5f6d8d4285475/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/stevetsa/atacflow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0b5ec520559485679de1dbe742c7a9b7538b06c25cef7fd29d64052ae652f422/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f73746576657473612f61746163666c6f772e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/stevetsa/atacflow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNCBI-Hackathons/ATACFlow: This pipeline performs ATAC Seq using Nextflow\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReference\u003c/h3\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-label=\"Permalink: Reference\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHave you found this pipeline useful to your research? You can cite it using DOI 10.17605/OSF.IO/UCWRH (\u003ca href=\"https://osf.io/ucwrh/\" rel=\"nofollow\"\u003eOSF project\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe NCBI-Hackathons/ATACFlow pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eDocker installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your HPC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003ePipeline Components\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/jupyter.md\"\u003eOperating with Jupyter Notebook\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was written by ATACFlow Team @ \u003ca href=\"https://hackathon.colorado.edu/rmgh-2018-hackathon/\" rel=\"nofollow\"\u003eRMGCH-18\u003c/a\u003e (\u003ca href=\"https://github.com/NCBI-Hackathons\"\u003eNCBI-Hackathons\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIgnacio Tripodi (ignacio.tripodi at colorado.edu): Computer Scientist, closet wet-lab enthusiast\u003c/li\u003e\n\u003cli\u003eSteve Tsang (hsinyi.tsang at nih.gov): Computational Biologist\u003c/li\u003e\n\u003cli\u003eJingjing Zhao (jjzhao123 at gmail.com): Bioinformatics Postdoctoral Scholar\u003c/li\u003e\n\u003cli\u003eEvan Floden (evanfloden at gmail.com): Workflow Wrangler\u003c/li\u003e\n\u003cli\u003eChi Zhang (chzh1418 at colorado.edu):\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "MASILab/cornn_tractography", + "latest_release": "v1.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCoRNN Tractography\u003c/h1\u003e\u003ca id=\"user-content-cornn-tractography\" class=\"anchor\" aria-label=\"Permalink: CoRNN Tractography\" href=\"#cornn-tractography\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/MASILab/cornn_tractography/blob/master/CoRNN.png?raw=true\"\u003e\u003cimg src=\"https://github.com/MASILab/cornn_tractography/raw/master/CoRNN.png?raw=true\" alt=\"itscornn\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTractography on T1-weighted MRI, no diffusion needed!\u003c/h2\u003e\u003ca id=\"user-content-tractography-on-t1-weighted-mri-no-diffusion-needed\" class=\"anchor\" aria-label=\"Permalink: Tractography on T1-weighted MRI, no diffusion needed!\" href=\"#tractography-on-t1-weighted-mri-no-diffusion-needed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#authors-and-reference\"\u003eAuthors and Reference\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#containerization-of-source-code\"\u003eContainerization of Source Code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#command\"\u003eCommand\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#arguments-and-io\"\u003eArguments and I/O\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#options\"\u003eOptions\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the model weights, source code, and containerized implementation of convolutional-recurrent neural network (CoRNN) tractography on T1w MRI with associated \u003ca href=\"https://github.com/MASILab/SLANTbrainSeg\"\u003eSLANT\u003c/a\u003e and \u003ca href=\"https://github.com/MASILab/WM_learning_release\"\u003eWM learning (WML)\u003c/a\u003e TractSeg segmentations.\u003c/p\u003e\n\u003cp\u003ePlease note that this methodology is still actively being characterized, validated, and extended. If you\u0027re interested in working with us to explore what it means to run tractography on T1w MRI, please \u003ca href=\"#authors-and-reference\"\u003econtact us\u003c/a\u003e and let us know!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors and Reference\u003c/h2\u003e\u003ca id=\"user-content-authors-and-reference\" class=\"anchor\" aria-label=\"Permalink: Authors and Reference\" href=\"#authors-and-reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"mailto:leon.y.cai@vanderbilt.edu\"\u003eLeon Y. Cai\u003c/a\u003e, Ho Hin Lee, Nancy R. Newlin, Cailey I. Kerley, Praitayini Kanakaraj, Qi Yang, Graham W. Johnson, Daniel Moyer, Kurt G. Schilling, Francois Rheault, and Bennett A. Landman. \u003ca href=\"https://www.biorxiv.org/content/10.1101/2023.02.25.530046v1\" rel=\"nofollow\"\u003eConvolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context\u003c/a\u003e. Proceedings of Machine Learning Reseach. In press. 2023.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://my.vanderbilt.edu/masi\" rel=\"nofollow\"\u003eMedical-image Analysis and Statistical Interpretation (MASI) Lab\u003c/a\u003e, Vanderbilt University, Nashville, TN, USA\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainerization of Source Code\u003c/h2\u003e\u003ca id=\"user-content-containerization-of-source-code\" class=\"anchor\" aria-label=\"Permalink: Containerization of Source Code\" href=\"#containerization-of-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/MASILab/cornn_tractography.git\ncd /path/to/repo/cornn_tractography\ngit checkout v1.0.0\nsudo singularity build /path/to/CoRNN_v1.0.0.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe use Singularity version 3.8 CE with root permissions.\u003c/p\u003e\n\u003cp\u003eAlternatively, a pre-built container can be downloaded \u003ca href=\"https://masi.vuse.vanderbilt.edu/CoRNN/CoRNN_v1.0.0.sif\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommand\u003c/h2\u003e\u003ca id=\"user-content-command\" class=\"anchor\" aria-label=\"Permalink: Command\" href=\"#command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run \n-e \n--contain\n-B \u0026lt;t1_file\u0026gt;:/data/T1.nii.gz\n-B \u0026lt;out_dir\u0026gt;:/data\n-B \u0026lt;slant_dir\u0026gt;:/data/slant\n-B \u0026lt;wml_dir\u0026gt;:/data/wml\n-B /tmp:/tmp\n--nv\n/path/to/CoRNN_v1.0.0.sif\n/data/T1.nii.gz\n/data/\u0026lt;out_name\u0026gt;\n--slant /data/slant\n--wml /data/wml\n[options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBinding \u003ccode\u003e/tmp\u003c/code\u003e is required with \u003ccode\u003e--contain\u003c/code\u003e when \u003ccode\u003e--work_dir\u003c/code\u003e is not specified.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--nv\u003c/code\u003e is optional. See \u003ccode\u003e--device\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eArguments and I/O\u003c/h2\u003e\u003ca id=\"user-content-arguments-and-io\" class=\"anchor\" aria-label=\"Permalink: Arguments and I/O\" href=\"#arguments-and-io\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;t1_file\u0026gt;\u003c/code\u003e\u003c/strong\u003e Path on the host machine to the T1-weighted MRI with which tractography is to be performed in NIFTI format (either compressed or not).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;out_dir\u0026gt;\u003c/code\u003e\u003c/strong\u003e Path on the host machine to the \u003cem\u003edirectory\u003c/em\u003e in which the output tractogram will be saved.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;out_name\u0026gt;\u003c/code\u003e\u003c/strong\u003e \u003cem\u003eName\u003c/em\u003e (i.e., no directory) of the output tractogram with extension in trk, tck, vtk, fib, or dpy format.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;slant_dir\u0026gt;\u003c/code\u003e\u003c/strong\u003e Path on the host machine to the SLANT output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;wml_dir\u0026gt;\u003c/code\u003e\u003c/strong\u003e Path on the host machine to the TractSeg WM Learning output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptions\u003c/h2\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: Options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--help\u003c/code\u003e\u003c/strong\u003e Print help statement.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--device cuda/cpu\u003c/code\u003e\u003c/strong\u003e A string indicating the device on which to perform inference. If \"cuda\" is selected, container option \u003ccode\u003e--nv\u003c/code\u003e must be included. Default = \"cpu\"\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--num_streamlines N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the number of streamlines to identify. Default = 1000000\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--num_seeds N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the number of streamlines to seed per batch. One GB of GPU memory can handle approximately 10000 seeds. Default = 100000\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--min_steps N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the minimum number of 1mm steps per streamline. Default = 50\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--max_steps N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the maximum number of 1mm steps per streamline. Default = 250\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--buffer_steps N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the number of 1mm steps where the angle stopping criteria are ignored at the beginning of tracking. Default = 5\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--unidirectional\u003c/code\u003e\u003c/strong\u003e A flag indicating that bidirectional tracking should not be performed. The buffer steps are NOT removed in this case. Default = Perform bidirectional tracking\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--work_dir /data/work_dir\u003c/code\u003e\u003c/strong\u003e A string indicating the working directory to use. The location of the working directory on the host machine, \u003ccode\u003e\u0026lt;work_dir\u0026gt;\u003c/code\u003e, must also exist and be bound into the container with \u003ccode\u003e-B \u0026lt;work_dir\u0026gt;:/data/work_dir\u003c/code\u003e in the \u003ca href=\"#command\"\u003ecommand\u003c/a\u003e. If the working directory contains previously generated intermediates, the corresponding steps will not be rerun. Default = create a new working directory in \u003ccode\u003e/tmp\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--keep_work\u003c/code\u003e\u003c/strong\u003e A flag indicating that the intermediates in the working directory should NOT be cleared. Default = Clear working directory after completion\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--num_threads N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the number of threads to use during multithreaded steps. Default = 1\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--force\u003c/code\u003e\u003c/strong\u003e A flag indicating that the output file should be overwritten if it already exists. Default = Do NOT override existing output file\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 14, - "subscribers_count": 10, + "subscribers_count": 3, "topics": [ - "atac-seq", - "nextflow", - "jupyter-notebook", - "ncbi", - "beta-release" + "deep-learning", + "diffusion-mri", + "tractography", + "convolutional-recurrent-neural-network", + "t1-weighted-mri" ], - "updated_at": 1719921792.0 + "updated_at": 1731323806.0 }, { "data_format": 2, - "description": "Singularity Global Client for container management", + "description": "Pipeline for processing FASTQ data from an Illumina MiSeq to genotype human RNA viruses like HIV and hepatitis C", "filenames": [ "Singularity" ], - "full_name": "singularityhub/sregistry-cli", - "latest_release": "v0.1.41", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Global Client\u003c/h1\u003e\u003ca id=\"user-content-singularity-global-client\" class=\"anchor\" aria-label=\"Permalink: Singularity Global Client\" href=\"#singularity-global-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://stanford-rc.github.io//rse-services/docs/tools/software-checklist/badge?label=100%25\u0026amp;color=#59BF40\u0026amp;ids=r1,r2,r3,r4,r5,r6,d1,d2,d3,d4,d5,d6,d7,a1,a2,a3,ci1,ci2\u0026amp;title=singularityhub/sregistry-cli\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cab68996537d3b9bd6a284de8194e3f5059ab8c7ced51e48c1760ab82d4132fd/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736f667477617265253230636865636b6c6973742d3130302532352d353942463430\" alt=\"https://img.shields.io/badge/software%20checklist-100%25-59BF40\" data-canonical-src=\"https://img.shields.io/badge/software%20checklist-100%25-59BF40\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/singularityhub/sregistry-cli/actions?query=branch%3Amaster+workflow%3Asregistry-ci\"\u003e\u003cimg src=\"https://github.com/singularityhub/sregistry-cli/workflows/sregistry-ci/badge.svg?branch=master\" alt=\"GitHub actions status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHi Friends! Are your containers lonely? Singularity containers thrive in happiness when they are shared. This means that wherever you might have them in these cloudy places, they are easy to find and move around.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is this?\u003c/h2\u003e\u003ca id=\"user-content-what-is-this\" class=\"anchor\" aria-label=\"Permalink: What is this?\" href=\"#what-is-this\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Global Client is an interface to interact with Singularity containers in many different storage locations. We are able to use modern APIs by way of providing and using the software within a Singularity container! For older architectures, we provide a \u003ca href=\"Singularity\"\u003eSingularity container\u003c/a\u003e for you to use instead. You can build it from this repository, or use the provided container on \u003ca href=\"https://www.singularity-hub.org/collections/379\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf used for the Singularity Registry client, Python 3 is required. See our \u003ca href=\"https://singularityhub.github.io/sregistry-cli/install\" rel=\"nofollow\"\u003einstallation guide\u003c/a\u003e to get started. For more details, please refer to our \u003ca href=\"docs\"\u003edocumentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation instructions\u003c/h2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-label=\"Permalink: Installation instructions\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith pip:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install sregistry[all]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWith conda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install -c conda-forge sregistry\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMore detailed instructions can be found \u003ca href=\"https://singularityhub.github.io/sregistry-cli/install\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython Versions Under 3\u003c/h2\u003e\u003ca id=\"user-content-python-versions-under-3\" class=\"anchor\" aria-label=\"Permalink: Python Versions Under 3\" href=\"#python-versions-under-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are looking for a version that works with Python 2.* see \u003ca href=\"https://github.com/singularityhub/sregistry-cli/releases/tag/v0.1.41\"\u003ethis branch\u003c/a\u003e, or all releases / branches prior to 0.2.0.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the RPM\u003c/h2\u003e\u003ca id=\"user-content-building-the-rpm\" class=\"anchor\" aria-label=\"Permalink: Building the RPM\" href=\"#building-the-rpm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe file \u003ca href=\"sregistry-cli.spec\"\u003esregistry-cli.spec\u003c/a\u003e is provided to build an rpm for a specified version,\ntypcailly the current release on pypi, and was discussed \u003ca href=\"https://github.com/singularityhub/sregistry-cli/issues/138#issuecomment-413323717\"\u003ehere\u003c/a\u003e.\nYou should do the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eUpdate the version to be the current in pypi specified in \u003ca href=\"sregistry/version.py\"\u003esregistry/version.py\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eVersion: 0.0.89\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate a \u003ca href=\"https://github.com/singularityhub/sregistry-cli/releases/new\"\u003enew release\u003c/a\u003e on Github with the version spec file added.\u003c/li\u003e\n\u003cli\u003eDownload the .tar.gz file from the release\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eVERSION=0.0.92\nwget https://github.com/singularityhub/sregistry-cli/archive/sregistry-cli-\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eUse rpmbuild to build it.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erpmbuild -ta sregistry-cli-\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should get an srpm which that can be distributed and anyone can be rebuilt:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erpmbuild --rebuild sregistry-cli.srpm\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code is licensed under the MPL 2.0 \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "cfe-lab/MiCall", + "latest_release": "v7.15.13", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMiCall\u003c/h1\u003e\u003ca id=\"user-content-micall\" class=\"anchor\" aria-label=\"Permalink: MiCall\" href=\"#micall\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProcessing FASTQ data from an Illumina MiSeq\u003c/h2\u003e\u003ca id=\"user-content-processing-fastq-data-from-an-illumina-miseq\" class=\"anchor\" aria-label=\"Permalink: Processing FASTQ data from an Illumina MiSeq\" href=\"#processing-fastq-data-from-an-illumina-miseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://codecov.io/github/cfe-lab/MiCall?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34f20f230b5de7f64e8bff799e3ca566be45bf925a73d904927b09540581d4c0/68747470733a2f2f636f6465636f762e696f2f6769746875622f6366652d6c61622f4d6943616c6c2f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"Code Coverage\" data-canonical-src=\"https://codecov.io/github/cfe-lab/MiCall/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.1289989\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/077775bfdea31e28bd85b3bd4b9186c43ceed2803094e8e5482921274668b2ec/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313238393938392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1289989.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMaps all the reads from a sample against a set of reference sequences, then\nstitches all the reads into consensus sequences and coverage maps.\u003c/p\u003e\n\u003cp\u003eA monitoring system regularly checks the file system for unprocessed runs,\ntransfers FASTQ.gz files to the cluster and executes the pipeline.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://cfe-lab.github.io/MiCall/steps\" rel=\"nofollow\"\u003elist of steps and files\u003c/a\u003e for details of what the pipeline does.\nThe \u003ca href=\"https://cfe-lab.github.io/MiCall/admin\" rel=\"nofollow\"\u003eadmin\u003c/a\u003e page describes how to look after the pipeline in Kive, and the\n\u003ca href=\"https://cfe-lab.github.io/MiCall/getting_started\" rel=\"nofollow\"\u003egetting started\u003c/a\u003e page describes how to get the docker version set up and run it\non your own data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDual Licensing\u003c/h2\u003e\u003ca id=\"user-content-dual-licensing\" class=\"anchor\" aria-label=\"Permalink: Dual Licensing\" href=\"#dual-licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopyright (C) 2016, University of British Columbia\u003c/p\u003e\n\u003cp\u003eThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU Affero General Public License as published\nby the Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\u003c/p\u003e\n\u003cp\u003eThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU Affero General Public License for more details.\u003c/p\u003e\n\u003cp\u003eYou should have received a copy of the GNU Affero General Public License\nalong with this program. If not, visit \u003ca href=\"https://www.gnu.org/licenses/\" rel=\"nofollow\"\u003egnu.org\u003c/a\u003e. The source code for\nthis program is available from \u003ca href=\"https://github.com/cfe-lab/MiCall\"\u003egithub.com\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe program is also available for a fee under a more permissive license. For\nexample, if you want to run a changed version of the program on a network server\nwithout publishing the changed source code, \u003ca href=\"mailto:micalldev@cfenet.ubc.ca\"\u003econtact us\u003c/a\u003e about\npurchasing a license.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThird Party Components\u003c/h2\u003e\u003ca id=\"user-content-third-party-components\" class=\"anchor\" aria-label=\"Permalink: Third Party Components\" href=\"#third-party-components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMiCall makes use of several open-source tools. Here is a list of tools with\ntheir licenses.\u003c/p\u003e\n\u003cp\u003eRequests is distributed under the Apache 2.0 license.\u003c/p\u003e\n\u003cp\u003ePython 3 is distributed under the \u003ca href=\"https://docs.python.org/3/license.html\" rel=\"nofollow\"\u003ePython 3 license\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBowtie2, IVA, and Python-Levenshtein are distributed under the GNU General\nPublic License (GPL).\u003c/p\u003e\n\u003cp\u003eMatplotlib is distributed under the \u003ca href=\"https://matplotlib.org/users/license.html\" rel=\"nofollow\"\u003eMatplotlib license\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eReportlab is distributed under the BSD license.\u003c/p\u003e\n\u003cp\u003ePyyaml and Cutadapt are distributed under the MIT license.\u003c/p\u003e\n", "stargazers_count": 14, - "subscribers_count": 8, + "subscribers_count": 12, "topics": [ - "singularity-container", - "singularity", - "singularity-hub", - "singularity-registry", - "client" + "bioinformatics", + "fastq", + "python", + "resistance", + "genotype" ], - "updated_at": 1681389292.0 + "updated_at": 1727290291.0 }, { "data_format": 2, - "description": "official build specifications for jupyter", + "description": "CNN based brain masking", "filenames": [ "Singularity" ], - "full_name": "singularityhub/jupyter", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJupyter\u003c/h1\u003e\u003ca id=\"user-content-jupyter\" class=\"anchor\" aria-label=\"Permalink: Jupyter\" href=\"#jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis example will show how to run a jupyter notebook server with nginx, from a container (singularity container in this case).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eperhaps you ran an analysis when you created the container, and want to serve the notebook as a result) or\u003c/li\u003e\n\u003cli\u003eperhaps you want this to be like a working container, to store a particular version of your software to use on local files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you are interested in more proper container orchestration with \u003ca href=\"https://singularityhub.github.io/singularity-compose/\" rel=\"nofollow\"\u003esingularity-compose\u003c/a\u003e, see the \u003ca href=\"https://github.com/singularityhub/singularity-compose-examples/tree/master/jupyter-simple\"\u003esingularity-compose jupyter example\u003c/a\u003e that can more easily handle adding other containers as services, volumes, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBranches\u003c/h2\u003e\u003ca id=\"user-content-branches\" class=\"anchor\" aria-label=\"Permalink: Branches\" href=\"#branches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/singularityhub/jupyter/tree/cifs\"\u003eWindows Filesystem Support\u003c/a\u003e A basic example for Windows Filesystem Support is on this cifs branch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you haven\u0027t installed singularity, do that with \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ethese instructions\u003c/a\u003e. Then download the repo if you haven\u0027t already:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://www.github.com/singularityhub/jupyter\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e jupyter\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLet\u0027s now create a jupyter notebook!\nFirst, we will create the writable container image in a \u003cem\u003ewritable\u003c/em\u003e \u003cem\u003eext3\u003c/em\u003e file system, instead of the \u003cem\u003esquashfs\u003c/em\u003e which only allows \u003cem\u003eread-only\u003c/em\u003e. \u003ca href=\"http://singularity.lbl.gov/docs-build-container\" rel=\"nofollow\"\u003eread more\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build --sandbox jupyter-box Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen to run our container, since we need to write files to \u003ccode\u003e/opt/notebooks\u003c/code\u003e inside the container, we must use sudo and add the \u003ccode\u003e--writable\u003c/code\u003e command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity run --writable jupyter-box\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen we open the browser, we see our server! Cool!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"jupyter.png\"\u003e\u003cimg src=\"jupyter.png\" alt=\"jupyter.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant\u003c/strong\u003e using this container requires the allow-root flag, which isn\u0027t great practice.\nIf you really need to run a notebook in a container, you might be better off building one\nthat installs the notebook with your user (e.g. see \u003ca href=\"https://github.com/hpsee/discourse-cluster/blob/master/Dockerfile\"\u003ethis Docker example\u003c/a\u003e that could be translated to Singularity). You would want to change\nthe user jovyan to your username. If you can, you can also just use Docker! EIther you\ncan use the image linked there, or you can check out \u003ca href=\"https://github.com/jupyter/repo2docker\"\u003erepo2docker\u003c/a\u003e to build\na custom container.\u003c/p\u003e\n\u003cp\u003eSince the notebooks are being written to the image, this means that all of our work is preserved in it. I can finish working, close up shop, and hand my image to someone else, and it\u0027s preserved. Here, I\u0027ll show you. Let\u0027s shell into the container after we\u0027ve shut down the server (note that I didn\u0027t need to use sudo for this).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell jupyter-box\nSingularity: Invoking an interactive shell within container...\n\nSingularity.jupyter.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ls /opt/notebooks\nUntitled.ipynb\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere it is! I really should work on naming my files better :) That is so cool.\u003c/p\u003e\n\u003cp\u003eYou can also map to a folder on your local machine, if you don\u0027t want to save the notebooks inside:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity run -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/opt/notebooks --writable jupyter-box\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand here I am sitting in my local directory, but the entire software and depdencies are provided by my container. STILL really cool.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"local.png\"\u003e\u003cimg src=\"local.png\" alt=\"local.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNote on port forwarding\u003c/h2\u003e\u003ca id=\"user-content-note-on-port-forwarding\" class=\"anchor\" aria-label=\"Permalink: Note on port forwarding\" href=\"#note-on-port-forwarding\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are running Singularity in Windows through vagrant, you will need to configure port forwarding in the Vagrantfile that you use to set up the Singularity container as well.\nAs an example, you should add a line that might look like this.\n\u003ccode\u003econfig.vm.network \"forwarded_port\", guest: 8888, host: 8888, host_ip: \"127.0.0.1\"\u003c/code\u003e\u003c/p\u003e\n", + "full_name": "pnlbwh/CNN-Diffusion-MRIBrain-Segmentation", + "latest_release": "v0.3", + "stargazers_count": 14, + "subscribers_count": 6, + "topics": [], + "updated_at": 1721061708.0 + }, + { + "data_format": 2, + "description": "Easy black-box access to state-of-the-art language models", + "filenames": [ + "models/RNNG/Singularity.rnng", + "models/gpt2/Singularity.gpt2", + "models/ngram/Singularity.ngram", + "models/JRNN/Singularity.jrnn", + "models/GRNN/Singularity.grnn", + "models/ordered-neurons/Singularity.ordered-neurons", + "models/transformer-xl/Singularity" + ], + "full_name": "cpllab/lm-zoo", + "latest_release": "v1.3", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-language-model-zoo\" class=\"anchor\" aria-hidden=\"true\" href=\"#language-model-zoo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLanguage Model Zoo\u003c/h1\u003e\n\u003cp\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e \u003cstrong\u003eThis project is no longer actively maintained by the Computational Psycholinguistics Laboratory.\u003c/strong\u003e \u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003c/p\u003e\n\u003cp\u003eWe do not guarantee the functionality or accuracy of the LM Zoo framework \u2014 use at your own risk!\u003c/p\u003e\n\u003cp\u003eYou may be interested in the following active projects (as of June 2023):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kanishkamisra/minicons\"\u003e\u003ccode\u003eminicons\u003c/code\u003e\u003c/a\u003e enables easy Python access to neural network language model representations and probability/surprisal estimates.\u003c/li\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/brain-score/language\"\u003eBrain Score Language\u003c/a\u003e project provides tools for extracting behavioral and representational quantities from computational language models, and many benchmarks for evaluating the human-likeness of these models\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://huggingface.co/spaces/cpllab/syntaxgym\" rel=\"nofollow\"\u003ean experimental SyntaxGym implementation\u003c/a\u003e built directly into the Huggingface \u003ccode\u003eevaluate\u003c/code\u003e framework\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/403d7bdd315b18ed5ef1a50ff8de7e3ce954c9ec3c878170e5082369cf576e96/68747470733a2f2f63706c6c61622e6769746875622e696f2f6c6d2d7a6f6f2f5f696d616765732f6c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/403d7bdd315b18ed5ef1a50ff8de7e3ce954c9ec3c878170e5082369cf576e96/68747470733a2f2f63706c6c61622e6769746875622e696f2f6c6d2d7a6f6f2f5f696d616765732f6c6f676f2e706e67\" alt=\"zoo-logo\" data-canonical-src=\"https://cpllab.github.io/lm-zoo/_images/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/cpllab/lm-zoo/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b44c1a2b3c9d844572d5aeb04f8445769f8fd5259d13d77b90e5bdd1be17457/68747470733a2f2f636972636c6563692e636f6d2f67682f63706c6c61622f6c6d2d7a6f6f2f747265652f6d61737465722e7376673f7374796c653d73766726636972636c652d746f6b656e3d64393037383234323439646235616436336330336266636333623430336336643961643834356532\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/cpllab/lm-zoo/tree/master.svg?style=svg\u0026amp;circle-token=d907824249db5ad63c03bfcc3b403c6d9ad845e2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/lm-zoo/community\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1903fba36a208e9404f0d330b5b77e123feaf5daf7a19332c6741425ee56c5c/68747470733a2f2f6261646765732e6769747465722e696d2f6c6d2d7a6f6f2f636f6d6d756e6974792e706e67\" alt=\"Gitter chat\" data-canonical-src=\"https://badges.gitter.im/lm-zoo/community.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe Language Model Zoo is an open-source repository of state-of-the-art\nlanguage models, designed to support black-box access to model predictions and\nrepresentations. It provides the command line tool \u003ccode\u003elm-zoo\u003c/code\u003e, a standard\ninterface for interacting with language models.\u003c/p\u003e\n\u003cp\u003eYou can use \u003ccode\u003elm-zoo\u003c/code\u003e to\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecompute language model predictions at the word level,\u003c/li\u003e\n\u003cli\u003eextract token-level surprisal data (popularly used in psycholinguistic\nexperiments), and\u003c/li\u003e\n\u003cli\u003epreprocess corpora according to a language model\u0027s particular tokenization\nstandards.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eQuick links:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cpllab.github.io/lm-zoo/quickstart.html\" rel=\"nofollow\"\u003eQuickstart\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cpllab.github.io/lm-zoo/models.html\" rel=\"nofollow\"\u003eSupported models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cpllab.github.io/lm-zoo/contributing.html\" rel=\"nofollow\"\u003eContributing models\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003eRunning language models from this repository requires \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eYou can install the \u003ccode\u003elm-zoo\u003c/code\u003e via \u003ccode\u003epip\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install lm-zoo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eList available language models:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lm-zoo list\ngpt2\n Image URI: docker.io/cpllab/language-models:gpt2\n Full name: None\n Reference URL: https://openai.com/blog/better-language-models/\n Maintainer: None\n Last updated: None\nRNNG\n Image URI: docker.io/cpllab/language-models:rnng\n Full name: None\n Reference URL: TODO\n Maintainer: None\n Last updated: None\nordered-neurons\n Image URI: docker.io/cpllab/language-models:ordered-neurons\n Full name: None\n Reference URL: https://github.com/yikangshen/Ordered-Neurons\n Maintainer: None\n Last updated: None\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTokenize some text according to a language model\u0027s standard:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ wget https://cpllab.github.io/lm-zoo/metamorphosis.txt -O metamorphosis.txt\n$ lm-zoo tokenize gpt2 metamorphosis.txt\nPulling latest Docker image for cpllab/language-models:gpt2.\nOne \u0120morning , \u0120when \u0120Greg or \u0120Sam sa \u0120woke \u0120from \u0120troubled \u0120dreams , \u0120he \u0120found \u0120himself \u0120transformed \u0120in \u0120his \u0120bed \u0120into \u0120a \u0120horrible \u0120ver min .\nHe \u0120lay \u0120on \u0120his \u0120armour - like \u0120back , \u0120and \u0120if \u0120he \u0120lifted \u0120his \u0120head \u0120a \u0120little \u0120he \u0120could \u0120see \u0120his \u0120brown \u0120belly , \u0120slightly \u0120dom ed \u0120and \u0120divided \u0120by \u0120ar ches \u0120into \u0120stiff \u0120sections .\nThe \u0120bed ding \u0120was \u0120hardly \u0120able \u0120to \u0120cover \u0120it \u0120and \u0120seemed \u0120ready \u0120to \u0120slide \u0120off \u0120any \u0120moment .\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGet token-level surprisals for text data:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lm-zoo get-surprisals ngram metamorphosis.txt\nsentence_id token_id token surprisal\n1 1 one 7.76847\n1 2 morning 9.40638\n1 3 , 1.05009\n1 4 when 7.08489\n1 5 gregor 18.8963\n1 6 \u0026lt;unk\u0026gt; 4.27466\n1 7 woke 19.0607\n1 8 from 10.3404\n1 9 troubled 17.478\n1 10 dreams 10.671\n1 11 , 3.39374\n1 12 he 5.99193\n1 13 found 8.07358\n1 14 himself 2.92718\n1 15 transformed 16.7328\n1 16 in 5.32057\n1 17 his 7.26454\n1 18 bed 9.78166\n1 19 into 8.90954\n1 20 a 3.72355\n1 21 horrible 14.2477\n1 22 \u0026lt;unk\u0026gt; 3.56907\n1 23 . 3.90242\n1 24 \u0026lt;/s\u0026gt; 22.8395\n2 1 he 4.43708\n2 2 lay 14.1721\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, see our \u003ca href=\"https://cpllab.github.io/lm-zoo/quickstart.html\" rel=\"nofollow\"\u003eQuickstart tutorial\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 14, "subscribers_count": 5, - "topics": [ - "singularity", - "jupyter" + "topics": [], + "updated_at": 1686152476.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "envs/containers/Singularity" ], - "updated_at": 1690510865.0 + "full_name": "Microbial-Ecology-Group/AMRplusplus", + "latest_release": "v3.0.2", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-amr-bioinformatic-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-bioinformatic-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ bioinformatic pipeline\u003c/h1\u003e\n\u003cp\u003e(\u003ca href=\"https://megares.meglab.org/\" rel=\"nofollow\"\u003ehttps://megares.meglab.org/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eAMR++ is a bioinformatic pipeline meant to aid in the analysis of raw sequencing reads to characterize the profile of antimicrobial resistance genes, or resistome. AMR++ was developed to work in conjuction with the the MEGARes database which contains sequence data for approximately 9,000 hand-curated antimicrobial resistance genes accompanied by an annotation structure that is optimized for use with high throughput sequencing and metagenomic analysis. The acyclical annotation graph of MEGARes allows for accurate, count-based, hierarchical statistical analysis of resistance at the population level, much like microbiome analysis, and is also designed to be used as a training database for the creation of statistical classifiers.\u003c/p\u003e\n\u003cp\u003eThe goal of many metagenomics studies is to characterize the content and relative abundance of sequences of interest from the DNA of a given sample or set of samples. You may want to know what is contained within your sample or how abundant a given sequence is relative to another.\u003c/p\u003e\n\u003cp\u003eOften, metagenomics is performed when the answer to these questions must be obtained for a large number of targets where techniques like multiplex PCR and other targeted methods would be too cumbersome to perform. AMR++ can process the raw data from the sequencer, identify the fragments of DNA, and count them. It also provides a count of the polymorphisms that occur in each DNA fragment with respect to the reference database.\u003c/p\u003e\n\u003cp\u003eAdditionally, you may want to know if the depth of your sequencing (how many reads you obtain that are on target) is high enough to identify rare organisms (organisms with low abundance relative to others) in your population. This is referred to as rarefaction and is calculated by randomly subsampling your sequence data at intervals between 0% and 100% in order to determine how many targets are found at each depth.\u003c/p\u003e\n\u003cp\u003eWith AMR++, you will obtain alignment count files for each sample that are combined into a count matrix that can be analyzed using any statistical and mathematical techniques that can operate on a matrix of observations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/FAQs.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/update_details.md\"\u003eDetails on AMR++ updates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-amr-demonstration\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ demonstration\u003c/h2\u003e\n\u003cp\u003eIf anaconda is already installed and nextflow is working, we\u0027ll just need to download the AMR++ github repository. Please review the \u003ca href=\"docs/installation.md\"\u003einstallation document\u003c/a\u003e for alternative methods to install AMR++ in your computing environment.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install mamba for faster installation\u003c/span\u003e\nconda install mamba -n base -c conda-forge\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eClone the AMR++ repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Microbial-Ecology-Group/AMRplusplus.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNavigate into the AMR++ repository and run the test command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e AMRplusplus\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run command to perform the demonstration pipeline using the conda profile.\u003c/span\u003e\nnextflow run main_AMR++.nf -profile conda\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The first time this can take 5-10 mins (or more) depending on your internet speed because it is installing a conda environment. Subsequent runs will skip this step automatically.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow, you can check out the results in the newly created \"test_results\" directory.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-using-amr-to-analyze-your-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-amr-to-analyze-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing AMR++ to analyze your data\u003c/h1\u003e\n\u003cp\u003eAMR++ is customizable to suit your computing needs and analyze your data. Primarily, the \u003ccode\u003e-profile\u003c/code\u003e paramater allows you to choose between running AMR++ using a singularity container, docker container, anaconda packages, or a local installation of your software.\nAll parameters used to control how AMR++ analyzes your data can also be changed as needed in a variety of ways. For full information, review this \u003ca href=\"docs/configuration.md\"\u003econfiguration document.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a brief example, the default parameters were run using this command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile conda\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo change the reads that were analyzed, you should specify the ```--reads`` parameters. Here, we can use regular expressions to point to your samples in a different directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main_AMR++.nf -profile conda --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epath/to/your/reads/*_R{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-optional-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional-flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional flags\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-snp-verification\" class=\"anchor\" aria-hidden=\"true\" href=\"#snp-verification\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSNP verification\u003c/h2\u003e\n\u003cp\u003eAMR++ now works in conjuction with a \u003ca href=\"https://github.com/Isabella136/AmrPlusPlus_SNP\"\u003ecustom SNP verification software\u003c/a\u003e to evaluate alignments to gene accessions requiring SNP confirmation to confer resistance. To include this workflow, include the \u003ccode\u003e--snp Y\u003c/code\u003e flag in your command like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main_AMR++.nf -profile conda --snp Y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create with the standard count table (AMR_analytic_matrix.csv) in addition to a count matrix with SNP confirmed counts (SNPconfirmed_AMR_analytic_matrix.csv).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deduplicated-counts\" class=\"anchor\" aria-hidden=\"true\" href=\"#deduplicated-counts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeduplicated counts\u003c/h2\u003e\n\u003cp\u003eAnother option is to include results for deduplicated counts by using the \u003ccode\u003e--deduped Y\u003c/code\u003e flag in your command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main_AMR++.nf -profile conda --snp Y --deduped Y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWith this flag, AMR++ will extract the deduplicated alignments to MEGARes also output a count matrix with deduplicated counts. Since also we included the \u003ccode\u003e--snp Y\u003c/code\u003e flag, we will end up with 4 total output count matrices.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-choosing-the-right-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#choosing-the-right-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoosing the right pipeline\u003c/h1\u003e\n\u003cp\u003eAMR++ analyzes data by combining workflows that takes a set of sequencing reads through various bioinformatic software. We recommend our standard AMR++ pipeline as a comprehensive way to start from raw sequencing reads, QC assessment, host DNA removal, and resistome analysis with MEGARes. However, users might only want to replicate portions of the pipeline and have more control over their computing needs. Using the \u003ccode\u003e--pipeline\u003c/code\u003e parameter, users can now change how AMR++ runs.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-workflows\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline workflows\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eomitting the \u003ccode\u003e--pipeline\u003c/code\u003e flag or using \u003ccode\u003e--pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimple demonstration on test data\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSteps: QC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline fast_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis workflow simply skips host removal to speed up analysis.\u003c/li\u003e\n\u003cli\u003eSteps: QC trimming \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR_wKraken\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis workflow adds microbiome analysis with kraken. It requires having a local kraken database. The minikraken_8GB_202003 will be downloaded automatically and requires ~8GB of space. Otherwise, you can specify the location to your own database with the flag, \u003ccode\u003e--kraken_db \"/Path/to/KrakenDb/\"\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSteps:\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003cli\u003eNon-host reads \u0026gt; Microbiome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-subworkflows\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-subworkflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline subworkflows\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline eval_qc\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate sample QC\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline trim_qc\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming using trimmomatic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline rm_host\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to host DNA using bwa and remove contaminants\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline resistome\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to MEGARes using bwa, perform rarefaction and resistome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline kraken\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eClassify reads taxonomically using kraken.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline bam_resistome\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eThis will run the resistome pipeline starting with bam files from a previous alignment to MEGARes.\u003c/li\u003e\n\u003cli\u003eNeed to include \u003ccode\u003e--bam_files \"Path/to/BAM/*.bam\"\u003c/code\u003e in the command line.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-command\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample command\u003c/h2\u003e\n\u003cp\u003eIn the following example, we\u0027ll choose to run the standard AMR++ workflow, which includes QC trimming, host removal, and Resistome analysis. Since we included the \u003ccode\u003e--snp Y --deduped Y\u003c/code\u003e flags, we\u0027ll also get ouput for deduped counts and SNP confirmed counts.\u003c/p\u003e\n\u003cp\u003eAlternatively, you can modify all of these variables and more in the \"params.config\" file which will be loaded automatically. Just make sure to include the \"-profile\" and \"--pipeline\" flags. More information \u003ca href=\"docs/configuration.md\"\u003ein this document\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Remember to update the --reads flag to match your read location\u003c/span\u003e\nnextflow run main_AMR++.nf -profile conda --pipeline standard_AMR --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epath/to/your/reads/*_R{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --snp Y --deduped Y\u003c/pre\u003e\u003c/div\u003e\n", + "stargazers_count": 14, + "subscribers_count": 2, + "topics": [], + "updated_at": 1682325765.0 }, { "data_format": 2, @@ -38064,104 +38108,116 @@ var data = }, { "data_format": 2, - "description": null, + "description": "official build specifications for jupyter", "filenames": [ - "envs/containers/Singularity" + "Singularity" ], - "full_name": "Microbial-Ecology-Group/AMRplusplus", - "latest_release": "v3.0.2", - "readme": "\u003ch2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-hidden=\"true\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOverview\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b7af09ab5d3e54feb3acda4c7b70aef9718f2928a49a50c92ea6ce95e96b2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-amr-bioinformatic-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-bioinformatic-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ bioinformatic pipeline\u003c/h1\u003e\n\u003cp\u003e(\u003ca href=\"https://megares.meglab.org/\" rel=\"nofollow\"\u003ehttps://megares.meglab.org/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eAMR++ is a bioinformatic pipeline meant to aid in the analysis of raw sequencing reads to characterize the profile of antimicrobial resistance genes, or resistome. AMR++ was developed to work in conjuction with the the MEGARes database which contains sequence data for approximately 9,000 hand-curated antimicrobial resistance genes accompanied by an annotation structure that is optimized for use with high throughput sequencing and metagenomic analysis. The acyclical annotation graph of MEGARes allows for accurate, count-based, hierarchical statistical analysis of resistance at the population level, much like microbiome analysis, and is also designed to be used as a training database for the creation of statistical classifiers.\u003c/p\u003e\n\u003cp\u003eThe goal of many metagenomics studies is to characterize the content and relative abundance of sequences of interest from the DNA of a given sample or set of samples. You may want to know what is contained within your sample or how abundant a given sequence is relative to another.\u003c/p\u003e\n\u003cp\u003eOften, metagenomics is performed when the answer to these questions must be obtained for a large number of targets where techniques like multiplex PCR and other targeted methods would be too cumbersome to perform. AMR++ can process the raw data from the sequencer, identify the fragments of DNA, and count them. It also provides a count of the polymorphisms that occur in each DNA fragment with respect to the reference database.\u003c/p\u003e\n\u003cp\u003eAdditionally, you may want to know if the depth of your sequencing (how many reads you obtain that are on target) is high enough to identify rare organisms (organisms with low abundance relative to others) in your population. This is referred to as rarefaction and is calculated by randomly subsampling your sequence data at intervals between 0% and 100% in order to determine how many targets are found at each depth.\u003c/p\u003e\n\u003cp\u003eWith AMR++, you will obtain alignment count files for each sample that are combined into a count matrix that can be analyzed using any statistical and mathematical techniques that can operate on a matrix of observations.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-hidden=\"true\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMore Information\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/FAQs.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/update_details.md\"\u003eDetails on AMR++ updates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-amr-demonstration\" class=\"anchor\" aria-hidden=\"true\" href=\"#amr-demonstration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAMR++ demonstration\u003c/h2\u003e\n\u003cp\u003eIf anaconda is already installed and nextflow is working, we\u0027ll just need to download the AMR++ github repository. Please review the \u003ca href=\"docs/installation.md\"\u003einstallation document\u003c/a\u003e for alternative methods to install AMR++ in your computing environment.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install mamba for faster installation\u003c/span\u003e\nconda install mamba -n base -c conda-forge\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eClone the AMR++ repository.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/Microbial-Ecology-Group/AMRplusplus.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNavigate into the AMR++ repository and run the test command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e AMRplusplus\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run command to perform the demonstration pipeline using the conda profile.\u003c/span\u003e\nnextflow run main_AMR++.nf -profile conda\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The first time this can take 5-10 mins (or more) depending on your internet speed because it is installing a conda environment. Subsequent runs will skip this step automatically.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow, you can check out the results in the newly created \"test_results\" directory.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-using-amr-to-analyze-your-data\" class=\"anchor\" aria-hidden=\"true\" href=\"#using-amr-to-analyze-your-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsing AMR++ to analyze your data\u003c/h1\u003e\n\u003cp\u003eAMR++ is customizable to suit your computing needs and analyze your data. Primarily, the \u003ccode\u003e-profile\u003c/code\u003e paramater allows you to choose between running AMR++ using a singularity container, docker container, anaconda packages, or a local installation of your software.\nAll parameters used to control how AMR++ analyzes your data can also be changed as needed in a variety of ways. For full information, review this \u003ca href=\"docs/configuration.md\"\u003econfiguration document.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBelow is a brief example, the default parameters were run using this command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003enextflow run main_AMR++.nf -profile conda\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eTo change the reads that were analyzed, you should specify the ```--reads`` parameters. Here, we can use regular expressions to point to your samples in a different directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main_AMR++.nf -profile conda --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epath/to/your/reads/*_R{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003ch1\u003e\u003ca id=\"user-content-optional-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional-flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional flags\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-snp-verification\" class=\"anchor\" aria-hidden=\"true\" href=\"#snp-verification\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSNP verification\u003c/h2\u003e\n\u003cp\u003eAMR++ now works in conjuction with a \u003ca href=\"https://github.com/Isabella136/AmrPlusPlus_SNP\"\u003ecustom SNP verification software\u003c/a\u003e to evaluate alignments to gene accessions requiring SNP confirmation to confer resistance. To include this workflow, include the \u003ccode\u003e--snp Y\u003c/code\u003e flag in your command like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main_AMR++.nf -profile conda --snp Y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create with the standard count table (AMR_analytic_matrix.csv) in addition to a count matrix with SNP confirmed counts (SNPconfirmed_AMR_analytic_matrix.csv).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-deduplicated-counts\" class=\"anchor\" aria-hidden=\"true\" href=\"#deduplicated-counts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDeduplicated counts\u003c/h2\u003e\n\u003cp\u003eAnother option is to include results for deduplicated counts by using the \u003ccode\u003e--deduped Y\u003c/code\u003e flag in your command.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run main_AMR++.nf -profile conda --snp Y --deduped Y\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWith this flag, AMR++ will extract the deduplicated alignments to MEGARes also output a count matrix with deduplicated counts. Since also we included the \u003ccode\u003e--snp Y\u003c/code\u003e flag, we will end up with 4 total output count matrices.\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-choosing-the-right-pipeline\" class=\"anchor\" aria-hidden=\"true\" href=\"#choosing-the-right-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChoosing the right pipeline\u003c/h1\u003e\n\u003cp\u003eAMR++ analyzes data by combining workflows that takes a set of sequencing reads through various bioinformatic software. We recommend our standard AMR++ pipeline as a comprehensive way to start from raw sequencing reads, QC assessment, host DNA removal, and resistome analysis with MEGARes. However, users might only want to replicate portions of the pipeline and have more control over their computing needs. Using the \u003ccode\u003e--pipeline\u003c/code\u003e parameter, users can now change how AMR++ runs.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-workflows\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline workflows\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eomitting the \u003ccode\u003e--pipeline\u003c/code\u003e flag or using \u003ccode\u003e--pipeline demo\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimple demonstration on test data\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSteps: QC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline fast_AMR\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis workflow simply skips host removal to speed up analysis.\u003c/li\u003e\n\u003cli\u003eSteps: QC trimming \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--pipeline standard_AMR_wKraken\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis workflow adds microbiome analysis with kraken. It requires having a local kraken database. The minikraken_8GB_202003 will be downloaded automatically and requires ~8GB of space. Otherwise, you can specify the location to your own database with the flag, \u003ccode\u003e--kraken_db \"/Path/to/KrakenDb/\"\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eSteps:\n\u003cul\u003e\n\u003cli\u003eQC trimming \u0026gt; Host DNA removal \u0026gt; Resistome alignment \u0026gt; Resistome results\u003c/li\u003e\n\u003cli\u003eNon-host reads \u0026gt; Microbiome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipeline-subworkflows\" class=\"anchor\" aria-hidden=\"true\" href=\"#pipeline-subworkflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipeline subworkflows\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline eval_qc\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate sample QC\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline trim_qc\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eQC trimming using trimmomatic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline rm_host\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to host DNA using bwa and remove contaminants\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline resistome\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eAlign reads to MEGARes using bwa, perform rarefaction and resistome analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline kraken\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eClassify reads taxonomically using kraken.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--pipeline bam_resistome\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eThis will run the resistome pipeline starting with bam files from a previous alignment to MEGARes.\u003c/li\u003e\n\u003cli\u003eNeed to include \u003ccode\u003e--bam_files \"Path/to/BAM/*.bam\"\u003c/code\u003e in the command line.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-example-command\" class=\"anchor\" aria-hidden=\"true\" href=\"#example-command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExample command\u003c/h2\u003e\n\u003cp\u003eIn the following example, we\u0027ll choose to run the standard AMR++ workflow, which includes QC trimming, host removal, and Resistome analysis. Since we included the \u003ccode\u003e--snp Y --deduped Y\u003c/code\u003e flags, we\u0027ll also get ouput for deduped counts and SNP confirmed counts.\u003c/p\u003e\n\u003cp\u003eAlternatively, you can modify all of these variables and more in the \"params.config\" file which will be loaded automatically. Just make sure to include the \"-profile\" and \"--pipeline\" flags. More information \u003ca href=\"docs/configuration.md\"\u003ein this document\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Remember to update the --reads flag to match your read location\u003c/span\u003e\nnextflow run main_AMR++.nf -profile conda --pipeline standard_AMR --reads \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epath/to/your/reads/*_R{1,2}.fastq.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --snp Y --deduped Y\u003c/pre\u003e\u003c/div\u003e\n", + "full_name": "singularityhub/jupyter", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eJupyter\u003c/h1\u003e\u003ca id=\"user-content-jupyter\" class=\"anchor\" aria-label=\"Permalink: Jupyter\" href=\"#jupyter\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis example will show how to run a jupyter notebook server with nginx, from a container (singularity container in this case).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eperhaps you ran an analysis when you created the container, and want to serve the notebook as a result) or\u003c/li\u003e\n\u003cli\u003eperhaps you want this to be like a working container, to store a particular version of your software to use on local files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you are interested in more proper container orchestration with \u003ca href=\"https://singularityhub.github.io/singularity-compose/\" rel=\"nofollow\"\u003esingularity-compose\u003c/a\u003e, see the \u003ca href=\"https://github.com/singularityhub/singularity-compose-examples/tree/master/jupyter-simple\"\u003esingularity-compose jupyter example\u003c/a\u003e that can more easily handle adding other containers as services, volumes, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBranches\u003c/h2\u003e\u003ca id=\"user-content-branches\" class=\"anchor\" aria-label=\"Permalink: Branches\" href=\"#branches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/singularityhub/jupyter/tree/cifs\"\u003eWindows Filesystem Support\u003c/a\u003e A basic example for Windows Filesystem Support is on this cifs branch.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you haven\u0027t installed singularity, do that with \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ethese instructions\u003c/a\u003e. Then download the repo if you haven\u0027t already:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://www.github.com/singularityhub/jupyter\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e jupyter\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eLet\u0027s now create a jupyter notebook!\nFirst, we will create the writable container image in a \u003cem\u003ewritable\u003c/em\u003e \u003cem\u003eext3\u003c/em\u003e file system, instead of the \u003cem\u003esquashfs\u003c/em\u003e which only allows \u003cem\u003eread-only\u003c/em\u003e. \u003ca href=\"http://singularity.lbl.gov/docs-build-container\" rel=\"nofollow\"\u003eread more\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build --sandbox jupyter-box Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen to run our container, since we need to write files to \u003ccode\u003e/opt/notebooks\u003c/code\u003e inside the container, we must use sudo and add the \u003ccode\u003e--writable\u003c/code\u003e command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity run --writable jupyter-box\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen we open the browser, we see our server! Cool!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"jupyter.png\"\u003e\u003cimg src=\"jupyter.png\" alt=\"jupyter.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImportant\u003c/strong\u003e using this container requires the allow-root flag, which isn\u0027t great practice.\nIf you really need to run a notebook in a container, you might be better off building one\nthat installs the notebook with your user (e.g. see \u003ca href=\"https://github.com/hpsee/discourse-cluster/blob/master/Dockerfile\"\u003ethis Docker example\u003c/a\u003e that could be translated to Singularity). You would want to change\nthe user jovyan to your username. If you can, you can also just use Docker! EIther you\ncan use the image linked there, or you can check out \u003ca href=\"https://github.com/jupyter/repo2docker\"\u003erepo2docker\u003c/a\u003e to build\na custom container.\u003c/p\u003e\n\u003cp\u003eSince the notebooks are being written to the image, this means that all of our work is preserved in it. I can finish working, close up shop, and hand my image to someone else, and it\u0027s preserved. Here, I\u0027ll show you. Let\u0027s shell into the container after we\u0027ve shut down the server (note that I didn\u0027t need to use sudo for this).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity shell jupyter-box\nSingularity: Invoking an interactive shell within container...\n\nSingularity.jupyter.sif\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e ls /opt/notebooks\nUntitled.ipynb\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThere it is! I really should work on naming my files better :) That is so cool.\u003c/p\u003e\n\u003cp\u003eYou can also map to a folder on your local machine, if you don\u0027t want to save the notebooks inside:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity run -B \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/opt/notebooks --writable jupyter-box\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand here I am sitting in my local directory, but the entire software and depdencies are provided by my container. STILL really cool.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"local.png\"\u003e\u003cimg src=\"local.png\" alt=\"local.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNote on port forwarding\u003c/h2\u003e\u003ca id=\"user-content-note-on-port-forwarding\" class=\"anchor\" aria-label=\"Permalink: Note on port forwarding\" href=\"#note-on-port-forwarding\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are running Singularity in Windows through vagrant, you will need to configure port forwarding in the Vagrantfile that you use to set up the Singularity container as well.\nAs an example, you should add a line that might look like this.\n\u003ccode\u003econfig.vm.network \"forwarded_port\", guest: 8888, host: 8888, host_ip: \"127.0.0.1\"\u003c/code\u003e\u003c/p\u003e\n", "stargazers_count": 14, - "subscribers_count": 2, - "topics": [], - "updated_at": 1682325765.0 + "subscribers_count": 5, + "topics": [ + "singularity", + "jupyter" + ], + "updated_at": 1690510865.0 }, { "data_format": 2, - "description": "Easy black-box access to state-of-the-art language models", + "description": "Singularity Global Client for container management", "filenames": [ - "models/RNNG/Singularity.rnng", - "models/gpt2/Singularity.gpt2", - "models/ngram/Singularity.ngram", - "models/JRNN/Singularity.jrnn", - "models/GRNN/Singularity.grnn", - "models/ordered-neurons/Singularity.ordered-neurons", - "models/transformer-xl/Singularity" + "Singularity" ], - "full_name": "cpllab/lm-zoo", - "latest_release": "v1.3", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-language-model-zoo\" class=\"anchor\" aria-hidden=\"true\" href=\"#language-model-zoo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLanguage Model Zoo\u003c/h1\u003e\n\u003cp\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e \u003cstrong\u003eThis project is no longer actively maintained by the Computational Psycholinguistics Laboratory.\u003c/strong\u003e \u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\" fallback-src=\"https://github.githubassets.com/images/icons/emoji/unicode/26a0.png\"\u003e\u26a0\ufe0f\u003c/g-emoji\u003e\u003c/p\u003e\n\u003cp\u003eWe do not guarantee the functionality or accuracy of the LM Zoo framework \u2014 use at your own risk!\u003c/p\u003e\n\u003cp\u003eYou may be interested in the following active projects (as of June 2023):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/kanishkamisra/minicons\"\u003e\u003ccode\u003eminicons\u003c/code\u003e\u003c/a\u003e enables easy Python access to neural network language model representations and probability/surprisal estimates.\u003c/li\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/brain-score/language\"\u003eBrain Score Language\u003c/a\u003e project provides tools for extracting behavioral and representational quantities from computational language models, and many benchmarks for evaluating the human-likeness of these models\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://huggingface.co/spaces/cpllab/syntaxgym\" rel=\"nofollow\"\u003ean experimental SyntaxGym implementation\u003c/a\u003e built directly into the Huggingface \u003ccode\u003eevaluate\u003c/code\u003e framework\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/403d7bdd315b18ed5ef1a50ff8de7e3ce954c9ec3c878170e5082369cf576e96/68747470733a2f2f63706c6c61622e6769746875622e696f2f6c6d2d7a6f6f2f5f696d616765732f6c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/403d7bdd315b18ed5ef1a50ff8de7e3ce954c9ec3c878170e5082369cf576e96/68747470733a2f2f63706c6c61622e6769746875622e696f2f6c6d2d7a6f6f2f5f696d616765732f6c6f676f2e706e67\" alt=\"zoo-logo\" data-canonical-src=\"https://cpllab.github.io/lm-zoo/_images/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/cpllab/lm-zoo/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b44c1a2b3c9d844572d5aeb04f8445769f8fd5259d13d77b90e5bdd1be17457/68747470733a2f2f636972636c6563692e636f6d2f67682f63706c6c61622f6c6d2d7a6f6f2f747265652f6d61737465722e7376673f7374796c653d73766726636972636c652d746f6b656e3d64393037383234323439646235616436336330336266636333623430336336643961643834356532\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/cpllab/lm-zoo/tree/master.svg?style=svg\u0026amp;circle-token=d907824249db5ad63c03bfcc3b403c6d9ad845e2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/lm-zoo/community\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1903fba36a208e9404f0d330b5b77e123feaf5daf7a19332c6741425ee56c5c/68747470733a2f2f6261646765732e6769747465722e696d2f6c6d2d7a6f6f2f636f6d6d756e6974792e706e67\" alt=\"Gitter chat\" data-canonical-src=\"https://badges.gitter.im/lm-zoo/community.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe Language Model Zoo is an open-source repository of state-of-the-art\nlanguage models, designed to support black-box access to model predictions and\nrepresentations. It provides the command line tool \u003ccode\u003elm-zoo\u003c/code\u003e, a standard\ninterface for interacting with language models.\u003c/p\u003e\n\u003cp\u003eYou can use \u003ccode\u003elm-zoo\u003c/code\u003e to\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ecompute language model predictions at the word level,\u003c/li\u003e\n\u003cli\u003eextract token-level surprisal data (popularly used in psycholinguistic\nexperiments), and\u003c/li\u003e\n\u003cli\u003epreprocess corpora according to a language model\u0027s particular tokenization\nstandards.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eQuick links:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://cpllab.github.io/lm-zoo/quickstart.html\" rel=\"nofollow\"\u003eQuickstart\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cpllab.github.io/lm-zoo/models.html\" rel=\"nofollow\"\u003eSupported models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cpllab.github.io/lm-zoo/contributing.html\" rel=\"nofollow\"\u003eContributing models\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-hidden=\"true\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eGetting started\u003c/h2\u003e\n\u003cp\u003eRunning language models from this repository requires \u003ca href=\"https://docs.docker.com/get-docker/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eYou can install the \u003ccode\u003elm-zoo\u003c/code\u003e via \u003ccode\u003epip\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ pip install lm-zoo\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eList available language models:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lm-zoo list\ngpt2\n Image URI: docker.io/cpllab/language-models:gpt2\n Full name: None\n Reference URL: https://openai.com/blog/better-language-models/\n Maintainer: None\n Last updated: None\nRNNG\n Image URI: docker.io/cpllab/language-models:rnng\n Full name: None\n Reference URL: TODO\n Maintainer: None\n Last updated: None\nordered-neurons\n Image URI: docker.io/cpllab/language-models:ordered-neurons\n Full name: None\n Reference URL: https://github.com/yikangshen/Ordered-Neurons\n Maintainer: None\n Last updated: None\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTokenize some text according to a language model\u0027s standard:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ wget https://cpllab.github.io/lm-zoo/metamorphosis.txt -O metamorphosis.txt\n$ lm-zoo tokenize gpt2 metamorphosis.txt\nPulling latest Docker image for cpllab/language-models:gpt2.\nOne \u0120morning , \u0120when \u0120Greg or \u0120Sam sa \u0120woke \u0120from \u0120troubled \u0120dreams , \u0120he \u0120found \u0120himself \u0120transformed \u0120in \u0120his \u0120bed \u0120into \u0120a \u0120horrible \u0120ver min .\nHe \u0120lay \u0120on \u0120his \u0120armour - like \u0120back , \u0120and \u0120if \u0120he \u0120lifted \u0120his \u0120head \u0120a \u0120little \u0120he \u0120could \u0120see \u0120his \u0120brown \u0120belly , \u0120slightly \u0120dom ed \u0120and \u0120divided \u0120by \u0120ar ches \u0120into \u0120stiff \u0120sections .\nThe \u0120bed ding \u0120was \u0120hardly \u0120able \u0120to \u0120cover \u0120it \u0120and \u0120seemed \u0120ready \u0120to \u0120slide \u0120off \u0120any \u0120moment .\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGet token-level surprisals for text data:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ lm-zoo get-surprisals ngram metamorphosis.txt\nsentence_id token_id token surprisal\n1 1 one 7.76847\n1 2 morning 9.40638\n1 3 , 1.05009\n1 4 when 7.08489\n1 5 gregor 18.8963\n1 6 \u0026lt;unk\u0026gt; 4.27466\n1 7 woke 19.0607\n1 8 from 10.3404\n1 9 troubled 17.478\n1 10 dreams 10.671\n1 11 , 3.39374\n1 12 he 5.99193\n1 13 found 8.07358\n1 14 himself 2.92718\n1 15 transformed 16.7328\n1 16 in 5.32057\n1 17 his 7.26454\n1 18 bed 9.78166\n1 19 into 8.90954\n1 20 a 3.72355\n1 21 horrible 14.2477\n1 22 \u0026lt;unk\u0026gt; 3.56907\n1 23 . 3.90242\n1 24 \u0026lt;/s\u0026gt; 22.8395\n2 1 he 4.43708\n2 2 lay 14.1721\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, see our \u003ca href=\"https://cpllab.github.io/lm-zoo/quickstart.html\" rel=\"nofollow\"\u003eQuickstart tutorial\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "singularityhub/sregistry-cli", + "latest_release": "v0.1.41", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Global Client\u003c/h1\u003e\u003ca id=\"user-content-singularity-global-client\" class=\"anchor\" aria-label=\"Permalink: Singularity Global Client\" href=\"#singularity-global-client\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://stanford-rc.github.io//rse-services/docs/tools/software-checklist/badge?label=100%25\u0026amp;color=#59BF40\u0026amp;ids=r1,r2,r3,r4,r5,r6,d1,d2,d3,d4,d5,d6,d7,a1,a2,a3,ci1,ci2\u0026amp;title=singularityhub/sregistry-cli\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cab68996537d3b9bd6a284de8194e3f5059ab8c7ced51e48c1760ab82d4132fd/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736f667477617265253230636865636b6c6973742d3130302532352d353942463430\" alt=\"https://img.shields.io/badge/software%20checklist-100%25-59BF40\" data-canonical-src=\"https://img.shields.io/badge/software%20checklist-100%25-59BF40\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/singularityhub/sregistry-cli/actions?query=branch%3Amaster+workflow%3Asregistry-ci\"\u003e\u003cimg src=\"https://github.com/singularityhub/sregistry-cli/workflows/sregistry-ci/badge.svg?branch=master\" alt=\"GitHub actions status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHi Friends! Are your containers lonely? Singularity containers thrive in happiness when they are shared. This means that wherever you might have them in these cloudy places, they are easy to find and move around.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is this?\u003c/h2\u003e\u003ca id=\"user-content-what-is-this\" class=\"anchor\" aria-label=\"Permalink: What is this?\" href=\"#what-is-this\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity Global Client is an interface to interact with Singularity containers in many different storage locations. We are able to use modern APIs by way of providing and using the software within a Singularity container! For older architectures, we provide a \u003ca href=\"Singularity\"\u003eSingularity container\u003c/a\u003e for you to use instead. You can build it from this repository, or use the provided container on \u003ca href=\"https://www.singularity-hub.org/collections/379\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf used for the Singularity Registry client, Python 3 is required. See our \u003ca href=\"https://singularityhub.github.io/sregistry-cli/install\" rel=\"nofollow\"\u003einstallation guide\u003c/a\u003e to get started. For more details, please refer to our \u003ca href=\"docs\"\u003edocumentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation instructions\u003c/h2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-label=\"Permalink: Installation instructions\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWith pip:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install sregistry[all]\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWith conda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install -c conda-forge sregistry\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMore detailed instructions can be found \u003ca href=\"https://singularityhub.github.io/sregistry-cli/install\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePython Versions Under 3\u003c/h2\u003e\u003ca id=\"user-content-python-versions-under-3\" class=\"anchor\" aria-label=\"Permalink: Python Versions Under 3\" href=\"#python-versions-under-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are looking for a version that works with Python 2.* see \u003ca href=\"https://github.com/singularityhub/sregistry-cli/releases/tag/v0.1.41\"\u003ethis branch\u003c/a\u003e, or all releases / branches prior to 0.2.0.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding the RPM\u003c/h2\u003e\u003ca id=\"user-content-building-the-rpm\" class=\"anchor\" aria-label=\"Permalink: Building the RPM\" href=\"#building-the-rpm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe file \u003ca href=\"sregistry-cli.spec\"\u003esregistry-cli.spec\u003c/a\u003e is provided to build an rpm for a specified version,\ntypcailly the current release on pypi, and was discussed \u003ca href=\"https://github.com/singularityhub/sregistry-cli/issues/138#issuecomment-413323717\"\u003ehere\u003c/a\u003e.\nYou should do the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eUpdate the version to be the current in pypi specified in \u003ca href=\"sregistry/version.py\"\u003esregistry/version.py\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eVersion: 0.0.89\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eCreate a \u003ca href=\"https://github.com/singularityhub/sregistry-cli/releases/new\"\u003enew release\u003c/a\u003e on Github with the version spec file added.\u003c/li\u003e\n\u003cli\u003eDownload the .tar.gz file from the release\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eVERSION=0.0.92\nwget https://github.com/singularityhub/sregistry-cli/archive/sregistry-cli-\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eUse rpmbuild to build it.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erpmbuild -ta sregistry-cli-\u003cspan class=\"pl-smi\"\u003e$VERSION\u003c/span\u003e.tar.gz\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou should get an srpm which that can be distributed and anyone can be rebuilt:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003erpmbuild --rebuild sregistry-cli.srpm\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code is licensed under the MPL 2.0 \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 14, + "subscribers_count": 8, + "topics": [ + "singularity-container", + "singularity", + "singularity-hub", + "singularity-registry", + "client" + ], + "updated_at": 1681389292.0 + }, + { + "data_format": 2, + "description": "An ATAC-seq pipeline wrapped in NextFlow that can be run by Jupyter", + "filenames": [ + "Singularity" + ], + "full_name": "NCBI-Hackathons/ATACFlow", + "latest_release": "v0.1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNCBI-Hackathons/ATACFlow\u003c/h1\u003e\u003ca id=\"user-content-ncbi-hackathonsatacflow\" class=\"anchor\" aria-label=\"Permalink: NCBI-Hackathons/ATACFlow\" href=\"#ncbi-hackathonsatacflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/NCBI-Hackathons/ATACFlow/master/docs/ATAC_logo.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/NCBI-Hackathons/ATACFlow/master/docs/ATAC_logo.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis pipeline performs ATAC Seq using Nextflow\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/NCBI-Hackathons/ATACFlow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6e1f9a84515374fc6a40fb10d277aee09a57014c5b58db5c2a5667dab9154af0/68747470733a2f2f7472617669732d63692e6f72672f4e4342492d4861636b6174686f6e732f41544143466c6f772e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/NCBI-Hackathons/ATACFlow.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c42e6080d15f532168bbb6d26fb300c5ed31e7f5e8648abbe9d5f6d8d4285475/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33302e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.30.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/stevetsa/atacflow\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0b5ec520559485679de1dbe742c7a9b7538b06c25cef7fd29d64052ae652f422/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f73746576657473612f61746163666c6f772e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/stevetsa/atacflow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d61f850aa9d897f08db782f0c206f5ad06e430aa54660843c9c75fbd1b4dff64/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d617661696c61626c652d3745344337342e737667\" alt=\"Singularity Container available\" data-canonical-src=\"https://img.shields.io/badge/singularity-available-7E4C74.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNCBI-Hackathons/ATACFlow: This pipeline performs ATAC Seq using Nextflow\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReference\u003c/h3\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-label=\"Permalink: Reference\" href=\"#reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHave you found this pipeline useful to your research? You can cite it using DOI 10.17605/OSF.IO/UCWRH (\u003ca href=\"https://osf.io/ucwrh/\" rel=\"nofollow\"\u003eOSF project\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe NCBI-Hackathons/ATACFlow pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eDocker installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your HPC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/output.md\"\u003ePipeline Components\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/jupyter.md\"\u003eOperating with Jupyter Notebook\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCredits\u003c/h3\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline was written by ATACFlow Team @ \u003ca href=\"https://hackathon.colorado.edu/rmgh-2018-hackathon/\" rel=\"nofollow\"\u003eRMGCH-18\u003c/a\u003e (\u003ca href=\"https://github.com/NCBI-Hackathons\"\u003eNCBI-Hackathons\u003c/a\u003e).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIgnacio Tripodi (ignacio.tripodi at colorado.edu): Computer Scientist, closet wet-lab enthusiast\u003c/li\u003e\n\u003cli\u003eSteve Tsang (hsinyi.tsang at nih.gov): Computational Biologist\u003c/li\u003e\n\u003cli\u003eJingjing Zhao (jjzhao123 at gmail.com): Bioinformatics Postdoctoral Scholar\u003c/li\u003e\n\u003cli\u003eEvan Floden (evanfloden at gmail.com): Workflow Wrangler\u003c/li\u003e\n\u003cli\u003eChi Zhang (chzh1418 at colorado.edu):\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 14, - "subscribers_count": 5, - "topics": [], - "updated_at": 1686152476.0 + "subscribers_count": 10, + "topics": [ + "atac-seq", + "nextflow", + "jupyter-notebook", + "ncbi", + "beta-release" + ], + "updated_at": 1719921792.0 }, { "data_format": 2, - "description": "CNN based brain masking", + "description": "Fast Optical Monte Carlo Simulation With Surface-Based Geometries", "filenames": [ - "Singularity" + "installation/chroma3.nvidia/Singularity" ], - "full_name": "pnlbwh/CNN-Diffusion-MRIBrain-Segmentation", - "latest_release": "v0.3", + "full_name": "BenLand100/chroma", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eChroma: Ultra-fast Photon Monte Carlo\u003c/h1\u003e\u003ca id=\"user-content-chroma-ultra-fast-photon-monte-carlo\" class=\"anchor\" aria-label=\"Permalink: Chroma: Ultra-fast Photon Monte Carlo\" href=\"#chroma-ultra-fast-photon-monte-carlo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChroma is a high performance optical photon simulation for particle physics detectors originally written by A. LaTorre and S. Seibert. It tracks individual photons passing through a triangle-mesh detector geometry, simulating standard physics processes like diffuse and specular reflections, refraction, Rayleigh scattering and absorption.\u003c/p\u003e\n\u003cp\u003eWith the assistance of a CUDA-enabled GPU, Chroma can propagate 2.5 million photons per second in a detector with 29,000 photomultiplier tubes. This is 200x faster than the same simulation with GEANT4.\u003c/p\u003e\n\u003cp\u003eCheck out the \u003ca href=\"doc/source/chroma.pdf\"\u003eChroma whitepaper\u003c/a\u003e for information on how Chroma works.\u003c/p\u003e\n\u003cp\u003eInformation about the historical development of Chroma can be found at the \u003ca href=\"https://chroma.bitbucket.io/index.html\" rel=\"nofollow\"\u003ebitbucket repository\u003c/a\u003e this repository was forked from.\u003c/p\u003e\n\u003cp\u003eThis repository contains a modified version of Chroma that includes support for wavelength shifters, scintillators, and dichroic filters.\nThis version of Chroma has also been been updated for Python3, Geant4.10.05.p01, and Root 6.18.04, among other things.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer overview\u003c/h2\u003e\u003ca id=\"user-content-container-overview\" class=\"anchor\" aria-label=\"Permalink: Container overview\" href=\"#container-overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003einstallation\u003c/code\u003e directory contains a collection of \u003ccode\u003eDockerfile\u003c/code\u003es to build an ubuntu-derived image containing Chroma. This may be a useful reference for other systems. Note that properly linking to boost_python and boost_numpy is nontrivial on systems with both python2 and python3.\u003c/p\u003e\n\u003cp\u003eContainers are great for packaging dependencies, but CUDA throws an additional constraint that the NVIDIA driver version inside the container must match the host NVIDIA driver version that created NVIDIA device nodes. This is because the device nodes created by the host are passed directly to the container. Images will need to be built for each possible driver version, or the \u003ccode\u003envidia-docker\u003c/code\u003e project \u003ca href=\"https://github.com/NVIDIA/nvidia-docker\"\u003ehttps://github.com/NVIDIA/nvidia-docker\u003c/a\u003e must be utilized to automatically synchronize the driver version. Singularity also supports NVIDIA GPUs in a very graceful way, and has other nice features.\u003c/p\u003e\n\u003cp\u003eThe images pushed to DockerHub are built from the subdirectories in the \u003ccode\u003einstallation\u003c/code\u003e directory. \u003ccode\u003einstallation/chroma3.deps\u003c/code\u003e was used to build the image \u003ccode\u003ebenland100/chroma3:deps\u003c/code\u003e by running \u003ccode\u003edocker build -t benland100/chroma3:deps\u003c/code\u003e and contains all dependencies for chroma except nvidia-drivers, and can be used to build images for particular versions. \u003ccode\u003einstallation/chroma.latest\u003c/code\u003e builds an image using the default version of NVIDIA drivers for Ubuntu-20.04 and is pushed to \u003ccode\u003ebenland100/chroma3:latest\u003c/code\u003e. The remaining directories create the images \u003ccode\u003ebenland100/chroma3:440\u003c/code\u003e and \u003ccode\u003ebenland100/chroma3:435\u003c/code\u003e for other common versions of nvidia-drivers. If you need another version for your host machine, you will have to create an analogous \u003ccode\u003eDockerfile\u003c/code\u003e. Finally the \u003ccode\u003ebenland100/chroma3:nvidia\u003c/code\u003e image is built from \u003ccode\u003echroma3.nvidia\u003c/code\u003e which is derived from \u003ccode\u003envidia/cudagl:9.2-devel-ubuntu18.04\u003c/code\u003e and features full CUDA and OpenGL support with \u003ccode\u003envidia-docker\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo get a prebuilt image, run \u003ccode\u003edocker pull benland100/chroma3:[tag]\u003c/code\u003e where tag identifies the image you want.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker usage\u003c/h2\u003e\u003ca id=\"user-content-docker-usage\" class=\"anchor\" aria-label=\"Permalink: Docker usage\" href=\"#docker-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConnecting the container to the GPU requires either the \u003ccode\u003envidia-docker\u003c/code\u003e runtime or passing the GPU device nodes manually to the container. See subsections for details.\u003c/p\u003e\n\u003cp\u003eIn general, the containers can be run with \u003ccode\u003edocker run -it benland100/chroma3:[tag]\u003c/code\u003e, but to do something useful, you should mount some host directory containing your analysis code and/or data storage to the container with additional \u003ccode\u003e-v host_path:container_path\u003c/code\u003e flags, and work within those directories. Paths not mounted from the host will not be saved when the container exits.\u003c/p\u003e\n\u003cp\u003eConsider adding \u003ccode\u003e--net=host\u003c/code\u003e to your run command and running \u003ccode\u003ejupyter\u003c/code\u003e within the container. The default Python3 environment is setup for Chroma.\u003c/p\u003e\n\u003cp\u003eFor running visualizations, you will need to allow the container to access your X11 server. The easiest way to accomplish this is by adding these flags \u003ccode\u003e--net=host -v $HOME/.Xauthority:/root/.Xauthority:rw -e DISPLAY=$DISPLAY\u003c/code\u003e to the docker run command.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWith nvidia-docker\u003c/h3\u003e\u003ca id=\"user-content-with-nvidia-docker\" class=\"anchor\" aria-label=\"Permalink: With nvidia-docker\" href=\"#with-nvidia-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis tool must be installed on the host, and adds the \u003ccode\u003envidia-docker\u003c/code\u003e command, which modifies the container on the fly to synchronize the NVIDIA drivers in the container with the host. If it is available, it provides full CUDA and OpenGL functionality for simulation and rendering. The \u003ccode\u003ebenland100/chroma3:nvidia\u003c/code\u003e image is derived from a base that supports this functionality.\u003c/p\u003e\n\u003cp\u003eOn my machine, the minimal docker command to launch a shell is:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003envidia-docker run -it benland100/chroma3:nvidia\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWithout nvidia-docker\u003c/h3\u003e\u003ca id=\"user-content-without-nvidia-docker\" class=\"anchor\" aria-label=\"Permalink: Without nvidia-docker\" href=\"#without-nvidia-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use CUDA within a container, the host\u0027s NVIDIA device nodes must be passed to the container. This will not enable OpenGL functionality, but is sufficient for running Chroma on machines where \u003ccode\u003envidia-docker\u003c/code\u003e is unavailable. To see the required device nodes run \u003ccode\u003egrep /dev/*nvidia*\u003c/code\u003e. Each must be passed to docker with the \u003ccode\u003e--device\u003c/code\u003e flag as shown with \u003ccode\u003efor dev in /dev/*nvidia*; do echo --device $dev:$dev; done\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOn my machine, this results in a very concise minimal docker run command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --device /dev/nvidia-modeset:/dev/nvidia-modeset --device /dev/nvidia-uvm:/dev/nvidia-uvm --device /dev/nvidia-uvm-tools:/dev/nvidia-uvm-tools --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl -it benland100/chroma3:440\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity usage\u003c/h2\u003e\u003ca id=\"user-content-singularity-usage\" class=\"anchor\" aria-label=\"Permalink: Singularity usage\" href=\"#singularity-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity\u0027s \u003ccode\u003e--nv\u003c/code\u003e flag for detecting and synchronizing NVIDIA GPUs and drivers within Singularity containers is very attractive. Singularity is more likely to be supported on large compute clusters, as it does not require root access. It also provides nice features like mounting your home directory in the container and synchronizing aspects of the environment automatically. Fortunately, Singularity can make use of the Docker containers described above.\u003c/p\u003e\n\u003cp\u003eA Singularity image may be derived from any Chroma Docker image with a simple \u003ccode\u003eSingularity\u003c/code\u003e file as found in \u003ccode\u003einstallation/chroma3.nvidia/Singularity\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e:nvidia\u003c/code\u003e tagged image used here is likely the best choice, as Singularity\u0027s \u003ccode\u003e--nv\u003c/code\u003e flag is designed for the base it was derived from.\u003c/p\u003e\n\u003cp\u003eSingularity can then be used to build an image: \u003ccode\u003esudo singularity build chroma3.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRunning this image is pretty setraightforward. Your home directory will be available within the image, but other directories can be mounted as desired.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity run --nv chroma3.simg\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eVisualization with OpenGL and simulation with CUDA will work in this container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTest drive\u003c/h3\u003e\u003ca id=\"user-content-test-drive\" class=\"anchor\" aria-label=\"Permalink: Test drive\" href=\"#test-drive\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter deploying a container to a GPU-enabled host locally or via SSH with XForwarding enabled, you should be able to run the container and execute\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003echroma-cam @chroma.models.lionsolid\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhich should display a GPU-rendered visualization, ensuring everything is working properly.\u003c/p\u003e\n", "stargazers_count": 14, "subscribers_count": 6, "topics": [], - "updated_at": 1721061708.0 + "updated_at": 1729242950.0 }, { "data_format": 2, - "description": "Pipeline for processing FASTQ data from an Illumina MiSeq to genotype human RNA viruses like HIV and hepatitis C", + "description": "Variant call verification", "filenames": [ - "Singularity" - ], - "full_name": "cfe-lab/MiCall", - "latest_release": "v7.15.13", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMiCall\u003c/h1\u003e\u003ca id=\"user-content-micall\" class=\"anchor\" aria-label=\"Permalink: MiCall\" href=\"#micall\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProcessing FASTQ data from an Illumina MiSeq\u003c/h2\u003e\u003ca id=\"user-content-processing-fastq-data-from-an-illumina-miseq\" class=\"anchor\" aria-label=\"Permalink: Processing FASTQ data from an Illumina MiSeq\" href=\"#processing-fastq-data-from-an-illumina-miseq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://codecov.io/github/cfe-lab/MiCall?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34f20f230b5de7f64e8bff799e3ca566be45bf925a73d904927b09540581d4c0/68747470733a2f2f636f6465636f762e696f2f6769746875622f6366652d6c61622f4d6943616c6c2f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"Code Coverage\" data-canonical-src=\"https://codecov.io/github/cfe-lab/MiCall/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.1289989\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/077775bfdea31e28bd85b3bd4b9186c43ceed2803094e8e5482921274668b2ec/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e313238393938392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1289989.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMaps all the reads from a sample against a set of reference sequences, then\nstitches all the reads into consensus sequences and coverage maps.\u003c/p\u003e\n\u003cp\u003eA monitoring system regularly checks the file system for unprocessed runs,\ntransfers FASTQ.gz files to the cluster and executes the pipeline.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"https://cfe-lab.github.io/MiCall/steps\" rel=\"nofollow\"\u003elist of steps and files\u003c/a\u003e for details of what the pipeline does.\nThe \u003ca href=\"https://cfe-lab.github.io/MiCall/admin\" rel=\"nofollow\"\u003eadmin\u003c/a\u003e page describes how to look after the pipeline in Kive, and the\n\u003ca href=\"https://cfe-lab.github.io/MiCall/getting_started\" rel=\"nofollow\"\u003egetting started\u003c/a\u003e page describes how to get the docker version set up and run it\non your own data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDual Licensing\u003c/h2\u003e\u003ca id=\"user-content-dual-licensing\" class=\"anchor\" aria-label=\"Permalink: Dual Licensing\" href=\"#dual-licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopyright (C) 2016, University of British Columbia\u003c/p\u003e\n\u003cp\u003eThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU Affero General Public License as published\nby the Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\u003c/p\u003e\n\u003cp\u003eThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU Affero General Public License for more details.\u003c/p\u003e\n\u003cp\u003eYou should have received a copy of the GNU Affero General Public License\nalong with this program. If not, visit \u003ca href=\"https://www.gnu.org/licenses/\" rel=\"nofollow\"\u003egnu.org\u003c/a\u003e. The source code for\nthis program is available from \u003ca href=\"https://github.com/cfe-lab/MiCall\"\u003egithub.com\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe program is also available for a fee under a more permissive license. For\nexample, if you want to run a changed version of the program on a network server\nwithout publishing the changed source code, \u003ca href=\"mailto:micalldev@cfenet.ubc.ca\"\u003econtact us\u003c/a\u003e about\npurchasing a license.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThird Party Components\u003c/h2\u003e\u003ca id=\"user-content-third-party-components\" class=\"anchor\" aria-label=\"Permalink: Third Party Components\" href=\"#third-party-components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMiCall makes use of several open-source tools. Here is a list of tools with\ntheir licenses.\u003c/p\u003e\n\u003cp\u003eRequests is distributed under the Apache 2.0 license.\u003c/p\u003e\n\u003cp\u003ePython 3 is distributed under the \u003ca href=\"https://docs.python.org/3/license.html\" rel=\"nofollow\"\u003ePython 3 license\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBowtie2, IVA, and Python-Levenshtein are distributed under the GNU General\nPublic License (GPL).\u003c/p\u003e\n\u003cp\u003eMatplotlib is distributed under the \u003ca href=\"https://matplotlib.org/users/license.html\" rel=\"nofollow\"\u003eMatplotlib license\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eReportlab is distributed under the BSD license.\u003c/p\u003e\n\u003cp\u003ePyyaml and Cutadapt are distributed under the MIT license.\u003c/p\u003e\n", - "stargazers_count": 14, - "subscribers_count": 12, - "topics": [ - "bioinformatics", - "fastq", - "python", - "resistance", - "genotype" + "Singularity.def" ], - "updated_at": 1727290291.0 + "full_name": "iqbal-lab-org/varifier", + "latest_release": "v0.4.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003evarifier\u003c/h1\u003e\u003ca id=\"user-content-varifier\" class=\"anchor\" aria-label=\"Permalink: varifier\" href=\"#varifier\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: full documentation is under construction\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003econda\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/bioconda/varifier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4c77703a45fbd033943e0b5289a911e9072cc0381399b2ec90a594f207aaea49/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f766e2f62696f636f6e64612f7661726966696572\" alt=\"Conda (channel only)\" data-canonical-src=\"https://img.shields.io/conda/vn/bioconda/varifier\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/varifier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/53a23ec73ed044b1ac84ff287a35e972e125e9ea440d670f10215c8db336bd19/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f76617269666965722f6261646765732f706c6174666f726d732e737667\" alt=\"bioconda version\" data-canonical-src=\"https://anaconda.org/bioconda/varifier/badges/platforms.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePrerequisite: \u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/\" rel=\"nofollow\"\u003e\u003ccode\u003econda\u003c/code\u003e\u003c/a\u003e (and bioconda channel \u003ca href=\"https://bioconda.github.io/user/install.html#set-up-channels\" rel=\"nofollow\"\u003ecorrectly set up\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ conda install varifier\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainer\u003c/h3\u003e\u003ca id=\"user-content-container\" class=\"anchor\" aria-label=\"Permalink: Container\" href=\"#container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker images are hosted at \u003ca href=\"https://quay.io/repository/iqballab/varifier\" rel=\"nofollow\"\u003equay.io\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ccode\u003esingularity\u003c/code\u003e\u003c/h4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePrerequisite: \u003ca href=\"https://sylabs.io/guides/3.4/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003e\u003ccode\u003esingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ URI=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edocker://quay.io/iqballab/varifier\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$URI\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e varifier --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe above will use the latest version. If you want to specify a version/commit then use a\n\u003ca href=\"https://quay.io/repository/iqballab/varifier\" rel=\"nofollow\"\u003etag\u003c/a\u003e (or commit) like so.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ TAG=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e3c8152a\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n$ URI=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edocker://quay.io/iqballab/varifier:\u003cspan class=\"pl-smi\"\u003e${TAG}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ccode\u003edocker\u003c/code\u003e\u003c/h4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/iqballab/varifier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cc5de2f9f030fa2ab2da1195c29e332ad9a13dc4512cf3e8809dbecefd1fb277/68747470733a2f2f717561792e696f2f7265706f7369746f72792f697162616c6c61622f76617269666965722f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/iqballab/varifier/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePrerequisite: \u003ca href=\"https://docs.docker.com/v17.12/install/\" rel=\"nofollow\"\u003e\u003ccode\u003edocker\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre lang=\"shhell\"\u003e\u003ccode\u003e$ docker pull quay.io/iqballab/varifier\n$ docker run quay.io/iqballab/varifier varifier --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can find all the available tags on the \u003ca href=\"https://quay.io/repository/iqballab/varifier\" rel=\"nofollow\"\u003equay.io repository\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLocal\u003c/h3\u003e\u003ca id=\"user-content-local\" class=\"anchor\" aria-label=\"Permalink: Local\" href=\"#local\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3 (tested on version 3.6.9)\u003c/li\u003e\n\u003cli\u003emummer installed\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epaftools.js\u003c/code\u003e and \u003ccode\u003ek8\u003c/code\u003e in your path. See \u003ca href=\"https://github.com/lh3/minimap2/tree/master/misc\"\u003ehttps://github.com/lh3/minimap2/tree/master/misc\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInstall:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo verify calls in a VCF file, you will need:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003etest.vcf\u003c/code\u003e - the VCF file to be tested\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eref.fasta\u003c/code\u003e - FASTA file of reference corresponding to the VCF file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etruth.fasta\u003c/code\u003e - a truth genome FASTA file\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRun:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evarifier vcf_eval truth.fasta ref.fasta test.vcf out_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis makes a new directory called \u003ccode\u003eout_dir\u003c/code\u003e. The results are in the file\n\u003ccode\u003esummary_stats.json\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTests\u003c/h2\u003e\u003ca id=\"user-content-tests\" class=\"anchor\" aria-label=\"Permalink: Tests\" href=\"#tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the tests, run \u003ccode\u003etox\u003c/code\u003e from the root of the repository.\u003c/p\u003e\n", + "stargazers_count": 15, + "subscribers_count": 6, + "topics": [], + "updated_at": 1730217051.0 }, { "data_format": 2, - "description": "Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context", + "description": "High-performance (\u0026 with GPU support) implementation of Stochastic Reconfiguration and Stochastic Wavefunction Optimisation methods for Neural Quantum States", "filenames": [ "Singularity" ], - "full_name": "MASILab/cornn_tractography", - "latest_release": "v1.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eCoRNN Tractography\u003c/h1\u003e\u003ca id=\"user-content-cornn-tractography\" class=\"anchor\" aria-label=\"Permalink: CoRNN Tractography\" href=\"#cornn-tractography\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/MASILab/cornn_tractography/blob/master/CoRNN.png?raw=true\"\u003e\u003cimg src=\"https://github.com/MASILab/cornn_tractography/raw/master/CoRNN.png?raw=true\" alt=\"itscornn\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTractography on T1-weighted MRI, no diffusion needed!\u003c/h2\u003e\u003ca id=\"user-content-tractography-on-t1-weighted-mri-no-diffusion-needed\" class=\"anchor\" aria-label=\"Permalink: Tractography on T1-weighted MRI, no diffusion needed!\" href=\"#tractography-on-t1-weighted-mri-no-diffusion-needed\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#authors-and-reference\"\u003eAuthors and Reference\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#containerization-of-source-code\"\u003eContainerization of Source Code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#command\"\u003eCommand\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#arguments-and-io\"\u003eArguments and I/O\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#options\"\u003eOptions\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the model weights, source code, and containerized implementation of convolutional-recurrent neural network (CoRNN) tractography on T1w MRI with associated \u003ca href=\"https://github.com/MASILab/SLANTbrainSeg\"\u003eSLANT\u003c/a\u003e and \u003ca href=\"https://github.com/MASILab/WM_learning_release\"\u003eWM learning (WML)\u003c/a\u003e TractSeg segmentations.\u003c/p\u003e\n\u003cp\u003ePlease note that this methodology is still actively being characterized, validated, and extended. If you\u0027re interested in working with us to explore what it means to run tractography on T1w MRI, please \u003ca href=\"#authors-and-reference\"\u003econtact us\u003c/a\u003e and let us know!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors and Reference\u003c/h2\u003e\u003ca id=\"user-content-authors-and-reference\" class=\"anchor\" aria-label=\"Permalink: Authors and Reference\" href=\"#authors-and-reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"mailto:leon.y.cai@vanderbilt.edu\"\u003eLeon Y. Cai\u003c/a\u003e, Ho Hin Lee, Nancy R. Newlin, Cailey I. Kerley, Praitayini Kanakaraj, Qi Yang, Graham W. Johnson, Daniel Moyer, Kurt G. Schilling, Francois Rheault, and Bennett A. Landman. \u003ca href=\"https://www.biorxiv.org/content/10.1101/2023.02.25.530046v1\" rel=\"nofollow\"\u003eConvolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context\u003c/a\u003e. Proceedings of Machine Learning Reseach. In press. 2023.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://my.vanderbilt.edu/masi\" rel=\"nofollow\"\u003eMedical-image Analysis and Statistical Interpretation (MASI) Lab\u003c/a\u003e, Vanderbilt University, Nashville, TN, USA\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainerization of Source Code\u003c/h2\u003e\u003ca id=\"user-content-containerization-of-source-code\" class=\"anchor\" aria-label=\"Permalink: Containerization of Source Code\" href=\"#containerization-of-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/MASILab/cornn_tractography.git\ncd /path/to/repo/cornn_tractography\ngit checkout v1.0.0\nsudo singularity build /path/to/CoRNN_v1.0.0.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe use Singularity version 3.8 CE with root permissions.\u003c/p\u003e\n\u003cp\u003eAlternatively, a pre-built container can be downloaded \u003ca href=\"https://masi.vuse.vanderbilt.edu/CoRNN/CoRNN_v1.0.0.sif\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommand\u003c/h2\u003e\u003ca id=\"user-content-command\" class=\"anchor\" aria-label=\"Permalink: Command\" href=\"#command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run \n-e \n--contain\n-B \u0026lt;t1_file\u0026gt;:/data/T1.nii.gz\n-B \u0026lt;out_dir\u0026gt;:/data\n-B \u0026lt;slant_dir\u0026gt;:/data/slant\n-B \u0026lt;wml_dir\u0026gt;:/data/wml\n-B /tmp:/tmp\n--nv\n/path/to/CoRNN_v1.0.0.sif\n/data/T1.nii.gz\n/data/\u0026lt;out_name\u0026gt;\n--slant /data/slant\n--wml /data/wml\n[options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBinding \u003ccode\u003e/tmp\u003c/code\u003e is required with \u003ccode\u003e--contain\u003c/code\u003e when \u003ccode\u003e--work_dir\u003c/code\u003e is not specified.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--nv\u003c/code\u003e is optional. See \u003ccode\u003e--device\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eArguments and I/O\u003c/h2\u003e\u003ca id=\"user-content-arguments-and-io\" class=\"anchor\" aria-label=\"Permalink: Arguments and I/O\" href=\"#arguments-and-io\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;t1_file\u0026gt;\u003c/code\u003e\u003c/strong\u003e Path on the host machine to the T1-weighted MRI with which tractography is to be performed in NIFTI format (either compressed or not).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;out_dir\u0026gt;\u003c/code\u003e\u003c/strong\u003e Path on the host machine to the \u003cem\u003edirectory\u003c/em\u003e in which the output tractogram will be saved.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;out_name\u0026gt;\u003c/code\u003e\u003c/strong\u003e \u003cem\u003eName\u003c/em\u003e (i.e., no directory) of the output tractogram with extension in trk, tck, vtk, fib, or dpy format.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;slant_dir\u0026gt;\u003c/code\u003e\u003c/strong\u003e Path on the host machine to the SLANT output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e\u0026lt;wml_dir\u0026gt;\u003c/code\u003e\u003c/strong\u003e Path on the host machine to the TractSeg WM Learning output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptions\u003c/h2\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: Options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--help\u003c/code\u003e\u003c/strong\u003e Print help statement.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--device cuda/cpu\u003c/code\u003e\u003c/strong\u003e A string indicating the device on which to perform inference. If \"cuda\" is selected, container option \u003ccode\u003e--nv\u003c/code\u003e must be included. Default = \"cpu\"\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--num_streamlines N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the number of streamlines to identify. Default = 1000000\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--num_seeds N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the number of streamlines to seed per batch. One GB of GPU memory can handle approximately 10000 seeds. Default = 100000\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--min_steps N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the minimum number of 1mm steps per streamline. Default = 50\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--max_steps N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the maximum number of 1mm steps per streamline. Default = 250\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--buffer_steps N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the number of 1mm steps where the angle stopping criteria are ignored at the beginning of tracking. Default = 5\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--unidirectional\u003c/code\u003e\u003c/strong\u003e A flag indicating that bidirectional tracking should not be performed. The buffer steps are NOT removed in this case. Default = Perform bidirectional tracking\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--work_dir /data/work_dir\u003c/code\u003e\u003c/strong\u003e A string indicating the working directory to use. The location of the working directory on the host machine, \u003ccode\u003e\u0026lt;work_dir\u0026gt;\u003c/code\u003e, must also exist and be bound into the container with \u003ccode\u003e-B \u0026lt;work_dir\u0026gt;:/data/work_dir\u003c/code\u003e in the \u003ca href=\"#command\"\u003ecommand\u003c/a\u003e. If the working directory contains previously generated intermediates, the corresponding steps will not be rerun. Default = create a new working directory in \u003ccode\u003e/tmp\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--keep_work\u003c/code\u003e\u003c/strong\u003e A flag indicating that the intermediates in the working directory should NOT be cleared. Default = Clear working directory after completion\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--num_threads N\u003c/code\u003e\u003c/strong\u003e A positive integer indicating the number of threads to use during multithreaded steps. Default = 1\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003e--force\u003c/code\u003e\u003c/strong\u003e A flag indicating that the output file should be overwritten if it already exists. Default = Do NOT override existing output file\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 14, - "subscribers_count": 3, - "topics": [ - "deep-learning", - "diffusion-mri", - "tractography", - "convolutional-recurrent-neural-network", - "t1-weighted-mri" - ], - "updated_at": 1731323806.0 + "full_name": "twesterhout/nqs-playground", + "latest_release": null, + "readme": "\u003cp\u003ePyTorch-based implementation of SR and SWO for NQS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#conda\"\u003eConda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#building-from-source\"\u003eBuilding from source\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConda\u003c/h2\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWARNING:\u003c/strong\u003e The version available on Conda is currently out of date.\nPlease, build from source for the latest features.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThe simplest way to get started using \u003ccode\u003enqs_playground\u003c/code\u003e package is to install it\nusing \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install -c twesterhout nqs_playground\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding from source\u003c/h2\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-label=\"Permalink: Building from source\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCPU-only version\u003c/h3\u003e\u003ca id=\"user-content-cpu-only-version\" class=\"anchor\" aria-label=\"Permalink: CPU-only version\" href=\"#cpu-only-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you do not have access or do not wish to use a GPU you can use cpu-only\nversion PyToch and nqs_playground. For this, first clone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/twesterhout/nqs-playground.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow just run \u003ca href=\"./build_locally_cpu.sh\"\u003e\u003ccode\u003ebuild_locally_cpu.sh\u003c/code\u003e\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./build_locally_cpu.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFull version\u003c/h3\u003e\u003ca id=\"user-content-full-version\" class=\"anchor\" aria-label=\"Permalink: Full version\" href=\"#full-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n", + "stargazers_count": 15, + "subscribers_count": 4, + "topics": [], + "updated_at": 1694445047.0 }, { "data_format": 2, - "description": "BayesTME: A reference-free Bayesian method for analyzing spatial transcriptomics data", + "description": null, "filenames": [ "Singularity" ], - "full_name": "tansey-lab/bayestme", - "latest_release": "v0.0.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBayesTME: A unified statistical framework for spatial transcriptomics\u003c/h1\u003e\u003ca id=\"user-content-bayestme-a-unified-statistical-framework-for-spatial-transcriptomics\" class=\"anchor\" aria-label=\"Permalink: BayesTME: A unified statistical framework for spatial transcriptomics\" href=\"#bayestme-a-unified-statistical-framework-for-spatial-transcriptomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tansey-lab/bayestme/actions/workflows/tests.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/tansey-lab/bayestme/actions/workflows/tests.yml/badge.svg\" alt=\"tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://bayestme.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8b6d62894915cc2418c18b79c9a11edde0a91684f1a459f2c09d8ebd4af92fb1/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6261796573746d652f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/bayestme/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/511984802\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/40654d00d94f98f904a2b1be34f950c9398843a972d3041f17e2dd03174bbfef/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3531313938343830322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/511984802.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis package implements BayesTME, a fully Bayesian method for analyzing ST data without needing single-cell RNA-seq (scRNA) reference data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://bayestme.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ebayestme.readthedocs.io\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-bayestmereadthedocsio\" class=\"anchor\" aria-label=\"Permalink: bayestme.readthedocs.io\" href=\"#bayestmereadthedocsio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use this code, please cite the \u003ca href=\"https://doi.org/10.1016/j.cels.2023.06.003\" rel=\"nofollow\"\u003emanuscript\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eZhang H, Hunter MV, Chou J, Quinn JF, Zhou M, White RM, Tansey W. BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment. Cell Syst. 2023 Jul 19;14(7):605-619.e7. doi: 10.1016/j.cels.2023.06.003. PMID: 37473731; PMCID: PMC10368078.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBibtex citation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@article{Zhang:2023aa,\n\tauthor = {Zhang, Haoran and Hunter, Miranda V and Chou, Jacqueline and Quinn, Jeffrey F and Zhou, Mingyuan and White, Richard M and Tansey, Wesley},\n\tjournal = {Cell Syst},\n\tmonth = {Jul},\n\tnumber = {7},\n\tpages = {605--619},\n\ttitle = {BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment.},\n\tvolume = {14},\n\tyear = {2023}}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeveloper Setup\u003c/h2\u003e\u003ca id=\"user-content-developer-setup\" class=\"anchor\" aria-label=\"Permalink: Developer Setup\" href=\"#developer-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease run \u003ccode\u003emake install_precommit_hooks\u003c/code\u003e from the root of the repository\nto install the pre-commit hooks.\u003c/p\u003e\n\u003cp\u003eWhen you run any \u003ccode\u003egit commit\u003c/code\u003e command these pre-commit hooks will run and format any files that you changed in your commit.\u003c/p\u003e\n\u003cp\u003eAny unchanged files will not be formatted.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInternal Contributions\u003c/h3\u003e\u003ca id=\"user-content-internal-contributions\" class=\"anchor\" aria-label=\"Permalink: Internal Contributions\" href=\"#internal-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen contributing to this repository, please use the feature branch workflow documented here: \u003ca href=\"https://github.com/tansey-lab/wiki/blob/master/FEATURE_BRANCH_WORKFLOW.md\"\u003ehttps://github.com/tansey-lab/wiki/blob/master/FEATURE_BRANCH_WORKFLOW.md\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "dl-container-registry/ffmpeg", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNVIDIA accelerated ffmpeg\u003c/h1\u003e\u003ca id=\"user-content-nvidia-accelerated-ffmpeg\" class=\"anchor\" aria-label=\"Permalink: NVIDIA accelerated ffmpeg\" href=\"#nvidia-accelerated-ffmpeg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/dl-container-registry/ffmpeg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ee51861a83113fed63dcdeca92fda7e632e7ec446e1d851b72fd7ac8c7c536a7/68747470733a2f2f7472617669732d63692e6f72672f646c2d636f6e7461696e65722d72656769737472792f66666d7065672e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/dl-container-registry/ffmpeg.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/willprice/nvidia-ffmpeg/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/680701285475c874bf8f852e787b50b1a1f9f07c96a5f72aded98a8a1db5f1cf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65726875622d3232623865622e737667\" alt=\"Dockerhub link\" data-canonical-src=\"https://img.shields.io/badge/hosted-dockerhub-22b8eb.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/521\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity hub link\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/nvidia-video-codec-sdk#NVENCFeatures\" rel=\"nofollow\"\u003eNVENCODE acceleration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/nvidia-video-codec-sdk#NVDECFeatures\" rel=\"nofollow\"\u003eNVDECODE acceleration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.videolan.org/developers/x264.html\" rel=\"nofollow\"\u003evideo codec: x264\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.videolan.org/developers/x265.html\" rel=\"nofollow\"\u003evideo codec: x265\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mstorsjo/fdk-aac\"\u003eaudio codec: AAC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNVENCODE (nvenc) and NVDECODE (formerly CUVID) are packaged in the \u003ca href=\"https://developer.nvidia.com/nvidia-video-codec-sdk\" rel=\"nofollow\"\u003eNVIDIA Video Codec\nSDK\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Accelerated Encoders:\u003c/h3\u003e\u003ca id=\"user-content-hardware-accelerated-encoders\" class=\"anchor\" aria-label=\"Permalink: Hardware Accelerated Encoders:\" href=\"#hardware-accelerated-encoders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList options of an encoder using \u003ccode\u003effmpeg -h encoder=XXXX\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eh264_nvenc\u003c/code\u003e, \u003ccode\u003envenc\u003c/code\u003e, \u003ccode\u003envenc_h264\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003envenc_hevc\u003c/code\u003e, \u003ccode\u003ehevc_nvenc\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Accelerated Decoders:\u003c/h3\u003e\u003ca id=\"user-content-hardware-accelerated-decoders\" class=\"anchor\" aria-label=\"Permalink: Hardware Accelerated Decoders:\" href=\"#hardware-accelerated-decoders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList options of a decoder using \u003ccode\u003effmpeg -h decoder=XXXX\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eh264_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ehevc_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emjpeg_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003empeg1_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003empeg2_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003empeg4_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003evc1_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003evp8_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003evp9_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Accelerated Filters:\u003c/h3\u003e\u003ca id=\"user-content-hardware-accelerated-filters\" class=\"anchor\" aria-label=\"Permalink: Hardware Accelerated Filters:\" href=\"#hardware-accelerated-filters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList options of a filter using \u003ccode\u003effmpeg -h filter=XXXX\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003ehwupload_cuda\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003escale_cuda\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003escale_npp\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ethumnail_cuda\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the container mounting the current directory to \u003ccode\u003e/workspace\u003c/code\u003e processing\n\u003ccode\u003einput.mp4\u003c/code\u003e to \u003ccode\u003eoutput.mp4\u003c/code\u003e without any hardware acceleration\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -it --runtime=nvidia \\\n --volume \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/workspace \\\n willprice/nvidia-ffmpeg -i input.mp4 output.avi\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -it --runtime=nvidia \\\n --volume \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/workspace \\\n willprice/nvidia-ffmpeg \\\n -hwaccel_device 0 \\\n -hwaccel cuvid \\\n -c:v h264_cuvid \\\n -i input.mp4 \\\n -c:v hevc_nvenc\n out.mkv\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eGet a shell prompt inside the container, useful for debugging:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -it --runtime=nvidia \\\n --volume \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/workspace \\\n --entrypoint bash\n willprice/nvidia-ffmpeg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker image is a multistage build. The initial stage, the \u003cem\u003ebuild\u003c/em\u003e stage, builds a statically linked ffmpeg binary\nthat is then copied over into the runtime image. By statically linking we minimize the number of external dependencies\nand shrink the runtime image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResources\u003c/h2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-label=\"Permalink: Resources\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://trac.ffmpeg.org/wiki/HWAccelIntro\" rel=\"nofollow\"\u003eFFmpeg hardware acceleration guide with examples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/jniltinho/9c009e9771651aa4a004ad3d1f6857e3\"\u003eStatic FFmpeg build on Ubuntu 16.04 guide\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/Brainiarc7/18fca697891aea0e879f13ed092cb213\"\u003eUsing FFmpeg with GNU parallel\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/Brainiarc7/c6164520f082c27ae7bbea9556d4a3ba\"\u003eListing NVENC and NPP capabilities of FFmpeg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/Brainiarc7/8b471ff91319483cdb725f615908286e\"\u003eEncoding HEVC using FFmpeg with NVENC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/Brainiarc7/ebf3091efd2bf0a0ded0f9715cd43a38\"\u003eFFmpeg cheatsheet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/zimbatm/ffmpeg-static\"\u003eFFmpeg-static build scripts\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 15, "subscribers_count": 3, "topics": [], - "updated_at": 1732701041.0 + "updated_at": 1696226360.0 }, { "data_format": 2, @@ -38186,114 +38242,127 @@ var data = }, { "data_format": 2, - "description": null, + "description": "BayesTME: A reference-free Bayesian method for analyzing spatial transcriptomics data", "filenames": [ "Singularity" ], - "full_name": "dl-container-registry/ffmpeg", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eNVIDIA accelerated ffmpeg\u003c/h1\u003e\u003ca id=\"user-content-nvidia-accelerated-ffmpeg\" class=\"anchor\" aria-label=\"Permalink: NVIDIA accelerated ffmpeg\" href=\"#nvidia-accelerated-ffmpeg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/dl-container-registry/ffmpeg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ee51861a83113fed63dcdeca92fda7e632e7ec446e1d851b72fd7ac8c7c536a7/68747470733a2f2f7472617669732d63692e6f72672f646c2d636f6e7461696e65722d72656769737472792f66666d7065672e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/dl-container-registry/ffmpeg.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/willprice/nvidia-ffmpeg/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/680701285475c874bf8f852e787b50b1a1f9f07c96a5f72aded98a8a1db5f1cf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f686f737465642d646f636b65726875622d3232623865622e737667\" alt=\"Dockerhub link\" data-canonical-src=\"https://img.shields.io/badge/hosted-dockerhub-22b8eb.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/521\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"Singularity hub link\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/nvidia-video-codec-sdk#NVENCFeatures\" rel=\"nofollow\"\u003eNVENCODE acceleration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/nvidia-video-codec-sdk#NVDECFeatures\" rel=\"nofollow\"\u003eNVDECODE acceleration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.videolan.org/developers/x264.html\" rel=\"nofollow\"\u003evideo codec: x264\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.videolan.org/developers/x265.html\" rel=\"nofollow\"\u003evideo codec: x265\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mstorsjo/fdk-aac\"\u003eaudio codec: AAC\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNVENCODE (nvenc) and NVDECODE (formerly CUVID) are packaged in the \u003ca href=\"https://developer.nvidia.com/nvidia-video-codec-sdk\" rel=\"nofollow\"\u003eNVIDIA Video Codec\nSDK\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Accelerated Encoders:\u003c/h3\u003e\u003ca id=\"user-content-hardware-accelerated-encoders\" class=\"anchor\" aria-label=\"Permalink: Hardware Accelerated Encoders:\" href=\"#hardware-accelerated-encoders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList options of an encoder using \u003ccode\u003effmpeg -h encoder=XXXX\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eh264_nvenc\u003c/code\u003e, \u003ccode\u003envenc\u003c/code\u003e, \u003ccode\u003envenc_h264\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003envenc_hevc\u003c/code\u003e, \u003ccode\u003ehevc_nvenc\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Accelerated Decoders:\u003c/h3\u003e\u003ca id=\"user-content-hardware-accelerated-decoders\" class=\"anchor\" aria-label=\"Permalink: Hardware Accelerated Decoders:\" href=\"#hardware-accelerated-decoders\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList options of a decoder using \u003ccode\u003effmpeg -h decoder=XXXX\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eh264_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ehevc_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emjpeg_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003empeg1_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003empeg2_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003empeg4_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003evc1_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003evp8_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003evp9_cuvid\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Accelerated Filters:\u003c/h3\u003e\u003ca id=\"user-content-hardware-accelerated-filters\" class=\"anchor\" aria-label=\"Permalink: Hardware Accelerated Filters:\" href=\"#hardware-accelerated-filters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eList options of a filter using \u003ccode\u003effmpeg -h filter=XXXX\u003c/code\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003ehwupload_cuda\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003escale_cuda\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003escale_npp\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ethumnail_cuda\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the container mounting the current directory to \u003ccode\u003e/workspace\u003c/code\u003e processing\n\u003ccode\u003einput.mp4\u003c/code\u003e to \u003ccode\u003eoutput.mp4\u003c/code\u003e without any hardware acceleration\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -it --runtime=nvidia \\\n --volume \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/workspace \\\n willprice/nvidia-ffmpeg -i input.mp4 output.avi\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -it --runtime=nvidia \\\n --volume \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/workspace \\\n willprice/nvidia-ffmpeg \\\n -hwaccel_device 0 \\\n -hwaccel cuvid \\\n -c:v h264_cuvid \\\n -i input.mp4 \\\n -c:v hevc_nvenc\n out.mkv\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eGet a shell prompt inside the container, useful for debugging:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run --rm -it --runtime=nvidia \\\n --volume \u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e:/workspace \\\n --entrypoint bash\n willprice/nvidia-ffmpeg\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild\u003c/h2\u003e\u003ca id=\"user-content-build\" class=\"anchor\" aria-label=\"Permalink: Build\" href=\"#build\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker image is a multistage build. The initial stage, the \u003cem\u003ebuild\u003c/em\u003e stage, builds a statically linked ffmpeg binary\nthat is then copied over into the runtime image. By statically linking we minimize the number of external dependencies\nand shrink the runtime image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResources\u003c/h2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-label=\"Permalink: Resources\" href=\"#resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://trac.ffmpeg.org/wiki/HWAccelIntro\" rel=\"nofollow\"\u003eFFmpeg hardware acceleration guide with examples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/jniltinho/9c009e9771651aa4a004ad3d1f6857e3\"\u003eStatic FFmpeg build on Ubuntu 16.04 guide\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/Brainiarc7/18fca697891aea0e879f13ed092cb213\"\u003eUsing FFmpeg with GNU parallel\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/Brainiarc7/c6164520f082c27ae7bbea9556d4a3ba\"\u003eListing NVENC and NPP capabilities of FFmpeg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/Brainiarc7/8b471ff91319483cdb725f615908286e\"\u003eEncoding HEVC using FFmpeg with NVENC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/Brainiarc7/ebf3091efd2bf0a0ded0f9715cd43a38\"\u003eFFmpeg cheatsheet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/zimbatm/ffmpeg-static\"\u003eFFmpeg-static build scripts\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "tansey-lab/bayestme", + "latest_release": "v0.0.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBayesTME: A unified statistical framework for spatial transcriptomics\u003c/h1\u003e\u003ca id=\"user-content-bayestme-a-unified-statistical-framework-for-spatial-transcriptomics\" class=\"anchor\" aria-label=\"Permalink: BayesTME: A unified statistical framework for spatial transcriptomics\" href=\"#bayestme-a-unified-statistical-framework-for-spatial-transcriptomics\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tansey-lab/bayestme/actions/workflows/tests.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/tansey-lab/bayestme/actions/workflows/tests.yml/badge.svg\" alt=\"tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://bayestme.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8b6d62894915cc2418c18b79c9a11edde0a91684f1a459f2c09d8ebd4af92fb1/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6261796573746d652f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/bayestme/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/511984802\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/40654d00d94f98f904a2b1be34f950c9398843a972d3041f17e2dd03174bbfef/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3531313938343830322e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/511984802.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis package implements BayesTME, a fully Bayesian method for analyzing ST data without needing single-cell RNA-seq (scRNA) reference data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://bayestme.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ebayestme.readthedocs.io\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-bayestmereadthedocsio\" class=\"anchor\" aria-label=\"Permalink: bayestme.readthedocs.io\" href=\"#bayestmereadthedocsio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use this code, please cite the \u003ca href=\"https://doi.org/10.1016/j.cels.2023.06.003\" rel=\"nofollow\"\u003emanuscript\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eZhang H, Hunter MV, Chou J, Quinn JF, Zhou M, White RM, Tansey W. BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment. Cell Syst. 2023 Jul 19;14(7):605-619.e7. doi: 10.1016/j.cels.2023.06.003. PMID: 37473731; PMCID: PMC10368078.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBibtex citation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@article{Zhang:2023aa,\n\tauthor = {Zhang, Haoran and Hunter, Miranda V and Chou, Jacqueline and Quinn, Jeffrey F and Zhou, Mingyuan and White, Richard M and Tansey, Wesley},\n\tjournal = {Cell Syst},\n\tmonth = {Jul},\n\tnumber = {7},\n\tpages = {605--619},\n\ttitle = {BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment.},\n\tvolume = {14},\n\tyear = {2023}}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeveloper Setup\u003c/h2\u003e\u003ca id=\"user-content-developer-setup\" class=\"anchor\" aria-label=\"Permalink: Developer Setup\" href=\"#developer-setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease run \u003ccode\u003emake install_precommit_hooks\u003c/code\u003e from the root of the repository\nto install the pre-commit hooks.\u003c/p\u003e\n\u003cp\u003eWhen you run any \u003ccode\u003egit commit\u003c/code\u003e command these pre-commit hooks will run and format any files that you changed in your commit.\u003c/p\u003e\n\u003cp\u003eAny unchanged files will not be formatted.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInternal Contributions\u003c/h3\u003e\u003ca id=\"user-content-internal-contributions\" class=\"anchor\" aria-label=\"Permalink: Internal Contributions\" href=\"#internal-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen contributing to this repository, please use the feature branch workflow documented here: \u003ca href=\"https://github.com/tansey-lab/wiki/blob/master/FEATURE_BRANCH_WORKFLOW.md\"\u003ehttps://github.com/tansey-lab/wiki/blob/master/FEATURE_BRANCH_WORKFLOW.md\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 15, "subscribers_count": 3, "topics": [], - "updated_at": 1696226360.0 + "updated_at": 1732701041.0 }, { "data_format": 2, - "description": "High-performance (\u0026 with GPU support) implementation of Stochastic Reconfiguration and Stochastic Wavefunction Optimisation methods for Neural Quantum States", + "description": null, "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "twesterhout/nqs-playground", - "latest_release": null, - "readme": "\u003cp\u003ePyTorch-based implementation of SR and SWO for NQS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContents\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#conda\"\u003eConda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#building-from-source\"\u003eBuilding from source\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConda\u003c/h2\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eWARNING:\u003c/strong\u003e The version available on Conda is currently out of date.\nPlease, build from source for the latest features.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThe simplest way to get started using \u003ccode\u003enqs_playground\u003c/code\u003e package is to install it\nusing \u003ca href=\"https://docs.conda.io/en/latest/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda install -c twesterhout nqs_playground\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding from source\u003c/h2\u003e\u003ca id=\"user-content-building-from-source\" class=\"anchor\" aria-label=\"Permalink: Building from source\" href=\"#building-from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCPU-only version\u003c/h3\u003e\u003ca id=\"user-content-cpu-only-version\" class=\"anchor\" aria-label=\"Permalink: CPU-only version\" href=\"#cpu-only-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you do not have access or do not wish to use a GPU you can use cpu-only\nversion PyToch and nqs_playground. For this, first clone the repository:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/twesterhout/nqs-playground.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow just run \u003ca href=\"./build_locally_cpu.sh\"\u003e\u003ccode\u003ebuild_locally_cpu.sh\u003c/code\u003e\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./build_locally_cpu.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFull version\u003c/h3\u003e\u003ca id=\"user-content-full-version\" class=\"anchor\" aria-label=\"Permalink: Full version\" href=\"#full-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO\u003c/p\u003e\n", - "stargazers_count": 15, - "subscribers_count": 4, + "full_name": "iqbal-lab-org/viridian", + "latest_release": "v1.3.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/iqbal-lab-org/viridian/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/viridian/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eViridian\u003c/h1\u003e\u003ca id=\"user-content-viridian\" class=\"anchor\" aria-label=\"Permalink: Viridian\" href=\"#viridian\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUltra-careful amplicon-aware viral assembly for tiled amplicon schemes.\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/iqbal-lab-org/viridian/wiki\"\u003eViridian Wiki\u003c/a\u003e\nfor full documentation.\u003c/p\u003e\n\u003cp\u003ePreprint: \u003ca href=\"https://doi.org/10.1101/2024.04.29.591666\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2024.04.29.591666\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recommended method is to use a pre-built Docker or Singularity container\n(see the wiki for how to build your own).\u003c/p\u003e\n\u003cp\u003eBoth the Docker and Singularity container have the main script\n\u003ccode\u003eviridian\u003c/code\u003e installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet a Docker image of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/iqbal-lab-org/viridian:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll Docker images from v1.2.1 onwards are listed in the\n\u003ca href=\"https://github.com/iqbal-lab-org/viridian/pkgs/container/viridian\"\u003epackages page\u003c/a\u003e.\nOlder images can be found on the\n\u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/pkgs/container/viridian_workflow\"\u003eold packages page\u003c/a\u003e\n(viridian used to be called \"viridian_workflow\", and hence were put on a packages page\ncalled \"viridian_workflow\" by github).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/viridian/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download.\nEach release from v1.2.1 onwards has a singularity image file called\n\u003ccode\u003eviridian_vX.Y.Z.img\u003c/code\u003e, where \u003ccode\u003eX.Y.Z\u003c/code\u003e is the release version. Newer releases\nalso have an image for ARM architecture called \u003ccode\u003eviridian_vX.Y.Z.arm.img\u003c/code\u003e.\nOlder images (from when viridian was called \"viridian_workflow\") are\ncalled \u003ccode\u003eviridian_workflow_vX.Y.Z.img\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest\u003c/h2\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo quickly test if the installation is successful, this command\nwill download the FASTQ reads files for SARS-CoV-2 sequencing\nrun ERR8959214 and assemble it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample --run_accession ERR8959214 --outdir test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt should take less than 1 minute to run, and make a new directory\ncalled \u003ccode\u003etest\u003c/code\u003e containing the results. The output files are\ndescribed below. The important thing here is that the log\nfile \u003ccode\u003elog.json.gz\u003c/code\u003e should have the entry\n\u003ccode\u003e\"result\": \"Success\"\u003c/code\u003e (around line 40-50), indicating that\nthe run was successful.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese instructions assume that you are assembling SARS-CoV-2 data.\u003c/p\u003e\n\u003cp\u003eTo run on paired Illumina reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample \\\n --tech illumina \\\n --reads1 reads_1.fastq.gz \\\n --reads2 reads_2.fastq.gz \\\n --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run on unpaired nanopore reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample \\\n --tech ont \\\n --reads reads.fastq.gz \\\n --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run on paired or unpaired Ion Torrent reads, use either of the\nabove commands, but with the option \u003ccode\u003e--tech iontorrent\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eDownload reads with accession SRR12345678 and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample --run_accession SRR12345678 --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe sequencing tech and unpaired/paired is taken from the ENA metadata\nfor each run.\u003c/p\u003e\n\u003cp\u003eDownload and run on a batch of sequencing runs, using a file\n\u003ccode\u003eruns.txt\u003c/code\u003e of INSDC run accessions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian dl_and_run --acc_file runs.txt --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput files\u003c/h2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-label=\"Permalink: Output files\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe default files in the output directory are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econsensus.fa.gz\u003c/code\u003e: a gzipped FASTA file of the consensus sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evariants.vcf\u003c/code\u003e: a VCF file of the identified variants between the consensus\nsequence and the reference genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog.json.gz\u003c/code\u003e: a gzipped JSON file that contains logging information\nfor the viridian run.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eqc.tsv.gz\u003c/code\u003e: a gzipped tab-delimited file of per-base QC information\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme_id.depth_across_genome.pdf\u003c/code\u003e: a plot of the read depth across\nthe genome, with amplicons coloured in the background.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme_id.score_plot.pdf\u003c/code\u003e: a plot of the scoring for amplicon scheme\nidentification.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the option \u003ccode\u003e--keep_bam\u003c/code\u003e is used, then a sorted BAM file of the reads mapped\nto the reference will also be present, called\n\u003ccode\u003ereference_mapped.bam\u003c/code\u003e (and its index file \u003ccode\u003ereference_mapped.bam.bai\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUseful options\u003c/h2\u003e\u003ca id=\"user-content-useful-options\" class=\"anchor\" aria-label=\"Permalink: Useful options\" href=\"#useful-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--sample_name MY_NAME\u003c/code\u003e: use this to change the sample name\n(default is \"sample\") that is put in the final FASTA file, BAM file, and\nVCF file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads_bam MY_READS.bam\u003c/code\u003e: instead of providing FASTQ (or FASTA) files of\nreads, you can provide a sorted by genome coordinate and indexed BAM file.\nThe reference genome must be the same as that used by viridian\n(by default MN908947).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--keep_bam\u003c/code\u003e: use this option to keep the BAM file of original input reads\nmapped to the reference genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--decontam COVID\u003c/code\u003e: decontaminate the reads using ReadItAndKeep at the\nstart of the pipeline (this is incompatible with \u003ccode\u003e--reads_bam\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--force\u003c/code\u003e: use with caution - it will overwrite the output directory if\nit already exists.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--write_msa indel_as_ref\u003c/code\u003e: this will write a FASTA file\ncalled \u003ccode\u003emsa.indel_as_ref.fa\u003c/code\u003e that can be\nused to build trees. It has the consensus sequence aligned to the\nreference genome, but with insertions in the consensus ignored and\ndeletions replaced with the reference sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--run_accession RUN_ID\u003c/code\u003e: using this option will download the specified reads\nfrom the ENA, and infer the \u003ccode\u003e--tech\u003c/code\u003e option from the ENA metadata\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 16, + "subscribers_count": 7, "topics": [], - "updated_at": 1694445047.0 + "updated_at": 1732191950.0 }, { "data_format": 2, - "description": "Variant call verification", + "description": "Annotate non-coding regulatory vars using our GREEN-DB, prediction scores, conservation and pop AF", "filenames": [ - "Singularity.def" + "Singularity.GREEN-VARAN" ], - "full_name": "iqbal-lab-org/varifier", - "latest_release": "v0.4.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003evarifier\u003c/h1\u003e\u003ca id=\"user-content-varifier\" class=\"anchor\" aria-label=\"Permalink: varifier\" href=\"#varifier\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote: full documentation is under construction\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003econda\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/bioconda/varifier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4c77703a45fbd033943e0b5289a911e9072cc0381399b2ec90a594f207aaea49/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f766e2f62696f636f6e64612f7661726966696572\" alt=\"Conda (channel only)\" data-canonical-src=\"https://img.shields.io/conda/vn/bioconda/varifier\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/varifier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/53a23ec73ed044b1ac84ff287a35e972e125e9ea440d670f10215c8db336bd19/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f76617269666965722f6261646765732f706c6174666f726d732e737667\" alt=\"bioconda version\" data-canonical-src=\"https://anaconda.org/bioconda/varifier/badges/platforms.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePrerequisite: \u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/\" rel=\"nofollow\"\u003e\u003ccode\u003econda\u003c/code\u003e\u003c/a\u003e (and bioconda channel \u003ca href=\"https://bioconda.github.io/user/install.html#set-up-channels\" rel=\"nofollow\"\u003ecorrectly set up\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ conda install varifier\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContainer\u003c/h3\u003e\u003ca id=\"user-content-container\" class=\"anchor\" aria-label=\"Permalink: Container\" href=\"#container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker images are hosted at \u003ca href=\"https://quay.io/repository/iqballab/varifier\" rel=\"nofollow\"\u003equay.io\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ccode\u003esingularity\u003c/code\u003e\u003c/h4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePrerequisite: \u003ca href=\"https://sylabs.io/guides/3.4/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003e\u003ccode\u003esingularity\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ URI=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edocker://quay.io/iqballab/varifier\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n$ singularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$URI\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e varifier --help\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe above will use the latest version. If you want to specify a version/commit then use a\n\u003ca href=\"https://quay.io/repository/iqballab/varifier\" rel=\"nofollow\"\u003etag\u003c/a\u003e (or commit) like so.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ TAG=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e3c8152a\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n$ URI=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edocker://quay.io/iqballab/varifier:\u003cspan class=\"pl-smi\"\u003e${TAG}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ccode\u003edocker\u003c/code\u003e\u003c/h4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://quay.io/repository/iqballab/varifier\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cc5de2f9f030fa2ab2da1195c29e332ad9a13dc4512cf3e8809dbecefd1fb277/68747470733a2f2f717561792e696f2f7265706f7369746f72792f697162616c6c61622f76617269666965722f737461747573\" alt=\"Docker Repository on Quay\" title=\"Docker Repository on Quay\" data-canonical-src=\"https://quay.io/repository/iqballab/varifier/status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePrerequisite: \u003ca href=\"https://docs.docker.com/v17.12/install/\" rel=\"nofollow\"\u003e\u003ccode\u003edocker\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre lang=\"shhell\"\u003e\u003ccode\u003e$ docker pull quay.io/iqballab/varifier\n$ docker run quay.io/iqballab/varifier varifier --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can find all the available tags on the \u003ca href=\"https://quay.io/repository/iqballab/varifier\" rel=\"nofollow\"\u003equay.io repository\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLocal\u003c/h3\u003e\u003ca id=\"user-content-local\" class=\"anchor\" aria-label=\"Permalink: Local\" href=\"#local\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3 (tested on version 3.6.9)\u003c/li\u003e\n\u003cli\u003emummer installed\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epaftools.js\u003c/code\u003e and \u003ccode\u003ek8\u003c/code\u003e in your path. See \u003ca href=\"https://github.com/lh3/minimap2/tree/master/misc\"\u003ehttps://github.com/lh3/minimap2/tree/master/misc\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInstall:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo verify calls in a VCF file, you will need:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ccode\u003etest.vcf\u003c/code\u003e - the VCF file to be tested\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eref.fasta\u003c/code\u003e - FASTA file of reference corresponding to the VCF file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etruth.fasta\u003c/code\u003e - a truth genome FASTA file\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRun:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evarifier vcf_eval truth.fasta ref.fasta test.vcf out_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis makes a new directory called \u003ccode\u003eout_dir\u003c/code\u003e. The results are in the file\n\u003ccode\u003esummary_stats.json\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTests\u003c/h2\u003e\u003ca id=\"user-content-tests\" class=\"anchor\" aria-label=\"Permalink: Tests\" href=\"#tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run the tests, run \u003ccode\u003etox\u003c/code\u003e from the root of the repository.\u003c/p\u003e\n", - "stargazers_count": 15, - "subscribers_count": 6, + "full_name": "edg1983/GREEN-VARAN", + "latest_release": "v1.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGREEN-VARAN and the GREEN-DB\u003c/h1\u003e\u003ca id=\"user-content-green-varan-and-the-green-db\" class=\"anchor\" aria-label=\"Permalink: GREEN-VARAN and the GREEN-DB\" href=\"#green-varan-and-the-green-db\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic Regulatory Elements ENcyclopedia\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e _.-~` `~-.\n _.--~~~---,.__ _.,;; . -=(@\u0027`\\\n .-` ``~~~~--~~` \u0027;;; ____)\n _.\u0027 \u0027. \u0027;;;;; \u0027`_.\u0027\n .-~;` `\\ \u0027 \u0027;;;;;__.~`\n .\u0027 .\u0027 `\u0027. | / /;\u0027\u0027\n \\/ .---\u0027\u0027 ``) /\u0027-._____.--\u0027\\ \\\\\n _/| (` / /` `\\ \\__\n\u0027, `/- \\ \\ __/ (_ /-\\-\\-`\n `;\u0027-..___) | `/-\\-\\-`\n `-. .\u0027\njgs `~~~~``\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7d32539417115545ef0cb8b4946d01060b12c3920c7719818d7fe0524e647a4e/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f677265656e2d766172616e2f62616467652f3f76657273696f6e3d6c6174657374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7d32539417115545ef0cb8b4946d01060b12c3920c7719818d7fe0524e647a4e/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f677265656e2d766172616e2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"https://readthedocs.org/projects/green-varan/badge/?version=latest\" data-canonical-src=\"https://readthedocs.org/projects/green-varan/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/_container/618c1e989b47264715334728\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the home of the GREEN-DB and companion tools (GREEN-VARAN)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGREEN-DB\u003c/h4\u003e\u003ca id=\"user-content-green-db\" class=\"anchor\" aria-label=\"Permalink: GREEN-DB\" href=\"#green-db\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic Regulatory Elements ENcyclopedia Database\u003c/strong\u003e\nA collection of ~2.4M regulatory regions in the human genome, with information about controlled genes, tissues of activity and associated phenotypes. GREEN-DB is available for free for academic usage in a \u003ca href=\"https://zenodo.org/record/5636209\" rel=\"nofollow\"\u003eZenodo repository\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGREEN-VARAN\u003c/h4\u003e\u003ca id=\"user-content-green-varan\" class=\"anchor\" aria-label=\"Permalink: GREEN-VARAN\" href=\"#green-varan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic Regulatory Elements ENcyclopedia VARiant ANnotation\u003c/strong\u003e\nAnnotate non-coding regulatory variants in a VCF with information from GREEN-DB\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epossibly controlled genes\u003c/li\u003e\n\u003cli\u003eoverlapping regulatory region IDs and data sources\u003c/li\u003e\n\u003cli\u003eoverlapping regulatory regions max constraint value\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGREEN-VARAN workflow\u003c/h4\u003e\u003ca id=\"user-content-green-varan-workflow\" class=\"anchor\" aria-label=\"Permalink: GREEN-VARAN workflow\" href=\"#green-varan-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eA Nextflow workflow for complete VCF processing\u003c/strong\u003e\nGiven a VCF, ideally annotated for gene consequences with snpEff or bcftools, the workflow can be used to automate processing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eannotate with functional regions (TFBS, DNase, UCNE)\u003c/li\u003e\n\u003cli\u003eannotate with the 3 best non-coding variant prediction scores (ncER, FATHMM-MKL, ReMM)\u003c/li\u003e\n\u003cli\u003eannotate population AF from gnomAD genomes\u003c/li\u003e\n\u003cli\u003eperform regulatory variant prioritization using GREEN-VARAN\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\"workflow/README.md\"\u003eworkflow readme\u003c/a\u003e for more details or look at the full documentation.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://green-varan.readthedocs.io/en/latest\" rel=\"nofollow\"\u003eDetailed documentation\u003c/a\u003e on GREEN-DB and GREEN-VARAN tool and workflow is provided in ReadTheDocs\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGREEN-VARAN tools are written in Nim. GREEN-VARAN relies on \u003ca href=\"https://github.com/brentp/hts-nim\"\u003ehts-nim\u003c/a\u003e by Brent Pedersen for fast VCF processing. The GREEN-DB BED files are needed for annotation (see Download the supporting files)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGet the tool binaries from the repository\u003c/h3\u003e\u003ca id=\"user-content-get-the-tool-binaries-from-the-repository\" class=\"anchor\" aria-label=\"Permalink: Get the tool binaries from the repository\" href=\"#get-the-tool-binaries-from-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to run GREEN-VARAN is to download the pre-compiled binaries from the latest release at \u003ca href=\"https://github.com/edg1983/GREEN-VARAN\"\u003ehttps://github.com/edg1983/GREEN-VARAN\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompile the tool\u003c/h3\u003e\u003ca id=\"user-content-compile-the-tool\" class=\"anchor\" aria-label=\"Permalink: Compile the tool\" href=\"#compile-the-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, you can clone the repository\n\u003ccode\u003egit clone https://github.com/edg1983/GREEN-VARAN.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAnd then compile the greenvaran using \u003ca href=\"https://nim-lang.org/\" rel=\"nofollow\"\u003eNim compiler\u003c/a\u003e.\nGREEN-VARAN requires\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enim \u0026gt;= 0.10\u003c/li\u003e\n\u003cli\u003ehts-nim \u0026gt;= 0.3.4\u003c/li\u003e\n\u003cli\u003eargparse 0.10.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you have Singularity installed, you can use the script \u003ccode\u003enim_compile.sh\u003c/code\u003e to create a static binary with no dependencies\nThis uses musl-hts-nim as described in hts-nim repository (see \u003ca href=\"https://github.com/brentp/hts-nim#static-binary-with-singularity\"\u003ehttps://github.com/brentp/hts-nim#static-binary-with-singularity\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eThe accessory greendb_query tool can be compiled using \u003ccode\u003enim compile greendb_query.nim\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGREEN-VARAN performs annotation of small variants or structural variants VCF adding information on potential regulatory variants from GREEN-DB. Especially, it can annotate possible controlled genes and a prioritization level (this latter need the presence of some additional annotations, see below)\nIt provides also ability to tag variants linked to genes of interest and update existing gene-level annotations from SnpEff or bcftools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBasic usage\u003c/h3\u003e\u003ca id=\"user-content-basic-usage\" class=\"anchor\" aria-label=\"Permalink: Basic usage\" href=\"#basic-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egreenvaran [run mode] [options]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe running mode can be one of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003esmallvars\u003c/p\u003e\n\u003cp\u003eIn this mode the tool will perform annotation for a small variants VCF.\nIt will annotate variants with information on the possible regulatory role based on GREENDB and eventually provide prioritization levels\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esv\u003c/p\u003e\n\u003cp\u003eIn this mode the tool will perform annotation for a structural variants VCF.\nCapability in this case is limited to annotation of overlapping GREENDB regions and controlled genes. No prioritization is provided\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003equerytab\u003c/p\u003e\n\u003cp\u003eThis mode is a convenient way to automatically prepare input table to be used with the query tool to extract detailed information from GREENDB database.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eversion\u003c/p\u003e\n\u003cp\u003ePrint the tool version\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNB.\u003c/strong\u003e To perform prioritization of small variants some additional annotation fields are expected in the input VCF, see the prioritization section below. By default, when these information are not present the prioritization level will be set to zero for all annotated variants.\nWe also provide pre-processed datasets (see \u003ca href=\"resources/README.md\"\u003eresources\u003c/a\u003e) and Nextflow workflow to automate the whole process (see \u003ca href=\"workflow/README.md\"\u003eworkflow\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand line options\u003c/h3\u003e\u003ca id=\"user-content-command-line-options\" class=\"anchor\" aria-label=\"Permalink: Command line options\" href=\"#command-line-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003esmallvars and sv shared options\u003c/h4\u003e\u003ca id=\"user-content-smallvars-and-sv-shared-options\" class=\"anchor\" aria-label=\"Permalink: smallvars and sv shared options\" href=\"#smallvars-and-sv-shared-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eoption\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e-i, --invcf INVCF\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003epath to indexed input vcf.gz / bcf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-o, --outvcf OUTVCF\u003c/td\u003e\n\u003ctd\u003eoutput vcf / vcf.gz file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-d, --db DB\u003c/td\u003e\n\u003ctd\u003eGREEN-DB bed.gz file for your build (see download section)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-s, --dbschema DBSCHEMA\u003c/td\u003e\n\u003ctd\u003ejson file containing greendb column mapping \u003cbr\u003e A default configuration for GREENDB v2.5 is available in config folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-u, --noupdate\u003c/td\u003e\n\u003ctd\u003edo not update ANN / BCSQ field in the input VCF\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-f, --filter\u003c/td\u003e\n\u003ctd\u003efilter instead of annotate. Only variants with greendb overlap will be written. \u003cbr\u003e If --genes is active, the output will contain only variants connected to the input genes of interest\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-m, --impact IMPACT\u003c/td\u003e\n\u003ctd\u003eWhich impact to assign when updating snpEff field \u003cbr\u003e Possible values: [HIGH, MODERATE, LOWm MODIFIER] (default: MODIFIER)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--chrom CHROM\u003c/td\u003e\n\u003ctd\u003eAnnotate only for a specific chromosome \u003cbr\u003e Useful to parallelize across chromosomes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--nochr\u003c/td\u003e\n\u003ctd\u003eUse this when input VCF does not have chr prefix in chromosome names\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-g, --genes GENES\u003c/td\u003e\n\u003ctd\u003eGene symbols for genes of interest, variants connected to those will be flagged with greendb_VOI tag \u003cbr\u003e This can be a comma-separated list or a text file listing genes one per line\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--connection CONNECTION\u003c/td\u003e\n\u003ctd\u003eRegion-gene connections accepted for annotation \u003cbr\u003e Possible values: [all, closest, annotated] (default: all)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--log LOG\u003c/td\u003e\n\u003ctd\u003eLog file. Default is greenvaran_[now].log\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003esv specific options\u003c/h4\u003e\u003ca id=\"user-content-sv-specific-options\" class=\"anchor\" aria-label=\"Permalink: sv specific options\" href=\"#sv-specific-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eoption\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e-p, --padding PADDING\u003c/td\u003e\n\u003ctd\u003eValue to add on each side of BND/INS, this override the CIPOS when set\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cipos CIPOS\u003c/td\u003e\n\u003ctd\u003eINFO field listing the confidence interval around breakpoints (default: CIPOS) \u003cbr\u003e It is expected to have 2 comma-separated values\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-t, --minoverlap MINOVERLAP\u003c/td\u003e\n\u003ctd\u003eMin fraction of GREENDB region to be overlapped by a SV (default: 0.000001)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-b, --minbp MINBP\u003c/td\u003e\n\u003ctd\u003eMin number of bases of GREENDB region to be overlapped by a SV (default: 1)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003esmallvars specific options\u003c/h4\u003e\u003ca id=\"user-content-smallvars-specific-options\" class=\"anchor\" aria-label=\"Permalink: smallvars specific options\" href=\"#smallvars-specific-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eoption\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e-c, --config CONFIG\u003c/td\u003e\n\u003ctd\u003ejson config file for prioritization \u003cbr\u003e A default configuration for the four level described in the paper is provided in config folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--prioritization_strategy\u003c/td\u003e\n\u003ctd\u003eset the strategy used to compute prioritization levels. Possible values are: levels (default) or pileup\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-p, --permissive\u003c/td\u003e\n\u003ctd\u003ePerform prioritization even if one of the INFO fields required by prioritization config is missing \u003cbr\u003e By default, when one of the expected fields is not defined in the header, the prioritization is disabled and all variants will get level zero\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnnotations added by GREEN-VARAN\u003c/h2\u003e\u003ca id=\"user-content-annotations-added-by-green-varan\" class=\"anchor\" aria-label=\"Permalink: Annotations added by GREEN-VARAN\" href=\"#annotations-added-by-green-varan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eINFO fields\u003c/h3\u003e\u003ca id=\"user-content-info-fields\" class=\"anchor\" aria-label=\"Permalink: INFO fields\" href=\"#info-fields\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFields in the following table are added to INFO fields by GREEN-VARAN. greendb_level will be added only for small variants\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003etag\u003c/th\u003e\n\u003cth\u003edata type\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_id\u003c/td\u003e\n\u003ctd\u003eString\u003c/td\u003e\n\u003ctd\u003eComma-separated list of GREEN-DB IDs identifying the regions that overlap this variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_stdtype\u003c/td\u003e\n\u003ctd\u003eString\u003c/td\u003e\n\u003ctd\u003eComma-separated list of standard region types as annotated in GREEN-DB for regions overlapping the variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_dbsource\u003c/td\u003e\n\u003ctd\u003eString\u003c/td\u003e\n\u003ctd\u003eComma-separated list of data sources as annotated in GREEN-DB for regions overlapping the variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_level\u003c/td\u003e\n\u003ctd\u003eInteger\u003c/td\u003e\n\u003ctd\u003eVariant prioritization level computed by GREEN-VARAN. See Prioritization section below\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_more_support\u003c/td\u003e\n\u003ctd\u003eInteger\u003c/td\u003e\n\u003ctd\u003eSum up of the additional pieces of evidence that support this variant as configured in the prioritization JSON\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_constraint\u003c/td\u003e\n\u003ctd\u003eFloat\u003c/td\u003e\n\u003ctd\u003eThe maximum constraint value across GREEN-DB regions overlapping the variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_genes\u003c/td\u003e\n\u003ctd\u003eString\u003c/td\u003e\n\u003ctd\u003ePossibly controlled genes for regulatory regions overlapping this variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_VOI\u003c/td\u003e\n\u003ctd\u003eFlag\u003c/td\u003e\n\u003ctd\u003eWhen \u003ccode\u003e--genes\u003c/code\u003e option is active this flag is set when any of the input genes is among the possibly controlled genes for overlapping regulatory regions.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUpdated gene consequences\u003c/h3\u003e\u003ca id=\"user-content-updated-gene-consequences\" class=\"anchor\" aria-label=\"Permalink: Updated gene consequences\" href=\"#updated-gene-consequences\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, GREEN-VARAN update gene consequences in the SnpEff ANN field or the bcftools BCSQ if one is present in the input VCF file. In this way the annotation can be processed by most downstream tools evaluating segregation.\nIf none is found, GREEN-VARAN will create a new ANN field. To switch off gene consequence update use the \u003ccode\u003e--noupdate\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eThe tool will add a new consequence for each possibly controlled gene, limited by the \u003ccode\u003e--connection\u003c/code\u003e option.\nThe new consequence will follow standard format according to SnpEff or bcftools and have MODIFIER impact by default.\nThis can be adjusted using the \u003ccode\u003e--impact\u003c/code\u003e option.\nThe gene effect will be set according to the GREEN-DB region type, adding 5 one of the terms: \u003ccode\u003ebivalent, enhancer, insulator, promoter, silencer\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eExample ANN / BCSQ field added by GREEN-VARAN.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eANN=C|enhancer|MODIFIER|GeneA||||||||||||\nBCQS=enhancer|GeneA||\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrioritization of small variants\u003c/h2\u003e\u003ca id=\"user-content-prioritization-of-small-variants\" class=\"anchor\" aria-label=\"Permalink: Prioritization of small variants\" href=\"#prioritization-of-small-variants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGREEN-VARAN will consider GREEN-DB annotations, additional functional regions and non-coding impact prediction scores to provide a prioritization level for each annotated variant. This level is annotated under \u003ccode\u003egreenvaran_level\u003c/code\u003e tag in the INFO field.\u003c/p\u003e\n\u003cp\u003eThis fields is an integer from 0 to N which summarize evidences supporting a regulatory impact for the variant. Higher values are associated to a higher support of regulatory impact.\u003c/p\u003e\n\u003cp\u003eYou need 3 set of information in your input VCF to run prioritization mode when using the default config provided.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003egnomAD_AF, gnomAD_AF_nfe\u003c/strong\u003e: float values describing global and NFE population AF from gnomAD\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003encER, FATHMM-MKL and ReMM\u003c/strong\u003e: float values providing scores predictions\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTFBS, DNase and UCNE\u003c/strong\u003e: flags describing overlap with additional functional regions\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe prioritization schema can be adjusted by modifying the .json file passed to \u003ccode\u003e--config\u003c/code\u003e. A default file is provided in config folder.\u003c/p\u003e\n\u003cp\u003eThe default behaviour is \u003ccode\u003e--prioritization_strategy levels\u003c/code\u003e which reproduce the 4 levels as described in the paper.\nAlternatively, you can chose a \"pile-up\" approach setting \u003ccode\u003e--prioritization_strategy pileup\u003c/code\u003e which simply sum evidences across levels. This means that the criteria described above are tested independently and the level reported is increased by one for each satisfied criteria.\u003c/p\u003e\n\u003cp\u003eSee documentation for more details \u003ca href=\"https://green-varan.readthedocs.io/en/latest\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun using singularity\u003c/h2\u003e\u003ca id=\"user-content-run-using-singularity\" class=\"anchor\" aria-label=\"Permalink: Run using singularity\" href=\"#run-using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tool binaries should work on most linux based system. In case you have any issue, we also provide GREEN-VARAN as Singularity image (tested on singularity \u0026gt;= 3.2).\nA Singularity recipe is included in the repository or you can pull the image from Singularity Library using\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull library://edg1983/greenvaran/greenvaran:latest\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe image contains both greenvaran and greendb_query tools.\nThe general usage is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec \\\n greenvaran.sif \\\n tool_name [tool arguments]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBind specific folders for resources or data\u003c/h3\u003e\u003ca id=\"user-content-bind-specific-folders-for-resources-or-data\" class=\"anchor\" aria-label=\"Permalink: Bind specific folders for resources or data\" href=\"#bind-specific-folders-for-resources-or-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tool needs access to input VCF file, the GREEN-DB bed file and the config files so remember to bind the corresponding locations in the container\u003c/p\u003e\n\u003cp\u003eSee the following example where we use the current working directory for input/output, while other files are located\nin the default config / resources folder within greenvaran folder (greenvaran_path). In the example we use GRCh38 genome build\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec \\\n --bind /greenvaran_path/resources/GRCh38:/db_files \\\n --bind /greenvaran_path/config:/config_files \\\n --bind ${PWD}:/data \\\n greenvaran.sif \\\n greenvaran -i /data/input.vcf.gz \\\n -o /data/output.vcf.gz \\\n --db /db_files/GRCh38_GREEN-DB.bed.gz \\\n --dbschema /config_files/greendb_schema_v2.5.json \\\n --config /config_files/prioritize_smallvars.json\n [additional tool arguments]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample usage\u003c/h2\u003e\u003ca id=\"user-content-example-usage\" class=\"anchor\" aria-label=\"Permalink: Example usage\" href=\"#example-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esmall variants test\u003c/h3\u003e\u003ca id=\"user-content-small-variants-test\" class=\"anchor\" aria-label=\"Permalink: small variants test\" href=\"#small-variants-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egreenvaran smallvars \\\n --invcf test/VCF/GRCh38.test.smallvars.vcf.gz \\\n --outvcf test/out/smallvars.annotated.vcf.gz \\\n --config config/prioritize_smallvars.json \\\n --dbschema config/greendb_schema_v2.5.json \\\n --db resources/GRCh38/GRCh38_GREEN-DB.bed.gz \\\n --genes test/VCF/genes_list_example.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003estructural variants test\u003c/h3\u003e\u003ca id=\"user-content-structural-variants-test\" class=\"anchor\" aria-label=\"Permalink: structural variants test\" href=\"#structural-variants-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egreenvaran sv \\\n --invcf test/VCF/GRCh38.test.SV.vcf.gz \\\n --outvcf test/out/SV.annotated.vcf.gz \\\n --dbschema config/greendb_schema_v2.5.json \\\n --db resources/GRCh38/GRCh38_GREEN-DB.bed.gz \\\n --minbp 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen you use GREEN-DB or GREEN-VARAN tools please cite:\n\u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.09.17.301960\" rel=\"nofollow\"\u003eGREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants in whole-genome sequencing\u003c/a\u003e Giacopuzzi E., Popitsch N., Taylor JC. BiorXiv (2021)\u003c/p\u003e\n\u003cp\u003eWhen you use GREEN-VARAN workflow for small variants annotation please also cite:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0973-5\" rel=\"nofollow\"\u003eVcfanno: fast, flexible annotation of genetic variants\u003c/a\u003e\nBrent S. Pedersen, Ryan M. Layer \u0026amp; Aaron R. Quinlan. Genome Biology volume 17, Article number: 118 (2016)\u003c/p\u003e\n\u003cp\u003eAdditionally, when you use any prediction score for annotation, please cite the corresponding publication.\u003c/p\u003e\n", + "stargazers_count": 16, + "subscribers_count": 3, "topics": [], - "updated_at": 1730217051.0 + "updated_at": 1709804259.0 }, { "data_format": 2, - "description": "Implementation of the 3D reconstruction pipeline optimized for plant branching structures.", + "description": "Deep dive into containerizing scientific apps.", "filenames": [ - "Singularity_colmap_vsfm", - "Singularity_recipe/Singularity", - "model_preprocess/Singularity" + "PEARC22/files/nwp-docker-container/Singularity", + "SC21/files/nwp-docker-container/Singularity" ], - "full_name": "Computational-Plant-Science/3D_model_reconstruction_demo", + "full_name": "XSEDE/Container_Tutorial", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDIRT/3D: 3D root phenotyping system for field-grown maize roots\u003c/h1\u003e\u003ca id=\"user-content-dirt3d-3d-root-phenotyping-system-for-field-grown-maize-roots\" class=\"anchor\" aria-label=\"Permalink: DIRT/3D: 3D root phenotyping system for field-grown maize roots\" href=\"#dirt3d-3d-root-phenotyping-system-for-field-grown-maize-roots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline: Build 3D root models from images captured by 3D root scanner, and compute 3D root trait by analyzing 3D root models and computing 3D root model structures.\u003c/p\u003e\n\u003cp\u003eThis repo was to Reconstruct a 3D point cloud root model from images.\u003c/p\u003e\n\u003cp\u003eFor example, a real root and a reconstruction, side by side:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../master/media/3D_scanner.gif\"\u003e\u003cimg src=\"../master/media/3D_scanner.gif\" alt=\"3D root scanner prototype\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../master/media/3D_model.gif\"\u003e\u003cimg src=\"../master/media/3D_model.gif\" alt=\"3D root model reconstruction\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to use this software is with Docker or Singularity. A public Docker image definition is available: \u003ccode\u003ecomputationalplantscience/dirt3d-reconstruction\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull an image or a repository from a registry\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull computationalplantscience/dirt3d-reconstruction\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMount the current working directory and open an interactive shell:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:/opt/dev -w /opt/dev computationalplantscience/dirt3d-reconstruction bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo allow \u003ccode\u003ecolmap\u003c/code\u003e to use CUDA-enabled GPUs, use \u003ccode\u003e--gpus all\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpen a shell in your current working directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell docker://computationalplantscience/dirt3d-reconstruction\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo allow \u003ccode\u003ecolmap\u003c/code\u003e to use CUDA-enabled GPUs, use the \u003ccode\u003e--nv\u003c/code\u003e flag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReconstructing a 3D point cloud\u003c/h2\u003e\u003ca id=\"user-content-reconstructing-a-3d-point-cloud\" class=\"anchor\" aria-label=\"Permalink: Reconstructing a 3D point cloud\" href=\"#reconstructing-a-3d-point-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo reconstruct a point cloud from an image set, use \u003ccode\u003epipeline.py\u003c/code\u003e as such:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 /opt/code/pipeline.py -i \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003einput directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -o \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eoutput directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -g \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehow many GPUs to use\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOmit the \u003ccode\u003e-g \u0026lt;# of GPUs\u0026gt;\u003c/code\u003e argument or set it to 0 to perform the reconstruction with CPUs only. Note that \u003ccode\u003e-g \u0026lt;# GPUs\u0026gt;\u003c/code\u003e is short for \u003ccode\u003e--gpus \u0026lt;# GPUs\u0026gt;\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA successful reconstruction will produce several files in the output directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esparse.ply\u003c/code\u003e: sparse point cloud model\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edense.ply\u003c/code\u003e: dense point cloud model\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emesh.ply\u003c/code\u003e: dense mesh model\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etimes.csv\u003c/code\u003e: time costs per step\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePreprocessing\u003c/h3\u003e\u003ca id=\"user-content-preprocessing\" class=\"anchor\" aria-label=\"Permalink: Preprocessing\" href=\"#preprocessing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are several optional preprocessing steps, all of which accept values \u003ccode\u003eTrue\u003c/code\u003e or \u003ccode\u003eFalse\u003c/code\u003e (and default to \u003ccode\u003eFalse\u003c/code\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--segmentation\u003c/code\u003e: crops to the largest feature\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--blur_detection\u003c/code\u003e: detects and omits blurry images\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--gamma_correction\u003c/code\u003e: increases brightness of dark images\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePMVS2 vs. Colmap for dense reconstruction\u003c/h3\u003e\u003ca id=\"user-content-pmvs2-vs-colmap-for-dense-reconstruction\" class=\"anchor\" aria-label=\"Permalink: PMVS2 vs. Colmap for dense reconstruction\" href=\"#pmvs2-vs-colmap-for-dense-reconstruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, PMVS2 is used for dense reconstruction on both CPU and GPU. Colmap can optionally be used with GPUs. It tends to produce significantly denser models but may run up to an order of magnitude more slowly.\u003c/p\u003e\n\u003cp\u003eTo enable dense reconstruction with Colmap, use \u003ccode\u003e-d COLMAP\u003c/code\u003e (short for \u003ccode\u003e--dense_strategy COLMAP\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eColmap configuration\u003c/h4\u003e\u003ca id=\"user-content-colmap-configuration\" class=\"anchor\" aria-label=\"Permalink: Colmap configuration\" href=\"#colmap-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are several configurable values for colmap\u0027s patch matching step during dense reconstruction. Optimal values will vary by host machine.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--cache_size\u003c/code\u003e: cache size (in GB) to use during patch matching, defaults to \u003ccode\u003e32\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--window_step\u003c/code\u003e: patch window step size, defaults to \u003ccode\u003e1\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--window_radius\u003c/code\u003e: patch window radius, defaults to \u003ccode\u003e5\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_iterations\u003c/code\u003e: number of patch match iterations, defaults to \u003ccode\u003e5\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_samples\u003c/code\u003e: number of sampled views, defaults to \u003ccode\u003e15\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--geom_consistency\u003c/code\u003e: whether to perform geometric dense reconstruction, defaults to \u003ccode\u003eFalse\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a 3D point cloud\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-3d-point-cloud\" class=\"anchor\" aria-label=\"Permalink: Visualizing a 3D point cloud\" href=\"#visualizing-a-3d-point-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently this software does not support model visualization. PLY files can be visualized with e.g. \u003ca href=\"https://www.meshlab.net/\" rel=\"nofollow\"\u003eMeshlab\u003c/a\u003e or \u003ca href=\"https://www.danielgm.net/cc/\" rel=\"nofollow\"\u003ecloudcompare\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDependencies\u003c/h1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software is built on top of COLMAP, VSFM, \u0026amp; PMVS2.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVisualSFM\u003c/h3\u003e\u003ca id=\"user-content-visualsfm\" class=\"anchor\" aria-label=\"Permalink: VisualSFM\" href=\"#visualsfm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"mailto:adamsgaard@ucsd.edu\"\u003eAnders Damsgaard\u003c/a\u003e with contributions by Caleb Adams and Connor P Doherty.\nChangchang Wu ( \u003ca href=\"mailto:wucc1130@gmail.com\"\u003ewucc1130@gmail.com\u003c/a\u003e )\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStructure from Motion\n[1] Changchang Wu, \"Towards Linear-time Incremental Structure From Motion\", 3DV 2013\n[2] Changchang Wu, \"VisualSFM: A Visual Structure from Motion System\", \u003ca href=\"http://ccwu.me/vsfm/\" rel=\"nofollow\"\u003ehttp://ccwu.me/vsfm/\u003c/a\u003e, 2011\u003c/li\u003e\n\u003cli\u003eBundle Adjustment\n[3] Changchang Wu, Sameer Agarwal, Brian Curless, and Steven M. Seitz, \"Multicore Bundle Adjustment\", CVPR 2011\u003c/li\u003e\n\u003cli\u003eFeature Detection\n[4] Changchang Wu, \"SiftGPU: A GPU implementation of Scale Invaraint Feature Transform (SIFT)\", \u003ca href=\"http://cs.unc.edu/~ccwu/siftgpu\" rel=\"nofollow\"\u003ehttp://cs.unc.edu/~ccwu/siftgpu\u003c/a\u003e, 2007\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCOLMAP\u003c/h3\u003e\u003ca id=\"user-content-colmap\" class=\"anchor\" aria-label=\"Permalink: COLMAP\" href=\"#colmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://colmap.github.io\" rel=\"nofollow\"\u003ehttps://colmap.github.io\u003c/a\u003e\nAuthor: Johannes L. Schoenberger (jsch-at-demuc-dot-de)\n@inproceedings{schoenberger2016sfm,\nauthor={Sch\"{o}nberger, Johannes Lutz and Frahm, Jan-Michael},\ntitle={Structure-from-Motion Revisited},\nbooktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},\nyear={2016},\n}\u003c/p\u003e\n\u003cp\u003e@inproceedings{schoenberger2016mvs,\nauthor={Sch\"{o}nberger, Johannes Lutz and Zheng, Enliang and Pollefeys, Marc and Frahm, Jan-Michael},\ntitle={Pixelwise View Selection for Unstructured Multi-View Stereo},\nbooktitle={European Conference on Computer Vision (ECCV)},\nyear={2016},\n}\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSuxing Liu (\u003ca href=\"mailto:suxingliu@gmail.com\"\u003esuxingliu@gmail.com\u003c/a\u003e), Wesley Paul Bonelli(\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther contributions\u003c/h2\u003e\u003ca id=\"user-content-other-contributions\" class=\"anchor\" aria-label=\"Permalink: Other contributions\" href=\"#other-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker container was maintained and deployed to \u003ca href=\"https://portnoy.cyverse.org\" rel=\"nofollow\"\u003ePlantIT\u003c/a\u003e by Wes Bonelli (\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eSingularity container overlay issues were solved by [Saravanaraj Ayyampalayam] (\u003ca href=\"https://github.com/raj76\"\u003ehttps://github.com/raj76\u003c/a\u003e) (\u003ca href=\"mailto:raj76@uga.edu\"\u003emailto:raj76@uga.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eSpecial thanks to Chris Cotter building the Singularity container recipe for testing and debugging.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGNU Public License\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-xsede-container-tutorial-deep-dive-into-constructing-containers-for-scientific-computing-and-gateways\" class=\"anchor\" aria-hidden=\"true\" href=\"#xsede-container-tutorial-deep-dive-into-constructing-containers-for-scientific-computing-and-gateways\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eXSEDE Container Tutorial: Deep Dive into Constructing Containers for Scientific Computing and Gateways\u003c/h1\u003e\n\u003cp\u003eThe XSEDE Container Tutorial is presented by the XSEDE Cyberinfrastructure Resource Integration (XCRI) team. This repository contains a collection of slides, exercises, and files for the Container Tutorial presented at various conferences and other venues. Below is an abstract of what the Tutorial covers. A directory exists for most events we have presented at with slides and exercises. Some descriptions of how we have structured the tutorial at each event are included in the corresponding READMEs. With each presentation, we have taken feedback and followed industry trends to improve the tutorial.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-upcoming-accepted-presentations\" class=\"anchor\" aria-hidden=\"true\" href=\"#upcoming-accepted-presentations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUpcoming Accepted Presentations\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.hpcwire.com/off-the-wire/access-takes-over-as-xsede-ends-formal-operations/\" rel=\"nofollow\"\u003eXSEDE has ended\u003c/a\u003e, and thus this repository is primarily archival.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-previously-presented-at\" class=\"anchor\" aria-hidden=\"true\" href=\"#previously-presented-at\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreviously Presented At\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/PEARC22\"\u003ePEARC22\u003c/a\u003e - In-person, July 10, full-day\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/SC21\"\u003eSC21\u003c/a\u003e - In-person, November 15 2021, 8am - 5pm CST (Full-day)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/eScience2021\"\u003eeScience 2021\u003c/a\u003e - Virtual, full-day, \u003ca href=\"https://youtu.be/mPnrgWjW2jY\" rel=\"nofollow\"\u003eVideo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/PEARC21\"\u003ePEARC21\u003c/a\u003e - Virtual, full-day\u003c/li\u003e\n\u003cli\u003eCaRCC Researcher-Facing Track Call, Feb 2021 - Virtual, abbridged version, \u003ca href=\"https://youtu.be/TpWrOYS7nh0\" rel=\"nofollow\"\u003eVideo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/Gateways2020\"\u003eGateways 2020\u003c/a\u003e - Virtual, 2 half-day sessions\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/PEARC20\"\u003ePEARC20\u003c/a\u003e - Virtual, half-day\u003c/li\u003e\n\u003cli\u003eSGCI Coding Institute 2020 and \u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/SGCI2021\"\u003e2021\u003c/a\u003e - Virtual, half-day\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-abstract\" class=\"anchor\" aria-hidden=\"true\" href=\"#abstract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbstract\u003c/h2\u003e\n\u003cp\u003eIn recent years, using containers has been rapidly gaining traction as a solution to lower the barriers to using more software on HPC and cloud resources. However, significant barriers still exist to actually doing this in practice, particularly for well-established community codes which expect to run on a particular operating system version or resource. Additional barriers exist for researchers unfamiliar with containerization technologies. While many beginner tutorials are available for building containers, they often stop short of covering the complexities that can arise when containerizing scientific computing software. The goal of this full-day tutorial is to demonstrate and work through building and running non-trivial containers with users. We will containerize community scientific software, exhibit how to share with a larger community via a container registry, and then run on a completely separate HPC resource, with and without the use of a Science Gateway. The subject matter will be approachable for intermediate to advanced users, and is expected to be of interest to a diverse audience including researchers, support staff, and teams building science gateways.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authorspresenters\" class=\"anchor\" aria-hidden=\"true\" href=\"#authorspresenters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors/Presenters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ECoulter\"\u003eEric Coulter\u003c/a\u003e, Georgia Institute of Technology\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sk8forether\"\u003ePeter Vaillancourt\u003c/a\u003e, Cornell University\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sanjanasudarshan\"\u003eSanjana Sudarshan\u003c/a\u003e, Indiana University\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/stebird\"\u003eStephen Bird\u003c/a\u003e, Indiana University\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jlf599\"\u003eJeremy Fischer\u003c/a\u003e, Indiana University\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/smarru\"\u003eSuresh Marru\u003c/a\u003e, Indiana University\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions?\u003c/h2\u003e\n\u003cp\u003eIf you have Questions, please contact us via\n\u003ccode\u003ehelp@xsede.org\u003c/code\u003e with XCRI in the subject line.\u003c/p\u003e\n", "stargazers_count": 16, - "subscribers_count": 5, + "subscribers_count": 18, + "topics": [], + "updated_at": 1673980921.0 + }, + { + "data_format": 2, + "description": "Astronomical Calibration and Imaging Software", + "filenames": [ + "deploy/singularity/Singularity.openmpi" + ], + "full_name": "ATNF/yandasoft", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eYandaSoft\u003c/h1\u003e\u003ca id=\"user-content-yandasoft\" class=\"anchor\" aria-label=\"Permalink: YandaSoft\" href=\"#yandasoft\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYandasoft is a suite of applications and software developed by CSIRO for the calibration and imaging of Interferometric Radio Telescope data.\u003c/p\u003e\n\u003cp\u003eThis is a \u003ca href=\"https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow\" rel=\"nofollow\"\u003eGitflow\u003c/a\u003e repo and you should install the \u003ca href=\"https://github.com/petervanderdoes/gitflow-avh/wiki/Installation\"\u003egitflow extensions\u003c/a\u003e to get the best milage. Gitflow affords a stricter release policy that other workflows and, as this package is used to build official versions of ASKAP software, it is important that this process is reliable and repeatable and so this package is a controlled dependency.\u003c/p\u003e\n\u003cp\u003eIn accordance with Gitflow workflow:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf you are after the latest release then take it from \u003ccode\u003etags/\u0026lt;ver\u0026gt;\u003c/code\u003e or the head of \u003ccode\u003emaster\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopment and fixes should only proceed in features/branches and then via pull requests into \u003ccode\u003edevelop\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eReleases are prepared in \u003ccode\u003erelease\u003c/code\u003e branches and then canonised to \u003ccode\u003emaster\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOfficial releases are tagged on \u003ccode\u003emaster\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA git submodule for the full calibration and imaging documentation is included\nin the docs sibdirectory. On an initial clone of this repository you have to\nrun, \u003ccode\u003egit submodule init\u003c/code\u003e and \u003ccode\u003egit submodule update\u003c/code\u003e to obtain the latest\nversions. Also you must have the sphinx document tool installed and run \u003ccode\u003emake html\u003c/code\u003e to generate the documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIn this version\u003c/h2\u003e\u003ca id=\"user-content-in-this-version\" class=\"anchor\" aria-label=\"Permalink: In this version\" href=\"#in-this-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis release of the software is the first and consequently \u003cem\u003ebeta\u003c/em\u003e release. It contains the (at least) the following applications:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMeasurement Set creation and manipulation\u003c/h3\u003e\u003ca id=\"user-content-measurement-set-creation-and-manipulation\" class=\"anchor\" aria-label=\"Permalink: Measurement Set creation and manipulation\" href=\"#measurement-set-creation-and-manipulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecsimulator\u003c/code\u003e: simulation of visibilities.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eccontsubtract\u003c/code\u003e: continuum subtraction.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emslist\u003c/code\u003e: measurement set interogation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emsconcat\u003c/code\u003e: concatenation of measurement sets.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emsmerge\u003c/code\u003e: merging of measurement sets.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCalibration tools\u003c/h3\u003e\u003ca id=\"user-content-calibration-tools\" class=\"anchor\" aria-label=\"Permalink: Calibration tools\" href=\"#calibration-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecbpcalibrator\u003c/code\u003e: bandpass calibrator.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eccalibrator\u003c/code\u003e: for performing gain calibration\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eccalapply\u003c/code\u003e: for the application of calibration solutions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImaging tasks\u003c/h3\u003e\u003ca id=\"user-content-imaging-tasks\" class=\"anchor\" aria-label=\"Permalink: Imaging tasks\" href=\"#imaging-tasks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecimager\u003c/code\u003e: Original ASKAP imager.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eimager\u003c/code\u003e: New imager - permits more parallisation options.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elinmos\u003c/code\u003e: Linear mosaicking of images\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline and Analysis tasks\u003c/h3\u003e\u003ca id=\"user-content-pipeline-and-analysis-tasks\" class=\"anchor\" aria-label=\"Permalink: Pipeline and Analysis tasks\" href=\"#pipeline-and-analysis-tasks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emsplit\u003c/code\u003e: Manipulate measurement sets\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecmodel\u003c/code\u003e: Generate model images from component lists\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eselavy\u003c/code\u003e: Source detection tools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout Yandasoft\u003c/h2\u003e\u003ca id=\"user-content-about-yandasoft\" class=\"anchor\" aria-label=\"Permalink: About Yandasoft\" href=\"#about-yandasoft\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese tasks were originally developed to form the real-time calibration and imaging pipeline for the ASKAP telescope. In order to distribute this software more widely we have extracted these tools from the main codebase of the telescope system and distributed it separately.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe dependencies are listed in detail in the INSTALL.txt file. But it should be noted that there are internal \"ASKAP\" dependencies that are required. They are all public and are automatically pulled from their respective repositories by the included \u003ccode\u003ebuild_all.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to get it\u003c/h3\u003e\u003ca id=\"user-content-how-to-get-it\" class=\"anchor\" aria-label=\"Permalink: How to get it\" href=\"#how-to-get-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYadasoft and its required ASKAP dependencies are available from the CSIRO bitbucket server at:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/lofar-common.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/lofar-common.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/lofar-blob.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/lofar-blob.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-askap.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-askap.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-logfilters.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-logfilters.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-imagemath.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-imagemath.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-scimath.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-scimath.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-askapparallel.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-askapparallel.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-accessors.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-accessors.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/yandasoft.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/yandasoft.git\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere are some extra tasks available from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/askap-pipelinetasks.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/askap-pipelinetasks.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/askap-analysis.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/askap-analysis.git\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 16, + "subscribers_count": 13, "topics": [ - "phenotyping", - "phenotyping-algorithms", - "root" + "astronomy", + "astronomical-algorithms" ], - "updated_at": 1730360243.0 + "updated_at": 1685695246.0 }, { "data_format": 2, - "description": "N-Ways to Multi-GPU Programming", + "description": "This an example app that can serve as a template.", "filenames": [ "Singularity" ], - "full_name": "openhackathons-org/nways_multi_gpu", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eN-Ways to Multi-GPU Programming\u003c/h1\u003e\u003ca id=\"user-content-n-ways-to-multi-gpu-programming\" class=\"anchor\" aria-label=\"Permalink: N-Ways to Multi-GPU Programming\" href=\"#n-ways-to-multi-gpu-programming\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains mini applications for GPU Bootcamps. This bootcamp focuses on multi-GPU programming models.\u003c/p\u003e\n\u003cp\u003eScaling applications to multiple GPUs across multiple nodes requires one to be adept at not just the programming models and optimization techniques, but also at performing root-cause analysis using in-depth profiling to identify and minimize bottlenecks. In this bootcamp, participants will learn to improve the performance of an application step-by-step, taking cues from profilers along the way. Moreover, understanding of the underlying technologies and communication topology will help us utilize high-performance NVIDIA libraries to extract more performance out of the system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootcamp Outline\u003c/h2\u003e\u003ca id=\"user-content-bootcamp-outline\" class=\"anchor\" aria-label=\"Permalink: Bootcamp Outline\" href=\"#bootcamp-outline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOverview of single-GPU code and Nsight Systems Profiler\u003c/li\u003e\n\u003cli\u003eSingle Node Multi-GPU:\n\u003cul\u003e\n\u003cli\u003eCUDA Memcpy and Peer-to-Peer Memory Access\u003c/li\u003e\n\u003cli\u003eIntra-node topology\u003c/li\u003e\n\u003cli\u003eCUDA Streams and Events\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMulti-Node Multi-GPU:\n\u003cul\u003e\n\u003cli\u003eIntroduction to MPI and Multi-Node execution overview\u003c/li\u003e\n\u003cli\u003eMPI with CUDA Memcpy\u003c/li\u003e\n\u003cli\u003eCUDA-aware MPI\u003c/li\u003e\n\u003cli\u003eSupplemental: Configuring MPI in a containerized environment\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eNVIDIA Collectives Communications Library (NCCL)\u003c/li\u003e\n\u003cli\u003eNVHSMEM Library\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis bootcamp requires a multi-node system with multiple GPUs in each node (atleast 2 GPUs/ node).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial Duration\u003c/h2\u003e\u003ca id=\"user-content-tutorial-duration\" class=\"anchor\" aria-label=\"Permalink: Tutorial Duration\" href=\"#tutorial-duration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe total bootcamp material would take approximately 8 hours .\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing NVIDIA HPC SDK\u003c/h3\u003e\u003ca id=\"user-content-using-nvidia-hpc-sdk\" class=\"anchor\" aria-label=\"Permalink: Using NVIDIA HPC SDK\" href=\"#using-nvidia-hpc-sdk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA multi-node installation of \u003ca href=\"https://developer.nvidia.com/hpc-sdk\" rel=\"nofollow\"\u003eNVIDIA\u0027s HPC SDK\u003c/a\u003e is desired. Refer to \u003ca href=\"https://docs.nvidia.com/hpc-sdk/hpc-sdk-install-guide/index.html\" rel=\"nofollow\"\u003eNVIDIA HPC SDK Installation Guide\u003c/a\u003e for detailed instructions. Ensure that your installation contains HPCX with UCX.\u003c/p\u003e\n\u003cp\u003eAfter installation, make sure to add HPC SDK to the environment as follows(For example the PATH highlighted below is for HPC SDK 21.5):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add HPC-SDK to PATH:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/compilers/bin:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/cuda/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add HPC-SDK to LD_LIBRARY_PATH:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/comm_libs/nvshmem/lib:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/comm_libs/nccl/lib:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/comm_libs/mpi/lib:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/math_libs/lib64:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/compilers/lib:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/cuda/extras/CUPTI/lib64:\u0026lt;path-nvidia-hpc-sdk\u0026gt;\u0026gt;/Linux_x86_64/21.5/cuda/lib64:\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eADD NVSHMEM HOME DIRECTORY PATH\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CUDA_HOME=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/cuda\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NVSHMEM_HOME=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/comm_libs/nvshmem\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e If you don\u0027t use Slurm workload manager, remove \u003ccode\u003e--with-slurm\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eThen, install OpenMPI as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download and extract OpenMPI Tarfile\u003c/span\u003e\nwget https://download.open-mpi.org/release/open-mpi/v4.1/openmpi-4.1.1.tar.gz\ntar -xvzf openmpi-4.1.1.tar.gz\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e openmpi-4.1.1/\nmkdir -p build\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Configure OpenMPI\u003c/span\u003e\n./configure --prefix=\u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/build --with-libevent=internal --with-xpmem --with-cuda=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/cuda/ --with-slurm --enable-mpi1-compatibility --with-verbs --with-hcoll=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/comm_libs/hpcx/hpcx-2.8.1/hcoll/lib --with-ucx=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/comm_libs/hpcx/hpcx-2.8.1/ucx/\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install OpenMPI\u003c/span\u003e\nmake all install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow, add OpenMPI to the environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0026lt;path-to-openmpi\u0026gt;/build/bin/:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0026lt;path-to-openmpi/build/lib:\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEnsure that the custom-built OpenMPI is in use by running \u003ccode\u003ewhich mpirun\u003c/code\u003e which should point the \u003ccode\u003empirun\u003c/code\u003e binary in \u003ccode\u003e\u0026lt;path-to-openmpi\u0026gt;/build/bin\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWithout Using NVIDIA HPC SDK\u003c/h3\u003e\u003ca id=\"user-content-without-using-nvidia-hpc-sdk\" class=\"anchor\" aria-label=\"Permalink: Without Using NVIDIA HPC SDK\" href=\"#without-using-nvidia-hpc-sdk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMulti-node compatible versions of the following are required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.open-mpi.org/\" rel=\"nofollow\"\u003eOpenMPI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/networking/hpc-x\" rel=\"nofollow\"\u003eHPCX\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/cuda-toolkit\" rel=\"nofollow\"\u003eCUDA Toolkit\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/nccl\" rel=\"nofollow\"\u003eNCCL\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/nvshmem\" rel=\"nofollow\"\u003eNVSHMEM\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have tested all the codes with CUDA drivers 460.32.03 with CUDA 11.3.0.0, OpenMPI 4.1.1, HPCX 2.8.1, Singularity 3.6.1, NCCL 2.9.9.1, and NVSHMEM 2.1.2. Note that OpenMPI in our cluster was compiled with CUDA, HCOLL, and UCX support.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Jupyter Lab\u003c/h2\u003e\u003ca id=\"user-content-running-jupyter-lab\" class=\"anchor\" aria-label=\"Permalink: Running Jupyter Lab\" href=\"#running-jupyter-lab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs this bootcamp covers multi-node CUDA-aware MPI concepts, it is primarily designed to run without any containers. After the prerequisite softwares have been installed, follow these steps to install and run Jupyter Lab:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Anaconda3\u003c/span\u003e\nwget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \nbash Miniconda3-latest-Linux-x86_64.sh -b -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add conda to PATH\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/bin/\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Jupyter Lab\u003c/span\u003e\nconda install -c conda-forge jupyterlab\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run Jupyter Lab\u003c/span\u003e\njupyter lab --notebook-dir=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-gpubootcamp-repo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/hpc/multi_gpu_nways/labs/ --port=8000 --ip=0.0.0.0 --no-browser --NotebookApp.token=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running Jupyter Lab, open \u003ca href=\"http://localhost:8888/\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e in a web browser and start the \u003ccode\u003eintroduction.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptional: Containerized Build with Singularity\u003c/h2\u003e\u003ca id=\"user-content-optional-containerized-build-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Optional: Containerized Build with Singularity\" href=\"#optional-containerized-build-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis material is designed to primarily run in containerless environments, that is, directly on the cluster. Thus, building the Singularity container is OPTIONAL.\u003c/p\u003e\n\u003cp\u003eIf containerization is desired, follow the steps outlined in the notebook \u003ca href=\"labs/CFD/English/C/jupyter_notebook/mpi/containers_and_mpi.ipynb\"\u003eMPI in Containerized Environments\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFollow the steps below to build the Singularity container image and run Jupyter Lab:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build the container\u003c/span\u003e\nsingularity build multi_gpu_nways.simg Singularity\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run Jupyter Lab\u003c/span\u003e\nsingularity run --nv multi_gpu_nways.simg jupyter lab --notebook-dir=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-gpubootcamp-repo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/hpc/multi_gpu_nways/labs/ --port=8000 --ip=0.0.0.0 --no-browser --NotebookApp.token=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, access Jupyter Lab on \u003ca href=\"http://localhost:8888/\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompiler throws errors\u003c/h4\u003e\u003ca id=\"user-content-compiler-throws-errors\" class=\"anchor\" aria-label=\"Permalink: Compiler throws errors\" href=\"#compiler-throws-errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf compiling any program throws an error related to CUDA/ NCCL/ NVHSMEM/ MPI libraries or header files being not found, ensure that \u003ccode\u003eLD_LIBRARY_PATH\u003c/code\u003e is correctly set. Moreover, make sure environment variables \u003ccode\u003eCUDA_HOME\u003c/code\u003e, \u003ccode\u003eNCCL_HOME\u003c/code\u003e, and \u003ccode\u003eNVSHMEM_HOME\u003c/code\u003e are set either during installation or manually inside each \u003ccode\u003eMakefile\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlease go through the list of exisiting bugs/issues or file a new issue at \u003ca href=\"https://github.com/gpuhackathons-org/gpubootcamp/issues\"\u003eGithub\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuestions?\u003c/h2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-label=\"Permalink: Questions?\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease join \u003ca href=\"https://openacclang.slack.com/messages/openaccusergroup\" rel=\"nofollow\"\u003eOpenACC Slack Channel\u003c/a\u003e to raise questions.\u003c/p\u003e\n\u003cp\u003eIf you observe any errors or issues, please file an issue on \u003ca href=\"https://github.com/gpuhackathons-org/gpubootcamp\"\u003eGPUBootcamp GitHuB repository\u003c/a\u003e.\u003c/p\u003e\n", + "full_name": "bids-apps/example", + "latest_release": "0.0.7", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAn example BIDS App (template repository)\u003c/h2\u003e\u003ca id=\"user-content-an-example-bids-app-template-repository\" class=\"anchor\" aria-label=\"Permalink: An example BIDS App (template repository)\" href=\"#an-example-bids-app-template-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEvery BIDS App needs to follow a minimal set of command arguments common across\nall of the Apps. This allows users and developers to easily use and integrate\nBIDS Apps with their environment.\u003c/p\u003e\n\u003cp\u003eThis is a minimalist example of a BIDS App consisting of a Dockerfile and a simple\nentry point script (written in this case in Python) accepting the standard BIDS\nApps command line arguments. This repository can be used as a template for new BIDS Apps.\u003c/p\u003e\n\u003cp\u003eFor more information about the specification of BIDS Apps see \u003ca href=\"https://docs.google.com/document/d/1E1Wi5ONvOVVnGhj21S1bmJJ4kyHFT7tkxnV3C23sjIE/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDescription\u003c/h3\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a placeholder for a short description explaining to the user what your App will doing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvide a link to the documentation of your pipeline.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to report errors\u003c/h3\u003e\u003ca id=\"user-content-how-to-report-errors\" class=\"anchor\" aria-label=\"Permalink: How to report errors\" href=\"#how-to-report-errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvide instructions for users on how to get help and report errors.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknowledgments\u003c/h3\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDescribe how would you would like users to acknowledge use of your App in their papers (citation, a paragraph that can be copy pasted, etc.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis App has the following command line arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: run.py [-h] [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]] [--skip_bids_validator] [-v]\n\t\t\tbids_dir output_dir {participant,group}\n\nExample BIDS App entrypoint script.\n\npositional arguments:\nbids_dir The directory with the input dataset formatted according to the BIDS standard.\noutput_dir The directory where the output files should be stored. If you are running group level analysis this folder should\n\t\t\t\t\t\tbe prepopulated with the results of theparticipant level analysis.\n{participant,group} Level of the analysis that will be performed. Multiple participant level analyses can be run independently (in\n\t\t\t\t\t\tparallel) using the same output_dir.\n\noptions:\n-h, --help show this help message and exit\n--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]\n\t\t\t\t\t\tThe label(s) of the participant(s) that should be analyzed. The label corresponds to sub-\u0026lt;participant_label\u0026gt; from\n\t\t\t\t\t\tthe BIDS spec (so it does not include \"sub-\"). If this parameter is not provided all subjects should be analyzed.\n\t\t\t\t\t\tMultiple participants can be specified with a space separated list.\n--skip_bids_validator\n\t\t\t\t\t\tWhether or not to perform BIDS dataset validation\n-v, --version show program\u0027s version number and exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run it in participant level mode (for one participant):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --rm \\\n\t-v /Users/filo/data/ds005:/bids_dataset:ro \\\n\t-v /Users/filo/outputs:/outputs \\\n\tbids/example \\\n\t/bids_dataset /outputs participant --participant_label 01\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter doing this for all subjects (potentially in parallel), the group level analysis\ncan be run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --rm \\\n\t-v /Users/filo/data/ds005:/bids_dataset:ro \\\n\t-v /Users/filo/outputs:/outputs \\\n\tbids/example \\\n\t/bids_dataset /outputs group\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSpecial considerations\u003c/h3\u003e\u003ca id=\"user-content-special-considerations\" class=\"anchor\" aria-label=\"Permalink: Special considerations\" href=\"#special-considerations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDescribe whether your app has any special requirements. For example:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMultiple map reduce steps (participant, group, participant2, group2 etc.)\u003c/li\u003e\n\u003cli\u003eUnusual memory requirements\u003c/li\u003e\n\u003cli\u003eetc.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 16, - "subscribers_count": 1, + "subscribers_count": 3, "topics": [ - "cuda", - "hpc", - "mpi", - "nccl", - "nsight-systems", - "nvshmem" + "bids", + "bids-apps" ], - "updated_at": 1732318936.0 + "updated_at": 1716899176.0 }, { "data_format": 2, - "description": "Variant call adjudication", + "description": "A RNN trained on Donald Trumps tweets", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "iqbal-lab-org/minos", - "latest_release": "v0.12.6", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/iqbal-lab-org/minos/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/minos/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eminos\u003c/h1\u003e\u003ca id=\"user-content-minos\" class=\"anchor\" aria-label=\"Permalink: minos\" href=\"#minos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVariant call adjudication.\u003c/p\u003e\n\u003cp\u003eMinimal instructions are below. Please see the \u003ca href=\"https://github.com/iqbal-lab-org/minos/wiki\"\u003eminos wiki page\u003c/a\u003e\nfor more details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet a Docker image of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/iqbal-lab-org/minos:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll Docker images are listed in the\n\u003ca href=\"https://github.com/iqbal-lab-org/minos/pkgs/container/minos\"\u003epackages page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, build your own Docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker build --network=host .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/minos/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download (from version 0.12.1 onwards).\u003c/p\u003e\n\u003cp\u003eAlternatively, build your own Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build minos.simg Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3 (tested on version 3.6.9)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/iqbal-lab-org/gramtools\"\u003egramtools\u003c/a\u003e commit\n04c4ba717399507b643fd4b77a61c048ef2ed83f\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://samtools.github.io/bcftools/\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/atks/vt.git\"\u003evt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vcflib/vcflib.git\"\u003evcflib\u003c/a\u003e. Specifically,\neither \u003ccode\u003evcflib\u003c/code\u003e, or all three of\n\u003ccode\u003evcfbreakmulti\u003c/code\u003e, \u003ccode\u003evcfallelicprimitives\u003c/code\u003e, and \u003ccode\u003evcfuniq\u003c/code\u003e must be installed.\u003c/li\u003e\n\u003cli\u003eOptionally, \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e and \u003ca href=\"https://github.com/iqbal-lab-org/ivcfmerge\"\u003eivcfmerge\u003c/a\u003e if you want to use the\npipeline to regenotype a large number of samples.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInstall by cloning this repository (or downloading the latest release), and\nrunning:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run on one sample, you will need:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA FASTA file of the reference genome.\u003c/li\u003e\n\u003cli\u003eOne or more VCF files of variant calls.\nThe only requirement of these files is that they must contain the genotype field \u003ccode\u003eGT\u003c/code\u003e,\nand correspond to the reference FASTA file. All variants with a non-reference genotype\ncall will be used (both alleles are considered for diploid calls)\u003c/li\u003e\n\u003cli\u003eIllumina reads in FASTQ file(s).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor example, if you have two call sets in the files \u003ccode\u003ecalls1.vcf\u003c/code\u003e and \u003ccode\u003ecalls2.vcf\u003c/code\u003e,\nthen run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eminos adjudicate --reads reads1.fq --reads reads2.fq out ref.fasta calls1.vcf calls2.vcf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003ereads1.fq\u003c/code\u003e and \u003ccode\u003ereads2.fq\u003c/code\u003e are FASTQ files of the reads and \u003ccode\u003eref.fasta\u003c/code\u003e\nis a FASTA of the reference corresponding to the two input VCF files.\nThe final call set will be \u003ccode\u003eout/final.vcf\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUnit tests\u003c/h2\u003e\u003ca id=\"user-content-unit-tests\" class=\"anchor\" aria-label=\"Permalink: Unit tests\" href=\"#unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \u003ccode\u003etox\u003c/code\u003e to run all unit tests.\nThey require \u003ccode\u003enextflow\u003c/code\u003e, \u003ccode\u003egramtools\u003c/code\u003e, \u003ccode\u003evt\u003c/code\u003e, \u003ccode\u003evcfbreakmulti\u003c/code\u003e,\n\u003ccode\u003evcfallelicprimitives\u003c/code\u003e, \u003ccode\u003evcfuniq\u003c/code\u003e in your \u003ccode\u003e$PATH\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eRun an individual test file with \u003ccode\u003etox tests/for_test.py::TestSpam::test_eggs\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eRun the main entry point with \u003ccode\u003epython3 -m minos\u003c/code\u003e.\u003c/p\u003e\n", + "full_name": "wyattferguson/trumpbot-rnn", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTrumpbot v0.1\u003c/h1\u003e\u003ca id=\"user-content-trumpbot-v01\" class=\"anchor\" aria-label=\"Permalink: Trumpbot v0.1\" href=\"#trumpbot-v01\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTrumpbot was my attempt at creating a RNN trained on Donald Trumps(DT) tweets. I used this as a sort of practice project for learning a bit about RNN\u0027s and Tensorflow 2. The result was a chaos and a learning experience so let\u0027s dive in.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun with Containers\u003c/h2\u003e\u003ca id=\"user-content-run-with-containers\" class=\"anchor\" aria-label=\"Permalink: Run with Containers\" href=\"#run-with-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to install dependencies to your host, you can build a Docker container\nwith the included Dockerfile:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build -t trumpbot \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe entrypoint is the script to generate the tweets:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run trumpbot\n...\n obamas Top and France at 900 PM on FoxNews. Anderson Congratulations to the House vote \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e MittRomney o\n\n hillary Clinton has been a total disaster. I have an idea \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eher great speech on CNN\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the world a great honor \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e me and his partisan hotel and every spor\n\n friends support Trump International Golf Club on the Paris About that Right School is started by the DNC and Clinton and the DNC that will be a great show with t\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to interact with the container (perhaps training first) you can shell inside instead:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run -it --entrypoint bash trumpbot\nroot@b53b98f12c34:/code# ls\nDockerfile README.md __init__.py learn.py raw_tweets.txt requirements.txt\ttraining_checkpoints trumpbot.py tweets.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll be in the \u003ccode\u003e/code\u003c/code\u003e directory that contains the source code.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor users that want to perhaps use GPU (or better leverage the host) the recommendation is to\nuse a \u003ca href=\"https://www.sylabs.io/guides/3.2/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, and a recipe file \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e is provided\nto build the container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build trumpbot.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then to run (add the --nv flag if you want to leverage any host libraries).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run trumpbot.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to change the way that tensorflow or numpy are installed, you can edit the Singularity or Docker recipes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSetup is pretty straightforward. It only needs numpy and tensorflow 2 alpha just run the start pip install:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataset\u003c/h2\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entire dataset was just tweets scraped from the DT twitter account. I used Jefferson Henrique\u0027s library \u003ca href=\"https://github.com/Jefferson-Henrique/GetOldTweets-python\"\u003eGetOldTweets-python\u003c/a\u003e that I modified a little bit. All the raw tweets can be found in the raw_tweets.txt file FYI all the links in any tweet have been removed.\u003c/p\u003e\n\u003cp\u003eThe first thing about using Tweets as a dataset for training is that they are filled with garbage that wreaks havoc when training. Heres what I did:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRemoved any links or urls to photos\u003c/li\u003e\n\u003cli\u003eSimplified all the puncuation, with Trump this is a big thing, his tweets are a clown fiesta of periods and exclemation marks.\u003c/li\u003e\n\u003cli\u003eCleaned out any invisible or non-english characters, any foreign characters just casuases trouble.\u003c/li\u003e\n\u003cli\u003eRemoved the \u0027@\u0027 symbol, I\u0027ll explain why later.\u003c/li\u003e\n\u003cli\u003eRemoved the first couple of months of tweets, they were mostly about the celebrity apprentice and not really core to what I was trying to capture.\u003c/li\u003e\n\u003cli\u003eRemoved any retweets or super short @replies\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe final training text is in tweets.txt which altogether is about 20,000 tweets.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining\u003c/h2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI trained the model twice, the first time for 30 epochs which took around 6 hours. The result was absolute garbage, at the time I hadn\u0027t removed hidden or foreign characters so it took 6 hours to spit out complete nonsense. So after I cleaned out the tweets again, I ran the training overnight for 50 epochs this time.\u003c/p\u003e\n\u003cp\u003eJust run the learn.py file to train it again if you want, the model check points are stored in the \u0027training_checkpoints\u0027 folder\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 learn.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenerating Tweets\u003c/h2\u003e\u003ca id=\"user-content-generating-tweets\" class=\"anchor\" aria-label=\"Permalink: Generating Tweets\" href=\"#generating-tweets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSo now the fun part, you can run the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 trumpbot.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will generate 10 tweets from a random group of topics. If you open the trumpbot.py file theres a few things you can play with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etweets - Number of messages you want generated\n\ntemperature - This controls how predictable the tweet will be, by \n default its random from 0.1 -\u0026gt; 0.4, anything above about 0.7 generates\n garbage.\n\ntalking_points - Is a list of inputs to feed the network, try out \n differnt words and see what works.\n\nnum_generate - This controls the length of the message you want to\n get generated.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResult\u003c/h2\u003e\u003ca id=\"user-content-result\" class=\"anchor\" aria-label=\"Permalink: Result\" href=\"#result\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor my first crack at text generation Im happy with the results. Here are some sample tweets:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehillary Clinton has been a total disaster. If you cant admit that \nthe U.S. more than has been treated big baster I am a g\n\nDonald Trump is 45% Iran\n\nhealthe lobbyist now wants to raise taxes for our country in the \nfirst place! If only one thing is clea\n\nfriends support Trump Rally Anger Golf Club of Caporate legislation \nat the WhiteHouse today! #MakeAmericaGreatAgain Thank you for your\n support! #Trump2016 \n\nkoreau like you it was great being in the last election then will be\n a great show. I have a fan o\n\nkoreau lies and losers and losers will be a great show with the U.S.\n The President has a various past c\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat I learned\u003c/h2\u003e\u003ca id=\"user-content-what-i-learned\" class=\"anchor\" aria-label=\"Permalink: What I learned\" href=\"#what-i-learned\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTweets make for a tough training set. Things like @ mentions just pollute the hell out of the text so unless you want your bot to be constantly @ing everything I need to find a better way to deal with that.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThings I thought the bot would love talking about stuff like #MAGA, Russia, China, and collusion just generate garbage strings.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eText generation is really hard, and takes a ton of training time.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eI could probably get a bit better results if I let it train a bit longer but for any drastic improvements I probably need to try another method or spend alot more time tuning the training set.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePick a subject that doesn\u0027t tweet like hes a dad yelling at a little league game. I think because his tweets are short little outbursts its hard to generate a predictable pattern across them.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe words it groups together for differnt topics is probably worth looking at, like whenever you use \u0027hillary\u0027 as a input it usually has the words \u0027liar\u0027 or \u0027disaster\u0027 in the sentence. or how it loves telling you when its gonna be on @Foxandfriends\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWith the method I used spelling its like to add random \u0027u\u0027 infront of words.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eI feel like this is good starting point, and with some work we might have a digital orange man bot in our future.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udcee Contact \u0026amp; Support\u003c/h2\u003e\u003ca id=\"user-content-postbox-contact--support\" class=\"anchor\" aria-label=\"Permalink: :postbox: Contact \u0026amp; Support\" href=\"#postbox-contact--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreated by \u003ca href=\"@wyattxdev@mastodon.social\"\u003eWyatt Ferguson\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor any comments or questions message me on \u003ca href=\"@wyattxdev@mastodon.social\"\u003eMastodon\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.buymeacoffee.com/wyattferguson\" rel=\"nofollow\"\u003e\u2615 Buy Me A Coffee\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 16, - "subscribers_count": 4, - "topics": [], - "updated_at": 1718272889.0 + "subscribers_count": 3, + "topics": [ + "tensorflow", + "python" + ], + "updated_at": 1704291383.0 }, { "data_format": 2, - "description": null, + "description": "Build and deploy Singularity containers to GitHub releases, and pull with the singularity-hpc client", "filenames": [ - "Singularity.def" + "Singularity.pokemon", + "Singularity.salad", + "Singularity" ], - "full_name": "iqbal-lab-org/viridian", - "latest_release": "v1.3.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/iqbal-lab-org/viridian/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/viridian/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eViridian\u003c/h1\u003e\u003ca id=\"user-content-viridian\" class=\"anchor\" aria-label=\"Permalink: Viridian\" href=\"#viridian\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUltra-careful amplicon-aware viral assembly for tiled amplicon schemes.\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/iqbal-lab-org/viridian/wiki\"\u003eViridian Wiki\u003c/a\u003e\nfor full documentation.\u003c/p\u003e\n\u003cp\u003ePreprint: \u003ca href=\"https://doi.org/10.1101/2024.04.29.591666\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2024.04.29.591666\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe recommended method is to use a pre-built Docker or Singularity container\n(see the wiki for how to build your own).\u003c/p\u003e\n\u003cp\u003eBoth the Docker and Singularity container have the main script\n\u003ccode\u003eviridian\u003c/code\u003e installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet a Docker image of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/iqbal-lab-org/viridian:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll Docker images from v1.2.1 onwards are listed in the\n\u003ca href=\"https://github.com/iqbal-lab-org/viridian/pkgs/container/viridian\"\u003epackages page\u003c/a\u003e.\nOlder images can be found on the\n\u003ca href=\"https://github.com/iqbal-lab-org/viridian_workflow/pkgs/container/viridian_workflow\"\u003eold packages page\u003c/a\u003e\n(viridian used to be called \"viridian_workflow\", and hence were put on a packages page\ncalled \"viridian_workflow\" by github).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/viridian/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download.\nEach release from v1.2.1 onwards has a singularity image file called\n\u003ccode\u003eviridian_vX.Y.Z.img\u003c/code\u003e, where \u003ccode\u003eX.Y.Z\u003c/code\u003e is the release version. Newer releases\nalso have an image for ARM architecture called \u003ccode\u003eviridian_vX.Y.Z.arm.img\u003c/code\u003e.\nOlder images (from when viridian was called \"viridian_workflow\") are\ncalled \u003ccode\u003eviridian_workflow_vX.Y.Z.img\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest\u003c/h2\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo quickly test if the installation is successful, this command\nwill download the FASTQ reads files for SARS-CoV-2 sequencing\nrun ERR8959214 and assemble it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample --run_accession ERR8959214 --outdir test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt should take less than 1 minute to run, and make a new directory\ncalled \u003ccode\u003etest\u003c/code\u003e containing the results. The output files are\ndescribed below. The important thing here is that the log\nfile \u003ccode\u003elog.json.gz\u003c/code\u003e should have the entry\n\u003ccode\u003e\"result\": \"Success\"\u003c/code\u003e (around line 40-50), indicating that\nthe run was successful.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese instructions assume that you are assembling SARS-CoV-2 data.\u003c/p\u003e\n\u003cp\u003eTo run on paired Illumina reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample \\\n --tech illumina \\\n --reads1 reads_1.fastq.gz \\\n --reads2 reads_2.fastq.gz \\\n --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run on unpaired nanopore reads:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample \\\n --tech ont \\\n --reads reads.fastq.gz \\\n --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run on paired or unpaired Ion Torrent reads, use either of the\nabove commands, but with the option \u003ccode\u003e--tech iontorrent\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eDownload reads with accession SRR12345678 and run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian run_one_sample --run_accession SRR12345678 --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe sequencing tech and unpaired/paired is taken from the ENA metadata\nfor each run.\u003c/p\u003e\n\u003cp\u003eDownload and run on a batch of sequencing runs, using a file\n\u003ccode\u003eruns.txt\u003c/code\u003e of INSDC run accessions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eviridian dl_and_run --acc_file runs.txt --outdir OUT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput files\u003c/h2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-label=\"Permalink: Output files\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe default files in the output directory are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003econsensus.fa.gz\u003c/code\u003e: a gzipped FASTA file of the consensus sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003evariants.vcf\u003c/code\u003e: a VCF file of the identified variants between the consensus\nsequence and the reference genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elog.json.gz\u003c/code\u003e: a gzipped JSON file that contains logging information\nfor the viridian run.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eqc.tsv.gz\u003c/code\u003e: a gzipped tab-delimited file of per-base QC information\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme_id.depth_across_genome.pdf\u003c/code\u003e: a plot of the read depth across\nthe genome, with amplicons coloured in the background.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003escheme_id.score_plot.pdf\u003c/code\u003e: a plot of the scoring for amplicon scheme\nidentification.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the option \u003ccode\u003e--keep_bam\u003c/code\u003e is used, then a sorted BAM file of the reads mapped\nto the reference will also be present, called\n\u003ccode\u003ereference_mapped.bam\u003c/code\u003e (and its index file \u003ccode\u003ereference_mapped.bam.bai\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUseful options\u003c/h2\u003e\u003ca id=\"user-content-useful-options\" class=\"anchor\" aria-label=\"Permalink: Useful options\" href=\"#useful-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--sample_name MY_NAME\u003c/code\u003e: use this to change the sample name\n(default is \"sample\") that is put in the final FASTA file, BAM file, and\nVCF file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads_bam MY_READS.bam\u003c/code\u003e: instead of providing FASTQ (or FASTA) files of\nreads, you can provide a sorted by genome coordinate and indexed BAM file.\nThe reference genome must be the same as that used by viridian\n(by default MN908947).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--keep_bam\u003c/code\u003e: use this option to keep the BAM file of original input reads\nmapped to the reference genome.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--decontam COVID\u003c/code\u003e: decontaminate the reads using ReadItAndKeep at the\nstart of the pipeline (this is incompatible with \u003ccode\u003e--reads_bam\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--force\u003c/code\u003e: use with caution - it will overwrite the output directory if\nit already exists.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--write_msa indel_as_ref\u003c/code\u003e: this will write a FASTA file\ncalled \u003ccode\u003emsa.indel_as_ref.fa\u003c/code\u003e that can be\nused to build trees. It has the consensus sequence aligned to the\nreference genome, but with insertions in the consensus ignored and\ndeletions replaced with the reference sequence.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--run_accession RUN_ID\u003c/code\u003e: using this option will download the specified reads\nfrom the ENA, and infer the \u003ccode\u003e--tech\u003c/code\u003e option from the ENA metadata\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "singularityhub/singularity-deploy", + "latest_release": "0.0.12", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Deploy\u003c/h1\u003e\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" aria-label=\"Permalink: Singularity Deploy\" href=\"#singularity-deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/shpc.png\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Template or Fork\u003c/h3\u003e\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" aria-label=\"Permalink: 1. Template or Fork\" href=\"#1-template-or-fork\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" aria-label=\"Permalink: 1. Write your Singularity Recipe(s)\" href=\"#1-write-your-singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Update the VERSION file\u003c/h3\u003e\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" aria-label=\"Permalink: 2. Update the VERSION file\" href=\"#2-update-the-version-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/releases.png\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. How to Develop\u003c/h3\u003e\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" aria-label=\"Permalink: 3. How to Develop\" href=\"#3-how-to-develop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. How to pull\u003c/h3\u003e\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" aria-label=\"Permalink: 4. How to pull\" href=\"#4-how-to-pull\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", "stargazers_count": 16, - "subscribers_count": 7, + "subscribers_count": 6, "topics": [], - "updated_at": 1732191950.0 + "updated_at": 1711957178.0 }, { "data_format": 2, @@ -38330,160 +38399,160 @@ var data = }, { "data_format": 2, - "description": "Build and deploy Singularity containers to GitHub releases, and pull with the singularity-hpc client", + "description": "Variant call adjudication", "filenames": [ - "Singularity.pokemon", - "Singularity.salad", - "Singularity" + "Singularity.def" ], - "full_name": "singularityhub/singularity-deploy", - "latest_release": "0.0.12", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Deploy\u003c/h1\u003e\u003ca id=\"user-content-singularity-deploy\" class=\"anchor\" aria-label=\"Permalink: Singularity Deploy\" href=\"#singularity-deploy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/shpc.png\"\u003e\u003cimg src=\"img/shpc.png\" alt=\"img/shpc.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWouldn\u0027t it be nice to build Singularity images without a registry proper,\nand just keep them alongside the GitHub codebase? This is now possible!\nThis small repository provides an example to get you started. It will\nbuild one or more images (whatever Singularity.* files that are present at\nthe root) and then release them as assets to your GitHub repository so\nthat they can be programatically obtained. It is associated with\n\u003ca href=\"https://github.com/singularityhub/singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e to allow\nyou to then define LMOD modules for these same containers.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eCan I upload the largest of chonkers?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eYes and no. Note that assets are limited to 2 GB in size, which is still fairly good. You can use\nit as a template for your own recipes as is, or modify it for your custom\nuse case. Instructions are below!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e Currently recipe extensions are associated with tags, and the GitHub release is\nassociated with a digest. We likely will change this in the next few weeks so that GitHub\nreleases are tags, and the \"digests\" are flie names. Stay tuned for updates!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Template or Fork\u003c/h3\u003e\u003ca id=\"user-content-1-template-or-fork\" class=\"anchor\" aria-label=\"Permalink: 1. Template or Fork\" href=\"#1-template-or-fork\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you haven\u0027t already, template or fork this repository. You can then clone\nyour fork:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eusername\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/singularity-deploy\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou likely want to name the repository by the container. For example, if I would\nhave created a container on Docker Hub or similar with the name \u003ccode\u003evsoch/salad\u003c/code\u003e,\nhere I\u0027d call the repository \u003ccode\u003esalad\u003c/code\u003e. You obviously are limited to your username\nor an organizational namespace.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Write your Singularity Recipe(s)\u003c/h3\u003e\u003ca id=\"user-content-1-write-your-singularity-recipes\" class=\"anchor\" aria-label=\"Permalink: 1. Write your Singularity Recipe(s)\" href=\"#1-write-your-singularity-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, you should write your container recipe(s) in the present working directory.\nFor good practice, when you are updating recipes you should checkout a new branch\nand open a pull request, as the repository comes with a workflow to trigger on a PR\nto \u003ca href=\".github/workflows/test.yml\"\u003etest your container build\u003c/a\u003e. Note that in the main workflow\nthat deploys the releases, the current branch is set to be \u003ccode\u003emain-branch\u003c/code\u003e. You should\nupdate this to be your main \"production\" branch that you want to deploy releases on merge.\nYou are also free to choose a different trigger and release strategy. You can add any additional\ntests that that you might need. By default, any Singularity.* file will be automatically detected.\nIf there is no extension (the name Singularity), the name used will be \"latest.\"\nYou can use these tags across multiple releases of your containers. For example,\nthese files would generate packages with sifs named as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.pokemon\"\u003eSingularity.pokemon\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.pokemon.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"Singularity.salad\"\u003eSingularity.salad\u003c/a\u003e maps to \u003ca href=\"https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\"\u003ehttps://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.salad.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor each name, you can see the direct download URL contains the repository (singularityhub/singularity-deploy),\nYou should not use any \u003ccode\u003e:\u003c/code\u003e characters in either your container tag (the GitHub extension) or\nthe GitHub tags (the release tags) as this might interfere with parsing.\nThe GitHub release tag (0.0.1 in the example above) is discussed next.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Update the VERSION file\u003c/h3\u003e\u003ca id=\"user-content-2-update-the-version-file\" class=\"anchor\" aria-label=\"Permalink: 2. Update the VERSION file\" href=\"#2-update-the-version-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAny time that you prepare new container recipes, you should update the \u003ca href=\"VERSION\"\u003eVERSION\u003c/a\u003e\nfile. The way that this repository works is to generate a release based on the\nstring in \u003ccode\u003eVERSION\u003c/code\u003e. A version is just a tag, so it could be something like\n\u003ccode\u003e0.0.1\u003c/code\u003e or \u003ccode\u003e0.0.1-slim\u003c/code\u003e. Keep in mind that GitHub releases cannot have duplicated\nnames, so you should not repeat the same tag. Do not use \u003ccode\u003e:\u003c/code\u003e in your tag names.\nIf you do need to re-release a tag (not recommended if a user might be using it and then it\u0027s changed) you can manually delete\nthe release and the tag in the GitHub interface. This is a nice structure because it\nmeans you can have containers with different names under the same tag. In the example\nabove, we have each of \"pokemon,\" \"latest,\" and \"salad\" released under tag 0.0.1.\nThis is how it looks on GitHub:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/releases.png\"\u003e\u003cimg src=\"img/releases.png\" alt=\"img/releases.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. How to Develop\u003c/h3\u003e\u003ca id=\"user-content-3-how-to-develop\" class=\"anchor\" aria-label=\"Permalink: 3. How to Develop\" href=\"#3-how-to-develop\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs we mentioned previously, the container builds will be tested on a pull request,\nand the release will trigger on merge into your main branch (main). See the \u003ca href=\".github/workflows/builder.yml\"\u003e.github/workflows/builder.yml\u003c/a\u003e)\nto edit this. The idea is that you can:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop your container via a development branch\u003c/li\u003e\n\u003cli\u003eOpen a pull request to test the container (the \u003ca href=\".github/workflows/test.yml\"\u003e.github/workflows/test.yml\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eOn merge, your container will be released!\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. How to pull\u003c/h3\u003e\u003ca id=\"user-content-4-how-to-pull\" class=\"anchor\" aria-label=\"Permalink: 4. How to pull\" href=\"#4-how-to-pull\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTechnically, Singularity can pull just knowing the URL. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull https://github.com/singularityhub/singularity-deploy/releases/download/0.0.1/singularityhub-singularity-deploy.latest.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eHowever, the \u003ca href=\"singularity-hpc\"\u003esingularity-hpc\u003c/a\u003e tool (will be) designed to be able to parse and handle\nthese container uris automatically. For the containers here, you could do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:latest\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:salad\n$ shpc pull gh://singularityhub/singularity-deploy/0.0.1:pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor write the container URI into a registry entry:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egh: singularityhub/singularity-deploy\nlatest:\n latest: \"0.0.1\"\ntags:\n \"latest\": \"0.0.1\"\n \"salad\": \"0.0.1\"\n \"pokemon\": \"0.0.1\"\nmaintainer: \"@vsoch\"\nurl: https://github.com/singularityhub/singularity-deploy\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(This part is still under development!)\u003c/p\u003e\n", + "full_name": "iqbal-lab-org/minos", + "latest_release": "v0.12.6", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/iqbal-lab-org/minos/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/iqbal-lab-org/minos/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eminos\u003c/h1\u003e\u003ca id=\"user-content-minos\" class=\"anchor\" aria-label=\"Permalink: minos\" href=\"#minos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVariant call adjudication.\u003c/p\u003e\n\u003cp\u003eMinimal instructions are below. Please see the \u003ca href=\"https://github.com/iqbal-lab-org/minos/wiki\"\u003eminos wiki page\u003c/a\u003e\nfor more details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet a Docker image of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/iqbal-lab-org/minos:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll Docker images are listed in the\n\u003ca href=\"https://github.com/iqbal-lab-org/minos/pkgs/container/minos\"\u003epackages page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, build your own Docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker build --network=host .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/iqbal-lab-org/minos/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download (from version 0.12.1 onwards).\u003c/p\u003e\n\u003cp\u003eAlternatively, build your own Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity build minos.simg Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3 (tested on version 3.6.9)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/iqbal-lab-org/gramtools\"\u003egramtools\u003c/a\u003e commit\n04c4ba717399507b643fd4b77a61c048ef2ed83f\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://samtools.github.io/bcftools/\" rel=\"nofollow\"\u003ebcftools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/atks/vt.git\"\u003evt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/vcflib/vcflib.git\"\u003evcflib\u003c/a\u003e. Specifically,\neither \u003ccode\u003evcflib\u003c/code\u003e, or all three of\n\u003ccode\u003evcfbreakmulti\u003c/code\u003e, \u003ccode\u003evcfallelicprimitives\u003c/code\u003e, and \u003ccode\u003evcfuniq\u003c/code\u003e must be installed.\u003c/li\u003e\n\u003cli\u003eOptionally, \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e and \u003ca href=\"https://github.com/iqbal-lab-org/ivcfmerge\"\u003eivcfmerge\u003c/a\u003e if you want to use the\npipeline to regenotype a large number of samples.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInstall by cloning this repository (or downloading the latest release), and\nrunning:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run on one sample, you will need:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA FASTA file of the reference genome.\u003c/li\u003e\n\u003cli\u003eOne or more VCF files of variant calls.\nThe only requirement of these files is that they must contain the genotype field \u003ccode\u003eGT\u003c/code\u003e,\nand correspond to the reference FASTA file. All variants with a non-reference genotype\ncall will be used (both alleles are considered for diploid calls)\u003c/li\u003e\n\u003cli\u003eIllumina reads in FASTQ file(s).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor example, if you have two call sets in the files \u003ccode\u003ecalls1.vcf\u003c/code\u003e and \u003ccode\u003ecalls2.vcf\u003c/code\u003e,\nthen run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eminos adjudicate --reads reads1.fq --reads reads2.fq out ref.fasta calls1.vcf calls2.vcf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003ereads1.fq\u003c/code\u003e and \u003ccode\u003ereads2.fq\u003c/code\u003e are FASTQ files of the reads and \u003ccode\u003eref.fasta\u003c/code\u003e\nis a FASTA of the reference corresponding to the two input VCF files.\nThe final call set will be \u003ccode\u003eout/final.vcf\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUnit tests\u003c/h2\u003e\u003ca id=\"user-content-unit-tests\" class=\"anchor\" aria-label=\"Permalink: Unit tests\" href=\"#unit-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun \u003ccode\u003etox\u003c/code\u003e to run all unit tests.\nThey require \u003ccode\u003enextflow\u003c/code\u003e, \u003ccode\u003egramtools\u003c/code\u003e, \u003ccode\u003evt\u003c/code\u003e, \u003ccode\u003evcfbreakmulti\u003c/code\u003e,\n\u003ccode\u003evcfallelicprimitives\u003c/code\u003e, \u003ccode\u003evcfuniq\u003c/code\u003e in your \u003ccode\u003e$PATH\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eRun an individual test file with \u003ccode\u003etox tests/for_test.py::TestSpam::test_eggs\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eRun the main entry point with \u003ccode\u003epython3 -m minos\u003c/code\u003e.\u003c/p\u003e\n", "stargazers_count": 16, - "subscribers_count": 6, + "subscribers_count": 4, "topics": [], - "updated_at": 1711957178.0 + "updated_at": 1718272889.0 }, { "data_format": 2, - "description": "A RNN trained on Donald Trumps tweets", + "description": "N-Ways to Multi-GPU Programming", "filenames": [ "Singularity" ], - "full_name": "wyattferguson/trumpbot-rnn", + "full_name": "openhackathons-org/nways_multi_gpu", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTrumpbot v0.1\u003c/h1\u003e\u003ca id=\"user-content-trumpbot-v01\" class=\"anchor\" aria-label=\"Permalink: Trumpbot v0.1\" href=\"#trumpbot-v01\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTrumpbot was my attempt at creating a RNN trained on Donald Trumps(DT) tweets. I used this as a sort of practice project for learning a bit about RNN\u0027s and Tensorflow 2. The result was a chaos and a learning experience so let\u0027s dive in.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun with Containers\u003c/h2\u003e\u003ca id=\"user-content-run-with-containers\" class=\"anchor\" aria-label=\"Permalink: Run with Containers\" href=\"#run-with-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to install dependencies to your host, you can build a Docker container\nwith the included Dockerfile:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker build -t trumpbot \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe entrypoint is the script to generate the tweets:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run trumpbot\n...\n obamas Top and France at 900 PM on FoxNews. Anderson Congratulations to the House vote \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e MittRomney o\n\n hillary Clinton has been a total disaster. I have an idea \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eher great speech on CNN\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the world a great honor \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e me and his partisan hotel and every spor\n\n friends support Trump International Golf Club on the Paris About that Right School is started by the DNC and Clinton and the DNC that will be a great show with t\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to interact with the container (perhaps training first) you can shell inside instead:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ docker run -it --entrypoint bash trumpbot\nroot@b53b98f12c34:/code# ls\nDockerfile README.md __init__.py learn.py raw_tweets.txt requirements.txt\ttraining_checkpoints trumpbot.py tweets.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll be in the \u003ccode\u003e/code\u003c/code\u003e directory that contains the source code.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor users that want to perhaps use GPU (or better leverage the host) the recommendation is to\nuse a \u003ca href=\"https://www.sylabs.io/guides/3.2/user-guide/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container, and a recipe file \u003ca href=\"Singularity\"\u003eSingularity\u003c/a\u003e is provided\nto build the container.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ sudo singularity build trumpbot.sif Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then to run (add the --nv flag if you want to leverage any host libraries).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity run trumpbot.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you need to change the way that tensorflow or numpy are installed, you can edit the Singularity or Docker recipes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSetup is pretty straightforward. It only needs numpy and tensorflow 2 alpha just run the start pip install:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataset\u003c/h2\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entire dataset was just tweets scraped from the DT twitter account. I used Jefferson Henrique\u0027s library \u003ca href=\"https://github.com/Jefferson-Henrique/GetOldTweets-python\"\u003eGetOldTweets-python\u003c/a\u003e that I modified a little bit. All the raw tweets can be found in the raw_tweets.txt file FYI all the links in any tweet have been removed.\u003c/p\u003e\n\u003cp\u003eThe first thing about using Tweets as a dataset for training is that they are filled with garbage that wreaks havoc when training. Heres what I did:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRemoved any links or urls to photos\u003c/li\u003e\n\u003cli\u003eSimplified all the puncuation, with Trump this is a big thing, his tweets are a clown fiesta of periods and exclemation marks.\u003c/li\u003e\n\u003cli\u003eCleaned out any invisible or non-english characters, any foreign characters just casuases trouble.\u003c/li\u003e\n\u003cli\u003eRemoved the \u0027@\u0027 symbol, I\u0027ll explain why later.\u003c/li\u003e\n\u003cli\u003eRemoved the first couple of months of tweets, they were mostly about the celebrity apprentice and not really core to what I was trying to capture.\u003c/li\u003e\n\u003cli\u003eRemoved any retweets or super short @replies\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe final training text is in tweets.txt which altogether is about 20,000 tweets.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining\u003c/h2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI trained the model twice, the first time for 30 epochs which took around 6 hours. The result was absolute garbage, at the time I hadn\u0027t removed hidden or foreign characters so it took 6 hours to spit out complete nonsense. So after I cleaned out the tweets again, I ran the training overnight for 50 epochs this time.\u003c/p\u003e\n\u003cp\u003eJust run the learn.py file to train it again if you want, the model check points are stored in the \u0027training_checkpoints\u0027 folder\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 learn.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGenerating Tweets\u003c/h2\u003e\u003ca id=\"user-content-generating-tweets\" class=\"anchor\" aria-label=\"Permalink: Generating Tweets\" href=\"#generating-tweets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSo now the fun part, you can run the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 trumpbot.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will generate 10 tweets from a random group of topics. If you open the trumpbot.py file theres a few things you can play with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etweets - Number of messages you want generated\n\ntemperature - This controls how predictable the tweet will be, by \n default its random from 0.1 -\u0026gt; 0.4, anything above about 0.7 generates\n garbage.\n\ntalking_points - Is a list of inputs to feed the network, try out \n differnt words and see what works.\n\nnum_generate - This controls the length of the message you want to\n get generated.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResult\u003c/h2\u003e\u003ca id=\"user-content-result\" class=\"anchor\" aria-label=\"Permalink: Result\" href=\"#result\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor my first crack at text generation Im happy with the results. Here are some sample tweets:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ehillary Clinton has been a total disaster. If you cant admit that \nthe U.S. more than has been treated big baster I am a g\n\nDonald Trump is 45% Iran\n\nhealthe lobbyist now wants to raise taxes for our country in the \nfirst place! If only one thing is clea\n\nfriends support Trump Rally Anger Golf Club of Caporate legislation \nat the WhiteHouse today! #MakeAmericaGreatAgain Thank you for your\n support! #Trump2016 \n\nkoreau like you it was great being in the last election then will be\n a great show. I have a fan o\n\nkoreau lies and losers and losers will be a great show with the U.S.\n The President has a various past c\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat I learned\u003c/h2\u003e\u003ca id=\"user-content-what-i-learned\" class=\"anchor\" aria-label=\"Permalink: What I learned\" href=\"#what-i-learned\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTweets make for a tough training set. Things like @ mentions just pollute the hell out of the text so unless you want your bot to be constantly @ing everything I need to find a better way to deal with that.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThings I thought the bot would love talking about stuff like #MAGA, Russia, China, and collusion just generate garbage strings.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eText generation is really hard, and takes a ton of training time.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eI could probably get a bit better results if I let it train a bit longer but for any drastic improvements I probably need to try another method or spend alot more time tuning the training set.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePick a subject that doesn\u0027t tweet like hes a dad yelling at a little league game. I think because his tweets are short little outbursts its hard to generate a predictable pattern across them.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe words it groups together for differnt topics is probably worth looking at, like whenever you use \u0027hillary\u0027 as a input it usually has the words \u0027liar\u0027 or \u0027disaster\u0027 in the sentence. or how it loves telling you when its gonna be on @Foxandfriends\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWith the method I used spelling its like to add random \u0027u\u0027 infront of words.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eI feel like this is good starting point, and with some work we might have a digital orange man bot in our future.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udcee Contact \u0026amp; Support\u003c/h2\u003e\u003ca id=\"user-content-postbox-contact--support\" class=\"anchor\" aria-label=\"Permalink: :postbox: Contact \u0026amp; Support\" href=\"#postbox-contact--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCreated by \u003ca href=\"@wyattxdev@mastodon.social\"\u003eWyatt Ferguson\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor any comments or questions message me on \u003ca href=\"@wyattxdev@mastodon.social\"\u003eMastodon\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.buymeacoffee.com/wyattferguson\" rel=\"nofollow\"\u003e\u2615 Buy Me A Coffee\u003c/a\u003e\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eN-Ways to Multi-GPU Programming\u003c/h1\u003e\u003ca id=\"user-content-n-ways-to-multi-gpu-programming\" class=\"anchor\" aria-label=\"Permalink: N-Ways to Multi-GPU Programming\" href=\"#n-ways-to-multi-gpu-programming\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains mini applications for GPU Bootcamps. This bootcamp focuses on multi-GPU programming models.\u003c/p\u003e\n\u003cp\u003eScaling applications to multiple GPUs across multiple nodes requires one to be adept at not just the programming models and optimization techniques, but also at performing root-cause analysis using in-depth profiling to identify and minimize bottlenecks. In this bootcamp, participants will learn to improve the performance of an application step-by-step, taking cues from profilers along the way. Moreover, understanding of the underlying technologies and communication topology will help us utilize high-performance NVIDIA libraries to extract more performance out of the system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootcamp Outline\u003c/h2\u003e\u003ca id=\"user-content-bootcamp-outline\" class=\"anchor\" aria-label=\"Permalink: Bootcamp Outline\" href=\"#bootcamp-outline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOverview of single-GPU code and Nsight Systems Profiler\u003c/li\u003e\n\u003cli\u003eSingle Node Multi-GPU:\n\u003cul\u003e\n\u003cli\u003eCUDA Memcpy and Peer-to-Peer Memory Access\u003c/li\u003e\n\u003cli\u003eIntra-node topology\u003c/li\u003e\n\u003cli\u003eCUDA Streams and Events\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMulti-Node Multi-GPU:\n\u003cul\u003e\n\u003cli\u003eIntroduction to MPI and Multi-Node execution overview\u003c/li\u003e\n\u003cli\u003eMPI with CUDA Memcpy\u003c/li\u003e\n\u003cli\u003eCUDA-aware MPI\u003c/li\u003e\n\u003cli\u003eSupplemental: Configuring MPI in a containerized environment\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eNVIDIA Collectives Communications Library (NCCL)\u003c/li\u003e\n\u003cli\u003eNVHSMEM Library\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrerequisites\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis bootcamp requires a multi-node system with multiple GPUs in each node (atleast 2 GPUs/ node).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial Duration\u003c/h2\u003e\u003ca id=\"user-content-tutorial-duration\" class=\"anchor\" aria-label=\"Permalink: Tutorial Duration\" href=\"#tutorial-duration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe total bootcamp material would take approximately 8 hours .\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing NVIDIA HPC SDK\u003c/h3\u003e\u003ca id=\"user-content-using-nvidia-hpc-sdk\" class=\"anchor\" aria-label=\"Permalink: Using NVIDIA HPC SDK\" href=\"#using-nvidia-hpc-sdk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA multi-node installation of \u003ca href=\"https://developer.nvidia.com/hpc-sdk\" rel=\"nofollow\"\u003eNVIDIA\u0027s HPC SDK\u003c/a\u003e is desired. Refer to \u003ca href=\"https://docs.nvidia.com/hpc-sdk/hpc-sdk-install-guide/index.html\" rel=\"nofollow\"\u003eNVIDIA HPC SDK Installation Guide\u003c/a\u003e for detailed instructions. Ensure that your installation contains HPCX with UCX.\u003c/p\u003e\n\u003cp\u003eAfter installation, make sure to add HPC SDK to the environment as follows(For example the PATH highlighted below is for HPC SDK 21.5):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add HPC-SDK to PATH:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/compilers/bin:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/cuda/bin:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add HPC-SDK to LD_LIBRARY_PATH:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/comm_libs/nvshmem/lib:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/comm_libs/nccl/lib:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/comm_libs/mpi/lib:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/math_libs/lib64:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/compilers/lib:\u0026lt;path-to-nvidia-hpc-sdk\u0026gt;/Linux_x86_64/21.5/cuda/extras/CUPTI/lib64:\u0026lt;path-nvidia-hpc-sdk\u0026gt;\u0026gt;/Linux_x86_64/21.5/cuda/lib64:\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eADD NVSHMEM HOME DIRECTORY PATH\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CUDA_HOME=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/cuda\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NVSHMEM_HOME=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/comm_libs/nvshmem\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e If you don\u0027t use Slurm workload manager, remove \u003ccode\u003e--with-slurm\u003c/code\u003e flag.\u003c/p\u003e\n\u003cp\u003eThen, install OpenMPI as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download and extract OpenMPI Tarfile\u003c/span\u003e\nwget https://download.open-mpi.org/release/open-mpi/v4.1/openmpi-4.1.1.tar.gz\ntar -xvzf openmpi-4.1.1.tar.gz\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e openmpi-4.1.1/\nmkdir -p build\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Configure OpenMPI\u003c/span\u003e\n./configure --prefix=\u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/build --with-libevent=internal --with-xpmem --with-cuda=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/cuda/ --with-slurm --enable-mpi1-compatibility --with-verbs --with-hcoll=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/comm_libs/hpcx/hpcx-2.8.1/hcoll/lib --with-ucx=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-nvidia-hpc-sdk\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/Linux_x86_64/21.5/comm_libs/hpcx/hpcx-2.8.1/ucx/\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install OpenMPI\u003c/span\u003e\nmake all install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNow, add OpenMPI to the environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0026lt;path-to-openmpi\u0026gt;/build/bin/:\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e LD_LIBRARY_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0026lt;path-to-openmpi/build/lib:\u003cspan class=\"pl-smi\"\u003e$LD_LIBRARY_PATH\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eEnsure that the custom-built OpenMPI is in use by running \u003ccode\u003ewhich mpirun\u003c/code\u003e which should point the \u003ccode\u003empirun\u003c/code\u003e binary in \u003ccode\u003e\u0026lt;path-to-openmpi\u0026gt;/build/bin\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWithout Using NVIDIA HPC SDK\u003c/h3\u003e\u003ca id=\"user-content-without-using-nvidia-hpc-sdk\" class=\"anchor\" aria-label=\"Permalink: Without Using NVIDIA HPC SDK\" href=\"#without-using-nvidia-hpc-sdk\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMulti-node compatible versions of the following are required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.open-mpi.org/\" rel=\"nofollow\"\u003eOpenMPI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/networking/hpc-x\" rel=\"nofollow\"\u003eHPCX\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/cuda-toolkit\" rel=\"nofollow\"\u003eCUDA Toolkit\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/nccl\" rel=\"nofollow\"\u003eNCCL\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/nvshmem\" rel=\"nofollow\"\u003eNVSHMEM\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have tested all the codes with CUDA drivers 460.32.03 with CUDA 11.3.0.0, OpenMPI 4.1.1, HPCX 2.8.1, Singularity 3.6.1, NCCL 2.9.9.1, and NVSHMEM 2.1.2. Note that OpenMPI in our cluster was compiled with CUDA, HCOLL, and UCX support.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Jupyter Lab\u003c/h2\u003e\u003ca id=\"user-content-running-jupyter-lab\" class=\"anchor\" aria-label=\"Permalink: Running Jupyter Lab\" href=\"#running-jupyter-lab\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs this bootcamp covers multi-node CUDA-aware MPI concepts, it is primarily designed to run without any containers. After the prerequisite softwares have been installed, follow these steps to install and run Jupyter Lab:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Anaconda3\u003c/span\u003e\nwget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \nbash Miniconda3-latest-Linux-x86_64.sh -b -p \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add conda to PATH\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PATH=\u003cspan class=\"pl-smi\"\u003e$PATH\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emy_dir\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/bin/\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install Jupyter Lab\u003c/span\u003e\nconda install -c conda-forge jupyterlab\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run Jupyter Lab\u003c/span\u003e\njupyter lab --notebook-dir=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-gpubootcamp-repo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/hpc/multi_gpu_nways/labs/ --port=8000 --ip=0.0.0.0 --no-browser --NotebookApp.token=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAfter running Jupyter Lab, open \u003ca href=\"http://localhost:8888/\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e in a web browser and start the \u003ccode\u003eintroduction.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptional: Containerized Build with Singularity\u003c/h2\u003e\u003ca id=\"user-content-optional-containerized-build-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Optional: Containerized Build with Singularity\" href=\"#optional-containerized-build-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis material is designed to primarily run in containerless environments, that is, directly on the cluster. Thus, building the Singularity container is OPTIONAL.\u003c/p\u003e\n\u003cp\u003eIf containerization is desired, follow the steps outlined in the notebook \u003ca href=\"labs/CFD/English/C/jupyter_notebook/mpi/containers_and_mpi.ipynb\"\u003eMPI in Containerized Environments\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFollow the steps below to build the Singularity container image and run Jupyter Lab:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build the container\u003c/span\u003e\nsingularity build multi_gpu_nways.simg Singularity\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run Jupyter Lab\u003c/span\u003e\nsingularity run --nv multi_gpu_nways.simg jupyter lab --notebook-dir=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath-to-gpubootcamp-repo\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/hpc/multi_gpu_nways/labs/ --port=8000 --ip=0.0.0.0 --no-browser --NotebookApp.token=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen, access Jupyter Lab on \u003ca href=\"http://localhost:8888/\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCompiler throws errors\u003c/h4\u003e\u003ca id=\"user-content-compiler-throws-errors\" class=\"anchor\" aria-label=\"Permalink: Compiler throws errors\" href=\"#compiler-throws-errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf compiling any program throws an error related to CUDA/ NCCL/ NVHSMEM/ MPI libraries or header files being not found, ensure that \u003ccode\u003eLD_LIBRARY_PATH\u003c/code\u003e is correctly set. Moreover, make sure environment variables \u003ccode\u003eCUDA_HOME\u003c/code\u003e, \u003ccode\u003eNCCL_HOME\u003c/code\u003e, and \u003ccode\u003eNVSHMEM_HOME\u003c/code\u003e are set either during installation or manually inside each \u003ccode\u003eMakefile\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlease go through the list of exisiting bugs/issues or file a new issue at \u003ca href=\"https://github.com/gpuhackathons-org/gpubootcamp/issues\"\u003eGithub\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuestions?\u003c/h2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-label=\"Permalink: Questions?\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease join \u003ca href=\"https://openacclang.slack.com/messages/openaccusergroup\" rel=\"nofollow\"\u003eOpenACC Slack Channel\u003c/a\u003e to raise questions.\u003c/p\u003e\n\u003cp\u003eIf you observe any errors or issues, please file an issue on \u003ca href=\"https://github.com/gpuhackathons-org/gpubootcamp\"\u003eGPUBootcamp GitHuB repository\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 16, - "subscribers_count": 3, + "subscribers_count": 1, "topics": [ - "tensorflow", - "python" + "cuda", + "hpc", + "mpi", + "nccl", + "nsight-systems", + "nvshmem" ], - "updated_at": 1704291383.0 + "updated_at": 1732318936.0 }, { "data_format": 2, - "description": "This an example app that can serve as a template.", + "description": "Implementation of the 3D reconstruction pipeline optimized for plant branching structures.", "filenames": [ - "Singularity" + "Singularity_colmap_vsfm", + "Singularity_recipe/Singularity", + "model_preprocess/Singularity" ], - "full_name": "bids-apps/example", - "latest_release": "0.0.7", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAn example BIDS App (template repository)\u003c/h2\u003e\u003ca id=\"user-content-an-example-bids-app-template-repository\" class=\"anchor\" aria-label=\"Permalink: An example BIDS App (template repository)\" href=\"#an-example-bids-app-template-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEvery BIDS App needs to follow a minimal set of command arguments common across\nall of the Apps. This allows users and developers to easily use and integrate\nBIDS Apps with their environment.\u003c/p\u003e\n\u003cp\u003eThis is a minimalist example of a BIDS App consisting of a Dockerfile and a simple\nentry point script (written in this case in Python) accepting the standard BIDS\nApps command line arguments. This repository can be used as a template for new BIDS Apps.\u003c/p\u003e\n\u003cp\u003eFor more information about the specification of BIDS Apps see \u003ca href=\"https://docs.google.com/document/d/1E1Wi5ONvOVVnGhj21S1bmJJ4kyHFT7tkxnV3C23sjIE/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDescription\u003c/h3\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is a placeholder for a short description explaining to the user what your App will doing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvide a link to the documentation of your pipeline.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to report errors\u003c/h3\u003e\u003ca id=\"user-content-how-to-report-errors\" class=\"anchor\" aria-label=\"Permalink: How to report errors\" href=\"#how-to-report-errors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eProvide instructions for users on how to get help and report errors.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAcknowledgments\u003c/h3\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDescribe how would you would like users to acknowledge use of your App in their papers (citation, a paragraph that can be copy pasted, etc.)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis App has the following command line arguments:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eusage: run.py [-h] [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]] [--skip_bids_validator] [-v]\n\t\t\tbids_dir output_dir {participant,group}\n\nExample BIDS App entrypoint script.\n\npositional arguments:\nbids_dir The directory with the input dataset formatted according to the BIDS standard.\noutput_dir The directory where the output files should be stored. If you are running group level analysis this folder should\n\t\t\t\t\t\tbe prepopulated with the results of theparticipant level analysis.\n{participant,group} Level of the analysis that will be performed. Multiple participant level analyses can be run independently (in\n\t\t\t\t\t\tparallel) using the same output_dir.\n\noptions:\n-h, --help show this help message and exit\n--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]\n\t\t\t\t\t\tThe label(s) of the participant(s) that should be analyzed. The label corresponds to sub-\u0026lt;participant_label\u0026gt; from\n\t\t\t\t\t\tthe BIDS spec (so it does not include \"sub-\"). If this parameter is not provided all subjects should be analyzed.\n\t\t\t\t\t\tMultiple participants can be specified with a space separated list.\n--skip_bids_validator\n\t\t\t\t\t\tWhether or not to perform BIDS dataset validation\n-v, --version show program\u0027s version number and exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo run it in participant level mode (for one participant):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --rm \\\n\t-v /Users/filo/data/ds005:/bids_dataset:ro \\\n\t-v /Users/filo/outputs:/outputs \\\n\tbids/example \\\n\t/bids_dataset /outputs participant --participant_label 01\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter doing this for all subjects (potentially in parallel), the group level analysis\ncan be run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --rm \\\n\t-v /Users/filo/data/ds005:/bids_dataset:ro \\\n\t-v /Users/filo/outputs:/outputs \\\n\tbids/example \\\n\t/bids_dataset /outputs group\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSpecial considerations\u003c/h3\u003e\u003ca id=\"user-content-special-considerations\" class=\"anchor\" aria-label=\"Permalink: Special considerations\" href=\"#special-considerations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDescribe whether your app has any special requirements. For example:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMultiple map reduce steps (participant, group, participant2, group2 etc.)\u003c/li\u003e\n\u003cli\u003eUnusual memory requirements\u003c/li\u003e\n\u003cli\u003eetc.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "Computational-Plant-Science/3D_model_reconstruction_demo", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDIRT/3D: 3D root phenotyping system for field-grown maize roots\u003c/h1\u003e\u003ca id=\"user-content-dirt3d-3d-root-phenotyping-system-for-field-grown-maize-roots\" class=\"anchor\" aria-label=\"Permalink: DIRT/3D: 3D root phenotyping system for field-grown maize roots\" href=\"#dirt3d-3d-root-phenotyping-system-for-field-grown-maize-roots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePipeline: Build 3D root models from images captured by 3D root scanner, and compute 3D root trait by analyzing 3D root models and computing 3D root model structures.\u003c/p\u003e\n\u003cp\u003eThis repo was to Reconstruct a 3D point cloud root model from images.\u003c/p\u003e\n\u003cp\u003eFor example, a real root and a reconstruction, side by side:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../master/media/3D_scanner.gif\"\u003e\u003cimg src=\"../master/media/3D_scanner.gif\" alt=\"3D root scanner prototype\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"../master/media/3D_model.gif\"\u003e\u003cimg src=\"../master/media/3D_model.gif\" alt=\"3D root model reconstruction\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to use this software is with Docker or Singularity. A public Docker image definition is available: \u003ccode\u003ecomputationalplantscience/dirt3d-reconstruction\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePull an image or a repository from a registry\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull computationalplantscience/dirt3d-reconstruction\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eMount the current working directory and open an interactive shell:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e:/opt/dev -w /opt/dev computationalplantscience/dirt3d-reconstruction bash\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo allow \u003ccode\u003ecolmap\u003c/code\u003e to use CUDA-enabled GPUs, use \u003ccode\u003e--gpus all\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpen a shell in your current working directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity shell docker://computationalplantscience/dirt3d-reconstruction\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo allow \u003ccode\u003ecolmap\u003c/code\u003e to use CUDA-enabled GPUs, use the \u003ccode\u003e--nv\u003c/code\u003e flag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUsage\u003c/h1\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReconstructing a 3D point cloud\u003c/h2\u003e\u003ca id=\"user-content-reconstructing-a-3d-point-cloud\" class=\"anchor\" aria-label=\"Permalink: Reconstructing a 3D point cloud\" href=\"#reconstructing-a-3d-point-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo reconstruct a point cloud from an image set, use \u003ccode\u003epipeline.py\u003c/code\u003e as such:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython3 /opt/code/pipeline.py -i \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003einput directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -o \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eoutput directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e -g \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ehow many GPUs to use\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOmit the \u003ccode\u003e-g \u0026lt;# of GPUs\u0026gt;\u003c/code\u003e argument or set it to 0 to perform the reconstruction with CPUs only. Note that \u003ccode\u003e-g \u0026lt;# GPUs\u0026gt;\u003c/code\u003e is short for \u003ccode\u003e--gpus \u0026lt;# GPUs\u0026gt;\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eA successful reconstruction will produce several files in the output directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esparse.ply\u003c/code\u003e: sparse point cloud model\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edense.ply\u003c/code\u003e: dense point cloud model\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emesh.ply\u003c/code\u003e: dense mesh model\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etimes.csv\u003c/code\u003e: time costs per step\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePreprocessing\u003c/h3\u003e\u003ca id=\"user-content-preprocessing\" class=\"anchor\" aria-label=\"Permalink: Preprocessing\" href=\"#preprocessing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are several optional preprocessing steps, all of which accept values \u003ccode\u003eTrue\u003c/code\u003e or \u003ccode\u003eFalse\u003c/code\u003e (and default to \u003ccode\u003eFalse\u003c/code\u003e):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--segmentation\u003c/code\u003e: crops to the largest feature\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--blur_detection\u003c/code\u003e: detects and omits blurry images\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--gamma_correction\u003c/code\u003e: increases brightness of dark images\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePMVS2 vs. Colmap for dense reconstruction\u003c/h3\u003e\u003ca id=\"user-content-pmvs2-vs-colmap-for-dense-reconstruction\" class=\"anchor\" aria-label=\"Permalink: PMVS2 vs. Colmap for dense reconstruction\" href=\"#pmvs2-vs-colmap-for-dense-reconstruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, PMVS2 is used for dense reconstruction on both CPU and GPU. Colmap can optionally be used with GPUs. It tends to produce significantly denser models but may run up to an order of magnitude more slowly.\u003c/p\u003e\n\u003cp\u003eTo enable dense reconstruction with Colmap, use \u003ccode\u003e-d COLMAP\u003c/code\u003e (short for \u003ccode\u003e--dense_strategy COLMAP\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eColmap configuration\u003c/h4\u003e\u003ca id=\"user-content-colmap-configuration\" class=\"anchor\" aria-label=\"Permalink: Colmap configuration\" href=\"#colmap-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are several configurable values for colmap\u0027s patch matching step during dense reconstruction. Optimal values will vary by host machine.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--cache_size\u003c/code\u003e: cache size (in GB) to use during patch matching, defaults to \u003ccode\u003e32\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--window_step\u003c/code\u003e: patch window step size, defaults to \u003ccode\u003e1\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--window_radius\u003c/code\u003e: patch window radius, defaults to \u003ccode\u003e5\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_iterations\u003c/code\u003e: number of patch match iterations, defaults to \u003ccode\u003e5\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--num_samples\u003c/code\u003e: number of sampled views, defaults to \u003ccode\u003e15\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--geom_consistency\u003c/code\u003e: whether to perform geometric dense reconstruction, defaults to \u003ccode\u003eFalse\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eVisualizing a 3D point cloud\u003c/h2\u003e\u003ca id=\"user-content-visualizing-a-3d-point-cloud\" class=\"anchor\" aria-label=\"Permalink: Visualizing a 3D point cloud\" href=\"#visualizing-a-3d-point-cloud\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently this software does not support model visualization. PLY files can be visualized with e.g. \u003ca href=\"https://www.meshlab.net/\" rel=\"nofollow\"\u003eMeshlab\u003c/a\u003e or \u003ca href=\"https://www.danielgm.net/cc/\" rel=\"nofollow\"\u003ecloudcompare\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDependencies\u003c/h1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software is built on top of COLMAP, VSFM, \u0026amp; PMVS2.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVisualSFM\u003c/h3\u003e\u003ca id=\"user-content-visualsfm\" class=\"anchor\" aria-label=\"Permalink: VisualSFM\" href=\"#visualsfm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"mailto:adamsgaard@ucsd.edu\"\u003eAnders Damsgaard\u003c/a\u003e with contributions by Caleb Adams and Connor P Doherty.\nChangchang Wu ( \u003ca href=\"mailto:wucc1130@gmail.com\"\u003ewucc1130@gmail.com\u003c/a\u003e )\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStructure from Motion\n[1] Changchang Wu, \"Towards Linear-time Incremental Structure From Motion\", 3DV 2013\n[2] Changchang Wu, \"VisualSFM: A Visual Structure from Motion System\", \u003ca href=\"http://ccwu.me/vsfm/\" rel=\"nofollow\"\u003ehttp://ccwu.me/vsfm/\u003c/a\u003e, 2011\u003c/li\u003e\n\u003cli\u003eBundle Adjustment\n[3] Changchang Wu, Sameer Agarwal, Brian Curless, and Steven M. Seitz, \"Multicore Bundle Adjustment\", CVPR 2011\u003c/li\u003e\n\u003cli\u003eFeature Detection\n[4] Changchang Wu, \"SiftGPU: A GPU implementation of Scale Invaraint Feature Transform (SIFT)\", \u003ca href=\"http://cs.unc.edu/~ccwu/siftgpu\" rel=\"nofollow\"\u003ehttp://cs.unc.edu/~ccwu/siftgpu\u003c/a\u003e, 2007\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCOLMAP\u003c/h3\u003e\u003ca id=\"user-content-colmap\" class=\"anchor\" aria-label=\"Permalink: COLMAP\" href=\"#colmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://colmap.github.io\" rel=\"nofollow\"\u003ehttps://colmap.github.io\u003c/a\u003e\nAuthor: Johannes L. Schoenberger (jsch-at-demuc-dot-de)\n@inproceedings{schoenberger2016sfm,\nauthor={Sch\"{o}nberger, Johannes Lutz and Frahm, Jan-Michael},\ntitle={Structure-from-Motion Revisited},\nbooktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},\nyear={2016},\n}\u003c/p\u003e\n\u003cp\u003e@inproceedings{schoenberger2016mvs,\nauthor={Sch\"{o}nberger, Johannes Lutz and Zheng, Enliang and Pollefeys, Marc and Frahm, Jan-Michael},\ntitle={Pixelwise View Selection for Unstructured Multi-View Stereo},\nbooktitle={European Conference on Computer Vision (ECCV)},\nyear={2016},\n}\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSuxing Liu (\u003ca href=\"mailto:suxingliu@gmail.com\"\u003esuxingliu@gmail.com\u003c/a\u003e), Wesley Paul Bonelli(\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther contributions\u003c/h2\u003e\u003ca id=\"user-content-other-contributions\" class=\"anchor\" aria-label=\"Permalink: Other contributions\" href=\"#other-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocker container was maintained and deployed to \u003ca href=\"https://portnoy.cyverse.org\" rel=\"nofollow\"\u003ePlantIT\u003c/a\u003e by Wes Bonelli (\u003ca href=\"mailto:wbonelli@uga.edu\"\u003ewbonelli@uga.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eSingularity container overlay issues were solved by [Saravanaraj Ayyampalayam] (\u003ca href=\"https://github.com/raj76\"\u003ehttps://github.com/raj76\u003c/a\u003e) (\u003ca href=\"mailto:raj76@uga.edu\"\u003emailto:raj76@uga.edu\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eSpecial thanks to Chris Cotter building the Singularity container recipe for testing and debugging.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGNU Public License\u003c/p\u003e\n", "stargazers_count": 16, - "subscribers_count": 3, + "subscribers_count": 5, "topics": [ - "bids", - "bids-apps" + "phenotyping", + "phenotyping-algorithms", + "root" ], - "updated_at": 1716899176.0 + "updated_at": 1730360243.0 }, { "data_format": 2, - "description": "Astronomical Calibration and Imaging Software", + "description": "SELFISH is a tool for finding differential chromatin interactions between two Hi-C contact maps.", "filenames": [ - "deploy/singularity/Singularity.openmpi" + "Singularity" ], - "full_name": "ATNF/yandasoft", + "full_name": "ay-lab/selfish", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eYandaSoft\u003c/h1\u003e\u003ca id=\"user-content-yandasoft\" class=\"anchor\" aria-label=\"Permalink: YandaSoft\" href=\"#yandasoft\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYandasoft is a suite of applications and software developed by CSIRO for the calibration and imaging of Interferometric Radio Telescope data.\u003c/p\u003e\n\u003cp\u003eThis is a \u003ca href=\"https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow\" rel=\"nofollow\"\u003eGitflow\u003c/a\u003e repo and you should install the \u003ca href=\"https://github.com/petervanderdoes/gitflow-avh/wiki/Installation\"\u003egitflow extensions\u003c/a\u003e to get the best milage. Gitflow affords a stricter release policy that other workflows and, as this package is used to build official versions of ASKAP software, it is important that this process is reliable and repeatable and so this package is a controlled dependency.\u003c/p\u003e\n\u003cp\u003eIn accordance with Gitflow workflow:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf you are after the latest release then take it from \u003ccode\u003etags/\u0026lt;ver\u0026gt;\u003c/code\u003e or the head of \u003ccode\u003emaster\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eDevelopment and fixes should only proceed in features/branches and then via pull requests into \u003ccode\u003edevelop\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eReleases are prepared in \u003ccode\u003erelease\u003c/code\u003e branches and then canonised to \u003ccode\u003emaster\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eOfficial releases are tagged on \u003ccode\u003emaster\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA git submodule for the full calibration and imaging documentation is included\nin the docs sibdirectory. On an initial clone of this repository you have to\nrun, \u003ccode\u003egit submodule init\u003c/code\u003e and \u003ccode\u003egit submodule update\u003c/code\u003e to obtain the latest\nversions. Also you must have the sphinx document tool installed and run \u003ccode\u003emake html\u003c/code\u003e to generate the documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIn this version\u003c/h2\u003e\u003ca id=\"user-content-in-this-version\" class=\"anchor\" aria-label=\"Permalink: In this version\" href=\"#in-this-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis release of the software is the first and consequently \u003cem\u003ebeta\u003c/em\u003e release. It contains the (at least) the following applications:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMeasurement Set creation and manipulation\u003c/h3\u003e\u003ca id=\"user-content-measurement-set-creation-and-manipulation\" class=\"anchor\" aria-label=\"Permalink: Measurement Set creation and manipulation\" href=\"#measurement-set-creation-and-manipulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecsimulator\u003c/code\u003e: simulation of visibilities.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eccontsubtract\u003c/code\u003e: continuum subtraction.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emslist\u003c/code\u003e: measurement set interogation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emsconcat\u003c/code\u003e: concatenation of measurement sets.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emsmerge\u003c/code\u003e: merging of measurement sets.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCalibration tools\u003c/h3\u003e\u003ca id=\"user-content-calibration-tools\" class=\"anchor\" aria-label=\"Permalink: Calibration tools\" href=\"#calibration-tools\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecbpcalibrator\u003c/code\u003e: bandpass calibrator.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eccalibrator\u003c/code\u003e: for performing gain calibration\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eccalapply\u003c/code\u003e: for the application of calibration solutions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eImaging tasks\u003c/h3\u003e\u003ca id=\"user-content-imaging-tasks\" class=\"anchor\" aria-label=\"Permalink: Imaging tasks\" href=\"#imaging-tasks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ecimager\u003c/code\u003e: Original ASKAP imager.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eimager\u003c/code\u003e: New imager - permits more parallisation options.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elinmos\u003c/code\u003e: Linear mosaicking of images\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline and Analysis tasks\u003c/h3\u003e\u003ca id=\"user-content-pipeline-and-analysis-tasks\" class=\"anchor\" aria-label=\"Permalink: Pipeline and Analysis tasks\" href=\"#pipeline-and-analysis-tasks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003emsplit\u003c/code\u003e: Manipulate measurement sets\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecmodel\u003c/code\u003e: Generate model images from component lists\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eselavy\u003c/code\u003e: Source detection tools\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout Yandasoft\u003c/h2\u003e\u003ca id=\"user-content-about-yandasoft\" class=\"anchor\" aria-label=\"Permalink: About Yandasoft\" href=\"#about-yandasoft\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese tasks were originally developed to form the real-time calibration and imaging pipeline for the ASKAP telescope. In order to distribute this software more widely we have extracted these tools from the main codebase of the telescope system and distributed it separately.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe dependencies are listed in detail in the INSTALL.txt file. But it should be noted that there are internal \"ASKAP\" dependencies that are required. They are all public and are automatically pulled from their respective repositories by the included \u003ccode\u003ebuild_all.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to get it\u003c/h3\u003e\u003ca id=\"user-content-how-to-get-it\" class=\"anchor\" aria-label=\"Permalink: How to get it\" href=\"#how-to-get-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYadasoft and its required ASKAP dependencies are available from the CSIRO bitbucket server at:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/lofar-common.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/lofar-common.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/lofar-blob.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/lofar-blob.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-askap.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-askap.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-logfilters.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-logfilters.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-imagemath.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-imagemath.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-scimath.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-scimath.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-askapparallel.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-askapparallel.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/base-accessors.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/base-accessors.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/yandasoft.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/yandasoft.git\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere are some extra tasks available from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/askap-pipelinetasks.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/askap-pipelinetasks.git\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bitbucket.csiro.au/scm/askapsdp/askap-analysis.git\" rel=\"nofollow\"\u003ehttps://bitbucket.csiro.au/scm/askapsdp/askap-analysis.git\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 16, - "subscribers_count": 13, - "topics": [ - "astronomy", - "astronomical-algorithms" - ], - "updated_at": 1685695246.0 + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSelfish\u003c/h1\u003e\u003ca id=\"user-content-selfish\" class=\"anchor\" aria-label=\"Permalink: Selfish\" href=\"#selfish\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSelfish (Discovery of Differential Chromatin Interactions via a Self-Similarity Measure) is a tool by Abbas Roayaei Ardakany, Ferhat Ay (\u003ca href=\"mailto:ferhatay@lji.org\"\u003eferhatay@lji.org\u003c/a\u003e), and Stefano Lonardi. This Python implementation is currently maintained by Tuvan Gezer (\u003ca href=\"mailto:tgezer@sabanciuniv.edu\"\u003etgezer@sabanciuniv.edu\u003c/a\u003e). The original MATLAB implementation by Abbas (\u003ca href=\"mailto:roayaei@gmail.com\"\u003eroayaei@gmail.com\u003c/a\u003e) can be found at \u003ca href=\"https://github.com/ucrbioinfo/SELFISH\"\u003ehttps://github.com/ucrbioinfo/SELFISH\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSELFISH is a tool for finding differential chromatin interactions between two Hi-C contact maps. It uses self-similarity to model interactions in a robust way. For more information read the full\npaper: \u003ca href=\"https://academic.oup.com/bioinformatics/article/35/14/i145/5529135\" rel=\"nofollow\"\u003eSelfish: discovery of differential chromatin interactions via a self-similarity measure\u003c/a\u003e.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/demo.png\"\u003e\u003cimg src=\"/demo.png\" alt=\"DCI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation and usage\u003c/h2\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-label=\"Permalink: Installation and usage\" href=\"#installation-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePIP\u003c/h3\u003e\u003ca id=\"user-content-pip\" class=\"anchor\" aria-label=\"Permalink: PIP\" href=\"#pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install selfish-hic\nselfish -f1 /path/to/contact/map1.txt \\\n -f2 /path/to/contact/map2.txt \\\n\t-ch 2 \\\n -r 100kb -o ./output.npy\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGithub\u003c/h3\u003e\u003ca id=\"user-content-github\" class=\"anchor\" aria-label=\"Permalink: Github\" href=\"#github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake sure you have Python 3 installed, along with all the dependencies listed.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/ay-lab/selfish\n./selfish/selfish/selfish.py -f1 /path/to/contact/map1.txt \\\n -f2 /path/to/contact/map2.txt \\\n\t -ch 2 \\\n -r 100kb -o ./output.npy\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNextflow\u003c/h3\u003e\u003ca id=\"user-content-nextflow\" class=\"anchor\" aria-label=\"Permalink: Nextflow\" href=\"#nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have any problem regarding dependencies or version mismatches, we recommend using \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e with a container technology like \u003ca href=\"https://www.docker.com/get-started\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. These methods require Nextflow(Can be installed with a single command that doesn\u0027t require special permissions.), and the desired container technology to be available.\nProgram arguments are given to Nextflow with two dashes and the short format listed below.\n\u003cstrong\u003eUpdating:\u003c/strong\u003e If Nextflow warns that your project is outdated, use \u003ccode\u003enextflow pull ay-lab/selfish\u003c/code\u003e in order to update to latest version.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstall Nextflow\u003c/h4\u003e\u003ca id=\"user-content-install-nextflow\" class=\"anchor\" aria-label=\"Permalink: Install Nextflow\" href=\"#install-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNextflow works for Linux and OS X. Install it using \u003cstrong\u003eone\u003c/strong\u003e of the commands listed below. \u003cstrong\u003eRequires Java 8+\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget -qO- https://get.nextflow.io \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e bash\nOR\ncurl -s https://get.nextflow.io \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e bash\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWith Docker\u003c/h4\u003e\u003ca id=\"user-content-with-docker\" class=\"anchor\" aria-label=\"Permalink: With Docker\" href=\"#with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./nextflow run ay-lab/selfish --f1=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/contact/map1.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --f2=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/contact/map2.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t --ch=2 \\\n --r=100kb -profile docker\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWith Singularity\u003c/h4\u003e\u003ca id=\"user-content-with-singularity\" class=\"anchor\" aria-label=\"Permalink: With Singularity\" href=\"#with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./nextflow run ay-lab/selfish --f1=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/.../map1.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --f2=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/.../map2.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t --ch=2 \\\n --r=100kb -profile singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBioconda\u003c/h3\u003e\u003ca id=\"user-content-bioconda\" class=\"anchor\" aria-label=\"Permalink: Bioconda\" href=\"#bioconda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioconda install isn\u0027t currently available.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSelfish uses some python packages to accomplish its mission. These are the packages used by selfish:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003ematplotlib\u003c/li\u003e\n\u003cli\u003eseaborn\u003c/li\u003e\n\u003cli\u003escipy\u003c/li\u003e\n\u003cli\u003estatsmodels\u003c/li\u003e\n\u003cli\u003ehic-straw\u003c/li\u003e\n\u003cli\u003epathlib\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eShort\u003c/th\u003e\n\u003cth\u003eLong\u003c/th\u003e\n\u003cth\u003eMeaning\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eRequired Parameters\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-f1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--file1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of contact map 1. (See below for format.) Not required for HiC-Pro input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-f2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--file2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of contact map 2. (See below for format.) Not required for HiC-Pro input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-r\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--resolution\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eResolution of the provided contact maps.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-o\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--outfile\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eName of the output file.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-ch\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--chromosome\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eSpecify which chromosome to run the program for.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-m1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--matrix1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of matrix file, \u003cstrong\u003eonly\u003c/strong\u003e for HiC-Pro type input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-m2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--matrix2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of matrix file, \u003cstrong\u003eonly\u003c/strong\u003e for HiC-Pro type input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-bed1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--bed1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of bed file, \u003cstrong\u003eonly\u003c/strong\u003e for HiC-Pro type input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-bed2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--bed2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of bed file, \u003cstrong\u003eonly\u003c/strong\u003e for HiC-Pro type input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eOptional Parameters\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-t\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--tsvout\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eIf specified, outputs will be written as a TSV file. Specify the q-value threshold for which the results will be written to the file (i.e. -t 0.05)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-d\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--distanceFilter\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eMaximum distance between interacting loci.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-c\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--changes\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eName of the output file that has the log fold changes (log2((a+1)/(b+1))) between the inputs.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-b1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--biases1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of bias/normalization vector file for contact map 1. (See below for format.)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-b2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--biases2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of bias/normalization vector file for contact map 2. (See below for format.)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-sz\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--sigmaZero\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eSigma0 parameter for Selfish. Default is experimentally chosen for 5Kb resolution.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-i\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--iterations\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eIteration count parameter for Selfish. Default is experimentally chosen for 5Kb resolution.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-v\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--verbose\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eWhether the program prints its progress while running. Default is True.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-st\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--sparsityThreshold\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eThe sparsity threshold selfish uses to filter differential interactions. Differential interactions falling in sparse regions are filtered out. The default is 0.7.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-ns\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--no-sparsityCheck\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eDo not use sparsity checking.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-nm\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--no-mutual\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eInclude also the interactions that are zero in one of the contact maps. By defalut, selfish only considers interactions that are not zero in any of the contact maps.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-norm\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--normalization\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eFor .[m]cool or .hic files, you can specify what normalization selfish should use (-norm KR). For .[m]cool files, by default, selfish assumes that bias values are stored in the \u0027weight\u0027 column when this parameter is not specified. For .hic format, the default is \u0027KR\u0027 normalization if not specified.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-p\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--plot\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eWhether the program plots its results. Highly discouraged for high resolutions(\u0026lt;50kb) as it will take a lot of time to compute the plots. For high resolutions, we recommend using the output matrix and plotting small sections of it manually. Default is False.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-lm\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--lowmem\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eUse float32 instead of float64. Uses less memory at the cost of precision. Default is False.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-V\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--version\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eShows the version of the tool.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput Formats\u003c/h3\u003e\u003ca id=\"user-content-input-formats\" class=\"anchor\" aria-label=\"Permalink: Input Formats\" href=\"#input-formats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSELFISH supports 3 different input formats. \u003cstrong\u003ePlain text\u003c/strong\u003e, \u003cstrong\u003e.hic\u003c/strong\u003e, \u003cstrong\u003e.cool\u003c/strong\u003e, \u003cstrong\u003e.bed/.matrix\u003c/strong\u003e pairs (HiC-Pro format).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eText Contact Maps\u003c/h4\u003e\u003ca id=\"user-content-text-contact-maps\" class=\"anchor\" aria-label=\"Permalink: Text Contact Maps\" href=\"#text-contact-maps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContact maps need to have the following format. They must not have a header.\nValues must be separated by either a space, a tab, or a comma.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eChromosome\u003c/th\u003e\n\u003cth\u003eMidpoint 1\u003c/th\u003e\n\u003cth\u003eChromosome\u003c/th\u003e\n\u003cth\u003eMidpoint 2\u003c/th\u003e\n\u003cth\u003eContact Count\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e5000\u003c/td\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e65000\u003c/td\u003e\n\u003ctd\u003e438\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e5000\u003c/td\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e85000\u003c/td\u003e\n\u003ctd\u003e12\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBed-Matrix pairs (HiC-Pro format)\u003c/h4\u003e\u003ca id=\"user-content-bed-matrix-pairs-hic-pro-format\" class=\"anchor\" aria-label=\"Permalink: Bed-Matrix pairs (HiC-Pro format)\" href=\"#bed-matrix-pairs-hic-pro-format\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUser must provide a chromosome with the -ch argument.\u003c/strong\u003e\n.bed and .matrix files must have the same name other than the extension.\nEither file name can be provided as an input for selfish and the program will search for the second file automatically.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHiC file\u003c/h4\u003e\u003ca id=\"user-content-hic-file\" class=\"anchor\" aria-label=\"Permalink: HiC file\" href=\"#hic-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUser must provide a chromosome with the -ch argument.\u003c/strong\u003e\nSelfish uses juicer\u0027s \u003cem\u003estraw\u003c/em\u003e tool to read .hic files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e.cool file\u003c/h4\u003e\u003ca id=\"user-content-cool-file\" class=\"anchor\" aria-label=\"Permalink: .cool file\" href=\"#cool-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUser must provide a chromosome with the -ch argument.\u003c/strong\u003e\nSelfish uses \u003cem\u003ecooler\u003c/em\u003e to read .cool files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBias (normalization) File\u003c/h4\u003e\u003ca id=\"user-content-bias-normalization-file\" class=\"anchor\" aria-label=\"Permalink: Bias (normalization) File\" href=\"#bias-normalization-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBias file need to have the following format.\nBias file must use the same midpoint format as the contact maps.\nBias file must not have a header.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eChromosome\u003c/th\u003e\n\u003cth\u003eMidpoint\u003c/th\u003e\n\u003cth\u003eBias\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e5000\u003c/td\u003e\n\u003ctd\u003e1.012\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e65000\u003c/td\u003e\n\u003ctd\u003e1.158\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput\u003c/h3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutput of Selfish is a matrix of q-values indicating the probability of differential conformation (Smaller values mean more significant.).\nX and Y coordinates indicate the bin midpoints.\u003cbr\u003e\nAnother optional output is the log fold changes file. It is simply produced by \u003ccode\u003elog2((map1 + 1) / (map2 + 1))\u003c/code\u003e\u003cbr\u003e\nFile format of the outputs is a binary numpy file. It can be read by using Numpy as follows.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enumpy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ematrix\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eload\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/path/to/output/selfish.npy\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use Selfish in your work, please cite our \u003ca href=\"https://academic.oup.com/bioinformatics/article/35/14/i145/5529135\" rel=\"nofollow\"\u003eBionformatics paper\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAbbas Roayaei Ardakany, Ferhat Ay, Stefano Lonardi, Selfish: discovery of differential chromatin interactions via a self-similarity measure, Bioinformatics, Volume 35, Issue 14, July 2019, Pages i145\u2013i153, \u003ca href=\"https://doi.org/10.1093/bioinformatics/btz362\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btz362\u003c/a\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-abbas-roayaei-ardakany-ferhat-ay-stefano-lonardi-selfish-discovery-of-differential-chromatin-interactions-via-a-self-similarity-measure-bioinformatics-volume-35-issue-14-july-2019-pages-i145i153-httpsdoiorg101093bioinformaticsbtz362\" class=\"anchor\" aria-label=\"Permalink: Abbas Roayaei Ardakany, Ferhat Ay, Stefano Lonardi, Selfish: discovery of differential chromatin interactions via a self-similarity measure, Bioinformatics, Volume 35, Issue 14, July 2019, Pages i145\u2013i153, https://doi.org/10.1093/bioinformatics/btz362\" href=\"#abbas-roayaei-ardakany-ferhat-ay-stefano-lonardi-selfish-discovery-of-differential-chromatin-interactions-via-a-self-similarity-measure-bioinformatics-volume-35-issue-14-july-2019-pages-i145i153-httpsdoiorg101093bioinformaticsbtz362\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", + "stargazers_count": 17, + "subscribers_count": 3, + "topics": [], + "updated_at": 1685812501.0 }, { "data_format": 2, - "description": "Deep dive into containerizing scientific apps.", + "description": null, "filenames": [ - "PEARC22/files/nwp-docker-container/Singularity", - "SC21/files/nwp-docker-container/Singularity" + "Singularity" ], - "full_name": "XSEDE/Container_Tutorial", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-xsede-container-tutorial-deep-dive-into-constructing-containers-for-scientific-computing-and-gateways\" class=\"anchor\" aria-hidden=\"true\" href=\"#xsede-container-tutorial-deep-dive-into-constructing-containers-for-scientific-computing-and-gateways\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eXSEDE Container Tutorial: Deep Dive into Constructing Containers for Scientific Computing and Gateways\u003c/h1\u003e\n\u003cp\u003eThe XSEDE Container Tutorial is presented by the XSEDE Cyberinfrastructure Resource Integration (XCRI) team. This repository contains a collection of slides, exercises, and files for the Container Tutorial presented at various conferences and other venues. Below is an abstract of what the Tutorial covers. A directory exists for most events we have presented at with slides and exercises. Some descriptions of how we have structured the tutorial at each event are included in the corresponding READMEs. With each presentation, we have taken feedback and followed industry trends to improve the tutorial.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-upcoming-accepted-presentations\" class=\"anchor\" aria-hidden=\"true\" href=\"#upcoming-accepted-presentations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUpcoming Accepted Presentations\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://www.hpcwire.com/off-the-wire/access-takes-over-as-xsede-ends-formal-operations/\" rel=\"nofollow\"\u003eXSEDE has ended\u003c/a\u003e, and thus this repository is primarily archival.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-previously-presented-at\" class=\"anchor\" aria-hidden=\"true\" href=\"#previously-presented-at\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePreviously Presented At\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/PEARC22\"\u003ePEARC22\u003c/a\u003e - In-person, July 10, full-day\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/SC21\"\u003eSC21\u003c/a\u003e - In-person, November 15 2021, 8am - 5pm CST (Full-day)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/eScience2021\"\u003eeScience 2021\u003c/a\u003e - Virtual, full-day, \u003ca href=\"https://youtu.be/mPnrgWjW2jY\" rel=\"nofollow\"\u003eVideo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/PEARC21\"\u003ePEARC21\u003c/a\u003e - Virtual, full-day\u003c/li\u003e\n\u003cli\u003eCaRCC Researcher-Facing Track Call, Feb 2021 - Virtual, abbridged version, \u003ca href=\"https://youtu.be/TpWrOYS7nh0\" rel=\"nofollow\"\u003eVideo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/Gateways2020\"\u003eGateways 2020\u003c/a\u003e - Virtual, 2 half-day sessions\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/PEARC20\"\u003ePEARC20\u003c/a\u003e - Virtual, half-day\u003c/li\u003e\n\u003cli\u003eSGCI Coding Institute 2020 and \u003ca href=\"https://github.com/XSEDE/Container_Tutorial/tree/main/SGCI2021\"\u003e2021\u003c/a\u003e - Virtual, half-day\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-abstract\" class=\"anchor\" aria-hidden=\"true\" href=\"#abstract\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAbstract\u003c/h2\u003e\n\u003cp\u003eIn recent years, using containers has been rapidly gaining traction as a solution to lower the barriers to using more software on HPC and cloud resources. However, significant barriers still exist to actually doing this in practice, particularly for well-established community codes which expect to run on a particular operating system version or resource. Additional barriers exist for researchers unfamiliar with containerization technologies. While many beginner tutorials are available for building containers, they often stop short of covering the complexities that can arise when containerizing scientific computing software. The goal of this full-day tutorial is to demonstrate and work through building and running non-trivial containers with users. We will containerize community scientific software, exhibit how to share with a larger community via a container registry, and then run on a completely separate HPC resource, with and without the use of a Science Gateway. The subject matter will be approachable for intermediate to advanced users, and is expected to be of interest to a diverse audience including researchers, support staff, and teams building science gateways.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-authorspresenters\" class=\"anchor\" aria-hidden=\"true\" href=\"#authorspresenters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors/Presenters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ECoulter\"\u003eEric Coulter\u003c/a\u003e, Georgia Institute of Technology\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sk8forether\"\u003ePeter Vaillancourt\u003c/a\u003e, Cornell University\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sanjanasudarshan\"\u003eSanjana Sudarshan\u003c/a\u003e, Indiana University\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/stebird\"\u003eStephen Bird\u003c/a\u003e, Indiana University\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jlf599\"\u003eJeremy Fischer\u003c/a\u003e, Indiana University\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/smarru\"\u003eSuresh Marru\u003c/a\u003e, Indiana University\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuestions?\u003c/h2\u003e\n\u003cp\u003eIf you have Questions, please contact us via\n\u003ccode\u003ehelp@xsede.org\u003c/code\u003e with XCRI in the subject line.\u003c/p\u003e\n", - "stargazers_count": 16, - "subscribers_count": 18, + "full_name": "NCBI-Hackathons/RNA_mapping", + "latest_release": "v0.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/NCBI-Hackathons/RNA_mapping/blob/master/images/RNAmappermatchup.png\"\u003e\u003cimg src=\"https://github.com/NCBI-Hackathons/RNA_mapping/raw/master/images/RNAmappermatchup.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDo you want to learn how to align RNA-sequences to a reference genome?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDo you want to bypass the process of installing alignment software and get a preview into a panel of different alignment strategies?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDo you want to compare how popular mapping algorithms perform on your data?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThen this is the tutorial for you! \u003ca href=\"https://github.com/NCBI-Hackathons/RNA_mapping/wiki\"\u003ePlease proceed to our wiki\u003c/a\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-then-this-is-the-tutorial-for-you--please-proceed-to-our-wiki\" class=\"anchor\" aria-label=\"Permalink: Then this is the tutorial for you! Please proceed to our wiki\" href=\"#then-this-is-the-tutorial-for-you--please-proceed-to-our-wiki\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003cem\u003eJust interested in comparing two or more .bam files? Check out \u003ca href=\"https://github.com/NCBI-Hackathons/RNA_mapping/tree/master/build/bamdiff\"\u003ebamdiff\u003c/a\u003e\u003c/em\u003e\u003c/h4\u003e\u003ca id=\"user-content-just-interested-in-comparing-two-or-more-bam-files--check-out-bamdiff\" class=\"anchor\" aria-label=\"Permalink: Just interested in comparing two or more .bam files? Check out bamdiff\" href=\"#just-interested-in-comparing-two-or-more-bam-files--check-out-bamdiff\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDocker\u003c/h4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Docker image is available for this project. For a detailed description of docker, please refer to this \u003ca href=\"https://www.docker.com/what-docker\" rel=\"nofollow\"\u003eoverview.\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eInstall Docker\u003c/h5\u003e\u003ca id=\"user-content-install-docker\" class=\"anchor\" aria-label=\"Permalink: Install Docker\" href=\"#install-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow \u003ca href=\"https://www.docker.com/docker-mac\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e to install Docker for your environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eGet a pre-built image from DockerHub and run the server\u003c/h5\u003e\u003ca id=\"user-content-get-a-pre-built-image-from-dockerhub-and-run-the-server\" class=\"anchor\" aria-label=\"Permalink: Get a pre-built image from DockerHub and run the server\" href=\"#get-a-pre-built-image-from-dockerhub-and-run-the-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull stevetsa/rna_mapping\ndocker run -it stevetsa/rna_mapping\n\nAt the root prompt inside the container, execute the script - \n/RNA_mapping/build/doAll.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingularity\u003c/h4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun this image on the NIH biowulf cluster\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esinteractive --cpus-per-task=16 --mem=10g\nmodule load singularity\nsingularity shell docker://stevetsa/rna_mapping\n \ninside Singularity container, execute the script - \n/RNA_mapping/build/doAll.sh\n\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 17, + "subscribers_count": 19, "topics": [], - "updated_at": 1673980921.0 + "updated_at": 1685980837.0 }, { "data_format": 2, - "description": "Annotate non-coding regulatory vars using our GREEN-DB, prediction scores, conservation and pop AF", + "description": "Super-fast modelling of dynamic compound flooding in Coastal Systems", "filenames": [ - "Singularity.GREEN-VARAN" + "source/Singularityfile-gpu.def", + "source/Singularityfile-cpu.def" ], - "full_name": "edg1983/GREEN-VARAN", - "latest_release": "v1.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGREEN-VARAN and the GREEN-DB\u003c/h1\u003e\u003ca id=\"user-content-green-varan-and-the-green-db\" class=\"anchor\" aria-label=\"Permalink: GREEN-VARAN and the GREEN-DB\" href=\"#green-varan-and-the-green-db\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic Regulatory Elements ENcyclopedia\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e _.-~` `~-.\n _.--~~~---,.__ _.,;; . -=(@\u0027`\\\n .-` ``~~~~--~~` \u0027;;; ____)\n _.\u0027 \u0027. \u0027;;;;; \u0027`_.\u0027\n .-~;` `\\ \u0027 \u0027;;;;;__.~`\n .\u0027 .\u0027 `\u0027. | / /;\u0027\u0027\n \\/ .---\u0027\u0027 ``) /\u0027-._____.--\u0027\\ \\\\\n _/| (` / /` `\\ \\__\n\u0027, `/- \\ \\ __/ (_ /-\\-\\-`\n `;\u0027-..___) | `/-\\-\\-`\n `-. .\u0027\njgs `~~~~``\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7d32539417115545ef0cb8b4946d01060b12c3920c7719818d7fe0524e647a4e/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f677265656e2d766172616e2f62616467652f3f76657273696f6e3d6c6174657374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7d32539417115545ef0cb8b4946d01060b12c3920c7719818d7fe0524e647a4e/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f677265656e2d766172616e2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"https://readthedocs.org/projects/green-varan/badge/?version=latest\" data-canonical-src=\"https://readthedocs.org/projects/green-varan/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/_container/618c1e989b47264715334728\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7469733561e035f80445de1299989fe45296536c720fd668020063d0e8b66355/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is the home of the GREEN-DB and companion tools (GREEN-VARAN)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGREEN-DB\u003c/h4\u003e\u003ca id=\"user-content-green-db\" class=\"anchor\" aria-label=\"Permalink: GREEN-DB\" href=\"#green-db\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic Regulatory Elements ENcyclopedia Database\u003c/strong\u003e\nA collection of ~2.4M regulatory regions in the human genome, with information about controlled genes, tissues of activity and associated phenotypes. GREEN-DB is available for free for academic usage in a \u003ca href=\"https://zenodo.org/record/5636209\" rel=\"nofollow\"\u003eZenodo repository\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGREEN-VARAN\u003c/h4\u003e\u003ca id=\"user-content-green-varan\" class=\"anchor\" aria-label=\"Permalink: GREEN-VARAN\" href=\"#green-varan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic Regulatory Elements ENcyclopedia VARiant ANnotation\u003c/strong\u003e\nAnnotate non-coding regulatory variants in a VCF with information from GREEN-DB\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epossibly controlled genes\u003c/li\u003e\n\u003cli\u003eoverlapping regulatory region IDs and data sources\u003c/li\u003e\n\u003cli\u003eoverlapping regulatory regions max constraint value\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGREEN-VARAN workflow\u003c/h4\u003e\u003ca id=\"user-content-green-varan-workflow\" class=\"anchor\" aria-label=\"Permalink: GREEN-VARAN workflow\" href=\"#green-varan-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eA Nextflow workflow for complete VCF processing\u003c/strong\u003e\nGiven a VCF, ideally annotated for gene consequences with snpEff or bcftools, the workflow can be used to automate processing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eannotate with functional regions (TFBS, DNase, UCNE)\u003c/li\u003e\n\u003cli\u003eannotate with the 3 best non-coding variant prediction scores (ncER, FATHMM-MKL, ReMM)\u003c/li\u003e\n\u003cli\u003eannotate population AF from gnomAD genomes\u003c/li\u003e\n\u003cli\u003eperform regulatory variant prioritization using GREEN-VARAN\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\"workflow/README.md\"\u003eworkflow readme\u003c/a\u003e for more details or look at the full documentation.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://green-varan.readthedocs.io/en/latest\" rel=\"nofollow\"\u003eDetailed documentation\u003c/a\u003e on GREEN-DB and GREEN-VARAN tool and workflow is provided in ReadTheDocs\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGREEN-VARAN tools are written in Nim. GREEN-VARAN relies on \u003ca href=\"https://github.com/brentp/hts-nim\"\u003ehts-nim\u003c/a\u003e by Brent Pedersen for fast VCF processing. The GREEN-DB BED files are needed for annotation (see Download the supporting files)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGet the tool binaries from the repository\u003c/h3\u003e\u003ca id=\"user-content-get-the-tool-binaries-from-the-repository\" class=\"anchor\" aria-label=\"Permalink: Get the tool binaries from the repository\" href=\"#get-the-tool-binaries-from-the-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe easiest way to run GREEN-VARAN is to download the pre-compiled binaries from the latest release at \u003ca href=\"https://github.com/edg1983/GREEN-VARAN\"\u003ehttps://github.com/edg1983/GREEN-VARAN\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCompile the tool\u003c/h3\u003e\u003ca id=\"user-content-compile-the-tool\" class=\"anchor\" aria-label=\"Permalink: Compile the tool\" href=\"#compile-the-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, you can clone the repository\n\u003ccode\u003egit clone https://github.com/edg1983/GREEN-VARAN.git\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAnd then compile the greenvaran using \u003ca href=\"https://nim-lang.org/\" rel=\"nofollow\"\u003eNim compiler\u003c/a\u003e.\nGREEN-VARAN requires\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enim \u0026gt;= 0.10\u003c/li\u003e\n\u003cli\u003ehts-nim \u0026gt;= 0.3.4\u003c/li\u003e\n\u003cli\u003eargparse 0.10.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you have Singularity installed, you can use the script \u003ccode\u003enim_compile.sh\u003c/code\u003e to create a static binary with no dependencies\nThis uses musl-hts-nim as described in hts-nim repository (see \u003ca href=\"https://github.com/brentp/hts-nim#static-binary-with-singularity\"\u003ehttps://github.com/brentp/hts-nim#static-binary-with-singularity\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eThe accessory greendb_query tool can be compiled using \u003ccode\u003enim compile greendb_query.nim\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGREEN-VARAN performs annotation of small variants or structural variants VCF adding information on potential regulatory variants from GREEN-DB. Especially, it can annotate possible controlled genes and a prioritization level (this latter need the presence of some additional annotations, see below)\nIt provides also ability to tag variants linked to genes of interest and update existing gene-level annotations from SnpEff or bcftools.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBasic usage\u003c/h3\u003e\u003ca id=\"user-content-basic-usage\" class=\"anchor\" aria-label=\"Permalink: Basic usage\" href=\"#basic-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003egreenvaran [run mode] [options]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe running mode can be one of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003esmallvars\u003c/p\u003e\n\u003cp\u003eIn this mode the tool will perform annotation for a small variants VCF.\nIt will annotate variants with information on the possible regulatory role based on GREENDB and eventually provide prioritization levels\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003esv\u003c/p\u003e\n\u003cp\u003eIn this mode the tool will perform annotation for a structural variants VCF.\nCapability in this case is limited to annotation of overlapping GREENDB regions and controlled genes. No prioritization is provided\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003equerytab\u003c/p\u003e\n\u003cp\u003eThis mode is a convenient way to automatically prepare input table to be used with the query tool to extract detailed information from GREENDB database.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eversion\u003c/p\u003e\n\u003cp\u003ePrint the tool version\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNB.\u003c/strong\u003e To perform prioritization of small variants some additional annotation fields are expected in the input VCF, see the prioritization section below. By default, when these information are not present the prioritization level will be set to zero for all annotated variants.\nWe also provide pre-processed datasets (see \u003ca href=\"resources/README.md\"\u003eresources\u003c/a\u003e) and Nextflow workflow to automate the whole process (see \u003ca href=\"workflow/README.md\"\u003eworkflow\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand line options\u003c/h3\u003e\u003ca id=\"user-content-command-line-options\" class=\"anchor\" aria-label=\"Permalink: Command line options\" href=\"#command-line-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003esmallvars and sv shared options\u003c/h4\u003e\u003ca id=\"user-content-smallvars-and-sv-shared-options\" class=\"anchor\" aria-label=\"Permalink: smallvars and sv shared options\" href=\"#smallvars-and-sv-shared-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eoption\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003e-i, --invcf INVCF\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003epath to indexed input vcf.gz / bcf\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-o, --outvcf OUTVCF\u003c/td\u003e\n\u003ctd\u003eoutput vcf / vcf.gz file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-d, --db DB\u003c/td\u003e\n\u003ctd\u003eGREEN-DB bed.gz file for your build (see download section)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-s, --dbschema DBSCHEMA\u003c/td\u003e\n\u003ctd\u003ejson file containing greendb column mapping \u003cbr\u003e A default configuration for GREENDB v2.5 is available in config folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-u, --noupdate\u003c/td\u003e\n\u003ctd\u003edo not update ANN / BCSQ field in the input VCF\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-f, --filter\u003c/td\u003e\n\u003ctd\u003efilter instead of annotate. Only variants with greendb overlap will be written. \u003cbr\u003e If --genes is active, the output will contain only variants connected to the input genes of interest\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-m, --impact IMPACT\u003c/td\u003e\n\u003ctd\u003eWhich impact to assign when updating snpEff field \u003cbr\u003e Possible values: [HIGH, MODERATE, LOWm MODIFIER] (default: MODIFIER)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--chrom CHROM\u003c/td\u003e\n\u003ctd\u003eAnnotate only for a specific chromosome \u003cbr\u003e Useful to parallelize across chromosomes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--nochr\u003c/td\u003e\n\u003ctd\u003eUse this when input VCF does not have chr prefix in chromosome names\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-g, --genes GENES\u003c/td\u003e\n\u003ctd\u003eGene symbols for genes of interest, variants connected to those will be flagged with greendb_VOI tag \u003cbr\u003e This can be a comma-separated list or a text file listing genes one per line\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--connection CONNECTION\u003c/td\u003e\n\u003ctd\u003eRegion-gene connections accepted for annotation \u003cbr\u003e Possible values: [all, closest, annotated] (default: all)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--log LOG\u003c/td\u003e\n\u003ctd\u003eLog file. Default is greenvaran_[now].log\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003esv specific options\u003c/h4\u003e\u003ca id=\"user-content-sv-specific-options\" class=\"anchor\" aria-label=\"Permalink: sv specific options\" href=\"#sv-specific-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eoption\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e-p, --padding PADDING\u003c/td\u003e\n\u003ctd\u003eValue to add on each side of BND/INS, this override the CIPOS when set\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cipos CIPOS\u003c/td\u003e\n\u003ctd\u003eINFO field listing the confidence interval around breakpoints (default: CIPOS) \u003cbr\u003e It is expected to have 2 comma-separated values\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-t, --minoverlap MINOVERLAP\u003c/td\u003e\n\u003ctd\u003eMin fraction of GREENDB region to be overlapped by a SV (default: 0.000001)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-b, --minbp MINBP\u003c/td\u003e\n\u003ctd\u003eMin number of bases of GREENDB region to be overlapped by a SV (default: 1)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003esmallvars specific options\u003c/h4\u003e\u003ca id=\"user-content-smallvars-specific-options\" class=\"anchor\" aria-label=\"Permalink: smallvars specific options\" href=\"#smallvars-specific-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eoption\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e-c, --config CONFIG\u003c/td\u003e\n\u003ctd\u003ejson config file for prioritization \u003cbr\u003e A default configuration for the four level described in the paper is provided in config folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--prioritization_strategy\u003c/td\u003e\n\u003ctd\u003eset the strategy used to compute prioritization levels. Possible values are: levels (default) or pileup\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e-p, --permissive\u003c/td\u003e\n\u003ctd\u003ePerform prioritization even if one of the INFO fields required by prioritization config is missing \u003cbr\u003e By default, when one of the expected fields is not defined in the header, the prioritization is disabled and all variants will get level zero\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnnotations added by GREEN-VARAN\u003c/h2\u003e\u003ca id=\"user-content-annotations-added-by-green-varan\" class=\"anchor\" aria-label=\"Permalink: Annotations added by GREEN-VARAN\" href=\"#annotations-added-by-green-varan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eINFO fields\u003c/h3\u003e\u003ca id=\"user-content-info-fields\" class=\"anchor\" aria-label=\"Permalink: INFO fields\" href=\"#info-fields\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFields in the following table are added to INFO fields by GREEN-VARAN. greendb_level will be added only for small variants\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003etag\u003c/th\u003e\n\u003cth\u003edata type\u003c/th\u003e\n\u003cth\u003edescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_id\u003c/td\u003e\n\u003ctd\u003eString\u003c/td\u003e\n\u003ctd\u003eComma-separated list of GREEN-DB IDs identifying the regions that overlap this variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_stdtype\u003c/td\u003e\n\u003ctd\u003eString\u003c/td\u003e\n\u003ctd\u003eComma-separated list of standard region types as annotated in GREEN-DB for regions overlapping the variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_dbsource\u003c/td\u003e\n\u003ctd\u003eString\u003c/td\u003e\n\u003ctd\u003eComma-separated list of data sources as annotated in GREEN-DB for regions overlapping the variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_level\u003c/td\u003e\n\u003ctd\u003eInteger\u003c/td\u003e\n\u003ctd\u003eVariant prioritization level computed by GREEN-VARAN. See Prioritization section below\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_more_support\u003c/td\u003e\n\u003ctd\u003eInteger\u003c/td\u003e\n\u003ctd\u003eSum up of the additional pieces of evidence that support this variant as configured in the prioritization JSON\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_constraint\u003c/td\u003e\n\u003ctd\u003eFloat\u003c/td\u003e\n\u003ctd\u003eThe maximum constraint value across GREEN-DB regions overlapping the variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_genes\u003c/td\u003e\n\u003ctd\u003eString\u003c/td\u003e\n\u003ctd\u003ePossibly controlled genes for regulatory regions overlapping this variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egreendb_VOI\u003c/td\u003e\n\u003ctd\u003eFlag\u003c/td\u003e\n\u003ctd\u003eWhen \u003ccode\u003e--genes\u003c/code\u003e option is active this flag is set when any of the input genes is among the possibly controlled genes for overlapping regulatory regions.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUpdated gene consequences\u003c/h3\u003e\u003ca id=\"user-content-updated-gene-consequences\" class=\"anchor\" aria-label=\"Permalink: Updated gene consequences\" href=\"#updated-gene-consequences\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, GREEN-VARAN update gene consequences in the SnpEff ANN field or the bcftools BCSQ if one is present in the input VCF file. In this way the annotation can be processed by most downstream tools evaluating segregation.\nIf none is found, GREEN-VARAN will create a new ANN field. To switch off gene consequence update use the \u003ccode\u003e--noupdate\u003c/code\u003e option.\u003c/p\u003e\n\u003cp\u003eThe tool will add a new consequence for each possibly controlled gene, limited by the \u003ccode\u003e--connection\u003c/code\u003e option.\nThe new consequence will follow standard format according to SnpEff or bcftools and have MODIFIER impact by default.\nThis can be adjusted using the \u003ccode\u003e--impact\u003c/code\u003e option.\nThe gene effect will be set according to the GREEN-DB region type, adding 5 one of the terms: \u003ccode\u003ebivalent, enhancer, insulator, promoter, silencer\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eExample ANN / BCSQ field added by GREEN-VARAN.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eANN=C|enhancer|MODIFIER|GeneA||||||||||||\nBCQS=enhancer|GeneA||\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePrioritization of small variants\u003c/h2\u003e\u003ca id=\"user-content-prioritization-of-small-variants\" class=\"anchor\" aria-label=\"Permalink: Prioritization of small variants\" href=\"#prioritization-of-small-variants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGREEN-VARAN will consider GREEN-DB annotations, additional functional regions and non-coding impact prediction scores to provide a prioritization level for each annotated variant. This level is annotated under \u003ccode\u003egreenvaran_level\u003c/code\u003e tag in the INFO field.\u003c/p\u003e\n\u003cp\u003eThis fields is an integer from 0 to N which summarize evidences supporting a regulatory impact for the variant. Higher values are associated to a higher support of regulatory impact.\u003c/p\u003e\n\u003cp\u003eYou need 3 set of information in your input VCF to run prioritization mode when using the default config provided.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003egnomAD_AF, gnomAD_AF_nfe\u003c/strong\u003e: float values describing global and NFE population AF from gnomAD\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003encER, FATHMM-MKL and ReMM\u003c/strong\u003e: float values providing scores predictions\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTFBS, DNase and UCNE\u003c/strong\u003e: flags describing overlap with additional functional regions\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe prioritization schema can be adjusted by modifying the .json file passed to \u003ccode\u003e--config\u003c/code\u003e. A default file is provided in config folder.\u003c/p\u003e\n\u003cp\u003eThe default behaviour is \u003ccode\u003e--prioritization_strategy levels\u003c/code\u003e which reproduce the 4 levels as described in the paper.\nAlternatively, you can chose a \"pile-up\" approach setting \u003ccode\u003e--prioritization_strategy pileup\u003c/code\u003e which simply sum evidences across levels. This means that the criteria described above are tested independently and the level reported is increased by one for each satisfied criteria.\u003c/p\u003e\n\u003cp\u003eSee documentation for more details \u003ca href=\"https://green-varan.readthedocs.io/en/latest\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun using singularity\u003c/h2\u003e\u003ca id=\"user-content-run-using-singularity\" class=\"anchor\" aria-label=\"Permalink: Run using singularity\" href=\"#run-using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tool binaries should work on most linux based system. In case you have any issue, we also provide GREEN-VARAN as Singularity image (tested on singularity \u0026gt;= 3.2).\nA Singularity recipe is included in the repository or you can pull the image from Singularity Library using\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull library://edg1983/greenvaran/greenvaran:latest\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsage\u003c/h3\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe image contains both greenvaran and greendb_query tools.\nThe general usage is:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec \\\n greenvaran.sif \\\n tool_name [tool arguments]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBind specific folders for resources or data\u003c/h3\u003e\u003ca id=\"user-content-bind-specific-folders-for-resources-or-data\" class=\"anchor\" aria-label=\"Permalink: Bind specific folders for resources or data\" href=\"#bind-specific-folders-for-resources-or-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tool needs access to input VCF file, the GREEN-DB bed file and the config files so remember to bind the corresponding locations in the container\u003c/p\u003e\n\u003cp\u003eSee the following example where we use the current working directory for input/output, while other files are located\nin the default config / resources folder within greenvaran folder (greenvaran_path). In the example we use GRCh38 genome build\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec \\\n --bind /greenvaran_path/resources/GRCh38:/db_files \\\n --bind /greenvaran_path/config:/config_files \\\n --bind ${PWD}:/data \\\n greenvaran.sif \\\n greenvaran -i /data/input.vcf.gz \\\n -o /data/output.vcf.gz \\\n --db /db_files/GRCh38_GREEN-DB.bed.gz \\\n --dbschema /config_files/greendb_schema_v2.5.json \\\n --config /config_files/prioritize_smallvars.json\n [additional tool arguments]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample usage\u003c/h2\u003e\u003ca id=\"user-content-example-usage\" class=\"anchor\" aria-label=\"Permalink: Example usage\" href=\"#example-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003esmall variants test\u003c/h3\u003e\u003ca id=\"user-content-small-variants-test\" class=\"anchor\" aria-label=\"Permalink: small variants test\" href=\"#small-variants-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egreenvaran smallvars \\\n --invcf test/VCF/GRCh38.test.smallvars.vcf.gz \\\n --outvcf test/out/smallvars.annotated.vcf.gz \\\n --config config/prioritize_smallvars.json \\\n --dbschema config/greendb_schema_v2.5.json \\\n --db resources/GRCh38/GRCh38_GREEN-DB.bed.gz \\\n --genes test/VCF/genes_list_example.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003estructural variants test\u003c/h3\u003e\u003ca id=\"user-content-structural-variants-test\" class=\"anchor\" aria-label=\"Permalink: structural variants test\" href=\"#structural-variants-test\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egreenvaran sv \\\n --invcf test/VCF/GRCh38.test.SV.vcf.gz \\\n --outvcf test/out/SV.annotated.vcf.gz \\\n --dbschema config/greendb_schema_v2.5.json \\\n --db resources/GRCh38/GRCh38_GREEN-DB.bed.gz \\\n --minbp 10\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen you use GREEN-DB or GREEN-VARAN tools please cite:\n\u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.09.17.301960\" rel=\"nofollow\"\u003eGREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants in whole-genome sequencing\u003c/a\u003e Giacopuzzi E., Popitsch N., Taylor JC. BiorXiv (2021)\u003c/p\u003e\n\u003cp\u003eWhen you use GREEN-VARAN workflow for small variants annotation please also cite:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0973-5\" rel=\"nofollow\"\u003eVcfanno: fast, flexible annotation of genetic variants\u003c/a\u003e\nBrent S. Pedersen, Ryan M. Layer \u0026amp; Aaron R. Quinlan. Genome Biology volume 17, Article number: 118 (2016)\u003c/p\u003e\n\u003cp\u003eAdditionally, when you use any prediction score for annotation, please cite the corresponding publication.\u003c/p\u003e\n", - "stargazers_count": 16, - "subscribers_count": 3, + "full_name": "Deltares/SFINCS", + "latest_release": "v2.0.3_Cauberg_release", + "stargazers_count": 17, + "subscribers_count": 7, "topics": [], - "updated_at": 1709804259.0 + "updated_at": 1714924613.0 }, { "data_format": 2, - "description": "DataLad extension for neuroimaging research", + "description": "Code supporting the recent preprint Markello et al., 2020", "filenames": [ - "tools/Singularity.devmaster", - "tools/Singularity" + "container/Singularity" ], - "full_name": "datalad/datalad-neuroimaging", - "latest_release": "0.3.5", - "readme": "\u003cpre\u003e\u003ccode\u003e ____ _ _ _\n| _ \\ __ _ | |_ __ _ | | __ _ __| |\n| | | | / _` || __| / _` || | / _` | / _` |\n| |_| || (_| || |_ | (_| || |___ | (_| || (_| |\n|____/ \\__,_| \\__| \\__,_||_____| \\__,_| \\__,_|\n Neuroimaging\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://ci.appveyor.com/project/mih/datalad-neuroimaging/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b786a11ed4c548bdb80df5d8e56408ec512cd85ba703a321cc0be2bb805d30d9/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f646174616c61642f646174616c61642d6e6575726f696d6167696e673f6272616e63683d6d6173746572267376673d74727565\" alt=\"Travis tests status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/datalad/datalad-neuroimaging?branch=master\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://codecov.io/github/datalad/datalad-neuroimaging?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31b8a3e8dada4f28be012c0bcf21f29445d8b0ffd53ee7e11e245e53b1ef46c6/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d6e6575726f696d6167696e672f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-neuroimaging/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://datalad-neuroimaging.rtfd.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/626ca7e31129e36bbe9e89422fb0242d25fca9afe39a1e3b20b522b8ed6b9301/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f646174616c61642d6e6575726f696d6167696e672f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation\" data-canonical-src=\"https://readthedocs.org/projects/datalad-neuroimaging/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/841\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6cd0120cc4c5ac11d28b2c60f76033b52db98dac641de3b2644bb054b449d60c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://GitHub.com/datalad/datalad-neuroimaging/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3b46b08339d91628c7de50a03f6a444b1917f024a808651064938dfe01663b8b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://pypi.python.org/pypi/datalad-neuroimaging/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4466cdd0dfd7c35e5746d86fadfb58edabac888c4b411d77bdd6ed0d324502b4/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://isitmaintained.com/project/datalad/datalad-neuroimaging\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/921d3c777a6aff6a3bffb115066492816d00ad523d1c9daa5abfeef4ff02e310/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://isitmaintained.com/project/datalad/datalad-neuroimaging\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ed9a33e559409cb8b5d6770412b8b1eb45bae52db2def86e2da4f11b110d697/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis extension enhances DataLad (\u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003ehttp://datalad.org\u003c/a\u003e) for working with\nneuroimaging data and workflows. Please see the \u003ca href=\"http://datalad-neuroimaging.rtfd.org\" rel=\"nofollow\"\u003eextension\ndocumentation\u003c/a\u003e\nfor a description on additional commands and functionality.\u003c/p\u003e\n\u003cp\u003eFor general information on how to use or contribute to DataLad (and this\nextension), please see the \u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003eDataLad website\u003c/a\u003e or the\n\u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003emain GitHub project page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore you install this package, please make sure that you \u003ca href=\"https://git-annex.branchable.com/install\" rel=\"nofollow\"\u003einstall a recent\nversion of git-annex\u003c/a\u003e. Afterwards,\ninstall the latest version of \u003ccode\u003edatalad-neuroimaging\u003c/code\u003e from\n\u003ca href=\"https://pypi.org/project/datalad-neuroimaging\" rel=\"nofollow\"\u003ePyPi\u003c/a\u003e. It is recommended to use\na dedicated \u003ca href=\"https://virtualenv.pypa.io\" rel=\"nofollow\"\u003evirtualenv\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# create and enter a new virtual environment (optional)\nvirtualenv --system-site-packages --python=python3 ~/env/dataladni\n. ~/env/dataladni/bin/activate\n\n# install from PyPi\npip install datalad_neuroimaging\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere is also a \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity container\u003c/a\u003e available.\nThe latest release version can be obtained by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://datalad/datalad-neuroimaging\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe documentation of this project is found here:\n\u003ca href=\"http://docs.datalad.org/projects/neuroimaging\" rel=\"nofollow\"\u003ehttp://docs.datalad.org/projects/neuroimaging\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAll bugs, concerns and enhancement requests for this software can be submitted here:\n\u003ca href=\"https://github.com/datalad/datalad-neuroimaging/issues\"\u003ehttps://github.com/datalad/datalad-neuroimaging/issues\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you have a problem or would like to ask a question about how to use DataLad,\nplease \u003ca href=\"https://neurostars.org/tags/datalad\" rel=\"nofollow\"\u003esubmit a question to\nNeuroStars.org\u003c/a\u003e with a \u003ccode\u003edatalad\u003c/code\u003e tag.\nNeuroStars.org is a platform similar to StackOverflow but dedicated to\nneuroinformatics.\u003c/p\u003e\n\u003cp\u003eAll previous DataLad questions are available here:\n\u003ca href=\"http://neurostars.org/tags/datalad/\" rel=\"nofollow\"\u003ehttp://neurostars.org/tags/datalad/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDataLad development is supported by a US-German collaboration in computational\nneuroscience (CRCNS) project \"DataGit: converging catalogues, warehouses, and\ndeployment logistics into a federated \u0027data distribution\u0027\" (Halchenko/Hanke),\nco-funded by the US National Science Foundation (NSF 1429999) and the German\nFederal Ministry of Education and Research (BMBF 01GQ1411). Additional support\nis provided by the German federal state of Saxony-Anhalt and the European\nRegional Development Fund (ERDF), Project: Center for Behavioral Brain\nSciences, Imaging Platform. This work is further facilitated by the ReproNim\nproject (NIH 1P41EB019936-01A1).\u003c/p\u003e\n", + "full_name": "netneurolab/markello_ppmisnf", + "latest_release": "0.1", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-similarity-network-fusion-in-parkinsons-disease\" class=\"anchor\" aria-hidden=\"true\" href=\"#similarity-network-fusion-in-parkinsons-disease\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimilarity network fusion in Parkinson\u0027s disease\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/245268776\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b54624e2e59ecddb7b78002cff4e39e47013c88ff0d58f1944cc28f16185036b/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3234353236383737362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/245268776.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-whats-in-this-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#whats-in-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"What\u0027s in this repository?\"\u003c/h2\u003e\n\u003cp\u003eThis repository contains data, code, and results for the manuscript \"\u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.03.05.979526v1\" rel=\"nofollow\"\u003eIntegrated morphometric, molecular, and clinical characterization of Parkinson\u0027s disease pathology\u003c/a\u003e.\"\nThe study examines the application of similarity network fusion to Parkinson\u0027s disease using data from the \u003ca href=\"https://www.ppmi-info.org\" rel=\"nofollow\"\u003eParkinson\u0027s Progression Markers Initiative\u003c/a\u003e (PPMI).\u003c/p\u003e\n\u003cp\u003eWe\u0027ve tried to document the various aspects of this repository with a whole bunch of README files, so feel free to jump around and check things out.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-just-let-me-run-the-things\" class=\"anchor\" aria-hidden=\"true\" href=\"#just-let-me-run-the-things\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"Just let me run the things!\"\u003c/h2\u003e\n\u003cp\u003eItching to just run the analyses?\nYou\u0027ll need to make sure you have \u003ca href=\"./walkthrough/01_accessing_data.md\"\u003eaccess to the PPMI\u003c/a\u003e and have set the appropriate environmental variables (\u003ccode\u003e$PPMI_USER\u003c/code\u003e) and (\u003ccode\u003e$PPMI_PASSWORD\u003c/code\u003e).\nOnce you\u0027ve done that, you can get going with the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recurse-submodules https://github.com/netneurolab/markello_ppmisnf\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_ppmisnf\npip install -r requirements.txt\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PYTHONPATH=\u003cspan class=\"pl-smi\"\u003e$PYTHONPATH\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/code\nmake all\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to deal with the hassle of creating a new Python environment, download the Singularity image that we used to run our analyses and run things in there:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recurse-submodules https://github.com/netneurolab/markello_ppmisnf\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_ppmisnf\nwget -O container/ppmi_snf.simg https://osf.io/h6jwx/download\nbash container/run.sh\nmake all\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-want-to-take-things-slow\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-want-to-take-things-slow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"I want to take things slow.\"\u003c/h2\u003e\n\u003cp\u003eIf you want a step-by-step through all the methods + analyses, take a look at out our \u003ca href=\"./walkthrough\"\u003ewalkthrough\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-have-some-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-have-some-questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"I have some questions...\"\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/netneurolab/markello_ppmisnf/issues\"\u003eOpen an issue\u003c/a\u003e on this repository and someone will try and get back to you as soon as possible!\u003c/p\u003e\n", "stargazers_count": 17, - "subscribers_count": 12, - "topics": [ - "datalad", - "neuroimaging" - ], - "updated_at": 1730736409.0 + "subscribers_count": 6, + "topics": [], + "updated_at": 1681409115.0 }, { "data_format": 2, - "description": "MFannot is a program for the annotation of mitochondrial and plastid genomes", + "description": "PuntSeq - Chasing the microbial diversity of Cambridge\u0027s freshwater", "filenames": [ - "Singularity.v1.0" + "analysis/containers/Singularity.puntseq" ], - "full_name": "BFL-lab/Mfannot", + "full_name": "d-j-k/puntseq", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMFannot\u003c/h1\u003e\u003ca id=\"user-content-mfannot\" class=\"anchor\" aria-label=\"Permalink: MFannot\" href=\"#mfannot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMFannot is a program for the annotation of mitochondrial and plastid genomes.\nIt is a PERL wrapper around a set of diverse, external independent tools.\u003c/p\u003e\n\u003cp\u003eIt makes intense use of RNA/intron detection tools including \u003ca href=\"http://hmmer.org/\" rel=\"nofollow\"\u003eHMMER\u003c/a\u003e, \u003ca href=\"https://github.com/nathanweeks/exonerate\"\u003eExonerate\u003c/a\u003e, \u003ca href=\"https://bioinformatics.ca/links_directory/tool/9822/erpin\" rel=\"nofollow\"\u003eErpin\u003c/a\u003e and others.\u003c/p\u003e\n\u003cp\u003eMFannot is particularly helpful with organelle genomes that contain lots of introns. Intron-exon boundaries are identified by a combination of secondary structure, intron splice rules and exon similarities, and are thus precise in most instances.\nNote that not all introns may be detected by MFannot, which requires expert manual curation/completion of gene structure annotations before GenBank submission.\u003c/p\u003e\n\u003cp\u003eThe output of MFannot is a listings of gene coordinates either in \u003ca href=\"https://www.ncbi.nlm.nih.gov/Sequin/\" rel=\"nofollow\"\u003eSequin format\u003c/a\u003e, a format that can be directly loaded into NCBI\u0027s sequence submission tools, or in \u003ca href=\"http://megasun.bch.umontreal.ca/ogmp/masterfile/intro.html\" rel=\"nofollow\"\u003eMasterfile\u003c/a\u003e format (computer-parsible as well as human-readable; annotations embedded into the FASTA sequence).\u003c/p\u003e\n\u003cp\u003eThis package is based on activities of the OGMP (Organelle Genome Megasequencing Project, D\u00e9partement de Biochimie, Universit\u00e9 de Montr\u00e9al, circa 1990-1998) and further developed since then by the labs of \u003ca href=\"https://biochimie.umontreal.ca/en/department/professors/franz-bernd-lang/\" rel=\"nofollow\"\u003eB.F. Lang\u003c/a\u003e and \u003ca href=\"https://biochimie.umontreal.ca/en/department/professors/gertraud-burger/\" rel=\"nofollow\"\u003eG. Burger\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: At this point the installation of MFannot is only possible on Unix systems (e.g. Ubuntu and CentOS).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBioPerl\u003c/h4\u003e\u003ca id=\"user-content-bioperl\" class=\"anchor\" aria-label=\"Permalink: BioPerl\" href=\"#bioperl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMFannot is a PERL pipeline that use \u003ca href=\"http://bioperl.org/\" rel=\"nofollow\"\u003eBioPerl\u003c/a\u003e, so you need to install BioPerl first and at least install the \u003ca href=\"http://search.cpan.org/dist/BioPerl/Bio/AlignIO.pm\" rel=\"nofollow\"\u003eBio::AlignIO\u003c/a\u003e and all their dependencies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExternal programs\u003c/h4\u003e\u003ca id=\"user-content-external-programs\" class=\"anchor\" aria-label=\"Permalink: External programs\" href=\"#external-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMFannot uses some well known programs, taht need to be installed on your system::\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBlast (requires version 2.2.26 and version \u0026gt;= 2.2.27+): to install Blast see the documentation at the \u003ca href=\"http://www.ncbi.nlm.nih.gov/guide/howto/run-blast-local/\" rel=\"nofollow\"\u003eNCBI website\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 2.2.26 and 2.2.31+\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eHMMER: to install HMMER see the documentation at the \u003ca href=\"http://hmmer.org/download.html\" rel=\"nofollow\"\u003eHMMER website\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 3.2.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eExonerate: to install Exonerate see the documentation at the following \u003ca href=\"https://github.com/nathanweeks/exonerate\"\u003eGitHub repo\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 2.2.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMuscle: to install Muscle see the documentation \u003ca href=\"http://www.drive5.com/muscle/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version muscle-3.8.1551\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEMBOSS: to install EMBOSS see the documentation \u003ca href=\"http://emboss.sourceforge.net/download/#Stable/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 6.6.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eErpin: to install Erpin see the documentation \u003ca href=\"http://rna.igmors.u-psud.fr/Software/erpin.php\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 5.5.4.serv\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003etbl2asn: to install tbl2asn see the documentation \u003ca href=\"https://www.ncbi.nlm.nih.gov/genbank/tbl2asn2/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePirObject: to install this Perl library see the documentation in this \u003ca href=\"https://github.com/prioux/PirObject\"\u003eGitHub repo\u003c/a\u003e,\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInternal programs\u003c/h4\u003e\u003ca id=\"user-content-internal-programs\" class=\"anchor\" aria-label=\"Permalink: Internal programs\" href=\"#internal-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFurther external programs and libraries that were developed in parallel to MFannot:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePirModels: all the PirModels needed by umac, HMMsearchWC and other programs developped at OGMP, to install the PirModels see the documentation in this \u003ca href=\"https://github.com/BFL-lab/PirModels\"\u003eGitHub repo\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFlip:to install Flip see the documentation in this \u003ca href=\"https://github.com/BFL-lab/flip\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUmac: to install Umac see the documentation in this \u003ca href=\"https://github.com/BFL-lab/umac\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHMMSearchWC: to install HMMSearchWC see the documentation in this \u003ca href=\"https://github.com/BFL-lab/HMMSearchWC\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRNAfinder: to install RNAfinder see the documentation in this \u003ca href=\"https://github.com/BFL-lab/RNAfinder\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMf2sqn: to install Mf2sqn see the documentation in this \u003ca href=\"https://github.com/BFL-lab/mf2sqn\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003egrab-seq and grab-fasta: to install this two tools see the documentation in this \u003ca href=\"https://github.com/BFL-lab/grab-fasta\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMFannot: to install MFannot see the documentation in this \u003ca href=\"https://github.com/BFL-lab/MFannot\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eStatic Data\u003c/h4\u003e\u003ca id=\"user-content-static-data\" class=\"anchor\" aria-label=\"Permalink: Static Data\" href=\"#static-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMFannot needs some static data files to run; these contain data defining splice sites, reference protein sequences, HMM models, etc.\nThese files are all available as a single bundle in this \u003ca href=\"https://github.com/BFL-lab/MFannot_data\"\u003eGitHub repo\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn order to run MFannot you should setup the following environment variable:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEGC to point to MFannot_data/EGC.\u003c/li\u003e\n\u003cli\u003eMFANNOT_EXT_CFG_PATH to point to MFannot_data/config.\u003c/li\u003e\n\u003cli\u003eMFANNOT_MOD_PATH to point to MFannot_data/models.\u003c/li\u003e\n\u003cli\u003eERPIN_MOD_PATH to point to MFannot_data/models/Erpin_models.\u003c/li\u003e\n\u003cli\u003eMFANNOT_LIB_PATH to point to MFannot_data/protein_collections.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBLAST matrices\u003c/h4\u003e\u003ca id=\"user-content-blast-matrices\" class=\"anchor\" aria-label=\"Permalink: BLAST matrices\" href=\"#blast-matrices\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload BLAST matrices from the ncbi \u003ccode\u003eftp://ftp.ncbi.nlm.nih.gov/blast/matrices/*\u003c/code\u003e (eg: \u003ccode\u003ewget -r -np -nd -P /path/to/mfannot/blast_matrices ftp://ftp.ncbi.nlm.nih.gov/blast/matrices\u003c/code\u003e)\nand set the environment variable \u003ccode\u003eBLASTMAT\u003c/code\u003e to point to the directory where you have download the matrices.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker container\u003c/h2\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-label=\"Permalink: Docker container\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entire software package, including data files, is available in a Docker container. You can find this docker container on \u003ca href=\"https://hub.docker.com/r/nbeck/mfannot/\" rel=\"nofollow\"\u003eDockerHub\u003c/a\u003e and use it in order to run MFannot locally if you do not want to install everything.\u003c/p\u003e\n\u003cp\u003eThe process to install MFannot on Ubuntu14 within a Docker image is documented in the [Dockerfile] present in this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to get the help page of MFannot you need to type \u003ccode\u003emfannot -h\u003c/code\u003e in your terminal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e and \u003ca href=\"CONDUCT.md\"\u003eCONDUCT\u003c/a\u003e for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BFL-lab/mfannot/graphs/contributors\"\u003eAll Contributors\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGNU General Public License v3.0. Please see \u003ca href=\"LICENSE.md\"\u003eLicense File\u003c/a\u003e for more information.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2599d6e3e819d2a92c833ebf20dbbd24babe049e2540cc62d5aa6e34276a72d2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c616e67756167652d507974686f6e2c5f525f265f426173682d79656c6c6f772e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2599d6e3e819d2a92c833ebf20dbbd24babe049e2540cc62d5aa6e34276a72d2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c616e67756167652d507974686f6e2c5f525f265f426173682d79656c6c6f772e737667\" alt=\"Scripting\" data-canonical-src=\"https://img.shields.io/badge/Language-Python,_R_\u0026amp;_Bash-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"License\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90d0f6b7537a36ae88260b9414d1ddb607b26e7c6b8e9bf015f555e6ff0ec1ef/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f707972696768742d2863295f323032305f50756e745365712d677265656e2e737667\" alt=\"Copyright\" data-canonical-src=\"https://img.shields.io/badge/Copyright-(c)_2020_PuntSeq-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.1101/2020.02.06.936302\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/746875823522b8fa892a01b0edfccad9de0ef9ea9c411374f5c9c4c4ce5b3121/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f444f492d31302e313130312f323032302e30322e30362e3933363330322d626c75652e737667\" alt=\"DOI\" data-canonical-src=\"https://img.shields.io/badge/DOI-10.1101/2020.02.06.936302-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/d-j-k/puntseq/blob/master/figure1.png\"\u003e\u003cimg src=\"https://github.com/d-j-k/puntseq/raw/master/figure1.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFreshwater monitoring by nanopore sequencing\u003c/h1\u003e\u003ca id=\"user-content-freshwater-monitoring-by-nanopore-sequencing\" class=\"anchor\" aria-label=\"Permalink: Freshwater monitoring by nanopore sequencing\" href=\"#freshwater-monitoring-by-nanopore-sequencing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eLara Urban, Andre Holzer, J Jotautas Baronas, Michael Hall, Philipp Braeuninger-Weimer, Michael J Scherm, Daniel J Kunz, Surangi N Perera, Daniel E Martin-Herranz, Edward T Tipper, Susannah J Salter, and Maximilian R Stammnitz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClean freshwater lies at the heart of human society. While most traditional water monitoring approaches test for specific chemicals or pathogens, the direct tracing of aquatic DNA poses a more holistic alternative which has hitherto been underappreciated due to challenges in logistics and investment. Here we present a simple, fast, inexpensive and reliable freshwater diagnostics workflow centred around portable nanopore DNA sequencing. Using bacterial mock communities and spatiotemporal microbiata from an example river in Cambridge (UK), our study shows how nanopore sequencing can be readily integrated for the assessment of aquatic bacterial diversity and pollution. We provide a computational benchmark that features more than ten taxonomic classification tools to derive guidelines for bacterial DNA analyses with nanopore data. Through complementary physicochemical measurements, we find that nanopore metagenomics can depict fine temporal gradients along the main hydrological axis of an urban-rural interface, in addition to yielding high-resolution pathogen maps that address concerns of public health.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/d-j-k/puntseq/blob/master/figure2.png\"\u003e\u003cimg src=\"https://github.com/d-j-k/puntseq/raw/master/figure2.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cem\u003eFigure: Overview of the experimental design of this study\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere, we provide additional environmental data, the final classifications (using \u003ca href=\"https://github.com/lh3/minimap2\"\u003eMinimap2\u003c/a\u003e, -k = 15) of all nanopore sequencing reads from three sampling dates (April, June, and August 2018) across nine sampling locations, including rarefied datasets.\nWe additionally provide a Snakemake framework that integrates all data pre-processing steps and a Singularity that contains all necessary software. We further provide scripts for the downstream analyses (written in R and python, integrated in a markdown file) and an appropriate conda environment.\u003c/p\u003e\n\u003cp\u003eUsing this platform, the user will be able to replicate all results presented in the corresponding study \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.02.06.936302\" rel=\"nofollow\"\u003epreprint\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDownload the raw data from our \u003ca href=\"https://www.ebi.ac.uk/ena/data/view/PRJEB34900\" rel=\"nofollow\"\u003eENA repository\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/d-j-k/puntseq/tree/master/analysis\"\u003ehere\u003c/a\u003e for the detailed description of our raw nanopore data pre-processing steps.\u003c/p\u003e\n\u003cp\u003eCheck out the \u003ca href=\"https://www.puntseq.co.uk/\" rel=\"nofollow\"\u003eproject website\u003c/a\u003e and follow us on \u003ca href=\"https://twitter.com/puntseq\" rel=\"nofollow\"\u003eTwitter\u003c/a\u003e for more updates!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use any part or modified version of the here provided code in your work, you should:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eState explicitly that you\u0027ve used the PuntSeq code (or a modified version of it, if this is the case) and follow all our code license conditions.\u003c/li\u003e\n\u003cli\u003eRead and cite the following paper:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eUrban L, Holzer A, Baronas JJ, Hall M, Braeuninger-Weimer P, Scherm MJ, Kunz DJ, Perera SN, Martin-Herranz DE, Tipper ET, Salter SJ and Stammnitz MR (2020), \u003cstrong\u003eFreshwater monitoring by nanopore sequencing\u003c/strong\u003e, bioRxiv 2020.02.06.936302; doi: \u003ca href=\"https://doi.org/10.1101/2020.02.06.936302\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2020.02.06.936302\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 17, - "subscribers_count": 3, - "topics": [ - "genome", - "mitochondrial", - "erpin", - "hmmer", - "blast", - "muscle", - "pipeline", - "bioperl", - "docker-container", - "umac", - "tbl2asn" + "subscribers_count": 11, + "topics": [], + "updated_at": 1714950384.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "Singularity" ], - "updated_at": 1722444937.0 + "full_name": "Neo-X/SMiRL_Code", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBayesian Surprise\u003c/h1\u003e\u003ca id=\"user-content-bayesian-surprise\" class=\"anchor\" aria-label=\"Permalink: Bayesian Surprise\" href=\"#bayesian-surprise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRepo for environments, gym wrappers, and scripts for the SMiRL project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFor distributing experiments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003edoodad: \u003ca href=\"https://github.com/montrealrobotics/doodad\"\u003ehttps://github.com/montrealrobotics/doodad\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRL library\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003erlkit: \u003ca href=\"https://github.com/Neo-X/rlkit/tree/surprise\"\u003ehttps://github.com/Neo-X/rlkit/tree/surprise\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Instruction\u003c/h3\u003e\u003ca id=\"user-content-build-instruction\" class=\"anchor\" aria-label=\"Permalink: Build Instruction\" href=\"#build-instruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name smirl_code python=3.7 pip\nconda activate smirl_code\npip install -r requirements.txt\npip install -e ./\ncd ../\ngit clone git@github.com:montrealrobotics/doodad.git\ncd doodad\npip install -e ./\ncd ../smirl_code\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you will need copy the \u003ca href=\"https://github.com/Neo-X/doodad/blob/master/doodad/easy_launch/config.py\"\u003e\u003ccode\u003econfig.py\u003c/code\u003e\u003c/a\u003e file locally to \u003ccode\u003elaunchers.config.py\u003c/code\u003e and update the paths in the file.\nYou need to update \u003ccode\u003eBASE_CODE_DIR\u003c/code\u003e to the location you have saved SMiRL_Code.\nAlso update \u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e to the location you would like the logging data to be saved on your computer.\nYou can look at the \u003ca href=\"https://github.com/Neo-X/doodad/\"\u003edoodad\u003c/a\u003e for more details on this configuration.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommands:\u003c/h2\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands:\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA basic examples.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 scripts/dqn_smirl.py --config=configs/tetris_SMiRL.json --run_mode=local --exp_name=test_smirl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003epython3 scripts/dqn_smirl.py --config=configs/Carnival_Small_SMiRL.json --run_mode=local --exp_name=test_smirl --training_processor_type=gpu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith docker locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 scripts/dqn_smirl.py --config=configs/tetris_SMiRL.json --exp-name=test --run_mode=local_docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e###Run Vizdoom SMiRL experiments\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/VizDoom_TakeCover_Small.json --exp_name=vizdoom_small_test --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/VizDoom_DefendTheLine_Small.json --exp_name=vizdoom_DTL_small_smirl --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/VizDoom_DefendTheLine_Small_Bonus.json --exp_name=vizdoom_DTL_small_smirl_bonus --run_mode=ssh --ssh_host=newton1 --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Atari Experiments\u003c/h3\u003e\u003ca id=\"user-content-run-atari-experiments\" class=\"anchor\" aria-label=\"Permalink: Run Atari Experiments\" href=\"#run-atari-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/Carnival_Small_SMiRL.json --exp_name=Atari_Carnival__small_smirl --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/Carnival_Small_SMiRL_Bonus.json --exp_name=Atari_Carnival_small_smirl_bonus --run_mode=ssh --ssh_host=newton1 --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/IceHockey_Small_SMiRL.json --exp_name=Atari_IceHockey_small_smirl --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/RiverRaid_Small_SMiRL.json --exp_name=Atari_RiverRaid_small_smirl --run_mode=ssh --ssh_host=newton1 --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n", + "stargazers_count": 17, + "subscribers_count": 2, + "topics": [], + "updated_at": 1729540195.0 }, { "data_format": 2, - "description": "A software framework of conservation-law solvers that use the space-time Conservation Element and Solution Element (CESE) method.", + "description": "Modify C++ test coverage reports to show uninstantiated templates", "filenames": [ - "contrib/singularity/Singularity.1.0.0-0.1.4+", - "contrib/singularity/Singularity.0.1.0", - "contrib/singularity/Singularity" + "Singularity" ], - "full_name": "solvcon/solvcon", - "latest_release": "0.1.4", + "full_name": "emilydolson/force-cover", + "latest_release": "v3.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eForce-cover\u003c/h1\u003e\u003ca id=\"user-content-force-cover\" class=\"anchor\" aria-label=\"Permalink: Force-cover\" href=\"#force-cover\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/emilydolson/force-cover/actions/workflows/tests.yml\"\u003e\u003cimg src=\"https://github.com/emilydolson/force-cover/actions/workflows/tests.yml/badge.svg\" alt=\"Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://codecov.io/gh/emilydolson/force-cover\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/24cc05b0e1d757b8751eeb3676ded348feceadca3dbe56415ec19ab7a721abf2/68747470733a2f2f636f6465636f762e696f2f67682f656d696c79646f6c736f6e2f666f7263652d636f7665722f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/emilydolson/force-cover/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ec478b486065cb0425b568fade40c5bb89281db693d0431a9f222ef0217b7a39/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f656d696c79646f6c736f6e2f666f7263652d636f7665722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ec478b486065cb0425b568fade40c5bb89281db693d0431a9f222ef0217b7a39/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f656d696c79646f6c736f6e2f666f7263652d636f7665722e737667\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/emilydolson/force-cover.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/emilydolson/force-cover/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a93286920599112849c7c2af9d239294be27738b440248e434813b1bd0ffb368/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e747269627574696f6e732d77656c636f6d652d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"contributions welcome\" data-canonical-src=\"https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/3916\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGetting accurate test coverage information about C++ code containing templates is challenging; uninstantiated templates don\u0027t make it into the compiled binary, so compilers don\u0027t instrument them for coverage tracking (i.e. if you never use a template the compiler thinks it isn\u0027t runnable code and doesn\u0027t count it as lines that should be covered). Since templates with no test coverage are likely to never get instantiated this results in overly accurate test coverage metrics.\u003c/p\u003e\n\u003cp\u003eForce-cover is a set of tools for dealing with this problem. It consists of two parts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea C++ program (built with Clang Libtooling) that reads your C++ code, finds the templates, and sticks comments before and after them to indicate that they should be covered.\u003c/li\u003e\n\u003cli\u003ea python program that looks at the final test coverage output, finds the macros, and adjusts the file as necessary to indicate that uncovered template code should be counted as uncovered code.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePython (any version)\u003c/li\u003e\n\u003cli\u003eclang (version 7+) (for version 6, use \u003ca href=\"https://github.com/emilydolson/force-cover/releases/tag/v1.5\"\u003ethis release of force-cover\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003elibclang-dev (version 7+ - must be same version as clang)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTheoretically force-cover should work on any operating system, but it\u0027s currently only been tested on Ubuntu and Linux Mint.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install the requirements on Ubuntu-flavored Linux with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt install -y clang llvm-dev libclang-dev\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can build force-cover by cloning this repo and running Make inside it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/emilydolson/force-cover.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e force-cover\nmake\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create the force_cover executable. No additional work is needed to set up the Python script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTroubleshooting\u003c/h3\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have multiple versions of clang or llvm on your computer, the Make command may fail. You may be able to fix this by changing the default version as described at the bottom of \u003ca href=\"https://blog.kowalczyk.info/article/k/how-to-install-latest-clang-6.0-on-ubuntu-16.04-xenial-wsl.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e. Alternatively, you can modify the Makefile to include absolute paths to the installation location. Set LLVM_SRC_PATH equal to the path to your llvm installation location (e.g. \u003ccode\u003e/usr/lib/llvm-11\u003c/code\u003e). Uncomment the \u003ccode\u003eLLVM_CONFIG := $(LLVM_BIN_PATH)/llvm-config\u003c/code\u003e line and comment out the line above it.\u003c/p\u003e\n\u003cp\u003eAlternately, save yourself a trip through install hell by using a containerized environment a la \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e!\nBuild from our handy-dandy Singularity recipe (\u003ccode\u003esudo singularity build force-cover.simg Singularity\u003c/code\u003e) or grab a pre-built container from SingularityHub (\u003ccode\u003esingularity pull --name \"force-cover.simg\" shub://emilydolson/force-cover\u003c/code\u003e).\nThen, hop on to an interactive shell by \u003ccode\u003esingularity shell force-cover.simg\u003c/code\u003e.\nCowabunga!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick-start guide\u003c/h2\u003e\u003ca id=\"user-content-quick-start-guide\" class=\"anchor\" aria-label=\"Permalink: Quick-start guide\" href=\"#quick-start-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is the basic sequence of commands you need to execute to use force-cover with LLVM Source-Based coverage (the recommended approach):\u003c/p\u003e\n\u003cpre lang=\"none\"\u003e\u003ccode\u003e./force_cover [C++ code file to be evaluated] -- [any flags you would pass to the compiler when compiling this program] \u0026gt; [name of file to store modified code in]\nclang++ -fprofile-instr-generate -fcoverage-mapping -O0 -fno-inline -fno-elide-constructors [.cpp file] -o [executable name]\n[run executable]\nllvm-profdata merge default.profraw -o default.profdata\nllvm-cov show [executable name] -instr-profile=default.profdata \u0026gt; coverage.txt\npython fix_coverage.py coverage.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample (using included example.cc file):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./force_cover examples/example.cc -- --language c++ -std=c++11 \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e examples/example_with_template_coverage_info.cc\nclang++ -fprofile-instr-generate -fcoverage-mapping -O0 -fno-inline -fno-elide-constructors examples/example_with_template_coverage_info.cc -o example\n./example\nllvm-profdata merge default.profraw -o default.profdata\nllvm-cov show ./example -instr-profile=default.profdata \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e coverage.txt\npython fix_coverage.py coverage.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing force-cover (in detail)\u003c/h2\u003e\u003ca id=\"user-content-using-force-cover-in-detail\" class=\"anchor\" aria-label=\"Permalink: Using force-cover (in detail)\" href=\"#using-force-cover-in-detail\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow for using force-cover is as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun all of your C++ code through the force_cover C++ program to insert comments.\u003c/li\u003e\n\u003cli\u003eCompile your program using appropriate flags for your compiler to indicate that you want to measure test coverage on this program\u003c/li\u003e\n\u003cli\u003eRun your program\u003c/li\u003e\n\u003cli\u003eRun your coverage program\u003c/li\u003e\n\u003cli\u003eRun the python script on the coverage program\u0027s output\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn theory, this should be possible with a variety of compilers and code coverage programs. Thus far, I have only tested it with LLVM Source Based coverage. If you have tested it and found that it worked with a different toolchain, let me know so I can add it to this documentation!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Run force_cover on your code\u003c/h3\u003e\u003ca id=\"user-content-step-1-run-force_cover-on-your-code\" class=\"anchor\" aria-label=\"Permalink: Step 1: Run force_cover on your code\" href=\"#step-1-run-force_cover-on-your-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe syntax for running the force_cover C++ program is:\u003c/p\u003e\n\u003cpre lang=\"none\"\u003e\u003ccode\u003e./force_cover [C++ code file to be evaluated] -- [any flags you would pass to the compiler when compiling this program]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor instance, to run it on the example you could use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./force_cover examples/example.cc -- --language c++ -std=c++11\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, it prints the modified version of the code to stdout. In order to compile programs using the modified code, you\u0027ll need to pipe this new code to a file. For instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./force_cover examples/example.cc -- --language c++ -std=c++11 \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e examples/example_with_template_coverage_info.cc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor larger code-bases, one option is to make a copy of your code, rewrite all of the files in the copy, and use those files to compile your tests. This can be achieved with a few lines of bash code. For instance, let\u0027s say you\u0027re writing a header-only library and all of the headers live in a directory called \u003ccode\u003esource\u003c/code\u003e. You could run the following code:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp -r \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e coverage_source\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003efilename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003efind ../coverage_source -name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*.h\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n ./force_cover \u003cspan class=\"pl-smi\"\u003e$filename\u003c/span\u003e -- -I../coverage_source --language c++ -std=c++14 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e xargs -0 \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$filename\u003c/span\u003e.temp\n mv \u003cspan class=\"pl-smi\"\u003e$filename\u003c/span\u003e.temp \u003cspan class=\"pl-smi\"\u003e$filename\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen when you go to compile your tests for coverage, instead of including \u003ccode\u003esource\u003c/code\u003e you would include \u003ccode\u003ecoverage_source\u003c/code\u003e (i.e. replace \u003ccode\u003e-Isource\u003c/code\u003e with \u003ccode\u003e-Icoverage_source\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eIf you are running tests on a continuous integration platform you may choose to skip the step of copying the code to a different directory. Just be aware that \u003cstrong\u003ethis is dangerous because it will overwrite your code\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Compile your program\u003c/h3\u003e\u003ca id=\"user-content-step-2-compile-your-program\" class=\"anchor\" aria-label=\"Permalink: Step 2: Compile your program\" href=\"#step-2-compile-your-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to get coverage information, you need to compile your program with coverage instrumentation turned on. This can be achieved by passing a few flags to the compiler. In LLVM, there are a number of different systems of coverage instrumentation. The one I have had by far the most luck with is Source Based coverage, which can be enabled with the \u003ccode\u003e-fprofile-instr-generate\u003c/code\u003e and \u003ccode\u003e-fcoverage-mapping\u003c/code\u003e flags. The other version, which mirrors GCC\u0027s gcov system, sometimes optimizes unused class methods out of the binary, preventing them from getting appropriately flagged as not covered.\u003c/p\u003e\n\u003cp\u003eSome other useful flags to prevent the compiler from making optimizations that hide uncovered code are: \u003ccode\u003e-O0 -fno-inline -fno-elide-constructors\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSo your compilation step will probably look something like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eclang++ -fprofile-instr-generate -fcoverage-mapping -O0 -fno-inline -fno-elide-constructors examples/example_with_template_coverage_info.cc -o example\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that Source Based coverage is only available in clang. Theoretically, the tools in this repo should work on code instrumented in other ways but, as mentioned before, it hasn\u0027t been tested on them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Run your program\u003c/h3\u003e\u003ca id=\"user-content-step-3-run-your-program\" class=\"anchor\" aria-label=\"Permalink: Step 3: Run your program\" href=\"#step-3-run-your-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most straightforward step! Run your program so that the coverage instrumentation can record which lines were executed.\u003c/p\u003e\n\u003cp\u003eFor instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./example\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 4: Extract coverage information\u003c/h3\u003e\u003ca id=\"user-content-step-4-extract-coverage-information\" class=\"anchor\" aria-label=\"Permalink: Step 4: Extract coverage information\" href=\"#step-4-extract-coverage-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow that you\u0027ve run your program, coverage data exists but it\u0027s probably not in an easy-to-interpret form. You\u0027ll have to run a program to extract it. For LLVM Source Based coverage, that will look like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ellvm-profdata merge default.profraw -o default.profdata\nllvm-cov show ./example -instr-profile=default.profdata \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e coverage.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis processes the raw coverage data and then compares that information to the executable to generate a report indicating the number of time each line was executed. Specifically, the format should look like this:\u003c/p\u003e\n\u003cpre lang=\"none\"\u003e\u003ccode\u003e[line_number] | [times_line_executed]| [code from source file]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhatever compiler and tools you used, you need to end up with data in this format for step 5 to work. Fortunately, it seems to be a relatively common format (Note: if anyone knows the actual name of this format, send me a PR! I wrote this tool because I needed it and thought others might too, not because I\u0027m some kind of code coverage expert).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 5: Run fix_coverage.py\u003c/h3\u003e\u003ca id=\"user-content-step-5-run-fix_coveragepy\" class=\"anchor\" aria-label=\"Permalink: Step 5: Run fix_coverage.py\" href=\"#step-5-run-fix_coveragepy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the final step, run fix_coverage.py on your output file from the previous step. \u003cstrong\u003eNote that this will overwrite your output file\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython fix_coverage.py coverage.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis script will go through and find all of the regions that are erroneously being excluded from coverage analysis and modify the coverage file to indicate that they should be covered but are not.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 6: Profit!\u003c/h3\u003e\u003ca id=\"user-content-step-6-profit\" class=\"anchor\" aria-label=\"Permalink: Step 6: Profit!\" href=\"#step-6-profit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTa-da! You have code coverage data that includes uninstantiated templates! You can look at the file directly, or pass it along to a service like \u003ca href=\"https://codecov.io\" rel=\"nofollow\"\u003ecodecov\u003c/a\u003e that will give you a more user-friendly way to examine your coverage (codecov\u0027s documentation on using llvm-cov isn\u0027t super clear, but it will accept files in this format with names matching the pattern \u003ccode\u003ecoverage*.txt\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCaveats\u003c/h2\u003e\u003ca id=\"user-content-caveats\" class=\"anchor\" aria-label=\"Permalink: Caveats\" href=\"#caveats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode coverage is a flawed metric. Just because a line of code is executed doesn\u0027t mean it\u0027s being rigorously tested. This is especially true for templates, since different instantiations of the same template could be wildly different from each other. That\u0027s the whole reason uninstantiated templates don\u0027t get included in the binary in the first place: template definitions only have a meaning with an appropriate set of arguments. Force-cover can increase the accuracy of your code coverage and alert you to uninstantiated templates, but it can\u0027t guarantee that your tests are actually good.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBugs? Contributions?\u003c/h2\u003e\u003ca id=\"user-content-bugs-contributions\" class=\"anchor\" aria-label=\"Permalink: Bugs? Contributions?\" href=\"#bugs-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpen an issue or send me a PR! I\u0027m not an expert on this stuff, so I\u0027m sure there are myriad ways force-cover could be better. I welcome all contributions. The code is pretty succinct, so hopefully it\u0027s not too overwhelming to wade into.\u003c/p\u003e\n\u003cp\u003eIn particular I would love to receive:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAdditional rules for \u003ccode\u003evalidate_line\u003c/code\u003e in \u003ccode\u003efix_coverage.py\u003c/code\u003e. Its goal is to detect lines that should not be marked as potentially coverable (e.g. lines containing only comments). I wrote some very basic rules, but I\u0027m sure there are a bunch of edge cases it\u0027s missing.\u003c/li\u003e\n\u003cli\u003eImprovements to the AST matching rules in \u003ccode\u003eforce_cover.cpp\u003c/code\u003e. I\u0027m sure there are edge cases that they\u0027re currently missing. Also in general they\u0027re a little overzealous at this point (in mostly harmless ways).\u003c/li\u003e\n\u003cli\u003eThere is probably a smoother way to do all of this (e.g. one that doesn\u0027t require both a pre-processing step and a post-processing step). Potential options (some of which I tried and gave up on):\n\u003cul\u003e\n\u003cli\u003eAutomatically add code that instantiates templates. Problem: you need to know what types to instantiate them with.\u003c/li\u003e\n\u003cli\u003eDetect uninstantiated templates and replace them with an equivalent number of lines of non-templated code. Problem: detecting uninstantiated templates is non-trivial.\u003c/li\u003e\n\u003cli\u003eDitch the preprocessing script and let Python find templates in the coverage output. Problem: probably requires parsing C++ in Python (although there are Python bindings for clang libtools... they\u0027re just really poorly documented).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 17, - "subscribers_count": 9, + "subscribers_count": 3, "topics": [ - "computational-science" + "test-coverage", + "llvm-cov", + "libtooling" ], - "updated_at": 1721725320.0 + "updated_at": 1731319757.0 }, { "data_format": 2, @@ -38507,155 +38576,156 @@ var data = }, { "data_format": 2, - "description": "Modify C++ test coverage reports to show uninstantiated templates", + "description": "A software framework of conservation-law solvers that use the space-time Conservation Element and Solution Element (CESE) method.", "filenames": [ - "Singularity" + "contrib/singularity/Singularity.1.0.0-0.1.4+", + "contrib/singularity/Singularity.0.1.0", + "contrib/singularity/Singularity" ], - "full_name": "emilydolson/force-cover", - "latest_release": "v3.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eForce-cover\u003c/h1\u003e\u003ca id=\"user-content-force-cover\" class=\"anchor\" aria-label=\"Permalink: Force-cover\" href=\"#force-cover\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/emilydolson/force-cover/actions/workflows/tests.yml\"\u003e\u003cimg src=\"https://github.com/emilydolson/force-cover/actions/workflows/tests.yml/badge.svg\" alt=\"Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://codecov.io/gh/emilydolson/force-cover\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/24cc05b0e1d757b8751eeb3676ded348feceadca3dbe56415ec19ab7a721abf2/68747470733a2f2f636f6465636f762e696f2f67682f656d696c79646f6c736f6e2f666f7263652d636f7665722f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/emilydolson/force-cover/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ec478b486065cb0425b568fade40c5bb89281db693d0431a9f222ef0217b7a39/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f656d696c79646f6c736f6e2f666f7263652d636f7665722e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ec478b486065cb0425b568fade40c5bb89281db693d0431a9f222ef0217b7a39/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f656d696c79646f6c736f6e2f666f7263652d636f7665722e737667\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/emilydolson/force-cover.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/emilydolson/force-cover/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a93286920599112849c7c2af9d239294be27738b440248e434813b1bd0ffb368/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e747269627574696f6e732d77656c636f6d652d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"contributions welcome\" data-canonical-src=\"https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/3916\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eGetting accurate test coverage information about C++ code containing templates is challenging; uninstantiated templates don\u0027t make it into the compiled binary, so compilers don\u0027t instrument them for coverage tracking (i.e. if you never use a template the compiler thinks it isn\u0027t runnable code and doesn\u0027t count it as lines that should be covered). Since templates with no test coverage are likely to never get instantiated this results in overly accurate test coverage metrics.\u003c/p\u003e\n\u003cp\u003eForce-cover is a set of tools for dealing with this problem. It consists of two parts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea C++ program (built with Clang Libtooling) that reads your C++ code, finds the templates, and sticks comments before and after them to indicate that they should be covered.\u003c/li\u003e\n\u003cli\u003ea python program that looks at the final test coverage output, finds the macros, and adjusts the file as necessary to indicate that uncovered template code should be counted as uncovered code.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePython (any version)\u003c/li\u003e\n\u003cli\u003eclang (version 7+) (for version 6, use \u003ca href=\"https://github.com/emilydolson/force-cover/releases/tag/v1.5\"\u003ethis release of force-cover\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003elibclang-dev (version 7+ - must be same version as clang)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTheoretically force-cover should work on any operating system, but it\u0027s currently only been tested on Ubuntu and Linux Mint.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install the requirements on Ubuntu-flavored Linux with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt install -y clang llvm-dev libclang-dev\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can build force-cover by cloning this repo and running Make inside it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/emilydolson/force-cover.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e force-cover\nmake\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create the force_cover executable. No additional work is needed to set up the Python script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTroubleshooting\u003c/h3\u003e\u003ca id=\"user-content-troubleshooting\" class=\"anchor\" aria-label=\"Permalink: Troubleshooting\" href=\"#troubleshooting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have multiple versions of clang or llvm on your computer, the Make command may fail. You may be able to fix this by changing the default version as described at the bottom of \u003ca href=\"https://blog.kowalczyk.info/article/k/how-to-install-latest-clang-6.0-on-ubuntu-16.04-xenial-wsl.html\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e. Alternatively, you can modify the Makefile to include absolute paths to the installation location. Set LLVM_SRC_PATH equal to the path to your llvm installation location (e.g. \u003ccode\u003e/usr/lib/llvm-11\u003c/code\u003e). Uncomment the \u003ccode\u003eLLVM_CONFIG := $(LLVM_BIN_PATH)/llvm-config\u003c/code\u003e line and comment out the line above it.\u003c/p\u003e\n\u003cp\u003eAlternately, save yourself a trip through install hell by using a containerized environment a la \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e!\nBuild from our handy-dandy Singularity recipe (\u003ccode\u003esudo singularity build force-cover.simg Singularity\u003c/code\u003e) or grab a pre-built container from SingularityHub (\u003ccode\u003esingularity pull --name \"force-cover.simg\" shub://emilydolson/force-cover\u003c/code\u003e).\nThen, hop on to an interactive shell by \u003ccode\u003esingularity shell force-cover.simg\u003c/code\u003e.\nCowabunga!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick-start guide\u003c/h2\u003e\u003ca id=\"user-content-quick-start-guide\" class=\"anchor\" aria-label=\"Permalink: Quick-start guide\" href=\"#quick-start-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere is the basic sequence of commands you need to execute to use force-cover with LLVM Source-Based coverage (the recommended approach):\u003c/p\u003e\n\u003cpre lang=\"none\"\u003e\u003ccode\u003e./force_cover [C++ code file to be evaluated] -- [any flags you would pass to the compiler when compiling this program] \u0026gt; [name of file to store modified code in]\nclang++ -fprofile-instr-generate -fcoverage-mapping -O0 -fno-inline -fno-elide-constructors [.cpp file] -o [executable name]\n[run executable]\nllvm-profdata merge default.profraw -o default.profdata\nllvm-cov show [executable name] -instr-profile=default.profdata \u0026gt; coverage.txt\npython fix_coverage.py coverage.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExample (using included example.cc file):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./force_cover examples/example.cc -- --language c++ -std=c++11 \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e examples/example_with_template_coverage_info.cc\nclang++ -fprofile-instr-generate -fcoverage-mapping -O0 -fno-inline -fno-elide-constructors examples/example_with_template_coverage_info.cc -o example\n./example\nllvm-profdata merge default.profraw -o default.profdata\nllvm-cov show ./example -instr-profile=default.profdata \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e coverage.txt\npython fix_coverage.py coverage.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing force-cover (in detail)\u003c/h2\u003e\u003ca id=\"user-content-using-force-cover-in-detail\" class=\"anchor\" aria-label=\"Permalink: Using force-cover (in detail)\" href=\"#using-force-cover-in-detail\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow for using force-cover is as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun all of your C++ code through the force_cover C++ program to insert comments.\u003c/li\u003e\n\u003cli\u003eCompile your program using appropriate flags for your compiler to indicate that you want to measure test coverage on this program\u003c/li\u003e\n\u003cli\u003eRun your program\u003c/li\u003e\n\u003cli\u003eRun your coverage program\u003c/li\u003e\n\u003cli\u003eRun the python script on the coverage program\u0027s output\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn theory, this should be possible with a variety of compilers and code coverage programs. Thus far, I have only tested it with LLVM Source Based coverage. If you have tested it and found that it worked with a different toolchain, let me know so I can add it to this documentation!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Run force_cover on your code\u003c/h3\u003e\u003ca id=\"user-content-step-1-run-force_cover-on-your-code\" class=\"anchor\" aria-label=\"Permalink: Step 1: Run force_cover on your code\" href=\"#step-1-run-force_cover-on-your-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe syntax for running the force_cover C++ program is:\u003c/p\u003e\n\u003cpre lang=\"none\"\u003e\u003ccode\u003e./force_cover [C++ code file to be evaluated] -- [any flags you would pass to the compiler when compiling this program]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor instance, to run it on the example you could use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./force_cover examples/example.cc -- --language c++ -std=c++11\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eBy default, it prints the modified version of the code to stdout. In order to compile programs using the modified code, you\u0027ll need to pipe this new code to a file. For instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./force_cover examples/example.cc -- --language c++ -std=c++11 \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e examples/example_with_template_coverage_info.cc\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor larger code-bases, one option is to make a copy of your code, rewrite all of the files in the copy, and use those files to compile your tests. This can be achieved with a few lines of bash code. For instance, let\u0027s say you\u0027re writing a header-only library and all of the headers live in a directory called \u003ccode\u003esource\u003c/code\u003e. You could run the following code:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecp -r \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e coverage_source\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003efilename\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003efind ../coverage_source -name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*.h\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n ./force_cover \u003cspan class=\"pl-smi\"\u003e$filename\u003c/span\u003e -- -I../coverage_source --language c++ -std=c++14 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e xargs -0 \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$filename\u003c/span\u003e.temp\n mv \u003cspan class=\"pl-smi\"\u003e$filename\u003c/span\u003e.temp \u003cspan class=\"pl-smi\"\u003e$filename\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen when you go to compile your tests for coverage, instead of including \u003ccode\u003esource\u003c/code\u003e you would include \u003ccode\u003ecoverage_source\u003c/code\u003e (i.e. replace \u003ccode\u003e-Isource\u003c/code\u003e with \u003ccode\u003e-Icoverage_source\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eIf you are running tests on a continuous integration platform you may choose to skip the step of copying the code to a different directory. Just be aware that \u003cstrong\u003ethis is dangerous because it will overwrite your code\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Compile your program\u003c/h3\u003e\u003ca id=\"user-content-step-2-compile-your-program\" class=\"anchor\" aria-label=\"Permalink: Step 2: Compile your program\" href=\"#step-2-compile-your-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to get coverage information, you need to compile your program with coverage instrumentation turned on. This can be achieved by passing a few flags to the compiler. In LLVM, there are a number of different systems of coverage instrumentation. The one I have had by far the most luck with is Source Based coverage, which can be enabled with the \u003ccode\u003e-fprofile-instr-generate\u003c/code\u003e and \u003ccode\u003e-fcoverage-mapping\u003c/code\u003e flags. The other version, which mirrors GCC\u0027s gcov system, sometimes optimizes unused class methods out of the binary, preventing them from getting appropriately flagged as not covered.\u003c/p\u003e\n\u003cp\u003eSome other useful flags to prevent the compiler from making optimizations that hide uncovered code are: \u003ccode\u003e-O0 -fno-inline -fno-elide-constructors\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eSo your compilation step will probably look something like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eclang++ -fprofile-instr-generate -fcoverage-mapping -O0 -fno-inline -fno-elide-constructors examples/example_with_template_coverage_info.cc -o example\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that Source Based coverage is only available in clang. Theoretically, the tools in this repo should work on code instrumented in other ways but, as mentioned before, it hasn\u0027t been tested on them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Run your program\u003c/h3\u003e\u003ca id=\"user-content-step-3-run-your-program\" class=\"anchor\" aria-label=\"Permalink: Step 3: Run your program\" href=\"#step-3-run-your-program\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most straightforward step! Run your program so that the coverage instrumentation can record which lines were executed.\u003c/p\u003e\n\u003cp\u003eFor instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./example\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 4: Extract coverage information\u003c/h3\u003e\u003ca id=\"user-content-step-4-extract-coverage-information\" class=\"anchor\" aria-label=\"Permalink: Step 4: Extract coverage information\" href=\"#step-4-extract-coverage-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNow that you\u0027ve run your program, coverage data exists but it\u0027s probably not in an easy-to-interpret form. You\u0027ll have to run a program to extract it. For LLVM Source Based coverage, that will look like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ellvm-profdata merge default.profraw -o default.profdata\nllvm-cov show ./example -instr-profile=default.profdata \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e coverage.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis processes the raw coverage data and then compares that information to the executable to generate a report indicating the number of time each line was executed. Specifically, the format should look like this:\u003c/p\u003e\n\u003cpre lang=\"none\"\u003e\u003ccode\u003e[line_number] | [times_line_executed]| [code from source file]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhatever compiler and tools you used, you need to end up with data in this format for step 5 to work. Fortunately, it seems to be a relatively common format (Note: if anyone knows the actual name of this format, send me a PR! I wrote this tool because I needed it and thought others might too, not because I\u0027m some kind of code coverage expert).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 5: Run fix_coverage.py\u003c/h3\u003e\u003ca id=\"user-content-step-5-run-fix_coveragepy\" class=\"anchor\" aria-label=\"Permalink: Step 5: Run fix_coverage.py\" href=\"#step-5-run-fix_coveragepy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor the final step, run fix_coverage.py on your output file from the previous step. \u003cstrong\u003eNote that this will overwrite your output file\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epython fix_coverage.py coverage.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis script will go through and find all of the regions that are erroneously being excluded from coverage analysis and modify the coverage file to indicate that they should be covered but are not.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 6: Profit!\u003c/h3\u003e\u003ca id=\"user-content-step-6-profit\" class=\"anchor\" aria-label=\"Permalink: Step 6: Profit!\" href=\"#step-6-profit\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTa-da! You have code coverage data that includes uninstantiated templates! You can look at the file directly, or pass it along to a service like \u003ca href=\"https://codecov.io\" rel=\"nofollow\"\u003ecodecov\u003c/a\u003e that will give you a more user-friendly way to examine your coverage (codecov\u0027s documentation on using llvm-cov isn\u0027t super clear, but it will accept files in this format with names matching the pattern \u003ccode\u003ecoverage*.txt\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCaveats\u003c/h2\u003e\u003ca id=\"user-content-caveats\" class=\"anchor\" aria-label=\"Permalink: Caveats\" href=\"#caveats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCode coverage is a flawed metric. Just because a line of code is executed doesn\u0027t mean it\u0027s being rigorously tested. This is especially true for templates, since different instantiations of the same template could be wildly different from each other. That\u0027s the whole reason uninstantiated templates don\u0027t get included in the binary in the first place: template definitions only have a meaning with an appropriate set of arguments. Force-cover can increase the accuracy of your code coverage and alert you to uninstantiated templates, but it can\u0027t guarantee that your tests are actually good.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBugs? Contributions?\u003c/h2\u003e\u003ca id=\"user-content-bugs-contributions\" class=\"anchor\" aria-label=\"Permalink: Bugs? Contributions?\" href=\"#bugs-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOpen an issue or send me a PR! I\u0027m not an expert on this stuff, so I\u0027m sure there are myriad ways force-cover could be better. I welcome all contributions. The code is pretty succinct, so hopefully it\u0027s not too overwhelming to wade into.\u003c/p\u003e\n\u003cp\u003eIn particular I would love to receive:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAdditional rules for \u003ccode\u003evalidate_line\u003c/code\u003e in \u003ccode\u003efix_coverage.py\u003c/code\u003e. Its goal is to detect lines that should not be marked as potentially coverable (e.g. lines containing only comments). I wrote some very basic rules, but I\u0027m sure there are a bunch of edge cases it\u0027s missing.\u003c/li\u003e\n\u003cli\u003eImprovements to the AST matching rules in \u003ccode\u003eforce_cover.cpp\u003c/code\u003e. I\u0027m sure there are edge cases that they\u0027re currently missing. Also in general they\u0027re a little overzealous at this point (in mostly harmless ways).\u003c/li\u003e\n\u003cli\u003eThere is probably a smoother way to do all of this (e.g. one that doesn\u0027t require both a pre-processing step and a post-processing step). Potential options (some of which I tried and gave up on):\n\u003cul\u003e\n\u003cli\u003eAutomatically add code that instantiates templates. Problem: you need to know what types to instantiate them with.\u003c/li\u003e\n\u003cli\u003eDetect uninstantiated templates and replace them with an equivalent number of lines of non-templated code. Problem: detecting uninstantiated templates is non-trivial.\u003c/li\u003e\n\u003cli\u003eDitch the preprocessing script and let Python find templates in the coverage output. Problem: probably requires parsing C++ in Python (although there are Python bindings for clang libtools... they\u0027re just really poorly documented).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "solvcon/solvcon", + "latest_release": "0.1.4", "stargazers_count": 17, - "subscribers_count": 3, + "subscribers_count": 9, "topics": [ - "test-coverage", - "llvm-cov", - "libtooling" + "computational-science" ], - "updated_at": 1731319757.0 + "updated_at": 1721725320.0 }, { "data_format": 2, - "description": null, + "description": "MFannot is a program for the annotation of mitochondrial and plastid genomes", "filenames": [ - "Singularity" + "Singularity.v1.0" ], - "full_name": "Neo-X/SMiRL_Code", + "full_name": "BFL-lab/Mfannot", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBayesian Surprise\u003c/h1\u003e\u003ca id=\"user-content-bayesian-surprise\" class=\"anchor\" aria-label=\"Permalink: Bayesian Surprise\" href=\"#bayesian-surprise\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRepo for environments, gym wrappers, and scripts for the SMiRL project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eFor distributing experiments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003edoodad: \u003ca href=\"https://github.com/montrealrobotics/doodad\"\u003ehttps://github.com/montrealrobotics/doodad\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRL library\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003erlkit: \u003ca href=\"https://github.com/Neo-X/rlkit/tree/surprise\"\u003ehttps://github.com/Neo-X/rlkit/tree/surprise\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Instruction\u003c/h3\u003e\u003ca id=\"user-content-build-instruction\" class=\"anchor\" aria-label=\"Permalink: Build Instruction\" href=\"#build-instruction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003econda create --name smirl_code python=3.7 pip\nconda activate smirl_code\npip install -r requirements.txt\npip install -e ./\ncd ../\ngit clone git@github.com:montrealrobotics/doodad.git\ncd doodad\npip install -e ./\ncd ../smirl_code\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen you will need copy the \u003ca href=\"https://github.com/Neo-X/doodad/blob/master/doodad/easy_launch/config.py\"\u003e\u003ccode\u003econfig.py\u003c/code\u003e\u003c/a\u003e file locally to \u003ccode\u003elaunchers.config.py\u003c/code\u003e and update the paths in the file.\nYou need to update \u003ccode\u003eBASE_CODE_DIR\u003c/code\u003e to the location you have saved SMiRL_Code.\nAlso update \u003ccode\u003eLOCAL_LOG_DIR\u003c/code\u003e to the location you would like the logging data to be saved on your computer.\nYou can look at the \u003ca href=\"https://github.com/Neo-X/doodad/\"\u003edoodad\u003c/a\u003e for more details on this configuration.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommands:\u003c/h2\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands:\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA basic examples.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 scripts/dqn_smirl.py --config=configs/tetris_SMiRL.json --run_mode=local --exp_name=test_smirl\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003epython3 scripts/dqn_smirl.py --config=configs/Carnival_Small_SMiRL.json --run_mode=local --exp_name=test_smirl --training_processor_type=gpu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith docker locally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 scripts/dqn_smirl.py --config=configs/tetris_SMiRL.json --exp-name=test --run_mode=local_docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e###Run Vizdoom SMiRL experiments\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/VizDoom_TakeCover_Small.json --exp_name=vizdoom_small_test --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/VizDoom_DefendTheLine_Small.json --exp_name=vizdoom_DTL_small_smirl --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/VizDoom_DefendTheLine_Small_Bonus.json --exp_name=vizdoom_DTL_small_smirl_bonus --run_mode=ssh --ssh_host=newton1 --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun Atari Experiments\u003c/h3\u003e\u003ca id=\"user-content-run-atari-experiments\" class=\"anchor\" aria-label=\"Permalink: Run Atari Experiments\" href=\"#run-atari-experiments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/Carnival_Small_SMiRL.json --exp_name=Atari_Carnival__small_smirl --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/Carnival_Small_SMiRL_Bonus.json --exp_name=Atari_Carnival_small_smirl_bonus --run_mode=ssh --ssh_host=newton1 --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/IceHockey_Small_SMiRL.json --exp_name=Atari_IceHockey_small_smirl --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n\u003cp\u003epython3 scripts/dqn_smirl.py --config=configs/RiverRaid_Small_SMiRL.json --exp_name=Atari_RiverRaid_small_smirl --run_mode=ssh --ssh_host=newton1 --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMFannot\u003c/h1\u003e\u003ca id=\"user-content-mfannot\" class=\"anchor\" aria-label=\"Permalink: MFannot\" href=\"#mfannot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMFannot is a program for the annotation of mitochondrial and plastid genomes.\nIt is a PERL wrapper around a set of diverse, external independent tools.\u003c/p\u003e\n\u003cp\u003eIt makes intense use of RNA/intron detection tools including \u003ca href=\"http://hmmer.org/\" rel=\"nofollow\"\u003eHMMER\u003c/a\u003e, \u003ca href=\"https://github.com/nathanweeks/exonerate\"\u003eExonerate\u003c/a\u003e, \u003ca href=\"https://bioinformatics.ca/links_directory/tool/9822/erpin\" rel=\"nofollow\"\u003eErpin\u003c/a\u003e and others.\u003c/p\u003e\n\u003cp\u003eMFannot is particularly helpful with organelle genomes that contain lots of introns. Intron-exon boundaries are identified by a combination of secondary structure, intron splice rules and exon similarities, and are thus precise in most instances.\nNote that not all introns may be detected by MFannot, which requires expert manual curation/completion of gene structure annotations before GenBank submission.\u003c/p\u003e\n\u003cp\u003eThe output of MFannot is a listings of gene coordinates either in \u003ca href=\"https://www.ncbi.nlm.nih.gov/Sequin/\" rel=\"nofollow\"\u003eSequin format\u003c/a\u003e, a format that can be directly loaded into NCBI\u0027s sequence submission tools, or in \u003ca href=\"http://megasun.bch.umontreal.ca/ogmp/masterfile/intro.html\" rel=\"nofollow\"\u003eMasterfile\u003c/a\u003e format (computer-parsible as well as human-readable; annotations embedded into the FASTA sequence).\u003c/p\u003e\n\u003cp\u003eThis package is based on activities of the OGMP (Organelle Genome Megasequencing Project, D\u00e9partement de Biochimie, Universit\u00e9 de Montr\u00e9al, circa 1990-1998) and further developed since then by the labs of \u003ca href=\"https://biochimie.umontreal.ca/en/department/professors/franz-bernd-lang/\" rel=\"nofollow\"\u003eB.F. Lang\u003c/a\u003e and \u003ca href=\"https://biochimie.umontreal.ca/en/department/professors/gertraud-burger/\" rel=\"nofollow\"\u003eG. Burger\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: At this point the installation of MFannot is only possible on Unix systems (e.g. Ubuntu and CentOS).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBioPerl\u003c/h4\u003e\u003ca id=\"user-content-bioperl\" class=\"anchor\" aria-label=\"Permalink: BioPerl\" href=\"#bioperl\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMFannot is a PERL pipeline that use \u003ca href=\"http://bioperl.org/\" rel=\"nofollow\"\u003eBioPerl\u003c/a\u003e, so you need to install BioPerl first and at least install the \u003ca href=\"http://search.cpan.org/dist/BioPerl/Bio/AlignIO.pm\" rel=\"nofollow\"\u003eBio::AlignIO\u003c/a\u003e and all their dependencies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eExternal programs\u003c/h4\u003e\u003ca id=\"user-content-external-programs\" class=\"anchor\" aria-label=\"Permalink: External programs\" href=\"#external-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMFannot uses some well known programs, taht need to be installed on your system::\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBlast (requires version 2.2.26 and version \u0026gt;= 2.2.27+): to install Blast see the documentation at the \u003ca href=\"http://www.ncbi.nlm.nih.gov/guide/howto/run-blast-local/\" rel=\"nofollow\"\u003eNCBI website\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 2.2.26 and 2.2.31+\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eHMMER: to install HMMER see the documentation at the \u003ca href=\"http://hmmer.org/download.html\" rel=\"nofollow\"\u003eHMMER website\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 3.2.1\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eExonerate: to install Exonerate see the documentation at the following \u003ca href=\"https://github.com/nathanweeks/exonerate\"\u003eGitHub repo\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 2.2.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMuscle: to install Muscle see the documentation \u003ca href=\"http://www.drive5.com/muscle/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version muscle-3.8.1551\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEMBOSS: to install EMBOSS see the documentation \u003ca href=\"http://emboss.sourceforge.net/download/#Stable/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 6.6.0\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eErpin: to install Erpin see the documentation \u003ca href=\"http://rna.igmors.u-psud.fr/Software/erpin.php\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003etested on version 5.5.4.serv\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003etbl2asn: to install tbl2asn see the documentation \u003ca href=\"https://www.ncbi.nlm.nih.gov/genbank/tbl2asn2/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePirObject: to install this Perl library see the documentation in this \u003ca href=\"https://github.com/prioux/PirObject\"\u003eGitHub repo\u003c/a\u003e,\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInternal programs\u003c/h4\u003e\u003ca id=\"user-content-internal-programs\" class=\"anchor\" aria-label=\"Permalink: Internal programs\" href=\"#internal-programs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFurther external programs and libraries that were developed in parallel to MFannot:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePirModels: all the PirModels needed by umac, HMMsearchWC and other programs developped at OGMP, to install the PirModels see the documentation in this \u003ca href=\"https://github.com/BFL-lab/PirModels\"\u003eGitHub repo\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFlip:to install Flip see the documentation in this \u003ca href=\"https://github.com/BFL-lab/flip\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eUmac: to install Umac see the documentation in this \u003ca href=\"https://github.com/BFL-lab/umac\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHMMSearchWC: to install HMMSearchWC see the documentation in this \u003ca href=\"https://github.com/BFL-lab/HMMSearchWC\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRNAfinder: to install RNAfinder see the documentation in this \u003ca href=\"https://github.com/BFL-lab/RNAfinder\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMf2sqn: to install Mf2sqn see the documentation in this \u003ca href=\"https://github.com/BFL-lab/mf2sqn\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003egrab-seq and grab-fasta: to install this two tools see the documentation in this \u003ca href=\"https://github.com/BFL-lab/grab-fasta\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMFannot: to install MFannot see the documentation in this \u003ca href=\"https://github.com/BFL-lab/MFannot\"\u003eGitHub repo\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eStatic Data\u003c/h4\u003e\u003ca id=\"user-content-static-data\" class=\"anchor\" aria-label=\"Permalink: Static Data\" href=\"#static-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMFannot needs some static data files to run; these contain data defining splice sites, reference protein sequences, HMM models, etc.\nThese files are all available as a single bundle in this \u003ca href=\"https://github.com/BFL-lab/MFannot_data\"\u003eGitHub repo\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn order to run MFannot you should setup the following environment variable:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEGC to point to MFannot_data/EGC.\u003c/li\u003e\n\u003cli\u003eMFANNOT_EXT_CFG_PATH to point to MFannot_data/config.\u003c/li\u003e\n\u003cli\u003eMFANNOT_MOD_PATH to point to MFannot_data/models.\u003c/li\u003e\n\u003cli\u003eERPIN_MOD_PATH to point to MFannot_data/models/Erpin_models.\u003c/li\u003e\n\u003cli\u003eMFANNOT_LIB_PATH to point to MFannot_data/protein_collections.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBLAST matrices\u003c/h4\u003e\u003ca id=\"user-content-blast-matrices\" class=\"anchor\" aria-label=\"Permalink: BLAST matrices\" href=\"#blast-matrices\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload BLAST matrices from the ncbi \u003ccode\u003eftp://ftp.ncbi.nlm.nih.gov/blast/matrices/*\u003c/code\u003e (eg: \u003ccode\u003ewget -r -np -nd -P /path/to/mfannot/blast_matrices ftp://ftp.ncbi.nlm.nih.gov/blast/matrices\u003c/code\u003e)\nand set the environment variable \u003ccode\u003eBLASTMAT\u003c/code\u003e to point to the directory where you have download the matrices.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker container\u003c/h2\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-label=\"Permalink: Docker container\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe entire software package, including data files, is available in a Docker container. You can find this docker container on \u003ca href=\"https://hub.docker.com/r/nbeck/mfannot/\" rel=\"nofollow\"\u003eDockerHub\u003c/a\u003e and use it in order to run MFannot locally if you do not want to install everything.\u003c/p\u003e\n\u003cp\u003eThe process to install MFannot on Ubuntu14 within a Docker image is documented in the [Dockerfile] present in this repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to get the help page of MFannot you need to type \u003ccode\u003emfannot -h\u003c/code\u003e in your terminal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING\u003c/a\u003e and \u003ca href=\"CONDUCT.md\"\u003eCONDUCT\u003c/a\u003e for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCredits\u003c/h2\u003e\u003ca id=\"user-content-credits\" class=\"anchor\" aria-label=\"Permalink: Credits\" href=\"#credits\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BFL-lab/mfannot/graphs/contributors\"\u003eAll Contributors\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGNU General Public License v3.0. Please see \u003ca href=\"LICENSE.md\"\u003eLicense File\u003c/a\u003e for more information.\u003c/p\u003e\n", "stargazers_count": 17, - "subscribers_count": 2, - "topics": [], - "updated_at": 1729540195.0 + "subscribers_count": 3, + "topics": [ + "genome", + "mitochondrial", + "erpin", + "hmmer", + "blast", + "muscle", + "pipeline", + "bioperl", + "docker-container", + "umac", + "tbl2asn" + ], + "updated_at": 1722444937.0 }, { "data_format": 2, - "description": null, + "description": "DataLad extension for neuroimaging research", "filenames": [ - "Singularity" + "tools/Singularity.devmaster", + "tools/Singularity" ], - "full_name": "NCBI-Hackathons/RNA_mapping", - "latest_release": "v0.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/NCBI-Hackathons/RNA_mapping/blob/master/images/RNAmappermatchup.png\"\u003e\u003cimg src=\"https://github.com/NCBI-Hackathons/RNA_mapping/raw/master/images/RNAmappermatchup.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDo you want to learn how to align RNA-sequences to a reference genome?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDo you want to bypass the process of installing alignment software and get a preview into a panel of different alignment strategies?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDo you want to compare how popular mapping algorithms perform on your data?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eThen this is the tutorial for you! \u003ca href=\"https://github.com/NCBI-Hackathons/RNA_mapping/wiki\"\u003ePlease proceed to our wiki\u003c/a\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-then-this-is-the-tutorial-for-you--please-proceed-to-our-wiki\" class=\"anchor\" aria-label=\"Permalink: Then this is the tutorial for you! Please proceed to our wiki\" href=\"#then-this-is-the-tutorial-for-you--please-proceed-to-our-wiki\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003cem\u003eJust interested in comparing two or more .bam files? Check out \u003ca href=\"https://github.com/NCBI-Hackathons/RNA_mapping/tree/master/build/bamdiff\"\u003ebamdiff\u003c/a\u003e\u003c/em\u003e\u003c/h4\u003e\u003ca id=\"user-content-just-interested-in-comparing-two-or-more-bam-files--check-out-bamdiff\" class=\"anchor\" aria-label=\"Permalink: Just interested in comparing two or more .bam files? Check out bamdiff\" href=\"#just-interested-in-comparing-two-or-more-bam-files--check-out-bamdiff\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDocker\u003c/h4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Docker image is available for this project. For a detailed description of docker, please refer to this \u003ca href=\"https://www.docker.com/what-docker\" rel=\"nofollow\"\u003eoverview.\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eInstall Docker\u003c/h5\u003e\u003ca id=\"user-content-install-docker\" class=\"anchor\" aria-label=\"Permalink: Install Docker\" href=\"#install-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow \u003ca href=\"https://www.docker.com/docker-mac\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e to install Docker for your environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eGet a pre-built image from DockerHub and run the server\u003c/h5\u003e\u003ca id=\"user-content-get-a-pre-built-image-from-dockerhub-and-run-the-server\" class=\"anchor\" aria-label=\"Permalink: Get a pre-built image from DockerHub and run the server\" href=\"#get-a-pre-built-image-from-dockerhub-and-run-the-server\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull stevetsa/rna_mapping\ndocker run -it stevetsa/rna_mapping\n\nAt the root prompt inside the container, execute the script - \n/RNA_mapping/build/doAll.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingularity\u003c/h4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun this image on the NIH biowulf cluster\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esinteractive --cpus-per-task=16 --mem=10g\nmodule load singularity\nsingularity shell docker://stevetsa/rna_mapping\n \ninside Singularity container, execute the script - \n/RNA_mapping/build/doAll.sh\n\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "datalad/datalad-neuroimaging", + "latest_release": "0.3.5", + "readme": "\u003cpre\u003e\u003ccode\u003e ____ _ _ _\n| _ \\ __ _ | |_ __ _ | | __ _ __| |\n| | | | / _` || __| / _` || | / _` | / _` |\n| |_| || (_| || |_ | (_| || |___ | (_| || (_| |\n|____/ \\__,_| \\__| \\__,_||_____| \\__,_| \\__,_|\n Neuroimaging\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ca href=\"https://ci.appveyor.com/project/mih/datalad-neuroimaging/branch/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b786a11ed4c548bdb80df5d8e56408ec512cd85ba703a321cc0be2bb805d30d9/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f646174616c61642f646174616c61642d6e6575726f696d6167696e673f6272616e63683d6d6173746572267376673d74727565\" alt=\"Travis tests status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/datalad/datalad-neuroimaging?branch=master\u0026amp;svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://codecov.io/github/datalad/datalad-neuroimaging?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/31b8a3e8dada4f28be012c0bcf21f29445d8b0ffd53ee7e11e245e53b1ef46c6/68747470733a2f2f636f6465636f762e696f2f6769746875622f646174616c61642f646174616c61642d6e6575726f696d6167696e672f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/datalad/datalad-neuroimaging/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://datalad-neuroimaging.rtfd.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/626ca7e31129e36bbe9e89422fb0242d25fca9afe39a1e3b20b522b8ed6b9301/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f646174616c61642d6e6575726f696d6167696e672f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation\" data-canonical-src=\"https://readthedocs.org/projects/datalad-neuroimaging/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://singularity-hub.org/collections/841\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6cd0120cc4c5ac11d28b2c60f76033b52db98dac641de3b2644bb054b449d60c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://GitHub.com/datalad/datalad-neuroimaging/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3b46b08339d91628c7de50a03f6a444b1917f024a808651064938dfe01663b8b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f646174616c61642f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/datalad/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://pypi.python.org/pypi/datalad-neuroimaging/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4466cdd0dfd7c35e5746d86fadfb58edabac888c4b411d77bdd6ed0d324502b4/68747470733a2f2f62616467652e667572792e696f2f70792f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://isitmaintained.com/project/datalad/datalad-neuroimaging\" title=\"Average time to resolve an issue\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/921d3c777a6aff6a3bffb115066492816d00ad523d1c9daa5abfeef4ff02e310/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f7265736f6c7574696f6e2f646174616c61642f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"Average time to resolve an issue\" data-canonical-src=\"http://isitmaintained.com/badge/resolution/datalad/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://isitmaintained.com/project/datalad/datalad-neuroimaging\" title=\"Percentage of issues still open\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ed9a33e559409cb8b5d6770412b8b1eb45bae52db2def86e2da4f11b110d697/687474703a2f2f697369746d61696e7461696e65642e636f6d2f62616467652f6f70656e2f646174616c61642f646174616c61642d6e6575726f696d6167696e672e737667\" alt=\"Percentage of issues still open\" data-canonical-src=\"http://isitmaintained.com/badge/open/datalad/datalad-neuroimaging.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis extension enhances DataLad (\u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003ehttp://datalad.org\u003c/a\u003e) for working with\nneuroimaging data and workflows. Please see the \u003ca href=\"http://datalad-neuroimaging.rtfd.org\" rel=\"nofollow\"\u003eextension\ndocumentation\u003c/a\u003e\nfor a description on additional commands and functionality.\u003c/p\u003e\n\u003cp\u003eFor general information on how to use or contribute to DataLad (and this\nextension), please see the \u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003eDataLad website\u003c/a\u003e or the\n\u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003emain GitHub project page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBefore you install this package, please make sure that you \u003ca href=\"https://git-annex.branchable.com/install\" rel=\"nofollow\"\u003einstall a recent\nversion of git-annex\u003c/a\u003e. Afterwards,\ninstall the latest version of \u003ccode\u003edatalad-neuroimaging\u003c/code\u003e from\n\u003ca href=\"https://pypi.org/project/datalad-neuroimaging\" rel=\"nofollow\"\u003ePyPi\u003c/a\u003e. It is recommended to use\na dedicated \u003ca href=\"https://virtualenv.pypa.io\" rel=\"nofollow\"\u003evirtualenv\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# create and enter a new virtual environment (optional)\nvirtualenv --system-site-packages --python=python3 ~/env/dataladni\n. ~/env/dataladni/bin/activate\n\n# install from PyPi\npip install datalad_neuroimaging\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThere is also a \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity container\u003c/a\u003e available.\nThe latest release version can be obtained by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://datalad/datalad-neuroimaging\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe documentation of this project is found here:\n\u003ca href=\"http://docs.datalad.org/projects/neuroimaging\" rel=\"nofollow\"\u003ehttp://docs.datalad.org/projects/neuroimaging\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAll bugs, concerns and enhancement requests for this software can be submitted here:\n\u003ca href=\"https://github.com/datalad/datalad-neuroimaging/issues\"\u003ehttps://github.com/datalad/datalad-neuroimaging/issues\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you have a problem or would like to ask a question about how to use DataLad,\nplease \u003ca href=\"https://neurostars.org/tags/datalad\" rel=\"nofollow\"\u003esubmit a question to\nNeuroStars.org\u003c/a\u003e with a \u003ccode\u003edatalad\u003c/code\u003e tag.\nNeuroStars.org is a platform similar to StackOverflow but dedicated to\nneuroinformatics.\u003c/p\u003e\n\u003cp\u003eAll previous DataLad questions are available here:\n\u003ca href=\"http://neurostars.org/tags/datalad/\" rel=\"nofollow\"\u003ehttp://neurostars.org/tags/datalad/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDataLad development is supported by a US-German collaboration in computational\nneuroscience (CRCNS) project \"DataGit: converging catalogues, warehouses, and\ndeployment logistics into a federated \u0027data distribution\u0027\" (Halchenko/Hanke),\nco-funded by the US National Science Foundation (NSF 1429999) and the German\nFederal Ministry of Education and Research (BMBF 01GQ1411). Additional support\nis provided by the German federal state of Saxony-Anhalt and the European\nRegional Development Fund (ERDF), Project: Center for Behavioral Brain\nSciences, Imaging Platform. This work is further facilitated by the ReproNim\nproject (NIH 1P41EB019936-01A1).\u003c/p\u003e\n", "stargazers_count": 17, - "subscribers_count": 19, - "topics": [], - "updated_at": 1685980837.0 + "subscribers_count": 12, + "topics": [ + "datalad", + "neuroimaging" + ], + "updated_at": 1730736409.0 }, { "data_format": 2, - "description": "PuntSeq - Chasing the microbial diversity of Cambridge\u0027s freshwater", + "description": "Destructive deep learning estimators and functions that are compatible with scikit-learn.", "filenames": [ - "analysis/containers/Singularity.puntseq" + "Singularity" ], - "full_name": "d-j-k/puntseq", + "full_name": "davidinouye/destructive-deep-learning", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2599d6e3e819d2a92c833ebf20dbbd24babe049e2540cc62d5aa6e34276a72d2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c616e67756167652d507974686f6e2c5f525f265f426173682d79656c6c6f772e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2599d6e3e819d2a92c833ebf20dbbd24babe049e2540cc62d5aa6e34276a72d2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c616e67756167652d507974686f6e2c5f525f265f426173682d79656c6c6f772e737667\" alt=\"Scripting\" data-canonical-src=\"https://img.shields.io/badge/Language-Python,_R_\u0026amp;_Bash-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"License\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90d0f6b7537a36ae88260b9414d1ddb607b26e7c6b8e9bf015f555e6ff0ec1ef/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f707972696768742d2863295f323032305f50756e745365712d677265656e2e737667\" alt=\"Copyright\" data-canonical-src=\"https://img.shields.io/badge/Copyright-(c)_2020_PuntSeq-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.1101/2020.02.06.936302\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/746875823522b8fa892a01b0edfccad9de0ef9ea9c411374f5c9c4c4ce5b3121/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f444f492d31302e313130312f323032302e30322e30362e3933363330322d626c75652e737667\" alt=\"DOI\" data-canonical-src=\"https://img.shields.io/badge/DOI-10.1101/2020.02.06.936302-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/d-j-k/puntseq/blob/master/figure1.png\"\u003e\u003cimg src=\"https://github.com/d-j-k/puntseq/raw/master/figure1.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFreshwater monitoring by nanopore sequencing\u003c/h1\u003e\u003ca id=\"user-content-freshwater-monitoring-by-nanopore-sequencing\" class=\"anchor\" aria-label=\"Permalink: Freshwater monitoring by nanopore sequencing\" href=\"#freshwater-monitoring-by-nanopore-sequencing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eLara Urban, Andre Holzer, J Jotautas Baronas, Michael Hall, Philipp Braeuninger-Weimer, Michael J Scherm, Daniel J Kunz, Surangi N Perera, Daniel E Martin-Herranz, Edward T Tipper, Susannah J Salter, and Maximilian R Stammnitz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClean freshwater lies at the heart of human society. While most traditional water monitoring approaches test for specific chemicals or pathogens, the direct tracing of aquatic DNA poses a more holistic alternative which has hitherto been underappreciated due to challenges in logistics and investment. Here we present a simple, fast, inexpensive and reliable freshwater diagnostics workflow centred around portable nanopore DNA sequencing. Using bacterial mock communities and spatiotemporal microbiata from an example river in Cambridge (UK), our study shows how nanopore sequencing can be readily integrated for the assessment of aquatic bacterial diversity and pollution. We provide a computational benchmark that features more than ten taxonomic classification tools to derive guidelines for bacterial DNA analyses with nanopore data. Through complementary physicochemical measurements, we find that nanopore metagenomics can depict fine temporal gradients along the main hydrological axis of an urban-rural interface, in addition to yielding high-resolution pathogen maps that address concerns of public health.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/d-j-k/puntseq/blob/master/figure2.png\"\u003e\u003cimg src=\"https://github.com/d-j-k/puntseq/raw/master/figure2.png\" alt=\"alt text\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cem\u003eFigure: Overview of the experimental design of this study\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere, we provide additional environmental data, the final classifications (using \u003ca href=\"https://github.com/lh3/minimap2\"\u003eMinimap2\u003c/a\u003e, -k = 15) of all nanopore sequencing reads from three sampling dates (April, June, and August 2018) across nine sampling locations, including rarefied datasets.\nWe additionally provide a Snakemake framework that integrates all data pre-processing steps and a Singularity that contains all necessary software. We further provide scripts for the downstream analyses (written in R and python, integrated in a markdown file) and an appropriate conda environment.\u003c/p\u003e\n\u003cp\u003eUsing this platform, the user will be able to replicate all results presented in the corresponding study \u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.02.06.936302\" rel=\"nofollow\"\u003epreprint\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDownload the raw data from our \u003ca href=\"https://www.ebi.ac.uk/ena/data/view/PRJEB34900\" rel=\"nofollow\"\u003eENA repository\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/d-j-k/puntseq/tree/master/analysis\"\u003ehere\u003c/a\u003e for the detailed description of our raw nanopore data pre-processing steps.\u003c/p\u003e\n\u003cp\u003eCheck out the \u003ca href=\"https://www.puntseq.co.uk/\" rel=\"nofollow\"\u003eproject website\u003c/a\u003e and follow us on \u003ca href=\"https://twitter.com/puntseq\" rel=\"nofollow\"\u003eTwitter\u003c/a\u003e for more updates!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use any part or modified version of the here provided code in your work, you should:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eState explicitly that you\u0027ve used the PuntSeq code (or a modified version of it, if this is the case) and follow all our code license conditions.\u003c/li\u003e\n\u003cli\u003eRead and cite the following paper:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eUrban L, Holzer A, Baronas JJ, Hall M, Braeuninger-Weimer P, Scherm MJ, Kunz DJ, Perera SN, Martin-Herranz DE, Tipper ET, Salter SJ and Stammnitz MR (2020), \u003cstrong\u003eFreshwater monitoring by nanopore sequencing\u003c/strong\u003e, bioRxiv 2020.02.06.936302; doi: \u003ca href=\"https://doi.org/10.1101/2020.02.06.936302\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/2020.02.06.936302\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 17, - "subscribers_count": 11, + "stargazers_count": 18, + "subscribers_count": 3, "topics": [], - "updated_at": 1714950384.0 + "updated_at": 1732782818.0 }, { "data_format": 2, - "description": "Code supporting the recent preprint Markello et al., 2020", + "description": " The Oceanographic Multi-purpose Software Environment: a package for multi-physics and multi-scale earth science simulations.", "filenames": [ - "container/Singularity" + "Singularity" ], - "full_name": "netneurolab/markello_ppmisnf", - "latest_release": "0.1", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-similarity-network-fusion-in-parkinsons-disease\" class=\"anchor\" aria-hidden=\"true\" href=\"#similarity-network-fusion-in-parkinsons-disease\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSimilarity network fusion in Parkinson\u0027s disease\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/245268776\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b54624e2e59ecddb7b78002cff4e39e47013c88ff0d58f1944cc28f16185036b/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3234353236383737362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/245268776.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-whats-in-this-repository\" class=\"anchor\" aria-hidden=\"true\" href=\"#whats-in-this-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"What\u0027s in this repository?\"\u003c/h2\u003e\n\u003cp\u003eThis repository contains data, code, and results for the manuscript \"\u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.03.05.979526v1\" rel=\"nofollow\"\u003eIntegrated morphometric, molecular, and clinical characterization of Parkinson\u0027s disease pathology\u003c/a\u003e.\"\nThe study examines the application of similarity network fusion to Parkinson\u0027s disease using data from the \u003ca href=\"https://www.ppmi-info.org\" rel=\"nofollow\"\u003eParkinson\u0027s Progression Markers Initiative\u003c/a\u003e (PPMI).\u003c/p\u003e\n\u003cp\u003eWe\u0027ve tried to document the various aspects of this repository with a whole bunch of README files, so feel free to jump around and check things out.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-just-let-me-run-the-things\" class=\"anchor\" aria-hidden=\"true\" href=\"#just-let-me-run-the-things\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"Just let me run the things!\"\u003c/h2\u003e\n\u003cp\u003eItching to just run the analyses?\nYou\u0027ll need to make sure you have \u003ca href=\"./walkthrough/01_accessing_data.md\"\u003eaccess to the PPMI\u003c/a\u003e and have set the appropriate environmental variables (\u003ccode\u003e$PPMI_USER\u003c/code\u003e) and (\u003ccode\u003e$PPMI_PASSWORD\u003c/code\u003e).\nOnce you\u0027ve done that, you can get going with the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recurse-submodules https://github.com/netneurolab/markello_ppmisnf\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_ppmisnf\npip install -r requirements.txt\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PYTHONPATH=\u003cspan class=\"pl-smi\"\u003e$PYTHONPATH\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e$PWD\u003c/span\u003e/code\nmake all\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you don\u0027t want to deal with the hassle of creating a new Python environment, download the Singularity image that we used to run our analyses and run things in there:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recurse-submodules https://github.com/netneurolab/markello_ppmisnf\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e markello_ppmisnf\nwget -O container/ppmi_snf.simg https://osf.io/h6jwx/download\nbash container/run.sh\nmake all\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-want-to-take-things-slow\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-want-to-take-things-slow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"I want to take things slow.\"\u003c/h2\u003e\n\u003cp\u003eIf you want a step-by-step through all the methods + analyses, take a look at out our \u003ca href=\"./walkthrough\"\u003ewalkthrough\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-i-have-some-questions\" class=\"anchor\" aria-hidden=\"true\" href=\"#i-have-some-questions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\"I have some questions...\"\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/netneurolab/markello_ppmisnf/issues\"\u003eOpen an issue\u003c/a\u003e on this repository and someone will try and get back to you as soon as possible!\u003c/p\u003e\n", - "stargazers_count": 17, - "subscribers_count": 6, - "topics": [], - "updated_at": 1681409115.0 - }, - { - "data_format": 2, - "description": "Super-fast modelling of dynamic compound flooding in Coastal Systems", - "filenames": [ - "source/Singularityfile-gpu.def", - "source/Singularityfile-cpu.def" + "full_name": "omuse-geoscience/omuse", + "latest_release": "v2021.6.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOMUSE\u003c/h1\u003e\u003ca id=\"user-content-omuse\" class=\"anchor\" aria-label=\"Permalink: OMUSE\" href=\"#omuse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOMUSE stands for Oceanographic MUltipurpose Software Environment. It is a\npackage to conduct numerical experiments in oceanography and other Earth\nsciences. Example OMUSE applications can be found in the examples\n\u003ca href=\"https://github.com/omuse-geoscience/omuse-examples\"\u003erepository\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFor whom is OMUSE?\u003c/h3\u003e\u003ca id=\"user-content-for-whom-is-omuse\" class=\"anchor\" aria-label=\"Permalink: For whom is OMUSE?\" href=\"#for-whom-is-omuse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOMUSE aims to be useable by any researcher or student with a basic knowledge of\nthe Python programming language.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat is this repository for?\u003c/h3\u003e\u003ca id=\"user-content-what-is-this-repository-for\" class=\"anchor\" aria-label=\"Permalink: What is this repository for?\" href=\"#what-is-this-repository-for\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source tree for OMUSE, including OMUSE specific framework\ncomponents and community codes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow do I get set up?\u003c/h3\u003e\u003ca id=\"user-content-how-do-i-get-set-up\" class=\"anchor\" aria-label=\"Permalink: How do I get set up?\" href=\"#how-do-i-get-set-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhile there are some packages available on \u003ca href=\"www.pypi.org\"\u003epipy\u003c/a\u003e, we recommend at the moment\nto do a pip developer install:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esetup a python environment, e.g. using virtualenv, and activate it.\u003c/li\u003e\n\u003cli\u003ein a suitable working directory clone the \u003ca href=\"https://github.com/amusecode/amuse\"\u003eAMUSE\u003c/a\u003e repository: \u003ccode\u003egit clone https://github.com/amusecode/amuse\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ego into the created directory: \u003ccode\u003ecd amuse\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003edo the developer install from here: \u003ccode\u003epip install -e . [MPI]\u003c/code\u003e The MPI is optional.\u003c/li\u003e\n\u003cli\u003eGoing back to the working directory (\u003ccode\u003ecd ..\u003c/code\u003e) also clone the OMUSE repository: \u003ccode\u003egit clone https://github.com/omuse-geoscience/omuse\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003ego into the source directory \u003ccode\u003ecd omuse\u003c/code\u003e and set the environment variable \u003ccode\u003eDOWNLOAD_CODES\u003c/code\u003e, e.g. \u003ccode\u003eexport DOWNLOAD_CODES=latest\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003enow, do \u003ccode\u003epip install -e .\u003c/code\u003e from the root of the package\u003c/li\u003e\n\u003cli\u003etype \u003ccode\u003epython setup.py build_codes --inplace\u003c/code\u003e to build the codes.\u003c/li\u003e\n\u003cli\u003ethe file \u003ccode\u003ebuild.log\u003c/code\u003e will report any errors in the build process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis installs amuse-devel and omuse-devel. The community codes of OMUSE can\nbe build manually by going into each directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esrc/omuse/community/adcirc\u003c/li\u003e\n\u003cli\u003esrc/omuse/community/swan\u003c/li\u003e\n\u003cli\u003eetc\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eand typing: first \u003ccode\u003emake download\u003c/code\u003e (for some) and then \u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOMUSE has been tested on OSX and linux machines, with ifort and gfortran\ncompilers, on desktop machines and on the Carthesius supercomputer.\u003c/p\u003e\n\u003cp\u003eIn addition to the AMUSE dependencies, OMUSE needs/ can use the following\npackages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ematplotlib basemap\u003c/li\u003e\n\u003cli\u003enetCDF and netCDF for fortran and the python bindings\u003c/li\u003e\n\u003cli\u003eGRIB_API\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation can be found \u003ca href=\"https://omuse.readthedocs.io\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. In addition the base \u003ca href=\"https://amuse.readthedocs.io\" rel=\"nofollow\"\u003eAMUSE documentation\u003c/a\u003e can be consulted.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReporting issues\u003c/h3\u003e\u003ca id=\"user-content-reporting-issues\" class=\"anchor\" aria-label=\"Permalink: Reporting issues\" href=\"#reporting-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIssues can be reported at the OMUSE issue tracker; for framework issues,\nreport them at the AMUSE \u003ca href=\"https://github.com/amusecode/amuse\"\u003erepository\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContribution guidelines\u003c/h3\u003e\u003ca id=\"user-content-contribution-guidelines\" class=\"anchor\" aria-label=\"Permalink: Contribution guidelines\" href=\"#contribution-guidelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContributions are welcome. Note that most framework development happens at\nthe AMUSE \u003ca href=\"https://github.com/amusecode/amuse\"\u003erepository\u003c/a\u003e A primer for\nwriting code interfaces and other documentation can be found on the amuse\n\u003ca href=\"www.amusecode.org\"\u003ewebsite\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 18, + "subscribers_count": 5, + "topics": [ + "oceanography", + "earth-science", + "python" ], - "full_name": "Deltares/SFINCS", - "latest_release": "v2.0.3_Cauberg_release", - "stargazers_count": 17, - "subscribers_count": 7, - "topics": [], - "updated_at": 1714924613.0 + "updated_at": 1715790947.0 }, { "data_format": 2, - "description": "SELFISH is a tool for finding differential chromatin interactions between two Hi-C contact maps.", + "description": "A comprehensive tutorial for running OpenGL apps on containers", "filenames": [ "Singularity" ], - "full_name": "ay-lab/selfish", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSelfish\u003c/h1\u003e\u003ca id=\"user-content-selfish\" class=\"anchor\" aria-label=\"Permalink: Selfish\" href=\"#selfish\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSelfish (Discovery of Differential Chromatin Interactions via a Self-Similarity Measure) is a tool by Abbas Roayaei Ardakany, Ferhat Ay (\u003ca href=\"mailto:ferhatay@lji.org\"\u003eferhatay@lji.org\u003c/a\u003e), and Stefano Lonardi. This Python implementation is currently maintained by Tuvan Gezer (\u003ca href=\"mailto:tgezer@sabanciuniv.edu\"\u003etgezer@sabanciuniv.edu\u003c/a\u003e). The original MATLAB implementation by Abbas (\u003ca href=\"mailto:roayaei@gmail.com\"\u003eroayaei@gmail.com\u003c/a\u003e) can be found at \u003ca href=\"https://github.com/ucrbioinfo/SELFISH\"\u003ehttps://github.com/ucrbioinfo/SELFISH\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSELFISH is a tool for finding differential chromatin interactions between two Hi-C contact maps. It uses self-similarity to model interactions in a robust way. For more information read the full\npaper: \u003ca href=\"https://academic.oup.com/bioinformatics/article/35/14/i145/5529135\" rel=\"nofollow\"\u003eSelfish: discovery of differential chromatin interactions via a self-similarity measure\u003c/a\u003e.\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/demo.png\"\u003e\u003cimg src=\"/demo.png\" alt=\"DCI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation and usage\u003c/h2\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-label=\"Permalink: Installation and usage\" href=\"#installation-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePIP\u003c/h3\u003e\u003ca id=\"user-content-pip\" class=\"anchor\" aria-label=\"Permalink: PIP\" href=\"#pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip3 install selfish-hic\nselfish -f1 /path/to/contact/map1.txt \\\n -f2 /path/to/contact/map2.txt \\\n\t-ch 2 \\\n -r 100kb -o ./output.npy\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGithub\u003c/h3\u003e\u003ca id=\"user-content-github\" class=\"anchor\" aria-label=\"Permalink: Github\" href=\"#github\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake sure you have Python 3 installed, along with all the dependencies listed.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/ay-lab/selfish\n./selfish/selfish/selfish.py -f1 /path/to/contact/map1.txt \\\n -f2 /path/to/contact/map2.txt \\\n\t -ch 2 \\\n -r 100kb -o ./output.npy\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eNextflow\u003c/h3\u003e\u003ca id=\"user-content-nextflow\" class=\"anchor\" aria-label=\"Permalink: Nextflow\" href=\"#nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have any problem regarding dependencies or version mismatches, we recommend using \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e with a container technology like \u003ca href=\"https://www.docker.com/get-started\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. These methods require Nextflow(Can be installed with a single command that doesn\u0027t require special permissions.), and the desired container technology to be available.\nProgram arguments are given to Nextflow with two dashes and the short format listed below.\n\u003cstrong\u003eUpdating:\u003c/strong\u003e If Nextflow warns that your project is outdated, use \u003ccode\u003enextflow pull ay-lab/selfish\u003c/code\u003e in order to update to latest version.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInstall Nextflow\u003c/h4\u003e\u003ca id=\"user-content-install-nextflow\" class=\"anchor\" aria-label=\"Permalink: Install Nextflow\" href=\"#install-nextflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNextflow works for Linux and OS X. Install it using \u003cstrong\u003eone\u003c/strong\u003e of the commands listed below. \u003cstrong\u003eRequires Java 8+\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ewget -qO- https://get.nextflow.io \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e bash\nOR\ncurl -s https://get.nextflow.io \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e bash\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWith Docker\u003c/h4\u003e\u003ca id=\"user-content-with-docker\" class=\"anchor\" aria-label=\"Permalink: With Docker\" href=\"#with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./nextflow run ay-lab/selfish --f1=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/contact/map1.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --f2=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/contact/map2.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t --ch=2 \\\n --r=100kb -profile docker\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWith Singularity\u003c/h4\u003e\u003ca id=\"user-content-with-singularity\" class=\"anchor\" aria-label=\"Permalink: With Singularity\" href=\"#with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./nextflow run ay-lab/selfish --f1=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/.../map1.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --f2=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/.../map2.txt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n\t --ch=2 \\\n --r=100kb -profile singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBioconda\u003c/h3\u003e\u003ca id=\"user-content-bioconda\" class=\"anchor\" aria-label=\"Permalink: Bioconda\" href=\"#bioconda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioconda install isn\u0027t currently available.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSelfish uses some python packages to accomplish its mission. These are the packages used by selfish:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003ematplotlib\u003c/li\u003e\n\u003cli\u003eseaborn\u003c/li\u003e\n\u003cli\u003escipy\u003c/li\u003e\n\u003cli\u003estatsmodels\u003c/li\u003e\n\u003cli\u003ehic-straw\u003c/li\u003e\n\u003cli\u003epathlib\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eShort\u003c/th\u003e\n\u003cth\u003eLong\u003c/th\u003e\n\u003cth\u003eMeaning\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eRequired Parameters\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-f1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--file1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of contact map 1. (See below for format.) Not required for HiC-Pro input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-f2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--file2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of contact map 2. (See below for format.) Not required for HiC-Pro input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-r\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--resolution\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eResolution of the provided contact maps.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-o\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--outfile\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eName of the output file.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-ch\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--chromosome\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eSpecify which chromosome to run the program for.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-m1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--matrix1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of matrix file, \u003cstrong\u003eonly\u003c/strong\u003e for HiC-Pro type input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-m2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--matrix2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of matrix file, \u003cstrong\u003eonly\u003c/strong\u003e for HiC-Pro type input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-bed1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--bed1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of bed file, \u003cstrong\u003eonly\u003c/strong\u003e for HiC-Pro type input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-bed2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--bed2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of bed file, \u003cstrong\u003eonly\u003c/strong\u003e for HiC-Pro type input.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eOptional Parameters\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-t\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--tsvout\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eIf specified, outputs will be written as a TSV file. Specify the q-value threshold for which the results will be written to the file (i.e. -t 0.05)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-d\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--distanceFilter\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eMaximum distance between interacting loci.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-c\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--changes\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eName of the output file that has the log fold changes (log2((a+1)/(b+1))) between the inputs.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-b1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--biases1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of bias/normalization vector file for contact map 1. (See below for format.)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-b2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--biases2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eLocation of bias/normalization vector file for contact map 2. (See below for format.)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-sz\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--sigmaZero\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eSigma0 parameter for Selfish. Default is experimentally chosen for 5Kb resolution.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-i\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--iterations\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eIteration count parameter for Selfish. Default is experimentally chosen for 5Kb resolution.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-v\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--verbose\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eWhether the program prints its progress while running. Default is True.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-st\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--sparsityThreshold\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eThe sparsity threshold selfish uses to filter differential interactions. Differential interactions falling in sparse regions are filtered out. The default is 0.7.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-ns\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--no-sparsityCheck\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eDo not use sparsity checking.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-nm\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--no-mutual\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eInclude also the interactions that are zero in one of the contact maps. By defalut, selfish only considers interactions that are not zero in any of the contact maps.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-norm\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--normalization\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eFor .[m]cool or .hic files, you can specify what normalization selfish should use (-norm KR). For .[m]cool files, by default, selfish assumes that bias values are stored in the \u0027weight\u0027 column when this parameter is not specified. For .hic format, the default is \u0027KR\u0027 normalization if not specified.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-p\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--plot\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eWhether the program plots its results. Highly discouraged for high resolutions(\u0026lt;50kb) as it will take a lot of time to compute the plots. For high resolutions, we recommend using the output matrix and plotting small sections of it manually. Default is False.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-lm\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--lowmem\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eUse float32 instead of float64. Uses less memory at the cost of precision. Default is False.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e-V\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e--version\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003eShows the version of the tool.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInput Formats\u003c/h3\u003e\u003ca id=\"user-content-input-formats\" class=\"anchor\" aria-label=\"Permalink: Input Formats\" href=\"#input-formats\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSELFISH supports 3 different input formats. \u003cstrong\u003ePlain text\u003c/strong\u003e, \u003cstrong\u003e.hic\u003c/strong\u003e, \u003cstrong\u003e.cool\u003c/strong\u003e, \u003cstrong\u003e.bed/.matrix\u003c/strong\u003e pairs (HiC-Pro format).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eText Contact Maps\u003c/h4\u003e\u003ca id=\"user-content-text-contact-maps\" class=\"anchor\" aria-label=\"Permalink: Text Contact Maps\" href=\"#text-contact-maps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContact maps need to have the following format. They must not have a header.\nValues must be separated by either a space, a tab, or a comma.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eChromosome\u003c/th\u003e\n\u003cth\u003eMidpoint 1\u003c/th\u003e\n\u003cth\u003eChromosome\u003c/th\u003e\n\u003cth\u003eMidpoint 2\u003c/th\u003e\n\u003cth\u003eContact Count\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e5000\u003c/td\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e65000\u003c/td\u003e\n\u003ctd\u003e438\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e5000\u003c/td\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e85000\u003c/td\u003e\n\u003ctd\u003e12\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBed-Matrix pairs (HiC-Pro format)\u003c/h4\u003e\u003ca id=\"user-content-bed-matrix-pairs-hic-pro-format\" class=\"anchor\" aria-label=\"Permalink: Bed-Matrix pairs (HiC-Pro format)\" href=\"#bed-matrix-pairs-hic-pro-format\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUser must provide a chromosome with the -ch argument.\u003c/strong\u003e\n.bed and .matrix files must have the same name other than the extension.\nEither file name can be provided as an input for selfish and the program will search for the second file automatically.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHiC file\u003c/h4\u003e\u003ca id=\"user-content-hic-file\" class=\"anchor\" aria-label=\"Permalink: HiC file\" href=\"#hic-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUser must provide a chromosome with the -ch argument.\u003c/strong\u003e\nSelfish uses juicer\u0027s \u003cem\u003estraw\u003c/em\u003e tool to read .hic files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e.cool file\u003c/h4\u003e\u003ca id=\"user-content-cool-file\" class=\"anchor\" aria-label=\"Permalink: .cool file\" href=\"#cool-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eUser must provide a chromosome with the -ch argument.\u003c/strong\u003e\nSelfish uses \u003cem\u003ecooler\u003c/em\u003e to read .cool files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eBias (normalization) File\u003c/h4\u003e\u003ca id=\"user-content-bias-normalization-file\" class=\"anchor\" aria-label=\"Permalink: Bias (normalization) File\" href=\"#bias-normalization-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBias file need to have the following format.\nBias file must use the same midpoint format as the contact maps.\nBias file must not have a header.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eChromosome\u003c/th\u003e\n\u003cth\u003eMidpoint\u003c/th\u003e\n\u003cth\u003eBias\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003echr1\u003c/td\u003e\n\u003ctd\u003e5000\u003c/td\u003e\n\u003ctd\u003e1.012\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e65000\u003c/td\u003e\n\u003ctd\u003e1.158\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOutput\u003c/h3\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOutput of Selfish is a matrix of q-values indicating the probability of differential conformation (Smaller values mean more significant.).\nX and Y coordinates indicate the bin midpoints.\u003cbr\u003e\nAnother optional output is the log fold changes file. It is simply produced by \u003ccode\u003elog2((map1 + 1) / (map2 + 1))\u003c/code\u003e\u003cbr\u003e\nFile format of the outputs is a binary numpy file. It can be read by using Numpy as follows.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enumpy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ematrix\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/path/to/output/selfish.npy\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use Selfish in your work, please cite our \u003ca href=\"https://academic.oup.com/bioinformatics/article/35/14/i145/5529135\" rel=\"nofollow\"\u003eBionformatics paper\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAbbas Roayaei Ardakany, Ferhat Ay, Stefano Lonardi, Selfish: discovery of differential chromatin interactions via a self-similarity measure, Bioinformatics, Volume 35, Issue 14, July 2019, Pages i145\u2013i153, \u003ca href=\"https://doi.org/10.1093/bioinformatics/btz362\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btz362\u003c/a\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-abbas-roayaei-ardakany-ferhat-ay-stefano-lonardi-selfish-discovery-of-differential-chromatin-interactions-via-a-self-similarity-measure-bioinformatics-volume-35-issue-14-july-2019-pages-i145i153-httpsdoiorg101093bioinformaticsbtz362\" class=\"anchor\" aria-label=\"Permalink: Abbas Roayaei Ardakany, Ferhat Ay, Stefano Lonardi, Selfish: discovery of differential chromatin interactions via a self-similarity measure, Bioinformatics, Volume 35, Issue 14, July 2019, Pages i145\u2013i153, https://doi.org/10.1093/bioinformatics/btz362\" href=\"#abbas-roayaei-ardakany-ferhat-ay-stefano-lonardi-selfish-discovery-of-differential-chromatin-interactions-via-a-self-similarity-measure-bioinformatics-volume-35-issue-14-july-2019-pages-i145i153-httpsdoiorg101093bioinformaticsbtz362\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n", - "stargazers_count": 17, - "subscribers_count": 3, + "full_name": "tashrifbillah/glxgears-containers", + "latest_release": null, + "readme": "\u003cp\u003eRunning GUI app through containers is a bit of challenge. It becomes more challenging when the app requires OpenGL support.\u003c/p\u003e\n\u003cp\u003eAfter digging a lot on the web, I seem to find a solution. I am noting my solution here for everyone.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#docker\"\u003eDocker\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#build-container\"\u003eBuild container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#push-the-container\"\u003ePush the container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pull-the-container\"\u003ePull the container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#run-the-container\"\u003eRun the container\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#linuxmac\"\u003eLinux/MAC\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#isolated-way\"\u003eIsolated way\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#hacky-way\"\u003eHacky way\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#windows\"\u003eWindows\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#docker-references\"\u003eDocker references\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#docker-known-issues\"\u003eDocker known issues\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#build-image\"\u003eBuild image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#push-the-image\"\u003ePush the image\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#linuxmac-1\"\u003eLinux/MAC\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pull-the-image\"\u003ePull the image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#run-the-image\"\u003eRun the image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#windows-1\"\u003eWindows\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#from-centos7-iso-recommended\"\u003eFrom CentOS7 ISO (recommended)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#from-standard-centos7-box\"\u003eFrom standard CentOS7 box\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pull-the-image-1\"\u003ePull the image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#run-the-image-1\"\u003eRun the image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity-references\"\u003eSingularity references\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity-known-issues\"\u003eSingularity known issues\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#useful-tips\"\u003eUseful tips\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#1-enablediable-hyper-v\"\u003e1. Enable/Disable Hyper-V\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#2-display-for-root\"\u003e2. Display for root\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3-3d-acceleration\"\u003e3. 3D acceleration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#4-x-forwarding-on-windows-host\"\u003e4. X forwarding on Windows host\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable of Contents created by \u003ca href=\"https://github.com/ekalinin/github-markdown-toc\"\u003egh-md-toc\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker\u003c/h1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning GUI apps on Docker containers is relatively easier in the sense that it doesn\u0027t require a GUI desktop. You should\nbe able to forward X11 configuration from the host and run GUI apps on the container smoothly.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild container\u003c/h2\u003e\u003ca id=\"user-content-build-container\" class=\"anchor\" aria-label=\"Permalink: Build container\" href=\"#build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeither \u003ccode\u003emesa-dri-drivers\u003c/code\u003e nor \u003ccode\u003emesa-libGL\u003c/code\u003e library could provide compatible environment for display\nin a docker container. Hence, I had to install \u003ccode\u003eNVIDIA-Linux-x86_64-430.40.run\u003c/code\u003e driver. See the full list of\nNVIDIA drivers at \u003ca href=\"\"\u003ehttps://www.nvidia.com/en-us/drivers/unix/\u003c/a\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker build -t glxgears-docker -f Dockerfile .\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePush the container\u003c/h2\u003e\u003ca id=\"user-content-push-the-container\" class=\"anchor\" aria-label=\"Permalink: Push the container\" href=\"#push-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker tag -t glxgears-docker tbillah/glxgears-docker\ndocker login\ndocker push tbillah/glxgears-docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull the container\u003c/h2\u003e\u003ca id=\"user-content-pull-the-container\" class=\"anchor\" aria-label=\"Permalink: Pull the container\" href=\"#pull-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker pull tbillah/glxgears-docker\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the container\u003c/h2\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-label=\"Permalink: Run the container\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLinux/MAC\u003c/h3\u003e\u003ca id=\"user-content-linuxmac\" class=\"anchor\" aria-label=\"Permalink: Linux/MAC\" href=\"#linuxmac\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIsolated way\u003c/h4\u003e\u003ca id=\"user-content-isolated-way\" class=\"anchor\" aria-label=\"Permalink: Isolated way\" href=\"#isolated-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY --privileged glxgears-docker glxgears\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eOr get in the container first and then run \u003ccode\u003eglxgears\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -ti -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY --privileged glxgears-docker\n(inside the docker container) glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003e--privileged\u003c/code\u003e enables \u003ccode\u003edirect rendering\u003c/code\u003e required for \u003ccode\u003eglxgears\u003c/code\u003e. Before forwarding the \u003ccode\u003eDISPLAY\u003c/code\u003e port like above,\n\u003ccode\u003exhost +\u003c/code\u003e might be necessary:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003exhost +\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eaccess control disabled, clients can connect from any host\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHacky way\u003c/h4\u003e\u003ca id=\"user-content-hacky-way\" class=\"anchor\" aria-label=\"Permalink: Hacky way\" href=\"#hacky-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI was able to run \u003ccode\u003eglxgears\u003c/code\u003e on docker container and Linux host by mounting the whole \u003ccode\u003e/usr/lib64\u003c/code\u003e directory without having to install\nany NVIDIA or mesa drivers on the container:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti -v /tmp/.X11-unix:/tmp/.X11-unix -v /usr/lib64:/usr/hostLib64 -e DISPLAY=$DISPLAY\n-e LD_LIBRARY_PATH=/usr/hostLib64 --privileged glxgears-docker glxgears\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWindows\u003c/h3\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall X server \u003ca href=\"\"\u003ehttps://sourceforge.net/projects/vcxsrv/\u003c/a\u003e and have it running in the background.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou must uncheck the \u003ccode\u003eNative opengl\u003c/code\u003e option that exports \u003ccode\u003eLIBGL_ALWAYS_INDIRECT\u003c/code\u003e variable. Checking\n\u003ccode\u003eDisable access control\u003c/code\u003e might be necessary. See the image below:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/LIBGL_ALWAYS_INDIRECT.png\"\u003e\u003cimg src=\"screenshots/LIBGL_ALWAYS_INDIRECT.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFinally, save the configuration file:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/Save_configuration.PNG\"\u003e\u003cimg src=\"screenshots/Save_configuration.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eLaunch Docker Desktop. See a few important settings below:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/Resources.PNG\"\u003e\u003cimg src=\"screenshots/Resources.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/ShareDrive.PNG\"\u003e\u003cimg src=\"screenshots/ShareDrive.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eLaunch Windows PowerShell. I DIDN\u0027T require to launch PowerShell with \u003ccode\u003eRun as administrator\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain your IP address:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eipconfig\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eWireless LAN adapter Wi-Fi:\n\n Connection-specific DNS Suffix . : partners.org\n Link-local IPv6 Address . . . . . : fe80::111e:3245:4393:ed21%24\n IPv4 Address. . . . . . . . . . . : 10.22.138.136\n Subnet Mask . . . . . . . . . . . : 255.255.248.0\n Default Gateway . . . . . . . . . : 10.22.136.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eIPv4\u003c/code\u003e should be your IP address. The above command should print a bunch of IPv4 addresses. Any of them can be used.\nMoreover, you won\u0027t need an active internet connection to enable display in a docker container.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eSet up DISPLAY variable in the PowerShell:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSet-Variable -name DISPLAY -Value 10.22.138.136:0.0\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eFinally:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti -e DISPLAY=$DISPLAY --privileged glxgears-docker glxgears\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eOr get in the container first and then run \u003ccode\u003eglxgears\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -ti -e DISPLAY=$DISPLAY --privileged glxgears-docker\n(inside the docker container) glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker references\u003c/h2\u003e\u003ca id=\"user-content-docker-references\" class=\"anchor\" aria-label=\"Permalink: Docker references\" href=\"#docker-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://gernotklingler.com/blog/docker-replaced-virtual-machines-chroots/\" rel=\"nofollow\"\u003ehttp://gernotklingler.com/blog/docker-replaced-virtual-machines-chroots/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://wiki.ros.org/docker/Tutorials/GUI\" rel=\"nofollow\"\u003ehttp://wiki.ros.org/docker/Tutorials/GUI\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://dev.to/darksmile92/run-gui-app-in-linux-docker-container-on-windows-host-4kde\" rel=\"nofollow\"\u003ehttps://dev.to/darksmile92/run-gui-app-in-linux-docker-container-on-windows-host-4kde\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker known issues\u003c/h2\u003e\u003ca id=\"user-content-docker-known-issues\" class=\"anchor\" aria-label=\"Permalink: Docker known issues\" href=\"#docker-known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/NVIDIA/nvidia-docker/issues/586\"\u003ehttps://github.com/NVIDIA/nvidia-docker/issues/586\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/NVIDIA/nvidia-docker/issues/136\"\u003ehttps://github.com/NVIDIA/nvidia-docker/issues/136\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpon closing \u003ccode\u003efsleyes\u003c/code\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-viml\"\u003e\u003cpre\u003e (fsleye\u003cspan class=\"pl-smi\"\u003e\u003cspan class=\"pl-k\"\u003es:\u003c/span\u003e203\u003c/span\u003e): Gdk\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eERROR\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e01\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e14.549\u003c/span\u003e: The program \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003efsleyes\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e received an X Window System \u003cspan class=\"pl-c1\"\u003eerror\u003c/span\u003e.\n This probably reflects \u003cspan class=\"pl-c1\"\u003ea\u003c/span\u003e bug \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the program.\n The \u003cspan class=\"pl-c1\"\u003eerror\u003c/span\u003e was \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eGLXBadCurrentWindow\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e.\n (Detail\u003cspan class=\"pl-smi\"\u003e\u003cspan class=\"pl-k\"\u003es:\u003c/span\u003e\u003c/span\u003e serial \u003cspan class=\"pl-c1\"\u003e95219\u003c/span\u003e error_code \u003cspan class=\"pl-c1\"\u003e159\u003c/span\u003e request_code \u003cspan class=\"pl-c1\"\u003e149\u003c/span\u003e minor_code \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e)\n (Note \u003cspan class=\"pl-c1\"\u003eto\u003c/span\u003e programmer\u003cspan class=\"pl-smi\"\u003e\u003cspan class=\"pl-k\"\u003es:\u003c/span\u003e\u003c/span\u003e normally, X errors are reported asynchronously;\n that \u003cspan class=\"pl-k\"\u003eis\u003c/span\u003e, you will receive the \u003cspan class=\"pl-c1\"\u003eerror\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ewhile\u003c/span\u003e after causing it.\n To \u003cspan class=\"pl-c1\"\u003edebug\u003c/span\u003e your program, run it with the \u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003esync\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecommand\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eline\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eoption\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eto\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003echange\u003c/span\u003e this behavior. You can then \u003cspan class=\"pl-c1\"\u003eget\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ea\u003c/span\u003e meaningful\n backtrace from your debugger \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e you \u003cspan class=\"pl-c1\"\u003ebreak\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eon\u003c/span\u003e the \u003cspan class=\"pl-en\"\u003egdk_x_error\u003c/span\u003e() \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e.)\n \u003cspan class=\"pl-sr\"\u003e/root/\u003c/span\u003efsl\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5.0\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e11\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003ecentos7\u003cspan class=\"pl-sr\"\u003e/bin/\u003c/span\u003efsleye\u003cspan class=\"pl-smi\"\u003e\u003cspan class=\"pl-k\"\u003es:\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eline\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e202\u003c/span\u003e Trace\u003cspan class=\"pl-sr\"\u003e/breakpoint trap ${FSLDIR}/\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003ebin\u003c/span\u003e\u003cspan class=\"pl-sr\"\u003e/FSLeyes/\u003c/span\u003efsleyes \u003cspan class=\"pl-smi\"\u003e$@\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis issue didn\u0027t affect anything I know of, so I labeled this as \u003cem\u003eWon\u0027t Fix\u003c/em\u003e.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eSince there is no way to reboot a docker container, NVIDIA driver won\u0027t load properly. Rather, it would only\nprovide libraries required to run \u003ccode\u003eglxgears\u003c/code\u003e or \u003ccode\u003efsleyes\u003c/code\u003e. Hence, commands like below run into errors:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003envidia-smi\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNVIDIA-SMI has failed because it couldn\u0027t communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eHowever, I was able to eliminate this error by installing a version of NVIDIA driver on docker container that matches\nwith my host machine. Again, See the full list of NVIDIA drivers at \u003ca href=\"\"\u003ehttps://www.nvidia.com/en-us/drivers/unix/\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity containers i.e. \"sylabs/singularity-3.3-centos-7-64\" don\u0027t come with GUI enabled. You may forward X11 from\nwindows host by means of an X server. This approach worked fine for lightweight apps like \u003ccode\u003exclock\u003c/code\u003e and \u003ccode\u003exeyes\u003c/code\u003e but\nwasn\u0027t able to run OpenGL hungry apps like glxgears. So, the most manageable way of running OpenGL apps on\nSingularity containers should be through a GUI desktop.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild image\u003c/h2\u003e\u003ca id=\"user-content-build-image\" class=\"anchor\" aria-label=\"Permalink: Build image\" href=\"#build-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity build xclock-glxgears Singularity\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePush the image\u003c/h2\u003e\u003ca id=\"user-content-push-the-image\" class=\"anchor\" aria-label=\"Permalink: Push the image\" href=\"#push-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate new access token, give it a nice name i.e. tuna-salad\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca href=\"\"\u003ehttps://cloud.sylabs.io/auth\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the access token into your clipboard. Save the access token to \u003ccode\u003e~/.singularity/sylabs-token\u003c/code\u003e file for future use.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLogin to Singularity cloud:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity remote login SylabsCloud\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWhen asked, just paste the access token. After login is complete, you should be able to push your images to Singularity Cloud:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity push tuna-image library://tbillah/collection/tuna-image:0.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eBefore pushing an image, you will be asked to sign and verify it. Follow the link below to be able to do that:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/signNverify.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/signNverify.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBut, you can push an unsigned image with \u0027-U\u0027 flag:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity push -U ~/Documents/tuna-image library://tbillah/collection/tuna-image:0.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLinux/MAC\u003c/h3\u003e\u003ca id=\"user-content-linuxmac-1\" class=\"anchor\" aria-label=\"Permalink: Linux/MAC\" href=\"#linuxmac-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning OpenGL apps on singularity container for a Linux/MAC host is easier than running on docker container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePull the image\u003c/h4\u003e\u003ca id=\"user-content-pull-the-image\" class=\"anchor\" aria-label=\"Permalink: Pull the image\" href=\"#pull-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull xlock-glxgears library://tbillah/collection/xclock-glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the image\u003c/h4\u003e\u003ca id=\"user-content-run-the-image\" class=\"anchor\" aria-label=\"Permalink: Run the image\" href=\"#run-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --writable-tmpfs xclock-glxgears\n(inside the singularity shell) glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have NVIDIA driver in your host machine, you can also use \u003ccode\u003e--nv\u003c/code\u003e flag to export host NVIDIA libraries.\nIn this case, you wouldn\u0027t need another \u003ccode\u003emesa-dri-drivers\u003c/code\u003e when you build the container.\u003c/p\u003e\n\u003cp\u003eNot using \u003ccode\u003e--nv\u003c/code\u003e flag may result in \u003ca href=\"#singularity-known-issues\"\u003eSingularity known issues #2\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWindows\u003c/h3\u003e\u003ca id=\"user-content-windows-1\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload Oracle VirtualBox from \u003ca href=\"https://www.virtualbox.org/wiki/Downloads\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFrom CentOS7 ISO (recommended)\u003c/h4\u003e\u003ca id=\"user-content-from-centos7-iso-recommended\" class=\"anchor\" aria-label=\"Permalink: From CentOS7 ISO (recommended)\" href=\"#from-centos7-iso-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownload a complete CentOS7 image from your \u003ca href=\"http://isoredirect.centos.org/centos/7/isos/x86_64/\" rel=\"nofollow\"\u003epreferred mirror\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOpen \u003cem\u003eOracle VM VirtualBox Manager\u003c/em\u003e and create a new \u003cem\u003eVirtual Machine\u003c/em\u003e.\nAllocate memory and other resources as you see like. You can choose dynamic allocation.\nSee important steps below (there should be other steps in between):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/OracleNewVM.png\"\u003e\u003cimg src=\"screenshots/OracleNewVM.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/create-vhd.PNG\"\u003e\u003cimg src=\"screenshots/create-vhd.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/type-vdi.PNG\"\u003e\u003cimg src=\"screenshots/type-vdi.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eStart the virtual machine, provide the ISO you downloaded.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/point-to-centos7-iso.PNG\"\u003e\u003cimg src=\"screenshots/point-to-centos7-iso.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eCentOS7 installation requires little user intervention. You need to \u003cem\u003eSelect the device\u003c/em\u003e,\nswitch \u003cem\u003eON\u003c/em\u003e internet connection, and create \u003cem\u003eROOT PASSWORD\u003c/em\u003e. The rest of the settings could be left as\nthey are. See important steps below (there should be other steps in between):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/installation-disk.PNG\"\u003e\u003cimg src=\"screenshots/installation-disk.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/ethernet.PNG\"\u003e\u003cimg src=\"screenshots/ethernet.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content--1\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/select-root-pswd.PNG\"\u003e\u003cimg src=\"screenshots/select-root-pswd.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eAfter \u003cem\u003einitramfs\u003c/em\u003e creation is complete, the machine should reboot itself. The Virtual Machine should\nautomatically eject the ISO you provided. However, if it does not and upon reboot it goes back to\nCentOS installation prompt, eject the ISO manually:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/remove-iso.png\"\u003e\u003cimg src=\"screenshots/remove-iso.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNow \u003cem\u003eStart\u003c/em\u003e your virtual machine again.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003e\n\u003cp\u003eAfter successful installation of CentOS7, open a terminal on GUI desktop, switch to root,\nand install the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo su -\n yum -y install epel-release\n yum -y install singularity\n exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFrom standard CentOS7 box\u003c/h4\u003e\u003ca id=\"user-content-from-standard-centos7-box\" class=\"anchor\" aria-label=\"Permalink: From standard CentOS7 box\" href=\"#from-standard-centos7-box\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section is put here for learning purpose:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Vagrant for Windows \u003ca href=\"\"\u003ehttps://www.vagrantup.com/downloads.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Vagrant manager for Windows \u003ca href=\"\"\u003ehttp://vagrantmanager.com/downloads/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLaunch \u003cem\u003eWindows PowerShell\u003c/em\u003e and do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e mkdir gui-container\n New-Item Vagrantfile\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eSave the following configuration in the \u003cem\u003eVagrantfile\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eVagrant.configure(\"2\") do |config|\n \n config.vm.box = \"centos/7\"\n config.vm.provider \"virtualbox\" do |v|\n v.gui = true\n v.memory = 2048\n v.cpus = 2\n end\n\nend \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eKeep \u003cem\u003eOracle VM VirtualBox Manager\u003c/em\u003e running in the background and start the virtual machine defined by\n\u003cem\u003eVagrantfile\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e vagrant up\n vagrant ssh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOnce in the Virtual Machine, open a terminal, switch to root, and install the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo su -\n\nyum -y groupinstall \u0027gnome desktop\u0027\nyum -y install \u0027xorg*\u0027\nyum -y install epel-release\nyum -y install singularity\n\nyum remove -y initial-setup initial-setup-gui\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow switch to GUI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esystemctl isolate graphical.target\nsystemctl set-default graphical.target\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUpon doing the above, you should see GUI desktop in your virtual box window.\nIt may be useful to reboot at this point (from the host terminal):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant halt\nvagrant up\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePull the image\u003c/h4\u003e\u003ca id=\"user-content-pull-the-image-1\" class=\"anchor\" aria-label=\"Permalink: Pull the image\" href=\"#pull-the-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinally, open a terminal on GUI desktop and pull the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull xclock-glxgears library://tbillah/collection/xclock-glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the image\u003c/h4\u003e\u003ca id=\"user-content-run-the-image-1\" class=\"anchor\" aria-label=\"Permalink: Run the image\" href=\"#run-the-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --writable-tmpfs xclock-glxgears\n(inside the singularity shell) glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity references\u003c/h2\u003e\u003ca id=\"user-content-singularity-references\" class=\"anchor\" aria-label=\"Permalink: Singularity references\" href=\"#singularity-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://codingbee.net/vagrant/vagrant-enabling-a-centos-vms-gui-mode\" rel=\"nofollow\"\u003ehttps://codingbee.net/vagrant/vagrant-enabling-a-centos-vms-gui-mode\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity known issues\u003c/h2\u003e\u003ca id=\"user-content-singularity-known-issues\" class=\"anchor\" aria-label=\"Permalink: Singularity known issues\" href=\"#singularity-known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sylabs/singularity/issues/4290\"\u003ehttps://github.com/sylabs/singularity/issues/4290\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpon running \u003ccode\u003exclock-glxgears\u003c/code\u003e, you may get the following error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e libGL: screen 0 does not appear to be DRI2 capable\n libGL: OpenDriver: trying /usr/lib64/dri/tls/swrast_dri.so\n libGL: OpenDriver: trying /usr/lib64/dri/swrast_dri.so\n libGL: Can\u0027t open configuration file /root/.drirc: No such file or directory\n libGL: Can\u0027t open configuration file /root/.drirc: No such file or directory\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf you have matching libraries across host and image accompanied by X configuration, this error might go away.\nOne solution is to have NVIDIA driver on the host and use \u003ccode\u003e--nv\u003c/code\u003e flag while running singularity image.\nHowever, I labeled this issue as \u003cem\u003eWon\u0027t Fix\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUseful tips\u003c/h1\u003e\u003ca id=\"user-content-useful-tips\" class=\"anchor\" aria-label=\"Permalink: Useful tips\" href=\"#useful-tips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Enable/Disable Hyper-V\u003c/h2\u003e\u003ca id=\"user-content-1-enabledisable-hyper-v\" class=\"anchor\" aria-label=\"Permalink: 1. Enable/Disable Hyper-V\" href=\"#1-enabledisable-hyper-v\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eDocker Desktop\u003c/em\u003e requires Hyper-V enabled while \u003cem\u003eOracle/Vagrant VirtualBox\u003c/em\u003e requires it disabled.\nFollow Microsoft documentation to do the required:\n\u003ca href=\"\"\u003ehttps://support.microsoft.com/en-us/help/3204980/virtualization-applications-do-not-work-together-with-hyper-v-device-g\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHowever, enabling/disabling Hyper-V required me to restart my machine twice instead of once asked.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Display for root\u003c/h2\u003e\u003ca id=\"user-content-2-display-for-root\" class=\"anchor\" aria-label=\"Permalink: 2. Display for root\" href=\"#2-display-for-root\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou usually log into Singularity container as \u003ccode\u003evagrant\u003c/code\u003e. You might need to switch to root\nfor running certain GUI applications. See the instruction below to set up display for root\nuser:\u003c/p\u003e\n\u003cp\u003e(i) Obtain your display parameters\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eecho $DISPLAY\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eYOUR-DISPLAY-SOCKET\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003exuath list\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eYOUR-DISPLAY-COOKIE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(ii) Export display parameters to root\u003c/p\u003e\n\u003cp\u003eNow switch to root user:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esudo su -\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eAdd the display parameters for root user:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport DISPLAY=${YOUR-DISPLAY-SOCKET}\nxauth add ${YOUR-DISPLAY-COOKIE}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTest display for root user now:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003exeyes\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eglxgears\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iii) But, if you need to use GUI with \u003cem\u003eroot\u003c/em\u003e user, it may be useful to log out and log in as \u003cem\u003eroot\u003c/em\u003e directly.\nUse cases include running containers with root privileges.\u003c/p\u003e\n\u003cp\u003e(iv) If needed, you may follow this suggestion to create \u003cem\u003e.XAuthority\u003c/em\u003e files:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"\"\u003ehttps://superuser.com/questions/806637/xauth-not-creating-xauthority-file\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. 3D acceleration\u003c/h2\u003e\u003ca id=\"user-content-3-3d-acceleration\" class=\"anchor\" aria-label=\"Permalink: 3. 3D acceleration\" href=\"#3-3d-acceleration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning fsleyes required me to disable 3D acceleration from \u003cem\u003eVirtualBox Manager Settings\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/disable-3d-accel.PNG\"\u003e\u003cimg src=\"screenshots/disable-3d-accel.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. X forwarding on Windows host\u003c/h2\u003e\u003ca id=\"user-content-4-x-forwarding-on-windows-host\" class=\"anchor\" aria-label=\"Permalink: 4. X forwarding on Windows host\" href=\"#4-x-forwarding-on-windows-host\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to install \u003ca href=\"https://gitforwindows.org/\" rel=\"nofollow\"\u003eGit Bash\u003c/a\u003e first. The following should be included to \u003cem\u003eVagrantfile\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econfig.ssh.forward_x11 = true\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDetails can be found below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"\"\u003ehttps://stackoverflow.com/questions/40056227/warning-no-xauth-data-using-fake-authentication-data-for-x11-forwarding\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 18, + "subscribers_count": 4, "topics": [], - "updated_at": 1685812501.0 + "updated_at": 1712559721.0 }, { "data_format": 2, - "description": "User-friendly exact diagonalization package written in Haskell. Can treat systems of up to \ud835\udcaa(42) spins! ", + "description": "DRL-VO navigation policy for BARN Challenge", "filenames": [ - "Singularity" + "Singularityfile_melodic.def", + "Singularityfile.def" ], - "full_name": "twesterhout/spin-ed", + "full_name": "TempleRAIL/nav-competition-icra2022-drl-vo", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSpinED\u003c/h1\u003e\u003ca id=\"user-content-spined\" class=\"anchor\" aria-label=\"Permalink: SpinED\" href=\"#spined\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/twesterhout/spin-ed/actions\"\u003e\u003cimg src=\"https://github.com/twesterhout/spin-ed/workflows/CI/badge.svg\" alt=\"GitHub CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/twesterhout/spin-ed/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ed6fdcd3ea81f453cfe5533a0ab95517babd43a2eab4a8a62302098463c3ebc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74776573746572686f75742f7370696e2d65643f696e636c7564655f70726572656c6561736573\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/v/release/twesterhout/spin-ed?include_prereleases\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5116bcc5e23725d16fc3dedb92367e72b3d6316596c5bca21f4df29d23b17971/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4253442d2d332d2d436c617573652d626c75652e737667\" alt=\"BSD-3-Clause license\" data-canonical-src=\"https://img.shields.io/badge/license-BSD--3--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUser-friendly exact diagonalization package for quantum many-body systems.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udd27 Installing\u003c/h2\u003e\u003ca id=\"user-content-wrench-installing\" class=\"anchor\" aria-label=\"Permalink: :wrench: Installing\" href=\"#wrench-installing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide pre-built static executables for Linux. Go to\n\u003ca href=\"https://github.com/twesterhout/spin-ed/releases\"\u003eReleases\u003c/a\u003e page and download the\nexecutable to your location of choice. That\u0027s it! \ud83e\udd73\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u2139\ufe0f \u003cstrong\u003eNote:\u003c/strong\u003e executables are currently tagged by git\ncommits from which they were built. It is suggested that after downloading\nthe application you create a symbolic link to it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eln --symbolic SpinED SpinED-8b0138b \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the commit hash may differ in your case\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udcdd Usage\u003c/h2\u003e\u003ca id=\"user-content--usage\" class=\"anchor\" aria-label=\"Permalink: \ud83d\udcdd Usage\" href=\"#-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing \u003ccode\u003eSpinED\u003c/code\u003e is quite simple. Just feed it your input \u003ca href=\"https://en.wikipedia.org/wiki/YAML\" rel=\"nofollow\"\u003eyaml\nfile\u003c/a\u003e. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./SpinED my_system.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003emy_system.yaml\u003c/code\u003e looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ebasis\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003enumber_spins\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esymmetries\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[]\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ehamiltonian\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHeisenberg Hamiltonian\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eterms\u003c/span\u003e:\n - \u003cspan class=\"pl-ent\"\u003ematrix\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[[1, 0, 0, 0],\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e[0, -1, 2, 0],\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e[0, 2, -1, 0],\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e[0, 0, 0, 1]]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esites\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[[0, 1], [1, 2], [2, 3], [3, 0]]\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eobservables\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[]\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a file \u003ccode\u003eexact_diagonalization_result.h5\u003c/code\u003e which will contain\nthe ground state of the Hamiltonian.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003eh5dump -H exact_diagonalization_result.h5\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHDF5 \"exact_diagonalization_result.h5\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGROUP \"/\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e GROUP \"basis\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASET \"representatives\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_STD_U64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 16 ) / ( 16 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e GROUP \"hamiltonian\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASET \"eigenvalues\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_IEEE_F64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 1 ) / ( 1 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASET \"eigenvectors\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_IEEE_F64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 16, 1 ) / ( 16, 1 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASET \"residuals\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_IEEE_F64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 1 ) / ( 1 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd we can check that it computed the correct energy:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003eh5dump -d /hamiltonian/eigenvalues exact_diagonalization_result.h5\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHDF5 \"exact_diagonalization_result.h5\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eDATASET \"/hamiltonian/eigenvalues\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_IEEE_F64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 1 ) / ( 1 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATA {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e (0): -8\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis was a very simple example! Have a look at \u003ca href=\"./template.yaml\"\u003e\u003ccode\u003etemplate.yaml\u003c/code\u003e\u003c/a\u003e\nwhich describes all supported fields. \u003ca href=\"./example/\"\u003e\u003ccode\u003eexample/\u003c/code\u003e\u003c/a\u003e folder also\ncontains various usage examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing and support\u003c/h2\u003e\u003ca id=\"user-content-contributing-and-support\" class=\"anchor\" aria-label=\"Permalink: Contributing and support\" href=\"#contributing-and-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use this package for your research and have questions or suggestions,\nplease, don\u0027t hesitate to contact me on Github or\n\u003ca href=\"https://www.ru.nl/tcm/about-us/phd-students/westerhout/\" rel=\"nofollow\"\u003eemail\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlso, if the fact that most code here is written in Haskell doesn\u0027t scare you,\nfeel free to create a pull request implementing new features or fixing bugs!\u003c/p\u003e\n", + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/BARN_Challenge.png\"\u003e\u003cimg width=\"100%\" src=\"res/BARN_Challenge.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDRL-VO control policy for ICRA 2022 BARN Challenge\u003c/h1\u003e\u003ca id=\"user-content-drl-vo-control-policy-for-icra-2022-barn-challenge\" class=\"anchor\" aria-label=\"Permalink: DRL-VO control policy for ICRA 2022 BARN Challenge\" href=\"#drl-vo-control-policy-for-icra-2022-barn-challenge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOur DRL-VO control policy ranked 1st in the simulated competition and 3rd in the final physical competition of the ICRA 2022 BARN Challenge.\nImplementation details can be found in our paper \u003ca href=\"https://doi.org/10.1109/TRO.2023.3257549\" rel=\"nofollow\"\u003e\"DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles\"\u003c/a\u003e(\u003ca href=\"https://arxiv.org/pdf/2301.06512.pdf\" rel=\"nofollow\"\u003earXiv\u003c/a\u003e) in IEEE Transactions on Robotics (T-RO) 2023.\nVideo demos can be found at \u003ca href=\"https://www.youtube.com/watch?v=KneELRT8GzU\u0026amp;list=PLouWbAcP4zIvPgaARrV223lf2eiSR-eSS\u0026amp;index=2\u0026amp;ab_channel=PhilipDames\" rel=\"nofollow\"\u003emultimedia demonstrations\u003c/a\u003e. The original training and implementation code can be found in our \u003ca href=\"https://github.com/TempleRAIL/drl_vo_nav.git\"\u003edrl_vo_nav\u003c/a\u003e repository.\u003c/p\u003e\n\u003cp\u003eThe details of the BARN Challenge can be found in our paper \u003ca href=\"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9975161\" rel=\"nofollow\"\u003e\"Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 [Competitions]\"\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enavigation metric: 0.2339\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUbuntu 20.04/18.04\u003c/li\u003e\n\u003cli\u003eROS-Noetic/ROS Melodic\u003c/li\u003e\n\u003cli\u003ePython 3.7\u003c/li\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage:\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, download the pre-created \u003ca href=\"https://doi.org/10.5281/zenodo.7968623\" rel=\"nofollow\"\u003e\"nav_competition_image.sif\"\u003c/a\u003e container to the home directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSimulation:\u003c/h3\u003e\u003ca id=\"user-content-simulation\" class=\"anchor\" aria-label=\"Permalink: Simulation:\" href=\"#simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# clone this project:\ngit clone -b master https://github.com/TempleRAIL/nav-competition-icra2022-drl-vo.git\ncd nav-competition-icra2022-drl-vo\n\n# move nav_competition_image.sif container to current directory:\nmv ~/nav_competition_image.sif ./\n\n# single world test:\n./singularity_run.sh ./nav_competition_image.sif python run.py --out ~/drl_vo_out.txt\n\n# 50 worlds test: 1 trial\n./singularity_run.sh ./nav_competition_image.sif python run_drl_vo.py --out ~/drl_vo_out.txt --trials 1\n\n# 50 worlds test: 10 trial\n./singularity_run.sh ./nav_competition_image.sif python run_drl_vo.py --out ~/drl_vo_out.txt --trials 10\n\n# report results:\n./singularity_run.sh ./nav_competition_image.sif python report_test.py --out_path ~/drl_vo_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware:\u003c/h3\u003e\u003ca id=\"user-content-hardware\" class=\"anchor\" aria-label=\"Permalink: Hardware:\" href=\"#hardware\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# enter the directory of nav_competition_image.sif container and run the container: home directory\ncd ~\nsingularity shell --nv nav_competition_image.sif\nsource /etc/.bashrc\n\n# set the appropriate goal point and run the DRL-VO policy: the robot\u0027s initial local coordinate system when the robot is powered on (right hand rule)\nroslaunch jackal_helper move_base_drl_vo.launch goal_x:=\"20\" goal_y:=\"15\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eModify code in hardware:\u003c/h3\u003e\u003ca id=\"user-content-modify-code-in-hardware\" class=\"anchor\" aria-label=\"Permalink: Modify code in hardware:\" href=\"#modify-code-in-hardware\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# enter the directory of nav_competition_image.sif container and run the container:\ncd ~\nsingularity shell --nv nav_competition_image.sif\nsource /etc/.bashrc\n\n# create ros workspace and clone this project:\nmkdir -p jackal_ws/src\ncd jackal_ws/src\ngit clone -b master https://github.com/TempleRAIL/nav-competition-icra2022-drl-vo.git\n\n# modify the corresponding code as needed\n\n# compile:\ncd ..\ncatkin_make\nsource devel/setup.sh\n\n# set the appropriate goal point and run the DRL-VO policy: the robot\u0027s initial local coordinate system when the robot is powered on (right hand rule)\nroslaunch jackal_helper move_base_drl_vo.launch goal_x:=\"20\" goal_y:=\"15\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@article{xie2023drl,\n author={Xie, Zhanteng and Dames, Philip},\n journal={IEEE Transactions on Robotics}, \n title={DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles}, \n year={2023},\n volume={39},\n number={4},\n pages={2700-2719},\n doi={10.1109/TRO.2023.3257549}\n}\n\n@article{xiao2022autonomous,\n title={Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 [Competitions]},\n author={Xiao, Xuesu and Xu, Zifan and Wang, Zizhao and Song, Yunlong and Warnell, Garrett and Stone, Peter and Zhang, Tingnan and Ravi, Shravan and Wang, Gary and Karnan, Haresh and others},\n journal={IEEE Robotics \\\u0026amp; Automation Magazine},\n volume={29},\n number={4},\n pages={148--156},\n year={2022},\n publisher={IEEE}\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 18, - "subscribers_count": 4, + "subscribers_count": 2, "topics": [ - "exact-diagonalization", - "quantum", - "many-body", - "high-performance", - "haskell", - "numerical-methods" + "collision-avoidance", + "gazebo-simulator", + "robot-navigation" ], - "updated_at": 1730550333.0 + "updated_at": 1712803686.0 }, { "data_format": 2, - "description": null, + "description": "Simulation tools for LiteBIRD", "filenames": [ - "basebuilds/Singularity.jupyterbase", - "basebuilds/Singularity.juliabase" + "singularity/Singularity.m4" ], - "full_name": "Crown421/Singularity.jl", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package was presented at JuliaCon, and the presentation is available \u003ca href=\"https://musing-pare-2ba365.netlify.app/#/\" rel=\"nofollow\"\u003eunder this link\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis package provides a rough interface to create \u003ca href=\"https://github.com/sylabs/singularity\"\u003eSingularity containers\u003c/a\u003e from DrWatson Projects.\nIt currently works best on Linux systems, as the build command currently not available on Mac.\u003c/p\u003e\n\u003cp\u003eMost of the code is still very WIP and based on my own processes and needs. If you use Singularity, or have a use case that I haven\u0027t considered yet, please reach out either by email or by opening an issue.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall singularity\u003c/h2\u003e\u003ca id=\"user-content-install-singularity\" class=\"anchor\" aria-label=\"Permalink: Install singularity\" href=\"#install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eSylab documentation\u003c/a\u003e contains instructions to install Singularity, but appears to be slightly out of date.\nYou can find additional information for\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinux on this \u003ca href=\"https://github.com/hpcng/singularity/blob/master/INSTALL.md\"\u003eGithub readme\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMac on the \u003ca href=\"https://sylabs.io/singularity-desktop-macos/\" rel=\"nofollow\"\u003edownload page for the beta release\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eWindows, see this \u003ca href=\"https://github.com/hpcng/singularity/issues/4518\"\u003ethis issue\u003c/a\u003e stating that WSL 2 is required (\u003ca href=\"https://docs.microsoft.com/en-us/windows/wsl/install-win10\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e). After installing it, follow the instructions for Linux.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlternatively, you can use the \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html#install-on-windows-or-mac\" rel=\"nofollow\"\u003einstructions for Vagrant\u003c/a\u003e on Windows and Mac.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBasis\u003c/h3\u003e\u003ca id=\"user-content-basis\" class=\"anchor\" aria-label=\"Permalink: Basis\" href=\"#basis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package uses a minimal debian-based container with Julia installed as a base. On the \u003ca href=\"https://cloud.sylabs.io/home\" rel=\"nofollow\"\u003eSylab cloud\u003c/a\u003e you can find the Juliabase image, and an experimental container also including jupyter\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/_container/5e418a1b2758e9ed1175de24\" rel=\"nofollow\"\u003ejuliabase\u003c/a\u003e: 1.4.2, 1.3.1\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/_container/5f20adbeae86dd3232dec1d1\" rel=\"nofollow\"\u003ejupyterbase\u003c/a\u003e: 1.4.2\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you would prefer to build them yourself, the def files are available in the \u003ccode\u003ebasebuilds\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAssumptions:\u003c/h3\u003e\u003ca id=\"user-content-assumptions\" class=\"anchor\" aria-label=\"Permalink: Assumptions:\" href=\"#assumptions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package assumes that that the folder structure contains the following elements\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u251c\u2500\u2500 scripts\n\u2502 \u251c\u2500\u2500 run\n\u251c\u2500\u2500 src\n\u2502 \u251c\u2500\u2500 module1\n\u2502 \u251c\u2500\u2500 module2\n\u251c\u2500\u2500 container\n\u251c\u2500\u2500 \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eother folders\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u251c\u2500\u2500 Project.toml\n\u251c\u2500\u2500 Manifest.toml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand everything is under the control of a single git repository. This will be automatically the case if the project folder was created by \u003ca href=\"https://github.com/JuliaDynamics/DrWatson.jl\"\u003eDrWatson.jl\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe src and scripts folder will be copied into the container, and it is further assumed that the modules in it are registered as \u003ccode\u003edev\u003c/code\u003eed in the project \u003ccode\u003eManifest.toml\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWarning:\u003c/h3\u003e\u003ca id=\"user-content-warning\" class=\"anchor\" aria-label=\"Permalink: Warning:\" href=\"#warning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCalling \u003ccode\u003ebuildsif\u003c/code\u003e will ask for root privileges, as the underlying \u003ccode\u003esingularity build\u003c/code\u003e commands requires it. This is clearly a potential security risk, so if you are unsure, please inspect the \u003ccode\u003eSingularity.pack\u003c/code\u003e file in the \u003ccode\u003econtainer\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package provides the following functions. All these functions work from any folder as long as the correct project environment is loaded. They are also still WIP, so there is very little error checking being done.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e generate_deffile(; excludepkgs = [], commit = \"master\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCreates the \u003ccode\u003econtainer\u003c/code\u003e folder if it does not exist yet, and generates the \u003ccode\u003eSingularity.pack\u003c/code\u003e def file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eexcludepkgs\u003c/code\u003e accepts and array of package names. These packages will be removed from \u003ccode\u003eProject.toml\u003c/code\u003e inside the container. This is for packages that are needed locally, for example for visualization, but are not needed in the container and would only add bloat.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecommit\u003c/code\u003e accept any project commit hash, and will build the container using the \u003ccode\u003esrc\u003c/code\u003e and \u003ccode\u003escript\u003c/code\u003e folder from that commit. Requires the git setting below.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e buildsif(;verbose = false, force = true)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuilds the container image into the \u003ccode\u003econtainer\u003c/code\u003e folder based on the existing def file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003everbose\u003c/code\u003e sends all the output of the build process to the REPL if set to \u003ccode\u003etrue\u003c/code\u003e, otherwise it will be written to file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eforce\u003c/code\u003e set to \u003ccode\u003etrue\u003c/code\u003e causes an existing image to be overwritten without asking for confirmation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e recreatedata(file::String; dir = [])\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExtracts the git commit hash and script name from a \u003ccode\u003eDrWatson.@tagsave\u003c/code\u003ed file, and generated a def file. The resulting container, when \u003ccode\u003esingularity run\u003c/code\u003e, should recreate the initial file directly.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edir\u003c/code\u003e allows the specification of a subdirectory of the \u003ccode\u003eDrWatson.datadir()\u003c/code\u003e directory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e servertransfer(host)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTransfers the image to the \u003ccode\u003ehost\u003c/code\u003e into a folder in the home directory of the same name as the project folder. This assumed that everything is configured such that \u003ccode\u003essh host\u003c/code\u003e just works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFurther info\u003c/h2\u003e\u003ca id=\"user-content-further-info\" class=\"anchor\" aria-label=\"Permalink: Further info\" href=\"#further-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently, the commands build a single read-only image. This means, that after any change in the project the entire image needs to be rebuilt. This is partly as intended, as the result is a tamper-proof complete environment, that can be used at any point in the future the return the exact same results.\nHowever for projects that are still under more rapid development, I have possible ideas to make that initial phase not require frequent lengthy rebuilds.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFurther work:\u003c/h2\u003e\u003ca id=\"user-content-further-work\" class=\"anchor\" aria-label=\"Permalink: Further work:\" href=\"#further-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eGenerate different def files\u003c/li\u003e\n\u003cli\u003eadd interaction with singularity cloud and hub (pushing and pulling)\u003c/li\u003e\n\u003cli\u003esigning\u003c/li\u003e\n\u003cli\u003eadd tests\u003c/li\u003e\n\u003cli\u003eadd various error handling and options\u003c/li\u003e\n\u003cli\u003e(big) do some remote builder magic to make this work on windows/ mac\n\u003cul\u003e\n\u003cli\u003eAutomate image build on repo push, as mentioned on \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/builds/automated\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e(bigger) add singularity binary ?\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "litebird/litebird_sim", + "latest_release": "v0.12.0", + "readme": "\n\n\n\u003cp\u003e\u003ca href=\"https://litebird-sim.readthedocs.io/en/master/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce182a0ee721b9f0d6200d4870f9f95d066b8909acc7ff51a706dfb03a9a3ff7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732d737461626c652d626c75652e737667\" alt=\"Stable\" data-canonical-src=\"https://img.shields.io/badge/docs-stable-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/litebird/litebird_sim/actions?query=workflow%3ATests+branch%3Amaster\"\u003e\u003cimg src=\"https://github.com/litebird/litebird_sim/workflows/Tests/badge.svg?branch=master\u0026amp;event=push\" alt=\"Tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ci.appveyor.com/project/litebird/litebird-sim\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37e2c30e5dbb40d11988dd9f1dd19e8ff7eed15fcf660dd50d3c22dfeb424a0f/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f6c697465626972642f6c697465626972642d73696d3f7376673d74727565\" alt=\"Build Status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/litebird/litebird-sim?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/litebird/litebird_sim/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84b0ad87ea5f5705eff97416ba1750ef8c494b5c47dda5bae02233f23b7200f4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6c697465626972642f6c697465626972645f73696d3f7374796c653d666c61742d737175617265\" alt=\"Issues\" data-canonical-src=\"https://img.shields.io/github/issues/litebird/litebird_sim?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/litebird/litebird_sim/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1189015d278ceaa1d49ad185ee55d651db46ad6fc1888e9362debe51efbb9632/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6c697465626972642f6c697465626972645f73696d2e7376673f7374796c653d666c61742d737175617265\" alt=\"GPL3 License\" data-canonical-src=\"https://img.shields.io/github/license/litebird/litebird_sim.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cbr\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://github.com/litebird/litebird_sim\"\u003e\n \u003cimg src=\"images/logo.png\" alt=\"Logo\" width=\"80\" height=\"80\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/p\u003e\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 align=\"center\" class=\"heading-element\"\u003eLiteBIRD Simulation Framework\u003c/h3\u003e\u003ca id=\"user-content-litebird-simulation-framework\" class=\"anchor\" aria-label=\"Permalink: LiteBIRD Simulation Framework\" href=\"#litebird-simulation-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n \u003cp align=\"center\"\u003e\n Main repository of the LiteBIRD Simulation Framework, a set of Python modules to simulate the instruments onboard the LiteBIRD spacecraft.\n \u003cbr\u003e\n \u003ca href=\"https://litebird-sim.readthedocs.io/en/master/\" rel=\"nofollow\"\u003e\u003cstrong\u003eExplore the docs \u00bb\u003c/strong\u003e\u003c/a\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca href=\"https://litebird-sim.readthedocs.io/en/master/tutorial.html\" rel=\"nofollow\"\u003eView Demo\u003c/a\u003e\n \u00b7\n \u003ca href=\"https://github.com/litebird/litebird_sim/issues\"\u003eReport Bug\u003c/a\u003e\n \u00b7\n \u003ca href=\"https://github.com/litebird/litebird_sim/issues\"\u003eRequest Feature\u003c/a\u003e\n \u003c/p\u003e\n\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#about-the-project\"\u003eAbout the Project\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#built-with\"\u003eBuilt With\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#roadmap\"\u003eRoadmap\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-cite-this-code\"\u003eHow to cite this code\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout The Project\u003c/h2\u003e\u003ca id=\"user-content-about-the-project\" class=\"anchor\" aria-label=\"Permalink: About The Project\" href=\"#about-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe LiteBIRD Simulation Framework is being developed for the\n\u003ca href=\"http://litebird.jp/eng/\" rel=\"nofollow\"\u003eLiteBIRD collaboration\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilt With\u003c/h3\u003e\u003ca id=\"user-content-built-with\" class=\"anchor\" aria-label=\"Permalink: Built With\" href=\"#built-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLove!\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.python.org\" rel=\"nofollow\"\u003ePython 3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://python-poetry.org/\" rel=\"nofollow\"\u003ePoetry\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://numpy.org\" rel=\"nofollow\"\u003eNumPy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.astropy.org\" rel=\"nofollow\"\u003eAstropy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://healpix.jpl.nasa.gov\" rel=\"nofollow\"\u003eHealpix\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sphinx-doc.org/en/master/\" rel=\"nofollow\"\u003eSphinx\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://numba.pydata.org/\" rel=\"nofollow\"\u003eNumba\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/litebird/ducc\"\u003educc\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRefer to the\n\u003ca href=\"https://litebird-sim.readthedocs.io/en/master/installation.html\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e\nto learn how to install the LiteBIRD simulation framework on your\ncomputer or on a HPC cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn example notebook is avalable \u003ca href=\"https://github.com/litebird/litebird_sim/blob/master/notebooks/litebird_sim_example.ipynb\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe documentation is available online at\n\u003ca href=\"https://litebird-sim.readthedocs.io/en/master/\" rel=\"nofollow\"\u003elitebird-sim.readthedocs.io/en/master/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo create a local copy of the documentation, make sure you ran\n\u003ccode\u003epoetry\u003c/code\u003e with the flag \u003ccode\u003e--extras=docs\u003c/code\u003e, then run the following\ncommand:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eLinux or Mac OS X:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./refresh_docs.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWindows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epoetry shell\ncd docs\nmake.bat html\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRoadmap\u003c/h2\u003e\u003ca id=\"user-content-roadmap\" class=\"anchor\" aria-label=\"Permalink: Roadmap\" href=\"#roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"https://github.com/litebird/litebird_sim/issues\"\u003eopen issues\u003c/a\u003e\nfor a list of proposed features (and known issues).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are part of the LiteBIRD collaboration and have something that\nmight fit in this framework, you\u0027re encouraged to contact us! Any\ncontributions you make are \u003cstrong\u003egreatly appreciated\u003c/strong\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRead \u003ca href=\"https://github.com/litebird/litebird_sim/blob/master/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFork the project\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b feature/AmazingFeature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -m \u0027Add some AmazingFeature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the Branch (\u003ccode\u003egit push origin feature/AmazingFeature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eOpen a Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDistributed under the \u003ca href=\"https://github.com/litebird/litebird_sim/blob/master/LICENSE\"\u003eGPL3 License\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLiteBIRD Simulation Team - \u003ca href=\"mailto:litebird_pipe@db.ipmu.jp\"\u003elitebird_pipe@db.ipmu.jp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eProject Link: \u003ca href=\"https://github.com/litebird/litebird_sim\"\u003ehttps://github.com/litebird/litebird_sim\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to cite this code\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite-this-code\" class=\"anchor\" aria-label=\"Permalink: How to cite this code\" href=\"#how-to-cite-this-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO!\u003c/p\u003e\n\n\n\n", "stargazers_count": 18, - "subscribers_count": 2, + "subscribers_count": 19, "topics": [], - "updated_at": 1683227438.0 + "updated_at": 1715092209.0 }, { "data_format": 2, - "description": "Run singularity containers on the Comet Supercomputer at San Diego Supercomputer Center", + "description": "Powerlifted Planner", "filenames": [ - "ubuntu_anaconda/Singularity" + "Singularity" ], - "full_name": "zonca/singularity-comet", + "full_name": "abcorrea/powerlifted", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun singularity containers on SDSC Comet with MPI support\u003c/h1\u003e\u003ca id=\"user-content-run-singularity-containers-on-sdsc-comet-with-mpi-support\" class=\"anchor\" aria-label=\"Permalink: Run singularity containers on SDSC Comet with MPI support\" href=\"#run-singularity-containers-on-sdsc-comet-with-mpi-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1309\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is a project by Lawrence Berkeley Labs to bring container technology like Docker to High Performance Computing.\u003c/p\u003e\n\u003cp\u003eComet at the San Diego Supercomputer Center is a Supercomputer funded by National Science Foundation that focuses on boosting computing resources of new HPC users.\u003c/p\u003e\n\u003cp\u003eIn \u003ca href=\"https://github.com/zonca/singularity-comet\"\u003ethis repository\u003c/a\u003e I gathered some information on how to run Singularity on Comet computing nodes.\u003c/p\u003e\n\u003cp\u003eSee an introduction to this tutorial on my blog: \u003ca href=\"https://zonca.github.io/2017/01/singularity-hpc-comet.html\" rel=\"nofollow\"\u003ehttps://zonca.github.io/2017/01/singularity-hpc-comet.html\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse a pre-made Singularity container\u003c/h2\u003e\u003ca id=\"user-content-use-a-pre-made-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Use a pre-made Singularity container\" href=\"#use-a-pre-made-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAvailable on Comet at:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/oasis/scratch/comet/zonca/temp_project/ubuntu_anaconda_2018.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAvailable on DockerHub, see the \u003ccode\u003eDockerfile\u003c/code\u003e in \u003ccode\u003ejupyter_datascience_docker/\u003c/code\u003e, see \u003ca href=\"https://hub.docker.com/r/zonca/jupyter-datascience-comet/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/zonca/jupyter-datascience-comet/\u003c/a\u003e, you can transform it into a singularity container directly on Comet with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\nsingularity pull docker://zonca/jupyter-datascience-comet\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAvailable on SingularityHub at \u003ca href=\"https://www.singularity-hub.org/collections/1309\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/1309\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\nsingularity pull shub://zonca/singularity-comet\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild a Ubuntu 16.04 container\u003c/h2\u003e\u003ca id=\"user-content-build-a-ubuntu-1604-container\" class=\"anchor\" aria-label=\"Permalink: Build a Ubuntu 16.04 container\" href=\"#build-a-ubuntu-1604-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements on the Host\u003c/h3\u003e\u003ca id=\"user-content-requirements-on-the-host\" class=\"anchor\" aria-label=\"Permalink: Requirements on the Host\" href=\"#requirements-on-the-host\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst of all we need to build a container on a machine where we have \u003ccode\u003eroot\u003c/code\u003e access, we cannot do this on Comet.\nI tested the following on Ubuntu 16.04.\u003c/p\u003e\n\u003cp\u003eIf are interested in testing MPI locally on the Host, you\u0027ll need to install \u003ccode\u003emvapich2\u003c/code\u003e on the Host machine, you can follow the commands inside \u003ccode\u003eubuntu.def\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to build the container with singularity\u003c/h3\u003e\u003ca id=\"user-content-how-to-build-the-container-with-singularity\" class=\"anchor\" aria-label=\"Permalink: How to build the container with singularity\" href=\"#how-to-build-the-container-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently the kernel on Comet does not support Ubuntu 18.04.\u003c/p\u003e\n\u003cp\u003eInstall the \u003ccode\u003edebootstrap\u003c/code\u003e package into the Host machine.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003esingularity\u003c/code\u003e, see \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate an image of potentially 4GB:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export IMAGE=/tmp/ubuntu_anaconda_2018.simg\n sudo singularity create -s 4096 $IMAGE\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository and \u003ccode\u003ecd\u003c/code\u003e into the folder\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBootstrap the image with the Ubuntu 16.04 OS and also install MPI support with \u003ccode\u003emvapich2\u003c/code\u003e version 2.1, the same currently available on Comet. See \u003ccode\u003eubuntu_anaconda/Singularity\u003c/code\u003e in this repository for details (it is going to take some time):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build $IMAGE ubuntu_anaconda\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf you installed \u003ccode\u003emvapich2\u003c/code\u003e on the host, you can check that you can execute the hello world command using the Host MPI installation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e mpirun -np 2 singularity exec $IMAGE /usr/bin/hellow\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest the container on Comet\u003c/h2\u003e\u003ca id=\"user-content-test-the-container-on-comet\" class=\"anchor\" aria-label=\"Permalink: Test the container on Comet\" href=\"#test-the-container-on-comet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the container on your \u003ccode\u003escratch\u003c/code\u003e folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e scp $IMAGE comet.sdsc.edu:/oasis/scratch/comet/$USER/temp_project/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSSH to Comet\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository and \u003ccode\u003ecd\u003c/code\u003e into the folder\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSubmit the job to the SLURM scheduler\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sbatch run_singularity.slurm\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the output file \u003ccode\u003esingularity.*.out\u003c/code\u003e that the output shows all processes sending a \"Hello World\" string\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-powerlifted-planner\" class=\"anchor\" aria-hidden=\"true\" href=\"#powerlifted-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePowerlifted Planner\u003c/h1\u003e\n\u003cp\u003ePowerlifted is a domain-independent classical planner that uses only lifted\nrepresentations.\u003c/p\u003e\n\u003cp\u003e(See \u003ca href=\"#references\"\u003eReferences\u003c/a\u003e for more details.)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003epowerlifted.py\u003c/code\u003e script solves a PDDL task provided as input. It also builds\nthe planner if the \u003ccode\u003e--build\u003c/code\u003e parameter is passed. To run a single search, you\ncan use the following algorithms:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py [-d DOMAIN] -i INSTANCE -s SEARCH -e EVALUATOR -g GENERATOR [--state STATE REPR.] [ADDITIONAL OPTIONS] [--build]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe options for each parameter are described below. If you do not pass any value for \u003ccode\u003eSEARCH\u003c/code\u003e, \u003ccode\u003eEVALUATOR\u003c/code\u003e, and \u003ccode\u003eGENERATOR\u003c/code\u003e, the planner will use the best (known) configuration for \u003cem\u003esatisficing\u003c/em\u003e planning (i.e., no optimality guaranteed). (See next section for more details.)\u003c/p\u003e\n\u003cp\u003eIt is also possible to perform multiple search algorithms on the same task iteratively. See the section \"Multiple Search Algorithms\" below.\u003c/p\u003e\n\u003cp\u003eYou can either use the \u003ccode\u003ebuild.py\u003c/code\u003e script to build the planner first, or pass the \u003ccode\u003e--build\u003c/code\u003e flag to build the planner prior to the search execution.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-best-configuration-for-satisficing-planning\" class=\"anchor\" aria-hidden=\"true\" href=\"#best-configuration-for-satisficing-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBest Configuration for Satisficing Planning\u003c/h3\u003e\n\u003cp\u003eCurrently, the best configuration for satisficing planning (with respect to\ntotal coverage) is the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py [-d DOMAIN] -i INSTANCE -s alt-bfws1 -e ff -g yannakakis [ADDITIONAL OPTIONS] [--build]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThese are also the default values for \u003ccode\u003e-s\u003c/code\u003e, \u003ccode\u003e-e\u003c/code\u003e, and \u003ccode\u003e-g\u003c/code\u003e. To maximize\ncoverage, we also recommend adding \u003ccode\u003e--unit-cost\u003c/code\u003e (see below) to the \u003ccode\u003eADDITIONAL OPTIONS\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-search\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-search\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eSEARCH\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ealt-bfws1\u003c/code\u003e and \u003ccode\u003ealt-bfws2\u003c/code\u003e: [R_x, h] with w=1 and w=2, respectively. The choice of h is\ngiven the \u003ccode\u003eEVALUATOR\u003c/code\u003e option. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eastar\u003c/code\u003e: A* Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebfs\u003c/code\u003e: Breadth-First Search (This option was previously called \u003ccode\u003enaive\u003c/code\u003e. You\ncan still use \u003ccode\u003enaive\u003c/code\u003e with the \u003ccode\u003epowerlifted.py\u003c/code\u003e script but the planner will internally\nuse the new keyword \u003ccode\u003ebfs\u003c/code\u003e.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebfws1\u003c/code\u003e and \u003ccode\u003ebfws2\u003c/code\u003e: Best-First Width Search with w=1 and w=2, respectively.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebfws1-rx\u003c/code\u003e and \u003ccode\u003ebfws2-rx\u003c/code\u003e: BFWS(R_x) with w=1 and w=2, respectively. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edq-bfws1-rx\u003c/code\u003e and \u003ccode\u003edq-bfws2-rx\u003c/code\u003e: DQ(R_x) with w=1 and w=2, respectively. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egbfs\u003c/code\u003e: Greedy Best-First Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eiw1\u003c/code\u003e and \u003ccode\u003eiw2\u003c/code\u003e: Iterated Width Search (with w=1 and w=2, respectively)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy\u003c/code\u003e: Lazy Best-First Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy-po\u003c/code\u003e: Lazy Best-First Search with Boosted Dual-Queue\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy-prune\u003c/code\u003e: Lazy Best-First Search with pruning of states generated by\nnon-preferred operators\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-evaluator\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-evaluator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eEVALUATOR\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eadd\u003c/code\u003e: The additive heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eblind\u003c/code\u003e: No Heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eff\u003c/code\u003e: The FF heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egoalcount\u003c/code\u003e: The goal-count/STRIPS heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ehmax\u003c/code\u003e: The hmax heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erff\u003c/code\u003e: The rule-based FF heuristic\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-generator\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-generator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eGENERATOR\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ejoin\u003c/code\u003e: Join program using the predicate order given in the PDDL file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erandom_join\u003c/code\u003e: Randomly ordered join program\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eordered_join\u003c/code\u003e: Join program ordered by the arity of the predicates\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efull_reducer\u003c/code\u003e: Generate successor for acyclic schemas using the full\nreducer method; for cyclic schemas it uses a partial reducer and a join\nprogram.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eyannakakis\u003c/code\u003e: Same as above but replaces the final join of the full\nreducer method by the Yannakakis\u0027 project-join program.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-state-repr\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-state-repr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eSTATE REPR.\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esparse\u003c/code\u003e: Use the sparse state representation where a state is only\nrepresented by the facts that are true in this state.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eextensional\u003c/code\u003e: Use the extensional representation where a state is a bitset\nwhere the ith-bit is true if the fact associated to it is true in this\nstate. This representation requires the grounding of facts (but not of\nactions) which, right now, is performed in the search component. \u003cem\u003eWarning\u003c/em\u003e:\nthis setting does not support all \u003ccode\u003eEVALUATOR\u003c/code\u003e options.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-additional-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-additional-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable \u003ccode\u003eADDITIONAL OPTIONS\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e[--only-effects-novelty-check]\u003c/code\u003e: Flag if the novelty evaluation of a state\nshould only consider atoms in the applied action effect. \u003cem\u003eWarning\u003c/em\u003e: for\nstate-of-the-art performance, you must use this option when running BFWS-based\nsearch engines. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--novelty-early-stop]\u003c/code\u003e: Flag if the novelty evaluation of a state should\nstop as soon as the return value is defined. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--seed RANDOM SEED]\u003c/code\u003e: Random seed for the random number generator.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--translator-output-file TRANSLATOR_FILE]\u003c/code\u003e: Output of the intermediate\nrepresentation to be parsed by the search component will be saved into\n\u003ccode\u003eTRANSLATOR_FILE\u003c/code\u003e. (Default: \u003ccode\u003eoutput.lifted\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--unit-cost]\u003c/code\u003e: Use unit cost (i.e., all costs are equal to 1) instead of\nthe costs specified in the domain file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--validate]\u003c/code\u003e: Runs VAL after a plan is found to validate it. This requires\n\u003ca href=\"https://github.com/KCL-Planning/VAL\"\u003eVAL\u003c/a\u003e to be added as \u003ccode\u003evalidate\u003c/code\u003e to the \u003ccode\u003ePATH\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-multiple-search-algorithms\" class=\"anchor\" aria-hidden=\"true\" href=\"#multiple-search-algorithms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMultiple Search Algorithms\u003c/h2\u003e\n\u003cp\u003eYou can use the flag \u003ccode\u003e--iteration\u003c/code\u003e to specify one single search iteration for\nthe planner. You can pass as many \u003ccode\u003e--iteration\u003c/code\u003e arguments as you wish, and each\nargument will execute a different search.\u003c/p\u003e\n\u003cp\u003eThe syntax to specify a search iteration is the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py -i INSTANCE --iteration S,E,G\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003ccode\u003eS\u003c/code\u003e is a search algorithm, \u003ccode\u003eE\u003c/code\u003e is an evaluator, and \u003ccode\u003eG\u003c/code\u003e a successor generator. For example, to execute Greedy Best-First Search with FF followed by a Lazy Best-First Search with the additive heuristic (and both using the Yannakakis\u0027 algorithm for successor generation), you should run\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py -i INSTANCE --iteration gbfs,ff,yannakakis --iteration lazy,add,yannakakis\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe plan founds are then numbered based on its iterations. If the first iteration finds a plan, it will be called \u003ccode\u003eplan.1\u003c/code\u003e; the second will be called `plan.2; etc.\u003c/p\u003e\n\u003cp\u003eUnfortunately, the planner has the limitation that additional options are set\n\u003cem\u003efor all the iterations\u003c/em\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-powerlifted-as-a-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-powerlifted-as-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Powerlifted as a Singularity container\u003c/h2\u003e\n\u003cp\u003eYou can also build a Singularity image to run the planner. This might be useful\nin the case where you are not able to compile the planner locally, for\nexample. To do so, first remove the \u003ccode\u003ebuilds/\u003c/code\u003e directory, in case you have any\nbuilds already in your system. Then, you can run the following command to create\nthe planner image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e sudo singularity build powerlifted.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBe aware that this might take a while. Once the image \u003ccode\u003epowerlifted.sif\u003c/code\u003e is\ncreated, you can run it with the same parameters as the \u003ccode\u003epowerlifted.py\u003c/code\u003e\nscript. The only exception is that, by default, VAL is not installed in the\ncontainer, so it is not possible to use the \u003ccode\u003e--validate\u003c/code\u003e flag with the\nSingularity image. However, you can run VAL with the \u003ccode\u003esas_plan\u003c/code\u003e file created by\nthe planner after the execution. The following command is a usage example on\nhow to run the planner with the Singularity image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./powerlifted.sif -i /path/to/instance.pddl -s lazy-po -e add -g yannakakis --datalog-file model.lp --translator-output-file output.lifted\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eA C++17-compliant compiler\u003c/li\u003e\n\u003cli\u003eCMake 3.9+\u003c/li\u003e\n\u003cli\u003ePython 3.5+\u003c/li\u003e\n\u003cli\u003eBoost\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-limitations\" class=\"anchor\" aria-hidden=\"true\" href=\"#limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLimitations\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAxioms\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConditional effects\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNegated preconditions\u003c/strong\u003e: only inequality\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQuantifiers\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Pommerening, F.; Helmert, M.; and Franc\u00e8s, G. 2020. Lifted Successor Generation using Query Optimization Techniques. In Proc. ICAPS 2020, pp. 80-89. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-et-al-icaps2020.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Franc\u00e8s, G.; Pommerening, F.; and Helmert, M. 2021. Delete-Relaxation Heuristics for Lifted Classical Planning. In Proc. ICAPS 2021, pp. 94-102. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-et-al-icaps2021.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Pommerening, F.; Helmert, M.; and Franc\u00e8s, G. 2022. The\nFF Heuristic for Lifted Classical Planning. In Proc. AAAI 2022. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-et-al-aaai2022.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; and Seipp, J. 2022. Best-First Width Search for Lifted\nClassical Planning. In Proc. ICAPS 2022. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-seipp-icaps2022.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 18, - "subscribers_count": 3, + "subscribers_count": 4, "topics": [], - "updated_at": 1595652972.0 + "updated_at": 1679477473.0 }, { "data_format": 2, @@ -38674,100 +38744,133 @@ var data = }, { "data_format": 2, - "description": "Powerlifted Planner", + "description": "Run singularity containers on the Comet Supercomputer at San Diego Supercomputer Center", "filenames": [ - "Singularity" + "ubuntu_anaconda/Singularity" ], - "full_name": "abcorrea/powerlifted", + "full_name": "zonca/singularity-comet", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-powerlifted-planner\" class=\"anchor\" aria-hidden=\"true\" href=\"#powerlifted-planner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePowerlifted Planner\u003c/h1\u003e\n\u003cp\u003ePowerlifted is a domain-independent classical planner that uses only lifted\nrepresentations.\u003c/p\u003e\n\u003cp\u003e(See \u003ca href=\"#references\"\u003eReferences\u003c/a\u003e for more details.)\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eThe \u003ccode\u003epowerlifted.py\u003c/code\u003e script solves a PDDL task provided as input. It also builds\nthe planner if the \u003ccode\u003e--build\u003c/code\u003e parameter is passed. To run a single search, you\ncan use the following algorithms:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py [-d DOMAIN] -i INSTANCE -s SEARCH -e EVALUATOR -g GENERATOR [--state STATE REPR.] [ADDITIONAL OPTIONS] [--build]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe options for each parameter are described below. If you do not pass any value for \u003ccode\u003eSEARCH\u003c/code\u003e, \u003ccode\u003eEVALUATOR\u003c/code\u003e, and \u003ccode\u003eGENERATOR\u003c/code\u003e, the planner will use the best (known) configuration for \u003cem\u003esatisficing\u003c/em\u003e planning (i.e., no optimality guaranteed). (See next section for more details.)\u003c/p\u003e\n\u003cp\u003eIt is also possible to perform multiple search algorithms on the same task iteratively. See the section \"Multiple Search Algorithms\" below.\u003c/p\u003e\n\u003cp\u003eYou can either use the \u003ccode\u003ebuild.py\u003c/code\u003e script to build the planner first, or pass the \u003ccode\u003e--build\u003c/code\u003e flag to build the planner prior to the search execution.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-best-configuration-for-satisficing-planning\" class=\"anchor\" aria-hidden=\"true\" href=\"#best-configuration-for-satisficing-planning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBest Configuration for Satisficing Planning\u003c/h3\u003e\n\u003cp\u003eCurrently, the best configuration for satisficing planning (with respect to\ntotal coverage) is the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py [-d DOMAIN] -i INSTANCE -s alt-bfws1 -e ff -g yannakakis [ADDITIONAL OPTIONS] [--build]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThese are also the default values for \u003ccode\u003e-s\u003c/code\u003e, \u003ccode\u003e-e\u003c/code\u003e, and \u003ccode\u003e-g\u003c/code\u003e. To maximize\ncoverage, we also recommend adding \u003ccode\u003e--unit-cost\u003c/code\u003e (see below) to the \u003ccode\u003eADDITIONAL OPTIONS\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-search\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-search\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eSEARCH\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ealt-bfws1\u003c/code\u003e and \u003ccode\u003ealt-bfws2\u003c/code\u003e: [R_x, h] with w=1 and w=2, respectively. The choice of h is\ngiven the \u003ccode\u003eEVALUATOR\u003c/code\u003e option. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eastar\u003c/code\u003e: A* Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebfs\u003c/code\u003e: Breadth-First Search (This option was previously called \u003ccode\u003enaive\u003c/code\u003e. You\ncan still use \u003ccode\u003enaive\u003c/code\u003e with the \u003ccode\u003epowerlifted.py\u003c/code\u003e script but the planner will internally\nuse the new keyword \u003ccode\u003ebfs\u003c/code\u003e.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebfws1\u003c/code\u003e and \u003ccode\u003ebfws2\u003c/code\u003e: Best-First Width Search with w=1 and w=2, respectively.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebfws1-rx\u003c/code\u003e and \u003ccode\u003ebfws2-rx\u003c/code\u003e: BFWS(R_x) with w=1 and w=2, respectively. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003edq-bfws1-rx\u003c/code\u003e and \u003ccode\u003edq-bfws2-rx\u003c/code\u003e: DQ(R_x) with w=1 and w=2, respectively. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egbfs\u003c/code\u003e: Greedy Best-First Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eiw1\u003c/code\u003e and \u003ccode\u003eiw2\u003c/code\u003e: Iterated Width Search (with w=1 and w=2, respectively)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy\u003c/code\u003e: Lazy Best-First Search\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy-po\u003c/code\u003e: Lazy Best-First Search with Boosted Dual-Queue\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003elazy-prune\u003c/code\u003e: Lazy Best-First Search with pruning of states generated by\nnon-preferred operators\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-evaluator\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-evaluator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eEVALUATOR\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eadd\u003c/code\u003e: The additive heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eblind\u003c/code\u003e: No Heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eff\u003c/code\u003e: The FF heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003egoalcount\u003c/code\u003e: The goal-count/STRIPS heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ehmax\u003c/code\u003e: The hmax heuristic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erff\u003c/code\u003e: The rule-based FF heuristic\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-generator\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-generator\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eGENERATOR\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ejoin\u003c/code\u003e: Join program using the predicate order given in the PDDL file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erandom_join\u003c/code\u003e: Randomly ordered join program\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eordered_join\u003c/code\u003e: Join program ordered by the arity of the predicates\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efull_reducer\u003c/code\u003e: Generate successor for acyclic schemas using the full\nreducer method; for cyclic schemas it uses a partial reducer and a join\nprogram.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eyannakakis\u003c/code\u003e: Same as above but replaces the final join of the full\nreducer method by the Yannakakis\u0027 project-join program.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-options-for-state-repr\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-options-for-state-repr\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable Options for \u003ccode\u003eSTATE REPR.\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esparse\u003c/code\u003e: Use the sparse state representation where a state is only\nrepresented by the facts that are true in this state.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eextensional\u003c/code\u003e: Use the extensional representation where a state is a bitset\nwhere the ith-bit is true if the fact associated to it is true in this\nstate. This representation requires the grounding of facts (but not of\nactions) which, right now, is performed in the search component. \u003cem\u003eWarning\u003c/em\u003e:\nthis setting does not support all \u003ccode\u003eEVALUATOR\u003c/code\u003e options.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-available-additional-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#available-additional-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAvailable \u003ccode\u003eADDITIONAL OPTIONS\u003c/code\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e[--only-effects-novelty-check]\u003c/code\u003e: Flag if the novelty evaluation of a state\nshould only consider atoms in the applied action effect. \u003cem\u003eWarning\u003c/em\u003e: for\nstate-of-the-art performance, you must use this option when running BFWS-based\nsearch engines. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--novelty-early-stop]\u003c/code\u003e: Flag if the novelty evaluation of a state should\nstop as soon as the return value is defined. (See Corr\u00eaa and Seipp 2022.)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--seed RANDOM SEED]\u003c/code\u003e: Random seed for the random number generator.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--translator-output-file TRANSLATOR_FILE]\u003c/code\u003e: Output of the intermediate\nrepresentation to be parsed by the search component will be saved into\n\u003ccode\u003eTRANSLATOR_FILE\u003c/code\u003e. (Default: \u003ccode\u003eoutput.lifted\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--unit-cost]\u003c/code\u003e: Use unit cost (i.e., all costs are equal to 1) instead of\nthe costs specified in the domain file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e[--validate]\u003c/code\u003e: Runs VAL after a plan is found to validate it. This requires\n\u003ca href=\"https://github.com/KCL-Planning/VAL\"\u003eVAL\u003c/a\u003e to be added as \u003ccode\u003evalidate\u003c/code\u003e to the \u003ccode\u003ePATH\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-multiple-search-algorithms\" class=\"anchor\" aria-hidden=\"true\" href=\"#multiple-search-algorithms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMultiple Search Algorithms\u003c/h2\u003e\n\u003cp\u003eYou can use the flag \u003ccode\u003e--iteration\u003c/code\u003e to specify one single search iteration for\nthe planner. You can pass as many \u003ccode\u003e--iteration\u003c/code\u003e arguments as you wish, and each\nargument will execute a different search.\u003c/p\u003e\n\u003cp\u003eThe syntax to specify a search iteration is the following:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py -i INSTANCE --iteration S,E,G\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003ccode\u003eS\u003c/code\u003e is a search algorithm, \u003ccode\u003eE\u003c/code\u003e is an evaluator, and \u003ccode\u003eG\u003c/code\u003e a successor generator. For example, to execute Greedy Best-First Search with FF followed by a Lazy Best-First Search with the additive heuristic (and both using the Yannakakis\u0027 algorithm for successor generation), you should run\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ ./powerlifted.py -i INSTANCE --iteration gbfs,ff,yannakakis --iteration lazy,add,yannakakis\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe plan founds are then numbered based on its iterations. If the first iteration finds a plan, it will be called \u003ccode\u003eplan.1\u003c/code\u003e; the second will be called `plan.2; etc.\u003c/p\u003e\n\u003cp\u003eUnfortunately, the planner has the limitation that additional options are set\n\u003cem\u003efor all the iterations\u003c/em\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-running-powerlifted-as-a-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#running-powerlifted-as-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRunning Powerlifted as a Singularity container\u003c/h2\u003e\n\u003cp\u003eYou can also build a Singularity image to run the planner. This might be useful\nin the case where you are not able to compile the planner locally, for\nexample. To do so, first remove the \u003ccode\u003ebuilds/\u003c/code\u003e directory, in case you have any\nbuilds already in your system. Then, you can run the following command to create\nthe planner image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e sudo singularity build powerlifted.sif Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBe aware that this might take a while. Once the image \u003ccode\u003epowerlifted.sif\u003c/code\u003e is\ncreated, you can run it with the same parameters as the \u003ccode\u003epowerlifted.py\u003c/code\u003e\nscript. The only exception is that, by default, VAL is not installed in the\ncontainer, so it is not possible to use the \u003ccode\u003e--validate\u003c/code\u003e flag with the\nSingularity image. However, you can run VAL with the \u003ccode\u003esas_plan\u003c/code\u003e file created by\nthe planner after the execution. The following command is a usage example on\nhow to run the planner with the Singularity image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./powerlifted.sif -i /path/to/instance.pddl -s lazy-po -e add -g yannakakis --datalog-file model.lp --translator-output-file output.lifted\u003c/code\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eA C++17-compliant compiler\u003c/li\u003e\n\u003cli\u003eCMake 3.9+\u003c/li\u003e\n\u003cli\u003ePython 3.5+\u003c/li\u003e\n\u003cli\u003eBoost\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-limitations\" class=\"anchor\" aria-hidden=\"true\" href=\"#limitations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLimitations\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAxioms\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConditional effects\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNegated preconditions\u003c/strong\u003e: only inequality\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eQuantifiers\u003c/strong\u003e: not supported\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Pommerening, F.; Helmert, M.; and Franc\u00e8s, G. 2020. Lifted Successor Generation using Query Optimization Techniques. In Proc. ICAPS 2020, pp. 80-89. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-et-al-icaps2020.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Franc\u00e8s, G.; Pommerening, F.; and Helmert, M. 2021. Delete-Relaxation Heuristics for Lifted Classical Planning. In Proc. ICAPS 2021, pp. 94-102. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-et-al-icaps2021.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; Pommerening, F.; Helmert, M.; and Franc\u00e8s, G. 2022. The\nFF Heuristic for Lifted Classical Planning. In Proc. AAAI 2022. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-et-al-aaai2022.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCorr\u00eaa, A. B.; and Seipp, J. 2022. Best-First Width Search for Lifted\nClassical Planning. In Proc. ICAPS 2022. \u003ca href=\"https://ai.dmi.unibas.ch/papers/correa-seipp-icaps2022.pdf\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eRun singularity containers on SDSC Comet with MPI support\u003c/h1\u003e\u003ca id=\"user-content-run-singularity-containers-on-sdsc-comet-with-mpi-support\" class=\"anchor\" aria-label=\"Permalink: Run singularity containers on SDSC Comet with MPI support\" href=\"#run-singularity-containers-on-sdsc-comet-with-mpi-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/1309\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e is a project by Lawrence Berkeley Labs to bring container technology like Docker to High Performance Computing.\u003c/p\u003e\n\u003cp\u003eComet at the San Diego Supercomputer Center is a Supercomputer funded by National Science Foundation that focuses on boosting computing resources of new HPC users.\u003c/p\u003e\n\u003cp\u003eIn \u003ca href=\"https://github.com/zonca/singularity-comet\"\u003ethis repository\u003c/a\u003e I gathered some information on how to run Singularity on Comet computing nodes.\u003c/p\u003e\n\u003cp\u003eSee an introduction to this tutorial on my blog: \u003ca href=\"https://zonca.github.io/2017/01/singularity-hpc-comet.html\" rel=\"nofollow\"\u003ehttps://zonca.github.io/2017/01/singularity-hpc-comet.html\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUse a pre-made Singularity container\u003c/h2\u003e\u003ca id=\"user-content-use-a-pre-made-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Use a pre-made Singularity container\" href=\"#use-a-pre-made-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAvailable on Comet at:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e/oasis/scratch/comet/zonca/temp_project/ubuntu_anaconda_2018.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAvailable on DockerHub, see the \u003ccode\u003eDockerfile\u003c/code\u003e in \u003ccode\u003ejupyter_datascience_docker/\u003c/code\u003e, see \u003ca href=\"https://hub.docker.com/r/zonca/jupyter-datascience-comet/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/zonca/jupyter-datascience-comet/\u003c/a\u003e, you can transform it into a singularity container directly on Comet with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\nsingularity pull docker://zonca/jupyter-datascience-comet\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAvailable on SingularityHub at \u003ca href=\"https://www.singularity-hub.org/collections/1309\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/1309\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodule load singularity\nsingularity pull shub://zonca/singularity-comet\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild a Ubuntu 16.04 container\u003c/h2\u003e\u003ca id=\"user-content-build-a-ubuntu-1604-container\" class=\"anchor\" aria-label=\"Permalink: Build a Ubuntu 16.04 container\" href=\"#build-a-ubuntu-1604-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRequirements on the Host\u003c/h3\u003e\u003ca id=\"user-content-requirements-on-the-host\" class=\"anchor\" aria-label=\"Permalink: Requirements on the Host\" href=\"#requirements-on-the-host\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst of all we need to build a container on a machine where we have \u003ccode\u003eroot\u003c/code\u003e access, we cannot do this on Comet.\nI tested the following on Ubuntu 16.04.\u003c/p\u003e\n\u003cp\u003eIf are interested in testing MPI locally on the Host, you\u0027ll need to install \u003ccode\u003emvapich2\u003c/code\u003e on the Host machine, you can follow the commands inside \u003ccode\u003eubuntu.def\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow to build the container with singularity\u003c/h3\u003e\u003ca id=\"user-content-how-to-build-the-container-with-singularity\" class=\"anchor\" aria-label=\"Permalink: How to build the container with singularity\" href=\"#how-to-build-the-container-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently the kernel on Comet does not support Ubuntu 18.04.\u003c/p\u003e\n\u003cp\u003eInstall the \u003ccode\u003edebootstrap\u003c/code\u003e package into the Host machine.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ccode\u003esingularity\u003c/code\u003e, see \u003ca href=\"http://singularity.lbl.gov/\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate an image of potentially 4GB:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e export IMAGE=/tmp/ubuntu_anaconda_2018.simg\n sudo singularity create -s 4096 $IMAGE\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository and \u003ccode\u003ecd\u003c/code\u003e into the folder\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBootstrap the image with the Ubuntu 16.04 OS and also install MPI support with \u003ccode\u003emvapich2\u003c/code\u003e version 2.1, the same currently available on Comet. See \u003ccode\u003eubuntu_anaconda/Singularity\u003c/code\u003e in this repository for details (it is going to take some time):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity build $IMAGE ubuntu_anaconda\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf you installed \u003ccode\u003emvapich2\u003c/code\u003e on the host, you can check that you can execute the hello world command using the Host MPI installation:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e mpirun -np 2 singularity exec $IMAGE /usr/bin/hellow\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest the container on Comet\u003c/h2\u003e\u003ca id=\"user-content-test-the-container-on-comet\" class=\"anchor\" aria-label=\"Permalink: Test the container on Comet\" href=\"#test-the-container-on-comet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the container on your \u003ccode\u003escratch\u003c/code\u003e folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e scp $IMAGE comet.sdsc.edu:/oasis/scratch/comet/$USER/temp_project/\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSSH to Comet\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repository and \u003ccode\u003ecd\u003c/code\u003e into the folder\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSubmit the job to the SLURM scheduler\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sbatch run_singularity.slurm\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck the output file \u003ccode\u003esingularity.*.out\u003c/code\u003e that the output shows all processes sending a \"Hello World\" string\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 18, - "subscribers_count": 4, + "subscribers_count": 3, "topics": [], - "updated_at": 1679477473.0 + "updated_at": 1595652972.0 }, { "data_format": 2, - "description": "Simulation tools for LiteBIRD", + "description": null, "filenames": [ - "singularity/Singularity.m4" + "basebuilds/Singularity.jupyterbase", + "basebuilds/Singularity.juliabase" ], - "full_name": "litebird/litebird_sim", - "latest_release": "v0.12.0", - "readme": "\n\n\n\u003cp\u003e\u003ca href=\"https://litebird-sim.readthedocs.io/en/master/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce182a0ee721b9f0d6200d4870f9f95d066b8909acc7ff51a706dfb03a9a3ff7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732d737461626c652d626c75652e737667\" alt=\"Stable\" data-canonical-src=\"https://img.shields.io/badge/docs-stable-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/litebird/litebird_sim/actions?query=workflow%3ATests+branch%3Amaster\"\u003e\u003cimg src=\"https://github.com/litebird/litebird_sim/workflows/Tests/badge.svg?branch=master\u0026amp;event=push\" alt=\"Tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ci.appveyor.com/project/litebird/litebird-sim\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37e2c30e5dbb40d11988dd9f1dd19e8ff7eed15fcf660dd50d3c22dfeb424a0f/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f6769746875622f6c697465626972642f6c697465626972642d73696d3f7376673d74727565\" alt=\"Build Status\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/github/litebird/litebird-sim?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/litebird/litebird_sim/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/84b0ad87ea5f5705eff97416ba1750ef8c494b5c47dda5bae02233f23b7200f4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6c697465626972642f6c697465626972645f73696d3f7374796c653d666c61742d737175617265\" alt=\"Issues\" data-canonical-src=\"https://img.shields.io/github/issues/litebird/litebird_sim?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/litebird/litebird_sim/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1189015d278ceaa1d49ad185ee55d651db46ad6fc1888e9362debe51efbb9632/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6c697465626972642f6c697465626972645f73696d2e7376673f7374796c653d666c61742d737175617265\" alt=\"GPL3 License\" data-canonical-src=\"https://img.shields.io/github/license/litebird/litebird_sim.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cbr\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://github.com/litebird/litebird_sim\"\u003e\n \u003cimg src=\"images/logo.png\" alt=\"Logo\" width=\"80\" height=\"80\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/p\u003e\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 align=\"center\" class=\"heading-element\"\u003eLiteBIRD Simulation Framework\u003c/h3\u003e\u003ca id=\"user-content-litebird-simulation-framework\" class=\"anchor\" aria-label=\"Permalink: LiteBIRD Simulation Framework\" href=\"#litebird-simulation-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n \u003cp align=\"center\"\u003e\n Main repository of the LiteBIRD Simulation Framework, a set of Python modules to simulate the instruments onboard the LiteBIRD spacecraft.\n \u003cbr\u003e\n \u003ca href=\"https://litebird-sim.readthedocs.io/en/master/\" rel=\"nofollow\"\u003e\u003cstrong\u003eExplore the docs \u00bb\u003c/strong\u003e\u003c/a\u003e\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca href=\"https://litebird-sim.readthedocs.io/en/master/tutorial.html\" rel=\"nofollow\"\u003eView Demo\u003c/a\u003e\n \u00b7\n \u003ca href=\"https://github.com/litebird/litebird_sim/issues\"\u003eReport Bug\u003c/a\u003e\n \u00b7\n \u003ca href=\"https://github.com/litebird/litebird_sim/issues\"\u003eRequest Feature\u003c/a\u003e\n \u003c/p\u003e\n\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#about-the-project\"\u003eAbout the Project\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#built-with\"\u003eBuilt With\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#roadmap\"\u003eRoadmap\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-cite-this-code\"\u003eHow to cite this code\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout The Project\u003c/h2\u003e\u003ca id=\"user-content-about-the-project\" class=\"anchor\" aria-label=\"Permalink: About The Project\" href=\"#about-the-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe LiteBIRD Simulation Framework is being developed for the\n\u003ca href=\"http://litebird.jp/eng/\" rel=\"nofollow\"\u003eLiteBIRD collaboration\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilt With\u003c/h3\u003e\u003ca id=\"user-content-built-with\" class=\"anchor\" aria-label=\"Permalink: Built With\" href=\"#built-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eLove!\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.python.org\" rel=\"nofollow\"\u003ePython 3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://python-poetry.org/\" rel=\"nofollow\"\u003ePoetry\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://numpy.org\" rel=\"nofollow\"\u003eNumPy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.astropy.org\" rel=\"nofollow\"\u003eAstropy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://healpix.jpl.nasa.gov\" rel=\"nofollow\"\u003eHealpix\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sphinx-doc.org/en/master/\" rel=\"nofollow\"\u003eSphinx\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://numba.pydata.org/\" rel=\"nofollow\"\u003eNumba\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/litebird/ducc\"\u003educc\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRefer to the\n\u003ca href=\"https://litebird-sim.readthedocs.io/en/master/installation.html\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e\nto learn how to install the LiteBIRD simulation framework on your\ncomputer or on a HPC cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn example notebook is avalable \u003ca href=\"https://github.com/litebird/litebird_sim/blob/master/notebooks/litebird_sim_example.ipynb\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe documentation is available online at\n\u003ca href=\"https://litebird-sim.readthedocs.io/en/master/\" rel=\"nofollow\"\u003elitebird-sim.readthedocs.io/en/master/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTo create a local copy of the documentation, make sure you ran\n\u003ccode\u003epoetry\u003c/code\u003e with the flag \u003ccode\u003e--extras=docs\u003c/code\u003e, then run the following\ncommand:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eLinux or Mac OS X:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./refresh_docs.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWindows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epoetry shell\ncd docs\nmake.bat html\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRoadmap\u003c/h2\u003e\u003ca id=\"user-content-roadmap\" class=\"anchor\" aria-label=\"Permalink: Roadmap\" href=\"#roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"https://github.com/litebird/litebird_sim/issues\"\u003eopen issues\u003c/a\u003e\nfor a list of proposed features (and known issues).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are part of the LiteBIRD collaboration and have something that\nmight fit in this framework, you\u0027re encouraged to contact us! Any\ncontributions you make are \u003cstrong\u003egreatly appreciated\u003c/strong\u003e.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRead \u003ca href=\"https://github.com/litebird/litebird_sim/blob/master/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFork the project\u003c/li\u003e\n\u003cli\u003eCreate your feature branch (\u003ccode\u003egit checkout -b feature/AmazingFeature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eCommit your changes (\u003ccode\u003egit commit -m \u0027Add some AmazingFeature\u0027\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePush to the Branch (\u003ccode\u003egit push origin feature/AmazingFeature\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eOpen a Pull Request\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDistributed under the \u003ca href=\"https://github.com/litebird/litebird_sim/blob/master/LICENSE\"\u003eGPL3 License\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLiteBIRD Simulation Team - \u003ca href=\"mailto:litebird_pipe@db.ipmu.jp\"\u003elitebird_pipe@db.ipmu.jp\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eProject Link: \u003ca href=\"https://github.com/litebird/litebird_sim\"\u003ehttps://github.com/litebird/litebird_sim\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to cite this code\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite-this-code\" class=\"anchor\" aria-label=\"Permalink: How to cite this code\" href=\"#how-to-cite-this-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTODO!\u003c/p\u003e\n\n\n\n", + "full_name": "Crown421/Singularity.jl", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package was presented at JuliaCon, and the presentation is available \u003ca href=\"https://musing-pare-2ba365.netlify.app/#/\" rel=\"nofollow\"\u003eunder this link\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis package provides a rough interface to create \u003ca href=\"https://github.com/sylabs/singularity\"\u003eSingularity containers\u003c/a\u003e from DrWatson Projects.\nIt currently works best on Linux systems, as the build command currently not available on Mac.\u003c/p\u003e\n\u003cp\u003eMost of the code is still very WIP and based on my own processes and needs. If you use Singularity, or have a use case that I haven\u0027t considered yet, please reach out either by email or by opening an issue.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall singularity\u003c/h2\u003e\u003ca id=\"user-content-install-singularity\" class=\"anchor\" aria-label=\"Permalink: Install singularity\" href=\"#install-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003eSylab documentation\u003c/a\u003e contains instructions to install Singularity, but appears to be slightly out of date.\nYou can find additional information for\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinux on this \u003ca href=\"https://github.com/hpcng/singularity/blob/master/INSTALL.md\"\u003eGithub readme\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMac on the \u003ca href=\"https://sylabs.io/singularity-desktop-macos/\" rel=\"nofollow\"\u003edownload page for the beta release\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eWindows, see this \u003ca href=\"https://github.com/hpcng/singularity/issues/4518\"\u003ethis issue\u003c/a\u003e stating that WSL 2 is required (\u003ca href=\"https://docs.microsoft.com/en-us/windows/wsl/install-win10\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e). After installing it, follow the instructions for Linux.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAlternatively, you can use the \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html#install-on-windows-or-mac\" rel=\"nofollow\"\u003einstructions for Vagrant\u003c/a\u003e on Windows and Mac.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBasis\u003c/h3\u003e\u003ca id=\"user-content-basis\" class=\"anchor\" aria-label=\"Permalink: Basis\" href=\"#basis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis package uses a minimal debian-based container with Julia installed as a base. On the \u003ca href=\"https://cloud.sylabs.io/home\" rel=\"nofollow\"\u003eSylab cloud\u003c/a\u003e you can find the Juliabase image, and an experimental container also including jupyter\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/_container/5e418a1b2758e9ed1175de24\" rel=\"nofollow\"\u003ejuliabase\u003c/a\u003e: 1.4.2, 1.3.1\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://cloud.sylabs.io/library/_container/5f20adbeae86dd3232dec1d1\" rel=\"nofollow\"\u003ejupyterbase\u003c/a\u003e: 1.4.2\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you would prefer to build them yourself, the def files are available in the \u003ccode\u003ebasebuilds\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAssumptions:\u003c/h3\u003e\u003ca id=\"user-content-assumptions\" class=\"anchor\" aria-label=\"Permalink: Assumptions:\" href=\"#assumptions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package assumes that that the folder structure contains the following elements\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u251c\u2500\u2500 scripts\n\u2502 \u251c\u2500\u2500 run\n\u251c\u2500\u2500 src\n\u2502 \u251c\u2500\u2500 module1\n\u2502 \u251c\u2500\u2500 module2\n\u251c\u2500\u2500 container\n\u251c\u2500\u2500 \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eother folders\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n\u251c\u2500\u2500 Project.toml\n\u251c\u2500\u2500 Manifest.toml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand everything is under the control of a single git repository. This will be automatically the case if the project folder was created by \u003ca href=\"https://github.com/JuliaDynamics/DrWatson.jl\"\u003eDrWatson.jl\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe src and scripts folder will be copied into the container, and it is further assumed that the modules in it are registered as \u003ccode\u003edev\u003c/code\u003eed in the project \u003ccode\u003eManifest.toml\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWarning:\u003c/h3\u003e\u003ca id=\"user-content-warning\" class=\"anchor\" aria-label=\"Permalink: Warning:\" href=\"#warning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCalling \u003ccode\u003ebuildsif\u003c/code\u003e will ask for root privileges, as the underlying \u003ccode\u003esingularity build\u003c/code\u003e commands requires it. This is clearly a potential security risk, so if you are unsure, please inspect the \u003ccode\u003eSingularity.pack\u003c/code\u003e file in the \u003ccode\u003econtainer\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package provides the following functions. All these functions work from any folder as long as the correct project environment is loaded. They are also still WIP, so there is very little error checking being done.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e generate_deffile(; excludepkgs = [], commit = \"master\")\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eCreates the \u003ccode\u003econtainer\u003c/code\u003e folder if it does not exist yet, and generates the \u003ccode\u003eSingularity.pack\u003c/code\u003e def file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eexcludepkgs\u003c/code\u003e accepts and array of package names. These packages will be removed from \u003ccode\u003eProject.toml\u003c/code\u003e inside the container. This is for packages that are needed locally, for example for visualization, but are not needed in the container and would only add bloat.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ecommit\u003c/code\u003e accept any project commit hash, and will build the container using the \u003ccode\u003esrc\u003c/code\u003e and \u003ccode\u003escript\u003c/code\u003e folder from that commit. Requires the git setting below.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e buildsif(;verbose = false, force = true)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBuilds the container image into the \u003ccode\u003econtainer\u003c/code\u003e folder based on the existing def file.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003everbose\u003c/code\u003e sends all the output of the build process to the REPL if set to \u003ccode\u003etrue\u003c/code\u003e, otherwise it will be written to file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eforce\u003c/code\u003e set to \u003ccode\u003etrue\u003c/code\u003e causes an existing image to be overwritten without asking for confirmation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e recreatedata(file::String; dir = [])\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExtracts the git commit hash and script name from a \u003ccode\u003eDrWatson.@tagsave\u003c/code\u003ed file, and generated a def file. The resulting container, when \u003ccode\u003esingularity run\u003c/code\u003e, should recreate the initial file directly.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edir\u003c/code\u003e allows the specification of a subdirectory of the \u003ccode\u003eDrWatson.datadir()\u003c/code\u003e directory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e servertransfer(host)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTransfers the image to the \u003ccode\u003ehost\u003c/code\u003e into a folder in the home directory of the same name as the project folder. This assumed that everything is configured such that \u003ccode\u003essh host\u003c/code\u003e just works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFurther info\u003c/h2\u003e\u003ca id=\"user-content-further-info\" class=\"anchor\" aria-label=\"Permalink: Further info\" href=\"#further-info\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently, the commands build a single read-only image. This means, that after any change in the project the entire image needs to be rebuilt. This is partly as intended, as the result is a tamper-proof complete environment, that can be used at any point in the future the return the exact same results.\nHowever for projects that are still under more rapid development, I have possible ideas to make that initial phase not require frequent lengthy rebuilds.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFurther work:\u003c/h2\u003e\u003ca id=\"user-content-further-work\" class=\"anchor\" aria-label=\"Permalink: Further work:\" href=\"#further-work\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eGenerate different def files\u003c/li\u003e\n\u003cli\u003eadd interaction with singularity cloud and hub (pushing and pulling)\u003c/li\u003e\n\u003cli\u003esigning\u003c/li\u003e\n\u003cli\u003eadd tests\u003c/li\u003e\n\u003cli\u003eadd various error handling and options\u003c/li\u003e\n\u003cli\u003e(big) do some remote builder magic to make this work on windows/ mac\n\u003cul\u003e\n\u003cli\u003eAutomate image build on repo push, as mentioned on \u003ca href=\"https://singularityhub.github.io/singularityhub-docs/docs/builds/automated\" rel=\"nofollow\"\u003esingularity hub\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e(bigger) add singularity binary ?\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 18, - "subscribers_count": 19, + "subscribers_count": 2, "topics": [], - "updated_at": 1715092209.0 + "updated_at": 1683227438.0 }, { "data_format": 2, - "description": "DRL-VO navigation policy for BARN Challenge", + "description": "User-friendly exact diagonalization package written in Haskell. Can treat systems of up to \ud835\udcaa(42) spins! ", "filenames": [ - "Singularityfile_melodic.def", - "Singularityfile.def" + "Singularity" ], - "full_name": "TempleRAIL/nav-competition-icra2022-drl-vo", + "full_name": "twesterhout/spin-ed", "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/BARN_Challenge.png\"\u003e\u003cimg width=\"100%\" src=\"res/BARN_Challenge.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDRL-VO control policy for ICRA 2022 BARN Challenge\u003c/h1\u003e\u003ca id=\"user-content-drl-vo-control-policy-for-icra-2022-barn-challenge\" class=\"anchor\" aria-label=\"Permalink: DRL-VO control policy for ICRA 2022 BARN Challenge\" href=\"#drl-vo-control-policy-for-icra-2022-barn-challenge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOur DRL-VO control policy ranked 1st in the simulated competition and 3rd in the final physical competition of the ICRA 2022 BARN Challenge.\nImplementation details can be found in our paper \u003ca href=\"https://doi.org/10.1109/TRO.2023.3257549\" rel=\"nofollow\"\u003e\"DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles\"\u003c/a\u003e(\u003ca href=\"https://arxiv.org/pdf/2301.06512.pdf\" rel=\"nofollow\"\u003earXiv\u003c/a\u003e) in IEEE Transactions on Robotics (T-RO) 2023.\nVideo demos can be found at \u003ca href=\"https://www.youtube.com/watch?v=KneELRT8GzU\u0026amp;list=PLouWbAcP4zIvPgaARrV223lf2eiSR-eSS\u0026amp;index=2\u0026amp;ab_channel=PhilipDames\" rel=\"nofollow\"\u003emultimedia demonstrations\u003c/a\u003e. The original training and implementation code can be found in our \u003ca href=\"https://github.com/TempleRAIL/drl_vo_nav.git\"\u003edrl_vo_nav\u003c/a\u003e repository.\u003c/p\u003e\n\u003cp\u003eThe details of the BARN Challenge can be found in our paper \u003ca href=\"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9975161\" rel=\"nofollow\"\u003e\"Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 [Competitions]\"\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003enavigation metric: 0.2339\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements:\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements:\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUbuntu 20.04/18.04\u003c/li\u003e\n\u003cli\u003eROS-Noetic/ROS Melodic\u003c/li\u003e\n\u003cli\u003ePython 3.7\u003c/li\u003e\n\u003cli\u003eSingularity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage:\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage:\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, download the pre-created \u003ca href=\"https://doi.org/10.5281/zenodo.7968623\" rel=\"nofollow\"\u003e\"nav_competition_image.sif\"\u003c/a\u003e container to the home directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSimulation:\u003c/h3\u003e\u003ca id=\"user-content-simulation\" class=\"anchor\" aria-label=\"Permalink: Simulation:\" href=\"#simulation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# clone this project:\ngit clone -b master https://github.com/TempleRAIL/nav-competition-icra2022-drl-vo.git\ncd nav-competition-icra2022-drl-vo\n\n# move nav_competition_image.sif container to current directory:\nmv ~/nav_competition_image.sif ./\n\n# single world test:\n./singularity_run.sh ./nav_competition_image.sif python run.py --out ~/drl_vo_out.txt\n\n# 50 worlds test: 1 trial\n./singularity_run.sh ./nav_competition_image.sif python run_drl_vo.py --out ~/drl_vo_out.txt --trials 1\n\n# 50 worlds test: 10 trial\n./singularity_run.sh ./nav_competition_image.sif python run_drl_vo.py --out ~/drl_vo_out.txt --trials 10\n\n# report results:\n./singularity_run.sh ./nav_competition_image.sif python report_test.py --out_path ~/drl_vo_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware:\u003c/h3\u003e\u003ca id=\"user-content-hardware\" class=\"anchor\" aria-label=\"Permalink: Hardware:\" href=\"#hardware\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# enter the directory of nav_competition_image.sif container and run the container: home directory\ncd ~\nsingularity shell --nv nav_competition_image.sif\nsource /etc/.bashrc\n\n# set the appropriate goal point and run the DRL-VO policy: the robot\u0027s initial local coordinate system when the robot is powered on (right hand rule)\nroslaunch jackal_helper move_base_drl_vo.launch goal_x:=\"20\" goal_y:=\"15\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eModify code in hardware:\u003c/h3\u003e\u003ca id=\"user-content-modify-code-in-hardware\" class=\"anchor\" aria-label=\"Permalink: Modify code in hardware:\" href=\"#modify-code-in-hardware\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e# enter the directory of nav_competition_image.sif container and run the container:\ncd ~\nsingularity shell --nv nav_competition_image.sif\nsource /etc/.bashrc\n\n# create ros workspace and clone this project:\nmkdir -p jackal_ws/src\ncd jackal_ws/src\ngit clone -b master https://github.com/TempleRAIL/nav-competition-icra2022-drl-vo.git\n\n# modify the corresponding code as needed\n\n# compile:\ncd ..\ncatkin_make\nsource devel/setup.sh\n\n# set the appropriate goal point and run the DRL-VO policy: the robot\u0027s initial local coordinate system when the robot is powered on (right hand rule)\nroslaunch jackal_helper move_base_drl_vo.launch goal_x:=\"20\" goal_y:=\"15\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e@article{xie2023drl,\n author={Xie, Zhanteng and Dames, Philip},\n journal={IEEE Transactions on Robotics}, \n title={DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles}, \n year={2023},\n volume={39},\n number={4},\n pages={2700-2719},\n doi={10.1109/TRO.2023.3257549}\n}\n\n@article{xiao2022autonomous,\n title={Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 [Competitions]},\n author={Xiao, Xuesu and Xu, Zifan and Wang, Zizhao and Song, Yunlong and Warnell, Garrett and Stone, Peter and Zhang, Tingnan and Ravi, Shravan and Wang, Gary and Karnan, Haresh and others},\n journal={IEEE Robotics \\\u0026amp; Automation Magazine},\n volume={29},\n number={4},\n pages={148--156},\n year={2022},\n publisher={IEEE}\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSpinED\u003c/h1\u003e\u003ca id=\"user-content-spined\" class=\"anchor\" aria-label=\"Permalink: SpinED\" href=\"#spined\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/twesterhout/spin-ed/actions\"\u003e\u003cimg src=\"https://github.com/twesterhout/spin-ed/workflows/CI/badge.svg\" alt=\"GitHub CI\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/twesterhout/spin-ed/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7ed6fdcd3ea81f453cfe5533a0ab95517babd43a2eab4a8a62302098463c3ebc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f74776573746572686f75742f7370696e2d65643f696e636c7564655f70726572656c6561736573\" alt=\"GitHub Release\" data-canonical-src=\"https://img.shields.io/github/v/release/twesterhout/spin-ed?include_prereleases\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5116bcc5e23725d16fc3dedb92367e72b3d6316596c5bca21f4df29d23b17971/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4253442d2d332d2d436c617573652d626c75652e737667\" alt=\"BSD-3-Clause license\" data-canonical-src=\"https://img.shields.io/badge/license-BSD--3--Clause-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUser-friendly exact diagonalization package for quantum many-body systems.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udd27 Installing\u003c/h2\u003e\u003ca id=\"user-content-wrench-installing\" class=\"anchor\" aria-label=\"Permalink: :wrench: Installing\" href=\"#wrench-installing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe provide pre-built static executables for Linux. Go to\n\u003ca href=\"https://github.com/twesterhout/spin-ed/releases\"\u003eReleases\u003c/a\u003e page and download the\nexecutable to your location of choice. That\u0027s it! \ud83e\udd73\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u2139\ufe0f \u003cstrong\u003eNote:\u003c/strong\u003e executables are currently tagged by git\ncommits from which they were built. It is suggested that after downloading\nthe application you create a symbolic link to it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eln --symbolic SpinED SpinED-8b0138b \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the commit hash may differ in your case\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udcdd Usage\u003c/h2\u003e\u003ca id=\"user-content--usage\" class=\"anchor\" aria-label=\"Permalink: \ud83d\udcdd Usage\" href=\"#-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing \u003ccode\u003eSpinED\u003c/code\u003e is quite simple. Just feed it your input \u003ca href=\"https://en.wikipedia.org/wiki/YAML\" rel=\"nofollow\"\u003eyaml\nfile\u003c/a\u003e. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./SpinED my_system.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ewhere \u003ccode\u003emy_system.yaml\u003c/code\u003e looks like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003ebasis\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003enumber_spins\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esymmetries\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[]\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003ehamiltonian\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eHeisenberg Hamiltonian\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eterms\u003c/span\u003e:\n - \u003cspan class=\"pl-ent\"\u003ematrix\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[[1, 0, 0, 0],\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e[0, -1, 2, 0],\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e[0, 2, -1, 0],\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e[0, 0, 0, 1]]\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003esites\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[[0, 1], [1, 2], [2, 3], [3, 0]]\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eobservables\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e[]\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis will create a file \u003ccode\u003eexact_diagonalization_result.h5\u003c/code\u003e which will contain\nthe ground state of the Hamiltonian.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003eh5dump -H exact_diagonalization_result.h5\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHDF5 \"exact_diagonalization_result.h5\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eGROUP \"/\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e GROUP \"basis\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASET \"representatives\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_STD_U64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 16 ) / ( 16 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e GROUP \"hamiltonian\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASET \"eigenvalues\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_IEEE_F64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 1 ) / ( 1 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASET \"eigenvectors\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_IEEE_F64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 16, 1 ) / ( 16, 1 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASET \"residuals\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_IEEE_F64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 1 ) / ( 1 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd we can check that it computed the correct energy:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-shell-session\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-s1\"\u003eh5dump -d /hamiltonian/eigenvalues exact_diagonalization_result.h5\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eHDF5 \"exact_diagonalization_result.h5\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eDATASET \"/hamiltonian/eigenvalues\" {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATATYPE H5T_IEEE_F64LE\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATASPACE SIMPLE { ( 1 ) / ( 1 ) }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e DATA {\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e (0): -8\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e }\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis was a very simple example! Have a look at \u003ca href=\"./template.yaml\"\u003e\u003ccode\u003etemplate.yaml\u003c/code\u003e\u003c/a\u003e\nwhich describes all supported fields. \u003ca href=\"./example/\"\u003e\u003ccode\u003eexample/\u003c/code\u003e\u003c/a\u003e folder also\ncontains various usage examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing and support\u003c/h2\u003e\u003ca id=\"user-content-contributing-and-support\" class=\"anchor\" aria-label=\"Permalink: Contributing and support\" href=\"#contributing-and-support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use this package for your research and have questions or suggestions,\nplease, don\u0027t hesitate to contact me on Github or\n\u003ca href=\"https://www.ru.nl/tcm/about-us/phd-students/westerhout/\" rel=\"nofollow\"\u003eemail\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlso, if the fact that most code here is written in Haskell doesn\u0027t scare you,\nfeel free to create a pull request implementing new features or fixing bugs!\u003c/p\u003e\n", "stargazers_count": 18, - "subscribers_count": 2, + "subscribers_count": 4, "topics": [ - "collision-avoidance", - "gazebo-simulator", - "robot-navigation" + "exact-diagonalization", + "quantum", + "many-body", + "high-performance", + "haskell", + "numerical-methods" ], - "updated_at": 1712803686.0 + "updated_at": 1730550333.0 }, { "data_format": 2, - "description": "A comprehensive tutorial for running OpenGL apps on containers", + "description": "A data processing platform for ChIP-seq, RNA-seq, MNase-seq, DNase-seq, ATAC-seq and GRO-seq datasets. Please ignore information on cipher.readthedocs.io, it is currently out of date. Follow information in README.", "filenames": [ "Singularity" ], - "full_name": "tashrifbillah/glxgears-containers", + "full_name": "c-guzman/cipher-workflow-platform", "latest_release": null, - "readme": "\u003cp\u003eRunning GUI app through containers is a bit of challenge. It becomes more challenging when the app requires OpenGL support.\u003c/p\u003e\n\u003cp\u003eAfter digging a lot on the web, I seem to find a solution. I am noting my solution here for everyone.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#docker\"\u003eDocker\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#build-container\"\u003eBuild container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#push-the-container\"\u003ePush the container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pull-the-container\"\u003ePull the container\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#run-the-container\"\u003eRun the container\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#linuxmac\"\u003eLinux/MAC\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#isolated-way\"\u003eIsolated way\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#hacky-way\"\u003eHacky way\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#windows\"\u003eWindows\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#docker-references\"\u003eDocker references\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#docker-known-issues\"\u003eDocker known issues\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#singularity\"\u003eSingularity\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#build-image\"\u003eBuild image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#push-the-image\"\u003ePush the image\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"#linuxmac-1\"\u003eLinux/MAC\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pull-the-image\"\u003ePull the image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#run-the-image\"\u003eRun the image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#windows-1\"\u003eWindows\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#from-centos7-iso-recommended\"\u003eFrom CentOS7 ISO (recommended)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#from-standard-centos7-box\"\u003eFrom standard CentOS7 box\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pull-the-image-1\"\u003ePull the image\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#run-the-image-1\"\u003eRun the image\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity-references\"\u003eSingularity references\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity-known-issues\"\u003eSingularity known issues\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#useful-tips\"\u003eUseful tips\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#1-enablediable-hyper-v\"\u003e1. Enable/Disable Hyper-V\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#2-display-for-root\"\u003e2. Display for root\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#3-3d-acceleration\"\u003e3. 3D acceleration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#4-x-forwarding-on-windows-host\"\u003e4. X forwarding on Windows host\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable of Contents created by \u003ca href=\"https://github.com/ekalinin/github-markdown-toc\"\u003egh-md-toc\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker\u003c/h1\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning GUI apps on Docker containers is relatively easier in the sense that it doesn\u0027t require a GUI desktop. You should\nbe able to forward X11 configuration from the host and run GUI apps on the container smoothly.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild container\u003c/h2\u003e\u003ca id=\"user-content-build-container\" class=\"anchor\" aria-label=\"Permalink: Build container\" href=\"#build-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeither \u003ccode\u003emesa-dri-drivers\u003c/code\u003e nor \u003ccode\u003emesa-libGL\u003c/code\u003e library could provide compatible environment for display\nin a docker container. Hence, I had to install \u003ccode\u003eNVIDIA-Linux-x86_64-430.40.run\u003c/code\u003e driver. See the full list of\nNVIDIA drivers at \u003ca href=\"\"\u003ehttps://www.nvidia.com/en-us/drivers/unix/\u003c/a\u003e.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker build -t glxgears-docker -f Dockerfile .\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePush the container\u003c/h2\u003e\u003ca id=\"user-content-push-the-container\" class=\"anchor\" aria-label=\"Permalink: Push the container\" href=\"#push-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003edocker tag -t glxgears-docker tbillah/glxgears-docker\ndocker login\ndocker push tbillah/glxgears-docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePull the container\u003c/h2\u003e\u003ca id=\"user-content-pull-the-container\" class=\"anchor\" aria-label=\"Permalink: Pull the container\" href=\"#pull-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker pull tbillah/glxgears-docker\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun the container\u003c/h2\u003e\u003ca id=\"user-content-run-the-container\" class=\"anchor\" aria-label=\"Permalink: Run the container\" href=\"#run-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLinux/MAC\u003c/h3\u003e\u003ca id=\"user-content-linuxmac\" class=\"anchor\" aria-label=\"Permalink: Linux/MAC\" href=\"#linuxmac\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIsolated way\u003c/h4\u003e\u003ca id=\"user-content-isolated-way\" class=\"anchor\" aria-label=\"Permalink: Isolated way\" href=\"#isolated-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY --privileged glxgears-docker glxgears\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eOr get in the container first and then run \u003ccode\u003eglxgears\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -ti -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY --privileged glxgears-docker\n(inside the docker container) glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003e--privileged\u003c/code\u003e enables \u003ccode\u003edirect rendering\u003c/code\u003e required for \u003ccode\u003eglxgears\u003c/code\u003e. Before forwarding the \u003ccode\u003eDISPLAY\u003c/code\u003e port like above,\n\u003ccode\u003exhost +\u003c/code\u003e might be necessary:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003exhost +\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eaccess control disabled, clients can connect from any host\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHacky way\u003c/h4\u003e\u003ca id=\"user-content-hacky-way\" class=\"anchor\" aria-label=\"Permalink: Hacky way\" href=\"#hacky-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI was able to run \u003ccode\u003eglxgears\u003c/code\u003e on docker container and Linux host by mounting the whole \u003ccode\u003e/usr/lib64\u003c/code\u003e directory without having to install\nany NVIDIA or mesa drivers on the container:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti -v /tmp/.X11-unix:/tmp/.X11-unix -v /usr/lib64:/usr/hostLib64 -e DISPLAY=$DISPLAY\n-e LD_LIBRARY_PATH=/usr/hostLib64 --privileged glxgears-docker glxgears\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWindows\u003c/h3\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eInstall X server \u003ca href=\"\"\u003ehttps://sourceforge.net/projects/vcxsrv/\u003c/a\u003e and have it running in the background.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou must uncheck the \u003ccode\u003eNative opengl\u003c/code\u003e option that exports \u003ccode\u003eLIBGL_ALWAYS_INDIRECT\u003c/code\u003e variable. Checking\n\u003ccode\u003eDisable access control\u003c/code\u003e might be necessary. See the image below:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/LIBGL_ALWAYS_INDIRECT.png\"\u003e\u003cimg src=\"screenshots/LIBGL_ALWAYS_INDIRECT.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFinally, save the configuration file:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/Save_configuration.PNG\"\u003e\u003cimg src=\"screenshots/Save_configuration.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eLaunch Docker Desktop. See a few important settings below:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/Resources.PNG\"\u003e\u003cimg src=\"screenshots/Resources.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/ShareDrive.PNG\"\u003e\u003cimg src=\"screenshots/ShareDrive.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\n\u003cp\u003eLaunch Windows PowerShell. I DIDN\u0027T require to launch PowerShell with \u003ccode\u003eRun as administrator\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain your IP address:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eipconfig\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eWireless LAN adapter Wi-Fi:\n\n Connection-specific DNS Suffix . : partners.org\n Link-local IPv6 Address . . . . . : fe80::111e:3245:4393:ed21%24\n IPv4 Address. . . . . . . . . . . : 10.22.138.136\n Subnet Mask . . . . . . . . . . . : 255.255.248.0\n Default Gateway . . . . . . . . . : 10.22.136.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eIPv4\u003c/code\u003e should be your IP address. The above command should print a bunch of IPv4 addresses. Any of them can be used.\nMoreover, you won\u0027t need an active internet connection to enable display in a docker container.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eSet up DISPLAY variable in the PowerShell:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSet-Variable -name DISPLAY -Value 10.22.138.136:0.0\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eFinally:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003edocker run --rm -ti -e DISPLAY=$DISPLAY --privileged glxgears-docker glxgears\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eOr get in the container first and then run \u003ccode\u003eglxgears\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -ti -e DISPLAY=$DISPLAY --privileged glxgears-docker\n(inside the docker container) glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker references\u003c/h2\u003e\u003ca id=\"user-content-docker-references\" class=\"anchor\" aria-label=\"Permalink: Docker references\" href=\"#docker-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://gernotklingler.com/blog/docker-replaced-virtual-machines-chroots/\" rel=\"nofollow\"\u003ehttp://gernotklingler.com/blog/docker-replaced-virtual-machines-chroots/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://wiki.ros.org/docker/Tutorials/GUI\" rel=\"nofollow\"\u003ehttp://wiki.ros.org/docker/Tutorials/GUI\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://dev.to/darksmile92/run-gui-app-in-linux-docker-container-on-windows-host-4kde\" rel=\"nofollow\"\u003ehttps://dev.to/darksmile92/run-gui-app-in-linux-docker-container-on-windows-host-4kde\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker known issues\u003c/h2\u003e\u003ca id=\"user-content-docker-known-issues\" class=\"anchor\" aria-label=\"Permalink: Docker known issues\" href=\"#docker-known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/NVIDIA/nvidia-docker/issues/586\"\u003ehttps://github.com/NVIDIA/nvidia-docker/issues/586\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/NVIDIA/nvidia-docker/issues/136\"\u003ehttps://github.com/NVIDIA/nvidia-docker/issues/136\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpon closing \u003ccode\u003efsleyes\u003c/code\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-viml\"\u003e\u003cpre\u003e (fsleye\u003cspan class=\"pl-smi\"\u003e\u003cspan class=\"pl-k\"\u003es:\u003c/span\u003e203\u003c/span\u003e): Gdk\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eERROR\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e01\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e14.549\u003c/span\u003e: The program \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003efsleyes\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e received an X Window System \u003cspan class=\"pl-c1\"\u003eerror\u003c/span\u003e.\n This probably reflects \u003cspan class=\"pl-c1\"\u003ea\u003c/span\u003e bug \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e the program.\n The \u003cspan class=\"pl-c1\"\u003eerror\u003c/span\u003e was \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eGLXBadCurrentWindow\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e.\n (Detail\u003cspan class=\"pl-smi\"\u003e\u003cspan class=\"pl-k\"\u003es:\u003c/span\u003e\u003c/span\u003e serial \u003cspan class=\"pl-c1\"\u003e95219\u003c/span\u003e error_code \u003cspan class=\"pl-c1\"\u003e159\u003c/span\u003e request_code \u003cspan class=\"pl-c1\"\u003e149\u003c/span\u003e minor_code \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e)\n (Note \u003cspan class=\"pl-c1\"\u003eto\u003c/span\u003e programmer\u003cspan class=\"pl-smi\"\u003e\u003cspan class=\"pl-k\"\u003es:\u003c/span\u003e\u003c/span\u003e normally, X errors are reported asynchronously;\n that \u003cspan class=\"pl-k\"\u003eis\u003c/span\u003e, you will receive the \u003cspan class=\"pl-c1\"\u003eerror\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ewhile\u003c/span\u003e after causing it.\n To \u003cspan class=\"pl-c1\"\u003edebug\u003c/span\u003e your program, run it with the \u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003esync\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecommand\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eline\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eoption\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eto\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003echange\u003c/span\u003e this behavior. You can then \u003cspan class=\"pl-c1\"\u003eget\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ea\u003c/span\u003e meaningful\n backtrace from your debugger \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e you \u003cspan class=\"pl-c1\"\u003ebreak\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eon\u003c/span\u003e the \u003cspan class=\"pl-en\"\u003egdk_x_error\u003c/span\u003e() \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e.)\n \u003cspan class=\"pl-sr\"\u003e/root/\u003c/span\u003efsl\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5.0\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e11\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003ecentos7\u003cspan class=\"pl-sr\"\u003e/bin/\u003c/span\u003efsleye\u003cspan class=\"pl-smi\"\u003e\u003cspan class=\"pl-k\"\u003es:\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eline\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e202\u003c/span\u003e Trace\u003cspan class=\"pl-sr\"\u003e/breakpoint trap ${FSLDIR}/\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003ebin\u003c/span\u003e\u003cspan class=\"pl-sr\"\u003e/FSLeyes/\u003c/span\u003efsleyes \u003cspan class=\"pl-smi\"\u003e$@\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis issue didn\u0027t affect anything I know of, so I labeled this as \u003cem\u003eWon\u0027t Fix\u003c/em\u003e.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eSince there is no way to reboot a docker container, NVIDIA driver won\u0027t load properly. Rather, it would only\nprovide libraries required to run \u003ccode\u003eglxgears\u003c/code\u003e or \u003ccode\u003efsleyes\u003c/code\u003e. Hence, commands like below run into errors:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp\u003envidia-smi\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNVIDIA-SMI has failed because it couldn\u0027t communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eHowever, I was able to eliminate this error by installing a version of NVIDIA driver on docker container that matches\nwith my host machine. Again, See the full list of NVIDIA drivers at \u003ca href=\"\"\u003ehttps://www.nvidia.com/en-us/drivers/unix/\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity\u003c/h1\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity containers i.e. \"sylabs/singularity-3.3-centos-7-64\" don\u0027t come with GUI enabled. You may forward X11 from\nwindows host by means of an X server. This approach worked fine for lightweight apps like \u003ccode\u003exclock\u003c/code\u003e and \u003ccode\u003exeyes\u003c/code\u003e but\nwasn\u0027t able to run OpenGL hungry apps like glxgears. So, the most manageable way of running OpenGL apps on\nSingularity containers should be through a GUI desktop.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild image\u003c/h2\u003e\u003ca id=\"user-content-build-image\" class=\"anchor\" aria-label=\"Permalink: Build image\" href=\"#build-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003esingularity build xclock-glxgears Singularity\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePush the image\u003c/h2\u003e\u003ca id=\"user-content-push-the-image\" class=\"anchor\" aria-label=\"Permalink: Push the image\" href=\"#push-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCreate new access token, give it a nice name i.e. tuna-salad\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca href=\"\"\u003ehttps://cloud.sylabs.io/auth\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eCopy the access token into your clipboard. Save the access token to \u003ccode\u003e~/.singularity/sylabs-token\u003c/code\u003e file for future use.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLogin to Singularity cloud:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity remote login SylabsCloud\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWhen asked, just paste the access token. After login is complete, you should be able to push your images to Singularity Cloud:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity push tuna-image library://tbillah/collection/tuna-image:0.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eBefore pushing an image, you will be asked to sign and verify it. Follow the link below to be able to do that:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://sylabs.io/guides/3.0/user-guide/signNverify.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/signNverify.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBut, you can push an unsigned image with \u0027-U\u0027 flag:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity push -U ~/Documents/tuna-image library://tbillah/collection/tuna-image:0.0.0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLinux/MAC\u003c/h3\u003e\u003ca id=\"user-content-linuxmac-1\" class=\"anchor\" aria-label=\"Permalink: Linux/MAC\" href=\"#linuxmac-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning OpenGL apps on singularity container for a Linux/MAC host is easier than running on docker container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePull the image\u003c/h4\u003e\u003ca id=\"user-content-pull-the-image\" class=\"anchor\" aria-label=\"Permalink: Pull the image\" href=\"#pull-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull xlock-glxgears library://tbillah/collection/xclock-glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the image\u003c/h4\u003e\u003ca id=\"user-content-run-the-image\" class=\"anchor\" aria-label=\"Permalink: Run the image\" href=\"#run-the-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --writable-tmpfs xclock-glxgears\n(inside the singularity shell) glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you have NVIDIA driver in your host machine, you can also use \u003ccode\u003e--nv\u003c/code\u003e flag to export host NVIDIA libraries.\nIn this case, you wouldn\u0027t need another \u003ccode\u003emesa-dri-drivers\u003c/code\u003e when you build the container.\u003c/p\u003e\n\u003cp\u003eNot using \u003ccode\u003e--nv\u003c/code\u003e flag may result in \u003ca href=\"#singularity-known-issues\"\u003eSingularity known issues #2\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWindows\u003c/h3\u003e\u003ca id=\"user-content-windows-1\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload Oracle VirtualBox from \u003ca href=\"https://www.virtualbox.org/wiki/Downloads\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFrom CentOS7 ISO (recommended)\u003c/h4\u003e\u003ca id=\"user-content-from-centos7-iso-recommended\" class=\"anchor\" aria-label=\"Permalink: From CentOS7 ISO (recommended)\" href=\"#from-centos7-iso-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDownload a complete CentOS7 image from your \u003ca href=\"http://isoredirect.centos.org/centos/7/isos/x86_64/\" rel=\"nofollow\"\u003epreferred mirror\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOpen \u003cem\u003eOracle VM VirtualBox Manager\u003c/em\u003e and create a new \u003cem\u003eVirtual Machine\u003c/em\u003e.\nAllocate memory and other resources as you see like. You can choose dynamic allocation.\nSee important steps below (there should be other steps in between):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/OracleNewVM.png\"\u003e\u003cimg src=\"screenshots/OracleNewVM.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/create-vhd.PNG\"\u003e\u003cimg src=\"screenshots/create-vhd.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003c/li\u003e\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/type-vdi.PNG\"\u003e\u003cimg src=\"screenshots/type-vdi.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eStart the virtual machine, provide the ISO you downloaded.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/point-to-centos7-iso.PNG\"\u003e\u003cimg src=\"screenshots/point-to-centos7-iso.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eCentOS7 installation requires little user intervention. You need to \u003cem\u003eSelect the device\u003c/em\u003e,\nswitch \u003cem\u003eON\u003c/em\u003e internet connection, and create \u003cem\u003eROOT PASSWORD\u003c/em\u003e. The rest of the settings could be left as\nthey are. See important steps below (there should be other steps in between):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/installation-disk.PNG\"\u003e\u003cimg src=\"screenshots/installation-disk.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/ethernet.PNG\"\u003e\u003cimg src=\"screenshots/ethernet.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content--1\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/select-root-pswd.PNG\"\u003e\u003cimg src=\"screenshots/select-root-pswd.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eAfter \u003cem\u003einitramfs\u003c/em\u003e creation is complete, the machine should reboot itself. The Virtual Machine should\nautomatically eject the ISO you provided. However, if it does not and upon reboot it goes back to\nCentOS installation prompt, eject the ISO manually:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/remove-iso.png\"\u003e\u003cimg src=\"screenshots/remove-iso.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNow \u003cem\u003eStart\u003c/em\u003e your virtual machine again.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003e\n\u003cp\u003eAfter successful installation of CentOS7, open a terminal on GUI desktop, switch to root,\nand install the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo su -\n yum -y install epel-release\n yum -y install singularity\n exit\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFrom standard CentOS7 box\u003c/h4\u003e\u003ca id=\"user-content-from-standard-centos7-box\" class=\"anchor\" aria-label=\"Permalink: From standard CentOS7 box\" href=\"#from-standard-centos7-box\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis section is put here for learning purpose:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Vagrant for Windows \u003ca href=\"\"\u003ehttps://www.vagrantup.com/downloads.html\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Vagrant manager for Windows \u003ca href=\"\"\u003ehttp://vagrantmanager.com/downloads/\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLaunch \u003cem\u003eWindows PowerShell\u003c/em\u003e and do the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e mkdir gui-container\n New-Item Vagrantfile\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eSave the following configuration in the \u003cem\u003eVagrantfile\u003c/em\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eVagrant.configure(\"2\") do |config|\n \n config.vm.box = \"centos/7\"\n config.vm.provider \"virtualbox\" do |v|\n v.gui = true\n v.memory = 2048\n v.cpus = 2\n end\n\nend \n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\n\u003cp\u003eKeep \u003cem\u003eOracle VM VirtualBox Manager\u003c/em\u003e running in the background and start the virtual machine defined by\n\u003cem\u003eVagrantfile\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e vagrant up\n vagrant ssh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOnce in the Virtual Machine, open a terminal, switch to root, and install the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo su -\n\nyum -y groupinstall \u0027gnome desktop\u0027\nyum -y install \u0027xorg*\u0027\nyum -y install epel-release\nyum -y install singularity\n\nyum remove -y initial-setup initial-setup-gui\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow switch to GUI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esystemctl isolate graphical.target\nsystemctl set-default graphical.target\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUpon doing the above, you should see GUI desktop in your virtual box window.\nIt may be useful to reboot at this point (from the host terminal):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant halt\nvagrant up\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePull the image\u003c/h4\u003e\u003ca id=\"user-content-pull-the-image-1\" class=\"anchor\" aria-label=\"Permalink: Pull the image\" href=\"#pull-the-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinally, open a terminal on GUI desktop and pull the image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull xclock-glxgears library://tbillah/collection/xclock-glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRun the image\u003c/h4\u003e\u003ca id=\"user-content-run-the-image-1\" class=\"anchor\" aria-label=\"Permalink: Run the image\" href=\"#run-the-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell --writable-tmpfs xclock-glxgears\n(inside the singularity shell) glxgears\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity references\u003c/h2\u003e\u003ca id=\"user-content-singularity-references\" class=\"anchor\" aria-label=\"Permalink: Singularity references\" href=\"#singularity-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://codingbee.net/vagrant/vagrant-enabling-a-centos-vms-gui-mode\" rel=\"nofollow\"\u003ehttps://codingbee.net/vagrant/vagrant-enabling-a-centos-vms-gui-mode\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity known issues\u003c/h2\u003e\u003ca id=\"user-content-singularity-known-issues\" class=\"anchor\" aria-label=\"Permalink: Singularity known issues\" href=\"#singularity-known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/sylabs/singularity/issues/4290\"\u003ehttps://github.com/sylabs/singularity/issues/4290\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpon running \u003ccode\u003exclock-glxgears\u003c/code\u003e, you may get the following error:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e libGL: screen 0 does not appear to be DRI2 capable\n libGL: OpenDriver: trying /usr/lib64/dri/tls/swrast_dri.so\n libGL: OpenDriver: trying /usr/lib64/dri/swrast_dri.so\n libGL: Can\u0027t open configuration file /root/.drirc: No such file or directory\n libGL: Can\u0027t open configuration file /root/.drirc: No such file or directory\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf you have matching libraries across host and image accompanied by X configuration, this error might go away.\nOne solution is to have NVIDIA driver on the host and use \u003ccode\u003e--nv\u003c/code\u003e flag while running singularity image.\nHowever, I labeled this issue as \u003cem\u003eWon\u0027t Fix\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUseful tips\u003c/h1\u003e\u003ca id=\"user-content-useful-tips\" class=\"anchor\" aria-label=\"Permalink: Useful tips\" href=\"#useful-tips\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e1. Enable/Disable Hyper-V\u003c/h2\u003e\u003ca id=\"user-content-1-enabledisable-hyper-v\" class=\"anchor\" aria-label=\"Permalink: 1. Enable/Disable Hyper-V\" href=\"#1-enabledisable-hyper-v\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eDocker Desktop\u003c/em\u003e requires Hyper-V enabled while \u003cem\u003eOracle/Vagrant VirtualBox\u003c/em\u003e requires it disabled.\nFollow Microsoft documentation to do the required:\n\u003ca href=\"\"\u003ehttps://support.microsoft.com/en-us/help/3204980/virtualization-applications-do-not-work-together-with-hyper-v-device-g\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHowever, enabling/disabling Hyper-V required me to restart my machine twice instead of once asked.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e2. Display for root\u003c/h2\u003e\u003ca id=\"user-content-2-display-for-root\" class=\"anchor\" aria-label=\"Permalink: 2. Display for root\" href=\"#2-display-for-root\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou usually log into Singularity container as \u003ccode\u003evagrant\u003c/code\u003e. You might need to switch to root\nfor running certain GUI applications. See the instruction below to set up display for root\nuser:\u003c/p\u003e\n\u003cp\u003e(i) Obtain your display parameters\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eecho $DISPLAY\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eYOUR-DISPLAY-SOCKET\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003exuath list\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003eYOUR-DISPLAY-COOKIE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(ii) Export display parameters to root\u003c/p\u003e\n\u003cp\u003eNow switch to root user:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003esudo su -\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eAdd the display parameters for root user:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport DISPLAY=${YOUR-DISPLAY-SOCKET}\nxauth add ${YOUR-DISPLAY-COOKIE}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTest display for root user now:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003exeyes\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eglxgears\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003e(iii) But, if you need to use GUI with \u003cem\u003eroot\u003c/em\u003e user, it may be useful to log out and log in as \u003cem\u003eroot\u003c/em\u003e directly.\nUse cases include running containers with root privileges.\u003c/p\u003e\n\u003cp\u003e(iv) If needed, you may follow this suggestion to create \u003cem\u003e.XAuthority\u003c/em\u003e files:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"\"\u003ehttps://superuser.com/questions/806637/xauth-not-creating-xauthority-file\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e3. 3D acceleration\u003c/h2\u003e\u003ca id=\"user-content-3-3d-acceleration\" class=\"anchor\" aria-label=\"Permalink: 3. 3D acceleration\" href=\"#3-3d-acceleration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRunning fsleyes required me to disable 3D acceleration from \u003cem\u003eVirtualBox Manager Settings\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"screenshots/disable-3d-accel.PNG\"\u003e\u003cimg src=\"screenshots/disable-3d-accel.PNG\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e4. X forwarding on Windows host\u003c/h2\u003e\u003ca id=\"user-content-4-x-forwarding-on-windows-host\" class=\"anchor\" aria-label=\"Permalink: 4. X forwarding on Windows host\" href=\"#4-x-forwarding-on-windows-host\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou need to install \u003ca href=\"https://gitforwindows.org/\" rel=\"nofollow\"\u003eGit Bash\u003c/a\u003e first. The following should be included to \u003cem\u003eVagrantfile\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econfig.ssh.forward_x11 = true\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eDetails can be found below:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"\"\u003ehttps://stackoverflow.com/questions/40056227/warning-no-xauth-data-using-fake-authentication-data-for-x11-forwarding\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 18, - "subscribers_count": 4, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eC I P H E R\u003c/h1\u003e\u003ca id=\"user-content-c-i-p-h-e-r\" class=\"anchor\" aria-label=\"Permalink: C I P H E R\" href=\"#c-i-p-h-e-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVersion 1.0.0 | Updated August 2017\u003c/p\u003e\n\u003cp\u003eAuthor: Carlos Guzman\u003c/p\u003e\n\u003cp\u003eE-mail: \u003ca href=\"mailto:cag104@ucsd.edu\"\u003ecag104@ucsd.edu\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCIPHER is a data processing workflow platform for next generation sequencing data including ChIP-seq, RNA-seq, DNase-seq, MNase-seq, ATAC-seq and GRO-seq. By taking advantage of the Nextflow language, and Singularity containers, CIPHER is an extremely easy to use, and reproducible pre-processing workflow toolkit.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHELP\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: HELP\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCIPHER has a built in help command. For more information regarding possible parameters and their meanings, open up the command line terminal and type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run cipher.nf --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload or \u003ccode\u003egit clone\u003c/code\u003e this repository and install dependencies.\u003c/p\u003e\n\u003cp\u003eThe only required dependencies to run CIPHER is:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextflow (\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSingularity (\u003ca href=\"http://singularity.lbl.gov/index.html\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/index.html\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCONFIG Files\u003c/h2\u003e\u003ca id=\"user-content-config-files\" class=\"anchor\" aria-label=\"Permalink: CONFIG Files\" href=\"#config-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConfig files are tab separated text files with 5 columns for single-ended data and 6 columns for pair ended data.\u003c/p\u003e\n\u003cp\u003eSingle-ended CONFIG:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esample1\t\tsample1_rep1\t/path/to/fastq.gz \tcontrol1\tsample1\nsample2\t\tsample2_rep1\t/path/to/fastq.gz \tcontrol1\tinput\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePair-ended CONFIG:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esample1\t\tsample1_rep1\t/path/to/fastq_R1.gz \t/path/to/fastq_R2.gz\tcontrol1\tsample1\nsample2\t\tsample2_rep1\t/path/to/fastq_R1.gz \t/path/to/fastq_R2.gz\tcontrol1\tinput\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eDO NOT MIX AND MATCH SINGLE AND PAIR ENDED DATA INTO THE SAME CONFIG FILE. CIPHER DOES NOT HANDLE THIS USE-CASE YET.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhere columns refer to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col\u003e\n\u003cli\u003eMergeID - Prefix used for naming files that are merged together.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eSampleID - Prefix used for naming files that are not merged together. Typically includes replicate information.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003ePath 1 - The file path to first FASTQ file. Typically the R1 file in pair-ended data.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003ePath 2 - The file path to second FASTQ file. Only required for pair-ended data. Typically the R2 file in pair-ended data.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInputID - Used to pair sample and input files for various types of sequencing data. Use \u003ccode\u003e-\u003c/code\u003e if no input file is available or needed (as is the case in RNA-seq/GRO-seq/MNase-seq/etc.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eMark - Used to differentiate sample files from input files. Use the keyword \u003ccode\u003einput\u003c/code\u003e if that sample corresponds to an input file. Otherwise use \u003ccode\u003eMergeID\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CIPHER\u003c/h2\u003e\u003ca id=\"user-content-running-cipher\" class=\"anchor\" aria-label=\"Permalink: Running CIPHER\" href=\"#running-cipher\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall required dependencies\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate Singularity container (will require \u003ccode\u003esudo\u003c/code\u003e access, so a container can be created on a local laptop/desktop and then transferred to the appopriate location/machine/cluster)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create -s 8000 cipher.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity bootstrap cipher.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun your workflow\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run cipher.nf -with-singularity \u0026lt;cipher.img\u0026gt; --mode \u0026lt;MODE\u0026gt; --config \u0026lt;CONFIG\u0026gt; --fa \u0026lt;FASTA\u0026gt; --gtf \u0026lt;GTF\u0026gt; --lib \u0026lt;LIB\u0026gt; --readLen \u0026lt;LENGTH\u0026gt; [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e If not running on a cluster please set the \u003ccode\u003e-qs \u0026lt;INT\u0026gt;\u003c/code\u003e flag in order to control the number of processes that CIPHER parallelizes. Too many and the workflow will abruptly end because it runs out of memory. \u003ccode\u003enextflow run -qs \u0026lt;INT\u0026gt; cipher.nf ...\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e If you would like to run CIPHER without using Singularity containers, please make sure that you have installed all the required software for your specific pipeline. Tools used can be found inside the main cipher.nf script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample Data\u003c/h2\u003e\u003ca id=\"user-content-example-data\" class=\"anchor\" aria-label=\"Permalink: Example Data\" href=\"#example-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome example data to test CIPHER\u0027s workflows can be found in the \u003ccode\u003eexample_data\u003c/code\u003e folder. The user should alter the config file fastq paths before running the workflow otherwise the run will fail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CIPHER on a Cluster\u003c/h2\u003e\u003ca id=\"user-content-running-cipher-on-a-cluster\" class=\"anchor\" aria-label=\"Permalink: Running CIPHER on a Cluster\" href=\"#running-cipher-on-a-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCIPHER is possible to execute it on your computer or any cluster resource\nmanager without modifying it.\u003c/p\u003e\n\u003cp\u003eCurrently the following platforms are supported:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOracle/Univa/Open Grid Engine (SGE)\u003c/li\u003e\n\u003cli\u003ePlatform LSF\u003c/li\u003e\n\u003cli\u003eSLURM\u003c/li\u003e\n\u003cli\u003ePBS/Torque\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default the pipeline is parallelized by spanning multiple threads in the machine where the script is launched.\u003c/p\u003e\n\u003cp\u003eFor example, to submit the execution to a SGE cluster edit the file named \u003ccode\u003enextflow.config\u003c/code\u003e, in the directory\nwhere the cipher.nf file is found, with the following content:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eprocess {\n executor=\u0027sge\u0027\n queue=\u0027\u0026lt;your queue name\u0026gt;\u0027\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn doing that, tasks will be executed through the \u003ccode\u003eqsub\u003c/code\u003e SGE command, and so your pipeline will behave like any\nother SGE job script, with the benefit that \u003cem\u003eNextflow\u003c/em\u003e will automatically and transparently manage the tasks\nsynchronisation, file(s) staging/un-staging, etc.\u003c/p\u003e\n\u003cp\u003eMore information regarding the platforms Nextflow supports and how to run them can be found \u003ca href=\"https://www.nextflow.io/docs/latest/executor.html\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 19, + "subscribers_count": 1, "topics": [], - "updated_at": 1712559721.0 + "updated_at": 1667468054.0 }, { "data_format": 2, - "description": " The Oceanographic Multi-purpose Software Environment: a package for multi-physics and multi-scale earth science simulations.", + "description": "VGEA (Viral Genomes Easily Analyzed) is a pipeline for analysis of RNA virus next-generation sequencing data.", "filenames": [ "Singularity" ], - "full_name": "omuse-geoscience/omuse", - "latest_release": "v2021.6.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOMUSE\u003c/h1\u003e\u003ca id=\"user-content-omuse\" class=\"anchor\" aria-label=\"Permalink: OMUSE\" href=\"#omuse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOMUSE stands for Oceanographic MUltipurpose Software Environment. It is a\npackage to conduct numerical experiments in oceanography and other Earth\nsciences. Example OMUSE applications can be found in the examples\n\u003ca href=\"https://github.com/omuse-geoscience/omuse-examples\"\u003erepository\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFor whom is OMUSE?\u003c/h3\u003e\u003ca id=\"user-content-for-whom-is-omuse\" class=\"anchor\" aria-label=\"Permalink: For whom is OMUSE?\" href=\"#for-whom-is-omuse\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOMUSE aims to be useable by any researcher or student with a basic knowledge of\nthe Python programming language.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat is this repository for?\u003c/h3\u003e\u003ca id=\"user-content-what-is-this-repository-for\" class=\"anchor\" aria-label=\"Permalink: What is this repository for?\" href=\"#what-is-this-repository-for\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository contains the source tree for OMUSE, including OMUSE specific framework\ncomponents and community codes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow do I get set up?\u003c/h3\u003e\u003ca id=\"user-content-how-do-i-get-set-up\" class=\"anchor\" aria-label=\"Permalink: How do I get set up?\" href=\"#how-do-i-get-set-up\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhile there are some packages available on \u003ca href=\"www.pypi.org\"\u003epipy\u003c/a\u003e, we recommend at the moment\nto do a pip developer install:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esetup a python environment, e.g. using virtualenv, and activate it.\u003c/li\u003e\n\u003cli\u003ein a suitable working directory clone the \u003ca href=\"https://github.com/amusecode/amuse\"\u003eAMUSE\u003c/a\u003e repository: \u003ccode\u003egit clone https://github.com/amusecode/amuse\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ego into the created directory: \u003ccode\u003ecd amuse\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003edo the developer install from here: \u003ccode\u003epip install -e . [MPI]\u003c/code\u003e The MPI is optional.\u003c/li\u003e\n\u003cli\u003eGoing back to the working directory (\u003ccode\u003ecd ..\u003c/code\u003e) also clone the OMUSE repository: \u003ccode\u003egit clone https://github.com/omuse-geoscience/omuse\u003c/code\u003e,\u003c/li\u003e\n\u003cli\u003ego into the source directory \u003ccode\u003ecd omuse\u003c/code\u003e and set the environment variable \u003ccode\u003eDOWNLOAD_CODES\u003c/code\u003e, e.g. \u003ccode\u003eexport DOWNLOAD_CODES=latest\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003enow, do \u003ccode\u003epip install -e .\u003c/code\u003e from the root of the package\u003c/li\u003e\n\u003cli\u003etype \u003ccode\u003epython setup.py build_codes --inplace\u003c/code\u003e to build the codes.\u003c/li\u003e\n\u003cli\u003ethe file \u003ccode\u003ebuild.log\u003c/code\u003e will report any errors in the build process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis installs amuse-devel and omuse-devel. The community codes of OMUSE can\nbe build manually by going into each directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esrc/omuse/community/adcirc\u003c/li\u003e\n\u003cli\u003esrc/omuse/community/swan\u003c/li\u003e\n\u003cli\u003eetc\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eand typing: first \u003ccode\u003emake download\u003c/code\u003e (for some) and then \u003ccode\u003emake\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOMUSE has been tested on OSX and linux machines, with ifort and gfortran\ncompilers, on desktop machines and on the Carthesius supercomputer.\u003c/p\u003e\n\u003cp\u003eIn addition to the AMUSE dependencies, OMUSE needs/ can use the following\npackages:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ematplotlib basemap\u003c/li\u003e\n\u003cli\u003enetCDF and netCDF for fortran and the python bindings\u003c/li\u003e\n\u003cli\u003eGRIB_API\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDocumentation can be found \u003ca href=\"https://omuse.readthedocs.io\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. In addition the base \u003ca href=\"https://amuse.readthedocs.io\" rel=\"nofollow\"\u003eAMUSE documentation\u003c/a\u003e can be consulted.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eReporting issues\u003c/h3\u003e\u003ca id=\"user-content-reporting-issues\" class=\"anchor\" aria-label=\"Permalink: Reporting issues\" href=\"#reporting-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIssues can be reported at the OMUSE issue tracker; for framework issues,\nreport them at the AMUSE \u003ca href=\"https://github.com/amusecode/amuse\"\u003erepository\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eContribution guidelines\u003c/h3\u003e\u003ca id=\"user-content-contribution-guidelines\" class=\"anchor\" aria-label=\"Permalink: Contribution guidelines\" href=\"#contribution-guidelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eContributions are welcome. Note that most framework development happens at\nthe AMUSE \u003ca href=\"https://github.com/amusecode/amuse\"\u003erepository\u003c/a\u003e A primer for\nwriting code interfaces and other documentation can be found on the amuse\n\u003ca href=\"www.amusecode.org\"\u003ewebsite\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 18, - "subscribers_count": 5, + "full_name": "pauloluniyi/VGEA", + "latest_release": "v1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVGEA \u003ca href=\"https://zenodo.org/badge/latestdoi/236802599\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f4144c21d8e6fe4bafe17833ca7ade3db5f320ce97ec3923a4c25f71a34ccfcc/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3233363830323539392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/236802599.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-vgea-\" class=\"anchor\" aria-label=\"Permalink: VGEA \" href=\"#vgea-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVGEA (Viral Genomes Easily Analyzed) is an RNA viral assembly toolkit.\u003c/p\u003e\n\u003cp\u003eVGEA was developed to aid in the analysis of next generation sequencing data. Users can do the following with this pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRemove adapters, low quality bases/positions, and perform read-level QC\u003c/li\u003e\n\u003cli\u003eAlign paired-end sequencing reads to the human reference genome.\u003c/li\u003e\n\u003cli\u003eExtract unmapped/unaligned reads.\u003c/li\u003e\n\u003cli\u003eSplit bam files into forward and reverse reads.\u003c/li\u003e\n\u003cli\u003eCarry out de novo assembly of forward and reverse reads to generate contigs.\u003c/li\u003e\n\u003cli\u003ePre-process reads for quality and contamination.\u003c/li\u003e\n\u003cli\u003eMap reads to a reference tailored to the sample using corrected contigs supplemented by the user\u2019s choice of reference sequences.\u003c/li\u003e\n\u003cli\u003eEvaluate/assess the quality of genome assemblies.\u003c/li\u003e\n\u003cli\u003eCollate results in a multiqc summary..\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDependencies:\u003c/p\u003e\n\u003cp\u003eThe VGEA pipeline requires the following dependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSnakemake (K\u00f6ster et al., 2012)\u003c/li\u003e\n\u003cli\u003eFastp (Chen et al., 2018)\u003c/li\u003e\n\u003cli\u003eBWA (Li and Durbin, 2009)\u003c/li\u003e\n\u003cli\u003eSamtools (Li et al., 2009)\u003c/li\u003e\n\u003cli\u003eIVA (Hunt et al., 2015)\u003c/li\u003e\n\u003cli\u003eShiver (Wymant et al., 2018)\u003c/li\u003e\n\u003cli\u003eSeqKit (Shen et al., 2016)\u003c/li\u003e\n\u003cli\u003eQuast (Gurevich et al., 2013)\u003c/li\u003e\n\u003cli\u003eMultiqc (Ewels et al., 2016)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVGEA was built on the Snakemake workflow management system and utilizes existing tools for each step: \u003cstrong\u003efastp\u003c/strong\u003e (Chen et al., 2018) for read trimming and quality control, \u003cstrong\u003ebwa\u003c/strong\u003e (Li and Durbin, 2009) for mapping sequencing reads to the human reference genome, \u003cstrong\u003esamtools\u003c/strong\u003e (Li et al., 2009) for extracting unmapped reads and also for splitting bam files into fastq files, \u003cstrong\u003eiva\u003c/strong\u003e (Hunt et al., 2015) for de novo assembly to generate contigs, \u003cstrong\u003eshiver\u003c/strong\u003e (Wymant et al., 2018) to pre-process reads for quality and contamination, then map to a reference tailored to the sample using corrected contigs supplemented with the user\u2019s choice of existing reference sequences, \u003cstrong\u003eseqkit\u003c/strong\u003e for cleaning shiver assembly for QUAST and \u003cstrong\u003equast\u003c/strong\u003e (Gurevich et al., 2013) to evaluate/assess the quality of genome assemblies.\nFinally, an interactive report of results are generated by \u003cstrong\u003eMultiQC\u003c/strong\u003e (Ewels et al., 2016).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Conda\u003c/h3\u003e\u003ca id=\"user-content-1-conda\" class=\"anchor\" aria-label=\"Permalink: 1. Conda\" href=\"#1-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow requires conda to be installed and available on the system.\u003c/p\u003e\n\u003cp\u003eTo do this install conda via the miniconda installers found \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and instructions \u003ca href=\"https://docs.conda.io/projects/continuumio-conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBriefly:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLinux\u003c/h4\u003e\u003ca id=\"user-content-linux\" class=\"anchor\" aria-label=\"Permalink: Linux\" href=\"#linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo obtain the installer for linux use the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, install miniconda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esh Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMacOS\u003c/h4\u003e\u003ca id=\"user-content-macos\" class=\"anchor\" aria-label=\"Permalink: MacOS\" href=\"#macos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo obtain the installer for MacOS, you can \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003edownload\u003c/a\u003e it manually or use wget:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, install miniconda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esh Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Snakemake\u003c/h3\u003e\u003ca id=\"user-content-2-snakemake\" class=\"anchor\" aria-label=\"Permalink: 2. Snakemake\" href=\"#2-snakemake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThen install snakemake as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -c bioconda -c conda-forge --name snakemake snakemake \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. VGEA\u003c/h3\u003e\u003ca id=\"user-content-3-vgea\" class=\"anchor\" aria-label=\"Permalink: 3. VGEA\" href=\"#3-vgea\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo complete installation of VGEA clone the directory and enter it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:pauloluniyi/VGEA.git\ncd VGEA\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Data Dependency\u003c/h3\u003e\u003ca id=\"user-content-4-data-dependency\" class=\"anchor\" aria-label=\"Permalink: 4. Data Dependency\" href=\"#4-data-dependency\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinally, you need to download a human reference genome.\nThere is a convenience script provided to do this that can run as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd resources/\nbash get_human_reference.sh \ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are 2 key files when running VGEA:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA yaml config file containing key parameters e.g., \u003ccode\u003econfig/config.yaml\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA 3 column tab-seperated sheet with a name and paths to r1 and r2 for each sample e.g., \u003ccode\u003e./tests/integration/sample_table.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfig\u003c/h3\u003e\u003ca id=\"user-content-config\" class=\"anchor\" aria-label=\"Permalink: Config\" href=\"#config\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esample_table\u003c/code\u003e is the path to the tsv created above with names and read locations for each sample (under headings id, r1, r2)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eshiver_config_file\u003c/code\u003e is the path to the config file for shiver, by default this is \u003ccode\u003econfig/shiver_config.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ehuman_reference_genome\u003c/code\u003e is the path to the fasta file containing the human genome reference, if you used \u003ccode\u003eget_human_reference.sh\u003c/code\u003e this will be \u003ccode\u003eresources/GRCh38_latest_genomic.fna\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_species\u003c/code\u003e is one of the 4 pre-supported viral references (\u003ccode\u003eHIV-1\u003c/code\u003e, \u003ccode\u003eSARS-CoV-2\u003c/code\u003e, \u003ccode\u003eLASV_L\u003c/code\u003e, \u003ccode\u003eLASV_S\u003c/code\u003e) and is used to autofill the paths to various reference files used by default for these. If you supply your own viral reference/adapter/primer files then this doesn\u0027t need to be supplied\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_reference_alignment\u003c/code\u003e path to a reference alignment of the appropriate viral genomes for your samples (by default \u003ccode\u003eresources/{viral_species}/MyRefAlignment.fasta\u003c/code\u003e). This reference alignment should be carefully created.\n\"An alignment of existing reference sequences is required as input for shiver. Construction of a custom reference for mapping involves identifying the existing references that are closest to the sample under consideration. The greater the number and diversity of existing references given as input, the denser and broader the coverage of sequence space is, and the closer the closest reference is expected to be, with corresponding benefits for the accuracy of the results. However these existing references should be aligned to each other accurately, in order for the addition of each sample\u2019s contigs to the alignment to be meaningful; this means that producing such an input by automatically aligning a large number of diverse sequences without checking the results would be a bad idea. This alignment will be used as input for every sample in a dataset processed by shiver, and so the user is advised to put a little thought into sequence selection and manually curating the alignment if needed.\" (Wymant et al., 2018).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_reference_genome\u003c/code\u003e path to a singular reference genome for your samples for QUAST based assembly assessment (by default \u003ccode\u003eresources/{viral_species}/MyRefGenome.fasta\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_reference_gene_features\u003c/code\u003e path to a GFF3 containing gene features for the supplied viral reference genome (by default \u003ccode\u003eresources/{viral_species}/MyRefFeatures.gff3\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_sequencing_adapters\u003c/code\u003e path to a fasta file containing the sequencing adapters used for your samples (by default \u003ccode\u003eresources/{viral_species}/MyAdapters.fasta\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_sequencing_primers\u003c/code\u003e path to a fasta file containing the sequencing primers used for your samples (by default \u003ccode\u003eresources/{viral_species}/MyRefAlignment.fasta\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSample Table\u003c/h3\u003e\u003ca id=\"user-content-sample-table\" class=\"anchor\" aria-label=\"Permalink: Sample Table\" href=\"#sample-table\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA 3 column, tab-separated file with an \u003cem\u003eid\u003c/em\u003e column containing sample names, \u003cem\u003er1\u003c/em\u003e with the path to the forward reads for that sample, and \u003cem\u003er2\u003c/em\u003e with the path to reverse reads for that sample.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eid\tr1\tr2\ntest1\t.tests/integration/test1_r1.fq.gz\t.tests/integration/test1_r2.fq.gz\ntest2\t.tests/integration/test2_r1.fq.gz\t.tests/integration/test2_r2.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExecuting the workflow\u003c/h3\u003e\u003ca id=\"user-content-executing-the-workflow\" class=\"anchor\" aria-label=\"Permalink: Executing the workflow\" href=\"#executing-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow will automatically install dependencies using conda/mamba if executed with \u003ccode\u003e--use-conda\u003c/code\u003e otherwise all dependencies listed at the start of the README must be manually installed via conda.\u003c/p\u003e\n\u003cp\u003eTo run the workflow complete the above config and sample tables and execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --cores $n --use-conda --configfile $your_config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003e$n\u003c/code\u003e is the number of cores with which to execute the workflow and \u003ccode\u003e$your_config\u003c/code\u003e is the path to \u003ccode\u003econfig.yaml\u003c/code\u003e you\u0027ve created.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eResults\u003c/h3\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVGEA will output all results in the \u003ccode\u003eresults/\u003c/code\u003e directory with a subfolder containing results for each sample and top-level folders collecting all log files, tool benchmarks, and an interactive multiQC html summary of results.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eresults/\n\u251c\u2500\u2500 benchmarks # all benchmark files with hardware usage for each sample\n\u251c\u2500\u2500 logs # all log files for each rule and sample\n\u251c\u2500\u2500 multiqc # multiqc summary of quast, fastp, and de-hosting results\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 multiqc_data\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 multiqc_report.html # the interactive multiqc report\n\u251c\u2500\u2500 test1 # all results for the sample \"test1\" \n\u2502\u00a0\u00a0 \u251c\u2500\u2500 shiver # folder containing all shiver output files \n\u2502 \u251c\u2500\u2500 test1_1.fastq # fastp trimmed and de-hosted reads for the sample \n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1_2.fastq\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.bam # alignment against human reference \n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.fasta # final cleaned assembly from IVA and shiver\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.fastp.html # fastp report in html format\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.fastp.json # fastp report in json format\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.flagstat # dehosting mapping statistics\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1_iva # folder containing all IVA assembly files\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.quast_results # folder containing all QUAST assembly assessment of test1.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1_r1_trimmed.fq # fastp trimmed reads\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1_r2_trimmed.fq \n\u2502\u00a0\u00a0 \u2514\u2500\u2500 test1.sam # sam file containing all the reads that didn\u0027t map to the human reference\n\u2514\u2500\u2500 test2\n \u251c\u2500\u2500 shiver\n \u251c\u2500\u2500 test2_1.fastq\n \u251c\u2500\u2500 test2_2.fastq\n \u251c\u2500\u2500 test2.bam\n \u251c\u2500\u2500 test2.fasta\n \u251c\u2500\u2500 test2.fastp.html\n \u251c\u2500\u2500 test2.fastp.json\n \u251c\u2500\u2500 test2.flagstat\n \u251c\u2500\u2500 test2_iva\n \u251c\u2500\u2500 test2.quast_results\n \u251c\u2500\u2500 test2_r1_trimmed.fq\n \u251c\u2500\u2500 test2_r2_trimmed.fq\n \u2514\u2500\u2500 test2.sam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainerized Singularity (Beta)\u003c/h2\u003e\u003ca id=\"user-content-containerized-singularity-beta\" class=\"anchor\" aria-label=\"Permalink: Containerized Singularity (Beta)\" href=\"#containerized-singularity-beta\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, users can run the VGEA pipeline with all dependencies installed in a docker/singularity container.\u003c/p\u003e\n\u003cp\u003eThis requires singularity and snakemake to be installed on the system but in theory provides a more reproducible version of the conda environments (not fully tested compared to just conda).\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for instructions to install Singularity.\u003c/p\u003e\n\u003cp\u003eThen the workflow can be run as normal with \u003ccode\u003e--use-singularity\u003c/code\u003e added e.g.,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-conda --use-singularity --configfile .tests/integration/test_config.yaml -j 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run a minimal integration test once snakemake and conda are installed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-conda --configfile .tests/integration/test_config.yaml -j 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContributions\u003c/h1\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePaul Eniola Oluniyi\u003c/li\u003e\n\u003cli\u003eGerry Tonkin-Hill (\u003ca href=\"https://gtonkinhill.github.io/\" rel=\"nofollow\"\u003ehttps://gtonkinhill.github.io/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eFinlay Maguire (\u003ca href=\"https://finlaymagui.re\" rel=\"nofollow\"\u003ehttps://finlaymagui.re\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/236802599\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f4144c21d8e6fe4bafe17833ca7ade3db5f320ce97ec3923a4c25f71a34ccfcc/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3233363830323539392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/236802599.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 19, + "subscribers_count": 1, + "topics": [], + "updated_at": 1719708921.0 + }, + { + "data_format": 2, + "description": null, + "filenames": [ + "container/Singularity.gnu", + "container/Singularity.intel_netcdf", + "container/Singularity.intel_am4" + ], + "full_name": "NOAA-GFDL/AM4", + "latest_release": "highres_aquaplanet_2022", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGFDL AM4 Model\u003c/h1\u003e\u003ca id=\"user-content-gfdl-am4-model\" class=\"anchor\" aria-label=\"Permalink: GFDL AM4 Model\" href=\"#gfdl-am4-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/102487636\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fea9cd2271674ad89e1a2cc6411a28dbebf6652947fe704e31cc8a612460e113/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3130323438373633362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/102487636.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository includes the public release of the GFDL AM4 model\ncode. The AM4 model is described in the\n\u003ca href=\"https://doi.org/10.1002/2017MS001208\" rel=\"nofollow\"\u003etwo\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.1002/2017MS001209\" rel=\"nofollow\"\u003earticles\u003c/a\u003e published in the\n\u003ca href=\"https://agupubs.onlinelibrary.wiley.com/journal/19422466\" rel=\"nofollow\"\u003eJournal of Advances in Modeling Earth Systems\n(JAMES)\u003c/a\u003e.\nMore information on the model and access to the output is available on\nthe \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eAM4 data and code\nsite\u003c/a\u003e at the\n\u003ca href=\"https://www.gfdl.noaa.gov\" rel=\"nofollow\"\u003eGeophysical Fluid Dynamics Laboratory\n(GFDL)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe layout of this package includes the following directories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esrc - The source code for the AM4 model\u003c/li\u003e\n\u003cli\u003eexec - The build directory with Makefiles for building the AM4 model executable\u003c/li\u003e\n\u003cli\u003eidealized_exec - The build directory with Makefiles for building the aquaplanet\nand doubly periodic executable\u003c/li\u003e\n\u003cli\u003erun - Sample run script and updated files needed for running\u003c/li\u003e\n\u003cli\u003eanalysis - Sample analysis scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning Instructions\u003c/h2\u003e\u003ca id=\"user-content-cloning-instructions\" class=\"anchor\" aria-label=\"Permalink: Cloning Instructions\" href=\"#cloning-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository uses \u003ca href=\"https://git-scm.com/book/en/v2/Git-Tools-Submodules\" rel=\"nofollow\"\u003egit\nsubmodules\u003c/a\u003e to\npoint to other repositories. Thus, care should be taken when cloning,\nand updating the source to ensure all source. To obtain all source,\nuse the following git command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NOAA-GFDL/AM4.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--recursive\u003c/code\u003e option to \u003ccode\u003egit clone\u003c/code\u003e instructs git to recursively\nclone all submodules. In the event the repository was not cloned\nusing the \u003ccode\u003e--recursive\u003c/code\u003e option, the following step must be taken to\nobtain all sources:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# From within the AM4 parent directory\ngit submodule update --init --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSource Code\u003c/h2\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-label=\"Permalink: Source Code\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll model source is contained in the \u003ca href=\"src\"\u003esrc\u003c/a\u003e directory. GFDL\ntracks code using the git version control system. This package\nincludes a single version of the following GFDL model components. The\ngit hash listed corresponds to the commit hash in the internal GFDL\ngit repository.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComponent\u003c/th\u003e\n\u003cth\u003eCommit Hash\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_drivers\u003c/td\u003e\n\u003ctd\u003e5ee95d6abf0879594551dd7e6635dff4004c4010\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_param\u003c/td\u003e\n\u003ctd\u003e2e94acfd8621e85216bf822c395a8c3f15a511a5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_shared\u003c/td\u003e\n\u003ctd\u003ea557d4d7bab033ef1ad1d400a62fe07a97ccb477\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eice_param\u003c/td\u003e\n\u003ctd\u003e1553c8bc4f9a66791c89367b6f327147523155ed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eice_sis\u003c/td\u003e\n\u003ctd\u003eccc7328dcd79706dd5c17c8bab660222886fc80b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eland_lad2\u003c/td\u003e\n\u003ctd\u003ea220288ecb289bf9d793d051fc5076072874ce07\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe following components are available in the\n\u003ca href=\"https://github.com/NOAA-GFDL\"\u003eNOAA-GFDL\u003c/a\u003e github organization:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/MOM6\"\u003eMOM6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/coupler\"\u003ecoupler\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/FMS\"\u003eFMS\u003c/a\u003e (as \u003ca href=\"src/shared\"\u003eshared\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/GFDL_atmos_cubed_sphere\"\u003eGFDL_atmos_cubed_sphere (tag AM4.0)\u003c/a\u003e (as \u003ca href=\"src/atmos_cubed_sphere\"\u003eatmos_cubed_sphere\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding AM4\u003c/h2\u003e\u003ca id=\"user-content-building-am4\" class=\"anchor\" aria-label=\"Permalink: Building AM4\" href=\"#building-am4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e###Containers\nThe \u003ca href=\"container\"\u003econtainer folder\u003c/a\u003e provides example Dockerfiles and Signularity\ndefinition files to use to build AM4 containers using either GCC/GFORTAN or\nIntel oneAPI. There is a script that can be used to build the intel\nsingularity containers, and the first step of this script can be used with the\nother GFDL climate models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"exec\"\u003eexec\u003c/a\u003e directory contains Makefiles that can be used to\nbuild the AM4 executable. These Makefiles were generated using the\n\u003ca href=\"https://github.com/NOAA-GFDL/mkmf\"\u003eMake Makefile (mkmf)\u003c/a\u003e program.\nIncluded in the exec direcgtory is a sample make template file for the\nIntel compilers (\u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e). This make\ntemplate can be used on any system with a relatively recent version of\nthe Intel compilers, the netCDF 4 library and the MPICH2 MPI library.\nIncluded in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file are\nadditional settings that can be modified during the build.\u003c/p\u003e\n\u003cp\u003eTo run the default build (-O3 -msse2), go to the exec directory and\nenter the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you would like to change some of the compiler options, there are several different\noptions to add to the make command. For example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake ISA=-xhost BLD_TYPE=REPRO\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill replace -msse with -xhost and -O3 with -O2. The three options for\n\u003ccode\u003eBLD_TYPE\u003c/code\u003e are\u003cbr\u003e\n\u003ccode\u003ePROD\u003c/code\u003e (-O3)\u003cbr\u003e\n\u003ccode\u003eREPRO\u003c/code\u003e (-O2)\u003cbr\u003e\n\u003ccode\u003eDEBUG\u003c/code\u003e (-O0 and other traps)\u003cbr\u003e\nAll of the make line options can be\nfound in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eObtaining the input data\u003c/h2\u003e\u003ca id=\"user-content-obtaining-the-input-data\" class=\"anchor\" aria-label=\"Permalink: Obtaining the input data\" href=\"#obtaining-the-input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe input data required for running the AM4 model can be found on\n\u003ca href=\"https://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eGFDL\u0027s data\nportal\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eAM4.tar.gz\u003c/code\u003e contains a configured run directory to run a\nsample experiment of the AM4 model. Included in the tar file is a\nREADME.AM4_run with more instructions on how to configure the AM4 run\ndirectory.\u003c/p\u003e\n\u003cp\u003eOn Linux systems, the \u003ccode\u003ewget\u003c/code\u003e command is usually sufficient to download the data\nfile:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo ensure the file downloaded is complete and not corrupted, download one of the two files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz.sha256\nwget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz.sig\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand run the following command that corresponds to the signature file downloaded:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esha256sum -c AM4_run.tar.gz.sha256\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003egpg --verify AM4_run.tar.gz.sig\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning AM4\u003c/h2\u003e\u003ca id=\"user-content-running-am4\" class=\"anchor\" aria-label=\"Permalink: Running AM4\" href=\"#running-am4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIncluded in the run directory is a sample run script for reference.\nTo run the AM4 sample experiment, first download the data file\nmentioned in \u003ca href=\"#obtaining-the-input-data\"\u003eObtaining the Input data\u003c/a\u003e\nsection. Replace diag_table and input.nml in the top level of the\nuntar\u0027d directory with the corresponding files in the run directory\nof this repository. Modify the variables in the configuration section\nin the sample run script, and then run the script.\u003c/p\u003e\n\u003cp\u003eThe sample data and run script are configured to run on 216\nprocessors. To run on a different number of processors, or modify the\nexperiment, refer to the \u003ccode\u003eREADME.AM4_run\u003c/code\u003e file included in the AM4\ndata tarball.\u003c/p\u003e\n\u003cp\u003eNote: The \u003ccode\u003einput.nml\u003c/code\u003e file (found in the AM4 data tarball) contains\nFortran namelists and namelist variables that modify, at run time, the\nmodel. To learn more about the settings in the \u003ccode\u003einput.nml\u003c/code\u003e file,\nplease refer to source code where the namelist/variable are defined.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalysis Scripts\u003c/h2\u003e\u003ca id=\"user-content-analysis-scripts\" class=\"anchor\" aria-label=\"Permalink: Analysis Scripts\" href=\"#analysis-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome of the climate analysis scripts run at NOAA GFDL and used in the\nAM4 documentation papers are located in the analysis directory.\nWithin each analysis suite, is a \u003ca href=\"https://jupyter-notebook.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ejupyter\nnotebook\u003c/a\u003e, both\nreadable and runnable from your local jupyter environment, provided\nall dependencies are installed.\u003c/p\u003e\n\u003cp\u003eE.g.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/cjs1/radiation_atmos_av_mon/radiation_atmos_av_mon.ipynb\"\u003eRadiation processor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/bw/bw_atmos_cru_ts_a1r/bw_atmos_monthly_cru_ts.1980-2014.ipynb\"\u003eLong-term DJF seasonal mean\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/bw/bw_atmos_zm_atl_pac_a1r/bw_atmos_atl_pac.1980-2014.ipynb\"\u003eZonal_mean_zonal_wind_stress\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/pcmdimetrics/portraitPlot-AM4.AMIP.ipynb\"\u003ePCMDI Metrics Portrait Plot\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel output and Other References\u003c/h2\u003e\u003ca id=\"user-content-model-output-and-other-references\" class=\"anchor\" aria-label=\"Permalink: Model output and Other References\" href=\"#model-output-and-other-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease refer to the \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eAM4 data and code\nsite\u003c/a\u003e for details\nabout where to find model and OBS data used in the papers.\u003c/p\u003e\n\u003cp\u003eFor all analysis figures and pertaining data, please use the AM4\ndocumentation papers as the original reference.\u003c/p\u003e\n\u003cp\u003ePlease direct your questions and feedback to\n\u003ca href=\"mailto:gfdl.climate.model.info@noaa.gov\"\u003egfdl.climate.model.info@noaa.gov\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is\nprovided on an \u0027as is\u0027 basis and the user assumes responsibility for\nits use. DOC has relinquished control of the information and no\nlonger has responsibility to protect the integrity, confidentiality,\nor availability of the information. Any claims against the Department\nof Commerce stemming from the use of its GitHub project will be\ngoverned by all applicable Federal law. Any reference to specific\ncommercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply\ntheir endorsement, recommendation or favoring by the Department of\nCommerce. The Department of Commerce seal and logo, or the seal and\nlogo of a DOC bureau, shall not be used in any manner to imply\nendorsement of any commercial product or activity by DOC or the United\nStates Government.\u003c/p\u003e\n\u003cp\u003eThis project code is made available through GitHub but is managed by\nNOAA-GFDL at \u003ca href=\"https://gitlab.gfdl.noaa.gov\" rel=\"nofollow\"\u003ehttps://gitlab.gfdl.noaa.gov\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 19, + "subscribers_count": 7, "topics": [ - "oceanography", - "earth-science", - "python" + "fortran", + "jupyter-notebook", + "shell-script", + "ncl" ], - "updated_at": 1715790947.0 + "updated_at": 1713319517.0 }, { "data_format": 2, - "description": "Easy and versatile open-source code to explore Kepler, K2 and TESS data in the search for exoplanets", + "description": "Create and maintain phylogenetic \"reference packages\" of biological sequences.", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "franpoz/SHERLOCK", + "full_name": "fhcrc/taxtastic", "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/franpoz/SHERLOCK/blob/master/images/sherlock3.png?raw=true\"\u003e\u003cimg width=\"350\" src=\"https://github.com/franpoz/SHERLOCK/raw/master/images/sherlock3.png?raw=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eSHERLOCK\u003c/b\u003e is an end-to-end pipeline that allows the users to explore the data from space-based missions to search for planetary candidates. It can be used to recover alerted candidates by the automatic pipelines such as SPOC and the QLP, the so-called Kepler objects of interest (KOIs) and TESS objects of interest (TOIs), and to search for candidates that remain unnoticed due to detection thresholds, lack of data exploration or poor photometric quality. To this end, SHERLOCK has six different modules to (1) acquire and prepare the light curves from their repositories, (2) search for planetary candidates, (3) vet the interesting signals, (4) perform a statistical validation, (5) model the signals to refine their ephemerides, and (6) compute the observational windows from ground-based observatories to trigger a follow-up campaign. To execute all these modules, the user only needs to fill in an initial YAML file with some basic information such as the star ID (KIC-ID, EPIC-ID, TIC-ID), the cadence to be used, etc., and use sequentially a few lines of code to pass from one step to the next. Alternatively, the user may provide with the light curve in a csv file, where the time, the normalized flux, and the flux error need to be given in columns comma-separated format.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have already published a specific work presenting SHERLOCK! Hence, the best way to cite the software is referencing \u003ca href=\"https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.4752D/abstract\" rel=\"nofollow\"\u003eD\u00e9vora-Pajares et al. (2024)\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@article{10.1093/mnras/stae1740,\n author = {D\u00e9vora-Pajares, Mart\u00edn and Pozuelos, Francisco J and Thuillier, Antoine and Timmermans, Mathilde and Van\u00a0Grootel, Val\u00e9rie and Bonidie, Victoria and Mota, Luis Cerde\u00f1o and Su\u00e1rez, Juan C},\n title = \"{The sherlock pipeline: new exoplanet candidates in the WASP-16, HAT-P-27, HAT-P-26, and TOI-2411 systems}\",\n journal = {Monthly Notices of the Royal Astronomical Society},\n volume = {532},\n number = {4},\n pages = {4752-4773},\n year = {2024},\n month = {07},\n abstract = \"{The launches of NASA Kepler and Transiting Exoplanet Survey Satellite (TESS) missions have significantly enhanced the interest in the exoplanet field during the last 15\u00a0yr, providing a vast amount of public data that are being exploited by the community thanks to the continuous development of new analysis tools. However, using these tools is not straightforward, and users must dive into different codes, input\u2013output formats, and methodologies, hindering an efficient and robust exploration of the available data. We present the sherlock pipeline, an end-to-end public software that allows the users to easily explore observations from space-based missions such as TESS or Kepler to recover known planets and candidates issued by the official pipelines and search for new planetary candidates that remained unnoticed. The pipeline incorporates all the steps to search for transit-like features, vet potential candidates, provide statistical validation, conduct a Bayesian fitting, and compute observational windows from ground-based observatories. Its performance is tested against a catalogue of known and confirmed planets from the TESS mission, trying to recover the official TESS Objects of Interest (TOIs), explore the existence of companions that have been missed, and release them as new planetary candidates. sherlock demonstrated an excellent performance, recovering 98\u00a0per\u00a0cent of the TOIs and confirmed planets in our test sample and finding new candidates. Specifically, we release four new planetary candidates around the systems WASP-16 (with P\u00a0\\\\$\\\\sim\\\\$\u00a010.46\u00a0d and R\u00a0\\\\$\\\\sim\\\\$\u00a02.20\u00a0\\\\$\\\\mathrm\\\\{ R\\\\}\\_\\\\{\\\\oplus \\\\}\\\\$), HAT-P-27 (with P\u00a0\\\\$\\\\sim\\\\$\u00a01.20\u00a0d and R\u00a0\\\\$\\\\sim\\\\$\u00a04.33\u00a0\\\\$\\\\mathrm\\\\{ R\\\\}\\_\\\\{\\\\oplus \\\\}\\\\$), HAT-P-26 (with P\u00a0\\\\$\\\\sim\\\\$\u00a06.59\u00a0d and R\u00a0\\\\$\\\\sim\\\\$\u00a01.97\u00a0\\\\$\\\\mathrm\\\\{ R\\\\}\\_\\\\{\\\\oplus \\\\}\\\\$), and TOI-2411 (with P\u00a0\\\\$\\\\sim\\\\$\u00a018.75\u00a0d and R\u00a0\\\\$\\\\sim\\\\$\u00a02.88\u00a0\\\\$\\\\mathrm\\\\{ R\\\\}\\_\\\\{\\\\oplus \\\\}\\\\$).}\",\n issn = {0035-8711},\n doi = {10.1093/mnras/stae1740},\n url = {https://doi.org/10.1093/mnras/stae1740},\n eprint = {https://academic.oup.com/mnras/article-pdf/532/4/4752/58747111/stae1740.pdf},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdditionally, we also encourage the citation of \u003ca href=\"https://ui.adsabs.harvard.edu/abs/2020A%26A...641A..23P/abstract\" rel=\"nofollow\"\u003ePozuelos et al. (2020)\u003c/a\u003e because it is the first work where the first preliminary version of SHERLOCK was used:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@ARTICLE{2020A\u0026amp;A...641A..23P,\n author = {{Pozuelos}, Francisco J. and {Su{\\\u0027a}rez}, Juan C. and {de El{\\\u0027\\i}a}, Gonzalo C. and {Berdi{\\~n}as}, Zaira M. and {Bonfanti}, Andrea and {Dugaro}, Agust{\\\u0027\\i}n and {Gillon}, Micha{\\\"e}l and {Jehin}, Emmanu{\\\"e}l and {G{\\\"u}nther}, Maximilian N. and {Van Grootel}, Val{\\\u0027e}rie and {Garcia}, Lionel J. and {Thuillier}, Antoine and {Delrez}, Laetitia and {Rod{\\\u0027o}n}, Jose R.},\n title = \"{GJ 273: on the formation, dynamical evolution, and habitability of a planetary system hosted by an M dwarf at 3.75 parsec}\",\n journal = {\\aap},\n keywords = {planets and satellites: dynamical evolution and stability, planets and satellites: formation, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics},\n year = 2020,\n month = sep,\n volume = {641},\n eid = {A23},\n pages = {A23},\n doi = {10.1051/0004-6361/202038047},\narchivePrefix = {arXiv},\n eprint = {2006.09403},\n primaryClass = {astro-ph.EP},\n adsurl = {https://ui.adsabs.harvard.edu/abs/2020A\u0026amp;A...641A..23P},\n adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlso, you may be interested in having a look at recent papers that used SHERLOCK: \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2023A%26A...672A..70P/abstract\" rel=\"nofollow\"\u003ePozuelos et al. (2023)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2022arXiv220902831D/abstract\" rel=\"nofollow\"\u003eDelrez et al. (2022)\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2022MNRAS.tmp.1364D/abstract\" rel=\"nofollow\"\u003eDransfield et al. (2022)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2022arXiv220410261L/abstract\" rel=\"nofollow\"\u003eLuque et al. (2022)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2022A%26A...657A..45S/abstract\" rel=\"nofollow\"\u003eSchanche et al. (2022)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2021A%26A...653A..97W/abstract\" rel=\"nofollow\"\u003eWells et al. (2021)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.4956B/abstract\" rel=\"nofollow\"\u003eBenni et al. (2021)\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2021A%26A...650A.205V/abstract\" rel=\"nofollow\"\u003eVan Grootel et al. (2021)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2020A%26A...642A..49D/abstract\" rel=\"nofollow\"\u003eDemory et al. (2020)\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFull Tutorials\u003c/h2\u003e\u003ca id=\"user-content-full-tutorials\" class=\"anchor\" aria-label=\"Permalink: Full Tutorials\" href=\"#full-tutorials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have conducted dedicated workshops to teach SHERLOCK\u0027s usage and best practices. The last one was held on June 2023 at the\n\u003ca href=\"https://www.iaa.csic.es/en\" rel=\"nofollow\"\u003eInstituto de Astrof\u00edsica de Andaluc\u00eda-CSIC\u003c/a\u003e.\nYou can find all the material used (Jupyter notebooks, full examples, presentations, etc.) in this link: \u003ca href=\"https://github.com/iaa-so-training/sherlock-tutorial\"\u003eSHERLOCK Workshop IAA-CSIC\u003c/a\u003e.\nLet us know if you or your lab are interested in the SHERLOCK package! We might organize an introduction and a hands-on session to help you get familiar with the code and/or implement new functionalities.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMain Developers\u003c/h2\u003e\u003ca id=\"user-content-main-developers\" class=\"anchor\" aria-label=\"Permalink: Main Developers\" href=\"#main-developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eActive: \u003ci\u003e\u003ca href=\"https://github.com/franpoz\"\u003eF.J. Pozuelos\u003c/a\u003e,\n\u003ca href=\"https://github.com/martindevora\"\u003eM. D\u00e9vora\u003c/a\u003e \u003c/i\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdditional contributors\u003c/h2\u003e\u003ca id=\"user-content-additional-contributors\" class=\"anchor\" aria-label=\"Permalink: Additional contributors\" href=\"#additional-contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ci\u003eA. Thuillier\u003c/i\u003e \u0026amp; \u003ci\u003e\u003ca href=\"https://github.com/LionelGarcia\"\u003eL. Garc\u00eda\u003c/a\u003e \u003c/i\u003e \u0026amp; \u003ci\u003e\u003ca href=\"https://github.com/LuisCerdenoMota\"\u003eLuis Cerde\u00f1o Mota\u003c/a\u003e\u003c/i\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease visit \u003ca href=\"https://sherlock-ph.readthedocs.io\" rel=\"nofollow\"\u003ehttps://sherlock-ph.readthedocs.io\u003c/a\u003e to get a complete set of explanations and tutorials to get started with \u003cb\u003eSHERLOCK\u003c/b\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLaunch\u003c/h2\u003e\u003ca id=\"user-content-launch\" class=\"anchor\" aria-label=\"Permalink: Launch\" href=\"#launch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can run SHERLOCK PIPEline as a standalone package by using:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe --properties my_properties.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou only need to provide a YAML file with any of the properties contained in the internal\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/blob/master/sherlockpipe/properties.yaml\"\u003eproperties.yaml\u003c/a\u003e\nprovided by the pipeline. The most important keys to be defined in your YAML file are those under\nthe \u003ccode\u003eGLOBAL OBJECTS RUN SETUP\u003c/code\u003e and \u003ccode\u003eSECTOR OBJECTS RUN SETUP\u003c/code\u003e sections because they contain the object ids\nor files to be analysed in the execution. You\u0027d need to fill at least one of those keys for the\npipeline to do anything. If you still have any doubts please refer to the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/properties\"\u003eexamples/properties\u003c/a\u003e directory\u003c/p\u003e\n\u003cp\u003eAdditionally, you could only want to inspect the preparation stage of SHERLOCK and therefore, you can execute it without\nrunning the analyse phase so you can watch the light curve, the periodogram and the initial report to take better\ndecisions to tune the execution parameters. Just launch SHERLOCK with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe --properties my_properties.yaml --explore\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand it will end as soon as it has processed the preparation stages for each object.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates\u003c/h2\u003e\u003ca id=\"user-content-updates\" class=\"anchor\" aria-label=\"Permalink: Updates\" href=\"#updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK uses third party data to know TOIs, KOIs, EPICs and to handle FFIs and the vetting process.\nThis data gets frequently updated from the active missions and therefore SHERLOCK will perform better\nif the metadata gets refreshed. You can simply run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.update\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand SHERLOCK will download the dependencies. It will store a timestamp to remember the last time it was\nrefreshed to prevent several unneeded calls. However, if you find that there are more updates and you need\nthem now, you can call:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.update --force\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand SHERLOCK will ignore the timestamps and perform the update process. In addition, you could be interested\nin wiping all the metadata and build it again. That\u0027s why you could execute:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.update --clean\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis last command implies a \u003ccode\u003eforce\u003c/code\u003e statement and the last executed time will be ignored too.\u003c/p\u003e\n\u003cp\u003eYou can additionally let SHERLOCK refresh the OIs list before running your current execution by adding to the\nYAML file the next line:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eUPDATE_OIS=True\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVetting\u003c/h3\u003e\u003ca id=\"user-content-vetting\" class=\"anchor\" aria-label=\"Permalink: Vetting\" href=\"#vetting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline comes with a submodule to examine the most promising transit candidates\nfound by any of its executions. This is done via \u003ca href=\"https://github.com/PlanetHunters/watson\"\u003eWATSON\u003c/a\u003e, capable of vetting\nTESS and Kepler targets.\nYou should be able to execute the vetting by calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.vet --properties my_properties.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThrough that command you will run the vetting process for the given parameters within your provided YAML file.\nYou could watch the generated results under \u003ccode\u003e$your_sherlock_object_results_dir/vetting\u003c/code\u003e directory.\nPlease go to\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/vetting\"\u003eexamples/vetting/\u003c/a\u003e\nto learn how to inject the proper properties for the vetting process.\u003c/p\u003e\n\u003cp\u003eThere is an additional simplified option which can be used to run the vetting. In case you are sure\nthere is a candidate from the Sherlock results which matches your desired parameters, you can run\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.vet --candidate ${theCandidateNumber}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003efrom the sherlock results directory. This execution will automatically read the transit\nparameters from the Sherlock generated files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFitting\u003c/h3\u003e\u003ca id=\"user-content-fitting\" class=\"anchor\" aria-label=\"Permalink: Fitting\" href=\"#fitting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline comes with another submodule to fit the most promising transit candidates\nfound by any of its executions. This fit is done via\n\u003ca href=\"https://github.com/MNGuenther/allesfitter\"\u003eALLESFITTER\u003c/a\u003e code. By calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.fit --properties my_properties.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eyou will run the fitting process for the given parameters within your provided YAML file.\nYou could watch the generated results under \u003ccode\u003e$your_sherlock_object_results_dir/fit\u003c/code\u003e directory.\nPlease go to\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/fitting\"\u003eexamples/fitting/\u003c/a\u003e\nto learn how to inject the proper properties for the fitting process.\u003c/p\u003e\n\u003cp\u003eThere is an additional simplified option which can be used to run the fit. In case you are sure\nthere is a candidate from the Sherlock results which matches your desired parameters, you can run\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.fit --candidate ${theCandidateNumber}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003efrom the sherlock results directory. This execution will automatically read the transit and star\nparameters from the Sherlock generated files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eValidation\u003c/h3\u003e\u003ca id=\"user-content-validation\" class=\"anchor\" aria-label=\"Permalink: Validation\" href=\"#validation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline implements a module to execute a statistical validation of a candidate by the usage\nof\n\u003ca href=\"https://github.com/stevengiacalone/triceratops\"\u003eTRICERATOPS\u003c/a\u003e. By calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.validate --candidate ${theCandidateNumber}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eyou will run the validation for one of the Sherlock candidates.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStability\u003c/h3\u003e\u003ca id=\"user-content-stability\" class=\"anchor\" aria-label=\"Permalink: Stability\" href=\"#stability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline also implements a module to execute a system stability computation by the usage\nof\n\u003ca href=\"https://github.com/hannorein/rebound\"\u003eRebound\u003c/a\u003e and \u003ca href=\"https://github.com/dtamayo/spock\"\u003eSPOCK\u003c/a\u003e. By calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.stability --bodies 1,2,4\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhere the \u003ccode\u003e--bodies\u003c/code\u003e parameter is the set of the SHERLOCK accepted signals as CSV to be used in the scenarios\nsimulation. You can also provide a\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/properties/stability.yaml\"\u003estability properties file\u003c/a\u003e)\nto run a custom stability simulation:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.stability --properties stability.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand you can even combine SHERLOCK accepted signals with some additional bodies provided by the properties file:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.stability --bodies 1,2,4 --properties stability.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe results will be stored into a \u003ccode\u003estability\u003c/code\u003e directory containing the execution log and a \u003ccode\u003estability.csv\u003c/code\u003e\ncontaining one line per simulated scenario, sorted by the best results score.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eObservation plan\u003c/h3\u003e\u003ca id=\"user-content-observation-plan\" class=\"anchor\" aria-label=\"Permalink: Observation plan\" href=\"#observation-plan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline also adds now a tool to plan your observations from ground-based observatories by using\n\u003ca href=\"https://github.com/astropy/astropy\"\u003eastropy\u003c/a\u003e and \u003ca href=\"https://github.com/astropy/astroplan\"\u003eastroplan\u003c/a\u003e. By calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.plan --candidate ${theCandidateNumber} --observatories observatories.csv\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eon the resulting \u003ccode\u003esherlockpipe.fit\u003c/code\u003e directory, where the precise candidate ephemeris are placed.\nThe \u003ccode\u003eobservatories.csv\u003c/code\u003e file should contain the list of available observatories for your candidate follow-up.\nAs an example, you can look at\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/blob/master/examples/observatories.csv\"\u003ethis file\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSHERLOCK PIPEline Workflow\u003c/h2\u003e\u003ca id=\"user-content-sherlock-pipeline-workflow\" class=\"anchor\" aria-label=\"Permalink: SHERLOCK PIPEline Workflow\" href=\"#sherlock-pipeline-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is important to note that SHERLOCK PIPEline uses some csv files with TOIs, KOIs and EPIC IDs\nfrom the TESS, Kepler and K2 missions. Therefore your first execution of the pipeline might\ntake longer because it will download the information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProvisioning of light curve\u003c/h3\u003e\u003ca id=\"user-content-provisioning-of-light-curve\" class=\"anchor\" aria-label=\"Permalink: Provisioning of light curve\" href=\"#provisioning-of-light-curve\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe light curve for every input object needs to be obtained from its mission database. For this we\nuse the high level API of \u003ca href=\"https://github.com/KeplerGO/lightkurve\"\u003eLightkurve\u003c/a\u003e, which enables the\ndownload of the desired light curves for TESS, Kepler and K2 missions. We also include Full Frame\nImages from the TESS mission by the usage of \u003ca href=\"https://adina.feinste.in/eleanor/\" rel=\"nofollow\"\u003eELEANOR\u003c/a\u003e. We\nalways use the PDCSAP signal from the ones provided by any of those two packages.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePre-processing of light curve\u003c/h3\u003e\u003ca id=\"user-content-pre-processing-of-light-curve\" class=\"anchor\" aria-label=\"Permalink: Pre-processing of light curve\" href=\"#pre-processing-of-light-curve\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn many cases we will find light curves which contain several systematics like noise, high dispersion\nbeside the borders, high-amplitude periodicities caused by pulsators, fast rotators, etc. SHERLOCK PIPEline\nprovides some methods to reduce these most important systematics.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLocal noise reduction\u003c/h4\u003e\u003ca id=\"user-content-local-noise-reduction\" class=\"anchor\" aria-label=\"Permalink: Local noise reduction\" href=\"#local-noise-reduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor local noise, where very close measurements show high deviation from the local trend, we apply a\nSavitzky-Golay filter. This has proved a highly increment of the SNR of found transits. This feature\ncan be disabled with a flag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHigh RMS areas masking\u003c/h4\u003e\u003ca id=\"user-content-high-rms-areas-masking\" class=\"anchor\" aria-label=\"Permalink: High RMS areas masking\" href=\"#high-rms-areas-masking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSometimes the spacecrafts have to perform reaction wheels momentum dumps by firing thrusters,\nsometimes there is high light scattering and sometimes the spacecraft can infer some jitter into\nthe signal. For all of those systematics we found that in many cases the data from those regions\nshould be discarded. Thus, SHERLOCK PIPEline includes a binned RMS computation where bins whose\nRMS value is higher than a configurable factor multiplied by the median get automatically masked.\nThis feature can be disabled with a flag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput time ranges masking\u003c/h4\u003e\u003ca id=\"user-content-input-time-ranges-masking\" class=\"anchor\" aria-label=\"Permalink: Input time ranges masking\" href=\"#input-time-ranges-masking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf enabled, this feature automatically disables\n\u003ca href=\"https://github.com/franpoz/SHERLOCK#high-rms-areas-masking\"\u003eHigh RMS areas masking\u003c/a\u003e\nfor the assigned object. The user can input an array of time ranges to be masked into the\noriginal signal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDetrend of high-amplitude periodicities\u003c/h4\u003e\u003ca id=\"user-content-detrend-of-high-amplitude-periodicities\" class=\"anchor\" aria-label=\"Permalink: Detrend of high-amplitude periodicities\" href=\"#detrend-of-high-amplitude-periodicities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOur most common foes with high periodicities are fast-rotators, which infer a high sinusoidal-like\ntrend in the PDCSAP signal. This is why SHERLOCK PIPEline includes an automatic high-amplitude periodicities\ndetection and detrending during its preparation stage. This feature can be disabled with a flag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput period detrend\u003c/h4\u003e\u003ca id=\"user-content-input-period-detrend\" class=\"anchor\" aria-label=\"Permalink: Input period detrend\" href=\"#input-period-detrend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf enabled, this feature automatically disables\n\u003ca href=\"https://github.com/franpoz/SHERLOCK#detrend-of-high-amplitude-periodicities\"\u003eDetrend of high-amplitude periodicities\u003c/a\u003e\nfor the assigned object. The user can input a period to be used for an initial detrend of the\noriginal signal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCustom user code\u003c/h4\u003e\u003ca id=\"user-content-custom-user-code\" class=\"anchor\" aria-label=\"Permalink: Custom user code\" href=\"#custom-user-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can even inject your own python code to perform:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA custom signal preparation task by implementing the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/sherlockpipe/sherlockpipe/curve_preparer/CurvePreparer.py\"\u003eCurvePreparer\u003c/a\u003e\nclass that we provide. Then, inject your python file into the \u003ccode\u003eCUSTOM_PREPARER\u003c/code\u003e property and let SHERLOCK\nuse your code.\u003c/li\u003e\n\u003cli\u003eA custom best signal selection algorithm by implementing the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/sherlockpipe/sherlockpipe/scoring/SignalSelector.py\"\u003eSignalSelector\u003c/a\u003e.\nclass that we provide. Then, inject your python file into the \u003ccode\u003eCUSTOM_ALGORITHM\u003c/code\u003e property and let SHERLOCK use your code.\u003c/li\u003e\n\u003cli\u003eA custom search zone definition by implementing the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/sherlockpipe/sherlockpipe/search_zones/SearchZone.py\"\u003eSearchZone\u003c/a\u003e.\nclass that we provide. Then, inject your python file into the \u003ccode\u003eCUSTOM_SEARCH_ZONE\u003c/code\u003e property and let SHERLOCK use your code.\u003c/li\u003e\n\u003cli\u003eCustom search modes: \u0027tls\u0027, \u0027bls\u0027, \u0027grazing\u0027, \u0027comet\u0027 or \u0027custom\u0027. You can search for transits by using TLS, BLS,\nTLS for a grazing template, TLS for a comet template or even inject your custom transit template (this is currently\nincluded as an experimental feature).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor better understanding of usage please see the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/properties/custom_algorithms.yaml\"\u003eexamples\u003c/a\u003e,\nwhich references custom implementations that you can inspect in our\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/custom_algorithms\"\u003ecustom algorithms directory\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 19, - "subscribers_count": 3, + "subscribers_count": 9, + "topics": [], + "updated_at": 1647885742.0 + }, + { + "data_format": 2, + "description": "Genotype Imputation Pipeline for H3Africa", + "filenames": [ + "singularity/Singularity.minimac4" + ], + "full_name": "h3abionet/chipimputation", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eImputation Workflow h3abionet/chipimputation\u003c/h1\u003e\u003ca id=\"user-content-imputation-workflow-h3abionetchipimputation\" class=\"anchor\" aria-label=\"Permalink: Imputation Workflow h3abionet/chipimputation\" href=\"#imputation-workflow-h3abionetchipimputation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/h3abionet/chipimputation\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/613f3026f3cde9349d4ad1ff0e6842e170600d7473949e030192e71b08edaabc/68747470733a2f2f7472617669732d63692e6f72672f68336162696f6e65742f63686970696d7075746174696f6e2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/h3abionet/chipimputation.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e4ecbdd6cf229cefa5beca8f0f926bd02782b9ef5f30ace294a1819053330fd/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30342e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.04.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://quay.io/h3abionet_org/imputation_tools\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3d0e0a9d959b890a16348beca6cc1543a49ebfa4bf4211297aa8a7bb410f9e2a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b657225323072656769737472792d517561792e696f2d726564\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/badge/docker%20registry-Quay.io-red\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/405f1b67ec217cf9909f64ae9a3c57aef234c5b6ccf794da4b5fff14b1ec139d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538422532302532302545322539372538422532302532302545322539372538422d6f72616e6765\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B%20%20%E2%97%8B%20%20%E2%97%8B-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImputation is likely to be run in the context of a GWAS, studying population structure, and admixture studies. It is computationally expensive in comparison to other GWAS steps.\nThe basic steps of the pipeline is described in the diagram below:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9e8f174f0f41656245c4ac854115d78269b81b4967cd1f539cf990ca80a87b2a/68747470733a2f2f7777772e68336162696f6e65742e6f72672f696d616765732f776f726b666c6f77732f736e705f696d7075746174696f6e5f776f726b666c6f772e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e8f174f0f41656245c4ac854115d78269b81b4967cd1f539cf990ca80a87b2a/68747470733a2f2f7777772e68336162696f6e65742e6f72672f696d616765732f776f726b666c6f77732f736e705f696d7075746174696f6e5f776f726b666c6f772e706e67\" alt=\"chipimputation pipeline workflow diagram\" data-canonical-src=\"https://www.h3abionet.org/images/workflows/snp_imputation_workflow.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe workflow is developed using \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e4ecbdd6cf229cefa5beca8f0f926bd02782b9ef5f30ace294a1819053330fd/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30342e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.04.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e and imputation performed using \u003ca href=\"https://genome.sph.umich.edu/wiki/Minimac4\" rel=\"nofollow\"\u003eMinimac4\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eIt identifies regions to be imputed on the basis of an input file in VCF format, split the regions into small chunks, phase each chunk using the phasing tool \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/\" rel=\"nofollow\"\u003eEagle2\u003c/a\u003e and produces output in VCF format that can subsequently be used in a \u003ca href=\"https://github.com/h3abionet/h3agwas\"\u003eGWAS\u003c/a\u003e workflow.\u003c/li\u003e\n\u003cli\u003eIt also produce basic plots and reports of the imputation process including the imputation performance report, the imputation accuracy, the allele frequency of the imputed vs of the reference panel and other metrics.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eThis pipeline comes with docker/singularity containers making installation trivial and results highly reproducible.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the pipeline with test dataset\u003c/h3\u003e\u003ca id=\"user-content-running-the-pipeline-with-test-dataset\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline with test dataset\" href=\"#running-the-pipeline-with-test-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline itself needs no installation - NextFlow will automatically fetch it from GitHub.\nYou can run the pipeline using test data hosted in github with singularity without have to install or change any parameters.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run h3abionet/chipimputation/main.nf -profile test,singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest\u003c/code\u003e profile will download the testdata from \u003ca href=\"https://github.com/h3abionet/chipimputation_test_data/tree/master/testdata_imputation\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity\u003c/code\u003e profile will download the singularity image from \u003ca href=\"https://quay.io/h3abionet_org/imputation_tools\" rel=\"nofollow\"\u003equay registry\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCheck for results in \u003ccode\u003e./output\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart running your own analysis\u003c/h3\u003e\u003ca id=\"user-content-start-running-your-own-analysis\" class=\"anchor\" aria-label=\"Permalink: Start running your own analysis\" href=\"#start-running-your-own-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the \u003ccode\u003etest.config\u003c/code\u003e file from the \u003ccode\u003econf\u003c/code\u003e folder by doing \u003ccode\u003ecp \u0026lt;conf dir\u0026gt;/test.config .\u003c/code\u003e and edit it to suit the path to where your files are stored.\u003c/p\u003e\n\u003cp\u003eOnce you have edited the config file, run the command below.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run h3abionet/chipimputation -c \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ename of your config file\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -profile singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity\u003c/code\u003e profile will download the singularity image from \u003ca href=\"https://quay.io/h3abionet_org/imputation_tools\" rel=\"nofollow\"\u003equay registry\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCheck for results in \u003ccode\u003e./output\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe h3achipimputation pipeline comes with detailed documentation about the pipeline.\nThis is found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"docs/configuration/config_files.md\"\u003ePipeline configuration\u003c/a\u003e\u003cbr\u003e\n2.1. \u003ca href=\"docs/configs.md\"\u003eConfiguration files\u003c/a\u003e\u003cbr\u003e\n2.2. \u003ca href=\"docs/soft_requirements.md\"\u003eSoftware requirements\u003c/a\u003e\u003cbr\u003e\n2.3. \u003ca href=\"docs/other_clusters.md\"\u003eOther clusters\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline with test data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline with your own config\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/other_clusters.md\"\u003eRunning on local machine or cluster\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/soft_requirements.md\"\u003eRunning docker and singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe track our open tasks using github\u0027s \u003ca href=\"https://github.com/h3abionet/chipimputation/issues\"\u003eissues\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow which was developed as part of the H3ABioNet Hackathon held in Pretoria, SA in 2016. Should want to reference it, please use:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eBaichoo S, Souilmi Y, Panji S, Botha G, Meintjes A, Hazelhurst S, Bendou H, Beste E, Mpangase PT, Souiai O, Alghali M, Yi L, O\u0027Connor BD, Crusoe M, Armstrong D, Aron S, Joubert F, Ahmed AE, Mbiyavanga M, Heusden PV, Magosi LE, Zermeno J, Mainzer LS, Fadlelmola FM, Jongeneel CV, Mulder N. Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics. BMC Bioinformatics. 2018 Nov 29;19(1):457. doi: 10.1186/s12859-018-2446-1. PubMed PMID: 30486782; PubMed Central PMCID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264621/\" rel=\"nofollow\"\u003ePMC6264621\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "stargazers_count": 19, + "subscribers_count": 11, "topics": [ - "exoplanets", - "tess", - "kepler" + "nextflow", + "imputation", + "bioinformatics-pipeline" ], - "updated_at": 1723807956.0 + "updated_at": 1704367018.0 }, { "data_format": 2, @@ -38798,163 +38901,120 @@ var data = }, { "data_format": 2, - "description": "Genotype Imputation Pipeline for H3Africa", + "description": "Easy and versatile open-source code to explore Kepler, K2 and TESS data in the search for exoplanets", "filenames": [ - "singularity/Singularity.minimac4" + "Singularity" ], - "full_name": "h3abionet/chipimputation", + "full_name": "franpoz/SHERLOCK", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eImputation Workflow h3abionet/chipimputation\u003c/h1\u003e\u003ca id=\"user-content-imputation-workflow-h3abionetchipimputation\" class=\"anchor\" aria-label=\"Permalink: Imputation Workflow h3abionet/chipimputation\" href=\"#imputation-workflow-h3abionetchipimputation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/h3abionet/chipimputation\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/613f3026f3cde9349d4ad1ff0e6842e170600d7473949e030192e71b08edaabc/68747470733a2f2f7472617669732d63692e6f72672f68336162696f6e65742f63686970696d7075746174696f6e2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/h3abionet/chipimputation.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e4ecbdd6cf229cefa5beca8f0f926bd02782b9ef5f30ace294a1819053330fd/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30342e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.04.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://quay.io/h3abionet_org/imputation_tools\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3d0e0a9d959b890a16348beca6cc1543a49ebfa4bf4211297aa8a7bb410f9e2a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b657225323072656769737472792d517561792e696f2d726564\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/badge/docker%20registry-Quay.io-red\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://fair-software.eu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/405f1b67ec217cf9909f64ae9a3c57aef234c5b6ccf794da4b5fff14b1ec139d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f666169722d2d736f6674776172652e65752d2545322539372538462532302532302545322539372538462532302532302545322539372538422532302532302545322539372538422532302532302545322539372538422d6f72616e6765\" alt=\"fair-software.eu\" data-canonical-src=\"https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8B%20%20%E2%97%8B%20%20%E2%97%8B-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImputation is likely to be run in the context of a GWAS, studying population structure, and admixture studies. It is computationally expensive in comparison to other GWAS steps.\nThe basic steps of the pipeline is described in the diagram below:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9e8f174f0f41656245c4ac854115d78269b81b4967cd1f539cf990ca80a87b2a/68747470733a2f2f7777772e68336162696f6e65742e6f72672f696d616765732f776f726b666c6f77732f736e705f696d7075746174696f6e5f776f726b666c6f772e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9e8f174f0f41656245c4ac854115d78269b81b4967cd1f539cf990ca80a87b2a/68747470733a2f2f7777772e68336162696f6e65742e6f72672f696d616765732f776f726b666c6f77732f736e705f696d7075746174696f6e5f776f726b666c6f772e706e67\" alt=\"chipimputation pipeline workflow diagram\" data-canonical-src=\"https://www.h3abionet.org/images/workflows/snp_imputation_workflow.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe workflow is developed using \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e4ecbdd6cf229cefa5beca8f0f926bd02782b9ef5f30ace294a1819053330fd/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d25453225383925413532302e30342e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A520.04.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e and imputation performed using \u003ca href=\"https://genome.sph.umich.edu/wiki/Minimac4\" rel=\"nofollow\"\u003eMinimac4\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eIt identifies regions to be imputed on the basis of an input file in VCF format, split the regions into small chunks, phase each chunk using the phasing tool \u003ca href=\"https://data.broadinstitute.org/alkesgroup/Eagle/\" rel=\"nofollow\"\u003eEagle2\u003c/a\u003e and produces output in VCF format that can subsequently be used in a \u003ca href=\"https://github.com/h3abionet/h3agwas\"\u003eGWAS\u003c/a\u003e workflow.\u003c/li\u003e\n\u003cli\u003eIt also produce basic plots and reports of the imputation process including the imputation performance report, the imputation accuracy, the allele frequency of the imputed vs of the reference panel and other metrics.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eThis pipeline comes with docker/singularity containers making installation trivial and results highly reproducible.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRunning the pipeline with test dataset\u003c/h3\u003e\u003ca id=\"user-content-running-the-pipeline-with-test-dataset\" class=\"anchor\" aria-label=\"Permalink: Running the pipeline with test dataset\" href=\"#running-the-pipeline-with-test-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis pipeline itself needs no installation - NextFlow will automatically fetch it from GitHub.\nYou can run the pipeline using test data hosted in github with singularity without have to install or change any parameters.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run h3abionet/chipimputation/main.nf -profile test,singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003etest\u003c/code\u003e profile will download the testdata from \u003ca href=\"https://github.com/h3abionet/chipimputation_test_data/tree/master/testdata_imputation\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity\u003c/code\u003e profile will download the singularity image from \u003ca href=\"https://quay.io/h3abionet_org/imputation_tools\" rel=\"nofollow\"\u003equay registry\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCheck for results in \u003ccode\u003e./output\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStart running your own analysis\u003c/h3\u003e\u003ca id=\"user-content-start-running-your-own-analysis\" class=\"anchor\" aria-label=\"Permalink: Start running your own analysis\" href=\"#start-running-your-own-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the \u003ccode\u003etest.config\u003c/code\u003e file from the \u003ccode\u003econf\u003c/code\u003e folder by doing \u003ccode\u003ecp \u0026lt;conf dir\u0026gt;/test.config .\u003c/code\u003e and edit it to suit the path to where your files are stored.\u003c/p\u003e\n\u003cp\u003eOnce you have edited the config file, run the command below.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run h3abionet/chipimputation -c \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ename of your config file\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -profile singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esingularity\u003c/code\u003e profile will download the singularity image from \u003ca href=\"https://quay.io/h3abionet_org/imputation_tools\" rel=\"nofollow\"\u003equay registry\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCheck for results in \u003ccode\u003e./output\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe h3achipimputation pipeline comes with detailed documentation about the pipeline.\nThis is found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"docs/configuration/config_files.md\"\u003ePipeline configuration\u003c/a\u003e\u003cbr\u003e\n2.1. \u003ca href=\"docs/configs.md\"\u003eConfiguration files\u003c/a\u003e\u003cbr\u003e\n2.2. \u003ca href=\"docs/soft_requirements.md\"\u003eSoftware requirements\u003c/a\u003e\u003cbr\u003e\n2.3. \u003ca href=\"docs/other_clusters.md\"\u003eOther clusters\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline with test data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline with your own config\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/other_clusters.md\"\u003eRunning on local machine or cluster\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/soft_requirements.md\"\u003eRunning docker and singularity\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe track our open tasks using github\u0027s \u003ca href=\"https://github.com/h3abionet/chipimputation/issues\"\u003eissues\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow which was developed as part of the H3ABioNet Hackathon held in Pretoria, SA in 2016. Should want to reference it, please use:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eBaichoo S, Souilmi Y, Panji S, Botha G, Meintjes A, Hazelhurst S, Bendou H, Beste E, Mpangase PT, Souiai O, Alghali M, Yi L, O\u0027Connor BD, Crusoe M, Armstrong D, Aron S, Joubert F, Ahmed AE, Mbiyavanga M, Heusden PV, Magosi LE, Zermeno J, Mainzer LS, Fadlelmola FM, Jongeneel CV, Mulder N. Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics. BMC Bioinformatics. 2018 Nov 29;19(1):457. doi: 10.1186/s12859-018-2446-1. PubMed PMID: 30486782; PubMed Central PMCID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264621/\" rel=\"nofollow\"\u003ePMC6264621\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/franpoz/SHERLOCK/blob/master/images/sherlock3.png?raw=true\"\u003e\u003cimg width=\"350\" src=\"https://github.com/franpoz/SHERLOCK/raw/master/images/sherlock3.png?raw=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eSHERLOCK\u003c/b\u003e is an end-to-end pipeline that allows the users to explore the data from space-based missions to search for planetary candidates. It can be used to recover alerted candidates by the automatic pipelines such as SPOC and the QLP, the so-called Kepler objects of interest (KOIs) and TESS objects of interest (TOIs), and to search for candidates that remain unnoticed due to detection thresholds, lack of data exploration or poor photometric quality. To this end, SHERLOCK has six different modules to (1) acquire and prepare the light curves from their repositories, (2) search for planetary candidates, (3) vet the interesting signals, (4) perform a statistical validation, (5) model the signals to refine their ephemerides, and (6) compute the observational windows from ground-based observatories to trigger a follow-up campaign. To execute all these modules, the user only needs to fill in an initial YAML file with some basic information such as the star ID (KIC-ID, EPIC-ID, TIC-ID), the cadence to be used, etc., and use sequentially a few lines of code to pass from one step to the next. Alternatively, the user may provide with the light curve in a csv file, where the time, the normalized flux, and the flux error need to be given in columns comma-separated format.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have already published a specific work presenting SHERLOCK! Hence, the best way to cite the software is referencing \u003ca href=\"https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.4752D/abstract\" rel=\"nofollow\"\u003eD\u00e9vora-Pajares et al. (2024)\u003c/a\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@article{10.1093/mnras/stae1740,\n author = {D\u00e9vora-Pajares, Mart\u00edn and Pozuelos, Francisco J and Thuillier, Antoine and Timmermans, Mathilde and Van\u00a0Grootel, Val\u00e9rie and Bonidie, Victoria and Mota, Luis Cerde\u00f1o and Su\u00e1rez, Juan C},\n title = \"{The sherlock pipeline: new exoplanet candidates in the WASP-16, HAT-P-27, HAT-P-26, and TOI-2411 systems}\",\n journal = {Monthly Notices of the Royal Astronomical Society},\n volume = {532},\n number = {4},\n pages = {4752-4773},\n year = {2024},\n month = {07},\n abstract = \"{The launches of NASA Kepler and Transiting Exoplanet Survey Satellite (TESS) missions have significantly enhanced the interest in the exoplanet field during the last 15\u00a0yr, providing a vast amount of public data that are being exploited by the community thanks to the continuous development of new analysis tools. However, using these tools is not straightforward, and users must dive into different codes, input\u2013output formats, and methodologies, hindering an efficient and robust exploration of the available data. We present the sherlock pipeline, an end-to-end public software that allows the users to easily explore observations from space-based missions such as TESS or Kepler to recover known planets and candidates issued by the official pipelines and search for new planetary candidates that remained unnoticed. The pipeline incorporates all the steps to search for transit-like features, vet potential candidates, provide statistical validation, conduct a Bayesian fitting, and compute observational windows from ground-based observatories. Its performance is tested against a catalogue of known and confirmed planets from the TESS mission, trying to recover the official TESS Objects of Interest (TOIs), explore the existence of companions that have been missed, and release them as new planetary candidates. sherlock demonstrated an excellent performance, recovering 98\u00a0per\u00a0cent of the TOIs and confirmed planets in our test sample and finding new candidates. Specifically, we release four new planetary candidates around the systems WASP-16 (with P\u00a0\\\\$\\\\sim\\\\$\u00a010.46\u00a0d and R\u00a0\\\\$\\\\sim\\\\$\u00a02.20\u00a0\\\\$\\\\mathrm\\\\{ R\\\\}\\_\\\\{\\\\oplus \\\\}\\\\$), HAT-P-27 (with P\u00a0\\\\$\\\\sim\\\\$\u00a01.20\u00a0d and R\u00a0\\\\$\\\\sim\\\\$\u00a04.33\u00a0\\\\$\\\\mathrm\\\\{ R\\\\}\\_\\\\{\\\\oplus \\\\}\\\\$), HAT-P-26 (with P\u00a0\\\\$\\\\sim\\\\$\u00a06.59\u00a0d and R\u00a0\\\\$\\\\sim\\\\$\u00a01.97\u00a0\\\\$\\\\mathrm\\\\{ R\\\\}\\_\\\\{\\\\oplus \\\\}\\\\$), and TOI-2411 (with P\u00a0\\\\$\\\\sim\\\\$\u00a018.75\u00a0d and R\u00a0\\\\$\\\\sim\\\\$\u00a02.88\u00a0\\\\$\\\\mathrm\\\\{ R\\\\}\\_\\\\{\\\\oplus \\\\}\\\\$).}\",\n issn = {0035-8711},\n doi = {10.1093/mnras/stae1740},\n url = {https://doi.org/10.1093/mnras/stae1740},\n eprint = {https://academic.oup.com/mnras/article-pdf/532/4/4752/58747111/stae1740.pdf},\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAdditionally, we also encourage the citation of \u003ca href=\"https://ui.adsabs.harvard.edu/abs/2020A%26A...641A..23P/abstract\" rel=\"nofollow\"\u003ePozuelos et al. (2020)\u003c/a\u003e because it is the first work where the first preliminary version of SHERLOCK was used:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@ARTICLE{2020A\u0026amp;A...641A..23P,\n author = {{Pozuelos}, Francisco J. and {Su{\\\u0027a}rez}, Juan C. and {de El{\\\u0027\\i}a}, Gonzalo C. and {Berdi{\\~n}as}, Zaira M. and {Bonfanti}, Andrea and {Dugaro}, Agust{\\\u0027\\i}n and {Gillon}, Micha{\\\"e}l and {Jehin}, Emmanu{\\\"e}l and {G{\\\"u}nther}, Maximilian N. and {Van Grootel}, Val{\\\u0027e}rie and {Garcia}, Lionel J. and {Thuillier}, Antoine and {Delrez}, Laetitia and {Rod{\\\u0027o}n}, Jose R.},\n title = \"{GJ 273: on the formation, dynamical evolution, and habitability of a planetary system hosted by an M dwarf at 3.75 parsec}\",\n journal = {\\aap},\n keywords = {planets and satellites: dynamical evolution and stability, planets and satellites: formation, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics},\n year = 2020,\n month = sep,\n volume = {641},\n eid = {A23},\n pages = {A23},\n doi = {10.1051/0004-6361/202038047},\narchivePrefix = {arXiv},\n eprint = {2006.09403},\n primaryClass = {astro-ph.EP},\n adsurl = {https://ui.adsabs.harvard.edu/abs/2020A\u0026amp;A...641A..23P},\n adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n}\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlso, you may be interested in having a look at recent papers that used SHERLOCK: \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2023A%26A...672A..70P/abstract\" rel=\"nofollow\"\u003ePozuelos et al. (2023)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2022arXiv220902831D/abstract\" rel=\"nofollow\"\u003eDelrez et al. (2022)\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2022MNRAS.tmp.1364D/abstract\" rel=\"nofollow\"\u003eDransfield et al. (2022)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2022arXiv220410261L/abstract\" rel=\"nofollow\"\u003eLuque et al. (2022)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2022A%26A...657A..45S/abstract\" rel=\"nofollow\"\u003eSchanche et al. (2022)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2021A%26A...653A..97W/abstract\" rel=\"nofollow\"\u003eWells et al. (2021)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.4956B/abstract\" rel=\"nofollow\"\u003eBenni et al. (2021)\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2021A%26A...650A.205V/abstract\" rel=\"nofollow\"\u003eVan Grootel et al. (2021)\u003c/a\u003e \u003cbr\u003e\n\u003ca href=\"https://ui.adsabs.harvard.edu/abs/2020A%26A...642A..49D/abstract\" rel=\"nofollow\"\u003eDemory et al. (2020)\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFull Tutorials\u003c/h2\u003e\u003ca id=\"user-content-full-tutorials\" class=\"anchor\" aria-label=\"Permalink: Full Tutorials\" href=\"#full-tutorials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have conducted dedicated workshops to teach SHERLOCK\u0027s usage and best practices. The last one was held on June 2023 at the\n\u003ca href=\"https://www.iaa.csic.es/en\" rel=\"nofollow\"\u003eInstituto de Astrof\u00edsica de Andaluc\u00eda-CSIC\u003c/a\u003e.\nYou can find all the material used (Jupyter notebooks, full examples, presentations, etc.) in this link: \u003ca href=\"https://github.com/iaa-so-training/sherlock-tutorial\"\u003eSHERLOCK Workshop IAA-CSIC\u003c/a\u003e.\nLet us know if you or your lab are interested in the SHERLOCK package! We might organize an introduction and a hands-on session to help you get familiar with the code and/or implement new functionalities.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMain Developers\u003c/h2\u003e\u003ca id=\"user-content-main-developers\" class=\"anchor\" aria-label=\"Permalink: Main Developers\" href=\"#main-developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eActive: \u003ci\u003e\u003ca href=\"https://github.com/franpoz\"\u003eF.J. Pozuelos\u003c/a\u003e,\n\u003ca href=\"https://github.com/martindevora\"\u003eM. D\u00e9vora\u003c/a\u003e \u003c/i\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdditional contributors\u003c/h2\u003e\u003ca id=\"user-content-additional-contributors\" class=\"anchor\" aria-label=\"Permalink: Additional contributors\" href=\"#additional-contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ci\u003eA. Thuillier\u003c/i\u003e \u0026amp; \u003ci\u003e\u003ca href=\"https://github.com/LionelGarcia\"\u003eL. Garc\u00eda\u003c/a\u003e \u003c/i\u003e \u0026amp; \u003ci\u003e\u003ca href=\"https://github.com/LuisCerdenoMota\"\u003eLuis Cerde\u00f1o Mota\u003c/a\u003e\u003c/i\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease visit \u003ca href=\"https://sherlock-ph.readthedocs.io\" rel=\"nofollow\"\u003ehttps://sherlock-ph.readthedocs.io\u003c/a\u003e to get a complete set of explanations and tutorials to get started with \u003cb\u003eSHERLOCK\u003c/b\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLaunch\u003c/h2\u003e\u003ca id=\"user-content-launch\" class=\"anchor\" aria-label=\"Permalink: Launch\" href=\"#launch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can run SHERLOCK PIPEline as a standalone package by using:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe --properties my_properties.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eYou only need to provide a YAML file with any of the properties contained in the internal\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/blob/master/sherlockpipe/properties.yaml\"\u003eproperties.yaml\u003c/a\u003e\nprovided by the pipeline. The most important keys to be defined in your YAML file are those under\nthe \u003ccode\u003eGLOBAL OBJECTS RUN SETUP\u003c/code\u003e and \u003ccode\u003eSECTOR OBJECTS RUN SETUP\u003c/code\u003e sections because they contain the object ids\nor files to be analysed in the execution. You\u0027d need to fill at least one of those keys for the\npipeline to do anything. If you still have any doubts please refer to the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/properties\"\u003eexamples/properties\u003c/a\u003e directory\u003c/p\u003e\n\u003cp\u003eAdditionally, you could only want to inspect the preparation stage of SHERLOCK and therefore, you can execute it without\nrunning the analyse phase so you can watch the light curve, the periodogram and the initial report to take better\ndecisions to tune the execution parameters. Just launch SHERLOCK with:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe --properties my_properties.yaml --explore\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand it will end as soon as it has processed the preparation stages for each object.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates\u003c/h2\u003e\u003ca id=\"user-content-updates\" class=\"anchor\" aria-label=\"Permalink: Updates\" href=\"#updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK uses third party data to know TOIs, KOIs, EPICs and to handle FFIs and the vetting process.\nThis data gets frequently updated from the active missions and therefore SHERLOCK will perform better\nif the metadata gets refreshed. You can simply run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.update\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand SHERLOCK will download the dependencies. It will store a timestamp to remember the last time it was\nrefreshed to prevent several unneeded calls. However, if you find that there are more updates and you need\nthem now, you can call:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.update --force\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand SHERLOCK will ignore the timestamps and perform the update process. In addition, you could be interested\nin wiping all the metadata and build it again. That\u0027s why you could execute:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.update --clean\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThis last command implies a \u003ccode\u003eforce\u003c/code\u003e statement and the last executed time will be ignored too.\u003c/p\u003e\n\u003cp\u003eYou can additionally let SHERLOCK refresh the OIs list before running your current execution by adding to the\nYAML file the next line:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eUPDATE_OIS=True\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVetting\u003c/h3\u003e\u003ca id=\"user-content-vetting\" class=\"anchor\" aria-label=\"Permalink: Vetting\" href=\"#vetting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline comes with a submodule to examine the most promising transit candidates\nfound by any of its executions. This is done via \u003ca href=\"https://github.com/PlanetHunters/watson\"\u003eWATSON\u003c/a\u003e, capable of vetting\nTESS and Kepler targets.\nYou should be able to execute the vetting by calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.vet --properties my_properties.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThrough that command you will run the vetting process for the given parameters within your provided YAML file.\nYou could watch the generated results under \u003ccode\u003e$your_sherlock_object_results_dir/vetting\u003c/code\u003e directory.\nPlease go to\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/vetting\"\u003eexamples/vetting/\u003c/a\u003e\nto learn how to inject the proper properties for the vetting process.\u003c/p\u003e\n\u003cp\u003eThere is an additional simplified option which can be used to run the vetting. In case you are sure\nthere is a candidate from the Sherlock results which matches your desired parameters, you can run\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.vet --candidate ${theCandidateNumber}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003efrom the sherlock results directory. This execution will automatically read the transit\nparameters from the Sherlock generated files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFitting\u003c/h3\u003e\u003ca id=\"user-content-fitting\" class=\"anchor\" aria-label=\"Permalink: Fitting\" href=\"#fitting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline comes with another submodule to fit the most promising transit candidates\nfound by any of its executions. This fit is done via\n\u003ca href=\"https://github.com/MNGuenther/allesfitter\"\u003eALLESFITTER\u003c/a\u003e code. By calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.fit --properties my_properties.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eyou will run the fitting process for the given parameters within your provided YAML file.\nYou could watch the generated results under \u003ccode\u003e$your_sherlock_object_results_dir/fit\u003c/code\u003e directory.\nPlease go to\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/fitting\"\u003eexamples/fitting/\u003c/a\u003e\nto learn how to inject the proper properties for the fitting process.\u003c/p\u003e\n\u003cp\u003eThere is an additional simplified option which can be used to run the fit. In case you are sure\nthere is a candidate from the Sherlock results which matches your desired parameters, you can run\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.fit --candidate ${theCandidateNumber}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003efrom the sherlock results directory. This execution will automatically read the transit and star\nparameters from the Sherlock generated files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eValidation\u003c/h3\u003e\u003ca id=\"user-content-validation\" class=\"anchor\" aria-label=\"Permalink: Validation\" href=\"#validation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline implements a module to execute a statistical validation of a candidate by the usage\nof\n\u003ca href=\"https://github.com/stevengiacalone/triceratops\"\u003eTRICERATOPS\u003c/a\u003e. By calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.validate --candidate ${theCandidateNumber}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eyou will run the validation for one of the Sherlock candidates.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStability\u003c/h3\u003e\u003ca id=\"user-content-stability\" class=\"anchor\" aria-label=\"Permalink: Stability\" href=\"#stability\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline also implements a module to execute a system stability computation by the usage\nof\n\u003ca href=\"https://github.com/hannorein/rebound\"\u003eRebound\u003c/a\u003e and \u003ca href=\"https://github.com/dtamayo/spock\"\u003eSPOCK\u003c/a\u003e. By calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.stability --bodies 1,2,4\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewhere the \u003ccode\u003e--bodies\u003c/code\u003e parameter is the set of the SHERLOCK accepted signals as CSV to be used in the scenarios\nsimulation. You can also provide a\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/properties/stability.yaml\"\u003estability properties file\u003c/a\u003e)\nto run a custom stability simulation:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.stability --properties stability.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eand you can even combine SHERLOCK accepted signals with some additional bodies provided by the properties file:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.stability --bodies 1,2,4 --properties stability.yaml\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe results will be stored into a \u003ccode\u003estability\u003c/code\u003e directory containing the execution log and a \u003ccode\u003estability.csv\u003c/code\u003e\ncontaining one line per simulated scenario, sorted by the best results score.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eObservation plan\u003c/h3\u003e\u003ca id=\"user-content-observation-plan\" class=\"anchor\" aria-label=\"Permalink: Observation plan\" href=\"#observation-plan\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSHERLOCK PIPEline also adds now a tool to plan your observations from ground-based observatories by using\n\u003ca href=\"https://github.com/astropy/astropy\"\u003eastropy\u003c/a\u003e and \u003ca href=\"https://github.com/astropy/astroplan\"\u003eastroplan\u003c/a\u003e. By calling:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003epython3 -m sherlockpipe.plan --candidate ${theCandidateNumber} --observatories observatories.csv\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eon the resulting \u003ccode\u003esherlockpipe.fit\u003c/code\u003e directory, where the precise candidate ephemeris are placed.\nThe \u003ccode\u003eobservatories.csv\u003c/code\u003e file should contain the list of available observatories for your candidate follow-up.\nAs an example, you can look at\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/blob/master/examples/observatories.csv\"\u003ethis file\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSHERLOCK PIPEline Workflow\u003c/h2\u003e\u003ca id=\"user-content-sherlock-pipeline-workflow\" class=\"anchor\" aria-label=\"Permalink: SHERLOCK PIPEline Workflow\" href=\"#sherlock-pipeline-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is important to note that SHERLOCK PIPEline uses some csv files with TOIs, KOIs and EPIC IDs\nfrom the TESS, Kepler and K2 missions. Therefore your first execution of the pipeline might\ntake longer because it will download the information.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eProvisioning of light curve\u003c/h3\u003e\u003ca id=\"user-content-provisioning-of-light-curve\" class=\"anchor\" aria-label=\"Permalink: Provisioning of light curve\" href=\"#provisioning-of-light-curve\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe light curve for every input object needs to be obtained from its mission database. For this we\nuse the high level API of \u003ca href=\"https://github.com/KeplerGO/lightkurve\"\u003eLightkurve\u003c/a\u003e, which enables the\ndownload of the desired light curves for TESS, Kepler and K2 missions. We also include Full Frame\nImages from the TESS mission by the usage of \u003ca href=\"https://adina.feinste.in/eleanor/\" rel=\"nofollow\"\u003eELEANOR\u003c/a\u003e. We\nalways use the PDCSAP signal from the ones provided by any of those two packages.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePre-processing of light curve\u003c/h3\u003e\u003ca id=\"user-content-pre-processing-of-light-curve\" class=\"anchor\" aria-label=\"Permalink: Pre-processing of light curve\" href=\"#pre-processing-of-light-curve\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn many cases we will find light curves which contain several systematics like noise, high dispersion\nbeside the borders, high-amplitude periodicities caused by pulsators, fast rotators, etc. SHERLOCK PIPEline\nprovides some methods to reduce these most important systematics.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLocal noise reduction\u003c/h4\u003e\u003ca id=\"user-content-local-noise-reduction\" class=\"anchor\" aria-label=\"Permalink: Local noise reduction\" href=\"#local-noise-reduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor local noise, where very close measurements show high deviation from the local trend, we apply a\nSavitzky-Golay filter. This has proved a highly increment of the SNR of found transits. This feature\ncan be disabled with a flag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eHigh RMS areas masking\u003c/h4\u003e\u003ca id=\"user-content-high-rms-areas-masking\" class=\"anchor\" aria-label=\"Permalink: High RMS areas masking\" href=\"#high-rms-areas-masking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSometimes the spacecrafts have to perform reaction wheels momentum dumps by firing thrusters,\nsometimes there is high light scattering and sometimes the spacecraft can infer some jitter into\nthe signal. For all of those systematics we found that in many cases the data from those regions\nshould be discarded. Thus, SHERLOCK PIPEline includes a binned RMS computation where bins whose\nRMS value is higher than a configurable factor multiplied by the median get automatically masked.\nThis feature can be disabled with a flag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput time ranges masking\u003c/h4\u003e\u003ca id=\"user-content-input-time-ranges-masking\" class=\"anchor\" aria-label=\"Permalink: Input time ranges masking\" href=\"#input-time-ranges-masking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf enabled, this feature automatically disables\n\u003ca href=\"https://github.com/franpoz/SHERLOCK#high-rms-areas-masking\"\u003eHigh RMS areas masking\u003c/a\u003e\nfor the assigned object. The user can input an array of time ranges to be masked into the\noriginal signal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDetrend of high-amplitude periodicities\u003c/h4\u003e\u003ca id=\"user-content-detrend-of-high-amplitude-periodicities\" class=\"anchor\" aria-label=\"Permalink: Detrend of high-amplitude periodicities\" href=\"#detrend-of-high-amplitude-periodicities\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOur most common foes with high periodicities are fast-rotators, which infer a high sinusoidal-like\ntrend in the PDCSAP signal. This is why SHERLOCK PIPEline includes an automatic high-amplitude periodicities\ndetection and detrending during its preparation stage. This feature can be disabled with a flag.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eInput period detrend\u003c/h4\u003e\u003ca id=\"user-content-input-period-detrend\" class=\"anchor\" aria-label=\"Permalink: Input period detrend\" href=\"#input-period-detrend\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf enabled, this feature automatically disables\n\u003ca href=\"https://github.com/franpoz/SHERLOCK#detrend-of-high-amplitude-periodicities\"\u003eDetrend of high-amplitude periodicities\u003c/a\u003e\nfor the assigned object. The user can input a period to be used for an initial detrend of the\noriginal signal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eCustom user code\u003c/h4\u003e\u003ca id=\"user-content-custom-user-code\" class=\"anchor\" aria-label=\"Permalink: Custom user code\" href=\"#custom-user-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can even inject your own python code to perform:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA custom signal preparation task by implementing the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/sherlockpipe/sherlockpipe/curve_preparer/CurvePreparer.py\"\u003eCurvePreparer\u003c/a\u003e\nclass that we provide. Then, inject your python file into the \u003ccode\u003eCUSTOM_PREPARER\u003c/code\u003e property and let SHERLOCK\nuse your code.\u003c/li\u003e\n\u003cli\u003eA custom best signal selection algorithm by implementing the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/sherlockpipe/sherlockpipe/scoring/SignalSelector.py\"\u003eSignalSelector\u003c/a\u003e.\nclass that we provide. Then, inject your python file into the \u003ccode\u003eCUSTOM_ALGORITHM\u003c/code\u003e property and let SHERLOCK use your code.\u003c/li\u003e\n\u003cli\u003eA custom search zone definition by implementing the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/sherlockpipe/sherlockpipe/search_zones/SearchZone.py\"\u003eSearchZone\u003c/a\u003e.\nclass that we provide. Then, inject your python file into the \u003ccode\u003eCUSTOM_SEARCH_ZONE\u003c/code\u003e property and let SHERLOCK use your code.\u003c/li\u003e\n\u003cli\u003eCustom search modes: \u0027tls\u0027, \u0027bls\u0027, \u0027grazing\u0027, \u0027comet\u0027 or \u0027custom\u0027. You can search for transits by using TLS, BLS,\nTLS for a grazing template, TLS for a comet template or even inject your custom transit template (this is currently\nincluded as an experimental feature).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor better understanding of usage please see the\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/properties/custom_algorithms.yaml\"\u003eexamples\u003c/a\u003e,\nwhich references custom implementations that you can inspect in our\n\u003ca href=\"https://github.com/franpoz/SHERLOCK/tree/master/examples/custom_algorithms\"\u003ecustom algorithms directory\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 19, - "subscribers_count": 11, + "subscribers_count": 3, "topics": [ - "nextflow", - "imputation", - "bioinformatics-pipeline" + "exoplanets", + "tess", + "kepler" ], - "updated_at": 1704367018.0 + "updated_at": 1723807956.0 }, { "data_format": 2, - "description": "Create and maintain phylogenetic \"reference packages\" of biological sequences.", + "description": "detection of burned in pixels using OCR (under development)", "filenames": [ - "singularity/Singularity" + "ocr/Singularity" ], - "full_name": "fhcrc/taxtastic", + "full_name": "pydicom/dicom-cleaner", "latest_release": null, - "stargazers_count": 19, - "subscribers_count": 9, - "topics": [], - "updated_at": 1647885742.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "container/Singularity.gnu", - "container/Singularity.intel_netcdf", - "container/Singularity.intel_am4" - ], - "full_name": "NOAA-GFDL/AM4", - "latest_release": "highres_aquaplanet_2022", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGFDL AM4 Model\u003c/h1\u003e\u003ca id=\"user-content-gfdl-am4-model\" class=\"anchor\" aria-label=\"Permalink: GFDL AM4 Model\" href=\"#gfdl-am4-model\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/102487636\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fea9cd2271674ad89e1a2cc6411a28dbebf6652947fe704e31cc8a612460e113/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3130323438373633362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/102487636.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository includes the public release of the GFDL AM4 model\ncode. The AM4 model is described in the\n\u003ca href=\"https://doi.org/10.1002/2017MS001208\" rel=\"nofollow\"\u003etwo\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.1002/2017MS001209\" rel=\"nofollow\"\u003earticles\u003c/a\u003e published in the\n\u003ca href=\"https://agupubs.onlinelibrary.wiley.com/journal/19422466\" rel=\"nofollow\"\u003eJournal of Advances in Modeling Earth Systems\n(JAMES)\u003c/a\u003e.\nMore information on the model and access to the output is available on\nthe \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eAM4 data and code\nsite\u003c/a\u003e at the\n\u003ca href=\"https://www.gfdl.noaa.gov\" rel=\"nofollow\"\u003eGeophysical Fluid Dynamics Laboratory\n(GFDL)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe layout of this package includes the following directories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003esrc - The source code for the AM4 model\u003c/li\u003e\n\u003cli\u003eexec - The build directory with Makefiles for building the AM4 model executable\u003c/li\u003e\n\u003cli\u003eidealized_exec - The build directory with Makefiles for building the aquaplanet\nand doubly periodic executable\u003c/li\u003e\n\u003cli\u003erun - Sample run script and updated files needed for running\u003c/li\u003e\n\u003cli\u003eanalysis - Sample analysis scripts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCloning Instructions\u003c/h2\u003e\u003ca id=\"user-content-cloning-instructions\" class=\"anchor\" aria-label=\"Permalink: Cloning Instructions\" href=\"#cloning-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository uses \u003ca href=\"https://git-scm.com/book/en/v2/Git-Tools-Submodules\" rel=\"nofollow\"\u003egit\nsubmodules\u003c/a\u003e to\npoint to other repositories. Thus, care should be taken when cloning,\nand updating the source to ensure all source. To obtain all source,\nuse the following git command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/NOAA-GFDL/AM4.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003ccode\u003e--recursive\u003c/code\u003e option to \u003ccode\u003egit clone\u003c/code\u003e instructs git to recursively\nclone all submodules. In the event the repository was not cloned\nusing the \u003ccode\u003e--recursive\u003c/code\u003e option, the following step must be taken to\nobtain all sources:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# From within the AM4 parent directory\ngit submodule update --init --recursive\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSource Code\u003c/h2\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-label=\"Permalink: Source Code\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll model source is contained in the \u003ca href=\"src\"\u003esrc\u003c/a\u003e directory. GFDL\ntracks code using the git version control system. This package\nincludes a single version of the following GFDL model components. The\ngit hash listed corresponds to the commit hash in the internal GFDL\ngit repository.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComponent\u003c/th\u003e\n\u003cth\u003eCommit Hash\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_drivers\u003c/td\u003e\n\u003ctd\u003e5ee95d6abf0879594551dd7e6635dff4004c4010\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_param\u003c/td\u003e\n\u003ctd\u003e2e94acfd8621e85216bf822c395a8c3f15a511a5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eatmos_shared\u003c/td\u003e\n\u003ctd\u003ea557d4d7bab033ef1ad1d400a62fe07a97ccb477\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eice_param\u003c/td\u003e\n\u003ctd\u003e1553c8bc4f9a66791c89367b6f327147523155ed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eice_sis\u003c/td\u003e\n\u003ctd\u003eccc7328dcd79706dd5c17c8bab660222886fc80b\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eland_lad2\u003c/td\u003e\n\u003ctd\u003ea220288ecb289bf9d793d051fc5076072874ce07\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe following components are available in the\n\u003ca href=\"https://github.com/NOAA-GFDL\"\u003eNOAA-GFDL\u003c/a\u003e github organization:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/MOM6\"\u003eMOM6\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NOAA-GFDL/coupler\"\u003ecoupler\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/FMS\"\u003eFMS\u003c/a\u003e (as \u003ca href=\"src/shared\"\u003eshared\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/NOAA-GFDL/GFDL_atmos_cubed_sphere\"\u003eGFDL_atmos_cubed_sphere (tag AM4.0)\u003c/a\u003e (as \u003ca href=\"src/atmos_cubed_sphere\"\u003eatmos_cubed_sphere\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding AM4\u003c/h2\u003e\u003ca id=\"user-content-building-am4\" class=\"anchor\" aria-label=\"Permalink: Building AM4\" href=\"#building-am4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e###Containers\nThe \u003ca href=\"container\"\u003econtainer folder\u003c/a\u003e provides example Dockerfiles and Signularity\ndefinition files to use to build AM4 containers using either GCC/GFORTAN or\nIntel oneAPI. There is a script that can be used to build the intel\nsingularity containers, and the first step of this script can be used with the\nother GFDL climate models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ca href=\"exec\"\u003eexec\u003c/a\u003e directory contains Makefiles that can be used to\nbuild the AM4 executable. These Makefiles were generated using the\n\u003ca href=\"https://github.com/NOAA-GFDL/mkmf\"\u003eMake Makefile (mkmf)\u003c/a\u003e program.\nIncluded in the exec direcgtory is a sample make template file for the\nIntel compilers (\u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e). This make\ntemplate can be used on any system with a relatively recent version of\nthe Intel compilers, the netCDF 4 library and the MPICH2 MPI library.\nIncluded in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file are\nadditional settings that can be modified during the build.\u003c/p\u003e\n\u003cp\u003eTo run the default build (-O3 -msse2), go to the exec directory and\nenter the command\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you would like to change some of the compiler options, there are several different\noptions to add to the make command. For example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake ISA=-xhost BLD_TYPE=REPRO\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewill replace -msse with -xhost and -O3 with -O2. The three options for\n\u003ccode\u003eBLD_TYPE\u003c/code\u003e are\u003cbr\u003e\n\u003ccode\u003ePROD\u003c/code\u003e (-O3)\u003cbr\u003e\n\u003ccode\u003eREPRO\u003c/code\u003e (-O2)\u003cbr\u003e\n\u003ccode\u003eDEBUG\u003c/code\u003e (-O0 and other traps)\u003cbr\u003e\nAll of the make line options can be\nfound in the \u003ca href=\"exec/templates/intel.mk\"\u003eintel.mk\u003c/a\u003e file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eObtaining the input data\u003c/h2\u003e\u003ca id=\"user-content-obtaining-the-input-data\" class=\"anchor\" aria-label=\"Permalink: Obtaining the input data\" href=\"#obtaining-the-input-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe input data required for running the AM4 model can be found on\n\u003ca href=\"https://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eGFDL\u0027s data\nportal\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eThe file \u003ccode\u003eAM4.tar.gz\u003c/code\u003e contains a configured run directory to run a\nsample experiment of the AM4 model. Included in the tar file is a\nREADME.AM4_run with more instructions on how to configure the AM4 run\ndirectory.\u003c/p\u003e\n\u003cp\u003eOn Linux systems, the \u003ccode\u003ewget\u003c/code\u003e command is usually sufficient to download the data\nfile:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo ensure the file downloaded is complete and not corrupted, download one of the two files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz.sha256\nwget ftp://nomads.gfdl.noaa.gov/users/Ming.Zhao/AM4Documentation/GFDL-AM4.0/inputData/AM4_run.tar.gz.sig\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand run the following command that corresponds to the signature file downloaded:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esha256sum -c AM4_run.tar.gz.sha256\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003egpg --verify AM4_run.tar.gz.sig\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning AM4\u003c/h2\u003e\u003ca id=\"user-content-running-am4\" class=\"anchor\" aria-label=\"Permalink: Running AM4\" href=\"#running-am4\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIncluded in the run directory is a sample run script for reference.\nTo run the AM4 sample experiment, first download the data file\nmentioned in \u003ca href=\"#obtaining-the-input-data\"\u003eObtaining the Input data\u003c/a\u003e\nsection. Replace diag_table and input.nml in the top level of the\nuntar\u0027d directory with the corresponding files in the run directory\nof this repository. Modify the variables in the configuration section\nin the sample run script, and then run the script.\u003c/p\u003e\n\u003cp\u003eThe sample data and run script are configured to run on 216\nprocessors. To run on a different number of processors, or modify the\nexperiment, refer to the \u003ccode\u003eREADME.AM4_run\u003c/code\u003e file included in the AM4\ndata tarball.\u003c/p\u003e\n\u003cp\u003eNote: The \u003ccode\u003einput.nml\u003c/code\u003e file (found in the AM4 data tarball) contains\nFortran namelists and namelist variables that modify, at run time, the\nmodel. To learn more about the settings in the \u003ccode\u003einput.nml\u003c/code\u003e file,\nplease refer to source code where the namelist/variable are defined.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnalysis Scripts\u003c/h2\u003e\u003ca id=\"user-content-analysis-scripts\" class=\"anchor\" aria-label=\"Permalink: Analysis Scripts\" href=\"#analysis-scripts\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome of the climate analysis scripts run at NOAA GFDL and used in the\nAM4 documentation papers are located in the analysis directory.\nWithin each analysis suite, is a \u003ca href=\"https://jupyter-notebook.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ejupyter\nnotebook\u003c/a\u003e, both\nreadable and runnable from your local jupyter environment, provided\nall dependencies are installed.\u003c/p\u003e\n\u003cp\u003eE.g.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"analysis/cjs1/radiation_atmos_av_mon/radiation_atmos_av_mon.ipynb\"\u003eRadiation processor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/bw/bw_atmos_cru_ts_a1r/bw_atmos_monthly_cru_ts.1980-2014.ipynb\"\u003eLong-term DJF seasonal mean\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/bw/bw_atmos_zm_atl_pac_a1r/bw_atmos_atl_pac.1980-2014.ipynb\"\u003eZonal_mean_zonal_wind_stress\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"analysis/pcmdimetrics/portraitPlot-AM4.AMIP.ipynb\"\u003ePCMDI Metrics Portrait Plot\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModel output and Other References\u003c/h2\u003e\u003ca id=\"user-content-model-output-and-other-references\" class=\"anchor\" aria-label=\"Permalink: Model output and Other References\" href=\"#model-output-and-other-references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease refer to the \u003ca href=\"http://data1.gfdl.noaa.gov/nomads/forms/am4.0/\" rel=\"nofollow\"\u003eAM4 data and code\nsite\u003c/a\u003e for details\nabout where to find model and OBS data used in the papers.\u003c/p\u003e\n\u003cp\u003eFor all analysis figures and pertaining data, please use the AM4\ndocumentation papers as the original reference.\u003c/p\u003e\n\u003cp\u003ePlease direct your questions and feedback to\n\u003ca href=\"mailto:gfdl.climate.model.info@noaa.gov\"\u003egfdl.climate.model.info@noaa.gov\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe United States Department of Commerce (DOC) GitHub project code is\nprovided on an \u0027as is\u0027 basis and the user assumes responsibility for\nits use. DOC has relinquished control of the information and no\nlonger has responsibility to protect the integrity, confidentiality,\nor availability of the information. Any claims against the Department\nof Commerce stemming from the use of its GitHub project will be\ngoverned by all applicable Federal law. Any reference to specific\ncommercial products, processes, or services by service mark,\ntrademark, manufacturer, or otherwise, does not constitute or imply\ntheir endorsement, recommendation or favoring by the Department of\nCommerce. The Department of Commerce seal and logo, or the seal and\nlogo of a DOC bureau, shall not be used in any manner to imply\nendorsement of any commercial product or activity by DOC or the United\nStates Government.\u003c/p\u003e\n\u003cp\u003eThis project code is made available through GitHub but is managed by\nNOAA-GFDL at \u003ca href=\"https://gitlab.gfdl.noaa.gov\" rel=\"nofollow\"\u003ehttps://gitlab.gfdl.noaa.gov\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 19, - "subscribers_count": 7, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDicom Cleaner\u003c/h1\u003e\u003ca id=\"user-content-dicom-cleaner\" class=\"anchor\" aria-label=\"Permalink: Dicom Cleaner\" href=\"#dicom-cleaner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository provides tools intended for use to scrape personal health information (PHI)\nthat is represented as text from image headers and pixels. Each subfolder here corresponds\nto a different kind of cleaner, and a docker container.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"ocr\"\u003eocr\u003c/a\u003e \"optical character recognititon\" is an image that runs text (letter detection) on a demo image. You can either detect (just find and report) or clean the data (and save cleaned png images).\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"header\"\u003eheader\u003c/a\u003e uses the \u003ca href=\"https://www.github.com/pydicom/deid\"\u003edeid\u003c/a\u003e python module to target known coordinates based on manufacturer and machine types to flag images. The coordinates are then cleaned (covered with a black box).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth containers are available on Docker Hub.\u003c/p\u003e\n", + "stargazers_count": 20, + "subscribers_count": 5, "topics": [ - "fortran", - "jupyter-notebook", - "shell-script", - "ncl" - ], - "updated_at": 1713319517.0 - }, - { - "data_format": 2, - "description": "VGEA (Viral Genomes Easily Analyzed) is a pipeline for analysis of RNA virus next-generation sequencing data.", - "filenames": [ - "Singularity" + "dicom", + "ocr", + "pixel-scrubbing", + "scrubber", + "deid", + "deidentification" ], - "full_name": "pauloluniyi/VGEA", - "latest_release": "v1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eVGEA \u003ca href=\"https://zenodo.org/badge/latestdoi/236802599\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f4144c21d8e6fe4bafe17833ca7ade3db5f320ce97ec3923a4c25f71a34ccfcc/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3233363830323539392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/236802599.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-vgea-\" class=\"anchor\" aria-label=\"Permalink: VGEA \" href=\"#vgea-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVGEA (Viral Genomes Easily Analyzed) is an RNA viral assembly toolkit.\u003c/p\u003e\n\u003cp\u003eVGEA was developed to aid in the analysis of next generation sequencing data. Users can do the following with this pipeline:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRemove adapters, low quality bases/positions, and perform read-level QC\u003c/li\u003e\n\u003cli\u003eAlign paired-end sequencing reads to the human reference genome.\u003c/li\u003e\n\u003cli\u003eExtract unmapped/unaligned reads.\u003c/li\u003e\n\u003cli\u003eSplit bam files into forward and reverse reads.\u003c/li\u003e\n\u003cli\u003eCarry out de novo assembly of forward and reverse reads to generate contigs.\u003c/li\u003e\n\u003cli\u003ePre-process reads for quality and contamination.\u003c/li\u003e\n\u003cli\u003eMap reads to a reference tailored to the sample using corrected contigs supplemented by the user\u2019s choice of reference sequences.\u003c/li\u003e\n\u003cli\u003eEvaluate/assess the quality of genome assemblies.\u003c/li\u003e\n\u003cli\u003eCollate results in a multiqc summary..\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDependencies:\u003c/p\u003e\n\u003cp\u003eThe VGEA pipeline requires the following dependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSnakemake (K\u00f6ster et al., 2012)\u003c/li\u003e\n\u003cli\u003eFastp (Chen et al., 2018)\u003c/li\u003e\n\u003cli\u003eBWA (Li and Durbin, 2009)\u003c/li\u003e\n\u003cli\u003eSamtools (Li et al., 2009)\u003c/li\u003e\n\u003cli\u003eIVA (Hunt et al., 2015)\u003c/li\u003e\n\u003cli\u003eShiver (Wymant et al., 2018)\u003c/li\u003e\n\u003cli\u003eSeqKit (Shen et al., 2016)\u003c/li\u003e\n\u003cli\u003eQuast (Gurevich et al., 2013)\u003c/li\u003e\n\u003cli\u003eMultiqc (Ewels et al., 2016)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVGEA was built on the Snakemake workflow management system and utilizes existing tools for each step: \u003cstrong\u003efastp\u003c/strong\u003e (Chen et al., 2018) for read trimming and quality control, \u003cstrong\u003ebwa\u003c/strong\u003e (Li and Durbin, 2009) for mapping sequencing reads to the human reference genome, \u003cstrong\u003esamtools\u003c/strong\u003e (Li et al., 2009) for extracting unmapped reads and also for splitting bam files into fastq files, \u003cstrong\u003eiva\u003c/strong\u003e (Hunt et al., 2015) for de novo assembly to generate contigs, \u003cstrong\u003eshiver\u003c/strong\u003e (Wymant et al., 2018) to pre-process reads for quality and contamination, then map to a reference tailored to the sample using corrected contigs supplemented with the user\u2019s choice of existing reference sequences, \u003cstrong\u003eseqkit\u003c/strong\u003e for cleaning shiver assembly for QUAST and \u003cstrong\u003equast\u003c/strong\u003e (Gurevich et al., 2013) to evaluate/assess the quality of genome assemblies.\nFinally, an interactive report of results are generated by \u003cstrong\u003eMultiQC\u003c/strong\u003e (Ewels et al., 2016).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Conda\u003c/h3\u003e\u003ca id=\"user-content-1-conda\" class=\"anchor\" aria-label=\"Permalink: 1. Conda\" href=\"#1-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis workflow requires conda to be installed and available on the system.\u003c/p\u003e\n\u003cp\u003eTo do this install conda via the miniconda installers found \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and instructions \u003ca href=\"https://docs.conda.io/projects/continuumio-conda/en/latest/user-guide/install/index.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBriefly:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eLinux\u003c/h4\u003e\u003ca id=\"user-content-linux\" class=\"anchor\" aria-label=\"Permalink: Linux\" href=\"#linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo obtain the installer for linux use the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, install miniconda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esh Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMacOS\u003c/h4\u003e\u003ca id=\"user-content-macos\" class=\"anchor\" aria-label=\"Permalink: MacOS\" href=\"#macos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo obtain the installer for MacOS, you can \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003edownload\u003c/a\u003e it manually or use wget:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, install miniconda,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esh Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Snakemake\u003c/h3\u003e\u003ca id=\"user-content-2-snakemake\" class=\"anchor\" aria-label=\"Permalink: 2. Snakemake\" href=\"#2-snakemake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThen install snakemake as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -c bioconda -c conda-forge --name snakemake snakemake \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. VGEA\u003c/h3\u003e\u003ca id=\"user-content-3-vgea\" class=\"anchor\" aria-label=\"Permalink: 3. VGEA\" href=\"#3-vgea\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo complete installation of VGEA clone the directory and enter it.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:pauloluniyi/VGEA.git\ncd VGEA\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Data Dependency\u003c/h3\u003e\u003ca id=\"user-content-4-data-dependency\" class=\"anchor\" aria-label=\"Permalink: 4. Data Dependency\" href=\"#4-data-dependency\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFinally, you need to download a human reference genome.\nThere is a convenience script provided to do this that can run as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd resources/\nbash get_human_reference.sh \ncd ..\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are 2 key files when running VGEA:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eA yaml config file containing key parameters e.g., \u003ccode\u003econfig/config.yaml\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA 3 column tab-seperated sheet with a name and paths to r1 and r2 for each sample e.g., \u003ccode\u003e./tests/integration/sample_table.tsv\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfig\u003c/h3\u003e\u003ca id=\"user-content-config\" class=\"anchor\" aria-label=\"Permalink: Config\" href=\"#config\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003esample_table\u003c/code\u003e is the path to the tsv created above with names and read locations for each sample (under headings id, r1, r2)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eshiver_config_file\u003c/code\u003e is the path to the config file for shiver, by default this is \u003ccode\u003econfig/shiver_config.sh\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003ehuman_reference_genome\u003c/code\u003e is the path to the fasta file containing the human genome reference, if you used \u003ccode\u003eget_human_reference.sh\u003c/code\u003e this will be \u003ccode\u003eresources/GRCh38_latest_genomic.fna\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_species\u003c/code\u003e is one of the 4 pre-supported viral references (\u003ccode\u003eHIV-1\u003c/code\u003e, \u003ccode\u003eSARS-CoV-2\u003c/code\u003e, \u003ccode\u003eLASV_L\u003c/code\u003e, \u003ccode\u003eLASV_S\u003c/code\u003e) and is used to autofill the paths to various reference files used by default for these. If you supply your own viral reference/adapter/primer files then this doesn\u0027t need to be supplied\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_reference_alignment\u003c/code\u003e path to a reference alignment of the appropriate viral genomes for your samples (by default \u003ccode\u003eresources/{viral_species}/MyRefAlignment.fasta\u003c/code\u003e). This reference alignment should be carefully created.\n\"An alignment of existing reference sequences is required as input for shiver. Construction of a custom reference for mapping involves identifying the existing references that are closest to the sample under consideration. The greater the number and diversity of existing references given as input, the denser and broader the coverage of sequence space is, and the closer the closest reference is expected to be, with corresponding benefits for the accuracy of the results. However these existing references should be aligned to each other accurately, in order for the addition of each sample\u2019s contigs to the alignment to be meaningful; this means that producing such an input by automatically aligning a large number of diverse sequences without checking the results would be a bad idea. This alignment will be used as input for every sample in a dataset processed by shiver, and so the user is advised to put a little thought into sequence selection and manually curating the alignment if needed.\" (Wymant et al., 2018).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_reference_genome\u003c/code\u003e path to a singular reference genome for your samples for QUAST based assembly assessment (by default \u003ccode\u003eresources/{viral_species}/MyRefGenome.fasta\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_reference_gene_features\u003c/code\u003e path to a GFF3 containing gene features for the supplied viral reference genome (by default \u003ccode\u003eresources/{viral_species}/MyRefFeatures.gff3\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_sequencing_adapters\u003c/code\u003e path to a fasta file containing the sequencing adapters used for your samples (by default \u003ccode\u003eresources/{viral_species}/MyAdapters.fasta\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eviral_sequencing_primers\u003c/code\u003e path to a fasta file containing the sequencing primers used for your samples (by default \u003ccode\u003eresources/{viral_species}/MyRefAlignment.fasta\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSample Table\u003c/h3\u003e\u003ca id=\"user-content-sample-table\" class=\"anchor\" aria-label=\"Permalink: Sample Table\" href=\"#sample-table\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA 3 column, tab-separated file with an \u003cem\u003eid\u003c/em\u003e column containing sample names, \u003cem\u003er1\u003c/em\u003e with the path to the forward reads for that sample, and \u003cem\u003er2\u003c/em\u003e with the path to reverse reads for that sample.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eid\tr1\tr2\ntest1\t.tests/integration/test1_r1.fq.gz\t.tests/integration/test1_r2.fq.gz\ntest2\t.tests/integration/test2_r1.fq.gz\t.tests/integration/test2_r2.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExecuting the workflow\u003c/h3\u003e\u003ca id=\"user-content-executing-the-workflow\" class=\"anchor\" aria-label=\"Permalink: Executing the workflow\" href=\"#executing-the-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow will automatically install dependencies using conda/mamba if executed with \u003ccode\u003e--use-conda\u003c/code\u003e otherwise all dependencies listed at the start of the README must be manually installed via conda.\u003c/p\u003e\n\u003cp\u003eTo run the workflow complete the above config and sample tables and execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --cores $n --use-conda --configfile $your_config\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere \u003ccode\u003e$n\u003c/code\u003e is the number of cores with which to execute the workflow and \u003ccode\u003e$your_config\u003c/code\u003e is the path to \u003ccode\u003econfig.yaml\u003c/code\u003e you\u0027ve created.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eResults\u003c/h3\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVGEA will output all results in the \u003ccode\u003eresults/\u003c/code\u003e directory with a subfolder containing results for each sample and top-level folders collecting all log files, tool benchmarks, and an interactive multiQC html summary of results.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eresults/\n\u251c\u2500\u2500 benchmarks # all benchmark files with hardware usage for each sample\n\u251c\u2500\u2500 logs # all log files for each rule and sample\n\u251c\u2500\u2500 multiqc # multiqc summary of quast, fastp, and de-hosting results\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 multiqc_data\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 multiqc_report.html # the interactive multiqc report\n\u251c\u2500\u2500 test1 # all results for the sample \"test1\" \n\u2502\u00a0\u00a0 \u251c\u2500\u2500 shiver # folder containing all shiver output files \n\u2502 \u251c\u2500\u2500 test1_1.fastq # fastp trimmed and de-hosted reads for the sample \n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1_2.fastq\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.bam # alignment against human reference \n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.fasta # final cleaned assembly from IVA and shiver\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.fastp.html # fastp report in html format\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.fastp.json # fastp report in json format\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.flagstat # dehosting mapping statistics\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1_iva # folder containing all IVA assembly files\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1.quast_results # folder containing all QUAST assembly assessment of test1.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1_r1_trimmed.fq # fastp trimmed reads\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 test1_r2_trimmed.fq \n\u2502\u00a0\u00a0 \u2514\u2500\u2500 test1.sam # sam file containing all the reads that didn\u0027t map to the human reference\n\u2514\u2500\u2500 test2\n \u251c\u2500\u2500 shiver\n \u251c\u2500\u2500 test2_1.fastq\n \u251c\u2500\u2500 test2_2.fastq\n \u251c\u2500\u2500 test2.bam\n \u251c\u2500\u2500 test2.fasta\n \u251c\u2500\u2500 test2.fastp.html\n \u251c\u2500\u2500 test2.fastp.json\n \u251c\u2500\u2500 test2.flagstat\n \u251c\u2500\u2500 test2_iva\n \u251c\u2500\u2500 test2.quast_results\n \u251c\u2500\u2500 test2_r1_trimmed.fq\n \u251c\u2500\u2500 test2_r2_trimmed.fq\n \u2514\u2500\u2500 test2.sam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainerized Singularity (Beta)\u003c/h2\u003e\u003ca id=\"user-content-containerized-singularity-beta\" class=\"anchor\" aria-label=\"Permalink: Containerized Singularity (Beta)\" href=\"#containerized-singularity-beta\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAlternatively, users can run the VGEA pipeline with all dependencies installed in a docker/singularity container.\u003c/p\u003e\n\u003cp\u003eThis requires singularity and snakemake to be installed on the system but in theory provides a more reproducible version of the conda environments (not fully tested compared to just conda).\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"https://www.sylabs.io/docs/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for instructions to install Singularity.\u003c/p\u003e\n\u003cp\u003eThen the workflow can be run as normal with \u003ccode\u003e--use-singularity\u003c/code\u003e added e.g.,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-conda --use-singularity --configfile .tests/integration/test_config.yaml -j 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run a minimal integration test once snakemake and conda are installed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-conda --configfile .tests/integration/test_config.yaml -j 1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContributions\u003c/h1\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePaul Eniola Oluniyi\u003c/li\u003e\n\u003cli\u003eGerry Tonkin-Hill (\u003ca href=\"https://gtonkinhill.github.io/\" rel=\"nofollow\"\u003ehttps://gtonkinhill.github.io/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eFinlay Maguire (\u003ca href=\"https://finlaymagui.re\" rel=\"nofollow\"\u003ehttps://finlaymagui.re\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/236802599\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f4144c21d8e6fe4bafe17833ca7ade3db5f320ce97ec3923a4c25f71a34ccfcc/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3233363830323539392e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/236802599.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 19, - "subscribers_count": 1, - "topics": [], - "updated_at": 1719708921.0 + "updated_at": 1702563654.0 }, { "data_format": 2, - "description": "Destructive deep learning estimators and functions that are compatible with scikit-learn.", + "description": "Repository for managing the builds of OpenStudio Docker images.", "filenames": [ - "Singularity" + "singularity/Singularity" ], - "full_name": "davidinouye/destructive-deep-learning", - "latest_release": null, - "stargazers_count": 19, - "subscribers_count": 3, + "full_name": "NREL/docker-openstudio", + "latest_release": "2.9.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOpenStudio\u003c/h1\u003e\u003ca id=\"user-content-openstudio\" class=\"anchor\" aria-label=\"Permalink: OpenStudio\" href=\"#openstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/NREL/docker-openstudio/actions/workflows/docker-openstudio.yml\"\u003e\u003cimg src=\"https://github.com/NREL/docker-openstudio/actions/workflows/docker-openstudio.yml/badge.svg\" alt=\"openstudio-docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repo provides a container for OpenStudio as well as several dependencies, including Ruby 2.x, Bundler,\nbuild-essentials and various development libraries for gem support.\u003c/p\u003e\n\u003cp\u003eAt the moment, there is only one target container for this project, however, we are working on containers for a\nslim version and a version compatible with \u003ca href=\"https://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Tags\u003c/h2\u003e\u003ca id=\"user-content-docker-tags\" class=\"anchor\" aria-label=\"Permalink: Docker Tags\" href=\"#docker-tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBelow is a table of the various docker tags and their meanings as seen on \u003ca href=\"https://hub.docker.com/r/nrel/openstudio/tags/\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTag\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ex.y.z\u003c/td\u003e\n\u003ctd\u003eBuild of official OpenStudio release (recommended use)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003elatest\u003c/td\u003e\n\u003ctd\u003eLatest official release of OpenStudio (e.g. 2.5.1)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edevelop\u003c/td\u003e\n\u003ctd\u003eRelease of \u003ca href=\"https://github.com/NREL/docker-openstudio/tree/develop\"\u003edevelop branch\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding OpenStudio Container\u003c/h2\u003e\u003ca id=\"user-content-building-openstudio-container\" class=\"anchor\" aria-label=\"Permalink: Building OpenStudio Container\" href=\"#building-openstudio-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese images are automatically built in TravisCI. To trigger TravisCI for a new build do the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eOn develop branch update the \u003ca href=\".travis.yml\"\u003e.travis.yml\u003c/a\u003e with the new version of OpenStudio and SHA\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003eOPENSTUDIO_VERSION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e2.6.0\u003c/span\u003e\n - \u003cspan class=\"pl-ent\"\u003eOPENSTUDIO_SHA\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ee3cb91f98a\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePush changes to the feature branch, make and merge a pull request to develop\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWait for CI to finish and verify new develop image is available on \u003ca href=\"https://hub.docker.com/r/nrel/openstudio/tags/\" rel=\"nofollow\"\u003edocker hub\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTest locally (if needed)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull nrel/openstudio:develop\ndocker run -it --rm nrel/openstudio:develop bash\nirb\nrequire \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eopenstudio\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nputs OpenStudio.getOpenStudioCLI\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Locally\u003c/h3\u003e\u003ca id=\"user-content-build-locally\" class=\"anchor\" aria-label=\"Permalink: Build Locally\" href=\"#build-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBegin by installing the \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003edocker toolkit\u003c/a\u003e version 17.03.1 or later, as\ndescribed in the linked documentation. Once the toolkit is installed and activated, run the command below to build the base image with OpenStudio 2.6.1.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --target base -t openstudio-local --build-arg OPENSTUDIO_VERSION=2.6.1 --build-arg OPENSTUDIO_SHA=ab0dddde0b .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe version of OpenStudio and the SHAs are listed \u003ca href=\"https://github.com/NREL/OpenStudio/wiki/OpenStudio-Version-Compatibility-Matrix\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf the \u003ccode\u003e--target\u003c/code\u003e is not passed, then the docker build will contain only the CLI.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecuting OpenStudio Container\u003c/h2\u003e\u003ca id=\"user-content-executing-openstudio-container\" class=\"anchor\" aria-label=\"Permalink: Executing OpenStudio Container\" href=\"#executing-openstudio-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two options to acquire the docker container required for execution. Both assume that the\n\u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003edocker tool-kit\u003c/a\u003e version 17.03.1 or later is installed. The first option,\nbuilding the container from a GitHub checkout, is outlined above. Additionally, it is typically easiest to\n\u003ca href=\"https://docs.docker.com/engine/reference/commandline/tag/\" rel=\"nofollow\"\u003etag the resulting container\u003c/a\u003e as \u003ccode\u003enrel/openstudio:latest\u003c/code\u003e.\nFor a more extensive discussion of the latest tag and associated best practices\n\u003ca href=\"https://medium.com/@mccode/the-misunderstood-docker-tag-latest-af3babfd6375\" rel=\"nofollow\"\u003eplease refer to this Medium article\u003c/a\u003e.\nThe second option, downloading a release from DockerHub, requires determining the\n\u003ca href=\"https://hub.docker.com/r/nrel/openstudio/tags/\" rel=\"nofollow\"\u003edocker-openstudio tagged release\u003c/a\u003e that is desired, and then running\n\u003ccode\u003edocker pull nrel/openstudio:\u0026lt;tag\u0026gt;\u003c/code\u003e. As an example, to download the 2.1.1 docker-openstudio image, the command would\nbe \u003ccode\u003edocker pull nrel/openstudio:2.1.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the desired container is available, either through a build or pull process, the next step is to run the container.\nTo simply access the container tagged with \u0027tag\u0027, (where tag was respectively \u0027latest\u0027 or \u00272.1.1\u0027 in the above\nparagraph,) run \u003ccode\u003edocker run -it --rm nrel/openstudio:tag /bin/bash\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo execute an OpenStudio Workflow directly from the command line requires\n\u003ca href=\"https://docs.docker.com/engine/reference/run/#volume-shared-filesystems\" rel=\"nofollow\"\u003emounting the requisite files to the container\u003c/a\u003e,\nas well as invoking the \u003ca href=\"https://nrel.github.io/OpenStudio-user-documentation/reference/command_line_interface/\" rel=\"nofollow\"\u003eOpenStudio CLI\u003c/a\u003e\n(command line interface.) The docker container by default executes commands in the \u003ccode\u003e/var/simdata/openstudio\u003c/code\u003e directory,\n(this is defined in the \u003ca href=\"./Dockerfile\"\u003eDockerfile\u003c/a\u003e.) If the desired OSW was located at\n\u003ccode\u003e/Users/myuser/demo_os_files/example.osw\u003c/code\u003e on the host computer, the appropriate docker command to execute the OSW would\nbe \u003ccode\u003edocker run -it --rm -v=/Users/myuser/demo_os_files:/var/simdata/openstudio nrel/openstudio /usr/bin/openstudio run -w example.osw\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf gem dependencies are required as part of the CLI outside of those\n\u003ca href=\"https://github.com/NREL/OpenStudio/blob/develop/dependencies/ruby/Gemfile\"\u003epacked with OpenStudio\u003c/a\u003e please contact\nKyle Benne, Ry Horsey, and Dan Macumber at their NREL email addresses.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIssues\u003c/h1\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-label=\"Permalink: Issues\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease submit issues on the project\u0027s \u003ca href=\"https://github.com/nrel/docker-openstudio\"\u003eGithub\u003c/a\u003e page.\u003c/p\u003e\n", + "stargazers_count": 20, + "subscribers_count": 14, "topics": [], - "updated_at": 1700225283.0 + "updated_at": 1714618187.0 }, { "data_format": 2, - "description": "A data processing platform for ChIP-seq, RNA-seq, MNase-seq, DNase-seq, ATAC-seq and GRO-seq datasets. Please ignore information on cipher.readthedocs.io, it is currently out of date. Follow information in README.", + "description": "Alignment-based retrieval and concatenation of phylogenetic markers from whole genome sequence (WGS) data", "filenames": [ - "Singularity" + "docker/Singularity.def" ], - "full_name": "c-guzman/cipher-workflow-platform", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eC I P H E R\u003c/h1\u003e\u003ca id=\"user-content-c-i-p-h-e-r\" class=\"anchor\" aria-label=\"Permalink: C I P H E R\" href=\"#c-i-p-h-e-r\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVersion 1.0.0 | Updated August 2017\u003c/p\u003e\n\u003cp\u003eAuthor: Carlos Guzman\u003c/p\u003e\n\u003cp\u003eE-mail: \u003ca href=\"mailto:cag104@ucsd.edu\"\u003ecag104@ucsd.edu\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCIPHER is a data processing workflow platform for next generation sequencing data including ChIP-seq, RNA-seq, DNase-seq, MNase-seq, ATAC-seq and GRO-seq. By taking advantage of the Nextflow language, and Singularity containers, CIPHER is an extremely easy to use, and reproducible pre-processing workflow toolkit.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHELP\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: HELP\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCIPHER has a built in help command. For more information regarding possible parameters and their meanings, open up the command line terminal and type:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run cipher.nf --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload or \u003ccode\u003egit clone\u003c/code\u003e this repository and install dependencies.\u003c/p\u003e\n\u003cp\u003eThe only required dependencies to run CIPHER is:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNextflow (\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003ehttps://www.nextflow.io/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSingularity (\u003ca href=\"http://singularity.lbl.gov/index.html\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/index.html\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCONFIG Files\u003c/h2\u003e\u003ca id=\"user-content-config-files\" class=\"anchor\" aria-label=\"Permalink: CONFIG Files\" href=\"#config-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConfig files are tab separated text files with 5 columns for single-ended data and 6 columns for pair ended data.\u003c/p\u003e\n\u003cp\u003eSingle-ended CONFIG:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esample1\t\tsample1_rep1\t/path/to/fastq.gz \tcontrol1\tsample1\nsample2\t\tsample2_rep1\t/path/to/fastq.gz \tcontrol1\tinput\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePair-ended CONFIG:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esample1\t\tsample1_rep1\t/path/to/fastq_R1.gz \t/path/to/fastq_R2.gz\tcontrol1\tsample1\nsample2\t\tsample2_rep1\t/path/to/fastq_R1.gz \t/path/to/fastq_R2.gz\tcontrol1\tinput\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eDO NOT MIX AND MATCH SINGLE AND PAIR ENDED DATA INTO THE SAME CONFIG FILE. CIPHER DOES NOT HANDLE THIS USE-CASE YET.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhere columns refer to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003col\u003e\n\u003cli\u003eMergeID - Prefix used for naming files that are merged together.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eSampleID - Prefix used for naming files that are not merged together. Typically includes replicate information.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003ePath 1 - The file path to first FASTQ file. Typically the R1 file in pair-ended data.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003ePath 2 - The file path to second FASTQ file. Only required for pair-ended data. Typically the R2 file in pair-ended data.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInputID - Used to pair sample and input files for various types of sequencing data. Use \u003ccode\u003e-\u003c/code\u003e if no input file is available or needed (as is the case in RNA-seq/GRO-seq/MNase-seq/etc.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eMark - Used to differentiate sample files from input files. Use the keyword \u003ccode\u003einput\u003c/code\u003e if that sample corresponds to an input file. Otherwise use \u003ccode\u003eMergeID\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CIPHER\u003c/h2\u003e\u003ca id=\"user-content-running-cipher\" class=\"anchor\" aria-label=\"Permalink: Running CIPHER\" href=\"#running-cipher\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall required dependencies\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate Singularity container (will require \u003ccode\u003esudo\u003c/code\u003e access, so a container can be created on a local laptop/desktop and then transferred to the appopriate location/machine/cluster)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity create -s 8000 cipher.img\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity bootstrap cipher.img Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun your workflow\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run cipher.nf -with-singularity \u0026lt;cipher.img\u0026gt; --mode \u0026lt;MODE\u0026gt; --config \u0026lt;CONFIG\u0026gt; --fa \u0026lt;FASTA\u0026gt; --gtf \u0026lt;GTF\u0026gt; --lib \u0026lt;LIB\u0026gt; --readLen \u0026lt;LENGTH\u0026gt; [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e If not running on a cluster please set the \u003ccode\u003e-qs \u0026lt;INT\u0026gt;\u003c/code\u003e flag in order to control the number of processes that CIPHER parallelizes. Too many and the workflow will abruptly end because it runs out of memory. \u003ccode\u003enextflow run -qs \u0026lt;INT\u0026gt; cipher.nf ...\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e If you would like to run CIPHER without using Singularity containers, please make sure that you have installed all the required software for your specific pipeline. Tools used can be found inside the main cipher.nf script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExample Data\u003c/h2\u003e\u003ca id=\"user-content-example-data\" class=\"anchor\" aria-label=\"Permalink: Example Data\" href=\"#example-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome example data to test CIPHER\u0027s workflows can be found in the \u003ccode\u003eexample_data\u003c/code\u003e folder. The user should alter the config file fastq paths before running the workflow otherwise the run will fail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CIPHER on a Cluster\u003c/h2\u003e\u003ca id=\"user-content-running-cipher-on-a-cluster\" class=\"anchor\" aria-label=\"Permalink: Running CIPHER on a Cluster\" href=\"#running-cipher-on-a-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCIPHER is possible to execute it on your computer or any cluster resource\nmanager without modifying it.\u003c/p\u003e\n\u003cp\u003eCurrently the following platforms are supported:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOracle/Univa/Open Grid Engine (SGE)\u003c/li\u003e\n\u003cli\u003ePlatform LSF\u003c/li\u003e\n\u003cli\u003eSLURM\u003c/li\u003e\n\u003cli\u003ePBS/Torque\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default the pipeline is parallelized by spanning multiple threads in the machine where the script is launched.\u003c/p\u003e\n\u003cp\u003eFor example, to submit the execution to a SGE cluster edit the file named \u003ccode\u003enextflow.config\u003c/code\u003e, in the directory\nwhere the cipher.nf file is found, with the following content:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eprocess {\n executor=\u0027sge\u0027\n queue=\u0027\u0026lt;your queue name\u0026gt;\u0027\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn doing that, tasks will be executed through the \u003ccode\u003eqsub\u003c/code\u003e SGE command, and so your pipeline will behave like any\nother SGE job script, with the benefit that \u003cem\u003eNextflow\u003c/em\u003e will automatically and transparently manage the tasks\nsynchronisation, file(s) staging/un-staging, etc.\u003c/p\u003e\n\u003cp\u003eMore information regarding the platforms Nextflow supports and how to run them can be found \u003ca href=\"https://www.nextflow.io/docs/latest/executor.html\" rel=\"nofollow\"\u003eHERE\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 19, + "full_name": "fethalen/Patchwork", + "latest_release": "v0.5.2", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/fethalen/Patchwork/blob/main/patchwork_logo_500px.png\"\u003e\u003cimg src=\"https://github.com/fethalen/Patchwork/raw/main/patchwork_logo_500px.png\" alt=\"Patchwork logo\" width=\"225\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f7853eb8ea0bc39f9ca567e8129bae603ce31b5c88563490315eb26bdbc05f43/687474703a2f2f7777772e676e752e6f72672f67726170686963732f67706c76332d38387833312e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f7853eb8ea0bc39f9ca567e8129bae603ce31b5c88563490315eb26bdbc05f43/687474703a2f2f7777772e676e752e6f72672f67726170686963732f67706c76332d38387833312e706e67\" alt=\"GPLv3\" data-canonical-src=\"http://www.gnu.org/graphics/gplv3-88x31.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://www.gnu.org/copyleft/gpl.html\" rel=\"nofollow\"\u003eGNU General Public License, GPLv3\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePatchwork is an alignment-based program for retrieving and concatenating\nphylogenetic markers from whole-genome sequencing (WGS) data. The program\nsearches the provided DNA query contigs against one or more amino acid reference\nsequences. Multiple, overlapping hits are merged to derive a single, continuous\nsequence for each reference sequence.\u003c/p\u003e\n\u003ch3 id=\"user-content-features\"\u003e\u003ca class=\"heading-link\" href=\"#features\"\u003eFeatures\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eAlign nucleotide sequences to one or more protein sequences\u003c/li\u003e\n\u003cli\u003eWorks with already assembled contigs \u003cem\u003eor\u003c/em\u003e raw reads\u003c/li\u003e\n\u003cli\u003eStitch overlapping or gappy sequences together based on a reference\u003c/li\u003e\n\u003cli\u003eFind homologs, even in distantly-related taxa\u003c/li\u003e\n\u003cli\u003e\ud83d\udc07 Written in \u003ca href=\"https://julialang.org/\" rel=\"nofollow\"\u003eJulia\u003c/a\u003e and utilizing\n\u003ca href=\"https://github.com/bbuchfink/diamond\"\u003eDIAMOND\u003c/a\u003e for maximum speed\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"user-content-graphical-overview\"\u003e\u003ca class=\"heading-link\" href=\"#graphical-overview\"\u003eGraphical Overview\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/fethalen/patchwork/blob/main/overview.png?raw=true\"\u003e\u003cimg src=\"https://github.com/fethalen/patchwork/raw/main/overview.png?raw=true\" alt=\"Graphical Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3 id=\"user-content-quick-installation\"\u003e\u003ca class=\"heading-link\" href=\"#quick-installation\"\u003eQuick installation\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eWe are currently working on a Conda build. In the future,\nthe user will be able to install this program by running \u003ccode\u003econda install -c bioconda patchwork\u003c/code\u003e. Until then, please refer to\n\u003ca href=\"https://github.com/fethalen/Patchwork/wiki/4.-Installation\"\u003ethese instructions\u003c/a\u003e\nfor installing from source. It is now also possible to \u003ca href=\"https://github.com/fethalen/Patchwork/wiki/4.-Installation#installing-patchwork-with-docker\"\u003einstall\nPatchwork using Docker\u003c/a\u003e.\u003c/p\u003e\n\u003ch3 id=\"user-content-documentation\"\u003e\u003ca class=\"heading-link\" href=\"#documentation\"\u003eDocumentation\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003ePlease see our \u003ca href=\"https://github.com/fethalen/Patchwork/wiki\"\u003eWiki\u003c/a\u003e.\u003c/p\u003e\n\u003ch3 id=\"user-content-cite\"\u003e\u003ca class=\"heading-link\" href=\"#cite\"\u003eCite\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eOur manuscript is still in preparation, it will be posted here once a preprint\nof the article is available.\u003c/p\u003e\n\u003cp\u003e\u00a9 \u003ca href=\"https://www.uni-goettingen.de/en/80149.html\" rel=\"nofollow\"\u003eDept. for Animal Evolution and Biodiversity\u003c/a\u003e 2020\u003c/p\u003e\n", + "stargazers_count": 20, "subscribers_count": 1, "topics": [], - "updated_at": 1667468054.0 + "updated_at": 1675872349.0 }, { "data_format": 2, - "description": "This repository is an AI bootcamp material that consist of a workflow for computer vision ", + "description": "A Nextflow full-length 16S profiling pipeline for ONT reads", "filenames": [ - "Singularity_deepstream", - "Singularity_tao", - "Singularity_triton" + "environments/Singularity" ], - "full_name": "openhackathons-org/End-to-End-Computer-Vision", + "full_name": "microgenlab/porefile", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnd-to-End Computer Vision Bootcamp\u003c/h1\u003e\u003ca id=\"user-content-end-to-end-computer-vision-bootcamp\" class=\"anchor\" aria-label=\"Permalink: End-to-End Computer Vision Bootcamp\" href=\"#end-to-end-computer-vision-bootcamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003cstrong\u003eEnd-to-End Computer Vision Bootcamp\u003c/strong\u003e is designed from a real-world perspective and follows the data processing, development, and deployment pipeline paradigm using a variety of tools. Through hands-on exercises, attendees will learn the fundamentals of preprocessing custom images, speeding the development process using transfer learning for model training, and deployment of trained models for fast and scalable AI in production.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootcamp Content\u003c/h2\u003e\u003ca id=\"user-content-bootcamp-content\" class=\"anchor\" aria-label=\"Permalink: Bootcamp Content\" href=\"#bootcamp-content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe content is structured in five modules with an additional introductory notebook and two challenge notebooks:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWelcome to \u003cstrong\u003eend-to-end computer vision\u003c/strong\u003e bootcamp\u003c/li\u003e\n\u003cli\u003eLab 1: Data labeling and preprocessing\u003c/li\u003e\n\u003cli\u003eLab 2: Object detection using TAO YOLOv4\u003c/li\u003e\n\u003cli\u003eLab 3: Model deployment with Triton Inference Server\u003c/li\u003e\n\u003cli\u003eLab 4: Model deployment with DeepStream\u003c/li\u003e\n\u003cli\u003eLab 5: Measure object size using OpenCV\u003c/li\u003e\n\u003cli\u003eChallenge 1: DeepStream SDK\u003c/li\u003e\n\u003cli\u003eChallenge 2: Triton Inference Server\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools and Frameworks\u003c/h2\u003e\u003ca id=\"user-content-tools-and-frameworks\" class=\"anchor\" aria-label=\"Permalink: Tools and Frameworks\" href=\"#tools-and-frameworks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tools and frameworks used in the bootcamp are as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/tao-toolkit\" rel=\"nofollow\"\u003eNVIDIA\u00ae TAO Toolkit\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/deepstream-sdk\" rel=\"nofollow\"\u003eNVIDIA DeepStream SDK\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nvidia.com/en-us/ai-data-science/products/triton-inference-server/\" rel=\"nofollow\"\u003eNVIDIA Triton\u2122 Inference Server\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/tensorrt\" rel=\"nofollow\"\u003eNVIDIA TensorRT\u2122\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://opencv.org/\" rel=\"nofollow\"\u003eOpenCV\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://labelstud.io/\" rel=\"nofollow\"\u003eLabel Studio\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootcamp Duration\u003c/h2\u003e\u003ca id=\"user-content-bootcamp-duration\" class=\"anchor\" aria-label=\"Permalink: Bootcamp Duration\" href=\"#bootcamp-duration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe total bootcamp material would take approximately 8.5 hours. It is recommended to divide the teaching of the material into two days, covering the first two notebooks (Lab 1 and Lab 2) in one session and the rest in the next session.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootcamp Prerequisites\u003c/h2\u003e\u003ca id=\"user-content-bootcamp-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Bootcamp Prerequisites\" href=\"#bootcamp-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA basic understanding of Deep Learning, Python programming, and familiarity with NVIDIA\u00ae NGC\u2122 is required.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploying the Bootcamp materials:\u003c/h2\u003e\u003ca id=\"user-content-deploying-the-bootcamp-materials\" class=\"anchor\" aria-label=\"Permalink: Deploying the Bootcamp materials:\" href=\"#deploying-the-bootcamp-materials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo deploy the Labs, please refer to the Deployment guide presented \u003ca href=\"https://github.com/openhackathons-org/End-to-End-Computer-Vision/blob/main/Deployment_Guide.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAttribution\u003c/h2\u003e\u003ca id=\"user-content-attribution\" class=\"anchor\" aria-label=\"Permalink: Attribution\" href=\"#attribution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis material originates from the OpenHackathons Github repository. Check out additional materials \u003ca href=\"https://github.com/openhackathons-org\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDon\u0027t forget to check out additional \u003ca href=\"https://www.openhackathons.org/s/technical-resources\" rel=\"nofollow\"\u003eOpen Hackathons Resources\u003c/a\u003e and join our \u003ca href=\"https://www.openacc.org/community#slack\" rel=\"nofollow\"\u003eOpenACC and Hackathons Slack Channel\u003c/a\u003e to share your experience and get more help from the community.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicensing\u003c/h2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-label=\"Permalink: Licensing\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopyright \u00a9 2023 OpenACC-Standard.org. This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0). These materials may include references to hardware and software developed by other entities; all applicable licensing and copyrights apply.\u003c/p\u003e\n", + "readme": "\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/16a291cc7c1e2accb0e461a9cbb655698d02d361f12027450f31622491867d1d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d32322e31302e322d627269676874677265656e\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-22.10.2-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePorefile: a Nextflow full-length 16S profiling pipeline for ONT reads\u003c/h1\u003e\u003ca id=\"user-content-porefile-a-nextflow-full-length-16s-profiling-pipeline-for-ont-reads\" class=\"anchor\" aria-label=\"Permalink: Porefile: a Nextflow full-length 16S profiling pipeline for ONT reads\" href=\"#porefile-a-nextflow-full-length-16s-profiling-pipeline-for-ont-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ePorefile\u003c/code\u003e is a Nextflow pipeline that wraps a bunch of third-party software to process and classify full length 16S (SSU) long reads generated using Oxford Nanopore sequencing, against the \u003ca href=\"https://www.arb-silva.de/\" rel=\"nofollow\"\u003eSILVAdb\u003c/a\u003e SSU NR99 database, which is downloaded on the fly if not provided by the user.\u003c/p\u003e\n\u003cp\u003eReads are then classified by \u003ca href=\"https://software-ab.informatik.uni-tuebingen.de/download/megan6/welcome.html\" rel=\"nofollow\"\u003eMEGAN6 CE\u003c/a\u003e tools, and using a SILVA-to-NCBI accession mapping file generated on-the-fly.\u003c/p\u003e\n\u003cp\u003ePorefile uses SILVA SSU NR99 version 138.1 by default, which is the latest available up to this date (Feb 2023). If a new version were released, users can manually provide the new links to tell \u003ccode\u003ePorefile\u003c/code\u003e to download it.\u003c/p\u003e\n\u003cp\u003eContents:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#workflow-scheme\"\u003eWorkflow scheme\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-porefile\"\u003eRunning Porefile\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#help\"\u003eHelp\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#dependencies\"\u003eDependencies\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#dependencies-included-in-the-container\"\u003eDependencies included in the container\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#profiles\"\u003eProfiles\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#container-engines\"\u003eContainer engines\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#other-configuration-for-dev-mostly\"\u003eOther configuration (for dev mostly)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output-files\"\u003eOutput files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow scheme\u003c/h2\u003e\u003ca id=\"user-content-workflow-scheme\" class=\"anchor\" aria-label=\"Permalink: Workflow scheme\" href=\"#workflow-scheme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-mermaid\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eflowchart\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTD\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep1\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e(((\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--fq \u0027./path/to/*fastq\u0027\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e)))\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep2\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003e--isDemultiplexed\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eDemultiplex\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep3\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eConcatenate\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep4\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003ePorechop\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep2\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e \u003cspan class=\"pl-s\"\u003eNo\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep3\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep2\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e \u003cspan class=\"pl-s\"\u003eYes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep5\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eNanoFilt\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eQFilt\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep5\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eNanofilt\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep6\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eAutomap\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep6\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep7\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eYacrd\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep4\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep5\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eQCheck\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep8\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eNanoplotRaw\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep9\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eNanoplotFilt\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep9\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep10\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eSummaryTable\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eQFilt\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep8\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMain\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep11\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMakeDB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep13\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMinimap2\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep12\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eFastq2Fasta\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep13\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep13\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep14\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMeganLCA\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep14\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep15\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eGetReadInfo\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep15\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep16\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eComputeAbundances\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep7\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep12\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep23\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[(\u003c/span\u003e\u003c/span\u003eSILVAdb\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e)]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003eInternet\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eSetSilva\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eInternet\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep17\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep20\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003edownloadSilvaTaxNcbiSp\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep21\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003edownloadSilvaTaxmap\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep17\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003edownload SILVA fasta\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep18\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003e--fullSilva\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep18\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e \u003cspan class=\"pl-s\"\u003eNo\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep19\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003ereduceSilva\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep20\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eGenerateSynonyms\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep21\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep19\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep11\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep18\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e \u003cspan class=\"pl-s\"\u003eYes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep11\u003c/span\u003e \n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ePolish\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep24\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eSubsetSilva\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep25\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMakeDB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep26\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eSubsetReads\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep27\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMinimap2\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep25\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep27\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep27\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep28\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMeganLCA\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep28\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep29\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eGetReadInfo\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep29\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep30\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eCorrectAssignments\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep30\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep31\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eComputeAbundances\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep19\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep24\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep24\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep16\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep24\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep12\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep26\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep16\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep26\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep28\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep14\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Porefile\u003c/h2\u003e\u003ca id=\"user-content-running-porefile\" class=\"anchor\" aria-label=\"Permalink: Running Porefile\" href=\"#running-porefile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA typical command for running the pipeline would be as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run microgenlab/porefile --fq \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003epath/to/*.fastq\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf a local copy of the required SILVAdb files were provided, the workflow avoids re downloading it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run microgenlab/porefile --fq \"./fastq/*.fastq\" \\\n --silvaFasta \"./SILVA_138.1_SSURef_NR99_tax_silva.fasta.gz\" \\\n --silvaTaxNcbiSp \"./tax_ncbi-species_ssu_ref_nr99_138.1.txt.gz\" \\\n --silvaTaxmap \"./taxmap_slv_ssu_ref_nr_138.1.txt.gz\" \\\n -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the following for more details about parameter tuning:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run microgenlab/porefile --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e and at least one of the following container engines: Docker, Singularity/Apptainer, Podman.\u003c/p\u003e\n\u003cp\u003eAll workflow dependencies have been packaged into a \u003ca href=\"https://hub.docker.com/repository/docker/iferres/porefile\" rel=\"nofollow\"\u003edocker container\u003c/a\u003e, which is automatically downloaded when the pipeline is executed. That\u0027s it, you don\u0027t need to install any other software on your own.\u003c/p\u003e\n\u003cp\u003ePorefile has been tested with each three mencioned container technologies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDependencies included in the container\u003c/h4\u003e\u003ca id=\"user-content-dependencies-included-in-the-container\" class=\"anchor\" aria-label=\"Permalink: Dependencies included in the container\" href=\"#dependencies-included-in-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDependencies used by the pipeline and included in the container are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/rrwick/Porechop\"\u003ePorechop\u003c/a\u003e (Demultiplex)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/wdecoster/nanofilt/\"\u003eNanoFilt\u003c/a\u003e (Quality filtering)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/natir/yacrd\"\u003eYacrd\u003c/a\u003e (Chimera removal)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/wdecoster/NanoPlot\"\u003eNanoPlot\u003c/a\u003e (Quality check)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/shenwei356/seqkit/\"\u003eseqkit\u003c/a\u003e (fastq/fasta manipulation)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e (Alignment)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e (Processing)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://software-ab.informatik.uni-tuebingen.de/download/megan6/welcome.html\" rel=\"nofollow\"\u003eMEGAN6\u003c/a\u003e (Taxonomy assignment)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you use Porefile, please also cite them since we are \u003cem\u003e\u003cstrong\u003estanding on the shoulders of giants\u003c/strong\u003e\u003c/em\u003e. Also cite \u003ca href=\"https://www.arb-silva.de/\" rel=\"nofollow\"\u003eSILVAdb\u003c/a\u003e, and \u003ca href=\"https://www.nature.com/articles/nbt.3820%7B\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProfiles\u003c/h2\u003e\u003ca id=\"user-content-profiles\" class=\"anchor\" aria-label=\"Permalink: Profiles\" href=\"#profiles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePorefile comes with a minimal set of configuration profiles. Please, refer to \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow documentation\u003c/a\u003e to create a configuration file for your HPC infrastructure.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eContainer engines\u003c/h4\u003e\u003ca id=\"user-content-container-engines\" class=\"anchor\" aria-label=\"Permalink: Container engines\" href=\"#container-engines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse \u003ccode\u003e-profile docker\u003c/code\u003e to run the pipeline using \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e-profile singularity\u003c/code\u003e to run the pipeline using \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eSingularity/Apptainer\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e-profile podman\u003c/code\u003e to run the pipeline using \u003ca href=\"https://podman.io/\" rel=\"nofollow\"\u003ePodman\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOther configuration (for dev mostly)\u003c/h4\u003e\u003ca id=\"user-content-other-configuration-for-dev-mostly\" class=\"anchor\" aria-label=\"Permalink: Other configuration (for dev mostly)\" href=\"#other-configuration-for-dev-mostly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-profile test\u003c/code\u003e: Tests the pipeline on a local machine with low resources using a toy dataset (5K ONT reads) included in the repo. Mostly used to develop on my desktop machine. Assigns at most 16Gb of RAM and 4 cpus per process. To run the test using (say) Singularity as container engine (takes about ~5min on a Intel Core i7-4790, 32Gb RAM):\n\u003ccode\u003enextflow run microgenlab/porefile -profile test,singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-profile nagual\u003c/code\u003e: Configuration to use at IPMont servers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eUsage:\nA typical command for running the pipeline would be as follows:\n\n nextflow run microgenlab/porefile --fq \u0027data/*.fastq\u0027\n\nInput fastq file(s):\n --fq Path to input data (must be surrounded with quotes).\n\nOther:\n --silvaFasta Path to SILVA_*_SSURef_NR99_tax_silva.fasta.gz file. You can provide it\n either compressed (.gz) or not. If not provided, the workflow automatically\n adds a download step (you must have internet connection).\n --silvaFastaURL URL to SILVA_*_SSURef_NR99_tax_silva.fasta.gz file. It will be used if you\n don\u0027t provide the --silvaFasta parameter (above). Default is:\n \u0027https://www.arb-silva.de/fileadmin/silva_databases/current/Exports/SILVA_138.1_SSURef_NR99_tax_silva.fasta.gz\u0027.\n\n --silvaTaxNcbiSp Path to tax_ncbi-species_ssu_ref_nr99_*.txt.gz file. You can provide it\n either compressed (.gz) or not. If not provided, the workflow automatically\n adds a download step.\n --silvaTaxNcbiSpURL URL to tax_ncbi-species_ssu_ref_nr99_*.txt.gz file. It will be used if you\n don\u0027t provide the --silvaFasta parameter (above). Default is:\n \u0027https://www.arb-silva.de/fileadmin/silva_databases/current/Exports/taxonomy/ncbi/tax_ncbi-species_ssu_ref_nr99_138.1.txt.gz\u0027.\n\n --silvaTaxmap Path to taxmap_slv_ssu_ref_nr_*.txt.gz file. You can provide it\n either compressed (.gz) or not. If not provided, the workflow automatically\n adds a download step.\n --silvaTaxmapURL URL to taxmap_slv_ssu_ref_nr_*.txt.gz file. It will be used if you\n don\u0027t provide the --silvaFasta parameter (above). Default is:\n \u0027https://www.arb-silva.de/fileadmin/silva_databases/current/Exports/taxonomy/taxmap_slv_ssu_ref_nr_138.1.txt.gz\u0027.\n\n --fullSilva By default, porefile reduces SILVA to prokatyote SSU (16S). Use this flag\n to deactivate the reducing step and use the full SILVA database.\n\n --outdir Name of the results directory. Default: \"results\".\n\n\nProcess specific parameters:\n Porechop parameters:\n --porechop_extra_end_trim The \u0027--extra_end_trim\u0027 parameter of Porechop. Default: 0.\n\n NanoFilt parameters:\n --nanofilt_quality The \u0027--quality\u0027 parameter of NanoFilt. Default: 8.\n --nanofilt_length The \u0027--length\u0027 parameter of NanoFilt (minimum length). Default: 1000.\n --nanofilt_maxlength The \u0027--maxlength\u0027 parameter of NanoFilt. Default: 1700.\n --nanofilt_headcrop The \u0027--headcrop\u0027 parameter of NanoFilt. Default: 0.\n --nanofilt_tailcrop The \u0027--tailcrop\u0027 parameter of NanoFilt. Default: 0.\n\n Yacrd parameters:\n --yacrd_c The \u0027-c\u0027 parameter of Yacrd (minimum coverage). Default: 4 .\n --yacrd_n The \u0027-n\u0027 parameter of Yacrd (minimum coverage of read). Default: 0.4 .\n\n Minimap2 parameters:\n --minimap2_k The \u0027-k\u0027 parameter of minimap2. Default: 15.\n --minimap2_x The \u0027-x\u0027 parameter of minimap2. Default: \u0027map-ont\u0027. Possible values: \u0027map-ont\u0027,\n \u0027asm5\u0027, \u0027asm10\u0027, \u0027asm20\u0027, \u0027map-pb\u0027, or \u0027map-hifi\u0027. \n --minimap2_f The \u0027-f\u0027 parameter of minimap2. Default: 1000. Only applied in the Automap module.\n --minimap2_KM The \u0027-K\u0027 parameter of minimap2, in Megabases. Default: 200.\n\n Megan6 parameters:\n --megan_lcaAlgorithm The \u0027--lcaAlgorithm\u0027 parameter of sam2rma tool (Megan6). Default: \u0027naive\u0027.\n Possible values are: \u0027naive\u0027, \u0027weighted\u0027, or \u0027longReads\u0027.\n --megan_topPercent The \u0027--topPercent\u0027 parameter of sam2rma tool (Megan6). Default: 10.\n --megan_topPercentPolish The \u0027--topPercent\u0027 parameter of sam2rma tool (Megan6) applied when polishing step\n is activated. Default: 5.\n --megan_minPercentReadCover The \u0027--minPercentReadCover\u0027 parameter of sam2rma and blast2rma tools (Megan6).\n Default: 70.\n --megan_lcaCoveragePercent The \u0027--lcaCoveragePercent\u0027 parameter of sam2rma and blast2rma tools (Megan6).\n Default: 100.\n\n\nOther control options:\n --isDemultiplexed Set this flag to avoid Demultiplex sub-workflow. If set, each fastq file is\n --removeChimeras Set this flag to activate the chimera-removing step with Yacrd.\n processed as a different barcode.\n --noNanoplot Set this flag to avoid QCheck sub-workflow.\n --noSpeciesPolishing Avoid the polishing sub-workflow.\n --lowAbundanceThreshold The threshold of total abundance (counts) to be considered as \"low\", and\n which the pipeline will try to re assign.\n\n\nContainer options (note single dash usage!):\n -profile docker Use docker as container engine (default).\n -profile singularity Use singularity as container engine.\n -profile podman Use podman as container engine.\n\nHelp:\n --help Print this help and exit.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput files\u003c/h2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-label=\"Permalink: Output files\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eresults\u003c/code\u003e directory contains the following directories/files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eresults/\n\u251c\u2500\u2500 COUNTS.tsv\n\u251c\u2500\u2500 COUNTS_polished.tsv\n\u251c\u2500\u2500 TAXCLA.tsv\n\u251c\u2500\u2500 TAXCLA_polished.tsv\n\u251c\u2500\u2500 Read_Assignments/\n\u2502 \u251c\u2500\u2500 BC01.read_info\n\u2502 \u251c\u2500\u2500 BC02.read_info\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 Read_Assignments_Polished/\n\u2502 \u251c\u2500\u2500 BC01_polished.read_info\n\u2502 \u251c\u2500\u2500 BC02_polished.read_info\n\u2502 \u2514\u2500\u2500...\n\u251c\u2500\u2500 NanoPlots/\n\u2502 \u251c\u2500\u2500 BC01/\n\u2502 \u251c\u2500\u2500 BC02/\n\u2502 \u251c\u2500\u2500 ...\n\u2502 \u2514\u2500\u2500 summary.tsv\n\u251c\u2500\u2500 Rma/\n\u2502 \u251c\u2500\u2500 BC01.rma\n\u2502 \u251c\u2500\u2500 BC02.rma\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 silva_to_NCBI_synonyms.map\n\u2514\u2500\u2500 versions.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eCOUNTS.tsv\u003c/code\u003e and \u003ccode\u003eCOUNTS_polished.tsv\u003c/code\u003e are a tabular text files with the counts for each taxa (rows), on each barcode (columns).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e BC01 BC02 BC03 ...\nTAXA_001 1 0 0 ...\nTAXA_002 4 0 0 ...\nTAXA_003 0 3 10 ... \n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eTAXCLA.tsv\u003c/code\u003e and \u003ccode\u003eTAXCLA_polished.tsv\u003c/code\u003e are tabular text files with the taxon path classification of each taxa.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Kingdom Phylum Class Order Family Genus Species\nTAXA_001 Bacteria NA NA NA NA NA NA\nTAXA_002 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Paracoccus NA\nTAXA_003 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas NA\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTake into account that the \u003ccode\u003eTAXA\u003c/code\u003e labels are arbitrarily generated for each pipeline, so the \u003ccode\u003eTAXA\u003c/code\u003e labels in \u003ccode\u003eTAXCLA.tsv\u003c/code\u003e do not match the ones in \u003ccode\u003eTAXCLA_polished.tsv\u003c/code\u003e (and the same to the \u003ccode\u003eCOUNTS*\u003c/code\u003e files).\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eRead_Assignments\u003c/code\u003e and \u003ccode\u003eRead_Assignments_Polished\u003c/code\u003e contains taxonomic classification for each read.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e001e48d4-cacc-4f78-b5f4-1b578a652ab2_0_1409 C 186801 [D] Bacteria; [P] Firmicutes; [C] Clostridia;\n027b1258-66df-4703-bfe8-bf93957a142d_0_1409 F 171552 [D] Bacteria; [P] Bacteroidetes; [C] Bacteroidia; [O] Bacteroidales; [F] Prevotellaceae;\n029f8418-4d6a-46b9-a98f-e0784e620fa2_0_1464 F 541000 [D] Bacteria; [P] Firmicutes; [C] Clostridia; [O] Clostridiales; [F] Ruminococcaceae;\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe columns correspond to: 1) Read id (header); 2) Taxonomic rank at which each read was possible to assign; 3) NCBI id of each taxon; 4) The taxon path assigned to each read.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eNanoPlots/\u003c/code\u003e directory contain QC plots (see \u003ca href=\"https://github.com/wdecoster/NanoPlot\"\u003eNanoPlot\u003c/a\u003e), pre and post filtering, and a summary tabular data file.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eRma/\u003c/code\u003e directory contains binary \u003ccode\u003e.rma\u003c/code\u003e files which can be analyzed with MEGAN. There isn\u0027t an equivalent directory for the polished pipeline since the second LCA assignment is done only with a subset of reads, and then \u003ccode\u003eporefile\u003c/code\u003e re-writes the base algorithm\u0027s assignments.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esilva_to_NCBI_synonyms.map\u003c/code\u003e is the SILVA to NCBI synonyms mapping file generated on-the-fly by using SILVA\u0027s \u003ccode\u003etax_ncbi-species\u003c/code\u003e and \u003ccode\u003etaxmap\u003c/code\u003e files.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eversions.txt\u003c/code\u003e prints the versions of the porefile dependencies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA manuscript is under preparation.\u003c/p\u003e\n", "stargazers_count": 20, - "subscribers_count": 2, + "subscribers_count": 5, "topics": [ - "computer-vision", - "deep-learning", - "deep-neural-networks", - "deepstream", - "image-processing", - "image-recognition", - "object-detection", - "object-tracking", - "opencv", - "tao", - "tensorrt", - "triton-inference-server" + "nextflow", + "pipeline", + "16s", + "profiling", + "docker", + "singularity", + "nanopore" ], - "updated_at": 1731009638.0 + "updated_at": 1706826446.0 }, { "data_format": 2, - "description": "Get Your Brain Together Neuroimage Registration Workshops and Hackathons", + "description": "A tool for weighted model counting through tensor network contraction", "filenames": [ - "HCK01_2022_Virtual/Tutorials/GetYourBrainPipelined/Example-Registration/Singularity.def", - "HCK01_2022_Virtual/Tutorials/GetYourBrainPipelined/Example-Easy/Singularity.def" + "Singularity" ], - "full_name": "InsightSoftwareConsortium/GetYourBrainTogether", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"common/logo.png\"\u003e\u003cimg alt=\"Get Your Brain Together\" src=\"common/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003cstrong\u003eGet Your Brain Together\u003c/strong\u003e hackathons \u003cstrong\u003ebring together neuroimage data\ngenerators, image registration researchers, and neurodata compute\ninfrastructure providers for a hands-on, collaborative event\u003c/strong\u003e. This community\ncollaboration aims to \u003cstrong\u003ecreate reproducible, open source resources\u003c/strong\u003e that enable\ndiscovery of the structure and function of brains.\u003c/p\u003e\n\u003cp\u003eThere are three components to the hackathons.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eTutorial\u003c/strong\u003e sessions share computational and biological knowledge around open source registration tools, open access datasets, or neurodata archives.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBirds-of-a-Feather (BOF) Breakout\u003c/strong\u003e sessions enable participants interested in collaborating to work on relevant topics.\u003c/li\u003e\n\u003cli\u003eEach hackathon generates a \u003cstrong\u003eReproducible Resource\u003c/strong\u003e for registration and analysis of neuroimaging datasets.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHackathon Events\u003c/h2\u003e\u003ca id=\"user-content-hackathon-events\" class=\"anchor\" aria-label=\"Permalink: Hackathon Events\" href=\"#hackathon-events\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUpcoming Events\u003c/h3\u003e\u003ca id=\"user-content-upcoming-events\" class=\"anchor\" aria-label=\"Permalink: Upcoming Events\" href=\"#upcoming-events\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePast Events\u003c/h3\u003e\u003ca id=\"user-content-past-events\" class=\"anchor\" aria-label=\"Permalink: Past Events\" href=\"#past-events\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"HCK03_2024_UNC_Hybrid/README.md\"\u003eHackathon 3: July 2024\u003c/a\u003e{:target=\"_top\"} - The \u003cstrong\u003e3rd Hackathon\u003c/strong\u003e will be held at the University of North Carolina-Chapel Hill and online July 26-28th, 2024.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"HCK02_2023_Allen_Institute_Hybrid/README.md\"\u003eHackathon 2: May 2023\u003c/a\u003e{:target=\"_top\"} - The \u003cstrong\u003e2nd Hackathon\u003c/strong\u003e was held at the Allen Institute and online May 22nd-23rd, 2023.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"HCK01_2022_Virtual/README.md\"\u003eHackathon 1: March 2022\u003c/a\u003e{:target=\"_top\"} - The \u003cstrong\u003e1st Hackathon\u003c/strong\u003e will be held April 4th-7th online.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWhat is the history of the hackathons?\u003c/h4\u003e\u003ca id=\"user-content-what-is-the-history-of-the-hackathons\" class=\"anchor\" aria-label=\"Permalink: What is the history of the hackathons?\" href=\"#what-is-the-history-of-the-hackathons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis hackathon is inspired by and based on the successful \u003ca href=\"https://neurodatawithoutborders.github.io/nwb_hackathons/\" rel=\"nofollow\"\u003eNeurodataWithoutBorders (NWB)\u003c/a\u003e and \u003ca href=\"https://projectweek.na-mic.org/\" rel=\"nofollow\"\u003eNA-MIC Project Week\u003c/a\u003e hackathons.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eThis page is hosted from the InsightSoftwareConsortium organization\u0027s \u003ca href=\"https://github.com/InsightSoftwareConsortium/GetYourBrainTogether\"\u003eGetYourBrainTogether\u003c/a\u003e repository on github.com and is published at \u003ca href=\"https://insightsoftwareconsortium.github.io/GetYourBrainTogether/\" rel=\"nofollow\"\u003einsightsoftwareconsortium.github.io/GetYourBrainTogether/\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "vardigroup/TensorOrder", + "latest_release": "v2.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTensorOrder\u003c/h1\u003e\u003ca id=\"user-content-tensororder\" class=\"anchor\" aria-label=\"Permalink: TensorOrder\" href=\"#tensororder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Python 3 tool for automatically contracting tensor networks for weighted model counting on multiple CPUs and on a GPU.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with docker\u003c/h2\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running with docker\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBecause of the variety of dependencies used in the various graph decomposition tools, it is recommended to use the docker container to run TensorOrder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker container (for singlecore and multi-core) can be built with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensororder .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to leverage a GPU, you must compile the (larger) docker container with TensorFlow and gpu drivers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensororder-gpu -f Dockerfile-gpu .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container\u003c/h3\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce built, docker containers can be used as follows to run TensorOrder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i tensororder:latest python /src/tensororder.py --planner=\"line-Flow\" --weights=\"unweighted\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, this runs the tensor network contraction on all available CPU cores with numpy. One can also choose to use the GPU to perform the contraction. This requires \u003ca href=\"https://nvidia.github.io/nvidia-container-runtime/\" rel=\"nofollow\"\u003envidia-container-runtime\u003c/a\u003e to be installed.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --gpus all tensororder-gpu:latest python /src/tensororder.py --planner=\"line-Flow\" --weights=\"unweighted\" --tensor_library=\"tensorflow-gpu\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is also possible to connect to a TPU on Google Cloud to perform the tensor contractions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i tensororder:latest python /src/tensororder.py --timeout=\"1000\" --planner=\"line-Flow\" --weights=\"unweighted\" --tensor_library=\"jax-tpu\" --entry_type=\"float32\" --tpu=\"10.6.165.2\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBoth docker containers are compatible with \u003ca href=\"https://github.com/Kasekopf/Turbine\"\u003eTurbine\u003c/a\u003e to run experiments on Google Cloud.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with Singularity\u003c/h2\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere is also a \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available for TensorOrder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container-1\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity container can be built with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build tensororder Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container\u003c/h3\u003e\u003ca id=\"user-content-using-the-container-1\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce built, Singularity containers can be used as follows to run TensorOrder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./tensororder --method=\"line-Flow\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning without containers\u003c/h2\u003e\u003ca id=\"user-content-running-without-containers\" class=\"anchor\" aria-label=\"Permalink: Running without containers\" href=\"#running-without-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTensorOrder can also be used directly as a Python 3 tool. Since TensorOrder uses \u003ca href=\"https://cython.org/\" rel=\"nofollow\"\u003eCython\u003c/a\u003e, it must be compiled:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake -C src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMoreover, the various tensor methods each require additional setup. Consult the \u003ca href=\"Dockerfile\"\u003eDocker file\u003c/a\u003e for an example set of installation commands.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor KCMR-metis and KCMR-gn, METIS must be installed using the instructions \u003ca href=\"src/tensorcsp\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-Tamaki and factor-Tamaki, the tree-decomposition solver Tamaki must be compiled using the \u003ccode\u003eheuristic\u003c/code\u003e instructions \u003ca href=\"solvers/TCS-Meiji\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-Flow and factor-Flow, the tree-decomposition solver FlowCutter must be compiled using the instructions \u003ca href=\"solvers/flow-cutter-pace17\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-htd and factor-htd, the tree-decomposition solver htd must be compiled using the instructions \u003ca href=\"solvers/htd-master\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor factor-hicks, the branch-decomposition solver Hicks must be compiled using the Makefile \u003ca href=\"solvers/hicks\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-portfolio3 and line-portfolio3, all tree-decompositions solvers must be compiled, and the portfolio must be compiled using the instructions \u003ca href=\"solvers/portfolio\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce everything has been built, the primary script is located in \u003ccode\u003esrc/tensororder.py\u003c/code\u003e. Example usage is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython src/tensororder.py --method=\"line-Flow\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTensorOrder requires the following python packages (see \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e for a working set of exact version information if needed):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eclick\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enumpy\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython-igraph\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enetworkx\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecython\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ethreadpoolctl\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etensorflow\u003c/code\u003e (optional)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePublications\u003c/h3\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease cite the following article if you use our code in a publication:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://arxiv.org/abs/2006.15512\" rel=\"nofollow\"\u003eParallel Weighted Model Counting with Tensor Networks\u003c/a\u003e. Jeffrey M. Dudek and Moshe Y. Vardi. Proceedings of MCW\u002720.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 20, - "subscribers_count": 33, - "topics": [ - "neuroscience", - "neuroimaging", - "registration", - "hackathons" - ], - "updated_at": 1731614823.0 + "subscribers_count": 3, + "topics": [], + "updated_at": 1712172800.0 }, { "data_format": 2, - "description": null, + "description": "Genomics datastructures using Apache Arrow", "filenames": [ - "singularity_training/exercise_04_GPUBurn/Singularity_04", - "singularity_training/exercise_04_GPUBurn/Singularity_04_multistage", - "singularity_training/exercise_02_PureMPI/Singularity_02_PureMPI", - "singularity_training/exercise_03_HPL/Singularity_03_HPL_complete", - "singularity_training/exercise_01_OpenMP/Singularity_01_OpenMP" + "Singularity/Singularity" ], - "full_name": "abdulrahmanazab/docker-training-neic", + "full_name": "abs-tudelft/ArrowSAM", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContainers Tutorial - PRACE training, 2021\u003c/h1\u003e\u003ca id=\"user-content-containers-tutorial---prace-training-2021\" class=\"anchor\" aria-label=\"Permalink: Containers Tutorial - PRACE training, 2021\" href=\"#containers-tutorial---prace-training-2021\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe training infrastructure is offered by\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.cscs.ch/computers/piz-daint/\" rel=\"nofollow\"\u003ePiz Daint\u003c/a\u003e at CSCS, Switserland.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nrec.no/\" rel=\"nofollow\"\u003eNorwegian Research and Education cloud\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConnect to your VM\u003c/h2\u003e\u003ca id=\"user-content-connect-to-your-vm\" class=\"anchor\" aria-label=\"Permalink: Connect to your VM\" href=\"#connect-to-your-vm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will get the key file \u003ccode\u003enrec.pem\u003c/code\u003e from your instructor\u003c/li\u003e\n\u003cli\u003eNow use it to connect to your VM:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod 600 nrec.pem \nssh -i nrec.pem debian@\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eTerminal-IP-Address\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial contents\u003c/h2\u003e\u003ca id=\"user-content-tutorial-contents\" class=\"anchor\" aria-label=\"Permalink: Tutorial contents\" href=\"#tutorial-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/blob/prace-training-2021/docker.md\"\u003eDocker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/tree/prace-training-2021/singularity_training\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/blob/prace-training-2021/sarus.md\"\u003eSarus\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/blob/prace-training-2021/Charliecloud/Charliecloud.md\"\u003eCharliecloud\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/blob/prace-training-2021/unikernels.md\"\u003eUnikernels\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eArrowSAM\u003c/h1\u003e\u003ca id=\"user-content-arrowsam\" class=\"anchor\" aria-label=\"Permalink: ArrowSAM\" href=\"#arrowsam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eArrowSAM is an in-memory Sequence Alignment/Map (SAM) representation which uses \u003ca href=\"https://arrow.apache.org/\" rel=\"nofollow\"\u003eApache Arrow framework\u003c/a\u003e (A cross-language development platform for in-memory data) and \u003ca href=\"https://arrow.apache.org/blog/2017/08/08/plasma-in-memory-object-store/\" rel=\"nofollow\"\u003ePlasma (Shared-Memory) Object Store\u003c/a\u003e to store and process SAM columnar data in-memory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-cite\"\u003e\u003c/a\u003eCiting ArrowSAM\u003c/h3\u003e\u003ca id=\"user-content-citing-arrowsam\" class=\"anchor\" aria-label=\"Permalink: Citing ArrowSAM\" href=\"#citing-arrowsam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following paper describes the ArrowSAM format and its usage to speedup genomics pipelines. If you use ArrowSAM in your work, please cite the following paper.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAhmad et al., (2020). \"ArrowSAM: In-Memory Genomics Data Processing Using Apache Arrow\",\n\u003cem\u003eICCAIS\u003c/em\u003e. \u003ca href=\"https://doi.org/10.1109/ICCAIS48893.2020.9096725\" rel=\"nofollow\"\u003edoi.org/10.1109/ICCAIS48893.2020.9096725\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAhmad et al., \"Optimizing performance of GATK workflows using Apache Arrow In-Memory data framework\",\n\u003cem\u003eBMC Genomics, presented at APBC2020\u003c/em\u003e. \u003ca href=\"https://doi.org/10.1186/s12864-020-07013-y\" rel=\"nofollow\"\u003ehttps://doi.org/10.1186/s12864-020-07013-y\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis repo contains following three components:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eArrowSAM (In-memory SAM data representation) integrated \u003ca href=\"https://github.com/tahashmi/bwa\"\u003eBWA-MEM\u003c/a\u003e, Picard and GATK tools.\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA Singularity container def file (To create an environment to use all Apache Arrow related tools and libraries for ArrowSAM).\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eScripts to run different GATK best practices recommended workflows (using different in-memory data placement techniques like ArrowSAM, ramDisk and pipes for fast processing) to run complete DNA analysis pipeline efficiently.\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNote: ArrowSAM and all other workflows are based on single node, multi-core machines.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload our Singularity \u003ca href=\"https://github.com/abs-tudelft/arrow-gen/tree/master/Singularity\"\u003escript\u003c/a\u003e and generate singularity image (this image contains all Arrow related packges necessary for building/compiling BWA-MEM, Picard and GATK)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNow enter into generated image using command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell \u0026lt;image_name\u0026gt;.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload \u003ca href=\"https://github.com/tahashmi/bwa\"\u003eBWA-MEM\u003c/a\u003e inside image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e git clone https://github.com/tahashmi/bwa.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo into bwa dir and compile BWA-MEM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd bwa\n make\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNow you can run BWA-MEM.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", "stargazers_count": 20, - "subscribers_count": 6, + "subscribers_count": 5, "topics": [], - "updated_at": 1700404011.0 + "updated_at": 1726076586.0 }, { "data_format": 2, @@ -38980,152 +39040,159 @@ var data = }, { "data_format": 2, - "description": "Genomics datastructures using Apache Arrow", + "description": null, "filenames": [ - "Singularity/Singularity" + "singularity_training/exercise_04_GPUBurn/Singularity_04", + "singularity_training/exercise_04_GPUBurn/Singularity_04_multistage", + "singularity_training/exercise_02_PureMPI/Singularity_02_PureMPI", + "singularity_training/exercise_03_HPL/Singularity_03_HPL_complete", + "singularity_training/exercise_01_OpenMP/Singularity_01_OpenMP" ], - "full_name": "abs-tudelft/ArrowSAM", + "full_name": "abdulrahmanazab/docker-training-neic", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eArrowSAM\u003c/h1\u003e\u003ca id=\"user-content-arrowsam\" class=\"anchor\" aria-label=\"Permalink: ArrowSAM\" href=\"#arrowsam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eArrowSAM is an in-memory Sequence Alignment/Map (SAM) representation which uses \u003ca href=\"https://arrow.apache.org/\" rel=\"nofollow\"\u003eApache Arrow framework\u003c/a\u003e (A cross-language development platform for in-memory data) and \u003ca href=\"https://arrow.apache.org/blog/2017/08/08/plasma-in-memory-object-store/\" rel=\"nofollow\"\u003ePlasma (Shared-Memory) Object Store\u003c/a\u003e to store and process SAM columnar data in-memory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ca name=\"user-content-cite\"\u003e\u003c/a\u003eCiting ArrowSAM\u003c/h3\u003e\u003ca id=\"user-content-citing-arrowsam\" class=\"anchor\" aria-label=\"Permalink: Citing ArrowSAM\" href=\"#citing-arrowsam\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe following paper describes the ArrowSAM format and its usage to speedup genomics pipelines. If you use ArrowSAM in your work, please cite the following paper.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAhmad et al., (2020). \"ArrowSAM: In-Memory Genomics Data Processing Using Apache Arrow\",\n\u003cem\u003eICCAIS\u003c/em\u003e. \u003ca href=\"https://doi.org/10.1109/ICCAIS48893.2020.9096725\" rel=\"nofollow\"\u003edoi.org/10.1109/ICCAIS48893.2020.9096725\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAhmad et al., \"Optimizing performance of GATK workflows using Apache Arrow In-Memory data framework\",\n\u003cem\u003eBMC Genomics, presented at APBC2020\u003c/em\u003e. \u003ca href=\"https://doi.org/10.1186/s12864-020-07013-y\" rel=\"nofollow\"\u003ehttps://doi.org/10.1186/s12864-020-07013-y\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis repo contains following three components:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eArrowSAM (In-memory SAM data representation) integrated \u003ca href=\"https://github.com/tahashmi/bwa\"\u003eBWA-MEM\u003c/a\u003e, Picard and GATK tools.\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA Singularity container def file (To create an environment to use all Apache Arrow related tools and libraries for ArrowSAM).\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eScripts to run different GATK best practices recommended workflows (using different in-memory data placement techniques like ArrowSAM, ramDisk and pipes for fast processing) to run complete DNA analysis pipeline efficiently.\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNote: ArrowSAM and all other workflows are based on single node, multi-core machines.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to run\u003c/h2\u003e\u003ca id=\"user-content-how-to-run\" class=\"anchor\" aria-label=\"Permalink: How to run\" href=\"#how-to-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload our Singularity \u003ca href=\"https://github.com/abs-tudelft/arrow-gen/tree/master/Singularity\"\u003escript\u003c/a\u003e and generate singularity image (this image contains all Arrow related packges necessary for building/compiling BWA-MEM, Picard and GATK)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNow enter into generated image using command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo singularity shell \u0026lt;image_name\u0026gt;.simg\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload \u003ca href=\"https://github.com/tahashmi/bwa\"\u003eBWA-MEM\u003c/a\u003e inside image\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e git clone https://github.com/tahashmi/bwa.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGo into bwa dir and compile BWA-MEM:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e cd bwa\n make\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNow you can run BWA-MEM.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContainers Tutorial - PRACE training, 2021\u003c/h1\u003e\u003ca id=\"user-content-containers-tutorial---prace-training-2021\" class=\"anchor\" aria-label=\"Permalink: Containers Tutorial - PRACE training, 2021\" href=\"#containers-tutorial---prace-training-2021\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe training infrastructure is offered by\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.cscs.ch/computers/piz-daint/\" rel=\"nofollow\"\u003ePiz Daint\u003c/a\u003e at CSCS, Switserland.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nrec.no/\" rel=\"nofollow\"\u003eNorwegian Research and Education cloud\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConnect to your VM\u003c/h2\u003e\u003ca id=\"user-content-connect-to-your-vm\" class=\"anchor\" aria-label=\"Permalink: Connect to your VM\" href=\"#connect-to-your-vm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eYou will get the key file \u003ccode\u003enrec.pem\u003c/code\u003e from your instructor\u003c/li\u003e\n\u003cli\u003eNow use it to connect to your VM:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003echmod 600 nrec.pem \nssh -i nrec.pem debian@\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eTerminal-IP-Address\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial contents\u003c/h2\u003e\u003ca id=\"user-content-tutorial-contents\" class=\"anchor\" aria-label=\"Permalink: Tutorial contents\" href=\"#tutorial-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/blob/prace-training-2021/docker.md\"\u003eDocker\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/tree/prace-training-2021/singularity_training\"\u003eSingularity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/blob/prace-training-2021/sarus.md\"\u003eSarus\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/blob/prace-training-2021/Charliecloud/Charliecloud.md\"\u003eCharliecloud\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abdulrahmanazab/docker-training-neic/blob/prace-training-2021/unikernels.md\"\u003eUnikernels\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 20, - "subscribers_count": 5, + "subscribers_count": 6, "topics": [], - "updated_at": 1726076586.0 + "updated_at": 1700404011.0 }, { "data_format": 2, - "description": "A tool for weighted model counting through tensor network contraction", + "description": "Get Your Brain Together Neuroimage Registration Workshops and Hackathons", "filenames": [ - "Singularity" + "HCK01_2022_Virtual/Tutorials/GetYourBrainPipelined/Example-Registration/Singularity.def", + "HCK01_2022_Virtual/Tutorials/GetYourBrainPipelined/Example-Easy/Singularity.def" ], - "full_name": "vardigroup/TensorOrder", - "latest_release": "v2.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTensorOrder\u003c/h1\u003e\u003ca id=\"user-content-tensororder\" class=\"anchor\" aria-label=\"Permalink: TensorOrder\" href=\"#tensororder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA Python 3 tool for automatically contracting tensor networks for weighted model counting on multiple CPUs and on a GPU.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with docker\u003c/h2\u003e\u003ca id=\"user-content-running-with-docker\" class=\"anchor\" aria-label=\"Permalink: Running with docker\" href=\"#running-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBecause of the variety of dependencies used in the various graph decomposition tools, it is recommended to use the docker container to run TensorOrder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe docker container (for singlecore and multi-core) can be built with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensororder .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to leverage a GPU, you must compile the (larger) docker container with TensorFlow and gpu drivers:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --tag tensororder-gpu -f Dockerfile-gpu .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container\u003c/h3\u003e\u003ca id=\"user-content-using-the-container\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce built, docker containers can be used as follows to run TensorOrder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i tensororder:latest python /src/tensororder.py --planner=\"line-Flow\" --weights=\"unweighted\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, this runs the tensor network contraction on all available CPU cores with numpy. One can also choose to use the GPU to perform the contraction. This requires \u003ca href=\"https://nvidia.github.io/nvidia-container-runtime/\" rel=\"nofollow\"\u003envidia-container-runtime\u003c/a\u003e to be installed.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i --gpus all tensororder-gpu:latest python /src/tensororder.py --planner=\"line-Flow\" --weights=\"unweighted\" --tensor_library=\"tensorflow-gpu\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt is also possible to connect to a TPU on Google Cloud to perform the tensor contractions:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -i tensororder:latest python /src/tensororder.py --timeout=\"1000\" --planner=\"line-Flow\" --weights=\"unweighted\" --tensor_library=\"jax-tpu\" --entry_type=\"float32\" --tpu=\"10.6.165.2\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBoth docker containers are compatible with \u003ca href=\"https://github.com/Kasekopf/Turbine\"\u003eTurbine\u003c/a\u003e to run experiments on Google Cloud.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning with Singularity\u003c/h2\u003e\u003ca id=\"user-content-running-with-singularity\" class=\"anchor\" aria-label=\"Permalink: Running with Singularity\" href=\"#running-with-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere is also a \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container available for TensorOrder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuilding the container\u003c/h3\u003e\u003ca id=\"user-content-building-the-container-1\" class=\"anchor\" aria-label=\"Permalink: Building the container\" href=\"#building-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Singularity container can be built with the following commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build tensororder Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the container\u003c/h3\u003e\u003ca id=\"user-content-using-the-container-1\" class=\"anchor\" aria-label=\"Permalink: Using the container\" href=\"#using-the-container-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce built, Singularity containers can be used as follows to run TensorOrder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./tensororder --method=\"line-Flow\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning without containers\u003c/h2\u003e\u003ca id=\"user-content-running-without-containers\" class=\"anchor\" aria-label=\"Permalink: Running without containers\" href=\"#running-without-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTensorOrder can also be used directly as a Python 3 tool. Since TensorOrder uses \u003ca href=\"https://cython.org/\" rel=\"nofollow\"\u003eCython\u003c/a\u003e, it must be compiled:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emake -C src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMoreover, the various tensor methods each require additional setup. Consult the \u003ca href=\"Dockerfile\"\u003eDocker file\u003c/a\u003e for an example set of installation commands.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor KCMR-metis and KCMR-gn, METIS must be installed using the instructions \u003ca href=\"src/tensorcsp\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-Tamaki and factor-Tamaki, the tree-decomposition solver Tamaki must be compiled using the \u003ccode\u003eheuristic\u003c/code\u003e instructions \u003ca href=\"solvers/TCS-Meiji\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-Flow and factor-Flow, the tree-decomposition solver FlowCutter must be compiled using the instructions \u003ca href=\"solvers/flow-cutter-pace17\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-htd and factor-htd, the tree-decomposition solver htd must be compiled using the instructions \u003ca href=\"solvers/htd-master\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor factor-hicks, the branch-decomposition solver Hicks must be compiled using the Makefile \u003ca href=\"solvers/hicks\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFor line-portfolio3 and line-portfolio3, all tree-decompositions solvers must be compiled, and the portfolio must be compiled using the instructions \u003ca href=\"solvers/portfolio\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce everything has been built, the primary script is located in \u003ccode\u003esrc/tensororder.py\u003c/code\u003e. Example usage is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython src/tensororder.py --method=\"line-Flow\" \u0026lt; \"benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTensorOrder requires the following python packages (see \u003ca href=\"requirements.txt\"\u003erequirements.txt\u003c/a\u003e for a working set of exact version information if needed):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003eclick\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enumpy\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epython-igraph\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003enetworkx\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecython\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ethreadpoolctl\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etensorflow\u003c/code\u003e (optional)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePublications\u003c/h3\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease cite the following article if you use our code in a publication:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://arxiv.org/abs/2006.15512\" rel=\"nofollow\"\u003eParallel Weighted Model Counting with Tensor Networks\u003c/a\u003e. Jeffrey M. Dudek and Moshe Y. Vardi. Proceedings of MCW\u002720.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "InsightSoftwareConsortium/GetYourBrainTogether", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"common/logo.png\"\u003e\u003cimg alt=\"Get Your Brain Together\" src=\"common/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003cstrong\u003eGet Your Brain Together\u003c/strong\u003e hackathons \u003cstrong\u003ebring together neuroimage data\ngenerators, image registration researchers, and neurodata compute\ninfrastructure providers for a hands-on, collaborative event\u003c/strong\u003e. This community\ncollaboration aims to \u003cstrong\u003ecreate reproducible, open source resources\u003c/strong\u003e that enable\ndiscovery of the structure and function of brains.\u003c/p\u003e\n\u003cp\u003eThere are three components to the hackathons.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eTutorial\u003c/strong\u003e sessions share computational and biological knowledge around open source registration tools, open access datasets, or neurodata archives.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBirds-of-a-Feather (BOF) Breakout\u003c/strong\u003e sessions enable participants interested in collaborating to work on relevant topics.\u003c/li\u003e\n\u003cli\u003eEach hackathon generates a \u003cstrong\u003eReproducible Resource\u003c/strong\u003e for registration and analysis of neuroimaging datasets.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHackathon Events\u003c/h2\u003e\u003ca id=\"user-content-hackathon-events\" class=\"anchor\" aria-label=\"Permalink: Hackathon Events\" href=\"#hackathon-events\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUpcoming Events\u003c/h3\u003e\u003ca id=\"user-content-upcoming-events\" class=\"anchor\" aria-label=\"Permalink: Upcoming Events\" href=\"#upcoming-events\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePast Events\u003c/h3\u003e\u003ca id=\"user-content-past-events\" class=\"anchor\" aria-label=\"Permalink: Past Events\" href=\"#past-events\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"HCK03_2024_UNC_Hybrid/README.md\"\u003eHackathon 3: July 2024\u003c/a\u003e{:target=\"_top\"} - The \u003cstrong\u003e3rd Hackathon\u003c/strong\u003e will be held at the University of North Carolina-Chapel Hill and online July 26-28th, 2024.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"HCK02_2023_Allen_Institute_Hybrid/README.md\"\u003eHackathon 2: May 2023\u003c/a\u003e{:target=\"_top\"} - The \u003cstrong\u003e2nd Hackathon\u003c/strong\u003e was held at the Allen Institute and online May 22nd-23rd, 2023.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"HCK01_2022_Virtual/README.md\"\u003eHackathon 1: March 2022\u003c/a\u003e{:target=\"_top\"} - The \u003cstrong\u003e1st Hackathon\u003c/strong\u003e will be held April 4th-7th online.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eWhat is the history of the hackathons?\u003c/h4\u003e\u003ca id=\"user-content-what-is-the-history-of-the-hackathons\" class=\"anchor\" aria-label=\"Permalink: What is the history of the hackathons?\" href=\"#what-is-the-history-of-the-hackathons\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis hackathon is inspired by and based on the successful \u003ca href=\"https://neurodatawithoutborders.github.io/nwb_hackathons/\" rel=\"nofollow\"\u003eNeurodataWithoutBorders (NWB)\u003c/a\u003e and \u003ca href=\"https://projectweek.na-mic.org/\" rel=\"nofollow\"\u003eNA-MIC Project Week\u003c/a\u003e hackathons.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eThis page is hosted from the InsightSoftwareConsortium organization\u0027s \u003ca href=\"https://github.com/InsightSoftwareConsortium/GetYourBrainTogether\"\u003eGetYourBrainTogether\u003c/a\u003e repository on github.com and is published at \u003ca href=\"https://insightsoftwareconsortium.github.io/GetYourBrainTogether/\" rel=\"nofollow\"\u003einsightsoftwareconsortium.github.io/GetYourBrainTogether/\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 20, - "subscribers_count": 3, - "topics": [], - "updated_at": 1712172800.0 + "subscribers_count": 33, + "topics": [ + "neuroscience", + "neuroimaging", + "registration", + "hackathons" + ], + "updated_at": 1731614823.0 }, { "data_format": 2, - "description": "A Nextflow full-length 16S profiling pipeline for ONT reads", + "description": "This repository is an AI bootcamp material that consist of a workflow for computer vision ", "filenames": [ - "environments/Singularity" + "Singularity_deepstream", + "Singularity_tao", + "Singularity_triton" ], - "full_name": "microgenlab/porefile", + "full_name": "openhackathons-org/End-to-End-Computer-Vision", "latest_release": null, - "readme": "\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/16a291cc7c1e2accb0e461a9cbb655698d02d361f12027450f31622491867d1d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d32322e31302e322d627269676874677265656e\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-22.10.2-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePorefile: a Nextflow full-length 16S profiling pipeline for ONT reads\u003c/h1\u003e\u003ca id=\"user-content-porefile-a-nextflow-full-length-16s-profiling-pipeline-for-ont-reads\" class=\"anchor\" aria-label=\"Permalink: Porefile: a Nextflow full-length 16S profiling pipeline for ONT reads\" href=\"#porefile-a-nextflow-full-length-16s-profiling-pipeline-for-ont-reads\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003ePorefile\u003c/code\u003e is a Nextflow pipeline that wraps a bunch of third-party software to process and classify full length 16S (SSU) long reads generated using Oxford Nanopore sequencing, against the \u003ca href=\"https://www.arb-silva.de/\" rel=\"nofollow\"\u003eSILVAdb\u003c/a\u003e SSU NR99 database, which is downloaded on the fly if not provided by the user.\u003c/p\u003e\n\u003cp\u003eReads are then classified by \u003ca href=\"https://software-ab.informatik.uni-tuebingen.de/download/megan6/welcome.html\" rel=\"nofollow\"\u003eMEGAN6 CE\u003c/a\u003e tools, and using a SILVA-to-NCBI accession mapping file generated on-the-fly.\u003c/p\u003e\n\u003cp\u003ePorefile uses SILVA SSU NR99 version 138.1 by default, which is the latest available up to this date (Feb 2023). If a new version were released, users can manually provide the new links to tell \u003ccode\u003ePorefile\u003c/code\u003e to download it.\u003c/p\u003e\n\u003cp\u003eContents:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#workflow-scheme\"\u003eWorkflow scheme\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-porefile\"\u003eRunning Porefile\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#help\"\u003eHelp\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#dependencies\"\u003eDependencies\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#dependencies-included-in-the-container\"\u003eDependencies included in the container\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#profiles\"\u003eProfiles\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#container-engines\"\u003eContainer engines\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#other-configuration-for-dev-mostly\"\u003eOther configuration (for dev mostly)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#output-files\"\u003eOutput files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflow scheme\u003c/h2\u003e\u003ca id=\"user-content-workflow-scheme\" class=\"anchor\" aria-label=\"Permalink: Workflow scheme\" href=\"#workflow-scheme\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-mermaid\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eflowchart\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTD\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep1\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e(((\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--fq \u0027./path/to/*fastq\u0027\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e)))\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep2\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003e--isDemultiplexed\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eDemultiplex\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep3\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eConcatenate\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep4\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003ePorechop\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep2\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e \u003cspan class=\"pl-s\"\u003eNo\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep3\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep2\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e \u003cspan class=\"pl-s\"\u003eYes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep5\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eNanoFilt\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eQFilt\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep5\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eNanofilt\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep6\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eAutomap\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep6\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep7\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eYacrd\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep4\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep5\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eQCheck\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep8\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eNanoplotRaw\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep9\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eNanoplotFilt\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep9\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep10\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eSummaryTable\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003eQFilt\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep8\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMain\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep11\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMakeDB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep13\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMinimap2\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep12\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eFastq2Fasta\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep13\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep13\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep14\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMeganLCA\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep14\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep15\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eGetReadInfo\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep15\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep16\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eComputeAbundances\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep7\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep12\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep23\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[(\u003c/span\u003e\u003c/span\u003eSILVAdb\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e)]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003eInternet\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eSetSilva\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eInternet\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep17\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep20\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003edownloadSilvaTaxNcbiSp\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep21\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003edownloadSilvaTaxmap\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep17\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003edownload SILVA fasta\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep18\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e{\u003c/span\u003e\u003c/span\u003e--fullSilva\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e}\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep18\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e \u003cspan class=\"pl-s\"\u003eNo\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep19\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003ereduceSilva\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep20\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eGenerateSynonyms\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep21\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep19\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep11\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep18\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e--\u003c/span\u003e \u003cspan class=\"pl-s\"\u003eYes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep11\u003c/span\u003e \n \u003cspan class=\"pl-k\"\u003esubgraph\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ePolish\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep24\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eSubsetSilva\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep25\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMakeDB\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep26\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eSubsetReads\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep27\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMinimap2\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep25\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep27\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep27\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep28\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eMeganLCA\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep28\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep29\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eGetReadInfo\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep29\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep30\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eCorrectAssignments\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep30\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep31\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e[\u003c/span\u003e\u003c/span\u003eComputeAbundances\u003cspan class=\"pl-pds\"\u003e\u003cspan class=\"pl-sg\"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eend\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep19\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep24\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep24\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep16\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep24\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep12\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep26\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep16\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep26\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep28\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003ep22\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e--\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-ent\"\u003ep14\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning Porefile\u003c/h2\u003e\u003ca id=\"user-content-running-porefile\" class=\"anchor\" aria-label=\"Permalink: Running Porefile\" href=\"#running-porefile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA typical command for running the pipeline would be as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run microgenlab/porefile --fq \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003epath/to/*.fastq\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e -profile docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf a local copy of the required SILVAdb files were provided, the workflow avoids re downloading it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run microgenlab/porefile --fq \"./fastq/*.fastq\" \\\n --silvaFasta \"./SILVA_138.1_SSURef_NR99_tax_silva.fasta.gz\" \\\n --silvaTaxNcbiSp \"./tax_ncbi-species_ssu_ref_nr99_138.1.txt.gz\" \\\n --silvaTaxmap \"./taxmap_slv_ssu_ref_nr_138.1.txt.gz\" \\\n -profile docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHelp\u003c/h2\u003e\u003ca id=\"user-content-help\" class=\"anchor\" aria-label=\"Permalink: Help\" href=\"#help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRun the following for more details about parameter tuning:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003enextflow run microgenlab/porefile --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e and at least one of the following container engines: Docker, Singularity/Apptainer, Podman.\u003c/p\u003e\n\u003cp\u003eAll workflow dependencies have been packaged into a \u003ca href=\"https://hub.docker.com/repository/docker/iferres/porefile\" rel=\"nofollow\"\u003edocker container\u003c/a\u003e, which is automatically downloaded when the pipeline is executed. That\u0027s it, you don\u0027t need to install any other software on your own.\u003c/p\u003e\n\u003cp\u003ePorefile has been tested with each three mencioned container technologies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDependencies included in the container\u003c/h4\u003e\u003ca id=\"user-content-dependencies-included-in-the-container\" class=\"anchor\" aria-label=\"Permalink: Dependencies included in the container\" href=\"#dependencies-included-in-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDependencies used by the pipeline and included in the container are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/rrwick/Porechop\"\u003ePorechop\u003c/a\u003e (Demultiplex)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/wdecoster/nanofilt/\"\u003eNanoFilt\u003c/a\u003e (Quality filtering)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/natir/yacrd\"\u003eYacrd\u003c/a\u003e (Chimera removal)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/wdecoster/NanoPlot\"\u003eNanoPlot\u003c/a\u003e (Quality check)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/shenwei356/seqkit/\"\u003eseqkit\u003c/a\u003e (fastq/fasta manipulation)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e (Alignment)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org/\" rel=\"nofollow\"\u003eR\u003c/a\u003e (Processing)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://software-ab.informatik.uni-tuebingen.de/download/megan6/welcome.html\" rel=\"nofollow\"\u003eMEGAN6\u003c/a\u003e (Taxonomy assignment)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you use Porefile, please also cite them since we are \u003cem\u003e\u003cstrong\u003estanding on the shoulders of giants\u003c/strong\u003e\u003c/em\u003e. Also cite \u003ca href=\"https://www.arb-silva.de/\" rel=\"nofollow\"\u003eSILVAdb\u003c/a\u003e, and \u003ca href=\"https://www.nature.com/articles/nbt.3820%7B\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProfiles\u003c/h2\u003e\u003ca id=\"user-content-profiles\" class=\"anchor\" aria-label=\"Permalink: Profiles\" href=\"#profiles\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePorefile comes with a minimal set of configuration profiles. Please, refer to \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow documentation\u003c/a\u003e to create a configuration file for your HPC infrastructure.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eContainer engines\u003c/h4\u003e\u003ca id=\"user-content-container-engines\" class=\"anchor\" aria-label=\"Permalink: Container engines\" href=\"#container-engines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse \u003ccode\u003e-profile docker\u003c/code\u003e to run the pipeline using \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e-profile singularity\u003c/code\u003e to run the pipeline using \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eSingularity/Apptainer\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eUse \u003ccode\u003e-profile podman\u003c/code\u003e to run the pipeline using \u003ca href=\"https://podman.io/\" rel=\"nofollow\"\u003ePodman\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOther configuration (for dev mostly)\u003c/h4\u003e\u003ca id=\"user-content-other-configuration-for-dev-mostly\" class=\"anchor\" aria-label=\"Permalink: Other configuration (for dev mostly)\" href=\"#other-configuration-for-dev-mostly\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e-profile test\u003c/code\u003e: Tests the pipeline on a local machine with low resources using a toy dataset (5K ONT reads) included in the repo. Mostly used to develop on my desktop machine. Assigns at most 16Gb of RAM and 4 cpus per process. To run the test using (say) Singularity as container engine (takes about ~5min on a Intel Core i7-4790, 32Gb RAM):\n\u003ccode\u003enextflow run microgenlab/porefile -profile test,singularity\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-profile nagual\u003c/code\u003e: Configuration to use at IPMont servers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eUsage:\nA typical command for running the pipeline would be as follows:\n\n nextflow run microgenlab/porefile --fq \u0027data/*.fastq\u0027\n\nInput fastq file(s):\n --fq Path to input data (must be surrounded with quotes).\n\nOther:\n --silvaFasta Path to SILVA_*_SSURef_NR99_tax_silva.fasta.gz file. You can provide it\n either compressed (.gz) or not. If not provided, the workflow automatically\n adds a download step (you must have internet connection).\n --silvaFastaURL URL to SILVA_*_SSURef_NR99_tax_silva.fasta.gz file. It will be used if you\n don\u0027t provide the --silvaFasta parameter (above). Default is:\n \u0027https://www.arb-silva.de/fileadmin/silva_databases/current/Exports/SILVA_138.1_SSURef_NR99_tax_silva.fasta.gz\u0027.\n\n --silvaTaxNcbiSp Path to tax_ncbi-species_ssu_ref_nr99_*.txt.gz file. You can provide it\n either compressed (.gz) or not. If not provided, the workflow automatically\n adds a download step.\n --silvaTaxNcbiSpURL URL to tax_ncbi-species_ssu_ref_nr99_*.txt.gz file. It will be used if you\n don\u0027t provide the --silvaFasta parameter (above). Default is:\n \u0027https://www.arb-silva.de/fileadmin/silva_databases/current/Exports/taxonomy/ncbi/tax_ncbi-species_ssu_ref_nr99_138.1.txt.gz\u0027.\n\n --silvaTaxmap Path to taxmap_slv_ssu_ref_nr_*.txt.gz file. You can provide it\n either compressed (.gz) or not. If not provided, the workflow automatically\n adds a download step.\n --silvaTaxmapURL URL to taxmap_slv_ssu_ref_nr_*.txt.gz file. It will be used if you\n don\u0027t provide the --silvaFasta parameter (above). Default is:\n \u0027https://www.arb-silva.de/fileadmin/silva_databases/current/Exports/taxonomy/taxmap_slv_ssu_ref_nr_138.1.txt.gz\u0027.\n\n --fullSilva By default, porefile reduces SILVA to prokatyote SSU (16S). Use this flag\n to deactivate the reducing step and use the full SILVA database.\n\n --outdir Name of the results directory. Default: \"results\".\n\n\nProcess specific parameters:\n Porechop parameters:\n --porechop_extra_end_trim The \u0027--extra_end_trim\u0027 parameter of Porechop. Default: 0.\n\n NanoFilt parameters:\n --nanofilt_quality The \u0027--quality\u0027 parameter of NanoFilt. Default: 8.\n --nanofilt_length The \u0027--length\u0027 parameter of NanoFilt (minimum length). Default: 1000.\n --nanofilt_maxlength The \u0027--maxlength\u0027 parameter of NanoFilt. Default: 1700.\n --nanofilt_headcrop The \u0027--headcrop\u0027 parameter of NanoFilt. Default: 0.\n --nanofilt_tailcrop The \u0027--tailcrop\u0027 parameter of NanoFilt. Default: 0.\n\n Yacrd parameters:\n --yacrd_c The \u0027-c\u0027 parameter of Yacrd (minimum coverage). Default: 4 .\n --yacrd_n The \u0027-n\u0027 parameter of Yacrd (minimum coverage of read). Default: 0.4 .\n\n Minimap2 parameters:\n --minimap2_k The \u0027-k\u0027 parameter of minimap2. Default: 15.\n --minimap2_x The \u0027-x\u0027 parameter of minimap2. Default: \u0027map-ont\u0027. Possible values: \u0027map-ont\u0027,\n \u0027asm5\u0027, \u0027asm10\u0027, \u0027asm20\u0027, \u0027map-pb\u0027, or \u0027map-hifi\u0027. \n --minimap2_f The \u0027-f\u0027 parameter of minimap2. Default: 1000. Only applied in the Automap module.\n --minimap2_KM The \u0027-K\u0027 parameter of minimap2, in Megabases. Default: 200.\n\n Megan6 parameters:\n --megan_lcaAlgorithm The \u0027--lcaAlgorithm\u0027 parameter of sam2rma tool (Megan6). Default: \u0027naive\u0027.\n Possible values are: \u0027naive\u0027, \u0027weighted\u0027, or \u0027longReads\u0027.\n --megan_topPercent The \u0027--topPercent\u0027 parameter of sam2rma tool (Megan6). Default: 10.\n --megan_topPercentPolish The \u0027--topPercent\u0027 parameter of sam2rma tool (Megan6) applied when polishing step\n is activated. Default: 5.\n --megan_minPercentReadCover The \u0027--minPercentReadCover\u0027 parameter of sam2rma and blast2rma tools (Megan6).\n Default: 70.\n --megan_lcaCoveragePercent The \u0027--lcaCoveragePercent\u0027 parameter of sam2rma and blast2rma tools (Megan6).\n Default: 100.\n\n\nOther control options:\n --isDemultiplexed Set this flag to avoid Demultiplex sub-workflow. If set, each fastq file is\n --removeChimeras Set this flag to activate the chimera-removing step with Yacrd.\n processed as a different barcode.\n --noNanoplot Set this flag to avoid QCheck sub-workflow.\n --noSpeciesPolishing Avoid the polishing sub-workflow.\n --lowAbundanceThreshold The threshold of total abundance (counts) to be considered as \"low\", and\n which the pipeline will try to re assign.\n\n\nContainer options (note single dash usage!):\n -profile docker Use docker as container engine (default).\n -profile singularity Use singularity as container engine.\n -profile podman Use podman as container engine.\n\nHelp:\n --help Print this help and exit.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput files\u003c/h2\u003e\u003ca id=\"user-content-output-files\" class=\"anchor\" aria-label=\"Permalink: Output files\" href=\"#output-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003eresults\u003c/code\u003e directory contains the following directories/files:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eresults/\n\u251c\u2500\u2500 COUNTS.tsv\n\u251c\u2500\u2500 COUNTS_polished.tsv\n\u251c\u2500\u2500 TAXCLA.tsv\n\u251c\u2500\u2500 TAXCLA_polished.tsv\n\u251c\u2500\u2500 Read_Assignments/\n\u2502 \u251c\u2500\u2500 BC01.read_info\n\u2502 \u251c\u2500\u2500 BC02.read_info\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 Read_Assignments_Polished/\n\u2502 \u251c\u2500\u2500 BC01_polished.read_info\n\u2502 \u251c\u2500\u2500 BC02_polished.read_info\n\u2502 \u2514\u2500\u2500...\n\u251c\u2500\u2500 NanoPlots/\n\u2502 \u251c\u2500\u2500 BC01/\n\u2502 \u251c\u2500\u2500 BC02/\n\u2502 \u251c\u2500\u2500 ...\n\u2502 \u2514\u2500\u2500 summary.tsv\n\u251c\u2500\u2500 Rma/\n\u2502 \u251c\u2500\u2500 BC01.rma\n\u2502 \u251c\u2500\u2500 BC02.rma\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 silva_to_NCBI_synonyms.map\n\u2514\u2500\u2500 versions.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eCOUNTS.tsv\u003c/code\u003e and \u003ccode\u003eCOUNTS_polished.tsv\u003c/code\u003e are a tabular text files with the counts for each taxa (rows), on each barcode (columns).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e BC01 BC02 BC03 ...\nTAXA_001 1 0 0 ...\nTAXA_002 4 0 0 ...\nTAXA_003 0 3 10 ... \n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003ccode\u003eTAXCLA.tsv\u003c/code\u003e and \u003ccode\u003eTAXCLA_polished.tsv\u003c/code\u003e are tabular text files with the taxon path classification of each taxa.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Kingdom Phylum Class Order Family Genus Species\nTAXA_001 Bacteria NA NA NA NA NA NA\nTAXA_002 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Paracoccus NA\nTAXA_003 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas NA\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTake into account that the \u003ccode\u003eTAXA\u003c/code\u003e labels are arbitrarily generated for each pipeline, so the \u003ccode\u003eTAXA\u003c/code\u003e labels in \u003ccode\u003eTAXCLA.tsv\u003c/code\u003e do not match the ones in \u003ccode\u003eTAXCLA_polished.tsv\u003c/code\u003e (and the same to the \u003ccode\u003eCOUNTS*\u003c/code\u003e files).\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eRead_Assignments\u003c/code\u003e and \u003ccode\u003eRead_Assignments_Polished\u003c/code\u003e contains taxonomic classification for each read.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e001e48d4-cacc-4f78-b5f4-1b578a652ab2_0_1409 C 186801 [D] Bacteria; [P] Firmicutes; [C] Clostridia;\n027b1258-66df-4703-bfe8-bf93957a142d_0_1409 F 171552 [D] Bacteria; [P] Bacteroidetes; [C] Bacteroidia; [O] Bacteroidales; [F] Prevotellaceae;\n029f8418-4d6a-46b9-a98f-e0784e620fa2_0_1464 F 541000 [D] Bacteria; [P] Firmicutes; [C] Clostridia; [O] Clostridiales; [F] Ruminococcaceae;\n...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe columns correspond to: 1) Read id (header); 2) Taxonomic rank at which each read was possible to assign; 3) NCBI id of each taxon; 4) The taxon path assigned to each read.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eNanoPlots/\u003c/code\u003e directory contain QC plots (see \u003ca href=\"https://github.com/wdecoster/NanoPlot\"\u003eNanoPlot\u003c/a\u003e), pre and post filtering, and a summary tabular data file.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eRma/\u003c/code\u003e directory contains binary \u003ccode\u003e.rma\u003c/code\u003e files which can be analyzed with MEGAN. There isn\u0027t an equivalent directory for the polished pipeline since the second LCA assignment is done only with a subset of reads, and then \u003ccode\u003eporefile\u003c/code\u003e re-writes the base algorithm\u0027s assignments.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003esilva_to_NCBI_synonyms.map\u003c/code\u003e is the SILVA to NCBI synonyms mapping file generated on-the-fly by using SILVA\u0027s \u003ccode\u003etax_ncbi-species\u003c/code\u003e and \u003ccode\u003etaxmap\u003c/code\u003e files.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eversions.txt\u003c/code\u003e prints the versions of the porefile dependencies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA manuscript is under preparation.\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eEnd-to-End Computer Vision Bootcamp\u003c/h1\u003e\u003ca id=\"user-content-end-to-end-computer-vision-bootcamp\" class=\"anchor\" aria-label=\"Permalink: End-to-End Computer Vision Bootcamp\" href=\"#end-to-end-computer-vision-bootcamp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003cstrong\u003eEnd-to-End Computer Vision Bootcamp\u003c/strong\u003e is designed from a real-world perspective and follows the data processing, development, and deployment pipeline paradigm using a variety of tools. Through hands-on exercises, attendees will learn the fundamentals of preprocessing custom images, speeding the development process using transfer learning for model training, and deployment of trained models for fast and scalable AI in production.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootcamp Content\u003c/h2\u003e\u003ca id=\"user-content-bootcamp-content\" class=\"anchor\" aria-label=\"Permalink: Bootcamp Content\" href=\"#bootcamp-content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe content is structured in five modules with an additional introductory notebook and two challenge notebooks:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWelcome to \u003cstrong\u003eend-to-end computer vision\u003c/strong\u003e bootcamp\u003c/li\u003e\n\u003cli\u003eLab 1: Data labeling and preprocessing\u003c/li\u003e\n\u003cli\u003eLab 2: Object detection using TAO YOLOv4\u003c/li\u003e\n\u003cli\u003eLab 3: Model deployment with Triton Inference Server\u003c/li\u003e\n\u003cli\u003eLab 4: Model deployment with DeepStream\u003c/li\u003e\n\u003cli\u003eLab 5: Measure object size using OpenCV\u003c/li\u003e\n\u003cli\u003eChallenge 1: DeepStream SDK\u003c/li\u003e\n\u003cli\u003eChallenge 2: Triton Inference Server\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTools and Frameworks\u003c/h2\u003e\u003ca id=\"user-content-tools-and-frameworks\" class=\"anchor\" aria-label=\"Permalink: Tools and Frameworks\" href=\"#tools-and-frameworks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tools and frameworks used in the bootcamp are as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/tao-toolkit\" rel=\"nofollow\"\u003eNVIDIA\u00ae TAO Toolkit\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/deepstream-sdk\" rel=\"nofollow\"\u003eNVIDIA DeepStream SDK\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nvidia.com/en-us/ai-data-science/products/triton-inference-server/\" rel=\"nofollow\"\u003eNVIDIA Triton\u2122 Inference Server\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/tensorrt\" rel=\"nofollow\"\u003eNVIDIA TensorRT\u2122\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://opencv.org/\" rel=\"nofollow\"\u003eOpenCV\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://labelstud.io/\" rel=\"nofollow\"\u003eLabel Studio\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootcamp Duration\u003c/h2\u003e\u003ca id=\"user-content-bootcamp-duration\" class=\"anchor\" aria-label=\"Permalink: Bootcamp Duration\" href=\"#bootcamp-duration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe total bootcamp material would take approximately 8.5 hours. It is recommended to divide the teaching of the material into two days, covering the first two notebooks (Lab 1 and Lab 2) in one session and the rest in the next session.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBootcamp Prerequisites\u003c/h2\u003e\u003ca id=\"user-content-bootcamp-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Bootcamp Prerequisites\" href=\"#bootcamp-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA basic understanding of Deep Learning, Python programming, and familiarity with NVIDIA\u00ae NGC\u2122 is required.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploying the Bootcamp materials:\u003c/h2\u003e\u003ca id=\"user-content-deploying-the-bootcamp-materials\" class=\"anchor\" aria-label=\"Permalink: Deploying the Bootcamp materials:\" href=\"#deploying-the-bootcamp-materials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo deploy the Labs, please refer to the Deployment guide presented \u003ca href=\"https://github.com/openhackathons-org/End-to-End-Computer-Vision/blob/main/Deployment_Guide.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAttribution\u003c/h2\u003e\u003ca id=\"user-content-attribution\" class=\"anchor\" aria-label=\"Permalink: Attribution\" href=\"#attribution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis material originates from the OpenHackathons Github repository. Check out additional materials \u003ca href=\"https://github.com/openhackathons-org\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDon\u0027t forget to check out additional \u003ca href=\"https://www.openhackathons.org/s/technical-resources\" rel=\"nofollow\"\u003eOpen Hackathons Resources\u003c/a\u003e and join our \u003ca href=\"https://www.openacc.org/community#slack\" rel=\"nofollow\"\u003eOpenACC and Hackathons Slack Channel\u003c/a\u003e to share your experience and get more help from the community.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicensing\u003c/h2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-label=\"Permalink: Licensing\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopyright \u00a9 2023 OpenACC-Standard.org. This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0). These materials may include references to hardware and software developed by other entities; all applicable licensing and copyrights apply.\u003c/p\u003e\n", "stargazers_count": 20, - "subscribers_count": 5, + "subscribers_count": 2, "topics": [ - "nextflow", - "pipeline", - "16s", - "profiling", - "docker", - "singularity", - "nanopore" + "computer-vision", + "deep-learning", + "deep-neural-networks", + "deepstream", + "image-processing", + "image-recognition", + "object-detection", + "object-tracking", + "opencv", + "tao", + "tensorrt", + "triton-inference-server" ], - "updated_at": 1706826446.0 + "updated_at": 1731009638.0 }, { "data_format": 2, - "description": "Alignment-based retrieval and concatenation of phylogenetic markers from whole genome sequence (WGS) data", + "description": "A simple wrapper of ENTERPRISE and ceffyl that allows for new-physics searches in PTA data.", "filenames": [ - "docker/Singularity.def" + "Singularity" ], - "full_name": "fethalen/Patchwork", - "latest_release": "v0.5.2", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/fethalen/Patchwork/blob/main/patchwork_logo_500px.png\"\u003e\u003cimg src=\"https://github.com/fethalen/Patchwork/raw/main/patchwork_logo_500px.png\" alt=\"Patchwork logo\" width=\"225\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f7853eb8ea0bc39f9ca567e8129bae603ce31b5c88563490315eb26bdbc05f43/687474703a2f2f7777772e676e752e6f72672f67726170686963732f67706c76332d38387833312e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f7853eb8ea0bc39f9ca567e8129bae603ce31b5c88563490315eb26bdbc05f43/687474703a2f2f7777772e676e752e6f72672f67726170686963732f67706c76332d38387833312e706e67\" alt=\"GPLv3\" data-canonical-src=\"http://www.gnu.org/graphics/gplv3-88x31.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://www.gnu.org/copyleft/gpl.html\" rel=\"nofollow\"\u003eGNU General Public License, GPLv3\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePatchwork is an alignment-based program for retrieving and concatenating\nphylogenetic markers from whole-genome sequencing (WGS) data. The program\nsearches the provided DNA query contigs against one or more amino acid reference\nsequences. Multiple, overlapping hits are merged to derive a single, continuous\nsequence for each reference sequence.\u003c/p\u003e\n\u003ch3 id=\"user-content-features\"\u003e\u003ca class=\"heading-link\" href=\"#features\"\u003eFeatures\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eAlign nucleotide sequences to one or more protein sequences\u003c/li\u003e\n\u003cli\u003eWorks with already assembled contigs \u003cem\u003eor\u003c/em\u003e raw reads\u003c/li\u003e\n\u003cli\u003eStitch overlapping or gappy sequences together based on a reference\u003c/li\u003e\n\u003cli\u003eFind homologs, even in distantly-related taxa\u003c/li\u003e\n\u003cli\u003e\ud83d\udc07 Written in \u003ca href=\"https://julialang.org/\" rel=\"nofollow\"\u003eJulia\u003c/a\u003e and utilizing\n\u003ca href=\"https://github.com/bbuchfink/diamond\"\u003eDIAMOND\u003c/a\u003e for maximum speed\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"user-content-graphical-overview\"\u003e\u003ca class=\"heading-link\" href=\"#graphical-overview\"\u003eGraphical Overview\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/fethalen/patchwork/blob/main/overview.png?raw=true\"\u003e\u003cimg src=\"https://github.com/fethalen/patchwork/raw/main/overview.png?raw=true\" alt=\"Graphical Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3 id=\"user-content-quick-installation\"\u003e\u003ca class=\"heading-link\" href=\"#quick-installation\"\u003eQuick installation\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eWe are currently working on a Conda build. In the future,\nthe user will be able to install this program by running \u003ccode\u003econda install -c bioconda patchwork\u003c/code\u003e. Until then, please refer to\n\u003ca href=\"https://github.com/fethalen/Patchwork/wiki/4.-Installation\"\u003ethese instructions\u003c/a\u003e\nfor installing from source. It is now also possible to \u003ca href=\"https://github.com/fethalen/Patchwork/wiki/4.-Installation#installing-patchwork-with-docker\"\u003einstall\nPatchwork using Docker\u003c/a\u003e.\u003c/p\u003e\n\u003ch3 id=\"user-content-documentation\"\u003e\u003ca class=\"heading-link\" href=\"#documentation\"\u003eDocumentation\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003ePlease see our \u003ca href=\"https://github.com/fethalen/Patchwork/wiki\"\u003eWiki\u003c/a\u003e.\u003c/p\u003e\n\u003ch3 id=\"user-content-cite\"\u003e\u003ca class=\"heading-link\" href=\"#cite\"\u003eCite\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eOur manuscript is still in preparation, it will be posted here once a preprint\nof the article is available.\u003c/p\u003e\n\u003cp\u003e\u00a9 \u003ca href=\"https://www.uni-goettingen.de/en/80149.html\" rel=\"nofollow\"\u003eDept. for Animal Evolution and Biodiversity\u003c/a\u003e 2020\u003c/p\u003e\n", - "stargazers_count": 20, - "subscribers_count": 1, - "topics": [], - "updated_at": 1675872349.0 + "full_name": "andrea-mitridate/PTArcade", + "latest_release": "v1.1.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePTArcade\u003c/h1\u003e\u003ca id=\"user-content-ptarcade\" class=\"anchor\" aria-label=\"Permalink: PTArcade\" href=\"#ptarcade\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/conda-forge/ptarcade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9c51f3ab97fab5205672c6edbb7b9ac357b9355dc33a848e011f655948452e6c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f70746172636164652f6261646765732f76657273696f6e2e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/ptarcade/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://badge.fury.io/py/ptarcade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/555ae1b8611ad496d719f5f76d1440f4a1c155d9274ab159ac86a05c2dc8d971/68747470733a2f2f62616467652e667572792e696f2f70792f70746172636164652e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/ptarcade.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/ngnewphy/ptarcade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/552bd3645f40236e0513a38789e0a23cc76a6cb33d4b459790ab3ee0d04292e6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65726875622d70746172636164652d696d706f7274616e742e7376673f6c6f676f3d446f636b6572\" data-canonical-src=\"https://img.shields.io/badge/dockerhub-ptarcade-important.svg?logo=Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://anaconda.org/conda-forge/ptarcade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80ad5865e388fc04bb85f0eeaa8912ec2cd6852e231731458af099276f785a8d/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f70746172636164652f6261646765732f6c61746573745f72656c656173655f646174652e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/ptarcade/badges/latest_release_date.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePTArcade provides an interface to the ENTERPRISE analysis suite and allows for simple implementation of new-physics searches in PTA data.\u003c/p\u003e\n\u003cp\u003eThe user can specify a new physics signal (either deterministic or stochastic), and the code will output Monte Carlo chains that can be used to reconstruct the model\u0027s parameter posterior distributions.\u003c/p\u003e\n\u003cp\u003ePlease see our detailed documentation \u003ca href=\"https://andrea-mitridate.github.io/PTArcade/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 21, + "subscribers_count": 8, + "topics": [ + "particle-physics", + "physics", + "pulsar-timing-array" + ], + "updated_at": 1725533010.0 }, { "data_format": 2, - "description": "Repository for managing the builds of OpenStudio Docker images.", + "description": "A snakemake pipeline to assembly, polishing, correction and quality check from Oxford nanopore reads.", "filenames": [ - "singularity/Singularity" + "Containers/Singularity.culebront_tools.def", + "Containers/Singularity.report.def" ], - "full_name": "NREL/docker-openstudio", - "latest_release": "2.9.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOpenStudio\u003c/h1\u003e\u003ca id=\"user-content-openstudio\" class=\"anchor\" aria-label=\"Permalink: OpenStudio\" href=\"#openstudio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/NREL/docker-openstudio/actions/workflows/docker-openstudio.yml\"\u003e\u003cimg src=\"https://github.com/NREL/docker-openstudio/actions/workflows/docker-openstudio.yml/badge.svg\" alt=\"openstudio-docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repo provides a container for OpenStudio as well as several dependencies, including Ruby 2.x, Bundler,\nbuild-essentials and various development libraries for gem support.\u003c/p\u003e\n\u003cp\u003eAt the moment, there is only one target container for this project, however, we are working on containers for a\nslim version and a version compatible with \u003ca href=\"https://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Tags\u003c/h2\u003e\u003ca id=\"user-content-docker-tags\" class=\"anchor\" aria-label=\"Permalink: Docker Tags\" href=\"#docker-tags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBelow is a table of the various docker tags and their meanings as seen on \u003ca href=\"https://hub.docker.com/r/nrel/openstudio/tags/\" rel=\"nofollow\"\u003ethis page\u003c/a\u003e.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTag\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ex.y.z\u003c/td\u003e\n\u003ctd\u003eBuild of official OpenStudio release (recommended use)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003elatest\u003c/td\u003e\n\u003ctd\u003eLatest official release of OpenStudio (e.g. 2.5.1)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edevelop\u003c/td\u003e\n\u003ctd\u003eRelease of \u003ca href=\"https://github.com/NREL/docker-openstudio/tree/develop\"\u003edevelop branch\u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding OpenStudio Container\u003c/h2\u003e\u003ca id=\"user-content-building-openstudio-container\" class=\"anchor\" aria-label=\"Permalink: Building OpenStudio Container\" href=\"#building-openstudio-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese images are automatically built in TravisCI. To trigger TravisCI for a new build do the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eOn develop branch update the \u003ca href=\".travis.yml\"\u003e.travis.yml\u003c/a\u003e with the new version of OpenStudio and SHA\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003eOPENSTUDIO_VERSION\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e2.6.0\u003c/span\u003e\n - \u003cspan class=\"pl-ent\"\u003eOPENSTUDIO_SHA\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003ee3cb91f98a\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePush changes to the feature branch, make and merge a pull request to develop\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWait for CI to finish and verify new develop image is available on \u003ca href=\"https://hub.docker.com/r/nrel/openstudio/tags/\" rel=\"nofollow\"\u003edocker hub\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTest locally (if needed)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker pull nrel/openstudio:develop\ndocker run -it --rm nrel/openstudio:develop bash\nirb\nrequire \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003eopenstudio\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\nputs OpenStudio.getOpenStudioCLI\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBuild Locally\u003c/h3\u003e\u003ca id=\"user-content-build-locally\" class=\"anchor\" aria-label=\"Permalink: Build Locally\" href=\"#build-locally\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBegin by installing the \u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003edocker toolkit\u003c/a\u003e version 17.03.1 or later, as\ndescribed in the linked documentation. Once the toolkit is installed and activated, run the command below to build the base image with OpenStudio 2.6.1.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --target base -t openstudio-local --build-arg OPENSTUDIO_VERSION=2.6.1 --build-arg OPENSTUDIO_SHA=ab0dddde0b .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe version of OpenStudio and the SHAs are listed \u003ca href=\"https://github.com/NREL/OpenStudio/wiki/OpenStudio-Version-Compatibility-Matrix\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf the \u003ccode\u003e--target\u003c/code\u003e is not passed, then the docker build will contain only the CLI.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecuting OpenStudio Container\u003c/h2\u003e\u003ca id=\"user-content-executing-openstudio-container\" class=\"anchor\" aria-label=\"Permalink: Executing OpenStudio Container\" href=\"#executing-openstudio-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two options to acquire the docker container required for execution. Both assume that the\n\u003ca href=\"https://docs.docker.com/engine/installation/\" rel=\"nofollow\"\u003edocker tool-kit\u003c/a\u003e version 17.03.1 or later is installed. The first option,\nbuilding the container from a GitHub checkout, is outlined above. Additionally, it is typically easiest to\n\u003ca href=\"https://docs.docker.com/engine/reference/commandline/tag/\" rel=\"nofollow\"\u003etag the resulting container\u003c/a\u003e as \u003ccode\u003enrel/openstudio:latest\u003c/code\u003e.\nFor a more extensive discussion of the latest tag and associated best practices\n\u003ca href=\"https://medium.com/@mccode/the-misunderstood-docker-tag-latest-af3babfd6375\" rel=\"nofollow\"\u003eplease refer to this Medium article\u003c/a\u003e.\nThe second option, downloading a release from DockerHub, requires determining the\n\u003ca href=\"https://hub.docker.com/r/nrel/openstudio/tags/\" rel=\"nofollow\"\u003edocker-openstudio tagged release\u003c/a\u003e that is desired, and then running\n\u003ccode\u003edocker pull nrel/openstudio:\u0026lt;tag\u0026gt;\u003c/code\u003e. As an example, to download the 2.1.1 docker-openstudio image, the command would\nbe \u003ccode\u003edocker pull nrel/openstudio:2.1.1\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eOnce the desired container is available, either through a build or pull process, the next step is to run the container.\nTo simply access the container tagged with \u0027tag\u0027, (where tag was respectively \u0027latest\u0027 or \u00272.1.1\u0027 in the above\nparagraph,) run \u003ccode\u003edocker run -it --rm nrel/openstudio:tag /bin/bash\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo execute an OpenStudio Workflow directly from the command line requires\n\u003ca href=\"https://docs.docker.com/engine/reference/run/#volume-shared-filesystems\" rel=\"nofollow\"\u003emounting the requisite files to the container\u003c/a\u003e,\nas well as invoking the \u003ca href=\"https://nrel.github.io/OpenStudio-user-documentation/reference/command_line_interface/\" rel=\"nofollow\"\u003eOpenStudio CLI\u003c/a\u003e\n(command line interface.) The docker container by default executes commands in the \u003ccode\u003e/var/simdata/openstudio\u003c/code\u003e directory,\n(this is defined in the \u003ca href=\"./Dockerfile\"\u003eDockerfile\u003c/a\u003e.) If the desired OSW was located at\n\u003ccode\u003e/Users/myuser/demo_os_files/example.osw\u003c/code\u003e on the host computer, the appropriate docker command to execute the OSW would\nbe \u003ccode\u003edocker run -it --rm -v=/Users/myuser/demo_os_files:/var/simdata/openstudio nrel/openstudio /usr/bin/openstudio run -w example.osw\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf gem dependencies are required as part of the CLI outside of those\n\u003ca href=\"https://github.com/NREL/OpenStudio/blob/develop/dependencies/ruby/Gemfile\"\u003epacked with OpenStudio\u003c/a\u003e please contact\nKyle Benne, Ry Horsey, and Dan Macumber at their NREL email addresses.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eIssues\u003c/h1\u003e\u003ca id=\"user-content-issues\" class=\"anchor\" aria-label=\"Permalink: Issues\" href=\"#issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease submit issues on the project\u0027s \u003ca href=\"https://github.com/nrel/docker-openstudio\"\u003eGithub\u003c/a\u003e page.\u003c/p\u003e\n", - "stargazers_count": 20, - "subscribers_count": 14, + "full_name": "SouthGreenPlatform/CulebrONT_pipeline", + "latest_release": "1.7.0", + "readme": "\u003cp\u003e\u003ca href=\"./docs/source/_images/culebront_logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./docs/source/_images/culebront_logo.png\" alt=\"Culebront Logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.python.org/downloads\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e4779c52a0f8acf7c62517ff771deebcf8ab8913544dd508ccdd6cec2f2b400a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e372532422d626c7565\" alt=\"PythonVersions\" data-canonical-src=\"https://img.shields.io/badge/python-3.7%2B-blue\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/35030e6ddc253302ffcdf599ce8a8e387c27d88eb3de9cfe4e103b3ec6161f96/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135352e31302e302d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"SnakemakeVersions\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%E2%89%A55.10.0-brightgreen.svg?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a324f41bf4495d7dc95ac4693962834b38ff77e1a6ed7f5c4dca9c3e3f92a6d3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d254532253839254135332e332e302d3745344337342e737667\" alt=\"Singularity\" data-canonical-src=\"https://img.shields.io/badge/singularity-%E2%89%A53.3.0-7E4C74.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/index.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cacdb0b19bd30d76ae4faaee3355a6d65ecc448b587bac638adbd5eb04339c20/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e64612d342e382e352532302d677265656e\" alt=\"Conda\" data-canonical-src=\"https://img.shields.io/badge/conda-4.8.5%20-green\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsing data from long reads obtained by Oxford Nanopore Technologies sequencing makes genome assembly easier, in particular to solve repeats and structural variants, in prokaryotic as well as in eukaryotic genomes, resulting in increased contiguity and accuracy.\u003c/p\u003e\n\u003cp\u003eBunch of softwares and tools are released or updated every week, and a lot of species see their genome assembled using those.\u003c/p\u003e\n\u003cp\u003eThat\u2019s right.\u003c/p\u003e\n\u003cp\u003e\"\u003cem\u003eBut which assembly tool could give the best results for my favorite organism?\u003c/em\u003e\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCulebrONT can help you!\u003c/strong\u003e CulebrONT is an open-source, scalable, modulable and traceable snakemake pipeline, able to launch multiple assembly tools in parallel and providing help for choosing the best possible assembly between all possibilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHomepage: \u003ca href=\"https://culebront-pipeline.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://culebront-pipeline.readthedocs.io/en/latest/\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca name=\"user-content-citation\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003e@Authors:\u003c/p\u003e\n\u003cp\u003eJulie Orjuela (IRD), Aurore Comte(IRD), S\u00e9bastien Ravel(CIRAD), Florian Charriat(INRAE), Tram Vi(IRD, AGI), Francois Sabot(IRD) and S\u00e9bastien Cunnac(IRD).\u003c/p\u003e\n\u003cp\u003e\u003ca name=\"user-content-notes\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-useful-notes\" class=\"anchor\" href=\"#useful-notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful notes\u003c/h2\u003e\n\u003cp\u003eBefore launching CulebrONT, you could base-calling of arbitrarily multiplexed libraries across several Minion runs with sequencing quality control and gather the output files by genome for subsequent steps. For that use \u003ca href=\"https://github.com/vibaotram/baseDmux\"\u003ehttps://github.com/vibaotram/baseDmux\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-thanks\" class=\"anchor\" href=\"#thanks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThanks\u003c/h4\u003e\n\u003cp\u003eThanks to Ndomassi Tando (i-Trop IRD) by administration support.\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the IRD i-Trop HPC (South Green Platform) at IRD Montpellier for providing HPC resources that have contributed to this work. \u003ca href=\"https://bioinfo.ird.fr/\" rel=\"nofollow\"\u003ehttps://bioinfo.ird.fr/\u003c/a\u003e - \u003ca href=\"http://www.southgreen.fr\" rel=\"nofollow\"\u003ehttp://www.southgreen.fr\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThanks to Yann Delorme for this beautiful logo \u003ca href=\"https://nimarell.github.io/resume\" rel=\"nofollow\"\u003ehttps://nimarell.github.io/resume\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca name=\"user-content-licence\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eLicencied under CeCill-C (\u003ca href=\"http://www.cecill.info/licences/Licence_CeCILL-C_V1-en.html\" rel=\"nofollow\"\u003ehttp://www.cecill.info/licences/Licence_CeCILL-C_V1-en.html\u003c/a\u003e) and GPLv3\nIntellectual property belongs to IRD and authors.\u003c/p\u003e\n", + "stargazers_count": 21, + "subscribers_count": 16, "topics": [], - "updated_at": 1714618187.0 + "updated_at": 1641455420.0 }, { "data_format": 2, - "description": "detection of burned in pixels using OCR (under development)", + "description": "RNAseq analysis pipeline", "filenames": [ - "ocr/Singularity" + "Singularity/Singularity.v2.2", + "Singularity/Singularity.v2.4", + "Singularity/Singularity.v2.3" ], - "full_name": "pydicom/dicom-cleaner", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDicom Cleaner\u003c/h1\u003e\u003ca id=\"user-content-dicom-cleaner\" class=\"anchor\" aria-label=\"Permalink: Dicom Cleaner\" href=\"#dicom-cleaner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository provides tools intended for use to scrape personal health information (PHI)\nthat is represented as text from image headers and pixels. Each subfolder here corresponds\nto a different kind of cleaner, and a docker container.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"ocr\"\u003eocr\u003c/a\u003e \"optical character recognititon\" is an image that runs text (letter detection) on a demo image. You can either detect (just find and report) or clean the data (and save cleaned png images).\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"header\"\u003eheader\u003c/a\u003e uses the \u003ca href=\"https://www.github.com/pydicom/deid\"\u003edeid\u003c/a\u003e python module to target known coordinates based on manufacturer and machine types to flag images. The coordinates are then cleaned (covered with a black box).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth containers are available on Docker Hub.\u003c/p\u003e\n", - "stargazers_count": 20, - "subscribers_count": 5, + "full_name": "IARCbioinfo/RNAseq-nf", + "latest_release": "v2.4a", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-rnaseq-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#rnaseq-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRNAseq-nf\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow-pipeline-for-rna-seq-processing\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-for-rna-seq-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline for RNA seq processing\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/RNAseq-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3243c4851a1ea38652c4f4e27327d1ecd991fb65bb3763a33bb47e2a077f40e3/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f524e417365712d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/RNAseq-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/rnaseq-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4271\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"RNAseqpipeline.png?raw=true\"\u003e\u003cimg src=\"RNAseqpipeline.png?raw=true\" alt=\"workflow\" title=\"Scheme of alignment/realignment Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-decription\" class=\"anchor\" aria-hidden=\"true\" href=\"#decription\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDecription\u003c/h2\u003e\n\u003cp\u003eNextflow pipeline for RNA sequencing mapping, quality control, reads counting, and unsupervised analysis\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eNextflow: for common installation procedures see the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc/INSTALL.txt\" rel=\"nofollow\"\u003e\u003cem\u003efastqc\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://rseqc.sourceforge.net/\" rel=\"nofollow\"\u003e\u003cem\u003eRESeQC\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://multiqc.info/docs/\" rel=\"nofollow\"\u003e\u003cem\u003emultiQC\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf\"\u003e\u003cem\u003eSTAR\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://www-huber.embl.de/HTSeq/doc/install.html#install\" rel=\"nofollow\"\u003e\u003cem\u003ehtseq\u003c/em\u003e\u003c/a\u003e; the python script htseq-count must also be in the PATH\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eA singularity container is available with all the tools needed to run the pipeline (see \"Usage\")\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h3\u003e\n\u003cp\u003eA bundle with reference genome and corresponding annotations for STAR is available at \u003ca href=\"https://data.broadinstitute.org/Trinity/CTAT_RESOURCE_LIB/\" rel=\"nofollow\"\u003ehttps://data.broadinstitute.org/Trinity/CTAT_RESOURCE_LIB/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, STAR genome indices can be generated from a genome fasta file ref.fa and a splice junction annotation file ref.gtf using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSTAR --runThreadN n --runMode genomeGenerate --genomeDir ref --genomeFastaFiles ref.fa --sjdbGTFfile ref.gtf --sjdbOverhang 99\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can provide a config file to customize the multiqc report (see \u003ca href=\"https://multiqc.info/docs/#configuring-multiqc\" rel=\"nofollow\"\u003ehttps://multiqc.info/docs/#configuring-multiqc\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reads-adapter-trimming-with-cutadapt\" class=\"anchor\" aria-hidden=\"true\" href=\"#reads-adapter-trimming-with-cutadapt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReads adapter trimming with cutadapt\u003c/h3\u003e\n\u003cp\u003eIn order to perform the optional adapter trimming of reads before mapping the following software must be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://cutadapt.readthedocs.io/en/stable/installation.html\" rel=\"nofollow\"\u003e\u003cem\u003ecutadapt\u003c/em\u003e\u003c/a\u003e version \u0026gt; 1.15, which requires Python version \u0026gt; 2.7\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/FelixKrueger/TrimGalore\"\u003e\u003cem\u003etrim_galore\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alignment-with-hisat2\" class=\"anchor\" aria-hidden=\"true\" href=\"#alignment-with-hisat2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlignment with hisat2\u003c/h3\u003e\n\u003cp\u003eIn order to perform the optional alignment with hisat2, hisat2 must be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://ccb.jhu.edu/software/hisat2/index.shtml\" rel=\"nofollow\"\u003e\u003cem\u003ehisat2\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition, indexes files \u003cem\u003e.ht2\u003c/em\u003e must be downloaded from generated from \u003ca href=\"https://ccb.jhu.edu/software/hisat2/index.shtml\" rel=\"nofollow\"\u003e\u003cem\u003ehisat2\u003c/em\u003e\u003c/a\u003e, or generated from a reference fasta file (e.g., reference.fa) and a GTF annotation file (e.g., reference.gtf) using the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eextract_splice_sites.py reference.gtf \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e genome.ss\nextract_exons.py reference.gtf \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e genome.exon\nhisat2-build reference.fa --ss genome.ss --exon genome.exon genome_tran\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reads-trimming-at-splice-junctions\" class=\"anchor\" aria-hidden=\"true\" href=\"#reads-trimming-at-splice-junctions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReads trimming at splice junctions\u003c/h3\u003e\n\u003cp\u003eIn order to perform the optional reads trimming at splice junctions, GATK4 must be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://software.broadinstitute.org/gatk/guide/quickstart\" rel=\"nofollow\"\u003e\u003cem\u003eGATK\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition, index \u003cem\u003e.fai\u003c/em\u003e and dictionnary \u003cem\u003e.dict\u003c/em\u003e must be generated from the fasta reference genome using the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esamtools faidx ref.fa\njava -jar picard.jar CreateSequenceDictionary R= ref.fa O= ref.dict\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-base-quality-score-recalibration\" class=\"anchor\" aria-hidden=\"true\" href=\"#base-quality-score-recalibration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBase quality score recalibration\u003c/h3\u003e\n\u003cp\u003eIn order to perform the optional base quality score recalibration, several files are required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://software.broadinstitute.org/gatk/guide/quickstart\" rel=\"nofollow\"\u003e\u003cem\u003eGATK4\u003c/em\u003e\u003c/a\u003e must be in the PATH variable\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://software.broadinstitute.org/gatk/download/bundle\" rel=\"nofollow\"\u003eGATK bundle\u003c/a\u003e VCF files with lists of indels and SNVs (recommended: 1000 genomes indels, dbsnp VCF)\u003c/li\u003e\n\u003cli\u003ebed file with intervals to be considered\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing BAM or fastq files to be aligned\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_file\u003c/td\u003e\n\u003ctd\u003eInput tabulation-separated values file with columns SM (sample name), RG (read group), pair1 (first fastq pair file), and pair2 (second fastq pair file)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that there are two input methods: folder and file. Although the input folder method is the easiest because it does not require to create an input file with the right format, the input file mode is recommended in cases when a single sample has multiple paired files (e.g., due to multiplexed sequencing); in that case, users should have one line per pair of file and put a same SM identifier so that the workflow can group them into the same output bam file. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSM RG pair1 pair2\nsample1\t\tsample1_1.fq.gz\tsample1_2.fq.gz\nsample2\tRG1\tsample2_RG1_1.fq.gz\tsample2_RG1_2.fq.gz\nsample2\tRG2\tsample2_RG2_1.fq.gz\tsample2_RG2_2.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth align=\"right\"\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref_folder\u003c/td\u003e\n\u003ctd align=\"right\"\u003eref\u003c/td\u003e\n\u003ctd\u003eFolder with genome reference files (with index)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--gtf\u003c/td\u003e\n\u003ctd align=\"right\"\u003eHomo_sapiens.GRCh38.79.gtf\u003c/td\u003e\n\u003ctd\u003eAnnotation GTF file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bed\u003c/td\u003e\n\u003ctd align=\"right\"\u003egene.bed\u003c/td\u003e\n\u003ctd\u003ebed file with genes for RESeQC (interval list)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003eNumber of cpu used by bwa mem and sambamba\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu_gatk\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of CPUs for GATK processes (SJ trimming and BQSR)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu_trim\u003c/td\u003e\n\u003ctd\u003e15\u003c/td\u003e\n\u003ctd\u003eNumber of CPUs for reads trimming (cutadapt)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e50\u003c/td\u003e\n\u003ctd\u003eSize of memory used for mapping (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem_QC\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eSize of memory used for QC and cutadapt (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fastq_ext\u003c/td\u003e\n\u003ctd\u003efq.gz\u003c/td\u003e\n\u003ctd\u003eExtension of fastq files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--suffix1\u003c/td\u003e\n\u003ctd\u003e_1\u003c/td\u003e\n\u003ctd\u003eSuffix of fastq files 1 (first element of read files pair)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--suffix2\u003c/td\u003e\n\u003ctd\u003e_2\u003c/td\u003e\n\u003ctd\u003eSuffix of fastq files 2(second element of read files pair)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003e.\u003c/td\u003e\n\u003ctd\u003eOutput folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd\u003eref.fa\u003c/td\u003e\n\u003ctd\u003eReference fasta file (with index) for GATK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--snp_vcf\u003c/td\u003e\n\u003ctd\u003edbsnp.vcf\u003c/td\u003e\n\u003ctd\u003ePath to SNP VCF from GATK bundle\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--indel_vcf\u003c/td\u003e\n\u003ctd\u003eMills_100G_indels.vcf\u003c/td\u003e\n\u003ctd\u003ePath to indel VCF from GATK bundle\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--STAR_mapqUnique\u003c/td\u003e\n\u003ctd\u003e255\u003c/td\u003e\n\u003ctd\u003eSTAR default mapping quality for unique mappers\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--RG\u003c/td\u003e\n\u003ctd\u003ePL:ILLUMINA\u003c/td\u003e\n\u003ctd\u003eSamtools read group specification\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--stranded\u003c/td\u003e\n\u003ctd\u003eno\u003c/td\u003e\n\u003ctd\u003eStrand information for counting with htseq [no, yes, reverse]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hisat2_idx\u003c/td\u003e\n\u003ctd\u003egenome_tran\u003c/td\u003e\n\u003ctd\u003ehisat2 index file prefix\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--htseq_maxreads\u003c/td\u003e\n\u003ctd\u003e30000000\u003c/td\u003e\n\u003ctd\u003eMaximum number of reads taken into account by htseq-count\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--multiqc_config\u003c/td\u003e\n\u003ctd\u003enull\u003c/td\u003e\n\u003ctd\u003eConfig yaml file for multiqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eprint usage and optional parameters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cutadapt\u003c/td\u003e\n\u003ctd\u003eenable adapter and quality reads trimming before alignment\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--sjtrim\u003c/td\u003e\n\u003ctd\u003eenable reads trimming at splice junctions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hisat2\u003c/td\u003e\n\u003ctd\u003euse hisat2 instead of STAR for mapping\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--recalibration\u003c/td\u003e\n\u003ctd\u003eperform quality score recalibration (GATK)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eTo run the pipeline on a series of paired-end fastq files (with suffixes \u003cem\u003e_1\u003c/em\u003e and \u003cem\u003e_2\u003c/em\u003e) in folder \u003cem\u003efastq\u003c/em\u003e, a reference genome with indexes in folder \u003cem\u003eref_genome\u003c/em\u003e, an annotation file ref.gtf, and a bed file ref.bed, one can type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf -r v2.4 -profile singularity --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the pipeline using conda instead of singularity, replace \"-profile singularity\" by \"-profile conda\". To run with your own local software installation, just remove \"-profile singularity\".\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-single-end-fastq-mode\" class=\"anchor\" aria-hidden=\"true\" href=\"#single-end-fastq-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingle-end fastq mode\u003c/h3\u003e\n\u003cp\u003eDefault is adapted to paired-end libraries. To use single-end libraries as input, you must specify the option \"--suffix2 null\".\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf -r v2.4 -profile singularity --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --suffix2 null\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf using \"--input_file\", you must additionally set the values in column \"pair2\" to \"NO_fastq2\". For example the following file input.txt:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSM RG pair1 pair2\nsample1\t\tsample1.fq.gz\tNO_fastq2\nsample2\tRG1\tsample2_RG1.fq.gz\tNO_fastq2\nsample2\tRG2\tsample2_RG2.fq.gz\tNO_fastq2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecan be processed with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf -r v2.4 -profile singularity --input_file input.txt --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --suffix2 null\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-hisat2-for-mapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-hisat2-for-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse hisat2 for mapping\u003c/h3\u003e\n\u003cp\u003eTo use hisat2 instead of STAR for the reads mapping, you must add the \u003cstrong\u003e\u003cem\u003e--hisat2\u003c/em\u003e option\u003c/strong\u003e, specify the path to the folder containing the hisat2 index files (genome_tran.1.ht2 to genome_tran.8.ht2), as well as satisfy the requirements above mentionned. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --hisat2 --hisat2_idx genome_tran \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that parameter \u0027--hisat2_idx\u0027 is the prefix of the index files, not the entire path to .ht2 files.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-enable-reads-trimming-at-splice-junctions\" class=\"anchor\" aria-hidden=\"true\" href=\"#enable-reads-trimming-at-splice-junctions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnable reads trimming at splice junctions\u003c/h3\u003e\n\u003cp\u003eTo use the reads trimming at splice junctions step, you must add the \u003cstrong\u003e\u003cem\u003e--sjtrim\u003c/em\u003e option\u003c/strong\u003e as well as satisfy the requirements above mentionned. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --sjtrim\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-enable-base-quality-score-recalibration\" class=\"anchor\" aria-hidden=\"true\" href=\"#enable-base-quality-score-recalibration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnable Base Quality Score Recalibration\u003c/h3\u003e\n\u003cp\u003eTo use the base quality score recalibration step, you must add the \u003cstrong\u003e\u003cem\u003e--recalibration\u003c/em\u003e option\u003c/strong\u003e, specify the path to the known snps and indels from the GATK bundle, as well as satisfy the requirements above mentionned. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --recalibration --snp_vcf GATK_bundle/dbsnp_146.hg38.vcf.gz --indel_vcf GATK_bundle/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eBAM/file.bam\u003c/td\u003e\n\u003ctd\u003eBAM files of alignments or realignments\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBAM/file.bam.bai\u003c/td\u003e\n\u003ctd\u003eBAI files of alignments or realignments\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBAM/STAR.file.Chimeric.SJ.out.junction\u003c/td\u003e\n\u003ctd\u003eSTAR chimeric junction output\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBAM/STAR.file.SJ.out.tab\u003c/td\u003e\n\u003ctd\u003eSTAR junction tab output\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecounts/file_count.txt\u003c/td\u003e\n\u003ctd\u003ehtseq-count output file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/multiqc_pretrim_report.html\u003c/td\u003e\n\u003ctd\u003emultiqc report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/multiqc_pretrim_report_data\u003c/td\u003e\n\u003ctd\u003efolder with data used to compute multiqc report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/multiqc_posttrim_report.html\u003c/td\u003e\n\u003ctd\u003emultiqc report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/multiqc_posttrim_report_data\u003c/td\u003e\n\u003ctd\u003efolder with data used to compute multiqc report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/adapter_trimming/file_{12}.fq.gz_trimming_report.txt\u003c/td\u003e\n\u003ctd\u003etrim_galore report\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/adapter_trimming/file_{12}\u003cem\u003eval\u003c/em\u003e{12}_fastqc.zip\u003c/td\u003e\n\u003ctd\u003eFastQC report after trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/alignment/STAR.file.Log.final.out, STAR.file.Log.out, STAR.file.Log.progress.out\u003c/td\u003e\n\u003ctd\u003eSTAR logs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/bam/file_readdist.txt, file_clipping_profile*, file_jun_stauration*\u003c/td\u003e\n\u003ctd\u003eRSeQC reports\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/fastq/file_{12}_pretrim_fastqc.zip\u003c/td\u003e\n\u003ctd\u003eFastQC report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe output_folder directory contains three subfolders: BAM, counts, and QC\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-hidden=\"true\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-default-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-default-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith default options\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/RNAseq-nf/blob/dev/dag_STAR.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag_STAR.png\" alt=\"DAG STAR\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-option---hisat2\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-option---hisat2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith option --hisat2\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/RNAseq-nf/blob/dev/dag_hisat2.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag_hisat2.png\" alt=\"DAG hisat2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-options---sjtrim-and---recalibration\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-options---sjtrim-and---recalibration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith options --sjtrim and --recalibration\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/RNAseq-nf/blob/dev/dag_STAR_sjtrim_recal.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag_STAR_sjtrim_recal.png\" alt=\"DAG STAR_sjtrim_recal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@fellows.iarc.fr\"\u003eAlcalaN@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNoemie Leblay\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:LeblayN@students.iarc.fr\"\u003eLeblayN@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eTester\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAlexis Robitaille\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:RobitailleA@students.iarc.fr\"\u003eRobitailleA@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eTester\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", + "stargazers_count": 21, + "subscribers_count": 6, "topics": [ - "dicom", - "ocr", - "pixel-scrubbing", - "scrubber", - "deid", - "deidentification" - ], - "updated_at": 1702563654.0 - }, - { - "data_format": 2, - "description": null, - "filenames": [ - "scripts/Singularity.def" + "nextflow", + "rna-seq", + "ngs", + "pipeline" ], - "full_name": "Garden-AI/garden", - "latest_release": "2.2.8", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\ud83c\udf31 Garden: FAIR AI/ML Model Publishing Framework\u003c/h1\u003e\u003ca id=\"user-content--garden-fair-aiml-model-publishing-framework\" class=\"anchor\" aria-label=\"Permalink: \ud83c\udf31 Garden: FAIR AI/ML Model Publishing Framework\" href=\"#-garden-fair-aiml-model-publishing-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209892\u0026amp;HistoricalAwards=false\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/feda11d23fe72028c0b0bd0a18795af859a5ce3895675d9e4846a40a52991dac/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e53462d323230393839322d626c7565\" alt=\"NSF-2209892\" data-canonical-src=\"https://img.shields.io/badge/NSF-2209892-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/garden-ai\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33da481681fafc9f5512a8f522ffd492fef7d2570fc1969f6e50cb05b81924a3/68747470733a2f2f62616467652e667572792e696f2f70792f67617264656e2d61692e737667\" alt=\"PyPI\" data-canonical-src=\"https://badge.fury.io/py/garden-ai.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/Garden-AI/garden/actions/workflows/pypi.yaml\"\u003e\u003cimg src=\"https://github.com/Garden-AI/garden/actions/workflows/pypi.yaml/badge.svg\" alt=\"Tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/Garden-AI/garden/actions/workflows/ci.yaml\"\u003e\u003cimg src=\"https://github.com/Garden-AI/garden/actions/workflows/ci.yaml/badge.svg\" alt=\"tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/Garden-AI/garden\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/205f6f0835fb5998b4e2ad43f686d36833b6c35e9554edd82f51ea5a5d2d9fb9/68747470733a2f2f636f6465636f762e696f2f6769746875622f47617264656e2d41492f67617264656e2f67726170682f62616467652e7376673f746f6b656e3d5759494e414746305334\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/github/Garden-AI/garden/graph/badge.svg?token=WYINAGF0S4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAt a Glance:\u003c/h2\u003e\u003ca id=\"user-content-at-a-glance\" class=\"anchor\" aria-label=\"Permalink: At a Glance:\" href=\"#at-a-glance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eEasy Model Publishing\u003c/strong\u003e: Publish pre-trained AI/ML models from a notebook with just a few commands\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReproducible Environments\u003c/strong\u003e: Use containers to ensure consistent execution across different systems\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRemote Execution\u003c/strong\u003e: Run your models (or others) remotely on HPC resources seamlessly\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDiscoverable Collections\u003c/strong\u003e: Organize models into \"Gardens\" for easy discovery and comparison\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMetadata Management\u003c/strong\u003e: Capture and manage metadata of related datasets, papers, or code repositories for better searchability\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhy Garden?\u003c/h2\u003e\u003ca id=\"user-content-why-garden\" class=\"anchor\" aria-label=\"Permalink: Why Garden?\" href=\"#why-garden\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGarden addresses key challenges faced by academic researchers in discovering, reproducing, and running AI/ML models:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eReproducibility\u003c/strong\u003e: Garden eliminates environment inconsistencies by containerizing models, ensuring they run consistently across different systems.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDiscoverability\u003c/strong\u003e: With curated \"Gardens\" of models, researchers can easily find, compare, and curate relevant models for their work.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAccessibility\u003c/strong\u003e: Garden simplifies the process of running models on diverse computing resources, from local machines to HPC clusters, via Globus Compute integration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTime-saving\u003c/strong\u003e: By handling environment management and system-specific quirks, Garden significantly reduces the time researchers spend on setup and configuration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCollaboration\u003c/strong\u003e: FAIR principles (Findable, Accessible, Interoperable, Reusable) and standardized publishing make it easier for researchers to share their work and build upon others\u0027 contributions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eGarden aims to let researchers focus on their science, not on the intricacies of software environments and computing infrastructure.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat\u0027s a Garden?\u003c/h3\u003e\u003ca id=\"user-content-whats-a-garden\" class=\"anchor\" aria-label=\"Permalink: What\u0027s a Garden?\" href=\"#whats-a-garden\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA \"Garden\" is a citable collection of published pre-trained AI/ML models, called \"Entrypoints\".\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOk, What\u0027s an Entrypoint?\u003c/h3\u003e\u003ca id=\"user-content-ok-whats-an-entrypoint\" class=\"anchor\" aria-label=\"Permalink: Ok, What\u0027s an Entrypoint?\" href=\"#ok-whats-an-entrypoint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn \"Entrypoint\" is just a python function you define in a regular jupyter notebook which typically invokes one or more of your pre-trained models.\u003c/p\u003e\n\u003cp\u003eWhen you give us that notebook, we \"freeze it in amber\" by containerizing it (along with any environment dependencies) and give you a citable DOI in return.\u003c/p\u003e\n\u003cp\u003eNow, anyone with the DOI can easily invoke that exact function, in the exact same environment, on any remote compute resources they have access to (via \u003ca href=\"https://www.globus.org/compute\" rel=\"nofollow\"\u003eGlobus Compute\u003c/a\u003e):\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/user-attachments/assets/3cdae3bf-9add-4523-84c8-5a227cb3c0f8\"\u003e\u003cimg width=\"916\" alt=\"remote-inference-screenshot\" src=\"https://github.com/user-attachments/assets/3cdae3bf-9add-4523-84c8-5a227cb3c0f8\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the garden CLI:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epipx install garden-ai\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet up Docker on your system (required for local development and testing)\u003c/p\u003e\n\u003cp\u003eWe recommend installing \u003ca href=\"https://www.docker.com/products/docker-desktop/\" rel=\"nofollow\"\u003eDocker Desktop\u003c/a\u003e for most users.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStart a notebook in an isolated environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egarden-ai notebook start my_model.ipynb --base-image=3.10-sklearn\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDefine a function invoking your model in the notebook and publish it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egarden-ai notebook publish my_model.ipynb\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor a more detailed walkthrough, check out our \u003ca href=\"https://garden-ai.readthedocs.io/en/latest/user_guide/tutorial/\" rel=\"nofollow\"\u003e15-minute tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more documentation, including installation instructions, tutorials, and API references, see our \u003ca href=\"https://garden-ai.readthedocs.io/\" rel=\"nofollow\"\u003elatest docs\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe welcome contributions from the community! Please see our \u003ca href=\"https://garden-ai.readthedocs.io/en/latest/developer_guide/contributing/\" rel=\"nofollow\"\u003eContributing Guide\u003c/a\u003e for more information on how to get started.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was supported by the National Science Foundation under NSF Award Number: 2209892 \"Frameworks: Garden: A FAIR Framework for Publishing and Applying AI Models for Translational Research in Science, Engineering, Education, and Industry\".\u003c/p\u003e\n", - "stargazers_count": 21, - "subscribers_count": 7, - "topics": [], - "updated_at": 1732141575.0 + "updated_at": 1658424915.0 }, { "data_format": 2, - "description": "The ProteoWizard Library and Tools are a set of modular and extensible open-source, cross-platform tools and software libraries that facilitate proteomics data analysis, developed by the Proteowizard Team at http://proteowizard.sourceforge.net/. This repository contains the Docker image to convert from many vendor raw data formats to mzML via Linux WINE.", + "description": "benchmarking and containerization of tools for analysis of complex non-clinical metagenomes.", "filenames": [ - "Singularity" + "container_images/fastqc/Singularityfile" ], - "full_name": "phnmnl/container-pwiz", + "full_name": "dahak-metagenomics/dahak", "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"pwiz-logo.png\"\u003e\u003cimg src=\"pwiz-logo.png\" alt=\"Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProteowizard msconvert\u003c/h1\u003e\u003ca id=\"user-content-proteowizard-msconvert\" class=\"anchor\" aria-label=\"Permalink: Proteowizard msconvert\" href=\"#proteowizard-msconvert\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVersion: 3.0.18205\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThis container is unmaintained\u003c/h2\u003e\u003ca id=\"user-content-this-container-is-unmaintained\" class=\"anchor\" aria-label=\"Permalink: This container is unmaintained\" href=\"#this-container-is-unmaintained\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease head over to \u003ca href=\"https://github.com/ProteoWizard/container\"\u003ehttps://github.com/ProteoWizard/container\u003c/a\u003e for the new upstream home.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShort Description\u003c/h2\u003e\u003ca id=\"user-content-short-description\" class=\"anchor\" aria-label=\"Permalink: Short Description\" href=\"#short-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConversion of mass spectrometry vendor formats to mzML.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease note that there is now a container by the Proteowizard team\nas part of their continuous integration pipeline\nwhich should be used instead of this \u003ccode\u003ephnmnl/container-pwiz\u003c/code\u003e:\n\u003ca href=\"https://hub.docker.com/r/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe first step in a metabolomics data processing workflow with Open\nSource tools is the conversion to an open raw data format like\n\u003ca href=\"https://github.com/HUPO-PSI/mzML/\"\u003emzML\u003c/a\u003e. One of the main routes to mzML-formatted data is using Open Source converter\nmsconvert developed by the Proteowizard team (Chambers et al. 2012),\nwhich is one of the reference implementations for mzML. It can convert\nto mzML from Sciex, Bruker, Thermo, Agilent, Shimadzu, Waters\nand also the earlier file formats like mzData or mzXML.\nAlthough Proteowizard was initially targeting LC/MS data, it can also readily\nconvert GC/MS data for example from the Waters GCT Premier or Agilent instruments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKey features\u003c/h2\u003e\u003ca id=\"user-content-key-features\" class=\"anchor\" aria-label=\"Permalink: Key features\" href=\"#key-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMS raw data conversion\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFunctionality\u003c/h2\u003e\u003ca id=\"user-content-functionality\" class=\"anchor\" aria-label=\"Permalink: Functionality\" href=\"#functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApproaches\u003c/h2\u003e\u003ca id=\"user-content-approaches\" class=\"anchor\" aria-label=\"Permalink: Approaches\" href=\"#approaches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMetabolomics\u003c/li\u003e\n\u003cli\u003eLipidomics\u003c/li\u003e\n\u003cli\u003eGlycomics\u003c/li\u003e\n\u003cli\u003eProteomics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstrument Data Types\u003c/h2\u003e\u003ca id=\"user-content-instrument-data-types\" class=\"anchor\" aria-label=\"Permalink: Instrument Data Types\" href=\"#instrument-data-types\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMS\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScreenshots\u003c/h2\u003e\u003ca id=\"user-content-screenshots\" class=\"anchor\" aria-label=\"Permalink: Screenshots\" href=\"#screenshots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"MSconvertGUI.png\"\u003e\u003cimg src=\"MSconvertGUI.png\" alt=\"screenshot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTool Authors\u003c/h2\u003e\u003ca id=\"user-content-tool-authors\" class=\"anchor\" aria-label=\"Permalink: Tool Authors\" href=\"#tool-authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSeveral hundred labs around the world are using ProteoWizard within their development processes and computational workflows. We\u0027d like to thank the many users who have contributed feedback to the project. We also thank the TPP team for their ongoing support.\u003c/li\u003e\n\u003cli\u003eSee \u003ca href=\"http://proteowizard.sourceforge.net/team.html\" rel=\"nofollow\"\u003ehttp://proteowizard.sourceforge.net/team.html\u003c/a\u003e for the full list of contributors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer Contributors\u003c/h2\u003e\u003ca id=\"user-content-container-contributors\" class=\"anchor\" aria-label=\"Permalink: Container Contributors\" href=\"#container-contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sneumann\"\u003eSteffen Neumann\u003c/a\u003e (IPB Halle)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meier-rene\"\u003eRene Meier\u003c/a\u003e (IPB Halle)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pcm32\"\u003ePablo Moreno\u003c/a\u003e (EMBL-EBI)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pkrog\"\u003ePierrick Roger\u003c/a\u003e (CAE)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWebsite\u003c/h2\u003e\u003ca id=\"user-content-website\" class=\"anchor\" aria-label=\"Permalink: Website\" href=\"#website\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://proteowizard.sourceforge.net/\" rel=\"nofollow\"\u003ehttp://proteowizard.sourceforge.net/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGit Repository\u003c/h2\u003e\u003ca id=\"user-content-git-repository\" class=\"anchor\" aria-label=\"Permalink: Git Repository\" href=\"#git-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/phnmnl/container-pwiz.git\"\u003ehttps://github.com/phnmnl/container-pwiz.git\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe conversion can be started with e.g.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run -v $PWD:/data:rw phnmnl/phnmnl/pwiz-i-agree-to-the-vendor-licenses:latest /data/neg-MM8_1-A,1_01_376.d -o /data/ --mzML\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe currently tested vendor formats are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emzXML: \u003ccode\u003edocker run -it -v $PWD:/data phnmnl/pwiz-i-agree-to-the-vendor-licenses:latest threonine_i2_e35_pH_tree.mzXML\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBruker .d: \u003ccode\u003edocker run -it -v $PWD:/data phnmnl/phnmnl/pwiz-i-agree-to-the-vendor-licenses:latest neg-MM8_1-A,1_01_376.d\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo run the MSconvertGUI as shown in the above screenshot, you have to enable X11 access on the client machine, and pass the X11 information to the container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $HOME/.Xauthority:/root/.Xauthority:r -v /tmp/.X11-unix:/tmp/.X11-unix:rw -v $HOME:/data:rw phnmnl/pwiz-i-agree-to-the-vendor-licenses wine MSconvertGUI\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGalaxy usage\u003c/h2\u003e\u003ca id=\"user-content-galaxy-usage\" class=\"anchor\" aria-label=\"Permalink: Galaxy usage\" href=\"#galaxy-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA rudimentary Galaxy node description is included as \u003ccode\u003emsconvert.xml\u003c/code\u003e,\nit was obtained from the \u003ccode\u003emsconvert.ctd\u003c/code\u003e using\n\u003ccode\u003epython CTD2Galaxy/generator.py -i /vol/phenomenal/vmis/docker-pwiz/msconvert.ctd -m sample_files/macros.xml -o /vol/phenomenal/vmis/docker-pwiz/msconvert.xml\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild instructions\u003c/h2\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-label=\"Permalink: Build instructions\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease note that for licensing reasons we can not include all required\nfiles in this repository. Upon container building, the Proteowizard files\nwill be downloaded from \u003ca href=\"http://proteowizard.sourceforge.net/downloads.shtml\" rel=\"nofollow\"\u003ehttp://proteowizard.sourceforge.net/downloads.shtml\u003c/a\u003e and included\nin the created container. By building this container, you agree\nto all the vendor licenses that are shown at the above download links,\nand also included in the container and Dockerfile repository. To build, please use\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker build --tag=\"phnmnl/pwiz-i-agree-to-the-vendor-licenses:latest\" .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAlso note that the build is known to fail with Docker-1.9, make sure to use Docker-1.10 or above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePublications\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, Gatto L,\nFischer B, Pratt B, Egertson J, Hoff K, Kessner D, Tasman N, Shulman N, Frewen B,\nBaker TA, Brusniak MY, Paulse C, Creasy D, Flashner L, Kani K, Moulding C,\nSeymour SL, Nuwaysir LM, Lefebvre B, Kuhlmann F, Roark J, Rainer P, Detlev S,\nHemenway T, Huhmer A, Langridge J, Connolly B, Chadick T, Holly K, Eckels J,\nDeutsch EW, Moritz RL, Katz JE, Agus DB, MacCoss M, Tabb DL, Mallick P. A\ncross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012\nOct;30(10):918-20. doi: 10.1038/nbt.2377. PubMed PMID: 23051804; PubMed Central\nPMCID: PMC3471674.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicensing: APACHE LICENSE\u003c/h2\u003e\u003ca id=\"user-content-licensing-apache-license\" class=\"anchor\" aria-label=\"Permalink: Licensing: APACHE LICENSE\" href=\"#licensing-apache-license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see LICENSES/LICENSE, this Apache License Covers Core ProteoWizard Tools and Library. This software does, however, depend on other software libraries which place further restrictions on its use and redistribution, see below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eADDENDUM TO APACHE LICENSE\u003c/h3\u003e\u003ca id=\"user-content-addendum-to-apache-license\" class=\"anchor\" aria-label=\"Permalink: ADDENDUM TO APACHE LICENSE\" href=\"#addendum-to-apache-license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo the best of our ability we deliver this software to you under the Apache 2.0 License listed below (the source code is available in the ProteoWizard project). This software does, however, depend on other software libraries which place further restrictions on its use and redistribution. By accepting the license terms for this software, you agree to comply with the restrictions imposed on you by the\n\u003ca href=\"LICENSES/VENDORLICENSES.html\"\u003elicense agreements of the software libraries\u003c/a\u003e\non which it depends:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAB Sciex WIFF Reader Library\u003c/li\u003e\n\u003cli\u003eAgilent Mass Hunter Data Access Component Library\u003c/li\u003e\n\u003cli\u003eBruker CompassXtract\u003c/li\u003e\n\u003cli\u003eShimadzu SFCS\u003c/li\u003e\n\u003cli\u003eThermo-Scientific MSFileReader Library\u003c/li\u003e\n\u003cli\u003eWaters Raw Data Access Component Library\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNOTE: If you do not plan to redistribute this software yourself, then you are the \"end-user\" in the above agreements.\u003c/p\u003e\n", + "readme": "\u003cp\u003eSee \u003ca href=\"docs/index.md\"\u003edocs/index.md\u003c/a\u003e for documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDahak\u003c/h1\u003e\u003ca id=\"user-content-dahak\" class=\"anchor\" aria-label=\"Permalink: Dahak\" href=\"#dahak\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDahak is a software suite that integrates state-of-the-art open source tools\nfor metagenomic analyses. Tools in the dahak software suite will perform\nvarious steps in metagenomic analysis workflows including data pre-processing,\nmetagenome assembly, taxonomic and functional classification, genome binning,\nand gene assignment. We aim to deliver the analytical framework as a robust and\nreliable containerized workflow system, which will be free from dependency,\ninstallation, and execution problems typically associated with other\nopen-source bioinformatics solutions. This will maximize the transparency, data\nprovenance (i.e., the process of tracing the origins of data and its movement\nthrough the workflow), and reproducibility.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBenchmarking Data\u003c/h2\u003e\u003ca id=\"user-content-benchmarking-data\" class=\"anchor\" aria-label=\"Permalink: Benchmarking Data\" href=\"#benchmarking-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor purposes of benchmarking this project will use the following datasets:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDataset\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eShakya complete\u003c/td\u003e\n\u003ctd\u003eComplete metagenomic dataset from Shakya et al., 2013\u003csup\u003e*\u003c/sup\u003e containing bacterial and archaeal genomes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShakya subset 50\u003c/td\u003e\n\u003ctd\u003e50 percent of the reads from Shakya complete\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShakya subset 25\u003c/td\u003e\n\u003ctd\u003e25 percent of the reads from Shakya complete\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShakya subset 10\u003c/td\u003e\n\u003ctd\u003e10 percent of the reads from Shakya complete\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3665634/\" rel=\"nofollow\"\u003eShakya, M., C. Quince, J. H. Campbell, Z. K. Yang, C. W. Schadt and M. Podar (2013). \"Comparative metagenomic and rRNA microbial diversity characterization using archaeal and bacterial synthetic communities.\" Environ Microbiol 15(6): 1882-1899.\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements and Installation\u003c/h2\u003e\u003ca id=\"user-content-requirements-and-installation\" class=\"anchor\" aria-label=\"Permalink: Requirements and Installation\" href=\"#requirements-and-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDahak is not a standalone program, but rather a collection of workflows\nthat are defined in Snakemake files. These workflows utilize Bioconda,\nBiocontainers, and Docker/Singularity containerization technologies to\ninstall and run software for different tasks.\u003c/p\u003e\n\u003cp\u003eThe following software is required to run Dahak workflows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eREQUIRED:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3\u003c/li\u003e\n\u003cli\u003eSnakemake\u003c/li\u003e\n\u003cli\u003eConda\u003c/li\u003e\n\u003cli\u003eSingularity or Docker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTARGET PLATFORM:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(Required) Singularity \u0026gt;= 2.4 (does not require sudo access) or Docker\n(requires sudo access)\u003c/li\u003e\n\u003cli\u003e(Optional) Sun Grid Compute Engine\u003c/li\u003e\n\u003cli\u003e(Optional) Ubuntu 16.04 (Xenial)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\"installing.md\"\u003eInstalling\u003c/a\u003e page for detailed instructions\non installing each of the required components listed above,\nincluding Singularity and Docker.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"quickstart.md\"\u003eQuickstart\u003c/a\u003e page for instructions on getting\nstarted running dahak workflows with Snakemake.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflows\u003c/h2\u003e\u003ca id=\"user-content-workflows\" class=\"anchor\" aria-label=\"Permalink: Workflows\" href=\"#workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDahak provides a set of workflow components that all fit together to perform\nvarious useful tasks.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"running_workflows.md\"\u003eRunning Workflows\u003c/a\u003e page for some background\non how to run workflows using singularity and snakemake.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"quickstart.md\"\u003eQuickstart\u003c/a\u003e guide if you just want to get\nup and running with workflows.\u003c/p\u003e\n\u003cp\u003eOur \u003cstrong\u003etarget compute system\u003c/strong\u003e is a generic cluster running Sun Grid Engine\nin an HPC environment; all Snakemake files are written for this target\nsystem.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWalkthroughs\u003c/h3\u003e\u003ca id=\"user-content-walkthroughs\" class=\"anchor\" aria-label=\"Permalink: Walkthroughs\" href=\"#walkthroughs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach workflow contains a walkthrough, which is a step-by-step guide\nof shell commands to run to execute each step of the workflow.\nThese workflows use docker and require sudo access. While they are\nuseful \"by hand\" guides to the workflows, they cannot be scaled,\nso they are provided primarily for instructional purposes.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"readfilt_walkthru.md\"\u003eRead Filtering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"taxclass_walkthru.md\"\u003eTaxonomic Classification\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"assembly_walkthru.md\"\u003eAssembly\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eMetagenomic Comparison\u003c/li\u003e\n\u003cli\u003eVariant Calling\u003c/li\u003e\n\u003cli\u003eFunctional Inference\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters and Configuration\u003c/h2\u003e\u003ca id=\"user-content-parameters-and-configuration\" class=\"anchor\" aria-label=\"Permalink: Parameters and Configuration\" href=\"#parameters-and-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"config.md\"\u003eParameters and Configuration\u003c/a\u003e page for details about\ncontrolling how each workflow operates, and how to use parameter presets.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContributing\u003c/h1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease read\n\u003ca href=\"https://github.com/dahak-metagenomics/dahak/blob/master/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e\nfor details on our code of conduct and the process for submitting pull requests to us.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePhillip Brooks\u003csup\u003e1\u003c/sup\u003e, Charles Reid\u003csup\u003e1\u003c/sup\u003e, Bruce Budowle\u003csup\u003e2\u003c/sup\u003e, Chris Grahlmann\u003csup\u003e3\u003c/sup\u003e, Stephanie L. Guertin\u003csup\u003e3\u003c/sup\u003e, F. Curtis Hewitt\u003csup\u003e3\u003c/sup\u003e, Alexander F. Koeppel\u003csup\u003e4\u003c/sup\u003e, Oana I. Lungu\u003csup\u003e3\u003c/sup\u003e, Krista L. Ternus\u003csup\u003e3\u003c/sup\u003e, Stephen D. Turner\u003csup\u003e4,\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e, C. Titus Brown\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSchool of Veterinary Medicine, University of California Davis, Davis, CA, United States of America\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eInstitute of Applied Genetics, Department of Molecular and Medical Genetics, University of North Texas Health Science Center, Fort Worth, Texas, United States of America\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eSignature Science, LLC, Austin, Texas, United States of America\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDepartment of Public Health Sciences, University of Virginia, Charlottesville, VA, United States of America\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eBioinformatics Core, University of Virginia School of Medicine, Charlottesville, VA, United States of America\u003c/p\u003e\n\u003cp\u003eSee also the list of \u003ca href=\"https://github.com/dahak-metagenomics/dahak/graphs/contributors\"\u003econtributors\u003c/a\u003e who participated in this project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the BSD 3-Clause License - see the\n\u003ca href=\"https://github.com/dahak-metagenomics/dahak/blob/master/LICENSE\"\u003eLICENSE\u003c/a\u003e file\nfor details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://bioconda.github.io\" rel=\"nofollow\"\u003eBioconda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eHat tip to anyone whose code was used\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 21, - "subscribers_count": 22, + "subscribers_count": 9, "topics": [], - "updated_at": 1729252335.0 + "updated_at": 1689623036.0 }, { "data_format": 2, - "description": "Utils and modules for Speech Language and Multimodal processing using pytorch and pytorch lightning", + "description": "An ensemble method to recover corrupted FASTQ files, drop or fix pesky lines, remove unpaired reads, and settle reads interleaving.", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "georgepar/slp", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eslp\u003c/h1\u003e\u003ca id=\"user-content-slp\" class=\"anchor\" aria-label=\"Permalink: slp\" href=\"#slp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/georgepar/slp/actions/workflows/ci.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/georgepar/slp/actions/workflows/ci.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/georgepar/slp/actions/workflows/docs.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/georgepar/slp/actions/workflows/docs.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://codeclimate.com/github/georgepar/slp/maintainability\" alt=\"Maintainability\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/f76fd65cd2efaf89a763298fcee9a20b5e18921527df4f363c4524eb0b959d81/68747470733a2f2f6170692e636f6465636c696d6174652e636f6d2f76312f6261646765732f64336164393732396164333061613135383733372f6d61696e7461696e6162696c697479\" data-canonical-src=\"https://api.codeclimate.com/v1/badges/d3ad9729ad30aa158737/maintainability\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://choosealicense.com/licenses/mit/\" alt=\"License: MIT\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://img.shields.io/pypi/pyversions/slp\" rel=\"nofollow\"\u003e\n \u003cimg alt=\"Python Version\" src=\"https://camo.githubusercontent.com/186996bdca2e037c39cf32efd8e84fb108208d88bbd318b86f7542b605118f57/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f736c70\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/slp\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://black.readthedocs.io/en/stable/\" alt=\"Code Style: Black\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/5bf9e9fa18966df7cb5fac7715bef6b72df15e01a6efa9d616c83f9fcb527fe2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e737667\" data-canonical-src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRepo:\u003c/strong\u003e \u003ca href=\"https://github.com/georgepar/slp\"\u003ehttps://github.com/georgepar/slp\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDocumentation:\u003c/strong\u003e \u003ca href=\"https://georgepar.github.io/slp/latest/\" rel=\"nofollow\"\u003ehttps://georgepar.github.io/slp/latest/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eslp is a framework for fast and reproducible development of multimodal models, with emphasis on\nNLP models.\u003c/p\u003e\n\u003cp\u003eIt started as a collection of scripts and code I wrote / collected during my PhD and it evolves\naccordingly.\u003c/p\u003e\n\u003cp\u003eAs such, the framework is opinionated and it follows a convention over configuration approach.\u003c/p\u003e\n\u003cp\u003eA heavy emphasis is put on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnforcing best practices and reproducibility of experiments\u003c/li\u003e\n\u003cli\u003eMaking common things fast at the top-level and not having to go through extensive configuration options\u003c/li\u003e\n\u003cli\u003eRemaining extendable. Extensions and modules for more use cases should be easy to add\u003c/li\u003e\n\u003cli\u003eOut of the box extensive logging and experiment management\u003c/li\u003e\n\u003cli\u003eSeparating dirty / scratch code (at the script level) for quick changes and clean / polished code at the library level\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis is currently in alpha release under active development, so things may break and new features\nwill be added.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe use \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePytorch\u003c/a\u003e (1.7) and the following libraries\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch-lightning.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ePytorch Lightning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/transformers/\" rel=\"nofollow\"\u003ehuggingface/transformers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://wandb.ai/\" rel=\"nofollow\"\u003eWandb\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePython 3.8\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use slp as an external library by installing from PyPI with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install slp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr you can clone it from github\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:georgepar/slp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe use \u003ca href=\"https://python-poetry.org/\" rel=\"nofollow\"\u003epoetry\u003c/a\u003e for dependency management\u003c/p\u003e\n\u003cp\u003eWhen you clone the repo run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install poetry\npoetry install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand a clean environment with all the dependencies will be created.\nYou can access it with \u003ccode\u003epoetry shell\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Wandb logging is enabled by default. You can either\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate an account and run \u003ccode\u003ewandb login\u003c/code\u003e when you clone the repo in a new machine to store the results in the online managed environment\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003ewandb offline\u003c/code\u003e when you clone the repo to disable remote sync or use the \u003ccode\u003e--offline\u003c/code\u003e command\nline argument in your scripts\u003c/li\u003e\n\u003cli\u003eUse one of their self-hosted solutions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate a new project based on slp\u003c/h2\u003e\u003ca id=\"user-content-create-a-new-project-based-on-slp\" class=\"anchor\" aria-label=\"Permalink: Create a new project based on slp\" href=\"#create-a-new-project-based-on-slp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use the template at \u003ca href=\"https://github.com/georgepar/cookiecutter-pytorch-slp\"\u003ehttps://github.com/georgepar/cookiecutter-pytorch-slp\u003c/a\u003e\nto create a new project based on slp\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install cookiecutter poetry\ncookiecutter gh:georgepar/cookiecutter-pytorch-slp\n# Follow the interactive configuration and a new folder with the project name you provided will appear\ncd $PROJECT_NAME\npoetry install # Installs slp and all other dependencies\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd you are good to go. Follow the instructions in the README of the new project you created. Happy coding\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou are welcome to open issues / PRs with improvements and bug fixes.\u003c/p\u003e\n\u003cp\u003eSince this is mostly a personal project based around workflows and practices that work for me, I don\u0027t guarantee I will accept every change, but I\u0027m always open to discussion.\u003c/p\u003e\n\u003cp\u003eIf you are going to contribute, please use the pre-commit hooks under \u003ccode\u003ehooks\u003c/code\u003e, otherwise the PR will not go through the CI. And never, ever touch \u003ccode\u003erequirements.txt\u003c/code\u003e by hand, it will automatically be exported from \u003ccode\u003epoetry\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecat \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u0026lt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003eEOT\u003c/span\u003e \u0026gt;\u0026gt; .git/hooks/pre-commit\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e#!/usr/bin/env bash\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003ebash hooks/export-requirements-txt\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003ebash hooks/checks\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-k\"\u003eEOT\u003c/span\u003e\u003c/span\u003e\n\nchmod +x .git/hooks/pre-commit \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Keep an up-to-date requirements.txt and run Linting, typechecking and tests\u003c/span\u003e\n\nln -s \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/hooks/commit-msg .git/hooks/commit-msg \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Sign-off your commit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCite\u003c/h2\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-label=\"Permalink: Cite\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use this code for your research, please include the following citation\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@ONLINE {,\n author = \"Georgios Paraskevopoulos\",\n title = \"slp\",\n year = \"2020\",\n url = \"https://github.com/georgepar/slp\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRoadmap\u003c/h2\u003e\u003ca id=\"user-content-roadmap\" class=\"anchor\" aria-label=\"Permalink: Roadmap\" href=\"#roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOptuna integration for hyperparameter tuning\u003c/li\u003e\n\u003cli\u003eAdd dataloaders for popular multimodal datasets\u003c/li\u003e\n\u003cli\u003eAdd multimodal architectures\u003c/li\u003e\n\u003cli\u003eAdd RIM, DNC and Kanerva machine implementations\u003c/li\u003e\n\u003cli\u003eWrite unit tests\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "mazzalab/fastqwiper", + "latest_release": "2024-colorama-ruleorder", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFastqWiper\u003c/h1\u003e\u003ca id=\"user-content-fastqwiper\" class=\"anchor\" aria-label=\"Permalink: FastqWiper\" href=\"#fastqwiper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/mazzalab/fastqwiper/actions/workflows/buildall_and_publish.yml\"\u003e\u003cimg src=\"https://github.com/mazzalab/fastqwiper/actions/workflows/buildall_and_publish.yml/badge.svg\" alt=\"Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://codecov.io/gh/mazzalab/fastqwiper\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a60e0498b235eb8394dbf16728f68a9bb137b876ddd4941c1a825091d4d51d9f/68747470733a2f2f636f6465636f762e696f2f67682f6d617a7a616c61622f666173747177697065722f67726170682f62616467652e7376673f746f6b656e3d3556344151544b363139\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/mazzalab/fastqwiper/graph/badge.svg?token=5V4AQTK619\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/mazzalab/fastqwiper/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ac95c4a29813db1e7f77eb12a91cef70c29dce14b373bfade8fddb494737bf3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d7261772f6d617a7a616c61622f66617374717769706572\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues-raw/mazzalab/fastqwiper\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/bfxcss/fastqwiper\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa36ecb28f429f3e7700e8f51fde11671a806674584732f49c24a4650d031597/68747470733a2f2f616e61636f6e64612e6f72672f6266786373732f666173747177697065722f6261646765732f76657273696f6e2e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/bfxcss/fastqwiper/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca 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href=\"https://anaconda.org/bfxcss/fastqwiper\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/16df64405e9e9dd37f9b11ea6b123b0e5265a5a13954ea71cf3feb0bfff1c0fd/68747470733a2f2f616e61636f6e64612e6f72672f6266786373732f666173747177697065722f6261646765732f646f776e6c6f6164732e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/bfxcss/fastqwiper/badges/downloads.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://badge.fury.io/py/fastqwiper\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad43a0384c058408307b20a0ca3b5405dbaefc38be3b94a08f2daba2de283aa2/68747470733a2f2f62616467652e667572792e696f2f70792f666173747177697065722e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/fastqwiper.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://pypi.python.org/pypi/fastqwiper/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7a6bd740e5ed8aeae12a8009eb0f94369731ab97c6bd6170df1ddb483a92ede9/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f666173747177697065722e737667\" alt=\"PyPI pyversions\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/fastqwiper.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5c81110a2686ee76915bb69dc8d1ea1b443abe4abc8f6ab5bad28404e5c71921/68747470733a2f2f696d672e736869656c64732e696f2f707970692f646d2f66617374717769706572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c81110a2686ee76915bb69dc8d1ea1b443abe4abc8f6ab5bad28404e5c71921/68747470733a2f2f696d672e736869656c64732e696f2f707970692f646d2f66617374717769706572\" alt=\"PyPI - Downloads\" data-canonical-src=\"https://img.shields.io/pypi/dm/fastqwiper\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/mazzalab/fastqwiper\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1f341bb4c267b5a2190542c60f1e84f3f48bf35638e34f5be756e42b3c6c88da/68747470733a2f2f62616467656e2e6e65742f62616467652f69636f6e2f646f636b65723f69636f6e3d646f636b6572266c6162656c\" alt=\"Docker\" data-canonical-src=\"https://badgen.net/badge/icon/docker?icon=docker\u0026amp;label\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c6b964472c81392a79f44f4f684214dfafd92aa2ce8a8722fa72a90fe17a1f02/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d617a7a616c61622f66617374717769706572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6b964472c81392a79f44f4f684214dfafd92aa2ce8a8722fa72a90fe17a1f02/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d617a7a616c61622f66617374717769706572\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/mazzalab/fastqwiper\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eFastqWiper\u003c/code\u003e is a Snakemake-enabled application that wipes out bad reads from broken FASTQ files. Additionally, the available and pre-designed Snakemake \u003ca href=\"https://github.com/mazzalab/fastqwiper/tree/main/pipeline\"\u003eworkflows\u003c/a\u003e allows \u003cstrong\u003erecovering\u003c/strong\u003e corrupted \u003ccode\u003efastq.gz\u003c/code\u003e, \u003cstrong\u003edropping\u003c/strong\u003e or \u003cstrong\u003efixing\u003c/strong\u003e pesky lines, \u003cstrong\u003eremoving\u003c/strong\u003e unpaired reads, and \u003cstrong\u003esettling\u003c/strong\u003e reads interleaving.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCompatibility: Python \u22653.7, \u0026lt;3.13\u003c/li\u003e\n\u003cli\u003eOS: Windows, Linux, Mac OS (Snakemake workflows only through Docker for Windows)\u003c/li\u003e\n\u003cli\u003eContributions: \u003ca href=\"bioinformatics@css-mendel.it\"\u003ebioinformatics@css-mendel.it\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDocker: \u003ca href=\"https://hub.docker.com/r/mazzalab/fastqwiper\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/mazzalab/fastqwiper\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity: \u003ca href=\"https://cloud.sylabs.io/library/mazzalab/fastqwiper/fastqwiper.sif\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/mazzalab/fastqwiper/fastqwiper.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBug report: \u003ca href=\"https://github.com/mazzalab/fastqwiper/issues\"\u003ehttps://github.com/mazzalab/fastqwiper/issues\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUSAGE\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: USAGE\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u003cstrong\u003eCase 1.\u003c/strong\u003e\u003c/code\u003eYou have one or a couple (R1\u0026amp;R2) of \u003cstrong\u003ecomputer readable\u003c/strong\u003e (meaning that the .gz files can be successfully decompressed or that the .fa/.fasta files can be viewed from the beginning to the EOF) FASTQ files which contain pesky, unformatted, uncompliant lines: Use \u003cem\u003eFastWiper\u003c/em\u003e to clean them;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u003cstrong\u003eCase 2.\u003c/strong\u003e\u003c/code\u003eYou have one or a couple (R1\u0026amp;R2) of \u003cstrong\u003ecomputer readable\u003c/strong\u003e FASTQ files that you want to drop unpaired reads from or fix reads interleaving: Use the FastqWiper\u0027s \u003cem\u003eSnakemake workflows\u003c/em\u003e;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u003cstrong\u003eCase 3.\u003c/strong\u003e\u003c/code\u003eYou have one \u003ccode\u003efastq.gz\u003c/code\u003e file or a couple (R1\u0026amp;R2) of \u003ccode\u003efastq.gz\u003c/code\u003e files which are corrupted (\u003cstrong\u003eunreadable\u003c/strong\u003e, meaning that the .gz files cannot be successfully decompressed) and you want to recover healthy reads and reformat them: Use the FastqWiper\u0027s \u003cem\u003eSnakemake workflows\u003c/em\u003e;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003eCase 1\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-case-1\" class=\"anchor\" aria-label=\"Permalink: Case 1\" href=\"#case-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis requires you to install FastqWiper and therefore not to use \u003cem\u003eworkflows\u003c/em\u003e. You can do it for all OSs:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUse Conda\u003c/h4\u003e\u003ca id=\"user-content-use-conda\" class=\"anchor\" aria-label=\"Permalink: Use Conda\" href=\"#use-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n fastqwiper python=3.11\nconda activate fastqwiper\nconda install -c bfxcss -c conda-forge fastqwiper\n\nfastqwiper --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eHint: for an healthier experience, use\u003c/em\u003e \u003cstrong\u003emamba\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUse Pypi\u003c/h4\u003e\u003ca id=\"user-content-use-pypi\" class=\"anchor\" aria-label=\"Permalink: Use Pypi\" href=\"#use-pypi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epip install fastqwiper\n\nfastqwiper --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cbr\u003e\n\u003cp\u003e\u003ccode\u003efastqwiper \u0026lt;options\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoptions:\n -i, --fastq_in TEXT The input FASTQ file to be cleaned [required]\n -o, --fastq_out TEXT The wiped FASTQ file [required]\n -l, --log_frequency INTEGER The number of reads you want to print a status message. Default: 500000\n -f, --log_out TEXT The file name of the final quality report summary. Print on the screen if not specified\n -a, --alphabet Allowed character in the SEQ line. Default: ACGTN\n -h, --help Show this message and exit.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt accepts strictly \u003cstrong\u003ereadable\u003c/strong\u003e \u003ccode\u003e*.fastq\u003c/code\u003e or \u003ccode\u003e*.fastq.gz\u003c/code\u003e files in input.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ccode\u003eCase 2\u003c/code\u003e \u0026amp; \u003ccode\u003eCase 3\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-case-2--case-3\" class=\"anchor\" aria-label=\"Permalink: Case 2 \u0026amp; Case 3\" href=\"#case-2--case-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are \u003cb\u003eQUICK\u003c/b\u003e and a \u003cb\u003eSLOW\u003c/b\u003e methods to configure \u003ccode\u003eFastqWiper\u003c/code\u003e\u0027s workflows.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOne quick way (Docker)\u003c/h4\u003e\u003ca id=\"user-content-one-quick-way-docker\" class=\"anchor\" aria-label=\"Permalink: One quick way (Docker)\" href=\"#one-quick-way-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePull the Docker image from DockerHub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ccode\u003edocker pull mazzalab/fastqwiper\u003c/code\u003e\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOnce downloaded the image, type:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCMD: \u003ccode\u003edocker run --rm -ti --name fastqwiper -v \"YOUR_LOCAL_PATH_TO_DATA_FOLDER:/fastqwiper/data\" mazzalab/fastqwiper paired 8 sample 50000000 33 ACGTN 500000\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAnother quick way (Singularity)\u003c/h4\u003e\u003ca id=\"user-content-another-quick-way-singularity\" class=\"anchor\" aria-label=\"Permalink: Another quick way (Singularity)\" href=\"#another-quick-way-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePull the Singularity image from the Cloud Library:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull library://mazzalab/fastqwiper/fastqwiper.sif\u003c/code\u003e\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOnce downloaded the image (e.g., fastqwiper.sif_2024.1.89.sif), type:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCMD \u003ccode\u003esingularity run --bind YOUR_LOCAL_PATH_TO_DATA_FOLDER:/fastqwiper/data --writable-tmpfs fastqwiper.sif_2024.1.89.sif paired 8 sample 50000000 33 ACGTN 500000\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you want to bind the \u003ccode\u003e.singularity\u003c/code\u003e cache folder and the \u003ccode\u003elogs\u003c/code\u003e folder, you can omit \u003ccode\u003e--writable-tmpfs\u003c/code\u003e, create the folders \u003ccode\u003e.singularity\u003c/code\u003e and \u003ccode\u003elogs\u003c/code\u003e (\u003ccode\u003emkdir .singularity logs\u003c/code\u003e) on the host system, and use this command instead:\u003c/p\u003e\n\u003cp\u003eCMD: \u003ccode\u003esingularity run --bind YOUR_LOCAL_PATH_TO_DATA_FOLDER/:/fastqwiper/data --bind YOUR_LOCAL_PATH_TO_.SNAKEMAKE_FOLDER/:/fastqwiper/.snakemake --bind YOUR_LOCAL_PATH_TO_LOGS_FOLDER/:/fastqwiper/logs fastqwiper.sif_2024.1.89.sif paired 8 sample 50000000 33 ACGTN\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor both \u003cstrong\u003eDocker\u003c/strong\u003e and \u003cstrong\u003eSingularity\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYOUR_LOCAL_PATH_TO_DATA_FOLDER\u003c/code\u003e is the path of the folder where the fastq.gz files to be wiped are located;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epaired\u003c/code\u003e triggers the cleaning of R1 and R2. Alternatively, \u003ccode\u003esingle\u003c/code\u003e will trigger the wipe of individual FASTQ files;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e8\u003c/code\u003e is the number of your choice of computing cores to be spawned;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esample\u003c/code\u003e is part of the names of the FASTQ files to be wiped. \u003cb\u003eBe aware\u003c/b\u003e that: for \u003cb\u003epaired-end\u003c/b\u003e files (e.g., \"sample_R1.fastq.gz\" and \"sample_R2.fastq.gz\"), your files must finish with \u003ccode\u003e_R1.fastq.gz\u003c/code\u003e and \u003ccode\u003e_R2.fastq.gz\u003c/code\u003e. Therefore, the argument to pass is everything before these texts: \u003ccode\u003esample\u003c/code\u003e in this case. For \u003cb\u003esingle end\u003c/b\u003e/individual files (e.g., \"excerpt_R1_001.fastq.gz\"), your file must end with the string \u003ccode\u003e.fastq.gz\u003c/code\u003e; the preceding text, i.e., \"excerpt_R1_001\" in this case, will be the text to be passed to the command as an argument.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e50000000\u003c/code\u003e (optional) is the number of rows-per-chunk (used when cores\u0026gt;1. It must be a number multiple of 4). Increasing this number too much would reduce the parallelism advantage. Decreasing this number too much would increase the number of chunks more than the number of available cpus, making parallelism unefficient. Choose this number wisely depending on the total number of reads in your starting file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e33\u003c/code\u003e (optional) is the ASCII offset (33=Sanger, 64=old Solexa)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eACGTN\u003c/code\u003e (optional) is the allowed alphabet in the SEQ line of the FASTQ file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e500000\u003c/code\u003e (optional) is the log frequency (# reads)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003eThe slow way (Linux \u0026amp; Mac OS)\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-the-slow-way-linux--mac-os\" class=\"anchor\" aria-label=\"Permalink: The slow way (Linux \u0026amp; Mac OS)\" href=\"#the-slow-way-linux--mac-os\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo enable the use of preconfigured \u003ca href=\"https://github.com/mazzalab/fastqwiper/tree/main/pipeline\"\u003epipelines\u003c/a\u003e, you need to install \u003cstrong\u003eSnakemake\u003c/strong\u003e. The recommended way to install Snakemake is via Conda, because it enables \u003cstrong\u003eSnakemake\u003c/strong\u003e to \u003ca href=\"https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#integrated-package-management\" rel=\"nofollow\"\u003ehandle software dependencies of your workflow\u003c/a\u003e.\nHowever, the default conda solver is slow and often hangs. Therefore, we recommend installing \u003ca href=\"https://github.com/mamba-org/mamba\"\u003eMamba\u003c/a\u003e as a drop-in replacement via\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ conda install -c conda-forge mamba\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eif you have anaconda/miniconda already installed, or directly installing \u003ccode\u003eMambaforge\u003c/code\u003e as described \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThen, create and activate a clean environment as above:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emamba create -n fastqwiper python=3.11\nmamba activate fastqwiper\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, install the Snakemake dependency:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mamba install -c bioconda snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsage\u003c/h4\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the FastqWiper repository in a folder of your choice and enter it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/mazzalab/fastqwiper.git\ncd fastqwiper\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt contains, in particular, a folder \u003ccode\u003edata\u003c/code\u003e containing the fastq files to be processed, a folder \u003ccode\u003epipeline\u003c/code\u003e containing the released pipelines and a folder \u003ccode\u003efastq_wiper\u003c/code\u003e with the source files of \u003ccode\u003eFastqWiper\u003c/code\u003e. \u003cbr\u003e\nInput files to be processed must be copied into the \u003cstrong\u003edata\u003c/strong\u003e folder.\u003c/p\u003e\n\u003cp\u003eCurrently, to run the \u003ccode\u003eFastqWiper\u003c/code\u003e pipelines, the following packages need to be installed manually:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erequired packages:\u003c/h3\u003e\u003ca id=\"user-content-required-packages\" class=\"anchor\" aria-label=\"Permalink: required packages:\" href=\"#required-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/arenn/gzrt\"\u003egzrt\u003c/a\u003e (Linux build from source \u003ca href=\"https://github.com/arenn/gzrt/blob/master/README.build\"\u003einstructions\u003c/a\u003e, Ubuntu install \u003ca href=\"https://howtoinstall.co/en/gzrt\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e, Mac OS install \u003ca href=\"https://formulae.brew.sh/formula/gzrt\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://jgi.doe.gov/data-and-tools/software-tools/bbtools/\" rel=\"nofollow\"\u003eBBTools\u003c/a\u003e (install \u003ca href=\"https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/installation-guide/\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eIf installed from source, \u003ccode\u003egzrt\u003c/code\u003e scripts need to be put on PATH. \u003ccode\u003ebbmap\u003c/code\u003e must be installed in the root folder of FastqWiper, as the image below\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"assets/hierarchy.png\"\u003e\u003cimg src=\"assets/hierarchy.png\" alt=\"FastqWiper folder yierarchy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommands:\u003c/h3\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands:\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the fastq files you want to fix in the \u003ccode\u003edata\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e\u003cstrong\u003eN.b.\u003c/strong\u003e: In all commands above, you will pass the name of the sample to be analyzed to the workflow through the config argument: \u003ccode\u003esample_name\u003c/code\u003e. Remember that your fastq files\u0027 names must finish with \u003ccode\u003e_R1.fastq.gz\u003c/code\u003e and \u003ccode\u003e_R2.fastq.gz\u003c/code\u003e, for paired fastq files, and with \u003ccode\u003e.fastq.gz\u003c/code\u003e, for individual fastq files, and, therefore, the text to be assigned to the variable \u003ccode\u003esample_name\u003c/code\u003e must be everything before them. E.g., if your files are \u003ccode\u003emy_sample_R1.fastq.gz\u003c/code\u003e and \u003ccode\u003emy_sample_R2.fastq.gz\u003c/code\u003e, then \u003ccode\u003e--config sample_name=my_sample\u003c/code\u003e.\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePaired-end files\u003c/h4\u003e\u003ca id=\"user-content-paired-end-files\" class=\"anchor\" aria-label=\"Permalink: Paired-end files\" href=\"#paired-end-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGet a dry run\u003c/strong\u003e of a pipeline (e.g., \u003ccode\u003efix_wipe_pairs_reads_sequential.smk\u003c/code\u003e):\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample qin=33 alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_pairs_reads_sequential.smk --use-conda --cores 4 -np\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGenerate the planned DAG\u003c/strong\u003e:\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample qin=33 alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_pairs_reads_sequential.smk --dag | dot -Tpdf \u0026gt; dag.pdf\u003c/code\u003e\u003cbr\u003e \u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mazzalab/fastqwiper/blob/main/pipeline/fix_wipe_pairs_reads.png?raw=true\"\u003e\u003cimg src=\"https://github.com/mazzalab/fastqwiper/raw/main/pipeline/fix_wipe_pairs_reads.png?raw=true\" width=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRun the pipeline\u003c/strong\u003e (n.b., during the first execution, Snakemake will download and install some required remote packages and may take longer). The number of computing cores can be tuned accordingly:\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_pairs_reads_sequential.smk --use-conda --cores 2\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFixed files will be copied in the \u003ccode\u003edata\u003c/code\u003e folder and will be suffixed with the string \u003ccode\u003e_fixed_wiped_paired_interleaving\u003c/code\u003e.\nWe remind that the \u003ccode\u003efix_wipe_pairs_reads_sequential.smk\u003c/code\u003e and \u003ccode\u003efix_wipe_pairs_reads_parallel.smk\u003c/code\u003e pipelines perform the following actions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexecute \u003ccode\u003egzrt\u003c/code\u003e on corrupted fastq.gz files (i.e., that cannot be unzipped because of errors) and recover readable reads;\u003c/li\u003e\n\u003cli\u003eexecute \u003ccode\u003eFastqWiper\u003c/code\u003e on recovered reads to make them compliant with the FASTQ format (source: \u003ca href=\"https://en.wikipedia.org/wiki/FASTQ_format\" rel=\"nofollow\"\u003eWipipedia\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eexecute \u003ccode\u003eTrimmomatic\u003c/code\u003e on wiped reads to remove residual unpaired reads\u003c/li\u003e\n\u003cli\u003eexecute \u003ccode\u003eBBmap (repair.sh)\u003c/code\u003e on paired reads to fix the correct interleaving and sort fastq files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingle-end files\u003c/h4\u003e\u003ca id=\"user-content-single-end-files\" class=\"anchor\" aria-label=\"Permalink: Single-end files\" href=\"#single-end-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003efix_wipe_single_reads_parallel.smk\u003c/code\u003e and \u003ccode\u003efix_wipe_single_reads_sequential.smk\u003c/code\u003e will not execute \u003ccode\u003etrimmomatic\u003c/code\u003e and BBmap\u0027s \u003ccode\u003erepair.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGet a dry run\u003c/strong\u003e of a pipeline (e.g., \u003ccode\u003efix_wipe_single_reads_sequential.smk\u003c/code\u003e):\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2 -np\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGenerate the planned DAG\u003c/strong\u003e:\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_single_reads_sequential.smk --dag | dot -Tpdf \u0026gt; dag.pdf\u003c/code\u003e\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mazzalab/fastqwiper/blob/main/pipeline/fix_wipe_single_reads.png?raw=true\"\u003e\u003cimg src=\"https://github.com/mazzalab/fastqwiper/raw/main/pipeline/fix_wipe_single_reads.png?raw=true\" width=\"200\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRun the pipeline\u003c/strong\u003e (n.b., The number of computing cores can be tuned accordingly):\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTommaso Mazza\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://twitter.com/irongraft\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7b7f5545e84dc5cd2f380455cc5f3fc5ed43458328bcf88782878011e4a41fb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f582d2532333030303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d58266c6f676f436f6c6f723d7768697465\" alt=\"X\" data-canonical-src=\"https://img.shields.io/badge/X-%23000000.svg?style=for-the-badge\u0026amp;logo=X\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://www.linkedin.com/in/tommasomazza/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/29ba59dbf61686238096822c7de916a9b41c40bf362b70e7f2c609551ce8f656/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c696e6b6564696e2d2532333030373742352e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d6c696e6b6564696e266c6f676f436f6c6f723d7768697465\" alt=\"LinkedIn\" data-canonical-src=\"https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge\u0026amp;logo=linkedin\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLaboratory of Bioinformatics\nFondazione IRCCS Casa Sollievo della Sofferenza\nViale Regina Margherita 261 - 00198 Roma IT\nTel: +39 06 44160526 - Fax: +39 06 44160548\nE-mail: \u003ca href=\"mailto:t.mazza@operapadrepio.it\"\u003et.mazza@operapadrepio.it\u003c/a\u003e\nWeb page: \u003ca href=\"http://www.css-mendel.it\" rel=\"nofollow\"\u003ehttp://www.css-mendel.it\u003c/a\u003e\nWeb page: \u003ca href=\"http://bioinformatics.css-mendel.it\" rel=\"nofollow\"\u003ehttp://bioinformatics.css-mendel.it\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 21, - "subscribers_count": 6, + "subscribers_count": 1, "topics": [ - "multimodal", - "multimodal-learning", - "multimodal-deep-learning", - "pytorch", - "pytorch-lightning", - "wandb", - "natural-language-processing" + "bioinformatics", + "corrupted", + "fastq", + "fix", + "ngs", + "recovery" ], - "updated_at": 1706707891.0 + "updated_at": 1723394980.0 }, { "data_format": 2, @@ -39152,74 +39219,53 @@ var data = }, { "data_format": 2, - "description": "An ensemble method to recover corrupted FASTQ files, drop or fix pesky lines, remove unpaired reads, and settle reads interleaving.", + "description": "Utils and modules for Speech Language and Multimodal processing using pytorch and pytorch lightning", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "mazzalab/fastqwiper", - "latest_release": "2024-colorama-ruleorder", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFastqWiper\u003c/h1\u003e\u003ca id=\"user-content-fastqwiper\" class=\"anchor\" aria-label=\"Permalink: FastqWiper\" href=\"#fastqwiper\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/mazzalab/fastqwiper/actions/workflows/buildall_and_publish.yml\"\u003e\u003cimg src=\"https://github.com/mazzalab/fastqwiper/actions/workflows/buildall_and_publish.yml/badge.svg\" alt=\"Build\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://codecov.io/gh/mazzalab/fastqwiper\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a60e0498b235eb8394dbf16728f68a9bb137b876ddd4941c1a825091d4d51d9f/68747470733a2f2f636f6465636f762e696f2f67682f6d617a7a616c61622f666173747177697065722f67726170682f62616467652e7376673f746f6b656e3d3556344151544b363139\" alt=\"codecov\" 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data-canonical-src=\"https://img.shields.io/pypi/dm/fastqwiper\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/mazzalab/fastqwiper\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1f341bb4c267b5a2190542c60f1e84f3f48bf35638e34f5be756e42b3c6c88da/68747470733a2f2f62616467656e2e6e65742f62616467652f69636f6e2f646f636b65723f69636f6e3d646f636b6572266c6162656c\" alt=\"Docker\" data-canonical-src=\"https://badgen.net/badge/icon/docker?icon=docker\u0026amp;label\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c6b964472c81392a79f44f4f684214dfafd92aa2ce8a8722fa72a90fe17a1f02/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d617a7a616c61622f66617374717769706572\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6b964472c81392a79f44f4f684214dfafd92aa2ce8a8722fa72a90fe17a1f02/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d617a7a616c61622f66617374717769706572\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/mazzalab/fastqwiper\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eFastqWiper\u003c/code\u003e is a Snakemake-enabled application that wipes out bad reads from broken FASTQ files. Additionally, the available and pre-designed Snakemake \u003ca href=\"https://github.com/mazzalab/fastqwiper/tree/main/pipeline\"\u003eworkflows\u003c/a\u003e allows \u003cstrong\u003erecovering\u003c/strong\u003e corrupted \u003ccode\u003efastq.gz\u003c/code\u003e, \u003cstrong\u003edropping\u003c/strong\u003e or \u003cstrong\u003efixing\u003c/strong\u003e pesky lines, \u003cstrong\u003eremoving\u003c/strong\u003e unpaired reads, and \u003cstrong\u003esettling\u003c/strong\u003e reads interleaving.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCompatibility: Python \u22653.7, \u0026lt;3.13\u003c/li\u003e\n\u003cli\u003eOS: Windows, Linux, Mac OS (Snakemake workflows only through Docker for Windows)\u003c/li\u003e\n\u003cli\u003eContributions: \u003ca href=\"bioinformatics@css-mendel.it\"\u003ebioinformatics@css-mendel.it\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eDocker: \u003ca href=\"https://hub.docker.com/r/mazzalab/fastqwiper\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/mazzalab/fastqwiper\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity: \u003ca href=\"https://cloud.sylabs.io/library/mazzalab/fastqwiper/fastqwiper.sif\" rel=\"nofollow\"\u003ehttps://cloud.sylabs.io/library/mazzalab/fastqwiper/fastqwiper.sif\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBug report: \u003ca href=\"https://github.com/mazzalab/fastqwiper/issues\"\u003ehttps://github.com/mazzalab/fastqwiper/issues\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUSAGE\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: USAGE\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e\u003cstrong\u003eCase 1.\u003c/strong\u003e\u003c/code\u003eYou have one or a couple (R1\u0026amp;R2) of \u003cstrong\u003ecomputer readable\u003c/strong\u003e (meaning that the .gz files can be successfully decompressed or that the .fa/.fasta files can be viewed from the beginning to the EOF) FASTQ files which contain pesky, unformatted, uncompliant lines: Use \u003cem\u003eFastWiper\u003c/em\u003e to clean them;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u003cstrong\u003eCase 2.\u003c/strong\u003e\u003c/code\u003eYou have one or a couple (R1\u0026amp;R2) of \u003cstrong\u003ecomputer readable\u003c/strong\u003e FASTQ files that you want to drop unpaired reads from or fix reads interleaving: Use the FastqWiper\u0027s \u003cem\u003eSnakemake workflows\u003c/em\u003e;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e\u003cstrong\u003eCase 3.\u003c/strong\u003e\u003c/code\u003eYou have one \u003ccode\u003efastq.gz\u003c/code\u003e file or a couple (R1\u0026amp;R2) of \u003ccode\u003efastq.gz\u003c/code\u003e files which are corrupted (\u003cstrong\u003eunreadable\u003c/strong\u003e, meaning that the .gz files cannot be successfully decompressed) and you want to recover healthy reads and reformat them: Use the FastqWiper\u0027s \u003cem\u003eSnakemake workflows\u003c/em\u003e;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003eCase 1\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-case-1\" class=\"anchor\" aria-label=\"Permalink: Case 1\" href=\"#case-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis requires you to install FastqWiper and therefore not to use \u003cem\u003eworkflows\u003c/em\u003e. You can do it for all OSs:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUse Conda\u003c/h4\u003e\u003ca id=\"user-content-use-conda\" class=\"anchor\" aria-label=\"Permalink: Use Conda\" href=\"#use-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n fastqwiper python=3.11\nconda activate fastqwiper\nconda install -c bfxcss -c conda-forge fastqwiper\n\nfastqwiper --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003eHint: for an healthier experience, use\u003c/em\u003e \u003cstrong\u003emamba\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUse Pypi\u003c/h4\u003e\u003ca id=\"user-content-use-pypi\" class=\"anchor\" aria-label=\"Permalink: Use Pypi\" href=\"#use-pypi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003epip install fastqwiper\n\nfastqwiper --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cbr\u003e\n\u003cp\u003e\u003ccode\u003efastqwiper \u0026lt;options\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eoptions:\n -i, --fastq_in TEXT The input FASTQ file to be cleaned [required]\n -o, --fastq_out TEXT The wiped FASTQ file [required]\n -l, --log_frequency INTEGER The number of reads you want to print a status message. Default: 500000\n -f, --log_out TEXT The file name of the final quality report summary. Print on the screen if not specified\n -a, --alphabet Allowed character in the SEQ line. Default: ACGTN\n -h, --help Show this message and exit.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt accepts strictly \u003cstrong\u003ereadable\u003c/strong\u003e \u003ccode\u003e*.fastq\u003c/code\u003e or \u003ccode\u003e*.fastq.gz\u003c/code\u003e files in input.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003ccode\u003eCase 2\u003c/code\u003e \u0026amp; \u003ccode\u003eCase 3\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-case-2--case-3\" class=\"anchor\" aria-label=\"Permalink: Case 2 \u0026amp; Case 3\" href=\"#case-2--case-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are \u003cb\u003eQUICK\u003c/b\u003e and a \u003cb\u003eSLOW\u003c/b\u003e methods to configure \u003ccode\u003eFastqWiper\u003c/code\u003e\u0027s workflows.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOne quick way (Docker)\u003c/h4\u003e\u003ca id=\"user-content-one-quick-way-docker\" class=\"anchor\" aria-label=\"Permalink: One quick way (Docker)\" href=\"#one-quick-way-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePull the Docker image from DockerHub:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ccode\u003edocker pull mazzalab/fastqwiper\u003c/code\u003e\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOnce downloaded the image, type:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCMD: \u003ccode\u003edocker run --rm -ti --name fastqwiper -v \"YOUR_LOCAL_PATH_TO_DATA_FOLDER:/fastqwiper/data\" mazzalab/fastqwiper paired 8 sample 50000000 33 ACGTN 500000\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eAnother quick way (Singularity)\u003c/h4\u003e\u003ca id=\"user-content-another-quick-way-singularity\" class=\"anchor\" aria-label=\"Permalink: Another quick way (Singularity)\" href=\"#another-quick-way-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003ePull the Singularity image from the Cloud Library:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ccode\u003esingularity pull library://mazzalab/fastqwiper/fastqwiper.sif\u003c/code\u003e\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOnce downloaded the image (e.g., fastqwiper.sif_2024.1.89.sif), type:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCMD \u003ccode\u003esingularity run --bind YOUR_LOCAL_PATH_TO_DATA_FOLDER:/fastqwiper/data --writable-tmpfs fastqwiper.sif_2024.1.89.sif paired 8 sample 50000000 33 ACGTN 500000\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIf you want to bind the \u003ccode\u003e.singularity\u003c/code\u003e cache folder and the \u003ccode\u003elogs\u003c/code\u003e folder, you can omit \u003ccode\u003e--writable-tmpfs\u003c/code\u003e, create the folders \u003ccode\u003e.singularity\u003c/code\u003e and \u003ccode\u003elogs\u003c/code\u003e (\u003ccode\u003emkdir .singularity logs\u003c/code\u003e) on the host system, and use this command instead:\u003c/p\u003e\n\u003cp\u003eCMD: \u003ccode\u003esingularity run --bind YOUR_LOCAL_PATH_TO_DATA_FOLDER/:/fastqwiper/data --bind YOUR_LOCAL_PATH_TO_.SNAKEMAKE_FOLDER/:/fastqwiper/.snakemake --bind YOUR_LOCAL_PATH_TO_LOGS_FOLDER/:/fastqwiper/logs fastqwiper.sif_2024.1.89.sif paired 8 sample 50000000 33 ACGTN\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor both \u003cstrong\u003eDocker\u003c/strong\u003e and \u003cstrong\u003eSingularity\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eYOUR_LOCAL_PATH_TO_DATA_FOLDER\u003c/code\u003e is the path of the folder where the fastq.gz files to be wiped are located;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epaired\u003c/code\u003e triggers the cleaning of R1 and R2. Alternatively, \u003ccode\u003esingle\u003c/code\u003e will trigger the wipe of individual FASTQ files;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e8\u003c/code\u003e is the number of your choice of computing cores to be spawned;\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esample\u003c/code\u003e is part of the names of the FASTQ files to be wiped. \u003cb\u003eBe aware\u003c/b\u003e that: for \u003cb\u003epaired-end\u003c/b\u003e files (e.g., \"sample_R1.fastq.gz\" and \"sample_R2.fastq.gz\"), your files must finish with \u003ccode\u003e_R1.fastq.gz\u003c/code\u003e and \u003ccode\u003e_R2.fastq.gz\u003c/code\u003e. Therefore, the argument to pass is everything before these texts: \u003ccode\u003esample\u003c/code\u003e in this case. For \u003cb\u003esingle end\u003c/b\u003e/individual files (e.g., \"excerpt_R1_001.fastq.gz\"), your file must end with the string \u003ccode\u003e.fastq.gz\u003c/code\u003e; the preceding text, i.e., \"excerpt_R1_001\" in this case, will be the text to be passed to the command as an argument.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e50000000\u003c/code\u003e (optional) is the number of rows-per-chunk (used when cores\u0026gt;1. It must be a number multiple of 4). Increasing this number too much would reduce the parallelism advantage. Decreasing this number too much would increase the number of chunks more than the number of available cpus, making parallelism unefficient. Choose this number wisely depending on the total number of reads in your starting file.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e33\u003c/code\u003e (optional) is the ASCII offset (33=Sanger, 64=old Solexa)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eACGTN\u003c/code\u003e (optional) is the allowed alphabet in the SEQ line of the FASTQ file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e500000\u003c/code\u003e (optional) is the log frequency (# reads)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ccode\u003eThe slow way (Linux \u0026amp; Mac OS)\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-the-slow-way-linux--mac-os\" class=\"anchor\" aria-label=\"Permalink: The slow way (Linux \u0026amp; Mac OS)\" href=\"#the-slow-way-linux--mac-os\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo enable the use of preconfigured \u003ca href=\"https://github.com/mazzalab/fastqwiper/tree/main/pipeline\"\u003epipelines\u003c/a\u003e, you need to install \u003cstrong\u003eSnakemake\u003c/strong\u003e. The recommended way to install Snakemake is via Conda, because it enables \u003cstrong\u003eSnakemake\u003c/strong\u003e to \u003ca href=\"https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#integrated-package-management\" rel=\"nofollow\"\u003ehandle software dependencies of your workflow\u003c/a\u003e.\nHowever, the default conda solver is slow and often hangs. Therefore, we recommend installing \u003ca href=\"https://github.com/mamba-org/mamba\"\u003eMamba\u003c/a\u003e as a drop-in replacement via\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ conda install -c conda-forge mamba\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eif you have anaconda/miniconda already installed, or directly installing \u003ccode\u003eMambaforge\u003c/code\u003e as described \u003ca href=\"https://github.com/conda-forge/miniforge#mambaforge\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThen, create and activate a clean environment as above:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emamba create -n fastqwiper python=3.11\nmamba activate fastqwiper\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFinally, install the Snakemake dependency:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mamba install -c bioconda snakemake\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eUsage\u003c/h4\u003e\u003ca id=\"user-content-usage-1\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eClone the FastqWiper repository in a folder of your choice and enter it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/mazzalab/fastqwiper.git\ncd fastqwiper\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIt contains, in particular, a folder \u003ccode\u003edata\u003c/code\u003e containing the fastq files to be processed, a folder \u003ccode\u003epipeline\u003c/code\u003e containing the released pipelines and a folder \u003ccode\u003efastq_wiper\u003c/code\u003e with the source files of \u003ccode\u003eFastqWiper\u003c/code\u003e. \u003cbr\u003e\nInput files to be processed must be copied into the \u003cstrong\u003edata\u003c/strong\u003e folder.\u003c/p\u003e\n\u003cp\u003eCurrently, to run the \u003ccode\u003eFastqWiper\u003c/code\u003e pipelines, the following packages need to be installed manually:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003erequired packages:\u003c/h3\u003e\u003ca id=\"user-content-required-packages\" class=\"anchor\" aria-label=\"Permalink: required packages:\" href=\"#required-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/arenn/gzrt\"\u003egzrt\u003c/a\u003e (Linux build from source \u003ca href=\"https://github.com/arenn/gzrt/blob/master/README.build\"\u003einstructions\u003c/a\u003e, Ubuntu install \u003ca href=\"https://howtoinstall.co/en/gzrt\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e, Mac OS install \u003ca href=\"https://formulae.brew.sh/formula/gzrt\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://jgi.doe.gov/data-and-tools/software-tools/bbtools/\" rel=\"nofollow\"\u003eBBTools\u003c/a\u003e (install \u003ca href=\"https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/installation-guide/\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eIf installed from source, \u003ccode\u003egzrt\u003c/code\u003e scripts need to be put on PATH. \u003ccode\u003ebbmap\u003c/code\u003e must be installed in the root folder of FastqWiper, as the image below\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"assets/hierarchy.png\"\u003e\u003cimg src=\"assets/hierarchy.png\" alt=\"FastqWiper folder yierarchy\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommands:\u003c/h3\u003e\u003ca id=\"user-content-commands\" class=\"anchor\" aria-label=\"Permalink: Commands:\" href=\"#commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy the fastq files you want to fix in the \u003ccode\u003edata\u003c/code\u003e folder.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e\u003cstrong\u003eN.b.\u003c/strong\u003e: In all commands above, you will pass the name of the sample to be analyzed to the workflow through the config argument: \u003ccode\u003esample_name\u003c/code\u003e. Remember that your fastq files\u0027 names must finish with \u003ccode\u003e_R1.fastq.gz\u003c/code\u003e and \u003ccode\u003e_R2.fastq.gz\u003c/code\u003e, for paired fastq files, and with \u003ccode\u003e.fastq.gz\u003c/code\u003e, for individual fastq files, and, therefore, the text to be assigned to the variable \u003ccode\u003esample_name\u003c/code\u003e must be everything before them. E.g., if your files are \u003ccode\u003emy_sample_R1.fastq.gz\u003c/code\u003e and \u003ccode\u003emy_sample_R2.fastq.gz\u003c/code\u003e, then \u003ccode\u003e--config sample_name=my_sample\u003c/code\u003e.\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePaired-end files\u003c/h4\u003e\u003ca id=\"user-content-paired-end-files\" class=\"anchor\" aria-label=\"Permalink: Paired-end files\" href=\"#paired-end-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGet a dry run\u003c/strong\u003e of a pipeline (e.g., \u003ccode\u003efix_wipe_pairs_reads_sequential.smk\u003c/code\u003e):\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample qin=33 alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_pairs_reads_sequential.smk --use-conda --cores 4 -np\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGenerate the planned DAG\u003c/strong\u003e:\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample qin=33 alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_pairs_reads_sequential.smk --dag | dot -Tpdf \u0026gt; dag.pdf\u003c/code\u003e\u003cbr\u003e \u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mazzalab/fastqwiper/blob/main/pipeline/fix_wipe_pairs_reads.png?raw=true\"\u003e\u003cimg src=\"https://github.com/mazzalab/fastqwiper/raw/main/pipeline/fix_wipe_pairs_reads.png?raw=true\" width=\"400\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRun the pipeline\u003c/strong\u003e (n.b., during the first execution, Snakemake will download and install some required remote packages and may take longer). The number of computing cores can be tuned accordingly:\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_pairs_reads_sequential.smk --use-conda --cores 2\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFixed files will be copied in the \u003ccode\u003edata\u003c/code\u003e folder and will be suffixed with the string \u003ccode\u003e_fixed_wiped_paired_interleaving\u003c/code\u003e.\nWe remind that the \u003ccode\u003efix_wipe_pairs_reads_sequential.smk\u003c/code\u003e and \u003ccode\u003efix_wipe_pairs_reads_parallel.smk\u003c/code\u003e pipelines perform the following actions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eexecute \u003ccode\u003egzrt\u003c/code\u003e on corrupted fastq.gz files (i.e., that cannot be unzipped because of errors) and recover readable reads;\u003c/li\u003e\n\u003cli\u003eexecute \u003ccode\u003eFastqWiper\u003c/code\u003e on recovered reads to make them compliant with the FASTQ format (source: \u003ca href=\"https://en.wikipedia.org/wiki/FASTQ_format\" rel=\"nofollow\"\u003eWipipedia\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eexecute \u003ccode\u003eTrimmomatic\u003c/code\u003e on wiped reads to remove residual unpaired reads\u003c/li\u003e\n\u003cli\u003eexecute \u003ccode\u003eBBmap (repair.sh)\u003c/code\u003e on paired reads to fix the correct interleaving and sort fastq files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingle-end files\u003c/h4\u003e\u003ca id=\"user-content-single-end-files\" class=\"anchor\" aria-label=\"Permalink: Single-end files\" href=\"#single-end-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003efix_wipe_single_reads_parallel.smk\u003c/code\u003e and \u003ccode\u003efix_wipe_single_reads_sequential.smk\u003c/code\u003e will not execute \u003ccode\u003etrimmomatic\u003c/code\u003e and BBmap\u0027s \u003ccode\u003erepair.sh\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGet a dry run\u003c/strong\u003e of a pipeline (e.g., \u003ccode\u003efix_wipe_single_reads_sequential.smk\u003c/code\u003e):\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2 -np\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGenerate the planned DAG\u003c/strong\u003e:\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_single_reads_sequential.smk --dag | dot -Tpdf \u0026gt; dag.pdf\u003c/code\u003e\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/mazzalab/fastqwiper/blob/main/pipeline/fix_wipe_single_reads.png?raw=true\"\u003e\u003cimg src=\"https://github.com/mazzalab/fastqwiper/raw/main/pipeline/fix_wipe_single_reads.png?raw=true\" width=\"200\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRun the pipeline\u003c/strong\u003e (n.b., The number of computing cores can be tuned accordingly):\u003cbr\u003e\n\u003ccode\u003esnakemake --config sample_name=my_sample alphabet=ACGTN log_freq=1000 -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAuthor\u003c/h1\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTommaso Mazza\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://twitter.com/irongraft\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7b7f5545e84dc5cd2f380455cc5f3fc5ed43458328bcf88782878011e4a41fb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f582d2532333030303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d58266c6f676f436f6c6f723d7768697465\" alt=\"X\" data-canonical-src=\"https://img.shields.io/badge/X-%23000000.svg?style=for-the-badge\u0026amp;logo=X\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://www.linkedin.com/in/tommasomazza/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/29ba59dbf61686238096822c7de916a9b41c40bf362b70e7f2c609551ce8f656/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c696e6b6564696e2d2532333030373742352e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d6c696e6b6564696e266c6f676f436f6c6f723d7768697465\" alt=\"LinkedIn\" data-canonical-src=\"https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge\u0026amp;logo=linkedin\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eLaboratory of Bioinformatics\nFondazione IRCCS Casa Sollievo della Sofferenza\nViale Regina Margherita 261 - 00198 Roma IT\nTel: +39 06 44160526 - Fax: +39 06 44160548\nE-mail: \u003ca href=\"mailto:t.mazza@operapadrepio.it\"\u003et.mazza@operapadrepio.it\u003c/a\u003e\nWeb page: \u003ca href=\"http://www.css-mendel.it\" rel=\"nofollow\"\u003ehttp://www.css-mendel.it\u003c/a\u003e\nWeb page: \u003ca href=\"http://bioinformatics.css-mendel.it\" rel=\"nofollow\"\u003ehttp://bioinformatics.css-mendel.it\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "georgepar/slp", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eslp\u003c/h1\u003e\u003ca id=\"user-content-slp\" class=\"anchor\" aria-label=\"Permalink: slp\" href=\"#slp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/georgepar/slp/actions/workflows/ci.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/georgepar/slp/actions/workflows/ci.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/georgepar/slp/actions/workflows/docs.yml/badge.svg\"\u003e\u003cimg src=\"https://github.com/georgepar/slp/actions/workflows/docs.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://codeclimate.com/github/georgepar/slp/maintainability\" alt=\"Maintainability\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/f76fd65cd2efaf89a763298fcee9a20b5e18921527df4f363c4524eb0b959d81/68747470733a2f2f6170692e636f6465636c696d6174652e636f6d2f76312f6261646765732f64336164393732396164333061613135383733372f6d61696e7461696e6162696c697479\" data-canonical-src=\"https://api.codeclimate.com/v1/badges/d3ad9729ad30aa158737/maintainability\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://choosealicense.com/licenses/mit/\" alt=\"License: MIT\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/44e7845c81a431dc740c9a7f76d0ea33e030e05d7a41d6164167ee435b17168f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d677265656e2e737667\" data-canonical-src=\"https://img.shields.io/badge/license-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://img.shields.io/pypi/pyversions/slp\" rel=\"nofollow\"\u003e\n \u003cimg alt=\"Python Version\" src=\"https://camo.githubusercontent.com/186996bdca2e037c39cf32efd8e84fb108208d88bbd318b86f7542b605118f57/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f736c70\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/slp\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://black.readthedocs.io/en/stable/\" alt=\"Code Style: Black\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/5bf9e9fa18966df7cb5fac7715bef6b72df15e01a6efa9d616c83f9fcb527fe2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e737667\" data-canonical-src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRepo:\u003c/strong\u003e \u003ca href=\"https://github.com/georgepar/slp\"\u003ehttps://github.com/georgepar/slp\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDocumentation:\u003c/strong\u003e \u003ca href=\"https://georgepar.github.io/slp/latest/\" rel=\"nofollow\"\u003ehttps://georgepar.github.io/slp/latest/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eslp is a framework for fast and reproducible development of multimodal models, with emphasis on\nNLP models.\u003c/p\u003e\n\u003cp\u003eIt started as a collection of scripts and code I wrote / collected during my PhD and it evolves\naccordingly.\u003c/p\u003e\n\u003cp\u003eAs such, the framework is opinionated and it follows a convention over configuration approach.\u003c/p\u003e\n\u003cp\u003eA heavy emphasis is put on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnforcing best practices and reproducibility of experiments\u003c/li\u003e\n\u003cli\u003eMaking common things fast at the top-level and not having to go through extensive configuration options\u003c/li\u003e\n\u003cli\u003eRemaining extendable. Extensions and modules for more use cases should be easy to add\u003c/li\u003e\n\u003cli\u003eOut of the box extensive logging and experiment management\u003c/li\u003e\n\u003cli\u003eSeparating dirty / scratch code (at the script level) for quick changes and clean / polished code at the library level\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis is currently in alpha release under active development, so things may break and new features\nwill be added.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe use \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePytorch\u003c/a\u003e (1.7) and the following libraries\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch-lightning.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ePytorch Lightning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/transformers/\" rel=\"nofollow\"\u003ehuggingface/transformers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://wandb.ai/\" rel=\"nofollow\"\u003eWandb\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePython 3.8\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use slp as an external library by installing from PyPI with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install slp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr you can clone it from github\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:georgepar/slp\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe use \u003ca href=\"https://python-poetry.org/\" rel=\"nofollow\"\u003epoetry\u003c/a\u003e for dependency management\u003c/p\u003e\n\u003cp\u003eWhen you clone the repo run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install poetry\npoetry install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand a clean environment with all the dependencies will be created.\nYou can access it with \u003ccode\u003epoetry shell\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Wandb logging is enabled by default. You can either\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate an account and run \u003ccode\u003ewandb login\u003c/code\u003e when you clone the repo in a new machine to store the results in the online managed environment\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003ewandb offline\u003c/code\u003e when you clone the repo to disable remote sync or use the \u003ccode\u003e--offline\u003c/code\u003e command\nline argument in your scripts\u003c/li\u003e\n\u003cli\u003eUse one of their self-hosted solutions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCreate a new project based on slp\u003c/h2\u003e\u003ca id=\"user-content-create-a-new-project-based-on-slp\" class=\"anchor\" aria-label=\"Permalink: Create a new project based on slp\" href=\"#create-a-new-project-based-on-slp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use the template at \u003ca href=\"https://github.com/georgepar/cookiecutter-pytorch-slp\"\u003ehttps://github.com/georgepar/cookiecutter-pytorch-slp\u003c/a\u003e\nto create a new project based on slp\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install cookiecutter poetry\ncookiecutter gh:georgepar/cookiecutter-pytorch-slp\n# Follow the interactive configuration and a new folder with the project name you provided will appear\ncd $PROJECT_NAME\npoetry install # Installs slp and all other dependencies\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd you are good to go. Follow the instructions in the README of the new project you created. Happy coding\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou are welcome to open issues / PRs with improvements and bug fixes.\u003c/p\u003e\n\u003cp\u003eSince this is mostly a personal project based around workflows and practices that work for me, I don\u0027t guarantee I will accept every change, but I\u0027m always open to discussion.\u003c/p\u003e\n\u003cp\u003eIf you are going to contribute, please use the pre-commit hooks under \u003ccode\u003ehooks\u003c/code\u003e, otherwise the PR will not go through the CI. And never, ever touch \u003ccode\u003erequirements.txt\u003c/code\u003e by hand, it will automatically be exported from \u003ccode\u003epoetry\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ecat \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-k\"\u003e\u0026lt;\u0026lt;\u003c/span\u003e\u003cspan class=\"pl-k\"\u003eEOT\u003c/span\u003e \u0026gt;\u0026gt; .git/hooks/pre-commit\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e#!/usr/bin/env bash\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003ebash hooks/export-requirements-txt\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003ebash hooks/checks\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-k\"\u003eEOT\u003c/span\u003e\u003c/span\u003e\n\nchmod +x .git/hooks/pre-commit \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Keep an up-to-date requirements.txt and run Linting, typechecking and tests\u003c/span\u003e\n\nln -s \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epwd\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e/hooks/commit-msg .git/hooks/commit-msg \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Sign-off your commit\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCite\u003c/h2\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-label=\"Permalink: Cite\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use this code for your research, please include the following citation\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@ONLINE {,\n author = \"Georgios Paraskevopoulos\",\n title = \"slp\",\n year = \"2020\",\n url = \"https://github.com/georgepar/slp\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRoadmap\u003c/h2\u003e\u003ca id=\"user-content-roadmap\" class=\"anchor\" aria-label=\"Permalink: Roadmap\" href=\"#roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eOptuna integration for hyperparameter tuning\u003c/li\u003e\n\u003cli\u003eAdd dataloaders for popular multimodal datasets\u003c/li\u003e\n\u003cli\u003eAdd multimodal architectures\u003c/li\u003e\n\u003cli\u003eAdd RIM, DNC and Kanerva machine implementations\u003c/li\u003e\n\u003cli\u003eWrite unit tests\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 21, - "subscribers_count": 1, + "subscribers_count": 6, "topics": [ - "bioinformatics", - "corrupted", - "fastq", - "fix", - "ngs", - "recovery" + "multimodal", + "multimodal-learning", + "multimodal-deep-learning", + "pytorch", + "pytorch-lightning", + "wandb", + "natural-language-processing" ], - "updated_at": 1723394980.0 + "updated_at": 1706707891.0 }, { "data_format": 2, - "description": "benchmarking and containerization of tools for analysis of complex non-clinical metagenomes.", + "description": "The ProteoWizard Library and Tools are a set of modular and extensible open-source, cross-platform tools and software libraries that facilitate proteomics data analysis, developed by the Proteowizard Team at http://proteowizard.sourceforge.net/. This repository contains the Docker image to convert from many vendor raw data formats to mzML via Linux WINE.", "filenames": [ - "container_images/fastqc/Singularityfile" + "Singularity" ], - "full_name": "dahak-metagenomics/dahak", + "full_name": "phnmnl/container-pwiz", "latest_release": null, - "readme": "\u003cp\u003eSee \u003ca href=\"docs/index.md\"\u003edocs/index.md\u003c/a\u003e for documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDahak\u003c/h1\u003e\u003ca id=\"user-content-dahak\" class=\"anchor\" aria-label=\"Permalink: Dahak\" href=\"#dahak\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDahak is a software suite that integrates state-of-the-art open source tools\nfor metagenomic analyses. Tools in the dahak software suite will perform\nvarious steps in metagenomic analysis workflows including data pre-processing,\nmetagenome assembly, taxonomic and functional classification, genome binning,\nand gene assignment. We aim to deliver the analytical framework as a robust and\nreliable containerized workflow system, which will be free from dependency,\ninstallation, and execution problems typically associated with other\nopen-source bioinformatics solutions. This will maximize the transparency, data\nprovenance (i.e., the process of tracing the origins of data and its movement\nthrough the workflow), and reproducibility.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBenchmarking Data\u003c/h2\u003e\u003ca id=\"user-content-benchmarking-data\" class=\"anchor\" aria-label=\"Permalink: Benchmarking Data\" href=\"#benchmarking-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor purposes of benchmarking this project will use the following datasets:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDataset\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eShakya complete\u003c/td\u003e\n\u003ctd\u003eComplete metagenomic dataset from Shakya et al., 2013\u003csup\u003e*\u003c/sup\u003e containing bacterial and archaeal genomes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShakya subset 50\u003c/td\u003e\n\u003ctd\u003e50 percent of the reads from Shakya complete\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShakya subset 25\u003c/td\u003e\n\u003ctd\u003e25 percent of the reads from Shakya complete\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShakya subset 10\u003c/td\u003e\n\u003ctd\u003e10 percent of the reads from Shakya complete\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3665634/\" rel=\"nofollow\"\u003eShakya, M., C. Quince, J. H. Campbell, Z. K. Yang, C. W. Schadt and M. Podar (2013). \"Comparative metagenomic and rRNA microbial diversity characterization using archaeal and bacterial synthetic communities.\" Environ Microbiol 15(6): 1882-1899.\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements and Installation\u003c/h2\u003e\u003ca id=\"user-content-requirements-and-installation\" class=\"anchor\" aria-label=\"Permalink: Requirements and Installation\" href=\"#requirements-and-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDahak is not a standalone program, but rather a collection of workflows\nthat are defined in Snakemake files. These workflows utilize Bioconda,\nBiocontainers, and Docker/Singularity containerization technologies to\ninstall and run software for different tasks.\u003c/p\u003e\n\u003cp\u003eThe following software is required to run Dahak workflows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eREQUIRED:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython 3\u003c/li\u003e\n\u003cli\u003eSnakemake\u003c/li\u003e\n\u003cli\u003eConda\u003c/li\u003e\n\u003cli\u003eSingularity or Docker\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTARGET PLATFORM:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e(Required) Singularity \u0026gt;= 2.4 (does not require sudo access) or Docker\n(requires sudo access)\u003c/li\u003e\n\u003cli\u003e(Optional) Sun Grid Compute Engine\u003c/li\u003e\n\u003cli\u003e(Optional) Ubuntu 16.04 (Xenial)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee the \u003ca href=\"installing.md\"\u003eInstalling\u003c/a\u003e page for detailed instructions\non installing each of the required components listed above,\nincluding Singularity and Docker.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"quickstart.md\"\u003eQuickstart\u003c/a\u003e page for instructions on getting\nstarted running dahak workflows with Snakemake.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWorkflows\u003c/h2\u003e\u003ca id=\"user-content-workflows\" class=\"anchor\" aria-label=\"Permalink: Workflows\" href=\"#workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDahak provides a set of workflow components that all fit together to perform\nvarious useful tasks.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"running_workflows.md\"\u003eRunning Workflows\u003c/a\u003e page for some background\non how to run workflows using singularity and snakemake.\u003c/p\u003e\n\u003cp\u003eSee the \u003ca href=\"quickstart.md\"\u003eQuickstart\u003c/a\u003e guide if you just want to get\nup and running with workflows.\u003c/p\u003e\n\u003cp\u003eOur \u003cstrong\u003etarget compute system\u003c/strong\u003e is a generic cluster running Sun Grid Engine\nin an HPC environment; all Snakemake files are written for this target\nsystem.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWalkthroughs\u003c/h3\u003e\u003ca id=\"user-content-walkthroughs\" class=\"anchor\" aria-label=\"Permalink: Walkthroughs\" href=\"#walkthroughs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach workflow contains a walkthrough, which is a step-by-step guide\nof shell commands to run to execute each step of the workflow.\nThese workflows use docker and require sudo access. While they are\nuseful \"by hand\" guides to the workflows, they cannot be scaled,\nso they are provided primarily for instructional purposes.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"readfilt_walkthru.md\"\u003eRead Filtering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"taxclass_walkthru.md\"\u003eTaxonomic Classification\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"assembly_walkthru.md\"\u003eAssembly\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eMetagenomic Comparison\u003c/li\u003e\n\u003cli\u003eVariant Calling\u003c/li\u003e\n\u003cli\u003eFunctional Inference\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters and Configuration\u003c/h2\u003e\u003ca id=\"user-content-parameters-and-configuration\" class=\"anchor\" aria-label=\"Permalink: Parameters and Configuration\" href=\"#parameters-and-configuration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"config.md\"\u003eParameters and Configuration\u003c/a\u003e page for details about\ncontrolling how each workflow operates, and how to use parameter presets.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContributing\u003c/h1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease read\n\u003ca href=\"https://github.com/dahak-metagenomics/dahak/blob/master/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e\nfor details on our code of conduct and the process for submitting pull requests to us.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePhillip Brooks\u003csup\u003e1\u003c/sup\u003e, Charles Reid\u003csup\u003e1\u003c/sup\u003e, Bruce Budowle\u003csup\u003e2\u003c/sup\u003e, Chris Grahlmann\u003csup\u003e3\u003c/sup\u003e, Stephanie L. Guertin\u003csup\u003e3\u003c/sup\u003e, F. Curtis Hewitt\u003csup\u003e3\u003c/sup\u003e, Alexander F. Koeppel\u003csup\u003e4\u003c/sup\u003e, Oana I. Lungu\u003csup\u003e3\u003c/sup\u003e, Krista L. Ternus\u003csup\u003e3\u003c/sup\u003e, Stephen D. Turner\u003csup\u003e4,\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e, C. Titus Brown\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSchool of Veterinary Medicine, University of California Davis, Davis, CA, United States of America\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eInstitute of Applied Genetics, Department of Molecular and Medical Genetics, University of North Texas Health Science Center, Fort Worth, Texas, United States of America\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eSignature Science, LLC, Austin, Texas, United States of America\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDepartment of Public Health Sciences, University of Virginia, Charlottesville, VA, United States of America\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eBioinformatics Core, University of Virginia School of Medicine, Charlottesville, VA, United States of America\u003c/p\u003e\n\u003cp\u003eSee also the list of \u003ca href=\"https://github.com/dahak-metagenomics/dahak/graphs/contributors\"\u003econtributors\u003c/a\u003e who participated in this project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the BSD 3-Clause License - see the\n\u003ca href=\"https://github.com/dahak-metagenomics/dahak/blob/master/LICENSE\"\u003eLICENSE\u003c/a\u003e file\nfor details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://bioconda.github.io\" rel=\"nofollow\"\u003eBioconda\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eHat tip to anyone whose code was used\u003c/li\u003e\n\u003c/ul\u003e\n", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"pwiz-logo.png\"\u003e\u003cimg src=\"pwiz-logo.png\" alt=\"Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eProteowizard msconvert\u003c/h1\u003e\u003ca id=\"user-content-proteowizard-msconvert\" class=\"anchor\" aria-label=\"Permalink: Proteowizard msconvert\" href=\"#proteowizard-msconvert\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVersion: 3.0.18205\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eThis container is unmaintained\u003c/h2\u003e\u003ca id=\"user-content-this-container-is-unmaintained\" class=\"anchor\" aria-label=\"Permalink: This container is unmaintained\" href=\"#this-container-is-unmaintained\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease head over to \u003ca href=\"https://github.com/ProteoWizard/container\"\u003ehttps://github.com/ProteoWizard/container\u003c/a\u003e for the new upstream home.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eShort Description\u003c/h2\u003e\u003ca id=\"user-content-short-description\" class=\"anchor\" aria-label=\"Permalink: Short Description\" href=\"#short-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConversion of mass spectrometry vendor formats to mzML.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease note that there is now a container by the Proteowizard team\nas part of their continuous integration pipeline\nwhich should be used instead of this \u003ccode\u003ephnmnl/container-pwiz\u003c/code\u003e:\n\u003ca href=\"https://hub.docker.com/r/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe first step in a metabolomics data processing workflow with Open\nSource tools is the conversion to an open raw data format like\n\u003ca href=\"https://github.com/HUPO-PSI/mzML/\"\u003emzML\u003c/a\u003e. One of the main routes to mzML-formatted data is using Open Source converter\nmsconvert developed by the Proteowizard team (Chambers et al. 2012),\nwhich is one of the reference implementations for mzML. It can convert\nto mzML from Sciex, Bruker, Thermo, Agilent, Shimadzu, Waters\nand also the earlier file formats like mzData or mzXML.\nAlthough Proteowizard was initially targeting LC/MS data, it can also readily\nconvert GC/MS data for example from the Waters GCT Premier or Agilent instruments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKey features\u003c/h2\u003e\u003ca id=\"user-content-key-features\" class=\"anchor\" aria-label=\"Permalink: Key features\" href=\"#key-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMS raw data conversion\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFunctionality\u003c/h2\u003e\u003ca id=\"user-content-functionality\" class=\"anchor\" aria-label=\"Permalink: Functionality\" href=\"#functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eApproaches\u003c/h2\u003e\u003ca id=\"user-content-approaches\" class=\"anchor\" aria-label=\"Permalink: Approaches\" href=\"#approaches\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMetabolomics\u003c/li\u003e\n\u003cli\u003eLipidomics\u003c/li\u003e\n\u003cli\u003eGlycomics\u003c/li\u003e\n\u003cli\u003eProteomics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstrument Data Types\u003c/h2\u003e\u003ca id=\"user-content-instrument-data-types\" class=\"anchor\" aria-label=\"Permalink: Instrument Data Types\" href=\"#instrument-data-types\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMS\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eScreenshots\u003c/h2\u003e\u003ca id=\"user-content-screenshots\" class=\"anchor\" aria-label=\"Permalink: Screenshots\" href=\"#screenshots\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"MSconvertGUI.png\"\u003e\u003cimg src=\"MSconvertGUI.png\" alt=\"screenshot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTool Authors\u003c/h2\u003e\u003ca id=\"user-content-tool-authors\" class=\"anchor\" aria-label=\"Permalink: Tool Authors\" href=\"#tool-authors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSeveral hundred labs around the world are using ProteoWizard within their development processes and computational workflows. We\u0027d like to thank the many users who have contributed feedback to the project. We also thank the TPP team for their ongoing support.\u003c/li\u003e\n\u003cli\u003eSee \u003ca href=\"http://proteowizard.sourceforge.net/team.html\" rel=\"nofollow\"\u003ehttp://proteowizard.sourceforge.net/team.html\u003c/a\u003e for the full list of contributors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainer Contributors\u003c/h2\u003e\u003ca id=\"user-content-container-contributors\" class=\"anchor\" aria-label=\"Permalink: Container Contributors\" href=\"#container-contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sneumann\"\u003eSteffen Neumann\u003c/a\u003e (IPB Halle)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/meier-rene\"\u003eRene Meier\u003c/a\u003e (IPB Halle)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pcm32\"\u003ePablo Moreno\u003c/a\u003e (EMBL-EBI)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pkrog\"\u003ePierrick Roger\u003c/a\u003e (CAE)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWebsite\u003c/h2\u003e\u003ca id=\"user-content-website\" class=\"anchor\" aria-label=\"Permalink: Website\" href=\"#website\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://proteowizard.sourceforge.net/\" rel=\"nofollow\"\u003ehttp://proteowizard.sourceforge.net/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGit Repository\u003c/h2\u003e\u003ca id=\"user-content-git-repository\" class=\"anchor\" aria-label=\"Permalink: Git Repository\" href=\"#git-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/phnmnl/container-pwiz.git\"\u003ehttps://github.com/phnmnl/container-pwiz.git\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe conversion can be started with e.g.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run -v $PWD:/data:rw phnmnl/phnmnl/pwiz-i-agree-to-the-vendor-licenses:latest /data/neg-MM8_1-A,1_01_376.d -o /data/ --mzML\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe currently tested vendor formats are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emzXML: \u003ccode\u003edocker run -it -v $PWD:/data phnmnl/pwiz-i-agree-to-the-vendor-licenses:latest threonine_i2_e35_pH_tree.mzXML\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBruker .d: \u003ccode\u003edocker run -it -v $PWD:/data phnmnl/phnmnl/pwiz-i-agree-to-the-vendor-licenses:latest neg-MM8_1-A,1_01_376.d\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo run the MSconvertGUI as shown in the above screenshot, you have to enable X11 access on the client machine, and pass the X11 information to the container:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -v $HOME/.Xauthority:/root/.Xauthority:r -v /tmp/.X11-unix:/tmp/.X11-unix:rw -v $HOME:/data:rw phnmnl/pwiz-i-agree-to-the-vendor-licenses wine MSconvertGUI\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGalaxy usage\u003c/h2\u003e\u003ca id=\"user-content-galaxy-usage\" class=\"anchor\" aria-label=\"Permalink: Galaxy usage\" href=\"#galaxy-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA rudimentary Galaxy node description is included as \u003ccode\u003emsconvert.xml\u003c/code\u003e,\nit was obtained from the \u003ccode\u003emsconvert.ctd\u003c/code\u003e using\n\u003ccode\u003epython CTD2Galaxy/generator.py -i /vol/phenomenal/vmis/docker-pwiz/msconvert.ctd -m sample_files/macros.xml -o /vol/phenomenal/vmis/docker-pwiz/msconvert.xml\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild instructions\u003c/h2\u003e\u003ca id=\"user-content-build-instructions\" class=\"anchor\" aria-label=\"Permalink: Build instructions\" href=\"#build-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease note that for licensing reasons we can not include all required\nfiles in this repository. Upon container building, the Proteowizard files\nwill be downloaded from \u003ca href=\"http://proteowizard.sourceforge.net/downloads.shtml\" rel=\"nofollow\"\u003ehttp://proteowizard.sourceforge.net/downloads.shtml\u003c/a\u003e and included\nin the created container. By building this container, you agree\nto all the vendor licenses that are shown at the above download links,\nand also included in the container and Dockerfile repository. To build, please use\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker build --tag=\"phnmnl/pwiz-i-agree-to-the-vendor-licenses:latest\" .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAlso note that the build is known to fail with Docker-1.9, make sure to use Docker-1.10 or above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePublications\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eChambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, Gatto L,\nFischer B, Pratt B, Egertson J, Hoff K, Kessner D, Tasman N, Shulman N, Frewen B,\nBaker TA, Brusniak MY, Paulse C, Creasy D, Flashner L, Kani K, Moulding C,\nSeymour SL, Nuwaysir LM, Lefebvre B, Kuhlmann F, Roark J, Rainer P, Detlev S,\nHemenway T, Huhmer A, Langridge J, Connolly B, Chadick T, Holly K, Eckels J,\nDeutsch EW, Moritz RL, Katz JE, Agus DB, MacCoss M, Tabb DL, Mallick P. A\ncross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012\nOct;30(10):918-20. doi: 10.1038/nbt.2377. PubMed PMID: 23051804; PubMed Central\nPMCID: PMC3471674.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicensing: APACHE LICENSE\u003c/h2\u003e\u003ca id=\"user-content-licensing-apache-license\" class=\"anchor\" aria-label=\"Permalink: Licensing: APACHE LICENSE\" href=\"#licensing-apache-license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see LICENSES/LICENSE, this Apache License Covers Core ProteoWizard Tools and Library. This software does, however, depend on other software libraries which place further restrictions on its use and redistribution, see below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eADDENDUM TO APACHE LICENSE\u003c/h3\u003e\u003ca id=\"user-content-addendum-to-apache-license\" class=\"anchor\" aria-label=\"Permalink: ADDENDUM TO APACHE LICENSE\" href=\"#addendum-to-apache-license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo the best of our ability we deliver this software to you under the Apache 2.0 License listed below (the source code is available in the ProteoWizard project). This software does, however, depend on other software libraries which place further restrictions on its use and redistribution. By accepting the license terms for this software, you agree to comply with the restrictions imposed on you by the\n\u003ca href=\"LICENSES/VENDORLICENSES.html\"\u003elicense agreements of the software libraries\u003c/a\u003e\non which it depends:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAB Sciex WIFF Reader Library\u003c/li\u003e\n\u003cli\u003eAgilent Mass Hunter Data Access Component Library\u003c/li\u003e\n\u003cli\u003eBruker CompassXtract\u003c/li\u003e\n\u003cli\u003eShimadzu SFCS\u003c/li\u003e\n\u003cli\u003eThermo-Scientific MSFileReader Library\u003c/li\u003e\n\u003cli\u003eWaters Raw Data Access Component Library\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNOTE: If you do not plan to redistribute this software yourself, then you are the \"end-user\" in the above agreements.\u003c/p\u003e\n", "stargazers_count": 21, - "subscribers_count": 9, + "subscribers_count": 22, "topics": [], - "updated_at": 1689623036.0 - }, - { - "data_format": 2, - "description": "RNAseq analysis pipeline", - "filenames": [ - "Singularity/Singularity.v2.2", - "Singularity/Singularity.v2.4", - "Singularity/Singularity.v2.3" - ], - "full_name": "IARCbioinfo/RNAseq-nf", - "latest_release": "v2.4a", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-rnaseq-nf\" class=\"anchor\" aria-hidden=\"true\" href=\"#rnaseq-nf\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRNAseq-nf\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-nextflow-pipeline-for-rna-seq-processing\" class=\"anchor\" aria-hidden=\"true\" href=\"#nextflow-pipeline-for-rna-seq-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNextflow pipeline for RNA seq processing\u003c/h2\u003e\n\u003cp\u003e\u003ca href=\"https://circleci.com/gh/IARCbioinfo/RNAseq-nf/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3243c4851a1ea38652c4f4e27327d1ecd991fb65bb3763a33bb47e2a077f40e3/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f524e417365712d6e662f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/RNAseq-nf/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/iarcbioinfo/rnaseq-nf/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5c5d5383ee3d6248c7d31b5fcaa21fc7d689b53ecb330dbfe628cd1fae38c853/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4271\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a07b23d4880320ac89cdc93bbbb603fa84c215d135e05dd227ba8633a9ff34be/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"RNAseqpipeline.png?raw=true\"\u003e\u003cimg src=\"RNAseqpipeline.png?raw=true\" alt=\"workflow\" title=\"Scheme of alignment/realignment Workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-decription\" class=\"anchor\" aria-hidden=\"true\" href=\"#decription\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDecription\u003c/h2\u003e\n\u003cp\u003eNextflow pipeline for RNA sequencing mapping, quality control, reads counting, and unsupervised analysis\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eNextflow: for common installation procedures see the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc/INSTALL.txt\" rel=\"nofollow\"\u003e\u003cem\u003efastqc\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://rseqc.sourceforge.net/\" rel=\"nofollow\"\u003e\u003cem\u003eRESeQC\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://multiqc.info/docs/\" rel=\"nofollow\"\u003e\u003cem\u003emultiQC\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf\"\u003e\u003cem\u003eSTAR\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"http://www-huber.embl.de/HTSeq/doc/install.html#install\" rel=\"nofollow\"\u003e\u003cem\u003ehtseq\u003c/em\u003e\u003c/a\u003e; the python script htseq-count must also be in the PATH\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eA singularity container is available with all the tools needed to run the pipeline (see \"Usage\")\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-hidden=\"true\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReferences\u003c/h3\u003e\n\u003cp\u003eA bundle with reference genome and corresponding annotations for STAR is available at \u003ca href=\"https://data.broadinstitute.org/Trinity/CTAT_RESOURCE_LIB/\" rel=\"nofollow\"\u003ehttps://data.broadinstitute.org/Trinity/CTAT_RESOURCE_LIB/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlternatively, STAR genome indices can be generated from a genome fasta file ref.fa and a splice junction annotation file ref.gtf using the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eSTAR --runThreadN n --runMode genomeGenerate --genomeDir ref --genomeFastaFiles ref.fa --sjdbGTFfile ref.gtf --sjdbOverhang 99\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can provide a config file to customize the multiqc report (see \u003ca href=\"https://multiqc.info/docs/#configuring-multiqc\" rel=\"nofollow\"\u003ehttps://multiqc.info/docs/#configuring-multiqc\u003c/a\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reads-adapter-trimming-with-cutadapt\" class=\"anchor\" aria-hidden=\"true\" href=\"#reads-adapter-trimming-with-cutadapt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReads adapter trimming with cutadapt\u003c/h3\u003e\n\u003cp\u003eIn order to perform the optional adapter trimming of reads before mapping the following software must be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://cutadapt.readthedocs.io/en/stable/installation.html\" rel=\"nofollow\"\u003e\u003cem\u003ecutadapt\u003c/em\u003e\u003c/a\u003e version \u0026gt; 1.15, which requires Python version \u0026gt; 2.7\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/FelixKrueger/TrimGalore\"\u003e\u003cem\u003etrim_galore\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-alignment-with-hisat2\" class=\"anchor\" aria-hidden=\"true\" href=\"#alignment-with-hisat2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAlignment with hisat2\u003c/h3\u003e\n\u003cp\u003eIn order to perform the optional alignment with hisat2, hisat2 must be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://ccb.jhu.edu/software/hisat2/index.shtml\" rel=\"nofollow\"\u003e\u003cem\u003ehisat2\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition, indexes files \u003cem\u003e.ht2\u003c/em\u003e must be downloaded from generated from \u003ca href=\"https://ccb.jhu.edu/software/hisat2/index.shtml\" rel=\"nofollow\"\u003e\u003cem\u003ehisat2\u003c/em\u003e\u003c/a\u003e, or generated from a reference fasta file (e.g., reference.fa) and a GTF annotation file (e.g., reference.gtf) using the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eextract_splice_sites.py reference.gtf \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e genome.ss\nextract_exons.py reference.gtf \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e genome.exon\nhisat2-build reference.fa --ss genome.ss --exon genome.exon genome_tran\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-reads-trimming-at-splice-junctions\" class=\"anchor\" aria-hidden=\"true\" href=\"#reads-trimming-at-splice-junctions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eReads trimming at splice junctions\u003c/h3\u003e\n\u003cp\u003eIn order to perform the optional reads trimming at splice junctions, GATK4 must be installed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://software.broadinstitute.org/gatk/guide/quickstart\" rel=\"nofollow\"\u003e\u003cem\u003eGATK\u003c/em\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn addition, index \u003cem\u003e.fai\u003c/em\u003e and dictionnary \u003cem\u003e.dict\u003c/em\u003e must be generated from the fasta reference genome using the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esamtools faidx ref.fa\njava -jar picard.jar CreateSequenceDictionary R= ref.fa O= ref.dict\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-base-quality-score-recalibration\" class=\"anchor\" aria-hidden=\"true\" href=\"#base-quality-score-recalibration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eBase quality score recalibration\u003c/h3\u003e\n\u003cp\u003eIn order to perform the optional base quality score recalibration, several files are required:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://software.broadinstitute.org/gatk/guide/quickstart\" rel=\"nofollow\"\u003e\u003cem\u003eGATK4\u003c/em\u003e\u003c/a\u003e must be in the PATH variable\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://software.broadinstitute.org/gatk/download/bundle\" rel=\"nofollow\"\u003eGATK bundle\u003c/a\u003e VCF files with lists of indels and SNVs (recommended: 1000 genomes indels, dbsnp VCF)\u003c/li\u003e\n\u003cli\u003ebed file with intervals to be considered\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-hidden=\"true\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_folder\u003c/td\u003e\n\u003ctd\u003eFolder containing BAM or fastq files to be aligned\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--input_file\u003c/td\u003e\n\u003ctd\u003eInput tabulation-separated values file with columns SM (sample name), RG (read group), pair1 (first fastq pair file), and pair2 (second fastq pair file)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that there are two input methods: folder and file. Although the input folder method is the easiest because it does not require to create an input file with the right format, the input file mode is recommended in cases when a single sample has multiple paired files (e.g., due to multiplexed sequencing); in that case, users should have one line per pair of file and put a same SM identifier so that the workflow can group them into the same output bam file. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSM RG pair1 pair2\nsample1\t\tsample1_1.fq.gz\tsample1_2.fq.gz\nsample2\tRG1\tsample2_RG1_1.fq.gz\tsample2_RG1_2.fq.gz\nsample2\tRG2\tsample2_RG2_1.fq.gz\tsample2_RG2_2.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-hidden=\"true\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eParameters\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-hidden=\"true\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMandatory\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth align=\"right\"\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref_folder\u003c/td\u003e\n\u003ctd align=\"right\"\u003eref\u003c/td\u003e\n\u003ctd\u003eFolder with genome reference files (with index)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--gtf\u003c/td\u003e\n\u003ctd align=\"right\"\u003eHomo_sapiens.GRCh38.79.gtf\u003c/td\u003e\n\u003ctd\u003eAnnotation GTF file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--bed\u003c/td\u003e\n\u003ctd align=\"right\"\u003egene.bed\u003c/td\u003e\n\u003ctd\u003ebed file with genes for RESeQC (interval list)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-hidden=\"true\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptional\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003eNumber of cpu used by bwa mem and sambamba\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu_gatk\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eNumber of CPUs for GATK processes (SJ trimming and BQSR)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cpu_trim\u003c/td\u003e\n\u003ctd\u003e15\u003c/td\u003e\n\u003ctd\u003eNumber of CPUs for reads trimming (cutadapt)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem\u003c/td\u003e\n\u003ctd\u003e50\u003c/td\u003e\n\u003ctd\u003eSize of memory used for mapping (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--mem_QC\u003c/td\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eSize of memory used for QC and cutadapt (in GB)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--fastq_ext\u003c/td\u003e\n\u003ctd\u003efq.gz\u003c/td\u003e\n\u003ctd\u003eExtension of fastq files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--suffix1\u003c/td\u003e\n\u003ctd\u003e_1\u003c/td\u003e\n\u003ctd\u003eSuffix of fastq files 1 (first element of read files pair)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--suffix2\u003c/td\u003e\n\u003ctd\u003e_2\u003c/td\u003e\n\u003ctd\u003eSuffix of fastq files 2(second element of read files pair)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--output_folder\u003c/td\u003e\n\u003ctd\u003e.\u003c/td\u003e\n\u003ctd\u003eOutput folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--ref\u003c/td\u003e\n\u003ctd\u003eref.fa\u003c/td\u003e\n\u003ctd\u003eReference fasta file (with index) for GATK\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--snp_vcf\u003c/td\u003e\n\u003ctd\u003edbsnp.vcf\u003c/td\u003e\n\u003ctd\u003ePath to SNP VCF from GATK bundle\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--indel_vcf\u003c/td\u003e\n\u003ctd\u003eMills_100G_indels.vcf\u003c/td\u003e\n\u003ctd\u003ePath to indel VCF from GATK bundle\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--STAR_mapqUnique\u003c/td\u003e\n\u003ctd\u003e255\u003c/td\u003e\n\u003ctd\u003eSTAR default mapping quality for unique mappers\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--RG\u003c/td\u003e\n\u003ctd\u003ePL:ILLUMINA\u003c/td\u003e\n\u003ctd\u003eSamtools read group specification\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--stranded\u003c/td\u003e\n\u003ctd\u003eno\u003c/td\u003e\n\u003ctd\u003eStrand information for counting with htseq [no, yes, reverse]\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hisat2_idx\u003c/td\u003e\n\u003ctd\u003egenome_tran\u003c/td\u003e\n\u003ctd\u003ehisat2 index file prefix\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--htseq_maxreads\u003c/td\u003e\n\u003ctd\u003e30000000\u003c/td\u003e\n\u003ctd\u003eMaximum number of reads taken into account by htseq-count\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--multiqc_config\u003c/td\u003e\n\u003ctd\u003enull\u003c/td\u003e\n\u003ctd\u003eConfig yaml file for multiqc\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-hidden=\"true\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFlags\u003c/h4\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e--help\u003c/td\u003e\n\u003ctd\u003eprint usage and optional parameters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--cutadapt\u003c/td\u003e\n\u003ctd\u003eenable adapter and quality reads trimming before alignment\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--sjtrim\u003c/td\u003e\n\u003ctd\u003eenable reads trimming at splice junctions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--hisat2\u003c/td\u003e\n\u003ctd\u003euse hisat2 instead of STAR for mapping\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e--recalibration\u003c/td\u003e\n\u003ctd\u003eperform quality score recalibration (GATK)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cp\u003eTo run the pipeline on a series of paired-end fastq files (with suffixes \u003cem\u003e_1\u003c/em\u003e and \u003cem\u003e_2\u003c/em\u003e) in folder \u003cem\u003efastq\u003c/em\u003e, a reference genome with indexes in folder \u003cem\u003eref_genome\u003c/em\u003e, an annotation file ref.gtf, and a bed file ref.bed, one can type:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf -r v2.4 -profile singularity --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo run the pipeline using conda instead of singularity, replace \"-profile singularity\" by \"-profile conda\". To run with your own local software installation, just remove \"-profile singularity\".\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-single-end-fastq-mode\" class=\"anchor\" aria-hidden=\"true\" href=\"#single-end-fastq-mode\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingle-end fastq mode\u003c/h3\u003e\n\u003cp\u003eDefault is adapted to paired-end libraries. To use single-end libraries as input, you must specify the option \"--suffix2 null\".\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf -r v2.4 -profile singularity --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --suffix2 null\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf using \"--input_file\", you must additionally set the values in column \"pair2\" to \"NO_fastq2\". For example the following file input.txt:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSM RG pair1 pair2\nsample1\t\tsample1.fq.gz\tNO_fastq2\nsample2\tRG1\tsample2_RG1.fq.gz\tNO_fastq2\nsample2\tRG2\tsample2_RG2.fq.gz\tNO_fastq2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ecan be processed with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf -r v2.4 -profile singularity --input_file input.txt --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --suffix2 null\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-use-hisat2-for-mapping\" class=\"anchor\" aria-hidden=\"true\" href=\"#use-hisat2-for-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUse hisat2 for mapping\u003c/h3\u003e\n\u003cp\u003eTo use hisat2 instead of STAR for the reads mapping, you must add the \u003cstrong\u003e\u003cem\u003e--hisat2\u003c/em\u003e option\u003c/strong\u003e, specify the path to the folder containing the hisat2 index files (genome_tran.1.ht2 to genome_tran.8.ht2), as well as satisfy the requirements above mentionned. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --hisat2 --hisat2_idx genome_tran \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eNote that parameter \u0027--hisat2_idx\u0027 is the prefix of the index files, not the entire path to .ht2 files.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-enable-reads-trimming-at-splice-junctions\" class=\"anchor\" aria-hidden=\"true\" href=\"#enable-reads-trimming-at-splice-junctions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnable reads trimming at splice junctions\u003c/h3\u003e\n\u003cp\u003eTo use the reads trimming at splice junctions step, you must add the \u003cstrong\u003e\u003cem\u003e--sjtrim\u003c/em\u003e option\u003c/strong\u003e as well as satisfy the requirements above mentionned. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --sjtrim\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca id=\"user-content-enable-base-quality-score-recalibration\" class=\"anchor\" aria-hidden=\"true\" href=\"#enable-base-quality-score-recalibration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eEnable Base Quality Score Recalibration\u003c/h3\u003e\n\u003cp\u003eTo use the base quality score recalibration step, you must add the \u003cstrong\u003e\u003cem\u003e--recalibration\u003c/em\u003e option\u003c/strong\u003e, specify the path to the known snps and indels from the GATK bundle, as well as satisfy the requirements above mentionned. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/RNAseq-nf --input_folder fastq --ref_folder ref_genome --gtf ref.gtf --bed ref.bed --recalibration --snp_vcf GATK_bundle/dbsnp_146.hg38.vcf.gz --indel_vcf GATK_bundle/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-hidden=\"true\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOutput\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eBAM/file.bam\u003c/td\u003e\n\u003ctd\u003eBAM files of alignments or realignments\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBAM/file.bam.bai\u003c/td\u003e\n\u003ctd\u003eBAI files of alignments or realignments\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBAM/STAR.file.Chimeric.SJ.out.junction\u003c/td\u003e\n\u003ctd\u003eSTAR chimeric junction output\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBAM/STAR.file.SJ.out.tab\u003c/td\u003e\n\u003ctd\u003eSTAR junction tab output\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ecounts/file_count.txt\u003c/td\u003e\n\u003ctd\u003ehtseq-count output file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/multiqc_pretrim_report.html\u003c/td\u003e\n\u003ctd\u003emultiqc report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/multiqc_pretrim_report_data\u003c/td\u003e\n\u003ctd\u003efolder with data used to compute multiqc report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/multiqc_posttrim_report.html\u003c/td\u003e\n\u003ctd\u003emultiqc report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/multiqc_posttrim_report_data\u003c/td\u003e\n\u003ctd\u003efolder with data used to compute multiqc report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/adapter_trimming/file_{12}.fq.gz_trimming_report.txt\u003c/td\u003e\n\u003ctd\u003etrim_galore report\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/adapter_trimming/file_{12}\u003cem\u003eval\u003c/em\u003e{12}_fastqc.zip\u003c/td\u003e\n\u003ctd\u003eFastQC report after trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/alignment/STAR.file.Log.final.out, STAR.file.Log.out, STAR.file.Log.progress.out\u003c/td\u003e\n\u003ctd\u003eSTAR logs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/bam/file_readdist.txt, file_clipping_profile*, file_jun_stauration*\u003c/td\u003e\n\u003ctd\u003eRSeQC reports\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQC/fastq/file_{12}_pretrim_fastqc.zip\u003c/td\u003e\n\u003ctd\u003eFastQC report before trimming\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe output_folder directory contains three subfolders: BAM, counts, and QC\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-directed-acyclic-graph\" class=\"anchor\" aria-hidden=\"true\" href=\"#directed-acyclic-graph\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDirected Acyclic Graph\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-default-options\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-default-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith default options\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/RNAseq-nf/blob/dev/dag_STAR.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag_STAR.png\" alt=\"DAG STAR\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-option---hisat2\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-option---hisat2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith option --hisat2\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/RNAseq-nf/blob/dev/dag_hisat2.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag_hisat2.png\" alt=\"DAG hisat2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-with-options---sjtrim-and---recalibration\" class=\"anchor\" aria-hidden=\"true\" href=\"#with-options---sjtrim-and---recalibration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWith options --sjtrim and --recalibration\u003c/h3\u003e\n\u003cp\u003e\u003ca href=\"http://htmlpreview.github.io/?https://github.com/IARCbioinfo/RNAseq-nf/blob/dev/dag_STAR_sjtrim_recal.html\" rel=\"nofollow\"\u003e\u003cimg src=\"dag_STAR_sjtrim_recal.png\" alt=\"DAG STAR_sjtrim_recal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-hidden=\"true\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributions\u003c/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:AlcalaN@fellows.iarc.fr\"\u003eAlcalaN@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDeveloper to contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNoemie Leblay\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:LeblayN@students.iarc.fr\"\u003eLeblayN@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eTester\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAlexis Robitaille\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:RobitailleA@students.iarc.fr\"\u003eRobitailleA@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eTester\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n", - "stargazers_count": 21, - "subscribers_count": 6, - "topics": [ - "nextflow", - "rna-seq", - "ngs", - "pipeline" - ], - "updated_at": 1658424915.0 + "updated_at": 1729252335.0 }, { "data_format": 2, - "description": "A snakemake pipeline to assembly, polishing, correction and quality check from Oxford nanopore reads.", + "description": null, "filenames": [ - "Containers/Singularity.culebront_tools.def", - "Containers/Singularity.report.def" + "scripts/Singularity.def" ], - "full_name": "SouthGreenPlatform/CulebrONT_pipeline", - "latest_release": "1.7.0", - "readme": "\u003cp\u003e\u003ca href=\"./docs/source/_images/culebront_logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"./docs/source/_images/culebront_logo.png\" alt=\"Culebront Logo\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.python.org/downloads\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e4779c52a0f8acf7c62517ff771deebcf8ab8913544dd508ccdd6cec2f2b400a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e372532422d626c7565\" alt=\"PythonVersions\" data-canonical-src=\"https://img.shields.io/badge/python-3.7%2B-blue\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/35030e6ddc253302ffcdf599ce8a8e387c27d88eb3de9cfe4e103b3ec6161f96/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135352e31302e302d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"SnakemakeVersions\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%E2%89%A55.10.0-brightgreen.svg?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a324f41bf4495d7dc95ac4693962834b38ff77e1a6ed7f5c4dca9c3e3f92a6d3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f73696e67756c61726974792d254532253839254135332e332e302d3745344337342e737667\" alt=\"Singularity\" data-canonical-src=\"https://img.shields.io/badge/singularity-%E2%89%A53.3.0-7E4C74.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/index.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cacdb0b19bd30d76ae4faaee3355a6d65ecc448b587bac638adbd5eb04339c20/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e64612d342e382e352532302d677265656e\" alt=\"Conda\" data-canonical-src=\"https://img.shields.io/badge/conda-4.8.5%20-green\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUsing data from long reads obtained by Oxford Nanopore Technologies sequencing makes genome assembly easier, in particular to solve repeats and structural variants, in prokaryotic as well as in eukaryotic genomes, resulting in increased contiguity and accuracy.\u003c/p\u003e\n\u003cp\u003eBunch of softwares and tools are released or updated every week, and a lot of species see their genome assembled using those.\u003c/p\u003e\n\u003cp\u003eThat\u2019s right.\u003c/p\u003e\n\u003cp\u003e\"\u003cem\u003eBut which assembly tool could give the best results for my favorite organism?\u003c/em\u003e\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCulebrONT can help you!\u003c/strong\u003e CulebrONT is an open-source, scalable, modulable and traceable snakemake pipeline, able to launch multiple assembly tools in parallel and providing help for choosing the best possible assembly between all possibilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHomepage: \u003ca href=\"https://culebront-pipeline.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://culebront-pipeline.readthedocs.io/en/latest/\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca name=\"user-content-citation\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-citation\" class=\"anchor\" href=\"#citation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003e@Authors:\u003c/p\u003e\n\u003cp\u003eJulie Orjuela (IRD), Aurore Comte(IRD), S\u00e9bastien Ravel(CIRAD), Florian Charriat(INRAE), Tram Vi(IRD, AGI), Francois Sabot(IRD) and S\u00e9bastien Cunnac(IRD).\u003c/p\u003e\n\u003cp\u003e\u003ca name=\"user-content-notes\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-useful-notes\" class=\"anchor\" href=\"#useful-notes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUseful notes\u003c/h2\u003e\n\u003cp\u003eBefore launching CulebrONT, you could base-calling of arbitrarily multiplexed libraries across several Minion runs with sequencing quality control and gather the output files by genome for subsequent steps. For that use \u003ca href=\"https://github.com/vibaotram/baseDmux\"\u003ehttps://github.com/vibaotram/baseDmux\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-thanks\" class=\"anchor\" href=\"#thanks\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eThanks\u003c/h4\u003e\n\u003cp\u003eThanks to Ndomassi Tando (i-Trop IRD) by administration support.\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the IRD i-Trop HPC (South Green Platform) at IRD Montpellier for providing HPC resources that have contributed to this work. \u003ca href=\"https://bioinfo.ird.fr/\" rel=\"nofollow\"\u003ehttps://bioinfo.ird.fr/\u003c/a\u003e - \u003ca href=\"http://www.southgreen.fr\" rel=\"nofollow\"\u003ehttp://www.southgreen.fr\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThanks to Yann Delorme for this beautiful logo \u003ca href=\"https://nimarell.github.io/resume\" rel=\"nofollow\"\u003ehttps://nimarell.github.io/resume\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca name=\"user-content-licence\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003eLicencied under CeCill-C (\u003ca href=\"http://www.cecill.info/licences/Licence_CeCILL-C_V1-en.html\" rel=\"nofollow\"\u003ehttp://www.cecill.info/licences/Licence_CeCILL-C_V1-en.html\u003c/a\u003e) and GPLv3\nIntellectual property belongs to IRD and authors.\u003c/p\u003e\n", + "full_name": "Garden-AI/garden", + "latest_release": "2.2.8", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\ud83c\udf31 Garden: FAIR AI/ML Model Publishing Framework\u003c/h1\u003e\u003ca id=\"user-content--garden-fair-aiml-model-publishing-framework\" class=\"anchor\" aria-label=\"Permalink: \ud83c\udf31 Garden: FAIR AI/ML Model Publishing Framework\" href=\"#-garden-fair-aiml-model-publishing-framework\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209892\u0026amp;HistoricalAwards=false\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/feda11d23fe72028c0b0bd0a18795af859a5ce3895675d9e4846a40a52991dac/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e53462d323230393839322d626c7565\" alt=\"NSF-2209892\" data-canonical-src=\"https://img.shields.io/badge/NSF-2209892-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/garden-ai\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33da481681fafc9f5512a8f522ffd492fef7d2570fc1969f6e50cb05b81924a3/68747470733a2f2f62616467652e667572792e696f2f70792f67617264656e2d61692e737667\" alt=\"PyPI\" data-canonical-src=\"https://badge.fury.io/py/garden-ai.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/Garden-AI/garden/actions/workflows/pypi.yaml\"\u003e\u003cimg src=\"https://github.com/Garden-AI/garden/actions/workflows/pypi.yaml/badge.svg\" alt=\"Tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/Garden-AI/garden/actions/workflows/ci.yaml\"\u003e\u003cimg src=\"https://github.com/Garden-AI/garden/actions/workflows/ci.yaml/badge.svg\" alt=\"tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/Garden-AI/garden\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/205f6f0835fb5998b4e2ad43f686d36833b6c35e9554edd82f51ea5a5d2d9fb9/68747470733a2f2f636f6465636f762e696f2f6769746875622f47617264656e2d41492f67617264656e2f67726170682f62616467652e7376673f746f6b656e3d5759494e414746305334\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/github/Garden-AI/garden/graph/badge.svg?token=WYINAGF0S4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAt a Glance:\u003c/h2\u003e\u003ca id=\"user-content-at-a-glance\" class=\"anchor\" aria-label=\"Permalink: At a Glance:\" href=\"#at-a-glance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eEasy Model Publishing\u003c/strong\u003e: Publish pre-trained AI/ML models from a notebook with just a few commands\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReproducible Environments\u003c/strong\u003e: Use containers to ensure consistent execution across different systems\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRemote Execution\u003c/strong\u003e: Run your models (or others) remotely on HPC resources seamlessly\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDiscoverable Collections\u003c/strong\u003e: Organize models into \"Gardens\" for easy discovery and comparison\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMetadata Management\u003c/strong\u003e: Capture and manage metadata of related datasets, papers, or code repositories for better searchability\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhy Garden?\u003c/h2\u003e\u003ca id=\"user-content-why-garden\" class=\"anchor\" aria-label=\"Permalink: Why Garden?\" href=\"#why-garden\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGarden addresses key challenges faced by academic researchers in discovering, reproducing, and running AI/ML models:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eReproducibility\u003c/strong\u003e: Garden eliminates environment inconsistencies by containerizing models, ensuring they run consistently across different systems.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDiscoverability\u003c/strong\u003e: With curated \"Gardens\" of models, researchers can easily find, compare, and curate relevant models for their work.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAccessibility\u003c/strong\u003e: Garden simplifies the process of running models on diverse computing resources, from local machines to HPC clusters, via Globus Compute integration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTime-saving\u003c/strong\u003e: By handling environment management and system-specific quirks, Garden significantly reduces the time researchers spend on setup and configuration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCollaboration\u003c/strong\u003e: FAIR principles (Findable, Accessible, Interoperable, Reusable) and standardized publishing make it easier for researchers to share their work and build upon others\u0027 contributions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eGarden aims to let researchers focus on their science, not on the intricacies of software environments and computing infrastructure.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWhat\u0027s a Garden?\u003c/h3\u003e\u003ca id=\"user-content-whats-a-garden\" class=\"anchor\" aria-label=\"Permalink: What\u0027s a Garden?\" href=\"#whats-a-garden\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA \"Garden\" is a citable collection of published pre-trained AI/ML models, called \"Entrypoints\".\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOk, What\u0027s an Entrypoint?\u003c/h3\u003e\u003ca id=\"user-content-ok-whats-an-entrypoint\" class=\"anchor\" aria-label=\"Permalink: Ok, What\u0027s an Entrypoint?\" href=\"#ok-whats-an-entrypoint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn \"Entrypoint\" is just a python function you define in a regular jupyter notebook which typically invokes one or more of your pre-trained models.\u003c/p\u003e\n\u003cp\u003eWhen you give us that notebook, we \"freeze it in amber\" by containerizing it (along with any environment dependencies) and give you a citable DOI in return.\u003c/p\u003e\n\u003cp\u003eNow, anyone with the DOI can easily invoke that exact function, in the exact same environment, on any remote compute resources they have access to (via \u003ca href=\"https://www.globus.org/compute\" rel=\"nofollow\"\u003eGlobus Compute\u003c/a\u003e):\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/user-attachments/assets/3cdae3bf-9add-4523-84c8-5a227cb3c0f8\"\u003e\u003cimg width=\"916\" alt=\"remote-inference-screenshot\" src=\"https://github.com/user-attachments/assets/3cdae3bf-9add-4523-84c8-5a227cb3c0f8\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the garden CLI:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epipx install garden-ai\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet up Docker on your system (required for local development and testing)\u003c/p\u003e\n\u003cp\u003eWe recommend installing \u003ca href=\"https://www.docker.com/products/docker-desktop/\" rel=\"nofollow\"\u003eDocker Desktop\u003c/a\u003e for most users.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStart a notebook in an isolated environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egarden-ai notebook start my_model.ipynb --base-image=3.10-sklearn\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDefine a function invoking your model in the notebook and publish it:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egarden-ai notebook publish my_model.ipynb\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor a more detailed walkthrough, check out our \u003ca href=\"https://garden-ai.readthedocs.io/en/latest/user_guide/tutorial/\" rel=\"nofollow\"\u003e15-minute tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more documentation, including installation instructions, tutorials, and API references, see our \u003ca href=\"https://garden-ai.readthedocs.io/\" rel=\"nofollow\"\u003elatest docs\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe welcome contributions from the community! Please see our \u003ca href=\"https://garden-ai.readthedocs.io/en/latest/developer_guide/contributing/\" rel=\"nofollow\"\u003eContributing Guide\u003c/a\u003e for more information on how to get started.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupport\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was supported by the National Science Foundation under NSF Award Number: 2209892 \"Frameworks: Garden: A FAIR Framework for Publishing and Applying AI Models for Translational Research in Science, Engineering, Education, and Industry\".\u003c/p\u003e\n", "stargazers_count": 21, - "subscribers_count": 16, + "subscribers_count": 7, "topics": [], - "updated_at": 1641455420.0 + "updated_at": 1732141575.0 }, { "data_format": 2, @@ -39228,8 +39274,8 @@ var data = "src/obsolete/Singularity" ], "full_name": "alexlancaster/pypop", - "latest_release": "v1.1.0", - "stargazers_count": 21, + "latest_release": "v1.1.2", + "stargazers_count": 22, "subscribers_count": 8, "topics": [ "population-genomics", @@ -39238,73 +39284,22 @@ var data = "open-source", "free-software" ], - "updated_at": 1721685890.0 - }, - { - "data_format": 2, - "description": "A simple wrapper of ENTERPRISE and ceffyl that allows for new-physics searches in PTA data.", - "filenames": [ - "Singularity" - ], - "full_name": "andrea-mitridate/PTArcade", - "latest_release": "v1.1.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePTArcade\u003c/h1\u003e\u003ca id=\"user-content-ptarcade\" class=\"anchor\" aria-label=\"Permalink: PTArcade\" href=\"#ptarcade\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/conda-forge/ptarcade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9c51f3ab97fab5205672c6edbb7b9ac357b9355dc33a848e011f655948452e6c/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f70746172636164652f6261646765732f76657273696f6e2e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/ptarcade/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://badge.fury.io/py/ptarcade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/555ae1b8611ad496d719f5f76d1440f4a1c155d9274ab159ac86a05c2dc8d971/68747470733a2f2f62616467652e667572792e696f2f70792f70746172636164652e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/ptarcade.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/ngnewphy/ptarcade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/552bd3645f40236e0513a38789e0a23cc76a6cb33d4b459790ab3ee0d04292e6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65726875622d70746172636164652d696d706f7274616e742e7376673f6c6f676f3d446f636b6572\" data-canonical-src=\"https://img.shields.io/badge/dockerhub-ptarcade-important.svg?logo=Docker\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://anaconda.org/conda-forge/ptarcade\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80ad5865e388fc04bb85f0eeaa8912ec2cd6852e231731458af099276f785a8d/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f70746172636164652f6261646765732f6c61746573745f72656c656173655f646174652e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/ptarcade/badges/latest_release_date.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePTArcade provides an interface to the ENTERPRISE analysis suite and allows for simple implementation of new-physics searches in PTA data.\u003c/p\u003e\n\u003cp\u003eThe user can specify a new physics signal (either deterministic or stochastic), and the code will output Monte Carlo chains that can be used to reconstruct the model\u0027s parameter posterior distributions.\u003c/p\u003e\n\u003cp\u003ePlease see our detailed documentation \u003ca href=\"https://andrea-mitridate.github.io/PTArcade/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 21, - "subscribers_count": 8, - "topics": [ - "particle-physics", - "physics", - "pulsar-timing-array" - ], - "updated_at": 1725533010.0 - }, - { - "data_format": 2, - "description": "Integrated toolkit for analysis and evaluation of annotated genomes", - "filenames": [ - "Singularity" - ], - "full_name": "BrendelGroup/AEGeAn", - "latest_release": "v0.16.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-aegean-toolkit-analysis-and-evaluation-of-genome-annotations\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#aegean-toolkit-analysis-and-evaluation-of-genome-annotations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAEGeAn Toolkit: analysis and evaluation of genome annotations\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/BrendelGroup/AEGeAn\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/06910269bc640daec72bca495674260f88f356237607ea5dc7ef5b692a624a6b/68747470733a2f2f6170692e7472617669732d63692e6f72672f4272656e64656c47726f75702f41454765416e2e7376673f6272616e63683d6d6173746572\" alt=\"AEGeAn build status\" data-canonical-src=\"https://api.travis-ci.org/BrendelGroup/AEGeAn.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://readthedocs.org/projects/aegean/badge/?version=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/30aacbc41b9d5ef8b62d7947568c70128148167e085712faef27278c90e734af/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f61656765616e2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"ReadTheDocs build status\" data-canonical-src=\"https://readthedocs.org/projects/aegean/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe AEGeAn Toolkit provides a bundle of software tools for evaluating gene structure annotations and genome organization.\nThe \u003cstrong\u003eLocusPocus\u003c/strong\u003e program is designed a break down an annotated eukaryotic genome into its constituent parts, and the \u003cstrong\u003efidibus\u003c/strong\u003e workflow brings in a variety of other programs and scripts to summarize genome content and the spacing genes.\nThe code conforms to our \u003ca href=\"https://brendelgroup.github.io/\" rel=\"nofollow\"\u003eRAMOSE\u003c/a\u003e philosophy: it generates \u003cstrong\u003ereproducible\u003c/strong\u003e, \u003cstrong\u003eaccurate\u003c/strong\u003e, and \u003cstrong\u003emeaningful\u003c/strong\u003e results; it is \u003cstrong\u003eopen\u003c/strong\u003e (source) and designed to be \u003cstrong\u003escalable\u003c/strong\u003e and \u003cstrong\u003eeasy\u003c/strong\u003e to use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the \u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003cp\u003ePlease see \u003ca href=\"https://github.com/BrendelGroup/AEGeAn/wiki/FAQ\"\u003eour wiki FAQ pages\u003c/a\u003e for usage examples and suggestions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe main AEGeAn Toolkit documentation is available at \u003ca href=\"https://aegean.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ehttps://aegean.readthedocs.io/en/stable/\u003c/a\u003e.\nThis documentation is focused on the core C library and the programs that directly call this library.\u003c/p\u003e\n\u003cp\u003eDocumentation for the fidibus module, recently merged into AEGeAn, is available from the original (and now deprecated) GenHub project.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003equick start: \u003ca href=\"https://github.com/standage/genhub/#quick-start-example-usages\"\u003ehttps://github.com/standage/genhub/#quick-start-example-usages\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003euser manual: \u003ca href=\"https://github.com/standage/genhub/blob/master/docs/MANUAL.md\"\u003ehttps://github.com/standage/genhub/blob/master/docs/MANUAL.md\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003edeveloper documentation: \u003ca href=\"https://github.com/standage/genhub/blob/master/docs/DEVELOP.md\"\u003ehttps://github.com/standage/genhub/blob/master/docs/DEVELOP.md\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe are in the process of combining and standardizing these separate sources of documentation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003ePlease direct all comments and suggestions to \u003ca href=\"mailto:vbrendel@indiana.edu\"\u003eVolker Brendel\u003c/a\u003e or \u003ca href=\"mailto:daniel.standage@nbacc.dhs.gov\"\u003eDaniel Standage\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 23, - "subscribers_count": 4, - "topics": [], - "updated_at": 1689860196.0 - }, - { - "data_format": 2, - "description": "Splice junction analysis and filtering from BAM files", - "filenames": [ - "Singularity" - ], - "full_name": "maplesond/portcullis", - "latest_release": "1.2.2", - "readme": "\u003cp\u003e\u003ca href=\"doc/source/images/portcullis_logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"doc/source/images/portcullis_logo.png\" alt=\"alt text\" title=\"Portcullis\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-portcullis\" class=\"anchor\" href=\"#portcullis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePortcullis\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/maplesond/portcullis/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b9c31b04d2671e6317cdfd9e4fdf893512936091302d1b1b56c99cb89ab43df7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f7461672f6d61706c65736f6e642f706f727463756c6c69732e737667\" alt=\"Version\" data-canonical-src=\"https://img.shields.io/github/tag/maplesond/portcullis.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://jenkins.sdlmapleson.net/job/portcullis/job/develop/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f696e3e0136cfb90f0e05f4f4e0a257ece7cd1e52ff19a0c8963b32df756d3a7/68747470733a2f2f6a656e6b696e732e73646c6d61706c65736f6e2e6e65742f6275696c645374617475732f69636f6e3f6a6f623d706f727463756c6c6973253246646576656c6f70\" alt=\"Build Status\" data-canonical-src=\"https://jenkins.sdlmapleson.net/buildStatus/icon?job=portcullis%2Fdevelop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/maplesond/portcullis/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad4d6f3e16da4f0dddcd142fa3b6088042b13242787f5ad939d2db28282d3eb5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d627269676874677265656e2e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/maplesond/portcullis/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d3bedf8e24750956939d66108f9ba197e72b83d1de8fc7305708ab2d67c20c17/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d7261772f6d61706c65736f6e642f706f727463756c6c69732e737667\" alt=\"Issues\" data-canonical-src=\"https://img.shields.io/github/issues-raw/maplesond/portcullis.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePortcullis stands for PORTable CULLing of Invalid Splice junctions from pre-aligned RNA-seq data. It is known that RNAseq mapping tools generate many invalid junction predictions, particularly in deep datasets with high coverage over splice sites. In order to address this, instead for creating a new RNAseq mapper, with a focus on SJ accuracy we created a tool that takes in a BAM file generated by an RNAseq mapper of the user\u0027s own choice (e.g. Tophat2, Gsnap, STAR2 or HISAT2) as input (i.e. it\u0027s portable). It then, analyses and quantifies all splice junctions in the file before, filtering (culling) those which are unlikely to be genuine. Portcullis output\u0027s junctions in a variety of formats making it suitable for downstream analysis (such as differential splicing analysis and gene modelling) without additional work. Portcullis can also filter the original BAM file removing alignments associated with \u003cem\u003ebad\u003c/em\u003e junctions. Both the filtered junctions and BAM files are cleaner and more usable resources which can more effectively be used to assist in downstream analyses such as gene prediction and genome annotation.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eWe support multiple methods for installing and running portcullis. Hopefully your favourite container or package manager is supported below. If not let us know and we\u0027ll try to work to get it integrated there.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDocker\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/maplesond/portcullis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/de966674ebe7a3dec2fed423683dd2c64e3630527fab6a691add53421292e384/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d61706c65736f6e642f706f727463756c6c69732e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/maplesond/portcullis.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Keep in mind you need to mount in any working directories to the container with the `-v` option.\n# Ideally, mount these into the /data directory which is the container\u0027s working directory.\ndocker run --it --rm -v /abspath/to/data/on/host:/data maplesond/portcullis:stable portcullis --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# First download the container:\nsingularity pull --name portcullis.img shub://maplesond/portcullis:master\n\n# Then to execute commands in the container:\nsingularity exec portcullis.img portcullis --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eConda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/bioconda/portcullis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/381a7739b713a2bae02343a6ac934de39148a7866dbf4e52b597391b2a07fd4b/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f706f727463756c6c69732f6261646765732f6c61746573745f72656c656173655f646174652e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/bioconda/portcullis/badges/latest_release_date.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/portcullis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/77a7c650d2675de3588df907d8e8aec11957abc95bcfd87d3b1b07f78a2bc4ec/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f706f727463756c6c69732f6261646765732f706c6174666f726d732e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/bioconda/portcullis/badges/platforms.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/portcullis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/83781f462972e76ba4f2d046533fd48deb7cb72a0512481ff304f79c51bc01e3/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f706f727463756c6c69732f6261646765732f646f776e6c6f6164732e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/bioconda/portcullis/badges/downloads.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install portcullis --channel=bioconda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eBrew\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebrew install brewsci/bio/portcullis\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eFrom source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/maplesond/portcullis/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3885a69f4777ec0c98cf3d0bee17eb7ca3d3eb69bbf850df2f36895b80168ade/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f646f776e6c6f6164732f6d61706c65736f6e642f706f727463756c6c69732f746f74616c2e737667\" alt=\"Downloads\" data-canonical-src=\"https://img.shields.io/github/downloads/maplesond/portcullis/total.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you wish to install from source please first confirm that first you have these dependencies are installed and configured:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eGCC\u003c/strong\u003e V4.8+\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eautoconf\u003c/strong\u003e V2.53+\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eautomake\u003c/strong\u003e V1.11+\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003emake\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003elibtool\u003c/strong\u003e V2.4.2+\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ezlib-dev\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003epthreads\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eboost-dev\u003c/strong\u003e V1.52+\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003esamtools\u003c/strong\u003e V1.2+\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePython3-dev\u003c/strong\u003e V3.5+ (Make sure the following packages are installed: \u003cem\u003epandas\u003c/em\u003e, \u003cem\u003ematplotlib\u003c/em\u003e, \u003cem\u003esetuptools\u003c/em\u003e, \u003cem\u003esphinx\u003c/em\u003e, \u003cem\u003etabulate\u003c/em\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen proceed with the following steps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Clone the repo\ngit clone git@github.com:maplesond/portcullis.git\n\n# Move into repo directory\ncd portcullis\n\n# Generate configure script\n./autogen.sh\n\n# Confirm dependencies and generate makefiles\n# Adding --prefix \u0026lt;dir\u0026gt; will tell make install to put everything in a \n# particular directory. Default is /usr/local.\n./configure\n\n# Compile (increasing -j will make it go faster!\nmake -j 2\n\n# Run some unit tests (you can increase -j here too)\nmake -j 2 check\n\n# Install to prefix dir\nmake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eCommon problems\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMany system python installations do not come with the C API immediately available, which prevents Portcullis from embedding python code. We typically would recommend installing anaconda3 as this would include the latest version of python, all required python packages as well as the C API. If you are running a debian system and the C libraries are not available by default and you wish to use the system python installation the you can install them using: \u003ccode\u003esudo apt-get install python-dev\u003c/code\u003e. Also, if you have installed python to a custom location please verify that the \u003cem\u003ebin\u003c/em\u003e directors on the \u003cem\u003ePATH\u003c/em\u003e environment variable, and the lib (or lib64) directory is on the \u003cem\u003eLD_LIBRARY_PATH\u003c/em\u003e or \u003cem\u003eLD_RUN_PATH\u003c/em\u003e as appropriate.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf Portcullis is failing at the \u003ccode\u003e./autogen.sh\u003c/code\u003e step you will likely need to install autotools. The following command should do this on MacOS: \u003ccode\u003ebrew install autoconf automake libtool\u003c/code\u003e. On a debian system this can be done with: \u003ccode\u003esudo apt-get install autoconf automake libtool\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quickstart\" class=\"anchor\" href=\"#quickstart\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eAfter portcullis has been installed, the \u003ccode\u003eportcullis\u003c/code\u003e executable should be available. Typing \u003ccode\u003eportcullis\u003c/code\u003e or \u003ccode\u003eportcullis --help\u003c/code\u003e at the command line will present you with the portcullis help message.\u003c/p\u003e\n\u003cp\u003eThese modes are available:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eprep\u003c/strong\u003e - Prepares input data so that it is suitable for junction analysis\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ejunc\u003c/strong\u003e - Calculates junction metrics for the prepared data\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efilter\u003c/strong\u003e - Separates alignments based on whether they are likely to represent genuine splice junctions or not\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebamfilt\u003c/strong\u003e - Filters a BAM to remove any reads associated with invalid junctions\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efull\u003c/strong\u003e - Runs prep, junc, filter and optionally bamfilt as a complete pipeline\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTyping \u003ccode\u003eportcullis \u0026lt;mode\u0026gt; --help\u003c/code\u003e will bring up help and usage information specific to that mode.\u003c/p\u003e\n\u003cp\u003eIn addition to portcullis, we provide a tool-suite for manipulating junction files called junctools. Typing \u003ccode\u003ejunctools --help\u003c/code\u003e will provide you with the program options.\u003c/p\u003e\n\u003cp\u003eFor much more information about portcullis\u0027 capabilities and how to configure and run it, an online version of the manual can be found here: \u003ca href=\"https://portcullis.readthedocs.org/en/latest/\" rel=\"nofollow\"\u003ehttps://portcullis.readthedocs.org/en/latest/\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-licensing\" class=\"anchor\" href=\"#licensing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003eGNU GPL V3. See COPYING file for more details.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-authors\" class=\"anchor\" href=\"#authors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDaniel Mapleson\u003c/li\u003e\n\u003cli\u003eLuca Venturini\u003c/li\u003e\n\u003cli\u003eDavid Swarbreck\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee AUTHORS file for more details.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" href=\"#acknowledgements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eAffiliation: The Earlham Institute (EI)\nFunding: The Biotechnology and Biological Sciences Research Council (BBSRC)\u003c/p\u003e\n", - "stargazers_count": 23, - "subscribers_count": 3, - "topics": [ - "portcullis", - "junction", - "splice-junctions", - "bam-files", - "filter" - ], - "updated_at": 1620067152.0 + "updated_at": 1732580917.0 }, { "data_format": 2, - "description": null, + "description": "MEGARes and AmrPlusPlus - A comprehensive database of antimicrobial resistance genes and user-friendly pipeline for analysis of high-throughput sequencing data", "filenames": [ - "Pytorch_Distributed_Deep_Learning/Singularity" + "containers/Singularity", + "containers/Singularity.RGI" ], - "full_name": "gfiameni/hpdl", - "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-welcome-to-this-repo-about-high-performance-deep-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcome-to-this-repo-about-high-performance-deep-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to this repo about High Performance Deep Learning\u003c/h1\u003e\n\u003cp\u003eThis repository contains different example Jupyter notebooks dealing with large-scale models training with PyTorch. They are inspired by materials, examples, exercies mainly taken from official PyTorch tutotials and other authors. Each notebook contains the list of reference material.\u003c/p\u003e\n\u003cp\u003eTopics covered in this PyTorch Multi-GPU approach to Deep learning Models include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eData and Model Parallelism\u003c/li\u003e\n\u003cli\u003eMessage Passing\u003c/li\u003e\n\u003cli\u003eDistributed training using Horovord\u003c/li\u003e\n\u003cli\u003eMixed Precision and Memory Format\u003c/li\u003e\n\u003cli\u003ePipeline Parallelism\u003c/li\u003e\n\u003cli\u003eand a challenge to test your knowledge\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h1\u003e\n\u003cp\u003eTo run this tutorial you will need a machine with NVIDIA GPU and also install any of the two listed below.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e, the primitives it provides for \u003ca href=\"https://pytorch.org/tutorials/intermediate/dist_tuto.html\" rel=\"nofollow\"\u003ewriting distributed applications\u003c/a\u003e as well as training \u003ca href=\"https://pytorch.org/tutorials/intermediate/ddp_tutorial.html\" rel=\"nofollow\"\u003edistributed models\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the latest \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. Then start you will have to build a Docker or Singularity container.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Container\u003c/h3\u003e\n\u003cp\u003eTo build a docker container, run:\n\u003ccode\u003esudo docker build --network=host -t \u0026lt;imagename\u0026gt;:\u0026lt;tagnumber\u0026gt; .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor instance:\n\u003ccode\u003esudo docker build -t pytorch:1.0 .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe code labs have been written using Jupyter notebooks and a Dockerfile has been built to simplify deployment. The following command would expose port 8888 inside the container as port 8888 on the lab machine:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -it --rm --network=host -v ~/hpdl/Pytorch_Distributed_Deep_Learning/workspace:/workspace pytorch:1.0 jupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--gpus\u003c/code\u003e flag is used to enable \u003ccode\u003eall\u003c/code\u003e NVIDIA GPUs during container runtime. The \u003ccode\u003e--rm\u003c/code\u003e flag is used to clean an temporary images created during the running of the container. The \u003ccode\u003e-it\u003c/code\u003e flag enables killing the jupyter server with \u003ccode\u003ectrl-c\u003c/code\u003e. The \u003ccode\u003e--ipc=host --ulimit memlock=-1 --ulimit stack=67108864\u003c/code\u003e enable sufficient memory allocation to run pytorch within the docker environment.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ejupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e command launch the jupyter notebook inside the container. The flag \u003ccode\u003e-v\u003c/code\u003e allows the mapping of working directory on your local machine \u003ccode\u003e~/hpdl/Pytorch_Distributed_Deep_Learning/workspace:/workspace\u003c/code\u003e to \u003ccode\u003eworspace\u003c/code\u003e directory inside the container.\u003c/p\u003e\n\u003cp\u003eThis command may be customized for your hosting environment. Now, open the jupyter notebook in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eStart by clicking on the \u003ccode\u003eStart_Here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container\u003c/h3\u003e\n\u003cp\u003eTo build the singularity container, run:\n\u003ccode\u003esudo singularity build --fakeroot \u0026lt;image_name\u0026gt;.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor example:\n\u003ccode\u003esingularity build --fakeroot pytorch.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, run the container:\n\u003ccode\u003esingularity run --nv --bind ~/hpdl/Pytorch_Distributed_Deep_Learning/workspace:/workspace pytorch.simg jupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, open the jupyter notebook in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e\nStart working on the lab by clicking on the \u003ccode\u003eStart_Here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cp\u003e#Tutorial Duration\nThe total bootcamp material would take approximately 4 hours.\u003c/p\u003e\n", + "full_name": "meglab-metagenomics/amrplusplus_v2", + "latest_release": "v2.0.2", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4426cbe5c21edb002526331c7a8fbfa089e84a550567b02a0d829a98b136ad0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d0858513f50b40ff4765c1310fa3edc59575c4c7934cd88a30991856dac51e2c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus\"\u003eAMR++ v3 now available!\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-amr-v3-now-available\" class=\"anchor\" aria-label=\"Permalink: AMR++ v3 now available!\" href=\"#amr-v3-now-available\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have migrated github repositories to a \u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus\"\u003enew location\u003c/a\u003e (to make it a group repository), and this repository will be deprecated. We apologize for any inconvenience and hope you find v3 useful for your research needs. Of note, version 3 includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSNP confirmation using a custom database and \u003ca href=\"https://github.com/Isabella136/AmrPlusPlus_SNP\"\u003eSNP verification software\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eimproved modularity to optimize a personalized workflow\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2022-08-22 : AMR++ update coming soon\u003c/h3\u003e\u003ca id=\"user-content-2022-08-22--amr-update-coming-soon\" class=\"anchor\" aria-label=\"Permalink: 2022-08-22 : AMR++ update coming soon\" href=\"#2022-08-22--amr-update-coming-soon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHello AMR++ users, we would like to sincerely apologize for the delay in addresssing your concerns and updating AMR++. As a lot of you likely experienced, COVID was challenging and we were not able dedicate the resources to AMR++ that it deserves. We are happy to announce that we have assembled a team for another major update to AMR++ and the MEGARes database in the next few months!\u003c/p\u003e\n\u003cp\u003eA few notes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWe are aware of the issues with integrating RGI results with the AMR++ pipeline. Unfortunately, we are discontinuing our support of integrating AMR++ results with the RGI software.\u003c/li\u003e\n\u003cli\u003eWe are attempting to remedy the issues that AMR++ users have reported, but we would also like to hear any other suggestions you might have. Please send any suggestions to \u003ca href=\"mailto:enriquedoster@gmail.com\"\u003eenriquedoster@gmail.com\u003c/a\u003e with the subject line, \"AMR++ update\".\u003c/li\u003e\n\u003cli\u003eA few upcoming updates: easy control over the amount of intermediate files that are stored, option to re-arrange pipeline processes, better sample summary statistics provided, and improved functionality through nextflow profiles.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2020-03-21 : AMR++ v2.0.2 update.\u003c/h3\u003e\u003ca id=\"user-content-2020-03-21--amr-v202-update\" class=\"anchor\" aria-label=\"Permalink: 2020-03-21 : AMR++ v2.0.2 update.\" href=\"#2020-03-21--amr-v202-update\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe identified issues in running RGI with the full AMR++ pipeline thanks to github users, AroArz and DiegoBrambilla. We are releasing v2.0.1 to continue AMR++ functionality, but we are planning further updates for the next stable release. As of this update, RGI developers are focused on contributing to the COVID-19 response, so we plan to reconvene with them when their schedule opens up.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlease view the \u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/CHANGELOG.md\"\u003eCHANGELOG\u003c/a\u003e for more details on changes included in AMR++ v2.0.1\u003c/li\u003e\n\u003cli\u003eTo run the AMR++ pipeline with RGI, you\u0027ll have to download the CARD database locally and specify it\u0027s location using the \"--card_db\" flag like this:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e# If you want to include RGI in your analysis, first download CARD with this command:\n# We tested AMR++ v2.0.2 with the CARD database v3.0.8, but we recommend using the command below to get the latest CARD db\nwget -q -O card-data.tar.bz2 https://card.mcmaster.ca/latest/data \u0026amp;\u0026amp; tar xfvj card-data.tar.bz2\n\n# In case the latest CARD database is causing issues, you can download the version we used for testing, v3.0.8:\nwget -q -O card-data.tar.bz2 https://card.mcmaster.ca/download/0/broadstreet-v3.0.8.tar.bz2 \u0026amp;\u0026amp; tar xfvj card-data.tar.bz2\n\n\n# If you run into an error regarding \"Issued certificate has expired.\", try this command:\nwget --no-check-certificate -q -O card-data.tar.bz2 https://card.mcmaster.ca/latest/data \u0026amp;\u0026amp; tar xfvj card-data.tar.bz2\n\n\n# Run the AMR++ pipeline with the \"--card_db\" flag\nnextflow run main_AmrPlusPlus_v2_withRGI.nf -profile singularity --card_db /path/to/card.json --reads \u0027/path/to/reads/*R{1,2}_001.R1.fastq.gz\u0027 --output AMR++_results -w work_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMicrobial Ecology Group (MEG)\u003c/h1\u003e\u003ca id=\"user-content-microbial-ecology-group-meg\" class=\"anchor\" aria-label=\"Permalink: Microbial Ecology Group (MEG)\" href=\"#microbial-ecology-group-meg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(\u003ca href=\"https://megares.meglab.org/\" rel=\"nofollow\"\u003ehttps://megares.meglab.org/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eOur international multidisciplinary group of scientists and educators is addressing the issues of antimicrobial resistance (AMR) and microbial ecology in agriculture through research, outreach, and education. By characterizing risks related to AMR and microbial ecology, our center will identify agricultural production practices that are harmful and can be avoided, while also identifying and promoting production practices and interventions that are beneficial or do no harm to the ecosystem or public health. This will allow society to realize \u201csustainable intensification\u201d of agriculture.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMEGARes and the AMR++ bioinformatic pipeline\u003c/h1\u003e\u003ca id=\"user-content-megares-and-the-amr-bioinformatic-pipeline\" class=\"anchor\" aria-label=\"Permalink: MEGARes and the AMR++ bioinformatic pipeline\" href=\"#megares-and-the-amr-bioinformatic-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(\u003ca href=\"http://megares.meglab.org/amrplusplus/latest/html/v2/\" rel=\"nofollow\"\u003ehttp://megares.meglab.org/amrplusplus/latest/html/v2/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eThe MEGARes database contains sequence data for approximately 8,000 hand-curated antimicrobial resistance genes accompanied by an annotation structure that is optimized for use with high throughput sequencing and metagenomic analysis. The acyclical annotation graph of MEGARes allows for accurate, count-based, hierarchical statistical analysis of resistance at the population level, much like microbiome analysis, and is also designed to be used as a training database for the creation of statistical classifiers.\u003c/p\u003e\n\u003cp\u003eThe goal of many metagenomics studies is to characterize the content and relative abundance of sequences of interest from the DNA of a given sample or set of samples. You may want to know what is contained within your sample or how abundant a given sequence is relative to another.\u003c/p\u003e\n\u003cp\u003eOften, metagenomics is performed when the answer to these questions must be obtained for a large number of targets where techniques like multiplex PCR and other targeted methods would be too cumbersome to perform. AmrPlusPlus can process the raw data from the sequencer, identify the fragments of DNA, and count them. It also provides a count of the polymorphisms that occur in each DNA fragment with respect to the reference database.\u003c/p\u003e\n\u003cp\u003eAdditionally, you may want to know if the depth of your sequencing (how many reads you obtain that are on target) is high enough to identify rare organisms (organisms with low abundance relative to others) in your population. This is referred to as rarefaction and is calculated by randomly subsampling your sequence data at intervals between 0% and 100% in order to determine how many targets are found at each depth.\u003c/p\u003e\n\u003cp\u003eWith AMR++, you will obtain alignment count files for each sample that are combined into a count matrix that can be analyzed using any statistical and mathematical techniques that can operate on a matrix of observations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMore Information\u003c/h2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-label=\"Permalink: More Information\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/accessing_AMR++.md\"\u003eAccessing AMR++\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/FAQs.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/update_details.md\"\u003eDetails on AMR++ updates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 23, - "subscribers_count": 2, + "subscribers_count": 7, "topics": [], - "updated_at": 1668106659.0 + "updated_at": 1683897635.0 }, { "data_format": 2, @@ -39332,63 +39327,65 @@ var data = }, { "data_format": 2, - "description": "MEGARes and AmrPlusPlus - A comprehensive database of antimicrobial resistance genes and user-friendly pipeline for analysis of high-throughput sequencing data", + "description": null, "filenames": [ - "containers/Singularity", - "containers/Singularity.RGI" + "Pytorch_Distributed_Deep_Learning/Singularity" ], - "full_name": "meglab-metagenomics/amrplusplus_v2", - "latest_release": "v2.0.2", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a4426cbe5c21edb002526331c7a8fbfa089e84a550567b02a0d829a98b136ad0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d0858513f50b40ff4765c1310fa3edc59575c4c7934cd88a30991856dac51e2c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e657874666c6f772d254532253839254135302e32352e312d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/Nextflow-%E2%89%A50.25.1-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus\"\u003eAMR++ v3 now available!\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-amr-v3-now-available\" class=\"anchor\" aria-label=\"Permalink: AMR++ v3 now available!\" href=\"#amr-v3-now-available\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe have migrated github repositories to a \u003ca href=\"https://github.com/Microbial-Ecology-Group/AMRplusplus\"\u003enew location\u003c/a\u003e (to make it a group repository), and this repository will be deprecated. We apologize for any inconvenience and hope you find v3 useful for your research needs. Of note, version 3 includes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSNP confirmation using a custom database and \u003ca href=\"https://github.com/Isabella136/AmrPlusPlus_SNP\"\u003eSNP verification software\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eimproved modularity to optimize a personalized workflow\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2022-08-22 : AMR++ update coming soon\u003c/h3\u003e\u003ca id=\"user-content-2022-08-22--amr-update-coming-soon\" class=\"anchor\" aria-label=\"Permalink: 2022-08-22 : AMR++ update coming soon\" href=\"#2022-08-22--amr-update-coming-soon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHello AMR++ users, we would like to sincerely apologize for the delay in addresssing your concerns and updating AMR++. As a lot of you likely experienced, COVID was challenging and we were not able dedicate the resources to AMR++ that it deserves. We are happy to announce that we have assembled a team for another major update to AMR++ and the MEGARes database in the next few months!\u003c/p\u003e\n\u003cp\u003eA few notes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWe are aware of the issues with integrating RGI results with the AMR++ pipeline. Unfortunately, we are discontinuing our support of integrating AMR++ results with the RGI software.\u003c/li\u003e\n\u003cli\u003eWe are attempting to remedy the issues that AMR++ users have reported, but we would also like to hear any other suggestions you might have. Please send any suggestions to \u003ca href=\"mailto:enriquedoster@gmail.com\"\u003eenriquedoster@gmail.com\u003c/a\u003e with the subject line, \"AMR++ update\".\u003c/li\u003e\n\u003cli\u003eA few upcoming updates: easy control over the amount of intermediate files that are stored, option to re-arrange pipeline processes, better sample summary statistics provided, and improved functionality through nextflow profiles.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2020-03-21 : AMR++ v2.0.2 update.\u003c/h3\u003e\u003ca id=\"user-content-2020-03-21--amr-v202-update\" class=\"anchor\" aria-label=\"Permalink: 2020-03-21 : AMR++ v2.0.2 update.\" href=\"#2020-03-21--amr-v202-update\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe identified issues in running RGI with the full AMR++ pipeline thanks to github users, AroArz and DiegoBrambilla. We are releasing v2.0.1 to continue AMR++ functionality, but we are planning further updates for the next stable release. As of this update, RGI developers are focused on contributing to the COVID-19 response, so we plan to reconvene with them when their schedule opens up.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlease view the \u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/CHANGELOG.md\"\u003eCHANGELOG\u003c/a\u003e for more details on changes included in AMR++ v2.0.1\u003c/li\u003e\n\u003cli\u003eTo run the AMR++ pipeline with RGI, you\u0027ll have to download the CARD database locally and specify it\u0027s location using the \"--card_db\" flag like this:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e# If you want to include RGI in your analysis, first download CARD with this command:\n# We tested AMR++ v2.0.2 with the CARD database v3.0.8, but we recommend using the command below to get the latest CARD db\nwget -q -O card-data.tar.bz2 https://card.mcmaster.ca/latest/data \u0026amp;\u0026amp; tar xfvj card-data.tar.bz2\n\n# In case the latest CARD database is causing issues, you can download the version we used for testing, v3.0.8:\nwget -q -O card-data.tar.bz2 https://card.mcmaster.ca/download/0/broadstreet-v3.0.8.tar.bz2 \u0026amp;\u0026amp; tar xfvj card-data.tar.bz2\n\n\n# If you run into an error regarding \"Issued certificate has expired.\", try this command:\nwget --no-check-certificate -q -O card-data.tar.bz2 https://card.mcmaster.ca/latest/data \u0026amp;\u0026amp; tar xfvj card-data.tar.bz2\n\n\n# Run the AMR++ pipeline with the \"--card_db\" flag\nnextflow run main_AmrPlusPlus_v2_withRGI.nf -profile singularity --card_db /path/to/card.json --reads \u0027/path/to/reads/*R{1,2}_001.R1.fastq.gz\u0027 --output AMR++_results -w work_dir\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMicrobial Ecology Group (MEG)\u003c/h1\u003e\u003ca id=\"user-content-microbial-ecology-group-meg\" class=\"anchor\" aria-label=\"Permalink: Microbial Ecology Group (MEG)\" href=\"#microbial-ecology-group-meg\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(\u003ca href=\"https://megares.meglab.org/\" rel=\"nofollow\"\u003ehttps://megares.meglab.org/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eOur international multidisciplinary group of scientists and educators is addressing the issues of antimicrobial resistance (AMR) and microbial ecology in agriculture through research, outreach, and education. By characterizing risks related to AMR and microbial ecology, our center will identify agricultural production practices that are harmful and can be avoided, while also identifying and promoting production practices and interventions that are beneficial or do no harm to the ecosystem or public health. This will allow society to realize \u201csustainable intensification\u201d of agriculture.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMEGARes and the AMR++ bioinformatic pipeline\u003c/h1\u003e\u003ca id=\"user-content-megares-and-the-amr-bioinformatic-pipeline\" class=\"anchor\" aria-label=\"Permalink: MEGARes and the AMR++ bioinformatic pipeline\" href=\"#megares-and-the-amr-bioinformatic-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e(\u003ca href=\"http://megares.meglab.org/amrplusplus/latest/html/v2/\" rel=\"nofollow\"\u003ehttp://megares.meglab.org/amrplusplus/latest/html/v2/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eThe MEGARes database contains sequence data for approximately 8,000 hand-curated antimicrobial resistance genes accompanied by an annotation structure that is optimized for use with high throughput sequencing and metagenomic analysis. The acyclical annotation graph of MEGARes allows for accurate, count-based, hierarchical statistical analysis of resistance at the population level, much like microbiome analysis, and is also designed to be used as a training database for the creation of statistical classifiers.\u003c/p\u003e\n\u003cp\u003eThe goal of many metagenomics studies is to characterize the content and relative abundance of sequences of interest from the DNA of a given sample or set of samples. You may want to know what is contained within your sample or how abundant a given sequence is relative to another.\u003c/p\u003e\n\u003cp\u003eOften, metagenomics is performed when the answer to these questions must be obtained for a large number of targets where techniques like multiplex PCR and other targeted methods would be too cumbersome to perform. AmrPlusPlus can process the raw data from the sequencer, identify the fragments of DNA, and count them. It also provides a count of the polymorphisms that occur in each DNA fragment with respect to the reference database.\u003c/p\u003e\n\u003cp\u003eAdditionally, you may want to know if the depth of your sequencing (how many reads you obtain that are on target) is high enough to identify rare organisms (organisms with low abundance relative to others) in your population. This is referred to as rarefaction and is calculated by randomly subsampling your sequence data at intervals between 0% and 100% in order to determine how many targets are found at each depth.\u003c/p\u003e\n\u003cp\u003eWith AMR++, you will obtain alignment count files for each sample that are combined into a count matrix that can be analyzed using any statistical and mathematical techniques that can operate on a matrix of observations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMore Information\u003c/h2\u003e\u003ca id=\"user-content-more-information\" class=\"anchor\" aria-label=\"Permalink: More Information\" href=\"#more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/usage.md\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/configuration.md\"\u003eConfiguration\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/accessing_AMR++.md\"\u003eAccessing AMR++\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/output.md\"\u003eOutput\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/dependencies.md\"\u003eDependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/requirements.md\"\u003eSoftware Requirements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/FAQs.md\"\u003eFAQs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/update_details.md\"\u003eDetails on AMR++ updates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/meglab-metagenomics/amrplusplus_v2/blob/master/docs/contact.md\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "gfiameni/hpdl", + "latest_release": null, + "readme": "\u003ch1\u003e\u003ca id=\"user-content-welcome-to-this-repo-about-high-performance-deep-learning\" class=\"anchor\" aria-hidden=\"true\" href=\"#welcome-to-this-repo-about-high-performance-deep-learning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to this repo about High Performance Deep Learning\u003c/h1\u003e\n\u003cp\u003eThis repository contains different example Jupyter notebooks dealing with large-scale models training with PyTorch. They are inspired by materials, examples, exercies mainly taken from official PyTorch tutotials and other authors. Each notebook contains the list of reference material.\u003c/p\u003e\n\u003cp\u003eTopics covered in this PyTorch Multi-GPU approach to Deep learning Models include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eData and Model Parallelism\u003c/li\u003e\n\u003cli\u003eMessage Passing\u003c/li\u003e\n\u003cli\u003eDistributed training using Horovord\u003c/li\u003e\n\u003cli\u003eMixed Precision and Memory Format\u003c/li\u003e\n\u003cli\u003ePipeline Parallelism\u003c/li\u003e\n\u003cli\u003eand a challenge to test your knowledge\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-hidden=\"true\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePrerequisites\u003c/h1\u003e\n\u003cp\u003eTo run this tutorial you will need a machine with NVIDIA GPU and also install any of the two listed below.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e, the primitives it provides for \u003ca href=\"https://pytorch.org/tutorials/intermediate/dist_tuto.html\" rel=\"nofollow\"\u003ewriting distributed applications\u003c/a\u003e as well as training \u003ca href=\"https://pytorch.org/tutorials/intermediate/ddp_tutorial.html\" rel=\"nofollow\"\u003edistributed models\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the latest \u003ca href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. Then start you will have to build a Docker or Singularity container.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-docker-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#docker-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocker Container\u003c/h3\u003e\n\u003cp\u003eTo build a docker container, run:\n\u003ccode\u003esudo docker build --network=host -t \u0026lt;imagename\u0026gt;:\u0026lt;tagnumber\u0026gt; .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor instance:\n\u003ccode\u003esudo docker build -t pytorch:1.0 .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe code labs have been written using Jupyter notebooks and a Dockerfile has been built to simplify deployment. The following command would expose port 8888 inside the container as port 8888 on the lab machine:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -it --rm --network=host -v ~/hpdl/Pytorch_Distributed_Deep_Learning/workspace:/workspace pytorch:1.0 jupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--gpus\u003c/code\u003e flag is used to enable \u003ccode\u003eall\u003c/code\u003e NVIDIA GPUs during container runtime. The \u003ccode\u003e--rm\u003c/code\u003e flag is used to clean an temporary images created during the running of the container. The \u003ccode\u003e-it\u003c/code\u003e flag enables killing the jupyter server with \u003ccode\u003ectrl-c\u003c/code\u003e. The \u003ccode\u003e--ipc=host --ulimit memlock=-1 --ulimit stack=67108864\u003c/code\u003e enable sufficient memory allocation to run pytorch within the docker environment.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003ejupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e command launch the jupyter notebook inside the container. The flag \u003ccode\u003e-v\u003c/code\u003e allows the mapping of working directory on your local machine \u003ccode\u003e~/hpdl/Pytorch_Distributed_Deep_Learning/workspace:/workspace\u003c/code\u003e to \u003ccode\u003eworspace\u003c/code\u003e directory inside the container.\u003c/p\u003e\n\u003cp\u003eThis command may be customized for your hosting environment. Now, open the jupyter notebook in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eStart by clicking on the \u003ccode\u003eStart_Here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-singularity-container\" class=\"anchor\" aria-hidden=\"true\" href=\"#singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSingularity Container\u003c/h3\u003e\n\u003cp\u003eTo build the singularity container, run:\n\u003ccode\u003esudo singularity build --fakeroot \u0026lt;image_name\u0026gt;.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFor example:\n\u003ccode\u003esingularity build --fakeroot pytorch.simg Singularity\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, run the container:\n\u003ccode\u003esingularity run --nv --bind ~/hpdl/Pytorch_Distributed_Deep_Learning/workspace:/workspace pytorch.simg jupyter-lab --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token=\"\" --notebook-dir=/workspace\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThen, open the jupyter notebook in browser: \u003ca href=\"http://localhost:8888\" rel=\"nofollow\"\u003ehttp://localhost:8888\u003c/a\u003e\nStart working on the lab by clicking on the \u003ccode\u003eStart_Here.ipynb\u003c/code\u003e notebook.\u003c/p\u003e\n\u003cp\u003e#Tutorial Duration\nThe total bootcamp material would take approximately 4 hours.\u003c/p\u003e\n", "stargazers_count": 23, - "subscribers_count": 7, + "subscribers_count": 2, "topics": [], - "updated_at": 1683897635.0 + "updated_at": 1668106659.0 }, { "data_format": 2, - "description": "AutoMATES: Automated Model Assembly from Text, Equations, and Software", + "description": "Splice junction analysis and filtering from BAM files", "filenames": [ - "automates/equation_reading/equation_extraction/containers/Singularity.im2markup", - "automates/equation_reading/equation_extraction/containers/Singularity.pytorch_skimage" + "Singularity" ], - "full_name": "ml4ai/automates", - "latest_release": "v1.4.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 align=\"center\" class=\"heading-element\"\u003eAutomated Model Assembly\u003cbr\u003efrom Text, Equations, and Software\u003c/h1\u003e\u003ca id=\"user-content-automated-model-assemblyfrom-text-equations-and-software\" class=\"anchor\" aria-label=\"Permalink: Automated Model Assemblyfrom Text, Equations, and Software\" href=\"#automated-model-assemblyfrom-text-equations-and-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n \n \n \u003ca href=\"https://github.com/ml4ai/automates/actions\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/26bab0b570017976d76f1fb6eb2de47c2bd98eceaa5545eadc4e255c98a0f2ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f776f726b666c6f772f7374617475732f6d6c3461692f6175746f6d617465732f436f6e74696e756f7573253230496e746567726174696f6e3f6c6162656c3d7465737473\" alt=\"GH Actions build status\" data-canonical-src=\"https://img.shields.io/github/workflow/status/ml4ai/automates/Continuous%20Integration?label=tests\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://codecov.io/gh/ml4ai/automates\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/681b94c316ad89b992991d018a5ed84bb2e2cc6b091292d244cb193ee5a505f0/68747470733a2f2f636f6465636f762e696f2f67682f6d6c3461692f6175746f6d617465732f6272616e63682f6d61737465722f67726170682f62616467652e737667\" data-canonical-src=\"https://codecov.io/gh/ml4ai/automates/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://www.codefactor.io/repository/github/ml4ai/automates\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/25ce80bae380fdfafba09fec84b05d741f6055f20d438f98abee2043116dec4f/68747470733a2f2f7777772e636f6465666163746f722e696f2f7265706f7369746f72792f6769746875622f6d6c3461692f6175746f6d617465732f6261646765\" alt=\"CodeFactor\" data-canonical-src=\"https://www.codefactor.io/repository/github/ml4ai/automates/badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eThis repository holds the source code for the AutoMATES documentation\nand several component pipelines.\u003c/p\u003e\n\u003cp\u003eFor documentation: \u003ca href=\"https://ml4ai.github.io/automates\" rel=\"nofollow\"\u003ehttps://ml4ai.github.io/automates\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation instructions\u003c/h2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-label=\"Permalink: Installation instructions\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor all operating systems, the first step of the installation process is to clone the AutoMATES repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLinux and macOS\u003c/h3\u003e\u003ca id=\"user-content-linux-and-macos\" class=\"anchor\" aria-label=\"Permalink: Linux and macOS\" href=\"#linux-and-macos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a new \u003ca href=\"https://docs.python.org/3/library/venv.html\" rel=\"nofollow\"\u003ePython virtualenv\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eActivate your new Python virtualenv\u003c/li\u003e\n\u003cli\u003eInstall Graphviz as defined below\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epip install -e .\u003c/code\u003e from the root of the AutoMATES directory\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGraphViz installation\u003c/h4\u003e\u003ca id=\"user-content-graphviz-installation\" class=\"anchor\" aria-label=\"Permalink: GraphViz installation\" href=\"#graphviz-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDebian flavored linux\u003c/h5\u003e\u003ca id=\"user-content-debian-flavored-linux\" class=\"anchor\" aria-label=\"Permalink: Debian flavored linux\" href=\"#debian-flavored-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse the command: \u003ccode\u003esudo apt-get install graphviz libgraphviz-dev pkg-config\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003emacOS with Homebrew\u003c/h5\u003e\u003ca id=\"user-content-macos-with-homebrew\" class=\"anchor\" aria-label=\"Permalink: macOS with Homebrew\" href=\"#macos-with-homebrew\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse the command: \u003ccode\u003ebrew install graphviz\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall PyGraphviz to your virtualenv with: \u003ccode\u003epip install --install-option=\"--include-path=/usr/local/include/\" --install-option=\"--library-path=/usr/local/lib\" pygraphviz\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWindows\u003c/h3\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload and install \u003ca href=\"https://www.anaconda.com/products/individual\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit the \u003ccode\u003ePYTHONPATH\u003c/code\u003e variable in \u003ccode\u003eenvironment.yml\u003c/code\u003e to be your local path to your checkout of the AutoMATES repo\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003econda env create --file environment.yml\u003c/code\u003e from the root of the AutoMATES directory\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 24, - "subscribers_count": 11, - "topics": [], - "updated_at": 1727628755.0 + "full_name": "maplesond/portcullis", + "latest_release": "1.2.2", + "readme": "\u003cp\u003e\u003ca href=\"doc/source/images/portcullis_logo.png\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\u003cimg src=\"doc/source/images/portcullis_logo.png\" alt=\"alt text\" title=\"Portcullis\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\n\u003ca id=\"user-content-portcullis\" class=\"anchor\" href=\"#portcullis\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePortcullis\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/maplesond/portcullis/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b9c31b04d2671e6317cdfd9e4fdf893512936091302d1b1b56c99cb89ab43df7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f7461672f6d61706c65736f6e642f706f727463756c6c69732e737667\" alt=\"Version\" data-canonical-src=\"https://img.shields.io/github/tag/maplesond/portcullis.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://jenkins.sdlmapleson.net/job/portcullis/job/develop/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f696e3e0136cfb90f0e05f4f4e0a257ece7cd1e52ff19a0c8963b32df756d3a7/68747470733a2f2f6a656e6b696e732e73646c6d61706c65736f6e2e6e65742f6275696c645374617475732f69636f6e3f6a6f623d706f727463756c6c6973253246646576656c6f70\" alt=\"Build Status\" data-canonical-src=\"https://jenkins.sdlmapleson.net/buildStatus/icon?job=portcullis%2Fdevelop\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/maplesond/portcullis/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad4d6f3e16da4f0dddcd142fa3b6088042b13242787f5ad939d2db28282d3eb5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c25323076332d627269676874677265656e2e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPL%20v3-brightgreen.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/maplesond/portcullis/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d3bedf8e24750956939d66108f9ba197e72b83d1de8fc7305708ab2d67c20c17/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d7261772f6d61706c65736f6e642f706f727463756c6c69732e737667\" alt=\"Issues\" data-canonical-src=\"https://img.shields.io/github/issues-raw/maplesond/portcullis.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePortcullis stands for PORTable CULLing of Invalid Splice junctions from pre-aligned RNA-seq data. It is known that RNAseq mapping tools generate many invalid junction predictions, particularly in deep datasets with high coverage over splice sites. In order to address this, instead for creating a new RNAseq mapper, with a focus on SJ accuracy we created a tool that takes in a BAM file generated by an RNAseq mapper of the user\u0027s own choice (e.g. Tophat2, Gsnap, STAR2 or HISAT2) as input (i.e. it\u0027s portable). It then, analyses and quantifies all splice junctions in the file before, filtering (culling) those which are unlikely to be genuine. Portcullis output\u0027s junctions in a variety of formats making it suitable for downstream analysis (such as differential splicing analysis and gene modelling) without additional work. Portcullis can also filter the original BAM file removing alignments associated with \u003cem\u003ebad\u003c/em\u003e junctions. Both the filtered junctions and BAM files are cleaner and more usable resources which can more effectively be used to assist in downstream analyses such as gene prediction and genome annotation.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eWe support multiple methods for installing and running portcullis. Hopefully your favourite container or package manager is supported below. If not let us know and we\u0027ll try to work to get it integrated there.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDocker\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/maplesond/portcullis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/de966674ebe7a3dec2fed423683dd2c64e3630527fab6a691add53421292e384/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d61706c65736f6e642f706f727463756c6c69732e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/maplesond/portcullis.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Keep in mind you need to mount in any working directories to the container with the `-v` option.\n# Ideally, mount these into the /data directory which is the container\u0027s working directory.\ndocker run --it --rm -v /abspath/to/data/on/host:/data maplesond/portcullis:stable portcullis --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# First download the container:\nsingularity pull --name portcullis.img shub://maplesond/portcullis:master\n\n# Then to execute commands in the container:\nsingularity exec portcullis.img portcullis --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eConda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/bioconda/portcullis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/381a7739b713a2bae02343a6ac934de39148a7866dbf4e52b597391b2a07fd4b/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f706f727463756c6c69732f6261646765732f6c61746573745f72656c656173655f646174652e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/bioconda/portcullis/badges/latest_release_date.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/portcullis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/77a7c650d2675de3588df907d8e8aec11957abc95bcfd87d3b1b07f78a2bc4ec/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f706f727463756c6c69732f6261646765732f706c6174666f726d732e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/bioconda/portcullis/badges/platforms.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/portcullis\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/83781f462972e76ba4f2d046533fd48deb7cb72a0512481ff304f79c51bc01e3/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f706f727463756c6c69732f6261646765732f646f776e6c6f6164732e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/bioconda/portcullis/badges/downloads.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install portcullis --channel=bioconda\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eBrew\u003c/strong\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebrew install brewsci/bio/portcullis\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eFrom source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/maplesond/portcullis/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3885a69f4777ec0c98cf3d0bee17eb7ca3d3eb69bbf850df2f36895b80168ade/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f646f776e6c6f6164732f6d61706c65736f6e642f706f727463756c6c69732f746f74616c2e737667\" alt=\"Downloads\" data-canonical-src=\"https://img.shields.io/github/downloads/maplesond/portcullis/total.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you wish to install from source please first confirm that first you have these dependencies are installed and configured:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eGCC\u003c/strong\u003e V4.8+\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eautoconf\u003c/strong\u003e V2.53+\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eautomake\u003c/strong\u003e V1.11+\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003emake\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003elibtool\u003c/strong\u003e V2.4.2+\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ezlib-dev\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003epthreads\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eboost-dev\u003c/strong\u003e V1.52+\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003esamtools\u003c/strong\u003e V1.2+\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePython3-dev\u003c/strong\u003e V3.5+ (Make sure the following packages are installed: \u003cem\u003epandas\u003c/em\u003e, \u003cem\u003ematplotlib\u003c/em\u003e, \u003cem\u003esetuptools\u003c/em\u003e, \u003cem\u003esphinx\u003c/em\u003e, \u003cem\u003etabulate\u003c/em\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThen proceed with the following steps:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Clone the repo\ngit clone git@github.com:maplesond/portcullis.git\n\n# Move into repo directory\ncd portcullis\n\n# Generate configure script\n./autogen.sh\n\n# Confirm dependencies and generate makefiles\n# Adding --prefix \u0026lt;dir\u0026gt; will tell make install to put everything in a \n# particular directory. Default is /usr/local.\n./configure\n\n# Compile (increasing -j will make it go faster!\nmake -j 2\n\n# Run some unit tests (you can increase -j here too)\nmake -j 2 check\n\n# Install to prefix dir\nmake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eCommon problems\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMany system python installations do not come with the C API immediately available, which prevents Portcullis from embedding python code. We typically would recommend installing anaconda3 as this would include the latest version of python, all required python packages as well as the C API. If you are running a debian system and the C libraries are not available by default and you wish to use the system python installation the you can install them using: \u003ccode\u003esudo apt-get install python-dev\u003c/code\u003e. Also, if you have installed python to a custom location please verify that the \u003cem\u003ebin\u003c/em\u003e directors on the \u003cem\u003ePATH\u003c/em\u003e environment variable, and the lib (or lib64) directory is on the \u003cem\u003eLD_LIBRARY_PATH\u003c/em\u003e or \u003cem\u003eLD_RUN_PATH\u003c/em\u003e as appropriate.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf Portcullis is failing at the \u003ccode\u003e./autogen.sh\u003c/code\u003e step you will likely need to install autotools. The following command should do this on MacOS: \u003ccode\u003ebrew install autoconf automake libtool\u003c/code\u003e. On a debian system this can be done with: \u003ccode\u003esudo apt-get install autoconf automake libtool\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-quickstart\" class=\"anchor\" href=\"#quickstart\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eQuickstart\u003c/h2\u003e\n\u003cp\u003eAfter portcullis has been installed, the \u003ccode\u003eportcullis\u003c/code\u003e executable should be available. Typing \u003ccode\u003eportcullis\u003c/code\u003e or \u003ccode\u003eportcullis --help\u003c/code\u003e at the command line will present you with the portcullis help message.\u003c/p\u003e\n\u003cp\u003eThese modes are available:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eprep\u003c/strong\u003e - Prepares input data so that it is suitable for junction analysis\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ejunc\u003c/strong\u003e - Calculates junction metrics for the prepared data\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efilter\u003c/strong\u003e - Separates alignments based on whether they are likely to represent genuine splice junctions or not\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ebamfilt\u003c/strong\u003e - Filters a BAM to remove any reads associated with invalid junctions\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003efull\u003c/strong\u003e - Runs prep, junc, filter and optionally bamfilt as a complete pipeline\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTyping \u003ccode\u003eportcullis \u0026lt;mode\u0026gt; --help\u003c/code\u003e will bring up help and usage information specific to that mode.\u003c/p\u003e\n\u003cp\u003eIn addition to portcullis, we provide a tool-suite for manipulating junction files called junctools. Typing \u003ccode\u003ejunctools --help\u003c/code\u003e will provide you with the program options.\u003c/p\u003e\n\u003cp\u003eFor much more information about portcullis\u0027 capabilities and how to configure and run it, an online version of the manual can be found here: \u003ca href=\"https://portcullis.readthedocs.org/en/latest/\" rel=\"nofollow\"\u003ehttps://portcullis.readthedocs.org/en/latest/\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-licensing\" class=\"anchor\" href=\"#licensing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003eGNU GPL V3. See COPYING file for more details.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-authors\" class=\"anchor\" href=\"#authors\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthors\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDaniel Mapleson\u003c/li\u003e\n\u003cli\u003eLuca Venturini\u003c/li\u003e\n\u003cli\u003eDavid Swarbreck\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee AUTHORS file for more details.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" href=\"#acknowledgements\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eAffiliation: The Earlham Institute (EI)\nFunding: The Biotechnology and Biological Sciences Research Council (BBSRC)\u003c/p\u003e\n", + "stargazers_count": 23, + "subscribers_count": 3, + "topics": [ + "portcullis", + "junction", + "splice-junctions", + "bam-files", + "filter" + ], + "updated_at": 1620067152.0 }, { "data_format": 2, - "description": "New main repository for the FieldOpt project", + "description": "Integrated toolkit for analysis and evaluation of annotated genomes", "filenames": [ - "Docker/Singularity", - "Docker/Release/Singularity", - "Docker/Develop/Singularity" + "Singularity" ], - "full_name": "PetroleumCyberneticsGroup/FieldOpt", - "latest_release": "v1.1-0", - "readme": "\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#FieldOpt\"\u003eFieldOpt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#License\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Setup\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Documentation\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFieldOpt\u003c/h1\u003e\u003ca id=\"user-content-fieldopt\" class=\"anchor\" aria-label=\"Permalink: FieldOpt\" href=\"#fieldopt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePetroleum Field Development Optimization Framework.\u003c/p\u003e\n\u003cp\u003eFieldOpt is a C++ programming framework implemented for rapid prototyping and testing of optimization procedures\ntargeting petroleum field development problems such as well placement optimization, reservoir control optimization\nand inflow-control device optimization. FieldOpt is an ongoing open-source research software designed to facilitate\ncollaboration projects.\u003c/p\u003e\n\u003cp\u003eFieldOpt contributes to research workflows by providing an extensible framework that facilitates both problem\nparametrization and customized algorithm development. It serves as a practical interface between optimization\nmethodology and reservoir simulation enabling the hybridization of algorithms and the use of different reservoir\nsimulators. Its execution layer provides efficient cost function parallelization across multiple realizations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe FieldOpt project as a whole, except for the\n\u003ca href=\"FieldOpt/WellIndexCalculator\"\u003eWellIndexCalculator\u003c/a\u003e, is\nprovided under the GNU General Public License Version 3.\nA verbatim copy of the license (copied September 9. 2016) can be\nfound \u003ca href=\"LICENSE.md\"\u003ehere\u003c/a\u003e.\nThe \u003ca href=\"FieldOpt/WellIndexCalculator\"\u003eWellIndexCalculator\u003c/a\u003e is\nprovided under the GNU \u003cem\u003eLesser\u003c/em\u003e Public License Version 3.\u003c/p\u003e\n\u003cp\u003eFieldOpt is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\u003c/p\u003e\n\u003cp\u003eFieldOpt is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\u003c/p\u003e\n\u003cp\u003eYou should have received a copy of the GNU General Public License\nalong with FieldOpt. If not, see \u003ca href=\"http://www.gnu.org/licenses/\" rel=\"nofollow\"\u003ehttp://www.gnu.org/licenses/\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"COMPILING.md\"\u003ecompilation guide\u003c/a\u003e for instructions on how\nto compile FieldOpt.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome documentation can be found in markdown formatted readme files\nin the appropriate directories (modules). However, most of the\ndocumentation is in form of Doxydoc comments. To compile this,\nrun \u003ccode\u003edoxygen Doxyfile\u003c/code\u003e in the root of this repository. This will\ngenerate a set of HTML files that can be opened in a browser.\u003c/p\u003e\n", - "stargazers_count": 24, - "subscribers_count": 13, + "full_name": "BrendelGroup/AEGeAn", + "latest_release": "v0.16.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-aegean-toolkit-analysis-and-evaluation-of-genome-annotations\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#aegean-toolkit-analysis-and-evaluation-of-genome-annotations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAEGeAn Toolkit: analysis and evaluation of genome annotations\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/BrendelGroup/AEGeAn\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/06910269bc640daec72bca495674260f88f356237607ea5dc7ef5b692a624a6b/68747470733a2f2f6170692e7472617669732d63692e6f72672f4272656e64656c47726f75702f41454765416e2e7376673f6272616e63683d6d6173746572\" alt=\"AEGeAn build status\" data-canonical-src=\"https://api.travis-ci.org/BrendelGroup/AEGeAn.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://readthedocs.org/projects/aegean/badge/?version=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/30aacbc41b9d5ef8b62d7947568c70128148167e085712faef27278c90e734af/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f61656765616e2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"ReadTheDocs build status\" data-canonical-src=\"https://readthedocs.org/projects/aegean/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe AEGeAn Toolkit provides a bundle of software tools for evaluating gene structure annotations and genome organization.\nThe \u003cstrong\u003eLocusPocus\u003c/strong\u003e program is designed a break down an annotated eukaryotic genome into its constituent parts, and the \u003cstrong\u003efidibus\u003c/strong\u003e workflow brings in a variety of other programs and scripts to summarize genome content and the spacing genes.\nThe code conforms to our \u003ca href=\"https://brendelgroup.github.io/\" rel=\"nofollow\"\u003eRAMOSE\u003c/a\u003e philosophy: it generates \u003cstrong\u003ereproducible\u003c/strong\u003e, \u003cstrong\u003eaccurate\u003c/strong\u003e, and \u003cstrong\u003emeaningful\u003c/strong\u003e results; it is \u003cstrong\u003eopen\u003c/strong\u003e (source) and designed to be \u003cstrong\u003escalable\u003c/strong\u003e and \u003cstrong\u003eeasy\u003c/strong\u003e to use.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003ePlease find detailed installation instructions and options in the \u003ca href=\"./INSTALL.md\"\u003eINSTALL\u003c/a\u003e document.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFAQ\u003c/h2\u003e\n\u003cp\u003ePlease see \u003ca href=\"https://github.com/BrendelGroup/AEGeAn/wiki/FAQ\"\u003eour wiki FAQ pages\u003c/a\u003e for usage examples and suggestions.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe main AEGeAn Toolkit documentation is available at \u003ca href=\"https://aegean.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ehttps://aegean.readthedocs.io/en/stable/\u003c/a\u003e.\nThis documentation is focused on the core C library and the programs that directly call this library.\u003c/p\u003e\n\u003cp\u003eDocumentation for the fidibus module, recently merged into AEGeAn, is available from the original (and now deprecated) GenHub project.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003equick start: \u003ca href=\"https://github.com/standage/genhub/#quick-start-example-usages\"\u003ehttps://github.com/standage/genhub/#quick-start-example-usages\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003euser manual: \u003ca href=\"https://github.com/standage/genhub/blob/master/docs/MANUAL.md\"\u003ehttps://github.com/standage/genhub/blob/master/docs/MANUAL.md\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003edeveloper documentation: \u003ca href=\"https://github.com/standage/genhub/blob/master/docs/DEVELOP.md\"\u003ehttps://github.com/standage/genhub/blob/master/docs/DEVELOP.md\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe are in the process of combining and standardizing these separate sources of documentation.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContact\u003c/h2\u003e\n\u003cp\u003ePlease direct all comments and suggestions to \u003ca href=\"mailto:vbrendel@indiana.edu\"\u003eVolker Brendel\u003c/a\u003e or \u003ca href=\"mailto:daniel.standage@nbacc.dhs.gov\"\u003eDaniel Standage\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 23, + "subscribers_count": 4, "topics": [], - "updated_at": 1700002550.0 + "updated_at": 1689860196.0 }, { "data_format": 2, - "description": "Read contamination removal", + "description": "ReproMan (AKA NICEMAN, AKA ReproNim TRD3)", "filenames": [ - "Singularity.def" + "Singularity" ], - "full_name": "GlobalPathogenAnalysisService/read-it-and-keep", - "latest_release": "v0.3.0", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.1093/bioinformatics/btac311\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/033b4176eb14e78d4f574595ea8fd0a9207eaf1da05ba1c48695391a4a9bbc99/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f444f492d31302e313039332f62696f696e666f726d61746963732f627461633331312d626c75652e737667\" alt=\"DOI\" data-canonical-src=\"https://img.shields.io/badge/DOI-10.1093/bioinformatics/btac311-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eread-it-and-keep\u003c/h1\u003e\u003ca id=\"user-content-read-it-and-keep\" class=\"anchor\" aria-label=\"Permalink: read-it-and-keep\" href=\"#read-it-and-keep\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRead contamination removal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake the executable \u003ccode\u003esrc/readItAndKeep\u003c/code\u003e by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd src \u0026amp;\u0026amp; make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBioconda \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a671c157d31352d4cef4a00f2a965f2cf79c11bf37ae71925018f9b22b88af6a/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f706c6174666f726d732e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a671c157d31352d4cef4a00f2a965f2cf79c11bf37ae71925018f9b22b88af6a/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f706c6174666f726d732e737667\" alt=\"Platforms\" data-canonical-src=\"https://anaconda.org/bioconda/read-it-and-keep/badges/platforms.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ade3515bd08d06d20b665d42ef19b60d72d622ccb548146835822ed4b7fb6ea5/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f76657273696f6e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ade3515bd08d06d20b665d42ef19b60d72d622ccb548146835822ed4b7fb6ea5/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f76657273696f6e2e737667\" alt=\"Versions\" data-canonical-src=\"https://anaconda.org/bioconda/read-it-and-keep/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e8e2e7f72fcee6345964bf34cfa732276ac65e5732faab166eb90dd2eb65b402/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f646f776e6c6f6164732e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8e2e7f72fcee6345964bf34cfa732276ac65e5732faab166eb90dd2eb65b402/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f646f776e6c6f6164732e737667\" alt=\"Downloads\" data-canonical-src=\"https://anaconda.org/bioconda/read-it-and-keep/badges/downloads.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-bioconda---\" class=\"anchor\" aria-label=\"Permalink: Bioconda \" href=\"#bioconda---\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom an existing environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c bioconda read-it-and-keep\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing a new environment (recommended):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n read-it-and-keep -c bioconda python=3 read-it-and-keep\nconda activate read-it-and-keep\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet a Docker container of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/globalpathogenanalysisservice/read-it-and-keep:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, build a docker container by cloning this repository and running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile -t \u0026lt;TAG\u0026gt; .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download, called\n\u003ccode\u003ereadItAndKeep_vX.Y.Z.img\u003c/code\u003e, where X.Y.Z is the release version.\u003c/p\u003e\n\u003cp\u003eAlternatively, build a singularity container by cloning this repository and running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build readItAndKeep.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning on SARS-CoV-2 data\u003c/h2\u003e\u003ca id=\"user-content-running-on-sars-cov-2-data\" class=\"anchor\" aria-label=\"Permalink: Running on SARS-CoV-2 data\" href=\"#running-on-sars-cov-2-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using \u003ccode\u003ereadItAndKeep\u003c/code\u003e for SARS-CoV-2 reads, then we recommend that\nyou use the reference genome MN908947.3, but with the poly-A tail removed.\nThis is explained in the publication\n\u003ca href=\"https://doi.org/10.1093/bioinformatics/btac311\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btac311\u003c/a\u003e.\nA FASTA file of MN908947.3 without the poly-A tail is available in\nthis repository: \u003ccode\u003etests/MN908947.3.no_poly_A.fa\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReadItAndKeep works by keeping the reads that match the provided target genome.\nTo run on paired Illumina reads, in two files \u003ccode\u003ereads1.fq.gz\u003c/code\u003e and \u003ccode\u003ereads2.fq.gz\u003c/code\u003e, keeping\nonly reads that match the genome in \u003ccode\u003eref_genome.fasta\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereadItAndKeep --ref_fasta ref_genome.fasta --reads1 reads1.fq.gz --reads2 reads2.fq.gz -o out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output \u003ccode\u003eout.reads_1.fastq.gz\u003c/code\u003e and \u003ccode\u003eout.reads_2.fastq.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo run on one file of nanopore reads \u003ccode\u003ereads.fq.gz\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereadItAndKeep --tech ont --ref_fasta ref_genome.fasta --reads1 reads.fq.gz -o out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output \u003ccode\u003eout.reads.fastq.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf the input reads files are in FASTA format, then it will output reads in FASTA format, calling the files \u003ccode\u003e*.fasta.*\u003c/code\u003e instead of \u003ccode\u003e*.fastq.*\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIt always writes the counts of input and output reads to \u003ccode\u003eSTDOUT\u003c/code\u003e in tab-delimited format, for example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eInput reads file 1\t1000\nInput reads file 2\t1000\nKept reads 1\t950\nKept reads 2\t950\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll logging messages are sent to \u003ccode\u003eSTDERR\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRequired arguments:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--ref_fasta\u003c/code\u003e: reference genome in FASTA format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads1\u003c/code\u003e: at least one reads file in FASTA[.GZ] or FASTQ[.GZ] format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o,--outprefix\u003c/code\u003e: prefix of output files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePlease note there is an option \u003ccode\u003e--tech\u003c/code\u003e, which defaults to \u003ccode\u003eillumina\u003c/code\u003e. Use \u003ccode\u003e--tech ont\u003c/code\u003e for nanopore reads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptional arguments:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads2\u003c/code\u003e: name of second reads file, i.e. mates file for paired reads\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--enumerate_names\u003c/code\u003e: rename the reads \u003ccode\u003e1\u003c/code\u003e,\u003ccode\u003e2\u003c/code\u003e,\u003ccode\u003e3\u003c/code\u003e,... (for paired reads, will also add \u003ccode\u003e/1\u003c/code\u003e or \u003ccode\u003e/2\u003c/code\u003e on the end of names)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--debug\u003c/code\u003e: debug mode. More verbose and writes debugging files\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--min_map_length\u003c/code\u003e: minimum length of match required to keep a read in bp (default \u003ccode\u003e50\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--min_map_length_pc\u003c/code\u003e: minimum length of match required to keep a read, as a percent of the read length (default \u003ccode\u003e50.0\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-V,--version\u003c/code\u003e: show version and exit\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker-1\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdditional arguments need to be supplied to allow Docker to access input and output files. Below is a functional example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run /path/to/read-it-and-keep/tests:/tests [-v /path/to/input:/input -v /path/to/output:/output] \u0026lt;TAG\u0026gt; --ref_fasta /tests/MN908947.3.fa --reads1 /input/\u0026lt;SAMPLE\u0026gt;_1.fastq.gz --reads2 /input/\u0026lt;SAMPLE\u0026gt;_2.fastq.gz --outprefix /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTests\u003c/h2\u003e\u003ca id=\"user-content-tests\" class=\"anchor\" aria-label=\"Permalink: Tests\" href=\"#tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are under development. To run them you will need:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePython 3\u003c/li\u003e\n\u003cli\u003ePython package \u003ca href=\"https://docs.pytest.org/en/stable/\" rel=\"nofollow\"\u003epytest\u003c/a\u003e (\u003ccode\u003epip install pytest\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePython package \u003ca href=\"https://github.com/sanger-pathogens/Fastaq\"\u003epyfastaq\u003c/a\u003e (\u003ccode\u003epip install pyfastaq\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm\" rel=\"nofollow\"\u003eART read simulator\u003c/a\u003e\ninstalled, so that \u003ccode\u003eart_illumina\u003c/code\u003e is in your \u003ccode\u003e$PATH\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/rrwick/Badread\"\u003ebadread\u003c/a\u003e for nanopore read simulation.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRun the tests after compiling the source code, ie:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd src\nmake\nmake test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository includes unedited copies of the code from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.cs.unc.edu/Research/compgeom/gzstream/\" rel=\"nofollow\"\u003egzstream\u003c/a\u003e, \u003ca href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/blob/main/src/ext/gzstream/COPYING.LIB\"\u003eLGPL 2.1 licence\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e, \u003ca href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/blob/main/src/ext/minimap2-2.22/LICENSE.txt\"\u003eMIT licence\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CLIUtils/CLI11\"\u003eCLI11\u003c/a\u003e header file, licence is at start of \u003ca href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/blob/main/src/CLI11.hpp\"\u003ecli11.hpp\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "ReproNim/reproman", + "latest_release": "v0.4.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReproMan\u003c/h1\u003e\u003ca id=\"user-content-reproman\" class=\"anchor\" aria-label=\"Permalink: ReproMan\" href=\"#reproman\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://pypi.org/project/datalad/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f3fee793de927a7b0cc53135790daf9d6931379c1c6b519f83e78e18ff5b2ee0/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f646174616c6164\" alt=\"Supports python version\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/datalad\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://GitHub.com/ReproNim/reproman/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b5e40db8e3e762cff6202d087dd44e4f97f65440c60c1714e0a7b8938ca2538e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f526570726f4e696d2f726570726f6d616e2e737667\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/ReproNim/reproman.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.python.org/pypi/reproman/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2e83bdbcea7e123ae8cd24a36459b653540326e88ffe6dd15253b8183d123127/68747470733a2f2f62616467652e667572792e696f2f70792f726570726f6d616e2e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/reproman.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/ReproNim/reproman/actions?query=workflow%3ATests\"\u003e\u003cimg src=\"https://github.com/ReproNim/reproman/workflows/Tests/badge.svg\" alt=\"Tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/ReproNim/reproman?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/909725271a797127cf2974cff459da11fc485fb428bcb844278ad1c81839dbb1/68747470733a2f2f636f6465636f762e696f2f6769746875622f526570726f4e696d2f726570726f6d616e2f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ReproNim/reproman/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://reproman.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e466c01f4743d4e7fa42578aca6a11e28b96d25234db44d0533535de28041773/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f726570726f6d616e2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation\" data-canonical-src=\"https://readthedocs.org/projects/reproman/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eReproMan aims to simplify creation and management of computing environments\nin Neuroimaging. While concentrating on Neuroimaging use-cases, it is\nby no means is limited to this field of science and tools will find\nutility in other fields as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eStatus\u003c/h1\u003e\u003ca id=\"user-content-status\" class=\"anchor\" aria-label=\"Permalink: Status\" href=\"#status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReproMan is under rapid development. While\nthe code base is still growing the focus is increasingly shifting towards\nrobust and safe operation with a sensible API. There has been no major public\nrelease yet, as organization and configuration are still subject of\nconsiderable reorganization and standardization.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e if you are interested in\ninternals and/or contributing to the project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReproMan requires Python 3 (\u0026gt;= 3.8).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinux\u0027es and OSX (Windows yet TODO) - via pip\u003c/h2\u003e\u003ca id=\"user-content-linuxes-and-osx-windows-yet-todo---via-pip\" class=\"anchor\" aria-label=\"Permalink: Linux\u0027es and OSX (Windows yet TODO) - via pip\" href=\"#linuxes-and-osx-windows-yet-todo---via-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, installation via pip (\u003ccode\u003epip install reproman\u003c/code\u003e) installs core functionality of reproman\nallowing for managing datasets etc. Additional installation schemes\nare available, so you could provide enhanced installation via\n\u003ccode\u003epip install \u0027reproman[SCHEME]\u0027\u003c/code\u003e where \u003ccode\u003eSCHEME\u003c/code\u003e could be\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etests\nto also install dependencies used by unit-tests battery of the reproman\u003c/li\u003e\n\u003cli\u003efull\nto install all of possible dependencies, e.g. \u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003eDataLad\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor installation through \u003ccode\u003epip\u003c/code\u003e you would need some external dependencies\nnot shipped from it (e.g. \u003ccode\u003edocker\u003c/code\u003e, \u003ccode\u003esingularity\u003c/code\u003e, etc.) for which please refer to\nthe next section.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDebian-based systems\u003c/h2\u003e\u003ca id=\"user-content-debian-based-systems\" class=\"anchor\" aria-label=\"Permalink: Debian-based systems\" href=\"#debian-based-systems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Debian-based systems we recommend to enable \u003ca href=\"http://neuro.debian.net\" rel=\"nofollow\"\u003eNeuroDebian\u003c/a\u003e\nfrom which we will soon provide recent releases of ReproMan. We will also provide backports of\nall necessary packages from that repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePython 3.8+ \u003cstrong\u003ewith header files\u003c/strong\u003e possibly needed to build some extensions without wheels. They are provided by \u003ccode\u003epython3-dev\u003c/code\u003e on\ndebian-based systems or \u003ccode\u003epython-devel\u003c/code\u003e on Red Hat systems.\u003c/p\u003e\n\u003cp\u003eOur \u003ccode\u003esetup.py\u003c/code\u003e and corresponding packaging describes all necessary python dependencies.\nOn Debian-based systems we recommend to enable \u003ca href=\"http://neuro.debian.net\" rel=\"nofollow\"\u003eNeuroDebian\u003c/a\u003e\nsince we use it to provide backports of recent fixed external modules we\ndepend upon. Additionally, if you would\nlike to develop and run our tests battery see \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e\nregarding additional dependencies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eA typical workflow for \u003ccode\u003ereproman run\u003c/code\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-a-typical-workflow-for-reproman-run\" class=\"anchor\" aria-label=\"Permalink: A typical workflow for reproman run\" href=\"#a-typical-workflow-for-reproman-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis example is heavily based on the \u003ca href=\"https://github.com/ReproNim/containers/#a-typical-workflow\"\u003e\"Typical workflow\"\u003c/a\u003e\nexample created for \u003ca href=\"https://github.com/ReproNim/containers/\"\u003e///repronim/containers\u003c/a\u003e\nwhich we refer you to discover more about YODA principles etc. In this reproman example we will\nfollow exactly the same goal -- running MRIQC on a sample dataset -- but this time utilizing\nReproMan\u0027s ability to run computation remotely. DataLad and \u003ccode\u003e///repronim/containers\u003c/code\u003e will\nstill be used for data and containers logistics, while reproman will establish a little \u003ca href=\"https://research.cs.wisc.edu/htcondor/\" rel=\"nofollow\"\u003eHTCondor\u003c/a\u003e\ncluster in the AWS cloud, run the analysis, and fetch the results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 1: Create the HTCondor AWS EC2 cluster\u003c/h2\u003e\u003ca id=\"user-content-step-1-create-the-htcondor-aws-ec2-cluster\" class=\"anchor\" aria-label=\"Permalink: Step 1: Create the HTCondor AWS EC2 cluster\" href=\"#step-1-create-the-htcondor-aws-ec2-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf it is the first time you are using ReproMan to interact with AWS cloud services, you should first provide\nReproMan with secret credentials to interact with AWS. For that edit its configuration file\n(\u003ccode\u003e~/.config/reproman/reproman.cfg\u003c/code\u003e on Linux, \u003ccode\u003e~/Library/Application Support/reproman/reproman.cfg\u003c/code\u003e on OSX)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[aws]\naccess_key_id = ...\nsecret_access_key = ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer/Warning: Never share or post those secrets publicly.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003efilling out the \u003ccode\u003e...\u003c/code\u003es. If \u003ccode\u003ereproman\u003c/code\u003e fails to find this information, error message \u003ccode\u003eUnable to locate credentials\u003c/code\u003e will appear.\u003c/p\u003e\n\u003cp\u003eRun (need to be done once, makes resource available for \u003ccode\u003ereproman login\u003c/code\u003e or \u003ccode\u003ereproman run\u003c/code\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ereproman create aws-hpc2 -t aws-condor -b size=2 -b instance_type=t2.medium\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto create a new ReproMan resource: 2 AWS EC2 instances, with HTCondor installed (we use \u003ca href=\"https://www.nitrc.org/projects/nitrc_es/\" rel=\"nofollow\"\u003eNITRC-CE\u003c/a\u003e instances).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer/Warning: It is important to monitor your cloud resources in the cloud provider dashboard(s)\nto ensure absent run away instances etc. to help avoid incuring heavy cost for used cloud services.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2: Create analysis DataLad dataset and run computation on aws-hpc2\u003c/h2\u003e\u003ca id=\"user-content-step-2-create-analysis-datalad-dataset-and-run-computation-on-aws-hpc2\" class=\"anchor\" aria-label=\"Permalink: Step 2: Create analysis DataLad dataset and run computation on aws-hpc2\" href=\"#step-2-create-analysis-datalad-dataset-and-run-computation-on-aws-hpc2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollowing script is an exact replica from \u003ca href=\"https://github.com/ReproNim/containers/#a-typical-workflow\"\u003e///repronim/containers\u003c/a\u003e\nwhere only the \u003ccode\u003edatalad containers-run\u003c/code\u003e command, which fetches data locally and runs computation locally and serially, is replaced with\n\u003ccode\u003ereproman run\u003c/code\u003e which publishes dataset (without data) to the remote resource, fetches the data, runs computation\nvia HTCondor in parallel across 2 nodes, and then fetches results back:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/sh\u003c/span\u003e\n( \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e so it could be just copy pasted or used as a script\u003c/span\u003e\nPS4=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u0026gt; \u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e -xeu \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to see what we are doing and exit upon error\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Work in some temporary directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003emktemp -d \u003cspan class=\"pl-smi\"\u003e${TMPDIR\u003cspan class=\"pl-k\"\u003e:-/\u003c/span\u003etmp}\u003c/span\u003e/repro-XXXXXXX\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a dataset to contain mriqc output\u003c/span\u003e\ndatalad create -d ds000003-qc -c text2git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ds000003-qc\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install our containers collection:\u003c/span\u003e\ndatalad install -d \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e ///repronim/containers\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (optionally) Freeze container of interest to the specific version desired\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to facilitate reproducibility of some older results\u003c/span\u003e\ndatalad run -m \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eDowngrade/Freeze mriqc container version\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n containers/scripts/freeze_versions bids-mriqc=0.16.0\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install input data:\u003c/span\u003e\ndatalad install -d \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e -s https://github.com/ReproNim/ds000003-demo sourcedata\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Setup git to ignore workdir to be used by pipelines\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworkdir/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e .gitignore \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e datalad save -m \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eIgnore workdir\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e .gitignore\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Execute desired preprocessing in parallel across two subjects\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e on remote AWS EC2 cluster, creating a provenance record\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e in git history containing all condor submission scripts and logs, and\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e fetching them locally\u003c/span\u003e\nreproman run -r aws-hpc2 \\\n --sub condor --orc datalad-pair \\\n --jp \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtainer=containers/bids-mriqc\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --bp subj=02,13 --follow \\\n --input \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003esourcedata/sub-{p[subj]}\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n --output \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \\\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e{inputs}\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e participant group -w workdir --participant_label \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e{p[subj]}\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://reproman.readthedocs.io/en/latest/execute.html\" rel=\"nofollow\"\u003eReproMan: Execute\u003c/a\u003e documentation section\nprovides more information on the underlying principles behind \u003ca href=\"https://reproman.readthedocs.io/en/latest/generated/man/reproman-run.html\" rel=\"nofollow\"\u003e\u003ccode\u003ereproman run\u003c/code\u003e\u003c/a\u003e\ncommand.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3: Remove resource\u003c/h2\u003e\u003ca id=\"user-content-step-3-remove-resource\" class=\"anchor\" aria-label=\"Permalink: Step 3: Remove resource\" href=\"#step-3-remove-resource\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhenever everything is computed and fetched, and you are satisfied with the results, use \u003ccode\u003ereproman delete aws-hpc2\u003c/code\u003e to terminate\nremote cluster in AWS, to not cause unnecessary charges.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMIT/Expat\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDisclaimer\u003c/h1\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is in a beta stage -- majority of the functionality is usable but\nDocumentation and API enhancements is WiP to make it better. Please do not be\nshy of filing an issue or a pull request. See \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e\nfor the guidance.\u003c/p\u003e\n", "stargazers_count": 24, - "subscribers_count": 5, + "subscribers_count": 10, "topics": [], - "updated_at": 1702328599.0 + "updated_at": 1698248762.0 }, { "data_format": 2, @@ -39415,56 +39412,104 @@ var data = }, { "data_format": 2, - "description": "ReproMan (AKA NICEMAN, AKA ReproNim TRD3)", + "description": "Read contamination removal", "filenames": [ - "Singularity" + "Singularity.def" ], - "full_name": "ReproNim/reproman", - "latest_release": "v0.4.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eReproMan\u003c/h1\u003e\u003ca id=\"user-content-reproman\" class=\"anchor\" aria-label=\"Permalink: ReproMan\" href=\"#reproman\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://pypi.org/project/datalad/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f3fee793de927a7b0cc53135790daf9d6931379c1c6b519f83e78e18ff5b2ee0/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f646174616c6164\" alt=\"Supports python version\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/datalad\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://GitHub.com/ReproNim/reproman/releases/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b5e40db8e3e762cff6202d087dd44e4f97f65440c60c1714e0a7b8938ca2538e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f526570726f4e696d2f726570726f6d616e2e737667\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/ReproNim/reproman.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.python.org/pypi/reproman/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2e83bdbcea7e123ae8cd24a36459b653540326e88ffe6dd15253b8183d123127/68747470733a2f2f62616467652e667572792e696f2f70792f726570726f6d616e2e737667\" alt=\"PyPI version fury.io\" data-canonical-src=\"https://badge.fury.io/py/reproman.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/ReproNim/reproman/actions?query=workflow%3ATests\"\u003e\u003cimg src=\"https://github.com/ReproNim/reproman/workflows/Tests/badge.svg\" alt=\"Tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/github/ReproNim/reproman?branch=master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/909725271a797127cf2974cff459da11fc485fb428bcb844278ad1c81839dbb1/68747470733a2f2f636f6465636f762e696f2f6769746875622f526570726f4e696d2f726570726f6d616e2f636f7665726167652e7376673f6272616e63683d6d6173746572\" alt=\"codecov.io\" data-canonical-src=\"https://codecov.io/github/ReproNim/reproman/coverage.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://reproman.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e466c01f4743d4e7fa42578aca6a11e28b96d25234db44d0533535de28041773/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f726570726f6d616e2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation\" data-canonical-src=\"https://readthedocs.org/projects/reproman/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eReproMan aims to simplify creation and management of computing environments\nin Neuroimaging. While concentrating on Neuroimaging use-cases, it is\nby no means is limited to this field of science and tools will find\nutility in other fields as well.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eStatus\u003c/h1\u003e\u003ca id=\"user-content-status\" class=\"anchor\" aria-label=\"Permalink: Status\" href=\"#status\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReproMan is under rapid development. While\nthe code base is still growing the focus is increasingly shifting towards\nrobust and safe operation with a sensible API. There has been no major public\nrelease yet, as organization and configuration are still subject of\nconsiderable reorganization and standardization.\u003c/p\u003e\n\u003cp\u003eSee \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e if you are interested in\ninternals and/or contributing to the project.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReproMan requires Python 3 (\u0026gt;= 3.8).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinux\u0027es and OSX (Windows yet TODO) - via pip\u003c/h2\u003e\u003ca id=\"user-content-linuxes-and-osx-windows-yet-todo---via-pip\" class=\"anchor\" aria-label=\"Permalink: Linux\u0027es and OSX (Windows yet TODO) - via pip\" href=\"#linuxes-and-osx-windows-yet-todo---via-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBy default, installation via pip (\u003ccode\u003epip install reproman\u003c/code\u003e) installs core functionality of reproman\nallowing for managing datasets etc. Additional installation schemes\nare available, so you could provide enhanced installation via\n\u003ccode\u003epip install \u0027reproman[SCHEME]\u0027\u003c/code\u003e where \u003ccode\u003eSCHEME\u003c/code\u003e could be\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etests\nto also install dependencies used by unit-tests battery of the reproman\u003c/li\u003e\n\u003cli\u003efull\nto install all of possible dependencies, e.g. \u003ca href=\"http://datalad.org\" rel=\"nofollow\"\u003eDataLad\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor installation through \u003ccode\u003epip\u003c/code\u003e you would need some external dependencies\nnot shipped from it (e.g. \u003ccode\u003edocker\u003c/code\u003e, \u003ccode\u003esingularity\u003c/code\u003e, etc.) for which please refer to\nthe next section.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDebian-based systems\u003c/h2\u003e\u003ca id=\"user-content-debian-based-systems\" class=\"anchor\" aria-label=\"Permalink: Debian-based systems\" href=\"#debian-based-systems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Debian-based systems we recommend to enable \u003ca href=\"http://neuro.debian.net\" rel=\"nofollow\"\u003eNeuroDebian\u003c/a\u003e\nfrom which we will soon provide recent releases of ReproMan. We will also provide backports of\nall necessary packages from that repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePython 3.8+ \u003cstrong\u003ewith header files\u003c/strong\u003e possibly needed to build some extensions without wheels. They are provided by \u003ccode\u003epython3-dev\u003c/code\u003e on\ndebian-based systems or \u003ccode\u003epython-devel\u003c/code\u003e on Red Hat systems.\u003c/p\u003e\n\u003cp\u003eOur \u003ccode\u003esetup.py\u003c/code\u003e and corresponding packaging describes all necessary python dependencies.\nOn Debian-based systems we recommend to enable \u003ca href=\"http://neuro.debian.net\" rel=\"nofollow\"\u003eNeuroDebian\u003c/a\u003e\nsince we use it to provide backports of recent fixed external modules we\ndepend upon. Additionally, if you would\nlike to develop and run our tests battery see \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e\nregarding additional dependencies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eA typical workflow for \u003ccode\u003ereproman run\u003c/code\u003e\n\u003c/h1\u003e\u003ca id=\"user-content-a-typical-workflow-for-reproman-run\" class=\"anchor\" aria-label=\"Permalink: A typical workflow for reproman run\" href=\"#a-typical-workflow-for-reproman-run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis example is heavily based on the \u003ca href=\"https://github.com/ReproNim/containers/#a-typical-workflow\"\u003e\"Typical workflow\"\u003c/a\u003e\nexample created for \u003ca href=\"https://github.com/ReproNim/containers/\"\u003e///repronim/containers\u003c/a\u003e\nwhich we refer you to discover more about YODA principles etc. In this reproman example we will\nfollow exactly the same goal -- running MRIQC on a sample dataset -- but this time utilizing\nReproMan\u0027s ability to run computation remotely. DataLad and \u003ccode\u003e///repronim/containers\u003c/code\u003e will\nstill be used for data and containers logistics, while reproman will establish a little \u003ca href=\"https://research.cs.wisc.edu/htcondor/\" rel=\"nofollow\"\u003eHTCondor\u003c/a\u003e\ncluster in the AWS cloud, run the analysis, and fetch the results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 1: Create the HTCondor AWS EC2 cluster\u003c/h2\u003e\u003ca id=\"user-content-step-1-create-the-htcondor-aws-ec2-cluster\" class=\"anchor\" aria-label=\"Permalink: Step 1: Create the HTCondor AWS EC2 cluster\" href=\"#step-1-create-the-htcondor-aws-ec2-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf it is the first time you are using ReproMan to interact with AWS cloud services, you should first provide\nReproMan with secret credentials to interact with AWS. For that edit its configuration file\n(\u003ccode\u003e~/.config/reproman/reproman.cfg\u003c/code\u003e on Linux, \u003ccode\u003e~/Library/Application Support/reproman/reproman.cfg\u003c/code\u003e on OSX)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e[aws]\naccess_key_id = ...\nsecret_access_key = ...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer/Warning: Never share or post those secrets publicly.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003efilling out the \u003ccode\u003e...\u003c/code\u003es. If \u003ccode\u003ereproman\u003c/code\u003e fails to find this information, error message \u003ccode\u003eUnable to locate credentials\u003c/code\u003e will appear.\u003c/p\u003e\n\u003cp\u003eRun (need to be done once, makes resource available for \u003ccode\u003ereproman login\u003c/code\u003e or \u003ccode\u003ereproman run\u003c/code\u003e):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ereproman create aws-hpc2 -t aws-condor -b size=2 -b instance_type=t2.medium\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eto create a new ReproMan resource: 2 AWS EC2 instances, with HTCondor installed (we use \u003ca href=\"https://www.nitrc.org/projects/nitrc_es/\" rel=\"nofollow\"\u003eNITRC-CE\u003c/a\u003e instances).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer/Warning: It is important to monitor your cloud resources in the cloud provider dashboard(s)\nto ensure absent run away instances etc. to help avoid incuring heavy cost for used cloud services.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 2: Create analysis DataLad dataset and run computation on aws-hpc2\u003c/h2\u003e\u003ca id=\"user-content-step-2-create-analysis-datalad-dataset-and-run-computation-on-aws-hpc2\" class=\"anchor\" aria-label=\"Permalink: Step 2: Create analysis DataLad dataset and run computation on aws-hpc2\" href=\"#step-2-create-analysis-datalad-dataset-and-run-computation-on-aws-hpc2\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollowing script is an exact replica from \u003ca href=\"https://github.com/ReproNim/containers/#a-typical-workflow\"\u003e///repronim/containers\u003c/a\u003e\nwhere only the \u003ccode\u003edatalad containers-run\u003c/code\u003e command, which fetches data locally and runs computation locally and serially, is replaced with\n\u003ccode\u003ereproman run\u003c/code\u003e which publishes dataset (without data) to the remote resource, fetches the data, runs computation\nvia HTCondor in parallel across 2 nodes, and then fetches results back:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/sh\u003c/span\u003e\n( \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e so it could be just copy pasted or used as a script\u003c/span\u003e\nPS4=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u0026gt; \u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e -xeu \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to see what we are doing and exit upon error\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Work in some temporary directory\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003emktemp -d \u003cspan class=\"pl-smi\"\u003e${TMPDIR\u003cspan class=\"pl-k\"\u003e:-/\u003c/span\u003etmp}\u003c/span\u003e/repro-XXXXXXX\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a dataset to contain mriqc output\u003c/span\u003e\ndatalad create -d ds000003-qc -c text2git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ds000003-qc\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install our containers collection:\u003c/span\u003e\ndatalad install -d \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e ///repronim/containers\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (optionally) Freeze container of interest to the specific version desired\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e to facilitate reproducibility of some older results\u003c/span\u003e\ndatalad run -m \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eDowngrade/Freeze mriqc container version\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n containers/scripts/freeze_versions bids-mriqc=0.16.0\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install input data:\u003c/span\u003e\ndatalad install -d \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e -s https://github.com/ReproNim/ds000003-demo sourcedata\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Setup git to ignore workdir to be used by pipelines\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eworkdir/\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e .gitignore \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e datalad save -m \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eIgnore workdir\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e .gitignore\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Execute desired preprocessing in parallel across two subjects\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e on remote AWS EC2 cluster, creating a provenance record\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e in git history containing all condor submission scripts and logs, and\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e fetching them locally\u003c/span\u003e\nreproman run -r aws-hpc2 \\\n --sub condor --orc datalad-pair \\\n --jp \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econtainer=containers/bids-mriqc\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --bp subj=02,13 --follow \\\n --input \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003esourcedata/sub-{p[subj]}\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \\\n --output \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e \\\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e{inputs}\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e participant group -w workdir --participant_label \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e{p[subj]}\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://reproman.readthedocs.io/en/latest/execute.html\" rel=\"nofollow\"\u003eReproMan: Execute\u003c/a\u003e documentation section\nprovides more information on the underlying principles behind \u003ca href=\"https://reproman.readthedocs.io/en/latest/generated/man/reproman-run.html\" rel=\"nofollow\"\u003e\u003ccode\u003ereproman run\u003c/code\u003e\u003c/a\u003e\ncommand.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eStep 3: Remove resource\u003c/h2\u003e\u003ca id=\"user-content-step-3-remove-resource\" class=\"anchor\" aria-label=\"Permalink: Step 3: Remove resource\" href=\"#step-3-remove-resource\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhenever everything is computed and fetched, and you are satisfied with the results, use \u003ccode\u003ereproman delete aws-hpc2\u003c/code\u003e to terminate\nremote cluster in AWS, to not cause unnecessary charges.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMIT/Expat\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDisclaimer\u003c/h1\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is in a beta stage -- majority of the functionality is usable but\nDocumentation and API enhancements is WiP to make it better. Please do not be\nshy of filing an issue or a pull request. See \u003ca href=\"CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e\nfor the guidance.\u003c/p\u003e\n", + "full_name": "GlobalPathogenAnalysisService/read-it-and-keep", + "latest_release": "v0.3.0", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/actions/workflows/build.yaml/badge.svg\"\u003e\u003cimg src=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/actions/workflows/build.yaml/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.1093/bioinformatics/btac311\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/033b4176eb14e78d4f574595ea8fd0a9207eaf1da05ba1c48695391a4a9bbc99/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f444f492d31302e313039332f62696f696e666f726d61746963732f627461633331312d626c75652e737667\" alt=\"DOI\" data-canonical-src=\"https://img.shields.io/badge/DOI-10.1093/bioinformatics/btac311-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eread-it-and-keep\u003c/h1\u003e\u003ca id=\"user-content-read-it-and-keep\" class=\"anchor\" aria-label=\"Permalink: read-it-and-keep\" href=\"#read-it-and-keep\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRead contamination removal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMake the executable \u003ccode\u003esrc/readItAndKeep\u003c/code\u003e by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd src \u0026amp;\u0026amp; make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBioconda \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a671c157d31352d4cef4a00f2a965f2cf79c11bf37ae71925018f9b22b88af6a/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f706c6174666f726d732e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a671c157d31352d4cef4a00f2a965f2cf79c11bf37ae71925018f9b22b88af6a/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f706c6174666f726d732e737667\" alt=\"Platforms\" data-canonical-src=\"https://anaconda.org/bioconda/read-it-and-keep/badges/platforms.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ade3515bd08d06d20b665d42ef19b60d72d622ccb548146835822ed4b7fb6ea5/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f76657273696f6e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ade3515bd08d06d20b665d42ef19b60d72d622ccb548146835822ed4b7fb6ea5/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f76657273696f6e2e737667\" alt=\"Versions\" data-canonical-src=\"https://anaconda.org/bioconda/read-it-and-keep/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e8e2e7f72fcee6345964bf34cfa732276ac65e5732faab166eb90dd2eb65b402/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f646f776e6c6f6164732e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e8e2e7f72fcee6345964bf34cfa732276ac65e5732faab166eb90dd2eb65b402/68747470733a2f2f616e61636f6e64612e6f72672f62696f636f6e64612f726561642d69742d616e642d6b6565702f6261646765732f646f776e6c6f6164732e737667\" alt=\"Downloads\" data-canonical-src=\"https://anaconda.org/bioconda/read-it-and-keep/badges/downloads.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-bioconda---\" class=\"anchor\" aria-label=\"Permalink: Bioconda \" href=\"#bioconda---\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom an existing environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c bioconda read-it-and-keep\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing a new environment (recommended):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n read-it-and-keep -c bioconda python=3 read-it-and-keep\nconda activate read-it-and-keep\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGet a Docker container of the latest release:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull ghcr.io/globalpathogenanalysisservice/read-it-and-keep:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, build a docker container by cloning this repository and running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -f Dockerfile -t \u0026lt;TAG\u0026gt; .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity\u003c/h3\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/releases\"\u003eReleases\u003c/a\u003e\ninclude a Singularity image to download, called\n\u003ccode\u003ereadItAndKeep_vX.Y.Z.img\u003c/code\u003e, where X.Y.Z is the release version.\u003c/p\u003e\n\u003cp\u003eAlternatively, build a singularity container by cloning this repository and running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build readItAndKeep.sif Singularity.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning on SARS-CoV-2 data\u003c/h2\u003e\u003ca id=\"user-content-running-on-sars-cov-2-data\" class=\"anchor\" aria-label=\"Permalink: Running on SARS-CoV-2 data\" href=\"#running-on-sars-cov-2-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are using \u003ccode\u003ereadItAndKeep\u003c/code\u003e for SARS-CoV-2 reads, then we recommend that\nyou use the reference genome MN908947.3, but with the poly-A tail removed.\nThis is explained in the publication\n\u003ca href=\"https://doi.org/10.1093/bioinformatics/btac311\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btac311\u003c/a\u003e.\nA FASTA file of MN908947.3 without the poly-A tail is available in\nthis repository: \u003ccode\u003etests/MN908947.3.no_poly_A.fa\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eReadItAndKeep works by keeping the reads that match the provided target genome.\nTo run on paired Illumina reads, in two files \u003ccode\u003ereads1.fq.gz\u003c/code\u003e and \u003ccode\u003ereads2.fq.gz\u003c/code\u003e, keeping\nonly reads that match the genome in \u003ccode\u003eref_genome.fasta\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereadItAndKeep --ref_fasta ref_genome.fasta --reads1 reads1.fq.gz --reads2 reads2.fq.gz -o out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output \u003ccode\u003eout.reads_1.fastq.gz\u003c/code\u003e and \u003ccode\u003eout.reads_2.fastq.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo run on one file of nanopore reads \u003ccode\u003ereads.fq.gz\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ereadItAndKeep --tech ont --ref_fasta ref_genome.fasta --reads1 reads.fq.gz -o out\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will output \u003ccode\u003eout.reads.fastq.gz\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIf the input reads files are in FASTA format, then it will output reads in FASTA format, calling the files \u003ccode\u003e*.fasta.*\u003c/code\u003e instead of \u003ccode\u003e*.fastq.*\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eIt always writes the counts of input and output reads to \u003ccode\u003eSTDOUT\u003c/code\u003e in tab-delimited format, for example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eInput reads file 1\t1000\nInput reads file 2\t1000\nKept reads 1\t950\nKept reads 2\t950\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll logging messages are sent to \u003ccode\u003eSTDERR\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRequired arguments:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--ref_fasta\u003c/code\u003e: reference genome in FASTA format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads1\u003c/code\u003e: at least one reads file in FASTA[.GZ] or FASTQ[.GZ] format.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-o,--outprefix\u003c/code\u003e: prefix of output files.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePlease note there is an option \u003ccode\u003e--tech\u003c/code\u003e, which defaults to \u003ccode\u003eillumina\u003c/code\u003e. Use \u003ccode\u003e--tech ont\u003c/code\u003e for nanopore reads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptional arguments:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003e--reads2\u003c/code\u003e: name of second reads file, i.e. mates file for paired reads\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--enumerate_names\u003c/code\u003e: rename the reads \u003ccode\u003e1\u003c/code\u003e,\u003ccode\u003e2\u003c/code\u003e,\u003ccode\u003e3\u003c/code\u003e,... (for paired reads, will also add \u003ccode\u003e/1\u003c/code\u003e or \u003ccode\u003e/2\u003c/code\u003e on the end of names)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--debug\u003c/code\u003e: debug mode. More verbose and writes debugging files\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--min_map_length\u003c/code\u003e: minimum length of match required to keep a read in bp (default \u003ccode\u003e50\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--min_map_length_pc\u003c/code\u003e: minimum length of match required to keep a read, as a percent of the read length (default \u003ccode\u003e50.0\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e-V,--version\u003c/code\u003e: show version and exit\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker-1\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdditional arguments need to be supplied to allow Docker to access input and output files. Below is a functional example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run /path/to/read-it-and-keep/tests:/tests [-v /path/to/input:/input -v /path/to/output:/output] \u0026lt;TAG\u0026gt; --ref_fasta /tests/MN908947.3.fa --reads1 /input/\u0026lt;SAMPLE\u0026gt;_1.fastq.gz --reads2 /input/\u0026lt;SAMPLE\u0026gt;_2.fastq.gz --outprefix /output/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTests\u003c/h2\u003e\u003ca id=\"user-content-tests\" class=\"anchor\" aria-label=\"Permalink: Tests\" href=\"#tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThese are under development. To run them you will need:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePython 3\u003c/li\u003e\n\u003cli\u003ePython package \u003ca href=\"https://docs.pytest.org/en/stable/\" rel=\"nofollow\"\u003epytest\u003c/a\u003e (\u003ccode\u003epip install pytest\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003ePython package \u003ca href=\"https://github.com/sanger-pathogens/Fastaq\"\u003epyfastaq\u003c/a\u003e (\u003ccode\u003epip install pyfastaq\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm\" rel=\"nofollow\"\u003eART read simulator\u003c/a\u003e\ninstalled, so that \u003ccode\u003eart_illumina\u003c/code\u003e is in your \u003ccode\u003e$PATH\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/rrwick/Badread\"\u003ebadread\u003c/a\u003e for nanopore read simulation.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRun the tests after compiling the source code, ie:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd src\nmake\nmake test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository includes unedited copies of the code from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.cs.unc.edu/Research/compgeom/gzstream/\" rel=\"nofollow\"\u003egzstream\u003c/a\u003e, \u003ca href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/blob/main/src/ext/gzstream/COPYING.LIB\"\u003eLGPL 2.1 licence\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e, \u003ca href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/blob/main/src/ext/minimap2-2.22/LICENSE.txt\"\u003eMIT licence\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CLIUtils/CLI11\"\u003eCLI11\u003c/a\u003e header file, licence is at start of \u003ca href=\"https://github.com/GlobalPathogenAnalysisService/read-it-and-keep/blob/main/src/CLI11.hpp\"\u003ecli11.hpp\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 24, - "subscribers_count": 10, + "subscribers_count": 5, "topics": [], - "updated_at": 1698248762.0 + "updated_at": 1702328599.0 }, { "data_format": 2, - "description": "Python 3 version of FabSim", + "description": "New main repository for the FieldOpt project", + "filenames": [ + "Docker/Singularity", + "Docker/Release/Singularity", + "Docker/Develop/Singularity" + ], + "full_name": "PetroleumCyberneticsGroup/FieldOpt", + "latest_release": "v1.1-0", + "readme": "\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#FieldOpt\"\u003eFieldOpt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#License\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Setup\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Documentation\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFieldOpt\u003c/h1\u003e\u003ca id=\"user-content-fieldopt\" class=\"anchor\" aria-label=\"Permalink: FieldOpt\" href=\"#fieldopt\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePetroleum Field Development Optimization Framework.\u003c/p\u003e\n\u003cp\u003eFieldOpt is a C++ programming framework implemented for rapid prototyping and testing of optimization procedures\ntargeting petroleum field development problems such as well placement optimization, reservoir control optimization\nand inflow-control device optimization. FieldOpt is an ongoing open-source research software designed to facilitate\ncollaboration projects.\u003c/p\u003e\n\u003cp\u003eFieldOpt contributes to research workflows by providing an extensible framework that facilitates both problem\nparametrization and customized algorithm development. It serves as a practical interface between optimization\nmethodology and reservoir simulation enabling the hybridization of algorithms and the use of different reservoir\nsimulators. Its execution layer provides efficient cost function parallelization across multiple realizations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe FieldOpt project as a whole, except for the\n\u003ca href=\"FieldOpt/WellIndexCalculator\"\u003eWellIndexCalculator\u003c/a\u003e, is\nprovided under the GNU General Public License Version 3.\nA verbatim copy of the license (copied September 9. 2016) can be\nfound \u003ca href=\"LICENSE.md\"\u003ehere\u003c/a\u003e.\nThe \u003ca href=\"FieldOpt/WellIndexCalculator\"\u003eWellIndexCalculator\u003c/a\u003e is\nprovided under the GNU \u003cem\u003eLesser\u003c/em\u003e Public License Version 3.\u003c/p\u003e\n\u003cp\u003eFieldOpt is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\u003c/p\u003e\n\u003cp\u003eFieldOpt is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\u003c/p\u003e\n\u003cp\u003eYou should have received a copy of the GNU General Public License\nalong with FieldOpt. If not, see \u003ca href=\"http://www.gnu.org/licenses/\" rel=\"nofollow\"\u003ehttp://www.gnu.org/licenses/\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"COMPILING.md\"\u003ecompilation guide\u003c/a\u003e for instructions on how\nto compile FieldOpt.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome documentation can be found in markdown formatted readme files\nin the appropriate directories (modules). However, most of the\ndocumentation is in form of Doxydoc comments. To compile this,\nrun \u003ccode\u003edoxygen Doxyfile\u003c/code\u003e in the root of this repository. This will\ngenerate a set of HTML files that can be opened in a browser.\u003c/p\u003e\n", + "stargazers_count": 24, + "subscribers_count": 13, + "topics": [], + "updated_at": 1700002550.0 + }, + { + "data_format": 2, + "description": "AutoMATES: Automated Model Assembly from Text, Equations, and Software", + "filenames": [ + "automates/equation_reading/equation_extraction/containers/Singularity.im2markup", + "automates/equation_reading/equation_extraction/containers/Singularity.pytorch_skimage" + ], + "full_name": "ml4ai/automates", + "latest_release": "v1.4.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 align=\"center\" class=\"heading-element\"\u003eAutomated Model Assembly\u003cbr\u003efrom Text, Equations, and Software\u003c/h1\u003e\u003ca id=\"user-content-automated-model-assemblyfrom-text-equations-and-software\" class=\"anchor\" aria-label=\"Permalink: Automated Model Assemblyfrom Text, Equations, and Software\" href=\"#automated-model-assemblyfrom-text-equations-and-software\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n \n \n \u003ca href=\"https://github.com/ml4ai/automates/actions\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/26bab0b570017976d76f1fb6eb2de47c2bd98eceaa5545eadc4e255c98a0f2ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f776f726b666c6f772f7374617475732f6d6c3461692f6175746f6d617465732f436f6e74696e756f7573253230496e746567726174696f6e3f6c6162656c3d7465737473\" alt=\"GH Actions build status\" data-canonical-src=\"https://img.shields.io/github/workflow/status/ml4ai/automates/Continuous%20Integration?label=tests\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://codecov.io/gh/ml4ai/automates\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/681b94c316ad89b992991d018a5ed84bb2e2cc6b091292d244cb193ee5a505f0/68747470733a2f2f636f6465636f762e696f2f67682f6d6c3461692f6175746f6d617465732f6272616e63682f6d61737465722f67726170682f62616467652e737667\" data-canonical-src=\"https://codecov.io/gh/ml4ai/automates/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://www.codefactor.io/repository/github/ml4ai/automates\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/25ce80bae380fdfafba09fec84b05d741f6055f20d438f98abee2043116dec4f/68747470733a2f2f7777772e636f6465666163746f722e696f2f7265706f7369746f72792f6769746875622f6d6c3461692f6175746f6d617465732f6261646765\" alt=\"CodeFactor\" data-canonical-src=\"https://www.codefactor.io/repository/github/ml4ai/automates/badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eThis repository holds the source code for the AutoMATES documentation\nand several component pipelines.\u003c/p\u003e\n\u003cp\u003eFor documentation: \u003ca href=\"https://ml4ai.github.io/automates\" rel=\"nofollow\"\u003ehttps://ml4ai.github.io/automates\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation instructions\u003c/h2\u003e\u003ca id=\"user-content-installation-instructions\" class=\"anchor\" aria-label=\"Permalink: Installation instructions\" href=\"#installation-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor all operating systems, the first step of the installation process is to clone the AutoMATES repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLinux and macOS\u003c/h3\u003e\u003ca id=\"user-content-linux-and-macos\" class=\"anchor\" aria-label=\"Permalink: Linux and macOS\" href=\"#linux-and-macos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a new \u003ca href=\"https://docs.python.org/3/library/venv.html\" rel=\"nofollow\"\u003ePython virtualenv\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eActivate your new Python virtualenv\u003c/li\u003e\n\u003cli\u003eInstall Graphviz as defined below\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003epip install -e .\u003c/code\u003e from the root of the AutoMATES directory\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eGraphViz installation\u003c/h4\u003e\u003ca id=\"user-content-graphviz-installation\" class=\"anchor\" aria-label=\"Permalink: GraphViz installation\" href=\"#graphviz-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eDebian flavored linux\u003c/h5\u003e\u003ca id=\"user-content-debian-flavored-linux\" class=\"anchor\" aria-label=\"Permalink: Debian flavored linux\" href=\"#debian-flavored-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse the command: \u003ccode\u003esudo apt-get install graphviz libgraphviz-dev pkg-config\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003emacOS with Homebrew\u003c/h5\u003e\u003ca id=\"user-content-macos-with-homebrew\" class=\"anchor\" aria-label=\"Permalink: macOS with Homebrew\" href=\"#macos-with-homebrew\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eUse the command: \u003ccode\u003ebrew install graphviz\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eInstall PyGraphviz to your virtualenv with: \u003ccode\u003epip install --install-option=\"--include-path=/usr/local/include/\" --install-option=\"--library-path=/usr/local/lib\" pygraphviz\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWindows\u003c/h3\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eDownload and install \u003ca href=\"https://www.anaconda.com/products/individual\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEdit the \u003ccode\u003ePYTHONPATH\u003c/code\u003e variable in \u003ccode\u003eenvironment.yml\u003c/code\u003e to be your local path to your checkout of the AutoMATES repo\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003econda env create --file environment.yml\u003c/code\u003e from the root of the AutoMATES directory\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 24, + "subscribers_count": 11, + "topics": [], + "updated_at": 1727628755.0 + }, + { + "data_format": 2, + "description": "[DEPRECATED] Docker Images which include a complete installation of all software needed to build all Bioconductor packages", "filenames": [ + "Singularity.RELEASE_3_9", + "Singularity.RELEASE_3_8", + "Singularity.RELEASE_3_10", "Singularity" ], - "full_name": "djgroen/FabSim3", - "latest_release": "v3.8", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFabSim\u003c/h1\u003e\u003ca id=\"user-content-fabsim\" class=\"anchor\" aria-label=\"Permalink: FabSim\" href=\"#fabsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/actions/workflows/Pytests.yml\"\u003e\u003cimg src=\"https://github.com/djgroen/FabSim3/actions/workflows/Pytests.yml/badge.svg?branch=master\" alt=\"Run Tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vecmafabsim3/fabsimdocker/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/92881bc002c155e480ba310eebbabf711c88f31e998ad3bb426bc5f8efbe8f24/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f7665636d6166616273696d332f66616273696d646f636b65722e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/vecmafabsim3/fabsimdocker.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vecmafabsim3/fabsimdocker/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b65f177412063ff2d0207e16a33cae77ba837ce9006098cb59fbff10e22e844/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f7665636d6166616273696d332f66616273696d646f636b65722e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/automated/vecmafabsim3/fabsimdocker.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/tags\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/baae1d1934af8f46ad0d31258d9d44d1caaf525e7d4388ae33d0e667fac2297f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f646a67726f656e2f46616253696d333f7374796c653d666c6174\" alt=\"GitHub tag (latest by date)\" data-canonical-src=\"https://img.shields.io/github/v/tag/djgroen/FabSim3?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d4fa02cb45ab9d8cf264944f49b67a1ed1b30df37c76421169c1fff5f1f8d2ad/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f646a67726f656e2f46616253696d332e737667\" alt=\"GitHub Issues\" data-canonical-src=\"https://img.shields.io/github/issues/djgroen/FabSim3.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/djgroen/FabSim3/commits/master\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2727377d9a0d56f2625f4bb78ab5e77de3eed9840bd4df91d10872fd64ad40d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f646a67726f656e2f46616253696d332e737667\" alt=\"GitHub last-commit\" data-canonical-src=\"https://img.shields.io/github/last-commit/djgroen/FabSim3.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFor the full FabSim3 documentation, please visit \u003ca href=\"https://fabsim3.readthedocs.io\" rel=\"nofollow\"\u003ehttps://fabsim3.readthedocs.io\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFabSim is a Python-based automation toolkit for scientific simulation and data processing workflows, licensed under the BSD 3-clause license. It aims to enable users to perform remote tasks from a local command-line, and to run applications while curating the environment variables and the input and output data in a systematic manner. To provide that curation, FabSim uses a basic data transfer functionalities such as rsync and ssh.\u003c/p\u003e\n\u003cp\u003eFabSim also contains a system for defining machine-specific configurations, including templates to execute jobs through schedulers such as PBSPro, Loadleveller and SGE. These machine-specific configurations are stored in the repository, apply to all applications run on that machine, and can be updated by any contributor who feels that a fix or improvement is required.\u003c/p\u003e\n\u003cp\u003eFabSim relies strongly on Fabric (\u003ca href=\"http://www.fabfile.org\" rel=\"nofollow\"\u003ehttp://www.fabfile.org\u003c/a\u003e, shown to work with versions 1.5.3 and 1.10.0) and PyYAML. Previous versions of FabSim (most notably FabHemeLB and FabMD) have been used to run simulation workflows on machines such as ARCHER, SuperMUC, BlueJoule, as well as local clusters and desktops.\u003c/p\u003e\n\u003cp\u003eFabSim is now publicly available at: \u003ca href=\"http://www.github.com/djgroen/FabSim\"\u003ehttp://www.github.com/djgroen/FabSim\u003c/a\u003e\nThe accompanying software paper can be found here: \u003ca href=\"https://doi.org/10.1016/j.cpc.2016.05.020\" rel=\"nofollow\"\u003ehttps://doi.org/10.1016/j.cpc.2016.05.020\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDerivative versions of FabSim include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFabHemeLB (previously known as \u003cem\u003eFabricHemeLB\u003c/em\u003e), which is used to automate workflows involving\nthe HemeLB lattice-Boltzmann simulation environment (see \u003ca href=\"http://www.github.com/UCL/hemelb\"\u003ehttp://www.github.com/UCL/hemelb\u003c/a\u003e for\nthe source code of that).\u003c/li\u003e\n\u003cli\u003eFabMD, which is used to semi-automatically coarse-grain polymer systems (part of this repository).\u003c/li\u003e\n\u003cli\u003eFabBioMD, which is used to facilitate protein-ligand binding affinity calculations (part of this repository).\u003c/li\u003e\n\u003cli\u003eFabFlee, which is under development and used to automate agent-based simulations of forced migration.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation and usage\u003c/h2\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-label=\"Permalink: Installation and usage\" href=\"#installation-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor instructions on how to install and test FabSim, please refer to \u003ccode\u003edocs/installation.md\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe FabMUSCLE plugin does not yet work correctly with the Eagle Supercomputer.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCiting FabSim\u003c/h3\u003e\u003ca id=\"user-content-citing-fabsim\" class=\"anchor\" aria-label=\"Permalink: Citing FabSim\" href=\"#citing-fabsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease find the BibTex reference below of our FabSim software paper in \u003cem\u003eComputer Physics Communications\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@article{GROEN2016375,\ntitle = \"FabSim: Facilitating computational research through automation on large-scale and distributed e-infrastructures\",\njournal = \"Computer Physics Communications\",\nvolume = \"207\",\nnumber = \"Supplement C\",\npages = \"375 - 385\",\nyear = \"2016\",\nissn = \"0010-4655\",\ndoi = \"https://doi.org/10.1016/j.cpc.2016.05.020\",\nurl = \"http://www.sciencedirect.com/science/article/pii/S0010465516301448\",\nauthor = \"Derek Groen and Agastya P. Bhati and James Suter and James Hetherington and Stefan J. Zasada and Peter V. Coveney\",\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "Bioconductor/bioconductor_full", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBioconductor Devel Docker image - DEPRECATED\u003c/h1\u003e\u003ca id=\"user-content-bioconductor-devel-docker-image---deprecated\" class=\"anchor\" aria-label=\"Permalink: Bioconductor Devel Docker image - DEPRECATED\" href=\"#bioconductor-devel-docker-image---deprecated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDeprecation Notice\u003c/strong\u003e : The \u003ccode\u003ebioconductor/bioconductor_full\u003c/code\u003e images have been deprecated in favor of the new \u003ccode\u003ebioconductor/bioconductor_docker\u003c/code\u003e images located at \u003ca href=\"https://github.com/bioconductor/bioconductor_docker\"\u003ehttps://github.com/bioconductor/bioconductor_docker\u003c/a\u003e . These new images will have the same functionality as \u003ccode\u003ebioconductor_full\u003c/code\u003e, and users should find it easy to switch between the new set of containers. Please refer to the \u003ca href=\"https://github.com/Bioconductor/bioconductor_docker/blob/master/README.md\"\u003eREADME.md\u003c/a\u003e of the new images for more information.\u003c/p\u003e\n\u003cp\u003eBioconductor Docker image with full set of system dependencies so that\nall Bioconductor packages can be installed.\u003c/p\u003e\n\u003cp\u003eThe Docker images have R and Bioconductor with different versions\nunder each \"branch\" in git.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: Docker image for bioconductor_full:devel is in the \u003ccode\u003emaster\u003c/code\u003e\nbranch, and all the release branches will be under the branch\n\u003ccode\u003eRELEASE_X_Y\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eImportant Links:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/bioconductor/bioconductor_full\" rel=\"nofollow\"\u003eDocker hub link for bioconductor_full\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Bioconductor/bioconductor_full\"\u003eGithub development link for bioconductor_full\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvantages of the \u003ccode\u003ebioconductor_full\u003c/code\u003e docker image\u003c/h2\u003e\u003ca id=\"user-content-advantages-of-the-bioconductor_full-docker-image\" class=\"anchor\" aria-label=\"Permalink: Advantages of the bioconductor_full docker image\" href=\"#advantages-of-the-bioconductor_full-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe bioconductor_full docker images can be used instead of\ninstalling complex dependencies needed for Bioconductor\npackages. The image comes with most of the dependencies installed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eQuick start up to start your analysis with all the Bioconductor\npackages if needed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe image will be regularly updated to reflect the build system on\nBioconductor. This is a very useful resource for maintainers who\nare actively developing their package to see if it works in tandem\nwith the bioconductor ecosystem. It provides a local testing outlet\nfor maintainers and developers.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation and quick start\u003c/h2\u003e\u003ca id=\"user-content-installation-and-quick-start\" class=\"anchor\" aria-label=\"Permalink: Installation and quick start\" href=\"#installation-and-quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis document assumes you have \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003edocker\u003c/a\u003e installed. Please check\n\u003ca href=\"https://www.docker.com/products/docker-desktop\" rel=\"nofollow\"\u003einstallation\u003c/a\u003e if you have more questions regarding this.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuick start\u003c/h3\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eStart docker on your machine.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn the command line, \"pull\" the bioconductor_full docker image with\nthe correct tag. These images are hosted on docker hub under the\n\u003ca href=\"https://cloud.docker.com/u/bioconductor/repository/registry-1.docker.io/bioconductor/bioconductor_full\" rel=\"nofollow\"\u003eBioconductor organization\u003c/a\u003e page.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull bioconductor/bioconductor_full:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull bioconductor/bioconductor_full:RELEASE_X_Y\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOnce the image is available on your local machine, you can check to\nsee if they are available.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker images\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo start using these images with RStudio, this will start the image\nunder the \u0027rstudio\u0027 user\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run \\\n -e PASSWORD=your_password \\\n -p 8787:8787 \\\n -v ~/host-site-library:/usr/local/lib/R/host-site-library \\\n bioconductor/bioconductor_full:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo start the image interactively using the \u003ccode\u003ebioc\u003c/code\u003e user\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run \\\n -it \\\n --user bioc \\\n -v ~/host-site-library:/usr/local/lib/R/host-site-library \\\n bioconductor/bioconductor_full:devel \\\n R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: The path \u003ccode\u003e/usr/local/lib/R/host-site-library\u003c/code\u003e is mapped to\n\u003ccode\u003e.libPaths()\u003c/code\u003e in R. So, when R is started, all the libraries in\nthe host directory, \u003ccode\u003ehost-site-library\u003c/code\u003e are available to R. It is\nstored on your machine mounted from the volume you fill in place\nof \u003ccode\u003ehost-site-library\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThese libraries will only work if they are pre-compiled with the\nsame version of R that is in the docker image. To explain further,\nyou would need the packages built with Bioconductor version \u00273.9\u0027\nto work with R-3.6. Similarly, you\u0027d need Bioconductor version\n\u00273.9\u0027 to work with R-3.6.z.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo start the docker image in deamon mode, i.e, have the container\nrunning in the background use the \u003ccode\u003e-d\u003c/code\u003e option.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo docker run -it \\\n -d \\\n -v host-site-library:/usr/local/lib/R/host-site-library \\\n --entrypoint /bin/bash \\\n bioconductor/bioconductor_full:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will start the container in the background and keep it\nrunning. You may check the running processes using \u003ccode\u003edocker ps\u003c/code\u003e,\nand copy the container id.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker ps\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo attach to a container which is running in the background\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker exec -it \u0026lt;container_id\u0026gt; bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: You can replace \u003ccode\u003ebash\u003c/code\u003e with R to start R directly in the\ncontainer.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker exec -it \u0026lt;container_id\u0026gt; R\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo run multiple RStudio instances, use a different external port\nmapping (the first port in \u003ccode\u003e-p XXXX:YYYY\u003c/code\u003e) for each instance.\nUse standard shell commands (e.g., adding a \u003ccode\u003e\u0026amp;\u003c/code\u003e at the end of the\nfirst docker command) to place docker processes in the\nbackground. The \u0027devel\u0027 instance will be available at\n\u003ca href=\"http://localhost:8787\" rel=\"nofollow\"\u003ehttp://localhost:8787\u003c/a\u003e, and the release image at\n\u003ca href=\"http://localhost:8788\" rel=\"nofollow\"\u003ehttp://localhost:8788\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run \\\n -e PASSWORD=your_password \\\n -p 8787:8787 \\\n bioconductor/bioconductor_full:devel\n\n docker run \\\n -e PASSWORD=your_password \\\n -p 8788:8787 \\\n bioconductor/bioconductor_full:RELEASE_3_10\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIssues in the devel image\u003c/h2\u003e\u003ca id=\"user-content-issues-in-the-devel-image\" class=\"anchor\" aria-label=\"Permalink: Issues in the devel image\" href=\"#issues-in-the-devel-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePackages which will never install\u003c/h3\u003e\u003ca id=\"user-content-packages-which-will-never-install\" class=\"anchor\" aria-label=\"Permalink: Packages which will never install\" href=\"#packages-which-will-never-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003exps\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRmpi\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eccfindR\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor more information on the issues for this image, please check\n\u003ccode\u003eissues.md\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioconductor provides singularity hub images. The files Singularity, Singularity.RELEASE_3_8, Singularity.RELEASE_3_9 allow the images to build on Singularity hub.\u003c/p\u003e\n\u003cp\u003eFind the singularity hub images, and download them by \u003ca href=\"https://www.singularity-hub.org/collections/3154/usage\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/3154/usage\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://Bioconductor/bioconductor_full\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFrequently Asked Questions (FAQ)\u003c/h2\u003e\u003ca id=\"user-content-frequently-asked-questions-faq\" class=\"anchor\" aria-label=\"Permalink: Frequently Asked Questions (FAQ)\" href=\"#frequently-asked-questions-faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow do I use LaTeX inside the container?\u003c/h3\u003e\u003ca id=\"user-content-how-do-i-use-latex-inside-the-container\" class=\"anchor\" aria-label=\"Permalink: How do I use LaTeX inside the container?\" href=\"#how-do-i-use-latex-inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the \u003ccode\u003etinytex\u003c/code\u003e package (\u003ca href=\"https://yihui.name/tinytex/\" rel=\"nofollow\"\u003ehttps://yihui.name/tinytex/\u003c/a\u003e) which has helpers for installing LaTeX functionality.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003etinytex\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-e\"\u003etinytex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_tinytex()\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 25, - "subscribers_count": 18, + "subscribers_count": 10, "topics": [ - "automation", - "multiscale-simulation", - "workflow-automation", - "high-performance-computing", - "python3" + "bioconductor", + "docker", + "containers" ], - "updated_at": 1729165766.0 + "updated_at": 1721819193.0 }, { "data_format": 2, - "description": "CheckQC inspects the content of an Illumina runfolder and determines if it passes a set of quality criteria", + "description": "Digital Imaging of Root Traits: Extract trait measurements from images of monocot and dicot roots.", "filenames": [ "Singularity" ], - "full_name": "Molmed/checkQC", - "latest_release": "v4.0.5", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003echeckQC\u003c/h1\u003e\u003ca id=\"user-content-checkqc\" class=\"anchor\" aria-label=\"Permalink: checkQC\" href=\"#checkqc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/Molmed/checkQC\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e9e21e9c23ad1b6a37b2db73831848ecaedc59c32bd87bcd287bb9b15938bcd/68747470733a2f2f7472617669732d63692e6f72672f4d6f6c6d65642f636865636b51432e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/Molmed/checkQC.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/Molmed/checkQC\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a2a6bd31033da332c8409f6fd2c5efefc00910d22271f45f9658647c1393f52a/68747470733a2f2f636f6465636f762e696f2f67682f4d6f6c6d65642f636865636b51432f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/Molmed/checkQC/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.python.org/pypi/checkQC\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db7452a52ebc3283465dfda35a2772e234632b5b1e29d02bf1098d064aa58293/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f636865636b71632e737667\" alt=\"PyPI\" data-canonical-src=\"https://img.shields.io/pypi/v/checkqc.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/checkqc\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ae21463d0db69d409fe412e62f7b3fac29086e07a4e28f2352ee9f7ceefd6575/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f762f62696f636f6e64612f636865636b7163\" alt=\"Conda\" data-canonical-src=\"https://img.shields.io/conda/v/bioconda/checkqc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://checkqc.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/705cd32bbd66b1ed45b28045e7584d1000bbfdb97a94a9da281008dbd130ad5f/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f636865636b71632f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/checkqc/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.21105/joss.00556\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9cb80ac47dec3434d4f398ac36efb3bbd61c3c5087dd53ea417b8619ae025eb6/687474703a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f31302e32313130352f6a6f73732e30303535362f7374617475732e737667\" alt=\"DOI\" data-canonical-src=\"http://joss.theoj.org/papers/10.21105/joss.00556/status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMore documentation is available at \u003ca href=\"http://checkqc.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttp://checkqc.readthedocs.io/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCheckQC is a program designed to check a set of quality criteria against an Illumina runfolder.\u003c/p\u003e\n\u003cp\u003eThis is useful as part of a pipeline, where one needs to evaluate a set of quality criteria after demultiplexing. CheckQC is fast, and\nshould finish within a few seconds. It will warn if there are problems breaching warning criteria, and will emit a non-zero exit status if it finds\nany errors, thus making it easy to stop further processing if the run that is being evaluated needs troubleshooting.\u003c/p\u003e\n\u003cp\u003eCheckQC has been designed to be modular, and exactly which \"qc handlers\" are executed with which parameters for a specific run type (i.e. machine\ntype and run length) is determined by a configuration file.\u003c/p\u003e\n\u003cp\u003eInstrument types supported in checkQC are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHiSeqX\u003c/li\u003e\n\u003cli\u003eHiSeq2500\u003c/li\u003e\n\u003cli\u003eiSeq\u003c/li\u003e\n\u003cli\u003eMiSeq\u003c/li\u003e\n\u003cli\u003eNovaSeq\u003c/li\u003e\n\u003cli\u003eNovaSeq X Plus\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall instructions\u003c/h2\u003e\u003ca id=\"user-content-install-instructions\" class=\"anchor\" aria-label=\"Permalink: Install instructions\" href=\"#install-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheckQC requires \u003cstrong\u003ePython 3.10\u003c/strong\u003e. CheckQC can be installed with pip.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install checkqc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively it can be installed with conda using the bioconda channel:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c bioconda checkqc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CheckQC\u003c/h2\u003e\u003ca id=\"user-content-running-checkqc\" class=\"anchor\" aria-label=\"Permalink: Running CheckQC\" href=\"#running-checkqc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter installing CheckQC you can run it by specifying the path to the runfolder you want to\nanalyze like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echeckqc \u0026lt;RUNFOLDER\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will use the default configuration file packaged with CheckQC if you want to specify\nyour own custom file, you can do so by adding a path to the config like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echeckqc --config_file \u0026lt;path to your config\u0026gt; \u0026lt;RUNFOLDER\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen CheckQC starts and no path to the config file is specified it will give you\nthe path to where the default file is located on your system, if you want a template\nthat you can customize according to your own needs.\u003c/p\u003e\n\u003cp\u003eWhen you run CheckQC you can expect to see output similar to this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echeckqc tests/resources/170726_D00118_0303_BCB1TVANXX/\nINFO ------------------------\nINFO Starting checkQC (1.1.2)\nINFO ------------------------\nINFO Runfolder is: tests/resources/170726_D00118_0303_BCB1TVANXX/\nINFO No config file specified, using default config from /home/MOLMED/johda411/workspace/checkQC/checkQC/default_config/config.yaml.\nINFO Run summary\nINFO -----------\nINFO Instrument and reagent version: hiseq2500_rapidhighoutput_v4\nINFO Read length: 125-125\nINFO Enabled handlers and their config values were:\nINFO ClusterPFHandler Error=unknown Warning=180\nINFO Q30Handler Error=unknown Warning=80\nINFO ErrorRateHandler Error=unknown Warning=2\nINFO ReadsPerSampleHandler Error=90 Warning=unknown\nINFO UndeterminedPercentageHandler Error=10 Warning=unknown\nWARNING QC warning: Cluster PF was to low on lane 1, it was: 117.93 M\nWARNING QC warning: Cluster PF was to low on lane 7, it was: 122.26 M\nWARNING QC warning: Cluster PF was to low on lane 8, it was: 177.02 M\nERROR Fatal QC error: Number of reads for sample Sample_pq-27 was too low on lane 7, it was: 6.893 M\nERROR Fatal QC error: Number of reads for sample Sample_pq-28 was too low on lane 7, it was: 7.104 M\nINFO Finished with fatal qc errors and will exit with non-zero exit status.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe program will summarize the type of run it has identified and output any warnings and/or errors in finds.\nIf any qc errors were found the CheckQC will output a non-zero exit status. This means it can easily be used to\ndecide if a further steps should run or not, e.g. in a workflow.\u003c/p\u003e\n\u003cp\u003eIn addition to the normal output CheckQC has a json mode, enabled by adding \u003ccode\u003e--json\u003c/code\u003e to the commandline.\nThis outputs the results normally shown in the log as json on \u003ccode\u003estdout\u003c/code\u003e (while the log itself is written to \u003ccode\u003estderr\u003c/code\u003e),\nso that this can either be written to a file, or redirected to other programs which can parse the data further.\nIn this example we use the python json tool to pretty print the json output:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echeckqc --json tests/resources/170726_D00118_0303_BCB1TVANXX/ | python -m json.tool\nINFO ------------------------\nINFO Starting checkQC (1.1.2)\nINFO ------------------------\nINFO Runfolder is: tests/resources/170726_D00118_0303_BCB1TVANXX/\nINFO No config file specified, using default config from /home/MOLMED/johda411/workspace/checkQC/checkQC/default_config/config.yaml.\nINFO Run summary\nINFO -----------\nINFO Instrument and reagent version: hiseq2500_rapidhighoutput_v4\nINFO Read length: 125-125\nINFO Enabled handlers and their config values were:\nINFO \tClusterPFHandler Error=unknown Warning=180\nINFO \tQ30Handler Error=unknown Warning=80\nINFO \tErrorRateHandler Error=unknown Warning=2\nINFO \tReadsPerSampleHandler Error=90 Warning=unknown\nINFO \tUndeterminedPercentageHandler Error=10 Warning=unknown\nWARNING QC warning: Cluster PF was to low on lane 1, it was: 117.93 M\nWARNING QC warning: Cluster PF was to low on lane 7, it was: 122.26 M\nWARNING QC warning: Cluster PF was to low on lane 8, it was: 177.02 M\nERROR Fatal QC error: Number of reads for sample Sample_pq-27 was too low on lane 7, it was: 6.893 M\nERROR Fatal QC error: Number of reads for sample Sample_pq-28 was too low on lane 7, it was: 7.104 M\nINFO Finished with fatal qc errors and will exit with non-zero exit status.\n{\n \"exit_status\": 1,\n \"ClusterPFHandler\": [\n {\n \"type\": \"warning\",\n \"message\": \"Cluster PF was to low on lane 1, it was: 117.93 M\",\n \"data\": {\n \"lane\": 1,\n \"lane_pf\": 117929896,\n \"threshold\": 180\n }\n },\n {\n \"type\": \"warning\",\n \"message\": \"Cluster PF was to low on lane 7, it was: 122.26 M\",\n \"data\": {\n \"lane\": 7,\n \"lane_pf\": 122263375,\n \"threshold\": 180\n }\n },\n {\n \"type\": \"warning\",\n \"message\": \"Cluster PF was to low on lane 8, it was: 177.02 M\",\n \"data\": {\n \"lane\": 8,\n \"lane_pf\": 177018999,\n \"threshold\": 180\n }\n }\n ],\n \"ReadsPerSampleHandler\": [\n {\n \"type\": \"error\",\n \"message\": \"Number of reads for sample Sample_pq-27 was too low on lane 7, it was: 6.893 M\",\n \"data\": {\n \"lane\": 7,\n \"number_of_samples\": 12,\n \"sample_id\": \"Sample_pq-27\",\n \"sample_reads\": 6.893002,\n \"threshold\": 90\n }\n },\n {\n \"type\": \"error\",\n \"message\": \"Number of reads for sample Sample_pq-28 was too low on lane 7, it was: 7.104 M\",\n \"data\": {\n \"lane\": 7,\n \"number_of_samples\": 12,\n \"sample_id\": \"Sample_pq-28\",\n \"sample_reads\": 7.10447,\n \"threshold\": 90\n }\n }\n ],\n \"run_summary\": {\n \"instrument_and_reagent_type\": \"hiseq2500_rapidhighoutput_v4\",\n \"read_length\": \"125-125\",\n \"handlers\": [\n {\n \"handler\": \"ClusterPFHandler\",\n \"error\": \"unknown\",\n \"warning\": 180\n },\n {\n \"handler\": \"Q30Handler\",\n \"error\": \"unknown\",\n \"warning\": 80\n },\n {\n \"handler\": \"ErrorRateHandler\",\n \"error\": \"unknown\",\n \"warning\": 2\n },\n {\n \"handler\": \"ReadsPerSampleHandler\",\n \"error\": 90,\n \"warning\": \"unknown\"\n },\n {\n \"handler\": \"UndeterminedPercentageHandler\",\n \"error\": 10,\n \"warning\": \"unknown\"\n }\n ]\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CheckQC as a webservice\u003c/h2\u003e\u003ca id=\"user-content-running-checkqc-as-a-webservice\" class=\"anchor\" aria-label=\"Permalink: Running CheckQC as a webservice\" href=\"#running-checkqc-as-a-webservice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn addition to running like a commandline application, CheckQC can be run as a simple webservice.\u003c/p\u003e\n\u003cp\u003eTo run it you simply need to provide the path to a directory where runfolders that you want to\nbe able to check are located. This is given as \u003ccode\u003eMONITOR_PATH\u003c/code\u003e below. There are also a number\nof optional arguments that can be passed to the service.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ checkqc-ws --help\nUsage: checkqc-ws [OPTIONS] MONITOR_PATH\n\nOptions:\n --port INTEGER Port which checkqc-ws will listen to (default: 9999).\n --config PATH Path to the checkQC configuration file (optional)\n --log_config PATH Path to the checkQC logging configuration file (optional)\n --debug Enable debug mode.\n --help Show this message and exit.\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the webserver is running you can query the \u003ccode\u003e/qc/\u003c/code\u003e endpoint and get any errors and warnings back as json.\nHere is an example how to query the endpoint, and what type of results it will return:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -s -w\u0027\\n\u0027 localhost:9999/qc/170726_D00118_0303_BCB1TVANXX | python -m json.tool\n{\n \"ClusterPFHandler\": [\n {\n \"data\": {\n \"lane\": 1,\n \"lane_pf\": 117929896,\n \"threshold\": 180\n },\n \"message\": \"Cluster PF was to low on lane 1, it was: 117.93 M\",\n \"type\": \"warning\"\n },\n {\n \"data\": {\n \"lane\": 7,\n \"lane_pf\": 122263375,\n \"threshold\": 180\n },\n \"message\": \"Cluster PF was to low on lane 7, it was: 122.26 M\",\n \"type\": \"warning\"\n },\n {\n \"data\": {\n \"lane\": 8,\n \"lane_pf\": 177018999,\n \"threshold\": 180\n },\n \"message\": \"Cluster PF was to low on lane 8, it was: 177.02 M\",\n \"type\": \"warning\"\n }\n ],\n \"ReadsPerSampleHandler\": [\n {\n \"data\": {\n \"lane\": 7,\n \"number_of_samples\": 12,\n \"sample_id\": \"Sample_pq-27\",\n \"sample_reads\": 6.893002,\n \"threshold\": 90\n },\n \"message\": \"Number of reads for sample Sample_pq-27 was too low on lane 7, it was: 6.893 M\",\n \"type\": \"warning\"\n },\n {\n \"data\": {\n \"lane\": 7,\n \"number_of_samples\": 12,\n \"sample_id\": \"Sample_pq-28\",\n \"sample_reads\": 7.10447,\n \"threshold\": 90\n },\n \"message\": \"Number of reads for sample Sample_pq-28 was too low on lane 7, it was: 7.104 M\",\n \"type\": \"warning\"\n }\n ],\n \"exit_status\": 0,\n \"version\": \"1.1.0\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "Computational-Plant-Science/DIRT", + "latest_release": null, + "readme": "\u003chr\u003e\n\u003cp\u003eDIRT 1.1 - An automatic high throughput root phenotyping platform\n(c) 2014,2016 Alexander Bucksch - \u003ca href=\"mailto:bucksch@uga.edu\"\u003ebucksch@uga.edu\u003c/a\u003e\nWeb application by Abhiram Das - \u003ca href=\"mailto:abhiram.das@gmail.com\"\u003eabhiram.das@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://dirt.iplantcollaborative.org\" rel=\"nofollow\"\u003ehttp://dirt.iplantcollaborative.org\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUser and developer group:\n\u003ca href=\"https://groups.google.com/forum/#!forum/dirt-users\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/dirt-users\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUniversity of Georgia, Athens\u003c/h2\u003e\u003ca id=\"user-content-university-of-georgia-athens\" class=\"anchor\" aria-label=\"Permalink: University of Georgia, Athens\" href=\"#university-of-georgia-athens\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe software is written and tested in:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython 2.7 (\u003ca href=\"https://www.python.org\" rel=\"nofollow\"\u003ehttps://www.python.org\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe software depends on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe graphtools package (\u003ca href=\"http://graph-tool.skewed.de\" rel=\"nofollow\"\u003ehttp://graph-tool.skewed.de\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ethe mahotas package (\u003ca href=\"http://luispedro.org/software/mahotas\" rel=\"nofollow\"\u003ehttp://luispedro.org/software/mahotas\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ethe numpy package (\u003ca href=\"http://sourceforge.net/projects/numpy/\" rel=\"nofollow\"\u003ehttp://sourceforge.net/projects/numpy/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ethe scipy package (\u003ca href=\"http://www.scipy.org/SciPy\" rel=\"nofollow\"\u003ehttp://www.scipy.org/SciPy\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptionally binaries of standard OCR and BarCode software can be used for tag recognition:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003etesseract (\u003ca href=\"https://code.google.com/p/tesseract-ocr/\" rel=\"nofollow\"\u003ehttps://code.google.com/p/tesseract-ocr/\u003c/a\u003e)\npaths have to be adjusted in /DIRTocr/pytesser.py (line 12-14)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ezbar (\u003ca href=\"http://zbar.sourceforge.net\" rel=\"nofollow\"\u003ehttp://zbar.sourceforge.net\u003c/a\u003e)\npath has to be adjusted in /DIRTocr/\u003cstrong\u003einit\u003c/strong\u003e.py (line 28)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsage:\n full path to the root image\n ID which will be a folder name in the working directory. Integer value needed.\n multiplier for the automatically determined mask threshold. 1.0 works fine and is default. For example, if a flashlight is used to take root images, then 0.6 is a good choice.\n number of roots placed at the right of the root crown, 0 - excised root analysis is off\n 1 - crown root analysis is on, 0 - crown root analysis is off\n 1 - is on, 0 - is off. Off refers to a pre-existing segmention done with DIRT. Binary masks as input images are detected automatically.\n a simple decimal e.g. 25.4. If 0.0 is used, then the output will have pixels as unit.\n 1 - reconstruction is turned on, 0 - reconstruction is turned off\n 1 - plotting data is stored, 0 - plotting data is not stored\n 1 - the full trait set is put into one excel file containing empty cells for traits that were not computed, 0 - only computed files are written to the output file\n full path to folder were the result is stored\n full path to .csv file containing the traits to be computed\u0027\u003c/p\u003e\n\u003cp\u003eExample:\npython main.py /Documents/image_name.jpg 8 25.0 1 1 1 25.1 0 0 0 /Documents/image_folder/ /Documents/traits.csv\u003c/p\u003e\n\u003cp\u003eNotes on common questions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInput is restricted to .jpg, .png and .tif images\u003c/li\u003e\n\u003cli\u003eIt is not possible to analyze only an excised root when a root crown is in the image. However, it is possible to analyze compute images containing only excised roots.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003eFor convenience we provide the runOnFolder script, that executes DIRT on all images in a specified folder.\nNote we made the masking threshold available on the command line because of user requests.\u003c/p\u003e\n\u003cp\u003eExample: python runOnFolder.py /Users/image_folder/ \u003c/p\u003e\n\u003cp\u003ePlease adjust line 86 according to the description above and note that the script uses 6 cores to compute images in parallel. The number of cores can be adjusted in line 80.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates in DIRT 1.1 (21 June 2019):\u003c/h2\u003e\u003ca id=\"user-content-updates-in-dirt-11-21-june-2019\" class=\"anchor\" aria-label=\"Permalink: Updates in DIRT 1.1 (21 June 2019):\" href=\"#updates-in-dirt-11-21-june-2019\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSome bug fixes on the avg. root density. There was a problem with very young and sparse root system. The formula changed and is now normed to the max. width instead of the max. width of the line.\nThe bug was found by Peng Wang at the University of Nebraska.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates in DIRT 1.1 (11 January 2016):\u003c/h2\u003e\u003ca id=\"user-content-updates-in-dirt-11-11-january-2016\" class=\"anchor\" aria-label=\"Permalink: Updates in DIRT 1.1 (11 January 2016):\" href=\"#updates-in-dirt-11-11-january-2016\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMinor bug fixes in Preprocessing.py to allow smaller circle markers and fix a possible missdetection of the experiment tag as the circle.\nThanks to Linda Zamariola (U Bologna) for finding this issue.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates in DIRT 1.1 (4 November 2015):\u003c/h2\u003e\u003ca id=\"user-content-updates-in-dirt-11-4-november-2015\" class=\"anchor\" aria-label=\"Permalink: Updates in DIRT 1.1 (4 November 2015):\" href=\"#updates-in-dirt-11-4-november-2015\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMinor bug fixes in the excised root calculations. Thanks to Alexandre Grondin (U Nebraska) for discovering and validating the fixes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChanges in DIRT 1.1 (14 January 2015):\u003c/h2\u003e\u003ca id=\"user-content-changes-in-dirt-11-14-january-2015\" class=\"anchor\" aria-label=\"Permalink: Changes in DIRT 1.1 (14 January 2015):\" href=\"#changes-in-dirt-11-14-january-2015\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003estorage of trait values is changed from a list data structure to a dictionary to allow trait selection controlled by the file traits.csv\u003c/li\u003e\n\u003cli\u003eadded support for trait selection to reduce computation time. See example file traits.csv (1 - trait is computed, 0 - trait is not computed)\u003c/li\u003e\n\u003cli\u003eremoved unused tip-diameter switch on the command line\u003c/li\u003e\n\u003cli\u003eadd stem reconstruction switch on the command line to turn the experimental stem reconstruction on/off\u003c/li\u003e\n\u003cli\u003eoutput file now uses the codes in the trait.csv file and only contains selected traits\u003c/li\u003e\n\u003cli\u003eremoved several unused variables and minor bugs fixed\u003c/li\u003e\n\u003cli\u003eadded command line option to turn storage of numpy arrays on/off. These files can be used to plot the individual root statistics and can be found in the \"Plots\" folders.\u003c/li\u003e\n\u003cli\u003enew (experimental, not validated) traits added due to community requests: projected root area, width and depth of the skeleton (medial axis), top and bottom angle for monocots, segmentation of adventious and basal roots for legumes to retrieve taproot and hypocotyl diameter and adventious and basal root counts.\u003c/li\u003e\n\u003cli\u003eadded computational statistics such as computation time and graph size to help balancing grid installations\u003c/li\u003e\n\u003cli\u003eadded an option to have an output file with all possible traits that contains empty cells for not computed traits in the output.csv file. This was a developer request to enable faster ingestion into data bases\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 25, - "subscribers_count": 12, + "subscribers_count": 7, "topics": [ - "genomics", - "quality-control", - "sequencing", - "illumina" + "phenotyping", + "phenotyping-algorithms", + "root", + "imaging", + "image-processing", + "computer-vis" ], - "updated_at": 1726737988.0 + "updated_at": 1730363822.0 + }, + { + "data_format": 2, + "description": "BLADE: Bayesian Log-normAl DEconvolution for enhanced in silico microdissection of bulk gene expression data", + "filenames": [ + "Singularity" + ], + "full_name": "tgac-vumc/BLADE", + "latest_release": "v1.0", + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/BLADE/blob/master/logo_final_small.png\"\u003e\u003cimg width=\"254\" height=\"281\" src=\"https://github.com/tgac-vumc/BLADE/raw/master/logo_final_small.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBLADE: Bayesian Log-normAl DEconvolution\u003c/h1\u003e\u003ca id=\"user-content-blade-bayesian-log-normal-deconvolution\" class=\"anchor\" aria-label=\"Permalink: BLADE: Bayesian Log-normAl DEconvolution\" href=\"#blade-bayesian-log-normal-deconvolution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/297362131\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df86a4efa94bed6eea97ae612ec93651e7850ba9f4feaf52b5d041b748b6d08e/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3239373336323133312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/297362131.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.python.org/downloads/release/python-360/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/113d6bc817de7305ae86870d1912944885c0d4078c0b0f40ea80d33d513e62c4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e362d626c75652e737667\" alt=\"Python 3.6\" data-canonical-src=\"https://img.shields.io/badge/python-3.6-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/BLADE-Deconvolution\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fc3ec29c80fed33ee2e5f75eef5b82be145f484024851e149843ce372ba49ae8/68747470733a2f2f62616467652e667572792e696f2f70792f424c4144452d4465636f6e766f6c7574696f6e2e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/BLADE-Deconvolution.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4861\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/tgac-vumc/BLADE/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBLADE (Bayesian Log-normAl DEconvolution) was designed to jointly estimate cell type composition and gene expression profiles per cell type in a single-step while accounting for the observed gene expression variability in single-cell RNA-seq data.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/BLADE/blob/master/framework.png\"\u003e\u003cimg width=\"100%\" height=\"100%\" src=\"https://github.com/tgac-vumc/BLADE/raw/master/framework.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eBLADE framework. To construct a prior knowledge of BLADE, we used single-cell sequencing data. Cell are subject to phenotyping, clustering and differential gene expression analysis. Then, for each cell type, we retrieve average expression profiles (red cross and top heatmap), and standard deviation per gene (blue circle and bottom heatmap). This prior knowledge is then used in the hierarchical Bayesian model (bottom right) to deconvolute bulk gene expression data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDemo notebook is available \u003ca href=\"https://github.com/tgac-vumc/BLADE/blob/master/jupyter/BLADE%20-%20Demo%20script.ipynb\"\u003ehere\u003c/a\u003e. You can also run the demo using \u003ca href=\"https://mybinder.org/v2/gh/tgac-vumc/BLADE/master\" rel=\"nofollow\"\u003eBinder\u003c/a\u003e.\u003c/h4\u003e\u003ca id=\"user-content-demo-notebook-is-available-here-you-can-also-run-the-demo-using-binder\" class=\"anchor\" aria-label=\"Permalink: Demo notebook is available here. You can also run the demo using Binder.\" href=\"#demo-notebook-is-available-here-you-can-also-run-the-demo-using-binder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that for the testing on Binder, parallel processing has to be disabled by setting \u003ccode\u003eNjob\u003c/code\u003e to 1. BLADE significantly performs better with high number of cores, epecially when \u003ccode\u003eNsample\u003c/code\u003e, \u003ccode\u003eNgene\u003c/code\u003e and \u003ccode\u003eNcell\u003c/code\u003e is high. In case of Binder, we recommend the following setting:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eNcell=3\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eNgene=50\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eNsample=10\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt takes about 30 minutes to complete the demo execution on Binder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSystem Requirements\u003c/h2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-label=\"Permalink: System Requirements\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Requirements\u003c/h3\u003e\u003ca id=\"user-content-hardware-requirements\" class=\"anchor\" aria-label=\"Permalink: Hardware Requirements\" href=\"#hardware-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBLADE can run on the minimal computer spec, such as Binder (1 CPU, 2GB RAM on Google Cloud), when data size is small. However, BLADE can significantly benefit from the larger amount of CPUs and RAM. Empirical Bayes procedure of BLADE runs independent optimization procedure that can be parallelized. In our evaluation, we used a computing node with the following spec:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e40 threads (Xeon 2.60GHz)\u003c/li\u003e\n\u003cli\u003e128 GB RAM\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOS Requirements\u003c/h3\u003e\u003ca id=\"user-content-os-requirements\" class=\"anchor\" aria-label=\"Permalink: OS Requirements\" href=\"#os-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package development version is tested on Linux operating systems. (CentOS 7 and Ubuntu 16.04).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing pip\u003c/h3\u003e\u003ca id=\"user-content-using-pip\" class=\"anchor\" aria-label=\"Permalink: Using pip\" href=\"#using-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package of BLADE is available on pip.\nYou can simply (takes only \u0026lt;1min):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install BLADE_Deconvolution\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe tested BLADE with \u003ccode\u003epython =\u0026gt; 3.6\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Conda\u003c/h3\u003e\u003ca id=\"user-content-using-conda\" class=\"anchor\" aria-label=\"Permalink: Using Conda\" href=\"#using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne can create a conda environment contains BLADE and also other dependencies to run \u003ca href=\"https://github.com/tgac-vumc/BLADE/blob/master/jupyter/BLADE%20-%20Demo%20script.ipynb\"\u003eDemo\u003c/a\u003e.\nThe environment definition is in \u003ca href=\"https://github.com/tgac-vumc/BLADE/environment.yml\"\u003eenvironment.yml\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Installing Miniconda 3\u003c/h3\u003e\u003ca id=\"user-content-step-1-installing-miniconda-3\" class=\"anchor\" aria-label=\"Permalink: Step 1: Installing Miniconda 3\" href=\"#step-1-installing-miniconda-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, please open a terminal or make sure you are logged into your Linux VM. Assuming that you have a 64-bit system, on Linux, download and install Miniconda 3 with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn MacOS X, download and install with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh\nbash Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Create a conda environment\u003c/h3\u003e\u003ca id=\"user-content-step-2-create-a-conda-environment\" class=\"anchor\" aria-label=\"Permalink: Step 2: Create a conda environment\" href=\"#step-2-create-a-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install all the necessary dependency using the following command (may takes few minutes).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create --file environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, the \u003ccode\u003eBLADE\u003c/code\u003e environment can be activate by:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate BLADE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Singularity\u003c/h3\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-label=\"Permalink: Using Singularity\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have Singularity, you can simply pull the singularity container with all dependency resolved (in few minutes, depends on the network speed).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tgac-vumc/BLADE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of BLADE\u003c/h2\u003e\u003ca id=\"user-content-overview-of-blade\" class=\"anchor\" aria-label=\"Permalink: Overview of BLADE\" href=\"#overview-of-blade\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the BLADE package, you can load the following functions and modules.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eBLADE\u003c/code\u003e: A class object contains core algorithms of \u003ccode\u003eBLADE\u003c/code\u003e. Users can reach internal variables (\u003ccode\u003eNu\u003c/code\u003e, \u003ccode\u003eOmega\u003c/code\u003e, and \u003ccode\u003eBeta\u003c/code\u003e) and functions for calculating objective functions (ELBO function) and gradients with respect to the variational parameters. There also is an optimization function (\u003ccode\u003eBLADE.Optimize()\u003c/code\u003e) for performing L-BFGS optimization. Though this is the core, we also provide a more accessible function (\u003ccode\u003eBLADE_framework\u003c/code\u003e) that performs deconvolution. See below to obtain the current estimate of cellualr fractions, gene expression profiles per cell type and per sample:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eExpF(self.Beta)\u003c/code\u003e : returns a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNgene\u003c/code\u003e matrix contains estimated fraction of each cell type in each sample.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eself.Nu\u003c/code\u003e: a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e multidimensional array contains estimated gene expression levels of each gene in each cell type for each sample.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003enumpy.mean(self.Nu,0)\u003c/code\u003e: To obtain a estimated gene expression profile per cell type, we can simply take an average across the samples.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eFramework\u003c/code\u003e: A framework based on the \u003ccode\u003eBLADE\u003c/code\u003e class module above. Users need to provide the following input/output arguments.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInput arguments\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eX\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains average gene expression profiles per cell type (a signature matrix) in log-scale.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estdX\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains standard deviation per gene per cell type (a signature matrix of gene expression variability).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eY\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNsample\u003c/code\u003e matrix contains bulk gene expression data. This should be in linear-scale data without log-transformation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInd_Marker\u003c/code\u003e: Index for marker genes. By default, \u003ccode\u003e[True]*Ngene\u003c/code\u003e (all genes used without filtering). For the genes with \u003ccode\u003eFalse\u003c/code\u003e they are excluded in the first phase (Empirical Bayes) for finidng the best hyperparameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInd_sample\u003c/code\u003e: Index for the samples used in the first phase (Empirical Bayes). By default, \u003ccode\u003e[True]*Nsample\u003c/code\u003e (all samples used).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAlphas\u003c/code\u003e, \u003ccode\u003eAlpha0s\u003c/code\u003e, \u003ccode\u003eKappa0s\u003c/code\u003e and \u003ccode\u003eSYs\u003c/code\u003e: all possible hyperparameters considered in the phase of Empirical Bayes. A default parameters are offered as described in the manuscript (to appear): \u003ccode\u003eAlphas=[1,10]\u003c/code\u003e, \u003ccode\u003eAlpha0s=[0.1, 1, 5]\u003c/code\u003e, \u003ccode\u003eKappa0s=[1,0.5,0.1]\u003c/code\u003e and \u003ccode\u003eSYs=[1,0.3,0.5]\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNrep\u003c/code\u003e: Number of repeat for evaluating each parameter configuration in Empirical Bayes phase. By default, \u003ccode\u003eNrep=3\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNrepfinal\u003c/code\u003e: Number of repeated optimizations for the final parameter set. By default, \u003ccode\u003eNrepfinal=10\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNjob\u003c/code\u003e: Number of jobs executed in parallel. By default, \u003ccode\u003eNjob=10\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOutput values\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efinal_obj\u003c/code\u003e: A final \u003ccode\u003eBLADE\u003c/code\u003e object with optimized variational parameters and hyperparameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebest_obj\u003c/code\u003e: The best object form Empirical Bayes step. If no genes and samples are filtered, \u003ccode\u003ebest_obj\u003c/code\u003e is the same as \u003ccode\u003efinal_obj\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebest_set\u003c/code\u003e: A list contains the hyperparameters selected in the Empirical Bayes step.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAll_out\u003c/code\u003e: A list of \u003ccode\u003eBLADE\u003c/code\u003e objects from the Empirical Bayes step.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eBLADE_job\u003c/code\u003e/\u003ccode\u003eOptimize\u003c/code\u003e: Internal functions used by \u003ccode\u003eFramework\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 25, + "subscribers_count": 5, + "topics": [], + "updated_at": 1726682704.0 }, { "data_format": 2, @@ -39492,59 +39537,153 @@ var data = }, { "data_format": 2, - "description": "BLADE: Bayesian Log-normAl DEconvolution for enhanced in silico microdissection of bulk gene expression data", + "description": "CheckQC inspects the content of an Illumina runfolder and determines if it passes a set of quality criteria", "filenames": [ "Singularity" ], - "full_name": "tgac-vumc/BLADE", - "latest_release": "v1.0", - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/BLADE/blob/master/logo_final_small.png\"\u003e\u003cimg width=\"254\" height=\"281\" src=\"https://github.com/tgac-vumc/BLADE/raw/master/logo_final_small.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBLADE: Bayesian Log-normAl DEconvolution\u003c/h1\u003e\u003ca id=\"user-content-blade-bayesian-log-normal-deconvolution\" class=\"anchor\" aria-label=\"Permalink: BLADE: Bayesian Log-normAl DEconvolution\" href=\"#blade-bayesian-log-normal-deconvolution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/297362131\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df86a4efa94bed6eea97ae612ec93651e7850ba9f4feaf52b5d041b748b6d08e/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3239373336323133312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/297362131.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.python.org/downloads/release/python-360/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/113d6bc817de7305ae86870d1912944885c0d4078c0b0f40ea80d33d513e62c4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e362d626c75652e737667\" alt=\"Python 3.6\" data-canonical-src=\"https://img.shields.io/badge/python-3.6-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/BLADE-Deconvolution\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fc3ec29c80fed33ee2e5f75eef5b82be145f484024851e149843ce372ba49ae8/68747470733a2f2f62616467652e667572792e696f2f70792f424c4144452d4465636f6e766f6c7574696f6e2e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/BLADE-Deconvolution.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/4861\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mybinder.org/v2/gh/tgac-vumc/BLADE/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBLADE (Bayesian Log-normAl DEconvolution) was designed to jointly estimate cell type composition and gene expression profiles per cell type in a single-step while accounting for the observed gene expression variability in single-cell RNA-seq data.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/tgac-vumc/BLADE/blob/master/framework.png\"\u003e\u003cimg width=\"100%\" height=\"100%\" src=\"https://github.com/tgac-vumc/BLADE/raw/master/framework.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003eBLADE framework. To construct a prior knowledge of BLADE, we used single-cell sequencing data. Cell are subject to phenotyping, clustering and differential gene expression analysis. Then, for each cell type, we retrieve average expression profiles (red cross and top heatmap), and standard deviation per gene (blue circle and bottom heatmap). This prior knowledge is then used in the hierarchical Bayesian model (bottom right) to deconvolute bulk gene expression data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDemo notebook is available \u003ca href=\"https://github.com/tgac-vumc/BLADE/blob/master/jupyter/BLADE%20-%20Demo%20script.ipynb\"\u003ehere\u003c/a\u003e. You can also run the demo using \u003ca href=\"https://mybinder.org/v2/gh/tgac-vumc/BLADE/master\" rel=\"nofollow\"\u003eBinder\u003c/a\u003e.\u003c/h4\u003e\u003ca id=\"user-content-demo-notebook-is-available-here-you-can-also-run-the-demo-using-binder\" class=\"anchor\" aria-label=\"Permalink: Demo notebook is available here. You can also run the demo using Binder.\" href=\"#demo-notebook-is-available-here-you-can-also-run-the-demo-using-binder\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNote that for the testing on Binder, parallel processing has to be disabled by setting \u003ccode\u003eNjob\u003c/code\u003e to 1. BLADE significantly performs better with high number of cores, epecially when \u003ccode\u003eNsample\u003c/code\u003e, \u003ccode\u003eNgene\u003c/code\u003e and \u003ccode\u003eNcell\u003c/code\u003e is high. In case of Binder, we recommend the following setting:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eNcell=3\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eNgene=50\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eNsample=10\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt takes about 30 minutes to complete the demo execution on Binder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSystem Requirements\u003c/h2\u003e\u003ca id=\"user-content-system-requirements\" class=\"anchor\" aria-label=\"Permalink: System Requirements\" href=\"#system-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHardware Requirements\u003c/h3\u003e\u003ca id=\"user-content-hardware-requirements\" class=\"anchor\" aria-label=\"Permalink: Hardware Requirements\" href=\"#hardware-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBLADE can run on the minimal computer spec, such as Binder (1 CPU, 2GB RAM on Google Cloud), when data size is small. However, BLADE can significantly benefit from the larger amount of CPUs and RAM. Empirical Bayes procedure of BLADE runs independent optimization procedure that can be parallelized. In our evaluation, we used a computing node with the following spec:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e40 threads (Xeon 2.60GHz)\u003c/li\u003e\n\u003cli\u003e128 GB RAM\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOS Requirements\u003c/h3\u003e\u003ca id=\"user-content-os-requirements\" class=\"anchor\" aria-label=\"Permalink: OS Requirements\" href=\"#os-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe package development version is tested on Linux operating systems. (CentOS 7 and Ubuntu 16.04).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing pip\u003c/h3\u003e\u003ca id=\"user-content-using-pip\" class=\"anchor\" aria-label=\"Permalink: Using pip\" href=\"#using-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe python package of BLADE is available on pip.\nYou can simply (takes only \u0026lt;1min):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install BLADE_Deconvolution\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe tested BLADE with \u003ccode\u003epython =\u0026gt; 3.6\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Conda\u003c/h3\u003e\u003ca id=\"user-content-using-conda\" class=\"anchor\" aria-label=\"Permalink: Using Conda\" href=\"#using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOne can create a conda environment contains BLADE and also other dependencies to run \u003ca href=\"https://github.com/tgac-vumc/BLADE/blob/master/jupyter/BLADE%20-%20Demo%20script.ipynb\"\u003eDemo\u003c/a\u003e.\nThe environment definition is in \u003ca href=\"https://github.com/tgac-vumc/BLADE/environment.yml\"\u003eenvironment.yml\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Installing Miniconda 3\u003c/h3\u003e\u003ca id=\"user-content-step-1-installing-miniconda-3\" class=\"anchor\" aria-label=\"Permalink: Step 1: Installing Miniconda 3\" href=\"#step-1-installing-miniconda-3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, please open a terminal or make sure you are logged into your Linux VM. Assuming that you have a 64-bit system, on Linux, download and install Miniconda 3 with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOn MacOS X, download and install with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh\nbash Miniconda3-latest-MacOSX-x86_64.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Create a conda environment\u003c/h3\u003e\u003ca id=\"user-content-step-2-create-a-conda-environment\" class=\"anchor\" aria-label=\"Permalink: Step 2: Create a conda environment\" href=\"#step-2-create-a-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install all the necessary dependency using the following command (may takes few minutes).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create --file environment.yml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, the \u003ccode\u003eBLADE\u003c/code\u003e environment can be activate by:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate BLADE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Singularity\u003c/h3\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-label=\"Permalink: Using Singularity\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you have Singularity, you can simply pull the singularity container with all dependency resolved (in few minutes, depends on the network speed).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://tgac-vumc/BLADE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview of BLADE\u003c/h2\u003e\u003ca id=\"user-content-overview-of-blade\" class=\"anchor\" aria-label=\"Permalink: Overview of BLADE\" href=\"#overview-of-blade\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the BLADE package, you can load the following functions and modules.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eBLADE\u003c/code\u003e: A class object contains core algorithms of \u003ccode\u003eBLADE\u003c/code\u003e. Users can reach internal variables (\u003ccode\u003eNu\u003c/code\u003e, \u003ccode\u003eOmega\u003c/code\u003e, and \u003ccode\u003eBeta\u003c/code\u003e) and functions for calculating objective functions (ELBO function) and gradients with respect to the variational parameters. There also is an optimization function (\u003ccode\u003eBLADE.Optimize()\u003c/code\u003e) for performing L-BFGS optimization. Though this is the core, we also provide a more accessible function (\u003ccode\u003eBLADE_framework\u003c/code\u003e) that performs deconvolution. See below to obtain the current estimate of cellualr fractions, gene expression profiles per cell type and per sample:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eExpF(self.Beta)\u003c/code\u003e : returns a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNgene\u003c/code\u003e matrix contains estimated fraction of each cell type in each sample.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eself.Nu\u003c/code\u003e: a \u003ccode\u003eNsample\u003c/code\u003e by \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e multidimensional array contains estimated gene expression levels of each gene in each cell type for each sample.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003enumpy.mean(self.Nu,0)\u003c/code\u003e: To obtain a estimated gene expression profile per cell type, we can simply take an average across the samples.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eFramework\u003c/code\u003e: A framework based on the \u003ccode\u003eBLADE\u003c/code\u003e class module above. Users need to provide the following input/output arguments.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInput arguments\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eX\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains average gene expression profiles per cell type (a signature matrix) in log-scale.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003estdX\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNcell\u003c/code\u003e matrix contains standard deviation per gene per cell type (a signature matrix of gene expression variability).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eY\u003c/code\u003e: a \u003ccode\u003eNgene\u003c/code\u003e by \u003ccode\u003eNsample\u003c/code\u003e matrix contains bulk gene expression data. This should be in linear-scale data without log-transformation.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInd_Marker\u003c/code\u003e: Index for marker genes. By default, \u003ccode\u003e[True]*Ngene\u003c/code\u003e (all genes used without filtering). For the genes with \u003ccode\u003eFalse\u003c/code\u003e they are excluded in the first phase (Empirical Bayes) for finidng the best hyperparameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eInd_sample\u003c/code\u003e: Index for the samples used in the first phase (Empirical Bayes). By default, \u003ccode\u003e[True]*Nsample\u003c/code\u003e (all samples used).\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAlphas\u003c/code\u003e, \u003ccode\u003eAlpha0s\u003c/code\u003e, \u003ccode\u003eKappa0s\u003c/code\u003e and \u003ccode\u003eSYs\u003c/code\u003e: all possible hyperparameters considered in the phase of Empirical Bayes. A default parameters are offered as described in the manuscript (to appear): \u003ccode\u003eAlphas=[1,10]\u003c/code\u003e, \u003ccode\u003eAlpha0s=[0.1, 1, 5]\u003c/code\u003e, \u003ccode\u003eKappa0s=[1,0.5,0.1]\u003c/code\u003e and \u003ccode\u003eSYs=[1,0.3,0.5]\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNrep\u003c/code\u003e: Number of repeat for evaluating each parameter configuration in Empirical Bayes phase. By default, \u003ccode\u003eNrep=3\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNrepfinal\u003c/code\u003e: Number of repeated optimizations for the final parameter set. By default, \u003ccode\u003eNrepfinal=10\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNjob\u003c/code\u003e: Number of jobs executed in parallel. By default, \u003ccode\u003eNjob=10\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eOutput values\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003efinal_obj\u003c/code\u003e: A final \u003ccode\u003eBLADE\u003c/code\u003e object with optimized variational parameters and hyperparameters.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebest_obj\u003c/code\u003e: The best object form Empirical Bayes step. If no genes and samples are filtered, \u003ccode\u003ebest_obj\u003c/code\u003e is the same as \u003ccode\u003efinal_obj\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003ebest_set\u003c/code\u003e: A list contains the hyperparameters selected in the Empirical Bayes step.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eAll_out\u003c/code\u003e: A list of \u003ccode\u003eBLADE\u003c/code\u003e objects from the Empirical Bayes step.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003eBLADE_job\u003c/code\u003e/\u003ccode\u003eOptimize\u003c/code\u003e: Internal functions used by \u003ccode\u003eFramework\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "Molmed/checkQC", + "latest_release": "v4.0.5", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003echeckQC\u003c/h1\u003e\u003ca id=\"user-content-checkqc\" class=\"anchor\" aria-label=\"Permalink: checkQC\" href=\"#checkqc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/Molmed/checkQC\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e9e21e9c23ad1b6a37b2db73831848ecaedc59c32bd87bcd287bb9b15938bcd/68747470733a2f2f7472617669732d63692e6f72672f4d6f6c6d65642f636865636b51432e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/Molmed/checkQC.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/Molmed/checkQC\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a2a6bd31033da332c8409f6fd2c5efefc00910d22271f45f9658647c1393f52a/68747470733a2f2f636f6465636f762e696f2f67682f4d6f6c6d65642f636865636b51432f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/Molmed/checkQC/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.python.org/pypi/checkQC\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/db7452a52ebc3283465dfda35a2772e234632b5b1e29d02bf1098d064aa58293/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f636865636b71632e737667\" alt=\"PyPI\" data-canonical-src=\"https://img.shields.io/pypi/v/checkqc.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/bioconda/checkqc\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ae21463d0db69d409fe412e62f7b3fac29086e07a4e28f2352ee9f7ceefd6575/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f762f62696f636f6e64612f636865636b7163\" alt=\"Conda\" data-canonical-src=\"https://img.shields.io/conda/v/bioconda/checkqc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://checkqc.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/705cd32bbd66b1ed45b28045e7584d1000bbfdb97a94a9da281008dbd130ad5f/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f636865636b71632f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/checkqc/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.21105/joss.00556\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9cb80ac47dec3434d4f398ac36efb3bbd61c3c5087dd53ea417b8619ae025eb6/687474703a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f31302e32313130352f6a6f73732e30303535362f7374617475732e737667\" alt=\"DOI\" data-canonical-src=\"http://joss.theoj.org/papers/10.21105/joss.00556/status.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMore documentation is available at \u003ca href=\"http://checkqc.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttp://checkqc.readthedocs.io/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eCheckQC is a program designed to check a set of quality criteria against an Illumina runfolder.\u003c/p\u003e\n\u003cp\u003eThis is useful as part of a pipeline, where one needs to evaluate a set of quality criteria after demultiplexing. CheckQC is fast, and\nshould finish within a few seconds. It will warn if there are problems breaching warning criteria, and will emit a non-zero exit status if it finds\nany errors, thus making it easy to stop further processing if the run that is being evaluated needs troubleshooting.\u003c/p\u003e\n\u003cp\u003eCheckQC has been designed to be modular, and exactly which \"qc handlers\" are executed with which parameters for a specific run type (i.e. machine\ntype and run length) is determined by a configuration file.\u003c/p\u003e\n\u003cp\u003eInstrument types supported in checkQC are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHiSeqX\u003c/li\u003e\n\u003cli\u003eHiSeq2500\u003c/li\u003e\n\u003cli\u003eiSeq\u003c/li\u003e\n\u003cli\u003eMiSeq\u003c/li\u003e\n\u003cli\u003eNovaSeq\u003c/li\u003e\n\u003cli\u003eNovaSeq X Plus\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall instructions\u003c/h2\u003e\u003ca id=\"user-content-install-instructions\" class=\"anchor\" aria-label=\"Permalink: Install instructions\" href=\"#install-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCheckQC requires \u003cstrong\u003ePython 3.10\u003c/strong\u003e. CheckQC can be installed with pip.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epip install checkqc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively it can be installed with conda using the bioconda channel:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install -c bioconda checkqc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CheckQC\u003c/h2\u003e\u003ca id=\"user-content-running-checkqc\" class=\"anchor\" aria-label=\"Permalink: Running CheckQC\" href=\"#running-checkqc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter installing CheckQC you can run it by specifying the path to the runfolder you want to\nanalyze like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echeckqc \u0026lt;RUNFOLDER\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will use the default configuration file packaged with CheckQC if you want to specify\nyour own custom file, you can do so by adding a path to the config like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echeckqc --config_file \u0026lt;path to your config\u0026gt; \u0026lt;RUNFOLDER\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhen CheckQC starts and no path to the config file is specified it will give you\nthe path to where the default file is located on your system, if you want a template\nthat you can customize according to your own needs.\u003c/p\u003e\n\u003cp\u003eWhen you run CheckQC you can expect to see output similar to this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echeckqc tests/resources/170726_D00118_0303_BCB1TVANXX/\nINFO ------------------------\nINFO Starting checkQC (1.1.2)\nINFO ------------------------\nINFO Runfolder is: tests/resources/170726_D00118_0303_BCB1TVANXX/\nINFO No config file specified, using default config from /home/MOLMED/johda411/workspace/checkQC/checkQC/default_config/config.yaml.\nINFO Run summary\nINFO -----------\nINFO Instrument and reagent version: hiseq2500_rapidhighoutput_v4\nINFO Read length: 125-125\nINFO Enabled handlers and their config values were:\nINFO ClusterPFHandler Error=unknown Warning=180\nINFO Q30Handler Error=unknown Warning=80\nINFO ErrorRateHandler Error=unknown Warning=2\nINFO ReadsPerSampleHandler Error=90 Warning=unknown\nINFO UndeterminedPercentageHandler Error=10 Warning=unknown\nWARNING QC warning: Cluster PF was to low on lane 1, it was: 117.93 M\nWARNING QC warning: Cluster PF was to low on lane 7, it was: 122.26 M\nWARNING QC warning: Cluster PF was to low on lane 8, it was: 177.02 M\nERROR Fatal QC error: Number of reads for sample Sample_pq-27 was too low on lane 7, it was: 6.893 M\nERROR Fatal QC error: Number of reads for sample Sample_pq-28 was too low on lane 7, it was: 7.104 M\nINFO Finished with fatal qc errors and will exit with non-zero exit status.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe program will summarize the type of run it has identified and output any warnings and/or errors in finds.\nIf any qc errors were found the CheckQC will output a non-zero exit status. This means it can easily be used to\ndecide if a further steps should run or not, e.g. in a workflow.\u003c/p\u003e\n\u003cp\u003eIn addition to the normal output CheckQC has a json mode, enabled by adding \u003ccode\u003e--json\u003c/code\u003e to the commandline.\nThis outputs the results normally shown in the log as json on \u003ccode\u003estdout\u003c/code\u003e (while the log itself is written to \u003ccode\u003estderr\u003c/code\u003e),\nso that this can either be written to a file, or redirected to other programs which can parse the data further.\nIn this example we use the python json tool to pretty print the json output:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003echeckqc --json tests/resources/170726_D00118_0303_BCB1TVANXX/ | python -m json.tool\nINFO ------------------------\nINFO Starting checkQC (1.1.2)\nINFO ------------------------\nINFO Runfolder is: tests/resources/170726_D00118_0303_BCB1TVANXX/\nINFO No config file specified, using default config from /home/MOLMED/johda411/workspace/checkQC/checkQC/default_config/config.yaml.\nINFO Run summary\nINFO -----------\nINFO Instrument and reagent version: hiseq2500_rapidhighoutput_v4\nINFO Read length: 125-125\nINFO Enabled handlers and their config values were:\nINFO \tClusterPFHandler Error=unknown Warning=180\nINFO \tQ30Handler Error=unknown Warning=80\nINFO \tErrorRateHandler Error=unknown Warning=2\nINFO \tReadsPerSampleHandler Error=90 Warning=unknown\nINFO \tUndeterminedPercentageHandler Error=10 Warning=unknown\nWARNING QC warning: Cluster PF was to low on lane 1, it was: 117.93 M\nWARNING QC warning: Cluster PF was to low on lane 7, it was: 122.26 M\nWARNING QC warning: Cluster PF was to low on lane 8, it was: 177.02 M\nERROR Fatal QC error: Number of reads for sample Sample_pq-27 was too low on lane 7, it was: 6.893 M\nERROR Fatal QC error: Number of reads for sample Sample_pq-28 was too low on lane 7, it was: 7.104 M\nINFO Finished with fatal qc errors and will exit with non-zero exit status.\n{\n \"exit_status\": 1,\n \"ClusterPFHandler\": [\n {\n \"type\": \"warning\",\n \"message\": \"Cluster PF was to low on lane 1, it was: 117.93 M\",\n \"data\": {\n \"lane\": 1,\n \"lane_pf\": 117929896,\n \"threshold\": 180\n }\n },\n {\n \"type\": \"warning\",\n \"message\": \"Cluster PF was to low on lane 7, it was: 122.26 M\",\n \"data\": {\n \"lane\": 7,\n \"lane_pf\": 122263375,\n \"threshold\": 180\n }\n },\n {\n \"type\": \"warning\",\n \"message\": \"Cluster PF was to low on lane 8, it was: 177.02 M\",\n \"data\": {\n \"lane\": 8,\n \"lane_pf\": 177018999,\n \"threshold\": 180\n }\n }\n ],\n \"ReadsPerSampleHandler\": [\n {\n \"type\": \"error\",\n \"message\": \"Number of reads for sample Sample_pq-27 was too low on lane 7, it was: 6.893 M\",\n \"data\": {\n \"lane\": 7,\n \"number_of_samples\": 12,\n \"sample_id\": \"Sample_pq-27\",\n \"sample_reads\": 6.893002,\n \"threshold\": 90\n }\n },\n {\n \"type\": \"error\",\n \"message\": \"Number of reads for sample Sample_pq-28 was too low on lane 7, it was: 7.104 M\",\n \"data\": {\n \"lane\": 7,\n \"number_of_samples\": 12,\n \"sample_id\": \"Sample_pq-28\",\n \"sample_reads\": 7.10447,\n \"threshold\": 90\n }\n }\n ],\n \"run_summary\": {\n \"instrument_and_reagent_type\": \"hiseq2500_rapidhighoutput_v4\",\n \"read_length\": \"125-125\",\n \"handlers\": [\n {\n \"handler\": \"ClusterPFHandler\",\n \"error\": \"unknown\",\n \"warning\": 180\n },\n {\n \"handler\": \"Q30Handler\",\n \"error\": \"unknown\",\n \"warning\": 80\n },\n {\n \"handler\": \"ErrorRateHandler\",\n \"error\": \"unknown\",\n \"warning\": 2\n },\n {\n \"handler\": \"ReadsPerSampleHandler\",\n \"error\": 90,\n \"warning\": \"unknown\"\n },\n {\n \"handler\": \"UndeterminedPercentageHandler\",\n \"error\": 10,\n \"warning\": \"unknown\"\n }\n ]\n }\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning CheckQC as a webservice\u003c/h2\u003e\u003ca id=\"user-content-running-checkqc-as-a-webservice\" class=\"anchor\" aria-label=\"Permalink: Running CheckQC as a webservice\" href=\"#running-checkqc-as-a-webservice\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn addition to running like a commandline application, CheckQC can be run as a simple webservice.\u003c/p\u003e\n\u003cp\u003eTo run it you simply need to provide the path to a directory where runfolders that you want to\nbe able to check are located. This is given as \u003ccode\u003eMONITOR_PATH\u003c/code\u003e below. There are also a number\nof optional arguments that can be passed to the service.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ checkqc-ws --help\nUsage: checkqc-ws [OPTIONS] MONITOR_PATH\n\nOptions:\n --port INTEGER Port which checkqc-ws will listen to (default: 9999).\n --config PATH Path to the checkQC configuration file (optional)\n --log_config PATH Path to the checkQC logging configuration file (optional)\n --debug Enable debug mode.\n --help Show this message and exit.\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the webserver is running you can query the \u003ccode\u003e/qc/\u003c/code\u003e endpoint and get any errors and warnings back as json.\nHere is an example how to query the endpoint, and what type of results it will return:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ curl -s -w\u0027\\n\u0027 localhost:9999/qc/170726_D00118_0303_BCB1TVANXX | python -m json.tool\n{\n \"ClusterPFHandler\": [\n {\n \"data\": {\n \"lane\": 1,\n \"lane_pf\": 117929896,\n \"threshold\": 180\n },\n \"message\": \"Cluster PF was to low on lane 1, it was: 117.93 M\",\n \"type\": \"warning\"\n },\n {\n \"data\": {\n \"lane\": 7,\n \"lane_pf\": 122263375,\n \"threshold\": 180\n },\n \"message\": \"Cluster PF was to low on lane 7, it was: 122.26 M\",\n \"type\": \"warning\"\n },\n {\n \"data\": {\n \"lane\": 8,\n \"lane_pf\": 177018999,\n \"threshold\": 180\n },\n \"message\": \"Cluster PF was to low on lane 8, it was: 177.02 M\",\n \"type\": \"warning\"\n }\n ],\n \"ReadsPerSampleHandler\": [\n {\n \"data\": {\n \"lane\": 7,\n \"number_of_samples\": 12,\n \"sample_id\": \"Sample_pq-27\",\n \"sample_reads\": 6.893002,\n \"threshold\": 90\n },\n \"message\": \"Number of reads for sample Sample_pq-27 was too low on lane 7, it was: 6.893 M\",\n \"type\": \"warning\"\n },\n {\n \"data\": {\n \"lane\": 7,\n \"number_of_samples\": 12,\n \"sample_id\": \"Sample_pq-28\",\n \"sample_reads\": 7.10447,\n \"threshold\": 90\n },\n \"message\": \"Number of reads for sample Sample_pq-28 was too low on lane 7, it was: 7.104 M\",\n \"type\": \"warning\"\n }\n ],\n \"exit_status\": 0,\n \"version\": \"1.1.0\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 25, - "subscribers_count": 5, - "topics": [], - "updated_at": 1726682704.0 + "subscribers_count": 12, + "topics": [ + "genomics", + "quality-control", + "sequencing", + "illumina" + ], + "updated_at": 1726737988.0 }, { "data_format": 2, - "description": "Digital Imaging of Root Traits: Extract trait measurements from images of monocot and dicot roots.", + "description": "Python 3 version of FabSim", "filenames": [ "Singularity" ], - "full_name": "Computational-Plant-Science/DIRT", - "latest_release": null, - "readme": "\u003chr\u003e\n\u003cp\u003eDIRT 1.1 - An automatic high throughput root phenotyping platform\n(c) 2014,2016 Alexander Bucksch - \u003ca href=\"mailto:bucksch@uga.edu\"\u003ebucksch@uga.edu\u003c/a\u003e\nWeb application by Abhiram Das - \u003ca href=\"mailto:abhiram.das@gmail.com\"\u003eabhiram.das@gmail.com\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://dirt.iplantcollaborative.org\" rel=\"nofollow\"\u003ehttp://dirt.iplantcollaborative.org\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUser and developer group:\n\u003ca href=\"https://groups.google.com/forum/#!forum/dirt-users\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/dirt-users\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUniversity of Georgia, Athens\u003c/h2\u003e\u003ca id=\"user-content-university-of-georgia-athens\" class=\"anchor\" aria-label=\"Permalink: University of Georgia, Athens\" href=\"#university-of-georgia-athens\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe software is written and tested in:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython 2.7 (\u003ca href=\"https://www.python.org\" rel=\"nofollow\"\u003ehttps://www.python.org\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe software depends on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe graphtools package (\u003ca href=\"http://graph-tool.skewed.de\" rel=\"nofollow\"\u003ehttp://graph-tool.skewed.de\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ethe mahotas package (\u003ca href=\"http://luispedro.org/software/mahotas\" rel=\"nofollow\"\u003ehttp://luispedro.org/software/mahotas\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ethe numpy package (\u003ca href=\"http://sourceforge.net/projects/numpy/\" rel=\"nofollow\"\u003ehttp://sourceforge.net/projects/numpy/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003ethe scipy package (\u003ca href=\"http://www.scipy.org/SciPy\" rel=\"nofollow\"\u003ehttp://www.scipy.org/SciPy\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptionally binaries of standard OCR and BarCode software can be used for tag recognition:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003etesseract (\u003ca href=\"https://code.google.com/p/tesseract-ocr/\" rel=\"nofollow\"\u003ehttps://code.google.com/p/tesseract-ocr/\u003c/a\u003e)\npaths have to be adjusted in /DIRTocr/pytesser.py (line 12-14)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ezbar (\u003ca href=\"http://zbar.sourceforge.net\" rel=\"nofollow\"\u003ehttp://zbar.sourceforge.net\u003c/a\u003e)\npath has to be adjusted in /DIRTocr/\u003cstrong\u003einit\u003c/strong\u003e.py (line 28)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUsage:\n full path to the root image\n ID which will be a folder name in the working directory. Integer value needed.\n multiplier for the automatically determined mask threshold. 1.0 works fine and is default. For example, if a flashlight is used to take root images, then 0.6 is a good choice.\n number of roots placed at the right of the root crown, 0 - excised root analysis is off\n 1 - crown root analysis is on, 0 - crown root analysis is off\n 1 - is on, 0 - is off. Off refers to a pre-existing segmention done with DIRT. Binary masks as input images are detected automatically.\n a simple decimal e.g. 25.4. If 0.0 is used, then the output will have pixels as unit.\n 1 - reconstruction is turned on, 0 - reconstruction is turned off\n 1 - plotting data is stored, 0 - plotting data is not stored\n 1 - the full trait set is put into one excel file containing empty cells for traits that were not computed, 0 - only computed files are written to the output file\n full path to folder were the result is stored\n full path to .csv file containing the traits to be computed\u0027\u003c/p\u003e\n\u003cp\u003eExample:\npython main.py /Documents/image_name.jpg 8 25.0 1 1 1 25.1 0 0 0 /Documents/image_folder/ /Documents/traits.csv\u003c/p\u003e\n\u003cp\u003eNotes on common questions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eInput is restricted to .jpg, .png and .tif images\u003c/li\u003e\n\u003cli\u003eIt is not possible to analyze only an excised root when a root crown is in the image. However, it is possible to analyze compute images containing only excised roots.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003eFor convenience we provide the runOnFolder script, that executes DIRT on all images in a specified folder.\nNote we made the masking threshold available on the command line because of user requests.\u003c/p\u003e\n\u003cp\u003eExample: python runOnFolder.py /Users/image_folder/ \u003c/p\u003e\n\u003cp\u003ePlease adjust line 86 according to the description above and note that the script uses 6 cores to compute images in parallel. The number of cores can be adjusted in line 80.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates in DIRT 1.1 (21 June 2019):\u003c/h2\u003e\u003ca id=\"user-content-updates-in-dirt-11-21-june-2019\" class=\"anchor\" aria-label=\"Permalink: Updates in DIRT 1.1 (21 June 2019):\" href=\"#updates-in-dirt-11-21-june-2019\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eSome bug fixes on the avg. root density. There was a problem with very young and sparse root system. The formula changed and is now normed to the max. width instead of the max. width of the line.\nThe bug was found by Peng Wang at the University of Nebraska.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates in DIRT 1.1 (11 January 2016):\u003c/h2\u003e\u003ca id=\"user-content-updates-in-dirt-11-11-january-2016\" class=\"anchor\" aria-label=\"Permalink: Updates in DIRT 1.1 (11 January 2016):\" href=\"#updates-in-dirt-11-11-january-2016\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMinor bug fixes in Preprocessing.py to allow smaller circle markers and fix a possible missdetection of the experiment tag as the circle.\nThanks to Linda Zamariola (U Bologna) for finding this issue.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates in DIRT 1.1 (4 November 2015):\u003c/h2\u003e\u003ca id=\"user-content-updates-in-dirt-11-4-november-2015\" class=\"anchor\" aria-label=\"Permalink: Updates in DIRT 1.1 (4 November 2015):\" href=\"#updates-in-dirt-11-4-november-2015\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eMinor bug fixes in the excised root calculations. Thanks to Alexandre Grondin (U Nebraska) for discovering and validating the fixes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChanges in DIRT 1.1 (14 January 2015):\u003c/h2\u003e\u003ca id=\"user-content-changes-in-dirt-11-14-january-2015\" class=\"anchor\" aria-label=\"Permalink: Changes in DIRT 1.1 (14 January 2015):\" href=\"#changes-in-dirt-11-14-january-2015\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003estorage of trait values is changed from a list data structure to a dictionary to allow trait selection controlled by the file traits.csv\u003c/li\u003e\n\u003cli\u003eadded support for trait selection to reduce computation time. See example file traits.csv (1 - trait is computed, 0 - trait is not computed)\u003c/li\u003e\n\u003cli\u003eremoved unused tip-diameter switch on the command line\u003c/li\u003e\n\u003cli\u003eadd stem reconstruction switch on the command line to turn the experimental stem reconstruction on/off\u003c/li\u003e\n\u003cli\u003eoutput file now uses the codes in the trait.csv file and only contains selected traits\u003c/li\u003e\n\u003cli\u003eremoved several unused variables and minor bugs fixed\u003c/li\u003e\n\u003cli\u003eadded command line option to turn storage of numpy arrays on/off. These files can be used to plot the individual root statistics and can be found in the \"Plots\" folders.\u003c/li\u003e\n\u003cli\u003enew (experimental, not validated) traits added due to community requests: projected root area, width and depth of the skeleton (medial axis), top and bottom angle for monocots, segmentation of adventious and basal roots for legumes to retrieve taproot and hypocotyl diameter and adventious and basal root counts.\u003c/li\u003e\n\u003cli\u003eadded computational statistics such as computation time and graph size to help balancing grid installations\u003c/li\u003e\n\u003cli\u003eadded an option to have an output file with all possible traits that contains empty cells for not computed traits in the output.csv file. This was a developer request to enable faster ingestion into data bases\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "djgroen/FabSim3", + "latest_release": "v3.8", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFabSim\u003c/h1\u003e\u003ca id=\"user-content-fabsim\" class=\"anchor\" aria-label=\"Permalink: FabSim\" href=\"#fabsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/actions/workflows/Pytests.yml\"\u003e\u003cimg src=\"https://github.com/djgroen/FabSim3/actions/workflows/Pytests.yml/badge.svg?branch=master\" alt=\"Run Tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vecmafabsim3/fabsimdocker/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/92881bc002c155e480ba310eebbabf711c88f31e998ad3bb426bc5f8efbe8f24/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f7665636d6166616273696d332f66616273696d646f636b65722e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/vecmafabsim3/fabsimdocker.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/vecmafabsim3/fabsimdocker/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6b65f177412063ff2d0207e16a33cae77ba837ce9006098cb59fbff10e22e844/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f7665636d6166616273696d332f66616273696d646f636b65722e737667\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/automated/vecmafabsim3/fabsimdocker.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/tags\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/baae1d1934af8f46ad0d31258d9d44d1caaf525e7d4388ae33d0e667fac2297f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f646a67726f656e2f46616253696d333f7374796c653d666c6174\" alt=\"GitHub tag (latest by date)\" data-canonical-src=\"https://img.shields.io/github/v/tag/djgroen/FabSim3?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"https://github.com/djgroen/FabSim3/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d4fa02cb45ab9d8cf264944f49b67a1ed1b30df37c76421169c1fff5f1f8d2ad/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f646a67726f656e2f46616253696d332e737667\" alt=\"GitHub Issues\" data-canonical-src=\"https://img.shields.io/github/issues/djgroen/FabSim3.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/djgroen/FabSim3/commits/master\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2727377d9a0d56f2625f4bb78ab5e77de3eed9840bd4df91d10872fd64ad40d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f646a67726f656e2f46616253696d332e737667\" alt=\"GitHub last-commit\" data-canonical-src=\"https://img.shields.io/github/last-commit/djgroen/FabSim3.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFor the full FabSim3 documentation, please visit \u003ca href=\"https://fabsim3.readthedocs.io\" rel=\"nofollow\"\u003ehttps://fabsim3.readthedocs.io\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFabSim is a Python-based automation toolkit for scientific simulation and data processing workflows, licensed under the BSD 3-clause license. It aims to enable users to perform remote tasks from a local command-line, and to run applications while curating the environment variables and the input and output data in a systematic manner. To provide that curation, FabSim uses a basic data transfer functionalities such as rsync and ssh.\u003c/p\u003e\n\u003cp\u003eFabSim also contains a system for defining machine-specific configurations, including templates to execute jobs through schedulers such as PBSPro, Loadleveller and SGE. These machine-specific configurations are stored in the repository, apply to all applications run on that machine, and can be updated by any contributor who feels that a fix or improvement is required.\u003c/p\u003e\n\u003cp\u003eFabSim relies strongly on Fabric (\u003ca href=\"http://www.fabfile.org\" rel=\"nofollow\"\u003ehttp://www.fabfile.org\u003c/a\u003e, shown to work with versions 1.5.3 and 1.10.0) and PyYAML. Previous versions of FabSim (most notably FabHemeLB and FabMD) have been used to run simulation workflows on machines such as ARCHER, SuperMUC, BlueJoule, as well as local clusters and desktops.\u003c/p\u003e\n\u003cp\u003eFabSim is now publicly available at: \u003ca href=\"http://www.github.com/djgroen/FabSim\"\u003ehttp://www.github.com/djgroen/FabSim\u003c/a\u003e\nThe accompanying software paper can be found here: \u003ca href=\"https://doi.org/10.1016/j.cpc.2016.05.020\" rel=\"nofollow\"\u003ehttps://doi.org/10.1016/j.cpc.2016.05.020\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDerivative versions of FabSim include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFabHemeLB (previously known as \u003cem\u003eFabricHemeLB\u003c/em\u003e), which is used to automate workflows involving\nthe HemeLB lattice-Boltzmann simulation environment (see \u003ca href=\"http://www.github.com/UCL/hemelb\"\u003ehttp://www.github.com/UCL/hemelb\u003c/a\u003e for\nthe source code of that).\u003c/li\u003e\n\u003cli\u003eFabMD, which is used to semi-automatically coarse-grain polymer systems (part of this repository).\u003c/li\u003e\n\u003cli\u003eFabBioMD, which is used to facilitate protein-ligand binding affinity calculations (part of this repository).\u003c/li\u003e\n\u003cli\u003eFabFlee, which is under development and used to automate agent-based simulations of forced migration.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation and usage\u003c/h2\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-label=\"Permalink: Installation and usage\" href=\"#installation-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor instructions on how to install and test FabSim, please refer to \u003ccode\u003edocs/installation.md\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe FabMUSCLE plugin does not yet work correctly with the Eagle Supercomputer.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCiting FabSim\u003c/h3\u003e\u003ca id=\"user-content-citing-fabsim\" class=\"anchor\" aria-label=\"Permalink: Citing FabSim\" href=\"#citing-fabsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease find the BibTex reference below of our FabSim software paper in \u003cem\u003eComputer Physics Communications\u003c/em\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@article{GROEN2016375,\ntitle = \"FabSim: Facilitating computational research through automation on large-scale and distributed e-infrastructures\",\njournal = \"Computer Physics Communications\",\nvolume = \"207\",\nnumber = \"Supplement C\",\npages = \"375 - 385\",\nyear = \"2016\",\nissn = \"0010-4655\",\ndoi = \"https://doi.org/10.1016/j.cpc.2016.05.020\",\nurl = \"http://www.sciencedirect.com/science/article/pii/S0010465516301448\",\nauthor = \"Derek Groen and Agastya P. Bhati and James Suter and James Hetherington and Stefan J. Zasada and Peter V. Coveney\",\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 25, - "subscribers_count": 7, + "subscribers_count": 18, "topics": [ - "phenotyping", - "phenotyping-algorithms", - "root", - "imaging", - "image-processing", - "computer-vis" + "automation", + "multiscale-simulation", + "workflow-automation", + "high-performance-computing", + "python3" ], - "updated_at": 1730363822.0 + "updated_at": 1729165766.0 }, { "data_format": 2, - "description": "[DEPRECATED] Docker Images which include a complete installation of all software needed to build all Bioconductor packages", + "description": "A Jupyter kernel for CASA", "filenames": [ - "Singularity.RELEASE_3_9", - "Singularity.RELEASE_3_8", - "Singularity.RELEASE_3_10", - "Singularity" + "singularity/Singularity.docker", + "singularity/Singularity", + "singularity/Singularity.centos7" ], - "full_name": "Bioconductor/bioconductor_full", + "full_name": "aardk/jupyter-casa", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBioconductor Devel Docker image - DEPRECATED\u003c/h1\u003e\u003ca id=\"user-content-bioconductor-devel-docker-image---deprecated\" class=\"anchor\" aria-label=\"Permalink: Bioconductor Devel Docker image - DEPRECATED\" href=\"#bioconductor-devel-docker-image---deprecated\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDeprecation Notice\u003c/strong\u003e : The \u003ccode\u003ebioconductor/bioconductor_full\u003c/code\u003e images have been deprecated in favor of the new \u003ccode\u003ebioconductor/bioconductor_docker\u003c/code\u003e images located at \u003ca href=\"https://github.com/bioconductor/bioconductor_docker\"\u003ehttps://github.com/bioconductor/bioconductor_docker\u003c/a\u003e . These new images will have the same functionality as \u003ccode\u003ebioconductor_full\u003c/code\u003e, and users should find it easy to switch between the new set of containers. Please refer to the \u003ca href=\"https://github.com/Bioconductor/bioconductor_docker/blob/master/README.md\"\u003eREADME.md\u003c/a\u003e of the new images for more information.\u003c/p\u003e\n\u003cp\u003eBioconductor Docker image with full set of system dependencies so that\nall Bioconductor packages can be installed.\u003c/p\u003e\n\u003cp\u003eThe Docker images have R and Bioconductor with different versions\nunder each \"branch\" in git.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: Docker image for bioconductor_full:devel is in the \u003ccode\u003emaster\u003c/code\u003e\nbranch, and all the release branches will be under the branch\n\u003ccode\u003eRELEASE_X_Y\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eImportant Links:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://hub.docker.com/r/bioconductor/bioconductor_full\" rel=\"nofollow\"\u003eDocker hub link for bioconductor_full\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/Bioconductor/bioconductor_full\"\u003eGithub development link for bioconductor_full\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdvantages of the \u003ccode\u003ebioconductor_full\u003c/code\u003e docker image\u003c/h2\u003e\u003ca id=\"user-content-advantages-of-the-bioconductor_full-docker-image\" class=\"anchor\" aria-label=\"Permalink: Advantages of the bioconductor_full docker image\" href=\"#advantages-of-the-bioconductor_full-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe bioconductor_full docker images can be used instead of\ninstalling complex dependencies needed for Bioconductor\npackages. The image comes with most of the dependencies installed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eQuick start up to start your analysis with all the Bioconductor\npackages if needed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe image will be regularly updated to reflect the build system on\nBioconductor. This is a very useful resource for maintainers who\nare actively developing their package to see if it works in tandem\nwith the bioconductor ecosystem. It provides a local testing outlet\nfor maintainers and developers.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation and quick start\u003c/h2\u003e\u003ca id=\"user-content-installation-and-quick-start\" class=\"anchor\" aria-label=\"Permalink: Installation and quick start\" href=\"#installation-and-quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis document assumes you have \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003edocker\u003c/a\u003e installed. Please check\n\u003ca href=\"https://www.docker.com/products/docker-desktop\" rel=\"nofollow\"\u003einstallation\u003c/a\u003e if you have more questions regarding this.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuick start\u003c/h3\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eStart docker on your machine.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn the command line, \"pull\" the bioconductor_full docker image with\nthe correct tag. These images are hosted on docker hub under the\n\u003ca href=\"https://cloud.docker.com/u/bioconductor/repository/registry-1.docker.io/bioconductor/bioconductor_full\" rel=\"nofollow\"\u003eBioconductor organization\u003c/a\u003e page.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull bioconductor/bioconductor_full:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker pull bioconductor/bioconductor_full:RELEASE_X_Y\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOnce the image is available on your local machine, you can check to\nsee if they are available.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker images\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo start using these images with RStudio, this will start the image\nunder the \u0027rstudio\u0027 user\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run \\\n -e PASSWORD=your_password \\\n -p 8787:8787 \\\n -v ~/host-site-library:/usr/local/lib/R/host-site-library \\\n bioconductor/bioconductor_full:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo start the image interactively using the \u003ccode\u003ebioc\u003c/code\u003e user\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run \\\n -it \\\n --user bioc \\\n -v ~/host-site-library:/usr/local/lib/R/host-site-library \\\n bioconductor/bioconductor_full:devel \\\n R\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: The path \u003ccode\u003e/usr/local/lib/R/host-site-library\u003c/code\u003e is mapped to\n\u003ccode\u003e.libPaths()\u003c/code\u003e in R. So, when R is started, all the libraries in\nthe host directory, \u003ccode\u003ehost-site-library\u003c/code\u003e are available to R. It is\nstored on your machine mounted from the volume you fill in place\nof \u003ccode\u003ehost-site-library\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eThese libraries will only work if they are pre-compiled with the\nsame version of R that is in the docker image. To explain further,\nyou would need the packages built with Bioconductor version \u00273.9\u0027\nto work with R-3.6. Similarly, you\u0027d need Bioconductor version\n\u00273.9\u0027 to work with R-3.6.z.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo start the docker image in deamon mode, i.e, have the container\nrunning in the background use the \u003ccode\u003e-d\u003c/code\u003e option.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e sudo docker run -it \\\n -d \\\n -v host-site-library:/usr/local/lib/R/host-site-library \\\n --entrypoint /bin/bash \\\n bioconductor/bioconductor_full:devel\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will start the container in the background and keep it\nrunning. You may check the running processes using \u003ccode\u003edocker ps\u003c/code\u003e,\nand copy the container id.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker ps\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo attach to a container which is running in the background\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker exec -it \u0026lt;container_id\u0026gt; bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNOTE: You can replace \u003ccode\u003ebash\u003c/code\u003e with R to start R directly in the\ncontainer.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker exec -it \u0026lt;container_id\u0026gt; R\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo run multiple RStudio instances, use a different external port\nmapping (the first port in \u003ccode\u003e-p XXXX:YYYY\u003c/code\u003e) for each instance.\nUse standard shell commands (e.g., adding a \u003ccode\u003e\u0026amp;\u003c/code\u003e at the end of the\nfirst docker command) to place docker processes in the\nbackground. The \u0027devel\u0027 instance will be available at\n\u003ca href=\"http://localhost:8787\" rel=\"nofollow\"\u003ehttp://localhost:8787\u003c/a\u003e, and the release image at\n\u003ca href=\"http://localhost:8788\" rel=\"nofollow\"\u003ehttp://localhost:8788\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e docker run \\\n -e PASSWORD=your_password \\\n -p 8787:8787 \\\n bioconductor/bioconductor_full:devel\n\n docker run \\\n -e PASSWORD=your_password \\\n -p 8788:8787 \\\n bioconductor/bioconductor_full:RELEASE_3_10\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIssues in the devel image\u003c/h2\u003e\u003ca id=\"user-content-issues-in-the-devel-image\" class=\"anchor\" aria-label=\"Permalink: Issues in the devel image\" href=\"#issues-in-the-devel-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePackages which will never install\u003c/h3\u003e\u003ca id=\"user-content-packages-which-will-never-install\" class=\"anchor\" aria-label=\"Permalink: Packages which will never install\" href=\"#packages-which-will-never-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003exps\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRmpi\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eccfindR\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor more information on the issues for this image, please check\n\u003ccode\u003eissues.md\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioconductor provides singularity hub images. The files Singularity, Singularity.RELEASE_3_8, Singularity.RELEASE_3_9 allow the images to build on Singularity hub.\u003c/p\u003e\n\u003cp\u003eFind the singularity hub images, and download them by \u003ca href=\"https://www.singularity-hub.org/collections/3154/usage\" rel=\"nofollow\"\u003ehttps://www.singularity-hub.org/collections/3154/usage\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull shub://Bioconductor/bioconductor_full\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFrequently Asked Questions (FAQ)\u003c/h2\u003e\u003ca id=\"user-content-frequently-asked-questions-faq\" class=\"anchor\" aria-label=\"Permalink: Frequently Asked Questions (FAQ)\" href=\"#frequently-asked-questions-faq\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eHow do I use LaTeX inside the container?\u003c/h3\u003e\u003ca id=\"user-content-how-do-i-use-latex-inside-the-container\" class=\"anchor\" aria-label=\"Permalink: How do I use LaTeX inside the container?\" href=\"#how-do-i-use-latex-inside-the-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstall the \u003ccode\u003etinytex\u003c/code\u003e package (\u003ca href=\"https://yihui.name/tinytex/\" rel=\"nofollow\"\u003ehttps://yihui.name/tinytex/\u003c/a\u003e) which has helpers for installing LaTeX functionality.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-r\"\u003e\u003cpre\u003einstall.packages(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003etinytex\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-e\"\u003etinytex\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e::\u003c/span\u003einstall_tinytex()\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 25, - "subscribers_count": 10, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejupyter-casa\u003c/h1\u003e\u003ca id=\"user-content-jupyter-casa\" class=\"anchor\" aria-label=\"Permalink: jupyter-casa\" href=\"#jupyter-casa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA \u003ca href=\"http://jupyter.org/\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e kernel for \u003ca href=\"https://casa.nrao.edu/\" rel=\"nofollow\"\u003eCASA\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJupyter is a web-based application which allows users to create interactive notebooks which can\ninclude annotated text and graphics as well as executable code. The notebook format has the great advantage that all\nsteps of the data reduction are preserved inside the notebook. This means that the whole data reduction process is\nself-documenting and fully repeatable. It also allows users to very easily make changes to their pipeline and then rerun\nthe pipeline steps affected.\u003c/p\u003e\n\u003cp\u003eAs part of the EU funded \u003ca href=\"https://projectescape.eu/\" rel=\"nofollow\"\u003eESCAPE\u003c/a\u003e project we have created a\nJupyter kernel for CASA, a widely-used software package for processing astronomical data.\nThe kernel allows all CASA tasks to be run from inside a Jupyter notebook, albeit non-interactively. Tasks which normally\nspawn a GUI window are wrapped so that their output is displayed inside the notebook instead.\u003c/p\u003e\n\u003cp\u003eThe Jupyter kernel is distributed as a \u003ca href=\"https://hub.docker.com/r/penngwyn/jupytercasa\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image which includes the latest\nversion of CASA and a number of additional software (see below).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Docker images can be pulled using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull penngwyn/jupytercasa\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhile it is possible to use this Docker image directly, for end users it is recommended to use the Docker images through\neither \u003ca href=\"https://www.vagrantup.com/\" rel=\"nofollow\"\u003eVagrant\u003c/a\u003e or \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer/Singularity\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVagrant\u003c/h3\u003e\u003ca id=\"user-content-vagrant\" class=\"anchor\" aria-label=\"Permalink: Vagrant\" href=\"#vagrant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVagrant is a front-end for various containerization and virtualization technologies, including Docker.\nInstallation instructions for Vagrant can he found here: \u003ca href=\"https://www.vagrantup.com/downloads\" rel=\"nofollow\"\u003ehttps://www.vagrantup.com/downloads\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAfter cloning this Git repository, change to the Vagrant directory inside the repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/aardk/jupyter-casa.git\ncd jupyter-casa/vagrant\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBefore jupyter-casa can launched, you first need to tell Vagrant where your data lives.\nVagrant is controlled through a file called \u003ca href=\"vagrant/Vagrantfile\"\u003eVagrantfile\u003c/a\u003e.\nIn the provided Vagrantfile there is one line which is commented out:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e #config.vm.synced_folder \"/path/to/data\", \"/home/jupyter/work\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis line needs to be uncommented (remove the #), and `/path/to/data\u0027 has to be replaced by the absolute path to where your data is stored.\nFor example, if the data are stored in a directory called \u003cem\u003e/opt/shared-data\u003c/em\u003e then we add\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e config.vm.synced_folder \"/opt/shared-data\", \"/home/jupyter/work\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen Vagrant can be started by executing (still inside the vagrant directory)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant up\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter the Vagrant virtual machine has been started you can connect to it via \u003cem\u003essh\u003c/em\u003e by executing (still inside the vagrant directory)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant ssh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will give you a shell within a Vagrant VM, jupyter can then be started by executing\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejupyter lab\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eApptainer/Singularity\u003c/h3\u003e\u003ca id=\"user-content-apptainersingularity\" class=\"anchor\" aria-label=\"Permalink: Apptainer/Singularity\" href=\"#apptainersingularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eApptainer is the new name for the Singularity container system. All commands below use Apptainer, but if you still have\nSingularity installed then you can simply replace apptainer with singularity in each command.\u003c/p\u003e\n\u003cp\u003eFirst the Docker image needs to be converted to an Apptainer container by executing\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eapptainer pull docker://penngwyn/jupytercasa:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eUnlike Docker, an Apptainer container runs with UID of the current user (i.e. the user executing \u003ccode\u003esingularity run\u003c/code\u003e).\nThe home directory of the user on the local filesystem will also be accessible inside the container, but by default\nonly the home directory is shared with the container. Therefore any symbolic links which point to locations outside of the\nhome directory will not be valid inside the container.\u003c/p\u003e\n\u003cp\u003eFortunately, it is fairly straightforward to make your local filesystem accessible to the container using the \u003cem\u003e-B\u003c/em\u003e option.\nFor example to mount a directory called \u003cem\u003e/data\u003c/em\u003e inside the container execute:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eapptainer run -B /data:$HOME/data jupytercasa_latest.sif\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEven though we wrap all CASA tasks so that they will not launch a GUI window, the QT based CASA tasks still require X11, unfortunately.\nTasks such as \u003cem\u003eplotms\u003c/em\u003e won\u0027t start unless X11 is working even when it doesn\u0027t even open a window.\nTherefore the local X11 socket needs to be shared with Docker container.\u003c/p\u003e\n\u003cp\u003eThe simplest incantation to start JUPYTER on a recent Ubuntu:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -p 8888:8888 -i -t -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY penngwyn/jupytercasa \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNote that the \u003ccode\u003e\u0027--rm\u0027\u003c/code\u003e option will make DOCKER delete the container after use.\u003c/p\u003e\n\u003cp\u003eOf course the above example is not very useful as the container will not be able to access locally stored \u003cem\u003emeasurement sets\u003c/em\u003e.\nTo add a data directory to the DOCKER container is, fortunately, very simple using the \u003ccode\u003e-v\u003c/code\u003e option:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -p 8888:8888 -i -t -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v PATH_TO_DATA_DIR:/home/jupyter/data penngwyn/jupytercasa\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhere \u003ccode\u003ePATH_TO_DATA_DIR\u003c/code\u003e should be replaced with the full path to your local data directory.\u003c/p\u003e\n\u003cp\u003eThe above examples use a JUPYTER kernel which is baked into the DOCKER image. It is also possible to use the GITHUB development version\nwithin the CASA container, from the root of the source tree run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -p 8888:8888 -i -t -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $PWD/jupyter:/home/jupyter/.local/share/jupyter -v $PWD/python/casapy:/home/jupyter/.local/lib/python2.7/site-packages/casapy -v PATH_TO_DATA_DIR:/home/jupyter/data penngwyn/jupytercasa \u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the \u003cem\u003eexamples\u003c/em\u003e directory there is a notebook which contains the NRAO continuum VLA tutorial. To run that code locally\nbe sure to download the data files from the \u003ca href=\"https://casaguides.nrao.edu/index.php?title=VLA_Continuum_Tutorial_3C391\" rel=\"nofollow\"\u003eNRAO wiki\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlso don\u0027t forget to make the directory available to the DOCKER container using the \u003ccode\u003e-v\u003c/code\u003e option as is explained above.\u003c/p\u003e\n", + "stargazers_count": 26, + "subscribers_count": 4, + "topics": [], + "updated_at": 1723165784.0 + }, + { + "data_format": 2, + "description": "An outdoor environment simulator with real-world imagery for Deep Reinforcement Learning on navigation tasks. ", + "filenames": [ + "Singularity" + ], + "full_name": "mweiss17/SEVN", + "latest_release": "1.0", + "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3288\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSEVN: A Sidewalk Environment for Visual Navigation\u003c/h1\u003e\u003ca id=\"user-content-sevn-a-sidewalk-environment-for-visual-navigation\" class=\"anchor\" aria-label=\"Permalink: SEVN: A Sidewalk Environment for Visual Navigation\" href=\"#sevn-a-sidewalk-environment-for-visual-navigation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSEVN contains around 5,000 full panoramic images and labels for house numbers, doors, and street name signs, which can be used for several different navigation tasks.\nAgents trained with SEVN have access to variable-resolution images, visible text, and simulated GPS data to navigate the environment.\nThe SEVN Simulator is OpenAI Gym-compatible to allow the use of state-of-the-art deep reinforcement learning algorithms. An instance of the simulator using low-resolution imagery can be run at 400-800 frames per second on a machine with 2 CPU cores and 2 GB of RAM.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eLow Resolution Views (84x84px)\u003c/th\u003e\n\u003cth align=\"center\"\u003eHigh Resolution Views (1280x1280px)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/low-res-viewer.png\"\u003e\u003cimg src=\"docs/img/low-res-viewer.png\" alt=\"game.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/high-res-viewer.png\"\u003e\u003cimg src=\"docs/img/high-res-viewer.png\" alt=\"game.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/spatial_graph.png\"\u003e\u003cimg src=\"docs/img/spatial_graph.png\" alt=\"spatial_graph.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e4,988 panoramic images across 6.3 kilometers with 3,259 labels on house numbers, doors, and street name signs.\u003c/p\u003e\n\u003cp\u003eA longer introduction can be found here: \u003ca href=\"https://github.com/mweiss17/SEVN/blob/master/docs/01-article-env-introduction.md\"\u003eCreating a Navigation Assistant for the Visually Impaired\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to setup the environment, do something like the following. If using a fresh Ubuntu install, ensure that build-essential is installed (i.e., \u003ccode\u003esudo apt-get build-essential\u003c/code\u003e). We\u0027ll need GCC for this, and that installs it.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install the code\u003c/span\u003e\ngit clone https://github.com/mweiss17/SEVN.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e SEVN\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a new conda environment for the depenencies\u003c/span\u003e\nconda create -n sevn python=3.7\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install the dependencies\u003c/span\u003e\nconda activate sevn\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the low resolution image data, labels, and spatial graph\u003c/span\u003e\npython scripts/download.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Test that your environment is correctly setup\u003c/span\u003e\npython scripts/01-play.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e WARNING! Running this step downloads 28GB of image data and is not required to run the model or play with the environment.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e python scripts/download.py --high-res\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e python scripts/01-play.py --high-res\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataset\u003c/h2\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can manually download the dataset here (in case you don\u0027t want to follow the installation instructions above).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://zenodo.org/record/3526490/files/coord.hdf5\" rel=\"nofollow\"\u003eCoordinates of Panoramas\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zenodo.org/record/3526490/files/graph.pkl\" rel=\"nofollow\"\u003eConnectvity Graph\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zenodo.org/record/3526490/files/labels.hdf5\" rel=\"nofollow\"\u003eImage Labels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zenodo.org/record/files/images.hdf5\" rel=\"nofollow\"\u003ePanoramas (Low resolution)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://zenodo.org/record/3526490/files/high-res-panos.zip\" rel=\"nofollow\"\u003ePanoramas (High resolution)\u003c/a\u003e (Warning! 48 GB of images in a zip file)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDataset pre-processing\u003c/h3\u003e\u003ca id=\"user-content-dataset-pre-processing\" class=\"anchor\" aria-label=\"Permalink: Dataset pre-processing\" href=\"#dataset-pre-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information about the data-preprocessing and the data format consult the \u003ccode\u003eREADME\u003c/code\u003e in the \u003ca href=\"https://github.com/mweiss17/SEVN-data\"\u003eSEVN-data\u003c/a\u003e github repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining\u003c/h2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information about how to train an agent on SEVN consult the \u003ccode\u003eREADME\u003c/code\u003e in the \u003ca href=\"https://github.com/mweiss17/SEVN-model\"\u003eSEVN Model Github repo\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting\u003c/h2\u003e\u003ca id=\"user-content-citing\" class=\"anchor\" aria-label=\"Permalink: Citing\" href=\"#citing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was accepted at \u003ca href=\"https://www.robot-learning.org/\" rel=\"nofollow\"\u003eConference on Robot Learning (CoRL) 2019\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe paper was also published to Arxiv: \u003ca href=\"http://arxiv.org/abs/1910.13249\" rel=\"nofollow\"\u003eNavigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you use this work, please cite us. Here\u0027s the Bibtex for our paper.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{weiss2019navigation,\n title={Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments},\n author={Martin Weiss and Simon Chamorro and Roger Girgis and Margaux Luck and\n Samira E. Kahou and Joseph P. Cohen and Derek Nowrouzezahrai and\n Doina Precup and Florian Golemo and Chris Pal},\n year={2019},\n eprint={1910.13249},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTeam\u003c/h2\u003e\u003ca id=\"user-content-team\" class=\"anchor\" aria-label=\"Permalink: Team\" href=\"#team\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mweiss17\"\u003eMartin Weiss\u003c/a\u003e, PhD student (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/simonchamorro\"\u003eSimon Chamorro\u003c/a\u003e, Undergrad student (USherbrooke)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/roggirg\"\u003eRoger Girgis\u003c/a\u003e, PhD student (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Museau\"\u003eMargaux Luck\u003c/a\u003e, Postdoctoral fellow (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://sites.google.com/site/samiraekahou/\" rel=\"nofollow\"\u003eSamira Ebrahimi Kahou\u003c/a\u003e, Postdoctoral fellow (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://josephpcohen.com/w/\" rel=\"nofollow\"\u003eJoseph Paul Cohen\u003c/a\u003e, Postdoctoral fellow (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cim.mcgill.ca/~derek/\" rel=\"nofollow\"\u003eDerek Nowrouzezahrai\u003c/a\u003e, Professor (Mila \u0026amp; McGill)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.cs.mcgill.ca/~dprecup/\" rel=\"nofollow\"\u003eDoina Precup\u003c/a\u003e, Professor (Mila \u0026amp; McGill)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://fgolemo.github.io/\" rel=\"nofollow\"\u003eFlorian Golemo\u003c/a\u003e, Postdoctoral fellow (Mila \u0026amp; ElementAI)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mila.quebec/en/person/pal-christopher/\" rel=\"nofollow\"\u003eChris Pal\u003c/a\u003e, Professor (Mila, Polytechnique Montreal \u0026amp; ElementAI)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilt With\u003c/h2\u003e\u003ca id=\"user-content-built-with\" class=\"anchor\" aria-label=\"Permalink: Built With\" href=\"#built-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/openai/gym\"\u003eOpenAI Gym\u003c/a\u003e - A toolkit for developing and comparing reinforcement learning algorithms\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the MIT licence - please see the \u003ccode\u003eLICENCE\u003c/code\u003e file in the repository.\u003c/p\u003e\n", + "stargazers_count": 26, + "subscribers_count": 6, + "topics": [], + "updated_at": 1704386005.0 + }, + { + "data_format": 2, + "description": "PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S rRNA, ITS and COI marker genes", + "filenames": [ + "Singularity", + "singularity/Singularity.v.1.3.1", + "singularity/Singularity.v.1.3.2", + "singularity/Singularity.v.1.1", + "singularity/Singularity.v.2.1.3", + "singularity/Singularity.v.2.1.0", + "singularity/Singularity.v.1.3", + "singularity/Singularity.v.2.0.3", + "singularity/Singularity.v.2.1..5-beta", + "singularity/Singularity.latest", + "singularity/Singularity.v.2.0.2" + ], + "full_name": "hariszaf/pema", + "latest_release": "v.2.1.4", + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/63aabb7b86961fdca97af908726d5ff8f12a858b1d1575da6fc62b1097c3ac1b/68747470733a2f2f692e70617374652e706963732f38373031383966616466363638613935386338616163383366333865373939632e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/63aabb7b86961fdca97af908726d5ff8f12a858b1d1575da6fc62b1097c3ac1b/68747470733a2f2f692e70617374652e706963732f38373031383966616466363638613935386338616163383366333865373939632e706e67\" width=\"300\" align=\"left\" data-canonical-src=\"https://i.paste.pics/870189fadf668a958c8aac83f38e799c.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePEMA:\u003c/h1\u003e\u003ca id=\"user-content-pema\" class=\"anchor\" aria-label=\"Permalink: PEMA:\" href=\"#pema\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ea flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S rRNA, ITS and COI marker genes\u003c/h3\u003e\u003ca id=\"user-content-a-flexible-pipeline-for-environmental-dna-metabarcoding-analysis-of-the-16s18s-rrna-its-and-coi-marker-genes\" class=\"anchor\" aria-label=\"Permalink: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S rRNA, ITS and COI marker genes\" href=\"#a-flexible-pipeline-for-environmental-dna-metabarcoding-analysis-of-the-16s18s-rrna-its-and-coi-marker-genes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003ePEMA is reposited in\u003c/em\u003e \u003ca href=\"https://hub.docker.com/r/hariszaf/pema\" rel=\"nofollow\"\u003e\u003cem\u003eDocker Hub\u003c/em\u003e\u003c/a\u003e \u003cem\u003eas well as in\u003c/em\u003e \u003ca href=\"https://singularity-hub.org/collections/2295\" rel=\"nofollow\"\u003e\u003cem\u003eSingularity Hub\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePEMA website along with \u003cem\u003ehow to\u003c/em\u003e documentation can be found \u003ca href=\"https://hariszaf.github.io/pema_documentation/\" rel=\"nofollow\"\u003e\u003cstrong\u003ehere\u003c/strong\u003e\u003c/a\u003e.\u003c/h4\u003e\u003ca id=\"user-content-pema-website-along-with-how-to-documentation-can-be-found-here\" class=\"anchor\" aria-label=\"Permalink: PEMA website along with how to documentation can be found here.\" href=\"#pema-website-along-with-how-to-documentation-can-be-found-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFor any troubles you may have when running PEMA or for any potential improvevments you would like to suggest, please share on the \u003ca href=\"https://gitter.im/pema-helpdesk/community\" rel=\"nofollow\"\u003ePEMA Gitter community\u003c/a\u003e.\u003c/h4\u003e\u003ca id=\"user-content-for-any-troubles-you-may-have-when-running-pema-or-for-any-potential-improvevments-you-would-like-to-suggest-please-share-on-the-pema-gitter-community\" class=\"anchor\" aria-label=\"Permalink: For any troubles you may have when running PEMA or for any potential improvevments you would like to suggest, please share on the PEMA Gitter community.\" href=\"#for-any-troubles-you-may-have-when-running-pema-or-for-any-potential-improvevments-you-would-like-to-suggest-please-share-on-the-pema-gitter-community\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://gitter.im/pema-helpdesk/community?utm_source=badge\u0026amp;utm_medium=badge\u0026amp;utm_campaign=pr-badge\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bd95e844fe5af14955a630c8385523494c1122fc0c3ffde83701f230a88d08bc/68747470733a2f2f6261646765732e6769747465722e696d2f70656d612d68656c706465736b2f636f6d6d756e6974792e737667\" alt=\"Gitter\" data-canonical-src=\"https://badges.gitter.im/pema-helpdesk/community.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6be00b1b55f8b9b6f6d85fa810b501a37e0d48bf99e98bb4381e92caeff02619/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pema-biodiversity-in-all-its-different-levels\"\u003ePEMA: biodiversity in all its different levels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#a-container-based-tool\"\u003eA container-based tool\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#get-set-go-pema\"\u003eHow to run PEMA\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#get\"\u003eGet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#set\"\u003eSet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#go\"\u003eGo\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#parameters-file\"\u003eThe Parameters\u0027 file\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#downstream-ecological-analysis\"\u003eDownstream ecological analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#acknowledgments\"\u003eAcknowledgments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\n\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePEMA: biodiversity in all its different levels\u003c/h2\u003e\u003ca id=\"user-content-pema-biodiversity-in-all-its-different-levels\" class=\"anchor\" aria-label=\"Permalink: PEMA: biodiversity in all its different levels\" href=\"#pema-biodiversity-in-all-its-different-levels\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA supports the metabarcoding analysis of four marker genes, \u003cstrong\u003e16S rRNA\u003c/strong\u003e (Bacteria), \u003cstrong\u003eITS\u003c/strong\u003e (Fungi) as well as \u003cstrong\u003eCOI\u003c/strong\u003e and \u003cstrong\u003e18S rRNA\u003c/strong\u003e (metazoa). As input, PEMA accepts .fastq.gz files as returned by Illumina sequencing platforms.\u003c/p\u003e\n\u003cp\u003eSince the \u003ccode\u003ev.2.1.4\u003c/code\u003e release, PEMA supports also the analysis of the 12S rRNA marker gene!\u003c/p\u003e\n\u003cp\u003ePEMA processes the reads from each sample and \u003cstrong\u003ereturns an OTU- or an ASV-table with the taxonomies\u003c/strong\u003e of the taxa found and their abundances in each sample. It also returns statistics and a FASTQC diagram about the quality of the reads for each sample. Finally, PEMA supports \u003cstrong\u003edownstream ecological analysis\u003c/strong\u003e of the profiles retrieved, facilitated by the \u003ca href=\"http://joey711.github.io/phyloseq/index.html\" rel=\"nofollow\"\u003ephyloseq\u003c/a\u003e R package.\u003c/p\u003e\n\u003cp\u003ePEMA supports both OTU clustering (VSEARCH) and ASV inference (Swarm).\u003c/p\u003e\n\u003cp\u003eMore specifically:\u003c/p\u003e\n\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMarker gene\u003c/th\u003e\n\u003cth\u003eOTUs /VSEARCH\u003c/th\u003e\n\u003cth\u003eASVs / Swarm\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e16S rRNA\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e18S rRNA\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12S rRNA\u003c/td\u003e\n\u003ctd\u003e\u2610\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eITS\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCOI\u003c/td\u003e\n\u003ctd\u003e\u2610\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor the case of the 16S rRNA marker gene, PEMA includes two separate approaches for taxonomy assignment: alignment-based and phylogenetic-based.\nFor the latter, a reference tree of 1000 taxa was created using SILVA_132_SSURef, EPA-ng and RaxML-ng.\u003c/p\u003e\n\u003cp\u003ePEMA has been implemented in \u003ca href=\"https://pcingola.github.io/BigDataScript/\" rel=\"nofollow\"\u003eBigDataScript\u003c/a\u003e programming language. BDS\u2019s ad hoc task parallelism and task synchronization, supports heavyweight computation. Thus, PEMA inherits such features and it also supports roll-back checkpoints and on-demand partial pipeline execution. In addition, PEMA takes advantage of all the computational power available on a specific machine; for example, if PEMA is executed on a personal laptop with 4 cores, it is going to use all four of them.\u003c/p\u003e\n\u003cp\u003eFinally, container-based technologies such as Docker and Singularity, make PEMA easy accessible for all operating systems.\nAs you can see in the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/help_files/GitHub%20tutorial.pdf\"\u003ePEMA_tutorial.pdf\u003c/a\u003e, once you have either Docker or Singularity on your computational environment (see below which suits your case better), running PEMA is cakewalk. You can also find the \u003ca href=\"https://docs.google.com/presentation/d/1lVH23DPa2NDNBhVvOTRoip8mraw8zfw8VQwbK4vkB1U/edit?usp=sharing\" rel=\"nofollow\"\u003e\u003cstrong\u003ePEMA tutorial\u003c/strong\u003e\u003c/a\u003e as a Google Slides file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA container-based tool\u003c/h2\u003e\u003ca id=\"user-content-a-container-based-tool\" class=\"anchor\" aria-label=\"Permalink: A container-based tool\" href=\"#a-container-based-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA can run either on a HPC environment (server, cluster etc) or on a simple PC.\nHowever, we definitely suggest to run it on an HPC environment to exploit the full potential of PEMA. Running on a powerful server or a cluster can be time-saving since it would require significantly\nless computational time than in a common PC.\nHowever, in some cases, for analyses with a small number of samples, a common PC can suffice.\nFor COI, a minimum of 20 GB of RAM for the taxonomy assignment step is required.\u003c/p\u003e\n\u003cp\u003ePEMA runs either as a \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003e\u003cstrong\u003eDocker\u003c/strong\u003e\u003c/a\u003e or as a \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/a\u003e image. On the following chapters, you can find how to install PEMA both in Docker and Singlularity including examples.\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet-set-go PEMA!\u003c/h2\u003e\u003ca id=\"user-content-get-set-go-pema\" class=\"anchor\" aria-label=\"Permalink: Get-set-go PEMA!\" href=\"#get-set-go-pema\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGet\u003c/h3\u003e\u003ca id=\"user-content-get\" class=\"anchor\" aria-label=\"Permalink: Get\" href=\"#get\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get PEMA running you first need to make sure you either have \u003cstrong\u003e\u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/strong\u003e ,\nor Docker on your computing environment.\u003c/p\u003e\n\u003cp\u003eIn case you are working on Singularity, you may run the following command to get the PEMA Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull pema_v.2.1.4.sif https://gitlab.com/microbactions/pema-singularity-images-v.2.1.4/-/raw/main/pema_v.2.1.4.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will take some time but once it\u0027s downloaded you have PEMA ready to go!\u003c/p\u003e\n\u003cp\u003eSimilarly, in case you are working on Docker you need to run:\u003c/p\u003e\n\u003cpre lang=\"bash=\"\u003e\u003ccode\u003edocker pull hariszaf/pema:v.2.1.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003einstead.\u003c/p\u003e\n\u003cp\u003eA version of Docker is avalable for all Windows, Mac and Linux.\nIf you have Windows 10 Pro or your Mac\u0027s hardware in after 2010, then you can insall Docker straightforward.\nOtherwise, you need to install the \u003ca href=\"https://docs.docker.com/toolbox/\" rel=\"nofollow\"\u003eDocker toolbox\u003c/a\u003e instead.\nYou can check if your System Requirements are according to the ones mentioned below in order to be sure what you need to do.\u003c/p\u003e\n\u003cp\u003eYou are now ready to set up your analysis PEMA run!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSet\u003c/h3\u003e\u003ca id=\"user-content-set\" class=\"anchor\" aria-label=\"Permalink: Set\" href=\"#set\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the step, you need to create a directory where you will have everything PEMA needs to\nperform an analysis. We will call this the \u003cem\u003e\u003cstrong\u003eanalysis directory\u003c/strong\u003e\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn the \u003cem\u003eanalysis directory\u003c/em\u003e, you need to add the following \u003cstrong\u003emandatory\u003c/strong\u003e files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003e\u003cstrong\u003eparameters.tsv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file\n(you can download it from this repository and then \u003cstrong\u003ecomplete it\u003c/strong\u003e according to the needs of your analysis)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eATTENTION!\u003c/strong\u003e You always need to check that you have the corresponding version of the parameters file with the pema version you are about to use! For example, if you are about to use \u003ccode\u003epema:v.2.1.4\u003c/code\u003e then, your parameters file needs to be the \u003ca href=\"https://github.com/hariszaf/pema/blob/ARMS/analysis_directory/parameters.tsv\"\u003e\u003ccode\u003ev.2.1.4\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003ea subdirectory called \u003cem\u003e\u003cstrong\u003emydata\u003c/strong\u003e\u003c/em\u003e where your .fastq.gz files will be located \u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf your need to perform phyloseq, in the analysis directory you also need to add the following \u003cstrong\u003eoptionally\u003c/strong\u003e files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ethe \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/phyloseq_in_PEMA.R\"\u003e\u003cem\u003e\u003cstrong\u003ephyloseq_in_PEMA.R\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e which you can also download from this repository and set it the way you want (that is an R script which we have implemented and has some main features that need to stay always the same in order to be executed as part of PEMA and some parts where the user can set what exactly needs to get from the phyloseq package)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ethe \u003ca href=\"https://raw.githubusercontent.com/hariszaf/pema/master/analysis_directory/metadata.csv\" rel=\"nofollow\"\u003e\u003cem\u003e\u003cstrong\u003emetadata.csv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file which has to be in a \u003cstrong\u003ecomma separated\u003c/strong\u003e format (you can find an example of this file on PEMA\u0027s GitHub repository).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eAttention!\u003c/strong\u003e \u003cbr\u003e\nPEMA will \u003cstrong\u003efail\u003c/strong\u003e unless you name the aforementioned files and directories \u003cstrong\u003eexactly\u003c/strong\u003e as described above.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cbr\u003e\n\u003cp\u003eHere is an example of how your \u003cem\u003eanalysis directory\u003c/em\u003e should be in case you do want a phyloseq analysis:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003euser@home-PC:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Desktop/analysis_directory$ ls\nmydata parameters.tsv phyloseq_in_PEMA.R metadata.csv\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand in case you do not:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003euser@home-PC:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Desktop/analysis_directory$ ls\nmydata parameters.tsv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/hariszaf/pema/tree/master/analysis_directory\"\u003e\u003cstrong\u003eHere\u003c/strong\u003e\u003c/a\u003e you can find an example of an \u003cem\u003eanalysis directory\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAn extended list with PEMA\u0027s ouput can be found \u003ca href=\"https://github.com/hariszaf/pema/blob/master/help_files/PEMA\u0027s%20output%20files.md\"\u003e\u003cstrong\u003ehere\u003c/strong\u003e\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNow you are ready to run!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGo\u003c/h3\u003e\u003ca id=\"user-content-go\" class=\"anchor\" aria-label=\"Permalink: Go\" href=\"#go\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEither you are working on Docker or in Singularity, running PEMA has two discrete steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA container needs to be initiated having your analysis directory mounted under a certain directory\u003c/li\u003e\n\u003cli\u003eRun pema\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDocker\u003c/h4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo do so using Docker, you will first run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_analysis_directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/:/mnt/analysis hariszaf/pema\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e-v\u003c/code\u003e flag allows to the container you built, have access to everything included under your \u003ccode\u003e\u0026lt;path_to_analysis_directory\u0026gt;\u003c/code\u003e.\nAll PEMA\u0027s outuput will be there too\u003c/p\u003e\n\u003cp\u003eFrom the inside of the PEMA container, the only thing remaining to do now, is to run PEMA:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pema_latest.bds\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePEMA is now running!\u003c/p\u003e\n\u003cp\u003eThe runtime of PEMA depends on the computational features of your environment, on the size of your data, as well as the parameters you chose.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingularity\u003c/h4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn case you are working on Singularity, you may implement both these steps with a single command\nby running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_analysis_directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/:/mnt/analysis /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/pema_v.2.1.4.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003epema_v.2.1.4.sif\u003c/code\u003e may be called differently according to the pema version you \u0027re using.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eFor further information, you can always check on \u003ca href=\"https://hariszaf.github.io/pema_documentation/2.running_general/\" rel=\"nofollow\"\u003ePEMA\u0027s website\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u0027 file\u003c/h2\u003e\u003ca id=\"user-content-parameters-file\" class=\"anchor\" aria-label=\"Permalink: Parameters\u0027 file\" href=\"#parameters-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most crucial component in running PEMA is the parameters file.\nThis file must be located \u003cstrong\u003ein\u003c/strong\u003e the \u003cem\u003eanalysis directory\u003c/em\u003e and the user needs to fill it \u003cstrong\u003eevery time\u003c/strong\u003e PEMA is about to be called.\nIf you need more than one analyses to run, then you need to make copies of the parameters\u0027 file and have one of those in eah of the analysis directrories you create.\u003c/p\u003e\n\u003cp\u003eSo, here is the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003e\u003cstrong\u003eparameters.tsv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file as it looks like, in a study case of our own.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlways remember\u003c/strong\u003e to have the same version of the parameters file with the pema version you are about to use!\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownstream ecological analysis\u003c/h2\u003e\u003ca id=\"user-content-downstream-ecological-analysis\" class=\"anchor\" aria-label=\"Permalink: Downstream ecological analysis\" href=\"#downstream-ecological-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA performs all the basic functions of the \"phyloseq\" R package. In addition, it performs certain functions of the \u003ca href=\"https://cran.r-project.org/web/packages/vegan/index.html\" rel=\"nofollow\"\u003e\u003cem\u003e\u003cstrong\u003evegan\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e R package.\u003c/p\u003e\n\u003cp\u003eWhen the user asks for a downstream analysis using the \"phyloseq\" R package, then an extra input file called \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/phyloseq_in_PEMA.R\"\u003e\u003cem\u003e\u003cstrong\u003e\"phyloseq_script.R\"\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e needs to be imported in the \"analysis_directory\". In PEMA\u0027s main repository, you can find a template of this file; this file needs to be as it would run on your own computer, as you would run \u003cem\u003ephyloseq\u003c/em\u003e in any case. PEMA will create the \u003cem\u003e\"phyloseq object\"\u003c/em\u003e automatically and then it will perform the analysis as asked. The output will be placed in an extra subfolder in the main output directory of PEMA called \u003cem\u003ephyloseq_analysis\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition, the \u003cem\u003e\u003cstrong\u003emetadata.tsv\u003c/strong\u003e\u003c/em\u003e file is also required when the phyloseq option has been selected. An example of this file you can find \u003ca href=\"https://raw.githubusercontent.com/hariszaf/pema/master/analysis_directory/metadata.csv\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA uses a series of tools, datasets as well as Big Data Script language. We thank all the groups that developed them.\nThe tools \u0026amp; databases that PEMA uses are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBigDataScript programming language - \u003ca href=\"https://pcingola.github.io/BigDataScript/\" rel=\"nofollow\"\u003ehttps://pcingola.github.io/BigDataScript/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFASTQC - \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eTrimmomatic - \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003ehttp://www.usadellab.org/cms/?page=trimmomatic\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCutadapt - \u003ca href=\"https://cutadapt.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ehttps://cutadapt.readthedocs.io/en/stable/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBayesHammer - included in SPAdes - \u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003ehttp://cab.spbu.ru/software/spades/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePANDAseq - \u003ca href=\"https://github.com/neufeld/pandaseq\"\u003ehttps://github.com/neufeld/pandaseq\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOBITools - \u003ca href=\"https://pythonhosted.org/OBITools/welcome.html\" rel=\"nofollow\"\u003ehttps://pythonhosted.org/OBITools/welcome.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBLAST Command Line Applications - \u003ca href=\"https://www.ncbi.nlm.nih.gov/books/NBK52640/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK52640/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVSEARCH-2.9.1 - \u003ca href=\"https://github.com/torognes/vsearch/releases/tag/v2.9.1\"\u003ehttps://github.com/torognes/vsearch/releases/tag/v2.9.1\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSWARM - \u003ca href=\"https://github.com/torognes/swarm\"\u003ehttps://github.com/torognes/swarm\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCROP - \u003ca href=\"https://github.com/tingchenlab/CROP\"\u003ehttps://github.com/tingchenlab/CROP\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCREST - \u003ca href=\"https://github.com/lanzen/CREST\"\u003ehttps://github.com/lanzen/CREST\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRDPClassifier - \u003ca href=\"https://github.com/rdpstaff/classifier\"\u003ehttps://github.com/rdpstaff/classifier\u003c/a\u003e\n(RPDtools are required in order to execute RDPClassifier)\u003c/li\u003e\n\u003cli\u003eSILVA db - \u003ca href=\"https://www.arb-silva.de/no_cache/download/archive/current/Exports/\" rel=\"nofollow\"\u003ehttps://www.arb-silva.de/no_cache/download/archive/current/Exports/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMIDORI db - \u003ca href=\"http://reference-midori.info/index.html\" rel=\"nofollow\"\u003ehttp://reference-midori.info/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePR2 db - \u003ca href=\"https://pr2-database.org/\" rel=\"nofollow\"\u003ehttps://pr2-database.org/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\"phat\" algorithm, from the \"gappa\" package - \u003ca href=\"https://github.com/lczech/gappa/wiki/Subcommand:-phat\"\u003ehttps://github.com/lczech/gappa/wiki/Subcommand:-phat\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMAFFT - \u003ca href=\"https://mafft.cbrc.jp/alignment/software/\" rel=\"nofollow\"\u003ehttps://mafft.cbrc.jp/alignment/software/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRAxML -ng - \u003ca href=\"https://github.com/amkozlov/raxml-ng\"\u003ehttps://github.com/amkozlov/raxml-ng\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePaPaRa - \u003ca href=\"https://cme.h-its.org/exelixis/web/software/papara/index.html\" rel=\"nofollow\"\u003ehttps://cme.h-its.org/exelixis/web/software/papara/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEPA-ng - \u003ca href=\"https://github.com/Pbdas/epa-ng\"\u003ehttps://github.com/Pbdas/epa-ng\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ephyloseq R package - \u003ca href=\"http://joey711.github.io/phyloseq/index.html\" rel=\"nofollow\"\u003ehttp://joey711.github.io/phyloseq/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003evegan R package - \u003ca href=\"https://cran.r-project.org/web/packages/vegan/index.html\" rel=\"nofollow\"\u003ehttps://cran.r-project.org/web/packages/vegan/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003encbi-taxonomist - \u003ca href=\"https://ncbi-taxonomist.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://ncbi-taxonomist.readthedocs.io/en/latest/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnd of course the container-based technologies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker - \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehttps://www.docker.com/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity - \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003ehttps://sylabs.io/singularity/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA is under the GNU GPLv3 license (for 3rd party components separate licenses apply).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHaris Zafeiropoulos, Ha Quoc Viet, Katerina Vasileiadou, Antonis Potirakis, Christos Arvanitidis, Pantelis Topalis, Christina Pavloudi, Evangelos Pafilis, PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes, GigaScience, Volume 9, Issue 3, March 2020, giaa022, \u003ca href=\"https://doi.org/10.1093/gigascience/giaa022\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/gigascience/giaa022\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 26, + "subscribers_count": 3, + "topics": [], + "updated_at": 1719986679.0 + }, + { + "data_format": 2, + "description": "Configurable Pipeline for the Analysis of Connectomes", + "filenames": [ + "Singularity.latest-ABCD-HCP", + "Singularity", + "Singularity.latest-lite" + ], + "full_name": "FCP-INDI/C-PAC", + "latest_release": "v1.8.2", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-c-pac-configurable-pipeline-for-the-analysis-of-connectomes\" class=\"anchor\" href=\"#c-pac-configurable-pipeline-for-the-analysis-of-connectomes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eC-PAC: Configurable Pipeline for the Analysis of Connectomes\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://openneuro.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/bids-standard/bids-website/gh-pages/old_website/openneuro_badge.svg?sanitize=true\" alt=\"OpenNeuro\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.164638\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f0d19be234e7ec74a9be968abf17667adad63377953a2750545746250066ab36/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e3136343633382e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.164638.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA configurable, open-source, Nipype-based, automated processing pipeline for resting state fMRI data.\nDesigned for use by both novice users and experts, C-PAC brings the power, flexibility and elegance\nof Nipype to users in a plug-and-play fashion; no programming required.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-website\" class=\"anchor\" href=\"#website\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWebsite\u003c/h2\u003e\n\u003cp\u003eThe C-PAC website is located here: \u003ca href=\"http://fcp-indi.github.com/\"\u003ehttp://fcp-indi.github.com/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-run\" class=\"anchor\" href=\"#how-to-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Run\u003c/h2\u003e\n\u003cp\u003eInstructions can be found within our quick-start guide: \u003ca href=\"https://fcp-indi.github.io/docs/latest/user/quick\" rel=\"nofollow\"\u003ehttps://fcp-indi.github.io/docs/latest/user/quick\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eUser documentation can be found here: \u003ca href=\"https://fcp-indi.github.io/docs/latest/user\" rel=\"nofollow\"\u003ehttps://fcp-indi.github.io/docs/latest/user\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDeveloper documentation can be found here: \u003ca href=\"https://fcp-indi.github.io/docs/latest/developer\" rel=\"nofollow\"\u003ehttps://fcp-indi.github.io/docs/latest/developer\u003c/a\u003e and here: \u003ca href=\"https://github.com/FCP-INDI/.github/blob/primary/CONTRIBUTING.md\"\u003ehttps://github.com/FCP-INDI/.github/blob/primary/CONTRIBUTING.md\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDocumentation pertaining to this latest release can be found here: \u003ca href=\"https://fcp-indi.github.io/docs/latest/user/release_notes/latest\" rel=\"nofollow\"\u003ehttps://fcp-indi.github.io/docs/latest/user/release_notes/latest\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-discussion-forum\" class=\"anchor\" href=\"#discussion-forum\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscussion Forum\u003c/h2\u003e\n\u003cp\u003eIf you are stuck and need help or have any other questions or comments about C-PAC, there is a C-PAC discussion forum here: \u003ca href=\"https://groups.google.com/forum/#!forum/cpax_forum\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/cpax_forum\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-issue-tracker-and-bugs\" class=\"anchor\" href=\"#issue-tracker-and-bugs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssue Tracker and Bugs\u003c/h2\u003e\n\u003cp\u003eThis is a beta version of C-PAC, which means that it is still under active development. As such, although we have done our best to ensure a stable pipeline, there will likely still be a few bugs that we did not catch. If you find a bug or would like to suggest a new feature, please open an issue on the the C-PAC Github issue tracker: \u003ca href=\"https://github.com/FCP-INDI/C-PAC/issues?state=open\"\u003ehttps://github.com/FCP-INDI/C-PAC/issues?state=open\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you would like to suggest revisions to the user documentation, please open an issue on the C-PAC website\u0027s GitHub issue tracker: \u003ca href=\"https://github.com/FCP-INDI/fcp-indi.github.com/issues\"\u003ehttps://github.com/FCP-INDI/fcp-indi.github.com/issues\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 26, + "subscribers_count": 7, "topics": [ - "bioconductor", - "docker", - "containers" + "hacktoberfest", + "closember" ], - "updated_at": 1721819193.0 + "updated_at": 1638611927.0 + }, + { + "data_format": 2, + "description": "StaG Metagenomic Workflow Collaboration", + "filenames": [ + "workflow/envs/Singularity.krakenuniq", + "workflow/envs/Singularity.snakemake", + "workflow/envs/Singularity.stag-mwc", + "workflow/envs/Singularity.biobakery" + ], + "full_name": "ctmrbio/stag-mwc", + "latest_release": "v0.7.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eStaG Metagenomic Workflow Collaboration (mwc)\u003c/h1\u003e\u003ca id=\"user-content-stag-metagenomic-workflow-collaboration-mwc\" class=\"anchor\" aria-label=\"Permalink: StaG Metagenomic Workflow Collaboration (mwc)\" href=\"#stag-metagenomic-workflow-collaboration-mwc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/125840716\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/92ad24a4fa50cdfc1d4478ded3050d22c276b5024721b114a5e0fcd96b0a32da/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3132353834303731362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/125840716.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://snakemake.bitbucket.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ed10a5610613b244a62d167ab48f7c89729fe009afd8c23f6e20591b6b6af2b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135372e32342e302d627269676874677265656e2e737667\" alt=\"Snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%E2%89%A57.24.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/source/img/stag_head_text.png\"\u003e\u003cimg src=\"docs/source/img/stag_head_text.png\" alt=\"StaG mwc logo\" title=\"StaG mwc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe StaG Metagenomic Workflow Collaboration (mwc) project focuses on providing\na metagenomics analysis workflow suitable for microbiome research and general\nmetagenomics analyses.\u003c/p\u003e\n\u003cp\u003ePlease visit \u003ca href=\"https://stag-mwc.readthedocs.io\" rel=\"nofollow\"\u003ehttps://stag-mwc.readthedocs.io\u003c/a\u003e for the full documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 0: Install conda and Snakemake\u003c/h3\u003e\u003ca id=\"user-content-step-0-install-conda-and-snakemake\" class=\"anchor\" aria-label=\"Permalink: Step 0: Install conda and Snakemake\" href=\"#step-0-install-conda-and-snakemake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://conda.io/docs/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e and\n\u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e are required to be able to use\nStaG-mwc. Most people would probably want to install\n\u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eMiniconda\u003c/a\u003e and install Snakemake into their\nbase environment. When running StaG with the \u003ccode\u003e--use-conda\u003c/code\u003e or\n\u003ccode\u003e--use-singularity\u003c/code\u003e flags, all dependencies are managed automatically. If\nusing conda it will automatically install the required versions of all tools\nrequired to run StaG-mwc. There is no need to combine the conda and singularity\nflags: the Singularity images used by the workflow already contain all required\ndependencies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Clone workflow\u003c/h3\u003e\u003ca id=\"user-content-step-1-clone-workflow\" class=\"anchor\" aria-label=\"Permalink: Step 1: Clone workflow\" href=\"#step-1-clone-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use StaG-mwc, you need a local copy of the workflow repository. Start by\nmaking a clone of the repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:ctmrbio/stag-mwc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you use StaG-mwc in a publication, please credit the authors by citing\neither the URL of this repository or the project\u0027s DOI. Also, don\u0027t forget to\ncite the publications of the other tools used in your workflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Configure workflow\u003c/h3\u003e\u003ca id=\"user-content-step-2-configure-workflow\" class=\"anchor\" aria-label=\"Permalink: Step 2: Configure workflow\" href=\"#step-2-configure-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConfigure the workflow according to your needs by editing the file\n\u003ccode\u003econfig/config.yaml\u003c/code\u003e. The most common changes include setting the paths to\ninput and output folders, and configuring what steps of the workflow should be\nincluded when running the workflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Execute workflow\u003c/h3\u003e\u003ca id=\"user-content-step-3-execute-workflow\" class=\"anchor\" aria-label=\"Permalink: Step 3: Execute workflow\" href=\"#step-3-execute-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTest your configuration by performing a dry-run via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-conda -n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the workflow locally via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-conda --cores N\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the workflow locally using \u003ccode\u003eN\u003c/code\u003e cores. It is also possible to run\nit in a cluster environment by using one of the available profiles, or creating\nyour own, e.g. to run on CTMR\u0027s Gandalf cluster:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --profile profiles/ctmr_gandalf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure you specify the Slurm account and partition in\n\u003ccode\u003eprofiles/ctmr_gandalf/config.yaml\u003c/code\u003e. Refer to the official \u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003eSnakemake\ndocumentation\u003c/a\u003e for further details on how to\nrun Snakemake workflows on other types of cluster resources.\u003c/p\u003e\n\u003cp\u003eNote that in all examples above, \u003ccode\u003e--use-conda\u003c/code\u003e can be replaced with\n\u003ccode\u003e--use-singularity\u003c/code\u003e to run in Singularity containers instead of using a locally\ninstalled conda. Read more about it under the Running section in the docs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA very basic continuous integration test is currently in place. It merely\nvalidates the syntax by trying to let Snakemake build the dependency graph if\nall outputs are activated. Suggestions for how to improve the automated\ntesting of StaG-mwc are very welcome!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRefer to the contributing guidelines in \u003ccode\u003eCONTRIBUTING.md\u003c/code\u003e for instructions on\nhow to contribute to StaG-mwc.\u003c/p\u003e\n\u003cp\u003eIf you intend to modify or further develop this workflow, you are welcome to\nfork this reposity. Please consider sharing potential improvements via a pull\nrequest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting\u003c/h2\u003e\u003ca id=\"user-content-citing\" class=\"anchor\" aria-label=\"Permalink: Citing\" href=\"#citing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you find StaG-mwc useful in your research, please cite the Zenodo DOI:\n\u003ca href=\"https://zenodo.org/badge/latestdoi/125840716\" rel=\"nofollow\"\u003ehttps://zenodo.org/badge/latestdoi/125840716\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLogo attribution\u003c/h1\u003e\u003ca id=\"user-content-logo-attribution\" class=\"anchor\" aria-label=\"Permalink: Logo attribution\" href=\"#logo-attribution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.freepik.com/free-photos-vectors/animal\" rel=\"nofollow\"\u003eAnimal vector created by Patrickss - Freepik.com\u003c/a\u003e\u003c/p\u003e\n", + "stargazers_count": 26, + "subscribers_count": 6, + "topics": [ + "snakemake-workflows", + "metagenomic-analysis", + "metagenomics", + "research", + "microbiome" + ], + "updated_at": 1714145307.0 + }, + { + "data_format": 2, + "description": "A simple multi camera library using PySpin", + "filenames": [ + "Singularity", + "Singularity.1.0.0" + ], + "full_name": "justinblaber/multi_pyspin", + "latest_release": "1.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emulti_pyspin\u003c/h1\u003e\u003ca id=\"user-content-multi_pyspin\" class=\"anchor\" aria-label=\"Permalink: multi_pyspin\" href=\"#multi_pyspin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple multi camera library using PySpin.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation and User Guide (Spinnaker USB cameras on Linux)\u003c/h1\u003e\u003ca id=\"user-content-installation-and-user-guide-spinnaker-usb-cameras-on-linux\" class=\"anchor\" aria-label=\"Permalink: Installation and User Guide (Spinnaker USB cameras on Linux)\" href=\"#installation-and-user-guide-spinnaker-usb-cameras-on-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eIncrease USB file system memory. In this case, I\u0027ve set it to 16gb (16000 mb). Please update the number to whatever you think is appropriate for your machine. There are two ways:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePermanent way:\u003c/h4\u003e\u003ca id=\"user-content-permanent-way\" class=\"anchor\" aria-label=\"Permalink: Permanent way:\" href=\"#permanent-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOpen the \u003ccode\u003e/etc/default/grub\u003c/code\u003e file in any text editor. Find and replace:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eGRUB_CMDLINE_LINUX_DEFAULT=\"quiet splash\"\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewith this:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eGRUB_CMDLINE_LINUX_DEFAULT=\"quiet splash usbcore.usbfs_memory_mb=16000\"\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpdate grub with these settings:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ sudo update-grub\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReboot and test a USB camera.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTemporary way:\u003c/h4\u003e\u003ca id=\"user-content-temporary-way\" class=\"anchor\" aria-label=\"Permalink: Temporary way:\" href=\"#temporary-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eRun the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo sh -c \u0027echo 16000 \u0026gt; /sys/module/usbcore/parameters/usbfs_memory_mb\u0027\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAfter doing either the permanent or temporary way, confirm that you have successfully updated the memory limit by running the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecat /sys/module/usbcore/parameters/usbfs_memory_mb\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe output should be 16000.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConfigure udev rules to allow access to USB devices:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /tmp\nwget https://github.com/justinblaber/multi_pyspin/blob/master/spinnaker-1.23.0.27-amd64-Ubuntu18.04-pkg.tar.gz?raw=true -O spinnaker-1.23.0.27-amd64-Ubuntu18.04-pkg.tar.gz\ntar xvfz spinnaker-1.23.0.27-amd64-Ubuntu18.04-pkg.tar.gz\ncd spinnaker-1.23.0.27-amd64\nsudo ./configure_spinnaker.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003eInstall singularity\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p ~/multi_pyspin\ncd ~/multi_pyspin\nsingularity pull --name multi_pyspin.simg shub://justinblaber/multi_pyspin\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the singularity image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./multi_pyspin.simg\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eA gui should appear like so:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/651e60f6c8d673ace2541b3cb133e369e75dcb023404d8c565f147ace9fb48ca/68747470733a2f2f692e696d6775722e636f6d2f6a506b613375322e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/651e60f6c8d673ace2541b3cb133e369e75dcb023404d8c565f147ace9fb48ca/68747470733a2f2f692e696d6775722e636f6d2f6a506b613375322e706e67\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/jPka3u2.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet the number of cameras, set/configure the yaml files, press the \"setup\" buttons, set gain/exposure/fps, then (optionally) start the streams. If successful, the gui should look like:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d3552fdd373c625d3b1fdc91fb162808281238d5afbe95d4a85e279d1cc19ee9/68747470733a2f2f692e696d6775722e636f6d2f644363695933552e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d3552fdd373c625d3b1fdc91fb162808281238d5afbe95d4a85e279d1cc19ee9/68747470733a2f2f692e696d6775722e636f6d2f644363695933552e706e67\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/dCciY3U.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSaving \"multi\" images should result in something like:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSERIAL_19061245_DATETIME_2019-05-28-22:28:10-795899_CAM_1_FRAMEID_0_COUNTER_1.png\u003c/code\u003e:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5f384924970cd48dc32d6e21ccdcd9f9e9379e8ec0a8125096653238c6fd1481/68747470733a2f2f692e696d6775722e636f6d2f557a4e414c774a2e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5f384924970cd48dc32d6e21ccdcd9f9e9379e8ec0a8125096653238c6fd1481/68747470733a2f2f692e696d6775722e636f6d2f557a4e414c774a2e6a7067\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/UzNALwJ.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSERIAL_16276941_DATETIME_2019-05-28-22:28:10-795920_CAM_2_FRAMEID_0_COUNTER_1.png\u003c/code\u003e:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/170d38168f8197baeeb9a7a9a3ff95d0d8abf5380f087955cfae6269bfb92939/68747470733a2f2f692e696d6775722e636f6d2f756776533641522e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/170d38168f8197baeeb9a7a9a3ff95d0d8abf5380f087955cfae6269bfb92939/68747470733a2f2f692e696d6775722e636f6d2f756776533641522e6a7067\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/ugvS6AR.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSERIAL_16276942_DATETIME_2019-05-28-22:28:10-795941_CAM_3_FRAMEID_0_COUNTER_1.png\u003c/code\u003e:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a60724b0e2df6df94bb727ac4a2c4b5631fb54ec760552ddf6bf01c3b329bb2d/68747470733a2f2f692e696d6775722e636f6d2f725655354a45342e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a60724b0e2df6df94bb727ac4a2c4b5631fb54ec760552ddf6bf01c3b329bb2d/68747470733a2f2f692e696d6775722e636f6d2f725655354a45342e6a7067\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/rVU5JE4.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIt\u0027s important to note that the images should be acquired very closely in time (especially if a hardware trigger is used, in which case the times should be ~1e-3 seconds apart at most) and their frameid\u0027s should match.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", + "stargazers_count": 26, + "subscribers_count": 3, + "topics": [], + "updated_at": 1728059030.0 }, { "data_format": 2, @@ -39902,114 +40041,22 @@ var data = }, { "data_format": 2, - "description": "A simple multi camera library using PySpin", - "filenames": [ - "Singularity", - "Singularity.1.0.0" - ], - "full_name": "justinblaber/multi_pyspin", - "latest_release": "1.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003emulti_pyspin\u003c/h1\u003e\u003ca id=\"user-content-multi_pyspin\" class=\"anchor\" aria-label=\"Permalink: multi_pyspin\" href=\"#multi_pyspin\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA simple multi camera library using PySpin.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation and User Guide (Spinnaker USB cameras on Linux)\u003c/h1\u003e\u003ca id=\"user-content-installation-and-user-guide-spinnaker-usb-cameras-on-linux\" class=\"anchor\" aria-label=\"Permalink: Installation and User Guide (Spinnaker USB cameras on Linux)\" href=\"#installation-and-user-guide-spinnaker-usb-cameras-on-linux\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eIncrease USB file system memory. In this case, I\u0027ve set it to 16gb (16000 mb). Please update the number to whatever you think is appropriate for your machine. There are two ways:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePermanent way:\u003c/h4\u003e\u003ca id=\"user-content-permanent-way\" class=\"anchor\" aria-label=\"Permalink: Permanent way:\" href=\"#permanent-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOpen the \u003ccode\u003e/etc/default/grub\u003c/code\u003e file in any text editor. Find and replace:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eGRUB_CMDLINE_LINUX_DEFAULT=\"quiet splash\"\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ewith this:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eGRUB_CMDLINE_LINUX_DEFAULT=\"quiet splash usbcore.usbfs_memory_mb=16000\"\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUpdate grub with these settings:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ sudo update-grub\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReboot and test a USB camera.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTemporary way:\u003c/h4\u003e\u003ca id=\"user-content-temporary-way\" class=\"anchor\" aria-label=\"Permalink: Temporary way:\" href=\"#temporary-way\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eRun the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003esudo sh -c \u0027echo 16000 \u0026gt; /sys/module/usbcore/parameters/usbfs_memory_mb\u0027\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAfter doing either the permanent or temporary way, confirm that you have successfully updated the memory limit by running the following command:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecat /sys/module/usbcore/parameters/usbfs_memory_mb\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe output should be 16000.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConfigure udev rules to allow access to USB devices:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /tmp\nwget https://github.com/justinblaber/multi_pyspin/blob/master/spinnaker-1.23.0.27-amd64-Ubuntu18.04-pkg.tar.gz?raw=true -O spinnaker-1.23.0.27-amd64-Ubuntu18.04-pkg.tar.gz\ntar xvfz spinnaker-1.23.0.27-amd64-Ubuntu18.04-pkg.tar.gz\ncd spinnaker-1.23.0.27-amd64\nsudo ./configure_spinnaker.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ca href=\"https://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003eInstall singularity\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDownload singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p ~/multi_pyspin\ncd ~/multi_pyspin\nsingularity pull --name multi_pyspin.simg shub://justinblaber/multi_pyspin\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the singularity image:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e./multi_pyspin.simg\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eA gui should appear like so:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/651e60f6c8d673ace2541b3cb133e369e75dcb023404d8c565f147ace9fb48ca/68747470733a2f2f692e696d6775722e636f6d2f6a506b613375322e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/651e60f6c8d673ace2541b3cb133e369e75dcb023404d8c565f147ace9fb48ca/68747470733a2f2f692e696d6775722e636f6d2f6a506b613375322e706e67\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/jPka3u2.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet the number of cameras, set/configure the yaml files, press the \"setup\" buttons, set gain/exposure/fps, then (optionally) start the streams. If successful, the gui should look like:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d3552fdd373c625d3b1fdc91fb162808281238d5afbe95d4a85e279d1cc19ee9/68747470733a2f2f692e696d6775722e636f6d2f644363695933552e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d3552fdd373c625d3b1fdc91fb162808281238d5afbe95d4a85e279d1cc19ee9/68747470733a2f2f692e696d6775722e636f6d2f644363695933552e706e67\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/dCciY3U.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSaving \"multi\" images should result in something like:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSERIAL_19061245_DATETIME_2019-05-28-22:28:10-795899_CAM_1_FRAMEID_0_COUNTER_1.png\u003c/code\u003e:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5f384924970cd48dc32d6e21ccdcd9f9e9379e8ec0a8125096653238c6fd1481/68747470733a2f2f692e696d6775722e636f6d2f557a4e414c774a2e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5f384924970cd48dc32d6e21ccdcd9f9e9379e8ec0a8125096653238c6fd1481/68747470733a2f2f692e696d6775722e636f6d2f557a4e414c774a2e6a7067\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/UzNALwJ.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSERIAL_16276941_DATETIME_2019-05-28-22:28:10-795920_CAM_2_FRAMEID_0_COUNTER_1.png\u003c/code\u003e:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/170d38168f8197baeeb9a7a9a3ff95d0d8abf5380f087955cfae6269bfb92939/68747470733a2f2f692e696d6775722e636f6d2f756776533641522e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/170d38168f8197baeeb9a7a9a3ff95d0d8abf5380f087955cfae6269bfb92939/68747470733a2f2f692e696d6775722e636f6d2f756776533641522e6a7067\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/ugvS6AR.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eSERIAL_16276942_DATETIME_2019-05-28-22:28:10-795941_CAM_3_FRAMEID_0_COUNTER_1.png\u003c/code\u003e:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a60724b0e2df6df94bb727ac4a2c4b5631fb54ec760552ddf6bf01c3b329bb2d/68747470733a2f2f692e696d6775722e636f6d2f725655354a45342e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a60724b0e2df6df94bb727ac4a2c4b5631fb54ec760552ddf6bf01c3b329bb2d/68747470733a2f2f692e696d6775722e636f6d2f725655354a45342e6a7067\" alt=\"alt text\" data-canonical-src=\"https://i.imgur.com/rVU5JE4.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIt\u0027s important to note that the images should be acquired very closely in time (especially if a hardware trigger is used, in which case the times should be ~1e-3 seconds apart at most) and their frameid\u0027s should match.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n", - "stargazers_count": 26, - "subscribers_count": 3, - "topics": [], - "updated_at": 1728059030.0 - }, - { - "data_format": 2, - "description": "StaG Metagenomic Workflow Collaboration", - "filenames": [ - "workflow/envs/Singularity.krakenuniq", - "workflow/envs/Singularity.snakemake", - "workflow/envs/Singularity.stag-mwc", - "workflow/envs/Singularity.biobakery" - ], - "full_name": "ctmrbio/stag-mwc", - "latest_release": "v0.7.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eStaG Metagenomic Workflow Collaboration (mwc)\u003c/h1\u003e\u003ca id=\"user-content-stag-metagenomic-workflow-collaboration-mwc\" class=\"anchor\" aria-label=\"Permalink: StaG Metagenomic Workflow Collaboration (mwc)\" href=\"#stag-metagenomic-workflow-collaboration-mwc\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://zenodo.org/badge/latestdoi/125840716\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/92ad24a4fa50cdfc1d4478ded3050d22c276b5024721b114a5e0fcd96b0a32da/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f3132353834303731362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/125840716.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://snakemake.bitbucket.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4ed10a5610613b244a62d167ab48f7c89729fe009afd8c23f6e20591b6b6af2b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736e616b656d616b652d254532253839254135372e32342e302d627269676874677265656e2e737667\" alt=\"Snakemake\" data-canonical-src=\"https://img.shields.io/badge/snakemake-%E2%89%A57.24.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/source/img/stag_head_text.png\"\u003e\u003cimg src=\"docs/source/img/stag_head_text.png\" alt=\"StaG mwc logo\" title=\"StaG mwc\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe StaG Metagenomic Workflow Collaboration (mwc) project focuses on providing\na metagenomics analysis workflow suitable for microbiome research and general\nmetagenomics analyses.\u003c/p\u003e\n\u003cp\u003ePlease visit \u003ca href=\"https://stag-mwc.readthedocs.io\" rel=\"nofollow\"\u003ehttps://stag-mwc.readthedocs.io\u003c/a\u003e for the full documentation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 0: Install conda and Snakemake\u003c/h3\u003e\u003ca id=\"user-content-step-0-install-conda-and-snakemake\" class=\"anchor\" aria-label=\"Permalink: Step 0: Install conda and Snakemake\" href=\"#step-0-install-conda-and-snakemake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://conda.io/docs/\" rel=\"nofollow\"\u003eConda\u003c/a\u003e and\n\u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003eSnakemake\u003c/a\u003e are required to be able to use\nStaG-mwc. Most people would probably want to install\n\u003ca href=\"https://conda.io/miniconda.html\" rel=\"nofollow\"\u003eMiniconda\u003c/a\u003e and install Snakemake into their\nbase environment. When running StaG with the \u003ccode\u003e--use-conda\u003c/code\u003e or\n\u003ccode\u003e--use-singularity\u003c/code\u003e flags, all dependencies are managed automatically. If\nusing conda it will automatically install the required versions of all tools\nrequired to run StaG-mwc. There is no need to combine the conda and singularity\nflags: the Singularity images used by the workflow already contain all required\ndependencies.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 1: Clone workflow\u003c/h3\u003e\u003ca id=\"user-content-step-1-clone-workflow\" class=\"anchor\" aria-label=\"Permalink: Step 1: Clone workflow\" href=\"#step-1-clone-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo use StaG-mwc, you need a local copy of the workflow repository. Start by\nmaking a clone of the repository:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone git@github.com:ctmrbio/stag-mwc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you use StaG-mwc in a publication, please credit the authors by citing\neither the URL of this repository or the project\u0027s DOI. Also, don\u0027t forget to\ncite the publications of the other tools used in your workflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 2: Configure workflow\u003c/h3\u003e\u003ca id=\"user-content-step-2-configure-workflow\" class=\"anchor\" aria-label=\"Permalink: Step 2: Configure workflow\" href=\"#step-2-configure-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConfigure the workflow according to your needs by editing the file\n\u003ccode\u003econfig/config.yaml\u003c/code\u003e. The most common changes include setting the paths to\ninput and output folders, and configuring what steps of the workflow should be\nincluded when running the workflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStep 3: Execute workflow\u003c/h3\u003e\u003ca id=\"user-content-step-3-execute-workflow\" class=\"anchor\" aria-label=\"Permalink: Step 3: Execute workflow\" href=\"#step-3-execute-workflow\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTest your configuration by performing a dry-run via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-conda -n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the workflow locally via\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-conda --cores N\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will run the workflow locally using \u003ccode\u003eN\u003c/code\u003e cores. It is also possible to run\nit in a cluster environment by using one of the available profiles, or creating\nyour own, e.g. to run on CTMR\u0027s Gandalf cluster:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --profile profiles/ctmr_gandalf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eMake sure you specify the Slurm account and partition in\n\u003ccode\u003eprofiles/ctmr_gandalf/config.yaml\u003c/code\u003e. Refer to the official \u003ca href=\"https://snakemake.readthedocs.io\" rel=\"nofollow\"\u003eSnakemake\ndocumentation\u003c/a\u003e for further details on how to\nrun Snakemake workflows on other types of cluster resources.\u003c/p\u003e\n\u003cp\u003eNote that in all examples above, \u003ccode\u003e--use-conda\u003c/code\u003e can be replaced with\n\u003ccode\u003e--use-singularity\u003c/code\u003e to run in Singularity containers instead of using a locally\ninstalled conda. Read more about it under the Running section in the docs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA very basic continuous integration test is currently in place. It merely\nvalidates the syntax by trying to let Snakemake build the dependency graph if\nall outputs are activated. Suggestions for how to improve the automated\ntesting of StaG-mwc are very welcome!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRefer to the contributing guidelines in \u003ccode\u003eCONTRIBUTING.md\u003c/code\u003e for instructions on\nhow to contribute to StaG-mwc.\u003c/p\u003e\n\u003cp\u003eIf you intend to modify or further develop this workflow, you are welcome to\nfork this reposity. Please consider sharing potential improvements via a pull\nrequest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting\u003c/h2\u003e\u003ca id=\"user-content-citing\" class=\"anchor\" aria-label=\"Permalink: Citing\" href=\"#citing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you find StaG-mwc useful in your research, please cite the Zenodo DOI:\n\u003ca href=\"https://zenodo.org/badge/latestdoi/125840716\" rel=\"nofollow\"\u003ehttps://zenodo.org/badge/latestdoi/125840716\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLogo attribution\u003c/h1\u003e\u003ca id=\"user-content-logo-attribution\" class=\"anchor\" aria-label=\"Permalink: Logo attribution\" href=\"#logo-attribution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.freepik.com/free-photos-vectors/animal\" rel=\"nofollow\"\u003eAnimal vector created by Patrickss - Freepik.com\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 26, - "subscribers_count": 6, - "topics": [ - "snakemake-workflows", - "metagenomic-analysis", - "metagenomics", - "research", - "microbiome" - ], - "updated_at": 1714145307.0 - }, - { - "data_format": 2, - "description": "Configurable Pipeline for the Analysis of Connectomes", + "description": "Geant4 Example Application with Rich features and Small footprints", "filenames": [ - "Singularity.latest-ABCD-HCP", - "Singularity", - "Singularity.latest-lite" + "INSTALL/Singularity" ], - "full_name": "FCP-INDI/C-PAC", - "latest_release": "v1.8.2", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-c-pac-configurable-pipeline-for-the-analysis-of-connectomes\" class=\"anchor\" href=\"#c-pac-configurable-pipeline-for-the-analysis-of-connectomes\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eC-PAC: Configurable Pipeline for the Analysis of Connectomes\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://openneuro.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/bids-standard/bids-website/gh-pages/old_website/openneuro_badge.svg?sanitize=true\" alt=\"OpenNeuro\" style=\"max-width:100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://doi.org/10.5281/zenodo.164638\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f0d19be234e7ec74a9be968abf17667adad63377953a2750545746250066ab36/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e3136343633382e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.164638.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA configurable, open-source, Nipype-based, automated processing pipeline for resting state fMRI data.\nDesigned for use by both novice users and experts, C-PAC brings the power, flexibility and elegance\nof Nipype to users in a plug-and-play fashion; no programming required.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-website\" class=\"anchor\" href=\"#website\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWebsite\u003c/h2\u003e\n\u003cp\u003eThe C-PAC website is located here: \u003ca href=\"http://fcp-indi.github.com/\"\u003ehttp://fcp-indi.github.com/\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-run\" class=\"anchor\" href=\"#how-to-run\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to Run\u003c/h2\u003e\n\u003cp\u003eInstructions can be found within our quick-start guide: \u003ca href=\"https://fcp-indi.github.io/docs/latest/user/quick\" rel=\"nofollow\"\u003ehttps://fcp-indi.github.io/docs/latest/user/quick\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-documentation\" class=\"anchor\" href=\"#documentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eUser documentation can be found here: \u003ca href=\"https://fcp-indi.github.io/docs/latest/user\" rel=\"nofollow\"\u003ehttps://fcp-indi.github.io/docs/latest/user\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDeveloper documentation can be found here: \u003ca href=\"https://fcp-indi.github.io/docs/latest/developer\" rel=\"nofollow\"\u003ehttps://fcp-indi.github.io/docs/latest/developer\u003c/a\u003e and here: \u003ca href=\"https://github.com/FCP-INDI/.github/blob/primary/CONTRIBUTING.md\"\u003ehttps://github.com/FCP-INDI/.github/blob/primary/CONTRIBUTING.md\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDocumentation pertaining to this latest release can be found here: \u003ca href=\"https://fcp-indi.github.io/docs/latest/user/release_notes/latest\" rel=\"nofollow\"\u003ehttps://fcp-indi.github.io/docs/latest/user/release_notes/latest\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-discussion-forum\" class=\"anchor\" href=\"#discussion-forum\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDiscussion Forum\u003c/h2\u003e\n\u003cp\u003eIf you are stuck and need help or have any other questions or comments about C-PAC, there is a C-PAC discussion forum here: \u003ca href=\"https://groups.google.com/forum/#!forum/cpax_forum\" rel=\"nofollow\"\u003ehttps://groups.google.com/forum/#!forum/cpax_forum\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-issue-tracker-and-bugs\" class=\"anchor\" href=\"#issue-tracker-and-bugs\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIssue Tracker and Bugs\u003c/h2\u003e\n\u003cp\u003eThis is a beta version of C-PAC, which means that it is still under active development. As such, although we have done our best to ensure a stable pipeline, there will likely still be a few bugs that we did not catch. If you find a bug or would like to suggest a new feature, please open an issue on the the C-PAC Github issue tracker: \u003ca href=\"https://github.com/FCP-INDI/C-PAC/issues?state=open\"\u003ehttps://github.com/FCP-INDI/C-PAC/issues?state=open\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you would like to suggest revisions to the user documentation, please open an issue on the C-PAC website\u0027s GitHub issue tracker: \u003ca href=\"https://github.com/FCP-INDI/fcp-indi.github.com/issues\"\u003ehttps://github.com/FCP-INDI/fcp-indi.github.com/issues\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 26, - "subscribers_count": 7, + "full_name": "jintonic/gears", + "latest_release": "v1.5.0", + "readme": "\u003cp\u003e\u003ca href=\"https://codedocs.xyz/jintonic/gears/annotated.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/69e499a9ac348bd92b62e3eac1391967c594141c2855f42783e17f07527553c4/68747470733a2f2f636f6465646f63732e78797a2f6a696e746f6e69632f67656172732e737667\" alt=\"Doxygen\" data-canonical-src=\"https://codedocs.xyz/jintonic/gears.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"examples\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11e2b1b7847229fdbb3430cf4a5d908964287c64b59e0119936bce5359e4efb9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f67656172732d6578616d706c65732d626c75653f7374796c653d666c6174\" alt=\"Examples\" data-canonical-src=\"https://img.shields.io/badge/gears-examples-blue?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"INSTALL\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/04a8d93a22dbf7fc5499c5c388432c900fac24a8e4061f7794fa5c2260199c46/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6765742d737461727465642d6f72616e67653f7374796c653d666c6174\" alt=\"Get Started\" data-canonical-src=\"https://img.shields.io/badge/get-started-orange?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"#how-to-contribute\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b02b9d3708d187a71e102cc95c8a8ad5fcdffb8241ba9b4a102a4272862f1216/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6765742d696e766f6c7665642d6666363962343f7374796c653d666c6174\" alt=\"Get Involved\" data-canonical-src=\"https://img.shields.io/badge/get-involved-ff69b4?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"examples/detector/visualization/gearsX3D.html\"\u003e\u003cimg align=\"right\" width=\"120px\" src=\"examples/detector/visualization/gears.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/jintonic/gears\"\u003eGEARS\u003c/a\u003e is a \u003ca href=\"http://geant4.cern.ch\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e \u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Examples/examples.html\" rel=\"nofollow\"\u003eExample\u003c/a\u003e Application with \u003ca href=\"#features\"\u003eRich features\u003c/a\u003e yet Small footprint. The entire C++ coding is minimized down to a single file with about 550 \u003ca href=\"https://en.wikipedia.org/wiki/Source_lines_of_code\" rel=\"nofollow\"\u003eSLOC\u003c/a\u003e. This is achieved mainly by utilizing \u003ca href=\"http://geant4.cern.ch\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e plain \u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Detector/Geometry/geomASCII.html\" rel=\"nofollow\"\u003etext geometry description\u003c/a\u003e, \u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Control/commands.html\" rel=\"nofollow\"\u003ebuilt-in UI commands\u003c/a\u003e (macros), and C++ inheritance. It is ideal for student training and fast implementation of small to medium-sized experiments.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-features\" class=\"anchor\" href=\"#features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"gears.cc\"\u003eSingle small C++ file\u003c/a\u003e, easy to manage, fast to \u003ca href=\"INSTALL#compile-gears\"\u003ecompile\u003c/a\u003e (a few second on a regular PC)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/physics\"\u003eEasy switching between well maintained Geant4 reference physics lists without recompilation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/physics#physics-processes\"\u003eIndividual processes can be turned on/off without recompilation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/physics#optical-properties-of-materials-and-surfaces\"\u003eFast implementation of optical properties without recompilation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/physics#radioactive-decay\"\u003eOptional radioactive decay simulation\u003c/a\u003e with the possibility to \u003ca href=\"examples/physics#split-decay-chain\"\u003esave the parent and daughter decays into different events if the later happens after a user specified time interval\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/sources#common-sources\"\u003eFrequently used source spectra (AmBe, Am-241, etc.)\u003c/a\u003e in addition to \u003ca href=\"examples/sources\"\u003eGPS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/output\"\u003eOutput in multiple data format\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"examples/output#root\"\u003eROOT\u003c/a\u003e TTree format (default, no \u003ca href=\"https://root.cern.ch\" rel=\"nofollow\"\u003eROOT\u003c/a\u003e installation is needed)\n\u003cul\u003e\n\u003cli\u003eBuild-in data compression, well suitable for large data processing\u003c/li\u003e\n\u003cli\u003eFast access to independent data members\u003c/li\u003e\n\u003cli\u003eFlat tree (no nested branches or arrays) with short leaf names\n\u003cul\u003e\n\u003cli\u003eEasy to use in TTree::Draw\u003c/li\u003e\n\u003cli\u003eNo need to load extra library to open\u003c/li\u003e\n\u003cli\u003eCan be easily analyzed in \u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython\u003c/a\u003e through \u003ca href=\"https://github.com/scikit-hep/uproot4\"\u003eUproot\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.hdfgroup.org/downloads/hdf5/\" rel=\"nofollow\"\u003eHDF5\u003c/a\u003e, universal data format, easy to read by different tools\u003c/li\u003e\n\u003cli\u003eCSV or XML, Human readable ASCII file, capable of dealing with multiple dimensional arrays\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/output#record-information-of-step-0\"\u003eRecord information of step 0\u003c/a\u003e (initStep), which is not available through \u003ca href=\"http://www-geant4.kek.jp/lxr/source/tracking/include/G4UserSteppingAction.hh\" rel=\"nofollow\"\u003eG4UserSteppingAction\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Detector/Geometry/geomASCII.html\" rel=\"nofollow\"\u003esimple text\u003c/a\u003e or \u003ca href=\"https://gdml.web.cern.ch/GDML/\" rel=\"nofollow\"\u003eGDML\u003c/a\u003e geometry I/O\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"examples/detector\"\u003eFast implementation of detector geometry\u003c/a\u003e without C++ programming\u003c/li\u003e\n\u003cli\u003eCreate/Change geometry without re-compilation\u003c/li\u003e\n\u003cli\u003eTurn off data saving in a volume by assigning it a non-positive copy number\u003c/li\u003e\n\u003cli\u003eTurn any volume to a \u003ca href=\"examples/detector#sensitive-volume\"\u003esensitive detector\u003c/a\u003e by adding \"(S)\" in its name\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/detector/optical\"\u003eAssign optical properties in Geant4 plain text geometry description\u003c/a\u003e, which is not available in the official \u003ca href=\"http://geant4.cern.ch\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e release\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/detector/syntax\"\u003eSyntax highlighting of the simple text geometry description files\u003c/a\u003e in \u003ca href=\"examples/detector/syntax#emacs\"\u003eEmacs\u003c/a\u003e, \u003ca href=\"examples/detector/syntax#vim\"\u003eVim\u003c/a\u003e, \u003ca href=\"examples/detector/syntax#micro\"\u003eMicro\u003c/a\u003e, and \u003ca href=\"examples/detector/syntax#sublime-text\"\u003eSublime Text\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Detector/commandScore.html\" rel=\"nofollow\"\u003eCreating 3D mesh to record and visualize physical variables in it without any change of the C++ code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://codedocs.xyz/jintonic/gears/\" rel=\"nofollow\"\u003eDoxygen documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eMany \u003ca href=\"examples\"\u003esample macros\u003c/a\u003e and \u003ca href=\"examples/detector\"\u003egeometry descriptions\u003c/a\u003e for feature demonstration\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-contribute\" class=\"anchor\" href=\"#how-to-contribute\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to contribute\u003c/h2\u003e\n\u003cp\u003ePlease \u003ca href=\"https://help.github.com/en/github/getting-started-with-github/fork-a-repo\"\u003efork GEARS on GitHub\u003c/a\u003e. Run the following to get a local copy of the forked repository and link it to the \u003ca href=\"https://github.com/jintonic/gears\"\u003eoriginal GEARS repository\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:yourGitHubAccount/gears.git \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e get forked repository\u003c/span\u003e\n$ git remote add upstream git@github.com:jintonic/gears.git \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e link to original repository\u003c/span\u003e\n$ git remote -v \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run a check\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun the following to keep your local repository updated with the \u003ca href=\"https://github.com/jintonic/gears\"\u003eoriginal GEARS repository\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git fetch upstream \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e updates are saved in a new branch upstream/master\u003c/span\u003e\n$ git merge upstream/master \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e merge 2 branches: upstream/master and master\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf the merge is successful, run \u003ccode\u003egit push\u003c/code\u003e to update your forked GEARS repository on GitHub.\u003c/p\u003e\n\u003cp\u003eYou can initiate a \u003ca href=\"https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests\"\u003epull request on GitHub\u003c/a\u003e if you\u0027d like to have your update being absorbed in \u003ca href=\"https://github.com/jintonic/gears\"\u003ethe original GEARS repository\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-coding-convention\" class=\"anchor\" href=\"#coding-convention\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCoding convention\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-g4cout-vs-stdcout\" class=\"anchor\" href=\"#g4cout-vs-stdcout\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eG4cout VS std::cout\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003eG4cout\u003c/code\u003e and \u003ccode\u003eG4endl\u003c/code\u003e is preferred over \u003ccode\u003estd:cout\u003c/code\u003e and \u003ccode\u003estd:endl\u003c/code\u003e because the former handle the output in \u003ca href=\"http://geant4.cern.ch\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e GUI correctly, while the later can only output to terminal.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-indentation\" class=\"anchor\" href=\"#indentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIndentation\u003c/h4\u003e\n\u003cp\u003eTwo spaces instead of a tab are used to indent a line in \u003ca href=\"gears.cc\"\u003egears.cc\u003c/a\u003e to insure a consistent appearance in different text editors, and to avoid wasting space in front of deeply nested code blocks. The following mode lines are added to the end of \u003ca href=\"gears.cc\"\u003egears.cc\u003c/a\u003e to insure that in \u003ca href=\"https://www.vim.org/\" rel=\"nofollow\"\u003eVim\u003c/a\u003e and \u003ca href=\"https://www.gnu.org/software/emacs/\" rel=\"nofollow\"\u003eEmacs\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e -*- C++; indent-tabs-mode:nil; tab-width:2 -*-\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e vim: ft=cpp:ts=2:sts=2:sw=2:et\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-dos\" class=\"anchor\" href=\"#to-dos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo-do\u0027s\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eexamples\n\u003cul\u003e\n\u003cli\u003eadd an example to show how QE can be implemented\u003c/li\u003e\n\u003cli\u003eadd examples to show how one can distribute source in a volume or surface\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 27, + "subscribers_count": 11, "topics": [ - "hacktoberfest", - "closember" - ], - "updated_at": 1638611927.0 - }, - { - "data_format": 2, - "description": "PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S rRNA, ITS and COI marker genes", - "filenames": [ - "Singularity", - "singularity/Singularity.v.1.3.1", - "singularity/Singularity.v.1.3.2", - "singularity/Singularity.v.1.1", - "singularity/Singularity.v.2.1.3", - "singularity/Singularity.v.2.1.0", - "singularity/Singularity.v.1.3", - "singularity/Singularity.v.2.0.3", - "singularity/Singularity.v.2.1..5-beta", - "singularity/Singularity.latest", - "singularity/Singularity.v.2.0.2" - ], - "full_name": "hariszaf/pema", - "latest_release": "v.2.1.4", - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/63aabb7b86961fdca97af908726d5ff8f12a858b1d1575da6fc62b1097c3ac1b/68747470733a2f2f692e70617374652e706963732f38373031383966616466363638613935386338616163383366333865373939632e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/63aabb7b86961fdca97af908726d5ff8f12a858b1d1575da6fc62b1097c3ac1b/68747470733a2f2f692e70617374652e706963732f38373031383966616466363638613935386338616163383366333865373939632e706e67\" width=\"300\" align=\"left\" data-canonical-src=\"https://i.paste.pics/870189fadf668a958c8aac83f38e799c.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePEMA:\u003c/h1\u003e\u003ca id=\"user-content-pema\" class=\"anchor\" aria-label=\"Permalink: PEMA:\" href=\"#pema\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ea flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S rRNA, ITS and COI marker genes\u003c/h3\u003e\u003ca id=\"user-content-a-flexible-pipeline-for-environmental-dna-metabarcoding-analysis-of-the-16s18s-rrna-its-and-coi-marker-genes\" class=\"anchor\" aria-label=\"Permalink: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S rRNA, ITS and COI marker genes\" href=\"#a-flexible-pipeline-for-environmental-dna-metabarcoding-analysis-of-the-16s18s-rrna-its-and-coi-marker-genes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003ePEMA is reposited in\u003c/em\u003e \u003ca href=\"https://hub.docker.com/r/hariszaf/pema\" rel=\"nofollow\"\u003e\u003cem\u003eDocker Hub\u003c/em\u003e\u003c/a\u003e \u003cem\u003eas well as in\u003c/em\u003e \u003ca href=\"https://singularity-hub.org/collections/2295\" rel=\"nofollow\"\u003e\u003cem\u003eSingularity Hub\u003c/em\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePEMA website along with \u003cem\u003ehow to\u003c/em\u003e documentation can be found \u003ca href=\"https://hariszaf.github.io/pema_documentation/\" rel=\"nofollow\"\u003e\u003cstrong\u003ehere\u003c/strong\u003e\u003c/a\u003e.\u003c/h4\u003e\u003ca id=\"user-content-pema-website-along-with-how-to-documentation-can-be-found-here\" class=\"anchor\" aria-label=\"Permalink: PEMA website along with how to documentation can be found here.\" href=\"#pema-website-along-with-how-to-documentation-can-be-found-here\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFor any troubles you may have when running PEMA or for any potential improvevments you would like to suggest, please share on the \u003ca href=\"https://gitter.im/pema-helpdesk/community\" rel=\"nofollow\"\u003ePEMA Gitter community\u003c/a\u003e.\u003c/h4\u003e\u003ca id=\"user-content-for-any-troubles-you-may-have-when-running-pema-or-for-any-potential-improvevments-you-would-like-to-suggest-please-share-on-the-pema-gitter-community\" class=\"anchor\" aria-label=\"Permalink: For any troubles you may have when running PEMA or for any potential improvevments you would like to suggest, please share on the PEMA Gitter community.\" href=\"#for-any-troubles-you-may-have-when-running-pema-or-for-any-potential-improvevments-you-would-like-to-suggest-please-share-on-the-pema-gitter-community\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://gitter.im/pema-helpdesk/community?utm_source=badge\u0026amp;utm_medium=badge\u0026amp;utm_campaign=pr-badge\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bd95e844fe5af14955a630c8385523494c1122fc0c3ffde83701f230a88d08bc/68747470733a2f2f6261646765732e6769747465722e696d2f70656d612d68656c706465736b2f636f6d6d756e6974792e737667\" alt=\"Gitter\" data-canonical-src=\"https://badges.gitter.im/pema-helpdesk/community.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6be00b1b55f8b9b6f6d85fa810b501a37e0d48bf99e98bb4381e92caeff02619/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d47504c76332d626c75652e737667\" alt=\"License: GPL v3\" data-canonical-src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTable of Contents\u003c/h1\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#pema-biodiversity-in-all-its-different-levels\"\u003ePEMA: biodiversity in all its different levels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#a-container-based-tool\"\u003eA container-based tool\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#get-set-go-pema\"\u003eHow to run PEMA\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#get\"\u003eGet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#set\"\u003eSet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#go\"\u003eGo\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#parameters-file\"\u003eThe Parameters\u0027 file\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#downstream-ecological-analysis\"\u003eDownstream ecological analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#acknowledgments\"\u003eAcknowledgments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\n\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePEMA: biodiversity in all its different levels\u003c/h2\u003e\u003ca id=\"user-content-pema-biodiversity-in-all-its-different-levels\" class=\"anchor\" aria-label=\"Permalink: PEMA: biodiversity in all its different levels\" href=\"#pema-biodiversity-in-all-its-different-levels\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA supports the metabarcoding analysis of four marker genes, \u003cstrong\u003e16S rRNA\u003c/strong\u003e (Bacteria), \u003cstrong\u003eITS\u003c/strong\u003e (Fungi) as well as \u003cstrong\u003eCOI\u003c/strong\u003e and \u003cstrong\u003e18S rRNA\u003c/strong\u003e (metazoa). As input, PEMA accepts .fastq.gz files as returned by Illumina sequencing platforms.\u003c/p\u003e\n\u003cp\u003eSince the \u003ccode\u003ev.2.1.4\u003c/code\u003e release, PEMA supports also the analysis of the 12S rRNA marker gene!\u003c/p\u003e\n\u003cp\u003ePEMA processes the reads from each sample and \u003cstrong\u003ereturns an OTU- or an ASV-table with the taxonomies\u003c/strong\u003e of the taxa found and their abundances in each sample. It also returns statistics and a FASTQC diagram about the quality of the reads for each sample. Finally, PEMA supports \u003cstrong\u003edownstream ecological analysis\u003c/strong\u003e of the profiles retrieved, facilitated by the \u003ca href=\"http://joey711.github.io/phyloseq/index.html\" rel=\"nofollow\"\u003ephyloseq\u003c/a\u003e R package.\u003c/p\u003e\n\u003cp\u003ePEMA supports both OTU clustering (VSEARCH) and ASV inference (Swarm).\u003c/p\u003e\n\u003cp\u003eMore specifically:\u003c/p\u003e\n\n\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMarker gene\u003c/th\u003e\n\u003cth\u003eOTUs /VSEARCH\u003c/th\u003e\n\u003cth\u003eASVs / Swarm\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e16S rRNA\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e18S rRNA\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e12S rRNA\u003c/td\u003e\n\u003ctd\u003e\u2610\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eITS\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCOI\u003c/td\u003e\n\u003ctd\u003e\u2610\u003c/td\u003e\n\u003ctd\u003e\u2611\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor the case of the 16S rRNA marker gene, PEMA includes two separate approaches for taxonomy assignment: alignment-based and phylogenetic-based.\nFor the latter, a reference tree of 1000 taxa was created using SILVA_132_SSURef, EPA-ng and RaxML-ng.\u003c/p\u003e\n\u003cp\u003ePEMA has been implemented in \u003ca href=\"https://pcingola.github.io/BigDataScript/\" rel=\"nofollow\"\u003eBigDataScript\u003c/a\u003e programming language. BDS\u2019s ad hoc task parallelism and task synchronization, supports heavyweight computation. Thus, PEMA inherits such features and it also supports roll-back checkpoints and on-demand partial pipeline execution. In addition, PEMA takes advantage of all the computational power available on a specific machine; for example, if PEMA is executed on a personal laptop with 4 cores, it is going to use all four of them.\u003c/p\u003e\n\u003cp\u003eFinally, container-based technologies such as Docker and Singularity, make PEMA easy accessible for all operating systems.\nAs you can see in the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/help_files/GitHub%20tutorial.pdf\"\u003ePEMA_tutorial.pdf\u003c/a\u003e, once you have either Docker or Singularity on your computational environment (see below which suits your case better), running PEMA is cakewalk. You can also find the \u003ca href=\"https://docs.google.com/presentation/d/1lVH23DPa2NDNBhVvOTRoip8mraw8zfw8VQwbK4vkB1U/edit?usp=sharing\" rel=\"nofollow\"\u003e\u003cstrong\u003ePEMA tutorial\u003c/strong\u003e\u003c/a\u003e as a Google Slides file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA container-based tool\u003c/h2\u003e\u003ca id=\"user-content-a-container-based-tool\" class=\"anchor\" aria-label=\"Permalink: A container-based tool\" href=\"#a-container-based-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA can run either on a HPC environment (server, cluster etc) or on a simple PC.\nHowever, we definitely suggest to run it on an HPC environment to exploit the full potential of PEMA. Running on a powerful server or a cluster can be time-saving since it would require significantly\nless computational time than in a common PC.\nHowever, in some cases, for analyses with a small number of samples, a common PC can suffice.\nFor COI, a minimum of 20 GB of RAM for the taxonomy assignment step is required.\u003c/p\u003e\n\u003cp\u003ePEMA runs either as a \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003e\u003cstrong\u003eDocker\u003c/strong\u003e\u003c/a\u003e or as a \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003e\u003cstrong\u003eSingularity\u003c/strong\u003e\u003c/a\u003e image. On the following chapters, you can find how to install PEMA both in Docker and Singlularity including examples.\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet-set-go PEMA!\u003c/h2\u003e\u003ca id=\"user-content-get-set-go-pema\" class=\"anchor\" aria-label=\"Permalink: Get-set-go PEMA!\" href=\"#get-set-go-pema\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGet\u003c/h3\u003e\u003ca id=\"user-content-get\" class=\"anchor\" aria-label=\"Permalink: Get\" href=\"#get\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo get PEMA running you first need to make sure you either have \u003cstrong\u003e\u003ca href=\"https://www.sylabs.io/guides/3.0/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e\u003c/strong\u003e ,\nor Docker on your computing environment.\u003c/p\u003e\n\u003cp\u003eIn case you are working on Singularity, you may run the following command to get the PEMA Singularity image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e singularity pull pema_v.2.1.4.sif https://gitlab.com/microbactions/pema-singularity-images-v.2.1.4/-/raw/main/pema_v.2.1.4.sif \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will take some time but once it\u0027s downloaded you have PEMA ready to go!\u003c/p\u003e\n\u003cp\u003eSimilarly, in case you are working on Docker you need to run:\u003c/p\u003e\n\u003cpre lang=\"bash=\"\u003e\u003ccode\u003edocker pull hariszaf/pema:v.2.1.4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003einstead.\u003c/p\u003e\n\u003cp\u003eA version of Docker is avalable for all Windows, Mac and Linux.\nIf you have Windows 10 Pro or your Mac\u0027s hardware in after 2010, then you can insall Docker straightforward.\nOtherwise, you need to install the \u003ca href=\"https://docs.docker.com/toolbox/\" rel=\"nofollow\"\u003eDocker toolbox\u003c/a\u003e instead.\nYou can check if your System Requirements are according to the ones mentioned below in order to be sure what you need to do.\u003c/p\u003e\n\u003cp\u003eYou are now ready to set up your analysis PEMA run!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSet\u003c/h3\u003e\u003ca id=\"user-content-set\" class=\"anchor\" aria-label=\"Permalink: Set\" href=\"#set\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the step, you need to create a directory where you will have everything PEMA needs to\nperform an analysis. We will call this the \u003cem\u003e\u003cstrong\u003eanalysis directory\u003c/strong\u003e\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn the \u003cem\u003eanalysis directory\u003c/em\u003e, you need to add the following \u003cstrong\u003emandatory\u003c/strong\u003e files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003e\u003cstrong\u003eparameters.tsv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file\n(you can download it from this repository and then \u003cstrong\u003ecomplete it\u003c/strong\u003e according to the needs of your analysis)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eATTENTION!\u003c/strong\u003e You always need to check that you have the corresponding version of the parameters file with the pema version you are about to use! For example, if you are about to use \u003ccode\u003epema:v.2.1.4\u003c/code\u003e then, your parameters file needs to be the \u003ca href=\"https://github.com/hariszaf/pema/blob/ARMS/analysis_directory/parameters.tsv\"\u003e\u003ccode\u003ev.2.1.4\u003c/code\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003ea subdirectory called \u003cem\u003e\u003cstrong\u003emydata\u003c/strong\u003e\u003c/em\u003e where your .fastq.gz files will be located \u003cbr\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf your need to perform phyloseq, in the analysis directory you also need to add the following \u003cstrong\u003eoptionally\u003c/strong\u003e files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ethe \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/phyloseq_in_PEMA.R\"\u003e\u003cem\u003e\u003cstrong\u003ephyloseq_in_PEMA.R\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e which you can also download from this repository and set it the way you want (that is an R script which we have implemented and has some main features that need to stay always the same in order to be executed as part of PEMA and some parts where the user can set what exactly needs to get from the phyloseq package)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ethe \u003ca href=\"https://raw.githubusercontent.com/hariszaf/pema/master/analysis_directory/metadata.csv\" rel=\"nofollow\"\u003e\u003cem\u003e\u003cstrong\u003emetadata.csv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file which has to be in a \u003cstrong\u003ecomma separated\u003c/strong\u003e format (you can find an example of this file on PEMA\u0027s GitHub repository).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cstrong\u003eAttention!\u003c/strong\u003e \u003cbr\u003e\nPEMA will \u003cstrong\u003efail\u003c/strong\u003e unless you name the aforementioned files and directories \u003cstrong\u003eexactly\u003c/strong\u003e as described above.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cbr\u003e\n\u003cp\u003eHere is an example of how your \u003cem\u003eanalysis directory\u003c/em\u003e should be in case you do want a phyloseq analysis:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003euser@home-PC:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Desktop/analysis_directory$ ls\nmydata parameters.tsv phyloseq_in_PEMA.R metadata.csv\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eand in case you do not:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003euser@home-PC:\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Desktop/analysis_directory$ ls\nmydata parameters.tsv \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/hariszaf/pema/tree/master/analysis_directory\"\u003e\u003cstrong\u003eHere\u003c/strong\u003e\u003c/a\u003e you can find an example of an \u003cem\u003eanalysis directory\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAn extended list with PEMA\u0027s ouput can be found \u003ca href=\"https://github.com/hariszaf/pema/blob/master/help_files/PEMA\u0027s%20output%20files.md\"\u003e\u003cstrong\u003ehere\u003c/strong\u003e\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNow you are ready to run!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGo\u003c/h3\u003e\u003ca id=\"user-content-go\" class=\"anchor\" aria-label=\"Permalink: Go\" href=\"#go\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEither you are working on Docker or in Singularity, running PEMA has two discrete steps:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eA container needs to be initiated having your analysis directory mounted under a certain directory\u003c/li\u003e\n\u003cli\u003eRun pema\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eDocker\u003c/h4\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo do so using Docker, you will first run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run -it -v /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_analysis_directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/:/mnt/analysis hariszaf/pema\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003e-v\u003c/code\u003e flag allows to the container you built, have access to everything included under your \u003ccode\u003e\u0026lt;path_to_analysis_directory\u0026gt;\u003c/code\u003e.\nAll PEMA\u0027s outuput will be there too\u003c/p\u003e\n\u003cp\u003eFrom the inside of the PEMA container, the only thing remaining to do now, is to run PEMA:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pema_latest.bds\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003ePEMA is now running!\u003c/p\u003e\n\u003cp\u003eThe runtime of PEMA depends on the computational features of your environment, on the size of your data, as well as the parameters you chose.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSingularity\u003c/h4\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn case you are working on Singularity, you may implement both these steps with a single command\nby running:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity run -B /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_analysis_directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/:/mnt/analysis /\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/pema_v.2.1.4.sif\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe \u003ccode\u003epema_v.2.1.4.sif\u003c/code\u003e may be called differently according to the pema version you \u0027re using.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eFor further information, you can always check on \u003ca href=\"https://hariszaf.github.io/pema_documentation/2.running_general/\" rel=\"nofollow\"\u003ePEMA\u0027s website\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u0027 file\u003c/h2\u003e\u003ca id=\"user-content-parameters-file\" class=\"anchor\" aria-label=\"Permalink: Parameters\u0027 file\" href=\"#parameters-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe most crucial component in running PEMA is the parameters file.\nThis file must be located \u003cstrong\u003ein\u003c/strong\u003e the \u003cem\u003eanalysis directory\u003c/em\u003e and the user needs to fill it \u003cstrong\u003eevery time\u003c/strong\u003e PEMA is about to be called.\nIf you need more than one analyses to run, then you need to make copies of the parameters\u0027 file and have one of those in eah of the analysis directrories you create.\u003c/p\u003e\n\u003cp\u003eSo, here is the \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/parameters.tsv\"\u003e\u003cem\u003e\u003cstrong\u003eparameters.tsv\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e file as it looks like, in a study case of our own.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlways remember\u003c/strong\u003e to have the same version of the parameters file with the pema version you are about to use!\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDownstream ecological analysis\u003c/h2\u003e\u003ca id=\"user-content-downstream-ecological-analysis\" class=\"anchor\" aria-label=\"Permalink: Downstream ecological analysis\" href=\"#downstream-ecological-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA performs all the basic functions of the \"phyloseq\" R package. In addition, it performs certain functions of the \u003ca href=\"https://cran.r-project.org/web/packages/vegan/index.html\" rel=\"nofollow\"\u003e\u003cem\u003e\u003cstrong\u003evegan\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e R package.\u003c/p\u003e\n\u003cp\u003eWhen the user asks for a downstream analysis using the \"phyloseq\" R package, then an extra input file called \u003ca href=\"https://github.com/hariszaf/pema/blob/master/analysis_directory/phyloseq_in_PEMA.R\"\u003e\u003cem\u003e\u003cstrong\u003e\"phyloseq_script.R\"\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e needs to be imported in the \"analysis_directory\". In PEMA\u0027s main repository, you can find a template of this file; this file needs to be as it would run on your own computer, as you would run \u003cem\u003ephyloseq\u003c/em\u003e in any case. PEMA will create the \u003cem\u003e\"phyloseq object\"\u003c/em\u003e automatically and then it will perform the analysis as asked. The output will be placed in an extra subfolder in the main output directory of PEMA called \u003cem\u003ephyloseq_analysis\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition, the \u003cem\u003e\u003cstrong\u003emetadata.tsv\u003c/strong\u003e\u003c/em\u003e file is also required when the phyloseq option has been selected. An example of this file you can find \u003ca href=\"https://raw.githubusercontent.com/hariszaf/pema/master/analysis_directory/metadata.csv\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA uses a series of tools, datasets as well as Big Data Script language. We thank all the groups that developed them.\nThe tools \u0026amp; databases that PEMA uses are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBigDataScript programming language - \u003ca href=\"https://pcingola.github.io/BigDataScript/\" rel=\"nofollow\"\u003ehttps://pcingola.github.io/BigDataScript/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eFASTQC - \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" rel=\"nofollow\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eTrimmomatic - \u003ca href=\"http://www.usadellab.org/cms/?page=trimmomatic\" rel=\"nofollow\"\u003ehttp://www.usadellab.org/cms/?page=trimmomatic\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCutadapt - \u003ca href=\"https://cutadapt.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003ehttps://cutadapt.readthedocs.io/en/stable/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBayesHammer - included in SPAdes - \u003ca href=\"http://cab.spbu.ru/software/spades/\" rel=\"nofollow\"\u003ehttp://cab.spbu.ru/software/spades/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePANDAseq - \u003ca href=\"https://github.com/neufeld/pandaseq\"\u003ehttps://github.com/neufeld/pandaseq\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eOBITools - \u003ca href=\"https://pythonhosted.org/OBITools/welcome.html\" rel=\"nofollow\"\u003ehttps://pythonhosted.org/OBITools/welcome.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eBLAST Command Line Applications - \u003ca href=\"https://www.ncbi.nlm.nih.gov/books/NBK52640/\" rel=\"nofollow\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK52640/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eVSEARCH-2.9.1 - \u003ca href=\"https://github.com/torognes/vsearch/releases/tag/v2.9.1\"\u003ehttps://github.com/torognes/vsearch/releases/tag/v2.9.1\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSWARM - \u003ca href=\"https://github.com/torognes/swarm\"\u003ehttps://github.com/torognes/swarm\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCROP - \u003ca href=\"https://github.com/tingchenlab/CROP\"\u003ehttps://github.com/tingchenlab/CROP\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eCREST - \u003ca href=\"https://github.com/lanzen/CREST\"\u003ehttps://github.com/lanzen/CREST\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRDPClassifier - \u003ca href=\"https://github.com/rdpstaff/classifier\"\u003ehttps://github.com/rdpstaff/classifier\u003c/a\u003e\n(RPDtools are required in order to execute RDPClassifier)\u003c/li\u003e\n\u003cli\u003eSILVA db - \u003ca href=\"https://www.arb-silva.de/no_cache/download/archive/current/Exports/\" rel=\"nofollow\"\u003ehttps://www.arb-silva.de/no_cache/download/archive/current/Exports/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMIDORI db - \u003ca href=\"http://reference-midori.info/index.html\" rel=\"nofollow\"\u003ehttp://reference-midori.info/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePR2 db - \u003ca href=\"https://pr2-database.org/\" rel=\"nofollow\"\u003ehttps://pr2-database.org/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\"phat\" algorithm, from the \"gappa\" package - \u003ca href=\"https://github.com/lczech/gappa/wiki/Subcommand:-phat\"\u003ehttps://github.com/lczech/gappa/wiki/Subcommand:-phat\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMAFFT - \u003ca href=\"https://mafft.cbrc.jp/alignment/software/\" rel=\"nofollow\"\u003ehttps://mafft.cbrc.jp/alignment/software/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRAxML -ng - \u003ca href=\"https://github.com/amkozlov/raxml-ng\"\u003ehttps://github.com/amkozlov/raxml-ng\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ePaPaRa - \u003ca href=\"https://cme.h-its.org/exelixis/web/software/papara/index.html\" rel=\"nofollow\"\u003ehttps://cme.h-its.org/exelixis/web/software/papara/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eEPA-ng - \u003ca href=\"https://github.com/Pbdas/epa-ng\"\u003ehttps://github.com/Pbdas/epa-ng\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ephyloseq R package - \u003ca href=\"http://joey711.github.io/phyloseq/index.html\" rel=\"nofollow\"\u003ehttp://joey711.github.io/phyloseq/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003evegan R package - \u003ca href=\"https://cran.r-project.org/web/packages/vegan/index.html\" rel=\"nofollow\"\u003ehttps://cran.r-project.org/web/packages/vegan/index.html\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003encbi-taxonomist - \u003ca href=\"https://ncbi-taxonomist.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003ehttps://ncbi-taxonomist.readthedocs.io/en/latest/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnd of course the container-based technologies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocker - \u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003ehttps://www.docker.com/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSingularity - \u003ca href=\"https://sylabs.io/singularity/\" rel=\"nofollow\"\u003ehttps://sylabs.io/singularity/\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePEMA is under the GNU GPLv3 license (for 3rd party components separate licenses apply).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHaris Zafeiropoulos, Ha Quoc Viet, Katerina Vasileiadou, Antonis Potirakis, Christos Arvanitidis, Pantelis Topalis, Christina Pavloudi, Evangelos Pafilis, PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes, GigaScience, Volume 9, Issue 3, March 2020, giaa022, \u003ca href=\"https://doi.org/10.1093/gigascience/giaa022\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/gigascience/giaa022\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 26, - "subscribers_count": 3, - "topics": [], - "updated_at": 1719986679.0 - }, - { - "data_format": 2, - "description": "An outdoor environment simulator with real-world imagery for Deep Reinforcement Learning on navigation tasks. ", - "filenames": [ - "Singularity" - ], - "full_name": "mweiss17/SEVN", - "latest_release": "1.0", - "readme": "\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/3288\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSEVN: A Sidewalk Environment for Visual Navigation\u003c/h1\u003e\u003ca id=\"user-content-sevn-a-sidewalk-environment-for-visual-navigation\" class=\"anchor\" aria-label=\"Permalink: SEVN: A Sidewalk Environment for Visual Navigation\" href=\"#sevn-a-sidewalk-environment-for-visual-navigation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSEVN contains around 5,000 full panoramic images and labels for house numbers, doors, and street name signs, which can be used for several different navigation tasks.\nAgents trained with SEVN have access to variable-resolution images, visible text, and simulated GPS data to navigate the environment.\nThe SEVN Simulator is OpenAI Gym-compatible to allow the use of state-of-the-art deep reinforcement learning algorithms. An instance of the simulator using low-resolution imagery can be run at 400-800 frames per second on a machine with 2 CPU cores and 2 GB of RAM.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eLow Resolution Views (84x84px)\u003c/th\u003e\n\u003cth align=\"center\"\u003eHigh Resolution Views (1280x1280px)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/low-res-viewer.png\"\u003e\u003cimg src=\"docs/img/low-res-viewer.png\" alt=\"game.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/high-res-viewer.png\"\u003e\u003cimg src=\"docs/img/high-res-viewer.png\" alt=\"game.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/img/spatial_graph.png\"\u003e\u003cimg src=\"docs/img/spatial_graph.png\" alt=\"spatial_graph.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e4,988 panoramic images across 6.3 kilometers with 3,259 labels on house numbers, doors, and street name signs.\u003c/p\u003e\n\u003cp\u003eA longer introduction can be found here: \u003ca href=\"https://github.com/mweiss17/SEVN/blob/master/docs/01-article-env-introduction.md\"\u003eCreating a Navigation Assistant for the Visually Impaired\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to setup the environment, do something like the following. If using a fresh Ubuntu install, ensure that build-essential is installed (i.e., \u003ccode\u003esudo apt-get build-essential\u003c/code\u003e). We\u0027ll need GCC for this, and that installs it.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install the code\u003c/span\u003e\ngit clone https://github.com/mweiss17/SEVN.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e SEVN\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a new conda environment for the depenencies\u003c/span\u003e\nconda create -n sevn python=3.7\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install the dependencies\u003c/span\u003e\nconda activate sevn\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the low resolution image data, labels, and spatial graph\u003c/span\u003e\npython scripts/download.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Test that your environment is correctly setup\u003c/span\u003e\npython scripts/01-play.py\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e WARNING! Running this step downloads 28GB of image data and is not required to run the model or play with the environment.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e python scripts/download.py --high-res\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e python scripts/01-play.py --high-res\u003c/span\u003e\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDataset\u003c/h2\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can manually download the dataset here (in case you don\u0027t want to follow the installation instructions above).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://zenodo.org/record/3526490/files/coord.hdf5\" rel=\"nofollow\"\u003eCoordinates of Panoramas\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zenodo.org/record/3526490/files/graph.pkl\" rel=\"nofollow\"\u003eConnectvity Graph\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zenodo.org/record/3526490/files/labels.hdf5\" rel=\"nofollow\"\u003eImage Labels\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://zenodo.org/record/files/images.hdf5\" rel=\"nofollow\"\u003ePanoramas (Low resolution)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://zenodo.org/record/3526490/files/high-res-panos.zip\" rel=\"nofollow\"\u003ePanoramas (High resolution)\u003c/a\u003e (Warning! 48 GB of images in a zip file)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDataset pre-processing\u003c/h3\u003e\u003ca id=\"user-content-dataset-pre-processing\" class=\"anchor\" aria-label=\"Permalink: Dataset pre-processing\" href=\"#dataset-pre-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information about the data-preprocessing and the data format consult the \u003ccode\u003eREADME\u003c/code\u003e in the \u003ca href=\"https://github.com/mweiss17/SEVN-data\"\u003eSEVN-data\u003c/a\u003e github repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTraining\u003c/h2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor more information about how to train an agent on SEVN consult the \u003ccode\u003eREADME\u003c/code\u003e in the \u003ca href=\"https://github.com/mweiss17/SEVN-model\"\u003eSEVN Model Github repo\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCiting\u003c/h2\u003e\u003ca id=\"user-content-citing\" class=\"anchor\" aria-label=\"Permalink: Citing\" href=\"#citing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis work was accepted at \u003ca href=\"https://www.robot-learning.org/\" rel=\"nofollow\"\u003eConference on Robot Learning (CoRL) 2019\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe paper was also published to Arxiv: \u003ca href=\"http://arxiv.org/abs/1910.13249\" rel=\"nofollow\"\u003eNavigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIf you use this work, please cite us. Here\u0027s the Bibtex for our paper.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e@misc{weiss2019navigation,\n title={Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments},\n author={Martin Weiss and Simon Chamorro and Roger Girgis and Margaux Luck and\n Samira E. Kahou and Joseph P. Cohen and Derek Nowrouzezahrai and\n Doina Precup and Florian Golemo and Chris Pal},\n year={2019},\n eprint={1910.13249},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTeam\u003c/h2\u003e\u003ca id=\"user-content-team\" class=\"anchor\" aria-label=\"Permalink: Team\" href=\"#team\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mweiss17\"\u003eMartin Weiss\u003c/a\u003e, PhD student (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/simonchamorro\"\u003eSimon Chamorro\u003c/a\u003e, Undergrad student (USherbrooke)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/roggirg\"\u003eRoger Girgis\u003c/a\u003e, PhD student (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/Museau\"\u003eMargaux Luck\u003c/a\u003e, Postdoctoral fellow (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://sites.google.com/site/samiraekahou/\" rel=\"nofollow\"\u003eSamira Ebrahimi Kahou\u003c/a\u003e, Postdoctoral fellow (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://josephpcohen.com/w/\" rel=\"nofollow\"\u003eJoseph Paul Cohen\u003c/a\u003e, Postdoctoral fellow (Mila)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.cim.mcgill.ca/~derek/\" rel=\"nofollow\"\u003eDerek Nowrouzezahrai\u003c/a\u003e, Professor (Mila \u0026amp; McGill)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.cs.mcgill.ca/~dprecup/\" rel=\"nofollow\"\u003eDoina Precup\u003c/a\u003e, Professor (Mila \u0026amp; McGill)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://fgolemo.github.io/\" rel=\"nofollow\"\u003eFlorian Golemo\u003c/a\u003e, Postdoctoral fellow (Mila \u0026amp; ElementAI)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://mila.quebec/en/person/pal-christopher/\" rel=\"nofollow\"\u003eChris Pal\u003c/a\u003e, Professor (Mila, Polytechnique Montreal \u0026amp; ElementAI)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilt With\u003c/h2\u003e\u003ca id=\"user-content-built-with\" class=\"anchor\" aria-label=\"Permalink: Built With\" href=\"#built-with\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/openai/gym\"\u003eOpenAI Gym\u003c/a\u003e - A toolkit for developing and comparing reinforcement learning algorithms\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project is licensed under the MIT licence - please see the \u003ccode\u003eLICENCE\u003c/code\u003e file in the repository.\u003c/p\u003e\n", - "stargazers_count": 26, - "subscribers_count": 6, - "topics": [], - "updated_at": 1704386005.0 - }, - { - "data_format": 2, - "description": "A Jupyter kernel for CASA", - "filenames": [ - "singularity/Singularity.docker", - "singularity/Singularity", - "singularity/Singularity.centos7" + "geant4", + "detector", + "physics", + "monte-carlo-simulation" ], - "full_name": "aardk/jupyter-casa", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ejupyter-casa\u003c/h1\u003e\u003ca id=\"user-content-jupyter-casa\" class=\"anchor\" aria-label=\"Permalink: jupyter-casa\" href=\"#jupyter-casa\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA \u003ca href=\"http://jupyter.org/\" rel=\"nofollow\"\u003eJupyter\u003c/a\u003e kernel for \u003ca href=\"https://casa.nrao.edu/\" rel=\"nofollow\"\u003eCASA\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJupyter is a web-based application which allows users to create interactive notebooks which can\ninclude annotated text and graphics as well as executable code. The notebook format has the great advantage that all\nsteps of the data reduction are preserved inside the notebook. This means that the whole data reduction process is\nself-documenting and fully repeatable. It also allows users to very easily make changes to their pipeline and then rerun\nthe pipeline steps affected.\u003c/p\u003e\n\u003cp\u003eAs part of the EU funded \u003ca href=\"https://projectescape.eu/\" rel=\"nofollow\"\u003eESCAPE\u003c/a\u003e project we have created a\nJupyter kernel for CASA, a widely-used software package for processing astronomical data.\nThe kernel allows all CASA tasks to be run from inside a Jupyter notebook, albeit non-interactively. Tasks which normally\nspawn a GUI window are wrapped so that their output is displayed inside the notebook instead.\u003c/p\u003e\n\u003cp\u003eThe Jupyter kernel is distributed as a \u003ca href=\"https://hub.docker.com/r/penngwyn/jupytercasa\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e image which includes the latest\nversion of CASA and a number of additional software (see below).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Docker images can be pulled using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull penngwyn/jupytercasa\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhile it is possible to use this Docker image directly, for end users it is recommended to use the Docker images through\neither \u003ca href=\"https://www.vagrantup.com/\" rel=\"nofollow\"\u003eVagrant\u003c/a\u003e or \u003ca href=\"https://apptainer.org/\" rel=\"nofollow\"\u003eApptainer/Singularity\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eVagrant\u003c/h3\u003e\u003ca id=\"user-content-vagrant\" class=\"anchor\" aria-label=\"Permalink: Vagrant\" href=\"#vagrant\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVagrant is a front-end for various containerization and virtualization technologies, including Docker.\nInstallation instructions for Vagrant can he found here: \u003ca href=\"https://www.vagrantup.com/downloads\" rel=\"nofollow\"\u003ehttps://www.vagrantup.com/downloads\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAfter cloning this Git repository, change to the Vagrant directory inside the repository\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/aardk/jupyter-casa.git\ncd jupyter-casa/vagrant\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBefore jupyter-casa can launched, you first need to tell Vagrant where your data lives.\nVagrant is controlled through a file called \u003ca href=\"vagrant/Vagrantfile\"\u003eVagrantfile\u003c/a\u003e.\nIn the provided Vagrantfile there is one line which is commented out:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e #config.vm.synced_folder \"/path/to/data\", \"/home/jupyter/work\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis line needs to be uncommented (remove the #), and `/path/to/data\u0027 has to be replaced by the absolute path to where your data is stored.\nFor example, if the data are stored in a directory called \u003cem\u003e/opt/shared-data\u003c/em\u003e then we add\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e config.vm.synced_folder \"/opt/shared-data\", \"/home/jupyter/work\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen Vagrant can be started by executing (still inside the vagrant directory)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant up\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAfter the Vagrant virtual machine has been started you can connect to it via \u003cem\u003essh\u003c/em\u003e by executing (still inside the vagrant directory)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evagrant ssh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will give you a shell within a Vagrant VM, jupyter can then be started by executing\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejupyter lab\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eApptainer/Singularity\u003c/h3\u003e\u003ca id=\"user-content-apptainersingularity\" class=\"anchor\" aria-label=\"Permalink: Apptainer/Singularity\" href=\"#apptainersingularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eApptainer is the new name for the Singularity container system. All commands below use Apptainer, but if you still have\nSingularity installed then you can simply replace apptainer with singularity in each command.\u003c/p\u003e\n\u003cp\u003eFirst the Docker image needs to be converted to an Apptainer container by executing\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eapptainer pull docker://penngwyn/jupytercasa:latest\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eUnlike Docker, an Apptainer container runs with UID of the current user (i.e. the user executing \u003ccode\u003esingularity run\u003c/code\u003e).\nThe home directory of the user on the local filesystem will also be accessible inside the container, but by default\nonly the home directory is shared with the container. Therefore any symbolic links which point to locations outside of the\nhome directory will not be valid inside the container.\u003c/p\u003e\n\u003cp\u003eFortunately, it is fairly straightforward to make your local filesystem accessible to the container using the \u003cem\u003e-B\u003c/em\u003e option.\nFor example to mount a directory called \u003cem\u003e/data\u003c/em\u003e inside the container execute:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eapptainer run -B /data:$HOME/data jupytercasa_latest.sif\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEven though we wrap all CASA tasks so that they will not launch a GUI window, the QT based CASA tasks still require X11, unfortunately.\nTasks such as \u003cem\u003eplotms\u003c/em\u003e won\u0027t start unless X11 is working even when it doesn\u0027t even open a window.\nTherefore the local X11 socket needs to be shared with Docker container.\u003c/p\u003e\n\u003cp\u003eThe simplest incantation to start JUPYTER on a recent Ubuntu:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -p 8888:8888 -i -t -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY penngwyn/jupytercasa \u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eNote that the \u003ccode\u003e\u0027--rm\u0027\u003c/code\u003e option will make DOCKER delete the container after use.\u003c/p\u003e\n\u003cp\u003eOf course the above example is not very useful as the container will not be able to access locally stored \u003cem\u003emeasurement sets\u003c/em\u003e.\nTo add a data directory to the DOCKER container is, fortunately, very simple using the \u003ccode\u003e-v\u003c/code\u003e option:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -p 8888:8888 -i -t -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v PATH_TO_DATA_DIR:/home/jupyter/data penngwyn/jupytercasa\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhere \u003ccode\u003ePATH_TO_DATA_DIR\u003c/code\u003e should be replaced with the full path to your local data directory.\u003c/p\u003e\n\u003cp\u003eThe above examples use a JUPYTER kernel which is baked into the DOCKER image. It is also possible to use the GITHUB development version\nwithin the CASA container, from the root of the source tree run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003edocker run --rm -p 8888:8888 -i -t -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $PWD/jupyter:/home/jupyter/.local/share/jupyter -v $PWD/python/casapy:/home/jupyter/.local/lib/python2.7/site-packages/casapy -v PATH_TO_DATA_DIR:/home/jupyter/data penngwyn/jupytercasa \u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn the \u003cem\u003eexamples\u003c/em\u003e directory there is a notebook which contains the NRAO continuum VLA tutorial. To run that code locally\nbe sure to download the data files from the \u003ca href=\"https://casaguides.nrao.edu/index.php?title=VLA_Continuum_Tutorial_3C391\" rel=\"nofollow\"\u003eNRAO wiki\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAlso don\u0027t forget to make the directory available to the DOCKER container using the \u003ccode\u003e-v\u003c/code\u003e option as is explained above.\u003c/p\u003e\n", - "stargazers_count": 26, - "subscribers_count": 4, - "topics": [], - "updated_at": 1723165784.0 + "updated_at": 1627212360.0 }, { "data_format": 2, @@ -40037,22 +40084,55 @@ var data = }, { "data_format": 2, - "description": "Geant4 Example Application with Rich features and Small footprints", + "description": "Workflow for SARS-CoV-2 genome Assembly at FioCruz/IAM", "filenames": [ - "INSTALL/Singularity" + "vfnext/containers/Singularity_nextclade", + "vfnext/containers/Singularity_pangolin", + "vfnext/containers/Singularity_snpEff" ], - "full_name": "jintonic/gears", - "latest_release": "v1.5.0", - "readme": "\u003cp\u003e\u003ca href=\"https://codedocs.xyz/jintonic/gears/annotated.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/69e499a9ac348bd92b62e3eac1391967c594141c2855f42783e17f07527553c4/68747470733a2f2f636f6465646f63732e78797a2f6a696e746f6e69632f67656172732e737667\" alt=\"Doxygen\" data-canonical-src=\"https://codedocs.xyz/jintonic/gears.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78f47a09877ba9d28da1887a93e5c3bc2efb309c1e910eb21135becd2998238a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d79656c6c6f772e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"examples\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11e2b1b7847229fdbb3430cf4a5d908964287c64b59e0119936bce5359e4efb9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f67656172732d6578616d706c65732d626c75653f7374796c653d666c6174\" alt=\"Examples\" data-canonical-src=\"https://img.shields.io/badge/gears-examples-blue?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"INSTALL\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/04a8d93a22dbf7fc5499c5c388432c900fac24a8e4061f7794fa5c2260199c46/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6765742d737461727465642d6f72616e67653f7374796c653d666c6174\" alt=\"Get Started\" data-canonical-src=\"https://img.shields.io/badge/get-started-orange?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"#how-to-contribute\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b02b9d3708d187a71e102cc95c8a8ad5fcdffb8241ba9b4a102a4272862f1216/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6765742d696e766f6c7665642d6666363962343f7374796c653d666c6174\" alt=\"Get Involved\" data-canonical-src=\"https://img.shields.io/badge/get-involved-ff69b4?style=flat\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"examples/detector/visualization/gearsX3D.html\"\u003e\u003cimg align=\"right\" width=\"120px\" src=\"examples/detector/visualization/gears.png\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/jintonic/gears\"\u003eGEARS\u003c/a\u003e is a \u003ca href=\"http://geant4.cern.ch\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e \u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Examples/examples.html\" rel=\"nofollow\"\u003eExample\u003c/a\u003e Application with \u003ca href=\"#features\"\u003eRich features\u003c/a\u003e yet Small footprint. The entire C++ coding is minimized down to a single file with about 550 \u003ca href=\"https://en.wikipedia.org/wiki/Source_lines_of_code\" rel=\"nofollow\"\u003eSLOC\u003c/a\u003e. This is achieved mainly by utilizing \u003ca href=\"http://geant4.cern.ch\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e plain \u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Detector/Geometry/geomASCII.html\" rel=\"nofollow\"\u003etext geometry description\u003c/a\u003e, \u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Control/commands.html\" rel=\"nofollow\"\u003ebuilt-in UI commands\u003c/a\u003e (macros), and C++ inheritance. It is ideal for student training and fast implementation of small to medium-sized experiments.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-features\" class=\"anchor\" href=\"#features\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eFeatures\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"gears.cc\"\u003eSingle small C++ file\u003c/a\u003e, easy to manage, fast to \u003ca href=\"INSTALL#compile-gears\"\u003ecompile\u003c/a\u003e (a few second on a regular PC)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/physics\"\u003eEasy switching between well maintained Geant4 reference physics lists without recompilation\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"examples/physics#physics-processes\"\u003eIndividual processes can be turned on/off without recompilation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"examples/physics#optical-properties-of-materials-and-surfaces\"\u003eFast implementation of optical properties without recompilation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/physics#radioactive-decay\"\u003eOptional radioactive decay simulation\u003c/a\u003e with the possibility to \u003ca href=\"examples/physics#split-decay-chain\"\u003esave the parent and daughter decays into different events if the later happens after a user specified time interval\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/sources#common-sources\"\u003eFrequently used source spectra (AmBe, Am-241, etc.)\u003c/a\u003e in addition to \u003ca href=\"examples/sources\"\u003eGPS\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/output\"\u003eOutput in multiple data format\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"examples/output#root\"\u003eROOT\u003c/a\u003e TTree format (default, no \u003ca href=\"https://root.cern.ch\" rel=\"nofollow\"\u003eROOT\u003c/a\u003e installation is needed)\n\u003cul\u003e\n\u003cli\u003eBuild-in data compression, well suitable for large data processing\u003c/li\u003e\n\u003cli\u003eFast access to independent data members\u003c/li\u003e\n\u003cli\u003eFlat tree (no nested branches or arrays) with short leaf names\n\u003cul\u003e\n\u003cli\u003eEasy to use in TTree::Draw\u003c/li\u003e\n\u003cli\u003eNo need to load extra library to open\u003c/li\u003e\n\u003cli\u003eCan be easily analyzed in \u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython\u003c/a\u003e through \u003ca href=\"https://github.com/scikit-hep/uproot4\"\u003eUproot\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.hdfgroup.org/downloads/hdf5/\" rel=\"nofollow\"\u003eHDF5\u003c/a\u003e, universal data format, easy to read by different tools\u003c/li\u003e\n\u003cli\u003eCSV or XML, Human readable ASCII file, capable of dealing with multiple dimensional arrays\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/output#record-information-of-step-0\"\u003eRecord information of step 0\u003c/a\u003e (initStep), which is not available through \u003ca href=\"http://www-geant4.kek.jp/lxr/source/tracking/include/G4UserSteppingAction.hh\" rel=\"nofollow\"\u003eG4UserSteppingAction\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Detector/Geometry/geomASCII.html\" rel=\"nofollow\"\u003esimple text\u003c/a\u003e or \u003ca href=\"https://gdml.web.cern.ch/GDML/\" rel=\"nofollow\"\u003eGDML\u003c/a\u003e geometry I/O\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"examples/detector\"\u003eFast implementation of detector geometry\u003c/a\u003e without C++ programming\u003c/li\u003e\n\u003cli\u003eCreate/Change geometry without re-compilation\u003c/li\u003e\n\u003cli\u003eTurn off data saving in a volume by assigning it a non-positive copy number\u003c/li\u003e\n\u003cli\u003eTurn any volume to a \u003ca href=\"examples/detector#sensitive-volume\"\u003esensitive detector\u003c/a\u003e by adding \"(S)\" in its name\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/detector/optical\"\u003eAssign optical properties in Geant4 plain text geometry description\u003c/a\u003e, which is not available in the official \u003ca href=\"http://geant4.cern.ch\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e release\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"examples/detector/syntax\"\u003eSyntax highlighting of the simple text geometry description files\u003c/a\u003e in \u003ca href=\"examples/detector/syntax#emacs\"\u003eEmacs\u003c/a\u003e, \u003ca href=\"examples/detector/syntax#vim\"\u003eVim\u003c/a\u003e, \u003ca href=\"examples/detector/syntax#micro\"\u003eMicro\u003c/a\u003e, and \u003ca href=\"examples/detector/syntax#sublime-text\"\u003eSublime Text\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://geant4-userdoc.web.cern.ch/geant4-userdoc/UsersGuides/ForApplicationDeveloper/html/Detector/commandScore.html\" rel=\"nofollow\"\u003eCreating 3D mesh to record and visualize physical variables in it without any change of the C++ code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://codedocs.xyz/jintonic/gears/\" rel=\"nofollow\"\u003eDoxygen documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eMany \u003ca href=\"examples\"\u003esample macros\u003c/a\u003e and \u003ca href=\"examples/detector\"\u003egeometry descriptions\u003c/a\u003e for feature demonstration\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-how-to-contribute\" class=\"anchor\" href=\"#how-to-contribute\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHow to contribute\u003c/h2\u003e\n\u003cp\u003ePlease \u003ca href=\"https://help.github.com/en/github/getting-started-with-github/fork-a-repo\"\u003efork GEARS on GitHub\u003c/a\u003e. Run the following to get a local copy of the forked repository and link it to the \u003ca href=\"https://github.com/jintonic/gears\"\u003eoriginal GEARS repository\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone git@github.com:yourGitHubAccount/gears.git \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e get forked repository\u003c/span\u003e\n$ git remote add upstream git@github.com:jintonic/gears.git \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e link to original repository\u003c/span\u003e\n$ git remote -v \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run a check\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eRun the following to keep your local repository updated with the \u003ca href=\"https://github.com/jintonic/gears\"\u003eoriginal GEARS repository\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git fetch upstream \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e updates are saved in a new branch upstream/master\u003c/span\u003e\n$ git merge upstream/master \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e merge 2 branches: upstream/master and master\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf the merge is successful, run \u003ccode\u003egit push\u003c/code\u003e to update your forked GEARS repository on GitHub.\u003c/p\u003e\n\u003cp\u003eYou can initiate a \u003ca href=\"https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests\"\u003epull request on GitHub\u003c/a\u003e if you\u0027d like to have your update being absorbed in \u003ca href=\"https://github.com/jintonic/gears\"\u003ethe original GEARS repository\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-coding-convention\" class=\"anchor\" href=\"#coding-convention\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCoding convention\u003c/h3\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-g4cout-vs-stdcout\" class=\"anchor\" href=\"#g4cout-vs-stdcout\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eG4cout VS std::cout\u003c/h4\u003e\n\u003cp\u003e\u003ccode\u003eG4cout\u003c/code\u003e and \u003ccode\u003eG4endl\u003c/code\u003e is preferred over \u003ccode\u003estd:cout\u003c/code\u003e and \u003ccode\u003estd:endl\u003c/code\u003e because the former handle the output in \u003ca href=\"http://geant4.cern.ch\" rel=\"nofollow\"\u003eGeant4\u003c/a\u003e GUI correctly, while the later can only output to terminal.\u003c/p\u003e\n\u003ch4\u003e\n\u003ca id=\"user-content-indentation\" class=\"anchor\" href=\"#indentation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eIndentation\u003c/h4\u003e\n\u003cp\u003eTwo spaces instead of a tab are used to indent a line in \u003ca href=\"gears.cc\"\u003egears.cc\u003c/a\u003e to insure a consistent appearance in different text editors, and to avoid wasting space in front of deeply nested code blocks. The following mode lines are added to the end of \u003ca href=\"gears.cc\"\u003egears.cc\u003c/a\u003e to insure that in \u003ca href=\"https://www.vim.org/\" rel=\"nofollow\"\u003eVim\u003c/a\u003e and \u003ca href=\"https://www.gnu.org/software/emacs/\" rel=\"nofollow\"\u003eEmacs\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e -*- C++; indent-tabs-mode:nil; tab-width:2 -*-\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e vim: ft=cpp:ts=2:sts=2:sw=2:et\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-to-dos\" class=\"anchor\" href=\"#to-dos\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTo-do\u0027s\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eexamples\n\u003cul\u003e\n\u003cli\u003eadd an example to show how QE can be implemented\u003c/li\u003e\n\u003cli\u003eadd examples to show how one can distribute source in a volume or surface\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 27, - "subscribers_count": 11, + "full_name": "dezordi/ViralFlow", + "latest_release": "v1.0.1", + "stargazers_count": 28, + "subscribers_count": 2, + "topics": [], + "updated_at": 1727528170.0 + }, + { + "data_format": 2, + "description": "MultiResolution Chemistry", + "filenames": [ + "recipes/Singularity.nompi", + "recipes/Singularity.openmpi4.0" + ], + "full_name": "MRChemSoft/mrchem", + "latest_release": "v1.1.4", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/MRChemSoft/mrchem/raw/master/doc/gfx/logo_full.png\"\u003e\u003cimg src=\"https://github.com/MRChemSoft/mrchem/raw/master/doc/gfx/logo_full.png\" alt=\"MRChem logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.3606658\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/afb3a88588c984bc1e55041237b387c5f85eb0cf58e96264e20251a58d500c46/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333630363635382e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3606658.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"../master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3c83acd4cf66df8a2dd158ebbf38a2bad0308114a5d3657b99321e6116adbd92/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d2532304c47504c76332d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/license-%20LGPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://mrchem.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a0c737379612d75b46494e5de6ad021c462abecc7ed54e056f81018c4f96f83a/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6d726368656d2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/mrchem/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/MRChemSoft/mrchem/workflows/Build%20and%20test%20MRChem/badge.svg\"\u003e\u003cimg src=\"https://github.com/MRChemSoft/mrchem/workflows/Build%20and%20test%20MRChem/badge.svg\" alt=\"Build and test MRChem\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/MRChemSoft/mrchem/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b922c3f231702f8297e12a8f05806afd692f45a07c271bbc09681dbbf2a50c1/68747470733a2f2f636972636c6563692e636f6d2f67682f4d524368656d536f66742f6d726368656d2f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/MRChemSoft/mrchem/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/MRChemSoft/mrchem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/89b14fc80b739179bc75859edf014c93dc24e2630ddc93bd1307311f0074ad8b/68747470733a2f2f636f6465636f762e696f2f67682f4d524368656d536f66742f6d726368656d2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/MRChemSoft/mrchem/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMRChem is a numerical real-space code for molecular electronic structure\ncalculations within the self-consistent field (SCF) approximations of quantum\nchemistry (Hartree-Fock and Density Functional Theory).\u003c/p\u003e\n\u003cp\u003eThe code is being developed at the Hylleraas Centre for Quantum Molecular\nSciences at UiT - The Arctic University of Norway.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation: \u003ca href=\"http://mrchem.readthedocs.io\" rel=\"nofollow\"\u003emrchem.readthedocs.io\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-documentation-mrchemreadthedocsio\" class=\"anchor\" aria-label=\"Permalink: Documentation: mrchem.readthedocs.io\" href=\"#documentation-mrchemreadthedocsio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor optimal performance it is recommended to build from source, as the packaged\nbuilds are quite generic without architecture specific optimizations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build MRChem from source with MPI+OpenMP parallelization:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/MRChemSoft/mrchem.git\n$ cd mrchem\n$ ./setup --prefix=\u0026lt;install-dir\u0026gt; --omp --mpi --cxx=\u0026lt;mpi-compiler\u0026gt; \u0026lt;build-dir\u0026gt;\n$ cd \u0026lt;build-dir\u0026gt;\n$ make\n$ make test\n$ make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll dependencies will be fetched at configure time, if not already available.\nFor more information on different kinds of builds, see\n\u003ca href=\"http://mrchem.readthedocs.io/en/latest/installation.html\" rel=\"nofollow\"\u003einstallation instructions\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Conda\u003c/h3\u003e\u003ca id=\"user-content-using-conda\" class=\"anchor\" aria-label=\"Permalink: Using Conda\" href=\"#using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/conda-forge/mrchem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96c2948aea8be9cf16575caedaa233453a12ee49ae4dae9f2d45871d1ac9cdaa/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6d726368656d2f6261646765732f76657273696f6e2e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/mrchem/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/mrchem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a91cb50b92a732bf836b93664029dc514f1fa033a220abd80414c93fe6accd7f/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6d726368656d2f6261646765732f6c61746573745f72656c656173655f646174652e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/mrchem/badges/latest_release_date.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/mrchem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b34c2c10ca16aa7d235ed69597acc44c0ee0bf9f328c42da6803f992d73dedb3/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6d726368656d2f6261646765732f646f776e6c6f6164732e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/mrchem/badges/downloads.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo install MRChem in a Conda environment \u003ccode\u003emyenv\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create -n myenv\n$ conda activate myenv\n$ conda install -c conda-forge mrchem # latest version (OpenMP only)\n$ conda install -c conda-forge mrchem=1.0.0 # tagged version (OpenMP only)\n$ conda install -c conda-forge mrchem=*=*openmpi* # latest version (MPI+OpenMP)\n$ conda install -c conda-forge mrchem=*=*mpich* # latest version (MPI+OpenMP)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo list all available versions\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda search -c conda-forge mrchem\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Spack\u003c/h3\u003e\u003ca id=\"user-content-using-spack\" class=\"anchor\" aria-label=\"Permalink: Using Spack\" href=\"#using-spack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install MRChem in a Spack environment \u003ccode\u003emyenv\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ spack env create myenv\n$ spack env activate myenv\n$ spack install mrchem # latest version (MPI+OpenMP)\n$ spack install mrchem @1.0.0 # tagged version (MPI+OpenMP)\n$ spack install mrchem -mpi # latest version (OpenMP only)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor information on available Spack builds:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ spack info mrchem\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing EasyBuild\u003c/h3\u003e\u003ca id=\"user-content-using-easybuild\" class=\"anchor\" aria-label=\"Permalink: Using EasyBuild\" href=\"#using-easybuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install MRChem in an EasyBuild/Lmod environment (only MPI+OpenMP version\navailable):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ eb MRChem-\u0026lt;version\u0026gt;-\u0026lt;toolchain\u0026gt; --fetch\n$ eb MRChem-\u0026lt;version\u0026gt;-\u0026lt;toolchain\u0026gt; --robot\n$ module load MRChem/\u0026lt;version\u0026gt;-\u0026lt;toolchain\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee\n\u003ca href=\"https://github.com/easybuilders/easybuild-easyconfigs/tree/develop/easybuild/easyconfigs/m/MRChem\"\u003eEasyBuild\u003c/a\u003e\nfor available \u003ccode\u003e\u0026lt;versions\u0026gt;\u003c/code\u003e and \u003ccode\u003e\u0026lt;toolchains\u0026gt;\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Singularity\u003c/h3\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-label=\"Permalink: Using Singularity\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe files are provided under \u003ccode\u003erecipes/\u003c/code\u003e for building local container images using\nthe current state of the source. Requires Singularity \u0026gt;= v3.2 as well as \u003ccode\u003esudo\u003c/code\u003e rights on the\nmachine you are building on:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$\u00a0sudo singularity build \u0026lt;image_name\u0026gt;.sif recipes/Singularity.\u0026lt;variant\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRecipes are provided for a pure OpenMP build (\u003ccode\u003erecipes/Singularity.nompi\u003c/code\u003e) and one MPI+OpenMP version,\nusing \u003ccode\u003eOpenMPI-4.0\u003c/code\u003e (\u003ccode\u003erecipes/Singularity.openmpi4.0\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eOfficial MRChem images can also be downloaded from the GitHub Container Registry.\u003c/p\u003e\n\u003cp\u003eLatest \u003ccode\u003emaster\u003c/code\u003e version (here OpenMP variant):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull oras://ghcr.io/MRChemSoft/mrchem/mrchem_nompi:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTagged version (here MRChem-v1.0.2 using OpenMPI-v4.0):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull oras://ghcr.io/MRChemSoft/mrchem/mrchem_openmpi4.0:v1.0.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the MPI image requires that a compatible MPI library is installed and\navailable on the host. For information on how to launch the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run-help mrchem-mpi.sif\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 28, + "subscribers_count": 9, "topics": [ - "geant4", - "detector", + "multiwavelets", + "computational-chemistry", + "chemistry", "physics", - "monte-carlo-simulation" + "c-plus-plus", + "python", + "density-functional-theory" ], - "updated_at": 1627212360.0 + "updated_at": 1727902627.0 + }, + { + "data_format": 2, + "description": "ROS-Jackal environment for RL", + "filenames": [ + "Singularityfile.def" + ], + "full_name": "Daffan/ros_jackal", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eROS-Jackal\u003c/h1\u003e\u003ca id=\"user-content-ros-jackal\" class=\"anchor\" aria-label=\"Permalink: ROS-Jackal\" href=\"#ros-jackal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the repository for the paper \"\u003ca href=\"https://arxiv.org/abs/2210.04839\" rel=\"nofollow\"\u003eBenchmarking Reinforcement Learning Techniques for Autonomous Navigation\u003c/a\u003e\".\u003c/p\u003e\n\u003cp\u003eThe results shown in the paper use Condor Cluster to distribute 100 actors for collecting trajectories. This setting can greatly speed up the training and make it feasible to finish all the experiments presented in the paper, however Condor Cluster is relatively inaccessible to most users. Instead, to guarantee reproducibility, we provide this version of repository that distributes the actors over 10 Singularity containers that can run locally on a single machine.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/ros_jackal.git\ncd ros_jackal\n\u003c/code\u003e\u003c/pre\u003e\n\u003col\u003e\n\u003cli\u003eIn your virtual environment, install the python dependencies:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\" rel=\"nofollow\"\u003ehttps://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 is \u003cstrong\u003erequired\u003c/strong\u003e to build the image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Only do following step if you really need!) The code does not require ROS installation, since the rollout happens in the container, but if you have need to develop based on our repo, running ROS and Gazebo simulation out of the container enables GUI and is easier to debug. Follow steps below to install ROS dependencies (assume \u003ccode\u003emelodic\u003c/code\u003e ROS installed already):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo and required ros packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/ros_jackal.git\ngit clone https://github.com/jackal/jackal.git --branch melodic-devel\ngit clone https://github.com/jackal/jackal_simulator.git --branch melodic-devel\ngit clone https://github.com/jackal/jackal_desktop.git --branch melodic-devel\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eInstall ROS package dependencies\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/melodic/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=melodic\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBuild the workspace\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecatkin_make\nsource devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eVerify your installation: (this script will run open-ai gym environment for 5 episodes)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePull image file (modify the \u0026lt;FOLDER_PATH_TO_SAVE_IMAGE\u0026gt; in the command, image file size ~ 3G\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \u0026lt;PATH_TO_THIS_REPO\u0026gt;/local_buffer/image:latest.sif library://zifanxu/ros_jackal_image/image:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh \u0026lt;PATH_TO_THIS_REPO\u0026gt;/local_buffer/nav_benchmark.sif python3 test_env.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrain a deep RL navigation policy\u003c/h2\u003e\u003ca id=\"user-content-train-a-deep-rl-navigation-policy\" class=\"anchor\" aria-label=\"Permalink: Train a deep RL navigation policy\" href=\"#train-a-deep-rl-navigation-policy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train a navigation policy, you just need to specify a \u003ccode\u003e.yaml\u003c/code\u003e file that includes the parameters for specific experiment. For instance,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython train.py --config configs/e2e_default_TD3.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe provide the full list of \u003ccode\u003e.yaml\u003c/code\u003e files used in our experiment in the end.\u003c/p\u003e\n\u003cp\u003eThis repo saves the collected trajectories from each actor in a local buffer folder, also actors load the recent policy from this folder. By default, buffer folder is a folder named \u003ccode\u003elocal_buffer\u003c/code\u003e in current dictionary. You can specify a new folder as \u003ccode\u003eexport BUFFER_FOLDER=/PATH/TO/YOUR/BUFFER_FOLDER\u003c/code\u003e. The logging files can be found under folder \u003ccode\u003elogging\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrain in computing cluster\u003c/h2\u003e\u003ca id=\"user-content-train-in-computing-cluster\" class=\"anchor\" aria-label=\"Permalink: Train in computing cluster\" href=\"#train-in-computing-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCluster requires a shared file system, where multiple actors load the lastest policy, rollout, and save the trajectory in the \u003ccode\u003eBUFFER_FOLDER\u003c/code\u003e. Then, a critic collects trajectories from \u003ccode\u003eBUFFER_FOLDER\u003c/code\u003e and updates the policy.\u003c/p\u003e\n\u003cp\u003eThis is asyncronized training pipeline, namely the actors might fall behind and do not generate trajectories from the latest policy.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the Singularity image\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif library://zifanxu/ros_jackal_image/image:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOn critic computing node\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport BUFFER_PATH=\u0026lt;BUFFER_PATH\u0026gt;\n./singularity_run.sh \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif python train.py --config configs/e2e_default_TD3_cluster.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOn actor computing node 0 (you need to run \u003ccode\u003e0-50\u003c/code\u003e computing nodes as defined in line 60 in \u003ccode\u003econtainer_config.yaml\u003c/code\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport BUFFER_PATH=\u0026lt;BUFFER_PATH\u0026gt;\n./singularity_run.sh \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif python actor.py --id 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSuccess rate of policies trained with different neural network architectures and history lengths in static (top) and dynamic-wall (bottom) environments.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eStatic\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e57 \u00b1 7%\u003c/td\u003e\n\u003ctd\u003e42 \u00b1 2%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e51 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e55 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e68 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e46 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eDynamic box\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e50 \u00b1 5%\u003c/td\u003e\n\u003ctd\u003e35 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e46 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e48 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 1%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e42 \u00b1 5%\u003c/td\u003e\n\u003ctd\u003e40 \u00b1 1%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e52 \u00b1 1%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e44 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eDynamic wall\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e67 \u00b1 7%\u003c/td\u003e\n\u003ctd\u003e72 \u00b1 1%\u003c/td\u003e\n\u003ctd\u003e69 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e82 \u00b1 4%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e78 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e63 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e33 \u00b1 28%\u003c/td\u003e\n\u003ctd\u003e15 \u00b1 13%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate, survival time and traversal time of policies trained with different safe-RL methods, MPC with probabilistic transition model and DWA.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSafe-RL method\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMLP\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eLagrangian\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMPC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDWA\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuccess rate\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e74 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e70 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e43%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSurvival time\u003c/td\u003e\n\u003ctd\u003e8.0 \u00b1 1.5s\u003c/td\u003e\n\u003ctd\u003e16.2 \u00b1 2.5s\u003c/td\u003e\n\u003ctd\u003e55.7 \u00b1 4.9s\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e88.6s\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTraversal time\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e7.5 \u00b1 0.3s\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e8.6 \u00b1 0.2s\u003c/td\u003e\n\u003ctd\u003e24.7 \u00b1 2.0s\u003c/td\u003e\n\u003ctd\u003e38.5s\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate of policies trained with different model-based methods and different number of transition samples\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTransition samples\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e100k\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e500k\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e2000k\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e13 \u00b1 7%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e58 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDyna-style deterministic\u003c/td\u003e\n\u003ctd\u003e8 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e30 \u00b1 10%\u003c/td\u003e\n\u003ctd\u003e66 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMPC deterministic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e21 \u00b1 10%\u003c/td\u003e\n\u003ctd\u003e62 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDyna-style probabilistic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e48 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e70 \u00b1 1%\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMPC probabilistic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e70 \u00b1 3%\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate of policies trained with different number of training environments\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eEnvironments\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e250\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuccess rate\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e54 \u00b1 8%\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e72 \u00b1 6%\u003c/td\u003e\n\u003ctd\u003e74 \u00b1 2 %\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(See below for all the config files used to reproduce the experiments)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e \u2514\u2500configs\n \u2502 \u2514\u2500safe_rl\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2502 \u2514\u2500mlp.yaml\n \u2502 \u2502 \u2514\u2500lagrangian.yaml\n \u2502 \u2514\u2500architecture_static\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_wall\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_box\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500model_based\n \u2502 \u2502 \u2514\u2500dyna.yaml\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2514\u2500generalization\n \u2502 \u2502 \u2514\u2500num_world_50.yaml\n \u2502 \u2502 \u2514\u2500num_world_5.yaml\n \u2502 \u2502 \u2514\u2500num_world_10.yaml\n \u2502 \u2502 \u2514\u2500num_world_100.yaml\n \u2502 \u2502 \u2514\u2500num_world_250.yamlconfigs\n \u2502 \u2514\u2500safe_rl\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2502 \u2514\u2500mlp.yaml\n \u2502 \u2502 \u2514\u2500lagrangian.yaml\n \u2502 \u2514\u2500architecture_static\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_wall\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_box\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500model_based\n \u2502 \u2502 \u2514\u2500dyna.yaml\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2514\u2500generalization\n \u2502 \u2502 \u2514\u2500num_world_50.yaml\n \u2502 \u2502 \u2514\u2500num_world_5.yaml\n \u2502 \u2502 \u2514\u2500num_world_10.yaml\n \u2502 \u2502 \u2514\u2500num_world_100.yaml\n \u2502 \u2502 \u2514\u2500num_world_250.yaml\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 28, + "subscribers_count": 4, + "topics": [], + "updated_at": 1724141499.0 }, { "data_format": 2, @@ -40311,55 +40391,32 @@ var data = }, { "data_format": 2, - "description": "ROS-Jackal environment for RL", + "description": "APSIM", "filenames": [ - "Singularityfile.def" + "Release/Containers/Singularity/Singularity.ubuntu" ], - "full_name": "Daffan/ros_jackal", + "full_name": "APSIMInitiative/APSIMClassic", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eROS-Jackal\u003c/h1\u003e\u003ca id=\"user-content-ros-jackal\" class=\"anchor\" aria-label=\"Permalink: ROS-Jackal\" href=\"#ros-jackal\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is the repository for the paper \"\u003ca href=\"https://arxiv.org/abs/2210.04839\" rel=\"nofollow\"\u003eBenchmarking Reinforcement Learning Techniques for Autonomous Navigation\u003c/a\u003e\".\u003c/p\u003e\n\u003cp\u003eThe results shown in the paper use Condor Cluster to distribute 100 actors for collecting trajectories. This setting can greatly speed up the training and make it feasible to finish all the experiments presented in the paper, however Condor Cluster is relatively inaccessible to most users. Instead, to guarantee reproducibility, we provide this version of repository that distributes the actors over 10 Singularity containers that can run locally on a single machine.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col start=\"0\"\u003e\n\u003cli\u003eClone this repository\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/ros_jackal.git\ncd ros_jackal\n\u003c/code\u003e\u003c/pre\u003e\n\u003col\u003e\n\u003cli\u003eIn your virtual environment, install the python dependencies:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\" rel=\"nofollow\"\u003ehttps://docs.sylabs.io/guides/latest/admin-guide/installation.html#installation-on-linux\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 is \u003cstrong\u003erequired\u003c/strong\u003e to build the image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Only do following step if you really need!) The code does not require ROS installation, since the rollout happens in the container, but if you have need to develop based on our repo, running ROS and Gazebo simulation out of the container enables GUI and is easier to debug. Follow steps below to install ROS dependencies (assume \u003ccode\u003emelodic\u003c/code\u003e ROS installed already):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eClone this repo and required ros packages\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/ros_jackal.git\ngit clone https://github.com/jackal/jackal.git --branch melodic-devel\ngit clone https://github.com/jackal/jackal_simulator.git --branch melodic-devel\ngit clone https://github.com/jackal/jackal_desktop.git --branch melodic-devel\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eInstall ROS package dependencies\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/melodic/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=melodic\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBuild the workspace\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003ecatkin_make\nsource devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eVerify your installation: (this script will run open-ai gym environment for 5 episodes)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePull image file (modify the \u0026lt;FOLDER_PATH_TO_SAVE_IMAGE\u0026gt; in the command, image file size ~ 3G\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \u0026lt;PATH_TO_THIS_REPO\u0026gt;/local_buffer/image:latest.sif library://zifanxu/ros_jackal_image/image:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh \u0026lt;PATH_TO_THIS_REPO\u0026gt;/local_buffer/nav_benchmark.sif python3 test_env.py\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrain a deep RL navigation policy\u003c/h2\u003e\u003ca id=\"user-content-train-a-deep-rl-navigation-policy\" class=\"anchor\" aria-label=\"Permalink: Train a deep RL navigation policy\" href=\"#train-a-deep-rl-navigation-policy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo train a navigation policy, you just need to specify a \u003ccode\u003e.yaml\u003c/code\u003e file that includes the parameters for specific experiment. For instance,\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython train.py --config configs/e2e_default_TD3.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe provide the full list of \u003ccode\u003e.yaml\u003c/code\u003e files used in our experiment in the end.\u003c/p\u003e\n\u003cp\u003eThis repo saves the collected trajectories from each actor in a local buffer folder, also actors load the recent policy from this folder. By default, buffer folder is a folder named \u003ccode\u003elocal_buffer\u003c/code\u003e in current dictionary. You can specify a new folder as \u003ccode\u003eexport BUFFER_FOLDER=/PATH/TO/YOUR/BUFFER_FOLDER\u003c/code\u003e. The logging files can be found under folder \u003ccode\u003elogging\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTrain in computing cluster\u003c/h2\u003e\u003ca id=\"user-content-train-in-computing-cluster\" class=\"anchor\" aria-label=\"Permalink: Train in computing cluster\" href=\"#train-in-computing-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCluster requires a shared file system, where multiple actors load the lastest policy, rollout, and save the trajectory in the \u003ccode\u003eBUFFER_FOLDER\u003c/code\u003e. Then, a critic collects trajectories from \u003ccode\u003eBUFFER_FOLDER\u003c/code\u003e and updates the policy.\u003c/p\u003e\n\u003cp\u003eThis is asyncronized training pipeline, namely the actors might fall behind and do not generate trajectories from the latest policy.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDownload the Singularity image\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --name \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif library://zifanxu/ros_jackal_image/image:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eOn critic computing node\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport BUFFER_PATH=\u0026lt;BUFFER_PATH\u0026gt;\n./singularity_run.sh \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif python train.py --config configs/e2e_default_TD3_cluster.yaml\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eOn actor computing node 0 (you need to run \u003ccode\u003e0-50\u003c/code\u003e computing nodes as defined in line 60 in \u003ccode\u003econtainer_config.yaml\u003c/code\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eexport BUFFER_PATH=\u0026lt;BUFFER_PATH\u0026gt;\n./singularity_run.sh \u0026lt;PATH/TO/IMAGE\u0026gt;/image:latest.sif python actor.py --id 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSuccess rate of policies trained with different neural network architectures and history lengths in static (top) and dynamic-wall (bottom) environments.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eStatic\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e57 \u00b1 7%\u003c/td\u003e\n\u003ctd\u003e42 \u00b1 2%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e51 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e55 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e68 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e46 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eDynamic box\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e50 \u00b1 5%\u003c/td\u003e\n\u003ctd\u003e35 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e46 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e48 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 1%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e42 \u00b1 5%\u003c/td\u003e\n\u003ctd\u003e40 \u00b1 1%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e52 \u00b1 1%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e44 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eDynamic wall\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHistory length\u003c/td\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003e8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e67 \u00b1 7%\u003c/td\u003e\n\u003ctd\u003e72 \u00b1 1%\u003c/td\u003e\n\u003ctd\u003e69 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGRU\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e82 \u00b1 4%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e78 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCNN\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e63 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformer\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e33 \u00b1 28%\u003c/td\u003e\n\u003ctd\u003e15 \u00b1 13%\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate, survival time and traversal time of policies trained with different safe-RL methods, MPC with probabilistic transition model and DWA.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSafe-RL method\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMLP\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eLagrangian\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMPC\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDWA\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuccess rate\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e74 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e70 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e43%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSurvival time\u003c/td\u003e\n\u003ctd\u003e8.0 \u00b1 1.5s\u003c/td\u003e\n\u003ctd\u003e16.2 \u00b1 2.5s\u003c/td\u003e\n\u003ctd\u003e55.7 \u00b1 4.9s\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e88.6s\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTraversal time\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e7.5 \u00b1 0.3s\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e8.6 \u00b1 0.2s\u003c/td\u003e\n\u003ctd\u003e24.7 \u00b1 2.0s\u003c/td\u003e\n\u003ctd\u003e38.5s\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate of policies trained with different model-based methods and different number of transition samples\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eTransition samples\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e100k\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e500k\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e2000k\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLP\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e13 \u00b1 7%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e58 \u00b1 2%\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDyna-style deterministic\u003c/td\u003e\n\u003ctd\u003e8 \u00b1 2%\u003c/td\u003e\n\u003ctd\u003e30 \u00b1 10%\u003c/td\u003e\n\u003ctd\u003e66 \u00b1 5%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMPC deterministic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e21 \u00b1 10%\u003c/td\u003e\n\u003ctd\u003e62 \u00b1 3%\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDyna-style probabilistic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e48 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e70 \u00b1 1%\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMPC probabilistic\u003c/td\u003e\n\u003ctd\u003e0 \u00b1 0%\u003c/td\u003e\n\u003ctd\u003e45 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e70 \u00b1 3%\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSuccess rate of policies trained with different number of training environments\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eEnvironments\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003e250\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuccess rate\u003c/td\u003e\n\u003ctd\u003e43 \u00b1 3%\u003c/td\u003e\n\u003ctd\u003e54 \u00b1 8%\u003c/td\u003e\n\u003ctd\u003e65 \u00b1 4%\u003c/td\u003e\n\u003ctd\u003e72 \u00b1 6%\u003c/td\u003e\n\u003ctd\u003e74 \u00b1 2 %\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(See below for all the config files used to reproduce the experiments)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e \u2514\u2500configs\n \u2502 \u2514\u2500safe_rl\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2502 \u2514\u2500mlp.yaml\n \u2502 \u2502 \u2514\u2500lagrangian.yaml\n \u2502 \u2514\u2500architecture_static\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_wall\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_box\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500model_based\n \u2502 \u2502 \u2514\u2500dyna.yaml\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2514\u2500generalization\n \u2502 \u2502 \u2514\u2500num_world_50.yaml\n \u2502 \u2502 \u2514\u2500num_world_5.yaml\n \u2502 \u2502 \u2514\u2500num_world_10.yaml\n \u2502 \u2502 \u2514\u2500num_world_100.yaml\n \u2502 \u2502 \u2514\u2500num_world_250.yamlconfigs\n \u2502 \u2514\u2500safe_rl\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2502 \u2514\u2500mlp.yaml\n \u2502 \u2502 \u2514\u2500lagrangian.yaml\n \u2502 \u2514\u2500architecture_static\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_wall\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500architecture_dynamic_box\n \u2502 \u2502 \u2514\u2500cnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500cnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500mlp_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500rnn_history_length_8.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_1.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_4.yaml\n \u2502 \u2502 \u2514\u2500transformer_history_length_8.yaml\n \u2502 \u2514\u2500model_based\n \u2502 \u2502 \u2514\u2500dyna.yaml\n \u2502 \u2502 \u2514\u2500mpc.yaml\n \u2502 \u2514\u2500generalization\n \u2502 \u2502 \u2514\u2500num_world_50.yaml\n \u2502 \u2502 \u2514\u2500num_world_5.yaml\n \u2502 \u2502 \u2514\u2500num_world_10.yaml\n \u2502 \u2502 \u2514\u2500num_world_100.yaml\n \u2502 \u2502 \u2514\u2500num_world_250.yaml\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 28, - "subscribers_count": 4, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAPSIM\u003c/h1\u003e\u003ca id=\"user-content-apsim\" class=\"anchor\" aria-label=\"Permalink: APSIM\" href=\"#apsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Agricultural Production Systems sIMulator (APSIM) is internationally recognised as a highly advanced simulator of agricultural systems. It contains a suite of modules which enable the simulation of systems that cover a range of plant, animal, soil, climate and management interactions. APSIM is undergoing continual development, with new capability added to regular releases of official versions. Its development and maintenance is underpinned by rigorous science and software engineering standards. The APSIM Initiative has been established to promote the development and use of the science modules and infrastructure software of APSIM.\u003c/p\u003e\n\u003cp\u003eCI builds of this repository can be found \u003ca href=\"https://apsimdev.apsim.info/APSIM.Builds.Portal/Bob.aspx\" rel=\"nofollow\"\u003eHere\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 29, + "subscribers_count": 16, "topics": [], - "updated_at": 1724141499.0 - }, - { - "data_format": 2, - "description": "MultiResolution Chemistry", - "filenames": [ - "recipes/Singularity.nompi", - "recipes/Singularity.openmpi4.0" - ], - "full_name": "MRChemSoft/mrchem", - "latest_release": "v1.1.4", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/MRChemSoft/mrchem/raw/master/doc/gfx/logo_full.png\"\u003e\u003cimg src=\"https://github.com/MRChemSoft/mrchem/raw/master/doc/gfx/logo_full.png\" alt=\"MRChem logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.3606658\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/afb3a88588c984bc1e55041237b387c5f85eb0cf58e96264e20251a58d500c46/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333630363635382e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3606658.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"../master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3c83acd4cf66df8a2dd158ebbf38a2bad0308114a5d3657b99321e6116adbd92/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d2532304c47504c76332d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/license-%20LGPLv3-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://mrchem.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a0c737379612d75b46494e5de6ad021c462abecc7ed54e056f81018c4f96f83a/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6d726368656d2f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/mrchem/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/MRChemSoft/mrchem/workflows/Build%20and%20test%20MRChem/badge.svg\"\u003e\u003cimg src=\"https://github.com/MRChemSoft/mrchem/workflows/Build%20and%20test%20MRChem/badge.svg\" alt=\"Build and test MRChem\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/MRChemSoft/mrchem/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7b922c3f231702f8297e12a8f05806afd692f45a07c271bbc09681dbbf2a50c1/68747470733a2f2f636972636c6563692e636f6d2f67682f4d524368656d536f66742f6d726368656d2f747265652f6d61737465722e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/MRChemSoft/mrchem/tree/master.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/MRChemSoft/mrchem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/89b14fc80b739179bc75859edf014c93dc24e2630ddc93bd1307311f0074ad8b/68747470733a2f2f636f6465636f762e696f2f67682f4d524368656d536f66742f6d726368656d2f6272616e63682f6d61737465722f67726170682f62616467652e737667\" alt=\"codecov\" data-canonical-src=\"https://codecov.io/gh/MRChemSoft/mrchem/branch/master/graph/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMRChem is a numerical real-space code for molecular electronic structure\ncalculations within the self-consistent field (SCF) approximations of quantum\nchemistry (Hartree-Fock and Density Functional Theory).\u003c/p\u003e\n\u003cp\u003eThe code is being developed at the Hylleraas Centre for Quantum Molecular\nSciences at UiT - The Arctic University of Norway.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation: \u003ca href=\"http://mrchem.readthedocs.io\" rel=\"nofollow\"\u003emrchem.readthedocs.io\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-documentation-mrchemreadthedocsio\" class=\"anchor\" aria-label=\"Permalink: Documentation: mrchem.readthedocs.io\" href=\"#documentation-mrchemreadthedocsio\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor optimal performance it is recommended to build from source, as the packaged\nbuilds are quite generic without architecture specific optimizations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build MRChem from source with MPI+OpenMP parallelization:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ git clone https://github.com/MRChemSoft/mrchem.git\n$ cd mrchem\n$ ./setup --prefix=\u0026lt;install-dir\u0026gt; --omp --mpi --cxx=\u0026lt;mpi-compiler\u0026gt; \u0026lt;build-dir\u0026gt;\n$ cd \u0026lt;build-dir\u0026gt;\n$ make\n$ make test\n$ make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAll dependencies will be fetched at configure time, if not already available.\nFor more information on different kinds of builds, see\n\u003ca href=\"http://mrchem.readthedocs.io/en/latest/installation.html\" rel=\"nofollow\"\u003einstallation instructions\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Conda\u003c/h3\u003e\u003ca id=\"user-content-using-conda\" class=\"anchor\" aria-label=\"Permalink: Using Conda\" href=\"#using-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/conda-forge/mrchem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96c2948aea8be9cf16575caedaa233453a12ee49ae4dae9f2d45871d1ac9cdaa/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6d726368656d2f6261646765732f76657273696f6e2e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/mrchem/badges/version.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/mrchem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a91cb50b92a732bf836b93664029dc514f1fa033a220abd80414c93fe6accd7f/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6d726368656d2f6261646765732f6c61746573745f72656c656173655f646174652e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/mrchem/badges/latest_release_date.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/mrchem\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b34c2c10ca16aa7d235ed69597acc44c0ee0bf9f328c42da6803f992d73dedb3/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6d726368656d2f6261646765732f646f776e6c6f6164732e737667\" alt=\"Anaconda-Server Badge\" data-canonical-src=\"https://anaconda.org/conda-forge/mrchem/badges/downloads.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo install MRChem in a Conda environment \u003ccode\u003emyenv\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda create -n myenv\n$ conda activate myenv\n$ conda install -c conda-forge mrchem # latest version (OpenMP only)\n$ conda install -c conda-forge mrchem=1.0.0 # tagged version (OpenMP only)\n$ conda install -c conda-forge mrchem=*=*openmpi* # latest version (MPI+OpenMP)\n$ conda install -c conda-forge mrchem=*=*mpich* # latest version (MPI+OpenMP)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo list all available versions\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ conda search -c conda-forge mrchem\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Spack\u003c/h3\u003e\u003ca id=\"user-content-using-spack\" class=\"anchor\" aria-label=\"Permalink: Using Spack\" href=\"#using-spack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install MRChem in a Spack environment \u003ccode\u003emyenv\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ spack env create myenv\n$ spack env activate myenv\n$ spack install mrchem # latest version (MPI+OpenMP)\n$ spack install mrchem @1.0.0 # tagged version (MPI+OpenMP)\n$ spack install mrchem -mpi # latest version (OpenMP only)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor information on available Spack builds:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ spack info mrchem\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing EasyBuild\u003c/h3\u003e\u003ca id=\"user-content-using-easybuild\" class=\"anchor\" aria-label=\"Permalink: Using EasyBuild\" href=\"#using-easybuild\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo install MRChem in an EasyBuild/Lmod environment (only MPI+OpenMP version\navailable):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ eb MRChem-\u0026lt;version\u0026gt;-\u0026lt;toolchain\u0026gt; --fetch\n$ eb MRChem-\u0026lt;version\u0026gt;-\u0026lt;toolchain\u0026gt; --robot\n$ module load MRChem/\u0026lt;version\u0026gt;-\u0026lt;toolchain\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSee\n\u003ca href=\"https://github.com/easybuilders/easybuild-easyconfigs/tree/develop/easybuild/easyconfigs/m/MRChem\"\u003eEasyBuild\u003c/a\u003e\nfor available \u003ccode\u003e\u0026lt;versions\u0026gt;\u003c/code\u003e and \u003ccode\u003e\u0026lt;toolchains\u0026gt;\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing Singularity\u003c/h3\u003e\u003ca id=\"user-content-using-singularity\" class=\"anchor\" aria-label=\"Permalink: Using Singularity\" href=\"#using-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSingularity recipe files are provided under \u003ccode\u003erecipes/\u003c/code\u003e for building local container images using\nthe current state of the source. Requires Singularity \u0026gt;= v3.2 as well as \u003ccode\u003esudo\u003c/code\u003e rights on the\nmachine you are building on:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$\u00a0sudo singularity build \u0026lt;image_name\u0026gt;.sif recipes/Singularity.\u0026lt;variant\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eRecipes are provided for a pure OpenMP build (\u003ccode\u003erecipes/Singularity.nompi\u003c/code\u003e) and one MPI+OpenMP version,\nusing \u003ccode\u003eOpenMPI-4.0\u003c/code\u003e (\u003ccode\u003erecipes/Singularity.openmpi4.0\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eOfficial MRChem images can also be downloaded from the GitHub Container Registry.\u003c/p\u003e\n\u003cp\u003eLatest \u003ccode\u003emaster\u003c/code\u003e version (here OpenMP variant):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull oras://ghcr.io/MRChemSoft/mrchem/mrchem_nompi:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTagged version (here MRChem-v1.0.2 using OpenMPI-v4.0):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity pull oras://ghcr.io/MRChemSoft/mrchem/mrchem_openmpi4.0:v1.0.2\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the MPI image requires that a compatible MPI library is installed and\navailable on the host. For information on how to launch the container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ singularity run-help mrchem-mpi.sif\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 28, - "subscribers_count": 9, - "topics": [ - "multiwavelets", - "computational-chemistry", - "chemistry", - "physics", - "c-plus-plus", - "python", - "density-functional-theory" - ], - "updated_at": 1727902627.0 + "updated_at": 1708973907.0 }, { "data_format": 2, - "description": "Workflow for SARS-CoV-2 genome Assembly at FioCruz/IAM", + "description": "gemBS is a bioinformatics pipeline designed for high throughput analysis of DNA methylation from Whole Genome Bisulfite Sequencing data (WGBS).", "filenames": [ - "vfnext/containers/Singularity_nextclade", - "vfnext/containers/Singularity_pangolin", - "vfnext/containers/Singularity_snpEff" + "Singularity", + "IHEC/Singularity.ihec" ], - "full_name": "dezordi/ViralFlow", - "latest_release": "v1.0.1", - "stargazers_count": 28, - "subscribers_count": 2, + "full_name": "heathsc/gemBS", + "latest_release": "v3.5.1_IHEC", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-hidden=\"true\" href=\"#news\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNews\u003c/h1\u003e\n\u003cp\u003eFirst release of gemBS-rs, a complete rewrite of the gemBS pipeline (apart from the mapper) in Rust bringing increased\nstability while maintaining the high performance of gemBS: \u003ca href=\"https://github.com/heathsc/gemBS-rs.git\"\u003ehttps://github.com/heathsc/gemBS-rs.git\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-gembs\" class=\"anchor\" aria-hidden=\"true\" href=\"#gembs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egemBS\u003c/h1\u003e\n\u003cp\u003egemBS is a high performance bioinformatic pipeline designed for highthroughput analysis\nof DNA methylation data from whole genome bisulfites sequencing data\n(WGBS). It combines GEM3, a high performance read aligner and\nbs_call, a high performance variant and methyation caller, into a streamlined and efficient pipeline for\nbisulfite sueqnce analysis.\u003c/p\u003e\n\u003cp\u003eThe manuscript describing the pipeline is available \u003ca href=\"https://www.biorxiv.org/content/early/2017/10/11/201988\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-hidden=\"true\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003egemBS is licensed under GPL.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h2\u003e\n\u003cp\u003eUse \u003ccode\u003egit clone --recursive\u003c/code\u003e to retrieve the complete source code including the code from external projects such as \u003ccode\u003ebs_call\u003c/code\u003e and \u003ccode\u003egem3-mapper\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBefore starting the installation of gemBS, you will need to install\nor check the installation of several packages.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ea) gcc with development libraries\nb) python3, pip3, matplotlib, multiprocess\nc) zlib, lzma, openssl, libcurl, libncurses, wget, pigz\u003c/p\u003e\n\u003cp\u003eIf you are working on a clean (fairly recent) Ubuntu installation, you\ncan install everything required with the followiwg commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get update\nsudo apt-get install -y python3 build-essential git python3-pip wget pigz\nsudo apt-get install -y zlib1g-dev libbz2-dev\nsudo apt-get install -y libncurses5-dev liblzma-dev libssl-dev libcurl4-openssl-dev\npip3 install matplotlib multiprocess\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the gemBS distribution if you haven\u0027t already done so:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS.git\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse python install command:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo install to the standard system location (i.e., so that all users\ncan use gemBS):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e``python3 setup.py install``\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install to the user\u0027s home directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e``python3 setup.py install --user``\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-check-your-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#check-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheck your installation\u003c/h2\u003e\n\u003cp\u003eFor checking your installation follow this\n\u003ca href=\"http://statgen.cnag.cat/gemBS/v3/UserGuide/_build/html/example.html\" rel=\"nofollow\"\u003eworked example\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eDocumentation can be found at\n\u003ca href=\"http://statgen.cnag.cat/gemBS/v3/UserGuide/_build/html/index.html\" rel=\"nofollow\"\u003egemBS\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-hidden=\"true\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChangelog:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e3.5.5 Fix logging bug caused by trimming change in 3.5.3\n3.5.4 Fix bug in the output of strand specific cpg txt files (not\n encode Bed files) where the \u0027C\u0027 entry was not being printed\n3.5.3 Allow for read end specific trimming in bs_call\n3.5.3 Enable range checks and asserts in bs_call all target; add bs_call debug target\n3.5.2 Correct problems with gcc10. Move to htslib/samtools/bcftools version 1.11\n3.5.1 Check if C compiler requires --std=c99 flag for standards conformant behaviour\n3.5.1 Make sure bgzip is copied correctly during installation\n3.5.0 Make bs_call process contig pools from largest to smallest (this change alters the sqlite db format so\n if you have a previously started gemBS run you should (a) remove the .gemBS directory, (b) redo the\n \u0027gemBS prepare\u0027 step to recreate the db file and (3) run \u0027gemBS db-sync\u0027. \n3.5.0 Switch bs_call and snpxtr to use the new dbSNP index format\n3.5.0 Add ability of dbSNP to read the new JSON and VCF dbSNP format files\n that are now used for human and non-human species respectively\n3.5.0 Add multithreading to dbSNP_idx\n3.5.0 Change format of dbSNP index to allow (a) efficient loading\n of SNP data for individual contigs and (b) parallel index creation \n3.5.0 Rewrite mextr and snpxtr as standalone tools rather than\n bcftools plugins. Now multithreaded and (relatively) memoryefficient\n3.5.0 Replace bedToBigBed and wigToBigWig to reduce memory usage\n and improve speed\n3.4.5 Fix crash when using the -k (keep-mismatch) flag, and fix rare hangs at end of processing\n3.4.4 Sort input bcf files to bcftools concat stage to ensure reproducibility.\n3.4.4 Add extra sort keys when generating pools to ensure stability of pool membership in the event of multiple contigs\n having the same size\n3.4.3 Remove calculation of the goodness of filter (GOF) as this is expensive, non-standard and unreliable. Removing this\n removes the dependency on GSL.\n3.4.3 Add autodetection of output format to bs_call (unless explicitly specified on the command line)\n3.4.2 Add CRAM support (via make_cram option in configuration file)\n3.4.1 Add benchmark-mode that does not write date or program version numbers into SAM/BAM or VCF/BCF files\n Switch to samtools, bcftools and htslib v1.10\n3.4.0 Move to new bs_call version (2.1.0) which is more efficient\n in memory use and can read BAMs and write BCFs natively.\n The new bs_call requires a faidx indexed reference, so gemBS\n no creates this during indexing.\n3.4.0 Add switches to give more control to threads and memory\n usage in mapping and calling stages\n3.3.3 Remove legacy pathway for config files with no header line (fix issue \u0027error in gemBS index #65)\n3.3.2 Fix error where header line for wig files could be omitted\n3.3.2 Fix generation of non_cpg files\n3.3.1 Fix Attribute error bug due to not checking if conversion is a list\n3.3.0 Make new release for IHEC\n3.3.0 Switch conversion default in IHEC_standard configuration to 0.01,0.05 rather than auto, which can give odd results if conversion controls not present or not working correctly\n3.3.0 Fix bug where conversion parameters could be ignored\n3.2.13 Fix formatting bug in mextr with multiple samples (not triggered in normal gemBS use)\n3.2.12 Ensure that conversion statistics are correctly calculated for non-stranded or reverse conversion protocols\n3.2.11 Introduce reverse_conversion option for mapping where read 1 is G2A converted and read 2 is C2T converted\n3.2.10 Correct regex patch for single end reads\n3.2.9 Update Singularity and Dockerfile recipes to allow kemp utils to be built correctly\n3.2.9 Make setup.py and gemBS/commands.py read the version information from gemBS/version.py, so ensuring consistency\n3.2.9 Fix bug added in last version where options in config file were not being taken into account\n3.2.8 Fix mis specification errors in long options for mextr. \n3.2.8 Fix bug where mextr (methyl extract plugin for bcftools) would crash if cpg output option was not set.\n3.2.7 Apply patches for bugs in handling single end reads (suggested by I. Moghul)\n3.2.7 Changed regex for filenames to make it more general (suggested by I. Moghul)\n3.2.7 Fixed bug (reported by chhylp123) where zero arguments to some options were being ignored\n3.2.6 Cleaned up compilation and cleaning of gemBS tools\n3.2.6 Fixed python error if either the over conversion reference sequence was not defined\n3.2.6 Added check in bs_call that conversion parameters are valid (between 0 and 1)\n3.2.6 Perform more stringent sanity checking on conversion vaalues when autocomputed by gemBS\n3.2.6 Use --diasble-lzma configuration flag for samtools and bcftools as we don\u0027t need it and it removes an unneccesary dependency\n3.2.6 Add install options --disable-cuda (on by default) and --enable-cuda that affect GEM3 comppilation\n3.2.6 Bug fix with incorrect handling of duplicate reads\n3.2.5 Minor bug fix - correct error with non-paired end non-bisulfite reads\n3.2.4 Modify the bisulfite processing in gem-mapper to be more efficient (in particular for the non-stranded option)\n3.2.4 Modify gemBS to use the new conversion options for gem-mapper\n3.2.4 Switch gem-mapper to use option --underconversion-sequence and --overconversion-sequence rather than --underconversion_sequence to be consistent with other options\n3.2.3 Fixed bug if conversion parameters were not set\n3.2.2 Rework non-stranded mode so that both possible conversions are tried and the results merged\n3.2.2 Fix bug where non-stranded flag was not being passed to mapper in paired end mode\n3.2.1 Move warning message from bscall from stdout to stderr\n3.2.1 Switch Singularity build to use Ubuntu 16.04 rather than 18.04 to allow the image to work in CentOS 6 (Docker build changed as well to keep the two in sync)\n3.2.1 Fix undeclared variable bugs and missing --ignore-deps option in merge-bcfs\n3.2.1 Add default for dbSNP_index if dbSNP_files is set\n3.2.1 Add gsl-path install option\n3.2.0 Make new release\n3.1.0 Make installation process more modular. Allow for sub-installs\n3.1.0 Add support for reading config from ${index_dir}/gemBS.json if it exists\n3.1.0 Add --reference-bias option to mextr and gemBS extract\n3.1.0 Add support for non-bisulfite mapping of individual datasets\n3.1.0 Allow white space in variable values\n3.1.0 Allow fallback to gzip if pigz not present\n3.1.0 Add --dry-run, --json, --ignore-db and --ignore-dep to extract command\n3.1.0 Add --ignore-dep option to call and merge-bcfs commands\n3.1.0 Add SNP extraction function to extract command\n3.0 Make v3.0 release\n3.0 Merge with master branch.\n3.0 Bump samtools sort memory limit to 2G\n3.0 Add extra_references option for reference generation\n3.0 Allow input files to mapping to be shell commands\n3.0 Add links to documentation\n3.0 Upload new yeast example and add documentation\n3.0 Add --dir option to gemBS\n3.0 Add --ignore-db options for --dry-run / --json\n3.0 Add --json output option for dry runs\n3.0 Update help text to match new functions\n3.0 Introduce standard analysis configurations stored within distribution\n3.0 Switch gem3-mapper distribution to gembs branch on official gem3-mapper repo\n3.0 Removal of incomplete files and roll back of db in the event of pipeline failure\n3.0 Automatic removal of individual BAMs and BCFs after successful merging\n3.0 Prevent pipelines hanging in event of failure\n3.0 Generate ENCODE bed and bigbed files\n3.0 Switch to python 3\n3.0 Switch to mextr for BCF filtering\n3.0 Include fetch and build of samtools / bcftools during build process\n3.0 Add dry-run capability to map and call commands\n3.0 Introduce contig pools to automatically group small contigs\n3.0 Automatic generation of contig.size files from index build\n3.0 Allow use of in memory sqlite3 db as an option\n3.0 Allow multiple instances of gemBS (possible on different hosts) to work \n simultaneously on the same analysis\n3.0 Reduce and simply commands\n3.0 Add Dockerfile\n3.0 Add multi-threading and multi-processing options for most commands\n3.0 Use sqlite3 to track progress of analyses, file paths etc.\n3.0 Added more flexible configuration options (new csv format + new configuration file)\n3.0 Remove test dataset from distribution (distribute from web site)\n2.1.0 Ensure commands run during pipeline come from installation\n2.1.0 Added Singularity build recipe\n2.1.0 Add new command gemBS direct-mapping\n2.1.0 Fixed Makefile clean in tools\n2.0.2 Fixed bug related with the percentage of High Quality Variant in Variants summary report.\n2.0.2 Check temporary directory existence.\n2.0.2 Fixed QualityNonRefCpg sample name in png image.\n2.0.2 Fixed mapper issues related with aligning performace.\n2.0.2 Fixed arguments for Under/Over Conversion sequence name in gem3-mapper\n2.0.1 On bscall repository, fixed argument -k about discarded reads that do not form proper pairs.\n2.0 Check tmp folder before starting mapping process.\n2.0 Added Left and Right Trimming optional arguments to gemBS bscall.\n2.0 Added GC Coverage correlation value to BS Call Stats Summary.\n2.0 Fixed error when reporting complete path to not found bam files.\n2.0 Fixed iteration over sampleBams dictionary in MergeAll method.\n2.0 Updated: Avoid redo indexing when merging just one file.\n2.0 Changed conversion formula.\n2.0 Added parameter for dbSNP.\n2.0 Added threads to bscall.\n2.0 Removed CpGs reports. Already done from bscall report.\n2.0 Fixed bs_call makefile for the gcc to be used.\n2.0 New bscall version. Generates JSON report.\n2.0 Removed gemBS options snp-stats,cpg-report,cpg-stats.\n2.0 Added summary report from the bs_call json stats\n2.0 New BSCall Report. From bscall son file generates three types of reports:\n Mapping and Coverage Report\n Bs-Genotypes Calls Report\n Methylation Statistics report\n1.7 Added non stranded read conversion parameter\n1.7 Fixed SE crash when estimating overlapped bases.\n1.7 Fixed gem-index (gem3) to follow fastq and SAM specifications. \n Modified gem3-mapper repository external module.\n New external module https://github.com/heathsc/gem3-mapper.git\n1.7 Fixed threads parameter to samtools merge\n1.7 Fixed threads parameter to gem-mapper\n1.7 Removed Indels Field on Variants Report.\n1.7 Added Overlapping Bases at Mapping Report\n1.7 Modified Base Counts Overall, removed Base Counts general and Base Counts Overall\n1.7 New Dinucleotide CpGs Report\n New table dinucleotide stats\n New plots for Informative Reads and CpGs\n Methylation levels plots for different types of CpGs\n Summary Table\n1.7 New Readme file to inform about report test\n1.7 New basic statis table for Variants Report\n1.7 Removed parameter -r (reference length) parameter for mapping reports command (gemBS bsMap).\n1.6 New CpGs Density plot, include box plos, bar plot and fitting curve\n1.6 Change name at CpG report:\n \"Heterozygous\" for \"Alternative CX\"\n \"De Novo CpGs Methylation Status\" for \"Non Reference CpGs\"\n \"CpGs with SNP\" for \"SNPs (CX) at Reference CpGs\"\n1.6 CpGs Report Simplified to Q\u0026gt;20\n1.6 BigWig Default parameters for filtering CpG per a given quality and a total number of supported informative reads \n1.5 Initial Release \n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-developers\" class=\"anchor\" aria-hidden=\"true\" href=\"#developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopers\u003c/h2\u003e\n\u003cp\u003egemBS:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarcos Fernandez-Callejo - \u003ca href=\"mailto:marcos.fernandez@cnag.crg.eu\"\u003emarcos.fernandez@cnag.crg.eu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimon Heath - \u003ca href=\"mailto:simon.heath@gmail.com\"\u003esimon.heath@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003egem mapper:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSantiago Marco-Sola - \u003ca href=\"mailto:santiagomsola@gmail.com\"\u003esantiagomsola@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ebisulfite caller and filtering:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimon Heath - \u003ca href=\"mailto:simon.heath@gmail.com\"\u003esimon.heath@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 29, + "subscribers_count": 5, "topics": [], - "updated_at": 1727528170.0 + "updated_at": 1680849725.0 }, { "data_format": 2, @@ -40377,48 +40434,40 @@ var data = }, { "data_format": 2, - "description": "gemBS is a bioinformatics pipeline designed for high throughput analysis of DNA methylation from Whole Genome Bisulfite Sequencing data (WGBS).", + "description": "scientific filesystem: a filesystem organization for scientific software and metadata", "filenames": [ - "Singularity", - "IHEC/Singularity.ihec" + "Singularity" ], - "full_name": "heathsc/gemBS", - "latest_release": "v3.5.1_IHEC", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-hidden=\"true\" href=\"#news\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eNews\u003c/h1\u003e\n\u003cp\u003eFirst release of gemBS-rs, a complete rewrite of the gemBS pipeline (apart from the mapper) in Rust bringing increased\nstability while maintaining the high performance of gemBS: \u003ca href=\"https://github.com/heathsc/gemBS-rs.git\"\u003ehttps://github.com/heathsc/gemBS-rs.git\u003c/a\u003e\u003c/p\u003e\n\u003ch1\u003e\u003ca id=\"user-content-gembs\" class=\"anchor\" aria-hidden=\"true\" href=\"#gembs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003egemBS\u003c/h1\u003e\n\u003cp\u003egemBS is a high performance bioinformatic pipeline designed for highthroughput analysis\nof DNA methylation data from whole genome bisulfites sequencing data\n(WGBS). It combines GEM3, a high performance read aligner and\nbs_call, a high performance variant and methyation caller, into a streamlined and efficient pipeline for\nbisulfite sueqnce analysis.\u003c/p\u003e\n\u003cp\u003eThe manuscript describing the pipeline is available \u003ca href=\"https://www.biorxiv.org/content/early/2017/10/11/201988\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-licensing\" class=\"anchor\" aria-hidden=\"true\" href=\"#licensing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicensing\u003c/h2\u003e\n\u003cp\u003egemBS is licensed under GPL.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-download\" class=\"anchor\" aria-hidden=\"true\" href=\"#download\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDownload\u003c/h2\u003e\n\u003cp\u003eUse \u003ccode\u003egit clone --recursive\u003c/code\u003e to retrieve the complete source code including the code from external projects such as \u003ccode\u003ebs_call\u003c/code\u003e and \u003ccode\u003egem3-mapper\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBefore starting the installation of gemBS, you will need to install\nor check the installation of several packages.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ea) gcc with development libraries\nb) python3, pip3, matplotlib, multiprocess\nc) zlib, lzma, openssl, libcurl, libncurses, wget, pigz\u003c/p\u003e\n\u003cp\u003eIf you are working on a clean (fairly recent) Ubuntu installation, you\ncan install everything required with the followiwg commands:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt-get update\nsudo apt-get install -y python3 build-essential git python3-pip wget pigz\nsudo apt-get install -y zlib1g-dev libbz2-dev\nsudo apt-get install -y libncurses5-dev liblzma-dev libssl-dev libcurl4-openssl-dev\npip3 install matplotlib multiprocess\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\n\u003cp\u003eDownload the gemBS distribution if you haven\u0027t already done so:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egit clone --recursive https://github.com/heathsc/gemBS.git\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse python install command:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo install to the standard system location (i.e., so that all users\ncan use gemBS):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e``python3 setup.py install``\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install to the user\u0027s home directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e``python3 setup.py install --user``\n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-check-your-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#check-your-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCheck your installation\u003c/h2\u003e\n\u003cp\u003eFor checking your installation follow this\n\u003ca href=\"http://statgen.cnag.cat/gemBS/v3/UserGuide/_build/html/example.html\" rel=\"nofollow\"\u003eworked example\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eDocumentation can be found at\n\u003ca href=\"http://statgen.cnag.cat/gemBS/v3/UserGuide/_build/html/index.html\" rel=\"nofollow\"\u003egemBS\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-changelog\" class=\"anchor\" aria-hidden=\"true\" href=\"#changelog\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChangelog:\u003c/h2\u003e\n\u003cpre\u003e\u003ccode\u003e3.5.5 Fix logging bug caused by trimming change in 3.5.3\n3.5.4 Fix bug in the output of strand specific cpg txt files (not\n encode Bed files) where the \u0027C\u0027 entry was not being printed\n3.5.3 Allow for read end specific trimming in bs_call\n3.5.3 Enable range checks and asserts in bs_call all target; add bs_call debug target\n3.5.2 Correct problems with gcc10. Move to htslib/samtools/bcftools version 1.11\n3.5.1 Check if C compiler requires --std=c99 flag for standards conformant behaviour\n3.5.1 Make sure bgzip is copied correctly during installation\n3.5.0 Make bs_call process contig pools from largest to smallest (this change alters the sqlite db format so\n if you have a previously started gemBS run you should (a) remove the .gemBS directory, (b) redo the\n \u0027gemBS prepare\u0027 step to recreate the db file and (3) run \u0027gemBS db-sync\u0027. \n3.5.0 Switch bs_call and snpxtr to use the new dbSNP index format\n3.5.0 Add ability of dbSNP to read the new JSON and VCF dbSNP format files\n that are now used for human and non-human species respectively\n3.5.0 Add multithreading to dbSNP_idx\n3.5.0 Change format of dbSNP index to allow (a) efficient loading\n of SNP data for individual contigs and (b) parallel index creation \n3.5.0 Rewrite mextr and snpxtr as standalone tools rather than\n bcftools plugins. Now multithreaded and (relatively) memoryefficient\n3.5.0 Replace bedToBigBed and wigToBigWig to reduce memory usage\n and improve speed\n3.4.5 Fix crash when using the -k (keep-mismatch) flag, and fix rare hangs at end of processing\n3.4.4 Sort input bcf files to bcftools concat stage to ensure reproducibility.\n3.4.4 Add extra sort keys when generating pools to ensure stability of pool membership in the event of multiple contigs\n having the same size\n3.4.3 Remove calculation of the goodness of filter (GOF) as this is expensive, non-standard and unreliable. Removing this\n removes the dependency on GSL.\n3.4.3 Add autodetection of output format to bs_call (unless explicitly specified on the command line)\n3.4.2 Add CRAM support (via make_cram option in configuration file)\n3.4.1 Add benchmark-mode that does not write date or program version numbers into SAM/BAM or VCF/BCF files\n Switch to samtools, bcftools and htslib v1.10\n3.4.0 Move to new bs_call version (2.1.0) which is more efficient\n in memory use and can read BAMs and write BCFs natively.\n The new bs_call requires a faidx indexed reference, so gemBS\n no creates this during indexing.\n3.4.0 Add switches to give more control to threads and memory\n usage in mapping and calling stages\n3.3.3 Remove legacy pathway for config files with no header line (fix issue \u0027error in gemBS index #65)\n3.3.2 Fix error where header line for wig files could be omitted\n3.3.2 Fix generation of non_cpg files\n3.3.1 Fix Attribute error bug due to not checking if conversion is a list\n3.3.0 Make new release for IHEC\n3.3.0 Switch conversion default in IHEC_standard configuration to 0.01,0.05 rather than auto, which can give odd results if conversion controls not present or not working correctly\n3.3.0 Fix bug where conversion parameters could be ignored\n3.2.13 Fix formatting bug in mextr with multiple samples (not triggered in normal gemBS use)\n3.2.12 Ensure that conversion statistics are correctly calculated for non-stranded or reverse conversion protocols\n3.2.11 Introduce reverse_conversion option for mapping where read 1 is G2A converted and read 2 is C2T converted\n3.2.10 Correct regex patch for single end reads\n3.2.9 Update Singularity and Dockerfile recipes to allow kemp utils to be built correctly\n3.2.9 Make setup.py and gemBS/commands.py read the version information from gemBS/version.py, so ensuring consistency\n3.2.9 Fix bug added in last version where options in config file were not being taken into account\n3.2.8 Fix mis specification errors in long options for mextr. \n3.2.8 Fix bug where mextr (methyl extract plugin for bcftools) would crash if cpg output option was not set.\n3.2.7 Apply patches for bugs in handling single end reads (suggested by I. Moghul)\n3.2.7 Changed regex for filenames to make it more general (suggested by I. Moghul)\n3.2.7 Fixed bug (reported by chhylp123) where zero arguments to some options were being ignored\n3.2.6 Cleaned up compilation and cleaning of gemBS tools\n3.2.6 Fixed python error if either the over conversion reference sequence was not defined\n3.2.6 Added check in bs_call that conversion parameters are valid (between 0 and 1)\n3.2.6 Perform more stringent sanity checking on conversion vaalues when autocomputed by gemBS\n3.2.6 Use --diasble-lzma configuration flag for samtools and bcftools as we don\u0027t need it and it removes an unneccesary dependency\n3.2.6 Add install options --disable-cuda (on by default) and --enable-cuda that affect GEM3 comppilation\n3.2.6 Bug fix with incorrect handling of duplicate reads\n3.2.5 Minor bug fix - correct error with non-paired end non-bisulfite reads\n3.2.4 Modify the bisulfite processing in gem-mapper to be more efficient (in particular for the non-stranded option)\n3.2.4 Modify gemBS to use the new conversion options for gem-mapper\n3.2.4 Switch gem-mapper to use option --underconversion-sequence and --overconversion-sequence rather than --underconversion_sequence to be consistent with other options\n3.2.3 Fixed bug if conversion parameters were not set\n3.2.2 Rework non-stranded mode so that both possible conversions are tried and the results merged\n3.2.2 Fix bug where non-stranded flag was not being passed to mapper in paired end mode\n3.2.1 Move warning message from bscall from stdout to stderr\n3.2.1 Switch Singularity build to use Ubuntu 16.04 rather than 18.04 to allow the image to work in CentOS 6 (Docker build changed as well to keep the two in sync)\n3.2.1 Fix undeclared variable bugs and missing --ignore-deps option in merge-bcfs\n3.2.1 Add default for dbSNP_index if dbSNP_files is set\n3.2.1 Add gsl-path install option\n3.2.0 Make new release\n3.1.0 Make installation process more modular. Allow for sub-installs\n3.1.0 Add support for reading config from ${index_dir}/gemBS.json if it exists\n3.1.0 Add --reference-bias option to mextr and gemBS extract\n3.1.0 Add support for non-bisulfite mapping of individual datasets\n3.1.0 Allow white space in variable values\n3.1.0 Allow fallback to gzip if pigz not present\n3.1.0 Add --dry-run, --json, --ignore-db and --ignore-dep to extract command\n3.1.0 Add --ignore-dep option to call and merge-bcfs commands\n3.1.0 Add SNP extraction function to extract command\n3.0 Make v3.0 release\n3.0 Merge with master branch.\n3.0 Bump samtools sort memory limit to 2G\n3.0 Add extra_references option for reference generation\n3.0 Allow input files to mapping to be shell commands\n3.0 Add links to documentation\n3.0 Upload new yeast example and add documentation\n3.0 Add --dir option to gemBS\n3.0 Add --ignore-db options for --dry-run / --json\n3.0 Add --json output option for dry runs\n3.0 Update help text to match new functions\n3.0 Introduce standard analysis configurations stored within distribution\n3.0 Switch gem3-mapper distribution to gembs branch on official gem3-mapper repo\n3.0 Removal of incomplete files and roll back of db in the event of pipeline failure\n3.0 Automatic removal of individual BAMs and BCFs after successful merging\n3.0 Prevent pipelines hanging in event of failure\n3.0 Generate ENCODE bed and bigbed files\n3.0 Switch to python 3\n3.0 Switch to mextr for BCF filtering\n3.0 Include fetch and build of samtools / bcftools during build process\n3.0 Add dry-run capability to map and call commands\n3.0 Introduce contig pools to automatically group small contigs\n3.0 Automatic generation of contig.size files from index build\n3.0 Allow use of in memory sqlite3 db as an option\n3.0 Allow multiple instances of gemBS (possible on different hosts) to work \n simultaneously on the same analysis\n3.0 Reduce and simply commands\n3.0 Add Dockerfile\n3.0 Add multi-threading and multi-processing options for most commands\n3.0 Use sqlite3 to track progress of analyses, file paths etc.\n3.0 Added more flexible configuration options (new csv format + new configuration file)\n3.0 Remove test dataset from distribution (distribute from web site)\n2.1.0 Ensure commands run during pipeline come from installation\n2.1.0 Added Singularity build recipe\n2.1.0 Add new command gemBS direct-mapping\n2.1.0 Fixed Makefile clean in tools\n2.0.2 Fixed bug related with the percentage of High Quality Variant in Variants summary report.\n2.0.2 Check temporary directory existence.\n2.0.2 Fixed QualityNonRefCpg sample name in png image.\n2.0.2 Fixed mapper issues related with aligning performace.\n2.0.2 Fixed arguments for Under/Over Conversion sequence name in gem3-mapper\n2.0.1 On bscall repository, fixed argument -k about discarded reads that do not form proper pairs.\n2.0 Check tmp folder before starting mapping process.\n2.0 Added Left and Right Trimming optional arguments to gemBS bscall.\n2.0 Added GC Coverage correlation value to BS Call Stats Summary.\n2.0 Fixed error when reporting complete path to not found bam files.\n2.0 Fixed iteration over sampleBams dictionary in MergeAll method.\n2.0 Updated: Avoid redo indexing when merging just one file.\n2.0 Changed conversion formula.\n2.0 Added parameter for dbSNP.\n2.0 Added threads to bscall.\n2.0 Removed CpGs reports. Already done from bscall report.\n2.0 Fixed bs_call makefile for the gcc to be used.\n2.0 New bscall version. Generates JSON report.\n2.0 Removed gemBS options snp-stats,cpg-report,cpg-stats.\n2.0 Added summary report from the bs_call json stats\n2.0 New BSCall Report. From bscall son file generates three types of reports:\n Mapping and Coverage Report\n Bs-Genotypes Calls Report\n Methylation Statistics report\n1.7 Added non stranded read conversion parameter\n1.7 Fixed SE crash when estimating overlapped bases.\n1.7 Fixed gem-index (gem3) to follow fastq and SAM specifications. \n Modified gem3-mapper repository external module.\n New external module https://github.com/heathsc/gem3-mapper.git\n1.7 Fixed threads parameter to samtools merge\n1.7 Fixed threads parameter to gem-mapper\n1.7 Removed Indels Field on Variants Report.\n1.7 Added Overlapping Bases at Mapping Report\n1.7 Modified Base Counts Overall, removed Base Counts general and Base Counts Overall\n1.7 New Dinucleotide CpGs Report\n New table dinucleotide stats\n New plots for Informative Reads and CpGs\n Methylation levels plots for different types of CpGs\n Summary Table\n1.7 New Readme file to inform about report test\n1.7 New basic statis table for Variants Report\n1.7 Removed parameter -r (reference length) parameter for mapping reports command (gemBS bsMap).\n1.6 New CpGs Density plot, include box plos, bar plot and fitting curve\n1.6 Change name at CpG report:\n \"Heterozygous\" for \"Alternative CX\"\n \"De Novo CpGs Methylation Status\" for \"Non Reference CpGs\"\n \"CpGs with SNP\" for \"SNPs (CX) at Reference CpGs\"\n1.6 CpGs Report Simplified to Q\u0026gt;20\n1.6 BigWig Default parameters for filtering CpG per a given quality and a total number of supported informative reads \n1.5 Initial Release \n\u003c/code\u003e\u003c/pre\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-developers\" class=\"anchor\" aria-hidden=\"true\" href=\"#developers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDevelopers\u003c/h2\u003e\n\u003cp\u003egemBS:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMarcos Fernandez-Callejo - \u003ca href=\"mailto:marcos.fernandez@cnag.crg.eu\"\u003emarcos.fernandez@cnag.crg.eu\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eSimon Heath - \u003ca href=\"mailto:simon.heath@gmail.com\"\u003esimon.heath@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003egem mapper:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSantiago Marco-Sola - \u003ca href=\"mailto:santiagomsola@gmail.com\"\u003esantiagomsola@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ebisulfite caller and filtering:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSimon Heath - \u003ca href=\"mailto:simon.heath@gmail.com\"\u003esimon.heath@gmail.com\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 29, - "subscribers_count": 5, - "topics": [], - "updated_at": 1680849725.0 - }, - { - "data_format": 2, - "description": "APSIM", - "filenames": [ - "Release/Containers/Singularity/Singularity.ubuntu" + "full_name": "vsoch/scif", + "latest_release": "0.0.84", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScientific Filesystem (SCI-F)\u003c/h1\u003e\u003ca id=\"user-content-scientific-filesystem-sci-f\" class=\"anchor\" aria-label=\"Permalink: Scientific Filesystem (SCI-F)\" href=\"#scientific-filesystem-sci-f\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/vsoch/scif/actions?query=branch%3Amaster+workflow%3Aci\"\u003e\u003cimg src=\"https://github.com/vsoch/scif/workflows/ci/badge.svg?branch=master\" alt=\"GitHub actions status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/vsoch/scif\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c61f28d8e304215fd7b1c5de3cb6abefbde84605b8d1b21a27124383856dc134/68747470733a2f2f636972636c6563692e636f6d2f67682f76736f63682f736369662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/vsoch/scif.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1de917a4e424c0d9364420086cc662902cce635afa8665a878e741bf0bd4f0c8/68747470733a2f2f7363692d662e6769746875622e696f2f696d672f6c6f676f2f736369662d736c6173682d677265656e2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1de917a4e424c0d9364420086cc662902cce635afa8665a878e741bf0bd4f0c8/68747470733a2f2f7363692d662e6769746875622e696f2f696d672f6c6f676f2f736369662d736c6173682d677265656e2e706e67\" alt=\"https://sci-f.github.io/img/logo/scif-slash-green.png\" data-canonical-src=\"https://sci-f.github.io/img/logo/scif-slash-green.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/156490?speed=2\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7db5330e459424b489e3771b5c23682f4853582a3652b5ea305ea2e79d7c4e3/68747470733a2f2f61736369696e656d612e6f72672f612f3135363439302e706e67\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/156490.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe The Scientific Filesystem is an organizational format for scientific software and metadata.\nOur goals are centered around \u003cstrong\u003econsistency\u003c/strong\u003e, \u003cstrong\u003etransparency\u003c/strong\u003e, \u003cstrong\u003eprogrammatic accessibility\u003c/strong\u003e,\nand \u003cstrong\u003emodularity\u003c/strong\u003e. \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eRead about\u003c/a\u003e the format and\nplease \u003ca href=\"https://github.com/vsoch/scif/issues\"\u003econtribute\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCitation\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVanessa Sochat; The Scientific Filesystem, GigaScience, Volume 7, Issue 5, 1 May 2018, giy023, \u003ca href=\"https://doi.org/10.1093/gigascience/giy023\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/gigascience/giy023\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is this?\u003c/h2\u003e\u003ca id=\"user-content-what-is-this\" class=\"anchor\" aria-label=\"Permalink: What is this?\" href=\"#what-is-this\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis module will provide tools for generating and interacting with scientific\nfilesystems, optimized for use on a host or inside a container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code is licensed under the Mozilla, version 2.0 or later \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 30, + "subscribers_count": 7, + "topics": [ + "singularity", + "docker", + "container", + "scientific-filesystems", + "organization", + "metadata", + "standard", + "containers" ], - "full_name": "APSIMInitiative/APSIMClassic", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAPSIM\u003c/h1\u003e\u003ca id=\"user-content-apsim\" class=\"anchor\" aria-label=\"Permalink: APSIM\" href=\"#apsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Agricultural Production Systems sIMulator (APSIM) is internationally recognised as a highly advanced simulator of agricultural systems. It contains a suite of modules which enable the simulation of systems that cover a range of plant, animal, soil, climate and management interactions. APSIM is undergoing continual development, with new capability added to regular releases of official versions. Its development and maintenance is underpinned by rigorous science and software engineering standards. The APSIM Initiative has been established to promote the development and use of the science modules and infrastructure software of APSIM.\u003c/p\u003e\n\u003cp\u003eCI builds of this repository can be found \u003ca href=\"https://apsimdev.apsim.info/APSIM.Builds.Portal/Bob.aspx\" rel=\"nofollow\"\u003eHere\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 29, - "subscribers_count": 16, - "topics": [], - "updated_at": 1708973907.0 + "updated_at": 1706123380.0 }, { "data_format": 2, - "description": "Webin command line submission program.", + "description": "CRyPTIC data processing pipelines", "filenames": [ - "image/Singularity", - "image/Singularity.2.0.0-rc-2", - "image/Singularity.2.0.0-rc-1" + "Singularity.def" ], - "full_name": "enasequence/webin-cli", - "latest_release": "8.0.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebin command line submission interface (Webin-CLI)\u003c/h1\u003e\u003ca id=\"user-content-webin-command-line-submission-interface-webin-cli\" class=\"anchor\" aria-label=\"Permalink: Webin command line submission interface (Webin-CLI)\" href=\"#webin-command-line-submission-interface-webin-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://app.codacy.com/app/enasequence/webin-cli?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=enasequence/webin-cli\u0026amp;utm_campaign=badger\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/55c28959ed177495f77ebc7901377cf1f6fa76ab4a964ab30f64d78c1ac87f2b/68747470733a2f2f6170692e636f646163792e636f6d2f70726f6a6563742f62616467652f47726164652f6334666132626366353539333433366461396561323731343966383465653665\" alt=\"Codacy Badge\" data-canonical-src=\"https://api.codacy.com/project/badge/Grade/c4fa2bcf5593436da9ea27149f84ee6e\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eData submissions to ENA can be made using the Webin command line submission interface (Webin-CLI). Webin submission account credentials are required to use the program.\u003c/p\u003e\n\u003cp\u003eThe following types of submissions are supported:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egenome assemblies\u003c/li\u003e\n\u003cli\u003etranscriptome assemblies\u003c/li\u003e\n\u003cli\u003eannotated sequences\u003c/li\u003e\n\u003cli\u003eread data submissions (Fastq, BAM, CRAM)\u003c/li\u003e\n\u003cli\u003etaxonomy reference sets\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor further information about Webin-CLI please refer to:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://ena-docs.readthedocs.io/en/latest/submit/general-guide/webin-cli.html\" rel=\"nofollow\"\u003ehttps://ena-docs.readthedocs.io/en/latest/submit/general-guide/webin-cli.html\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecutable Java JAR\u003c/h2\u003e\u003ca id=\"user-content-executable-java-jar\" class=\"anchor\" aria-label=\"Permalink: Executable Java JAR\" href=\"#executable-java-jar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe latest version of the Webin-CLI can be downloaded from:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/enasequence/webin-cli/releases\"\u003ehttps://github.com/enasequence/webin-cli/releases\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe program requires Java 1.8 or a newer which can be downloaded from:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://java.com/en/download/\" rel=\"nofollow\"\u003ehttps://java.com/en/download/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe program is run using the java command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejava -jar webin-cli-\u0026lt;version\u0026gt;.jar \u0026lt;options\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejava -jar webin-cli-2.0.0.jar -help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo increase the memory available to Webin-CLI please use the -Xms java option:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejava -Xms2G -jar webin-cli-2.0.0.jar -help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun\u003c/h3\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince version 1.8.12 Webin-CLI is available as a docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull enasequence/webin-cli\ndocker run --rm -v \u0026lt;local data directory\u0026gt;:/data enasequence/webin-cli -help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo increase the memory available to Webin-CLI please set the JAVA_TOOL_OPTIONS environment variable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -v \u0026lt;local data directory\u0026gt;:/data -e JAVA_TOOL_OPTIONS=\"-Xms2G\" enasequence/webin-cli -help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePublishing\u003c/h3\u003e\u003ca id=\"user-content-publishing\" class=\"anchor\" aria-label=\"Permalink: Publishing\" href=\"#publishing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCreate docker image with default tags by running \u003ccode\u003egradle dockerTag\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting requires the following environmental variables to be set:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ewebin-cli-username or webinCliUsername\u003c/li\u003e\n\u003cli\u003ewebin-cli-password or webinCliPassword\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLibrary Jar Publishing\u003c/h2\u003e\u003ca id=\"user-content-library-jar-publishing\" class=\"anchor\" aria-label=\"Permalink: Library Jar Publishing\" href=\"#library-jar-publishing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo publish webin-cli as a library :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egradle publish\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "iqbal-lab-org/clockwork", + "latest_release": "v0.12.3", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-clockwork\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#clockwork\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClockwork\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipelines-for-processing-bacterial-sequence-data-illumina-only--and-variant-calling\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#pipelines-for-processing-bacterial-sequence-data-illumina-only--and-variant-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipelines for processing bacterial sequence data (Illumina only) and variant calling\u003c/h2\u003e\n\u003cp\u003eNote: these pipelines were developed for the \u003ca href=\"http://www.crypticproject.org/\" rel=\"nofollow\"\u003eCRyPTIC\u003c/a\u003e project which studies \u003cem\u003eM. tuberculosis\u003c/em\u003e, but in principle can be used on any bacteria.\u003c/p\u003e\n\u003cp\u003eClockwork takes fastq input and outputs standard VCF files as output\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/iqbal-lab-org/clockwork/wiki\"\u003eclockwork wiki page\u003c/a\u003e for documentation.\u003c/p\u003e\n", "stargazers_count": 30, "subscribers_count": 8, "topics": [], - "updated_at": 1727577546.0 + "updated_at": 1700735751.0 }, { "data_format": 2, @@ -40438,86 +40487,38 @@ var data = }, { "data_format": 2, - "description": "CRyPTIC data processing pipelines", + "description": "Webin command line submission program.", "filenames": [ - "Singularity.def" + "image/Singularity", + "image/Singularity.2.0.0-rc-2", + "image/Singularity.2.0.0-rc-1" ], - "full_name": "iqbal-lab-org/clockwork", - "latest_release": "v0.12.3", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-clockwork\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#clockwork\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eClockwork\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-pipelines-for-processing-bacterial-sequence-data-illumina-only--and-variant-calling\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#pipelines-for-processing-bacterial-sequence-data-illumina-only--and-variant-calling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003ePipelines for processing bacterial sequence data (Illumina only) and variant calling\u003c/h2\u003e\n\u003cp\u003eNote: these pipelines were developed for the \u003ca href=\"http://www.crypticproject.org/\" rel=\"nofollow\"\u003eCRyPTIC\u003c/a\u003e project which studies \u003cem\u003eM. tuberculosis\u003c/em\u003e, but in principle can be used on any bacteria.\u003c/p\u003e\n\u003cp\u003eClockwork takes fastq input and outputs standard VCF files as output\u003c/p\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/iqbal-lab-org/clockwork/wiki\"\u003eclockwork wiki page\u003c/a\u003e for documentation.\u003c/p\u003e\n", + "full_name": "enasequence/webin-cli", + "latest_release": "8.0.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWebin command line submission interface (Webin-CLI)\u003c/h1\u003e\u003ca id=\"user-content-webin-command-line-submission-interface-webin-cli\" class=\"anchor\" aria-label=\"Permalink: Webin command line submission interface (Webin-CLI)\" href=\"#webin-command-line-submission-interface-webin-cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://app.codacy.com/app/enasequence/webin-cli?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=enasequence/webin-cli\u0026amp;utm_campaign=badger\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/55c28959ed177495f77ebc7901377cf1f6fa76ab4a964ab30f64d78c1ac87f2b/68747470733a2f2f6170692e636f646163792e636f6d2f70726f6a6563742f62616467652f47726164652f6334666132626366353539333433366461396561323731343966383465653665\" alt=\"Codacy Badge\" data-canonical-src=\"https://api.codacy.com/project/badge/Grade/c4fa2bcf5593436da9ea27149f84ee6e\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eData submissions to ENA can be made using the Webin command line submission interface (Webin-CLI). Webin submission account credentials are required to use the program.\u003c/p\u003e\n\u003cp\u003eThe following types of submissions are supported:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egenome assemblies\u003c/li\u003e\n\u003cli\u003etranscriptome assemblies\u003c/li\u003e\n\u003cli\u003eannotated sequences\u003c/li\u003e\n\u003cli\u003eread data submissions (Fastq, BAM, CRAM)\u003c/li\u003e\n\u003cli\u003etaxonomy reference sets\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor further information about Webin-CLI please refer to:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://ena-docs.readthedocs.io/en/latest/submit/general-guide/webin-cli.html\" rel=\"nofollow\"\u003ehttps://ena-docs.readthedocs.io/en/latest/submit/general-guide/webin-cli.html\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExecutable Java JAR\u003c/h2\u003e\u003ca id=\"user-content-executable-java-jar\" class=\"anchor\" aria-label=\"Permalink: Executable Java JAR\" href=\"#executable-java-jar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe latest version of the Webin-CLI can be downloaded from:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/enasequence/webin-cli/releases\"\u003ehttps://github.com/enasequence/webin-cli/releases\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe program requires Java 1.8 or a newer which can be downloaded from:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://java.com/en/download/\" rel=\"nofollow\"\u003ehttps://java.com/en/download/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe program is run using the java command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejava -jar webin-cli-\u0026lt;version\u0026gt;.jar \u0026lt;options\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003efor example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejava -jar webin-cli-2.0.0.jar -help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo increase the memory available to Webin-CLI please use the -Xms java option:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ejava -Xms2G -jar webin-cli-2.0.0.jar -help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eRun\u003c/h3\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince version 1.8.12 Webin-CLI is available as a docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull enasequence/webin-cli\ndocker run --rm -v \u0026lt;local data directory\u0026gt;:/data enasequence/webin-cli -help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo increase the memory available to Webin-CLI please set the JAVA_TOOL_OPTIONS environment variable:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run --rm -v \u0026lt;local data directory\u0026gt;:/data -e JAVA_TOOL_OPTIONS=\"-Xms2G\" enasequence/webin-cli -help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePublishing\u003c/h3\u003e\u003ca id=\"user-content-publishing\" class=\"anchor\" aria-label=\"Permalink: Publishing\" href=\"#publishing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eCreate docker image with default tags by running \u003ccode\u003egradle dockerTag\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting\u003c/h2\u003e\u003ca id=\"user-content-testing\" class=\"anchor\" aria-label=\"Permalink: Testing\" href=\"#testing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTesting requires the following environmental variables to be set:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ewebin-cli-username or webinCliUsername\u003c/li\u003e\n\u003cli\u003ewebin-cli-password or webinCliPassword\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLibrary Jar Publishing\u003c/h2\u003e\u003ca id=\"user-content-library-jar-publishing\" class=\"anchor\" aria-label=\"Permalink: Library Jar Publishing\" href=\"#library-jar-publishing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo publish webin-cli as a library :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egradle publish\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 30, "subscribers_count": 8, "topics": [], - "updated_at": 1700735751.0 - }, - { - "data_format": 2, - "description": "scientific filesystem: a filesystem organization for scientific software and metadata", - "filenames": [ - "Singularity" - ], - "full_name": "vsoch/scif", - "latest_release": "0.0.84", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eScientific Filesystem (SCI-F)\u003c/h1\u003e\u003ca id=\"user-content-scientific-filesystem-sci-f\" class=\"anchor\" aria-label=\"Permalink: Scientific Filesystem (SCI-F)\" href=\"#scientific-filesystem-sci-f\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/vsoch/scif/actions?query=branch%3Amaster+workflow%3Aci\"\u003e\u003cimg src=\"https://github.com/vsoch/scif/workflows/ci/badge.svg?branch=master\" alt=\"GitHub actions status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/vsoch/scif\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c61f28d8e304215fd7b1c5de3cb6abefbde84605b8d1b21a27124383856dc134/68747470733a2f2f636972636c6563692e636f6d2f67682f76736f63682f736369662e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/vsoch/scif.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/1de917a4e424c0d9364420086cc662902cce635afa8665a878e741bf0bd4f0c8/68747470733a2f2f7363692d662e6769746875622e696f2f696d672f6c6f676f2f736369662d736c6173682d677265656e2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1de917a4e424c0d9364420086cc662902cce635afa8665a878e741bf0bd4f0c8/68747470733a2f2f7363692d662e6769746875622e696f2f696d672f6c6f676f2f736369662d736c6173682d677265656e2e706e67\" alt=\"https://sci-f.github.io/img/logo/scif-slash-green.png\" data-canonical-src=\"https://sci-f.github.io/img/logo/scif-slash-green.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://asciinema.org/a/156490?speed=2\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7db5330e459424b489e3771b5c23682f4853582a3652b5ea305ea2e79d7c4e3/68747470733a2f2f61736369696e656d612e6f72672f612f3135363439302e706e67\" alt=\"asciicast\" data-canonical-src=\"https://asciinema.org/a/156490.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe The Scientific Filesystem is an organizational format for scientific software and metadata.\nOur goals are centered around \u003cstrong\u003econsistency\u003c/strong\u003e, \u003cstrong\u003etransparency\u003c/strong\u003e, \u003cstrong\u003eprogrammatic accessibility\u003c/strong\u003e,\nand \u003cstrong\u003emodularity\u003c/strong\u003e. \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eRead about\u003c/a\u003e the format and\nplease \u003ca href=\"https://github.com/vsoch/scif/issues\"\u003econtribute\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCitation\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eVanessa Sochat; The Scientific Filesystem, GigaScience, Volume 7, Issue 5, 1 May 2018, giy023, \u003ca href=\"https://doi.org/10.1093/gigascience/giy023\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/gigascience/giy023\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is this?\u003c/h2\u003e\u003ca id=\"user-content-what-is-this\" class=\"anchor\" aria-label=\"Permalink: What is this?\" href=\"#what-is-this\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis module will provide tools for generating and interacting with scientific\nfilesystems, optimized for use on a host or inside a container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code is licensed under the Mozilla, version 2.0 or later \u003ca href=\"LICENSE\"\u003eLICENSE\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 30, - "subscribers_count": 7, - "topics": [ - "singularity", - "docker", - "container", - "scientific-filesystems", - "organization", - "metadata", - "standard", - "containers" - ], - "updated_at": 1706123380.0 + "updated_at": 1727577546.0 }, { "data_format": 2, "description": "Bioinformatics tool outputs converter to JSON or YAML", "filenames": [ - "singularity/Singularity" - ], - "full_name": "bow/crimson", - "latest_release": "v0.5.1", - "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-crimson\" class=\"anchor\" href=\"#crimson\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003ecrimson\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://pypi.org/project/crimson\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b88d8e72d91a58b5e0d8651dfb03de679b9d91a059cb88b8f4809639801721f3/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6372696d736f6e\" alt=\"pypi\" data-canonical-src=\"https://img.shields.io/pypi/v/crimson\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://builds.sr.ht/~bow/crimson?\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/21063ea62384043fb1ba2e562d346bea30f475aa5b3a714dbce1776ad5f0da70/68747470733a2f2f6275696c64732e73722e68742f7e626f772f6372696d736f6e2e737667\" alt=\"sourcehut\" data-canonical-src=\"https://builds.sr.ht/~bow/crimson.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecrimson\u003c/code\u003e converts non-standard bioinformatics tool outputs to JSON or YAML.\u003c/p\u003e\n\u003cp\u003eCurrently it can convert outputs of the following tools:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc/%3E\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e (\u003ccode\u003efastqc\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ndaniel/fusioncatcher\"\u003eFusionCatcher\u003c/a\u003e (\u003ccode\u003efusioncatcher\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.htslib.org/doc/samtools.html\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e flagstat (\u003ccode\u003eflagstat\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://broadinstitute.github.io/picard/\" rel=\"nofollow\"\u003ePicard\u003c/a\u003e metrics tools (\u003ccode\u003epicard\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTAR\u003c/a\u003e log file (\u003ccode\u003estar\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/STAR-Fusion/STAR-Fusion\"\u003eSTAR-Fusion\u003c/a\u003e hits table (\u003ccode\u003estar-fusion\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.ensembl.org/info/docs/tools/vep/index.html\" rel=\"nofollow\"\u003eVariant Effect Predictor\u003c/a\u003e\nplain text output (\u003ccode\u003evep\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe conversion can be done using the command line interface or by calling the\ntool-specificparser functions in your Python script.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecrimson\u003c/code\u003e is available on the \u003ca href=\"https://pypi.org/project/crimson/\" rel=\"nofollow\"\u003ePython Package Index\u003c/a\u003e\nand you can install it via \u003ccode\u003epip\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install crimson\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt is also available on\n\u003ca href=\"https://bioconda.github.io/recipes/crimson/README.html\" rel=\"nofollow\"\u003eBioConda\u003c/a\u003e, both through the\n\u003ccode\u003econda\u003c/code\u003e package manager or as a\n\u003ca href=\"https://quay.io/repository/biocontainers/crimson?tab=tags\" rel=\"nofollow\"\u003eDocker container\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-as-a-command-line-tool\" class=\"anchor\" href=\"#as-a-command-line-tool\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAs a command line tool\u003c/h3\u003e\n\u003cp\u003eThe general command is \u003ccode\u003ecrimson {program_name}\u003c/code\u003e and by default the output is written to\n\u003ccode\u003estdout\u003c/code\u003e. For example, to use the \u003ccode\u003epicard\u003c/code\u003e parser, you would execute:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson picard /path/to/a/picard.metrics\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also specify a file name directly to write to a file. The following command will\nwrite the output to a file named \u003ccode\u003econverted.json\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson picard /path/to/a/picard.metrics converted.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSome parsers may also accept additional input format. The FastQC parser, for example, also\nworks if you specify a path to a FastQC output directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson fastqc /path/to/a/fastqc/dir\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor path to a zipped result:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson fastqc /path/to/a/fastqc_result.zip\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen in doubt, use the \u003ccode\u003e--help\u003c/code\u003e flag:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson --help \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for the general help\u003c/span\u003e\n$ crimson fastqc --help \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for parser-specific (FastQC) help\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-as-a-python-library-function\" class=\"anchor\" href=\"#as-a-python-library-function\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAs a Python library function\u003c/h3\u003e\n\u003cp\u003eGenerally, the function to import is located at \u003ccode\u003ecrimson.{program_name}.parser\u003c/code\u003e. For\nexample, to use the \u003ccode\u003epicard\u003c/code\u003e parser in your script, you can do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecrimson\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epicard\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# You can specify the input file name as a string ...\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eparsed\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epicard\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eparse\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/path/to/a/picard.metrics\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# ... or a file handle\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/path/to/a/picard.metrics\"\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esrc\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eparsed\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epicard\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eparse\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esrc\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why\" class=\"anchor\" href=\"#why\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eNot enough tools use standard output formats.\u003c/li\u003e\n\u003cli\u003eWriting and re-writing the same parsers across different scripts is not a productive\nway to spend the day.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-local-development\" class=\"anchor\" href=\"#local-development\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal Development\u003c/h2\u003e\n\u003cp\u003eSetting up a local development requires that you set up all of the supported Python\nversions. We recommend using \u003ca href=\"https://github.com/pyenv/pyenv\"\u003epyenv\u003c/a\u003e for this.\u003c/p\u003e\n\u003cp\u003eThe following steps can be your guide for your local development setup:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Clone the repository and cd into it.\u003c/span\u003e\n$ git clone https://git.sr.ht/~bow/crimson\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e crimson\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create your virtualenv.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you already have pyenv installed, you may use the Makefile rule below.\u003c/span\u003e\n$ make dev-pyenv\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install the package along with its development dependencies.\u003c/span\u003e\n$ make dev\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run the test and linter suite to verify the setup.\u003c/span\u003e\n$ make lint \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eIf you are interested, \u003ccode\u003ecrimson\u003c/code\u003e accepts the following types contribution:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocumentation additions (if anything seems unclear, feel free to open an issue)\u003c/li\u003e\n\u003cli\u003eBug reports\u003c/li\u003e\n\u003cli\u003eSupport for tools\u0027 outputs which can be converted to JSON or YAML.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor any of these, feel free to open an issue in the \u003ca href=\"https://github.com/bow/crimson/issues%3E\"\u003eissue\ntracker\u003c/a\u003e or submit a pull request.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecrimson\u003c/code\u003e is BSD-licensed. Refer to the \u003ccode\u003eLICENSE\u003c/code\u003e file for the full license.\u003c/p\u003e\n", - "stargazers_count": 31, - "subscribers_count": 4, - "topics": [ - "bioinformatics", - "json", - "yaml", - "converter" - ], - "updated_at": 1628018649.0 - }, - { - "data_format": 2, - "description": "Multi-modal Image Registration And Connectivity anaLysis", - "filenames": [ - "Singularity" - ], - "full_name": "AICONSlab/MIRACL", - "latest_release": "v2.4.0", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/mgoubran/MIRACL/blob/master/LICENSE.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/844437fd48ab8b39d37aa9c69c0404fd162507b7d975cda0f3adae5f47327a61/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6d676f756272616e2f4d495241434c\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/mgoubran/MIRACL\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e658fffe37df6b5a4ecf60479588013b14d8fead1cc509f8ebcda2c439b62a4c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d676f756272616e2f6d697261636c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e658fffe37df6b5a4ecf60479588013b14d8fead1cc509f8ebcda2c439b62a4c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d676f756272616e2f6d697261636c\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/mgoubran/miracl\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/gallery/images/icon.png\"\u003e\u003cimg src=\"docs/gallery/images/icon.png\" alt=\"alt text\" width=\"400\" height=\"250\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eMIRACL\u003c/strong\u003e (\u003cstrong\u003eM\u003c/strong\u003eulti-modal \u003cstrong\u003eI\u003c/strong\u003emage \u003cstrong\u003eR\u003c/strong\u003eegistration \u003cstrong\u003eA\u003c/strong\u003end \u003cstrong\u003eC\u003c/strong\u003eonnectivity ana\u003cstrong\u003eL\u003c/strong\u003eysis) is a general-purpose, open-source pipeline for automated:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRegistration of cleared and imaging data (ex. LSFM and MRI) to atlases (ex. Allen Reference Atlas)\u003c/li\u003e\n\u003cli\u003e3D Segmentation and feature extraction of cleared data\u003c/li\u003e\n\u003cli\u003eTract-specific or network-level connectivity analysis\u003c/li\u003e\n\u003cli\u003eStatistical analysis of cleared and imaging data\u003c/li\u003e\n\u003cli\u003eComparison of dMRI/tractography, virus tracing, and connectivity atlases\u003c/li\u003e\n\u003cli\u003eAtlas generation and Label manipulation\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cp\u003e**\u003cstrong\u003eNEW WORKFLOW/FEATURE RELEASE\u003c/strong\u003e**\u003c/p\u003e\n\u003cp\u003eWe have released our AI-based Cartography of Ensembles (ACE) workflow, an end-to-end, automated pipeline that integrates cutting-edge deep learning\nsegmentation models and advanced statistical methods to enable unbiased and generalizable brain-wide mapping of 3D alterations in neuronal activity,\nmorphology, or connectivity at the sub-regional and laminar levels beyond atlas-defined regions.\u003c/p\u003e\n\u003cp\u003eACE is now available. Tutorials and usage examples for ACE can be found in our \u003ca href=\"https://miracl.readthedocs.io/en/latest/tutorials/workflows/ace_flow/ace_flow.html\" rel=\"nofollow\"\u003edocs\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eWe recommend using MIRACL with the Docker or Singularity containers we provide but it can also be installed locally. For more details, see our\n\u003ca href=\"https://miracl.readthedocs.io\" rel=\"nofollow\"\u003edocs\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright (c) 2023 Maged Goubran\u003c/p\u003e\n\u003cp\u003eAll Rights Reserved\u003c/p\u003e\n", - "stargazers_count": 32, - "subscribers_count": 7, + "singularity/Singularity" + ], + "full_name": "bow/crimson", + "latest_release": "v0.5.1", + "readme": "\u003ch1\u003e\n\u003ca id=\"user-content-crimson\" class=\"anchor\" href=\"#crimson\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003ccode\u003ecrimson\u003c/code\u003e\n\u003c/h1\u003e\n\u003cp\u003e\u003ca href=\"https://pypi.org/project/crimson\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b88d8e72d91a58b5e0d8651dfb03de679b9d91a059cb88b8f4809639801721f3/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6372696d736f6e\" alt=\"pypi\" data-canonical-src=\"https://img.shields.io/pypi/v/crimson\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://builds.sr.ht/~bow/crimson?\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/21063ea62384043fb1ba2e562d346bea30f475aa5b3a714dbce1776ad5f0da70/68747470733a2f2f6275696c64732e73722e68742f7e626f772f6372696d736f6e2e737667\" alt=\"sourcehut\" data-canonical-src=\"https://builds.sr.ht/~bow/crimson.svg\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003ecrimson\u003c/code\u003e converts non-standard bioinformatics tool outputs to JSON or YAML.\u003c/p\u003e\n\u003cp\u003eCurrently it can convert outputs of the following tools:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc/%3E\" rel=\"nofollow\"\u003eFastQC\u003c/a\u003e (\u003ccode\u003efastqc\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ndaniel/fusioncatcher\"\u003eFusionCatcher\u003c/a\u003e (\u003ccode\u003efusioncatcher\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.htslib.org/doc/samtools.html\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e flagstat (\u003ccode\u003eflagstat\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://broadinstitute.github.io/picard/\" rel=\"nofollow\"\u003ePicard\u003c/a\u003e metrics tools (\u003ccode\u003epicard\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/alexdobin/STAR\"\u003eSTAR\u003c/a\u003e log file (\u003ccode\u003estar\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/STAR-Fusion/STAR-Fusion\"\u003eSTAR-Fusion\u003c/a\u003e hits table (\u003ccode\u003estar-fusion\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"http://www.ensembl.org/info/docs/tools/vep/index.html\" rel=\"nofollow\"\u003eVariant Effect Predictor\u003c/a\u003e\nplain text output (\u003ccode\u003evep\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe conversion can be done using the command line interface or by calling the\ntool-specificparser functions in your Python script.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-installation\" class=\"anchor\" href=\"#installation\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecrimson\u003c/code\u003e is available on the \u003ca href=\"https://pypi.org/project/crimson/\" rel=\"nofollow\"\u003ePython Package Index\u003c/a\u003e\nand you can install it via \u003ccode\u003epip\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install crimson\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIt is also available on\n\u003ca href=\"https://bioconda.github.io/recipes/crimson/README.html\" rel=\"nofollow\"\u003eBioConda\u003c/a\u003e, both through the\n\u003ccode\u003econda\u003c/code\u003e package manager or as a\n\u003ca href=\"https://quay.io/repository/biocontainers/crimson?tab=tags\" rel=\"nofollow\"\u003eDocker container\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-usage\" class=\"anchor\" href=\"#usage\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-as-a-command-line-tool\" class=\"anchor\" href=\"#as-a-command-line-tool\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAs a command line tool\u003c/h3\u003e\n\u003cp\u003eThe general command is \u003ccode\u003ecrimson {program_name}\u003c/code\u003e and by default the output is written to\n\u003ccode\u003estdout\u003c/code\u003e. For example, to use the \u003ccode\u003epicard\u003c/code\u003e parser, you would execute:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson picard /path/to/a/picard.metrics\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also specify a file name directly to write to a file. The following command will\nwrite the output to a file named \u003ccode\u003econverted.json\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson picard /path/to/a/picard.metrics converted.json\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSome parsers may also accept additional input format. The FastQC parser, for example, also\nworks if you specify a path to a FastQC output directory:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson fastqc /path/to/a/fastqc/dir\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor path to a zipped result:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson fastqc /path/to/a/fastqc_result.zip\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhen in doubt, use the \u003ccode\u003e--help\u003c/code\u003e flag:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ crimson --help \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for the general help\u003c/span\u003e\n$ crimson fastqc --help \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for parser-specific (FastQC) help\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3\u003e\n\u003ca id=\"user-content-as-a-python-library-function\" class=\"anchor\" href=\"#as-a-python-library-function\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAs a Python library function\u003c/h3\u003e\n\u003cp\u003eGenerally, the function to import is located at \u003ccode\u003ecrimson.{program_name}.parser\u003c/code\u003e. For\nexample, to use the \u003ccode\u003epicard\u003c/code\u003e parser in your script, you can do:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecrimson\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epicard\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# You can specify the input file name as a string ...\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eparsed\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epicard\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eparse\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/path/to/a/picard.metrics\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# ... or a file handle\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"/path/to/a/picard.metrics\"\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esrc\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eparsed\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epicard\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eparse\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esrc\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-why\" class=\"anchor\" href=\"#why\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWhy?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eNot enough tools use standard output formats.\u003c/li\u003e\n\u003cli\u003eWriting and re-writing the same parsers across different scripts is not a productive\nway to spend the day.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-local-development\" class=\"anchor\" href=\"#local-development\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLocal Development\u003c/h2\u003e\n\u003cp\u003eSetting up a local development requires that you set up all of the supported Python\nversions. We recommend using \u003ca href=\"https://github.com/pyenv/pyenv\"\u003epyenv\u003c/a\u003e for this.\u003c/p\u003e\n\u003cp\u003eThe following steps can be your guide for your local development setup:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Clone the repository and cd into it.\u003c/span\u003e\n$ git clone https://git.sr.ht/~bow/crimson\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e crimson\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create your virtualenv.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If you already have pyenv installed, you may use the Makefile rule below.\u003c/span\u003e\n$ make dev-pyenv\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install the package along with its development dependencies.\u003c/span\u003e\n$ make dev\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run the test and linter suite to verify the setup.\u003c/span\u003e\n$ make lint \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-contributing\" class=\"anchor\" href=\"#contributing\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eContributing\u003c/h2\u003e\n\u003cp\u003eIf you are interested, \u003ccode\u003ecrimson\u003c/code\u003e accepts the following types contribution:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDocumentation additions (if anything seems unclear, feel free to open an issue)\u003c/li\u003e\n\u003cli\u003eBug reports\u003c/li\u003e\n\u003cli\u003eSupport for tools\u0027 outputs which can be converted to JSON or YAML.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor any of these, feel free to open an issue in the \u003ca href=\"https://github.com/bow/crimson/issues%3E\"\u003eissue\ntracker\u003c/a\u003e or submit a pull request.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca id=\"user-content-license\" class=\"anchor\" href=\"#license\" aria-hidden=\"true\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLicense\u003c/h2\u003e\n\u003cp\u003e\u003ccode\u003ecrimson\u003c/code\u003e is BSD-licensed. Refer to the \u003ccode\u003eLICENSE\u003c/code\u003e file for the full license.\u003c/p\u003e\n", + "stargazers_count": 31, + "subscribers_count": 4, "topics": [ - "neuroscience", - "brain", - "atlases", - "connectivity", - "network", - "clarity", - "mri", - "neuroimaging", - "biomedical", - "image-registration", - "image-segmentation", - "python" + "bioinformatics", + "json", + "yaml", + "converter" ], - "updated_at": 1732202474.0 + "updated_at": 1628018649.0 }, { "data_format": 2, @@ -40533,20 +40534,6 @@ var data = "topics": [], "updated_at": 1714025770.0 }, - { - "data_format": 2, - "description": null, - "filenames": [ - "Singularityfile.def" - ], - "full_name": "Daffan/nav-competition-icra2022", - "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/BARN_Challenge.png\"\u003e\u003cimg width=\"100%\" src=\"res/BARN_Challenge.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/p\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-icra-2022-barn-challenge\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#icra-2022-barn-challenge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eICRA 2022 BARN Challenge\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eIf you run it on a local machine without containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eROS version at least Kinetic\u003c/li\u003e\n\u003cli\u003eCMake version at least 3.0.2\u003c/li\u003e\n\u003cli\u003ePython version at least 3.6\u003c/li\u003e\n\u003cli\u003ePython packages: defusedxml, rospkg, netifaces, numpy\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run it in Singularity containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGo version at least 1.13\u003c/li\u003e\n\u003cli\u003eSingularity version at least 3.6.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe requirements above are just suggestions. If you run into any issue, please contact organizers for help (\u003ca href=\"mailto:zfxu@utexas.edu\"\u003ezfxu@utexas.edu\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eFollow the instructions below to run simulations on your local machines. (You can skip 1-6 if you only use Singularity container)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a virtual environment (we show examples with python venv, you can use conda instead)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eapt -y update; apt-get -y install python3-venv\npython3 -m venv /\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge\nexport PATH=\"/\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge/bin:$PATH\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eInstall Python dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install defusedxml rospkg netifaces numpy\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eClone this repo and required ros packages: (replace \u003ccode\u003e\u0026lt;YOUR_ROS_VERSION\u0026gt;\u003c/code\u003e with your own, e.g. melodic)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/nav-competition-icra2022.git\ngit clone https://github.com/jackal/jackal.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/jackal/jackal_simulator.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/jackal/jackal_desktop.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInstall ROS package dependencies: (replace \u003ccode\u003e\u0026lt;YOUR_ROS_VERSION\u0026gt;\u003c/code\u003e with your own, e.g. melodic)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/\u0026lt;YOUR_ROS_VERSION\u0026gt;/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=\u0026lt;YOUR_ROS_VERSION\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eBuild the workspace (if \u003ccode\u003ecatkin_make\u003c/code\u003e fails, try changing \u003ccode\u003e-std=c++11\u003c/code\u003e to \u003ccode\u003e-std=c++17\u003c/code\u003e in \u003ccode\u003ejackal_helper/CMakeLists.txt\u003c/code\u003e line 3)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esource devel/setup.bash\ncatkin_make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollow the instruction below to run simulations in Singularity containers.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 is required to successfully build the image!\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repo\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/nav-competition-icra2022.git\ncd nav-competition-icra2022\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild Singularity image (sudo access required)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --notest nav_competition_image.sif Singularityfile.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-simulations\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#run-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Simulations\u003c/h2\u003e\n\u003cp\u003eNavigate to the folder of this repo. Below is the example to run move_base with DWA as local planner.\u003c/p\u003e\n\u003cp\u003eIf you run it on your local machines: (the example below runs \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e with DWA local planner in world 0)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource ../../devel/setup.sh\npython3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you run it in a Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA successful run should print the episode status (collided/succeeded/timeout) and the time cost in second:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation collided with time 27.2930 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation succeeded with time 29.4610 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation timeout with time 100.0000 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-your-own-navigation-stack\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#test-your-own-navigation-stack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest your own navigation stack\u003c/h2\u003e\n\u003cp\u003eWe currently don\u0027t provide a lot of instructions or a standard API for implementing the navigation stack, but we might add more in this section depending on people\u0027s feedback. If you are new to the ROS or mobile robot navigation, we suggest checking \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e which provides basic interface to manipulate a robot.\u003c/p\u003e\n\u003cp\u003eThe suggested work flow is to edit section 1 in \u003ccode\u003erun.py\u003c/code\u003e file (line 89-109) that initialize your own navigation stack. You should not edit other parts in this file. We provide a bash script \u003ccode\u003etest.sh\u003c/code\u003e to run your navigation stack on 50 uniformly sampled BARN worlds with 10 runs for each world. Once the tests finish, run \u003ccode\u003epython report_test.py --out_path /path/to/out/file\u003c/code\u003e to report the test. Below is an example of DWA:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython report_test.py --out_path res/dwa_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see the report as this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAvg Time: 33.4715, Avg Metric: 0.1693, Avg Success: 0.8800, Avg Collision: 0.0480, Avg Timeout: 0.0720\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eExcept for \u003ccode\u003eDWA\u003c/code\u003e, we also provide three learning-based navigation stack as examples (see branch \u003ccode\u003eLfH\u003c/code\u003e, \u003ccode\u003eapplr\u003c/code\u003e and \u003ccode\u003ee2e\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-submission\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#submission\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubmission\u003c/h2\u003e\n\u003cp\u003eSubmit a link that downloads your customized repository to this \u003ca href=\"https://docs.google.com/forms/d/e/1FAIpQLScGCMwVm-Kzg2c3kkXd_IUNwHw8D3s06ydCg4lPgOJYkEy8aQ/viewform?usp=sf_link\" rel=\"nofollow\"\u003eGoogle form\u003c/a\u003e. Your navigation stack will be tested in the Singularity container on 50 hold-out BARN worlds sampled from the same distribution as the 300 BARN worlds. In the repository, make sure the \u003ccode\u003erun.py\u003c/code\u003e runs your navigation stack and \u003ccode\u003eSingularityfile.def\u003c/code\u003e installs all the dependencies of your repo. We suggest to actually build an image and test it with \u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\u003c/code\u003e. You can also refer to branch \u003ccode\u003eLfH\u003c/code\u003e, \u003ccode\u003eapplr\u003c/code\u003e and \u003ccode\u003ee2e\u003c/code\u003e, which are in the correct form for submissions.\u003c/p\u003e\n", - "stargazers_count": 32, - "subscribers_count": 4, - "topics": [], - "updated_at": 1706251456.0 - }, { "data_format": 2, "description": "Singularity recipe for AlphaFold", @@ -40573,17 +40560,44 @@ var data = }, { "data_format": 2, - "description": "APSIM", + "description": null, "filenames": [ - "Release/Containers/Singularity/Singularity.ubuntu" + "Singularityfile.def" ], - "full_name": "APSIMInitiative/APSIM710", + "full_name": "Daffan/nav-competition-icra2022", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAPSIM\u003c/h1\u003e\u003ca id=\"user-content-apsim\" class=\"anchor\" aria-label=\"Permalink: APSIM\" href=\"#apsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Agricultural Production Systems sIMulator (APSIM) is internationally recognised as a highly advanced simulator of agricultural systems. It contains a suite of modules which enable the simulation of systems that cover a range of plant, animal, soil, climate and management interactions. APSIM is undergoing continual development, with new capability added to regular releases of official versions. Its development and maintenance is underpinned by rigorous science and software engineering standards. The APSIM Initiative has been established to promote the development and use of the science modules and infrastructure software of APSIM.\u003c/p\u003e\n\u003cp\u003eCI builds of this repository can be found \u003ca href=\"https://apsimdev.apsim.info/APSIM.Builds.Portal/Bob.aspx\" rel=\"nofollow\"\u003eHere\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 33, - "subscribers_count": 16, + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/BARN_Challenge.png\"\u003e\u003cimg width=\"100%\" src=\"res/BARN_Challenge.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/p\u003e\n\u003chr\u003e\n\u003ch1\u003e\u003ca id=\"user-content-icra-2022-barn-challenge\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#icra-2022-barn-challenge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eICRA 2022 BARN Challenge\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eIf you run it on a local machine without containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eROS version at least Kinetic\u003c/li\u003e\n\u003cli\u003eCMake version at least 3.0.2\u003c/li\u003e\n\u003cli\u003ePython version at least 3.6\u003c/li\u003e\n\u003cli\u003ePython packages: defusedxml, rospkg, netifaces, numpy\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run it in Singularity containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGo version at least 1.13\u003c/li\u003e\n\u003cli\u003eSingularity version at least 3.6.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe requirements above are just suggestions. If you run into any issue, please contact organizers for help (\u003ca href=\"mailto:zfxu@utexas.edu\"\u003ezfxu@utexas.edu\u003c/a\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eFollow the instructions below to run simulations on your local machines. (You can skip 1-6 if you only use Singularity container)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a virtual environment (we show examples with python venv, you can use conda instead)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eapt -y update; apt-get -y install python3-venv\npython3 -m venv /\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge\nexport PATH=\"/\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge/bin:$PATH\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eInstall Python dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install defusedxml rospkg netifaces numpy\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eClone this repo and required ros packages: (replace \u003ccode\u003e\u0026lt;YOUR_ROS_VERSION\u0026gt;\u003c/code\u003e with your own, e.g. melodic)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/nav-competition-icra2022.git\ngit clone https://github.com/jackal/jackal.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/jackal/jackal_simulator.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/jackal/jackal_desktop.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInstall ROS package dependencies: (replace \u003ccode\u003e\u0026lt;YOUR_ROS_VERSION\u0026gt;\u003c/code\u003e with your own, e.g. melodic)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/\u0026lt;YOUR_ROS_VERSION\u0026gt;/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=\u0026lt;YOUR_ROS_VERSION\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eBuild the workspace (if \u003ccode\u003ecatkin_make\u003c/code\u003e fails, try changing \u003ccode\u003e-std=c++11\u003c/code\u003e to \u003ccode\u003e-std=c++17\u003c/code\u003e in \u003ccode\u003ejackal_helper/CMakeLists.txt\u003c/code\u003e line 3)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esource devel/setup.bash\ncatkin_make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollow the instruction below to run simulations in Singularity containers.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 is required to successfully build the image!\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repo\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/nav-competition-icra2022.git\ncd nav-competition-icra2022\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild Singularity image (sudo access required)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --notest nav_competition_image.sif Singularityfile.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-run-simulations\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#run-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRun Simulations\u003c/h2\u003e\n\u003cp\u003eNavigate to the folder of this repo. Below is the example to run move_base with DWA as local planner.\u003c/p\u003e\n\u003cp\u003eIf you run it on your local machines: (the example below runs \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e with DWA local planner in world 0)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource ../../devel/setup.sh\npython3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you run it in a Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA successful run should print the episode status (collided/succeeded/timeout) and the time cost in second:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation collided with time 27.2930 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation succeeded with time 29.4610 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation timeout with time 100.0000 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2\u003e\u003ca id=\"user-content-test-your-own-navigation-stack\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#test-your-own-navigation-stack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eTest your own navigation stack\u003c/h2\u003e\n\u003cp\u003eWe currently don\u0027t provide a lot of instructions or a standard API for implementing the navigation stack, but we might add more in this section depending on people\u0027s feedback. If you are new to the ROS or mobile robot navigation, we suggest checking \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e which provides basic interface to manipulate a robot.\u003c/p\u003e\n\u003cp\u003eThe suggested work flow is to edit section 1 in \u003ccode\u003erun.py\u003c/code\u003e file (line 89-109) that initialize your own navigation stack. You should not edit other parts in this file. We provide a bash script \u003ccode\u003etest.sh\u003c/code\u003e to run your navigation stack on 50 uniformly sampled BARN worlds with 10 runs for each world. Once the tests finish, run \u003ccode\u003epython report_test.py --out_path /path/to/out/file\u003c/code\u003e to report the test. Below is an example of DWA:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython report_test.py --out_path res/dwa_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see the report as this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAvg Time: 33.4715, Avg Metric: 0.1693, Avg Success: 0.8800, Avg Collision: 0.0480, Avg Timeout: 0.0720\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eExcept for \u003ccode\u003eDWA\u003c/code\u003e, we also provide three learning-based navigation stack as examples (see branch \u003ccode\u003eLfH\u003c/code\u003e, \u003ccode\u003eapplr\u003c/code\u003e and \u003ccode\u003ee2e\u003c/code\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-submission\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#submission\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSubmission\u003c/h2\u003e\n\u003cp\u003eSubmit a link that downloads your customized repository to this \u003ca href=\"https://docs.google.com/forms/d/e/1FAIpQLScGCMwVm-Kzg2c3kkXd_IUNwHw8D3s06ydCg4lPgOJYkEy8aQ/viewform?usp=sf_link\" rel=\"nofollow\"\u003eGoogle form\u003c/a\u003e. Your navigation stack will be tested in the Singularity container on 50 hold-out BARN worlds sampled from the same distribution as the 300 BARN worlds. In the repository, make sure the \u003ccode\u003erun.py\u003c/code\u003e runs your navigation stack and \u003ccode\u003eSingularityfile.def\u003c/code\u003e installs all the dependencies of your repo. We suggest to actually build an image and test it with \u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\u003c/code\u003e. You can also refer to branch \u003ccode\u003eLfH\u003c/code\u003e, \u003ccode\u003eapplr\u003c/code\u003e and \u003ccode\u003ee2e\u003c/code\u003e, which are in the correct form for submissions.\u003c/p\u003e\n", + "stargazers_count": 32, + "subscribers_count": 4, "topics": [], - "updated_at": 1732336211.0 + "updated_at": 1706251456.0 + }, + { + "data_format": 2, + "description": "Multi-modal Image Registration And Connectivity anaLysis", + "filenames": [ + "Singularity" + ], + "full_name": "AICONSlab/MIRACL", + "latest_release": "v2.4.0", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/mgoubran/MIRACL/blob/master/LICENSE.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/844437fd48ab8b39d37aa9c69c0404fd162507b7d975cda0f3adae5f47327a61/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f6d676f756272616e2f4d495241434c\" alt=\"GitHub license\" data-canonical-src=\"https://img.shields.io/github/license/mgoubran/MIRACL\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e658fffe37df6b5a4ecf60479588013b14d8fead1cc509f8ebcda2c439b62a4c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d676f756272616e2f6d697261636c\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e658fffe37df6b5a4ecf60479588013b14d8fead1cc509f8ebcda2c439b62a4c/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d676f756272616e2f6d697261636c\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/mgoubran/miracl\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/gallery/images/icon.png\"\u003e\u003cimg src=\"docs/gallery/images/icon.png\" alt=\"alt text\" width=\"400\" height=\"250\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eMIRACL\u003c/strong\u003e (\u003cstrong\u003eM\u003c/strong\u003eulti-modal \u003cstrong\u003eI\u003c/strong\u003emage \u003cstrong\u003eR\u003c/strong\u003eegistration \u003cstrong\u003eA\u003c/strong\u003end \u003cstrong\u003eC\u003c/strong\u003eonnectivity ana\u003cstrong\u003eL\u003c/strong\u003eysis) is a general-purpose, open-source pipeline for automated:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRegistration of cleared and imaging data (ex. LSFM and MRI) to atlases (ex. Allen Reference Atlas)\u003c/li\u003e\n\u003cli\u003e3D Segmentation and feature extraction of cleared data\u003c/li\u003e\n\u003cli\u003eTract-specific or network-level connectivity analysis\u003c/li\u003e\n\u003cli\u003eStatistical analysis of cleared and imaging data\u003c/li\u003e\n\u003cli\u003eComparison of dMRI/tractography, virus tracing, and connectivity atlases\u003c/li\u003e\n\u003cli\u003eAtlas generation and Label manipulation\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cp\u003e**\u003cstrong\u003eNEW WORKFLOW/FEATURE RELEASE\u003c/strong\u003e**\u003c/p\u003e\n\u003cp\u003eWe have released our AI-based Cartography of Ensembles (ACE) workflow, an end-to-end, automated pipeline that integrates cutting-edge deep learning\nsegmentation models and advanced statistical methods to enable unbiased and generalizable brain-wide mapping of 3D alterations in neuronal activity,\nmorphology, or connectivity at the sub-regional and laminar levels beyond atlas-defined regions.\u003c/p\u003e\n\u003cp\u003eACE is now available. Tutorials and usage examples for ACE can be found in our \u003ca href=\"https://miracl.readthedocs.io/en/latest/tutorials/workflows/ace_flow/ace_flow.html\" rel=\"nofollow\"\u003edocs\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eWe recommend using MIRACL with the Docker or Singularity containers we provide but it can also be installed locally. For more details, see our\n\u003ca href=\"https://miracl.readthedocs.io\" rel=\"nofollow\"\u003edocs\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eCopyright (c) 2023 Maged Goubran\u003c/p\u003e\n\u003cp\u003eAll Rights Reserved\u003c/p\u003e\n", + "stargazers_count": 32, + "subscribers_count": 7, + "topics": [ + "neuroscience", + "brain", + "atlases", + "connectivity", + "network", + "clarity", + "mri", + "neuroimaging", + "biomedical", + "image-registration", + "image-segmentation", + "python" + ], + "updated_at": 1732202474.0 }, { "data_format": 2, @@ -40599,6 +40613,20 @@ var data = "topics": [], "updated_at": 1646079611.0 }, + { + "data_format": 2, + "description": "APSIM", + "filenames": [ + "Release/Containers/Singularity/Singularity.ubuntu" + ], + "full_name": "APSIMInitiative/APSIM710", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAPSIM\u003c/h1\u003e\u003ca id=\"user-content-apsim\" class=\"anchor\" aria-label=\"Permalink: APSIM\" href=\"#apsim\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Agricultural Production Systems sIMulator (APSIM) is internationally recognised as a highly advanced simulator of agricultural systems. It contains a suite of modules which enable the simulation of systems that cover a range of plant, animal, soil, climate and management interactions. APSIM is undergoing continual development, with new capability added to regular releases of official versions. Its development and maintenance is underpinned by rigorous science and software engineering standards. The APSIM Initiative has been established to promote the development and use of the science modules and infrastructure software of APSIM.\u003c/p\u003e\n\u003cp\u003eCI builds of this repository can be found \u003ca href=\"https://apsimdev.apsim.info/APSIM.Builds.Portal/Bob.aspx\" rel=\"nofollow\"\u003eHere\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 33, + "subscribers_count": 16, + "topics": [], + "updated_at": 1732336211.0 + }, { "data_format": 2, "description": "Tools related to the Genomics of Drug Sensitivity in Cancer (GDSC) projects (http://www.cancerrxgene.org/ )", @@ -40612,6 +40640,20 @@ var data = "topics": [], "updated_at": 1718807146.0 }, + { + "data_format": 2, + "description": "A snakemake pipeline to assembly, polishing, correction and quality check from Oxford nanopore reads.", + "filenames": [ + "culebrONT/containers/Singularity.report.def", + "culebrONT/containers/Singularity.culebront_tools.def" + ], + "full_name": "SouthGreenPlatform/culebrONT", + "latest_release": "2.2.0", + "stargazers_count": 35, + "subscribers_count": 17, + "topics": [], + "updated_at": 1706716570.0 + }, { "data_format": 2, "description": "\ud83c\udf08", @@ -40628,17 +40670,17 @@ var data = }, { "data_format": 2, - "description": "A snakemake pipeline to assembly, polishing, correction and quality check from Oxford nanopore reads.", + "description": "This is gromacs interface with cp2k by singularity which is convenient for users to migrate this programe", "filenames": [ - "culebrONT/containers/Singularity.report.def", - "culebrONT/containers/Singularity.culebront_tools.def" + "Singularity.def" ], - "full_name": "SouthGreenPlatform/culebrONT", - "latest_release": "2.2.0", - "stargazers_count": 35, - "subscribers_count": 17, + "full_name": "CondaPereira/gromacs_cp2k_singularity", + "latest_release": "gromacs_cp2k_singularity_v3.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-gromacs-interface-with-cp2k\" class=\"anchor\" aria-hidden=\"true\" href=\"#gromacs-interface-with-cp2k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003egromacs interface with cp2k\u003c/strong\u003e\u003c/em\u003e\u003c/h1\u003e\n\u003cp\u003eThis is a repository which will provide a portable gromacs \u003cem\u003e\u003cstrong\u003e(interface with cp2k)\u003c/strong\u003e\u003c/em\u003e version, and the singularity image file can solve the problem of software installation when there is a lack of dependencies during the service period. The version of the compiled programs now support CPU\u0027s \u003cem\u003e\u003cstrong\u003eAVX2\u003c/strong\u003e\u003c/em\u003e instruction settings \u003cem\u003e\u003cstrong\u003e(Ryzen 7 5800H)\u003c/strong\u003e\u003c/em\u003e, Other instruction sets have not been tested yet \u003cem\u003e\u003cstrong\u003e(such as SSE)\u003c/strong\u003e\u003c/em\u003e ,which may be OK and I also want to invite you to provide issues and we can solve these difficult together, thanks! :)\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-singularity-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-singularity-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Singularity installation\u003c/h3\u003e\n\u003cp\u003eBased on Singularity\u0027s high-performance computing container technology, compared with the container technology used in cloud computing environments such as Docker, Singularity supports both root and non-root user startup, and the user context remains unchanged before and after the container startup, which makes user privileges the same inside and outside of the container. Singularity emphasizes the convenience, portability, and scalability of container services while weakening the high isolation of container processes, resulting in lighter weight, fewer namespaces in the kernel, and less loss of performance. On the other hand, when you transplant a program by sif mirror, you\u0027d better install the singularity on the machine. Nevertheless, it must fail which will prompt \u003cem\u003e\u003cstrong\u003e\"/usr/bin/env run-singularity not found .\"\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e1. Conda Environment\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e conda create -n singularity python=3.8\u003c/span\u003e\nconda activate singularity\nconda install -c conda-forge singularity\nsingularity --version\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity version 3.8.6\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-manually-compile-ubuntu-2004\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-manually-compile-ubuntu-2004\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e2. Manually Compile (Ubuntu-20.04)\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThis way is recommended when you are an administrator or root user:)\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall dependencies\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get update\nsudo apt upgrade -y\nsudo apt-get install -y \\\n build-essential \\\n libseccomp-dev \\\n libglib2.0-dev \\\n pkg-config \\\n squashfs-tools \\\n cryptsetup \\\n runc\u003c/pre\u003e\u003c/div\u003e\n\u003ch5\u003e\u003ca id=\"user-content-install-go\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-go\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einstall Go\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e VERSION=1.19.3 OS=linux ARCH=amd64 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e change this as you need\u003c/span\u003e\n\nwget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\nsudo tar -C /usr/local -xzf /tmp/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then set your own environment variable\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch5\u003e\u003ca id=\"user-content-compiling-singularityce\" class=\"anchor\" aria-hidden=\"true\" href=\"#compiling-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling SingularityCE\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recurse-submodules https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\n\n./mconfig\nmake -C builddir\nsudo make -C builddir install\n\n./mconfig -b ./buildtree -p /usr/local\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eif you want to check out more information about the singularity installation progress, please visit this \u003ca href=\"https://github.com/sylabs/singularity/blob/main/INSTALL.md\"\u003ewebsite\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-3-use-of-the-gromacs_cp2k-fsif\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-use-of-the-gromacs_cp2k-fsif\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e3. Use of the gromacs_cp2k-f.sif\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eOnly need download it and then put it in your familiar file location.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pan.baidu.com/s/11R9Mf56B8OiAiw0ybUzU0Q?pwd=1234\" rel=\"nofollow\"\u003eDownload location_1 (mainland)\u003c/a\u003e \u003cem\u003e\u003cstrong\u003ecode\u003c/strong\u003e\u003c/em\u003e: 1234\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"\"\u003eDownload location_2 (All users)\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e /path to gromacs_cp2k-f.sif gmx_cp2k --version\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e this progress might produce huge cache file so i recommend you change the singularity tmp_dir to the proper location \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-4-submit-mission-on-the-slurm-cluster\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-submit-mission-on-the-slurm-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e4. Submit mission on the slurm cluster\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eif you want to run gromacs_cp2k_singularity on the slurm cluster, i have provided a slurm template below, and this will not be influenced by the mpirun version which have been installed on your cluster.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -J nma-em\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -p CondaPereira-PC\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -N 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH --mail-user=szkchris@sina.com\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH --mail-type=ALL\u003c/span\u003e\n\nmpirun -np 8 /software/gromacs_cp2k_v2/gromacs_cp2k-v2.sif gmx_cp2k mdrun -v -deffnm nma-em\u003c/pre\u003e\u003c/div\u003e\n", + "stargazers_count": 36, + "subscribers_count": 1, "topics": [], - "updated_at": 1706716570.0 + "updated_at": 1683912696.0 }, { "data_format": 2, @@ -40667,31 +40709,17 @@ var data = }, { "data_format": 2, - "description": "This is gromacs interface with cp2k by singularity which is convenient for users to migrate this programe", - "filenames": [ - "Singularity.def" - ], - "full_name": "CondaPereira/gromacs_cp2k_singularity", - "latest_release": "gromacs_cp2k_singularity_v3.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-gromacs-interface-with-cp2k\" class=\"anchor\" aria-hidden=\"true\" href=\"#gromacs-interface-with-cp2k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003egromacs interface with cp2k\u003c/strong\u003e\u003c/em\u003e\u003c/h1\u003e\n\u003cp\u003eThis is a repository which will provide a portable gromacs \u003cem\u003e\u003cstrong\u003e(interface with cp2k)\u003c/strong\u003e\u003c/em\u003e version, and the singularity image file can solve the problem of software installation when there is a lack of dependencies during the service period. The version of the compiled programs now support CPU\u0027s \u003cem\u003e\u003cstrong\u003eAVX2\u003c/strong\u003e\u003c/em\u003e instruction settings \u003cem\u003e\u003cstrong\u003e(Ryzen 7 5800H)\u003c/strong\u003e\u003c/em\u003e, Other instruction sets have not been tested yet \u003cem\u003e\u003cstrong\u003e(such as SSE)\u003c/strong\u003e\u003c/em\u003e ,which may be OK and I also want to invite you to provide issues and we can solve these difficult together, thanks! :)\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-singularity-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-singularity-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Singularity installation\u003c/h3\u003e\n\u003cp\u003eBased on Singularity\u0027s high-performance computing container technology, compared with the container technology used in cloud computing environments such as Docker, Singularity supports both root and non-root user startup, and the user context remains unchanged before and after the container startup, which makes user privileges the same inside and outside of the container. Singularity emphasizes the convenience, portability, and scalability of container services while weakening the high isolation of container processes, resulting in lighter weight, fewer namespaces in the kernel, and less loss of performance. On the other hand, when you transplant a program by sif mirror, you\u0027d better install the singularity on the machine. Nevertheless, it must fail which will prompt \u003cem\u003e\u003cstrong\u003e\"/usr/bin/env run-singularity not found .\"\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-conda-environment\" class=\"anchor\" aria-hidden=\"true\" href=\"#1-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e1. Conda Environment\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e conda create -n singularity python=3.8\u003c/span\u003e\nconda activate singularity\nconda install -c conda-forge singularity\nsingularity --version\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity version 3.8.6\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-manually-compile-ubuntu-2004\" class=\"anchor\" aria-hidden=\"true\" href=\"#2-manually-compile-ubuntu-2004\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e2. Manually Compile (Ubuntu-20.04)\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThis way is recommended when you are an administrator or root user:)\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall dependencies\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get update\nsudo apt upgrade -y\nsudo apt-get install -y \\\n build-essential \\\n libseccomp-dev \\\n libglib2.0-dev \\\n pkg-config \\\n squashfs-tools \\\n cryptsetup \\\n runc\u003c/pre\u003e\u003c/div\u003e\n\u003ch5\u003e\u003ca id=\"user-content-install-go\" class=\"anchor\" aria-hidden=\"true\" href=\"#install-go\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einstall Go\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e VERSION=1.19.3 OS=linux ARCH=amd64 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e change this as you need\u003c/span\u003e\n\nwget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\nsudo tar -C /usr/local -xzf /tmp/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then set your own environment variable\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch5\u003e\u003ca id=\"user-content-compiling-singularityce\" class=\"anchor\" aria-hidden=\"true\" href=\"#compiling-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling SingularityCE\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recurse-submodules https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\n\n./mconfig\nmake -C builddir\nsudo make -C builddir install\n\n./mconfig -b ./buildtree -p /usr/local\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eif you want to check out more information about the singularity installation progress, please visit this \u003ca href=\"https://github.com/sylabs/singularity/blob/main/INSTALL.md\"\u003ewebsite\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-3-use-of-the-gromacs_cp2k-fsif\" class=\"anchor\" aria-hidden=\"true\" href=\"#3-use-of-the-gromacs_cp2k-fsif\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e3. Use of the gromacs_cp2k-f.sif\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eOnly need download it and then put it in your familiar file location.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pan.baidu.com/s/11R9Mf56B8OiAiw0ybUzU0Q?pwd=1234\" rel=\"nofollow\"\u003eDownload location_1 (mainland)\u003c/a\u003e \u003cem\u003e\u003cstrong\u003ecode\u003c/strong\u003e\u003c/em\u003e: 1234\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"\"\u003eDownload location_2 (All users)\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e /path to gromacs_cp2k-f.sif gmx_cp2k --version\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e this progress might produce huge cache file so i recommend you change the singularity tmp_dir to the proper location \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-4-submit-mission-on-the-slurm-cluster\" class=\"anchor\" aria-hidden=\"true\" href=\"#4-submit-mission-on-the-slurm-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e4. Submit mission on the slurm cluster\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eif you want to run gromacs_cp2k_singularity on the slurm cluster, i have provided a slurm template below, and this will not be influenced by the mpirun version which have been installed on your cluster.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -J nma-em\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -p CondaPereira-PC\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -N 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH --mail-user=szkchris@sina.com\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH --mail-type=ALL\u003c/span\u003e\n\nmpirun -np 8 /software/gromacs_cp2k_v2/gromacs_cp2k-v2.sif gmx_cp2k mdrun -v -deffnm nma-em\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 36, - "subscribers_count": 1, - "topics": [], - "updated_at": 1683912696.0 - }, - { - "data_format": 2, - "description": "Dockerfile to create an image with OpenFOAM-plus and PyTorch support", + "description": "A High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology", "filenames": [ - "Singularity.def" + "docker/Singularity" ], - "full_name": "AndreWeiner/of_pytorch_docker", + "full_name": "jlevy44/PathFlowAI", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker/Singularity/Apptainer + OpenFOAM\u00ae + PyTorch\u003c/h1\u003e\u003ca id=\"user-content-dockersingularityapptainer--openfoam--pytorch\" class=\"anchor\" aria-label=\"Permalink: Docker/Singularity/Apptainer + OpenFOAM\u00ae + PyTorch\" href=\"#dockersingularityapptainer--openfoam--pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Dockerfile in this repository creates an image with \u003ca href=\"https://openfoam.com/\" rel=\"nofollow\"\u003eESI-OpenFOAM\u003c/a\u003e and \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e support. The image is currently based on\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUbuntu 24.04,\u003c/li\u003e\n\u003cli\u003eOpenFOAM-v2406, and\u003c/li\u003e\n\u003cli\u003ePyTorch 2.5.0 (only CPU).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere are also convenience scripts for working with the images. The \u003cem\u003etest\u003c/em\u003e directory contains two examples demonstrating how to compile applications using \u003cem\u003ecmake\u003c/em\u003e and \u003cem\u003ewmake\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOpenFOAM is not compiled from scratch but installed via the package manager (\u003ca href=\"https://develop.openfoam.com/Development/openfoam/-/wikis/precompiled/debian\" rel=\"nofollow\"\u003eread more\u003c/a\u003e). Also for PyTorch, only the pre-compiled C++ part of the library, named \u003cem\u003elibtorch\u003c/em\u003e, is contained on the image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to build the images\u003c/h2\u003e\u003ca id=\"user-content-how-to-build-the-images\" class=\"anchor\" aria-label=\"Permalink: How to build the images\" href=\"#how-to-build-the-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker image\u003c/h3\u003e\u003ca id=\"user-content-docker-image\" class=\"anchor\" aria-label=\"Permalink: Docker image\" href=\"#docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the image yourself, copy this repository and navigate into the top-level folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/AndreWeiner/of_pytorch_docker.git\ncd of_pytorch_docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to upload the image to a Docker registry, consider the following naming convention when running the build command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t user_name/of_pytorch:of2404-py2.5.0-cpu -f Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePushing the image to Dockerhub works as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker login\ndocker push user_name/of_pytorch:of2406-py2.5.0-cpu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA word on \u003cem\u003ePodman\u003c/em\u003e: due to the strong compatibility between Docker and Podman (builder), building and using the image with Podman instead of Docker should work, too, but I have not tested it yet.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity/Apptainer image\u003c/h3\u003e\u003ca id=\"user-content-singularityapptainer-image\" class=\"anchor\" aria-label=\"Permalink: Singularity/Apptainer image\" href=\"#singularityapptainer-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor university clusters, \u003ca href=\"https://apptainer.org/docs/user/main/introduction.html\" rel=\"nofollow\"\u003eSingularity/Apptainer\u003c/a\u003e is often the only supported container tool. Note that Singularity was renamed to \u003cstrong\u003eApptainer\u003c/strong\u003e after joining the Linux Foundation. In contrast to the default Docker workflow, the \u003cstrong\u003eexecution\u003c/strong\u003e of Apptainer containers does not require root-privileges (the image creation does, though). Moreover, Apptainer works out of the box with Schedulers like SLURM and was build with focus on MPI-parallel applications. The Docker image built before can be easily converted to Apptainer by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apptainer build of2406-py2.5.0-cpu.sif docker://andreweiner/of_pytorch:of2406-py2.5.0-cpu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe image may be used similarly to the Docker image. Convenience scripts like \u003cem\u003ecreate_openfoam_container.sh\u003c/em\u003e or \u003cem\u003estart_openfoam.sh\u003c/em\u003e are not necessary because Apptainer performs similar actions by default (e.g., mapping of the user and important directories). To start an interactive shell, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapptainer shell of2406-py2.5.0-cpu.sif\n# first thing to do inside the container\n. /usr/lib/openfoam/openfoam2406/etc/bashrc\n# now you are ready to run and build OpenFOAM+PyTorch applications\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnroot image\u003c/h3\u003e\u003ca id=\"user-content-enroot-image\" class=\"anchor\" aria-label=\"Permalink: Enroot image\" href=\"#enroot-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs pointed out in \u003ca href=\"https://github.com/AndreWeiner/of_pytorch_docker/issues/4\"\u003ethis issue\u003c/a\u003e by \u003ca href=\"https://github.com/dr-br\"\u003e@dr-br\u003c/a\u003e, it is fairly easy to use the Docker image with Nvidia\u0027s \u003ca href=\"https://github.com/NVIDIA/enroot\"\u003eenroot\u003c/a\u003e. After installing enroot, the following commands pull and convert the Docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eenroot import docker://andreweiner/of_pytorch:of2406-py2.5.0-cpu\nenroot create --name of2406-py2.5.0-cpu andreweiner+of_pytorch+of2406-py2.5.0-cpu.sqsh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage and examples\u003c/h2\u003e\u003ca id=\"user-content-usage-and-examples\" class=\"anchor\" aria-label=\"Permalink: Usage and examples\" href=\"#usage-and-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker image\u003c/h3\u003e\u003ca id=\"user-content-docker-image-1\" class=\"anchor\" aria-label=\"Permalink: Docker image\" href=\"#docker-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy this repository and navigate into the top-level folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/AndreWeiner/of_pytorch_docker.git\ncd of_pytorch_docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe script \u003cem\u003ecreate_openfoam_container.sh\u003c/em\u003e creates a container with suitable settings (e.g. mapping the user into the container, mounting the current directory, setting the working directory to \u003cem\u003e./test/\u003c/em\u003e). The script also pulls the Docker image from \u003ca href=\"https://hub.docker.com/repository/docker/andreweiner/of_pytorch\" rel=\"nofollow\"\u003eDockerhub\u003c/a\u003e if it cannot be found locally. The default image and container names can be changed by passing them as command line arguments.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# default settings\n./create_openfoam_container.sh\n\n# use different image, e.g., to use an older version\n./create_openfoam_container.sh \"andreweiner/of_pytorch:of2012-py1.7.1-cpu\" \"of2012-py1.7.1-cpu\" \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003cem\u003estart_openfoam.sh\u003c/em\u003e script starts an interactive shell instance in the running container. If you modified the container name in the previous step, provide the modified name as command line argument.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# default\n./start_openfoam.sh\n\n# custom container name\n./start_openfoam.sh \"of2012-py1.7.1-cpu\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe container\u0027s entry point is set to the \u003cem\u003etest\u003c/em\u003e directory. There you are presented with two examples:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003etensorCreation\u003c/strong\u003e: PyTorch tensor basics; compiled with \u003cstrong\u003ewmake\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003esimpleMLP\u003c/strong\u003e: implementation of a simple \u003cem\u003emultilayer perceptron\u003c/em\u003e (MLP) class; compiled with \u003cstrong\u003ecmake\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cem\u003etensorCreation\u003c/em\u003e\u003c/h3\u003e\u003ca id=\"user-content-tensorcreation\" class=\"anchor\" aria-label=\"Permalink: tensorCreation\" href=\"#tensorcreation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo compile and run \u003cem\u003etensorCreation\u003c/em\u003e, execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# you must be inside the container for this to work\n# execute ./start_openfoam.sh to launch a shell instance\ncd tensorCreation\nwmake\n./tensorCreation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cem\u003esimpleMLP\u003c/em\u003e\u003c/h3\u003e\u003ca id=\"user-content-simplemlp\" class=\"anchor\" aria-label=\"Permalink: simpleMLP\" href=\"#simplemlp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo compile and run \u003cem\u003esimpleMLP\u003c/em\u003e, execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# you must be inside the container for this to work\n# execute ./start_openfoam.sh to launch a shell instance\ncd simpleMLP\nmkdir build\ncd build\ncmake ..\nmake\n./simpleMLP\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity/Apptainer image\u003c/h3\u003e\u003ca id=\"user-content-singularityapptainer-image-1\" class=\"anchor\" aria-label=\"Permalink: Singularity/Apptainer image\" href=\"#singularityapptainer-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom the top-level folder of this repository, you can build and run the examples as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapptainer shell of2406-py2.5.0-cpu.sif\n# first thing to do inside the container\n. /usr/lib/openfoam/openfoam2406/etc/bashrc\n# go to the tensorCreation example, compile, and run\ncd test/tensorCreation\nwmake\n./tensorCreation\n# go to the simpleMLP example and execute the test script\ncd ../simpleMLP\n./compileAndRun.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo hide the additional complexity from using containers, one can also define shell functions that assemble suitable commands in the background. E.g., in \u003ca href=\"https://github.com/FlowModelingControl/naca0012_shock_buffet/blob/main/functions\"\u003ethis\u003c/a\u003e file, the function \u003ccode\u003esingularityRun\u003c/code\u003e works the same way as the frequently used \u003ccode\u003erunApplication\u003c/code\u003e but uses a Singularity/Apptainer image in the background.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnroot image\u003c/h3\u003e\u003ca id=\"user-content-enroot-image-1\" class=\"anchor\" aria-label=\"Permalink: Enroot image\" href=\"#enroot-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing enroot is very similar to working with Apptainer. The main difference is that we have to bind the \u003cem\u003etest\u003c/em\u003e directory manually. The command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# assuming to be at the top-level of this repository\nenroot start -m \"$PWD/test\":\"/home/$USER\" of2406-py2.5.0-cpu bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ebinds the \u003cem\u003etest\u003c/em\u003e folder on the host to \u003cem\u003e/home/$USER\u003c/em\u003e inside the container and starts a new shell. Once the shell is open, compiling and running the tests works as described before.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet in touch\u003c/h2\u003e\u003ca id=\"user-content-get-in-touch\" class=\"anchor\" aria-label=\"Permalink: Get in touch\" href=\"#get-in-touch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you would like to suggest changes or improvements regarding the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild process,\u003c/li\u003e\n\u003cli\u003epre-installed packages,\u003c/li\u003e\n\u003cli\u003eexamples,\u003c/li\u003e\n\u003cli\u003edocumentation,\u003c/li\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eplease use the \u003ca href=\"https://github.com/AndreWeiner/of_pytorch_docker/issues\"\u003eissue tracker\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMiscellaneous\u003c/h2\u003e\u003ca id=\"user-content-miscellaneous\" class=\"anchor\" aria-label=\"Permalink: Miscellaneous\" href=\"#miscellaneous\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIssue with Sourceforge\u003c/h3\u003e\u003ca id=\"user-content-issue-with-sourceforge\" class=\"anchor\" aria-label=\"Permalink: Issue with Sourceforge\" href=\"#issue-with-sourceforge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuilding the Docker image sometimes fails with the error message:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eE: Failed to fetch \u003ca href=\"https://nav.dl.sourceforge.net/project/openfoam/repos/deb/dists/focal/main/pool/2012_1/binary-amd64/openfoam2012_1-2_amd64.deb\" rel=\"nofollow\"\u003ehttps://nav.dl.sourceforge.net/project/openfoam/repos/deb/dists/focal/main/pool/2012_1/binary-amd64/openfoam2012_1-2_amd64.deb\u003c/a\u003e\u003cbr\u003e\nCould not connect to nav.dl.sourceforge.net:443 (5.154.224.27), connection timed out\nE: Unable to fetch some archives, maybe run apt-get update or try with --fix-missing?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThe OpenFOAM Debian package is hosted on Sourceforge, and sometimes their servers are not available. Usually waiting a couple of minutes before rebuilding the image helps to solve this issue.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOlder versions of the Dockerfile\u003c/h3\u003e\u003ca id=\"user-content-older-versions-of-the-dockerfile\" class=\"anchor\" aria-label=\"Permalink: Older versions of the Dockerfile\" href=\"#older-versions-of-the-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two more Dockerfiles which were used to build previous versions of the Docker image. They remain part of the repository since they might be helpful to some users.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eDockerfile.abi_source\u003c/em\u003e: OpenFOAM is compiled from scratch; only some third-party packages are installed and, hence, some applications are missing\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eDockerfile.no_abi\u003c/em\u003e: OpenFOAM is compiled from scratch with modified compiler flags to be compatible with \u003cem\u003elibtorch\u003c/em\u003e versions prior to version 1.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default, the OpenFOAM library will be compiled running two jobs in parallel. If you prefer to use more jobs, set the \u003cem\u003eNP\u003c/em\u003e build argument, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --build-args NP=8 -t user_name/of_pytorch:of1912-py1.5-cpu -f Dockerfile.abi .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI also recommend to save the Docker output in a log-file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --build-args NP=8 -t user_name/of_pytorch:of1912-py1.5-cpu -f Dockerfile.abi . \u0026amp;\u0026gt; log.docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eABI\u003c/h3\u003e\u003ca id=\"user-content-abi\" class=\"anchor\" aria-label=\"Permalink: ABI\" href=\"#abi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince version 1.3 of PyTorch, there is a version of libtorch compiled with ABI enabled (\u003ca href=\"https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html\" rel=\"nofollow\"\u003eread more\u003c/a\u003e). The only change in prior versions compared to a regular compilation is the additional flag\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-D_GLIBCXX_USE_CXX11_ABI=0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhen compiling OpenFOAM. The flag is related to backwards compatibility for standards older than C++11 (\u003ca href=\"https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html\" rel=\"nofollow\"\u003eread more\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLibtorch\u003c/h3\u003e\u003ca id=\"user-content-libtorch\" class=\"anchor\" aria-label=\"Permalink: Libtorch\" href=\"#libtorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe PyTorch library files are located in \u003cem\u003e/opt/libtorch\u003c/em\u003e. The environment variable \u003cstrong\u003eTORCH_LIBRARIES\u003c/strong\u003e can be used to indicate the location of certain header and library files to the compiler and linker. To compile PyTorch C++ code using \u003cem\u003ewmake\u003c/em\u003e, add\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eEXE_INC = \\\n -I$(TORCH_LIBRARIES)/include \\\n -I$(TORCH_LIBRARIES)/include/torch/csrc/api/include \\\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto the include paths, and\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eEXE_LIBS = \\\n -Wl,-rpath,$(TORCH_LIBRARIES)/lib $(TORCH_LIBRARIES)/lib/libtorch.so $(TORCH_LIBRARIES)/lib/libc10.so \\\n -Wl,--no-as-needed,$(TORCH_LIBRARIES)/lib/libtorch_cpu.so \\\n -Wl,--as-needed $(TORCH_LIBRARIES)/lib/libc10.so \\\n -Wl,--no-as-needed,$(TORCH_LIBRARIES)/lib/libtorch.so\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto the library paths.\u003c/p\u003e\n", + "readme": "\u003ch1 align=\"center\"\u003e\u003ca id=\"user-content-welcome-to-pathflowai-\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#welcome-to-pathflowai-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to PathFlowAI \u003c/h1\u003e\n\u003cp\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/04326a93c9e29389e4820d081197228f17c813d31ef892ec634161db000840a8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e312d626c75652e7376673f63616368655365636f6e64733d32353932303030\"\u003e\u003cimg alt=\"Version\" src=\"https://camo.githubusercontent.com/04326a93c9e29389e4820d081197228f17c813d31ef892ec634161db000840a8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e312d626c75652e7376673f63616368655365636f6e64733d32353932303030\" data-canonical-src=\"https://img.shields.io/badge/version-0.1-blue.svg?cacheSeconds=2592000\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://jlevy44.github.io/PathFlowAI/\" rel=\"nofollow\"\u003e\n \u003cimg alt=\"Documentation\" src=\"https://camo.githubusercontent.com/d548501ddd04019606593dd09918078c6aca34cbcd1af0e9728381106bf5755c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d7965732d627269676874677265656e2e737667\" data-canonical-src=\"https://img.shields.io/badge/documentation-yes-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA Convenient High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content--homepage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#-homepage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\ud83c\udfe0 \u003ca href=\"https://github.com/jlevy44/PathFlowAI\"\u003eHomepage\u003c/a\u003e\n\u003c/h3\u003e\n\u003cp\u003ePublished in the Proceedings of the Pacific Symposium for Biocomputing 2020, Manuscript: \u003ca href=\"https://psb.stanford.edu/psb-online/proceedings/psb20/Levy.pdf\" rel=\"nofollow\"\u003ehttps://psb.stanford.edu/psb-online/proceedings/psb20/Levy.pdf\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eFirst, install \u003ca href=\"https://openslide.org/download/\" rel=\"nofollow\"\u003eopenslide\u003c/a\u003e. Note: may need to install libiconv and shapely using conda. Will update with more installation information, please submit issues as well.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install pathflowai\ninstall_apex\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epathflowai-preprocess -h\npathflowai-train_model -h\npathflowai-monitor -h\npathflowai-visualize -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/jlevy44/PathFlowAI/wiki\"\u003eWiki\u003c/a\u003e for more information on setting up and running the workflow. Please submit feedback as issues and let me know if there is any trouble with installation and I am more than happy to provide advice and fixes.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthor\u003c/h2\u003e\n\u003cp\u003e\ud83d\udc64 \u003cstrong\u003eJoshua Levy\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGithub: \u003ca href=\"https://github.com/jlevy44\"\u003e@jlevy44\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content--contributing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#-contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\ud83e\udd1d Contributing\u003c/h2\u003e\n\u003cp\u003eContributions, issues and feature requests are welcome!\u003cbr\u003eFeel free to check \u003ca href=\"https://github.com/jlevy44/PathFlowAI/issues\"\u003eissues page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigures from the Paper\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/19698023/62230963-0199d780-b391-11e9-96eb-ac9b86686723.jpeg\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/19698023/62230963-0199d780-b391-11e9-96eb-ac9b86686723.jpeg\" alt=\"1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFig. 1. PathFlowAI Framework: a) Annotations and whole slide images are preprocessed in parallel using\nDask; b) Deep learning prediction model is trained on the model; c) Results are visualized; d) UMAP\nembeddings provide diagnostics; e) SHAP framework is used to find important regions for the prediction\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/19698023/62231545-41ad8a00-b392-11e9-8d47-f9f83f4b764a.jpeg\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/19698023/62231545-41ad8a00-b392-11e9-8d47-f9f83f4b764a.jpeg\" alt=\"2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFig. 2. Comparison of PathFlowAI to Preprocessing WSI in Series for: a) Preprocessing time, b) Storage\nSpace, c) Impact on the filesystem. The PathFlowAI method of parallel processing followed by\ncentralized storage saves both time and storage space\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/19698023/62231546-41ad8a00-b392-11e9-9b16-ea3b2b92bf3f.jpeg\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/19698023/62231546-41ad8a00-b392-11e9-9b16-ea3b2b92bf3f.jpeg\" alt=\"3\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFig. 3. Segmentation: Original (a) Annotations Compared to Predicted (b) Annotations; (c) Pathologist\nannotations guided by the classification model\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/19698023/62230966-02326e00-b391-11e9-989c-155ff0a9be67.jpeg\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/19698023/62230966-02326e00-b391-11e9-989c-155ff0a9be67.jpeg\" alt=\"4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFig. 4. Portal Classification Results: a) Darker tiles indicate a higher assigned probability of portal classification, b)\nAUC-ROC curves for the test images that estimate overall accuracy given different sensitivity cutoffs, c) H\u0026amp;E patch\n(left) with corresponding SHAP interpretations (right) for four patches; the probability value of portal classification\nis shown, and on the SHAP value scale, red indicates regions that the model attributes to portal prediction, d) Model trained UMAP embeddings of patches colored by original portal coverage (area of patch covered by portal) as judged\nby pathologist and visualization of individual patches\u003c/p\u003e\n", "stargazers_count": 37, "subscribers_count": 3, "topics": [], - "updated_at": 1731722941.0 + "updated_at": 1700475823.0 }, { "data_format": 2, @@ -40716,33 +40744,31 @@ var data = }, { "data_format": 2, - "description": "A High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology", + "description": "Dockerfile to create an image with OpenFOAM-plus and PyTorch support", "filenames": [ - "docker/Singularity" + "Singularity.def" ], - "full_name": "jlevy44/PathFlowAI", + "full_name": "AndreWeiner/of_pytorch_docker", "latest_release": null, - "readme": "\u003ch1 align=\"center\"\u003e\u003ca id=\"user-content-welcome-to-pathflowai-\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#welcome-to-pathflowai-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eWelcome to PathFlowAI \u003c/h1\u003e\n\u003cp\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/04326a93c9e29389e4820d081197228f17c813d31ef892ec634161db000840a8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e312d626c75652e7376673f63616368655365636f6e64733d32353932303030\"\u003e\u003cimg alt=\"Version\" src=\"https://camo.githubusercontent.com/04326a93c9e29389e4820d081197228f17c813d31ef892ec634161db000840a8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f76657273696f6e2d302e312d626c75652e7376673f63616368655365636f6e64733d32353932303030\" data-canonical-src=\"https://img.shields.io/badge/version-0.1-blue.svg?cacheSeconds=2592000\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://jlevy44.github.io/PathFlowAI/\" rel=\"nofollow\"\u003e\n \u003cimg alt=\"Documentation\" src=\"https://camo.githubusercontent.com/d548501ddd04019606593dd09918078c6aca34cbcd1af0e9728381106bf5755c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d7965732d627269676874677265656e2e737667\" data-canonical-src=\"https://img.shields.io/badge/documentation-yes-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eA Convenient High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3\u003e\u003ca id=\"user-content--homepage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#-homepage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\ud83c\udfe0 \u003ca href=\"https://github.com/jlevy44/PathFlowAI\"\u003eHomepage\u003c/a\u003e\n\u003c/h3\u003e\n\u003cp\u003ePublished in the Proceedings of the Pacific Symposium for Biocomputing 2020, Manuscript: \u003ca href=\"https://psb.stanford.edu/psb-online/proceedings/psb20/Levy.pdf\" rel=\"nofollow\"\u003ehttps://psb.stanford.edu/psb-online/proceedings/psb20/Levy.pdf\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall\u003c/h2\u003e\n\u003cp\u003eFirst, install \u003ca href=\"https://openslide.org/download/\" rel=\"nofollow\"\u003eopenslide\u003c/a\u003e. Note: may need to install libiconv and shapely using conda. Will update with more installation information, please submit issues as well.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install pathflowai\ninstall_apex\u003c/pre\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epathflowai-preprocess -h\npathflowai-train_model -h\npathflowai-monitor -h\npathflowai-visualize -h\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/jlevy44/PathFlowAI/wiki\"\u003eWiki\u003c/a\u003e for more information on setting up and running the workflow. Please submit feedback as issues and let me know if there is any trouble with installation and I am more than happy to provide advice and fixes.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#author\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAuthor\u003c/h2\u003e\n\u003cp\u003e\ud83d\udc64 \u003cstrong\u003eJoshua Levy\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGithub: \u003ca href=\"https://github.com/jlevy44\"\u003e@jlevy44\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content--contributing\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#-contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\ud83e\udd1d Contributing\u003c/h2\u003e\n\u003cp\u003eContributions, issues and feature requests are welcome!\u003cbr\u003eFeel free to check \u003ca href=\"https://github.com/jlevy44/PathFlowAI/issues\"\u003eissues page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigures from the Paper\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/19698023/62230963-0199d780-b391-11e9-96eb-ac9b86686723.jpeg\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/19698023/62230963-0199d780-b391-11e9-96eb-ac9b86686723.jpeg\" alt=\"1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFig. 1. PathFlowAI Framework: a) Annotations and whole slide images are preprocessed in parallel using\nDask; b) Deep learning prediction model is trained on the model; c) Results are visualized; d) UMAP\nembeddings provide diagnostics; e) SHAP framework is used to find important regions for the prediction\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/19698023/62231545-41ad8a00-b392-11e9-8d47-f9f83f4b764a.jpeg\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/19698023/62231545-41ad8a00-b392-11e9-8d47-f9f83f4b764a.jpeg\" alt=\"2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFig. 2. Comparison of PathFlowAI to Preprocessing WSI in Series for: a) Preprocessing time, b) Storage\nSpace, c) Impact on the filesystem. The PathFlowAI method of parallel processing followed by\ncentralized storage saves both time and storage space\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/19698023/62231546-41ad8a00-b392-11e9-9b16-ea3b2b92bf3f.jpeg\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/19698023/62231546-41ad8a00-b392-11e9-9b16-ea3b2b92bf3f.jpeg\" alt=\"3\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFig. 3. Segmentation: Original (a) Annotations Compared to Predicted (b) Annotations; (c) Pathologist\nannotations guided by the classification model\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/19698023/62230966-02326e00-b391-11e9-989c-155ff0a9be67.jpeg\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/19698023/62230966-02326e00-b391-11e9-989c-155ff0a9be67.jpeg\" alt=\"4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFig. 4. Portal Classification Results: a) Darker tiles indicate a higher assigned probability of portal classification, b)\nAUC-ROC curves for the test images that estimate overall accuracy given different sensitivity cutoffs, c) H\u0026amp;E patch\n(left) with corresponding SHAP interpretations (right) for four patches; the probability value of portal classification\nis shown, and on the SHAP value scale, red indicates regions that the model attributes to portal prediction, d) Model trained UMAP embeddings of patches colored by original portal coverage (area of patch covered by portal) as judged\nby pathologist and visualization of individual patches\u003c/p\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDocker/Singularity/Apptainer + OpenFOAM\u00ae + PyTorch\u003c/h1\u003e\u003ca id=\"user-content-dockersingularityapptainer--openfoam--pytorch\" class=\"anchor\" aria-label=\"Permalink: Docker/Singularity/Apptainer + OpenFOAM\u00ae + PyTorch\" href=\"#dockersingularityapptainer--openfoam--pytorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Dockerfile in this repository creates an image with \u003ca href=\"https://openfoam.com/\" rel=\"nofollow\"\u003eESI-OpenFOAM\u003c/a\u003e and \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e support. The image is currently based on\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUbuntu 24.04,\u003c/li\u003e\n\u003cli\u003eOpenFOAM-v2406, and\u003c/li\u003e\n\u003cli\u003ePyTorch 2.5.0 (only CPU).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere are also convenience scripts for working with the images. The \u003cem\u003etest\u003c/em\u003e directory contains two examples demonstrating how to compile applications using \u003cem\u003ecmake\u003c/em\u003e and \u003cem\u003ewmake\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOpenFOAM is not compiled from scratch but installed via the package manager (\u003ca href=\"https://develop.openfoam.com/Development/openfoam/-/wikis/precompiled/debian\" rel=\"nofollow\"\u003eread more\u003c/a\u003e). Also for PyTorch, only the pre-compiled C++ part of the library, named \u003cem\u003elibtorch\u003c/em\u003e, is contained on the image.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to build the images\u003c/h2\u003e\u003ca id=\"user-content-how-to-build-the-images\" class=\"anchor\" aria-label=\"Permalink: How to build the images\" href=\"#how-to-build-the-images\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker image\u003c/h3\u003e\u003ca id=\"user-content-docker-image\" class=\"anchor\" aria-label=\"Permalink: Docker image\" href=\"#docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo build the image yourself, copy this repository and navigate into the top-level folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/AndreWeiner/of_pytorch_docker.git\ncd of_pytorch_docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to upload the image to a Docker registry, consider the following naming convention when running the build command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t user_name/of_pytorch:of2404-py2.5.0-cpu -f Dockerfile .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ePushing the image to Dockerhub works as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker login\ndocker push user_name/of_pytorch:of2406-py2.5.0-cpu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA word on \u003cem\u003ePodman\u003c/em\u003e: due to the strong compatibility between Docker and Podman (builder), building and using the image with Podman instead of Docker should work, too, but I have not tested it yet.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity/Apptainer image\u003c/h3\u003e\u003ca id=\"user-content-singularityapptainer-image\" class=\"anchor\" aria-label=\"Permalink: Singularity/Apptainer image\" href=\"#singularityapptainer-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor university clusters, \u003ca href=\"https://apptainer.org/docs/user/main/introduction.html\" rel=\"nofollow\"\u003eSingularity/Apptainer\u003c/a\u003e is often the only supported container tool. Note that Singularity was renamed to \u003cstrong\u003eApptainer\u003c/strong\u003e after joining the Linux Foundation. In contrast to the default Docker workflow, the \u003cstrong\u003eexecution\u003c/strong\u003e of Apptainer containers does not require root-privileges (the image creation does, though). Moreover, Apptainer works out of the box with Schedulers like SLURM and was build with focus on MPI-parallel applications. The Docker image built before can be easily converted to Apptainer by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apptainer build of2406-py2.5.0-cpu.sif docker://andreweiner/of_pytorch:of2406-py2.5.0-cpu\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe image may be used similarly to the Docker image. Convenience scripts like \u003cem\u003ecreate_openfoam_container.sh\u003c/em\u003e or \u003cem\u003estart_openfoam.sh\u003c/em\u003e are not necessary because Apptainer performs similar actions by default (e.g., mapping of the user and important directories). To start an interactive shell, run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapptainer shell of2406-py2.5.0-cpu.sif\n# first thing to do inside the container\n. /usr/lib/openfoam/openfoam2406/etc/bashrc\n# now you are ready to run and build OpenFOAM+PyTorch applications\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnroot image\u003c/h3\u003e\u003ca id=\"user-content-enroot-image\" class=\"anchor\" aria-label=\"Permalink: Enroot image\" href=\"#enroot-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAs pointed out in \u003ca href=\"https://github.com/AndreWeiner/of_pytorch_docker/issues/4\"\u003ethis issue\u003c/a\u003e by \u003ca href=\"https://github.com/dr-br\"\u003e@dr-br\u003c/a\u003e, it is fairly easy to use the Docker image with Nvidia\u0027s \u003ca href=\"https://github.com/NVIDIA/enroot\"\u003eenroot\u003c/a\u003e. After installing enroot, the following commands pull and convert the Docker image:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eenroot import docker://andreweiner/of_pytorch:of2406-py2.5.0-cpu\nenroot create --name of2406-py2.5.0-cpu andreweiner+of_pytorch+of2406-py2.5.0-cpu.sqsh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage and examples\u003c/h2\u003e\u003ca id=\"user-content-usage-and-examples\" class=\"anchor\" aria-label=\"Permalink: Usage and examples\" href=\"#usage-and-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocker image\u003c/h3\u003e\u003ca id=\"user-content-docker-image-1\" class=\"anchor\" aria-label=\"Permalink: Docker image\" href=\"#docker-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCopy this repository and navigate into the top-level folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/AndreWeiner/of_pytorch_docker.git\ncd of_pytorch_docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe script \u003cem\u003ecreate_openfoam_container.sh\u003c/em\u003e creates a container with suitable settings (e.g. mapping the user into the container, mounting the current directory, setting the working directory to \u003cem\u003e./test/\u003c/em\u003e). The script also pulls the Docker image from \u003ca href=\"https://hub.docker.com/repository/docker/andreweiner/of_pytorch\" rel=\"nofollow\"\u003eDockerhub\u003c/a\u003e if it cannot be found locally. The default image and container names can be changed by passing them as command line arguments.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# default settings\n./create_openfoam_container.sh\n\n# use different image, e.g., to use an older version\n./create_openfoam_container.sh \"andreweiner/of_pytorch:of2012-py1.7.1-cpu\" \"of2012-py1.7.1-cpu\" \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe \u003cem\u003estart_openfoam.sh\u003c/em\u003e script starts an interactive shell instance in the running container. If you modified the container name in the previous step, provide the modified name as command line argument.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# default\n./start_openfoam.sh\n\n# custom container name\n./start_openfoam.sh \"of2012-py1.7.1-cpu\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe container\u0027s entry point is set to the \u003cem\u003etest\u003c/em\u003e directory. There you are presented with two examples:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003etensorCreation\u003c/strong\u003e: PyTorch tensor basics; compiled with \u003cstrong\u003ewmake\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003esimpleMLP\u003c/strong\u003e: implementation of a simple \u003cem\u003emultilayer perceptron\u003c/em\u003e (MLP) class; compiled with \u003cstrong\u003ecmake\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cem\u003etensorCreation\u003c/em\u003e\u003c/h3\u003e\u003ca id=\"user-content-tensorcreation\" class=\"anchor\" aria-label=\"Permalink: tensorCreation\" href=\"#tensorcreation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo compile and run \u003cem\u003etensorCreation\u003c/em\u003e, execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# you must be inside the container for this to work\n# execute ./start_openfoam.sh to launch a shell instance\ncd tensorCreation\nwmake\n./tensorCreation\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cem\u003esimpleMLP\u003c/em\u003e\u003c/h3\u003e\u003ca id=\"user-content-simplemlp\" class=\"anchor\" aria-label=\"Permalink: simpleMLP\" href=\"#simplemlp\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo compile and run \u003cem\u003esimpleMLP\u003c/em\u003e, execute:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# you must be inside the container for this to work\n# execute ./start_openfoam.sh to launch a shell instance\ncd simpleMLP\nmkdir build\ncd build\ncmake ..\nmake\n./simpleMLP\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eSingularity/Apptainer image\u003c/h3\u003e\u003ca id=\"user-content-singularityapptainer-image-1\" class=\"anchor\" aria-label=\"Permalink: Singularity/Apptainer image\" href=\"#singularityapptainer-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFrom the top-level folder of this repository, you can build and run the examples as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eapptainer shell of2406-py2.5.0-cpu.sif\n# first thing to do inside the container\n. /usr/lib/openfoam/openfoam2406/etc/bashrc\n# go to the tensorCreation example, compile, and run\ncd test/tensorCreation\nwmake\n./tensorCreation\n# go to the simpleMLP example and execute the test script\ncd ../simpleMLP\n./compileAndRun.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo hide the additional complexity from using containers, one can also define shell functions that assemble suitable commands in the background. E.g., in \u003ca href=\"https://github.com/FlowModelingControl/naca0012_shock_buffet/blob/main/functions\"\u003ethis\u003c/a\u003e file, the function \u003ccode\u003esingularityRun\u003c/code\u003e works the same way as the frequently used \u003ccode\u003erunApplication\u003c/code\u003e but uses a Singularity/Apptainer image in the background.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnroot image\u003c/h3\u003e\u003ca id=\"user-content-enroot-image-1\" class=\"anchor\" aria-label=\"Permalink: Enroot image\" href=\"#enroot-image-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsing enroot is very similar to working with Apptainer. The main difference is that we have to bind the \u003cem\u003etest\u003c/em\u003e directory manually. The command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# assuming to be at the top-level of this repository\nenroot start -m \"$PWD/test\":\"/home/$USER\" of2406-py2.5.0-cpu bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ebinds the \u003cem\u003etest\u003c/em\u003e folder on the host to \u003cem\u003e/home/$USER\u003c/em\u003e inside the container and starts a new shell. Once the shell is open, compiling and running the tests works as described before.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGet in touch\u003c/h2\u003e\u003ca id=\"user-content-get-in-touch\" class=\"anchor\" aria-label=\"Permalink: Get in touch\" href=\"#get-in-touch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you would like to suggest changes or improvements regarding the\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebuild process,\u003c/li\u003e\n\u003cli\u003epre-installed packages,\u003c/li\u003e\n\u003cli\u003eexamples,\u003c/li\u003e\n\u003cli\u003edocumentation,\u003c/li\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eplease use the \u003ca href=\"https://github.com/AndreWeiner/of_pytorch_docker/issues\"\u003eissue tracker\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMiscellaneous\u003c/h2\u003e\u003ca id=\"user-content-miscellaneous\" class=\"anchor\" aria-label=\"Permalink: Miscellaneous\" href=\"#miscellaneous\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIssue with Sourceforge\u003c/h3\u003e\u003ca id=\"user-content-issue-with-sourceforge\" class=\"anchor\" aria-label=\"Permalink: Issue with Sourceforge\" href=\"#issue-with-sourceforge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBuilding the Docker image sometimes fails with the error message:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eE: Failed to fetch \u003ca href=\"https://nav.dl.sourceforge.net/project/openfoam/repos/deb/dists/focal/main/pool/2012_1/binary-amd64/openfoam2012_1-2_amd64.deb\" rel=\"nofollow\"\u003ehttps://nav.dl.sourceforge.net/project/openfoam/repos/deb/dists/focal/main/pool/2012_1/binary-amd64/openfoam2012_1-2_amd64.deb\u003c/a\u003e\u003cbr\u003e\nCould not connect to nav.dl.sourceforge.net:443 (5.154.224.27), connection timed out\nE: Unable to fetch some archives, maybe run apt-get update or try with --fix-missing?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThe OpenFOAM Debian package is hosted on Sourceforge, and sometimes their servers are not available. Usually waiting a couple of minutes before rebuilding the image helps to solve this issue.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eOlder versions of the Dockerfile\u003c/h3\u003e\u003ca id=\"user-content-older-versions-of-the-dockerfile\" class=\"anchor\" aria-label=\"Permalink: Older versions of the Dockerfile\" href=\"#older-versions-of-the-dockerfile\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThere are two more Dockerfiles which were used to build previous versions of the Docker image. They remain part of the repository since they might be helpful to some users.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eDockerfile.abi_source\u003c/em\u003e: OpenFOAM is compiled from scratch; only some third-party packages are installed and, hence, some applications are missing\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eDockerfile.no_abi\u003c/em\u003e: OpenFOAM is compiled from scratch with modified compiler flags to be compatible with \u003cem\u003elibtorch\u003c/em\u003e versions prior to version 1.3\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default, the OpenFOAM library will be compiled running two jobs in parallel. If you prefer to use more jobs, set the \u003cem\u003eNP\u003c/em\u003e build argument, e.g.:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --build-args NP=8 -t user_name/of_pytorch:of1912-py1.5-cpu -f Dockerfile.abi .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eI also recommend to save the Docker output in a log-file:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build --build-args NP=8 -t user_name/of_pytorch:of1912-py1.5-cpu -f Dockerfile.abi . \u0026amp;\u0026gt; log.docker\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eABI\u003c/h3\u003e\u003ca id=\"user-content-abi\" class=\"anchor\" aria-label=\"Permalink: ABI\" href=\"#abi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince version 1.3 of PyTorch, there is a version of libtorch compiled with ABI enabled (\u003ca href=\"https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html\" rel=\"nofollow\"\u003eread more\u003c/a\u003e). The only change in prior versions compared to a regular compilation is the additional flag\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-D_GLIBCXX_USE_CXX11_ABI=0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhen compiling OpenFOAM. The flag is related to backwards compatibility for standards older than C++11 (\u003ca href=\"https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html\" rel=\"nofollow\"\u003eread more\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLibtorch\u003c/h3\u003e\u003ca id=\"user-content-libtorch\" class=\"anchor\" aria-label=\"Permalink: Libtorch\" href=\"#libtorch\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe PyTorch library files are located in \u003cem\u003e/opt/libtorch\u003c/em\u003e. The environment variable \u003cstrong\u003eTORCH_LIBRARIES\u003c/strong\u003e can be used to indicate the location of certain header and library files to the compiler and linker. To compile PyTorch C++ code using \u003cem\u003ewmake\u003c/em\u003e, add\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eEXE_INC = \\\n -I$(TORCH_LIBRARIES)/include \\\n -I$(TORCH_LIBRARIES)/include/torch/csrc/api/include \\\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto the include paths, and\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eEXE_LIBS = \\\n -Wl,-rpath,$(TORCH_LIBRARIES)/lib $(TORCH_LIBRARIES)/lib/libtorch.so $(TORCH_LIBRARIES)/lib/libc10.so \\\n -Wl,--no-as-needed,$(TORCH_LIBRARIES)/lib/libtorch_cpu.so \\\n -Wl,--as-needed $(TORCH_LIBRARIES)/lib/libc10.so \\\n -Wl,--no-as-needed,$(TORCH_LIBRARIES)/lib/libtorch.so\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto the library paths.\u003c/p\u003e\n", "stargazers_count": 37, "subscribers_count": 3, "topics": [], - "updated_at": 1700475823.0 + "updated_at": 1731722941.0 }, { "data_format": 2, - "description": "Tomography Reconstructon Pipeline", + "description": null, "filenames": [ - "install/savu_singularity/singularity-recipes/Singularity.SavuDeps", - "install/savu_singularity/singularity-recipes/Singularity.SavuAstra", - "install/savu_singularity/singularity-recipes/Singularity.SavuCore", - "install/savu_singularity/conda-recipes/Singularity" + "Singularityfile.def" ], - "full_name": "DiamondLightSource/Savu", - "latest_release": "v4.2", + "full_name": "Daffan/the-barn-challenge", + "latest_release": null, + "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/BARN_Challenge.png\"\u003e\u003cimg width=\"100%\" src=\"res/BARN_Challenge.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eICRA BARN Navigation Challenge\u003c/h1\u003e\u003ca id=\"user-content-icra-barn-navigation-challenge\" class=\"anchor\" aria-label=\"Permalink: ICRA BARN Navigation Challenge\" href=\"#icra-barn-navigation-challenge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates:\u003c/h2\u003e\u003ca id=\"user-content-updates\" class=\"anchor\" aria-label=\"Permalink: Updates:\" href=\"#updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e02/04/2024: Adding 60 \u003ca href=\"https://github.com/aninair1905/DynaBARN\"\u003eDynaBARN\u003c/a\u003e environments. DynaBARN environments can be accessed by world indexes from 300-359.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you run it on a local machine without containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eROS version at least Kinetic\u003c/li\u003e\n\u003cli\u003eCMake version at least 3.0.2\u003c/li\u003e\n\u003cli\u003ePython version at least 3.6\u003c/li\u003e\n\u003cli\u003ePython packages: defusedxml, rospkg, netifaces, numpy\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run it in Singularity containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGo version at least 1.13\u003c/li\u003e\n\u003cli\u003eSingularity version at least 3.6.3 and less than 4.02\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe requirements above are just suggestions. If you run into any issue, please contact organizers for help (\u003ca href=\"mailto:zfxu@utexas.edu\"\u003ezfxu@utexas.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow the instructions below to run simulations on your local machines. (You can skip 1-6 if you only use Singularity container)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a virtual environment (we show examples with python venv, you can use conda instead)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eapt -y update; apt-get -y install python3-venv\npython3 -m venv /\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge\nexport PATH=\"/\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge/bin:$PATH\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eInstall Python dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install defusedxml rospkg netifaces numpy\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eClone this repo and required ros packages: (replace \u003ccode\u003e\u0026lt;YOUR_ROS_VERSION\u0026gt;\u003c/code\u003e with your own, e.g. melodic)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/the-barn-challenge.git\ngit clone https://github.com/jackal/jackal.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/jackal/jackal_simulator.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/jackal/jackal_desktop.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInstall ROS package dependencies: (replace \u003ccode\u003e\u0026lt;YOUR_ROS_VERSION\u0026gt;\u003c/code\u003e with your own, e.g. melodic)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/\u0026lt;YOUR_ROS_VERSION\u0026gt;/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=\u0026lt;YOUR_ROS_VERSION\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eBuild the workspace (if \u003ccode\u003ecatkin_make\u003c/code\u003e fails, try changing \u003ccode\u003e-std=c++11\u003c/code\u003e to \u003ccode\u003e-std=c++17\u003c/code\u003e in \u003ccode\u003ejackal_helper/CMakeLists.txt\u003c/code\u003e line 3)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecatkin_make\nsource devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollow the instruction below to run simulations in Singularity containers.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 and \u0026lt;= 4.02 is required to successfully build the image!\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repo\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/the-barn-challenge.git\ncd the-barn-challenge\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild Singularity image (sudo access required)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --notest nav_competition_image.sif Singularityfile.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Simulations\u003c/h2\u003e\u003ca id=\"user-content-run-simulations\" class=\"anchor\" aria-label=\"Permalink: Run Simulations\" href=\"#run-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNavigate to the folder of this repo. Below is the example to run move_base with DWA as local planner.\u003c/p\u003e\n\u003cp\u003eIf you run it on your local machines: (the example below runs \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e with DWA local planner in world 0)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource ../../devel/setup.sh\npython3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you run it in a Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA successful run should print the episode status (collided/succeeded/timeout) and the time cost in second:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation collided with time 27.2930 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation succeeded with time 29.4610 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation timeout with time 100.0000 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest your own navigation stack\u003c/h2\u003e\u003ca id=\"user-content-test-your-own-navigation-stack\" class=\"anchor\" aria-label=\"Permalink: Test your own navigation stack\" href=\"#test-your-own-navigation-stack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe currently don\u0027t provide a lot of instructions or a standard API for implementing the navigation stack, but we might add more in this section depending on people\u0027s feedback. If you are new to the ROS or mobile robot navigation, we suggest checking \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e which provides basic interface to manipulate a robot.\u003c/p\u003e\n\u003cp\u003eThe suggested work flow is to edit section 1 in \u003ccode\u003erun.py\u003c/code\u003e file (line 89-109) that initialize your own navigation stack. You should not edit other parts in this file. We provide a bash script \u003ccode\u003etest.sh\u003c/code\u003e to run your navigation stack on 50 uniformly sampled BARN worlds with 10 runs for each world. Once the tests finish, run \u003ccode\u003epython report_test.py --out_path /path/to/out/file\u003c/code\u003e to report the test. Below is an example of DWA:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython report_test.py --out_path res/dwa_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see the report as this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAvg Time: 33.4715, Avg Metric: 0.1693, Avg Success: 0.8800, Avg Collision: 0.0480, Avg Timeout: 0.0720\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eExcept for \u003ccode\u003eDWA\u003c/code\u003e, we also provide three learning-based navigation stack as examples (see branch \u003ccode\u003eLfH\u003c/code\u003e, \u003ccode\u003eapplr\u003c/code\u003e and \u003ccode\u003ee2e\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSubmission\u003c/h2\u003e\u003ca id=\"user-content-submission\" class=\"anchor\" aria-label=\"Permalink: Submission\" href=\"#submission\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSubmit a link that downloads your customized repository to this \u003ca href=\"https://docs.google.com/forms/d/e/1FAIpQLSfZLMVluXE-HWnV9lNP00LuBi3e9HFOeLi30p9tsHUViWpqrA/viewform\" rel=\"nofollow\"\u003eGoogle form\u003c/a\u003e. Your navigation stack will be tested in the Singularity container on 50 hold-out BARN worlds sampled from the same distribution as the 300 BARN worlds. In the repository, make sure the \u003ccode\u003erun.py\u003c/code\u003e runs your navigation stack and \u003ccode\u003eSingularityfile.def\u003c/code\u003e installs all the dependencies of your repo. We suggest to actually build an image and test it with \u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\u003c/code\u003e. You can also refer to branch \u003ccode\u003eLfH\u003c/code\u003e, \u003ccode\u003eapplr\u003c/code\u003e and \u003ccode\u003ee2e\u003c/code\u003e, which are in the correct form for submissions.\u003c/p\u003e\n", "stargazers_count": 38, - "subscribers_count": 17, + "subscribers_count": 4, "topics": [], - "updated_at": 1722494936.0 + "updated_at": 1712740335.0 }, { "data_format": 2, @@ -40766,17 +40792,19 @@ var data = }, { "data_format": 2, - "description": null, + "description": "Tomography Reconstructon Pipeline", "filenames": [ - "Singularityfile.def" + "install/savu_singularity/singularity-recipes/Singularity.SavuDeps", + "install/savu_singularity/singularity-recipes/Singularity.SavuAstra", + "install/savu_singularity/singularity-recipes/Singularity.SavuCore", + "install/savu_singularity/conda-recipes/Singularity" ], - "full_name": "Daffan/the-barn-challenge", - "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"res/BARN_Challenge.png\"\u003e\u003cimg width=\"100%\" src=\"res/BARN_Challenge.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eICRA BARN Navigation Challenge\u003c/h1\u003e\u003ca id=\"user-content-icra-barn-navigation-challenge\" class=\"anchor\" aria-label=\"Permalink: ICRA BARN Navigation Challenge\" href=\"#icra-barn-navigation-challenge\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdates:\u003c/h2\u003e\u003ca id=\"user-content-updates\" class=\"anchor\" aria-label=\"Permalink: Updates:\" href=\"#updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e02/04/2024: Adding 60 \u003ca href=\"https://github.com/aninair1905/DynaBARN\"\u003eDynaBARN\u003c/a\u003e environments. DynaBARN environments can be accessed by world indexes from 300-359.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you run it on a local machine without containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eROS version at least Kinetic\u003c/li\u003e\n\u003cli\u003eCMake version at least 3.0.2\u003c/li\u003e\n\u003cli\u003ePython version at least 3.6\u003c/li\u003e\n\u003cli\u003ePython packages: defusedxml, rospkg, netifaces, numpy\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you run it in Singularity containers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGo version at least 1.13\u003c/li\u003e\n\u003cli\u003eSingularity version at least 3.6.3 and less than 4.02\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe requirements above are just suggestions. If you run into any issue, please contact organizers for help (\u003ca href=\"mailto:zfxu@utexas.edu\"\u003ezfxu@utexas.edu\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFollow the instructions below to run simulations on your local machines. (You can skip 1-6 if you only use Singularity container)\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate a virtual environment (we show examples with python venv, you can use conda instead)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003eapt -y update; apt-get -y install python3-venv\npython3 -m venv /\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge\nexport PATH=\"/\u0026lt;YOUR_HOME_DIR\u0026gt;/nav_challenge/bin:$PATH\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eInstall Python dependencies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003epip3 install defusedxml rospkg netifaces numpy\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eCreate ROS workspace\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003emkdir -p /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\ncd /\u0026lt;YOUR_HOME_DIR\u0026gt;/jackal_ws/src\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eClone this repo and required ros packages: (replace \u003ccode\u003e\u0026lt;YOUR_ROS_VERSION\u0026gt;\u003c/code\u003e with your own, e.g. melodic)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/the-barn-challenge.git\ngit clone https://github.com/jackal/jackal.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/jackal/jackal_simulator.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/jackal/jackal_desktop.git --branch \u0026lt;YOUR_ROS_VERSION\u0026gt;-devel\ngit clone https://github.com/utexas-bwi/eband_local_planner.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eInstall ROS package dependencies: (replace \u003ccode\u003e\u0026lt;YOUR_ROS_VERSION\u0026gt;\u003c/code\u003e with your own, e.g. melodic)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecd ..\nsource /opt/ros/\u0026lt;YOUR_ROS_VERSION\u0026gt;/setup.bash\nrosdep init; rosdep update\nrosdep install -y --from-paths . --ignore-src --rosdistro=\u0026lt;YOUR_ROS_VERSION\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eBuild the workspace (if \u003ccode\u003ecatkin_make\u003c/code\u003e fails, try changing \u003ccode\u003e-std=c++11\u003c/code\u003e to \u003ccode\u003e-std=c++17\u003c/code\u003e in \u003ccode\u003ejackal_helper/CMakeLists.txt\u003c/code\u003e line 3)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003ecatkin_make\nsource devel/setup.bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFollow the instruction below to run simulations in Singularity containers.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFollow this instruction to install Singularity: \u003ca href=\"https://sylabs.io/guides/3.0/user-guide/installation.html\" rel=\"nofollow\"\u003ehttps://sylabs.io/guides/3.0/user-guide/installation.html\u003c/a\u003e. Singularity version \u0026gt;= 3.6.3 and \u0026lt;= 4.02 is required to successfully build the image!\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone this repo\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/Daffan/the-barn-challenge.git\ncd the-barn-challenge\n\u003c/code\u003e\u003c/pre\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eBuild Singularity image (sudo access required)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build --notest nav_competition_image.sif Singularityfile.def\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun Simulations\u003c/h2\u003e\u003ca id=\"user-content-run-simulations\" class=\"anchor\" aria-label=\"Permalink: Run Simulations\" href=\"#run-simulations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNavigate to the folder of this repo. Below is the example to run move_base with DWA as local planner.\u003c/p\u003e\n\u003cp\u003eIf you run it on your local machines: (the example below runs \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e with DWA local planner in world 0)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esource ../../devel/setup.sh\npython3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you run it in a Singularity container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA successful run should print the episode status (collided/succeeded/timeout) and the time cost in second:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation collided with time 27.2930 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation succeeded with time 29.4610 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt; Test finished! \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003eNavigation timeout with time 100.0000 (s)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest your own navigation stack\u003c/h2\u003e\u003ca id=\"user-content-test-your-own-navigation-stack\" class=\"anchor\" aria-label=\"Permalink: Test your own navigation stack\" href=\"#test-your-own-navigation-stack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe currently don\u0027t provide a lot of instructions or a standard API for implementing the navigation stack, but we might add more in this section depending on people\u0027s feedback. If you are new to the ROS or mobile robot navigation, we suggest checking \u003ca href=\"http://wiki.ros.org/move_base\" rel=\"nofollow\"\u003emove_base\u003c/a\u003e which provides basic interface to manipulate a robot.\u003c/p\u003e\n\u003cp\u003eThe suggested work flow is to edit section 1 in \u003ccode\u003erun.py\u003c/code\u003e file (line 89-109) that initialize your own navigation stack. You should not edit other parts in this file. We provide a bash script \u003ccode\u003etest.sh\u003c/code\u003e to run your navigation stack on 50 uniformly sampled BARN worlds with 10 runs for each world. Once the tests finish, run \u003ccode\u003epython report_test.py --out_path /path/to/out/file\u003c/code\u003e to report the test. Below is an example of DWA:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython report_test.py --out_path res/dwa_out.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should see the report as this:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eAvg Time: 33.4715, Avg Metric: 0.1693, Avg Success: 0.8800, Avg Collision: 0.0480, Avg Timeout: 0.0720\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eExcept for \u003ccode\u003eDWA\u003c/code\u003e, we also provide three learning-based navigation stack as examples (see branch \u003ccode\u003eLfH\u003c/code\u003e, \u003ccode\u003eapplr\u003c/code\u003e and \u003ccode\u003ee2e\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSubmission\u003c/h2\u003e\u003ca id=\"user-content-submission\" class=\"anchor\" aria-label=\"Permalink: Submission\" href=\"#submission\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSubmit a link that downloads your customized repository to this \u003ca href=\"https://docs.google.com/forms/d/e/1FAIpQLSfZLMVluXE-HWnV9lNP00LuBi3e9HFOeLi30p9tsHUViWpqrA/viewform\" rel=\"nofollow\"\u003eGoogle form\u003c/a\u003e. Your navigation stack will be tested in the Singularity container on 50 hold-out BARN worlds sampled from the same distribution as the 300 BARN worlds. In the repository, make sure the \u003ccode\u003erun.py\u003c/code\u003e runs your navigation stack and \u003ccode\u003eSingularityfile.def\u003c/code\u003e installs all the dependencies of your repo. We suggest to actually build an image and test it with \u003ccode\u003e./singularity_run.sh /path/to/image/file python3 run.py --world_idx 0\u003c/code\u003e. You can also refer to branch \u003ccode\u003eLfH\u003c/code\u003e, \u003ccode\u003eapplr\u003c/code\u003e and \u003ccode\u003ee2e\u003c/code\u003e, which are in the correct form for submissions.\u003c/p\u003e\n", + "full_name": "DiamondLightSource/Savu", + "latest_release": "v4.2", "stargazers_count": 38, - "subscribers_count": 4, + "subscribers_count": 17, "topics": [], - "updated_at": 1712740335.0 + "updated_at": 1722494936.0 }, { "data_format": 2, @@ -40798,6 +40826,20 @@ var data = ], "updated_at": 1732446525.0 }, + { + "data_format": 2, + "description": "This is gromacs interface with cp2k by singularity which is convenient for users to migrate this programe", + "filenames": [ + "Singularity.def" + ], + "full_name": "KeithTab/gromacs_cp2k_singularity", + "latest_release": "gromacs_cp2k_singularity_v3.0", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-gromacs-interface-with-cp2k\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#gromacs-interface-with-cp2k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003egromacs interface with cp2k\u003c/strong\u003e\u003c/em\u003e\u003c/h1\u003e\n\u003cp\u003eThis is a repository which will provide a portable gromacs \u003cem\u003e\u003cstrong\u003e(interface with cp2k)\u003c/strong\u003e\u003c/em\u003e version, and the singularity image file can solve the problem of software installation when there is a lack of dependencies during the service period. The version of the compiled programs now support CPU\u0027s \u003cem\u003e\u003cstrong\u003eAVX2\u003c/strong\u003e\u003c/em\u003e instruction settings \u003cem\u003e\u003cstrong\u003e(Ryzen 7 5800H)\u003c/strong\u003e\u003c/em\u003e, Other instruction sets have not been tested yet \u003cem\u003e\u003cstrong\u003e(such as SSE)\u003c/strong\u003e\u003c/em\u003e ,which may be OK and I also want to invite you to provide issues and we can solve these difficult together, thanks! :)\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-singularity-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#1-singularity-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Singularity installation\u003c/h3\u003e\n\u003cp\u003eBased on Singularity\u0027s high-performance computing container technology, compared with the container technology used in cloud computing environments such as Docker, Singularity supports both root and non-root user startup, and the user context remains unchanged before and after the container startup, which makes user privileges the same inside and outside of the container. Singularity emphasizes the convenience, portability, and scalability of container services while weakening the high isolation of container processes, resulting in lighter weight, fewer namespaces in the kernel, and less loss of performance. On the other hand, when you transplant a program by sif mirror, you\u0027d better install the singularity on the machine. Nevertheless, it must fail which will prompt \u003cem\u003e\u003cstrong\u003e\"/usr/bin/env run-singularity not found .\"\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-conda-environment\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#1-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e1. Conda Environment\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e conda create -n singularity python=3.8\u003c/span\u003e\nconda activate singularity\nconda install -c conda-forge singularity\nsingularity --version\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity version 3.8.6\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-manually-compile-ubuntu-2004\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#2-manually-compile-ubuntu-2004\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e2. Manually Compile (Ubuntu-20.04)\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThis way is recommended when you are an administrator or root user:)\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall dependencies\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get update\nsudo apt upgrade -y\nsudo apt-get install -y \\\n build-essential \\\n libseccomp-dev \\\n libglib2.0-dev \\\n pkg-config \\\n squashfs-tools \\\n cryptsetup \\\n runc\u003c/pre\u003e\u003c/div\u003e\n\u003ch5\u003e\u003ca id=\"user-content-install-go\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#install-go\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einstall Go\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e VERSION=1.19.3 OS=linux ARCH=amd64 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e change this as you need\u003c/span\u003e\n\nwget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\nsudo tar -C /usr/local -xzf /tmp/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then set your own environment variable\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch5\u003e\u003ca id=\"user-content-compiling-singularityce\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#compiling-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling SingularityCE\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recurse-submodules https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\n\n./mconfig\nmake -C builddir\nsudo make -C builddir install\n\n./mconfig -b ./buildtree -p /usr/local\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eif you want to check out more information about the singularity installation progress, please visit this \u003ca href=\"https://github.com/sylabs/singularity/blob/main/INSTALL.md\"\u003ewebsite\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-3-use-of-the-gromacs_cp2k-fsif\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#3-use-of-the-gromacs_cp2k-fsif\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e3. Use of the gromacs_cp2k-f.sif\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eOnly need download it and then put it in your familiar file location.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pan.baidu.com/s/11R9Mf56B8OiAiw0ybUzU0Q?pwd=1234\" rel=\"nofollow\"\u003eDownload location_1 (mainland)\u003c/a\u003e \u003cem\u003e\u003cstrong\u003ecode\u003c/strong\u003e\u003c/em\u003e: 1234\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"\"\u003eDownload location_2 (All users)\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e /path to gromacs_cp2k-f.sif gmx_cp2k --version\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e this progress might produce huge cache file so i recommend you change the singularity tmp_dir to the proper location \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-4-submit-mission-on-the-slurm-cluster\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#4-submit-mission-on-the-slurm-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e4. Submit mission on the slurm cluster\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eif you want to run gromacs_cp2k_singularity on the slurm cluster, i have provided a slurm template below, and this will not be influenced by the mpirun version which have been installed on your cluster.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -J nma-em\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -p CondaPereira-PC\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -N 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH --mail-user=szkchris@sina.com\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH --mail-type=ALL\u003c/span\u003e\n\nmpirun -np 8 /software/gromacs_cp2k_v2/gromacs_cp2k-v2.sif gmx_cp2k mdrun -v -deffnm nma-em\u003c/pre\u003e\u003c/div\u003e\n", + "stargazers_count": 39, + "subscribers_count": 1, + "topics": [], + "updated_at": 1693646305.0 + }, { "data_format": 2, "description": "Statistically Significant loops from HiChIP data", @@ -40816,20 +40858,6 @@ var data = ], "updated_at": 1727938129.0 }, - { - "data_format": 2, - "description": "This is gromacs interface with cp2k by singularity which is convenient for users to migrate this programe", - "filenames": [ - "Singularity.def" - ], - "full_name": "KeithTab/gromacs_cp2k_singularity", - "latest_release": "gromacs_cp2k_singularity_v3.0", - "readme": "\u003ch1\u003e\u003ca id=\"user-content-gromacs-interface-with-cp2k\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#gromacs-interface-with-cp2k\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003egromacs interface with cp2k\u003c/strong\u003e\u003c/em\u003e\u003c/h1\u003e\n\u003cp\u003eThis is a repository which will provide a portable gromacs \u003cem\u003e\u003cstrong\u003e(interface with cp2k)\u003c/strong\u003e\u003c/em\u003e version, and the singularity image file can solve the problem of software installation when there is a lack of dependencies during the service period. The version of the compiled programs now support CPU\u0027s \u003cem\u003e\u003cstrong\u003eAVX2\u003c/strong\u003e\u003c/em\u003e instruction settings \u003cem\u003e\u003cstrong\u003e(Ryzen 7 5800H)\u003c/strong\u003e\u003c/em\u003e, Other instruction sets have not been tested yet \u003cem\u003e\u003cstrong\u003e(such as SSE)\u003c/strong\u003e\u003c/em\u003e ,which may be OK and I also want to invite you to provide issues and we can solve these difficult together, thanks! :)\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-1-singularity-installation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#1-singularity-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e1. Singularity installation\u003c/h3\u003e\n\u003cp\u003eBased on Singularity\u0027s high-performance computing container technology, compared with the container technology used in cloud computing environments such as Docker, Singularity supports both root and non-root user startup, and the user context remains unchanged before and after the container startup, which makes user privileges the same inside and outside of the container. Singularity emphasizes the convenience, portability, and scalability of container services while weakening the high isolation of container processes, resulting in lighter weight, fewer namespaces in the kernel, and less loss of performance. On the other hand, when you transplant a program by sif mirror, you\u0027d better install the singularity on the machine. Nevertheless, it must fail which will prompt \u003cem\u003e\u003cstrong\u003e\"/usr/bin/env run-singularity not found .\"\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-1-conda-environment\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#1-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e1. Conda Environment\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e conda create -n singularity python=3.8\u003c/span\u003e\nconda activate singularity\nconda install -c conda-forge singularity\nsingularity --version\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e singularity version 3.8.6\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-2-manually-compile-ubuntu-2004\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#2-manually-compile-ubuntu-2004\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e2. Manually Compile (Ubuntu-20.04)\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThis way is recommended when you are an administrator or root user:)\u003c/p\u003e\n\u003ch5\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstall dependencies\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get update\nsudo apt upgrade -y\nsudo apt-get install -y \\\n build-essential \\\n libseccomp-dev \\\n libglib2.0-dev \\\n pkg-config \\\n squashfs-tools \\\n cryptsetup \\\n runc\u003c/pre\u003e\u003c/div\u003e\n\u003ch5\u003e\u003ca id=\"user-content-install-go\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#install-go\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003einstall Go\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e VERSION=1.19.3 OS=linux ARCH=amd64 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e change this as you need\u003c/span\u003e\n\nwget -O /tmp/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz \\\n https://dl.google.com/go/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\nsudo tar -C /usr/local -xzf /tmp/go\u003cspan class=\"pl-smi\"\u003e${VERSION}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${OS}\u003c/span\u003e-\u003cspan class=\"pl-smi\"\u003e${ARCH}\u003c/span\u003e.tar.gz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e then set your own environment variable\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch5\u003e\u003ca id=\"user-content-compiling-singularityce\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#compiling-singularityce\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCompiling SingularityCE\u003c/h5\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --recurse-submodules https://github.com/sylabs/singularity.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e singularity\n\n./mconfig\nmake -C builddir\nsudo make -C builddir install\n\n./mconfig -b ./buildtree -p /usr/local\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eif you want to check out more information about the singularity installation progress, please visit this \u003ca href=\"https://github.com/sylabs/singularity/blob/main/INSTALL.md\"\u003ewebsite\u003c/a\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003ca id=\"user-content-3-use-of-the-gromacs_cp2k-fsif\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#3-use-of-the-gromacs_cp2k-fsif\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e3. Use of the gromacs_cp2k-f.sif\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eOnly need download it and then put it in your familiar file location.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://pan.baidu.com/s/11R9Mf56B8OiAiw0ybUzU0Q?pwd=1234\" rel=\"nofollow\"\u003eDownload location_1 (mainland)\u003c/a\u003e \u003cem\u003e\u003cstrong\u003ecode\u003c/strong\u003e\u003c/em\u003e: 1234\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"\"\u003eDownload location_2 (All users)\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity \u003cspan class=\"pl-c1\"\u003eexec\u003c/span\u003e /path to gromacs_cp2k-f.sif gmx_cp2k --version\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e this progress might produce huge cache file so i recommend you change the singularity tmp_dir to the proper location \u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch4\u003e\u003ca id=\"user-content-4-submit-mission-on-the-slurm-cluster\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#4-submit-mission-on-the-slurm-cluster\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003cem\u003e\u003cstrong\u003e4. Submit mission on the slurm cluster\u003c/strong\u003e\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eif you want to run gromacs_cp2k_singularity on the slurm cluster, i have provided a slurm template below, and this will not be influenced by the mpirun version which have been installed on your cluster.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#!\u003c/span\u003e/bin/bash\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -J nma-em\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -p CondaPereira-PC\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH -N 1\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH --mail-user=szkchris@sina.com\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSBATCH --mail-type=ALL\u003c/span\u003e\n\nmpirun -np 8 /software/gromacs_cp2k_v2/gromacs_cp2k-v2.sif gmx_cp2k mdrun -v -deffnm nma-em\u003c/pre\u003e\u003c/div\u003e\n", - "stargazers_count": 39, - "subscribers_count": 1, - "topics": [], - "updated_at": 1693646305.0 - }, { "data_format": 2, "description": "BIDS app wrapping recon-all from FreeSurfer", @@ -40871,6 +40899,22 @@ var data = ], "updated_at": 1725472145.0 }, + { + "data_format": 2, + "description": "MONET : MOdularising NEtwork Toolbox - https://doi.org/10.1093/bioinformatics/btaa236", + "filenames": [ + ".containers/K1/singularity/Singularity", + ".containers/R1/singularity/Singularity", + ".containers/M1/singularity/Singularity" + ], + "full_name": "BergmannLab/MONET", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMONET\u003c/h1\u003e\u003ca id=\"user-content-monet\" class=\"anchor\" aria-label=\"Permalink: MONET\" href=\"#monet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository holds the source code for \u003cstrong\u003eMONET\u003c/strong\u003e, a Linux/MacOS command-line toolbox to mine molecular and genetic networks, leveraging the top performing methods of the \u003cstrong\u003eDisease Module Identification (DMI) DREAM Challenge\u003c/strong\u003e (see DREAM Challenge paper under section PUBLICATIONS and \u003ca href=\"https://www.synapse.org/modulechallenge\" rel=\"nofollow\"\u003ehttps://www.synapse.org/modulechallenge\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePREREQUISITES\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: PREREQUISITES\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOperating System\u003c/strong\u003e: MONET can be run on \u003cstrong\u003eeither\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinux (it was tested on \u003cem\u003eUbuntu Linux\u003c/em\u003e 20.04, \u003cem\u003eCentOS Linux\u003c/em\u003e 7.5)\u003c/li\u003e\n\u003cli\u003eMacOS (it was tested on \u003cem\u003eBig Sur\u003c/em\u003e 11.4 and \u003cem\u003eSierra\u003c/em\u003e 10.12)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware\u003c/strong\u003e: MONET requires \u003cstrong\u003eeither\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eDocker\u003c/code\u003e (see \"Install using the repository\" \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity\u003c/code\u003e (see \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eHardware\u003c/strong\u003e: MONET was tested both on server and on commodity hardware (i.e., regular desktop). For details, please refer to section COMPUTATIONAL RESOURCES below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eINSTALLATION\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: INSTALLATION\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eJust like you can \u003ccode\u003els\u003c/code\u003e a folder, after installation will be able to \u003ccode\u003emonet\u003c/code\u003e a network\u003c/strong\u003e from any location on your system.\u003c/p\u003e\n\u003cp\u003eSimply run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git clone https://github.com/BergmannLab/MONET.git \u0026amp;\u0026amp; cd MONET \u0026amp;\u0026amp; ./install.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eA folder MONET will have been created with the source code: you are free to remove it, if you are not interested. This will not affect MONET, which has now been installed in your system: the command \u003ccode\u003emonet\u003c/code\u003e can be invoked from any location on the system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIF YOU NEED SOME MORE GUIDANCE\u003c/h4\u003e\u003ca id=\"user-content-if-you-need-some-more-guidance\" class=\"anchor\" aria-label=\"Permalink: IF YOU NEED SOME MORE GUIDANCE\" href=\"#if-you-need-some-more-guidance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can follow this \u003ca href=\"https://form.jotform.com/tomasonimattia/monet-installation\" rel=\"nofollow\"\u003esurvey-tutorial\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eit will guide you step by step (assumes no prior knowledge)\u003c/li\u003e\n\u003cli\u003e(optionally) guides you through running some examples (feel free to skip those)\u003c/li\u003e\n\u003cli\u003eit will help us collect information about possible errors on different platforms\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIF YOU ARE ON WINDOWS\u003c/h4\u003e\u003ca id=\"user-content-if-you-are-on-windows\" class=\"anchor\" aria-label=\"Permalink: IF YOU ARE ON WINDOWS\" href=\"#if-you-are-on-windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsers using Windows are encouraged to install a hypervisor (i.e., a software that allows to creates and run virtual machines): for example, install VirtualBox \u003ca href=\"https://www.virtualbox.org/wiki/Downloads\" rel=\"nofollow\"\u003ehttps://www.virtualbox.org/wiki/Downloads\u003c/a\u003e and configure it up to run a virtual Ubuntu Linux inside which to install MONET (using the instructions above).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIF YOU ARE A SINGULARITY USER WITHOUT SUDO RIGHTS\u003c/h4\u003e\u003ca id=\"user-content-if-you-are-a-singularity-user-without-sudo-rights\" class=\"anchor\" aria-label=\"Permalink: IF YOU ARE A SINGULARITY USER WITHOUT SUDO RIGHTS\" href=\"#if-you-are-a-singularity-user-without-sudo-rights\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSudo rights will be required at installation time for Singularity users: Singularity users will not need sudo rights while running MONET (i.e., Singularity does not require sudo right to run containers), but they will need it at installation time (i.e., at the time the Singularity images are first created).\u003c/p\u003e\n\u003cp\u003eUsers that don\u0027t have sudo rights should follow the regular installation procedure explained above, then refer to MONET/docs/installation_no_sudo.txt where they will find a workaround to complete the installation manually without needing sudo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTESTING THE INSTALLATION\u003c/h2\u003e\u003ca id=\"user-content-testing-the-installation\" class=\"anchor\" aria-label=\"Permalink: TESTING THE INSTALLATION\" href=\"#testing-the-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAt the end of the install process, you will be asked whether you want to test MONET. This test is completely automatic.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMONET HELP COMMAND\u003c/h2\u003e\u003ca id=\"user-content-monet-help-command\" class=\"anchor\" aria-label=\"Permalink: MONET HELP COMMAND\" href=\"#monet-help-command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter installing MONET, the help command \u003ccode\u003emonet --help\u003c/code\u003e will be available from any location on your system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRUNNING\u003c/h2\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: RUNNING\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce installed, from any location on your system, you can run the following example command: it will run a method called M1 (see section METHODS for details), on a network contained in your /tmp folder (see section INPUT for details), using docker virtualization (see section PREREQUISITES for details). In the remainder of this document, you will find details about what parameters you can use, what to expect as an output and resource usage (in the PARAMETERS, OUTPUT and COMPUTATIONAL RESOURCES sections respectively).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ monet --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ monet --input=/tmp/input_network.txt \u2014-method=M1 --container=docker\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eINPUT\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: INPUT\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe input file is provided to MONET using the \u003ccode\u003e--input\u003c/code\u003e parameter (see section RUNNING and section PARAMETERS).\u003c/p\u003e\n\u003cp\u003eThe format for the input network is the following: a \u003cstrong\u003etab-separated\u003c/strong\u003e file containing one line for each edge.\u003c/p\u003e\n\u003cp\u003eIf an edge is connecting two nodes, gene_a and gene_b, with a certain weight, the file will contain the line:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egene_a \\t gene_b \\t weight \\n\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eDetails:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egene_a and gene_b, the gene ids, can be either \u003cem\u003estring\u003c/em\u003e or \u003cem\u003einteger\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003eweight can be of type \u003cem\u003einteger\u003c/em\u003e or \u003cem\u003efloat\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003e\"\\t\" indicates the tab character and \"\\n\" the newline character\u003c/li\u003e\n\u003cli\u003eno blank spaces should appear, neither as separators nor as part of the gene ids\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor an example, see MONET/.test/system_test/input/zachary_karate_club.txt. The same folder containing the actual inputs to the Disease Module Identification (DMI) DREAM Challenge. Beware that some of the inputs will require high amounts of computational resources and are not suited to be run on a simple laptop or desktop computer; please refer to section COMPUTATIONAL RESOURCES for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOUTPUT\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: OUTPUT\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe output location is provided to MONET using the \u003ccode\u003e--output\u003c/code\u003e parameter (see section OPTIONAL PARAMETERS).\u003c/p\u003e\n\u003cp\u003eTwo output files will be generated in the directory where you run the command. They are marked with a timestamp, the name of the selected method and the name of your input network. For example, let\u0027s assume if you run M1 on 1st January 2020 at midday on a file called input_network.txt:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea \u003cstrong\u003econsole-output\u003c/strong\u003e file, which will contain the run-time outputs generated by the method you have selected, providing details about the steps that the M1 algorithm took to generate your output. Any errors would also be redirected here. The file would be called: \u003ccode\u003e2020-01-01-120000__M1__console-output__input_network.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ea \u003cstrong\u003eresult-modules\u003c/strong\u003e file, containing the results of your analysis and it will not be generated in case of errors. The file would be called: \u003ccode\u003e2020-01-01-120000__M1__result-modules__input_network.txt\u003c/code\u003e. It will be in tab-separated format, containing one module per line:\n\u003cul\u003e\n\u003cli\u003ethe first value of each line will be a module identifier (in the form of an integer number starting from 1)\u003c/li\u003e\n\u003cli\u003ethe second is a fixed numerical value and can be ignored (curerntly set to \u003ccode\u003e1.0\u003c/code\u003e, it was originally used in the DREAM Challenge to provide module-level confidence scores)\u003c/li\u003e\n\u003cli\u003ethe rest of the values on the line will be the gene ids container in the input (like gene_a and gene_b, see section INPUT)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMETHODS\u003c/h2\u003e\u003ca id=\"user-content-methods\" class=\"anchor\" aria-label=\"Permalink: METHODS\" href=\"#methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThree methods are available as part of MONET, which emerged as the top-performing methods of the DREAM Challenge.\u003c/p\u003e\n\u003cp\u003eIn order to run one of the three methods, adapt the example command provided in section RUNNING providing the --method option with the name of the chosen method (--method=[K1|M1|R1], for details, see section PARAMETERS).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eK1\u003c/strong\u003e: KERNEL CLUSTERING OPTIMISATION algorithm. K1 is based on the \u201cDiffusion State Distance\u201d (DSD), a novel graph metric which is built on the premise that paths through low-degree nodes are stronger indications of functional similarity than paths that traverse high degree nodes by Cao et al. (2014). The DSD metric is used to define a pairwise distance matrix between all nodes, on which a spectral clustering algorithm is applied. In parallel, dense bipartite sub-graphs are identified using standard graph techniques. Finally, results are merged into a single set of non-overlapping clusters. For further details, please see: \u003ca href=\"https://www.synapse.org/#!Synapse:syn7349492/wiki/407359\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7349492/wiki/407359\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eM1\u003c/strong\u003e: MODULARITY OPTIMIZATION algorithm. M1 employs an original technique named Multiresolution introduced by (Arenas et al., 2008) to explore all topological scales at which modules may be found. The novelty of this approach relies on the introduction of a parameter, called resistance, which controls the aversion of nodes to form modules. Modularity (Newman and Girvan, 2004; Arenas et al., 2007) is optimized using an ensemble of algorithms: Extremal optimization (Duch and Arenas, 2005), Spectral optimization (Newman, 2006), Fast algorithm (Newman, 2004), Tabu search (Arenas et al., 2008), and fine-tuning by iterative repositioning of individual nodes in adjacent modules. For further details, please see: \u003ca href=\"https://www.synapse.org/#!Synapse:syn7352969/wiki/407384\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7352969/wiki/407384\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eR1\u003c/strong\u003e: RANDOM-WALK-BASED algorithm. R1 is based on a variant of Markov Cluster Algorithm known as balanced Multi-layer Regularized Markov Cluster Algorithm(bMLRMCL) (Satuluriet al., 2010) which scales well to large graphs and minimizes the number of oversized clusters. First, a pre-processing step is applied so that edges with low weights are discarded and all remaining edges are scaled to integer values. Then, bMLRMCL is applied iteratively on modules of size grater than a user-defined threshold. For further details, please see: \u003ca href=\"https://www.synapse.org/#!Synapse:syn7286597/wiki/406659\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7286597/wiki/406659\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePARAMETERS\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: PARAMETERS\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease, provide values for the following MANDATORY parameters:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--input\u003c/strong\u003e: path to the network file to be analysed\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--method\u003c/strong\u003e: method to be used to analyse the input: [K1|M1|R1]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--container\u003c/strong\u003e: virtualisation technology available on the system: [docker|singularity]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOPTIONAL PARAMETERS\u003c/h2\u003e\u003ca id=\"user-content-optional-parameters\" class=\"anchor\" aria-label=\"Permalink: OPTIONAL PARAMETERS\" href=\"#optional-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--output\u003c/strong\u003e: directory in which to output results (default is current directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eif you select K1\u003c/strong\u003e as a method, you may additionally provide the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--nclusters\u003c/strong\u003e: initial number of output clusters for spectral clustering step; final number may differ (default is 100)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eif you select M1\u003c/strong\u003e as a method, you may additionally provide the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--smallest\u003c/strong\u003e: min size of output clusters (default is 3)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--largest\u003c/strong\u003e: max size of output clusters (default is 100)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--linksdir\u003c/strong\u003e: directionality of links: [undirected|directed] (default is undirected)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--avgk\u003c/strong\u003e: desired average degree for nodes in output (default is 25)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eif you select R1\u003c/strong\u003e as a method, you may additionally provide the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--smallest\u003c/strong\u003e: min size of output clusters (default is 3)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--largest\u003c/strong\u003e: max size of output clusters (default is 100)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--c\u003c/strong\u003e: trade-off parameter for computational efficiency; for larger c, the algorithm will run slower, but may provide more accurate results (default is 800)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--i\u003c/strong\u003e: inflation parameter for standard Markov Clustering algorithm on which R1 is based (default is 2)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--b\u003c/strong\u003e: parameter controlling how balanced the clustering results should be; for b=0, R1 behaves like standard Regularized Markov Cluster (default is 2)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--threshold\u003c/strong\u003e: remove edges smaller than threshold from the input (default is 4)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--post\u003c/strong\u003e: decide whether to recursively cluster (recluster) or discard too large output clusters: [recluster|discard] (default is discard)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--c2\u003c/strong\u003e: (only used if --post=recluster) sets --c for reclustering round (default is 500)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--i2\u003c/strong\u003e: (only used if --post=recluster) sets --i for reclustering round (default is 2)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--b2\u003c/strong\u003e: (only used if --post=recluster) sets --b for reclustering round (default is 2)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCOMPUTATIONAL RESOURCES\u003c/h2\u003e\u003ca id=\"user-content-computational-resources\" class=\"anchor\" aria-label=\"Permalink: COMPUTATIONAL RESOURCES\" href=\"#computational-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome of the methods require large amount of resources, depending on your input (please, refer to the MONET paper in the PUBLICATIONS section for details about how resource needs will scale with the size of the input, for the different methods).\u003c/p\u003e\n\u003cp\u003eTo reproduce the results of the DREAM Challenge, you can run MONET/.test/system_test/reproduce_challenge/reproduce_challenge.sh. This might fail on commodity hardware (i.e., a regular laptop or desktop) as about 8GB or RAM need to be available. In that case, you can allocate a larger SWAP partition (on Linux) or run the experiment on more powerful hardware, such as a server. Please browser the rest of the contents of MONET/.test/system_test/reproduce_challenge to view the exact RAM usage (ram_usage.txt) and the challenge outputs produced by MONET (disease_modules_output directory).\u003c/p\u003e\n\u003cp\u003eTo monitor resource usage when running on your own input (and thus determine the amount or RAM / swap needed by your particular input network for a particular method), two simple scripts have been added to MONET/.test/helper_scripts (for Unix and one for MacOS systems): launch them before execution of MONET and redirect their output to file for simple inspection (no other task should be running).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBENCHMARKING\u003c/h2\u003e\u003ca id=\"user-content-benchmarking\" class=\"anchor\" aria-label=\"Permalink: BENCHMARKING\" href=\"#benchmarking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor details about the modularization performance of the MONET methods on a set of artificial benchmarks (Louvain algorithm is shown as a baseline), please refer to the MONET paper in the PUBLICATIONS section; in particular, Fig. 1. MONET/.test/benchmarking for a detailed output of the experiments that have been carried out.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSOURCE CODE\u003c/h2\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-label=\"Permalink: SOURCE CODE\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe source code is hosted at: \u003ca href=\"https://github.com/BergmannLab/MONET.git\"\u003ehttps://github.com/BergmannLab/MONET.git\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCONTRIBUTING\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: CONTRIBUTING\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are interested in contributing to MONET, we encourage you to get in touch! We will be happy to add you to the list of our developers \u003ca href=\"https://github.com/BergmannLab/MONET/graphs/contributors\"\u003ehttps://github.com/BergmannLab/MONET/graphs/contributors\u003c/a\u003e. \u003cstrong\u003eTHANK YOU!\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONTRIBUTING - CREATING A BRANCH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we will create an issue for the specific feature you are willing to contribute; let\u0027s say yours will happen to be issue 999. You will be then asked to create a new git branch where to implement your changes; run the following from the cloned MONET directory:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git checkout -b issues_999\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git push origin issues_999\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAt this point, you are free to make changes to your local code in your laptop. Don\u0027t worry if you mess things up, it\u0027s no problem to add mistakes to a branch.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONTRIBUTING - TESTING YOUR CHANGES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce you are done with your changes, you can test them locally by \u003cstrong\u003ereinstalling\u003c/strong\u003e from the modified MONET directory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONTRIBUTING - PUBLISHING YOUR CHANGES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce you have tested your changes, run the following from the cloned MONET directory:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git add .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git commit -m \"adding code for feature # issues_999\"\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git push --set-upstream origin issues_999\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git checkout master\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOne of the MONET developers will test the changes in your branch then merge to Master.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIMPLEMENTING LOCAL CHANGES TO MONET\u003c/h2\u003e\u003ca id=\"user-content-implementing-local-changes-to-monet\" class=\"anchor\" aria-label=\"Permalink: IMPLEMENTING LOCAL CHANGES TO MONET\" href=\"#implementing-local-changes-to-monet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you wish to implement local changes to MONET, independently from our github repository, you can simply modify the code in your local cloned repository and \u003cstrong\u003ereinstall\u003c/strong\u003e after having made those changes (i.e. run or re-run the \u003ccode\u003einstall.sh\u003c/code\u003e script and confirm if you are asked to reinstall). This procedure can be repeated as many times as you like.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTROUBLESHOOTING COMMON PROBLEMS\u003c/h2\u003e\u003ca id=\"user-content-troubleshooting-common-problems\" class=\"anchor\" aria-label=\"Permalink: TROUBLESHOOTING COMMON PROBLEMS\" href=\"#troubleshooting-common-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf a MONET run is suddenly interrupted or if the expected outputs has not been generated, here are few common problems that can occur:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elack of RAM: if the console-output file (see section OUTPUT) contains the word \"Killed\", the MONET processed were stopped by the Operating System, likely due to a lack of RAM. To confirm this, please read section COMPUTATIONAL RESOURCES to learn how to monitor your resource usage while running MONET.\u003c/li\u003e\n\u003cli\u003eoutdated kernel: Singularity users that work on Linux distributions with old kernels (e.g. CentOS 6.1, kernel 2.6) will encounter trouble during the install process; they need to contact their system administrator to inquire whether a kernel upgrade is possible.\u003c/li\u003e\n\u003cli\u003ecan\u0027t implement local changes: please, refer to section IMPLEMENTING LOCAL CHANGES TO MONET.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBUG-REPORTS\u003c/h2\u003e\u003ca id=\"user-content-bug-reports\" class=\"anchor\" aria-label=\"Permalink: BUG-REPORTS\" href=\"#bug-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease, address your questions and bug reports to Mattia Tomasoni, \u0026lt;mattia.tomasoni AT unil.ch\u0026gt;. An issue will be opened here to address your problem: \u003ca href=\"https://github.com/BergmannLab/MONET/issues\"\u003ehttps://github.com/BergmannLab/MONET/issues\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePUBLICATIONS\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: PUBLICATIONS\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMONET paper\u003c/strong\u003e: Mattia Tomasoni, Sergio G\u00f3mez, Jake Crawford, Weijia Zhang, Sarvenaz Choobdar, Daniel Marbach and Sven Bergmann. MONET: a toolbox integrating top-performing methods for network modularization. Bioinformatics 36 (12), 3920-3921. doi: \u003ca href=\"https://doi.org/10.1093/bioinformatics/btaa236\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btaa236\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDREAM Challenge paper\u003c/strong\u003e: Sarvenaz Choobdar, Mehmet Ahsen, Jake Crawford, Mattia Tomasoni, Tao Fang, David Lamparter, Junyuan Lin, Benjamin Hescott, Xiaozhe Hu, Johnathan Mercer, Ted Natoli, Rajiv Narayan, The DREAM Module Identification Challenge Consortium, Aravind Subramanian, Jitao David Zhang, Gustavo Stolovitzky, Zolt\u00e1n Kutalik, Kasper Lage, Donna Slonim, Julio Saez-Rodriguez, Lenore Cowen, Sven Bergmann, Daniel Marbach. Assessment of network module identification across complex diseases. Nature Methods 16 (2019) 843-852. doi: \u003ca href=\"https://doi.org/10.1038/s41592-019-0509-5\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41592-019-0509-5\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 42, + "subscribers_count": 6, + "topics": [], + "updated_at": 1727270865.0 + }, { "data_format": 2, "description": "babette is an R package to work with BEAST2", @@ -40894,59 +40938,37 @@ var data = }, { "data_format": 2, - "description": "MONET : MOdularising NEtwork Toolbox - https://doi.org/10.1093/bioinformatics/btaa236", - "filenames": [ - ".containers/K1/singularity/Singularity", - ".containers/R1/singularity/Singularity", - ".containers/M1/singularity/Singularity" - ], - "full_name": "BergmannLab/MONET", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMONET\u003c/h1\u003e\u003ca id=\"user-content-monet\" class=\"anchor\" aria-label=\"Permalink: MONET\" href=\"#monet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository holds the source code for \u003cstrong\u003eMONET\u003c/strong\u003e, a Linux/MacOS command-line toolbox to mine molecular and genetic networks, leveraging the top performing methods of the \u003cstrong\u003eDisease Module Identification (DMI) DREAM Challenge\u003c/strong\u003e (see DREAM Challenge paper under section PUBLICATIONS and \u003ca href=\"https://www.synapse.org/modulechallenge\" rel=\"nofollow\"\u003ehttps://www.synapse.org/modulechallenge\u003c/a\u003e)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePREREQUISITES\u003c/h2\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: PREREQUISITES\" href=\"#prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eOperating System\u003c/strong\u003e: MONET can be run on \u003cstrong\u003eeither\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinux (it was tested on \u003cem\u003eUbuntu Linux\u003c/em\u003e 20.04, \u003cem\u003eCentOS Linux\u003c/em\u003e 7.5)\u003c/li\u003e\n\u003cli\u003eMacOS (it was tested on \u003cem\u003eBig Sur\u003c/em\u003e 11.4 and \u003cem\u003eSierra\u003c/em\u003e 10.12)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware\u003c/strong\u003e: MONET requires \u003cstrong\u003eeither\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eDocker\u003c/code\u003e (see \"Install using the repository\" \u003ca href=\"https://docs.docker.com/engine/install/\" rel=\"nofollow\"\u003ehttps://docs.docker.com/engine/install/\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eSingularity\u003c/code\u003e (see \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eHardware\u003c/strong\u003e: MONET was tested both on server and on commodity hardware (i.e., regular desktop). For details, please refer to section COMPUTATIONAL RESOURCES below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eINSTALLATION\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: INSTALLATION\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eJust like you can \u003ccode\u003els\u003c/code\u003e a folder, after installation will be able to \u003ccode\u003emonet\u003c/code\u003e a network\u003c/strong\u003e from any location on your system.\u003c/p\u003e\n\u003cp\u003eSimply run:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git clone https://github.com/BergmannLab/MONET.git \u0026amp;\u0026amp; cd MONET \u0026amp;\u0026amp; ./install.sh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eA folder MONET will have been created with the source code: you are free to remove it, if you are not interested. This will not affect MONET, which has now been installed in your system: the command \u003ccode\u003emonet\u003c/code\u003e can be invoked from any location on the system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIF YOU NEED SOME MORE GUIDANCE\u003c/h4\u003e\u003ca id=\"user-content-if-you-need-some-more-guidance\" class=\"anchor\" aria-label=\"Permalink: IF YOU NEED SOME MORE GUIDANCE\" href=\"#if-you-need-some-more-guidance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can follow this \u003ca href=\"https://form.jotform.com/tomasonimattia/monet-installation\" rel=\"nofollow\"\u003esurvey-tutorial\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eit will guide you step by step (assumes no prior knowledge)\u003c/li\u003e\n\u003cli\u003e(optionally) guides you through running some examples (feel free to skip those)\u003c/li\u003e\n\u003cli\u003eit will help us collect information about possible errors on different platforms\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIF YOU ARE ON WINDOWS\u003c/h4\u003e\u003ca id=\"user-content-if-you-are-on-windows\" class=\"anchor\" aria-label=\"Permalink: IF YOU ARE ON WINDOWS\" href=\"#if-you-are-on-windows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eUsers using Windows are encouraged to install a hypervisor (i.e., a software that allows to creates and run virtual machines): for example, install VirtualBox \u003ca href=\"https://www.virtualbox.org/wiki/Downloads\" rel=\"nofollow\"\u003ehttps://www.virtualbox.org/wiki/Downloads\u003c/a\u003e and configure it up to run a virtual Ubuntu Linux inside which to install MONET (using the instructions above).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eIF YOU ARE A SINGULARITY USER WITHOUT SUDO RIGHTS\u003c/h4\u003e\u003ca id=\"user-content-if-you-are-a-singularity-user-without-sudo-rights\" class=\"anchor\" aria-label=\"Permalink: IF YOU ARE A SINGULARITY USER WITHOUT SUDO RIGHTS\" href=\"#if-you-are-a-singularity-user-without-sudo-rights\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSudo rights will be required at installation time for Singularity users: Singularity users will not need sudo rights while running MONET (i.e., Singularity does not require sudo right to run containers), but they will need it at installation time (i.e., at the time the Singularity images are first created).\u003c/p\u003e\n\u003cp\u003eUsers that don\u0027t have sudo rights should follow the regular installation procedure explained above, then refer to MONET/docs/installation_no_sudo.txt where they will find a workaround to complete the installation manually without needing sudo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTESTING THE INSTALLATION\u003c/h2\u003e\u003ca id=\"user-content-testing-the-installation\" class=\"anchor\" aria-label=\"Permalink: TESTING THE INSTALLATION\" href=\"#testing-the-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAt the end of the install process, you will be asked whether you want to test MONET. This test is completely automatic.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMONET HELP COMMAND\u003c/h2\u003e\u003ca id=\"user-content-monet-help-command\" class=\"anchor\" aria-label=\"Permalink: MONET HELP COMMAND\" href=\"#monet-help-command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter installing MONET, the help command \u003ccode\u003emonet --help\u003c/code\u003e will be available from any location on your system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRUNNING\u003c/h2\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: RUNNING\" href=\"#running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOnce installed, from any location on your system, you can run the following example command: it will run a method called M1 (see section METHODS for details), on a network contained in your /tmp folder (see section INPUT for details), using docker virtualization (see section PREREQUISITES for details). In the remainder of this document, you will find details about what parameters you can use, what to expect as an output and resource usage (in the PARAMETERS, OUTPUT and COMPUTATIONAL RESOURCES sections respectively).\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ monet --help\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ monet --input=/tmp/input_network.txt \u2014-method=M1 --container=docker\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eINPUT\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: INPUT\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe input file is provided to MONET using the \u003ccode\u003e--input\u003c/code\u003e parameter (see section RUNNING and section PARAMETERS).\u003c/p\u003e\n\u003cp\u003eThe format for the input network is the following: a \u003cstrong\u003etab-separated\u003c/strong\u003e file containing one line for each edge.\u003c/p\u003e\n\u003cp\u003eIf an edge is connecting two nodes, gene_a and gene_b, with a certain weight, the file will contain the line:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003egene_a \\t gene_b \\t weight \\n\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eDetails:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egene_a and gene_b, the gene ids, can be either \u003cem\u003estring\u003c/em\u003e or \u003cem\u003einteger\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003eweight can be of type \u003cem\u003einteger\u003c/em\u003e or \u003cem\u003efloat\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003e\"\\t\" indicates the tab character and \"\\n\" the newline character\u003c/li\u003e\n\u003cli\u003eno blank spaces should appear, neither as separators nor as part of the gene ids\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor an example, see MONET/.test/system_test/input/zachary_karate_club.txt. The same folder containing the actual inputs to the Disease Module Identification (DMI) DREAM Challenge. Beware that some of the inputs will require high amounts of computational resources and are not suited to be run on a simple laptop or desktop computer; please refer to section COMPUTATIONAL RESOURCES for details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOUTPUT\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: OUTPUT\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe output location is provided to MONET using the \u003ccode\u003e--output\u003c/code\u003e parameter (see section OPTIONAL PARAMETERS).\u003c/p\u003e\n\u003cp\u003eTwo output files will be generated in the directory where you run the command. They are marked with a timestamp, the name of the selected method and the name of your input network. For example, let\u0027s assume if you run M1 on 1st January 2020 at midday on a file called input_network.txt:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea \u003cstrong\u003econsole-output\u003c/strong\u003e file, which will contain the run-time outputs generated by the method you have selected, providing details about the steps that the M1 algorithm took to generate your output. Any errors would also be redirected here. The file would be called: \u003ccode\u003e2020-01-01-120000__M1__console-output__input_network.txt\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ea \u003cstrong\u003eresult-modules\u003c/strong\u003e file, containing the results of your analysis and it will not be generated in case of errors. The file would be called: \u003ccode\u003e2020-01-01-120000__M1__result-modules__input_network.txt\u003c/code\u003e. It will be in tab-separated format, containing one module per line:\n\u003cul\u003e\n\u003cli\u003ethe first value of each line will be a module identifier (in the form of an integer number starting from 1)\u003c/li\u003e\n\u003cli\u003ethe second is a fixed numerical value and can be ignored (curerntly set to \u003ccode\u003e1.0\u003c/code\u003e, it was originally used in the DREAM Challenge to provide module-level confidence scores)\u003c/li\u003e\n\u003cli\u003ethe rest of the values on the line will be the gene ids container in the input (like gene_a and gene_b, see section INPUT)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eMETHODS\u003c/h2\u003e\u003ca id=\"user-content-methods\" class=\"anchor\" aria-label=\"Permalink: METHODS\" href=\"#methods\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThree methods are available as part of MONET, which emerged as the top-performing methods of the DREAM Challenge.\u003c/p\u003e\n\u003cp\u003eIn order to run one of the three methods, adapt the example command provided in section RUNNING providing the --method option with the name of the chosen method (--method=[K1|M1|R1], for details, see section PARAMETERS).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eK1\u003c/strong\u003e: KERNEL CLUSTERING OPTIMISATION algorithm. K1 is based on the \u201cDiffusion State Distance\u201d (DSD), a novel graph metric which is built on the premise that paths through low-degree nodes are stronger indications of functional similarity than paths that traverse high degree nodes by Cao et al. (2014). The DSD metric is used to define a pairwise distance matrix between all nodes, on which a spectral clustering algorithm is applied. In parallel, dense bipartite sub-graphs are identified using standard graph techniques. Finally, results are merged into a single set of non-overlapping clusters. For further details, please see: \u003ca href=\"https://www.synapse.org/#!Synapse:syn7349492/wiki/407359\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7349492/wiki/407359\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eM1\u003c/strong\u003e: MODULARITY OPTIMIZATION algorithm. M1 employs an original technique named Multiresolution introduced by (Arenas et al., 2008) to explore all topological scales at which modules may be found. The novelty of this approach relies on the introduction of a parameter, called resistance, which controls the aversion of nodes to form modules. Modularity (Newman and Girvan, 2004; Arenas et al., 2007) is optimized using an ensemble of algorithms: Extremal optimization (Duch and Arenas, 2005), Spectral optimization (Newman, 2006), Fast algorithm (Newman, 2004), Tabu search (Arenas et al., 2008), and fine-tuning by iterative repositioning of individual nodes in adjacent modules. For further details, please see: \u003ca href=\"https://www.synapse.org/#!Synapse:syn7352969/wiki/407384\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7352969/wiki/407384\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eR1\u003c/strong\u003e: RANDOM-WALK-BASED algorithm. R1 is based on a variant of Markov Cluster Algorithm known as balanced Multi-layer Regularized Markov Cluster Algorithm(bMLRMCL) (Satuluriet al., 2010) which scales well to large graphs and minimizes the number of oversized clusters. First, a pre-processing step is applied so that edges with low weights are discarded and all remaining edges are scaled to integer values. Then, bMLRMCL is applied iteratively on modules of size grater than a user-defined threshold. For further details, please see: \u003ca href=\"https://www.synapse.org/#!Synapse:syn7286597/wiki/406659\" rel=\"nofollow\"\u003ehttps://www.synapse.org/#!Synapse:syn7286597/wiki/406659\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePARAMETERS\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: PARAMETERS\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease, provide values for the following MANDATORY parameters:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--input\u003c/strong\u003e: path to the network file to be analysed\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--method\u003c/strong\u003e: method to be used to analyse the input: [K1|M1|R1]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--container\u003c/strong\u003e: virtualisation technology available on the system: [docker|singularity]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOPTIONAL PARAMETERS\u003c/h2\u003e\u003ca id=\"user-content-optional-parameters\" class=\"anchor\" aria-label=\"Permalink: OPTIONAL PARAMETERS\" href=\"#optional-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--output\u003c/strong\u003e: directory in which to output results (default is current directory)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eif you select K1\u003c/strong\u003e as a method, you may additionally provide the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--nclusters\u003c/strong\u003e: initial number of output clusters for spectral clustering step; final number may differ (default is 100)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eif you select M1\u003c/strong\u003e as a method, you may additionally provide the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--smallest\u003c/strong\u003e: min size of output clusters (default is 3)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--largest\u003c/strong\u003e: max size of output clusters (default is 100)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--linksdir\u003c/strong\u003e: directionality of links: [undirected|directed] (default is undirected)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--avgk\u003c/strong\u003e: desired average degree for nodes in output (default is 25)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eif you select R1\u003c/strong\u003e as a method, you may additionally provide the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--smallest\u003c/strong\u003e: min size of output clusters (default is 3)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--largest\u003c/strong\u003e: max size of output clusters (default is 100)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--c\u003c/strong\u003e: trade-off parameter for computational efficiency; for larger c, the algorithm will run slower, but may provide more accurate results (default is 800)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--i\u003c/strong\u003e: inflation parameter for standard Markov Clustering algorithm on which R1 is based (default is 2)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--b\u003c/strong\u003e: parameter controlling how balanced the clustering results should be; for b=0, R1 behaves like standard Regularized Markov Cluster (default is 2)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--threshold\u003c/strong\u003e: remove edges smaller than threshold from the input (default is 4)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--post\u003c/strong\u003e: decide whether to recursively cluster (recluster) or discard too large output clusters: [recluster|discard] (default is discard)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--c2\u003c/strong\u003e: (only used if --post=recluster) sets --c for reclustering round (default is 500)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--i2\u003c/strong\u003e: (only used if --post=recluster) sets --i for reclustering round (default is 2)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e--b2\u003c/strong\u003e: (only used if --post=recluster) sets --b for reclustering round (default is 2)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCOMPUTATIONAL RESOURCES\u003c/h2\u003e\u003ca id=\"user-content-computational-resources\" class=\"anchor\" aria-label=\"Permalink: COMPUTATIONAL RESOURCES\" href=\"#computational-resources\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSome of the methods require large amount of resources, depending on your input (please, refer to the MONET paper in the PUBLICATIONS section for details about how resource needs will scale with the size of the input, for the different methods).\u003c/p\u003e\n\u003cp\u003eTo reproduce the results of the DREAM Challenge, you can run MONET/.test/system_test/reproduce_challenge/reproduce_challenge.sh. This might fail on commodity hardware (i.e., a regular laptop or desktop) as about 8GB or RAM need to be available. In that case, you can allocate a larger SWAP partition (on Linux) or run the experiment on more powerful hardware, such as a server. Please browser the rest of the contents of MONET/.test/system_test/reproduce_challenge to view the exact RAM usage (ram_usage.txt) and the challenge outputs produced by MONET (disease_modules_output directory).\u003c/p\u003e\n\u003cp\u003eTo monitor resource usage when running on your own input (and thus determine the amount or RAM / swap needed by your particular input network for a particular method), two simple scripts have been added to MONET/.test/helper_scripts (for Unix and one for MacOS systems): launch them before execution of MONET and redirect their output to file for simple inspection (no other task should be running).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBENCHMARKING\u003c/h2\u003e\u003ca id=\"user-content-benchmarking\" class=\"anchor\" aria-label=\"Permalink: BENCHMARKING\" href=\"#benchmarking\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor details about the modularization performance of the MONET methods on a set of artificial benchmarks (Louvain algorithm is shown as a baseline), please refer to the MONET paper in the PUBLICATIONS section; in particular, Fig. 1. MONET/.test/benchmarking for a detailed output of the experiments that have been carried out.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSOURCE CODE\u003c/h2\u003e\u003ca id=\"user-content-source-code\" class=\"anchor\" aria-label=\"Permalink: SOURCE CODE\" href=\"#source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe source code is hosted at: \u003ca href=\"https://github.com/BergmannLab/MONET.git\"\u003ehttps://github.com/BergmannLab/MONET.git\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCONTRIBUTING\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: CONTRIBUTING\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are interested in contributing to MONET, we encourage you to get in touch! We will be happy to add you to the list of our developers \u003ca href=\"https://github.com/BergmannLab/MONET/graphs/contributors\"\u003ehttps://github.com/BergmannLab/MONET/graphs/contributors\u003c/a\u003e. \u003cstrong\u003eTHANK YOU!\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONTRIBUTING - CREATING A BRANCH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we will create an issue for the specific feature you are willing to contribute; let\u0027s say yours will happen to be issue 999. You will be then asked to create a new git branch where to implement your changes; run the following from the cloned MONET directory:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git checkout -b issues_999\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git push origin issues_999\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAt this point, you are free to make changes to your local code in your laptop. Don\u0027t worry if you mess things up, it\u0027s no problem to add mistakes to a branch.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONTRIBUTING - TESTING YOUR CHANGES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce you are done with your changes, you can test them locally by \u003cstrong\u003ereinstalling\u003c/strong\u003e from the modified MONET directory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONTRIBUTING - PUBLISHING YOUR CHANGES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce you have tested your changes, run the following from the cloned MONET directory:\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git add .\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git commit -m \"adding code for feature # issues_999\"\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git push --set-upstream origin issues_999\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e$ git checkout master\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOne of the MONET developers will test the changes in your branch then merge to Master.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIMPLEMENTING LOCAL CHANGES TO MONET\u003c/h2\u003e\u003ca id=\"user-content-implementing-local-changes-to-monet\" class=\"anchor\" aria-label=\"Permalink: IMPLEMENTING LOCAL CHANGES TO MONET\" href=\"#implementing-local-changes-to-monet\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you wish to implement local changes to MONET, independently from our github repository, you can simply modify the code in your local cloned repository and \u003cstrong\u003ereinstall\u003c/strong\u003e after having made those changes (i.e. run or re-run the \u003ccode\u003einstall.sh\u003c/code\u003e script and confirm if you are asked to reinstall). This procedure can be repeated as many times as you like.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTROUBLESHOOTING COMMON PROBLEMS\u003c/h2\u003e\u003ca id=\"user-content-troubleshooting-common-problems\" class=\"anchor\" aria-label=\"Permalink: TROUBLESHOOTING COMMON PROBLEMS\" href=\"#troubleshooting-common-problems\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf a MONET run is suddenly interrupted or if the expected outputs has not been generated, here are few common problems that can occur:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003elack of RAM: if the console-output file (see section OUTPUT) contains the word \"Killed\", the MONET processed were stopped by the Operating System, likely due to a lack of RAM. To confirm this, please read section COMPUTATIONAL RESOURCES to learn how to monitor your resource usage while running MONET.\u003c/li\u003e\n\u003cli\u003eoutdated kernel: Singularity users that work on Linux distributions with old kernels (e.g. CentOS 6.1, kernel 2.6) will encounter trouble during the install process; they need to contact their system administrator to inquire whether a kernel upgrade is possible.\u003c/li\u003e\n\u003cli\u003ecan\u0027t implement local changes: please, refer to section IMPLEMENTING LOCAL CHANGES TO MONET.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBUG-REPORTS\u003c/h2\u003e\u003ca id=\"user-content-bug-reports\" class=\"anchor\" aria-label=\"Permalink: BUG-REPORTS\" href=\"#bug-reports\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease, address your questions and bug reports to Mattia Tomasoni, \u0026lt;mattia.tomasoni AT unil.ch\u0026gt;. An issue will be opened here to address your problem: \u003ca href=\"https://github.com/BergmannLab/MONET/issues\"\u003ehttps://github.com/BergmannLab/MONET/issues\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePUBLICATIONS\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: PUBLICATIONS\" href=\"#publications\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMONET paper\u003c/strong\u003e: Mattia Tomasoni, Sergio G\u00f3mez, Jake Crawford, Weijia Zhang, Sarvenaz Choobdar, Daniel Marbach and Sven Bergmann. MONET: a toolbox integrating top-performing methods for network modularization. Bioinformatics 36 (12), 3920-3921. doi: \u003ca href=\"https://doi.org/10.1093/bioinformatics/btaa236\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btaa236\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDREAM Challenge paper\u003c/strong\u003e: Sarvenaz Choobdar, Mehmet Ahsen, Jake Crawford, Mattia Tomasoni, Tao Fang, David Lamparter, Junyuan Lin, Benjamin Hescott, Xiaozhe Hu, Johnathan Mercer, Ted Natoli, Rajiv Narayan, The DREAM Module Identification Challenge Consortium, Aravind Subramanian, Jitao David Zhang, Gustavo Stolovitzky, Zolt\u00e1n Kutalik, Kasper Lage, Donna Slonim, Julio Saez-Rodriguez, Lenore Cowen, Sven Bergmann, Daniel Marbach. Assessment of network module identification across complex diseases. Nature Methods 16 (2019) 843-852. doi: \u003ca href=\"https://doi.org/10.1038/s41592-019-0509-5\" rel=\"nofollow\"\u003ehttps://doi.org/10.1038/s41592-019-0509-5\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 42, - "subscribers_count": 6, - "topics": [], - "updated_at": 1727270865.0 - }, - { - "data_format": 2, - "description": "Dynamical Neuroimaging Spatiotemporal Representations (DyNeuSR)", + "description": "A flexible Nextflow-based framework for the definition of sequencing data processing pipelines", "filenames": [ "Singularity" ], - "full_name": "braindynamicslab/dyneusr", + "full_name": "montilab/pipeliner", "latest_release": null, - "readme": "\u003cp align=\"center\"\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/logo.png\" height=\"250\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/haxby_decoding_mapper_stages.png\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eDynamical Neuroimaging Spatiotemporal Representations\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-dynamical-neuroimaging-spatiotemporal-representations\" class=\"anchor\" aria-label=\"Permalink: Dynamical Neuroimaging Spatiotemporal Representations\" href=\"#dynamical-neuroimaging-spatiotemporal-representations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4218\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://braindynamicslab.github.io/dyneusr/\" rel=\"nofollow\"\u003eDyNeuSR\u003c/a\u003e is a Python visualization library for topological representations of neuroimaging data.\u003c/p\u003e\n\u003cp\u003eDyNeuSR was designed specifically for working with shape graphs produced by the Mapper algorithm from topological data analysis (TDA), as described in the papers \"\u003ca href=\"https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093\" rel=\"nofollow\"\u003eGenerating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis\u003c/a\u003e\" (Geniesse et al., 2019) and \"\u003ca href=\"https://www.nature.com/articles/s41467-018-03664-4\" rel=\"nofollow\"\u003eTowards a new approach to reveal dynamical organization of the brain using topological data analysis\u003c/a\u003e\" (Saggar et al., 2018). Check out this \u003ca href=\"https://bdl.stanford.edu/blog/tda-cme-paper/\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e for more about the initial work that inspired the development of DyNeuSR.\u003c/p\u003e\n\u003cp\u003eDeveloped with neuroimaging data analysis in mind, DyNeuSR connects existing implementations of Mapper (e.g. \u003ca href=\"https://kepler-mapper.scikit-tda.org\" rel=\"nofollow\"\u003eKeplerMapper\u003c/a\u003e) with network analysis tools (e.g. \u003ca href=\"https://networkx.github.io/\" rel=\"nofollow\"\u003eNetworkX\u003c/a\u003e) and other neuroimaging data visualization libraries (e.g. \u003ca href=\"https://nilearn.github.io/\" rel=\"nofollow\"\u003eNilearn\u003c/a\u003e) and provides a high-level interface for interacting with and manipulating shape graph representations of neuroimaging data and relating these representations back to neurophysiology.\u003c/p\u003e\n\u003cp\u003eDyNeuSR also provides an interactive web interface for visualizing and exploring shape graphs. To see this visual interface in action, check out the \u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/\" rel=\"nofollow\"\u003edemos\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eDemos\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-demos\" class=\"anchor\" aria-label=\"Permalink: Demos\" href=\"#demos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/trefoil-knot/\" rel=\"nofollow\"\u003eTrefoil knot\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/trefoil-knot/trefoil_knot.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/trefoil-knot/\" rel=\"nofollow\"\u003eTrefoil knot (custom layouts)\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/trefoil-knot-custom-layouts/trefoil_knot_custom_layouts.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/haxby-tsne/\" rel=\"nofollow\"\u003eHaxby fMRI data (t-SNE lens)\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/haxby-tsne/haxby_tsne.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/haxby-umap/\" rel=\"nofollow\"\u003eHaxby fMRI data (UMAP lens)\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/haxby-umap/haxby_umap.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/haxby-umap-supervised/\" rel=\"nofollow\"\u003eHaxby fMRI data (supervised UMAP lens)\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/haxby-umap-supervised/haxby_umap_supervised.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eRelated Projects\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-related-projects\" class=\"anchor\" aria-label=\"Permalink: Related Projects\" href=\"#related-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr-fire/\" rel=\"nofollow\"\u003eDyNeuSR Fire\u003c/a\u003e is a new project that provides a command line interface for DyNeuSR. It wraps \u003ccode\u003ekmapper\u003c/code\u003e and \u003ccode\u003edyneusr\u003c/code\u003e into a single pipeline, and uses the \u003ca href=\"https://github.com/google/python-fire\"\u003ePython Fire\u003c/a\u003e library to automatically generate a simple command line interface that accepts several important options and allows users to customize this pipeline. For more information about DyNeuSR Fire, check out the \u003ca href=\"https://braindynamicslab.github.io/dyneusr-fire/\" rel=\"nofollow\"\u003edocs\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you find DyNeuSR useful, please consider citing:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eGeniesse, C., Sporns, O., Petri, G., \u0026amp; Saggar, M. (2019). \u003ca href=\"https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093\" rel=\"nofollow\"\u003eGenerating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis\u003c/a\u003e. \u003cem\u003eNetwork Neuroscience\u003c/em\u003e. Advance publication. doi:10.1162/netn_a_00093\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eFor more information about the Mapper approach, please see:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSaggar, M., Sporns, O., Gonzalez-Castillo, J., Bandettini, P.A., Carlsson, G., Glover, G., \u0026amp; Reiss, A.L. (2018). \u003ca href=\"https://www.nature.com/articles/s41467-018-03664-4\" rel=\"nofollow\"\u003eTowards a new approach to reveal dynamical organization of the brain using topological data analysis\u003c/a\u003e. \u003cem\u003eNature Communications, 9\u003c/em\u003e(1). doi:10.1038/s41467-018-03664-4\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eAPI Usage \u0026amp; Examples\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-api-usage--examples\" class=\"anchor\" aria-label=\"Permalink: API Usage \u0026amp; Examples\" href=\"#api-usage--examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDyNeuSR provides a Python API for working with and visualizing shape graphs generated by Mapper. This repository includes several \u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/\"\u003eexamples\u003c/a\u003e that introduce DyNeuSR\u0027s API and highlight different aspects of analysis with DyNeuSR. For more detailed tutorials, check out \u003ca href=\"https://github.com/braindynamicslab/dyneusr-notebooks/\"\u003edyneusr-notebooks\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003cstrong\u003e\u003cem\u003eShape Graph Visualization\u003c/em\u003e\u003c/strong\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/examples/trefoil_knot/trefoil_knot.py\"\u003etrefoil knot\u003c/a\u003e)\u003c/h3\u003e\u003ca id=\"user-content-shape-graph-visualization-trefoil-knot\" class=\"anchor\" aria-label=\"Permalink: Shape Graph Visualization (trefoil knot)\" href=\"#shape-graph-visualization-trefoil-knot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edyneusr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDyNeuGraph\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edyneusr\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emake_trefoil\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edyneusr\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etools\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evisualize_mapper_stages\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekmapper\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Generate synthetic dataset\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emake_trefoil\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e)\n\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Generate shape graph using KeplerMapper\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit_transform\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e])\n\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emap\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enr_cubes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoverlap_perc\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.2\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Visualize the shape graph using DyNeuSR\u0027s DyNeuGraph \u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edG\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDyNeuGraph\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eG\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edG\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003evisualize\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027dyneusr_output.html\u0027\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/trefoil_knot\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/dyneusr_trefoil_knot.png\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003cstrong\u003e\u003cem\u003eMapper Parameter Comparison\u003c/em\u003e\u003c/strong\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/examples/trefoil_knot/compare_lenses.py\"\u003etrefoil knot\u003c/a\u003e)\u003c/h3\u003e\u003ca id=\"user-content-mapper-parameter-comparison-trefoil-knot\" class=\"anchor\" aria-label=\"Permalink: Mapper Parameter Comparison (trefoil knot)\" href=\"#mapper-parameter-comparison-trefoil-knot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Define projections to compare\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eprojections\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e ([\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e], [\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e], [\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e], [\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# Compare different sets of columns as lenses\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprojections\u003c/span\u003e:\n\n\t\u003cspan class=\"pl-c\"\u003e# Generate shape graph using KeplerMapper\u003c/span\u003e\n\t\u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\t\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit_transform\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e)\n\t\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emap\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enr_cubes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoverlap_perc\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.3\u003c/span\u003e)\n\n\t\u003cspan class=\"pl-c\"\u003e# Visualize the stages of Mapper\u003c/span\u003e\n\t\u003cspan class=\"pl-s1\"\u003efig\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eaxes\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003evisualize_mapper_stages\u003c/span\u003e(\n\t\t\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecover\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecover\u003c/span\u003e, \n\t\t\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"spectral\"\u003c/span\u003e)\n\t\t\u003c/pre\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/trefoil_knot\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/trefoil_knot_mapper_lens_0.png\" style=\"max-width: 100%;\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/trefoil_knot_mapper_lens_0_1.png\" style=\"max-width: 100%;\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/trefoil_knot_mapper_lens_0_2.png\" style=\"max-width: 100%;\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/trefoil_knot_mapper_lens_1_2.png\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003cstrong\u003e\u003cem\u003eNeuroimaging Applications\u003c/em\u003e\u003c/strong\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/haxby_decoding\"\u003ehaxby decoding\u003c/a\u003e)\u003c/h3\u003e\u003ca id=\"user-content-neuroimaging-applications-haxby-decoding\" class=\"anchor\" aria-label=\"Permalink: Neuroimaging Applications (haxby decoding)\" href=\"#neuroimaging-applications-haxby-decoding\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enumpy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epandas\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enilearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efetch_haxby\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enilearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003einput_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eNiftiMasker\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekmapper\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eCover\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emanifold\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTSNE\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecluster\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDBSCAN\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Fetch dataset, extract time-series from ventral temporal (VT) mask\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003efetch_haxby\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003emasker\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eNiftiMasker\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emask_vt\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e], \n \u003cspan class=\"pl-s1\"\u003estandardize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edetrend\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esmoothing_fwhm\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4.0\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elow_pass\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.09\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ehigh_pass\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.008\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003et_r\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2.5\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ememory\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"nilearn_cache\"\u003c/span\u003e)\n\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emasker\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit_transform\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003efunc\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# Encode labels as integers\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eread_csv\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003esession_target\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003esep\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\" \"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efactorize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalues\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eDataFrame\u003c/span\u003e({\u003cspan class=\"pl-s1\"\u003el\u003c/span\u003e:(\u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e).\u003cspan class=\"pl-en\"\u003eastype\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eint\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003el\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eenumerate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e)})\n\n\u003cspan class=\"pl-c\"\u003e# Generate shape graph using KeplerMapper\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit_transform\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eTSNE\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e))\n\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emap\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecover\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eCover\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003eclusterer\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eDBSCAN\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eeps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e20.\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e# Visualize the shape graph using DyNeuSR\u0027s DyNeuGraph \u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edG\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDyNeuGraph\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eG\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edG\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003evisualize\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027dyneusr_output.html\u0027\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/haxby_decoding\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/dyneusr_haxby_decoding.png\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eSetup\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cstrong\u003e\u003cem\u003eDependencies\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython 3.6\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-python-36\" class=\"anchor\" aria-label=\"Permalink: Python 3.6\" href=\"#python-36\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRequired Python Packages\u003c/h4\u003e\u003ca id=\"user-content-required-python-packages\" class=\"anchor\" aria-label=\"Permalink: Required Python Packages\" href=\"#required-python-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"www.numpy.org\"\u003enumpy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"pandas.pydata.org\"\u003epandas\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"www.scipy.org\"\u003escipy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"scikit-learn.org\"\u003escikit-learn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"matplotlib.sourceforge.net\"\u003ematplotlib\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"stanford.edu/~mwaskom/software/seaborn\"\u003eseaborn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"networkx.github.io\"\u003enetworkx\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"nilearn.github.io\"\u003enilearn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"kepler-mapper.scikit-tda.org\"\u003ekmapper\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eFor a full list of packages and required versions, see \u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/requirements.txt\"\u003e\u003ccode\u003erequirements.txt\u003c/code\u003e\u003c/a\u003e and \u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/requirements-versions.txt\"\u003e\u003ccode\u003erequirements-versions.txt\u003c/code\u003e\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cstrong\u003e\u003cem\u003eInstall with PIP\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\u003ca id=\"user-content-install-with-pip\" class=\"anchor\" aria-label=\"Permalink: Install with PIP\" href=\"#install-with-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTo install with pip:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install dyneusr\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTo install from source:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/braindynamicslab/dyneusr.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dyneusr\n\npip install -r requirements.txt\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\npytest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cstrong\u003e\u003cem\u003eInstall with Conda\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\u003ca id=\"user-content-install-with-conda\" class=\"anchor\" aria-label=\"Permalink: Install with Conda\" href=\"#install-with-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf your default environment is Python 2, we recommend that you install \u003ccode\u003edyneusr\u003c/code\u003e in a separate Python 3 environment. You can find more information about creating a separate environment for Python 3 \u003ca href=\"https://salishsea-meopar-docs.readthedocs.io/en/latest/work_env/python3_conda_environment.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you don\u0027t have conda, or if you are new to scientific python, we recommend that you download the \u003ca href=\"https://store.continuum.io/cshop/anaconda/\" rel=\"nofollow\"\u003eAnaconda scientific python distribution\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTo create a new conda environment and install from source:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n dyneusr python=3.6\nconda activate dyneusr\n\ngit clone https://github.com/braindynamicslab/dyneusr.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dyneusr\n\nconda install --file requirements-conda.txt\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\npytest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis creates a new conda environment \u003ccode\u003edyneusr\u003c/code\u003e and installs in it the dependencies that are needed. To access it, use the \u003ccode\u003econda activate dyneusr\u003c/code\u003e command (if your conda version \u0026gt;= 4.4) and use \u003ccode\u003esource activate dyneusr\u003c/code\u003e command (if your conda version \u0026lt; 4.4).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cstrong\u003e\u003cem\u003eRun in a Singularity Container\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\u003ca id=\"user-content-run-in-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Run in a Singularity Container\" href=\"#run-in-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run dyneusr in a Singularity container, for use on clusters where you do not have root access for example, first install singularity. Then, you can use the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run shub://braindynamicslab/dyneusr\nsource activate neuro\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will download and run a singularity container from \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003esingularity-hub\u003c/a\u003e running centos 7 with dyneusr, jupyter, and all dependencies. It will also activate the conda environement \u003ccode\u003eneuro\u003c/code\u003e where these are installed. For more information on how to use singularity, see the \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e. The singularity recipe for this file was built with \u003ca href=\"https://github.com/ReproNim/neurodocker\"\u003eneurodocker\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eSupport\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease feel free to \u003ca href=\"https://github.com/braindynamicslab/dyneusr/issues/new\"\u003ereport\u003c/a\u003e any issues, \u003ca href=\"https://github.com/braindynamicslab/dyneusr/issues/new\"\u003erequest\u003c/a\u003e new features, or \u003ca href=\"https://github.com/braindynamicslab/dyneusr/compare\"\u003epropose\u003c/a\u003e improvements. You can also contact Caleb Geniesse at geniesse [at] stanford [dot] edu.\u003c/p\u003e\n\u003cp\u003eIf you contribute to DyNeuSR, please feel free to add your name to the \u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/contributors.md\"\u003elist of contributors\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eCitation\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eGeniesse, C., Sporns, O., Petri, G., \u0026amp; Saggar, M. (2019). \u003ca href=\"https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093\" rel=\"nofollow\"\u003eGenerating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis\u003c/a\u003e. \u003cem\u003eNetwork Neuroscience\u003c/em\u003e. Advance publication. doi:10.1162/netn_a_00093\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeliner\u003c/h1\u003e\u003ca id=\"user-content-pipeliner\" class=\"anchor\" aria-label=\"Permalink: Pipeliner\" href=\"#pipeliner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ci\u003eA flexible Nextflow-based framework for the definition of sequencing data processing pipelines\u003c/i\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/508d3cf3e60de07f1708a87005b68d010a24744fc1f459d7228cc2ef296cf041/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4275696c74253230576974682d4e657874666c6f772d627269676874677265656e2e737667\" alt=\"Built With\" data-canonical-src=\"https://img.shields.io/badge/Built%20With-Nextflow-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3e5756eff05217c8a60b4879897d09c74b9176e01b508c41903b58ee571f9b87/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4672616d65776f726b2d507974686f6e253230332e362d626c75652e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3e5756eff05217c8a60b4879897d09c74b9176e01b508c41903b58ee571f9b87/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4672616d65776f726b2d507974686f6e253230332e362d626c75652e737667\" alt=\"Python\" data-canonical-src=\"https://img.shields.io/badge/Framework-Python%203.6-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3b655db49bd9b450ab53d4dbfb4a675374c91a0648f2b4e09cabd7d68db7a6f3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6d7061746962696c6974792d4c696e75782532302532462532304f53582d6f72616e67652e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3b655db49bd9b450ab53d4dbfb4a675374c91a0648f2b4e09cabd7d68db7a6f3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6d7061746962696c6974792d4c696e75782532302532462532304f53582d6f72616e67652e737667\" alt=\"Compatibility\" data-canonical-src=\"https://img.shields.io/badge/Compatibility-Linux%20%2F%20OSX-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/eec32502ffa8eabfe95cc204a8a2ba285df42dbc7c747a9ad86b33305375d581/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646570656e64656e636965732d7570253230746f253230646174652d627269676874677265656e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eec32502ffa8eabfe95cc204a8a2ba285df42dbc7c747a9ad86b33305375d581/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646570656e64656e636965732d7570253230746f253230646174652d627269676874677265656e2e737667\" alt=\"Dependencies\" data-canonical-src=\"https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/montilab/pipeliner/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33507c2956695a97fd44292ae43445abd05bfdf68234ff52042d1f418e06f110/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6d6f6e74696c61622f706970656c696e65722e737667\" alt=\"GitHub Issues\" data-canonical-src=\"https://img.shields.io/github/issues/montilab/pipeliner.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"left\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/montilab/pipeliner/master/media/framework_schematic.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/montilab/pipeliner/master/media/framework_schematic.png\" width=\"80%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\n\u003c/p\u003e\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eModular directory structure: It is designed to generate automated result directory based on the names of the samples and tools used to process them.\u003c/li\u003e\n\u003cli\u003ePlatform independent: It is bundled with an anaconda repository which contains pre-compiled tools as well as pre-built environments that can be used directly.\u003c/li\u003e\n\u003cli\u003eModular architecture: It allows the expert users to customize, modify processes, or add additional tools based on their needs.\u003c/li\u003e\n\u003cli\u003eAutomated job parallelization, job recovery, and reproducibility.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuickstart\u003c/h2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eFor more information, please refer to the \u003ca href=\"https://pipeliner.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003efull documentation\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClone Repository\u003c/h3\u003e\u003ca id=\"user-content-clone-repository\" class=\"anchor\" aria-label=\"Permalink: Clone Repository\" href=\"#clone-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/montilab/pipeliner\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall Dependencies\u003c/h3\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install Dependencies\" href=\"#install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ conda env create -f pipeliner/envs/linux_env.yml \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Linux\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ conda env create -f pipeliner/envs/osx_env.yml \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Mac\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eActivate Conda Environment\u003c/h3\u003e\u003ca id=\"user-content-activate-conda-environment\" class=\"anchor\" aria-label=\"Permalink: Activate Conda Environment\" href=\"#activate-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate pipeliner\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUpdate Local Paths\u003c/h3\u003e\u003ca id=\"user-content-update-local-paths\" class=\"anchor\" aria-label=\"Permalink: Update Local Paths\" href=\"#update-local-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ python pipeliner/scripts/paths.py\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Nextflow Executable\u003c/h3\u003e\u003ca id=\"user-content-download-nextflow-executable\" class=\"anchor\" aria-label=\"Permalink: Download Nextflow Executable\" href=\"#download-nextflow-executable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd pipeliner/pipelines\n$ curl -s https://get.nextflow.io | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLocally Run Example Data\u003c/h3\u003e\u003ca id=\"user-content-locally-run-example-data\" class=\"anchor\" aria-label=\"Permalink: Locally Run Example Data\" href=\"#locally-run-example-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./nextflow rnaseq.nf -c rnaseq.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExpected Output\u003c/h3\u003e\u003ca id=\"user-content-expected-output\" class=\"anchor\" aria-label=\"Permalink: Expected Output\" href=\"#expected-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eN E X T F L O W ~ version 0.31.1\nLaunching `rnaseq.nf` [nasty_pauling] - revision: cd3f572ab2\n[warm up] executor \u0026gt; local\n[31/1b2066] Submitted process \u0026gt; pre_fastqc (ggal_alpha)\n[23/de6d60] Submitted process \u0026gt; pre_fastqc (ggal_theta)\n[7c/28ee53] Submitted process \u0026gt; pre_fastqc (ggal_gamma)\n[97/9ad6c1] Submitted process \u0026gt; check_reads (ggal_alpha)\n[ab/c3eedf] Submitted process \u0026gt; check_reads (ggal_theta)\n[2d/050633] Submitted process \u0026gt; check_reads (ggal_gamma)\n[1d/f3af6d] Submitted process \u0026gt; pre_multiqc\n[32/b1db1d] Submitted process \u0026gt; hisat_indexing (genome_reference.fa)\n[3b/d93c6d] Submitted process \u0026gt; trim_galore (ggal_alpha)\n[9c/3fa50b] Submitted process \u0026gt; trim_galore (ggal_theta)\n[62/25fce0] Submitted process \u0026gt; trim_galore (ggal_gamma)\n[66/ccc9db] Submitted process \u0026gt; hisat_mapping (ggal_alpha)\n[28/69fff5] Submitted process \u0026gt; hisat_mapping (ggal_theta)\n[5c/5ed2b6] Submitted process \u0026gt; hisat_mapping (ggal_gamma)\n[b4/e559ab] Submitted process \u0026gt; gtftobed (genome_annotation.gtf)\n[bc/6f490c] Submitted process \u0026gt; rseqc (ggal_alpha)\n[71/80aa9e] Submitted process \u0026gt; rseqc (ggal_theta)\n[17/ca0d9f] Submitted process \u0026gt; rseqc (ggal_gamma)\n[d7/7d391b] Submitted process \u0026gt; counting (ggal_alpha)\n[df/936854] Submitted process \u0026gt; counting (ggal_theta)\n[11/143c2c] Submitted process \u0026gt; counting (ggal_gamma)\n[31/4c11f9] Submitted process \u0026gt; expression_matrix\n[1f/3af548] Submitted process \u0026gt; multiqc\nSuccess: Pipeline Completed!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003ePlease refer to \u003ca href=\"https://github.com/montilab/pipeliner/blob/master/docs/reports.md\"\u003ereports\u003c/a\u003e for examples\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCite\u003c/h2\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-label=\"Permalink: Cite\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFederico A, Karagiannis T, Karri K, Kishore D, Koga Y, Campbell J, Monti S (2019) Pipeliner: A Nextflow-based framework for the definition of sequencing data processing pipelines. \u003cem\u003eFrontiers in Genetics\u003c/em\u003e. \u003ca href=\"https://doi.org/10.3389/fgene.2019.00614\" rel=\"nofollow\"\u003ehttps://doi.org/10.3389/fgene.2019.00614\u003c/a\u003e.\u003c/p\u003e\n", "stargazers_count": 44, - "subscribers_count": 8, + "subscribers_count": 9, "topics": [ - "brain-dynamics", - "topological-data-analysis", - "neuroimaging", - "brain-networks", - "mapper-algorithm", - "visualization" + "nextflow", + "workflow", + "bioinformatics", + "rna-seq", + "computational-biology" ], - "updated_at": 1728873619.0 + "updated_at": 1718911278.0 }, { "data_format": 2, - "description": "Generate subject connectomes from raw BIDS data \u0026 perform inter-subject connection density normalisation, using the MRtrix3 software package.", + "description": "The python implementation of the Stochastic Self-Consistent Harmonic Approximation (SSCHA).", "filenames": [ - "Singularity" + "docker/Singularity.def" ], - "full_name": "bids-apps/MRtrix3_connectome", - "latest_release": null, - "readme": "\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eThis BIDS App enables generation and subsequent group analysis of structural connectomes\ngenerated from diffusion MRI data. The analysis pipeline relies primarily on the \u003cem\u003eMRtrix3\u003c/em\u003e\nsoftware package, and includes a number of state-of-the-art methods for image processing,\ntractography reconstruction, connectome generation and inter-subject connection density\nnormalisation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: App is still under development; script is not guaranteed to be operational\nfor all use cases.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eDue to use of the Anatomically-Constrained Tractography (ACT) framework, correction of\nEPI susceptibility distortions is a prerequisite for this pipeline. Currently, this is\nonly possible within this pipeline through use of the FSL tool \u003ccode\u003etopup\u003c/code\u003e, which relies\non the presence of spin-echo EPI images with differences in phase encoding to estimate\nthe causative inhomogeneity field. In the absence of such data, this pipeline is not\ncurrently applicable; though recommendations for alternative mechanisms for such\ncorrection in the Issues page are welcome, and development of novel techniques for\nperforming this correction are additionally underway.\u003c/p\u003e\n\u003cp\u003eWhile many common DICOM conversion software are capable of providing data characterising\nthe phase and slice encoding performed in the acquisition protocol, which are subsequently\nused by this pipeline to automate DWI data pre-processing, for some softwares and/or\nsome data (particularly those not acquired on a Siemens platform), such data may not be\npresent in the sidecar JSON files for files in the BIDS \u003ccode\u003edwi/\u003c/code\u003e and \u003ccode\u003efmap/\u003c/code\u003e directories.\nIn this circumstance, it will be necessary for users to manually enter the relevant\ninformation into these files in order for this script to be capable of processing the\ndata. Every JSON file in these two directories should contain the BIDS fields\n\u003ccode\u003ePhaseEncodingDirection\u003c/code\u003e and \u003ccode\u003eTotalReadoutTime\u003c/code\u003e. For DWI data, it is also preferable to\nprovide the \u003ccode\u003eSliceEncodingDirection\u003c/code\u003e and \u003ccode\u003eSliceTiming\u003c/code\u003e fields. More information on these\ndata can be found in the \u003ca href=\"http://bids.neuroimaging.io/\" rel=\"nofollow\"\u003eBIDS documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-multi-stage-analysis-pipeline\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#multi-stage-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-stage analysis pipeline\u003c/h3\u003e\n\u003cp\u003eThe tool consists of three explicit analysis levels:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\"\u003ccode\u003epreproc\u003c/code\u003e\": This performs basic DWI (and if necessary T1-weighted image) pre-processing\nbased on the raw BIDS data input, writing the resulting pre-processed data to\nsub-directory \"\u003ccode\u003eMRtrix3_connectome-preproc\u003c/code\u003e\" within the specified output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\"\u003ccode\u003eparticipant\u003c/code\u003e\": This operates upon the pre-processed DWI (and possibly T1-weighted image)\ndata generated by the \"\u003ccode\u003epreproc\u003c/code\u003e\" analysis level, performing CSD, streamlines reconstruction\nand connectome construction. Results are written to sub-directory \"\u003ccode\u003eMRtrix3_connectome-participant\u003c/code\u003e\"\nwithin the specified output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\"\u003ccode\u003egroup\u003c/code\u003e\": This operates on the outputs of the \"\u003ccode\u003eparticipant\u003c/code\u003e\" analysis level, performing\nappropriate inter-subject connection density normalisation as described in\n\u003ca href=\"https://osf.io/c67kn/\" rel=\"nofollow\"\u003ethis manuscript\u003c/a\u003e. Results are written to sub-directory\n\"\u003ccode\u003eMRtrix3_connectome-group\u003c/code\u003e\" within the specified output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-execution-platforms\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#execution-platforms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution platforms\u003c/h3\u003e\n\u003cp\u003eThis script can be utilised in one of three ways:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eAs a stand-alone \u003cem\u003eMRtrix3\u003c/em\u003e script\u003c/p\u003e\n\u003cp\u003eThe script \u003ccode\u003emrtrix3_connectome.py\u003c/code\u003e can additionally be used \u003cem\u003eoutside\u003c/em\u003e of this Docker\ncontainer, as a stand-alone Python script build against the \u003cem\u003eMRtrix3\u003c/em\u003e Python libraries.\nUsing the script in this way requires setting the \u003ccode\u003ePYTHONPATH\u003c/code\u003e environment variable to\ninclude the path to the \u003cem\u003eMRtrix3\u003c/em\u003e \u003ccode\u003elib/\u003c/code\u003e directory where it is installed on your local\nsystem, as described \u003ca href=\"https://community.mrtrix.org/t/the-mrtrix3-python-script-library/2243\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nWhen used in this way, the command-line interface of the script will be more consistent\nwith the rest of \u003cem\u003eMRtrix3\u003c/em\u003e. Note that this usage will require version \u003ccode\u003e3.0.0\u003c/code\u003e of \u003cem\u003eMRtrix3\u003c/em\u003e\nto be installed and configured appropriately on your local system.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs a Docker container\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://hub.docker.com/r/bids/mrtrix3_connectome/\" rel=\"nofollow\"\u003ebids/MRtrix3_connectome\u003c/a\u003e Docker\ncontainer enables users to generate structural connectomes from diffusion MRI data using\nstate-of-the-art techniques. The pipeline requires that data be organized in accordance\nwith the \u003ca href=\"http://bids.neuroimaging.io\" rel=\"nofollow\"\u003eBIDS specification\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn your terminal, type:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker pull bids/mrtrix3_connectome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing the \"\u003ccode\u003epreproc\u003c/code\u003e\"-level analysis as an exemplar, the tool is executed as e.g.:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker run -i --rm \\\n -v /Users/yourname/data:/bids_dataset \\\n -v /Users/yourname/output:/output \\\n bids/mrtrix3_connectome \\\n /bids_dataset /output preproc --participant_label 01\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs a Singularity container\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eMRtrix3_connectome\u003c/em\u003e BIDS App can also be built locally as a Singularity container.\nThis is particularly useful for subsequent utilisation on high-performance computing\nhardware, as unlike Docker there are no super-user privileges or user group\nmemberships required for execution. I have also personally been able to utilise the\nCUDA version of FSL\u0027s \u003ccode\u003eeddy\u003c/code\u003e command within this tool when running on a computing\ncluster node with GPU capability (though this can require explicit configuration;\nspeak to your system administrator).\u003c/p\u003e\n\u003cp\u003eWithin the location in which the \u003cem\u003eMRtrix3_connectome\u003c/em\u003e source code has been cloned,\ntype:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ sudo singularity build MRtrix3_connectome.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting container file \"\u003ccode\u003eMRtrix3_connectome.sif\u003c/code\u003e\" can be run as a stand-alone\nexecutable, as long as the system on which the file is executed has a version of\nSingularity installed that is compatible with that of the system used to build the\ncontainer.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe help page of the tool itself can be generated by executing the script without\nproviding any command-line options. The help page is additionally presented at the\nbottom of this README page for reference. Documentation regarding the underlying\n\u003cem\u003eMRtrix3\u003c/em\u003e tools can be found in the official\n\u003ca href=\"http://mrtrix.readthedocs.org\" rel=\"nofollow\"\u003e\u003cem\u003eMRtrix3\u003c/em\u003e documentation\u003c/a\u003e. Additional information\nmay be found in the \u003ca href=\"http://community.mrtrix.org\" rel=\"nofollow\"\u003eonline \u003cem\u003eMRtrix3\u003c/em\u003e community forum\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-error-reporting\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#error-reporting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eError Reporting\u003c/h2\u003e\n\u003cp\u003eExperiencing problems? You can either post a private message to me on the\n\u003ca href=\"http://community.mrtrix.org/u/rsmith\" rel=\"nofollow\"\u003e\u003cem\u003eMRtrix3\u003c/em\u003e community forum\u003c/a\u003e, or you can report it\ndirectly to the \u003ca href=\"https://github.com/BIDS-Apps/MRtrix3_connectome/issues\"\u003eGitHub issues list\u003c/a\u003e.\nIn both cases, please include as much information as possible; this may include re-running\nthe script using the \u003ccode\u003e--debug\u003c/code\u003e option, which will provide additional information at the\nterminal, and preserve temporary files generated by the script within your target output\ndirectory, which can be forwarded to the developer.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eDevelopment of this tool was made possible through funding from the National Health\nand Medical Research Council (NHMRC) of Australia.\u003c/p\u003e\n\u003cp\u003eThe developer acknowledges the facilities and scientific and technical assistance of\nthe National Imaging Facility, a National Collaborative Research Infrastructure Strategy\n(NCRIS) capability, at the Florey Institute of Neuroscience and Mental Health.\u003c/p\u003e\n\u003cp\u003eThe Florey Institute of Neuroscience and Mental Health acknowledges support from the\nVictorian Government and in particular the funding from the Operational Infrastructure\nSupport Grant.\u003c/p\u003e\n\u003cp\u003eRobert Smith is supported by fellowship funding from the National Imaging Facility (NIF),\nan Australian Government National Collaborative Research Infrastructure Strategy (NCRIS)\ncapability.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eWhen using this pipeline, please use the following snippet to acknowledge the relevant\nwork (amend as appropriate depending on options used):\u003c/p\u003e\n\u003cp\u003eStructural connectomes were generated using the \u003cem\u003eMRtrix3_connectome\u003c/em\u003e BIDS App (Smith\net al., 2019), which operates principally using tools provided in the \u003cem\u003eMRtrix3\u003c/em\u003e\nsoftware package (Tournier et al., 2019; \u003ca href=\"http://mrtrix.org\" rel=\"nofollow\"\u003ehttp://mrtrix.org\u003c/a\u003e). This included: DWI\ndenoising (Veraart et al., 2016), Gibbs ringing removal (Kellner et al., 2016),\npre-processing (Andersson et al., 2003; Andersson and Sotiropoulos, 2016; Andersson\net al., 2016; (IF USING EDDY_CUDA: Andersson et al., 2017)); and bias field correction\n(Tustison et al., 2010 OR Zhang et al., 2001); inter-modal registration (Bhushan et al.,\n2015); brain extraction (Smith, 2002 OR Iglesias et al., 2011), T1 tissue segmentation\n(Zhang et al., 2001; Smith, 2002; Patenaude et al., 2011; Smith et al., 2012); spherical\ndeconvolution (Tournier et al., 2004; Jeurissen et al., 2014); probabilistic tractography\n(Tournier et al., 2010) utilizing Anatomically-Constrained Tractography (Smith et al.,\n2012) and dynamic seeding (Smith et al., 2015b); SIFT2 (Smith et al., 2015b); T1\nparcellation ((((Avants et al., 2008 AND Tustison et al., 2013) OR Andersson et al.,\n2010) AND (Tzourio-Mazoyer et al., 2002 OR Yeo et al., 2011 OR Craddock et al., 2012\n2011) OR Fan et al., 2016 OR (Zalesky et al., 2010 AND Perry et al., 2017))) OR\n2012) (Dale et al., 1999 AND (Desikan et al., 2006 OR Destrieux et al., 2010 OR\n2013) Glasser et al., 2016))); robust structural connectome construction (Smith et al.,\n2014) 2015a; Yeh et al., 2016); inter-subject connection density normalisation\n2015) (Smith et al., 2020).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSmith, R. E.; Connelly, A. MRtrix3_connectome: A BIDS Application for quantitative structural connectome construction. In Proc OHBM, 2019, W610\nAndersson, J. L.; Skare, S. \u0026amp; Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 2003, 20, 870-888\nAndersson, J. L. R.; Jenkinson, M. \u0026amp; Smith, S. Non-linear registration, aka spatial normalisation. FMRIB technical report, 2010, TR07JA2\nAndersson, J. L. \u0026amp; Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 2016, 125, 1063-1078\nAndersson, J. L. R. \u0026amp; Graham, M. S. \u0026amp; Zsoldos, E. \u0026amp; Sotiropoulos, S. N. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. NeuroImage, 2016, 141, 556-572\nAndersson, J. L. R.; Graham, M. S.; Drobnjak, I.; Zhang, H.; Filippini, N. \u0026amp; Bastiani, M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. NeuroImage, 2017, 152, 450-466\nAndersson, J. L. R.; Graham, M. S.; Drobnjak, I.; Zhang, H. \u0026amp; Campbell, J. Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data. NeuroImage, 2018, 171, 277-295\nAvants, B. B.; Epstein, C. L.; Grossman, M. \u0026amp; Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 2008, 12, 26-41\nBhushan, C.; Haldar, J. P.; Choi, S.; Joshi, A. A.; Shattuck, D. W. \u0026amp; Leahy, R. M. Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization. NeuroImage, 2015, 115, 269-280\nCraddock, R. C.; James, G. A.; Holtzheimer, P. E.; Hu, X. P.; Mayberg, H. S. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 2012, 33(8), 1914-1928\nDale, A. M.; Fischl, B. \u0026amp; Sereno, M. I. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage, 1999, 9, 179-194\nDesikan, R. S.; S\u00e9gonne, F.; Fischl, B.; Quinn, B. T.; Dickerson, B. C.; Blacker, D.; Buckner, R. L.; Dale, A. M.; Maguire, R. P.; Hyman, B. T.; Albert, M. S. \u0026amp; Killiany, R. J. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 2006, 31, 968-980\nDestrieux, C.; Fischl, B.; Dale, A. \u0026amp; Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage, 2010, 53, 1-15\nFan, L.; Li, H.; Zhuo, J.; Zhang, Y.; Wang, J.; Chen, L.; Yang, Z.; Chu, C.; Xie, S.; Laird, A.R.; Fox, P.T.; Eickhoff, S.B.; Yu, C.; Jiang, T. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cerebral Cortex, 2016, 26 (8), 3508-3526\nGlasser, M. F.; Coalson, T. S.; Robinson, E. C.; Hacker, C. D.; Harwell, J.; Yacoub, E.; Ugurbil, K.; Andersson, J.; Beckmann, C. F.; Jenkinson, M.; Smith, S. M. \u0026amp; Van Essen, D. C. A multi-modal parcellation of human cerebral cortex. Nature, 2016, 536, 171-178\nIglesias, J. E.; Liu, C. Y.; Thompson, P. M. \u0026amp; Tu, Z. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods. IEEE Transactions on Medical Imaging, 2011, 30, 1617-1634\nJeurissen, B; Tournier, J-D; Dhollander, T; Connelly, A \u0026amp; Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 2014, 103, 411-426\nKellner, E.; Dhital, B.; Kiselev, V. G.; Reisert, M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine, 2006, 76(5), 1574-1581\nPatenaude, B.; Smith, S. M.; Kennedy, D. N. \u0026amp; Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 2011, 56, 907-922\nPerry, A.; Wen, W.; Kochan, N. A.; Thalamuthu, A.; Sachdev, P. S.; Breakspear, M. The independent influences of age and education on functional brain networks and cognition in healthy older adults. Human Brain Mapping, 2017, 38(10), 5094-5114\nSmith, S. M. Fast robust automated brain extraction. Human Brain Mapping, 2002, 17, 143-155\nSmith, R. E.; Tournier, J.-D.; Calamante, F. \u0026amp; Connelly, A. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage, 2012, 62, 1924-1938\nSmith, R. E.; Tournier, J.-D.; Calamante, F. \u0026amp; Connelly, A. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. NeuroImage, 2015a, 104, 253-265\nSmith, R. E.; Tournier, J.-D.; Calamante, F. \u0026amp; Connelly, A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage, 2015b, 119, 338-351\nSmith, R. E.; Raffelt, D.; Tournier, J.-D.; Connelly, A. Quantitative streamlines tractography: methods and inter-subject normalisation. Preprint, 2020, OSF, https://doi.org/10.31219/osf.io/c67kn\nTournier, J.-D.; Calamante, F., Gadian, D.G. \u0026amp; Connelly, A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 2004, 23, 1176-1185\nTournier, J.-D.; Calamante, F. \u0026amp; Connelly, A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proceedings of the International Society for Magnetic Resonance in Medicine, 2010, 1670\nTournier, J.-D.; Smith, R. E.; Raffelt, D. A.; Tabbara, R.; Dhollander, T.; Pietsch, M; Christiaens, D.; Jeurissen, B.; Y, C.-H.; Connelly, A. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 2019, 202, 116137\nTustison, N.; Avants, B.; Cook, P.; Zheng, Y.; Egan, A.; Yushkevich, P. \u0026amp; Gee, J. N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 2010, 29, 1310-1320\nTustison, N.; Avants, B. Explicit B-spline regularization in diffeomorphic image registration. Frontiers in Neuroinformatics, 2013, 7, 39\nTzourio-Mazoyer, N.; Landeau, B.; Papathanassiou, D.; Crivello, F.; Etard, O.; Delcroix, N.; Mazoyer, B. \u0026amp; Joliot, M. Automated Anatomical Labeling of activations in SPM using a Macroscopic Anatomical Parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273\u2013289\nVeraart, J.; Novikov, D. S.; Christiaens, D.; Ades-aron, B.; Sijbers, J. \u0026amp; Fieremans, E. Denoising of diffusion MRI using random matrix theory. NeuroImage, 2016, 142, 394-406\nYeh, C.-H.; Smith, R. E.; Liang, X.; Calamante, F. \u0026amp; Connelly, A. Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics. NeuroImage, 2016, 142, 150-162\nYeo, B.T.; Krienen, F.M.; Sepulcre, J.; Sabuncu, M.R.; Lashkari, D.; Hollinshead, M.; Roffman, J.L.; Smoller, J.W.; Zollei, L.; Polimeni, J.R.; Fischl, B.; Liu, H. \u0026amp; Buckner, R.L. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol, 2011, 106(3), 1125-1165\nZalesky, A.; Fornito, A.; Harding, I. H.; Cocchi, L.; Y\u00fccel, M.; Pantelis, C. \u0026amp; Bullmore, E. T. Whole-brain anatomical networks: Does the choice of nodes matter? NeuroImage, 2010, 50, 970-983\nZhang, Y.; Brady, M. \u0026amp; Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 2001, 20, 45-57\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-help-page\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#help-page\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHelp page\u003c/h2\u003e\n\u003cp\u003eThe following help page can equivalently be generated by executing the tool without\nproviding any command-line arguments (within a container environment; note interface\nis slightly different if run natively).\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-synopsis\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#synopsis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSynopsis\u003c/h2\u003e\n\u003cp\u003eGenerate structural connectomes based on diffusion-weighted and T1-weighted image data using state-of-the-art reconstruction tools, particularly those provided in \u003cem\u003eMRtrix3\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emrtrix3_connectome.py bids_dir output_dir analysis_level [ options ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003ebids_dir\u003c/em\u003e: The directory with the input dataset formatted according to the BIDS standard.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eoutput_dir\u003c/em\u003e: The directory where the output files should be stored.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eanalysis_level\u003c/em\u003e: Level of analysis that will be performed; options are: preproc, participant, group.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-description-1\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#description-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eWhile preproc-level analysis only requires data within the BIDS directory, participant-level analysis requires that the output directory be pre-populated with the results from preproc-level processing; similarly, group-level analysis requires that the output directory be pre-populated with the results from participant-level analysis.\u003c/p\u003e\n\u003cp\u003eThe operations performed by each of the three levels of analysis are as follows:\u003c/p\u003e\n\u003cp\u003e\"preproc\": DWI: Denoising; Gibbs ringing removal; motion, eddy current and EPI distortion correction and outlier detection \u0026amp; replacement; brain masking, bias field correction and intensity normalisation; rigid-body registration \u0026amp; transformation to T1-weighted image. T1-weighted image: bias field correction; brain masking.\u003c/p\u003e\n\u003cp\u003e\"participant\": DWI: Response function estimation; FOD estimation. T1-weighted image (if -parcellation is not none): Tissue segmentation; grey matter parcellation. Combined (if -parcellation is not none, or -streamlines is provided): Whole-brain streamlines tractography; SIFT2; connectome construction.\u003c/p\u003e\n\u003cp\u003e\"group\": Generation of FA-based population template; warping of template-based white matter mask to subject spaces; calculation of group mean white matter response function; scaling of connectomes based on white matter b=0 intensity, response function used during participant-level analysis, and SIFT model proportioinality coefficient; generation of group mean connectome.\u003c/p\u003e\n\u003cp\u003eThe label(s) provided to the -participant_label and -session_label options correspond(s) to sub-\u0026lt;participant_label\u0026gt; and ses-\u0026lt;session_label\u0026gt; from the BIDS spec (so they do \u003cem\u003enot\u003c/em\u003e include \"sub-\" or \"ses-\"). Multiple participants / sessions can be specified with a space-separated list.\u003c/p\u003e\n\u003cp\u003eFor both preproc-level and participant-level analyses, if no specific participants or sessions are nominated by the user (or the user explicitly specifies multiple participants / sessions), the script will process each of these in series. It is additionally possible for the user to invoke multiple instances of this script in order to process multiple subjects at once in parallel, ensuring that no single participant / session is being processed in parallel, and that preproc-level output data are written fully before commencing participant-level analysis.\u003c/p\u003e\n\u003cp\u003eThe -output_verbosity option principally affects the participant-level analysis, modulating how many derivative files are written to the output directory. Permitted values are from 1 to 4: 1 writes only those files requisite for group-level analysis; 2 additionally writes files typically useful for post-hoc analysis (the default); 3 additionally generates files for enhanced connectome visualisation and copies the entire whole-brain tractogram; 4 additionally generates a full copy of the script scratch directory (with all intermediate files retained) to the output directory (and this applies to all analysis levels)\u003c/p\u003e\n\u003cp\u003eIf running participant-level analysis using the script as a standalone tool rather than inside the provided container, data pertaining to atlas parcellations can no longer be guaranteed to be stored at a specific location on the filesystem. In this case, the user will most likely need to manually specify the location where the corresponding parcellation is stored using the -atlas_path option.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--output_verbosity\u003c/strong\u003e\u003cbr\u003eThe verbosity of script output (number from 1 to 4).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-options-that-are-relevant-to-participant-level-analysis\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#options-that-are-relevant-to-participant-level-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions that are relevant to participant-level analysis\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--parcellation\u003c/strong\u003e\u003cbr\u003eThe choice of connectome parcellation scheme (compulsory for participant-level analysis); options are: aal, aal2, brainnetome246fs, brainnetome246mni, craddock200, craddock400, desikan, destrieux, hcpmmp1, none, perry512, yeo7fs, yeo7mni, yeo17fs, yeo17mni.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--streamlines\u003c/strong\u003e\u003cbr\u003eThe number of streamlines to generate for each subject (will be determined heuristically if not explicitly set).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--template_reg software\u003c/strong\u003e\u003cbr\u003eThe choice of registration software for mapping subject to template space; options are: ants, fsl.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-options-that-are-relevant-to-both-preproc-level-and-participant-level-analyses\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#options-that-are-relevant-to-both-preproc-level-and-participant-level-analyses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions that are relevant to both preproc-level and participant-level analyses\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--t1w_preproc path\u003c/strong\u003e\u003cbr\u003eProvide a path by which pre-processed T1-weighted image data may be found for the processed participant(s) / session(s)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-options-specific-to-the-batch-processing-of-participant-data\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#options-specific-to-the-batch-processing-of-participant-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions specific to the batch processing of participant data\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--participant_label\u003c/strong\u003e\u003cbr\u003eThe label(s) of the participant(s) that should be analyzed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--session_label\u003c/strong\u003e\u003cbr\u003eThe session(s) within each participant that should be analyzed.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-standard-options\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#standard-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStandard options\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e-d/--debug\u003c/strong\u003e\u003cbr\u003edisplay debugging messages.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e-h/--help\u003c/strong\u003e\u003cbr\u003edisplay this information page and exit.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e-n/--n_cpus number\u003c/strong\u003e\u003cbr\u003euse this number of threads in multi-threaded applications (set to 0 to disable multi-threading).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--scratch /path/to/scratch/\u003c/strong\u003e\u003cbr\u003emanually specify the path in which to generate the scratch directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--skip-bids-validator\u003c/strong\u003e\u003cbr\u003eSkip BIDS validation\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e-v/--version\u003c/strong\u003e\u003cbr\u003edisplay version information and exit.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Robert E. Smith (\u003ca href=\"mailto:robert.smith@florey.edu.au\"\u003erobert.smith@florey.edu.au\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCopyright:\u003c/strong\u003e Copyright (c) 2016-2020 The Florey Institute of Neuroscience\nand Mental Health.\u003c/p\u003e\n\u003cp\u003eThis Source Code Form is subject to the terms of the Mozilla Public\nLicense, v. 2.0. If a copy of the MPL was not distributed with this\nfile, You can obtain one at \u003ca href=\"http://mozilla.org/MPL/2.0/\" rel=\"nofollow\"\u003ehttp://mozilla.org/MPL/2.0/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eCovered Software is provided under this License on an \"as is\"\nbasis, without warranty of any kind, either expressed, implied, or\nstatutory, including, without limitation, warranties that the\nCovered Software is free of defects, merchantable, fit for a\nparticular purpose or non-infringing.\nSee the Mozilla Public License v. 2.0 for more details.\u003c/p\u003e\n\u003chr\u003e\n", + "full_name": "SSCHAcode/python-sscha", + "latest_release": "v1.2", + "readme": "\u003ch1 id=\"user-content-sscha\"\u003e\u003ca class=\"heading-link\" href=\"#sscha\"\u003eSSCHA\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003eThe stochastic self-consistent harmonic approximation (SSCHA) is a full computational python package that simulates thermodynamic and phononic properties of materials accounting for anharmonicity at a nonperturbative level, fully including quantum and thermal fluctuations.\u003c/p\u003e\n\u003cp\u003eSee more info on the webpage:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://sscha.eu\" rel=\"nofollow\"\u003ewww.sscha.eu\u003c/a\u003e\u003c/p\u003e\n\u003ch2 id=\"user-content-easy-installation-through-anaconda\"\u003e\u003ca class=\"heading-link\" href=\"#easy-installation-through-anaconda\"\u003eEasy installation through Anaconda\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe SSCHA code comes as a python library, with computationally intense part speedup with C, Fortran and Julia. The easiest way to install is through Anaconda (\u003ca href=\"https://www.anaconda.com/download\" rel=\"nofollow\"\u003ehow to install anaconda\u003c/a\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n sscha -c conda-forge python=3.10 gfortran libblas lapack openmpi julia openmpi-mpicc pip numpy scipy spglib\nconda activate sscha\npip install ase julia mpi4py\npip install cellconstructor python-sscha tdscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2 id=\"user-content-video-lessons-from--the-2023-school-are-available\"\u003e\u003ca class=\"heading-link\" href=\"#video-lessons-from--the-2023-school-are-available\"\u003eVideo lessons from the 2023 School are available\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe full recordings, both of theoretical lectures, tutorials and Hands-on sessions can be found\nin our youtube channel \u003ccode\u003eSSCHAcode \u0026lt;https://www.youtube.com/@SSCHAcode\u0026gt;\u003c/code\u003e_\u003c/p\u003e\n\u003cp\u003eThis is the safest and best way to install the SSCHA. The first line creates a new pristine python environment with all the required libraries to compile the source code. The second line activates the newly installed environment. Then, the thrid command installs the additional dependencies, the last line compiles and install the SSCHA code.\u003c/p\u003e\n\u003cp\u003eTo use the SSCHA, you must activate the python environment with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate sscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis installation method should work also on clusters and with computers with custom configurations. You must remember to activate the \u003ccode\u003esscha\u003c/code\u003e environment even in your submission scripts on clusters.\u003c/p\u003e\n\u003cp\u003eTo activate the julia speedup on the SSCHA minimization, you must ensure julia dependencies are correctly setup. To do this, run the following line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -c \u0027import julia; julia.install()\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2 id=\"user-content-installing-without-anaconda\"\u003e\u003ca class=\"heading-link\" href=\"#installing-without-anaconda\"\u003eInstalling without Anaconda\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eIf you do not have anaconda to handle your dependencies you need to manually compile the code.\u003c/p\u003e\n\u003cp\u003eMost of the codes require a fortran or C compiler and MPI configured. Here we install all the requirements to properly setup the SSCHA code. To properly compile and install the SSCHA code, you need a fortran compiler and LAPACK/BLAS available.\u003c/p\u003e\n\u003cp\u003eOn Debian-based Linux distribution, all the software required is installed with (Tested on Quantum Mobile and ubuntu 20.04):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt install libblas-dev liblapack-dev liblapacke-dev gfortran openmpi-bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that some of the names of the libraries may change slightly in different linux versions or on MacOS.\u003c/p\u003e\n\u003ch3 id=\"user-content-python-installation\"\u003e\u003ca class=\"heading-link\" href=\"#python-installation\"\u003ePython installation\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eUp to version 1.4 of SSCHA, it supports only python \u0026lt;= 3.10. If you are using the default python in the system, make sure to have installed the development header files. On ubuntu, they can be installed with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install python-dev\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you use anaconda, they are automatically installed.\u003c/p\u003e\n\u003ch3 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eThe SSCHA code is a collection of 3 python packages: CellConstructor, python-sscha and tdscha.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCellConstructor \u0026lt;https://github.com/SSCHAcode/CellConstructor\u0026gt;\u003c/code\u003e_ : utility to manage phonon dispersions, atomic structures and crystal symmetries\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esscha \u0026lt;https://github.com/SSCHAcode/python-sscha\u0026gt;\u003c/code\u003e_ : This repository, relax with anharmonicity and compute static linear response properties.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etdscha \u0026lt;https://github.com/SSCHAcode/tdscha\u0026gt;\u003c/code\u003e_ : Compute the dynamical linear response (Raman and IR, spectral functions)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMore details about installations are in the official website \u003ccode\u003ewww.sscha.eu \u0026lt;https://sscha.eu/download\u0026gt;\u003c/code\u003e_\u003c/p\u003e\n\u003ch2 id=\"user-content-install-with-anaconda\"\u003e\u003ca class=\"heading-link\" href=\"#install-with-anaconda\"\u003eInstall with Anaconda\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe easiest way to install the code is through anaconda.\nFirst make sure you have anaconda installed \u003ccode\u003e(install anaconda) \u0026lt;https://www.anaconda.com/download\u0026gt;\u003c/code\u003e_\u003c/p\u003e\n\u003cp\u003eThe following commands are sufficient to install the full sscha suite and its dependencies.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda create -n sscha -c conda-forge python=3.10 gfortran libblas lapack openmpi julia openmpi-mpicc pip numpy scipy spglib\n conda activate sscha\n pip install ase julia mpi4py\n pip install cellconstructor python-sscha tdscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo activate the environment and execute the SSCHA, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda activate sscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2 id=\"user-content-manual-installation\"\u003e\u003ca class=\"heading-link\" href=\"#manual-installation\"\u003eManual installation\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe SSCHA benefits from julia being installed in the system. If present,\nit will be automatically used to speedup the calculation.\u003c/p\u003e\n\u003cp\u003eTo install julia, refer to the official website \u003ca href=\"https://julialang.org/downloads/\" rel=\"nofollow\"\u003ejulialang.org/downloads/\u003c/a\u003e\nAlternatively, to install julia on linux we can employ juliaup:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e curl -fsSL https://install.julialang.org | sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHit enter when asked to install julia.\u003c/p\u003e\n\u003cp\u003eThen, install the python bindings for julia with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install julia\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe tdscha extension to compute Raman and IR requires some additional julia packages that can be installed within a julia terminal. Update your configuration to have access to the newly installed julia\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e source ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, open a terminal and type \u003ccode\u003ejulia\u003c/code\u003e. Inside the julia prompt, type \u003ccode\u003e]\u003c/code\u003e. The prompt should change color and display the julia version ending with \u003ccode\u003epkg\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eInstall the required julia libraries\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pkg\u0026gt; add SparseArrays, LinearAlgebra, InteractiveUtils, PyCall\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should install the required libraries. Press backspace to return to the standard julia prompt and exit with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e julia\u0026gt; exit()\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, you should be able to exploit the julia speedup in the TDSCHA calculations. It is not required to install julia before TDSCHA, it can also be done in a later moment.\u003c/p\u003e\n\u003ch3 id=\"user-content-compiling-sscha\"\u003e\u003ca class=\"heading-link\" href=\"#compiling-sscha\"\u003eCompiling SSCHA\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eOnce the prerequisites have been installed, python-sscha can be downloaded and installed with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install cellconstructor python-sscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, it is possible to use the most recent version from the \u003ca href=\"https://github.com/SSCHAcode\"\u003eSSCHA GitHub\u003c/a\u003e repository, under CellConstructor and python-sscha repositories. The installation is performed in this case with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3 id=\"user-content-personalize-the-compiler\"\u003e\u003ca class=\"heading-link\" href=\"#personalize-the-compiler\"\u003ePersonalize the compiler\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eIf you have multiple compilers installed, and want to force pip to employ a specific fortran compiler, you can specify its path in the FC environment variable. Remember that the compiler employed to compile the code should match with the linker, indicated in the LDSHARED variable.\u003c/p\u003e\n\u003cp\u003eFor example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e FC=gfortran LDSHARED=gfortran pip install cellconstructor python-sscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor the development version of the code, subtitute the pip call with the python setup.py install.\u003c/p\u003e\n", "stargazers_count": 44, - "subscribers_count": 5, - "topics": [ - "bids", - "bidsapp", - "diffusion-mri", - "mri" - ], - "updated_at": 1708416908.0 + "subscribers_count": 7, + "topics": [], + "updated_at": 1695963113.0 }, { "data_format": 2, @@ -40967,71 +40989,43 @@ var data = }, { "data_format": 2, - "description": "The python implementation of the Stochastic Self-Consistent Harmonic Approximation (SSCHA).", - "filenames": [ - "docker/Singularity.def" - ], - "full_name": "SSCHAcode/python-sscha", - "latest_release": "v1.2", - "readme": "\u003ch1 id=\"user-content-sscha\"\u003e\u003ca class=\"heading-link\" href=\"#sscha\"\u003eSSCHA\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h1\u003e\n\u003cp\u003eThe stochastic self-consistent harmonic approximation (SSCHA) is a full computational python package that simulates thermodynamic and phononic properties of materials accounting for anharmonicity at a nonperturbative level, fully including quantum and thermal fluctuations.\u003c/p\u003e\n\u003cp\u003eSee more info on the webpage:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://sscha.eu\" rel=\"nofollow\"\u003ewww.sscha.eu\u003c/a\u003e\u003c/p\u003e\n\u003ch2 id=\"user-content-easy-installation-through-anaconda\"\u003e\u003ca class=\"heading-link\" href=\"#easy-installation-through-anaconda\"\u003eEasy installation through Anaconda\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe SSCHA code comes as a python library, with computationally intense part speedup with C, Fortran and Julia. The easiest way to install is through Anaconda (\u003ca href=\"https://www.anaconda.com/download\" rel=\"nofollow\"\u003ehow to install anaconda\u003c/a\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n sscha -c conda-forge python=3.10 gfortran libblas lapack openmpi julia openmpi-mpicc pip numpy scipy spglib\nconda activate sscha\npip install ase julia mpi4py\npip install cellconstructor python-sscha tdscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2 id=\"user-content-video-lessons-from--the-2023-school-are-available\"\u003e\u003ca class=\"heading-link\" href=\"#video-lessons-from--the-2023-school-are-available\"\u003eVideo lessons from the 2023 School are available\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe full recordings, both of theoretical lectures, tutorials and Hands-on sessions can be found\nin our youtube channel \u003ccode\u003eSSCHAcode \u0026lt;https://www.youtube.com/@SSCHAcode\u0026gt;\u003c/code\u003e_\u003c/p\u003e\n\u003cp\u003eThis is the safest and best way to install the SSCHA. The first line creates a new pristine python environment with all the required libraries to compile the source code. The second line activates the newly installed environment. Then, the thrid command installs the additional dependencies, the last line compiles and install the SSCHA code.\u003c/p\u003e\n\u003cp\u003eTo use the SSCHA, you must activate the python environment with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda activate sscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis installation method should work also on clusters and with computers with custom configurations. You must remember to activate the \u003ccode\u003esscha\u003c/code\u003e environment even in your submission scripts on clusters.\u003c/p\u003e\n\u003cp\u003eTo activate the julia speedup on the SSCHA minimization, you must ensure julia dependencies are correctly setup. To do this, run the following line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -c \u0027import julia; julia.install()\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2 id=\"user-content-installing-without-anaconda\"\u003e\u003ca class=\"heading-link\" href=\"#installing-without-anaconda\"\u003eInstalling without Anaconda\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eIf you do not have anaconda to handle your dependencies you need to manually compile the code.\u003c/p\u003e\n\u003cp\u003eMost of the codes require a fortran or C compiler and MPI configured. Here we install all the requirements to properly setup the SSCHA code. To properly compile and install the SSCHA code, you need a fortran compiler and LAPACK/BLAS available.\u003c/p\u003e\n\u003cp\u003eOn Debian-based Linux distribution, all the software required is installed with (Tested on Quantum Mobile and ubuntu 20.04):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt update\nsudo apt install libblas-dev liblapack-dev liblapacke-dev gfortran openmpi-bin\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that some of the names of the libraries may change slightly in different linux versions or on MacOS.\u003c/p\u003e\n\u003ch3 id=\"user-content-python-installation\"\u003e\u003ca class=\"heading-link\" href=\"#python-installation\"\u003ePython installation\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eUp to version 1.4 of SSCHA, it supports only python \u0026lt;= 3.10. If you are using the default python in the system, make sure to have installed the development header files. On ubuntu, they can be installed with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo apt install python-dev\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you use anaconda, they are automatically installed.\u003c/p\u003e\n\u003ch3 id=\"user-content-prerequisites\"\u003e\u003ca class=\"heading-link\" href=\"#prerequisites\"\u003ePrerequisites\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eThe SSCHA code is a collection of 3 python packages: CellConstructor, python-sscha and tdscha.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eCellConstructor \u0026lt;https://github.com/SSCHAcode/CellConstructor\u0026gt;\u003c/code\u003e_ : utility to manage phonon dispersions, atomic structures and crystal symmetries\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esscha \u0026lt;https://github.com/SSCHAcode/python-sscha\u0026gt;\u003c/code\u003e_ : This repository, relax with anharmonicity and compute static linear response properties.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etdscha \u0026lt;https://github.com/SSCHAcode/tdscha\u0026gt;\u003c/code\u003e_ : Compute the dynamical linear response (Raman and IR, spectral functions)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMore details about installations are in the official website \u003ccode\u003ewww.sscha.eu \u0026lt;https://sscha.eu/download\u0026gt;\u003c/code\u003e_\u003c/p\u003e\n\u003ch2 id=\"user-content-install-with-anaconda\"\u003e\u003ca class=\"heading-link\" href=\"#install-with-anaconda\"\u003eInstall with Anaconda\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe easiest way to install the code is through anaconda.\nFirst make sure you have anaconda installed \u003ccode\u003e(install anaconda) \u0026lt;https://www.anaconda.com/download\u0026gt;\u003c/code\u003e_\u003c/p\u003e\n\u003cp\u003eThe following commands are sufficient to install the full sscha suite and its dependencies.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda create -n sscha -c conda-forge python=3.10 gfortran libblas lapack openmpi julia openmpi-mpicc pip numpy scipy spglib\n conda activate sscha\n pip install ase julia mpi4py\n pip install cellconstructor python-sscha tdscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo activate the environment and execute the SSCHA, run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e conda activate sscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2 id=\"user-content-manual-installation\"\u003e\u003ca class=\"heading-link\" href=\"#manual-installation\"\u003eManual installation\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h2\u003e\n\u003cp\u003eThe SSCHA benefits from julia being installed in the system. If present,\nit will be automatically used to speedup the calculation.\u003c/p\u003e\n\u003cp\u003eTo install julia, refer to the official website \u003ca href=\"https://julialang.org/downloads/\" rel=\"nofollow\"\u003ejulialang.org/downloads/\u003c/a\u003e\nAlternatively, to install julia on linux we can employ juliaup:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e curl -fsSL https://install.julialang.org | sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eHit enter when asked to install julia.\u003c/p\u003e\n\u003cp\u003eThen, install the python bindings for julia with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install julia\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe tdscha extension to compute Raman and IR requires some additional julia packages that can be installed within a julia terminal. Update your configuration to have access to the newly installed julia\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e source ~/.bashrc\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, open a terminal and type \u003ccode\u003ejulia\u003c/code\u003e. Inside the julia prompt, type \u003ccode\u003e]\u003c/code\u003e. The prompt should change color and display the julia version ending with \u003ccode\u003epkg\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eInstall the required julia libraries\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pkg\u0026gt; add SparseArrays, LinearAlgebra, InteractiveUtils, PyCall\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should install the required libraries. Press backspace to return to the standard julia prompt and exit with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e julia\u0026gt; exit()\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow, you should be able to exploit the julia speedup in the TDSCHA calculations. It is not required to install julia before TDSCHA, it can also be done in a later moment.\u003c/p\u003e\n\u003ch3 id=\"user-content-compiling-sscha\"\u003e\u003ca class=\"heading-link\" href=\"#compiling-sscha\"\u003eCompiling SSCHA\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eOnce the prerequisites have been installed, python-sscha can be downloaded and installed with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install cellconstructor python-sscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAlternatively, it is possible to use the most recent version from the \u003ca href=\"https://github.com/SSCHAcode\"\u003eSSCHA GitHub\u003c/a\u003e repository, under CellConstructor and python-sscha repositories. The installation is performed in this case with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e pip install .\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3 id=\"user-content-personalize-the-compiler\"\u003e\u003ca class=\"heading-link\" href=\"#personalize-the-compiler\"\u003ePersonalize the compiler\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/h3\u003e\n\u003cp\u003eIf you have multiple compilers installed, and want to force pip to employ a specific fortran compiler, you can specify its path in the FC environment variable. Remember that the compiler employed to compile the code should match with the linker, indicated in the LDSHARED variable.\u003c/p\u003e\n\u003cp\u003eFor example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e FC=gfortran LDSHARED=gfortran pip install cellconstructor python-sscha\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor the development version of the code, subtitute the pip call with the python setup.py install.\u003c/p\u003e\n", - "stargazers_count": 44, - "subscribers_count": 7, - "topics": [], - "updated_at": 1695963113.0 - }, - { - "data_format": 2, - "description": "A flexible Nextflow-based framework for the definition of sequencing data processing pipelines", + "description": "Generate subject connectomes from raw BIDS data \u0026 perform inter-subject connection density normalisation, using the MRtrix3 software package.", "filenames": [ "Singularity" ], - "full_name": "montilab/pipeliner", + "full_name": "bids-apps/MRtrix3_connectome", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePipeliner\u003c/h1\u003e\u003ca id=\"user-content-pipeliner\" class=\"anchor\" aria-label=\"Permalink: Pipeliner\" href=\"#pipeliner\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ci\u003eA flexible Nextflow-based framework for the definition of sequencing data processing pipelines\u003c/i\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/508d3cf3e60de07f1708a87005b68d010a24744fc1f459d7228cc2ef296cf041/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4275696c74253230576974682d4e657874666c6f772d627269676874677265656e2e737667\" alt=\"Built With\" data-canonical-src=\"https://img.shields.io/badge/Built%20With-Nextflow-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3e5756eff05217c8a60b4879897d09c74b9176e01b508c41903b58ee571f9b87/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4672616d65776f726b2d507974686f6e253230332e362d626c75652e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3e5756eff05217c8a60b4879897d09c74b9176e01b508c41903b58ee571f9b87/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4672616d65776f726b2d507974686f6e253230332e362d626c75652e737667\" alt=\"Python\" data-canonical-src=\"https://img.shields.io/badge/Framework-Python%203.6-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3b655db49bd9b450ab53d4dbfb4a675374c91a0648f2b4e09cabd7d68db7a6f3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6d7061746962696c6974792d4c696e75782532302532462532304f53582d6f72616e67652e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3b655db49bd9b450ab53d4dbfb4a675374c91a0648f2b4e09cabd7d68db7a6f3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6d7061746962696c6974792d4c696e75782532302532462532304f53582d6f72616e67652e737667\" alt=\"Compatibility\" data-canonical-src=\"https://img.shields.io/badge/Compatibility-Linux%20%2F%20OSX-orange.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/eec32502ffa8eabfe95cc204a8a2ba285df42dbc7c747a9ad86b33305375d581/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646570656e64656e636965732d7570253230746f253230646174652d627269676874677265656e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eec32502ffa8eabfe95cc204a8a2ba285df42dbc7c747a9ad86b33305375d581/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646570656e64656e636965732d7570253230746f253230646174652d627269676874677265656e2e737667\" alt=\"Dependencies\" data-canonical-src=\"https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/montilab/pipeliner/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/33507c2956695a97fd44292ae43445abd05bfdf68234ff52042d1f418e06f110/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6d6f6e74696c61622f706970656c696e65722e737667\" alt=\"GitHub Issues\" data-canonical-src=\"https://img.shields.io/github/issues/montilab/pipeliner.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"left\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/montilab/pipeliner/master/media/framework_schematic.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/montilab/pipeliner/master/media/framework_schematic.png\" width=\"80%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\n\u003c/p\u003e\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eModular directory structure: It is designed to generate automated result directory based on the names of the samples and tools used to process them.\u003c/li\u003e\n\u003cli\u003ePlatform independent: It is bundled with an anaconda repository which contains pre-compiled tools as well as pre-built environments that can be used directly.\u003c/li\u003e\n\u003cli\u003eModular architecture: It allows the expert users to customize, modify processes, or add additional tools based on their needs.\u003c/li\u003e\n\u003cli\u003eAutomated job parallelization, job recovery, and reproducibility.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuickstart\u003c/h2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eFor more information, please refer to the \u003ca href=\"https://pipeliner.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003efull documentation\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eClone Repository\u003c/h3\u003e\u003ca id=\"user-content-clone-repository\" class=\"anchor\" aria-label=\"Permalink: Clone Repository\" href=\"#clone-repository\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ git clone https://github.com/montilab/pipeliner\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eInstall Dependencies\u003c/h3\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install Dependencies\" href=\"#install-dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ conda env create -f pipeliner/envs/linux_env.yml \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Linux\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ conda env create -f pipeliner/envs/osx_env.yml \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Mac\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eActivate Conda Environment\u003c/h3\u003e\u003ca id=\"user-content-activate-conda-environment\" class=\"anchor\" aria-label=\"Permalink: Activate Conda Environment\" href=\"#activate-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e activate pipeliner\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUpdate Local Paths\u003c/h3\u003e\u003ca id=\"user-content-update-local-paths\" class=\"anchor\" aria-label=\"Permalink: Update Local Paths\" href=\"#update-local-paths\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ python pipeliner/scripts/paths.py\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDownload Nextflow Executable\u003c/h3\u003e\u003ca id=\"user-content-download-nextflow-executable\" class=\"anchor\" aria-label=\"Permalink: Download Nextflow Executable\" href=\"#download-nextflow-executable\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd pipeliner/pipelines\n$ curl -s https://get.nextflow.io | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eLocally Run Example Data\u003c/h3\u003e\u003ca id=\"user-content-locally-run-example-data\" class=\"anchor\" aria-label=\"Permalink: Locally Run Example Data\" href=\"#locally-run-example-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ ./nextflow rnaseq.nf -c rnaseq.config\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eExpected Output\u003c/h3\u003e\u003ca id=\"user-content-expected-output\" class=\"anchor\" aria-label=\"Permalink: Expected Output\" href=\"#expected-output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre lang=\"text\"\u003e\u003ccode\u003eN E X T F L O W ~ version 0.31.1\nLaunching `rnaseq.nf` [nasty_pauling] - revision: cd3f572ab2\n[warm up] executor \u0026gt; local\n[31/1b2066] Submitted process \u0026gt; pre_fastqc (ggal_alpha)\n[23/de6d60] Submitted process \u0026gt; pre_fastqc (ggal_theta)\n[7c/28ee53] Submitted process \u0026gt; pre_fastqc (ggal_gamma)\n[97/9ad6c1] Submitted process \u0026gt; check_reads (ggal_alpha)\n[ab/c3eedf] Submitted process \u0026gt; check_reads (ggal_theta)\n[2d/050633] Submitted process \u0026gt; check_reads (ggal_gamma)\n[1d/f3af6d] Submitted process \u0026gt; pre_multiqc\n[32/b1db1d] Submitted process \u0026gt; hisat_indexing (genome_reference.fa)\n[3b/d93c6d] Submitted process \u0026gt; trim_galore (ggal_alpha)\n[9c/3fa50b] Submitted process \u0026gt; trim_galore (ggal_theta)\n[62/25fce0] Submitted process \u0026gt; trim_galore (ggal_gamma)\n[66/ccc9db] Submitted process \u0026gt; hisat_mapping (ggal_alpha)\n[28/69fff5] Submitted process \u0026gt; hisat_mapping (ggal_theta)\n[5c/5ed2b6] Submitted process \u0026gt; hisat_mapping (ggal_gamma)\n[b4/e559ab] Submitted process \u0026gt; gtftobed (genome_annotation.gtf)\n[bc/6f490c] Submitted process \u0026gt; rseqc (ggal_alpha)\n[71/80aa9e] Submitted process \u0026gt; rseqc (ggal_theta)\n[17/ca0d9f] Submitted process \u0026gt; rseqc (ggal_gamma)\n[d7/7d391b] Submitted process \u0026gt; counting (ggal_alpha)\n[df/936854] Submitted process \u0026gt; counting (ggal_theta)\n[11/143c2c] Submitted process \u0026gt; counting (ggal_gamma)\n[31/4c11f9] Submitted process \u0026gt; expression_matrix\n[1f/3af548] Submitted process \u0026gt; multiqc\nSuccess: Pipeline Completed!\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cem\u003ePlease refer to \u003ca href=\"https://github.com/montilab/pipeliner/blob/master/docs/reports.md\"\u003ereports\u003c/a\u003e for examples\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCite\u003c/h2\u003e\u003ca id=\"user-content-cite\" class=\"anchor\" aria-label=\"Permalink: Cite\" href=\"#cite\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFederico A, Karagiannis T, Karri K, Kishore D, Koga Y, Campbell J, Monti S (2019) Pipeliner: A Nextflow-based framework for the definition of sequencing data processing pipelines. \u003cem\u003eFrontiers in Genetics\u003c/em\u003e. \u003ca href=\"https://doi.org/10.3389/fgene.2019.00614\" rel=\"nofollow\"\u003ehttps://doi.org/10.3389/fgene.2019.00614\u003c/a\u003e.\u003c/p\u003e\n", + "readme": "\u003ch2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h2\u003e\n\u003cp\u003eThis BIDS App enables generation and subsequent group analysis of structural connectomes\ngenerated from diffusion MRI data. The analysis pipeline relies primarily on the \u003cem\u003eMRtrix3\u003c/em\u003e\nsoftware package, and includes a number of state-of-the-art methods for image processing,\ntractography reconstruction, connectome generation and inter-subject connection density\nnormalisation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: App is still under development; script is not guaranteed to be operational\nfor all use cases.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eRequirements\u003c/h2\u003e\n\u003cp\u003eDue to use of the Anatomically-Constrained Tractography (ACT) framework, correction of\nEPI susceptibility distortions is a prerequisite for this pipeline. Currently, this is\nonly possible within this pipeline through use of the FSL tool \u003ccode\u003etopup\u003c/code\u003e, which relies\non the presence of spin-echo EPI images with differences in phase encoding to estimate\nthe causative inhomogeneity field. In the absence of such data, this pipeline is not\ncurrently applicable; though recommendations for alternative mechanisms for such\ncorrection in the Issues page are welcome, and development of novel techniques for\nperforming this correction are additionally underway.\u003c/p\u003e\n\u003cp\u003eWhile many common DICOM conversion software are capable of providing data characterising\nthe phase and slice encoding performed in the acquisition protocol, which are subsequently\nused by this pipeline to automate DWI data pre-processing, for some softwares and/or\nsome data (particularly those not acquired on a Siemens platform), such data may not be\npresent in the sidecar JSON files for files in the BIDS \u003ccode\u003edwi/\u003c/code\u003e and \u003ccode\u003efmap/\u003c/code\u003e directories.\nIn this circumstance, it will be necessary for users to manually enter the relevant\ninformation into these files in order for this script to be capable of processing the\ndata. Every JSON file in these two directories should contain the BIDS fields\n\u003ccode\u003ePhaseEncodingDirection\u003c/code\u003e and \u003ccode\u003eTotalReadoutTime\u003c/code\u003e. For DWI data, it is also preferable to\nprovide the \u003ccode\u003eSliceEncodingDirection\u003c/code\u003e and \u003ccode\u003eSliceTiming\u003c/code\u003e fields. More information on these\ndata can be found in the \u003ca href=\"http://bids.neuroimaging.io/\" rel=\"nofollow\"\u003eBIDS documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-instructions\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstructions\u003c/h2\u003e\n\u003ch3\u003e\u003ca id=\"user-content-multi-stage-analysis-pipeline\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#multi-stage-analysis-pipeline\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eMulti-stage analysis pipeline\u003c/h3\u003e\n\u003cp\u003eThe tool consists of three explicit analysis levels:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\"\u003ccode\u003epreproc\u003c/code\u003e\": This performs basic DWI (and if necessary T1-weighted image) pre-processing\nbased on the raw BIDS data input, writing the resulting pre-processed data to\nsub-directory \"\u003ccode\u003eMRtrix3_connectome-preproc\u003c/code\u003e\" within the specified output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\"\u003ccode\u003eparticipant\u003c/code\u003e\": This operates upon the pre-processed DWI (and possibly T1-weighted image)\ndata generated by the \"\u003ccode\u003epreproc\u003c/code\u003e\" analysis level, performing CSD, streamlines reconstruction\nand connectome construction. Results are written to sub-directory \"\u003ccode\u003eMRtrix3_connectome-participant\u003c/code\u003e\"\nwithin the specified output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\"\u003ccode\u003egroup\u003c/code\u003e\": This operates on the outputs of the \"\u003ccode\u003eparticipant\u003c/code\u003e\" analysis level, performing\nappropriate inter-subject connection density normalisation as described in\n\u003ca href=\"https://osf.io/c67kn/\" rel=\"nofollow\"\u003ethis manuscript\u003c/a\u003e. Results are written to sub-directory\n\"\u003ccode\u003eMRtrix3_connectome-group\u003c/code\u003e\" within the specified output directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca id=\"user-content-execution-platforms\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#execution-platforms\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eExecution platforms\u003c/h3\u003e\n\u003cp\u003eThis script can be utilised in one of three ways:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eAs a stand-alone \u003cem\u003eMRtrix3\u003c/em\u003e script\u003c/p\u003e\n\u003cp\u003eThe script \u003ccode\u003emrtrix3_connectome.py\u003c/code\u003e can additionally be used \u003cem\u003eoutside\u003c/em\u003e of this Docker\ncontainer, as a stand-alone Python script build against the \u003cem\u003eMRtrix3\u003c/em\u003e Python libraries.\nUsing the script in this way requires setting the \u003ccode\u003ePYTHONPATH\u003c/code\u003e environment variable to\ninclude the path to the \u003cem\u003eMRtrix3\u003c/em\u003e \u003ccode\u003elib/\u003c/code\u003e directory where it is installed on your local\nsystem, as described \u003ca href=\"https://community.mrtrix.org/t/the-mrtrix3-python-script-library/2243\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nWhen used in this way, the command-line interface of the script will be more consistent\nwith the rest of \u003cem\u003eMRtrix3\u003c/em\u003e. Note that this usage will require version \u003ccode\u003e3.0.0\u003c/code\u003e of \u003cem\u003eMRtrix3\u003c/em\u003e\nto be installed and configured appropriately on your local system.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs a Docker container\u003c/p\u003e\n\u003cp\u003eThe \u003ca href=\"https://hub.docker.com/r/bids/mrtrix3_connectome/\" rel=\"nofollow\"\u003ebids/MRtrix3_connectome\u003c/a\u003e Docker\ncontainer enables users to generate structural connectomes from diffusion MRI data using\nstate-of-the-art techniques. The pipeline requires that data be organized in accordance\nwith the \u003ca href=\"http://bids.neuroimaging.io\" rel=\"nofollow\"\u003eBIDS specification\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn your terminal, type:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker pull bids/mrtrix3_connectome\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUsing the \"\u003ccode\u003epreproc\u003c/code\u003e\"-level analysis as an exemplar, the tool is executed as e.g.:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ docker run -i --rm \\\n -v /Users/yourname/data:/bids_dataset \\\n -v /Users/yourname/output:/output \\\n bids/mrtrix3_connectome \\\n /bids_dataset /output preproc --participant_label 01\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs a Singularity container\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eMRtrix3_connectome\u003c/em\u003e BIDS App can also be built locally as a Singularity container.\nThis is particularly useful for subsequent utilisation on high-performance computing\nhardware, as unlike Docker there are no super-user privileges or user group\nmemberships required for execution. I have also personally been able to utilise the\nCUDA version of FSL\u0027s \u003ccode\u003eeddy\u003c/code\u003e command within this tool when running on a computing\ncluster node with GPU capability (though this can require explicit configuration;\nspeak to your system administrator).\u003c/p\u003e\n\u003cp\u003eWithin the location in which the \u003cem\u003eMRtrix3_connectome\u003c/em\u003e source code has been cloned,\ntype:\u003c/p\u003e\n\u003cpre lang=\"{bash}\"\u003e\u003ccode\u003e$ sudo singularity build MRtrix3_connectome.sif Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe resulting container file \"\u003ccode\u003eMRtrix3_connectome.sif\u003c/code\u003e\" can be run as a stand-alone\nexecutable, as long as the system on which the file is executed has a version of\nSingularity installed that is compatible with that of the system used to build the\ncontainer.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDocumentation\u003c/h2\u003e\n\u003cp\u003eThe help page of the tool itself can be generated by executing the script without\nproviding any command-line options. The help page is additionally presented at the\nbottom of this README page for reference. Documentation regarding the underlying\n\u003cem\u003eMRtrix3\u003c/em\u003e tools can be found in the official\n\u003ca href=\"http://mrtrix.readthedocs.org\" rel=\"nofollow\"\u003e\u003cem\u003eMRtrix3\u003c/em\u003e documentation\u003c/a\u003e. Additional information\nmay be found in the \u003ca href=\"http://community.mrtrix.org\" rel=\"nofollow\"\u003eonline \u003cem\u003eMRtrix3\u003c/em\u003e community forum\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-error-reporting\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#error-reporting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eError Reporting\u003c/h2\u003e\n\u003cp\u003eExperiencing problems? You can either post a private message to me on the\n\u003ca href=\"http://community.mrtrix.org/u/rsmith\" rel=\"nofollow\"\u003e\u003cem\u003eMRtrix3\u003c/em\u003e community forum\u003c/a\u003e, or you can report it\ndirectly to the \u003ca href=\"https://github.com/BIDS-Apps/MRtrix3_connectome/issues\"\u003eGitHub issues list\u003c/a\u003e.\nIn both cases, please include as much information as possible; this may include re-running\nthe script using the \u003ccode\u003e--debug\u003c/code\u003e option, which will provide additional information at the\nterminal, and preserve temporary files generated by the script within your target output\ndirectory, which can be forwarded to the developer.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eDevelopment of this tool was made possible through funding from the National Health\nand Medical Research Council (NHMRC) of Australia.\u003c/p\u003e\n\u003cp\u003eThe developer acknowledges the facilities and scientific and technical assistance of\nthe National Imaging Facility, a National Collaborative Research Infrastructure Strategy\n(NCRIS) capability, at the Florey Institute of Neuroscience and Mental Health.\u003c/p\u003e\n\u003cp\u003eThe Florey Institute of Neuroscience and Mental Health acknowledges support from the\nVictorian Government and in particular the funding from the Operational Infrastructure\nSupport Grant.\u003c/p\u003e\n\u003cp\u003eRobert Smith is supported by fellowship funding from the National Imaging Facility (NIF),\nan Australian Government National Collaborative Research Infrastructure Strategy (NCRIS)\ncapability.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCitation\u003c/h2\u003e\n\u003cp\u003eWhen using this pipeline, please use the following snippet to acknowledge the relevant\nwork (amend as appropriate depending on options used):\u003c/p\u003e\n\u003cp\u003eStructural connectomes were generated using the \u003cem\u003eMRtrix3_connectome\u003c/em\u003e BIDS App (Smith\net al., 2019), which operates principally using tools provided in the \u003cem\u003eMRtrix3\u003c/em\u003e\nsoftware package (Tournier et al., 2019; \u003ca href=\"http://mrtrix.org\" rel=\"nofollow\"\u003ehttp://mrtrix.org\u003c/a\u003e). This included: DWI\ndenoising (Veraart et al., 2016), Gibbs ringing removal (Kellner et al., 2016),\npre-processing (Andersson et al., 2003; Andersson and Sotiropoulos, 2016; Andersson\net al., 2016; (IF USING EDDY_CUDA: Andersson et al., 2017)); and bias field correction\n(Tustison et al., 2010 OR Zhang et al., 2001); inter-modal registration (Bhushan et al.,\n2015); brain extraction (Smith, 2002 OR Iglesias et al., 2011), T1 tissue segmentation\n(Zhang et al., 2001; Smith, 2002; Patenaude et al., 2011; Smith et al., 2012); spherical\ndeconvolution (Tournier et al., 2004; Jeurissen et al., 2014); probabilistic tractography\n(Tournier et al., 2010) utilizing Anatomically-Constrained Tractography (Smith et al.,\n2012) and dynamic seeding (Smith et al., 2015b); SIFT2 (Smith et al., 2015b); T1\nparcellation ((((Avants et al., 2008 AND Tustison et al., 2013) OR Andersson et al.,\n2010) AND (Tzourio-Mazoyer et al., 2002 OR Yeo et al., 2011 OR Craddock et al., 2012\n2011) OR Fan et al., 2016 OR (Zalesky et al., 2010 AND Perry et al., 2017))) OR\n2012) (Dale et al., 1999 AND (Desikan et al., 2006 OR Destrieux et al., 2010 OR\n2013) Glasser et al., 2016))); robust structural connectome construction (Smith et al.,\n2014) 2015a; Yeh et al., 2016); inter-subject connection density normalisation\n2015) (Smith et al., 2020).\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eSmith, R. E.; Connelly, A. MRtrix3_connectome: A BIDS Application for quantitative structural connectome construction. In Proc OHBM, 2019, W610\nAndersson, J. L.; Skare, S. \u0026amp; Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 2003, 20, 870-888\nAndersson, J. L. R.; Jenkinson, M. \u0026amp; Smith, S. Non-linear registration, aka spatial normalisation. FMRIB technical report, 2010, TR07JA2\nAndersson, J. L. \u0026amp; Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 2016, 125, 1063-1078\nAndersson, J. L. R. \u0026amp; Graham, M. S. \u0026amp; Zsoldos, E. \u0026amp; Sotiropoulos, S. N. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. NeuroImage, 2016, 141, 556-572\nAndersson, J. L. R.; Graham, M. S.; Drobnjak, I.; Zhang, H.; Filippini, N. \u0026amp; Bastiani, M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. NeuroImage, 2017, 152, 450-466\nAndersson, J. L. R.; Graham, M. S.; Drobnjak, I.; Zhang, H. \u0026amp; Campbell, J. Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data. NeuroImage, 2018, 171, 277-295\nAvants, B. B.; Epstein, C. L.; Grossman, M. \u0026amp; Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 2008, 12, 26-41\nBhushan, C.; Haldar, J. P.; Choi, S.; Joshi, A. A.; Shattuck, D. W. \u0026amp; Leahy, R. M. Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization. NeuroImage, 2015, 115, 269-280\nCraddock, R. C.; James, G. A.; Holtzheimer, P. E.; Hu, X. P.; Mayberg, H. S. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 2012, 33(8), 1914-1928\nDale, A. M.; Fischl, B. \u0026amp; Sereno, M. I. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage, 1999, 9, 179-194\nDesikan, R. S.; S\u00e9gonne, F.; Fischl, B.; Quinn, B. T.; Dickerson, B. C.; Blacker, D.; Buckner, R. L.; Dale, A. M.; Maguire, R. P.; Hyman, B. T.; Albert, M. S. \u0026amp; Killiany, R. J. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 2006, 31, 968-980\nDestrieux, C.; Fischl, B.; Dale, A. \u0026amp; Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage, 2010, 53, 1-15\nFan, L.; Li, H.; Zhuo, J.; Zhang, Y.; Wang, J.; Chen, L.; Yang, Z.; Chu, C.; Xie, S.; Laird, A.R.; Fox, P.T.; Eickhoff, S.B.; Yu, C.; Jiang, T. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cerebral Cortex, 2016, 26 (8), 3508-3526\nGlasser, M. F.; Coalson, T. S.; Robinson, E. C.; Hacker, C. D.; Harwell, J.; Yacoub, E.; Ugurbil, K.; Andersson, J.; Beckmann, C. F.; Jenkinson, M.; Smith, S. M. \u0026amp; Van Essen, D. C. A multi-modal parcellation of human cerebral cortex. Nature, 2016, 536, 171-178\nIglesias, J. E.; Liu, C. Y.; Thompson, P. M. \u0026amp; Tu, Z. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods. IEEE Transactions on Medical Imaging, 2011, 30, 1617-1634\nJeurissen, B; Tournier, J-D; Dhollander, T; Connelly, A \u0026amp; Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 2014, 103, 411-426\nKellner, E.; Dhital, B.; Kiselev, V. G.; Reisert, M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine, 2006, 76(5), 1574-1581\nPatenaude, B.; Smith, S. M.; Kennedy, D. N. \u0026amp; Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 2011, 56, 907-922\nPerry, A.; Wen, W.; Kochan, N. A.; Thalamuthu, A.; Sachdev, P. S.; Breakspear, M. The independent influences of age and education on functional brain networks and cognition in healthy older adults. Human Brain Mapping, 2017, 38(10), 5094-5114\nSmith, S. M. Fast robust automated brain extraction. Human Brain Mapping, 2002, 17, 143-155\nSmith, R. E.; Tournier, J.-D.; Calamante, F. \u0026amp; Connelly, A. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage, 2012, 62, 1924-1938\nSmith, R. E.; Tournier, J.-D.; Calamante, F. \u0026amp; Connelly, A. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. NeuroImage, 2015a, 104, 253-265\nSmith, R. E.; Tournier, J.-D.; Calamante, F. \u0026amp; Connelly, A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage, 2015b, 119, 338-351\nSmith, R. E.; Raffelt, D.; Tournier, J.-D.; Connelly, A. Quantitative streamlines tractography: methods and inter-subject normalisation. Preprint, 2020, OSF, https://doi.org/10.31219/osf.io/c67kn\nTournier, J.-D.; Calamante, F., Gadian, D.G. \u0026amp; Connelly, A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 2004, 23, 1176-1185\nTournier, J.-D.; Calamante, F. \u0026amp; Connelly, A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proceedings of the International Society for Magnetic Resonance in Medicine, 2010, 1670\nTournier, J.-D.; Smith, R. E.; Raffelt, D. A.; Tabbara, R.; Dhollander, T.; Pietsch, M; Christiaens, D.; Jeurissen, B.; Y, C.-H.; Connelly, A. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 2019, 202, 116137\nTustison, N.; Avants, B.; Cook, P.; Zheng, Y.; Egan, A.; Yushkevich, P. \u0026amp; Gee, J. N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 2010, 29, 1310-1320\nTustison, N.; Avants, B. Explicit B-spline regularization in diffeomorphic image registration. Frontiers in Neuroinformatics, 2013, 7, 39\nTzourio-Mazoyer, N.; Landeau, B.; Papathanassiou, D.; Crivello, F.; Etard, O.; Delcroix, N.; Mazoyer, B. \u0026amp; Joliot, M. Automated Anatomical Labeling of activations in SPM using a Macroscopic Anatomical Parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273\u2013289\nVeraart, J.; Novikov, D. S.; Christiaens, D.; Ades-aron, B.; Sijbers, J. \u0026amp; Fieremans, E. Denoising of diffusion MRI using random matrix theory. NeuroImage, 2016, 142, 394-406\nYeh, C.-H.; Smith, R. E.; Liang, X.; Calamante, F. \u0026amp; Connelly, A. Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics. NeuroImage, 2016, 142, 150-162\nYeo, B.T.; Krienen, F.M.; Sepulcre, J.; Sabuncu, M.R.; Lashkari, D.; Hollinshead, M.; Roffman, J.L.; Smoller, J.W.; Zollei, L.; Polimeni, J.R.; Fischl, B.; Liu, H. \u0026amp; Buckner, R.L. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol, 2011, 106(3), 1125-1165\nZalesky, A.; Fornito, A.; Harding, I. H.; Cocchi, L.; Y\u00fccel, M.; Pantelis, C. \u0026amp; Bullmore, E. T. Whole-brain anatomical networks: Does the choice of nodes matter? NeuroImage, 2010, 50, 970-983\nZhang, Y.; Brady, M. \u0026amp; Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 2001, 20, 45-57\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-help-page\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#help-page\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eHelp page\u003c/h2\u003e\n\u003cp\u003eThe following help page can equivalently be generated by executing the tool without\nproviding any command-line arguments (within a container environment; note interface\nis slightly different if run natively).\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003ca id=\"user-content-synopsis\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#synopsis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eSynopsis\u003c/h2\u003e\n\u003cp\u003eGenerate structural connectomes based on diffusion-weighted and T1-weighted image data using state-of-the-art reconstruction tools, particularly those provided in \u003cem\u003eMRtrix3\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eUsage\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003emrtrix3_connectome.py bids_dir output_dir analysis_level [ options ]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003ebids_dir\u003c/em\u003e: The directory with the input dataset formatted according to the BIDS standard.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eoutput_dir\u003c/em\u003e: The directory where the output files should be stored.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eanalysis_level\u003c/em\u003e: Level of analysis that will be performed; options are: preproc, participant, group.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca id=\"user-content-description-1\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#description-1\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDescription\u003c/h3\u003e\n\u003cp\u003eWhile preproc-level analysis only requires data within the BIDS directory, participant-level analysis requires that the output directory be pre-populated with the results from preproc-level processing; similarly, group-level analysis requires that the output directory be pre-populated with the results from participant-level analysis.\u003c/p\u003e\n\u003cp\u003eThe operations performed by each of the three levels of analysis are as follows:\u003c/p\u003e\n\u003cp\u003e\"preproc\": DWI: Denoising; Gibbs ringing removal; motion, eddy current and EPI distortion correction and outlier detection \u0026amp; replacement; brain masking, bias field correction and intensity normalisation; rigid-body registration \u0026amp; transformation to T1-weighted image. T1-weighted image: bias field correction; brain masking.\u003c/p\u003e\n\u003cp\u003e\"participant\": DWI: Response function estimation; FOD estimation. T1-weighted image (if -parcellation is not none): Tissue segmentation; grey matter parcellation. Combined (if -parcellation is not none, or -streamlines is provided): Whole-brain streamlines tractography; SIFT2; connectome construction.\u003c/p\u003e\n\u003cp\u003e\"group\": Generation of FA-based population template; warping of template-based white matter mask to subject spaces; calculation of group mean white matter response function; scaling of connectomes based on white matter b=0 intensity, response function used during participant-level analysis, and SIFT model proportioinality coefficient; generation of group mean connectome.\u003c/p\u003e\n\u003cp\u003eThe label(s) provided to the -participant_label and -session_label options correspond(s) to sub-\u0026lt;participant_label\u0026gt; and ses-\u0026lt;session_label\u0026gt; from the BIDS spec (so they do \u003cem\u003enot\u003c/em\u003e include \"sub-\" or \"ses-\"). Multiple participants / sessions can be specified with a space-separated list.\u003c/p\u003e\n\u003cp\u003eFor both preproc-level and participant-level analyses, if no specific participants or sessions are nominated by the user (or the user explicitly specifies multiple participants / sessions), the script will process each of these in series. It is additionally possible for the user to invoke multiple instances of this script in order to process multiple subjects at once in parallel, ensuring that no single participant / session is being processed in parallel, and that preproc-level output data are written fully before commencing participant-level analysis.\u003c/p\u003e\n\u003cp\u003eThe -output_verbosity option principally affects the participant-level analysis, modulating how many derivative files are written to the output directory. Permitted values are from 1 to 4: 1 writes only those files requisite for group-level analysis; 2 additionally writes files typically useful for post-hoc analysis (the default); 3 additionally generates files for enhanced connectome visualisation and copies the entire whole-brain tractogram; 4 additionally generates a full copy of the script scratch directory (with all intermediate files retained) to the output directory (and this applies to all analysis levels)\u003c/p\u003e\n\u003cp\u003eIf running participant-level analysis using the script as a standalone tool rather than inside the provided container, data pertaining to atlas parcellations can no longer be guaranteed to be stored at a specific location on the filesystem. In this case, the user will most likely need to manually specify the location where the corresponding parcellation is stored using the -atlas_path option.\u003c/p\u003e\n\u003ch3\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--output_verbosity\u003c/strong\u003e\u003cbr\u003eThe verbosity of script output (number from 1 to 4).\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-options-that-are-relevant-to-participant-level-analysis\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#options-that-are-relevant-to-participant-level-analysis\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions that are relevant to participant-level analysis\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--parcellation\u003c/strong\u003e\u003cbr\u003eThe choice of connectome parcellation scheme (compulsory for participant-level analysis); options are: aal, aal2, brainnetome246fs, brainnetome246mni, craddock200, craddock400, desikan, destrieux, hcpmmp1, none, perry512, yeo7fs, yeo7mni, yeo17fs, yeo17mni.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--streamlines\u003c/strong\u003e\u003cbr\u003eThe number of streamlines to generate for each subject (will be determined heuristically if not explicitly set).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--template_reg software\u003c/strong\u003e\u003cbr\u003eThe choice of registration software for mapping subject to template space; options are: ants, fsl.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-options-that-are-relevant-to-both-preproc-level-and-participant-level-analyses\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#options-that-are-relevant-to-both-preproc-level-and-participant-level-analyses\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions that are relevant to both preproc-level and participant-level analyses\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e--t1w_preproc path\u003c/strong\u003e\u003cbr\u003eProvide a path by which pre-processed T1-weighted image data may be found for the processed participant(s) / session(s)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-options-specific-to-the-batch-processing-of-participant-data\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#options-specific-to-the-batch-processing-of-participant-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eOptions specific to the batch processing of participant data\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--participant_label\u003c/strong\u003e\u003cbr\u003eThe label(s) of the participant(s) that should be analyzed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--session_label\u003c/strong\u003e\u003cbr\u003eThe session(s) within each participant that should be analyzed.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003ca id=\"user-content-standard-options\" class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#standard-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eStandard options\u003c/h4\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e-d/--debug\u003c/strong\u003e\u003cbr\u003edisplay debugging messages.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e-h/--help\u003c/strong\u003e\u003cbr\u003edisplay this information page and exit.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e-n/--n_cpus number\u003c/strong\u003e\u003cbr\u003euse this number of threads in multi-threaded applications (set to 0 to disable multi-threading).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--scratch /path/to/scratch/\u003c/strong\u003e\u003cbr\u003emanually specify the path in which to generate the scratch directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e--skip-bids-validator\u003c/strong\u003e\u003cbr\u003eSkip BIDS validation\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e-v/--version\u003c/strong\u003e\u003cbr\u003edisplay version information and exit.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Robert E. Smith (\u003ca href=\"mailto:robert.smith@florey.edu.au\"\u003erobert.smith@florey.edu.au\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCopyright:\u003c/strong\u003e Copyright (c) 2016-2020 The Florey Institute of Neuroscience\nand Mental Health.\u003c/p\u003e\n\u003cp\u003eThis Source Code Form is subject to the terms of the Mozilla Public\nLicense, v. 2.0. If a copy of the MPL was not distributed with this\nfile, You can obtain one at \u003ca href=\"http://mozilla.org/MPL/2.0/\" rel=\"nofollow\"\u003ehttp://mozilla.org/MPL/2.0/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eCovered Software is provided under this License on an \"as is\"\nbasis, without warranty of any kind, either expressed, implied, or\nstatutory, including, without limitation, warranties that the\nCovered Software is free of defects, merchantable, fit for a\nparticular purpose or non-infringing.\nSee the Mozilla Public License v. 2.0 for more details.\u003c/p\u003e\n\u003chr\u003e\n", "stargazers_count": 44, - "subscribers_count": 9, + "subscribers_count": 5, "topics": [ - "nextflow", - "workflow", - "bioinformatics", - "rna-seq", - "computational-biology" + "bids", + "bidsapp", + "diffusion-mri", + "mri" ], - "updated_at": 1718911278.0 + "updated_at": 1708416908.0 }, { "data_format": 2, - "description": "An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images", + "description": "Dynamical Neuroimaging Spatiotemporal Representations (DyNeuSR)", "filenames": [ "Singularity" ], - "full_name": "MASILab/PreQual", - "latest_release": "v1.1.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePreQual (dtiQA v7 Multi) User Guide\u003c/h1\u003e\u003ca id=\"user-content-prequal-dtiqa-v7-multi-user-guide\" class=\"anchor\" aria-label=\"Permalink: PreQual (dtiQA v7 Multi) User Guide\" href=\"#prequal-dtiqa-v7-multi-user-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#authors-and-reference\"\u003eAuthors and Reference\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#containerization-of-source-code\"\u003eContainerization of Source Code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#command\"\u003eCommand\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#arguments-and-io\"\u003eArguments and I/O\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#configuration-file\"\u003eConfiguration File\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#examples\"\u003eExamples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-bids-data\"\u003eRunning BIDS Data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#options\"\u003eOptions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pipeline-assumptions\"\u003ePipeline Assumptions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pipeline-processing-steps\"\u003ePipeline Processing Steps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pipeline-quality-assurance-steps\"\u003ePipeline Quality Assurance Steps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#note-on-versioning-for-vuiis-xnat-users\"\u003eNote on Versioning for VUIIS XNAT Users\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/MASILab/PreQual/blob/master/overview.png?raw=true\"\u003e\u003cimg src=\"https://github.com/MASILab/PreQual/raw/master/overview.png?raw=true\" alt=\"Pipeline Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSummary:\u003c/strong\u003e Perform integrated preprocessing and quality assurance of diffusion MRI data\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePreprocessing Steps:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMP-PCA denoising (default on)\u003c/li\u003e\n\u003cli\u003eGibbs de-ringing (default off)\u003c/li\u003e\n\u003cli\u003eRician correction (default off)\u003c/li\u003e\n\u003cli\u003eInter-scan normalization (default on)\u003c/li\u003e\n\u003cli\u003eSusceptibility-induced distortion correction, with or without reverse gradient images or field maps\u003c/li\u003e\n\u003cli\u003eEddy current-induced distortion correction\u003c/li\u003e\n\u003cli\u003eInter-volume motion correction\u003c/li\u003e\n\u003cli\u003eSlice-wise signal dropout imputation\u003c/li\u003e\n\u003cli\u003eN4 B1 bias field correction (default off)\u003c/li\u003e\n\u003cli\u003eGradient nonlinearity correction (default off)\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Assurance Steps:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eVerification of phase encoding schemes\u003c/li\u003e\n\u003cli\u003eAnalysis of gradient directions\u003c/li\u003e\n\u003cli\u003eShell-wise analysis of signal-to-noise and contrast-to-noise ratios\u003c/li\u003e\n\u003cli\u003eVisualization of Gibbs de-ringing changes (if applicable)\u003c/li\u003e\n\u003cli\u003eVisualization of within brain intensity distributions before and after Rician correction (if applicable)\u003c/li\u003e\n\u003cli\u003eCorrection (if applicable) or visualization of inter-scan intensity relationships\u003c/li\u003e\n\u003cli\u003eShell-wise analysis of distortion corrections\u003c/li\u003e\n\u003cli\u003eAnalysis of inter-volume motion and slice-wise signal dropout\u003c/li\u003e\n\u003cli\u003eAnalysis of B1 bias fields (if applicable)\u003c/li\u003e\n\u003cli\u003eAnalysis of gradient nonlinear fields (if applicable)\u003c/li\u003e\n\u003cli\u003eVerification of intra-pipeline masking\u003c/li\u003e\n\u003cli\u003eAnalysis of tensor goodness-of-fit\u003c/li\u003e\n\u003cli\u003eVoxel-wise and region-wise quantification of FA\u003c/li\u003e\n\u003cli\u003eVoxel-wise quantification of MD\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors and Reference\u003c/h2\u003e\u003ca id=\"user-content-authors-and-reference\" class=\"anchor\" aria-label=\"Permalink: Authors and Reference\" href=\"#authors-and-reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"mailto:leon.y.cai@vanderbilt.edu\"\u003eLeon Y. Cai\u003c/a\u003e, Qi Yang, Colin B. Hansen, Vishwesh Nath, Karthik Ramadass, Graham W. Johnson, Benjamin N. Conrad, Brian D. Boyd, John P. Begnoche, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Warren D. Taylor, Gavin R. Price, Victoria L. Morgan, Baxter P. Rogers, Kurt G. Schilling, Bennett A. Landman. \u003cem\u003ePreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images\u003c/em\u003e. \u003ca href=\"https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.28678\" rel=\"nofollow\"\u003eMagnetic Resonance in Medicine\u003c/a\u003e, 2021.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://my.vanderbilt.edu/masi\" rel=\"nofollow\"\u003eMedical-image Analysis and Statistical Interpretation (MASI) Lab\u003c/a\u003e, Vanderbilt University, Nashville, TN, USA\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe PreQual software is designed to run inside a \u003ca href=\"#containerization-of-source-code\"\u003eSingularity container\u003c/a\u003e. The container requires an \"\u003ca href=\"#arguments-and-io\"\u003einputs\u003c/a\u003e\" folder that holds all required input diffusion image files (i.e., .nii.gz, .bval, and .bvec files) and a \u003ca href=\"#configuration-file\"\u003econfiguration file\u003c/a\u003e. For those running Synb0-DisCo to correct susceptibility distortions without reverse phase-encoded images, this folder will also contain the \u003ca href=\"#arguments-and-io\"\u003estructural T1 image\u003c/a\u003e. The container also requires an \"\u003ca href=\"#arguments-and-io\"\u003eoutputs\u003c/a\u003e\" folder that will hold all the outputs after the pipeline runs. We also need to know the image \u003cem\u003e\u003ca href=\"#arguments-and-io\"\u003eaxis\u003c/a\u003e\u003c/em\u003e on which phase encoding was performed for all inputs (i.e., \"i\" for the first dimension, \"j\" for the second). To build the configuration file, we need to know the \u003cem\u003e\u003ca href=\"#configuration-file\"\u003edirection\u003c/a\u003e\u003c/em\u003e along said axis in which each image was phase encoded (i.e., \"+\" for positive direction and \"-\" for the negative direction) and the \u003ca href=\"#configuration-file\"\u003ereadout time\u003c/a\u003e for each input image. Once we have this information, we bind the inputs and outputs directories into the container to \u003ca href=\"#command\"\u003erun the pipeline\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eNote: The phase encoding axis, direction, and readout time must be known ahead of time, as this information is not stored in NIFTI headers. Depending on the scanner used, they may be available in JSON sidecars when NIFTIs are converted from DICOMs with \u003ca href=\"#pipeline-assumptions\"\u003edcm2niix\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainerization of Source Code\u003c/h2\u003e\u003ca id=\"user-content-containerization-of-source-code\" class=\"anchor\" aria-label=\"Permalink: Containerization of Source Code\" href=\"#containerization-of-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/MASILab/PreQual.git\ncd /path/to/repo/PreQual\ngit checkout v1.1.0\nsudo singularity build /path/to/prequal.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe use Singularity version 3.8 CE with root permissions.\u003c/p\u003e\n\u003cp\u003eAlternatively, a pre-built container can be downloaded \u003ca href=\"https://masi.vuse.vanderbilt.edu/PreQual/PreQual_v1.0.8.simg\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommand\u003c/h2\u003e\u003ca id=\"user-content-command\" class=\"anchor\" aria-label=\"Permalink: Command\" href=\"#command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run \n-e \n--contain\n--home /path/to/inputs/directory/\n-B /path/to/inputs/directory/:/INPUTS\n-B /path/to/outputs/directory/:/OUTPUTS\n-B /tmp:/tmp\n-B /path/to/freesurfer/license.txt:/APPS/freesurfer/license.txt\n-B /path/to/cuda:/usr/local/cuda\n--nv\n/path/to/prequal.simg\npe_axis\n[options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBinding the freesurfer license is optional and only needed for Synb0-DisCo\u003c/li\u003e\n\u003cli\u003eBinding the tmp directory is necessary when running the image with \u003ccode\u003e--contain\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eBinding --home is necessary for matlab since it uses home for temp storage.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--nv\u003c/code\u003e and \u003ccode\u003e-B /path/to/cuda:/usr/local/cuda\u003c/code\u003e are optional. See options \u003ccode\u003e--eddy_cuda\u003c/code\u003e and \u003ccode\u003e--eddy_extra_args\u003c/code\u003e. \u003cstrong\u003eGPU support is currently experimental.\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eArguments and I/O\u003c/h2\u003e\u003ca id=\"user-content-arguments-and-io\" class=\"anchor\" aria-label=\"Permalink: Arguments and I/O\" href=\"#arguments-and-io\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eInput Directory:\u003c/strong\u003e The dtiQA_config.csv configuration file and at least one diffusion weighted image must be provided.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003edtiQA_config.csv (see \u003ca href=\"#configuration-file\"\u003ebelow\u003c/a\u003e for format, must be named exactly)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;image1\u0026gt;.nii.gz (diffusion weighted image)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;image1\u0026gt;.bval (units of s/mm\u003csup\u003e2\u003c/sup\u003e, in the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Diffusion_data_in_FSL\" rel=\"nofollow\"\u003eFSL format\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;image1\u0026gt;.bvec (normalized unit vectors in the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Diffusion_data_in_FSL\" rel=\"nofollow\"\u003eFSL format\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;imageN\u0026gt;.nii.gz (diffusion weighted image)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;imageN\u0026gt;.bval (units of s/mm\u003csup\u003e2\u003c/sup\u003e, in the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Diffusion_data_in_FSL\" rel=\"nofollow\"\u003eFSL format\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;imageN\u0026gt;.bvec (normalized unit vectors in the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Diffusion_data_in_FSL\" rel=\"nofollow\"\u003eFSL format\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003et1.nii.gz (Optional, used for Synb0-DisCo, must be named exactly)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egradtensor.nii (Optional, used for --correct_grad, must be named exactly)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOther files as needed (see \u003ccode\u003e--extra_eddy_args\u003c/code\u003e for more information)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eOutput Directory:\u003c/strong\u003e Full outputs listed at the \u003ca href=\"#outputs\"\u003eend\u003c/a\u003e of this document\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe output preprocessed images are available in the PREPROCESSED subfolder in the output directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePREPROCESSED/dwmri.nii.gz\u003c/li\u003e\n\u003cli\u003ePREPROCESSED/dwmri.bval\u003c/li\u003e\n\u003cli\u003ePREPROCESSED/dwmri.bvec\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe QA document is available in the PDF subfolder in the output directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePDF/dtiQA.pdf\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003epe_axis:\u003c/strong\u003e Phase encoding axis of all the input images. We do NOT support different phase encoding axes between different input images at this time. The options are i and j and correspond to the first and second dimension of the input images, respectively. Note that FSL does not currently support phase encoding in the third dimension (i.e. k, the dimension in which the image slices were acquired, commonly axial for RAS and LAS oriented images). \u003cstrong\u003eThis parameter is direction AGNOSTIC\u003c/strong\u003e. The phase encoding directions of the input images along this axis are specified in the dtiQA_config.csv file. See \u003ca href=\"#configuration-file\"\u003eConfiguration File\u003c/a\u003e and \u003ca href=\"#examples\"\u003eExamples\u003c/a\u003e for more information.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration File\u003c/h2\u003e\u003ca id=\"user-content-configuration-file\" class=\"anchor\" aria-label=\"Permalink: Configuration File\" href=\"#configuration-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe format for the lines of the configuration CSV file, dtiQA_config.csv (must be named exactly), are as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;image1\u0026gt;,pe_dir,readout_time\n:\n\u0026lt;imageN\u0026gt;,pe_dir,readout_time\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;image\u0026gt;\u003c/strong\u003e is the shared file PREFIX between the corresponding NIFTI, BVAL, and BVEC files for that particular image in the input directory (i.e., my_dwi.nii.gz/.bval/.bvec -\u0026gt; my_dwi). Do NOT include the path to the input directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003epe_dir\u003c/strong\u003e is either + or -, corresponding to the direction along the phase encoding axis (as defined by the parameter \u003ccode\u003epe_axis\u003c/code\u003e) on which the image is phase encoded.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNote that a combination of phase encoding axis and direction map to specific anatomical (i.e. APA, APP, etc.) directions based on the orientation of the image. So, for instance in a RAS image, an axis of j and direction of + map to APP. We infer the orientation of the image from the header of the NIFTI using nibabel tools and output the best anatomical phase encoding direction interpretation of the input direction in the PDF for QA.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ereadout_time\u003c/strong\u003e is a non-negative number, the readout_time parameter required by FSL\u2019s eddy. The absolute value of this parameter is used to scale the estimated b0 field. Note a value of 0 indicates that the images are infinite bandwidth (i.e. no susceptibility distortion). See \u003ca href=\"#examples\"\u003eExamples\u003c/a\u003e for more information.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere are some different example combinations of pe_axis, pe_dir, and readout_time parameters and the corresponding FSL acquisition parameters lines:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003epe_axis\u003c/th\u003e\n\u003cth\u003epe_dir\u003c/th\u003e\n\u003cth\u003ereadout_time\u003c/th\u003e\n\u003cth\u003eacqparams line\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ei\u003c/td\u003e\n\u003ctd\u003e+\u003c/td\u003e\n\u003ctd\u003e0.05\u003c/td\u003e\n\u003ctd\u003e1, 0, 0, 0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ej\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e0.1\u003c/td\u003e\n\u003ctd\u003e0, -1, 0, 0.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThese are examples of common use cases. They also all share the same command, as detailed above. The PREPROCESSED output folder will contain the final outputs and the PDF folder will contain the QA report.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePhase Encoding\u003cbr\u003eAxis\u003c/th\u003e\n\u003cth\u003eReverse Phase\u003cbr\u003eEncoded (RPE) Image\u003c/th\u003e\n\u003cth\u003eT1\u003cbr\u003eImage\u003c/th\u003e\n\u003cth\u003eContents of\u003cbr\u003eInput Directory\u003c/th\u003e\n\u003cth\u003eContents of\u003cbr\u003edtiQA_config.csv\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ej\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003ctd\u003eN/A\u003c/td\u003e\n\u003ctd\u003edti1.nii.gz\u003cbr\u003edti1.bval\u003cbr\u003edti1.bvec\u003cbr\u003edti2.nii.gz\u003cbr\u003edti2.bval\u003cbr\u003edti2.bvec\u003cbr\u003erpe.nii.gz\u003cbr\u003erpe.bval\u003cbr\u003erpe.bvec\u003cbr\u003edtiQA_config.csv\u003c/td\u003e\n\u003ctd\u003edti1,+,0.05\u003cbr\u003edti2,+,0.05\u003cbr\u003erpe,-,0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ej\u003c/td\u003e\n\u003ctd\u003eNo\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003ctd\u003edti1.nii.gz\u003cbr\u003edti1.bval\u003cbr\u003edti1.bvec\u003cbr\u003edti2.nii.gz\u003cbr\u003edti2.bval\u003cbr\u003edti2.bvec\u003cbr\u003et1.nii.gz\u003cbr\u003edtiQA_config.csv\u003c/td\u003e\n\u003ctd\u003edti1,+,0.05\u003cbr\u003edti2,+,0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ej\u003c/td\u003e\n\u003ctd\u003eNo\u003c/td\u003e\n\u003ctd\u003eNo\u003c/td\u003e\n\u003ctd\u003edti1.nii.gz\u003cbr\u003edti1.bval\u003cbr\u003edti1.bvec\u003cbr\u003edti2.nii.gz\u003cbr\u003edti2.bval\u003cbr\u003edti2.bvec\u003cbr\u003edtiQA_config.csv\u003c/td\u003e\n\u003ctd\u003edti1,+,0.05\u003cbr\u003edti2,+,0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning BIDS Data\u003c/h2\u003e\u003ca id=\"user-content-running-bids-data\" class=\"anchor\" aria-label=\"Permalink: Running BIDS Data\" href=\"#running-bids-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhile not a BIDS pipeline, data in BIDS format can be run with PreQual without moving or copying data. The key is that the input directory structure must be as described relative to \u003cem\u003einside the container\u003c/em\u003e. By creatively binding files/folders into the container, we can achieve the same effect:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-B /path/to/sub-X/ses-X/dwi/:/INPUTS\n-B /path/to/sub-X/ses-X/anat/sub-X_ses-X_T1w.nii.gz:/INPUTS/t1.nii.gz (optional, Synb0-DisCo only)\n-B /path/to/config/file.csv:/INPUTS/dtiQA_config.csv\n-B /path/to/outputs/directory/:/OUTPUTS\n-B /tmp:/tmp\n-B /path/to/freesurfer/license.txt:/APPS/freesurfer/license.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe outputs directory and configuration file can be created wherever makes the most sense for the user. The contents of the configuration file will look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esub-X_ses-X_acq-1_dwi,pe_dir,readout_time\n:\nsub-X_ses-X_acq-N_dwi,pe_dir,readout_time\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptions\u003c/h2\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: Options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e--bval_threshold N\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA non-negative integer threshold under which to consider a b-value to be zero. Useful when some MRI machines do not allow for more than one b0 volume to be acquired so some users acquire scans with extremely low b-values to be treated like b0 volumes. Setting this value to 0 results in no thresholding. Units = s/mm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDefault = 50\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--nonzero_shells s1,s2,...,sn/auto\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comma separated list of positive integers (s/mm\u003csup\u003e2\u003c/sup\u003e) indicating nonzero shells for SNR/CNR analysis when there are more unique b-values than shells determined by eddy or automatically determine shells by rounding to nearest 100. Useful when b-values are modulated around a shell value instead of set exactly at that value. Only used when determining shells for SNR/CNR analysis. Original b-values used elsewhere in pipeline.\u003c/p\u003e\n\u003cp\u003eDefault = auto\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--denoise on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDenoise images prior to preprocessing using Marchenko-Pastur PCA \u003ca href=\"https://mrtrix.readthedocs.io/en/latest/reference/commands/dwidenoise.html\" rel=\"nofollow\"\u003eimplemented in MRTrix3\u003c/a\u003e. The SNR of the b0s of the final preprocessed images are reported in the PDF output regardless of whether this option is on or off.\u003c/p\u003e\n\u003cp\u003eDefault = on\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--degibbs on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRemove Gibbs ringing artifacts using the local subvoxel-shifts method as \u003ca href=\"https://mrtrix.readthedocs.io/en/latest/reference/commands/mrdegibbs.html\" rel=\"nofollow\"\u003eimplemented in MRTrix3\u003c/a\u003e. We caution against using this feature as it not designed for the partial Fourier schemes with which most echo planar diffusion images are acquired. It is also difficult to quality check, but we include a visualization of averaged residuals across all b = 0 s/mm\u003csup\u003e2\u003c/sup\u003e volumes, looking for larger signals near high contrast (i.e. parenchyma-CSF) interfaces.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--rician on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform Rician correction using the method of moments. We normally do not perform this step as we empirically do not find it to affect results drastically. It is also difficult to quality check, but we include a plot of the shell-wise within brain intensity distributions for each input before and after correction, looking for a slight drop in intensity with correction.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--prenormalize on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntensity normalize images prior to preprocessing by maximizing the intra-mask intensity-histogram intersections between the averaged b0s of the scans. If this option is on, these histograms before and after prenormalization will be reported in the output PDF. This is done to avoid gain differences between different diffusion scans. If this option is off, we assume that the various input images all have the same gain. That being said, we still estimate and report the gain factors and intensity histograms in a gain QA page and report warnings if estimated gains greater than 5% are found.\u003c/p\u003e\n\u003cp\u003eDefault = on\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--synb0 raw/stripped/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003etopup\u003c/code\u003e with a synthetic b0 generated with the Synb0-DisCo deep-learning framework if no reverse phase encoded images are supplied and a raw or skull-stripped T1 image is supplied. Synb0-DisCo requires at least 24GB of RAM.\u003c/p\u003e\n\u003cp\u003eDefault = raw\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--topup_first_b0s_only\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003etopup\u003c/code\u003e with only the first b0 from each input image. At the time of writing, \u003cstrong\u003eFSL\u0027s topup cannot be parallelized\u003c/strong\u003e, and the runtime of topup can increase dramatically as more b0 volumes are included. This flag allows for faster processing at the expense of information lost from any interleaved b0s.\u003c/p\u003e\n\u003cp\u003eDefault = use ALL b0s\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--extra_topup_args=string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtra arguments to pass to FSL\u2019s \u003ccode\u003etopup\u003c/code\u003e. \u003ccode\u003eTopup\u003c/code\u003e will run with the following by default (as listed in the \u003ccode\u003e/SUPPLEMENTAL/topup.cnf\u003c/code\u003e configuration file) but will be overwritten by arguments passed to \u003ccode\u003e--extra_topup_args\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Resolution (knot-spacing) of warps in mm\n--warpres=20,16,14,12,10,6,4,4,4\n# Subsampling level (a value of 2 indicates that a 2x2x2 neighbourhood is collapsed to 1 voxel)\n--subsamp=1,1,1,1,1,1,1,1,1\n# FWHM of gaussian smoothing\n--fwhm=8,6,4,3,3,2,1,0,0\n# Maximum number of iterations\n--miter=10,10,10,10,10,20,20,30,30\n# Relative weight of regularisation\n--lambda=0.00033,0.000067,0.0000067,0.000001,0.00000033,0.000000033,0.0000000033,0.000000000033,0.00000000000067\n# If set to 1 lambda is multiplied by the current average squared difference\n--ssqlambda=1\n# Regularisation model\n--regmod=bending_energy\n# If set to 1 movements are estimated along with the field\n--estmov=1,1,1,1,1,0,0,0,0\n# 0=Levenberg-Marquardt, 1=Scaled Conjugate Gradient\n--minmet=0,0,0,0,0,1,1,1,1\n# Quadratic or cubic splines\n--splineorder=3\n# Precision for calculation and storage of Hessian\n--numprec=double\n# Linear or spline interpolation\n--interp=spline\n# If set to 1 the images are individually scaled to a common mean intensity \n--scale=0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThese parameters should be formatted as a list separated by +\u0027s with no spaces (i.e., \u003ccode\u003e--extra_topup_args=--scale=1+--regrid=0\u003c/code\u003e). For \u003ccode\u003etopup\u003c/code\u003e options that require additional inputs, place the file in the inputs directory and use the following syntax: \u003ccode\u003e--\u0026lt;myinputoption\u0026gt;=/INPUTS/\u0026lt;file.ext\u0026gt;\u003c/code\u003e. For \u003ccode\u003etopup\u003c/code\u003e options that produce additional outputs, the file will save in the output directory under the \u201cTOPUP\u201d folder by using the following syntax: \u003ccode\u003e--\u0026lt;myoutputoption\u0026gt;=/OUTPUTS/TOPUP/\u0026lt;file.ext\u0026gt;\u003c/code\u003e. Note that in this case \u003ccode\u003e/INPUTS\u003c/code\u003e and \u003ccode\u003e/OUTPUTS\u003c/code\u003e should be named exactly as is and are NOT the path to the input and output directory on your file system.\u003c/p\u003e\n\u003cp\u003eDefault = none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--eddy_cuda 8.0/9.1/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun FSL\u2019s \u003ccode\u003eeddy\u003c/code\u003e with NVIDIA GPU acceleration. If this parameter is 8.0 or 9.1, either CUDA 8.0 or 9.1 must be installed, properly configured on your system, and bound into the container, respectively. Additionally the \u003ccode\u003e--nv\u003c/code\u003e flag must be run in the singularity command. If this parameter is off, \u003ccode\u003eeddy\u003c/code\u003e is run with OPENMP CPU multithreading. See \u003ccode\u003e--num_threads\u003c/code\u003e for more information. CUDA is required to run \u003ccode\u003eeddy\u003c/code\u003e with \u003ccode\u003e--mporder\u003c/code\u003e (intra-volume slice-wise motion correction). See \u003ccode\u003e--extra_eddy_args\u003c/code\u003e for more information.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--eddy_mask on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003eeddy\u003c/code\u003e with or without a brain mask. If on, FSL\u2019s brain extraction tool (\u003ccode\u003ebet\u003c/code\u003e) is used with a low threshold to create a rough brain mask for \u003ccode\u003eeddy\u003c/code\u003e. This can sometimes produce poor results. If off, no mask is used and produces empirically minor differences in results than when a mask is used. If this option is on, the contour of this mask is drawn in the PDF.\u003c/p\u003e\n\u003cp\u003eDefault = on\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--eddy_bval_scale N/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003eeddy\u003c/code\u003e with b-values scaled by the positive number N. All other steps of the pipeline use the original b-values. This can help \u003ccode\u003eeddy\u003c/code\u003e finish distortion correction when extremely low b-values (\u0026lt;200) are involved. If off, no scaling of b-values is used.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--extra_eddy_args=string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtra arguments to pass to FSL\u2019s \u003ccode\u003eeddy\u003c/code\u003e. \u003ccode\u003eEddy\u003c/code\u003e will always run with the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--repol\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that if \u003ccode\u003e--mporder\u003c/code\u003e is passed here, \u003ccode\u003e--eddy_cuda\u003c/code\u003e must be 8.0 or 9.1 and the singularity option \u003ccode\u003e--nv\u003c/code\u003e must be passed into the container, as intra-volume slice-wise motion correction requires GPU acceleration.\u003c/p\u003e\n\u003cp\u003eThese parameters should be formatted as a list separated by +\u0027s with no spaces (i.e., \u003ccode\u003e--extra_eddy_args=--data_is_shelled+--ol_nstd=1\u003c/code\u003e). For \u003ccode\u003eeddy\u003c/code\u003e options that require additional inputs, place the file in the inputs directory and use the following syntax: \u003ccode\u003e--\u0026lt;myinputoption\u0026gt;=/INPUTS/\u0026lt;file.ext\u0026gt;\u003c/code\u003e. For \u003ccode\u003eeddy\u003c/code\u003e options that produce additional outputs, the file will save in the output directory under the \u201cEDDY\u201d folder by using the following syntax: \u003ccode\u003e--\u0026lt;myoutputoption\u0026gt;=/OUTPUTS/EDDY/\u0026lt;file.ext\u0026gt;\u003c/code\u003e. Note that in this case \u003ccode\u003e/INPUTS\u003c/code\u003e and \u003ccode\u003e/OUTPUTS\u003c/code\u003e should be named exactly as is and are NOT the path to the input and output directory on your file system.\u003c/p\u003e\n\u003cp\u003eDefault = none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--postnormalize on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntensity normalize images after preprocessing by maximizing the intra-mask intensity-histogram intersections between the averaged b0s of the scans. If this option is on, these histograms before and after postnormalization will be reported in the output PDF.\u003c/p\u003e\n\u003cp\u003eNote: This option was intended for testing and is left for posterity. It is not recommended at this time and will be deprecated.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--correct_bias on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform \u003ca href=\"https://manpages.debian.org/testing/ants/N4BiasFieldCorrection.1.en.html\" rel=\"nofollow\"\u003eANTs N4 bias field correction\u003c/a\u003e as \u003ca href=\"https://mrtrix.readthedocs.io/en/latest/reference/commands/dwibiascorrect.html\" rel=\"nofollow\"\u003ecalled in MRTrix3\u003c/a\u003e. If this option is on, the calculated bias field will be visualized in the output PDF.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--correct_grad on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform gradient nonlinearity correction. First, corrected voxelwise b-table is calculated as in [\u003ca href=\"https://github.com/baxpr/gradtensor\"\u003ehttps://github.com/baxpr/gradtensor\u003c/a\u003e]. These results are used to compute the corrected diffusion weighted signal. If this option is on, the determinant nonlinear gradient field will be visualized in the output PDF.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--improbable_mask on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreate an additional mask on the preprocessed data that omits voxels where the minimum b0 signal is smaller than the minimum diffusion weighted signal. This can be helpful for reducing artifacts near the mask border when fitting models.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--glyph_type tensor/vector\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisualize either tensors or principal eigenvectors in the QA document.\u003c/p\u003e\n\u003cp\u003eDefault = tensor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--atlas_reg_type FA/b0\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform JHU white matter atlas registration to the subject by either deformably registering the subject\u0027s FA map or average b0 to the MNI FA or T2 template, respectively. Empirically, the FA approach tends to be more accurate for white matter whereas the b0 approach tends to be more accurate globally. The b0 approach is more robust for acquisitions with low shells (i.e., b \u0026lt; 500 s/mm\u003csup\u003e2\u003c/sup\u003e) or poor masking that result in the inclusion of a lot of facial structure.\u003c/p\u003e\n\u003cp\u003eDefault = FA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--split_outputs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSplit the fully preprocessed output (a concatenation of the input images) back into their component parts and do NOT keep the concatenated preprocessed output.\u003c/p\u003e\n\u003cp\u003eDefault = Do NOT split and return only the concatenated output\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--keep_intermediates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKeep intermediate copies of diffusion data (i.e. denoised, prenormalized, bias-corrected, etc.) used to generate final preprocessed data. Using this flag may result in a large consumption of hard disk space.\u003c/p\u003e\n\u003cp\u003eNote: Due to space concerns, special permission needed to use this option on XNAT.\u003c/p\u003e\n\u003cp\u003eDefault = do NOT keep intermediates\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--num_threads N\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA positive integer indicating the number of threads to use when running portions of the pipeline that can be multithreaded (i.e. MRTrix3, ANTs, and FSL\u2019s eddy without GPU acceleration). Please note that at the time of writing, \u003cstrong\u003eFSL\u0027s topup cannot be parallelized\u003c/strong\u003e, and that the runtime of topup can increase dramatically as more b0 volumes are included. See \u003ccode\u003e--topup_first_b0s_only\u003c/code\u003e for more information.\u003c/p\u003e\n\u003cp\u003eNote: Due to resource concerns, special permission needed to multi-thread on XNAT.\u003c/p\u003e\n\u003cp\u003eDefault = 1 (do NOT multithread)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--project string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eString describing project in which the input data belong to label PDF output\u003c/p\u003e\n\u003cp\u003eDefault = proj\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--subject string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eString describing subject from which the input data were acquired to label PDF output\u003c/p\u003e\n\u003cp\u003eDefault = subj\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--session string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eString describing session in which the input data were acquired to label PDF output\u003c/p\u003e\n\u003cp\u003eDefault = sess\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--help, -h\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline Assumptions\u003c/h2\u003e\u003ca id=\"user-content-pipeline-assumptions\" class=\"anchor\" aria-label=\"Permalink: Pipeline Assumptions\" href=\"#pipeline-assumptions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAll NIFTI images are consistent with a conversion from a DICOM using \u003ccode\u003edcm2niix\u003c/code\u003e (\u003ca href=\"https://github.com/rordenlab/dcm2niix/releases/tag/v1.0.20180622\"\u003eat least v1.0.20180622\u003c/a\u003e) by Chris Rorden and are raw NIFTIs without distortion correction. We require this as dcm2niix exports b-value/b-vector files in FSL format and removes ADC or trace images auto-generated in some Philips DICOMs. In addition \u003ccode\u003edcm2niix\u003c/code\u003e correctly moves the gradients from scanner to subject space and does not re-order volumes, both of which can cause spurious results or pipeline failure.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWe expect raw volumes only, no ADC or trace volumes.\u003c/strong\u003e ADC volumes are sometimes encoded as having a b-value greater than 0 with a corresponding b-vector of (0,0,0) and trace volumes are sometimes encoded as having a b-value of 0 with a corresponding non-unit normalized b-vector, as in the case of some Philips PARREC converters. We check for these cases, remove the affected volumes, and report a warning in the console and in the PDF.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe cannot, unfortunately, account for failure of reorientation of gradients into subject space. Visualization of tensor glyphs or principal eigenvectors can be helpful in distinguishing this. However, this error can be subtle so we suggest proper DICOM to NIFTI conversion with the above release of \u003ccode\u003edcm2niix\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eImages will be processed in the order they are listed in dtiQA_config.csv.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe size of all the volumes across all images must all be the same.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe location of b0 images inside the input images do not matter.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs per the FSL format, we do not support non-unit normalized gradients. We also do not support gradient directions of 0,0,0 when the corresponding b-value is non-zero. Gradients with the latter configurations may cause pipeline failure. We report warnings in the output PDF if we identify these.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe phase encoding axis of all volumes across all images is the same.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe phase encoding direction along the axis is the same for all volumes inside an image and is specified in the dtiQA_config.csv file.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUnless \u003ccode\u003e--prenormalize\u003c/code\u003e is on, we assume all input images have the same gain.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe will preferentially preprocess images with FSL\u2019s topup using available images with complementary phase encoding directions (i.e. + and -, \"reverse phase encodings\"). If none are available and a T1 is available, we will synthesize a susceptibility-corrected b0 from the first image listed in dtiQA_config.csv with Synb0-DisCo for use with topup, unless the user turns the \u003ccode\u003e--synb0\u003c/code\u003e parameter off. The readout time of this synthetic b0 will be zero and the phase encoding direction will be equal to that of the first image in dtiQA_config.csv. Otherwise, we will preprocess without topup and move straight to FSL\u2019s eddy.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe use topup and eddy for preprocessing, both of which at the present moment do NOT officially support DSI acquisitions but only single- and multi-shell. We will force topup and eddy to run on DSI data, but may not produce quality results. Please carefully check the PDF output as we report a warning if eddy detected non-shelled data and thus required the use of the force flag.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNote that eddy may erroneously detect data as non-shelled if there are fewer directions in one of the shells than others. Because we merge the images for preprocessing, a notable example of this is when a reverse-phase encoded image uses a different shell than the forward images and has significantly fewer directions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor preprocessing, eddy will motion correct to the first b0 of each image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNIFTI files inherently have three transformations in the header: the sform, qform, and the fall-back. Different software prefer to use different transformations. We follow the \u003ca href=\"https://nipy.org/nibabel/nifti_images.html#choosing-the-image-affine\" rel=\"nofollow\"\u003eNibabel standard\u003c/a\u003e (sform \u0026gt; qform \u0026gt; fall-back). To explicitly ensure this, we check all NIFTI inputs to determine their optimal affines as captured by Nibabel, then resave all inputs placing the optimal affines in both the sform (code = 2) and qform (code = 0) fields. Additionally, if the optimal affines are not the sform, we report warnings on the output PDF.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo b0 drift correction is performed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe use the fit tensor model primarily for QA. If b-values less than 500 s/mm\u003csup\u003e2\u003c/sup\u003e or greater than 1500 s/mm\u003csup\u003e2\u003c/sup\u003e are present, we suggest careful review of the fit prior to use for non-QA purposes.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline Processing Steps\u003c/h2\u003e\u003ca id=\"user-content-pipeline-processing-steps\" class=\"anchor\" aria-label=\"Permalink: Pipeline Processing Steps\" href=\"#pipeline-processing-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThreshold all b-values such that values less than the \u003ccode\u003e--bval_threshold\u003c/code\u003e parameter are 0.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck that all b-vectors are unit normalized and all b-values greater than zero have associated non-zero b-vectors. For any volumes where this is not the case, we remove them, flag a warning for the output PDF, and continue the pipeline.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf applicable, denoise all diffusion scans with \u003ccode\u003edwidenoise\u003c/code\u003e (Marchenko-Pastur PCA) from MrTrix3 and save the noise profiles (needed for Rician correction later).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf applicable, perform Gibbs de-ringing on all diffusion scans with \u003ccode\u003emrdegibbs\u003c/code\u003e from MRTrix3.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf applicable, perform Rician correction on all diffusion scans with the method of moments.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf applicable, prenormalize all diffusion scans. To accomplish this, extract all b0 images from each diffusion scan and average them. Then find a rough brain-mask with FSL\u2019s bet and calculate an intensity scale factor such that the histogram intersection between the intra-mask histogram of the different scans\u2019 averaged b0s to that of the first scan is maximized. Apply this scale factor to the entire diffusion weighted scan. This is done to avoid gain differences between different diffusion scans.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIf prenormalization is not indicated, we still run the prenormalization algorithms to calculate rough gain differences and report the gain factors and intensity histograms in a gain QA page. The outputs of the algorithms, however, are NOT propagated through to the rest of the pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePrepare data for and run preprocessing with topup and eddy\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTopup:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eExtract all b0s from all scans, maintaining their relative order.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Optional) If a T1 is supplied and no complementary (i.e. reverse) phase encoded images are provided, use Synb0-DisCo to convert the first b0 of the first scan to a susceptibility-corrected b0.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the acquisition parameters file required by both topup and eddy\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFor the number of b0s from each image, add the same phase encoding and readout time line to the acquisition parameters file, as outlined in \"Example Phase Encoding Schemes\".\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eExample: In the case where we have a phase encoding axis of j and two images, one with 7 b0s, + direction, and 0.05 readout time and one with 3 b0s, - direction, and 0.02 readout time, this file will have 10 lines. The first 7 lines are identical and equal to [0, 1, 0, 0.05]. The last three lines are also identical and equal to [0, -1, 0, 0.02].\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Optional) If Synb0-DisCo is run because no complementary phase encoding directions are supplied and --synb0 is not off, we add an additional line to the end of the file. This line is the same as the first line of the file except that the readout time is 0 instead.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eExample: In the case where we have a phase encoding axis of j and two images, one with 7 b0s, + direction, and 0.05 readout time and one with 3 b0s, + direction, and 0.02 readout time, this file will have 11 lines. The first 7 lines are identical and equal to [0, 1, 0, 0.05]. The next three lines are also identical and equal to [0, 1, 0, 0.02]. Finally, the last line is equal to [0, 1, 0, 0].\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then concatenate all the b0s maintaining their order and run topup with the acquisition parameters file if images with complementary phase encoding directions are supplied or if a T1 was supplied. Otherwise, we move on to the next step, eddy.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEddy\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eUsing the acquisition parameters file from the topup step, regardless of whether topup was performed, we build the eddy index file such that each volume in each image corresponds to the line in the acquisition parameters file associated with the first b0 of each scan. This is done to tell eddy that each volume in a given scan has the same underlying phase encoding scheme as the first b0 of that scan.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eExample: In the case where we have two images, one with 7 b0s and 100 total volumes and one with 3 b0s and 10 total volumes, the eddy index file has 100 1\u2019s followed by 10 8\u2019s.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEddy is then run with either a mask generated with bet and the -f 0.25 and -R options or without a mask (aka with a mask of all 1\u2019s), depending on user input (see the --eddy_mask option) and with the output of topup if topup was run. Eddy also runs with the --repol option for outlier slice replacement. We also first run eddy with a check looking for shelled data. If the check fails, eddy is then run with the --data_is_shelled flag to force eddy to run on all scans, DSI included. Note that DSI data is not officially supported by FSL\u2026 yet?\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eIf eddy detects data is not shelled, we report this as a warning\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs noted in the assumptions section above, eddy may erroneously detect data as non-shelled if there are fewer directions in one of the shells than others. Because we merge the images for preprocessing, a notable example of this is when a reverse-phase encoded image uses a different shell than the forward images and has significantly fewer directions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEddy also performs bvec rotation correction and calculates the voxel-wise signal-to-noise ratios of the b0 images and the voxel-wise contrast-to-noise ratios for the diffusion weighted images. SNR is defined as the mean value divided by the standard deviation. CNR is defined as the standard deviation of the Gaussian Process predictions (GP) divided by the standard deviation of the residuals between the measured data and the GP predictions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf the user chooses to, we then perform post-normalization in the same fashion as pre-normalization.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf the user chooses to, we then wrap up preprocessing with an N4 bias field correction as implemented in ANTs via MRTrix3\u2019s dwibiascorrect.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe generate a brain mask using FSL\u2019s bet2 with the following options. If applicable, we omit the voxels where the minimum b0 signal is less than the minimum diffusion weighted signal in an additional \"improbable mask\".\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-f 0.25 -R\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf the user chooses to, we then perform gradient nonliear field correction by first calculating the voxel-wise b-table and then corrected diffusion weighted signal.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then apply the mask to the preprocessed images while we calculate tensors using MRTrix3\u2019s dwi2tensor function. For visualization we discard tensors that have diagonal elements greater than 3 times the apparent diffusion coefficient of water at 37\u00b0C (~0.01).\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eWe also reconstruct the preprocessed image from the tensor fit for further analysis later. dwi2tensor does this for us.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then convert the tensor to FA and MD images (and visualize them later too) as well as AD, RD, and V1 eigenvector images for the user. The latter 3 are not visualized.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline Quality Assurance Steps\u003c/h2\u003e\u003ca id=\"user-content-pipeline-quality-assurance-steps\" class=\"anchor\" aria-label=\"Permalink: Pipeline Quality Assurance Steps\" href=\"#pipeline-quality-assurance-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eWe start with the brain mask generated above to generate a mask used for the following quantification of tensor fit using a chi-squared statistic.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFirst, we calculate the mean image for each unique b-value (0 not included). Then we run FSL\u2019s FAST to isolate the CSF on each meaned image. We then take the average probability of a voxel being CSF across all unique b-values and assign \u0026gt;15% probability to be a positive CSF voxel.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThen we call the final chi-squared mask to be the intersection of the inverted CSF mask and a 1-pixel eroded version of the brain mask.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn the voxels inside the chi-squared mask, we perform the following quality assurance:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eWe perform a chi-squared analysis for each slice for each volume in the main image by calculating the ratio between the sum-squared error of the fit and the sum-squared intensities of the slice.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe extract the average FA for a number of white matter ROIs defined by the Hopkins atlas. We do this by non-rigidly registering the atlas to our FA output and extracting the FA values contained in each ROI.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe check the gradients output by eddy (i.e. the preprocessed gradients) with \u003ca href=\"https://mrtrix.readthedocs.io/en/3.0.0/reference/commands/dwigradcheck.html\" rel=\"nofollow\"\u003edwigradcheck from MRTrix3\u003c/a\u003e. This performs tractography and finds the optimal sign and order permutation of the b-vectors such that the average tract length in the brain is most physiological.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThese optimized gradients are saved in the OPTIMIZED_BVECS output folder, and the gradients output by eddy in the PREPROCESSED folder are NOT overwritten.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe original, preprocessed, and preprocessed + optimized gradients are visualized as outlined below.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize the entire pipeline.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOn the first page we describe the methods used for that run of the pipeline (what inputs were provided, what sort of preprocessing happened, etc.).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize the raw images with the interpreted phase encoding schemes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf Gibbs de-ringing was run, we visualize central slices of the averaged residuals across b0 volumes before and after Gibbs de-ringing, looking for large residuals near high contrast interfaces (i.e. parenchyma against CSF)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf Rician correction was performed, we visualize the within brain intensity distributions of each shell of each image before and after correction, looking for downward shifts after correction.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf Synb0-DisCo was run, we then visualize the distorted b0 (first b0 of first scan) and T1 used as inputs as well as the output susceptibility corrected b0 in their native space.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf pre- or post-normalization was performed, we then visualize the intra-mask histograms before and after these steps as well as the calculated scaling factors. If pre-normalization is not performed, we visualize the histograms that would have been generated with pre-normalization ONLY as a check for gain differences.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize the first b0 of the images before and after preprocessing with the contours of the brain and stats masks overlaid as well as the contours of the eddy mask overlaid if it is used. We also report the percent of \"improbable voxels\" in the preprocessed mask, regardless of whether the improbable mask is saved.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe plot the motion and angle correction done by eddy as well as the RMS displacement and median intensity for each volume and the volume\u2019s associated b-value. These values are read in from an eddy output text file and we also compute and save the average of these values. In addition, we plot the outlier slices removed and then imputed by eddy as well as the chi-squared fit, with maximal bounds 0 to 0.2. The median chi-squared values are shown across volumes and slices.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then plot the original raw b-vectors scaled by their b-values, the preprocessed ones output by eddy, and the optimized ones determined by \u003ccode\u003edwigradcheck\u003c/code\u003e applied to the preprocessed ones.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf bias field correction was performed, we then visualize the calculated fields.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf bias field correction was performed, we then visualize the calculated image and gradient fields.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize some central slices of the average volumes for all unique b-values, including b = 0 and report the median intra-mask SNR or CNR calculated by eddy as appropriate. If there are more unique b-values than shells deteremined by eddy, we round the b-values to the nearest 100 by default to assign volumes to shells or we choose the nearest shell indicated by the user (see \u003ccode\u003e--nonzero_shells\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe visualize the tensors (or principal eigenvectors depending on \u003ccode\u003e--glyph_type\u003c/code\u003e) using MRTrix3\u2019s mrview, omitting the tensors with negative eigenvalues or eigenvalues greater than 3 times the ADC of water at 37\u00b0C.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize some central slices of the FA map clipped from 0 to 1 as well as the average FA for the Hopkins ROIs and the quality of the atlas registration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLastly, we visualize some central slices of the MD map clipped from 0 to 0.003 (ADC of water at 37\u00b0C).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u0026lt;imageN_%\u0026gt; denotes the original prefix of imageN with the preceding preprocessing step descriptors tacked on the end. For example, in the case of the PRENORMALIZED directory, the prefix for imageJ may or may not include \"_denoised\" depending on whether the denoising step was run.\u003c/p\u003e\n\u003cp\u003eFolders and files in \u003cstrong\u003ebold\u003c/strong\u003e are always included.\u003c/p\u003e\n\u003cp\u003eFolders and files in \u003cem\u003eitalics\u003c/em\u003e are removed if \u003ccode\u003e--keep_intermediates\u003c/code\u003e is NOT indicated\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTHRESHOLDED_BVALS\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;image1\u0026gt;.bval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;imageN\u0026gt;.bval\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eCHECKED\u003c/em\u003e (these contain the volumes that have passed the bval/bvec checks)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1\u0026gt;_checked.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1\u0026gt;_checked.bval\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1\u0026gt;_checked.bvec\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN\u0026gt;_checked.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN\u0026gt;_checked.bval\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN\u0026gt;_checked.bvec\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eDENOISED\u003c/em\u003e (these files are only created if \u003ccode\u003e--denoise\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_denoised.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_noise.nii.gz\u003c/em\u003e (needed for Rician correction)\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_denoised.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_noise.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eDEGIBBS\u003c/em\u003e (these files are only created if \u003ccode\u003e--degibbs\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_degibbs.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_degibbs.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eRICIAN\u003c/em\u003e (these files are only created if \u003ccode\u003e--rician\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_rician.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_rician.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003ePRENORMALIZED\u003c/em\u003e (these files are only created if \u003ccode\u003e--prenormalize\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eGAIN_CHECK\u003c/em\u003e (these files are only created if \u003ccode\u003e--prenormalize\u003c/code\u003e is off)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTOPUP\u003c/strong\u003e (these files are only created if \u003ccode\u003etopup\u003c/code\u003e was run)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eacqparams.txt (same as OUTPUTS/EDDY/acqparams.txt)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_b0_first.nii.gz\u003c/em\u003e (only if Synb0-DisCo is run)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eb0_syn.nii.gz (only if Synb0-DisCo is run)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_b0_all.nii.gz\u003c/em\u003e or \u003cem\u003epreproc_input_b0_all_smooth_with_b0_syn.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_b0_all_topped_up.nii.gz\u003c/em\u003e or \u003cem\u003epreproc_input_b0_all_smooth_with_b0_syn_topped_up.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epreproc_input_b0_all.topup_log or preproc_input_b0_all_smooth_with_b0_syn.topup_log\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etopup_field.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etopup_results_fieldcoef.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etopup_results_movpar.txt\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEDDY\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eacqparams.txt\u003c/strong\u003e (same as OUTPUTS/TOPUP/acqparams.txt)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eindex.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input.bval\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input.bvec\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_eddyed.nii.gz\u003c/em\u003e (renamed from \"eddy_results.nii.gz\")\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_eddyed.bval\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_eddyed.bvec\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eeddy_mask.nii.gz (only included if \u003ccode\u003e--eddy_mask\u003c/code\u003e is on)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_command_txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_movement_rms\u003c/strong\u003e (describes volume-wise RMS displacement)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_free_data.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_map\u003c/strong\u003e (describes which slices were deemed outliers)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_n_sqr_stdev_map\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_n_stdev_map\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_report\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_parameters\u003c/strong\u003e (describes volume-wise rotation and translation)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_post_eddy_shell_alignment_parameters\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_post_eddy_shell_PE_translation_parameters\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_restricted_movement_rms\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_rotated_bvecs (describes properly rotated b-vectors)\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_values_of_all_input_parameters\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_cnr_maps.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003ePOSTNORMALIZED\u003c/em\u003e (these files are only created if \u003ccode\u003e--postnormalize\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_topup_eddy_norm.nii.gz\u003c/em\u003e (\"_topup\" only applies if topup was run)\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_topup_eddy_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eUNBIASED\u003c/em\u003e (these files are only created if \u003ccode\u003e--correct_bias\u003c/code\u003e is on; this folder is removed if \u003ccode\u003e--correct_bias\u003c/code\u003e is off)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003enormed_unbiased.nii.gz\u003c/em\u003e (if postnormalization is run) or \u003cem\u003epreproc_input_eddyed_unbiased.nii.gz\u003c/em\u003e (if postnormalization is not run)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ebias_field.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eUNBIASED\u003c/em\u003e (these files are only created if \u003ccode\u003e--correct_grad\u003c/code\u003e is on; this folder is removed if \u003ccode\u003e--correct_grad\u003c/code\u003e is off)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ecorrected_sig.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egradtensor_fa.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eL_resamp.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eorg.bval\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eorg.bvec\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;bval_1\u0026gt;.nii.gz\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;bval_N\u0026gt;.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;bvec_1\u0026gt;.nii.gz\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;bvec_N\u0026gt;.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePREPROCESSED\u003c/strong\u003e (these represent the final output of 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or \u003cstrong\u003efa2template_0GenericAffine.mat\u003c/strong\u003e depending on \u003ccode\u003e--atlas_reg_type\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eb02template_1Warp.nii.gz\u003c/strong\u003e or \u003cstrong\u003efa2template_1Warp.nii.gz\u003c/strong\u003e depending on \u003ccode\u003e--atlas_reg_type\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eb02template_1InverseWarp.nii.gz\u003c/strong\u003e or \u003cstrong\u003efa2template_1InverseWarp.nii.gz\u003c/strong\u003e depending on \u003ccode\u003e--atlas_reg_type\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003echisq_mask.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003echisq_matrix.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_avg_abs_displacement.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_median_cnr.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_avg_rel_displacement.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_avg_rotations.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_avg_translations.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eroi_avg_fa.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003estats.csv\u003c/strong\u003e (contains summary of all motion, SNR/CNR, and average FA stats)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eOPTIMIZED_BVECS\u003c/strong\u003e (these are sign/axis permuted per \u003ccode\u003edwigradcheck\u003c/code\u003e and are only used for QA purposes)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri.bval\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri.bvec\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePDF\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003edtiQA.pdf\u003c/strong\u003e (final QA document)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNote on Versioning for VUIIS XNAT Users\u003c/h2\u003e\u003ca id=\"user-content-note-on-versioning-for-vuiis-xnat-users\" class=\"anchor\" aria-label=\"Permalink: Note on Versioning for VUIIS XNAT Users\" href=\"#note-on-versioning-for-vuiis-xnat-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePreQual was developed at Vanderbilt under the project name \"dtiQA v7 Multi\". PreQual v1.0.0 represents dtiQA v7.2.0. Thus, on XNAT, dtiQA v7.2.x refers to PreQual v1.0.x.\u003c/p\u003e\n", - "stargazers_count": 45, - "subscribers_count": 1, + "full_name": "braindynamicslab/dyneusr", + "latest_release": null, + "readme": "\u003cp align=\"center\"\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/logo.png\" height=\"250\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/haxby_decoding_mapper_stages.png\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eDynamical Neuroimaging Spatiotemporal Representations\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-dynamical-neuroimaging-spatiotemporal-representations\" class=\"anchor\" aria-label=\"Permalink: Dynamical Neuroimaging Spatiotemporal Representations\" href=\"#dynamical-neuroimaging-spatiotemporal-representations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://singularity-hub.org/collections/4218\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://braindynamicslab.github.io/dyneusr/\" rel=\"nofollow\"\u003eDyNeuSR\u003c/a\u003e is a Python visualization library for topological representations of neuroimaging data.\u003c/p\u003e\n\u003cp\u003eDyNeuSR was designed specifically for working with shape graphs produced by the Mapper algorithm from topological data analysis (TDA), as described in the papers \"\u003ca href=\"https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093\" rel=\"nofollow\"\u003eGenerating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis\u003c/a\u003e\" (Geniesse et al., 2019) and \"\u003ca href=\"https://www.nature.com/articles/s41467-018-03664-4\" rel=\"nofollow\"\u003eTowards a new approach to reveal dynamical organization of the brain using topological data analysis\u003c/a\u003e\" (Saggar et al., 2018). Check out this \u003ca href=\"https://bdl.stanford.edu/blog/tda-cme-paper/\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e for more about the initial work that inspired the development of DyNeuSR.\u003c/p\u003e\n\u003cp\u003eDeveloped with neuroimaging data analysis in mind, DyNeuSR connects existing implementations of Mapper (e.g. \u003ca href=\"https://kepler-mapper.scikit-tda.org\" rel=\"nofollow\"\u003eKeplerMapper\u003c/a\u003e) with network analysis tools (e.g. \u003ca href=\"https://networkx.github.io/\" rel=\"nofollow\"\u003eNetworkX\u003c/a\u003e) and other neuroimaging data visualization libraries (e.g. \u003ca href=\"https://nilearn.github.io/\" rel=\"nofollow\"\u003eNilearn\u003c/a\u003e) and provides a high-level interface for interacting with and manipulating shape graph representations of neuroimaging data and relating these representations back to neurophysiology.\u003c/p\u003e\n\u003cp\u003eDyNeuSR also provides an interactive web interface for visualizing and exploring shape graphs. To see this visual interface in action, check out the \u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/\" rel=\"nofollow\"\u003edemos\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eDemos\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-demos\" class=\"anchor\" aria-label=\"Permalink: Demos\" href=\"#demos\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/trefoil-knot/\" rel=\"nofollow\"\u003eTrefoil knot\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/trefoil-knot/trefoil_knot.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/trefoil-knot/\" rel=\"nofollow\"\u003eTrefoil knot (custom layouts)\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/trefoil-knot-custom-layouts/trefoil_knot_custom_layouts.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/haxby-tsne/\" rel=\"nofollow\"\u003eHaxby fMRI data (t-SNE lens)\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/haxby-tsne/haxby_tsne.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/haxby-umap/\" rel=\"nofollow\"\u003eHaxby fMRI data (UMAP lens)\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/haxby-umap/haxby_umap.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr/demo/haxby-umap-supervised/\" rel=\"nofollow\"\u003eHaxby fMRI data (supervised UMAP lens)\u003c/a\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/docs/demo/haxby-umap-supervised/haxby_umap_supervised.py\"\u003ecode\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eRelated Projects\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-related-projects\" class=\"anchor\" aria-label=\"Permalink: Related Projects\" href=\"#related-projects\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://braindynamicslab.github.io/dyneusr-fire/\" rel=\"nofollow\"\u003eDyNeuSR Fire\u003c/a\u003e is a new project that provides a command line interface for DyNeuSR. It wraps \u003ccode\u003ekmapper\u003c/code\u003e and \u003ccode\u003edyneusr\u003c/code\u003e into a single pipeline, and uses the \u003ca href=\"https://github.com/google/python-fire\"\u003ePython Fire\u003c/a\u003e library to automatically generate a simple command line interface that accepts several important options and allows users to customize this pipeline. For more information about DyNeuSR Fire, check out the \u003ca href=\"https://braindynamicslab.github.io/dyneusr-fire/\" rel=\"nofollow\"\u003edocs\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you find DyNeuSR useful, please consider citing:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eGeniesse, C., Sporns, O., Petri, G., \u0026amp; Saggar, M. (2019). \u003ca href=\"https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093\" rel=\"nofollow\"\u003eGenerating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis\u003c/a\u003e. \u003cem\u003eNetwork Neuroscience\u003c/em\u003e. Advance publication. doi:10.1162/netn_a_00093\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eFor more information about the Mapper approach, please see:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eSaggar, M., Sporns, O., Gonzalez-Castillo, J., Bandettini, P.A., Carlsson, G., Glover, G., \u0026amp; Reiss, A.L. (2018). \u003ca href=\"https://www.nature.com/articles/s41467-018-03664-4\" rel=\"nofollow\"\u003eTowards a new approach to reveal dynamical organization of the brain using topological data analysis\u003c/a\u003e. \u003cem\u003eNature Communications, 9\u003c/em\u003e(1). doi:10.1038/s41467-018-03664-4\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eAPI Usage \u0026amp; Examples\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-api-usage--examples\" class=\"anchor\" aria-label=\"Permalink: API Usage \u0026amp; Examples\" href=\"#api-usage--examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDyNeuSR provides a Python API for working with and visualizing shape graphs generated by Mapper. This repository includes several \u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/\"\u003eexamples\u003c/a\u003e that introduce DyNeuSR\u0027s API and highlight different aspects of analysis with DyNeuSR. For more detailed tutorials, check out \u003ca href=\"https://github.com/braindynamicslab/dyneusr-notebooks/\"\u003edyneusr-notebooks\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003cstrong\u003e\u003cem\u003eShape Graph Visualization\u003c/em\u003e\u003c/strong\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/examples/trefoil_knot/trefoil_knot.py\"\u003etrefoil knot\u003c/a\u003e)\u003c/h3\u003e\u003ca id=\"user-content-shape-graph-visualization-trefoil-knot\" class=\"anchor\" aria-label=\"Permalink: Shape Graph Visualization (trefoil knot)\" href=\"#shape-graph-visualization-trefoil-knot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edyneusr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDyNeuGraph\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edyneusr\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emake_trefoil\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edyneusr\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etools\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evisualize_mapper_stages\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekmapper\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Generate synthetic dataset\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emake_trefoil\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e)\n\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Generate shape graph using KeplerMapper\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit_transform\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e])\n\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emap\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enr_cubes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e6\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoverlap_perc\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.2\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Visualize the shape graph using DyNeuSR\u0027s DyNeuGraph \u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edG\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDyNeuGraph\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eG\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edG\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003evisualize\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027dyneusr_output.html\u0027\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/trefoil_knot\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/dyneusr_trefoil_knot.png\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003cstrong\u003e\u003cem\u003eMapper Parameter Comparison\u003c/em\u003e\u003c/strong\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/examples/trefoil_knot/compare_lenses.py\"\u003etrefoil knot\u003c/a\u003e)\u003c/h3\u003e\u003ca id=\"user-content-mapper-parameter-comparison-trefoil-knot\" class=\"anchor\" aria-label=\"Permalink: Mapper Parameter Comparison (trefoil knot)\" href=\"#mapper-parameter-comparison-trefoil-knot\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Define projections to compare\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eprojections\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e ([\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e], [\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e], [\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e], [\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# Compare different sets of columns as lenses\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eprojections\u003c/span\u003e:\n\n\t\u003cspan class=\"pl-c\"\u003e# Generate shape graph using KeplerMapper\u003c/span\u003e\n\t\u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\t\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit_transform\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e)\n\t\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emap\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enr_cubes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoverlap_perc\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.3\u003c/span\u003e)\n\n\t\u003cspan class=\"pl-c\"\u003e# Visualize the stages of Mapper\u003c/span\u003e\n\t\u003cspan class=\"pl-s1\"\u003efig\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eaxes\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003evisualize_mapper_stages\u003c/span\u003e(\n\t\t\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecover\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecover\u003c/span\u003e, \n\t\t\u003cspan class=\"pl-s1\"\u003elayout\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"spectral\"\u003c/span\u003e)\n\t\t\u003c/pre\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/trefoil_knot\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/trefoil_knot_mapper_lens_0.png\" style=\"max-width: 100%;\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/trefoil_knot_mapper_lens_0_1.png\" style=\"max-width: 100%;\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/trefoil_knot_mapper_lens_0_2.png\" style=\"max-width: 100%;\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/trefoil_knot_mapper_lens_1_2.png\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\n\u003cstrong\u003e\u003cem\u003eNeuroimaging Applications\u003c/em\u003e\u003c/strong\u003e (\u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/haxby_decoding\"\u003ehaxby decoding\u003c/a\u003e)\u003c/h3\u003e\u003ca id=\"user-content-neuroimaging-applications-haxby-decoding\" class=\"anchor\" aria-label=\"Permalink: Neuroimaging Applications (haxby decoding)\" href=\"#neuroimaging-applications-haxby-decoding\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enumpy\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epandas\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enilearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efetch_haxby\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enilearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003einput_data\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eNiftiMasker\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekmapper\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eCover\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emanifold\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTSNE\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esklearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecluster\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDBSCAN\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Fetch dataset, extract time-series from ventral temporal (VT) mask\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003efetch_haxby\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003emasker\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eNiftiMasker\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emask_vt\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e], \n \u003cspan class=\"pl-s1\"\u003estandardize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edetrend\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esmoothing_fwhm\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4.0\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elow_pass\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.09\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ehigh_pass\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.008\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003et_r\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2.5\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ememory\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"nilearn_cache\"\u003c/span\u003e)\n\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emasker\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit_transform\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003efunc\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# Encode labels as integers\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eread_csv\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003esession_target\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003esep\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\" \"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efactorize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003evalues\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eDataFrame\u003c/span\u003e({\u003cspan class=\"pl-s1\"\u003el\u003c/span\u003e:(\u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e==\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e).\u003cspan class=\"pl-en\"\u003eastype\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eint\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003el\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eenumerate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e)})\n\n\u003cspan class=\"pl-c\"\u003e# Generate shape graph using KeplerMapper\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKeplerMapper\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003everbose\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit_transform\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprojection\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eTSNE\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e))\n\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emapper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003emap\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elens\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eX\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecover\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eCover\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003eclusterer\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eDBSCAN\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eeps\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e20.\u003c/span\u003e))\n\n\u003cspan class=\"pl-c\"\u003e# Visualize the shape graph using DyNeuSR\u0027s DyNeuGraph \u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edG\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDyNeuGraph\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eG\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egraph\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edG\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003evisualize\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u0027dyneusr_output.html\u0027\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\u003ca href=\"https://github.com/braindynamicslab/dyneusr/tree/master/examples/haxby_decoding\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/dyneusr_haxby_decoding.png\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eSetup\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cstrong\u003e\u003cem\u003eDependencies\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003e\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003ePython 3.6\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-python-36\" class=\"anchor\" aria-label=\"Permalink: Python 3.6\" href=\"#python-36\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eRequired Python Packages\u003c/h4\u003e\u003ca id=\"user-content-required-python-packages\" class=\"anchor\" aria-label=\"Permalink: Required Python Packages\" href=\"#required-python-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"www.numpy.org\"\u003enumpy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"pandas.pydata.org\"\u003epandas\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"www.scipy.org\"\u003escipy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"scikit-learn.org\"\u003escikit-learn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"matplotlib.sourceforge.net\"\u003ematplotlib\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"stanford.edu/~mwaskom/software/seaborn\"\u003eseaborn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"networkx.github.io\"\u003enetworkx\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"nilearn.github.io\"\u003enilearn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"kepler-mapper.scikit-tda.org\"\u003ekmapper\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eFor a full list of packages and required versions, see \u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/requirements.txt\"\u003e\u003ccode\u003erequirements.txt\u003c/code\u003e\u003c/a\u003e and \u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/requirements-versions.txt\"\u003e\u003ccode\u003erequirements-versions.txt\u003c/code\u003e\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cstrong\u003e\u003cem\u003eInstall with PIP\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\u003ca id=\"user-content-install-with-pip\" class=\"anchor\" aria-label=\"Permalink: Install with PIP\" href=\"#install-with-pip\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTo install with pip:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epip install dyneusr\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTo install from source:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/braindynamicslab/dyneusr.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dyneusr\n\npip install -r requirements.txt\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\npytest\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cstrong\u003e\u003cem\u003eInstall with Conda\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\u003ca id=\"user-content-install-with-conda\" class=\"anchor\" aria-label=\"Permalink: Install with Conda\" href=\"#install-with-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf your default environment is Python 2, we recommend that you install \u003ccode\u003edyneusr\u003c/code\u003e in a separate Python 3 environment. You can find more information about creating a separate environment for Python 3 \u003ca href=\"https://salishsea-meopar-docs.readthedocs.io/en/latest/work_env/python3_conda_environment.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you don\u0027t have conda, or if you are new to scientific python, we recommend that you download the \u003ca href=\"https://store.continuum.io/cshop/anaconda/\" rel=\"nofollow\"\u003eAnaconda scientific python distribution\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTo create a new conda environment and install from source:\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003econda create -n dyneusr python=3.6\nconda activate dyneusr\n\ngit clone https://github.com/braindynamicslab/dyneusr.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e dyneusr\n\nconda install --file requirements-conda.txt\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\npytest\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis creates a new conda environment \u003ccode\u003edyneusr\u003c/code\u003e and installs in it the dependencies that are needed. To access it, use the \u003ccode\u003econda activate dyneusr\u003c/code\u003e command (if your conda version \u0026gt;= 4.4) and use \u003ccode\u003esource activate dyneusr\u003c/code\u003e command (if your conda version \u0026lt; 4.4).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003cstrong\u003e\u003cem\u003eRun in a Singularity Container\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\u003ca id=\"user-content-run-in-a-singularity-container\" class=\"anchor\" aria-label=\"Permalink: Run in a Singularity Container\" href=\"#run-in-a-singularity-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo run dyneusr in a Singularity container, for use on clusters where you do not have root access for example, first install singularity. Then, you can use the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run shub://braindynamicslab/dyneusr\nsource activate neuro\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis will download and run a singularity container from \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003esingularity-hub\u003c/a\u003e running centos 7 with dyneusr, jupyter, and all dependencies. It will also activate the conda environement \u003ccode\u003eneuro\u003c/code\u003e where these are installed. For more information on how to use singularity, see the \u003ca href=\"https://sylabs.io/docs/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e. The singularity recipe for this file was built with \u003ca href=\"https://github.com/ReproNim/neurodocker\"\u003eneurodocker\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eSupport\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease feel free to \u003ca href=\"https://github.com/braindynamicslab/dyneusr/issues/new\"\u003ereport\u003c/a\u003e any issues, \u003ca href=\"https://github.com/braindynamicslab/dyneusr/issues/new\"\u003erequest\u003c/a\u003e new features, or \u003ca href=\"https://github.com/braindynamicslab/dyneusr/compare\"\u003epropose\u003c/a\u003e improvements. You can also contact Caleb Geniesse at geniesse [at] stanford [dot] edu.\u003c/p\u003e\n\u003cp\u003eIf you contribute to DyNeuSR, please feel free to add your name to the \u003ca href=\"https://github.com/braindynamicslab/dyneusr/blob/master/contributors.md\"\u003elist of contributors\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u003cstrong\u003eCitation\u003c/strong\u003e\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp\u003eGeniesse, C., Sporns, O., Petri, G., \u0026amp; Saggar, M. (2019). \u003ca href=\"https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093\" rel=\"nofollow\"\u003eGenerating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis\u003c/a\u003e. \u003cem\u003eNetwork Neuroscience\u003c/em\u003e. Advance publication. doi:10.1162/netn_a_00093\u003c/p\u003e\n\u003c/blockquote\u003e\n", + "stargazers_count": 44, + "subscribers_count": 8, "topics": [ - "diffusion", - "mri", - "preprocessing", - "quality", - "assurance" - ], - "updated_at": 1732140542.0 - }, - { - "data_format": 2, - "description": "Portable eQTL analysis and statistical fine mapping workflow used by the eQTL Catalogue", - "filenames": [ - "Singularity" + "brain-dynamics", + "topological-data-analysis", + "neuroimaging", + "brain-networks", + "mapper-algorithm", + "visualization" ], - "full_name": "eQTL-Catalogue/qtlmap", - "latest_release": "v24.01.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eeQTL-Catalogue/qtlmap\u003c/h1\u003e\u003ca id=\"user-content-eqtl-catalogueqtlmap\" class=\"anchor\" aria-label=\"Permalink: eQTL-Catalogue/qtlmap\" href=\"#eqtl-catalogueqtlmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePortable eQTL analysis and statistical fine mapping workflow used by the eQTL Catalogue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/kerimoff/qtlmap\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22d4cb3a7b5f8ed3c8cd4e79485c6a8f532cc76bb17c6fb0bfaf7335dbc7cf5e/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6b6572696d6f66662f71746c6d61702e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/kerimoff/qtlmap.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2842\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eeQTL-Catalogue/qtlmap\u003c/strong\u003e is a bioinformatics analysis pipeline used for QTL Analysis.\u003c/p\u003e\n\u003cp\u003eThe workflow takes phenotype count matrix (normalized and quality controlled) and genotype data as input, and finds associations between them with the help of sample metadata and phenotype metadata files (See \u003ca href=\"docs/inputs_expl.md\"\u003eInput formats and preparation\u003c/a\u003e for required input file details). To map QTLs, pipeline uses \u003ca href=\"https://qtltools.github.io/qtltools/\" rel=\"nofollow\"\u003eQTLTools\u0027s\u003c/a\u003e PCA and RUN methods. For manipulation of files \u003ca href=\"https://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003eBcfTools\u003c/a\u003e, \u003ca href=\"http://www.htslib.org/doc/tabix.html\" rel=\"nofollow\"\u003eTabix\u003c/a\u003e and custom \u003ca href=\"https://www.rdocumentation.org/packages/utils/versions/3.5.3/topics/Rscript\" rel=\"nofollow\"\u003eRscript\u003c/a\u003e scripts are used.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a bioinformatics workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe eQTL-Catalogue/qtlmap pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/inputs_expl.md\"\u003eInput formats and preparation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline Description\u003c/h3\u003e\u003ca id=\"user-content-pipeline-description\" class=\"anchor\" aria-label=\"Permalink: Pipeline Description\" href=\"#pipeline-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMapping QTLs is a process of finding statistically significant associations between phenotypes and genetic variants located nearby (within a specific window around phenotype, a.k.a cis window)\nThis pipeline is designed to perform QTL mapping. It is intended to add this pipeline to the nf-core framework in the future.\nHigh level representation of the pipeline is shown below:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/metromap.png\"\u003e\u003cimg src=\"docs/images/metromap.png\" alt=\"High_level_schema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eResults\u003c/h3\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe output directory of the workflow contains the following subdirectories:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePCA - genotype and gene expression PCA values used as covariates for QTL analysis.\u003c/li\u003e\n\u003cli\u003esumstats - QTL summary statistics from nominal and permutation passes.\u003c/li\u003e\n\u003cli\u003esusie - SuSiE fine mapping credible sets.\u003c/li\u003e\n\u003cli\u003esusie_full - full set of susie results for all tested variants (very large files).\u003c/li\u003e\n\u003cli\u003esusie_merged - susie credible sets merged with summary statistics from univariate QTL analysis.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eColumn names of the output files are explained \u003ca href=\"https://github.com/eQTL-Catalogue/eQTL-Catalogue-resources/blob/master/tabix/Columns.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContributors\u003c/h1\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNurlan Kerimov\u003c/li\u003e\n\u003cli\u003eKaur Alasoo\u003c/li\u003e\n\u003cli\u003eMasahiro Kanai\u003c/li\u003e\n\u003cli\u003eRalf Tambets\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 45, - "subscribers_count": 3, - "topics": [], - "updated_at": 1730962083.0 + "updated_at": 1728873619.0 }, { "data_format": 2, @@ -41048,33 +41042,37 @@ var data = }, { "data_format": 2, - "description": "Bayesian co-estimation of phylogenies and multiple alignments via MCMC", + "description": "Portable eQTL analysis and statistical fine mapping workflow used by the eQTL Catalogue", "filenames": [ "Singularity" ], - "full_name": "bredelings/BAli-Phy", - "latest_release": "3.6.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bredelings/BAli-Phy/workflows/Build%20and%20test/badge.svg\"\u003e\u003cimg src=\"https://github.com/bredelings/BAli-Phy/workflows/Build%20and%20test/badge.svg\" alt=\"Build and test\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you just want to install bali-phy, please visit the \u003ca href=\"http://www.bali-phy.org/download.php\" rel=\"nofollow\"\u003erelease page\u003c/a\u003e. If you want to compile BAli-phy from source, the quick start instructions are below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling\u003c/h2\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need a C++ compiler that understands C++20.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egcc 11 (or higher) works\u003c/li\u003e\n\u003cli\u003eclang 14 (or higher) works\u003c/li\u003e\n\u003cli\u003eXCode 14 (or higher) works\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall Prerequisites\u003c/h2\u003e\u003ca id=\"user-content-install-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Install Prerequisites\" href=\"#install-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Ubuntu 24.04 (or higher), or on Debian testing, you can use apt-get:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get install g++ libcairo2-dev meson libboost-all-dev\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn Mac (or Linux, actually) you can use homebrew:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew install cairo meson boost pkg-config\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn miniconda, you can use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n devel -c conda-forge --strict-channel-priority\nconda activate devel\nconda install meson gxx boost-cpp cmake pkg-config cairo\nexport BOOST_ROOT=$CONDA_PREFIX\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMeson version\u003c/h3\u003e\u003ca id=\"user-content-meson-version\" class=\"anchor\" aria-label=\"Permalink: Meson version\" href=\"#meson-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter installing prerequisites above, check that the meson version is at least 1.1.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emeson --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf your version of meson is less than 1.1, you need to install the most recent version of meson into a python virtual environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 -m venv meson_venv\nsource meson_venv/bin/activate\npip install meson ninja\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild BAli-Phy\u003c/h2\u003e\u003ca id=\"user-content-build-bali-phy\" class=\"anchor\" aria-label=\"Permalink: Build BAli-Phy\" href=\"#build-bali-phy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will build the latest unreleased beta version of BAli-Phy, which fixes some memory issues in 3.6. There are some changes to the model language. Check the NEWS file for the details.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/bredelings/BAli-Phy.git\ncd BAli-Phy\nmeson setup build --prefix=$HOME/Applications/bali-phy\nninja -C build install\nninja -C build test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdding bali-phy to your \u003ccode\u003e$PATH\u003c/code\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-adding-bali-phy-to-your-path\" class=\"anchor\" aria-label=\"Permalink: Adding bali-phy to your $PATH\" href=\"#adding-bali-phy-to-your-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run the installed software, you should \u003ca href=\"http://bali-phy.org/README.xhtml#path\" rel=\"nofollow\"\u003eadd bali-phy to your $PATH\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalled locations\u003c/h2\u003e\u003ca id=\"user-content-installed-locations\" class=\"anchor\" aria-label=\"Permalink: Installed locations\" href=\"#installed-locations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you installed in \u003ccode\u003e$HOME/Applications/bali-phy/\u003c/code\u003e as recommended above, then files will be in:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eLocation\u003c/th\u003e\n\u003cth\u003eFiles\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e~/Applications/bali-phy/bin\u003c/td\u003e\n\u003ctd\u003eBinary executables.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e~/Applications/bali-phy/share/bali-phy/examples/sequences\u003c/td\u003e\n\u003ctd\u003eExample files.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e~/Applications/bali-phy/share/doc/bali-phy/\u003c/td\u003e\n\u003ctd\u003eDocumentation.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFurther Documentation\u003c/h2\u003e\u003ca id=\"user-content-further-documentation\" class=\"anchor\" aria-label=\"Permalink: Further Documentation\" href=\"#further-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://bali-phy.org/\" rel=\"nofollow\"\u003ehttp://bali-phy.org/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://bali-phy.org/README.xhtml\" rel=\"nofollow\"\u003eManual\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://bali-phy.org/Tutorial4.html\" rel=\"nofollow\"\u003eTutorial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Manual describes \u003ca href=\"http://bali-phy.org/README.xhtml#installation\" rel=\"nofollow\"\u003ehow to install\u003c/a\u003e bali-phy in greater detail.\u003c/p\u003e\n", - "stargazers_count": 46, - "subscribers_count": 6, + "full_name": "eQTL-Catalogue/qtlmap", + "latest_release": "v24.01.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eeQTL-Catalogue/qtlmap\u003c/h1\u003e\u003ca id=\"user-content-eqtl-catalogueqtlmap\" class=\"anchor\" aria-label=\"Permalink: eQTL-Catalogue/qtlmap\" href=\"#eqtl-catalogueqtlmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePortable eQTL analysis and statistical fine mapping workflow used by the eQTL Catalogue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/37eb119ce1183ca2fe2e3dafe2327bab5c0ead31ba107e9782c7ae5de4b66aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e657874666c6f772d254532253839254135302e33322e302d627269676874677265656e2e737667\" alt=\"Nextflow\" data-canonical-src=\"https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://bioconda.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/72f2095dcf3d481aeee0ce4fb71c79ddca1c67b85ac8e47af4490d091e721ef4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e737667\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/kerimoff/qtlmap\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22d4cb3a7b5f8ed3c8cd4e79485c6a8f532cc76bb17c6fb0bfaf7335dbc7cf5e/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f6175746f6d617465642f6b6572696d6f66662f71746c6d61702e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/docker/automated/kerimoff/qtlmap.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/2842\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eeQTL-Catalogue/qtlmap\u003c/strong\u003e is a bioinformatics analysis pipeline used for QTL Analysis.\u003c/p\u003e\n\u003cp\u003eThe workflow takes phenotype count matrix (normalized and quality controlled) and genotype data as input, and finds associations between them with the help of sample metadata and phenotype metadata files (See \u003ca href=\"docs/inputs_expl.md\"\u003eInput formats and preparation\u003c/a\u003e for required input file details). To map QTLs, pipeline uses \u003ca href=\"https://qtltools.github.io/qtltools/\" rel=\"nofollow\"\u003eQTLTools\u0027s\u003c/a\u003e PCA and RUN methods. For manipulation of files \u003ca href=\"https://samtools.github.io/bcftools/bcftools.html\" rel=\"nofollow\"\u003eBcfTools\u003c/a\u003e, \u003ca href=\"http://www.htslib.org/doc/tabix.html\" rel=\"nofollow\"\u003eTabix\u003c/a\u003e and custom \u003ca href=\"https://www.rdocumentation.org/packages/utils/versions/3.5.3/topics/Rscript\" rel=\"nofollow\"\u003eRscript\u003c/a\u003e scripts are used.\u003c/p\u003e\n\u003cp\u003eThe pipeline is built using \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e, a bioinformatics workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDocumentation\u003c/h3\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe eQTL-Catalogue/qtlmap pipeline comes with documentation about the pipeline, found in the \u003ccode\u003edocs/\u003c/code\u003e directory:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"docs/installation.md\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePipeline configuration\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/local.md\"\u003eLocal installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/configuration/adding_your_own.md\"\u003eAdding your own system\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/inputs_expl.md\"\u003eInput formats and preparation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/usage.md\"\u003eRunning the pipeline\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"docs/troubleshooting.md\"\u003eTroubleshooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePipeline Description\u003c/h3\u003e\u003ca id=\"user-content-pipeline-description\" class=\"anchor\" aria-label=\"Permalink: Pipeline Description\" href=\"#pipeline-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eMapping QTLs is a process of finding statistically significant associations between phenotypes and genetic variants located nearby (within a specific window around phenotype, a.k.a cis window)\nThis pipeline is designed to perform QTL mapping. It is intended to add this pipeline to the nf-core framework in the future.\nHigh level representation of the pipeline is shown below:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"docs/images/metromap.png\"\u003e\u003cimg src=\"docs/images/metromap.png\" alt=\"High_level_schema\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eResults\u003c/h3\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe output directory of the workflow contains the following subdirectories:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePCA - genotype and gene expression PCA values used as covariates for QTL analysis.\u003c/li\u003e\n\u003cli\u003esumstats - QTL summary statistics from nominal and permutation passes.\u003c/li\u003e\n\u003cli\u003esusie - SuSiE fine mapping credible sets.\u003c/li\u003e\n\u003cli\u003esusie_full - full set of susie results for all tested variants (very large files).\u003c/li\u003e\n\u003cli\u003esusie_merged - susie credible sets merged with summary statistics from univariate QTL analysis.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eColumn names of the output files are explained \u003ca href=\"https://github.com/eQTL-Catalogue/eQTL-Catalogue-resources/blob/master/tabix/Columns.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContributors\u003c/h1\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNurlan Kerimov\u003c/li\u003e\n\u003cli\u003eKaur Alasoo\u003c/li\u003e\n\u003cli\u003eMasahiro Kanai\u003c/li\u003e\n\u003cli\u003eRalf Tambets\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 45, + "subscribers_count": 3, "topics": [], - "updated_at": 1731801196.0 + "updated_at": 1730962083.0 }, { "data_format": 2, - "description": "Computational workflows for metagenomics tasks, by the Bhatt lab", + "description": "An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images", "filenames": [ - "singularity/Singularity.example_recipe", - "singularity/Singularity.align", - "singularity/Singularity.plotting" + "Singularity" ], - "full_name": "bhattlab/bhattlab_workflows", - "latest_release": "v1.01", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBhattlab workflows\u003c/h1\u003e\u003ca id=\"user-content-bhattlab-workflows\" class=\"anchor\" aria-label=\"Permalink: Bhattlab workflows\" href=\"#bhattlab-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputational workflows for metagenomics tasks, packaged with Snakemake, Singularity and Conda.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTable of contents\u003c/h3\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"manual/setup.md\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/running.md\"\u003eRunning a workflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eAvailable workflows\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"manual/preprocessing.md\"\u003e\u003cstrong\u003ePreprocessing\u003c/strong\u003e metagenomic data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/assembly.md\"\u003eMetagenomic \u003cstrong\u003eAssembly\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/binning.md\"\u003eMetagenomic \u003cstrong\u003eBinning\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/dRep.md\"\u003e\u003cstrong\u003eDeReplication\u003c/strong\u003e of binned genomes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/inStrain.md\"\u003e\u003cstrong\u003einStrain\u003c/strong\u003e strain-diversity aware comparison of samples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bhattlab/kraken2_classification\"\u003eMetagenomic classification with \u003cstrong\u003eKraken2\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/sourmash.md\"\u003e\u003cstrong\u003eSourmash\u003c/strong\u003e read comparison\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/download_sra.md\"\u003e\u003cstrong\u003eDownload SRA\u003c/strong\u003e data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/arg.md\"\u003e\u003cstrong\u003eARG detection\u003c/strong\u003e with RGI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/viral.md\"\u003e\u003cstrong\u003eViral\u003c/strong\u003e contig prediction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/comparative_genomics.md\"\u003eComparative microbial genomics pipelines\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"metariboseq/README.md\"\u003eMetaRibo-Seq\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuickstart\u003c/h3\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re in the Bhatt lab and working on SCG, this command is an example of how to run the workflows. Other users will need to change these options (see \u003ca href=\"manual/running.md\"\u003eRunning a workflow\u003c/a\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --configfile config_preprocessing.yaml \\\n--snakefile ~/projects/bhattlab_workflows/preprocessing/preprocessing.snakefile \\\n--profile scg --jobs 100 --use-singularity \\\n--singularity-args \u0027--bind /labs/,/oak/,/home/\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eImportant\u003c/strong\u003e after running the preprocessing or assembly pipelines, run the cleanup rule in the snakefile. This will delete all the unnecessary files and save us space on SCG!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake cleanup -s PATH/TO/SNAKEFILE --configfile YOUR_CONFIGFILE.yaml\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also remove the \u003ccode\u003e.snakemake\u003c/code\u003e folder, which only contains metadata and cache from the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erm -rf .snakemake \n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 46, - "subscribers_count": 11, - "topics": [], - "updated_at": 1721049025.0 + "full_name": "MASILab/PreQual", + "latest_release": "v1.1.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003ePreQual (dtiQA v7 Multi) User Guide\u003c/h1\u003e\u003ca id=\"user-content-prequal-dtiqa-v7-multi-user-guide\" class=\"anchor\" aria-label=\"Permalink: PreQual (dtiQA v7 Multi) User Guide\" href=\"#prequal-dtiqa-v7-multi-user-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#authors-and-reference\"\u003eAuthors and Reference\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#containerization-of-source-code\"\u003eContainerization of Source Code\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#command\"\u003eCommand\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#arguments-and-io\"\u003eArguments and I/O\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#configuration-file\"\u003eConfiguration File\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#examples\"\u003eExamples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#running-bids-data\"\u003eRunning BIDS Data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#options\"\u003eOptions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pipeline-assumptions\"\u003ePipeline Assumptions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pipeline-processing-steps\"\u003ePipeline Processing Steps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pipeline-quality-assurance-steps\"\u003ePipeline Quality Assurance Steps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#note-on-versioning-for-vuiis-xnat-users\"\u003eNote on Versioning for VUIIS XNAT Users\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/MASILab/PreQual/blob/master/overview.png?raw=true\"\u003e\u003cimg src=\"https://github.com/MASILab/PreQual/raw/master/overview.png?raw=true\" alt=\"Pipeline Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSummary:\u003c/strong\u003e Perform integrated preprocessing and quality assurance of diffusion MRI data\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePreprocessing Steps:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eMP-PCA denoising (default on)\u003c/li\u003e\n\u003cli\u003eGibbs de-ringing (default off)\u003c/li\u003e\n\u003cli\u003eRician correction (default off)\u003c/li\u003e\n\u003cli\u003eInter-scan normalization (default on)\u003c/li\u003e\n\u003cli\u003eSusceptibility-induced distortion correction, with or without reverse gradient images or field maps\u003c/li\u003e\n\u003cli\u003eEddy current-induced distortion correction\u003c/li\u003e\n\u003cli\u003eInter-volume motion correction\u003c/li\u003e\n\u003cli\u003eSlice-wise signal dropout imputation\u003c/li\u003e\n\u003cli\u003eN4 B1 bias field correction (default off)\u003c/li\u003e\n\u003cli\u003eGradient nonlinearity correction (default off)\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Assurance Steps:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eVerification of phase encoding schemes\u003c/li\u003e\n\u003cli\u003eAnalysis of gradient directions\u003c/li\u003e\n\u003cli\u003eShell-wise analysis of signal-to-noise and contrast-to-noise ratios\u003c/li\u003e\n\u003cli\u003eVisualization of Gibbs de-ringing changes (if applicable)\u003c/li\u003e\n\u003cli\u003eVisualization of within brain intensity distributions before and after Rician correction (if applicable)\u003c/li\u003e\n\u003cli\u003eCorrection (if applicable) or visualization of inter-scan intensity relationships\u003c/li\u003e\n\u003cli\u003eShell-wise analysis of distortion corrections\u003c/li\u003e\n\u003cli\u003eAnalysis of inter-volume motion and slice-wise signal dropout\u003c/li\u003e\n\u003cli\u003eAnalysis of B1 bias fields (if applicable)\u003c/li\u003e\n\u003cli\u003eAnalysis of gradient nonlinear fields (if applicable)\u003c/li\u003e\n\u003cli\u003eVerification of intra-pipeline masking\u003c/li\u003e\n\u003cli\u003eAnalysis of tensor goodness-of-fit\u003c/li\u003e\n\u003cli\u003eVoxel-wise and region-wise quantification of FA\u003c/li\u003e\n\u003cli\u003eVoxel-wise quantification of MD\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors and Reference\u003c/h2\u003e\u003ca id=\"user-content-authors-and-reference\" class=\"anchor\" aria-label=\"Permalink: Authors and Reference\" href=\"#authors-and-reference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"mailto:leon.y.cai@vanderbilt.edu\"\u003eLeon Y. Cai\u003c/a\u003e, Qi Yang, Colin B. Hansen, Vishwesh Nath, Karthik Ramadass, Graham W. Johnson, Benjamin N. Conrad, Brian D. Boyd, John P. Begnoche, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Warren D. Taylor, Gavin R. Price, Victoria L. Morgan, Baxter P. Rogers, Kurt G. Schilling, Bennett A. Landman. \u003cem\u003ePreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images\u003c/em\u003e. \u003ca href=\"https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.28678\" rel=\"nofollow\"\u003eMagnetic Resonance in Medicine\u003c/a\u003e, 2021.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://my.vanderbilt.edu/masi\" rel=\"nofollow\"\u003eMedical-image Analysis and Statistical Interpretation (MASI) Lab\u003c/a\u003e, Vanderbilt University, Nashville, TN, USA\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe PreQual software is designed to run inside a \u003ca href=\"#containerization-of-source-code\"\u003eSingularity container\u003c/a\u003e. The container requires an \"\u003ca href=\"#arguments-and-io\"\u003einputs\u003c/a\u003e\" folder that holds all required input diffusion image files (i.e., .nii.gz, .bval, and .bvec files) and a \u003ca href=\"#configuration-file\"\u003econfiguration file\u003c/a\u003e. For those running Synb0-DisCo to correct susceptibility distortions without reverse phase-encoded images, this folder will also contain the \u003ca href=\"#arguments-and-io\"\u003estructural T1 image\u003c/a\u003e. The container also requires an \"\u003ca href=\"#arguments-and-io\"\u003eoutputs\u003c/a\u003e\" folder that will hold all the outputs after the pipeline runs. We also need to know the image \u003cem\u003e\u003ca href=\"#arguments-and-io\"\u003eaxis\u003c/a\u003e\u003c/em\u003e on which phase encoding was performed for all inputs (i.e., \"i\" for the first dimension, \"j\" for the second). To build the configuration file, we need to know the \u003cem\u003e\u003ca href=\"#configuration-file\"\u003edirection\u003c/a\u003e\u003c/em\u003e along said axis in which each image was phase encoded (i.e., \"+\" for positive direction and \"-\" for the negative direction) and the \u003ca href=\"#configuration-file\"\u003ereadout time\u003c/a\u003e for each input image. Once we have this information, we bind the inputs and outputs directories into the container to \u003ca href=\"#command\"\u003erun the pipeline\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eNote: The phase encoding axis, direction, and readout time must be known ahead of time, as this information is not stored in NIFTI headers. Depending on the scanner used, they may be available in JSON sidecars when NIFTIs are converted from DICOMs with \u003ca href=\"#pipeline-assumptions\"\u003edcm2niix\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainerization of Source Code\u003c/h2\u003e\u003ca id=\"user-content-containerization-of-source-code\" class=\"anchor\" aria-label=\"Permalink: Containerization of Source Code\" href=\"#containerization-of-source-code\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/MASILab/PreQual.git\ncd /path/to/repo/PreQual\ngit checkout v1.1.0\nsudo singularity build /path/to/prequal.simg Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe use Singularity version 3.8 CE with root permissions.\u003c/p\u003e\n\u003cp\u003eAlternatively, a pre-built container can be downloaded \u003ca href=\"https://masi.vuse.vanderbilt.edu/PreQual/PreQual_v1.0.8.simg\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommand\u003c/h2\u003e\u003ca id=\"user-content-command\" class=\"anchor\" aria-label=\"Permalink: Command\" href=\"#command\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run \n-e \n--contain\n--home /path/to/inputs/directory/\n-B /path/to/inputs/directory/:/INPUTS\n-B /path/to/outputs/directory/:/OUTPUTS\n-B /tmp:/tmp\n-B /path/to/freesurfer/license.txt:/APPS/freesurfer/license.txt\n-B /path/to/cuda:/usr/local/cuda\n--nv\n/path/to/prequal.simg\npe_axis\n[options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eBinding the freesurfer license is optional and only needed for Synb0-DisCo\u003c/li\u003e\n\u003cli\u003eBinding the tmp directory is necessary when running the image with \u003ccode\u003e--contain\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eBinding --home is necessary for matlab since it uses home for temp storage.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003e--nv\u003c/code\u003e and \u003ccode\u003e-B /path/to/cuda:/usr/local/cuda\u003c/code\u003e are optional. See options \u003ccode\u003e--eddy_cuda\u003c/code\u003e and \u003ccode\u003e--eddy_extra_args\u003c/code\u003e. \u003cstrong\u003eGPU support is currently experimental.\u003c/strong\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eArguments and I/O\u003c/h2\u003e\u003ca id=\"user-content-arguments-and-io\" class=\"anchor\" aria-label=\"Permalink: Arguments and I/O\" href=\"#arguments-and-io\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eInput Directory:\u003c/strong\u003e The dtiQA_config.csv configuration file and at least one diffusion weighted image must be provided.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003edtiQA_config.csv (see \u003ca href=\"#configuration-file\"\u003ebelow\u003c/a\u003e for format, must be named exactly)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;image1\u0026gt;.nii.gz (diffusion weighted image)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;image1\u0026gt;.bval (units of s/mm\u003csup\u003e2\u003c/sup\u003e, in the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Diffusion_data_in_FSL\" rel=\"nofollow\"\u003eFSL format\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;image1\u0026gt;.bvec (normalized unit vectors in the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Diffusion_data_in_FSL\" rel=\"nofollow\"\u003eFSL format\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;imageN\u0026gt;.nii.gz (diffusion weighted image)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;imageN\u0026gt;.bval (units of s/mm\u003csup\u003e2\u003c/sup\u003e, in the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Diffusion_data_in_FSL\" rel=\"nofollow\"\u003eFSL format\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;imageN\u0026gt;.bvec (normalized unit vectors in the \u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Diffusion_data_in_FSL\" rel=\"nofollow\"\u003eFSL format\u003c/a\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003et1.nii.gz (Optional, used for Synb0-DisCo, must be named exactly)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egradtensor.nii (Optional, used for --correct_grad, must be named exactly)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOther files as needed (see \u003ccode\u003e--extra_eddy_args\u003c/code\u003e for more information)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eOutput Directory:\u003c/strong\u003e Full outputs listed at the \u003ca href=\"#outputs\"\u003eend\u003c/a\u003e of this document\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe output preprocessed images are available in the PREPROCESSED subfolder in the output directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePREPROCESSED/dwmri.nii.gz\u003c/li\u003e\n\u003cli\u003ePREPROCESSED/dwmri.bval\u003c/li\u003e\n\u003cli\u003ePREPROCESSED/dwmri.bvec\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe QA document is available in the PDF subfolder in the output directory:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePDF/dtiQA.pdf\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003epe_axis:\u003c/strong\u003e Phase encoding axis of all the input images. We do NOT support different phase encoding axes between different input images at this time. The options are i and j and correspond to the first and second dimension of the input images, respectively. Note that FSL does not currently support phase encoding in the third dimension (i.e. k, the dimension in which the image slices were acquired, commonly axial for RAS and LAS oriented images). \u003cstrong\u003eThis parameter is direction AGNOSTIC\u003c/strong\u003e. The phase encoding directions of the input images along this axis are specified in the dtiQA_config.csv file. See \u003ca href=\"#configuration-file\"\u003eConfiguration File\u003c/a\u003e and \u003ca href=\"#examples\"\u003eExamples\u003c/a\u003e for more information.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eConfiguration File\u003c/h2\u003e\u003ca id=\"user-content-configuration-file\" class=\"anchor\" aria-label=\"Permalink: Configuration File\" href=\"#configuration-file\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe format for the lines of the configuration CSV file, dtiQA_config.csv (must be named exactly), are as follows:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u0026lt;image1\u0026gt;,pe_dir,readout_time\n:\n\u0026lt;imageN\u0026gt;,pe_dir,readout_time\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;image\u0026gt;\u003c/strong\u003e is the shared file PREFIX between the corresponding NIFTI, BVAL, and BVEC files for that particular image in the input directory (i.e., my_dwi.nii.gz/.bval/.bvec -\u0026gt; my_dwi). Do NOT include the path to the input directory.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003epe_dir\u003c/strong\u003e is either + or -, corresponding to the direction along the phase encoding axis (as defined by the parameter \u003ccode\u003epe_axis\u003c/code\u003e) on which the image is phase encoded.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNote that a combination of phase encoding axis and direction map to specific anatomical (i.e. APA, APP, etc.) directions based on the orientation of the image. So, for instance in a RAS image, an axis of j and direction of + map to APP. We infer the orientation of the image from the header of the NIFTI using nibabel tools and output the best anatomical phase encoding direction interpretation of the input direction in the PDF for QA.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ereadout_time\u003c/strong\u003e is a non-negative number, the readout_time parameter required by FSL\u2019s eddy. The absolute value of this parameter is used to scale the estimated b0 field. Note a value of 0 indicates that the images are infinite bandwidth (i.e. no susceptibility distortion). See \u003ca href=\"#examples\"\u003eExamples\u003c/a\u003e for more information.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHere are some different example combinations of pe_axis, pe_dir, and readout_time parameters and the corresponding FSL acquisition parameters lines:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003epe_axis\u003c/th\u003e\n\u003cth\u003epe_dir\u003c/th\u003e\n\u003cth\u003ereadout_time\u003c/th\u003e\n\u003cth\u003eacqparams line\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ei\u003c/td\u003e\n\u003ctd\u003e+\u003c/td\u003e\n\u003ctd\u003e0.05\u003c/td\u003e\n\u003ctd\u003e1, 0, 0, 0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ej\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003e0.1\u003c/td\u003e\n\u003ctd\u003e0, -1, 0, 0.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThese are examples of common use cases. They also all share the same command, as detailed above. The PREPROCESSED output folder will contain the final outputs and the PDF folder will contain the QA report.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePhase Encoding\u003cbr\u003eAxis\u003c/th\u003e\n\u003cth\u003eReverse Phase\u003cbr\u003eEncoded (RPE) Image\u003c/th\u003e\n\u003cth\u003eT1\u003cbr\u003eImage\u003c/th\u003e\n\u003cth\u003eContents of\u003cbr\u003eInput Directory\u003c/th\u003e\n\u003cth\u003eContents of\u003cbr\u003edtiQA_config.csv\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ej\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003ctd\u003eN/A\u003c/td\u003e\n\u003ctd\u003edti1.nii.gz\u003cbr\u003edti1.bval\u003cbr\u003edti1.bvec\u003cbr\u003edti2.nii.gz\u003cbr\u003edti2.bval\u003cbr\u003edti2.bvec\u003cbr\u003erpe.nii.gz\u003cbr\u003erpe.bval\u003cbr\u003erpe.bvec\u003cbr\u003edtiQA_config.csv\u003c/td\u003e\n\u003ctd\u003edti1,+,0.05\u003cbr\u003edti2,+,0.05\u003cbr\u003erpe,-,0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ej\u003c/td\u003e\n\u003ctd\u003eNo\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003ctd\u003edti1.nii.gz\u003cbr\u003edti1.bval\u003cbr\u003edti1.bvec\u003cbr\u003edti2.nii.gz\u003cbr\u003edti2.bval\u003cbr\u003edti2.bvec\u003cbr\u003et1.nii.gz\u003cbr\u003edtiQA_config.csv\u003c/td\u003e\n\u003ctd\u003edti1,+,0.05\u003cbr\u003edti2,+,0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ej\u003c/td\u003e\n\u003ctd\u003eNo\u003c/td\u003e\n\u003ctd\u003eNo\u003c/td\u003e\n\u003ctd\u003edti1.nii.gz\u003cbr\u003edti1.bval\u003cbr\u003edti1.bvec\u003cbr\u003edti2.nii.gz\u003cbr\u003edti2.bval\u003cbr\u003edti2.bvec\u003cbr\u003edtiQA_config.csv\u003c/td\u003e\n\u003ctd\u003edti1,+,0.05\u003cbr\u003edti2,+,0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRunning BIDS Data\u003c/h2\u003e\u003ca id=\"user-content-running-bids-data\" class=\"anchor\" aria-label=\"Permalink: Running BIDS Data\" href=\"#running-bids-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhile not a BIDS pipeline, data in BIDS format can be run with PreQual without moving or copying data. The key is that the input directory structure must be as described relative to \u003cem\u003einside the container\u003c/em\u003e. By creatively binding files/folders into the container, we can achieve the same effect:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-B /path/to/sub-X/ses-X/dwi/:/INPUTS\n-B /path/to/sub-X/ses-X/anat/sub-X_ses-X_T1w.nii.gz:/INPUTS/t1.nii.gz (optional, Synb0-DisCo only)\n-B /path/to/config/file.csv:/INPUTS/dtiQA_config.csv\n-B /path/to/outputs/directory/:/OUTPUTS\n-B /tmp:/tmp\n-B /path/to/freesurfer/license.txt:/APPS/freesurfer/license.txt\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe outputs directory and configuration file can be created wherever makes the most sense for the user. The contents of the configuration file will look something like this:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esub-X_ses-X_acq-1_dwi,pe_dir,readout_time\n:\nsub-X_ses-X_acq-N_dwi,pe_dir,readout_time\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptions\u003c/h2\u003e\u003ca id=\"user-content-options\" class=\"anchor\" aria-label=\"Permalink: Options\" href=\"#options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e--bval_threshold N\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA non-negative integer threshold under which to consider a b-value to be zero. Useful when some MRI machines do not allow for more than one b0 volume to be acquired so some users acquire scans with extremely low b-values to be treated like b0 volumes. Setting this value to 0 results in no thresholding. Units = s/mm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDefault = 50\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--nonzero_shells s1,s2,...,sn/auto\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comma separated list of positive integers (s/mm\u003csup\u003e2\u003c/sup\u003e) indicating nonzero shells for SNR/CNR analysis when there are more unique b-values than shells determined by eddy or automatically determine shells by rounding to nearest 100. Useful when b-values are modulated around a shell value instead of set exactly at that value. Only used when determining shells for SNR/CNR analysis. Original b-values used elsewhere in pipeline.\u003c/p\u003e\n\u003cp\u003eDefault = auto\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--denoise on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDenoise images prior to preprocessing using Marchenko-Pastur PCA \u003ca href=\"https://mrtrix.readthedocs.io/en/latest/reference/commands/dwidenoise.html\" rel=\"nofollow\"\u003eimplemented in MRTrix3\u003c/a\u003e. The SNR of the b0s of the final preprocessed images are reported in the PDF output regardless of whether this option is on or off.\u003c/p\u003e\n\u003cp\u003eDefault = on\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--degibbs on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRemove Gibbs ringing artifacts using the local subvoxel-shifts method as \u003ca href=\"https://mrtrix.readthedocs.io/en/latest/reference/commands/mrdegibbs.html\" rel=\"nofollow\"\u003eimplemented in MRTrix3\u003c/a\u003e. We caution against using this feature as it not designed for the partial Fourier schemes with which most echo planar diffusion images are acquired. It is also difficult to quality check, but we include a visualization of averaged residuals across all b = 0 s/mm\u003csup\u003e2\u003c/sup\u003e volumes, looking for larger signals near high contrast (i.e. parenchyma-CSF) interfaces.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--rician on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform Rician correction using the method of moments. We normally do not perform this step as we empirically do not find it to affect results drastically. It is also difficult to quality check, but we include a plot of the shell-wise within brain intensity distributions for each input before and after correction, looking for a slight drop in intensity with correction.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--prenormalize on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntensity normalize images prior to preprocessing by maximizing the intra-mask intensity-histogram intersections between the averaged b0s of the scans. If this option is on, these histograms before and after prenormalization will be reported in the output PDF. This is done to avoid gain differences between different diffusion scans. If this option is off, we assume that the various input images all have the same gain. That being said, we still estimate and report the gain factors and intensity histograms in a gain QA page and report warnings if estimated gains greater than 5% are found.\u003c/p\u003e\n\u003cp\u003eDefault = on\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--synb0 raw/stripped/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003etopup\u003c/code\u003e with a synthetic b0 generated with the Synb0-DisCo deep-learning framework if no reverse phase encoded images are supplied and a raw or skull-stripped T1 image is supplied. Synb0-DisCo requires at least 24GB of RAM.\u003c/p\u003e\n\u003cp\u003eDefault = raw\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--topup_first_b0s_only\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003etopup\u003c/code\u003e with only the first b0 from each input image. At the time of writing, \u003cstrong\u003eFSL\u0027s topup cannot be parallelized\u003c/strong\u003e, and the runtime of topup can increase dramatically as more b0 volumes are included. This flag allows for faster processing at the expense of information lost from any interleaved b0s.\u003c/p\u003e\n\u003cp\u003eDefault = use ALL b0s\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--extra_topup_args=string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtra arguments to pass to FSL\u2019s \u003ccode\u003etopup\u003c/code\u003e. \u003ccode\u003eTopup\u003c/code\u003e will run with the following by default (as listed in the \u003ccode\u003e/SUPPLEMENTAL/topup.cnf\u003c/code\u003e configuration file) but will be overwritten by arguments passed to \u003ccode\u003e--extra_topup_args\u003c/code\u003e:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# Resolution (knot-spacing) of warps in mm\n--warpres=20,16,14,12,10,6,4,4,4\n# Subsampling level (a value of 2 indicates that a 2x2x2 neighbourhood is collapsed to 1 voxel)\n--subsamp=1,1,1,1,1,1,1,1,1\n# FWHM of gaussian smoothing\n--fwhm=8,6,4,3,3,2,1,0,0\n# Maximum number of iterations\n--miter=10,10,10,10,10,20,20,30,30\n# Relative weight of regularisation\n--lambda=0.00033,0.000067,0.0000067,0.000001,0.00000033,0.000000033,0.0000000033,0.000000000033,0.00000000000067\n# If set to 1 lambda is multiplied by the current average squared difference\n--ssqlambda=1\n# Regularisation model\n--regmod=bending_energy\n# If set to 1 movements are estimated along with the field\n--estmov=1,1,1,1,1,0,0,0,0\n# 0=Levenberg-Marquardt, 1=Scaled Conjugate Gradient\n--minmet=0,0,0,0,0,1,1,1,1\n# Quadratic or cubic splines\n--splineorder=3\n# Precision for calculation and storage of Hessian\n--numprec=double\n# Linear or spline interpolation\n--interp=spline\n# If set to 1 the images are individually scaled to a common mean intensity \n--scale=0\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThese parameters should be formatted as a list separated by +\u0027s with no spaces (i.e., \u003ccode\u003e--extra_topup_args=--scale=1+--regrid=0\u003c/code\u003e). For \u003ccode\u003etopup\u003c/code\u003e options that require additional inputs, place the file in the inputs directory and use the following syntax: \u003ccode\u003e--\u0026lt;myinputoption\u0026gt;=/INPUTS/\u0026lt;file.ext\u0026gt;\u003c/code\u003e. For \u003ccode\u003etopup\u003c/code\u003e options that produce additional outputs, the file will save in the output directory under the \u201cTOPUP\u201d folder by using the following syntax: \u003ccode\u003e--\u0026lt;myoutputoption\u0026gt;=/OUTPUTS/TOPUP/\u0026lt;file.ext\u0026gt;\u003c/code\u003e. Note that in this case \u003ccode\u003e/INPUTS\u003c/code\u003e and \u003ccode\u003e/OUTPUTS\u003c/code\u003e should be named exactly as is and are NOT the path to the input and output directory on your file system.\u003c/p\u003e\n\u003cp\u003eDefault = none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--eddy_cuda 8.0/9.1/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun FSL\u2019s \u003ccode\u003eeddy\u003c/code\u003e with NVIDIA GPU acceleration. If this parameter is 8.0 or 9.1, either CUDA 8.0 or 9.1 must be installed, properly configured on your system, and bound into the container, respectively. Additionally the \u003ccode\u003e--nv\u003c/code\u003e flag must be run in the singularity command. If this parameter is off, \u003ccode\u003eeddy\u003c/code\u003e is run with OPENMP CPU multithreading. See \u003ccode\u003e--num_threads\u003c/code\u003e for more information. CUDA is required to run \u003ccode\u003eeddy\u003c/code\u003e with \u003ccode\u003e--mporder\u003c/code\u003e (intra-volume slice-wise motion correction). See \u003ccode\u003e--extra_eddy_args\u003c/code\u003e for more information.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--eddy_mask on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003eeddy\u003c/code\u003e with or without a brain mask. If on, FSL\u2019s brain extraction tool (\u003ccode\u003ebet\u003c/code\u003e) is used with a low threshold to create a rough brain mask for \u003ccode\u003eeddy\u003c/code\u003e. This can sometimes produce poor results. If off, no mask is used and produces empirically minor differences in results than when a mask is used. If this option is on, the contour of this mask is drawn in the PDF.\u003c/p\u003e\n\u003cp\u003eDefault = on\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--eddy_bval_scale N/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRun \u003ccode\u003eeddy\u003c/code\u003e with b-values scaled by the positive number N. All other steps of the pipeline use the original b-values. This can help \u003ccode\u003eeddy\u003c/code\u003e finish distortion correction when extremely low b-values (\u0026lt;200) are involved. If off, no scaling of b-values is used.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--extra_eddy_args=string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtra arguments to pass to FSL\u2019s \u003ccode\u003eeddy\u003c/code\u003e. \u003ccode\u003eEddy\u003c/code\u003e will always run with the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e--repol\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that if \u003ccode\u003e--mporder\u003c/code\u003e is passed here, \u003ccode\u003e--eddy_cuda\u003c/code\u003e must be 8.0 or 9.1 and the singularity option \u003ccode\u003e--nv\u003c/code\u003e must be passed into the container, as intra-volume slice-wise motion correction requires GPU acceleration.\u003c/p\u003e\n\u003cp\u003eThese parameters should be formatted as a list separated by +\u0027s with no spaces (i.e., \u003ccode\u003e--extra_eddy_args=--data_is_shelled+--ol_nstd=1\u003c/code\u003e). For \u003ccode\u003eeddy\u003c/code\u003e options that require additional inputs, place the file in the inputs directory and use the following syntax: \u003ccode\u003e--\u0026lt;myinputoption\u0026gt;=/INPUTS/\u0026lt;file.ext\u0026gt;\u003c/code\u003e. For \u003ccode\u003eeddy\u003c/code\u003e options that produce additional outputs, the file will save in the output directory under the \u201cEDDY\u201d folder by using the following syntax: \u003ccode\u003e--\u0026lt;myoutputoption\u0026gt;=/OUTPUTS/EDDY/\u0026lt;file.ext\u0026gt;\u003c/code\u003e. Note that in this case \u003ccode\u003e/INPUTS\u003c/code\u003e and \u003ccode\u003e/OUTPUTS\u003c/code\u003e should be named exactly as is and are NOT the path to the input and output directory on your file system.\u003c/p\u003e\n\u003cp\u003eDefault = none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--postnormalize on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntensity normalize images after preprocessing by maximizing the intra-mask intensity-histogram intersections between the averaged b0s of the scans. If this option is on, these histograms before and after postnormalization will be reported in the output PDF.\u003c/p\u003e\n\u003cp\u003eNote: This option was intended for testing and is left for posterity. It is not recommended at this time and will be deprecated.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--correct_bias on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform \u003ca href=\"https://manpages.debian.org/testing/ants/N4BiasFieldCorrection.1.en.html\" rel=\"nofollow\"\u003eANTs N4 bias field correction\u003c/a\u003e as \u003ca href=\"https://mrtrix.readthedocs.io/en/latest/reference/commands/dwibiascorrect.html\" rel=\"nofollow\"\u003ecalled in MRTrix3\u003c/a\u003e. If this option is on, the calculated bias field will be visualized in the output PDF.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--correct_grad on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform gradient nonlinearity correction. First, corrected voxelwise b-table is calculated as in [\u003ca href=\"https://github.com/baxpr/gradtensor\"\u003ehttps://github.com/baxpr/gradtensor\u003c/a\u003e]. These results are used to compute the corrected diffusion weighted signal. If this option is on, the determinant nonlinear gradient field will be visualized in the output PDF.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--improbable_mask on/off\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreate an additional mask on the preprocessed data that omits voxels where the minimum b0 signal is smaller than the minimum diffusion weighted signal. This can be helpful for reducing artifacts near the mask border when fitting models.\u003c/p\u003e\n\u003cp\u003eDefault = off\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--glyph_type tensor/vector\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisualize either tensors or principal eigenvectors in the QA document.\u003c/p\u003e\n\u003cp\u003eDefault = tensor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--atlas_reg_type FA/b0\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform JHU white matter atlas registration to the subject by either deformably registering the subject\u0027s FA map or average b0 to the MNI FA or T2 template, respectively. Empirically, the FA approach tends to be more accurate for white matter whereas the b0 approach tends to be more accurate globally. The b0 approach is more robust for acquisitions with low shells (i.e., b \u0026lt; 500 s/mm\u003csup\u003e2\u003c/sup\u003e) or poor masking that result in the inclusion of a lot of facial structure.\u003c/p\u003e\n\u003cp\u003eDefault = FA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--split_outputs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSplit the fully preprocessed output (a concatenation of the input images) back into their component parts and do NOT keep the concatenated preprocessed output.\u003c/p\u003e\n\u003cp\u003eDefault = Do NOT split and return only the concatenated output\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--keep_intermediates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKeep intermediate copies of diffusion data (i.e. denoised, prenormalized, bias-corrected, etc.) used to generate final preprocessed data. Using this flag may result in a large consumption of hard disk space.\u003c/p\u003e\n\u003cp\u003eNote: Due to space concerns, special permission needed to use this option on XNAT.\u003c/p\u003e\n\u003cp\u003eDefault = do NOT keep intermediates\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--num_threads N\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA positive integer indicating the number of threads to use when running portions of the pipeline that can be multithreaded (i.e. MRTrix3, ANTs, and FSL\u2019s eddy without GPU acceleration). Please note that at the time of writing, \u003cstrong\u003eFSL\u0027s topup cannot be parallelized\u003c/strong\u003e, and that the runtime of topup can increase dramatically as more b0 volumes are included. See \u003ccode\u003e--topup_first_b0s_only\u003c/code\u003e for more information.\u003c/p\u003e\n\u003cp\u003eNote: Due to resource concerns, special permission needed to multi-thread on XNAT.\u003c/p\u003e\n\u003cp\u003eDefault = 1 (do NOT multithread)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--project string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eString describing project in which the input data belong to label PDF output\u003c/p\u003e\n\u003cp\u003eDefault = proj\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--subject string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eString describing subject from which the input data were acquired to label PDF output\u003c/p\u003e\n\u003cp\u003eDefault = subj\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--session string\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eString describing session in which the input data were acquired to label PDF output\u003c/p\u003e\n\u003cp\u003eDefault = sess\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--help, -h\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline Assumptions\u003c/h2\u003e\u003ca id=\"user-content-pipeline-assumptions\" class=\"anchor\" aria-label=\"Permalink: Pipeline Assumptions\" href=\"#pipeline-assumptions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAll NIFTI images are consistent with a conversion from a DICOM using \u003ccode\u003edcm2niix\u003c/code\u003e (\u003ca href=\"https://github.com/rordenlab/dcm2niix/releases/tag/v1.0.20180622\"\u003eat least v1.0.20180622\u003c/a\u003e) by Chris Rorden and are raw NIFTIs without distortion correction. We require this as dcm2niix exports b-value/b-vector files in FSL format and removes ADC or trace images auto-generated in some Philips DICOMs. In addition \u003ccode\u003edcm2niix\u003c/code\u003e correctly moves the gradients from scanner to subject space and does not re-order volumes, both of which can cause spurious results or pipeline failure.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWe expect raw volumes only, no ADC or trace volumes.\u003c/strong\u003e ADC volumes are sometimes encoded as having a b-value greater than 0 with a corresponding b-vector of (0,0,0) and trace volumes are sometimes encoded as having a b-value of 0 with a corresponding non-unit normalized b-vector, as in the case of some Philips PARREC converters. We check for these cases, remove the affected volumes, and report a warning in the console and in the PDF.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe cannot, unfortunately, account for failure of reorientation of gradients into subject space. Visualization of tensor glyphs or principal eigenvectors can be helpful in distinguishing this. However, this error can be subtle so we suggest proper DICOM to NIFTI conversion with the above release of \u003ccode\u003edcm2niix\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eImages will be processed in the order they are listed in dtiQA_config.csv.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe size of all the volumes across all images must all be the same.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe location of b0 images inside the input images do not matter.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs per the FSL format, we do not support non-unit normalized gradients. We also do not support gradient directions of 0,0,0 when the corresponding b-value is non-zero. Gradients with the latter configurations may cause pipeline failure. We report warnings in the output PDF if we identify these.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe phase encoding axis of all volumes across all images is the same.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe phase encoding direction along the axis is the same for all volumes inside an image and is specified in the dtiQA_config.csv file.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUnless \u003ccode\u003e--prenormalize\u003c/code\u003e is on, we assume all input images have the same gain.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe will preferentially preprocess images with FSL\u2019s topup using available images with complementary phase encoding directions (i.e. + and -, \"reverse phase encodings\"). If none are available and a T1 is available, we will synthesize a susceptibility-corrected b0 from the first image listed in dtiQA_config.csv with Synb0-DisCo for use with topup, unless the user turns the \u003ccode\u003e--synb0\u003c/code\u003e parameter off. The readout time of this synthetic b0 will be zero and the phase encoding direction will be equal to that of the first image in dtiQA_config.csv. Otherwise, we will preprocess without topup and move straight to FSL\u2019s eddy.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe use topup and eddy for preprocessing, both of which at the present moment do NOT officially support DSI acquisitions but only single- and multi-shell. We will force topup and eddy to run on DSI data, but may not produce quality results. Please carefully check the PDF output as we report a warning if eddy detected non-shelled data and thus required the use of the force flag.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNote that eddy may erroneously detect data as non-shelled if there are fewer directions in one of the shells than others. Because we merge the images for preprocessing, a notable example of this is when a reverse-phase encoded image uses a different shell than the forward images and has significantly fewer directions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor preprocessing, eddy will motion correct to the first b0 of each image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNIFTI files inherently have three transformations in the header: the sform, qform, and the fall-back. Different software prefer to use different transformations. We follow the \u003ca href=\"https://nipy.org/nibabel/nifti_images.html#choosing-the-image-affine\" rel=\"nofollow\"\u003eNibabel standard\u003c/a\u003e (sform \u0026gt; qform \u0026gt; fall-back). To explicitly ensure this, we check all NIFTI inputs to determine their optimal affines as captured by Nibabel, then resave all inputs placing the optimal affines in both the sform (code = 2) and qform (code = 0) fields. Additionally, if the optimal affines are not the sform, we report warnings on the output PDF.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo b0 drift correction is performed.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe use the fit tensor model primarily for QA. If b-values less than 500 s/mm\u003csup\u003e2\u003c/sup\u003e or greater than 1500 s/mm\u003csup\u003e2\u003c/sup\u003e are present, we suggest careful review of the fit prior to use for non-QA purposes.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline Processing Steps\u003c/h2\u003e\u003ca id=\"user-content-pipeline-processing-steps\" class=\"anchor\" aria-label=\"Permalink: Pipeline Processing Steps\" href=\"#pipeline-processing-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThreshold all b-values such that values less than the \u003ccode\u003e--bval_threshold\u003c/code\u003e parameter are 0.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCheck that all b-vectors are unit normalized and all b-values greater than zero have associated non-zero b-vectors. For any volumes where this is not the case, we remove them, flag a warning for the output PDF, and continue the pipeline.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf applicable, denoise all diffusion scans with \u003ccode\u003edwidenoise\u003c/code\u003e (Marchenko-Pastur PCA) from MrTrix3 and save the noise profiles (needed for Rician correction later).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf applicable, perform Gibbs de-ringing on all diffusion scans with \u003ccode\u003emrdegibbs\u003c/code\u003e from MRTrix3.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf applicable, perform Rician correction on all diffusion scans with the method of moments.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf applicable, prenormalize all diffusion scans. To accomplish this, extract all b0 images from each diffusion scan and average them. Then find a rough brain-mask with FSL\u2019s bet and calculate an intensity scale factor such that the histogram intersection between the intra-mask histogram of the different scans\u2019 averaged b0s to that of the first scan is maximized. Apply this scale factor to the entire diffusion weighted scan. This is done to avoid gain differences between different diffusion scans.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIf prenormalization is not indicated, we still run the prenormalization algorithms to calculate rough gain differences and report the gain factors and intensity histograms in a gain QA page. The outputs of the algorithms, however, are NOT propagated through to the rest of the pipeline.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePrepare data for and run preprocessing with topup and eddy\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTopup:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eExtract all b0s from all scans, maintaining their relative order.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Optional) If a T1 is supplied and no complementary (i.e. reverse) phase encoded images are provided, use Synb0-DisCo to convert the first b0 of the first scan to a susceptibility-corrected b0.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuild the acquisition parameters file required by both topup and eddy\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFor the number of b0s from each image, add the same phase encoding and readout time line to the acquisition parameters file, as outlined in \"Example Phase Encoding Schemes\".\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eExample: In the case where we have a phase encoding axis of j and two images, one with 7 b0s, + direction, and 0.05 readout time and one with 3 b0s, - direction, and 0.02 readout time, this file will have 10 lines. The first 7 lines are identical and equal to [0, 1, 0, 0.05]. The last three lines are also identical and equal to [0, -1, 0, 0.02].\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e(Optional) If Synb0-DisCo is run because no complementary phase encoding directions are supplied and --synb0 is not off, we add an additional line to the end of the file. This line is the same as the first line of the file except that the readout time is 0 instead.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eExample: In the case where we have a phase encoding axis of j and two images, one with 7 b0s, + direction, and 0.05 readout time and one with 3 b0s, + direction, and 0.02 readout time, this file will have 11 lines. The first 7 lines are identical and equal to [0, 1, 0, 0.05]. The next three lines are also identical and equal to [0, 1, 0, 0.02]. Finally, the last line is equal to [0, 1, 0, 0].\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then concatenate all the b0s maintaining their order and run topup with the acquisition parameters file if images with complementary phase encoding directions are supplied or if a T1 was supplied. Otherwise, we move on to the next step, eddy.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEddy\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eUsing the acquisition parameters file from the topup step, regardless of whether topup was performed, we build the eddy index file such that each volume in each image corresponds to the line in the acquisition parameters file associated with the first b0 of each scan. This is done to tell eddy that each volume in a given scan has the same underlying phase encoding scheme as the first b0 of that scan.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eExample: In the case where we have two images, one with 7 b0s and 100 total volumes and one with 3 b0s and 10 total volumes, the eddy index file has 100 1\u2019s followed by 10 8\u2019s.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEddy is then run with either a mask generated with bet and the -f 0.25 and -R options or without a mask (aka with a mask of all 1\u2019s), depending on user input (see the --eddy_mask option) and with the output of topup if topup was run. Eddy also runs with the --repol option for outlier slice replacement. We also first run eddy with a check looking for shelled data. If the check fails, eddy is then run with the --data_is_shelled flag to force eddy to run on all scans, DSI included. Note that DSI data is not officially supported by FSL\u2026 yet?\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eIf eddy detects data is not shelled, we report this as a warning\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs noted in the assumptions section above, eddy may erroneously detect data as non-shelled if there are fewer directions in one of the shells than others. Because we merge the images for preprocessing, a notable example of this is when a reverse-phase encoded image uses a different shell than the forward images and has significantly fewer directions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEddy also performs bvec rotation correction and calculates the voxel-wise signal-to-noise ratios of the b0 images and the voxel-wise contrast-to-noise ratios for the diffusion weighted images. SNR is defined as the mean value divided by the standard deviation. CNR is defined as the standard deviation of the Gaussian Process predictions (GP) divided by the standard deviation of the residuals between the measured data and the GP predictions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf the user chooses to, we then perform post-normalization in the same fashion as pre-normalization.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf the user chooses to, we then wrap up preprocessing with an N4 bias field correction as implemented in ANTs via MRTrix3\u2019s dwibiascorrect.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe generate a brain mask using FSL\u2019s bet2 with the following options. If applicable, we omit the voxels where the minimum b0 signal is less than the minimum diffusion weighted signal in an additional \"improbable mask\".\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003e-f 0.25 -R\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf the user chooses to, we then perform gradient nonliear field correction by first calculating the voxel-wise b-table and then corrected diffusion weighted signal.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then apply the mask to the preprocessed images while we calculate tensors using MRTrix3\u2019s dwi2tensor function. For visualization we discard tensors that have diagonal elements greater than 3 times the apparent diffusion coefficient of water at 37\u00b0C (~0.01).\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eWe also reconstruct the preprocessed image from the tensor fit for further analysis later. dwi2tensor does this for us.\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then convert the tensor to FA and MD images (and visualize them later too) as well as AD, RD, and V1 eigenvector images for the user. The latter 3 are not visualized.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePipeline Quality Assurance Steps\u003c/h2\u003e\u003ca id=\"user-content-pipeline-quality-assurance-steps\" class=\"anchor\" aria-label=\"Permalink: Pipeline Quality Assurance Steps\" href=\"#pipeline-quality-assurance-steps\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eWe start with the brain mask generated above to generate a mask used for the following quantification of tensor fit using a chi-squared statistic.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFirst, we calculate the mean image for each unique b-value (0 not included). Then we run FSL\u2019s FAST to isolate the CSF on each meaned image. We then take the average probability of a voxel being CSF across all unique b-values and assign \u0026gt;15% probability to be a positive CSF voxel.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThen we call the final chi-squared mask to be the intersection of the inverted CSF mask and a 1-pixel eroded version of the brain mask.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn the voxels inside the chi-squared mask, we perform the following quality assurance:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eWe perform a chi-squared analysis for each slice for each volume in the main image by calculating the ratio between the sum-squared error of the fit and the sum-squared intensities of the slice.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe extract the average FA for a number of white matter ROIs defined by the Hopkins atlas. We do this by non-rigidly registering the atlas to our FA output and extracting the FA values contained in each ROI.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe check the gradients output by eddy (i.e. the preprocessed gradients) with \u003ca href=\"https://mrtrix.readthedocs.io/en/3.0.0/reference/commands/dwigradcheck.html\" rel=\"nofollow\"\u003edwigradcheck from MRTrix3\u003c/a\u003e. This performs tractography and finds the optimal sign and order permutation of the b-vectors such that the average tract length in the brain is most physiological.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThese optimized gradients are saved in the OPTIMIZED_BVECS output folder, and the gradients output by eddy in the PREPROCESSED folder are NOT overwritten.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe original, preprocessed, and preprocessed + optimized gradients are visualized as outlined below.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize the entire pipeline.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOn the first page we describe the methods used for that run of the pipeline (what inputs were provided, what sort of preprocessing happened, etc.).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize the raw images with the interpreted phase encoding schemes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf Gibbs de-ringing was run, we visualize central slices of the averaged residuals across b0 volumes before and after Gibbs de-ringing, looking for large residuals near high contrast interfaces (i.e. parenchyma against CSF)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf Rician correction was performed, we visualize the within brain intensity distributions of each shell of each image before and after correction, looking for downward shifts after correction.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf Synb0-DisCo was run, we then visualize the distorted b0 (first b0 of first scan) and T1 used as inputs as well as the output susceptibility corrected b0 in their native space.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf pre- or post-normalization was performed, we then visualize the intra-mask histograms before and after these steps as well as the calculated scaling factors. If pre-normalization is not performed, we visualize the histograms that would have been generated with pre-normalization ONLY as a check for gain differences.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize the first b0 of the images before and after preprocessing with the contours of the brain and stats masks overlaid as well as the contours of the eddy mask overlaid if it is used. We also report the percent of \"improbable voxels\" in the preprocessed mask, regardless of whether the improbable mask is saved.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe plot the motion and angle correction done by eddy as well as the RMS displacement and median intensity for each volume and the volume\u2019s associated b-value. These values are read in from an eddy output text file and we also compute and save the average of these values. In addition, we plot the outlier slices removed and then imputed by eddy as well as the chi-squared fit, with maximal bounds 0 to 0.2. The median chi-squared values are shown across volumes and slices.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then plot the original raw b-vectors scaled by their b-values, the preprocessed ones output by eddy, and the optimized ones determined by \u003ccode\u003edwigradcheck\u003c/code\u003e applied to the preprocessed ones.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf bias field correction was performed, we then visualize the calculated fields.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf bias field correction was performed, we then visualize the calculated image and gradient fields.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize some central slices of the average volumes for all unique b-values, including b = 0 and report the median intra-mask SNR or CNR calculated by eddy as appropriate. If there are more unique b-values than shells deteremined by eddy, we round the b-values to the nearest 100 by default to assign volumes to shells or we choose the nearest shell indicated by the user (see \u003ccode\u003e--nonzero_shells\u003c/code\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe visualize the tensors (or principal eigenvectors depending on \u003ccode\u003e--glyph_type\u003c/code\u003e) using MRTrix3\u2019s mrview, omitting the tensors with negative eigenvalues or eigenvalues greater than 3 times the ADC of water at 37\u00b0C.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe then visualize some central slices of the FA map clipped from 0 to 1 as well as the average FA for the Hopkins ROIs and the quality of the atlas registration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLastly, we visualize some central slices of the MD map clipped from 0 to 0.003 (ADC of water at 37\u00b0C).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u0026lt;imageN_%\u0026gt; denotes the original prefix of imageN with the preceding preprocessing step descriptors tacked on the end. For example, in the case of the PRENORMALIZED directory, the prefix for imageJ may or may not include \"_denoised\" depending on whether the denoising step was run.\u003c/p\u003e\n\u003cp\u003eFolders and files in \u003cstrong\u003ebold\u003c/strong\u003e are always included.\u003c/p\u003e\n\u003cp\u003eFolders and files in \u003cem\u003eitalics\u003c/em\u003e are removed if \u003ccode\u003e--keep_intermediates\u003c/code\u003e is NOT indicated\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTHRESHOLDED_BVALS\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;image1\u0026gt;.bval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;imageN\u0026gt;.bval\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eCHECKED\u003c/em\u003e (these contain the volumes that have passed the bval/bvec checks)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1\u0026gt;_checked.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1\u0026gt;_checked.bval\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1\u0026gt;_checked.bvec\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN\u0026gt;_checked.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN\u0026gt;_checked.bval\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN\u0026gt;_checked.bvec\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eDENOISED\u003c/em\u003e (these files are only created if \u003ccode\u003e--denoise\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_denoised.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_noise.nii.gz\u003c/em\u003e (needed for Rician correction)\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_denoised.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_noise.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eDEGIBBS\u003c/em\u003e (these files are only created if \u003ccode\u003e--degibbs\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_degibbs.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_degibbs.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eRICIAN\u003c/em\u003e (these files are only created if \u003ccode\u003e--rician\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_rician.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_rician.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003ePRENORMALIZED\u003c/em\u003e (these files are only created if \u003ccode\u003e--prenormalize\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eGAIN_CHECK\u003c/em\u003e (these files are only created if \u003ccode\u003e--prenormalize\u003c/code\u003e is off)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTOPUP\u003c/strong\u003e (these files are only created if \u003ccode\u003etopup\u003c/code\u003e was run)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eacqparams.txt (same as OUTPUTS/EDDY/acqparams.txt)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_b0_first.nii.gz\u003c/em\u003e (only if Synb0-DisCo is run)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eb0_syn.nii.gz (only if Synb0-DisCo is run)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_b0_all.nii.gz\u003c/em\u003e or \u003cem\u003epreproc_input_b0_all_smooth_with_b0_syn.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_b0_all_topped_up.nii.gz\u003c/em\u003e or \u003cem\u003epreproc_input_b0_all_smooth_with_b0_syn_topped_up.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epreproc_input_b0_all.topup_log or preproc_input_b0_all_smooth_with_b0_syn.topup_log\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etopup_field.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etopup_results_fieldcoef.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etopup_results_movpar.txt\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEDDY\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eacqparams.txt\u003c/strong\u003e (same as OUTPUTS/TOPUP/acqparams.txt)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eindex.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input.bval\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input.bvec\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_eddyed.nii.gz\u003c/em\u003e (renamed from \"eddy_results.nii.gz\")\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_eddyed.bval\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003epreproc_input_eddyed.bvec\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eeddy_mask.nii.gz (only included if \u003ccode\u003e--eddy_mask\u003c/code\u003e is on)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_command_txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_movement_rms\u003c/strong\u003e (describes volume-wise RMS displacement)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_free_data.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_map\u003c/strong\u003e (describes which slices were deemed outliers)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_n_sqr_stdev_map\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_n_stdev_map\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_outlier_report\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_parameters\u003c/strong\u003e (describes volume-wise rotation and translation)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_post_eddy_shell_alignment_parameters\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_post_eddy_shell_PE_translation_parameters\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_restricted_movement_rms\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_rotated_bvecs (describes properly rotated b-vectors)\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_values_of_all_input_parameters\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_results.eddy_cnr_maps.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003ePOSTNORMALIZED\u003c/em\u003e (these files are only created if \u003ccode\u003e--postnormalize\u003c/code\u003e is on)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;image1_%\u0026gt;_topup_eddy_norm.nii.gz\u003c/em\u003e (\"_topup\" only applies if topup was run)\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003e\u0026lt;imageN_%\u0026gt;_topup_eddy_norm.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eUNBIASED\u003c/em\u003e (these files are only created if \u003ccode\u003e--correct_bias\u003c/code\u003e is on; this folder is removed if \u003ccode\u003e--correct_bias\u003c/code\u003e is off)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003enormed_unbiased.nii.gz\u003c/em\u003e (if postnormalization is run) or \u003cem\u003epreproc_input_eddyed_unbiased.nii.gz\u003c/em\u003e (if postnormalization is not run)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ebias_field.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eUNBIASED\u003c/em\u003e (these files are only created if \u003ccode\u003e--correct_grad\u003c/code\u003e is on; this folder is removed if \u003ccode\u003e--correct_grad\u003c/code\u003e is off)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ecorrected_sig.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egradtensor_fa.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eL_resamp.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eorg.bval\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eorg.bvec\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;bval_1\u0026gt;.nii.gz\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;bval_N\u0026gt;.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;bvec_1\u0026gt;.nii.gz\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;bvec_N\u0026gt;.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePREPROCESSED\u003c/strong\u003e (these represent the final output of the pipeline)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003edwmri.nii.gz\u003c/em\u003e (dwmri* files deleted only if \u003ccode\u003e--split_outputs\u003c/code\u003e is also set)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003edwmri.bval\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003edwmri.bvec\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;image1\u0026gt;_preproc.nii.gz (*_preproc files exist only if \u003ccode\u003e--split_outputs\u003c/code\u003e is set)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;image1\u0026gt;_preproc.bval\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;image1\u0026gt;_preproc.bvec\u003c/p\u003e\n\u003cp\u003e:\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;imageN\u0026gt;_preproc.nii.gz\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;imageN\u0026gt;_preproc.bval\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u0026lt;imageN\u0026gt;_preproc.bvec\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003emask.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eimprobable_mask.nii.gz (only included if \u003ccode\u003e--improbable_mask\u003c/code\u003e is on)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTENSOR\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri_tensor.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003edwmri_recon.nii.gz\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSCALARS\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri_tensor_fa.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri_tensor_md.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri_tensor_ad.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri_tensor_rd.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri_tensor_v1.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSTATS\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eatlas2subj.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eb02template_0GenericAffine.mat\u003c/strong\u003e or \u003cstrong\u003efa2template_0GenericAffine.mat\u003c/strong\u003e depending on \u003ccode\u003e--atlas_reg_type\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eb02template_1Warp.nii.gz\u003c/strong\u003e or \u003cstrong\u003efa2template_1Warp.nii.gz\u003c/strong\u003e depending on \u003ccode\u003e--atlas_reg_type\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eb02template_1InverseWarp.nii.gz\u003c/strong\u003e or \u003cstrong\u003efa2template_1InverseWarp.nii.gz\u003c/strong\u003e depending on \u003ccode\u003e--atlas_reg_type\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003echisq_mask.nii.gz\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003echisq_matrix.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_avg_abs_displacement.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_median_cnr.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_avg_rel_displacement.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_avg_rotations.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eeddy_avg_translations.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eroi_avg_fa.txt\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003estats.csv\u003c/strong\u003e (contains summary of all motion, SNR/CNR, and average FA stats)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eOPTIMIZED_BVECS\u003c/strong\u003e (these are sign/axis permuted per \u003ccode\u003edwigradcheck\u003c/code\u003e and are only used for QA purposes)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri.bval\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003edwmri.bvec\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePDF\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003edtiQA.pdf\u003c/strong\u003e (final QA document)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNote on Versioning for VUIIS XNAT Users\u003c/h2\u003e\u003ca id=\"user-content-note-on-versioning-for-vuiis-xnat-users\" class=\"anchor\" aria-label=\"Permalink: Note on Versioning for VUIIS XNAT Users\" href=\"#note-on-versioning-for-vuiis-xnat-users\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePreQual was developed at Vanderbilt under the project name \"dtiQA v7 Multi\". PreQual v1.0.0 represents dtiQA v7.2.0. Thus, on XNAT, dtiQA v7.2.x refers to PreQual v1.0.x.\u003c/p\u003e\n", + "stargazers_count": 45, + "subscribers_count": 1, + "topics": [ + "diffusion", + "mri", + "preprocessing", + "quality", + "assurance" + ], + "updated_at": 1732140542.0 }, { "data_format": 2, @@ -41105,6 +41103,36 @@ var data = ], "updated_at": 1706609974.0 }, + { + "data_format": 2, + "description": "Computational workflows for metagenomics tasks, by the Bhatt lab", + "filenames": [ + "singularity/Singularity.example_recipe", + "singularity/Singularity.align", + "singularity/Singularity.plotting" + ], + "full_name": "bhattlab/bhattlab_workflows", + "latest_release": "v1.01", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eBhattlab workflows\u003c/h1\u003e\u003ca id=\"user-content-bhattlab-workflows\" class=\"anchor\" aria-label=\"Permalink: Bhattlab workflows\" href=\"#bhattlab-workflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputational workflows for metagenomics tasks, packaged with Snakemake, Singularity and Conda.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTable of contents\u003c/h3\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"manual/setup.md\"\u003eSetup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/running.md\"\u003eRunning a workflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eAvailable workflows\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"manual/preprocessing.md\"\u003e\u003cstrong\u003ePreprocessing\u003c/strong\u003e metagenomic data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/assembly.md\"\u003eMetagenomic \u003cstrong\u003eAssembly\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/binning.md\"\u003eMetagenomic \u003cstrong\u003eBinning\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/dRep.md\"\u003e\u003cstrong\u003eDeReplication\u003c/strong\u003e of binned genomes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/inStrain.md\"\u003e\u003cstrong\u003einStrain\u003c/strong\u003e strain-diversity aware comparison of samples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bhattlab/kraken2_classification\"\u003eMetagenomic classification with \u003cstrong\u003eKraken2\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/sourmash.md\"\u003e\u003cstrong\u003eSourmash\u003c/strong\u003e read comparison\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/download_sra.md\"\u003e\u003cstrong\u003eDownload SRA\u003c/strong\u003e data\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/arg.md\"\u003e\u003cstrong\u003eARG detection\u003c/strong\u003e with RGI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/viral.md\"\u003e\u003cstrong\u003eViral\u003c/strong\u003e contig prediction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"manual/comparative_genomics.md\"\u003eComparative microbial genomics pipelines\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"metariboseq/README.md\"\u003eMetaRibo-Seq\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eQuickstart\u003c/h3\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you\u0027re in the Bhatt lab and working on SCG, this command is an example of how to run the workflows. Other users will need to change these options (see \u003ca href=\"manual/running.md\"\u003eRunning a workflow\u003c/a\u003e)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --configfile config_preprocessing.yaml \\\n--snakefile ~/projects/bhattlab_workflows/preprocessing/preprocessing.snakefile \\\n--profile scg --jobs 100 --use-singularity \\\n--singularity-args \u0027--bind /labs/,/oak/,/home/\u0027\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eImportant\u003c/strong\u003e after running the preprocessing or assembly pipelines, run the cleanup rule in the snakefile. This will delete all the unnecessary files and save us space on SCG!\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake cleanup -s PATH/TO/SNAKEFILE --configfile YOUR_CONFIGFILE.yaml\n\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou can also remove the \u003ccode\u003e.snakemake\u003c/code\u003e folder, which only contains metadata and cache from the run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003erm -rf .snakemake \n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 46, + "subscribers_count": 11, + "topics": [], + "updated_at": 1721049025.0 + }, + { + "data_format": 2, + "description": "Bayesian co-estimation of phylogenies and multiple alignments via MCMC", + "filenames": [ + "Singularity" + ], + "full_name": "bredelings/BAli-Phy", + "latest_release": "3.6.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/bredelings/BAli-Phy/workflows/Build%20and%20test/badge.svg\"\u003e\u003cimg src=\"https://github.com/bredelings/BAli-Phy/workflows/Build%20and%20test/badge.svg\" alt=\"Build and test\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you just want to install bali-phy, please visit the \u003ca href=\"http://www.bali-phy.org/download.php\" rel=\"nofollow\"\u003erelease page\u003c/a\u003e. If you want to compile BAli-phy from source, the quick start instructions are below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompiling\u003c/h2\u003e\u003ca id=\"user-content-compiling\" class=\"anchor\" aria-label=\"Permalink: Compiling\" href=\"#compiling\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need a C++ compiler that understands C++20.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003egcc 11 (or higher) works\u003c/li\u003e\n\u003cli\u003eclang 14 (or higher) works\u003c/li\u003e\n\u003cli\u003eXCode 14 (or higher) works\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstall Prerequisites\u003c/h2\u003e\u003ca id=\"user-content-install-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Install Prerequisites\" href=\"#install-prerequisites\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOn Ubuntu 24.04 (or higher), or on Debian testing, you can use apt-get:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo apt-get install g++ libcairo2-dev meson libboost-all-dev\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn Mac (or Linux, actually) you can use homebrew:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew install cairo meson boost pkg-config\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eOn miniconda, you can use:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda create -n devel -c conda-forge --strict-channel-priority\nconda activate devel\nconda install meson gxx boost-cpp cmake pkg-config cairo\nexport BOOST_ROOT=$CONDA_PREFIX\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMeson version\u003c/h3\u003e\u003ca id=\"user-content-meson-version\" class=\"anchor\" aria-label=\"Permalink: Meson version\" href=\"#meson-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter installing prerequisites above, check that the meson version is at least 1.1.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emeson --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf your version of meson is less than 1.1, you need to install the most recent version of meson into a python virtual environment:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython3 -m venv meson_venv\nsource meson_venv/bin/activate\npip install meson ninja\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuild BAli-Phy\u003c/h2\u003e\u003ca id=\"user-content-build-bali-phy\" class=\"anchor\" aria-label=\"Permalink: Build BAli-Phy\" href=\"#build-bali-phy\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis will build the latest unreleased beta version of BAli-Phy, which fixes some memory issues in 3.6. There are some changes to the model language. Check the NEWS file for the details.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/bredelings/BAli-Phy.git\ncd BAli-Phy\nmeson setup build --prefix=$HOME/Applications/bali-phy\nninja -C build install\nninja -C build test\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAdding bali-phy to your \u003ccode\u003e$PATH\u003c/code\u003e\n\u003c/h2\u003e\u003ca id=\"user-content-adding-bali-phy-to-your-path\" class=\"anchor\" aria-label=\"Permalink: Adding bali-phy to your $PATH\" href=\"#adding-bali-phy-to-your-path\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run the installed software, you should \u003ca href=\"http://bali-phy.org/README.xhtml#path\" rel=\"nofollow\"\u003eadd bali-phy to your $PATH\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstalled locations\u003c/h2\u003e\u003ca id=\"user-content-installed-locations\" class=\"anchor\" aria-label=\"Permalink: Installed locations\" href=\"#installed-locations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you installed in \u003ccode\u003e$HOME/Applications/bali-phy/\u003c/code\u003e as recommended above, then files will be in:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eLocation\u003c/th\u003e\n\u003cth\u003eFiles\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e~/Applications/bali-phy/bin\u003c/td\u003e\n\u003ctd\u003eBinary executables.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e~/Applications/bali-phy/share/bali-phy/examples/sequences\u003c/td\u003e\n\u003ctd\u003eExample files.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e~/Applications/bali-phy/share/doc/bali-phy/\u003c/td\u003e\n\u003ctd\u003eDocumentation.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFurther Documentation\u003c/h2\u003e\u003ca id=\"user-content-further-documentation\" class=\"anchor\" aria-label=\"Permalink: Further Documentation\" href=\"#further-documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://bali-phy.org/\" rel=\"nofollow\"\u003ehttp://bali-phy.org/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://bali-phy.org/README.xhtml\" rel=\"nofollow\"\u003eManual\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://bali-phy.org/Tutorial4.html\" rel=\"nofollow\"\u003eTutorial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Manual describes \u003ca href=\"http://bali-phy.org/README.xhtml#installation\" rel=\"nofollow\"\u003ehow to install\u003c/a\u003e bali-phy in greater detail.\u003c/p\u003e\n", + "stargazers_count": 46, + "subscribers_count": 6, + "topics": [], + "updated_at": 1731801196.0 + }, { "data_format": 2, "description": "A setup for automatic generation of shareable, version-controlled BIDS datasets from MR scanners", @@ -41161,20 +41189,28 @@ var data = }, { "data_format": 2, - "description": "Finite Element Multiphysics PARallel solvers. Official mirror from https://gitlab.com/fempar/fempar", + "description": "Multi-sample somatic variant caller", "filenames": [ - "Containers/Singularity/Singularity.gnu-release_p4est-parallel", - "Containers/Singularity/Singularity.gnu-debug_p4est-serial", - "Containers/Singularity/Singularity.gnu-debug_p4est-parallel", - "Containers/Singularity/Singularity.gnu-release_p4est-serial" + "Singularity" ], - "full_name": "fempar/fempar", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFEMPAR\u003c/h1\u003e\u003ca id=\"user-content-fempar\" class=\"anchor\" aria-label=\"Permalink: FEMPAR\" href=\"#fempar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eFinite Element Multiphysics PARallel solvers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://gitlab.com/fempar/fempar/commits/experimental\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9cafa391ff15a4005e434db9f678594cb7b1f0c6cab27930d401e6d9a8df5dd9/68747470733a2f2f6769746c61622e636f6d2f66656d7061722f66656d7061722f6261646765732f6578706572696d656e74616c2f6275696c642e737667\" alt=\"build status\" data-canonical-src=\"https://gitlab.com/fempar/fempar/badges/experimental/build.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitlab.com/fempar/fempar/commits/experimental\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d8043c7775be04e352875590b5f1f8a3ba1ff2abb486d387cd32b3832440b668/68747470733a2f2f6769746c61622e636f6d2f66656d7061722f66656d7061722f6261646765732f6578706572696d656e74616c2f636f7665726167652e737667\" alt=\"coverage report\" data-canonical-src=\"https://gitlab.com/fempar/fempar/badges/experimental/coverage.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFEMPAR\u003c/strong\u003e is a scientific software for the simulation of problems governed by partial differential equations (PDEs). It provides a set of state-of-the-art numerical discretizations, including finite element methods, discontinuous Galerkin methods, XFEM, and spline-based functional spaces. The library was originally designed to efficiently exploit distributed-memory supercomputers and easily handle multiphysics problems. It also provides a set of highly scalable numerical linear algebra solvers based on multilevel domain decomposition for the systems of equations that arise from PDE discretizations. Some applications of FEMPAR include the simulation of metal additive manufacturing processes, superconductor devices, breeding blankets in fusion reactors, and nuclear waste repositories.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSoftware design\u003c/h2\u003e\u003ca id=\"user-content-software-design\" class=\"anchor\" aria-label=\"Permalink: Software design\" href=\"#software-design\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor those who are interested on the design and rationale behind the mathematically-supported software abstractions in FEMPAR, a very through presentation is available at the following reference:\u003c/p\u003e\n\u003cp\u003eSantiago Badia, Alberto F. Mart\u00edn and Javier Principe. \u003cbr\u003e\nFEMPAR: An object-oriented parallel finite element framework. \u003cbr\u003e\n\u003cem\u003eArchives of Computational Methods in Engineering\u003c/em\u003e 25, 2 (2018), 195\u2013271. \u003cbr\u003e\n\u003ca href=\"https://arxiv.org/abs/1708.01773\" rel=\"nofollow\"\u003e[ArXiv link]\u003c/a\u003e \u003ca href=\"https://link.springer.com/article/10.1007%2Fs11831-017-9244-1\" rel=\"nofollow\"\u003e[DOI]\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial-Driven Introduction\u003c/h2\u003e\u003ca id=\"user-content-tutorial-driven-introduction\" class=\"anchor\" aria-label=\"Permalink: Tutorial-Driven Introduction\" href=\"#tutorial-driven-introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA very nice tutorial-driven introduction to the software library (v1.0.0) can be found at the following reference:\u003c/p\u003e\n\u003cp\u003eSantiago Badia, and Alberto F. Mart\u00edn. \u003cbr\u003e\nA tutorial-driven introduction to the parallel finite element library FEMPAR v1.0.0 \u003cbr\u003e\n\u003cem\u003eComputer Physics Communications\u003c/em\u003e Volume 248, March 2020, 107059. \u003cbr\u003e\n\u003ca href=\"https://arxiv.org/abs/1908.00891\" rel=\"nofollow\"\u003e[ArXiv link]\u003c/a\u003e \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S0010465519303832\" rel=\"nofollow\"\u003e[DOI]\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://www.fempar.org/\" rel=\"nofollow\"\u003eWeb page\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.gitlab.com/fempar/fempar\" rel=\"nofollow\"\u003eOfficial repository\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.github.com/fempar/fempar\"\u003eMirror repository\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cdash.cimne.upc.edu/user.php\" rel=\"nofollow\"\u003eTesting dashboard\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://fempar.gitlab.io/documentation/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://listas.cimne.upc.edu/mailman/listinfo/fempar-dev\" rel=\"nofollow\"\u003eDevelopers mailing list\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest and easiest way to start with FEMPAR is using \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e. \u003ca href=\"https://opensource.com/resources/what-docker\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e is a tool designed to make it easier to create, deploy, and run applications by using containers.\u003c/p\u003e\n\u003cp\u003eFEMPAR provides a Docker \u003ca href=\"https://hub.docker.com/u/fempar\" rel=\"nofollow\"\u003econtainer with the required environment\u003c/a\u003e to compile the project source code and to run tutorials and tests. Click on \u003ca href=\"https://gitlab.com/fempar/fempar/tree/experimental/Containers/Docker\" rel=\"nofollow\"\u003ethis link\u003c/a\u003e for additional details.\u003c/p\u003e\n\u003cp\u003ePlease, follow the steps below to compile FEMPAR using the Docker container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You must Sing up at https://hub.docker.com/ to get your_username_at_dockerhub\u003c/span\u003e\n$ sudo docker login --username=your_username_at_dockerhub\n$ sudo docker pull fempar/fempar-env:gnu-debug_p4est-serial \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Get Docker image from Docker Hub\u003c/span\u003e\n$ sudo docker run -ti fempar/fempar-env:gnu-debug_p4est-serial\n$ WORKDIR=/data\n$ SOURCES_DIR=\u003cspan class=\"pl-smi\"\u003e$WORKDIR\u003c/span\u003e/sources\n$ FEMPAR_DIR=\u003cspan class=\"pl-smi\"\u003e$WORKDIR\u003c/span\u003e/FEMPAR\n$ git clone --recursive https://github.com/fempar/fempar \u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$WORKDIR\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e invokes CMake while setting up appropriate values for CMake variables\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run $SOURCES_DIR/Tools/configure -h to get an informative message on screen\u003c/span\u003e\n$ \u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e/Tools/configure -s \u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e/SuperBuild -c GNU --without-tests \n$ make\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are new to FEMPAR, the very first point to start with are the \u003ca href=\"https://gitlab.com/fempar/fempar/tree/experimental/Tutorials\" rel=\"nofollow\"\u003etutorial programs\u003c/a\u003e available at the official FEMPAR repository.\nAfter completing the previous compilation steps, you can compile and run FEMPAR tutorials with the following steps:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e...\n$ FEMPAR_TUTORIALS_DIR=\u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e/Tutorials\n$ mkdir -p \u003cspan class=\"pl-smi\"\u003e$FEMPAR_TUTORIALS_DIR\u003c/span\u003e\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$FEMPAR_TUTORIALS_DIR\u003c/span\u003e\n$ cmake -DFEMPAR_DIR=\u003cspan class=\"pl-smi\"\u003e$FEMPAR_DIR\u003c/span\u003e -DFEMPAR_TUTORIAL=tutorial_01_poisson_sharp_circular_wave \u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e/Tutorials\n$ make -j 4\n$ bin/tutorial_01_poisson_sharp_circular_wave --help \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e get informative message on screen\u003c/span\u003e\n$ bin/tutorial_01_poisson_sharp_circular_wave [optional \u003cspan class=\"pl-c1\"\u003ecommand\u003c/span\u003e line arguments] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e execute the tutorial\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis particular set of commands compiles the tutorial program named \u003ccode\u003etutorial_01_poisson_sharp_circular_wave\u003c/code\u003e. You may use any of the tutorial names at \u003ccode\u003e$SOURCES_DIR/Tutorials\u003c/code\u003e as well.\nThe main source code folder of each tutorial is supplied with the \u003ccode\u003etutorial_invokation_examples\u003c/code\u003e file. This file contains command-line invokation examples of the corresponding tutorial, and thus is\na good starting point to start playing with the tutorial.\u003c/p\u003e\n\u003cp\u003eAt present, we only offer a reduced set of tutorial programs which show the usage of the most simple FEMPAR features. However, we plan in the near future to extend the current\ntutorial suite towards demonstration of the various aspects of the library.\u003c/p\u003e\n\u003cp\u003eIn the meantime, you can also take a look at the\n\u003ca href=\"https://gitlab.com/fempar/fempar/tree/experimental/Sources/Tests/Serial\" rel=\"nofollow\"\u003eserial\u003c/a\u003e and \u003ca href=\"https://gitlab.com/fempar/fempar/tree/experimental/Sources/Tests/Par\" rel=\"nofollow\"\u003eMPI-parallel test programs\u003c/a\u003e available at the official FEMPAR repository.\nWhile these programs go far beyond the current tutorial programs in exploiting many of the most advanced FEMPAR features, they are, though, not fully documented,\nso they are only recommended for advanced users. Test programs are compiled as a final stage of the compilation of FEMPAR. In order to activate the compilation of tests, you have to replace\n\u003ccode\u003e--without-tests\u003c/code\u003e by \u003ccode\u003e--with-tests\u003c/code\u003e in the steps above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNative compilation\u003c/h2\u003e\u003ca id=\"user-content-native-compilation\" class=\"anchor\" aria-label=\"Permalink: Native compilation\" href=\"#native-compilation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCompiler Vendor Support\u003c/th\u003e\n\u003cth\u003eNotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34ed91c2637295daf1750150794a2b4b7c7666aa82793ede055e9e8c4a8d6328/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f474e552d76382e312e302b2d627269676874677265656e2e737667\" alt=\"Compiler\" data-canonical-src=\"https://img.shields.io/badge/GNU-v8.1.0+-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFull support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e6c6a361fb7121abef590361a8cc3f343d017d24a45a226317c462608dd733ca/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f496e74656c2d7631392e302e302b2d627269676874677265656e2e737667\" alt=\"Compiler\" data-canonical-src=\"https://img.shields.io/badge/Intel-v19.0.0+-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFull support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFEMPAR\u003c/strong\u003e uses \u003ca href=\"https://cmake.org/\" rel=\"nofollow\"\u003eCMake\u003c/a\u003e as a portable compilation system.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBuild Managgers Support\u003c/th\u003e\n\u003cth\u003eNotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b292e19c3c7ec481687750c442ba6fb684e73fc517f8a88bfbd97bf0057051ce/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f434d616b652d76322e382e31312b2d79656c6c6f772e737667\" alt=\"Compiler\" data-canonical-src=\"https://img.shields.io/badge/CMake-v2.8.11+-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDoes not support \u003ccode\u003efortran_tester\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/408f81d2680e5a8ee6c9f00e2c9077962126c152dc4456ce041451a3b8646617/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f434d616b652d76332e312e302b2d627269676874677265656e2e737667\" alt=\"Compiler\" data-canonical-src=\"https://img.shields.io/badge/CMake-v3.1.0+-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFull support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNative compilation is only recommended for experienced users. It requires to set up (configure, compile, install, etc) in your own all the mandatory (and optional) software dependencies required to deploy FEMPAR in your Desktop/Laptop or HPC cluster computing environment. This approach is not fully documented here yet.\nIn the meantime, you may take a look at the \u003cstrong\u003eDockerfile recipes\u003c/strong\u003e (\u003ca href=\"https://gitlab.com/fempar/fempar/blob/experimental/Containers/Docker/gnu/Dockerfile\" rel=\"nofollow\"\u003e1\u003c/a\u003e, \u003ca href=\"https://gitlab.com/fempar/fempar/blob/experimental/Containers/Docker/gnu/debug/Dockerfile\" rel=\"nofollow\"\u003e2\u003c/a\u003e, \u003ca href=\"https://gitlab.com/fempar/fempar/blob/experimental/Containers/Docker/gnu/debug/p4est_serial/Dockerfile\" rel=\"nofollow\"\u003e3\u003c/a\u003e) which are used to create the \u003ccode\u003efempar/fempar-env:gnu-debug_p4est-serial\u003c/code\u003e Docker image above in order to grasp how you may compile FEMPAR\u0027s dependencies on your Desktop/Laptop or HPC infrastructure.\u003c/p\u003e\n\u003cp\u003eWe strongly recommend to use the \u003ccode\u003econfigure\u003c/code\u003e script in \u003ccode\u003e$FEMPARDIR/Tools\u003c/code\u003e, where we assume hereafter that \u003ccode\u003eFEMPARDIR\u003c/code\u003e is an environment variable pointing to the path of the root directory of FEMPAR\u0027s git repository. Information of the script can be obtained by typing \u003ccode\u003econfigure --help\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWe also strongly recommend to use the \u003ccode\u003emodule\u003c/code\u003e functionality, in order to easily switch between different compilers and compiler versions, and to automatically have a well-defined environment.\nInstructions to set up this functionality can be found \u003ca href=\"XXX\"\u003ehere\u003c/a\u003e.\nFor instance, the compilation of \u003cstrong\u003eFEMPAR\u003c/strong\u003e using the previous two functionalities using \u003cstrong\u003eGNU\u003c/strong\u003e compilers would read:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir build\n$ cd build\n$ module load gcc/8.2.0\n$ $FEMPARDIR/Tools/configure -c GNU -s $FEMPARDIR/SuperBuild\n$ make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstead, for INTEL compilers, the last part would read:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ module load ifc/19.0.0\n$ $FEMPARDIR/Tools/configure -s $FEMPARDIR/SuperBuild\n$ make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe compiler by default is the \u003cstrong\u003eINTEL\u003c/strong\u003e compiler. The tests are compiled unless otherwise stated with \u003ccode\u003e--without-tests\u003c/code\u003e.\nOne driver can be included in the compilation by adding \u003ccode\u003e-d drivername\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor stubborn users that want not to take profit from the previous tools, a manual way to compile \u003cstrong\u003eFEMPAR\u003c/strong\u003e under Linux is (with compilation of tests included):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir build\n$ cd build\n$ cmake ../fempar/SuperBuild -DFEMPAR_ENABLE_TESTS=ON\n$ make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eassuming that the right environment configuration is in place through, e.g., \u003ccode\u003e.bashrc\u003c/code\u003e. But this approach is prone to error when switching environments.\nAnother approach is to directly include in the \u003ccode\u003ecmake\u003c/code\u003e call the right compilation flags and values (see one of the notes below).\u003c/p\u003e\n\u003cp\u003eIn order to configure the compilation of a driver (right after the previous steps):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build\n$ cmake . -DFEMPAR_DRIVER=driver_folder_name\n$ make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to compile FEMPAR library and tests if the first block of commands has been executed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build/FEMPAR\n$ make -jP\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewith \u003ccode\u003eP\u003c/code\u003e being the number of parallel processes involved in the compilation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFEMPAR\u003c/strong\u003e compiles with GNU Fortran compiler 5.3.0 (and newer versions) and Intel Fortran compiler 16.0.0 (and newer versions).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun tests\u003c/h2\u003e\u003ca id=\"user-content-run-tests\" class=\"anchor\" aria-label=\"Permalink: Run tests\" href=\"#run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run the tests, we need the right environment. If we are relying on \u003ccode\u003emodule\u003c/code\u003e functionalities, we must be sure that the required modules are load in the terminal in which we want to run the tests.\nIt seems that the only module that has to be loaded is \u003ccode\u003emkl\u003c/code\u003e, whereas the path to other dynamic libraries is hard-coded, e.g., \u003ccode\u003eopenmpi\u003c/code\u003e. In any case, one can use the initialization suggested in the \u003ccode\u003emodule\u003c/code\u003e manual and pre-load some modules in \u003ccode\u003emodulerc\u003c/code\u003e.\nAssuming the right environment is in place, to run all tests in fast mode, we just do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build/FEMPAR\n$ ctest -R fast -VV\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build/FEMPAR\n$ ctest -R test_name\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto run a particular test.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun drivers\u003c/h2\u003e\u003ca id=\"user-content-run-drivers\" class=\"anchor\" aria-label=\"Permalink: Run drivers\" href=\"#run-drivers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven a driver \u003ccode\u003edriver_name\u003c/code\u003e, to run it (assuming it has been compiled, see above), we do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build/DRIVERS/driver_name/bin\n$ mpirun -np P ./driver_name [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003eP\u003c/code\u003e is the number of MPI processes to be used. Clearly, \u003ccode\u003empirun -np P\u003c/code\u003e must be eliminated to run serial drivers.\u003c/p\u003e\n\u003cp\u003eTo see the different options and default values we can do\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./driver_name --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting dashboard (CDash)\u003c/h2\u003e\u003ca id=\"user-content-testing-dashboard-cdash\" class=\"anchor\" aria-label=\"Permalink: Testing dashboard (CDash)\" href=\"#testing-dashboard-cdash\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project offers to its users/developers a testing dashboard service. This service is powered by \u003ca href=\"https://www.cdash.org/\" rel=\"nofollow\"\u003eCDash\u003c/a\u003e on a\nserver hosted by CIMNE. Click \u003ca href=\"https://cdash.cimne.upc.edu/user.php\" rel=\"nofollow\"\u003ehere\u003c/a\u003e in order to access to the web interface of the service.\nThe CDash server gathers and displays rich information regarding the execution of tests which are performed each\ntime you push into a branch of the fempar repository, and thus, lets you know, e.g., which tests failed,\nwith which compiler, amount of code, and code covered by the tests, memory defects (e.g., leaks), etc.\nIf you want to access to this (highly recommended) service, then you have to follow the instructions available\n\u003ca href=\"https://cdash.cimne.upc.edu/user.php\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Once you are provided with a new user account, then \u003ca href=\"mailto:amartin@cimne.upc.edu\"\u003ee-mail us\u003c/a\u003e,\nso that we can add you to the fempar project at the CDash server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: we have detected that some tests (e.g., \u003ccode\u003etest_poisson_unffited\u003c/code\u003e) do \u003cstrong\u003eNOT\u003c/strong\u003e pass with GNU Fortran compiler 5.5.0, 6.3.0, \u0026amp; 7.3.0 for \u003ccode\u003eexperimental\u003c/code\u003e\ncommit f7b4199e due to what it seems to be a compiler BUG. See issue #259 for more details.\nPlease also note that we do not actually know since\nwhich commit in \u003ccode\u003eexperimental\u003c/code\u003e this is happening, but only that it happens at this one. Thus, avoid using these GNU Fortran compiler versions.\nWe neither know whether this also happens for GNU compiler version different from the ones above. It does NOT happen with 5.4.0.\n\u003cstrong\u003eUPDATE\u003c/strong\u003e: As pointed out by @principe, the issue disappears with \u003ccode\u003egfortran\u003c/code\u003e v8.1.0. This version can easily be installed in Ubuntu as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo apt-get install gfortran-8\n$ sudo apt-get install g++-8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you do not currently use or do not plan to install the modules environment,\nthen one can use this compiler version when configuring FEMPAR as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake -DCMAKE_Fortran_COMPILER=gfortran-8 -DCMAKE_C_COMPILER=gcc-8 \\\n-DCMAKE_CXX_COMPILER=g++-8 -DFEMPAR_ENABLE_TESTS=ON -DFEMPAR_ENABLE_OPENMP=OFF \\\n-DFORTRAN_EXTRA_FLAGS= -DC_EXTRA_FLAGS= -DCMAKE_BUILD_TYPE=Debug -DMPIEXEC_PREFLAGS= \\\n-DFEMPAR_ENABLE_BLAS=ON -DFEMPAR_ENABLE_LAPACK=ON -DFEMPAR_ENABLE_MKL=ON \\\n-DFEMPAR_ENABLE_P4EST=ON -DFEMPAR_ENABLE_UMFPACK=ON -DFEMPAR_ENABLE_METIS=ON \\\n-DFEMPAR_ENABLE_GIDPOST=OFF ../fempar/SuperBuild/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: there is also an open issue with gfortran 6.4.1 and gfortran 7.3.1 (\u003ca href=\"https://gitlab.com/fempar/XH5For/issues/7\" rel=\"nofollow\"\u003ehttps://gitlab.com/fempar/XH5For/issues/7\u003c/a\u003e). An internal\ncompiler error raises when compiling FoX, a third party library of XH5For. \u003cstrong\u003eUPDATE\u003c/strong\u003e: This issue has been already by-passed in FEMPAR\u0027s experimental\nbranch from commit 47b947f2. See \u003ca href=\"https://gitlab.com/fempar/XH5For/issues/7\" rel=\"nofollow\"\u003ehttps://gitlab.com/fempar/XH5For/issues/7\u003c/a\u003e for more details\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: we also detected a BUG with Intel Fortran compiler 18.0.0 related to missing initialization of member variables to default values in the case of\npolymorphic allocatable variables. For example, \u003ccode\u003etest_transient_poisson\u003c/code\u003e do not pass with Intel Fortran compiler 18.0.0 for commit 5176d2976659c64f45e35022bfea5dcb1e72045e.\ndue to a compiler BUG (see issue #250). Thus, avoid using this Intel Fortran compiler version. With Intel compiler 18.0.1 this issue disappears\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: if you plan to use \u003ccode\u003eIntel Parallel Studio XE 2019\u003c/code\u003e in order to compile FEMPAR with the Intel compilers in your machine (this is indeed the only version currently supported\nby \u003ccode\u003eUbuntu 18.04\u003c/code\u003e), please note the following. The most annoying issue is related to the compilation of \u003ccode\u003eSISL\u003c/code\u003e. If you use the \u003ccode\u003eicc\u003c/code\u003e compiler, the \u003ccode\u003eicpc\u003c/code\u003e C++ compiler must be used as well.\nThis is already achieved by fempar\u0027s \u003ccode\u003econfigure\u003c/code\u003e script in \u003ccode\u003eTools\u003c/code\u003e whenever you specify \u003ccode\u003e-c Intel\u003c/code\u003e, but it won\u0027t be if you call \u003ccode\u003ecmake\u003c/code\u003e directly.\nIn the latter case, you must specify \u003ccode\u003e-DCMAKE_CXX_COMPILER=icpc\u003c/code\u003e explicitly when invoking cmake.\u003c/p\u003e\n", + "full_name": "IARCbioinfo/needlestack", + "latest_release": "v1.1", + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://cloud.githubusercontent.com/assets/3366818/12507528/99afd6fa-c0f6-11e5-993b-5ac925d178fa.png\"\u003e\u003cimg align=\"center\" src=\"https://cloud.githubusercontent.com/assets/3366818/12507528/99afd6fa-c0f6-11e5-993b-5ac925d178fa.png\" width=\"700\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA multi-sample somatic variant caller\u003c/h2\u003e\u003ca id=\"user-content-a-multi-sample-somatic-variant-caller\" class=\"anchor\" aria-label=\"Permalink: A multi-sample somatic variant caller\" href=\"#a-multi-sample-somatic-variant-caller\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://cloud.githubusercontent.com/assets/3366818/10489240/7da79144-729c-11e5-8cb3-0225106d9b06.jpg\"\u003e\u003cimg align=\"center\" src=\"https://cloud.githubusercontent.com/assets/3366818/10489240/7da79144-729c-11e5-8cb3-0225106d9b06.jpg\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://gitter.im/iarcbioinfo/needlestack?utm_source=badge\u0026amp;utm_medium=badge\u0026amp;utm_campaign=pr-badge\u0026amp;utm_content=badge\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ef3705254e766b5edea93f49291c6d9239f29b942cfdb84f3296d0e37898b067/68747470733a2f2f6261646765732e6769747465722e696d2f4a6f696e253230436861742e737667\" alt=\"Join the chat at https://gitter.im/iarcbioinfo/needlestack\" data-canonical-src=\"https://badges.gitter.im/Join%20Chat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://circleci.com/gh/IARCbioinfo/needlestack\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8819d9a6aedffc4a1ee81defa062a9e52a34ee25b1e662b73f40d3abc3897b0f/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f6e6565646c65737461636b2e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/needlestack.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/iarcbioinfo/needlestack/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA preprint describing needlestack is available on biorxiv: \u003ca href=\"https://www.biorxiv.org/content/10.1101/639377v1\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/639377v1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContact: \u003ca href=\"mailto:follm@iarc.fr\"\u003efollm@iarc.fr\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eJump over \u003ca href=\"https://github.com/IARCbioinfo/needlestack#usage\"\u003ehere\u003c/a\u003e for a quick start!\u003c/p\u003e\n\u003cp\u003eNeedlestack development is support by the US National Cancer Institute (grant number R21CA175979) and the French Institut national du cancer.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"needlestack.png\"\u003e\u003cimg src=\"needlestack.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWARNING\u003c/h2\u003e\u003ca id=\"user-content-warning\" class=\"anchor\" aria-label=\"Permalink: WARNING\" href=\"#warning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince the last release, we have harmonized our \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003epipelines\u003c/a\u003e and the following option names changed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ebam_folder\u003c/code\u003e is now \u003ccode\u003einput_bams\u003c/code\u003e and accepts text file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efasta_ref\u003c/code\u003e is now \u003ccode\u003eref\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emax_DP\u003c/code\u003e is now \u003ccode\u003emax_dp\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eout_folder\u003c/code\u003e is now \u003ccode\u003eoutput_folder\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eout_vcf\u003c/code\u003e is now \u003ccode\u003eoutput_vcf\u003c/code\u003e and is now a mandatory argument\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eout_annotated_vcf\u003c/code\u003e is now \u003ccode\u003eoutput_annotated_vcf\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epairs_file\u003c/code\u003e is now \u003ccode\u003etn_pairs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eno_plots\u003c/code\u003e is now \u003ccode\u003eplots\u003c/code\u003e (see the Detailed description section)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeedlestack is an ultra-sensitive multi-sample variant caller for Next Generation Sequencing (NGS) data. It is based on the idea that analysing several samples together can help estimate the distribution of sequencing errors to accurately identify variants. It has been initially developed for somatic variant calling using very deep NGS data from circulating free DNA, but is also applicable to lower coverage data like Whole Exome Sequencing (WES) or even Whole Genome Sequencing (WGS). It is a highly scalable and reproducible pipeline thanks to the use of \u003ca href=\"http://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e and \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e/\u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container technologies.\u003c/p\u003e\n\u003cp\u003eHere is a summary of the method:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAt each position and for each candidate variant, we model sequencing errors using a negative binomial regression with a linear link and a zero intercept. The data is extracted from the BAM files using \u003ca href=\"http://www.htslib.org\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eGenetic variants are detected as being outliers from the error model. To avoid these outliers biasing the regression we use a robust estimator for the negative binomial regression (published \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/25156188\" rel=\"nofollow\"\u003ehere\u003c/a\u003e with code available \u003ca href=\"https://github.com/williamaeberhard/glmrob.nb\"\u003ehere\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003eWe calculate for each sample a p-value for being a variant (outlier from the regression) that we further transform into q-values to account for multiple testing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that needlestack is not performing a re-assembly of reads to determine haplotypes as some other modern variant callers are doing (\u003ca href=\"https://gatkforums.broadinstitute.org/gatk/discussion/11076/local-re-assembly-and-haplotype-determination-haplotypecaller-mutect2\" rel=\"nofollow\"\u003eMutect2\u003c/a\u003e, Strelka2 etc.). For this reason we recommend using \u003ca href=\"https://github.com/mozack/abra2\"\u003eABRA2\u003c/a\u003e, a realigner for NGS data, before running needlestack. We also host a \u003ca href=\"https://github.com/IARCbioinfo/abra-nf\"\u003enextflow pipeline for ABRA2\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe detection of a variant can be visualized graphically. Here are two examples of a variant detected, one from a series of samples with a coverage of 120X (left) and one with a very high coverage (50000X, right). Each dot represents a sequenced sample colored according to its \u003ca href=\"https://en.wikipedia.org/wiki/Phred_quality_score\" rel=\"nofollow\"\u003ePhred scale\u003c/a\u003e q-value. The black regression line shows the estimated sequencing-error rate along with the 99% confidence interval (black dotted lines) containing samples. Colored-dotted lines correspond to the limits of regions defined for different significance q-value thresholds. In both case one sample appears as outlier from the regression (in red), and is therefore classified as carrying the given mutation. Note that the position on the left has a high error rate, while the position on the right has a very low error rate, allowing the detection of a variant with a tiny allelic fraction (0.025%).\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"reg_ex.png\"\u003e\u003cimg src=\"reg_ex.png\" alt=\"Example of an alignment plot\" title=\"Example of an regression plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeedlestack works under most Linux distributions and Apple OS X.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we host several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExternal software:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://bedtools.readthedocs.org/en/latest/\" rel=\"nofollow\"\u003ebedtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.htslib.org\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org\" rel=\"nofollow\"\u003eR\u003c/a\u003e with Rscript\u003c/li\u003e\n\u003cli\u003eCompile the file \u003cem\u003e\u003ccode\u003empileup2readcounts.cc\u003c/code\u003e\u003c/em\u003e located \u003ca href=\"https://github.com/IARCbioinfo/mpileup2readcounts\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd the previous tools to your path (executables are assumed to be respectively called \u003ccode\u003esamtools\u003c/code\u003e, \u003ccode\u003eRscript\u003c/code\u003e and \u003ccode\u003empileup2readcounts\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eOptionally, the bioconductor packages required for the alignments plots (see the Detailed description section)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software by using \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information. Warning: the docker/singularity containers don\u0027t include the bioconductor packages needed to plot alignments.\u003c/p\u003e\n\u003cp\u003eTo use needlestack without nextflow, in addition to the previous tools, download the files in this \u003ca href=\"https://github.com/IARCbioinfo/needlestack/tree/gabriela_cpp/bin\"\u003ebin\u003c/a\u003e directory and add them to your path. See the Usage section to run needlestack without nextflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://samtools.github.io/hts-specs/\" rel=\"nofollow\"\u003eBAM files\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eBAM files (called \u003ccode\u003e*.bam\u003c/code\u003e) grouped in a single folder or described in a text file (one BAM path per line) along with their \u003ca href=\"http://www.htslib.org/doc/samtools.html\" rel=\"nofollow\"\u003eindex files\u003c/a\u003e (called \u003ccode\u003e*.bam.bai\u003c/code\u003e or \u003ccode\u003e*.bai\u003c/code\u003e). A minimum of 20 BAM files is recommended.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://en.wikipedia.org/wiki/FASTA_format\" rel=\"nofollow\"\u003efasta file\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eA reference fasta file (eventually compressed with \u003ca href=\"http://www.htslib.org/doc/tabix.html\" rel=\"nofollow\"\u003ebgzip\u003c/a\u003e) along with its \u003ca href=\"http://www.htslib.org/doc/faidx.html\" rel=\"nofollow\"\u003efaidx index\u003c/a\u003e (and \u003ccode\u003e*.gzi\u003c/code\u003e faidx index if compressed).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://genome.ucsc.edu/FAQ/FAQformat.html#format1\" rel=\"nofollow\"\u003ebed file\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eOptional input file. Otherwise the variant calling is performed on the whole reference provided.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA sample dataset is available \u003ca href=\"https://github.com/mfoll/NGS_data_test.git\"\u003ehere\u003c/a\u003e for testing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eType \u003ccode\u003e--help\u003c/code\u003e to get the full list of options. All parameters are prefixed with a double-dash like in \u003ccode\u003e--help\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003einput_bams\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eBAM/\u003c/code\u003e or \u003ccode\u003ebams.txt\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003eFolder containing the BAM files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eref\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003efasta_name.fa\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eReference fasta file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eoutput_vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003evcf_name.vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eName of the output VCF file generated by needlestack\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003emin_dp\u003c/code\u003e *\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e30\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum median coverage to consider a site. In addition, at least 10 samples have to be covered by \u003ccode\u003emin_dp\u003c/code\u003e.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003emin_ao\u003c/code\u003e *\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e3\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum number of non-ref reads in at least one sample to consider a site\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003emin_af\u003c/code\u003e *\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum allelic fraction in at least one sample to consider a site\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ensplit\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e1\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSplit the bed file in nsplit pieces and run in parallel\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emin_qval\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e50\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eqvalue threshold in \u003ca href=\"https://en.wikipedia.org/wiki/Phred_quality_score\" rel=\"nofollow\"\u003ePhred scale\u003c/a\u003e to consider a variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esb_type\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eSOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eStrand bias measure, either \u003ccode\u003eSOR\u003c/code\u003e, \u003ccode\u003eRVSB\u003c/code\u003e or \u003ccode\u003eFS\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esb_snv\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003e100\u003c/code\u003e or \u003ccode\u003e1000\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003eStrand bias threshold for SNVs. The default of 100 (1000 if \u003ccode\u003esb_type\u003c/code\u003e is \u003ccode\u003eFS\u003c/code\u003e) means no filter\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esb_indel\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003e100\u003c/code\u003e or \u003ccode\u003e1000\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003eStrand bias threshold for indels. The default of 100 (1000 if \u003ccode\u003esb_type\u003c/code\u003e is \u003ccode\u003eFS\u003c/code\u003e) means no filter\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emap_qual\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMin mapping quality (passed to \u003ccode\u003esamtools\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ebase_qual\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e13\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMin base quality (passed to \u003ccode\u003esamtools\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emax_dp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e50000\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDownsample coverage per sample (passed to \u003ccode\u003esamtools\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eplots\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eSOMATIC\u003c/code\u003e if \u003ccode\u003etn_pairs\u003c/code\u003e provided, \u003ccode\u003eALL\u003c/code\u003e if not\u003c/td\u003e\n\u003ctd\u003eCreates pdf plots of regressions in the output. To remove plots set \u003ccode\u003e--plots\u003c/code\u003e to \u003ccode\u003eNONE\u003c/code\u003e (See \u003cem\u003ePlot options\u003c/em\u003e paragraph).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003egenome_release\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eReference genome for the annotions on the alignments plots. Examples: \u003cem\u003e\u003ccode\u003eHsapiens.UCSC.hg19\u003c/code\u003e\u003c/em\u003e, \u003cem\u003e\u003ccode\u003eHsapiens.UCSC.hg18\u003c/code\u003e\u003c/em\u003e, \u003cem\u003e\u003ccode\u003eHsapiens.UCSC.hg38\u003c/code\u003e\u003c/em\u003e, \u003cem\u003e\u003ccode\u003eMmusculus.UCSC.mm10\u003c/code\u003e\u003c/em\u003e. The right terminology is described below (\u003cem\u003ePlot options\u003c/em\u003e paragraph). WARNING: this option is mandatory if the \u003ccode\u003e--do_alignments\u003c/code\u003e flag is used\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eoutput_folder\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e--bam_folder\u003c/td\u003e\n\u003ctd\u003eOutput folder, by default equals to the input bam folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ebed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eBED file containing a list of regions (or positions) where needlestack will perform variant calling\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eregion\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eA region in format CHR:START-END where calling should be done\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003etn_pairs\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eA tab-delimited file containing two columns (normal and tumor sample names) for each sample in line. This enables matched tumor/normal pair calling features (see below)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esigma_normal\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0.1\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSigma parameter for negative binomial modeling of expected germline allelic fraction. We strongly recommend not to change this parameter unless you really know what it means\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emin_af_extra_rob\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0.2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum allelic fraction to exclude a sample at a position for extra-robust regression\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emin_prop_extra_rob\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0.1\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum proportion of samples having an allelic fraction to be excluded from extra-robust regression\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emax_prop_extra_rob\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0.8\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMaximum proportion of samples having an allelic fraction to be excluded from extra-robust regression\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epower_min_af\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eAllelic fraction used to classify genotypes as 0/0 or ./. depending of the power to detect a variant at this fraction (see below)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003einput_vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eA VCF file (from GATK) where calling should be done. Needlestack will extract DP and AO from this VCF (DP and AD fields) and annotate it with phred q-value score (\u003ccode\u003eFORMAT/QVAL\u003c/code\u003e field), error rate (\u003ccode\u003eINFO/ERR\u003c/code\u003e) and over-dispersion sigma parameter (\u003ccode\u003eINFO/SIG\u003c/code\u003e). WARNING: by default, only works with split (coreutils) version \u0026gt; 8.13\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Caution: \u003ccode\u003emin_dp\u003c/code\u003e, \u003ccode\u003emin_ao\u003c/code\u003e and \u003ccode\u003emin_af\u003c/code\u003e are position-based filters, which means that needlestack might still identify a variant with an allelic fraction lower than \u003ccode\u003emin_af\u003c/code\u003e in a sample if at the same position, another sample has an allelic fraction larger than \u003ccode\u003emin_af\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eBy default, if neither \u003ccode\u003e--bed\u003c/code\u003e nor \u003ccode\u003e--region\u003c/code\u003e are provided, needlestack runs on the whole reference genome provided, building a bed file from fasta index.\nIf \u003ccode\u003e--bed\u003c/code\u003e and \u003ccode\u003e--region\u003c/code\u003e are both provided, it runs on the region only.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ehelp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eall_SNVs\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eOutput all SNVs, even when no variant is detected\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eextra_robust_gl\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePerform extra-robust regression (useful for common germline SNPs, see below)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eno_labels\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eNo label for the outliers on regression plots\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eno_indels\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDo not perform variant calling for insertions and deletions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eno_contours\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDo not plot q-values contours (for q-value threshold={10,30,50,70,100} by default) and do not plot minimum detectable allelic fraction as a function function of coverage\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003euse_file_name\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eUse the bam file names as sample names. By default the sample name is extracted from the bam file \u003ccode\u003eSM\u003c/code\u003e tag\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edo_alignments\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAdd alignments plots to the pdf plots of regressions\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOptionally download a sample dataset.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --depth=1 https://github.com/IARCbioinfo/data_test\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline using nextflow (here using docker).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e data_test\nnextflow run iarcbioinfo/needlestack -with-docker \\\n --bed BED/TP53_all.bed \\\n --input_bams BAM/BAM_multiple/ \\\n --ref REF/17.fasta \\\n --output_vcf all_variants.vcf \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will find a \u003ca href=\"https://samtools.github.io/hts-specs/\" rel=\"nofollow\"\u003eVCF file\u003c/a\u003e called \u003ccode\u003eall_variants.vcf\u003c/code\u003e in the \u003ccode\u003eBAM_multiple/\u003c/code\u003e folder once done.\u003c/p\u003e\n\u003cp\u003eThe first time it will take more time as the pipeline will be downloaded from github and the docker container from \u003ca href=\"https://hub.docker.com/r/iarcbioinfo/needlestack/\" rel=\"nofollow\"\u003edockerhub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOfficial releases can be found \u003ca href=\"https://github.com/iarcbioinfo/needlestack/releases/\"\u003ehere\u003c/a\u003e. There is a corresponding official \u003ca href=\"https://hub.docker.com/r/iarcbioinfo/needlestack/\" rel=\"nofollow\"\u003edocker container\u003c/a\u003e for each release and one can run a particular version using (for example for v0.3):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/needlestack -r v0.3 -with-docker \\\n\t --bed BED/TP53_all.bed --bam_folder BAM/BAM_multiple/ --fasta_ref REF/17.fasta --out_vcf all_variants.vcf\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIt is also possible to run the pipeline without nextflow (not recommended) using the \u003ccode\u003eneedlestack.sh\u003c/code\u003e. Here on the example dataset downloaded above:\n\u003ccode\u003ebash cd data_test/ needlestack.sh --region=17:7572814-7573814 --bam_folder=BAM/BAM_multiple --ref=REF/17.fasta --output_vcf=all_variants.vcf \u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://samtools.github.io/hts-specs/\" rel=\"nofollow\"\u003eVCF file\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFile containing all the variants detected. It contains a header of meta-information and data lines containing information about each variant position\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePDF files\u003c/td\u003e\n\u003ctd\u003eFor each variant detected a pdf is generated, this pdf file contains the regression plots and alignments plots if the --do_alignments flag is used\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed description\u003c/h2\u003e\u003ca id=\"user-content-detailed-description\" class=\"anchor\" aria-label=\"Permalink: Detailed description\" href=\"#detailed-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGermline, somatic, matched Tumor-Normal pairs calling and contamination\u003c/h3\u003e\u003ca id=\"user-content-germline-somatic-matched-tumor-normal-pairs-calling-and-contamination\" class=\"anchor\" aria-label=\"Permalink: Germline, somatic, matched Tumor-Normal pairs calling and contamination\" href=\"#germline-somatic-matched-tumor-normal-pairs-calling-and-contamination\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen using matched tumor/normal, Needlestack can classify variants (VCF \u003ccode\u003eFORMAT/STATUS\u003c/code\u003e field) according to the following table:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"STATUS_TABLE.png\"\u003e\u003cimg src=\"STATUS_TABLE.png\" alt=\"Status Table\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor this, one need to provide a tab-delimited file containing two columns with normal and tumor sample names using the \u003ccode\u003e--tn_pairs\u003c/code\u003e option. The first line of this file is a header with \u003ccode\u003eTUMOR\u003c/code\u003e and \u003ccode\u003eNORMAL\u003c/code\u003e keywords. When one normal or one tumor is missing, one can write \u003ccode\u003eNA\u003c/code\u003e. In this mode, the parameter \u003ccode\u003epower_min_af\u003c/code\u003e defines the allelic fraction in the tumor one is trying to detect to classify genotypes as \u003ccode\u003e./.\u003c/code\u003e or \u003ccode\u003e0/0\u003c/code\u003e depending on the power to detect this allelic fraction. Variants found as somatic in a tumor, but germline in another sample of the series will be flagged as \u003ccode\u003ePOSSIBLE_CONTAMINATION\u003c/code\u003e. We found this particularly important, as needlestack is very sensitive to low allelic fractions, to filter out contamination among samples, coming for example from pooled exome capture.\u003c/p\u003e\n\u003cp\u003eIn other cases (when there is no \u003ccode\u003e--tn_pairs\u003c/code\u003e parameter defined), genotypes are defined as \u003ccode\u003e./.\u003c/code\u003e or \u003ccode\u003e0/0\u003c/code\u003e assuming one is looking for allelic fractions expected for germline variants (negative binomial distribution centered at 0.5 with over-dispersion parameter sigma=\u003ccode\u003esigma_normal\u003c/code\u003e, with \u003ccode\u003esigma_normal=0.1\u003c/code\u003e by default). If you are looking for somatic variants without matched-normal and assuming you are interesting to correctly distinguish \u003ccode\u003e./.\u003c/code\u003e and \u003ccode\u003e0/0\u003c/code\u003egenotypes, you can set the \u003ccode\u003epower_min_af\u003c/code\u003e parameter to the lowest allelic fraction of somatic variants you are interested with (and your coverage allows you to find). Note that this is by far not the most common situation, and that in most cases you don\u0027t have to worry about the \u003ccode\u003epower_min_af\u003c/code\u003e parameter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlot options\u003c/h3\u003e\u003ca id=\"user-content-plot-options\" class=\"anchor\" aria-label=\"Permalink: Plot options\" href=\"#plot-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTwo options to consider:\u003c/h4\u003e\u003ca id=\"user-content-two-options-to-consider\" class=\"anchor\" aria-label=\"Permalink: Two options to consider:\" href=\"#two-options-to-consider\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--plots\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSOMATIC\u003c/code\u003e: produce pdf regression plots (like in the description above) only for somatic variants (option not available in the annotation mode). Default value when using matched tumor/normal.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eALL\u003c/code\u003e: produce pdf regression plots for all variants. Default value when not using matched tumor/normal or when using annotation mode.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNONE\u003c/code\u003e: remove pdf regression plots from the output.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--do_alignments\u003c/code\u003e: To add the alignments plots after the regression plots. If this flag is used, the name of the reference genome (\u003ccode\u003e--genome_release\u003c/code\u003e option) needs to be provided to choose the correct annotation (See \u003cem\u003e\u003ccode\u003e--genome_release\u003c/code\u003e option\u003c/em\u003e below).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe following \u003ca href=\"https://bioconductor.org/\" rel=\"nofollow\"\u003ebioconductor\u003c/a\u003e packages need to be installed for plotting the alignments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/Gviz.html\" rel=\"nofollow\"\u003eGviz\u003c/a\u003e package\u003c/li\u003e\n\u003cli\u003eAn \u003ca href=\"http://bioconductor.org/packages/release/BiocViews.html#___TxDb\" rel=\"nofollow\"\u003eAnnotation package for TxDb objects\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://bioconductor.org/packages/release/BiocViews.html#___OrgDb\" rel=\"nofollow\"\u003eGenome wide annotation\u003c/a\u003e, it contains mappings between Entrez Gene identifiers and GenBank accession numbers. Examples: the Genome wide annotation package for Human: \u003cem\u003e\u003ccode\u003eorg.Hs.eg.db\u003c/code\u003e\u003c/em\u003e and for the mouse: \u003cem\u003e\u003ccode\u003eorg.Mm.eg.db\u003c/code\u003e\u003c/em\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample: If the hg19 release of the human genome is used, the following packages should be installed: \u003cem\u003e\u003ccode\u003eGviz\u003c/code\u003e\u003c/em\u003e, \u003cem\u003e\u003ccode\u003eTxDb.Hsapiens.UCSC.hg19.knownGene\u003c/code\u003e\u003c/em\u003e (hg18 and hg38 UCSC version can also be used) and \u003cem\u003e\u003ccode\u003eorg.Hs.eg.db\u003c/code\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThese packages exist for other organisms than Human but have not been tested. One can for example generate the alignments plot for mouse data by installing \u003cem\u003e\u003ccode\u003eTxDb.Mmusculus.UCSC.mm10.knownGene\u003c/code\u003e\u003c/em\u003e (mm9 UCSC version can also be used) and \u003cem\u003e\u003ccode\u003eorg.Mm.eg.db\u003c/code\u003e\u003c/em\u003e. For the other organisms the packages need to have the same nomenclature as the ones listed above.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--genome_release\u003c/code\u003e option needs to be provided and corresponds to the TxDb annotation package name without its prefix and suffix. For the hg19 release of the human genome, one needs to set \u003ccode\u003e--genome_release\u003c/code\u003e to \u003cem\u003e\u003ccode\u003eHsapiens.UCSC.hg19\u003c/code\u003e\u003c/em\u003e.\nNote that the packages chosen for the annotations are compatible with the UCSC notations since most of the Gviz fonctionalities can handle these notations. The reference genome used for the BAM alignments can be based on GENCODE, UCSC or ENSEMBL genome varieties.\u003c/p\u003e\n\u003cp\u003eWarning: the docker/singularity containers don\u0027t include the bioconductor packages needed to plot alignments.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"alignments.png\"\u003e\u003cimg src=\"alignments.png\" alt=\"Example of an alignment plot\" title=\"Example of an alignment plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe alignment plot represents from top to bottom:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eChromosome representation: a red vertical line shows the position of the variant.\u003c/li\u003e\n\u003cli\u003eGenomic axis associated with the alignment.\u003c/li\u003e\n\u003cli\u003eBAM alignment(s) (50 bases on both sides of the variant): the variant position is highlighted in red. Note that insertions and deletions are \u003ca href=\"https://support.bioconductor.org/p/95750/\" rel=\"nofollow\"\u003enot currently supported\u003c/a\u003e by Gviz.\u003c/li\u003e\n\u003cli\u003eThe reference genome: the variant position is highlighted in red.\u003c/li\u003e\n\u003cli\u003eGenome annotation: the yellow blocks represent exons, the variant position is highlighted in red. The annotation is represented only if the variant is not in an intergenic region.\u003c/li\u003e\n\u003cli\u003eZoom-out on the genome annotation: representation of the whole gene with its name on the left side. The annotation is represented only if the variant is not in an intergenic region. A red vertical line shows the position of the variant.\u003c/li\u003e\n\u003cli\u003eGenomic axis corresponding to the previous gene annotation. A red vertical line shows the position of the variant.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommon variants\u003c/h3\u003e\u003ca id=\"user-content-common-variants\" class=\"anchor\" aria-label=\"Permalink: Common variants\" href=\"#common-variants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeedlestack is designed to identify rare variants (i.e. only a few samples in your set of samples have a particular variant), because of the robust regression used. Therefore, common SNPs (\u0026gt;10%) or strong somatic hotspots will be missed. The optional \u003ccode\u003eextra_robust_gl\u003c/code\u003e can overcome partially this issue for common germline mutations: it first discard high allelic fraction (\u0026gt;20%, assuming these are likely true variants) before fitting the regression model when between 10% and 50% of sample have such a high allelic fraction. A flag is written in the VCF \u003ccode\u003eINFO/WARN\u003c/code\u003e field when this happened (\u003ccode\u003eEXTRA_ROBUST_GL\u003c/code\u003e). Additionally, when an other allele than the reference allele is the most common, it is taken as the reference allele and a flag is also written in the VCF (\u003ccode\u003eINFO/WARN=INV_REF\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStrand bias\u003c/h3\u003e\u003ca id=\"user-content-strand-bias\" class=\"anchor\" aria-label=\"Permalink: Strand bias\" href=\"#strand-bias\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor conventional variant callers, GATK WES/WGS \u003ca href=\"http://gatkforums.broadinstitute.org/discussion/5533/strandoddsratio-computation\" rel=\"nofollow\"\u003erecommended values\u003c/a\u003e for SOR strand bias are SOR \u0026lt; 4 for SNVs and \u0026lt; 10 for indels. We haven\u0027t found this particularly useful, but a more detailed evaluation is necessary. For amplicon based targeted sequencing, RVSB\u0026gt;0.85 seems to reduce some errors. There is no hard filter by default as this is easy to do afterward using \u003ca href=\"http://samtools.github.io/bcftools/bcftools.html#filter\" rel=\"nofollow\"\u003ebcftools filter\u003c/a\u003e command.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMatthieu Foll*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:follm@iarc.fr\"\u003efollm@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eLead developer, contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:delhommet@students.iarc.fr\"\u003edelhommet@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMain developer, contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:alcalan@fellows.iarc.fr\"\u003ealcalan@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003etumor/normal pair mode, power calculation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAurelie Gabriel\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:gabriela@students.iarc.fr\"\u003egabriela@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eAlignment plot, nextflow pipeline, mpileup2readcounts, bash version\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNeedlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data\nTiffany M. Delhomme, Patrice H. Avogbe, Aurelie A.G. Gabriel, Nicolas Alcala, Noemie Leblay, Catherine Voegele, Maxime Vallee, Priscilia Chopard, Amelie Chabrier, Behnoush Abedi-Ardekani, Valerie Gaborieau, Ivana Holcatova, Vladimir Janout, Lenka Foretova, Sasa Milosavljevic, David Zaridze, Anush Mukeriya, Elisabeth Brambilla, Paul Brennan, Ghislaine Scelo, Lynnette Fernandez-Cuesta, Graham Byrnes, Florence Le Calvez-Kelm, James D D. McKay, Matthieu Foll\nbioRxiv 639377; doi: \u003ca href=\"https://doi.org/10.1101/639377\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/639377\u003c/a\u003e. \u003ca href=\"https://www.biorxiv.org/content/10.1101/639377v1\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/639377v1\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNeedlestack has been used in these papers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUrinary TERT promoter mutations as non-invasive biomarkers for the comprehensive detection of urothelial cancer\nAvogbe et al. 2019 \u003ca href=\"https://www.ebiomedicine.com/article/S2352-3964(19)30305-6/fulltext\" rel=\"nofollow\"\u003eEBioMedicine\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFernandez-Cuesta L, Perdomo S, Avogbe PH, Leblay N, Delhomme TM, Gaborieau V, Abedi-Ardekani B, Chanudet E, Olivier M, Zaridze D, Mukeria A, Vilensky M, Holcatova I, Polesel J, Simonato L, Canova C, Lagiou P, Brambilla C, Brambilla E, Byrnes G, Scelo G, Le Calvez-Kelm F, Foll M, McKay JD, Brennan P. Identification of Circulating Tumor DNA for the Early Detection of Small-cell Lung Cancer. \u003ca href=\"http://www.sciencedirect.com/science/article/pii/S2352396416302894\" rel=\"nofollow\"\u003eEBioMedicine\u003c/a\u003e. 2016 Aug;10:117-23. doi: \u003ca href=\"http://dx.doi.org/10.1016/j.ebiom.2016.06.032\" rel=\"nofollow\"\u003e10.1016/j.ebiom.2016.06.032\u003c/a\u003e. Epub 2016 Jun 25. PubMed PMID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/27377626\" rel=\"nofollow\"\u003e27377626\u003c/a\u003e; PubMed Central PMCID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036515/\" rel=\"nofollow\"\u003ePMC5036515\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eLe Calvez-Kelm F, Foll M, Wozniak MB, Delhomme TM, Durand G, Chopard P, Pertesi M, Fabianova E, Adamcakova Z, Holcatova I, Foretova L, Janout V, Vallee MP, Rinaldi S, Brennan P, McKay JD, Byrnes GB, Scelo G. KRAS mutations in blood circulating cell-free DNA: a pancreatic cancer case-control. \u003ca href=\"http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget\u0026amp;page=article\u0026amp;op=view\u0026amp;path%5b%5d=12386\" rel=\"nofollow\"\u003eOncotarget\u003c/a\u003e. 2016 Nov 29;7(48):78827-78840. doi: \u003ca href=\"https://doi.org/10.18632/oncotarget.12386\" rel=\"nofollow\"\u003e10.18632/oncotarget.12386\u003c/a\u003e. PubMed PMID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/27705932\" rel=\"nofollow\"\u003e27705932\u003c/a\u003e; PubMed Central PMCID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5346680/\" rel=\"nofollow\"\u003ePMC5346680\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", "stargazers_count": 49, - "subscribers_count": 10, - "topics": [], - "updated_at": 1710751230.0 + "subscribers_count": 11, + "topics": [ + "nextflow", + "pipeline", + "regression", + "bam-files", + "variants", + "ngs", + "somatic-mutations", + "negative-binomial-regression", + "samtools", + "docker" + ], + "updated_at": 1729066855.0 }, { "data_format": 2, @@ -41192,28 +41228,20 @@ var data = }, { "data_format": 2, - "description": "Multi-sample somatic variant caller", + "description": "Finite Element Multiphysics PARallel solvers. Official mirror from https://gitlab.com/fempar/fempar", "filenames": [ - "Singularity" + "Containers/Singularity/Singularity.gnu-release_p4est-parallel", + "Containers/Singularity/Singularity.gnu-debug_p4est-serial", + "Containers/Singularity/Singularity.gnu-debug_p4est-parallel", + "Containers/Singularity/Singularity.gnu-release_p4est-serial" ], - "full_name": "IARCbioinfo/needlestack", - "latest_release": "v1.1", - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://cloud.githubusercontent.com/assets/3366818/12507528/99afd6fa-c0f6-11e5-993b-5ac925d178fa.png\"\u003e\u003cimg align=\"center\" src=\"https://cloud.githubusercontent.com/assets/3366818/12507528/99afd6fa-c0f6-11e5-993b-5ac925d178fa.png\" width=\"700\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eA multi-sample somatic variant caller\u003c/h2\u003e\u003ca id=\"user-content-a-multi-sample-somatic-variant-caller\" class=\"anchor\" aria-label=\"Permalink: A multi-sample somatic variant caller\" href=\"#a-multi-sample-somatic-variant-caller\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://cloud.githubusercontent.com/assets/3366818/10489240/7da79144-729c-11e5-8cb3-0225106d9b06.jpg\"\u003e\u003cimg align=\"center\" src=\"https://cloud.githubusercontent.com/assets/3366818/10489240/7da79144-729c-11e5-8cb3-0225106d9b06.jpg\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://gitter.im/iarcbioinfo/needlestack?utm_source=badge\u0026amp;utm_medium=badge\u0026amp;utm_campaign=pr-badge\u0026amp;utm_content=badge\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ef3705254e766b5edea93f49291c6d9239f29b942cfdb84f3296d0e37898b067/68747470733a2f2f6261646765732e6769747465722e696d2f4a6f696e253230436861742e737667\" alt=\"Join the chat at https://gitter.im/iarcbioinfo/needlestack\" data-canonical-src=\"https://badges.gitter.im/Join%20Chat.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://circleci.com/gh/IARCbioinfo/needlestack\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8819d9a6aedffc4a1ee81defa062a9e52a34ee25b1e662b73f40d3abc3897b0f/68747470733a2f2f636972636c6563692e636f6d2f67682f4941524362696f696e666f2f6e6565646c65737461636b2e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/IARCbioinfo/needlestack.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://hub.docker.com/r/iarcbioinfo/needlestack/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7c75972dff54a1fb1f126b20e299a216be70a65ee3a654f3c3015b545570e18/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f636b65722d72656164792d626c75652e737667\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/badge/docker-ready-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA preprint describing needlestack is available on biorxiv: \u003ca href=\"https://www.biorxiv.org/content/10.1101/639377v1\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/639377v1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eContact: \u003ca href=\"mailto:follm@iarc.fr\"\u003efollm@iarc.fr\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eJump over \u003ca href=\"https://github.com/IARCbioinfo/needlestack#usage\"\u003ehere\u003c/a\u003e for a quick start!\u003c/p\u003e\n\u003cp\u003eNeedlestack development is support by the US National Cancer Institute (grant number R21CA175979) and the French Institut national du cancer.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"needlestack.png\"\u003e\u003cimg src=\"needlestack.png\" alt=\"Workflow representation\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWARNING\u003c/h2\u003e\u003ca id=\"user-content-warning\" class=\"anchor\" aria-label=\"Permalink: WARNING\" href=\"#warning\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSince the last release, we have harmonized our \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003epipelines\u003c/a\u003e and the following option names changed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003ebam_folder\u003c/code\u003e is now \u003ccode\u003einput_bams\u003c/code\u003e and accepts text file\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efasta_ref\u003c/code\u003e is now \u003ccode\u003eref\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003emax_DP\u003c/code\u003e is now \u003ccode\u003emax_dp\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eout_folder\u003c/code\u003e is now \u003ccode\u003eoutput_folder\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eout_vcf\u003c/code\u003e is now \u003ccode\u003eoutput_vcf\u003c/code\u003e and is now a mandatory argument\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eout_annotated_vcf\u003c/code\u003e is now \u003ccode\u003eoutput_annotated_vcf\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003epairs_file\u003c/code\u003e is now \u003ccode\u003etn_pairs\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eno_plots\u003c/code\u003e is now \u003ccode\u003eplots\u003c/code\u003e (see the Detailed description section)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeedlestack is an ultra-sensitive multi-sample variant caller for Next Generation Sequencing (NGS) data. It is based on the idea that analysing several samples together can help estimate the distribution of sequencing errors to accurately identify variants. It has been initially developed for somatic variant calling using very deep NGS data from circulating free DNA, but is also applicable to lower coverage data like Whole Exome Sequencing (WES) or even Whole Genome Sequencing (WGS). It is a highly scalable and reproducible pipeline thanks to the use of \u003ca href=\"http://www.nextflow.io/\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e and \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e/\u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e container technologies.\u003c/p\u003e\n\u003cp\u003eHere is a summary of the method:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAt each position and for each candidate variant, we model sequencing errors using a negative binomial regression with a linear link and a zero intercept. The data is extracted from the BAM files using \u003ca href=\"http://www.htslib.org\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eGenetic variants are detected as being outliers from the error model. To avoid these outliers biasing the regression we use a robust estimator for the negative binomial regression (published \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/25156188\" rel=\"nofollow\"\u003ehere\u003c/a\u003e with code available \u003ca href=\"https://github.com/williamaeberhard/glmrob.nb\"\u003ehere\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003eWe calculate for each sample a p-value for being a variant (outlier from the regression) that we further transform into q-values to account for multiple testing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that needlestack is not performing a re-assembly of reads to determine haplotypes as some other modern variant callers are doing (\u003ca href=\"https://gatkforums.broadinstitute.org/gatk/discussion/11076/local-re-assembly-and-haplotype-determination-haplotypecaller-mutect2\" rel=\"nofollow\"\u003eMutect2\u003c/a\u003e, Strelka2 etc.). For this reason we recommend using \u003ca href=\"https://github.com/mozack/abra2\"\u003eABRA2\u003c/a\u003e, a realigner for NGS data, before running needlestack. We also host a \u003ca href=\"https://github.com/IARCbioinfo/abra-nf\"\u003enextflow pipeline for ABRA2\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe detection of a variant can be visualized graphically. Here are two examples of a variant detected, one from a series of samples with a coverage of 120X (left) and one with a very high coverage (50000X, right). Each dot represents a sequenced sample colored according to its \u003ca href=\"https://en.wikipedia.org/wiki/Phred_quality_score\" rel=\"nofollow\"\u003ePhred scale\u003c/a\u003e q-value. The black regression line shows the estimated sequencing-error rate along with the 99% confidence interval (black dotted lines) containing samples. Colored-dotted lines correspond to the limits of regions defined for different significance q-value thresholds. In both case one sample appears as outlier from the regression (in red), and is therefore classified as carrying the given mutation. Note that the position on the left has a high error rate, while the position on the right has a very low error rate, allowing the detection of a variant with a tiny allelic fraction (0.025%).\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"reg_ex.png\"\u003e\u003cimg src=\"reg_ex.png\" alt=\"Example of an alignment plot\" title=\"Example of an regression plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeedlestack works under most Linux distributions and Apple OS X.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThis pipeline is based on \u003ca href=\"https://www.nextflow.io\" rel=\"nofollow\"\u003enextflow\u003c/a\u003e. As we host several nextflow pipelines, we have centralized the common information in the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExternal software:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://bedtools.readthedocs.org/en/latest/\" rel=\"nofollow\"\u003ebedtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.htslib.org\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.r-project.org\" rel=\"nofollow\"\u003eR\u003c/a\u003e with Rscript\u003c/li\u003e\n\u003cli\u003eCompile the file \u003cem\u003e\u003ccode\u003empileup2readcounts.cc\u003c/code\u003e\u003c/em\u003e located \u003ca href=\"https://github.com/IARCbioinfo/mpileup2readcounts\"\u003ehere\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAdd the previous tools to your path (executables are assumed to be respectively called \u003ccode\u003esamtools\u003c/code\u003e, \u003ccode\u003eRscript\u003c/code\u003e and \u003ccode\u003empileup2readcounts\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eOptionally, the bioconductor packages required for the alignments plots (see the Detailed description section)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can avoid installing all the external software by using \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"http://singularity.lbl.gov\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e. See the \u003ca href=\"https://github.com/IARCbioinfo/IARC-nf\"\u003eIARC-nf\u003c/a\u003e repository for more information. Warning: the docker/singularity containers don\u0027t include the bioconductor packages needed to plot alignments.\u003c/p\u003e\n\u003cp\u003eTo use needlestack without nextflow, in addition to the previous tools, download the files in this \u003ca href=\"https://github.com/IARCbioinfo/needlestack/tree/gabriela_cpp/bin\"\u003ebin\u003c/a\u003e directory and add them to your path. See the Usage section to run needlestack without nextflow.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInput\u003c/h2\u003e\u003ca id=\"user-content-input\" class=\"anchor\" aria-label=\"Permalink: Input\" href=\"#input\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://samtools.github.io/hts-specs/\" rel=\"nofollow\"\u003eBAM files\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eBAM files (called \u003ccode\u003e*.bam\u003c/code\u003e) grouped in a single folder or described in a text file (one BAM path per line) along with their \u003ca href=\"http://www.htslib.org/doc/samtools.html\" rel=\"nofollow\"\u003eindex files\u003c/a\u003e (called \u003ccode\u003e*.bam.bai\u003c/code\u003e or \u003ccode\u003e*.bai\u003c/code\u003e). A minimum of 20 BAM files is recommended.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://en.wikipedia.org/wiki/FASTA_format\" rel=\"nofollow\"\u003efasta file\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eA reference fasta file (eventually compressed with \u003ca href=\"http://www.htslib.org/doc/tabix.html\" rel=\"nofollow\"\u003ebgzip\u003c/a\u003e) along with its \u003ca href=\"http://www.htslib.org/doc/faidx.html\" rel=\"nofollow\"\u003efaidx index\u003c/a\u003e (and \u003ccode\u003e*.gzi\u003c/code\u003e faidx index if compressed).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://genome.ucsc.edu/FAQ/FAQformat.html#format1\" rel=\"nofollow\"\u003ebed file\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eOptional input file. Otherwise the variant calling is performed on the whole reference provided.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA sample dataset is available \u003ca href=\"https://github.com/mfoll/NGS_data_test.git\"\u003ehere\u003c/a\u003e for testing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eParameters\u003c/h2\u003e\u003ca id=\"user-content-parameters\" class=\"anchor\" aria-label=\"Permalink: Parameters\" href=\"#parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eType \u003ccode\u003e--help\u003c/code\u003e to get the full list of options. All parameters are prefixed with a double-dash like in \u003ccode\u003e--help\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eMandatory\u003c/h4\u003e\u003ca id=\"user-content-mandatory\" class=\"anchor\" aria-label=\"Permalink: Mandatory\" href=\"#mandatory\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eExample value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003einput_bams\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eBAM/\u003c/code\u003e or \u003ccode\u003ebams.txt\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003eFolder containing the BAM files\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eref\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003efasta_name.fa\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eReference fasta file\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eoutput_vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003evcf_name.vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eName of the output VCF file generated by needlestack\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eOptional\u003c/h4\u003e\u003ca id=\"user-content-optional\" class=\"anchor\" aria-label=\"Permalink: Optional\" href=\"#optional\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDefault value\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003emin_dp\u003c/code\u003e *\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e30\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum median coverage to consider a site. In addition, at least 10 samples have to be covered by \u003ccode\u003emin_dp\u003c/code\u003e.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003emin_ao\u003c/code\u003e *\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e3\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum number of non-ref reads in at least one sample to consider a site\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003emin_af\u003c/code\u003e *\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum allelic fraction in at least one sample to consider a site\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ensplit\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e1\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSplit the bed file in nsplit pieces and run in parallel\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emin_qval\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e50\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eqvalue threshold in \u003ca href=\"https://en.wikipedia.org/wiki/Phred_quality_score\" rel=\"nofollow\"\u003ePhred scale\u003c/a\u003e to consider a variant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esb_type\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eSOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eStrand bias measure, either \u003ccode\u003eSOR\u003c/code\u003e, \u003ccode\u003eRVSB\u003c/code\u003e or \u003ccode\u003eFS\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esb_snv\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003e100\u003c/code\u003e or \u003ccode\u003e1000\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003eStrand bias threshold for SNVs. The default of 100 (1000 if \u003ccode\u003esb_type\u003c/code\u003e is \u003ccode\u003eFS\u003c/code\u003e) means no filter\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esb_indel\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003e100\u003c/code\u003e or \u003ccode\u003e1000\u003c/code\u003e\n\u003c/td\u003e\n\u003ctd\u003eStrand bias threshold for indels. The default of 100 (1000 if \u003ccode\u003esb_type\u003c/code\u003e is \u003ccode\u003eFS\u003c/code\u003e) means no filter\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emap_qual\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMin mapping quality (passed to \u003ccode\u003esamtools\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ebase_qual\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e13\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMin base quality (passed to \u003ccode\u003esamtools\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emax_dp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e50000\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDownsample coverage per sample (passed to \u003ccode\u003esamtools\u003c/code\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eplots\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003ccode\u003eSOMATIC\u003c/code\u003e if \u003ccode\u003etn_pairs\u003c/code\u003e provided, \u003ccode\u003eALL\u003c/code\u003e if not\u003c/td\u003e\n\u003ctd\u003eCreates pdf plots of regressions in the output. To remove plots set \u003ccode\u003e--plots\u003c/code\u003e to \u003ccode\u003eNONE\u003c/code\u003e (See \u003cem\u003ePlot options\u003c/em\u003e paragraph).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003egenome_release\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eReference genome for the annotions on the alignments plots. Examples: \u003cem\u003e\u003ccode\u003eHsapiens.UCSC.hg19\u003c/code\u003e\u003c/em\u003e, \u003cem\u003e\u003ccode\u003eHsapiens.UCSC.hg18\u003c/code\u003e\u003c/em\u003e, \u003cem\u003e\u003ccode\u003eHsapiens.UCSC.hg38\u003c/code\u003e\u003c/em\u003e, \u003cem\u003e\u003ccode\u003eMmusculus.UCSC.mm10\u003c/code\u003e\u003c/em\u003e. The right terminology is described below (\u003cem\u003ePlot options\u003c/em\u003e paragraph). WARNING: this option is mandatory if the \u003ccode\u003e--do_alignments\u003c/code\u003e flag is used\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eoutput_folder\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e--bam_folder\u003c/td\u003e\n\u003ctd\u003eOutput folder, by default equals to the input bam folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ebed\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eBED file containing a list of regions (or positions) where needlestack will perform variant calling\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eregion\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eA region in format CHR:START-END where calling should be done\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003etn_pairs\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e-\u003c/td\u003e\n\u003ctd\u003eA tab-delimited file containing two columns (normal and tumor sample names) for each sample in line. This enables matched tumor/normal pair calling features (see below)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esigma_normal\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0.1\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSigma parameter for negative binomial modeling of expected germline allelic fraction. We strongly recommend not to change this parameter unless you really know what it means\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emin_af_extra_rob\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0.2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum allelic fraction to exclude a sample at a position for extra-robust regression\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emin_prop_extra_rob\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0.1\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMinimum proportion of samples having an allelic fraction to be excluded from extra-robust regression\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emax_prop_extra_rob\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e0.8\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eMaximum proportion of samples having an allelic fraction to be excluded from extra-robust regression\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epower_min_af\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eAllelic fraction used to classify genotypes as 0/0 or ./. depending of the power to detect a variant at this fraction (see below)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003einput_vcf\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eA VCF file (from GATK) where calling should be done. Needlestack will extract DP and AO from this VCF (DP and AD fields) and annotate it with phred q-value score (\u003ccode\u003eFORMAT/QVAL\u003c/code\u003e field), error rate (\u003ccode\u003eINFO/ERR\u003c/code\u003e) and over-dispersion sigma parameter (\u003ccode\u003eINFO/SIG\u003c/code\u003e). WARNING: by default, only works with split (coreutils) version \u0026gt; 8.13\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Caution: \u003ccode\u003emin_dp\u003c/code\u003e, \u003ccode\u003emin_ao\u003c/code\u003e and \u003ccode\u003emin_af\u003c/code\u003e are position-based filters, which means that needlestack might still identify a variant with an allelic fraction lower than \u003ccode\u003emin_af\u003c/code\u003e in a sample if at the same position, another sample has an allelic fraction larger than \u003ccode\u003emin_af\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eBy default, if neither \u003ccode\u003e--bed\u003c/code\u003e nor \u003ccode\u003e--region\u003c/code\u003e are provided, needlestack runs on the whole reference genome provided, building a bed file from fasta index.\nIf \u003ccode\u003e--bed\u003c/code\u003e and \u003ccode\u003e--region\u003c/code\u003e are both provided, it runs on the region only.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eFlags\u003c/h4\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFlags are parameters without value.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ehelp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDisplay help\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eall_SNVs\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eOutput all SNVs, even when no variant is detected\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eextra_robust_gl\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003ePerform extra-robust regression (useful for common germline SNPs, see below)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eno_labels\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eNo label for the outliers on regression plots\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eno_indels\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDo not perform variant calling for insertions and deletions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eno_contours\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDo not plot q-values contours (for q-value threshold={10,30,50,70,100} by default) and do not plot minimum detectable allelic fraction as a function function of coverage\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003euse_file_name\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eUse the bam file names as sample names. By default the sample name is extracted from the bam file \u003ccode\u003eSM\u003c/code\u003e tag\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edo_alignments\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAdd alignments plots to the pdf plots of regressions\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eOptionally download a sample dataset.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone --depth=1 https://github.com/IARCbioinfo/data_test\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRun the pipeline using nextflow (here using docker).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e data_test\nnextflow run iarcbioinfo/needlestack -with-docker \\\n --bed BED/TP53_all.bed \\\n --input_bams BAM/BAM_multiple/ \\\n --ref REF/17.fasta \\\n --output_vcf all_variants.vcf \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou will find a \u003ca href=\"https://samtools.github.io/hts-specs/\" rel=\"nofollow\"\u003eVCF file\u003c/a\u003e called \u003ccode\u003eall_variants.vcf\u003c/code\u003e in the \u003ccode\u003eBAM_multiple/\u003c/code\u003e folder once done.\u003c/p\u003e\n\u003cp\u003eThe first time it will take more time as the pipeline will be downloaded from github and the docker container from \u003ca href=\"https://hub.docker.com/r/iarcbioinfo/needlestack/\" rel=\"nofollow\"\u003edockerhub\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eOfficial releases can be found \u003ca href=\"https://github.com/iarcbioinfo/needlestack/releases/\"\u003ehere\u003c/a\u003e. There is a corresponding official \u003ca href=\"https://hub.docker.com/r/iarcbioinfo/needlestack/\" rel=\"nofollow\"\u003edocker container\u003c/a\u003e for each release and one can run a particular version using (for example for v0.3):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003enextflow run iarcbioinfo/needlestack -r v0.3 -with-docker \\\n\t --bed BED/TP53_all.bed --bam_folder BAM/BAM_multiple/ --fasta_ref REF/17.fasta --out_vcf all_variants.vcf\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIt is also possible to run the pipeline without nextflow (not recommended) using the \u003ccode\u003eneedlestack.sh\u003c/code\u003e. Here on the example dataset downloaded above:\n\u003ccode\u003ebash cd data_test/ needlestack.sh --region=17:7572814-7573814 --bam_folder=BAM/BAM_multiple --ref=REF/17.fasta --output_vcf=all_variants.vcf \u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutput\u003c/h2\u003e\u003ca id=\"user-content-output\" class=\"anchor\" aria-label=\"Permalink: Output\" href=\"#output\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eType\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://samtools.github.io/hts-specs/\" rel=\"nofollow\"\u003eVCF file\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFile containing all the variants detected. It contains a header of meta-information and data lines containing information about each variant position\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePDF files\u003c/td\u003e\n\u003ctd\u003eFor each variant detected a pdf is generated, this pdf file contains the regression plots and alignments plots if the --do_alignments flag is used\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDetailed description\u003c/h2\u003e\u003ca id=\"user-content-detailed-description\" class=\"anchor\" aria-label=\"Permalink: Detailed description\" href=\"#detailed-description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGermline, somatic, matched Tumor-Normal pairs calling and contamination\u003c/h3\u003e\u003ca id=\"user-content-germline-somatic-matched-tumor-normal-pairs-calling-and-contamination\" class=\"anchor\" aria-label=\"Permalink: Germline, somatic, matched Tumor-Normal pairs calling and contamination\" href=\"#germline-somatic-matched-tumor-normal-pairs-calling-and-contamination\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen using matched tumor/normal, Needlestack can classify variants (VCF \u003ccode\u003eFORMAT/STATUS\u003c/code\u003e field) according to the following table:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"STATUS_TABLE.png\"\u003e\u003cimg src=\"STATUS_TABLE.png\" alt=\"Status Table\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor this, one need to provide a tab-delimited file containing two columns with normal and tumor sample names using the \u003ccode\u003e--tn_pairs\u003c/code\u003e option. The first line of this file is a header with \u003ccode\u003eTUMOR\u003c/code\u003e and \u003ccode\u003eNORMAL\u003c/code\u003e keywords. When one normal or one tumor is missing, one can write \u003ccode\u003eNA\u003c/code\u003e. In this mode, the parameter \u003ccode\u003epower_min_af\u003c/code\u003e defines the allelic fraction in the tumor one is trying to detect to classify genotypes as \u003ccode\u003e./.\u003c/code\u003e or \u003ccode\u003e0/0\u003c/code\u003e depending on the power to detect this allelic fraction. Variants found as somatic in a tumor, but germline in another sample of the series will be flagged as \u003ccode\u003ePOSSIBLE_CONTAMINATION\u003c/code\u003e. We found this particularly important, as needlestack is very sensitive to low allelic fractions, to filter out contamination among samples, coming for example from pooled exome capture.\u003c/p\u003e\n\u003cp\u003eIn other cases (when there is no \u003ccode\u003e--tn_pairs\u003c/code\u003e parameter defined), genotypes are defined as \u003ccode\u003e./.\u003c/code\u003e or \u003ccode\u003e0/0\u003c/code\u003e assuming one is looking for allelic fractions expected for germline variants (negative binomial distribution centered at 0.5 with over-dispersion parameter sigma=\u003ccode\u003esigma_normal\u003c/code\u003e, with \u003ccode\u003esigma_normal=0.1\u003c/code\u003e by default). If you are looking for somatic variants without matched-normal and assuming you are interesting to correctly distinguish \u003ccode\u003e./.\u003c/code\u003e and \u003ccode\u003e0/0\u003c/code\u003egenotypes, you can set the \u003ccode\u003epower_min_af\u003c/code\u003e parameter to the lowest allelic fraction of somatic variants you are interested with (and your coverage allows you to find). Note that this is by far not the most common situation, and that in most cases you don\u0027t have to worry about the \u003ccode\u003epower_min_af\u003c/code\u003e parameter.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePlot options\u003c/h3\u003e\u003ca id=\"user-content-plot-options\" class=\"anchor\" aria-label=\"Permalink: Plot options\" href=\"#plot-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eTwo options to consider:\u003c/h4\u003e\u003ca id=\"user-content-two-options-to-consider\" class=\"anchor\" aria-label=\"Permalink: Two options to consider:\" href=\"#two-options-to-consider\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--plots\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003eSOMATIC\u003c/code\u003e: produce pdf regression plots (like in the description above) only for somatic variants (option not available in the annotation mode). Default value when using matched tumor/normal.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eALL\u003c/code\u003e: produce pdf regression plots for all variants. Default value when not using matched tumor/normal or when using annotation mode.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eNONE\u003c/code\u003e: remove pdf regression plots from the output.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e--do_alignments\u003c/code\u003e: To add the alignments plots after the regression plots. If this flag is used, the name of the reference genome (\u003ccode\u003e--genome_release\u003c/code\u003e option) needs to be provided to choose the correct annotation (See \u003cem\u003e\u003ccode\u003e--genome_release\u003c/code\u003e option\u003c/em\u003e below).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe following \u003ca href=\"https://bioconductor.org/\" rel=\"nofollow\"\u003ebioconductor\u003c/a\u003e packages need to be installed for plotting the alignments:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ca href=\"https://bioconductor.org/packages/release/bioc/html/Gviz.html\" rel=\"nofollow\"\u003eGviz\u003c/a\u003e package\u003c/li\u003e\n\u003cli\u003eAn \u003ca href=\"http://bioconductor.org/packages/release/BiocViews.html#___TxDb\" rel=\"nofollow\"\u003eAnnotation package for TxDb objects\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eA \u003ca href=\"https://bioconductor.org/packages/release/BiocViews.html#___OrgDb\" rel=\"nofollow\"\u003eGenome wide annotation\u003c/a\u003e, it contains mappings between Entrez Gene identifiers and GenBank accession numbers. Examples: the Genome wide annotation package for Human: \u003cem\u003e\u003ccode\u003eorg.Hs.eg.db\u003c/code\u003e\u003c/em\u003e and for the mouse: \u003cem\u003e\u003ccode\u003eorg.Mm.eg.db\u003c/code\u003e\u003c/em\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExample: If the hg19 release of the human genome is used, the following packages should be installed: \u003cem\u003e\u003ccode\u003eGviz\u003c/code\u003e\u003c/em\u003e, \u003cem\u003e\u003ccode\u003eTxDb.Hsapiens.UCSC.hg19.knownGene\u003c/code\u003e\u003c/em\u003e (hg18 and hg38 UCSC version can also be used) and \u003cem\u003e\u003ccode\u003eorg.Hs.eg.db\u003c/code\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThese packages exist for other organisms than Human but have not been tested. One can for example generate the alignments plot for mouse data by installing \u003cem\u003e\u003ccode\u003eTxDb.Mmusculus.UCSC.mm10.knownGene\u003c/code\u003e\u003c/em\u003e (mm9 UCSC version can also be used) and \u003cem\u003e\u003ccode\u003eorg.Mm.eg.db\u003c/code\u003e\u003c/em\u003e. For the other organisms the packages need to have the same nomenclature as the ones listed above.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003e--genome_release\u003c/code\u003e option needs to be provided and corresponds to the TxDb annotation package name without its prefix and suffix. For the hg19 release of the human genome, one needs to set \u003ccode\u003e--genome_release\u003c/code\u003e to \u003cem\u003e\u003ccode\u003eHsapiens.UCSC.hg19\u003c/code\u003e\u003c/em\u003e.\nNote that the packages chosen for the annotations are compatible with the UCSC notations since most of the Gviz fonctionalities can handle these notations. The reference genome used for the BAM alignments can be based on GENCODE, UCSC or ENSEMBL genome varieties.\u003c/p\u003e\n\u003cp\u003eWarning: the docker/singularity containers don\u0027t include the bioconductor packages needed to plot alignments.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"alignments.png\"\u003e\u003cimg src=\"alignments.png\" alt=\"Example of an alignment plot\" title=\"Example of an alignment plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe alignment plot represents from top to bottom:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eChromosome representation: a red vertical line shows the position of the variant.\u003c/li\u003e\n\u003cli\u003eGenomic axis associated with the alignment.\u003c/li\u003e\n\u003cli\u003eBAM alignment(s) (50 bases on both sides of the variant): the variant position is highlighted in red. Note that insertions and deletions are \u003ca href=\"https://support.bioconductor.org/p/95750/\" rel=\"nofollow\"\u003enot currently supported\u003c/a\u003e by Gviz.\u003c/li\u003e\n\u003cli\u003eThe reference genome: the variant position is highlighted in red.\u003c/li\u003e\n\u003cli\u003eGenome annotation: the yellow blocks represent exons, the variant position is highlighted in red. The annotation is represented only if the variant is not in an intergenic region.\u003c/li\u003e\n\u003cli\u003eZoom-out on the genome annotation: representation of the whole gene with its name on the left side. The annotation is represented only if the variant is not in an intergenic region. A red vertical line shows the position of the variant.\u003c/li\u003e\n\u003cli\u003eGenomic axis corresponding to the previous gene annotation. A red vertical line shows the position of the variant.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommon variants\u003c/h3\u003e\u003ca id=\"user-content-common-variants\" class=\"anchor\" aria-label=\"Permalink: Common variants\" href=\"#common-variants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNeedlestack is designed to identify rare variants (i.e. only a few samples in your set of samples have a particular variant), because of the robust regression used. Therefore, common SNPs (\u0026gt;10%) or strong somatic hotspots will be missed. The optional \u003ccode\u003eextra_robust_gl\u003c/code\u003e can overcome partially this issue for common germline mutations: it first discard high allelic fraction (\u0026gt;20%, assuming these are likely true variants) before fitting the regression model when between 10% and 50% of sample have such a high allelic fraction. A flag is written in the VCF \u003ccode\u003eINFO/WARN\u003c/code\u003e field when this happened (\u003ccode\u003eEXTRA_ROBUST_GL\u003c/code\u003e). Additionally, when an other allele than the reference allele is the most common, it is taken as the reference allele and a flag is also written in the VCF (\u003ccode\u003eINFO/WARN=INV_REF\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStrand bias\u003c/h3\u003e\u003ca id=\"user-content-strand-bias\" class=\"anchor\" aria-label=\"Permalink: Strand bias\" href=\"#strand-bias\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor conventional variant callers, GATK WES/WGS \u003ca href=\"http://gatkforums.broadinstitute.org/discussion/5533/strandoddsratio-computation\" rel=\"nofollow\"\u003erecommended values\u003c/a\u003e for SOR strand bias are SOR \u0026lt; 4 for SNVs and \u0026lt; 10 for indels. We haven\u0027t found this particularly useful, but a more detailed evaluation is necessary. For amplicon based targeted sequencing, RVSB\u0026gt;0.85 seems to reduce some errors. There is no hard filter by default as this is easy to do afterward using \u003ca href=\"http://samtools.github.io/bcftools/bcftools.html#filter\" rel=\"nofollow\"\u003ebcftools filter\u003c/a\u003e command.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributions\u003c/h2\u003e\u003ca id=\"user-content-contributions\" class=\"anchor\" aria-label=\"Permalink: Contributions\" href=\"#contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eEmail\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMatthieu Foll*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:follm@iarc.fr\"\u003efollm@iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eLead developer, contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTiffany Delhomme*\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:delhommet@students.iarc.fr\"\u003edelhommet@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMain developer, contact for support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNicolas Alcala\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:alcalan@fellows.iarc.fr\"\u003ealcalan@fellows.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003etumor/normal pair mode, power calculation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAurelie Gabriel\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"mailto:gabriela@students.iarc.fr\"\u003egabriela@students.iarc.fr\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eAlignment plot, nextflow pipeline, mpileup2readcounts, bash version\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eNeedlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data\nTiffany M. Delhomme, Patrice H. Avogbe, Aurelie A.G. Gabriel, Nicolas Alcala, Noemie Leblay, Catherine Voegele, Maxime Vallee, Priscilia Chopard, Amelie Chabrier, Behnoush Abedi-Ardekani, Valerie Gaborieau, Ivana Holcatova, Vladimir Janout, Lenka Foretova, Sasa Milosavljevic, David Zaridze, Anush Mukeriya, Elisabeth Brambilla, Paul Brennan, Ghislaine Scelo, Lynnette Fernandez-Cuesta, Graham Byrnes, Florence Le Calvez-Kelm, James D D. McKay, Matthieu Foll\nbioRxiv 639377; doi: \u003ca href=\"https://doi.org/10.1101/639377\" rel=\"nofollow\"\u003ehttps://doi.org/10.1101/639377\u003c/a\u003e. \u003ca href=\"https://www.biorxiv.org/content/10.1101/639377v1\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/639377v1\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNeedlestack has been used in these papers:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUrinary TERT promoter mutations as non-invasive biomarkers for the comprehensive detection of urothelial cancer\nAvogbe et al. 2019 \u003ca href=\"https://www.ebiomedicine.com/article/S2352-3964(19)30305-6/fulltext\" rel=\"nofollow\"\u003eEBioMedicine\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eFernandez-Cuesta L, Perdomo S, Avogbe PH, Leblay N, Delhomme TM, Gaborieau V, Abedi-Ardekani B, Chanudet E, Olivier M, Zaridze D, Mukeria A, Vilensky M, Holcatova I, Polesel J, Simonato L, Canova C, Lagiou P, Brambilla C, Brambilla E, Byrnes G, Scelo G, Le Calvez-Kelm F, Foll M, McKay JD, Brennan P. Identification of Circulating Tumor DNA for the Early Detection of Small-cell Lung Cancer. \u003ca href=\"http://www.sciencedirect.com/science/article/pii/S2352396416302894\" rel=\"nofollow\"\u003eEBioMedicine\u003c/a\u003e. 2016 Aug;10:117-23. doi: \u003ca href=\"http://dx.doi.org/10.1016/j.ebiom.2016.06.032\" rel=\"nofollow\"\u003e10.1016/j.ebiom.2016.06.032\u003c/a\u003e. Epub 2016 Jun 25. PubMed PMID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/27377626\" rel=\"nofollow\"\u003e27377626\u003c/a\u003e; PubMed Central PMCID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036515/\" rel=\"nofollow\"\u003ePMC5036515\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eLe Calvez-Kelm F, Foll M, Wozniak MB, Delhomme TM, Durand G, Chopard P, Pertesi M, Fabianova E, Adamcakova Z, Holcatova I, Foretova L, Janout V, Vallee MP, Rinaldi S, Brennan P, McKay JD, Byrnes GB, Scelo G. KRAS mutations in blood circulating cell-free DNA: a pancreatic cancer case-control. \u003ca href=\"http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget\u0026amp;page=article\u0026amp;op=view\u0026amp;path%5b%5d=12386\" rel=\"nofollow\"\u003eOncotarget\u003c/a\u003e. 2016 Nov 29;7(48):78827-78840. doi: \u003ca href=\"https://doi.org/10.18632/oncotarget.12386\" rel=\"nofollow\"\u003e10.18632/oncotarget.12386\u003c/a\u003e. PubMed PMID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/27705932\" rel=\"nofollow\"\u003e27705932\u003c/a\u003e; PubMed Central PMCID: \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5346680/\" rel=\"nofollow\"\u003ePMC5346680\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n", + "full_name": "fempar/fempar", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFEMPAR\u003c/h1\u003e\u003ca id=\"user-content-fempar\" class=\"anchor\" aria-label=\"Permalink: FEMPAR\" href=\"#fempar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eFinite Element Multiphysics PARallel solvers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://gitlab.com/fempar/fempar/commits/experimental\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9cafa391ff15a4005e434db9f678594cb7b1f0c6cab27930d401e6d9a8df5dd9/68747470733a2f2f6769746c61622e636f6d2f66656d7061722f66656d7061722f6261646765732f6578706572696d656e74616c2f6275696c642e737667\" alt=\"build status\" data-canonical-src=\"https://gitlab.com/fempar/fempar/badges/experimental/build.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitlab.com/fempar/fempar/commits/experimental\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d8043c7775be04e352875590b5f1f8a3ba1ff2abb486d387cd32b3832440b668/68747470733a2f2f6769746c61622e636f6d2f66656d7061722f66656d7061722f6261646765732f6578706572696d656e74616c2f636f7665726167652e737667\" alt=\"coverage report\" data-canonical-src=\"https://gitlab.com/fempar/fempar/badges/experimental/coverage.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFEMPAR\u003c/strong\u003e is a scientific software for the simulation of problems governed by partial differential equations (PDEs). It provides a set of state-of-the-art numerical discretizations, including finite element methods, discontinuous Galerkin methods, XFEM, and spline-based functional spaces. The library was originally designed to efficiently exploit distributed-memory supercomputers and easily handle multiphysics problems. It also provides a set of highly scalable numerical linear algebra solvers based on multilevel domain decomposition for the systems of equations that arise from PDE discretizations. Some applications of FEMPAR include the simulation of metal additive manufacturing processes, superconductor devices, breeding blankets in fusion reactors, and nuclear waste repositories.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSoftware design\u003c/h2\u003e\u003ca id=\"user-content-software-design\" class=\"anchor\" aria-label=\"Permalink: Software design\" href=\"#software-design\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor those who are interested on the design and rationale behind the mathematically-supported software abstractions in FEMPAR, a very through presentation is available at the following reference:\u003c/p\u003e\n\u003cp\u003eSantiago Badia, Alberto F. Mart\u00edn and Javier Principe. \u003cbr\u003e\nFEMPAR: An object-oriented parallel finite element framework. \u003cbr\u003e\n\u003cem\u003eArchives of Computational Methods in Engineering\u003c/em\u003e 25, 2 (2018), 195\u2013271. \u003cbr\u003e\n\u003ca href=\"https://arxiv.org/abs/1708.01773\" rel=\"nofollow\"\u003e[ArXiv link]\u003c/a\u003e \u003ca href=\"https://link.springer.com/article/10.1007%2Fs11831-017-9244-1\" rel=\"nofollow\"\u003e[DOI]\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial-Driven Introduction\u003c/h2\u003e\u003ca id=\"user-content-tutorial-driven-introduction\" class=\"anchor\" aria-label=\"Permalink: Tutorial-Driven Introduction\" href=\"#tutorial-driven-introduction\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA very nice tutorial-driven introduction to the software library (v1.0.0) can be found at the following reference:\u003c/p\u003e\n\u003cp\u003eSantiago Badia, and Alberto F. Mart\u00edn. \u003cbr\u003e\nA tutorial-driven introduction to the parallel finite element library FEMPAR v1.0.0 \u003cbr\u003e\n\u003cem\u003eComputer Physics Communications\u003c/em\u003e Volume 248, March 2020, 107059. \u003cbr\u003e\n\u003ca href=\"https://arxiv.org/abs/1908.00891\" rel=\"nofollow\"\u003e[ArXiv link]\u003c/a\u003e \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S0010465519303832\" rel=\"nofollow\"\u003e[DOI]\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinks\u003c/h2\u003e\u003ca id=\"user-content-links\" class=\"anchor\" aria-label=\"Permalink: Links\" href=\"#links\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"http://www.fempar.org/\" rel=\"nofollow\"\u003eWeb page\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.gitlab.com/fempar/fempar\" rel=\"nofollow\"\u003eOfficial repository\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.github.com/fempar/fempar\"\u003eMirror repository\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cdash.cimne.upc.edu/user.php\" rel=\"nofollow\"\u003eTesting dashboard\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://fempar.gitlab.io/documentation/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://listas.cimne.upc.edu/mailman/listinfo/fempar-dev\" rel=\"nofollow\"\u003eDevelopers mailing list\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe quickest and easiest way to start with FEMPAR is using \u003ca href=\"https://www.docker.com\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e. \u003ca href=\"https://opensource.com/resources/what-docker\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e is a tool designed to make it easier to create, deploy, and run applications by using containers.\u003c/p\u003e\n\u003cp\u003eFEMPAR provides a Docker \u003ca href=\"https://hub.docker.com/u/fempar\" rel=\"nofollow\"\u003econtainer with the required environment\u003c/a\u003e to compile the project source code and to run tutorials and tests. Click on \u003ca href=\"https://gitlab.com/fempar/fempar/tree/experimental/Containers/Docker\" rel=\"nofollow\"\u003ethis link\u003c/a\u003e for additional details.\u003c/p\u003e\n\u003cp\u003ePlease, follow the steps below to compile FEMPAR using the Docker container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e You must Sing up at https://hub.docker.com/ to get your_username_at_dockerhub\u003c/span\u003e\n$ sudo docker login --username=your_username_at_dockerhub\n$ sudo docker pull fempar/fempar-env:gnu-debug_p4est-serial \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Get Docker image from Docker Hub\u003c/span\u003e\n$ sudo docker run -ti fempar/fempar-env:gnu-debug_p4est-serial\n$ WORKDIR=/data\n$ SOURCES_DIR=\u003cspan class=\"pl-smi\"\u003e$WORKDIR\u003c/span\u003e/sources\n$ FEMPAR_DIR=\u003cspan class=\"pl-smi\"\u003e$WORKDIR\u003c/span\u003e/FEMPAR\n$ git clone --recursive https://github.com/fempar/fempar \u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$WORKDIR\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e invokes CMake while setting up appropriate values for CMake variables\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run $SOURCES_DIR/Tools/configure -h to get an informative message on screen\u003c/span\u003e\n$ \u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e/Tools/configure -s \u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e/SuperBuild -c GNU --without-tests \n$ make\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf you are new to FEMPAR, the very first point to start with are the \u003ca href=\"https://gitlab.com/fempar/fempar/tree/experimental/Tutorials\" rel=\"nofollow\"\u003etutorial programs\u003c/a\u003e available at the official FEMPAR repository.\nAfter completing the previous compilation steps, you can compile and run FEMPAR tutorials with the following steps:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e...\n$ FEMPAR_TUTORIALS_DIR=\u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e/Tutorials\n$ mkdir -p \u003cspan class=\"pl-smi\"\u003e$FEMPAR_TUTORIALS_DIR\u003c/span\u003e\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$FEMPAR_TUTORIALS_DIR\u003c/span\u003e\n$ cmake -DFEMPAR_DIR=\u003cspan class=\"pl-smi\"\u003e$FEMPAR_DIR\u003c/span\u003e -DFEMPAR_TUTORIAL=tutorial_01_poisson_sharp_circular_wave \u003cspan class=\"pl-smi\"\u003e$SOURCES_DIR\u003c/span\u003e/Tutorials\n$ make -j 4\n$ bin/tutorial_01_poisson_sharp_circular_wave --help \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e get informative message on screen\u003c/span\u003e\n$ bin/tutorial_01_poisson_sharp_circular_wave [optional \u003cspan class=\"pl-c1\"\u003ecommand\u003c/span\u003e line arguments] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e execute the tutorial\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis particular set of commands compiles the tutorial program named \u003ccode\u003etutorial_01_poisson_sharp_circular_wave\u003c/code\u003e. You may use any of the tutorial names at \u003ccode\u003e$SOURCES_DIR/Tutorials\u003c/code\u003e as well.\nThe main source code folder of each tutorial is supplied with the \u003ccode\u003etutorial_invokation_examples\u003c/code\u003e file. This file contains command-line invokation examples of the corresponding tutorial, and thus is\na good starting point to start playing with the tutorial.\u003c/p\u003e\n\u003cp\u003eAt present, we only offer a reduced set of tutorial programs which show the usage of the most simple FEMPAR features. However, we plan in the near future to extend the current\ntutorial suite towards demonstration of the various aspects of the library.\u003c/p\u003e\n\u003cp\u003eIn the meantime, you can also take a look at the\n\u003ca href=\"https://gitlab.com/fempar/fempar/tree/experimental/Sources/Tests/Serial\" rel=\"nofollow\"\u003eserial\u003c/a\u003e and \u003ca href=\"https://gitlab.com/fempar/fempar/tree/experimental/Sources/Tests/Par\" rel=\"nofollow\"\u003eMPI-parallel test programs\u003c/a\u003e available at the official FEMPAR repository.\nWhile these programs go far beyond the current tutorial programs in exploiting many of the most advanced FEMPAR features, they are, though, not fully documented,\nso they are only recommended for advanced users. Test programs are compiled as a final stage of the compilation of FEMPAR. In order to activate the compilation of tests, you have to replace\n\u003ccode\u003e--without-tests\u003c/code\u003e by \u003ccode\u003e--with-tests\u003c/code\u003e in the steps above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNative compilation\u003c/h2\u003e\u003ca id=\"user-content-native-compilation\" class=\"anchor\" aria-label=\"Permalink: Native compilation\" href=\"#native-compilation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCompiler Vendor Support\u003c/th\u003e\n\u003cth\u003eNotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/34ed91c2637295daf1750150794a2b4b7c7666aa82793ede055e9e8c4a8d6328/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f474e552d76382e312e302b2d627269676874677265656e2e737667\" alt=\"Compiler\" data-canonical-src=\"https://img.shields.io/badge/GNU-v8.1.0+-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFull support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e6c6a361fb7121abef590361a8cc3f343d017d24a45a226317c462608dd733ca/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f496e74656c2d7631392e302e302b2d627269676874677265656e2e737667\" alt=\"Compiler\" data-canonical-src=\"https://img.shields.io/badge/Intel-v19.0.0+-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFull support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFEMPAR\u003c/strong\u003e uses \u003ca href=\"https://cmake.org/\" rel=\"nofollow\"\u003eCMake\u003c/a\u003e as a portable compilation system.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBuild Managgers Support\u003c/th\u003e\n\u003cth\u003eNotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b292e19c3c7ec481687750c442ba6fb684e73fc517f8a88bfbd97bf0057051ce/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f434d616b652d76322e382e31312b2d79656c6c6f772e737667\" alt=\"Compiler\" data-canonical-src=\"https://img.shields.io/badge/CMake-v2.8.11+-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDoes not support \u003ccode\u003efortran_tester\u003c/code\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/408f81d2680e5a8ee6c9f00e2c9077962126c152dc4456ce041451a3b8646617/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f434d616b652d76332e312e302b2d627269676874677265656e2e737667\" alt=\"Compiler\" data-canonical-src=\"https://img.shields.io/badge/CMake-v3.1.0+-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFull support\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNative compilation is only recommended for experienced users. It requires to set up (configure, compile, install, etc) in your own all the mandatory (and optional) software dependencies required to deploy FEMPAR in your Desktop/Laptop or HPC cluster computing environment. This approach is not fully documented here yet.\nIn the meantime, you may take a look at the \u003cstrong\u003eDockerfile recipes\u003c/strong\u003e (\u003ca href=\"https://gitlab.com/fempar/fempar/blob/experimental/Containers/Docker/gnu/Dockerfile\" rel=\"nofollow\"\u003e1\u003c/a\u003e, \u003ca href=\"https://gitlab.com/fempar/fempar/blob/experimental/Containers/Docker/gnu/debug/Dockerfile\" rel=\"nofollow\"\u003e2\u003c/a\u003e, \u003ca href=\"https://gitlab.com/fempar/fempar/blob/experimental/Containers/Docker/gnu/debug/p4est_serial/Dockerfile\" rel=\"nofollow\"\u003e3\u003c/a\u003e) which are used to create the \u003ccode\u003efempar/fempar-env:gnu-debug_p4est-serial\u003c/code\u003e Docker image above in order to grasp how you may compile FEMPAR\u0027s dependencies on your Desktop/Laptop or HPC infrastructure.\u003c/p\u003e\n\u003cp\u003eWe strongly recommend to use the \u003ccode\u003econfigure\u003c/code\u003e script in \u003ccode\u003e$FEMPARDIR/Tools\u003c/code\u003e, where we assume hereafter that \u003ccode\u003eFEMPARDIR\u003c/code\u003e is an environment variable pointing to the path of the root directory of FEMPAR\u0027s git repository. Information of the script can be obtained by typing \u003ccode\u003econfigure --help\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eWe also strongly recommend to use the \u003ccode\u003emodule\u003c/code\u003e functionality, in order to easily switch between different compilers and compiler versions, and to automatically have a well-defined environment.\nInstructions to set up this functionality can be found \u003ca href=\"XXX\"\u003ehere\u003c/a\u003e.\nFor instance, the compilation of \u003cstrong\u003eFEMPAR\u003c/strong\u003e using the previous two functionalities using \u003cstrong\u003eGNU\u003c/strong\u003e compilers would read:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir build\n$ cd build\n$ module load gcc/8.2.0\n$ $FEMPARDIR/Tools/configure -c GNU -s $FEMPARDIR/SuperBuild\n$ make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstead, for INTEL compilers, the last part would read:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ module load ifc/19.0.0\n$ $FEMPARDIR/Tools/configure -s $FEMPARDIR/SuperBuild\n$ make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe compiler by default is the \u003cstrong\u003eINTEL\u003c/strong\u003e compiler. The tests are compiled unless otherwise stated with \u003ccode\u003e--without-tests\u003c/code\u003e.\nOne driver can be included in the compilation by adding \u003ccode\u003e-d drivername\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor stubborn users that want not to take profit from the previous tools, a manual way to compile \u003cstrong\u003eFEMPAR\u003c/strong\u003e under Linux is (with compilation of tests included):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ mkdir build\n$ cd build\n$ cmake ../fempar/SuperBuild -DFEMPAR_ENABLE_TESTS=ON\n$ make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eassuming that the right environment configuration is in place through, e.g., \u003ccode\u003e.bashrc\u003c/code\u003e. But this approach is prone to error when switching environments.\nAnother approach is to directly include in the \u003ccode\u003ecmake\u003c/code\u003e call the right compilation flags and values (see one of the notes below).\u003c/p\u003e\n\u003cp\u003eIn order to configure the compilation of a driver (right after the previous steps):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build\n$ cmake . -DFEMPAR_DRIVER=driver_folder_name\n$ make\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn order to compile FEMPAR library and tests if the first block of commands has been executed:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build/FEMPAR\n$ make -jP\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewith \u003ccode\u003eP\u003c/code\u003e being the number of parallel processes involved in the compilation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFEMPAR\u003c/strong\u003e compiles with GNU Fortran compiler 5.3.0 (and newer versions) and Intel Fortran compiler 16.0.0 (and newer versions).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun tests\u003c/h2\u003e\u003ca id=\"user-content-run-tests\" class=\"anchor\" aria-label=\"Permalink: Run tests\" href=\"#run-tests\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to run the tests, we need the right environment. If we are relying on \u003ccode\u003emodule\u003c/code\u003e functionalities, we must be sure that the required modules are load in the terminal in which we want to run the tests.\nIt seems that the only module that has to be loaded is \u003ccode\u003emkl\u003c/code\u003e, whereas the path to other dynamic libraries is hard-coded, e.g., \u003ccode\u003eopenmpi\u003c/code\u003e. In any case, one can use the initialization suggested in the \u003ccode\u003emodule\u003c/code\u003e manual and pre-load some modules in \u003ccode\u003emodulerc\u003c/code\u003e.\nAssuming the right environment is in place, to run all tests in fast mode, we just do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build/FEMPAR\n$ ctest -R fast -VV\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build/FEMPAR\n$ ctest -R test_name\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eto run a particular test.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun drivers\u003c/h2\u003e\u003ca id=\"user-content-run-drivers\" class=\"anchor\" aria-label=\"Permalink: Run drivers\" href=\"#run-drivers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGiven a driver \u003ccode\u003edriver_name\u003c/code\u003e, to run it (assuming it has been compiled, see above), we do:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cd build/DRIVERS/driver_name/bin\n$ mpirun -np P ./driver_name [options]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere \u003ccode\u003eP\u003c/code\u003e is the number of MPI processes to be used. Clearly, \u003ccode\u003empirun -np P\u003c/code\u003e must be eliminated to run serial drivers.\u003c/p\u003e\n\u003cp\u003eTo see the different options and default values we can do\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ ./driver_name --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTesting dashboard (CDash)\u003c/h2\u003e\u003ca id=\"user-content-testing-dashboard-cdash\" class=\"anchor\" aria-label=\"Permalink: Testing dashboard (CDash)\" href=\"#testing-dashboard-cdash\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis project offers to its users/developers a testing dashboard service. This service is powered by \u003ca href=\"https://www.cdash.org/\" rel=\"nofollow\"\u003eCDash\u003c/a\u003e on a\nserver hosted by CIMNE. Click \u003ca href=\"https://cdash.cimne.upc.edu/user.php\" rel=\"nofollow\"\u003ehere\u003c/a\u003e in order to access to the web interface of the service.\nThe CDash server gathers and displays rich information regarding the execution of tests which are performed each\ntime you push into a branch of the fempar repository, and thus, lets you know, e.g., which tests failed,\nwith which compiler, amount of code, and code covered by the tests, memory defects (e.g., leaks), etc.\nIf you want to access to this (highly recommended) service, then you have to follow the instructions available\n\u003ca href=\"https://cdash.cimne.upc.edu/user.php\" rel=\"nofollow\"\u003ehere\u003c/a\u003e. Once you are provided with a new user account, then \u003ca href=\"mailto:amartin@cimne.upc.edu\"\u003ee-mail us\u003c/a\u003e,\nso that we can add you to the fempar project at the CDash server.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKnown issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known issues\" href=\"#known-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: we have detected that some tests (e.g., \u003ccode\u003etest_poisson_unffited\u003c/code\u003e) do \u003cstrong\u003eNOT\u003c/strong\u003e pass with GNU Fortran compiler 5.5.0, 6.3.0, \u0026amp; 7.3.0 for \u003ccode\u003eexperimental\u003c/code\u003e\ncommit f7b4199e due to what it seems to be a compiler BUG. See issue #259 for more details.\nPlease also note that we do not actually know since\nwhich commit in \u003ccode\u003eexperimental\u003c/code\u003e this is happening, but only that it happens at this one. Thus, avoid using these GNU Fortran compiler versions.\nWe neither know whether this also happens for GNU compiler version different from the ones above. It does NOT happen with 5.4.0.\n\u003cstrong\u003eUPDATE\u003c/strong\u003e: As pointed out by @principe, the issue disappears with \u003ccode\u003egfortran\u003c/code\u003e v8.1.0. This version can easily be installed in Ubuntu as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ sudo apt-get install gfortran-8\n$ sudo apt-get install g++-8\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you do not currently use or do not plan to install the modules environment,\nthen one can use this compiler version when configuring FEMPAR as:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecmake -DCMAKE_Fortran_COMPILER=gfortran-8 -DCMAKE_C_COMPILER=gcc-8 \\\n-DCMAKE_CXX_COMPILER=g++-8 -DFEMPAR_ENABLE_TESTS=ON -DFEMPAR_ENABLE_OPENMP=OFF \\\n-DFORTRAN_EXTRA_FLAGS= -DC_EXTRA_FLAGS= -DCMAKE_BUILD_TYPE=Debug -DMPIEXEC_PREFLAGS= \\\n-DFEMPAR_ENABLE_BLAS=ON -DFEMPAR_ENABLE_LAPACK=ON -DFEMPAR_ENABLE_MKL=ON \\\n-DFEMPAR_ENABLE_P4EST=ON -DFEMPAR_ENABLE_UMFPACK=ON -DFEMPAR_ENABLE_METIS=ON \\\n-DFEMPAR_ENABLE_GIDPOST=OFF ../fempar/SuperBuild/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: there is also an open issue with gfortran 6.4.1 and gfortran 7.3.1 (\u003ca href=\"https://gitlab.com/fempar/XH5For/issues/7\" rel=\"nofollow\"\u003ehttps://gitlab.com/fempar/XH5For/issues/7\u003c/a\u003e). An internal\ncompiler error raises when compiling FoX, a third party library of XH5For. \u003cstrong\u003eUPDATE\u003c/strong\u003e: This issue has been already by-passed in FEMPAR\u0027s experimental\nbranch from commit 47b947f2. See \u003ca href=\"https://gitlab.com/fempar/XH5For/issues/7\" rel=\"nofollow\"\u003ehttps://gitlab.com/fempar/XH5For/issues/7\u003c/a\u003e for more details\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: we also detected a BUG with Intel Fortran compiler 18.0.0 related to missing initialization of member variables to default values in the case of\npolymorphic allocatable variables. For example, \u003ccode\u003etest_transient_poisson\u003c/code\u003e do not pass with Intel Fortran compiler 18.0.0 for commit 5176d2976659c64f45e35022bfea5dcb1e72045e.\ndue to a compiler BUG (see issue #250). Thus, avoid using this Intel Fortran compiler version. With Intel compiler 18.0.1 this issue disappears\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: if you plan to use \u003ccode\u003eIntel Parallel Studio XE 2019\u003c/code\u003e in order to compile FEMPAR with the Intel compilers in your machine (this is indeed the only version currently supported\nby \u003ccode\u003eUbuntu 18.04\u003c/code\u003e), please note the following. The most annoying issue is related to the compilation of \u003ccode\u003eSISL\u003c/code\u003e. If you use the \u003ccode\u003eicc\u003c/code\u003e compiler, the \u003ccode\u003eicpc\u003c/code\u003e C++ compiler must be used as well.\nThis is already achieved by fempar\u0027s \u003ccode\u003econfigure\u003c/code\u003e script in \u003ccode\u003eTools\u003c/code\u003e whenever you specify \u003ccode\u003e-c Intel\u003c/code\u003e, but it won\u0027t be if you call \u003ccode\u003ecmake\u003c/code\u003e directly.\nIn the latter case, you must specify \u003ccode\u003e-DCMAKE_CXX_COMPILER=icpc\u003c/code\u003e explicitly when invoking cmake.\u003c/p\u003e\n", "stargazers_count": 49, - "subscribers_count": 11, - "topics": [ - "nextflow", - "pipeline", - "regression", - "bam-files", - "variants", - "ngs", - "somatic-mutations", - "negative-binomial-regression", - "samtools", - "docker" - ], - "updated_at": 1729066855.0 + "subscribers_count": 10, + "topics": [], + "updated_at": 1710751230.0 }, { "data_format": 2, @@ -41224,10 +41252,10 @@ var data = "full_name": "wsjeon/maddpg-rllib", "latest_release": null, "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMulti-Agent DDPG in Ray/RLlib\u003c/h1\u003e\u003ca id=\"user-content-multi-agent-ddpg-in-rayrllib\" class=\"anchor\" aria-label=\"Permalink: Multi-Agent DDPG in Ray/RLlib\" href=\"#multi-agent-ddpg-in-rayrllib\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe codes in \u003ca href=\"https://github.com/openai/maddpg\"\u003eOpenAI/MADDPG\u003c/a\u003e were refactored in RLlib, and test results are given in \u003ccode\u003e./plots\u003c/code\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIt was tested on 7 scenarios of \u003ca href=\"https://github.com/openai/multiagent-particle-envs\"\u003eOpenAI/Multi-Agent Particle Environment (MPE)\u003c/a\u003e.\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esimple\u003c/code\u003e, \u003ccode\u003esimple_adversary\u003c/code\u003e, \u003ccode\u003esimple_crypto\u003c/code\u003e, \u003ccode\u003esimple_push\u003c/code\u003e, \u003ccode\u003esimple_speaker_listener\u003c/code\u003e, \u003ccode\u003esimple_spread\u003c/code\u003e, \u003ccode\u003esimple_tag\u003c/code\u003e\n\u003cul\u003e\n\u003cli\u003eRLlib MADDPG shows the similar performance as OpenAI MADDPG on 7 scenarios except \u003ccode\u003esimple_crypto\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eHyperparameters were set to follow the original hyperparameter setting in \u003ca href=\"https://github.com/openai/maddpg\"\u003eOpenAI/MADDPG\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEmpirically, \u003cem\u003eremoving lz4\u003c/em\u003e makes running much faster. I guess this is due to the small-size observation in MPE.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/openai/maddpg\"\u003eOpenAI/MADDPG\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/openai/multiagent-particle-envs\"\u003eOpenAI/Multi-Agent Particle Environment\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/wsjeon/multiagent-particle-envs\"\u003ewsjeon/Multi-Agent Particle Environment\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003eIt includes the minor change for MPE to work with recent OpenAI Gym.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 51, + "stargazers_count": 50, "subscribers_count": 2, "topics": [], - "updated_at": 1731152852.0 + "updated_at": 1731949950.0 }, { "data_format": 2, @@ -41315,6 +41343,24 @@ var data = ], "updated_at": 1727418358.0 }, + { + "data_format": 2, + "description": "Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps", + "filenames": [ + "Singularity" + ], + "full_name": "MASILab/Synb0-DISCO", + "latest_release": "v3.1", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSynb0-DISCO\u003c/h1\u003e\u003ca id=\"user-content-synb0-disco\" class=\"anchor\" aria-label=\"Permalink: Synb0-DISCO\" href=\"#synb0-disco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#dockerized-application\"\u003eDockerized Application\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#docker-instructions\"\u003eDocker Instructions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity-instructions\"\u003eSingularity Instructions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#non-containerized-instructions\"\u003eNon-containerized Instructions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#flags\"\u003eFlags\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#inputs\"\u003eInputs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#after-running\"\u003eAfter Running\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository implements the paper \"Synthesized b0 for diffusion distortion correction\" and \"Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps\".\u003c/p\u003e\n\u003cp\u003eThis tool aims to enable susceptibility distortion correction with historical and/or limited datasets that do not include specific sequences for distortion correction (i.e. reverse phase-encoded scans). In short, we synthesize an \"undistorted\" b=0 image that matches the geometry of structural T1w images and also matches the contrast from diffusion images. We can then use this \u0027undistorted\u0027 image in standard pipelines (i.e. TOPUP) and tell the algorithm that this synthetic image has an infinite bandwidth. Note that the processing below enables both image synthesis, and also synthesis + full pipeline correction, if desired.\u003c/p\u003e\n\u003cp\u003ePlease use the following citations to refer to this work:\u003c/p\u003e\n\u003cp\u003eSchilling KG, Blaber J, Hansen C, Cai L, Rogers B, Anderson AW, Smith S, Kanakaraj P, Rex T, Resnick SM, Shafer AT, Cutting LE, Woodward N, Zald D, Landman BA. Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps. PLoS One. 2020 Jul 31;15(7):e0236418. doi: 10.1371/journal.pone.0236418. PMID: 32735601; PMCID: PMC7394453.\u003c/p\u003e\n\u003cp\u003eSchilling KG, Blaber J, Huo Y, Newton A, Hansen C, Nath V, Shafer AT, Williams O, Resnick SM, Rogers B, Anderson AW, Landman BA. Synthesized b0 for diffusion distortion correction (Synb0-DisCo). Magn Reson Imaging. 2019 Dec;64:62-70. doi: 10.1016/j.mri.2019.05.008. Epub 2019 May 7. PMID: 31075422; PMCID: PMC6834894.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDockerized Application\u003c/h2\u003e\u003ca id=\"user-content-dockerized-application\" class=\"anchor\" aria-label=\"Permalink: Dockerized Application\" href=\"#dockerized-application\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor deployment we provide a \u003ca href=\"https://hub.docker.com/repository/docker/leonyichencai/synb0-disco\" rel=\"nofollow\"\u003eDocker container\u003c/a\u003e which uses the trained model to predict the undistorted b0 to be used in susceptability distortion correction for diffusion weighted MRI. For those who prefer, Docker containers can be converted to Singularity containers (see below).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Instructions:\u003c/h2\u003e\u003ca id=\"user-content-docker-instructions\" class=\"anchor\" aria-label=\"Permalink: Docker Instructions:\" href=\"#docker-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run --rm \\\n-v $(pwd)/INPUTS/:/INPUTS/ \\\n-v $(pwd)/OUTPUTS:/OUTPUTS/ \\\n-v \u0026lt;path to license.txt\u0026gt;:/extra/freesurfer/license.txt \\\n--user $(id -u):$(id -g) \\\nleonyichencai/synb0-disco:v3.1\n\u0026lt;flags\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf within your current directory you have your INPUTS and OUTPUTS folder, you can run this command copy/paste with the only change being \u0026lt;path to license.txt\u0026gt; should point to the freesurfer license.txt file on your system.\u003c/li\u003e\n\u003cli\u003eIf INPUTS and OUTPUTS are not within your current directory, you will need to change $(pwd)/INPUTS/ to the full path to your input directory, and similarly for OUTPUTS.\u003c/li\u003e\n\u003cli\u003eFor Mac users, Docker defaults allows only 2gb of RAM and 2 cores - we suggest giving Docker access to \u0026gt;13Gb of RAM\u003c/li\u003e\n\u003cli\u003eAdditionally on MAC, if permissions issues prevent binding the path to the license.txt file, we suggest moving the freesurfer license.txt file to the current path and replacing the path line to \" $(pwd)/license.txt:/extra/freesurfer/license.txt \"\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Instructions\u003c/h2\u003e\u003ca id=\"user-content-singularity-instructions\" class=\"anchor\" aria-label=\"Permalink: Singularity Instructions\" href=\"#singularity-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, build the synb0.simg container in the current directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull docker://leonyichencai/synb0-disco:v3.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, to run the synb0.simg container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e \\\n-B INPUTS/:/INPUTS \\\n-B OUTPUTS/:/OUTPUTS \\\n-B \u0026lt;path to license.txt\u0026gt;:/extra/freesurfer/license.txt \\\n\u0026lt;path to synb0.simg\u0026gt;\n\u0026lt;flags\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\u0026lt;path to license.txt\u0026gt; should point to freesurfer licesnse.txt file\u003c/li\u003e\n\u003cli\u003e\u0026lt;path to synb0.simg\u0026gt; should point to the singularity container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNon-containerized Instructions\u003c/h2\u003e\u003ca id=\"user-content-non-containerized-instructions\" class=\"anchor\" aria-label=\"Permalink: Non-containerized Instructions\" href=\"#non-containerized-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you choose to run this in bash, the primary script is located in src/pipeline.sh. The paths in pipeline.sh are specific to the docker/singularity file\nsystems, but the processing can be replicated using the scripts in src. These utilize freesurfer, FSL, ANTS, and a python environment with pytorch.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFlags:\u003c/h2\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags:\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e--notopup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSkip the application of FSL\u0027s topup susceptibility correction. As a default, we run topup for you, although you may want to run this on your own (for example with your own config file, or if you would like to utilize multiple b0\u0027s). If desired, you can concatenate the original (distorted) b=0 and the sythesized b=0 with: \"fslmerge -t /OUTPUTS/b0_all.nii.gz /OUTPUTS/b0_d_smooth.nii.gz /OUTPUTS/b0_u.nii.gz\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--stripped\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLets the container know the supplied T1 has already been skull stripped. As a default, we assume it is not skull stripped. \u003cem\u003ePlease note this feature requires a well-stripped T1 as stripping artifacts can affect performance.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe INPUTS directory must contain the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0.nii.gz: the non-diffusion weighted image, must be a 3D image (either one b=0, or the average of all b=0 volumes)\u003c/li\u003e\n\u003cli\u003eT1.nii.gz: the T1-weighted image (either raw or skull-stripped, see \u003ca href=\"#flags\"\u003eFlags\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eacqparams.txt: A text file that describes the acqusition parameters, and is described in detail on the FslWiki for topup (\u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup\u003c/a\u003e). Briefly,\nit describes the direction of distortion and tells TOPUP that the synthesized image has an effective echo spacing of 0 (infinite bandwidth). An example acqparams.txt is\ndisplayed below, in which distortion is in the second dimension, note that the second row corresponds to the synthesized, undistorted, b0:\n\u003cpre\u003e\u003ccode\u003e$ cat acqparams.txt \n0 1 0 0.062\n0 1 0 0.000\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter running, the OUTPUTS directory contains the following preprocessing files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eT1_mask.nii.gz: brain extracted (skull-stripped) T1 (a copy of the input if T1.nii.gz is already skull-stripped)\u003c/li\u003e\n\u003cli\u003eT1_norm.nii.gz: normalized T1\u003c/li\u003e\n\u003cli\u003eepi_reg_d.mat: epi_reg b0 to T1 in FSL format\u003c/li\u003e\n\u003cli\u003eepi_reg_d_ANTS.txt: epi_reg to T1 in ANTS format\u003c/li\u003e\n\u003cli\u003eANTS0GenericAffine.mat: Affine ANTs registration of T1_norm to/from MNI space\u003c/li\u003e\n\u003cli\u003eANTS1Warp.nii.gz: Deformable ANTs registration of T1_norm to/from MNI space\u003c/li\u003e\n\u003cli\u003eANTS1InverseWarp.nii.gz: Inverse deformable ANTs registration of T1_norm to/from MNI space\u003c/li\u003e\n\u003cli\u003eT1_norm_lin_atlas_2_5.nii.gz: linear transform T1 to MNI\u003c/li\u003e\n\u003cli\u003eb0_d_lin_atlas_2_5.nii.gz: linear transform distorted b0 in MNI space\u003c/li\u003e\n\u003cli\u003eT1_norm_nonlin_atlas_2_5.nii.gz: nonlinear transform T1 to MNI\u003c/li\u003e\n\u003cli\u003eb0_d_nonlin_atlas_2_5.nii.gz: nonlinear transform distorted b0 in MNI space\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe OUTPUTS directory also contains inferences (predictions) for each of five folds utilizing T1_norm_lin_atlas_2_5.nii.gz and b0_d_lin_atlas_2_5.nii.gz as inputs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_1.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_2.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_3.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_4.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_5.nii.gz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAfter inference the ensemble average is taken in atlas space:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_merged.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5.nii.gz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is then moved to native space for the undistorted, synthetic output:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0_u.nii.gz: Synthetic b0 native space\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe undistorted synthetic output, and a smoothed distorted input can then be stacked together for topup:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0_d_smooth.nii.gz: smoothed b0\u003c/li\u003e\n\u003cli\u003eb0_all.nii.gz: stack of distorted and synthetized image as input to topup\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, the topup outputs to be used for eddy:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etopup_movpar.txt\u003c/li\u003e\n\u003cli\u003eb0_all_topup.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_all.topup_log\u003c/li\u003e\n\u003cli\u003etopup_fieldcoef.nii.gz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAfter Running\u003c/h2\u003e\u003ca id=\"user-content-after-running\" class=\"anchor\" aria-label=\"Permalink: After Running\" href=\"#after-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter running, we envision using the topup outputs directly with FSL\u0027s eddy command, exactly as would be done if a full set of reverse PE scans was acquired. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eeddy --imain=path/to/diffusiondata.nii.gz --mask=path/to/brainmask.nii.gz \\\n--acqp=path/to/acqparams.txt --index=path/to/index.txt \\\n--bvecs=path/to/bvecs.txt --bvals=path/to/bvals.txt \n--topup=path/to/OUTPUTS/topup --out=eddy_unwarped_images\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere imain is the original diffusion data, mask is a brain mask, acqparams is from before, index is the traditional eddy index file which contains an index (most likely a 1) for every volume in the diffusion dataset, topup points to the output of the singularity/docker pipeline, and out is the eddy-corrected images utilizing the field coefficients from the previous step.\u003c/p\u003e\n\u003cp\u003eAlternatively, if you choose to run --notopup flag, the file you are interested in is b0_all. This is a concatenation of the real b0 and the synthesized undistorted b0. We run topup with this file, although you may chose to do so utilizing your topup version or config file.\u003c/p\u003e\n", + "stargazers_count": 60, + "subscribers_count": 14, + "topics": [ + "deep-learning", + "diffusion-mri", + "distortion-correction" + ], + "updated_at": 1731422714.0 + }, { "data_format": 2, "description": "Supervised and RL Models for No Press Diplomacy", @@ -41335,21 +41381,22 @@ var data = }, { "data_format": 2, - "description": "Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps", + "description": "ascii database of pokemon... in python!", "filenames": [ "Singularity" ], - "full_name": "MASILab/Synb0-DISCO", - "latest_release": "v3.1", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSynb0-DISCO\u003c/h1\u003e\u003ca id=\"user-content-synb0-disco\" class=\"anchor\" aria-label=\"Permalink: Synb0-DISCO\" href=\"#synb0-disco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContents\u003c/h2\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#dockerized-application\"\u003eDockerized Application\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#docker-instructions\"\u003eDocker Instructions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#singularity-instructions\"\u003eSingularity Instructions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#non-containerized-instructions\"\u003eNon-containerized Instructions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#flags\"\u003eFlags\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#inputs\"\u003eInputs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#outputs\"\u003eOutputs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#after-running\"\u003eAfter Running\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis repository implements the paper \"Synthesized b0 for diffusion distortion correction\" and \"Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps\".\u003c/p\u003e\n\u003cp\u003eThis tool aims to enable susceptibility distortion correction with historical and/or limited datasets that do not include specific sequences for distortion correction (i.e. reverse phase-encoded scans). In short, we synthesize an \"undistorted\" b=0 image that matches the geometry of structural T1w images and also matches the contrast from diffusion images. We can then use this \u0027undistorted\u0027 image in standard pipelines (i.e. TOPUP) and tell the algorithm that this synthetic image has an infinite bandwidth. Note that the processing below enables both image synthesis, and also synthesis + full pipeline correction, if desired.\u003c/p\u003e\n\u003cp\u003ePlease use the following citations to refer to this work:\u003c/p\u003e\n\u003cp\u003eSchilling KG, Blaber J, Hansen C, Cai L, Rogers B, Anderson AW, Smith S, Kanakaraj P, Rex T, Resnick SM, Shafer AT, Cutting LE, Woodward N, Zald D, Landman BA. Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps. PLoS One. 2020 Jul 31;15(7):e0236418. doi: 10.1371/journal.pone.0236418. PMID: 32735601; PMCID: PMC7394453.\u003c/p\u003e\n\u003cp\u003eSchilling KG, Blaber J, Huo Y, Newton A, Hansen C, Nath V, Shafer AT, Williams O, Resnick SM, Rogers B, Anderson AW, Landman BA. Synthesized b0 for diffusion distortion correction (Synb0-DisCo). Magn Reson Imaging. 2019 Dec;64:62-70. doi: 10.1016/j.mri.2019.05.008. Epub 2019 May 7. PMID: 31075422; PMCID: PMC6834894.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDockerized Application\u003c/h2\u003e\u003ca id=\"user-content-dockerized-application\" class=\"anchor\" aria-label=\"Permalink: Dockerized Application\" href=\"#dockerized-application\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor deployment we provide a \u003ca href=\"https://hub.docker.com/repository/docker/leonyichencai/synb0-disco\" rel=\"nofollow\"\u003eDocker container\u003c/a\u003e which uses the trained model to predict the undistorted b0 to be used in susceptability distortion correction for diffusion weighted MRI. For those who prefer, Docker containers can be converted to Singularity containers (see below).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker Instructions:\u003c/h2\u003e\u003ca id=\"user-content-docker-instructions\" class=\"anchor\" aria-label=\"Permalink: Docker Instructions:\" href=\"#docker-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esudo docker run --rm \\\n-v $(pwd)/INPUTS/:/INPUTS/ \\\n-v $(pwd)/OUTPUTS:/OUTPUTS/ \\\n-v \u0026lt;path to license.txt\u0026gt;:/extra/freesurfer/license.txt \\\n--user $(id -u):$(id -g) \\\nleonyichencai/synb0-disco:v3.1\n\u0026lt;flags\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eIf within your current directory you have your INPUTS and OUTPUTS folder, you can run this command copy/paste with the only change being \u0026lt;path to license.txt\u0026gt; should point to the freesurfer license.txt file on your system.\u003c/li\u003e\n\u003cli\u003eIf INPUTS and OUTPUTS are not within your current directory, you will need to change $(pwd)/INPUTS/ to the full path to your input directory, and similarly for OUTPUTS.\u003c/li\u003e\n\u003cli\u003eFor Mac users, Docker defaults allows only 2gb of RAM and 2 cores - we suggest giving Docker access to \u0026gt;13Gb of RAM\u003c/li\u003e\n\u003cli\u003eAdditionally on MAC, if permissions issues prevent binding the path to the license.txt file, we suggest moving the freesurfer license.txt file to the current path and replacing the path line to \" $(pwd)/license.txt:/extra/freesurfer/license.txt \"\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity Instructions\u003c/h2\u003e\u003ca id=\"user-content-singularity-instructions\" class=\"anchor\" aria-label=\"Permalink: Singularity Instructions\" href=\"#singularity-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, build the synb0.simg container in the current directory:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull docker://leonyichencai/synb0-disco:v3.1\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen, to run the synb0.simg container:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity run -e \\\n-B INPUTS/:/INPUTS \\\n-B OUTPUTS/:/OUTPUTS \\\n-B \u0026lt;path to license.txt\u0026gt;:/extra/freesurfer/license.txt \\\n\u0026lt;path to synb0.simg\u0026gt;\n\u0026lt;flags\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\u0026lt;path to license.txt\u0026gt; should point to freesurfer licesnse.txt file\u003c/li\u003e\n\u003cli\u003e\u0026lt;path to synb0.simg\u0026gt; should point to the singularity container\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eNon-containerized Instructions\u003c/h2\u003e\u003ca id=\"user-content-non-containerized-instructions\" class=\"anchor\" aria-label=\"Permalink: Non-containerized Instructions\" href=\"#non-containerized-instructions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you choose to run this in bash, the primary script is located in src/pipeline.sh. The paths in pipeline.sh are specific to the docker/singularity file\nsystems, but the processing can be replicated using the scripts in src. These utilize freesurfer, FSL, ANTS, and a python environment with pytorch.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFlags:\u003c/h2\u003e\u003ca id=\"user-content-flags\" class=\"anchor\" aria-label=\"Permalink: Flags:\" href=\"#flags\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e--notopup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSkip the application of FSL\u0027s topup susceptibility correction. As a default, we run topup for you, although you may want to run this on your own (for example with your own config file, or if you would like to utilize multiple b0\u0027s). If desired, you can concatenate the original (distorted) b=0 and the sythesized b=0 with: \"fslmerge -t /OUTPUTS/b0_all.nii.gz /OUTPUTS/b0_d_smooth.nii.gz /OUTPUTS/b0_u.nii.gz\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e--stripped\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLets the container know the supplied T1 has already been skull stripped. As a default, we assume it is not skull stripped. \u003cem\u003ePlease note this feature requires a well-stripped T1 as stripping artifacts can affect performance.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe INPUTS directory must contain the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0.nii.gz: the non-diffusion weighted image, must be a 3D image (either one b=0, or the average of all b=0 volumes)\u003c/li\u003e\n\u003cli\u003eT1.nii.gz: the T1-weighted image (either raw or skull-stripped, see \u003ca href=\"#flags\"\u003eFlags\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eacqparams.txt: A text file that describes the acqusition parameters, and is described in detail on the FslWiki for topup (\u003ca href=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup\" rel=\"nofollow\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup\u003c/a\u003e). Briefly,\nit describes the direction of distortion and tells TOPUP that the synthesized image has an effective echo spacing of 0 (infinite bandwidth). An example acqparams.txt is\ndisplayed below, in which distortion is in the second dimension, note that the second row corresponds to the synthesized, undistorted, b0:\n\u003cpre\u003e\u003ccode\u003e$ cat acqparams.txt \n0 1 0 0.062\n0 1 0 0.000\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter running, the OUTPUTS directory contains the following preprocessing files:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eT1_mask.nii.gz: brain extracted (skull-stripped) T1 (a copy of the input if T1.nii.gz is already skull-stripped)\u003c/li\u003e\n\u003cli\u003eT1_norm.nii.gz: normalized T1\u003c/li\u003e\n\u003cli\u003eepi_reg_d.mat: epi_reg b0 to T1 in FSL format\u003c/li\u003e\n\u003cli\u003eepi_reg_d_ANTS.txt: epi_reg to T1 in ANTS format\u003c/li\u003e\n\u003cli\u003eANTS0GenericAffine.mat: Affine ANTs registration of T1_norm to/from MNI space\u003c/li\u003e\n\u003cli\u003eANTS1Warp.nii.gz: Deformable ANTs registration of T1_norm to/from MNI space\u003c/li\u003e\n\u003cli\u003eANTS1InverseWarp.nii.gz: Inverse deformable ANTs registration of T1_norm to/from MNI space\u003c/li\u003e\n\u003cli\u003eT1_norm_lin_atlas_2_5.nii.gz: linear transform T1 to MNI\u003c/li\u003e\n\u003cli\u003eb0_d_lin_atlas_2_5.nii.gz: linear transform distorted b0 in MNI space\u003c/li\u003e\n\u003cli\u003eT1_norm_nonlin_atlas_2_5.nii.gz: nonlinear transform T1 to MNI\u003c/li\u003e\n\u003cli\u003eb0_d_nonlin_atlas_2_5.nii.gz: nonlinear transform distorted b0 in MNI space\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe OUTPUTS directory also contains inferences (predictions) for each of five folds utilizing T1_norm_lin_atlas_2_5.nii.gz and b0_d_lin_atlas_2_5.nii.gz as inputs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_1.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_2.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_3.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_4.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_FOLD_5.nii.gz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAfter inference the ensemble average is taken in atlas space:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5_merged.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_u_lin_atlas_2_5.nii.gz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIt is then moved to native space for the undistorted, synthetic output:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0_u.nii.gz: Synthetic b0 native space\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe undistorted synthetic output, and a smoothed distorted input can then be stacked together for topup:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eb0_d_smooth.nii.gz: smoothed b0\u003c/li\u003e\n\u003cli\u003eb0_all.nii.gz: stack of distorted and synthetized image as input to topup\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFinally, the topup outputs to be used for eddy:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etopup_movpar.txt\u003c/li\u003e\n\u003cli\u003eb0_all_topup.nii.gz\u003c/li\u003e\n\u003cli\u003eb0_all.topup_log\u003c/li\u003e\n\u003cli\u003etopup_fieldcoef.nii.gz\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAfter Running\u003c/h2\u003e\u003ca id=\"user-content-after-running\" class=\"anchor\" aria-label=\"Permalink: After Running\" href=\"#after-running\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAfter running, we envision using the topup outputs directly with FSL\u0027s eddy command, exactly as would be done if a full set of reverse PE scans was acquired. For example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eeddy --imain=path/to/diffusiondata.nii.gz --mask=path/to/brainmask.nii.gz \\\n--acqp=path/to/acqparams.txt --index=path/to/index.txt \\\n--bvecs=path/to/bvecs.txt --bvals=path/to/bvals.txt \n--topup=path/to/OUTPUTS/topup --out=eddy_unwarped_images\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003ewhere imain is the original diffusion data, mask is a brain mask, acqparams is from before, index is the traditional eddy index file which contains an index (most likely a 1) for every volume in the diffusion dataset, topup points to the output of the singularity/docker pipeline, and out is the eddy-corrected images utilizing the field coefficients from the previous step.\u003c/p\u003e\n\u003cp\u003eAlternatively, if you choose to run --notopup flag, the file you are interested in is b0_all. This is a concatenation of the real b0 and the synthesized undistorted b0. We run topup with this file, although you may chose to do so utilizing your topup version or config file.\u003c/p\u003e\n", - "stargazers_count": 60, - "subscribers_count": 14, + "full_name": "vsoch/pokemon", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epokemon\u003c/h1\u003e\u003ca id=\"user-content-pokemon\" class=\"anchor\" aria-label=\"Permalink: pokemon\" href=\"#pokemon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWatch the pokemon ascii being born!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/vsoch/pokemon/raw/master/img/generation.gif\"\u003e\u003cimg src=\"https://github.com/vsoch/pokemon/raw/master/img/generation.gif\" alt=\"img/generation.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a module for generating ascii art for any of the 890 pokemon, across 8 generations, in the Pokedex. The package includes functions for generating \"gravatars\" (pokemon associated with an identifier like an email address), and functions for searching and exploring the database. The library includes a \u003ca href=\"pokemon/database/db.json\"\u003eversion of the database\u003c/a\u003e generated with \u003ca href=\"pokemon/make_db.py\"\u003epokemon/make_db.py\u003c/a\u003e that can be updated by re-running the script. The choice of ascii art is to produce pokemon images or avatars that are suited for command line tools.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pokemon\nusage: pokemon [-h] [--avatar AVATAR] [--pokemon POKEMON] [--message MESSAGE]\n [--catch] [--list]\n\ngenerate pokemon ascii art and avatars\n\noptional arguments:\n -h, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n --avatar AVATAR generate a pokemon avatar \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e some unique id.\n --pokemon POKEMON generate ascii \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e a particular pokemon (by name)\n --message MESSAGE add a custom message to your ascii\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\n --catch catch a random pokemon\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\n --list list pokemon available\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install directly from pip:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor for the development version, clone the repo and install manually:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vsoch/pokemon\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pokemon\npip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProduce an avatar\u003c/h2\u003e\u003ca id=\"user-content-produce-an-avatar\" class=\"anchor\" aria-label=\"Permalink: Produce an avatar\" href=\"#produce-an-avatar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn \"avatar\" is an image that is consistently associated with some unique ID. In our case, this is an ascii avatar. For example,\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/avatar.png\"\u003e\u003cimg src=\"img/avatar.png\" alt=\"img/avatar.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo do this, I take the hash of a string, and then use modulus to get the remainder of that hash divided by the number of pokemon in the database. This means that, given that the database doesn\u0027t change, and given that the pokemon have unique IDs in the range of 1 to 721, you should always get the same image for some unique id (like an email).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e the database was updated between version 0.34 and version 0.35, so you will\nget different avatars depending on the version you are using. There are Docker tags\nand pip installs available for each, and version 0.35 is suggested to use with Python 3.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pokemon --avatar vsoch\n\n@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e:::::::::::::::+.+.@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n@@@@@@@\u003cspan class=\"pl-k\"\u003e*?\u003c/span\u003e%:::::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e::\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSSSSS%.**S+@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@\u003cspan class=\"pl-k\"\u003e*???\u003c/span\u003e:::::::\u003cspan class=\"pl-k\"\u003e*********\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e...+****++++S:@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@::SSS............S+.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e....\u003cspan class=\"pl-k\"\u003e*****?\u003c/span\u003e%S+\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@@.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003eSS.S.....S\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%%%%%%..\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e+....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@@@..%\u003cspan class=\"pl-k\"\u003e???????\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e%%%%%%%....**++.....?@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@@..+++%\u003cspan class=\"pl-k\"\u003e????????????\u003c/span\u003e%%%%%%\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.......%++%@@@@@@@@@@\n@@@@@@@@@@@@S.+++++S%+++SS%..\u003cspan class=\"pl-k\"\u003e????\u003c/span\u003e%%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e..............@@@@@@@@@\n@@@@@@@@@@@%++++S+S++++.......\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@%%%%%......SSSSSS:@@@@@@@@@@\n@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.++.+++++%\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.....?.%%%%%,@@@@@@@@@@@#.%@@@@@@@@@\u003c/span\u003e\n@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.....+.\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.....%.....+++%@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e+++S.%?+++.%@@@@@@@@@\u003c/span\u003e\n@@@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e***......%:.**++%........++++#.#+++++.++++.S@@@@@@@@@@\u003c/span\u003e\n@@@@,+%.......\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+++++......\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e....#.**+++#@@%++++.SS@@@@@@@@@@@\u003c/span\u003e\n@@@:+\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+...\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e..S.......S%%%...S+++::.+++@%++++.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e...S%%\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e@@.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%++SS.S++.+S...\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.+++S++..@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.@SS+.SS....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e....\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e++%:+..S@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+S.%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e+@@@@@S...#???%%%S@+:::...@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@++S....\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@@@@@@S.%...:::+#...@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e++.....S@@@@@@@@@@@@@.S..++..%?:...+?...@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@.......\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@+.....+++......\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.++....*@@@@@@@@@@@@@\u003c/span\u003e\n@@.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e+...@@@@@@@@@@@@S.....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.S+..S....++...S....@@@@@@@@@@@@@\n@+\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e++@@@@@@@@@@@@,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e........@:%.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003eSS\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+++++..+.....%@@@@@@@@@@@\n@:+%@@@@@@@@@@@:\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e........\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e:::.+++++#...+.....#@@@@@@@@@@\u003c/span\u003e\n@S@@@@@@@@@@@@+.........\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@+..++++....+......S.,@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@S%\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#.....S@@@@@@@@@*.??.......#@%...??.#@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@S%@....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003eS@@@@@@@@@@@@@%.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e...\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.@@@.S+.....@@@@@@\n@@@@@@@@@@@@@@@@S.....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@@@@.%S.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@+++S....@@@@@\n@@@@@@@@@@@@@@@@+......@@@@@@@@@@@@@@@@@@@@@@@@.+++S....@@@@\n@@@@@@@@@@@@@@@.%......@@@@@@@@@@@@@@@@@@@@@@@@@++++......@@\n@@@@@@@@@@@@@@.%......S@@@@@@@@@@@@@@@@@@@@@@@@@++++......@@\n@@@@@@@@@@@@@S.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.....++@@@@@@@@@@@@@@@@@@@@@@@@@..+..+....S@\u003c/span\u003e\n@@@@@@@@@@@.::++S.@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@@@@@@+...\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eS+?..SS\u003c/span\u003e\n@@@@@@@@@@@@,@,@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.+..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e++,@\n@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.::+@@\n\nvsoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also use the functions on command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epokemon\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eskills\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eget_avatar\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Just get the string!\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eavatar\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eget_avatar\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"vsoch\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprint_screen\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eavatar\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Remove the name at the bottom, print to screen (default)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eavatar\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eget_avatar\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"vsoch\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einclude_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eList Pokemon\u003c/h2\u003e\u003ca id=\"user-content-list-pokemon\" class=\"anchor\" aria-label=\"Permalink: List Pokemon\" href=\"#list-pokemon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWant a complete listing of your Pokemon choices in the database?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epokemon --list\n\nSlugma\nMachop\nDruddigon\nMagby\nClawitzer\nGrowlithe\nEmpoleon\nDusknoir\nRhydon\nKrookodile\nHoppip\nSwellow\nOddish\nScrafty\nBoldore\nPancham\nBeheeyem\nHonedge\n...\nJumpluff\nRotom\nFrillish\nLapras\nClamperl\nWingull\nVespiquen\nKeldeo\nMareep\nPhantump\nMedicham\nShuckle\nLickitung\nChingling\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou could use this to parse through a function. Here we show a simple loop to print the name of the Pokemon, but you would be more creative!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egotcha\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epokemon --list\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$gotcha\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRandomly select a Pokemon\u003c/h2\u003e\u003ca id=\"user-content-randomly-select-a-pokemon\" class=\"anchor\" aria-label=\"Permalink: Randomly select a Pokemon\" href=\"#randomly-select-a-pokemon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou might want to just randomly get a pokemon! Do this with the \u003ccode\u003e--catch\u003c/code\u003e command line argument!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e pokemon --catch\n\n @%,@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n .\u003cspan class=\"pl-k\"\u003e????\u003c/span\u003e.@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n .\u003cspan class=\"pl-k\"\u003e???????\u003c/span\u003eS@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n :\u003cspan class=\"pl-k\"\u003e?????????\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e*?????????????*\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @\u003cspan class=\"pl-k\"\u003e???????\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e?????###@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@,*.??#\u003c/span\u003e\n @\u003cspan class=\"pl-k\"\u003e?????\u003c/span\u003e,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#,S???#####@@@@@@@@@@@@@@@@@@@@@@@@@@S##????????????\u003c/span\u003e\n @\u003cspan class=\"pl-k\"\u003e?????*\u003c/span\u003e,,,,,,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#######@@@@@@@@@@@@@@@@@:###????????????????#@\u003c/span\u003e\n @\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#????,,,,,,,,,#####@@@@@@@@@@@@@.######?????#?:#????????@@\u003c/span\u003e\n @\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e###?#,,,,,,,,,,,##@@@@@@@@@@@@@@#######*,,,,,*##+?????+@@@\u003c/span\u003e\n @\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#####,,,,,,,,,,,S@@@@@@@@@@@@@@#.,,,,,,,,,,,,,,:?####@@@@@\u003c/span\u003e\n @\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#####,,,,,,,,,,%@@,S.S.,@@@@@@@,,,,,,,,,,,,,,,######@@@@@@\u003c/span\u003e\n @@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e####,,,,,,,,.,,,,,,,,,,,,,,,*#,,,,,,,,,,,,,.#####:@@@@@@@\u003c/span\u003e\n @@@@@@@@@@.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,######@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,+\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#####@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@%,,,,,++:,,,,,,,,,,,,,,,,,,,,,@@:.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#####:@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@:,,,:\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#@@@#,,,,,,,,,,,,?@S#,,,,,,@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e,,,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e######,,,,,,,,,,,#.@:##,,,:?@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@.,,S,\u003cspan class=\"pl-k\"\u003e??\u003c/span\u003e%\u003cspan class=\"pl-k\"\u003e?*\u003c/span\u003e,,,,,,,,,,,,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e###?%+,::%@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e+,,,,,,\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,,,,,,,,,,+\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eS,::::*@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@%..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,,,,,,,,,,,,,,,,,,:.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...%@@@@@@@@@@@@@@@@@@@@@\n @@@@@@@@@@.\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e::::::,,:::::::+.....@@@@@@@@@@@@@@@@@@@@@@@\n @@@@@@@@.@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e:::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e::::::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e,,,,,,,,,:,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#S::::**:::S#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@.,,,,,,:S#\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#?########:#****#,@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@,%:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%,\u003cspan class=\"pl-k\"\u003e??\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e,,,,:*S##**:..****:,.*@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@+,,,,,,,,,,,,,,,,,,\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,.,@@@@@@@@@@@@@@@@@@@\n @@@@@@@@@@@@@+,,,,,,,,,,,,,,,,,,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e?@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,,,,,,,,,,,,,,,,,.@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e########?@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,,,,,,,,,,,,,,:.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#%,?#####????:@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.....\u003cspan class=\"pl-k\"\u003e*******\u003c/span\u003e....S@@@@@@:\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#?????@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@@S.+..\u003cspan class=\"pl-k\"\u003e********\u003c/span\u003e...\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e%@@@@@@@@@##,@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e*,,,,*.#@@@@@@@..*:,,*S@@@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@+@,%,,,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@,S,,,%,,:@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n\n Pichu\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can equivalently use the \u003ccode\u003e--message\u003c/code\u003e argument to add a custom message to your catch!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e pokemon --catch --message \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eYou got me!\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n @@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@@@@@@...+@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@@@@@@++++@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n :..+,@@+.+++%@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @..++++S++++++.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e...@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@:S.S+SSS.S%++.+++@@@@@@@@@@+.%.@@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@@:SSSSSSSSSS,@@@@@@@,:,:.SS+.....+.@@@@@@@@@@@@@@@@@@@@@@\n @@@@,:%SS++SS.,.%,:,S,,,,+..%.........S.@@@@@@@@@@@@@@@@@@@@\n @@@@@,:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...:,,+,.,,,,,,,\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%%%++++..+++SSS+@@@@@@@@@@@@@@@@@@@\n @@@@@@,,.....%:,,,:.:.,:.%%.SSSS++SS+%+S%,+@@@@@@@@@@@@@@@@@\n @@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.....S...\u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e+,,,%..%++,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003eSSS.%.%%%:,.,@@@@@@@@@@@@@@@\n @@@@@@@@,+\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e........,,,,....++S@,+%..\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e..%,,S..@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e..:,,,,,%..%++S%%.%%.S%%,,\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e+.+@@@@@@@@@@@@@\n @@@@@@@@@@@@@@@@S,,,,,,,,,%%%..SS..%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%,,,S+...@@@@@@@@@@@@\n @@@@@@@@@@@@@@@@S.:::::::::%.%%S...%%%%:::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.....\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e@@@@@@@@@@\n @@@@@@@@@@@@@@@@.%%..:::::::S%%.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%%%:::....\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e,S,,:@@@@@@@@\n @@@@@@@@@@@@@@:::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%%%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:::,.%%%%.,:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.%@@.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,,,:,,S@....@@\n @@@@@@@@@@@@@:,:,::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%%%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%%%+@@,,,,,,,.++%++@@@\n @@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,,,,\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e...\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%%%%%%%%%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e++++++.@,,,,,SS+SS++@@@\n @@@@@@@@@@@@@,,.,S,,,,:....\u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%++++++++++.@.,,+SSSSS.S+@@\n @@@@@@@@@@@@,,SSSS..:.%,:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e..\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%\u003cspan class=\"pl-k\"\u003e??\u003c/span\u003e%%++++++.+S+@@@.S..%S.%.S++\n @@@@@@@@@@@,,S.....S::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.@@@%%%%@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e+++++%%%?S@@@@@.%.,@@...\u003c/span\u003e\n @@@@@@@@@@@:,,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.%%%::::@@@...%.@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.%.++++.+%%%%.@@@@..++@@@@@\n @@@@@@@@@@S,.%%.:,,,,,S@@@@@.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@+SS,S..........@@@@@,@@@@@@@\n @@@@@@@@@@@+S...++.,,:@@@@@@@@@@@@@@@%....SSS+SS@@@@@@@@@@@@\n\n You got me\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also catch pokemon in your python applications. If you are going to be generating many, it is recommended to load the database once and provide it to the function, otherwise it will be loaded each time.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003efrom pokemon.master import catch_em_all, get_pokemon\n\npokemons = \u003cspan class=\"pl-en\"\u003ecatch_em_all\u003c/span\u003e()\ncatch = get_pokemon(pokemons=pokemons)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe catch is a dictionary, with keys as the pokemon ID, and the value being another dictionary with various meta data (height, weight, japanese, link, ascii, etc).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdating the database\u003c/h2\u003e\u003ca id=\"user-content-updating-the-database\" class=\"anchor\" aria-label=\"Permalink: Updating the database\" href=\"#updating-the-database\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe database was generated by running the script make_db.py, and you can update it by running it yourself, if at some point in the future new pokemon are added to the index.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vsoch/pokemon\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pokemon\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e scripts\npip install -r requirements.txt\npython make_db.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen move your old database (and you can do this to keep it in case you don\u0027t want changes to persist):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emv pokemon/database dbbackup\nmv ./database pokemon/database\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe file pokemons.json will be saved under \u003ca href=\"pokemon/databases\"\u003epokemon/databases\u003c/a\u003e. Next, install as usual.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can also use the \u003ca href=\"https://hub.docker.com/r/vanessa/pokemon/\" rel=\"nofollow\"\u003eDocker image\u003c/a\u003e,\nwhich provides the various functions and \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eScientific Filesystem\u003c/a\u003e apps.\nThe 0.35 tag was developed with Python 2, and the 0.35 tag is Python 3 and later\n(with an updated database).\u003c/p\u003e\n\u003cp\u003eWhat can I do?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon apps\n list\n catch\n avatar\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eGive me my avatar!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon run avatar vsoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCatch a random Pokemon\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon run catch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat Pokemon can I catch?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon run list\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCatch me Venusaur!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon run catch Venusaur\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also build the image locally:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t vanessa/pokemon \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe can do the same with Singularity containers!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build pokemons Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat can I do?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons apps\n avatar\n catch\n list\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eGive me my avatar!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons run avatar vsoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCatch a random Pokemon\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons run catch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat Pokemons can I catch?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons list\n...\nPhantump\nTrevenant\nPumpkaboo\nGourgeist\nBergmite\nAvalugg\nNoibat\nNoivern\nXerneas\nYveltal\nZygarde\nDiancie\nHoopa\nVolcanion\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCatch a specific Pokemon\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons run catch Pikachu\n[catch] executing /bin/bash /scif/apps/catch/scif/runscript Pikachu\n@@@@@@@@@@@@@.@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@,\u003cspan class=\"pl-k\"\u003e??\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e##@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#:,@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@*?@@\u003c/span\u003e\n@@@@@@@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e::::@@@@@@@@@@@@@@@@@@@@@@@@@,*.???%@@@@@@@@*,,,@@\u003c/span\u003e\n@@@@@@@@::,,::@@@@@@@@@@@@@@@@@@%:,,:\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e####??,@@@@@@*,,,,,,:@\u003c/span\u003e\n@@@@@@@@%:,,:.@@@@@@@@@@@@@@.:::::::.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e####@@@@@@@.::,,,,,::@\u003c/span\u003e\n@@@@@@@@%::::.,,,,:,:%@@:,:::::::::S#\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#@@@@@@@@%,:::::,::,:%\u003c/span\u003e\n@@@@@@@@.S,,,,,,,,::::::::::::::::\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e::::::::::::::\n@@@@@@@:,,,,,,,:,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.#?::::::+.,@@@@@@@@@@@@@.::::::::::::::::\u003c/span\u003e\n@@@@@,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e:S,,:,::::*#.,:::::::*@@@@@@@@@@@@,::::::::::::::::+@\u003c/span\u003e\n@@@@@:%S::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,:::...+.::::S@@@@@@@@@@:::::::::::::::%@@@@\n@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.::::,SSSS%::::+++++:::::%@@@@@@@@:::::::::::::%@@@@@@@\n@@@@@.+:,,::S%+S::::.+++:::::::,@@@@@@@@@:::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e::::S@@@@@@@@@@\n@@@@@@.S:::::.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.::::::::::::::::@@@@@@@@@,\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e%@@@@@@@@@@@@@\n@@@@@@@@.:::::::::::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e::::,@@@@@@@@,\u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e@@@@@@@@@@@@@@\n@@@@,%,::::::::::::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e::,:S%@@@@@@......@@@@@@@@@@@@@\n,\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e::::,,,,,,:::::::::::+:\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e:::::,::@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.....S@@@@@@@@@@@@@@@\n%:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,:::,,,,,,,,,,,::::::%::::::,,,::,@S..+@@@@@@@@@@@@@@@@@\n@@@@@,S%+::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,:,,::,:,,,,::::::::::::::\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@%SS\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@@@@\n@@@@@@@@@@@@.:,,,,:,,,,,,,:::::::::::::+SSSSS.@@@@@@@@@@@@@@\n@@@@@@@@@@@@@:,,,:::::,::::,:::::::::::\u003cspan class=\"pl-k\"\u003e*?\u003c/span\u003e.@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@+,:,:,::::::::::,,::,::::\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e.SS@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@S,,:,,,,::::::::::::::::\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@:::::::::\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e::::::\u003cspan class=\"pl-k\"\u003e*******\u003c/span\u003eS@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e**********\u003c/span\u003e.%..\u003cspan class=\"pl-k\"\u003e***********\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@@@@,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+S%@@@@@@@@@@@@......@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@@@@@@+..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e@@@@@@@@@@@@@:+\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIssues and updates\u003c/h2\u003e\u003ca id=\"user-content-issues-and-updates\" class=\"anchor\" aria-label=\"Permalink: Issues and updates\" href=\"#issues-and-updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWould you like different or updated functionality?\nPlease ping me by adding an \u003ca href=\"https://github.com/vsoch/pokemon/issues\"\u003eissue\u003c/a\u003e!\nI did this for fun, might sneak it into a few command line applications,\nand it\u0027s pretty simple so far! I hope you have fun with it! :D\u003c/p\u003e\n", + "stargazers_count": 61, + "subscribers_count": 4, "topics": [ - "deep-learning", - "diffusion-mri", - "distortion-correction" + "pokemon", + "avatar", + "fun", + "python" ], - "updated_at": 1731422714.0 + "updated_at": 1728229254.0 }, { "data_format": 2, @@ -41360,7 +41407,7 @@ var data = "full_name": "miykael/fmriflows", "latest_release": null, "readme": "\u003cp\u003e\u003ca href=\"https://circleci.com/gh/miykael/fmriflows/tree/master\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0c6a8ce979c28f168df11ee4c8ae6a5d3055d0bb8fef3180a3f25255c439bb69/68747470733a2f2f636972636c6563692e636f6d2f67682f6d69796b61656c2f666d7269666c6f77732e7376673f7374796c653d736869656c64\" alt=\"CircleCi\" data-canonical-src=\"https://circleci.com/gh/miykael/fmriflows.svg?style=shield\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/fmriflows/issues/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc5375c1882998384f08971d01ef9c77dae164e60636ef0d81458ce31b2499e6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6d69796b61656c2f666d7269666c6f77732e737667\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/miykael/fmriflows.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/fmriflows/pulls/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e2e6439fbe86f8923df17b183425873d54c5a10f1b533b25c58b7e8afc3c8cd5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732d70722f6d69796b61656c2f666d7269666c6f77732e737667\" alt=\"GitHub pull-requests\" data-canonical-src=\"https://img.shields.io/github/issues-pr/miykael/fmriflows.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://GitHub.com/miykael/fmriflows/graphs/contributors/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aa7ded1f73515f48fecd4bc04c6a07f146bc64484e17dd74a2ec98c8c665a61a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f6d69796b61656c2f666d7269666c6f77732e737667\" alt=\"GitHub contributors\" data-canonical-src=\"https://img.shields.io/github/contributors/miykael/fmriflows.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/fmriflows/commits/master\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1752eea04c0375dfd4cfbb1524e45d2b9920c5df3a74c78b0e2c54f85b982db8/68747470733a2f2f6769746875622d62617369632d6261646765732e6865726f6b756170702e636f6d2f636f6d6d6974732f6d69796b61656c2f666d7269666c6f77732e737667\" alt=\"GitHub Commits\" data-canonical-src=\"https://github-basic-badges.herokuapp.com/commits/miykael/fmriflows.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/miykael/fmriflows/archive/master.zip\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/91b7d6d3ed972c5ed5d2d0497df271400c516cbc2a7d194c0b25d7b404ab5e67/68747470733a2f2f6769746875622d73697a652d62616467652e6865726f6b756170702e636f6d2f6d69796b61656c2f666d7269666c6f77732e737667\" alt=\"GitHub size\" data-canonical-src=\"https://github-size-badge.herokuapp.com/miykael/fmriflows.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/r/miykael/fmriflows/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9880d57db8d95ae3d45006b37a6aff612c4784a1d0a5b0e0ef224d054d69ab69/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6d69796b61656c2f666d7269666c6f77732e7376673f6d61784167653d32353932303030\" alt=\"Docker Hub\" data-canonical-src=\"https://img.shields.io/docker/pulls/miykael/fmriflows.svg?maxAge=2592000\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003efMRIflows\u003c/h1\u003e\u003ca id=\"user-content-fmriflows\" class=\"anchor\" aria-label=\"Permalink: fMRIflows\" href=\"#fmriflows\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"notebooks/templates/logo_fmriflows.gif\"\u003e\u003cimg src=\"notebooks/templates/logo_fmriflows.gif\" width=\"400px\" align=\"right\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ccode\u003efMRIflows\u003c/code\u003e\u003c/strong\u003e is a collection of fully autonomous uni- \u0026amp; multivariate fMRI processing pipelines. This comprises anatomical and functional preprocessing, estimation of singal confounds, as well as univariate and multivariate analysis on the subject and group level.\u003c/p\u003e\n\u003cp\u003eAs is rather obvious, \u003ccode\u003efMRIflows\u003c/code\u003e was greatly inspired by \u003ca href=\"https://github.com/poldracklab/fmriprep\"\u003efmriprep\u003c/a\u003e and other open source projects and borrows strongly from their ideas and solutions. But while \u003ca href=\"https://github.com/poldracklab/fmriprep\"\u003efmriprep\u003c/a\u003e can be described as a \"glass\" box software, \u003ccode\u003efMRIflows\u003c/code\u003e is more like a shoebox. Simple to open, easy to understand what\u0027s inside and uncomplicated to replace and change components inside. This is all due to the fact that all the source code of \u003ccode\u003efMRIflows\u003c/code\u003e is stored within the notebooks.\u003c/p\u003e\n\u003cp\u003eIf you are using \u003cstrong\u003e\u003ccode\u003efMRIflows\u003c/code\u003e\u003c/strong\u003e in your publication, please contact the author \u003ca href=\"https://github.com/miykael\"\u003eMichael Notter\u003c/a\u003e to get further information about how to cite this toolbox, as the publication is currently \u003cem\u003ein preparation for submission\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation and Usage\u003c/h1\u003e\u003ca id=\"user-content-installation-and-usage\" class=\"anchor\" aria-label=\"Permalink: Installation and Usage\" href=\"#installation-and-usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing Containers\u003c/h2\u003e\u003ca id=\"user-content-using-containers\" class=\"anchor\" aria-label=\"Permalink: Using Containers\" href=\"#using-containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe best way to use \u003ccode\u003efMRIflows\u003c/code\u003e is to run it directly within the corresponding container (\u003ca href=\"https://www.docker.com/\" rel=\"nofollow\"\u003eDocker\u003c/a\u003e or \u003ca href=\"https://www.sylabs.io\" rel=\"nofollow\"\u003eSingularity\u003c/a\u003e). The docker image for this can be downloaded from \u003ca href=\"https://hub.docker.com/r/miykael/fmriflows/\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e with the command \u003ccode\u003edocker pull miykael/fmriflows\u003c/code\u003e, and can be run with the command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker run -it --rm -p 9999:8888 -v /home/user/ds001:/data miykael/fmriflows\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003cstrong\u003e\u003ccode\u003e-it\u003c/code\u003e\u003c/strong\u003e means that docker will be run in an interactive mode.\u003c/li\u003e\n\u003cli\u003eThe \u003cstrong\u003e\u003ccode\u003e--rm\u003c/code\u003e\u003c/strong\u003e means that the docker container (not the image!) will be removed from your system, once you close the container.\u003c/li\u003e\n\u003cli\u003eThe \u003cstrong\u003e\u003ccode\u003e-p\u003c/code\u003e\u003c/strong\u003e flag specifies which port should be used to access the jupyter notebook inside the docker container. The first 4 numbers can be changed (we recommend \u003ccode\u003e9999\u003c/code\u003e), but the second half needs to be fixed to \u003ccode\u003e:8888\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eThe \u003cstrong\u003e\u003ccode\u003e-v\u003c/code\u003e\u003c/strong\u003e flag tells docker which folder to mount within the docker container. The path \u003ccode\u003e/home/user/ds001\u003c/code\u003e should therefore point to your BIDS conform dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce this command has been executed (note: the terminal will not go back to a new input prompt, but rather stays on this command) you need to copy paste/open the link \u003ca href=\"http://127.0.0.1:9999/?token=fmriflows\" rel=\"nofollow\"\u003ehttp://127.0.0.1:9999/?token=fmriflows\u003c/a\u003e or \u003ca href=\"http://localhost:9999/?token=fmriflows\" rel=\"nofollow\"\u003ehttp://localhost:9999/?token=fmriflows\u003c/a\u003e in your web browser to access the jupyter notebooks required to run fMRIflows.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Should you by any chance encounter a web page that asks you for \"Password or token\", use the token \u003ccode\u003efmriflows\u003c/code\u003e to login.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsing your native environment\u003c/h2\u003e\u003ca id=\"user-content-using-your-native-environment\" class=\"anchor\" aria-label=\"Permalink: Using your native environment\" href=\"#using-your-native-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is also possible to run \u003ccode\u003efMRIflows\u003c/code\u003e outside of a container. All the important code is stored within the \u003ca href=\"https://github.com/miykael/fmriflows/tree/master/notebooks\"\u003enotebooks\u003c/a\u003e. But for this, you need to make sure that all necessary neuroimaging and python dependencies are installed on your system. For a list of what needs to be done, see the \u003ca href=\"https://github.com/miykael/fmriflows/blob/master/generate.sh\"\u003egenerating\u003c/a\u003e file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\n\u003ccode\u003efMRIflows\u003c/code\u003e pipelines\u003c/h1\u003e\u003ca id=\"user-content-fmriflows-pipelines\" class=\"anchor\" aria-label=\"Permalink: fMRIflows pipelines\" href=\"#fmriflows-pipelines\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003efMRIflows\u003c/code\u003e contains many different pipelines. The following is a short summary and explanation of each individual notebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e01_spec_preparation.ipynb\u003c/h2\u003e\u003ca id=\"user-content-01_spec_preparationipynb\" class=\"anchor\" aria-label=\"Permalink: 01_spec_preparation.ipynb\" href=\"#01_spec_preparationipynb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe notebook \u003ca href=\"https://nbviewer.jupyter.org/github/miykael/fmriflows/blob/master/notebooks/01_spec_preparation.ipynb\" rel=\"nofollow\"\u003e01_spec_preparation.ipynb\u003c/a\u003e helps you to create the \u003ccode\u003eJSON\u003c/code\u003e files that contain the specifiation to run \u003ccode\u003efMRIflows\u003c/code\u003e. This is the most direct way to specify the processing parameters for \u003ccode\u003efMRIflows\u003c/code\u003e. Open the notebook, run it, change a few parameters as you want and you\u0027re good to go. Alternatively, you can also directly change the \u003ccode\u003eJSON\u003c/code\u003e specification file within your dataset folder (but you need to run \u003ccode\u003e01_spec_preparation.ipynb\u003c/code\u003e for this to work at least once).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e02_preproc_anat.ipynb\u003c/h2\u003e\u003ca id=\"user-content-02_preproc_anatipynb\" class=\"anchor\" aria-label=\"Permalink: 02_preproc_anat.ipynb\" href=\"#02_preproc_anatipynb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe notebook \u003ca href=\"https://nbviewer.jupyter.org/github/miykael/fmriflows/blob/master/notebooks/02_preproc_anat.ipynb\" rel=\"nofollow\"\u003e02_preproc_anat.ipynb\u003c/a\u003e contains perform anatomical preprocessing. Parameters that can be specified in the \u003ca href=\"https://github.com/miykael/fmriflows/blob/master/static/fmriflows_spec_preproc.json\"\u003e\u003ccode\u003efmriflows_spec_preproc.json\u003c/code\u003e\u003c/a\u003e file are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esubject_list_anat\u003c/code\u003e: List of subject identifier to preprocess\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esession_list_anat\u003c/code\u003e: List of session identifier to preprocess (set to \u003ccode\u003e[]\u003c/code\u003e if dataset doesn\u0027t contain session identifiers)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eT1w_id\u003c/code\u003e: T1-image identifier, usually \u003ccode\u003eT1w\u003c/code\u003e. For MP2RAGE acquisiton, this can for example be changed to \u003ccode\u003eT1w_UNI-DEN\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eres_norm\u003c/code\u003e: Requested voxel resolution after normalization\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003enorm_accuracy\u003c/code\u003e: Precision accuracy of the normalization. This can be either \u003ccode\u003eprecise\u003c/code\u003e or \u003ccode\u003efast\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe simplified workflow of the anatomical preprocessing looks as follows:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/preproc_anat.png\"\u003e\u003cimg src=\"static/preproc_anat.png\" width=\"400px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAfter anatomical preprocessing the following output figures are generated:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTissue Segmentation of T1w\u003c/h3\u003e\u003ca id=\"user-content-tissue-segmentation-of-t1w\" class=\"anchor\" aria-label=\"Permalink: Tissue Segmentation of T1w\" href=\"#tissue-segmentation-of-t1w\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows quality of the T1-weighted image segmentation, with brain tissues highlighted in green for Gray Matter (GM), in beige for White Matter (WM) and in blue for Cerebrospinal Fluid (CSF).\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_anat_segmentation.png\"\u003e\u003cimg src=\"static/outputs/preproc_anat_segmentation.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eBrain Extraction of T1w\u003c/h3\u003e\u003ca id=\"user-content-brain-extraction-of-t1w\" class=\"anchor\" aria-label=\"Permalink: Brain Extraction of T1w\" href=\"#brain-extraction-of-t1w\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows the quality of the brain extraction. The brain highlighted in red is overlayed on the subject specific T1-weighted image in gray.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_anat_brain.png\"\u003e\u003cimg src=\"static/outputs/preproc_anat_brain.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eT1w to MNI registration\u003c/h3\u003e\u003ca id=\"user-content-t1w-to-mni-registration\" class=\"anchor\" aria-label=\"Permalink: T1w to MNI registration\" href=\"#t1w-to-mni-registration\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows the quality of the nonlinear template registration. The normalized brain highlighted in yellow is overlayed on the ICBM T1-weighted template image in gray. Regions in red and blue show negative and positive deformation discrepancy between the normalized subject image and the template.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_anat_normalized.png\"\u003e\u003cimg src=\"static/outputs/preproc_anat_normalized.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e03_preproc_func.ipynb\u003c/h2\u003e\u003ca id=\"user-content-03_preproc_funcipynb\" class=\"anchor\" aria-label=\"Permalink: 03_preproc_func.ipynb\" href=\"#03_preproc_funcipynb\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe notebook \u003ca href=\"https://nbviewer.jupyter.org/github/miykael/fmriflows/blob/master/notebooks/03_preproc_func.ipynb\" rel=\"nofollow\"\u003e03_preproc_func.ipynb\u003c/a\u003e contains perform functional preprocessing. Parameters that can be specified in the \u003ca href=\"https://github.com/miykael/fmriflows/blob/master/static/fmriflows_spec_preproc.json\"\u003e\u003ccode\u003efmriflows_spec_preproc.json\u003c/code\u003e\u003c/a\u003e file are the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003esubject_list_func\u003c/code\u003e: List of subject identifier to preprocess\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esession_list_func\u003c/code\u003e: List of session identifier to preprocess (set to \u003ccode\u003e[]\u003c/code\u003e if dataset doesn\u0027t contain session identifiers)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003etask_list\u003c/code\u003e: List of task identifier to preprocess\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erun_list\u003c/code\u003e: List of run identifiers (\u003ccode\u003eint\u003c/code\u003e) to preprocess (set to \u003ccode\u003e[]\u003c/code\u003e if dataset doesn\u0027t contain session identifiers)\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eref_timepoint\u003c/code\u003e: Reference timepoint for slicetime correction, in milliseconds\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eres_func\u003c/code\u003e: Isometric resample resolution for after image coregistration\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efilters_spatial\u003c/code\u003e: List of spatial filters to apply. Use \u003ccode\u003eLP\u003c/code\u003e for lowpass, \u003ccode\u003eHP\u003c/code\u003e for highpass and \u003ccode\u003eBP\u003c/code\u003e for bandpass spatial filtering. For example [[\u003ccode\u003eLP\u003c/code\u003e, 6.0], [\u003ccode\u003eHP\u003c/code\u003e, 2.0]] will run the analysis once with a spatial lowpass filter of FWHM=6mm and once with a spatial highpass filter of FWHM=2mm.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efilters_temporal\u003c/code\u003e: List of temporal filters to apply. First element will define lowpass, second highpass cutoff. Use \u003ccode\u003enull\u003c/code\u003e to not use given filter. For example \u003ccode\u003e[[5.0, 100.0], [null, 100.0]]\u003c/code\u003e will run the analysis once with a temporal lowpass filter of 5Hz and highpass filter of 100Hz, and once with a temporal filter of only a highpass filter of 100Hz.,\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003en_compcor_confounds\u003c/code\u003e: Number of anatomical and functional CompCor componenets to compute. Defautl is 5.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eoutlier_thresholds\u003c/code\u003e: Thresholds used to performe outlier detection on FD, DVARS, TV, GM, WM and CSF. Use \u003ccode\u003eNone\u003c/code\u003e if parameter shouldn\u0027t be used for outlier detection.\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003en_independent_components\u003c/code\u003e: Number of ICA components that should be estimaed. Default is 10.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe simplified workflow of the anatomical preprocessing looks as follows:\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/preproc_func.png\"\u003e\u003cimg src=\"static/preproc_func.png\" width=\"500px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally to the preprocessing, the following signal confounds are computed:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFriston\u0027s 24-parameter model for motion parameters\u003c/li\u003e\n\u003cli\u003eFramewise Displacement (FD) and DVARS\u003c/li\u003e\n\u003cli\u003eAverage signal in total volume (TV), in GM, in WM and in CSF\u003c/li\u003e\n\u003cli\u003eAnatomical and temporal CompCor components\u003c/li\u003e\n\u003cli\u003eIndependent components in image before smoothing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAfter functional preprocessing the following output figures are generated:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMean Image and CompCor Masks\u003c/h3\u003e\u003ca id=\"user-content-mean-image-and-compcor-masks\" class=\"anchor\" aria-label=\"Permalink: Mean Image and CompCor Masks\" href=\"#mean-image-and-compcor-masks\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows the mean functional image in gray, and the brainmask (red) that was used to compute DVARS and the temporal (green) and anatomical (blue) CompCor mask.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_func_overlays.png\"\u003e\u003cimg src=\"static/outputs/preproc_func_overlays.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCarpet plot of the temporal filtered functional image\u003c/h3\u003e\u003ca id=\"user-content-carpet-plot-of-the-temporal-filtered-functional-image\" class=\"anchor\" aria-label=\"Permalink: Carpet plot of the temporal filtered functional image\" href=\"#carpet-plot-of-the-temporal-filtered-functional-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows the signal for (almost) every voxel (y-axis), over time in volumes (x-axis). The panel shows voxel in the gray matter (top part), white matter (between blue and red line) and CSF (bottom section). The data was standardized to the global signal, and ordered within a given region according to correlation coefficient to the average signal.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_func_carpet_nofilt.png\"\u003e\u003cimg src=\"static/outputs/preproc_func_carpet_nofilt.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMain confounds with highlighted outliers\u003c/h3\u003e\u003ca id=\"user-content-main-confounds-with-highlighted-outliers\" class=\"anchor\" aria-label=\"Permalink: Main confounds with highlighted outliers\" href=\"#main-confounds-with-highlighted-outliers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows the main confounds. Shown are FD (Framewise Displacement) and DVARS, as well as the average signal in TV (total brain volume), GM (gray matter), WM (white matter) and CSF (cerebral spinal fluid). Vertical lines in black indicate outliers, as defined by threshold values.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_func_confounds_main_nofilt.png\"\u003e\u003cimg src=\"static/outputs/preproc_func_confounds_main_nofilt.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMotion Parameters\u003c/h3\u003e\u003ca id=\"user-content-motion-parameters\" class=\"anchor\" aria-label=\"Permalink: Motion Parameters\" href=\"#motion-parameters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows the motion parameters (3 rotation and 3 translation parameters). Colored lines are motion parameters after the application of a temporal low-pass filter at 5Hz. Gray lines indicate motion parameters before temporal filter were applied.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_func_confounds_motion_filt.png\"\u003e\u003cimg src=\"static/outputs/preproc_func_confounds_motion_filt.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAnatomical CompCor Components\u003c/h3\u003e\u003ca id=\"user-content-anatomical-compcor-components\" class=\"anchor\" aria-label=\"Permalink: Anatomical CompCor Components\" href=\"#anatomical-compcor-components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows the anatomical CompCor components.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_func_confounds_compA_nofilt.png\"\u003e\u003cimg src=\"static/outputs/preproc_func_confounds_compA_nofilt.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTemporal CompCor Components\u003c/h3\u003e\u003ca id=\"user-content-temporal-compcor-components\" class=\"anchor\" aria-label=\"Permalink: Temporal CompCor Components\" href=\"#temporal-compcor-components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows the temporal CompCor components.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_func_confounds_compT_nofilt.png\"\u003e\u003cimg src=\"static/outputs/preproc_func_confounds_compT_nofilt.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eICA Components\u003c/h3\u003e\u003ca id=\"user-content-ica-components\" class=\"anchor\" aria-label=\"Permalink: ICA Components\" href=\"#ica-components\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis panel shows the ICA components. Left side of the figure shows the correlation between the component and the functional image, over time. The right panel shows the power spectrum density of this component, with values in Hz on the x-axis.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_func_ICA_comp_signal_nofilt.png\"\u003e\u003cimg src=\"static/outputs/preproc_func_ICA_comp_signal_nofilt.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis panel shows the load of the ICA components within the brain volume.\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"static/outputs/preproc_func_ICA_comp_brain_nofilt.png\"\u003e\u003cimg src=\"static/outputs/preproc_func_ICA_comp_brain_nofilt.png\" width=\"800px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFeedback, Help \u0026amp; Support\u003c/h1\u003e\u003ca id=\"user-content-feedback-help--support\" class=\"anchor\" aria-label=\"Permalink: Feedback, Help \u0026amp; Support\" href=\"#feedback-help--support\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to help with this project or have any questions, fell free to fork the repo, send us a pull request or open a new \u003ca href=\"https://github.com/miykael/fmriflows/issues\"\u003eissue on github\u003c/a\u003e. Every help is highly appreciated, every feature request or bug report is very welcomed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThanks and Acknowledgment\u003c/h1\u003e\u003ca id=\"user-content-thanks-and-acknowledgment\" class=\"anchor\" aria-label=\"Permalink: Thanks and Acknowledgment\" href=\"#thanks-and-acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe would like to thank everybody who\u0027s developing code for \u003ca href=\"https://github.com/nipy/nipype\"\u003eNipype\u003c/a\u003e, \u003ca href=\"https://fmriprep.readthedocs.io/en/stable/\" rel=\"nofollow\"\u003efmriprep\u003c/a\u003e, \u003ca href=\"https://github.com/nipy/nipype\"\u003eNilearn\u003c/a\u003e, \u003ca href=\"http://www.pymvpa.org/\" rel=\"nofollow\"\u003ePyMVPA\u003c/a\u003e and many other related open-source toolboxes. We were able to integrate their knowledge, code and approaches in \u003ccode\u003efMRIflows\u003c/code\u003e, because their code is open.\u003c/p\u003e\n\u003cp\u003eThanks also to all the contributors, tester, bug reporter and feedback giver to \u003ccode\u003efMRIflows\u003c/code\u003e. And a special thanks to \u003ca href=\"https://github.com/ofgulban\"\u003eFaruk\u003c/a\u003e who created the great \u003ccode\u003efMRIflows\u003c/code\u003e logo.\u003c/p\u003e\n", - "stargazers_count": 60, + "stargazers_count": 62, "subscribers_count": 7, "topics": [ "python", @@ -41368,26 +41415,26 @@ var data = "fmri", "bids" ], - "updated_at": 1721799562.0 + "updated_at": 1731393825.0 }, { "data_format": 2, - "description": "ascii database of pokemon... in python!", + "description": "SKA Radio Telescope Simulator", "filenames": [ - "Singularity" + "singularity/Singularity.base-dep", + "singularity/Singularity.python3" ], - "full_name": "vsoch/pokemon", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003epokemon\u003c/h1\u003e\u003ca id=\"user-content-pokemon\" class=\"anchor\" aria-label=\"Permalink: pokemon\" href=\"#pokemon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWatch the pokemon ascii being born!\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/vsoch/pokemon/raw/master/img/generation.gif\"\u003e\u003cimg src=\"https://github.com/vsoch/pokemon/raw/master/img/generation.gif\" alt=\"img/generation.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a module for generating ascii art for any of the 890 pokemon, across 8 generations, in the Pokedex. The package includes functions for generating \"gravatars\" (pokemon associated with an identifier like an email address), and functions for searching and exploring the database. The library includes a \u003ca href=\"pokemon/database/db.json\"\u003eversion of the database\u003c/a\u003e generated with \u003ca href=\"pokemon/make_db.py\"\u003epokemon/make_db.py\u003c/a\u003e that can be updated by re-running the script. The choice of ascii art is to produce pokemon images or avatars that are suited for command line tools.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pokemon\nusage: pokemon [-h] [--avatar AVATAR] [--pokemon POKEMON] [--message MESSAGE]\n [--catch] [--list]\n\ngenerate pokemon ascii art and avatars\n\noptional arguments:\n -h, --help show this \u003cspan class=\"pl-c1\"\u003ehelp\u003c/span\u003e message and \u003cspan class=\"pl-c1\"\u003eexit\u003c/span\u003e\n --avatar AVATAR generate a pokemon avatar \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e some unique id.\n --pokemon POKEMON generate ascii \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e a particular pokemon (by name)\n --message MESSAGE add a custom message to your ascii\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\n --catch catch a random pokemon\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\n --list list pokemon available\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can install directly from pip:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pip install pokemon\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eor for the development version, clone the repo and install manually:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vsoch/pokemon\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pokemon\npip install \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eProduce an avatar\u003c/h2\u003e\u003ca id=\"user-content-produce-an-avatar\" class=\"anchor\" aria-label=\"Permalink: Produce an avatar\" href=\"#produce-an-avatar\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAn \"avatar\" is an image that is consistently associated with some unique ID. In our case, this is an ascii avatar. For example,\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/avatar.png\"\u003e\u003cimg src=\"img/avatar.png\" alt=\"img/avatar.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTo do this, I take the hash of a string, and then use modulus to get the remainder of that hash divided by the number of pokemon in the database. This means that, given that the database doesn\u0027t change, and given that the pokemon have unique IDs in the range of 1 to 721, you should always get the same image for some unique id (like an email).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e the database was updated between version 0.34 and version 0.35, so you will\nget different avatars depending on the version you are using. There are Docker tags\nand pip installs available for each, and version 0.35 is suggested to use with Python 3.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ pokemon --avatar vsoch\n\n@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e:::::::::::::::+.+.@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n@@@@@@@\u003cspan class=\"pl-k\"\u003e*?\u003c/span\u003e%:::::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e::\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eSSSSS%.**S+@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@\u003cspan class=\"pl-k\"\u003e*???\u003c/span\u003e:::::::\u003cspan class=\"pl-k\"\u003e*********\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e...+****++++S:@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@::SSS............S+.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e....\u003cspan class=\"pl-k\"\u003e*****?\u003c/span\u003e%S+\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@@.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003eSS.S.....S\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%%%%%%..\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e+....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@@@..%\u003cspan class=\"pl-k\"\u003e???????\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e%%%%%%%....**++.....?@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@@..+++%\u003cspan class=\"pl-k\"\u003e????????????\u003c/span\u003e%%%%%%\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.......%++%@@@@@@@@@@\n@@@@@@@@@@@@S.+++++S%+++SS%..\u003cspan class=\"pl-k\"\u003e????\u003c/span\u003e%%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e..............@@@@@@@@@\n@@@@@@@@@@@%++++S+S++++.......\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@%%%%%......SSSSSS:@@@@@@@@@@\n@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.++.+++++%\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.....?.%%%%%,@@@@@@@@@@@#.%@@@@@@@@@\u003c/span\u003e\n@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.....+.\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.....%.....+++%@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e+++S.%?+++.%@@@@@@@@@\u003c/span\u003e\n@@@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e***......%:.**++%........++++#.#+++++.++++.S@@@@@@@@@@\u003c/span\u003e\n@@@@,+%.......\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+++++......\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e....#.**+++#@@%++++.SS@@@@@@@@@@@\u003c/span\u003e\n@@@:+\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+...\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e..S.......S%%%...S+++::.+++@%++++.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e...S%%\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e@@.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%++SS.S++.+S...\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.+++S++..@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.@SS+.SS....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e....\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e++%:+..S@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+S.%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e+@@@@@S...#???%%%S@+:::...@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@++S....\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@@@@@@S.%...:::+#...@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e++.....S@@@@@@@@@@@@@.S..++..%?:...+?...@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@.......\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@+.....+++......\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.++....*@@@@@@@@@@@@@\u003c/span\u003e\n@@.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e+...@@@@@@@@@@@@S.....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.S+..S....++...S....@@@@@@@@@@@@@\n@+\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e++@@@@@@@@@@@@,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e........@:%.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003eSS\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+++++..+.....%@@@@@@@@@@@\n@:+%@@@@@@@@@@@:\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e........\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e:::.+++++#...+.....#@@@@@@@@@@\u003c/span\u003e\n@S@@@@@@@@@@@@+.........\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@+..++++....+......S.,@@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@S%\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#.....S@@@@@@@@@*.??.......#@%...??.#@@@@@@@\u003c/span\u003e\n@@@@@@@@@@@@@S%@....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003eS@@@@@@@@@@@@@%.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e...\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.@@@.S+.....@@@@@@\n@@@@@@@@@@@@@@@@S.....\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@@@@.%S.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@+++S....@@@@@\n@@@@@@@@@@@@@@@@+......@@@@@@@@@@@@@@@@@@@@@@@@.+++S....@@@@\n@@@@@@@@@@@@@@@.%......@@@@@@@@@@@@@@@@@@@@@@@@@++++......@@\n@@@@@@@@@@@@@@.%......S@@@@@@@@@@@@@@@@@@@@@@@@@++++......@@\n@@@@@@@@@@@@@S.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.....++@@@@@@@@@@@@@@@@@@@@@@@@@..+..+....S@\u003c/span\u003e\n@@@@@@@@@@@.::++S.@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@@@@@@+...\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eS+?..SS\u003c/span\u003e\n@@@@@@@@@@@@,@,@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.+..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e++,@\n@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@.::+@@\n\nvsoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also use the functions on command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epokemon\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eskills\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eget_avatar\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Just get the string!\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eavatar\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eget_avatar\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"vsoch\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprint_screen\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eavatar\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Remove the name at the bottom, print to screen (default)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eavatar\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eget_avatar\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"vsoch\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einclude_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eList Pokemon\u003c/h2\u003e\u003ca id=\"user-content-list-pokemon\" class=\"anchor\" aria-label=\"Permalink: List Pokemon\" href=\"#list-pokemon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWant a complete listing of your Pokemon choices in the database?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epokemon --list\n\nSlugma\nMachop\nDruddigon\nMagby\nClawitzer\nGrowlithe\nEmpoleon\nDusknoir\nRhydon\nKrookodile\nHoppip\nSwellow\nOddish\nScrafty\nBoldore\nPancham\nBeheeyem\nHonedge\n...\nJumpluff\nRotom\nFrillish\nLapras\nClamperl\nWingull\nVespiquen\nKeldeo\nMareep\nPhantump\nMedicham\nShuckle\nLickitung\nChingling\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou could use this to parse through a function. Here we show a simple loop to print the name of the Pokemon, but you would be more creative!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003egotcha\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epokemon --list\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$gotcha\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRandomly select a Pokemon\u003c/h2\u003e\u003ca id=\"user-content-randomly-select-a-pokemon\" class=\"anchor\" aria-label=\"Permalink: Randomly select a Pokemon\" href=\"#randomly-select-a-pokemon\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou might want to just randomly get a pokemon! Do this with the \u003ccode\u003e--catch\u003c/code\u003e command line argument!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e pokemon --catch\n\n @%,@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n .\u003cspan class=\"pl-k\"\u003e????\u003c/span\u003e.@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n .\u003cspan class=\"pl-k\"\u003e???????\u003c/span\u003eS@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n :\u003cspan class=\"pl-k\"\u003e?????????\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e*?????????????*\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @\u003cspan class=\"pl-k\"\u003e???????\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e?????###@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@,*.??#\u003c/span\u003e\n @\u003cspan class=\"pl-k\"\u003e?????\u003c/span\u003e,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#,S???#####@@@@@@@@@@@@@@@@@@@@@@@@@@S##????????????\u003c/span\u003e\n @\u003cspan class=\"pl-k\"\u003e?????*\u003c/span\u003e,,,,,,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#######@@@@@@@@@@@@@@@@@:###????????????????#@\u003c/span\u003e\n @\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#????,,,,,,,,,#####@@@@@@@@@@@@@.######?????#?:#????????@@\u003c/span\u003e\n @\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e###?#,,,,,,,,,,,##@@@@@@@@@@@@@@#######*,,,,,*##+?????+@@@\u003c/span\u003e\n @\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#####,,,,,,,,,,,S@@@@@@@@@@@@@@#.,,,,,,,,,,,,,,:?####@@@@@\u003c/span\u003e\n @\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#####,,,,,,,,,,%@@,S.S.,@@@@@@@,,,,,,,,,,,,,,,######@@@@@@\u003c/span\u003e\n @@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e####,,,,,,,,.,,,,,,,,,,,,,,,*#,,,,,,,,,,,,,.#####:@@@@@@@\u003c/span\u003e\n @@@@@@@@@@.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,######@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,+\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#####@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@%,,,,,++:,,,,,,,,,,,,,,,,,,,,,@@:.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#####:@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@:,,,:\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#@@@#,,,,,,,,,,,,?@S#,,,,,,@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e,,,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e######,,,,,,,,,,,#.@:##,,,:?@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@.,,S,\u003cspan class=\"pl-k\"\u003e??\u003c/span\u003e%\u003cspan class=\"pl-k\"\u003e?*\u003c/span\u003e,,,,,,,,,,,,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e###?%+,::%@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e+,,,,,,\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,,,,,,,,,,+\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003eS,::::*@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@%..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,,,,,,,,,,,,,,,,,,:.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...%@@@@@@@@@@@@@@@@@@@@@\n @@@@@@@@@@.\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e::::::,,:::::::+.....@@@@@@@@@@@@@@@@@@@@@@@\n @@@@@@@@.@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e:\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e:::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e::::::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e,,,,,,,,,:,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#S::::**:::S#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@.,,,,,,:S#\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#?########:#****#,@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@,%:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%,\u003cspan class=\"pl-k\"\u003e??\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e,,,,:*S##**:..****:,.*@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@+,,,,,,,,,,,,,,,,,,\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,.,@@@@@@@@@@@@@@@@@@@\n @@@@@@@@@@@@@+,,,,,,,,,,,,,,,,,,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e?@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,,,,,,,,,,,,,,,,,.@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e########?@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,,,,,,,,,,,,,,:.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#%,?#####????:@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.....\u003cspan class=\"pl-k\"\u003e*******\u003c/span\u003e....S@@@@@@:\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#?????@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@@S.+..\u003cspan class=\"pl-k\"\u003e********\u003c/span\u003e...\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e%@@@@@@@@@##,@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e*,,,,*.#@@@@@@@..*:,,*S@@@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@+@,%,,,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e@@@@@@@@@@,S,,,%,,:@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n\n Pichu\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can equivalently use the \u003ccode\u003e--message\u003c/code\u003e argument to add a custom message to your catch!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e pokemon --catch --message \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eYou got me!\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n @@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@@@@@@...+@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@@@@@@++++@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n :..+,@@+.+++%@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @..++++S++++++.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e...@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@:S.S+SSS.S%++.+++@@@@@@@@@@+.%.@@@@@@@@@@@@@@@@@@@@@@@@@@\n @@@@:SSSSSSSSSS,@@@@@@@,:,:.SS+.....+.@@@@@@@@@@@@@@@@@@@@@@\n @@@@,:%SS++SS.,.%,:,S,,,,+..%.........S.@@@@@@@@@@@@@@@@@@@@\n @@@@@,:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e...:,,+,.,,,,,,,\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%%%++++..+++SSS+@@@@@@@@@@@@@@@@@@@\n @@@@@@,,.....%:,,,:.:.,:.%%.SSSS++SS+%+S%,+@@@@@@@@@@@@@@@@@\n @@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.....S...\u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e+,,,%..%++,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003eSSS.%.%%%:,.,@@@@@@@@@@@@@@@\n @@@@@@@@,+\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e........,,,,....++S@,+%..\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e..%,,S..@@@@@@@@@@@@@@\u003c/span\u003e\n @@@@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e..:,,,,,%..%++S%%.%%.S%%,,\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e+.+@@@@@@@@@@@@@\n @@@@@@@@@@@@@@@@S,,,,,,,,,%%%..SS..%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%,,,S+...@@@@@@@@@@@@\n @@@@@@@@@@@@@@@@S.:::::::::%.%%S...%%%%:::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.....\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e@@@@@@@@@@\n @@@@@@@@@@@@@@@@.%%..:::::::S%%.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%%%:::....\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e,S,,:@@@@@@@@\n @@@@@@@@@@@@@@:::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%%%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:::,.%%%%.,:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.%@@.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,,,:,,S@....@@\n @@@@@@@@@@@@@:,:,::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%%%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%%%%+@@,,,,,,,.++%++@@@\n @@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,,,,\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e...\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e%%%%%%%%%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e++++++.@,,,,,SS+SS++@@@\n @@@@@@@@@@@@@,,.,S,,,,:....\u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e%%\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%++++++++++.@.,,+SSSSS.S+@@\n @@@@@@@@@@@@,,SSSS..:.%,:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e..\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%\u003cspan class=\"pl-k\"\u003e??\u003c/span\u003e%%++++++.+S+@@@.S..%S.%.S++\n @@@@@@@@@@@,,S.....S::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.@@@%%%%@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e%%\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e+++++%%%?S@@@@@.%.,@@...\u003c/span\u003e\n @@@@@@@@@@@:,,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.%%%::::@@@...%.@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.%.++++.+%%%%.@@@@..++@@@@@\n @@@@@@@@@@S,.%%.:,,,,,S@@@@@.\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@+SS,S..........@@@@@,@@@@@@@\n @@@@@@@@@@@+S...++.,,:@@@@@@@@@@@@@@@%....SSS+SS@@@@@@@@@@@@\n\n You got me\u003cspan class=\"pl-k\"\u003e!\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also catch pokemon in your python applications. If you are going to be generating many, it is recommended to load the database once and provide it to the function, otherwise it will be loaded each time.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003efrom pokemon.master import catch_em_all, get_pokemon\n\npokemons = \u003cspan class=\"pl-en\"\u003ecatch_em_all\u003c/span\u003e()\ncatch = get_pokemon(pokemons=pokemons)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe catch is a dictionary, with keys as the pokemon ID, and the value being another dictionary with various meta data (height, weight, japanese, link, ascii, etc).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUpdating the database\u003c/h2\u003e\u003ca id=\"user-content-updating-the-database\" class=\"anchor\" aria-label=\"Permalink: Updating the database\" href=\"#updating-the-database\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe database was generated by running the script make_db.py, and you can update it by running it yourself, if at some point in the future new pokemon are added to the index.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/vsoch/pokemon\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pokemon\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e scripts\npip install -r requirements.txt\npython make_db.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThen move your old database (and you can do this to keep it in case you don\u0027t want changes to persist):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003emv pokemon/database dbbackup\nmv ./database pokemon/database\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe file pokemons.json will be saved under \u003ca href=\"pokemon/databases\"\u003epokemon/databases\u003c/a\u003e. Next, install as usual.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocker\u003c/h2\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can also use the \u003ca href=\"https://hub.docker.com/r/vanessa/pokemon/\" rel=\"nofollow\"\u003eDocker image\u003c/a\u003e,\nwhich provides the various functions and \u003ca href=\"https://sci-f.github.io\" rel=\"nofollow\"\u003eScientific Filesystem\u003c/a\u003e apps.\nThe 0.35 tag was developed with Python 2, and the 0.35 tag is Python 3 and later\n(with an updated database).\u003c/p\u003e\n\u003cp\u003eWhat can I do?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon apps\n list\n catch\n avatar\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eGive me my avatar!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon run avatar vsoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCatch a random Pokemon\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon run catch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat Pokemon can I catch?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon run list\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCatch me Venusaur!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker run vanessa/pokemon run catch Venusaur\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou can also build the image locally:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003edocker build -t vanessa/pokemon \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSingularity\u003c/h2\u003e\u003ca id=\"user-content-singularity\" class=\"anchor\" aria-label=\"Permalink: Singularity\" href=\"#singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe can do the same with Singularity containers!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo singularity build pokemons Singularity\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat can I do?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons apps\n avatar\n catch\n list\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eGive me my avatar!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons run avatar vsoch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCatch a random Pokemon\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons run catch\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eWhat Pokemons can I catch?\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons list\n...\nPhantump\nTrevenant\nPumpkaboo\nGourgeist\nBergmite\nAvalugg\nNoibat\nNoivern\nXerneas\nYveltal\nZygarde\nDiancie\nHoopa\nVolcanion\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eCatch a specific Pokemon\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e./pokemons run catch Pikachu\n[catch] executing /bin/bash /scif/apps/catch/scif/runscript Pikachu\n@@@@@@@@@@@@@.@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@,\u003cspan class=\"pl-k\"\u003e??\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e##@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\u003c/span\u003e\n@@@@@@@@@,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#:,@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@*?@@\u003c/span\u003e\n@@@@@@@@@\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e::::@@@@@@@@@@@@@@@@@@@@@@@@@,*.???%@@@@@@@@*,,,@@\u003c/span\u003e\n@@@@@@@@::,,::@@@@@@@@@@@@@@@@@@%:,,:\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e####??,@@@@@@*,,,,,,:@\u003c/span\u003e\n@@@@@@@@%:,,:.@@@@@@@@@@@@@@.:::::::.\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e####@@@@@@@.::,,,,,::@\u003c/span\u003e\n@@@@@@@@%::::.,,,,:,:%@@:,:::::::::S#\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e#@@@@@@@@%,:::::,::,:%\u003c/span\u003e\n@@@@@@@@.S,,,,,,,,::::::::::::::::\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e::::::::::::::\n@@@@@@@:,,,,,,,:,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e.#?::::::+.,@@@@@@@@@@@@@.::::::::::::::::\u003c/span\u003e\n@@@@@,\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e:S,,:,::::*#.,:::::::*@@@@@@@@@@@@,::::::::::::::::+@\u003c/span\u003e\n@@@@@:%S::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,,:::...+.::::S@@@@@@@@@@:::::::::::::::%@@@@\n@@@@\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.::::,SSSS%::::+++++:::::%@@@@@@@@:::::::::::::%@@@@@@@\n@@@@@.+:,,::S%+S::::.+++:::::::,@@@@@@@@@:::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e::::S@@@@@@@@@@\n@@@@@@.S:::::.\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.::::::::::::::::@@@@@@@@@,\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e%@@@@@@@@@@@@@\n@@@@@@@@.:::::::::::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e::::,@@@@@@@@,\u003cspan class=\"pl-k\"\u003e***\u003c/span\u003e@@@@@@@@@@@@@@\n@@@@,%,::::::::::::::::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e.\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e::,:S%@@@@@@......@@@@@@@@@@@@@\n,\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e::::,,,,,,:::::::::::+:\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e:::::,::@@\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e.....S@@@@@@@@@@@@@@@\n%:\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e:,:::,,,,,,,,,,,::::::%::::::,,,::,@S..+@@@@@@@@@@@@@@@@@\n@@@@@,S%+::\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e,:,,::,:,,,,::::::::::::::\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@%SS\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e@@@@@@@@@@@@@@@\n@@@@@@@@@@@@.:,,,,:,,,,,,,:::::::::::::+SSSSS.@@@@@@@@@@@@@@\n@@@@@@@@@@@@@:,,,:::::,::::,:::::::::::\u003cspan class=\"pl-k\"\u003e*?\u003c/span\u003e.@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@+,:,:,::::::::::,,::,::::\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e.SS@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@S,,:,,,,::::::::::::::::\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@:::::::::\u003cspan class=\"pl-k\"\u003e****\u003c/span\u003e::::::\u003cspan class=\"pl-k\"\u003e*******\u003c/span\u003eS@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@@\u003cspan class=\"pl-k\"\u003e**********\u003c/span\u003e.%..\u003cspan class=\"pl-k\"\u003e***********\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@@@@,\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e+S%@@@@@@@@@@@@......@@@@@@@@@@@@@@@@@@@@\n@@@@@@@@@@@@@@@@@@@+..\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e@@@@@@@@@@@@@:+\u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e@@@@@@@@@@@@@@@@@@@@\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eIssues and updates\u003c/h2\u003e\u003ca id=\"user-content-issues-and-updates\" class=\"anchor\" aria-label=\"Permalink: Issues and updates\" href=\"#issues-and-updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWould you like different or updated functionality?\nPlease ping me by adding an \u003ca href=\"https://github.com/vsoch/pokemon/issues\"\u003eissue\u003c/a\u003e!\nI did this for fun, might sneak it into a few command line applications,\nand it\u0027s pretty simple so far! I hope you have fun with it! :D\u003c/p\u003e\n", - "stargazers_count": 61, - "subscribers_count": 4, + "full_name": "OxfordSKA/OSKAR", + "latest_release": "2.8.3", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22b41a369bf679916b20b7555194b2892b6d1effa6c050482db987de07ce3c44/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f4f78666f7264534b412f4f534b41522e7376673f7374796c653d666c61742d737175617265\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/OxfordSKA/OSKAR.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.3758491\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1681f5ab8e878d59912e950496faa519def4d3d1c14b2aa5250266a1ca7d037/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333735383439312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3758491.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOSKAR: A GPU-accelerated simulator for the Square Kilometre Array\u003c/h1\u003e\u003ca id=\"user-content-oskar-a-gpu-accelerated-simulator-for-the-square-kilometre-array\" class=\"anchor\" aria-label=\"Permalink: OSKAR: A GPU-accelerated simulator for the Square Kilometre Array\" href=\"#oskar-a-gpu-accelerated-simulator-for-the-square-kilometre-array\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOSKAR has been designed to produce simulated visibility data from radio\ntelescopes containing aperture arrays, such as those envisaged for the\nSquare Kilometre Array.\u003c/p\u003e\n\u003cp\u003eA source code archive, and pre-built binary packages for Linux (using\nSingularity), macOS and Windows platforms are available to download from\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003ehttps://github.com/OxfordSKA/OSKAR/releases\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOSKAR is licensed under the terms of the 3-clause BSD License,\nand makes use of a number of third-party libraries.\nPlease see the OSKAR documentation for license details\nof third-party libraries used by OSKAR.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePackage documentation is built automatically as part of the\nGitLab CI/CD pipeline, and can be viewed at\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://ska-telescope.gitlab.io/sim/oskar/\" rel=\"nofollow\"\u003ehttps://ska-telescope.gitlab.io/sim/oskar/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation Guide\u003c/h2\u003e\u003ca id=\"user-content-installation-guide\" class=\"anchor\" aria-label=\"Permalink: Installation Guide\" href=\"#installation-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf compiling from source, see the\n\u003ca href=\"https://ska-telescope.gitlab.io/sim/oskar/install/install.html\" rel=\"nofollow\"\u003eInstallation Guide\u003c/a\u003e\nin the documentation.\u003c/p\u003e\n", + "stargazers_count": 63, + "subscribers_count": 11, "topics": [ - "pokemon", - "avatar", - "fun", - "python" + "astronomy", + "radio-telescopes", + "simulator" ], - "updated_at": 1728229254.0 + "updated_at": 1711907296.0 }, { "data_format": 2, @@ -41578,36 +41625,17 @@ var data = }, { "data_format": 2, - "description": "SKA Radio Telescope Simulator", - "filenames": [ - "singularity/Singularity.base-dep", - "singularity/Singularity.python3" - ], - "full_name": "OxfordSKA/OSKAR", - "latest_release": "2.8.3", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22b41a369bf679916b20b7555194b2892b6d1effa6c050482db987de07ce3c44/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f4f78666f7264534b412f4f534b41522e7376673f7374796c653d666c61742d737175617265\" alt=\"GitHub release\" data-canonical-src=\"https://img.shields.io/github/release/OxfordSKA/OSKAR.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.3758491\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e1681f5ab8e878d59912e950496faa519def4d3d1c14b2aa5250266a1ca7d037/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e333735383439312e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.3758491.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eOSKAR: A GPU-accelerated simulator for the Square Kilometre Array\u003c/h1\u003e\u003ca id=\"user-content-oskar-a-gpu-accelerated-simulator-for-the-square-kilometre-array\" class=\"anchor\" aria-label=\"Permalink: OSKAR: A GPU-accelerated simulator for the Square Kilometre Array\" href=\"#oskar-a-gpu-accelerated-simulator-for-the-square-kilometre-array\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eOSKAR has been designed to produce simulated visibility data from radio\ntelescopes containing aperture arrays, such as those envisaged for the\nSquare Kilometre Array.\u003c/p\u003e\n\u003cp\u003eA source code archive, and pre-built binary packages for Linux (using\nSingularity), macOS and Windows platforms are available to download from\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OxfordSKA/OSKAR/releases\"\u003ehttps://github.com/OxfordSKA/OSKAR/releases\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOSKAR is licensed under the terms of the 3-clause BSD License,\nand makes use of a number of third-party libraries.\nPlease see the OSKAR documentation for license details\nof third-party libraries used by OSKAR.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePackage documentation is built automatically as part of the\nGitLab CI/CD pipeline, and can be viewed at\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://ska-telescope.gitlab.io/sim/oskar/\" rel=\"nofollow\"\u003ehttps://ska-telescope.gitlab.io/sim/oskar/\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation Guide\u003c/h2\u003e\u003ca id=\"user-content-installation-guide\" class=\"anchor\" aria-label=\"Permalink: Installation Guide\" href=\"#installation-guide\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf compiling from source, see the\n\u003ca href=\"https://ska-telescope.gitlab.io/sim/oskar/install/install.html\" rel=\"nofollow\"\u003eInstallation Guide\u003c/a\u003e\nin the documentation.\u003c/p\u003e\n", - "stargazers_count": 63, - "subscribers_count": 11, - "topics": [ - "astronomy", - "radio-telescopes", - "simulator" - ], - "updated_at": 1711907296.0 - }, - { - "data_format": 2, - "description": "TIDDIT - structural variant calling", + "description": "nanodisco: a toolbox for discovering and exploiting multiple types of DNA methylation from individual bacteria and microbiomes using nanopore sequencing.", "filenames": [ "Singularity" ], - "full_name": "SciLifeLab/TIDDIT", - "latest_release": "TIDDIT-3.8.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDESCRIPTION\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: DESCRIPTION\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTIDDIT: Is a tool to used to identify chromosomal rearrangements using Mate Pair or Paired End sequencing data. TIDDIT identifies intra and inter-chromosomal translocations, deletions, tandem-duplications and inversions, using supplementary alignments as well as discordant pairs.\nTIDDIT searches for discordant reads and splti reads (supplementary alignments). The supplementary alignments are assembled and aligned using a fermikit-like workflow.\nNext all signals (contigs, split-reads, and discordant pairs) are clustered using DBSCAN. The resulting clusters are filtered and annotated, and reported as SV depending on the statistics.\nTIDDIT has two analysis modules. The sv mode, which is used to search for structural variants. And the cov mode that analyse the read depth of a bam file and generates a coverage report.\nOn a 30X human genome, the TIDDIT SV module typically completetes within 5 hours, and requires less than 10Gb ram.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eINSTALLATION\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: INSTALLATION\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTIDDIT requires python3 (=\u0026gt; 3.8), cython, pysam, and Numpy.\u003c/p\u003e\n\u003cp\u003eBy default, tiddit will require, bwa, fermi2 and ropebwt2 for local assembly; local assembly may be disabled through the \"--skip_assembly\" parameter.\u003c/p\u003e\n\u003cp\u003eInstallation\u003c/p\u003e\n\u003cp\u003eCloning from Git Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/SciLifeLab/TIDDIT.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install TIDDIT:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd tiddit\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext install fermi2, ropebwt2, and bwa, I recommend using conda:\u003c/p\u003e\n\u003cp\u003econda install fermi2 ropebwt2 bwa\u003c/p\u003e\n\u003cp\u003eYou may also compile bwa, fermi2, and ropebwt2 yourself. Remember to add executables to path, or provide path through the command line parameters.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etiddit --help\ntiddit --sv --help\ntiddit --cov --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOptionally, the assembly calling may be turned off using the \"--skip_assembly\" option.\u003c/p\u003e\n\u003cp\u003eTIDDIT may be installed using bioconda:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install tiddit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor using the docker image on biocontainers\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull quay.io/biocontainers/tiddit:\u0026lt;tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003evisit \u003ca href=\"https://quay.io/repository/biocontainers/tiddit?tab=tags\" rel=\"nofollow\"\u003ehttps://quay.io/repository/biocontainers/tiddit?tab=tags\u003c/a\u003e for tags.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe SV module\u003c/h1\u003e\u003ca id=\"user-content-the-sv-module\" class=\"anchor\" aria-label=\"Permalink: The SV module\" href=\"#the-sv-module\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main TIDDIT module, detects structural variant using discordant pairs, split reads and coverage information\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython tiddit --sv [Options] --bam in.bam --ref reference.fa\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere bam is the input bam or cram file. And reference.fasta is the reference fasta used to align the sequencing data: TIDDIT will crash if the reference fasta is different from the one used to align the reads. The reads of the input bam file must be sorted on genome position.\u003c/p\u003e\n\u003cp\u003eTIDDIT may be fine-tuned by altering these optional parameters:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-o\toutput prefix(default=output)\n-i\tpaired reads maximum allowed insert size. Pairs aligning on the same chr at a distance higher than this are considered candidates for SV (default= 99.9th percentile of insert size)\n-d\texpected reads orientations, possible values \"innie\" (-\u0026gt; \u0026lt;-) or \"outtie\" (\u0026lt;- -\u0026gt;). Default: major orientation within the dataset\n-p\tMinimum number of supporting pairs in order to call a variant (default 3)\n-r\tMinimum number of supporting split reads to call a variant (default 3)\n--threads\tNumber of threads (default 1)\n-q\tMinimum mapping quality to consider an alignment (default 5)\n-n\tthe ploidy of the organism,(default = 2)\n-e\tclustering distance parameter, discordant pairs closer than this distance are considered to belong to the same variant(default = sqrt(insert-size*2)*12)\n-c\taverage coverage, overwrites the estimated average coverage (useful for exome or panel data)\n-l\tmin-pts parameter (default=3),must be set \u0026gt;= 2\n-s\tNumber of reads to sample when computing library statistics(default=25000000)\n-z \tminimum variant size (default=50), variants smaller than this will not be printed ( z \u0026lt; 10 is not recomended)\n--force_ploidy\tforce the ploidy to be set to -n across the entire genome (i.e skip coverage normalisation of chromosomes)\n --force_overwrite force the analysis and overwrite any data in the output folder\n--n_mask\texclude regions from coverage calculation if they contain more than this fraction of N (default = 0.5)\n--skip_assembly\tSkip running local assembly, tiddit will perform worse, but wont require fermi2, bwa, ropebwt and bwa indexed ref\n--bwa\tpath to bwa executable file(default=bwa)\n--fermi2\tpath to fermi2 executable file (default=fermi2)\n--ropebwt2\tpath to ropebwt2 executable file (default=ropebwt2)\n--p_ratio\tminimum discordant pair/normal pair ratio at the breakpoint junction(default=0.1)\n--r_ratio\tminimum split read/coverage ratio at the breakpoint junction(default=0.1)\n--max_coverage\tfilter call if X times higher than chromosome average coverage (default=4)\n--min_contig\t Skip calling on small contigs (default \u0026lt; 10000 bp)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eoutput:\u003c/p\u003e\n\u003cp\u003eTIDDIT SV module produces two output files, a vcf file containing SV calls, and a tab file dscribing the estimated ploidy and coverage across each contig.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe cov module\u003c/h1\u003e\u003ca id=\"user-content-the-cov-module\" class=\"anchor\" aria-label=\"Permalink: The cov module\" href=\"#the-cov-module\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputes the coverge of different regions of the bam file\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython TIDDIT.py --cov [Options] --bam bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eoptional parameters:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-o - the prefix of the output files\n-z - compute the coverage within bins of a specified size across the entire genome, default bin size is 500\n-w - generate a wig file instead of bed\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e--ref - reference sequence (fasta), required for reading cram file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFilters\u003c/h1\u003e\u003ca id=\"user-content-filters\" class=\"anchor\" aria-label=\"Permalink: Filters\" href=\"#filters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTIDDIT uses four different filters to detect low quality calls. The filter field of variants passing these tests are set to \"PASS\". If a variant fail any of these tests, the filter field is set to the filter failing that variant. These are the four filters empoyed by TIDDIT:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eExpectedlinks\nLess than \u0026lt;p_ratio\u0026gt; fraction of the spanning pairs or \u0026lt;r_ratio\u0026gt; fraction reads support the variant\nFewLinks\n The number of discordant pairs supporting the variant is too low compared to the number of discordant pairs within that genomic region.\nUnexpectedcoverage\n High coverage\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFailed Variants may be removed using tools such as VCFtools or grep. Removing these variants greatly improves the precision of TIDDIT, but may reduce the sensitivity. It is adviced to remove filtered variants or prioritize the variants that have passed the quality checks.\nThis command may be usedto filter the TIDDIT vcf:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egrep -E \"#|PASS\" input.vcf \u0026gt; output.filtered.vcf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuality column\u003c/h1\u003e\u003ca id=\"user-content-quality-column\" class=\"anchor\" aria-label=\"Permalink: Quality column\" href=\"#quality-column\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe scores in the quality column are calculated using non parametric sampling: 1000 points/genomic positions are sampled across each chromosome. And the number of read-pairs and reads spanning these points are counted.\nThe variant support of each call is compared to these values, and the quality column is set to he lowest percentile higher than the (variant support*ploidy).\u003c/p\u003e\n\u003cp\u003eNote: SVs usually occur in repetetive regions, hence these scores are expected to be relatively low. A true variant may have a low score, and the score itself depends on the input data (mate-pair vs pe for instance).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMerging the vcf files\u003c/h1\u003e\u003ca id=\"user-content-merging-the-vcf-files\" class=\"anchor\" aria-label=\"Permalink: Merging the vcf files\" href=\"#merging-the-vcf-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI usually merge vcf files using SVDB (\u003ca href=\"https://github.com/J35P312\"\u003ehttps://github.com/J35P312\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003esvdb --merge --vcf file1.vcf file2.vcf --bnd_distance 500 --overlap 0.6 \u0026gt; merged.vcf\u003c/p\u003e\n\u003cp\u003eMerging of vcf files could be useful for tumor-normal analysis or for analysing a pedigree. But also to combine the output of multiple callers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTumor normal example\u003c/h1\u003e\u003ca id=\"user-content-tumor-normal-example\" class=\"anchor\" aria-label=\"Permalink: Tumor normal example\" href=\"#tumor-normal-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003erun the tumor sample using a lower ratio treshold (to allow for subclonal events, and to account for low purity)\u003c/p\u003e\n\u003cp\u003epython TIDDIT.py --sv --p_ratio 0.10 --bam tumor.bam -o tumor --ref reference.fasta\ngrep -E \"#|PASS\" tumor.vcf \u0026gt; tumor.pass.vcf\u003c/p\u003e\n\u003cp\u003erun the normal sample\u003c/p\u003e\n\u003cp\u003epython TIDDIT.py --sv --bam normal.bam -o normal --ref reference.fasta\ngrep -E \"#|PASS\" normal.vcf \u0026gt; normal.pass.vcf\u003c/p\u003e\n\u003cp\u003emerge files:\u003c/p\u003e\n\u003cp\u003esvdb --merge --vcf tumor.pass.vcf normal.pass.vcf --bnd_distance 500 --overlap 0.6 \u0026gt; Tumor_normal.vcf\u003c/p\u003e\n\u003cp\u003eThe output vcf should be filtered further and annotated (using a local-frequency database for instance)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAnnotation\u003c/h1\u003e\u003ca id=\"user-content-annotation\" class=\"anchor\" aria-label=\"Permalink: Annotation\" href=\"#annotation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003egenes may be annotated using vep or snpeff. NIRVANA may be used for annotating CNVs, and SVDB may be used as a frequency database\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAlgorithm\u003c/h1\u003e\u003ca id=\"user-content-algorithm\" class=\"anchor\" aria-label=\"Permalink: Algorithm\" href=\"#algorithm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDiscordant pairs, split reads (supplementary alignments), and contigs are extracted. A discordant pair is any pair having a larger insert size than the -i paramater, or a pair where the reads map to different chromosomes.\nsupplementary alignments and discordant pairs are only extracted if their mapping quality exceed the -q parameter. Contigs are generated by assembling all reads with supplementary alignment using fermi2\u003c/p\u003e\n\u003cp\u003eThe most recent version of TIDDIT uses an algorithm similar to DBSCAN: A cluster is formed if -l or more signals are located within the -e distance. Once a cluster is formed, more signals may be added if these signals are within the\n-e distance of -l signals within a cluster.\u003c/p\u003e\n\u003cp\u003eA cluster is rejected if it contains less than -r plus -p signals. If the cluster is rejected, it will not be printed to the vcf file.\u003c/p\u003e\n\u003cp\u003eIf the cluster is not rejected, it will be printed to file, even if it fails any quality filter.\u003c/p\u003e\n\u003cp\u003eThe sensitivity and precision may be controlled using the -q,r,p, and -l parameters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLICENSE\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll the tools distributed with this package are distributed under GNU General Public License version 3.0 (GPLv3).\u003c/p\u003e\n", + "full_name": "fanglab/nanodisco", + "latest_release": "v1.0.3", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enanodisco\u003c/h1\u003e\u003ca id=\"user-content-nanodisco\" class=\"anchor\" aria-label=\"Permalink: nanodisco\" href=\"#nanodisco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003enanodisco\u003c/code\u003e is a toolbox for \u003cem\u003ede novo\u003c/em\u003e discovery of all the three types (6mA, 5mC and 4mC) of DNA methylation from individual bacteria and microbiomes using nanopore sequencing. For microbiomes, nanodisco also supports the use of DNA methylation patterns as natural epigenetic barcodes to facilitate high resolution metagenomic binning. Specifically, nanodisco can be used to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eDe novo\u003c/em\u003e discover DNA methylation motifs, identify specific type (6mA, 5mC or 4mC, namely \u003cem\u003etyping\u003c/em\u003e) of a methylation motif, and identify which specific position within the motif is methylated (namely \u003cem\u003efine mapping\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003ePerform metagenomic binning based on microbial DNA methylation pattern by constructing and clustering a methylation profile matrix.\u003c/li\u003e\n\u003cli\u003eIntegrate the two functionalities above together for \u003cem\u003ede novo\u003c/em\u003e methylation motif discovery from microbiomes, and metagenomic analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u0027 notes\u003c/h2\u003e\u003ca id=\"user-content-authors-notes\" class=\"anchor\" aria-label=\"Permalink: Authors\u0027 notes\" href=\"#authors-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are actively developing \u003ccode\u003enanodisco\u003c/code\u003e to facilitate usage and broaden features. All feedback is more than welcome. You can reach us on twitter (\u003ca href=\"https://twitter.com/iamfanggang\" rel=\"nofollow\"\u003e@iamfanggang\u003c/a\u003e and \u003ca href=\"https://twitter.com/AlanTourancheau\" rel=\"nofollow\"\u003e@AlanTourancheau\u003c/a\u003e) or directly through the \u003ca href=\"https://github.com/fanglab/nanodisco/issues\"\u003eGitHub issues system\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e02/13/21: Updated to \u003ccode\u003ev1.0.2\u003c/code\u003e, including a new \u003ccode\u003enanodisco score\u003c/code\u003e command and the \u003ccode\u003e--split_fasta\u003c/code\u003e option to generate binned fasta files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContent\u003c/h2\u003e\u003ca id=\"user-content-content\" class=\"anchor\" aria-label=\"Permalink: Content\" href=\"#content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#Installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#Tool-showcase\"\u003eTool showcase\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#Prepare-the-container-for-examples\"\u003ePrepare the container for examples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Methylation-typing-and-fine-mapping\"\u003eMethylation typing and fine mapping\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Methylation-binning-of-metagenomic-contigs\"\u003eMethylation binning of metagenomic contigs\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Documentation\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003enanodisco\u003c/code\u003e is distributed as a fully functional image bypassing the need to install any dependencies others than the virtualization software. We currently recommend using Singularity (v3.2.1 and above), which can be installed on Linux systems and is often the preferred solution by HPC administrators (\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eQuick Start\u003c/a\u003e). \u003ccode\u003enanodisco\u003c/code\u003e was tested extensively with Singularity v3.2.1 and v3.5.2.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name nanodisco.sif library://fanglab/default/nanodisco \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the image from cloud.sylabs.io\u003c/span\u003e\nsingularity build nd_env nanodisco.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a container named nd_env\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTool showcase\u003c/h2\u003e\u003ca id=\"user-content-tool-showcase\" class=\"anchor\" aria-label=\"Permalink: Tool showcase\" href=\"#tool-showcase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo showcase the toolbox applications and facilitate an understanding of the methods, we provide examples for the analysis of two datasets presented in our preprint. Those datasets can be download with the following commands from within a \u003ccode\u003enanodisco\u003c/code\u003e container: \u003ccode\u003eget_data_bacteria\u003c/code\u003e and \u003ccode\u003eget_data_microbiome\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrepare the container for examples\u003c/h3\u003e\u003ca id=\"user-content-prepare-the-container-for-examples\" class=\"anchor\" aria-label=\"Permalink: Prepare the container for examples\" href=\"#prepare-the-container-for-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build --sandbox nd_example nanodisco.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a writable container (directory) named nd_example\u003c/span\u003e\nsingularity run --no-home -w nd_example \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start an interactive shell to use nanodisco, type `exit` to leave\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe image retrieved from \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with \u003ccode\u003esingularity pull\u003c/code\u003e (nanodisco.sif) is already build and can be reused at will. Containers built with those instructions are writable meaning that results from nanodisco analysis can be retrieved when the container is not running. Outputs for the following commands can be found at \u003ccode\u003e./path/to/nd_example/home/nanodisco/analysis\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMethylation typing and fine mapping\u003c/h3\u003e\u003ca id=\"user-content-methylation-typing-and-fine-mapping\" class=\"anchor\" aria-label=\"Permalink: Methylation typing and fine mapping\" href=\"#methylation-typing-and-fine-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGoal:\u003c/strong\u003e Identify the specific type (6mA, 5mC or 4mC, namely \u003cem\u003etyping\u003c/em\u003e) of a methylation motif, and identify which specific position within the motif is methylated (namely \u003cem\u003efine mapping\u003c/em\u003e). The detailed method is described in the preprint.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInputs:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCurrent differences file (pre-computed in the following example, can be generated with \u003ccode\u003enanodisco difference\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eReference genome file (.fasta)\u003c/li\u003e\n\u003cli\u003eMethylation motifs for which one wants to perform typing and fine mapping\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eOutputs:\u003c/strong\u003e For each queried methylation motif, \u003ccode\u003enanodisco\u003c/code\u003e identifies the methylation type and the methylated position summarized in a heatmap (\u003ccode\u003eanalysis/Ecoli_motifs/Motifs_classification_Ecoli_nn_model.pdf\u003c/code\u003e). See Figure 4d in the preprint as an example. In addition, the predicted methylation type and methylated position for each motif is compiled in a text file (\u003ccode\u003eanalysis/Ecoli_motifs/Motifs_classification_Ecoli_nn_model.tsv\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/Motifs_classification_Ecoli_nn_model.png\"\u003e\u003cimg src=\"/docs/figures/Motifs_classification_Ecoli_nn_model.png\" alt=\"Output Characterize\" title=\"E. coli methylation motifs classification results\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003csub\u003e\u003cem\u003e1. AACNNNNNNGTGC: highest value (85) is on the 6mA row with offset +1 (relative to the first base), meaning that the second base (A) is 6mA\u003c/em\u003e\u003c/sub\u003e\u003cbr\u003e\n\u003csub\u003e\u003cem\u003e2. CCWGG: highest value (95) is on the 5mC row with offset +1 (relative to the first base), meaning that the second base (C) is 5mC\u003c/em\u003e\u003c/sub\u003e\u003cbr\u003e\n\u003csub\u003e\u003cem\u003e3. GATC: highest value (91) is on the 6mA row with offset +1 (relative to the first base), meaning that the second base (A) is 6mA\u003c/em\u003e\u003c/sub\u003e\u003cbr\u003e\n\u003csub\u003e\u003cem\u003e4. GCACNNNNNNGTT: highest value (84) is on the 6mA row with offset +2 (relative to the first base), meaning that the third base (A) is 6mA\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExample commands:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eget_data_bacteria \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Retrieve E. coli current differences and reference genome\u003c/span\u003e\nnanodisco characterize -p 4 -b Ecoli -d dataset/EC_difference.RDS -o analysis/Ecoli_motifs -m GATC,CCWGG,GCACNNNNNNGTT,AACNNNNNNGTGC -t nn -r reference/Ecoli_K12_MG1655_ATCC47076.fasta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn this example, the current differences file (\u003ccode\u003eEC_difference.RDS\u003c/code\u003e) was generated on a whole \u003cem\u003eE. coli\u003c/em\u003e nanopore sequencing dataset, from the preprint, using \u003ccode\u003enanodisco difference\u003c/code\u003e. \u003cstrong\u003eRuntime is ~1 min with 4 threads\u003c/strong\u003e (~6.5 GB memory used).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMethylation binning of metagenomic contigs\u003c/h3\u003e\u003ca id=\"user-content-methylation-binning-of-metagenomic-contigs\" class=\"anchor\" aria-label=\"Permalink: Methylation binning of metagenomic contigs\" href=\"#methylation-binning-of-metagenomic-contigs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGoal:\u003c/strong\u003e Construct methylation profiles for metagenomic contigs, identify informative features, and perform methylation binning for high-resolution metagenomic analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInputs:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCurrent differences file (pre-computed in the following example)\u003c/li\u003e\n\u003cli\u003eMetagenomic \u003cem\u003ede novo\u003c/em\u003e assembly (.fasta)\u003c/li\u003e\n\u003cli\u003eMetagenomic contigs coverage files\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eDe novo\u003c/em\u003e discovered methylation motifs\u003c/li\u003e\n\u003cli\u003e(Optional) Annotation for metagenome contigs (e.g. species of origin) and List of contigs from Mobile Genetic Elements (MGEs)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eOutputs:\u003c/strong\u003e t-SNE scatter plots that demonstrates the species level clustering of metagenomic contigs (\u003ccode\u003eanalysis/binning/Contigs_methylation_tsne_MGM1_motif.pdf\u003c/code\u003e) as presented in the preprint Figure 5a.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/Contigs_methylation_tsne_MGM1_motif.png\"\u003e\u003cimg src=\"/docs/figures/Contigs_methylation_tsne_MGM1_motif.png\" alt=\"MGM1 guided metagenomic contigs binning\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExample commands:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eget_data_microbiome \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Retrieve current differences, de novo metagenome assembly, etc\u003c/span\u003e\nnanodisco profile -p 4 -r reference/metagenome.fasta -d dataset/metagenome_subset_difference.RDS -w dataset/metagenome_WGA.cov -n dataset/metagenome_NAT.cov -b MGM1_motif -o analysis/binning --motifs_file dataset/list_de_novo_discovered_motifs.txt\nnanodisco binning -r reference/metagenome.fasta -s dataset/methylation_profile_MGM1_motif.RDS -b MGM1_motif -o analysis/binning\nnanodisco plot_binning -r reference/metagenome.fasta -u analysis/binning/methylation_binning_MGM1_motif.RDS -b MGM1_motif -o analysis/binning -a reference/motif_binning_annotation.RDS --MGEs_file dataset/list_MGE_contigs.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn this example, the current differences file (\u003ccode\u003emetagenome_subset_difference.RDS\u003c/code\u003e) was generated on a mouse gut microbiome nanopore sequencing dataset, MGM1 from the preprint, using \u003ccode\u003enanodisco difference\u003c/code\u003e. This examples correspond to the procedure refered to as guided methylation binning where methylation motifs were already \u003cem\u003ede novo\u003c/em\u003e discovered. \u003cstrong\u003eRuntime is ~10 min with 4 threads\u003c/strong\u003e and ~4 GB of memory used.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a comprehensive description of \u003ccode\u003enanodisco\u003c/code\u003e including installation guide, and a detailed tutorial, please consult the \u003ca href=\"https://nanodisco.readthedocs.io/en/latest/overview.html\" rel=\"nofollow\"\u003ecomplete documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTourancheau, A., Mead, E.A., Zhang, XS. \u0026amp; Fang, G. Discovering multiple types of DNA methylation from bacteria and microbiome using nanopore sequencing. \u003cem\u003eNat Methods\u003c/em\u003e (2021). doi:\u003ca href=\"https://doi.org/10.1038/s41592-021-01109-3\" rel=\"nofollow\"\u003e10.1038/s41592-021-01109-3\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 67, - "subscribers_count": 18, + "subscribers_count": 7, "topics": [], - "updated_at": 1727535119.0 + "updated_at": 1726405639.0 }, { "data_format": 2, @@ -41625,36 +41653,77 @@ var data = }, { "data_format": 2, - "description": "nanodisco: a toolbox for discovering and exploiting multiple types of DNA methylation from individual bacteria and microbiomes using nanopore sequencing.", + "description": "TIDDIT - structural variant calling", "filenames": [ "Singularity" ], - "full_name": "fanglab/nanodisco", - "latest_release": "v1.0.3", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003enanodisco\u003c/h1\u003e\u003ca id=\"user-content-nanodisco\" class=\"anchor\" aria-label=\"Permalink: nanodisco\" href=\"#nanodisco\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003enanodisco\u003c/code\u003e is a toolbox for \u003cem\u003ede novo\u003c/em\u003e discovery of all the three types (6mA, 5mC and 4mC) of DNA methylation from individual bacteria and microbiomes using nanopore sequencing. For microbiomes, nanodisco also supports the use of DNA methylation patterns as natural epigenetic barcodes to facilitate high resolution metagenomic binning. Specifically, nanodisco can be used to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cem\u003eDe novo\u003c/em\u003e discover DNA methylation motifs, identify specific type (6mA, 5mC or 4mC, namely \u003cem\u003etyping\u003c/em\u003e) of a methylation motif, and identify which specific position within the motif is methylated (namely \u003cem\u003efine mapping\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003ePerform metagenomic binning based on microbial DNA methylation pattern by constructing and clustering a methylation profile matrix.\u003c/li\u003e\n\u003cli\u003eIntegrate the two functionalities above together for \u003cem\u003ede novo\u003c/em\u003e methylation motif discovery from microbiomes, and metagenomic analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors\u0027 notes\u003c/h2\u003e\u003ca id=\"user-content-authors-notes\" class=\"anchor\" aria-label=\"Permalink: Authors\u0027 notes\" href=\"#authors-notes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe are actively developing \u003ccode\u003enanodisco\u003c/code\u003e to facilitate usage and broaden features. All feedback is more than welcome. You can reach us on twitter (\u003ca href=\"https://twitter.com/iamfanggang\" rel=\"nofollow\"\u003e@iamfanggang\u003c/a\u003e and \u003ca href=\"https://twitter.com/AlanTourancheau\" rel=\"nofollow\"\u003e@AlanTourancheau\u003c/a\u003e) or directly through the \u003ca href=\"https://github.com/fanglab/nanodisco/issues\"\u003eGitHub issues system\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e02/13/21: Updated to \u003ccode\u003ev1.0.2\u003c/code\u003e, including a new \u003ccode\u003enanodisco score\u003c/code\u003e command and the \u003ccode\u003e--split_fasta\u003c/code\u003e option to generate binned fasta files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContent\u003c/h2\u003e\u003ca id=\"user-content-content\" class=\"anchor\" aria-label=\"Permalink: Content\" href=\"#content\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#Installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"#Tool-showcase\"\u003eTool showcase\u003c/a\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#Prepare-the-container-for-examples\"\u003ePrepare the container for examples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Methylation-typing-and-fine-mapping\"\u003eMethylation typing and fine mapping\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Methylation-binning-of-metagenomic-contigs\"\u003eMethylation binning of metagenomic contigs\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Documentation\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#Citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003enanodisco\u003c/code\u003e is distributed as a fully functional image bypassing the need to install any dependencies others than the virtualization software. We currently recommend using Singularity (v3.2.1 and above), which can be installed on Linux systems and is often the preferred solution by HPC administrators (\u003ca href=\"https://sylabs.io/guides/3.5/user-guide/quick_start.html\" rel=\"nofollow\"\u003eQuick Start\u003c/a\u003e). \u003ccode\u003enanodisco\u003c/code\u003e was tested extensively with Singularity v3.2.1 and v3.5.2.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity pull --name nanodisco.sif library://fanglab/default/nanodisco \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download the image from cloud.sylabs.io\u003c/span\u003e\nsingularity build nd_env nanodisco.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a container named nd_env\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTool showcase\u003c/h2\u003e\u003ca id=\"user-content-tool-showcase\" class=\"anchor\" aria-label=\"Permalink: Tool showcase\" href=\"#tool-showcase\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo showcase the toolbox applications and facilitate an understanding of the methods, we provide examples for the analysis of two datasets presented in our preprint. Those datasets can be download with the following commands from within a \u003ccode\u003enanodisco\u003c/code\u003e container: \u003ccode\u003eget_data_bacteria\u003c/code\u003e and \u003ccode\u003eget_data_microbiome\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePrepare the container for examples\u003c/h3\u003e\u003ca id=\"user-content-prepare-the-container-for-examples\" class=\"anchor\" aria-label=\"Permalink: Prepare the container for examples\" href=\"#prepare-the-container-for-examples\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esingularity build --sandbox nd_example nanodisco.sif \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a writable container (directory) named nd_example\u003c/span\u003e\nsingularity run --no-home -w nd_example \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start an interactive shell to use nanodisco, type `exit` to leave\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe image retrieved from \u003ca href=\"https://singularity-hub.org/\" rel=\"nofollow\"\u003eSingularity Hub\u003c/a\u003e with \u003ccode\u003esingularity pull\u003c/code\u003e (nanodisco.sif) is already build and can be reused at will. Containers built with those instructions are writable meaning that results from nanodisco analysis can be retrieved when the container is not running. Outputs for the following commands can be found at \u003ccode\u003e./path/to/nd_example/home/nanodisco/analysis\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMethylation typing and fine mapping\u003c/h3\u003e\u003ca id=\"user-content-methylation-typing-and-fine-mapping\" class=\"anchor\" aria-label=\"Permalink: Methylation typing and fine mapping\" href=\"#methylation-typing-and-fine-mapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGoal:\u003c/strong\u003e Identify the specific type (6mA, 5mC or 4mC, namely \u003cem\u003etyping\u003c/em\u003e) of a methylation motif, and identify which specific position within the motif is methylated (namely \u003cem\u003efine mapping\u003c/em\u003e). The detailed method is described in the preprint.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInputs:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCurrent differences file (pre-computed in the following example, can be generated with \u003ccode\u003enanodisco difference\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eReference genome file (.fasta)\u003c/li\u003e\n\u003cli\u003eMethylation motifs for which one wants to perform typing and fine mapping\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eOutputs:\u003c/strong\u003e For each queried methylation motif, \u003ccode\u003enanodisco\u003c/code\u003e identifies the methylation type and the methylated position summarized in a heatmap (\u003ccode\u003eanalysis/Ecoli_motifs/Motifs_classification_Ecoli_nn_model.pdf\u003c/code\u003e). See Figure 4d in the preprint as an example. In addition, the predicted methylation type and methylated position for each motif is compiled in a text file (\u003ccode\u003eanalysis/Ecoli_motifs/Motifs_classification_Ecoli_nn_model.tsv\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/Motifs_classification_Ecoli_nn_model.png\"\u003e\u003cimg src=\"/docs/figures/Motifs_classification_Ecoli_nn_model.png\" alt=\"Output Characterize\" title=\"E. coli methylation motifs classification results\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003csub\u003e\u003cem\u003e1. AACNNNNNNGTGC: highest value (85) is on the 6mA row with offset +1 (relative to the first base), meaning that the second base (A) is 6mA\u003c/em\u003e\u003c/sub\u003e\u003cbr\u003e\n\u003csub\u003e\u003cem\u003e2. CCWGG: highest value (95) is on the 5mC row with offset +1 (relative to the first base), meaning that the second base (C) is 5mC\u003c/em\u003e\u003c/sub\u003e\u003cbr\u003e\n\u003csub\u003e\u003cem\u003e3. GATC: highest value (91) is on the 6mA row with offset +1 (relative to the first base), meaning that the second base (A) is 6mA\u003c/em\u003e\u003c/sub\u003e\u003cbr\u003e\n\u003csub\u003e\u003cem\u003e4. GCACNNNNNNGTT: highest value (84) is on the 6mA row with offset +2 (relative to the first base), meaning that the third base (A) is 6mA\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExample commands:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eget_data_bacteria \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Retrieve E. coli current differences and reference genome\u003c/span\u003e\nnanodisco characterize -p 4 -b Ecoli -d dataset/EC_difference.RDS -o analysis/Ecoli_motifs -m GATC,CCWGG,GCACNNNNNNGTT,AACNNNNNNGTGC -t nn -r reference/Ecoli_K12_MG1655_ATCC47076.fasta\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn this example, the current differences file (\u003ccode\u003eEC_difference.RDS\u003c/code\u003e) was generated on a whole \u003cem\u003eE. coli\u003c/em\u003e nanopore sequencing dataset, from the preprint, using \u003ccode\u003enanodisco difference\u003c/code\u003e. \u003cstrong\u003eRuntime is ~1 min with 4 threads\u003c/strong\u003e (~6.5 GB memory used).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMethylation binning of metagenomic contigs\u003c/h3\u003e\u003ca id=\"user-content-methylation-binning-of-metagenomic-contigs\" class=\"anchor\" aria-label=\"Permalink: Methylation binning of metagenomic contigs\" href=\"#methylation-binning-of-metagenomic-contigs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGoal:\u003c/strong\u003e Construct methylation profiles for metagenomic contigs, identify informative features, and perform methylation binning for high-resolution metagenomic analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInputs:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCurrent differences file (pre-computed in the following example)\u003c/li\u003e\n\u003cli\u003eMetagenomic \u003cem\u003ede novo\u003c/em\u003e assembly (.fasta)\u003c/li\u003e\n\u003cli\u003eMetagenomic contigs coverage files\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eDe novo\u003c/em\u003e discovered methylation motifs\u003c/li\u003e\n\u003cli\u003e(Optional) Annotation for metagenome contigs (e.g. species of origin) and List of contigs from Mobile Genetic Elements (MGEs)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eOutputs:\u003c/strong\u003e t-SNE scatter plots that demonstrates the species level clustering of metagenomic contigs (\u003ccode\u003eanalysis/binning/Contigs_methylation_tsne_MGM1_motif.pdf\u003c/code\u003e) as presented in the preprint Figure 5a.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/docs/figures/Contigs_methylation_tsne_MGM1_motif.png\"\u003e\u003cimg src=\"/docs/figures/Contigs_methylation_tsne_MGM1_motif.png\" alt=\"MGM1 guided metagenomic contigs binning\" width=\"500\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExample commands:\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003eget_data_microbiome \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Retrieve current differences, de novo metagenome assembly, etc\u003c/span\u003e\nnanodisco profile -p 4 -r reference/metagenome.fasta -d dataset/metagenome_subset_difference.RDS -w dataset/metagenome_WGA.cov -n dataset/metagenome_NAT.cov -b MGM1_motif -o analysis/binning --motifs_file dataset/list_de_novo_discovered_motifs.txt\nnanodisco binning -r reference/metagenome.fasta -s dataset/methylation_profile_MGM1_motif.RDS -b MGM1_motif -o analysis/binning\nnanodisco plot_binning -r reference/metagenome.fasta -u analysis/binning/methylation_binning_MGM1_motif.RDS -b MGM1_motif -o analysis/binning -a reference/motif_binning_annotation.RDS --MGEs_file dataset/list_MGE_contigs.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIn this example, the current differences file (\u003ccode\u003emetagenome_subset_difference.RDS\u003c/code\u003e) was generated on a mouse gut microbiome nanopore sequencing dataset, MGM1 from the preprint, using \u003ccode\u003enanodisco difference\u003c/code\u003e. This examples correspond to the procedure refered to as guided methylation binning where methylation motifs were already \u003cem\u003ede novo\u003c/em\u003e discovered. \u003cstrong\u003eRuntime is ~10 min with 4 threads\u003c/strong\u003e and ~4 GB of memory used.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor a comprehensive description of \u003ccode\u003enanodisco\u003c/code\u003e including installation guide, and a detailed tutorial, please consult the \u003ca href=\"https://nanodisco.readthedocs.io/en/latest/overview.html\" rel=\"nofollow\"\u003ecomplete documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTourancheau, A., Mead, E.A., Zhang, XS. \u0026amp; Fang, G. Discovering multiple types of DNA methylation from bacteria and microbiome using nanopore sequencing. \u003cem\u003eNat Methods\u003c/em\u003e (2021). doi:\u003ca href=\"https://doi.org/10.1038/s41592-021-01109-3\" rel=\"nofollow\"\u003e10.1038/s41592-021-01109-3\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "SciLifeLab/TIDDIT", + "latest_release": "TIDDIT-3.8.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDESCRIPTION\u003c/h1\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: DESCRIPTION\" href=\"#description\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTIDDIT: Is a tool to used to identify chromosomal rearrangements using Mate Pair or Paired End sequencing data. TIDDIT identifies intra and inter-chromosomal translocations, deletions, tandem-duplications and inversions, using supplementary alignments as well as discordant pairs.\nTIDDIT searches for discordant reads and splti reads (supplementary alignments). The supplementary alignments are assembled and aligned using a fermikit-like workflow.\nNext all signals (contigs, split-reads, and discordant pairs) are clustered using DBSCAN. The resulting clusters are filtered and annotated, and reported as SV depending on the statistics.\nTIDDIT has two analysis modules. The sv mode, which is used to search for structural variants. And the cov mode that analyse the read depth of a bam file and generates a coverage report.\nOn a 30X human genome, the TIDDIT SV module typically completetes within 5 hours, and requires less than 10Gb ram.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eINSTALLATION\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: INSTALLATION\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTIDDIT requires python3 (=\u0026gt; 3.8), cython, pysam, and Numpy.\u003c/p\u003e\n\u003cp\u003eBy default, tiddit will require, bwa, fermi2 and ropebwt2 for local assembly; local assembly may be disabled through the \"--skip_assembly\" parameter.\u003c/p\u003e\n\u003cp\u003eInstallation\u003c/p\u003e\n\u003cp\u003eCloning from Git Hub:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/SciLifeLab/TIDDIT.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install TIDDIT:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd tiddit\npip install -e .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext install fermi2, ropebwt2, and bwa, I recommend using conda:\u003c/p\u003e\n\u003cp\u003econda install fermi2 ropebwt2 bwa\u003c/p\u003e\n\u003cp\u003eYou may also compile bwa, fermi2, and ropebwt2 yourself. Remember to add executables to path, or provide path through the command line parameters.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etiddit --help\ntiddit --sv --help\ntiddit --cov --help\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOptionally, the assembly calling may be turned off using the \"--skip_assembly\" option.\u003c/p\u003e\n\u003cp\u003eTIDDIT may be installed using bioconda:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install tiddit\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor using the docker image on biocontainers\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull quay.io/biocontainers/tiddit:\u0026lt;tag\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003evisit \u003ca href=\"https://quay.io/repository/biocontainers/tiddit?tab=tags\" rel=\"nofollow\"\u003ehttps://quay.io/repository/biocontainers/tiddit?tab=tags\u003c/a\u003e for tags.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe SV module\u003c/h1\u003e\u003ca id=\"user-content-the-sv-module\" class=\"anchor\" aria-label=\"Permalink: The SV module\" href=\"#the-sv-module\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main TIDDIT module, detects structural variant using discordant pairs, split reads and coverage information\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython tiddit --sv [Options] --bam in.bam --ref reference.fa\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWhere bam is the input bam or cram file. And reference.fasta is the reference fasta used to align the sequencing data: TIDDIT will crash if the reference fasta is different from the one used to align the reads. The reads of the input bam file must be sorted on genome position.\u003c/p\u003e\n\u003cp\u003eTIDDIT may be fine-tuned by altering these optional parameters:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-o\toutput prefix(default=output)\n-i\tpaired reads maximum allowed insert size. Pairs aligning on the same chr at a distance higher than this are considered candidates for SV (default= 99.9th percentile of insert size)\n-d\texpected reads orientations, possible values \"innie\" (-\u0026gt; \u0026lt;-) or \"outtie\" (\u0026lt;- -\u0026gt;). Default: major orientation within the dataset\n-p\tMinimum number of supporting pairs in order to call a variant (default 3)\n-r\tMinimum number of supporting split reads to call a variant (default 3)\n--threads\tNumber of threads (default 1)\n-q\tMinimum mapping quality to consider an alignment (default 5)\n-n\tthe ploidy of the organism,(default = 2)\n-e\tclustering distance parameter, discordant pairs closer than this distance are considered to belong to the same variant(default = sqrt(insert-size*2)*12)\n-c\taverage coverage, overwrites the estimated average coverage (useful for exome or panel data)\n-l\tmin-pts parameter (default=3),must be set \u0026gt;= 2\n-s\tNumber of reads to sample when computing library statistics(default=25000000)\n-z \tminimum variant size (default=50), variants smaller than this will not be printed ( z \u0026lt; 10 is not recomended)\n--force_ploidy\tforce the ploidy to be set to -n across the entire genome (i.e skip coverage normalisation of chromosomes)\n --force_overwrite force the analysis and overwrite any data in the output folder\n--n_mask\texclude regions from coverage calculation if they contain more than this fraction of N (default = 0.5)\n--skip_assembly\tSkip running local assembly, tiddit will perform worse, but wont require fermi2, bwa, ropebwt and bwa indexed ref\n--bwa\tpath to bwa executable file(default=bwa)\n--fermi2\tpath to fermi2 executable file (default=fermi2)\n--ropebwt2\tpath to ropebwt2 executable file (default=ropebwt2)\n--p_ratio\tminimum discordant pair/normal pair ratio at the breakpoint junction(default=0.1)\n--r_ratio\tminimum split read/coverage ratio at the breakpoint junction(default=0.1)\n--max_coverage\tfilter call if X times higher than chromosome average coverage (default=4)\n--min_contig\t Skip calling on small contigs (default \u0026lt; 10000 bp)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eoutput:\u003c/p\u003e\n\u003cp\u003eTIDDIT SV module produces two output files, a vcf file containing SV calls, and a tab file dscribing the estimated ploidy and coverage across each contig.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eThe cov module\u003c/h1\u003e\u003ca id=\"user-content-the-cov-module\" class=\"anchor\" aria-label=\"Permalink: The cov module\" href=\"#the-cov-module\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eComputes the coverge of different regions of the bam file\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython TIDDIT.py --cov [Options] --bam bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eoptional parameters:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e-o - the prefix of the output files\n-z - compute the coverage within bins of a specified size across the entire genome, default bin size is 500\n-w - generate a wig file instead of bed\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e--ref - reference sequence (fasta), required for reading cram file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eFilters\u003c/h1\u003e\u003ca id=\"user-content-filters\" class=\"anchor\" aria-label=\"Permalink: Filters\" href=\"#filters\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTIDDIT uses four different filters to detect low quality calls. The filter field of variants passing these tests are set to \"PASS\". If a variant fail any of these tests, the filter field is set to the filter failing that variant. These are the four filters empoyed by TIDDIT:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eExpectedlinks\nLess than \u0026lt;p_ratio\u0026gt; fraction of the spanning pairs or \u0026lt;r_ratio\u0026gt; fraction reads support the variant\nFewLinks\n The number of discordant pairs supporting the variant is too low compared to the number of discordant pairs within that genomic region.\nUnexpectedcoverage\n High coverage\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFailed Variants may be removed using tools such as VCFtools or grep. Removing these variants greatly improves the precision of TIDDIT, but may reduce the sensitivity. It is adviced to remove filtered variants or prioritize the variants that have passed the quality checks.\nThis command may be usedto filter the TIDDIT vcf:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egrep -E \"#|PASS\" input.vcf \u0026gt; output.filtered.vcf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eQuality column\u003c/h1\u003e\u003ca id=\"user-content-quality-column\" class=\"anchor\" aria-label=\"Permalink: Quality column\" href=\"#quality-column\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe scores in the quality column are calculated using non parametric sampling: 1000 points/genomic positions are sampled across each chromosome. And the number of read-pairs and reads spanning these points are counted.\nThe variant support of each call is compared to these values, and the quality column is set to he lowest percentile higher than the (variant support*ploidy).\u003c/p\u003e\n\u003cp\u003eNote: SVs usually occur in repetetive regions, hence these scores are expected to be relatively low. A true variant may have a low score, and the score itself depends on the input data (mate-pair vs pe for instance).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMerging the vcf files\u003c/h1\u003e\u003ca id=\"user-content-merging-the-vcf-files\" class=\"anchor\" aria-label=\"Permalink: Merging the vcf files\" href=\"#merging-the-vcf-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI usually merge vcf files using SVDB (\u003ca href=\"https://github.com/J35P312\"\u003ehttps://github.com/J35P312\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003esvdb --merge --vcf file1.vcf file2.vcf --bnd_distance 500 --overlap 0.6 \u0026gt; merged.vcf\u003c/p\u003e\n\u003cp\u003eMerging of vcf files could be useful for tumor-normal analysis or for analysing a pedigree. But also to combine the output of multiple callers.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eTumor normal example\u003c/h1\u003e\u003ca id=\"user-content-tumor-normal-example\" class=\"anchor\" aria-label=\"Permalink: Tumor normal example\" href=\"#tumor-normal-example\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003erun the tumor sample using a lower ratio treshold (to allow for subclonal events, and to account for low purity)\u003c/p\u003e\n\u003cp\u003epython TIDDIT.py --sv --p_ratio 0.10 --bam tumor.bam -o tumor --ref reference.fasta\ngrep -E \"#|PASS\" tumor.vcf \u0026gt; tumor.pass.vcf\u003c/p\u003e\n\u003cp\u003erun the normal sample\u003c/p\u003e\n\u003cp\u003epython TIDDIT.py --sv --bam normal.bam -o normal --ref reference.fasta\ngrep -E \"#|PASS\" normal.vcf \u0026gt; normal.pass.vcf\u003c/p\u003e\n\u003cp\u003emerge files:\u003c/p\u003e\n\u003cp\u003esvdb --merge --vcf tumor.pass.vcf normal.pass.vcf --bnd_distance 500 --overlap 0.6 \u0026gt; Tumor_normal.vcf\u003c/p\u003e\n\u003cp\u003eThe output vcf should be filtered further and annotated (using a local-frequency database for instance)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAnnotation\u003c/h1\u003e\u003ca id=\"user-content-annotation\" class=\"anchor\" aria-label=\"Permalink: Annotation\" href=\"#annotation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003egenes may be annotated using vep or snpeff. NIRVANA may be used for annotating CNVs, and SVDB may be used as a frequency database\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eAlgorithm\u003c/h1\u003e\u003ca id=\"user-content-algorithm\" class=\"anchor\" aria-label=\"Permalink: Algorithm\" href=\"#algorithm\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDiscordant pairs, split reads (supplementary alignments), and contigs are extracted. A discordant pair is any pair having a larger insert size than the -i paramater, or a pair where the reads map to different chromosomes.\nsupplementary alignments and discordant pairs are only extracted if their mapping quality exceed the -q parameter. Contigs are generated by assembling all reads with supplementary alignment using fermi2\u003c/p\u003e\n\u003cp\u003eThe most recent version of TIDDIT uses an algorithm similar to DBSCAN: A cluster is formed if -l or more signals are located within the -e distance. Once a cluster is formed, more signals may be added if these signals are within the\n-e distance of -l signals within a cluster.\u003c/p\u003e\n\u003cp\u003eA cluster is rejected if it contains less than -r plus -p signals. If the cluster is rejected, it will not be printed to the vcf file.\u003c/p\u003e\n\u003cp\u003eIf the cluster is not rejected, it will be printed to file, even if it fails any quality filter.\u003c/p\u003e\n\u003cp\u003eThe sensitivity and precision may be controlled using the -q,r,p, and -l parameters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLICENSE\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll the tools distributed with this package are distributed under GNU General Public License version 3.0 (GPLv3).\u003c/p\u003e\n", "stargazers_count": 67, - "subscribers_count": 7, + "subscribers_count": 18, "topics": [], - "updated_at": 1726405639.0 + "updated_at": 1727535119.0 }, { "data_format": 2, - "description": "An example GitHub Action (CI) to build a Singularity container", + "description": "Fast visualization tool for large-scale and high dimensional single-cell data", "filenames": [ "Singularity" ], - "full_name": "singularityhub/github-ci", + "full_name": "aertslab/SCope", + "latest_release": "untagged-64f13ec6d922418df08e", + "readme": "\u003cp\u003e\u003ca href=\"https://www.codefactor.io/repository/github/aertslab/scope\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1a16c1766f7d3c5d0013371ab9e20e734523c6bfc7c731b7cdeaea22eb3b38b8/68747470733a2f2f7777772e636f6465666163746f722e696f2f7265706f7369746f72792f6769746875622f61657274736c61622f73636f70652f6261646765\" alt=\"CodeFactor\" data-canonical-src=\"https://www.codefactor.io/repository/github/aertslab/scope/badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSCope v1.8.2: Visualization of large-scale and high dimensional single cell data\u003c/h1\u003e\u003ca id=\"user-content-scope-v182-visualization-of-large-scale-and-high-dimensional-single-cell-data\" class=\"anchor\" aria-label=\"Permalink: SCope v1.8.2: Visualization of large-scale and high dimensional single cell data\" href=\"#scope-v182-visualization-of-large-scale-and-high-dimensional-single-cell-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/SCope_Logo.png\"\u003e\u003cimg src=\"images/SCope_Logo.png\" width=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets.\nCurrently the data format supported by SCope is \u003ccode\u003e.loom\u003c/code\u003e. This file format for very large omics datasets is maintained by the Linnarsson Lab through the \u003ccode\u003eloompy\u003c/code\u003e Python package (\u003ca href=\"https://github.com/linnarsson-lab/loompy\"\u003ehttps://github.com/linnarsson-lab/loompy\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eView the \u003ca href=\"CHANGELOG.md\"\u003echange log here\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDemo\u003c/h2\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-label=\"Permalink: Demo\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVisit \u003ca href=\"https://scope.aertslab.org\" rel=\"nofollow\"\u003ehttps://scope.aertslab.org\u003c/a\u003e to test out SCope on several published datasets! Personal loom file files can be uploaded but will only be kept for 5 days.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLoom File Generation\u003c/h2\u003e\u003ca id=\"user-content-loom-file-generation\" class=\"anchor\" aria-label=\"Permalink: Loom File Generation\" href=\"#loom-file-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently there are two packages to generate extended loom files compatible with SCope.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR: \u003ca href=\"https://github.com/aertslab/SCopeLoomR\"\u003eSCopeLoomR\u003c/a\u003e - Dedicated R package\u003c/li\u003e\n\u003cli\u003ePython: \u003ca href=\"https://github.com/aertslab/pySCENIC\"\u003epySCENIC\u003c/a\u003e - Single function for generation from SCENIC results\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEventually the functionality from pySCENIC will be expanded and put in its own python package.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun SCope\u003c/h2\u003e\u003ca id=\"user-content-run-scope\" class=\"anchor\" aria-label=\"Permalink: Run SCope\" href=\"#run-scope\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStandalone App\u003c/h3\u003e\u003ca id=\"user-content-standalone-app\" class=\"anchor\" aria-label=\"Permalink: Standalone App\" href=\"#standalone-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStandalone apps for \u003cstrong\u003emacOS\u003c/strong\u003e and \u003cstrong\u003eLinux\u003c/strong\u003e can be downloaded from \u003ca href=\"https://github.com/aertslab/SCope/releases\"\u003ethe releases page.\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u2757 SCope standalone app requires Node.js (\u0026gt; v9). To install it, go to \u003ca href=\"https://nodejs.org/en/download/\" rel=\"nofollow\"\u003ehttps://nodejs.org/en/download/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eA \u003cstrong\u003eWindows\u003c/strong\u003e app is under development, but currently has no ETA.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand Line\u003c/h3\u003e\u003ca id=\"user-content-command-line\" class=\"anchor\" aria-label=\"Permalink: Command Line\" href=\"#command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need access to at least Python 3.7 do run this.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone the GitHub repository and install,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Define where you want to clone the SCope repository.\u003c/span\u003e\nLOCAL_SCOPE_REPO=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HOME}\u003c/span\u003e/repos/SCope\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Clone SCope git repository.\u003c/span\u003e\ngit clone https://github.com/aertslab/SCope \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Go to your local cloned SCope repository.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install SCope.\u003c/span\u003e\nnpm install\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Go to your local cloned SCope repository.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nSCOPE_CONFIG=config.json npm run scope\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy a Cloud-based Instance\u003c/h2\u003e\u003ca id=\"user-content-deploy-a-cloud-based-instance\" class=\"anchor\" aria-label=\"Permalink: Deploy a Cloud-based Instance\" href=\"#deploy-a-cloud-based-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAmazon Web Services\u003c/h3\u003e\u003ca id=\"user-content-amazon-web-services\" class=\"anchor\" aria-label=\"Permalink: Amazon Web Services\" href=\"#amazon-web-services\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePublic AMI\u003c/h4\u003e\u003ca id=\"user-content-public-ami\" class=\"anchor\" aria-label=\"Permalink: Public AMI\" href=\"#public-ami\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNo ETA.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSource\u003c/h4\u003e\u003ca id=\"user-content-source\" class=\"anchor\" aria-label=\"Permalink: Source\" href=\"#source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo create a SCope AWS instance from scratch please read the tutorial \u003ca href=\"https://github.com/aertslab/SCope/tree/master/tutorials/aws-deployment-source\"\u003eaws-deployment-source\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnabling ORCID Functionality\u003c/h3\u003e\u003ca id=\"user-content-enabling-orcid-functionality\" class=\"anchor\" aria-label=\"Permalink: Enabling ORCID Functionality\" href=\"#enabling-orcid-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo enable colaborative annotations and login via ORCID ID, API credentials (\u003ccode\u003eorcidAPIClientID\u003c/code\u003e, \u003ccode\u003eorcidAPIClientSecret\u003c/code\u003e and \u003ccode\u003eorcidAPIRedirectURI\u003c/code\u003e) must be added to the config file provided.\nThese can be generated at the \u003ca href=\"https://orcid.org/developer-tools\" rel=\"nofollow\"\u003eorcid developer tools page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003edataHashSecret\u003c/code\u003e entry in the config file should be filled in with a randomly generated string for example from the python \u003ca href=\"https://docs.python.org/3/library/secrets.html\" rel=\"nofollow\"\u003esecrets package\u003c/a\u003e.\nThis string will be used to salt all annotation data, allowing validation of data generated on the instance of SCope. Any changes in this string will invalidate all pre-existing annotations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the GitHub repository and install,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Define where you want to clone the SCope repository.\u003c/span\u003e\nLOCAL_SCOPE_REPO=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HOME}\u003c/span\u003e/repos/SCope\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Clone SCope git repository.\u003c/span\u003e\ngit clone https://github.com/aertslab/SCope \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Go to your local cloned SCope repository.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install SCope.\u003c/span\u003e\nnpm install\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Go to your local cloned SCope repository.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start SCope Server (terminal 1).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e opt\npoetry run hypercorn main:scope_api --reload\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start SCope Client (terminal 2).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\nnpm run dev\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfiguration file (\u003ccode\u003econfig.json\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-configuration-file-configjson\" class=\"anchor\" aria-label=\"Permalink: Configuration file (config.json)\" href=\"#configuration-file-configjson\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eKeys:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edata\u003c/code\u003e: This is a directory containing data files (e.g. the \u003ccode\u003emotd.txt\u003c/code\u003e message of the day).\nCan be an absolute path or a relative path from where you start SCope. By default it is\n\u003ccode\u003e./data/\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeploying SCope with Docker\u003c/h3\u003e\u003ca id=\"user-content-deploying-scope-with-docker\" class=\"anchor\" aria-label=\"Permalink: Deploying SCope with Docker\" href=\"#deploying-scope-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edocker-compose.yml\u003c/code\u003e is configured to spin up 2 containers: One to run the SCope backend and another to run an Apache\nreverse proxy server.\u003c/p\u003e\n\u003cp\u003eThe SCope application will be available on port \u003ccode\u003e80\u003c/code\u003e by default. You can specify a port by using env variable: \u003ccode\u003eSCOPE_PORT\u003c/code\u003e\nbefore running the docker-compose command. Apache will proxy requests through to the appropriate port inside the container.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003edocker-compose.yml\u003c/code\u003e will serve the assets from inside the scope container, and the \u003ccode\u003edocker-compose.host.yml\u003c/code\u003e will serve them from the host.\nThis supports as many use cases as possible, because you can either build the assets on the host yourself using whatever configuration you need,\nor serve them from the container if your environment doesn\u0027t allow for that (e.g. you don\u0027t have npm installed on the host).\u003c/p\u003e\n\u003cp\u003eBefore running the compose build, you can specify a SCOPE_PORT with: \u003ccode\u003edocker-compose build --build-arg SCOPE_PORT=8080\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe scope webpack assets will have to be built with the config: \u003ccode\u003e\"reverseProxyOn\": true\u003c/code\u003e.\nYou can use environment variable: \u003ccode\u003eSCOPE_CONFIG=path to your config\u003c/code\u003e to specify a config file instead of changing the main one.\u003c/p\u003e\n\u003cp\u003eYou can configure where the dockerised SCope data directories should be located\non the host machine by using the env var \u003ccode\u003eSCOPE_DATA_DIR\u003c/code\u003e before launching the docker-compose.\nThe default location is \u003ccode\u003e./scope_data\u003c/code\u003e which will be created if you do not specify one.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: in this config, you do not need to specify the port in \u003ccode\u003epublicHostAddress\u003c/code\u003e. The env var \u003ccode\u003eSCOPE_PORT\u003c/code\u003e gets appended for you.\u003c/p\u003e\n\u003cp\u003eIf deploying the container on a specific port with another external apache reverse-proxy server,\nyou may have to add a config to the external apache site config to allow http and websocket reverse-proxying.\nHere is an example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ProxyPass / http://0.0.0.0:8080/\n RewriteEngine on\n RewriteCond %{HTTP:Upgrade} websocket [NC]\n RewriteCond %{HTTP:Connection} upgrade [NC]\n RewriteRule ^/?(.*) \"ws://0.0.0.0:8080/$1\" [P,L]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExample serve from container\u003c/h5\u003e\u003ca id=\"user-content-example-serve-from-container\" class=\"anchor\" aria-label=\"Permalink: Example serve from container\" href=\"#example-serve-from-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCopy \u003ccode\u003econfig.json\u003c/code\u003e to a new file and modify with \u003ccode\u003e\"reverseProxyOn\": true,\u003c/code\u003e and \u003ccode\u003epublicHostAddress\u003c/code\u003e set to your domain\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker-compose build --build-arg SCOPE_PORT=8080\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSCOPE_DATA_DIR=$HOME/scope_data SCOPE_PORT=8080 docker-compose up -d\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOR Serve from host\u003c/h5\u003e\u003ca id=\"user-content-or-serve-from-host\" class=\"anchor\" aria-label=\"Permalink: OR Serve from host\" href=\"#or-serve-from-host\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003enpm run build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSCOPE_DATA_DIR=$HOME/scope_data SCOPE_PORT=8080 docker-compose -f docker-compose.host.yml up -d\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou should be able to visit \u003ccode\u003ehttp://localhost:8080\u003c/code\u003e and see the app!\u003c/p\u003e\n", + "stargazers_count": 68, + "subscribers_count": 9, + "topics": [ + "single-cell", + "large-scale-data-visualization", + "gene-expression", + "gene-regulatory-network", + "aws", + "cloud", + "loom", + "reactjs", + "grpc" + ], + "updated_at": 1725421665.0 + }, + { + "data_format": 2, + "description": "Unified kit with all the scripts required for maintaining the repository \ud83e\uddf0", + "filenames": [ + "guest/Singularity" + ], + "full_name": "chaotic-aur/toolbox", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Builder GitHub CI\u003c/h1\u003e\u003ca id=\"user-content-singularity-builder-github-ci\" class=\"anchor\" aria-label=\"Permalink: Singularity Builder GitHub CI\" href=\"#singularity-builder-github-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/sregistry-github-small.png\"\u003e\u003cimg src=\"img/sregistry-github-small.png\" alt=\"img/sregistry-github-small.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a simple example of how you can achieve:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eversion control of your recipes\u003c/li\u003e\n\u003cli\u003ebuild of associated container and\u003c/li\u003e\n\u003cli\u003epush to a storage endpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003efor a reproducible build workflow! By default, we will build on all pull requests and deploy\non push to main. The containers will go to an enabled GitHub package registry thanks to\nthe Singularity oras endpoint.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eupdated\u003c/strong\u003e August 2021, we can now push containers to the GitHub package registry! Woohoo!\u003c/p\u003e\n\u003cp\u003eThere are three workflows configured as examples to build and deploy Singularity containers:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\".github/workflows/native-install.yml\"\u003enative install\u003c/a\u003e discovers Singularity* changed files, and builds Singularity 3.x (with GoLang) natively, deploys to GitHub packages.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\".github/workflows/container.yml\"\u003edocker image\u003c/a\u003e discovers Singularity* changed files, and builds in a \u003ca href=\"https://quay.io/repository/singularity/singularity\" rel=\"nofollow\"\u003edocker image\u003c/a\u003e, also deploys to GitHub packages.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\".github/workflows/manual-deploy.yml\"\u003emanual deploy\u003c/a\u003e takes a list of manually specified Singularity recipes (that aren\u0027t required to be changed), builds Singularity 3.x natively, and deploys to GitHub packages.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWhile the \"build in a container\" option is faster to complete and a more simple workflow, it should be noted that docker runs with\n\u003ccode\u003e--privileged\u003c/code\u003e which may lead to issues with the resulting container in a non privileged situation. Also note that you\nare free to mix and match the above recipes to your liking, or \u003ca href=\"https://github.com/singularityhub/github-ci/issues\"\u003eopen an issue\u003c/a\u003e if you want to ask for help!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should this be managed via Github?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGithub, by way of easy integration with \u003cstrong\u003enative\u003c/strong\u003e continuous integration, is an easy way\nto have a workflow set up where multiple people can collaborate on a container recipe,\nthe recipe can be tested (with whatever testing you need), discussed in pull requests,\nand tested on merge to master. Further, now with GitHub packages we can push our containers\ndirectly to the GitHub package registry!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should I use this instead of a service?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou could use a remote builder, but if you do the build in a continuous integration\nservice you get complete control over it. This means everything from the version of\nSingularity to use, to the tests that you run for your container. You have a lot more\nfreedom in the rate of building, and organization of your repository, because it\u0027s you\nthat writes the configuration.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Enable Packages\u003c/h3\u003e\u003ca id=\"user-content-1-enable-packages\" class=\"anchor\" aria-label=\"Permalink: 1. Enable Packages\" href=\"#1-enable-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to use the \u003ca href=\"https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry\"\u003eGitHub package registry\u003c/a\u003e\nyou\u0027ll need to follow the instructions there to enable packages for your organization, specifically \"public\" and \"internal\" packages should be allowed to be created. You\u0027ll also want to add a username associated with your GitHub organization to the repository secret \u003ccode\u003eGHCR_USERNAME\u003c/code\u003e. If a package already exists for your repository, you may need to \u003ca href=\"https://docs.github.com/en/packages/managing-github-packages-using-github-actions-workflows/publishing-and-installing-a-package-with-github-actions#upgrading-a-workflow-that-accesses-ghcrio\"\u003efollow instructions\u003c/a\u003e to update a package from using personal access tokens to the safer \u003ccode\u003eGITHUB_TOKEN\u003c/code\u003e. This means that:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIf you want to be able to push to the package registry without extra work, don\u0027t push a container to the same package namespace that matches the repository before (e.g., from the command line).\u003c/li\u003e\n\u003cli\u003eIf you have already pushed a container to the repository package namespace, or you want to test and don\u0027t mind making the manual changes, you will need to \u003ca href=\"https://docs.github.com/en/packages/managing-github-packages-using-github-actions-workflows/publishing-and-installing-a-package-with-github-actions#upgrading-a-workflow-that-accesses-ghcrio\"\u003efollow these instructions\u003c/a\u003e to associate the package with your repository.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Add Your Recipes\u003c/h3\u003e\u003ca id=\"user-content-2-add-your-recipes\" class=\"anchor\" aria-label=\"Permalink: 2. Add Your Recipes\" href=\"#2-add-your-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdd your Singularity recipes to this repository, which should be named \u003ccode\u003eSingularity.\u0026lt;tag\u0026gt;\u003c/code\u003e\nor just \u003ccode\u003eSingularity\u003c/code\u003e to follow the previously published convention. You can then choose your file in \u003ca href=\".github/workflows\"\u003e.github/workflows\u003c/a\u003e.\nIf you choose the \u003ccode\u003emanual-deploy.yml\u003c/code\u003e you can manually specify recipes in the matrix variable \"recipe.\"\nIf you choose either of the other two workflows, changed files that start with Singularity.* will\nbe automatically detected and built.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Test your Container\u003c/h3\u003e\u003ca id=\"user-content-3-test-your-container\" class=\"anchor\" aria-label=\"Permalink: 3. Test your Container\" href=\"#3-test-your-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImportantly, we suggest that you add some steps to test your container! Whether that\u0027s running it,\nexec\u0027ing a custom command, or invoking the test command, there is more than\none way to eat a reeses:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTest Container\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003erun\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e singularity exec smokey.sif python run_tests.py\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e singularity test smokey.sif\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e singularity run toasty.sif\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis step is not provided in the workflows, but it\u0027s recommended that you think about it and add if necessary.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Check Triggers\u003c/h3\u003e\u003ca id=\"user-content-4-check-triggers\" class=\"anchor\" aria-label=\"Permalink: 4. Check Triggers\" href=\"#4-check-triggers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow files each have a section at the top that indicates when the workflow will\ntrigger. By default, we will do builds on pull requests, and deploys on pushes to a main\nbranch. If you want to change this logic, edit the top of the recipe files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Push to a registry\u003c/h3\u003e\u003ca id=\"user-content-5-push-to-a-registry\" class=\"anchor\" aria-label=\"Permalink: 5. Push to a registry\" href=\"#5-push-to-a-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are good with GitHub packages, then you are good to go! Otherwise,\nif you want to push to other kinds of storage, you can install the \u003ca href=\"http://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003eSingularity Registry Client\u003c/a\u003e and push to your cloud storage of choice! You will want to add python and python-dev to the dependency\ninstall:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eInstall Dependencies\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003erun\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e sudo apt-get update \u0026amp;\u0026amp; sudo apt-get install -y \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e build-essential \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e libssl-dev \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e uuid-dev \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e libgpgme11-dev \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e squashfs-tools \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e libseccomp-dev \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e pkg-config \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e python-dev python python3-pip\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then install and use sregistry client. Here are many examples:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eDeploy Container\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003erun\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e sudo pip3 install sregistry\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e SREGISTRY_CLIENT=google-storage sregistry push --name username/reponame smokey.sif\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e SREGISTRY_CLIENT=s3 sregistry push --name username/reponame smokey.sif\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e SREGISTRY_CLIENT=registry sregistry push --name username/reponame smokey.sif\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e SREGISTRY_CLIENT=dropbox sregistry push --name username/reponame smokey.sif\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclients page\u003c/a\u003e for all the options.\nIf you want to change the recipe triggers, see \u003ca href=\"https://help.github.com/en/articles/about-github-actions#core-concepts-for-github-actions\"\u003ehere\u003c/a\u003e\nfor getting started with GitHub actions, and \u003ca href=\"https://www.github.com/singularityhub/github-ci/issues\"\u003eplease open an issue\u003c/a\u003e\nif you need any help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e6. Pull Your Container!\u003c/h3\u003e\u003ca id=\"user-content-6-pull-your-container\" class=\"anchor\" aria-label=\"Permalink: 6. Pull Your Container!\" href=\"#6-pull-your-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe example container here is published to \u003ca href=\"https://github.com/singularityhub/github-ci/pkgs/container/github-ci\"\u003esingularithub/github-ci\u003c/a\u003e\nand can be pulled as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull oras://ghcr.io/singularityhub/github-ci:latest\nINFO: Downloading oras image\n$ ls\ngithub-ci_latest.sif img README.md Singularity\n\n$ ./github-ci_latest.sif \nHold me closer... tiny container :) :D\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther Options\u003c/h2\u003e\u003ca id=\"user-content-other-options\" class=\"anchor\" aria-label=\"Permalink: Other Options\" href=\"#other-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can customize this base recipe in so many ways! For example:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to build a Docker container and pull down to Singularity, that\u0027s a good approach too! We have a \u003ca href=\"https://github.com/autamus/container-builder-template\"\u003econtainer-builder-template\u003c/a\u003e to help you authenticate with several popular registries.\u003c/li\u003e\n\u003cli\u003eThe action matrix can be extended to run builds on multiple platforms.\u003c/li\u003e\n\u003cli\u003eYou can also do the same, but test multiple versions of Singularity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHave fun!\u003c/p\u003e\n", + "readme": "\u003ch1\u003e\u003ca id=\"user-content-chaotic-aur\" class=\"anchor\" aria-hidden=\"true\" href=\"#chaotic-aur\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChaotic AUR\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-hidden=\"true\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h2\u003e\n\u003cp\u003eI receive questions like \"Why didn\u0027t you write it in language X? Why didn\u0027t you use sudoers instead of \u003ccode\u003esetuid\u003c/code\u003e? Why don\u0027t you guarantee reproducible builds? Why don\u0027t you use submodules and review every PKGBUILD change?\"\u003c/p\u003e\n\u003cp\u003eBecause I can\u0027t, I am the only person, taking care of all the steps required for updating 3800 packages non-stop, treating all the differences between those PKGBUILDs and their sources, in an infra that runs in donated VMs which are not similar.\u003c/p\u003e\n\u003cp\u003eIf at some point you see something that could be better, then please open a PR. And keep it simple, the more complex the codebase becomes, the more complicated will be in the future for one man alone to maintain it.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cli\" class=\"anchor\" aria-hidden=\"true\" href=\"#cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCLI\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic pr{,epare} ${INPUTDIR} $@\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt generates a building script to be later run in a containerized environment.\n\u003ccode\u003e$INPUTDIR\u003c/code\u003e is the name of the directory in \"$PWD\" which contains a PKGBUILD.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {lw,lowerstrap}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt generates a lowerdir for later chrooting.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {mk,makepkg} ${INPUTDIR} $@\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBuilds the package in a container using systemd-nspawn.\n\u003ccode\u003e$INPUTDIR\u003c/code\u003e is the result of a \u003ccode\u003eprepare\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {mkd,makepwd} [${PACKAGES[@]}]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ePrepare and build all packages in the current directory.\nIf \u003ccode\u003ePACKAGES\u003c/code\u003e are not provided then it will try to build all sub-directories.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {si,interfere-sync}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSync packages\u0027 interference repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {bump,interfere-bump} [${PACKAGES[@]}]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eGenerate and push a PKGREL_BUMPS file for use in interfere. Old entries will automatically be removed.\nUses \u003ccode\u003erepoctl\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {sp,package-lists-sync}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSync package list repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {dp,deploy} ${INPUTDIR}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSign the package and send to primary node.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {dbb,db-bump}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAdd recently deployed packages to the database, while moving replaced packages to the archive.\nUses \u003ccode\u003erepoctl\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic db-rebuild\u003c/code\u003e\nCompletely rebuild the database and files database from scratch, keeping the original DB around until the rebuild is finished.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {rm,remove} ${PACKAGES[@]}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRemove and archive all listed packages.\nUses \u003ccode\u003erepoctl\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {get,aur-download} [-r] ${PACKAGES[@]}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eDownload listed packages\u0027 sources from AUR.\nUses \u003ccode\u003erepoctl\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic cl{,eanup} ${INPUTDIR}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSafely deletes old package sources.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic help {syncthing,rsync}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eInstructions to the mirroring services.\nRSync is one-way (primary-\u0026gt;cluster) only, and Syncthing is both ways.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic routine {hourly,morning,afternoon,nightly,midnight}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRun the specified routine.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic routine clean-archive\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen on a primary node, clean up the archive folder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {clean-duplicates,dedup}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMoves packages which have been replaced by newer (modify time) packages to the archive folder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {clg,clean-logs}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAfter a \u003ccode\u003echaotic makepwd\u003c/code\u003e, remove successfull and \"already built\" logs.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {cls,clean-srccache} ${PACKAGE}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRemoves cached sources from a specific package.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic clean-sigs {,-q}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMoves package files or archive files without their corresponding signature or archive file to the package archive.\nFiles have to be at least 1 hour old to be considered.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {srt,sort-logs}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eUnclutters and sorts the raw log directory into easy to read subcategories\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic find-discarded\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFind and print discarded packages. All packages that are in the database, but do not exist in the package list repo, are considered discarded, but have to be deleted manually.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-involved-directories\" class=\"anchor\" aria-hidden=\"true\" href=\"#involved-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInvolved directories\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/cache/chaotic/sources/${PACKAGETAG}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ePer-package \u003ccode\u003eSRCDEST\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/cache/chaotic/lower/{latest,$DATESTAMP}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eLowerdirs.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/cache/chaotic/cc/{PACKAGETAG}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ePer-package \u003ccode\u003e~/.ccache\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/cache/chaotic/packages\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eContainer-shared pacman\u0027s cache.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/lib/chaotic/interfere\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCloned version of \u003ca href=\"https://github.com/chaotic-aur/interfere\"\u003einterfere repository\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003epacman -S --needed base-devel git arch-install-scripts repoctl fuse-overlayfs rsync python-telegram-send openssh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOne needs an active mirror or a setting (in /etc/chaotic.conf) like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_URL=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehttps://builds.garudalinux.org/repos/chaotic-aur/x86_64\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e REPOCTL_CONFIG=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/etc/chaotic/repoctl.conf\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_REPOCTL_DB_URL=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${CAUR_URL}\u003c/span\u003e/chaotic-aur.db.tar.zst\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_REPOCTL_DB_FILE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/tmp/chaotic/db.tar.zst\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo create a gpg key for the root user refer to this \u003ca href=\"https://wiki.archlinux.org/index.php/GnuPG#Create_a_key_pair\" rel=\"nofollow\"\u003eArchWiki article\u003c/a\u003e for more information. If you find problems when using \"sudo\", read the \"\u003ca href=\"https://wiki.archlinux.org/index.php/GnuPG#su\" rel=\"nofollow\"\u003esu\u003c/a\u003e\" subsection.\nThen generate a ssh keypair for the root user.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo ssh-keygen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe ssh public key (cat /root/.ssh/id_rsa.pub) then needs to be added to the primary servers\u0027 root authorized keys (/root/.ssh/authorized_keys). After that follow these \u003ca href=\"https://wiki.archlinux.org/index.php/GnuPG#Export_your_public_key\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e to export the gpg public key. This key will have to be \u003ca href=\"https://wiki.archlinux.org/index.php/GnuPG#Sending_keys\" rel=\"nofollow\"\u003euploaded\u003c/a\u003e to \u003ca href=\"keyserver.ubuntu.com\"\u003ekeyserver.ubuntu.com\u003c/a\u003e in order for the key to be verified.\nThen, configure it as follows in \u003ccode\u003e/etc/chaotic.conf\u003c/code\u003e, like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_DEPLOY_PKGS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/var/www/chaotic-aur/x86_64\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_URL=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttp://localhost:8080/chaotic-aur/x86_64\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_SIGN_KEY=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e8A9E14A07010F7E3\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_TYPE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecluster\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e REPOCTL_CONFIG=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/etc/chaotic/repoctl.toml\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll find more options in \u003ccode\u003esrc/chaotic\u003c/code\u003e first lines.\u003c/p\u003e\n\u003cp\u003eSupported \u003ccode\u003etype\u003c/code\u003e values are \u003ccode\u003eprimary\u003c/code\u003e, \u003ccode\u003ecluster\u003c/code\u003e, and \u003ccode\u003edev\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, a valid \u003ccode\u003e.gitconfig\u003c/code\u003e needs to be supplied in \u003ccode\u003e/root/.gitconfig\u003c/code\u003e to allow bumping packages via \u003ccode\u003echaotic bump\u003c/code\u003e with a meaningful \u003ccode\u003eemail\u003c/code\u003e and \u003ccode\u003ename\u003c/code\u003e supplied:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[user]\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eemail\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eteam@chaotic.cx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003ename\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eChaotic Dragon (Node) \u262d\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo allow pushing to the interfere repo, a fitting ssh key also needs to be added to authenticate the node.\u003c/p\u003e\n\u003cp\u003eTo have clean logs \u0026amp; less bandwidth usage \u003ccode\u003e/etc/pacman.conf\u003c/code\u003e settings need to be adjusted:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEnable \u003ccode\u003eNoProgressBar\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse \u003ccode\u003eServer = file:///path-to-local-repo\u003c/code\u003e as repo link if a local mirror is available\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDon\u0027t use \u003ccode\u003eILoveCandy\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo deploy faster replace \u003ccode\u003eopenssh\u003c/code\u003e with \u003ccode\u003eopenssh-hpn\u003c/code\u003e on all nodes (adds performance-related \u003ca href=\"https://www.psc.edu/research/networking/hpn-ssh/\" rel=\"nofollow\"\u003epatches\u003c/a\u003e). It should be noted that \u003ccode\u003eopenssh-hpn\u003c/code\u003e recently changed its configuration and behavior and therefore doesn\u0027t provide a 1:1 replacement for \u003ccode\u003eopenssh\u003c/code\u003e anymore.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstall dependencies, then:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo groupadd chaotic_op\nsudo usermod -aG chaotic_op $(whoami)\n\nmake build \u0026amp;\u0026amp; sudo make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-lint\" class=\"anchor\" aria-hidden=\"true\" href=\"#lint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLint\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epacman -S --needed yarn shellcheck\nyarn install\nyarn run lint\u003c/pre\u003e\u003c/div\u003e\n", "stargazers_count": 68, - "subscribers_count": 5, + "subscribers_count": 15, "topics": [ - "singularity-ci", - "github-ci", - "singularity", - "container" + "archlinux", + "repository-management", + "automation", + "continuous-deployment", + "pkgbuild", + "aur" ], - "updated_at": 1728016545.0 + "updated_at": 1685536204.0 + }, + { + "data_format": 2, + "description": "Turn an existing conda environment into a Singularity container", + "filenames": [ + "Singularity.template", + "conda-pack/Singularity" + ], + "full_name": "grst/containerize-conda", + "latest_release": "v0.2.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContainerize an existing conda environment\u003c/h1\u003e\u003ca id=\"user-content-containerize-an-existing-conda-environment\" class=\"anchor\" aria-label=\"Permalink: Containerize an existing conda environment\" href=\"#containerize-an-existing-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI use conda environments for working on data analysis projects.\nSometimes I need to revert to install using \u003ccode\u003epip\u003c/code\u003e or \u003ccode\u003eR\u003c/code\u003e\u0027s\n\u003ccode\u003einstall.packages\u003c/code\u003e if a package is not on bioconda or conda-forge.\u003c/p\u003e\n\u003cp\u003eThis makes it very hard to reproduce the environment, and therefore,\nthe analysis, on another system. Even pure conda environments stored\nas an \u003ccode\u003eenvironment.yml\u003c/code\u003e file tend to \u003ca href=\"https://github.com/conda/conda/issues/9257\"\u003ebreak after a\nwhile\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eUsing the instructions below allows to package an existing environment\ninto a Singularity container which should be more portable\nand can also easily be integrated into a \u003ca href=\"https://grst.github.io/bioinformatics/2019/12/23/reportsrender.html\" rel=\"nofollow\"\u003efully reproducible\ndata analysis\nworkflow\u003c/a\u003e\nbased on e.g. \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eusage: conda_to_singularity.py [-h] [--template TEMPLATE] CONDA_ENV OUTPUT_CONTAINER\n\nConvert a conda env to a singularity container.\n\npositional arguments:\n CONDA_ENV Absolute path to the conda enviornment. Must be exactely the path as it shows up in `conda env list`, not a symbolic link to it, nor a realpath.\n OUTPUT_CONTAINER Output path where the singularity container will be safed.\n\noptional arguments:\n -h, --help show this help message and exit\n --template TEMPLATE Path to a Singularity template file. Must contain a `{conda_env}` placeholder. If not specified, uses the default template shipped with this script.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda_to_singularity.py /home/sturm/.conda/envs/whatever whatever.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, the image will be based on CentOS 7. If you want a different base image,\nyou can modify \u003ccode\u003eSingularity.template\u003c/code\u003e, and specify it with the \u003ccode\u003e--template\u003c/code\u003e argument.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow it works\u003c/h2\u003e\u003ca id=\"user-content-how-it-works\" class=\"anchor\" aria-label=\"Permalink: How it works\" href=\"#how-it-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConda envs cannot simply be \"moved\" as some paths are hardcoded into the environment.\nI previously applied \u003ccode\u003econda-pack\u003c/code\u003e to solve this issue, which works fine in most cases\nbut breaks in some (especially for old environments that have a long history\nof manually installing stuff through R or pip).\u003c/p\u003e\n\u003cp\u003eThis is an other appraoch where the issue is solved by copying the conda environment\nwith its full absolute path to the container and append a line to the Singularity environment\nfile that activates the conda environment from that path once the container is started:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eecho \"source /opt/conda/bin/activate {conda_env}\" \u0026gt;\u0026gt;$SINGULARITY_ENVIRONMENT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNaively, this could be solved with \u003ccode\u003e%files /path/to/env\u003c/code\u003e, however, this dereferences\nall symbolic links, which breaks some environments. Instead, I build a tar archive\nthat keeps all symbolic links intact \u003cem\u003ewithin\u003c/em\u003e the conda environment, but at the\nsame time include all files that are outside the conda env, but referenced\nby a symbolic link.\u003c/p\u003e\n\u003cp\u003eI don\u0027t have a lot of experience yet if it is really more stable than conda-pack\nor just happens to fail in different cases.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhere\u0027s the conda-pack/Docker version?\u003c/h2\u003e\u003ca id=\"user-content-wheres-the-conda-packdocker-version\" class=\"anchor\" aria-label=\"Permalink: Where\u0027s the conda-pack/Docker version?\" href=\"#wheres-the-conda-packdocker-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is an updated version of my scripts that works without \u003ccode\u003econda-pack\u003c/code\u003e and turned out\nto work even in cases where the conda-pack variant failed. It works only with Singularity at the moment.\nI don\u0027t see any major issues porting the new approach to Docker, but don\u0027t have need for that myself.\u003c/p\u003e\n\u003cp\u003eIf you are looking for the previous scripts based on \u003ccode\u003econda-pack\u003c/code\u003e, because you need a Docker variant, or they just\nwork for you, they are in the \u003ca href=\"conda-pack\"\u003econda-pack\u003c/a\u003e folder with a dedicated \u003ca href=\"conda-pack/README.md\"\u003eREADME\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 68, + "subscribers_count": 5, + "topics": [], + "updated_at": 1712084153.0 }, { "data_format": 2, @@ -41689,63 +41758,38 @@ var data = }, { "data_format": 2, - "description": "Turn an existing conda environment into a Singularity container", - "filenames": [ - "Singularity.template", - "conda-pack/Singularity" - ], - "full_name": "grst/containerize-conda", - "latest_release": "v0.2.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContainerize an existing conda environment\u003c/h1\u003e\u003ca id=\"user-content-containerize-an-existing-conda-environment\" class=\"anchor\" aria-label=\"Permalink: Containerize an existing conda environment\" href=\"#containerize-an-existing-conda-environment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eI use conda environments for working on data analysis projects.\nSometimes I need to revert to install using \u003ccode\u003epip\u003c/code\u003e or \u003ccode\u003eR\u003c/code\u003e\u0027s\n\u003ccode\u003einstall.packages\u003c/code\u003e if a package is not on bioconda or conda-forge.\u003c/p\u003e\n\u003cp\u003eThis makes it very hard to reproduce the environment, and therefore,\nthe analysis, on another system. Even pure conda environments stored\nas an \u003ccode\u003eenvironment.yml\u003c/code\u003e file tend to \u003ca href=\"https://github.com/conda/conda/issues/9257\"\u003ebreak after a\nwhile\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eUsing the instructions below allows to package an existing environment\ninto a Singularity container which should be more portable\nand can also easily be integrated into a \u003ca href=\"https://grst.github.io/bioinformatics/2019/12/23/reportsrender.html\" rel=\"nofollow\"\u003efully reproducible\ndata analysis\nworkflow\u003c/a\u003e\nbased on e.g. \u003ca href=\"https://www.nextflow.io/\" rel=\"nofollow\"\u003eNextflow\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003eusage: conda_to_singularity.py [-h] [--template TEMPLATE] CONDA_ENV OUTPUT_CONTAINER\n\nConvert a conda env to a singularity container.\n\npositional arguments:\n CONDA_ENV Absolute path to the conda enviornment. Must be exactely the path as it shows up in `conda env list`, not a symbolic link to it, nor a realpath.\n OUTPUT_CONTAINER Output path where the singularity container will be safed.\n\noptional arguments:\n -h, --help show this help message and exit\n --template TEMPLATE Path to a Singularity template file. Must contain a `{conda_env}` placeholder. If not specified, uses the default template shipped with this script.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda_to_singularity.py /home/sturm/.conda/envs/whatever whatever.sif\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eBy default, the image will be based on CentOS 7. If you want a different base image,\nyou can modify \u003ccode\u003eSingularity.template\u003c/code\u003e, and specify it with the \u003ccode\u003e--template\u003c/code\u003e argument.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow it works\u003c/h2\u003e\u003ca id=\"user-content-how-it-works\" class=\"anchor\" aria-label=\"Permalink: How it works\" href=\"#how-it-works\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eConda envs cannot simply be \"moved\" as some paths are hardcoded into the environment.\nI previously applied \u003ccode\u003econda-pack\u003c/code\u003e to solve this issue, which works fine in most cases\nbut breaks in some (especially for old environments that have a long history\nof manually installing stuff through R or pip).\u003c/p\u003e\n\u003cp\u003eThis is an other appraoch where the issue is solved by copying the conda environment\nwith its full absolute path to the container and append a line to the Singularity environment\nfile that activates the conda environment from that path once the container is started:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eecho \"source /opt/conda/bin/activate {conda_env}\" \u0026gt;\u0026gt;$SINGULARITY_ENVIRONMENT\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNaively, this could be solved with \u003ccode\u003e%files /path/to/env\u003c/code\u003e, however, this dereferences\nall symbolic links, which breaks some environments. Instead, I build a tar archive\nthat keeps all symbolic links intact \u003cem\u003ewithin\u003c/em\u003e the conda environment, but at the\nsame time include all files that are outside the conda env, but referenced\nby a symbolic link.\u003c/p\u003e\n\u003cp\u003eI don\u0027t have a lot of experience yet if it is really more stable than conda-pack\nor just happens to fail in different cases.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhere\u0027s the conda-pack/Docker version?\u003c/h2\u003e\u003ca id=\"user-content-wheres-the-conda-packdocker-version\" class=\"anchor\" aria-label=\"Permalink: Where\u0027s the conda-pack/Docker version?\" href=\"#wheres-the-conda-packdocker-version\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis is an updated version of my scripts that works without \u003ccode\u003econda-pack\u003c/code\u003e and turned out\nto work even in cases where the conda-pack variant failed. It works only with Singularity at the moment.\nI don\u0027t see any major issues porting the new approach to Docker, but don\u0027t have need for that myself.\u003c/p\u003e\n\u003cp\u003eIf you are looking for the previous scripts based on \u003ccode\u003econda-pack\u003c/code\u003e, because you need a Docker variant, or they just\nwork for you, they are in the \u003ca href=\"conda-pack\"\u003econda-pack\u003c/a\u003e folder with a dedicated \u003ca href=\"conda-pack/README.md\"\u003eREADME\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 68, - "subscribers_count": 5, - "topics": [], - "updated_at": 1712084153.0 - }, - { - "data_format": 2, - "description": "Unified kit with all the scripts required for maintaining the repository \ud83e\uddf0", + "description": "An example GitHub Action (CI) to build a Singularity container", "filenames": [ - "guest/Singularity" + "Singularity" ], - "full_name": "chaotic-aur/toolbox", + "full_name": "singularityhub/github-ci", "latest_release": null, - "readme": "\u003ch1\u003e\u003ca id=\"user-content-chaotic-aur\" class=\"anchor\" aria-hidden=\"true\" href=\"#chaotic-aur\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eChaotic AUR\u003c/h1\u003e\n\u003ch2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-hidden=\"true\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDisclaimer\u003c/h2\u003e\n\u003cp\u003eI receive questions like \"Why didn\u0027t you write it in language X? Why didn\u0027t you use sudoers instead of \u003ccode\u003esetuid\u003c/code\u003e? Why don\u0027t you guarantee reproducible builds? Why don\u0027t you use submodules and review every PKGBUILD change?\"\u003c/p\u003e\n\u003cp\u003eBecause I can\u0027t, I am the only person, taking care of all the steps required for updating 3800 packages non-stop, treating all the differences between those PKGBUILDs and their sources, in an infra that runs in donated VMs which are not similar.\u003c/p\u003e\n\u003cp\u003eIf at some point you see something that could be better, then please open a PR. And keep it simple, the more complex the codebase becomes, the more complicated will be in the future for one man alone to maintain it.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-cli\" class=\"anchor\" aria-hidden=\"true\" href=\"#cli\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eCLI\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic pr{,epare} ${INPUTDIR} $@\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt generates a building script to be later run in a containerized environment.\n\u003ccode\u003e$INPUTDIR\u003c/code\u003e is the name of the directory in \"$PWD\" which contains a PKGBUILD.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {lw,lowerstrap}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eIt generates a lowerdir for later chrooting.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {mk,makepkg} ${INPUTDIR} $@\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eBuilds the package in a container using systemd-nspawn.\n\u003ccode\u003e$INPUTDIR\u003c/code\u003e is the result of a \u003ccode\u003eprepare\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {mkd,makepwd} [${PACKAGES[@]}]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ePrepare and build all packages in the current directory.\nIf \u003ccode\u003ePACKAGES\u003c/code\u003e are not provided then it will try to build all sub-directories.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {si,interfere-sync}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSync packages\u0027 interference repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {bump,interfere-bump} [${PACKAGES[@]}]\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eGenerate and push a PKGREL_BUMPS file for use in interfere. Old entries will automatically be removed.\nUses \u003ccode\u003erepoctl\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {sp,package-lists-sync}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSync package list repo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {dp,deploy} ${INPUTDIR}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSign the package and send to primary node.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {dbb,db-bump}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAdd recently deployed packages to the database, while moving replaced packages to the archive.\nUses \u003ccode\u003erepoctl\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic db-rebuild\u003c/code\u003e\nCompletely rebuild the database and files database from scratch, keeping the original DB around until the rebuild is finished.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {rm,remove} ${PACKAGES[@]}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRemove and archive all listed packages.\nUses \u003ccode\u003erepoctl\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {get,aur-download} [-r] ${PACKAGES[@]}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eDownload listed packages\u0027 sources from AUR.\nUses \u003ccode\u003erepoctl\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic cl{,eanup} ${INPUTDIR}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eSafely deletes old package sources.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic help {syncthing,rsync}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eInstructions to the mirroring services.\nRSync is one-way (primary-\u0026gt;cluster) only, and Syncthing is both ways.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic routine {hourly,morning,afternoon,nightly,midnight}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRun the specified routine.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic routine clean-archive\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eWhen on a primary node, clean up the archive folder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {clean-duplicates,dedup}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMoves packages which have been replaced by newer (modify time) packages to the archive folder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {clg,clean-logs}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eAfter a \u003ccode\u003echaotic makepwd\u003c/code\u003e, remove successfull and \"already built\" logs.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {cls,clean-srccache} ${PACKAGE}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eRemoves cached sources from a specific package.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic clean-sigs {,-q}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eMoves package files or archive files without their corresponding signature or archive file to the package archive.\nFiles have to be at least 1 hour old to be considered.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic {srt,sort-logs}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eUnclutters and sorts the raw log directory into easy to read subcategories\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003echaotic find-discarded\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eFind and print discarded packages. All packages that are in the database, but do not exist in the package list repo, are considered discarded, but have to be deleted manually.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca id=\"user-content-involved-directories\" class=\"anchor\" aria-hidden=\"true\" href=\"#involved-directories\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInvolved directories\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/cache/chaotic/sources/${PACKAGETAG}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ePer-package \u003ccode\u003eSRCDEST\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/cache/chaotic/lower/{latest,$DATESTAMP}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eLowerdirs.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/cache/chaotic/cc/{PACKAGETAG}\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003ePer-package \u003ccode\u003e~/.ccache\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/cache/chaotic/packages\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eContainer-shared pacman\u0027s cache.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003ccode\u003e/var/lib/chaotic/interfere\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eCloned version of \u003ca href=\"https://github.com/chaotic-aur/interfere\"\u003einterfere repository\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch1\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-hidden=\"true\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eDependencies\u003c/h1\u003e\n\u003cp\u003e\u003ccode\u003epacman -S --needed base-devel git arch-install-scripts repoctl fuse-overlayfs rsync python-telegram-send openssh\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eOne needs an active mirror or a setting (in /etc/chaotic.conf) like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_URL=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ehttps://builds.garudalinux.org/repos/chaotic-aur/x86_64\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e REPOCTL_CONFIG=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/etc/chaotic/repoctl.conf\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_REPOCTL_DB_URL=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${CAUR_URL}\u003c/span\u003e/chaotic-aur.db.tar.zst\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_REPOCTL_DB_FILE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/tmp/chaotic/db.tar.zst\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo create a gpg key for the root user refer to this \u003ca href=\"https://wiki.archlinux.org/index.php/GnuPG#Create_a_key_pair\" rel=\"nofollow\"\u003eArchWiki article\u003c/a\u003e for more information. If you find problems when using \"sudo\", read the \"\u003ca href=\"https://wiki.archlinux.org/index.php/GnuPG#su\" rel=\"nofollow\"\u003esu\u003c/a\u003e\" subsection.\nThen generate a ssh keypair for the root user.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003esudo ssh-keygen\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe ssh public key (cat /root/.ssh/id_rsa.pub) then needs to be added to the primary servers\u0027 root authorized keys (/root/.ssh/authorized_keys). After that follow these \u003ca href=\"https://wiki.archlinux.org/index.php/GnuPG#Export_your_public_key\" rel=\"nofollow\"\u003einstructions\u003c/a\u003e to export the gpg public key. This key will have to be \u003ca href=\"https://wiki.archlinux.org/index.php/GnuPG#Sending_keys\" rel=\"nofollow\"\u003euploaded\u003c/a\u003e to \u003ca href=\"keyserver.ubuntu.com\"\u003ekeyserver.ubuntu.com\u003c/a\u003e in order for the key to be verified.\nThen, configure it as follows in \u003ccode\u003e/etc/chaotic.conf\u003c/code\u003e, like this:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_DEPLOY_PKGS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/var/www/chaotic-aur/x86_64\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_URL=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttp://localhost:8080/chaotic-aur/x86_64\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_SIGN_KEY=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e8A9E14A07010F7E3\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CAUR_TYPE=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003ecluster\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e REPOCTL_CONFIG=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e/etc/chaotic/repoctl.toml\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eYou\u0027ll find more options in \u003ccode\u003esrc/chaotic\u003c/code\u003e first lines.\u003c/p\u003e\n\u003cp\u003eSupported \u003ccode\u003etype\u003c/code\u003e values are \u003ccode\u003eprimary\u003c/code\u003e, \u003ccode\u003ecluster\u003c/code\u003e, and \u003ccode\u003edev\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, a valid \u003ccode\u003e.gitconfig\u003c/code\u003e needs to be supplied in \u003ccode\u003e/root/.gitconfig\u003c/code\u003e to allow bumping packages via \u003ccode\u003echaotic bump\u003c/code\u003e with a meaningful \u003ccode\u003eemail\u003c/code\u003e and \u003ccode\u003ename\u003c/code\u003e supplied:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[user]\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eemail\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eteam@chaotic.cx\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003ename\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eChaotic Dragon (Node) \u262d\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eTo allow pushing to the interfere repo, a fitting ssh key also needs to be added to authenticate the node.\u003c/p\u003e\n\u003cp\u003eTo have clean logs \u0026amp; less bandwidth usage \u003ccode\u003e/etc/pacman.conf\u003c/code\u003e settings need to be adjusted:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEnable \u003ccode\u003eNoProgressBar\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUse \u003ccode\u003eServer = file:///path-to-local-repo\u003c/code\u003e as repo link if a local mirror is available\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDon\u0027t use \u003ccode\u003eILoveCandy\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo deploy faster replace \u003ccode\u003eopenssh\u003c/code\u003e with \u003ccode\u003eopenssh-hpn\u003c/code\u003e on all nodes (adds performance-related \u003ca href=\"https://www.psc.edu/research/networking/hpn-ssh/\" rel=\"nofollow\"\u003epatches\u003c/a\u003e). It should be noted that \u003ccode\u003eopenssh-hpn\u003c/code\u003e recently changed its configuration and behavior and therefore doesn\u0027t provide a 1:1 replacement for \u003ccode\u003eopenssh\u003c/code\u003e anymore.\u003c/p\u003e\n\u003ch2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-hidden=\"true\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eInstallation\u003c/h2\u003e\n\u003cp\u003eInstall dependencies, then:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo groupadd chaotic_op\nsudo usermod -aG chaotic_op $(whoami)\n\nmake build \u0026amp;\u0026amp; sudo make install\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch2\u003e\u003ca id=\"user-content-lint\" class=\"anchor\" aria-hidden=\"true\" href=\"#lint\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003eLint\u003c/h2\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003epacman -S --needed yarn shellcheck\nyarn install\nyarn run lint\u003c/pre\u003e\u003c/div\u003e\n", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSingularity Builder GitHub CI\u003c/h1\u003e\u003ca id=\"user-content-singularity-builder-github-ci\" class=\"anchor\" aria-label=\"Permalink: Singularity Builder GitHub CI\" href=\"#singularity-builder-github-ci\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"img/sregistry-github-small.png\"\u003e\u003cimg src=\"img/sregistry-github-small.png\" alt=\"img/sregistry-github-small.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis is a simple example of how you can achieve:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eversion control of your recipes\u003c/li\u003e\n\u003cli\u003ebuild of associated container and\u003c/li\u003e\n\u003cli\u003epush to a storage endpoint\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003efor a reproducible build workflow! By default, we will build on all pull requests and deploy\non push to main. The containers will go to an enabled GitHub package registry thanks to\nthe Singularity oras endpoint.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eupdated\u003c/strong\u003e August 2021, we can now push containers to the GitHub package registry! Woohoo!\u003c/p\u003e\n\u003cp\u003eThere are three workflows configured as examples to build and deploy Singularity containers:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003ca href=\".github/workflows/native-install.yml\"\u003enative install\u003c/a\u003e discovers Singularity* changed files, and builds Singularity 3.x (with GoLang) natively, deploys to GitHub packages.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\".github/workflows/container.yml\"\u003edocker image\u003c/a\u003e discovers Singularity* changed files, and builds in a \u003ca href=\"https://quay.io/repository/singularity/singularity\" rel=\"nofollow\"\u003edocker image\u003c/a\u003e, also deploys to GitHub packages.\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\".github/workflows/manual-deploy.yml\"\u003emanual deploy\u003c/a\u003e takes a list of manually specified Singularity recipes (that aren\u0027t required to be changed), builds Singularity 3.x natively, and deploys to GitHub packages.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWhile the \"build in a container\" option is faster to complete and a more simple workflow, it should be noted that docker runs with\n\u003ccode\u003e--privileged\u003c/code\u003e which may lead to issues with the resulting container in a non privileged situation. Also note that you\nare free to mix and match the above recipes to your liking, or \u003ca href=\"https://github.com/singularityhub/github-ci/issues\"\u003eopen an issue\u003c/a\u003e if you want to ask for help!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should this be managed via Github?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGithub, by way of easy integration with \u003cstrong\u003enative\u003c/strong\u003e continuous integration, is an easy way\nto have a workflow set up where multiple people can collaborate on a container recipe,\nthe recipe can be tested (with whatever testing you need), discussed in pull requests,\nand tested on merge to master. Further, now with GitHub packages we can push our containers\ndirectly to the GitHub package registry!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy should I use this instead of a service?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou could use a remote builder, but if you do the build in a continuous integration\nservice you get complete control over it. This means everything from the version of\nSingularity to use, to the tests that you run for your container. You have a lot more\nfreedom in the rate of building, and organization of your repository, because it\u0027s you\nthat writes the configuration.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Enable Packages\u003c/h3\u003e\u003ca id=\"user-content-1-enable-packages\" class=\"anchor\" aria-label=\"Permalink: 1. Enable Packages\" href=\"#1-enable-packages\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you want to use the \u003ca href=\"https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry\"\u003eGitHub package registry\u003c/a\u003e\nyou\u0027ll need to follow the instructions there to enable packages for your organization, specifically \"public\" and \"internal\" packages should be allowed to be created. You\u0027ll also want to add a username associated with your GitHub organization to the repository secret \u003ccode\u003eGHCR_USERNAME\u003c/code\u003e. If a package already exists for your repository, you may need to \u003ca href=\"https://docs.github.com/en/packages/managing-github-packages-using-github-actions-workflows/publishing-and-installing-a-package-with-github-actions#upgrading-a-workflow-that-accesses-ghcrio\"\u003efollow instructions\u003c/a\u003e to update a package from using personal access tokens to the safer \u003ccode\u003eGITHUB_TOKEN\u003c/code\u003e. This means that:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIf you want to be able to push to the package registry without extra work, don\u0027t push a container to the same package namespace that matches the repository before (e.g., from the command line).\u003c/li\u003e\n\u003cli\u003eIf you have already pushed a container to the repository package namespace, or you want to test and don\u0027t mind making the manual changes, you will need to \u003ca href=\"https://docs.github.com/en/packages/managing-github-packages-using-github-actions-workflows/publishing-and-installing-a-package-with-github-actions#upgrading-a-workflow-that-accesses-ghcrio\"\u003efollow these instructions\u003c/a\u003e to associate the package with your repository.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Add Your Recipes\u003c/h3\u003e\u003ca id=\"user-content-2-add-your-recipes\" class=\"anchor\" aria-label=\"Permalink: 2. Add Your Recipes\" href=\"#2-add-your-recipes\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAdd your Singularity recipes to this repository, which should be named \u003ccode\u003eSingularity.\u0026lt;tag\u0026gt;\u003c/code\u003e\nor just \u003ccode\u003eSingularity\u003c/code\u003e to follow the previously published convention. You can then choose your file in \u003ca href=\".github/workflows\"\u003e.github/workflows\u003c/a\u003e.\nIf you choose the \u003ccode\u003emanual-deploy.yml\u003c/code\u003e you can manually specify recipes in the matrix variable \"recipe.\"\nIf you choose either of the other two workflows, changed files that start with Singularity.* will\nbe automatically detected and built.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Test your Container\u003c/h3\u003e\u003ca id=\"user-content-3-test-your-container\" class=\"anchor\" aria-label=\"Permalink: 3. Test your Container\" href=\"#3-test-your-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eImportantly, we suggest that you add some steps to test your container! Whether that\u0027s running it,\nexec\u0027ing a custom command, or invoking the test command, there is more than\none way to eat a reeses:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eTest Container\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003erun\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e singularity exec smokey.sif python run_tests.py\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e singularity test smokey.sif\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e singularity run toasty.sif\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThis step is not provided in the workflows, but it\u0027s recommended that you think about it and add if necessary.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Check Triggers\u003c/h3\u003e\u003ca id=\"user-content-4-check-triggers\" class=\"anchor\" aria-label=\"Permalink: 4. Check Triggers\" href=\"#4-check-triggers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe workflow files each have a section at the top that indicates when the workflow will\ntrigger. By default, we will do builds on pull requests, and deploys on pushes to a main\nbranch. If you want to change this logic, edit the top of the recipe files.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Push to a registry\u003c/h3\u003e\u003ca id=\"user-content-5-push-to-a-registry\" class=\"anchor\" aria-label=\"Permalink: 5. Push to a registry\" href=\"#5-push-to-a-registry\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you are good with GitHub packages, then you are good to go! Otherwise,\nif you want to push to other kinds of storage, you can install the \u003ca href=\"http://singularityhub.github.io/sregistry-cli\" rel=\"nofollow\"\u003eSingularity Registry Client\u003c/a\u003e and push to your cloud storage of choice! You will want to add python and python-dev to the dependency\ninstall:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eInstall Dependencies\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003erun\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e sudo apt-get update \u0026amp;\u0026amp; sudo apt-get install -y \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e build-essential \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e libssl-dev \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e uuid-dev \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e libgpgme11-dev \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e squashfs-tools \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e libseccomp-dev \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e pkg-config \\\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e python-dev python python3-pip\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAnd then install and use sregistry client. Here are many examples:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml\"\u003e\u003cpre\u003e - \u003cspan class=\"pl-ent\"\u003ename\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eDeploy Container\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003erun\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e|\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e sudo pip3 install sregistry\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e SREGISTRY_CLIENT=google-storage sregistry push --name username/reponame smokey.sif\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e SREGISTRY_CLIENT=s3 sregistry push --name username/reponame smokey.sif\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e SREGISTRY_CLIENT=registry sregistry push --name username/reponame smokey.sif\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e SREGISTRY_CLIENT=dropbox sregistry push --name username/reponame smokey.sif\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eSee the \u003ca href=\"https://singularityhub.github.io/sregistry-cli/clients\" rel=\"nofollow\"\u003eclients page\u003c/a\u003e for all the options.\nIf you want to change the recipe triggers, see \u003ca href=\"https://help.github.com/en/articles/about-github-actions#core-concepts-for-github-actions\"\u003ehere\u003c/a\u003e\nfor getting started with GitHub actions, and \u003ca href=\"https://www.github.com/singularityhub/github-ci/issues\"\u003eplease open an issue\u003c/a\u003e\nif you need any help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e6. Pull Your Container!\u003c/h3\u003e\u003ca id=\"user-content-6-pull-your-container\" class=\"anchor\" aria-label=\"Permalink: 6. Pull Your Container!\" href=\"#6-pull-your-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe example container here is published to \u003ca href=\"https://github.com/singularityhub/github-ci/pkgs/container/github-ci\"\u003esingularithub/github-ci\u003c/a\u003e\nand can be pulled as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e$ singularity pull oras://ghcr.io/singularityhub/github-ci:latest\nINFO: Downloading oras image\n$ ls\ngithub-ci_latest.sif img README.md Singularity\n\n$ ./github-ci_latest.sif \nHold me closer... tiny container :) :D\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOther Options\u003c/h2\u003e\u003ca id=\"user-content-other-options\" class=\"anchor\" aria-label=\"Permalink: Other Options\" href=\"#other-options\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can customize this base recipe in so many ways! For example:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf you want to build a Docker container and pull down to Singularity, that\u0027s a good approach too! We have a \u003ca href=\"https://github.com/autamus/container-builder-template\"\u003econtainer-builder-template\u003c/a\u003e to help you authenticate with several popular registries.\u003c/li\u003e\n\u003cli\u003eThe action matrix can be extended to run builds on multiple platforms.\u003c/li\u003e\n\u003cli\u003eYou can also do the same, but test multiple versions of Singularity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHave fun!\u003c/p\u003e\n", "stargazers_count": 68, - "subscribers_count": 15, + "subscribers_count": 5, "topics": [ - "archlinux", - "repository-management", - "automation", - "continuous-deployment", - "pkgbuild", - "aur" + "singularity-ci", + "github-ci", + "singularity", + "container" ], - "updated_at": 1685536204.0 + "updated_at": 1728016545.0 }, { "data_format": 2, - "description": "Fast visualization tool for large-scale and high dimensional single-cell data", + "description": "A tool for generating bacterial genomes from metagenomes with nanopore long read sequencing", "filenames": [ - "Singularity" - ], - "full_name": "aertslab/SCope", - "latest_release": "untagged-64f13ec6d922418df08e", - "readme": "\u003cp\u003e\u003ca href=\"https://www.codefactor.io/repository/github/aertslab/scope\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1a16c1766f7d3c5d0013371ab9e20e734523c6bfc7c731b7cdeaea22eb3b38b8/68747470733a2f2f7777772e636f6465666163746f722e696f2f7265706f7369746f72792f6769746875622f61657274736c61622f73636f70652f6261646765\" alt=\"CodeFactor\" data-canonical-src=\"https://www.codefactor.io/repository/github/aertslab/scope/badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eSCope v1.8.2: Visualization of large-scale and high dimensional single cell data\u003c/h1\u003e\u003ca id=\"user-content-scope-v182-visualization-of-large-scale-and-high-dimensional-single-cell-data\" class=\"anchor\" aria-label=\"Permalink: SCope v1.8.2: Visualization of large-scale and high dimensional single cell data\" href=\"#scope-v182-visualization-of-large-scale-and-high-dimensional-single-cell-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/SCope_Logo.png\"\u003e\u003cimg src=\"images/SCope_Logo.png\" width=\"640\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets.\nCurrently the data format supported by SCope is \u003ccode\u003e.loom\u003c/code\u003e. This file format for very large omics datasets is maintained by the Linnarsson Lab through the \u003ccode\u003eloompy\u003c/code\u003e Python package (\u003ca href=\"https://github.com/linnarsson-lab/loompy\"\u003ehttps://github.com/linnarsson-lab/loompy\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eView the \u003ca href=\"CHANGELOG.md\"\u003echange log here\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDemo\u003c/h2\u003e\u003ca id=\"user-content-demo\" class=\"anchor\" aria-label=\"Permalink: Demo\" href=\"#demo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eVisit \u003ca href=\"https://scope.aertslab.org\" rel=\"nofollow\"\u003ehttps://scope.aertslab.org\u003c/a\u003e to test out SCope on several published datasets! Personal loom file files can be uploaded but will only be kept for 5 days.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLoom File Generation\u003c/h2\u003e\u003ca id=\"user-content-loom-file-generation\" class=\"anchor\" aria-label=\"Permalink: Loom File Generation\" href=\"#loom-file-generation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurrently there are two packages to generate extended loom files compatible with SCope.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eR: \u003ca href=\"https://github.com/aertslab/SCopeLoomR\"\u003eSCopeLoomR\u003c/a\u003e - Dedicated R package\u003c/li\u003e\n\u003cli\u003ePython: \u003ca href=\"https://github.com/aertslab/pySCENIC\"\u003epySCENIC\u003c/a\u003e - Single function for generation from SCENIC results\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEventually the functionality from pySCENIC will be expanded and put in its own python package.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRun SCope\u003c/h2\u003e\u003ca id=\"user-content-run-scope\" class=\"anchor\" aria-label=\"Permalink: Run SCope\" href=\"#run-scope\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eStandalone App\u003c/h3\u003e\u003ca id=\"user-content-standalone-app\" class=\"anchor\" aria-label=\"Permalink: Standalone App\" href=\"#standalone-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eStandalone apps for \u003cstrong\u003emacOS\u003c/strong\u003e and \u003cstrong\u003eLinux\u003c/strong\u003e can be downloaded from \u003ca href=\"https://github.com/aertslab/SCope/releases\"\u003ethe releases page.\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u2757 SCope standalone app requires Node.js (\u0026gt; v9). To install it, go to \u003ca href=\"https://nodejs.org/en/download/\" rel=\"nofollow\"\u003ehttps://nodejs.org/en/download/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eA \u003cstrong\u003eWindows\u003c/strong\u003e app is under development, but currently has no ETA.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCommand Line\u003c/h3\u003e\u003ca id=\"user-content-command-line\" class=\"anchor\" aria-label=\"Permalink: Command Line\" href=\"#command-line\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou will need access to at least Python 3.7 do run this.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eClone the GitHub repository and install,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Define where you want to clone the SCope repository.\u003c/span\u003e\nLOCAL_SCOPE_REPO=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HOME}\u003c/span\u003e/repos/SCope\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Clone SCope git repository.\u003c/span\u003e\ngit clone https://github.com/aertslab/SCope \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Go to your local cloned SCope repository.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install SCope.\u003c/span\u003e\nnpm install\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Go to your local cloned SCope repository.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nSCOPE_CONFIG=config.json npm run scope\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDeploy a Cloud-based Instance\u003c/h2\u003e\u003ca id=\"user-content-deploy-a-cloud-based-instance\" class=\"anchor\" aria-label=\"Permalink: Deploy a Cloud-based Instance\" href=\"#deploy-a-cloud-based-instance\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAmazon Web Services\u003c/h3\u003e\u003ca id=\"user-content-amazon-web-services\" class=\"anchor\" aria-label=\"Permalink: Amazon Web Services\" href=\"#amazon-web-services\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003ePublic AMI\u003c/h4\u003e\u003ca id=\"user-content-public-ami\" class=\"anchor\" aria-label=\"Permalink: Public AMI\" href=\"#public-ami\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eNo ETA.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eSource\u003c/h4\u003e\u003ca id=\"user-content-source\" class=\"anchor\" aria-label=\"Permalink: Source\" href=\"#source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo create a SCope AWS instance from scratch please read the tutorial \u003ca href=\"https://github.com/aertslab/SCope/tree/master/tutorials/aws-deployment-source\"\u003eaws-deployment-source\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eEnabling ORCID Functionality\u003c/h3\u003e\u003ca id=\"user-content-enabling-orcid-functionality\" class=\"anchor\" aria-label=\"Permalink: Enabling ORCID Functionality\" href=\"#enabling-orcid-functionality\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTo enable colaborative annotations and login via ORCID ID, API credentials (\u003ccode\u003eorcidAPIClientID\u003c/code\u003e, \u003ccode\u003eorcidAPIClientSecret\u003c/code\u003e and \u003ccode\u003eorcidAPIRedirectURI\u003c/code\u003e) must be added to the config file provided.\nThese can be generated at the \u003ca href=\"https://orcid.org/developer-tools\" rel=\"nofollow\"\u003eorcid developer tools page\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003edataHashSecret\u003c/code\u003e entry in the config file should be filled in with a randomly generated string for example from the python \u003ca href=\"https://docs.python.org/3/library/secrets.html\" rel=\"nofollow\"\u003esecrets package\u003c/a\u003e.\nThis string will be used to salt all annotation data, allowing validation of data generated on the instance of SCope. Any changes in this string will invalidate all pre-existing annotations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eClone the GitHub repository and install,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Define where you want to clone the SCope repository.\u003c/span\u003e\nLOCAL_SCOPE_REPO=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${HOME}\u003c/span\u003e/repos/SCope\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Clone SCope git repository.\u003c/span\u003e\ngit clone https://github.com/aertslab/SCope \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Go to your local cloned SCope repository.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install SCope.\u003c/span\u003e\nnpm install\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eRun,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Go to your local cloned SCope repository.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${LOCAL_SCOPE_REPO}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start SCope Server (terminal 1).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e opt\npoetry run hypercorn main:scope_api --reload\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start SCope Client (terminal 2).\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\nnpm run dev\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eConfiguration file (\u003ccode\u003econfig.json\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-configuration-file-configjson\" class=\"anchor\" aria-label=\"Permalink: Configuration file (config.json)\" href=\"#configuration-file-configjson\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eKeys:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003edata\u003c/code\u003e: This is a directory containing data files (e.g. the \u003ccode\u003emotd.txt\u003c/code\u003e message of the day).\nCan be an absolute path or a relative path from where you start SCope. By default it is\n\u003ccode\u003e./data/\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDeploying SCope with Docker\u003c/h3\u003e\u003ca id=\"user-content-deploying-scope-with-docker\" class=\"anchor\" aria-label=\"Permalink: Deploying SCope with Docker\" href=\"#deploying-scope-with-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ccode\u003edocker-compose.yml\u003c/code\u003e is configured to spin up 2 containers: One to run the SCope backend and another to run an Apache\nreverse proxy server.\u003c/p\u003e\n\u003cp\u003eThe SCope application will be available on port \u003ccode\u003e80\u003c/code\u003e by default. You can specify a port by using env variable: \u003ccode\u003eSCOPE_PORT\u003c/code\u003e\nbefore running the docker-compose command. Apache will proxy requests through to the appropriate port inside the container.\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003edocker-compose.yml\u003c/code\u003e will serve the assets from inside the scope container, and the \u003ccode\u003edocker-compose.host.yml\u003c/code\u003e will serve them from the host.\nThis supports as many use cases as possible, because you can either build the assets on the host yourself using whatever configuration you need,\nor serve them from the container if your environment doesn\u0027t allow for that (e.g. you don\u0027t have npm installed on the host).\u003c/p\u003e\n\u003cp\u003eBefore running the compose build, you can specify a SCOPE_PORT with: \u003ccode\u003edocker-compose build --build-arg SCOPE_PORT=8080\u003c/code\u003e\u003c/p\u003e\n\u003cp\u003eThe scope webpack assets will have to be built with the config: \u003ccode\u003e\"reverseProxyOn\": true\u003c/code\u003e.\nYou can use environment variable: \u003ccode\u003eSCOPE_CONFIG=path to your config\u003c/code\u003e to specify a config file instead of changing the main one.\u003c/p\u003e\n\u003cp\u003eYou can configure where the dockerised SCope data directories should be located\non the host machine by using the env var \u003ccode\u003eSCOPE_DATA_DIR\u003c/code\u003e before launching the docker-compose.\nThe default location is \u003ccode\u003e./scope_data\u003c/code\u003e which will be created if you do not specify one.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: in this config, you do not need to specify the port in \u003ccode\u003epublicHostAddress\u003c/code\u003e. The env var \u003ccode\u003eSCOPE_PORT\u003c/code\u003e gets appended for you.\u003c/p\u003e\n\u003cp\u003eIf deploying the container on a specific port with another external apache reverse-proxy server,\nyou may have to add a config to the external apache site config to allow http and websocket reverse-proxying.\nHere is an example:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ProxyPass / http://0.0.0.0:8080/\n RewriteEngine on\n RewriteCond %{HTTP:Upgrade} websocket [NC]\n RewriteCond %{HTTP:Connection} upgrade [NC]\n RewriteRule ^/?(.*) \"ws://0.0.0.0:8080/$1\" [P,L]\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eExample serve from container\u003c/h5\u003e\u003ca id=\"user-content-example-serve-from-container\" class=\"anchor\" aria-label=\"Permalink: Example serve from container\" href=\"#example-serve-from-container\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eCopy \u003ccode\u003econfig.json\u003c/code\u003e to a new file and modify with \u003ccode\u003e\"reverseProxyOn\": true,\u003c/code\u003e and \u003ccode\u003epublicHostAddress\u003c/code\u003e set to your domain\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edocker-compose build --build-arg SCOPE_PORT=8080\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSCOPE_DATA_DIR=$HOME/scope_data SCOPE_PORT=8080 docker-compose up -d\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch5 class=\"heading-element\"\u003eOR Serve from host\u003c/h5\u003e\u003ca id=\"user-content-or-serve-from-host\" class=\"anchor\" aria-label=\"Permalink: OR Serve from host\" href=\"#or-serve-from-host\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode\u003enpm run build\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eSCOPE_DATA_DIR=$HOME/scope_data SCOPE_PORT=8080 docker-compose -f docker-compose.host.yml up -d\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYou should be able to visit \u003ccode\u003ehttp://localhost:8080\u003c/code\u003e and see the app!\u003c/p\u003e\n", - "stargazers_count": 68, - "subscribers_count": 9, - "topics": [ - "single-cell", - "large-scale-data-visualization", - "gene-expression", - "gene-regulatory-network", - "aws", - "cloud", - "loom", - "reactjs", - "grpc" + "singularity/Singularity.htsbox", + "singularity/Singularity.longread", + "singularity/Singularity.quickmerge" ], - "updated_at": 1725421665.0 + "full_name": "bhattlab/lathe", + "latest_release": null, + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLathe\u003c/h1\u003e\u003ca id=\"user-content-lathe\" class=\"anchor\" aria-label=\"Permalink: Lathe\" href=\"#lathe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA tool for generating bacterial genomes from metagenomes with Nanopore long read sequencing\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, install \u003ca href=\"https://conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda3\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen install snakemake. This can be done with the following.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install snakemake\nsnakemake --version #please ensure this is \u0026gt;=5.4.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, clone this github directory to some location where it can be stored permanently. Remember to keep it updated with \u003ccode\u003egit pull\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/elimoss/lathe.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstructions to enable cluster execution with SLURM can be found at \u003ca href=\"https://github.com/bhattlab/slurm\"\u003ehttps://github.com/bhattlab/slurm\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eTypical installation time: 5-10 minutes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChange as of 2021-02-03\u003c/h2\u003e\u003ca id=\"user-content-change-as-of-2021-02-03\" class=\"anchor\" aria-label=\"Permalink: Change as of 2021-02-03\" href=\"#change-as-of-2021-02-03\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLathe has been adapted to run on multiple samples simultaneously, instead of one sample per snakemake command. The pipeline can now take in either .fast5 raw data from a nanopore run, or basecalled fastq files. The config file has been changed to reflect this. You now provide sample information and datasets in a tab-delimited file, and indicate this file in the \u003ccode\u003econfig.yaml\u003c/code\u003e. Provide this file to the \u003ccode\u003efile_names_txt\u003c/code\u003e argument in the configfile.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efile_names_txt is a 2 or 3 column tsv with the following columns\nSAMPLE_NAME FAST5/FASTQ_READS SHORT_READS_1,SHORT_READS_2\nsample name in the first column will be used to name ouptut\nthe second column can be a directory containing fast5 files (the output of a nanopore run)\n -OR- a single fastq file containing basecalled data \nOptionally, a short read sequencing dataset can be provided in the third column, \n with pairs separated by a comma. If this option is selected, short read\n polishing will be turned on. \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAlter config.yaml to provide the following:\u003c/h3\u003e\u003ca id=\"user-content-alter-configyaml-to-provide-the-following\" class=\"anchor\" aria-label=\"Permalink: Alter config.yaml to provide the following:\" href=\"#alter-configyaml-to-provide-the-following\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003efile_names_txt\u003c/strong\u003e: Tab delimited file describing sample names and input datasets. See config.yaml for a description.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eflowcell\u003c/strong\u003e: flowcell code, e.g. FLO-MIN106, passed to basecaller\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ekit\u003c/strong\u003e: kit code, e.g. SQK-LSK109, passed to basecaller\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003egenome_size\u003c/strong\u003e: Estimated genome size, e.g. 50m, passed to Canu.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esingularity\u003c/strong\u003e: location (including on the internet) of a singularity image to be used for the workflow. Don\u0027t change this.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003euse_grid\u003c/strong\u003e: should Canu execute in distributed mode on a cluster?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003egrid_options\u003c/strong\u003e: Extra options for execution on a cluster\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ecanu_args\u003c/strong\u003e: Extra options for Canu\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eskip_circularization\u003c/strong\u003e: Should circularization be omitted from the workflow?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLathe uses the Flye assembler by default. For Canu, please specify \u0027canu\u0027 for the assembler parameter in the config. For cluster Canu execution, please note: if set to True, you will need to install Canu, e.g. \u003ccode\u003econda install -c conda-forge -c bioconda Canu=1.8\u003c/code\u003e as well as provide any additional required parameters for your job scheduler in the config.yaml file. Please see the example config file. When executing on a cluster, Canu will appear to fail, as the first process does not produce an assembly and instead spawns subsequent jobs on the cluster. Don\u0027t worry, just re-run Lathe when the assembly completes.\u003c/p\u003e\n\u003cp\u003eTo execute please run the following. Please note, you must substitute a parent directory containing all of your data and working directories for \u003ccode\u003e/labs/\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity --singularity-args \u0027--bind /labs/,/scg/,/home/ \u0027 -s /path/to/lathe/Snakefile \\\n--configfile path/to/modified_config.yaml --restart-times 0 --keep-going --latency-wait 30\n# --profile scg #enable cluster support, highly recommended. See above.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe outputs generated by this workflow will look like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esamplename/\n\u251c\u2500\u2500 0.basecall\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename.fq\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 nanoplots\n\u251c\u2500\u2500 1.assemble\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_merged.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.amb\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.ann\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.bwt\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.fai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.pac\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.paf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.sa\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 assemble_100m (if specified)\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 assemble_250m (if specified)\n\u251c\u2500\u2500 2.polish\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.corrected.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.bam.bai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.fai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.misassemblies.tsv\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 medaka (if specified)\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 pilon (if specified)\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 racon (if specified)\n\u251c\u2500\u2500 3.circularization\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 1.candidate_genomes\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 2.circularization\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 3.circular_sequences #circularized genomes\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.corrected.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.bam.bai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.fai\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 4.samplename_circularized.fasta.misassemblies.tsv\n\u2514\u2500\u2500 5.final\n \u00a0\u00a0 \u251c\u2500\u2500 samplename_final.fa\n \u00a0\u00a0 \u2514\u2500\u2500 samplename_final.fa.fai\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial\u003c/h2\u003e\u003ca id=\"user-content-tutorial\" class=\"anchor\" aria-label=\"Permalink: Tutorial\" href=\"#tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tutorial can be run using the provided config file and input data within the tutorial folder. This tutorial uses pre-basecalled long read data (to reduce total file sizes) and performs assembly with Flye and short read polishing. To reduce runtime, this tutorial skips basecalling, long read polishing, and circularization steps. With cluster execution enabled, this tutorial should be completed in under 6 hours. Successful completion will be indicated by the presence of a atcc_tutorial_final.fa file in the 5.final directory. To run the tutorial:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eunzip the short read (tutorial/inputdata/atcc_100000_sr.fastq.gz) and long read (tutorial/atcc_tutorial/0.basecall/atcc_tutorial.fq.gz) data\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eedit the config file to provide the absolute path to the short read input data (atcc_100000_sr.fastq)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun Lathe using the command:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity --singularity-args \u0027--bind /yourrootdirectories/ \u0027 -s /path/to/lathe/Snakefile \\\n--configfile path/to/config_nobasecalling.yaml --restart-times 0 --keep-going --latency-wait 30\n# --profile clusterconfiguration #enable cluster support, highly recommended. See above.\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 69, + "subscribers_count": 8, + "topics": [], + "updated_at": 1709358414.0 }, { "data_format": 2, @@ -41765,19 +41809,21 @@ var data = }, { "data_format": 2, - "description": "A tool for generating bacterial genomes from metagenomes with nanopore long read sequencing", + "description": "physically-based rendering engine implemented with Rust.", "filenames": [ - "singularity/Singularity.htsbox", - "singularity/Singularity.longread", - "singularity/Singularity.quickmerge" + "Singularity" ], - "full_name": "bhattlab/lathe", + "full_name": "beltegeuse/rustlight", "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eLathe\u003c/h1\u003e\u003ca id=\"user-content-lathe\" class=\"anchor\" aria-label=\"Permalink: Lathe\" href=\"#lathe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA tool for generating bacterial genomes from metagenomes with Nanopore long read sequencing\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, install \u003ca href=\"https://conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda3\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThen install snakemake. This can be done with the following.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda install snakemake\nsnakemake --version #please ensure this is \u0026gt;=5.4.3\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNext, clone this github directory to some location where it can be stored permanently. Remember to keep it updated with \u003ccode\u003egit pull\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/elimoss/lathe.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eInstructions to enable cluster execution with SLURM can be found at \u003ca href=\"https://github.com/bhattlab/slurm\"\u003ehttps://github.com/bhattlab/slurm\u003c/a\u003e .\u003c/p\u003e\n\u003cp\u003eTypical installation time: 5-10 minutes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eChange as of 2021-02-03\u003c/h2\u003e\u003ca id=\"user-content-change-as-of-2021-02-03\" class=\"anchor\" aria-label=\"Permalink: Change as of 2021-02-03\" href=\"#change-as-of-2021-02-03\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eLathe has been adapted to run on multiple samples simultaneously, instead of one sample per snakemake command. The pipeline can now take in either .fast5 raw data from a nanopore run, or basecalled fastq files. The config file has been changed to reflect this. You now provide sample information and datasets in a tab-delimited file, and indicate this file in the \u003ccode\u003econfig.yaml\u003c/code\u003e. Provide this file to the \u003ccode\u003efile_names_txt\u003c/code\u003e argument in the configfile.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efile_names_txt is a 2 or 3 column tsv with the following columns\nSAMPLE_NAME FAST5/FASTQ_READS SHORT_READS_1,SHORT_READS_2\nsample name in the first column will be used to name ouptut\nthe second column can be a directory containing fast5 files (the output of a nanopore run)\n -OR- a single fastq file containing basecalled data \nOptionally, a short read sequencing dataset can be provided in the third column, \n with pairs separated by a comma. If this option is selected, short read\n polishing will be turned on. \n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInputs\u003c/h2\u003e\u003ca id=\"user-content-inputs\" class=\"anchor\" aria-label=\"Permalink: Inputs\" href=\"#inputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAlter config.yaml to provide the following:\u003c/h3\u003e\u003ca id=\"user-content-alter-configyaml-to-provide-the-following\" class=\"anchor\" aria-label=\"Permalink: Alter config.yaml to provide the following:\" href=\"#alter-configyaml-to-provide-the-following\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003efile_names_txt\u003c/strong\u003e: Tab delimited file describing sample names and input datasets. See config.yaml for a description.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eflowcell\u003c/strong\u003e: flowcell code, e.g. FLO-MIN106, passed to basecaller\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ekit\u003c/strong\u003e: kit code, e.g. SQK-LSK109, passed to basecaller\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003egenome_size\u003c/strong\u003e: Estimated genome size, e.g. 50m, passed to Canu.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003esingularity\u003c/strong\u003e: location (including on the internet) of a singularity image to be used for the workflow. Don\u0027t change this.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003euse_grid\u003c/strong\u003e: should Canu execute in distributed mode on a cluster?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003egrid_options\u003c/strong\u003e: Extra options for execution on a cluster\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ecanu_args\u003c/strong\u003e: Extra options for Canu\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eskip_circularization\u003c/strong\u003e: Should circularization be omitted from the workflow?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLathe uses the Flye assembler by default. For Canu, please specify \u0027canu\u0027 for the assembler parameter in the config. For cluster Canu execution, please note: if set to True, you will need to install Canu, e.g. \u003ccode\u003econda install -c conda-forge -c bioconda Canu=1.8\u003c/code\u003e as well as provide any additional required parameters for your job scheduler in the config.yaml file. Please see the example config file. When executing on a cluster, Canu will appear to fail, as the first process does not produce an assembly and instead spawns subsequent jobs on the cluster. Don\u0027t worry, just re-run Lathe when the assembly completes.\u003c/p\u003e\n\u003cp\u003eTo execute please run the following. Please note, you must substitute a parent directory containing all of your data and working directories for \u003ccode\u003e/labs/\u003c/code\u003e.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity --singularity-args \u0027--bind /labs/,/scg/,/home/ \u0027 -s /path/to/lathe/Snakefile \\\n--configfile path/to/modified_config.yaml --restart-times 0 --keep-going --latency-wait 30\n# --profile scg #enable cluster support, highly recommended. See above.\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOutputs\u003c/h2\u003e\u003ca id=\"user-content-outputs\" class=\"anchor\" aria-label=\"Permalink: Outputs\" href=\"#outputs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe outputs generated by this workflow will look like the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esamplename/\n\u251c\u2500\u2500 0.basecall\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename.fq\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 nanoplots\n\u251c\u2500\u2500 1.assemble\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_merged.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.amb\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.ann\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.bwt\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.fai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.pac\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.paf\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_raw_assembly.fa.sa\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 assemble_100m (if specified)\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 assemble_250m (if specified)\n\u251c\u2500\u2500 2.polish\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.corrected.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.bam.bai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.fai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 samplename_polished.fasta.misassemblies.tsv\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 medaka (if specified)\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 pilon (if specified)\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 racon (if specified)\n\u251c\u2500\u2500 3.circularization\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 1.candidate_genomes\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 2.circularization\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 3.circular_sequences #circularized genomes\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.corrected.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.bam\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.bam.bai\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 4.samplename_circularized.fasta.fai\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 4.samplename_circularized.fasta.misassemblies.tsv\n\u2514\u2500\u2500 5.final\n \u00a0\u00a0 \u251c\u2500\u2500 samplename_final.fa\n \u00a0\u00a0 \u2514\u2500\u2500 samplename_final.fa.fai\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorial\u003c/h2\u003e\u003ca id=\"user-content-tutorial\" class=\"anchor\" aria-label=\"Permalink: Tutorial\" href=\"#tutorial\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe tutorial can be run using the provided config file and input data within the tutorial folder. This tutorial uses pre-basecalled long read data (to reduce total file sizes) and performs assembly with Flye and short read polishing. To reduce runtime, this tutorial skips basecalling, long read polishing, and circularization steps. With cluster execution enabled, this tutorial should be completed in under 6 hours. Successful completion will be indicated by the presence of a atcc_tutorial_final.fa file in the 5.final directory. To run the tutorial:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eunzip the short read (tutorial/inputdata/atcc_100000_sr.fastq.gz) and long read (tutorial/atcc_tutorial/0.basecall/atcc_tutorial.fq.gz) data\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eedit the config file to provide the absolute path to the short read input data (atcc_100000_sr.fastq)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003erun Lathe using the command:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003esnakemake --use-singularity --singularity-args \u0027--bind /yourrootdirectories/ \u0027 -s /path/to/lathe/Snakefile \\\n--configfile path/to/config_nobasecalling.yaml --restart-times 0 --keep-going --latency-wait 30\n# --profile clusterconfiguration #enable cluster support, highly recommended. See above.\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 69, - "subscribers_count": 8, - "topics": [], - "updated_at": 1709358414.0 + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\nRustlight \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7994c57c4856bdfdaefaab1cf44072b868fa3083ff704c27e14aa87f6e8aaff5/687474703a2f2f62656c746567657573652e73332d776562736974652d61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f727573746c696768742f6c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7994c57c4856bdfdaefaab1cf44072b868fa3083ff704c27e14aa87f6e8aaff5/687474703a2f2f62656c746567657573652e73332d776562736974652d61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f727573746c696768742f6c6f676f2e706e67\" width=\"96\" data-canonical-src=\"http://beltegeuse.s3-website-ap-northeast-1.amazonaws.com/rustlight/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \n\u003c/h1\u003e\u003ca id=\"user-content-rustlight--\" class=\"anchor\" aria-label=\"Permalink: \nRustlight \" href=\"#rustlight--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePhysically-based rendering engine implemented with \u003cstrong\u003eRust\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use it\u003c/h2\u003e\u003ca id=\"user-content-how-to-use-it\" class=\"anchor\" aria-label=\"Permalink: How to use it\" href=\"#how-to-use-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can easily uses Rustlight via the provided command line tool (via examples/cli.rs):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cargo run --example=cli --release -- -h\nrustlight 0.2.0\nAdrien Gruson \u0026lt;adrien.gruson@gmail.com\u0026gt;\nA Rusty Light Transport simulation program\n\nUSAGE:\n rustlight [FLAGS] [OPTIONS] \u0026lt;scene\u0026gt; [SUBCOMMAND]\n\nFLAGS:\n -d debug output\n -h, --help Prints help information\n -V, --version Prints version information\n\nOPTIONS:\n -a \u0026lt;average\u0026gt; average several pass of the integrator with a time limit (\u0027inf\u0027 is possible)\n -s \u0026lt;image_scale\u0026gt; image scaling factor [default: 1.0]\n -m \u0026lt;medium\u0026gt; add medium with defined density [default: 0.0]\n -n \u0026lt;nbsamples\u0026gt; integration technique\n -t \u0026lt;nbthreads\u0026gt; number of thread for the computation [default: auto]\n -o \u0026lt;output\u0026gt; output image file\n\nARGS:\n \u0026lt;scene\u0026gt; JSON file description\n\nSUBCOMMANDS:\n ao ambiant occlusion\n direct direct lighting\n gradient-path gradient path tracing\n gradient-path-explicit gradient path tracing\n help Prints this message or the help of the given subcommand(s)\n light light tracing generating path from the lights\n path path tracing generating path from the sensor\n path_kulla path tracing for single scattering\n plane_single Prototype implementation of \u0027Photon surfaces for robust, unbiased volumetric\n density estimation\u0027\n pssmlt path tracing with MCMC sampling\n uncorrelated_plane_single Prototype implementation of \u0027Photon surfaces for robust, unbiased volumetric\n density estimation\u0027\n vol_primitives BRE/Beam/Planes estimators\n vpl brute force virtual point light integrator\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, to use path tracing using 128 spp:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cargo run --example=cli --release --features=\"pbrt openexr\" -- -a inf -n 128 -o path.pfm ./data/cbox.json path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOther examples (wasm, viewer) are planned.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptional Features\u003c/h2\u003e\u003ca id=\"user-content-optional-features\" class=\"anchor\" aria-label=\"Permalink: Optional Features\" href=\"#optional-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to activate/desactivate some features of rustlight depending of your needs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eimage\u003c/strong\u003e(*): load and save LDR images (via image)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eopenexr\u003c/strong\u003e: load and save EXR images (via \u003ca href=\"https://github.com/cessen/openexr-rs\"\u003eopenexr-rs\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003epbrt\u003c/strong\u003e(*): read PBRT files (via pbrt_rs) [Not that only support a subset PBRT primitives]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003emitsuba\u003c/strong\u003e(*): read Mitsuba files (via mitsuba_rs) [Not that only support a subset Mitsuba primitives]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eprogress-bar\u003c/strong\u003e(*): show progress bar (via pbr)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eembree\u003c/strong\u003e: fast intersection (via \u003ca href=\"https://github.com/Twinklebear/embree-rs\"\u003eembree-rs\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e(*) These features are activated by default.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor now, these are the following features implemented:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntegrators (most of them using a common graph to represent the light transport):\n\u003cul\u003e\n\u003cli\u003eAmbiant occlusion\u003c/li\u003e\n\u003cli\u003eDirect with MIS\u003c/li\u003e\n\u003cli\u003ePath-tracing with NEE\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e[*]\u003c/strong\u003e Gradient-path tracing [1]\u003c/li\u003e\n\u003cli\u003ePrimary-sample space MLT [2]\u003c/li\u003e\n\u003cli\u003eEnergy redistribution PT (in PSS) [10]\u003c/li\u003e\n\u003cli\u003eLight tracing\u003c/li\u003e\n\u003cli\u003eVirtual Point Light\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSpecial volumetric integrators (via vol_primitives):\n\u003cul\u003e\n\u003cli\u003eBeam radiance estimate (2D kernel) [3]\u003c/li\u003e\n\u003cli\u003ePhoton beams (1D kernel) [4]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e[*]\u003c/strong\u003e Photon planes (0D kernel) [5]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e[*]\u003c/strong\u003e Naive Virtual ray light [6]\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSpecial single scattering intergrators:\n\u003cul\u003e\n\u003cli\u003e(Un)correlated photon planes [7]\u003c/li\u003e\n\u003cli\u003eKulla importance sampling [8]\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFiltering:\n\u003cul\u003e\n\u003cli\u003eImage-space control variate with uniform and variance-based weights [7]\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMaterials:\n\u003cul\u003e\n\u003cli\u003eDiffuse\u003c/li\u003e\n\u003cli\u003ePhong lobe\u003c/li\u003e\n\u003cli\u003eSpecular\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEmitters:\n\u003cul\u003e\n\u003cli\u003eMultiple tri-mesh lights support\u003c/li\u003e\n\u003cli\u003ePoint, Directional and Envmap\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eVolumes:\n\u003cul\u003e\n\u003cli\u003eInfinite homogenous participating media\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePhase functions:\n\u003cul\u003e\n\u003cli\u003eIsotropic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e[*]\u003c/strong\u003e Techniques that could contains bugs or are incomplete (only naive implementation)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRendering\u003c/h2\u003e\u003ca id=\"user-content-rendering\" class=\"anchor\" aria-label=\"Permalink: Rendering\" href=\"#rendering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e46414e99eacc2bf6753af1b4268784cc537299855043ee2f610107bdad785a1/687474703a2f2f62656c746567657573652e73332d776562736974652d61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f727573746c696768742f706272745f72732e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e46414e99eacc2bf6753af1b4268784cc537299855043ee2f610107bdad785a1/687474703a2f2f62656c746567657573652e73332d776562736974652d61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f727573746c696768742f706272745f72732e706e67\" alt=\"Cornel Box gradient-domain pt\" data-canonical-src=\"http://beltegeuse.s3-website-ap-northeast-1.amazonaws.com/rustlight/pbrt_rs.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRoadmap\u003c/h2\u003e\u003ca id=\"user-content-roadmap\" class=\"anchor\" aria-label=\"Permalink: Roadmap\" href=\"#roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRendering algorithms for path-tracing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003euse the explict layout to do implement gradient-domain path tracing\u003c/li\u003e\n\u003cli\u003efixing gradient-domain path tracing: seems to have wrong gradient when the light source is not visible from the base path\u003c/li\u003e\n\u003cli\u003egradient-domain path reuse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOther rendering features:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMaterials: glass, microfacet with Beckert distribution.\u003c/li\u003e\n\u003cli\u003eEmitters: Environmental and point lights\u003c/li\u003e\n\u003cli\u003eScene format support: PBRT\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInspirations\u003c/h2\u003e\u003ca id=\"user-content-inspirations\" class=\"anchor\" aria-label=\"Permalink: Inspirations\" href=\"#inspirations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code has been inspired from several repositories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ers_pbrt project: \u003ca href=\"https://github.com/wahn/rs_pbrt\"\u003ehttps://github.com/wahn/rs_pbrt\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ethe blog post from Brook Heisler: \u003ca href=\"https://bheisler.github.io/post/writing-raytracer-in-rust-part-1/\" rel=\"nofollow\"\u003ehttps://bheisler.github.io/post/writing-raytracer-in-rust-part-1/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003etray_rust project: \u003ca href=\"https://github.com/Twinklebear/tray_rust\"\u003ehttps://github.com/Twinklebear/tray_rust\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003emitsuba: \u003ca href=\"https://github.com/mitsuba-renderer/mitsuba\"\u003ehttps://github.com/mitsuba-renderer/mitsuba\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e[1] Kettunen et al. \"Gradient-domain path tracing\" (SIGGRAPH 2015) \u003cbr\u003e\n[2] Csaba et al. \"A simple and robust mutation strategy for the metropolis light transport algorithm. (CGF 2002) \u003cbr\u003e\n[3] Jarosz et al. \"The beam radiance estimate for volumetric photon mapping\" (EG 2008) \u003cbr\u003e\n[4] Jarosz et al. \"Progressive photon beams\" (SIGGRAPH Asia 2011) \u003cbr\u003e\n[5] Bitterli and Jarosz \"Beyond points and beams: Higher-dimensional photon samples for volumetric light transport\" (SIGGRAPH 2017) \u003cbr\u003e\n[6] Novak et al. \"Virtual ray lights for rendering scenes with participating media\" (SIGGRAPH 2012) \u003cbr\u003e\n[7] Rousselle et al. \"Image-space control variates for rendering\" (SIGGRAPH 2016) \u003cbr\u003e\n[8] Deng et al. \"Photon surfaces for robust, unbiased volumetric density estimation\" (SIGGRAPH 2019) \u003cbr\u003e\n[9] Kulla et al. \"Importance Sampling Techniques for Path Tracing in Participating Media\" (EGSR 2012) \u003cbr\u003e\n[10] Cline et al. \"energy redistribution path tracing\" (SIGGRAPH 2012)\u003c/p\u003e\n", + "stargazers_count": 72, + "subscribers_count": 6, + "topics": [ + "rust", + "rendering", + "path-tracer" + ], + "updated_at": 1716825098.0 }, { "data_format": 2, @@ -41793,24 +41839,6 @@ var data = "topics": [], "updated_at": 1728504493.0 }, - { - "data_format": 2, - "description": "physically-based rendering engine implemented with Rust.", - "filenames": [ - "Singularity" - ], - "full_name": "beltegeuse/rustlight", - "latest_release": null, - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003e\nRustlight \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7994c57c4856bdfdaefaab1cf44072b868fa3083ff704c27e14aa87f6e8aaff5/687474703a2f2f62656c746567657573652e73332d776562736974652d61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f727573746c696768742f6c6f676f2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7994c57c4856bdfdaefaab1cf44072b868fa3083ff704c27e14aa87f6e8aaff5/687474703a2f2f62656c746567657573652e73332d776562736974652d61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f727573746c696768742f6c6f676f2e706e67\" width=\"96\" data-canonical-src=\"http://beltegeuse.s3-website-ap-northeast-1.amazonaws.com/rustlight/logo.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \n\u003c/h1\u003e\u003ca id=\"user-content-rustlight--\" class=\"anchor\" aria-label=\"Permalink: \nRustlight \" href=\"#rustlight--\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePhysically-based rendering engine implemented with \u003cstrong\u003eRust\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use it\u003c/h2\u003e\u003ca id=\"user-content-how-to-use-it\" class=\"anchor\" aria-label=\"Permalink: How to use it\" href=\"#how-to-use-it\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can easily uses Rustlight via the provided command line tool (via examples/cli.rs):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cargo run --example=cli --release -- -h\nrustlight 0.2.0\nAdrien Gruson \u0026lt;adrien.gruson@gmail.com\u0026gt;\nA Rusty Light Transport simulation program\n\nUSAGE:\n rustlight [FLAGS] [OPTIONS] \u0026lt;scene\u0026gt; [SUBCOMMAND]\n\nFLAGS:\n -d debug output\n -h, --help Prints help information\n -V, --version Prints version information\n\nOPTIONS:\n -a \u0026lt;average\u0026gt; average several pass of the integrator with a time limit (\u0027inf\u0027 is possible)\n -s \u0026lt;image_scale\u0026gt; image scaling factor [default: 1.0]\n -m \u0026lt;medium\u0026gt; add medium with defined density [default: 0.0]\n -n \u0026lt;nbsamples\u0026gt; integration technique\n -t \u0026lt;nbthreads\u0026gt; number of thread for the computation [default: auto]\n -o \u0026lt;output\u0026gt; output image file\n\nARGS:\n \u0026lt;scene\u0026gt; JSON file description\n\nSUBCOMMANDS:\n ao ambiant occlusion\n direct direct lighting\n gradient-path gradient path tracing\n gradient-path-explicit gradient path tracing\n help Prints this message or the help of the given subcommand(s)\n light light tracing generating path from the lights\n path path tracing generating path from the sensor\n path_kulla path tracing for single scattering\n plane_single Prototype implementation of \u0027Photon surfaces for robust, unbiased volumetric\n density estimation\u0027\n pssmlt path tracing with MCMC sampling\n uncorrelated_plane_single Prototype implementation of \u0027Photon surfaces for robust, unbiased volumetric\n density estimation\u0027\n vol_primitives BRE/Beam/Planes estimators\n vpl brute force virtual point light integrator\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor example, to use path tracing using 128 spp:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e$ cargo run --example=cli --release --features=\"pbrt openexr\" -- -a inf -n 128 -o path.pfm ./data/cbox.json path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOther examples (wasm, viewer) are planned.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eOptional Features\u003c/h2\u003e\u003ca id=\"user-content-optional-features\" class=\"anchor\" aria-label=\"Permalink: Optional Features\" href=\"#optional-features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is possible to activate/desactivate some features of rustlight depending of your needs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eimage\u003c/strong\u003e(*): load and save LDR images (via image)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eopenexr\u003c/strong\u003e: load and save EXR images (via \u003ca href=\"https://github.com/cessen/openexr-rs\"\u003eopenexr-rs\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003epbrt\u003c/strong\u003e(*): read PBRT files (via pbrt_rs) [Not that only support a subset PBRT primitives]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003emitsuba\u003c/strong\u003e(*): read Mitsuba files (via mitsuba_rs) [Not that only support a subset Mitsuba primitives]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eprogress-bar\u003c/strong\u003e(*): show progress bar (via pbr)\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eembree\u003c/strong\u003e: fast intersection (via \u003ca href=\"https://github.com/Twinklebear/embree-rs\"\u003eembree-rs\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e(*) These features are activated by default.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFor now, these are the following features implemented:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntegrators (most of them using a common graph to represent the light transport):\n\u003cul\u003e\n\u003cli\u003eAmbiant occlusion\u003c/li\u003e\n\u003cli\u003eDirect with MIS\u003c/li\u003e\n\u003cli\u003ePath-tracing with NEE\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e[*]\u003c/strong\u003e Gradient-path tracing [1]\u003c/li\u003e\n\u003cli\u003ePrimary-sample space MLT [2]\u003c/li\u003e\n\u003cli\u003eEnergy redistribution PT (in PSS) [10]\u003c/li\u003e\n\u003cli\u003eLight tracing\u003c/li\u003e\n\u003cli\u003eVirtual Point Light\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSpecial volumetric integrators (via vol_primitives):\n\u003cul\u003e\n\u003cli\u003eBeam radiance estimate (2D kernel) [3]\u003c/li\u003e\n\u003cli\u003ePhoton beams (1D kernel) [4]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e[*]\u003c/strong\u003e Photon planes (0D kernel) [5]\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e[*]\u003c/strong\u003e Naive Virtual ray light [6]\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSpecial single scattering intergrators:\n\u003cul\u003e\n\u003cli\u003e(Un)correlated photon planes [7]\u003c/li\u003e\n\u003cli\u003eKulla importance sampling [8]\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFiltering:\n\u003cul\u003e\n\u003cli\u003eImage-space control variate with uniform and variance-based weights [7]\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMaterials:\n\u003cul\u003e\n\u003cli\u003eDiffuse\u003c/li\u003e\n\u003cli\u003ePhong lobe\u003c/li\u003e\n\u003cli\u003eSpecular\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEmitters:\n\u003cul\u003e\n\u003cli\u003eMultiple tri-mesh lights support\u003c/li\u003e\n\u003cli\u003ePoint, Directional and Envmap\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eVolumes:\n\u003cul\u003e\n\u003cli\u003eInfinite homogenous participating media\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ePhase functions:\n\u003cul\u003e\n\u003cli\u003eIsotropic\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e[*]\u003c/strong\u003e Techniques that could contains bugs or are incomplete (only naive implementation)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRendering\u003c/h2\u003e\u003ca id=\"user-content-rendering\" class=\"anchor\" aria-label=\"Permalink: Rendering\" href=\"#rendering\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e46414e99eacc2bf6753af1b4268784cc537299855043ee2f610107bdad785a1/687474703a2f2f62656c746567657573652e73332d776562736974652d61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f727573746c696768742f706272745f72732e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e46414e99eacc2bf6753af1b4268784cc537299855043ee2f610107bdad785a1/687474703a2f2f62656c746567657573652e73332d776562736974652d61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f727573746c696768742f706272745f72732e706e67\" alt=\"Cornel Box gradient-domain pt\" data-canonical-src=\"http://beltegeuse.s3-website-ap-northeast-1.amazonaws.com/rustlight/pbrt_rs.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRoadmap\u003c/h2\u003e\u003ca id=\"user-content-roadmap\" class=\"anchor\" aria-label=\"Permalink: Roadmap\" href=\"#roadmap\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRendering algorithms for path-tracing:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003euse the explict layout to do implement gradient-domain path tracing\u003c/li\u003e\n\u003cli\u003efixing gradient-domain path tracing: seems to have wrong gradient when the light source is not visible from the base path\u003c/li\u003e\n\u003cli\u003egradient-domain path reuse\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOther rendering features:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMaterials: glass, microfacet with Beckert distribution.\u003c/li\u003e\n\u003cli\u003eEmitters: Environmental and point lights\u003c/li\u003e\n\u003cli\u003eScene format support: PBRT\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInspirations\u003c/h2\u003e\u003ca id=\"user-content-inspirations\" class=\"anchor\" aria-label=\"Permalink: Inspirations\" href=\"#inspirations\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis code has been inspired from several repositories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ers_pbrt project: \u003ca href=\"https://github.com/wahn/rs_pbrt\"\u003ehttps://github.com/wahn/rs_pbrt\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ethe blog post from Brook Heisler: \u003ca href=\"https://bheisler.github.io/post/writing-raytracer-in-rust-part-1/\" rel=\"nofollow\"\u003ehttps://bheisler.github.io/post/writing-raytracer-in-rust-part-1/\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003etray_rust project: \u003ca href=\"https://github.com/Twinklebear/tray_rust\"\u003ehttps://github.com/Twinklebear/tray_rust\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003emitsuba: \u003ca href=\"https://github.com/mitsuba-renderer/mitsuba\"\u003ehttps://github.com/mitsuba-renderer/mitsuba\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e[1] Kettunen et al. \"Gradient-domain path tracing\" (SIGGRAPH 2015) \u003cbr\u003e\n[2] Csaba et al. \"A simple and robust mutation strategy for the metropolis light transport algorithm. (CGF 2002) \u003cbr\u003e\n[3] Jarosz et al. \"The beam radiance estimate for volumetric photon mapping\" (EG 2008) \u003cbr\u003e\n[4] Jarosz et al. \"Progressive photon beams\" (SIGGRAPH Asia 2011) \u003cbr\u003e\n[5] Bitterli and Jarosz \"Beyond points and beams: Higher-dimensional photon samples for volumetric light transport\" (SIGGRAPH 2017) \u003cbr\u003e\n[6] Novak et al. \"Virtual ray lights for rendering scenes with participating media\" (SIGGRAPH 2012) \u003cbr\u003e\n[7] Rousselle et al. \"Image-space control variates for rendering\" (SIGGRAPH 2016) \u003cbr\u003e\n[8] Deng et al. \"Photon surfaces for robust, unbiased volumetric density estimation\" (SIGGRAPH 2019) \u003cbr\u003e\n[9] Kulla et al. \"Importance Sampling Techniques for Path Tracing in Participating Media\" (EGSR 2012) \u003cbr\u003e\n[10] Cline et al. \"energy redistribution path tracing\" (SIGGRAPH 2012)\u003c/p\u003e\n", - "stargazers_count": 72, - "subscribers_count": 6, - "topics": [ - "rust", - "rendering", - "path-tracer" - ], - "updated_at": 1716825098.0 - }, { "data_format": 2, "description": "Docker Containers for Bioconductor", @@ -41879,6 +41907,19 @@ var data = "topics": [], "updated_at": 1732443189.0 }, + { + "data_format": 2, + "description": "A tool for analyzing trascriptomes of millions of single cells.", + "filenames": [ + "Singularity" + ], + "full_name": "lilab-bcb/pegasus", + "latest_release": null, + "stargazers_count": 81, + "subscribers_count": 7, + "topics": [], + "updated_at": 1729539551.0 + }, { "data_format": 2, "description": "Python package and CLI for whole-genome duplication related analyses. This package is deprecated in favor of https://github.com/heche-psb/wgd.", @@ -41900,19 +41941,6 @@ var data = ], "updated_at": 1721207666.0 }, - { - "data_format": 2, - "description": "A tool for analyzing trascriptomes of millions of single cells.", - "filenames": [ - "Singularity" - ], - "full_name": "lilab-bcb/pegasus", - "latest_release": null, - "stargazers_count": 81, - "subscribers_count": 7, - "topics": [], - "updated_at": 1729539551.0 - }, { "data_format": 2, "description": "A Perl script allowing to identify CRISPR arrays and associated Cas proteins from DNA sequences", @@ -41961,20 +41989,6 @@ var data = "topics": [], "updated_at": 1671708595.0 }, - { - "data_format": 2, - "description": "Antibiotic resistance prediction in minutes", - "filenames": [ - "Singularity.def" - ], - "full_name": "Mykrobe-tools/mykrobe", - "latest_release": "v0.13.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMykrobe\u003c/h1\u003e\u003ca id=\"user-content-mykrobe\" class=\"anchor\" aria-label=\"Permalink: Mykrobe\" href=\"#mykrobe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAntibiotic resistance prediction in minutes.\u003c/p\u003e\n\u003cp\u003eCurrently supports \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eShigella sonnei\u003c/em\u003e, \u003cem\u003eSalmonella typhi\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.mykrobe.com\" rel=\"nofollow\"\u003ehttp://www.mykrobe.com\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/Mykrobe-tools/mykrobe/wiki\"\u003emykrobe wiki\u003c/a\u003e for documentation.\nFor \u003cem\u003eS. typhi\u003c/em\u003e also see \u003ca href=\"https://github.com/katholt/genotyphi\"\u003ehttps://github.com/katholt/genotyphi\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eInstall\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebioconda - \u003ccode\u003econda install -c bioconda mykrobe\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003efrom source - \u003ccode\u003epip3 install . \u0026amp;\u0026amp; mykrobe panels update_metadata \u0026amp;\u0026amp; mykrobe panels update_species all\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eor using singularity or docker (see wiki for details)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eRun\u003c/strong\u003e on Mtb, making a JSON file of results:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emykrobe predict --sample my_sample_name \\\n --species tb \\\n --output out.json \\\n --format json \\\n --seq reads.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTest reads can be obtained by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget -O reads.fq.gz https://ndownloader.figshare.com/files/21059229\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 88, - "subscribers_count": 9, - "topics": [], - "updated_at": 1711081849.0 - }, { "data_format": 2, "description": "DeepLearning - Camelyon16 dataset", @@ -41990,6 +42004,20 @@ var data = "topics": [], "updated_at": 1683878528.0 }, + { + "data_format": 2, + "description": "Antibiotic resistance prediction in minutes", + "filenames": [ + "Singularity.def" + ], + "full_name": "Mykrobe-tools/mykrobe", + "latest_release": "v0.13.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMykrobe\u003c/h1\u003e\u003ca id=\"user-content-mykrobe\" class=\"anchor\" aria-label=\"Permalink: Mykrobe\" href=\"#mykrobe\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAntibiotic resistance prediction in minutes.\u003c/p\u003e\n\u003cp\u003eCurrently supports \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eShigella sonnei\u003c/em\u003e, \u003cem\u003eSalmonella typhi\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.mykrobe.com\" rel=\"nofollow\"\u003ehttp://www.mykrobe.com\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease see the \u003ca href=\"https://github.com/Mykrobe-tools/mykrobe/wiki\"\u003emykrobe wiki\u003c/a\u003e for documentation.\nFor \u003cem\u003eS. typhi\u003c/em\u003e also see \u003ca href=\"https://github.com/katholt/genotyphi\"\u003ehttps://github.com/katholt/genotyphi\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eInstall\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ebioconda - \u003ccode\u003econda install -c bioconda mykrobe\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003efrom source - \u003ccode\u003epip3 install . \u0026amp;\u0026amp; mykrobe panels update_metadata \u0026amp;\u0026amp; mykrobe panels update_species all\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eor using singularity or docker (see wiki for details)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eRun\u003c/strong\u003e on Mtb, making a JSON file of results:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emykrobe predict --sample my_sample_name \\\n --species tb \\\n --output out.json \\\n --format json \\\n --seq reads.fq.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTest reads can be obtained by running:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget -O reads.fq.gz https://ndownloader.figshare.com/files/21059229\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 88, + "subscribers_count": 9, + "topics": [], + "updated_at": 1711081849.0 + }, { "data_format": 2, "description": "SHIFT Dataset DevKit - CVPR2022", @@ -42027,33 +42055,6 @@ var data = "topics": [], "updated_at": 1731758813.0 }, - { - "data_format": 2, - "description": "BioDynaMo is a high-performance and modular, agent-based simulation platform.", - "filenames": [ - "Singularity" - ], - "full_name": "BioDynaMo/biodynamo", - "latest_release": "v1.04", - "readme": "\u003cp align=\"center\"\u003e\n \u003ca href=\"https://www.biodynamo.org/\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://github.com/BioDynaMo/biodynamo.github.io/raw/master/images/bdm_logo_large.png\" alt=\"BioDynaMo logo\" width=\"72\" height=\"72\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 align=\"center\" class=\"heading-element\"\u003e\n \u003ca href=\"http://www.biodynamo.org/\" rel=\"nofollow\"\u003eBioDynaMo\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content---biodynamo\" class=\"anchor\" aria-label=\"Permalink: BioDynaMo\" href=\"#--biodynamo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n The open-source Biology Dynamics Modeller\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca href=\"https://www.biodynamo.org/user-guide/\" rel=\"nofollow\"\u003eUser\u0027s guide\u003c/a\u003e\n |\n \u003ca href=\"https://www.biodynamo.org/developer-guide/\" rel=\"nofollow\"\u003eDeveloper\u0027s guide\u003c/a\u003e\n |\n \u003ca href=\"https://github.com/BioDynaMo/biodynamo/issues/new\"\u003eReport an issue or bug\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/ubuntu-system-ci.yml\"\u003e\u003cimg src=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/ubuntu-system-ci.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/centos-system-ci.yml\"\u003e\u003cimg src=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/centos-system-ci.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/macos-system-ci.yml\"\u003e\u003cimg src=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/macos-system-ci.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://sonarcloud.io/project/overview?id=BioDynaMo_biodynamo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90e99743bbf4001c54ee5d572bb7b7e7e1931db533742123662d2649a57246a8/68747470733a2f2f736f6e6172636c6f75642e696f2f6170692f70726f6a6563745f6261646765732f6d6561737572653f70726f6a6563743d42696f44796e614d6f5f62696f64796e616d6f266d65747269633d616c6572745f737461747573\" data-canonical-src=\"https://sonarcloud.io/api/project_badges/measure?project=BioDynaMo_biodynamo\u0026amp;metric=alert_status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://discord.com/invite/kTcTTNFy\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6c9449de7042ebd0b7d9141691e2e571eafd7817234be0e22651602d30d16e9/68747470733a2f2f696d672e736869656c64732e696f2f646973636f72642f31303239363930343534353734333730383136\" data-canonical-src=\"https://img.shields.io/discord/1029690454574370816\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is BioDynaMo?\u003c/h2\u003e\u003ca id=\"user-content-what-is-biodynamo\" class=\"anchor\" aria-label=\"Permalink: What is BioDynaMo?\" href=\"#what-is-biodynamo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioDynaMo is a software platform to easily create, run, and visualise 3D agent-based simulations.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eBlazing fast:\u003c/strong\u003e The core of the platform is written in C++ and is highly optimized to harness the computational power of modern hardware.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eModular:\u003c/strong\u003e Reuse, adapt, or create modules that represents a specific biological behavior or entity. With BioDynaMo you don\u0027t need to start from scratch anymore!\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVisual:\u003c/strong\u003e Effortlessly create astonishing images and animations of your simulations. Your publications and presentations will stand out even more!\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimply run the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://biodynamo.github.io/install | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, check out our \u003ca href=\"https://www.biodynamo.org/user-guide\" rel=\"nofollow\"\u003euser\u0027s guide\u003c/a\u003e documentation that is designed for people who will use BioDynaMo on a day-to-day basis, and aims at providing users with all essential info to make full use of BioDynaMo.\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main purpose of this repository is to evolve BioDynaMo, making it faster and easier to use. Development of BioDynaMo happens in the open on Github, and we are grateful to the community for contributing bugfixes and improvements. Please read our \u003ca href=\"https://github.com/BioDynaMo/biodynamo/blob/master/doc/dev_guide/contribute.md\"\u003econtribution guide\u003c/a\u003e and learn about our development process, how to propose bugfixes and improvements, and how to build and test your changes to BioDynaMo.\u003c/p\u003e\n\u003cp\u003eWe pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community in BioDynaMo. Thus, we expect all contributors to adhere to our \u003ca href=\"https://www.biodynamo.org/developer-guide/contributor-covenant\" rel=\"nofollow\"\u003ecovenant for contributors\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen publishing work that relies on BioDynaMo, please cite the following manuscripts, which underwent rigorous peer review.\nCitations are crucial to improve your work\u0027s reproducibility, show funding agencies that our work is valuable to the community, and increase your peers\u0027 knowledge about our terrific tool.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eLukas Breitwieser, Ahmad Hesam, Jean de Montigny, Vasileios Vavourakis, Alexandros Iosif, Jack Jennings, Marcus Kaiser, Marco Manca, Alberto Di Meglio, Zaid Al-Ars, Fons Rademakers, Onur Mutlu, Roman Bauer,\n\u003cstrong\u003eBioDynaMo: a modular platform for high-performance agent-based simulation\u003c/strong\u003e, Bioinformatics, Volume 38, Issue 2, January 2022, Pages 453\u2013460, \u003ca href=\"https://doi.org/10.1093/bioinformatics/btab649\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btab649\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e@article{breitwieser_biodynamo_2022,\n author = {Breitwieser, Lukas and Hesam, Ahmad and de Montigny, Jean and Vavourakis, Vasileios and Iosif, Alexandros and Jennings, Jack and Kaiser, Marcus and Manca, Marco and Di Meglio, Alberto and Al-Ars, Zaid and Rademakers, Fons and Mutlu, Onur and Bauer, Roman},\n title = \"{BioDynaMo: a modular platform for high-performance agent-based simulation}\",\n journal = {Bioinformatics},\n volume = {38},\n number = {2},\n pages = {453-460},\n year = {2021},\n month = {09},\n issn = {1367-4803},\n doi = {10.1093/bioinformatics/btab649},\n url = {https://doi.org/10.1093/bioinformatics/btab649}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eLukas Breitwieser, Ahmad Hesam, Fons Rademakers, Juan G\u00f3mez Luna, and Onur Mutlu. 2023.\n\u003cstrong\u003eHigh-Performance and Scalable Agent-Based Simulation with BioDynaMo.\u003c/strong\u003e\nIn Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (Montreal, QC, Canada) (PPoPP \u201923).\nAssociation for Computing Machinery, New York, NY, USA, 174\u2013188. \u003ca href=\"https://doi.org/10.1145/3572848.3577480\" rel=\"nofollow\"\u003ehttps://doi.org/10.1145/3572848.3577480\u003c/a\u003e arXiv:\u003ca href=\"https://arxiv.org/abs/2301.06984\" rel=\"nofollow\"\u003e2301.06984\u003c/a\u003e [cs.DC]\u003c/p\u003e\n\u003cdiv\u003e\n\u003ca href=\"https://blog.biodynamo.org/2023/04/PPoPP23-best-artifact-biodynamo.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/solid/trophy.svg\" width=\"25\" height=\"25\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nReceived \u003ca href=\"https://blog.biodynamo.org/2023/04/PPoPP23-best-artifact-biodynamo.html\" rel=\"nofollow\"\u003eBest Artifact Award\u003c/a\u003e.\n\u003c/div\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{breitwieser_biodynamo_2023,\n author = {Breitwieser, Lukas and Hesam, Ahmad and Rademakers, Fons and Luna, Juan G\\\u0027{o}mez and Mutlu, Onur},\n title = {High-Performance and Scalable Agent-Based Simulation with BioDynaMo},\n year = {2023},\n isbn = {9798400700156},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n url = {https://doi.org/10.1145/3572848.3577480},\n doi = {10.1145/3572848.3577480},\n booktitle = {Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming},\n pages = {174\u2013188},\n numpages = {15},\n keywords = {NUMA, HPC, performance evaluation, parallel computing, space-filling curve, high-performance simulation, performance optimization, agent-based modeling, memory layout optimization, memory allocation, scalability},\n location = {Montreal, QC, Canada},\n series = {PPoPP \u002723},\n archivePrefix = \"arXiv\",\n eprint = \"2301.06984\",\n primaryClass = \"cs.DC\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe recommend specifying the exact BioDynaMo version used in the methods section.\nFor example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-tex-latex\"\u003e\u003cpre\u003eWe use BioDynaMo v1.04.5-e9db03a1 \u003cspan class=\"pl-k\"\u003e\\cite\u003c/span\u003e{\u003cspan class=\"pl-c1\"\u003ebreitwieser_biodynamo_2022\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003ebreitwieser_biodynamo_2023\u003c/span\u003e} \nfor all simulations in this article. \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe version can be obtained by executing \u003ccode\u003ebiodynamo --version\u003c/code\u003e and can be interpreted as follows: \u003ccode\u003evMAJOR.MINOR.PATCH-SHA\u003c/code\u003e\n\u003ccode\u003eSHA\u003c/code\u003e is the git commit hash that can be used to check out the exact BioDynaMo version with \u003ccode\u003egit checkout SHA\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioDynaMo is \u003ca href=\"./LICENSE\"\u003eApache 2.0 licensed\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 98, - "subscribers_count": 13, - "topics": [ - "simulation", - "high-performance", - "neuroscience", - "modular-design", - "agent-based", - "agent-based-modelling", - "agent-based-framework", - "biology", - "epidemiology", - "cancer", - "large-scale", - "parallel" - ], - "updated_at": 1729181995.0 - }, { "data_format": 2, "description": "HIPAA \u0026 GDPR compliant ready parse-server with postgres/mongo, parse-hipaa-dashboard. Compatible with ParseCareKit", @@ -42088,18 +42089,17 @@ var data = }, { "data_format": 2, - "description": "Automatic atomic model building program for cryo-EM maps", + "description": "A diagnostic wind model developed for use in wildland fire modeling.", "filenames": [ - "Singularity.from.ghcr.io", - "Singularity.from.scratch" + "Singularity" ], - "full_name": "3dem/model-angelo", - "latest_release": "v1.0.12", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eModelAngelo\u003c/h1\u003e\u003ca id=\"user-content-modelangelo\" class=\"anchor\" aria-label=\"Permalink: ModelAngelo\" href=\"#modelangelo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModelAngelo is an automatic atomic model building program for cryo-EM maps.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompute requirements\u003c/h2\u003e\u003ca id=\"user-content-compute-requirements\" class=\"anchor\" aria-label=\"Permalink: Compute requirements\" href=\"#compute-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is highly recommended to have access to GPUs with at least 8GB of memory. ModelAngelo performs well on NVIDIA GPUs such as 2080\u0027s and beyond.\u003c/p\u003e\n\u003cp\u003ePlease note that the weight files required by both ModelAngelo and the language model it uses combined are around 10 GB. So you need to have more disk space than that.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdetails\u003e\n\u003csummary\u003ePersonal use\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003e(If you manage a computational cluster, please skip to the next section)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 1: Install Conda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo install ModelAngelo, you need Anaconda. We recommend installing miniconda3, as it is lighter. You can find that here: \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOnce you have a version of Anaconda installed, check that it actually runs with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda info\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eStep 2: Clone this repo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNow, you can install ModelAngelo. First, you need to clone this Github repository with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/3dem/model-angelo.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eStep 3: Run install script\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter, all you need to do is go into the \u003ccode\u003emodel-angelo\u003c/code\u003e directory and run the install script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd model-angelo\nsource install_script.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will now have a conda environment called \u003ccode\u003emodel_angelo\u003c/code\u003e that is able to run the program.\nYou need to activate this conda environment with \u003ccode\u003econda activate model_angelo\u003c/code\u003e.\nNow, you can run \u003ccode\u003emodel_angelo build -h\u003c/code\u003e to see if the installation worked!\n\u003cbr\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eShared computational environment\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003eIf you manage a computational cluster with many users and would like to install ModelAngelo once to be used everywhere,\nyou should complete the above steps 1 and 2 for a public account.\u003c/p\u003e\n\u003cp\u003eNext, you should designate a folder to save the shared weights of the model such that it can be \u003cem\u003ereadable and executable\u003c/em\u003e\nby all users of your cluster. Let\u0027s say that path is \u003ccode\u003e/public/model_angelo_weights\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNow, you run the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport TORCH_HOME=/public/model_angelo_weights\ncd model-angelo\nsource install_script.sh --download-weights\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the script is finished running, make sure that where it installed the weights is in the directory you set.\u003c/p\u003e\n\u003cp\u003eFinally, you can make the following bash script available for all users to run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsource `which activate` model_angelo\nmodel_angelo \"$@\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cbr\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eInstallation issues\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003e\u003cstrong\u003e1. Binary activate not found\u003c/strong\u003e\nIt appears that miniconda\u0027s activate binary is not added to \u003ccode\u003ePATH\u003c/code\u003e by default. You can either fix this by appending it yourself, like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PATH=\"$PATH:/path/to/miniconda3/bin\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor running \u003ccode\u003econda init\u003c/code\u003e and restarting your shell.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, make sure to run \u003ccode\u003emodel_angelo build --help\u003c/code\u003e or \u003ccode\u003emodel_angelo build_no_seq --help\u003c/code\u003e to familiarize yourself with all of the options available.\u003c/p\u003e\n\u003cdetails\u003e\n\u003csummary\u003eBuilding a map with FASTA sequence\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003eThis is the recommended use case, when you have access to a medium-high resolution cryo-EM map (resolutions exceeding 4 \u00c5) as well as a FASTA files with all of your protein, RNA, and DNA sequences.\u003c/p\u003e\n\u003cp\u003eLet\u0027s say the map\u0027s name is \u003ccode\u003emap.mrc\u003c/code\u003e and the (protein) sequence file is \u003ccode\u003eprot.fasta\u003c/code\u003e. To build your model in a directory named \u003ccode\u003eoutput\u003c/code\u003e, you run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodel_angelo build -v map.mrc -pf prot.fasta -o output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you would like to build nucleotides as well, you need to provide the RNA and DNA portions of your sequences in different files like so\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodel_angelo build -v map.mrc -pf prot.fasta -df dna.fasta -rf rna.fasta -o output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you only have RNA or DNA, you can drop the other input.\u003c/p\u003e\n\u003cp\u003eIf the output of the program halts before the completion of \u003ccode\u003eGNN model refinement, round 3 / 3\u003c/code\u003e, there was a bug that you can see in \u003ccode\u003eoutput/model_angelo.log\u003c/code\u003e. Otherwise, you can find your model in \u003ccode\u003eoutput/output.cif\u003c/code\u003e. The name of the mmCIF file is based on the output folder name, so if you specify, for example, \u003ccode\u003e-o testing/test/model_building\u003c/code\u003e, the model will be in \u003ccode\u003etesting/test/model_building/model_building.cif\u003c/code\u003e.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eBuilding a map with no FASTA sequence\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003eIf you have a sample where you do not know all of the protein sequences that occur in the map, you can run \u003ccode\u003emodel_angelo build_no_seq\u003c/code\u003e instead.\nThis version of the program uses a network that was not trained with input sequences, nor does it do post-processing on the built map.\u003c/p\u003e\n\u003cp\u003eInstead, in addition to a built model, it provides you with HMM profile files that you can use to search a database such as UniRef with HHblits.\u003c/p\u003e\n\u003cp\u003eYou run this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodel_angelo build_no_seq -v map.mrc -o output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe model will be in \u003ccode\u003eoutput/output.cif\u003c/code\u003e as before.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eUsing the HMM search procedure\u003c/summary\u003e\n\u003cbr\u003e\nIf you have a map and would like to identify new proteins, first build it with the no sequence option as above.\nYou will have the model as an mmCIF.\n\u003cp\u003eNow there are also HMM profiles for each chain in HMMER3 format here: \u003ccode\u003eoutput/hmm_profiles\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo do a sequence search, you should first download a fasta file that includes your proteins of interest. Often, this will be the proteome of the organism in question. Here are some example proteomes in a \u003ccode\u003eFASTA\u003c/code\u003e format:\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOrganism\u003c/th\u003e\n\u003cth\u003eLink\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003eE.coli\u003c/em\u003e (K12)\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Bacteria/UP000000625/UP000000625_83333.fasta.gz\" rel=\"nofollow\"\u003eDownload\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHuman\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000005640/UP000005640_9606.fasta.gz\" rel=\"nofollow\"\u003eDownload\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000002311/UP000002311_559292.fasta.gz\" rel=\"nofollow\"\u003eDownload\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAfter downloading this file, you will need to uncompress it with \u003ccode\u003egunzip\u003c/code\u003e. Then, you can run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodel_angelo hmm_search --i output --f PATH_TO_DB --o hmm_output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will have your results as a series of HMM output files with the extension .hhr, for example: \u003ccode\u003ehmm_output/A.hhr\u003c/code\u003e. These are named by chain according to the model built by ModelAngelo in \u003ccode\u003eoutput/output.cif\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor further convenience, especially for large maps, we also now have two csv files named \u003ccode\u003eoutput/all_hits.csv\u003c/code\u003e and \u003ccode\u003eoutput/best_hits.csv\u003c/code\u003e. The first will have all the hits for all the chains sorted by the E-value. The second has all the \u003cem\u003eunique\u003c/em\u003e hits, with the information of the best hit.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQs\u003c/h2\u003e\u003ca id=\"user-content-faqs\" class=\"anchor\" aria-label=\"Permalink: FAQs\" href=\"#faqs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eHow do I change which GPU ModelAngelo runs on?\u003c/strong\u003e You can specify the device(s) ModelAngelo runs on by using the \u003ccode\u003e--device\u003c/code\u003e flag. So, for example, to use GPU with Id 0, you write \u003ccode\u003e--device 0\u003c/code\u003e. To use the first two GPUs of your computer, you can write \u003ccode\u003e--device 0,1\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDo I need to repeat the sequence of a dimer twice in the FASTA file?\u003c/strong\u003e No, each \u003cem\u003eunique\u003c/em\u003e sequence only needs to show up once in the FASTA file. Duplicates are always removed.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHow does ModelAngelo deal with glycosylation sites, non standard amino acids, etc?\u003c/strong\u003e It \u003cem\u003edoesn\u0027t\u003c/em\u003e. These parts of the model should be checked manually.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHow does ModelAngelo deal with cis prolines?\u003c/strong\u003e It \u003cem\u003edoesn\u0027t\u003c/em\u003e. However, we find that a round of refinement (with REFMAC, for example) fixes this issue.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommon issues\u003c/h2\u003e\u003ca id=\"user-content-common-issues\" class=\"anchor\" aria-label=\"Permalink: Common issues\" href=\"#common-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eIf the result looks very bad, with many disconnected chains, take a look at the alpha helices. If these are made of short and disconnected chains, the map was probably in the wrong hand. If you flip the map and run again, you should see much better results.\u003c/li\u003e\n\u003cli\u003eIf the map is processed using deepEMhancer, we have noticed less than satisfactory results. Please try with a map post-processed with a conventional algorithm and try again.\u003c/li\u003e\n\u003cli\u003eAlways check your input sequence files to make sure that they correspond to a correct FASTA format. Please make sure that the sequences are all capital letters, as is the convention.\u003c/li\u003e\n\u003cli\u003eIf the output model is shifted with respect to your map, make sure that the map provided to ModelAngelo is cubic. Otherwise, it might get shifted when ModelAngelo internally makes the map cubic.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eModelAngelo 0.3\u003c/strong\u003e: Published in the proceedings of the International Conference on Learning Representations (\u003cem\u003eICLR\u003c/em\u003e) 2023. You can find the paper on \u003ca href=\"https://openreview.net/forum?id=65XDF_nwI61\" rel=\"nofollow\"\u003eOpenreview\u003c/a\u003e. Bibtex:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{jamali2023modelangelo,\n title={A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps},\n author={Kiarash Jamali and Dari Kimanius and Sjors HW Scheres},\n booktitle={International Conference on Learning Representations},\n year={2023},\n url={https://openreview.net/forum?id=65XDF_nwI61}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eModelAngelo 1.0\u003c/strong\u003e: Paper available in \u003ca href=\"https://www.nature.com/articles/s41586-024-07215-4\" rel=\"nofollow\"\u003eNature\u003c/a\u003e . Bibtex:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e@article{jamali2024automated,\n\tauthor = {Kiarash Jamali and Lukas Kall and Rui Zhang and Alan Brown and Dari Kimanius and Sjors Scheres},\n\ttitle = {Automated model building and protein identification in cryo-EM maps},\n\tyear = {2024},\n\tdoi = {10.1038/s41586-024-07215-4},\n\tURL = {https://www.nature.com/articles/s41586-024-07215-4},\n\tjournal = {Nature}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", + "full_name": "firelab/windninja", + "latest_release": "3.10.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWindNinja\u003c/h1\u003e\u003ca id=\"user-content-windninja\" class=\"anchor\" aria-label=\"Permalink: WindNinja\" href=\"#windninja\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/firelab/windninja/actions\"\u003e\u003cimg src=\"https://github.com/firelab/windninja/actions/workflows/testing.yml/badge.svg\" alt=\"example workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/21244/firelab/windninja\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0759cb3e12021c2473f1903df4d3511a777e4203248a16bec481a6bbba8b502a/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f32313234342f666972656c61622f77696e646e696e6a612e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/21244/firelab/windninja.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWindNinja is a diagnostic wind model developed for use in wildland fire modeling.\u003c/p\u003e\n\u003cp\u003eWeb:\n\u003ca href=\"http://firelab.org/project/windninja\" rel=\"nofollow\"\u003ehttp://firelab.org/project/windninja\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSource \u0026amp; wiki:\n\u003ca href=\"https://github.com/firelab/windninja\"\u003ehttps://github.com/firelab/windninja\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFAQ:\n\u003ca href=\"https://github.com/firelab/windninja/wiki/Frequently-Asked-Questions\"\u003ehttps://github.com/firelab/windninja/wiki/Frequently-Asked-Questions\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIRC:\nirc://irc.freenode.net/#windninja\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/firelab/windninja/wiki/Building-WindNinja-on-Linux\"\u003eBuilding on Linux\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/firelab/windninja/wiki/Building-WindNinja-on-Windows-using-the-MSVC-compiler-and-gisinternals.com-dependencies\"\u003eBuilding on Windows\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDirectories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eautotest -\u0026gt; testing suite\u003c/li\u003e\n\u003cli\u003ecmake -\u0026gt; cmake support scripts\u003c/li\u003e\n\u003cli\u003edata -\u0026gt; testing data\u003c/li\u003e\n\u003cli\u003edoc -\u0026gt; documentation\u003c/li\u003e\n\u003cli\u003eimages -\u0026gt; splash image and icons for gui\u003c/li\u003e\n\u003cli\u003esrc -\u0026gt; source files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDependencies (versions are versions we build against for the Windows installer):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBoost 1.46:\n\u003cul\u003e\n\u003cli\u003eboost_date_time\u003c/li\u003e\n\u003cli\u003eboost_program_options\u003c/li\u003e\n\u003cli\u003eboost_test\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eNetCDF 4.1.1\u003c/li\u003e\n\u003cli\u003eGDAL 2.2.2\n\u003cul\u003e\n\u003cli\u003eNetCDF support\u003c/li\u003e\n\u003cli\u003ePROJ.4 support\u003c/li\u003e\n\u003cli\u003eGEOS support\u003c/li\u003e\n\u003cli\u003eCURL support\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eQt 4.8.5\n\u003cul\u003e\n\u003cli\u003eQtGui\u003c/li\u003e\n\u003cli\u003eQtCore\u003c/li\u003e\n\u003cli\u003eQtNetwork/Phonon\u003c/li\u003e\n\u003cli\u003eQtWebKit\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OpenFOAM/OpenFOAM-2.2.x\"\u003eOpenFOAM 2.2.x\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee INSTALL for more information (coming soon)\u003c/p\u003e\n\u003cp\u003eSee CREDITS for authors\u003c/p\u003e\n\u003cp\u003eSee NEWS for release information\u003c/p\u003e\n\u003cp\u003e=========\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/bsb.jpg\"\u003e\u003cimg src=\"images/bsb.jpg\" alt=\"Example output\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", "stargazers_count": 101, - "subscribers_count": 8, + "subscribers_count": 23, "topics": [], - "updated_at": 1714673579.0 + "updated_at": 1707132009.0 }, { "data_format": 2, @@ -42118,17 +42118,18 @@ var data = }, { "data_format": 2, - "description": "A diagnostic wind model developed for use in wildland fire modeling.", + "description": "Automatic atomic model building program for cryo-EM maps", "filenames": [ - "Singularity" + "Singularity.from.ghcr.io", + "Singularity.from.scratch" ], - "full_name": "firelab/windninja", - "latest_release": "3.10.0", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eWindNinja\u003c/h1\u003e\u003ca id=\"user-content-windninja\" class=\"anchor\" aria-label=\"Permalink: WindNinja\" href=\"#windninja\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/firelab/windninja/actions\"\u003e\u003cimg src=\"https://github.com/firelab/windninja/actions/workflows/testing.yml/badge.svg\" alt=\"example workflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/21244/firelab/windninja\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0759cb3e12021c2473f1903df4d3511a777e4203248a16bec481a6bbba8b502a/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f32313234342f666972656c61622f77696e646e696e6a612e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/21244/firelab/windninja.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eWindNinja is a diagnostic wind model developed for use in wildland fire modeling.\u003c/p\u003e\n\u003cp\u003eWeb:\n\u003ca href=\"http://firelab.org/project/windninja\" rel=\"nofollow\"\u003ehttp://firelab.org/project/windninja\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSource \u0026amp; wiki:\n\u003ca href=\"https://github.com/firelab/windninja\"\u003ehttps://github.com/firelab/windninja\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFAQ:\n\u003ca href=\"https://github.com/firelab/windninja/wiki/Frequently-Asked-Questions\"\u003ehttps://github.com/firelab/windninja/wiki/Frequently-Asked-Questions\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIRC:\nirc://irc.freenode.net/#windninja\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/firelab/windninja/wiki/Building-WindNinja-on-Linux\"\u003eBuilding on Linux\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/firelab/windninja/wiki/Building-WindNinja-on-Windows-using-the-MSVC-compiler-and-gisinternals.com-dependencies\"\u003eBuilding on Windows\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eDirectories:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eautotest -\u0026gt; testing suite\u003c/li\u003e\n\u003cli\u003ecmake -\u0026gt; cmake support scripts\u003c/li\u003e\n\u003cli\u003edata -\u0026gt; testing data\u003c/li\u003e\n\u003cli\u003edoc -\u0026gt; documentation\u003c/li\u003e\n\u003cli\u003eimages -\u0026gt; splash image and icons for gui\u003c/li\u003e\n\u003cli\u003esrc -\u0026gt; source files\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDependencies (versions are versions we build against for the Windows installer):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBoost 1.46:\n\u003cul\u003e\n\u003cli\u003eboost_date_time\u003c/li\u003e\n\u003cli\u003eboost_program_options\u003c/li\u003e\n\u003cli\u003eboost_test\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eNetCDF 4.1.1\u003c/li\u003e\n\u003cli\u003eGDAL 2.2.2\n\u003cul\u003e\n\u003cli\u003eNetCDF support\u003c/li\u003e\n\u003cli\u003ePROJ.4 support\u003c/li\u003e\n\u003cli\u003eGEOS support\u003c/li\u003e\n\u003cli\u003eCURL support\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eQt 4.8.5\n\u003cul\u003e\n\u003cli\u003eQtGui\u003c/li\u003e\n\u003cli\u003eQtCore\u003c/li\u003e\n\u003cli\u003eQtNetwork/Phonon\u003c/li\u003e\n\u003cli\u003eQtWebKit\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OpenFOAM/OpenFOAM-2.2.x\"\u003eOpenFOAM 2.2.x\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSee INSTALL for more information (coming soon)\u003c/p\u003e\n\u003cp\u003eSee CREDITS for authors\u003c/p\u003e\n\u003cp\u003eSee NEWS for release information\u003c/p\u003e\n\u003cp\u003e=========\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"images/bsb.jpg\"\u003e\u003cimg src=\"images/bsb.jpg\" alt=\"Example output\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n", + "full_name": "3dem/model-angelo", + "latest_release": "v1.0.12", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eModelAngelo\u003c/h1\u003e\u003ca id=\"user-content-modelangelo\" class=\"anchor\" aria-label=\"Permalink: ModelAngelo\" href=\"#modelangelo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eModelAngelo is an automatic atomic model building program for cryo-EM maps.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCompute requirements\u003c/h2\u003e\u003ca id=\"user-content-compute-requirements\" class=\"anchor\" aria-label=\"Permalink: Compute requirements\" href=\"#compute-requirements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIt is highly recommended to have access to GPUs with at least 8GB of memory. ModelAngelo performs well on NVIDIA GPUs such as 2080\u0027s and beyond.\u003c/p\u003e\n\u003cp\u003ePlease note that the weight files required by both ModelAngelo and the language model it uses combined are around 10 GB. So you need to have more disk space than that.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdetails\u003e\n\u003csummary\u003ePersonal use\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003e(If you manage a computational cluster, please skip to the next section)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 1: Install Conda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo install ModelAngelo, you need Anaconda. We recommend installing miniconda3, as it is lighter. You can find that here: \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOnce you have a version of Anaconda installed, check that it actually runs with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda info\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eStep 2: Clone this repo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNow, you can install ModelAngelo. First, you need to clone this Github repository with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/3dem/model-angelo.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eStep 3: Run install script\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter, all you need to do is go into the \u003ccode\u003emodel-angelo\u003c/code\u003e directory and run the install script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd model-angelo\nsource install_script.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will now have a conda environment called \u003ccode\u003emodel_angelo\u003c/code\u003e that is able to run the program.\nYou need to activate this conda environment with \u003ccode\u003econda activate model_angelo\u003c/code\u003e.\nNow, you can run \u003ccode\u003emodel_angelo build -h\u003c/code\u003e to see if the installation worked!\n\u003cbr\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eShared computational environment\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003eIf you manage a computational cluster with many users and would like to install ModelAngelo once to be used everywhere,\nyou should complete the above steps 1 and 2 for a public account.\u003c/p\u003e\n\u003cp\u003eNext, you should designate a folder to save the shared weights of the model such that it can be \u003cem\u003ereadable and executable\u003c/em\u003e\nby all users of your cluster. Let\u0027s say that path is \u003ccode\u003e/public/model_angelo_weights\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eNow, you run the following:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport TORCH_HOME=/public/model_angelo_weights\ncd model-angelo\nsource install_script.sh --download-weights\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce the script is finished running, make sure that where it installed the weights is in the directory you set.\u003c/p\u003e\n\u003cp\u003eFinally, you can make the following bash script available for all users to run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#!/bin/bash\nsource `which activate` model_angelo\nmodel_angelo \"$@\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cbr\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eInstallation issues\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003e\u003cstrong\u003e1. Binary activate not found\u003c/strong\u003e\nIt appears that miniconda\u0027s activate binary is not added to \u003ccode\u003ePATH\u003c/code\u003e by default. You can either fix this by appending it yourself, like so:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PATH=\"$PATH:/path/to/miniconda3/bin\"\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor running \u003ccode\u003econda init\u003c/code\u003e and restarting your shell.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst, make sure to run \u003ccode\u003emodel_angelo build --help\u003c/code\u003e or \u003ccode\u003emodel_angelo build_no_seq --help\u003c/code\u003e to familiarize yourself with all of the options available.\u003c/p\u003e\n\u003cdetails\u003e\n\u003csummary\u003eBuilding a map with FASTA sequence\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003eThis is the recommended use case, when you have access to a medium-high resolution cryo-EM map (resolutions exceeding 4 \u00c5) as well as a FASTA files with all of your protein, RNA, and DNA sequences.\u003c/p\u003e\n\u003cp\u003eLet\u0027s say the map\u0027s name is \u003ccode\u003emap.mrc\u003c/code\u003e and the (protein) sequence file is \u003ccode\u003eprot.fasta\u003c/code\u003e. To build your model in a directory named \u003ccode\u003eoutput\u003c/code\u003e, you run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodel_angelo build -v map.mrc -pf prot.fasta -o output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you would like to build nucleotides as well, you need to provide the RNA and DNA portions of your sequences in different files like so\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodel_angelo build -v map.mrc -pf prot.fasta -df dna.fasta -rf rna.fasta -o output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you only have RNA or DNA, you can drop the other input.\u003c/p\u003e\n\u003cp\u003eIf the output of the program halts before the completion of \u003ccode\u003eGNN model refinement, round 3 / 3\u003c/code\u003e, there was a bug that you can see in \u003ccode\u003eoutput/model_angelo.log\u003c/code\u003e. Otherwise, you can find your model in \u003ccode\u003eoutput/output.cif\u003c/code\u003e. The name of the mmCIF file is based on the output folder name, so if you specify, for example, \u003ccode\u003e-o testing/test/model_building\u003c/code\u003e, the model will be in \u003ccode\u003etesting/test/model_building/model_building.cif\u003c/code\u003e.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eBuilding a map with no FASTA sequence\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp\u003eIf you have a sample where you do not know all of the protein sequences that occur in the map, you can run \u003ccode\u003emodel_angelo build_no_seq\u003c/code\u003e instead.\nThis version of the program uses a network that was not trained with input sequences, nor does it do post-processing on the built map.\u003c/p\u003e\n\u003cp\u003eInstead, in addition to a built model, it provides you with HMM profile files that you can use to search a database such as UniRef with HHblits.\u003c/p\u003e\n\u003cp\u003eYou run this command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodel_angelo build_no_seq -v map.mrc -o output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe model will be in \u003ccode\u003eoutput/output.cif\u003c/code\u003e as before.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eUsing the HMM search procedure\u003c/summary\u003e\n\u003cbr\u003e\nIf you have a map and would like to identify new proteins, first build it with the no sequence option as above.\nYou will have the model as an mmCIF.\n\u003cp\u003eNow there are also HMM profiles for each chain in HMMER3 format here: \u003ccode\u003eoutput/hmm_profiles\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eTo do a sequence search, you should first download a fasta file that includes your proteins of interest. Often, this will be the proteome of the organism in question. Here are some example proteomes in a \u003ccode\u003eFASTA\u003c/code\u003e format:\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOrganism\u003c/th\u003e\n\u003cth\u003eLink\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cem\u003eE.coli\u003c/em\u003e (K12)\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Bacteria/UP000000625/UP000000625_83333.fasta.gz\" rel=\"nofollow\"\u003eDownload\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHuman\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000005640/UP000005640_9606.fasta.gz\" rel=\"nofollow\"\u003eDownload\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000002311/UP000002311_559292.fasta.gz\" rel=\"nofollow\"\u003eDownload\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAfter downloading this file, you will need to uncompress it with \u003ccode\u003egunzip\u003c/code\u003e. Then, you can run\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003emodel_angelo hmm_search --i output --f PATH_TO_DB --o hmm_output\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will have your results as a series of HMM output files with the extension .hhr, for example: \u003ccode\u003ehmm_output/A.hhr\u003c/code\u003e. These are named by chain according to the model built by ModelAngelo in \u003ccode\u003eoutput/output.cif\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eFor further convenience, especially for large maps, we also now have two csv files named \u003ccode\u003eoutput/all_hits.csv\u003c/code\u003e and \u003ccode\u003eoutput/best_hits.csv\u003c/code\u003e. The first will have all the hits for all the chains sorted by the E-value. The second has all the \u003cem\u003eunique\u003c/em\u003e hits, with the information of the best hit.\u003c/p\u003e\n\u003c/details\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFAQs\u003c/h2\u003e\u003ca id=\"user-content-faqs\" class=\"anchor\" aria-label=\"Permalink: FAQs\" href=\"#faqs\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eHow do I change which GPU ModelAngelo runs on?\u003c/strong\u003e You can specify the device(s) ModelAngelo runs on by using the \u003ccode\u003e--device\u003c/code\u003e flag. So, for example, to use GPU with Id 0, you write \u003ccode\u003e--device 0\u003c/code\u003e. To use the first two GPUs of your computer, you can write \u003ccode\u003e--device 0,1\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDo I need to repeat the sequence of a dimer twice in the FASTA file?\u003c/strong\u003e No, each \u003cem\u003eunique\u003c/em\u003e sequence only needs to show up once in the FASTA file. Duplicates are always removed.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHow does ModelAngelo deal with glycosylation sites, non standard amino acids, etc?\u003c/strong\u003e It \u003cem\u003edoesn\u0027t\u003c/em\u003e. These parts of the model should be checked manually.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHow does ModelAngelo deal with cis prolines?\u003c/strong\u003e It \u003cem\u003edoesn\u0027t\u003c/em\u003e. However, we find that a round of refinement (with REFMAC, for example) fixes this issue.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCommon issues\u003c/h2\u003e\u003ca id=\"user-content-common-issues\" class=\"anchor\" aria-label=\"Permalink: Common issues\" href=\"#common-issues\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003eIf the result looks very bad, with many disconnected chains, take a look at the alpha helices. If these are made of short and disconnected chains, the map was probably in the wrong hand. If you flip the map and run again, you should see much better results.\u003c/li\u003e\n\u003cli\u003eIf the map is processed using deepEMhancer, we have noticed less than satisfactory results. Please try with a map post-processed with a conventional algorithm and try again.\u003c/li\u003e\n\u003cli\u003eAlways check your input sequence files to make sure that they correspond to a correct FASTA format. Please make sure that the sequences are all capital letters, as is the convention.\u003c/li\u003e\n\u003cli\u003eIf the output model is shifted with respect to your map, make sure that the map provided to ModelAngelo is cubic. Otherwise, it might get shifted when ModelAngelo internally makes the map cubic.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eModelAngelo 0.3\u003c/strong\u003e: Published in the proceedings of the International Conference on Learning Representations (\u003cem\u003eICLR\u003c/em\u003e) 2023. You can find the paper on \u003ca href=\"https://openreview.net/forum?id=65XDF_nwI61\" rel=\"nofollow\"\u003eOpenreview\u003c/a\u003e. Bibtex:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{jamali2023modelangelo,\n title={A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps},\n author={Kiarash Jamali and Dari Kimanius and Sjors HW Scheres},\n booktitle={International Conference on Learning Representations},\n year={2023},\n url={https://openreview.net/forum?id=65XDF_nwI61}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eModelAngelo 1.0\u003c/strong\u003e: Paper available in \u003ca href=\"https://www.nature.com/articles/s41586-024-07215-4\" rel=\"nofollow\"\u003eNature\u003c/a\u003e . Bibtex:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e@article{jamali2024automated,\n\tauthor = {Kiarash Jamali and Lukas Kall and Rui Zhang and Alan Brown and Dari Kimanius and Sjors Scheres},\n\ttitle = {Automated model building and protein identification in cryo-EM maps},\n\tyear = {2024},\n\tdoi = {10.1038/s41586-024-07215-4},\n\tURL = {https://www.nature.com/articles/s41586-024-07215-4},\n\tjournal = {Nature}\n}\n\u003c/code\u003e\u003c/pre\u003e\n", "stargazers_count": 101, - "subscribers_count": 23, + "subscribers_count": 8, "topics": [], - "updated_at": 1707132009.0 + "updated_at": 1714673579.0 }, { "data_format": 2, @@ -42146,6 +42147,33 @@ var data = "topics": [], "updated_at": 1707715375.0 }, + { + "data_format": 2, + "description": "BioDynaMo is a high-performance and modular, agent-based simulation platform.", + "filenames": [ + "Singularity" + ], + "full_name": "BioDynaMo/biodynamo", + "latest_release": "v1.04", + "readme": "\u003cp align=\"center\"\u003e\n \u003ca href=\"https://www.biodynamo.org/\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://github.com/BioDynaMo/biodynamo.github.io/raw/master/images/bdm_logo_large.png\" alt=\"BioDynaMo logo\" width=\"72\" height=\"72\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 align=\"center\" class=\"heading-element\"\u003e\n \u003ca href=\"http://www.biodynamo.org/\" rel=\"nofollow\"\u003eBioDynaMo\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content---biodynamo\" class=\"anchor\" aria-label=\"Permalink: BioDynaMo\" href=\"#--biodynamo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n The open-source Biology Dynamics Modeller\n \u003cbr\u003e\n \u003cbr\u003e\n \u003ca href=\"https://www.biodynamo.org/user-guide/\" rel=\"nofollow\"\u003eUser\u0027s guide\u003c/a\u003e\n |\n \u003ca href=\"https://www.biodynamo.org/developer-guide/\" rel=\"nofollow\"\u003eDeveloper\u0027s guide\u003c/a\u003e\n |\n \u003ca href=\"https://github.com/BioDynaMo/biodynamo/issues/new\"\u003eReport an issue or bug\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/ubuntu-system-ci.yml\"\u003e\u003cimg src=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/ubuntu-system-ci.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/centos-system-ci.yml\"\u003e\u003cimg src=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/centos-system-ci.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/macos-system-ci.yml\"\u003e\u003cimg src=\"https://github.com/BioDynaMo/biodynamo/actions/workflows/macos-system-ci.yml/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://sonarcloud.io/project/overview?id=BioDynaMo_biodynamo\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/90e99743bbf4001c54ee5d572bb7b7e7e1931db533742123662d2649a57246a8/68747470733a2f2f736f6e6172636c6f75642e696f2f6170692f70726f6a6563745f6261646765732f6d6561737572653f70726f6a6563743d42696f44796e614d6f5f62696f64796e616d6f266d65747269633d616c6572745f737461747573\" data-canonical-src=\"https://sonarcloud.io/api/project_badges/measure?project=BioDynaMo_biodynamo\u0026amp;metric=alert_status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://discord.com/invite/kTcTTNFy\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b6c9449de7042ebd0b7d9141691e2e571eafd7817234be0e22651602d30d16e9/68747470733a2f2f696d672e736869656c64732e696f2f646973636f72642f31303239363930343534353734333730383136\" data-canonical-src=\"https://img.shields.io/discord/1029690454574370816\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is BioDynaMo?\u003c/h2\u003e\u003ca id=\"user-content-what-is-biodynamo\" class=\"anchor\" aria-label=\"Permalink: What is BioDynaMo?\" href=\"#what-is-biodynamo\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioDynaMo is a software platform to easily create, run, and visualise 3D agent-based simulations.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eBlazing fast:\u003c/strong\u003e The core of the platform is written in C++ and is highly optimized to harness the computational power of modern hardware.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eModular:\u003c/strong\u003e Reuse, adapt, or create modules that represents a specific biological behavior or entity. With BioDynaMo you don\u0027t need to start from scratch anymore!\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVisual:\u003c/strong\u003e Effortlessly create astonishing images and animations of your simulations. Your publications and presentations will stand out even more!\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSimply run the following command:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://biodynamo.github.io/install | bash\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor more information, check out our \u003ca href=\"https://www.biodynamo.org/user-guide\" rel=\"nofollow\"\u003euser\u0027s guide\u003c/a\u003e documentation that is designed for people who will use BioDynaMo on a day-to-day basis, and aims at providing users with all essential info to make full use of BioDynaMo.\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe main purpose of this repository is to evolve BioDynaMo, making it faster and easier to use. Development of BioDynaMo happens in the open on Github, and we are grateful to the community for contributing bugfixes and improvements. Please read our \u003ca href=\"https://github.com/BioDynaMo/biodynamo/blob/master/doc/dev_guide/contribute.md\"\u003econtribution guide\u003c/a\u003e and learn about our development process, how to propose bugfixes and improvements, and how to build and test your changes to BioDynaMo.\u003c/p\u003e\n\u003cp\u003eWe pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community in BioDynaMo. Thus, we expect all contributors to adhere to our \u003ca href=\"https://www.biodynamo.org/developer-guide/contributor-covenant\" rel=\"nofollow\"\u003ecovenant for contributors\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWhen publishing work that relies on BioDynaMo, please cite the following manuscripts, which underwent rigorous peer review.\nCitations are crucial to improve your work\u0027s reproducibility, show funding agencies that our work is valuable to the community, and increase your peers\u0027 knowledge about our terrific tool.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eLukas Breitwieser, Ahmad Hesam, Jean de Montigny, Vasileios Vavourakis, Alexandros Iosif, Jack Jennings, Marcus Kaiser, Marco Manca, Alberto Di Meglio, Zaid Al-Ars, Fons Rademakers, Onur Mutlu, Roman Bauer,\n\u003cstrong\u003eBioDynaMo: a modular platform for high-performance agent-based simulation\u003c/strong\u003e, Bioinformatics, Volume 38, Issue 2, January 2022, Pages 453\u2013460, \u003ca href=\"https://doi.org/10.1093/bioinformatics/btab649\" rel=\"nofollow\"\u003ehttps://doi.org/10.1093/bioinformatics/btab649\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e@article{breitwieser_biodynamo_2022,\n author = {Breitwieser, Lukas and Hesam, Ahmad and de Montigny, Jean and Vavourakis, Vasileios and Iosif, Alexandros and Jennings, Jack and Kaiser, Marcus and Manca, Marco and Di Meglio, Alberto and Al-Ars, Zaid and Rademakers, Fons and Mutlu, Onur and Bauer, Roman},\n title = \"{BioDynaMo: a modular platform for high-performance agent-based simulation}\",\n journal = {Bioinformatics},\n volume = {38},\n number = {2},\n pages = {453-460},\n year = {2021},\n month = {09},\n issn = {1367-4803},\n doi = {10.1093/bioinformatics/btab649},\n url = {https://doi.org/10.1093/bioinformatics/btab649}\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cblockquote\u003e\n\u003cp\u003eLukas Breitwieser, Ahmad Hesam, Fons Rademakers, Juan G\u00f3mez Luna, and Onur Mutlu. 2023.\n\u003cstrong\u003eHigh-Performance and Scalable Agent-Based Simulation with BioDynaMo.\u003c/strong\u003e\nIn Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (Montreal, QC, Canada) (PPoPP \u201923).\nAssociation for Computing Machinery, New York, NY, USA, 174\u2013188. \u003ca href=\"https://doi.org/10.1145/3572848.3577480\" rel=\"nofollow\"\u003ehttps://doi.org/10.1145/3572848.3577480\u003c/a\u003e arXiv:\u003ca href=\"https://arxiv.org/abs/2301.06984\" rel=\"nofollow\"\u003e2301.06984\u003c/a\u003e [cs.DC]\u003c/p\u003e\n\u003cdiv\u003e\n\u003ca href=\"https://blog.biodynamo.org/2023/04/PPoPP23-best-artifact-biodynamo.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/solid/trophy.svg\" width=\"25\" height=\"25\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nReceived \u003ca href=\"https://blog.biodynamo.org/2023/04/PPoPP23-best-artifact-biodynamo.html\" rel=\"nofollow\"\u003eBest Artifact Award\u003c/a\u003e.\n\u003c/div\u003e\n\u003c/blockquote\u003e\n\u003cpre\u003e\u003ccode\u003e@inproceedings{breitwieser_biodynamo_2023,\n author = {Breitwieser, Lukas and Hesam, Ahmad and Rademakers, Fons and Luna, Juan G\\\u0027{o}mez and Mutlu, Onur},\n title = {High-Performance and Scalable Agent-Based Simulation with BioDynaMo},\n year = {2023},\n isbn = {9798400700156},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n url = {https://doi.org/10.1145/3572848.3577480},\n doi = {10.1145/3572848.3577480},\n booktitle = {Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming},\n pages = {174\u2013188},\n numpages = {15},\n keywords = {NUMA, HPC, performance evaluation, parallel computing, space-filling curve, high-performance simulation, performance optimization, agent-based modeling, memory layout optimization, memory allocation, scalability},\n location = {Montreal, QC, Canada},\n series = {PPoPP \u002723},\n archivePrefix = \"arXiv\",\n eprint = \"2301.06984\",\n primaryClass = \"cs.DC\"\n}\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWe recommend specifying the exact BioDynaMo version used in the methods section.\nFor example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-tex-latex\"\u003e\u003cpre\u003eWe use BioDynaMo v1.04.5-e9db03a1 \u003cspan class=\"pl-k\"\u003e\\cite\u003c/span\u003e{\u003cspan class=\"pl-c1\"\u003ebreitwieser_biodynamo_2022\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003ebreitwieser_biodynamo_2023\u003c/span\u003e} \nfor all simulations in this article. \u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe version can be obtained by executing \u003ccode\u003ebiodynamo --version\u003c/code\u003e and can be interpreted as follows: \u003ccode\u003evMAJOR.MINOR.PATCH-SHA\u003c/code\u003e\n\u003ccode\u003eSHA\u003c/code\u003e is the git commit hash that can be used to check out the exact BioDynaMo version with \u003ccode\u003egit checkout SHA\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eBioDynaMo is \u003ca href=\"./LICENSE\"\u003eApache 2.0 licensed\u003c/a\u003e.\u003c/p\u003e\n", + "stargazers_count": 104, + "subscribers_count": 14, + "topics": [ + "simulation", + "high-performance", + "neuroscience", + "modular-design", + "agent-based", + "agent-based-modelling", + "agent-based-framework", + "biology", + "epidemiology", + "cancer", + "large-scale", + "parallel" + ], + "updated_at": 1733548383.0 + }, { "data_format": 2, "description": "This repository containts materials for End-to-End AI for Science", @@ -42207,6 +42235,23 @@ var data = "topics": [], "updated_at": 1732793549.0 }, + { + "data_format": 2, + "description": "A deep learning python package for neuroimaging data. Made by:", + "filenames": [ + "deepneuro/pipelines/Skull_Stripping/Singularity.deepneuro_skullstripping", + "deepneuro/pipelines/Segment_GBM/Singularity.deepneuro_segment_gbm", + "deepneuro/pipelines/Ischemic_Stroke/Singularity.deepneuro_segment_ischemic_stroke", + "deepneuro/pipelines/Segment_Brain_Mets/Singularity.deepneuro_segment_mets" + ], + "full_name": "QTIM-Lab/DeepNeuro", + "latest_release": null, + "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./package_resources/logos/DeepNeuro_alt.PNG?raw=true\"\u003e\u003cimg src=\"./package_resources/logos/DeepNeuro_alt.PNG?raw=true\" alt=\"Alt text\" title=\"DeepNeuro\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/QTIM-Lab/DeepNeuro\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/12319d58eb5556899c6d69bffb294bbaef54a8ff509d4196c53f000d141d619f/68747470733a2f2f7472617669732d63692e6f72672f5154494d2d4c61622f446565704e6575726f2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/QTIM-Lab/DeepNeuro.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDeepNeuro\u003c/h1\u003e\u003ca id=\"user-content-deepneuro\" class=\"anchor\" aria-label=\"Permalink: DeepNeuro\" href=\"#deepneuro\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA deep learning python package for neuroimaging data. Focused on validated command-line tools you can use today. Created by the Quantitative Tumor Imaging Lab at the Martinos Center (Harvard-MIT Program in Health, Sciences, and Technology / Massachusetts General Hospital).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#about\"\u003e\u2753 About\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003e\ud83d\udcbe Installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#tutorials\"\u003e\ud83c\udf93 Tutorials\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#modules\"\u003e\ud83c\udf81 Modules\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003e\ud83d\udcac Contact\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003e\ud83d\udce3 Citation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#acknowledgements\"\u003e\ud83d\udc9b Acknowledgements\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDeepNeuro is an open-source toolset of deep learning applications for neuroimaging. We have several goals for this package:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eProvide easy-to-use command line tools for neuroimaging using deep learning.\u003c/li\u003e\n\u003cli\u003eCreate Docker containers for each tool and all out-of-package pre-processing steps, so they can each can be run without having install prerequisite libraries.\u003c/li\u003e\n\u003cli\u003eProvide freely available deep learning models trained on a wealth of neuroimaging data.\u003c/li\u003e\n\u003cli\u003eProvide training scripts and links to publically-available data to replicate the results of DeepNeuro\u0027s models.\u003c/li\u003e\n\u003cli\u003eProvide implementations of popular models for medical imaging data, and pre-processed datasets for educational purposes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis package is under active development, but we encourage users to both try the modules with pre-trained modules highlighted below, and try their hand at making their own DeepNeuro modules using the tutorials below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Docker from Docker\u0027s website here: \u003ca href=\"https://www.docker.com/get-started\" rel=\"nofollow\"\u003ehttps://www.docker.com/get-started\u003c/a\u003e. Follow instructions on that link to get Docker set up properly on your workstation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the Docker Engine Utility for NVIDIA GPUs, AKA nvidia-docker. You can find installation instructions at their Github page, here: \u003ca href=\"https://github.com/NVIDIA/nvidia-docker\"\u003ehttps://github.com/NVIDIA/nvidia-docker\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePull the DeepNeuro Docker container from \u003ca href=\"https://hub.docker.com/r/qtimlab/deepneuro_segment_gbm/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/qtimlab/deepneuro_segment_gbm/\u003c/a\u003e. Use the command \"docker pull qtimlab/deepneuro\"\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf you want to run DeepNeuro outside of a Docker container, you can install the DeepNeuro Python package locally using the pip package manager. On the command line, run \u003ccode\u003epip install deepneuro\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorials\u003c/h2\u003e\u003ca id=\"user-content-tutorials\" class=\"anchor\" aria-label=\"Permalink: Tutorials\" href=\"#tutorials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://colab.research.google.com/github/QTIM-Lab/DeepNeuro/blob/master/notebooks/Preprocess_and_Augment.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"./notebooks/resources/train_preprocess_icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://colab.research.google.com/github/QTIM-Lab/DeepNeuro/blob/master/notebooks/Train_Model.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"./notebooks/resources/train_model_icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://colab.research.google.com/github/QTIM-Lab/DeepNeuro/blob/master/notebooks/Run_Inference.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"./notebooks/resources/model_inference_icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModules\u003c/h2\u003e\u003ca id=\"user-content-modules\" class=\"anchor\" aria-label=\"Permalink: Modules\" href=\"#modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/QTIM-Lab/DeepNeuro/tree/master/deepneuro/pipelines/Segment_GBM\"\u003e\n\u003cimg src=\"./deepneuro/pipelines/Segment_GBM/resources/icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/QTIM-Lab/DeepNeuro/tree/master/deepneuro/pipelines/Skull_Stripping\"\u003e\n\u003cimg src=\"./deepneuro/pipelines/Skull_Stripping/resources/icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/QTIM-Lab/DeepNeuro/tree/master/deepneuro/pipelines/Segment_Brain_Mets\"\u003e\n\u003cimg src=\"./deepneuro/pipelines/Segment_Brain_Mets/resources/icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/QTIM-Lab/DeepNeuro/tree/master/deepneuro/pipelines/Ischemic_Stroke\"\u003e\n\u003cimg src=\"./deepneuro/pipelines/Ischemic_Stroke/resources/icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use DeepNeuro in your published work, please cite:\u003c/p\u003e\n\u003cp\u003eBeers, A., Brown, J., Chang, K., Hoebel, K., Patel, J., Ly, K. Ina, Tolaney, S.M., Brastianos, P., Rosen, B., Gerstner, E., and Kalpathy-Cramer, J. (2020). \u003ca href=\"https://link.springer.com/article/10.1007/s12021-020-09477-5\" rel=\"nofollow\"\u003eDeepNeuro: an open-source deep learning toolbox for neuroimaging\u003c/a\u003e. Neuroinformatics. DOI: 10.1007/s12021-020-09477-5. PMID: 32578020\u003c/p\u003e\n\u003cp\u003eIf you use the MRI skull-stripping or glioblastoma segmentation modules, please cite:\u003c/p\u003e\n\u003cp\u003eChang, K., Beers, A.L., Bai, H.X., Brown, J.M., Ly, K.I., Li, X., Senders, J.T., Kavouridis, V.K., Boaro, A., Su, C., Bi, W.L., Rapalino, O., Liao, W., Shen, Q., Zhou, H., Xiao, B., Wang, Y., Zhang, P.J., Pinho, M.C., Wen, P.Y., Batchelor, T.T., Boxerman, J.L., Arnaout, O., Rosen, B.R., Gerstner, E.R., Yang, L., Huang, R.Y., and Kalpathy-Cramer, J., 2019. \u003ca href=\"https://academic.oup.com/neuro-oncology/advance-article/doi/10.1093/neuonc/noz106/5514498?searchresult=1\" rel=\"nofollow\"\u003eAutomatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement\u003c/a\u003e. Neuro-Oncology. DOI: 10.1093/neuonc/noz106. PMID: 31190077\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDeepNeuro is under active development, and you may run into errors or want additional features. Send any questions or requests for methods to \u003ca href=\"mailto:qtimlab@gmail.com\"\u003eqtimlab@gmail.com\u003c/a\u003e. You can also submit a Github issue if you run into a bug.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Center for Clinical Data Science at Massachusetts General Hospital and the Brigham and Woman\u0027s Hospital provided technical and hardware support for the development of DeepNeuro, including access to graphics processing units. The DeepNeuro project is also indebted to the following \u003ca href=\"https://github.com/ellisdg/3DUnetCNN\"\u003eGithub repository\u003c/a\u003e for the 3D UNet by user ellisdg, which formed the original kernel for much of its code in early stages. Long live open source deep learning :)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software package and the deep learning models within are intended for research purposes only and have not yet been validated for clinical use.\u003c/p\u003e\n", + "stargazers_count": 125, + "subscribers_count": 14, + "topics": [], + "updated_at": 1731450305.0 + }, { "data_format": 2, "description": "A CF-compliant Earth Science data analysis library", @@ -42232,23 +42277,6 @@ var data = ], "updated_at": 1731592368.0 }, - { - "data_format": 2, - "description": "A deep learning python package for neuroimaging data. Made by:", - "filenames": [ - "deepneuro/pipelines/Skull_Stripping/Singularity.deepneuro_skullstripping", - "deepneuro/pipelines/Segment_GBM/Singularity.deepneuro_segment_gbm", - "deepneuro/pipelines/Ischemic_Stroke/Singularity.deepneuro_segment_ischemic_stroke", - "deepneuro/pipelines/Segment_Brain_Mets/Singularity.deepneuro_segment_mets" - ], - "full_name": "QTIM-Lab/DeepNeuro", - "latest_release": null, - "readme": "\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"./package_resources/logos/DeepNeuro_alt.PNG?raw=true\"\u003e\u003cimg src=\"./package_resources/logos/DeepNeuro_alt.PNG?raw=true\" alt=\"Alt text\" title=\"DeepNeuro\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.org/QTIM-Lab/DeepNeuro\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/12319d58eb5556899c6d69bffb294bbaef54a8ff509d4196c53f000d141d619f/68747470733a2f2f7472617669732d63692e6f72672f5154494d2d4c61622f446565704e6575726f2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.org/QTIM-Lab/DeepNeuro.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eDeepNeuro\u003c/h1\u003e\u003ca id=\"user-content-deepneuro\" class=\"anchor\" aria-label=\"Permalink: DeepNeuro\" href=\"#deepneuro\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA deep learning python package for neuroimaging data. Focused on validated command-line tools you can use today. Created by the Quantitative Tumor Imaging Lab at the Martinos Center (Harvard-MIT Program in Health, Sciences, and Technology / Massachusetts General Hospital).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#about\"\u003e\u2753 About\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003e\ud83d\udcbe Installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#tutorials\"\u003e\ud83c\udf93 Tutorials\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#modules\"\u003e\ud83c\udf81 Modules\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contact\"\u003e\ud83d\udcac Contact\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003e\ud83d\udce3 Citation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#acknowledgements\"\u003e\ud83d\udc9b Acknowledgements\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAbout\u003c/h2\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDeepNeuro is an open-source toolset of deep learning applications for neuroimaging. We have several goals for this package:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eProvide easy-to-use command line tools for neuroimaging using deep learning.\u003c/li\u003e\n\u003cli\u003eCreate Docker containers for each tool and all out-of-package pre-processing steps, so they can each can be run without having install prerequisite libraries.\u003c/li\u003e\n\u003cli\u003eProvide freely available deep learning models trained on a wealth of neuroimaging data.\u003c/li\u003e\n\u003cli\u003eProvide training scripts and links to publically-available data to replicate the results of DeepNeuro\u0027s models.\u003c/li\u003e\n\u003cli\u003eProvide implementations of popular models for medical imaging data, and pre-processed datasets for educational purposes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis package is under active development, but we encourage users to both try the modules with pre-trained modules highlighted below, and try their hand at making their own DeepNeuro modules using the tutorials below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall Docker from Docker\u0027s website here: \u003ca href=\"https://www.docker.com/get-started\" rel=\"nofollow\"\u003ehttps://www.docker.com/get-started\u003c/a\u003e. Follow instructions on that link to get Docker set up properly on your workstation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInstall the Docker Engine Utility for NVIDIA GPUs, AKA nvidia-docker. You can find installation instructions at their Github page, here: \u003ca href=\"https://github.com/NVIDIA/nvidia-docker\"\u003ehttps://github.com/NVIDIA/nvidia-docker\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePull the DeepNeuro Docker container from \u003ca href=\"https://hub.docker.com/r/qtimlab/deepneuro_segment_gbm/\" rel=\"nofollow\"\u003ehttps://hub.docker.com/r/qtimlab/deepneuro_segment_gbm/\u003c/a\u003e. Use the command \"docker pull qtimlab/deepneuro\"\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf you want to run DeepNeuro outside of a Docker container, you can install the DeepNeuro Python package locally using the pip package manager. On the command line, run \u003ccode\u003epip install deepneuro\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTutorials\u003c/h2\u003e\u003ca id=\"user-content-tutorials\" class=\"anchor\" aria-label=\"Permalink: Tutorials\" href=\"#tutorials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://colab.research.google.com/github/QTIM-Lab/DeepNeuro/blob/master/notebooks/Preprocess_and_Augment.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"./notebooks/resources/train_preprocess_icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://colab.research.google.com/github/QTIM-Lab/DeepNeuro/blob/master/notebooks/Train_Model.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"./notebooks/resources/train_model_icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://colab.research.google.com/github/QTIM-Lab/DeepNeuro/blob/master/notebooks/Run_Inference.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"./notebooks/resources/model_inference_icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eModules\u003c/h2\u003e\u003ca id=\"user-content-modules\" class=\"anchor\" aria-label=\"Permalink: Modules\" href=\"#modules\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/QTIM-Lab/DeepNeuro/tree/master/deepneuro/pipelines/Segment_GBM\"\u003e\n\u003cimg src=\"./deepneuro/pipelines/Segment_GBM/resources/icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/QTIM-Lab/DeepNeuro/tree/master/deepneuro/pipelines/Skull_Stripping\"\u003e\n\u003cimg src=\"./deepneuro/pipelines/Skull_Stripping/resources/icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/QTIM-Lab/DeepNeuro/tree/master/deepneuro/pipelines/Segment_Brain_Mets\"\u003e\n\u003cimg src=\"./deepneuro/pipelines/Segment_Brain_Mets/resources/icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/QTIM-Lab/DeepNeuro/tree/master/deepneuro/pipelines/Ischemic_Stroke\"\u003e\n\u003cimg src=\"./deepneuro/pipelines/Ischemic_Stroke/resources/icon.png?raw=true\" width=\"684\" alt=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf you use DeepNeuro in your published work, please cite:\u003c/p\u003e\n\u003cp\u003eBeers, A., Brown, J., Chang, K., Hoebel, K., Patel, J., Ly, K. Ina, Tolaney, S.M., Brastianos, P., Rosen, B., Gerstner, E., and Kalpathy-Cramer, J. (2020). \u003ca href=\"https://link.springer.com/article/10.1007/s12021-020-09477-5\" rel=\"nofollow\"\u003eDeepNeuro: an open-source deep learning toolbox for neuroimaging\u003c/a\u003e. Neuroinformatics. DOI: 10.1007/s12021-020-09477-5. PMID: 32578020\u003c/p\u003e\n\u003cp\u003eIf you use the MRI skull-stripping or glioblastoma segmentation modules, please cite:\u003c/p\u003e\n\u003cp\u003eChang, K., Beers, A.L., Bai, H.X., Brown, J.M., Ly, K.I., Li, X., Senders, J.T., Kavouridis, V.K., Boaro, A., Su, C., Bi, W.L., Rapalino, O., Liao, W., Shen, Q., Zhou, H., Xiao, B., Wang, Y., Zhang, P.J., Pinho, M.C., Wen, P.Y., Batchelor, T.T., Boxerman, J.L., Arnaout, O., Rosen, B.R., Gerstner, E.R., Yang, L., Huang, R.Y., and Kalpathy-Cramer, J., 2019. \u003ca href=\"https://academic.oup.com/neuro-oncology/advance-article/doi/10.1093/neuonc/noz106/5514498?searchresult=1\" rel=\"nofollow\"\u003eAutomatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement\u003c/a\u003e. Neuro-Oncology. DOI: 10.1093/neuonc/noz106. PMID: 31190077\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDeepNeuro is under active development, and you may run into errors or want additional features. Send any questions or requests for methods to \u003ca href=\"mailto:qtimlab@gmail.com\"\u003eqtimlab@gmail.com\u003c/a\u003e. You can also submit a Github issue if you run into a bug.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe Center for Clinical Data Science at Massachusetts General Hospital and the Brigham and Woman\u0027s Hospital provided technical and hardware support for the development of DeepNeuro, including access to graphics processing units. The DeepNeuro project is also indebted to the following \u003ca href=\"https://github.com/ellisdg/3DUnetCNN\"\u003eGithub repository\u003c/a\u003e for the 3D UNet by user ellisdg, which formed the original kernel for much of its code in early stages. Long live open source deep learning :)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThis software package and the deep learning models within are intended for research purposes only and have not yet been validated for clinical use.\u003c/p\u003e\n", - "stargazers_count": 125, - "subscribers_count": 14, - "topics": [], - "updated_at": 1731450305.0 - }, { "data_format": 2, "description": "Training pipelines for Firefox Translations neural machine translation models", @@ -42296,33 +42324,6 @@ var data = ], "updated_at": 1705658745.0 }, - { - "data_format": 2, - "description": ":floppy_disk: C++ \u0026 Python API for Scientific I/O", - "filenames": [ - "Singularity" - 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src=\"https://camo.githubusercontent.com/e4b8098329c41cd66986174e9faf37b0539226d70164d16a7171349d2d964e42/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6f70656e706d642d6170692f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/openpmd-api/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/openPMD/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/490fb264bdb35bdf19440e9cc7d1da41f3da133156f6539961692812da5f5af4/68747470733a2f2f7472617669732d63692e636f6d2f6f70656e504d442f6f70656e504d442d6170692e7376673f6272616e63683d646576\" alt=\"Linux/OSX Build Status dev\" data-canonical-src=\"https://travis-ci.com/openPMD/openPMD-api.svg?branch=dev\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ci.appveyor.com/project/ax3l/openpmd-api/branch/dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d5b914ab0955b82622790f9da53bd4278625895c4c4262d8a23c9e808743819e/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f78393571346e36323070716b306530742f6272616e63682f6465763f7376673d74727565\" alt=\"Windows Build Status dev\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/x95q4n620pqk0e0t/branch/dev?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/openPMD/openPMD-api/actions?query=workflow%3Awheels\"\u003e\u003cimg src=\"https://github.com/openPMD/openPMD-api/workflows/wheels/badge.svg?branch=wheels\u0026amp;event=push\" alt=\"PyPI Wheel Release\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://dev.azure.com/axelhuebl/openPMD-api/_build/latest?definitionId=1\u0026amp;branchName=azure_install\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/908f0e30f8b3ac85f9f6358a95b124d62f6b88c80334cc3699e138b6dc8bc4e8/68747470733a2f2f6465762e617a7572652e636f6d2f6178656c687565626c2f6f70656e504d442d6170692f5f617069732f6275696c642f7374617475732f6f70656e504d442e6f70656e504d442d6170693f6272616e63684e616d653d617a7572655f696e7374616c6c266c6162656c3d6e696768746c792532307061636b61676573\" alt=\"Nightly Packages Status\" data-canonical-src=\"https://dev.azure.com/axelhuebl/openPMD-api/_apis/build/status/openPMD.openPMD-api?branchName=azure_install\u0026amp;label=nightly%20packages\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://scan.coverity.com/projects/openpmd-openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9dad0ea91ecfbb199146489c9a9ae245caf07e43538209f129770248707d3d06/68747470733a2f2f7363616e2e636f7665726974792e636f6d2f70726f6a656374732f31373630322f62616467652e737667\" alt=\"Coverity Scan Build Status\" data-canonical-src=\"https://scan.coverity.com/projects/17602/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eopenPMD is an open meta-data schema that provides meaning and self-description for data sets in science and engineering.\nSee \u003ca href=\"https://github.com/openPMD/openPMD-standard\"\u003ethe openPMD standard\u003c/a\u003e for details of this schema.\u003c/p\u003e\n\u003cp\u003eThis library provides a reference API for openPMD data handling.\nSince openPMD is a schema (or markup) on top of portable, hierarchical file formats, this library implements various backends such as HDF5, ADIOS2 and JSON.\nWriting \u0026amp; reading through those backends and their associated files are supported for serial and \u003ca href=\"https://www.mpi-forum.org/docs/\" rel=\"nofollow\"\u003eMPI-parallel\u003c/a\u003e workflows.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eC++\u003c/h3\u003e\u003ca id=\"user-content-c\" class=\"anchor\" aria-label=\"Permalink: C++\" href=\"#c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://isocpp.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0ff407625ba02420a0a7140b8c72db8547c6153501e5e5d58641f7afc2533723/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c616e67756167652d4325324225324231372d79656c6c6f77677265656e\" alt=\"C++17\" title=\"C++17 API\" data-canonical-src=\"https://img.shields.io/badge/language-C%2B%2B17-yellowgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ad313c29546a0ea1b644d34f3369cb4e08ae406244bb695a6251e174d725b67f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70686173652d626574612d79656c6c6f77677265656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad313c29546a0ea1b644d34f3369cb4e08ae406244bb695a6251e174d725b67f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70686173652d626574612d79656c6c6f77677265656e\" alt=\"C++17 API: Beta\" title=\"Status: Beta\" data-canonical-src=\"https://img.shields.io/badge/phase-beta-yellowgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++\"\u003e\u003cpre\u003e#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003eopenPMD/openPMD.hpp\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003eiostream\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e ...\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e s = openPMD::Series(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esamples/git-sample/data%T.h5\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, openPMD::Access::READ_ONLY);\n\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u0026amp; [step, it] : s.iterations ) {\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eIteration: \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; step \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e;\n\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u0026amp; [name, mesh] : it.\u003cspan class=\"pl-smi\"\u003emeshes\u003c/span\u003e ) {\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e Mesh \u0027\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; name \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0027 attributes:\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e;\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e\u0026amp; val : mesh.\u003cspan class=\"pl-c1\"\u003eattributes\u003c/span\u003e() )\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; val \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e;\n }\n\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u0026amp; [name, species] : it.\u003cspan class=\"pl-smi\"\u003eparticles\u003c/span\u003e ) {\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e Particle species \u0027\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; name \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0027 attributes:\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e;\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e\u0026amp; val : species.\u003cspan class=\"pl-c1\"\u003eattributes\u003c/span\u003e() )\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; val \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e;\n }\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython\u003c/h3\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aec38eb6cf1bc5def88ff14888758ca17ada302ff6ba39c8df0c284a2b552548/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c616e67756167652d507974686f6e332d79656c6c6f77677265656e\" alt=\"Python3\" title=\"Python3 API\" data-canonical-src=\"https://img.shields.io/badge/language-Python3-yellowgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ad313c29546a0ea1b644d34f3369cb4e08ae406244bb695a6251e174d725b67f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70686173652d626574612d79656c6c6f77677265656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad313c29546a0ea1b644d34f3369cb4e08ae406244bb695a6251e174d725b67f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70686173652d626574612d79656c6c6f77677265656e\" alt=\"Python3 API: Beta\" title=\"Status: Beta\" data-canonical-src=\"https://img.shields.io/badge/phase-beta-yellowgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eopenpmd_api\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eio\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# ...\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eio\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eSeries\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"samples/git-sample/data%T.h5\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eio\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eAccess\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eread_only\u003c/span\u003e)\n\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ek_i\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eiterations\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"Iteration: {0}\"\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ek_i\u003c/span\u003e))\n\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ek_m\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003em\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emeshes\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\" Mesh \u0027{0}\u0027 attributes:\"\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ek_m\u003c/span\u003e))\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003em\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eattributes\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\" {0}\"\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e))\n\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ek_p\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ep\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eparticles\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\" Particle species \u0027{0}\u0027 attributes:\"\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ek_p\u003c/span\u003e))\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ep\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eattributes\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\" {0}\"\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMore!\u003c/h3\u003e\u003ca id=\"user-content-more\" class=\"anchor\" aria-label=\"Permalink: More!\" href=\"#more\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurious?\nOur manual shows full \u003ca href=\"https://openpmd-api.readthedocs.io/en/latest/usage/firstwrite.html\" rel=\"nofollow\"\u003eread \u0026amp; write examples\u003c/a\u003e, both serial and MPI-parallel!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequired:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCMake 3.22.0+\u003c/li\u003e\n\u003cli\u003eC++17 capable compiler, e.g., g++ 7+, clang 7+, MSVC 19.15+, icpc 19+, icpx\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eShipped internally (downloaded by CMake unless \u003ccode\u003eopenPMD_SUPERBUILD=OFF\u003c/code\u003e is set):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/catchorg/Catch2\"\u003eCatch2\u003c/a\u003e 2.13.10+ (\u003ca href=\"https://github.com/catchorg/Catch2/blob/master/LICENSE.txt\"\u003eBSL-1.0\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pybind/pybind11\"\u003epybind11\u003c/a\u003e 2.13.0+ (\u003ca href=\"https://github.com/pybind/pybind11/blob/master/LICENSE\"\u003enew BSD\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/nlohmann/json\"\u003eNLohmann-JSON\u003c/a\u003e 3.9.1+ (\u003ca href=\"https://github.com/nlohmann/json/blob/develop/LICENSE.MIT\"\u003eMIT\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ToruNiina/toml11\"\u003etoml11\u003c/a\u003e 3.7.1+ (\u003ca href=\"https://github.com/ToruNiina/toml11/blob/master/LICENSE\"\u003eMIT\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eI/O backends:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://en.wikipedia.org/wiki/JSON\" rel=\"nofollow\"\u003eJSON\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://support.hdfgroup.org/HDF5\" rel=\"nofollow\"\u003eHDF5\u003c/a\u003e 1.8.13+ (optional)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ornladios/ADIOS2\"\u003eADIOS2\u003c/a\u003e 2.7.0+ (optional)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewhile those can be built either with or without:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMPI 2.1+, e.g. OpenMPI 1.6.5+ or MPICH2\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional language bindings:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython:\n\u003cul\u003e\n\u003cli\u003ePython 3.8 - 3.13\u003c/li\u003e\n\u003cli\u003epybind11 2.13.0+\u003c/li\u003e\n\u003cli\u003enumpy 1.15+\u003c/li\u003e\n\u003cli\u003empi4py 2.1+ (optional, for MPI)\u003c/li\u003e\n\u003cli\u003epandas 1.0+ (optional, for dataframes)\u003c/li\u003e\n\u003cli\u003edask 2021+ (optional, for dask dataframes)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCUDA C++ (optional, currently used only in tests)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/570e394f6d7f2d921755bcc40d7089eb4ff00081d33c591e780ae132b0d20db0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f737061636b2e696f2d6f70656e706d642d2d6170692d627269676874677265656e\" alt=\"Spack Package\" data-canonical-src=\"https://img.shields.io/badge/spack.io-openpmd--api-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6cd332b7b2264ec786b56a1024949bd103946f78db75c461f427e0ffad2b4f69/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e64612e696f2d6f70656e706d642d2d6170692d627269676874677265656e\" alt=\"Conda Package\" data-canonical-src=\"https://img.shields.io/badge/conda.io-openpmd--api-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/openPMD/homebrew-openPMD\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb7b1fc3bd94ae34834f512ce11f991084f3945094a8458d152a9e8f8426f353/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f627265772e73682d6f70656e706d642d2d6170692d627269676874677265656e\" alt=\"Brew Package\" data-canonical-src=\"https://img.shields.io/badge/brew.sh-openpmd--api-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fea34f845f3f0da6dcf49275d104c5d1215beccc98adf0486e7b04190ee3f2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707970692e6f72672d6f70656e706d642d2d6170692d627269676874677265656e\" alt=\"PyPI Package\" data-canonical-src=\"https://img.shields.io/badge/pypi.org-openpmd--api-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f501404192a503a406de97a5067605fbf37a8412c706d11cf90fe66c0009a31a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f66726f6d5f736f757263652d434d616b652d627269676874677265656e\" alt=\"From Source\" data-canonical-src=\"https://img.shields.io/badge/from_source-CMake-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOur community loves to help each other.\nPlease \u003ca href=\"https://github.com/openPMD/openPMD-api/issues/new?labels=install\u0026amp;template=install_problem.md\"\u003ereport installation problems\u003c/a\u003e in case you should get stuck.\u003c/p\u003e\n\u003cp\u003eChoose \u003cem\u003eone\u003c/em\u003e of the install methods below to get started:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://spack.io\" rel=\"nofollow\"\u003eSpack\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-spack\" class=\"anchor\" aria-label=\"Permalink: Spack\" href=\"#spack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ab09a38910c62b1ce2e2d9636b4468cacb0bc6e9f82d30a1a3f7f7d71741510a/68747470733a2f2f696d672e736869656c64732e696f2f737061636b2f762f6f70656e706d642d617069\" alt=\"Spack Version\" data-canonical-src=\"https://img.shields.io/spack/v/openpmd-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://spack.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f975eca3ac9c73e5bb6ae43f488cf35c73ab3e4a636cf59856323ff8b4e100cf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f73782532302d626c7565\" alt=\"Spack Platforms\" data-canonical-src=\"https://img.shields.io/badge/platforms-linux%20%7C%20osx%20-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80cd0c6e5264f590c79f51a1f2a346b8af64b9cddedbc6aa0fc2d17c37a79a9b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d6465736b746f705f253238432532422532422c5f70792532392c5f646576656c6f706d656e742c5f4850432d627269676874677265656e\" alt=\"Spack Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-desktop_%28C%2B%2B,_py%29,_development,_HPC-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: +python -adios2 -hdf5 -mpi\u003c/span\u003e\nspack install openpmd-api\nspack load openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://conda.io\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/299268c2c9940187a59751abe0de2ce596ff28e8bd0a514247785d427510a021/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f766e2f636f6e64612d666f7267652f6f70656e706d642d617069\" alt=\"Conda Version\" data-canonical-src=\"https://img.shields.io/conda/vn/conda-forge/openpmd-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9d9ebb8b7e6322fcd166bfae60f71af84c736366c66eb3530c0dba93f188b21a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f737825323025374325323077696e2d626c7565\" alt=\"Conda Platforms\" data-canonical-src=\"https://img.shields.io/badge/platforms-linux%20%7C%20osx%20%7C%20win-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11d69a1db5d4aa49394b2650e53db0344313a2e0cd1dac5c36dd83e2d8fc0925/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d6465736b746f705f25323870792532392d627269676874677265656e\" alt=\"Conda Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-desktop_%28py%29-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5bfef69ca60c5c6b8a1094bd5ef056625c336337ec043cfc0cca3c24d657d375/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f646e2f636f6e64612d666f7267652f6f70656e706d642d617069\" alt=\"Conda Downloads\" data-canonical-src=\"https://img.shields.io/conda/dn/conda-forge/openpmd-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: OpenMPI support =*=mpi_openmpi*\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: MPICH support =*=mpi_mpich*\u003c/span\u003e\nconda create -n openpmd -c conda-forge openpmd-api\nconda activate openpmd\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://brew.sh\" rel=\"nofollow\"\u003eBrew\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-brew\" class=\"anchor\" aria-label=\"Permalink: Brew\" href=\"#brew\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/openPMD/homebrew-openPMD\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dd40671507b4ba3ebce204e1cfe6d92daa387ed33075ea6300a116456865eec8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f627265772d6c61746573745f76657273696f6e2d6f72616e6765\" alt=\"Brew Version\" data-canonical-src=\"https://img.shields.io/badge/brew-latest_version-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://docs.brew.sh/Homebrew-on-Linux\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f975eca3ac9c73e5bb6ae43f488cf35c73ab3e4a636cf59856323ff8b4e100cf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f73782532302d626c7565\" alt=\"Brew Platforms\" data-canonical-src=\"https://img.shields.io/badge/platforms-linux%20%7C%20osx%20-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://brew.sh\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b7ea9e1ffd8fbbc4684ccbf39cc3db9d7e7b0385ca3998f1d2691939a56b3906/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d6465736b746f705f253238432532422532422c5f70792532392d627269676874677265656e\" alt=\"Brew Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-desktop_%28C%2B%2B,_py%29-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew tap openpmd/openpmd\nbrew install openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://pypi.org\" rel=\"nofollow\"\u003ePyPI\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-pypi\" class=\"anchor\" aria-label=\"Permalink: PyPI\" href=\"#pypi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b4af06805977086b7b7f7785cff649c52d91d497d308edaf74f06a64366a3999/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6f70656e504d442d617069\" alt=\"PyPI Version\" data-canonical-src=\"https://img.shields.io/pypi/v/openPMD-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api/#files\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9d9ebb8b7e6322fcd166bfae60f71af84c736366c66eb3530c0dba93f188b21a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f737825323025374325323077696e2d626c7565\" alt=\"PyPI Platforms\" data-canonical-src=\"https://img.shields.io/badge/platforms-linux%20%7C%20osx%20%7C%20win-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11d69a1db5d4aa49394b2650e53db0344313a2e0cd1dac5c36dd83e2d8fc0925/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d6465736b746f705f25323870792532392d627269676874677265656e\" alt=\"PyPI Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-desktop_%28py%29-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/86d50b4ea99b20b0cc7c49fd4317f4972dc475416682bb2a0640d37ea025797e/68747470733a2f2f696d672e736869656c64732e696f2f707970692f666f726d61742f6f70656e504d442d617069\" alt=\"PyPI Format\" data-canonical-src=\"https://img.shields.io/pypi/format/openPMD-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0030fc55eab10086e5b4b1ad72be1cfcdf8403df2a9f5120765a2dd2c364ea6b/68747470733a2f2f696d672e736869656c64732e696f2f707970692f646d2f6f70656e504d442d617069\" alt=\"PyPI Downloads\" data-canonical-src=\"https://img.shields.io/pypi/dm/openPMD-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOn very old macOS versions (\u0026lt;10.9) or on exotic processor architectures, this install method \u003cem\u003ecompiles from source\u003c/em\u003e against the found installations of HDF5, ADIOS2, and/or MPI (in system paths, from other package managers, or loaded via a module system, ...).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e we need pip 19 or newer\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\npython3 -m pip install -U pip\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\npython3 -m pip install openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf MPI-support shall be enabled, we always have to recompile:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\npython3 -m pip install -U pip packaging setuptools wheel\npython3 -m pip install -U cmake\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\nopenPMD_USE_MPI=ON python3 -m pip install openpmd-api --no-binary openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor some exotic architectures and compilers, you might need to disable a compiler feature called \u003ca href=\"https://en.wikipedia.org/wiki/Interprocedural_optimization\" rel=\"nofollow\"\u003elink-time/interprocedural optimization\u003c/a\u003e if you encounter linking problems:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CMAKE_INTERPROCEDURAL_OPTIMIZATION=OFF\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\npython3 -m pip install openpmd-api --no-binary openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditional CMake options can be passed via individual environment variables, which need to be prefixed with \u003ccode\u003eopenPMD_CMAKE_\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom Source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From Source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7f9966b22cbf700d42e2110f2855f6d6a7d645bc8b786fdcae2019a1379d58d6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d646576656c6f706d656e742d627269676874677265656e\" alt=\"Source Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-development-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eopenPMD-api can also be built and installed from source using \u003ca href=\"https://cmake.org/\" rel=\"nofollow\"\u003eCMake\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/openPMD/openPMD-api.git\n\nmkdir openPMD-api-build\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e openPMD-api-build\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: for full tests, with unzip\u003c/span\u003e\n../openPMD-api/share/openPMD/download_samples.sh\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for own install prefix append:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -DCMAKE_INSTALL_PREFIX=$HOME/somepath\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for options append:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -DopenPMD_USE_...=...\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e e.g. for python support add:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -DopenPMD_USE_PYTHON=ON -DPython_EXECUTABLE=$(which python3)\u003c/span\u003e\ncmake ../openPMD-api\n\ncmake --build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional\u003c/span\u003e\nctest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e sudo might be required for system paths\u003c/span\u003e\ncmake --build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --target install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe following options can be added to the \u003ccode\u003ecmake\u003c/code\u003e call to control features.\nCMake controls options with prefixed \u003ccode\u003e-D\u003c/code\u003e, e.g. \u003ccode\u003e-DopenPMD_USE_MPI=OFF\u003c/code\u003e:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCMake Option\u003c/th\u003e\n\u003cth\u003eValues\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_MPI\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eAUTO\u003c/strong\u003e/ON/OFF\u003c/td\u003e\n\u003ctd\u003eParallel, Multi-Node I/O for clusters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_HDF5\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eAUTO\u003c/strong\u003e/ON/OFF\u003c/td\u003e\n\u003ctd\u003eHDF5 backend (\u003ccode\u003e.h5\u003c/code\u003e files)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_ADIOS2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eAUTO\u003c/strong\u003e/ON/OFF\u003c/td\u003e\n\u003ctd\u003eADIOS2 backend (\u003ccode\u003e.bp\u003c/code\u003e files in BP3, BP4 or higher)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_PYTHON\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eAUTO\u003c/strong\u003e/ON/OFF\u003c/td\u003e\n\u003ctd\u003eEnable Python bindings\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_INVASIVE_TESTS\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eON/\u003cstrong\u003eOFF\u003c/strong\u003e\n\u003c/td\u003e\n\u003ctd\u003eEnable unit tests that modify source code \u003csup\u003e1\u003c/sup\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_VERIFY\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eEnable internal VERIFY (assert) macro independent of build type \u003csup\u003e2\u003c/sup\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_INSTALL\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eAdd installation targets\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_INSTALL_RPATH\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eAdd RPATHs to installed binaries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ePython_EXECUTABLE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e(newest found)\u003c/td\u003e\n\u003ctd\u003ePath to Python executable\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e \u003cem\u003ee.g. changes C++ visibility keywords, breaks MSVC\u003c/em\u003e\n\u003csup\u003e2\u003c/sup\u003e \u003cem\u003ethis includes most pre-/post-condition checks, disabling without specific cause is highly discouraged\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, the following libraries are downloaded via \u003ca href=\"https://cmake.org/cmake/help/latest/module/FetchContent.html\" rel=\"nofollow\"\u003eFetchContent\u003c/a\u003e\nduring the configuration of the project or, if the corresponding \u003ccode\u003e\u0026lt;PACKAGENAME\u0026gt;_ROOT\u003c/code\u003e variable is provided, can be provided externally:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/catchorg/Catch2\"\u003eCatch2\u003c/a\u003e (2.13.10+)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pybind/pybind11\"\u003ePyBind11\u003c/a\u003e (2.13.0+)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/nlohmann/json\"\u003eNLohmann-JSON\u003c/a\u003e (3.9.1+)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ToruNiina/toml11\"\u003etoml11\u003c/a\u003e (3.7.1+)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default, this will build as a shared library (\u003ccode\u003elibopenPMD.[so|dylib|dll]\u003c/code\u003e) and installs also its headers.\nIn order to build a static library, append \u003ccode\u003e-DBUILD_SHARED_LIBS=OFF\u003c/code\u003e to the \u003ccode\u003ecmake\u003c/code\u003e command.\nYou can only build a static or a shared library at a time.\u003c/p\u003e\n\u003cp\u003eBy default, the \u003ccode\u003eRelease\u003c/code\u003e version is built.\nIn order to build with debug symbols, pass \u003ccode\u003e-DCMAKE_BUILD_TYPE=Debug\u003c/code\u003e to your \u003ccode\u003ecmake\u003c/code\u003e command.\u003c/p\u003e\n\u003cp\u003eBy default, tests, examples and command line tools are built.\nIn order to skip building those, pass \u003ccode\u003eOFF\u003c/code\u003e to these \u003ccode\u003ecmake\u003c/code\u003e options:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCMake Option\u003c/th\u003e\n\u003cth\u003eValues\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_BUILD_TESTING\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eBuild tests\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_BUILD_EXAMPLES\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eBuild examples\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_BUILD_CLI_TOOLS\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eBuild command-line tools\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_CUDA_EXAMPLES\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eON/\u003cstrong\u003eOFF\u003c/strong\u003e\n\u003c/td\u003e\n\u003ctd\u003eUse CUDA in examples\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinking to your project\u003c/h2\u003e\u003ca id=\"user-content-linking-to-your-project\" class=\"anchor\" aria-label=\"Permalink: Linking to your project\" href=\"#linking-to-your-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe install will contain header files and libraries in the path set with \u003ccode\u003e-DCMAKE_INSTALL_PREFIX\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCMake\u003c/h3\u003e\u003ca id=\"user-content-cmake\" class=\"anchor\" aria-label=\"Permalink: CMake\" href=\"#cmake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf your project is using CMake for its build, one can conveniently use our provided \u003ccode\u003eopenPMDConfig.cmake\u003c/code\u003e package, which is installed alongside the library.\u003c/p\u003e\n\u003cp\u003eFirst set the following environment hint if openPMD-api was \u003cem\u003enot\u003c/em\u003e installed in a system path:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: only needed if installed outside of system paths\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CMAKE_PREFIX_PATH=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/somepath:\u003cspan class=\"pl-smi\"\u003e$CMAKE_PREFIX_PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse the following lines in your project\u0027s \u003ccode\u003eCMakeLists.txt\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-cmake\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e supports: COMPONENTS MPI NOMPI HDF5 ADIOS2\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003efind_package\u003c/span\u003e(openPMD 0.16.0 \u003cspan class=\"pl-k\"\u003eCONFIG\u003c/span\u003e)\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e(openPMD_FOUND)\n \u003cspan class=\"pl-c1\"\u003etarget_link_libraries\u003c/span\u003e(YourTarget \u003cspan class=\"pl-k\"\u003ePRIVATE\u003c/span\u003e openPMD::openPMD)\n\u003cspan class=\"pl-k\"\u003eendif\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAlternatively\u003c/em\u003e, add the openPMD-api repository source directly to your project and use it via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-cmake\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003eadd_subdirectory\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"path/to/source/of/openPMD-api\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c1\"\u003etarget_link_libraries\u003c/span\u003e(YourTarget \u003cspan class=\"pl-k\"\u003ePRIVATE\u003c/span\u003e openPMD::openPMD)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor development workflows, you can even automatically download and build openPMD-api from within a depending CMake project.\nJust replace the \u003ccode\u003eadd_subdirectory\u003c/code\u003e call with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-cmake\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003einclude\u003c/span\u003e(FetchContent)\n\u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e(CMAKE_POLICY_DEFAULT_CMP0077 \u003cspan class=\"pl-k\"\u003eNEW\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e(openPMD_BUILD_CLI_TOOLS \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e(openPMD_BUILD_EXAMPLES \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e(openPMD_BUILD_TESTING \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e(openPMD_BUILD_SHARED_LIBS \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e precedence over BUILD_SHARED_LIBS if needed\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e(openPMD_INSTALL \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or instead use:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e set(openPMD_INSTALL ${BUILD_SHARED_LIBS}) # only install if used as a shared library\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003eset\u003c/span\u003e(openPMD_USE_PYTHON \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e)\nFetchContent_Declare(openPMD\n GIT_REPOSITORY \u003cspan class=\"pl-s\"\u003e\"https://github.com/openPMD/openPMD-api.git\"\u003c/span\u003e\n GIT_TAG \u003cspan class=\"pl-s\"\u003e\"0.16.0\"\u003c/span\u003e)\nFetchContent_MakeAvailable(openPMD)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eManually\u003c/h3\u003e\u003ca id=\"user-content-manually\" class=\"anchor\" aria-label=\"Permalink: Manually\" href=\"#manually\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf your (Linux/OSX) project is build by calling the compiler directly or uses a manually written \u003ccode\u003eMakefile\u003c/code\u003e, consider using our \u003ccode\u003eopenPMD.pc\u003c/code\u003e helper file for \u003ccode\u003epkg-config\u003c/code\u003e, which are installed alongside the library.\u003c/p\u003e\n\u003cp\u003eFirst set the following environment hint if openPMD-api was \u003cem\u003enot\u003c/em\u003e installed in a system path:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: only needed if installed outside of system paths\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PKG_CONFIG_PATH=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/somepath/lib/pkgconfig:\u003cspan class=\"pl-smi\"\u003e$PKG_CONFIG_PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditional linker and compiler flags for your project are available via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e switch to check if openPMD-api was build as static library\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (via BUILD_SHARED_LIBS=OFF) or as shared library (default)\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e [ \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epkg-config --variable=static openPMD\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etrue\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e ]\n\u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n pkg-config --libs --static openPMD\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -L/usr/local/lib -L/usr/lib/x86_64-linux-gnu/openmpi/lib -lopenPMD -pthread /usr/lib/libmpi.so -pthread /usr/lib/x86_64-linux-gnu/openmpi/lib/libmpi_cxx.so /usr/lib/libmpi.so /usr/lib/x86_64-linux-gnu/hdf5/openmpi/libhdf5.so /usr/lib/x86_64-linux-gnu/libsz.so /usr/lib/x86_64-linux-gnu/libz.so /usr/lib/x86_64-linux-gnu/libdl.so /usr/lib/x86_64-linux-gnu/libm.so -pthread /usr/lib/libmpi.so -pthread /usr/lib/x86_64-linux-gnu/openmpi/lib/libmpi_cxx.so /usr/lib/libmpi.so\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e\n pkg-config --libs openPMD\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -L${HOME}/somepath/lib -lopenPMD\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\n\npkg-config --cflags openPMD\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -I${HOME}/somepath/include\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthor Contributions\u003c/h2\u003e\u003ca id=\"user-content-author-contributions\" class=\"anchor\" aria-label=\"Permalink: Author Contributions\" href=\"#author-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eopenPMD-api is developed by many people.\nIt was initially started by the \u003ca href=\"https://hzdr.de/crp\" rel=\"nofollow\"\u003eComputational Radiation Physics Group\u003c/a\u003e at \u003ca href=\"https://www.hzdr.de/\" rel=\"nofollow\"\u003eHZDR\u003c/a\u003e as successor to \u003ca href=\"https://github.com/ComputationalRadiationPhysics/libSplash/\"\u003elibSplash\u003c/a\u003e, generalizing the \u003ca href=\"https://arxiv.org/abs/1706.00522\" rel=\"nofollow\"\u003esuccessful HDF5 \u0026amp; ADIOS1 implementations\u003c/a\u003e in \u003ca href=\"https://github.com/ComputationalRadiationPhysics/picongpu\"\u003ePIConGPU\u003c/a\u003e.\nThe following people and institutions \u003ca href=\"https://github.com/openPMD/openPMD-api/graphs/contributors\"\u003econtributed\u003c/a\u003e to openPMD-api:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ax3l\"\u003eAxel Huebl (LBNL, previously HZDR)\u003c/a\u003e:\nproject lead, releases, documentation, automated CI/CD, Python bindings, Dask, installation \u0026amp; packaging, prior reference implementations\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/franzpoeschel\"\u003eFranz Poeschel (CASUS)\u003c/a\u003e:\nJSON \u0026amp; ADIOS2 backend, data staging/streaming, reworked class design\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/C0nsultant\"\u003eFabian Koller (HZDR)\u003c/a\u003e:\ninitial library design and implementation with HDF5 \u0026amp; ADIOS1 backend\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/guj\"\u003eJunmin Gu (LBNL)\u003c/a\u003e:\nnon-collective parallel I/O fixes, ADIOS improvements, benchmarks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMaintained by the following research groups:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.casus.science/casus/team/\" rel=\"nofollow\"\u003eComputational Radiation Physics (CRD)\u003c/a\u003e at CASUS/HZDR, led by \u003ca href=\"https://github.com/bussmann\"\u003eMichael Bussmann\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://atap.lbl.gov/accelerator-modeling-program/\" rel=\"nofollow\"\u003eAccelerator Modeling Program (AMP)\u003c/a\u003e at LBNL, led by \u003ca href=\"https://github.com/jlvay\"\u003eJean-Luc Vay\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://crd.lbl.gov/divisions/scidata/sdm/\" rel=\"nofollow\"\u003eScientific Data Management (SDM)\u003c/a\u003e at LBNL, led by \u003ca href=\"https://github.com/john18\"\u003eKesheng (John) Wu\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFurther thanks go to improvements and contributions from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CFGrote\"\u003eCarsten Fortmann-Grote (EU XFEL GmbH, now MPI-EvolBio)\u003c/a\u003e:\ndraft of our Python unit tests\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/StanczakDominik\"\u003eDominik Sta\u0144czak (Warsaw University of Technology)\u003c/a\u003e:\ndocumentation improvements\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mingwandroid\"\u003eRay Donnelly (Anaconda, Inc.)\u003c/a\u003e:\nsupport on conda packaging and libc++ quirks\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/amundson\"\u003eJames Amundson (FNAL)\u003c/a\u003e:\ncompile fix for newer compilers\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/psychocoderHPC\"\u003eRen\u00e9 Widera (HZDR)\u003c/a\u003e:\ndesign improvements for initial API design\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/erikzenker\"\u003eErik Zenker (HZDR)\u003c/a\u003e:\ndesign improvements for initial API design\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sbastrakov\"\u003eSergei Bastrakov (HZDR)\u003c/a\u003e:\ndocumentation improvements (windows)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/RemiLehe\"\u003eR\u00e9mi Lehe (LBNL)\u003c/a\u003e:\npackage integration testing on macOS and Linux\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/LDAmorim\"\u003eL\u00edgia Diana Amorim (LBNL)\u003c/a\u003e:\npackage integration testing on macOS\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/KseniaBastrakova\"\u003eKseniia Bastrakova (HZDR)\u003c/a\u003e:\ncompatibility testing\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/PrometheusPi\"\u003eRichard Pausch (HZDR)\u003c/a\u003e:\ncompatibility testing, documentation improvements\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pordyna\"\u003ePawe\u0142 Ordyna (HZDR)\u003c/a\u003e:\nreport on NVCC warnings\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/dmitry-ganyushin\"\u003eDmitry Ganyushin (ORNL)\u003c/a\u003e:\nDask prototyping \u0026amp; ADIOS2 benchmarking\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jakirkham\"\u003eJohn Kirkham (NVIDIA)\u003c/a\u003e:\nDask guidance \u0026amp; reviews\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/eschnett\"\u003eErik Schnetter (PITP)\u003c/a\u003e:\nC++ API bug fixes\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jeanbez\"\u003eJean Luca Bez (LBNL)\u003c/a\u003e:\nHDF5 performance tuning\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/bernhardmgruber\"\u003eBernhard Manfred Gruber (CERN)\u003c/a\u003e:\nCMake fix for parallel HDF5\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/DerNils-git\"\u003eNils Schild (IPP)\u003c/a\u003e:\nCMake improvements for subprojects\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGrants\u003c/h3\u003e\u003ca id=\"user-content-grants\" class=\"anchor\" aria-label=\"Permalink: Grants\" href=\"#grants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe openPMD-api authors acknowledge support via the following programs.\nSupported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program.\nPreviously supported by the Consortium for Advanced Modeling of Particles Accelerators (CAMPA), funded by the U.S. DOE Office of Science under Contract No. DE-AC02-05CH11231.\nSupported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of two U.S. Department of Energy organizations (Office of Science and the National Nuclear Security Administration).\nThis project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 654220.\nThis work was partially funded by the Center of Advanced Systems Understanding (CASUS), which is financed by Germany\u0027s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament.\nSupported by the HElmholtz Laser Plasma Metadata Initiative (HELPMI) project (ZT-I-PF-3-066), funded by the \"Initiative and Networking Fund\" of the Helmholtz Association in the framework of the \"Helmholtz Metadata Collaboration\" project call 2022.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTransitive Contributions\u003c/h3\u003e\u003ca id=\"user-content-transitive-contributions\" class=\"anchor\" aria-label=\"Permalink: Transitive Contributions\" href=\"#transitive-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eopenPMD-api stands on the shoulders of giants and we are grateful for the following projects included as direct dependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ornladios/ADIOS2\"\u003eADIOS2\u003c/a\u003e by \u003ca href=\"https://csmd.ornl.gov/adios\" rel=\"nofollow\"\u003eS. Klasky, N. Podhorszki, W.F. Godoy (ORNL), team, collaborators\u003c/a\u003e and \u003ca href=\"https://github.com/ornladios/ADIOS2/graphs/contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/catchorg/Catch2\"\u003eCatch2\u003c/a\u003e by \u003ca href=\"https://github.com/philsquared\"\u003ePhil Nash\u003c/a\u003e, \u003ca href=\"https://github.com/horenmar\"\u003eMartin Ho\u0159e\u0148ovsk\u00fd\u003c/a\u003e and \u003ca href=\"https://github.com/catchorg/Catch2/graphs/contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHDF5 by \u003ca href=\"https://www.hdfgroup.org\" rel=\"nofollow\"\u003ethe HDF group\u003c/a\u003e and community\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/nlohmann/json\"\u003ejson\u003c/a\u003e by \u003ca href=\"https://github.com/nlohmann\"\u003eNiels Lohmann\u003c/a\u003e and \u003ca href=\"https://github.com/nlohmann/json/graphs/contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ToruNiina/toml11\"\u003etoml11\u003c/a\u003e by \u003ca href=\"https://github.com/ToruNiina\"\u003eToru Niina\u003c/a\u003e and \u003ca href=\"https://github.com/ToruNiina/toml11#Contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pybind/pybind11\"\u003epybind11\u003c/a\u003e by \u003ca href=\"https://github.com/wjakob\"\u003eWenzel Jakob (EPFL)\u003c/a\u003e and \u003ca href=\"https://github.com/pybind/pybind11/graphs/contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eall contributors to the evolution of modern C++ and early library preview developers, e.g. \u003ca href=\"https://github.com/mpark\"\u003eMichael Park (Facebook)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ethe \u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003eCMake build system\u003c/a\u003e and \u003ca href=\"https://github.com/Kitware/CMake/blob/master/Copyright.txt\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003epackaging support by the \u003ca href=\"https://conda-forge.org\" rel=\"nofollow\"\u003econda-forge\u003c/a\u003e, \u003ca href=\"https://pypi.org\" rel=\"nofollow\"\u003ePyPI\u003c/a\u003e and \u003ca href=\"https://spack.io\" rel=\"nofollow\"\u003eSpack\u003c/a\u003e communities, among others\u003c/li\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/openPMD/openPMD-standard\"\u003eopenPMD-standard\u003c/a\u003e by \u003ca href=\"https://github.com/ax3l\"\u003eAxel Huebl (HZDR, now LBNL)\u003c/a\u003e and \u003ca href=\"https://github.com/openPMD/openPMD-standard/blob/latest/AUTHORS.md\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", - "stargazers_count": 138, - "subscribers_count": 11, - "topics": [ - "openpmd", - "openscience", - "hdf5", - "adios", - "mpi", - "hpc", - "research", - "file-handling", - "python3", - "opendata", - "metadata", - "cpp17" - ], - "updated_at": 1728680719.0 - }, { "data_format": 2, "description": "Collection of hyperparameter optimization benchmark problems", @@ -42358,6 +42359,33 @@ var data = ], "updated_at": 1731737157.0 }, + { + "data_format": 2, + "description": ":floppy_disk: C++ \u0026 Python API for Scientific I/O", + "filenames": [ + "Singularity" + ], + "full_name": "openPMD/openPMD-api", + "latest_release": "0.16.0", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eC++ \u0026amp; Python API for Scientific I/O with openPMD\u003c/h1\u003e\u003ca id=\"user-content-c--python-api-for-scientific-io-with-openpmd\" class=\"anchor\" aria-label=\"Permalink: C++ \u0026amp; Python API for Scientific I/O with openPMD\" href=\"#c--python-api-for-scientific-io-with-openpmd\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/openPMD/openPMD-standard/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/856e360c940f46d9bac8e21908b2217503055f3636de47e7060c086e15747cf6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6f70656e504d442d312e302e302d2d312e312e302d626c7565\" alt=\"Supported openPMD Standard\" data-canonical-src=\"https://img.shields.io/badge/openPMD-1.0.0--1.1.0-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.openpmd.org/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6ffd0967bd6146f90bb03752b5a2adaa759e4388862cbc65a3ca900ab5b37925/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4150492d446f787967656e2d626c7565\" alt=\"Doxygen\" data-canonical-src=\"https://img.shields.io/badge/API-Doxygen-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://gitter.im/openPMD/API\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3c1f88ad219e6eca329d985fdd950b8cecdb7d76aade916bb9f09ddf93e88c25/68747470733a2f2f696d672e736869656c64732e696f2f6769747465722f726f6f6d2f6f70656e504d442f415049\" alt=\"Gitter chat\" data-canonical-src=\"https://img.shields.io/gitter/room/openPMD/API\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9d9ebb8b7e6322fcd166bfae60f71af84c736366c66eb3530c0dba93f188b21a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f737825323025374325323077696e2d626c7565\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9d9ebb8b7e6322fcd166bfae60f71af84c736366c66eb3530c0dba93f188b21a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f737825323025374325323077696e2d626c7565\" alt=\"Supported Platforms\" title=\"Supported Platforms\" data-canonical-src=\"https://img.shields.io/badge/platforms-linux%20%7C%20osx%20%7C%20win-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.gnu.org/licenses/lgpl-3.0.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8bbb8fc2409e7de1c4eb29ed0f41ba11e81ba5ce419d9bab46c79893c272dc90/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4c47504c76332d626c7565\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/license-LGPLv3-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.14278/rodare.27\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c0a80422628eb90e6d2595aece4b924c1382f100879b455a5db935e4482f93ae/68747470733a2f2f726f646172652e687a64722e64652f62616467652f444f492f31302e31343237382f726f646172652e32372e737667\" alt=\"DOI\" data-canonical-src=\"https://rodare.hzdr.de/badge/DOI/10.14278/rodare.27.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.codefactor.io/repository/github/openpmd/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/88955957098b42444419c531710d633d56e40c8448bfac5d7731c066f1e9b226/68747470733a2f2f7777772e636f6465666163746f722e696f2f7265706f7369746f72792f6769746875622f6f70656e706d642f6f70656e706d642d6170692f6261646765\" alt=\"CodeFactor\" data-canonical-src=\"https://www.codefactor.io/repository/github/openpmd/openpmd-api/badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://coveralls.io/github/openPMD/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a65cfb12d73b7a5755c52903379270a45eca34e2c734c753201c05b4614ab631/68747470733a2f2f636f766572616c6c732e696f2f7265706f732f6769746875622f6f70656e504d442f6f70656e504d442d6170692f6261646765\" alt=\"Coverage Status\" data-canonical-src=\"https://coveralls.io/repos/github/openPMD/openPMD-api/badge\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://openpmd-api.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e4b8098329c41cd66986174e9faf37b0539226d70164d16a7171349d2d964e42/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6f70656e706d642d6170692f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/openpmd-api/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.com/openPMD/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/490fb264bdb35bdf19440e9cc7d1da41f3da133156f6539961692812da5f5af4/68747470733a2f2f7472617669732d63692e636f6d2f6f70656e504d442f6f70656e504d442d6170692e7376673f6272616e63683d646576\" alt=\"Linux/OSX Build Status dev\" data-canonical-src=\"https://travis-ci.com/openPMD/openPMD-api.svg?branch=dev\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ci.appveyor.com/project/ax3l/openpmd-api/branch/dev\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d5b914ab0955b82622790f9da53bd4278625895c4c4262d8a23c9e808743819e/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f78393571346e36323070716b306530742f6272616e63682f6465763f7376673d74727565\" alt=\"Windows Build Status dev\" data-canonical-src=\"https://ci.appveyor.com/api/projects/status/x95q4n620pqk0e0t/branch/dev?svg=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/openPMD/openPMD-api/actions?query=workflow%3Awheels\"\u003e\u003cimg src=\"https://github.com/openPMD/openPMD-api/workflows/wheels/badge.svg?branch=wheels\u0026amp;event=push\" alt=\"PyPI Wheel Release\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://dev.azure.com/axelhuebl/openPMD-api/_build/latest?definitionId=1\u0026amp;branchName=azure_install\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/908f0e30f8b3ac85f9f6358a95b124d62f6b88c80334cc3699e138b6dc8bc4e8/68747470733a2f2f6465762e617a7572652e636f6d2f6178656c687565626c2f6f70656e504d442d6170692f5f617069732f6275696c642f7374617475732f6f70656e504d442e6f70656e504d442d6170693f6272616e63684e616d653d617a7572655f696e7374616c6c266c6162656c3d6e696768746c792532307061636b61676573\" alt=\"Nightly Packages Status\" data-canonical-src=\"https://dev.azure.com/axelhuebl/openPMD-api/_apis/build/status/openPMD.openPMD-api?branchName=azure_install\u0026amp;label=nightly%20packages\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://scan.coverity.com/projects/openpmd-openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9dad0ea91ecfbb199146489c9a9ae245caf07e43538209f129770248707d3d06/68747470733a2f2f7363616e2e636f7665726974792e636f6d2f70726f6a656374732f31373630322f62616467652e737667\" alt=\"Coverity Scan Build Status\" data-canonical-src=\"https://scan.coverity.com/projects/17602/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eopenPMD is an open meta-data schema that provides meaning and self-description for data sets in science and engineering.\nSee \u003ca href=\"https://github.com/openPMD/openPMD-standard\"\u003ethe openPMD standard\u003c/a\u003e for details of this schema.\u003c/p\u003e\n\u003cp\u003eThis library provides a reference API for openPMD data handling.\nSince openPMD is a schema (or markup) on top of portable, hierarchical file formats, this library implements various backends such as HDF5, ADIOS2 and JSON.\nWriting \u0026amp; reading through those backends and their associated files are supported for serial and \u003ca href=\"https://www.mpi-forum.org/docs/\" rel=\"nofollow\"\u003eMPI-parallel\u003c/a\u003e workflows.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eC++\u003c/h3\u003e\u003ca id=\"user-content-c\" class=\"anchor\" aria-label=\"Permalink: C++\" href=\"#c\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://isocpp.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0ff407625ba02420a0a7140b8c72db8547c6153501e5e5d58641f7afc2533723/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c616e67756167652d4325324225324231372d79656c6c6f77677265656e\" alt=\"C++17\" title=\"C++17 API\" data-canonical-src=\"https://img.shields.io/badge/language-C%2B%2B17-yellowgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ad313c29546a0ea1b644d34f3369cb4e08ae406244bb695a6251e174d725b67f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70686173652d626574612d79656c6c6f77677265656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad313c29546a0ea1b644d34f3369cb4e08ae406244bb695a6251e174d725b67f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70686173652d626574612d79656c6c6f77677265656e\" alt=\"C++17 API: Beta\" title=\"Status: Beta\" data-canonical-src=\"https://img.shields.io/badge/phase-beta-yellowgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++\"\u003e\u003cpre\u003e#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003eopenPMD/openPMD.hpp\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003eiostream\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e ...\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e s = openPMD::Series(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esamples/git-sample/data%T.h5\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, openPMD::Access::READ_ONLY);\n\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u0026amp; [step, it] : s.iterations ) {\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eIteration: \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; step \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e;\n\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u0026amp; [name, mesh] : it.\u003cspan class=\"pl-smi\"\u003emeshes\u003c/span\u003e ) {\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e Mesh \u0027\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; name \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0027 attributes:\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e;\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e\u0026amp; val : mesh.\u003cspan class=\"pl-c1\"\u003eattributes\u003c/span\u003e() )\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; val \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e;\n }\n\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u0026amp; [name, species] : it.\u003cspan class=\"pl-smi\"\u003eparticles\u003c/span\u003e ) {\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e Particle species \u0027\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; name \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0027 attributes:\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e;\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e( \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e\u0026amp; val : species.\u003cspan class=\"pl-c1\"\u003eattributes\u003c/span\u003e() )\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; val \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\u0027\u003c/span\u003e\u003c/span\u003e;\n }\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003ePython\u003c/h3\u003e\u003ca id=\"user-content-python\" class=\"anchor\" aria-label=\"Permalink: Python\" href=\"#python\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.python.org/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/aec38eb6cf1bc5def88ff14888758ca17ada302ff6ba39c8df0c284a2b552548/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c616e67756167652d507974686f6e332d79656c6c6f77677265656e\" alt=\"Python3\" title=\"Python3 API\" data-canonical-src=\"https://img.shields.io/badge/language-Python3-yellowgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/ad313c29546a0ea1b644d34f3369cb4e08ae406244bb695a6251e174d725b67f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70686173652d626574612d79656c6c6f77677265656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ad313c29546a0ea1b644d34f3369cb4e08ae406244bb695a6251e174d725b67f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70686173652d626574612d79656c6c6f77677265656e\" alt=\"Python3 API: Beta\" title=\"Status: Beta\" data-canonical-src=\"https://img.shields.io/badge/phase-beta-yellowgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eopenpmd_api\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eio\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# ...\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eSeries\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"samples/git-sample/data%T.h5\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eAccess\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread_only\u003c/span\u003e)\n\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ek_i\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eiterations\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"Iteration: {0}\"\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ek_i\u003c/span\u003e))\n\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ek_m\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003em\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emeshes\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\" Mesh \u0027{0}\u0027 attributes:\"\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ek_m\u003c/span\u003e))\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003em\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eattributes\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\" {0}\"\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e))\n\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ek_p\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ep\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eparticles\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eitems\u003c/span\u003e():\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\" Particle species \u0027{0}\u0027 attributes:\"\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ek_p\u003c/span\u003e))\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ep\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eattributes\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\" {0}\"\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eMore!\u003c/h3\u003e\u003ca id=\"user-content-more\" class=\"anchor\" aria-label=\"Permalink: More!\" href=\"#more\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eCurious?\nOur manual shows full \u003ca href=\"https://openpmd-api.readthedocs.io/en/latest/usage/firstwrite.html\" rel=\"nofollow\"\u003eread \u0026amp; write examples\u003c/a\u003e, both serial and MPI-parallel!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eDependencies\u003c/h2\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequired:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCMake 3.22.0+\u003c/li\u003e\n\u003cli\u003eC++17 capable compiler, e.g., g++ 7+, clang 7+, MSVC 19.15+, icpc 19+, icpx\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eShipped internally (downloaded by CMake unless \u003ccode\u003eopenPMD_SUPERBUILD=OFF\u003c/code\u003e is set):\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/catchorg/Catch2\"\u003eCatch2\u003c/a\u003e 2.13.10+ (\u003ca href=\"https://github.com/catchorg/Catch2/blob/master/LICENSE.txt\"\u003eBSL-1.0\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pybind/pybind11\"\u003epybind11\u003c/a\u003e 2.13.0+ (\u003ca href=\"https://github.com/pybind/pybind11/blob/master/LICENSE\"\u003enew BSD\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/nlohmann/json\"\u003eNLohmann-JSON\u003c/a\u003e 3.9.1+ (\u003ca href=\"https://github.com/nlohmann/json/blob/develop/LICENSE.MIT\"\u003eMIT\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ToruNiina/toml11\"\u003etoml11\u003c/a\u003e 3.7.1+ (\u003ca href=\"https://github.com/ToruNiina/toml11/blob/master/LICENSE\"\u003eMIT\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eI/O backends:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://en.wikipedia.org/wiki/JSON\" rel=\"nofollow\"\u003eJSON\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://support.hdfgroup.org/HDF5\" rel=\"nofollow\"\u003eHDF5\u003c/a\u003e 1.8.13+ (optional)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ornladios/ADIOS2\"\u003eADIOS2\u003c/a\u003e 2.7.0+ (optional)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ewhile those can be built either with or without:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMPI 2.1+, e.g. OpenMPI 1.6.5+ or MPICH2\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOptional language bindings:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython:\n\u003cul\u003e\n\u003cli\u003ePython 3.8 - 3.13\u003c/li\u003e\n\u003cli\u003epybind11 2.13.0+\u003c/li\u003e\n\u003cli\u003enumpy 1.15+\u003c/li\u003e\n\u003cli\u003empi4py 2.1+ (optional, for MPI)\u003c/li\u003e\n\u003cli\u003epandas 1.0+ (optional, for dataframes)\u003c/li\u003e\n\u003cli\u003edask 2021+ (optional, for dask dataframes)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCUDA C++ (optional, currently used only in tests)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/570e394f6d7f2d921755bcc40d7089eb4ff00081d33c591e780ae132b0d20db0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f737061636b2e696f2d6f70656e706d642d2d6170692d627269676874677265656e\" alt=\"Spack Package\" data-canonical-src=\"https://img.shields.io/badge/spack.io-openpmd--api-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6cd332b7b2264ec786b56a1024949bd103946f78db75c461f427e0ffad2b4f69/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f6e64612e696f2d6f70656e706d642d2d6170692d627269676874677265656e\" alt=\"Conda Package\" data-canonical-src=\"https://img.shields.io/badge/conda.io-openpmd--api-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/openPMD/homebrew-openPMD\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/eb7b1fc3bd94ae34834f512ce11f991084f3945094a8458d152a9e8f8426f353/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f627265772e73682d6f70656e706d642d2d6170692d627269676874677265656e\" alt=\"Brew Package\" data-canonical-src=\"https://img.shields.io/badge/brew.sh-openpmd--api-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fea34f845f3f0da6dcf49275d104c5d1215beccc98adf0486e7b04190ee3f2f7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707970692e6f72672d6f70656e706d642d2d6170692d627269676874677265656e\" alt=\"PyPI Package\" data-canonical-src=\"https://img.shields.io/badge/pypi.org-openpmd--api-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f501404192a503a406de97a5067605fbf37a8412c706d11cf90fe66c0009a31a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f66726f6d5f736f757263652d434d616b652d627269676874677265656e\" alt=\"From Source\" data-canonical-src=\"https://img.shields.io/badge/from_source-CMake-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOur community loves to help each other.\nPlease \u003ca href=\"https://github.com/openPMD/openPMD-api/issues/new?labels=install\u0026amp;template=install_problem.md\"\u003ereport installation problems\u003c/a\u003e in case you should get stuck.\u003c/p\u003e\n\u003cp\u003eChoose \u003cem\u003eone\u003c/em\u003e of the install methods below to get started:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://spack.io\" rel=\"nofollow\"\u003eSpack\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-spack\" class=\"anchor\" aria-label=\"Permalink: Spack\" href=\"#spack\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ab09a38910c62b1ce2e2d9636b4468cacb0bc6e9f82d30a1a3f7f7d71741510a/68747470733a2f2f696d672e736869656c64732e696f2f737061636b2f762f6f70656e706d642d617069\" alt=\"Spack Version\" data-canonical-src=\"https://img.shields.io/spack/v/openpmd-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://spack.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f975eca3ac9c73e5bb6ae43f488cf35c73ab3e4a636cf59856323ff8b4e100cf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f73782532302d626c7565\" alt=\"Spack Platforms\" data-canonical-src=\"https://img.shields.io/badge/platforms-linux%20%7C%20osx%20-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://spack.readthedocs.io/en/latest/package_list.html#openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/80cd0c6e5264f590c79f51a1f2a346b8af64b9cddedbc6aa0fc2d17c37a79a9b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d6465736b746f705f253238432532422532422c5f70792532392c5f646576656c6f706d656e742c5f4850432d627269676874677265656e\" alt=\"Spack Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-desktop_%28C%2B%2B,_py%29,_development,_HPC-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: +python -adios2 -hdf5 -mpi\u003c/span\u003e\nspack install openpmd-api\nspack load openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://conda.io\" rel=\"nofollow\"\u003eConda\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/299268c2c9940187a59751abe0de2ce596ff28e8bd0a514247785d427510a021/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f766e2f636f6e64612d666f7267652f6f70656e706d642d617069\" alt=\"Conda Version\" data-canonical-src=\"https://img.shields.io/conda/vn/conda-forge/openpmd-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9d9ebb8b7e6322fcd166bfae60f71af84c736366c66eb3530c0dba93f188b21a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f737825323025374325323077696e2d626c7565\" alt=\"Conda Platforms\" data-canonical-src=\"https://img.shields.io/badge/platforms-linux%20%7C%20osx%20%7C%20win-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11d69a1db5d4aa49394b2650e53db0344313a2e0cd1dac5c36dd83e2d8fc0925/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d6465736b746f705f25323870792532392d627269676874677265656e\" alt=\"Conda Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-desktop_%28py%29-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/openpmd-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5bfef69ca60c5c6b8a1094bd5ef056625c336337ec043cfc0cca3c24d657d375/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f646e2f636f6e64612d666f7267652f6f70656e706d642d617069\" alt=\"Conda Downloads\" data-canonical-src=\"https://img.shields.io/conda/dn/conda-forge/openpmd-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: OpenMPI support =*=mpi_openmpi*\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: MPICH support =*=mpi_mpich*\u003c/span\u003e\nconda create -n openpmd -c conda-forge openpmd-api\nconda activate openpmd\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://brew.sh\" rel=\"nofollow\"\u003eBrew\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-brew\" class=\"anchor\" aria-label=\"Permalink: Brew\" href=\"#brew\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/openPMD/homebrew-openPMD\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dd40671507b4ba3ebce204e1cfe6d92daa387ed33075ea6300a116456865eec8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f627265772d6c61746573745f76657273696f6e2d6f72616e6765\" alt=\"Brew Version\" data-canonical-src=\"https://img.shields.io/badge/brew-latest_version-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://docs.brew.sh/Homebrew-on-Linux\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f975eca3ac9c73e5bb6ae43f488cf35c73ab3e4a636cf59856323ff8b4e100cf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f73782532302d626c7565\" alt=\"Brew Platforms\" data-canonical-src=\"https://img.shields.io/badge/platforms-linux%20%7C%20osx%20-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://brew.sh\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b7ea9e1ffd8fbbc4684ccbf39cc3db9d7e7b0385ca3998f1d2691939a56b3906/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d6465736b746f705f253238432532422532422c5f70792532392d627269676874677265656e\" alt=\"Brew Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-desktop_%28C%2B%2B,_py%29-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003ebrew tap openpmd/openpmd\nbrew install openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e\u003ca href=\"https://pypi.org\" rel=\"nofollow\"\u003ePyPI\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-pypi\" class=\"anchor\" aria-label=\"Permalink: PyPI\" href=\"#pypi\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b4af06805977086b7b7f7785cff649c52d91d497d308edaf74f06a64366a3999/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6f70656e504d442d617069\" alt=\"PyPI Version\" data-canonical-src=\"https://img.shields.io/pypi/v/openPMD-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api/#files\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9d9ebb8b7e6322fcd166bfae60f71af84c736366c66eb3530c0dba93f188b21a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d6c696e75782532302537432532306f737825323025374325323077696e2d626c7565\" alt=\"PyPI Platforms\" data-canonical-src=\"https://img.shields.io/badge/platforms-linux%20%7C%20osx%20%7C%20win-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/11d69a1db5d4aa49394b2650e53db0344313a2e0cd1dac5c36dd83e2d8fc0925/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d6465736b746f705f25323870792532392d627269676874677265656e\" alt=\"PyPI Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-desktop_%28py%29-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/86d50b4ea99b20b0cc7c49fd4317f4972dc475416682bb2a0640d37ea025797e/68747470733a2f2f696d672e736869656c64732e696f2f707970692f666f726d61742f6f70656e504d442d617069\" alt=\"PyPI Format\" data-canonical-src=\"https://img.shields.io/pypi/format/openPMD-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/openPMD-api\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0030fc55eab10086e5b4b1ad72be1cfcdf8403df2a9f5120765a2dd2c364ea6b/68747470733a2f2f696d672e736869656c64732e696f2f707970692f646d2f6f70656e504d442d617069\" alt=\"PyPI Downloads\" data-canonical-src=\"https://img.shields.io/pypi/dm/openPMD-api\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOn very old macOS versions (\u0026lt;10.9) or on exotic processor architectures, this install method \u003cem\u003ecompiles from source\u003c/em\u003e against the found installations of HDF5, ADIOS2, and/or MPI (in system paths, from other package managers, or loaded via a module system, ...).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e we need pip 19 or newer\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\npython3 -m pip install -U pip\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\npython3 -m pip install openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eIf MPI-support shall be enabled, we always have to recompile:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\npython3 -m pip install -U pip packaging setuptools wheel\npython3 -m pip install -U cmake\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\nopenPMD_USE_MPI=ON python3 -m pip install openpmd-api --no-binary openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor some exotic architectures and compilers, you might need to disable a compiler feature called \u003ca href=\"https://en.wikipedia.org/wiki/Interprocedural_optimization\" rel=\"nofollow\"\u003elink-time/interprocedural optimization\u003c/a\u003e if you encounter linking problems:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CMAKE_INTERPROCEDURAL_OPTIMIZATION=OFF\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: --user\u003c/span\u003e\npython3 -m pip install openpmd-api --no-binary openpmd-api\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditional CMake options can be passed via individual environment variables, which need to be prefixed with \u003ccode\u003eopenPMD_CMAKE_\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eFrom Source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From Source\" href=\"#from-source\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7f9966b22cbf700d42e2110f2855f6d6a7d645bc8b786fdcae2019a1379d58d6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7573655f636173652d646576656c6f706d656e742d627269676874677265656e\" alt=\"Source Use Case\" data-canonical-src=\"https://img.shields.io/badge/use_case-development-brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eopenPMD-api can also be built and installed from source using \u003ca href=\"https://cmake.org/\" rel=\"nofollow\"\u003eCMake\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003egit clone https://github.com/openPMD/openPMD-api.git\n\nmkdir openPMD-api-build\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e openPMD-api-build\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: for full tests, with unzip\u003c/span\u003e\n../openPMD-api/share/openPMD/download_samples.sh\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for own install prefix append:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -DCMAKE_INSTALL_PREFIX=$HOME/somepath\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e for options append:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -DopenPMD_USE_...=...\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e e.g. for python support add:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -DopenPMD_USE_PYTHON=ON -DPython_EXECUTABLE=$(which python3)\u003c/span\u003e\ncmake ../openPMD-api\n\ncmake --build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional\u003c/span\u003e\nctest\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e sudo might be required for system paths\u003c/span\u003e\ncmake --build \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e --target install\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eThe following options can be added to the \u003ccode\u003ecmake\u003c/code\u003e call to control features.\nCMake controls options with prefixed \u003ccode\u003e-D\u003c/code\u003e, e.g. \u003ccode\u003e-DopenPMD_USE_MPI=OFF\u003c/code\u003e:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCMake Option\u003c/th\u003e\n\u003cth\u003eValues\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_MPI\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eAUTO\u003c/strong\u003e/ON/OFF\u003c/td\u003e\n\u003ctd\u003eParallel, Multi-Node I/O for clusters\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_HDF5\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eAUTO\u003c/strong\u003e/ON/OFF\u003c/td\u003e\n\u003ctd\u003eHDF5 backend (\u003ccode\u003e.h5\u003c/code\u003e files)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_ADIOS2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eAUTO\u003c/strong\u003e/ON/OFF\u003c/td\u003e\n\u003ctd\u003eADIOS2 backend (\u003ccode\u003e.bp\u003c/code\u003e files in BP3, BP4 or higher)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_PYTHON\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eAUTO\u003c/strong\u003e/ON/OFF\u003c/td\u003e\n\u003ctd\u003eEnable Python bindings\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_INVASIVE_TESTS\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eON/\u003cstrong\u003eOFF\u003c/strong\u003e\n\u003c/td\u003e\n\u003ctd\u003eEnable unit tests that modify source code \u003csup\u003e1\u003c/sup\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_VERIFY\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eEnable internal VERIFY (assert) macro independent of build type \u003csup\u003e2\u003c/sup\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_INSTALL\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eAdd installation targets\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_INSTALL_RPATH\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eAdd RPATHs to installed binaries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ePython_EXECUTABLE\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e(newest found)\u003c/td\u003e\n\u003ctd\u003ePath to Python executable\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e \u003cem\u003ee.g. changes C++ visibility keywords, breaks MSVC\u003c/em\u003e\n\u003csup\u003e2\u003c/sup\u003e \u003cem\u003ethis includes most pre-/post-condition checks, disabling without specific cause is highly discouraged\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, the following libraries are downloaded via \u003ca href=\"https://cmake.org/cmake/help/latest/module/FetchContent.html\" rel=\"nofollow\"\u003eFetchContent\u003c/a\u003e\nduring the configuration of the project or, if the corresponding \u003ccode\u003e\u0026lt;PACKAGENAME\u0026gt;_ROOT\u003c/code\u003e variable is provided, can be provided externally:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/catchorg/Catch2\"\u003eCatch2\u003c/a\u003e (2.13.10+)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pybind/pybind11\"\u003ePyBind11\u003c/a\u003e (2.13.0+)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/nlohmann/json\"\u003eNLohmann-JSON\u003c/a\u003e (3.9.1+)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ToruNiina/toml11\"\u003etoml11\u003c/a\u003e (3.7.1+)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy default, this will build as a shared library (\u003ccode\u003elibopenPMD.[so|dylib|dll]\u003c/code\u003e) and installs also its headers.\nIn order to build a static library, append \u003ccode\u003e-DBUILD_SHARED_LIBS=OFF\u003c/code\u003e to the \u003ccode\u003ecmake\u003c/code\u003e command.\nYou can only build a static or a shared library at a time.\u003c/p\u003e\n\u003cp\u003eBy default, the \u003ccode\u003eRelease\u003c/code\u003e version is built.\nIn order to build with debug symbols, pass \u003ccode\u003e-DCMAKE_BUILD_TYPE=Debug\u003c/code\u003e to your \u003ccode\u003ecmake\u003c/code\u003e command.\u003c/p\u003e\n\u003cp\u003eBy default, tests, examples and command line tools are built.\nIn order to skip building those, pass \u003ccode\u003eOFF\u003c/code\u003e to these \u003ccode\u003ecmake\u003c/code\u003e options:\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCMake Option\u003c/th\u003e\n\u003cth\u003eValues\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_BUILD_TESTING\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eBuild tests\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_BUILD_EXAMPLES\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eBuild examples\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_BUILD_CLI_TOOLS\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\n\u003cstrong\u003eON\u003c/strong\u003e/OFF\u003c/td\u003e\n\u003ctd\u003eBuild command-line tools\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eopenPMD_USE_CUDA_EXAMPLES\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eON/\u003cstrong\u003eOFF\u003c/strong\u003e\n\u003c/td\u003e\n\u003ctd\u003eUse CUDA in examples\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eLinking to your project\u003c/h2\u003e\u003ca id=\"user-content-linking-to-your-project\" class=\"anchor\" aria-label=\"Permalink: Linking to your project\" href=\"#linking-to-your-project\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe install will contain header files and libraries in the path set with \u003ccode\u003e-DCMAKE_INSTALL_PREFIX\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eCMake\u003c/h3\u003e\u003ca id=\"user-content-cmake\" class=\"anchor\" aria-label=\"Permalink: CMake\" href=\"#cmake\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf your project is using CMake for its build, one can conveniently use our provided \u003ccode\u003eopenPMDConfig.cmake\u003c/code\u003e package, which is installed alongside the library.\u003c/p\u003e\n\u003cp\u003eFirst set the following environment hint if openPMD-api was \u003cem\u003enot\u003c/em\u003e installed in a system path:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: only needed if installed outside of system paths\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CMAKE_PREFIX_PATH=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/somepath:\u003cspan class=\"pl-smi\"\u003e$CMAKE_PREFIX_PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eUse the following lines in your project\u0027s \u003ccode\u003eCMakeLists.txt\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-cmake\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# supports: COMPONENTS MPI NOMPI HDF5 ADIOS2\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efind_package\u003c/span\u003e(openPMD 0.17.0 CONFIG)\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e(openPMD_FOUND)\n\u003cspan class=\"pl-k\"\u003e target_link_libraries\u003c/span\u003e(YourTarget \u003cspan class=\"pl-e\"\u003ePRIVATE\u003c/span\u003e openPMD::openPMD)\n\u003cspan class=\"pl-k\"\u003eendif\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAlternatively\u003c/em\u003e, add the openPMD-api repository source directly to your project and use it via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-cmake\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eadd_subdirectory\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"path/to/source/of/openPMD-api\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-k\"\u003etarget_link_libraries\u003c/span\u003e(YourTarget \u003cspan class=\"pl-e\"\u003ePRIVATE\u003c/span\u003e openPMD::openPMD)\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eFor development workflows, you can even automatically download and build openPMD-api from within a depending CMake project.\nJust replace the \u003ccode\u003eadd_subdirectory\u003c/code\u003e call with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-cmake\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e(FetchContent)\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eCMAKE_POLICY_DEFAULT_CMP0077\u003c/span\u003e NEW)\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e(openPMD_BUILD_CLI_TOOLS \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e(openPMD_BUILD_EXAMPLES \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e(openPMD_BUILD_TESTING \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e(openPMD_BUILD_SHARED_LIBS \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# precedence over BUILD_SHARED_LIBS if needed\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e(openPMD_INSTALL \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# or instead use:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# set(openPMD_INSTALL ${BUILD_SHARED_LIBS}) # only install if used as a shared library\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e(openPMD_USE_PYTHON \u003cspan class=\"pl-k\"\u003eOFF\u003c/span\u003e)\nFetchContent_Declare(openPMD\n GIT_REPOSITORY \u003cspan class=\"pl-s\"\u003e\"https://github.com/openPMD/openPMD-api.git\"\u003c/span\u003e\n GIT_TAG \u003cspan class=\"pl-s\"\u003e\"0.17.0\"\u003c/span\u003e)\nFetchContent_MakeAvailable(openPMD)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eManually\u003c/h3\u003e\u003ca id=\"user-content-manually\" class=\"anchor\" aria-label=\"Permalink: Manually\" href=\"#manually\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIf your (Linux/OSX) project is build by calling the compiler directly or uses a manually written \u003ccode\u003eMakefile\u003c/code\u003e, consider using our \u003ccode\u003eopenPMD.pc\u003c/code\u003e helper file for \u003ccode\u003epkg-config\u003c/code\u003e, which are installed alongside the library.\u003c/p\u003e\n\u003cp\u003eFirst set the following environment hint if openPMD-api was \u003cem\u003enot\u003c/em\u003e installed in a system path:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional: only needed if installed outside of system paths\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PKG_CONFIG_PATH=\u003cspan class=\"pl-smi\"\u003e$HOME\u003c/span\u003e/somepath/lib/pkgconfig:\u003cspan class=\"pl-smi\"\u003e$PKG_CONFIG_PATH\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp\u003eAdditional linker and compiler flags for your project are available via:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e switch to check if openPMD-api was build as static library\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (via BUILD_SHARED_LIBS=OFF) or as shared library (default)\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e [ \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003epkg-config --variable=static openPMD\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e==\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etrue\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e ]\n\u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n pkg-config --libs --static openPMD\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -L/usr/local/lib -L/usr/lib/x86_64-linux-gnu/openmpi/lib -lopenPMD -pthread /usr/lib/libmpi.so -pthread /usr/lib/x86_64-linux-gnu/openmpi/lib/libmpi_cxx.so /usr/lib/libmpi.so /usr/lib/x86_64-linux-gnu/hdf5/openmpi/libhdf5.so /usr/lib/x86_64-linux-gnu/libsz.so /usr/lib/x86_64-linux-gnu/libz.so /usr/lib/x86_64-linux-gnu/libdl.so /usr/lib/x86_64-linux-gnu/libm.so -pthread /usr/lib/libmpi.so -pthread /usr/lib/x86_64-linux-gnu/openmpi/lib/libmpi_cxx.so /usr/lib/libmpi.so\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e\n pkg-config --libs openPMD\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -L${HOME}/somepath/lib -lopenPMD\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\n\npkg-config --cflags openPMD\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e -I${HOME}/somepath/include\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthor Contributions\u003c/h2\u003e\u003ca id=\"user-content-author-contributions\" class=\"anchor\" aria-label=\"Permalink: Author Contributions\" href=\"#author-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eopenPMD-api is developed by many people.\nIt was initially started by the \u003ca href=\"https://hzdr.de/crp\" rel=\"nofollow\"\u003eComputational Radiation Physics Group\u003c/a\u003e at \u003ca href=\"https://www.hzdr.de/\" rel=\"nofollow\"\u003eHZDR\u003c/a\u003e as successor to \u003ca href=\"https://github.com/ComputationalRadiationPhysics/libSplash/\"\u003elibSplash\u003c/a\u003e, generalizing the \u003ca href=\"https://arxiv.org/abs/1706.00522\" rel=\"nofollow\"\u003esuccessful HDF5 \u0026amp; ADIOS1 implementations\u003c/a\u003e in \u003ca href=\"https://github.com/ComputationalRadiationPhysics/picongpu\"\u003ePIConGPU\u003c/a\u003e.\nThe following people and institutions \u003ca href=\"https://github.com/openPMD/openPMD-api/graphs/contributors\"\u003econtributed\u003c/a\u003e to openPMD-api:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ax3l\"\u003eAxel Huebl (LBNL, previously HZDR)\u003c/a\u003e:\nproject lead, releases, documentation, automated CI/CD, Python bindings, Dask, installation \u0026amp; packaging, prior reference implementations\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/franzpoeschel\"\u003eFranz Poeschel (CASUS)\u003c/a\u003e:\nJSON \u0026amp; ADIOS2 backend, data staging/streaming, reworked class design\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/C0nsultant\"\u003eFabian Koller (HZDR)\u003c/a\u003e:\ninitial library design and implementation with HDF5 \u0026amp; ADIOS1 backend\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/guj\"\u003eJunmin Gu (LBNL)\u003c/a\u003e:\nnon-collective parallel I/O fixes, ADIOS improvements, benchmarks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMaintained by the following research groups:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.casus.science/casus/team/\" rel=\"nofollow\"\u003eComputational Radiation Physics (CRD)\u003c/a\u003e at CASUS/HZDR, led by \u003ca href=\"https://github.com/bussmann\"\u003eMichael Bussmann\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://atap.lbl.gov/accelerator-modeling-program/\" rel=\"nofollow\"\u003eAccelerator Modeling Program (AMP)\u003c/a\u003e at LBNL, led by \u003ca href=\"https://github.com/jlvay\"\u003eJean-Luc Vay\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://crd.lbl.gov/divisions/scidata/sdm/\" rel=\"nofollow\"\u003eScientific Data Management (SDM)\u003c/a\u003e at LBNL, led by \u003ca href=\"https://github.com/john18\"\u003eKesheng (John) Wu\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFurther thanks go to improvements and contributions from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/CFGrote\"\u003eCarsten Fortmann-Grote (EU XFEL GmbH, now MPI-EvolBio)\u003c/a\u003e:\ndraft of our Python unit tests\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/StanczakDominik\"\u003eDominik Sta\u0144czak (Warsaw University of Technology)\u003c/a\u003e:\ndocumentation improvements\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/mingwandroid\"\u003eRay Donnelly (Anaconda, Inc.)\u003c/a\u003e:\nsupport on conda packaging and libc++ quirks\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/amundson\"\u003eJames Amundson (FNAL)\u003c/a\u003e:\ncompile fix for newer compilers\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/psychocoderHPC\"\u003eRen\u00e9 Widera (HZDR)\u003c/a\u003e:\ndesign improvements for initial API design\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/erikzenker\"\u003eErik Zenker (HZDR)\u003c/a\u003e:\ndesign improvements for initial API design\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/sbastrakov\"\u003eSergei Bastrakov (HZDR)\u003c/a\u003e:\ndocumentation improvements (windows)\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/RemiLehe\"\u003eR\u00e9mi Lehe (LBNL)\u003c/a\u003e:\npackage integration testing on macOS and Linux\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/LDAmorim\"\u003eL\u00edgia Diana Amorim (LBNL)\u003c/a\u003e:\npackage integration testing on macOS\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/KseniaBastrakova\"\u003eKseniia Bastrakova (HZDR)\u003c/a\u003e:\ncompatibility testing\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/PrometheusPi\"\u003eRichard Pausch (HZDR)\u003c/a\u003e:\ncompatibility testing, documentation improvements\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pordyna\"\u003ePawe\u0142 Ordyna (HZDR)\u003c/a\u003e:\nreport on NVCC warnings\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/dmitry-ganyushin\"\u003eDmitry Ganyushin (ORNL)\u003c/a\u003e:\nDask prototyping \u0026amp; ADIOS2 benchmarking\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jakirkham\"\u003eJohn Kirkham (NVIDIA)\u003c/a\u003e:\nDask guidance \u0026amp; reviews\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/eschnett\"\u003eErik Schnetter (PITP)\u003c/a\u003e:\nC++ API bug fixes\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/jeanbez\"\u003eJean Luca Bez (LBNL)\u003c/a\u003e:\nHDF5 performance tuning\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/bernhardmgruber\"\u003eBernhard Manfred Gruber (CERN)\u003c/a\u003e:\nCMake fix for parallel HDF5\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/DerNils-git\"\u003eNils Schild (IPP)\u003c/a\u003e:\nCMake improvements for subprojects\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eGrants\u003c/h3\u003e\u003ca id=\"user-content-grants\" class=\"anchor\" aria-label=\"Permalink: Grants\" href=\"#grants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe openPMD-api authors acknowledge support via the following programs.\nSupported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program.\nPreviously supported by the Consortium for Advanced Modeling of Particles Accelerators (CAMPA), funded by the U.S. DOE Office of Science under Contract No. DE-AC02-05CH11231.\nSupported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of two U.S. Department of Energy organizations (Office of Science and the National Nuclear Security Administration).\nThis project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 654220.\nThis work was partially funded by the Center of Advanced Systems Understanding (CASUS), which is financed by Germany\u0027s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament.\nSupported by the HElmholtz Laser Plasma Metadata Initiative (HELPMI) project (ZT-I-PF-3-066), funded by the \"Initiative and Networking Fund\" of the Helmholtz Association in the framework of the \"Helmholtz Metadata Collaboration\" project call 2022.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eTransitive Contributions\u003c/h3\u003e\u003ca id=\"user-content-transitive-contributions\" class=\"anchor\" aria-label=\"Permalink: Transitive Contributions\" href=\"#transitive-contributions\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eopenPMD-api stands on the shoulders of giants and we are grateful for the following projects included as direct dependencies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ornladios/ADIOS2\"\u003eADIOS2\u003c/a\u003e by \u003ca href=\"https://csmd.ornl.gov/adios\" rel=\"nofollow\"\u003eS. Klasky, N. Podhorszki, W.F. Godoy (ORNL), team, collaborators\u003c/a\u003e and \u003ca href=\"https://github.com/ornladios/ADIOS2/graphs/contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/catchorg/Catch2\"\u003eCatch2\u003c/a\u003e by \u003ca href=\"https://github.com/philsquared\"\u003ePhil Nash\u003c/a\u003e, \u003ca href=\"https://github.com/horenmar\"\u003eMartin Ho\u0159e\u0148ovsk\u00fd\u003c/a\u003e and \u003ca href=\"https://github.com/catchorg/Catch2/graphs/contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eHDF5 by \u003ca href=\"https://www.hdfgroup.org\" rel=\"nofollow\"\u003ethe HDF group\u003c/a\u003e and community\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/nlohmann/json\"\u003ejson\u003c/a\u003e by \u003ca href=\"https://github.com/nlohmann\"\u003eNiels Lohmann\u003c/a\u003e and \u003ca href=\"https://github.com/nlohmann/json/graphs/contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ToruNiina/toml11\"\u003etoml11\u003c/a\u003e by \u003ca href=\"https://github.com/ToruNiina\"\u003eToru Niina\u003c/a\u003e and \u003ca href=\"https://github.com/ToruNiina/toml11#Contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pybind/pybind11\"\u003epybind11\u003c/a\u003e by \u003ca href=\"https://github.com/wjakob\"\u003eWenzel Jakob (EPFL)\u003c/a\u003e and \u003ca href=\"https://github.com/pybind/pybind11/graphs/contributors\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eall contributors to the evolution of modern C++ and early library preview developers, e.g. \u003ca href=\"https://github.com/mpark\"\u003eMichael Park (Facebook)\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003ethe \u003ca href=\"https://cmake.org\" rel=\"nofollow\"\u003eCMake build system\u003c/a\u003e and \u003ca href=\"https://github.com/Kitware/CMake/blob/master/Copyright.txt\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003epackaging support by the \u003ca href=\"https://conda-forge.org\" rel=\"nofollow\"\u003econda-forge\u003c/a\u003e, \u003ca href=\"https://pypi.org\" rel=\"nofollow\"\u003ePyPI\u003c/a\u003e and \u003ca href=\"https://spack.io\" rel=\"nofollow\"\u003eSpack\u003c/a\u003e communities, among others\u003c/li\u003e\n\u003cli\u003ethe \u003ca href=\"https://github.com/openPMD/openPMD-standard\"\u003eopenPMD-standard\u003c/a\u003e by \u003ca href=\"https://github.com/ax3l\"\u003eAxel Huebl (HZDR, now LBNL)\u003c/a\u003e and \u003ca href=\"https://github.com/openPMD/openPMD-standard/blob/latest/AUTHORS.md\"\u003econtributors\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n", + "stargazers_count": 142, + "subscribers_count": 11, + "topics": [ + "openpmd", + "openscience", + "hdf5", + "adios", + "mpi", + "hpc", + "research", + "file-handling", + "python3", + "opendata", + "metadata", + "cpp17" + ], + "updated_at": 1733337290.0 + }, { "data_format": 2, "description": "Apptainer: Application containers for Linux", @@ -42450,7 +42478,7 @@ var data = "full_name": "wheaton5/souporcell", "latest_release": "v2.5", "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003esouporcell\u003c/h1\u003e\u003ca id=\"user-content-souporcell\" class=\"anchor\" aria-label=\"Permalink: souporcell\" href=\"#souporcell\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/wheaton5/souporcell/blob/master/souporcell_star.png\"\u003e\u003cimg src=\"https://github.com/wheaton5/souporcell/raw/master/souporcell_star.png\" width=\"100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePreprint manuscript of this method available at \u003ca href=\"https://www.biorxiv.org/content/10.1101/699637v1\" rel=\"nofollow\"\u003ehttps://www.biorxiv.org/content/10.1101/699637v1\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003esouporcell is a method for clustering mixed-genotype scRNAseq experiments by individual.\u003c/p\u003e\n\u003cp\u003eThe inputs are just the possorted_genome_bam.bam, and barcodes.tsv as output from \u003ca href=\"https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger\" rel=\"nofollow\"\u003ecellranger\u003c/a\u003e.\nsouporcell is comprised of 6 steps with the first 3 using external tools and the final using the code provided here.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eRemapping (\u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCalling candidate variants (\u003ca href=\"https://github.com/ekg/freebayes\"\u003efreebayes\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eCell allele counting (\u003ca href=\"https://github.com/10XGenomics/vartrix\"\u003evartrix\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eClustering cells by genotype (souporcell.py)\u003c/li\u003e\n\u003cli\u003eCalling doublets (troublet)\u003c/li\u003e\n\u003cli\u003eCalling cluster genotypes and inferring amount of ambient RNA (consensus.py)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eEasy Installation (Linux) (recommended)\u003c/h2\u003e\u003ca id=\"user-content-easy-installation-linux-recommended\" class=\"anchor\" aria-label=\"Permalink: Easy Installation (Linux) (recommended)\" href=\"#easy-installation-linux-recommended\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload singularity image (1.3gb) (singularity is similar to docker but safe for clusters)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity pull --arch amd64 library://wheaton5/souporcell/souporcell:release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you are running on a scientific cluster, they will likely have singularity, contact your sysadmin for more details.\nIf you are running on your own linux box you may need to install \u003ca href=\"https://www.sylabs.io/guides/3.2/user-guide/quick_start.html#quick-installation-steps\" rel=\"nofollow\"\u003esingularity\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003erequires singularity \u0026gt;= 3.0 or apptainer version \u0026gt;= 1.0.0 (singularity rebranded as apptainer and changed its version numbers)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewhich singularity\nsingularity --version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou should now be able to run souporcell_pipeline.py through the singularity container. Singularity automatically mounts the current working directory and directories downstream from where you run it, otherwise you would need to manually mount those directories. Therefor you can run it from a directory that is upstream of all of the inputs. Input files are the cellranger bam, cellranger barcodes file, and a reference fasta. The cellranger bam is located in the cellranger outs directory and is called possorted_genome_bam.bam. The barcodes file is located in the cellranger outs/filtered_gene_bc_matrices/\u0026lt;ref_name\u0026gt;/barcodes.tsv. The reference fasta should be of the same species but does not necessarily need to be the exact cellranger reference.\u003c/p\u003e\n\u003cp\u003eThe options for using souporcell are:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec souporcell_latest.sif souporcell_pipeline.py -h\nusage: souporcell_pipeline.py [-h] -i BAM -b BARCODES -f FASTA -t THREADS -o\n OUT_DIR -k CLUSTERS [-p PLOIDY]\n [--min_alt MIN_ALT] [--min_ref MIN_REF]\n [--max_loci MAX_LOCI] [--restarts RESTARTS]\n [--common_variants COMMON_VARIANTS]\n [--known_genotypes KNOWN_GENOTYPES]\n [--known_genotypes_sample_names KNOWN_GENOTYPES_SAMPLE_NAMES [KNOWN_GENOTYPES_SAMPLE_NAMES ...]]\n [--skip_remap SKIP_REMAP] [--ignore IGNORE]\n\nsingle cell RNAseq mixed genotype clustering using sparse mixture model\nclustering with tensorflow.\n\noptional arguments:\n -h, --help show this help message and exit\n -i BAM, --bam BAM cellranger bam\n -b BARCODES, --barcodes BARCODES\n barcodes.tsv from cellranger\n -f FASTA, --fasta FASTA\n reference fasta file\n -t THREADS, --threads THREADS\n max threads to use\n -o OUT_DIR, --out_dir OUT_DIR\n name of directory to place souporcell files\n -k CLUSTERS, --clusters CLUSTERS\n number cluster, tbd add easy way to run on a range of\n k\n -p PLOIDY, --ploidy PLOIDY\n ploidy, must be 1 or 2, default = 2\n --min_alt MIN_ALT min alt to use locus, default = 10.\n --min_ref MIN_REF min ref to use locus, default = 10.\n --max_loci MAX_LOCI max loci per cell, affects speed, default = 2048.\n --restarts RESTARTS number of restarts in clustering, when there are \u0026gt; 12\n clusters we recommend increasing this to avoid local\n minima\n --common_variants COMMON_VARIANTS\n common variant loci or known variant loci vcf, must be\n vs same reference fasta\n --known_genotypes KNOWN_GENOTYPES\n known variants per clone in population vcf mode, must\n be .vcf right now we dont accept gzip or bcf sorry\n --known_genotypes_sample_names KNOWN_GENOTYPES_SAMPLE_NAMES [KNOWN_GENOTYPES_SAMPLE_NAMES ...]\n which samples in population vcf from known genotypes\n option represent the donors in your sample\n --skip_remap SKIP_REMAP\n don\u0027t remap with minimap2 (not recommended unless in\n conjunction with --common_variants\n --ignore IGNORE set to True to ignore data error assertions\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eA typical command looks like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /path/to/souporcell_latest.sif souporcell_pipeline.py -i /path/to/possorted_genome_bam.bam -b /path/to/barcodes.tsv -f /path/to/reference.fasta -t num_threads_to_use -o output_dir_name -k num_clusters\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe above command will run all six steps of the pipeline and it will require up to 24gb of ram for human (minimap2 bam index is high water mark for memory). For smaller genomes, fewer clusters, lower --max-loci will require less memory. Note that souporcell will require roughly 2x the amount of diskspace that the input bam file takes up. This dataset should take several hours to run on 8 threads mostly due to read processing, remapping, and variant calling.\u003c/p\u003e\n\u003cp\u003eIf you have a common snps file you may want to use the --common_variants option with or without the --skip_remap option. This option will skip conversion to fastq, remapping with minimap2, and reattaching barcodes, and the --common_variants will remove the freebayes step. Each which will save a significant amount of time, but --skip-remap isn\u0027t recommended without --common_variants.\u003c/p\u003e\n\u003cp\u003eCommon variant files from 1k genomes filtered to variants \u0026gt;= 2% allele frequency in the population and limited to SNPs can be found here for GRCh38\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl ftp://ftp.eng.auburn.edu/pub/whh0027/common_variants_grch38.vcf.gz -o common_variants_grch38.vcf.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor for hg19 here\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl ftp://ftp.eng.auburn.edu/pub/whh0027/filtered_2p_1kgenomes_hg19.vcf.gz -o common_variants_hg19.vcf.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003ePractice/Testing data set: Demuxlet paper data\u003c/h2\u003e\u003ca id=\"user-content-practicetesting-data-set-demuxlet-paper-data\" class=\"anchor\" aria-label=\"Permalink: Practice/Testing data set: Demuxlet paper data\" href=\"#practicetesting-data-set-demuxlet-paper-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003ewget https://sra-pub-src-1.s3.amazonaws.com/SRR5398235/A.merged.bam.1 -O A.merged.bam\nwget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM2560nnn/GSM2560245/suppl/GSM2560245_barcodes.tsv.gz\ngunzip GSM2560245_barcodes.tsv.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd if you don\u0027t have a human reference sitting around, grab one here\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ewget http://cf.10xgenomics.com/supp/cell-exp/refdata-cellranger-GRCh38-3.0.0.tar.gz\ntar -xzvf refdata-cellranger-GRCh38-3.0.0.tar.gz\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNow you should be ready to test it out\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec /path/to/souporcell_latest.sif souporcell_pipeline.py -i A.merged.bam -b GSM2560245_barcodes.tsv -f refdata-cellranger-GRCh38-3.0.0/fasta/genome.fa -t 8 -o demux_data_test -k 4\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis should require about 20gb of ram mostly because of the minimap2 indexing step. I might soon host an index and reference for human to make this less painful.\u003c/p\u003e\n\u003cp\u003eThe important files are\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eclusters.tsv\u003c/li\u003e\n\u003cli\u003ecluster_genotypes.vcf\u003c/li\u003e\n\u003cli\u003eambient_rna.txt\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eclusters.tsv will look like\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ebarcode status assignment log_loss_singleton log_loss_doublet cluster0 cluster1\nAAACCTGAGATCCGAG-1 singlet 0 -152.7778890920112 -190.5463095948822 -43.95302689281067 -101.63377524087669\nAAACCTGAGCACCGTC-1 singlet 0 -78.56014177554212 -96.66255440088581 -20.949294849836267 -52.57478083591962\nAAACCTGAGTACGATA-1 singlet 0 -216.0188863327174 -281.3888392065457 -63.059016939362536 -159.5450834682198\nAAACCTGGTACATGTC-1 singlet 1 -47.189434469216565 -96.30865717225866 -62.652900832546955 -15.284168900754413\nAAACCTGTCTACTCAT-1 singlet 0 -129.30104434183454 -167.87811467946756 -41.09158213888751 -106.3201962010145\nAAACCTGTCTTGTCAT-1 singlet 0 -85.99781433701455 -110.81701038967158 -24.518165091815554 -60.05279033826837\nAAACGGGCACTGTTAG-1 singlet 0 -154.26595878718032 -191.05465308213363 -31.356408693487197 -81.61186496254497\nAAACGGGCATCATCCC-1 singlet 1 -46.33205678267174 -80.24152434540565 -50.78221280006256 -14.615983876840312\nAAACGGGGTAGGGTAC-1 singlet 0 -240.5237900569412 -302.91575436035924 -71.79370547349878 -154.08594135029728\nAAACGGGTCGGCATCG-1 singlet 0 -166.66827966974532 -226.56795157885028 -51.08790637893961 -148.04625123166286\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eWith the cell barcode, singlet/doublet status, cluster, log_loss_singleton, log_loss_doublet, followed by log loss for each cluster.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003ecluster_genotypes.vcf is a vcf with genotypes for each cluster for each variant in the input vcf from freebayes\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eand\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eambient_rna.txt just contains the ambient RNA percentage detected\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHard install\u003c/h2\u003e\u003ca id=\"user-content-hard-install\" class=\"anchor\" aria-label=\"Permalink: Hard install\" href=\"#hard-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstead of using singularity you can install everything independently (not recommended, but shouldn\u0027t be too bad)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit clone https://github.com/wheaton5/souporcell.git\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eput souporcell directory on your PATH\nrequires samtools, bcftools, htslib, python3, freebayes, vartrix, minimap2 all on your PATH\nI suggest you use the conda env I have set up by using the following command if you have conda or miniconda\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f /path/to/souporcell/souporcell_env.yaml\nconda activate souporcell\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eYou will also need Rust and to compile the two rust binaries\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl --proto \u0027=https\u0027 --tlsv1.2 -sSf https://sh.rustup.rs | sh\ncd /path/to/souporcell/souporcell \u0026amp;\u0026amp; cargo build --release\ncd /path/to/souporcell/troublet \u0026amp;\u0026amp; cargo build --release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eotherwise python packages tensorflow, pyvcf, pystan, pyfaidx, numpy, scipy are required, but as the versions change, I do recommend using the presetup env.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run through the pipeline script\u003c/h2\u003e\u003ca id=\"user-content-to-run-through-the-pipeline-script\" class=\"anchor\" aria-label=\"Permalink: To run through the pipeline script\" href=\"#to-run-through-the-pipeline-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cpre\u003e\u003ccode\u003esouporcell_pipeline.py -i /path/to/possorted_genome_bam.bam -b /path/to/barcodes.tsv -f /path/to/reference.fasta -t num_threads_to_use -o output_dir_name -k num_clusters\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTo run things step by step not through the pipeline script\u003c/h2\u003e\u003ca id=\"user-content-to-run-things-step-by-step-not-through-the-pipeline-script\" class=\"anchor\" aria-label=\"Permalink: To run things step by step not through the pipeline script\" href=\"#to-run-things-step-by-step-not-through-the-pipeline-script\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e1. Remapping\u003c/h3\u003e\u003ca id=\"user-content-1-remapping\" class=\"anchor\" aria-label=\"Permalink: 1. Remapping\" href=\"#1-remapping\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eWe discuss the need for remapping in our manuscript. We need to keep track of cell barcodes and and UMIs, so we first create a fastq with those items encoded in the readname.\nRequires python 3.0, modules pysam, argparse (pip install/conda install depending on environment)\nEasiest to first add the souporcell directory to your PATH variable with\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eexport PATH=/path/to/souporcell:$PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen run the renamer.py script to put some of the quality information in the read name. For human data this step will typically take several hours and the output fq file will be somewhat larger than the input bam\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython renamer.py --bam possorted_genome_bam.bam --barcodes barcodes.tsv --out fq.fq\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we must remap these reads using minimap2 (similar results have been seen with hisat2)\nRequires \u003ca href=\"https://github.com/lh3/minimap2\"\u003eminimap2\u003c/a\u003e\nand add /path/to/minimap2 to your PATH. For human data the remapping will typically require more than 12 Gb memory and may take a few hours to run.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eminimap2 -ax splice -t 8 -G50k -k 21 -w 11 --sr -A2 -B8 -O12,32 -E2,1 -r200 -p.5 -N20 -f1000,5000 -n2 -m20 -s40 -g2000 -2K50m --secondary=no \u0026lt;reference_fasta_file\u0026gt; \u0026lt;fastq_file\u0026gt; \u0026gt; minimap.sam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(note the -t 8 as the number of threads, change this as needed)\nNow we must retag the reads with their cell barcodes and UMIs\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython retag.py --sam minimap.sam --out minitagged.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThen we must sort and index our bam.\nRequires \u003ca href=\"http://www.htslib.org/\" rel=\"nofollow\"\u003esamtools\u003c/a\u003e\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esamtools sort minitagged.bam minitagged_sorted.bam\nsamtools index minitagged_sorted.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e2. Calling candidate variants\u003c/h3\u003e\u003ca id=\"user-content-2-calling-candidate-variants\" class=\"anchor\" aria-label=\"Permalink: 2. Calling candidate variants\" href=\"#2-calling-candidate-variants\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou may wish to break this into multiple jobs such as 1 job per chromosome and merge after but the basic command is the following.\nRequires \u003ca href=\"https://github.com/ekg/freebayes\"\u003efreebayes\u003c/a\u003e and add /path/to/freebayes/bin to your PATH\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003efreebayes -f \u0026lt;reference_fasta\u0026gt; -iXu -C 2 -q 20 -n 3 -E 1 -m 30 --min-coverage 6 --limit-coverage 100000 minitagged_sorted.bam\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e3. Cell allele counting\u003c/h3\u003e\u003ca id=\"user-content-3-cell-allele-counting\" class=\"anchor\" aria-label=\"Permalink: 3. Cell allele counting\" href=\"#3-cell-allele-counting\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRequires \u003ca href=\"https://github.com/10XGenomics/vartrix\"\u003evartrix\u003c/a\u003e\nand add /path/to/vartrix to your PATH\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003evartrix --umi --mapq 30 -b \u0026lt;bam file\u0026gt; -c \u0026lt;barcode tsv\u0026gt; --scoring-method coverage --threads 8 --ref-matrix ref.mtx --out-matrix alt.mtx -v \u0026lt;freebayes vcf\u0026gt; --fasta \u0026lt;fasta file used for remapping\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003enote the --threads argument and use an appropriate number of threads for your system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e4. Clustering cells by genotype\u003c/h3\u003e\u003ca id=\"user-content-4-clustering-cells-by-genotype\" class=\"anchor\" aria-label=\"Permalink: 4. Clustering cells by genotype\" href=\"#4-clustering-cells-by-genotype\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRust required. To install rust:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecurl https://sh.rustup.rs -sSf | sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand to build souporcell clustering\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/souporcell/souporcell\ncargo build --release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd add /path/to/souporcell/souporcell/target/release to your path\nusage\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esouporcell -h\nsouporcell 2.4\nHaynes Heaton \u0026lt;whheaton@gmail.com\u0026gt;\nclustering scRNAseq cells by genotype\n\nUSAGE:\n souporcell [OPTIONS] --alt_matrix \u0026lt;alt_matrix\u0026gt; --barcodes \u0026lt;barcodes\u0026gt; --num_clusters \u0026lt;num_clusters\u0026gt; --ref_matrix \u0026lt;ref_matrix\u0026gt;\n\nFLAGS:\n -h, --help Prints help information\n -V, --version Prints version information\n\nOPTIONS:\n -a, --alt_matrix \u0026lt;alt_matrix\u0026gt; alt matrix from vartrix\n -b, --barcodes \u0026lt;barcodes\u0026gt; cell barcodes\n --initialization_strategy \u0026lt;initialization_strategy\u0026gt;\n cluster initialization strategy, defaults to kmeans++, valid values are kmeans++, random_uniform,\n middle_variance, random_cell_assignment\n --known_cell_assignments \u0026lt;known_cell_assignments\u0026gt;\n tsv with barcodes and their known assignments\n\n -g, --known_genotypes \u0026lt;known_genotypes\u0026gt;\n NOT YET IMPLEMENTED population vcf/bcf of known genotypes if available.\n \n --known_genotypes_sample_names \u0026lt;known_genotypes_sample_names\u0026gt;...\n NOT YET IMPLEMENTED sample names, must be samples from the known_genotypes vcf\n\n --min_alt \u0026lt;min_alt\u0026gt;\n minimum number of cells containing the alt allele for the variant to be used for clustering\n\n --min_alt_umis \u0026lt;min_alt_umis\u0026gt; min alt umis to use locus for clustering\n --min_ref \u0026lt;min_ref\u0026gt;\n minimum number of cells containing the ref allele for the variant to be used for clustering\n\n --min_ref_umis \u0026lt;min_ref_umis\u0026gt; min ref umis to use locus for clustering\n -k, --num_clusters \u0026lt;num_clusters\u0026gt; number of clusters\n -r, --ref_matrix \u0026lt;ref_matrix\u0026gt; ref matrix from vartrix\n -r, --restarts \u0026lt;restarts\u0026gt; number of random seedings\n --seed \u0026lt;seed\u0026gt; optional random seed\n -t, --threads \u0026lt;threads\u0026gt; number of threads to use\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo generally something along the lines of\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esouporcell -a alt.mtx -r ref.mtx -b barcodes.tsv -k \u0026lt;num_clusters\u0026gt; -t 8 \u0026gt; clusters_tmp.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e(note clusters_tmp.tsv output as the doublet caller outputs the final clusters file)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e5. Calling doublets\u003c/h3\u003e\u003ca id=\"user-content-5-calling-doublets\" class=\"anchor\" aria-label=\"Permalink: 5. Calling doublets\" href=\"#5-calling-doublets\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eRust required.\nBuild troublet:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003ecd /path/to/souporcell/troublet\ncargo build --release\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAnd add /path/to/souporcell/troublet/target/release to your path\nThe usage is\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etroublet -h\ntroublet 2.4\nHaynes Heaton \u0026lt;whheaton@gmail.com\u0026gt;\nIntergenotypic doublet detection given cluster assignments and cell allele counts\n\nUSAGE:\n troublet [OPTIONS] --alts \u0026lt;alts\u0026gt; --clusters \u0026lt;clusters\u0026gt;\n\nFLAGS:\n -h, --help Prints help information\n -V, --version Prints version information\n\nOPTIONS:\n -a, --alts \u0026lt;alts\u0026gt; alt allele counts per cell in sparse matrix format out of vartrix\n -c, --clusters \u0026lt;clusters\u0026gt; cluster file output from schism\n -b, --debug \u0026lt;debug\u0026gt;... print debug info for index of cells listed\n -d, --doublet_prior \u0026lt;doublet_prior\u0026gt; prior on doublets. Defaults to 0.5\n --doublet_threshold \u0026lt;doublet_threshold\u0026gt; doublet posterior threshold, defaults to 0.90\n -r, --refs \u0026lt;refs\u0026gt; ref allele counts per cell in sparse matrix format out of vartrix\n --singlet_threshold \u0026lt;singlet_threshold\u0026gt; singlet posterior threshold, defaults to 0.90\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo generally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etroublet -a alt.mtx -r ref.mtx --clusters clusters_tmp.tsv \u0026gt; clusters.tsv\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003e6. Genotype and ambient RNA coinference\u003c/h3\u003e\u003ca id=\"user-content-6-genotype-and-ambient-rna-coinference\" class=\"anchor\" aria-label=\"Permalink: 6. Genotype and ambient RNA coinference\" href=\"#6-genotype-and-ambient-rna-coinference\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePython3 required with modules pystan, pyvcf, pickle, math, scipy, gzip (pip install should work for each)\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econsensus.py -h\nusage: consensus.py [-h] -c CLUSTERS -a ALT_MATRIX -r REF_MATRIX [-p PLOIDY]\n --soup_out SOUP_OUT --vcf_out VCF_OUT --output_dir\n OUTPUT_DIR -v VCF\n\nconsensus genotype calling and ambient RNA estimation\n\noptional arguments:\n -h, --help show this help message and exit\n -c CLUSTERS, --clusters CLUSTERS\n tsv cluster file from the troublet output\n -a ALT_MATRIX, --alt_matrix ALT_MATRIX\n alt matrix file\n -r REF_MATRIX, --ref_matrix REF_MATRIX\n ref matrix file\n -p PLOIDY, --ploidy PLOIDY\n ploidy, must be 1 or 2, defaults to 2\n --soup_out SOUP_OUT soup output\n --vcf_out VCF_OUT vcf output\n --output_dir OUTPUT_DIR\n output directory\n -v VCF, --vcf VCF vcf file from which alt and ref matrix were created\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eSo generally\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econsensus.py -c clusters.tsv -a alt.mtx -r ref.mtx --soup_out soup.txt -v \u0026lt;freebayes vcf\u0026gt; --vcf_out cluster_genotypes.vcf --output_dir .\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 168, + "stargazers_count": 169, "subscribers_count": 11, "topics": [ "scrna-seq", @@ -42460,7 +42488,7 @@ var data = "computational-biology", "genomics" ], - "updated_at": 1730915536.0 + "updated_at": 1733285106.0 }, { "data_format": 2, @@ -42501,7 +42529,7 @@ var data = "cryo-em", "topaz-em" ], - "updated_at": 1732932591.0 + "updated_at": 1733240774.0 }, { "data_format": 2, @@ -42668,6 +42696,20 @@ var data = ], "updated_at": 1724415365.0 }, + { + "data_format": 2, + "description": "MRtrix3 provides a set of tools to perform various advanced diffusion MRI analyses, including constrained spherical deconvolution (CSD), probabilistic tractography, track-density imaging, and apparent fibre density", + "filenames": [ + "Singularity" + ], + "full_name": "MRtrix3/mrtrix3", + "latest_release": "3.0.4", + "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMRtrix\u003c/h1\u003e\u003ca id=\"user-content-mrtrix\" class=\"anchor\" aria-label=\"Permalink: MRtrix\" href=\"#mrtrix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/MRtrix3/mrtrix3/actions\"\u003e\u003cimg src=\"https://github.com/MRtrix3/mrtrix3/workflows/checks/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://twitter.com/MRtrix3\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b8da32aaf086aa8974aa3a6e8e20ec5b420021a0f49bb2517caab6e37623f91b/687474703a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f4d5274726978332e7376673f7374796c653d736f6369616c\" alt=\"@MRtrix3\" data-canonical-src=\"http://img.shields.io/twitter/follow/MRtrix3.svg?style=social\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMRtrix3\u003c/em\u003e can be installed / run through multiple avenues:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.mrtrix.org/download/\" rel=\"nofollow\"\u003eDirect download\u003c/a\u003e through mechanisms tailored for different OS platforms;\u003c/li\u003e\n\u003cli\u003eCompiled from the source code in this repository, for which \u003ca href=\"https://mrtrix.readthedocs.io/en/latest/installation/build_from_source.html\" rel=\"nofollow\"\u003ecomprehensive instructions\u003c/a\u003e are provided in the \u003ca href=\"https://mrtrix.readthedocs.io/en/\" rel=\"nofollow\"\u003eonline documentation\u003c/a\u003e;\u003c/li\u003e\n\u003cli\u003eVia containerisation technology using Docker or Singularity; see \u003ca href=\"https://mrtrix.readthedocs.org/en/latest/installation/using_containers.html\" rel=\"nofollow\"\u003eonline documentation page\u003c/a\u003e for details.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting help\u003c/h2\u003e\u003ca id=\"user-content-getting-help\" class=\"anchor\" aria-label=\"Permalink: Getting help\" href=\"#getting-help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions on software setup and use are provided in the \u003ca href=\"https://mrtrix.readthedocs.org\" rel=\"nofollow\"\u003eonline documentation\u003c/a\u003e.\nSupport and general discussion is hosted on the \u003ca href=\"http://community.mrtrix.org/\" rel=\"nofollow\"\u003e\u003cem\u003eMRtrix3\u003c/em\u003e Community Forum\u003c/a\u003e.\nPlease also look through the Frequently Asked Questions on the \u003ca href=\"http://community.mrtrix.org/c/wiki\" rel=\"nofollow\"\u003ewiki section of the forum\u003c/a\u003e.\nYou can address all \u003cem\u003eMRtrix3\u003c/em\u003e-related queries there, using your GitHub or Google login to post questions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick install\u003c/h2\u003e\u003ca id=\"user-content-quick-install\" class=\"anchor\" aria-label=\"Permalink: Quick install\" href=\"#quick-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall dependencies by whichever means your system uses.\nThese include: Python (\u0026gt;=2.6), a C++ compiler with full C++11 support (\u003ccode\u003eg++\u003c/code\u003e 4.9 or later, \u003ccode\u003eclang++\u003c/code\u003e),\nEigen (\u0026gt;=3.2.8), zlib, OpenGL (\u0026gt;=3.3), and Qt (\u0026gt;=4.8, or at least 5.1 on MacOSX).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone Git repository and compile:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ git clone https://github.com/MRtrix3/mrtrix3.git\n $ cd mrtrix3/\n $ ./configure\n $ ./build\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet the \u003ccode\u003ePATH\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eBash shell:\u003c/p\u003e\n\u003cp\u003erun the \u003ccode\u003eset_path\u003c/code\u003e script provided:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./set_path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor edit the startup \u003ccode\u003e~/.bashrc\u003c/code\u003e or \u003ccode\u003e/etc/bash.bashrc\u003c/code\u003e file manually by adding this line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ export PATH=/\u0026lt;edit as appropriate\u0026gt;/mrtrix3/bin:$PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eC shell:\u003c/p\u003e\n\u003cp\u003eedit the startup \u003ccode\u003e~/.cshrc\u003c/code\u003e or \u003ccode\u003e/etc/csh.cshrc\u003c/code\u003e file manually by adding this line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ setenv PATH /\u0026lt;edit as appropriate\u0026gt;/mrtrix3/bin:$PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTest installation:\u003c/p\u003e\n\u003cp\u003eCommand-line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ mrconvert\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGUI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ mrview\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKeeping MRtrix3 up to date\u003c/h2\u003e\u003ca id=\"user-content-keeping-mrtrix3-up-to-date\" class=\"anchor\" aria-label=\"Permalink: Keeping MRtrix3 up to date\" href=\"#keeping-mrtrix3-up-to-date\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eYou can update your installation at any time by opening a terminal in the mrtrix3 folder, and typing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e git pull\n ./build\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf this doesn\u0027t work immediately, it may be that you need to re-run the configure script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./configure\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand re-run step 1 again.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding a specific release of MRtrix3\u003c/h2\u003e\u003ca id=\"user-content-building-a-specific-release-of-mrtrix3\" class=\"anchor\" aria-label=\"Permalink: Building a specific release of MRtrix3\" href=\"#building-a-specific-release-of-mrtrix3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a particular release of MRtrix3 by checking out the corresponding \u003cem\u003etag\u003c/em\u003e, and using the same procedure as above to build it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit checkout 3.0_RC3\n./configure\n./build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThank you for your interest in contributing to \u003cem\u003eMRtrix3\u003c/em\u003e! Please read on \u003ca href=\"CONTRIBUTING.md\"\u003ehere\u003c/a\u003e to find out how to report issues, request features and make direct contributions.\u003c/p\u003e\n", + "stargazers_count": 294, + "subscribers_count": 32, + "topics": [], + "updated_at": 1731067546.0 + }, { "data_format": 2, "description": "This repository contains the code for our fast polygonal building extraction from overhead images pipeline.", @@ -42689,20 +42731,6 @@ var data = ], "updated_at": 1732682583.0 }, - { - "data_format": 2, - "description": "MRtrix3 provides a set of tools to perform various advanced diffusion MRI analyses, including constrained spherical deconvolution (CSD), probabilistic tractography, track-density imaging, and apparent fibre density", - "filenames": [ - "Singularity" - ], - "full_name": "MRtrix3/mrtrix3", - "latest_release": "3.0.4", - "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eMRtrix\u003c/h1\u003e\u003ca id=\"user-content-mrtrix\" class=\"anchor\" aria-label=\"Permalink: MRtrix\" href=\"#mrtrix\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/MRtrix3/mrtrix3/actions\"\u003e\u003cimg src=\"https://github.com/MRtrix3/mrtrix3/workflows/checks/badge.svg\" alt=\"Build Status\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://twitter.com/MRtrix3\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b8da32aaf086aa8974aa3a6e8e20ec5b420021a0f49bb2517caab6e37623f91b/687474703a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f4d5274726978332e7376673f7374796c653d736f6369616c\" alt=\"@MRtrix3\" data-canonical-src=\"http://img.shields.io/twitter/follow/MRtrix3.svg?style=social\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMRtrix3\u003c/em\u003e can be installed / run through multiple avenues:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://www.mrtrix.org/download/\" rel=\"nofollow\"\u003eDirect download\u003c/a\u003e through mechanisms tailored for different OS platforms;\u003c/li\u003e\n\u003cli\u003eCompiled from the source code in this repository, for which \u003ca href=\"https://mrtrix.readthedocs.io/en/latest/installation/build_from_source.html\" rel=\"nofollow\"\u003ecomprehensive instructions\u003c/a\u003e are provided in the \u003ca href=\"https://mrtrix.readthedocs.io/en/\" rel=\"nofollow\"\u003eonline documentation\u003c/a\u003e;\u003c/li\u003e\n\u003cli\u003eVia containerisation technology using Docker or Singularity; see \u003ca href=\"https://mrtrix.readthedocs.org/en/latest/installation/using_containers.html\" rel=\"nofollow\"\u003eonline documentation page\u003c/a\u003e for details.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eGetting help\u003c/h2\u003e\u003ca id=\"user-content-getting-help\" class=\"anchor\" aria-label=\"Permalink: Getting help\" href=\"#getting-help\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eInstructions on software setup and use are provided in the \u003ca href=\"https://mrtrix.readthedocs.org\" rel=\"nofollow\"\u003eonline documentation\u003c/a\u003e.\nSupport and general discussion is hosted on the \u003ca href=\"http://community.mrtrix.org/\" rel=\"nofollow\"\u003e\u003cem\u003eMRtrix3\u003c/em\u003e Community Forum\u003c/a\u003e.\nPlease also look through the Frequently Asked Questions on the \u003ca href=\"http://community.mrtrix.org/c/wiki\" rel=\"nofollow\"\u003ewiki section of the forum\u003c/a\u003e.\nYou can address all \u003cem\u003eMRtrix3\u003c/em\u003e-related queries there, using your GitHub or Google login to post questions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eQuick install\u003c/h2\u003e\u003ca id=\"user-content-quick-install\" class=\"anchor\" aria-label=\"Permalink: Quick install\" href=\"#quick-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eInstall dependencies by whichever means your system uses.\nThese include: Python (\u0026gt;=2.6), a C++ compiler with full C++11 support (\u003ccode\u003eg++\u003c/code\u003e 4.9 or later, \u003ccode\u003eclang++\u003c/code\u003e),\nEigen (\u0026gt;=3.2.8), zlib, OpenGL (\u0026gt;=3.3), and Qt (\u0026gt;=4.8, or at least 5.1 on MacOSX).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClone Git repository and compile:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ git clone https://github.com/MRtrix3/mrtrix3.git\n $ cd mrtrix3/\n $ ./configure\n $ ./build\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSet the \u003ccode\u003ePATH\u003c/code\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eBash shell:\u003c/p\u003e\n\u003cp\u003erun the \u003ccode\u003eset_path\u003c/code\u003e script provided:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ ./set_path\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eor edit the startup \u003ccode\u003e~/.bashrc\u003c/code\u003e or \u003ccode\u003e/etc/bash.bashrc\u003c/code\u003e file manually by adding this line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ export PATH=/\u0026lt;edit as appropriate\u0026gt;/mrtrix3/bin:$PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eC shell:\u003c/p\u003e\n\u003cp\u003eedit the startup \u003ccode\u003e~/.cshrc\u003c/code\u003e or \u003ccode\u003e/etc/csh.cshrc\u003c/code\u003e file manually by adding this line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ setenv PATH /\u0026lt;edit as appropriate\u0026gt;/mrtrix3/bin:$PATH\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTest installation:\u003c/p\u003e\n\u003cp\u003eCommand-line:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ mrconvert\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eGUI:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e $ mrview\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eKeeping MRtrix3 up to date\u003c/h2\u003e\u003ca id=\"user-content-keeping-mrtrix3-up-to-date\" class=\"anchor\" aria-label=\"Permalink: Keeping MRtrix3 up to date\" href=\"#keeping-mrtrix3-up-to-date\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eYou can update your installation at any time by opening a terminal in the mrtrix3 folder, and typing:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e git pull\n ./build\n\u003c/code\u003e\u003c/pre\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIf this doesn\u0027t work immediately, it may be that you need to re-run the configure script:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ./configure\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eand re-run step 1 again.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eBuilding a specific release of MRtrix3\u003c/h2\u003e\u003ca id=\"user-content-building-a-specific-release-of-mrtrix3\" class=\"anchor\" aria-label=\"Permalink: Building a specific release of MRtrix3\" href=\"#building-a-specific-release-of-mrtrix3\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can build a particular release of MRtrix3 by checking out the corresponding \u003cem\u003etag\u003c/em\u003e, and using the same procedure as above to build it:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003egit checkout 3.0_RC3\n./configure\n./build\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThank you for your interest in contributing to \u003cem\u003eMRtrix3\u003c/em\u003e! Please read on \u003ca href=\"CONTRIBUTING.md\"\u003ehere\u003c/a\u003e to find out how to report issues, request features and make direct contributions.\u003c/p\u003e\n", - "stargazers_count": 294, - "subscribers_count": 32, - "topics": [], - "updated_at": 1731067546.0 - }, { "data_format": 2, "description": "MLCommons Algorithmic Efficiency is a benchmark and competition measuring neural network training speedups due to algorithmic improvements in both training algorithms and models.", @@ -42717,6 +42745,25 @@ var data = "topics": [], "updated_at": 1716035946.0 }, + { + "data_format": 2, + "description": "Eukaryotic Genome Annotation Pipeline", + "filenames": [ + "Singularity" + ], + "full_name": "nextgenusfs/funannotate", + "latest_release": "v1.8.17", + "readme": "\u003cp\u003e\u003ca href=\"https://github.com/nextgenusfs/funannotate/releases/latest\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7adeb983d1d110a5fc93da05232464468e9c0a88362ceed5025106bc9f201a62/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6e65787467656e757366732f66756e616e6e6f746174652e737667\" alt=\"Latest Github release\" data-canonical-src=\"https://img.shields.io/github/release/nextgenusfs/funannotate.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/48254740\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f622c943ab3ca1ffeed95813d0e501dd68109f8e2da4db48750bbefaee2673a/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f34383235343734302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/48254740.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" 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src=\"https://camo.githubusercontent.com/3c28c66b5ffe053b28715e772373446d8600330882ccf560919a8c1337cfd887/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6e65787467656e757366732f66756e616e6e6f746174652f6c6174657374\" alt=\"Docker Image Size (tag)\" data-canonical-src=\"https://img.shields.io/docker/image-size/nextgenusfs/funannotate/latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f3fb86f1f236067cc536b7984721bee112324423084e72793c40cd98c0677e55/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6e65787467656e757366732f66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f3fb86f1f236067cc536b7984721bee112324423084e72793c40cd98c0677e55/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6e65787467656e757366732f66756e616e6e6f74617465\" alt=\"Docker Pulls\" 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src=\"https://camo.githubusercontent.com/bb354791ee1e875d63dae5aa7f9f7de6657419ecf92ebfe7b025fa4aa90a93ec/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"install with bioconda\" data-canonical-src=\"https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://usegalaxy.eu/root?tool_id=funannotate_annotate\" rel=\"nofollow\"\u003e\u003cimg 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data-canonical-src=\"https://img.shields.io/badge/usegalaxy-.eu-brightgreen?logo=data:image/png;base64,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\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"funannotate-logo.png?raw=true\"\u003e\u003cimg src=\"funannotate-logo.png?raw=true\" alt=\"Alt text\" title=\"Funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003efunannotate is a pipeline for genome annotation (built specifically for fungi, but will also work with higher eukaryotes). Installation, usage, and more information can be found at \u003ca href=\"http://funannotate.readthedocs.io\" rel=\"nofollow\"\u003ehttp://funannotate.readthedocs.io\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eQuickest start Docker:\u003c/h4\u003e\u003ca id=\"user-content-quickest-start-docker\" class=\"anchor\" aria-label=\"Permalink: Quickest start Docker:\" href=\"#quickest-start-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use docker to run \u003ccode\u003efunannotate\u003c/code\u003e. Caveats are that GeneMark is not included in the docker image (see licensing below and you can complain to the developers for making it difficult to distribute/use). I\u0027ve also written a bash script that can run the docker image and auto-detect/include the proper user/volume bindings. This docker image is built off of the latest code in master, so it will be ahead of the tagged releases. The image includes the required databases as well, if you want just funannotate without the databases then that is located on docker hub as well \u003ccode\u003enextgenusfs/funannotate-slim\u003c/code\u003e. So this route can be achieved with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# download/pull the image from docker hub\n$ docker pull nextgenusfs/funannotate\n\n# download bash wrapper script (optional)\n$ wget -O funannotate-docker https://raw.githubusercontent.com/nextgenusfs/funannotate/master/funannotate-docker\n\n# might need to make this executable on your system\n$ chmod +x /path/to/funannotate-docker\n\n# assuming it is in your PATH, now you can run this script as if it were the funannotate executable script\n$ funannotate-docker test -t predict --cpus 12\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eQuickstart Bioconda install:\u003c/h4\u003e\u003ca id=\"user-content-quickstart-bioconda-install\" class=\"anchor\" aria-label=\"Permalink: Quickstart Bioconda install:\" href=\"#quickstart-bioconda-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline can be installed with conda (via \u003ca href=\"https://bioconda.github.io/\" rel=\"nofollow\"\u003ebioconda\u003c/a\u003e):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#add appropriate channels\nconda config --add channels defaults\nconda config --add channels bioconda\nconda config --add channels conda-forge\n\n#then create environment\nconda create -n funannotate \"python\u0026gt;=3.6,\u0026lt;3.9\" funannotate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf \u003ccode\u003econda\u003c/code\u003e is taking forever to solve the environment, I would recommend giving \u003ca href=\"https://github.com/mamba-org/mamba\"\u003emamba\u003c/a\u003e a try:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#install mamba into base environment\nconda install -n base mamba\n\n#then use mamba as drop in replacmeent\nmamba create -n funannotate funannotate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to use GeneMark-ES/ET you will need to install that manually following developers instructions:\n\u003ca href=\"http://topaz.gatech.edu/GeneMark/license_download.cgi\" rel=\"nofollow\"\u003ehttp://topaz.gatech.edu/GeneMark/license_download.cgi\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that you will need to change the shebang line for all perl scripts in GeneMark to use \u003ccode\u003e/usr/bin/env perl\u003c/code\u003e.\nYou will then also need to add \u003ccode\u003egmes_petap.pl\u003c/code\u003e to the $PATH or set the environmental variable $GENEMARK_PATH to the gmes_petap directory.\u003c/p\u003e\n\u003cp\u003eTo install just the python funannotate package, you can do this with pip:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m pip install funannotate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install the most updated code in master you can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m pip install git+https://github.com/nextgenusfs/funannotate.git\n\u003c/code\u003e\u003c/pre\u003e\n", + "stargazers_count": 323, + "subscribers_count": 16, + "topics": [ + "genome-annotation", + "gene-models", + "comparative-genomics", + "ncbi-submission" + ], + "updated_at": 1733345263.0 + }, { "data_format": 2, "description": "Structural variant toolkit for VCFs", @@ -42726,8 +42773,8 @@ var data = "full_name": "ACEnglish/truvari", "latest_release": "v4.3.1", "readme": "\u003cp\u003e\u003ca href=\"https://badge.fury.io/py/Truvari\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6d571ab529b208bcabfe8eeface1271f2471f31690c08d83a72ff772dfeaa4cb/68747470733a2f2f62616467652e667572792e696f2f70792f547275766172692e737667\" alt=\"PyPI version\" data-canonical-src=\"https://badge.fury.io/py/Truvari.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/actions/workflows/pylint.yml\"\u003e\u003cimg src=\"imgs/pylint.svg\" alt=\"pylint\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/actions/workflows/func_tests.yml\"\u003e\u003cimg src=\"https://github.com/acenglish/truvari/actions/workflows/func_tests.yml/badge.svg?branch=develop\u0026amp;event=push\" alt=\"FuncTests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/actions/workflows/func_tests.yml\"\u003e\u003cimg src=\"imgs/coverage.svg\" alt=\"coverage\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/ACEnglish/truvari/compare/v4.3.1...develop\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/22ece66c377ec400fc04187de947f0553efa5220b53a5f16829c7ce0645fdb8e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6d6d6974732d73696e63652f6163656e676c6973682f747275766172692f76342e332e31\" alt=\"develop\" data-canonical-src=\"https://img.shields.io/github/commits-since/acenglish/truvari/v4.3.1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pepy.tech/project/truvari\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/097caa0c159e0e9a5500c02404f43d00f930ef5fea20e57405fc25d2ff516df2/68747470733a2f2f7374617469632e706570792e746563682f62616467652f74727576617269\" alt=\"Downloads\" data-canonical-src=\"https://static.pepy.tech/badge/truvari\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/ACEnglish/truvari/develop/imgs/BoxScale1_DarkBG.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/ACEnglish/truvari/develop/imgs/BoxScale1_DarkBG.png\" alt=\"Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nToolkit for benchmarking, merging, and annotating Structural Variants\u003c/p\u003e\n\u003cp\u003e\ud83d\udcda \u003ca href=\"https://github.com/acenglish/truvari/wiki\"\u003eWIKI page\u003c/a\u003e has detailed documentation.\u003cbr\u003e\n\ud83d\udcc8 See \u003ca href=\"https://github.com/acenglish/truvari/wiki/Updates\"\u003eUpdates\u003c/a\u003e on new versions.\u003cbr\u003e\n\ud83d\udcdd Read our Papers (\u003ca href=\"https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02840-6\" rel=\"nofollow\"\u003e#1\u003c/a\u003e, \u003ca href=\"https://rdcu.be/dFQNN\" rel=\"nofollow\"\u003e#2\u003c/a\u003e) to learn more.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udcbb Installation\u003c/h2\u003e\u003ca id=\"user-content--installation\" class=\"anchor\" aria-label=\"Permalink: \ud83d\udcbb Installation\" href=\"#-installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eTruvari uses Python 3.6+ and can be installed with pip:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e python3 -m pip install Truvari \n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFor details and more installation options, see \u003ca href=\"https://github.com/acenglish/truvari/wiki/Installation\"\u003eInstallation\u003c/a\u003e on the wiki.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\u23e9 Quick Start\u003c/h2\u003e\u003ca id=\"user-content--quick-start\" class=\"anchor\" aria-label=\"Permalink: \u23e9 Quick Start\" href=\"#-quick-start\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eEach sub-command contains help documentation. Start with \u003ccode\u003etruvari -h\u003c/code\u003e to see available commands.\u003c/p\u003e\n\u003cp\u003eThe current most common Truvari use case is for structural variation benchmarking:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e truvari bench -b base.vcf.gz -c comp.vcf.gz -o output_dir/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eFind more matches by harmonizing phased varians using refine:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e truvari refine -R -U -r reference.fa --regions output_dir/candidate.refine.bed output_dir/\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eUse Truvari\u0027s comparison engine to consolidate redundant variants in a merged multi-sample VCF:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e bcftools merge -m none sampleA.vcf.gz sampleB.vcf.gz | bgzip \u0026gt; merge.vcf.gz\n tabix merge.vcf.gz\n truvari collapse -i merge.vcf.gz -o truvari_merge.vcf\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83e\uddec Truvari Commands\u003c/h2\u003e\u003ca id=\"user-content--truvari-commands\" class=\"anchor\" aria-label=\"Permalink: \ud83e\uddec Truvari Commands\" href=\"#-truvari-commands\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/wiki/bench\"\u003ebench\u003c/a\u003e - Performance metrics from comparison of two VCFs\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/wiki/collapse\"\u003ecollapse\u003c/a\u003e - Collapse possibly redundant VCF entries\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ACEnglish/truvari/wiki/refine\"\u003erefine\u003c/a\u003e - Automated bench result refinement with phab\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/wiki/anno\"\u003eanno\u003c/a\u003e - Add SV annotations to a VCF\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ACEnglish/truvari/wiki/phab\"\u003ephab\u003c/a\u003e - Harmonize variant representations using MSA\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/wiki/consistency\"\u003econsistency\u003c/a\u003e - Consistency report between multiple VCFs\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/wiki/vcf2df\"\u003evcf2df\u003c/a\u003e - Turn a VCF into a pandas DataFrame\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/wiki/segment\"\u003esegment\u003c/a\u003e - Normalization of SVs into disjointed genomic regions\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/acenglish/truvari/wiki/stratify\"\u003estratify\u003c/a\u003e - Count variants per-region in vcf\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ACEnglish/truvari/wiki/divide\"\u003edivide\u003c/a\u003e - Divide a VCF into independent shards\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/ACEnglish/truvari/wiki/ga4gh\"\u003ega4gh\u003c/a\u003e - Consolidate benchmarking result VCFs\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003e\ud83d\udd0e More Information\u003c/h2\u003e\u003ca id=\"user-content--more-information\" class=\"anchor\" aria-label=\"Permalink: \ud83d\udd0e More Information\" href=\"#-more-information\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eAll documentation about Truvari is on the \u003ca href=\"https://github.com/acenglish/truvari/wiki\"\u003eWIKI\u003c/a\u003e. Additional information about using Truvari can be found in \u003ca href=\"https://github.com/ACEnglish/truvari/discussions\"\u003eDiscussions\u003c/a\u003e\u003c/p\u003e\n", - "stargazers_count": 321, - "subscribers_count": 14, + "stargazers_count": 324, + "subscribers_count": 13, "topics": [ "structural-variation", "vcf", @@ -42740,26 +42787,7 @@ var data = "benchmarking", "sequencing" ], - "updated_at": 1729911196.0 - }, - { - "data_format": 2, - "description": "Eukaryotic Genome Annotation Pipeline", - "filenames": [ - "Singularity" - ], - "full_name": "nextgenusfs/funannotate", - "latest_release": "v1.8.17", - "readme": "\u003cp\u003e\u003ca href=\"https://github.com/nextgenusfs/funannotate/releases/latest\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7adeb983d1d110a5fc93da05232464468e9c0a88362ceed5025106bc9f201a62/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f72656c656173652f6e65787467656e757366732f66756e616e6e6f746174652e737667\" alt=\"Latest Github release\" data-canonical-src=\"https://img.shields.io/github/release/nextgenusfs/funannotate.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/48254740\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f622c943ab3ca1ffeed95813d0e501dd68109f8e2da4db48750bbefaee2673a/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f34383235343734302e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/48254740.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/022f5cd71f819b3882de60c50fae0aff90574c5e3f5cbb1602b26022267274c0/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f646e2f62696f636f6e64612f66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/022f5cd71f819b3882de60c50fae0aff90574c5e3f5cbb1602b26022267274c0/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f646e2f62696f636f6e64612f66756e616e6e6f74617465\" alt=\"Conda\" data-canonical-src=\"https://img.shields.io/conda/dn/bioconda/funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/3c28c66b5ffe053b28715e772373446d8600330882ccf560919a8c1337cfd887/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6e65787467656e757366732f66756e616e6e6f746174652f6c6174657374\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3c28c66b5ffe053b28715e772373446d8600330882ccf560919a8c1337cfd887/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f696d6167652d73697a652f6e65787467656e757366732f66756e616e6e6f746174652f6c6174657374\" alt=\"Docker Image Size (tag)\" data-canonical-src=\"https://img.shields.io/docker/image-size/nextgenusfs/funannotate/latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/f3fb86f1f236067cc536b7984721bee112324423084e72793c40cd98c0677e55/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6e65787467656e757366732f66756e616e6e6f74617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f3fb86f1f236067cc536b7984721bee112324423084e72793c40cd98c0677e55/68747470733a2f2f696d672e736869656c64732e696f2f646f636b65722f70756c6c732f6e65787467656e757366732f66756e616e6e6f74617465\" alt=\"Docker Pulls\" data-canonical-src=\"https://img.shields.io/docker/pulls/nextgenusfs/funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://singularity-hub.org/collections/5068\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df010a2bcabf58c1b072442e95c5e1ae15d169798d425ec9423c2f5b79e08d6b/68747470733a2f2f7777772e73696e67756c61726974792d6875622e6f72672f7374617469632f696d672f686f737465642d73696e67756c61726974792d2d6875622d2532336533323932392e737667\" alt=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" data-canonical-src=\"https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"http://bioconda.github.io/recipes/funannotate/README.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bb354791ee1e875d63dae5aa7f9f7de6657419ecf92ebfe7b025fa4aa90a93ec/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c253230776974682d62696f636f6e64612d627269676874677265656e2e7376673f7374796c653d666c6174\" alt=\"install with bioconda\" 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src=\"https://camo.githubusercontent.com/91a0d48c1dac0e238909091cfc90a1fd1e378cbccfac3ce252c2cf49c9015553/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f75736567616c6178792d2e65752d627269676874677265656e3f6c6f676f3d646174613a696d6167652f706e673b6261736536342c6956424f5277304b47676f414141414e53556845556741414142674141414153434159414141424237423665414141414247644254554541414c4750432f7868425141414143426a53464a4e414142364a6741416749514141506f41414143413641414164544141414f7067414141366d41414146334363756c4538414141414358424957584d41414173544141414c457745416d7077594141414343326c555748525954557736593239744c6d466b62324a6c4c6e687463414141414141415048673665473177625756305953423462577875637a703450534a685a4739695a547075637a70745a5852684c7949676544703462584230617a30695745315149454e76636d55674e5334304c6a416950676f6749434138636d526d4f6c4a455269423462577875637a70795a475939496d6830644841364c79393364336375647a4d7562334a6e4c7a45354f546b764d4449764d6a4974636d526d4c584e35626e52686543317563794d6950676f674943416749434138636d526d4f6b526c63324e79615842306157397549484a6b5a6a7068596d39316444306949676f674943416749434167494341674943423462577875637a703061575a6d50534a6f644852774f693876626e4d7559575276596d5575593239744c3352705a6d59764d5334774c79492b4369416749434167494341674944783061575a6d4f6c4a6c633239736458527062323556626d6c30506a49384c3352705a6d5936556d567a6232783164476c76626c56756158512b4369416749434167494341674944783061575a6d4f6b4e76625842795a584e7a61573975506a45384c3352705a6d59365132397463484a6c63334e706232342b4369416749434167494341674944783061575a6d4f6b3979615756756447463061573975506a45384c3352705a6d593654334a705a573530595852706232342b4369416749434167494341674944783061575a6d4f6c426f6233527662575630636d6c6a535735305a584a77636d5630595852706232342b4d6a777664476c6d5a6a7051614739306232316c64484a7059306c756447567963484a6c644746306157397550676f6749434167494341384c334a6b5a6a70455a584e6a636d6c7764476c76626a344b49434167504339795a475936556b524750676f384c33673665473177625756305954344b443055716b774141416e394a524546554f42476c5645754c453045517275715a69667477447a345159543149594d3865466b4846772f344859582b4742332f42346c2f59502b4350384f424e5477704377464d51584151504b746e7367356e4a5a704b646e69362f366b7a487641594446745255543731663355774145626b4c6368396f675178634277524b4d66416e4d312f434277677262786b67504159716c424f79316a666f766c61507345695750524f5a6d716d5a4b4b7a4f59434a622f416264594c736f392f39423647707042527143726a5359596171755a71323045554b417a56706a6f31467a57524456724e617936432f48447854393277587241564348334153717135567145747631575a31334d647766384c4679794b45434e62674848414f62576853636634576e6a3943625170507a5759553355466f5833716b686c473841593242545174352f45413771614550517367474c57696564304138564b72484173434331654a3645466f5564317636476f504f615241744450566955722f77507a6b494656394161415a4774594235363856794a66696a562b5a427a6c565a4a335737584842325245534765346f705849477a5254646a634175704f4b30395241366b7a72314e5472546a37563175674d34566750475745772b65333943784f364a5577355868684b69686d614461635532476952304f68636334635a2b4b7133416a6c456e45655253617a4c73362f39622f6b68346554432b686e674533515144375979636c78737266336370787350586e2b634664656e463961716c42584d5861446945796679666177427a325271432f4f39574631797361634f7079746c55536f714e72746662533634322b34443443533956337862347538502f414349344f38313065665275364b734330516e6a484a47617134494f47556a57546f2f59445a444233785349786347794e6c576354756362345433696e2f33496175654e725a7958306c474f72576e6473744f722b773231556c56466f6b494c6a4a4c466850756b625659384f6d774e51336e5a674e4a4e6d4b44636375735362345549652b67746b492b392f62534c4a446a716e3736336635435135544c41706d49436b71775230516e55504b5a4649556e6f6f7a57635175526243304b6d30326b6e6a30745059783633667572477333782f69506e7a38337a4a44564e746450335141414141424a52553545726b4a6767673d3d\" 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style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"funannotate-logo.png?raw=true\"\u003e\u003cimg src=\"funannotate-logo.png?raw=true\" alt=\"Alt text\" title=\"Funannotate\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003efunannotate is a pipeline for genome annotation (built specifically for fungi, but will also work with higher eukaryotes). Installation, usage, and more information can be found at \u003ca href=\"http://funannotate.readthedocs.io\" rel=\"nofollow\"\u003ehttp://funannotate.readthedocs.io\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eQuickest start Docker:\u003c/h4\u003e\u003ca id=\"user-content-quickest-start-docker\" class=\"anchor\" aria-label=\"Permalink: Quickest start Docker:\" href=\"#quickest-start-docker\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eYou can use docker to run \u003ccode\u003efunannotate\u003c/code\u003e. Caveats are that GeneMark is not included in the docker image (see licensing below and you can complain to the developers for making it difficult to distribute/use). I\u0027ve also written a bash script that can run the docker image and auto-detect/include the proper user/volume bindings. This docker image is built off of the latest code in master, so it will be ahead of the tagged releases. The image includes the required databases as well, if you want just funannotate without the databases then that is located on docker hub as well \u003ccode\u003enextgenusfs/funannotate-slim\u003c/code\u003e. So this route can be achieved with:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e# download/pull the image from docker hub\n$ docker pull nextgenusfs/funannotate\n\n# download bash wrapper script (optional)\n$ wget -O funannotate-docker https://raw.githubusercontent.com/nextgenusfs/funannotate/master/funannotate-docker\n\n# might need to make this executable on your system\n$ chmod +x /path/to/funannotate-docker\n\n# assuming it is in your PATH, now you can run this script as if it were the funannotate executable script\n$ funannotate-docker test -t predict --cpus 12\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch4 class=\"heading-element\"\u003eQuickstart Bioconda install:\u003c/h4\u003e\u003ca id=\"user-content-quickstart-bioconda-install\" class=\"anchor\" aria-label=\"Permalink: Quickstart Bioconda install:\" href=\"#quickstart-bioconda-install\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe pipeline can be installed with conda (via \u003ca href=\"https://bioconda.github.io/\" rel=\"nofollow\"\u003ebioconda\u003c/a\u003e):\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#add appropriate channels\nconda config --add channels defaults\nconda config --add channels bioconda\nconda config --add channels conda-forge\n\n#then create environment\nconda create -n funannotate \"python\u0026gt;=3.6,\u0026lt;3.9\" funannotate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf \u003ccode\u003econda\u003c/code\u003e is taking forever to solve the environment, I would recommend giving \u003ca href=\"https://github.com/mamba-org/mamba\"\u003emamba\u003c/a\u003e a try:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e#install mamba into base environment\nconda install -n base mamba\n\n#then use mamba as drop in replacmeent\nmamba create -n funannotate funannotate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIf you want to use GeneMark-ES/ET you will need to install that manually following developers instructions:\n\u003ca href=\"http://topaz.gatech.edu/GeneMark/license_download.cgi\" rel=\"nofollow\"\u003ehttp://topaz.gatech.edu/GeneMark/license_download.cgi\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eNote that you will need to change the shebang line for all perl scripts in GeneMark to use \u003ccode\u003e/usr/bin/env perl\u003c/code\u003e.\nYou will then also need to add \u003ccode\u003egmes_petap.pl\u003c/code\u003e to the $PATH or set the environmental variable $GENEMARK_PATH to the gmes_petap directory.\u003c/p\u003e\n\u003cp\u003eTo install just the python funannotate package, you can do this with pip:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m pip install funannotate\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eTo install the most updated code in master you can run:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003epython -m pip install git+https://github.com/nextgenusfs/funannotate.git\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 322, - "subscribers_count": 16, - "topics": [ - "genome-annotation", - "gene-models", - "comparative-genomics", - "ncbi-submission" - ], - "updated_at": 1729779443.0 + "updated_at": 1732813185.0 }, { "data_format": 2, @@ -42851,10 +42879,10 @@ var data = "full_name": "nservant/HiC-Pro", "latest_release": "v3.1.0", "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eHiC-Pro\u003c/h1\u003e\u003ca id=\"user-content-hic-pro\" class=\"anchor\" aria-label=\"Permalink: HiC-Pro\" href=\"#hic-pro\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eAn optimized and flexible pipeline for Hi-C data processing\u003c/h3\u003e\u003ca id=\"user-content-an-optimized-and-flexible-pipeline-for-hi-c-data-processing\" class=\"anchor\" aria-label=\"Permalink: An optimized and flexible pipeline for Hi-C data processing\" href=\"#an-optimized-and-flexible-pipeline-for-hi-c-data-processing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://travis-ci.com/nservant/HiC-Pro\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0806ec3f9b18ef0a58c2d3c7a5af780decb468761569e4c899b9ec989aa38e66/68747470733a2f2f7472617669732d63692e636f6d2f6e73657276616e742f4869432d50726f2e7376673f6272616e63683d646576656c5f707933\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/nservant/HiC-Pro.svg?branch=devel_py3\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9a0752697dd62070d847847c8c9a238cce45d1e08467161cef1106e7bd17c2e0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6e64612d6275696c642d627269676874677265656e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9a0752697dd62070d847847c8c9a238cce45d1e08467161cef1106e7bd17c2e0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6e64612d6275696c642d627269676874677265656e2e737667\" alt=\"Conda\" data-canonical-src=\"https://img.shields.io/badge/Conda-build-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/537b130f521324548ffdfb3d39447a2afa5882cc864bf64db381650c1cfdacb6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f53696e67756c61726974792d6275696c642d627269676874677265656e2e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/537b130f521324548ffdfb3d39447a2afa5882cc864bf64db381650c1cfdacb6/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f53696e67756c61726974792d6275696c642d627269676874677265656e2e737667\" alt=\"Singularity\" data-canonical-src=\"https://img.shields.io/badge/Singularity-build-brightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hub.docker.com/repository/docker/nservant/hicpro\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/00af03ba36c4aa1e8f99c527cb1d80f5c73f8b26c7328d8fa1bf66fe40045362/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f446f636b65722d6d616e75616c2d79656c6c6f772e737667\" alt=\"Docker\" data-canonical-src=\"https://img.shields.io/badge/Docker-manual-yellow.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/261cd1cdd21aaa91e3ab35bb445bf943c9d512af3fd2c49fca57b0c025a1e5c7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4d756c746951432d312e382d626c75652e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/261cd1cdd21aaa91e3ab35bb445bf943c9d512af3fd2c49fca57b0c025a1e5c7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4d756c746951432d312e382d626c75652e737667\" alt=\"MultiQC\" data-canonical-src=\"https://img.shields.io/badge/MultiQC-1.8-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://groups.google.com/forum/#!forum/hic-pro\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a842892826251700e4cc3c666fea3560ab4b7e424f040abba13077bbddc0c861/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f47726f7570732d2532306a6f696e253230636861742532302545322538362539322d3466623939612e7376673f7374796c653d666c61742d737175617265\" alt=\"Forum\" data-canonical-src=\"https://img.shields.io/badge/Groups-%20join%20chat%20%E2%86%92-4fb99a.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.1186/s13059-015-0831-x\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8506627be830cebc6b4376129700d9be8df07e1f13213d9f48187e65a930446e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f444f492d31302e313138362532467331333035392d2d3031352d2d303833312d2d782d6c69676874677265792e7376673f7374796c653d666c61742d737175617265\" alt=\"DOI\" data-canonical-src=\"https://img.shields.io/badge/DOI-10.1186%2Fs13059--015--0831--x-lightgrey.svg?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003eFind documentation and examples at \u003ca href=\"http://nservant.github.io/HiC-Pro/\" rel=\"nofollow\"\u003ehttp://nservant.github.io/HiC-Pro/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eFor any question about HiC-Pro, please contact \u003ca href=\"mailto:nicolas.servant@curie.fr\"\u003enicolas.servant@curie.fr\u003c/a\u003e or use the \u003ca href=\"https://groups.google.com/forum/#!forum/hic-pro\" rel=\"nofollow\"\u003eHiC-Pro forum\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eWhat is HiC-Pro ?\u003c/h2\u003e\u003ca id=\"user-content-what-is-hic-pro-\" class=\"anchor\" aria-label=\"Permalink: What is HiC-Pro ?\" href=\"#what-is-hic-pro-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHiC-Pro was designed to process Hi-C data, from raw fastq files (paired-end Illumina data) to normalized contact maps. It supports the main Hi-C protocols, including digestion protocols as well as protocols that do not require restriction enzymes such as DNase Hi-C. In practice, HiC-Pro was successfully applied to many data-sets including dilution Hi-C, in situ Hi-C, DNase Hi-C, Micro-C, capture-C, capture Hi-C or HiChip data.\u003cbr\u003e\nThe pipeline is flexible, scalable and optimized. It can operate either on a single laptop or on a computational cluster. HiC-Pro is sequential and each step of the workflow can be run independantly.\u003cbr\u003e\nHiC-Pro includes a fast implementatation of the iterative correction method (see the \u003ca href=\"https://github.com/hiclib/iced\"\u003eiced python package\u003c/a\u003e for more information).\nFinally, HiC-Pro can use phasing data to build \u003ca href=\"doc/AS.md\"\u003eallele-specific contact maps\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIf you use HiC-Pro, please cite :\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eServant N., Varoquaux N., Lajoie BR., Viara E., Chen CJ., Vert JP., Dekker J., Heard E., Barillot E.\u003c/em\u003e HiC-Pro: An optimized and flexible pipeline for Hi-C processing. Genome Biology 2015, 16:259 \u003ca href=\"https://doi.org/10.1186/s13059-015-0831-x\" rel=\"nofollow\"\u003edoi:10.1186/s13059-015-0831-x\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContainers\u003c/h2\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing HiC-Pro through \u003ccode\u003econda\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-hic-pro-through-conda\" class=\"anchor\" aria-label=\"Permalink: Using HiC-Pro through conda\" href=\"#using-hic-pro-through-conda\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to ease the installation of HiC-Pro dependancies, we provide a \u003ccode\u003eyml\u003c/code\u003e file for conda with all required tools.\nIn order to build your conda environment, first install \u003ca href=\"https://docs.conda.io/en/latest/miniconda.html\" rel=\"nofollow\"\u003eminiconda\u003c/a\u003e and use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003econda env create -f MY_INSTALL_PATH/HiC-Pro/environment.yml -p WHERE_TO_INSTALL_MY_ENV\nconda activate WHERE_TO_INSTALL_MY_ENV\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing the HiC-Pro \u003ccode\u003eDocker\u003c/code\u003e image\u003c/h3\u003e\u003ca id=\"user-content-using-the-hic-pro-docker-image\" class=\"anchor\" aria-label=\"Permalink: Using the HiC-Pro Docker image\" href=\"#using-the-hic-pro-docker-image\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eA docker image is automatically build and available on \u003ca href=\"https://hub.docker.com/repository/docker/nservant/hicpro\" rel=\"nofollow\"\u003eDocker Hub\u003c/a\u003e\nTo pull a Docker image, simply use :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker pull nservant/hicpro:latest\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that the \u003ccode\u003etag\u003c/code\u003e may depend on the HiC-Pro version.\u003c/p\u003e\n\u003cp\u003eYou can also build your own image from the root folder using\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003edocker build -t hicpro:3.1.0 .\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eUsing HiC-Pro through \u003ccode\u003eSingularity\u003c/code\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-using-hic-pro-through-singularity\" class=\"anchor\" aria-label=\"Permalink: Using HiC-Pro through Singularity\" href=\"#using-hic-pro-through-singularity\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eHiC-Pro provides a Singularity container to ease its installation process.\nA ready-to-use container is available \u003ca href=\"https://zerkalo.curie.fr/partage/HiC-Pro/singularity_images/hicpro_latest_ubuntu.img\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn order to build you own Singularity image;\u003c/p\u003e\n\u003cp\u003e1- Install singularity\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinux : \u003ca href=\"http://singularity.lbl.gov/install-linux\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/install-linux\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eMAC : \u003ca href=\"http://singularity.lbl.gov/install-mac\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/install-mac\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eWindows : \u003ca href=\"http://singularity.lbl.gov/install-windows\" rel=\"nofollow\"\u003ehttp://singularity.lbl.gov/install-windows\u003c/a\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e2- Build the singularity HiC-Pro image using the \u0027Singularity\u0027 file available in the HiC-Pro root directory.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esudo singularity build hicpro_latest_ubuntu.img MY_INSTALL_PATH/HiC-Pro/envs/Singularity\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003e3- Run HiC-pro\u003c/p\u003e\n\u003cp\u003eYou can then either use HiC-Pro using the \u0027exec\u0027 command ;\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity exec hicpro_latest_ubuntu.img HiC-Pro -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOr directly use HiC-Pro within the Singularity shell\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003esingularity shell hicpro_latest_ubuntu.img\nHiC-Pro -h\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to install it ?\u003c/h2\u003e\u003ca id=\"user-content-how-to-install-it-\" class=\"anchor\" aria-label=\"Permalink: How to install it ?\" href=\"#how-to-install-it-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe HiC-Pro pipeline requires the following dependencies :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe \u003ca href=\"http://bowtie-bio.sourceforge.net/bowtie2/index.shtml\" rel=\"nofollow\"\u003ebowtie2\u003c/a\u003e mapper\u003c/li\u003e\n\u003cli\u003ePython (\u0026gt;3.7) with \u003cem\u003epysam (\u0026gt;=0.15.4)\u003c/em\u003e, \u003cem\u003ebx-python(\u0026gt;=0.8.8)\u003c/em\u003e, \u003cem\u003enumpy(\u0026gt;=1.18.1)\u003c/em\u003e, and \u003cem\u003escipy(\u0026gt;=1.4.1)\u003c/em\u003e libraries.\u003cbr\u003e\n\u003cstrong\u003eNote that the current version no longer supports python 2\u003c/strong\u003e\n\u003c/li\u003e\n\u003cli\u003eR with the \u003cem\u003eRColorBrewer\u003c/em\u003e and \u003cem\u003eggplot2 (\u0026gt;2.2.1)\u003c/em\u003e packages\u003c/li\u003e\n\u003cli\u003eg++ compiler\u003c/li\u003e\n\u003cli\u003esamtools (\u0026gt;1.9)\u003c/li\u003e\n\u003cli\u003eUnix sort (\u003cstrong\u003ewhich support -V option\u003c/strong\u003e) is required ! For Mac OS user, please install the GNU core utilities !\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNote that Bowtie \u0026gt;2.2.2 is strongly recommanded for allele specific analysis.\u003c/p\u003e\n\u003cp\u003eTo install HiC-Pro, be sure to have the appropriate rights and run :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003etar -zxvf HiC-Pro-master.tar.gz\ncd HiC-Pro-master\n## Edit config-install.txt file if necessary\nmake configure\nmake install\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eNote that if some of these dependencies are not installed (i.e. not detected in the $PATH), HiC-Pro will try to install them.\u003cbr\u003e\nYou can also edit the \u003cem\u003econfig-install.txt\u003c/em\u003e file and manually defined the paths to dependencies.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003eSYSTEM CONFIGURATION\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePREFIX\u003c/td\u003e\n\u003ctd\u003ePath to installation folder\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBOWTIE2_PATH\u003c/td\u003e\n\u003ctd\u003eFull path the bowtie2 installation directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSAMTOOLS_PATH\u003c/td\u003e\n\u003ctd\u003eFull path to the samtools installation directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eR_PATH\u003c/td\u003e\n\u003ctd\u003eFull path to the R installation directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePYTHON_PATH\u003c/td\u003e\n\u003ctd\u003eFull path to the python installation directory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCLUSTER_SYS\u003c/td\u003e\n\u003ctd\u003eScheduler to use for cluster submission. Must be TORQUE, SGE, SLURM or LSF\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAnnotation Files\u003c/h2\u003e\u003ca id=\"user-content-annotation-files\" class=\"anchor\" aria-label=\"Permalink: Annotation Files\" href=\"#annotation-files\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eIn order to process the raw data, HiC-Pro requires three annotation files. Note that the pipeline is provided with some Human and Mouse annotation files.\u003cbr\u003e\n\u003cstrong\u003ePlease be sure that the chromosome names are the same than the ones used in your bowtie indexes !\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eA BED file\u003c/strong\u003e of the restriction fragments after digestion. This file depends both of the restriction enzyme and the reference genome. See the \u003ca href=\"doc/FAQ.md\"\u003eFAQ\u003c/a\u003e and the \u003ca href=\"doc/UTILS.md\"\u003eHiC-Pro utilities\u003c/a\u003e for details about how to generate this file. A few annotation files are provided with the HiC-Pro sources as examples.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e chr1 0 16007 HIC_chr1_1 0 +\n chr1 16007 24571 HIC_chr1_2 0 +\n chr1 24571 27981 HIC_chr1_3 0 +\n chr1 27981 30429 HIC_chr1_4 0 +\n chr1 30429 32153 HIC_chr1_5 0 +\n chr1 32153 32774 HIC_chr1_6 0 +\n chr1 32774 37752 HIC_chr1_7 0 +\n chr1 37752 38369 HIC_chr1_8 0 +\n chr1 38369 38791 HIC_chr1_9 0 +\n chr1 38791 39255 HIC_chr1_10 0 +\n (...)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eA table file\u003c/strong\u003e of chromosomes\u0027 size. This file can be easily find on the UCSC genome browser. Of note, pay attention to the contigs or scaffolds, and be aware that HiC-pro will generate a map per chromosomes pair. For model organisms such as Human or Mouse, which are well annotated, we usually recommand to remove all scaffolds.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e chr1 249250621\n chr2 243199373\n chr3 198022430\n chr4 191154276\n chr5 180915260\n chr6 171115067\n chr7 159138663\n chr8 146364022\n chr9 141213431\n chr10 135534747\n (...)\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eThe bowtie2 indexes\u003c/strong\u003e. See the \u003ca href=\"http://bowtie-bio.sourceforge.net/bowtie2/index.shtml\" rel=\"nofollow\"\u003ebowtie2 manual page\u003c/a\u003e for details about how to create such indexes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eHow to use it ?\u003c/h2\u003e\u003ca id=\"user-content-how-to-use-it-\" class=\"anchor\" aria-label=\"Permalink: How to use it ?\" href=\"#how-to-use-it-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eFirst have a look at the help message !\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e HiC-Pro --help\n usage : HiC-Pro -i INPUT -o OUTPUT -c CONFIG [-s ANALYSIS_STEP] [-p] [-h] [-v]\n Use option -h|--help for more information\n\n HiC-Pro 3.1.0\n ---------------\n OPTIONS\n\n -i|--input INPUT : input data folder; Must contains a folder per sample with input files\n -o|--output OUTPUT : output folder\n -c|--conf CONFIG : configuration file for Hi-C processing\n [-p|--parallel] : if specified run HiC-Pro on a cluster\n [-s|--step ANALYSIS_STEP] : run only a subset of the HiC-Pro workflow; if not specified the complete workflow is run\n mapping: perform reads alignment - require fast files\n proc_hic: perform Hi-C filtering - require BAM files\n quality_checks: run Hi-C quality control plots\n merge_persample: merge multiple inputs and remove duplicates if specified - require .validPairs files\n build_contact_maps: Build raw inter/intrachromosomal contact maps - require .allValidPairs files\n ice_norm : run ICE normalization on contact maps - require .matrix files\n [-h|--help]: help\n [-v|--version]: version\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCopy and edit the configuration file \u003cem\u003e\u0027config-hicpro.txt\u0027\u003c/em\u003e in your local folder. See the \u003ca href=\"doc/MANUAL.md\"\u003emanual\u003c/a\u003e for details about the configuration file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePut all input files in a rawdata folder. The input files have to be organized with \u003cstrong\u003eone folder per sample\u003c/strong\u003e, such as;\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e + PATH_TO_MY_DATA\n + sample1\n ++ file1_R1.fastq.gz\n ++ file1_R2.fastq.gz\n ++ ...\n + sample2\n ++ file1_R1.fastq.gz\n ++ file1_R2.fastq.gz\n *...\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRun HiC-Pro on your laptop in standalone model\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e MY_INSTALL_PATH/bin/HiC-Pro -i FULL_PATH_TO_DATA_FOLDER -o FULL_PATH_TO_OUTPUTS -c MY_LOCAL_CONFIG_FILE\n\u003c/code\u003e\u003c/pre\u003e\n\u003cul\u003e\n\u003cli\u003eRun HiC-Pro on a cluster (TORQUE/SGE/SLURM/LSF)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cpre\u003e\u003ccode\u003e MY_INSTALL_PATH/bin/HiC-Pro -i FULL_PATH_TO_DATA_FOLDER -o FULL_PATH_TO_OUTPUTS -c MY_LOCAL_CONFIG_FILE -p\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eIn the latter case, you will have the following message :\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e Please run HiC-Pro in two steps :\n 1- The following command will launch the parallel workflow through 12 torque jobs:\n qsub HiCPro_step1.sh\n 2- The second command will merge all outputs to generate the contact maps:\n qsub HiCPro_step2.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eExecute the displayed command from the output folder:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e qsub HiCPro_step1.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eOnce executed succesfully (may take several hours), run the step using:\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e qsub HiCPro_step2.sh\n\u003c/code\u003e\u003c/pre\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eTest Dataset\u003c/h2\u003e\u003ca id=\"user-content-test-dataset\" class=\"anchor\" aria-label=\"Permalink: Test Dataset\" href=\"#test-dataset\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThe test dataset and associated results are available \u003ca href=\"https://zerkalo.curie.fr/partage/HiC-Pro/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\nSmall fastq files (2M reads) extracted from the Dixon et al. 2012 paper are available for test.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e ## Get the data. Will download a test_data folder and a configuration file\n wget https://zerkalo.curie.fr/partage/HiC-Pro/HiCPro_testdata.tar.gz \u0026amp;\u0026amp; tar -zxvf HiCPro_testdata.tar.gz\n\n ## Edit the configuration file and set the path to Human bowtie2 indexes\n\n ## Run HiC-Pro\n time HICPRO_INSTALL_DIR/bin/HiC-Pro -c config_test_latest.txt -i test_data -o hicpro_latest_test\n\nRun HiC-Pro 3.1.0\n--------------------------------------------\nThu Mar 19, 12:18:10 (UTC+0100)\nBowtie2 alignment step1 ...\nLogs: logs/dixon_2M_2/mapping_step1.log\nLogs: logs/dixon_2M/mapping_step1.log\n\n--------------------------------------------\nThu Mar 19, 12:18:57 (UTC+0100)\nBowtie2 alignment step2 ...\nLogs: logs/dixon_2M_2/mapping_step2.log\nLogs: logs/dixon_2M/mapping_step2.log\n\n--------------------------------------------\nThu Mar 19, 12:19:08 (UTC+0100)\nCombine R1/R2 alignment files ...\nLogs: logs/dixon_2M_2/mapping_combine.log\nLogs: logs/dixon_2M/mapping_combine.log\n\n--------------------------------------------\nThu Mar 19, 12:19:13 (UTC+0100)\nMapping statistics for R1 and R2 tags ...\nLogs: logs/dixon_2M_2/mapping_stats.log\nLogs: logs/dixon_2M/mapping_stats.log\n\n--------------------------------------------\nThu Mar 19, 12:19:15 (UTC+0100)\nPairing of R1 and R2 tags ...\nLogs: logs/dixon_2M_2/mergeSAM.log\nLogs: logs/dixon_2M/mergeSAM.log\n\n--------------------------------------------\nThu Mar 19, 12:19:25 (UTC+0100)\nAssign alignments to restriction fragments ...\nLogs: logs/dixon_2M_2/mapped_2hic_fragments.log\nLogs: logs/dixon_2M/mapped_2hic_fragments.log\n\n--------------------------------------------\nThu Mar 19, 12:20:10 (UTC+0100)\nMerge chunks from the same sample ...\nLogs: logs/dixon_2M/merge_valid_interactions.log\nLogs: logs/dixon_2M_2/merge_valid_interactions.log\n\n--------------------------------------------\nThu Mar 19, 12:20:11 (UTC+0100)\nMerge stat files per sample ...\nLogs: logs/dixon_2M/merge_stats.log\nLogs: logs/dixon_2M_2/merge_stats.log\n\n--------------------------------------------\nThu Mar 19, 12:20:11 (UTC+0100)\nRun quality checks for all samples ...\nLogs: logs/dixon_2M/make_Rplots.log\nLogs: logs/dixon_2M_2/make_Rplots.log\n\n--------------------------------------------\nThu Mar 19, 12:20:22 (UTC+0100)\nGenerate binned matrix files ...\nLogs: logs/dixon_2M/build_raw_maps.log\nLogs: logs/dixon_2M_2/build_raw_maps.log\n\n--------------------------------------------\nThu Mar 19, 12:20:22 (UTC+0100)\nRun ICE Normalization ...\nLogs: logs/dixon_2M/ice_500000.log\nLogs: logs/dixon_2M/ice_1000000.log\nLogs: logs/dixon_2M_2/ice_500000.log\nLogs: logs/dixon_2M_2/ice_1000000.log\n\nreal\t2m15,736s\nuser\t4m3,277s\nsys\t0m24,423s\n\n\u003c/code\u003e\u003c/pre\u003e\n", - "stargazers_count": 387, + "stargazers_count": 388, "subscribers_count": 18, "topics": [], - "updated_at": 1733028684.0 + "updated_at": 1733361034.0 }, { "data_format": 2, @@ -42918,7 +42946,7 @@ var data = "full_name": "openhackathons-org/gpubootcamp", "latest_release": null, "readme": "\u003cp\u003e\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/gpuhackathons-org/gpubootcamp/releases/latest\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b4951ef249d79bb7bd25025a1cfd47e18bd62dcab7bcdffdfc2aa21a40efc17c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f6770756861636b6174686f6e732d6f72672f677075626f6f7463616d703f696e636c7564655f70726572656c6561736573\" alt=\"GitHub release (latest by date including pre-releases)\" data-canonical-src=\"https://img.shields.io/github/v/release/gpuhackathons-org/gpubootcamp?include_prereleases\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/gpuhackathons-org/gpubootcamp/issues\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/18169f947e4dbf0ddcd939b21d43081aa227c086ca1e0253c9611d683c6c3cd1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6973737565732f6770756861636b6174686f6e732d6f72672f677075626f6f7463616d70\" alt=\"GitHub issues\" data-canonical-src=\"https://img.shields.io/github/issues/gpuhackathons-org/gpubootcamp\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e Please note this repository is deprecated and no longer maintained. Please refer to the new repositories under OpenHackathons\u003c/span\u003e \u003ca href=\"https://github.com/openhackathons-org\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eGPUBootcamp Training Materials\u003c/h1\u003e\u003ca id=\"user-content-gpubootcamp-training-materials\" class=\"anchor\" aria-label=\"Permalink: GPUBootcamp Training Materials\" href=\"#gpubootcamp-training-materials\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eGPU Bootcamps are designed to help build confidence in Accelerated Computing and eventually prepare developers to enroll for \u003ca href=\"http://gpuhackathons.org/\" rel=\"nofollow\"\u003eHackathons\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThis repository consists of GPU bootcamp material for HPC, AI and convergence of both:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eContribution\u003c/h1\u003e\u003ca id=\"user-content-contribution\" class=\"anchor\" aria-label=\"Permalink: Contribution\" href=\"#contribution\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003eThe repository uses Apache 2.0 license. For more details on folder structure developers may refer to CONTRIBUTING.md file.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eAuthors and Acknowledgment\u003c/h2\u003e\u003ca id=\"user-content-authors-and-acknowledgment\" class=\"anchor\" aria-label=\"Permalink: Authors and Acknowledgment\" href=\"#authors-and-acknowledgment\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eSee \u003ca href=\"https://github.com/gpuhackathons-org/gpubootcamp/graphs/contributors\"\u003eContributors\u003c/a\u003e for a list of contributors towards this Bootcamp.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eJoin OpenACC Community\u003c/h2\u003e\u003ca id=\"user-content-join-openacc-community\" class=\"anchor\" aria-label=\"Permalink: Join OpenACC Community\" href=\"#join-openacc-community\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003ePlease join \u003ca href=\"https://openacclang.slack.com/messages/openaccusergroup\" rel=\"nofollow\"\u003eOpenACC Slack Channel\u003c/a\u003e.\u003c/p\u003e\n", - "stargazers_count": 524, + "stargazers_count": 525, "subscribers_count": 23, "topics": [ "machine-learning", @@ -42934,7 +42962,7 @@ var data = "openmp", "ai4hpc" ], - "updated_at": 1732263806.0 + "updated_at": 1733451794.0 }, { "data_format": 2, @@ -43142,7 +43170,7 @@ var data = "full_name": "UniversalDataTool/universal-data-tool", "latest_release": "v0.14.26", "readme": "\u003cdiv class=\"markdown-heading\"\u003e\u003ch1 class=\"heading-element\"\u003eUniversal Data Tool\u003c/h1\u003e\u003ca id=\"user-content-universal-data-tool\" class=\"anchor\" aria-label=\"Permalink: Universal Data Tool\" href=\"#universal-data-tool\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://badge.fury.io/gh/UniversalDataTool%2Funiversal-data-tool\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/105737285d93ce10121bd19ad0d6b9b69c899b6aff05142623fe7c39f1ec7d0e/68747470733a2f2f62616467652e667572792e696f2f67682f556e6976657273616c44617461546f6f6c253246756e6976657273616c2d646174612d746f6f6c2e737667\" alt=\"GitHub version\" data-canonical-src=\"https://badge.fury.io/gh/UniversalDataTool%2Funiversal-data-tool.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/UniversalDataTool/universal-data-tool/workflows/Test/badge.svg\"\u003e\u003cimg src=\"https://github.com/UniversalDataTool/universal-data-tool/workflows/Test/badge.svg\" alt=\"Master Branch\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca 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href=\"https://github.com/UniversalDataTool/universal-data-tool/releases\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9ff076d70848abf376ae16c53c79fcc4a9bedef81d5ebf127d96f0a1e3dc1c27/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d732d57656225323057696e646f77732532304c696e75782532304d61632d626c756576696f6c6574\" alt=\"Platform Support Web/Win/Linux/Mac\" data-canonical-src=\"https://img.shields.io/badge/platforms-Web%20Windows%20Linux%20Mac-blueviolet\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://join.slack.com/t/universaldatatool/shared_invite/zt-d8teykwi-iOSOUfxugKR~M4AJN6VL3g\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/262c193bbf3bbdbcd30bf57e66ced6e6e81e630d0313d1325b4cb682bdbda610/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736c61636b2d556e6976657273616c25323044617461253230546f6f6c2d626c75652e7376673f6c6f676f3d736c61636b\" alt=\"Slack Image\" data-canonical-src=\"https://img.shields.io/badge/slack-Universal%20Data%20Tool-blue.svg?logo=slack\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://twitter.com/UniversalDataTl\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/de3b484faf3c6e15217c780c98c91ea4979e001ef09f69902266056ed2da1bc6/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f556e6976657273616c44617461546c3f7374796c653d736f6369616c\" alt=\"Twitter Logo\" data-canonical-src=\"https://img.shields.io/twitter/follow/UniversalDataTl?style=social\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTry it out at \u003ca href=\"https://udt.dev\" rel=\"nofollow\"\u003eudt.dev\u003c/a\u003e, \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/releases\"\u003edownload the desktop app\u003c/a\u003e or \u003ca href=\"https://docs.universaldatatool.com/running-on-premise\" rel=\"nofollow\"\u003erun on-premise\u003c/a\u003e.\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/1910070/91648687-729a3b80-ea38-11ea-92f2-7ce94ae04da6.gif\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/1910070/91648687-729a3b80-ea38-11ea-92f2-7ce94ae04da6.gif\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n \u003cb\u003e\n \u003ca href=\"https://docs.universaldatatool.com\" rel=\"nofollow\"\u003eDocs\u003c/a\u003e \u2022 \u003ca href=\"https://universaldatatool.com\" rel=\"nofollow\"\u003eWebsite\u003c/a\u003e \u2022 \u003ca href=\"https://udt.dev\" rel=\"nofollow\"\u003ePlayground\u003c/a\u003e \u2022 \u003ca href=\"https://docs.universaldatatool.com/integrate-with-any-web-page/integrate-with-the-javascript-library\" rel=\"nofollow\"\u003eLibrary Usage\u003c/a\u003e \u2022 \u003ca href=\"https://docs.universaldatatool.com/running-on-premise\" rel=\"nofollow\"\u003eOn-Premise\u003c/a\u003e\n \u003c/b\u003e\n\u003c/p\u003e\n\u003cp\u003eThe Universal Data Tool is a web/desktop app for editing and annotating images, text, audio, documents and to view and edit any data defined in the extensible \u003ca href=\"https://github.com/UniversalDataTool/udt-format\"\u003e.udt.json and .udt.csv standard\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSupported Data\u003c/h2\u003e\u003ca id=\"user-content-supported-data\" class=\"anchor\" aria-label=\"Permalink: Supported Data\" href=\"#supported-data\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://docs.universaldatatool.com/building-and-labeling-datasets/image-segmentation\" rel=\"nofollow\"\u003eImage Segmentation\u003c/a\u003e \u2022 \n \u003ca href=\"https://docs.universaldatatool.com/building-and-labeling-datasets/image-classification\" rel=\"nofollow\"\u003eImage Classification\u003c/a\u003e \u2022 \n \u003ca href=\"https://docs.universaldatatool.com/building-and-labeling-datasets/text-classification\" rel=\"nofollow\"\u003eText Classification\u003c/a\u003e \u2022 \n \u003ca href=\"https://docs.universaldatatool.com/building-and-labeling-datasets/named-entity-recognition\" rel=\"nofollow\"\u003eNamed Entity Recognition\u003c/a\u003e \u2022 \n \u003ca href=\"https://docs.universaldatatool.com/building-and-labeling-datasets/entity-relations-part-of-speech-tagging\" rel=\"nofollow\"\u003eNamed Entity Relations / Part of Speech Tagging\u003c/a\u003e \u2022 \n \u003ca href=\"https://docs.universaldatatool.com/building-and-labeling-datasets/audio-transcription\" rel=\"nofollow\"\u003eAudio Transcription\u003c/a\u003e \u2022 \n \u003ca href=\"https://docs.universaldatatool.com/building-and-labeling-datasets/data-entry\" rel=\"nofollow\"\u003eData Entry\u003c/a\u003e \u2022 \n \u003ca href=\"https://docs.universaldatatool.com/building-and-labeling-datasets/video-segmentation\" rel=\"nofollow\"\u003eVideo Segmentation\u003c/a\u003e \u2022 \n \u003ca href=\"https://docs.universaldatatool.com/building-and-labeling-datasets/landmark-annotation\" rel=\"nofollow\"\u003eLandmark / Pose Annotation\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eRecent Updates\u003c/h2\u003e\u003ca id=\"user-content-recent-updates\" class=\"anchor\" aria-label=\"Permalink: Recent Updates\" href=\"#recent-updates\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://www.youtube.com/channel/UCgFkrRN7CLt7_iTa2WDjf2g\" rel=\"nofollow\"\u003eFollow our development on Youtube!\u003c/a\u003e\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/q20WrCRcG4k\" rel=\"nofollow\"\u003eCommunity Update Video 9\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/watch?v=IBWOaw0jMmM\" rel=\"nofollow\"\u003eCommunity Update Video 8\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://youtu.be/glPPFgXibdw\" rel=\"nofollow\"\u003eCommunity Update Video 7\u003c/a\u003e \u003ca href=\"https://universaldatatool.substack.com/p/build-your-dataset-from-coco\" rel=\"nofollow\"\u003e(blog version)\u003c/a\u003e\n\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eCollaborate with others in real time, no sign up!\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003eUsable on \u003ca href=\"https://universaldatatool.com\" rel=\"nofollow\"\u003eweb\u003c/a\u003e or as \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/wiki/Installation\"\u003eWindows,Mac or Linux desktop application\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eConfigure your project with an easy-to-use GUI\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://universaldatatool.com/courses\" rel=\"nofollow\"\u003eEasily create courses to train your labelers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDownload/upload as easy-to-use CSV (\u003ca href=\"https://github.com/UniversalDataTool/udt-format/blob/master/SAMPLE.udt.csv\"\u003esample.udt.csv\u003c/a\u003e) or JSON (\u003ca href=\"https://github.com/UniversalDataTool/udt-format/blob/master/SAMPLE.udt.json\"\u003esample.udt.json\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eSupport for Images, Videos, PDFs, Text, Audio Transcription and many other formats\u003c/li\u003e\n\u003cli\u003eCan be \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/wiki/Usage-with-React\"\u003eeasily integrated into a React application\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eAnnotate images or videos with classifications, tags, bounding boxes, polygons and points\u003c/li\u003e\n\u003cli\u003eFast Automatic Smart Pixel Segmentation using WebWorkers and WebAssembly\u003c/li\u003e\n\u003cli\u003eImport data from Google Drive, Youtube, CSV, Clipboard and more\u003c/li\u003e\n\u003cli\u003eAnnotate NLP datasets with Named Entity Recognition (NER), classification and Part of Speech (PoS) tagging.\u003c/li\u003e\n\u003cli\u003eEasily \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/wiki/Usage-with-Pandas\"\u003eload into pandas\u003c/a\u003e or \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/wiki/Usage-with-Fast.ai\"\u003euse with fast.ai\u003c/a\u003e\n\u003c/li\u003e\n\u003cli\u003eRuns \u003ca href=\"https://hub.docker.com/r/universaldatatool/universaldatatool\" rel=\"nofollow\"\u003ewith docker\u003c/a\u003e \u003ccode\u003edocker run -p 3000:3000 universaldatatool/universaldatatool\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eRuns \u003ca href=\"https://singularity-hub.org/collections/4792\" rel=\"nofollow\"\u003ewith singularity\u003c/a\u003e \u003ccode\u003esingularity run universaldatatool/universaldatatool\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"center\"\u003e\u003ckbd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/1910070/76154066-06033d00-60a4-11ea-9bbd-69a62780769f.png\"\u003e\u003cimg width=\"600\" src=\"https://user-images.githubusercontent.com/1910070/76154066-06033d00-60a4-11ea-9bbd-69a62780769f.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/kbd\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\u003ckbd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/1910070/91648815-07516900-ea3a-11ea-9355-70dfbf5c8974.png\"\u003e\u003cimg width=\"600\" src=\"https://user-images.githubusercontent.com/1910070/91648815-07516900-ea3a-11ea-9355-70dfbf5c8974.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/kbd\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\u003ckbd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/1910070/76157343-9a39c800-60d5-11ea-8dd6-a67c516fcf63.png\"\u003e\u003cimg width=\"600\" src=\"https://user-images.githubusercontent.com/1910070/76157343-9a39c800-60d5-11ea-8dd6-a67c516fcf63.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/kbd\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\u003ckbd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/1910070/93283916-7b607080-f79f-11ea-838d-683829aff1b3.png\"\u003e\u003cimg width=\"600\" src=\"https://user-images.githubusercontent.com/1910070/93283916-7b607080-f79f-11ea-838d-683829aff1b3.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/kbd\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eSponsors\u003c/h2\u003e\u003ca id=\"user-content-sponsors\" class=\"anchor\" aria-label=\"Permalink: Sponsors\" href=\"#sponsors\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003e\u003ca href=\"https://wao.ai\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/1910070/107271376-20fbd100-6a1a-11eb-9f82-2d10607591ba.png\" alt=\"wao.ai sponsorship image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://momentum-tech.ca/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/1910070/107270943-8bf8d800-6a19-11eb-97c2-895b0280aa8a.png\" alt=\"momentum image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.enabledintelligence.net/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/1910070/107271756-aaab9e80-6a1a-11eb-887c-6f5d009f0fd2.png\" alt=\"enabled intelligence image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eWeb App\u003c/h3\u003e\u003ca id=\"user-content-web-app\" class=\"anchor\" aria-label=\"Permalink: Web App\" href=\"#web-app\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eJust visit \u003ca href=\"https://universaldatatool.com\" rel=\"nofollow\"\u003euniversaldatatool.com\u003c/a\u003e!\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTrying to run the web app locally? Run \u003ccode\u003enpm install\u003c/code\u003e then \u003ccode\u003enpm run start\u003c/code\u003e after cloning this repository to start the web server.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch3 class=\"heading-element\"\u003eDesktop Application\u003c/h3\u003e\u003ca id=\"user-content-desktop-application\" class=\"anchor\" aria-label=\"Permalink: Desktop Application\" href=\"#desktop-application\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eDownload the latest release from the \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/releases\"\u003ereleases page\u003c/a\u003e and run the executable you downloaded.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e(Optional) Say hi in the \u003ca href=\"https://join.slack.com/t/universaldatatool/shared_invite/zt-d8teykwi-iOSOUfxugKR~M4AJN6VL3g\" rel=\"nofollow\"\u003eSlack channel\u003c/a\u003e!\u003c/li\u003e\n\u003cli\u003eRead \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/wiki/Setup-for-Development\"\u003ethis guide to get started with development\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\"\u003e\u003ch2 class=\"heading-element\"\u003eContributors \u2728\u003c/h2\u003e\u003ca id=\"user-content-contributors-\" class=\"anchor\" aria-label=\"Permalink: Contributors \u2728\" href=\"#contributors-\"\u003e\u003cspan aria-hidden=\"true\" class=\"octicon octicon-link\"\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp\u003eThanks goes to these wonderful people (\u003ca href=\"https://allcontributors.org/docs/en/emoji-key\" rel=\"nofollow\"\u003eemoji key\u003c/a\u003e):\u003c/p\u003e\n\n\n\n\u003ctable\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://twitter.com/seveibar\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/1910070?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eSeverin Ibarluzea\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=seveibar\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=seveibar\" title=\"Documentation\"\u003e\ud83d\udcd6\u003c/a\u003e \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/pulls?q=is%3Apr+reviewed-by%3Aseveibar\" title=\"Reviewed Pull Requests\"\u003e\ud83d\udc40\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"http://puskuruk.github.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/22892227?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003ePuskuruk\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=puskuruk\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/pulls?q=is%3Apr+reviewed-by%3Apuskuruk\" title=\"Reviewed Pull Requests\"\u003e\ud83d\udc40\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/CedricJean\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/63243979?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eCedricJean\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=CedricJean\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"http://berupon.hatenablog.com/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/1131125?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eberu\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=beru\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/Ownmarc\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/24617457?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eMarc\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=Ownmarc\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=Ownmarc\" title=\"Documentation\"\u003e\ud83d\udcd6\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/Wafaa-arbash\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/59834878?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eWafaa-arbash\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=Wafaa-arbash\" title=\"Documentation\"\u003e\ud83d\udcd6\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/pgrimaud\"\u003e\u003cimg src=\"https://avatars1.githubusercontent.com/u/1866496?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003ePierre Grimaud\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=pgrimaud\" title=\"Documentation\"\u003e\ud83d\udcd6\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/sreevardhanreddi\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/31174432?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003esreevardhanreddi\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=sreevardhanreddi\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/mrdadah\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/11255121?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eMohammed Eldadah\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=mrdadah\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://x8795278.blogspot.com/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/9297254?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003ex213212\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=x213212\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/hysios\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/103227?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003ehysios \u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=hysios\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://congdv.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/8192210?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eCong Dao\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=congdv\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://www.linkedin.com/in/renato-gonsalves-499317125/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/47343193?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eRenato Junior\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"#translation-MrJunato\" title=\"Translation\"\u003e\ud83c\udf0d\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://gitlab.com/rickstaa\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars0.githubusercontent.com/u/17570430?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eRick\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"#translation-rickstaa\" title=\"Translation\"\u003e\ud83c\udf0d\u003c/a\u003e \u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=rickstaa\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/anaplian\"\u003e\u003cimg src=\"https://avatars3.githubusercontent.com/u/18647401?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eanaplian\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=anaplian\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://www.behance.net/MiguelCarvalho13\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/6718302?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eMiguel Carvalho\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"#translation-miguelcarvalho13\" title=\"Translation\"\u003e\ud83c\udf0d\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://kyleo.io\" rel=\"nofollow\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/27719893?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eKyle OBrien\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=obrien-k\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/hakkiyagiz\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/12295562?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eHakk\u0131 Ya\u011f\u0131z ERD\u0130N\u00c7\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=hakkiyagiz\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n\u003ca href=\"https://github.com/jvdavim\"\u003e\u003cimg src=\"https://avatars2.githubusercontent.com/u/16657663?v=4\" width=\"100px;\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003cbr\u003e\u003csub\u003e\u003cb\u003eJo\u00e3o Victor Davim\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/UniversalDataTool/universal-data-tool/commits?author=jvdavim\" title=\"Code\"\u003e\ud83d\udcbb\u003c/a\u003e\n\u003c/td\u003e\n \u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\n\n\n\u003cp\u003eThis project follows the \u003ca href=\"https://github.com/all-contributors/all-contributors\"\u003eall-contributors\u003c/a\u003e specification. Contributions of any kind welcome!\u003c/p\u003e\n", - "stargazers_count": 1964, + "stargazers_count": 1965, "subscribers_count": 37, "topics": [ "computer-vision", @@ -43166,7 +43194,7 @@ var data = "labeling-tool", "hacktoberfest" ], - "updated_at": 1732900462.0 + "updated_at": 1733335701.0 }, { "data_format": 2,